From c3c6780de7111b1a9bc2ddbae02360cb418af647 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Mon, 5 Feb 2024 17:45:26 -0500
Subject: [PATCH 001/623] Remving Python from build

---
 README.md              | 40 +++++++++++++++++++++++++++++++++++++++-
 ci/build_python.sh     | 21 ---------------------
 ci/build_wheel_cuvs.sh |  4 ++--
 ci/test_python.sh      | 26 +++++++++++++-------------
 ci/test_wheel_cuvs.sh  | 20 ++++++++++----------
 docs/source/index.rst  | 10 +++-------
 6 files changed, 67 insertions(+), 54 deletions(-)

diff --git a/README.md b/README.md
index e86eafda2e..428ab04a95 100755
--- a/README.md
+++ b/README.md
@@ -1,2 +1,40 @@
-# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuVS: Vector Search on the GPU</div>
+# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuVS: Vector Search and Clustering on the GPU</div>
+
+### NOTE: cuVS is currently being 
+
+## Contents
+<hr>
+
+1. [Useful Resources](#useful-resources)
+2. [What is cuVS?](#what-is-cuvs)
+3. [Getting Started](#getting-started)
+4. [Installing cuVS](#installing)
+5. [Contributing](#contributing)
+6. [References](#references)
+
+<hr>
+
+## Useful Resources
+
+- [cuVS Reference Documentation](https://docs.rapids.ai/api/cuvs/stable/): API Documentation.
+- [cuVS Getting Started](./docs/source/quick_start.md): Getting started with RAFT.
+- [Build and Install cuVS](./docs/source/build.md): Instructions for installing and building cuVS.
+- [Example Notebooks](./notebooks): Example jupyer notebooks
+- [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
+- [GitHub repository](https://github.com/rapidsai/cuvs): Download the cuVS source code.
+- [Issue tracker](https://github.com/rapidsai/cuvs/issues): Report issues or request features.
+
+## What is cuVS?
+
+cuVS contains many algorithms for running approximate nearest neighbors and clustering on the GPU.
+
+## Getting Started
+
+
+
+## Installing cuVS
+
+## Contributing
+
+## References
 
diff --git a/ci/build_python.sh b/ci/build_python.sh
index a54c3dcaa9..525d21c513 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -34,24 +34,3 @@ rapids-conda-retry mambabuild \
   --no-test \
   --channel "${CPP_CHANNEL}" \
   conda/recipes/cuvs
-
-
-# Build ann-bench for each cuda and python version
-rapids-conda-retry mambabuild \
---no-test \
---channel "${CPP_CHANNEL}" \
---channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
-conda/recipes/cuda-ann-bench
-
-# Build ann-bench-cpu only in CUDA 11 jobs since it only depends on python
-# version
-RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
-if [[ ${RAPIDS_CUDA_MAJOR} == "11" ]]; then
-  rapids-conda-retry mambabuild \
-  --no-test \
-  --channel "${CPP_CHANNEL}" \
-  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
-  conda/recipes/cuda-ann-bench-cpu
-fi
-
-rapids-upload-conda-to-s3 python
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index 9d2f969964..b4765be38a 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -1,9 +1,9 @@
 #!/bin/bash
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 
 set -euo pipefail
 
 # Set up skbuild options. Enable sccache in skbuild config options
 export SKBUILD_CONFIGURE_OPTIONS="-DRAFT_BUILD_WHEELS=ON -DDETECT_CONDA_ENV=OFF -DFIND_RAFT_CPP=OFF"
 
-ci/build_wheel.sh cuvs python/cuvs
+#ci/build_wheel.sh cuvs python/cuvs
diff --git a/ci/test_python.sh b/ci/test_python.sh
index a65469928c..9f0c9d6ee7 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 set -euo pipefail
 
@@ -31,7 +31,7 @@ rapids-print-env
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
   --channel "${PYTHON_CHANNEL}" \
-  libcuvs cuvs
+  libcuvs #cuvs
 
 rapids-logger "Check GPU usage"
 nvidia-smi
@@ -40,17 +40,17 @@ EXITCODE=0
 trap "EXITCODE=1" ERR
 set +e
 
-rapids-logger "pytest cuvs"
-pushd python/cuvs/cuvs
-pytest \
-  --cache-clear \
-  --junitxml="${RAPIDS_TESTS_DIR}/junit-cuvs.xml" \
-  --cov-config=../.coveragerc \
-  --cov=cuvs \
-  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cuvs-coverage.xml" \
-  --cov-report=term \
-  test
-popd
+#rapids-logger "pytest cuvs"
+#pushd python/cuvs/cuvs
+#pytest \
+#  --cache-clear \
+#  --junitxml="${RAPIDS_TESTS_DIR}/junit-cuvs.xml" \
+#  --cov-config=../.coveragerc \
+#  --cov=cuvs \
+#  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cuvs-coverage.xml" \
+#  --cov-report=term \
+#  test
+#popd
 
 rapids-logger "Test script exiting with value: $EXITCODE"
 exit ${EXITCODE}
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 6b213d399a..de75019155 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 
 set -euo pipefail
 
@@ -7,12 +7,12 @@ mkdir -p ./dist
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
 RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./dist
 
-# echo to expand wildcard before adding `[extra]` requires for pip
-python -m pip install $(echo ./dist/cuvs*.whl)[test]
-
-# Run smoke tests for aarch64 pull requests
-if [[ "$(arch)" == "aarch64" && "${RAPIDS_BUILD_TYPE}" == "pull-request" ]]; then
-    python ./ci/wheel_smoke_test_cuvs.py
-else
-    python -m pytest ./python/cuvs/cuvs/test
-fi
+## echo to expand wildcard before adding `[extra]` requires for pip
+#python -m pip install $(echo ./dist/cuvs*.whl)[test]
+#
+## Run smoke tests for aarch64 pull requests
+#if [[ "$(arch)" == "aarch64" && "${RAPIDS_BUILD_TYPE}" == "pull-request" ]]; then
+#    python ./ci/wheel_smoke_test_cuvs.py
+#else
+#    python -m pytest ./python/cuvs/cuvs/test
+#fi
diff --git a/docs/source/index.rst b/docs/source/index.rst
index a161efb42d..bf97906101 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,9 +1,5 @@
-cuVS: GPU-accelerated Vector Search
-===================================
-
-.. image:: ../../img/raft-tech-stack-vss.png
-  :width: 800
-  :alt: cuVS Tech Stack
+cuVS: Vector Search and Clustering on the GPU
+=============================================
 
 Useful Resources
 ################
@@ -19,7 +15,7 @@ Useful Resources
 What is cuVS?
 #############
 
-cuVS is a library for vector search on the GPU.
+cuVS is a library for vector search and clustering on the GPU.
 
 .. toctree::
    :maxdepth: 1

From 7ff6d70f79e8fe91772acaf831482ed718708984 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Wed, 28 Feb 2024 20:15:32 +0100
Subject: [PATCH 002/623] Adding necessary codeowner groups (#36)

Authors:
   - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
   - Ray Douglass (https://github.com/raydouglass)
---
 .github/CODEOWNERS | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 407c5448e5..01dbcfc835 100755
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -4,12 +4,18 @@ cpp/               @rapidsai/cuvs-cpp-codeowners
 #python code owners
 python/            @rapidsai/cuvs-python-codeowners
 
-#cmake code owners
-**/CMakeLists.txt  @rapidsai/cuvs-cmake-codeowners
-**/cmake/          @rapidsai/cuvs-cmake-codeowners
-python/setup.py    @rapidsai/cuvs-cmake-codeowners
-build.sh           @rapidsai/cuvs-cmake-codeowners
-**/build.sh        @rapidsai/cuvs-cmake-codeowners
+#rust code owners
+rust/              @rapidsai/cuvs-rust-codeowners
+
+#docs code owners
+docs/              @rapidsai/cuvs-docs-codeowners
+
+#build code owners
+**/CMakeLists.txt  @rapidsai/cuvs-build-codeowners
+**/cmake/          @rapidsai/cuvs-build-codeowners
+python/setup.py    @rapidsai/cuvs-build-codeowners
+build.sh           @rapidsai/cuvs-build-codeowners
+**/build.sh        @rapidsai/cuvs-build-codeowners
 
 #build/ops code owners
 .github/           @rapidsai/ops-codeowners

From 6040aa07c1b808769712f89e3077334a04a23bf7 Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <dante.gamadessavre@gmail.com>
Date: Mon, 4 Mar 2024 09:18:38 -0600
Subject: [PATCH 003/623] CAGRA Python API (#35)

PR does the following:

- [x] Initial check in of new CAGRA Python API
- [x] Initial check in of Cython using dlpack to call pylibcuvs
- [x] Initial check in of pytests of CAGRA and docstrings
- [x] Build updates for new files and algos
- [x] Enable Python conda package and pytests run in CI
- [x] Final cleanups

Todos:
- [x] Discussion regarding renaming `handle` to `resources` in Python API
- [x] Testing exceptions handling while using C API or design strategy to enable that
- [x] Finish todos that are not going to be solved in follow ups.

Follow up PRs:
- Moving cydlpack to RAFT.
- Enable serialization
- Enable update of dataset
- Python API for other algorithms

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/35
---
 .../cuda11.8-conda/devcontainer.json          |   2 +-
 .devcontainer/cuda11.8-pip/devcontainer.json  |   2 +-
 .../cuda12.0-conda/devcontainer.json          |   2 +-
 .devcontainer/cuda12.0-pip/devcontainer.json  |   2 +-
 .github/workflows/build.yaml                  |  12 +-
 .github/workflows/pr.yaml                     |  22 +-
 .github/workflows/test.yaml                   |   8 +-
 .pre-commit-config.yaml                       |   2 +-
 README.md                                     | 150 +++++-
 VERSION                                       |   2 +-
 build.sh                                      |  15 +-
 ci/build_docs.sh                              |  10 +-
 ci/build_python.sh                            |  13 +-
 ci/build_wheel.sh                             |   2 +
 ci/build_wheel_cuvs.sh                        |   4 +-
 ci/release/update-version.sh                  |   8 +-
 ci/test_python.sh                             |  23 +-
 ci/test_wheel_cuvs.sh                         |  18 +-
 ci/wheel_smoke_test_cuvs.py                   |  47 +-
 .../all_cuda-118_arch-aarch64.yaml            |  10 +-
 .../all_cuda-118_arch-x86_64.yaml             |  10 +-
 ...64.yaml => all_cuda-122_arch-aarch64.yaml} |  14 +-
 ..._64.yaml => all_cuda-122_arch-x86_64.yaml} |  14 +-
 conda/recipes/cuvs/build.sh                   |   2 +-
 conda/recipes/cuvs/meta.yaml                  |  10 +-
 ..._template.sh => build_libcuvs_examples.sh} |   4 +-
 conda/recipes/libcuvs/meta.yaml               |   6 +-
 cpp/CMakeLists.txt                            |  52 +-
 cpp/cmake/thirdparty/get_raft.cmake           |  33 +-
 cpp/doxygen/Doxyfile                          |   2 +-
 cpp/{template => examples}/CMakeLists.txt     |   0
 cpp/{template => examples}/README.md          |   8 +-
 cpp/{template => examples}/build.sh           |   2 +-
 .../cmake/thirdparty/fetch_rapids.cmake       |   4 +-
 .../cmake/thirdparty/get_cuvs.cmake           |   0
 .../src/cagra_example.cu                      |   0
 cpp/{template => examples}/src/common.cuh     |   2 +-
 .../cuvs/neighbors/{cagra_c.h => cagra.h}     |  72 +--
 cpp/src/neighbors/cagra_c.cpp                 |  58 +-
 cpp/test/CMakeLists.txt                       |   2 +-
 cpp/test/neighbors/ann_cagra_c.cu             |  12 +-
 cpp/test/neighbors/c_api.c                    |  10 +-
 dependencies.yaml                             | 174 +++---
 docs/source/build.md                          |   4 +-
 docs/source/conf.py                           |   4 +-
 docs/source/contributing.md                   |   8 +-
 docs/source/cpp_api/core_interop.rst          |   6 +-
 docs/source/developer_guide.md                |   8 +-
 fetch_rapids.cmake                            |  10 +-
 python/cuvs/CMakeLists.txt                    |  29 +-
 python/cuvs/README.md                         |   0
 python/cuvs/cuvs/__init__.py                  |   2 +-
 python/cuvs/cuvs/common/CMakeLists.txt        |  24 +
 python/cuvs/cuvs/common/__init__.pxd          |   0
 python/cuvs/cuvs/common/__init__.py           |  18 +
 python/cuvs/cuvs/common/c_api.pxd             |  32 ++
 python/cuvs/cuvs/common/cydlpack.pxd          |  70 +++
 python/cuvs/cuvs/common/cydlpack.pyx          | 102 ++++
 python/cuvs/cuvs/common/temp_raft.py          |  55 ++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |  15 +
 python/cuvs/cuvs/neighbors/__init__.pxd       |   0
 python/cuvs/cuvs/neighbors/__init__.py        |  18 +
 .../cuvs/cuvs/neighbors/cagra/CMakeLists.txt  |  24 +
 python/cuvs/cuvs/neighbors/cagra/__init__.pxd |   0
 python/cuvs/cuvs/neighbors/cagra/__init__.py  |  18 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |  95 ++++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    | 502 ++++++++++++++++++
 python/cuvs/cuvs/test/__init__py              |   0
 python/cuvs/cuvs/test/ann_utils.py            |  35 ++
 python/cuvs/cuvs/test/test_cagra.py           | 175 ++++++
 python/cuvs/cuvs/test/test_doctests.py        | 116 ++++
 python/cuvs/pyproject.toml                    |  32 +-
 python/cuvs/setup.cfg                         |   3 +-
 python/cuvs/setup.py                          |  37 --
 74 files changed, 1873 insertions(+), 414 deletions(-)
 rename conda/environments/{all_cuda-120_arch-aarch64.yaml => all_cuda-122_arch-aarch64.yaml} (85%)
 rename conda/environments/{all_cuda-120_arch-x86_64.yaml => all_cuda-122_arch-x86_64.yaml} (85%)
 rename conda/recipes/libcuvs/{build_libcuvs_template.sh => build_libcuvs_examples.sh} (61%)
 rename cpp/{template => examples}/CMakeLists.txt (100%)
 rename cpp/{template => examples}/README.md (82%)
 rename cpp/{template => examples}/build.sh (93%)
 rename cpp/{template => examples}/cmake/thirdparty/fetch_rapids.cmake (92%)
 rename cpp/{template => examples}/cmake/thirdparty/get_cuvs.cmake (100%)
 rename cpp/{template => examples}/src/cagra_example.cu (100%)
 rename cpp/{template => examples}/src/common.cuh (98%)
 rename cpp/include/cuvs/neighbors/{cagra_c.h => cagra.h} (79%)
 create mode 100644 python/cuvs/README.md
 create mode 100644 python/cuvs/cuvs/common/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/common/__init__.pxd
 create mode 100644 python/cuvs/cuvs/common/__init__.py
 create mode 100644 python/cuvs/cuvs/common/c_api.pxd
 create mode 100644 python/cuvs/cuvs/common/cydlpack.pxd
 create mode 100644 python/cuvs/cuvs/common/cydlpack.pyx
 create mode 100644 python/cuvs/cuvs/common/temp_raft.py
 create mode 100644 python/cuvs/cuvs/neighbors/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/cagra/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/cagra/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/cagra/cagra.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/cagra/cagra.pyx
 create mode 100644 python/cuvs/cuvs/test/__init__py
 create mode 100644 python/cuvs/cuvs/test/ann_utils.py
 create mode 100644 python/cuvs/cuvs/test/test_cagra.py
 create mode 100644 python/cuvs/cuvs/test/test_doctests.py
 delete mode 100644 python/cuvs/setup.py

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index 76ce8599a8..cefbea72b7 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.02-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 3a126b36ef..05518805aa 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.02-cpp-llvm16-cuda11.8-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda11.8-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
diff --git a/.devcontainer/cuda12.0-conda/devcontainer.json b/.devcontainer/cuda12.0-conda/devcontainer.json
index 426aaef98f..3f89836e26 100644
--- a/.devcontainer/cuda12.0-conda/devcontainer.json
+++ b/.devcontainer/cuda12.0-conda/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "12.0",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.02-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.04-cpp-mambaforge-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.0-pip/devcontainer.json
index 1ef2fdcb64..33f67cd3fb 100644
--- a/.devcontainer/cuda12.0-pip/devcontainer.json
+++ b/.devcontainer/cuda12.0-pip/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "12.0",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.02-cpp-llvm16-cuda12.0-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda12.0-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index f079b5e78a..34cf1f5b07 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -46,7 +46,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -57,7 +57,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.04
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -69,7 +69,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -79,7 +79,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index b190a2a62f..5799f51087 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -23,29 +23,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.04
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.04
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.04
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.04
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.04
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -53,19 +53,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.04
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.04
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.04
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -75,20 +75,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.04
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.04
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.04
     with:
       build_command: |
         sccache -z;
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index acea5755f0..0e66113f24 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9e3b1a38b3..a82fb74a57 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -99,7 +99,7 @@ repos:
                 args: ["--toml", "pyproject.toml"]
                 exclude: (?x)^(^CHANGELOG.md$)
       - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.5.1
+        rev: v1.8.0
         hooks:
             - id: rapids-dependency-file-generator
               args: ["--clean"]
diff --git a/README.md b/README.md
index 428ab04a95..dfba9eb4aa 100755
--- a/README.md
+++ b/README.md
@@ -1,19 +1,14 @@
 # <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuVS: Vector Search and Clustering on the GPU</div>
 
-### NOTE: cuVS is currently being 
-
 ## Contents
-<hr>
 
 1. [Useful Resources](#useful-resources)
 2. [What is cuVS?](#what-is-cuvs)
-3. [Getting Started](#getting-started)
-4. [Installing cuVS](#installing)
+3. [Installing cuVS](#installing)
+4. [Getting Started](#getting-started)
 5. [Contributing](#contributing)
 6. [References](#references)
 
-<hr>
-
 ## Useful Resources
 
 - [cuVS Reference Documentation](https://docs.rapids.ai/api/cuvs/stable/): API Documentation.
@@ -26,15 +21,152 @@
 
 ## What is cuVS?
 
-cuVS contains many algorithms for running approximate nearest neighbors and clustering on the GPU.
+cuVS contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.
+
+**Please note** that cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
+1. RAFT contains C++ and Python APIs for all of the approximate nearest neighbors and clustering algorithms.
+2. cuVS contains a growing support for different languages, including C, C++, Python, and Rust. We will be adding more language support to cuVS in the future but will not be improving the language support for RAFT.
+3. Once all of RAFT's approximate nearest neighbors and clustering algorithms are moved to cuVS, the RAFT APIs will be deprecated and eventually removed altogether. Once removed, RAFT will become a lightweight header-only library. In the meantime, there's no harm in using RAFT if support for additional languages is not needed.
+
+## Installing cuVS
+
+cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python). Different packages are available for the different languages supported by cuVS:
+
+| Python | C++ | C | Rust |
+|--------|-----|---|------|
+| `pycuvs`| `libcuvs` | `libcuvs_c` | `cuvs-rs` |
+
+### Stable release
+
+It is recommended to use [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) to install the desired packages. The following command will install the Python package. You can substitute `pycuvs` for any of the packages in the table above:
+```bash
+mamba install -c conda-forge -c nvidia -c rapidsai pycuvs
+```
+
+### Nightlies
+If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
+```bash
+mamba install -c conda-forge -c nvidia -c rapidsai-nightly pycuvs=24.04*
+```
+
+Please see the [Build and Install Guide](docs/source/build.md) for more information on installing cuVS and building from source.
 
 ## Getting Started
 
+The following code snippets train an approximate nearest neighbors index for the CAGRA algorithm.
 
+### Python API
+
+```python
+from cuvs.neighbors import cagra
+
+dataset = load_data()
+index_params = cagra.IndexParams()
+
+index = cagra.build_index(build_params, dataset)
+```
+
+### C++ API
+
+```c++
+#include <cuvs/neighbors/cagra.hpp>
+
+using namespace cuvs::neighbors;
+
+raft::device_matrix_view<float> dataset = load_dataset();
+raft::device_resources res;
+
+cagra::index_params index_params;
+
+auto index = cagra::build(res, index_params, dataset);
+```
+
+For more example of the C++ APIs, refer to [cpp/examples](https://github.com/rapidsai/cuvs/tree/HEAD/cpp/examples) directory in the codebase.
+
+### C API
+
+```c
+#include <cuvs/neighbors/cagra.h>
+
+cuvsResources_t res;
+cuvsCagraIndexParams_t index_params;
+cuvsCagraIndex_t index;
+
+DLManagedTensor *dataset;
+load_dataset(dataset);
+
+cuvsResourcesCreate(&res);
+cuvsCagraIndexParamsCreate(&index_params);
+cuvsCagraIndexCreate(&index);
+
+cuvsCagraBuild(res, index_params, dataset, index);
+
+cuvsCagraIndexDestroy(index);
+cuvsCagraIndexParamsDestroy(index_params);
+cuvsResourcesDestroy(res);
+```
 
-## Installing cuVS
 
 ## Contributing
 
+If you are interested in contributing to the cuVS library, please read our [Contributing guidelines](docs/source/contributing.md). Refer to the [Developer Guide](docs/source/developer_guide.md) for details on the developer guidelines, workflows, and principals.
+
 ## References
 
+When citing cuVS generally, please consider referencing this Github repository.
+```bibtex
+@misc{rapidsai,
+  title={Rapidsai/cuVS: Vector Search and Clustering on the GPU.},
+  url={https://github.com/rapidsai/cuvs},
+  journal={GitHub},
+  publisher={Nvidia RAPIDS},
+  author={Rapidsai},
+  year={2024}
+}
+```
+
+If citing CAGRA, please consider the following bibtex:
+```bibtex
+@misc{ootomo2023cagra,
+      title={CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs},
+      author={Hiroyuki Ootomo and Akira Naruse and Corey Nolet and Ray Wang and Tamas Feher and Yong Wang},
+      year={2023},
+      eprint={2308.15136},
+      archivePrefix={arXiv},
+      primaryClass={cs.DS}
+}
+```
+
+If citing the k-selection routines, please consider the following bibtex:
+```bibtex
+@proceedings{10.1145/3581784,
+    title = {SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
+    year = {2023},
+    isbn = {9798400701092},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    abstract = {Started in 1988, the SC Conference has become the annual nexus for researchers and practitioners from academia, industry and government to share information and foster collaborations to advance the state of the art in High Performance Computing (HPC), Networking, Storage, and Analysis.},
+    location = {, Denver, CO, USA, }
+}
+```
+
+If citing the nearest neighbors descent API, please consider the following bibtex:
+```bibtex
+@inproceedings{10.1145/3459637.3482344,
+    author = {Wang, Hui and Zhao, Wan-Lei and Zeng, Xiangxiang and Yang, Jianye},
+    title = {Fast K-NN Graph Construction by GPU Based NN-Descent},
+    year = {2021},
+    isbn = {9781450384469},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3459637.3482344},
+    doi = {10.1145/3459637.3482344},
+    abstract = {NN-Descent is a classic k-NN graph construction approach. It is still widely employed in machine learning, computer vision, and information retrieval tasks due to its efficiency and genericness. However, the current design only works well on CPU. In this paper, NN-Descent has been redesigned to adapt to the GPU architecture. A new graph update strategy called selective update is proposed. It reduces the data exchange between GPU cores and GPU global memory significantly, which is the processing bottleneck under GPU computation architecture. This redesign leads to full exploitation of the parallelism of the GPU hardware. In the meantime, the genericness, as well as the simplicity of NN-Descent, are well-preserved. Moreover, a procedure that allows to k-NN graph to be merged efficiently on GPU is proposed. It makes the construction of high-quality k-NN graphs for out-of-GPU-memory datasets tractable. Our approach is 100-250\texttimes{} faster than the single-thread NN-Descent and is 2.5-5\texttimes{} faster than the existing GPU-based approaches as we tested on million as well as billion scale datasets.},
+    booktitle = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
+    pages = {1929–1938},
+    numpages = {10},
+    keywords = {high-dimensional, nn-descent, gpu, k-nearest neighbor graph},
+    location = {Virtual Event, Queensland, Australia},
+    series = {CIKM '21}
+}
+```
diff --git a/VERSION b/VERSION
index 3c6c5e2b70..4a2fe8aa57 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.02.00
+24.04.00
diff --git a/build.sh b/build.sh
index 6dd250c51c..c6e09c5bf9 100755
--- a/build.sh
+++ b/build.sh
@@ -18,7 +18,7 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python docs tests template clean --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python docs tests examples clean --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
@@ -27,7 +27,7 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    python           - build the cuvs Python package
    docs             - build the documentation
    tests            - build the tests
-   template         - build the example CUVS application template
+   examples         - build the examples
 
  and <flag> is:
    -v                          - verbose build mode
@@ -60,7 +60,7 @@ BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR} ${CUVS_DASK_BUILD_DIR}"
 CMAKE_LOG_LEVEL=""
 VERBOSE_FLAG=""
 BUILD_ALL_GPU_ARCH=0
-BUILD_TESTS=OFF
+BUILD_TESTS=ON
 BUILD_TYPE=Release
 COMPILE_LIBRARY=OFF
 INSTALL_TARGET=install
@@ -305,6 +305,9 @@ if [[ ${CMAKE_TARGET} == "" ]]; then
 fi
 
 # Append `-DFIND_CUVS_CPP=ON` to EXTRA_CMAKE_ARGS unless a user specified the option.
+
+
+
 SKBUILD_EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS}"
 if [[ "${EXTRA_CMAKE_ARGS}" != *"DFIND_CUVS_CPP"* ]]; then
     SKBUILD_EXTRA_CMAKE_ARGS="${SKBUILD_EXTRA_CMAKE_ARGS} -DFIND_CUVS_CPP=ON"
@@ -433,10 +436,10 @@ if hasArg docs; then
 fi
 
 ################################################################################
-# Initiate build for example CUVS application template (if needed)
+# Initiate build for c++ examples (if needed)
 
-if hasArg template; then
-    pushd ${REPODIR}/cpp/template
+if hasArg examples; then
+    pushd ${REPODIR}/cpp/examples
     ./build.sh
     popd
 fi
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index 1193285da5..983e973854 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -18,17 +18,13 @@ rapids-print-env
 
 rapids-logger "Downloading artifacts from previous jobs"
 CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+#PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
 
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
-  --channel "${PYTHON_CHANNEL}" \
-  libcuvs \
-  libcuvs-headers \
-  cuvs \
-  raft-dask
+  libcuvs
 
-export RAPIDS_VERSION_NUMBER="24.02"
+export RAPIDS_VERSION_NUMBER="24.04"
 export RAPIDS_DOCS_DIR="$(mktemp -d)"
 
 rapids-logger "Build CPP docs"
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 525d21c513..c50a413c6e 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -15,18 +15,17 @@ rapids-print-env
 
 rapids-logger "Begin py build"
 
+package_name="cuvs"
+package_dir="python"
+
 CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
 
 version=$(rapids-generate-version)
 git_commit=$(git rev-parse HEAD)
-export RAPIDS_PACKAGE_VERSION=${version} 
+export RAPIDS_PACKAGE_VERSION=${version}
 echo "${version}" > VERSION
 
-package_dir="python"
-for package_name in cuvs raft-dask; do
-  underscore_package_name=$(echo "${package_name}" | tr "-" "_")
-  sed -i "/^__git_commit__/ s/= .*/= \"${git_commit}\"/g" "${package_dir}/${package_name}/${underscore_package_name}/_version.py"
-done
+sed -i "/^__git_commit__/ s/= .*/= \"${git_commit}\"/g" "${package_dir}/${package_name}/${package_name}/_version.py"
 
 # TODO: Remove `--no-test` flags once importing on a CPU
 # node works correctly
@@ -34,3 +33,5 @@ rapids-conda-retry mambabuild \
   --no-test \
   --channel "${CPP_CHANNEL}" \
   conda/recipes/cuvs
+
+rapids-upload-conda-to-s3 python
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 3cc7f06c83..4f9f96d19c 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -37,6 +37,8 @@ if ! rapids-is-release-build; then
 fi
 
 sed -r -i "s/rmm(.*)\"/rmm${PACKAGE_CUDA_SUFFIX}\1${alpha_spec}\"/g" ${pyproject_file}
+sed -r -i "s/pylibraft(.*)\"/pylibraft${PACKAGE_CUDA_SUFFIX}\1${alpha_spec}\"/g" ${pyproject_file}
+
 
 if [[ $PACKAGE_CUDA_SUFFIX == "-cu12" ]]; then
     sed -i "s/cuda-python[<=>\.,0-9a]*/cuda-python>=12.0,<13.0a0/g" ${pyproject_file}
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index b4765be38a..238483b6e5 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -4,6 +4,6 @@
 set -euo pipefail
 
 # Set up skbuild options. Enable sccache in skbuild config options
-export SKBUILD_CONFIGURE_OPTIONS="-DRAFT_BUILD_WHEELS=ON -DDETECT_CONDA_ENV=OFF -DFIND_RAFT_CPP=OFF"
+export SKBUILD_CONFIGURE_OPTIONS="-DDETECT_CONDA_ENV=OFF -DFIND_CUVS_CPP=OFF"
 
-#ci/build_wheel.sh cuvs python/cuvs
+ci/build_wheel.sh cuvs python/cuvs
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index f463aeb65b..b122e73bdf 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 ########################
 # RAFT Version Updater #
 ########################
@@ -38,7 +38,7 @@ function sed_runner() {
 
 sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" cpp/CMakeLists.txt
 sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" cpp/template/cmake/thirdparty/fetch_rapids.cmake
-sed_runner "s/set(RAFT_VERSION .*)/set(RAFT_VERSION \"${NEXT_FULL_TAG}\")/g" cpp/CMakeLists.txt
+sed_runner "s/set(CUVS_VERSION .*)/set(CUVS_VERSION \"${NEXT_FULL_TAG}\")/g" cpp/CMakeLists.txt
 sed_runner 's/'"cuvs_version .*)"'/'"cuvs_version ${NEXT_FULL_TAG})"'/g' python/cuvs/CMakeLists.txt
 sed_runner 's/'"branch-.*\/RAPIDS.cmake"'/'"branch-${NEXT_SHORT_TAG}\/RAPIDS.cmake"'/g' fetch_rapids.cmake
 
@@ -57,6 +57,8 @@ DEPENDENCIES=(
   cuvs
   cuvs-cu11
   cuvs-cu12
+  pylibraft-cu11
+  pylibraft-cu12
   rmm
   rmm-cu11
   rmm-cu12
@@ -85,7 +87,7 @@ sed_runner "s/RAPIDS_VERSION_NUMBER=\".*/RAPIDS_VERSION_NUMBER=\"${NEXT_SHORT_TA
 
 sed_runner "/^PROJECT_NUMBER/ s|\".*\"|\"${NEXT_SHORT_TAG}\"|g" cpp/doxygen/Doxyfile
 
-sed_runner "/^set(RAFT_VERSION/ s|\".*\"|\"${NEXT_SHORT_TAG}\"|g" docs/source/build.md
+sed_runner "/^set(CUVS_VERSION/ s|\".*\"|\"${NEXT_SHORT_TAG}\"|g" docs/source/build.md
 sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/build.md
 sed_runner "/rapidsai\/raft/ s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/developer_guide.md
 
diff --git a/ci/test_python.sh b/ci/test_python.sh
index 9f0c9d6ee7..17fbed4243 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -31,7 +31,7 @@ rapids-print-env
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
   --channel "${PYTHON_CHANNEL}" \
-  libcuvs #cuvs
+  libcuvs cuvs
 
 rapids-logger "Check GPU usage"
 nvidia-smi
@@ -40,17 +40,16 @@ EXITCODE=0
 trap "EXITCODE=1" ERR
 set +e
 
-#rapids-logger "pytest cuvs"
-#pushd python/cuvs/cuvs
-#pytest \
-#  --cache-clear \
-#  --junitxml="${RAPIDS_TESTS_DIR}/junit-cuvs.xml" \
-#  --cov-config=../.coveragerc \
-#  --cov=cuvs \
-#  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cuvs-coverage.xml" \
-#  --cov-report=term \
-#  test
-#popd
+rapids-logger "pytest cuvs"
+pushd python/cuvs/cuvs
+pytest \
+ --cache-clear \
+ --junitxml="${RAPIDS_TESTS_DIR}/junit-cuvs.xml" \
+ --cov-config=../.coveragerc \
+ --cov=cuvs \
+ --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cuvs-coverage.xml" \
+ --cov-report=term \
+ test
 
 rapids-logger "Test script exiting with value: $EXITCODE"
 exit ${EXITCODE}
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index de75019155..364e00a7cf 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -7,12 +7,12 @@ mkdir -p ./dist
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
 RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./dist
 
-## echo to expand wildcard before adding `[extra]` requires for pip
-#python -m pip install $(echo ./dist/cuvs*.whl)[test]
-#
-## Run smoke tests for aarch64 pull requests
-#if [[ "$(arch)" == "aarch64" && "${RAPIDS_BUILD_TYPE}" == "pull-request" ]]; then
-#    python ./ci/wheel_smoke_test_cuvs.py
-#else
-#    python -m pytest ./python/cuvs/cuvs/test
-#fi
+# echo to expand wildcard before adding `[extra]` requires for pip
+python -m pip install $(echo ./dist/cuvs*.whl)[test]
+
+# Run smoke tests for aarch64 pull requests
+if [[ "$(arch)" == "aarch64" && "${RAPIDS_BUILD_TYPE}" == "pull-request" ]]; then
+   python ./ci/wheel_smoke_test_cuvs.py
+else
+   python -m pytest ./python/cuvs/cuvs/test
+fi
diff --git a/ci/wheel_smoke_test_cuvs.py b/ci/wheel_smoke_test_cuvs.py
index 65b5fb8b2e..26ab5f6b50 100644
--- a/ci/wheel_smoke_test_cuvs.py
+++ b/ci/wheel_smoke_test_cuvs.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,41 +13,30 @@
 # limitations under the License.
 #
 
+import cupy as cp
 import numpy as np
-from scipy.spatial.distance import cdist
 
-from pylibrat.common import Handle, Stream, device_ndarray
-from cuvs.distance import pairwise_distance
+from cuvs.neighbors import cagra
+from pylibraft.common import Stream, DeviceResources
 
 
 if __name__ == "__main__":
-    metric = "euclidean"
-    n_rows = 1337
-    n_cols = 1337
+    n_samples = 1000
+    n_features = 50
+    n_queries = 1000
+    k = 10
 
-    input1 = np.random.random_sample((n_rows, n_cols))
-    input1 = np.asarray(input1, order="C").astype(np.float64)
+    dataset = cp.random.random_sample((n_samples,
+                                       n_features)).astype(cp.float32)
 
-    output = np.zeros((n_rows, n_rows), dtype=np.float64)
+    build_params = cagra.IndexParams(metric="sqeuclidean",
+                                     build_algo="nn_descent")
 
-    expected = cdist(input1, input1, metric)
+    index = cagra.build_index(build_params, dataset)
 
-    expected[expected <= 1e-5] = 0.0
+    distances, neighbors = cagra.search(cagra.SearchParams(),
+                                          index, dataset,
+                                          k)
 
-    input1_device = device_ndarray(input1)
-    output_device = None
-
-    s2 = Stream()
-    handle = Handle(stream=s2)
-    ret_output = pairwise_distance(
-        input1_device, input1_device, output_device, metric, handle=handle
-    )
-    handle.sync()
-
-    output_device = ret_output
-
-    actual = output_device.copy_to_host()
-
-    actual[actual <= 1e-5] = 0.0
-
-    assert np.allclose(expected, actual, rtol=1e-4)
+    distances = cp.asarray(distances)
+    neighbors = cp.asarray(neighbors)
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index dbf92ec475..e4f9223784 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -36,18 +36,18 @@ dependencies:
 - libcusparse=11.7.5.86
 - nccl>=2.9.9
 - ninja
-- numpy>=1.21
+- numpy>=1.23
 - numpydoc
 - nvcc_linux-aarch64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pytest
+- pylibraft==24.4.*
 - pytest-cov
+- pytest==7.*
 - recommonmark
-- rmm==24.2.*
-- scikit-build>=0.13.1
+- rmm==24.4.*
+- scikit-build-core>=0.7.0
 - scikit-learn
-- scipy
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sysroot_linux-aarch64==2.17
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 2fe184f964..a26314b229 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -36,18 +36,18 @@ dependencies:
 - libcusparse=11.7.5.86
 - nccl>=2.9.9
 - ninja
-- numpy>=1.21
+- numpy>=1.23
 - numpydoc
 - nvcc_linux-64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pytest
+- pylibraft==24.4.*
 - pytest-cov
+- pytest==7.*
 - recommonmark
-- rmm==24.2.*
-- scikit-build>=0.13.1
+- rmm==24.4.*
+- scikit-build-core>=0.7.0
 - scikit-learn
-- scipy
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sysroot_linux-64==2.17
diff --git a/conda/environments/all_cuda-120_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
similarity index 85%
rename from conda/environments/all_cuda-120_arch-aarch64.yaml
rename to conda/environments/all_cuda-122_arch-aarch64.yaml
index 1b7f3908ad..91d55917ad 100644
--- a/conda/environments/all_cuda-120_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.0,<13.0a0
-- cuda-version=12.0
+- cuda-version=12.2
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -33,18 +33,18 @@ dependencies:
 - libcusparse-dev
 - nccl>=2.9.9
 - ninja
-- numpy>=1.21
+- numpy>=1.23
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pytest
+- pylibraft==24.4.*
 - pytest-cov
+- pytest==7.*
 - recommonmark
-- rmm==24.2.*
-- scikit-build>=0.13.1
+- rmm==24.4.*
+- scikit-build-core>=0.7.0
 - scikit-learn
-- scipy
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sysroot_linux-aarch64==2.17
-name: all_cuda-120_arch-aarch64
+name: all_cuda-122_arch-aarch64
diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
similarity index 85%
rename from conda/environments/all_cuda-120_arch-x86_64.yaml
rename to conda/environments/all_cuda-122_arch-x86_64.yaml
index 3352279942..f27d131ffa 100644
--- a/conda/environments/all_cuda-120_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.0,<13.0a0
-- cuda-version=12.0
+- cuda-version=12.2
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -33,18 +33,18 @@ dependencies:
 - libcusparse-dev
 - nccl>=2.9.9
 - ninja
-- numpy>=1.21
+- numpy>=1.23
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pytest
+- pylibraft==24.4.*
 - pytest-cov
+- pytest==7.*
 - recommonmark
-- rmm==24.2.*
-- scikit-build>=0.13.1
+- rmm==24.4.*
+- scikit-build-core>=0.7.0
 - scikit-learn
-- scipy
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sysroot_linux-64==2.17
-name: all_cuda-120_arch-x86_64
+name: all_cuda-122_arch-x86_64
diff --git a/conda/recipes/cuvs/build.sh b/conda/recipes/cuvs/build.sh
index 81f762068b..767d066722 100644
--- a/conda/recipes/cuvs/build.sh
+++ b/conda/recipes/cuvs/build.sh
@@ -2,4 +2,4 @@
 #!/usr/bin/env bash
 
 # This assumes the script is executed from the root of the repo directory
-./build.sh python --no-nvtx
+./build.sh python --no-nvtx -v
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index f22bd01d51..0902dd15a5 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 # Usage:
 #   conda build . -c conda-forge -c numba -c rapidsai -c pytorch
@@ -46,19 +46,19 @@ requirements:
     {% endif %}
     - cuda-version ={{ cuda_version }}
     - cython >=3.0.0
-    - pylibraft {{ version }}
+    - dlpack >=0.8
+    - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
-    - numpy >=1.21
     - python x.x
     - rmm ={{ minor_version }}
-    - scikit-build >=0.13.1
+    - scikit-build-core >=0.7.0
     - setuptools
   run:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major == "11" %}
     - cudatoolkit
     {% endif %}
-    - pylibraft {{ version }}
+    - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
     - python x.x
     - rmm ={{ minor_version }}
diff --git a/conda/recipes/libcuvs/build_libcuvs_template.sh b/conda/recipes/libcuvs/build_libcuvs_examples.sh
similarity index 61%
rename from conda/recipes/libcuvs/build_libcuvs_template.sh
rename to conda/recipes/libcuvs/build_libcuvs_examples.sh
index bd7719af76..6286a530e6 100644
--- a/conda/recipes/libcuvs/build_libcuvs_template.sh
+++ b/conda/recipes/libcuvs/build_libcuvs_examples.sh
@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 # Just building template so we verify it uses libraft.so and fail if it doesn't build
-./build.sh template
+./build.sh examples
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index 4e437f3e62..4b6ff87e97 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -195,9 +195,9 @@ outputs:
       home: https://rapids.ai/
       license: Apache-2.0
       summary: libcuvs tests
-  - name: libcuvs-template
+  - name: libcuvs-examples
     version: {{ version }}
-    script: build_libcuvs_template.sh
+    script: build_libcuvs_examples.sh
     build:
       script_env: *script_env
       number: {{ GIT_DESCRIBE_NUMBER }}
@@ -241,4 +241,4 @@ outputs:
     about:
       home: https://rapids.ai/
       license: Apache-2.0
-      summary: libcuvs template
+      summary: libcuvs examples
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 3c9cd6d1b7..73dec92a22 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -10,8 +10,8 @@
 # is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
-set(RAPIDS_VERSION "24.02")
-set(CUVS_VERSION "24.02.00")
+set(RAPIDS_VERSION "24.04")
+set(CUVS_VERSION "24.04.00")
 
 cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
 include(../fetch_rapids.cmake)
@@ -28,12 +28,12 @@ set(lang_list "CXX")
 
 if(NOT BUILD_CPU_ONLY)
   include(rapids-cuda)
-  rapids_cuda_init_architectures(cuVS)
+  rapids_cuda_init_architectures(CUVS)
   list(APPEND lang_list "CUDA")
 endif()
 
 project(
-  cuVS
+  CUVS
   VERSION ${CUVS_VERSION}
   LANGUAGES ${lang_list}
 )
@@ -62,6 +62,7 @@ option(CUDA_ENABLE_LINEINFO
        "Enable the -lineinfo option for nvcc (useful for cuda-memcheck / profiler)" OFF
 )
 option(CUDA_STATIC_RUNTIME "Statically link the CUDA toolkit runtime and libraries" OFF)
+option(CUVS_USE_RAFT_STATIC "Build and statically link the RAFT libraries" OFF)
 option(CUDA_LOG_COMPILE_TIME "Write a log of compilation times to nvcc_compile_log.csv" OFF)
 option(DETECT_CONDA_ENV "Enable detection of conda environment for dependencies" ON)
 option(DISABLE_DEPRECATION_WARNINGS "Disable deprecaction warnings " ON)
@@ -102,6 +103,7 @@ message(VERBOSE "cuVS: Enable nvtx markers: ${CUVS_NVTX}")
 message(VERBOSE
         "cuVS: Statically link the CUDA toolkit runtime and libraries: ${CUDA_STATIC_RUNTIME}"
 )
+message(VERBOSE "cuVS: Build and statically link RAFT libraries: ${CUVS_USE_RAFT_STATIC}")
 
 # Set RMM logging level
 set(RMM_LOGGING_LEVEL
@@ -214,7 +216,16 @@ target_include_directories(
 
 if(NOT BUILD_CPU_ONLY)
   # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
-  target_link_libraries(cuvs PUBLIC raft::raft raft::compiled nvidia::cutlass::cutlass)
+  target_link_libraries(cuvs
+        PUBLIC
+            rmm::rmm
+            $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::raft>
+            $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::compiled>
+        PRIVATE
+            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
+            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::compiled_static>
+            nvidia::cutlass::cutlass
+    )
 endif()
 
 # Endian detection
@@ -269,11 +280,14 @@ endif()
 
 set_target_properties(
   cuvs
-  PROPERTIES CXX_STANDARD 17
-             CXX_STANDARD_REQUIRED ON
-             CUDA_STANDARD 17
-             CUDA_STANDARD_REQUIRED ON
-             POSITION_INDEPENDENT_CODE ON
+  PROPERTIES BUILD_RPATH                         "\$ORIGIN"
+             INSTALL_RPATH                       "\$ORIGIN"
+             CXX_STANDARD                        17
+             CXX_STANDARD_REQUIRED               ON
+             CUDA_STANDARD                       17
+             CUDA_STANDARD_REQUIRED              ON
+             INTERFACE_POSITION_INDEPENDENT_CODE ON
+             POSITION_INDEPENDENT_CODE           ON
 )
 
 target_compile_options(
@@ -292,10 +306,13 @@ if(BUILD_C_LIBRARY)
 
   set_target_properties(
     cuvs_c
-    PROPERTIES CXX_STANDARD 17
-               CXX_STANDARD_REQUIRED ON
-               POSITION_INDEPENDENT_CODE ON
-               EXPORT_NAME c_api
+    PROPERTIES BUILD_RPATH                         "\$ORIGIN"
+               INSTALL_RPATH                       "\$ORIGIN"
+               CXX_STANDARD                        17
+               CXX_STANDARD_REQUIRED               ON
+               POSITION_INDEPENDENT_CODE           ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+               EXPORT_NAME                         c_api
   )
 
   target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
@@ -306,7 +323,12 @@ if(BUILD_C_LIBRARY)
     INTERFACE "$<INSTALL_INTERFACE:include>"
   )
 
-  target_link_libraries(cuvs_c PUBLIC cuvs::cuvs)
+  target_link_libraries(cuvs_c
+        PUBLIC
+            cuvs::cuvs
+        PRIVATE
+            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
+    )
 
   # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
   target_link_options(cuvs_c PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index d57d27312f..ace0165f71 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -21,9 +21,24 @@ function(find_and_configure_raft)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
+    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "branch-${CUML_BRANCH_VERSION_raft}")
+        message(STATUS "cuVS: RAFT pinned tag found: ${PKG_PINNED_TAG}. Cloning raft locally.")
+        set(CPM_DOWNLOAD_raft ON)
+    elseif(PKG_USE_RAFT_STATIC AND (NOT CPM_raft_SOURCE))
+        message(STATUS "cuVS: Cloning raft locally to build static libraries.")
+        set(CPM_DOWNLOAD_raft ON)
+    endif()
+
     set(RAFT_COMPONENTS "")
+
     if(PKG_COMPILE_LIBRARY)
+      if(NOT PKG_USE_RAFT_STATIC)
         string(APPEND RAFT_COMPONENTS " compiled")
+        set(RAFT_COMPILED_LIB raft::compiled PARENT_SCOPE)
+      else()
+        string(APPEND RAFT_COMPONENTS " compiled_static")
+        set(RAFT_COMPILED_LIB raft::compiled_static PARENT_SCOPE)
+      endif()
     endif()
 
     if(PKG_ENABLE_MNMG_DEPENDENCIES)
@@ -39,15 +54,15 @@ function(find_and_configure_raft)
             INSTALL_EXPORT_SET  cuvs-exports
             COMPONENTS          ${RAFT_COMPONENTS}
             CPM_ARGS
-            GIT_REPOSITORY https://github.com/${PKG_FORK}/raft.git
-            GIT_TAG        ${PKG_PINNED_TAG}
-            SOURCE_SUBDIR  cpp
-            OPTIONS
-            "BUILD_TESTS OFF"
-            "BUILD_PRIMS_BENCH OFF"
-            "BUILD_ANN_BENCH OFF"
-            "RAFT_NVTX ${PKG_ENABLE_NVTX}"
-            "RAFT_COMPILE_LIBRARY ${PKG_COMPILE_LIBRARY}"
+              GIT_REPOSITORY        https://github.com/${PKG_FORK}/raft.git
+              GIT_TAG               ${PKG_PINNED_TAG}
+              SOURCE_SUBDIR         cpp
+              OPTIONS
+              "BUILD_TESTS OFF"
+              "BUILD_PRIMS_BENCH OFF"
+              "BUILD_ANN_BENCH OFF"
+              "RAFT_NVTX ${PKG_ENABLE_NVTX}"
+              "RAFT_COMPILE_LIBRARY ${PKG_COMPILE_LIBRARY}"
             )
 endfunction()
 
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index 0a2c7f8f61..94304afe02 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -38,7 +38,7 @@ PROJECT_NAME           = "cuVS C++ API"
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = "24.02"
+PROJECT_NUMBER         = "24.04"
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a
diff --git a/cpp/template/CMakeLists.txt b/cpp/examples/CMakeLists.txt
similarity index 100%
rename from cpp/template/CMakeLists.txt
rename to cpp/examples/CMakeLists.txt
diff --git a/cpp/template/README.md b/cpp/examples/README.md
similarity index 82%
rename from cpp/template/README.md
rename to cpp/examples/README.md
index 5393c02296..125c6dba22 100644
--- a/cpp/template/README.md
+++ b/cpp/examples/README.md
@@ -1,14 +1,14 @@
-# Example CUVS Project Template
+# cuVS C++ Examples
 
 This template project provides a drop-in sample to either start building a new application with, or using CUVS in an existing CMake project. 
 
-First, please refer to our [installation docs](https://docs.rapids.ai/api/cuvs/stable/build.html#cuda-gpu-requirements) for the minimum requirements to use CUVS.
+First, please refer to our [installation docs](https://docs.rapids.ai/api/cuvs/stable/build.html#cuda-gpu-requirements) for the minimum requirements to use cuVS.
 
 Once the minimum requirements are satisfied, this example template application can be built with the provided `build.sh` script. This is a bash script that calls the appropriate CMake commands, so you can look into it to see the typical CMake based build workflow.  
 
-This directory (`CUVS_SOURCE/cpp/template`) can be copied directly in order to build a new application with CUVS.
+This directory (`CUVS_SOURCE/cpp/examples`) can be copied directly in order to build a new application with CUVS.
 
-CUVS can be integrated into an existing CMake project by copying the contents in the `configure rapids-cmake` and `configure cuvs` sections of the provided `CMakeLists.txt` into your project, along with `cmake/thirdparty/get_cuvs.cmake`. 
+cuVS can be integrated into an existing CMake project by copying the contents in the `configure rapids-cmake` and `configure cuvs` sections of the provided `CMakeLists.txt` into your project, along with `cmake/thirdparty/get_cuvs.cmake`. 
 
 Make sure to link against the appropriate Cmake targets. Use `cuvs::cuvs` to utilize the shared library.
 
diff --git a/cpp/template/build.sh b/cpp/examples/build.sh
similarity index 93%
rename from cpp/template/build.sh
rename to cpp/examples/build.sh
index 25ccb34618..7a948d9a8a 100755
--- a/cpp/template/build.sh
+++ b/cpp/examples/build.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 
 # cuvs empty project template build script
 
diff --git a/cpp/template/cmake/thirdparty/fetch_rapids.cmake b/cpp/examples/cmake/thirdparty/fetch_rapids.cmake
similarity index 92%
rename from cpp/template/cmake/thirdparty/fetch_rapids.cmake
rename to cpp/examples/cmake/thirdparty/fetch_rapids.cmake
index 15b6c43a6f..c22f586cad 100644
--- a/cpp/template/cmake/thirdparty/fetch_rapids.cmake
+++ b/cpp/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and RAFT versions
-set(RAPIDS_VERSION "24.02")
+set(RAPIDS_VERSION "24.04")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/cpp/template/cmake/thirdparty/get_cuvs.cmake b/cpp/examples/cmake/thirdparty/get_cuvs.cmake
similarity index 100%
rename from cpp/template/cmake/thirdparty/get_cuvs.cmake
rename to cpp/examples/cmake/thirdparty/get_cuvs.cmake
diff --git a/cpp/template/src/cagra_example.cu b/cpp/examples/src/cagra_example.cu
similarity index 100%
rename from cpp/template/src/cagra_example.cu
rename to cpp/examples/src/cagra_example.cu
diff --git a/cpp/template/src/common.cuh b/cpp/examples/src/common.cuh
similarity index 98%
rename from cpp/template/src/common.cuh
rename to cpp/examples/src/common.cuh
index 0b72d3bf3b..757123cea5 100644
--- a/cpp/template/src/common.cuh
+++ b/cpp/examples/src/common.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/cpp/include/cuvs/neighbors/cagra_c.h b/cpp/include/cuvs/neighbors/cagra.h
similarity index 79%
rename from cpp/include/cuvs/neighbors/cagra_c.h
rename to cpp/include/cuvs/neighbors/cagra.h
index 59861b5029..64a26b9243 100644
--- a/cpp/include/cuvs/neighbors/cagra_c.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -33,7 +33,7 @@ extern "C" {
  * @brief Enum to denote which ANN algorithm is used to build CAGRA graph
  *
  */
-enum cagraGraphBuildAlgo {
+enum cuvsCagraGraphBuildAlgo {
   /* Use IVF-PQ to build all-neighbors knn graph */
   IVF_PQ,
   /* Experimental, use NN-Descent to build all-neighbors knn graph */
@@ -44,18 +44,18 @@ enum cagraGraphBuildAlgo {
  * @brief Supplemental parameters to build CAGRA Index
  *
  */
-struct cagraIndexParams {
+struct cuvsCagraIndexParams {
   /** Degree of input graph for pruning. */
   size_t intermediate_graph_degree;
   /** Degree of output graph. */
   size_t graph_degree;
   /** ANN algorithm to build knn graph. */
-  enum cagraGraphBuildAlgo build_algo;
+  enum cuvsCagraGraphBuildAlgo build_algo;
   /** Number of Iterations to run if building with NN_DESCENT */
   size_t nn_descent_niter;
 };
 
-typedef struct cagraIndexParams* cuvsCagraIndexParams_t;
+typedef struct cuvsCagraIndexParams* cuvsCagraIndexParams_t;
 
 /**
  * @brief Allocate CAGRA Index params, and populate with default values
@@ -77,7 +77,7 @@ cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t index);
  * @brief Enum to denote algorithm used to search CAGRA Index
  *
  */
-enum cagraSearchAlgo {
+enum cuvsCagraSearchAlgo {
   /** For large batch sizes. */
   SINGLE_CTA,
   /** For small batch sizes. */
@@ -90,13 +90,13 @@ enum cagraSearchAlgo {
  * @brief Enum to denote Hash Mode used while searching CAGRA index
  *
  */
-enum cagraHashMode { HASH, SMALL, AUTO_HASH };
+enum cuvsCagraHashMode { HASH, SMALL, AUTO_HASH };
 
 /**
  * @brief Supplemental parameters to search CAGRA index
  *
  */
-struct cagraSearchParams {
+struct cuvsCagraSearchParams {
   /** Maximum number of queries to search at the same time (batch size). Auto select when 0.*/
   size_t max_queries;
 
@@ -114,7 +114,7 @@ struct cagraSearchParams {
   // Reasonable default values are automatically chosen.
 
   /** Which search implementation to use. */
-  enum cagraSearchAlgo algo;
+  enum cuvsCagraSearchAlgo algo;
 
   /** Number of threads used to calculate a single distance. 4, 8, 16, or 32. */
   size_t team_size;
@@ -128,7 +128,7 @@ struct cagraSearchParams {
   /** Thread block size. 0, 64, 128, 256, 512, 1024. Auto selection when 0. */
   size_t thread_block_size;
   /** Hashmap type. Auto selection when AUTO. */
-  enum cagraHashMode hashmap_mode;
+  enum cuvsCagraHashMode hashmap_mode;
   /** Lower limit of hashmap bit length. More than 8. */
   size_t hashmap_min_bitlen;
   /** Upper limit of hashmap fill rate. More than 0.1, less than 0.9.*/
@@ -140,7 +140,7 @@ struct cagraSearchParams {
   uint64_t rand_xor_mask;
 };
 
-typedef struct cagraSearchParams* cuvsCagraSearchParams_t;
+typedef struct cuvsCagraSearchParams* cuvsCagraSearchParams_t;
 
 /**
  * @brief Allocate CAGRA search params, and populate with default values
@@ -166,24 +166,24 @@ typedef struct {
   uintptr_t addr;
   DLDataType dtype;
 
-} cagraIndex;
+} cuvsCagraIndex;
 
-typedef cagraIndex* cagraIndex_t;
+typedef cuvsCagraIndex* cuvsCagraIndex_t;
 
 /**
  * @brief Allocate CAGRA index
  *
- * @param[in] index cagraIndex_t to allocate
+ * @param[in] index cuvsCagraIndex_t to allocate
  * @return cagraError_t
  */
-cuvsError_t cagraIndexCreate(cagraIndex_t* index);
+cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index);
 
 /**
  * @brief De-allocate CAGRA index
  *
- * @param[in] index cagraIndex_t to de-allocate
+ * @param[in] index cuvsCagraIndex_t to de-allocate
  */
-cuvsError_t cagraIndexDestroy(cagraIndex_t index);
+cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index);
 
 /**
  * @brief Build a CAGRA index with a `DLManagedTensor` which has underlying
@@ -195,7 +195,7 @@ cuvsError_t cagraIndexDestroy(cagraIndex_t index);
  *
  * @code {.c}
  * #include <cuvs/core/c_api.h>
- * #include <cuvs/neighbors/cagra_c.h>
+ * #include <cuvs/neighbors/cagra.h>
  *
  * // Create cuvsResources_t
  * cuvsResources_t res;
@@ -209,28 +209,28 @@ cuvsError_t cagraIndexDestroy(cagraIndex_t index);
  * cuvsError_t params_create_status = cuvsCagraIndexParamsCreate(&params);
  *
  * // Create CAGRA index
- * cagraIndex_t index;
- * cuvsError_t index_create_status = cagraIndexCreate(&index);
+ * cuvsCagraIndex_t index;
+ * cuvsError_t index_create_status = cuvsCagraIndexCreate(&index);
  *
  * // Build the CAGRA Index
- * cuvsError_t build_status = cagraBuild(res, params, &dataset, index);
+ * cuvsError_t build_status = cuvsCagraBuild(res, params, &dataset, index);
  *
  * // de-allocate `params`, `index` and `res`
  * cuvsError_t params_destroy_status = cuvsCagraIndexParamsDestroy(params);
- * cuvsError_t index_destroy_status = cagraIndexDestroy(index);
+ * cuvsError_t index_destroy_status = cuvsCagraIndexDestroy(index);
  * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
  * @endcode
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] params cuvsCagraIndexParams_t used to build CAGRA index
  * @param[in] dataset DLManagedTensor* training dataset
- * @param[out] index cagraIndex_t Newly built CAGRA index
+ * @param[out] index cuvsCagraIndex_t Newly built CAGRA index
  * @return cuvsError_t
  */
-cuvsError_t cagraBuild(cuvsResources_t res,
-                       cuvsCagraIndexParams_t params,
-                       DLManagedTensor* dataset,
-                       cagraIndex_t index);
+cuvsError_t cuvsCagraBuild(cuvsResources_t res,
+                           cuvsCagraIndexParams_t params,
+                           DLManagedTensor* dataset,
+                           cuvsCagraIndex_t index);
 
 /**
  * @brief Search a CAGRA index with a `DLManagedTensor` which has underlying
@@ -244,7 +244,7 @@ cuvsError_t cagraBuild(cuvsResources_t res,
  *
  * @code {.c}
  * #include <cuvs/core/c_api.h>
- * #include <cuvs/neighbors/cagra_c.h>
+ * #include <cuvs/neighbors/cagra.h>
  *
  * // Create cuvsResources_t
  * cuvsResources_t res;
@@ -259,8 +259,8 @@ cuvsError_t cagraBuild(cuvsResources_t res,
  * cuvsCagraSearchParams_t params;
  * cuvsError_t params_create_status = cuvsCagraSearchParamsCreate(&params);
  *
- * // Search the `index` built using `cagraBuild`
- * cuvsError_t search_status = cagraSearch(res, params, index, queries, neighbors, distances);
+ * // Search the `index` built using `cuvsCagraBuild`
+ * cuvsError_t search_status = cuvsCagraSearch(res, params, index, queries, neighbors, distances);
  *
  * // de-allocate `params` and `res`
  * cuvsError_t params_destroy_status = cuvsCagraSearchParamsDestroy(params);
@@ -269,17 +269,17 @@ cuvsError_t cagraBuild(cuvsResources_t res,
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] params cuvsCagraSearchParams_t used to search CAGRA index
- * @param[in] index cagraIndex which has been returned by `cagraBuild`
+ * @param[in] index cuvsCagraIndex which has been returned by `cuvsCagraBuild`
  * @param[in] queries DLManagedTensor* queries dataset to search
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
  */
-cuvsError_t cagraSearch(cuvsResources_t res,
-                        cuvsCagraSearchParams_t params,
-                        cagraIndex_t index,
-                        DLManagedTensor* queries,
-                        DLManagedTensor* neighbors,
-                        DLManagedTensor* distances);
+cuvsError_t cuvsCagraSearch(cuvsResources_t res,
+                            cuvsCagraSearchParams_t params,
+                            cuvsCagraIndex_t index,
+                            DLManagedTensor* queries,
+                            DLManagedTensor* neighbors,
+                            DLManagedTensor* distances);
 
 #ifdef __cplusplus
 }
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 638c9a23d8..70e268fb2f 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -24,13 +24,13 @@
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/cagra.h>
 #include <cuvs/neighbors/cagra.hpp>
-#include <cuvs/neighbors/cagra_c.h>
 
 namespace {
 
 template <typename T>
-void* _build(cuvsResources_t res, cagraIndexParams params, DLManagedTensor* dataset_tensor)
+void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor* dataset_tensor)
 {
   auto dataset = dataset_tensor->dl_tensor;
 
@@ -53,14 +53,13 @@ void* _build(cuvsResources_t res, cagraIndexParams params, DLManagedTensor* data
     auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
     cuvs::neighbors::cagra::build_host(*res_ptr, build_params, mds, *index);
   }
-
   return index;
 }
 
 template <typename T>
 void _search(cuvsResources_t res,
-             cagraSearchParams params,
-             cagraIndex index,
+             cuvsCagraSearchParams params,
+             cuvsCagraIndex index,
              DLManagedTensor* queries_tensor,
              DLManagedTensor* neighbors_tensor,
              DLManagedTensor* distances_tensor)
@@ -95,17 +94,17 @@ void _search(cuvsResources_t res,
 
 }  // namespace
 
-extern "C" cuvsError_t cagraIndexCreate(cagraIndex_t* index)
+extern "C" cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index)
 {
   try {
-    *index = new cagraIndex{};
+    *index = new cuvsCagraIndex{};
     return CUVS_SUCCESS;
   } catch (...) {
     return CUVS_ERROR;
   }
 }
 
-extern "C" cuvsError_t cagraIndexDestroy(cagraIndex_t index_c_ptr)
+extern "C" cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index_c_ptr)
 {
   try {
     auto index = *index_c_ptr;
@@ -130,10 +129,10 @@ extern "C" cuvsError_t cagraIndexDestroy(cagraIndex_t index_c_ptr)
   }
 }
 
-extern "C" cuvsError_t cagraBuild(cuvsResources_t res,
-                                  cuvsCagraIndexParams_t params,
-                                  DLManagedTensor* dataset_tensor,
-                                  cagraIndex_t index)
+extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
+                                      cuvsCagraIndexParams_t params,
+                                      DLManagedTensor* dataset_tensor,
+                                      cuvsCagraIndex_t index)
 {
   try {
     auto dataset = dataset_tensor->dl_tensor;
@@ -153,17 +152,20 @@ extern "C" cuvsError_t cagraBuild(cuvsResources_t res,
                 dataset.dtype.bits);
     }
     return CUVS_SUCCESS;
+  } catch (const std::exception& ex) {
+    std::cerr << "Error occurred: " << ex.what() << std::endl;
+    return CUVS_ERROR;
   } catch (...) {
     return CUVS_ERROR;
   }
 }
 
-extern "C" cuvsError_t cagraSearch(cuvsResources_t res,
-                                   cuvsCagraSearchParams_t params,
-                                   cagraIndex_t index_c_ptr,
-                                   DLManagedTensor* queries_tensor,
-                                   DLManagedTensor* neighbors_tensor,
-                                   DLManagedTensor* distances_tensor)
+extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
+                                       cuvsCagraSearchParams_t params,
+                                       cuvsCagraIndex_t index_c_ptr,
+                                       DLManagedTensor* queries_tensor,
+                                       DLManagedTensor* neighbors_tensor,
+                                       DLManagedTensor* distances_tensor)
 {
   try {
     auto queries   = queries_tensor->dl_tensor;
@@ -197,6 +199,8 @@ extern "C" cuvsError_t cagraSearch(cuvsResources_t res,
                 queries.dtype.bits);
     }
     return CUVS_SUCCESS;
+  } catch (const std::exception& ex) {
+    std::cerr << "Error occurred: " << ex.what() << std::endl;
   } catch (...) {
     return CUVS_ERROR;
   }
@@ -205,10 +209,10 @@ extern "C" cuvsError_t cagraSearch(cuvsResources_t res,
 extern "C" cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params)
 {
   try {
-    *params = new cagraIndexParams{.intermediate_graph_degree = 128,
-                                   .graph_degree              = 64,
-                                   .build_algo                = IVF_PQ,
-                                   .nn_descent_niter          = 20};
+    *params = new cuvsCagraIndexParams{.intermediate_graph_degree = 128,
+                                       .graph_degree              = 64,
+                                       .build_algo                = IVF_PQ,
+                                       .nn_descent_niter          = 20};
     return CUVS_SUCCESS;
   } catch (...) {
     return CUVS_ERROR;
@@ -228,11 +232,11 @@ extern "C" cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params
 extern "C" cuvsError_t cuvsCagraSearchParamsCreate(cuvsCagraSearchParams_t* params)
 {
   try {
-    *params = new cagraSearchParams{.itopk_size            = 64,
-                                    .search_width          = 1,
-                                    .hashmap_max_fill_rate = 0.5,
-                                    .num_random_samplings  = 1,
-                                    .rand_xor_mask         = 0x128394};
+    *params = new cuvsCagraSearchParams{.itopk_size            = 64,
+                                        .search_width          = 1,
+                                        .hashmap_max_fill_rate = 0.5,
+                                        .num_random_samplings  = 1,
+                                        .rand_xor_mask         = 0x128394};
     return CUVS_SUCCESS;
   } catch (...) {
     return CUVS_ERROR;
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 609bc2d4ec..f33c141790 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -57,7 +57,7 @@ function(ConfigureTest)
   )
   set_target_properties(
     ${TEST_NAME}
-    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/gtests>"
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUVS_BINARY_DIR}/gtests>"
                INSTALL_RPATH "\$ORIGIN/../../../lib"
                CXX_STANDARD 17
                CXX_STANDARD_REQUIRED ON
diff --git a/cpp/test/neighbors/ann_cagra_c.cu b/cpp/test/neighbors/ann_cagra_c.cu
index 4870ac3b83..6e3a3cbd1f 100644
--- a/cpp/test/neighbors/ann_cagra_c.cu
+++ b/cpp/test/neighbors/ann_cagra_c.cu
@@ -19,7 +19,7 @@
 #include <dlpack/dlpack.h>
 
 #include <cstdint>
-#include <cuvs/neighbors/cagra_c.h>
+#include <cuvs/neighbors/cagra.h>
 
 #include <cuda_runtime.h>
 #include <gtest/gtest.h>
@@ -56,13 +56,13 @@ TEST(CagraC, BuildSearch)
   dataset_tensor.dl_tensor.strides            = nullptr;
 
   // create index
-  cagraIndex_t index;
-  cagraIndexCreate(&index);
+  cuvsCagraIndex_t index;
+  cuvsCagraIndexCreate(&index);
 
   // build index
   cuvsCagraIndexParams_t build_params;
   cuvsCagraIndexParamsCreate(&build_params);
-  cagraBuild(res, build_params, &dataset_tensor, index);
+  cuvsCagraBuild(res, build_params, &dataset_tensor, index);
 
   // create queries DLTensor
   float* queries_d;
@@ -113,7 +113,7 @@ TEST(CagraC, BuildSearch)
   // search index
   cuvsCagraSearchParams_t search_params;
   cuvsCagraSearchParamsCreate(&search_params);
-  cagraSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  cuvsCagraSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
 
   // verify output
   ASSERT_TRUE(cuvs::devArrMatchHost(neighbors_exp, neighbors_d, 4, cuvs::Compare<uint32_t>()));
@@ -128,6 +128,6 @@ TEST(CagraC, BuildSearch)
   // de-allocate index and res
   cuvsCagraSearchParamsDestroy(search_params);
   cuvsCagraIndexParamsDestroy(build_params);
-  cagraIndexDestroy(index);
+  cuvsCagraIndexDestroy(index);
   cuvsResourcesDestroy(res);
 }
diff --git a/cpp/test/neighbors/c_api.c b/cpp/test/neighbors/c_api.c
index d4f5ad08e0..0c476e95bd 100644
--- a/cpp/test/neighbors/c_api.c
+++ b/cpp/test/neighbors/c_api.c
@@ -15,17 +15,17 @@
  */
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/neighbors/cagra_c.h>
+#include <cuvs/neighbors/cagra.h>
 #include <stdio.h>
 #include <stdlib.h>
 
 int main()
 {
-  // simple smoke test to make sure that we can compile the cagra_c.h API
+  // simple smoke test to make sure that we can compile the cagra.h API
   // using a c compiler. This isn't aiming to be a full test, just checking
   // that the exposed C-API is valid C code and doesn't contain C++ features
-  cagraIndex_t index;
-  cagraIndexCreate(&index);
-  cagraIndexDestroy(index);
+  cuvsCagraIndex_t index;
+  cuvsCagraIndexCreate(&index);
+  cuvsCagraIndexDestroy(index);
   return 0;
 }
diff --git a/dependencies.yaml b/dependencies.yaml
index 6f9f10535a..f17b84dffa 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,33 +3,34 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["11.8", "12.0"]
+      cuda: ["11.8", "12.2"]
       arch: [x86_64, aarch64]
     includes:
       - build
-      - build_cuvs
-      - cudatoolkit
+      - build_py_cuvs
+      - cuda
+      - cuda_version
       - develop
       - checks
       - build_wheels
       - test_libcuvs
       - docs
-      - run_cuvs
+      - run_py_cuvs
       - test_python_common
-      - test_cuvs
+      - test_py_cuvs
       - cupy
   test_cpp:
     output: none
     includes:
-      - cudatoolkit
+      - cuda_version
       - test_libcuvs
   test_python:
     output: none
     includes:
-      - cudatoolkit
+      - cuda_version
       - py_version
       - test_python_common
-      - test_cuvs
+      - test_py_cuvs
       - cupy
   checks:
     output: none
@@ -39,28 +40,27 @@ files:
   docs:
     output: none
     includes:
-      - test_cuvs
+      - cuda_version
       - cupy
-      - cudatoolkit
       - docs
       - py_version
-  py_build_cuvs:
+      - test_py_cuvs
+  py_build_py_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
     extras:
       table: build-system
     includes:
       - build
-      - build_cuvs
-      - build_wheels
-  py_run_cuvs:
+      - build_py_cuvs
+  py_run_py_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
     extras:
       table: project
     includes:
-      - run_cuvs
-  py_test_cuvs:
+      - run_py_cuvs
+  py_test_py_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
     extras:
@@ -68,7 +68,7 @@ files:
       key: test
     includes:
       - test_python_common
-      - test_cuvs
+      - test_py_cuvs
       - cupy
 channels:
   - rapidsai
@@ -84,12 +84,15 @@ dependencies:
           - &cmake_ver cmake>=3.26.4
           - cython>=3.0.0
           - ninja
-          - scikit-build>=0.13.1
       - output_types: [conda]
         packages:
           - c-compiler
           - cxx-compiler
           - nccl>=2.9.9
+          - scikit-build-core>=0.7.0
+      - output_types: [requirements, pyproject]
+        packages:
+          - scikit-build-core[pyproject]>=0.7.0
     specific:
       - output_types: conda
         matrices:
@@ -105,8 +108,8 @@ dependencies:
               - sysroot_linux-aarch64==2.17
       - output_types: conda
         matrices:
-          - matrix: {cuda: "12.0"}
-            packages: [cuda-version=12.0, cuda-nvcc]
+          - matrix: {cuda: "12.*"}
+            packages: [cuda-nvcc]
           - matrix: {cuda: "11.8", arch: x86_64}
             packages: [nvcc_linux-64=11.8]
           - matrix: {cuda: "11.8", arch: aarch64}
@@ -124,21 +127,23 @@ dependencies:
           - matrix: {cuda: "11.2", arch: aarch64}
             packages: [nvcc_linux-aarch64=11.2]
 
-  build_cuvs:
+  build_py_cuvs:
     common:
       - output_types: [conda]
         packages:
-          - &rmm_conda rmm==24.2.*
+          - &rmm_conda rmm==24.4.*
+          - &pylibraft_conda pylibraft==24.4.*
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
           # This index is needed for rmm-cu{11,12}.
           - --extra-index-url=https://pypi.nvidia.com
+          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
     specific:
       - output_types: [conda, requirements, pyproject]
         matrices:
           - matrix:
-              cuda: "12.0"
+              cuda: "12.*"
             packages:
               - &cuda_python12 cuda-python>=12.0,<13.0a0
           - matrix: # All CUDA 11 versions
@@ -146,18 +151,15 @@ dependencies:
               - &cuda_python11 cuda-python>=11.7.1,<12.0a0
       - output_types: [requirements, pyproject]
         matrices:
-          - matrix: {cuda: "12.2"}
-            packages: &build_cuvs_packages_cu12
-              - &rmm_cu12 rmm-cu12==24.2.*
-          - {matrix: {cuda: "12.1"}, packages: *build_cuvs_packages_cu12}
-          - {matrix: {cuda: "12.0"}, packages: *build_cuvs_packages_cu12}
-          - matrix: {cuda: "11.8"}
-            packages: &build_cuvs_packages_cu11
-              - &rmm_cu11 rmm-cu11==24.2.*
-          - {matrix: {cuda: "11.5"}, packages: *build_cuvs_packages_cu11}
-          - {matrix: {cuda: "11.4"}, packages: *build_cuvs_packages_cu11}
-          - {matrix: {cuda: "11.2"}, packages: *build_cuvs_packages_cu11}
-          - {matrix: null, packages: [*rmm_conda] }
+          - matrix: {cuda: "12.*"}
+            packages:
+              - &rmm_cu12 rmm-cu12==24.4.*
+              - &pylibraft_cu12 pylibraft-cu12==24.4.*
+          - matrix: {cuda: "11.*"}
+            packages:
+              - &rmm_cu11 rmm-cu11==24.4.*
+              - &pylibraft_cu11 pylibraft-cu11==24.4.*
+          - {matrix: null, packages: [*rmm_conda, *pylibraft_conda] }
   checks:
     common:
       - output_types: [conda, requirements]
@@ -169,14 +171,41 @@ dependencies:
         packages:
           - clang==16.0.6
           - clang-tools=16.0.6
-  cudatoolkit:
+  cuda_version:
     specific:
       - output_types: conda
         matrices:
+          - matrix:
+              cuda: "11.2"
+            packages:
+              - cuda-version=11.2
+          - matrix:
+              cuda: "11.4"
+            packages:
+              - cuda-version=11.4
+          - matrix:
+              cuda: "11.5"
+            packages:
+              - cuda-version=11.5
+          - matrix:
+              cuda: "11.8"
+            packages:
+              - cuda-version=11.8
           - matrix:
               cuda: "12.0"
             packages:
               - cuda-version=12.0
+          - matrix:
+              cuda: "12.2"
+            packages:
+              - cuda-version=12.2
+  cuda:
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              cuda: "12.*"
+            packages:
               - cuda-nvtx-dev
               - cuda-cudart-dev
               - cuda-profiler-api
@@ -187,7 +216,6 @@ dependencies:
           - matrix:
               cuda: "11.8"
             packages:
-              - cuda-version=11.8
               - cudatoolkit
               - cuda-nvtx=11.8
               - cuda-profiler-api=11.8.86
@@ -202,7 +230,6 @@ dependencies:
           - matrix:
               cuda: "11.5"
             packages:
-              - cuda-version=11.5
               - cudatoolkit
               - cuda-nvtx=11.5
               - cuda-profiler-api>=11.4.240,<=11.8.86 # use any `11.x` version since pkg is missing several CUDA/arch packages
@@ -217,7 +244,6 @@ dependencies:
           - matrix:
               cuda: "11.4"
             packages:
-              - cuda-version=11.4
               - cudatoolkit
               - &cudanvtx114 cuda-nvtx=11.4
               - cuda-profiler-api>=11.4.240,<=11.8.86 # use any `11.x` version since pkg is missing several CUDA/arch packages
@@ -232,7 +258,6 @@ dependencies:
           - matrix:
               cuda: "11.2"
             packages:
-              - cuda-version=11.2
               - cudatoolkit
               - *cudanvtx114
               - cuda-profiler-api>=11.4.240,<=11.8.86 # use any `11.x` version since pkg is missing several CUDA/arch packages
@@ -255,34 +280,12 @@ dependencies:
     specific:
       - output_types: [requirements, pyproject]
         matrices:
-          # All CUDA 12 + x86_64 versions
-          - matrix: {cuda: "12.2", arch: x86_64}
-            packages: &cupy_packages_cu12_x86_64
-              - &cupy_cu12_x86_64 cupy-cuda12x>=12.0.0
-          - {matrix: {cuda: "12.1", arch: x86_64}, packages: *cupy_packages_cu12_x86_64}
-          - {matrix: {cuda: "12.0", arch: x86_64}, packages: *cupy_packages_cu12_x86_64}
-          # All CUDA 12 + aarch64 versions
-          - matrix: {cuda: "12.2", arch: aarch64}
-            packages: &cupy_packages_cu12_aarch64
-              - &cupy_cu12_aarch64 cupy-cuda12x -f https://pip.cupy.dev/aarch64 # TODO: Verify that this works.
-          - {matrix: {cuda: "12.1", arch: aarch64}, packages: *cupy_packages_cu12_aarch64}
-          - {matrix: {cuda: "12.0", arch: aarch64}, packages: *cupy_packages_cu12_aarch64}
-
-          # All CUDA 11 + x86_64 versions
-          - matrix: {cuda: "11.8", arch: x86_64}
-            packages: &cupy_packages_cu11_x86_64
+          - matrix: {cuda: "12.*"}
+            packages:
+              - cupy-cuda12x>=12.0.0
+          - matrix: {cuda: "11.*"}
+            packages:
               - cupy-cuda11x>=12.0.0
-          - {matrix: {cuda: "11.5", arch: x86_64}, packages: *cupy_packages_cu11_x86_64}
-          - {matrix: {cuda: "11.4", arch: x86_64}, packages: *cupy_packages_cu11_x86_64}
-          - {matrix: {cuda: "11.2", arch: x86_64}, packages: *cupy_packages_cu11_x86_64}
-
-          # All CUDA 11 + aarch64 versions
-          - matrix: {cuda: "11.8", arch: aarch64}
-            packages: &cupy_packages_cu11_aarch64
-              - cupy-cuda11x -f https://pip.cupy.dev/aarch64 # TODO: Verify that this works.
-          - {matrix: {cuda: "11.5", arch: aarch64}, packages: *cupy_packages_cu11_aarch64}
-          - {matrix: {cuda: "11.4", arch: aarch64}, packages: *cupy_packages_cu11_aarch64}
-          - {matrix: {cuda: "11.2", arch: aarch64}, packages: *cupy_packages_cu11_aarch64}
           - {matrix: null, packages: [cupy-cuda11x>=12.0.0]}
 
   test_libcuvs:
@@ -324,26 +327,32 @@ dependencies:
             packages:
               - python=3.10
           - matrix:
+              py: "3.11"
             packages:
-              - python>=3.9,<3.11
-  run_cuvs:
+              - python=3.11
+          - matrix:
+            packages:
+              - python>=3.9,<3.12
+  run_py_cuvs:
     common:
       - output_types: [conda, pyproject]
         packages:
-          - &numpy numpy>=1.21
+          - &numpy numpy>=1.23
       - output_types: [conda]
         packages:
           - *rmm_conda
+          - *pylibraft_conda
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
           # This index is needed for cudf and rmm.
           - --extra-index-url=https://pypi.nvidia.com
+          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
     specific:
       - output_types: [conda, requirements, pyproject]
         matrices:
           - matrix:
-              cuda: "12.0"
+              cuda: "12.*"
             packages:
               - *cuda_python12
           - matrix: # All CUDA 11 versions
@@ -351,27 +360,22 @@ dependencies:
               - *cuda_python11
       - output_types: [requirements, pyproject]
         matrices:
-          - matrix: {cuda: "12.2"}
-            packages: &run_cuvs_packages_cu12
-              - *rmm_cu12
-          - {matrix: {cuda: "12.1"}, packages: *run_cuvs_packages_cu12}
-          - {matrix: {cuda: "12.0"}, packages: *run_cuvs_packages_cu12}
-          - matrix: {cuda: "11.8"}
-            packages: &run_cuvs_packages_cu11
-              - *rmm_cu11
-          - {matrix: {cuda: "11.5"}, packages: *run_cuvs_packages_cu11}
-          - {matrix: {cuda: "11.4"}, packages: *run_cuvs_packages_cu11}
-          - {matrix: {cuda: "11.2"}, packages: *run_cuvs_packages_cu11}
+          - matrix: {cuda: "12.*"}
+            packages:
+              - *pylibraft_cu12
+          - matrix: {cuda: "11.*"}
+            packages:
+              - *pylibraft_cu11
           - {matrix: null, packages: [*rmm_conda]}
   test_python_common:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - pytest
+          - pytest==7.*
           - pytest-cov
-  test_cuvs:
+  test_py_cuvs:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
+          - *pylibraft_conda
           - scikit-learn
-          - scipy
diff --git a/docs/source/build.md b/docs/source/build.md
index ae7734d0ed..31de69b461 100644
--- a/docs/source/build.md
+++ b/docs/source/build.md
@@ -56,7 +56,7 @@ You can also install the conda packages individually using the `mamba` command a
 mamba install -c rapidsai -c conda-forge -c nvidia libraft libraft-headers cuda-version=12.0
 ```
 
-If installing the C++ APIs Please see [using libraft](https://docs.rapids.ai/api/raft/nightly/using_libraft/) for more information on using the pre-compiled shared library. You can also refer to the [example C++ template project](https://github.com/rapidsai/raft/tree/branch-24.02/cpp/template) for a ready-to-go CMake configuration that you can drop into your project and build against installed RAFT development artifacts above.
+If installing the C++ APIs Please see [using libraft](https://docs.rapids.ai/api/raft/nightly/using_libraft/) for more information on using the pre-compiled shared library. You can also refer to the [example C++ template project](https://github.com/rapidsai/raft/tree/branch-24.04/cpp/template) for a ready-to-go CMake configuration that you can drop into your project and build against installed RAFT development artifacts above.
 
 ## Installing Python through Pip
 
@@ -315,4 +315,4 @@ The `raft::raft` CMake target is made available when including RAFT into your CM
 |-------------|---------------------|----------------------------------------------------------|----------------------------------------|
 | n/a         | `raft::raft`        | Full RAFT header library                                 | CUDA toolkit, RMM, NVTX, CCCL, CUTLASS |
 | compiled    | `raft::compiled`    | Pre-compiled template instantiations and runtime library | raft::raft                             |
-| distributed | `raft::distributed` | Dependencies for `raft::comms` APIs                      | raft::raft, UCX, NCCL         
\ No newline at end of file
+| distributed | `raft::distributed` | Dependencies for `raft::comms` APIs                      | raft::raft, UCX, NCCL         
diff --git a/docs/source/conf.py b/docs/source/conf.py
index c09ab953f2..1a5c9dfe83 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -67,9 +67,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '24.02'
+version = '24.04'
 # The full version, including alpha/beta/rc tags.
-release = '24.02.00'
+release = '24.04.00'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/docs/source/contributing.md b/docs/source/contributing.md
index 090fd834b3..c426ce5340 100755
--- a/docs/source/contributing.md
+++ b/docs/source/contributing.md
@@ -1,9 +1,9 @@
 # Contributing
 
-If you are interested in contributing to CUVS, your contributions will fall
+If you are interested in contributing to cuVS, your contributions will fall
 into three categories:
 1. You want to report a bug, feature request, or documentation issue
-    - File an [issue](https://github.com/rapidsai/CUVS/issues/new/choose)
+    - File an [issue](https://github.com/rapidsai/cuvs/issues/new/choose)
     describing what you encountered or what you want to see changed.
     - The RAPIDS team will evaluate the issues and triage them, scheduling
     them for a release. If you believe the issue needs priority attention
@@ -26,10 +26,10 @@ into three categories:
 1. Read the project's [README.md](https://github.com/rapidsai/cuvs)
     to learn how to setup the development environment
 2. Find an issue to work on. The best way is to look for the [good first issue](https://github.com/rapidsai/CUVS/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
-    or [help wanted](https://github.com/rapidsai/CUVS/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels
+    or [help wanted](https://github.com/rapidsai/cuvs/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels
 3. Comment on the issue saying you are going to work on it
 4. Code! Make sure to update unit tests!
-5. When done, [create your pull request](https://github.com/rapidsai/CUVS/compare)
+5. When done, [create your pull request](https://github.com/rapidsai/cuvs/compare)
 6. Verify that CI passes all [status checks](https://help.github.com/articles/about-status-checks/). Fix if needed
 7. Wait for other developers to review your code and update code as needed
 8. Once reviewed and approved, a RAPIDS developer will merge your pull request
diff --git a/docs/source/cpp_api/core_interop.rst b/docs/source/cpp_api/core_interop.rst
index 034030db5c..b2ef05f275 100644
--- a/docs/source/cpp_api/core_interop.rst
+++ b/docs/source/cpp_api/core_interop.rst
@@ -6,11 +6,11 @@ Interop
    :class: highlight
 
 
-``#include <raft/core/interop.hpp>``
+``#include <cuvs/core/interop.hpp>``
 
-namespace *raft::core*
+namespace *cuvs::core*
 
  .. doxygengroup:: interop
-     :project: RAFT
+     :project: cuvs
      :members:
      :content-only:
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index c5bcd03f69..d29130add0 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.02/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.04/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.02/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.04/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
@@ -215,7 +215,7 @@ python ./cpp/scripts/include_checker.py --inplace [cpp/include cpp/test ... list
 ```
 
 ### Copyright header
-[copyright.py](https://github.com/rapidsai/raft/blob/branch-24.02/ci/checks/copyright.py) checks the Copyright header for all git-modified files
+[copyright.py](https://github.com/rapidsai/raft/blob/branch-24.04/ci/checks/copyright.py) checks the Copyright header for all git-modified files
 
 Manually, you can run the following to bulk-fix the header if only the years need to be updated:
 ```bash
@@ -229,7 +229,7 @@ Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY`
 ## Logging
 
 ### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.02/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
+Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.04/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
 
 ### Usage
 ```cpp
diff --git a/fetch_rapids.cmake b/fetch_rapids.cmake
index ca871c5759..330270d662 100644
--- a/fetch_rapids.cmake
+++ b/fetch_rapids.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -11,10 +11,10 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 # =============================================================================
-if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
-  file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-24.02/RAPIDS.cmake
-       ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake
+if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
+  file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-24.04/RAPIDS.cmake
+       ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake
   )
 endif()
 
-include(${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
+include(${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index ca9da8a9d3..5a5efe8f9c 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -16,7 +16,7 @@ cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
 
 include(../../fetch_rapids.cmake)
 
-set(cuvs_version 24.02.00)
+set(cuvs_version 24.04.00)
 
 # We always need CUDA for cuvs because the cuvs dependency brings in a header-only cuco dependency
 # that enables CUDA unconditionally.
@@ -37,29 +37,44 @@ option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulti
        OFF
 )
 
+message("- FIND_CUVS_CPP: ${FIND_CUVS_CPP}")
+
+include(../../fetch_rapids.cmake)
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-cython-core)
+include(rapids-export)
+include(rapids-find)
+
+rapids_cpm_init()
+
 # If the user requested it we attempt to find CUVS.
 if(FIND_CUVS_CPP)
   find_package(cuvs ${cuvs_version})
+  include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
 else()
   set(cuvs_FOUND OFF)
 endif()
 
-include(rapids-cython)
-
 if(NOT cuvs_FOUND)
   set(BUILD_TESTS OFF)
+  set(BUILD_C_LIBRARY ON)
+
+  # Statically link dependencies if building wheels
   set(CUDA_STATIC_RUNTIME ON)
+  set(CUVS_USE_RAFT_STATIC ON)
 
   add_subdirectory(../../cpp cuvs-cpp EXCLUDE_FROM_ALL)
 
-  # When building the C++ libraries from source we must copy libcuvs.so alongside the Cython
-  # libraries TODO: when we have a single 'compiled' cuvs library, we shouldn't need this
-  set(cython_lib_dir cuvs_py)
-  install(TARGETS cuvs DESTINATION ${cython_lib_dir})
+  set(cython_lib_dir cuvs)
+  install(TARGETS cuvs cuvs_c DESTINATION ${cython_lib_dir})
 endif()
 
 rapids_cython_init()
 
+add_subdirectory(cuvs/common)
+add_subdirectory(cuvs/neighbors)
+
 if(DEFINED cython_lib_dir)
   rapids_cython_add_rpath_entries(TARGET cuvs PATHS "${cython_lib_dir}")
 endif()
diff --git a/python/cuvs/README.md b/python/cuvs/README.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/__init__.py b/python/cuvs/cuvs/__init__.py
index 94b3a200b5..9f0481cb70 100644
--- a/python/cuvs/cuvs/__init__.py
+++ b/python/cuvs/cuvs/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
new file mode 100644
index 0000000000..2cf1ff19c1
--- /dev/null
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources cydlpack.pyx)
+set(linked_libraries cuvs::cuvs cuvs_c)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX common_
+)
diff --git a/python/cuvs/cuvs/common/__init__.pxd b/python/cuvs/cuvs/common/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/common/__init__.py b/python/cuvs/cuvs/common/__init__.py
new file mode 100644
index 0000000000..eb5666659f
--- /dev/null
+++ b/python/cuvs/cuvs/common/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .temp_raft import auto_sync_resources
+
+__all__ = ["auto_sync_resources"]
diff --git a/python/cuvs/cuvs/common/c_api.pxd b/python/cuvs/cuvs/common/c_api.pxd
new file mode 100644
index 0000000000..6addbf16e2
--- /dev/null
+++ b/python/cuvs/cuvs/common/c_api.pxd
@@ -0,0 +1,32 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+
+from cuda.ccudart cimport cudaStream_t
+from libc.stdint cimport uintptr_t
+
+
+cdef extern from "cuvs/core/c_api.h":
+    ctypedef uintptr_t cuvsResources_t
+
+    ctypedef enum cuvsError_t:
+        CUVS_ERROR,
+        CUVS_SUCCESS
+
+    cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
+    cuvsError_t cuvsResourcesDestroy(cuvsResources_t res)
+    cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream)
diff --git a/python/cuvs/cuvs/common/cydlpack.pxd b/python/cuvs/cuvs/common/cydlpack.pxd
new file mode 100644
index 0000000000..73334e5007
--- /dev/null
+++ b/python/cuvs/cuvs/common/cydlpack.pxd
@@ -0,0 +1,70 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport int32_t, int64_t, uint8_t, uint16_t, uint64_t
+
+
+cdef extern from "dlpack/dlpack.h" nogil:
+    ctypedef enum DLDeviceType:
+        kDLCPU
+        kDLCUDA
+        kDLCUDAHost
+        kDLOpenCL
+        kDLVulkan
+        kDLMetal
+        kDLVPI
+        kDLROCM
+        kDLROCMHost
+        kDLExtDev
+        kDLCUDAManaged
+        kDLOneAPI
+        kDLWebGPU
+        kDLHexagon
+
+    ctypedef struct DLDevice:
+        DLDeviceType device_type
+        int32_t device_id
+
+    ctypedef enum DLDataTypeCode:
+        kDLInt
+        kDLUInt
+        kDLFloat
+        kDLBfloat
+        kDLComplex
+        kDLBool
+
+    ctypedef struct DLDataType:
+        uint8_t code
+        uint8_t bits
+        uint16_t lanes
+
+    ctypedef struct DLTensor:
+        void* data
+        DLDevice device
+        int32_t ndim
+        DLDataType dtype
+        int64_t* shape
+        int64_t* strides
+        uint64_t byte_offset
+
+    ctypedef struct DLManagedTensor:
+        DLTensor dl_tensor
+        void* manager_ctx
+        void (*deleter)(DLManagedTensor*)  # noqa: E211
+
+
+cdef DLManagedTensor* dlpack_c(ary)
diff --git a/python/cuvs/cuvs/common/cydlpack.pyx b/python/cuvs/cuvs/common/cydlpack.pyx
new file mode 100644
index 0000000000..526f6c78e9
--- /dev/null
+++ b/python/cuvs/cuvs/common/cydlpack.pyx
@@ -0,0 +1,102 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from libc cimport stdlib
+from libc.stdint cimport uintptr_t
+
+
+cdef void deleter(DLManagedTensor* tensor) noexcept:
+    if tensor.manager_ctx is NULL:
+        return
+    stdlib.free(tensor.dl_tensor.shape)
+    tensor.manager_ctx = NULL
+    stdlib.free(tensor)
+
+
+cdef DLManagedTensor* dlpack_c(ary):
+    # todo(dgd): add checking options/parameters
+    cdef DLDeviceType dev_type
+    cdef DLDevice dev
+    cdef DLDataType dtype
+    cdef DLTensor tensor
+    cdef DLManagedTensor* dlm = \
+        <DLManagedTensor*>stdlib.malloc(sizeof(DLManagedTensor))
+
+    if ary.from_cai:
+        dev_type = DLDeviceType.kDLCUDA
+    else:
+        dev_type = DLDeviceType.kDLCPU
+
+    dev.device_type = dev_type
+    dev.device_id = 0
+
+    # todo (dgd): change to nice dict
+    if ary.dtype == np.float32:
+        dtype.code = DLDataTypeCode.kDLFloat
+        dtype.bits = 32
+    elif ary.dtype == np.float64:
+        dtype.code = DLDataTypeCode.kDLFloat
+        dtype.bits = 64
+    elif ary.dtype == np.int8:
+        dtype.code = DLDataTypeCode.kDLInt
+        dtype.bits = 8
+    elif ary.dtype == np.int32:
+        dtype.code = DLDataTypeCode.kDLInt
+        dtype.bits = 32
+    elif ary.dtype == np.int64:
+        dtype.code = DLDataTypeCode.kDLInt
+        dtype.bits = 64
+    elif ary.dtype == np.uint8:
+        dtype.code = DLDataTypeCode.kDLUInt
+        dtype.bits = 8
+    elif ary.dtype == np.uint32:
+        dtype.code = DLDataTypeCode.kDLUInt
+        dtype.bits = 32
+    elif ary.dtype == np.uint64:
+        dtype.code = DLDataTypeCode.kDLUInt
+        dtype.bits = 64
+    elif ary.dtype == np.bool_:
+        dtype.code = DLDataTypeCode.kDLFloat
+        dtype.bits = 8
+
+    dtype.lanes = 1
+
+    cdef size_t ndim = len(ary.shape)
+
+    cdef int64_t* shape = <int64_t*>stdlib.malloc(ndim * sizeof(int64_t))
+
+    for i in range(ndim):
+        shape[i] = ary.shape[i]
+
+    cdef uintptr_t tensor_ptr
+    tensor_ptr = <uintptr_t>ary.ai_["data"][0]
+
+    tensor.data = <void*> tensor_ptr
+    tensor.device = dev
+    tensor.dtype = dtype
+    tensor.strides = NULL
+    tensor.ndim = ndim
+    tensor.shape = shape
+    tensor.byte_offset = 0
+
+    dlm.dl_tensor = tensor
+    dlm.manager_ctx = NULL
+    dlm.deleter = deleter
+
+    return dlm
diff --git a/python/cuvs/cuvs/common/temp_raft.py b/python/cuvs/cuvs/common/temp_raft.py
new file mode 100644
index 0000000000..67944eeab7
--- /dev/null
+++ b/python/cuvs/cuvs/common/temp_raft.py
@@ -0,0 +1,55 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+
+# This file has code that will be upstreamed to RAFT
+
+import functools
+
+from pylibraft.common import DeviceResources
+
+_resources_param_string = """
+     handle : Optional RAFT resource handle for reusing CUDA resources.
+        If a handle isn't supplied, CUDA resources will be
+        allocated inside this function and synchronized before the
+        function exits. If a handle is supplied, you will need to
+        explicitly synchronize yourself by calling `handle.sync()`
+        before accessing the output.
+""".strip()
+
+
+def auto_sync_resources(f):
+    """
+    This is identical to auto_sync_handle except for the proposed name change.
+    """
+
+    @functools.wraps(f)
+    def wrapper(*args, resources=None, **kwargs):
+        sync_resources = resources is None
+        resources = resources if resources is not None else DeviceResources()
+
+        ret_value = f(*args, resources=resources, **kwargs)
+
+        if sync_resources:
+            resources.sync()
+
+        return ret_value
+
+    wrapper.__doc__ = wrapper.__doc__.format(
+        resources_docstring=_resources_param_string
+    )
+    return wrapper
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
new file mode 100644
index 0000000000..eaf418c60f
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -0,0 +1,15 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+add_subdirectory(cagra)
diff --git a/python/cuvs/cuvs/neighbors/__init__.pxd b/python/cuvs/cuvs/neighbors/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
new file mode 100644
index 0000000000..1f8f956d9c
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from cuvs.neighbors import cagra
+
+__all__ = ["common", "cagra"]
diff --git a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
new file mode 100644
index 0000000000..377cfe7793
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources cagra.pyx)
+set(linked_libraries cuvs::cuvs cuvs_c)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_cagra_
+)
diff --git a/python/cuvs/cuvs/neighbors/cagra/__init__.pxd b/python/cuvs/cuvs/neighbors/cagra/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/cagra/__init__.py b/python/cuvs/cuvs/neighbors/cagra/__init__.py
new file mode 100644
index 0000000000..657c7d366e
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/cagra/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .cagra import Index, IndexParams, SearchParams, build_index, search
+
+__all__ = ["Index", "IndexParams", "SearchParams", "build_index", "search"]
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
new file mode 100644
index 0000000000..4293bdc073
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport (
+    int8_t,
+    int64_t,
+    uint8_t,
+    uint32_t,
+    uint64_t,
+    uintptr_t,
+)
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+
+
+cdef extern from "cuvs/neighbors/cagra.h" nogil:
+
+    ctypedef enum cuvsCagraGraphBuildAlgo:
+        IVF_PQ
+        NN_DESCENT
+
+    ctypedef struct cuvsCagraIndexParams:
+        size_t intermediate_graph_degree
+        size_t graph_degree
+        cuvsCagraGraphBuildAlgo build_algo
+        size_t nn_descent_niter
+
+    ctypedef cuvsCagraIndexParams* cuvsCagraIndexParams_t
+
+    ctypedef enum cuvsCagraSearchAlgo:
+        SINGLE_CTA,
+        MULTI_CTA,
+        MULTI_KERNEL,
+        AUTO
+
+    ctypedef enum cuvsCagraHashMode:
+        HASH,
+        SMALL,
+        AUTO_HASH
+
+    ctypedef struct cuvsCagraSearchParams:
+        size_t max_queries
+        size_t itopk_size
+        size_t max_iterations
+        cuvsCagraSearchAlgo algo
+        size_t team_size
+        size_t search_width
+        size_t min_iterations
+        size_t thread_block_size
+        cuvsCagraHashMode hashmap_mode
+        size_t hashmap_min_bitlen
+        float hashmap_max_fill_rate
+        uint32_t num_random_samplings
+        uint64_t rand_xor_mask
+
+    ctypedef struct cuvsCagraIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsCagraIndex* cuvsCagraIndex_t
+
+    cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params)
+
+    cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t index)
+
+    cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index)
+
+    cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index)
+
+    cuvsError_t cuvsCagraBuild(cuvsResources_t res,
+                               cuvsCagraIndexParams* params,
+                               DLManagedTensor* dataset,
+                               cuvsCagraIndex_t index) except +
+
+    cuvsError_t cuvsCagraSearch(cuvsResources_t res,
+                                cuvsCagraSearchParams* params,
+                                cuvsCagraIndex_t index,
+                                DLManagedTensor* queries,
+                                DLManagedTensor* neighbors,
+                                DLManagedTensor* distances) except +
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
new file mode 100644
index 0000000000..bf17fe6a55
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -0,0 +1,502 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.temp_raft import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libcpp cimport bool, cast
+
+from cuvs.common cimport cydlpack
+
+from pylibraft.common import (
+    DeviceResources,
+    Stream,
+    auto_convert_output,
+    cai_wrapper,
+    device_ndarray,
+)
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+from pylibraft.neighbors.common import _check_input_array
+
+from libc.stdint cimport (
+    int8_t,
+    int64_t,
+    uint8_t,
+    uint32_t,
+    uint64_t,
+    uintptr_t,
+)
+
+from cuvs.common.c_api cimport (
+    cuvsError_t,
+    cuvsResources_t,
+    cuvsResourcesCreate,
+)
+
+
+cdef class IndexParams:
+    """
+    Parameters to build index for CAGRA nearest neighbor search
+
+    Parameters
+    ----------
+    metric : string denoting the metric type, default="sqeuclidean"
+        Valid values for metric: ["sqeuclidean"], where
+            - sqeuclidean is the euclidean distance without the square root
+              operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2
+    intermediate_graph_degree : int, default = 128
+
+    graph_degree : int, default = 64
+
+    build_algo: string denoting the graph building algorithm to use, \
+                default = "ivf_pq"
+        Valid values for algo: ["ivf_pq", "nn_descent"], where
+            - ivf_pq will use the IVF-PQ algorithm for building the knn graph
+            - nn_descent (experimental) will use the NN-Descent algorithm for
+              building the knn graph. It is expected to be generally
+              faster than ivf_pq.
+    """
+    cdef cuvsCagraIndexParams* params
+
+    def __init__(self, *,
+                 metric="sqeuclidean",
+                 intermediate_graph_degree=128,
+                 graph_degree=64,
+                 build_algo="ivf_pq",
+                 nn_descent_niter=20):
+
+        cuvsCagraIndexParamsCreate(&self.params)
+
+        # todo (dgd): enable once other metrics are present
+        # and exposed in cuVS C API
+        # self.params.metric = _get_metric(metric)
+        # self.params.metric_arg = 0
+        self.params.intermediate_graph_degree = intermediate_graph_degree
+        self.params.graph_degree = graph_degree
+        if build_algo == "ivf_pq":
+            self.params.build_algo = cuvsCagraGraphBuildAlgo.IVF_PQ
+        elif build_algo == "nn_descent":
+            self.params.build_algo = cuvsCagraGraphBuildAlgo.NN_DESCENT
+        self.params.nn_descent_niter = nn_descent_niter
+
+    # @property
+    # def metric(self):
+        # return self.params.metric
+
+    @property
+    def intermediate_graph_degree(self):
+        return self.params.intermediate_graph_degree
+
+    @property
+    def graph_degree(self):
+        return self.params.graph_degree
+
+    @property
+    def build_algo(self):
+        return self.params.build_algo
+
+    @property
+    def nn_descent_niter(self):
+        return self.params.nn_descent_niter
+
+
+cdef class Index:
+    cdef cuvsCagraIndex_t index
+    cdef bool trained
+
+    def __cinit__(self):
+        cdef cuvsError_t index_create_status
+        index_create_status = cuvsCagraIndexCreate(&self.index)
+        self.trained = False
+
+        if index_create_status == cuvsError_t.CUVS_ERROR:
+            raise RuntimeError("Failed to create index.")
+
+    def __dealloc__(self):
+        cdef cuvsError_t index_destroy_status
+        if self.index is not NULL:
+            index_destroy_status = cuvsCagraIndexDestroy(self.index)
+            if index_destroy_status == cuvsError_t.CUVS_ERROR:
+                raise Exception("Failed to deallocate index.")
+
+    @property
+    def trained(self):
+        return self.trained
+
+    def __repr__(self):
+        # todo(dgd): update repr as we expose data through C API
+        attr_str = []
+        return "Index(type=CAGRA, metric=L2" + (", ".join(attr_str)) + ")"
+
+
+@auto_sync_resources
+def build_index(IndexParams index_params, dataset, resources=None):
+    """
+    Build the CAGRA index from the dataset for efficient search.
+
+    The build performs two different steps- first an intermediate knn-graph is
+    constructed, then it's optimized it to create the final graph. The
+    index_params object controls the node degree of these graphs.
+
+    It is required that both the dataset and the optimized graph fit the
+    GPU memory.
+
+    The following distance metrics are supported:
+        - L2
+
+    Parameters
+    ----------
+    index_params : IndexParams object
+    dataset : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: cuvs.cagra.Index
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> build_params = cagra.IndexParams(metric="sqeuclidean")
+    >>> index = cagra.build_index(build_params, dataset)
+    >>> distances, neighbors = cagra.search(cagra.SearchParams(),
+    ...                                      index, dataset,
+    ...                                      k)
+    >>> distances = cp.asarray(distances)
+    >>> neighbors = cp.asarray(neighbors)
+    """
+
+    # todo(dgd): we can make the check of dtype a parameter of wrap_array
+    # in RAFT to make this a single call
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
+                                    np.dtype('ubyte')])
+
+    cdef cuvsResources_t res_
+    cdef cuvsError_t cstat
+
+    cstat = cuvsResourcesCreate(&res_)
+    if cstat == cuvsError_t.CUVS_ERROR:
+        raise RuntimeError("Error creating Device Reources.")
+
+    cdef Index idx = Index()
+    cdef cuvsError_t build_status
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cuvsCagraIndexParams* params = index_params.params
+
+    with cuda_interruptible():
+        build_status = cuvsCagraBuild(
+            res_,
+            params,
+            dataset_dlpack,
+            idx.index
+        )
+
+        if build_status == cuvsError_t.CUVS_ERROR:
+            raise RuntimeError("Index failed to build.")
+        else:
+            idx.trained = True
+
+    return idx
+
+
+cdef class SearchParams:
+    """
+    CAGRA search parameters
+
+    Parameters
+    ----------
+    max_queries: int, default = 0
+        Maximum number of queries to search at the same time (batch size).
+        Auto select when 0.
+    itopk_size: int, default = 64
+        Number of intermediate search results retained during the search.
+        This is the main knob to adjust trade off between accuracy and
+        search speed. Higher values improve the search accuracy.
+    max_iterations: int, default = 0
+        Upper limit of search iterations. Auto select when 0.
+    algo: string denoting the search algorithm to use, default = "auto"
+        Valid values for algo: ["auto", "single_cta", "multi_cta"], where
+            - auto will automatically select the best value based on query size
+            - single_cta is better when query contains larger number of
+              vectors (e.g >10)
+            - multi_cta is better when query contains only a few vectors
+    team_size: int, default = 0
+        Number of threads used to calculate a single distance. 4, 8, 16,
+        or 32.
+    search_width: int, default = 1
+        Number of graph nodes to select as the starting point for the
+        search in each iteration.
+    min_iterations: int, default = 0
+        Lower limit of search iterations.
+    thread_block_size: int, default = 0
+        Thread block size. 0, 64, 128, 256, 512, 1024.
+        Auto selection when 0.
+    hashmap_mode: string denoting the type of hash map to use.
+        It's usually better to allow the algorithm to select this value,
+        default = "auto".
+        Valid values for hashmap_mode: ["auto", "small", "hash"], where
+            - auto will automatically select the best value based on algo
+            - small will use the small shared memory hash table with resetting.
+            - hash will use a single hash table in global memory.
+    hashmap_min_bitlen: int, default = 0
+        Upper limit of hashmap fill rate. More than 0.1, less than 0.9.
+    hashmap_max_fill_rate: float, default = 0.5
+        Upper limit of hashmap fill rate. More than 0.1, less than 0.9.
+    num_random_samplings: int, default = 1
+        Number of iterations of initial random seed node selection. 1 or
+        more.
+    rand_xor_mask: int, default = 0x128394
+        Bit mask used for initial random seed node selection.
+    """
+    cdef cuvsCagraSearchParams params
+
+    def __init__(self, *,
+                 max_queries=0,
+                 itopk_size=64,
+                 max_iterations=0,
+                 algo="auto",
+                 team_size=0,
+                 search_width=1,
+                 min_iterations=0,
+                 thread_block_size=0,
+                 hashmap_mode="auto",
+                 hashmap_min_bitlen=0,
+                 hashmap_max_fill_rate=0.5,
+                 num_random_samplings=1,
+                 rand_xor_mask=0x128394):
+        self.params.max_queries = max_queries
+        self.params.itopk_size = itopk_size
+        self.params.max_iterations = max_iterations
+        if algo == "single_cta":
+            self.params.algo = cuvsCagraSearchAlgo.SINGLE_CTA
+        elif algo == "multi_cta":
+            self.params.algo = cuvsCagraSearchAlgo.MULTI_CTA
+        elif algo == "multi_kernel":
+            self.params.algo = cuvsCagraSearchAlgo.MULTI_KERNEL
+        elif algo == "auto":
+            self.params.algo = cuvsCagraSearchAlgo.AUTO
+        else:
+            raise ValueError("`algo` value not supported.")
+
+        self.params.team_size = team_size
+        self.params.search_width = search_width
+        self.params.min_iterations = min_iterations
+        self.params.thread_block_size = thread_block_size
+        if hashmap_mode == "hash":
+            self.params.hashmap_mode = cuvsCagraHashMode.HASH
+        elif hashmap_mode == "small":
+            self.params.hashmap_mode = cuvsCagraHashMode.SMALL
+        elif hashmap_mode == "auto":
+            self.params.hashmap_mode = cuvsCagraHashMode.AUTO_HASH
+        else:
+            raise ValueError("`hashmap_mode` value not supported.")
+
+        self.params.hashmap_min_bitlen = hashmap_min_bitlen
+        self.params.hashmap_max_fill_rate = hashmap_max_fill_rate
+        self.params.num_random_samplings = num_random_samplings
+        self.params.rand_xor_mask = rand_xor_mask
+
+    def __repr__(self):
+        attr_str = [attr + "=" + str(getattr(self, attr))
+                    for attr in [
+                        "max_queries", "itopk_size", "max_iterations", "algo",
+                        "team_size", "search_width", "min_iterations",
+                        "thread_block_size", "hashmap_mode",
+                        "hashmap_min_bitlen", "hashmap_max_fill_rate",
+                        "num_random_samplings", "rand_xor_mask"]]
+        return "SearchParams(type=CAGRA, " + (", ".join(attr_str)) + ")"
+
+    @property
+    def max_queries(self):
+        return self.params.max_queries
+
+    @property
+    def itopk_size(self):
+        return self.params.itopk_size
+
+    @property
+    def max_iterations(self):
+        return self.params.max_iterations
+
+    @property
+    def algo(self):
+        return self.params.algo
+
+    @property
+    def team_size(self):
+        return self.params.team_size
+
+    @property
+    def search_width(self):
+        return self.params.search_width
+
+    @property
+    def min_iterations(self):
+        return self.params.min_iterations
+
+    @property
+    def thread_block_size(self):
+        return self.params.thread_block_size
+
+    @property
+    def hashmap_mode(self):
+        return self.params.hashmap_mode
+
+    @property
+    def hashmap_min_bitlen(self):
+        return self.params.hashmap_min_bitlen
+
+    @property
+    def hashmap_max_fill_rate(self):
+        return self.params.hashmap_max_fill_rate
+
+    @property
+    def num_random_samplings(self):
+        return self.params.num_random_samplings
+
+    @property
+    def rand_xor_mask(self):
+        return self.params.rand_xor_mask
+
+
+@auto_sync_resources
+@auto_convert_output
+def search(SearchParams search_params,
+           Index index,
+           queries,
+           k,
+           neighbors=None,
+           distances=None,
+           resources=None):
+    """
+    Find the k nearest neighbors for each query.
+
+    Parameters
+    ----------
+    search_params : SearchParams
+    index : Index
+        Trained CAGRA index.
+    queries : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    k : int
+        The number of neighbors.
+    neighbors : Optional CUDA array interface compliant matrix shape
+                (n_queries, k), dtype int64_t. If supplied, neighbor
+                indices will be written here in-place. (default None)
+    distances : Optional CUDA array interface compliant matrix shape
+                (n_queries, k) If supplied, the distances to the
+                neighbors will be written here in-place. (default None)
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = cagra.build_index(cagra.IndexParams(), dataset)
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> k = 10
+    >>> search_params = cagra.SearchParams(
+    ...     max_queries=100,
+    ...     itopk_size=64
+    ... )
+    >>> # Using a pooling allocator reduces overhead of temporary array
+    >>> # creation during search. This is useful if multiple searches
+    >>> # are performad with same query size.
+    >>> distances, neighbors = cagra.search(search_params, index, queries,
+    ...                                     k)
+    >>> neighbors = cp.asarray(neighbors)
+    >>> distances = cp.asarray(distances)
+    """
+    if not index.trained:
+        raise ValueError("Index needs to be built before calling search.")
+
+    cdef cuvsResources_t res_
+    cdef cuvsError_t cstat
+
+    cstat = cuvsResourcesCreate(&res_)
+    if cstat == cuvsError_t.CUVS_ERROR:
+        raise RuntimeError("Error creating Device Reources.")
+
+    # todo(dgd): we can make the check of dtype a parameter of wrap_array
+    # in RAFT to make this a single call
+    queries_cai = wrap_array(queries)
+    _check_input_array(queries_cai, [np.dtype('float32'), np.dtype('byte'),
+                                     np.dtype('ubyte')])
+
+    cdef uint32_t n_queries = queries_cai.shape[0]
+
+    if neighbors is None:
+        neighbors = device_ndarray.empty((n_queries, k), dtype='uint32')
+
+    neighbors_cai = wrap_array(neighbors)
+    _check_input_array(neighbors_cai, [np.dtype('uint32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    if distances is None:
+        distances = device_ndarray.empty((n_queries, k), dtype='float32')
+
+    distances_cai = wrap_array(distances)
+    _check_input_array(distances_cai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    cdef cuvsCagraSearchParams* params = &search_params.params
+    cdef cuvsError_t search_status
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_cai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_cai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_cai)
+
+    with cuda_interruptible():
+        search_status = cuvsCagraSearch(
+            res_,
+            params,
+            index.index,
+            queries_dlpack,
+            neighbors_dlpack,
+            distances_dlpack
+        )
+
+        if search_status == cuvsError_t.CUVS_ERROR:
+            raise RuntimeError("Search failed.")
+
+    return (distances, neighbors)
diff --git a/python/cuvs/cuvs/test/__init__py b/python/cuvs/cuvs/test/__init__py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/test/ann_utils.py b/python/cuvs/cuvs/test/ann_utils.py
new file mode 100644
index 0000000000..60db7f3273
--- /dev/null
+++ b/python/cuvs/cuvs/test/ann_utils.py
@@ -0,0 +1,35 @@
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     h ttp://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+
+def generate_data(shape, dtype):
+    if dtype == np.byte:
+        x = np.random.randint(-127, 128, size=shape, dtype=np.byte)
+    elif dtype == np.ubyte:
+        x = np.random.randint(0, 255, size=shape, dtype=np.ubyte)
+    else:
+        x = np.random.random_sample(shape).astype(dtype)
+
+    return x
+
+
+def calc_recall(ann_idx, true_nn_idx):
+    assert ann_idx.shape == true_nn_idx.shape
+    n = 0
+    for i in range(ann_idx.shape[0]):
+        n += np.intersect1d(ann_idx[i, :], true_nn_idx[i, :]).size
+    recall = n / ann_idx.size
+    return recall
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
new file mode 100644
index 0000000000..6074eee3a7
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -0,0 +1,175 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     h ttp://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+
+from cuvs.neighbors import cagra
+from cuvs.test.ann_utils import calc_recall, generate_data
+
+
+def run_cagra_build_search_test(
+    n_rows=10000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    metric="euclidean",
+    intermediate_graph_degree=128,
+    graph_degree=64,
+    build_algo="ivf_pq",
+    array_type="device",
+    compare=True,
+    inplace=True,
+    add_data_on_build=True,
+    search_params={},
+):
+    dataset = generate_data((n_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+    dataset_device = device_ndarray(dataset)
+
+    build_params = cagra.IndexParams(
+        metric=metric,
+        intermediate_graph_degree=intermediate_graph_degree,
+        graph_degree=graph_degree,
+        build_algo=build_algo,
+    )
+
+    if array_type == "device":
+        index = cagra.build_index(build_params, dataset_device)
+    else:
+        index = cagra.build_index(build_params, dataset)
+
+    if not add_data_on_build:
+        dataset_1 = dataset[: n_rows // 2, :]
+        dataset_2 = dataset[n_rows // 2 :, :]
+        indices_1 = np.arange(n_rows // 2, dtype=np.uint32)
+        indices_2 = np.arange(n_rows // 2, n_rows, dtype=np.uint32)
+        if array_type == "device":
+            dataset_1_device = device_ndarray(dataset_1)
+            dataset_2_device = device_ndarray(dataset_2)
+            indices_1_device = device_ndarray(indices_1)
+            indices_2_device = device_ndarray(indices_2)
+            index = cagra.extend(index, dataset_1_device, indices_1_device)
+            index = cagra.extend(index, dataset_2_device, indices_2_device)
+        else:
+            index = cagra.extend(index, dataset_1, indices_1)
+            index = cagra.extend(index, dataset_2, indices_2)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    out_idx = np.zeros((n_queries, k), dtype=np.uint32)
+    out_dist = np.zeros((n_queries, k), dtype=np.float32)
+
+    queries_device = device_ndarray(queries)
+    out_idx_device = device_ndarray(out_idx) if inplace else None
+    out_dist_device = device_ndarray(out_dist) if inplace else None
+
+    search_params = cagra.SearchParams(**search_params)
+
+    ret_output = cagra.search(
+        search_params,
+        index,
+        queries_device,
+        k,
+        neighbors=out_idx_device,
+        distances=out_dist_device,
+    )
+
+    if not inplace:
+        out_dist_device, out_idx_device = ret_output
+
+    if not compare:
+        return
+
+    out_idx = out_idx_device.copy_to_host()
+    out_dist = out_dist_device.copy_to_host()
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    recall = calc_recall(out_idx, skl_idx)
+    assert recall > 0.7
+
+
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("array_type", ["device", "host"])
+@pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
+def test_cagra_dataset_dtype_host_device(
+    dtype, array_type, inplace, build_algo
+):
+    # Note that inner_product tests use normalized input which we cannot
+    # represent in int8, therefore we test only sqeuclidean metric here.
+    run_cagra_build_search_test(
+        dtype=dtype,
+        inplace=inplace,
+        array_type=array_type,
+        build_algo=build_algo,
+    )
+
+
+@pytest.mark.parametrize(
+    "params",
+    [
+        {
+            "intermediate_graph_degree": 64,
+            "graph_degree": 32,
+            "add_data_on_build": True,
+            "k": 1,
+            "metric": "euclidean",
+            "build_algo": "ivf_pq",
+        },
+        {
+            "intermediate_graph_degree": 32,
+            "graph_degree": 16,
+            "add_data_on_build": False,
+            "k": 5,
+            "metric": "sqeuclidean",
+            "build_algo": "ivf_pq",
+        },
+        {
+            "intermediate_graph_degree": 128,
+            "graph_degree": 32,
+            "add_data_on_build": True,
+            "k": 10,
+            "metric": "inner_product",
+            "build_algo": "nn_descent",
+        },
+    ],
+)
+def test_cagra_index_params(params):
+    # Note that inner_product tests use normalized input which we cannot
+    # represent in int8, therefore we test only sqeuclidean metric here.
+    run_cagra_build_search_test(
+        k=params["k"],
+        metric=params["metric"],
+        graph_degree=params["graph_degree"],
+        intermediate_graph_degree=params["intermediate_graph_degree"],
+        compare=False,
+        build_algo=params["build_algo"],
+    )
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
new file mode 100644
index 0000000000..6d56ffaa2f
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -0,0 +1,116 @@
+#
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import contextlib
+import doctest
+import inspect
+import io
+
+import pytest
+
+import cuvs.neighbors
+
+# Code adapted from https://github.com/rapidsai/cudf/blob/branch-23.02/python/cudf/cudf/tests/test_doctests.py  # noqa
+
+
+def _name_in_all(parent, name):
+    return name in getattr(parent, "__all__", [])
+
+
+def _is_public_name(parent, name):
+    return not name.startswith("_")
+
+
+def _find_doctests_in_obj(obj, finder=None, criteria=None):
+    """Find all doctests in an object.
+
+    Parameters
+    ----------
+    obj : module or class
+        The object to search for docstring examples.
+    finder : doctest.DocTestFinder, optional
+        The DocTestFinder object to use. If not provided, a DocTestFinder is
+        constructed.
+    criteria : callable, optional
+        Callable indicating whether to recurse over members of the provided
+        object. If not provided, names not defined in the object's ``__all__``
+        property are ignored.
+
+    Yields
+    ------
+    doctest.DocTest
+        The next doctest found in the object.
+    """
+    if finder is None:
+        finder = doctest.DocTestFinder()
+    if criteria is None:
+        criteria = _name_in_all
+    for docstring in finder.find(obj):
+        if docstring.examples:
+            yield docstring
+    for name, member in inspect.getmembers(obj):
+        # Only recurse over members matching the criteria
+        if not criteria(obj, name):
+            continue
+        # Recurse over the public API of modules (objects defined in the
+        # module's __all__)
+        if inspect.ismodule(member):
+            yield from _find_doctests_in_obj(
+                member, finder, criteria=_name_in_all
+            )
+        # Recurse over the public API of classes (attributes not prefixed with
+        # an underscore)
+        if inspect.isclass(member):
+            yield from _find_doctests_in_obj(
+                member, finder, criteria=_is_public_name
+            )
+
+        # doctest finder seems to dislike cython functions, since
+        # `inspect.isfunction` doesn't return true for them. hack around this
+        if callable(member) and not inspect.isfunction(member):
+            for docstring in finder.find(member):
+                if docstring.examples:
+                    yield docstring
+
+
+# since the root pylibraft module doesn't import submodules (or define an
+# __all__) we are explicitly adding all the submodules we want to run
+# doctests for here
+DOC_STRINGS = list(_find_doctests_in_obj(cuvs.neighbors))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.cagra))
+
+
+@pytest.mark.parametrize(
+    "docstring",
+    DOC_STRINGS,
+    ids=lambda docstring: docstring.name,
+)
+def test_docstring(docstring):
+    # We ignore differences in whitespace in the doctest output, and enable
+    # the use of an ellipsis "..." to match any string in the doctest
+    # output. An ellipsis is useful for, e.g., memory addresses or
+    # imprecise floating point values.
+    optionflags = doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE
+    runner = doctest.DocTestRunner(optionflags=optionflags)
+
+    # Capture stdout and include failing outputs in the traceback.
+    doctest_stdout = io.StringIO()
+    with contextlib.redirect_stdout(doctest_stdout):
+        runner.run(docstring)
+        results = runner.summarize()
+    assert not results.failed, (
+        f"{results.failed} of {results.attempted} doctests failed for "
+        f"{docstring.name}:\n{doctest_stdout.getvalue()}"
+    )
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index cba8d4adf0..664cf29692 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -19,12 +19,11 @@ requires = [
     "cuda-python>=11.7.1,<12.0a0",
     "cython>=3.0.0",
     "ninja",
-    "rmm==24.2.*",
-    "scikit-build>=0.13.1",
-    "setuptools",
-    "wheel",
+    "pylibraft==24.4.*",
+    "rmm==24.4.*",
+    "scikit-build-core[pyproject]>=0.7.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-build-backend = "setuptools.build_meta"
+build-backend = "scikit_build_core.build"
 
 [project]
 name = "cuvs"
@@ -38,8 +37,8 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.9"
 dependencies = [
     "cuda-python>=11.7.1,<12.0a0",
-    "numpy>=1.21",
-    "rmm==24.2.*",
+    "numpy>=1.23",
+    "rmm==24.4.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -51,10 +50,10 @@ classifiers = [
 [project.optional-dependencies]
 test = [
     "cupy-cuda11x>=12.0.0",
-    "pytest",
+    "pylibraft==24.4.*",
     "pytest-cov",
+    "pytest==7.*",
     "scikit-learn",
-    "scipy",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 
 [project.urls]
@@ -109,3 +108,16 @@ skip = [
     "dist",
     "__init__.py",
 ]
+
+[tool.scikit-build]
+build-dir = "build/{wheel_tag}"
+cmake.build-type = "Release"
+cmake.minimum-version = "3.26.4"
+ninja.make-fallback = true
+sdist.reproducible = true
+wheel.packages = ["cuvs"]
+
+[tool.scikit-build.metadata.version]
+provider = "scikit_build_core.metadata.regex"
+input = "cuvs/VERSION"
+regex = "(?P<value>.*)"
diff --git a/python/cuvs/setup.cfg b/python/cuvs/setup.cfg
index 3574b44160..57b4954bc6 100644
--- a/python/cuvs/setup.cfg
+++ b/python/cuvs/setup.cfg
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2024, NVIDIA CORPORATION.
 
 [isort]
 line_length=79
@@ -12,6 +12,7 @@ known_dask=
     distributed
     dask_cuda
 known_rapids=
+    cuvs
     nvtext
     cudf
     cuml
diff --git a/python/cuvs/setup.py b/python/cuvs/setup.py
deleted file mode 100644
index 4e825dab29..0000000000
--- a/python/cuvs/setup.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-from setuptools import find_packages
-from skbuild import setup
-
-
-def exclude_libcxx_symlink(cmake_manifest):
-    return list(
-        filter(
-            lambda name: not ("include/rapids/libcxx/include" in name),
-            cmake_manifest,
-        )
-    )
-
-
-packages = find_packages(include=["cuvs*"])
-setup(
-    # Don't want libcxx getting pulled into wheel builds.
-    cmake_process_manifest_hook=exclude_libcxx_symlink,
-    packages=packages,
-    package_data={key: ["VERSION", "*.pxd"] for key in packages},
-    zip_safe=False,
-)

From 8e6979f5548e447e155af6d728dc7c29a6e5af79 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Mon, 4 Mar 2024 11:30:10 -0800
Subject: [PATCH 004/623] Add Rust bindings for CAGRA (#34)

---
 .github/workflows/build.yaml         |  13 ++
 .github/workflows/pr.yaml            |  11 ++
 README.md                            |  61 +++++++++
 ci/build_rust.sh                     |  40 ++++++
 dependencies.yaml                    |  16 ++-
 rust/Cargo.toml                      |  16 +++
 rust/cuvs-sys/Cargo.toml             |  16 +++
 rust/cuvs-sys/build.rs               | 112 ++++++++++++++++
 rust/cuvs-sys/cuvs_c_wrapper.h       |  20 +++
 rust/cuvs-sys/src/lib.rs             |  54 ++++++++
 rust/cuvs/Cargo.toml                 |  16 +++
 rust/cuvs/build.rs                   |  29 ++++
 rust/cuvs/examples/cagra.rs          |  78 +++++++++++
 rust/cuvs/src/cagra/index.rs         | 141 ++++++++++++++++++++
 rust/cuvs/src/cagra/index_params.rs  | 110 ++++++++++++++++
 rust/cuvs/src/cagra/mod.rs           |  23 ++++
 rust/cuvs/src/cagra/search_params.rs | 168 +++++++++++++++++++++++
 rust/cuvs/src/dlpack.rs              | 190 +++++++++++++++++++++++++++
 rust/cuvs/src/error.rs               |  51 +++++++
 rust/cuvs/src/lib.rs                 |  24 ++++
 rust/cuvs/src/resources.rs           |  52 ++++++++
 21 files changed, 1240 insertions(+), 1 deletion(-)
 create mode 100755 ci/build_rust.sh
 create mode 100644 rust/Cargo.toml
 create mode 100644 rust/cuvs-sys/Cargo.toml
 create mode 100644 rust/cuvs-sys/build.rs
 create mode 100644 rust/cuvs-sys/cuvs_c_wrapper.h
 create mode 100644 rust/cuvs-sys/src/lib.rs
 create mode 100644 rust/cuvs/Cargo.toml
 create mode 100644 rust/cuvs/build.rs
 create mode 100644 rust/cuvs/examples/cagra.rs
 create mode 100644 rust/cuvs/src/cagra/index.rs
 create mode 100644 rust/cuvs/src/cagra/index_params.rs
 create mode 100644 rust/cuvs/src/cagra/mod.rs
 create mode 100644 rust/cuvs/src/cagra/search_params.rs
 create mode 100644 rust/cuvs/src/dlpack.rs
 create mode 100644 rust/cuvs/src/error.rs
 create mode 100644 rust/cuvs/src/lib.rs
 create mode 100644 rust/cuvs/src/resources.rs

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 34cf1f5b07..61ebdf0bca 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -34,6 +34,19 @@ jobs:
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
+  rust-build:
+    needs: cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      arch: "amd64"
+      date: ${{ inputs.date }}
+      container_image: "rapidsai/ci-conda:latest"
+      node_type: "gpu-v100-latest-1"
+      run_script: "ci/build_rust.sh"
+      sha: ${{ inputs.sha }}
   python-build:
     needs: [cpp-build]
     secrets: inherit
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 5799f51087..7c6db2c603 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -19,6 +19,7 @@ jobs:
       - conda-python-build
       - conda-python-tests
       - docs-build
+      - rust-build
       - wheel-build-cuvs
       - wheel-tests-cuvs
       - devcontainer
@@ -72,6 +73,16 @@ jobs:
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
       run_script: "ci/build_docs.sh"
+  rust-build:
+    needs: conda-cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/build_rust.sh"
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
diff --git a/README.md b/README.md
index dfba9eb4aa..14bb028123 100755
--- a/README.md
+++ b/README.md
@@ -106,6 +106,67 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
+### Rust API
+
+```rust
+use cuvs::cagra::{Index, IndexParams, SearchParams};
+use cuvs::{ManagedTensor, Resources, Result};
+
+use ndarray::s;
+use ndarray_rand::rand_distr::Uniform;
+use ndarray_rand::RandomExt;
+
+/// Example showing how to index and search data with CAGRA
+fn cagra_example() -> Result<()> {
+    let res = Resources::new()?;
+
+    // Create a new random dataset to index
+    let n_datapoints = 65536;
+    let n_features = 512;
+    let dataset =
+        ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+    // build the cagra index
+    let build_params = IndexParams::new()?;
+    let index = Index::build(&res, &build_params, &dataset)?;
+    println!(
+        "Indexed {}x{} datapoints into cagra index",
+        n_datapoints, n_features
+    );
+
+    // use the first 4 points from the dataset as queries : will test that we get them back
+    // as their own nearest neighbor
+    let n_queries = 4;
+    let queries = dataset.slice(s![0..n_queries, ..]);
+
+    let k = 10;
+
+    // CAGRA search API requires queries and outputs to be on device memory
+    // copy query data over, and allocate new device memory for the distances/ neighbors
+    // outputs
+    let queries = ManagedTensor::from(&queries).to_device(&res)?;
+    let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+    let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;
+
+    let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+    let distances = ManagedTensor::from(&distances_host).to_device(&res)?;
+
+    let search_params = SearchParams::new()?;
+
+    index.search(&res, &search_params, &queries, &neighbors, &distances)?;
+
+    // Copy back to host memory
+    distances.to_host(&res, &mut distances_host)?;
+    neighbors.to_host(&res, &mut neighbors_host)?;
+
+    // nearest neighbors should be themselves, since queries are from the
+    // dataset
+    println!("Neighbors {:?}", neighbors_host);
+    println!("Distances {:?}", distances_host);
+    Ok(())
+}
+```
+
 
 ## Contributing
 
diff --git a/ci/build_rust.sh b/ci/build_rust.sh
new file mode 100755
index 0000000000..895dd41e07
--- /dev/null
+++ b/ci/build_rust.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+rapids-logger "Create test conda environment"
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key rust \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n rust
+
+# seeing failures on activating the environment here on unbound locals
+# apply workaround from https://github.com/conda/conda/issues/8186#issuecomment-532874667
+set +eu
+conda activate rust
+set -eu
+
+rapids-print-env
+
+# we need to set up LIBCLANG_PATH to allow rust bindgen to work,
+# grab it from the conda env
+export LIBCLANG_PATH=$(dirname $(find /opt/conda -name libclang.so | head -n 1))
+echo "LIBCLANG_PATH=$LIBCLANG_PATH"
+
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+
+# installing libcuvs/libraft will speed up the rust build substantially
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  libcuvs  \
+  libraft
+
+# build and test the rust bindings
+cd rust
+cargo test
diff --git a/dependencies.yaml b/dependencies.yaml
index f17b84dffa..d7562ce573 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -44,7 +44,12 @@ files:
       - cupy
       - docs
       - py_version
-      - test_py_cuvs
+  rust:
+    output: none
+    includes:
+      - build
+      - cuda
+      - rust
   py_build_py_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
@@ -308,6 +313,15 @@ dependencies:
           - recommonmark
           - sphinx-copybutton
           - sphinx-markdown-tables
+  rust:
+    common:
+      - output_types: [conda]
+        packages:
+          - make
+          - rust
+          # clang/liblclang only needed for bindgen support
+          - clang  
+          - libclang
   build_wheels:
     common:
       - output_types: [requirements, pyproject]
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
new file mode 100644
index 0000000000..7e9bfe4ecb
--- /dev/null
+++ b/rust/Cargo.toml
@@ -0,0 +1,16 @@
+[workspace]
+members = [
+    "cuvs",
+    "cuvs-sys",
+]
+resolver = "2"
+
+[workspace.package]
+version = "0.1.0"
+edition = "2021"
+repository = "https://github.com/rapidsai/cuvs"
+homepage = "https://github.com/rapidsai/cuvs"
+description = "RAPIDS vector search library"
+authors = ["NVIDIA Corporation"]
+license = "Apache-2.0"
+
diff --git a/rust/cuvs-sys/Cargo.toml b/rust/cuvs-sys/Cargo.toml
new file mode 100644
index 0000000000..b011e6b374
--- /dev/null
+++ b/rust/cuvs-sys/Cargo.toml
@@ -0,0 +1,16 @@
+[package]
+name = "cuvs-sys"
+description = "Low-level rust bindings to libcuvs"
+links = "cuvs"
+version.workspace = true
+edition.workspace = true
+repository.workspace = true
+homepage.workspace = true
+authors.workspace = true
+license.workspace = true
+
+[dependencies]
+
+[build-dependencies]
+cmake = ">=0.1"
+bindgen = ">=0.69"
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
new file mode 100644
index 0000000000..816a6f2592
--- /dev/null
+++ b/rust/cuvs-sys/build.rs
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::env;
+use std::io::BufRead;
+use std::path::PathBuf;
+
+/*
+    TODO:
+    * would be nice to use already built versions of libcuvs_c / libcuvs
+        if they already existed, but this might not be possible here using cmake-rs
+        (https://github.com/rust-lang/cmake-rs/issues/111)
+    * figure out how this works with rust packaging: does the c++ code
+        need to be in a subdirectory? If so would a symlink work here
+        should we be using static linking ?
+*/
+fn main() {
+    // build the cuvs c-api library with cmake, and link it into this crate
+    let cuvs_build = cmake::Config::new("../../cpp")
+        .configure_arg("-DBUILD_TESTS:BOOL=OFF")
+        .configure_arg("-DBUILD_C_LIBRARY:BOOL=ON")
+        .build();
+
+    println!(
+        "cargo:rustc-link-search=native={}/lib",
+        cuvs_build.display()
+    );
+    println!("cargo:rustc-link-lib=dylib=cuvs_c");
+    println!("cargo:rustc-link-lib=dylib=cudart");
+
+    // we need some extra flags both to link against cuvs, and also to run bindgen
+    // specifically we need to:
+    //  * -I flags to set the include path to pick up cudaruntime.h during bindgen
+    //  * -rpath-link settings to link to libraft/libcuvs.so etc during the link
+    // Rather than redefine the logic to set all these things, lets pick up the values from
+    // the cuvs cmake build in its CMakeCache.txt and set from there
+    let out_path = PathBuf::from(env::var("OUT_DIR").unwrap());
+
+    let cmake_cache: Vec<String> = std::io::BufReader::new(
+        std::fs::File::open(format!("{}/build/CMakeCache.txt", out_path.display()))
+            .expect("Failed to open cuvs CMakeCache.txt"),
+    )
+    .lines()
+    .map(|x| x.expect("Couldn't parse line from CMakeCache.txt"))
+    .collect();
+
+    let cmake_cxx_flags = cmake_cache
+        .iter()
+        .find(|x| x.starts_with("CMAKE_CXX_FLAGS:STRING="))
+        .expect("failed to find CMAKE_CXX_FLAGS in CMakeCache.txt")
+        .strip_prefix("CMAKE_CXX_FLAGS:STRING=")
+        .unwrap();
+
+    let cmake_linker_flags = cmake_cache
+        .iter()
+        .find(|x| x.starts_with("CMAKE_EXE_LINKER_FLAGS:STRING="))
+        .expect("failed to find CMAKE_EXE_LINKER_FLAGS in CMakeCache.txt")
+        .strip_prefix("CMAKE_EXE_LINKER_FLAGS:STRING=")
+        .unwrap();
+
+    // need to propagate the rpath-link settings to dependent crates =(
+    // (this will get added as DEP_CUVS_CMAKE_LINKER_ARGS in dependent crates)
+    println!("cargo:cmake_linker_flags={}", cmake_linker_flags);
+
+    // add the required rpath-link flags to the cargo build
+    for flag in cmake_linker_flags.split(' ') {
+        if flag.starts_with("-Wl,-rpath-link") {
+            println!("cargo:rustc-link-arg={}", flag);
+        }
+    }
+
+    // run bindgen to automatically create rust bindings for the cuvs c-api
+    bindgen::Builder::default()
+        .header("cuvs_c_wrapper.h")
+        .clang_arg("-I../../cpp/include")
+        // needed to find cudaruntime.h
+        .clang_args(cmake_cxx_flags.split(' '))
+        // include dlpack from the cmake build dependencies
+        .clang_arg(format!(
+            "-I{}/build/_deps/dlpack-src/include/",
+            out_path.display()
+        ))
+        // add `must_use' declarations to functions returning cuvsError_t
+        // (so that if you don't check the error code a compile warning is
+        // generated)
+        .must_use_type("cuvsError_t")
+        // Only generate bindings for cuvs/cagra types and functions
+        .allowlist_type("(cuvs|cagra|DL).*")
+        .allowlist_function("(cuvs|cagra).*")
+        .rustified_enum("(cuvs|cagra|DL).*")
+        // also need some basic cuda mem functions
+        // (TODO: should we be adding in RMM support instead here?)
+        .allowlist_function("(cudaMalloc|cudaFree|cudaMemcpy)")
+        .rustified_enum("cudaError")
+        .generate()
+        .expect("Unable to generate cagra_c bindings")
+        .write_to_file(out_path.join("cuvs_bindings.rs"))
+        .expect("Failed to write generated rust bindings");
+}
diff --git a/rust/cuvs-sys/cuvs_c_wrapper.h b/rust/cuvs-sys/cuvs_c_wrapper.h
new file mode 100644
index 0000000000..ccca82632d
--- /dev/null
+++ b/rust/cuvs-sys/cuvs_c_wrapper.h
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// wrapper file containing all the C-API's we should automatically be creating rust
+// bindings for
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/cagra.h>
diff --git a/rust/cuvs-sys/src/lib.rs b/rust/cuvs-sys/src/lib.rs
new file mode 100644
index 0000000000..8a261a0524
--- /dev/null
+++ b/rust/cuvs-sys/src/lib.rs
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// ignore warnings from bindgen
+#![allow(non_upper_case_globals)]
+#![allow(non_camel_case_types)]
+#![allow(non_snake_case)]
+#![allow(unused_attributes)]
+
+// include the generated cuvs_bindings.rs file directly in here
+// (this file is automatically generated by bindgen in build.rs)
+include!(concat!(env!("OUT_DIR"), "/cuvs_bindings.rs"));
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    // some super basic tests here to make sure we can call into the cuvs library
+    // the actual logic will be tested out through the higher level bindings
+
+    #[test]
+    fn test_create_cagra_index() {
+        unsafe {
+            let mut index = core::mem::MaybeUninit::<cuvsCagraIndex_t>::uninit();
+            assert_eq!(
+                cuvsCagraIndexCreate(index.as_mut_ptr()),
+                cuvsError_t::CUVS_SUCCESS
+            );
+            let index = index.assume_init();
+            assert_eq!(cuvsCagraIndexDestroy(index), cuvsError_t::CUVS_SUCCESS);
+        }
+    }
+
+    #[test]
+    fn test_create_resources() {
+        unsafe {
+            let mut res: cuvsResources_t = 0;
+            assert_eq!(cuvsResourcesCreate(&mut res), cuvsError_t::CUVS_SUCCESS);
+            assert_eq!(cuvsResourcesDestroy(res), cuvsError_t::CUVS_SUCCESS);
+        }
+    }
+}
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
new file mode 100644
index 0000000000..cc52db026e
--- /dev/null
+++ b/rust/cuvs/Cargo.toml
@@ -0,0 +1,16 @@
+[package]
+name = "cuvs"
+description = "RAPIDS vector search library"
+version.workspace = true
+edition.workspace = true
+repository.workspace = true
+homepage.workspace = true
+authors.workspace = true
+license.workspace = true
+
+[dependencies]
+ffi = { package = "cuvs-sys", path = "../cuvs-sys" }
+ndarray = "0.15"
+
+[dev-dependencies]
+ndarray-rand = "*"
diff --git a/rust/cuvs/build.rs b/rust/cuvs/build.rs
new file mode 100644
index 0000000000..fde400dc40
--- /dev/null
+++ b/rust/cuvs/build.rs
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::env;
+
+fn main() {
+    // add the required rpath-link flags to the cargo build
+    // TODO: ... this isn't great, there must be a way to propagate this directly without hacks like
+    // this
+    let cmake_linker_flags = env::var("DEP_CUVS_CMAKE_LINKER_FLAGS").unwrap();
+    for flag in cmake_linker_flags.split(' ') {
+        if flag.starts_with("-Wl,-rpath-link") {
+            println!("cargo:rustc-link-arg={}", flag);
+        }
+    }
+}
diff --git a/rust/cuvs/examples/cagra.rs b/rust/cuvs/examples/cagra.rs
new file mode 100644
index 0000000000..ccc1466dd9
--- /dev/null
+++ b/rust/cuvs/examples/cagra.rs
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use cuvs::cagra::{Index, IndexParams, SearchParams};
+use cuvs::{ManagedTensor, Resources, Result};
+
+use ndarray::s;
+use ndarray_rand::rand_distr::Uniform;
+use ndarray_rand::RandomExt;
+
+/// Example showing how to index and search data with CAGRA
+fn cagra_example() -> Result<()> {
+    let res = Resources::new()?;
+
+    // Create a new random dataset to index
+    let n_datapoints = 65536;
+    let n_features = 512;
+    let dataset =
+        ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+    // build the cagra index
+    let build_params = IndexParams::new()?;
+    let index = Index::build(&res, &build_params, &dataset)?;
+    println!(
+        "Indexed {}x{} datapoints into cagra index",
+        n_datapoints, n_features
+    );
+
+    // use the first 4 points from the dataset as queries : will test that we get them back
+    // as their own nearest neighbor
+    let n_queries = 4;
+    let queries = dataset.slice(s![0..n_queries, ..]);
+
+    let k = 10;
+
+    // CAGRA search API requires queries and outputs to be on device memory
+    // copy query data over, and allocate new device memory for the distances/ neighbors
+    // outputs
+    let queries = ManagedTensor::from(&queries).to_device(&res)?;
+    let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+    let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;
+
+    let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+    let distances = ManagedTensor::from(&distances_host).to_device(&res)?;
+
+    let search_params = SearchParams::new()?;
+
+    index.search(&res, &search_params, &queries, &neighbors, &distances)?;
+
+    // Copy back to host memory
+    distances.to_host(&res, &mut distances_host)?;
+    neighbors.to_host(&res, &mut neighbors_host)?;
+
+    // nearest neighbors should be themselves, since queries are from the
+    // dataset
+    println!("Neighbors {:?}", neighbors_host);
+    println!("Distances {:?}", distances_host);
+    Ok(())
+}
+
+fn main() {
+    if let Err(e) = cagra_example() {
+        println!("Failed to run CAGRA: {:?}", e);
+    }
+}
diff --git a/rust/cuvs/src/cagra/index.rs b/rust/cuvs/src/cagra/index.rs
new file mode 100644
index 0000000000..43f0326766
--- /dev/null
+++ b/rust/cuvs/src/cagra/index.rs
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::io::{stderr, Write};
+
+use crate::cagra::{IndexParams, SearchParams};
+use crate::dlpack::ManagedTensor;
+use crate::error::{check_cuvs, Result};
+use crate::resources::Resources;
+
+#[derive(Debug)]
+pub struct Index(ffi::cuvsCagraIndex_t);
+
+impl Index {
+    /// Builds a new index
+    pub fn build<T: Into<ManagedTensor>>(
+        res: &Resources,
+        params: &IndexParams,
+        dataset: T,
+    ) -> Result<Index> {
+        let dataset: ManagedTensor = dataset.into();
+        let index = Index::new()?;
+        unsafe {
+            check_cuvs(ffi::cuvsCagraBuild(res.0, params.0, dataset.as_ptr(), index.0))?;
+        }
+        Ok(index)
+    }
+
+    /// Creates a new empty index
+    pub fn new() -> Result<Index> {
+        unsafe {
+            let mut index = core::mem::MaybeUninit::<ffi::cuvsCagraIndex_t>::uninit();
+            check_cuvs(ffi::cuvsCagraIndexCreate(index.as_mut_ptr()))?;
+            Ok(Index(index.assume_init()))
+        }
+    }
+
+    pub fn search(
+        self,
+        res: &Resources,
+        params: &SearchParams,
+        queries: &ManagedTensor,
+        neighbors: &ManagedTensor,
+        distances: &ManagedTensor,
+    ) -> Result<()> {
+        unsafe {
+            check_cuvs(ffi::cuvsCagraSearch(
+                res.0,
+                params.0,
+                self.0,
+                queries.as_ptr(),
+                neighbors.as_ptr(),
+                distances.as_ptr(),
+            ))
+        }
+    }
+}
+
+impl Drop for Index {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsCagraIndexDestroy(self.0) }) {
+            write!(stderr(), "failed to call cagraIndexDestroy {:?}", e)
+                .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::s;
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    #[test]
+    fn test_cagra_index() {
+        let res = Resources::new().unwrap();
+
+        // Create a new random dataset to index
+        let n_datapoints = 256;
+        let n_features = 16;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        // build the cagra index
+        let build_params = IndexParams::new().unwrap();
+        let index =
+            Index::build(&res, &build_params, &dataset).expect("failed to create cagra index");
+
+        // use the first 4 points from the dataset as queries : will test that we get them back
+        // as their own nearest neighbor
+        let n_queries = 4;
+        let queries = dataset.slice(s![0..n_queries, ..]);
+
+        let k = 10;
+
+        // CAGRA search API requires queries and outputs to be on device memory
+        // copy query data over, and allocate new device memory for the distances/ neighbors
+        // outputs
+        let queries = ManagedTensor::from(&queries).to_device(&res).unwrap();
+        let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+        let neighbors = ManagedTensor::from(&neighbors_host)
+            .to_device(&res)
+            .unwrap();
+
+        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+        let distances = ManagedTensor::from(&distances_host)
+            .to_device(&res)
+            .unwrap();
+
+        let search_params = SearchParams::new().unwrap();
+
+        index
+            .search(&res, &search_params, &queries, &neighbors, &distances)
+            .unwrap();
+
+        // Copy back to host memory
+        distances.to_host(&res, &mut distances_host).unwrap();
+        neighbors.to_host(&res, &mut neighbors_host).unwrap();
+
+        // nearest neighbors should be themselves, since queries are from the
+        // dataset
+        assert_eq!(neighbors_host[[0, 0]], 0);
+        assert_eq!(neighbors_host[[1, 0]], 1);
+        assert_eq!(neighbors_host[[2, 0]], 2);
+        assert_eq!(neighbors_host[[3, 0]], 3);
+    }
+}
diff --git a/rust/cuvs/src/cagra/index_params.rs b/rust/cuvs/src/cagra/index_params.rs
new file mode 100644
index 0000000000..656ab4a9c1
--- /dev/null
+++ b/rust/cuvs/src/cagra/index_params.rs
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::error::{check_cuvs, Result};
+use std::fmt;
+use std::io::{stderr, Write};
+
+pub type BuildAlgo = ffi::cuvsCagraGraphBuildAlgo;
+
+/// Supplemental parameters to build CAGRA Index
+pub struct IndexParams(pub ffi::cuvsCagraIndexParams_t);
+
+impl IndexParams {
+    pub fn new() -> Result<IndexParams> {
+        unsafe {
+            let mut params = core::mem::MaybeUninit::<ffi::cuvsCagraIndexParams_t>::uninit();
+            check_cuvs(ffi::cuvsCagraIndexParamsCreate(params.as_mut_ptr()))?;
+            Ok(IndexParams(params.assume_init()))
+        }
+    }
+
+    /// Degree of input graph for pruning
+    pub fn set_intermediate_graph_degree(self, intermediate_graph_degree: usize) -> IndexParams {
+        unsafe {
+            (*self.0).intermediate_graph_degree = intermediate_graph_degree;
+        }
+        self
+    }
+
+    /// Degree of output graph
+    pub fn set_graph_degree(self, graph_degree: usize) -> IndexParams {
+        unsafe {
+            (*self.0).graph_degree = graph_degree;
+        }
+        self
+    }
+
+    /// ANN algorithm to build knn graph
+    pub fn set_build_algo(self, build_algo: BuildAlgo) -> IndexParams {
+        unsafe {
+            (*self.0).build_algo = build_algo;
+        }
+        self
+    }
+
+    /// Number of iterations to run if building with NN_DESCENT
+    pub fn set_nn_descent_niter(self, nn_descent_niter: usize) -> IndexParams {
+        unsafe {
+            (*self.0).nn_descent_niter = nn_descent_niter;
+        }
+        self
+    }
+}
+
+impl fmt::Debug for IndexParams {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        // custom debug trait here, default value will show the pointer address
+        // for the inner params object which isn't that useful.
+        write!(f, "IndexParams {{ params: {:?} }}", unsafe { *self.0 })
+    }
+}
+
+impl Drop for IndexParams {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsCagraIndexParamsDestroy(self.0) }) {
+            write!(
+                stderr(),
+                "failed to call cuvsCagraIndexParamsDestroy {:?}",
+                e
+            )
+            .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_index_params() {
+        let params = IndexParams::new()
+            .unwrap()
+            .set_intermediate_graph_degree(128)
+            .set_graph_degree(16)
+            .set_build_algo(BuildAlgo::NN_DESCENT)
+            .set_nn_descent_niter(10);
+
+        // make sure the setters actually updated internal representation on the c-struct
+        unsafe {
+            assert_eq!((*params.0).graph_degree, 16);
+            assert_eq!((*params.0).intermediate_graph_degree, 128);
+            assert_eq!((*params.0).build_algo, BuildAlgo::NN_DESCENT);
+            assert_eq!((*params.0).nn_descent_niter, 10);
+        }
+    }
+}
diff --git a/rust/cuvs/src/cagra/mod.rs b/rust/cuvs/src/cagra/mod.rs
new file mode 100644
index 0000000000..55705c27a1
--- /dev/null
+++ b/rust/cuvs/src/cagra/mod.rs
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+mod index;
+mod index_params;
+mod search_params;
+
+pub use index::Index;
+pub use index_params::{BuildAlgo, IndexParams};
+pub use search_params::{HashMode, SearchAlgo, SearchParams};
diff --git a/rust/cuvs/src/cagra/search_params.rs b/rust/cuvs/src/cagra/search_params.rs
new file mode 100644
index 0000000000..11ac92bdd2
--- /dev/null
+++ b/rust/cuvs/src/cagra/search_params.rs
@@ -0,0 +1,168 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::error::{check_cuvs, Result};
+use std::fmt;
+use std::io::{stderr, Write};
+
+pub type SearchAlgo = ffi::cuvsCagraSearchAlgo;
+pub type HashMode = ffi::cuvsCagraHashMode;
+
+/// Supplemental parameters to search CAGRA index
+pub struct SearchParams(pub ffi::cuvsCagraSearchParams_t);
+
+impl SearchParams {
+    pub fn new() -> Result<SearchParams> {
+        unsafe {
+            let mut params = core::mem::MaybeUninit::<ffi::cuvsCagraSearchParams_t>::uninit();
+            check_cuvs(ffi::cuvsCagraSearchParamsCreate(params.as_mut_ptr()))?;
+            Ok(SearchParams(params.assume_init()))
+        }
+    }
+
+    /// Maximum number of queries to search at the same time (batch size). Auto select when 0
+    pub fn set_max_queries(self, max_queries: usize) -> SearchParams {
+        unsafe {
+            (*self.0).max_queries = max_queries;
+        }
+        self
+    }
+
+    /// Number of intermediate search results retained during the search.
+    /// This is the main knob to adjust trade off between accuracy and search speed.
+    /// Higher values improve the search accuracy
+    pub fn set_itopk_size(self, itopk_size: usize) -> SearchParams {
+        unsafe {
+            (*self.0).itopk_size = itopk_size;
+        }
+        self
+    }
+
+    /// Upper limit of search iterations. Auto select when 0.
+    pub fn set_max_iterations(self, max_iterations: usize) -> SearchParams {
+        unsafe {
+            (*self.0).max_iterations = max_iterations;
+        }
+        self
+    }
+
+    /// Which search implementation to use.
+    pub fn set_algo(self, algo: SearchAlgo) -> SearchParams {
+        unsafe {
+            (*self.0).algo = algo;
+        }
+        self
+    }
+
+    /// Number of threads used to calculate a single distance. 4, 8, 16, or 32.
+    pub fn set_team_size(self, team_size: usize) -> SearchParams {
+        unsafe {
+            (*self.0).team_size = team_size;
+        }
+        self
+    }
+
+    /// Lower limit of search iterations.
+    pub fn set_min_iterations(self, min_iterations: usize) -> SearchParams {
+        unsafe {
+            (*self.0).min_iterations = min_iterations;
+        }
+        self
+    }
+
+    /// Thread block size. 0, 64, 128, 256, 512, 1024. Auto selection when 0.
+    pub fn set_thread_block_size(self, thread_block_size: usize) -> SearchParams {
+        unsafe {
+            (*self.0).thread_block_size = thread_block_size;
+        }
+        self
+    }
+
+    /// Hashmap type. Auto selection when AUTO.
+    pub fn set_hashmap_mode(self, hashmap_mode: HashMode) -> SearchParams {
+        unsafe {
+            (*self.0).hashmap_mode = hashmap_mode;
+        }
+        self
+    }
+
+    /// Lower limit of hashmap bit length. More than 8.
+    pub fn set_hashmap_min_bitlen(self, hashmap_min_bitlen: usize) -> SearchParams {
+        unsafe {
+            (*self.0).hashmap_min_bitlen = hashmap_min_bitlen;
+        }
+        self
+    }
+
+    /// Upper limit of hashmap fill rate. More than 0.1, less than 0.9.
+    pub fn set_hashmap_max_fill_rate(self, hashmap_max_fill_rate: f32) -> SearchParams {
+        unsafe {
+            (*self.0).hashmap_max_fill_rate = hashmap_max_fill_rate;
+        }
+        self
+    }
+
+    /// Number of iterations of initial random seed node selection. 1 or more.
+    pub fn set_num_random_samplings(self, num_random_samplings: u32) -> SearchParams {
+        unsafe {
+            (*self.0).num_random_samplings = num_random_samplings;
+        }
+        self
+    }
+
+    /// Bit mask used for initial random seed node selection.
+    pub fn set_rand_xor_mask(self, rand_xor_mask: u64) -> SearchParams {
+        unsafe {
+            (*self.0).rand_xor_mask = rand_xor_mask;
+        }
+        self
+    }
+}
+
+impl fmt::Debug for SearchParams {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        // custom debug trait here, default value will show the pointer address
+        // for the inner params object which isn't that useful.
+        write!(f, "SearchParams {{ params: {:?} }}", unsafe { *self.0 })
+    }
+}
+
+impl Drop for SearchParams {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsCagraSearchParamsDestroy(self.0) }) {
+            write!(
+                stderr(),
+                "failed to call cuvsCagraSearchParamsDestroy {:?}",
+                e
+            )
+            .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_search_params() {
+        let params = SearchParams::new().unwrap().set_itopk_size(128);
+
+        unsafe {
+            assert_eq!((*params.0).itopk_size, 128);
+        }
+    }
+}
diff --git a/rust/cuvs/src/dlpack.rs b/rust/cuvs/src/dlpack.rs
new file mode 100644
index 0000000000..b86959db12
--- /dev/null
+++ b/rust/cuvs/src/dlpack.rs
@@ -0,0 +1,190 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::convert::From;
+
+use crate::error::{check_cuda, Result};
+use crate::resources::Resources;
+
+#[derive(Debug)]
+pub struct ManagedTensor(ffi::DLManagedTensor);
+
+pub trait IntoDtype {
+    fn ffi_dtype() -> ffi::DLDataType;
+}
+
+impl ManagedTensor {
+    pub fn as_ptr(&self) -> *mut ffi::DLManagedTensor {
+        &self.0 as *const _ as *mut _
+    }
+
+    fn bytes(&self) -> usize {
+        // figure out how many bytes to allocate
+        let mut bytes: usize = 1;
+        for x in 0..self.0.dl_tensor.ndim {
+            bytes *= unsafe { (*self.0.dl_tensor.shape.add(x as usize)) as usize };
+        }
+        bytes *= (self.0.dl_tensor.dtype.bits / 8) as usize;
+        bytes
+    }
+
+    pub fn to_device(&self, _res: &Resources) -> Result<ManagedTensor> {
+        unsafe {
+            let bytes = self.bytes();
+            let mut device_data: *mut std::ffi::c_void = std::ptr::null_mut();
+
+            // allocate storage, copy over
+            check_cuda(ffi::cudaMalloc(&mut device_data as *mut _, bytes))?;
+            check_cuda(ffi::cudaMemcpy(
+                device_data,
+                self.0.dl_tensor.data,
+                bytes,
+                ffi::cudaMemcpyKind_cudaMemcpyDefault,
+            ))?;
+
+            let mut ret = self.0.clone();
+            ret.dl_tensor.data = device_data;
+            // call cudaFree automatically to clean up data
+            ret.deleter = Some(cuda_free_tensor);
+            ret.dl_tensor.device.device_type = ffi::DLDeviceType::kDLCUDA;
+
+            Ok(ManagedTensor(ret))
+        }
+    }
+    pub fn to_host<
+        T: IntoDtype,
+        S: ndarray::RawData<Elem = T> + ndarray::RawDataMut,
+        D: ndarray::Dimension,
+    >(
+        &self,
+        _res: &Resources,
+        arr: &mut ndarray::ArrayBase<S, D>,
+    ) -> Result<()> {
+        unsafe {
+            let bytes = self.bytes();
+            check_cuda(ffi::cudaMemcpy(
+                arr.as_mut_ptr() as *mut std::ffi::c_void,
+                self.0.dl_tensor.data,
+                bytes,
+                ffi::cudaMemcpyKind_cudaMemcpyDefault,
+            ))?;
+
+            Ok(())
+        }
+    }
+}
+
+unsafe extern "C" fn cuda_free_tensor(self_: *mut ffi::DLManagedTensor) {
+    let _ = ffi::cudaFree((*self_).dl_tensor.data);
+}
+
+/// Create a non-owning view of a Tensor from a ndarray
+impl<T: IntoDtype, S: ndarray::RawData<Elem = T>, D: ndarray::Dimension>
+    From<&ndarray::ArrayBase<S, D>> for ManagedTensor
+{
+    fn from(arr: &ndarray::ArrayBase<S, D>) -> Self {
+        // There is a draft PR out right now for creating dlpack directly from ndarray
+        // right now, but until its merged we have to implement ourselves
+        //https://github.com/rust-ndarray/ndarray/pull/1306/files
+        unsafe {
+            let mut ret = std::mem::MaybeUninit::<ffi::DLTensor>::uninit();
+            let tensor = ret.as_mut_ptr();
+            (*tensor).data = arr.as_ptr() as *mut std::os::raw::c_void;
+            (*tensor).device = ffi::DLDevice {
+                device_type: ffi::DLDeviceType::kDLCPU,
+                device_id: 0,
+            };
+            (*tensor).byte_offset = 0;
+            (*tensor).strides = std::ptr::null_mut(); // TODO: error if not rowmajor
+            (*tensor).ndim = arr.ndim() as i32;
+            (*tensor).shape = arr.shape().as_ptr() as *mut _;
+            (*tensor).dtype = T::ffi_dtype();
+            ManagedTensor(ffi::DLManagedTensor {
+                dl_tensor: ret.assume_init(),
+                manager_ctx: std::ptr::null_mut(),
+                deleter: None,
+            })
+        }
+    }
+}
+
+impl Drop for ManagedTensor {
+    fn drop(&mut self) {
+        unsafe {
+            if let Some(deleter) = self.0.deleter {
+                deleter(&mut self.0 as *mut _);
+            }
+        }
+    }
+}
+
+impl IntoDtype for f32 {
+    fn ffi_dtype() -> ffi::DLDataType {
+        ffi::DLDataType {
+            code: ffi::DLDataTypeCode::kDLFloat as _,
+            bits: 32,
+            lanes: 1,
+        }
+    }
+}
+
+impl IntoDtype for f64 {
+    fn ffi_dtype() -> ffi::DLDataType {
+        ffi::DLDataType {
+            code: ffi::DLDataTypeCode::kDLFloat as _,
+            bits: 64,
+            lanes: 1,
+        }
+    }
+}
+
+impl IntoDtype for i32 {
+    fn ffi_dtype() -> ffi::DLDataType {
+        ffi::DLDataType {
+            code: ffi::DLDataTypeCode::kDLInt as _,
+            bits: 32,
+            lanes: 1,
+        }
+    }
+}
+
+impl IntoDtype for u32 {
+    fn ffi_dtype() -> ffi::DLDataType {
+        ffi::DLDataType {
+            code: ffi::DLDataTypeCode::kDLUInt as _,
+            bits: 32,
+            lanes: 1,
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_from_ndarray() {
+        let arr = ndarray::Array::<f32, _>::zeros((8, 4));
+
+        let tensor = unsafe { (*(ManagedTensor::from(&arr).as_ptr())).dl_tensor };
+
+        assert_eq!(tensor.ndim, 2);
+
+        // make sure we can get the shape ok
+        assert_eq!(unsafe { *tensor.shape }, 8);
+        assert_eq!(unsafe { *tensor.shape.add(1) }, 4);
+    }
+}
diff --git a/rust/cuvs/src/error.rs b/rust/cuvs/src/error.rs
new file mode 100644
index 0000000000..618106aba7
--- /dev/null
+++ b/rust/cuvs/src/error.rs
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::fmt;
+
+#[derive(Debug, Clone)]
+pub enum Error {
+    CudaError(ffi::cudaError_t),
+    CuvsError(ffi::cuvsError_t),
+}
+
+impl std::error::Error for Error {}
+
+pub type Result<T> = std::result::Result<T, Error>;
+
+impl fmt::Display for Error {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        match self {
+            Error::CudaError(cuda_error) => write!(f, "cudaError={:?}", cuda_error),
+            Error::CuvsError(cuvs_error) => write!(f, "cuvsError={:?}", cuvs_error),
+        }
+    }
+}
+
+/// Simple wrapper to convert a cuvsError_t into a Result
+pub fn check_cuvs(err: ffi::cuvsError_t) -> Result<()> {
+    match err {
+        ffi::cuvsError_t::CUVS_SUCCESS => Ok(()),
+        _ => Err(Error::CuvsError(err)),
+    }
+}
+
+pub fn check_cuda(err: ffi::cudaError_t) -> Result<()> {
+    match err {
+        ffi::cudaError::cudaSuccess => Ok(()),
+        _ => Err(Error::CudaError(err)),
+    }
+}
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
new file mode 100644
index 0000000000..7a6f847f5d
--- /dev/null
+++ b/rust/cuvs/src/lib.rs
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+pub mod cagra;
+mod dlpack;
+mod error;
+mod resources;
+
+pub use dlpack::ManagedTensor;
+pub use error::{Error, Result};
+pub use resources::Resources;
diff --git a/rust/cuvs/src/resources.rs b/rust/cuvs/src/resources.rs
new file mode 100644
index 0000000000..ad7113e6be
--- /dev/null
+++ b/rust/cuvs/src/resources.rs
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::error::{check_cuvs, Result};
+use std::io::{stderr, Write};
+
+#[derive(Debug)]
+pub struct Resources(pub ffi::cuvsResources_t);
+
+impl Resources {
+    pub fn new() -> Result<Resources> {
+        let mut res: ffi::cuvsResources_t = 0;
+        unsafe {
+            check_cuvs(ffi::cuvsResourcesCreate(&mut res))?;
+        }
+        Ok(Resources(res))
+    }
+}
+
+impl Drop for Resources {
+    fn drop(&mut self) {
+        unsafe {
+            if let Err(e) = check_cuvs(ffi::cuvsResourcesDestroy(self.0)) {
+                write!(stderr(), "failed to call cuvsResourcesDestroy {:?}", e)
+                    .expect("failed to write to stderr");
+            }
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_resources_create() {
+        let _ = Resources::new();
+    }
+}

From b94c1b531f9442ecfb50140c3311bf055a7d7aef Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 5 Mar 2024 11:20:32 -0800
Subject: [PATCH 005/623] Expose exception text to C, Python and Rust API's
 (#39)

Previously when something went wrong in the Rust or C API - all the end user would see is a `CUVS_ERROR` return code with no extra indication of what went wrong.

This change exposes the exception text to both the C and Rust api's, and provides a convenience method to automatically catch c++ exceptions, and convert the exception into an error code with the text set appropiately.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: https://github.com/rapidsai/cuvs/pull/39
---
 cpp/include/cuvs/core/c_api.h              | 10 ++++
 cpp/include/cuvs/core/exceptions.hpp       | 45 ++++++++++++++++
 cpp/src/core/c_api.cpp                     | 38 ++++++-------
 cpp/src/neighbors/cagra_c.cpp              | 62 +++++-----------------
 python/cuvs/cuvs/common/CMakeLists.txt     |  2 +-
 python/cuvs/cuvs/common/c_api.pxd          |  1 +
 python/cuvs/cuvs/common/exceptions.pyx     | 37 +++++++++++++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx | 37 ++++---------
 rust/cuvs/src/cagra/index.rs               |  7 ++-
 rust/cuvs/src/cagra/index_params.rs        |  2 +-
 rust/cuvs/src/error.rs                     | 26 ++++++++-
 11 files changed, 167 insertions(+), 100 deletions(-)
 create mode 100644 cpp/include/cuvs/core/exceptions.hpp
 create mode 100644 python/cuvs/cuvs/common/exceptions.pyx

diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index b500329169..eceb917ff3 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -67,6 +67,16 @@ cuvsError_t cuvsResourcesDestroy(cuvsResources_t res);
  */
 cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream);
 
+/** @brief Returns a string describing the last seen error on this thread, or
+ *         NULL if the last function succeeded.
+ */
+const char* cuvsGetLastErrorText();
+
+/**
+ * @brief Sets a string describing an error seen on the thread. Passing NULL
+ *        clears any previously seen error message.
+ */
+void cuvsSetLastErrorText(const char* error);
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/include/cuvs/core/exceptions.hpp b/cpp/include/cuvs/core/exceptions.hpp
new file mode 100644
index 0000000000..01ee42151d
--- /dev/null
+++ b/cpp/include/cuvs/core/exceptions.hpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "c_api.h"
+
+#include <exception>
+
+namespace cuvs::core {
+
+/**
+ * @brief Translates C++ exceptions into cuvs C-API error codes
+ */
+template <typename Fn>
+cuvsError_t translate_exceptions(Fn func)
+{
+  cuvsError_t status;
+  try {
+    func();
+    status = CUVS_SUCCESS;
+    cuvsSetLastErrorText(NULL);
+  } catch (const std::exception& e) {
+    cuvsSetLastErrorText(e.what());
+    status = CUVS_ERROR;
+  } catch (...) {
+    cuvsSetLastErrorText("unknown exception");
+    status = CUVS_ERROR;
+  }
+  return status;
+}
+}  // namespace cuvs::core
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 133021d77f..7ddb4f3e4c 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -16,46 +16,42 @@
 
 #include <cstdint>
 #include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
 #include <memory>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <rmm/cuda_stream_view.hpp>
+#include <thread>
 
 extern "C" cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
 {
-  cuvsError_t status;
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto res_ptr = new raft::resources{};
     *res         = reinterpret_cast<uintptr_t>(res_ptr);
-    status       = CUVS_SUCCESS;
-  } catch (...) {
-    status = CUVS_ERROR;
-  }
-  return status;
+  });
 }
 
 extern "C" cuvsError_t cuvsResourcesDestroy(cuvsResources_t res)
 {
-  cuvsError_t status;
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto res_ptr = reinterpret_cast<raft::resources*>(res);
     delete res_ptr;
-    status = CUVS_SUCCESS;
-  } catch (...) {
-    status = CUVS_ERROR;
-  }
-  return status;
+  });
 }
 
 extern "C" cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream)
 {
-  cuvsError_t status;
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto res_ptr = reinterpret_cast<raft::resources*>(res);
     raft::resource::set_cuda_stream(*res_ptr, static_cast<rmm::cuda_stream_view>(stream));
-    status = CUVS_SUCCESS;
-  } catch (...) {
-    status = CUVS_ERROR;
-  }
-  return status;
+  });
 }
+
+thread_local std::string last_error_text = "";
+
+extern "C" const char* cuvsGetLastErrorText()
+{
+  return last_error_text.empty() ? NULL : last_error_text.c_str();
+}
+
+extern "C" void cuvsSetLastErrorText(const char* error) { last_error_text = error ? error : ""; }
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 70e268fb2f..9fdfe2c1e7 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -23,6 +23,7 @@
 #include <raft/core/resources.hpp>
 
 #include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
 #include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/cagra.h>
 #include <cuvs/neighbors/cagra.hpp>
@@ -96,17 +97,12 @@ void _search(cuvsResources_t res,
 
 extern "C" cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index)
 {
-  try {
-    *index = new cuvsCagraIndex{};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsCagraIndex{}; });
 }
 
 extern "C" cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index_c_ptr)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
     if (index.dtype.code == kDLFloat) {
@@ -123,10 +119,7 @@ extern "C" cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index_c_ptr)
       delete index_ptr;
     }
     delete index_c_ptr;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
@@ -134,7 +127,7 @@ extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                                       DLManagedTensor* dataset_tensor,
                                       cuvsCagraIndex_t index)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
 
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
@@ -151,13 +144,7 @@ extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                 dataset.dtype.code,
                 dataset.dtype.bits);
     }
-    return CUVS_SUCCESS;
-  } catch (const std::exception& ex) {
-    std::cerr << "Error occurred: " << ex.what() << std::endl;
-    return CUVS_ERROR;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
@@ -167,7 +154,7 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
                                        DLManagedTensor* neighbors_tensor,
                                        DLManagedTensor* distances_tensor)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
     auto neighbors = neighbors_tensor->dl_tensor;
     auto distances = distances_tensor->dl_tensor;
@@ -198,57 +185,36 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
                 queries.dtype.code,
                 queries.dtype.bits);
     }
-    return CUVS_SUCCESS;
-  } catch (const std::exception& ex) {
-    std::cerr << "Error occurred: " << ex.what() << std::endl;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     *params = new cuvsCagraIndexParams{.intermediate_graph_degree = 128,
                                        .graph_degree              = 64,
                                        .build_algo                = IVF_PQ,
                                        .nn_descent_niter          = 20};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params)
 {
-  try {
-    delete params;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { delete params; });
 }
 
 extern "C" cuvsError_t cuvsCagraSearchParamsCreate(cuvsCagraSearchParams_t* params)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     *params = new cuvsCagraSearchParams{.itopk_size            = 64,
                                         .search_width          = 1,
                                         .hashmap_max_fill_rate = 0.5,
                                         .num_random_samplings  = 1,
                                         .rand_xor_mask         = 0x128394};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsCagraSearchParamsDestroy(cuvsCagraSearchParams_t params)
 {
-  try {
-    delete params;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { delete params; });
 }
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index 2cf1ff19c1..b477fdb322 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -13,7 +13,7 @@
 # =============================================================================
 
 # Set the list of Cython files to build
-set(cython_sources cydlpack.pyx)
+set(cython_sources cydlpack.pyx exceptions.pyx)
 set(linked_libraries cuvs::cuvs cuvs_c)
 
 # Build all of the Cython targets
diff --git a/python/cuvs/cuvs/common/c_api.pxd b/python/cuvs/cuvs/common/c_api.pxd
index 6addbf16e2..e4e62bf944 100644
--- a/python/cuvs/cuvs/common/c_api.pxd
+++ b/python/cuvs/cuvs/common/c_api.pxd
@@ -30,3 +30,4 @@ cdef extern from "cuvs/core/c_api.h":
     cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
     cuvsError_t cuvsResourcesDestroy(cuvsResources_t res)
     cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream)
+    const char * cuvsGetLastErrorText()
diff --git a/python/cuvs/cuvs/common/exceptions.pyx b/python/cuvs/cuvs/common/exceptions.pyx
new file mode 100644
index 0000000000..603e0711bc
--- /dev/null
+++ b/python/cuvs/cuvs/common/exceptions.pyx
@@ -0,0 +1,37 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsGetLastErrorText
+
+
+class CuvsException(Exception):
+    pass
+
+
+def get_last_error_text():
+    """ returns the last error description from the cuvs c-api """
+    cdef const char* c_err = cuvsGetLastErrorText()
+    if c_err is NULL:
+        return
+    cdef bytes err = c_err
+    return err.decode("utf8")
+
+
+def check_cuvs(status: cuvsError_t):
+    """ Converts a status code into an exception """
+    if status == cuvsError_t.CUVS_ERROR:
+        raise CuvsException(get_last_error_text())
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index bf17fe6a55..04031414e2 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -52,6 +52,8 @@ from cuvs.common.c_api cimport (
     cuvsResourcesCreate,
 )
 
+from cuvs.common.exceptions import check_cuvs
+
 
 cdef class IndexParams:
     """
@@ -124,19 +126,13 @@ cdef class Index:
     cdef bool trained
 
     def __cinit__(self):
-        cdef cuvsError_t index_create_status
-        index_create_status = cuvsCagraIndexCreate(&self.index)
         self.trained = False
-
-        if index_create_status == cuvsError_t.CUVS_ERROR:
-            raise RuntimeError("Failed to create index.")
+        check_cuvs(cuvsCagraIndexCreate(&self.index))
 
     def __dealloc__(self):
         cdef cuvsError_t index_destroy_status
         if self.index is not NULL:
-            index_destroy_status = cuvsCagraIndexDestroy(self.index)
-            if index_destroy_status == cuvsError_t.CUVS_ERROR:
-                raise Exception("Failed to deallocate index.")
+            check_cuvs(cuvsCagraIndexDestroy(self.index))
 
     @property
     def trained(self):
@@ -203,9 +199,7 @@ def build_index(IndexParams index_params, dataset, resources=None):
     cdef cuvsResources_t res_
     cdef cuvsError_t cstat
 
-    cstat = cuvsResourcesCreate(&res_)
-    if cstat == cuvsError_t.CUVS_ERROR:
-        raise RuntimeError("Error creating Device Reources.")
+    check_cuvs(cuvsResourcesCreate(&res_))
 
     cdef Index idx = Index()
     cdef cuvsError_t build_status
@@ -214,17 +208,13 @@ def build_index(IndexParams index_params, dataset, resources=None):
     cdef cuvsCagraIndexParams* params = index_params.params
 
     with cuda_interruptible():
-        build_status = cuvsCagraBuild(
+        check_cuvs(cuvsCagraBuild(
             res_,
             params,
             dataset_dlpack,
             idx.index
-        )
-
-        if build_status == cuvsError_t.CUVS_ERROR:
-            raise RuntimeError("Index failed to build.")
-        else:
-            idx.trained = True
+        ))
+        idx.trained = True
 
     return idx
 
@@ -451,9 +441,7 @@ def search(SearchParams search_params,
     cdef cuvsResources_t res_
     cdef cuvsError_t cstat
 
-    cstat = cuvsResourcesCreate(&res_)
-    if cstat == cuvsError_t.CUVS_ERROR:
-        raise RuntimeError("Error creating Device Reources.")
+    check_cuvs(cuvsResourcesCreate(&res_))
 
     # todo(dgd): we can make the check of dtype a parameter of wrap_array
     # in RAFT to make this a single call
@@ -487,16 +475,13 @@ def search(SearchParams search_params,
         cydlpack.dlpack_c(distances_cai)
 
     with cuda_interruptible():
-        search_status = cuvsCagraSearch(
+        check_cuvs(cuvsCagraSearch(
             res_,
             params,
             index.index,
             queries_dlpack,
             neighbors_dlpack,
             distances_dlpack
-        )
-
-        if search_status == cuvsError_t.CUVS_ERROR:
-            raise RuntimeError("Search failed.")
+        ))
 
     return (distances, neighbors)
diff --git a/rust/cuvs/src/cagra/index.rs b/rust/cuvs/src/cagra/index.rs
index 43f0326766..3c45efafd2 100644
--- a/rust/cuvs/src/cagra/index.rs
+++ b/rust/cuvs/src/cagra/index.rs
@@ -34,7 +34,12 @@ impl Index {
         let dataset: ManagedTensor = dataset.into();
         let index = Index::new()?;
         unsafe {
-            check_cuvs(ffi::cuvsCagraBuild(res.0, params.0, dataset.as_ptr(), index.0))?;
+            check_cuvs(ffi::cuvsCagraBuild(
+                res.0,
+                params.0,
+                dataset.as_ptr(),
+                index.0,
+            ))?;
         }
         Ok(index)
     }
diff --git a/rust/cuvs/src/cagra/index_params.rs b/rust/cuvs/src/cagra/index_params.rs
index 656ab4a9c1..7ff68d3f3f 100644
--- a/rust/cuvs/src/cagra/index_params.rs
+++ b/rust/cuvs/src/cagra/index_params.rs
@@ -69,7 +69,7 @@ impl fmt::Debug for IndexParams {
     fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
         // custom debug trait here, default value will show the pointer address
         // for the inner params object which isn't that useful.
-        write!(f, "IndexParams {{ params: {:?} }}", unsafe { *self.0 })
+        write!(f, "IndexParams({:?})", unsafe { *self.0 })
     }
 }
 
diff --git a/rust/cuvs/src/error.rs b/rust/cuvs/src/error.rs
index 618106aba7..53c68a9767 100644
--- a/rust/cuvs/src/error.rs
+++ b/rust/cuvs/src/error.rs
@@ -16,13 +16,20 @@
 
 use std::fmt;
 
+#[derive(Debug, Clone)]
+pub struct CuvsError {
+    code: ffi::cuvsError_t,
+    text: String,
+}
+
 #[derive(Debug, Clone)]
 pub enum Error {
     CudaError(ffi::cudaError_t),
-    CuvsError(ffi::cuvsError_t),
+    CuvsError(CuvsError),
 }
 
 impl std::error::Error for Error {}
+impl std::error::Error for CuvsError {}
 
 pub type Result<T> = std::result::Result<T, Error>;
 
@@ -35,11 +42,26 @@ impl fmt::Display for Error {
     }
 }
 
+impl fmt::Display for CuvsError {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        write!(f, "{:?}:{}", self.code, self.text)
+    }
+}
+
 /// Simple wrapper to convert a cuvsError_t into a Result
 pub fn check_cuvs(err: ffi::cuvsError_t) -> Result<()> {
     match err {
         ffi::cuvsError_t::CUVS_SUCCESS => Ok(()),
-        _ => Err(Error::CuvsError(err)),
+        _ => {
+            // get a description of the error from cuvs
+            let cstr = unsafe {
+                let text_ptr = ffi::cuvsGetLastErrorText();
+                std::ffi::CStr::from_ptr(text_ptr)
+            };
+            let text = std::string::String::from_utf8_lossy(cstr.to_bytes()).to_string();
+
+            Err(Error::CuvsError(CuvsError { code: err, text }))
+        }
     }
 }
 

From d3478f729ab8479a14c4e4197faae87d55bb01bd Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Fri, 8 Mar 2024 11:15:34 -0600
Subject: [PATCH 006/623] Fix 'cuvs' conda package to only include Python
 artifacts and clean build.sh (#40)

PR does the following:

- [x] Stops the inclusion of C/C++ artifacts in the `cuvs` conda package
- [x] Cleans `build.sh` from code that was copied from RAFT
- [x] Adds `dlpack` to development environments
- [x] Other small fixes and cleanups

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/40
---
 build.sh                                      | 52 +++-----------
 ci/build_wheel_cuvs.sh                        |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  1 +
 .../all_cuda-118_arch-x86_64.yaml             |  1 +
 .../all_cuda-122_arch-aarch64.yaml            |  1 +
 .../all_cuda-122_arch-x86_64.yaml             |  1 +
 conda/recipes/cuvs/meta.yaml                  |  6 ++
 conda/recipes/libcuvs/meta.yaml               | 72 ++++++++++++++++---
 dependencies.yaml                             |  7 +-
 python/cuvs/CMakeLists.txt                    | 13 +++-
 python/cuvs/pyproject.toml                    |  3 +-
 11 files changed, 96 insertions(+), 63 deletions(-)

diff --git a/build.sh b/build.sh
index c6e09c5bf9..d53f7c3545 100755
--- a/build.sh
+++ b/build.sh
@@ -48,13 +48,13 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
                                  Results can be interpreted with cpp/scripts/analyze_nvcc_log.py
    -h                          - print this text
 
- default action (no args) is to build libcuvs, tests, cuvs and cuvs-dask targets
+ default action (no args) is to build libcuvs, tests and cuvs targets
 "
 LIBCUVS_BUILD_DIR=${LIBCUVS_BUILD_DIR:=${REPODIR}/cpp/build}
 SPHINX_BUILD_DIR=${REPODIR}/docs
 DOXYGEN_BUILD_DIR=${REPODIR}/cpp/doxygen
 PYTHON_BUILD_DIR=${REPODIR}/python/cuvs/_skbuild
-BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR} ${CUVS_DASK_BUILD_DIR}"
+BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR}"
 
 # Set defaults for vars modified by flags to this script
 CMAKE_LOG_LEVEL=""
@@ -218,28 +218,6 @@ if hasArg --uninstall; then
         echo "Could not uninstall cuvs from pip or conda. cuvs package will need to be manually uninstalled"
       fi
     fi
-
-    if hasArg cuvs-dask || (( ${NUMARGS} == 1 )); then
-      echo "Uninstalling cuvs-dask package..."
-      if [ -e ${CUVS_DASK_BUILD_DIR}/install_manifest.txt ]; then
-          xargs rm -fv < ${CUVS_DASK_BUILD_DIR}/install_manifest.txt > /dev/null 2>&1
-      fi
-
-      # Try to uninstall via pip if it is installed
-      if [ -x "$(command -v pip)" ]; then
-        echo "Using pip to uninstall cuvs-dask"
-        pip uninstall -y cuvs-dask
-
-      # Otherwise, try to uninstall through conda if that's where things are installed
-      elif [ -x "$(command -v conda)" ] && [ "$INSTALL_PREFIX" == "$CONDA_PREFIX" ]; then
-        echo "Using conda to uninstall cuvs-dask"
-        conda uninstall -y cuvs-dask
-
-      # Otherwise, fail
-      else
-        echo "Could not uninstall cuvs-dask from pip or conda. cuvs-dask package will need to be manually uninstalled."
-      fi
-    fi
     exit 0
 fi
 
@@ -261,24 +239,14 @@ if hasArg --allgpuarch; then
     BUILD_ALL_GPU_ARCH=1
 fi
 
-
-# Append `-DFIND_CUVS_CPP=ON` to EXTRA_CMAKE_ARGS unless a user specified the option.
-if [[ "${EXTRA_CMAKE_ARGS}" != *"DFIND_CUVS_CPP"* ]]; then
-    EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DFIND_CUVS_CPP=ON"
-fi
-
 if hasArg tests || (( ${NUMARGS} == 0 )); then
     BUILD_TESTS=ON
     CMAKE_TARGET="${CMAKE_TARGET};${TEST_TARGETS}"
 
     # Force compile library when needed test targets are specified
-    if [[ $CMAKE_TARGET == *"CLUSTER_TEST"* || \
-          $CMAKE_TARGET == *"DISTANCE_TEST"* || \
-          $CMAKE_TARGET == *"NEIGHBORS_ANN_CAGRA_TEST"* || \
-          $CMAKE_TARGET == *"NEIGHBORS_ANN_IVF_TEST"* || \
-          $CMAKE_TARGET == *"NEIGHBORS_ANN_NN_DESCENT_TEST"* || \
-          $CMAKE_TARGET == *"NEIGHBORS_TEST"* || \
-          $CMAKE_TARGET == *"STATS_TEST"* ]]; then
+    if [[ $CMAKE_TARGET == *"CAGRA_C_TEST"* || \
+          $CMAKE_TARGET == *"INTEROP_TEST"* || \
+          $CMAKE_TARGET == *"NEIGHBORS_ANN_CAGRA_TEST"* ]]; then
       echo "-- Enabling compiled lib for gtests"
       COMPILE_LIBRARY=ON
     fi
@@ -304,13 +272,10 @@ if [[ ${CMAKE_TARGET} == "" ]]; then
     CMAKE_TARGET="all"
 fi
 
-# Append `-DFIND_CUVS_CPP=ON` to EXTRA_CMAKE_ARGS unless a user specified the option.
-
-
 
 SKBUILD_EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS}"
 if [[ "${EXTRA_CMAKE_ARGS}" != *"DFIND_CUVS_CPP"* ]]; then
-    SKBUILD_EXTRA_CMAKE_ARGS="${SKBUILD_EXTRA_CMAKE_ARGS} -DFIND_CUVS_CPP=ON"
+    SKBUILD_EXTRA_CMAKE_ARGS="${SKBUILD_EXTRA_CMAKE_ARGS};-DFIND_CUVS_CPP=ON"
 fi
 
 # If clean given, run it prior to any other steps
@@ -353,7 +318,6 @@ if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || has
           -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \
           -DCMAKE_CUDA_ARCHITECTURES=${CUVS_CMAKE_CUDA_ARCHITECTURES} \
           -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
-          -DCUVS_COMPILE_LIBRARY=${COMPILE_LIBRARY} \
           -DBUILD_C_LIBRARY=${COMPILE_LIBRARY} \
           -DCUVS_NVTX=${NVTX} \
           -DCUDA_LOG_COMPILE_TIME=${LOG_COMPILE_TIME} \
@@ -422,9 +386,9 @@ fi
 
 # Build and (optionally) install the cuvs Python package
 if (( ${NUMARGS} == 0 )) || hasArg python; then
-    SKBUILD_CONFIGURE_OPTIONS="${SKBUILD_EXTRA_CMAKE_ARGS}" \
+    SKBUILD_CMAKE_ARGS="${SKBUILD_EXTRA_CMAKE_ARGS}" \
         SKBUILD_BUILD_OPTIONS="-j${PARALLEL_LEVEL}" \
-        python -m pip install --no-build-isolation --no-deps ${REPODIR}/python/cuvs
+        python -m pip install --no-build-isolation --no-deps -vvv ${REPODIR}/python/cuvs
 fi
 
 if hasArg docs; then
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index 238483b6e5..0fe28c2f7f 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -4,6 +4,6 @@
 set -euo pipefail
 
 # Set up skbuild options. Enable sccache in skbuild config options
-export SKBUILD_CONFIGURE_OPTIONS="-DDETECT_CONDA_ENV=OFF -DFIND_CUVS_CPP=OFF"
+export SKBUILD_CMAKE_ARGS="-DDETECT_CONDA_ENV=OFF -DFIND_CUVS_CPP=OFF"
 
 ci/build_wheel.sh cuvs python/cuvs
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index e4f9223784..d711fc5554 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -20,6 +20,7 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
+- dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-aarch64=11.*
 - gmock>=1.13.0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index a26314b229..b4c2c6857b 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -20,6 +20,7 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
+- dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-64=11.*
 - gmock>=1.13.0
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index 91d55917ad..85d96e5c94 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -21,6 +21,7 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
+- dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-aarch64=11.*
 - gmock>=1.13.0
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index f27d131ffa..16cc655c63 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -21,6 +21,7 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
+- dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-64=11.*
 - gmock>=1.13.0
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index 0902dd15a5..b2a8074007 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -22,6 +22,9 @@ build:
   ignore_run_exports_from:
     {% if cuda_major == "11" %}
     - {{ compiler('cuda11') }}
+    {% else %}
+    - {{ compiler('cuda') }}
+    - cuda-cudart-dev
     {% endif %}
 
 requirements:
@@ -43,6 +46,7 @@ requirements:
     - cudatoolkit
     {% else %}
     - cuda-python >=12.0,<13.0a0
+    - cuda-cudart-dev
     {% endif %}
     - cuda-version ={{ cuda_version }}
     - cython >=3.0.0
@@ -57,6 +61,8 @@ requirements:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major == "11" %}
     - cudatoolkit
+    {% else %}
+    - cuda-cudart
     {% endif %}
     - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index 4b6ff87e97..4ad3618c38 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -1,13 +1,12 @@
 # Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 # Usage:
-  #   conda build . -c conda-forge -c nvidia -c rapidsai
-  {% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
-  {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
-  {% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
-  {% set cuda_major = cuda_version.split('.')[0] %}
-  {% set cuda_spec = ">=" + cuda_major ~ ",<" + (cuda_major | int + 1) ~ ".0a0" %} # i.e. >=11,<12.0a0
-  {% set date_string = environ['RAPIDS_DATE_STRING'] %}
+#   conda build . -c conda-forge -c nvidia -c rapidsai
+{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
+{% set cuda_major = cuda_version.split('.')[0] %}
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
 
 package:
   name: libcuvs-split
@@ -42,6 +41,13 @@ outputs:
       ignore_run_exports_from:
         {% if cuda_major == "11" %}
         - {{ compiler('cuda11') }}
+        {% else %}
+        - {{ compiler('cuda') }}
+        - cuda-cudart-dev
+        - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
         {% endif %}
     requirements:
       build:
@@ -70,6 +76,7 @@ outputs:
         - libcusparse {{ cuda11_libcusparse_host_version }}
         - libcusparse-dev {{ cuda11_libcusparse_host_version }}
         {% else %}
+        - cuda-cudart-dev
         - cuda-profiler-api
         - libcublas-dev
         - libcurand-dev
@@ -79,6 +86,13 @@ outputs:
       run:
         - libraft ={{ minor_version }}
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
+        {% if cuda_major != "11" %}
+        - cuda-cudart
+        - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
+        {% endif %}
     about:
       home: https://rapids.ai/
       license: Apache-2.0
@@ -93,6 +107,13 @@ outputs:
       ignore_run_exports_from:
         {% if cuda_major == "11" %}
         - {{ compiler('cuda11') }}
+        {% else %}
+        - {{ compiler('cuda') }}
+        - cuda-cudart-dev
+        - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
         {% endif %}
     requirements:
       build:
@@ -121,6 +142,7 @@ outputs:
         - libcusparse {{ cuda11_libcusparse_host_version }}
         - libcusparse-dev {{ cuda11_libcusparse_host_version }}
         {% else %}
+        - cuda-cudart-dev
         - cuda-profiler-api
         - libcublas-dev
         - libcurand-dev
@@ -130,6 +152,13 @@ outputs:
       run:
         - libraft ={{ minor_version }}
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
+        {% if cuda_major != "11" %}
+        - cuda-cudart
+        - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
+        {% endif %}
     about:
       home: https://rapids.ai/
       license: Apache-2.0
@@ -144,6 +173,13 @@ outputs:
       ignore_run_exports_from:
         {% if cuda_major == "11" %}
         - {{ compiler('cuda11') }}
+        {% else %}
+        - {{ compiler('cuda') }}
+        - cuda-cudart-dev
+        - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
         {% endif %}
     requirements:
       build:
@@ -160,7 +196,7 @@ outputs:
         - sysroot_{{ target_platform }} {{ sysroot_version }}
       host:
         - libraft ={{ minor_version }}
-        - libcuvs ={{ minor_version }}
+        - {{ pin_subpackage('libcuvs', exact=True) }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
@@ -186,9 +222,15 @@ outputs:
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
         {% if cuda_major == "11" %}
         - cudatoolkit
+        {% else %}
+        - cuda-cudart
+        - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
         {% endif %}
         - libraft ={{ minor_version }}
-        - libcuvs ={{ minor_version }}
+        - {{ pin_subpackage('libcuvs', exact=True) }}
         - gmock {{ gtest_version }}
         - gtest {{ gtest_version }}
     about:
@@ -205,6 +247,10 @@ outputs:
       ignore_run_exports_from:
         {% if cuda_major == "11" %}
         - {{ compiler('cuda11') }}
+        {% else %}
+        - {{ compiler('cuda') }}
+        - cuda-cudart-dev
+        - libcublas-dev
         {% endif %}
     requirements:
       build:
@@ -221,13 +267,14 @@ outputs:
         - sysroot_{{ target_platform }} {{ sysroot_version }}
       host:
         - libraft ={{ minor_version }}
-        - libcuvs ={{ minor_version }}
+        - {{ pin_subpackage('libcuvs', exact=True) }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
         - libcublas {{ cuda11_libcublas_host_version }}
         - libcublas-dev {{ cuda11_libcublas_host_version }}
         {% else %}
+        - cuda-cudart-dev
         - cuda-profiler-api
         - libcublas-dev
         {% endif %}
@@ -235,9 +282,12 @@ outputs:
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
         {% if cuda_major == "11" %}
         - cudatoolkit
+        {% else %}
+        - cuda-cudart
+        - libcublas
         {% endif %}
         - libraft ={{ minor_version }}
-        - libcuvs ={{ minor_version }}
+        - {{ pin_subpackage('libcuvs', exact=True) }}
     about:
       home: https://rapids.ai/
       license: Apache-2.0
diff --git a/dependencies.yaml b/dependencies.yaml
index d7562ce573..7fff7811a8 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -47,6 +47,7 @@ files:
   rust:
     output: none
     includes:
+      - cuda_version
       - build
       - cuda
       - rust
@@ -138,6 +139,7 @@ dependencies:
         packages:
           - &rmm_conda rmm==24.4.*
           - &pylibraft_conda pylibraft==24.4.*
+          - dlpack>=0.8,<1.0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -320,7 +322,7 @@ dependencies:
           - make
           - rust
           # clang/liblclang only needed for bindgen support
-          - clang  
+          - clang
           - libclang
   build_wheels:
     common:
@@ -380,7 +382,7 @@ dependencies:
           - matrix: {cuda: "11.*"}
             packages:
               - *pylibraft_cu11
-          - {matrix: null, packages: [*rmm_conda]}
+          - {matrix: null, packages: [*pylibraft_conda]}
   test_python_common:
     common:
       - output_types: [conda, requirements, pyproject]
@@ -391,5 +393,4 @@ dependencies:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - *pylibraft_conda
           - scikit-learn
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 5a5efe8f9c..33a9420e7f 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -33,11 +33,17 @@ project(
             C CXX CUDA
 )
 
+################################################################################
+# - User Options  --------------------------------------------------------------
+
 option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulting to local files"
        OFF
 )
 
-message("- FIND_CUVS_CPP: ${FIND_CUVS_CPP}")
+message("CUVS_PY: Searching for existing cuVS C/C++ installations before defaulting to local files: ${FIND_CUVS_CPP}")
+
+################################################################################
+# - Process User Options  ------------------------------------------------------
 
 include(../../fetch_rapids.cmake)
 include(rapids-cmake)
@@ -50,7 +56,7 @@ rapids_cpm_init()
 
 # If the user requested it we attempt to find CUVS.
 if(FIND_CUVS_CPP)
-  find_package(cuvs ${cuvs_version})
+  find_package(cuvs ${cuvs_version} REQUIRED)
   include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
 else()
   set(cuvs_FOUND OFF)
@@ -70,6 +76,9 @@ if(NOT cuvs_FOUND)
   install(TARGETS cuvs cuvs_c DESTINATION ${cython_lib_dir})
 endif()
 
+################################################################################
+# - Build Cython artifacts -----------------------------------------------------
+
 rapids_cython_init()
 
 add_subdirectory(cuvs/common)
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 664cf29692..07578abf3a 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -38,7 +38,7 @@ requires-python = ">=3.9"
 dependencies = [
     "cuda-python>=11.7.1,<12.0a0",
     "numpy>=1.23",
-    "rmm==24.4.*",
+    "pylibraft==24.4.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -50,7 +50,6 @@ classifiers = [
 [project.optional-dependencies]
 test = [
     "cupy-cuda11x>=12.0.0",
-    "pylibraft==24.4.*",
     "pytest-cov",
     "pytest==7.*",
     "scikit-learn",

From 506c14387878c6eb2eef8e998ca52b47f9ad3a4d Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Fri, 8 Mar 2024 23:32:40 +0100
Subject: [PATCH 007/623] API Documentation (#44)

Building out initial API documentation for all the supported languages and algorithms.

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/44
---
 build.sh                                      |   2 +-
 cpp/doxygen/Doxyfile                          |   1 +
 cpp/include/cuvs/core/c_api.h                 |  48 +--
 cpp/include/cuvs/neighbors/cagra.h            |  55 ++-
 cpp/include/cuvs/neighbors/cagra.hpp          | 241 ++++++++++----
 docs/source/api_docs.rst                      |  11 +
 docs/source/build.md                          | 312 +++++-------------
 docs/source/c_api.rst                         |   8 +-
 docs/source/c_api/core_c_api.rst              |  20 +-
 docs/source/c_api/neighbors.rst               |  14 +
 docs/source/c_api/neighbors_cagra_c.rst       |  48 ++-
 docs/source/conf.py                           |   8 +-
 docs/source/cpp_api.rst                       |   6 +-
 docs/source/cpp_api/core_interop.rst          |   1 -
 docs/source/cpp_api/neighbors.rst             |   6 +-
 docs/source/cpp_api/neighbors_cagra.rst       |  40 ++-
 docs/source/index.rst                         |   3 +-
 docs/source/python_api.rst                    |   8 +-
 docs/source/python_api/neighbors.rst          |  12 +
 docs/source/python_api/neighbors_cagra.rst    |  36 ++
 docs/source/rust_api.rst                      |   8 +
 docs/source/sphinxext/github_link.py          |   2 +-
 {cpp/examples => examples/cpp}/CMakeLists.txt |   0
 {cpp/examples => examples/cpp}/README.md      |   5 +-
 {cpp/examples => examples/cpp}/build.sh       |   0
 .../cpp}/cmake/thirdparty/fetch_rapids.cmake  |   0
 .../cpp}/cmake/thirdparty/get_cuvs.cmake      |   0
 .../cpp}/src/cagra_example.cu                 |   0
 {cpp/examples => examples/cpp}/src/common.cuh |   0
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |   7 +
 30 files changed, 537 insertions(+), 365 deletions(-)
 create mode 100644 docs/source/api_docs.rst
 create mode 100644 docs/source/c_api/neighbors.rst
 create mode 100644 docs/source/python_api/neighbors.rst
 create mode 100644 docs/source/python_api/neighbors_cagra.rst
 create mode 100644 docs/source/rust_api.rst
 rename {cpp/examples => examples/cpp}/CMakeLists.txt (100%)
 rename {cpp/examples => examples/cpp}/README.md (93%)
 rename {cpp/examples => examples/cpp}/build.sh (100%)
 rename {cpp/examples => examples/cpp}/cmake/thirdparty/fetch_rapids.cmake (100%)
 rename {cpp/examples => examples/cpp}/cmake/thirdparty/get_cuvs.cmake (100%)
 rename {cpp/examples => examples/cpp}/src/cagra_example.cu (100%)
 rename {cpp/examples => examples/cpp}/src/common.cuh (100%)

diff --git a/build.sh b/build.sh
index d53f7c3545..8571a574c1 100755
--- a/build.sh
+++ b/build.sh
@@ -403,7 +403,7 @@ fi
 # Initiate build for c++ examples (if needed)
 
 if hasArg examples; then
-    pushd ${REPODIR}/cpp/examples
+    pushd ${REPODIR}/examples/cpp
     ./build.sh
     popd
 fi
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index 94304afe02..faa31c25d0 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -864,6 +864,7 @@ INPUT_ENCODING         = UTF-8
 # *.vhdl, *.ucf, *.qsf and *.ice.
 
 FILE_PATTERNS          = *.hpp \
+                         *.h \
                          *.cuh
 
 # The RECURSIVE tag can be used to specify whether or not subdirectories should
diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index eceb917ff3..a15d7cd5c7 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -16,30 +16,46 @@
 
 #pragma once
 
+#include <cuda_runtime.h>
 #include <stdint.h>
 
-#include <cuda_runtime.h>
+#ifdef __cplusplus
+extern "C" {
+#endif
 
 /**
- * @defgroup c_api C API Core Types and Functions
+ * @defgroup error_c cuVS Error Messages
  * @{
  */
+/**
+ * @brief An enum denoting return values for function calls
+ *
+ */
+typedef enum { CUVS_ERROR, CUVS_SUCCESS } cuvsError_t;
 
-#ifdef __cplusplus
-extern "C" {
-#endif
+/** @brief Returns a string describing the last seen error on this thread, or
+ *         NULL if the last function succeeded.
+ */
+const char* cuvsGetLastErrorText();
 
 /**
- * @brief An opaque C handle for C++ type `raft::resources`
- *
+ * @brief Sets a string describing an error seen on the thread. Passing NULL
+ *        clears any previously seen error message.
  */
-typedef uintptr_t cuvsResources_t;
+void cuvsSetLastErrorText(const char* error);
+
+/** @} */
 
 /**
- * @brief An enum denoting return values for function calls
+ * @defgroup resources_c cuVS Resources Handle
+ * @{
+ */
+
+/**
+ * @brief An opaque C handle for C++ type `raft::resources`
  *
  */
-typedef enum { CUVS_ERROR, CUVS_SUCCESS } cuvsError_t;
+typedef uintptr_t cuvsResources_t;
 
 /**
  * @brief Create an Initialized opaque C handle for C++ type `raft::resources`
@@ -67,18 +83,8 @@ cuvsError_t cuvsResourcesDestroy(cuvsResources_t res);
  */
 cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream);
 
-/** @brief Returns a string describing the last seen error on this thread, or
- *         NULL if the last function succeeded.
- */
-const char* cuvsGetLastErrorText();
+/** @} */
 
-/**
- * @brief Sets a string describing an error seen on the thread. Passing NULL
- *        clears any previously seen error message.
- */
-void cuvsSetLastErrorText(const char* error);
 #ifdef __cplusplus
 }
 #endif
-
-/** @} */
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 64a26b9243..ae8f8ea01d 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -20,15 +20,15 @@
 #include <dlpack/dlpack.h>
 #include <stdint.h>
 
-/**
- * @defgroup cagra_c C API for CUDA ANN Graph-based nearest neighbor search
- * @{
- */
-
 #ifdef __cplusplus
 extern "C" {
 #endif
 
+/**
+ * @defgroup cagra_c_index_params C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
 /**
  * @brief Enum to denote which ANN algorithm is used to build CAGRA graph
  *
@@ -71,7 +71,16 @@ cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params);
  * @param[in] params
  * @return cuvsError_t
  */
-cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t index);
+cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_c_search_params C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
 
 /**
  * @brief Enum to denote algorithm used to search CAGRA Index
@@ -158,6 +167,15 @@ cuvsError_t cuvsCagraSearchParamsCreate(cuvsCagraSearchParams_t* params);
  */
 cuvsError_t cuvsCagraSearchParamsDestroy(cuvsCagraSearchParams_t params);
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_c_index C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
 /**
  * @brief Struct to hold address of cuvs::neighbors::cagra::index and its active trained dtype
  *
@@ -185,6 +203,15 @@ cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index);
  */
 cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index);
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_c_index_build C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
 /**
  * @brief Build a CAGRA index with a `DLManagedTensor` which has underlying
  *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
@@ -232,6 +259,14 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                            DLManagedTensor* dataset,
                            cuvsCagraIndex_t index);
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_c_index_search C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
 /**
  * @brief Search a CAGRA index with a `DLManagedTensor` which has underlying
  *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`.
@@ -281,10 +316,10 @@ cuvsError_t cuvsCagraSearch(cuvsResources_t res,
                             DLManagedTensor* neighbors,
                             DLManagedTensor* distances);
 
-#ifdef __cplusplus
-}
-#endif
-
 /**
  * @}
  */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 3a0c60b782..0f2919ff46 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -27,7 +27,7 @@
 
 namespace cuvs::neighbors::cagra {
 /**
- * @addtogroup cagra
+ * @defgroup cagra_cpp_index_params CAGRA index build parameters
  * @{
  */
 
@@ -68,6 +68,15 @@ struct index_params : ann::index_params {
   }
 };
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_cpp_search_params CAGRA index search parameters
+ * @{
+ */
+
 enum class search_algo {
   /** For large batch sizes. */
   SINGLE_CTA,
@@ -144,9 +153,17 @@ struct search_params : ann::search_params {
   }
 };
 
+/**
+ * @}
+ */
 static_assert(std::is_aggregate_v<index_params>);
 static_assert(std::is_aggregate_v<search_params>);
 
+/**
+ * @defgroup cagra_cpp_index CAGRA index type
+ * @{
+ */
+
 /**
  * @brief CAGRA index.
  *
@@ -344,70 +361,162 @@ struct index : ann::index {
   std::unique_ptr<raft::neighbors::cagra::index<T, IdxT>> raft_index_;
 };
 
-// Using device and host_matrix_view avoids needing to typedef multiple mdspans based on accessors
-#define CUVS_INST_CAGRA_FUNCS(T, IdxT)                                                   \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)        \
-    ->cuvs::neighbors::cagra::index<T, IdxT>;                                            \
-                                                                                         \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)          \
-    ->cuvs::neighbors::cagra::index<T, IdxT>;                                            \
-                                                                                         \
-  void build_device(raft::resources const& handle,                                       \
-                    const cuvs::neighbors::cagra::index_params& params,                  \
-                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset, \
-                    cuvs::neighbors::cagra::index<T, IdxT>& idx);                        \
-                                                                                         \
-  void build_host(raft::resources const& handle,                                         \
-                  const cuvs::neighbors::cagra::index_params& params,                    \
-                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,     \
-                  cuvs::neighbors::cagra::index<T, IdxT>& idx);                          \
-                                                                                         \
-  void search(raft::resources const& handle,                                             \
-              cuvs::neighbors::cagra::search_params const& params,                       \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                       \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,       \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,        \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances);      \
-  void serialize_file(raft::resources const& handle,                                     \
-                      const std::string& filename,                                       \
-                      const cuvs::neighbors::cagra::index<T, IdxT>& index,               \
-                      bool include_dataset = true);                                      \
-                                                                                         \
-  void deserialize_file(raft::resources const& handle,                                   \
-                        const std::string& filename,                                     \
-                        cuvs::neighbors::cagra::index<T, IdxT>* index);                  \
-  void serialize(raft::resources const& handle,                                          \
-                 std::string& str,                                                       \
-                 const cuvs::neighbors::cagra::index<T, IdxT>& index,                    \
-                 bool include_dataset = true);                                           \
-                                                                                         \
-  void deserialize(raft::resources const& handle,                                        \
-                   const std::string& str,                                               \
-                   cuvs::neighbors::cagra::index<T, IdxT>* index);
-
-CUVS_INST_CAGRA_FUNCS(float, uint32_t);
-CUVS_INST_CAGRA_FUNCS(int8_t, uint32_t);
-CUVS_INST_CAGRA_FUNCS(uint8_t, uint32_t);
-
-#undef CUVS_INST_CAGRA_FUNCS
-
-#define CUVS_INST_CAGRA_OPTIMIZE(IdxT)                                                     \
-  void optimize_device(raft::resources const& res,                                         \
-                       raft::device_matrix_view<IdxT, int64_t, raft::row_major> knn_graph, \
-                       raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph);  \
-                                                                                           \
-  void optimize_host(raft::resources const& res,                                           \
-                     raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,     \
-                     raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph);
-
-CUVS_INST_CAGRA_OPTIMIZE(uint32_t);
-
-#undef CUVS_INST_CAGRA_OPTIMIZE
-
-/** @} */
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_cpp_index_build CAGRA index build functions
+ * @{
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<float, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<float, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<int8_t, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<int8_t, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<uint8_t, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<uint8_t, uint32_t>;
+/**
+ * @}
+ */
+
+void build_device(raft::resources const& handle,
+                  const cuvs::neighbors::cagra::index_params& params,
+                  raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+                  cuvs::neighbors::cagra::index<float, uint32_t>& idx);
+
+void build_host(raft::resources const& handle,
+                const cuvs::neighbors::cagra::index_params& params,
+                raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
+                cuvs::neighbors::cagra::index<float, uint32_t>& idx);
+
+void build_device(raft::resources const& handle,
+                  const cuvs::neighbors::cagra::index_params& params,
+                  raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+                  cuvs::neighbors::cagra::index<int8_t, uint32_t>& idx);
+
+void build_host(raft::resources const& handle,
+                const cuvs::neighbors::cagra::index_params& params,
+                raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+                cuvs::neighbors::cagra::index<int8_t, uint32_t>& idx);
+
+void build_device(raft::resources const& handle,
+                  const cuvs::neighbors::cagra::index_params& params,
+                  raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+                  cuvs::neighbors::cagra::index<uint8_t, uint32_t>& idx);
+
+void build_host(raft::resources const& handle,
+                const cuvs::neighbors::cagra::index_params& params,
+                raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+                cuvs::neighbors::cagra::index<uint8_t, uint32_t>& idx);
+
+/**
+ * @defgroup cagra_cpp_index_search CAGRA search functions
+ * @{
+ */
+
+void search(raft::resources const& handle,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<float, uint32_t>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+void search(raft::resources const& handle,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+void search(raft::resources const& handle,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_cpp_serialize CAGRA serialize functions
+ * @{
+ */
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::cagra::index<float, uint32_t>& index,
+                    bool include_dataset = true);
+
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::cagra::index<float, uint32_t>* index);
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::cagra::index<float, uint32_t>& index,
+               bool include_dataset = true);
+
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::cagra::index<float, uint32_t>* index);
+
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
+                    bool include_dataset = true);
+
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::cagra::index<int8_t, uint32_t>* index);
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
+               bool include_dataset = true);
+
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::cagra::index<int8_t, uint32_t>* index);
+
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
+                    bool include_dataset = true);
+
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::cagra::index<uint8_t, uint32_t>* index);
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
+               bool include_dataset = true);
+
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::cagra::index<uint8_t, uint32_t>* index);
+/**
+ * @}
+ */
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/docs/source/api_docs.rst b/docs/source/api_docs.rst
new file mode 100644
index 0000000000..2a5a867e0a
--- /dev/null
+++ b/docs/source/api_docs.rst
@@ -0,0 +1,11 @@
+API Documentation
+=================
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+   c_api.rst
+   cpp_api.rst
+   python_api.rst
+   rust_api.rst
diff --git a/docs/source/build.md b/docs/source/build.md
index 31de69b461..939f1db346 100644
--- a/docs/source/build.md
+++ b/docs/source/build.md
@@ -1,318 +1,184 @@
-# Installation
+**# Installation
 
-RAFT currently provides libraries for C++ and Python. The C++ libraries, including the header-only and optional shared library, can be installed with Conda. 
+The cuVS software development kit provides APIs for C, C++, Python, and Rust languages. These 
 
 Both the C++ and Python APIs require CMake to build from source.
 
 ## Table of Contents
 
-- [Install C++ and Python through Conda](#installing-c-and-python-through-conda)
-- [Installing Python through Pip](#installing-python-through-pip)
-- [Building C++ and Python from source](#building-c-and-python-from-source)
-  - [CUDA/GPU requirements](#cudagpu-requirements)
-  - [Build dependencies](#build-dependencies)
-    - [Required](#required)
-    - [Optional](#optional)
-    - [Conda environment scripts](#conda-environment-scripts)
-  - [Header-only C++](#header-only-c)
-  - [C++ shared library](#c-shared-library-optional)
-  - [ccache and sccache](#ccache-and-sccache)
-  - [C++ tests](#c-tests)
-  - [C++ primitives microbenchmarks](#c-primitives-microbenchmarks)
-  - [Python libraries](#python-libraries)
-- [Using CMake directly](#using-cmake-directly)
-- [Build documentation](#build-documentation)
-- [Using RAFT in downstream projects](#using-raft-c-in-downstream-projects)
-  - [CMake targets](#cmake-targets)
+- [Install pre-compiled packages](#installing-precompiled-packages)
+  - [C, C++, and Python through Conda](#installing-through-conda)
+  - [Python through Pip](#installing-python-through-pip)
+  - [Rust through crates.io](#installing-rust-through-crates)
+- [Build from source](#building-c-and-python-from-source)
+  - [Prerequisites](#build-prerequisites)
+    - [CUDA/GPU requirements](#cudagpu-requirements)
+    - [Build dependencies](#build-dependencies)
+      - [Required](#required)
+  - [Create a build environment](#creating-a-build-environment)
+      - [ccache and sccache](#ccache-and-sccache)
+  - [C/C++ Libraries](#c-library)
+      - [Building the Googletests](#building-the-googletests)
+  - [Python Library](#python-library)
+  - [Rust Library](#rust-library)
+  - [Using Cmake Directly](#using-cmake-directly)
+- [Build Documentation](#build-documentation)
+- [Use cuVS in your application](#use-cuvs-in-your-application)
+
+[//]: # (- [Using cuVS in downstream projects]&#40;#using-raft-c-in-downstream-projects&#41;)
+
+[//]: # (  - [CMake targets]&#40;#cmake-targets&#41;_)
 
 ------
 
-## Installing C++ and Python through Conda
+## Installing Pre-compiled Packages
 
-The easiest way to install RAFT is through conda and several packages are provided.
-- `libraft-headers` C++ headers
-- `libraft` (optional) C++ shared library containing pre-compiled template instantiations and runtime API.
-- `pylibraft` (optional) Python library
-- `raft-dask` (optional) Python library for deployment of multi-node multi-GPU algorithms that use the RAFT `raft::comms` abstraction layer in Dask clusters.
-- `raft-ann-bench` (optional) Benchmarking tool for easily producing benchmarks that compare RAFT's vector search algorithms against other state-of-the-art implementations.
-- `raft-ann-bench-cpu` (optional) Reproducible benchmarking tool similar to above, but doesn't require CUDA to be installed on the machine. Can be used to test in environments with competitive CPUs.
+### C, C++, and Python through Conda
 
-Use the following command, depending on your CUDA version, to install all of the RAFT packages with conda (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
+The easiest way to install the pre-compiled C, C++, and Python packages is through [conda](https://docs.anaconda.com/free/miniconda/index.html). Use the following commands, depending on your CUDA version, to install cuVS packages (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
+
+#### C++ Package
 ```bash
-# for CUDA 11.8
-mamba install -c rapidsai -c conda-forge -c nvidia raft-dask pylibraft cuda-version=11.8
+mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=11.8
 ```
 
+#### C Package
 ```bash
-# for CUDA 12.0
-mamba install -c rapidsai -c conda-forge -c nvidia raft-dask pylibraft cuda-version=12.0
+mamba install -c rapidsai -c conda-forge -c nvidia libcuvs_c cuda-version=11.8
 ```
 
-Note that the above commands will also install `libraft-headers` and `libraft`.
-
-You can also install the conda packages individually using the `mamba` command above. For example, if you'd like to install RAFT's headers and pre-compiled shared library to use in your project:
+#### Python Package
 ```bash
-# for CUDA 12.0
-mamba install -c rapidsai -c conda-forge -c nvidia libraft libraft-headers cuda-version=12.0
+mamba install -c rapidsai -c conda-forge -c nvidia pycuvs cuda-version=11.8
 ```
 
-If installing the C++ APIs Please see [using libraft](https://docs.rapids.ai/api/raft/nightly/using_libraft/) for more information on using the pre-compiled shared library. You can also refer to the [example C++ template project](https://github.com/rapidsai/raft/tree/branch-24.04/cpp/template) for a ready-to-go CMake configuration that you can drop into your project and build against installed RAFT development artifacts above.
+### Python through Pip
 
-## Installing Python through Pip
-
-`pylibraft` and `raft-dask` both have packages that can be [installed through pip](https://rapids.ai/pip.html#install). 
+The cuVS Python package can also be [installed through pip](https://rapids.ai/pip.html#install). 
 
 For CUDA 11 packages:
 ```bash
-pip install pylibraft-cu11 --extra-index-url=https://pypi.nvidia.com
-pip install raft-dask-cu11 --extra-index-url=https://pypi.nvidia.com
+pip install pycuvs-cu11 --extra-index-url=https://pypi.nvidia.com
 ```
 
 And CUDA 12 packages:
 ```bash
-pip install pylibraft-cu12 --extra-index-url=https://pypi.nvidia.com
-pip install raft-dask-cu12 --extra-index-url=https://pypi.nvidia.com
+pip install pycuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 ```
 
-These packages statically build RAFT's pre-compiled instantiations, so the C++ headers and pre-compiled shared library won't be readily available to use in your code. 
+Note: these packages statically links the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code. 
 
-## Building C++ and Python from source
+### Rust through crates.io
 
-### CUDA/GPU Requirements
-- cmake 3.26.4+
-- GCC 9.3+ (9.5.0+ recommended)
-- CUDA Toolkit 11.2+
-- NVIDIA driver 450.80.02+
-- Pascal architecture or better (compute capability >= 6.0)
+## Build from source
 
-### Build Dependencies
+The core cuVS source code is written in C++ and wrapped through a C API. The C API is wrapped around the C++ APIs and the other supported languages are built around the C API. 
 
-In addition to the libraries included with cudatoolkit 11.0+, there are some other dependencies below for building RAFT from source. Many of the dependencies are optional and depend only on the primitives being used. All of these can be installed with cmake or [rapids-cpm](https://github.com/rapidsai/rapids-cmake#cpm) and many of them can be installed with [conda](https://anaconda.org).
 
-#### Required
-- [RMM](https://github.com/rapidsai/rmm) corresponding to RAFT version.
-- [Thrust](https://github.com/NVIDIA/thrust) v1.17 / [CUB](https://github.com/NVIDIA/cub)
-- [cuCollections](https://github.com/NVIDIA/cuCollections) - Used in `raft::sparse::distance` API.
-- [CUTLASS](https://github.com/NVIDIA/cutlass)  v2.9.1 - Used in `raft::distance` API.
+### Prerequisites
 
-#### Optional
-- [NCCL](https://github.com/NVIDIA/nccl) - Used in `raft::comms` API and needed to build `raft-dask`.
-- [UCX](https://github.com/openucx/ucx) - Used in `raft::comms` API and needed to build `raft-dask`.
-- [Googletest](https://github.com/google/googletest) - Needed to build tests
-- [Googlebench](https://github.com/google/benchmark) - Needed to build benchmarks
-- [Doxygen](https://github.com/doxygen/doxygen) - Needed to build docs
+- Cmake 3.26.4+
+- GCC 9.3+ (11.4+ recommended)
+- CUDA Toolkit 11.4+
+- Volta architecture or better (compute capability >= 7.0)
 
-#### Conda environment scripts
+### Create a build environment
 
-Conda environment scripts are provided for installing the necessary dependencies to build both the C++ and Python libraries from source. It is preferred to use `mamba`, as it provides significant speedup over `conda`:
+[Conda](https://docs.anaconda.com/free/miniconda/index.html) environment scripts are provided for installing the necessary dependencies to build cuVS from source. It is preferred to use `mamba`, as it provides significant speedup over `conda`:
 ```bash
-mamba env create --name rapids_raft -f conda/environments/all_cuda-120_arch-x86_64.yaml
-mamba activate rapids_raft
+mamba env create --name cuvs -f conda/environments/all_cuda-120_arch-x86_64.yaml
+mamba activate cuvs
 ```
 
-All of RAFT's C++ APIs can be used header-only and optional pre-compiled shared libraries provide some host-accessible runtime APIs and template instantiations to accelerate compile times.
-
 The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
 ```bash
-mamba env create --name rapids_raft -f conda/environments/all_cuda-118_arch-x86_64.yaml
-mamba activate rapids_raft
+mamba env create --name cuvs -f conda/environments/all_cuda-118_arch-x86_64.yaml
+mamba activate cuvs
 ```
 
-The recommended way to build and install RAFT from source is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides CMake options for building and installing the headers, tests, benchmarks, and the pre-compiled shared library.
-
-### Header-only C++
-
-`build.sh` uses [rapids-cmake](https://github.com/rapidsai/rapids-cmake), which will automatically download any dependencies which are not already installed. It's important to note that while all the headers will be installed and available, some parts of the RAFT API depend on libraries like CUTLASS, which will need to be explicitly enabled in `build.sh`.
-
-The following example will download the needed dependencies and install the RAFT headers into `$INSTALL_PREFIX/include/raft`. 
-```bash
-./build.sh libraft
-```
-The `-n` flag can be passed to just have the build download the needed dependencies. Since RAFT's C++ headers are primarily used during build-time in downstream projects, the dependencies will never be installed by the RAFT build.
-```bash
-./build.sh libraft -n
-```
+The recommended way to build and install cuVS from source is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides CMake options for building and installing the headers, tests, benchmarks, and the pre-compiled shared library.
 
-Once installed, `libraft` headers (and dependencies which were downloaded and installed using `rapids-cmake`) can be uninstalled also using `build.sh`:
-```bash
-./build.sh libraft --uninstall
-```
 
-### C++ Shared Library (optional)
+### C and C++ libraries
 
-A shared library can be built for speeding up compile times. The shared library also contains a runtime API that allows you to invoke RAFT APIs directly from C++ source files (without `nvcc`). The shared library can also significantly improve re-compile times both while developing RAFT and using its APIs to develop applications. Pass the `--compile-lib` flag to `build.sh` to build the library:
+The C and C++ shared libraries are built together using the following arguments to `build.sh`:
 ```bash
-./build.sh libraft --compile-lib
+./build.sh libcuvs
 ```
 
-In above example the shared library is installed by default into `$INSTALL_PREFIX/lib`. To disable this, pass `-n` flag.
+In above example the `libcuvs.so` and `libcuvs_c.so` shared libraries are installed by default into `$INSTALL_PREFIX/lib`. To disable this, pass `-n` flag.
 
-Once installed, the shared library, headers (and any dependencies downloaded and installed via `rapids-cmake`) can be uninstalled using `build.sh`:
+Once installed, the shared libraries, headers (and any dependencies downloaded and installed via `rapids-cmake`) can be uninstalled using `build.sh`:
 ```bash
-./build.sh libraft --uninstall
+./build.sh libcuvs --uninstall
 ```
 
 
-### ccache and sccache
+#### Building the Googletests
 
-`ccache` and `sccache` can be used to better cache parts of the build when rebuilding frequently, such as when working on a new feature. You can also use `ccache` or `sccache` with `build.sh`:
+Compile the C and C++ Googletests using the `tests` target in `build.sh`.
 
 ```bash
-./build.sh libraft --cache-tool=ccache
+./build.sh libcuvs tests
 ```
 
-### C++ Tests
+The tests will be written to the build directory, which is `cpp/build/` by default, and they will be named `*_TEST`.
 
-Compile the tests using the `tests` target in `build.sh`.
+It can take sometime to compile all of the tests. You can build individual tests by providing a semicolon-separated list to the `--limit-tests` option in `build.sh`. Make sure to pass the `-n` flag so the tests are not installed.
 
 ```bash
-./build.sh libraft tests
+./build.sh libcuvs tests -n --limit-tests=NEIGHBORS_TEST;CLUSTER_TEST
 ```
 
-Test compile times can be improved significantly by using the optional shared libraries. If installed, they will be used automatically when building the tests but `--compile-libs` can be used to add additional compilation units and compile them with the tests.
+### Python library
 
-```bash
-./build.sh libraft tests --compile-lib
-```
-
-The tests are broken apart by algorithm category, so you will find several binaries in `cpp/build/` named `*_TEST`.
+The Python library should be built and installed using the `build.sh` script:
 
-For example, to run the distance tests:
 ```bash
-./cpp/build/DISTANCE_TEST
+./build.sh python
 ```
 
-It can take sometime to compile all of the tests. You can build individual tests by providing a semicolon-separated list to the `--limit-tests` option in `build.sh`:
-
-```bash
-./build.sh libraft tests -n --limit-tests=NEIGHBORS_TEST;DISTANCE_TEST;MATRIX_TEST
-```
-
-### C++ Primitives Microbenchmarks
-
-The benchmarks are broken apart by algorithm category, so you will find several binaries in `cpp/build/` named `*_PRIMS_BENCH`.
-```bash
-./build.sh libraft bench-prims
-```
-
-It can take sometime to compile all of the benchmarks. You can build individual benchmarks by providing a semicolon-separated list to the `--limit-bench-prims` option in `build.sh`:
-
-```bash
-./build.sh libraft bench-prims -n --limit-bench=NEIGHBORS_PRIMS_BENCH;DISTANCE_PRIMS_BENCH;LINALG_PRIMS_BENCH
-```
-
-In addition to microbenchmarks for individual primitives, RAFT contains a reproducible benchmarking tool for evaluating the performance of RAFT's vector search algorithms against the existing state-of-the-art. Please refer to the [RAFT ANN Benchmarks](https://docs.rapids.ai/api/raft/nightly/raft_ann_benchmarks/) guide for more information on this tool.
-
-### Python libraries
-
-The Python libraries can be built and installed using the `build.sh` script:
-
-```bash
-# to build pylibraft
-./build.sh libraft pylibraft --compile-lib
-# to build raft-dask (depends on pylibraft)
-./build.sh libraft pylibraft raft-dask --compile-lib
-```
-
-`setup.py` can also be used to build the Python libraries manually:
-
+The Python packages can also be uninstalled using the `build.sh` script:
 ```bash
-cd python/raft-dask
-python setup.py build_ext --inplace
-python setup.py install
-
-cd python/pylibraft
-python setup.py build_ext --inplace
-python setup.py install
+./build.sh python --uninstall
 ```
 
-Python tests are automatically installed with the corresponding libraries. To run Python tests:
-```bash
-cd python/raft-dask
-py.test -s -v
+### Rust library
 
-cd python/pylibraft
-py.test -s -v
-```
 
-The Python packages can also be uninstalled using the `build.sh` script:
-```bash
-./build.sh pylibraft raft-dask --uninstall
-```
 
 ### Using CMake directly
 
-When building RAFT from source, the `build.sh` script offers a nice wrapper around the `cmake` commands to ease the burdens of manually configuring the various available cmake options. When more fine-grained control over the CMake configuration is desired, the `cmake` command can be invoked directly as the below example demonstrates. 
+When building cuVS from source, the `build.sh` script offers a nice wrapper around the `cmake` commands to ease the burdens of manually configuring the various available cmake options. When more fine-grained control over the CMake configuration is desired, the `cmake` command can be invoked directly as the below example demonstrates. 
 
-The `CMAKE_INSTALL_PREFIX` installs RAFT into a specific location. The example below installs RAFT into the current Conda environment:
+The `CMAKE_INSTALL_PREFIX` installs cuVS into a specific location. The example below installs cuVS into the current Conda environment:
 ```bash
 cd cpp
 mkdir build
 cd build
-cmake -D BUILD_TESTS=ON -DRAFT_COMPILE_LIBRARY=ON -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX ../
+cmake -D BUILD_TESTS=ON -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX ../
 make -j<parallel_level> install
 ```
 
-RAFT's CMake has the following configurable flags available:
-
-| Flag                            | Possible Values      | Default Value | Behavior                                                                     |
-|---------------------------------|----------------------| --- |------------------------------------------------------------------------------|
-| BUILD_TESTS                     | ON, OFF              | ON | Compile Googletests                                                          |
-| BUILD_PRIMS_BENCH               | ON, OFF              | OFF | Compile benchmarks                                                           |
-| BUILD_ANN_BENCH                 | ON, OFF              | OFF | Compile end-to-end ANN benchmarks |
-| CUDA_ENABLE_KERNELINFO          | ON, OFF              | OFF | Enables `kernelinfo` in nvcc. This is useful for `compute-sanitizer`         |
-| CUDA_ENABLE_LINEINFO            | ON, OFF              | OFF | Enable the -lineinfo option for nvcc                                         |
-| CUDA_STATIC_RUNTIME             | ON, OFF              | OFF | Statically link the CUDA runtime                                             |
-| DETECT_CONDA_ENV                | ON, OFF              | ON | Enable detection of conda environment for dependencies                       |
-| raft_FIND_COMPONENTS            | compiled distributed | | Configures the optional components as a space-separated list                 |
-| RAFT_COMPILE_LIBRARY            | ON, OFF              | ON if either BUILD_TESTS or BUILD_PRIMS_BENCH is ON; otherwise OFF | Compiles all `libraft` shared libraries (these are required for Googletests) |
-| RAFT_ENABLE_CUBLAS_DEPENDENCY   | ON, OFF | ON | Link against cublas library in `raft::raft`                                  | 
-| RAFT_ENABLE_CUSOLVER_DEPENDENCY | ON, OFF | ON | Link against cusolver library in `raft::raft`                                | 
-| RAFT_ENABLE_CUSPARSE_DEPENDENCY | ON, OFF | ON | Link against cusparse library in `raft::raft`                                | 
-| RAFT_ENABLE_CUSOLVER_DEPENDENCY | ON, OFF | ON | Link against curand library in `raft::raft`                                  | 
-| RAFT_NVTX                       | ON, OFF              | OFF | Enable NVTX Markers                                                          |
-
-### Build documentation
+cuVS has the following configurable cmake flags available:
 
-The documentation requires that the C++ and Python libraries have been built and installed. The following will build the docs along with the C++ and Python packages:
-
-```
-./build.sh libraft pylibraft raft-dask docs --compile-lib
-```
+| Flag                   | Possible Values      | Default Value | Behavior                                                                     |
+|------------------------|----------------------| --- |------------------------------------------------------------------------------|
+| BUILD_TESTS            | ON, OFF              | ON | Compile Googletests                                                          |
+| CUDA_ENABLE_KERNELINFO | ON, OFF              | OFF | Enables `kernelinfo` in nvcc. This is useful for `compute-sanitizer`         |
+| CUDA_ENABLE_LINEINFO   | ON, OFF              | OFF | Enable the -lineinfo option for nvcc                                         |
+| CUDA_STATIC_RUNTIME    | ON, OFF              | OFF | Statically link the CUDA runtime                                             |
+| DETECT_CONDA_ENV       | ON, OFF              | ON | Enable detection of conda environment for dependencies                       |
+| CUVS_NVTX              | ON, OFF              | OFF | Enable NVTX Markers                                                          |
 
-## Using RAFT C++ in downstream projects
-
-There are a few different strategies for including RAFT in downstream projects, depending on whether the [required build dependencies](#build-dependencies) have already been installed and are available on the `lib` and `include` search paths.
+### Build documentation
 
-When using the GPU parts of RAFT, you will need to enable CUDA support in your CMake project declaration:
-```cmake
-project(YOUR_PROJECT VERSION 0.1 LANGUAGES CXX CUDA)
-```
+The documentation requires that the C, C++ and Python libraries have been built and installed. The following will build the docs along with the necessary libraries:
 
-Note that some additional compiler flags might need to be added when building against RAFT. For example, if you see an error like this `The experimental flag '--expt-relaxed-constexpr' can be used to allow this.`. The necessary flags can be set with CMake:
-```cmake
-target_compile_options(your_target_name PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--expt-extended-lambda --expt-relaxed-constexpr>)
 ```
-
-Further, it's important that the language level be set to at least C++ 17. This can be done with cmake:
-```cmake
-set_target_properties(your_target_name
-PROPERTIES CXX_STANDARD                        17
-           CXX_STANDARD_REQUIRED               ON
-           CUDA_STANDARD                       17
-           CUDA_STANDARD_REQUIRED              ON
-           POSITION_INDEPENDENT_CODE           ON
-           INTERFACE_POSITION_INDEPENDENT_CODE ON)
+./build.sh libcuvs python docs
 ```
 
-The [C++ example template project](https://github.com/rapidsai/raft/tree/HEAD/cpp/template) provides an end-to-end buildable example of what a `CMakeLists.txt` that uses RAFT should look like. The items below point out some of the needed details.
-
-#### CMake Targets
-
-The `raft::raft` CMake target is made available when including RAFT into your CMake project but additional CMake targets can be made available by adding to the `COMPONENTS` option in CMake's `find_package(raft)` (refer to [CMake docs](https://cmake.org/cmake/help/latest/command/find_package.html#basic-signature) to learn more). The components should be separated by spaces. The `raft::raft` target will always be available. Note that the `distributed` component also exports additional dependencies.
+## Use cuVS in your application
 
-| Component   | Target              | Description                                              | Base Dependencies                      |
-|-------------|---------------------|----------------------------------------------------------|----------------------------------------|
-| n/a         | `raft::raft`        | Full RAFT header library                                 | CUDA toolkit, RMM, NVTX, CCCL, CUTLASS |
-| compiled    | `raft::compiled`    | Pre-compiled template instantiations and runtime library | raft::raft                             |
-| distributed | `raft::distributed` | Dependencies for `raft::comms` APIs                      | raft::raft, UCX, NCCL         
+The [examples/](https://github.com/rapidsai/raft/tree/HEAD/examples) directory at the root of the cuVS repository has self-contains drop-in projects to build and use the cuVS SDK in your applications.
\ No newline at end of file
diff --git a/docs/source/c_api.rst b/docs/source/c_api.rst
index 4daaa84dfd..3359a5dbec 100644
--- a/docs/source/c_api.rst
+++ b/docs/source/c_api.rst
@@ -1,6 +1,6 @@
-~~~~~
-C API
-~~~~~
+~~~~~~~~~~~~~~~~~~~
+C API Documentation
+~~~~~~~~~~~~~~~~~~~
 
 .. _api:
 
@@ -8,4 +8,4 @@ C API
    :maxdepth: 4
 
    c_api/core_c_api.rst
-   c_api/neighbors_cagra_c.rst
+   c_api/neighbors.rst
diff --git a/docs/source/c_api/core_c_api.rst b/docs/source/c_api/core_c_api.rst
index e0060bd5d4..e2796700fd 100644
--- a/docs/source/c_api/core_c_api.rst
+++ b/docs/source/c_api/core_c_api.rst
@@ -1,14 +1,24 @@
-C-API
-=====
+Core Routines
+=============
 
 .. role:: py(code)
    :language: c
    :class: highlight
 
-
 ``#include <cuvs/core/c_api.h>``
 
-.. doxygengroup:: c_api
-    :project: RAFT
+Resources Handle
+----------------
+
+.. doxygengroup:: resources_c
+    :project: cuvs
+    :members:
+    :content-only:
+
+Error Handling
+--------------
+
+.. doxygengroup:: error_c
+    :project: cuvs
     :members:
     :content-only:
diff --git a/docs/source/c_api/neighbors.rst b/docs/source/c_api/neighbors.rst
new file mode 100644
index 0000000000..6bbd3cc7f3
--- /dev/null
+++ b/docs/source/c_api/neighbors.rst
@@ -0,0 +1,14 @@
+Nearest Neighbors
+=================
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+
+
+   neighbors_cagra_c.rst
\ No newline at end of file
diff --git a/docs/source/c_api/neighbors_cagra_c.rst b/docs/source/c_api/neighbors_cagra_c.rst
index d4b7b0bf27..eb40d55782 100644
--- a/docs/source/c_api/neighbors_cagra_c.rst
+++ b/docs/source/c_api/neighbors_cagra_c.rst
@@ -1,7 +1,8 @@
-CAGRA C-API
-===========
+CAGRA
+=====
+
+CAGRA is a graph-based nearest neighbors algorithm that was built from the ground up for GPU acceleration. CAGRA demonstrates state-of-the art index build and query performance for both small- and large-batch sized search.
 
-CAGRA is a graph-based nearest neighbors implementation with state-of-the art query performance for both small- and large-batch sized search.
 
 .. role:: py(code)
    :language: c
@@ -9,7 +10,44 @@ CAGRA is a graph-based nearest neighbors implementation with state-of-the art qu
 
 ``#include <raft/neighbors/cagra.h>``
 
-.. doxygengroup:: cagra_c
-    :project: RAFT
+Index build parameters
+----------------------
+
+.. doxygengroup:: cagra_c_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: cagra_c_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: cagra_c_index
+    :project: cuvs
     :members:
     :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: cagra_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: cagra_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 1a5c9dfe83..1089f6a5ca 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -23,11 +23,11 @@
 # ones.
 extensions = [
     "numpydoc",
+    "sphinx.ext.linkcode",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.doctest",
     "sphinx.ext.intersphinx",
-    "sphinx.ext.linkcode",
     "IPython.sphinxext.ipython_console_highlighting",
     "IPython.sphinxext.ipython_directive",
     "breathe",
@@ -59,7 +59,7 @@
 
 # General information about the project.
 project = "cuvs"
-copyright = "2023, NVIDIA Corporation"
+copyright = "2024, NVIDIA Corporation"
 author = "NVIDIA Corporation"
 
 # The version info for the project you're documenting, acts as replacement for
@@ -193,8 +193,8 @@ def setup(app):
 # The following is used by sphinx.ext.linkcode to provide links to github
 linkcode_resolve = make_linkcode_resolve(
     "cuvs",
-    "https://github.com/rapidsai/cuvs"
-    "cuvs/blob/{revision}/python/cuvs"
+    "https://github.com/rapidsai/cuvs/"
+    "blob/{revision}/python/cuvs/cuvs/"
     "{package}/{path}#L{lineno}",
 )
 
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index 1916abde1a..67bc7f65b2 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -1,6 +1,6 @@
-~~~~~~~
-C++ API
-~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
+C++ API Documentation
+~~~~~~~~~~~~~~~~~~~~~
 
 .. _api:
 
diff --git a/docs/source/cpp_api/core_interop.rst b/docs/source/cpp_api/core_interop.rst
index b2ef05f275..a8fcb2890b 100644
--- a/docs/source/cpp_api/core_interop.rst
+++ b/docs/source/cpp_api/core_interop.rst
@@ -5,7 +5,6 @@ Interop
    :language: c++
    :class: highlight
 
-
 ``#include <cuvs/core/interop.hpp>``
 
 namespace *cuvs::core*
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index 6ba0014442..61898cec86 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -1,7 +1,5 @@
-Neighbors
-=========
-
-This page provides C++ class references for the publicly-exposed elements of the neighbors package.
+Nearest Neighbors
+=================
 
 .. role:: py(code)
    :language: c++
diff --git a/docs/source/cpp_api/neighbors_cagra.rst b/docs/source/cpp_api/neighbors_cagra.rst
index 65fdcb2bcd..aed843dad7 100644
--- a/docs/source/cpp_api/neighbors_cagra.rst
+++ b/docs/source/cpp_api/neighbors_cagra.rst
@@ -1,9 +1,7 @@
 CAGRA
 =====
 
-CAGRA is a graph-based nearest neighbors implementation with state-of-the art query performance for both small- and large-batch sized search.
-
-Please note that the CAGRA implementation is currently experimental and the API is subject to change from release to release. We are currently working on promoting CAGRA to a top-level stable API within RAFT.
+CAGRA is a graph-based nearest neighbors algorithm that was built from the ground up for GPU acceleration. CAGRA demonstrates state-of-the art index build and query performance for both small- and large-batch sized search.
 
 .. role:: py(code)
    :language: c++
@@ -13,19 +11,43 @@ Please note that the CAGRA implementation is currently experimental and the API
 
 namespace *cuvs::neighbors::cagra*
 
-.. doxygengroup:: cagra
+Index build parameters
+----------------------
+
+.. doxygengroup:: cagra_cpp_index_params
     :project: cuvs
     :members:
     :content-only:
 
+Index search parameters
+-----------------------
 
-Serializer Methods
-------------------
-``#include <cuvs/neighbors/cagra_serialize.cuh>``
+.. doxygengroup:: cagra_cpp_search_params
+    :project: cuvs
+    :members:
+    :content-only:
 
-namespace *cuvs::neighbors::cagra*
+Index
+-----
 
-.. doxygengroup:: cagra_serialize
+.. doxygengroup:: cagra_cpp_index
     :project: cuvs
     :members:
     :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: cagra_cpp_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: cagra_cpp_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
diff --git a/docs/source/index.rst b/docs/source/index.rst
index bf97906101..bbb6f93f27 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -23,8 +23,7 @@ cuVS is a library for vector search and clustering on the GPU.
 
    quick_start.md
    build.md
-   cpp_api.rst
-   python_api.rst
+   api_docs.rst
    developer_guide.md
    contributing.md
 
diff --git a/docs/source/python_api.rst b/docs/source/python_api.rst
index fba4d96ba5..999c40fd37 100644
--- a/docs/source/python_api.rst
+++ b/docs/source/python_api.rst
@@ -1,8 +1,10 @@
-~~~~~~~~~~
-Python API
-~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~
+Python API Documentation
+~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. _api:
 
 .. toctree::
    :maxdepth: 4
+
+   python_api/neighbors.rst
\ No newline at end of file
diff --git a/docs/source/python_api/neighbors.rst b/docs/source/python_api/neighbors.rst
new file mode 100644
index 0000000000..14e907002e
--- /dev/null
+++ b/docs/source/python_api/neighbors.rst
@@ -0,0 +1,12 @@
+Nearest Neighbors
+=================
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   neighbors_cagra.rst
\ No newline at end of file
diff --git a/docs/source/python_api/neighbors_cagra.rst b/docs/source/python_api/neighbors_cagra.rst
new file mode 100644
index 0000000000..6f64f77635
--- /dev/null
+++ b/docs/source/python_api/neighbors_cagra.rst
@@ -0,0 +1,36 @@
+CAGRA
+=====
+
+CAGRA is a graph-based nearest neighbors algorithm that was built from the ground up for GPU acceleration. CAGRA demonstrates state-of-the art index build and query performance for both small- and large-batch sized search.
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Index build parameters
+######################
+
+.. autoclass:: cuvs.neighbors.cagra.IndexParams
+    :members:
+
+Index search parameters
+#######################
+
+.. autoclass:: cuvs.neighbors.cagra.SearchParams
+    :members:
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.cagra.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.cagra.build_index
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.cagra.search
diff --git a/docs/source/rust_api.rst b/docs/source/rust_api.rst
new file mode 100644
index 0000000000..254136e3aa
--- /dev/null
+++ b/docs/source/rust_api.rst
@@ -0,0 +1,8 @@
+~~~~~~~~~~~~~~~~~~~~~~
+Rust API Documentation
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. _api:
+
+.. toctree::
+   :maxdepth: 4
diff --git a/docs/source/sphinxext/github_link.py b/docs/source/sphinxext/github_link.py
index a7a46fdd9d..2c52488caa 100644
--- a/docs/source/sphinxext/github_link.py
+++ b/docs/source/sphinxext/github_link.py
@@ -96,7 +96,7 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision):
             # fn is expected to be the absolute path.
             fn = os.path.relpath(source_file, start=package)
             print("{}:{}".format(
-                os.path.abspath(os.path.join("..", "python", "cuml", fn)),
+                os.path.abspath(os.path.join("..", "python", "cuvs", fn)),
                 lineno))
         else:
             return
diff --git a/cpp/examples/CMakeLists.txt b/examples/cpp/CMakeLists.txt
similarity index 100%
rename from cpp/examples/CMakeLists.txt
rename to examples/cpp/CMakeLists.txt
diff --git a/cpp/examples/README.md b/examples/cpp/README.md
similarity index 93%
rename from cpp/examples/README.md
rename to examples/cpp/README.md
index 125c6dba22..5a98dd3904 100644
--- a/cpp/examples/README.md
+++ b/examples/cpp/README.md
@@ -6,7 +6,7 @@ First, please refer to our [installation docs](https://docs.rapids.ai/api/cuvs/s
 
 Once the minimum requirements are satisfied, this example template application can be built with the provided `build.sh` script. This is a bash script that calls the appropriate CMake commands, so you can look into it to see the typical CMake based build workflow.  
 
-This directory (`CUVS_SOURCE/cpp/examples`) can be copied directly in order to build a new application with CUVS.
+This directory (`CUVS_SOURCE/examples/cpp`) can be copied directly in order to build a new application with CUVS.
 
 cuVS can be integrated into an existing CMake project by copying the contents in the `configure rapids-cmake` and `configure cuvs` sections of the provided `CMakeLists.txt` into your project, along with `cmake/thirdparty/get_cuvs.cmake`. 
 
@@ -14,5 +14,4 @@ Make sure to link against the appropriate Cmake targets. Use `cuvs::cuvs` to uti
 
 ```cmake
 target_link_libraries(your_app_target PRIVATE cuvs::cuvs)
-```
-
+```
\ No newline at end of file
diff --git a/cpp/examples/build.sh b/examples/cpp/build.sh
similarity index 100%
rename from cpp/examples/build.sh
rename to examples/cpp/build.sh
diff --git a/cpp/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cpp/cmake/thirdparty/fetch_rapids.cmake
similarity index 100%
rename from cpp/examples/cmake/thirdparty/fetch_rapids.cmake
rename to examples/cpp/cmake/thirdparty/fetch_rapids.cmake
diff --git a/cpp/examples/cmake/thirdparty/get_cuvs.cmake b/examples/cpp/cmake/thirdparty/get_cuvs.cmake
similarity index 100%
rename from cpp/examples/cmake/thirdparty/get_cuvs.cmake
rename to examples/cpp/cmake/thirdparty/get_cuvs.cmake
diff --git a/cpp/examples/src/cagra_example.cu b/examples/cpp/src/cagra_example.cu
similarity index 100%
rename from cpp/examples/src/cagra_example.cu
rename to examples/cpp/src/cagra_example.cu
diff --git a/cpp/examples/src/common.cuh b/examples/cpp/src/common.cuh
similarity index 100%
rename from cpp/examples/src/common.cuh
rename to examples/cpp/src/common.cuh
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 04031414e2..d64ac72ee0 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -77,6 +77,7 @@ cdef class IndexParams:
               building the knn graph. It is expected to be generally
               faster than ivf_pq.
     """
+
     cdef cuvsCagraIndexParams* params
 
     def __init__(self, *,
@@ -122,6 +123,11 @@ cdef class IndexParams:
 
 
 cdef class Index:
+    """
+    CAGRA index object. This object stores the trained CAGRA index state
+    which can be used to perform nearest neighbors searches.
+    """
+
     cdef cuvsCagraIndex_t index
     cdef bool trained
 
@@ -268,6 +274,7 @@ cdef class SearchParams:
     rand_xor_mask: int, default = 0x128394
         Bit mask used for initial random seed node selection.
     """
+
     cdef cuvsCagraSearchParams params
 
     def __init__(self, *,

From 49b95fffb4c228174ef6ac23154352bc8a8f09c8 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Fri, 8 Mar 2024 23:39:50 +0100
Subject: [PATCH 008/623] Docs 2404 api docs (#46)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/46
---
 docs/source/build.md  | 20 +++++++-------------
 docs/source/index.rst |  1 -
 2 files changed, 7 insertions(+), 14 deletions(-)

diff --git a/docs/source/build.md b/docs/source/build.md
index 939f1db346..976482725c 100644
--- a/docs/source/build.md
+++ b/docs/source/build.md
@@ -1,8 +1,6 @@
 **# Installation
 
-The cuVS software development kit provides APIs for C, C++, Python, and Rust languages. These 
-
-Both the C++ and Python APIs require CMake to build from source.
+The cuVS software development kit provides APIs for C, C++, Python, and Rust languages. This guide outlines how to install the pre-compiled packages, build it from source, and use it in downstream applications. 
 
 ## Table of Contents
 
@@ -25,10 +23,6 @@ Both the C++ and Python APIs require CMake to build from source.
 - [Build Documentation](#build-documentation)
 - [Use cuVS in your application](#use-cuvs-in-your-application)
 
-[//]: # (- [Using cuVS in downstream projects]&#40;#using-raft-c-in-downstream-projects&#41;)
-
-[//]: # (  - [CMake targets]&#40;#cmake-targets&#41;_)
-
 ------
 
 ## Installing Pre-compiled Packages
@@ -39,17 +33,17 @@ The easiest way to install the pre-compiled C, C++, and Python packages is throu
 
 #### C++ Package
 ```bash
-mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=11.8
+mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.0
 ```
 
 #### C Package
 ```bash
-mamba install -c rapidsai -c conda-forge -c nvidia libcuvs_c cuda-version=11.8
+mamba install -c rapidsai -c conda-forge -c nvidia libcuvs_c cuda-version=12.0
 ```
 
 #### Python Package
 ```bash
-mamba install -c rapidsai -c conda-forge -c nvidia pycuvs cuda-version=11.8
+mamba install -c rapidsai -c conda-forge -c nvidia pycuvs cuda-version=12.0
 ```
 
 ### Python through Pip
@@ -66,7 +60,7 @@ And CUDA 12 packages:
 pip install pycuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 ```
 
-Note: these packages statically links the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code. 
+Note: these packages statically link the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code. 
 
 ### Rust through crates.io
 
@@ -124,10 +118,10 @@ Compile the C and C++ Googletests using the `tests` target in `build.sh`.
 
 The tests will be written to the build directory, which is `cpp/build/` by default, and they will be named `*_TEST`.
 
-It can take sometime to compile all of the tests. You can build individual tests by providing a semicolon-separated list to the `--limit-tests` option in `build.sh`. Make sure to pass the `-n` flag so the tests are not installed.
+It can take some time to compile all of the tests. You can build individual tests by providing a semicolon-separated list to the `--limit-tests` option in `build.sh`. Make sure to pass the `-n` flag so the tests are not installed.
 
 ```bash
-./build.sh libcuvs tests -n --limit-tests=NEIGHBORS_TEST;CLUSTER_TEST
+./build.sh libcuvs tests -n --limit-tests=NEIGHBORS_TEST;CAGRA_C_TEST
 ```
 
 ### Python library
diff --git a/docs/source/index.rst b/docs/source/index.rst
index bbb6f93f27..ce8a8e1d25 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -24,7 +24,6 @@ cuVS is a library for vector search and clustering on the GPU.
    quick_start.md
    build.md
    api_docs.rst
-   developer_guide.md
    contributing.md
 
 

From 0e5aac6127aa325a4d01f5160af84fffc9b11401 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 12 Mar 2024 15:39:14 -0400
Subject: [PATCH 009/623] Remove hard-coding of RAPIDS version where possible
 (#45)

* Read `VERSION` file from CMake
* Read `cuvs.__version__` from docs build
* Read `VERSION` file from shell scripts
* Use variable substitution in Doxyfile
* Update `ci/release/update-version.sh`

Issue: https://github.com/rapidsai/build-planning/issues/15

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)
  - Jake Awe (https://github.com/AyodeAwe)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/45
---
 build.sh                            |  3 +++
 ci/build_docs.sh                    |  9 +++++---
 ci/release/update-version.sh        | 16 +-------------
 cpp/CMakeLists.txt                  |  7 ++----
 cpp/cmake/thirdparty/get_raft.cmake |  6 ++---
 cpp/doxygen/Doxyfile                |  2 +-
 docs/source/conf.py                 |  8 +++++--
 fetch_rapids.cmake                  | 20 -----------------
 python/cuvs/CMakeLists.txt          |  9 +++-----
 rapids_config.cmake                 | 34 +++++++++++++++++++++++++++++
 10 files changed, 59 insertions(+), 55 deletions(-)
 delete mode 100644 fetch_rapids.cmake
 create mode 100644 rapids_config.cmake

diff --git a/build.sh b/build.sh
index 8571a574c1..36ab3529c1 100755
--- a/build.sh
+++ b/build.sh
@@ -391,6 +391,9 @@ if (( ${NUMARGS} == 0 )) || hasArg python; then
         python -m pip install --no-build-isolation --no-deps -vvv ${REPODIR}/python/cuvs
 fi
 
+export RAPIDS_VERSION="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2.\3/' "${REPODIR}/VERSION")"
+export RAPIDS_VERSION_MAJOR_MINOR="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2/' "${REPODIR}/VERSION")"
+
 if hasArg docs; then
     set -x
     cd ${DOXYGEN_BUILD_DIR}
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index 983e973854..e756d09af2 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -18,13 +18,16 @@ rapids-print-env
 
 rapids-logger "Downloading artifacts from previous jobs"
 CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-#PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
 
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
-  libcuvs
+  --channel "${PYTHON_CHANNEL}" \
+  libcuvs cuvs
 
-export RAPIDS_VERSION_NUMBER="24.04"
+export RAPIDS_VERSION="$(rapids-version)"
+export RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
+export RAPIDS_VERSION_NUMBER="$RAPIDS_VERSION_MAJOR_MINOR"
 export RAPIDS_DOCS_DIR="$(mktemp -d)"
 
 rapids-logger "Build CPP docs"
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index b122e73bdf..7dc4d12738 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -36,22 +36,11 @@ function sed_runner() {
     sed -i.bak ''"$1"'' $2 && rm -f ${2}.bak
 }
 
-sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" cpp/CMakeLists.txt
-sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" cpp/template/cmake/thirdparty/fetch_rapids.cmake
-sed_runner "s/set(CUVS_VERSION .*)/set(CUVS_VERSION \"${NEXT_FULL_TAG}\")/g" cpp/CMakeLists.txt
-sed_runner 's/'"cuvs_version .*)"'/'"cuvs_version ${NEXT_FULL_TAG})"'/g' python/cuvs/CMakeLists.txt
-sed_runner 's/'"branch-.*\/RAPIDS.cmake"'/'"branch-${NEXT_SHORT_TAG}\/RAPIDS.cmake"'/g' fetch_rapids.cmake
+sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" cpp/examples/cmake/thirdparty/fetch_rapids.cmake
 
 # Centralized version file update
 echo "${NEXT_FULL_TAG}" > VERSION
 
-# Wheel testing script
-sed_runner "s/branch-.*/branch-${NEXT_SHORT_TAG}/g" ci/test_wheel_raft_dask.sh
-
-# Docs update
-sed_runner 's/version = .*/version = '"'${NEXT_SHORT_TAG}'"'/g' docs/source/conf.py
-sed_runner 's/release = .*/release = '"'${NEXT_FULL_TAG}'"'/g' docs/source/conf.py
-
 DEPENDENCIES=(
   dask-cuda
   cuvs
@@ -83,9 +72,6 @@ sed_runner "/^ucx_py_version:$/ {n;s/.*/  - \"${NEXT_UCX_PY_VERSION}\"/}" conda/
 for FILE in .github/workflows/*.yaml; do
   sed_runner "/shared-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}"
 done
-sed_runner "s/RAPIDS_VERSION_NUMBER=\".*/RAPIDS_VERSION_NUMBER=\"${NEXT_SHORT_TAG}\"/g" ci/build_docs.sh
-
-sed_runner "/^PROJECT_NUMBER/ s|\".*\"|\"${NEXT_SHORT_TAG}\"|g" cpp/doxygen/Doxyfile
 
 sed_runner "/^set(CUVS_VERSION/ s|\".*\"|\"${NEXT_SHORT_TAG}\"|g" docs/source/build.md
 sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/build.md
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 73dec92a22..f6d6a2223d 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -10,11 +10,8 @@
 # is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
-set(RAPIDS_VERSION "24.04")
-set(CUVS_VERSION "24.04.00")
-
 cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
-include(../fetch_rapids.cmake)
+include(../rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-export)
@@ -34,7 +31,7 @@ endif()
 
 project(
   CUVS
-  VERSION ${CUVS_VERSION}
+  VERSION "${RAPIDS_VERSION}"
   LANGUAGES ${lang_list}
 )
 
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index ace0165f71..3675136ee8 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -11,10 +11,10 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-# Use RAPIDS_VERSION from cmake/thirdparty/fetch_rapids.cmake
-set(RAFT_VERSION "${RAPIDS_VERSION}")
+# Use RAPIDS_VERSION_MAJOR_MINOR from rapids_config.cmake
+set(RAFT_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
 set(RAFT_FORK "rapidsai")
-set(RAFT_PINNED_TAG "branch-${RAPIDS_VERSION}")
+set(RAFT_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
 
 function(find_and_configure_raft)
     set(oneValueArgs VERSION FORK PINNED_TAG COMPILE_LIBRARY ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES)
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index faa31c25d0..614cf43d99 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -38,7 +38,7 @@ PROJECT_NAME           = "cuVS C++ API"
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = "24.04"
+PROJECT_NUMBER         = "$(RAPIDS_VERSION_MAJOR_MINOR)"
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 1089f6a5ca..0d667833ad 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -2,6 +2,9 @@
 
 import os
 import sys
+from packaging.version import Version
+
+import cuvs
 
 # If extensions (or modules to document with autodoc) are in another
 # directory, add these directories to sys.path here. If the directory
@@ -66,10 +69,11 @@
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
+CUVS_VERSION = Version(cuvs.__version__)
 # The short X.Y version.
-version = '24.04'
+version = f"{CUVS_VERSION.major:02}.{CUVS_VERSION.minor:02}"
 # The full version, including alpha/beta/rc tags.
-release = '24.04.00'
+release = f"{CUVS_VERSION.major:02}.{CUVS_VERSION.minor:02}.{CUVS_VERSION.micro:02}"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/fetch_rapids.cmake b/fetch_rapids.cmake
deleted file mode 100644
index 330270d662..0000000000
--- a/fetch_rapids.cmake
+++ /dev/null
@@ -1,20 +0,0 @@
-# =============================================================================
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
-# in compliance with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software distributed under the License
-# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
-# or implied. See the License for the specific language governing permissions and limitations under
-# the License.
-# =============================================================================
-if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
-  file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-24.04/RAPIDS.cmake
-       ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake
-  )
-endif()
-
-include(${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 33a9420e7f..36a62044ae 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -14,9 +14,7 @@
 
 cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
 
-include(../../fetch_rapids.cmake)
-
-set(cuvs_version 24.04.00)
+include(../../rapids_config.cmake)
 
 # We always need CUDA for cuvs because the cuvs dependency brings in a header-only cuco dependency
 # that enables CUDA unconditionally.
@@ -25,7 +23,7 @@ rapids_cuda_init_architectures(cuvs_py)
 
 project(
   cuvs_py
-  VERSION ${cuvs_version}
+  VERSION "${RAPIDS_VERSION}"
   LANGUAGES # TODO: Building Python extension modules via the python_extension_module requires the C
             # language to be enabled here. The test project that is built in scikit-build to verify
             # various linking options for the python library is hardcoded to build with C, so until
@@ -45,7 +43,6 @@ message("CUVS_PY: Searching for existing cuVS C/C++ installations before default
 ################################################################################
 # - Process User Options  ------------------------------------------------------
 
-include(../../fetch_rapids.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-cython-core)
@@ -56,7 +53,7 @@ rapids_cpm_init()
 
 # If the user requested it we attempt to find CUVS.
 if(FIND_CUVS_CPP)
-  find_package(cuvs ${cuvs_version} REQUIRED)
+  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED)
   include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
 else()
   set(cuvs_FOUND OFF)
diff --git a/rapids_config.cmake b/rapids_config.cmake
new file mode 100644
index 0000000000..19906dbc62
--- /dev/null
+++ b/rapids_config.cmake
@@ -0,0 +1,34 @@
+# =============================================================================
+# Copyright (c) 2018-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+file(READ "${CMAKE_CURRENT_LIST_DIR}/VERSION" _rapids_version)
+if(_rapids_version MATCHES [[^([0-9][0-9])\.([0-9][0-9])\.([0-9][0-9])]])
+  set(RAPIDS_VERSION_MAJOR "${CMAKE_MATCH_1}")
+  set(RAPIDS_VERSION_MINOR "${CMAKE_MATCH_2}")
+  set(RAPIDS_VERSION_PATCH "${CMAKE_MATCH_3}")
+  set(RAPIDS_VERSION_MAJOR_MINOR "${RAPIDS_VERSION_MAJOR}.${RAPIDS_VERSION_MINOR}")
+  set(RAPIDS_VERSION "${RAPIDS_VERSION_MAJOR}.${RAPIDS_VERSION_MINOR}.${RAPIDS_VERSION_PATCH}")
+else()
+  string(REPLACE "\n" "\n  " _rapids_version_formatted "  ${_rapids_version}")
+  message(
+    FATAL_ERROR
+      "Could not determine RAPIDS version. Contents of VERSION file:\n${_rapids_version_formatted}")
+endif()
+
+if(NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")
+  file(
+    DOWNLOAD
+    "https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION_MAJOR_MINOR}/RAPIDS.cmake"
+    "${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")
+endif()
+include("${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")

From fa4ccc3debac8c16ab1226c2c8cada369fb86e4c Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 12 Mar 2024 13:37:28 -0700
Subject: [PATCH 010/623] Add Rust API docs (#49)

This adds the rust API docs to the generated sphinx documentation. The rust docs are generated by `cargo doc` and then copied over to the sphinx `_static` html directory, and included in the generated docs site with an iframe.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/49
---
 build.sh                             |  4 ++
 ci/build_docs.sh                     |  5 +++
 dependencies.yaml                    |  2 +
 docs/source/rust_api.rst             | 13 ++++--
 rust/cuvs/src/cagra/index.rs         | 18 +++++++-
 rust/cuvs/src/cagra/index_params.rs  |  1 +
 rust/cuvs/src/cagra/mod.rs           | 63 ++++++++++++++++++++++++++++
 rust/cuvs/src/cagra/search_params.rs |  1 +
 rust/cuvs/src/dlpack.rs              |  6 +++
 rust/cuvs/src/lib.rs                 |  5 +++
 rust/cuvs/src/resources.rs           |  4 ++
 11 files changed, 118 insertions(+), 4 deletions(-)

diff --git a/build.sh b/build.sh
index 36ab3529c1..8c4ece05e4 100755
--- a/build.sh
+++ b/build.sh
@@ -54,6 +54,7 @@ LIBCUVS_BUILD_DIR=${LIBCUVS_BUILD_DIR:=${REPODIR}/cpp/build}
 SPHINX_BUILD_DIR=${REPODIR}/docs
 DOXYGEN_BUILD_DIR=${REPODIR}/cpp/doxygen
 PYTHON_BUILD_DIR=${REPODIR}/python/cuvs/_skbuild
+RUST_BUILD_DIR=${REPODIR}/rust
 BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR}"
 
 # Set defaults for vars modified by flags to this script
@@ -398,6 +399,9 @@ if hasArg docs; then
     set -x
     cd ${DOXYGEN_BUILD_DIR}
     doxygen Doxyfile
+    cd ${RUST_BUILD_DIR}
+    cargo doc -p cuvs --no-deps
+    rsync -av ${RUST_BUILD_DIR}/target/doc/ ${SPHINX_BUILD_DIR}/source/_static/rust
     cd ${SPHINX_BUILD_DIR}
     sphinx-build -b html source _html
 fi
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index e756d09af2..9b371d1f9e 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -12,7 +12,12 @@ rapids-dependency-file-generator \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
 rapids-mamba-retry env create --force -f env.yaml -n docs
+
+# seeing failures on activating the environment here on unbound locals
+# apply workaround from https://github.com/conda/conda/issues/8186#issuecomment-532874667
+set +eu
 conda activate docs
+set -eu
 
 rapids-print-env
 
diff --git a/dependencies.yaml b/dependencies.yaml
index 7fff7811a8..cd6cb0cf04 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -44,6 +44,8 @@ files:
       - cupy
       - docs
       - py_version
+      - rust
+      - build
   rust:
     output: none
     includes:
diff --git a/docs/source/rust_api.rst b/docs/source/rust_api.rst
index 254136e3aa..8297dbef94 100644
--- a/docs/source/rust_api.rst
+++ b/docs/source/rust_api.rst
@@ -2,7 +2,14 @@
 Rust API Documentation
 ~~~~~~~~~~~~~~~~~~~~~~
 
-.. _api:
+.. raw:: html
 
-.. toctree::
-   :maxdepth: 4
+    <iframe src="./_static/rust/cuvs/index.html" height="720px" width="100%"></iframe>
+
+    <!-- hide the 'view source' section here, since it doesn't work with the iframe
+    and we want the iframe to use the space -->
+    <style type="text/css">
+        .bd-sidebar-secondary {
+            display: none;
+        }
+    </style>
diff --git a/rust/cuvs/src/cagra/index.rs b/rust/cuvs/src/cagra/index.rs
index 3c45efafd2..3394889aad 100644
--- a/rust/cuvs/src/cagra/index.rs
+++ b/rust/cuvs/src/cagra/index.rs
@@ -21,11 +21,18 @@ use crate::dlpack::ManagedTensor;
 use crate::error::{check_cuvs, Result};
 use crate::resources::Resources;
 
+/// CAGRA ANN Index
 #[derive(Debug)]
 pub struct Index(ffi::cuvsCagraIndex_t);
 
 impl Index {
-    /// Builds a new index
+    /// Builds a new Index from the dataset for efficient search.
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `params` - Parameters for building the index
+    /// * `dataset` - A row-major matrix on either the host or device to index
     pub fn build<T: Into<ManagedTensor>>(
         res: &Resources,
         params: &IndexParams,
@@ -53,6 +60,15 @@ impl Index {
         }
     }
 
+    /// Perform a Approximate Nearest Neighbors search on the Index
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `params` - Parameters to use in searching the index
+    /// * `queries` - A matrix in device memory to query for
+    /// * `neighbors` - Matrix in device memory that receives the indices of the nearest neighbors
+    /// * `distances` - Matrix in device memory that receives the distances of the nearest neighbors
     pub fn search(
         self,
         res: &Resources,
diff --git a/rust/cuvs/src/cagra/index_params.rs b/rust/cuvs/src/cagra/index_params.rs
index 7ff68d3f3f..ecc6605313 100644
--- a/rust/cuvs/src/cagra/index_params.rs
+++ b/rust/cuvs/src/cagra/index_params.rs
@@ -24,6 +24,7 @@ pub type BuildAlgo = ffi::cuvsCagraGraphBuildAlgo;
 pub struct IndexParams(pub ffi::cuvsCagraIndexParams_t);
 
 impl IndexParams {
+    /// Returns a new IndexParams
     pub fn new() -> Result<IndexParams> {
         unsafe {
             let mut params = core::mem::MaybeUninit::<ffi::cuvsCagraIndexParams_t>::uninit();
diff --git a/rust/cuvs/src/cagra/mod.rs b/rust/cuvs/src/cagra/mod.rs
index 55705c27a1..417ed9b0d8 100644
--- a/rust/cuvs/src/cagra/mod.rs
+++ b/rust/cuvs/src/cagra/mod.rs
@@ -14,6 +14,69 @@
  * limitations under the License.
  */
 
+//! CAGRA is a graph-based nearest neighbors implementation with state-of-the art
+//! query performance for both small- and large-batch sized search.
+//!
+//! Example:
+//! ```
+//!
+//! use cuvs::cagra::{Index, IndexParams, SearchParams};
+//! use cuvs::{ManagedTensor, Resources, Result};
+//!
+//! use ndarray::s;
+//! use ndarray_rand::rand_distr::Uniform;
+//! use ndarray_rand::RandomExt;
+//!
+//! fn cagra_example() -> Result<()> {
+//!     let res = Resources::new()?;
+//!
+//!     // Create a new random dataset to index
+//!     let n_datapoints = 65536;
+//!     let n_features = 512;
+//!     let dataset =
+//!         ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+//!
+//!     // build the cagra index
+//!     let build_params = IndexParams::new()?;
+//!     let index = Index::build(&res, &build_params, &dataset)?;
+//!     println!(
+//!         "Indexed {}x{} datapoints into cagra index",
+//!         n_datapoints, n_features
+//!     );
+//!
+//!     // use the first 4 points from the dataset as queries : will test that we get them back
+//!     // as their own nearest neighbor
+//!     let n_queries = 4;
+//!     let queries = dataset.slice(s![0..n_queries, ..]);
+//!
+//!     let k = 10;
+//!
+//!     // CAGRA search API requires queries and outputs to be on device memory
+//!     // copy query data over, and allocate new device memory for the distances/ neighbors
+//!     // outputs
+//!     let queries = ManagedTensor::from(&queries).to_device(&res)?;
+//!     let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+//!     let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;
+//!
+//!     let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+//!     let distances = ManagedTensor::from(&distances_host).to_device(&res)?;
+//!
+//!     let search_params = SearchParams::new()?;
+//!
+//!     index.search(&res, &search_params, &queries, &neighbors, &distances)?;
+//!
+//!     // Copy back to host memory
+//!     distances.to_host(&res, &mut distances_host)?;
+//!     neighbors.to_host(&res, &mut neighbors_host)?;
+//!
+//!     // nearest neighbors should be themselves, since queries are from the
+//!     // dataset
+//!     println!("Neighbors {:?}", neighbors_host);
+//!     println!("Distances {:?}", distances_host);
+//!     Ok(())
+//! }
+//! ```
+
 mod index;
 mod index_params;
 mod search_params;
diff --git a/rust/cuvs/src/cagra/search_params.rs b/rust/cuvs/src/cagra/search_params.rs
index 11ac92bdd2..14956966ea 100644
--- a/rust/cuvs/src/cagra/search_params.rs
+++ b/rust/cuvs/src/cagra/search_params.rs
@@ -25,6 +25,7 @@ pub type HashMode = ffi::cuvsCagraHashMode;
 pub struct SearchParams(pub ffi::cuvsCagraSearchParams_t);
 
 impl SearchParams {
+    /// Returns a new SearchParams object
     pub fn new() -> Result<SearchParams> {
         unsafe {
             let mut params = core::mem::MaybeUninit::<ffi::cuvsCagraSearchParams_t>::uninit();
diff --git a/rust/cuvs/src/dlpack.rs b/rust/cuvs/src/dlpack.rs
index b86959db12..a1d4e41c6d 100644
--- a/rust/cuvs/src/dlpack.rs
+++ b/rust/cuvs/src/dlpack.rs
@@ -19,6 +19,8 @@ use std::convert::From;
 use crate::error::{check_cuda, Result};
 use crate::resources::Resources;
 
+/// ManagedTensor is a wrapper around a dlpack DLManagedTensor object.
+/// This lets you pass matrices in device or host memory into cuvs.
 #[derive(Debug)]
 pub struct ManagedTensor(ffi::DLManagedTensor);
 
@@ -41,6 +43,8 @@ impl ManagedTensor {
         bytes
     }
 
+    /// Creates a new ManagedTensor on the current GPU device, and copies
+    /// the data into it.
     pub fn to_device(&self, _res: &Resources) -> Result<ManagedTensor> {
         unsafe {
             let bytes = self.bytes();
@@ -64,6 +68,8 @@ impl ManagedTensor {
             Ok(ManagedTensor(ret))
         }
     }
+
+    /// Copies data from device memory into host memory
     pub fn to_host<
         T: IntoDtype,
         S: ndarray::RawData<Elem = T> + ndarray::RawDataMut,
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
index 7a6f847f5d..273f047237 100644
--- a/rust/cuvs/src/lib.rs
+++ b/rust/cuvs/src/lib.rs
@@ -14,6 +14,11 @@
  * limitations under the License.
  */
 
+//! cuVS: Rust bindings for Vector Search on the GPU
+//!
+//! This crate provides Rust bindings for cuVS, allowing you to run
+//! approximate nearest neighbors search on the GPU.
+
 pub mod cagra;
 mod dlpack;
 mod error;
diff --git a/rust/cuvs/src/resources.rs b/rust/cuvs/src/resources.rs
index ad7113e6be..a5c503dc53 100644
--- a/rust/cuvs/src/resources.rs
+++ b/rust/cuvs/src/resources.rs
@@ -17,10 +17,14 @@
 use crate::error::{check_cuvs, Result};
 use std::io::{stderr, Write};
 
+/// Resources are objects that are shared between function calls,
+/// and includes things like CUDA streams, cuBLAS handles and other
+/// resources that are expensive to create.
 #[derive(Debug)]
 pub struct Resources(pub ffi::cuvsResources_t);
 
 impl Resources {
+    /// Returns a new Resources object
     pub fn new() -> Result<Resources> {
         let mut res: ffi::cuvsResources_t = 0;
         unsafe {

From bfb7f57cbe964b83da83543e01c6c663a6c9064a Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 12 Mar 2024 14:06:43 -0700
Subject: [PATCH 011/623] add rust to conda environments (#50)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/50
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 4 ++++
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 4 ++++
 conda/environments/all_cuda-122_arch-aarch64.yaml | 4 ++++
 conda/environments/all_cuda-122_arch-x86_64.yaml  | 4 ++++
 dependencies.yaml                                 | 1 +
 5 files changed, 17 insertions(+)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index d711fc5554..e8854eae6a 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -9,6 +9,7 @@ channels:
 dependencies:
 - breathe
 - c-compiler
+- clang
 - clang-tools=16.0.6
 - clang==16.0.6
 - cmake>=3.26.4
@@ -27,6 +28,7 @@ dependencies:
 - graphviz
 - gtest>=1.13.0
 - ipython
+- libclang
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
@@ -35,6 +37,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- make
 - nccl>=2.9.9
 - ninja
 - numpy>=1.23
@@ -47,6 +50,7 @@ dependencies:
 - pytest==7.*
 - recommonmark
 - rmm==24.4.*
+- rust
 - scikit-build-core>=0.7.0
 - scikit-learn
 - sphinx-copybutton
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index b4c2c6857b..f8269df7b9 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -9,6 +9,7 @@ channels:
 dependencies:
 - breathe
 - c-compiler
+- clang
 - clang-tools=16.0.6
 - clang==16.0.6
 - cmake>=3.26.4
@@ -27,6 +28,7 @@ dependencies:
 - graphviz
 - gtest>=1.13.0
 - ipython
+- libclang
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
@@ -35,6 +37,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- make
 - nccl>=2.9.9
 - ninja
 - numpy>=1.23
@@ -47,6 +50,7 @@ dependencies:
 - pytest==7.*
 - recommonmark
 - rmm==24.4.*
+- rust
 - scikit-build-core>=0.7.0
 - scikit-learn
 - sphinx-copybutton
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index 85d96e5c94..e5940cbed8 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -9,6 +9,7 @@ channels:
 dependencies:
 - breathe
 - c-compiler
+- clang
 - clang-tools=16.0.6
 - clang==16.0.6
 - cmake>=3.26.4
@@ -28,10 +29,12 @@ dependencies:
 - graphviz
 - gtest>=1.13.0
 - ipython
+- libclang
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- make
 - nccl>=2.9.9
 - ninja
 - numpy>=1.23
@@ -43,6 +46,7 @@ dependencies:
 - pytest==7.*
 - recommonmark
 - rmm==24.4.*
+- rust
 - scikit-build-core>=0.7.0
 - scikit-learn
 - sphinx-copybutton
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 16cc655c63..1008c02700 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -9,6 +9,7 @@ channels:
 dependencies:
 - breathe
 - c-compiler
+- clang
 - clang-tools=16.0.6
 - clang==16.0.6
 - cmake>=3.26.4
@@ -28,10 +29,12 @@ dependencies:
 - graphviz
 - gtest>=1.13.0
 - ipython
+- libclang
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- make
 - nccl>=2.9.9
 - ninja
 - numpy>=1.23
@@ -43,6 +46,7 @@ dependencies:
 - pytest==7.*
 - recommonmark
 - rmm==24.4.*
+- rust
 - scikit-build-core>=0.7.0
 - scikit-learn
 - sphinx-copybutton
diff --git a/dependencies.yaml b/dependencies.yaml
index cd6cb0cf04..fedf4946ee 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -19,6 +19,7 @@ files:
       - test_python_common
       - test_py_cuvs
       - cupy
+      - rust
   test_cpp:
     output: none
     includes:

From 9c0e38005b9942cba3e3a52e8bf25594fb92bb88 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Wed, 13 Mar 2024 14:32:34 +0100
Subject: [PATCH 012/623] `quick start` and `integrations` guides for the docs
 (#47)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/47
---
 .gitignore                               |   1 +
 README.md                                |   8 +-
 docs/source/api_docs.rst                 |   4 +-
 docs/source/basics.rst                   |  90 +++++++++
 docs/source/build.md                     | 178 -----------------
 docs/source/build.rst                    | 233 +++++++++++++++++++++++
 docs/source/getting_started.rst          |  12 ++
 docs/source/index.rst                    |   5 +-
 docs/source/integrations.rst             |  40 ++++
 docs/source/interoperability.rst         |  72 +++++++
 docs/source/quick_start.md               | 183 ------------------
 docs/source/working_with_ann_indexes.rst | 102 ++++++++++
 12 files changed, 559 insertions(+), 369 deletions(-)
 create mode 100644 docs/source/basics.rst
 delete mode 100644 docs/source/build.md
 create mode 100644 docs/source/build.rst
 create mode 100644 docs/source/getting_started.rst
 create mode 100644 docs/source/integrations.rst
 create mode 100644 docs/source/interoperability.rst
 delete mode 100644 docs/source/quick_start.md
 create mode 100644 docs/source/working_with_ann_indexes.rst

diff --git a/.gitignore b/.gitignore
index 8cac2b5aed..9fcde3fb3b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -61,6 +61,7 @@ _xml
 # sphinx
 _html
 _text
+docs/source/_static/rust
 
 # clang tooling
 compile_commands.json
diff --git a/README.md b/README.md
index 14bb028123..4a2d2de166 100755
--- a/README.md
+++ b/README.md
@@ -11,10 +11,10 @@
 
 ## Useful Resources
 
-- [cuVS Reference Documentation](https://docs.rapids.ai/api/cuvs/stable/): API Documentation.
-- [cuVS Getting Started](./docs/source/quick_start.md): Getting started with RAFT.
-- [Build and Install cuVS](./docs/source/build.md): Instructions for installing and building cuVS.
-- [Example Notebooks](./notebooks): Example jupyer notebooks
+- [Code Examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples): Self-contained Code Examples.
+- [API Reference Documentation](https://docs.rapids.ai/api/cuvs/nightly/api_docs): API Documentation.
+- [Getting Started Guide](https://docs.rapids.ai/api/cuvs/nightly/getting_started): Getting started with RAFT.
+- [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build): Instructions for installing and building cuVS.
 - [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
 - [GitHub repository](https://github.com/rapidsai/cuvs): Download the cuVS source code.
 - [Issue tracker](https://github.com/rapidsai/cuvs/issues): Report issues or request features.
diff --git a/docs/source/api_docs.rst b/docs/source/api_docs.rst
index 2a5a867e0a..c911539b95 100644
--- a/docs/source/api_docs.rst
+++ b/docs/source/api_docs.rst
@@ -1,5 +1,5 @@
-API Documentation
-=================
+API Reference
+=============
 
 .. toctree::
    :maxdepth: 1
diff --git a/docs/source/basics.rst b/docs/source/basics.rst
new file mode 100644
index 0000000000..4842e27889
--- /dev/null
+++ b/docs/source/basics.rst
@@ -0,0 +1,90 @@
+cuVS API Basics
+===============
+
+- `Memory management`_
+- `Resource management`_
+
+Memory management
+-----------------
+
+Centralized memory management allows flexible configuration of allocation strategies, such as sharing the same CUDA memory pool across library boundaries. cuVS uses the [RMM](https://github.com/rapidsai/rmm) library, which eases the burden of configuring different allocation strategies globally across GPU-accelerated libraries.
+
+RMM currently has APIs for C++ and Python.
+
+C++
+^^^
+
+Here's an example of configuring RMM to use a pool allocator in C++ (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#example>`_):
+
+.. code-block:: c++
+
+    rmm::mr::cuda_memory_resource cuda_mr;
+    // Construct a resource that uses a coalescing best-fit pool allocator
+    // With the pool initially half of available device memory
+    auto initial_size = rmm::percent_of_free_device_memory(50);
+    rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource> pool_mr{&cuda_mr, initial_size};
+    rmm::mr::set_current_device_resource(&pool_mr); // Updates the current device resource pointer to `pool_mr`
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource(); // Points to `pool_mr`
+
+Python
+^^^^^^
+
+And the corresponding code in Python (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#memoryresource-objects>`_):
+
+.. code-block:: python
+
+    import rmm
+    pool = rmm.mr.PoolMemoryResource(
+      rmm.mr.CudaMemoryResource(),
+      initial_pool_size=2**30,
+      maximum_pool_size=2**32)
+    rmm.mr.set_current_device_resource(pool)
+
+
+Resource management
+-------------------
+
+cuVS uses an API from the `RAFT <https://github.com/rapidsai/raft>`_ library of ML and data mining primitives to centralize and reuse expensive resources, such as memory management. The below code examples demonstrate how to create these resources for use throughout this guide.
+
+See RAFT's `resource API documentation <https://docs.rapids.ai/api/raft/nightly/cpp_api/core_resources/>`_ for more information.
+
+C
+^
+
+.. code-block:: c
+
+    #include <cuda_runtime.h>
+    #include <cuvs/core/c_api.h>
+
+    cuvsResources_t res;
+    cuvsResourcesCreate(&res);
+
+    // ... do some processing ...
+
+    cuvsResourcesDestroy(res);
+
+C++
+^^^
+
+.. code-block:: c++
+
+    #include <raft/core/device_resources.hpp>
+
+    raft::device_resources res;
+
+Python
+^^^^^^
+
+.. code-block:: python
+
+    import pylibraft
+
+    res = pylibraft.common.DeviceResources()
+
+
+Rust
+^^^^
+
+.. code-block:: rust
+
+    let res = cuvs::Resources::new()?;
\ No newline at end of file
diff --git a/docs/source/build.md b/docs/source/build.md
deleted file mode 100644
index 976482725c..0000000000
--- a/docs/source/build.md
+++ /dev/null
@@ -1,178 +0,0 @@
-**# Installation
-
-The cuVS software development kit provides APIs for C, C++, Python, and Rust languages. This guide outlines how to install the pre-compiled packages, build it from source, and use it in downstream applications. 
-
-## Table of Contents
-
-- [Install pre-compiled packages](#installing-precompiled-packages)
-  - [C, C++, and Python through Conda](#installing-through-conda)
-  - [Python through Pip](#installing-python-through-pip)
-  - [Rust through crates.io](#installing-rust-through-crates)
-- [Build from source](#building-c-and-python-from-source)
-  - [Prerequisites](#build-prerequisites)
-    - [CUDA/GPU requirements](#cudagpu-requirements)
-    - [Build dependencies](#build-dependencies)
-      - [Required](#required)
-  - [Create a build environment](#creating-a-build-environment)
-      - [ccache and sccache](#ccache-and-sccache)
-  - [C/C++ Libraries](#c-library)
-      - [Building the Googletests](#building-the-googletests)
-  - [Python Library](#python-library)
-  - [Rust Library](#rust-library)
-  - [Using Cmake Directly](#using-cmake-directly)
-- [Build Documentation](#build-documentation)
-- [Use cuVS in your application](#use-cuvs-in-your-application)
-
-------
-
-## Installing Pre-compiled Packages
-
-### C, C++, and Python through Conda
-
-The easiest way to install the pre-compiled C, C++, and Python packages is through [conda](https://docs.anaconda.com/free/miniconda/index.html). Use the following commands, depending on your CUDA version, to install cuVS packages (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
-
-#### C++ Package
-```bash
-mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.0
-```
-
-#### C Package
-```bash
-mamba install -c rapidsai -c conda-forge -c nvidia libcuvs_c cuda-version=12.0
-```
-
-#### Python Package
-```bash
-mamba install -c rapidsai -c conda-forge -c nvidia pycuvs cuda-version=12.0
-```
-
-### Python through Pip
-
-The cuVS Python package can also be [installed through pip](https://rapids.ai/pip.html#install). 
-
-For CUDA 11 packages:
-```bash
-pip install pycuvs-cu11 --extra-index-url=https://pypi.nvidia.com
-```
-
-And CUDA 12 packages:
-```bash
-pip install pycuvs-cu12 --extra-index-url=https://pypi.nvidia.com
-```
-
-Note: these packages statically link the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code. 
-
-### Rust through crates.io
-
-## Build from source
-
-The core cuVS source code is written in C++ and wrapped through a C API. The C API is wrapped around the C++ APIs and the other supported languages are built around the C API. 
-
-
-### Prerequisites
-
-- Cmake 3.26.4+
-- GCC 9.3+ (11.4+ recommended)
-- CUDA Toolkit 11.4+
-- Volta architecture or better (compute capability >= 7.0)
-
-### Create a build environment
-
-[Conda](https://docs.anaconda.com/free/miniconda/index.html) environment scripts are provided for installing the necessary dependencies to build cuVS from source. It is preferred to use `mamba`, as it provides significant speedup over `conda`:
-```bash
-mamba env create --name cuvs -f conda/environments/all_cuda-120_arch-x86_64.yaml
-mamba activate cuvs
-```
-
-The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
-```bash
-mamba env create --name cuvs -f conda/environments/all_cuda-118_arch-x86_64.yaml
-mamba activate cuvs
-```
-
-The recommended way to build and install cuVS from source is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides CMake options for building and installing the headers, tests, benchmarks, and the pre-compiled shared library.
-
-
-### C and C++ libraries
-
-The C and C++ shared libraries are built together using the following arguments to `build.sh`:
-```bash
-./build.sh libcuvs
-```
-
-In above example the `libcuvs.so` and `libcuvs_c.so` shared libraries are installed by default into `$INSTALL_PREFIX/lib`. To disable this, pass `-n` flag.
-
-Once installed, the shared libraries, headers (and any dependencies downloaded and installed via `rapids-cmake`) can be uninstalled using `build.sh`:
-```bash
-./build.sh libcuvs --uninstall
-```
-
-
-#### Building the Googletests
-
-Compile the C and C++ Googletests using the `tests` target in `build.sh`.
-
-```bash
-./build.sh libcuvs tests
-```
-
-The tests will be written to the build directory, which is `cpp/build/` by default, and they will be named `*_TEST`.
-
-It can take some time to compile all of the tests. You can build individual tests by providing a semicolon-separated list to the `--limit-tests` option in `build.sh`. Make sure to pass the `-n` flag so the tests are not installed.
-
-```bash
-./build.sh libcuvs tests -n --limit-tests=NEIGHBORS_TEST;CAGRA_C_TEST
-```
-
-### Python library
-
-The Python library should be built and installed using the `build.sh` script:
-
-```bash
-./build.sh python
-```
-
-The Python packages can also be uninstalled using the `build.sh` script:
-```bash
-./build.sh python --uninstall
-```
-
-### Rust library
-
-
-
-### Using CMake directly
-
-When building cuVS from source, the `build.sh` script offers a nice wrapper around the `cmake` commands to ease the burdens of manually configuring the various available cmake options. When more fine-grained control over the CMake configuration is desired, the `cmake` command can be invoked directly as the below example demonstrates. 
-
-The `CMAKE_INSTALL_PREFIX` installs cuVS into a specific location. The example below installs cuVS into the current Conda environment:
-```bash
-cd cpp
-mkdir build
-cd build
-cmake -D BUILD_TESTS=ON -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX ../
-make -j<parallel_level> install
-```
-
-cuVS has the following configurable cmake flags available:
-
-| Flag                   | Possible Values      | Default Value | Behavior                                                                     |
-|------------------------|----------------------| --- |------------------------------------------------------------------------------|
-| BUILD_TESTS            | ON, OFF              | ON | Compile Googletests                                                          |
-| CUDA_ENABLE_KERNELINFO | ON, OFF              | OFF | Enables `kernelinfo` in nvcc. This is useful for `compute-sanitizer`         |
-| CUDA_ENABLE_LINEINFO   | ON, OFF              | OFF | Enable the -lineinfo option for nvcc                                         |
-| CUDA_STATIC_RUNTIME    | ON, OFF              | OFF | Statically link the CUDA runtime                                             |
-| DETECT_CONDA_ENV       | ON, OFF              | ON | Enable detection of conda environment for dependencies                       |
-| CUVS_NVTX              | ON, OFF              | OFF | Enable NVTX Markers                                                          |
-
-### Build documentation
-
-The documentation requires that the C, C++ and Python libraries have been built and installed. The following will build the docs along with the necessary libraries:
-
-```
-./build.sh libcuvs python docs
-```
-
-## Use cuVS in your application
-
-The [examples/](https://github.com/rapidsai/raft/tree/HEAD/examples) directory at the root of the cuVS repository has self-contains drop-in projects to build and use the cuVS SDK in your applications.
\ No newline at end of file
diff --git a/docs/source/build.rst b/docs/source/build.rst
new file mode 100644
index 0000000000..8140224656
--- /dev/null
+++ b/docs/source/build.rst
@@ -0,0 +1,233 @@
+Installation
+============
+
+The cuVS software development kit provides APIs for C, C++, Python, and Rust languages. This guide outlines how to install the pre-compiled packages, build it from source, and use it in downstream applications. 
+
+- `Installing pre-compiled packages`_
+
+  * `C, C++, and Python through Conda`_
+
+  * `Python through Pip`_
+
+  * `Rust through crates.io`_
+
+- `Build from source`_
+
+  * `Prerequisites`_
+
+  * `Create a build environment`_
+
+  * `C and C++ Libraries`_
+
+    * `Building the Googletests`_
+
+  * `Python Library`_
+
+  * `Rust Library`_
+
+  * `Using Cmake Directly`_
+
+- `Build Documentation`_
+
+
+Installing Pre-compiled Packages
+--------------------------------
+
+C, C++, and Python through Conda
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The easiest way to install the pre-compiled C, C++, and Python packages is through `conda <https://docs.anaconda.com/free/miniconda/index.html>`_. Use the following commands, depending on your CUDA version, to install cuVS packages (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
+
+C++ Package
+~~~~~~~~~~~
+
+.. code-block:: bash
+
+  mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.0
+
+C Package
+~~~~~~~~~
+
+.. code-block:: bash
+
+    mamba install -c rapidsai -c conda-forge -c nvidia libcuvs_c cuda-version=12.0
+
+Python Package
+~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+    mamba install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.0
+
+Python through Pip
+^^^^^^^^^^^^^^^^^^
+
+The cuVS Python package can also be `installed through pip <https://rapids.ai/pip.html#install>`_.
+
+For CUDA 11 packages:
+
+.. code-block:: bash
+
+    pip install cuvs-cu11 --extra-index-url=https://pypi.nvidia.com
+
+And CUDA 12 packages:
+
+.. code-block:: bash
+
+    pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
+
+Note: these packages statically link the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code. 
+
+Rust through crates.io
+^^^^^^^^^^^^^^^^^^^^^^
+
+Build from source
+-----------------
+
+The core cuVS source code is written in C++ and wrapped through a C API. The C API is wrapped around the C++ APIs and the other supported languages are built around the C API. 
+
+
+Prerequisites
+^^^^^^^^^^^^^
+
+- Cmake 3.26.4+
+- GCC 9.3+ (11.4+ recommended)
+- CUDA Toolkit 11.4+
+- Volta architecture or better (compute capability >= 7.0)
+
+Create a build environment
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`Conda <https://docs.anaconda.com/free/miniconda/index.html>`_ environment scripts are provided for installing the necessary dependencies to build cuVS from source. It is preferred to use `mamba`, as it provides significant speedup over `conda`:
+
+.. code-block:: bash
+
+    mamba env create --name cuvs -f conda/environments/all_cuda-120_arch-x86_64.yaml
+    mamba activate cuvs
+
+The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
+
+.. code-block:: bash
+
+    mamba env create --name cuvs -f conda/environments/all_cuda-118_arch-x86_64.yaml
+    mamba activate cuvs
+
+The recommended way to build and install cuVS from source is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides CMake options for building and installing the headers, tests, benchmarks, and the pre-compiled shared library.
+
+
+C and C++ libraries
+^^^^^^^^^^^^^^^^^^^
+
+The C and C++ shared libraries are built together using the following arguments to `build.sh`:
+
+.. code-block:: bash
+
+    ./build.sh libcuvs
+
+In above example the `libcuvs.so` and `libcuvs_c.so` shared libraries are installed by default into `$INSTALL_PREFIX/lib`. To disable this, pass `-n` flag.
+
+Once installed, the shared libraries, headers (and any dependencies downloaded and installed via `rapids-cmake`) can be uninstalled using `build.sh`:
+
+.. code-block:: bash
+
+    ./build.sh libcuvs --uninstall
+
+
+Building the Googletests
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Compile the C and C++ Googletests using the `tests` target in `build.sh`.
+
+.. code-block:: bash
+
+    ./build.sh libcuvs tests
+
+The tests will be written to the build directory, which is `cpp/build/` by default, and they will be named `*_TEST`.
+
+It can take some time to compile all of the tests. You can build individual tests by providing a semicolon-separated list to the `--limit-tests` option in `build.sh`. Make sure to pass the `-n` flag so the tests are not installed.
+
+.. code-block:: bash
+
+    ./build.sh libcuvs tests -n --limit-tests=NEIGHBORS_TEST;CAGRA_C_TEST
+
+Python library
+^^^^^^^^^^^^^^
+
+The Python library should be built and installed using the `build.sh` script:
+
+.. code-block:: bash
+
+    ./build.sh python
+
+The Python packages can also be uninstalled using the `build.sh` script:
+
+.. code-block:: bash
+
+    ./build.sh python --uninstall
+
+Rust library
+^^^^^^^^^^^^
+
+Using CMake directly
+^^^^^^^^^^^^^^^^^^^^
+
+When building cuVS from source, the `build.sh` script offers a nice wrapper around the `cmake` commands to ease the burdens of manually configuring the various available cmake options. When more fine-grained control over the CMake configuration is desired, the `cmake` command can be invoked directly as the below example demonstrates. 
+
+The `CMAKE_INSTALL_PREFIX` installs cuVS into a specific location. The example below installs cuVS into the current Conda environment:
+
+.. code-block:: bash
+
+    cd cpp
+    mkdir build
+    cd build
+    cmake -D BUILD_TESTS=ON -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX ../
+    make -j<parallel_level> install
+
+cuVS has the following configurable cmake flags available:
+
+.. list-table:: Cmake Flags
+
+ * - Flag
+   - Possible Values
+   - Default Value
+   - Behavior
+
+ * - BUILD_TESTS
+   - ON, OFF
+   - ON
+   - Compile Googletests
+
+ * - CUDA_ENABLE_KERNELINFO
+   - ON, OFF
+   - OFF
+   - Enables `kernelinfo` in nvcc. This is useful for `compute-sanitizer`
+
+ * - CUDA_ENABLE_LINEINFO
+   - ON, OFF
+   - OFF
+   - Enable the `-lineinfo` option for nvcc
+
+ * - CUDA_STATIC_RUNTIME
+   - ON, OFF
+   - OFF
+   - Statically link the CUDA runtime
+
+ * - DETECT_CONDA_ENV
+   - ON, OFF
+   - ON
+   - Enable detection of conda environment for dependencies
+
+ * - CUVS_NVTX
+   - ON, OFF
+   - OFF
+   - Enable NVTX markers
+
+
+Build documentation
+^^^^^^^^^^^^^^^^^^^
+
+The documentation requires that the C, C++ and Python libraries have been built and installed. The following will build the docs along with the necessary libraries:
+
+.. code-block:: bash
+
+    ./build.sh libcuvs python docs
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
new file mode 100644
index 0000000000..79b35c2d50
--- /dev/null
+++ b/docs/source/getting_started.rst
@@ -0,0 +1,12 @@
+Getting Started
+===============
+
+This guide provides an initial starting point of the basic concepts and using the various APIs in the cuVS software development kit.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+    
+   basics.rst
+   interoperability.rst
+   working_with_ann_indexes.rst
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index ce8a8e1d25..b8334e8589 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -6,7 +6,7 @@ Useful Resources
 
 .. _cuvs_reference: https://docs.rapids.ai/api/cuvs/stable/
 
-- `Example Notebooks <https://github.com/rapidsai/cuvs/tree/HEAD/notebooks>`_: Example Jupyter notebooks
+- `Example Notebooks <https://github.com/rapidsai/cuvs/tree/HEAD/exmples>`_: Self-contained Code examples
 - `RAPIDS Community <https://rapids.ai/community.html>`_: Get help, contribute, and collaborate.
 - `GitHub repository <https://github.com/rapidsai/cuvs>`_: Download the cuVS source code.
 - `Issue tracker <https://github.com/rapidsai/cuvs/issues>`_: Report issues or request features.
@@ -21,8 +21,9 @@ cuVS is a library for vector search and clustering on the GPU.
    :maxdepth: 1
    :caption: Contents:
 
-   quick_start.md
+   getting_started.rst
    build.md
+   integrations.rst
    api_docs.rst
    contributing.md
 
diff --git a/docs/source/integrations.rst b/docs/source/integrations.rst
new file mode 100644
index 0000000000..6c6e2ce8ca
--- /dev/null
+++ b/docs/source/integrations.rst
@@ -0,0 +1,40 @@
+Integrations
+============
+
+Aside from using cuVS directly, it can be consumed through a number of sdk and vector database integrations.
+
+- `FAISS`_
+- `Milvus`_
+- `Kinetica`_
+
+FAISS
+-----
+
+FAISS v1.8 provides a special conda package that enables a RAFT backend for the Flat, IVF-Flat and IVF-PQ indexes on the GPU. Like the classical FAISS GPU indexes, the RAFT backend also enables interoperability between FAISS CPU indexes, allowing an index to be trained on GPU, searched on CPU, and vice versa.
+
+The RAFT backend can be enabled by building FAISS from source with the `FAISS_USE_RAFT` cmake flag enabled and setting the `use_raft` configuration option for the RAFT-enabled GPU indexes.
+
+A pre-compiled conda package can also be installed using the following command:
+
+.. code-block:: bash
+
+    conda install -c conda-forge -c pytorch -c rapidsai -c nvidia -c "nvidia/label/cuda-11.8.0" faiss-gpu-raft
+
+The next release of FAISS will feature cuVS support as we continue to migrate the vector search algorithms from RAFT to cuVS.
+
+Milvus
+------
+
+In version 2.3, Milvus released support for IVF-Flat and IVF-PQ indexes on the GPU through RAFT. Version 2.4 adds support for brute-force and the graph-based CAGRA index on the GPU. Please refer to the `Milvus documentation <https://milvus.io/docs/install_standalone-docker-compose-gpu.md>`_ to install Milvus with GPU support.
+
+The GPU indexes can be enabled by using the index types prefixed with `GPU_`, as outlined in the `Milvus index build guide <https://milvus.io/docs/build_index.md#Prepare-index-parameter>`_.
+
+Milvus will be migrating their GPU support from RAFT to cuVS as we continue to move the vector search algorithms out of RAFT and into cuVS.
+
+
+Kinetica
+--------
+
+Starting with release 7.2, Kinetica supports the graph-based the CAGRA algorithm from RAFT. Kinetica will continue to improve its support over coming versions, while also migrating to cuVS as we work to move the vector search algorithms out of RAFT and into cuVS.
+
+Kinetica currently offers the ability to create a CAGRA index in a SQL `CREATE_TABLE` statement, as outlined in their `vector search indexing docs <https://docs.kinetica.com/7.2/concepts/indexes/#cagra-index>`_. Kinetica is not open source, but the RAFT indexes can be enabled in the developer edition, which can be installed `here <https://www.kinetica.com/try/#download_instructions>`_.
\ No newline at end of file
diff --git a/docs/source/interoperability.rst b/docs/source/interoperability.rst
new file mode 100644
index 0000000000..fc8d19ef43
--- /dev/null
+++ b/docs/source/interoperability.rst
@@ -0,0 +1,72 @@
+Interoperability
+================
+
+DLPack (C)
+^^^^^^^^^^
+
+Multi-dimensional span (C++)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+cuVS is built on top of the GPU-accelerated machine learning and data mining primitives in the `RAFT <https://github.com/rapidsai/raft>`_ library. Most of the C++ APIs in cuVS accept `mdspan <https://arxiv.org/abs/2010.06474>`_ multi-dimensional array view for representing data in higher dimensions similar to the `ndarray` in the Numpy Python library. RAFT also contains the corresponding owning `mdarray` structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory.
+
+The `mdarray` is an owning object that forms a convenience layer over RMM and can be constructed in RAFT using a number of different helper functions:
+
+.. code-block:: c++
+
+    #include <raft/core/device_mdarray.hpp>
+    
+    int n_rows = 10;
+    int n_cols = 10;
+    
+    auto scalar = raft::make_device_scalar<float>(handle, 1.0);
+    auto vector = raft::make_device_vector<float>(handle, n_cols);
+    auto matrix = raft::make_device_matrix<float>(handle, n_rows, n_cols);
+
+The `mdspan` is a lightweight non-owning view that can wrap around any pointer, maintaining shape, layout, and indexing information for accessing elements.
+
+We can construct `mdspan` instances directly from the above `mdarray` instances:
+
+.. code-block:: c++
+
+    // Scalar mdspan on device
+    auto scalar_view = scalar.view();
+
+    // Vector mdspan on device
+    auto vector_view = vector.view();
+
+    // Matrix mdspan on device
+    auto matrix_view = matrix.view();
+
+Since the `mdspan` is just a lightweight wrapper, we can also construct it from the underlying data handles in the `mdarray` instances above. We use the extent to get information about the `mdarray` or `mdspan`'s shape.
+
+.. code-block:: c++
+
+    #include <raft/core/device_mdspan.hpp>
+
+    auto scalar_view = raft::make_device_scalar_view(scalar.data_handle());
+    auto vector_view = raft::make_device_vector_view(vector.data_handle(), vector.extent(0));
+    auto matrix_view = raft::make_device_matrix_view(matrix.data_handle(), matrix.extent(0), matrix.extent(1));
+
+Of course, RAFT's `mdspan`/`mdarray` APIs aren't just limited to the `device`. You can also create `host` variants:
+
+.. code-block:: c++
+
+    #include <raft/core/host_mdarray.hpp>
+    #include <raft/core/host_mdspan.hpp>
+
+    int n_rows = 10;
+    int n_cols = 10;
+
+    auto scalar = raft::make_host_scalar<float>(handle, 1.0);
+    auto vector = raft::make_host_vector<float>(handle, n_cols);
+    auto matrix = raft::make_host_matrix<float>(handle, n_rows, n_cols);
+
+    auto scalar_view = raft::make_host_scalar_view(scalar.data_handle());
+    auto vector_view = raft::make_host_vector_view(vector.data_handle(), vector.extent(0));
+    auto matrix_view = raft::make_host_matrix_view(matrix.data_handle(), matrix.extent(0), matrix.extent(1));
+
+Please refer to RAFT's `mdspan` `documentation <https://docs.rapids.ai/api/raft/stable/cpp_api/mdspan/>`_ to learn more.
+
+
+CUDA array interface (Python)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/docs/source/quick_start.md b/docs/source/quick_start.md
deleted file mode 100644
index 3909b40f20..0000000000
--- a/docs/source/quick_start.md
+++ /dev/null
@@ -1,183 +0,0 @@
-# Quick Start
-
-This guide is meant to provide a quick-start tutorial for interacting with RAFT's C++ & Python APIs.
-
-## RAPIDS Memory Manager (RMM)
-
-RAFT relies heavily on the [RMM](https://github.com/rapidsai/rmm) library which eases the burden of configuring different allocation strategies globally across the libraries that use it.
-
-## Multi-dimensional Spans and Arrays
-
-Most of the APIs in RAFT accept  [mdspan](https://arxiv.org/abs/2010.06474) multi-dimensional array view for representing data in higher dimensions similar to the `ndarray` in the Numpy Python library. RAFT also contains the corresponding owning `mdarray` structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory.
-
-The `mdarray` is an owning object that forms a convenience layer over RMM and can be constructed in RAFT using a number of different helper functions:
-
-```c++
-#include <raft/core/device_mdarray.hpp>
-
-int n_rows = 10;
-int n_cols = 10;
-
-auto scalar = raft::make_device_scalar<float>(handle, 1.0);
-auto vector = raft::make_device_vector<float>(handle, n_cols);
-auto matrix = raft::make_device_matrix<float>(handle, n_rows, n_cols);
-```
-
-The `mdspan` is a lightweight non-owning view that can wrap around any pointer, maintaining shape, layout, and indexing information for accessing elements. 
-
-
-We can construct `mdspan` instances directly from the above `mdarray` instances:
-
-```c++
-// Scalar mdspan on device
-auto scalar_view = scalar.view();
-
-// Vector mdspan on device
-auto vector_view = vector.view();
-
-// Matrix mdspan on device
-auto matrix_view = matrix.view();
-```
-Since the `mdspan` is just a lightweight wrapper, we can also construct it from the underlying data handles in the `mdarray` instances above. We use the extent to get information about the `mdarray` or `mdspan`'s shape.
-
-```c++
-#include <raft/core/device_mdspan.hpp>
-
-auto scalar_view = raft::make_device_scalar_view(scalar.data_handle());
-auto vector_view = raft::make_device_vector_view(vector.data_handle(), vector.extent(0));
-auto matrix_view = raft::make_device_matrix_view(matrix.data_handle(), matrix.extent(0), matrix.extent(1));
-```
-
-Of course, RAFT's `mdspan`/`mdarray` APIs aren't just limited to the `device`. You can also create `host` variants:
-
-```c++
-#include <raft/core/host_mdarray.hpp>
-#include <raft/core/host_mdspan.hpp>
-
-int n_rows = 10;
-int n_cols = 10;
-
-auto scalar = raft::make_host_scalar<float>(handle, 1.0);
-auto vector = raft::make_host_vector<float>(handle, n_cols);
-auto matrix = raft::make_host_matrix<float>(handle, n_rows, n_cols);
-
-auto scalar_view = raft::make_host_scalar_view(scalar.data_handle());
-auto vector_view = raft::make_host_vector_view(vector.data_handle(), vector.extent(0));
-auto matrix_view = raft::make_host_matrix_view(matrix.data_handle(), matrix.extent(0), matrix.extent(1));
-```
-
-And `managed` variants:
-
-```c++
-#include <raft/core/device_mdspan.hpp>
-
-int n_rows = 10;
-int n_cols = 10;
-
-auto matrix = raft::make_managed_mdspan(managed_ptr, raft::make_matrix_extents(n_rows, n_cols));
-```
-
-You can also create strided mdspans:
-
-```c++
-
-#include <raft/core/device_mdspan.hpp>
-
-int n_elements = 10;
-int stride = 10;
-
-auto vector = raft::make_device_vector_view(vector_ptr, raft::make_vector_strided_layout(n_elements, stride));
-```
-
-
-## C++ Example
-
-Most of the primitives in RAFT accept a `raft::handle_t` object for the management of resources which are expensive to create, such CUDA streams, stream pools, and handles to other CUDA libraries like `cublas` and `cusolver`.
-
-The example below demonstrates creating a RAFT handle and using it with `device_matrix` and `device_vector` to allocate memory, generating random clusters, and computing
-pairwise Euclidean distances:
-
-```c++
-#include <raft/core/handle.hpp>
-#include <raft/core/device_mdarray.hpp>
-#include <raft/random/make_blobs.cuh>
-#include <raft/distance/distance.cuh>
-
-raft::handle_t handle;
-
-int n_samples = 5000;
-int n_features = 50;
-
-auto input = raft::make_device_matrix<float>(handle, n_samples, n_features);
-auto labels = raft::make_device_vector<int>(handle, n_samples);
-auto output = raft::make_device_matrix<float>(handle, n_samples, n_samples);
-
-raft::random::make_blobs(handle, input.view(), labels.view());
-
-auto metric = raft::distance::DistanceType::L2SqrtExpanded;
-raft::distance::pairwise_distance(handle, input.view(), input.view(), output.view(), metric);
-```
-
-## Python Example
-
-The `pylibraft` package contains a Python API for RAFT algorithms and primitives. `pylibraft` integrates nicely into other libraries by being very lightweight with minimal dependencies and accepting any object that supports the `__cuda_array_interface__`, such as [CuPy's ndarray](https://docs.cupy.dev/en/stable/user_guide/interoperability.html#rmm). The number of RAFT algorithms exposed in this package is continuing to grow from release to release.
-
-The example below demonstrates computing the pairwise Euclidean distances between CuPy arrays. Note that CuPy is not a required dependency for `pylibraft`.
-
-```python
-import cupy as cp
-
-from pylibraft.distance import pairwise_distance
-
-n_samples = 5000
-n_features = 50
-
-in1 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
-in2 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
-
-output = pairwise_distance(in1, in2, metric="euclidean")
-```
-
-The `output` array in the above example is of type `raft.common.device_ndarray`, which supports [__cuda_array_interface__](https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html#cuda-array-interface-version-2) making it interoperable with other libraries like CuPy, Numba, and PyTorch that also support it. CuPy supports DLPack, which also enables zero-copy conversion from `raft.common.device_ndarray` to JAX and Tensorflow.
-
-Below is an example of converting the output `pylibraft.common.device_ndarray` to a CuPy array:
-```python
-cupy_array = cp.asarray(output)
-```
-
-And converting to a PyTorch tensor:
-```python
-import torch
-
-torch_tensor = torch.as_tensor(output, device='cuda')
-```
-
-When the corresponding library has been installed and available in your environment, this conversion can also be done automatically by all RAFT compute APIs by setting a global configuration option:
-```python
-import pylibraft.config
-pylibraft.config.set_output_as("cupy")  # All compute APIs will return cupy arrays
-pylibraft.config.set_output_as("torch") # All compute APIs will return torch tensors
-```
-
-You can also specify a `callable` that accepts a `pylibraft.common.device_ndarray` and performs a custom conversion. The following example converts all output to `numpy` arrays:
-```python
-pylibraft.config.set_output_as(lambda device_ndarray: return device_ndarray.copy_to_host())
-```
-
-
-`pylibraft` also supports writing to a pre-allocated output array so any `__cuda_array_interface__` supported array can be written to in-place:
-
-```python
-import cupy as cp
-
-from pylibraft.distance import pairwise_distance
-
-n_samples = 5000
-n_features = 50
-
-in1 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
-in2 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
-output = cp.empty((n_samples, n_samples), dtype=cp.float32)
-
-pairwise_distance(in1, in2, out=output, metric="euclidean")
-```
diff --git a/docs/source/working_with_ann_indexes.rst b/docs/source/working_with_ann_indexes.rst
new file mode 100644
index 0000000000..f347a2f428
--- /dev/null
+++ b/docs/source/working_with_ann_indexes.rst
@@ -0,0 +1,102 @@
+Working with ANN Indexes
+========================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+C
+^
+
+.. code-block:: c
+
+    #include <cuvs/neighbors/cagra.h>
+
+    cuvsResources_t res;
+    cuvsCagraIndexParams_t index_params;
+    cuvsCagraIndex_t index;
+
+    DLManagedTensor *dataset;
+    load_dataset(dataset);
+
+    cuvsResourcesCreate(&res);
+    cuvsCagraIndexParamsCreate(&index_params);
+    cuvsCagraIndexCreate(&index);
+
+    cuvsCagraBuild(res, index_params, dataset, index);
+
+    cuvsCagraIndexDestroy(index);
+    cuvsCagraIndexParamsDestroy(index_params);
+    cuvsResourcesDestroy(res);
+
+C++
+^^^
+
+.. code-block:: c++
+
+    #include <cuvs/neighbors/cagra.hpp>
+
+    using namespace cuvs::neighbors;
+
+    raft::device_matrix_view<float> dataset = load_dataset();
+    raft::device_resources res;
+
+    cagra::index_params index_params;
+
+    auto index = cagra::build(res, index_params, dataset);
+
+
+Python
+^^^^^^
+
+.. code-block:: python
+
+    from cuvs.neighbors import cagra
+
+    dataset = load_data()
+    index_params = cagra.IndexParams()
+
+    index = cagra.build_index(build_params, dataset)
+
+Rust
+^^^^
+
+.. code-block:: rust
+
+    use cuvs::cagra::{Index, IndexParams};
+    use cuvs::{Resources, Result};
+
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    /// Example showing how to index and search data with CAGRA
+    fn cagra_example() -> Result<()> {
+        let res = Resources::new()?;
+
+        // Create a new random dataset to index
+        let n_datapoints = 65536;
+        let n_features = 512;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        // build the cagra index
+        let build_params = IndexParams::new()?;
+        let index = Index::build(&res, &build_params, &dataset)?;
+
+        Ok(())
+    }
+
+
+Searching an index
+------------------
+
+
+CPU/GPU interoperability
+------------------------
+
+Serializing an index
+--------------------
\ No newline at end of file

From 6eb9bdb4c92f2248c5e11be891ab75d0e7a70e16 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 13 Mar 2024 08:38:21 -0500
Subject: [PATCH 013/623] Add upper bound to prevent usage of NumPy 2 (#52)

NumPy 2 is expected to be released in the near future. For the RAPIDS 24.04 release, we will pin to `numpy>=1.23,<2.0a0`. This PR adds an upper bound to affected RAPIDS repositories.

xref: https://github.com/rapidsai/build-planning/issues/29

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/52
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/all_cuda-122_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-122_arch-x86_64.yaml  | 2 +-
 dependencies.yaml                                 | 2 +-
 python/cuvs/pyproject.toml                        | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index e8854eae6a..897e200074 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -40,7 +40,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23
+- numpy>=1.23,<2.0a0
 - numpydoc
 - nvcc_linux-aarch64=11.8
 - pre-commit
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index f8269df7b9..92c9c2d601 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -40,7 +40,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23
+- numpy>=1.23,<2.0a0
 - numpydoc
 - nvcc_linux-64=11.8
 - pre-commit
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index e5940cbed8..0e8fa75a20 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -37,7 +37,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23
+- numpy>=1.23,<2.0a0
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 1008c02700..06da6fecc0 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -37,7 +37,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23
+- numpy>=1.23,<2.0a0
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
diff --git a/dependencies.yaml b/dependencies.yaml
index fedf4946ee..ff3aa61b19 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -356,7 +356,7 @@ dependencies:
     common:
       - output_types: [conda, pyproject]
         packages:
-          - &numpy numpy>=1.23
+          - &numpy numpy>=1.23,<2.0a0
       - output_types: [conda]
         packages:
           - *rmm_conda
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 07578abf3a..ae56a37acd 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -37,7 +37,7 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.9"
 dependencies = [
     "cuda-python>=11.7.1,<12.0a0",
-    "numpy>=1.23",
+    "numpy>=1.23,<2.0a0",
     "pylibraft==24.4.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [

From 5b8393fd3de36ef8f81320bf7259630a2095e8a5 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 14 Mar 2024 21:54:55 +0100
Subject: [PATCH 014/623] More docs updates for 24.04 (#54)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/54
---
 README.md                                     |  12 +-
 docs/source/working_with_ann_indexes.rst      | 107 ++----------------
 docs/source/working_with_ann_indexes_c.rst    |  39 +++++++
 docs/source/working_with_ann_indexes_cpp.rst  |  35 ++++++
 .../working_with_ann_indexes_python.rst       |  30 +++++
 docs/source/working_with_ann_indexes_rust.rst |  43 +++++++
 6 files changed, 162 insertions(+), 104 deletions(-)
 create mode 100644 docs/source/working_with_ann_indexes_c.rst
 create mode 100644 docs/source/working_with_ann_indexes_cpp.rst
 create mode 100644 docs/source/working_with_ann_indexes_python.rst
 create mode 100644 docs/source/working_with_ann_indexes_rust.rst

diff --git a/README.md b/README.md
index 4a2d2de166..56aaec0791 100755
--- a/README.md
+++ b/README.md
@@ -1,5 +1,12 @@
 # <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuVS: Vector Search and Clustering on the GPU</div>
 
+> [!note]
+> cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
+> 1. RAFT contains C++ and Python APIs for all of the approximate nearest neighbors and clustering algorithms.
+> 2. cuVS contains a growing support for different languages, including C, C++, Python, and Rust. We will be adding more language support to cuVS in the future but will not be improving the language support for RAFT.
+> 3. Once all of RAFT's approximate nearest neighbors and clustering algorithms are moved to cuVS, the RAFT APIs will be deprecated and eventually removed altogether. Once removed, RAFT will become a lightweight header-only library. In the meantime, there's no harm in using RAFT if support for additional languages is not needed.
+
+
 ## Contents
 
 1. [Useful Resources](#useful-resources)
@@ -23,11 +30,6 @@
 
 cuVS contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.
 
-**Please note** that cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
-1. RAFT contains C++ and Python APIs for all of the approximate nearest neighbors and clustering algorithms.
-2. cuVS contains a growing support for different languages, including C, C++, Python, and Rust. We will be adding more language support to cuVS in the future but will not be improving the language support for RAFT.
-3. Once all of RAFT's approximate nearest neighbors and clustering algorithms are moved to cuVS, the RAFT APIs will be deprecated and eventually removed altogether. Once removed, RAFT will become a lightweight header-only library. In the meantime, there's no harm in using RAFT if support for additional languages is not needed.
-
 ## Installing cuVS
 
 cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python). Different packages are available for the different languages supported by cuVS:
diff --git a/docs/source/working_with_ann_indexes.rst b/docs/source/working_with_ann_indexes.rst
index f347a2f428..8e91fb4acd 100644
--- a/docs/source/working_with_ann_indexes.rst
+++ b/docs/source/working_with_ann_indexes.rst
@@ -1,102 +1,11 @@
 Working with ANN Indexes
 ========================
 
-- `Building an index`_
-- `Searching an index`_
-- `CPU/GPU Interoperability`_
-- `Serializing an index`_
-
-Building an index
------------------
-
-C
-^
-
-.. code-block:: c
-
-    #include <cuvs/neighbors/cagra.h>
-
-    cuvsResources_t res;
-    cuvsCagraIndexParams_t index_params;
-    cuvsCagraIndex_t index;
-
-    DLManagedTensor *dataset;
-    load_dataset(dataset);
-
-    cuvsResourcesCreate(&res);
-    cuvsCagraIndexParamsCreate(&index_params);
-    cuvsCagraIndexCreate(&index);
-
-    cuvsCagraBuild(res, index_params, dataset, index);
-
-    cuvsCagraIndexDestroy(index);
-    cuvsCagraIndexParamsDestroy(index_params);
-    cuvsResourcesDestroy(res);
-
-C++
-^^^
-
-.. code-block:: c++
-
-    #include <cuvs/neighbors/cagra.hpp>
-
-    using namespace cuvs::neighbors;
-
-    raft::device_matrix_view<float> dataset = load_dataset();
-    raft::device_resources res;
-
-    cagra::index_params index_params;
-
-    auto index = cagra::build(res, index_params, dataset);
-
-
-Python
-^^^^^^
-
-.. code-block:: python
-
-    from cuvs.neighbors import cagra
-
-    dataset = load_data()
-    index_params = cagra.IndexParams()
-
-    index = cagra.build_index(build_params, dataset)
-
-Rust
-^^^^
-
-.. code-block:: rust
-
-    use cuvs::cagra::{Index, IndexParams};
-    use cuvs::{Resources, Result};
-
-    use ndarray_rand::rand_distr::Uniform;
-    use ndarray_rand::RandomExt;
-
-    /// Example showing how to index and search data with CAGRA
-    fn cagra_example() -> Result<()> {
-        let res = Resources::new()?;
-
-        // Create a new random dataset to index
-        let n_datapoints = 65536;
-        let n_features = 512;
-        let dataset =
-            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
-
-        // build the cagra index
-        let build_params = IndexParams::new()?;
-        let index = Index::build(&res, &build_params, &dataset)?;
-
-        Ok(())
-    }
-
-
-Searching an index
-------------------
-
-
-CPU/GPU interoperability
-------------------------
-
-Serializing an index
---------------------
\ No newline at end of file
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+   working_with_ann_indexes_c.rst
+   working_with_ann_indexes_cpp.rst
+   working_with_ann_indexes_python.rst
+   working_with_ann_indexes_rust.rst
diff --git a/docs/source/working_with_ann_indexes_c.rst b/docs/source/working_with_ann_indexes_c.rst
new file mode 100644
index 0000000000..6632e1c96e
--- /dev/null
+++ b/docs/source/working_with_ann_indexes_c.rst
@@ -0,0 +1,39 @@
+Working with ANN Indexes in C
+=============================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: c
+
+    #include <cuvs/neighbors/cagra.h>
+
+    cuvsResources_t res;
+    cuvsCagraIndexParams_t index_params;
+    cuvsCagraIndex_t index;
+
+    DLManagedTensor *dataset;
+    load_dataset(dataset);
+
+    cuvsResourcesCreate(&res);
+    cuvsCagraIndexParamsCreate(&index_params);
+    cuvsCagraIndexCreate(&index);
+
+    cuvsCagraBuild(res, index_params, dataset, index);
+
+    cuvsCagraIndexDestroy(index);
+    cuvsCagraIndexParamsDestroy(index_params);
+    cuvsResourcesDestroy(res);
+
+
+Searching an index
+------------------
+
+
+Serializing an index
+--------------------
\ No newline at end of file
diff --git a/docs/source/working_with_ann_indexes_cpp.rst b/docs/source/working_with_ann_indexes_cpp.rst
new file mode 100644
index 0000000000..a74b4c88c1
--- /dev/null
+++ b/docs/source/working_with_ann_indexes_cpp.rst
@@ -0,0 +1,35 @@
+Working with ANN Indexes in C++
+===============================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: c++
+
+    #include <cuvs/neighbors/cagra.hpp>
+
+    using namespace cuvs::neighbors;
+
+    raft::device_matrix_view<float> dataset = load_dataset();
+    raft::device_resources res;
+
+    cagra::index_params index_params;
+
+    auto index = cagra::build(res, index_params, dataset);
+
+
+
+Searching an index
+------------------
+
+
+CPU/GPU interoperability
+------------------------
+
+Serializing an index
+--------------------
\ No newline at end of file
diff --git a/docs/source/working_with_ann_indexes_python.rst b/docs/source/working_with_ann_indexes_python.rst
new file mode 100644
index 0000000000..bc439bbd5b
--- /dev/null
+++ b/docs/source/working_with_ann_indexes_python.rst
@@ -0,0 +1,30 @@
+Working with ANN Indexes in Python
+==================================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: python
+
+    from cuvs.neighbors import cagra
+
+    dataset = load_data()
+    index_params = cagra.IndexParams()
+
+    index = cagra.build_index(build_params, dataset)
+
+
+Searching an index
+------------------
+
+
+CPU/GPU interoperability
+------------------------
+
+Serializing an index
+--------------------
\ No newline at end of file
diff --git a/docs/source/working_with_ann_indexes_rust.rst b/docs/source/working_with_ann_indexes_rust.rst
new file mode 100644
index 0000000000..e7da4960a8
--- /dev/null
+++ b/docs/source/working_with_ann_indexes_rust.rst
@@ -0,0 +1,43 @@
+Working with ANN Indexes in Rust
+================================
+
+- `Building an index`_
+- `Searching an index`_
+- `CPU/GPU Interoperability`_
+- `Serializing an index`_
+
+Building an index
+-----------------
+
+.. code-block:: rust
+
+    use cuvs::cagra::{Index, IndexParams};
+    use cuvs::{Resources, Result};
+
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    /// Example showing how to index and search data with CAGRA
+    fn cagra_example() -> Result<()> {
+        let res = Resources::new()?;
+
+        // Create a new random dataset to index
+        let n_datapoints = 65536;
+        let n_features = 512;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        // build the cagra index
+        let build_params = IndexParams::new()?;
+        let index = Index::build(&res, &build_params, &dataset)?;
+
+        Ok(())
+    }
+
+
+Searching an index
+------------------
+
+
+Serializing an index
+--------------------
\ No newline at end of file

From b4e6a9eb6a304baf3d722b2c78bba9b73bd31b57 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 14 Mar 2024 13:56:22 -0700
Subject: [PATCH 015/623] Update build.sh to build rust bindings (#53)

* Update build.sh to build the rust bindings
* update ci/release/update-version.sh to keep the rust version up to date
* remove references to the cuvs-rs conda package from the README
* Remove temporary copy of rust documentation from `docs/source/_static` - just copy directly to output folder

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/53
---
 README.md                    |  8 ++++----
 build.sh                     | 21 +++++++++++++++------
 ci/build_rust.sh             |  5 ++---
 ci/release/update-version.sh |  7 ++++++-
 docs/source/build.rst        |  6 ++++++
 rust/Cargo.toml              |  2 +-
 6 files changed, 34 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index 56aaec0791..160f466042 100755
--- a/README.md
+++ b/README.md
@@ -34,9 +34,9 @@ cuVS contains state-of-the-art implementations of several algorithms for running
 
 cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python). Different packages are available for the different languages supported by cuVS:
 
-| Python | C++ | C | Rust |
-|--------|-----|---|------|
-| `pycuvs`| `libcuvs` | `libcuvs_c` | `cuvs-rs` |
+| Python | C++ | C |
+|--------|-----|---|
+| `pycuvs`| `libcuvs` | `libcuvs_c` |
 
 ### Stable release
 
@@ -172,7 +172,7 @@ fn cagra_example() -> Result<()> {
 
 ## Contributing
 
-If you are interested in contributing to the cuVS library, please read our [Contributing guidelines](docs/source/contributing.md). Refer to the [Developer Guide](docs/source/developer_guide.md) for details on the developer guidelines, workflows, and principals.
+If you are interested in contributing to the cuVS library, please read our [Contributing guidelines](docs/source/contributing.md). Refer to the [Developer Guide](docs/source/developer_guide.md) for details on the developer guidelines, workflows, and principles.
 
 ## References
 
diff --git a/build.sh b/build.sh
index 8c4ece05e4..0c987ed962 100755
--- a/build.sh
+++ b/build.sh
@@ -18,13 +18,14 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python docs tests examples clean --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python rust docs tests examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
    libcuvs          - build the cuvs C++ code only. Also builds the C-wrapper library
                       around the C++ code.
    python           - build the cuvs Python package
+   rust             - build the cuvs Rust bindings
    docs             - build the documentation
    tests            - build the tests
    examples         - build the examples
@@ -54,8 +55,8 @@ LIBCUVS_BUILD_DIR=${LIBCUVS_BUILD_DIR:=${REPODIR}/cpp/build}
 SPHINX_BUILD_DIR=${REPODIR}/docs
 DOXYGEN_BUILD_DIR=${REPODIR}/cpp/doxygen
 PYTHON_BUILD_DIR=${REPODIR}/python/cuvs/_skbuild
-RUST_BUILD_DIR=${REPODIR}/rust
-BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR}"
+RUST_BUILD_DIR=${REPODIR}/rust/target
+BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR} ${RUST_BUILD_DIR}"
 
 # Set defaults for vars modified by flags to this script
 CMAKE_LOG_LEVEL=""
@@ -77,6 +78,7 @@ CLEAN=0
 UNINSTALL=0
 DISABLE_DEPRECATION_WARNINGS=ON
 CMAKE_TARGET=""
+EXTRA_CMAKE_ARGS=""
 
 # Set defaults for vars that may not have been defined externally
 INSTALL_PREFIX=${INSTALL_PREFIX:=${PREFIX:=${CONDA_PREFIX:=$LIBCUVS_BUILD_DIR/install}}}
@@ -392,6 +394,13 @@ if (( ${NUMARGS} == 0 )) || hasArg python; then
         python -m pip install --no-build-isolation --no-deps -vvv ${REPODIR}/python/cuvs
 fi
 
+# Build the cuvs Rust bindings
+if (( ${NUMARGS} == 0 )) || hasArg rust; then
+    cd ${REPODIR}/rust
+    cargo build --examples --lib
+    cargo test
+fi
+
 export RAPIDS_VERSION="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2.\3/' "${REPODIR}/VERSION")"
 export RAPIDS_VERSION_MAJOR_MINOR="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2/' "${REPODIR}/VERSION")"
 
@@ -399,11 +408,11 @@ if hasArg docs; then
     set -x
     cd ${DOXYGEN_BUILD_DIR}
     doxygen Doxyfile
-    cd ${RUST_BUILD_DIR}
-    cargo doc -p cuvs --no-deps
-    rsync -av ${RUST_BUILD_DIR}/target/doc/ ${SPHINX_BUILD_DIR}/source/_static/rust
     cd ${SPHINX_BUILD_DIR}
     sphinx-build -b html source _html
+    cd ${REPODIR}/rust
+    cargo doc -p cuvs --no-deps
+    rsync -av ${RUST_BUILD_DIR}/doc/ ${SPHINX_BUILD_DIR}/_html/_static/rust
 fi
 
 ################################################################################
diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index 895dd41e07..59f0924c80 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -35,6 +35,5 @@ rapids-mamba-retry install \
   libcuvs  \
   libraft
 
-# build and test the rust bindings
-cd rust
-cargo test
+export EXTRA_CMAKE_ARGS=""
+bash ./build.sh rust
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 7dc4d12738..589110ab12 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 # Copyright (c) 2020-2024, NVIDIA CORPORATION.
 ########################
-# RAFT Version Updater #
+# CUVS Version Updater #
 ########################
 
 ## Usage
@@ -21,6 +21,7 @@ CURRENT_SHORT_TAG=${CURRENT_MAJOR}.${CURRENT_MINOR}
 #Get <major>.<minor> for next version
 NEXT_MAJOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[1]}')
 NEXT_MINOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[2]}')
+NEXT_PATCH=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[3]}')
 NEXT_SHORT_TAG=${NEXT_MAJOR}.${NEXT_MINOR}
 NEXT_UCX_PY_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/${NEXT_SHORT_TAG})"
 NEXT_UCX_PY_VERSION="${NEXT_UCX_PY_SHORT_TAG}.*"
@@ -81,6 +82,10 @@ sed_runner "s|:[0-9][0-9].[0-9][0-9]|:${NEXT_SHORT_TAG}|g" docs/source/raft_ann_
 
 sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" README.md
 
+# rust can't handle leading 0's in the major/minor/patch version - remove
+NEXT_FULL_RUST_TAG=$(printf "%d.%d.%d" $((10#$NEXT_MAJOR)) $((10#$NEXT_MINOR)) $((10#$NEXT_PATCH)))
+sed_runner "s/version = \".*\"/version = \"${NEXT_FULL_RUST_TAG}\"/g" rust/Cargo.toml
+
 # .devcontainer files
 find .devcontainer/ -type f -name devcontainer.json -print0 | while IFS= read -r -d '' filename; do
     sed_runner "s@rapidsai/devcontainers:[0-9.]*@rapidsai/devcontainers:${NEXT_SHORT_TAG}@g" "${filename}"
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 8140224656..dbd882660f 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -168,6 +168,12 @@ The Python packages can also be uninstalled using the `build.sh` script:
 Rust library
 ^^^^^^^^^^^^
 
+The Rust bindings can be built with
+
+.. code-block:: bash
+
+    ./build.sh rust
+
 Using CMake directly
 ^^^^^^^^^^^^^^^^^^^^
 
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index 7e9bfe4ecb..689fd0565c 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "0.1.0"
+version = "24.4.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"

From 0f11d07f65b597c3397000b743e18ac13db26d49 Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Mon, 18 Mar 2024 09:27:42 -0400
Subject: [PATCH 016/623] DOC v24.06 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  4 ++--
 .devcontainer/cuda11.8-pip/devcontainer.json  |  6 ++---
 .../cuda12.0-conda/devcontainer.json          |  4 ++--
 .devcontainer/cuda12.0-pip/devcontainer.json  |  6 ++---
 .github/workflows/build.yaml                  | 14 +++++------
 .github/workflows/pr.yaml                     | 24 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++----
 README.md                                     |  2 +-
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-118_arch-x86_64.yaml             |  4 ++--
 .../all_cuda-122_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-122_arch-x86_64.yaml             |  4 ++--
 dependencies.yaml                             | 12 +++++-----
 docs/source/developer_guide.md                |  8 +++----
 .../cpp/cmake/thirdparty/fetch_rapids.cmake   |  2 +-
 python/cuvs/pyproject.toml                    |  6 ++---
 rust/Cargo.toml                               |  2 +-
 18 files changed, 58 insertions(+), 58 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index cefbea72b7..bea16385a0 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,12 +5,12 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.06-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 05518805aa..39b7971e79 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,13 +5,13 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda11.8-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.06-cpp-llvm16-cuda11.8-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.2": {"version": "1.14.1"},
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {"version": "1.14.1"},
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.0-conda/devcontainer.json b/.devcontainer/cuda12.0-conda/devcontainer.json
index 3f89836e26..4d9cbf33d8 100644
--- a/.devcontainer/cuda12.0-conda/devcontainer.json
+++ b/.devcontainer/cuda12.0-conda/devcontainer.json
@@ -5,12 +5,12 @@
     "args": {
       "CUDA": "12.0",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.04-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.06-cpp-mambaforge-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.0-pip/devcontainer.json
index 33f67cd3fb..2a77539cff 100644
--- a/.devcontainer/cuda12.0-pip/devcontainer.json
+++ b/.devcontainer/cuda12.0-pip/devcontainer.json
@@ -5,13 +5,13 @@
     "args": {
       "CUDA": "12.0",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda12.0-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.06-cpp-llvm16-cuda12.0-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.2": {"version": "1.14.1"},
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {"version": "1.14.1"},
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 61ebdf0bca..66945cb2ae 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 7c6db2c603..652092dde5 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.06
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.06
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.06
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.06
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.06
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.06
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.06
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,20 +86,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.06
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.06
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.06
     with:
       build_command: |
         sccache -z;
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 0e66113f24..4f49e87cfe 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.06
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.06
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.06
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.06
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/README.md b/README.md
index 160f466042..c787d4ded0 100755
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@ mamba install -c conda-forge -c nvidia -c rapidsai pycuvs
 ### Nightlies
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai-nightly pycuvs=24.04*
+mamba install -c conda-forge -c nvidia -c rapidsai-nightly pycuvs=24.06
 ```
 
 Please see the [Build and Install Guide](docs/source/build.md) for more information on installing cuVS and building from source.
diff --git a/VERSION b/VERSION
index 4a2fe8aa57..0bff6981a3 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.04.00
+24.06.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 897e200074..364d9c76af 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -45,11 +45,11 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.4.*
+- pylibraft==24.6.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.4.*
+- rmm==24.6.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 92c9c2d601..f269ff55ea 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -45,11 +45,11 @@ dependencies:
 - nvcc_linux-64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.4.*
+- pylibraft==24.6.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.4.*
+- rmm==24.6.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index 0e8fa75a20..d232a16318 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -41,11 +41,11 @@ dependencies:
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.4.*
+- pylibraft==24.6.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.4.*
+- rmm==24.6.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 06da6fecc0..a5dc50d762 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -41,11 +41,11 @@ dependencies:
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.4.*
+- pylibraft==24.6.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.4.*
+- rmm==24.6.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/dependencies.yaml b/dependencies.yaml
index ff3aa61b19..1951ee5cf0 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -140,8 +140,8 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
-          - &rmm_conda rmm==24.4.*
-          - &pylibraft_conda pylibraft==24.4.*
+          - &rmm_conda rmm==24.6.*
+          - &pylibraft_conda pylibraft==24.6.*
           - dlpack>=0.8,<1.0
       - output_types: requirements
         packages:
@@ -163,12 +163,12 @@ dependencies:
         matrices:
           - matrix: {cuda: "12.*"}
             packages:
-              - &rmm_cu12 rmm-cu12==24.4.*
-              - &pylibraft_cu12 pylibraft-cu12==24.4.*
+              - &rmm_cu12 rmm-cu12==24.6.*
+              - &pylibraft_cu12 pylibraft-cu12==24.6.*
           - matrix: {cuda: "11.*"}
             packages:
-              - &rmm_cu11 rmm-cu11==24.4.*
-              - &pylibraft_cu11 pylibraft-cu11==24.4.*
+              - &rmm_cu11 rmm-cu11==24.6.*
+              - &pylibraft_cu11 pylibraft-cu11==24.6.*
           - {matrix: null, packages: [*rmm_conda, *pylibraft_conda] }
   checks:
     common:
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index d29130add0..e10e8987af 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.04/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.06/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.04/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.06/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
@@ -215,7 +215,7 @@ python ./cpp/scripts/include_checker.py --inplace [cpp/include cpp/test ... list
 ```
 
 ### Copyright header
-[copyright.py](https://github.com/rapidsai/raft/blob/branch-24.04/ci/checks/copyright.py) checks the Copyright header for all git-modified files
+[copyright.py](https://github.com/rapidsai/raft/blob/branch-24.06/ci/checks/copyright.py) checks the Copyright header for all git-modified files
 
 Manually, you can run the following to bulk-fix the header if only the years need to be updated:
 ```bash
@@ -229,7 +229,7 @@ Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY`
 ## Logging
 
 ### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.04/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
+Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.06/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
 
 ### Usage
 ```cpp
diff --git a/examples/cpp/cmake/thirdparty/fetch_rapids.cmake b/examples/cpp/cmake/thirdparty/fetch_rapids.cmake
index c22f586cad..ec0e0816db 100644
--- a/examples/cpp/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cpp/cmake/thirdparty/fetch_rapids.cmake
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and RAFT versions
-set(RAPIDS_VERSION "24.04")
+set(RAPIDS_VERSION "24.06")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index ae56a37acd..d3eae51ea5 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -19,8 +19,8 @@ requires = [
     "cuda-python>=11.7.1,<12.0a0",
     "cython>=3.0.0",
     "ninja",
-    "pylibraft==24.4.*",
-    "rmm==24.4.*",
+    "pylibraft==24.6.*",
+    "rmm==24.6.*",
     "scikit-build-core[pyproject]>=0.7.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
@@ -38,7 +38,7 @@ requires-python = ">=3.9"
 dependencies = [
     "cuda-python>=11.7.1,<12.0a0",
     "numpy>=1.23,<2.0a0",
-    "pylibraft==24.4.*",
+    "pylibraft==24.6.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index 689fd0565c..700b65b40e 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "24.4.0"
+version = "24.6.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"

From 964f68600e2be7f4f0c566ba7cff7c8d340187e2 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Mon, 18 Mar 2024 12:24:24 -0400
Subject: [PATCH 017/623] Replace local copyright check with pre-commit-hooks
 verify-copyright (#48)

The local `copyright.py` script is bug-prone. Replace it with a more robust centralized script from `pre-commit-hooks`.

Issue: https://github.com/rapidsai/build-planning/issues/30

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/48
---
 .pre-commit-config.yaml |  22 ++-
 ci/checks/copyright.py  | 289 ----------------------------------------
 2 files changed, 16 insertions(+), 295 deletions(-)
 delete mode 100644 ci/checks/copyright.py

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a82fb74a57..ce5e8e0e4a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -79,12 +79,6 @@ repos:
                 verbose: true
                 require_serial: true
                 exclude: .*/thirdparty/.*
-              - id: copyright-check
-                name: copyright-check
-                entry: python ./ci/checks/copyright.py --git-modified-only --update-current-year
-                language: python
-                pass_filenames: false
-                additional_dependencies: [gitpython]
               - id: include-check
                 name: include-check
                 entry: python ./cpp/scripts/include_checker.py cpp/bench cpp/include cpp/test
@@ -107,6 +101,22 @@ repos:
         rev: v4.5.0
         hooks:
               - id: check-json
+      - repo: https://github.com/rapidsai/pre-commit-hooks
+        rev: v0.0.1
+        hooks:
+          - id: verify-copyright
+            files: |
+              (?x)
+                  [.](cmake|cpp|cu|cuh|h|hpp|sh|pxd|py|pyx|rs)$|
+                  CMakeLists[.]txt$|
+                  CMakeLists_standalone[.]txt$|
+                  meta[.]yaml$|
+                  setup[.]cfg$
+            exclude: |
+              (?x)
+                  cpp/include/cuvs/neighbors/detail/faiss_select/|
+                  docs/source/sphinxext/github_link\.py|
+                  cpp/cmake/modules/FindAVX\.cmake|
 
 default_language_version:
       python: python3
diff --git a/ci/checks/copyright.py b/ci/checks/copyright.py
deleted file mode 100644
index a49f27cd2c..0000000000
--- a/ci/checks/copyright.py
+++ /dev/null
@@ -1,289 +0,0 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-import datetime
-import re
-import argparse
-import io
-import os
-import sys
-
-import git
-
-SCRIPT_DIR = os.path.dirname(os.path.realpath(os.path.expanduser(__file__)))
-
-# Add the scripts dir for gitutils
-sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR,
-                                              "../../cpp/scripts")))
-
-# Now import gitutils. Ignore flake8 error here since there is no other way to
-# set up imports
-import gitutils  # noqa: E402
-
-FilesToCheck = [
-    re.compile(r"[.](cmake|cpp|cu|cuh|h|hpp|sh|pxd|py|pyx)$"),
-    re.compile(r"CMakeLists[.]txt$"),
-    re.compile(r"CMakeLists_standalone[.]txt$"),
-    re.compile(r"setup[.]cfg$"),
-    re.compile(r"meta[.]yaml$")
-]
-ExemptFiles = [
-    re.compile("cpp/include/cuvs/neighbors/detail/faiss_select/"),
-    re.compile("docs/source/sphinxext/github_link.py"),
-    re.compile("cpp/cmake/modules/FindAVX.cmake")
-]
-
-# this will break starting at year 10000, which is probably OK :)
-CheckSimple = re.compile(
-    r"Copyright *(?:\(c\))? *(\d{4}),? *NVIDIA C(?:ORPORATION|orporation)")
-CheckDouble = re.compile(
-    r"Copyright *(?:\(c\))? *(\d{4})-(\d{4}),? *NVIDIA C(?:ORPORATION|orporation)"  # noqa: E501
-)
-
-
-def checkThisFile(f):
-    if isinstance(f, git.Diff):
-        if f.deleted_file or f.b_blob.size == 0:
-            return False
-        f = f.b_path
-    elif not os.path.exists(f) or os.stat(f).st_size == 0:
-        # This check covers things like symlinks which point to files that DNE
-        return False
-    for exempt in ExemptFiles:
-        if exempt.search(f):
-            return False
-    for checker in FilesToCheck:
-        if checker.search(f):
-            return True
-    return False
-
-
-def modifiedFiles():
-    """Get a set of all modified files, as Diff objects.
-
-    The files returned have been modified in git since the merge base of HEAD
-    and the upstream of the target branch. We return the Diff objects so that
-    we can read only the staged changes.
-    """
-    repo = git.Repo()
-    # Use the environment variable TARGET_BRANCH or RAPIDS_BASE_BRANCH (defined in CI) if possible
-    target_branch = os.environ.get("TARGET_BRANCH", os.environ.get("RAPIDS_BASE_BRANCH"))
-    if target_branch is None:
-        # Fall back to the closest branch if not on CI
-        target_branch = repo.git.describe(
-            all=True, tags=True, match="branch-*", abbrev=0
-        ).lstrip("heads/")
-
-    upstream_target_branch = None
-    if target_branch in repo.heads:
-        # Use the tracking branch of the local reference if it exists. This
-        # returns None if no tracking branch is set.
-        upstream_target_branch = repo.heads[target_branch].tracking_branch()
-    if upstream_target_branch is None:
-        # Fall back to the remote with the newest target_branch. This code
-        # path is used on CI because the only local branch reference is
-        # current-pr-branch, and thus target_branch is not in repo.heads.
-        # This also happens if no tracking branch is defined for the local
-        # target_branch. We use the remote with the latest commit if
-        # multiple remotes are defined.
-        candidate_branches = [
-            remote.refs[target_branch] for remote in repo.remotes
-            if target_branch in remote.refs
-        ]
-        if len(candidate_branches) > 0:
-            upstream_target_branch = sorted(
-                candidate_branches,
-                key=lambda branch: branch.commit.committed_datetime,
-            )[-1]
-        else:
-            # If no remotes are defined, try to use the local version of the
-            # target_branch. If this fails, the repo configuration must be very
-            # strange and we can fix this script on a case-by-case basis.
-            upstream_target_branch = repo.heads[target_branch]
-    merge_base = repo.merge_base("HEAD", upstream_target_branch.commit)[0]
-    diff = merge_base.diff()
-    changed_files = {f for f in diff if f.b_path is not None}
-    return changed_files
-
-
-def getCopyrightYears(line):
-    res = CheckSimple.search(line)
-    if res:
-        return int(res.group(1)), int(res.group(1))
-    res = CheckDouble.search(line)
-    if res:
-        return int(res.group(1)), int(res.group(2))
-    return None, None
-
-
-def replaceCurrentYear(line, start, end):
-    # first turn a simple regex into double (if applicable). then update years
-    res = CheckSimple.sub(r"Copyright (c) \1-\1, NVIDIA CORPORATION", line)
-    res = CheckDouble.sub(
-        rf"Copyright (c) {start:04d}-{end:04d}, NVIDIA CORPORATION",
-        res,
-    )
-    return res
-
-
-def checkCopyright(f, update_current_year):
-    """Checks for copyright headers and their years."""
-    errs = []
-    thisYear = datetime.datetime.now().year
-    lineNum = 0
-    crFound = False
-    yearMatched = False
-
-    if isinstance(f, git.Diff):
-        path = f.b_path
-        lines = f.b_blob.data_stream.read().decode().splitlines(keepends=True)
-    else:
-        path = f
-        with open(f, encoding="utf-8") as fp:
-            lines = fp.readlines()
-
-    for line in lines:
-        lineNum += 1
-        start, end = getCopyrightYears(line)
-        if start is None:
-            continue
-        crFound = True
-        if start > end:
-            e = [
-                path,
-                lineNum,
-                "First year after second year in the copyright "
-                "header (manual fix required)",
-                None,
-            ]
-            errs.append(e)
-        elif thisYear < start or thisYear > end:
-            e = [
-                path,
-                lineNum,
-                "Current year not included in the copyright header",
-                None,
-            ]
-            if thisYear < start:
-                e[-1] = replaceCurrentYear(line, thisYear, end)
-            if thisYear > end:
-                e[-1] = replaceCurrentYear(line, start, thisYear)
-            errs.append(e)
-        else:
-            yearMatched = True
-    # copyright header itself not found
-    if not crFound:
-        e = [
-            path,
-            0,
-            "Copyright header missing or formatted incorrectly "
-            "(manual fix required)",
-            None,
-        ]
-        errs.append(e)
-    # even if the year matches a copyright header, make the check pass
-    if yearMatched:
-        errs = []
-
-    if update_current_year:
-        errs_update = [x for x in errs if x[-1] is not None]
-        if len(errs_update) > 0:
-            lines_changed = ", ".join(str(x[1]) for x in errs_update)
-            print(f"File: {path}. Changing line(s) {lines_changed}")
-            for _, lineNum, __, replacement in errs_update:
-                lines[lineNum - 1] = replacement
-            with open(path, "w", encoding="utf-8") as out_file:
-                out_file.writelines(lines)
-
-    return errs
-
-
-def getAllFilesUnderDir(root, pathFilter=None):
-    retList = []
-    for dirpath, dirnames, filenames in os.walk(root):
-        for fn in filenames:
-            filePath = os.path.join(dirpath, fn)
-            if pathFilter(filePath):
-                retList.append(filePath)
-    return retList
-
-
-def checkCopyright_main():
-    """
-    Checks for copyright headers in all the modified files. In case of local
-    repo, this script will just look for uncommitted files and in case of CI
-    it compares between branches "$PR_TARGET_BRANCH" and "current-pr-branch"
-    """
-    retVal = 0
-
-    argparser = argparse.ArgumentParser(
-        "Checks for a consistent copyright header in git's modified files"
-    )
-    argparser.add_argument(
-        "--update-current-year",
-        dest="update_current_year",
-        action="store_true",
-        required=False,
-        help="If set, "
-        "update the current year if a header is already "
-        "present and well formatted.",
-    )
-    argparser.add_argument(
-        "--git-modified-only",
-        dest="git_modified_only",
-        action="store_true",
-        required=False,
-        help="If set, "
-        "only files seen as modified by git will be "
-        "processed.",
-    )
-
-    args, dirs = argparser.parse_known_args()
-
-    if args.git_modified_only:
-        files = [f for f in modifiedFiles() if checkThisFile(f)]
-    else:
-        files = []
-        for d in [os.path.abspath(d) for d in dirs]:
-            if not os.path.isdir(d):
-                raise ValueError(f"{d} is not a directory.")
-            files += getAllFilesUnderDir(d, pathFilter=checkThisFile)
-
-    errors = []
-    for f in files:
-        errors += checkCopyright(f, args.update_current_year)
-
-    if len(errors) > 0:
-        if any(e[-1] is None for e in errors):
-            print("Copyright headers incomplete in some of the files!")
-        for e in errors:
-            print("  %s:%d Issue: %s" % (e[0], e[1], e[2]))
-        print("")
-        n_fixable = sum(1 for e in errors if e[-1] is not None)
-        path_parts = os.path.abspath(__file__).split(os.sep)
-        file_from_repo = os.sep.join(path_parts[path_parts.index("ci") :])
-        if n_fixable > 0 and not args.update_current_year:
-            print(
-                f"You can run `python {file_from_repo} --git-modified-only "
-                "--update-current-year` and stage the results in git to "
-                f"fix {n_fixable} of these errors.\n"
-            )
-        retVal = 1
-
-    return retVal
-
-
-if __name__ == "__main__":
-    sys.exit(checkCopyright_main())

From e5d5e3a1e7577ee6c571ca397307872da9a72ce5 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 19 Mar 2024 09:27:00 -0500
Subject: [PATCH 018/623] Remove unused get_*.cmake files. (#57)

This PR removes some unused `get_*.cmake` files. I know cuVS may need to add some of these back as functionality is migrated from RAFT. As that happens, I would like to copy them again from the latest versions in RAFT.

Specifically, files like `get_thrust.cmake` are outdated (we are moving to `rapids_cpm_cccl` and `rapids_cpm_thrust` is deprecated) so this will reduce the maintenance burden for CMake / build system developers in the interim period.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/57
---
 cpp/cmake/thirdparty/get_fmt.cmake    | 22 ------------
 cpp/cmake/thirdparty/get_glog.cmake   | 48 ---------------------------
 cpp/cmake/thirdparty/get_rmm.cmake    | 23 -------------
 cpp/cmake/thirdparty/get_spdlog.cmake | 33 ------------------
 cpp/cmake/thirdparty/get_thrust.cmake | 24 --------------
 5 files changed, 150 deletions(-)
 delete mode 100644 cpp/cmake/thirdparty/get_fmt.cmake
 delete mode 100644 cpp/cmake/thirdparty/get_glog.cmake
 delete mode 100644 cpp/cmake/thirdparty/get_rmm.cmake
 delete mode 100644 cpp/cmake/thirdparty/get_spdlog.cmake
 delete mode 100644 cpp/cmake/thirdparty/get_thrust.cmake

diff --git a/cpp/cmake/thirdparty/get_fmt.cmake b/cpp/cmake/thirdparty/get_fmt.cmake
deleted file mode 100644
index c06f8a78bb..0000000000
--- a/cpp/cmake/thirdparty/get_fmt.cmake
+++ /dev/null
@@ -1,22 +0,0 @@
-# =============================================================================
-# Copyright (c) 2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
-# in compliance with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software distributed under the License
-# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
-# or implied. See the License for the specific language governing permissions and limitations under
-# the License.
-# =============================================================================
-
-# Use CPM to find or clone fmt
-function(find_and_configure_fmt)
-
-    include(${rapids-cmake-dir}/cpm/fmt.cmake)
-    rapids_cpm_fmt(INSTALL_EXPORT_SET rmm-exports BUILD_EXPORT_SET rmm-exports)
-endfunction()
-
-find_and_configure_fmt()
\ No newline at end of file
diff --git a/cpp/cmake/thirdparty/get_glog.cmake b/cpp/cmake/thirdparty/get_glog.cmake
deleted file mode 100644
index 56ed452746..0000000000
--- a/cpp/cmake/thirdparty/get_glog.cmake
+++ /dev/null
@@ -1,48 +0,0 @@
-#=============================================================================
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#=============================================================================
-
-function(find_and_configure_glog)
-    set(oneValueArgs VERSION FORK PINNED_TAG EXCLUDE_FROM_ALL)
-    cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
-            "${multiValueArgs}" ${ARGN} )
-
-    rapids_cpm_find(glog ${PKG_VERSION}
-            GLOBAL_TARGETS      glog::glog
-            BUILD_EXPORT_SET    cuvs-exports
-            INSTALL_EXPORT_SET  cuvs-exports
-            CPM_ARGS
-            GIT_REPOSITORY         https://github.com/${PKG_FORK}/glog.git
-            GIT_TAG                ${PKG_PINNED_TAG}
-            EXCLUDE_FROM_ALL       ${PKG_EXCLUDE_FROM_ALL}
-            )
-
-    if(glog_ADDED)
-        message(VERBOSE "RAFT: Using glog located in ${glog_SOURCE_DIR}")
-    else()
-        message(VERBOSE "RAFT: Using glog located in ${glog_DIR}")
-    endif()
-
-
-endfunction()
-
-# Change pinned tag here to test a commit in CI
-# To use a different RAFT locally, set the CMake variable
-# CPM_glog_SOURCE=/path/to/local/glog
-find_and_configure_glog(VERSION 0.6.0
-        FORK             google
-        PINNED_TAG       v0.6.0
-        EXCLUDE_FROM_ALL ON
-        )
diff --git a/cpp/cmake/thirdparty/get_rmm.cmake b/cpp/cmake/thirdparty/get_rmm.cmake
deleted file mode 100644
index 97796215e3..0000000000
--- a/cpp/cmake/thirdparty/get_rmm.cmake
+++ /dev/null
@@ -1,23 +0,0 @@
-#=============================================================================
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#=============================================================================
-
-function(find_and_configure_rmm)
-    include(${rapids-cmake-dir}/cpm/rmm.cmake)
-    rapids_cpm_rmm(BUILD_EXPORT_SET cuvs-exports
-                   INSTALL_EXPORT_SET  cuvs-exports)
-endfunction()
-
-find_and_configure_rmm()
diff --git a/cpp/cmake/thirdparty/get_spdlog.cmake b/cpp/cmake/thirdparty/get_spdlog.cmake
deleted file mode 100644
index 7be7804c7e..0000000000
--- a/cpp/cmake/thirdparty/get_spdlog.cmake
+++ /dev/null
@@ -1,33 +0,0 @@
-# =============================================================================
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
-# in compliance with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software distributed under the License
-# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
-# or implied. See the License for the specific language governing permissions and limitations under
-# the License.
-# =============================================================================
-
-# Use CPM to find or clone speedlog
-function(find_and_configure_spdlog)
-
-    include(${rapids-cmake-dir}/cpm/spdlog.cmake)
-    rapids_cpm_spdlog(FMT_OPTION "EXTERNAL_FMT_HO" INSTALL_EXPORT_SET rmm-exports)
-    rapids_export_package(BUILD spdlog rmm-exports)
-
-    if(spdlog_ADDED)
-        rapids_export(
-                BUILD spdlog
-                EXPORT_SET spdlog
-                GLOBAL_TARGETS spdlog spdlog_header_only
-                NAMESPACE spdlog::)
-        include("${rapids-cmake-dir}/export/find_package_root.cmake")
-        rapids_export_find_package_root(BUILD spdlog [=[${CMAKE_CURRENT_LIST_DIR}]=] EXPORT_SET rmm-exports)
-    endif()
-endfunction()
-
-find_and_configure_spdlog()
\ No newline at end of file
diff --git a/cpp/cmake/thirdparty/get_thrust.cmake b/cpp/cmake/thirdparty/get_thrust.cmake
deleted file mode 100644
index d2d6cb3e6a..0000000000
--- a/cpp/cmake/thirdparty/get_thrust.cmake
+++ /dev/null
@@ -1,24 +0,0 @@
-# =============================================================================
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
-# in compliance with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software distributed under the License
-# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
-# or implied. See the License for the specific language governing permissions and limitations under
-# the License.
-# =============================================================================
-
-# Use CPM to find or clone thrust
-function(find_and_configure_thrust)
-        include(${rapids-cmake-dir}/cpm/thrust.cmake)
-
-        rapids_cpm_thrust( NAMESPACE cuvs
-                           BUILD_EXPORT_SET cuvs-exports
-                           INSTALL_EXPORT_SET cuvs-exports)
-endfunction()
-
-find_and_configure_thrust()

From 6981a08ec2e81a37275a45bb6806294cb20bc5b3 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 19 Mar 2024 13:38:01 -0700
Subject: [PATCH 019/623] Add ability to allocate with RMM to the c-api and
 rust api (#56)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/56
---
 ci/build_rust.sh                     |  1 -
 cpp/include/cuvs/core/c_api.h        | 44 ++++++++++++++++++++++++++++
 cpp/src/core/c_api.cpp               | 35 ++++++++++++++++++++++
 rust/cuvs-sys/build.rs               |  5 ++--
 rust/cuvs/src/cagra/index.rs         |  2 +-
 rust/cuvs/src/cagra/index_params.rs  |  2 +-
 rust/cuvs/src/cagra/search_params.rs |  2 +-
 rust/cuvs/src/dlpack.rs              | 44 +++++++++++++++-------------
 rust/cuvs/src/resources.rs           | 19 ++++++++++++
 9 files changed, 126 insertions(+), 28 deletions(-)

diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index 59f0924c80..ad2202734a 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -35,5 +35,4 @@ rapids-mamba-retry install \
   libcuvs  \
   libraft
 
-export EXTRA_CMAKE_ARGS=""
 bash ./build.sh rust
diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index a15d7cd5c7..d931d6c13d 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -83,6 +83,50 @@ cuvsError_t cuvsResourcesDestroy(cuvsResources_t res);
  */
 cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream);
 
+/**
+ * @brief Get the cudaStream_t from a cuvsResources_t t
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[out] stream cudaStream_t stream to queue CUDA kernels
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsStreamGet(cuvsResources_t res, cudaStream_t* stream);
+
+/**
+ * @brief Syncs the current CUDA stream on the resources object
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsStreamSync(cuvsResources_t res);
+/** @} */
+
+/**
+ * @defgroup memory_c cuVS Memory Allocation
+ * @{
+ */
+
+/**
+ * @brief Allocates device memory using RMM
+ *
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[out] ptr Pointer to allocated device memory
+ * @param[in] bytes Size in bytes to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsRMMAlloc(cuvsResources_t res, void** ptr, size_t bytes);
+
+/**
+ * @brief Deallocates device memory using RMM
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] ptr Pointer to allocated device memory to free
+ * @param[in] bytes Size in bytes to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsRMMFree(cuvsResources_t res, void* ptr, size_t bytes);
+
 /** @} */
 
 #ifdef __cplusplus
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 7ddb4f3e4c..96504a2fee 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -21,6 +21,7 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
 #include <thread>
 
 extern "C" cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
@@ -47,6 +48,40 @@ extern "C" cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream)
   });
 }
 
+extern "C" cuvsError_t cuvsStreamGet(cuvsResources_t res, cudaStream_t* stream)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto res_ptr = reinterpret_cast<raft::resources*>(res);
+    *stream      = raft::resource::get_cuda_stream(*res_ptr);
+  });
+}
+
+extern "C" cuvsError_t cuvsStreamSync(cuvsResources_t res)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto res_ptr = reinterpret_cast<raft::resources*>(res);
+    raft::resource::sync_stream(*res_ptr);
+  });
+}
+
+extern "C" cuvsError_t cuvsRMMAlloc(cuvsResources_t res, void** ptr, size_t bytes)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto res_ptr = reinterpret_cast<raft::resources*>(res);
+    auto mr      = rmm::mr::get_current_device_resource();
+    *ptr         = mr->allocate(bytes, raft::resource::get_cuda_stream(*res_ptr));
+  });
+}
+
+extern "C" cuvsError_t cuvsRMMFree(cuvsResources_t res, void* ptr, size_t bytes)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto res_ptr = reinterpret_cast<raft::resources*>(res);
+    auto mr      = rmm::mr::get_current_device_resource();
+    mr->deallocate(ptr, bytes, raft::resource::get_cuda_stream(*res_ptr));
+  });
+}
+
 thread_local std::string last_error_text = "";
 
 extern "C" const char* cuvsGetLastErrorText()
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
index 816a6f2592..ec96725695 100644
--- a/rust/cuvs-sys/build.rs
+++ b/rust/cuvs-sys/build.rs
@@ -101,9 +101,8 @@ fn main() {
         .allowlist_type("(cuvs|cagra|DL).*")
         .allowlist_function("(cuvs|cagra).*")
         .rustified_enum("(cuvs|cagra|DL).*")
-        // also need some basic cuda mem functions
-        // (TODO: should we be adding in RMM support instead here?)
-        .allowlist_function("(cudaMalloc|cudaFree|cudaMemcpy)")
+        // also need some basic cuda mem functions for copying data
+        .allowlist_function("(cudaMemcpyAsync|cudaMemcpy)")
         .rustified_enum("cudaError")
         .generate()
         .expect("Unable to generate cagra_c bindings")
diff --git a/rust/cuvs/src/cagra/index.rs b/rust/cuvs/src/cagra/index.rs
index 3394889aad..6a5149f073 100644
--- a/rust/cuvs/src/cagra/index.rs
+++ b/rust/cuvs/src/cagra/index.rs
@@ -54,7 +54,7 @@ impl Index {
     /// Creates a new empty index
     pub fn new() -> Result<Index> {
         unsafe {
-            let mut index = core::mem::MaybeUninit::<ffi::cuvsCagraIndex_t>::uninit();
+            let mut index = std::mem::MaybeUninit::<ffi::cuvsCagraIndex_t>::uninit();
             check_cuvs(ffi::cuvsCagraIndexCreate(index.as_mut_ptr()))?;
             Ok(Index(index.assume_init()))
         }
diff --git a/rust/cuvs/src/cagra/index_params.rs b/rust/cuvs/src/cagra/index_params.rs
index ecc6605313..2e3367e06e 100644
--- a/rust/cuvs/src/cagra/index_params.rs
+++ b/rust/cuvs/src/cagra/index_params.rs
@@ -27,7 +27,7 @@ impl IndexParams {
     /// Returns a new IndexParams
     pub fn new() -> Result<IndexParams> {
         unsafe {
-            let mut params = core::mem::MaybeUninit::<ffi::cuvsCagraIndexParams_t>::uninit();
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsCagraIndexParams_t>::uninit();
             check_cuvs(ffi::cuvsCagraIndexParamsCreate(params.as_mut_ptr()))?;
             Ok(IndexParams(params.assume_init()))
         }
diff --git a/rust/cuvs/src/cagra/search_params.rs b/rust/cuvs/src/cagra/search_params.rs
index 14956966ea..7b891b0028 100644
--- a/rust/cuvs/src/cagra/search_params.rs
+++ b/rust/cuvs/src/cagra/search_params.rs
@@ -28,7 +28,7 @@ impl SearchParams {
     /// Returns a new SearchParams object
     pub fn new() -> Result<SearchParams> {
         unsafe {
-            let mut params = core::mem::MaybeUninit::<ffi::cuvsCagraSearchParams_t>::uninit();
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsCagraSearchParams_t>::uninit();
             check_cuvs(ffi::cuvsCagraSearchParamsCreate(params.as_mut_ptr()))?;
             Ok(SearchParams(params.assume_init()))
         }
diff --git a/rust/cuvs/src/dlpack.rs b/rust/cuvs/src/dlpack.rs
index a1d4e41c6d..238caec9d7 100644
--- a/rust/cuvs/src/dlpack.rs
+++ b/rust/cuvs/src/dlpack.rs
@@ -16,7 +16,7 @@
 
 use std::convert::From;
 
-use crate::error::{check_cuda, Result};
+use crate::error::{check_cuda, check_cuvs, Result};
 use crate::resources::Resources;
 
 /// ManagedTensor is a wrapper around a dlpack DLManagedTensor object.
@@ -33,36 +33,27 @@ impl ManagedTensor {
         &self.0 as *const _ as *mut _
     }
 
-    fn bytes(&self) -> usize {
-        // figure out how many bytes to allocate
-        let mut bytes: usize = 1;
-        for x in 0..self.0.dl_tensor.ndim {
-            bytes *= unsafe { (*self.0.dl_tensor.shape.add(x as usize)) as usize };
-        }
-        bytes *= (self.0.dl_tensor.dtype.bits / 8) as usize;
-        bytes
-    }
-
     /// Creates a new ManagedTensor on the current GPU device, and copies
     /// the data into it.
-    pub fn to_device(&self, _res: &Resources) -> Result<ManagedTensor> {
+    pub fn to_device(&self, res: &Resources) -> Result<ManagedTensor> {
         unsafe {
-            let bytes = self.bytes();
+            let bytes = dl_tensor_bytes(&self.0.dl_tensor);
             let mut device_data: *mut std::ffi::c_void = std::ptr::null_mut();
 
             // allocate storage, copy over
-            check_cuda(ffi::cudaMalloc(&mut device_data as *mut _, bytes))?;
-            check_cuda(ffi::cudaMemcpy(
+            check_cuvs(ffi::cuvsRMMAlloc(res.0, &mut device_data as *mut _, bytes))?;
+
+            check_cuda(ffi::cudaMemcpyAsync(
                 device_data,
                 self.0.dl_tensor.data,
                 bytes,
                 ffi::cudaMemcpyKind_cudaMemcpyDefault,
+                res.get_cuda_stream()?,
             ))?;
 
             let mut ret = self.0.clone();
             ret.dl_tensor.data = device_data;
-            // call cudaFree automatically to clean up data
-            ret.deleter = Some(cuda_free_tensor);
+            ret.deleter = Some(rmm_free_tensor);
             ret.dl_tensor.device.device_type = ffi::DLDeviceType::kDLCUDA;
 
             Ok(ManagedTensor(ret))
@@ -80,21 +71,32 @@ impl ManagedTensor {
         arr: &mut ndarray::ArrayBase<S, D>,
     ) -> Result<()> {
         unsafe {
-            let bytes = self.bytes();
+            let bytes = dl_tensor_bytes(&self.0.dl_tensor);
             check_cuda(ffi::cudaMemcpy(
                 arr.as_mut_ptr() as *mut std::ffi::c_void,
                 self.0.dl_tensor.data,
                 bytes,
                 ffi::cudaMemcpyKind_cudaMemcpyDefault,
             ))?;
-
             Ok(())
         }
     }
 }
 
-unsafe extern "C" fn cuda_free_tensor(self_: *mut ffi::DLManagedTensor) {
-    let _ = ffi::cudaFree((*self_).dl_tensor.data);
+/// Figures out how many bytes are in a DLTensor
+fn dl_tensor_bytes(tensor: &ffi::DLTensor) -> usize {
+    let mut bytes: usize = 1;
+    for dim in 0..tensor.ndim {
+        bytes *= unsafe { (*tensor.shape.add(dim as usize)) as usize };
+    }
+    bytes *= (tensor.dtype.bits / 8) as usize;
+    bytes
+}
+
+unsafe extern "C" fn rmm_free_tensor(self_: *mut ffi::DLManagedTensor) {
+    let bytes = dl_tensor_bytes(&(*self_).dl_tensor);
+    let res = Resources::new().unwrap();
+    let _ = ffi::cuvsRMMFree(res.0, (*self_).dl_tensor.data as *mut _, bytes);
 }
 
 /// Create a non-owning view of a Tensor from a ndarray
diff --git a/rust/cuvs/src/resources.rs b/rust/cuvs/src/resources.rs
index a5c503dc53..0c60cf6697 100644
--- a/rust/cuvs/src/resources.rs
+++ b/rust/cuvs/src/resources.rs
@@ -32,6 +32,25 @@ impl Resources {
         }
         Ok(Resources(res))
     }
+
+    /// Sets the current cuda stream
+    pub fn set_cuda_stream(&self, stream: ffi::cudaStream_t) -> Result<()> {
+        unsafe { check_cuvs(ffi::cuvsStreamSet(self.0, stream)) }
+    }
+
+    /// Gets the current cuda stream
+    pub fn get_cuda_stream(&self) -> Result<ffi::cudaStream_t> {
+        unsafe {
+            let mut stream = std::mem::MaybeUninit::<ffi::cudaStream_t>::uninit();
+            check_cuvs(ffi::cuvsStreamGet(self.0, stream.as_mut_ptr()))?;
+            Ok(stream.assume_init())
+        }
+    }
+
+    /// Syncs the current cuda stream
+    pub fn sync_stream(&self) -> Result<()> {
+        unsafe { check_cuvs(ffi::cuvsStreamSync(self.0)) }
+    }
 }
 
 impl Drop for Resources {

From 18bab30e83cb793837224ce88478458f5571ea18 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Wed, 20 Mar 2024 22:56:23 +0100
Subject: [PATCH 020/623] KNN bruteforce and IVF methods C/C++ API (#33)

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/33
---
 .gitignore                                    |    6 +
 cpp/CMakeLists.txt                            |   35 +-
 cpp/include/cuvs/core/detail/interop.hpp      |    6 +-
 cpp/include/cuvs/core/interop.hpp             |    6 +-
 cpp/include/cuvs/distance/distance_types.h    |   70 ++
 cpp/include/cuvs/neighbors/brute_force.h      |  163 +++
 cpp/include/cuvs/neighbors/brute_force.hpp    |  146 +++
 cpp/include/cuvs/neighbors/cagra.h            |    2 +-
 cpp/include/cuvs/neighbors/ivf_flat.h         |  283 +++++
 cpp/include/cuvs/neighbors/ivf_flat.hpp       | 1026 +++++++++++++++++
 cpp/include/cuvs/neighbors/ivf_pq.h           |  356 ++++++
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  868 ++++++++++++++
 cpp/src/neighbors/brute_force.cu              |   55 +
 cpp/src/neighbors/brute_force_c.cpp           |  167 +++
 cpp/src/neighbors/brute_force_index.cu        |   86 ++
 cpp/src/neighbors/cagra_c.cpp                 |    6 +-
 .../neighbors/ivf_flat/generate_ivf_flat.py   |  174 +++
 .../ivf_flat/ivf_flat_build_float_int64_t.cpp |   52 +
 .../ivf_flat_build_int8_t_int64_t.cpp         |   52 +
 .../ivf_flat_build_uint8_t_int64_t.cpp        |   52 +
 .../ivf_flat_extend_float_int64_t.cpp         |   55 +
 .../ivf_flat_extend_int8_t_int64_t.cpp        |   55 +
 .../ivf_flat_extend_uint8_t_int64_t.cpp       |   55 +
 .../ivf_flat_search_float_int64_t.cpp         |   46 +
 .../ivf_flat_search_int8_t_int64_t.cpp        |   46 +
 .../ivf_flat_search_uint8_t_int64_t.cpp       |   46 +
 .../ivf_flat_serialize_float_int64_t.cpp      |   64 +
 .../ivf_flat_serialize_int8_t_int64_t.cpp     |   64 +
 .../ivf_flat_serialize_uint8_t_int64_t.cpp    |   64 +
 cpp/src/neighbors/ivf_flat_c.cpp              |  246 ++++
 cpp/src/neighbors/ivf_flat_index.cpp          |  163 +++
 cpp/src/neighbors/ivf_pq/generate_ivf_pq.py   |  138 +++
 .../ivf_pq/ivf_pq_build_float_int64_t.cpp     |   52 +
 .../ivf_pq/ivf_pq_build_int8_t_int64_t.cpp    |   52 +
 .../ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp   |   52 +
 .../ivf_pq/ivf_pq_extend_float_int64_t.cpp    |   55 +
 .../ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp   |   55 +
 .../ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp  |   55 +
 .../ivf_pq/ivf_pq_search_float_int64_t.cpp    |   46 +
 .../ivf_pq/ivf_pq_search_int8_t_int64_t.cpp   |   46 +
 .../ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp  |   46 +
 cpp/src/neighbors/ivf_pq_c.cpp                |  235 ++++
 cpp/src/neighbors/ivf_pq_index.cpp            |  237 ++++
 cpp/src/neighbors/ivf_pq_serialize.cpp        |   40 +
 cpp/test/CMakeLists.txt                       |   54 +
 cpp/test/neighbors/ann_ivf_flat.cuh           |  643 +++++++++++
 .../ann_ivf_flat/test_float_int64_t.cu        |   28 +
 .../ann_ivf_flat/test_int8_t_int64_t.cu       |   28 +
 .../ann_ivf_flat/test_uint8_t_int64_t.cu      |   28 +
 cpp/test/neighbors/ann_ivf_flat_c.cu          |  147 +++
 cpp/test/neighbors/ann_ivf_pq.cuh             |  981 ++++++++++++++++
 .../ann_ivf_pq/test_float_int64_t.cu          |   27 +
 .../ann_ivf_pq/test_int8_t_int64_t.cu         |   27 +
 .../ann_ivf_pq/test_uint8_t_int64_t.cu        |   27 +
 cpp/test/neighbors/ann_ivf_pq_c.cu            |  147 +++
 cpp/test/neighbors/brute_force.cu             |  182 +++
 cpp/test/neighbors/brute_force_c.cu           |  137 +++
 cpp/test/neighbors/ivf_pq_helpers.cuh         |  428 +++++++
 cpp/test/neighbors/run_brute_force_c.c        |   94 ++
 cpp/test/neighbors/run_ivf_flat_c.c           |  105 ++
 cpp/test/neighbors/run_ivf_pq_c.c             |  105 ++
 docs/source/c_api/neighbors.rst               |    7 +-
 docs/source/c_api/neighbors_bruteforce_c.rst  |   34 +
 docs/source/c_api/neighbors_ivf_flat_c.rst    |   50 +
 docs/source/c_api/neighbors_ivf_pq_c.rst      |   50 +
 docs/source/cpp_api.rst                       |    1 +
 docs/source/cpp_api/distance.rst              |   19 +
 docs/source/cpp_api/neighbors.rst             |    5 +-
 docs/source/cpp_api/neighbors_bruteforce.rst  |   36 +
 docs/source/cpp_api/neighbors_ivf_flat.rst    |   68 ++
 docs/source/cpp_api/neighbors_ivf_pq.rst      |   68 ++
 71 files changed, 9135 insertions(+), 15 deletions(-)
 create mode 100644 cpp/include/cuvs/distance/distance_types.h
 create mode 100644 cpp/include/cuvs/neighbors/brute_force.h
 create mode 100644 cpp/include/cuvs/neighbors/brute_force.hpp
 create mode 100644 cpp/include/cuvs/neighbors/ivf_flat.h
 create mode 100644 cpp/include/cuvs/neighbors/ivf_flat.hpp
 create mode 100644 cpp/include/cuvs/neighbors/ivf_pq.h
 create mode 100644 cpp/include/cuvs/neighbors/ivf_pq.hpp
 create mode 100644 cpp/src/neighbors/brute_force.cu
 create mode 100644 cpp/src/neighbors/brute_force_c.cpp
 create mode 100644 cpp/src/neighbors/brute_force_index.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat_c.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat_index.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/generate_ivf_pq.py
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq_c.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq_index.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq_serialize.cpp
 create mode 100644 cpp/test/neighbors/ann_ivf_flat.cuh
 create mode 100644 cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu
 create mode 100644 cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
 create mode 100644 cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
 create mode 100644 cpp/test/neighbors/ann_ivf_flat_c.cu
 create mode 100644 cpp/test/neighbors/ann_ivf_pq.cuh
 create mode 100644 cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
 create mode 100644 cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
 create mode 100644 cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
 create mode 100644 cpp/test/neighbors/ann_ivf_pq_c.cu
 create mode 100644 cpp/test/neighbors/brute_force.cu
 create mode 100644 cpp/test/neighbors/brute_force_c.cu
 create mode 100644 cpp/test/neighbors/ivf_pq_helpers.cuh
 create mode 100644 cpp/test/neighbors/run_brute_force_c.c
 create mode 100644 cpp/test/neighbors/run_ivf_flat_c.c
 create mode 100644 cpp/test/neighbors/run_ivf_pq_c.c
 create mode 100644 docs/source/c_api/neighbors_bruteforce_c.rst
 create mode 100644 docs/source/c_api/neighbors_ivf_flat_c.rst
 create mode 100644 docs/source/c_api/neighbors_ivf_pq_c.rst
 create mode 100644 docs/source/cpp_api/distance.rst
 create mode 100644 docs/source/cpp_api/neighbors_bruteforce.rst
 create mode 100644 docs/source/cpp_api/neighbors_ivf_flat.rst
 create mode 100644 docs/source/cpp_api/neighbors_ivf_pq.rst

diff --git a/.gitignore b/.gitignore
index 9fcde3fb3b..68996dbdf0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -66,3 +66,9 @@ docs/source/_static/rust
 # clang tooling
 compile_commands.json
 .clangd/
+
+# serialized ann indexes
+cagra_index
+ivf_flat_index
+ivf_pq_index
+
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index f6d6a2223d..c2d311bf23 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -187,6 +187,8 @@ include(cmake/thirdparty/get_cutlass.cmake)
 
 add_library(
   cuvs SHARED
+  src/neighbors/brute_force_index.cu
+  src/neighbors/brute_force.cu
   src/neighbors/cagra_build_float.cpp
   src/neighbors/cagra_build_int8.cpp
   src/neighbors/cagra_build_uint8.cpp
@@ -197,6 +199,30 @@ add_library(
   src/neighbors/cagra_serialize_float.cpp
   src/neighbors/cagra_serialize_int8.cpp
   src/neighbors/cagra_serialize_uint8.cpp
+  src/neighbors/ivf_flat_index.cpp
+  src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
+  src/neighbors/ivf_pq_index.cpp
+  src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp
+  src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp
+  src/neighbors/ivf_pq_serialize.cpp
 )
 
 target_compile_options(
@@ -297,7 +323,14 @@ target_link_options(cuvs PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
 # ##################################################################################################
 # * cuvs_c -------------------------------------------------------------------------------
 if(BUILD_C_LIBRARY)
-  add_library(cuvs_c SHARED src/core/c_api.cpp src/neighbors/cagra_c.cpp)
+  add_library(
+    cuvs_c SHARED
+    src/core/c_api.cpp
+    src/neighbors/brute_force_c.cpp
+    src/neighbors/ivf_flat_c.cpp
+    src/neighbors/ivf_pq_c.cpp
+    src/neighbors/cagra_c.cpp
+  )
 
   add_library(cuvs::c_api ALIAS cuvs_c)
 
diff --git a/cpp/include/cuvs/core/detail/interop.hpp b/cpp/include/cuvs/core/detail/interop.hpp
index f218dc554d..208daaae7c 100644
--- a/cpp/include/cuvs/core/detail/interop.hpp
+++ b/cpp/include/cuvs/core/detail/interop.hpp
@@ -53,20 +53,20 @@ DLDataType data_type_to_DLDataType()
   }
 }
 
-bool is_dlpack_device_compatible(DLTensor tensor)
+inline bool is_dlpack_device_compatible(DLTensor tensor)
 {
   return tensor.device.device_type == kDLCUDAManaged || tensor.device.device_type == kDLCUDAHost ||
          tensor.device.device_type == kDLCUDA;
 }
 
-bool is_dlpack_host_compatible(DLTensor tensor)
+inline bool is_dlpack_host_compatible(DLTensor tensor)
 {
   return tensor.device.device_type == kDLCUDAManaged || tensor.device.device_type == kDLCUDAHost ||
          tensor.device.device_type == kDLCPU;
 }
 
 template <typename MdspanType, typename = raft::is_mdspan_t<MdspanType>>
-MdspanType from_dlpack(DLManagedTensor* managed_tensor)
+inline MdspanType from_dlpack(DLManagedTensor* managed_tensor)
 {
   auto tensor = managed_tensor->dl_tensor;
 
diff --git a/cpp/include/cuvs/core/interop.hpp b/cpp/include/cuvs/core/interop.hpp
index 9510022db8..2462f02ec0 100644
--- a/cpp/include/cuvs/core/interop.hpp
+++ b/cpp/include/cuvs/core/interop.hpp
@@ -33,7 +33,7 @@ namespace cuvs::core {
  * @param[in] tensor DLTensor object to check underlying memory type
  * @return bool
  */
-bool is_dlpack_device_compatible(DLTensor tensor)
+inline bool is_dlpack_device_compatible(DLTensor tensor)
 {
   return detail::is_dlpack_device_compatible(tensor);
 }
@@ -46,7 +46,7 @@ bool is_dlpack_device_compatible(DLTensor tensor)
  * @param tensor DLTensor object to check underlying memory type
  * @return bool
  */
-bool is_dlpack_host_compatible(DLTensor tensor)
+inline bool is_dlpack_host_compatible(DLTensor tensor)
 {
   return detail::is_dlpack_host_compatible(tensor);
 }
@@ -72,7 +72,7 @@ bool is_dlpack_host_compatible(DLTensor tensor)
  * @return MdspanType
  */
 template <typename MdspanType, typename = raft::is_mdspan_t<MdspanType>>
-MdspanType from_dlpack(DLManagedTensor* managed_tensor)
+inline MdspanType from_dlpack(DLManagedTensor* managed_tensor)
 {
   return detail::from_dlpack<MdspanType>(managed_tensor);
 }
diff --git a/cpp/include/cuvs/distance/distance_types.h b/cpp/include/cuvs/distance/distance_types.h
new file mode 100644
index 0000000000..8e9a4149cb
--- /dev/null
+++ b/cpp/include/cuvs/distance/distance_types.h
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/** enum to tell how to compute distance */
+enum DistanceType {
+
+  /** evaluate as dist_ij = sum(x_ik^2) + sum(y_ij)^2 - 2*sum(x_ik * y_jk) */
+  L2Expanded = 0,
+  /** same as above, but inside the epilogue, perform square root operation */
+  L2SqrtExpanded = 1,
+  /** cosine distance */
+  CosineExpanded = 2,
+  /** L1 distance */
+  L1 = 3,
+  /** evaluate as dist_ij += (x_ik - y-jk)^2 */
+  L2Unexpanded = 4,
+  /** same as above, but inside the epilogue, perform square root operation */
+  L2SqrtUnexpanded = 5,
+  /** basic inner product **/
+  InnerProduct = 6,
+  /** Chebyshev (Linf) distance **/
+  Linf = 7,
+  /** Canberra distance **/
+  Canberra = 8,
+  /** Generalized Minkowski distance **/
+  LpUnexpanded = 9,
+  /** Correlation distance **/
+  CorrelationExpanded = 10,
+  /** Jaccard distance **/
+  JaccardExpanded = 11,
+  /** Hellinger distance **/
+  HellingerExpanded = 12,
+  /** Haversine distance **/
+  Haversine = 13,
+  /** Bray-Curtis distance **/
+  BrayCurtis = 14,
+  /** Jensen-Shannon distance**/
+  JensenShannon = 15,
+  /** Hamming distance **/
+  HammingUnexpanded = 16,
+  /** KLDivergence **/
+  KLDivergence = 17,
+  /** RusselRao **/
+  RusselRaoExpanded = 18,
+  /** Dice-Sorensen distance **/
+  DiceExpanded = 19,
+  /** Precomputed (special value) **/
+  Precomputed = 100
+};
+
+#ifdef __cplusplus
+}
+#endif
\ No newline at end of file
diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
new file mode 100644
index 0000000000..0bb4d6bdbf
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -0,0 +1,163 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance_types.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup bruteforce_c_index Bruteforce index
+ * @{
+ */
+/**
+ * @brief Struct to hold address of cuvs::neighbors::brute_force::index and its active trained dtype
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} bruteForceIndex;
+
+typedef bruteForceIndex* cuvsBruteForceIndex_t;
+
+/**
+ * @brief Allocate BRUTEFORCE index
+ *
+ * @param[in] index cuvsBruteForceIndex_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t bruteForceIndexCreate(cuvsBruteForceIndex_t* index);
+
+/**
+ * @brief De-allocate BRUTEFORCE index
+ *
+ * @param[in] index cuvsBruteForceIndex_t to de-allocate
+ */
+cuvsError_t bruteForceIndexDestroy(cuvsBruteForceIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup bruteforce_c_index_build Bruteforce index build
+ * @{
+ */
+/**
+ * @brief Build a BRUTEFORCE index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
+ *        or `kDLCPU`. Also, acceptable underlying types are:
+ *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/brute_force.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ *
+ * // Create BRUTEFORCE index
+ * cuvsBruteForceIndex_t index;
+ * cuvsError_t index_create_status = bruteForceIndexCreate(&index);
+ *
+ * // Build the BRUTEFORCE Index
+ * cuvsError_t build_status = bruteForceBuild(res, &dataset_tensor, L2Expanded, 0.f, index);
+ *
+ * // de-allocate `index` and `res`
+ * cuvsError_t index_destroy_status = bruteForceIndexDestroy(index);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] dataset DLManagedTensor* training dataset
+ * @param[in] metric metric
+ * @param[in] metric_arg metric_arg
+ * @param[out] index cuvsBruteForceIndex_t Newly built BRUTEFORCE index
+ * @return cuvsError_t
+ */
+cuvsError_t bruteForceBuild(cuvsResources_t res,
+                            DLManagedTensor* dataset,
+                            enum DistanceType metric,
+                            float metric_arg,
+                            cuvsBruteForceIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup bruteforce_c_index_search Bruteforce index search
+ * @{
+ */
+/**
+ * @brief Search a BRUTEFORCE index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`.
+ *        It is also important to note that the BRUTEFORCE index must have been built
+ *        with the same type of `queries`, such that `index.dtype.code ==
+ *        queries.dl_tensor.dtype.code` Types for input are:
+ *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
+ *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/brute_force.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ * DLManagedTensor queries;
+ * DLManagedTensor neighbors;
+ *
+ * // Search the `index` built using `bruteForceBuild`
+ * cuvsError_t search_status = bruteForceSearch(res, index, &queries, &neighbors, &distances);
+ *
+ * // de-allocate `res`
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index bruteForceIndex which has been returned by `bruteForceBuild`
+ * @param[in] queries DLManagedTensor* queries dataset to search
+ * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
+ * @param[out] distances DLManagedTensor* output `k` distances for queries
+ */
+cuvsError_t bruteForceSearch(cuvsResources_t res,
+                             cuvsBruteForceIndex_t index,
+                             DLManagedTensor* queries,
+                             DLManagedTensor* neighbors,
+                             DLManagedTensor* distances);
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
new file mode 100644
index 0000000000..26951e1ec3
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "ann_types.hpp"
+#include <cuvs/neighbors/ann_types.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/handle.hpp>
+
+namespace cuvs::neighbors::brute_force {
+
+/**
+ * @defgroup bruteforce_cpp_index Bruteforce index
+ * @{
+ */
+/**
+ * @brief Brute Force index.
+ *
+ * The index stores the dataset and norms for the dataset in device memory.
+ *
+ * @tparam T data element type
+ */
+template <typename T>
+struct index : cuvs::neighbors::ann::index {
+ public:
+  index(const index&)            = delete;
+  index(index&&)                 = default;
+  index& operator=(const index&) = delete;
+  index& operator=(index&&)      = default;
+  ~index()                       = default;
+  index(void* raft_index);
+
+  /** Distance metric used for retrieval */
+  cuvs::distance::DistanceType metric() const noexcept;
+
+  /** Metric argument */
+  T metric_arg() const noexcept;
+
+  /** Total length of the index (number of vectors). */
+  size_t size() const noexcept;
+
+  /** Dimensionality of the data. */
+  size_t dim() const noexcept;
+
+  /** Dataset [size, dim] */
+  raft::device_matrix_view<const T, int64_t, raft::row_major> dataset() const noexcept;
+
+  /** Dataset norms */
+  raft::device_vector_view<const T, int64_t, raft::row_major> norms() const;
+
+  /** Whether ot not this index has dataset norms */
+  bool has_norms() const noexcept;
+
+  // Get pointer to underlying RAFT index, not meant to be used outside of cuVS
+  inline const void* get_raft_index() const noexcept { return raft_index_.get(); }
+
+ private:
+  std::unique_ptr<void*> raft_index_;
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup bruteforce_cpp_index_build Bruteforce index build
+ * @{
+ */
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = brute_force::build(handle, dataset, metric);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ * @param[in] metric cuvs::distance::DistanceType
+ * @param[in] metric_arg metric argument
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+           float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<float>;
+/**
+ * @}
+ */
+
+/**
+ * @defgroup bruteforce_cpp_index_search Bruteforce index search
+ * @{
+ */
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`:
+ * @code{.cpp}
+ *   ...
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   brute_force::search(handle, index, queries1, out_inds1, out_dists1);
+ *   brute_force::search(handle, index, queries2, out_inds2, out_dists2);
+ *   brute_force::search(handle, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index ivf-flat constructed index
+ * @param[in] queries a device pointer to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::index<float>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index ae8f8ea01d..912430a47b 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -273,7 +273,7 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  *        It is also important to note that the CAGRA Index must have been built
  *        with the same type of `queries`, such that `index.dtype.code ==
  * queries.dl_tensor.dtype.code` Types for input are:
- *        1. `queries`: kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
new file mode 100644
index 0000000000..08200ae7da
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -0,0 +1,283 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance_types.h>
+#include <dlpack/dlpack.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup ivf_flat_c_index_params IVF-Flat index build parameters
+ * @{
+ */
+/**
+ * @brief Supplemental parameters to build IVF-Flat Index
+ *
+ */
+struct ivfFlatIndexParams {
+  /** Distance type. */
+  enum DistanceType metric;
+  /** The argument used by some distance metrics. */
+  float metric_arg;
+  /**
+   * Whether to add the dataset content to the index, i.e.:
+   *
+   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
+   * `build`.
+   *  - `false` means `build` only trains the underlying model (e.g. quantizer or clustering), but
+   * the index is left empty; you'd need to call `extend` on the index afterwards to populate it.
+   */
+  bool add_data_on_build;
+  /** The number of inverted lists (clusters) */
+  uint32_t n_lists;
+  /** The number of iterations searching for kmeans centers (index building). */
+  uint32_t kmeans_n_iters;
+  /** The fraction of data to use during iterative kmeans building. */
+  double kmeans_trainset_fraction;
+  /**
+   * By default (adaptive_centers = false), the cluster centers are trained in `ivf_flat::build`,
+   * and never modified in `ivf_flat::extend`. As a result, you may need to retrain the index
+   * from scratch after invoking (`ivf_flat::extend`) a few times with new data, the distribution of
+   * which is no longer representative of the original training set.
+   *
+   * The alternative behavior (adaptive_centers = true) is to update the cluster centers for new
+   * data when it is added. In this case, `index.centers()` are always exactly the centroids of the
+   * data in the corresponding clusters. The drawback of this behavior is that the centroids depend
+   * on the order of adding new data (through the classification of the added data); that is,
+   * `index.centers()` "drift" together with the changing distribution of the newly added data.
+   */
+  bool adaptive_centers;
+  /**
+   * By default, the algorithm allocates more space than necessary for individual clusters
+   * (`list_data`). This allows to amortize the cost of memory allocation and reduce the number of
+   * data copies during repeated calls to `extend` (extending the database).
+   *
+   * The alternative is the conservative allocation behavior; when enabled, the algorithm always
+   * allocates the minimum amount of memory required to store the given number of records. Set this
+   * flag to `true` if you prefer to use as little GPU memory for the database as possible.
+   */
+  bool conservative_memory_allocation;
+};
+
+typedef struct ivfFlatIndexParams* cuvsIvfFlatIndexParams_t;
+
+/**
+ * @brief Allocate IVF-Flat Index params, and populate with default values
+ *
+ * @param[in] index_params cuvsIvfFlatIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t* index_params);
+
+/**
+ * @brief De-allocate IVF-Flat Index params
+ *
+ * @param[in] index_params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_c_search_params IVF-Flat index search parameters
+ * @{
+ */
+/**
+ * @brief Supplemental parameters to search IVF-Flat index
+ *
+ */
+struct ivfFlatSearchParams {
+  /** The number of clusters to search. */
+  uint32_t n_probes;
+};
+
+typedef struct ivfFlatSearchParams* cuvsIvfFlatSearchParams_t;
+
+/**
+ * @brief Allocate IVF-Flat search params, and populate with default values
+ *
+ * @param[in] params cuvsIvfFlatSearchParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t* params);
+
+/**
+ * @brief De-allocate IVF-Flat search params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_c_index IVF-Flat index
+ * @{
+ */
+/**
+ * @brief Struct to hold address of cuvs::neighbors::ivf_flat::index and its active trained dtype
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} ivfFlatIndex;
+
+typedef ivfFlatIndex* cuvsIvfFlatIndex_t;
+
+/**
+ * @brief Allocate IVF-Flat index
+ *
+ * @param[in] index cuvsIvfFlatIndex_t to allocate
+ * @return ivfFlatError_t
+ */
+cuvsError_t ivfFlatIndexCreate(cuvsIvfFlatIndex_t* index);
+
+/**
+ * @brief De-allocate IVF-Flat index
+ *
+ * @param[in] index cuvsIvfFlatIndex_t to de-allocate
+ */
+cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_c_index_build IVF-Flat index build
+ * @{
+ */
+/**
+ * @brief Build a IVF-Flat index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
+ *        or `kDLCPU`. Also, acceptable underlying types are:
+ *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/ivf_flat.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ *
+ * // Create default index params
+ * cuvsIvfFlatIndexParams_t index_params;
+ * cuvsError_t params_create_status = cuvsIvfFlatIndexParamsCreate(&index_params);
+ *
+ * // Create IVF-Flat index
+ * cuvsIvfFlatIndex_t index;
+ * cuvsError_t index_create_status = ivfFlatIndexCreate(&index);
+ *
+ * // Build the IVF-Flat Index
+ * cuvsError_t build_status = ivfFlatBuild(res, index_params, &dataset, index);
+ *
+ * // de-allocate `index_params`, `index` and `res`
+ * cuvsError_t params_destroy_status = cuvsIvfFlatIndexParamsDestroy(index_params);
+ * cuvsError_t index_destroy_status = ivfFlatIndexDestroy(index);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index_params cuvsIvfFlatIndexParams_t used to build IVF-Flat index
+ * @param[in] dataset DLManagedTensor* training dataset
+ * @param[out] index cuvsIvfFlatIndex_t Newly built IVF-Flat index
+ * @return cuvsError_t
+ */
+cuvsError_t ivfFlatBuild(cuvsResources_t res,
+                         cuvsIvfFlatIndexParams_t index_params,
+                         DLManagedTensor* dataset,
+                         cuvsIvfFlatIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_c_index_search IVF-Flat index search
+ * @{
+ */
+/**
+ * @brief Search a IVF-Flat index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`.
+ *        It is also important to note that the IVF-Flat Index must have been built
+ *        with the same type of `queries`, such that `index.dtype.code ==
+ * queries.dl_tensor.dtype.code` Types for input are:
+ *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
+ *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/ivf_flat.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ * DLManagedTensor queries;
+ * DLManagedTensor neighbors;
+ *
+ * // Create default search params
+ * cuvsIvfFlatSearchParams_t search_params;
+ * cuvsError_t params_create_status = cuvsIvfFlatSearchParamsCreate(&search_params);
+ *
+ * // Search the `index` built using `ivfFlatBuild`
+ * cuvsError_t search_status = ivfFlatSearch(res, search_params, index, &queries, &neighbors,
+ * &distances);
+ *
+ * // de-allocate `search_params` and `res`
+ * cuvsError_t params_destroy_status = cuvsIvfFlatSearchParamsDestroy(search_params);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] search_params cuvsIvfFlatSearchParams_t used to search IVF-Flat index
+ * @param[in] index ivfFlatIndex which has been returned by `ivfFlatBuild`
+ * @param[in] queries DLManagedTensor* queries dataset to search
+ * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
+ * @param[out] distances DLManagedTensor* output `k` distances for queries
+ */
+cuvsError_t ivfFlatSearch(cuvsResources_t res,
+                          cuvsIvfFlatSearchParams_t search_params,
+                          cuvsIvfFlatIndex_t index,
+                          DLManagedTensor* queries,
+                          DLManagedTensor* neighbors,
+                          DLManagedTensor* distances);
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
new file mode 100644
index 0000000000..efb32e024d
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -0,0 +1,1026 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "ann_types.hpp"
+#include <raft/neighbors/ivf_flat_types.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+/**
+ * @defgroup ivf_flat_cpp_index_params IVF-Flat index build parameters
+ * @{
+ */
+struct index_params : ann::index_params {
+  /** The number of inverted lists (clusters) */
+  uint32_t n_lists = 1024;
+  /** The number of iterations searching for kmeans centers (index building). */
+  uint32_t kmeans_n_iters = 20;
+  /** The fraction of data to use during iterative kmeans building. */
+  double kmeans_trainset_fraction = 0.5;
+  /**
+   * By default (adaptive_centers = false), the cluster centers are trained in `ivf_flat::build`,
+   * and never modified in `ivf_flat::extend`. As a result, you may need to retrain the index
+   * from scratch after invoking (`ivf_flat::extend`) a few times with new data, the distribution of
+   * which is no longer representative of the original training set.
+   *
+   * The alternative behavior (adaptive_centers = true) is to update the cluster centers for new
+   * data when it is added. In this case, `index.centers()` are always exactly the centroids of the
+   * data in the corresponding clusters. The drawback of this behavior is that the centroids depend
+   * on the order of adding new data (through the classification of the added data); that is,
+   * `index.centers()` "drift" together with the changing distribution of the newly added data.
+   */
+  bool adaptive_centers = false;
+  /**
+   * By default, the algorithm allocates more space than necessary for individual clusters
+   * (`list_data`). This allows to amortize the cost of memory allocation and reduce the number of
+   * data copies during repeated calls to `extend` (extending the database).
+   *
+   * The alternative is the conservative allocation behavior; when enabled, the algorithm always
+   * allocates the minimum amount of memory required to store the given number of records. Set this
+   * flag to `true` if you prefer to use as little GPU memory for the database as possible.
+   */
+  bool conservative_memory_allocation = false;
+
+  /** Build a raft IVF_FLAT index params from an existing cuvs IVF_FLAT index params. */
+  operator raft::neighbors::ivf_flat::index_params() const
+  {
+    return raft::neighbors::ivf_flat::index_params{
+      {
+        .metric            = static_cast<raft::distance::DistanceType>((int)this->metric),
+        .metric_arg        = this->metric_arg,
+        .add_data_on_build = this->add_data_on_build,
+      },
+      .n_lists                        = n_lists,
+      .kmeans_n_iters                 = kmeans_n_iters,
+      .kmeans_trainset_fraction       = kmeans_trainset_fraction,
+      .adaptive_centers               = adaptive_centers,
+      .conservative_memory_allocation = conservative_memory_allocation};
+  }
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_cpp_search_params IVF-Flat index search parameters
+ * @{
+ */
+struct search_params : ann::search_params {
+  /** The number of clusters to search. */
+  uint32_t n_probes = 20;
+
+  /** Build a raft IVF_FLAT search params from an existing cuvs IVF_FLAT search params. */
+  operator raft::neighbors::ivf_flat::search_params() const
+  {
+    raft::neighbors::ivf_flat::search_params result = {{}, n_probes};
+    return result;
+  }
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_cpp_index IVF-Flat index
+ * @{
+ */
+/**
+ * @brief IVF-flat index.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ */
+template <typename T, typename IdxT>
+struct index : ann::index {
+  static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
+                "IdxT must be able to represent all values of uint32_t");
+
+ public:
+  index(const index&)            = delete;
+  index(index&&)                 = default;
+  index& operator=(const index&) = delete;
+  index& operator=(index&&)      = default;
+  ~index()                       = default;
+  /** Construct an empty index. It needs to be trained and then populated. */
+  index(raft::resources const& res, const index_params& params, uint32_t dim);
+  /** Construct an empty index. It needs to be trained and then populated. */
+  index(raft::resources const& res,
+        cuvs::distance::DistanceType metric,
+        uint32_t n_lists,
+        bool adaptive_centers,
+        bool conservative_memory_allocation,
+        uint32_t dim);
+  index(raft::neighbors::ivf_flat::index<T, IdxT>&& raft_idx);
+
+  /**
+   * Vectorized load/store size in elements, determines the size of interleaved data chunks.
+   */
+  uint32_t veclen() const noexcept;
+
+  /** Distance metric used for clustering. */
+  cuvs::distance::DistanceType metric() const noexcept;
+
+  /** Whether `centers()` change upon extending the index (ivf_flat::extend). */
+  bool adaptive_centers() const noexcept;
+
+  /**
+   * Inverted list data [size, dim].
+   *
+   * The data consists of the dataset rows, grouped by their labels (into clusters/lists).
+   * Within each list (cluster), the data is grouped into blocks of `kIndexGroupSize` interleaved
+   * vectors. Note, the total index length is slightly larger than the source dataset length,
+   * because each cluster is padded by `kIndexGroupSize` elements.
+   *
+   * Interleaving pattern:
+   * within groups of `kIndexGroupSize` rows, the data is interleaved with the block size equal to
+   * `veclen * sizeof(T)`. That is, a chunk of `veclen` consecutive components of one row is
+   * followed by a chunk of the same size of the next row, and so on.
+   *
+   * __Example__: veclen = 2, dim = 6, kIndexGroupSize = 32, list_size = 31
+   *
+   *     x[ 0, 0], x[ 0, 1], x[ 1, 0], x[ 1, 1], ... x[14, 0], x[14, 1], x[15, 0], x[15, 1],
+   *     x[16, 0], x[16, 1], x[17, 0], x[17, 1], ... x[30, 0], x[30, 1],    -    ,    -    ,
+   *     x[ 0, 2], x[ 0, 3], x[ 1, 2], x[ 1, 3], ... x[14, 2], x[14, 3], x[15, 2], x[15, 3],
+   *     x[16, 2], x[16, 3], x[17, 2], x[17, 3], ... x[30, 2], x[30, 3],    -    ,    -    ,
+   *     x[ 0, 4], x[ 0, 5], x[ 1, 4], x[ 1, 5], ... x[14, 4], x[14, 5], x[15, 4], x[15, 5],
+   *     x[16, 4], x[16, 5], x[17, 4], x[17, 5], ... x[30, 4], x[30, 5],    -    ,    -    ,
+   *
+   */
+  /** Sizes of the lists (clusters) [n_lists]
+   * NB: This may differ from the actual list size if the shared lists have been extended by another
+   * index
+   */
+  raft::device_vector_view<uint32_t, uint32_t> list_sizes() noexcept;
+  raft::device_vector_view<const uint32_t, uint32_t> list_sizes() const noexcept;
+
+  /** k-means cluster centers corresponding to the lists [n_lists, dim] */
+  raft::device_matrix_view<float, uint32_t, raft::row_major> centers() noexcept;
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> centers() const noexcept;
+
+  /**
+   * (Optional) Precomputed norms of the `centers` w.r.t. the chosen distance metric [n_lists].
+   *
+   * NB: this may be empty if the index is empty or if the metric does not require the center norms
+   * calculation.
+   */
+  std::optional<raft::device_vector_view<float, uint32_t>> center_norms() noexcept;
+  std::optional<raft::device_vector_view<const float, uint32_t>> center_norms() const noexcept;
+
+  /** Total length of the index. */
+  IdxT size() const noexcept;
+
+  /** Dimensionality of the data. */
+  uint32_t dim() const noexcept;
+
+  /** Number of clusters/inverted lists. */
+  uint32_t n_lists() const noexcept;
+  raft::device_vector_view<T*, uint32_t> data_ptrs() noexcept;
+  raft::device_vector_view<T* const, uint32_t> data_ptrs() const noexcept;
+
+  /** Pointers to the inverted lists (clusters) indices  [n_lists]. */
+  raft::device_vector_view<IdxT*, uint32_t> inds_ptrs() noexcept;
+  raft::device_vector_view<IdxT* const, uint32_t> inds_ptrs() const noexcept;
+
+  /**
+   * Whether to use convervative memory allocation when extending the list (cluster) data
+   * (see index_params.conservative_memory_allocation).
+   */
+  bool conservative_memory_allocation() const noexcept;
+
+  /** Lists' data and indices. */
+  std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>& lists() noexcept;
+  const std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>& lists()
+    const noexcept;
+
+  // Get pointer to underlying RAFT index, not meant to be used outside of cuVS
+  inline raft::neighbors::ivf_flat::index<T, IdxT>* get_raft_index() noexcept
+  {
+    return raft_index_.get();
+  }
+  inline const raft::neighbors::ivf_flat::index<T, IdxT>* get_raft_index() const noexcept
+  {
+    return raft_index_.get();
+  }
+
+ private:
+  std::unique_ptr<raft::neighbors::ivf_flat::index<T, IdxT>> raft_index_;
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_cpp_index_build IVF-Flat index build
+ * @{
+ */
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<float, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<float, int64_t>& idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<int8_t, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& idx);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_cpp_index_extend IVF-Flat index extend
+ * @{
+ */
+
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<float, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<float, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<float, int64_t>* idx);
+
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, dataset, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<int8_t, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<int8_t, int64_t>* idx);
+
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, dataset, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* idx);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_cpp_index_search IVF-Flat index search
+ * @{
+ */
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`:
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_flat::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_flat::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_flat::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_flat::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] index ivf-flat constructed index
+ * @param[in] queries raft::device_matrix_view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors raft::device_matrix_view to the indices of the neighbors in the source
+ * dataset [n_queries, k]
+ * @param[out] distances raft::device_matrix_view to the distances to the selected neighbors
+ * [n_queries, k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_flat::search_params& params,
+            cuvs::neighbors::ivf_flat::index<float, int64_t>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`:
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_flat::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_flat::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_flat::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_flat::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] index ivf-flat constructed index
+ * @param[in] queries raft::device_matrix_view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors raft::device_matrix_view to the indices of the neighbors in the source
+ * dataset [n_queries, k]
+ * @param[out] distances raft::device_matrix_view to the distances to the selected neighbors
+ * [n_queries, k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_flat::search_params& params,
+            cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`:
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_flat::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_flat::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_flat::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_flat::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] index ivf-flat constructed index
+ * @param[in] queries raft::device_matrix_view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors raft::device_matrix_view to the indices of the neighbors in the source
+ * dataset [n_queries, k]
+ * @param[out] distances raft::device_matrix_view to the distances to the selected neighbors
+ * [n_queries, k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_flat::search_params& params,
+            cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_flat_cpp_serialize IVF-Flat index serialize
+ * @{
+ */
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::serialize_file(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::ivf_flat::index<float, int64_t>& index);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using T    = float; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * cuvs::deserialize_file(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::ivf_flat::index<float, int64_t>* index);
+
+/**
+ * Write the index to an output string
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output string
+ * std::string str;
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::serialize(handle, str, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[out] str output string
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::ivf_flat::index<float, int64_t>& index);
+
+/**
+ * Load index from input string
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input string
+ * std::string str;
+ * using T    = float; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * auto index = cuvs::deserialize(handle, str, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] str output string
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::ivf_flat::index<float, int64_t>* index);
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::serialize_file(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using T    = float; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * cuvs::deserialize_file(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::ivf_flat::index<int8_t, int64_t>* index);
+
+/**
+ * Write the index to an output string
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output string
+ * std::string str;
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::serialize(handle, str, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[out] str output string
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index);
+
+/**
+ * Load index from input string
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input string
+ * std::string str;
+ * using T    = float; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * auto index = cuvs::deserialize(handle, str, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] str output string
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::ivf_flat::index<int8_t, int64_t>* index);
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::serialize_file(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using T    = float; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * cuvs::deserialize_file(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* index);
+
+/**
+ * Write the index to an output string
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output string
+ * std::string str;
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::serialize(handle, str, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[out] str output string
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index);
+
+/**
+ * Load index from input string
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input string
+ * std::string str;
+ * using T    = float; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * auto index = cuvs::deserialize(handle, str, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] str output string
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* index);
+
+/**
+ * @}
+ */
+}  // namespace cuvs::neighbors::ivf_flat
\ No newline at end of file
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
new file mode 100644
index 0000000000..c1fcaed86a
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -0,0 +1,356 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance_types.h>
+#include <dlpack/dlpack.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup ivf_pq_c_index_params IVF-PQ index build parameters
+ * @{
+ */
+/**
+ * @brief A type for specifying how PQ codebooks are created
+ *
+ */
+enum codebook_gen {  // NOLINT
+  PER_SUBSPACE = 0,  // NOLINT
+  PER_CLUSTER  = 1,  // NOLINT
+};
+
+/**
+ * @brief Supplemental parameters to build IVF-PQ Index
+ *
+ */
+struct ivfPqIndexParams {
+  /** Distance type. */
+  enum DistanceType metric;
+  /** The argument used by some distance metrics. */
+  float metric_arg;
+  /**
+   * Whether to add the dataset content to the index, i.e.:
+   *
+   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
+   * `build`.
+   *  - `false` means `build` only trains the underlying model (e.g. quantizer or clustering), but
+   * the index is left empty; you'd need to call `extend` on the index afterwards to populate it.
+   */
+  bool add_data_on_build;
+  /**
+   * The number of inverted lists (clusters)
+   *
+   * Hint: the number of vectors per cluster (`n_rows/n_lists`) should be approximately 1,000 to
+   * 10,000.
+   */
+  uint32_t n_lists;
+  /** The number of iterations searching for kmeans centers (index building). */
+  uint32_t kmeans_n_iters;
+  /** The fraction of data to use during iterative kmeans building. */
+  double kmeans_trainset_fraction;
+  /**
+   * The bit length of the vector element after compression by PQ.
+   *
+   * Possible values: [4, 5, 6, 7, 8].
+   *
+   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
+   * performance, but the lower the recall.
+   */
+  uint32_t pq_bits;
+  /**
+   * The dimensionality of the vector after compression by PQ. When zero, an optimal value is
+   * selected using a heuristic.
+   *
+   * NB: `pq_dim * pq_bits` must be a multiple of 8.
+   *
+   * Hint: a smaller 'pq_dim' results in a smaller index size and better search performance, but
+   * lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any number, but multiple of 8 are
+   * desirable for good performance. If 'pq_bits' is not 8, 'pq_dim' should be a multiple of 8.
+   * For good performance, it is desirable that 'pq_dim' is a multiple of 32. Ideally, 'pq_dim'
+   * should be also a divisor of the dataset dim.
+   */
+  uint32_t pq_dim;
+  /** How PQ codebooks are created. */
+  enum codebook_gen codebook_kind;
+  /**
+   * Apply a random rotation matrix on the input data and queries even if `dim % pq_dim == 0`.
+   *
+   * Note: if `dim` is not multiple of `pq_dim`, a random rotation is always applied to the input
+   * data and queries to transform the working space from `dim` to `rot_dim`, which may be slightly
+   * larger than the original space and and is a multiple of `pq_dim` (`rot_dim % pq_dim == 0`).
+   * However, this transform is not necessary when `dim` is multiple of `pq_dim`
+   *   (`dim == rot_dim`, hence no need in adding "extra" data columns / features).
+   *
+   * By default, if `dim == rot_dim`, the rotation transform is initialized with the identity
+   * matrix. When `force_random_rotation == true`, a random orthogonal transform matrix is generated
+   * regardless of the values of `dim` and `pq_dim`.
+   */
+  bool force_random_rotation;
+  /**
+   * By default, the algorithm allocates more space than necessary for individual clusters
+   * (`list_data`). This allows to amortize the cost of memory allocation and reduce the number of
+   * data copies during repeated calls to `extend` (extending the database).
+   *
+   * The alternative is the conservative allocation behavior; when enabled, the algorithm always
+   * allocates the minimum amount of memory required to store the given number of records. Set this
+   * flag to `true` if you prefer to use as little GPU memory for the database as possible.
+   */
+  bool conservative_memory_allocation;
+};
+
+typedef struct ivfPqIndexParams* cuvsIvfPqIndexParams_t;
+
+/**
+ * @brief Allocate IVF-PQ Index params, and populate with default values
+ *
+ * @param[in] index_params cuvsIvfPqIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t* index_params);
+
+/**
+ * @brief De-allocate IVF-PQ Index params
+ *
+ * @param[in] index_params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_c_search_params IVF-PQ index search parameters
+ * @{
+ */
+/**
+ * @brief Supplemental parameters to search IVF-PQ index
+ *
+ */
+struct ivfPqSearchParams {
+  /** The number of clusters to search. */
+  uint32_t n_probes;
+  /**
+   * Data type of look up table to be created dynamically at search time.
+   *
+   * Possible values: [CUDA_R_32F, CUDA_R_16F, CUDA_R_8U]
+   *
+   * The use of low-precision types reduces the amount of shared memory required at search time, so
+   * fast shared memory kernels can be used even for datasets with large dimansionality. Note that
+   * the recall is slightly degraded when low-precision type is selected.
+   */
+  cudaDataType_t lut_dtype;
+  /**
+   * Storage data type for distance/similarity computed at search time.
+   *
+   * Possible values: [CUDA_R_16F, CUDA_R_32F]
+   *
+   * If the performance limiter at search time is device memory access, selecting FP16 will improve
+   * performance slightly.
+   */
+  cudaDataType_t internal_distance_dtype;
+  /**
+   * Preferred fraction of SM's unified memory / L1 cache to be used as shared memory.
+   *
+   * Possible values: [0.0 - 1.0] as a fraction of the `sharedMemPerMultiprocessor`.
+   *
+   * One wants to increase the carveout to make sure a good GPU occupancy for the main search
+   * kernel, but not to keep it too high to leave some memory to be used as L1 cache. Note, this
+   * value is interpreted only as a hint. Moreover, a GPU usually allows only a fixed set of cache
+   * configurations, so the provided value is rounded up to the nearest configuration. Refer to the
+   * NVIDIA tuning guide for the target GPU architecture.
+   *
+   * Note, this is a low-level tuning parameter that can have drastic negative effects on the search
+   * performance if tweaked incorrectly.
+   */
+  double preferred_shmem_carveout;
+};
+
+typedef struct ivfPqSearchParams* cuvsIvfPqSearchParams_t;
+
+/**
+ * @brief Allocate IVF-PQ search params, and populate with default values
+ *
+ * @param[in] params cuvsIvfPqSearchParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t* params);
+
+/**
+ * @brief De-allocate IVF-PQ search params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_c_index IVF-PQ index
+ * @{
+ */
+/**
+ * @brief Struct to hold address of cuvs::neighbors::ivf_pq::index and its active trained dtype
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} ivfPqIndex;
+
+typedef ivfPqIndex* cuvsIvfPqIndex_t;
+
+/**
+ * @brief Allocate IVF-PQ index
+ *
+ * @param[in] index cuvsIvfPqIndex_t to allocate
+ * @return ivfPqError_t
+ */
+cuvsError_t ivfPqIndexCreate(cuvsIvfPqIndex_t* index);
+
+/**
+ * @brief De-allocate IVF-PQ index
+ *
+ * @param[in] index cuvsIvfPqIndex_t to de-allocate
+ */
+cuvsError_t ivfPqIndexDestroy(cuvsIvfPqIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_c_index_build IVF-PQ index build
+ * @{
+ */
+/**
+ * @brief Build a IVF-PQ index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
+ *        or `kDLCPU`. Also, acceptable underlying types are:
+ *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/ivf_pq.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ *
+ * // Create default index params
+ * cuvsIvfPqIndexParams_t index_params;
+ * cuvsError_t params_create_status = cuvsIvfPqIndexParamsCreate(&index_params);
+ *
+ * // Create IVF-PQ index
+ * cuvsIvfPqIndex_t index;
+ * cuvsError_t index_create_status = ivfPqIndexCreate(&index);
+ *
+ * // Build the IVF-PQ Index
+ * cuvsError_t build_status = ivfPqBuild(res, index_params, &dataset, index);
+ *
+ * // de-allocate `index_params`, `index` and `res`
+ * cuvsError_t params_destroy_status = cuvsIvfPqIndexParamsDestroy(index_params);
+ * cuvsError_t index_destroy_status = ivfPqIndexDestroy(index);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsIvfPqIndexParams_t used to build IVF-PQ index
+ * @param[in] dataset DLManagedTensor* training dataset
+ * @param[out] index cuvsIvfPqIndex_t Newly built IVF-PQ index
+ * @return cuvsError_t
+ */
+cuvsError_t ivfPqBuild(cuvsResources_t res,
+                       cuvsIvfPqIndexParams_t params,
+                       DLManagedTensor* dataset,
+                       cuvsIvfPqIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_c_index_search IVF-PQ index search
+ * @{
+ */
+/**
+ * @brief Search a IVF-PQ index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`.
+ *        It is also important to note that the IVF-PQ Index must have been built
+ *        with the same type of `queries`, such that `index.dtype.code ==
+ * queries.dl_tensor.dtype.code` Types for input are:
+ *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
+ *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/ivf_pq.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ * DLManagedTensor queries;
+ * DLManagedTensor neighbors;
+ *
+ * // Create default search params
+ * cuvsIvfPqSearchParams_t search_params;
+ * cuvsError_t params_create_status = cuvsIvfPqSearchParamsCreate(&search_params);
+ *
+ * // Search the `index` built using `ivfPqBuild`
+ * cuvsError_t search_status = ivfPqSearch(res, search_params, index, &queries, &neighbors,
+ * &distances);
+ *
+ * // de-allocate `search_params` and `res`
+ * cuvsError_t params_destroy_status = cuvsIvfPqSearchParamsDestroy(search_params);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] search_params cuvsIvfPqSearchParams_t used to search IVF-PQ index
+ * @param[in] index ivfPqIndex which has been returned by `ivfPqBuild`
+ * @param[in] queries DLManagedTensor* queries dataset to search
+ * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
+ * @param[out] distances DLManagedTensor* output `k` distances for queries
+ */
+cuvsError_t ivfPqSearch(cuvsResources_t res,
+                        cuvsIvfPqSearchParams_t search_params,
+                        cuvsIvfPqIndex_t index,
+                        DLManagedTensor* queries,
+                        DLManagedTensor* neighbors,
+                        DLManagedTensor* distances);
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
new file mode 100644
index 0000000000..b2fc9a3668
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -0,0 +1,868 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "ann_types.hpp"
+#include <raft/neighbors/ivf_pq_types.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+/**
+ * @defgroup ivf_pq_cpp_index_params IVF-PQ index build parameters
+ * @{
+ */
+/** A type for specifying how PQ codebooks are created. */
+enum class codebook_gen {  // NOLINT
+  PER_SUBSPACE = 0,        // NOLINT
+  PER_CLUSTER  = 1,        // NOLINT
+};
+
+struct index_params : ann::index_params {
+  /**
+   * The number of inverted lists (clusters)
+   *
+   * Hint: the number of vectors per cluster (`n_rows/n_lists`) should be approximately 1,000 to
+   * 10,000.
+   */
+  uint32_t n_lists = 1024;
+  /** The number of iterations searching for kmeans centers (index building). */
+  uint32_t kmeans_n_iters = 20;
+  /** The fraction of data to use during iterative kmeans building. */
+  double kmeans_trainset_fraction = 0.5;
+  /**
+   * The bit length of the vector element after compression by PQ.
+   *
+   * Possible values: [4, 5, 6, 7, 8].
+   *
+   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
+   * performance, but the lower the recall.
+   */
+  uint32_t pq_bits = 8;
+  /**
+   * The dimensionality of the vector after compression by PQ. When zero, an optimal value is
+   * selected using a heuristic.
+   *
+   * NB: `pq_dim * pq_bits` must be a multiple of 8.
+   *
+   * Hint: a smaller 'pq_dim' results in a smaller index size and better search performance, but
+   * lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any number, but multiple of 8 are
+   * desirable for good performance. If 'pq_bits' is not 8, 'pq_dim' should be a multiple of 8.
+   * For good performance, it is desirable that 'pq_dim' is a multiple of 32. Ideally, 'pq_dim'
+   * should be also a divisor of the dataset dim.
+   */
+  uint32_t pq_dim = 0;
+  /** How PQ codebooks are created. */
+  codebook_gen codebook_kind = codebook_gen::PER_SUBSPACE;
+  /**
+   * Apply a random rotation matrix on the input data and queries even if `dim % pq_dim == 0`.
+   *
+   * Note: if `dim` is not multiple of `pq_dim`, a random rotation is always applied to the input
+   * data and queries to transform the working space from `dim` to `rot_dim`, which may be slightly
+   * larger than the original space and and is a multiple of `pq_dim` (`rot_dim % pq_dim == 0`).
+   * However, this transform is not necessary when `dim` is multiple of `pq_dim`
+   *   (`dim == rot_dim`, hence no need in adding "extra" data columns / features).
+   *
+   * By default, if `dim == rot_dim`, the rotation transform is initialized with the identity
+   * matrix. When `force_random_rotation == true`, a random orthogonal transform matrix is generated
+   * regardless of the values of `dim` and `pq_dim`.
+   */
+  bool force_random_rotation = false;
+  /**
+   * By default, the algorithm allocates more space than necessary for individual clusters
+   * (`list_data`). This allows to amortize the cost of memory allocation and reduce the number of
+   * data copies during repeated calls to `extend` (extending the database).
+   *
+   * The alternative is the conservative allocation behavior; when enabled, the algorithm always
+   * allocates the minimum amount of memory required to store the given number of records. Set this
+   * flag to `true` if you prefer to use as little GPU memory for the database as possible.
+   */
+  bool conservative_memory_allocation = false;
+
+  /** Build a raft IVF_PQ index params from an existing cuvs IVF_PQ index params. */
+  operator raft::neighbors::ivf_pq::index_params() const
+  {
+    return raft::neighbors::ivf_pq::index_params{
+      {
+        .metric            = static_cast<raft::distance::DistanceType>((int)this->metric),
+        .metric_arg        = this->metric_arg,
+        .add_data_on_build = this->add_data_on_build,
+      },
+      .n_lists                  = n_lists,
+      .kmeans_n_iters           = kmeans_n_iters,
+      .kmeans_trainset_fraction = kmeans_trainset_fraction,
+      .pq_bits                  = pq_bits,
+      .pq_dim                   = pq_dim,
+      .codebook_kind = static_cast<raft::neighbors::ivf_pq::codebook_gen>((int)this->codebook_kind),
+      .force_random_rotation          = force_random_rotation,
+      .conservative_memory_allocation = conservative_memory_allocation};
+  }
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_cpp_search_params IVF-PQ index search parameters
+ * @{
+ */
+struct search_params : ann::search_params {
+  /** The number of clusters to search. */
+  uint32_t n_probes = 20;
+  /**
+   * Data type of look up table to be created dynamically at search time.
+   *
+   * Possible values: [CUDA_R_32F, CUDA_R_16F, CUDA_R_8U]
+   *
+   * The use of low-precision types reduces the amount of shared memory required at search time, so
+   * fast shared memory kernels can be used even for datasets with large dimansionality. Note that
+   * the recall is slightly degraded when low-precision type is selected.
+   */
+  cudaDataType_t lut_dtype = CUDA_R_32F;
+  /**
+   * Storage data type for distance/similarity computed at search time.
+   *
+   * Possible values: [CUDA_R_16F, CUDA_R_32F]
+   *
+   * If the performance limiter at search time is device memory access, selecting FP16 will improve
+   * performance slightly.
+   */
+  cudaDataType_t internal_distance_dtype = CUDA_R_32F;
+  /**
+   * Preferred fraction of SM's unified memory / L1 cache to be used as shared memory.
+   *
+   * Possible values: [0.0 - 1.0] as a fraction of the `sharedMemPerMultiprocessor`.
+   *
+   * One wants to increase the carveout to make sure a good GPU occupancy for the main search
+   * kernel, but not to keep it too high to leave some memory to be used as L1 cache. Note, this
+   * value is interpreted only as a hint. Moreover, a GPU usually allows only a fixed set of cache
+   * configurations, so the provided value is rounded up to the nearest configuration. Refer to the
+   * NVIDIA tuning guide for the target GPU architecture.
+   *
+   * Note, this is a low-level tuning parameter that can have drastic negative effects on the search
+   * performance if tweaked incorrectly.
+   */
+  double preferred_shmem_carveout = 1.0;
+
+  /** Build a raft IVF_PQ search params from an existing cuvs IVF_PQ search params. */
+  operator raft::neighbors::ivf_pq::search_params() const
+  {
+    raft::neighbors::ivf_pq::search_params result = {
+      {}, n_probes, lut_dtype, internal_distance_dtype, preferred_shmem_carveout};
+    return result;
+  }
+};
+/**
+ * @}
+ */
+
+template <typename IdxT, typename SizeT = uint32_t>
+using list_data = raft::neighbors::ivf_pq::list_data<IdxT, SizeT>;
+
+/**
+ * @defgroup ivf_pq_cpp_index IVF-PQ index
+ * @{
+ */
+/**
+ * @brief IVF-PQ index.
+ *
+ * In the IVF-PQ index, a database vector y is approximated with two level quantization:
+ *
+ * y = Q_1(y) + Q_2(y - Q_1(y))
+ *
+ * The first level quantizer (Q_1), maps the vector y to the nearest cluster center. The number of
+ * clusters is n_lists.
+ *
+ * The second quantizer encodes the residual, and it is defined as a product quantizer [1].
+ *
+ * A product quantizer encodes a `dim` dimensional vector with a `pq_dim` dimensional vector.
+ * First we split the input vector into `pq_dim` subvectors (denoted by u), where each u vector
+ * contains `pq_len` distinct components of y
+ *
+ * y_1, y_2, ... y_{pq_len}, y_{pq_len+1}, ... y_{2*pq_len}, ... y_{dim-pq_len+1} ... y_{dim}
+ *  \___________________/     \____________________________/      \______________________/
+ *         u_1                         u_2                          u_{pq_dim}
+ *
+ * Then each subvector encoded with a separate quantizer q_i, end the results are concatenated
+ *
+ * Q_2(y) = q_1(u_1),q_2(u_2),...,q_{pq_dim}(u_pq_dim})
+ *
+ * Each quantizer q_i outputs a code with pq_bit bits. The second level quantizers are also defined
+ * by k-means clustering in the corresponding sub-space: the reproduction values are the centroids,
+ * and the set of reproduction values is the codebook.
+ *
+ * When the data dimensionality `dim` is not multiple of `pq_dim`, the feature space is transformed
+ * using a random orthogonal matrix to have `rot_dim = pq_dim * pq_len` dimensions
+ * (`rot_dim >= dim`).
+ *
+ * The second-level quantizers are trained either for each subspace or for each cluster:
+ *   (a) codebook_gen::PER_SUBSPACE:
+ *         creates `pq_dim` second-level quantizers - one for each slice of the data along features;
+ *   (b) codebook_gen::PER_CLUSTER:
+ *         creates `n_lists` second-level quantizers - one for each first-level cluster.
+ * In either case, the centroids are again found using k-means clustering interpreting the data as
+ * having pq_len dimensions.
+ *
+ * [1] Product quantization for nearest neighbor search Herve Jegou, Matthijs Douze, Cordelia Schmid
+ *
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ */
+template <typename IdxT>
+struct index : ann::index {
+  static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
+                "IdxT must be able to represent all values of uint32_t");
+
+  using pq_centers_extents = typename raft::neighbors::ivf_pq::index<IdxT>::pq_centers_extents;
+
+ public:
+  index(const index&)                    = delete;
+  index(index&&)                         = default;
+  auto operator=(const index&) -> index& = delete;
+  auto operator=(index&&) -> index&      = default;
+  ~index()                               = default;
+
+  /** Construct an empty index. It needs to be trained and then populated. */
+  index(raft::resources const& handle, const index_params& params, uint32_t dim);
+  index(raft::neighbors::ivf_pq::index<IdxT>&& raft_idx);
+
+  /** Total length of the index. */
+  IdxT size() const noexcept;
+
+  /** Dimensionality of the input data. */
+  uint32_t dim() const noexcept;
+
+  /**
+   * Dimensionality of the cluster centers:
+   * input data dim extended with vector norms and padded to 8 elems.
+   */
+  uint32_t dim_ext() const noexcept;
+
+  /**
+   * Dimensionality of the data after transforming it for PQ processing
+   * (rotated and augmented to be muplitple of `pq_dim`).
+   */
+  uint32_t rot_dim() const noexcept;
+
+  /** The bit length of an encoded vector element after compression by PQ. */
+  uint32_t pq_bits() const noexcept;
+
+  /** The dimensionality of an encoded vector after compression by PQ. */
+  uint32_t pq_dim() const noexcept;
+
+  /** Dimensionality of a subspaces, i.e. the number of vector components mapped to a subspace */
+  uint32_t pq_len() const noexcept;
+
+  /** The number of vectors in a PQ codebook (`1 << pq_bits`). */
+  uint32_t pq_book_size() const noexcept;
+
+  /** Distance metric used for clustering. */
+  cuvs::distance::DistanceType metric() const noexcept;
+
+  /** How PQ codebooks are created. */
+  codebook_gen codebook_kind() const noexcept;
+
+  /** Number of clusters/inverted lists (first level quantization). */
+  uint32_t n_lists() const noexcept;
+
+  /**
+   * Whether to use convervative memory allocation when extending the list (cluster) data
+   * (see index_params.conservative_memory_allocation).
+   */
+  bool conservative_memory_allocation() const noexcept;
+
+  /**
+   * PQ cluster centers
+   *
+   *   - codebook_gen::PER_SUBSPACE: [pq_dim , pq_len, pq_book_size]
+   *   - codebook_gen::PER_CLUSTER:  [n_lists, pq_len, pq_book_size]
+   */
+  raft::mdspan<float, pq_centers_extents, raft::row_major> pq_centers() noexcept;
+  raft::mdspan<const float, pq_centers_extents, raft::row_major> pq_centers() const noexcept;
+
+  /** Lists' data and indices. */
+  std::vector<std::shared_ptr<list_data<IdxT>>>& lists() noexcept;
+  const std::vector<std::shared_ptr<list_data<IdxT>>>& lists() const noexcept;
+
+  /** Pointers to the inverted lists (clusters) data  [n_lists]. */
+  raft::device_vector_view<uint8_t*, uint32_t, raft::row_major> data_ptrs() noexcept;
+  raft::device_vector_view<const uint8_t* const, uint32_t, raft::row_major> data_ptrs()
+    const noexcept;
+
+  /** Pointers to the inverted lists (clusters) indices  [n_lists]. */
+  raft::device_vector_view<IdxT*, uint32_t, raft::row_major> inds_ptrs() noexcept;
+  raft::device_vector_view<const IdxT* const, uint32_t, raft::row_major> inds_ptrs() const noexcept;
+
+  /** The transform matrix (original space -> rotated padded space) [rot_dim, dim] */
+  raft::device_matrix_view<float, uint32_t, raft::row_major> rotation_matrix() noexcept;
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> rotation_matrix() const noexcept;
+
+  /**
+   * Accumulated list sizes, sorted in descending order [n_lists + 1].
+   * The last value contains the total length of the index.
+   * The value at index zero is always zero.
+   *
+   * That is, the content of this span is as if the `list_sizes` was sorted and then accumulated.
+   *
+   * This span is used during search to estimate the maximum size of the workspace.
+   */
+  raft::host_vector_view<IdxT, uint32_t, raft::row_major> accum_sorted_sizes() noexcept;
+  raft::host_vector_view<const IdxT, uint32_t, raft::row_major> accum_sorted_sizes() const noexcept;
+
+  /** Sizes of the lists [n_lists]. */
+  raft::device_vector_view<uint32_t, uint32_t, raft::row_major> list_sizes() noexcept;
+  raft::device_vector_view<const uint32_t, uint32_t, raft::row_major> list_sizes() const noexcept;
+
+  /** Cluster centers corresponding to the lists in the original space [n_lists, dim_ext] */
+  raft::device_matrix_view<float, uint32_t, raft::row_major> centers() noexcept;
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> centers() const noexcept;
+
+  /** Cluster centers corresponding to the lists in the rotated space [n_lists, rot_dim] */
+  raft::device_matrix_view<float, uint32_t, raft::row_major> centers_rot() noexcept;
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> centers_rot() const noexcept;
+
+  // Get pointer to underlying RAFT index, not meant to be used outside of cuVS
+  inline raft::neighbors::ivf_pq::index<IdxT>* get_raft_index() noexcept
+  {
+    return raft_index_.get();
+  }
+  inline const raft::neighbors::ivf_pq::index<IdxT>* get_raft_index() const noexcept
+  {
+    return raft_index_.get();
+  }
+
+ private:
+  std::unique_ptr<raft::neighbors::ivf_pq::index<IdxT>> raft_index_;
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_cpp_index_build IVF-PQ index build
+ * @{
+ */
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device matrix view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device matrix view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device matrix view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_cpp_index_extend IVF-PQ index extend
+ * @{
+ */
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_cpp_index_search IVF-PQ index search
+ * @{
+ */
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_pq::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_pq::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_pq::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] search_params configure the search
+ * @param[in] index ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_pq::search_params& search_params,
+            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_pq::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_pq::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_pq::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] search_params configure the search
+ * @param[in] index ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_pq::search_params& search_params,
+            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_pq::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_pq::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_pq::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] search_params configure the search
+ * @param[in] index ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_pq::search_params& search_params,
+            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup ivf_pq_cpp_serialize IVF-PQ index serialize
+ * @{
+ */
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = ivf_pq::build(...);`
+ * cuvs::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-PQ index
+ *
+ */
+void serialize(raft::resources const& handle,
+               std::string& filename,
+               const cuvs::neighbors::ivf_pq::index<int64_t>& index);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_pq::index<IdxT> index(handle, index_params, dim);`
+ * cuvs::deserialize(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index IVF-PQ index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_pq::index<int64_t>* index);
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
new file mode 100644
index 0000000000..33dc2088ca
--- /dev/null
+++ b/cpp/src/neighbors/brute_force.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/neighbors/brute_force-inl.cuh>
+
+namespace cuvs::neighbors::brute_force {
+
+#define CUVS_INST_BFKNN(T, IdxT)                                                             \
+  auto build(raft::resources const& res,                                                     \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
+             cuvs::distance::DistanceType metric,                                            \
+             T metric_arg)                                                                   \
+    ->cuvs::neighbors::brute_force::index<T>                                                 \
+  {                                                                                          \
+    auto index_on_stack = raft::neighbors::brute_force::build(                               \
+      res, dataset, static_cast<raft::distance::DistanceType>(metric), metric_arg);          \
+    auto index_on_heap =                                                                     \
+      new raft::neighbors::brute_force::index<float>(std::move(index_on_stack));             \
+    return cuvs::neighbors::brute_force::index<float>(index_on_heap);                        \
+  }                                                                                          \
+                                                                                             \
+  void search(raft::resources const& res,                                                    \
+              const cuvs::neighbors::brute_force::index<T>& idx,                             \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,              \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,               \
+              raft::device_matrix_view<T, IdxT, raft::row_major> distances)                  \
+  {                                                                                          \
+    auto raft_idx =                                                                          \
+      reinterpret_cast<const raft::neighbors::brute_force::index<T>*>(idx.get_raft_index()); \
+    raft::neighbors::brute_force::search(res, *raft_idx, queries, neighbors, distances);     \
+  }                                                                                          \
+                                                                                             \
+  template struct cuvs::neighbors::brute_force::index<T>;
+
+CUVS_INST_BFKNN(float, int64_t);
+// CUVS_INST_BFKNN(int8_t, int64_t);
+// CUVS_INST_BFKNN(uint8_t, int64_t);
+
+#undef CUVS_INST_BFKNN
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
new file mode 100644
index 0000000000..531fc9a57d
--- /dev/null
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -0,0 +1,167 @@
+
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/brute_force.h>
+#include <cuvs/neighbors/brute_force.hpp>
+
+namespace {
+
+template <typename T>
+void* _build(cuvsResources_t res,
+             DLManagedTensor* dataset_tensor,
+             enum DistanceType metric,
+             T metric_arg)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+
+  auto index_on_stack = cuvs::neighbors::brute_force::build(
+    *res_ptr, mds, static_cast<cuvs::distance::DistanceType>((int)metric), metric_arg);
+  auto index_on_heap = new cuvs::neighbors::brute_force::index<T>(std::move(index_on_stack));
+
+  return index_on_heap;
+}
+
+template <typename T>
+void _search(cuvsResources_t res,
+             bruteForceIndex index,
+             DLManagedTensor* queries_tensor,
+             DLManagedTensor* neighbors_tensor,
+             DLManagedTensor* distances_tensor)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T>*>(index.addr);
+
+  using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+  using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
+  auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  cuvs::neighbors::brute_force::search(
+    *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+}
+
+}  // namespace
+
+extern "C" cuvsError_t bruteForceIndexCreate(cuvsBruteForceIndex_t* index)
+{
+  try {
+    *index = new bruteForceIndex{};
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t bruteForceIndexDestroy(cuvsBruteForceIndex_t index_c_ptr)
+{
+  try {
+    auto index = *index_c_ptr;
+
+    if (index.dtype.code == kDLFloat) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<float>*>(index.addr);
+      delete index_ptr;
+    } else if (index.dtype.code == kDLInt) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<int8_t>*>(index.addr);
+      delete index_ptr;
+    } else if (index.dtype.code == kDLUInt) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<uint8_t>*>(index.addr);
+      delete index_ptr;
+    }
+    delete index_c_ptr;
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t bruteForceBuild(cuvsResources_t res,
+                                       DLManagedTensor* dataset_tensor,
+                                       enum DistanceType metric,
+                                       float metric_arg,
+                                       cuvsBruteForceIndex_t index)
+{
+  try {
+    auto dataset = dataset_tensor->dl_tensor;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<float>(res, dataset_tensor, metric, metric_arg));
+      index->dtype.code = kDLFloat;
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t bruteForceSearch(cuvsResources_t res,
+                                        cuvsBruteForceIndex_t index_c_ptr,
+                                        DLManagedTensor* queries_tensor,
+                                        DLManagedTensor* neighbors_tensor,
+                                        DLManagedTensor* distances_tensor)
+{
+  try {
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(queries),
+                 "queries should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(neighbors),
+                 "neighbors should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(distances),
+                 "distances should have device compatible memory");
+
+    RAFT_EXPECTS(neighbors.dtype.code == kDLInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    auto index = *index_c_ptr;
+    RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
+
+    if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
+      _search<float>(res, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
+                queries.dtype.code,
+                queries.dtype.bits);
+    }
+
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
diff --git a/cpp/src/neighbors/brute_force_index.cu b/cpp/src/neighbors/brute_force_index.cu
new file mode 100644
index 0000000000..b05fa7ced9
--- /dev/null
+++ b/cpp/src/neighbors/brute_force_index.cu
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/neighbors/brute_force-inl.cuh>
+
+namespace cuvs::neighbors::brute_force {
+
+template <typename T>
+inline const raft::neighbors::brute_force::index<T>* get_underlying_index(
+  const cuvs::neighbors::brute_force::index<T>* idx)
+{
+  return reinterpret_cast<const raft::neighbors::brute_force::index<T>*>(idx->get_raft_index());
+}
+
+template <typename T>
+index<T>::index(void* raft_index)
+  : cuvs::neighbors::ann::index(), raft_index_(reinterpret_cast<void**>(raft_index))
+{
+}
+
+template <typename T>
+cuvs::distance::DistanceType index<T>::metric() const noexcept
+{
+  auto raft_index = cuvs::neighbors::brute_force::get_underlying_index(this);
+  return static_cast<cuvs::distance::DistanceType>((int)raft_index->metric());
+}
+
+template <typename T>
+size_t index<T>::size() const noexcept
+{
+  auto raft_index = get_underlying_index(this);
+  return raft_index->size();
+}
+
+template <typename T>
+size_t index<T>::dim() const noexcept
+{
+  auto raft_index = get_underlying_index(this);
+  return raft_index->dim();
+}
+
+template <typename T>
+raft::device_matrix_view<const T, int64_t, raft::row_major> index<T>::dataset() const noexcept
+{
+  auto raft_index = get_underlying_index(this);
+  return raft_index->dataset();
+}
+
+template <typename T>
+raft::device_vector_view<const T, int64_t, raft::row_major> index<T>::norms() const
+{
+  auto raft_index = get_underlying_index(this);
+  return raft_index->norms();
+}
+
+template <typename T>
+bool index<T>::has_norms() const noexcept
+{
+  auto raft_index = get_underlying_index(this);
+  return raft_index->has_norms();
+}
+
+template <typename T>
+T index<T>::metric_arg() const noexcept
+{
+  auto raft_index = get_underlying_index(this);
+  return raft_index->metric_arg();
+}
+
+template struct index<float>;
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 9fdfe2c1e7..9e50870163 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -162,14 +162,14 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
     RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(queries),
                  "queries should have device compatible memory");
     RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(neighbors),
-                 "queries should have device compatible memory");
+                 "neighbors should have device compatible memory");
     RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(distances),
-                 "queries should have device compatible memory");
+                 "distances should have device compatible memory");
 
     RAFT_EXPECTS(neighbors.dtype.code == kDLUInt && neighbors.dtype.bits == 32,
                  "neighbors should be of type uint32_t");
     RAFT_EXPECTS(distances.dtype.code == kDLFloat && neighbors.dtype.bits == 32,
-                 "neighbors should be of type float32");
+                 "distances should be of type float32");
 
     auto index = *index_c_ptr;
     RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
diff --git a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
new file mode 100644
index 0000000000..bf0cad6d4a
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
@@ -0,0 +1,174 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+"""
+
+footer = """
+}  // namespace cuvs::neighbors::ivf_flat
+"""
+
+types = dict(
+    float_int64_t=("float", "int64_t"),
+    int8_t_int64_t=("int8_t", "int64_t"),
+    uint8_t_int64_t=("uint8_t", "int64_t"),
+)
+
+build_macro = """
+#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \\
+  auto build(raft::resources const& handle,                                                    \\
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \\
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \\
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \\
+  {                                                                                            \\
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \\
+      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \\
+  }                                                                                            \\
+                                                                                               \\
+  void build(raft::resources const& handle,                                                    \\
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \\
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \\
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \\
+  {                                                                                            \\
+    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \\
+  }
+"""
+
+extend_macro = """
+#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \\
+  auto extend(raft::resources const& handle,                                         \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \\
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \\
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \\
+  {                                                                                  \\
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \\
+      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \\
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \\
+  }                                                                                  \\
+                                                                                     \\
+  void extend(raft::resources const& handle,                                         \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \\
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \\
+  {                                                                                  \\
+    raft::runtime::neighbors::ivf_flat::extend(                                      \\
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \\
+  }                 
+"""
+
+search_macro = """
+#define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \\
+  void search(raft::resources const& handle,                                    \\
+              const cuvs::neighbors::ivf_flat::search_params& params,           \\
+              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \\
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \\
+  {                                                                             \\
+    raft::runtime::neighbors::ivf_flat::search(                                 \\
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \\
+  }
+"""
+
+serialize_macro = """
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \\
+  void serialize_file(raft::resources const& handle,                                               \\
+                      const std::string& filename,                                                 \\
+                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \\
+  {                                                                                                \\
+    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \\
+  }                                                                                                \\
+                                                                                                   \\
+  void deserialize_file(raft::resources const& handle,                                             \\
+                        const std::string& filename,                                               \\
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \\
+  {                                                                                                \\
+    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \\
+      handle, filename, index->get_raft_index());                                                  \\
+  }                                                                                                \\
+                                                                                                   \\
+  void serialize(raft::resources const& handle,                                                    \\
+                 std::string& str,                                                                 \\
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \\
+  {                                                                                                \\
+    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \\
+  }                                                                                                \\
+                                                                                                   \\
+  void deserialize(raft::resources const& handle,                                                  \\
+                   const std::string& str,                                                         \\
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \\
+  {                                                                                                \\
+    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \\
+  }
+"""
+
+macros = dict(
+    build=dict(
+        definition=build_macro,
+        name="CUVS_INST_IVF_FLAT_BUILD",
+    ),
+    extend=dict(
+        definition=extend_macro,
+        name="CUVS_INST_IVF_FLAT_EXTEND",
+    ),
+    search=dict(
+        definition=search_macro,
+        name="CUVS_INST_IVF_FLAT_SEARCH",
+    ),
+    serialize=dict(
+        definition=serialize_macro,
+        name="CUVS_INST_IVF_FLAT_SERIALIZE",
+    ),
+)
+
+for type_path, (T, IdxT) in types.items():
+    for macro_path, macro in macros.items():
+        path = f"ivf_flat_{macro_path}_{type_path}.cpp"
+        with open(path, "w") as f:
+            f.write(header)
+            f.write(macro["definition"])
+            f.write(f"{macro['name']}({T}, {IdxT});\n\n")
+            f.write(f"#undef {macro['name']}\n")
+            f.write(footer)
+
+        print(f"src/neighbors/ivf_flat/{path}")
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
new file mode 100644
index 0000000000..177aaac11f
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \
+  auto build(raft::resources const& handle,                                                    \
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \
+  {                                                                                            \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \
+      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \
+  }                                                                                            \
+                                                                                               \
+  void build(raft::resources const& handle,                                                    \
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \
+  {                                                                                            \
+    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \
+  }
+CUVS_INST_IVF_FLAT_BUILD(float, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
new file mode 100644
index 0000000000..6fe6e2b8d6
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \
+  auto build(raft::resources const& handle,                                                    \
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \
+  {                                                                                            \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \
+      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \
+  }                                                                                            \
+                                                                                               \
+  void build(raft::resources const& handle,                                                    \
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \
+  {                                                                                            \
+    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \
+  }
+CUVS_INST_IVF_FLAT_BUILD(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
new file mode 100644
index 0000000000..01098ed450
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \
+  auto build(raft::resources const& handle,                                                    \
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \
+  {                                                                                            \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \
+      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \
+  }                                                                                            \
+                                                                                               \
+  void build(raft::resources const& handle,                                                    \
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \
+  {                                                                                            \
+    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \
+  }
+CUVS_INST_IVF_FLAT_BUILD(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
new file mode 100644
index 0000000000..04ca3a50ff
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \
+  auto extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
+  }                                                                                  \
+                                                                                     \
+  void extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \
+  {                                                                                  \
+    raft::runtime::neighbors::ivf_flat::extend(                                      \
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \
+  }
+CUVS_INST_IVF_FLAT_EXTEND(float, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
new file mode 100644
index 0000000000..accc53e04d
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \
+  auto extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
+  }                                                                                  \
+                                                                                     \
+  void extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \
+  {                                                                                  \
+    raft::runtime::neighbors::ivf_flat::extend(                                      \
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \
+  }
+CUVS_INST_IVF_FLAT_EXTEND(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
new file mode 100644
index 0000000000..e44ae51b12
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \
+  auto extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
+  }                                                                                  \
+                                                                                     \
+  void extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \
+  {                                                                                  \
+    raft::runtime::neighbors::ivf_flat::extend(                                      \
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \
+  }
+CUVS_INST_IVF_FLAT_EXTEND(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp
new file mode 100644
index 0000000000..48a584e9ec
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_flat::search_params& params,           \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+  {                                                                             \
+    raft::runtime::neighbors::ivf_flat::search(                                 \
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+  }
+CUVS_INST_IVF_FLAT_SEARCH(float, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp
new file mode 100644
index 0000000000..5645c18e0f
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_flat::search_params& params,           \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+  {                                                                             \
+    raft::runtime::neighbors::ivf_flat::search(                                 \
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+  }
+CUVS_INST_IVF_FLAT_SEARCH(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp
new file mode 100644
index 0000000000..ab1bee8b60
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_flat::search_params& params,           \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+  {                                                                             \
+    raft::runtime::neighbors::ivf_flat::search(                                 \
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+  }
+CUVS_INST_IVF_FLAT_SEARCH(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp
new file mode 100644
index 0000000000..19a3d72d98
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \
+  void serialize_file(raft::resources const& handle,                                               \
+                      const std::string& filename,                                                 \
+                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \
+  }                                                                                                \
+                                                                                                   \
+  void deserialize_file(raft::resources const& handle,                                             \
+                        const std::string& filename,                                               \
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \
+      handle, filename, index->get_raft_index());                                                  \
+  }                                                                                                \
+                                                                                                   \
+  void serialize(raft::resources const& handle,                                                    \
+                 std::string& str,                                                                 \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \
+  }                                                                                                \
+                                                                                                   \
+  void deserialize(raft::resources const& handle,                                                  \
+                   const std::string& str,                                                         \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \
+  }
+CUVS_INST_IVF_FLAT_SERIALIZE(float, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SERIALIZE
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
new file mode 100644
index 0000000000..f65fe221d4
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \
+  void serialize_file(raft::resources const& handle,                                               \
+                      const std::string& filename,                                                 \
+                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \
+  }                                                                                                \
+                                                                                                   \
+  void deserialize_file(raft::resources const& handle,                                             \
+                        const std::string& filename,                                               \
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \
+      handle, filename, index->get_raft_index());                                                  \
+  }                                                                                                \
+                                                                                                   \
+  void serialize(raft::resources const& handle,                                                    \
+                 std::string& str,                                                                 \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \
+  }                                                                                                \
+                                                                                                   \
+  void deserialize(raft::resources const& handle,                                                  \
+                   const std::string& str,                                                         \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \
+  }
+CUVS_INST_IVF_FLAT_SERIALIZE(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SERIALIZE
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
new file mode 100644
index 0000000000..5f312dc0a4
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \
+  void serialize_file(raft::resources const& handle,                                               \
+                      const std::string& filename,                                                 \
+                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \
+  }                                                                                                \
+                                                                                                   \
+  void deserialize_file(raft::resources const& handle,                                             \
+                        const std::string& filename,                                               \
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \
+      handle, filename, index->get_raft_index());                                                  \
+  }                                                                                                \
+                                                                                                   \
+  void serialize(raft::resources const& handle,                                                    \
+                 std::string& str,                                                                 \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \
+  }                                                                                                \
+                                                                                                   \
+  void deserialize(raft::resources const& handle,                                                  \
+                   const std::string& str,                                                         \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \
+  {                                                                                                \
+    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \
+  }
+CUVS_INST_IVF_FLAT_SERIALIZE(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SERIALIZE
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
new file mode 100644
index 0000000000..b9488ec366
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -0,0 +1,246 @@
+
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/ivf_flat.h>
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+namespace {
+
+template <typename T, typename IdxT>
+void* _build(cuvsResources_t res, ivfFlatIndexParams params, DLManagedTensor* dataset_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto build_params              = cuvs::neighbors::ivf_flat::index_params();
+  build_params.metric            = static_cast<cuvs::distance::DistanceType>((int)params.metric),
+  build_params.metric_arg        = params.metric_arg;
+  build_params.add_data_on_build = params.add_data_on_build;
+  build_params.n_lists           = params.n_lists;
+  build_params.kmeans_n_iters    = params.kmeans_n_iters;
+  build_params.kmeans_trainset_fraction       = params.kmeans_trainset_fraction;
+  build_params.adaptive_centers               = params.adaptive_centers;
+  build_params.conservative_memory_allocation = params.conservative_memory_allocation;
+
+  auto dataset = dataset_tensor->dl_tensor;
+  auto dim     = dataset.shape[0];
+
+  auto index = new cuvs::neighbors::ivf_flat::index<T, IdxT>(*res_ptr, build_params, dim);
+
+  using mdspan_type = raft::device_matrix_view<T const, IdxT, raft::row_major>;
+  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+
+  cuvs::neighbors::ivf_flat::build(*res_ptr, build_params, mds, *index);
+
+  return index;
+}
+
+template <typename T, typename IdxT>
+void _search(cuvsResources_t res,
+             ivfFlatSearchParams params,
+             ivfFlatIndex index,
+             DLManagedTensor* queries_tensor,
+             DLManagedTensor* neighbors_tensor,
+             DLManagedTensor* distances_tensor)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
+
+  auto search_params     = cuvs::neighbors::ivf_flat::search_params();
+  search_params.n_probes = params.n_probes;
+
+  using queries_mdspan_type   = raft::device_matrix_view<T const, IdxT, raft::row_major>;
+  using neighbors_mdspan_type = raft::device_matrix_view<IdxT, IdxT, raft::row_major>;
+  using distances_mdspan_type = raft::device_matrix_view<float, IdxT, raft::row_major>;
+  auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  cuvs::neighbors::ivf_flat::search(
+    *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+}
+
+}  // namespace
+
+extern "C" cuvsError_t ivfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
+{
+  try {
+    *index = new ivfFlatIndex{};
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index_c_ptr)
+{
+  try {
+    auto index = *index_c_ptr;
+
+    if (index.dtype.code == kDLFloat) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index.addr);
+      delete index_ptr;
+    } else if (index.dtype.code == kDLInt) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index.addr);
+      delete index_ptr;
+    } else if (index.dtype.code == kDLUInt) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>*>(index.addr);
+      delete index_ptr;
+    }
+    delete index_c_ptr;
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t ivfFlatBuild(cuvsResources_t res,
+                                    cuvsIvfFlatIndexParams_t params,
+                                    DLManagedTensor* dataset_tensor,
+                                    cuvsIvfFlatIndex_t index)
+{
+  try {
+    auto dataset = dataset_tensor->dl_tensor;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<float, int64_t>(res, *params, dataset_tensor));
+      index->dtype.code = kDLFloat;
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<int8_t, int64_t>(res, *params, dataset_tensor));
+      index->dtype.code = kDLInt;
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<uint8_t, int64_t>(res, *params, dataset_tensor));
+      index->dtype.code = kDLUInt;
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t ivfFlatSearch(cuvsResources_t res,
+                                     cuvsIvfFlatSearchParams_t params,
+                                     cuvsIvfFlatIndex_t index_c_ptr,
+                                     DLManagedTensor* queries_tensor,
+                                     DLManagedTensor* neighbors_tensor,
+                                     DLManagedTensor* distances_tensor)
+{
+  try {
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(queries),
+                 "queries should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(neighbors),
+                 "neighbors should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(distances),
+                 "distances should have device compatible memory");
+
+    RAFT_EXPECTS(neighbors.dtype.code == kDLInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    auto index = *index_c_ptr;
+    RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
+
+    if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
+      _search<float, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
+      _search<int8_t, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
+      _search<uint8_t, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
+                queries.dtype.code,
+                queries.dtype.bits);
+    }
+
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t* params)
+{
+  try {
+    *params = new ivfFlatIndexParams{.metric                         = L2Expanded,
+                                     .metric_arg                     = 2.0f,
+                                     .add_data_on_build              = true,
+                                     .n_lists                        = 1024,
+                                     .kmeans_n_iters                 = 20,
+                                     .kmeans_trainset_fraction       = 0.5,
+                                     .adaptive_centers               = false,
+                                     .conservative_memory_allocation = false};
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t params)
+{
+  try {
+    delete params;
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t* params)
+{
+  try {
+    *params = new ivfFlatSearchParams{.n_probes = 20};
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
+{
+  try {
+    delete params;
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
new file mode 100644
index 0000000000..678bec32a5
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -0,0 +1,163 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+
+template <typename T, typename IdxT>
+index<T, IdxT>::index(raft::resources const& res, const index_params& params, uint32_t dim)
+  : ann::index(),
+    raft_index_(std::make_unique<raft::neighbors::ivf_flat::index<T, IdxT>>(
+      res,
+      static_cast<raft::distance::DistanceType>((int)params.metric),
+      params.n_lists,
+      params.adaptive_centers,
+      params.conservative_memory_allocation,
+      dim))
+{
+}
+
+template <typename T, typename IdxT>
+index<T, IdxT>::index(raft::neighbors::ivf_flat::index<T, IdxT>&& raft_idx)
+  : ann::index(),
+    raft_index_(std::make_unique<raft::neighbors::ivf_flat::index<T, IdxT>>(std::move(raft_idx)))
+{
+}
+
+template <typename T, typename IdxT>
+uint32_t index<T, IdxT>::veclen() const noexcept
+{
+  return raft_index_->veclen();
+}
+
+template <typename T, typename IdxT>
+cuvs::distance::DistanceType index<T, IdxT>::metric() const noexcept
+{
+  return static_cast<cuvs::distance::DistanceType>((int)raft_index_->metric());
+}
+
+template <typename T, typename IdxT>
+bool index<T, IdxT>::adaptive_centers() const noexcept
+{
+  return raft_index_->adaptive_centers();
+}
+
+template <typename T, typename IdxT>
+raft::device_vector_view<uint32_t, uint32_t> index<T, IdxT>::list_sizes() noexcept
+{
+  return raft_index_->list_sizes();
+}
+
+template <typename T, typename IdxT>
+raft::device_vector_view<const uint32_t, uint32_t> index<T, IdxT>::list_sizes() const noexcept
+{
+  return raft_index_->list_sizes();
+}
+
+template <typename T, typename IdxT>
+raft::device_matrix_view<float, uint32_t, raft::row_major> index<T, IdxT>::centers() noexcept
+{
+  return raft_index_->centers();
+}
+
+template <typename T, typename IdxT>
+raft::device_matrix_view<const float, uint32_t, raft::row_major> index<T, IdxT>::centers()
+  const noexcept
+{
+  return raft_index_->centers();
+}
+
+template <typename T, typename IdxT>
+std::optional<raft::device_vector_view<float, uint32_t>> index<T, IdxT>::center_norms() noexcept
+{
+  return raft_index_->center_norms();
+}
+
+template <typename T, typename IdxT>
+std::optional<raft::device_vector_view<const float, uint32_t>> index<T, IdxT>::center_norms()
+  const noexcept
+{
+  return raft_index_->center_norms();
+}
+
+template <typename T, typename IdxT>
+IdxT index<T, IdxT>::size() const noexcept
+{
+  return raft_index_->size();
+}
+
+template <typename T, typename IdxT>
+uint32_t index<T, IdxT>::dim() const noexcept
+{
+  return raft_index_->dim();
+}
+
+template <typename T, typename IdxT>
+uint32_t index<T, IdxT>::n_lists() const noexcept
+{
+  return raft_index_->n_lists();
+}
+
+template <typename T, typename IdxT>
+raft::device_vector_view<T*, uint32_t> index<T, IdxT>::data_ptrs() noexcept
+{
+  return raft_index_->data_ptrs();
+}
+
+template <typename T, typename IdxT>
+raft::device_vector_view<T* const, uint32_t> index<T, IdxT>::data_ptrs() const noexcept
+{
+  return raft_index_->data_ptrs();
+}
+
+template <typename T, typename IdxT>
+raft::device_vector_view<IdxT*, uint32_t> index<T, IdxT>::inds_ptrs() noexcept
+{
+  return raft_index_->inds_ptrs();
+}
+
+template <typename T, typename IdxT>
+raft::device_vector_view<IdxT* const, uint32_t> index<T, IdxT>::inds_ptrs() const noexcept
+{
+  return raft_index_->inds_ptrs();
+}
+
+template <typename T, typename IdxT>
+bool index<T, IdxT>::conservative_memory_allocation() const noexcept
+{
+  return raft_index_->conservative_memory_allocation();
+}
+
+template <typename T, typename IdxT>
+std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>&
+index<T, IdxT>::lists() noexcept
+{
+  return raft_index_->lists();
+}
+
+template <typename T, typename IdxT>
+const std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>&
+index<T, IdxT>::lists() const noexcept
+{
+  return raft_index_->lists();
+}
+
+template struct index<float, int64_t>;
+template struct index<int8_t, int64_t>;
+template struct index<uint8_t, int64_t>;
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_pq/generate_ivf_pq.py b/cpp/src/neighbors/ivf_pq/generate_ivf_pq.py
new file mode 100644
index 0000000000..d0a5d2b192
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/generate_ivf_pq.py
@@ -0,0 +1,138 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+"""
+
+footer = """
+}  // namespace cuvs::neighbors::ivf_pq
+"""
+
+types = dict(
+    float_int64_t=("float", "int64_t"),
+    int8_t_int64_t=("int8_t", "int64_t"),
+    uint8_t_int64_t=("uint8_t", "int64_t"),
+)
+
+build_macro = """
+#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \\
+  auto build(raft::resources const& handle,                                                  \\
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \\
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \\
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \\
+  {                                                                                          \\
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \\
+      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \\
+  }                                                                                          \\
+                                                                                             \\
+  void build(raft::resources const& handle,                                                  \\
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \\
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \\
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \\
+  {                                                                                          \\
+    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \\
+  }
+"""
+
+extend_macro = """
+#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \\
+  auto extend(raft::resources const& handle,                                         \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \\
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \\
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \\
+  {                                                                                  \\
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \\
+      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \\
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \\
+  }                                                                                  \\
+                                                                                     \\
+  void extend(raft::resources const& handle,                                         \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \\
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \\
+  {                                                                                  \\
+    raft::runtime::neighbors::ivf_pq::extend(                                        \\
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \\
+  }             
+"""
+
+search_macro = """
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \\
+  void search(raft::resources const& handle,                                    \\
+              const cuvs::neighbors::ivf_pq::search_params& params,             \\
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \\
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \\
+  {                                                                             \\
+    raft::runtime::neighbors::ivf_pq::search(                                   \\
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \\
+  }
+"""
+
+macros = dict(
+    build=dict(
+        definition=build_macro,
+        name="CUVS_INST_IVF_PQ_BUILD",
+    ),
+    extend=dict(
+        definition=extend_macro,
+        name="CUVS_INST_IVF_PQ_EXTEND",
+    ),
+    search=dict(
+        definition=search_macro,
+        name="CUVS_INST_IVF_PQ_SEARCH",
+    ),
+)
+
+for type_path, (T, IdxT) in types.items():
+    for macro_path, macro in macros.items():
+        path = f"ivf_pq_{macro_path}_{type_path}.cpp"
+        with open(path, "w") as f:
+            f.write(header)
+            f.write(macro["definition"])
+            f.write(f"{macro['name']}({T}, {IdxT});\n\n")
+            f.write(f"#undef {macro['name']}\n")
+            f.write(footer)
+
+        print(f"src/neighbors/ivf_pq/{path}")
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
new file mode 100644
index 0000000000..78c4a0f67d
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \
+  auto build(raft::resources const& handle,                                                  \
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \
+  {                                                                                          \
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \
+      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \
+  }                                                                                          \
+                                                                                             \
+  void build(raft::resources const& handle,                                                  \
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \
+  {                                                                                          \
+    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \
+  }
+CUVS_INST_IVF_PQ_BUILD(float, int64_t);
+
+#undef CUVS_INST_IVF_PQ_BUILD
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
new file mode 100644
index 0000000000..c9d7fc4c9c
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \
+  auto build(raft::resources const& handle,                                                  \
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \
+  {                                                                                          \
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \
+      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \
+  }                                                                                          \
+                                                                                             \
+  void build(raft::resources const& handle,                                                  \
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \
+  {                                                                                          \
+    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \
+  }
+CUVS_INST_IVF_PQ_BUILD(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_BUILD
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
new file mode 100644
index 0000000000..24e56592df
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \
+  auto build(raft::resources const& handle,                                                  \
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \
+  {                                                                                          \
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \
+      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \
+  }                                                                                          \
+                                                                                             \
+  void build(raft::resources const& handle,                                                  \
+             const cuvs::neighbors::ivf_pq::index_params& params,                            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \
+  {                                                                                          \
+    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \
+  }
+CUVS_INST_IVF_PQ_BUILD(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_BUILD
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
new file mode 100644
index 0000000000..ec189ca9a4
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \
+  auto extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \
+      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
+  }                                                                                  \
+                                                                                     \
+  void extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \
+  {                                                                                  \
+    raft::runtime::neighbors::ivf_pq::extend(                                        \
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \
+  }
+CUVS_INST_IVF_PQ_EXTEND(float, int64_t);
+
+#undef CUVS_INST_IVF_PQ_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
new file mode 100644
index 0000000000..27eadec727
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \
+  auto extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \
+      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
+  }                                                                                  \
+                                                                                     \
+  void extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \
+  {                                                                                  \
+    raft::runtime::neighbors::ivf_pq::extend(                                        \
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \
+  }
+CUVS_INST_IVF_PQ_EXTEND(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
new file mode 100644
index 0000000000..072b30bb00
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \
+  auto extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \
+      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \
+        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
+  }                                                                                  \
+                                                                                     \
+  void extend(raft::resources const& handle,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \
+  {                                                                                  \
+    raft::runtime::neighbors::ivf_pq::extend(                                        \
+      handle, new_vectors, new_indices, idx->get_raft_index());                      \
+  }
+CUVS_INST_IVF_PQ_EXTEND(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp
new file mode 100644
index 0000000000..69db44d9be
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_pq::search_params& params,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+  {                                                                             \
+    raft::runtime::neighbors::ivf_pq::search(                                   \
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+  }
+CUVS_INST_IVF_PQ_SEARCH(float, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp
new file mode 100644
index 0000000000..e2e96b9a78
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_pq::search_params& params,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+  {                                                                             \
+    raft::runtime::neighbors::ivf_pq::search(                                   \
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+  }
+CUVS_INST_IVF_PQ_SEARCH(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp
new file mode 100644
index 0000000000..18e7f46183
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_pq::search_params& params,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+  {                                                                             \
+    raft::runtime::neighbors::ivf_pq::search(                                   \
+      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+  }
+CUVS_INST_IVF_PQ_SEARCH(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
new file mode 100644
index 0000000000..27076cbc1e
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -0,0 +1,235 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace {
+
+template <typename IdxT>
+void* _build(cuvsResources_t res, ivfPqIndexParams params, DLManagedTensor* dataset_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto build_params              = cuvs::neighbors::ivf_pq::index_params();
+  build_params.metric            = static_cast<cuvs::distance::DistanceType>((int)params.metric),
+  build_params.metric_arg        = params.metric_arg;
+  build_params.add_data_on_build = params.add_data_on_build;
+  build_params.n_lists           = params.n_lists;
+  build_params.kmeans_n_iters    = params.kmeans_n_iters;
+  build_params.kmeans_trainset_fraction = params.kmeans_trainset_fraction;
+  build_params.pq_bits                  = params.pq_bits;
+  build_params.pq_dim                   = params.pq_dim;
+  build_params.codebook_kind =
+    static_cast<cuvs::neighbors::ivf_pq::codebook_gen>((int)params.codebook_kind);
+  build_params.force_random_rotation          = params.force_random_rotation;
+  build_params.conservative_memory_allocation = params.conservative_memory_allocation;
+
+  auto dataset = dataset_tensor->dl_tensor;
+  auto dim     = dataset.shape[0];
+
+  auto index = new cuvs::neighbors::ivf_pq::index<IdxT>(*res_ptr, build_params, dim);
+
+  using mdspan_type = raft::device_matrix_view<float const, IdxT, raft::row_major>;
+  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+
+  cuvs::neighbors::ivf_pq::build(*res_ptr, build_params, mds, index);
+
+  return index;
+}
+
+template <typename IdxT>
+void _search(cuvsResources_t res,
+             ivfPqSearchParams params,
+             ivfPqIndex index,
+             DLManagedTensor* queries_tensor,
+             DLManagedTensor* neighbors_tensor,
+             DLManagedTensor* distances_tensor)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
+
+  auto search_params                     = cuvs::neighbors::ivf_pq::search_params();
+  search_params.n_probes                 = params.n_probes;
+  search_params.lut_dtype                = params.lut_dtype;
+  search_params.internal_distance_dtype  = params.internal_distance_dtype;
+  search_params.preferred_shmem_carveout = params.preferred_shmem_carveout;
+
+  using queries_mdspan_type   = raft::device_matrix_view<float const, IdxT, raft::row_major>;
+  using neighbors_mdspan_type = raft::device_matrix_view<IdxT, IdxT, raft::row_major>;
+  using distances_mdspan_type = raft::device_matrix_view<float, IdxT, raft::row_major>;
+  auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  cuvs::neighbors::ivf_pq::search(
+    *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+}
+
+}  // namespace
+
+extern "C" cuvsError_t ivfPqIndexCreate(cuvsIvfPqIndex_t* index)
+{
+  try {
+    *index = new ivfPqIndex{};
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t ivfPqIndexDestroy(cuvsIvfPqIndex_t index_c_ptr)
+{
+  try {
+    auto index = *index_c_ptr;
+
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index.addr);
+    delete index_ptr;
+    delete index_c_ptr;
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t ivfPqBuild(cuvsResources_t res,
+                                  cuvsIvfPqIndexParams_t params,
+                                  DLManagedTensor* dataset_tensor,
+                                  cuvsIvfPqIndex_t index)
+{
+  try {
+    auto dataset = dataset_tensor->dl_tensor;
+
+    if ((dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) ||
+        (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) ||
+        (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8)) {
+      index->addr = reinterpret_cast<uintptr_t>(_build<int64_t>(res, *params, dataset_tensor));
+      index->dtype.code = dataset.dtype.code;
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t ivfPqSearch(cuvsResources_t res,
+                                   cuvsIvfPqSearchParams_t params,
+                                   cuvsIvfPqIndex_t index_c_ptr,
+                                   DLManagedTensor* queries_tensor,
+                                   DLManagedTensor* neighbors_tensor,
+                                   DLManagedTensor* distances_tensor)
+{
+  try {
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(queries),
+                 "queries should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(neighbors),
+                 "neighbors should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(distances),
+                 "distances should have device compatible memory");
+
+    RAFT_EXPECTS(neighbors.dtype.code == kDLInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    auto index = *index_c_ptr;
+    RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
+
+    if ((queries.dtype.code == kDLFloat && queries.dtype.bits == 32) ||
+        (queries.dtype.code == kDLInt && queries.dtype.bits == 8) ||
+        (queries.dtype.code == kDLUInt && queries.dtype.bits == 8)) {
+      _search<int64_t>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
+                queries.dtype.code,
+                queries.dtype.bits);
+    }
+
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t* params)
+{
+  try {
+    *params = new ivfPqIndexParams{.metric                         = L2Expanded,
+                                   .metric_arg                     = 2.0f,
+                                   .add_data_on_build              = true,
+                                   .n_lists                        = 1024,
+                                   .kmeans_n_iters                 = 20,
+                                   .kmeans_trainset_fraction       = 0.5,
+                                   .pq_bits                        = 8,
+                                   .pq_dim                         = 0,
+                                   .codebook_kind                  = codebook_gen::PER_SUBSPACE,
+                                   .force_random_rotation          = false,
+                                   .conservative_memory_allocation = false};
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t params)
+{
+  try {
+    delete params;
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t* params)
+{
+  try {
+    *params = new ivfPqSearchParams{.n_probes                 = 20,
+                                    .lut_dtype                = CUDA_R_32F,
+                                    .internal_distance_dtype  = CUDA_R_32F,
+                                    .preferred_shmem_carveout = 1.0};
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
+{
+  try {
+    delete params;
+    return CUVS_SUCCESS;
+  } catch (...) {
+    return CUVS_ERROR;
+  }
+}
diff --git a/cpp/src/neighbors/ivf_pq_index.cpp b/cpp/src/neighbors/ivf_pq_index.cpp
new file mode 100644
index 0000000000..b464da6706
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq_index.cpp
@@ -0,0 +1,237 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+template <typename IdxT>
+index<IdxT>::index(raft::resources const& handle, const index_params& params, uint32_t dim)
+  : ann::index(),
+    raft_index_(std::make_unique<raft::neighbors::ivf_pq::index<IdxT>>(
+      handle,
+      static_cast<raft::distance::DistanceType>((int)params.metric),
+      static_cast<raft::neighbors::ivf_pq::codebook_gen>((int)params.codebook_kind),
+      params.n_lists,
+      dim,
+      params.pq_bits,
+      params.pq_dim,
+      params.conservative_memory_allocation))
+{
+}
+
+template <typename IdxT>
+index<IdxT>::index(raft::neighbors::ivf_pq::index<IdxT>&& raft_idx)
+  : ann::index(),
+    raft_index_(std::make_unique<raft::neighbors::ivf_pq::index<IdxT>>(std::move(raft_idx)))
+{
+}
+
+template <typename IdxT>
+IdxT index<IdxT>::size() const noexcept
+{
+  return raft_index_->size();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::dim() const noexcept
+{
+  return raft_index_->dim();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::dim_ext() const noexcept
+{
+  return raft_index_->dim_ext();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::rot_dim() const noexcept
+{
+  return raft_index_->rot_dim();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::pq_bits() const noexcept
+{
+  return raft_index_->pq_bits();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::pq_dim() const noexcept
+{
+  return raft_index_->pq_dim();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::pq_len() const noexcept
+{
+  return raft_index_->pq_len();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::pq_book_size() const noexcept
+{
+  return raft_index_->pq_book_size();
+}
+
+template <typename IdxT>
+cuvs::distance::DistanceType index<IdxT>::metric() const noexcept
+{
+  return static_cast<cuvs::distance::DistanceType>((int)raft_index_->metric());
+}
+
+template <typename IdxT>
+codebook_gen index<IdxT>::codebook_kind() const noexcept
+{
+  return static_cast<codebook_gen>((int)raft_index_->codebook_kind());
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::n_lists() const noexcept
+{
+  return raft_index_->n_lists();
+}
+
+template <typename IdxT>
+bool index<IdxT>::conservative_memory_allocation() const noexcept
+{
+  return raft_index_->conservative_memory_allocation();
+}
+
+template <typename IdxT>
+raft::
+  mdspan<float, typename cuvs::neighbors::ivf_pq::index<IdxT>::pq_centers_extents, raft::row_major>
+  index<IdxT>::pq_centers() noexcept
+{
+  return raft_index_->pq_centers();
+}
+
+template <typename IdxT>
+raft::mdspan<const float,
+             typename cuvs::neighbors::ivf_pq::index<IdxT>::pq_centers_extents,
+             raft::row_major>
+index<IdxT>::pq_centers() const noexcept
+{
+  return raft_index_->pq_centers();
+}
+
+template <typename IdxT>
+std::vector<std::shared_ptr<list_data<IdxT>>>& index<IdxT>::lists() noexcept
+{
+  return raft_index_->lists();
+}
+
+template <typename IdxT>
+const std::vector<std::shared_ptr<list_data<IdxT>>>& index<IdxT>::lists() const noexcept
+{
+  return raft_index_->lists();
+}
+
+template <typename IdxT>
+raft::device_vector_view<uint8_t*, uint32_t, raft::row_major> index<IdxT>::data_ptrs() noexcept
+{
+  return raft_index_->data_ptrs();
+}
+
+template <typename IdxT>
+raft::device_vector_view<const uint8_t* const, uint32_t, raft::row_major> index<IdxT>::data_ptrs()
+  const noexcept
+{
+  return raft_index_->data_ptrs();
+}
+
+template <typename IdxT>
+raft::device_vector_view<IdxT*, uint32_t, raft::row_major> index<IdxT>::inds_ptrs() noexcept
+{
+  return raft_index_->inds_ptrs();
+}
+
+template <typename IdxT>
+raft::device_vector_view<const IdxT* const, uint32_t, raft::row_major> index<IdxT>::inds_ptrs()
+  const noexcept
+{
+  return raft_index_->inds_ptrs();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<float, uint32_t, raft::row_major> index<IdxT>::rotation_matrix() noexcept
+{
+  return raft_index_->rotation_matrix();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<const float, uint32_t, raft::row_major> index<IdxT>::rotation_matrix()
+  const noexcept
+{
+  return raft_index_->rotation_matrix();
+}
+
+template <typename IdxT>
+raft::host_vector_view<IdxT, uint32_t, raft::row_major> index<IdxT>::accum_sorted_sizes() noexcept
+{
+  return raft_index_->accum_sorted_sizes();
+}
+
+template <typename IdxT>
+raft::host_vector_view<const IdxT, uint32_t, raft::row_major> index<IdxT>::accum_sorted_sizes()
+  const noexcept
+{
+  return raft_index_->accum_sorted_sizes();
+}
+
+template <typename IdxT>
+raft::device_vector_view<uint32_t, uint32_t, raft::row_major> index<IdxT>::list_sizes() noexcept
+{
+  return raft_index_->list_sizes();
+}
+
+template <typename IdxT>
+raft::device_vector_view<const uint32_t, uint32_t, raft::row_major> index<IdxT>::list_sizes()
+  const noexcept
+{
+  return raft_index_->list_sizes();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<float, uint32_t, raft::row_major> index<IdxT>::centers() noexcept
+{
+  return raft_index_->centers();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<const float, uint32_t, raft::row_major> index<IdxT>::centers()
+  const noexcept
+{
+  return raft_index_->centers();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<float, uint32_t, raft::row_major> index<IdxT>::centers_rot() noexcept
+{
+  return raft_index_->centers_rot();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<const float, uint32_t, raft::row_major> index<IdxT>::centers_rot()
+  const noexcept
+{
+  return raft_index_->centers_rot();
+}
+
+template struct index<int64_t>;
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq_serialize.cpp b/cpp/src/neighbors/ivf_pq_serialize.cpp
new file mode 100644
index 0000000000..4bdd3b04bc
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq_serialize.cpp
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SERIALIZE(IdxT)                                                      \
+  void serialize(raft::resources const& handle,                                               \
+                 std::string& filename,                                                       \
+                 const cuvs::neighbors::ivf_pq::index<IdxT>& index)                           \
+  {                                                                                           \
+    raft::runtime::neighbors::ivf_pq::serialize(handle, filename, *index.get_raft_index());   \
+  }                                                                                           \
+  void deserialize(raft::resources const& handle,                                             \
+                   const std::string& filename,                                               \
+                   cuvs::neighbors::ivf_pq::index<IdxT>* index)                               \
+  {                                                                                           \
+    raft::runtime::neighbors::ivf_pq::deserialize(handle, filename, index->get_raft_index()); \
+  }
+
+CUVS_INST_IVF_PQ_SERIALIZE(int64_t);
+
+#undef CUVS_INST_IVF_PQ_SERIALIZE
+
+}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index f33c141790..841058f60f 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -88,6 +88,34 @@ endfunction()
 # ##################################################################################################
 
 if(BUILD_TESTS)
+  ConfigureTest(NAME NEIGHBORS_TEST PATH test/neighbors/brute_force.cu GPUS 1 PERCENT 100)
+
+  ConfigureTest(
+    NAME
+    NEIGHBORS_ANN_IVF_FLAT_TEST
+    PATH
+    test/neighbors/ann_ivf_flat/test_float_int64_t.cu
+    test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
+    test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
+    GPUS
+    1
+    PERCENT
+    100
+  )
+
+  ConfigureTest(
+    NAME
+    NEIGHBORS_ANN_IVF_PQ_TEST
+    PATH
+    test/neighbors/ann_ivf_pq/test_float_int64_t.cu
+    test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+    test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+    GPUS
+    1
+    PERCENT
+    100
+  )
+
   ConfigureTest(
     NAME
     NEIGHBORS_ANN_CAGRA_TEST
@@ -105,6 +133,32 @@ endif()
 if(BUILD_C_TESTS)
   ConfigureTest(NAME INTEROP_TEST PATH test/core/interop.cu C_LIB)
 
+  ConfigureTest(
+    NAME
+    BRUTEFORCE_C_TEST
+    PATH
+    test/neighbors/run_brute_force_c.c
+    test/neighbors/brute_force_c.cu
+    C_LIB
+  )
+
+  ConfigureTest(
+    NAME
+    IVF_FLAT_C_TEST
+    PATH
+    test/neighbors/run_ivf_flat_c.c
+    test/neighbors/ann_ivf_flat_c.cu
+    C_LIB
+  )
+
+  ConfigureTest(
+    NAME
+    IVF_PQ_C_TEST
+    PATH
+    test/neighbors/run_ivf_pq_c.c
+    test/neighbors/ann_ivf_pq_c.cu C_LIB
+  )
+
   ConfigureTest(NAME CAGRA_C_TEST PATH test/neighbors/ann_cagra_c.cu C_LIB)
 endif()
 
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
new file mode 100644
index 0000000000..90b70ba112
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -0,0 +1,643 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+#include "naive_knn.cuh"
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft/stats/mean.cuh>
+#include <thrust/sequence.h>
+
+#include <raft/spatial/knn/ann.cuh>
+
+namespace cuvs::neighbors::ivf_flat {
+
+struct test_ivf_sample_filter {
+  static constexpr unsigned offset = 300;
+};
+
+template <typename IdxT>
+struct AnnIvfFlatInputs {
+  IdxT num_queries;
+  IdxT num_db_vecs;
+  IdxT dim;
+  IdxT k;
+  IdxT nprobe;
+  IdxT nlist;
+  cuvs::distance::DistanceType metric;
+  bool adaptive_centers;
+  // bool host_dataset;
+};
+
+template <typename IdxT>
+::std::ostream& operator<<(::std::ostream& os, const AnnIvfFlatInputs<IdxT>& p)
+{
+  os << "{ " << p.num_queries << ", " << p.num_db_vecs << ", " << p.dim << ", " << p.k << ", "
+     << p.nprobe << ", " << p.nlist << ", " << static_cast<int>(p.metric) << ", "
+     << p.adaptive_centers << '}' << std::endl;
+  return os;
+}
+
+template <typename T, typename DataT, typename IdxT>
+class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
+ public:
+  AnnIVFFlatTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnIvfFlatInputs<IdxT>>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+  void testIVFFlat()
+  {
+    size_t queries_size = ps.num_queries * ps.k;
+    std::vector<IdxT> indices_ivfflat(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<T> distances_ivfflat(queries_size);
+    std::vector<T> distances_naive(queries_size);
+
+    {
+      rmm::device_uvector<T> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+      cuvs::neighbors::naive_knn<T, DataT, IdxT>(handle_,
+                                                 distances_naive_dev.data(),
+                                                 indices_naive_dev.data(),
+                                                 search_queries.data(),
+                                                 database.data(),
+                                                 ps.num_queries,
+                                                 ps.num_db_vecs,
+                                                 ps.dim,
+                                                 ps.k,
+                                                 ps.metric);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    {
+      // unless something is really wrong with clustering, this could serve as a lower bound on
+      // recall
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+
+      rmm::device_uvector<T> distances_ivfflat_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_ivfflat_dev(queries_size, stream_);
+
+      {
+        // legacy interface
+        raft::spatial::knn::IVFFlatParam ivfParams;
+        ivfParams.nprobe = ps.nprobe;
+        ivfParams.nlist  = ps.nlist;
+        raft::spatial::knn::knnIndex index;
+
+        raft::spatial::knn::approx_knn_build_index(
+          handle_,
+          &index,
+          dynamic_cast<raft::spatial::knn::knnIndexParam*>(&ivfParams),
+          static_cast<raft::distance::DistanceType>((int)ps.metric),
+          (IdxT)0,
+          database.data(),
+          ps.num_db_vecs,
+          ps.dim);
+
+        raft::resource::sync_stream(handle_);
+        raft::spatial::knn::approx_knn_search(handle_,
+                                              distances_ivfflat_dev.data(),
+                                              indices_ivfflat_dev.data(),
+                                              &index,
+                                              ps.k,
+                                              search_queries.data(),
+                                              ps.num_queries);
+
+        raft::update_host(
+          distances_ivfflat.data(), distances_ivfflat_dev.data(), queries_size, stream_);
+        raft::update_host(
+          indices_ivfflat.data(), indices_ivfflat_dev.data(), queries_size, stream_);
+        raft::resource::sync_stream(handle_);
+      }
+
+      ASSERT_TRUE(eval_neighbours(indices_naive,
+                                  indices_ivfflat,
+                                  distances_naive,
+                                  distances_ivfflat,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+      {
+        cuvs::neighbors::ivf_flat::index_params index_params;
+        cuvs::neighbors::ivf_flat::search_params search_params;
+        index_params.n_lists          = ps.nlist;
+        index_params.metric           = ps.metric;
+        index_params.adaptive_centers = ps.adaptive_centers;
+        search_params.n_probes        = ps.nprobe;
+
+        index_params.add_data_on_build        = false;
+        index_params.kmeans_trainset_fraction = 0.5;
+        index_params.metric_arg               = 0;
+
+        cuvs::neighbors::ivf_flat::index<DataT, IdxT> idx(handle_, index_params, ps.dim);
+        cuvs::neighbors::ivf_flat::index<DataT, IdxT> index_2(handle_, index_params, ps.dim);
+
+        // if (!ps.host_dataset) {
+
+        auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
+          (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
+        idx = cuvs::neighbors::ivf_flat::build(handle_, index_params, database_view);
+        rmm::device_uvector<IdxT> vector_indices(ps.num_db_vecs, stream_);
+        thrust::sequence(raft::resource::get_thrust_policy(handle_),
+                         thrust::device_pointer_cast(vector_indices.data()),
+                         thrust::device_pointer_cast(vector_indices.data() + ps.num_db_vecs));
+        raft::resource::sync_stream(handle_);
+
+        IdxT half_of_data = ps.num_db_vecs / 2;
+
+        auto half_of_data_view = raft::make_device_matrix_view<const DataT, IdxT>(
+          (const DataT*)database.data(), half_of_data, ps.dim);
+
+        const std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
+        index_2 = cuvs::neighbors::ivf_flat::extend(handle_, half_of_data_view, no_opt, idx);
+
+        auto new_half_of_data_view = raft::make_device_matrix_view<const DataT, IdxT>(
+          database.data() + half_of_data * ps.dim, IdxT(ps.num_db_vecs) - half_of_data, ps.dim);
+
+        auto new_half_of_data_indices_view = raft::make_device_vector_view<const IdxT, IdxT>(
+          vector_indices.data() + half_of_data, IdxT(ps.num_db_vecs) - half_of_data);
+
+        cuvs::neighbors::ivf_flat::extend(
+          handle_,
+          new_half_of_data_view,
+          std::make_optional<raft::device_vector_view<const IdxT, IdxT>>(
+            new_half_of_data_indices_view),
+          &index_2);
+
+        /*
+        } else {
+          auto host_database = raft::make_host_matrix<DataT, IdxT>(ps.num_db_vecs, ps.dim);
+          raft::copy(
+            host_database.data_handle(), database.data(), ps.num_db_vecs * ps.dim, stream_);
+          idx = ivf_flat::build(handle_, index_params,
+        raft::make_const_mdspan(host_database.view()));
+
+          auto vector_indices = raft::make_host_vector<IdxT>(handle_, ps.num_db_vecs);
+          std::iota(vector_indices.data_handle(), vector_indices.data_handle() + ps.num_db_vecs, 0);
+
+          IdxT half_of_data = ps.num_db_vecs / 2;
+
+          auto half_of_data_view = raft::make_host_matrix_view<const DataT, IdxT>(
+            (const DataT*)host_database.data_handle(), half_of_data, ps.dim);
+
+          const std::optional<raft::host_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
+          index_2 = ivf_flat::extend(handle_, half_of_data_view, no_opt, idx);
+
+          auto new_half_of_data_view = raft::make_host_matrix_view<const DataT, IdxT>(
+            host_database.data_handle() + half_of_data * ps.dim,
+            IdxT(ps.num_db_vecs) - half_of_data,
+            ps.dim);
+          auto new_half_of_data_indices_view = raft::make_host_vector_view<const IdxT, IdxT>(
+            vector_indices.data_handle() + half_of_data, IdxT(ps.num_db_vecs) - half_of_data);
+          ivf_flat::extend(handle_,
+                           new_half_of_data_view,
+                           std::make_optional<raft::host_vector_view<const IdxT, IdxT>>(
+                             new_half_of_data_indices_view),
+                           &index_2);
+        }
+        */
+
+        auto search_queries_view = raft::make_device_matrix_view<const DataT, IdxT>(
+          search_queries.data(), ps.num_queries, ps.dim);
+        auto indices_out_view = raft::make_device_matrix_view<IdxT, IdxT>(
+          indices_ivfflat_dev.data(), ps.num_queries, ps.k);
+        auto dists_out_view = raft::make_device_matrix_view<T, IdxT>(
+          distances_ivfflat_dev.data(), ps.num_queries, ps.k);
+        const std::string filename = "ivf_flat_index";
+        cuvs::neighbors::ivf_flat::serialize_file(handle_, filename, index_2);
+        cuvs::neighbors::ivf_flat::index<DataT, IdxT> index_loaded(handle_, index_params, ps.dim);
+        cuvs::neighbors::ivf_flat::deserialize_file(handle_, filename, &index_loaded);
+        ASSERT_EQ(index_2.size(), index_loaded.size());
+
+        cuvs::neighbors::ivf_flat::search(handle_,
+                                          search_params,
+                                          index_loaded,
+                                          search_queries_view,
+                                          indices_out_view,
+                                          dists_out_view);
+
+        raft::update_host(
+          distances_ivfflat.data(), distances_ivfflat_dev.data(), queries_size, stream_);
+        raft::update_host(
+          indices_ivfflat.data(), indices_ivfflat_dev.data(), queries_size, stream_);
+        raft::resource::sync_stream(handle_);
+
+        // Test the centroid invariants
+        if (index_2.adaptive_centers()) {
+          // The centers must be up-to-date with the corresponding data
+          std::vector<uint32_t> list_sizes(index_2.n_lists());
+          std::vector<IdxT*> list_indices(index_2.n_lists());
+          rmm::device_uvector<float> centroid(ps.dim, stream_);
+          raft::copy(
+            list_sizes.data(), index_2.list_sizes().data_handle(), index_2.n_lists(), stream_);
+          raft::copy(
+            list_indices.data(), index_2.inds_ptrs().data_handle(), index_2.n_lists(), stream_);
+          raft::resource::sync_stream(handle_);
+          for (uint32_t l = 0; l < index_2.n_lists(); l++) {
+            if (list_sizes[l] == 0) continue;
+            rmm::device_uvector<float> cluster_data(list_sizes[l] * ps.dim, stream_);
+            raft::spatial::knn::detail::utils::copy_selected<float>((IdxT)list_sizes[l],
+                                                                    (IdxT)ps.dim,
+                                                                    database.data(),
+                                                                    list_indices[l],
+                                                                    (IdxT)ps.dim,
+                                                                    cluster_data.data(),
+                                                                    (IdxT)ps.dim,
+                                                                    stream_);
+            raft::stats::mean<float, uint32_t>(
+              centroid.data(), cluster_data.data(), ps.dim, list_sizes[l], false, true, stream_);
+            ASSERT_TRUE(cuvs::devArrMatch(index_2.centers().data_handle() + ps.dim * l,
+                                          centroid.data(),
+                                          ps.dim,
+                                          cuvs::CompareApprox<float>(0.001),
+                                          stream_));
+          }
+        } else {
+          // The centers must be immutable
+          ASSERT_TRUE(cuvs::devArrMatch(index_2.centers().data_handle(),
+                                        idx.centers().data_handle(),
+                                        index_2.centers().size(),
+                                        cuvs::Compare<float>(),
+                                        stream_));
+        }
+      }
+      ASSERT_TRUE(eval_neighbours(indices_naive,
+                                  indices_ivfflat,
+                                  distances_naive,
+                                  distances_ivfflat,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+    }
+  }
+
+  /*
+    void testPacker()
+    {
+      ivf_flat::index_params index_params;
+      ivf_flat::search_params search_params;
+      index_params.n_lists          = ps.nlist;
+      index_params.metric           = ps.metric;
+      index_params.adaptive_centers = false;
+      search_params.n_probes        = ps.nprobe;
+
+      index_params.add_data_on_build        = false;
+      index_params.kmeans_trainset_fraction = 1.0;
+      index_params.metric_arg               = 0;
+
+      auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
+        (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
+
+      auto idx = ivf_flat::build(handle_, index_params, database_view);
+
+      const std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
+      index<DataT, IdxT> extend_index = ivf_flat::extend(handle_, database_view, no_opt, idx);
+
+      auto list_sizes = raft::make_host_vector<uint32_t>(idx.n_lists());
+      raft::update_host(list_sizes.data_handle(),
+                  extend_index.list_sizes().data_handle(),
+                  extend_index.n_lists(),
+                  stream_);
+      raft::resource::sync_stream(handle_);
+
+      auto& lists = idx.lists();
+
+      // conservative memory allocation for codepacking
+      auto list_device_spec = list_spec<uint32_t, DataT, IdxT>{idx.dim(), false};
+
+      for (uint32_t label = 0; label < idx.n_lists(); label++) {
+        uint32_t list_size = list_sizes.data_handle()[label];
+
+        ivf::resize_list(handle_, lists[label], list_device_spec, list_size, 0);
+      }
+
+      idx.recompute_internal_state(handle_);
+
+      using interleaved_group = Pow2<kIndexGroupSize>;
+
+      for (uint32_t label = 0; label < idx.n_lists(); label++) {
+        uint32_t list_size = list_sizes.data_handle()[label];
+
+        if (list_size > 0) {
+          uint32_t padded_list_size = interleaved_group::roundUp(list_size);
+          uint32_t n_elems          = padded_list_size * idx.dim();
+          auto list_data            = lists[label]->data;
+          auto list_inds            = extend_index.lists()[label]->indices;
+
+          // fetch the flat codes
+          auto flat_codes = make_device_matrix<DataT, uint32_t>(handle_, list_size, idx.dim());
+
+          matrix::gather(
+            handle_,
+            make_device_matrix_view<const DataT, uint32_t>(
+              (const DataT*)database.data(), static_cast<uint32_t>(ps.num_db_vecs), idx.dim()),
+            make_device_vector_view<const IdxT, uint32_t>((const IdxT*)list_inds.data_handle(),
+                                                          list_size),
+            flat_codes.view());
+
+          helpers::codepacker::pack<DataT, IdxT>(
+            handle_, make_const_mdspan(flat_codes.view()), idx.veclen(), 0, list_data.view());
+
+          {
+            auto mask = make_device_vector<bool>(handle_, n_elems);
+
+            linalg::map_offset(handle_,
+                               mask.view(),
+                               [dim = idx.dim(),
+                                list_size,
+                                padded_list_size,
+                                chunk_size = util::FastIntDiv(idx.veclen())] __device__(auto i) {
+                                 uint32_t max_group_offset =
+    interleaved_group::roundDown(list_size); if (i < max_group_offset * dim) { return true; }
+                                 uint32_t surplus    = (i - max_group_offset * dim);
+                                 uint32_t ingroup_id = interleaved_group::mod(surplus / chunk_size);
+                                 return ingroup_id < (list_size - max_group_offset);
+                               });
+
+            // ensure that the correct number of indices are masked out
+            ASSERT_TRUE(thrust::reduce(raft::resource::get_thrust_policy(handle_),
+                                       mask.data_handle(),
+                                       mask.data_handle() + n_elems,
+                                       0) == list_size * ps.dim);
+
+            auto packed_list_data = make_device_vector<DataT, uint32_t>(handle_, n_elems);
+
+            linalg::map_offset(handle_,
+                               packed_list_data.view(),
+                               [mask      = mask.data_handle(),
+                                list_data = list_data.data_handle()] __device__(uint32_t i) {
+                                 if (mask[i]) return list_data[i];
+                                 return DataT{0};
+                               });
+
+            auto extend_data          = extend_index.lists()[label]->data;
+            auto extend_data_filtered = make_device_vector<DataT, uint32_t>(handle_, n_elems);
+            linalg::map_offset(handle_,
+                               extend_data_filtered.view(),
+                               [mask        = mask.data_handle(),
+                                extend_data = extend_data.data_handle()] __device__(uint32_t i) {
+                                 if (mask[i]) return extend_data[i];
+                                 return DataT{0};
+                               });
+
+            ASSERT_TRUE(cuvs::devArrMatch(packed_list_data.data_handle(),
+                                          extend_data_filtered.data_handle(),
+                                          n_elems,
+                                          cuvs::Compare<DataT>(),
+                                          stream_));
+          }
+
+          auto unpacked_flat_codes =
+            make_device_matrix<DataT, uint32_t>(handle_, list_size, idx.dim());
+
+          helpers::codepacker::unpack<DataT, IdxT>(
+            handle_, list_data.view(), idx.veclen(), 0, unpacked_flat_codes.view());
+
+          ASSERT_TRUE(cuvs::devArrMatch(flat_codes.data_handle(),
+                                        unpacked_flat_codes.data_handle(),
+                                        list_size * ps.dim,
+                                        cuvs::Compare<DataT>(),
+                                        stream_));
+        }
+      }
+    }
+  */
+
+  void testFilter()
+  {
+    size_t queries_size = ps.num_queries * ps.k;
+    std::vector<IdxT> indices_ivfflat(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<T> distances_ivfflat(queries_size);
+    std::vector<T> distances_naive(queries_size);
+
+    {
+      rmm::device_uvector<T> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+      auto* database_filtered_ptr = database.data() + test_ivf_sample_filter::offset * ps.dim;
+      cuvs::neighbors::naive_knn<T, DataT, IdxT>(handle_,
+                                                 distances_naive_dev.data(),
+                                                 indices_naive_dev.data(),
+                                                 search_queries.data(),
+                                                 database_filtered_ptr,
+                                                 ps.num_queries,
+                                                 ps.num_db_vecs - test_ivf_sample_filter::offset,
+                                                 ps.dim,
+                                                 ps.k,
+                                                 ps.metric);
+      raft::linalg::addScalar(indices_naive_dev.data(),
+                              indices_naive_dev.data(),
+                              IdxT(test_ivf_sample_filter::offset),
+                              queries_size,
+                              stream_);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    /*
+    {
+      // unless something is really wrong with clustering, this could serve as a lower bound on
+      // recall
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+
+      auto distances_ivfflat_dev = raft::make_device_matrix<T, IdxT>(handle_, ps.num_queries, ps.k);
+      auto indices_ivfflat_dev =
+        raft::make_device_matrix<IdxT, IdxT>(handle_, ps.num_queries, ps.k);
+
+      {
+        ivf_flat::index_params index_params;
+        ivf_flat::search_params search_params;
+        index_params.n_lists          = ps.nlist;
+        index_params.metric           = ps.metric;
+        index_params.adaptive_centers = ps.adaptive_centers;
+        search_params.n_probes        = ps.nprobe;
+
+        index_params.add_data_on_build        = true;
+        index_params.kmeans_trainset_fraction = 0.5;
+        index_params.metric_arg               = 0;
+
+        // Create IVF Flat index
+        auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
+          (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
+        auto index = ivf_flat::build(handle_, index_params, database_view);
+
+        // Create Bitset filter
+        auto removed_indices =
+          raft::make_device_vector<IdxT, int64_t>(handle_, test_ivf_sample_filter::offset);
+        thrust::sequence(raft::resource::get_thrust_policy(handle_),
+                         thrust::device_pointer_cast(removed_indices.data_handle()),
+                         thrust::device_pointer_cast(removed_indices.data_handle() +
+                                                     test_ivf_sample_filter::offset));
+        raft::resource::sync_stream(handle_);
+
+        raft::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
+          handle_, removed_indices.view(), ps.num_db_vecs);
+
+        // Search with the filter
+        auto search_queries_view = raft::make_device_matrix_view<const DataT, IdxT>(
+          search_queries.data(), ps.num_queries, ps.dim);
+        ivf_flat::search_with_filtering(
+          handle_,
+          search_params,
+          index,
+          search_queries_view,
+          indices_ivfflat_dev.view(),
+          distances_ivfflat_dev.view(),
+          raft::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
+
+        raft::update_host(
+          distances_ivfflat.data(), distances_ivfflat_dev.data_handle(), queries_size, stream_);
+        raft::update_host(
+          indices_ivfflat.data(), indices_ivfflat_dev.data_handle(), queries_size, stream_);
+        raft::resource::sync_stream(handle_);
+      }
+      ASSERT_TRUE(eval_neighbours(indices_naive,
+                                  indices_ivfflat,
+                                  distances_naive,
+                                  distances_ivfflat,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+    }
+    */
+  }
+
+  void SetUp() override
+  {
+    database.resize(ps.num_db_vecs * ps.dim, stream_);
+    search_queries.resize(ps.num_queries * ps.dim, stream_);
+
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::uniform(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(0.1), DataT(2.0));
+      raft::random::uniform(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(1), DataT(20));
+      raft::random::uniformInt(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(1), DataT(20));
+    }
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnIvfFlatInputs<IdxT> ps;
+  rmm::device_uvector<DataT> database;
+  rmm::device_uvector<DataT> search_queries;
+};
+
+const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
+  // test various dims (aligned and not aligned to vector sizes)
+  {1000, 10000, 1, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 10000, 2, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 3, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 10000, 4, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 5, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 8, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 5, 16, 40, 1024, cuvs::distance::DistanceType::L2SqrtExpanded, false},
+  {1000, 10000, 8, 16, 40, 1024, cuvs::distance::DistanceType::L2SqrtExpanded, true},
+
+  // test dims that do not fit into kernel shared memory limits
+  {1000, 10000, 2048, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 2049, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 2050, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 2052, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 2053, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 10000, 2056, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+
+  // various random combinations
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::L2Expanded, false},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+
+  /*
+  // host input data
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  */
+
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::InnerProduct, true},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {10000, 131072, 8, 10, 50, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+
+  {1000, 10000, 4096, 20, 50, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+
+  // test splitting the big query batches  (> max gridDim.y) into smaller batches
+  {100000, 1024, 32, 10, 64, 64, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000000, 1024, 32, 10, 256, 256, cuvs::distance::DistanceType::InnerProduct, false},
+  {98306, 1024, 32, 10, 64, 64, cuvs::distance::DistanceType::InnerProduct, true},
+
+  // test radix_sort for getting the cluster selection
+  {1000,
+   10000,
+   16,
+   10,
+   raft::matrix::detail::select::warpsort::kMaxCapacity * 2,
+   raft::matrix::detail::select::warpsort::kMaxCapacity * 4,
+   cuvs::distance::DistanceType::L2Expanded,
+   false},
+  {1000,
+   10000,
+   16,
+   10,
+   raft::matrix::detail::select::warpsort::kMaxCapacity * 4,
+   raft::matrix::detail::select::warpsort::kMaxCapacity * 4,
+   cuvs::distance::DistanceType::InnerProduct,
+   false},
+
+  // The following two test cases should show very similar recall.
+  // num_queries, num_db_vecs, dim, k, nprobe, nlist, metric, adaptive_centers
+  {20000, 8712, 3, 10, 51, 66, cuvs::distance::DistanceType::L2Expanded, false},
+  {100000, 8712, 3, 10, 51, 66, cuvs::distance::DistanceType::L2Expanded, false}};
+
+}  // namespace cuvs::neighbors::ivf_flat
\ No newline at end of file
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu b/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu
new file mode 100644
index 0000000000..0ce168f5ef
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_ivf_flat.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+typedef AnnIVFFlatTest<float, float, int64_t> AnnIVFFlatTestF_float;
+TEST_P(AnnIVFFlatTestF_float, AnnIVFFlat) { this->testIVFFlat(); }
+
+INSTANTIATE_TEST_CASE_P(AnnIVFFlatTest, AnnIVFFlatTestF_float, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
new file mode 100644
index 0000000000..15935fd889
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_ivf_flat.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+typedef AnnIVFFlatTest<float, int8_t, int64_t> AnnIVFFlatTestF_int8;
+TEST_P(AnnIVFFlatTestF_int8, AnnIVFFlat) { this->testIVFFlat(); }
+
+INSTANTIATE_TEST_CASE_P(AnnIVFFlatTest, AnnIVFFlatTestF_int8, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..42a8dab2e1
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_ivf_flat.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+typedef AnnIVFFlatTest<float, uint8_t, int64_t> AnnIVFFlatTestF_uint8;
+TEST_P(AnnIVFFlatTestF_uint8, AnnIVFFlat) { this->testIVFFlat(); }
+
+INSTANTIATE_TEST_CASE_P(AnnIVFFlatTest, AnnIVFFlatTestF_uint8, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/test/neighbors/ann_ivf_flat_c.cu b/cpp/test/neighbors/ann_ivf_flat_c.cu
new file mode 100644
index 0000000000..e854504948
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_flat_c.cu
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/random/rng.cuh>
+
+#include "ann_utils.cuh"
+#include <cuvs/neighbors/ivf_flat.h>
+
+extern "C" void run_ivf_flat(int64_t n_rows,
+                             int64_t n_queries,
+                             int64_t n_dim,
+                             uint32_t n_neighbors,
+                             float* index_data,
+                             float* query_data,
+                             float* distances_data,
+                             int64_t* neighbors_data,
+                             enum DistanceType metric,
+                             size_t n_probes,
+                             size_t n_lists);
+
+template <typename T>
+void generate_random_data(T* devPtr, size_t size)
+{
+  raft::handle_t handle;
+  raft::random::RngState r(1234ULL);
+  raft::random::uniform(handle, r, devPtr, size, T(0.1), T(2.0));
+};
+
+template <typename T, typename IdxT>
+void recall_eval(T* query_data,
+                 T* index_data,
+                 IdxT* neighbors,
+                 T* distances,
+                 size_t n_queries,
+                 size_t n_rows,
+                 size_t n_dim,
+                 size_t n_neighbors,
+                 DistanceType metric,
+                 size_t n_probes,
+                 size_t n_lists)
+{
+  raft::handle_t handle;
+  auto distances_ref = raft::make_device_matrix<T, IdxT>(handle, n_queries, n_neighbors);
+  auto neighbors_ref = raft::make_device_matrix<IdxT, IdxT>(handle, n_queries, n_neighbors);
+  cuvs::neighbors::naive_knn<T, T, IdxT>(
+    handle,
+    distances_ref.data_handle(),
+    neighbors_ref.data_handle(),
+    query_data,
+    index_data,
+    n_queries,
+    n_rows,
+    n_dim,
+    n_neighbors,
+    static_cast<cuvs::distance::DistanceType>((uint16_t)metric));
+
+  size_t size = n_queries * n_neighbors;
+  std::vector<IdxT> neighbors_h(size);
+  std::vector<T> distances_h(size);
+  std::vector<IdxT> neighbors_ref_h(size);
+  std::vector<T> distances_ref_h(size);
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+  raft::copy(neighbors_h.data(), neighbors, size, stream);
+  raft::copy(distances_h.data(), distances, size, stream);
+  raft::copy(neighbors_ref_h.data(), neighbors_ref.data_handle(), size, stream);
+  raft::copy(distances_ref_h.data(), distances_ref.data_handle(), size, stream);
+
+  // verify output
+  double min_recall = static_cast<double>(n_probes) / static_cast<double>(n_lists);
+  ASSERT_TRUE(cuvs::neighbors::eval_neighbours(neighbors_ref_h,
+                                               neighbors_h,
+                                               distances_ref_h,
+                                               distances_h,
+                                               n_queries,
+                                               n_neighbors,
+                                               0.001,
+                                               min_recall));
+};
+
+TEST(IvfFlatC, BuildSearch)
+{
+  int64_t n_rows       = 8096;
+  int64_t n_queries    = 128;
+  int64_t n_dim        = 32;
+  uint32_t n_neighbors = 8;
+
+  enum DistanceType metric = L2Expanded;
+  size_t n_probes          = 20;
+  size_t n_lists           = 1024;
+
+  float *index_data, *query_data, *distances_data;
+  int64_t* neighbors_data;
+  cudaMalloc(&index_data, sizeof(float) * n_rows * n_dim);
+  cudaMalloc(&query_data, sizeof(float) * n_queries * n_dim);
+  cudaMalloc(&neighbors_data, sizeof(int64_t) * n_queries * n_neighbors);
+  cudaMalloc(&distances_data, sizeof(float) * n_queries * n_neighbors);
+
+  generate_random_data(index_data, n_rows * n_dim);
+  generate_random_data(query_data, n_queries * n_dim);
+
+  run_ivf_flat(n_rows,
+               n_queries,
+               n_dim,
+               n_neighbors,
+               index_data,
+               query_data,
+               distances_data,
+               neighbors_data,
+               metric,
+               n_probes,
+               n_lists);
+
+  recall_eval(query_data,
+              index_data,
+              neighbors_data,
+              distances_data,
+              n_queries,
+              n_rows,
+              n_dim,
+              n_neighbors,
+              metric,
+              n_probes,
+              n_lists);
+
+  // delete device memory
+  cudaFree(index_data);
+  cudaFree(query_data);
+  cudaFree(neighbors_data);
+  cudaFree(distances_data);
+}
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
new file mode 100644
index 0000000000..5e276cbf1c
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -0,0 +1,981 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+#include "ivf_pq_helpers.cuh"
+#include "naive_knn.cuh"
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include <raft/neighbors/ivf_pq-inl.cuh>
+#include <raft/neighbors/sample_filter.cuh>
+#include <thrust/sequence.h>
+
+namespace cuvs::neighbors::ivf_pq {
+
+using namespace raft;
+
+struct test_ivf_sample_filter {
+  static constexpr unsigned offset = 300;
+};
+
+struct ivf_pq_inputs {
+  uint32_t num_db_vecs             = 4096;
+  uint32_t num_queries             = 1024;
+  uint32_t dim                     = 64;
+  uint32_t k                       = 32;
+  std::optional<double> min_recall = std::nullopt;
+
+  cuvs::neighbors::ivf_pq::index_params index_params;
+  cuvs::neighbors::ivf_pq::search_params search_params;
+
+  // Set some default parameters for tests
+  ivf_pq_inputs()
+  {
+    index_params.n_lists                  = max(32u, min(1024u, num_db_vecs / 128u));
+    index_params.kmeans_trainset_fraction = 1.0;
+  }
+};
+
+inline auto operator<<(std::ostream& os, const ivf_pq::codebook_gen& p) -> std::ostream&
+{
+  switch (p) {
+    case ivf_pq::codebook_gen::PER_CLUSTER: os << "codebook_gen::PER_CLUSTER"; break;
+    case ivf_pq::codebook_gen::PER_SUBSPACE: os << "codebook_gen::PER_SUBSPACE"; break;
+    default: RAFT_FAIL("unreachable code");
+  }
+  return os;
+}
+
+inline auto operator<<(std::ostream& os, const ivf_pq_inputs& p) -> std::ostream&
+{
+  ivf_pq_inputs dflt;
+  bool need_comma = false;
+#define PRINT_DIFF_V(spec, val)       \
+  do {                                \
+    if (dflt spec != p spec) {        \
+      if (need_comma) { os << ", "; } \
+      os << #spec << " = " << val;    \
+      need_comma = true;              \
+    }                                 \
+  } while (0)
+#define PRINT_DIFF(spec) PRINT_DIFF_V(spec, p spec)
+
+  os << "ivf_pq_inputs {";
+  PRINT_DIFF(.num_db_vecs);
+  PRINT_DIFF(.num_queries);
+  PRINT_DIFF(.dim);
+  PRINT_DIFF(.k);
+  PRINT_DIFF_V(.min_recall, p.min_recall.value_or(0));
+  PRINT_DIFF_V(.index_params.metric,
+               cuvs::neighbors::print_metric{
+                 static_cast<cuvs::distance::DistanceType>((int)p.index_params.metric)});
+  PRINT_DIFF(.index_params.metric_arg);
+  PRINT_DIFF(.index_params.add_data_on_build);
+  PRINT_DIFF(.index_params.n_lists);
+  PRINT_DIFF(.index_params.kmeans_n_iters);
+  PRINT_DIFF(.index_params.kmeans_trainset_fraction);
+  PRINT_DIFF(.index_params.pq_bits);
+  PRINT_DIFF(.index_params.pq_dim);
+  PRINT_DIFF(.index_params.codebook_kind);
+  PRINT_DIFF(.index_params.force_random_rotation);
+  PRINT_DIFF(.search_params.n_probes);
+  PRINT_DIFF_V(.search_params.lut_dtype, cuvs::neighbors::print_dtype{p.search_params.lut_dtype});
+  PRINT_DIFF_V(.search_params.internal_distance_dtype,
+               cuvs::neighbors::print_dtype{p.search_params.internal_distance_dtype});
+  os << "}";
+  return os;
+}
+
+template <typename T>
+void compare_vectors_l2(
+  const raft::resources& res, T a, T b, uint32_t label, double compression_ratio, double eps)
+{
+  auto n_rows = a.extent(0);
+  auto dim    = a.extent(1);
+  rmm::mr::managed_memory_resource managed_memory;
+  auto dist = make_device_mdarray<double>(res, &managed_memory, make_extents<uint32_t>(n_rows));
+  linalg::map_offset(res, dist.view(), [a, b, dim] __device__(uint32_t i) {
+    spatial::knn::detail::utils::mapping<float> f{};
+    double d = 0.0f;
+    for (uint32_t j = 0; j < dim; j++) {
+      double t = f(a(i, j)) - f(b(i, j));
+      d += t * t;
+    }
+    return sqrt(d / double(dim));
+  });
+  resource::sync_stream(res);
+  for (uint32_t i = 0; i < n_rows; i++) {
+    double d = dist(i);
+    // The theoretical estimate of the error is hard to come up with,
+    // the estimate below is based on experimentation + curse of dimensionality
+    ASSERT_LE(d, 1.2 * eps * std::pow(2.0, compression_ratio))
+      << " (label = " << label << ", ix = " << i << ", eps = " << eps << ")";
+  }
+}
+
+template <typename IdxT>
+auto min_output_size(const raft::resources& handle,
+                     const ivf_pq::index<IdxT>& index,
+                     uint32_t n_probes) -> IdxT
+{
+  auto acc_sizes        = index.accum_sorted_sizes();
+  uint32_t last_nonzero = index.n_lists();
+  while (last_nonzero > 0 && acc_sizes(last_nonzero - 1) == acc_sizes(last_nonzero)) {
+    last_nonzero--;
+  }
+  return acc_sizes(last_nonzero) - acc_sizes(last_nonzero - std::min(last_nonzero, n_probes));
+}
+
+template <typename EvalT, typename DataT, typename IdxT>
+class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
+ public:
+  ivf_pq_test()
+    : stream_(resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<ivf_pq_inputs>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+  void gen_data()
+  {
+    database.resize(size_t{ps.num_db_vecs} * size_t{ps.dim}, stream_);
+    search_queries.resize(size_t{ps.num_queries} * size_t{ps.dim}, stream_);
+
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::uniform(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(0.1), DataT(2.0));
+      raft::random::uniform(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(1), DataT(20));
+      raft::random::uniformInt(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(1), DataT(20));
+    }
+    resource::sync_stream(handle_);
+  }
+
+  void calc_ref()
+  {
+    size_t queries_size = size_t{ps.num_queries} * size_t{ps.k};
+    rmm::device_uvector<EvalT> distances_naive_dev(queries_size, stream_);
+    rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+    cuvs::neighbors::naive_knn<EvalT, DataT, int64_t>(
+      handle_,
+      distances_naive_dev.data(),
+      indices_naive_dev.data(),
+      search_queries.data(),
+      database.data(),
+      ps.num_queries,
+      ps.num_db_vecs,
+      ps.dim,
+      ps.k,
+      static_cast<cuvs::distance::DistanceType>((int)ps.index_params.metric));
+    distances_ref.resize(queries_size);
+    update_host(distances_ref.data(), distances_naive_dev.data(), queries_size, stream_);
+    indices_ref.resize(queries_size);
+    update_host(indices_ref.data(), indices_naive_dev.data(), queries_size, stream_);
+    resource::sync_stream(handle_);
+  }
+
+  auto build_only()
+  {
+    auto ipams              = ps.index_params;
+    ipams.add_data_on_build = true;
+
+    auto index_view =
+      raft::make_device_matrix_view<DataT, IdxT>(database.data(), ps.num_db_vecs, ps.dim);
+    return cuvs::neighbors::ivf_pq::build(handle_, ipams, index_view);
+  }
+
+  auto build_2_extends()
+  {
+    auto db_indices = make_device_vector<IdxT>(handle_, ps.num_db_vecs);
+    linalg::map_offset(handle_, db_indices.view(), identity_op{});
+    resource::sync_stream(handle_);
+    auto size_1 = IdxT(ps.num_db_vecs) / 2;
+    auto size_2 = IdxT(ps.num_db_vecs) - size_1;
+    auto vecs_1 = database.data();
+    auto vecs_2 = database.data() + size_t(size_1) * size_t(ps.dim);
+    auto inds_1 = db_indices.data_handle();
+    auto inds_2 = db_indices.data_handle() + size_t(size_1);
+
+    auto ipams              = ps.index_params;
+    ipams.add_data_on_build = false;
+
+    auto database_view =
+      raft::make_device_matrix_view<DataT, IdxT>(database.data(), ps.num_db_vecs, ps.dim);
+    auto idx = cuvs::neighbors::ivf_pq::build(handle_, ipams, database_view);
+
+    auto vecs_2_view = raft::make_device_matrix_view<DataT, IdxT>(vecs_2, size_2, ps.dim);
+    auto inds_2_view = raft::make_device_vector_view<IdxT, IdxT>(inds_2, size_2);
+    cuvs::neighbors::ivf_pq::extend(handle_, vecs_2_view, inds_2_view, &idx);
+
+    auto vecs_1_view =
+      raft::make_device_matrix_view<DataT, IdxT, row_major>(vecs_1, size_1, ps.dim);
+    auto inds_1_view = raft::make_device_vector_view<const IdxT, IdxT>(inds_1, size_1);
+    cuvs::neighbors::ivf_pq::extend(handle_, vecs_1_view, inds_1_view, &idx);
+    return idx;
+  }
+
+  auto build_serialize()
+  {
+    std::string filename = "ivf_pq_index";
+    cuvs::neighbors::ivf_pq::serialize(handle_, filename, build_only());
+    cuvs::neighbors::ivf_pq::index<IdxT> index(handle_, ps.index_params, ps.dim);
+    cuvs::neighbors::ivf_pq::deserialize(handle_, filename, &index);
+    return index;
+  }
+
+  void check_reconstruction(const index<IdxT>& index,
+                            double compression_ratio,
+                            uint32_t label,
+                            uint32_t n_take,
+                            uint32_t n_skip)
+  {
+    auto& rec_list = index.lists()[label];
+    auto dim       = index.dim();
+    n_take         = std::min<uint32_t>(n_take, rec_list->size.load());
+    n_skip         = std::min<uint32_t>(n_skip, rec_list->size.load() - n_take);
+
+    if (n_take == 0) { return; }
+
+    auto rec_data  = make_device_matrix<DataT>(handle_, n_take, dim);
+    auto orig_data = make_device_matrix<DataT>(handle_, n_take, dim);
+
+    cuvs::neighbors::ivf_pq::helpers::reconstruct_list_data(
+      handle_, index, rec_data.view(), label, n_skip);
+
+    matrix::gather(database.data(),
+                   IdxT{dim},
+                   IdxT{n_take},
+                   rec_list->indices.data_handle() + n_skip,
+                   IdxT{n_take},
+                   orig_data.data_handle(),
+                   stream_);
+
+    compare_vectors_l2(handle_, rec_data.view(), orig_data.view(), label, compression_ratio, 0.06);
+  }
+
+  void check_reconstruct_extend(index<IdxT>* index, double compression_ratio, uint32_t label)
+  {
+    // NB: this is not reference, the list is retained; the index will have to create a new list on
+    // `erase_list` op.
+    auto old_list = index->lists()[label];
+    auto n_rows   = old_list->size.load();
+    if (n_rows == 0) { return; }
+
+    auto vectors_1 = make_device_matrix<EvalT>(handle_, n_rows, index->dim());
+    auto indices   = make_device_vector<IdxT>(handle_, n_rows);
+    copy(indices.data_handle(), old_list->indices.data_handle(), n_rows, stream_);
+
+    cuvs::neighbors::ivf_pq::helpers::reconstruct_list_data(
+      handle_, *index, vectors_1.view(), label, 0);
+    cuvs::neighbors::ivf_pq::helpers::erase_list(handle_, index, label);
+    // NB: passing the type parameter because const->non-const implicit conversion of the mdspans
+    // breaks type inference
+    cuvs::neighbors::ivf_pq::helpers::extend_list<EvalT, IdxT>(
+      handle_, index, vectors_1.view(), indices.view(), label);
+
+    auto& new_list = index->lists()[label];
+    ASSERT_NE(old_list.get(), new_list.get())
+      << "The old list should have been shared and retained after ivf_pq index has erased the "
+         "corresponding cluster.";
+
+    auto vectors_2 = make_device_matrix<EvalT>(handle_, n_rows, index->dim());
+    cuvs::neighbors::ivf_pq::helpers::reconstruct_list_data(
+      handle_, *index, vectors_2.view(), label, 0);
+    // The code search is unstable, and there's high chance of repeating values of the lvl-2 codes.
+    // Hence, encoding-decoding chain often leads to altering both the PQ codes and the
+    // reconstructed data.
+    compare_vectors_l2(
+      handle_, vectors_1.view(), vectors_2.view(), label, compression_ratio, 0.04);  // 0.025);
+  }
+
+  void check_packing(index<IdxT>* index, uint32_t label)
+  {
+    auto old_list = index->lists()[label];
+    auto n_rows   = old_list->size.load();
+
+    if (n_rows == 0) { return; }
+
+    auto codes   = make_device_matrix<uint8_t>(handle_, n_rows, index->pq_dim());
+    auto indices = make_device_vector<IdxT>(handle_, n_rows);
+    copy(indices.data_handle(), old_list->indices.data_handle(), n_rows, stream_);
+
+    cuvs::neighbors::ivf_pq::helpers::unpack_list_data(handle_, *index, codes.view(), label, 0);
+    cuvs::neighbors::ivf_pq::helpers::erase_list(handle_, index, label);
+    cuvs::neighbors::ivf_pq::helpers::extend_list_with_codes<IdxT>(
+      handle_, index, codes.view(), indices.view(), label);
+
+    auto& new_list = index->lists()[label];
+    ASSERT_NE(old_list.get(), new_list.get())
+      << "The old list should have been shared and retained after ivf_pq index has erased the "
+         "corresponding cluster.";
+    auto list_data_size = (n_rows / raft::neighbors::ivf_pq::kIndexGroupSize) *
+                          new_list->data.extent(1) * new_list->data.extent(2) *
+                          new_list->data.extent(3);
+
+    ASSERT_TRUE(old_list->data.size() >= list_data_size);
+    ASSERT_TRUE(new_list->data.size() >= list_data_size);
+    ASSERT_TRUE(cuvs::devArrMatch(old_list->data.data_handle(),
+                                  new_list->data.data_handle(),
+                                  list_data_size,
+                                  cuvs::Compare<uint8_t>{}));
+
+    // Pack a few vectors back to the list.
+    int row_offset = 9;
+    int n_vec      = 3;
+    ASSERT_TRUE(row_offset + n_vec < n_rows);
+    size_t offset      = row_offset * index->pq_dim();
+    auto codes_to_pack = make_device_matrix_view<const uint8_t, uint32_t>(
+      codes.data_handle() + offset, n_vec, index->pq_dim());
+    ivf_pq::helpers::pack_list_data(handle_, index, codes_to_pack, label, row_offset);
+    ASSERT_TRUE(cuvs::devArrMatch(old_list->data.data_handle(),
+                                  new_list->data.data_handle(),
+                                  list_data_size,
+                                  cuvs::Compare<uint8_t>{}));
+
+    // Another test with the API that take list_data directly
+    auto list_data  = index->lists()[label]->data.view();
+    uint32_t n_take = 4;
+    ASSERT_TRUE(row_offset + n_take < n_rows);
+    auto codes2 = raft::make_device_matrix<uint8_t>(handle_, n_take, index->pq_dim());
+    ivf_pq::helpers::codepacker::unpack(
+      handle_, list_data, index->pq_bits(), row_offset, codes2.view());
+
+    // Write it back
+    ivf_pq::helpers::codepacker::pack(
+      handle_, make_const_mdspan(codes2.view()), index->pq_bits(), row_offset, list_data);
+    ASSERT_TRUE(cuvs::devArrMatch(old_list->data.data_handle(),
+                                  new_list->data.data_handle(),
+                                  list_data_size,
+                                  cuvs::Compare<uint8_t>{}));
+  }
+
+  template <typename BuildIndex>
+  void run(BuildIndex build_index)
+  {
+    index<IdxT> index = build_index();
+
+    double compression_ratio =
+      static_cast<double>(ps.dim * 8) / static_cast<double>(index.pq_dim() * index.pq_bits());
+
+    for (uint32_t label = 0; label < index.n_lists(); label++) {
+      switch (label % 3) {
+        case 0: {
+          // Reconstruct and re-write vectors for one label
+          check_reconstruct_extend(&index, compression_ratio, label);
+        } break;
+        case 1: {
+          // Dump and re-write codes for one label
+          check_packing(&index, label);
+        } break;
+        default: {
+          // check a small subset of data in a randomly chosen cluster to see if the data
+          // reconstruction works well.
+          check_reconstruction(index, compression_ratio, label, 100, 7);
+        }
+      }
+    }
+
+    size_t queries_size = ps.num_queries * ps.k;
+    std::vector<IdxT> indices_ivf_pq(queries_size);
+    std::vector<EvalT> distances_ivf_pq(queries_size);
+
+    rmm::device_uvector<EvalT> distances_ivf_pq_dev(queries_size, stream_);
+    rmm::device_uvector<IdxT> indices_ivf_pq_dev(queries_size, stream_);
+
+    auto query_view =
+      raft::make_device_matrix_view<DataT, uint32_t>(search_queries.data(), ps.num_queries, ps.dim);
+    auto inds_view = raft::make_device_matrix_view<IdxT, uint32_t>(
+      indices_ivf_pq_dev.data(), ps.num_queries, ps.k);
+    auto dists_view = raft::make_device_matrix_view<EvalT, uint32_t>(
+      distances_ivf_pq_dev.data(), ps.num_queries, ps.k);
+
+    cuvs::neighbors::ivf_pq::search(
+      handle_, ps.search_params, index, query_view, inds_view, dists_view);
+
+    update_host(distances_ivf_pq.data(), distances_ivf_pq_dev.data(), queries_size, stream_);
+    update_host(indices_ivf_pq.data(), indices_ivf_pq_dev.data(), queries_size, stream_);
+    resource::sync_stream(handle_);
+
+    // A very conservative lower bound on recall
+    double min_recall =
+      static_cast<double>(ps.search_params.n_probes) / static_cast<double>(ps.index_params.n_lists);
+    // Using a heuristic to lower the required recall due to code-packing errors
+    min_recall =
+      std::min(std::erfc(0.05 * compression_ratio / std::max(min_recall, 0.5)), min_recall);
+    // Use explicit per-test min recall value if provided.
+    min_recall = ps.min_recall.value_or(min_recall);
+
+    ASSERT_TRUE(cuvs::neighbors::eval_neighbours(indices_ref,
+                                                 indices_ivf_pq,
+                                                 distances_ref,
+                                                 distances_ivf_pq,
+                                                 ps.num_queries,
+                                                 ps.k,
+                                                 0.0001 * compression_ratio,
+                                                 min_recall))
+      << ps;
+
+    // Test a few extra invariants
+    IdxT min_results = min_output_size(handle_, index, ps.search_params.n_probes);
+    IdxT max_oob     = ps.k <= min_results ? 0 : ps.k - min_results;
+    IdxT found_oob   = 0;
+    for (uint32_t query_ix = 0; query_ix < ps.num_queries; query_ix++) {
+      for (uint32_t k = 0; k < ps.k; k++) {
+        auto flat_i   = query_ix * ps.k + k;
+        auto found_ix = indices_ivf_pq[flat_i];
+        if (found_ix == raft::neighbors::ivf_pq::kOutOfBoundsRecord<IdxT>) {
+          found_oob++;
+          continue;
+        }
+        ASSERT_NE(found_ix, raft::neighbors::ivf::kInvalidRecord<IdxT>)
+          << "got an invalid record at query_ix = " << query_ix << ", k = " << k
+          << " (distance = " << distances_ivf_pq[flat_i] << ")";
+        ASSERT_LT(found_ix, ps.num_db_vecs)
+          << "got an impossible index = " << found_ix << " at query_ix = " << query_ix
+          << ", k = " << k << " (distance = " << distances_ivf_pq[flat_i] << ")";
+      }
+    }
+    ASSERT_LE(found_oob, max_oob)
+      << "got too many records out-of-bounds (see ivf_pq::kOutOfBoundsRecord<IdxT>).";
+    if (found_oob > 0) {
+      RAFT_LOG_WARN(
+        "Got %zu results out-of-bounds because of large top-k (%zu) and small n_probes (%u) and "
+        "small DB size/n_lists ratio (%zu / %u)",
+        size_t(found_oob),
+        size_t(ps.k),
+        ps.search_params.n_probes,
+        size_t(ps.num_db_vecs),
+        ps.index_params.n_lists);
+    }
+  }
+
+  void SetUp() override  // NOLINT
+  {
+    gen_data();
+    calc_ref();
+  }
+
+  void TearDown() override  // NOLINT
+  {
+    cudaGetLastError();
+    resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  ivf_pq_inputs ps;                           // NOLINT
+  rmm::device_uvector<DataT> database;        // NOLINT
+  rmm::device_uvector<DataT> search_queries;  // NOLINT
+  std::vector<IdxT> indices_ref;              // NOLINT
+  std::vector<EvalT> distances_ref;           // NOLINT
+};
+
+template <typename EvalT, typename DataT, typename IdxT>
+class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
+ public:
+  ivf_pq_filter_test()
+    : stream_(resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<ivf_pq_inputs>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+  void gen_data()
+  {
+    database.resize(size_t{ps.num_db_vecs} * size_t{ps.dim}, stream_);
+    search_queries.resize(size_t{ps.num_queries} * size_t{ps.dim}, stream_);
+
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::uniform(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(0.1), DataT(2.0));
+      raft::random::uniform(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(1), DataT(20));
+      raft::random::uniformInt(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(1), DataT(20));
+    }
+    resource::sync_stream(handle_);
+  }
+
+  void calc_ref()
+  {
+    size_t queries_size = size_t{ps.num_queries} * size_t{ps.k};
+    rmm::device_uvector<EvalT> distances_naive_dev(queries_size, stream_);
+    rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+    cuvs::neighbors::naive_knn<EvalT, DataT, IdxT>(
+      handle_,
+      distances_naive_dev.data(),
+      indices_naive_dev.data(),
+      search_queries.data(),
+      database.data() + test_ivf_sample_filter::offset * ps.dim,
+      ps.num_queries,
+      ps.num_db_vecs - test_ivf_sample_filter::offset,
+      ps.dim,
+      ps.k,
+      static_cast<cuvs::distance::DistanceType>((int)ps.index_params.metric));
+    raft::linalg::addScalar(indices_naive_dev.data(),
+                            indices_naive_dev.data(),
+                            IdxT(test_ivf_sample_filter::offset),
+                            queries_size,
+                            stream_);
+    distances_ref.resize(queries_size);
+    update_host(distances_ref.data(), distances_naive_dev.data(), queries_size, stream_);
+    indices_ref.resize(queries_size);
+    update_host(indices_ref.data(), indices_naive_dev.data(), queries_size, stream_);
+    resource::sync_stream(handle_);
+  }
+
+  auto build_only()
+  {
+    auto ipams              = ps.index_params;
+    ipams.add_data_on_build = true;
+
+    auto index_view =
+      raft::make_device_matrix_view<DataT, IdxT>(database.data(), ps.num_db_vecs, ps.dim);
+    return cuvs::neighbors::ivf_pq::build(handle_, ipams, index_view);
+  }
+
+  template <typename BuildIndex>
+  void run(BuildIndex build_index)
+  {
+    index<IdxT> index = build_index();
+
+    double compression_ratio =
+      static_cast<double>(ps.dim * 8) / static_cast<double>(index.pq_dim() * index.pq_bits());
+    size_t queries_size = ps.num_queries * ps.k;
+    std::vector<IdxT> indices_ivf_pq(queries_size);
+    std::vector<EvalT> distances_ivf_pq(queries_size);
+
+    rmm::device_uvector<EvalT> distances_ivf_pq_dev(queries_size, stream_);
+    rmm::device_uvector<IdxT> indices_ivf_pq_dev(queries_size, stream_);
+
+    auto query_view =
+      raft::make_device_matrix_view<DataT, uint32_t>(search_queries.data(), ps.num_queries, ps.dim);
+    auto inds_view = raft::make_device_matrix_view<IdxT, uint32_t>(
+      indices_ivf_pq_dev.data(), ps.num_queries, ps.k);
+    auto dists_view = raft::make_device_matrix_view<EvalT, uint32_t>(
+      distances_ivf_pq_dev.data(), ps.num_queries, ps.k);
+
+    // Create Bitset filter
+    auto removed_indices =
+      raft::make_device_vector<IdxT, int64_t>(handle_, test_ivf_sample_filter::offset);
+    thrust::sequence(
+      resource::get_thrust_policy(handle_),
+      thrust::device_pointer_cast(removed_indices.data_handle()),
+      thrust::device_pointer_cast(removed_indices.data_handle() + test_ivf_sample_filter::offset));
+    resource::sync_stream(handle_);
+
+    raft::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
+      handle_, removed_indices.view(), ps.num_db_vecs);
+    raft::neighbors::ivf_pq::search_with_filtering<DataT, IdxT>(
+      handle_,
+      ps.search_params,
+      index,
+      query_view,
+      inds_view,
+      dists_view,
+      raft::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
+
+    update_host(distances_ivf_pq.data(), distances_ivf_pq_dev.data(), queries_size, stream_);
+    update_host(indices_ivf_pq.data(), indices_ivf_pq_dev.data(), queries_size, stream_);
+    resource::sync_stream(handle_);
+
+    // A very conservative lower bound on recall
+    double min_recall =
+      static_cast<double>(ps.search_params.n_probes) / static_cast<double>(ps.index_params.n_lists);
+    // Using a heuristic to lower the required recall due to code-packing errors
+    min_recall =
+      std::min(std::erfc(0.05 * compression_ratio / std::max(min_recall, 0.5)), min_recall);
+    // Use explicit per-test min recall value if provided.
+    min_recall = ps.min_recall.value_or(min_recall);
+
+    ASSERT_TRUE(cuvs::neighbors::eval_neighbours(indices_ref,
+                                                 indices_ivf_pq,
+                                                 distances_ref,
+                                                 distances_ivf_pq,
+                                                 ps.num_queries,
+                                                 ps.k,
+                                                 0.0001 * compression_ratio,
+                                                 min_recall))
+      << ps;
+  }
+
+  void SetUp() override  // NOLINT
+  {
+    gen_data();
+    calc_ref();
+  }
+
+  void TearDown() override  // NOLINT
+  {
+    cudaGetLastError();
+    resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  ivf_pq_inputs ps;                           // NOLINT
+  rmm::device_uvector<DataT> database;        // NOLINT
+  rmm::device_uvector<DataT> search_queries;  // NOLINT
+  std::vector<IdxT> indices_ref;              // NOLINT
+  std::vector<EvalT> distances_ref;           // NOLINT
+};
+
+/* Test cases */
+using test_cases_t = std::vector<ivf_pq_inputs>;
+
+// concatenate parameter sets for different type
+template <typename T>
+auto operator+(const std::vector<T>& a, const std::vector<T>& b) -> std::vector<T>
+{
+  std::vector<T> res = a;
+  res.insert(res.end(), b.begin(), b.end());
+  return res;
+}
+
+inline auto defaults() -> test_cases_t { return {ivf_pq_inputs{}}; }
+
+template <typename B, typename A, typename F>
+auto map(const std::vector<A>& xs, F f) -> std::vector<B>
+{
+  std::vector<B> ys(xs.size());
+  std::transform(xs.begin(), xs.end(), ys.begin(), f);
+  return ys;
+}
+
+inline auto with_dims(const std::vector<uint32_t>& dims) -> test_cases_t
+{
+  return map<ivf_pq_inputs>(dims, [](uint32_t d) {
+    ivf_pq_inputs x;
+    x.dim = d;
+    return x;
+  });
+}
+
+/** These will surely trigger the fastest kernel available. */
+inline auto small_dims() -> test_cases_t { return with_dims({1, 2, 3, 4, 5, 8, 15, 16, 17}); }
+
+inline auto small_dims_per_cluster() -> test_cases_t
+{
+  return map<ivf_pq_inputs>(small_dims(), [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    y.index_params.codebook_kind = ivf_pq::codebook_gen::PER_CLUSTER;
+    return y;
+  });
+}
+
+inline auto big_dims() -> test_cases_t
+{
+  // with_dims({512, 513, 1023, 1024, 1025, 2048, 2049, 2050, 2053, 6144, 8192, 12288, 16384});
+  auto xs = with_dims({512, 513, 1023, 1024, 1025, 2048, 2049, 2050, 2053, 6144});
+  return map<ivf_pq_inputs>(xs, [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    uint32_t pq_len       = 2;
+    y.index_params.pq_dim = div_rounding_up_safe(x.dim, pq_len);
+    // This comes from pure experimentation, also the recall depens a lot on pq_len.
+    y.min_recall = 0.48 + 0.028 * std::log2(x.dim);
+    return y;
+  });
+}
+
+/** These will surely trigger no-smem-lut kernel.  */
+inline auto big_dims_moderate_lut() -> test_cases_t
+{
+  return map<ivf_pq_inputs>(big_dims(), [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    uint32_t pq_len           = 2;
+    y.index_params.pq_dim     = round_up_safe(div_rounding_up_safe(x.dim, pq_len), 4u);
+    y.index_params.pq_bits    = 6;
+    y.search_params.lut_dtype = CUDA_R_16F;
+    y.min_recall              = 0.69;
+    return y;
+  });
+}
+
+/** Some of these should trigger no-basediff kernel.  */
+inline auto big_dims_small_lut() -> test_cases_t
+{
+  return map<ivf_pq_inputs>(big_dims(), [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    uint32_t pq_len           = 8;
+    y.index_params.pq_dim     = round_up_safe(div_rounding_up_safe(x.dim, pq_len), 4u);
+    y.index_params.pq_bits    = 6;
+    y.search_params.lut_dtype = CUDA_R_8U;
+    y.min_recall              = 0.21;
+    return y;
+  });
+}
+
+/**
+ * A minimal set of tests to check various enum-like parameters.
+ */
+inline auto enum_variety() -> test_cases_t
+{
+  test_cases_t xs;
+#define ADD_CASE(f)                               \
+  do {                                            \
+    xs.push_back({});                             \
+    ([](ivf_pq_inputs & x) f)(xs[xs.size() - 1]); \
+  } while (0);
+
+  ADD_CASE({
+    x.index_params.codebook_kind = ivf_pq::codebook_gen::PER_CLUSTER;
+    x.min_recall                 = 0.86;
+  });
+  ADD_CASE({
+    x.index_params.codebook_kind = ivf_pq::codebook_gen::PER_SUBSPACE;
+    x.min_recall                 = 0.86;
+  });
+  ADD_CASE({
+    x.index_params.codebook_kind = ivf_pq::codebook_gen::PER_CLUSTER;
+    x.index_params.pq_bits       = 4;
+    x.min_recall                 = 0.79;
+  });
+  ADD_CASE({
+    x.index_params.codebook_kind = ivf_pq::codebook_gen::PER_CLUSTER;
+    x.index_params.pq_bits       = 5;
+    x.min_recall                 = 0.83;
+  });
+
+  ADD_CASE({
+    x.index_params.pq_bits = 6;
+    x.min_recall           = 0.84;
+  });
+  ADD_CASE({
+    x.index_params.pq_bits = 7;
+    x.min_recall           = 0.85;
+  });
+  ADD_CASE({
+    x.index_params.pq_bits = 8;
+    x.min_recall           = 0.86;
+  });
+
+  ADD_CASE({
+    x.index_params.force_random_rotation = true;
+    x.min_recall                         = 0.86;
+  });
+  ADD_CASE({
+    x.index_params.force_random_rotation = false;
+    x.min_recall                         = 0.86;
+  });
+
+  ADD_CASE({
+    x.search_params.lut_dtype = CUDA_R_32F;
+    x.min_recall              = 0.86;
+  });
+  ADD_CASE({
+    x.search_params.lut_dtype = CUDA_R_16F;
+    x.min_recall              = 0.86;
+  });
+  ADD_CASE({
+    x.search_params.lut_dtype = CUDA_R_8U;
+    x.min_recall              = 0.84;
+  });
+
+  ADD_CASE({
+    x.search_params.internal_distance_dtype = CUDA_R_32F;
+    x.min_recall                            = 0.86;
+  });
+  ADD_CASE({
+    x.search_params.internal_distance_dtype = CUDA_R_16F;
+    x.search_params.lut_dtype               = CUDA_R_16F;
+    x.min_recall                            = 0.86;
+  });
+
+  return xs;
+}
+
+inline auto enum_variety_l2() -> test_cases_t
+{
+  return map<ivf_pq_inputs>(enum_variety(), [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    y.index_params.metric = distance::DistanceType::L2Expanded;
+    return y;
+  });
+}
+
+inline auto enum_variety_ip() -> test_cases_t
+{
+  return map<ivf_pq_inputs>(enum_variety(), [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    if (y.min_recall.has_value()) {
+      if (y.search_params.lut_dtype == CUDA_R_8U) {
+        // InnerProduct score is signed,
+        // thus we're forced to used signed 8-bit representation,
+        // thus we have one bit less precision
+        y.min_recall = y.min_recall.value() * 0.90;
+      } else {
+        // In other cases it seems to perform a little bit better, still worse than L2
+        y.min_recall = y.min_recall.value() * 0.94;
+      }
+    }
+    y.index_params.metric = distance::DistanceType::InnerProduct;
+    return y;
+  });
+}
+
+inline auto enum_variety_l2sqrt() -> test_cases_t
+{
+  return map<ivf_pq_inputs>(enum_variety(), [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    y.index_params.metric = distance::DistanceType::L2SqrtExpanded;
+    return y;
+  });
+}
+
+/**
+ * Try different number of n_probes, some of which may trigger the non-fused version of the search
+ * kernel.
+ */
+inline auto var_n_probes() -> test_cases_t
+{
+  ivf_pq_inputs dflt;
+  std::vector<uint32_t> xs;
+  for (auto x = dflt.index_params.n_lists; x >= 1; x /= 2) {
+    xs.push_back(x);
+  }
+  return map<ivf_pq_inputs>(xs, [](uint32_t n_probes) {
+    ivf_pq_inputs x;
+    x.search_params.n_probes = n_probes;
+    return x;
+  });
+}
+
+/**
+ * Try different number of nearest neighbours.
+ * Values smaller than 32 test if the code behaves well when Capacity (== 32) does not change,
+ * but `k <= Capacity` changes.
+ *
+ * Values between `32 and ivf_pq::detail::kMaxCapacity` test various instantiations of the
+ * main kernel (Capacity-templated)
+ *
+ * Values above ivf_pq::detail::kMaxCapacity should trigger the non-fused version of the kernel
+ * (manage_local_topk = false).
+ *
+ * Also we test here various values that are close-but-not-power-of-two to catch any problems
+ * related to rounding/alignment.
+ *
+ * Note, we cannot control explicitly which instance of the search kernel to choose, hence it's
+ * important to try a variety of different values of `k` to make sure all paths are triggered.
+ *
+ * Set the log level to DEBUG (5) or above to inspect the selected kernel instances.
+ */
+inline auto var_k() -> test_cases_t
+{
+  return map<ivf_pq_inputs, uint32_t>(
+    {1, 2, 3, 5, 8, 15, 16, 32, 63, 65, 127, 128, 256, 257, 1023, 2048, 2049}, [](uint32_t k) {
+      ivf_pq_inputs x;
+      x.k = k;
+      // when there's not enough data, try more cluster probes
+      x.search_params.n_probes = max(x.search_params.n_probes, min(x.index_params.n_lists, k));
+      return x;
+    });
+}
+
+/**
+ * Cases brought up from downstream projects.
+ */
+inline auto special_cases() -> test_cases_t
+{
+  test_cases_t xs;
+
+#define ADD_CASE(f)                               \
+  do {                                            \
+    xs.push_back({});                             \
+    ([](ivf_pq_inputs & x) f)(xs[xs.size() - 1]); \
+  } while (0);
+
+  ADD_CASE({
+    x.num_db_vecs                = 1183514;
+    x.dim                        = 100;
+    x.num_queries                = 10000;
+    x.k                          = 10;
+    x.index_params.codebook_kind = ivf_pq::codebook_gen::PER_SUBSPACE;
+    x.index_params.pq_dim        = 10;
+    x.index_params.pq_bits       = 8;
+    x.index_params.n_lists       = 1024;
+    x.search_params.n_probes     = 50;
+  });
+
+  ADD_CASE({
+    x.num_db_vecs                = 10000;
+    x.dim                        = 16;
+    x.num_queries                = 500;
+    x.k                          = 128;
+    x.index_params.metric        = distance::DistanceType::L2Expanded;
+    x.index_params.codebook_kind = ivf_pq::codebook_gen::PER_SUBSPACE;
+    x.index_params.pq_bits       = 8;
+    x.index_params.n_lists       = 100;
+    x.search_params.n_probes     = 100;
+  });
+
+  ADD_CASE({
+    x.num_db_vecs                = 10000;
+    x.dim                        = 16;
+    x.num_queries                = 500;
+    x.k                          = 129;
+    x.index_params.metric        = distance::DistanceType::L2Expanded;
+    x.index_params.codebook_kind = ivf_pq::codebook_gen::PER_SUBSPACE;
+    x.index_params.pq_bits       = 8;
+    x.index_params.n_lists       = 100;
+    x.search_params.n_probes     = 100;
+  });
+
+  return xs;
+}
+
+/* Test instantiations */
+
+#define TEST_BUILD_SEARCH(type)                         \
+  TEST_P(type, build_search) /* NOLINT */               \
+  {                                                     \
+    this->run([this]() { return this->build_only(); }); \
+  }
+
+#define TEST_BUILD_EXTEND_SEARCH(type)                       \
+  TEST_P(type, build_extend_search) /* NOLINT */             \
+  {                                                          \
+    this->run([this]() { return this->build_2_extends(); }); \
+  }
+
+#define TEST_BUILD_SERIALIZE_SEARCH(type)                    \
+  TEST_P(type, build_serialize_search) /* NOLINT */          \
+  {                                                          \
+    this->run([this]() { return this->build_serialize(); }); \
+  }
+
+#define INSTANTIATE(type, vals) \
+  INSTANTIATE_TEST_SUITE_P(IvfPq, type, ::testing::ValuesIn(vals)); /* NOLINT */
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
new file mode 100644
index 0000000000..5ca4cde68d
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_ivf_pq.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+using f32_f32_i64 = ivf_pq_test<float, float, int64_t>;
+
+TEST_BUILD_EXTEND_SEARCH(f32_f32_i64)
+TEST_BUILD_SERIALIZE_SEARCH(f32_f32_i64)
+INSTANTIATE(f32_f32_i64, defaults() + small_dims() + big_dims_moderate_lut());
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
new file mode 100644
index 0000000000..0763b661e7
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_ivf_pq.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+using f32_i08_i64 = ivf_pq_test<float, int8_t, int64_t>;
+
+TEST_BUILD_SEARCH(f32_i08_i64)
+TEST_BUILD_SERIALIZE_SEARCH(f32_i08_i64)
+INSTANTIATE(f32_i08_i64, defaults() + big_dims() + var_k());
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..93e7d5336b
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_ivf_pq.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+using f32_u08_i64 = ivf_pq_test<float, uint8_t, int64_t>;
+
+TEST_BUILD_SEARCH(f32_u08_i64)
+TEST_BUILD_EXTEND_SEARCH(f32_u08_i64)
+INSTANTIATE(f32_u08_i64, small_dims_per_cluster() + enum_variety());
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq_c.cu b/cpp/test/neighbors/ann_ivf_pq_c.cu
new file mode 100644
index 0000000000..94d121ce2f
--- /dev/null
+++ b/cpp/test/neighbors/ann_ivf_pq_c.cu
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/random/rng.cuh>
+
+#include "ann_utils.cuh"
+#include <cuvs/neighbors/ivf_pq.h>
+
+extern "C" void run_ivf_pq(int64_t n_rows,
+                           int64_t n_queries,
+                           int64_t n_dim,
+                           uint32_t n_neighbors,
+                           float* index_data,
+                           float* query_data,
+                           float* distances_data,
+                           int64_t* neighbors_data,
+                           enum DistanceType metric,
+                           size_t n_probes,
+                           size_t n_lists);
+
+template <typename T>
+void generate_random_data(T* devPtr, size_t size)
+{
+  raft::handle_t handle;
+  raft::random::RngState r(1234ULL);
+  raft::random::uniform(handle, r, devPtr, size, T(0.1), T(2.0));
+};
+
+template <typename T, typename IdxT>
+void recall_eval(T* query_data,
+                 T* index_data,
+                 IdxT* neighbors,
+                 T* distances,
+                 size_t n_queries,
+                 size_t n_rows,
+                 size_t n_dim,
+                 size_t n_neighbors,
+                 DistanceType metric,
+                 size_t n_probes,
+                 size_t n_lists)
+{
+  raft::handle_t handle;
+  auto distances_ref = raft::make_device_matrix<T, IdxT>(handle, n_queries, n_neighbors);
+  auto neighbors_ref = raft::make_device_matrix<IdxT, IdxT>(handle, n_queries, n_neighbors);
+  cuvs::neighbors::naive_knn<T, T, IdxT>(
+    handle,
+    distances_ref.data_handle(),
+    neighbors_ref.data_handle(),
+    query_data,
+    index_data,
+    n_queries,
+    n_rows,
+    n_dim,
+    n_neighbors,
+    static_cast<cuvs::distance::DistanceType>((uint16_t)metric));
+
+  size_t size = n_queries * n_neighbors;
+  std::vector<IdxT> neighbors_h(size);
+  std::vector<T> distances_h(size);
+  std::vector<IdxT> neighbors_ref_h(size);
+  std::vector<T> distances_ref_h(size);
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+  raft::copy(neighbors_h.data(), neighbors, size, stream);
+  raft::copy(distances_h.data(), distances, size, stream);
+  raft::copy(neighbors_ref_h.data(), neighbors_ref.data_handle(), size, stream);
+  raft::copy(distances_ref_h.data(), distances_ref.data_handle(), size, stream);
+
+  // verify output
+  double min_recall = static_cast<double>(n_probes) / static_cast<double>(n_lists);
+  ASSERT_TRUE(cuvs::neighbors::eval_neighbours(neighbors_ref_h,
+                                               neighbors_h,
+                                               distances_ref_h,
+                                               distances_h,
+                                               n_queries,
+                                               n_neighbors,
+                                               0.001,
+                                               min_recall));
+};
+
+TEST(IvfPqC, BuildSearch)
+{
+  int64_t n_rows       = 8096;
+  int64_t n_queries    = 128;
+  int64_t n_dim        = 32;
+  uint32_t n_neighbors = 8;
+
+  enum DistanceType metric = L2Expanded;
+  size_t n_probes          = 20;
+  size_t n_lists           = 1024;
+
+  float *index_data, *query_data, *distances_data;
+  int64_t* neighbors_data;
+  cudaMalloc(&index_data, sizeof(float) * n_rows * n_dim);
+  cudaMalloc(&query_data, sizeof(float) * n_queries * n_dim);
+  cudaMalloc(&neighbors_data, sizeof(int64_t) * n_queries * n_neighbors);
+  cudaMalloc(&distances_data, sizeof(float) * n_queries * n_neighbors);
+
+  generate_random_data(index_data, n_rows * n_dim);
+  generate_random_data(query_data, n_queries * n_dim);
+
+  run_ivf_pq(n_rows,
+             n_queries,
+             n_dim,
+             n_neighbors,
+             index_data,
+             query_data,
+             distances_data,
+             neighbors_data,
+             metric,
+             n_probes,
+             n_lists);
+
+  recall_eval(query_data,
+              index_data,
+              neighbors_data,
+              distances_data,
+              n_queries,
+              n_rows,
+              n_dim,
+              n_neighbors,
+              metric,
+              n_probes,
+              n_lists);
+
+  // delete device memory
+  cudaFree(index_data);
+  cudaFree(query_data);
+  cudaFree(neighbors_data);
+  cudaFree(distances_data);
+}
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
new file mode 100644
index 0000000000..d058bfacef
--- /dev/null
+++ b/cpp/test/neighbors/brute_force.cu
@@ -0,0 +1,182 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/distance/distance_types.hpp>
+
+namespace cuvs::neighbors::brute_force {
+struct KNNInputs {
+  std::vector<std::vector<float>> input;
+  int k;
+  std::vector<int> labels;
+};
+
+template <typename IdxT>
+RAFT_KERNEL build_actual_output(
+  int* output, int n_rows, int k, const int* idx_labels, const IdxT* indices)
+{
+  int element = threadIdx.x + blockDim.x * blockIdx.x;
+  if (element >= n_rows * k) return;
+
+  output[element] = idx_labels[indices[element]];
+}
+
+RAFT_KERNEL build_expected_output(int* output, int n_rows, int k, const int* labels)
+{
+  int row = threadIdx.x + blockDim.x * blockIdx.x;
+  if (row >= n_rows) return;
+
+  int cur_label = labels[row];
+  for (int i = 0; i < k; i++) {
+    output[row * k + i] = cur_label;
+  }
+}
+
+template <typename T, typename IdxT>
+class KNNTest : public ::testing::TestWithParam<KNNInputs> {
+ public:
+  KNNTest()
+    : params_(::testing::TestWithParam<KNNInputs>::GetParam()),
+      stream(raft::resource::get_cuda_stream(handle)),
+      actual_labels_(0, stream),
+      expected_labels_(0, stream),
+      input_(0, stream),
+      search_data_(0, stream),
+      indices_(0, stream),
+      distances_(0, stream),
+      search_labels_(0, stream)
+  {
+  }
+
+ protected:
+  void testBruteForce()
+  {
+    // #if (RAFT_ACTIVE_LEVEL >= RAFT_LEVEL_DEBUG)
+    raft::print_device_vector("Input array: ", input_.data(), rows_ * cols_, std::cout);
+    std::cout << "K: " << k_ << std::endl;
+    raft::print_device_vector("Labels array: ", search_labels_.data(), rows_, std::cout);
+    // #endif
+
+    auto index = raft::make_device_matrix_view<const T, IdxT, raft::row_major>(
+      (const T*)(input_.data()), rows_, cols_);
+    auto search = raft::make_device_matrix_view<const T, IdxT, raft::row_major>(
+      (const T*)(search_data_.data()), rows_, cols_);
+    auto indices =
+      raft::make_device_matrix_view<IdxT, IdxT, raft::row_major>(indices_.data(), rows_, k_);
+    auto distances =
+      raft::make_device_matrix_view<T, IdxT, raft::row_major>(distances_.data(), rows_, k_);
+
+    auto metric = cuvs::distance::DistanceType::L2Unexpanded;
+    auto idx    = cuvs::neighbors::brute_force::build(handle, index, metric);
+    cuvs::neighbors::brute_force::search(handle, idx, search, indices, distances);
+
+    build_actual_output<<<raft::ceildiv(rows_ * k_, 32), 32, 0, stream>>>(
+      actual_labels_.data(), rows_, k_, search_labels_.data(), indices_.data());
+
+    build_expected_output<<<raft::ceildiv(rows_ * k_, 32), 32, 0, stream>>>(
+      expected_labels_.data(), rows_, k_, search_labels_.data());
+
+    ASSERT_TRUE(devArrMatch(
+      expected_labels_.data(), actual_labels_.data(), rows_ * k_, cuvs::Compare<int>(), stream));
+  }
+
+  void SetUp() override
+  {
+    rows_ = params_.input.size();
+    cols_ = params_.input[0].size();
+    k_    = params_.k;
+
+    actual_labels_.resize(rows_ * k_, stream);
+    expected_labels_.resize(rows_ * k_, stream);
+    input_.resize(rows_ * cols_, stream);
+    search_data_.resize(rows_ * cols_, stream);
+    indices_.resize(rows_ * k_, stream);
+    distances_.resize(rows_ * k_, stream);
+    search_labels_.resize(rows_, stream);
+
+    RAFT_CUDA_TRY(
+      cudaMemsetAsync(actual_labels_.data(), 0, actual_labels_.size() * sizeof(int), stream));
+    RAFT_CUDA_TRY(
+      cudaMemsetAsync(expected_labels_.data(), 0, expected_labels_.size() * sizeof(int), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(input_.data(), 0, input_.size() * sizeof(float), stream));
+    RAFT_CUDA_TRY(
+      cudaMemsetAsync(search_data_.data(), 0, search_data_.size() * sizeof(float), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(indices_.data(), 0, indices_.size() * sizeof(IdxT), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(distances_.data(), 0, distances_.size() * sizeof(float), stream));
+    RAFT_CUDA_TRY(
+      cudaMemsetAsync(search_labels_.data(), 0, search_labels_.size() * sizeof(int), stream));
+
+    std::vector<float> row_major_input;
+    for (std::size_t i = 0; i < params_.input.size(); ++i) {
+      for (std::size_t j = 0; j < params_.input[i].size(); ++j) {
+        row_major_input.push_back(params_.input[i][j]);
+      }
+    }
+    rmm::device_buffer input_d =
+      rmm::device_buffer(row_major_input.data(), row_major_input.size() * sizeof(float), stream);
+    float* input_ptr = static_cast<float*>(input_d.data());
+
+    rmm::device_buffer labels_d =
+      rmm::device_buffer(params_.labels.data(), params_.labels.size() * sizeof(int), stream);
+    int* labels_ptr = static_cast<int*>(labels_d.data());
+
+    raft::copy(input_.data(), input_ptr, rows_ * cols_, stream);
+    raft::copy(search_data_.data(), input_ptr, rows_ * cols_, stream);
+    raft::copy(search_labels_.data(), labels_ptr, rows_, stream);
+    raft::resource::sync_stream(handle, stream);
+  }
+
+ private:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  KNNInputs params_;
+  int rows_;
+  int cols_;
+  rmm::device_uvector<float> input_;
+  rmm::device_uvector<float> search_data_;
+  rmm::device_uvector<IdxT> indices_;
+  rmm::device_uvector<float> distances_;
+  int k_;
+
+  rmm::device_uvector<int> search_labels_;
+  rmm::device_uvector<int> actual_labels_;
+  rmm::device_uvector<int> expected_labels_;
+};
+
+const std::vector<KNNInputs> inputs = {
+  // 2D
+  {{
+     {2.7810836, 2.550537003},
+     {1.465489372, 2.362125076},
+     {3.396561688, 4.400293529},
+     {1.38807019, 1.850220317},
+     {3.06407232, 3.005305973},
+     {7.627531214, 2.759262235},
+     {5.332441248, 2.088626775},
+     {6.922596716, 1.77106367},
+     {8.675418651, -0.242068655},
+     {7.673756466, 3.508563011},
+   },
+   2,
+   {0, 0, 0, 0, 0, 1, 1, 1, 1, 1}}};
+
+typedef KNNTest<float, int64_t> KNNTestFint64_t;
+TEST_P(KNNTestFint64_t, BruteForce) { this->testBruteForce(); }
+
+INSTANTIATE_TEST_CASE_P(KNNTest, KNNTestFint64_t, ::testing::ValuesIn(inputs));
+}  // namespace cuvs::neighbors::brute_force
\ No newline at end of file
diff --git a/cpp/test/neighbors/brute_force_c.cu b/cpp/test/neighbors/brute_force_c.cu
new file mode 100644
index 0000000000..7730a98c63
--- /dev/null
+++ b/cpp/test/neighbors/brute_force_c.cu
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/random/rng.cuh>
+
+#include "ann_utils.cuh"
+#include <cuvs/neighbors/brute_force.h>
+
+extern "C" void run_brute_force(int64_t n_rows,
+                                int64_t n_queries,
+                                int64_t n_dim,
+                                uint32_t n_neighbors,
+                                float* index_data,
+                                float* query_data,
+                                float* distances_data,
+                                int64_t* neighbors_data,
+                                enum DistanceType metric);
+
+template <typename T>
+void generate_random_data(T* devPtr, size_t size)
+{
+  raft::handle_t handle;
+  raft::random::RngState r(1234ULL);
+  raft::random::uniform(handle, r, devPtr, size, T(0.1), T(2.0));
+};
+
+template <typename T, typename IdxT>
+void recall_eval(T* query_data,
+                 T* index_data,
+                 IdxT* neighbors,
+                 T* distances,
+                 size_t n_queries,
+                 size_t n_rows,
+                 size_t n_dim,
+                 size_t n_neighbors,
+                 DistanceType metric)
+{
+  raft::handle_t handle;
+  auto distances_ref = raft::make_device_matrix<T, IdxT>(handle, n_queries, n_neighbors);
+  auto neighbors_ref = raft::make_device_matrix<IdxT, IdxT>(handle, n_queries, n_neighbors);
+  cuvs::neighbors::naive_knn<T, T, IdxT>(
+    handle,
+    distances_ref.data_handle(),
+    neighbors_ref.data_handle(),
+    query_data,
+    index_data,
+    n_queries,
+    n_rows,
+    n_dim,
+    n_neighbors,
+    static_cast<cuvs::distance::DistanceType>((uint16_t)metric));
+
+  size_t size = n_queries * n_neighbors;
+  std::vector<IdxT> neighbors_h(size);
+  std::vector<T> distances_h(size);
+  std::vector<IdxT> neighbors_ref_h(size);
+  std::vector<T> distances_ref_h(size);
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+  raft::copy(neighbors_h.data(), neighbors, size, stream);
+  raft::copy(distances_h.data(), distances, size, stream);
+  raft::copy(neighbors_ref_h.data(), neighbors_ref.data_handle(), size, stream);
+  raft::copy(distances_ref_h.data(), distances_ref.data_handle(), size, stream);
+
+  // verify output
+  double min_recall = 0.95;
+  ASSERT_TRUE(cuvs::neighbors::eval_neighbours(neighbors_ref_h,
+                                               neighbors_h,
+                                               distances_ref_h,
+                                               distances_h,
+                                               n_queries,
+                                               n_neighbors,
+                                               0.001,
+                                               min_recall));
+};
+
+TEST(BruteForceC, BuildSearch)
+{
+  int64_t n_rows       = 8096;
+  int64_t n_queries    = 128;
+  int64_t n_dim        = 32;
+  uint32_t n_neighbors = 8;
+
+  enum DistanceType metric = L2Expanded;
+
+  float *index_data, *query_data, *distances_data;
+  int64_t* neighbors_data;
+  cudaMalloc(&index_data, sizeof(float) * n_rows * n_dim);
+  cudaMalloc(&query_data, sizeof(float) * n_queries * n_dim);
+  cudaMalloc(&neighbors_data, sizeof(int64_t) * n_queries * n_neighbors);
+  cudaMalloc(&distances_data, sizeof(float) * n_queries * n_neighbors);
+
+  generate_random_data(index_data, n_rows * n_dim);
+  generate_random_data(query_data, n_queries * n_dim);
+
+  run_brute_force(n_rows,
+                  n_queries,
+                  n_dim,
+                  n_neighbors,
+                  index_data,
+                  query_data,
+                  distances_data,
+                  neighbors_data,
+                  metric);
+
+  recall_eval(query_data,
+              index_data,
+              neighbors_data,
+              distances_data,
+              n_queries,
+              n_rows,
+              n_dim,
+              n_neighbors,
+              metric);
+
+  // delete device memory
+  cudaFree(index_data);
+  cudaFree(query_data);
+  cudaFree(neighbors_data);
+  cudaFree(distances_data);
+}
diff --git a/cpp/test/neighbors/ivf_pq_helpers.cuh b/cpp/test/neighbors/ivf_pq_helpers.cuh
new file mode 100644
index 0000000000..79a310f07b
--- /dev/null
+++ b/cpp/test/neighbors/ivf_pq_helpers.cuh
@@ -0,0 +1,428 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/neighbors/detail/ivf_pq_build.cuh>
+#include <raft/neighbors/ivf_pq_types.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq::helpers {
+
+using namespace raft;
+using namespace raft::neighbors;
+using namespace raft::neighbors::ivf_pq;
+
+/**
+ * @defgroup ivf_pq_helpers Helper functions for manipulationg IVF PQ Index
+ * @{
+ */
+
+namespace codepacker {
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Bit compression is removed, which means output will have pq_dim dimensional vectors (one code per
+ * byte, instead of ceildiv(pq_dim * pq_bits, 8) bytes of pq codes).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data = index.lists()[label]->data.view();
+ *   // allocate the buffer for the output
+ *   uint32_t n_take = 4;
+ *   auto codes = raft::make_device_matrix<uint8_t>(res, n_take, index.pq_dim());
+ *   uint32_t offset = 0;
+ *   // unpack n_take elements from the list
+ *   ivf_pq::helpers::codepacker::unpack(res, list_data, index.pq_bits(), offset, codes.view());
+ * @endcode
+ *
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res raft resource
+ * @param[in] list_data block to read from
+ * @param[in] pq_bits bit length of encoded vector elements
+ * @param[in] offset
+ *   How many records in the list to skip.
+ * @param[out] codes
+ *   the destination buffer [n_take, index.pq_dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be smaller than the list size.
+ */
+inline void unpack(
+  raft::resources const& res,
+  device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, row_major> list_data,
+  uint32_t pq_bits,
+  uint32_t offset,
+  device_matrix_view<uint8_t, uint32_t, row_major> codes)
+{
+  raft::neighbors::ivf_pq::detail::unpack_list_data(
+    codes, list_data, offset, pq_bits, resource::get_cuda_stream(res));
+}
+
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<uint8_t>(res, n_vec, index.pq_dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_pq::helpers::codepacker::pack(
+ *       res, make_const_mdspan(codes.view()), index.pq_bits(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] codes flat PQ codes, one code per byte [n_vec, pq_dim]
+ * @param[in] pq_bits bit length of encoded vector elements
+ * @param[in] offset how many records to skip before writing the data into the list
+ * @param[in] list_data block to write into
+ */
+inline void pack(
+  raft::resources const& res,
+  device_matrix_view<const uint8_t, uint32_t, row_major> codes,
+  uint32_t pq_bits,
+  uint32_t offset,
+  device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, row_major> list_data)
+{
+  raft::neighbors::ivf_pq::detail::pack_list_data(
+    list_data, codes, offset, pq_bits, resource::get_cuda_stream(res));
+}
+}  // namespace codepacker
+
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * The list is identified by its label.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will write into the 137th cluster
+ *   uint32_t label = 137;
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<const uint8_t>(res, n_vec, index.pq_dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_pq::helpers::pack_list_data(res, &index, codes_to_pack, label, 42);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[inout] index IVF-PQ index.
+ * @param[in] codes flat PQ codes, one code per byte [n_rows, pq_dim]
+ * @param[in] label The id of the list (cluster) into which we write.
+ * @param[in] offset how many records to skip before writing the data into the list
+ */
+template <typename IdxT>
+void pack_list_data(raft::resources const& res,
+                    cuvs::neighbors::ivf_pq::index<IdxT>* index,
+                    device_matrix_view<const uint8_t, uint32_t, row_major> codes,
+                    uint32_t label,
+                    uint32_t offset)
+{
+  raft::neighbors::ivf_pq::detail::pack_list_data(
+    res, index->get_raft_index(), codes, label, offset);
+}
+
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`, one code per byte (independently of pq_bits).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will unpack the fourth cluster
+ *   uint32_t label = 3;
+ *   // Get the list size
+ *   uint32_t list_size = 0;
+ *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
+ * resource::get_cuda_stream(res)); resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto codes = raft::make_device_matrix<float>(res, list_size, index.pq_dim());
+ *   // unpack the whole list
+ *   ivf_pq::helpers::unpack_list_data(res, index, codes.view(), label, 0);
+ * @endcode
+ *
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[out] out_codes
+ *   the destination buffer [n_take, index.pq_dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ * @param[in] offset
+ *   How many records in the list to skip.
+ */
+template <typename IdxT>
+void unpack_list_data(raft::resources const& res,
+                      const cuvs::neighbors::ivf_pq::index<IdxT>& index,
+                      device_matrix_view<uint8_t, uint32_t, row_major> out_codes,
+                      uint32_t label,
+                      uint32_t offset)
+{
+  return raft::neighbors::ivf_pq::detail::unpack_list_data<IdxT>(
+    res, *index.get_raft_index(), out_codes, label, offset);
+}
+
+/**
+ * @brief Unpack a series of records of a single list (cluster) in the compressed index
+ * by their in-list offsets, one code per byte (independently of pq_bits).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will unpack the fourth cluster
+ *   uint32_t label = 3;
+ *   // Create the selection vector
+ *   auto selected_indices = raft::make_device_vector<uint32_t>(res, 4);
+ *   ... fill the indices ...
+ *   resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto codes = raft::make_device_matrix<float>(res, selected_indices.size(), index.pq_dim());
+ *   // decode the whole list
+ *   ivf_pq::helpers::unpack_list_data(
+ *       res, index, selected_indices.view(), codes.view(), label);
+ * @endcode
+ *
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[in] in_cluster_indices
+ *   The offsets of the selected indices within the cluster.
+ * @param[out] out_codes
+ *   the destination buffer [n_take, index.pq_dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ */
+template <typename IdxT>
+void unpack_list_data(raft::resources const& res,
+                      const cuvs::neighbors::ivf_pq::index<IdxT>& index,
+                      device_vector_view<const uint32_t> in_cluster_indices,
+                      device_matrix_view<uint8_t, uint32_t, row_major> out_codes,
+                      uint32_t label)
+{
+  return raft::neighbors::ivf_pq::detail::unpack_list_data<IdxT>(
+    res, index, out_codes, label, in_cluster_indices);
+}
+
+/**
+ * @brief Decode `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will reconstruct the fourth cluster
+ *   uint32_t label = 3;
+ *   // Get the list size
+ *   uint32_t list_size = 0;
+ *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
+ * resource::get_cuda_stream(res)); resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto decoded_vectors = raft::make_device_matrix<float>(res, list_size, index.dim());
+ *   // decode the whole list
+ *   ivf_pq::helpers::reconstruct_list_data(res, index, decoded_vectors.view(), label, 0);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[out] out_vectors
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to reconstruct,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ * @param[in] offset
+ *   How many records in the list to skip.
+ */
+template <typename T, typename IdxT>
+void reconstruct_list_data(raft::resources const& res,
+                           const cuvs::neighbors::ivf_pq::index<IdxT>& index,
+                           device_matrix_view<T, uint32_t, row_major> out_vectors,
+                           uint32_t label,
+                           uint32_t offset)
+{
+  return raft::neighbors::ivf_pq::detail::reconstruct_list_data(
+    res, *index.get_raft_index(), out_vectors, label, offset);
+}
+
+/**
+ * @brief Decode a series of records of a single list (cluster) in the compressed index
+ * by their in-list offsets.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will reconstruct the fourth cluster
+ *   uint32_t label = 3;
+ *   // Create the selection vector
+ *   auto selected_indices = raft::make_device_vector<uint32_t>(res, 4);
+ *   ... fill the indices ...
+ *   resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto decoded_vectors = raft::make_device_matrix<float>(
+ *                             res, selected_indices.size(), index.dim());
+ *   // decode the whole list
+ *   ivf_pq::helpers::reconstruct_list_data(
+ *       res, index, selected_indices.view(), decoded_vectors.view(), label);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[in] in_cluster_indices
+ *   The offsets of the selected indices within the cluster.
+ * @param[out] out_vectors
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to reconstruct,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ */
+template <typename T, typename IdxT>
+void reconstruct_list_data(raft::resources const& res,
+                           const cuvs::neighbors::ivf_pq::index<IdxT>& index,
+                           device_vector_view<const uint32_t> in_cluster_indices,
+                           device_matrix_view<T, uint32_t, row_major> out_vectors,
+                           uint32_t label)
+{
+  return raft::neighbors::ivf_pq::detail::reconstruct_list_data(
+    res, index, out_vectors, label, in_cluster_indices);
+}
+
+/**
+ * @brief Extend one list of the index in-place, by the list label, skipping the classification and
+ * encoding steps.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will extend the fourth cluster
+ *   uint32_t label = 3;
+ *   // We will fill 4 new vectors
+ *   uint32_t n_vec = 4;
+ *   // Indices of the new vectors
+ *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
+ *   ... fill the indices ...
+ *   auto new_codes = raft::make_device_matrix<uint8_t, uint32_t, row_major> new_codes(
+ *       res, n_vec, index.pq_dim());
+ *   ... fill codes ...
+ *   // extend list with new codes
+ *   ivf_pq::helpers::extend_list_with_codes(
+ *       res, &index, codes.view(), indices.view(), label);
+ * @endcode
+ *
+ * @tparam IdxT
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] new_codes flat PQ codes, one code per byte [n_rows, index.pq_dim()]
+ * @param[in] new_indices source indices [n_rows]
+ * @param[in] label the id of the target list (cluster).
+ */
+template <typename IdxT>
+void extend_list_with_codes(raft::resources const& res,
+                            cuvs::neighbors::ivf_pq::index<IdxT>* index,
+                            device_matrix_view<const uint8_t, uint32_t, row_major> new_codes,
+                            device_vector_view<const IdxT, uint32_t, row_major> new_indices,
+                            uint32_t label)
+{
+  raft::neighbors::ivf_pq::detail::extend_list_with_codes(
+    res, index->get_raft_index(), new_codes, new_indices, label);
+}
+
+/**
+ * @brief Extend one list of the index in-place, by the list label, skipping the classification
+ * step.
+ *
+ *  Usage example:
+ * @code{.cpp}
+ *   // We will extend the fourth cluster
+ *   uint32_t label = 3;
+ *   // We will extend with 4 new vectors
+ *   uint32_t n_vec = 4;
+ *   // Indices of the new vectors
+ *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
+ *   ... fill the indices ...
+ *   auto new_vectors = raft::make_device_matrix<float, uint32_t, row_major> new_codes(
+ *       res, n_vec, index.dim());
+ *   ... fill vectors ...
+ *   // extend list with new vectors
+ *   ivf_pq::helpers::extend_list(
+ *       res, &index, new_vectors.view(), indices.view(), label);
+ * @endcode
+ *
+ * @tparam T
+ * @tparam IdxT
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] new_vectors data to encode [n_rows, index.dim()]
+ * @param[in] new_indices source indices [n_rows]
+ * @param[in] label the id of the target list (cluster).
+ *
+ */
+template <typename T, typename IdxT>
+void extend_list(raft::resources const& res,
+                 cuvs::neighbors::ivf_pq::index<IdxT>* index,
+                 device_matrix_view<const T, uint32_t, row_major> new_vectors,
+                 device_vector_view<const IdxT, uint32_t, row_major> new_indices,
+                 uint32_t label)
+{
+  raft::neighbors::ivf_pq::detail::extend_list(
+    res, index->get_raft_index(), new_vectors, new_indices, label);
+}
+
+/**
+ * @brief Remove all data from a single list (cluster) in the index.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will erase the fourth cluster (label = 3)
+ *   ivf_pq::helpers::erase_list(res, &index, 3);
+ * @endcode
+ *
+ * @tparam IdxT
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] label the id of the target list (cluster).
+ */
+template <typename IdxT>
+void erase_list(raft::resources const& res,
+                cuvs::neighbors::ivf_pq::index<IdxT>* index,
+                uint32_t label)
+{
+  raft::neighbors::ivf_pq::detail::erase_list(res, index->get_raft_index(), label);
+}
+
+/** @} */
+}  // namespace cuvs::neighbors::ivf_pq::helpers
diff --git a/cpp/test/neighbors/run_brute_force_c.c b/cpp/test/neighbors/run_brute_force_c.c
new file mode 100644
index 0000000000..9c7af13a60
--- /dev/null
+++ b/cpp/test/neighbors/run_brute_force_c.c
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/brute_force.h>
+
+void run_brute_force(int64_t n_rows,
+                     int64_t n_queries,
+                     int64_t n_dim,
+                     uint32_t n_neighbors,
+                     float* index_data,
+                     float* query_data,
+                     float* distances_data,
+                     int64_t* neighbors_data,
+                     enum DistanceType metric)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // create dataset DLTensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {n_rows, n_dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // create index
+  cuvsBruteForceIndex_t index;
+  bruteForceIndexCreate(&index);
+
+  // build index
+  bruteForceBuild(res, &dataset_tensor, metric, 0.0f, index);
+
+  // create queries DLTensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = (void*)query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {n_queries, n_dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // create neighbors DLTensor
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = (void*)neighbors_data;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {n_queries, n_neighbors};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
+
+  // create distances DLTensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = (void*)distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {n_queries, n_neighbors};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // search index
+  bruteForceSearch(res, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+
+  // de-allocate index and res
+  bruteForceIndexDestroy(index);
+  cuvsResourcesDestroy(res);
+}
diff --git a/cpp/test/neighbors/run_ivf_flat_c.c b/cpp/test/neighbors/run_ivf_flat_c.c
new file mode 100644
index 0000000000..badb507a50
--- /dev/null
+++ b/cpp/test/neighbors/run_ivf_flat_c.c
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/ivf_flat.h>
+
+void run_ivf_flat(int64_t n_rows,
+                  int64_t n_queries,
+                  int64_t n_dim,
+                  uint32_t n_neighbors,
+                  float* index_data,
+                  float* query_data,
+                  float* distances_data,
+                  int64_t* neighbors_data,
+                  enum DistanceType metric,
+                  size_t n_probes,
+                  size_t n_lists)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // create dataset DLTensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {n_rows, n_dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // create index
+  cuvsIvfFlatIndex_t index;
+  ivfFlatIndexCreate(&index);
+
+  // build index
+  cuvsIvfFlatIndexParams_t build_params;
+  cuvsIvfFlatIndexParamsCreate(&build_params);
+  build_params->metric  = metric;
+  build_params->n_lists = n_lists;
+  ivfFlatBuild(res, build_params, &dataset_tensor, index);
+
+  // create queries DLTensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = (void*)query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {n_queries, n_dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // create neighbors DLTensor
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = (void*)neighbors_data;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {n_queries, n_neighbors};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
+
+  // create distances DLTensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = (void*)distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {n_queries, n_neighbors};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // search index
+  cuvsIvfFlatSearchParams_t search_params;
+  cuvsIvfFlatSearchParamsCreate(&search_params);
+  search_params->n_probes = n_probes;
+  ivfFlatSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+
+  // de-allocate index and res
+  cuvsIvfFlatSearchParamsDestroy(search_params);
+  cuvsIvfFlatIndexParamsDestroy(build_params);
+  ivfFlatIndexDestroy(index);
+  cuvsResourcesDestroy(res);
+}
diff --git a/cpp/test/neighbors/run_ivf_pq_c.c b/cpp/test/neighbors/run_ivf_pq_c.c
new file mode 100644
index 0000000000..fece4a644c
--- /dev/null
+++ b/cpp/test/neighbors/run_ivf_pq_c.c
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/ivf_pq.h>
+
+void run_ivf_pq(int64_t n_rows,
+                int64_t n_queries,
+                int64_t n_dim,
+                uint32_t n_neighbors,
+                float* index_data,
+                float* query_data,
+                float* distances_data,
+                int64_t* neighbors_data,
+                enum DistanceType metric,
+                size_t n_probes,
+                size_t n_lists)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // create dataset DLTensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {n_rows, n_dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // create index
+  cuvsIvfPqIndex_t index;
+  ivfPqIndexCreate(&index);
+
+  // build index
+  cuvsIvfPqIndexParams_t build_params;
+  cuvsIvfPqIndexParamsCreate(&build_params);
+  build_params->metric  = metric;
+  build_params->n_lists = n_lists;
+  ivfPqBuild(res, build_params, &dataset_tensor, index);
+
+  // create queries DLTensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = (void*)query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {n_queries, n_dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // create neighbors DLTensor
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = (void*)neighbors_data;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {n_queries, n_neighbors};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
+
+  // create distances DLTensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = (void*)distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {n_queries, n_neighbors};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // search index
+  cuvsIvfPqSearchParams_t search_params;
+  cuvsIvfPqSearchParamsCreate(&search_params);
+  search_params->n_probes = n_probes;
+  ivfPqSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+
+  // de-allocate index and res
+  cuvsIvfPqSearchParamsDestroy(search_params);
+  cuvsIvfPqIndexParamsDestroy(build_params);
+  ivfPqIndexDestroy(index);
+  cuvsResourcesDestroy(res);
+}
diff --git a/docs/source/c_api/neighbors.rst b/docs/source/c_api/neighbors.rst
index 6bbd3cc7f3..dc55a74dca 100644
--- a/docs/source/c_api/neighbors.rst
+++ b/docs/source/c_api/neighbors.rst
@@ -9,6 +9,7 @@ Nearest Neighbors
    :maxdepth: 2
    :caption: Contents:
 
-
-
-   neighbors_cagra_c.rst
\ No newline at end of file
+   neighbors_bruteforce_c.rst
+   neighbors_ivf_flat_c.rst
+   neighbors_ivf_pq_c.rst
+   neighbors_cagra_c.rst
diff --git a/docs/source/c_api/neighbors_bruteforce_c.rst b/docs/source/c_api/neighbors_bruteforce_c.rst
new file mode 100644
index 0000000000..af0356eee4
--- /dev/null
+++ b/docs/source/c_api/neighbors_bruteforce_c.rst
@@ -0,0 +1,34 @@
+Bruteforce
+==========
+
+The bruteforce method is running the KNN algorithm. It performs an extensive search, and in contrast to ANN methods produces an exact result.
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+``#include <raft/neighbors/bruteforce.h>``
+
+Index
+-----
+
+.. doxygengroup:: bruteforce_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: bruteforce_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: bruteforce_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/c_api/neighbors_ivf_flat_c.rst b/docs/source/c_api/neighbors_ivf_flat_c.rst
new file mode 100644
index 0000000000..9e1ccc0d1d
--- /dev/null
+++ b/docs/source/c_api/neighbors_ivf_flat_c.rst
@@ -0,0 +1,50 @@
+IVF-Flat
+========
+
+The IVF-Flat method is an ANN algorithm. It uses an inverted file index (IVF) with unmodified (that is, flat) vectors. This algorithm provides simple knobs to reduce the overall search space and to trade-off accuracy for speed.
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+``#include <raft/neighbors/ivf_flat.h>``
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: ivf_flat_c_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: ivf_flat_c_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: ivf_flat_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: ivf_flat_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: ivf_flat_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/c_api/neighbors_ivf_pq_c.rst b/docs/source/c_api/neighbors_ivf_pq_c.rst
new file mode 100644
index 0000000000..0707196098
--- /dev/null
+++ b/docs/source/c_api/neighbors_ivf_pq_c.rst
@@ -0,0 +1,50 @@
+IVF-PQ
+======
+
+The IVF-PQ method is an ANN algorithm. Like IVF-Flat, IVF-PQ splits the points into a number of clusters (also specified by a parameter called n_lists) and searches the closest clusters to compute the nearest neighbors (also specified by a parameter called n_probes), but it shrinks the sizes of the vectors using a technique called product quantization.
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+``#include <raft/neighbors/ivf_pq.h>``
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: ivf_pq_c_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: ivf_pq_c_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: ivf_pq_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: ivf_pq_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: ivf_pq_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index 67bc7f65b2..e04fff0b87 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -7,4 +7,5 @@ C++ API Documentation
 .. toctree::
    :maxdepth: 4
 
+   cpp_api/distance.rst
    cpp_api/neighbors.rst
diff --git a/docs/source/cpp_api/distance.rst b/docs/source/cpp_api/distance.rst
new file mode 100644
index 0000000000..21f4558ecd
--- /dev/null
+++ b/docs/source/cpp_api/distance.rst
@@ -0,0 +1,19 @@
+Distance
+========
+
+This page provides C++ class references for the publicly-exposed elements of the `cuvs/distance` package. cuVS's
+distances have been highly optimized and support a wide assortment of different distance measures.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+Distance Types
+--------------
+
+``#include <cuvs/distance/distance_types.hpp>``
+
+namespace *cuvs::distance*
+
+.. doxygenenum:: cuvs::distance::DistanceType
+   :project: cuvs
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index 61898cec86..f9006412c9 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -9,4 +9,7 @@ Nearest Neighbors
    :maxdepth: 2
    :caption: Contents:
 
-   neighbors_cagra.rst
\ No newline at end of file
+   neighbors_bruteforce.rst
+   neighbors_ivf_flat.rst
+   neighbors_ivf_pq.rst
+   neighbors_cagra.rst
diff --git a/docs/source/cpp_api/neighbors_bruteforce.rst b/docs/source/cpp_api/neighbors_bruteforce.rst
new file mode 100644
index 0000000000..3adcb01c52
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_bruteforce.rst
@@ -0,0 +1,36 @@
+Bruteforce
+==========
+
+The bruteforce method is running the KNN algorithm. It performs an extensive search, and in contrast to ANN methods produces an exact result.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/bruteforce.hpp>``
+
+namespace *cuvs::neighbors::bruteforce*
+
+Index
+-----
+
+.. doxygengroup:: bruteforce_cpp_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: bruteforce_cpp_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: bruteforce_cpp_index_search
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cpp_api/neighbors_ivf_flat.rst b/docs/source/cpp_api/neighbors_ivf_flat.rst
new file mode 100644
index 0000000000..3836223e10
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_ivf_flat.rst
@@ -0,0 +1,68 @@
+IVF-Flat
+========
+
+The IVF-Flat method is an ANN algorithm. It uses an inverted file index (IVF) with unmodified (that is, flat) vectors. This algorithm provides simple knobs to reduce the overall search space and to trade-off accuracy for speed.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/ivf_flat.hpp>``
+
+namespace *cuvs::neighbors::ivf_flat*
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: ivf_flat_cpp_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: ivf_flat_cpp_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: ivf_flat_cpp_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: ivf_flat_cpp_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index extend
+------------
+
+.. doxygengroup:: ivf_flat_cpp_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: ivf_flat_cpp_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: ivf_flat_cpp_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cpp_api/neighbors_ivf_pq.rst b/docs/source/cpp_api/neighbors_ivf_pq.rst
new file mode 100644
index 0000000000..0d4d7061a6
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_ivf_pq.rst
@@ -0,0 +1,68 @@
+IVF-PQ
+======
+
+The IVF-PQ method is an ANN algorithm. Like IVF-Flat, IVF-PQ splits the points into a number of clusters (also specified by a parameter called n_lists) and searches the closest clusters to compute the nearest neighbors (also specified by a parameter called n_probes), but it shrinks the sizes of the vectors using a technique called product quantization.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/ivf_pq.hpp>``
+
+namespace *cuvs::neighbors::ivf_pq*
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: ivf_pq_cpp_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: ivf_pq_cpp_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: ivf_pq_cpp_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: ivf_pq_cpp_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index extend
+------------
+
+.. doxygengroup:: ivf_pq_cpp_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: ivf_pq_cpp_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: ivf_pq_cpp_serialize
+    :project: cuvs
+    :members:
+    :content-only:

From b25c184557a70e1a5ce6e4fe71dd22a52c57e2a7 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Thu, 21 Mar 2024 10:33:12 -0400
Subject: [PATCH 021/623] Update pre-commit-hooks to v0.0.3 (#60)

This fixes an issue with how the `verify-copyright` hook handles multiple merge bases.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/60
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ce5e8e0e4a..576b53907a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -102,7 +102,7 @@ repos:
         hooks:
               - id: check-json
       - repo: https://github.com/rapidsai/pre-commit-hooks
-        rev: v0.0.1
+        rev: v0.0.3
         hooks:
           - id: verify-copyright
             files: |

From 724ce680218a5598a56b64f9d95f02eadcce46f2 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 21 Mar 2024 12:17:56 -0700
Subject: [PATCH 022/623] expose cuvsResources_t to python (#58)

The current python code was creating a cuvsResources_t to pass to the CAGRA apis on the build/search code. This both ignored the passed in `resources` object, and also leaked memory since it wasn't calling cuvsResourcesDestroy.

Fix by exposing the cuvsResources_t object to python.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/58
---
 python/cuvs/cuvs/common/CMakeLists.txt     |   2 +-
 python/cuvs/cuvs/common/__init__.py        |   4 +-
 python/cuvs/cuvs/common/c_api.pxd          |   1 +
 python/cuvs/cuvs/common/resources.pxd      |  22 ++++
 python/cuvs/cuvs/common/resources.pyx      | 120 +++++++++++++++++++++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx |  35 ++----
 python/cuvs/cuvs/test/test_doctests.py     |   1 +
 7 files changed, 154 insertions(+), 31 deletions(-)
 create mode 100644 python/cuvs/cuvs/common/resources.pxd
 create mode 100644 python/cuvs/cuvs/common/resources.pyx

diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index b477fdb322..066a73d351 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -13,7 +13,7 @@
 # =============================================================================
 
 # Set the list of Cython files to build
-set(cython_sources cydlpack.pyx exceptions.pyx)
+set(cython_sources cydlpack.pyx exceptions.pyx resources.pyx)
 set(linked_libraries cuvs::cuvs cuvs_c)
 
 # Build all of the Cython targets
diff --git a/python/cuvs/cuvs/common/__init__.py b/python/cuvs/cuvs/common/__init__.py
index eb5666659f..e66418306b 100644
--- a/python/cuvs/cuvs/common/__init__.py
+++ b/python/cuvs/cuvs/common/__init__.py
@@ -13,6 +13,6 @@
 # limitations under the License.
 
 
-from .temp_raft import auto_sync_resources
+from .resources import Resources, auto_sync_resources
 
-__all__ = ["auto_sync_resources"]
+__all__ = ["auto_sync_resources", "Resources"]
diff --git a/python/cuvs/cuvs/common/c_api.pxd b/python/cuvs/cuvs/common/c_api.pxd
index e4e62bf944..f99fd5348e 100644
--- a/python/cuvs/cuvs/common/c_api.pxd
+++ b/python/cuvs/cuvs/common/c_api.pxd
@@ -30,4 +30,5 @@ cdef extern from "cuvs/core/c_api.h":
     cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
     cuvsError_t cuvsResourcesDestroy(cuvsResources_t res)
     cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream)
+    cuvsError_t cuvsStreamSync(cuvsResources_t res)
     const char * cuvsGetLastErrorText()
diff --git a/python/cuvs/cuvs/common/resources.pxd b/python/cuvs/cuvs/common/resources.pxd
new file mode 100644
index 0000000000..69a5c894f4
--- /dev/null
+++ b/python/cuvs/cuvs/common/resources.pxd
@@ -0,0 +1,22 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuvs.common.c_api cimport cuvsResources_t
+
+
+cdef class Resources:
+    cdef cuvsResources_t c_obj
diff --git a/python/cuvs/cuvs/common/resources.pyx b/python/cuvs/cuvs/common/resources.pyx
new file mode 100644
index 0000000000..c0b72ae348
--- /dev/null
+++ b/python/cuvs/cuvs/common/resources.pyx
@@ -0,0 +1,120 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import functools
+
+from cuda.ccudart cimport cudaStream_t
+
+from cuvs.common.c_api cimport (
+    cuvsResources_t,
+    cuvsResourcesCreate,
+    cuvsResourcesDestroy,
+    cuvsStreamSet,
+    cuvsStreamSync,
+)
+
+from cuvs.common.exceptions import check_cuvs
+
+
+cdef class Resources:
+    """
+    Resources  is a lightweight python wrapper around the corresponding
+    C++ class of resources exposed by RAFT's C++ interface. Refer to
+    the header file raft/core/resources.hpp for interface level
+    details of this struct.
+
+    Parameters
+    ----------
+    stream : Optional stream to use for ordering CUDA instructions
+
+    Examples
+    --------
+
+    Basic usage:
+
+    >>> from cuvs.common import Resources
+    >>> handle = Resources()
+    >>>
+    >>> # call algos here
+    >>>
+    >>> # final sync of all work launched in the stream of this handle
+    >>> handle.sync()
+
+    Using a cuPy stream with cuVS Resources:
+
+    >>> import cupy
+    >>> from cuvs.common import Resources
+    >>>
+    >>> cupy_stream = cupy.cuda.Stream()
+    >>> handle = Resources(stream=cupy_stream.ptr)
+    """
+
+    def __cinit__(self, stream=None):
+        check_cuvs(cuvsResourcesCreate(&self.c_obj))
+        if stream:
+            check_cuvs(cuvsStreamSet(self.c_obj, <cudaStream_t>stream))
+
+    def sync(self):
+        check_cuvs(cuvsStreamSync(self.c_obj))
+
+    def get_c_obj(self):
+        """
+        Return the pointer to the underlying c_obj as a size_t
+        """
+        return <size_t> self.c_obj
+
+    def __dealloc__(self):
+        check_cuvs(cuvsResourcesDestroy(self.c_obj))
+
+
+_resources_param_string = """
+     resources : Optional cuVS Resource handle for reusing CUDA resources.
+        If Resources aren't supplied, CUDA resources will be
+        allocated inside this function and synchronized before the
+        function exits. If resources are supplied, you will need to
+        explicitly synchronize yourself by calling `resources.sync()`
+        before accessing the output.
+""".strip()
+
+
+def auto_sync_resources(f):
+    """Decorator to automatically call sync on a cuVS Resources object when
+    it isn't passed to a function.
+
+    When a resources=None is passed to the wrapped function, this decorator
+    will automatically create a default resources for the function, and
+    call sync on that resources when the function exits.
+
+    This will also insert the appropriate docstring for the resources parameter
+    """
+
+    @functools.wraps(f)
+    def wrapper(*args, resources=None, **kwargs):
+        sync_resources = resources is None
+        resources = resources if resources is not None else Resources()
+
+        ret_value = f(*args, resources=resources, **kwargs)
+
+        if sync_resources:
+            resources.sync()
+
+        return ret_value
+
+    wrapper.__doc__ = wrapper.__doc__.format(
+        resources_docstring=_resources_param_string
+    )
+    return wrapper
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index d64ac72ee0..818c93940a 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -19,20 +19,14 @@ import numpy as np
 
 cimport cuvs.common.cydlpack
 
-from cuvs.common.temp_raft import auto_sync_resources
+from cuvs.common.resources import auto_sync_resources
 
 from cython.operator cimport dereference as deref
 from libcpp cimport bool, cast
 
 from cuvs.common cimport cydlpack
 
-from pylibraft.common import (
-    DeviceResources,
-    Stream,
-    auto_convert_output,
-    cai_wrapper,
-    device_ndarray,
-)
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 from pylibraft.neighbors.common import _check_input_array
@@ -46,12 +40,6 @@ from libc.stdint cimport (
     uintptr_t,
 )
 
-from cuvs.common.c_api cimport (
-    cuvsError_t,
-    cuvsResources_t,
-    cuvsResourcesCreate,
-)
-
 from cuvs.common.exceptions import check_cuvs
 
 
@@ -86,7 +74,6 @@ cdef class IndexParams:
                  graph_degree=64,
                  build_algo="ivf_pq",
                  nn_descent_niter=20):
-
         cuvsCagraIndexParamsCreate(&self.params)
 
         # todo (dgd): enable once other metrics are present
@@ -136,7 +123,6 @@ cdef class Index:
         check_cuvs(cuvsCagraIndexCreate(&self.index))
 
     def __dealloc__(self):
-        cdef cuvsError_t index_destroy_status
         if self.index is not NULL:
             check_cuvs(cuvsCagraIndexDestroy(self.index))
 
@@ -202,20 +188,17 @@ def build_index(IndexParams index_params, dataset, resources=None):
     _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
                                     np.dtype('ubyte')])
 
-    cdef cuvsResources_t res_
-    cdef cuvsError_t cstat
-
-    check_cuvs(cuvsResourcesCreate(&res_))
-
     cdef Index idx = Index()
     cdef cuvsError_t build_status
     cdef cydlpack.DLManagedTensor* dataset_dlpack = \
         cydlpack.dlpack_c(dataset_ai)
     cdef cuvsCagraIndexParams* params = index_params.params
 
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
     with cuda_interruptible():
         check_cuvs(cuvsCagraBuild(
-            res_,
+            res,
             params,
             dataset_dlpack,
             idx.index
@@ -445,11 +428,6 @@ def search(SearchParams search_params,
     if not index.trained:
         raise ValueError("Index needs to be built before calling search.")
 
-    cdef cuvsResources_t res_
-    cdef cuvsError_t cstat
-
-    check_cuvs(cuvsResourcesCreate(&res_))
-
     # todo(dgd): we can make the check of dtype a parameter of wrap_array
     # in RAFT to make this a single call
     queries_cai = wrap_array(queries)
@@ -480,10 +458,11 @@ def search(SearchParams search_params,
         cydlpack.dlpack_c(neighbors_cai)
     cdef cydlpack.DLManagedTensor* distances_dlpack = \
         cydlpack.dlpack_c(distances_cai)
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
     with cuda_interruptible():
         check_cuvs(cuvsCagraSearch(
-            res_,
+            res,
             params,
             index.index,
             queries_dlpack,
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
index 6d56ffaa2f..d334aa3228 100644
--- a/python/cuvs/cuvs/test/test_doctests.py
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -90,6 +90,7 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 # doctests for here
 DOC_STRINGS = list(_find_doctests_in_obj(cuvs.neighbors))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.cagra))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.common))
 
 
 @pytest.mark.parametrize(

From 21e6d89d98273be6b8cdfb3849ee574aeef5f9ff Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Mon, 1 Apr 2024 17:49:36 -0500
Subject: [PATCH 023/623] Use `conda env create --yes` instead of `--force`
 (#62)

conda dropped support for the `--force` flag to `conda env create`. This changes that flag name to `--yes`.
See https://github.com/conda/conda/blob/main/CHANGELOG.md#2430-2024-03-12 and https://github.com/rapidsai/miniforge-cuda/pull/63 for more info.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Ray Douglass (https://github.com/raydouglass)
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cuvs/pull/62
---
 ci/build_docs.sh  | 2 +-
 ci/build_rust.sh  | 2 +-
 ci/check_style.sh | 2 +-
 ci/test_cpp.sh    | 2 +-
 ci/test_python.sh | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index 9b371d1f9e..9d4509e08a 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -11,7 +11,7 @@ rapids-dependency-file-generator \
   --file_key docs \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
-rapids-mamba-retry env create --force -f env.yaml -n docs
+rapids-mamba-retry env create --yes -f env.yaml -n docs
 
 # seeing failures on activating the environment here on unbound locals
 # apply workaround from https://github.com/conda/conda/issues/8186#issuecomment-532874667
diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index ad2202734a..dc4d4cf720 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -11,7 +11,7 @@ rapids-dependency-file-generator \
   --file_key rust \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
-rapids-mamba-retry env create --force -f env.yaml -n rust
+rapids-mamba-retry env create --yes -f env.yaml -n rust
 
 # seeing failures on activating the environment here on unbound locals
 # apply workaround from https://github.com/conda/conda/issues/8186#issuecomment-532874667
diff --git a/ci/check_style.sh b/ci/check_style.sh
index 0ee6e88e58..58cc1b944a 100755
--- a/ci/check_style.sh
+++ b/ci/check_style.sh
@@ -11,7 +11,7 @@ rapids-dependency-file-generator \
   --file_key checks \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
-rapids-mamba-retry env create --force -f env.yaml -n checks
+rapids-mamba-retry env create --yes -f env.yaml -n checks
 conda activate checks
 
 # Run pre-commit checks
diff --git a/ci/test_cpp.sh b/ci/test_cpp.sh
index 95ae6a69bd..75ec7c5b65 100755
--- a/ci/test_cpp.sh
+++ b/ci/test_cpp.sh
@@ -11,7 +11,7 @@ rapids-dependency-file-generator \
   --file_key test_cpp \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch)" | tee env.yaml
 
-rapids-mamba-retry env create --force -f env.yaml -n test
+rapids-mamba-retry env create --yes -f env.yaml -n test
 
 # Temporarily allow unbound variables for conda activation.
 set +u
diff --git a/ci/test_python.sh b/ci/test_python.sh
index 17fbed4243..1c23c92273 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -11,7 +11,7 @@ rapids-dependency-file-generator \
   --file_key test_python \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
-rapids-mamba-retry env create --force -f env.yaml -n test
+rapids-mamba-retry env create --yes -f env.yaml -n test
 
 # Temporarily allow unbound variables for conda activation.
 set +u

From 813c54fdf3524e583e81c6596cf5471bb105f1f6 Mon Sep 17 00:00:00 2001
From: Yinze <37508512+yinze00@users.noreply.github.com>
Date: Wed, 3 Apr 2024 04:31:58 +0800
Subject: [PATCH 024/623] [fix] link anchor of "Installing cuVS" (#64)

This PR fixes the broken anchor link for 'installing cuVS' in the Readme file

Authors:
  - Yinze (https://github.com/yinze00)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/64
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 160f466042..6ded5515c7 100755
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
 
 1. [Useful Resources](#useful-resources)
 2. [What is cuVS?](#what-is-cuvs)
-3. [Installing cuVS](#installing)
+3. [Installing cuVS](#installing-cuvs)
 4. [Getting Started](#getting-started)
 5. [Contributing](#contributing)
 6. [References](#references)

From 1fa8bef86a955a0d8b7848e0ae4922775d4e7e97 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 2 Apr 2024 22:49:34 +0200
Subject: [PATCH 025/623] Add CAGRA-Q compression (#61)

Minimal code changes to add CAGRA-Q compression to the CAGRA index.
When built with compression, the index uses the compressed dataset automatically during search. At this moment, there's not new search parameters compared to standard CAGRA.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/61
---
 cpp/include/cuvs/neighbors/cagra.h   | 61 +++++++++++++++++++++++++
 cpp/include/cuvs/neighbors/cagra.hpp | 59 ++++++++++++++++++++++++-
 cpp/src/neighbors/cagra_c.cpp        | 31 ++++++++++++-
 cpp/test/neighbors/ann_cagra.cuh     | 66 ++++++++++++++++++++++------
 4 files changed, 202 insertions(+), 15 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 912430a47b..3a1a8c9fec 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -40,6 +40,45 @@ enum cuvsCagraGraphBuildAlgo {
   NN_DESCENT
 };
 
+/** Parameters for VPQ compression. */
+struct cuvsCagraCompressionParams {
+  /**
+   * The bit length of the vector element after compression by PQ.
+   *
+   * Possible values: [4, 5, 6, 7, 8].
+   *
+   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
+   * performance, but the lower the recall.
+   */
+  uint32_t pq_bits;
+  /**
+   * The dimensionality of the vector after compression by PQ.
+   * When zero, an optimal value is selected using a heuristic.
+   *
+   * TODO: at the moment `dim` must be a multiple `pq_dim`.
+   */
+  uint32_t pq_dim;
+  /**
+   * Vector Quantization (VQ) codebook size - number of "coarse cluster centers".
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  uint32_t vq_n_centers;
+  /** The number of iterations searching for kmeans centers (both VQ & PQ phases). */
+  uint32_t kmeans_n_iters;
+  /**
+   * The fraction of data to use during iterative kmeans building (VQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double vq_kmeans_trainset_fraction;
+  /**
+   * The fraction of data to use during iterative kmeans building (PQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double pq_kmeans_trainset_fraction;
+};
+
+typedef struct cuvsCagraCompressionParams* cuvsCagraCompressionParams_t;
+
 /**
  * @brief Supplemental parameters to build CAGRA Index
  *
@@ -53,6 +92,12 @@ struct cuvsCagraIndexParams {
   enum cuvsCagraGraphBuildAlgo build_algo;
   /** Number of Iterations to run if building with NN_DESCENT */
   size_t nn_descent_niter;
+  /**
+   * Optional: specify compression parameters if compression is desired.
+   *
+   * NOTE: this is experimental new API, consider it unsafe.
+   */
+  cuvsCagraCompressionParams_t compression;
 };
 
 typedef struct cuvsCagraIndexParams* cuvsCagraIndexParams_t;
@@ -73,6 +118,22 @@ cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params);
  */
 cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params);
 
+/**
+ * @brief Allocate CAGRA Compression params, and populate with default values
+ *
+ * @param[in] params cuvsCagraCompressionParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraCompressionParamsCreate(cuvsCagraCompressionParams_t* params);
+
+/**
+ * @brief De-allocate CAGRA Compression params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraCompressionParamsDestroy(cuvsCagraCompressionParams_t params);
+
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 0f2919ff46..d6f5fef496 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -25,6 +25,8 @@
 #include <raft/neighbors/cagra_types.hpp>
 #include <rmm/cuda_stream_view.hpp>
 
+#include <optional>
+
 namespace cuvs::neighbors::cagra {
 /**
  * @defgroup cagra_cpp_index_params CAGRA index build parameters
@@ -42,6 +44,54 @@ enum class graph_build_algo {
   NN_DESCENT
 };
 
+/** Parameters for VPQ compression. */
+struct vpq_params {
+  /**
+   * The bit length of the vector element after compression by PQ.
+   *
+   * Possible values: [4, 5, 6, 7, 8].
+   *
+   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
+   * performance, but the lower the recall.
+   */
+  uint32_t pq_bits = 8;
+  /**
+   * The dimensionality of the vector after compression by PQ.
+   * When zero, an optimal value is selected using a heuristic.
+   *
+   * TODO: at the moment `dim` must be a multiple `pq_dim`.
+   */
+  uint32_t pq_dim = 0;
+  /**
+   * Vector Quantization (VQ) codebook size - number of "coarse cluster centers".
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  uint32_t vq_n_centers = 0;
+  /** The number of iterations searching for kmeans centers (both VQ & PQ phases). */
+  uint32_t kmeans_n_iters = 25;
+  /**
+   * The fraction of data to use during iterative kmeans building (VQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double vq_kmeans_trainset_fraction = 0;
+  /**
+   * The fraction of data to use during iterative kmeans building (PQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double pq_kmeans_trainset_fraction = 0;
+
+  /** Build a raft CAGRA index params from an existing cuvs CAGRA index params. */
+  operator raft::neighbors::vpq_params() const
+  {
+    return {.pq_bits                     = pq_bits,
+            .pq_dim                      = pq_dim,
+            .vq_n_centers                = vq_n_centers,
+            .kmeans_n_iters              = kmeans_n_iters,
+            .vq_kmeans_trainset_fraction = vq_kmeans_trainset_fraction,
+            .pq_kmeans_trainset_fraction = pq_kmeans_trainset_fraction};
+  }
+};
+
 struct index_params : ann::index_params {
   /** Degree of input graph for pruning. */
   size_t intermediate_graph_degree = 128;
@@ -51,6 +101,12 @@ struct index_params : ann::index_params {
   graph_build_algo build_algo = graph_build_algo::IVF_PQ;
   /** Number of Iterations to run if building with NN_DESCENT */
   size_t nn_descent_niter = 20;
+  /**
+   * Specify compression parameters if compression is desired.
+   *
+   * NOTE: this is experimental new API, consider it unsafe.
+   */
+  std::optional<vpq_params> compression = std::nullopt;
 
   /** Build a raft CAGRA index params from an existing cuvs CAGRA index params. */
   operator raft::neighbors::cagra::index_params() const
@@ -64,7 +120,8 @@ struct index_params : ann::index_params {
       .intermediate_graph_degree = intermediate_graph_degree,
       .graph_degree              = graph_degree,
       .build_algo       = static_cast<raft::neighbors::cagra::graph_build_algo>((int)build_algo),
-      .nn_descent_niter = nn_descent_niter};
+      .nn_descent_niter = nn_descent_niter,
+      .compression      = compression};
   }
 };
 
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 9e50870163..2e96e8db68 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -45,6 +45,17 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
     static_cast<cuvs::neighbors::cagra::graph_build_algo>(params.build_algo);
   build_params.nn_descent_niter = params.nn_descent_niter;
 
+  if (auto* cparams = params.compression; cparams != nullptr) {
+    auto compression_params                        = cuvs::neighbors::cagra::vpq_params();
+    compression_params.pq_bits                     = cparams->pq_bits;
+    compression_params.pq_dim                      = cparams->pq_dim;
+    compression_params.vq_n_centers                = cparams->vq_n_centers;
+    compression_params.kmeans_n_iters              = cparams->kmeans_n_iters;
+    compression_params.vq_kmeans_trainset_fraction = cparams->vq_kmeans_trainset_fraction;
+    compression_params.pq_kmeans_trainset_fraction = cparams->pq_kmeans_trainset_fraction;
+    build_params.compression.emplace(compression_params);
+  }
+
   if (cuvs::core::is_dlpack_device_compatible(dataset)) {
     using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
     auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
@@ -129,7 +140,6 @@ extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
 {
   return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
-
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       index->addr       = reinterpret_cast<uintptr_t>(_build<float>(res, *params, dataset_tensor));
       index->dtype.code = kDLFloat;
@@ -203,6 +213,25 @@ extern "C" cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params
   return cuvs::core::translate_exceptions([=] { delete params; });
 }
 
+extern "C" cuvsError_t cuvsCagraCompressionParamsCreate(cuvsCagraCompressionParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto ps = cuvs::neighbors::cagra::vpq_params();
+    *params =
+      new cuvsCagraCompressionParams{.pq_bits                     = ps.pq_bits,
+                                     .pq_dim                      = ps.pq_dim,
+                                     .vq_n_centers                = ps.vq_n_centers,
+                                     .kmeans_n_iters              = ps.kmeans_n_iters,
+                                     .vq_kmeans_trainset_fraction = ps.vq_kmeans_trainset_fraction,
+                                     .pq_kmeans_trainset_fraction = ps.pq_kmeans_trainset_fraction};
+  });
+}
+
+extern "C" cuvsError_t cuvsCagraCompressionParamsDestroy(cuvsCagraCompressionParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
 extern "C" cuvsError_t cuvsCagraSearchParamsCreate(cuvsCagraSearchParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 9c58275c60..c9bc0808d2 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -39,6 +39,7 @@
 
 #include <cstddef>
 #include <iostream>
+#include <optional>
 #include <string>
 #include <vector>
 
@@ -144,6 +145,7 @@ struct AnnCagraInputs {
   bool include_serialized_dataset;
   // std::optional<double>
   double min_recall;  // = std::nullopt;
+  std::optional<vpq_params> compression = std::nullopt;
 };
 
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
@@ -154,7 +156,13 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
      << ", k=" << p.k << ", " << algo.at((int)p.algo) << ", max_queries=" << p.max_queries
      << ", itopk_size=" << p.itopk_size << ", search_width=" << p.search_width
      << ", metric=" << static_cast<int>(p.metric) << (p.host_dataset ? ", host" : ", device")
-     << ", build_algo=" << build_algo.at((int)p.build_algo) << '}' << std::endl;
+     << ", build_algo=" << build_algo.at((int)p.build_algo);
+  if (p.compression.has_value()) {
+    auto vpq = p.compression.value();
+    os << ", pq_bits=" << vpq.pq_bits << ", pq_dim=" << vpq.pq_dim
+       << ", vq_n_centers=" << vpq.vq_n_centers;
+  }
+  os << '}' << std::endl;
   return os;
 }
 
@@ -204,7 +212,8 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
         cagra::index_params index_params;
         index_params.metric = ps.metric;  // Note: currently ony the cagra::index_params metric is
                                           // not used for knn_graph building.
-        index_params.build_algo = ps.build_algo;
+        index_params.build_algo  = ps.build_algo;
+        index_params.compression = ps.compression;
         cagra::search_params search_params;
         search_params.algo        = ps.algo;
         search_params.max_queries = ps.max_queries;
@@ -261,17 +270,20 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
                                   ps.k,
                                   0.003,
                                   min_recall));
-      EXPECT_TRUE(eval_distances(handle_,
-                                 database.data(),
-                                 search_queries.data(),
-                                 indices_dev.data(),
-                                 distances_dev.data(),
-                                 ps.n_rows,
-                                 ps.dim,
-                                 ps.n_queries,
-                                 ps.k,
-                                 ps.metric,
-                                 1.0e-4));
+      if (!ps.compression.has_value()) {
+        // Don't evaluate distances for CAGRA-Q for now as the error can be somewhat large
+        EXPECT_TRUE(eval_distances(handle_,
+                                   database.data(),
+                                   search_queries.data(),
+                                   indices_dev.data(),
+                                   distances_dev.data(),
+                                   ps.n_rows,
+                                   ps.dim,
+                                   ps.n_queries,
+                                   ps.k,
+                                   ps.metric,
+                                   1.0e-4));
+      }
     }
   }
 
@@ -394,6 +406,34 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
+  // a few PQ configurations
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {10000},
+    {64, 128, 192, 256, 512, 1024},  // dim
+    {16},                            // k
+    {graph_build_algo::IVF_PQ},
+    {search_algo::AUTO},
+    {10},
+    {0},
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {true},
+    {0.6});                      // don't demand high recall without refinement
+  for (uint32_t pq_len : {2}) {  // for now, only pq_len = 2 is supported, more options coming soon
+    for (uint32_t vq_n_centers : {100, 1000}) {
+      for (auto input : inputs2) {
+        vpq_params ps{};
+        ps.pq_dim       = input.dim / pq_len;
+        ps.vq_n_centers = vq_n_centers;
+        input.compression.emplace(ps);
+        inputs.push_back(input);
+      }
+    }
+  }
+
   return inputs;
 }
 

From d62e1ce574077d08d5572d34c13fa7743bb57dad Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Tue, 2 Apr 2024 18:55:28 -0400
Subject: [PATCH 026/623] CAGRA C example and DlPack docs (#51)

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/51
---
 build.sh                                      |   2 +-
 docs/source/interoperability.rst              |  34 ++++
 examples/{cpp => }/README.md                  |   6 +-
 examples/build.sh                             |  55 +++++++
 examples/c/CMakeLists.txt                     |  37 +++++
 examples/c/src/cagra_c_example.c              | 148 ++++++++++++++++++
 .../cmake/thirdparty/fetch_rapids.cmake       |   0
 .../{cpp => }/cmake/thirdparty/get_cuvs.cmake |  14 +-
 examples/cmake/thirdparty/get_dlpack.cmake    |  41 +++++
 examples/cpp/CMakeLists.txt                   |   8 +-
 examples/cpp/build.sh                         |  41 -----
 examples/cpp/src/common.cuh                   |  67 ++++----
 12 files changed, 372 insertions(+), 81 deletions(-)
 rename examples/{cpp => }/README.md (73%)
 create mode 100755 examples/build.sh
 create mode 100644 examples/c/CMakeLists.txt
 create mode 100644 examples/c/src/cagra_c_example.c
 rename examples/{cpp => }/cmake/thirdparty/fetch_rapids.cmake (100%)
 rename examples/{cpp => }/cmake/thirdparty/get_cuvs.cmake (79%)
 create mode 100644 examples/cmake/thirdparty/get_dlpack.cmake
 delete mode 100755 examples/cpp/build.sh

diff --git a/build.sh b/build.sh
index 0c987ed962..822b845904 100755
--- a/build.sh
+++ b/build.sh
@@ -419,7 +419,7 @@ fi
 # Initiate build for c++ examples (if needed)
 
 if hasArg examples; then
-    pushd ${REPODIR}/examples/cpp
+    pushd ${REPODIR}/examples
     ./build.sh
     popd
 fi
diff --git a/docs/source/interoperability.rst b/docs/source/interoperability.rst
index fc8d19ef43..4dfe1e09af 100644
--- a/docs/source/interoperability.rst
+++ b/docs/source/interoperability.rst
@@ -4,6 +4,40 @@ Interoperability
 DLPack (C)
 ^^^^^^^^^^
 
+Approximate nearest neighbor (ANN) indexes provide an interface to build and search an index via a C API. [DLPack v0.8](https://github.com/dmlc/dlpack/blob/main/README.md), a tensor interface framework, is used as the standard to interact with our C API.
+
+Representing a tensor with DLPack is simple, as it is a POD struct that stores information about the tensor at runtime. At the moment, `DLManagedTensor` from DLPack v0.8 is compatible with out C API however we will soon upgrade to `DLManagedTensorVersioned` from DLPack v1.0 as it will help us maintain ABI and API compatibility.
+
+Here's an example on how to represent device memory using `DLManagedTensor`:
+
+.. code-block:: c
+
+    #include <dlpack/dlpack.h>
+
+    // Create data representation in host memory
+    float dataset[2][1] = {{0.2, 0.1}};
+    // copy data to device memory
+    float *dataset_dev;
+    cuvsRMMAlloc(&dataset_dev, sizeof(float) * 2 * 1);
+    cudaMemcpy(dataset_dev, dataset, sizeof(float) * 2 * 1, cudaMemcpyDefault);
+
+    // Use DLPack for representing the data as a tensor
+    DLManagedTensor dataset_tensor;
+    dataset_tensor.dl_tensor.data               = dataset;
+    dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+    dataset_tensor.dl_tensor.ndim               = 2;
+    dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+    dataset_tensor.dl_tensor.dtype.bits         = 32;
+    dataset_tensor.dl_tensor.dtype.lanes        = 1;
+    int64_t dataset_shape[2]                    = {2, 1};
+    dataset_tensor.dl_tensor.shape              = dataset_shape;
+    dataset_tensor.dl_tensor.strides            = nullptr;
+
+    // free memory after use
+    cuvsRMMFree(dataset_dev);
+
+Please refer to cuVS C API `documentation <c_api.rst>`_ to learn more.
+
 Multi-dimensional span (C++)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/examples/cpp/README.md b/examples/README.md
similarity index 73%
rename from examples/cpp/README.md
rename to examples/README.md
index 5a98dd3904..701fd0deee 100644
--- a/examples/cpp/README.md
+++ b/examples/README.md
@@ -1,4 +1,4 @@
-# cuVS C++ Examples
+# cuVS C and C++ Examples
 
 This template project provides a drop-in sample to either start building a new application with, or using CUVS in an existing CMake project. 
 
@@ -6,11 +6,11 @@ First, please refer to our [installation docs](https://docs.rapids.ai/api/cuvs/s
 
 Once the minimum requirements are satisfied, this example template application can be built with the provided `build.sh` script. This is a bash script that calls the appropriate CMake commands, so you can look into it to see the typical CMake based build workflow.  
 
-This directory (`CUVS_SOURCE/examples/cpp`) can be copied directly in order to build a new application with CUVS.
+The directories (`CUVS_SOURCE/examples/c`) or (`CUVS_SOURCE/examples/cpp`) can be copied directly in order to build a new application with cuVS.
 
 cuVS can be integrated into an existing CMake project by copying the contents in the `configure rapids-cmake` and `configure cuvs` sections of the provided `CMakeLists.txt` into your project, along with `cmake/thirdparty/get_cuvs.cmake`. 
 
-Make sure to link against the appropriate Cmake targets. Use `cuvs::cuvs` to utilize the shared library.
+Make sure to link against the appropriate CMake targets. Use `cuvs::c_api` and `cuvs::cuvs` to use the C and C++ shared libraries respectively.
 
 ```cmake
 target_link_libraries(your_app_target PRIVATE cuvs::cuvs)
diff --git a/examples/build.sh b/examples/build.sh
new file mode 100755
index 0000000000..cd021ca21d
--- /dev/null
+++ b/examples/build.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+
+# cuvs empty project template build script
+
+# Abort script on first error
+set -e
+
+PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
+
+BUILD_TYPE=Release
+BUILD_DIR=build/
+
+CUVS_REPO_REL=""
+EXTRA_CMAKE_ARGS=""
+set -e
+
+# Root of examples
+EXAMPLES_DIR=$(dirname "$(realpath "$0")")
+
+if [[ ${CUVS_REPO_REL} != "" ]]; then
+  CUVS_REPO_PATH="`readlink -f \"${CUVS_REPO_REL}\"`"
+  EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DCPM_cuvs_SOURCE=${CUVS_REPO_PATH}"
+else
+  LIB_BUILD_DIR=${LIB_BUILD_DIR:-$(readlink -f "${EXAMPLES_DIR}/../cpp/build")}
+  EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -Dcuvs_ROOT=${LIB_BUILD_DIR}"
+fi
+
+if [ "$1" == "clean" ]; then
+  rm -rf build
+  exit 0
+fi
+
+################################################################################
+# Add individual libcudf examples build scripts down below
+
+build_example() {
+  example_dir=${1}
+  example_dir="${EXAMPLES_DIR}/${example_dir}"
+  build_dir="${example_dir}/build"
+
+  # Configure
+  cmake -S ${example_dir} -B ${build_dir} \
+  -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+  -DCUVS_NVTX=OFF \
+  -DCMAKE_CUDA_ARCHITECTURES="native" \
+  -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
+  ${EXTRA_CMAKE_ARGS}
+  # Build
+  cmake --build ${build_dir} -j${PARALLEL_LEVEL}
+}
+
+build_example c
+build_example cpp
diff --git a/examples/c/CMakeLists.txt b/examples/c/CMakeLists.txt
new file mode 100644
index 0000000000..fb508728f0
--- /dev/null
+++ b/examples/c/CMakeLists.txt
@@ -0,0 +1,37 @@
+# =============================================================================
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+# ------------- configure rapids-cmake --------------#
+
+include(../cmake/thirdparty/fetch_rapids.cmake)
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-export)
+include(rapids-find)
+
+# ------------- configure project --------------#
+
+project(test_cuvs_c LANGUAGES C CXX CUDA)
+
+# ------------- configure cuvs -----------------#
+
+rapids_cpm_init()
+set(BUILD_CUVS_C_LIBRARY ON)
+include(../cmake/thirdparty/get_dlpack.cmake)
+include(../cmake/thirdparty/get_cuvs.cmake)
+
+add_executable(CAGRA_C_EXAMPLE src/cagra_c_example.c)
+target_include_directories(CAGRA_C_EXAMPLE PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
+target_link_libraries(CAGRA_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/c/src/cagra_c_example.c b/examples/c/src/cagra_c_example.c
new file mode 100644
index 0000000000..fdcbbf5712
--- /dev/null
+++ b/examples/c/src/cagra_c_example.c
@@ -0,0 +1,148 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/cagra.h>
+
+#include <dlpack/dlpack.h>
+
+#include <cuda_runtime.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+float dataset[4][2] = {{0.74021935, 0.9209938},
+                       {0.03902049, 0.9689629},
+                       {0.92514056, 0.4463501},
+                       {0.6673192, 0.10993068}};
+float queries[4][2] = {{0.48216683, 0.0428398},
+                       {0.5084142, 0.6545497},
+                       {0.51260436, 0.2643005},
+                       {0.05198065, 0.5789965}};
+
+void cagra_build_search_simple() {
+
+  int64_t n_rows = 4;
+  int64_t n_cols = 2;
+  int64_t topk = 2;
+  int64_t n_queries = 4;
+
+  // Create a cuvsResources_t object
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // Use DLPack to represent `dataset` as a tensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data = dataset;
+  dataset_tensor.dl_tensor.device.device_type = kDLCPU;
+  dataset_tensor.dl_tensor.ndim = 2;
+  dataset_tensor.dl_tensor.dtype.code = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits = 32;
+  dataset_tensor.dl_tensor.dtype.lanes = 1;
+  int64_t dataset_shape[2] = {n_rows, n_cols};
+  dataset_tensor.dl_tensor.shape = dataset_shape;
+  dataset_tensor.dl_tensor.strides = NULL;
+
+  // Build the CAGRA index
+  cuvsCagraIndexParams_t index_params;
+  cuvsCagraIndexParamsCreate(&index_params);
+
+  cuvsCagraIndex_t index;
+  cuvsCagraIndexCreate(&index);
+
+  cuvsCagraBuild(res, index_params, &dataset_tensor, index);
+
+  // Allocate memory for `queries`, `neighbors` and `distances` output
+  uint32_t *neighbors;
+  float *distances, *queries_d;
+  cuvsRMMAlloc(res, (void**) &queries_d, sizeof(float) * n_queries * n_cols);
+  cuvsRMMAlloc(res, (void**) &neighbors, sizeof(uint32_t) * n_queries * topk);
+  cuvsRMMAlloc(res, (void**) &distances, sizeof(float) * n_queries * topk);
+
+  // Use DLPack to represent `queries`, `neighbors` and `distances` as tensors
+  cudaMemcpy(queries_d, queries, sizeof(float) * 4 * 2, cudaMemcpyDefault);
+
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data = queries_d;
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;
+  queries_tensor.dl_tensor.ndim = 2;
+  queries_tensor.dl_tensor.dtype.code = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits = 32;
+  queries_tensor.dl_tensor.dtype.lanes = 1;
+  int64_t queries_shape[2] = {n_queries, n_cols};
+  queries_tensor.dl_tensor.shape = queries_shape;
+  queries_tensor.dl_tensor.strides = NULL;
+
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data = neighbors;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
+  neighbors_tensor.dl_tensor.ndim = 2;
+  neighbors_tensor.dl_tensor.dtype.code = kDLUInt;
+  neighbors_tensor.dl_tensor.dtype.bits = 32;
+  neighbors_tensor.dl_tensor.dtype.lanes = 1;
+  int64_t neighbors_shape[2] = {n_queries, topk};
+  neighbors_tensor.dl_tensor.shape = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides = NULL;
+
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data = distances;
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim = 2;
+  distances_tensor.dl_tensor.dtype.code = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits = 32;
+  distances_tensor.dl_tensor.dtype.lanes = 1;
+  int64_t distances_shape[2] = {n_queries, topk};
+  distances_tensor.dl_tensor.shape = distances_shape;
+  distances_tensor.dl_tensor.strides = NULL;
+
+  // Search the CAGRA index
+  cuvsCagraSearchParams_t search_params;
+  cuvsCagraSearchParamsCreate(&search_params);
+
+  cuvsCagraSearch(res, search_params, index, &queries_tensor, &neighbors_tensor,
+                  &distances_tensor);
+
+  // print results
+  uint32_t *neighbors_h =
+      (uint32_t *)malloc(sizeof(uint32_t) * n_queries * topk);
+  float *distances_h = (float *)malloc(sizeof(float) * n_queries * topk);
+  cudaMemcpy(neighbors_h, neighbors, sizeof(uint32_t) * n_queries * topk,
+             cudaMemcpyDefault);
+  cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk,
+             cudaMemcpyDefault);
+  printf("Query 0 neighbor indices: =[%d, %d]\n", neighbors_h[0],
+         neighbors_h[1]);
+  printf("Query 0 neighbor distances: =[%f, %f]\n", distances_h[0],
+         distances_h[1]);
+
+  // Free or destroy all allocations
+  free(neighbors_h);
+  free(distances_h);
+
+  cuvsCagraSearchParamsDestroy(search_params);
+
+  cuvsRMMFree(res, distances, sizeof(float) * n_queries * topk);
+  cuvsRMMFree(res, neighbors, sizeof(uint32_t) * n_queries * topk);
+  cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_cols);
+
+  cuvsCagraIndexDestroy(index);
+  cuvsCagraIndexParamsDestroy(index_params);
+  cuvsResourcesDestroy(res);
+}
+
+int main() {
+  // Simple build and search example.
+  cagra_build_search_simple();
+}
diff --git a/examples/cpp/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
similarity index 100%
rename from examples/cpp/cmake/thirdparty/fetch_rapids.cmake
rename to examples/cmake/thirdparty/fetch_rapids.cmake
diff --git a/examples/cpp/cmake/thirdparty/get_cuvs.cmake b/examples/cmake/thirdparty/get_cuvs.cmake
similarity index 79%
rename from examples/cpp/cmake/thirdparty/get_cuvs.cmake
rename to examples/cmake/thirdparty/get_cuvs.cmake
index 0b3821e207..a2a4e88420 100644
--- a/examples/cpp/cmake/thirdparty/get_cuvs.cmake
+++ b/examples/cmake/thirdparty/get_cuvs.cmake
@@ -17,22 +17,29 @@ set(CUVS_FORK "rapidsai")
 set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION}")
 
 function(find_and_configure_cuvs)
-    set(oneValueArgs VERSION FORK PINNED_TAG ENABLE_NVTX)
+    set(oneValueArgs VERSION FORK PINNED_TAG ENABLE_NVTX BUILD_CUVS_C_LIBRARY)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
+
+    set(CUVS_COMPONENTS "")
+    if(PKG_BUILD_CUVS_C_LIBRARY)
+        string(APPEND CUVS_COMPONENTS " c_api")
+    endif()
     #-----------------------------------------------------
     # Invoke CPM find_package()
     #-----------------------------------------------------
     rapids_cpm_find(cuvs ${PKG_VERSION}
             GLOBAL_TARGETS      cuvs::cuvs
-            BUILD_EXPORT_SET    cuvs-template-exports
-            INSTALL_EXPORT_SET  cuvs-template-exports
+            BUILD_EXPORT_SET    cuvs-examples-exports
+            INSTALL_EXPORT_SET  cuvs-examples-exports
+            COMPONENTS ${CUVS_COMPONENTS}
             CPM_ARGS
             GIT_REPOSITORY https://github.com/${PKG_FORK}/cuvs.git
             GIT_TAG        ${PKG_PINNED_TAG}
             SOURCE_SUBDIR  cpp
             OPTIONS
+            "BUILD_C_LIBRARY ${PKG_BUILD_CUVS_C_LIBRARY}"
             "BUILD_TESTS OFF"
             "CUVS_NVTX ${PKG_ENABLE_NVTX}"
             )
@@ -45,4 +52,5 @@ find_and_configure_cuvs(VERSION  ${CUVS_VERSION}.00
         FORK                     ${CUVS_FORK}
         PINNED_TAG               ${CUVS_PINNED_TAG}
         ENABLE_NVTX              OFF
+        BUILD_CUVS_C_LIBRARY     ${BUILD_CUVS_C_LIBRARY}
 )
diff --git a/examples/cmake/thirdparty/get_dlpack.cmake b/examples/cmake/thirdparty/get_dlpack.cmake
new file mode 100644
index 0000000000..7c90cb3d23
--- /dev/null
+++ b/examples/cmake/thirdparty/get_dlpack.cmake
@@ -0,0 +1,41 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds dlpack and sets any additional necessary environment variables.
+function(find_and_configure_dlpack VERSION)
+
+  include(${rapids-cmake-dir}/find/generate_module.cmake)
+  rapids_find_generate_module(DLPACK HEADER_NAMES dlpack.h)
+
+  rapids_cpm_find(
+    dlpack ${VERSION}
+    GIT_REPOSITORY https://github.com/dmlc/dlpack.git
+    GIT_TAG v${VERSION}
+    GIT_SHALLOW TRUE
+    DOWNLOAD_ONLY TRUE
+    OPTIONS "BUILD_MOCK OFF"
+  )
+
+  if(DEFINED dlpack_SOURCE_DIR)
+    # otherwise find_package(DLPACK) will set this variable
+    set(DLPACK_INCLUDE_DIR
+        "${dlpack_SOURCE_DIR}/include"
+        PARENT_SCOPE
+    )
+  endif()
+endfunction()
+
+set(CUVS_MIN_VERSION_dlpack 0.8)
+
+find_and_configure_dlpack(${CUVS_MIN_VERSION_dlpack})
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index dc8a70a5ff..5ff7bef612 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -15,7 +15,7 @@ cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
 
 # ------------- configure rapids-cmake --------------#
 
-include(cmake/thirdparty/fetch_rapids.cmake)
+include(../cmake/thirdparty/fetch_rapids.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-cuda)
@@ -31,8 +31,12 @@ project(test_cuvs LANGUAGES CXX CUDA)
 # ------------- configure cuvs -----------------#
 
 rapids_cpm_init()
-include(cmake/thirdparty/get_cuvs.cmake)
+set(BUILD_CUVS_C_LIBRARY OFF)
+include(../cmake/thirdparty/get_cuvs.cmake)
 
 # -------------- compile tasks ----------------- #
 add_executable(CAGRA_EXAMPLE src/cagra_example.cu)
+
+# `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets
+# are installed in a conda environment, if one exists
 target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/cpp/build.sh b/examples/cpp/build.sh
deleted file mode 100755
index 7a948d9a8a..0000000000
--- a/examples/cpp/build.sh
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/bin/bash
-
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
-
-# cuvs empty project template build script
-
-# Abort script on first error
-set -e
-
-PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
-
-BUILD_TYPE=Release
-BUILD_DIR=build/
-
-CUVS_REPO_REL=""
-EXTRA_CMAKE_ARGS=""
-set -e
-
-
-if [[ ${CUVS_REPO_REL} != "" ]]; then
-  CUVS_REPO_PATH="`readlink -f \"${CUVS_REPO_REL}\"`"
-  EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DCPM_cuvs_SOURCE=${CUVS_REPO_PATH}"
-fi
-
-if [ "$1" == "clean" ]; then
-  rm -rf build
-  exit 0
-fi
-
-mkdir -p $BUILD_DIR
-cd $BUILD_DIR
-
-cmake \
- -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
- -DCUVS_NVTX=OFF \
- -DCMAKE_CUDA_ARCHITECTURES="NATIVE" \
- -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
- ${EXTRA_CMAKE_ARGS} \
- ../
-
-cmake  --build . -j${PARALLEL_LEVEL}
diff --git a/examples/cpp/src/common.cuh b/examples/cpp/src/common.cuh
index 757123cea5..aade6d4afe 100644
--- a/examples/cpp/src/common.cuh
+++ b/examples/cpp/src/common.cuh
@@ -17,6 +17,7 @@
 #include <cstdint>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_resources.hpp>
+#include <raft/core/host_mdarray.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
 #include <raft/matrix/copy.cuh>
 #include <raft/random/make_blobs.cuh>
@@ -28,34 +29,36 @@
 #include <thrust/iterator/counting_iterator.h>
 
 // Fill dataset and queries with synthetic data.
-void generate_dataset(raft::device_resources const& dev_resources,
+void generate_dataset(raft::device_resources const &dev_resources,
                       raft::device_matrix_view<float, int64_t> dataset,
-                      raft::device_matrix_view<float, int64_t> queries)
-{
-  auto labels = raft::make_device_vector<int64_t, int64_t>(dev_resources, dataset.extent(0));
+                      raft::device_matrix_view<float, int64_t> queries) {
+  auto labels = raft::make_device_vector<int64_t, int64_t>(dev_resources,
+                                                           dataset.extent(0));
   raft::random::make_blobs(dev_resources, dataset, labels.view());
   raft::random::RngState r(1234ULL);
-  raft::random::uniform(dev_resources,
-                        r,
-                        raft::make_device_vector_view(queries.data_handle(), queries.size()),
-                        -1.0f,
-                        1.0f);
+  raft::random::uniform(
+      dev_resources, r,
+      raft::make_device_vector_view(queries.data_handle(), queries.size()),
+      -1.0f, 1.0f);
 }
 
 // Copy the results to host and print a few samples
 template <typename IdxT>
-void print_results(raft::device_resources const& dev_resources,
+void print_results(raft::device_resources const &dev_resources,
                    raft::device_matrix_view<IdxT, int64_t> neighbors,
-                   raft::device_matrix_view<float, int64_t> distances)
-{
-  int64_t topk        = neighbors.extent(1);
-  auto neighbors_host = raft::make_host_matrix<IdxT, int64_t>(neighbors.extent(0), topk);
-  auto distances_host = raft::make_host_matrix<float, int64_t>(distances.extent(0), topk);
+                   raft::device_matrix_view<float, int64_t> distances) {
+  int64_t topk = neighbors.extent(1);
+  auto neighbors_host =
+      raft::make_host_matrix<IdxT, int64_t>(neighbors.extent(0), topk);
+  auto distances_host =
+      raft::make_host_matrix<float, int64_t>(distances.extent(0), topk);
 
   cudaStream_t stream = raft::resource::get_cuda_stream(dev_resources);
 
-  raft::copy(neighbors_host.data_handle(), neighbors.data_handle(), neighbors.size(), stream);
-  raft::copy(distances_host.data_handle(), distances.data_handle(), distances.size(), stream);
+  raft::copy(neighbors_host.data_handle(), neighbors.data_handle(),
+             neighbors.size(), stream);
+  raft::copy(distances_host.data_handle(), distances.data_handle(),
+             distances.size(), stream);
 
   // The calls to RAFT algorithms and  raft::copy is asynchronous.
   // We need to sync the stream before accessing the data.
@@ -70,26 +73,28 @@ void print_results(raft::device_resources const& dev_resources,
 }
 
 /** Subsample the dataset to create a training set*/
-raft::device_matrix<float, int64_t> subsample(
-  raft::device_resources const& dev_resources,
-  raft::device_matrix_view<const float, int64_t> dataset,
-  raft::device_vector_view<const int64_t, int64_t> data_indices,
-  float fraction)
-{
+raft::device_matrix<float, int64_t>
+subsample(raft::device_resources const &dev_resources,
+          raft::device_matrix_view<const float, int64_t> dataset,
+          raft::device_vector_view<const int64_t, int64_t> data_indices,
+          float fraction) {
   int64_t n_samples = dataset.extent(0);
-  int64_t n_dim     = dataset.extent(1);
-  int64_t n_train   = n_samples * fraction;
-  auto trainset     = raft::make_device_matrix<float, int64_t>(dev_resources, n_train, n_dim);
+  int64_t n_dim = dataset.extent(1);
+  int64_t n_train = n_samples * fraction;
+  auto trainset =
+      raft::make_device_matrix<float, int64_t>(dev_resources, n_train, n_dim);
 
   int seed = 137;
   raft::random::RngState rng(seed);
-  auto train_indices = raft::make_device_vector<int64_t>(dev_resources, n_train);
+  auto train_indices =
+      raft::make_device_vector<int64_t>(dev_resources, n_train);
 
-  raft::random::sample_without_replacement(
-    dev_resources, rng, data_indices, std::nullopt, train_indices.view(), std::nullopt);
+  raft::random::sample_without_replacement(dev_resources, rng, data_indices,
+                                           std::nullopt, train_indices.view(),
+                                           std::nullopt);
 
-  raft::matrix::copy_rows(
-    dev_resources, dataset, trainset.view(), raft::make_const_mdspan(train_indices.view()));
+  raft::matrix::copy_rows(dev_resources, dataset, trainset.view(),
+                          raft::make_const_mdspan(train_indices.view()));
 
   return trainset;
 }

From d23de1b842aab059c17df37188b3b6b21a5caf4c Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 4 Apr 2024 09:41:05 -0700
Subject: [PATCH 027/623] Add Brute Force KNN to Python and Rust API's (#59)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/59
---
 cpp/include/cuvs/distance/distance_types.h    |   3 +-
 cpp/include/cuvs/neighbors/brute_force.h      |  40 ++--
 cpp/src/neighbors/brute_force_c.cpp           |  56 ++---
 cpp/src/neighbors/ivf_flat_c.cpp              |  59 ++---
 cpp/test/neighbors/run_brute_force_c.c        |   8 +-
 python/cuvs/cuvs/distance_type.pxd            |  41 ++++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 .../cuvs/neighbors/brute_force/CMakeLists.txt |  25 ++
 .../cuvs/neighbors/brute_force/__init__.pxd   |   0
 .../cuvs/neighbors/brute_force/__init__.py    |  18 ++
 .../neighbors/brute_force/brute_force.pxd     |  47 ++++
 .../neighbors/brute_force/brute_force.pyx     | 216 ++++++++++++++++++
 python/cuvs/cuvs/test/test_brute_force.py     |  90 ++++++++
 rust/cuvs-sys/build.rs                        |   6 +-
 rust/cuvs-sys/cuvs_c_wrapper.h                |   1 +
 rust/cuvs/src/brute_force.rs                  | 180 +++++++++++++++
 rust/cuvs/src/distance_type.rs                |  17 ++
 rust/cuvs/src/dlpack.rs                       |  15 +-
 rust/cuvs/src/lib.rs                          |   3 +-
 19 files changed, 715 insertions(+), 111 deletions(-)
 create mode 100644 python/cuvs/cuvs/distance_type.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/brute_force/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/brute_force/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
 create mode 100644 python/cuvs/cuvs/test/test_brute_force.py
 create mode 100644 rust/cuvs/src/brute_force.rs
 create mode 100644 rust/cuvs/src/distance_type.rs

diff --git a/cpp/include/cuvs/distance/distance_types.h b/cpp/include/cuvs/distance/distance_types.h
index 8e9a4149cb..6cc2a993b6 100644
--- a/cpp/include/cuvs/distance/distance_types.h
+++ b/cpp/include/cuvs/distance/distance_types.h
@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+#pragma once
 
 #ifdef __cplusplus
 extern "C" {
@@ -67,4 +68,4 @@ enum DistanceType {
 
 #ifdef __cplusplus
 }
-#endif
\ No newline at end of file
+#endif
diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
index 0bb4d6bdbf..cb7ee2a30e 100644
--- a/cpp/include/cuvs/neighbors/brute_force.h
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -36,9 +36,9 @@ extern "C" {
 typedef struct {
   uintptr_t addr;
   DLDataType dtype;
-} bruteForceIndex;
+} cuvsBruteForceIndex;
 
-typedef bruteForceIndex* cuvsBruteForceIndex_t;
+typedef cuvsBruteForceIndex* cuvsBruteForceIndex_t;
 
 /**
  * @brief Allocate BRUTEFORCE index
@@ -46,14 +46,14 @@ typedef bruteForceIndex* cuvsBruteForceIndex_t;
  * @param[in] index cuvsBruteForceIndex_t to allocate
  * @return cuvsError_t
  */
-cuvsError_t bruteForceIndexCreate(cuvsBruteForceIndex_t* index);
+cuvsError_t cuvsBruteForceIndexCreate(cuvsBruteForceIndex_t* index);
 
 /**
  * @brief De-allocate BRUTEFORCE index
  *
  * @param[in] index cuvsBruteForceIndex_t to de-allocate
  */
-cuvsError_t bruteForceIndexDestroy(cuvsBruteForceIndex_t index);
+cuvsError_t cuvsBruteForceIndexDestroy(cuvsBruteForceIndex_t index);
 /**
  * @}
  */
@@ -83,13 +83,13 @@ cuvsError_t bruteForceIndexDestroy(cuvsBruteForceIndex_t index);
  *
  * // Create BRUTEFORCE index
  * cuvsBruteForceIndex_t index;
- * cuvsError_t index_create_status = bruteForceIndexCreate(&index);
+ * cuvsError_t index_create_status = cuvsBruteForceIndexCreate(&index);
  *
  * // Build the BRUTEFORCE Index
- * cuvsError_t build_status = bruteForceBuild(res, &dataset_tensor, L2Expanded, 0.f, index);
+ * cuvsError_t build_status = cuvsBruteForceBuild(res, &dataset_tensor, L2Expanded, 0.f, index);
  *
  * // de-allocate `index` and `res`
- * cuvsError_t index_destroy_status = bruteForceIndexDestroy(index);
+ * cuvsError_t index_destroy_status = cuvsBruteForceIndexDestroy(index);
  * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
  * @endcode
  *
@@ -100,11 +100,11 @@ cuvsError_t bruteForceIndexDestroy(cuvsBruteForceIndex_t index);
  * @param[out] index cuvsBruteForceIndex_t Newly built BRUTEFORCE index
  * @return cuvsError_t
  */
-cuvsError_t bruteForceBuild(cuvsResources_t res,
-                            DLManagedTensor* dataset,
-                            enum DistanceType metric,
-                            float metric_arg,
-                            cuvsBruteForceIndex_t index);
+cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
+                                DLManagedTensor* dataset,
+                                enum DistanceType metric,
+                                float metric_arg,
+                                cuvsBruteForceIndex_t index);
 /**
  * @}
  */
@@ -136,24 +136,24 @@ cuvsError_t bruteForceBuild(cuvsResources_t res,
  * DLManagedTensor queries;
  * DLManagedTensor neighbors;
  *
- * // Search the `index` built using `bruteForceBuild`
- * cuvsError_t search_status = bruteForceSearch(res, index, &queries, &neighbors, &distances);
+ * // Search the `index` built using `cuvsBruteForceBuild`
+ * cuvsError_t search_status = cuvsBruteForceSearch(res, index, &queries, &neighbors, &distances);
  *
  * // de-allocate `res`
  * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
  * @endcode
  *
  * @param[in] res cuvsResources_t opaque C handle
- * @param[in] index bruteForceIndex which has been returned by `bruteForceBuild`
+ * @param[in] index cuvsBruteForceIndex which has been returned by `cuvsBruteForceBuild`
  * @param[in] queries DLManagedTensor* queries dataset to search
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
  */
-cuvsError_t bruteForceSearch(cuvsResources_t res,
-                             cuvsBruteForceIndex_t index,
-                             DLManagedTensor* queries,
-                             DLManagedTensor* neighbors,
-                             DLManagedTensor* distances);
+cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
+                                 cuvsBruteForceIndex_t index,
+                                 DLManagedTensor* queries,
+                                 DLManagedTensor* neighbors,
+                                 DLManagedTensor* distances);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index 531fc9a57d..88349e089e 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -23,6 +23,7 @@
 #include <raft/core/resources.hpp>
 
 #include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
 #include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/brute_force.hpp>
@@ -49,7 +50,7 @@ void* _build(cuvsResources_t res,
 
 template <typename T>
 void _search(cuvsResources_t res,
-             bruteForceIndex index,
+             cuvsBruteForceIndex index,
              DLManagedTensor* queries_tensor,
              DLManagedTensor* neighbors_tensor,
              DLManagedTensor* distances_tensor)
@@ -70,19 +71,14 @@ void _search(cuvsResources_t res,
 
 }  // namespace
 
-extern "C" cuvsError_t bruteForceIndexCreate(cuvsBruteForceIndex_t* index)
+extern "C" cuvsError_t cuvsBruteForceIndexCreate(cuvsBruteForceIndex_t* index)
 {
-  try {
-    *index = new bruteForceIndex{};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsBruteForceIndex{}; });
 }
 
-extern "C" cuvsError_t bruteForceIndexDestroy(cuvsBruteForceIndex_t index_c_ptr)
+extern "C" cuvsError_t cuvsBruteForceIndexDestroy(cuvsBruteForceIndex_t index_c_ptr)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
     if (index.dtype.code == kDLFloat) {
@@ -96,19 +92,16 @@ extern "C" cuvsError_t bruteForceIndexDestroy(cuvsBruteForceIndex_t index_c_ptr)
       delete index_ptr;
     }
     delete index_c_ptr;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
-extern "C" cuvsError_t bruteForceBuild(cuvsResources_t res,
-                                       DLManagedTensor* dataset_tensor,
-                                       enum DistanceType metric,
-                                       float metric_arg,
-                                       cuvsBruteForceIndex_t index)
+extern "C" cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
+                                           DLManagedTensor* dataset_tensor,
+                                           enum DistanceType metric,
+                                           float metric_arg,
+                                           cuvsBruteForceIndex_t index)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
 
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
@@ -120,19 +113,16 @@ extern "C" cuvsError_t bruteForceBuild(cuvsResources_t res,
                 dataset.dtype.code,
                 dataset.dtype.bits);
     }
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
-extern "C" cuvsError_t bruteForceSearch(cuvsResources_t res,
-                                        cuvsBruteForceIndex_t index_c_ptr,
-                                        DLManagedTensor* queries_tensor,
-                                        DLManagedTensor* neighbors_tensor,
-                                        DLManagedTensor* distances_tensor)
+extern "C" cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
+                                            cuvsBruteForceIndex_t index_c_ptr,
+                                            DLManagedTensor* queries_tensor,
+                                            DLManagedTensor* neighbors_tensor,
+                                            DLManagedTensor* distances_tensor)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
     auto neighbors = neighbors_tensor->dl_tensor;
     auto distances = distances_tensor->dl_tensor;
@@ -159,9 +149,5 @@ extern "C" cuvsError_t bruteForceSearch(cuvsResources_t res,
                 queries.dtype.code,
                 queries.dtype.bits);
     }
-
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index b9488ec366..d8185c4f77 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -23,6 +23,7 @@
 #include <raft/core/resources.hpp>
 
 #include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
 #include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/ivf_flat.h>
 #include <cuvs/neighbors/ivf_flat.hpp>
@@ -86,17 +87,12 @@ void _search(cuvsResources_t res,
 
 extern "C" cuvsError_t ivfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
 {
-  try {
-    *index = new ivfFlatIndex{};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { *index = new ivfFlatIndex{}; });
 }
 
 extern "C" cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index_c_ptr)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
     if (index.dtype.code == kDLFloat) {
@@ -113,10 +109,7 @@ extern "C" cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index_c_ptr)
       delete index_ptr;
     }
     delete index_c_ptr;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t ivfFlatBuild(cuvsResources_t res,
@@ -124,7 +117,7 @@ extern "C" cuvsError_t ivfFlatBuild(cuvsResources_t res,
                                     DLManagedTensor* dataset_tensor,
                                     cuvsIvfFlatIndex_t index)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
 
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
@@ -144,10 +137,7 @@ extern "C" cuvsError_t ivfFlatBuild(cuvsResources_t res,
                 dataset.dtype.code,
                 dataset.dtype.bits);
     }
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t ivfFlatSearch(cuvsResources_t res,
@@ -157,7 +147,7 @@ extern "C" cuvsError_t ivfFlatSearch(cuvsResources_t res,
                                      DLManagedTensor* neighbors_tensor,
                                      DLManagedTensor* distances_tensor)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
     auto neighbors = neighbors_tensor->dl_tensor;
     auto distances = distances_tensor->dl_tensor;
@@ -191,16 +181,12 @@ extern "C" cuvsError_t ivfFlatSearch(cuvsResources_t res,
                 queries.dtype.code,
                 queries.dtype.bits);
     }
-
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t* params)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     *params = new ivfFlatIndexParams{.metric                         = L2Expanded,
                                      .metric_arg                     = 2.0f,
                                      .add_data_on_build              = true,
@@ -209,38 +195,21 @@ extern "C" cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t* pa
                                      .kmeans_trainset_fraction       = 0.5,
                                      .adaptive_centers               = false,
                                      .conservative_memory_allocation = false};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t params)
 {
-  try {
-    delete params;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { delete params; });
 }
 
 extern "C" cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t* params)
 {
-  try {
-    *params = new ivfFlatSearchParams{.n_probes = 20};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions(
+    [=] { *params = new ivfFlatSearchParams{.n_probes = 20}; });
 }
 
 extern "C" cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
 {
-  try {
-    delete params;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { delete params; });
 }
diff --git a/cpp/test/neighbors/run_brute_force_c.c b/cpp/test/neighbors/run_brute_force_c.c
index 9c7af13a60..ed775a2d6c 100644
--- a/cpp/test/neighbors/run_brute_force_c.c
+++ b/cpp/test/neighbors/run_brute_force_c.c
@@ -44,10 +44,10 @@ void run_brute_force(int64_t n_rows,
 
   // create index
   cuvsBruteForceIndex_t index;
-  bruteForceIndexCreate(&index);
+  cuvsBruteForceIndexCreate(&index);
 
   // build index
-  bruteForceBuild(res, &dataset_tensor, metric, 0.0f, index);
+  cuvsBruteForceBuild(res, &dataset_tensor, metric, 0.0f, index);
 
   // create queries DLTensor
   DLManagedTensor queries_tensor;
@@ -86,9 +86,9 @@ void run_brute_force(int64_t n_rows,
   distances_tensor.dl_tensor.strides            = NULL;
 
   // search index
-  bruteForceSearch(res, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  cuvsBruteForceSearch(res, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
 
   // de-allocate index and res
-  bruteForceIndexDestroy(index);
+  cuvsBruteForceIndexDestroy(index);
   cuvsResourcesDestroy(res);
 }
diff --git a/python/cuvs/cuvs/distance_type.pxd b/python/cuvs/cuvs/distance_type.pxd
new file mode 100644
index 0000000000..a1f0366a56
--- /dev/null
+++ b/python/cuvs/cuvs/distance_type.pxd
@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+
+cdef extern from "cuvs/distance/distance_types.h" nogil:
+    ctypedef enum DistanceType:
+        L2Expanded
+        L2SqrtExpanded
+        CosineExpanded
+        L1
+        L2Unexpanded
+        L2SqrtUnexpanded
+        InnerProduct
+        Linf
+        Canberra
+        LpUnexpanded
+        CorrelationExpanded
+        JaccardExpanded
+        HellingerExpanded
+        Haversine
+        BrayCurtis
+        JensenShannon
+        HammingUnexpanded
+        KLDivergence
+        RusselRaoExpanded
+        DiceExpanded
+        Precomputed
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index eaf418c60f..481ec9915f 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -12,4 +12,5 @@
 # the License.
 # =============================================================================
 
+add_subdirectory(brute_force)
 add_subdirectory(cagra)
diff --git a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
new file mode 100644
index 0000000000..8534370aee
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
@@ -0,0 +1,25 @@
+# =============================================================================
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources brute_force.pyx)
+set(linked_libraries cuvs::cuvs cuvs_c)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
+                   neighbors_brute_force_
+)
diff --git a/python/cuvs/cuvs/neighbors/brute_force/__init__.pxd b/python/cuvs/cuvs/neighbors/brute_force/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/brute_force/__init__.py b/python/cuvs/cuvs/neighbors/brute_force/__init__.py
new file mode 100644
index 0000000000..b88c4b4648
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/brute_force/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .brute_force import Index, build, search
+
+__all__ = ["Index", "build", "search"]
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
new file mode 100644
index 0000000000..77e484fba2
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
@@ -0,0 +1,47 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uintptr_t
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport DistanceType
+
+
+cdef extern from "cuvs/neighbors/brute_force.h" nogil:
+
+    ctypedef struct cuvsBruteForceIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsBruteForceIndex* cuvsBruteForceIndex_t
+
+    cuvsError_t cuvsBruteForceIndexCreate(cuvsBruteForceIndex_t* index)
+
+    cuvsError_t cuvsBruteForceIndexDestroy(cuvsBruteForceIndex_t index)
+
+    cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
+                                    DLManagedTensor* dataset,
+                                    DistanceType metric,
+                                    float metric_arg,
+                                    cuvsBruteForceIndex_t index) except +
+
+    cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
+                                     cuvsBruteForceIndex_t index,
+                                     DLManagedTensor* queries,
+                                     DLManagedTensor* neighbors,
+                                     DLManagedTensor* distances) except +
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
new file mode 100644
index 0000000000..ccb10e3055
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -0,0 +1,216 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libc.stdint cimport uint32_t
+from libcpp cimport bool
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport DistanceType
+
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+from pylibraft.distance.pairwise_distance import DISTANCE_TYPES
+from pylibraft.neighbors.common import _check_input_array
+
+from cuvs.common.c_api cimport cuvsResources_t
+
+from cuvs.common.exceptions import check_cuvs
+
+
+cdef class Index:
+    """
+    Brute Force index object. This object stores the trained Brute Force
+    which can be used to perform nearest neighbors searches.
+    """
+
+    cdef cuvsBruteForceIndex_t index
+    cdef bool trained
+
+    def __cinit__(self):
+        self.trained = False
+        check_cuvs(cuvsBruteForceIndexCreate(&self.index))
+
+    def __dealloc__(self):
+        if self.index is not NULL:
+            check_cuvs(cuvsBruteForceIndexDestroy(self.index))
+
+    @property
+    def trained(self):
+        return self.trained
+
+    def __repr__(self):
+        return "Index(type=BruteForce)"
+
+
+@auto_sync_resources
+def build(dataset, metric="sqeuclidean", metric_arg=2.0, resources=None):
+    """
+    Build the Brute Force index from the dataset for efficient search.
+    The following distance metrics are supported:
+        - L2
+
+    Parameters
+    ----------
+    dataset : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    metric : Distance metric to use. Default is sqeuclidean
+    metric_arg : value of 'p' for Minkowski distances
+    {resources_docstring}
+
+    Returns
+    -------
+    index: cuvs.neighbors.brute_force.Index
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import brute_force
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> index = brute_force.build(dataset, metric="cosine")
+    >>> distances, neighbors = brute_force.search(index, dataset, k)
+    >>> distances = cp.asarray(distances)
+    >>> neighbors = cp.asarray(neighbors)
+    """
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32')])
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef DistanceType c_metric = <DistanceType>DISTANCE_TYPES[metric]
+    cdef Index idx = Index()
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsBruteForceBuild(
+            res,
+            dataset_dlpack,
+            c_metric,
+            <float>metric_arg,
+            idx.index
+        ))
+        idx.trained = True
+
+    return idx
+
+
+@auto_sync_resources
+@auto_convert_output
+def search(Index index,
+           queries,
+           k,
+           neighbors=None,
+           distances=None,
+           resources=None):
+    """
+    Find the k nearest neighbors for each query.
+
+    Parameters
+    ----------
+    index : Index
+        Trained Brute Force index.
+    queries : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    k : int
+        The number of neighbors.
+    neighbors : Optional CUDA array interface compliant matrix shape
+                (n_queries, k), dtype int64_t. If supplied, neighbor
+                indices will be written here in-place. (default None)
+    distances : Optional CUDA array interface compliant matrix shape
+                (n_queries, k) If supplied, the distances to the
+                neighbors will be written here in-place. (default None)
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import brute_force
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = brute_force.build(dataset, metric="sqeuclidean")
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> k = 10
+    >>> # Using a pooling allocator reduces overhead of temporary array
+    >>> # creation during search. This is useful if multiple searches
+    >>> # are performad with same query size.
+    >>> distances, neighbors = brute_force.search(index, queries, k)
+    >>> neighbors = cp.asarray(neighbors)
+    >>> distances = cp.asarray(distances)
+    """
+    if not index.trained:
+        raise ValueError("Index needs to be built before calling search.")
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    queries_cai = wrap_array(queries)
+    _check_input_array(queries_cai, [np.dtype('float32')])
+
+    cdef uint32_t n_queries = queries_cai.shape[0]
+
+    if neighbors is None:
+        neighbors = device_ndarray.empty((n_queries, k), dtype='int64')
+
+    neighbors_cai = wrap_array(neighbors)
+    _check_input_array(neighbors_cai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    if distances is None:
+        distances = device_ndarray.empty((n_queries, k), dtype='float32')
+
+    distances_cai = wrap_array(distances)
+    _check_input_array(distances_cai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    cdef cuvsError_t search_status
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_cai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_cai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_cai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsBruteForceSearch(
+            res,
+            index.index,
+            queries_dlpack,
+            neighbors_dlpack,
+            distances_dlpack
+        ))
+
+    return (distances, neighbors)
diff --git a/python/cuvs/cuvs/test/test_brute_force.py b/python/cuvs/cuvs/test/test_brute_force.py
new file mode 100644
index 0000000000..146ab59f01
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_brute_force.py
@@ -0,0 +1,90 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+from scipy.spatial.distance import cdist
+
+from cuvs.neighbors import brute_force
+
+
+@pytest.mark.parametrize("n_index_rows", [32, 100])
+@pytest.mark.parametrize("n_query_rows", [32, 100])
+@pytest.mark.parametrize("n_cols", [40, 100])
+@pytest.mark.parametrize("k", [1, 5, 32])
+@pytest.mark.parametrize(
+    "metric",
+    [
+        "euclidean",
+        "cityblock",
+        "chebyshev",
+        "canberra",
+        "correlation",
+        "russellrao",
+        "cosine",
+        "sqeuclidean",
+        # "inner_product",
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32])
+def test_brute_force_knn(
+    n_index_rows, n_query_rows, n_cols, k, inplace, metric, dtype
+):
+    index = np.random.random_sample((n_index_rows, n_cols)).astype(dtype)
+    queries = np.random.random_sample((n_query_rows, n_cols)).astype(dtype)
+
+    # RussellRao expects boolean arrays
+    if metric == "russellrao":
+        index[index < 0.5] = 0.0
+        index[index >= 0.5] = 1.0
+        queries[queries < 0.5] = 0.0
+        queries[queries >= 0.5] = 1.0
+
+    indices = np.zeros((n_query_rows, k), dtype="int64")
+    distances = np.zeros((n_query_rows, k), dtype=dtype)
+
+    index_device = device_ndarray(index)
+    queries_device = device_ndarray(queries)
+    indices_device = device_ndarray(indices)
+    distances_device = device_ndarray(distances)
+
+    brute_force_index = brute_force.build(index_device, metric)
+    ret_distances, ret_indices = brute_force.search(
+        brute_force_index,
+        queries_device,
+        k,
+        neighbors=indices_device,
+        distances=distances_device,
+    )
+
+    pw_dists = cdist(queries, index, metric=metric)
+
+    distances_device = ret_distances if not inplace else distances_device
+
+    actual_distances = distances_device.copy_to_host()
+
+    actual_distances[actual_distances <= 1e-5] = 0.0
+    argsort = np.argsort(pw_dists, axis=1)
+
+    for i in range(pw_dists.shape[0]):
+        expected_indices = argsort[i]
+        gpu_dists = actual_distances[i]
+
+        cpu_ordered = pw_dists[i, expected_indices]
+        np.testing.assert_allclose(
+            cpu_ordered[:k], gpu_dists, atol=1e-3, rtol=1e-3
+        )
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
index ec96725695..f521f8af3f 100644
--- a/rust/cuvs-sys/build.rs
+++ b/rust/cuvs-sys/build.rs
@@ -98,9 +98,9 @@ fn main() {
         // generated)
         .must_use_type("cuvsError_t")
         // Only generate bindings for cuvs/cagra types and functions
-        .allowlist_type("(cuvs|cagra|DL).*")
-        .allowlist_function("(cuvs|cagra).*")
-        .rustified_enum("(cuvs|cagra|DL).*")
+        .allowlist_type("(cuvs|bruteForce|cagra|DL).*")
+        .allowlist_function("(cuvs|bruteForce|cagra).*")
+        .rustified_enum("(cuvs|cagra|DL|DistanceType).*")
         // also need some basic cuda mem functions for copying data
         .allowlist_function("(cudaMemcpyAsync|cudaMemcpy)")
         .rustified_enum("cudaError")
diff --git a/rust/cuvs-sys/cuvs_c_wrapper.h b/rust/cuvs-sys/cuvs_c_wrapper.h
index ccca82632d..e2c35f12dc 100644
--- a/rust/cuvs-sys/cuvs_c_wrapper.h
+++ b/rust/cuvs-sys/cuvs_c_wrapper.h
@@ -17,4 +17,5 @@
 // wrapper file containing all the C-API's we should automatically be creating rust
 // bindings for
 #include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/cagra.h>
diff --git a/rust/cuvs/src/brute_force.rs b/rust/cuvs/src/brute_force.rs
new file mode 100644
index 0000000000..e2e03c2367
--- /dev/null
+++ b/rust/cuvs/src/brute_force.rs
@@ -0,0 +1,180 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::io::{stderr, Write};
+
+use crate::distance_type::DistanceType;
+use crate::dlpack::ManagedTensor;
+use crate::error::{check_cuvs, Result};
+use crate::resources::Resources;
+
+/// Brute Force KNN Index
+#[derive(Debug)]
+pub struct Index(ffi::cuvsBruteForceIndex_t);
+
+impl Index {
+    /// Builds a new Brute Force KNN Index from the dataset for efficient search.
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `metric` - DistanceType to use for building the index
+    /// * `metric_arg` - Optional value of `p` for Minkowski distances
+    /// * `dataset` - A row-major matrix on either the host or device to index
+    pub fn build<T: Into<ManagedTensor>>(
+        res: &Resources,
+        metric: DistanceType,
+        metric_arg: Option<f32>,
+        dataset: T,
+    ) -> Result<Index> {
+        let dataset: ManagedTensor = dataset.into();
+        let index = Index::new()?;
+        unsafe {
+            check_cuvs(ffi::cuvsBruteForceBuild(
+                res.0,
+                dataset.as_ptr(),
+                metric,
+                metric_arg.unwrap_or(2.0),
+                index.0,
+            ))?;
+        }
+        Ok(index)
+    }
+
+    /// Creates a new empty index
+    pub fn new() -> Result<Index> {
+        unsafe {
+            let mut index = std::mem::MaybeUninit::<ffi::cuvsBruteForceIndex_t>::uninit();
+            check_cuvs(ffi::cuvsBruteForceIndexCreate(index.as_mut_ptr()))?;
+            Ok(Index(index.assume_init()))
+        }
+    }
+
+    /// Perform a Nearest Neighbors search on the Index
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `queries` - A matrix in device memory to query for
+    /// * `neighbors` - Matrix in device memory that receives the indices of the nearest neighbors
+    /// * `distances` - Matrix in device memory that receives the distances of the nearest neighbors
+    pub fn search(
+        self,
+        res: &Resources,
+        queries: &ManagedTensor,
+        neighbors: &ManagedTensor,
+        distances: &ManagedTensor,
+    ) -> Result<()> {
+        unsafe {
+            check_cuvs(ffi::cuvsBruteForceSearch(
+                res.0,
+                self.0,
+                queries.as_ptr(),
+                neighbors.as_ptr(),
+                distances.as_ptr(),
+            ))
+        }
+    }
+}
+
+impl Drop for Index {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsBruteForceIndexDestroy(self.0) }) {
+            write!(stderr(), "failed to call cagraIndexDestroy {:?}", e)
+                .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::s;
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    fn test_bfknn(metric: DistanceType) {
+        let res = Resources::new().unwrap();
+
+        // Create a new random dataset to index
+        let n_datapoints = 16;
+        let n_features = 8;
+        let dataset_host =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        let dataset = ManagedTensor::from(&dataset_host)
+            .to_device(&res)
+            .unwrap();
+
+        println!("dataset {:#?}", dataset_host);
+
+        // build the brute force index
+        let index =
+            Index::build(&res, metric, None, dataset).expect("failed to create brute force index");
+
+        res.sync_stream().unwrap();
+        
+        // use the first 4 points from the dataset as queries : will test that we get them back
+        // as their own nearest neighbor
+        let n_queries = 4;
+        let queries = dataset_host.slice(s![0..n_queries, ..]);
+
+        let k = 4;
+
+        println!("queries! {:#?}", queries);
+        let queries = ManagedTensor::from(&queries).to_device(&res).unwrap();
+        let mut neighbors_host = ndarray::Array::<i64, _>::zeros((n_queries, k));
+        let neighbors = ManagedTensor::from(&neighbors_host)
+            .to_device(&res)
+            .unwrap();
+
+        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+        let distances = ManagedTensor::from(&distances_host)
+            .to_device(&res)
+            .unwrap();
+
+        index
+            .search(&res, &queries, &neighbors, &distances)
+            .unwrap();
+
+        // Copy back to host memory
+        distances.to_host(&res, &mut distances_host).unwrap();
+        neighbors.to_host(&res, &mut neighbors_host).unwrap();
+        res.sync_stream().unwrap();
+
+        println!("distances {:#?}", distances_host);
+        println!("neighbors {:#?}", neighbors_host);
+
+        // nearest neighbors should be themselves, since queries are from the
+        // dataset
+        assert_eq!(neighbors_host[[0, 0]], 0);
+        assert_eq!(neighbors_host[[1, 0]], 1);
+        assert_eq!(neighbors_host[[2, 0]], 2);
+        assert_eq!(neighbors_host[[3, 0]], 3);
+    }
+
+/*
+    #[test]
+    fn test_cosine() {
+        test_bfknn(DistanceType::CosineExpanded);
+    }
+*/
+
+    #[test]
+    fn test_l2() {
+        test_bfknn(DistanceType::L2Expanded);
+    }
+}
diff --git a/rust/cuvs/src/distance_type.rs b/rust/cuvs/src/distance_type.rs
new file mode 100644
index 0000000000..4ac3e9164b
--- /dev/null
+++ b/rust/cuvs/src/distance_type.rs
@@ -0,0 +1,17 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+pub type DistanceType = ffi::DistanceType;
diff --git a/rust/cuvs/src/dlpack.rs b/rust/cuvs/src/dlpack.rs
index 238caec9d7..b74484a5c9 100644
--- a/rust/cuvs/src/dlpack.rs
+++ b/rust/cuvs/src/dlpack.rs
@@ -67,16 +67,17 @@ impl ManagedTensor {
         D: ndarray::Dimension,
     >(
         &self,
-        _res: &Resources,
+        res: &Resources,
         arr: &mut ndarray::ArrayBase<S, D>,
     ) -> Result<()> {
         unsafe {
             let bytes = dl_tensor_bytes(&self.0.dl_tensor);
-            check_cuda(ffi::cudaMemcpy(
+            check_cuda(ffi::cudaMemcpyAsync(
                 arr.as_mut_ptr() as *mut std::ffi::c_void,
                 self.0.dl_tensor.data,
                 bytes,
                 ffi::cudaMemcpyKind_cudaMemcpyDefault,
+                res.get_cuda_stream()?,
             ))?;
             Ok(())
         }
@@ -169,6 +170,16 @@ impl IntoDtype for i32 {
     }
 }
 
+impl IntoDtype for i64 {
+    fn ffi_dtype() -> ffi::DLDataType {
+        ffi::DLDataType {
+            code: ffi::DLDataTypeCode::kDLInt as _,
+            bits: 64,
+            lanes: 1,
+        }
+    }
+}
+
 impl IntoDtype for u32 {
     fn ffi_dtype() -> ffi::DLDataType {
         ffi::DLDataType {
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
index 273f047237..de974dad7a 100644
--- a/rust/cuvs/src/lib.rs
+++ b/rust/cuvs/src/lib.rs
@@ -18,8 +18,9 @@
 //!
 //! This crate provides Rust bindings for cuVS, allowing you to run
 //! approximate nearest neighbors search on the GPU.
-
+pub mod brute_force;
 pub mod cagra;
+pub mod distance_type;
 mod dlpack;
 mod error;
 mod resources;

From 36926d2f21df8650d4ebb8f7b86b6b0c9ddfe7d8 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 9 Apr 2024 11:39:39 -0700
Subject: [PATCH 028/623] Update devcontainers to use cuda12.2 (#72)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/72
---
 .devcontainer/cuda11.8-pip/devcontainer.json  | 12 ++++++++++-
 .../devcontainer.json                         |  4 ++--
 .../devcontainer.json                         | 20 ++++++++++++++-----
 .github/workflows/pr.yaml                     |  2 ++
 python/cuvs/CMakeLists.txt                    | 18 +++++++++--------
 python/cuvs/cuvs/common/CMakeLists.txt        |  2 +-
 .../cuvs/cuvs/neighbors/cagra/CMakeLists.txt  |  2 +-
 7 files changed, 42 insertions(+), 18 deletions(-)
 rename .devcontainer/{cuda12.0-conda => cuda12.2-conda}/devcontainer.json (93%)
 rename .devcontainer/{cuda12.0-pip => cuda12.2-pip}/devcontainer.json (73%)

diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 39b7971e79..c4abd5b8c3 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -10,11 +10,21 @@
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {"version": "1.14.1"},
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
+      "version": "1.14.1"
+    },
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.6": {
+      "version": "11.8",
+      "installcuBLAS": true,
+      "installcuSOLVER": true,
+      "installcuRAND": true,
+      "installcuSPARSE": true
+    },
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
+    "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
   "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda11.8-venvs}"],
diff --git a/.devcontainer/cuda12.0-conda/devcontainer.json b/.devcontainer/cuda12.2-conda/devcontainer.json
similarity index 93%
rename from .devcontainer/cuda12.0-conda/devcontainer.json
rename to .devcontainer/cuda12.2-conda/devcontainer.json
index 4d9cbf33d8..6c15504977 100644
--- a/.devcontainer/cuda12.0-conda/devcontainer.json
+++ b/.devcontainer/cuda12.2-conda/devcontainer.json
@@ -15,7 +15,7 @@
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.0-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.2-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -24,7 +24,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.0-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.2-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.2-pip/devcontainer.json
similarity index 73%
rename from .devcontainer/cuda12.0-pip/devcontainer.json
rename to .devcontainer/cuda12.2-pip/devcontainer.json
index 2a77539cff..8dd06ae6c4 100644
--- a/.devcontainer/cuda12.0-pip/devcontainer.json
+++ b/.devcontainer/cuda12.2-pip/devcontainer.json
@@ -3,21 +3,31 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.0",
+      "CUDA": "12.2",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.06-cpp-llvm16-cuda12.0-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.06-cpp-cuda12.2-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {"version": "1.14.1"},
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
+      "version": "1.14.1"
+    },
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.6": {
+      "version": "12.2",
+      "installcuBLAS": true,
+      "installcuSOLVER": true,
+      "installcuRAND": true,
+      "installcuSPARSE": true
+    },
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
+    "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.0-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.2-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -25,7 +35,7 @@
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.0-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.2-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 652092dde5..3e244cc109 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -101,6 +101,8 @@ jobs:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.06
     with:
+      arch: '["amd64"]'
+      cuda: '["12.2"]'
       build_command: |
         sccache -z;
         build-all -DBUILD_PRIMS_BENCH=ON -DBUILD_ANN_BENCH=ON --verbose;
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 36a62044ae..db6f256751 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -31,17 +31,19 @@ project(
             C CXX CUDA
 )
 
-################################################################################
-# - User Options  --------------------------------------------------------------
+# ##################################################################################################
+# * User Options  --------------------------------------------------------------
 
 option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulting to local files"
        OFF
 )
 
-message("CUVS_PY: Searching for existing cuVS C/C++ installations before defaulting to local files: ${FIND_CUVS_CPP}")
+message(
+  "CUVS_PY: Searching for existing cuVS C/C++ installations before defaulting to local files: ${FIND_CUVS_CPP}"
+)
 
-################################################################################
-# - Process User Options  ------------------------------------------------------
+# ##################################################################################################
+# * Process User Options  ------------------------------------------------------
 
 include(rapids-cmake)
 include(rapids-cpm)
@@ -53,7 +55,7 @@ rapids_cpm_init()
 
 # If the user requested it we attempt to find CUVS.
 if(FIND_CUVS_CPP)
-  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED)
+  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS c_api)
   include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
 else()
   set(cuvs_FOUND OFF)
@@ -73,8 +75,8 @@ if(NOT cuvs_FOUND)
   install(TARGETS cuvs cuvs_c DESTINATION ${cython_lib_dir})
 endif()
 
-################################################################################
-# - Build Cython artifacts -----------------------------------------------------
+# ##################################################################################################
+# * Build Cython artifacts -----------------------------------------------------
 
 rapids_cython_init()
 
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index b477fdb322..3de3805af8 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 # Set the list of Cython files to build
 set(cython_sources cydlpack.pyx exceptions.pyx)
-set(linked_libraries cuvs::cuvs cuvs_c)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
 rapids_cython_create_modules(
diff --git a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
index 377cfe7793..87e6597fe1 100644
--- a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 # Set the list of Cython files to build
 set(cython_sources cagra.pyx)
-set(linked_libraries cuvs::cuvs cuvs_c)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
 rapids_cython_create_modules(

From 1e3206afe9674d52da013e7f93b45144067f4f37 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 9 Apr 2024 12:30:15 -0700
Subject: [PATCH 029/623] Enable forward-merger ops-bot plugin (#70)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Jake Awe (https://github.com/AyodeAwe)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/70
---
 .github/ops-bot.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/ops-bot.yaml b/.github/ops-bot.yaml
index 9a0b415503..2ed5231aef 100644
--- a/.github/ops-bot.yaml
+++ b/.github/ops-bot.yaml
@@ -6,3 +6,4 @@ branch_checker: true
 label_checker: true
 release_drafter: true
 recently_updated: true
+forward_merger: true

From e7e3f5684509eca24fca187b30d56ba62f66d0aa Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Tue, 9 Apr 2024 15:48:29 -0400
Subject: [PATCH 030/623] Fixing brute-force python build

---
 python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
index 8534370aee..4806fb9fc0 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 # Set the list of Cython files to build
 set(cython_sources brute_force.pyx)
-set(linked_libraries cuvs::cuvs cuvs_c)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
 rapids_cython_create_modules(

From 4488626373fda37656074e805d948875646c6647 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Tue, 9 Apr 2024 18:31:32 -0400
Subject: [PATCH 031/623] Add `lucene-cuvs` to integrations section of docs
 (#73)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/73
---
 docs/source/integrations.rst | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/docs/source/integrations.rst b/docs/source/integrations.rst
index 6c6e2ce8ca..46cbaa52c9 100644
--- a/docs/source/integrations.rst
+++ b/docs/source/integrations.rst
@@ -5,8 +5,10 @@ Aside from using cuVS directly, it can be consumed through a number of sdk and v
 
 - `FAISS`_
 - `Milvus`_
+- `Lucene` _
 - `Kinetica`_
 
+
 FAISS
 -----
 
@@ -32,9 +34,17 @@ The GPU indexes can be enabled by using the index types prefixed with `GPU_`, as
 Milvus will be migrating their GPU support from RAFT to cuVS as we continue to move the vector search algorithms out of RAFT and into cuVS.
 
 
+Lucene
+------
+
+An experimental Lucene connector for cuVS enables GPU-accelerated vector search indexes through Lucene. Initial benchmarks are showing that this connector can drastically improve the performance of both indexing and search in Lucene. This connector will continue to be improved over time and any interested developers are encouraged to contribute.
+
+Install and evaluate the `lucene-cuvs` connector on `Github <https://github.com/SearchScale/lucene-cuvs>`_.
+
+
 Kinetica
 --------
 
 Starting with release 7.2, Kinetica supports the graph-based the CAGRA algorithm from RAFT. Kinetica will continue to improve its support over coming versions, while also migrating to cuVS as we work to move the vector search algorithms out of RAFT and into cuVS.
 
-Kinetica currently offers the ability to create a CAGRA index in a SQL `CREATE_TABLE` statement, as outlined in their `vector search indexing docs <https://docs.kinetica.com/7.2/concepts/indexes/#cagra-index>`_. Kinetica is not open source, but the RAFT indexes can be enabled in the developer edition, which can be installed `here <https://www.kinetica.com/try/#download_instructions>`_.
\ No newline at end of file
+Kinetica currently offers the ability to create a CAGRA index in a SQL `CREATE_TABLE` statement, as outlined in their `vector search indexing docs <https://docs.kinetica.com/7.2/concepts/indexes/#cagra-index>`_. Kinetica is not open source, but the RAFT indexes can be enabled in the developer edition, which can be installed `here <https://www.kinetica.com/try/#download_instructions>`_.

From 6ad58e3f928b09794fc63f12c22f766be6027822 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 9 Apr 2024 15:34:51 -0700
Subject: [PATCH 032/623] Add Cagra-Q compression to the python and rust api's
 (#68)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/68
---
 python/cuvs/cuvs/neighbors/cagra/__init__.py |  18 ++-
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd   |  17 +++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx   |  97 +++++++++++++++-
 python/cuvs/cuvs/test/test_cagra.py          |  10 ++
 rust/cuvs/src/cagra/index.rs                 |  18 ++-
 rust/cuvs/src/cagra/index_params.rs          | 115 ++++++++++++++++++-
 rust/cuvs/src/cagra/mod.rs                   |   2 +-
 7 files changed, 266 insertions(+), 11 deletions(-)

diff --git a/python/cuvs/cuvs/neighbors/cagra/__init__.py b/python/cuvs/cuvs/neighbors/cagra/__init__.py
index 657c7d366e..0b487d7ab6 100644
--- a/python/cuvs/cuvs/neighbors/cagra/__init__.py
+++ b/python/cuvs/cuvs/neighbors/cagra/__init__.py
@@ -13,6 +13,20 @@
 # limitations under the License.
 
 
-from .cagra import Index, IndexParams, SearchParams, build_index, search
+from .cagra import (
+    CompressionParams,
+    Index,
+    IndexParams,
+    SearchParams,
+    build_index,
+    search,
+)
 
-__all__ = ["Index", "IndexParams", "SearchParams", "build_index", "search"]
+__all__ = [
+    "CompressionParams",
+    "Index",
+    "IndexParams",
+    "SearchParams",
+    "build_index",
+    "search",
+]
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index 4293bdc073..d229a3b09d 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -34,11 +34,22 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
         IVF_PQ
         NN_DESCENT
 
+    ctypedef struct cuvsCagraCompressionParams:
+        uint32_t pq_bits
+        uint32_t pq_dim
+        uint32_t vq_n_centers
+        uint32_t kmeans_n_iters
+        double vq_kmeans_trainset_fraction
+        double pq_kmeans_trainset_fraction
+
+    ctypedef cuvsCagraCompressionParams* cuvsCagraCompressionParams_t
+
     ctypedef struct cuvsCagraIndexParams:
         size_t intermediate_graph_degree
         size_t graph_degree
         cuvsCagraGraphBuildAlgo build_algo
         size_t nn_descent_niter
+        cuvsCagraCompressionParams_t compression
 
     ctypedef cuvsCagraIndexParams* cuvsCagraIndexParams_t
 
@@ -74,6 +85,12 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
 
     ctypedef cuvsCagraIndex* cuvsCagraIndex_t
 
+    cuvsError_t cuvsCagraCompressionParamsCreate(
+        cuvsCagraCompressionParams_t* params)
+
+    cuvsError_t cuvsCagraCompressionParamsDestroy(
+        cuvsCagraCompressionParams_t index)
+
     cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params)
 
     cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t index)
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 818c93940a..f5f94aba5e 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -43,6 +43,82 @@ from libc.stdint cimport (
 from cuvs.common.exceptions import check_cuvs
 
 
+cdef class CompressionParams:
+    """
+    Parameters for VPQ Compression
+
+    Parameters
+    ----------
+    pq_bits: int
+        The bit length of the vector element after compression by PQ.
+        Possible values: [4, 5, 6, 7, 8]. The smaller the 'pq_bits', the
+        smaller the index size and the better the search performance, but
+        the lower the recall.
+    pq_dim: int
+        The dimensionality of the vector after compression by PQ. When zero,
+        an optimal value is selected using a heuristic.
+    vq_n_centers: int
+        Vector Quantization (VQ) codebook size - number of "coarse cluster
+        centers". When zero, an optimal value is selected using a heuristic.
+    kmeans_n_iters: int
+        The number of iterations searching for kmeans centers (both VQ & PQ
+        phases).
+    vq_kmeans_trainset_fraction: float
+        The fraction of data to use during iterative kmeans building (VQ
+        phase). When zero, an optimal value is selected using a heuristic.
+    vq_kmeans_trainset_fraction: float
+        The fraction of data to use during iterative kmeans building (PQ
+        phase). When zero, an optimal value is selected using a heuristic.
+    """
+    cdef cuvsCagraCompressionParams * params
+
+    def __cinit__(self):
+        check_cuvs(cuvsCagraCompressionParamsCreate(&self.params))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsCagraCompressionParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 pq_bits=8,
+                 pq_dim=0,
+                 vq_n_centers=0,
+                 kmeans_n_iters=25,
+                 vq_kmeans_trainset_fraction=0.0,
+                 pq_kmeans_trainset_fraction=0.0):
+        self.params.pq_bits = pq_bits
+        self.params.pq_dim = pq_dim
+        self.params.vq_n_centers = vq_n_centers
+        self.params.kmeans_n_iters = kmeans_n_iters
+        self.params.vq_kmeans_trainset_fraction = vq_kmeans_trainset_fraction
+        self.params.pq_kmeans_trainset_fraction = pq_kmeans_trainset_fraction
+
+    @property
+    def pq_bits(self):
+        return self.params.pq_bits
+
+    @property
+    def pq_dim(self):
+        return self.params.pq_dim
+
+    @property
+    def vq_n_centers(self):
+        return self.params.vq_n_centers
+
+    @property
+    def kmeans_n_iters(self):
+        return self.params.kmeans_n_iters
+
+    @property
+    def vq_kmeans_trainset_fraction(self):
+        return self.params.vq_kmeans_trainset_fraction
+
+    @property
+    def pq_kmeans_trainset_fraction(self):
+        return self.params.pq_kmeans_trainset_fraction
+
+    def get_handle(self):
+        return <size_t>self.params
+
 cdef class IndexParams:
     """
     Parameters to build index for CAGRA nearest neighbor search
@@ -64,17 +140,30 @@ cdef class IndexParams:
             - nn_descent (experimental) will use the NN-Descent algorithm for
               building the knn graph. It is expected to be generally
               faster than ivf_pq.
+    compression: CompressionParams, optional
+        If compression is desired should be a CompressionParams object. If None
+        compression will be disabled.
     """
 
     cdef cuvsCagraIndexParams* params
 
+    # hold on to a reference to the compression, to keep from being GC'ed
+    cdef public object compression
+
+    def __cinit__(self):
+        check_cuvs(cuvsCagraIndexParamsCreate(&self.params))
+        self.compression = None
+
+    def __dealloc__(self):
+        check_cuvs(cuvsCagraIndexParamsDestroy(self.params))
+
     def __init__(self, *,
                  metric="sqeuclidean",
                  intermediate_graph_degree=128,
                  graph_degree=64,
                  build_algo="ivf_pq",
-                 nn_descent_niter=20):
-        cuvsCagraIndexParamsCreate(&self.params)
+                 nn_descent_niter=20,
+                 compression=None):
 
         # todo (dgd): enable once other metrics are present
         # and exposed in cuVS C API
@@ -87,6 +176,10 @@ cdef class IndexParams:
         elif build_algo == "nn_descent":
             self.params.build_algo = cuvsCagraGraphBuildAlgo.NN_DESCENT
         self.params.nn_descent_niter = nn_descent_niter
+        if compression is not None:
+            self.compression = compression
+            self.params.compression = \
+                <cuvsCagraCompressionParams_t><size_t>compression.get_handle()
 
     # @property
     # def metric(self):
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
index 6074eee3a7..a2b0184860 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -38,6 +38,7 @@ def run_cagra_build_search_test(
     inplace=True,
     add_data_on_build=True,
     search_params={},
+    compression=None,
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     if metric == "inner_product":
@@ -49,6 +50,7 @@ def run_cagra_build_search_test(
         intermediate_graph_degree=intermediate_graph_degree,
         graph_degree=graph_degree,
         build_algo=build_algo,
+        compression=compression,
     )
 
     if array_type == "device":
@@ -173,3 +175,11 @@ def test_cagra_index_params(params):
         compare=False,
         build_algo=params["build_algo"],
     )
+
+
+def test_cagra_vpq_compression():
+    dim = 64
+    pq_len = 2
+    run_cagra_build_search_test(
+        n_cols=dim, compression=cagra.CompressionParams(pq_dim=dim / pq_len)
+    )
diff --git a/rust/cuvs/src/cagra/index.rs b/rust/cuvs/src/cagra/index.rs
index 6a5149f073..959959f608 100644
--- a/rust/cuvs/src/cagra/index.rs
+++ b/rust/cuvs/src/cagra/index.rs
@@ -106,8 +106,7 @@ mod tests {
     use ndarray_rand::rand_distr::Uniform;
     use ndarray_rand::RandomExt;
 
-    #[test]
-    fn test_cagra_index() {
+    fn test_cagra(build_params: IndexParams) {
         let res = Resources::new().unwrap();
 
         // Create a new random dataset to index
@@ -117,7 +116,6 @@ mod tests {
             ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
 
         // build the cagra index
-        let build_params = IndexParams::new().unwrap();
         let index =
             Index::build(&res, &build_params, &dataset).expect("failed to create cagra index");
 
@@ -159,4 +157,18 @@ mod tests {
         assert_eq!(neighbors_host[[2, 0]], 2);
         assert_eq!(neighbors_host[[3, 0]], 3);
     }
+
+    #[test]
+    fn test_cagra_index() {
+        let build_params = IndexParams::new().unwrap();
+        test_cagra(build_params);
+    }
+
+    #[test]
+    fn test_cagra_compression() {
+        use crate::cagra::CompressionParams;
+        let build_params = IndexParams::new().unwrap()
+            .set_compression(CompressionParams::new().unwrap());
+        test_cagra(build_params);
+    }
 }
diff --git a/rust/cuvs/src/cagra/index_params.rs b/rust/cuvs/src/cagra/index_params.rs
index 2e3367e06e..9a481ef9e1 100644
--- a/rust/cuvs/src/cagra/index_params.rs
+++ b/rust/cuvs/src/cagra/index_params.rs
@@ -21,7 +21,79 @@ use std::io::{stderr, Write};
 pub type BuildAlgo = ffi::cuvsCagraGraphBuildAlgo;
 
 /// Supplemental parameters to build CAGRA Index
-pub struct IndexParams(pub ffi::cuvsCagraIndexParams_t);
+pub struct CompressionParams(pub ffi::cuvsCagraCompressionParams_t);
+
+impl CompressionParams {
+    /// Returns a new CompressionParams
+    pub fn new() -> Result<CompressionParams> {
+        unsafe {
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsCagraCompressionParams_t>::uninit();
+            check_cuvs(ffi::cuvsCagraCompressionParamsCreate(params.as_mut_ptr()))?;
+            Ok(CompressionParams(params.assume_init()))
+        }
+    }
+
+    /// The bit length of the vector element after compression by PQ.
+    pub fn set_pq_bits(self, pq_bits: u32) -> CompressionParams {
+        unsafe {
+            (*self.0).pq_bits = pq_bits;
+        }
+        self
+    }
+
+    /// The dimensionality of the vector after compression by PQ. When zero,
+    /// an optimal value is selected using a heuristic.
+    pub fn set_pq_dim(self, pq_dim: u32) -> CompressionParams {
+        unsafe {
+            (*self.0).pq_dim = pq_dim;
+        }
+        self
+    }
+
+    /// Vector Quantization (VQ) codebook size - number of "coarse cluster
+    /// centers". When zero, an optimal value is selected using a heuristic.
+    pub fn set_vq_n_centers(self, vq_n_centers: u32) -> CompressionParams {
+        unsafe {
+            (*self.0).vq_n_centers = vq_n_centers;
+        }
+        self
+    }
+
+    /// The number of iterations searching for kmeans centers (both VQ & PQ
+    /// phases).
+    pub fn set_kmeans_n_iters(self, kmeans_n_iters: u32) -> CompressionParams {
+        unsafe {
+            (*self.0).kmeans_n_iters = kmeans_n_iters;
+        }
+        self
+    }
+
+    /// The fraction of data to use during iterative kmeans building (VQ
+    /// phase). When zero, an optimal value is selected using a heuristic.
+    pub fn set_vq_kmeans_trainset_fraction(
+        self,
+        vq_kmeans_trainset_fraction: f64,
+    ) -> CompressionParams {
+        unsafe {
+            (*self.0).vq_kmeans_trainset_fraction = vq_kmeans_trainset_fraction;
+        }
+        self
+    }
+
+    /// The fraction of data to use during iterative kmeans building (PQ
+    /// phase). When zero, an optimal value is selected using a heuristic.
+    pub fn set_pq_kmeans_trainset_fraction(
+        self,
+        pq_kmeans_trainset_fraction: f64,
+    ) -> CompressionParams {
+        unsafe {
+            (*self.0).pq_kmeans_trainset_fraction = pq_kmeans_trainset_fraction;
+        }
+        self
+    }
+}
+
+pub struct IndexParams(pub ffi::cuvsCagraIndexParams_t, Option<CompressionParams>);
 
 impl IndexParams {
     /// Returns a new IndexParams
@@ -29,7 +101,7 @@ impl IndexParams {
         unsafe {
             let mut params = std::mem::MaybeUninit::<ffi::cuvsCagraIndexParams_t>::uninit();
             check_cuvs(ffi::cuvsCagraIndexParamsCreate(params.as_mut_ptr()))?;
-            Ok(IndexParams(params.assume_init()))
+            Ok(IndexParams(params.assume_init(), None))
         }
     }
 
@@ -64,6 +136,16 @@ impl IndexParams {
         }
         self
     }
+
+    pub fn set_compression(mut self, compression: CompressionParams) -> IndexParams {
+        unsafe {
+            (*self.0).compression = compression.0;
+        }
+        // Note: we're moving the ownership of compression here to avoid having it cleaned up
+        // and leaving a dangling pointer
+        self.1 = Some(compression);
+        self
+    }
 }
 
 impl fmt::Debug for IndexParams {
@@ -74,6 +156,12 @@ impl fmt::Debug for IndexParams {
     }
 }
 
+impl fmt::Debug for CompressionParams {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        write!(f, "CompressionParams({:?})", unsafe { *self.0 })
+    }
+}
+
 impl Drop for IndexParams {
     fn drop(&mut self) {
         if let Err(e) = check_cuvs(unsafe { ffi::cuvsCagraIndexParamsDestroy(self.0) }) {
@@ -87,6 +175,19 @@ impl Drop for IndexParams {
     }
 }
 
+impl Drop for CompressionParams {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsCagraCompressionParamsDestroy(self.0) }) {
+            write!(
+                stderr(),
+                "failed to call cuvsCagraCompressionParamsDestroy {:?}",
+                e
+            )
+            .expect("failed to write to stderr");
+        }
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -98,7 +199,13 @@ mod tests {
             .set_intermediate_graph_degree(128)
             .set_graph_degree(16)
             .set_build_algo(BuildAlgo::NN_DESCENT)
-            .set_nn_descent_niter(10);
+            .set_nn_descent_niter(10)
+            .set_compression(
+                CompressionParams::new()
+                    .unwrap()
+                    .set_pq_bits(4)
+                    .set_pq_dim(8),
+            );
 
         // make sure the setters actually updated internal representation on the c-struct
         unsafe {
@@ -106,6 +213,8 @@ mod tests {
             assert_eq!((*params.0).intermediate_graph_degree, 128);
             assert_eq!((*params.0).build_algo, BuildAlgo::NN_DESCENT);
             assert_eq!((*params.0).nn_descent_niter, 10);
+            assert_eq!((*(*params.0).compression).pq_dim, 8);
+            assert_eq!((*(*params.0).compression).pq_bits, 4);
         }
     }
 }
diff --git a/rust/cuvs/src/cagra/mod.rs b/rust/cuvs/src/cagra/mod.rs
index 417ed9b0d8..c7db858421 100644
--- a/rust/cuvs/src/cagra/mod.rs
+++ b/rust/cuvs/src/cagra/mod.rs
@@ -82,5 +82,5 @@ mod index_params;
 mod search_params;
 
 pub use index::Index;
-pub use index_params::{BuildAlgo, IndexParams};
+pub use index_params::{BuildAlgo, CompressionParams, IndexParams};
 pub use search_params::{HashMode, SearchAlgo, SearchParams};

From 26377efa2cbd932c2d468a1fa3e58941b3eecdaa Mon Sep 17 00:00:00 2001
From: GALI PREM SAGAR <sagarprem75@gmail.com>
Date: Wed, 10 Apr 2024 11:18:27 -0500
Subject: [PATCH 033/623] Enable all tests for `arm` jobs (#63)

This PR enables all tests in `arm` jobs.

xref: https://github.com/rapidsai/build-planning/issues/36

---------

Co-authored-by: Corey J. Nolet <cjnolet@gmail.com>
---
 ci/test_wheel_cuvs.sh       |  7 +------
 ci/wheel_smoke_test_cuvs.py | 42 -------------------------------------
 2 files changed, 1 insertion(+), 48 deletions(-)
 delete mode 100644 ci/wheel_smoke_test_cuvs.py

diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 364e00a7cf..7033003e95 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -10,9 +10,4 @@ RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from
 # echo to expand wildcard before adding `[extra]` requires for pip
 python -m pip install $(echo ./dist/cuvs*.whl)[test]
 
-# Run smoke tests for aarch64 pull requests
-if [[ "$(arch)" == "aarch64" && "${RAPIDS_BUILD_TYPE}" == "pull-request" ]]; then
-   python ./ci/wheel_smoke_test_cuvs.py
-else
-   python -m pytest ./python/cuvs/cuvs/test
-fi
+python -m pytest ./python/cuvs/cuvs/test
diff --git a/ci/wheel_smoke_test_cuvs.py b/ci/wheel_smoke_test_cuvs.py
deleted file mode 100644
index 26ab5f6b50..0000000000
--- a/ci/wheel_smoke_test_cuvs.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-import cupy as cp
-import numpy as np
-
-from cuvs.neighbors import cagra
-from pylibraft.common import Stream, DeviceResources
-
-
-if __name__ == "__main__":
-    n_samples = 1000
-    n_features = 50
-    n_queries = 1000
-    k = 10
-
-    dataset = cp.random.random_sample((n_samples,
-                                       n_features)).astype(cp.float32)
-
-    build_params = cagra.IndexParams(metric="sqeuclidean",
-                                     build_algo="nn_descent")
-
-    index = cagra.build_index(build_params, dataset)
-
-    distances, neighbors = cagra.search(cagra.SearchParams(),
-                                          index, dataset,
-                                          k)
-
-    distances = cp.asarray(distances)
-    neighbors = cp.asarray(neighbors)

From 8976bd133d879d855796ee22ba0b86e02b7b250f Mon Sep 17 00:00:00 2001
From: Ray Douglass <3107146+raydouglass@users.noreply.github.com>
Date: Thu, 11 Apr 2024 14:34:07 -0400
Subject: [PATCH 034/623] Fix for 24.04 release (#75)

Fixes some versions so `24.04` can be released.

#69 implements the changes in the script

This PR should replace #76

---------

Co-authored-by: Jake Awe <jawe@nvidia.com>
Co-authored-by: Corey J. Nolet <cjnolet@gmail.com>
Co-authored-by: ptaylor <paul.e.taylor@me.com>
---
 .../cuda11.8-conda/devcontainer.json          |  4 ++--
 .devcontainer/cuda11.8-pip/devcontainer.json  | 16 +++++++++++---
 .../devcontainer.json                         |  8 +++----
 .../devcontainer.json                         | 22 ++++++++++++++-----
 .github/workflows/build.yaml                  |  2 +-
 .github/workflows/pr.yaml                     |  6 +++--
 python/cuvs/CMakeLists.txt                    |  2 +-
 python/cuvs/cuvs/common/CMakeLists.txt        |  2 +-
 .../cuvs/neighbors/brute_force/CMakeLists.txt |  2 +-
 .../cuvs/cuvs/neighbors/cagra/CMakeLists.txt  |  2 +-
 10 files changed, 44 insertions(+), 22 deletions(-)
 rename .devcontainer/{cuda12.0-conda => cuda12.2-conda}/devcontainer.json (92%)
 rename .devcontainer/{cuda12.0-pip => cuda12.2-pip}/devcontainer.json (72%)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index cefbea72b7..007ff83b56 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,12 +5,12 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.04-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 05518805aa..aae6b0f953 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,16 +5,26 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda11.8-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.04-cpp-cuda11.8-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.2": {"version": "1.14.1"},
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.4": {
+      "version": "1.14.1"
+    },
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.4": {
+      "version": "11.8",
+      "installcuBLAS": true,
+      "installcuSOLVER": true,
+      "installcuRAND": true,
+      "installcuSPARSE": true
+    },
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
+    "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
   "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda11.8-venvs}"],
diff --git a/.devcontainer/cuda12.0-conda/devcontainer.json b/.devcontainer/cuda12.2-conda/devcontainer.json
similarity index 92%
rename from .devcontainer/cuda12.0-conda/devcontainer.json
rename to .devcontainer/cuda12.2-conda/devcontainer.json
index 3f89836e26..4349cd80a1 100644
--- a/.devcontainer/cuda12.0-conda/devcontainer.json
+++ b/.devcontainer/cuda12.2-conda/devcontainer.json
@@ -3,19 +3,19 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.0",
+      "CUDA": "12.2",
       "PYTHON_PACKAGE_MANAGER": "conda",
       "BASE": "rapidsai/devcontainers:24.04-cpp-mambaforge-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.0-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.2-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -24,7 +24,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.0-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.2-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.2-pip/devcontainer.json
similarity index 72%
rename from .devcontainer/cuda12.0-pip/devcontainer.json
rename to .devcontainer/cuda12.2-pip/devcontainer.json
index 33f67cd3fb..a8a8fdb5b8 100644
--- a/.devcontainer/cuda12.0-pip/devcontainer.json
+++ b/.devcontainer/cuda12.2-pip/devcontainer.json
@@ -3,21 +3,31 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.0",
+      "CUDA": "12.2",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.04-cpp-llvm16-cuda12.0-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.04-cpp-cuda12.2-ubuntu22.04"
     }
   },
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.2": {"version": "1.14.1"},
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.4": {
+      "version": "1.14.1"
+    },
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.4": {
+      "version": "12.2",
+      "installcuBLAS": true,
+      "installcuSOLVER": true,
+      "installcuRAND": true,
+      "installcuSPARSE": true
+    },
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
+    "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.0-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.2-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -25,7 +35,7 @@
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.0-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.2-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 61ebdf0bca..62f648d7e3 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 7c6db2c603..c87712a4f8 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.04
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -101,7 +101,9 @@ jobs:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.04
     with:
+      arch: '["amd64"]'
+      cuda: '["12.2"]'
       build_command: |
         sccache -z;
-        build-all -DBUILD_PRIMS_BENCH=ON -DBUILD_ANN_BENCH=ON --verbose;
+        build-all --verbose;
         sccache -s;
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 36a62044ae..b2a65797e5 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -53,7 +53,7 @@ rapids_cpm_init()
 
 # If the user requested it we attempt to find CUVS.
 if(FIND_CUVS_CPP)
-  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED)
+  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS c_api)
   include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
 else()
   set(cuvs_FOUND OFF)
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index 066a73d351..202919e012 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 # Set the list of Cython files to build
 set(cython_sources cydlpack.pyx exceptions.pyx resources.pyx)
-set(linked_libraries cuvs::cuvs cuvs_c)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
 rapids_cython_create_modules(
diff --git a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
index 8534370aee..4806fb9fc0 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 # Set the list of Cython files to build
 set(cython_sources brute_force.pyx)
-set(linked_libraries cuvs::cuvs cuvs_c)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
 rapids_cython_create_modules(
diff --git a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
index 377cfe7793..87e6597fe1 100644
--- a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 # Set the list of Cython files to build
 set(cython_sources cagra.pyx)
-set(linked_libraries cuvs::cuvs cuvs_c)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
 rapids_cython_create_modules(

From 8df8c49e1ab24b7754e26211467f9e264933e06e Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Mon, 15 Apr 2024 15:49:05 -0400
Subject: [PATCH 035/623] Update the developer's guide with new copyright hook
 (#81)

Issue: https://github.com/rapidsai/build-planning/issues/30

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/81
---
 docs/source/developer_guide.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index e10e8987af..5e288e9f2f 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -215,13 +215,14 @@ python ./cpp/scripts/include_checker.py --inplace [cpp/include cpp/test ... list
 ```
 
 ### Copyright header
-[copyright.py](https://github.com/rapidsai/raft/blob/branch-24.06/ci/checks/copyright.py) checks the Copyright header for all git-modified files
+RAPIDS [pre-commit-hooks](https://github.com/rapidsai/pre-commit-hooks) checks the Copyright
+header for all git-modified files.
 
-Manually, you can run the following to bulk-fix the header if only the years need to be updated:
+Manually, you can run the following to bulk-fix the header on all files in the repository:
 ```bash
-python ./ci/checks/copyright.py --update-current-year
+pre-commit run -a verify-copyright
 ```
-Keep in mind that this only applies to files tracked by git and having been modified.
+Keep in mind that this only applies to files tracked by git that have been modified.
 
 ## Error handling
 Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY` and `RAFT_CUSOLVER_TRY`. These macros take care of checking the return values of the used API calls and generate an exception when the command is not successful. If you need to avoid an exception, e.g. inside a destructor, use `RAFT_CUDA_TRY_NO_THROW`, `RAFT_CUBLAS_TRY_NO_THROW ` and `RAFT_CUSOLVER_TRY_NO_THROW`. These macros log the error but do not throw an exception.

From a23704989484980499cffaffceed7f5d2a404cf8 Mon Sep 17 00:00:00 2001
From: Robert Maynard <robertjmaynard@gmail.com>
Date: Tue, 16 Apr 2024 10:09:15 -0400
Subject: [PATCH 036/623] Use static gtest (#83)

Allows us to remove the gtest dependency from our conda env for testing.

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Jake Awe (https://github.com/AyodeAwe)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/83
---
 conda/recipes/libcuvs/conda_build_config.yaml |  3 ---
 conda/recipes/libcuvs/meta.yaml               |  4 ----
 cpp/CMakeLists.txt                            |  3 ++-
 cpp/cmake/thirdparty/get_gtest.cmake          | 22 -------------------
 4 files changed, 2 insertions(+), 30 deletions(-)
 delete mode 100644 cpp/cmake/thirdparty/get_gtest.cmake

diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index 73c3281e66..c833f68ba7 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -19,9 +19,6 @@ cmake_version:
 nccl_version:
   - ">=2.9.9"
 
-gtest_version:
-  - ">=1.13.0"
-
 h5py_version:
   - ">=3.8.0"
 
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index 4ad3618c38..7a09015ccc 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -216,8 +216,6 @@ outputs:
         - libcusolver-dev
         - libcusparse-dev
         {% endif %}
-        - gmock {{ gtest_version }}
-        - gtest {{ gtest_version }}
       run:
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
         {% if cuda_major == "11" %}
@@ -231,8 +229,6 @@ outputs:
         {% endif %}
         - libraft ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
-        - gmock {{ gtest_version }}
-        - gtest {{ gtest_version }}
     about:
       home: https://rapids.ai/
       license: Apache-2.0
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index c2d311bf23..eb9de7a9db 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -177,7 +177,8 @@ if(BUILD_C_LIBRARY)
 endif()
 
 if(BUILD_TESTS OR BUILD_C_TESTS)
-  include(cmake/thirdparty/get_gtest.cmake)
+  include(${rapids-cmake-dir}/cpm/gtest.cmake)
+  rapids_cpm_gtest(BUILD_STATIC)
 endif()
 
 include(cmake/thirdparty/get_cutlass.cmake)
diff --git a/cpp/cmake/thirdparty/get_gtest.cmake b/cpp/cmake/thirdparty/get_gtest.cmake
deleted file mode 100644
index 34fca4c7dc..0000000000
--- a/cpp/cmake/thirdparty/get_gtest.cmake
+++ /dev/null
@@ -1,22 +0,0 @@
-#=============================================================================
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#=============================================================================
-
-function(find_and_configure_gtest )
-    include(${rapids-cmake-dir}/cpm/gtest.cmake)
-    rapids_cpm_gtest()
-endfunction()
-
-find_and_configure_gtest()

From da1e63bab83ee9fd366abd1a746fb23afe08a91f Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 18 Apr 2024 10:12:09 -0700
Subject: [PATCH 037/623] Fix rust docs build (#84)

The rust API docs aren't being generated on docs.rapids.ai/cuvs . While the `build.sh docs` script was including the rust api docs, the `ci/build_docs.sh` wasn't. Fix.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/84
---
 ci/build_docs.sh  | 7 +++++++
 dependencies.yaml | 1 +
 2 files changed, 8 insertions(+)

diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index 9d4509e08a..fbf24b346d 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -40,10 +40,17 @@ pushd cpp/doxygen
 doxygen Doxyfile
 popd
 
+rapids-logger "Build Rust docs"
+pushd rust
+export LIBCLANG_PATH=$(dirname $(find /opt/conda -name libclang.so | head -n 1))
+cargo doc -p cuvs --no-deps
+popd
+
 rapids-logger "Build Python docs"
 pushd docs
 sphinx-build -b dirhtml source _html
 sphinx-build -b text source _text
+mv ../rust/target/doc ./_html/_static/rust
 mkdir -p "${RAPIDS_DOCS_DIR}/cuvs/"{html,txt}
 mv _html/* "${RAPIDS_DOCS_DIR}/cuvs/html"
 mv _text/* "${RAPIDS_DOCS_DIR}/cuvs/txt"
diff --git a/dependencies.yaml b/dependencies.yaml
index 1951ee5cf0..74b573c017 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -47,6 +47,7 @@ files:
       - py_version
       - rust
       - build
+      - cuda
   rust:
     output: none
     includes:

From aea7f5423fb78db2258442c208e2641298b6f399 Mon Sep 17 00:00:00 2001
From: Paul Taylor <178183+trxcllnt@users.noreply.github.com>
Date: Fri, 19 Apr 2024 14:24:22 -0700
Subject: [PATCH 038/623] Update pip devcontainers to UCX v1.15.0 (#88)

Authors:
  - Paul Taylor (https://github.com/trxcllnt)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/88
---
 .devcontainer/cuda11.8-pip/devcontainer.json | 2 +-
 .devcontainer/cuda12.2-pip/devcontainer.json | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index f34a6adc84..05f45a39a1 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -11,7 +11,7 @@
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
-      "version": "1.14.1"
+      "version": "1.15.0"
     },
     "ghcr.io/rapidsai/devcontainers/features/cuda:24.6": {
       "version": "11.8",
diff --git a/.devcontainer/cuda12.2-pip/devcontainer.json b/.devcontainer/cuda12.2-pip/devcontainer.json
index 8dd06ae6c4..7d57303497 100644
--- a/.devcontainer/cuda12.2-pip/devcontainer.json
+++ b/.devcontainer/cuda12.2-pip/devcontainer.json
@@ -11,7 +11,7 @@
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
-      "version": "1.14.1"
+      "version": "1.15.0"
     },
     "ghcr.io/rapidsai/devcontainers/features/cuda:24.6": {
       "version": "12.2",

From 5ff588dd1e3ddc8462026294918d85bc0a4b6d9f Mon Sep 17 00:00:00 2001
From: Shunya Ueta <hurutoriya@users.noreply.github.com>
Date: Wed, 24 Apr 2024 02:50:24 +0900
Subject: [PATCH 039/623] update: fix RAFT URL in README (#91)

## What
SSIA

## Why
Maintain Docs

Authors:
  - Shunya Ueta (https://github.com/hurutoriya)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/91
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c82294f570..6829c80f72 100755
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuVS: Vector Search and Clustering on the GPU</div>
 
 > [!note]
-> cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
+> cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai/raft) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
 > 1. RAFT contains C++ and Python APIs for all of the approximate nearest neighbors and clustering algorithms.
 > 2. cuVS contains a growing support for different languages, including C, C++, Python, and Rust. We will be adding more language support to cuVS in the future but will not be improving the language support for RAFT.
 > 3. Once all of RAFT's approximate nearest neighbors and clustering algorithms are moved to cuVS, the RAFT APIs will be deprecated and eventually removed altogether. Once removed, RAFT will become a lightweight header-only library. In the meantime, there's no harm in using RAFT if support for additional languages is not needed.

From b38dc11f0e4e1ec23356bced325079ca467037a1 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Tue, 23 Apr 2024 22:44:06 -0400
Subject: [PATCH 040/623] Remove gtest from dependencies.yaml (#87)

Missed these files in https://github.com/rapidsai/cuvs/pull/83

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/87
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 2 --
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 2 --
 conda/environments/all_cuda-122_arch-aarch64.yaml | 2 --
 conda/environments/all_cuda-122_arch-x86_64.yaml  | 2 --
 dependencies.yaml                                 | 2 --
 5 files changed, 10 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 364d9c76af..d1a6fa011e 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -24,9 +24,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-aarch64=11.*
-- gmock>=1.13.0
 - graphviz
-- gtest>=1.13.0
 - ipython
 - libclang
 - libcublas-dev=11.11.3.6
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index f269ff55ea..e604ed8f31 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -24,9 +24,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-64=11.*
-- gmock>=1.13.0
 - graphviz
-- gtest>=1.13.0
 - ipython
 - libclang
 - libcublas-dev=11.11.3.6
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index d232a16318..7b50ceb0a0 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -25,9 +25,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-aarch64=11.*
-- gmock>=1.13.0
 - graphviz
-- gtest>=1.13.0
 - ipython
 - libclang
 - libcublas-dev
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index a5dc50d762..6c933e193d 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -25,9 +25,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-64=11.*
-- gmock>=1.13.0
 - graphviz
-- gtest>=1.13.0
 - ipython
 - libclang
 - libcublas-dev
diff --git a/dependencies.yaml b/dependencies.yaml
index 74b573c017..eb1f99d4a1 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -304,8 +304,6 @@ dependencies:
       - output_types: [conda]
         packages:
           - *cmake_ver
-          - gtest>=1.13.0
-          - gmock>=1.13.0
   docs:
     common:
       - output_types: [conda]

From 32235a4ef22919ea57c3a9b2c4345be2fc3dd2aa Mon Sep 17 00:00:00 2001
From: Paul Taylor <178183+trxcllnt@users.noreply.github.com>
Date: Fri, 26 Apr 2024 15:31:37 -0700
Subject: [PATCH 041/623] add --rm and --name to devcontainer run args (#89)

* Remove the devcontainer when the VSCode window closes
* Adds a descriptive name to the running container:
  ```shell
  $ docker ps -a
  CONTAINER ID   IMAGE         ...  NAMES
  0dbb364fe544   vsc-cuvs-...  ...  rapids-cuvs-24.06-cuda12.2-conda

  $ docker rm -f rapids-cuvs-24.06-cuda12.2-conda
  ```

Authors:
  - Paul Taylor (https://github.com/trxcllnt)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/89
---
 .devcontainer/cuda11.8-conda/devcontainer.json | 5 +++++
 .devcontainer/cuda11.8-pip/devcontainer.json   | 5 +++++
 .devcontainer/cuda12.2-conda/devcontainer.json | 5 +++++
 .devcontainer/cuda12.2-pip/devcontainer.json   | 5 +++++
 ci/release/update-version.sh                   | 1 +
 5 files changed, 21 insertions(+)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index 72fbef26d6..bfba756fbe 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -8,6 +8,11 @@
       "BASE": "rapidsai/devcontainers:24.06-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
+  "runArgs": [
+    "--rm",
+    "--name",
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-conda"
+  ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 05f45a39a1..bb25e2ba31 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -8,6 +8,11 @@
       "BASE": "rapidsai/devcontainers:24.06-cpp-cuda11.8-ubuntu22.04"
     }
   },
+  "runArgs": [
+    "--rm",
+    "--name",
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-pip"
+  ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
diff --git a/.devcontainer/cuda12.2-conda/devcontainer.json b/.devcontainer/cuda12.2-conda/devcontainer.json
index d6b7796cb7..bc0548622b 100644
--- a/.devcontainer/cuda12.2-conda/devcontainer.json
+++ b/.devcontainer/cuda12.2-conda/devcontainer.json
@@ -8,6 +8,11 @@
       "BASE": "rapidsai/devcontainers:24.06-cpp-mambaforge-ubuntu22.04"
     }
   },
+  "runArgs": [
+    "--rm",
+    "--name",
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-conda"
+  ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
diff --git a/.devcontainer/cuda12.2-pip/devcontainer.json b/.devcontainer/cuda12.2-pip/devcontainer.json
index 7d57303497..19ab3e145e 100644
--- a/.devcontainer/cuda12.2-pip/devcontainer.json
+++ b/.devcontainer/cuda12.2-pip/devcontainer.json
@@ -8,6 +8,11 @@
       "BASE": "rapidsai/devcontainers:24.06-cpp-cuda12.2-ubuntu22.04"
     }
   },
+  "runArgs": [
+    "--rm",
+    "--name",
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-pip"
+  ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 589110ab12..b8f15aba78 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -91,4 +91,5 @@ find .devcontainer/ -type f -name devcontainer.json -print0 | while IFS= read -r
     sed_runner "s@rapidsai/devcontainers:[0-9.]*@rapidsai/devcontainers:${NEXT_SHORT_TAG}@g" "${filename}"
     sed_runner "s@rapidsai/devcontainers/features/ucx:[0-9.]*@rapidsai/devcontainers/features/ucx:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
     sed_runner "s@rapidsai/devcontainers/features/rapids-build-utils:[0-9.]*@rapidsai/devcontainers/features/rapids-build-utils:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
+    sed_runner "s@rapids-\${localWorkspaceFolderBasename}-${CURRENT_SHORT_TAG}@rapids-\${localWorkspaceFolderBasename}-${NEXT_SHORT_TAG}@g" "${filename}"
 done

From 8db4638ff376c195df48022f647321b258048bd6 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Mon, 29 Apr 2024 15:09:15 -0400
Subject: [PATCH 042/623] Moving and renaming distance namespaces from raft ->
 cuvs (#85)

This PR moves the `raft::distance` APIs over to cuVS. We're ripping the band-aid off quickly here so moving all over at once.

All of the non-public APIs are being moved to `src/distance` and only the public APIs being moved to `include/cuvs/distance`.

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/85
---
 .../all_cuda-122_arch-aarch64.yaml            |    1 +
 .../all_cuda-122_arch-x86_64.yaml             |    1 +
 cpp/CMakeLists.txt                            |   91 +-
 cpp/include/cuvs/distance/distance_types.h    |    4 +-
 cpp/include/cuvs/distance/distance_types.hpp  |   48 +-
 .../cuvs/distance/pairwise_distance.hpp       |  206 ++++
 cpp/include/cuvs/neighbors/brute_force.h      |    2 +-
 cpp/include/cuvs/neighbors/ivf_flat.h         |    2 +-
 cpp/include/cuvs/neighbors/ivf_pq.h           |    2 +-
 cpp/src/distance/detail/compress_to_bits.cuh  |  123 ++
 cpp/src/distance/detail/distance.cuh          |  815 +++++++++++++
 .../distance/detail/distance_ops/all_ops.cuh  |   35 +
 .../distance/detail/distance_ops/canberra.cuh |   71 ++
 .../detail/distance_ops/correlation.cuh       |  126 ++
 .../distance/detail/distance_ops/cosine.cuh   |   85 ++
 .../distance/detail/distance_ops/cutlass.cuh  |   40 +
 .../distance/detail/distance_ops/hamming.cuh  |   73 ++
 .../detail/distance_ops/hellinger.cuh         |   77 ++
 .../detail/distance_ops/jensen_shannon.cuh    |   81 ++
 .../detail/distance_ops/kl_divergence.cuh     |   99 ++
 cpp/src/distance/detail/distance_ops/l1.cuh   |   62 +
 .../distance/detail/distance_ops/l2_exp.cuh   |  136 +++
 .../distance/detail/distance_ops/l2_unexp.cuh |   79 ++
 .../distance/detail/distance_ops/l_inf.cuh    |   67 ++
 .../distance/detail/distance_ops/lp_unexp.cuh |   78 ++
 .../detail/distance_ops/russel_rao.cuh        |   74 ++
 .../distance/detail/distance_ops/template.cuh |   68 ++
 cpp/src/distance/detail/fused_distance_nn.cuh |   97 ++
 .../custom_epilogue_with_broadcast.h          |  668 +++++++++++
 .../detail/fused_distance_nn/cutlass_base.cuh |  177 +++
 .../detail/fused_distance_nn/epilogue.cuh     |  134 +++
 .../epilogue_elementwise.cuh                  |  220 ++++
 .../fused_distance_nn/fused_cosine_nn.cuh     |  136 +++
 .../detail/fused_distance_nn/fused_l2_nn.cuh  |  146 +++
 .../distance/detail/fused_distance_nn/gemm.h  |  409 +++++++
 .../fused_distance_nn/helper_structs.cuh      |  146 +++
 .../fused_distance_nn/persistent_gemm.h       |  512 ++++++++
 .../predicated_tile_iterator_normvec_smem.h   |  448 +++++++
 .../predicated_tile_iterator_reduced_vec.h    |  610 ++++++++++
 .../detail/fused_distance_nn/simt_kernel.cuh  |  187 +++
 cpp/src/distance/detail/fused_l2_nn.cuh       |  386 ++++++
 .../distance/detail/kernels/gram_matrix.cuh   |  488 ++++++++
 .../detail/kernels/kernel_factory.cuh         |   65 +
 .../detail/kernels/kernel_matrices.cuh        |  777 ++++++++++++
 .../distance/detail/kernels/rbf_fin_op.cuh    |   51 +
 .../distance/detail/masked_distance_base.cuh  |  326 +++++
 cpp/src/distance/detail/masked_nn.cuh         |  329 +++++
 .../detail/pairwise_distance_base.cuh         |  326 +++++
 .../detail/pairwise_distance_cutlass_base.cuh |  185 +++
 .../detail/pairwise_distance_epilogue.h       |  100 ++
 .../pairwise_distance_epilogue_elementwise.h  |  171 +++
 .../distance/detail/pairwise_distance_gemm.h  |  238 ++++
 .../detail/pairwise_matrix/dispatch-ext.cuh   |  194 +++
 .../detail/pairwise_matrix/dispatch-inl.cuh   |  127 ++
 .../detail/pairwise_matrix/dispatch.cuh       |   24 +
 .../pairwise_matrix/dispatch_00_generate.py   |  194 +++
 ...patch_canberra_double_double_double_int.cu |   55 +
 ...dispatch_canberra_float_float_float_int.cu |   50 +
 ...ch_correlation_double_double_double_int.cu |   55 +
 ...patch_correlation_float_float_float_int.cu |   55 +
 ...ispatch_cosine_double_double_double_int.cu |   51 +
 .../dispatch_cosine_float_float_float_int.cu  |   51 +
 ...ing_unexpanded_double_double_double_int.cu |   50 +
 ...amming_unexpanded_float_float_float_int.cu |   50 +
 ...inger_expanded_double_double_double_int.cu |   55 +
 ...ellinger_expanded_float_float_float_int.cu |   50 +
 ...jensen_shannon_double_double_double_int.cu |   55 +
 ...ch_jensen_shannon_float_float_float_int.cu |   55 +
 ..._kl_divergence_double_double_double_int.cu |   50 +
 ...tch_kl_divergence_float_float_float_int.cu |   50 +
 .../dispatch_l1_double_double_double_int.cu   |   51 +
 .../dispatch_l1_float_float_float_int.cu      |   50 +
 ...ch_l2_expanded_double_double_double_int.cu |   51 +
 ...patch_l2_expanded_float_float_float_int.cu |   51 +
 ..._l2_unexpanded_double_double_double_int.cu |   55 +
 ...tch_l2_unexpanded_float_float_float_int.cu |   50 +
 ...dispatch_l_inf_double_double_double_int.cu |   50 +
 .../dispatch_l_inf_float_float_float_int.cu   |   50 +
 .../pairwise_matrix/dispatch_layout.cuh       |  117 ++
 ..._lp_unexpanded_double_double_double_int.cu |   55 +
 ...tch_lp_unexpanded_float_float_float_int.cu |   50 +
 .../detail/pairwise_matrix/dispatch_rbf.cu    |   64 +
 ...tch_russel_rao_double_double_double_int.cu |   55 +
 ...spatch_russel_rao_float_float_float_int.cu |   50 +
 .../detail/pairwise_matrix/dispatch_sm60.cuh  |   85 ++
 .../detail/pairwise_matrix/dispatch_sm80.cuh  |   69 ++
 .../detail/pairwise_matrix/kernel_sm60.cuh    |  156 +++
 .../detail/pairwise_matrix/params.cuh         |   47 +
 .../detail/predicated_tile_iterator_normvec.h |  585 +++++++++
 cpp/src/distance/distance-ext.cuh             | 1066 +++++++++++++++++
 cpp/src/distance/distance-inl.cuh             |  478 ++++++++
 cpp/src/distance/distance.cu                  |  934 +++++++++++++++
 cpp/src/distance/distance.cuh                 |   24 +
 cpp/src/distance/pairwise_distance.cu         |  103 ++
 cpp/src/neighbors/brute_force_c.cpp           |    4 +-
 cpp/src/neighbors/cagra_build_float.cpp       |    2 +-
 cpp/test/CMakeLists.txt                       |   42 +-
 cpp/test/distance/dist_canberra.cu            |   70 ++
 cpp/test/distance/dist_correlation.cu         |   94 ++
 cpp/test/distance/dist_cos.cu                 |  112 ++
 cpp/test/distance/dist_hamming.cu             |   71 ++
 cpp/test/distance/dist_hellinger.cu           |   71 ++
 cpp/test/distance/dist_inner_product.cu       |   74 ++
 cpp/test/distance/dist_jensen_shannon.cu      |   71 ++
 cpp/test/distance/dist_kl_divergence.cu       |   71 ++
 cpp/test/distance/dist_l1.cu                  |   70 ++
 cpp/test/distance/dist_l2_exp.cu              |  115 ++
 cpp/test/distance/dist_l2_sqrt_exp.cu         |   74 ++
 cpp/test/distance/dist_l2_unexp.cu            |   71 ++
 cpp/test/distance/dist_l_inf.cu               |   70 ++
 cpp/test/distance/dist_lp_unexp.cu            |   71 ++
 cpp/test/distance/dist_russell_rao.cu         |   71 ++
 cpp/test/distance/distance_base.cuh           |  710 +++++++++++
 cpp/test/neighbors/ann_ivf_flat_c.cu          |   10 +-
 cpp/test/neighbors/ann_ivf_pq_c.cu            |   10 +-
 cpp/test/neighbors/ann_utils.cuh              |   50 +-
 cpp/test/neighbors/brute_force_c.cu           |    6 +-
 cpp/test/neighbors/run_brute_force_c.c        |    2 +-
 cpp/test/neighbors/run_ivf_flat_c.c           |    2 +-
 cpp/test/neighbors/run_ivf_pq_c.c             |    2 +-
 dependencies.yaml                             |    1 +
 docs/source/cpp_api/distance.rst              |   13 +
 python/cuvs/cuvs/distance_type.pxd            |    2 +-
 .../neighbors/brute_force/brute_force.pxd     |    4 +-
 .../neighbors/brute_force/brute_force.pyx     |    4 +-
 rust/cuvs/src/distance_type.rs                |    2 +-
 126 files changed, 17390 insertions(+), 147 deletions(-)
 create mode 100644 cpp/include/cuvs/distance/pairwise_distance.hpp
 create mode 100644 cpp/src/distance/detail/compress_to_bits.cuh
 create mode 100644 cpp/src/distance/detail/distance.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/all_ops.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/canberra.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/correlation.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/cosine.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/cutlass.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/hamming.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/hellinger.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/jensen_shannon.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/kl_divergence.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/l1.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/l2_exp.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/l2_unexp.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/l_inf.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/lp_unexp.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/russel_rao.cuh
 create mode 100644 cpp/src/distance/detail/distance_ops/template.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/epilogue.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/fused_cosine_nn.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/gemm.h
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/helper_structs.cuh
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h
 create mode 100644 cpp/src/distance/detail/fused_distance_nn/simt_kernel.cuh
 create mode 100644 cpp/src/distance/detail/fused_l2_nn.cuh
 create mode 100644 cpp/src/distance/detail/kernels/gram_matrix.cuh
 create mode 100644 cpp/src/distance/detail/kernels/kernel_factory.cuh
 create mode 100644 cpp/src/distance/detail/kernels/kernel_matrices.cuh
 create mode 100644 cpp/src/distance/detail/kernels/rbf_fin_op.cuh
 create mode 100644 cpp/src/distance/detail/masked_distance_base.cuh
 create mode 100644 cpp/src/distance/detail/masked_nn.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_distance_base.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_distance_epilogue.h
 create mode 100644 cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
 create mode 100644 cpp/src/distance/detail/pairwise_distance_gemm.h
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_layout.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_sm60.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_sm80.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/kernel_sm60.cuh
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/params.cuh
 create mode 100644 cpp/src/distance/detail/predicated_tile_iterator_normvec.h
 create mode 100644 cpp/src/distance/distance-ext.cuh
 create mode 100644 cpp/src/distance/distance-inl.cuh
 create mode 100644 cpp/src/distance/distance.cu
 create mode 100644 cpp/src/distance/distance.cuh
 create mode 100644 cpp/src/distance/pairwise_distance.cu
 create mode 100644 cpp/test/distance/dist_canberra.cu
 create mode 100644 cpp/test/distance/dist_correlation.cu
 create mode 100644 cpp/test/distance/dist_cos.cu
 create mode 100644 cpp/test/distance/dist_hamming.cu
 create mode 100644 cpp/test/distance/dist_hellinger.cu
 create mode 100644 cpp/test/distance/dist_inner_product.cu
 create mode 100644 cpp/test/distance/dist_jensen_shannon.cu
 create mode 100644 cpp/test/distance/dist_kl_divergence.cu
 create mode 100644 cpp/test/distance/dist_l1.cu
 create mode 100644 cpp/test/distance/dist_l2_exp.cu
 create mode 100644 cpp/test/distance/dist_l2_sqrt_exp.cu
 create mode 100644 cpp/test/distance/dist_l2_unexp.cu
 create mode 100644 cpp/test/distance/dist_l_inf.cu
 create mode 100644 cpp/test/distance/dist_lp_unexp.cu
 create mode 100644 cpp/test/distance/dist_russell_rao.cu
 create mode 100644 cpp/test/distance/distance_base.cuh

diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index 7b50ceb0a0..3c53191605 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -32,6 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- libnvjitlink
 - make
 - nccl>=2.9.9
 - ninja
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 6c933e193d..4e84731c72 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -32,6 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- libnvjitlink
 - make
 - nccl>=2.9.9
 - ninja
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index eb9de7a9db..8556e2941e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -188,6 +188,35 @@ include(cmake/thirdparty/get_cutlass.cmake)
 
 add_library(
   cuvs SHARED
+  src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_rbf.cu
+  src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
+  src/distance/distance.cu
+  src/distance/pairwise_distance.cu
   src/neighbors/brute_force_index.cu
   src/neighbors/brute_force.cu
   src/neighbors/cagra_build_float.cpp
@@ -240,16 +269,13 @@ target_include_directories(
 
 if(NOT BUILD_CPU_ONLY)
   # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
-  target_link_libraries(cuvs
-        PUBLIC
-            rmm::rmm
-            $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::raft>
-            $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::compiled>
-        PRIVATE
-            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
-            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::compiled_static>
-            nvidia::cutlass::cutlass
-    )
+  target_link_libraries(
+    cuvs
+    PUBLIC rmm::rmm $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::raft>
+           $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::compiled>
+    PRIVATE $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
+            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::compiled_static> nvidia::cutlass::cutlass
+  )
 endif()
 
 # Endian detection
@@ -304,14 +330,14 @@ endif()
 
 set_target_properties(
   cuvs
-  PROPERTIES BUILD_RPATH                         "\$ORIGIN"
-             INSTALL_RPATH                       "\$ORIGIN"
-             CXX_STANDARD                        17
-             CXX_STANDARD_REQUIRED               ON
-             CUDA_STANDARD                       17
-             CUDA_STANDARD_REQUIRED              ON
+  PROPERTIES BUILD_RPATH "\$ORIGIN"
+             INSTALL_RPATH "\$ORIGIN"
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             CUDA_STANDARD 17
+             CUDA_STANDARD_REQUIRED ON
              INTERFACE_POSITION_INDEPENDENT_CODE ON
-             POSITION_INDEPENDENT_CODE           ON
+             POSITION_INDEPENDENT_CODE ON
 )
 
 target_compile_options(
@@ -325,25 +351,21 @@ target_link_options(cuvs PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
 # * cuvs_c -------------------------------------------------------------------------------
 if(BUILD_C_LIBRARY)
   add_library(
-    cuvs_c SHARED
-    src/core/c_api.cpp
-    src/neighbors/brute_force_c.cpp
-    src/neighbors/ivf_flat_c.cpp
-    src/neighbors/ivf_pq_c.cpp
-    src/neighbors/cagra_c.cpp
+    cuvs_c SHARED src/core/c_api.cpp src/neighbors/brute_force_c.cpp src/neighbors/ivf_flat_c.cpp
+                  src/neighbors/ivf_pq_c.cpp src/neighbors/cagra_c.cpp
   )
 
   add_library(cuvs::c_api ALIAS cuvs_c)
 
   set_target_properties(
     cuvs_c
-    PROPERTIES BUILD_RPATH                         "\$ORIGIN"
-               INSTALL_RPATH                       "\$ORIGIN"
-               CXX_STANDARD                        17
-               CXX_STANDARD_REQUIRED               ON
-               POSITION_INDEPENDENT_CODE           ON
+    PROPERTIES BUILD_RPATH "\$ORIGIN"
+               INSTALL_RPATH "\$ORIGIN"
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
                INTERFACE_POSITION_INDEPENDENT_CODE ON
-               EXPORT_NAME                         c_api
+               EXPORT_NAME c_api
   )
 
   target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
@@ -354,12 +376,11 @@ if(BUILD_C_LIBRARY)
     INTERFACE "$<INSTALL_INTERFACE:include>"
   )
 
-  target_link_libraries(cuvs_c
-        PUBLIC
-            cuvs::cuvs
-        PRIVATE
-            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
-    )
+  target_link_libraries(
+    cuvs_c
+    PUBLIC cuvs::cuvs
+    PRIVATE $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
+  )
 
   # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
   target_link_options(cuvs_c PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
diff --git a/cpp/include/cuvs/distance/distance_types.h b/cpp/include/cuvs/distance/distance_types.h
index 6cc2a993b6..550221e8e6 100644
--- a/cpp/include/cuvs/distance/distance_types.h
+++ b/cpp/include/cuvs/distance/distance_types.h
@@ -20,7 +20,7 @@ extern "C" {
 #endif
 
 /** enum to tell how to compute distance */
-enum DistanceType {
+typedef enum {
 
   /** evaluate as dist_ij = sum(x_ik^2) + sum(y_ij)^2 - 2*sum(x_ik * y_jk) */
   L2Expanded = 0,
@@ -64,7 +64,7 @@ enum DistanceType {
   DiceExpanded = 19,
   /** Precomputed (special value) **/
   Precomputed = 100
-};
+} cuvsDistanceType;
 
 #ifdef __cplusplus
 }
diff --git a/cpp/include/cuvs/distance/distance_types.hpp b/cpp/include/cuvs/distance/distance_types.hpp
index 0b2fa4c263..7b18646471 100644
--- a/cpp/include/cuvs/distance/distance_types.hpp
+++ b/cpp/include/cuvs/distance/distance_types.hpp
@@ -14,56 +14,12 @@
  * limitations under the License.
  */
 
+#include "distance_types.h"
 #pragma once
 
 namespace cuvs::distance {
 
-/** enum to tell how to compute distance */
-enum DistanceType : unsigned short {
-
-  /** evaluate as dist_ij = sum(x_ik^2) + sum(y_ij)^2 - 2*sum(x_ik * y_jk) */
-  L2Expanded = 0,
-  /** same as above, but inside the epilogue, perform square root operation */
-  L2SqrtExpanded = 1,
-  /** cosine distance */
-  CosineExpanded = 2,
-  /** L1 distance */
-  L1 = 3,
-  /** evaluate as dist_ij += (x_ik - y-jk)^2 */
-  L2Unexpanded = 4,
-  /** same as above, but inside the epilogue, perform square root operation */
-  L2SqrtUnexpanded = 5,
-  /** basic inner product **/
-  InnerProduct = 6,
-  /** Chebyshev (Linf) distance **/
-  Linf = 7,
-  /** Canberra distance **/
-  Canberra = 8,
-  /** Generalized Minkowski distance **/
-  LpUnexpanded = 9,
-  /** Correlation distance **/
-  CorrelationExpanded = 10,
-  /** Jaccard distance **/
-  JaccardExpanded = 11,
-  /** Hellinger distance **/
-  HellingerExpanded = 12,
-  /** Haversine distance **/
-  Haversine = 13,
-  /** Bray-Curtis distance **/
-  BrayCurtis = 14,
-  /** Jensen-Shannon distance**/
-  JensenShannon = 15,
-  /** Hamming distance **/
-  HammingUnexpanded = 16,
-  /** KLDivergence **/
-  KLDivergence = 17,
-  /** RusselRao **/
-  RusselRaoExpanded = 18,
-  /** Dice-Sorensen distance **/
-  DiceExpanded = 19,
-  /** Precomputed (special value) **/
-  Precomputed = 100
-};
+using DistanceType = cuvsDistanceType;
 
 /**
  * Whether minimal distance corresponds to similar elements (using the given metric).
diff --git a/cpp/include/cuvs/distance/pairwise_distance.hpp b/cpp/include/cuvs/distance/pairwise_distance.hpp
new file mode 100644
index 0000000000..27d9af6c15
--- /dev/null
+++ b/cpp/include/cuvs/distance/pairwise_distance.hpp
@@ -0,0 +1,206 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::distance {
+
+/**
+ * @defgroup pairwise_distance Pairwise Distances API
+ * @{
+ */
+
+/**
+ * @brief Compute pairwise distances for two matrices
+ *
+ * Note: Only contiguous row- or column-major layouts supported currently.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/core/device_mdarray.hpp>
+ * #include <cuvs/distance/pairwise_distance.hpp>
+ *
+ * raft::raft::resources handle;
+ * int n_samples = 5000;
+ * int n_features = 50;
+ *
+ * auto input = raft::make_device_matrix<float>(handle, n_samples, n_features);
+ *
+ * // ... fill input with data ...
+ *
+ * auto output = raft::make_device_matrix<float>(handle, n_samples, n_samples);
+ *
+ * auto metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+ * cuvs::distance::pairwise_distance(handle,
+ *                                   raft::make_const(input.view()),
+ *                                   raft::make_const(input.view()),
+ *                                   output.view(),
+ *                                   metric);
+ * @endcode
+ *
+ * @param[in] handle raft handle for managing expensive resources
+ * @param[in] x first set of points (size n*k)
+ * @param[in] y second set of points (size m*k)
+ * @param[out] dist output distance matrix (size n*m)
+ * @param[in] metric distance to evaluate
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_c_contiguous> const x,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_c_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_c_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg = 2.0f);
+
+/**
+ * @brief Compute pairwise distances for two matrices
+ *
+ * Note: Only contiguous row- or column-major layouts supported currently.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/core/device_mdarray.hpp>
+ * #include <cuvs/distance/pairwise_distance.hpp>
+ *
+ * raft::raft::resources handle;
+ * int n_samples = 5000;
+ * int n_features = 50;
+ *
+ * auto input = raft::make_device_matrix<double>(handle, n_samples, n_features);
+ *
+ * // ... fill input with data ...
+ *
+ * auto output = raft::make_device_matrix<double>(handle, n_samples, n_samples);
+ *
+ * auto metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+ * cuvs::distance::pairwise_distance(handle,
+ *                                   raft::make_const(input.view()),
+ *                                   raft::make_const(input.view()),
+ *                                   output.view(),
+ *                                   metric);
+ * @endcode
+ *
+ * @param[in] handle raft handle for managing expensive resources
+ * @param[in] x first set of points (size n*k)
+ * @param[in] y second set of points (size m*k)
+ * @param[out] dist output distance matrix (size n*m)
+ * @param[in] metric distance to evaluate
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_c_contiguous> const x,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_c_contiguous> const y,
+  raft::device_matrix_view<double, std::int64_t, raft::layout_c_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  double metric_arg = 2.0f);
+
+/**
+ * @brief Compute pairwise distances for two matrices
+ *
+ * Note: Only contiguous row- or column-major layouts supported currently.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/core/device_mdarray.hpp>
+ * #include <cuvs/distance/pairwise_distance.hpp>
+ *
+ * raft::raft::resources handle;
+ * int n_samples = 5000;
+ * int n_features = 50;
+ *
+ * auto input = raft::make_device_matrix<float>(handle, n_samples, n_features);
+ *
+ * // ... fill input with data ...
+ *
+ * auto output = raft::make_device_matrix<float>(handle, n_samples, n_samples);
+ *
+ * auto metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+ * cuvs::distance::pairwise_distance(handle,
+ *                                   raft::make_const(input.view()),
+ *                                   raft::make_const(input.view()),
+ *                                   output.view(),
+ *                                   metric);
+ * @endcode
+ *
+ * @param[in] handle raft handle for managing expensive resources
+ * @param[in] x first set of points (size n*k)
+ * @param[in] y second set of points (size m*k)
+ * @param[out] dist output distance matrix (size n*m)
+ * @param[in] metric distance to evaluate
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_f_contiguous> const x,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_f_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_f_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg = 2.0f);
+/**
+ * @brief Compute pairwise distances for two matrices
+ *
+ * Note: Only contiguous row- or column-major layouts supported currently.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/core/device_mdarray.hpp>
+ * #include <cuvs/distance/pairwise_distance.hpp>
+ *
+ * raft::raft::resources handle;
+ * int n_samples = 5000;
+ * int n_features = 50;
+ *
+ * auto input = raft::make_device_matrix<double>(handle, n_samples, n_features);
+ *
+ * // ... fill input with data ...
+ *
+ * auto output = raft::make_device_matrix<double>(handle, n_samples, n_samples);
+ *
+ * auto metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+ * cuvs::distance::pairwise_distance(handle,
+ *                                   raft::make_const(input.view()),
+ *                                   raft::make_const(input.view()),
+ *                                   output.view(),
+ *                                   metric);
+ * @endcode
+ *
+ * @param[in] handle raft handle for managing expensive resources
+ * @param[in] x first set of points (size n*k)
+ * @param[in] y second set of points (size m*k)
+ * @param[out] dist output distance matrix (size n*m)
+ * @param[in] metric distance to evaluate
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_f_contiguous> const x,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_f_contiguous> const y,
+  raft::device_matrix_view<double, std::int64_t, raft::layout_f_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  double metric_arg = 2.0f);
+
+/** @} */  // end group pairwise_distance_runtime
+
+}  // namespace cuvs::distance
diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
index cb7ee2a30e..145bb55550 100644
--- a/cpp/include/cuvs/neighbors/brute_force.h
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -102,7 +102,7 @@ cuvsError_t cuvsBruteForceIndexDestroy(cuvsBruteForceIndex_t index);
  */
 cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
                                 DLManagedTensor* dataset,
-                                enum DistanceType metric,
+                                cuvsDistanceType metric,
                                 float metric_arg,
                                 cuvsBruteForceIndex_t index);
 /**
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 08200ae7da..22c4d361ca 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -36,7 +36,7 @@ extern "C" {
  */
 struct ivfFlatIndexParams {
   /** Distance type. */
-  enum DistanceType metric;
+  cuvsDistanceType metric;
   /** The argument used by some distance metrics. */
   float metric_arg;
   /**
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index c1fcaed86a..2a8269eb07 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -45,7 +45,7 @@ enum codebook_gen {  // NOLINT
  */
 struct ivfPqIndexParams {
   /** Distance type. */
-  enum DistanceType metric;
+  cuvsDistanceType metric;
   /** The argument used by some distance metrics. */
   float metric_arg;
   /**
diff --git a/cpp/src/distance/detail/compress_to_bits.cuh b/cpp/src/distance/detail/compress_to_bits.cuh
new file mode 100644
index 0000000000..9ce47774a6
--- /dev/null
+++ b/cpp/src/distance/detail/compress_to_bits.cuh
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <raft/core/handle.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/device_atomics.cuh>
+
+namespace cuvs::distance::detail {
+
+/**
+ * @brief Compress 2D boolean matrix to bitfield
+ *
+ * Utility kernel for masked_l2_nn.
+ *
+ * @tparam T
+ *
+ * @parameter[in]  in       An `m x n` boolean matrix. Row major.
+ * @parameter[out] out      An `(m / bits_per_elem) x n` matrix with elements of
+ *                          type T, where T is of size `bits_per_elem` bits.
+ *                          Note: the division (`/`) is a ceilDiv.
+ */
+template <typename T = uint64_t, typename = std::enable_if_t<std::is_integral<T>::value>>
+RAFT_KERNEL compress_to_bits_kernel(
+  raft::device_matrix_view<const bool, int, raft::layout_c_contiguous> in,
+  raft::device_matrix_view<T, int, raft::layout_c_contiguous> out)
+{
+  constexpr int bits_per_element = 8 * sizeof(T);
+  constexpr int tile_dim_m       = bits_per_element;
+  constexpr int nthreads         = 128;
+  constexpr int tile_dim_n       = nthreads;  // read 128 bools at once = 1 sector
+
+  // Tile in shared memory is transposed
+  __shared__ bool smem[tile_dim_n][tile_dim_m];
+
+  const int num_tiles_per_m = raft::ceildiv(in.extent(0), tile_dim_m);
+  const int num_tiles_per_n = raft::ceildiv(in.extent(1), tile_dim_n);
+
+  for (int lin_tile_idx = blockIdx.x; true; lin_tile_idx += gridDim.x) {
+    const int tile_idx_n = tile_dim_n * (lin_tile_idx % num_tiles_per_n);
+    const int tile_idx_m = tile_dim_m * (lin_tile_idx / num_tiles_per_n);
+
+    if (in.extent(0) <= tile_idx_m) { break; }
+    // Fill shared memory tile
+    bool reg_buf[tile_dim_m];
+#pragma unroll
+    for (int i = 0; i < tile_dim_m; ++i) {
+      const int in_m       = tile_idx_m + i;
+      const int in_n       = tile_idx_n + threadIdx.x;
+      bool in_bounds       = in_m < in.extent(0) && in_n < in.extent(1);
+      reg_buf[i]           = in_bounds ? in(in_m, in_n) : false;
+      smem[threadIdx.x][i] = reg_buf[i];
+    }
+    __syncthreads();
+
+    // Drain memory tile into single output element out_elem.
+    T out_elem{0};
+#pragma unroll
+    for (int j = 0; j < tile_dim_n; ++j) {
+      if (smem[threadIdx.x][j]) { out_elem |= T(1) << j; }
+    }
+    __syncthreads();
+
+    // Write output.
+    int out_m = tile_idx_m / bits_per_element;
+    int out_n = tile_idx_n + threadIdx.x;
+
+    if (out_m < out.extent(0) && out_n < out.extent(1)) { out(out_m, out_n) = out_elem; }
+  }
+}
+
+/**
+ * @brief Compress 2D boolean matrix to bitfield
+ *
+ * Utility kernel for masked_l2_nn.
+ *
+ * @tparam T
+ *
+ * @parameter[in]  in       An `m x n` boolean matrix. Row major.
+ * @parameter[out] out      An `(m / bits_per_elem) x n` matrix with elements of
+ *                          type T, where T is of size `bits_per_elem` bits.
+ *                          Note: the division (`/`) is a ceilDiv.
+ */
+template <typename T = uint64_t, typename = std::enable_if_t<std::is_integral<T>::value>>
+void compress_to_bits(raft::resources const& handle,
+                      raft::device_matrix_view<const bool, int, raft::layout_c_contiguous> in,
+                      raft::device_matrix_view<T, int, raft::layout_c_contiguous> out)
+{
+  auto stream                    = resource::get_cuda_stream(handle);
+  constexpr int bits_per_element = 8 * sizeof(T);
+
+  RAFT_EXPECTS(raft::ceildiv(in.extent(0), bits_per_element) == out.extent(0),
+               "Number of output rows must be ceildiv(input rows, bits_per_elem)");
+  RAFT_EXPECTS(in.extent(1) == out.extent(1), "Number of output columns must equal input columns.");
+
+  const int num_SMs           = raft::getMultiProcessorCount();
+  int blocks_per_sm           = 0;
+  constexpr int num_threads   = 128;
+  constexpr int dyn_smem_size = 0;
+  RAFT_CUDA_TRY(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &blocks_per_sm, compress_to_bits_kernel<T>, num_threads, dyn_smem_size));
+
+  dim3 grid(num_SMs * blocks_per_sm);
+  dim3 block(128);
+  compress_to_bits_kernel<<<grid, block, 0, stream>>>(in, out);
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+};  // namespace cuvs::distance::detail
diff --git a/cpp/src/distance/detail/distance.cuh b/cpp/src/distance/detail/distance.cuh
new file mode 100644
index 0000000000..7765bc672a
--- /dev/null
+++ b/cpp/src/distance/detail/distance.cuh
@@ -0,0 +1,815 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "distance_ops/all_ops.cuh"
+#include "pairwise_matrix/dispatch.cuh"
+#include "pairwise_matrix/dispatch_sm60.cuh"
+#include "pairwise_matrix/dispatch_sm80.cuh"
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/gemm.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/reduce.cuh>
+#include <raft/linalg/unary_op.cuh>
+
+#include <type_traits>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+
+/**
+ * @brief: A tag type for overload resolution based on DistanceType
+ *
+ * It is not possible to partially specialize function templates on a single
+ * parameter. Instead, it is often easier to use a combination of conventional
+ * method overloading and a parameter with a specific tag type. The following
+ * type is used to help method overloading based on the DistanceType enum.
+ */
+template <DistanceType d>
+using distance_tag = std::integral_constant<DistanceType, d>;
+
+/**
+ * @brief Implement pairwise_matrix for specific distance
+ *
+ * There are multiple overloads for this function, one for each distance type.
+ * They are implemented below. The documentation of this function serves as
+ * documentation for all functions. The following overloads are defined:
+ *
+ * - DistanceType::Canberra:
+ * - DistanceType::CorrelationExpanded:
+ * - DistanceType::CosineExpanded:
+ * - DistanceType::HammingUnexpanded:
+ * - DistanceType::HellingerExpanded:
+ * - DistanceType::JensenShannon:
+ * - DistanceType::KLDivergence:
+ * - DistanceType::L1:
+ * - DistanceType::L2Expanded:
+ * - DistanceType::L2SqrtExpanded:
+ * - DistanceType::L2Unexpanded:
+ * - DistanceType::L2SqrtUnexpanded:
+ * - DistanceType::Linf:
+ * - DistanceType::LpUnexpanded:
+ * - DistanceType::RusselRaoExpanded:
+ *
+ * @tparam DataT   Input data type
+ * @tparam AccT    Accumulation data type
+ * @tparam OutT    Output data type
+ * @tparam FinOpT  Type of final operation
+ * @tparam IdxT    Index type
+ *
+ * @param handle        RAFT resources handle
+ * @param distance_type A tag type to indicate which distance is calculated.
+ * @param x             First set of points
+ * @param y             Second set of points
+ * @param out           Output distance matrix
+ * @param m             Number of points in x
+ * @param n             Number of points in y
+ * @param k             Dimensionality of points in x, y
+ * @param workspace     Temporary workspace needed for computations
+ * @param worksize      Number of bytes of the workspace
+ * @param is_row_major  Whether the matrices are row-major or col-major
+ * @param metric_arg    The `p` argument for Lp.
+ */
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::Canberra> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT* workspace,  // unused
+                   size_t worksize,  // unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT metric_arg)  // unused
+{
+  ops::canberra_distance_op<DataT, AccT, IdxT> distance_op{};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::CorrelationExpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT* workspace,
+                   size_t worksize,
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // unused
+{
+  ASSERT(!(worksize < 2 * (m + n) * sizeof(AccT)), "workspace size error");
+  ASSERT(workspace != nullptr, "workspace is null");
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  AccT* x_norm    = workspace;
+  AccT* y_norm    = workspace;
+  AccT* sq_x_norm = workspace;
+  AccT* sq_y_norm = workspace;
+  // TODO: Column major case looks to have lower accuracy for X == Y,
+  // perhaps the use of stridedSummationKernel could be causing this,
+  // need to investigate and fix.
+  if (x == y && is_row_major) {
+    raft::linalg::reduce(x_norm,
+                         x,
+                         k,
+                         std::max(m, n),
+                         (AccT)0,
+                         is_row_major,
+                         true,
+                         stream,
+                         false,
+                         raft::identity_op(),
+                         raft::add_op());
+    sq_x_norm += std::max(m, n);
+    sq_y_norm = sq_x_norm;
+    raft::linalg::rowNorm(
+      sq_x_norm, x, k, std::max(m, n), raft::linalg::L2Norm, is_row_major, stream);
+  } else {
+    y_norm += m;
+    raft::linalg::reduce(x_norm,
+                         x,
+                         k,
+                         m,
+                         (AccT)0,
+                         is_row_major,
+                         true,
+                         stream,
+                         false,
+                         raft::identity_op(),
+                         raft::add_op());
+    raft::linalg::reduce(y_norm,
+                         y,
+                         k,
+                         n,
+                         (AccT)0,
+                         is_row_major,
+                         true,
+                         stream,
+                         false,
+                         raft::identity_op(),
+                         raft::add_op());
+
+    sq_x_norm += (m + n);
+    sq_y_norm = sq_x_norm + m;
+    raft::linalg::rowNorm(sq_x_norm, x, k, m, raft::linalg::L2Norm, is_row_major, stream);
+    raft::linalg::rowNorm(sq_y_norm, y, k, n, raft::linalg::L2Norm, is_row_major, stream);
+  }
+
+  using OpT = ops::correlation_distance_op<DataT, AccT, IdxT>;
+  OpT corr_op(is_row_major, sq_x_norm, sq_y_norm, m, n, k);
+  pairwise_matrix_dispatch<decltype(corr_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    corr_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::CosineExpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT* workspace,
+                   size_t worksize,
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // unused
+{
+  // raft distance support inputs as float/double and output as uint8_t/float/double.
+  static_assert(!((sizeof(OutT) > 1) && (sizeof(AccT) != sizeof(OutT))),
+                "OutT can be uint8_t, float, double,"
+                "if sizeof(OutT) > 1 then sizeof(AccT) == sizeof(OutT).");
+
+  ASSERT(!(worksize < (m + n) * sizeof(AccT)), "workspace size error");
+  ASSERT(workspace != nullptr, "workspace is null");
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  DataT* x_norm = workspace;
+  DataT* y_norm = workspace;
+  // TODO: Column major case looks to have lower accuracy for X == Y,
+  // perhaps the use of stridedSummationKernel could be causing this,
+  // need to investigate and fix.
+  if (x == y && is_row_major) {
+    raft::linalg::rowNorm(
+      x_norm, x, k, std::max(m, n), raft::linalg::L2Norm, is_row_major, stream, raft::sqrt_op{});
+  } else {
+    y_norm += m;
+    raft::linalg::rowNorm(
+      x_norm, x, k, m, raft::linalg::L2Norm, is_row_major, stream, raft::sqrt_op{});
+    raft::linalg::rowNorm(
+      y_norm, y, k, n, raft::linalg::L2Norm, is_row_major, stream, raft::sqrt_op{});
+  }
+
+  ops::cosine_distance_op<DataT, AccT, IdxT> distance_op{};
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::HammingUnexpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  ops::hamming_distance_op<DataT, AccT, IdxT> distance_op{k};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::InnerProduct> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  raft::linalg::gemm(handle,
+                     out,
+                     const_cast<DataT*>(x),
+                     const_cast<DataT*>(y),
+                     m,
+                     n,
+                     k,
+                     !is_row_major,
+                     !is_row_major,
+                     is_row_major,
+                     stream);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::HellingerExpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  // First sqrt x and y
+  const auto raft_sqrt = raft::linalg::unaryOp<DataT, raft::sqrt_op, IdxT>;
+
+  raft_sqrt((DataT*)x, x, m * k, raft::sqrt_op{}, stream);
+  if (x != y) { raft_sqrt((DataT*)y, y, n * k, raft::sqrt_op{}, stream); }
+
+  // Then calculate Hellinger distance
+  ops::hellinger_distance_op<DataT, AccT, IdxT> distance_op{};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+
+  // Finally revert sqrt of x and y
+  raft_sqrt((DataT*)x, x, m * k, raft::sqrt_op{}, stream);
+  if (x != y) { raft_sqrt((DataT*)y, y, n * k, raft::sqrt_op{}, stream); }
+
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::JensenShannon> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  ops::jensen_shannon_distance_op<DataT, AccT, IdxT> distance_op{};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::KLDivergence> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  auto unaryOp_lambda = [] __device__(DataT input) {
+    const bool x_zero = (input == 0);
+    return (!x_zero) * raft::log(input + x_zero);
+  };
+
+  auto unaryOp_lambda_reverse = [] __device__(DataT input) {
+    // reverse previous log (x) back to x using (e ^ log(x))
+    const bool x_zero = (input == 0);
+    return (!x_zero) * raft::exp(input);
+  };
+
+  if (x != y) {
+    raft::linalg::unaryOp<DataT, decltype(unaryOp_lambda), IdxT>(
+      (DataT*)y, y, n * k, unaryOp_lambda, stream);
+  }
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  // This op takes some shortcuts when x equals y. So its behavior changes based
+  // on this.
+  ops::kl_divergence_op<DataT, AccT, IdxT> distance_op{is_row_major, x == y};
+
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+
+  if (x != y) {
+    // Now reverse previous log (x) back to x using (e ^ log(x))
+    raft::linalg::unaryOp<DataT, decltype(unaryOp_lambda_reverse), IdxT>(
+      (DataT*)y, y, n * k, unaryOp_lambda_reverse, stream);
+  }
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::L1> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  ops::l1_distance_op<DataT, AccT, IdxT> distance_op{};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT,
+          typename AccT,
+          typename OutT,
+          typename FinOpT,
+          typename IdxT = int>
+void distance_impl_l2_expanded(  // NOTE: different name
+  bool perform_sqrt,             // dispatch on sqrt
+  const DataT* x,
+  const DataT* y,
+  OutT* out,
+  IdxT m,
+  IdxT n,
+  IdxT k,
+  AccT* workspace,
+  size_t worksize,
+  FinOpT fin_op,
+  cudaStream_t stream,
+  bool is_row_major)
+{
+  // raft distance support inputs as float/double and output as uint8_t/float/double.
+  static_assert(!((sizeof(OutT) > 1) && (sizeof(AccT) != sizeof(OutT))),
+                "OutT can be uint8_t, float, double,"
+                "if sizeof(OutT) > 1 then sizeof(AccT) == sizeof(OutT).");
+
+  ASSERT(!(worksize < (m + n) * sizeof(AccT)), "workspace size error");
+  ASSERT(workspace != nullptr, "workspace is null");
+
+  DataT* x_norm = workspace;
+  DataT* y_norm = workspace;
+  // TODO: Column major case looks to have lower accuracy for X == Y,
+  // perhaps the use of stridedSummationKernel could be causing this,
+  // need to investigate and fix.
+  if ((x == y) && is_row_major) {
+    raft::linalg::rowNorm(x_norm,
+                          x,
+                          k,
+                          std::max(m, n),
+                          raft::linalg::L2Norm,
+                          is_row_major,
+                          stream,
+                          raft::identity_op{});
+  } else {
+    y_norm += m;
+    raft::linalg::rowNorm(
+      x_norm, x, k, m, raft::linalg::L2Norm, is_row_major, stream, raft::identity_op{});
+    raft::linalg::rowNorm(
+      y_norm, y, k, n, raft::linalg::L2Norm, is_row_major, stream, raft::identity_op{});
+  }
+
+  ops::l2_exp_distance_op<DataT, AccT, IdxT> distance_op{perform_sqrt};
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::L2Expanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT* workspace,
+                   size_t worksize,
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  bool perform_sqrt   = false;
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  distance_impl_l2_expanded(
+    perform_sqrt, x, y, out, m, n, k, workspace, worksize, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::L2SqrtExpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT* workspace,
+                   size_t worksize,
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  bool perform_sqrt   = true;
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  distance_impl_l2_expanded(
+    perform_sqrt, x, y, out, m, n, k, workspace, worksize, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::L2Unexpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  bool perform_sqrt = false;
+  ops::l2_unexp_distance_op<DataT, AccT, IdxT> l2_op(perform_sqrt);
+
+  // The unexpanded L2 does not require the norms of a and b to be calculated.
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  pairwise_matrix_dispatch<decltype(l2_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    l2_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::L2SqrtUnexpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  bool perform_sqrt = true;
+  ops::l2_unexp_distance_op<DataT, AccT, IdxT> l2_op(perform_sqrt);
+
+  // The unexpanded L2 does not require the norms of a and b to be calculated.
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  pairwise_matrix_dispatch<decltype(l2_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    l2_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::Linf> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  ops::l_inf_distance_op<DataT, AccT, IdxT> distance_op{};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::LpUnexpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT metric_arg)
+{
+  ops::lp_unexp_distance_op<DataT, AccT, IdxT> distance_op{metric_arg};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+template <typename DataT, typename AccT, typename OutT, typename FinOpT, typename IdxT = int>
+void distance_impl(raft::resources const& handle,
+                   distance_tag<DistanceType::RusselRaoExpanded> distance_type,
+                   const DataT* x,
+                   const DataT* y,
+                   OutT* out,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   AccT*,   // workspace unused
+                   size_t,  // worksize unused
+                   FinOpT fin_op,
+                   bool is_row_major,
+                   DataT)  // metric_arg unused
+{
+  ops::russel_rao_distance_op<DataT, AccT, IdxT> distance_op{k};
+
+  const DataT* x_norm = nullptr;
+  const DataT* y_norm = nullptr;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
+    distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
+}
+
+/**
+ * @brief Evaluate pairwise distances with the user epilogue lamba allowed
+ * @tparam DistanceType which distance to evaluate
+ * @tparam InType input argument type
+ * @tparam AccType accumulation type
+ * @tparam OutType output type
+ * @tparam FinalLambda user-defined epilogue lamba
+ * @tparam Index_ Index type
+ *
+ * @param x first set of points
+ * @param y second set of points
+ * @param out output distance matrix
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ * @param workspace temporary workspace needed for computations
+ * @param worksize number of bytes of the workspace
+ * @param fin_op the final gemm epilogue lambda
+ * @param stream cuda stream
+ * @param isRowMajor whether the matrices are row-major or col-major
+ *
+ * @note fin_op: This is a device lambda which is supposed to operate upon the
+ * input which is AccType and returns the output in OutType. It's signature is
+ * as follows:  <pre>OutType fin_op(AccType in, int g_idx);</pre>. If one needs
+ * any other parameters, feel free to pass them via closure.
+ */
+template <cuvs::distance::DistanceType distanceType,
+          typename InType,
+          typename AccType,
+          typename OutType,
+          typename FinalLambda,
+          typename Index_ = int>
+void distance(raft::resources const& handle,
+              const InType* x,
+              const InType* y,
+              OutType* out,
+              Index_ m,
+              Index_ n,
+              Index_ k,
+              void* workspace,
+              size_t worksize,
+              FinalLambda fin_op,
+              bool isRowMajor   = true,
+              InType metric_arg = 2.0f)
+{
+  // raft distance support inputs as float/double and output as uint8_t/float/double.
+  static_assert(!((sizeof(OutType) > 1) && (sizeof(AccType) != sizeof(OutType))),
+                "OutType can be uint8_t, float, double,"
+                "if sizeof(OutType) > 1 then sizeof(AccType) == sizeof(OutType).");
+
+  distance_impl<InType, AccType, OutType, FinalLambda, Index_>(
+    handle,
+    distance_tag<distanceType>{},
+    x,
+    y,
+    out,
+    m,
+    n,
+    k,
+    reinterpret_cast<AccType*>(workspace),
+    worksize,
+    fin_op,
+    isRowMajor,
+    metric_arg);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/**
+ * @brief Evaluate pairwise distances for the simple use case
+ * @tparam DistanceType which distance to evaluate
+ * @tparam InType input argument type
+ * @tparam AccType accumulation type
+ * @tparam OutType output type
+ * @tparam Index_ Index type
+ * @param x first set of points
+ * @param y second set of points
+ * @param dist output distance matrix
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ * @param workspace temporary workspace needed for computations
+ * @param worksize number of bytes of the workspace
+ * @param stream cuda stream
+ * @param isRowMajor whether the matrices are row-major or col-major
+ */
+template <cuvs::distance::DistanceType distanceType,
+          typename InType,
+          typename AccType,
+          typename OutType,
+          typename Index_ = int>
+void distance(raft::resources const& handle,
+              const InType* x,
+              const InType* y,
+              OutType* out,
+              Index_ m,
+              Index_ n,
+              Index_ k,
+              void* workspace,
+              size_t worksize,
+              bool isRowMajor   = true,
+              InType metric_arg = 2.0f)
+{
+  auto fin_op = raft::identity_op();
+
+  distance<distanceType, InType, AccType, OutType, decltype(fin_op), Index_>(
+    handle, x, y, out, m, n, k, workspace, worksize, fin_op, isRowMajor, metric_arg);
+}
+
+/**
+ * @brief Return the exact workspace size to compute the distance
+ * @tparam DistanceType which distance to evaluate
+ * @tparam InType input argument type
+ * @tparam AccType accumulation type
+ * @tparam OutType output type
+ * @tparam Index_ Index type
+ * @param x first set of points
+ * @param y second set of points
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ *
+ * @note If the specified distanceType doesn't need the workspace at all, it
+ * returns 0.
+ */
+template <cuvs::distance::DistanceType distanceType,
+          typename InType,
+          typename AccType,
+          typename OutType,
+          typename Index_ = int>
+size_t getWorkspaceSize(const InType* x, const InType* y, Index_ m, Index_ n, Index_ k)
+{
+  size_t worksize             = 0;
+  constexpr bool is_allocated = (distanceType <= cuvs::distance::DistanceType::CosineExpanded) ||
+                                (distanceType == cuvs::distance::DistanceType::CorrelationExpanded);
+  constexpr int numOfBuffers =
+    (distanceType == cuvs::distance::DistanceType::CorrelationExpanded) ? 2 : 1;
+
+  if (is_allocated) {
+    // TODO : when X == Y allocate std::max(m, n) instead of m + n when column major input
+    // accuracy issue is resolved until then we allocate as m + n.
+    worksize += numOfBuffers * m * sizeof(AccType);
+    worksize += numOfBuffers * n * sizeof(AccType);
+  }
+
+  return worksize;
+}
+
+};  // namespace detail
+};  // namespace distance
+};  // namespace cuvs
diff --git a/cpp/src/distance/detail/distance_ops/all_ops.cuh b/cpp/src/distance/detail/distance_ops/all_ops.cuh
new file mode 100644
index 0000000000..55989697f7
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/all_ops.cuh
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// Defines a named requirement "has_cutlass_op"
+#include "../distance_ops/cutlass.cuh"
+
+// The distance operations:
+#include "../distance_ops/canberra.cuh"
+#include "../distance_ops/correlation.cuh"
+#include "../distance_ops/cosine.cuh"
+#include "../distance_ops/hamming.cuh"
+#include "../distance_ops/hellinger.cuh"
+#include "../distance_ops/jensen_shannon.cuh"
+#include "../distance_ops/kl_divergence.cuh"
+#include "../distance_ops/l1.cuh"
+#include "../distance_ops/l2_exp.cuh"
+#include "../distance_ops/l2_unexp.cuh"
+#include "../distance_ops/l_inf.cuh"
+#include "../distance_ops/lp_unexp.cuh"
+#include "../distance_ops/russel_rao.cuh"
diff --git a/cpp/src/distance/detail/distance_ops/canberra.cuh b/cpp/src/distance/detail/distance_ops/canberra.cuh
new file mode 100644
index 0000000000..8bbdc9945b
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/canberra.cuh
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/operators.hpp>            // raft::abs
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief The canberra distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ *  c_ij = sum_k |x_ik - y_kj| / ( |x_ik| + |y_kj| )
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct canberra_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = true;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    const auto diff = raft::abs(x - y);
+    const auto add  = raft::abs(x) + raft::abs(y);
+    // deal with potential for 0 in denominator by
+    // forcing 0/1 instead
+    acc += ((add != 0) * diff / (add + (add == 0)));
+  };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    return;
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/correlation.cuh b/cpp/src/distance/detail/distance_ops/correlation.cuh
new file mode 100644
index 0000000000..f033f3dfad
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/correlation.cuh
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/** @brief The correlation distance
+ *
+ * It computes the following equation:
+ *
+ * d(x, y) = ((x - mean(x)) ⋅ (y - mean(y)))
+ *           /
+ *           (|| x - mean(x) ||_2 || y - mean(y) ||_2)
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct correlation_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  const DataT* x2n;
+  const DataT* y2n;
+  IdxT m;
+  IdxT n;
+  IdxT k;
+
+  correlation_distance_op(
+    bool is_row_major, const DataT* x2n_, const DataT* y2n_, IdxT m_, IdxT n_, IdxT k_) noexcept
+    : x2n(x2n_), y2n(y2n_), m(m_), n(n_), k(k_)
+  {
+    // The distance op is typically created before the row-major/col-major
+    // swapping has been done. So we do it here.
+    if (!is_row_major) {
+      std::swap<const DataT*>(x2n, y2n);
+      std::swap(m, n);
+    }
+  }
+
+  // Load norms of input data
+  static constexpr bool use_norms = true;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize + (2 * (Policy::Mblk + Policy::Nblk) * sizeof(DataT));
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    // Note how we can sneakily get a pointer to shared memory here, to store
+    // more data. If the implementation of PairwiseDistanceMatKernel ever
+    // changes, this will be where we find the bugs.
+    extern __shared__ char smem[];
+
+    DataT regx2n[Policy::AccRowsPerTh], regy2n[Policy::AccColsPerTh];
+
+    DataT* sx2Norm =
+      (DataT*)(&smem[Policy::SmemSize + (Policy::Mblk + Policy::Nblk) * sizeof(DataT)]);
+    DataT* sy2Norm = (&sx2Norm[Policy::Mblk]);
+
+    // Load x & y norms required by this threadblock in shmem buffer
+    if (gridStrideX == blockIdx.x * Policy::Nblk) {
+      for (int i = threadIdx.x; i < Policy::Mblk; i += Policy::Nthreads) {
+        auto idx   = gridStrideY + i;
+        sx2Norm[i] = idx < m ? x2n[idx] : 0;
+      }
+    }
+
+    for (int i = threadIdx.x; i < Policy::Nblk; i += Policy::Nthreads) {
+      auto idx   = gridStrideX + i;
+      sy2Norm[i] = idx < n ? y2n[idx] : 0;
+    }
+    __syncthreads();
+
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+      regx2n[i] = sx2Norm[i * Policy::AccThRows + (threadIdx.x / Policy::AccThCols)];
+    }
+#pragma unroll
+    for (int i = 0; i < Policy::AccColsPerTh; ++i) {
+      regy2n[i] = sy2Norm[i * Policy::AccThCols + (threadIdx.x % Policy::AccThCols)];
+    }
+
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        auto numer   = k * acc[i][j] - (regxn[i] * regyn[j]);
+        auto Q_denom = k * regx2n[i] - (regxn[i] * regxn[i]);
+        auto R_denom = k * regy2n[j] - (regyn[j] * regyn[j]);
+
+        acc[i][j] = 1 - (numer / raft::sqrt(Q_denom * R_denom));
+      }
+    }
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/cosine.cuh b/cpp/src/distance/detail/distance_ops/cosine.cuh
new file mode 100644
index 0000000000..d487316513
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/cosine.cuh
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+// Epilogue operator for CUTLASS based kernel
+template <typename DataT, typename AccT>
+struct cosine_cutlass_op {
+  __device__ cosine_cutlass_op() noexcept {}
+  __device__ AccT operator()(DataT& aNorm, const DataT& bNorm, DataT& accVal) const noexcept
+  {
+    return static_cast<AccT>(1.0) - static_cast<AccT>(accVal / (aNorm * bNorm));
+  }
+  __device__ AccT operator()(DataT aData) const noexcept { return aData; }
+};
+
+/**
+ * @brief the expanded cosine distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ * d(x, y) = 1 - (x ⋅ y) / ( ||x||_2 ||y||_2)
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct cosine_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  // Load norms of input data
+  static constexpr bool use_norms = true;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize + ((Policy::Mblk + Policy::Nblk) * sizeof(DataT));
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        acc[i][j] = 1.0 - (acc[i][j] / (regxn[i] * regyn[j]));
+      }
+    }
+  }
+
+  constexpr cosine_cutlass_op<DataT, AccT> get_cutlass_op() const
+  {
+    return cosine_cutlass_op<DataT, AccT>();
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/cutlass.cuh b/cpp/src/distance/detail/distance_ops/cutlass.cuh
new file mode 100644
index 0000000000..6d928314d7
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/cutlass.cuh
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <type_traits>  // std::false_type
+#include <utility>      // std::declval
+
+namespace cuvs::distance::detail::ops {
+
+// This file defines the named requirement "has_cutlass_op" that can be used to
+// determine if a distance operation has a CUTLASS op that can be used to pass
+// to CUTLASS. Examples of distance operations that satisfy this requirement are
+// cosine_distance_op and l2_exp_distance_op.
+
+// Primary template handles types that do not support CUTLASS.
+// This pattern is described in:
+// https://en.cppreference.com/w/cpp/types/void_t
+template <typename, typename = void>
+struct has_cutlass_op : std::false_type {};
+
+// Specialization recognizes types that do support CUTLASS
+template <typename T>
+struct has_cutlass_op<T, std::void_t<decltype(std::declval<T>().get_cutlass_op())>>
+  : std::true_type {};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/hamming.cuh b/cpp/src/distance/detail/distance_ops/hamming.cuh
new file mode 100644
index 0000000000..7c6553f387
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/hamming.cuh
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the Hamming Unexpanded distance matrix calculation
+ *  It computes the following equation:
+ *
+ *    c_ij = sum_k (x_ik != y_kj) / k
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct hamming_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  IdxT k;
+
+  hamming_distance_op(IdxT k_) noexcept : k(k_) {}
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += (x != y); };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    const DataT one_over_k = DataT(1.0) / k;
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        acc[i][j] *= one_over_k;
+      }
+    }
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/hellinger.cuh b/cpp/src/distance/detail/distance_ops/hellinger.cuh
new file mode 100644
index 0000000000..ad5ca31564
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/hellinger.cuh
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the Hellinger distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ *  c_ij = sqrt(1 - sum_k sqrt(x_ik * y_kj))
+ *
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct hellinger_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    // This is sqrt(x) * sqrt(y).
+    const auto product = x * y;
+    acc += product;
+  };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        // Adjust to replace NaN in sqrt with 0 if input to sqrt is negative
+        const auto finalVal  = (1 - acc[i][j]);
+        const auto rectifier = (!signbit(finalVal));
+        acc[i][j]            = raft::sqrt(rectifier * finalVal);
+      }
+    }
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/jensen_shannon.cuh b/cpp/src/distance/detail/distance_ops/jensen_shannon.cuh
new file mode 100644
index 0000000000..2166394941
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/jensen_shannon.cuh
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <raft/core/operators.hpp>            // raft::log
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+// Describes the computation the jensen_shannon distance
+
+/**
+ * @brief the Jensen Shannon distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ * c_ij = sqrt(0.5 * sum( -x_i * (log(0.5 * (x_i + y_i)) - log(x_i))
+ *       + (-y_i * (log(0.5 * (x_i + y_i)) - log(y_i)))))
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct jensen_shannon_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = true;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    const DataT m     = 0.5f * (x + y);
+    const bool m_zero = (m == 0);
+    const auto logM   = (!m_zero) * raft::log(m + m_zero);
+
+    const bool x_zero = (x == 0);
+    const bool y_zero = (y == 0);
+    acc += (-x * (logM - raft::log(x + x_zero))) + (-y * (logM - raft::log(y + y_zero)));
+  };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        acc[i][j] = raft::sqrt(0.5 * acc[i][j]);
+      }
+    }
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/kl_divergence.cuh b/cpp/src/distance/detail/distance_ops/kl_divergence.cuh
new file mode 100644
index 0000000000..929c3a5596
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/kl_divergence.cuh
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <raft/core/operators.hpp>            // raft::log
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the KL Divergence distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ *   c_ij = 0.5 * sum(x * log (x / y));
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct kl_divergence_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  const bool is_row_major;
+  const bool x_equal_y;
+
+  kl_divergence_op(bool row_major_, bool x_equal_y_ = false) noexcept
+    : is_row_major(row_major_), x_equal_y(x_equal_y_)
+  {
+  }
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = true;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    // TODO: make sure that these branches get hoisted out of main loop.. Could
+    // be quite expensive otherwise.
+    if (x_equal_y) {
+      if (is_row_major) {
+        const bool x_zero = (x == 0);
+        const bool y_zero = (y == 0);
+        acc += x * (raft::log(x + x_zero) - (!y_zero) * raft::log(y + y_zero));
+      } else {
+        const bool y_zero = (y == 0);
+        const bool x_zero = (x == 0);
+        acc += y * (raft::log(y + y_zero) - (!x_zero) * raft::log(x + x_zero));
+      }
+    } else {
+      if (is_row_major) {
+        const bool x_zero = (x == 0);
+        acc += x * (raft::log(x + x_zero) - y);
+      } else {
+        const bool y_zero = (y == 0);
+        acc += y * (raft::log(y + y_zero) - x);
+      }
+    }
+  };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        acc[i][j] = (0.5f * acc[i][j]);
+      }
+    }
+  }
+};
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/l1.cuh b/cpp/src/distance/detail/distance_ops/l1.cuh
new file mode 100644
index 0000000000..76eaffaf30
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/l1.cuh
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the L1 distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ *   c_ij = sum_k abs(x_ik  - y_kj)
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct l1_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  // Do not load norms of data, the computation of L1 distance does not use them.
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += raft::abs(x - y); };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    return;
+  };
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/l2_exp.cuh b/cpp/src/distance/detail/distance_ops/l2_exp.cuh
new file mode 100644
index 0000000000..f45c412069
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/l2_exp.cuh
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/math.hpp>
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * Reserve 1 digit of precision from each floating-point type
+ * for round-off error tolerance.
+ * @tparam DataT
+ */
+template <typename DataT>
+__device__ constexpr DataT get_clamp_precision()
+{
+  switch (sizeof(DataT)) {
+    case 2: return 1e-3;
+    case 4: return 1e-6;
+    case 8: return 1e-15;
+    default: return 0;
+  }
+}
+
+// Epilogue operator for CUTLASS based kernel
+template <typename DataT, typename AccT>
+struct l2_exp_cutlass_op {
+  bool sqrt;
+
+  __device__ l2_exp_cutlass_op() noexcept : sqrt(false) {}
+  __device__ l2_exp_cutlass_op(bool isSqrt) noexcept : sqrt(isSqrt) {}
+  inline __device__ AccT operator()(DataT aNorm, DataT bNorm, DataT accVal) const noexcept
+  {
+    AccT outVal = aNorm + bNorm - DataT(2.0) * accVal;
+
+    /**
+     * Self-neighboring points should have (aNorm == bNorm) == accVal and the dot product (accVal)
+     * can sometimes have round-off errors, which will cause (aNorm == bNorm) ~ accVal instead.
+     */
+    outVal = outVal * !((outVal * outVal < get_clamp_precision<DataT>()) * (aNorm == bNorm));
+    return sqrt ? raft::sqrt(outVal * (outVal > 0)) : outVal;
+  }
+
+  __device__ AccT operator()(DataT aData) const noexcept { return aData; }
+};
+
+/**
+ * @brief the expanded euclidean distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ * c_ij = - 2 sum_k x_ik * y_kj + ||x_i.||_2 + ||y_.j||_2
+ *
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct l2_exp_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  const bool sqrt;
+
+  l2_exp_distance_op(bool sqrt_) noexcept : sqrt(sqrt_) {}
+
+  // Load norms of input data
+  static constexpr bool use_norms = true;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize + ((Policy::Mblk + Policy::Nblk) * sizeof(DataT));
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        DataT accVal = acc[i][j];
+        DataT val    = regxn[i] + regyn[j] - (DataT)2.0 * accVal;
+
+        /**
+         * Self-neighboring points should have (aNorm == bNorm) == accVal and the dot product
+         * (accVal) can sometimes have round-off errors, which will cause (aNorm == bNorm) ~ accVal
+         * instead.
+         */
+        acc[i][j] =
+          val * (val > 0) * !((val * val < get_clamp_precision<DataT>()) * (regxn[i] == regyn[j]));
+      }
+    }
+    if (sqrt) {
+#pragma unroll
+      for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+          acc[i][j] = raft::sqrt(acc[i][j]);
+        }
+      }
+    }
+  }
+
+  constexpr l2_exp_cutlass_op<DataT, AccT> get_cutlass_op() const
+  {
+    return l2_exp_cutlass_op<DataT, AccT>(sqrt);
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/l2_unexp.cuh b/cpp/src/distance/detail/distance_ops/l2_unexp.cuh
new file mode 100644
index 0000000000..aa6cc27f3e
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/l2_unexp.cuh
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the unexpanded euclidean distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ * c_ij = optional_sqrt ( sum_k (x_ik - y_kj)^2 )
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct l2_unexp_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  bool sqrt;
+
+  l2_unexp_distance_op(bool sqrt_) noexcept : sqrt(sqrt_) {}
+
+  // Do not load norms of data, the computation of L1 distance does not use them.
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    const auto diff = x - y;
+    acc += diff * diff;
+  };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    if (sqrt) {
+#pragma unroll
+      for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+          acc[i][j] = raft::sqrt(acc[i][j]);
+        }
+      }
+    }
+  };
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/l_inf.cuh b/cpp/src/distance/detail/distance_ops/l_inf.cuh
new file mode 100644
index 0000000000..d8f9384d7e
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/l_inf.cuh
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the L_inf (Chebyshev) distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ *  c_ij = max_k | x_ik - y_kj |
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct l_inf_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    const auto diff = raft::abs(x - y);
+    acc             = raft::max(acc, diff);
+  };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    return;
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/lp_unexp.cuh b/cpp/src/distance/detail/distance_ops/lp_unexp.cuh
new file mode 100644
index 0000000000..6136f9f3eb
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/lp_unexp.cuh
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <raft/core/operators.hpp>            // raft::pow, raft::abs
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the unexpanded Lp (Minkowski) distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ *   c_ij = (sum_k |x_ik - y_jk|^p)^(1/p)
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct lp_unexp_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  DataT p;
+
+  lp_unexp_distance_op(DataT p_) noexcept : p(p_) {}
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = true;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    const auto diff = raft::abs(x - y);
+    acc += raft::pow(diff, p);
+  };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    const auto one_over_p = 1.0f / p;
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        acc[i][j] = raft::pow(acc[i][j], one_over_p);
+      }
+    }
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/russel_rao.cuh b/cpp/src/distance/detail/distance_ops/russel_rao.cuh
new file mode 100644
index 0000000000..5dffdcdb89
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/russel_rao.cuh
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+/**
+ * @brief the Russell Rao distance matrix calculation
+ *
+ * It computes the following equation:
+ *
+ *  c_ij = (k - (sum_k x_ik * y_kj)) / k
+ */
+template <typename DataType, typename AccType, typename IdxType>
+struct russel_rao_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  IdxT k;
+  const float one_over_k;
+
+  russel_rao_distance_op(IdxT k_) noexcept : k(k_), one_over_k(1.0f / k_) {}
+
+  // Load norms of input data
+  static constexpr bool use_norms = false;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+#pragma unroll
+    for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+        acc[i][j] = (k - acc[i][j]) * one_over_k;
+      }
+    }
+  }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/distance_ops/template.cuh b/cpp/src/distance/detail/distance_ops/template.cuh
new file mode 100644
index 0000000000..bdb9332379
--- /dev/null
+++ b/cpp/src/distance/detail/distance_ops/template.cuh
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/util/cuda_dev_essentials.cuh>  // DI
+
+namespace cuvs::distance::detail::ops {
+
+// Describes the computation the template distance
+//
+// Fill in the TODO items.
+
+template <typename DataType, typename AccType, typename IdxType>
+struct template_distance_op {
+  using DataT = DataType;
+  using AccT  = AccType;
+  using IdxT  = IdxType;
+
+  TODO member;
+
+  template_distance_op(TODO member_) noexcept : member(member_) {}
+
+  // Load norms of input data
+  static constexpr bool use_norms = TODO;
+  // Whether the core function requires so many instructions that it makes sense
+  // to reduce loop unrolling, etc. We do this to keep compile times in check.
+  static constexpr bool expensive_inner_loop = false;
+
+  // Size of shared memory. This is normally decided by the kernel policy, but
+  // some ops such as correlation_distance_op use more.
+  template <typename Policy>
+  static constexpr size_t shared_mem_size()
+  {
+    return Policy::SmemSize + TODO;
+  }
+
+  DI void core(AccT& acc, DataT& x, DataT& y) const { TODO; };
+
+  template <typename Policy>
+  DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+                 DataT* regxn,
+                 DataT* regyn,
+                 IdxT gridStrideX,
+                 IdxT gridStrideY) const
+  {
+    TODO;
+  }
+
+  // If exist, returns a cutlass op that performs the same operation.
+  // See cosine and l2_exp distance ops for an example.
+  constexpr l2_exp_cutlass_op<DataT, AccT> get_cutlass_op() const { TODO; }
+};
+
+}  // namespace cuvs::distance::detail::ops
diff --git a/cpp/src/distance/detail/fused_distance_nn.cuh b/cpp/src/distance/detail/fused_distance_nn.cuh
new file mode 100644
index 0000000000..1bf8793fdc
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn.cuh
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "distance_ops/l2_exp.cuh"  // ops::l2_exp_distance_op
+#include "fused_distance_nn/cutlass_base.cuh"
+#include "fused_distance_nn/fused_cosine_nn.cuh"
+#include "fused_distance_nn/fused_l2_nn.cuh"
+#include "fused_distance_nn/helper_structs.cuh"
+#include "fused_distance_nn/simt_kernel.cuh"
+#include "pairwise_distance_base.cuh"  // PairwiseDistances
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/kvp.hpp>             // raft::KeyValuePair
+#include <raft/core/operators.hpp>       // raft::identity_op
+#include <raft/linalg/contractions.cuh>  // Policy
+#include <raft/util/arch.cuh>            // raft::util::arch::SM_*
+#include <raft/util/cuda_utils.cuh>      // raft::ceildiv, raft::shfl
+
+#include <cstddef>  // size_t
+#include <limits>   // std::numeric_limits
+
+namespace cuvs {
+namespace distance {
+
+namespace detail {
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename Policy,
+          typename ReduceOpT,
+          typename KVPReduceOpT>
+void fusedDistanceNNImpl(OutT* min,
+                         const DataT* x,
+                         const DataT* y,
+                         const DataT* xn,
+                         const DataT* yn,
+                         IdxT m,
+                         IdxT n,
+                         IdxT k,
+                         int* workspace,
+                         ReduceOpT redOp,
+                         KVPReduceOpT pairRedOp,
+                         bool sqrt,
+                         bool initOutBuffer,
+                         bool isRowMajor,
+                         cuvs::distance::DistanceType metric,
+                         float metric_arg,
+                         cudaStream_t stream)
+{
+  // The kernel policy is determined by fusedDistanceNN.
+  typedef Policy P;
+
+  dim3 blk(P::Nthreads);
+  auto nblks            = raft::ceildiv<int>(m, P::Nthreads);
+  constexpr auto maxVal = std::numeric_limits<DataT>::max();
+  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(workspace, 0, sizeof(int) * m, stream));
+  if (initOutBuffer) {
+    initKernel<DataT, OutT, IdxT, ReduceOpT>
+      <<<nblks, P::Nthreads, 0, stream>>>(min, m, maxVal, redOp);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+
+  switch (metric) {
+    case cuvs::distance::DistanceType::CosineExpanded:
+      fusedCosineNN<DataT, OutT, IdxT, P, ReduceOpT, KVPReduceOpT>(
+        min, x, y, xn, yn, m, n, k, workspace, redOp, pairRedOp, sqrt, stream);
+      break;
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded:
+      // initOutBuffer is take care by fusedDistanceNNImpl() so we set it false to fusedL2NNImpl.
+      fusedL2NNImpl<DataT, OutT, IdxT, P, ReduceOpT, KVPReduceOpT>(
+        min, x, y, xn, yn, m, n, k, workspace, redOp, pairRedOp, sqrt, false, stream);
+      break;
+    default: assert("only cosine/l2 metric is supported with fusedDistanceNN\n"); break;
+  }
+}
+
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h b/cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h
new file mode 100644
index 0000000000..186715851b
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h
@@ -0,0 +1,668 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*! \file
+
+  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
+
+  The epilogue rearranges the result of a matrix product through shared memory to match canonical
+  tensor layouts in global memory. Epilogues support conversion and reduction operations.
+
+This file contains a customized version of EpilogueWithBroadcast from CUTLASS 2.9.1
+(https://github.com/NVIDIA/cutlass/blob/v2.9.1/include/cutlass/epilogue/threadblock/epilogue_with_broadcast.h)
+
+Changes:
+- customized the compute_source_needed_() and apply_output_operator_() to suit the needs of per row
+reduction
+*/
+
+#pragma once
+
+#if defined(__CUDACC_RTC__)
+#include <cuda/std/cassert>
+#include <cuda/std/utility>
+#else
+#include <assert.h>
+
+#include <utility>
+#endif
+
+#include <cutlass/aligned_buffer.h>
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/threadblock/epilogue_base.h>
+#include <cutlass/epilogue/threadblock/predicated_tile_iterator.h>
+#include <cutlass/fast_math.h>
+#include <cutlass/functional.h>
+#include <cutlass/gemm/gemm.h>
+#include <cutlass/layout/tensor.h>
+#include <cutlass/layout/vector.h>
+#include <cutlass/numeric_conversion.h>
+#include <cutlass/numeric_types.h>
+#include <cutlass/tensor_coord.h>
+#include <cutlass/transform/pitch_linear_thread_map.h>
+#include <cutlass/transform/threadblock/regular_tile_iterator.h>
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace epilogue {
+namespace threadblock {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// This base class is meant to define the concept required of the
+/// EpilogueWithBroadcast::OutputOp
+template <typename ElementC_,
+          typename ElementAccumulator_,
+          typename ElementCompute_,
+          typename ElementZ_,
+          typename ElementT_,
+          int ElementsPerAccess,
+          bool StoreZ = true,
+          bool StoreT = true>
+struct EpilogueWithBroadcastOpBaseCustom {
+  using ElementOutput                 = ElementC_;
+  using ElementAccumulator            = ElementAccumulator_;
+  using ElementCompute                = ElementCompute_;
+  using ElementZ                      = ElementZ_;
+  using ElementT                      = ElementT_;
+  static int const kElementsPerAccess = ElementsPerAccess;
+
+  using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
+  using FragmentCompute     = Array<ElementCompute, kElementsPerAccess>;
+  using FragmentC           = Array<ElementOutput, kElementsPerAccess>;
+  using FragmentZ           = Array<ElementZ, kElementsPerAccess>;
+  using FragmentT           = Array<ElementT, kElementsPerAccess>;
+
+  /// If true, the 'Z' tensor is stored
+  static bool const kStoreZ = StoreZ;
+
+  /// If true, the 'T' tensor is stored
+  static bool const kStoreT = StoreT;
+
+  /// Parameters structure - required
+  struct Params {};
+
+  //
+  // Methods
+  //
+
+  /// Constructor from Params
+  EpilogueWithBroadcastOpBaseCustom(Params const& params_) {}
+
+  /// Determine if the source is needed. May return false if
+  bool is_source_needed() const { return true; }
+
+  CUTLASS_HOST_DEVICE
+  void set_k_partition(int k_partition, int k_partition_count) {}
+
+  /// Applies the operation when is_source_needed() is true
+  CUTLASS_HOST_DEVICE
+  void operator()(FragmentZ& frag_Z,
+                  FragmentT& frag_T,
+                  FragmentAccumulator const& AB,
+                  FragmentC const& frag_C,
+                  FragmentCompute const& V) const
+  {
+  }
+
+  /// Applies the operation when is_source_needed() is false
+  CUTLASS_HOST_DEVICE
+  void operator()(FragmentZ& frag_Z,
+                  FragmentT& frag_T,
+                  FragmentAccumulator const& AB,
+                  FragmentCompute const& V) const
+  {
+  }
+};
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Epilogue operator with bias vector broadcast over columns.
+///
+/// Computes the following:
+///
+///
+///  Z, T = OutputOp(AB, C, Broadcast)
+///
+///  if (ElementwiseOp::kStoreZ) {
+///    store(converted_u);
+///  }
+///
+///  if (ElementwiseOp::kStoreT) {
+///    store(v);
+///  }
+///
+template <
+  typename Shape_,               ///< Shape of threadblock tile (concept: GemmShape)
+  typename WarpMmaOperator_,     ///< Warp-level MMA operator (concept: gemm::warp::MmaTensorOp)
+  int PartitionsK,               ///< Number of partitions of the K dimension
+  typename OutputTileIterator_,  ///< Tile iterator reading and writing output tensors (z)
+  typename TensorTileIterator_,  ///< Additional tile iterator for tensor-valued operands (t)
+  typename ElementVector_,       ///< Pointer to broadcast vector
+  typename AccumulatorFragmentIterator_,  ///< Fragment iterator selecting accumulators
+  typename WarpTileIterator_,    ///< Warp-scoped tile iterator writing accumulators to SMEM
+  typename SharedLoadIterator_,  ///< Threadblock-scoped tile iterator loading from SMEM
+  typename OutputOp_,            ///< Output operator - concept is EpilogueWithBroadcastOp
+  typename Padding_,  ///< Padding added to SMEM allocation to avoid bank conflicts (concept:
+                      ///< MatrixShape)
+  int FragmentsPerPartition = 1,  ///< Used to coarsten the epilogue granularity
+  int IterationsUnroll      =     ///< Used to reduce binary size when epilogue op is large
+  (!IsEpilogueFunctorHeavy<OutputOp_>::value)>
+class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
+                                                        typename WarpMmaOperator_::Shape,
+                                                        PartitionsK,
+                                                        AccumulatorFragmentIterator_,
+                                                        WarpTileIterator_,
+                                                        Padding_,
+                                                        FragmentsPerPartition> {
+ public:
+  using Base = EpilogueBase<Shape_,
+                            typename WarpMmaOperator_::Shape,
+                            PartitionsK,
+                            AccumulatorFragmentIterator_,
+                            WarpTileIterator_,
+                            Padding_,
+                            FragmentsPerPartition>;
+
+  using Shape                       = Shape_;
+  using WarpMmaOperator             = WarpMmaOperator_;
+  static int const kPartitionsK     = PartitionsK;
+  using OutputTileIterator          = OutputTileIterator_;
+  using TensorTileIterator          = TensorTileIterator_;
+  using ElementVector               = ElementVector_;
+  using AccumulatorFragmentIterator = AccumulatorFragmentIterator_;
+  using WarpTileIterator            = WarpTileIterator_;
+  using SharedLoadIterator          = SharedLoadIterator_;
+  using OutputOp                    = OutputOp_;
+  using Padding                     = Padding_;
+
+  using Layout    = layout::RowMajor;
+  using LongIndex = typename Layout::LongIndex;
+
+  /// The complete warp-level accumulator tile
+  using AccumulatorTile = typename Base::AccumulatorTile;
+
+  /// Accumulator element
+  using ElementAccumulator = typename WarpTileIterator::Element;
+
+  /// Compute data type produced by the output op
+  using ElementCompute = typename OutputOp::ElementCompute;
+
+  /// Compute fragment
+  using FragmentCompute = Array<ElementCompute, OutputTileIterator::Fragment::kElements>;
+
+  /// Thread map used by output tile iterators
+  using ThreadMap = typename OutputTileIterator::ThreadMap;
+
+  /// Fragment object used to store the broadcast values
+  using BroadcastFragment =
+    Array<ElementCompute, ThreadMap::Iterations::kColumn * ThreadMap::kElementsPerAccess>;
+
+  /// Output element
+  using ElementOutput = typename OutputTileIterator::Element;
+
+  /// Data type of additional tensor
+  using ElementTensor = typename TensorTileIterator::Element;
+
+  /// Output access size
+  static int const kElementsPerAccess = OutputTileIterator::kElementsPerAccess;
+
+  /// Tensor reference to destination tensor
+  using TensorRef = typename OutputTileIterator::TensorRef;
+
+  /// Tensor reference to sync tensor
+  using SyncTensorRef = typename cutlass::TensorRef<int, cutlass::layout::PackedVectorLayout>;
+
+  /// Const tensor reference to source tensor
+  using ConstTensorRef = typename OutputTileIterator::ConstTensorRef;
+
+  /// Array type used to output
+  using OutputAccessType =
+    Array<typename OutputTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
+
+  /// Array type used by output functor
+  using AccumulatorAccessType =
+    Array<typename WarpTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
+
+  /// Array type used by output functor
+  using ComputeAccessType = Array<ElementCompute, OutputTileIterator::kElementsPerAccess>;
+
+  /// Tensor access type
+  using TensorAccessType = Array<ElementTensor, OutputTileIterator::kElementsPerAccess>;
+
+  /// Number of warps
+  using WarpCount = typename Base::WarpCount;
+
+  /// Shared memory allocation from epilogue base class
+  using BaseSharedStorage = typename Base::SharedStorage;
+
+  static int constexpr kSmemTiles =
+    Base::kFragmentsPerIteration > 1 ? Base::kFragmentsPerIteration : kPartitionsK;
+  static int constexpr kSmemPointerOffset = Base::SharedStorage::StorageShape::kCount / kSmemTiles;
+
+  /// Used for the broadcast
+  struct BroadcastDetail {
+    /// Number of threads per warp
+    static int const kWarpSize = 32;
+
+    static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
+
+    /// Number of distinct scalar column indices handled by each thread
+    static int const kColumnsPerThread =
+      ThreadMap::Iterations::kColumn * ThreadMap::kElementsPerAccess;
+
+    /// Number of distinct scalar row indices handled by each thread
+    static int const kRowsPerThread =
+      ThreadMap::Iterations::kCount / ThreadMap::Iterations::kColumn;
+
+    /// Number of threads per threadblock
+    static int const kThreadCount = kWarpSize * WarpCount::kCount;
+
+    /// Number of distinct threads per row of output tile
+    static int const kThreadsPerRow = (Shape::kN / kColumnsPerThread);
+
+    /// Number of distinct threads which must be reduced during the final reduction phase within the
+    /// threadblock.
+    static int const kThreadRows = kThreadCount / kThreadsPerRow;
+
+    /// I'm not sure what I meant here.
+    static int const kThreadAccessesPerRow =
+      const_max(1, (Shape::kN + kThreadCount - 1) / kThreadCount);
+
+    /// Shape of the shared memory allocation for the epilogue
+    using StorageShape = MatrixShape<kThreadRows, Shape::kN>;
+
+    /// Debug printing
+    CUTLASS_DEVICE
+    static void print()
+    {
+#if 0
+      printf("BroadcastDetail {\n");
+      printf(
+        "  kColumnsPerThread: %d\nkRowsPerThread: %d\n,kThreadCount: %d\nkThreadsPerRow: %d\n"
+        "kThreadRows: %d\nThreadAccessesPerRow: %d\nStorageShape: %d x %d (count: %d)\n",
+        kColumnsPerThread,
+        kRowsPerThread,
+        kThreadCount,
+        kThreadsPerRow,
+        kThreadRows,
+        kThreadAccessesPerRow,
+        StorageShape::kRow,
+        StorageShape::kColumn,
+        StorageShape::kCount
+      );
+      printf("};\n");
+#endif
+    }
+  };
+
+  /// Shared storage structure (shadows base) with additional SMEM buffer for reduction
+  struct SharedStorage {
+    union {
+      BaseSharedStorage base;
+    };
+
+    CUTLASS_HOST_DEVICE
+    SharedStorage() {}
+  };
+
+ public:
+  static_assert(SharedLoadIterator::Fragment::kElements == TensorTileIterator::Fragment::kElements,
+                "Mismatch between shared load iterator and output tile iterator.");
+
+  static_assert(OutputTileIterator::kElementsPerAccess,
+                "OutputTileIterator::kElementsPerAccess must not be zero.");
+
+  static_assert(!(OutputTileIterator::Fragment::kElements % OutputTileIterator::kElementsPerAccess),
+                "Divisibility");
+
+ private:
+  /// Loads fragment from shared memory aligned with output tensor
+  SharedLoadIterator shared_load_iterator_;
+
+  /// Thread index within the threadblock
+  int thread_idx_;
+
+ public:
+  /// Constructor
+  CUTLASS_DEVICE
+  EpilogueWithBroadcastCustom(SharedStorage& shared_storage,  ///< Shared storage object
+                              int thread_idx,  ///< ID of a thread within the threadblock
+                              int warp_idx,    ///< ID of warp within threadblock
+                              int lane_idx     ///< Id of thread within warp
+                              )
+    : Base(shared_storage.base, thread_idx, warp_idx, lane_idx),
+      shared_load_iterator_(shared_storage.base.reference(), thread_idx),
+      thread_idx_(thread_idx)
+  {
+  }
+
+  /// Streams the result to global memory
+  CUTLASS_DEVICE
+  void operator()(
+    OutputOp const& output_op,            ///< Output operator
+    ElementVector const* broadcast_ptr,   ///< Broadcast vector
+    AccumulatorTile const& accumulators,  ///< Complete warp-level accumulator tile
+    OutputTileIterator source_iterator,   ///< Tile iterator for source accumulator matrix
+    TensorTileIterator
+      tensor_iterator,  ///< Threadblock tile iterator for additional tensor operand
+    MatrixCoord const&
+      problem_size =  ///< Problem size needed to guard against out-of-bounds accesses
+    MatrixCoord(Shape::kM, Shape::kN),
+    MatrixCoord const&
+      threadblock_offset =  ///< Threadblock's initial offset within the problem size space
+    MatrixCoord())
+  {
+    BroadcastFragment broadcast_fragment;
+
+    load_broadcast_fragment_(broadcast_fragment, broadcast_ptr, problem_size, threadblock_offset);
+
+    compute_source_needed_(
+      output_op, broadcast_fragment, accumulators, source_iterator, tensor_iterator);
+  }
+
+ private:
+  CUTLASS_DEVICE
+  void load_broadcast_fragment_(
+    BroadcastFragment&
+      broadcast_fragment,  ///< Fragment containing the accumulated partial reduction over columns
+    ElementVector const* broadcast_ptr,  ///< Broadcast vector
+    MatrixCoord const&
+      problem_size,  ///< Problem size needed to guard against out-of-bounds accesses
+    MatrixCoord const&
+      threadblock_offset  ///< Threadblock's initial offset within the problem size space
+  )
+  {
+    broadcast_fragment.clear();
+
+    // If no pointer is supplied, set with all zeros and avoid memory accesses
+    if (!broadcast_ptr) { return; }
+
+    int thread_initial_column = ThreadMap::initial_offset(thread_idx_).column();
+
+    int thread_column_idx = threadblock_offset.column() + thread_initial_column;
+    broadcast_ptr += thread_initial_column;
+
+    NumericArrayConverter<ElementCompute, ElementVector, BroadcastDetail::kElementsPerAccess>
+      converter;
+    using AccessType          = AlignedArray<ElementVector, BroadcastDetail::kElementsPerAccess>;
+    using ComputeFragmentType = Array<ElementCompute, BroadcastDetail::kElementsPerAccess>;
+
+    ComputeFragmentType* frag_ptr = reinterpret_cast<ComputeFragmentType*>(&broadcast_fragment);
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int j = 0; j < ThreadMap::Iterations::kColumn; ++j) {
+      AccessType loaded;
+
+      loaded.clear();
+
+      if (thread_column_idx < problem_size.column()) {
+        loaded = *reinterpret_cast<AccessType const*>(broadcast_ptr);
+      }
+
+      ComputeFragmentType cvt = converter(loaded);
+      frag_ptr[j]             = cvt;
+
+      thread_column_idx += ThreadMap::Delta::kColumn;
+      broadcast_ptr += ThreadMap::Delta::kColumn;
+    }
+  }
+
+  template <class Seq>
+  struct acc2smem_source_not_needed;
+
+  template <size_t... Seq>
+  struct acc2smem_source_not_needed<cutlass::index_sequence<Seq...>> {
+    template <int Advance>
+    CUTLASS_DEVICE static void helper(AccumulatorFragmentIterator accum_fragment_iterator,
+                                      WarpTileIterator& warp_tile_iterator)
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < Advance; i++) {
+        ++accum_fragment_iterator;
+      }
+
+      CUTLASS_PRAGMA_UNROLL
+      for (int p = 0; p < Base::kFragmentsPerIteration; ++p) {
+        typename AccumulatorFragmentIterator::Fragment accum_fragment;
+
+        accum_fragment_iterator.load(accum_fragment);
+        ++accum_fragment_iterator;
+
+        warp_tile_iterator.store(accum_fragment);
+        if (p < Base::kFragmentsPerIteration - 1) {
+          warp_tile_iterator.add_pointer_offset(kSmemPointerOffset);
+        }
+      }
+
+      if (Base::kFragmentsPerIteration > 1) {
+        warp_tile_iterator.add_pointer_offset(kSmemPointerOffset *
+                                              (1 - Base::kFragmentsPerIteration));
+      }
+    }
+
+    CUTLASS_DEVICE
+    static void push(size_t pos,
+                     AccumulatorFragmentIterator const& iterator_begin,
+                     WarpTileIterator& warp_tile_iterator)
+    {
+      int dummy[] = {
+        (pos == (Seq * Base::kFragmentsPerIteration)) &&
+        (helper<Seq * Base::kFragmentsPerIteration>(iterator_begin, warp_tile_iterator), 0)...};
+
+      CUTLASS_UNUSED(dummy[0]);
+    }
+  };
+
+  /// Streams the result to global memory
+  CUTLASS_DEVICE
+  void compute_source_not_needed_(
+    OutputOp const& output_op,  ///< Output operator
+    BroadcastFragment const&
+      broadcast_fragment,  ///< Fragment containing the accumulated partial reduction over columns
+    OutputTileIterator destination_iterator,  ///< Tile iterator for destination
+    AccumulatorTile const& accumulators,      ///< Complete warp-level accumulator tile
+    TensorTileIterator tensor_iterator  ///< Threadblock tile iterator for additioanl tensor operand
+  )
+  {
+  }
+
+  template <class Seq>
+  struct acc2smem_source_needed;
+
+  template <size_t... Seq>
+  struct acc2smem_source_needed<cutlass::index_sequence<Seq...>> {
+    template <int Advance>
+    CUTLASS_DEVICE static void helper(AccumulatorFragmentIterator accum_fragment_iterator,
+                                      WarpTileIterator& warp_tile_iterator)
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < Advance; i++) {
+        ++accum_fragment_iterator;
+      }
+
+      typename AccumulatorFragmentIterator::Fragment accum_fragment;
+      accum_fragment_iterator.load(accum_fragment);
+      warp_tile_iterator.store(accum_fragment);
+    }
+
+    CUTLASS_DEVICE
+    static void push(size_t pos,
+                     AccumulatorFragmentIterator const& iterator_begin,
+                     WarpTileIterator& warp_tile_iterator)
+    {
+      int dummy[] = {(pos == Seq) && (helper<Seq>(iterator_begin, warp_tile_iterator), 0)...};
+    }
+  };
+
+  /// Streams the result to global memory
+  CUTLASS_DEVICE
+  void compute_source_needed_(
+    OutputOp const& output_op,  ///< Output operator
+    BroadcastFragment const&
+      broadcast_fragment,  ///< Fragment containing the accumulated partial reduction over columns
+    AccumulatorTile const& accumulators,  ///< Complete warp-level accumulator tile
+    OutputTileIterator
+      source_iterator,  ///< Threadblock tile coordinate in GEMM (in units of threadblock tiles)
+    TensorTileIterator tensor_iterator  ///< Threadblock tile iterator for additioanl tensor operand
+  )
+  {
+    typename OutputTileIterator::Fragment source_fragment;
+    source_fragment.clear();
+
+    //
+    // Iterator over warp-level accumulator fragment
+    //
+
+    AccumulatorFragmentIterator accum_fragment_iterator(accumulators);
+
+    //
+    // Iterate over accumulator tile
+    //
+
+#pragma unroll(IterationsUnroll ? OutputTileIterator::kIterations : 1)
+    for (int iter = 0; iter < OutputTileIterator::kIterations; ++iter) {
+      //
+      // Convert and store fragment
+      //
+
+      //__syncthreads();
+
+      acc2smem_source_needed<cutlass::make_index_sequence<OutputTileIterator::kIterations>>::push(
+        iter, accum_fragment_iterator, this->warp_tile_iterator_);
+
+      __syncthreads();
+
+      //
+      // Load fragments from shared memory
+      //
+
+      typename SharedLoadIterator::Fragment aligned_accum_fragment[kPartitionsK];
+
+      shared_load_iterator_.load(aligned_accum_fragment[0]);
+
+      //
+      // Apply output operation
+      //
+
+      typename TensorTileIterator::Fragment frag_T;
+
+      //
+      // Load the source
+      //
+
+      source_iterator.load(source_fragment);
+      ++source_iterator;
+
+      apply_output_operator_(
+        frag_T, output_op, aligned_accum_fragment[0], source_fragment, broadcast_fragment);
+
+      //
+      // Conditionally store fragments
+      //
+      if (OutputOp::kStoreT) {
+        tensor_iterator.store(frag_T);
+        ++tensor_iterator;
+      }
+    }
+    tensor_iterator.dumpToGmem();
+  }
+
+  /// Helper to invoke the output functor over each vector of output
+  CUTLASS_DEVICE
+  void apply_output_operator_(typename TensorTileIterator::Fragment& frag_T,
+                              OutputOp const& output_op,
+                              typename SharedLoadIterator::Fragment const& frag_AB,
+                              typename OutputTileIterator::Fragment const& frag_C,
+                              BroadcastFragment const& frag_Broadcast)
+  {
+    using AccessTypeT         = Array<typename TensorTileIterator::OutValT, kElementsPerAccess>;
+    using AccessTypeBroadcast = Array<ElementCompute, kElementsPerAccess>;
+
+    AccessTypeT* frag_T_ptr = reinterpret_cast<AccessTypeT*>(&frag_T);
+
+    AccumulatorAccessType const* frag_AB_ptr =
+      reinterpret_cast<AccumulatorAccessType const*>(&frag_AB);
+
+    OutputAccessType const* frag_C_ptr = reinterpret_cast<OutputAccessType const*>(&frag_C);
+
+    AccessTypeBroadcast const* frag_Broadcast_ptr =
+      reinterpret_cast<AccessTypeBroadcast const*>(&frag_Broadcast);
+
+    int const kOutputOpIterations =
+      TensorTileIterator::Fragment::kElements / TensorTileIterator::kElementsPerAccess;
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int i = 0; i < kOutputOpIterations; ++i) {
+      output_op(frag_T_ptr[i],
+                frag_AB_ptr[i],
+                frag_C_ptr[(i / ThreadMap::Iterations::kColumn)],
+                frag_Broadcast_ptr[i % ThreadMap::Iterations::kColumn]);
+    }
+  }
+
+  /// Helper to invoke the output functor over each vector of output
+  CUTLASS_DEVICE
+  void apply_output_operator_source_not_needed_(
+    typename OutputTileIterator::Fragment& frag_Z,
+    typename TensorTileIterator::Fragment& frag_T,
+    OutputOp const& output_op,
+    typename SharedLoadIterator::Fragment const& frag_AB,
+    BroadcastFragment const& frag_Broadcast)
+  {
+  }
+};
+
+////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace threadblock
+}  // namespace epilogue
+}  // namespace cutlass
+
+////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh b/cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh
new file mode 100644
index 0000000000..f58c48aff3
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh
@@ -0,0 +1,177 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wstrict-aliasing"
+#pragma GCC diagnostic ignored "-Wtautological-compare"
+
+// We define CUTLASS_NAMESPACE in case
+// RAFT cmake is not used
+#ifndef CUTLASS_NAMESPACE
+#define cutlass raft_cutlass
+#endif
+
+#include "epilogue_elementwise.cuh"     // FusedDistanceNNEpilogueElementwise
+#include "gemm.h"                       // FusedDistanceNNGemm
+#include <raft/util/cudart_utils.hpp>   // getMultiProcessorCount
+#include <raft/util/cutlass_utils.cuh>  // RAFT_CUTLASS_TRY
+
+#include <rmm/device_uvector.hpp>
+
+#include <cuda/semaphore>
+
+#include <cutlass/cutlass.h>
+#include <cutlass/gemm/device/gemm.h>
+#include <cutlass/gemm/device/gemm_grouped.h>
+#include <cutlass/gemm/device/gemm_universal_adapter.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/layout/tensor.h>
+#include <cutlass/matrix_coord.h>
+#include <cutlass/tensor_view.h>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+
+template <typename IdxT>
+RAFT_KERNEL initBinMutexKernel(cuda::binary_semaphore<cuda::thread_scope_device>* mut, IdxT m)
+{
+  auto tid = IdxT(blockIdx.x) * blockDim.x + threadIdx.x;
+
+  if (tid < m) { mut[tid].release(); }
+}
+
+template <typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT,
+          int VecLen,
+          typename CGReduceOpT,
+          typename DistanceFn,
+          typename ReduceOpT,
+          typename KVPReduceOpT>
+void cutlassFusedDistanceNN(const DataT* x,
+                            const DataT* y,
+                            const DataT* xn,
+                            const DataT* yn,
+                            IdxT m,
+                            IdxT n,
+                            IdxT k,
+                            IdxT lda,
+                            IdxT ldb,
+                            IdxT ldd,
+                            OutT* dOutput,
+                            int* mutexes,
+                            CGReduceOpT cg_reduce_op,
+                            DistanceFn dist_op,
+                            ReduceOpT redOp,
+                            KVPReduceOpT pairRedOp,
+                            cudaStream_t stream)
+{
+  using EpilogueOutputOp = cutlass::epilogue::thread::FusedDistanceNNEpilogueElementwise<
+    DataT,  // ElementC_
+    AccT,   // ElementAccumulator_
+    DataT,  // ElementCompute_
+    AccT,   // ElementZ_
+    OutT,   // ElementT_
+    // 128 / cutlass::sizeof_bits<DataT>::value,
+    1,  // Elements per access 1
+    DistanceFn,
+    CGReduceOpT,
+    ReduceOpT,
+    KVPReduceOpT>;
+  constexpr int batch_count = 1;
+
+  rmm::device_uvector<cuda::binary_semaphore<cuda::thread_scope_device>> bin_mutex(m, stream);
+
+  int blks_ = (m / 256) + 1;
+
+  initBinMutexKernel<<<blks_, 256, 0, stream>>>(bin_mutex.data(), m);
+
+  typename EpilogueOutputOp::Params epilog_op_param(
+    dist_op, cg_reduce_op, redOp, pairRedOp, mutexes, bin_mutex.data());
+
+  // Number of pipelines you want to use
+  constexpr int NumStages = 3;
+  // Alignment
+  constexpr int Alignment = VecLen;
+
+  // default initialize problem size with row major inputs
+  auto problem_size = cutlass::gemm::GemmCoord(m, n, k);
+
+  constexpr bool isRowMajor = true;
+
+  using fusedDistanceNNKernel =
+    typename cutlass::gemm::kernel::FusedDistanceNNGemm<DataT,
+                                                        Alignment,
+                                                        DataT,
+                                                        Alignment,
+                                                        AccT,
+                                                        AccT,
+                                                        EpilogueOutputOp,
+                                                        NumStages,  // Number of pipeline stages
+                                                        isRowMajor>::GemmKernel;
+
+  using fusedDistanceNN = cutlass::gemm::device::GemmGrouped<fusedDistanceNNKernel>;
+
+  int num_blocks_per_sm   = fusedDistanceNN::maximum_active_blocks();
+  int num_sms             = raft::getMultiProcessorCount();
+  int full_wave           = num_blocks_per_sm * num_sms;
+  constexpr int mmaShapeM = fusedDistanceNNKernel::Mma::Shape::kM;
+  constexpr int mmaShapeN = fusedDistanceNNKernel::Mma::Shape::kN;
+  int columnTiles         = (problem_size.n() - 1 + mmaShapeN) / mmaShapeN;
+  int rowTiles            = (problem_size.m() - 1 + mmaShapeM) / mmaShapeM;
+  int totalTiles          = columnTiles * rowTiles;
+  int thread_blocks =
+    rowTiles < full_wave ? (totalTiles < full_wave ? totalTiles : full_wave) : rowTiles;
+
+  typename fusedDistanceNN::Arguments arguments{
+    problem_size,
+    batch_count,  // num of problems.
+    thread_blocks,
+    epilog_op_param,
+    x,
+    y,
+    xn,            // C matrix eq vector param, which here is A norm
+    (DataT*)yn,    // this is broadcast vec, which is required to be non-const param
+    dOutput,       // Output distance matrix
+    (int64_t)lda,  // stride A
+    (int64_t)ldb,  // stride B
+    (int64_t)1,    // stride A norm
+    (int64_t)ldd   // stride Output matrix
+  };
+
+  // Using the arguments, query for extra workspace required for matrix multiplication computation
+  size_t workspace_size = fusedDistanceNN::get_workspace_size(arguments);
+  // Allocate workspace memory
+  rmm::device_uvector<uint8_t> workspace(workspace_size, stream);
+  // Instantiate CUTLASS kernel depending on templates
+  fusedDistanceNN fusedDistanceNN_op;
+  // Check the problem size is supported or not
+  RAFT_CUTLASS_TRY(fusedDistanceNN_op.can_implement(arguments));
+  // Initialize CUTLASS kernel with arguments and workspace pointer
+  RAFT_CUTLASS_TRY(fusedDistanceNN_op.initialize(arguments, workspace.data(), stream));
+  // Launch initialized CUTLASS kernel
+  RAFT_CUTLASS_TRY(fusedDistanceNN_op.run(stream));
+}
+
+};  // namespace detail
+};  // namespace distance
+};  // namespace cuvs
+
+#pragma GCC diagnostic pop
diff --git a/cpp/src/distance/detail/fused_distance_nn/epilogue.cuh b/cpp/src/distance/detail/fused_distance_nn/epilogue.cuh
new file mode 100644
index 0000000000..06939e2bfd
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/epilogue.cuh
@@ -0,0 +1,134 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*! \file
+  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
+
+This is adapted from DefaultEpilogueWithBroadcastTensorOp from CUTLASS 2.9.0
+(https://github.com/NVIDIA/cutlass/blob/master/include/cutlass/epilogue/threadblock/default_epilogue_with_broadcast.h#L75)
+
+This epilogue allows us to load norm buffers using PredicatedTileIteratorNormVec
+and EpilogueWithBroadcast used for distances L2/cosine as well as applies user-define elementwise
+operation.
+-- A norm load is provided PredicatedTileIteratorNormVec
+-- B norm load is provided by EpilogueWithBroadcast
+-- elementwise operation is provided by OutputOp
+*/
+
+#pragma once
+
+#include "custom_epilogue_with_broadcast.h"
+#include "predicated_tile_iterator_normvec_smem.h"
+#include "predicated_tile_iterator_reduced_vec.h"
+
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/threadblock/default_epilogue_tensor_op.h>
+#include <cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h>
+#include <cutlass/epilogue/threadblock/epilogue.h>
+#include <cutlass/gemm/gemm.h>
+#include <cutlass/numeric_types.h>
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace epilogue {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Defines sensible defaults for epilogues for TensorOps.
+template <typename Shape,
+          typename WarpMmaTensorOp,
+          int PartitionsK,
+          typename ElementOutput,
+          typename ElementTensor,
+          typename ElementVector,
+          typename OutputOp,
+          typename LayoutT,
+          int ElementsPerAccess,
+          bool ScatterD = false>
+struct FusedDistanceNNEpilogue {
+  /// Use defaults related to the existing epilogue
+  using Base =
+    DefaultEpilogueTensorOp<Shape, WarpMmaTensorOp, PartitionsK, OutputOp, ElementsPerAccess>;
+
+  //
+  // Stores the result z = (y = GEMM(A, B, C), broadcast)
+  //
+  using RowNormTileIterator = cutlass::epilogue::threadblock::
+    PredicatedTileIteratorNormVecSmem<typename Base::OutputTileThreadMap, ElementOutput, LayoutT>;
+
+  //
+  // Additional tensor tile iterator - stores t = Elementwise(z)
+  //
+  using OutputTileIterator = cutlass::epilogue::threadblock::PredicatedTileIteratorReducedVec<
+    typename Base::OutputTileThreadMap,
+    ElementTensor,
+    LayoutT,
+    typename OutputOp::Params>;
+
+  /// Define the epilogue
+  using Epilogue = cutlass::epilogue::threadblock::EpilogueWithBroadcastCustom<
+    Shape,
+    WarpMmaTensorOp,
+    PartitionsK,
+    RowNormTileIterator,
+    OutputTileIterator,
+    ElementVector,
+    typename Base::AccumulatorFragmentIterator,
+    typename Base::WarpTileIterator,
+    typename Base::SharedLoadIterator,
+    OutputOp,
+    typename Base::Padding,
+    Base::kFragmentsPerIteration>;
+};
+
+}  // namespace threadblock
+}  // namespace epilogue
+}  // namespace cutlass
+
+////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh b/cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh
new file mode 100644
index 0000000000..e69b2486df
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh
@@ -0,0 +1,220 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//
+/*! \file
+  \brief Functor performing distance operations used by epilogues of pairwise distance
+  * kernels.
+* This is adapted from LinearCombinationBiasElementwise from CUTLASS 2.9.0
+* customized for applying elementwise distance formula on accumulated GEMM value
+* and applying user-defined operation which can convert distance values to key-value pair.
+* .
+*/
+
+#pragma once
+
+#include <cuda/semaphore>
+
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/thread/activation.h>
+#include <cutlass/functional.h>
+#include <cutlass/numeric_conversion.h>
+#include <cutlass/numeric_types.h>
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace epilogue {
+namespace thread {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// This base class is meant to define the concept required of the
+/// EpilogueWithBroadcast::OutputOp
+template <typename ElementC_,
+          typename ElementAccumulator_,
+          typename ElementCompute_,
+          typename ElementZ_,
+          typename ElementT_,
+          int ElementsPerAccess,
+          typename DistanceOp_,
+          typename CGReduceOp_,
+          typename ReduceOpT_,
+          typename KVPReduceOpT_>
+class FusedDistanceNNEpilogueElementwise {
+ public:
+  using ElementOutput                 = ElementC_;
+  using ElementC                      = ElementC_;
+  using ElementAccumulator            = ElementAccumulator_;
+  using ElementCompute                = ElementCompute_;
+  using ElementZ                      = ElementZ_;
+  using ElementT                      = ElementT_;
+  static int const kElementsPerAccess = ElementsPerAccess;
+  static int const kCount             = kElementsPerAccess;
+
+  using DistanceOp = DistanceOp_;
+  using CGReduceOp = CGReduceOp_;
+
+  using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
+  using FragmentCompute     = Array<ElementCompute, kElementsPerAccess>;
+  using FragmentC           = Array<ElementOutput, kElementsPerAccess>;
+  using FragmentZ           = Array<ElementZ, kElementsPerAccess>;
+  using OutValT             = typename CGReduceOp::AccTypeT;
+  using FragmentT           = Array<OutValT, kElementsPerAccess>;
+
+  using FragmentOutput = FragmentZ;
+
+  static bool const kIsHeavy = true;  // ElementwiseOp::kIsHeavy;
+
+  /// If true, the 'Z' tensor is stored
+  static bool const kStoreZ = false;  // We don't store anything in Z,
+
+  /// If true, the 'T' tensor is stored
+  static bool const kStoreT = true;  // this is our final output storage.
+
+  /// Host-constructable parameters structure
+  struct Params {
+    CGReduceOp_ cg_reduce_op;
+    DistanceOp_ dist_op_;
+    KVPReduceOpT_ pair_redop_;
+    ReduceOpT_ red_op_;
+    int* mutexes_;
+    cuda::binary_semaphore<cuda::thread_scope_device>* bin_mutex_;
+    using CGReduceT = CGReduceOp_;
+    //
+    // Methods
+    //
+    CUTLASS_HOST_DEVICE
+    Params(DistanceOp_ dist_op,
+           CGReduceOp cg_reduce_op,
+           ReduceOpT_ red_op,
+           KVPReduceOpT_ pair_redop,
+           int* mutexes,
+           cuda::binary_semaphore<cuda::thread_scope_device>* bin_mutex)
+      : cg_reduce_op(cg_reduce_op),
+        dist_op_(dist_op),
+        pair_redop_(pair_redop),
+        red_op_(red_op),
+        mutexes_(mutexes),
+        bin_mutex_(bin_mutex)
+    {
+    }
+
+    CUTLASS_HOST_DEVICE
+    Params() {}
+  };
+
+ private:
+  //
+  // Data members
+  //
+  DistanceOp_ elementwise_op;
+  KVPReduceOpT_ pair_redop;
+
+ public:
+  ReduceOpT_ red_op;
+
+  //
+  // Methods
+  //
+
+  /// Constructor from Params
+  CUTLASS_HOST_DEVICE
+  FusedDistanceNNEpilogueElementwise(Params const& params)
+    : elementwise_op(params.dist_op_), pair_redop(params.pair_redop_), red_op(params.red_op_)
+  {
+  }
+
+  /// Returns true if source is needed
+  CUTLASS_HOST_DEVICE
+  bool is_source_needed() const
+  {
+    // we use for making sure C matrix is used for A mat norm.
+    return true;
+  }
+
+  /// Functionally required for serial reduction in the epilogue
+  CUTLASS_HOST_DEVICE
+  void set_k_partition(int k_partition, int k_partition_count) {}
+
+  /// Applies the operation when is_source_needed() is true
+  CUTLASS_HOST_DEVICE
+  void operator()(FragmentT& frag_T,
+                  FragmentAccumulator const& AB,
+                  FragmentC const& frag_C,
+                  FragmentCompute const& V) const
+  {
+    FragmentCompute tmp_Accum =
+      NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
+    FragmentCompute tmp_C =
+      NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(frag_C);
+    FragmentCompute result_Z;
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int i = 0; i < kElementsPerAccess; ++i) {
+      ElementCompute res_Z = elementwise_op(tmp_C[i], V[i], tmp_Accum[i]);
+      frag_T[i]            = res_Z;
+    }
+  }
+
+  /// Applies the operation when is_source_needed() is false
+  CUTLASS_HOST_DEVICE
+  void operator()(FragmentZ& frag_Z,
+                  FragmentT& frag_T,
+                  FragmentAccumulator const& AB,
+                  FragmentCompute const& V) const
+  {
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace thread
+}  // namespace epilogue
+}  // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/fused_cosine_nn.cuh b/cpp/src/distance/detail/fused_distance_nn/fused_cosine_nn.cuh
new file mode 100644
index 0000000000..bcbc6689c4
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/fused_cosine_nn.cuh
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../distance_ops/cosine.cuh"     // ops::l2_exp_distance_op
+#include "../pairwise_distance_base.cuh"  // PairwiseDistances
+#include "cutlass_base.cuh"
+#include "helper_structs.cuh"
+#include "simt_kernel.cuh"
+#include <raft/core/kvp.hpp>             // raft::KeyValuePair
+#include <raft/core/operators.hpp>       // raft::identity_op
+#include <raft/linalg/contractions.cuh>  // Policy
+#include <raft/util/arch.cuh>            // raft::util::arch::SM_*
+#include <raft/util/cuda_utils.cuh>      // raft::ceildiv, raft::shfl
+
+#include <cstddef>  // size_t
+#include <limits>   // std::numeric_limits
+
+namespace cuvs {
+namespace distance {
+
+namespace detail {
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename Policy,
+          typename ReduceOpT,
+          typename KVPReduceOpT>
+void fusedCosineNN(OutT* min,
+                   const DataT* x,
+                   const DataT* y,
+                   const DataT* xn,
+                   const DataT* yn,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   int* workspace,
+                   ReduceOpT redOp,
+                   KVPReduceOpT pairRedOp,
+                   bool sqrt,
+                   cudaStream_t stream)
+{
+  // The kernel policy is determined by fusedL2NN.
+  typedef Policy P;
+
+  dim3 blk(P::Nthreads);
+  constexpr auto maxVal = std::numeric_limits<DataT>::max();
+  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
+
+  namespace arch = raft::util::arch;
+  using AccT     = DataT;
+  ops::cosine_distance_op<DataT, AccT, IdxT> distance_op{};
+
+  raft::identity_op fin_op{};
+
+  auto kernel = fusedDistanceNNkernel<DataT,
+                                      OutT,
+                                      IdxT,
+                                      P,
+                                      ReduceOpT,
+                                      KVPReduceOpT,
+                                      decltype(distance_op),
+                                      decltype(fin_op)>;
+
+  // Get pointer to fp32 SIMT kernel to determine the runtime architecture of the
+  // current system. Other methods to determine the architecture (that do not
+  // require a pointer) can be error prone. See:
+  // https://github.com/NVIDIA/cub/issues/545
+  void* kernel_ptr   = reinterpret_cast<void*>(kernel);
+  auto runtime_arch  = arch::kernel_virtual_arch(kernel_ptr);
+  auto cutlass_range = arch::SM_range(arch::SM_80(), arch::SM_future());
+
+  if (cutlass_range.contains(runtime_arch)) {
+    // If device is SM_80 or later, use CUTLASS-based kernel.
+    using cosineOp              = cuvs::distance::detail::ops::cosine_cutlass_op<DataT, DataT>;
+    using kvp_cg_min_reduce_op_ = kvp_cg_min_reduce_op<DataT, IdxT, OutT>;
+    kvp_cg_min_reduce_op_ cg_reduce_op;
+    cosineOp cosine_dist_op;
+
+    IdxT lda, ldb, ldd;
+    lda = k, ldb = k, ldd = n;
+
+    cutlassFusedDistanceNN<DataT,
+                           DataT,
+                           OutT,
+                           IdxT,
+                           P::Veclen,
+                           decltype(cg_reduce_op),
+                           decltype(cosine_dist_op),
+                           ReduceOpT,
+                           KVPReduceOpT>(x,
+                                         y,
+                                         xn,
+                                         yn,
+                                         m,
+                                         n,
+                                         k,
+                                         lda,
+                                         ldb,
+                                         ldd,
+                                         min,
+                                         workspace,
+                                         cg_reduce_op,
+                                         cosine_dist_op,
+                                         redOp,
+                                         pairRedOp,
+                                         stream);
+  } else {
+    // If device less than SM_80, use fp32 SIMT kernel.
+    constexpr size_t shmemSize = P::SmemSize + ((P::Mblk + P::Nblk) * sizeof(DataT));
+    dim3 grid                  = launchConfigGenerator<P>(m, n, shmemSize, kernel);
+
+    kernel<<<grid, blk, shmemSize, stream>>>(
+      min, x, y, xn, yn, m, n, k, maxVal, workspace, redOp, pairRedOp, distance_op, fin_op);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+}
+
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh b/cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh
new file mode 100644
index 0000000000..59fe10ea00
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../distance_ops/l2_exp.cuh"     // ops::l2_exp_distance_op
+#include "../pairwise_distance_base.cuh"  // PairwiseDistances
+#include "cutlass_base.cuh"
+#include "helper_structs.cuh"
+#include "simt_kernel.cuh"
+#include <raft/core/kvp.hpp>             // raft::KeyValuePair
+#include <raft/core/operators.hpp>       // raft::identity_op
+#include <raft/linalg/contractions.cuh>  // Policy
+#include <raft/util/arch.cuh>            // raft::util::arch::SM_*
+#include <raft/util/cuda_utils.cuh>      // raft::ceildiv, raft::shfl
+
+#include <cstddef>  // size_t
+#include <limits>   // std::numeric_limits
+
+namespace cuvs {
+namespace distance {
+
+namespace detail {
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename Policy,
+          typename ReduceOpT,
+          typename KVPReduceOpT>
+void fusedL2NNImpl(OutT* min,
+                   const DataT* x,
+                   const DataT* y,
+                   const DataT* xn,
+                   const DataT* yn,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   int* workspace,
+                   ReduceOpT redOp,
+                   KVPReduceOpT pairRedOp,
+                   bool sqrt,
+                   bool initOutBuffer,
+                   cudaStream_t stream)
+{
+  // The kernel policy is determined by fusedL2NN.
+  typedef Policy P;
+
+  dim3 blk(P::Nthreads);
+  auto nblks            = raft::ceildiv<int>(m, P::Nthreads);
+  constexpr auto maxVal = std::numeric_limits<DataT>::max();
+  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(workspace, 0, sizeof(int) * m, stream));
+  if (initOutBuffer) {
+    initKernel<DataT, OutT, IdxT, ReduceOpT>
+      <<<nblks, P::Nthreads, 0, stream>>>(min, m, maxVal, redOp);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+
+  namespace arch = raft::util::arch;
+  using AccT     = DataT;
+  ops::l2_exp_distance_op<DataT, AccT, IdxT> distance_op{sqrt};
+
+  raft::identity_op fin_op{};
+
+  auto kernel = fusedDistanceNNkernel<DataT,
+                                      OutT,
+                                      IdxT,
+                                      P,
+                                      ReduceOpT,
+                                      KVPReduceOpT,
+                                      decltype(distance_op),
+                                      decltype(fin_op)>;
+
+  // Get pointer to fp32 SIMT kernel to determine the best compute architecture
+  // out of all for which the kernel was compiled for that matches closely
+  // to the current device. Other methods to determine the architecture (that do not
+  // require a pointer) can be error prone. See:
+  // https://github.com/NVIDIA/cub/issues/545
+  void* kernel_ptr   = reinterpret_cast<void*>(kernel);
+  auto runtime_arch  = arch::kernel_virtual_arch(kernel_ptr);
+  auto cutlass_range = arch::SM_range(arch::SM_80(), arch::SM_future());
+
+  if (cutlass_range.contains(runtime_arch)) {
+    // If device is SM_80 or later, use CUTLASS-based kernel.
+    using L2Op                  = cuvs::distance::detail::ops::l2_exp_cutlass_op<DataT, DataT>;
+    using kvp_cg_min_reduce_op_ = kvp_cg_min_reduce_op<DataT, IdxT, OutT>;
+    kvp_cg_min_reduce_op_ cg_reduce_op;
+    L2Op L2_dist_op(sqrt);
+
+    IdxT lda, ldb, ldd;
+    lda = k, ldb = k, ldd = n;
+
+    cutlassFusedDistanceNN<DataT,
+                           DataT,
+                           OutT,
+                           IdxT,
+                           P::Veclen,
+                           kvp_cg_min_reduce_op_,
+                           L2Op,
+                           ReduceOpT,
+                           KVPReduceOpT>(x,
+                                         y,
+                                         xn,
+                                         yn,
+                                         m,
+                                         n,
+                                         k,
+                                         lda,
+                                         ldb,
+                                         ldd,
+                                         min,
+                                         workspace,
+                                         cg_reduce_op,
+                                         L2_dist_op,
+                                         redOp,
+                                         pairRedOp,
+                                         stream);
+  } else {
+    // If device less than SM_80, use fp32 SIMT kernel.
+    constexpr size_t shmemSize = P::SmemSize + ((P::Mblk + P::Nblk) * sizeof(DataT));
+    dim3 grid                  = launchConfigGenerator<P>(m, n, shmemSize, kernel);
+
+    kernel<<<grid, blk, shmemSize, stream>>>(
+      min, x, y, xn, yn, m, n, k, maxVal, workspace, redOp, pairRedOp, distance_op, fin_op);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+}
+
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/fused_distance_nn/gemm.h b/cpp/src/distance/detail/fused_distance_nn/gemm.h
new file mode 100644
index 0000000000..0385b95cd1
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/gemm.h
@@ -0,0 +1,409 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "epilogue.cuh"
+#include "persistent_gemm.h"
+
+#include <cutlass/cutlass.h>
+#include <cutlass/gemm/kernel/default_gemm_universal.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/layout/tensor.h>
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace gemm {
+namespace kernel {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+/*
+ * This configuration is used for float inputs with veclen(kAlignmentA/B) = 2 or 4,
+ * ideal threadblock tile shape is 32x256x16 for such cases as there is no
+ * registers spills for it.
+ *
+ */
+template <
+  /// Element type for A matrix operand
+  typename ElementA_,
+  /// Layout type for A matrix operand
+  int kAlignmentA,
+  /// Element type for B matrix operand
+  typename ElementB_,
+  /// Layout type for B matrix operand
+  int kAlignmentB,
+  /// Element type for C and D matrix operands
+  typename ElementC_,
+  /// Element type for internal accumulation
+  typename ElementAccumulator,
+  /// Epilogue output operator      - must satisfy concept of 'EpilogueWithBroadcastOp'
+  typename EpilogueOutputOp,
+  /// Number of stages used in the pipelined mainloop
+  int Stages,
+  /// data layout row/column major of inputs
+  bool isRowMajor>
+struct FusedDistanceNNGemm {
+  // This struct is specialized for fp32/3xTF32
+
+  /// Threadblock-level tile size (concept: GemmShape)
+  // <- threadblock tile M = 32, N = 256, K = 16
+  // this is more performant but note that for veclen = 1
+  // this shape has register spills
+  using ThreadblockShape = cutlass::gemm::GemmShape<32, 256, 16>;
+
+  // <- threadblock tile M = 32, N = 128, K = 16
+  // this shape has high occupancy but less perf
+  // this is less performant but this shape has *no* register spills
+  // for any veclens(1, 2, 4)
+  // using ThreadblockShape = cutlass::gemm::GemmShape<32, 128, 16>;
+
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes tile size a warp will compute
+  // <- warp tile M = 64, N = 64, K = 16
+  // this is more performant for veclen 2,4.
+  using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
+
+  //  this shape has high occupancy but less perf used for 32x128x16
+  // using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
+
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes the size of MMA op
+  // <- MMA Op tile M = 16, N = 8, K = 4
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 4>;
+
+  /// Operation performed by GEMM
+  using Operator = cutlass::arch::OpMultiplyAddFastF32;
+  // using Operator = cutlass::arch::OpMultiplyAdd; // this runs only 1xTF32 for float inputs
+
+  // This code section describes whether you want to use tensor cores or regular SIMT cores on GPU
+  // SM
+  using OperatorClass = cutlass::arch::OpClassTensorOp;
+
+  // This code section describes CUDA SM architecture number
+  using ArchTag = cutlass::arch::Sm80;
+
+  // This code section describes how threadblocks are scheduled on GPU
+  /// Threadblock-level swizzling operator
+  using ThreadblockSwizzle = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>;
+
+  /// data layout for final output matrix.
+  // we keep this same layout even for column major inputs
+  using LayoutOutput = cutlass::layout::RowMajor;
+
+  typedef typename std::conditional<isRowMajor,
+                                    cutlass::layout::RowMajor,
+                                    cutlass::layout::ColumnMajor>::type NormXLayout;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::RowMajor, cutlass::layout::ColumnMajor>::type LayoutA_;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
+
+  using GemmBase = typename DefaultGemmUniversal<ElementA_,
+                                                 LayoutA_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 kAlignmentA,
+                                                 ElementB_,
+                                                 LayoutB_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 kAlignmentB,
+                                                 ElementC_,
+                                                 LayoutOutput,
+                                                 ElementAccumulator,
+                                                 OperatorClass,
+                                                 ArchTag,
+                                                 ThreadblockShape,
+                                                 WarpShape,
+                                                 InstructionShape,
+                                                 EpilogueOutputOp,
+                                                 ThreadblockSwizzle,
+                                                 Stages,
+                                                 Operator>::GemmKernel;
+
+  // Replace epilogue
+  using Epilogue = typename cutlass::epilogue::threadblock::FusedDistanceNNEpilogue<
+    typename GemmBase::Epilogue::Shape,
+    typename GemmBase::Epilogue::WarpMmaOperator,
+    GemmBase::Epilogue::kPartitionsK,
+    ElementAccumulator,
+    typename EpilogueOutputOp::ElementT,
+    ElementAccumulator,
+    EpilogueOutputOp,
+    NormXLayout,
+    GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
+
+  // Compose the GEMM kernel
+  using GemmKernel = FusedDistanceNNPersistent<typename GemmBase::Mma,
+                                               Epilogue,
+                                               ThreadblockSwizzle,
+                                               GroupScheduleMode::kDeviceOnly>;
+};
+
+/*
+ * This configuration is used for float inputs with veclen(kAlignmentA/B) = 1,
+ * ideal threadblock tile shape is 32x128x16 for such cases as there is no
+ * registers spills for it.
+ *
+ */
+template <
+  /// Element type for C and D matrix operands
+  typename ElementC_,
+  /// Element type for internal accumulation
+  typename ElementAccumulator,
+  /// Epilogue output operator      - must satisfy concept of 'EpilogueWithBroadcastOp'
+  typename EpilogueOutputOp,
+  /// Number of stages used in the pipelined mainloop
+  int Stages,
+  /// data layout row/column major of inputs
+  bool isRowMajor>
+struct FusedDistanceNNGemm<float,  /// Element type for A matrix operand
+                           1,      /// Layout type (veclen) for A matrix operand
+                           float,  /// Element type for B matrix operand
+                           1,      /// Layout type (veclen) for B matrix operand
+                           ElementC_,
+                           ElementAccumulator,
+                           EpilogueOutputOp,
+                           Stages,
+                           isRowMajor> {
+  // This struct is specialized for fp32/3xTF32
+  using ElementA_ = float;
+  using ElementB_ = float;
+
+  /// Threadblock-level tile size (concept: GemmShape)
+  // <- threadblock tile M = 32, N = 128, K = 16
+  // this shape has high occupancy and no register spills for veclen = 1.
+  using ThreadblockShape = cutlass::gemm::GemmShape<32, 128, 16>;
+
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes tile size a warp will compute
+  // <- warp tile M = 32, N = 32, K = 16
+  using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
+
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes the size of MMA op
+  // <- MMA Op tile M = 16, N = 8, K = 4
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 4>;
+
+  /// Operation performed by GEMM
+  using Operator = cutlass::arch::OpMultiplyAddFastF32;
+  // using Operator = cutlass::arch::OpMultiplyAdd; // this runs only 1xTF32 for float inputs
+
+  // This code section describes whether you want to use tensor cores or regular SIMT cores on GPU
+  // SM
+  using OperatorClass = cutlass::arch::OpClassTensorOp;
+
+  // This code section describes CUDA SM architecture number
+  using ArchTag = cutlass::arch::Sm80;
+
+  // This code section describes how threadblocks are scheduled on GPU
+  /// Threadblock-level swizzling operator
+  using ThreadblockSwizzle = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>;
+
+  /// data layout for final output matrix.
+  // we keep this same layout even for column major inputs
+  using LayoutOutput = cutlass::layout::RowMajor;
+
+  typedef typename std::conditional<isRowMajor,
+                                    cutlass::layout::RowMajor,
+                                    cutlass::layout::ColumnMajor>::type NormXLayout;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::RowMajor, cutlass::layout::ColumnMajor>::type LayoutA_;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
+
+  using GemmBase = typename DefaultGemmUniversal<ElementA_,
+                                                 LayoutA_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 1,
+                                                 ElementB_,
+                                                 LayoutB_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 1,
+                                                 ElementC_,
+                                                 LayoutOutput,
+                                                 ElementAccumulator,
+                                                 OperatorClass,
+                                                 ArchTag,
+                                                 ThreadblockShape,
+                                                 WarpShape,
+                                                 InstructionShape,
+                                                 EpilogueOutputOp,
+                                                 ThreadblockSwizzle,
+                                                 Stages,
+                                                 Operator>::GemmKernel;
+
+  // Replace epilogue
+  using Epilogue = typename cutlass::epilogue::threadblock::FusedDistanceNNEpilogue<
+    typename GemmBase::Epilogue::Shape,
+    typename GemmBase::Epilogue::WarpMmaOperator,
+    GemmBase::Epilogue::kPartitionsK,
+    ElementAccumulator,
+    typename EpilogueOutputOp::ElementT,
+    ElementAccumulator,
+    EpilogueOutputOp,
+    NormXLayout,
+    GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
+
+  // Compose the GEMM kernel
+  using GemmKernel = FusedDistanceNNPersistent<typename GemmBase::Mma,
+                                               Epilogue,
+                                               ThreadblockSwizzle,
+                                               GroupScheduleMode::kDeviceOnly>;
+};
+
+template <
+  /// Layout type for A matrix operand
+  int kAlignmentA,
+  /// Layout type for B matrix operand
+  int kAlignmentB,
+  /// Element type for C and D matrix operands
+  typename ElementC_,
+  /// Element type for internal accumulation
+  typename ElementAccumulator,
+  /// Epilogue output operator      - must satisfy concept of 'EpilogueWithBroadcastOp'
+  typename EpilogueOutputOp,
+  /// Number of stages used in the pipelined mainloop
+  int Stages,
+  /// data layout row/column major of inputs
+  bool isRowMajor>
+struct FusedDistanceNNGemm<double,
+                           kAlignmentA,
+                           double,
+                           kAlignmentB,
+                           ElementC_,
+                           ElementAccumulator,
+                           EpilogueOutputOp,
+                           Stages,
+                           isRowMajor> {
+  // Threadblock-level tile size (concept: GemmShape)
+  // <- threadblock tile M = 64, N = 64, K = 16
+  using ThreadblockShape = cutlass::gemm::GemmShape<64, 64, 16>;
+  // using ThreadblockShape = cutlass::gemm::GemmShape<16, 128, 16>;
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes tile size a warp will compute
+  // <- warp tile M = 32, N = 32, K = 16
+  using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
+  // using WarpShape = cutlass::gemm::GemmShape<16, 32, 16>;
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes the size of MMA op
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
+
+  // Operation performed by GEMM
+  using Operator = cutlass::arch::OpMultiplyAdd;
+  // This code section describes whether you want to use tensor cores or regular SIMT cores on GPU
+  // SM
+  using OperatorClass = cutlass::arch::OpClassTensorOp;
+
+  // This code section describes CUDA SM architecture number
+  using ArchTag = cutlass::arch::Sm80;
+
+  // This code section describes how threadblocks are scheduled on GPU
+  /// Threadblock-level swizzling operator
+  using ThreadblockSwizzle = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>;
+
+  /// data layout for final output matrix.
+  // we keep this same layout even for column major inputs
+  using LayoutOutput = cutlass::layout::RowMajor;
+
+  typedef typename std::conditional<isRowMajor,
+                                    cutlass::layout::RowMajor,
+                                    cutlass::layout::ColumnMajor>::type NormXLayout;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::RowMajor, cutlass::layout::ColumnMajor>::type LayoutA_;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
+
+  using GemmBase = typename DefaultGemmUniversal<double,
+                                                 LayoutA_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 1,
+                                                 double,
+                                                 LayoutB_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 1,
+                                                 ElementC_,
+                                                 LayoutOutput,
+                                                 ElementAccumulator,
+                                                 OperatorClass,
+                                                 ArchTag,
+                                                 ThreadblockShape,
+                                                 WarpShape,
+                                                 InstructionShape,
+                                                 EpilogueOutputOp,
+                                                 ThreadblockSwizzle,
+                                                 Stages,
+                                                 Operator>::GemmKernel;
+
+  // Replace epilogue
+  using Epilogue = typename cutlass::epilogue::threadblock::FusedDistanceNNEpilogue<
+    typename GemmBase::Epilogue::Shape,
+    typename GemmBase::Epilogue::WarpMmaOperator,
+    GemmBase::Epilogue::kPartitionsK,
+    ElementC_,
+    typename EpilogueOutputOp::ElementT,
+    ElementC_,
+    EpilogueOutputOp,
+    NormXLayout,
+    GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
+
+  // Compose the GEMM kernel
+  using GemmKernel = FusedDistanceNNPersistent<typename GemmBase::Mma,
+                                               Epilogue,
+                                               ThreadblockSwizzle,
+                                               GroupScheduleMode::kDeviceOnly>;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace kernel
+}  // namespace gemm
+}  // namespace cutlass
\ No newline at end of file
diff --git a/cpp/src/distance/detail/fused_distance_nn/helper_structs.cuh b/cpp/src/distance/detail/fused_distance_nn/helper_structs.cuh
new file mode 100644
index 0000000000..bd439e0a7d
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/helper_structs.cuh
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../distance_ops/l2_exp.cuh"     // ops::l2_exp_distance_op
+#include "../pairwise_distance_base.cuh"  // PairwiseDistances
+#include "cutlass_base.cuh"
+#include "simt_kernel.cuh"
+#include <raft/core/kvp.hpp>             // raft::KeyValuePair
+#include <raft/core/operators.hpp>       // raft::identity_op
+#include <raft/linalg/contractions.cuh>  // Policy
+#include <raft/util/arch.cuh>            // raft::util::arch::SM_*
+#include <raft/util/cuda_utils.cuh>      // raft::ceildiv, raft::shfl
+#include <raft/util/device_atomics.cuh>
+
+#include <cstddef>  // size_t
+#include <limits>   // std::numeric_limits
+
+namespace cuvs {
+namespace distance {
+
+namespace detail {
+
+template <typename LabelT, typename DataT>
+struct KVPMinReduceImpl {
+  typedef raft::KeyValuePair<LabelT, DataT> KVP;
+  DI KVP operator()(LabelT rit, const KVP& a, const KVP& b) { return b.value < a.value ? b : a; }
+  DI KVP operator()(const KVP& a, const KVP& b) { return b.value < a.value ? b : a; }
+
+};  // KVPMinReduce
+
+template <typename LabelT, typename DataT>
+struct MinAndDistanceReduceOpImpl {
+  typedef typename raft::KeyValuePair<LabelT, DataT> KVP;
+
+  DI void operator()(LabelT rid, KVP* out, const KVP& other) const
+  {
+    if (other.value < out->value) {
+      out->key   = other.key;
+      out->value = other.value;
+    }
+  }
+  DI void operator()(LabelT rid, volatile KVP* out, const KVP& other) const
+  {
+    if (other.value < out->value) {
+      out->key   = other.key;
+      out->value = other.value;
+    }
+  }
+
+  DI void operator()(LabelT rid, DataT* out, const KVP& other) const
+  {
+    if (other.value < *out) { *out = other.value; }
+  }
+
+  DI void operator()(LabelT rid, volatile DataT* out, const KVP& other) const
+  {
+    if (other.value < *out) { *out = other.value; }
+  }
+
+  DI void operator()(LabelT rid, DataT* out, const DataT& other) const
+  {
+    if (other < *out) { *out = other; }
+  }
+
+  DI void operator()(LabelT rid, volatile DataT* out, const DataT& other) const
+  {
+    if (other < *out) { *out = other; }
+  }
+
+  DI void init(DataT* out, DataT maxVal) const { *out = maxVal; }
+  DI void init(KVP* out, DataT maxVal) const
+  {
+    out->value = maxVal;
+    out->key   = 0xfffffff0;
+  }
+
+  DI void init_key(DataT& out, LabelT idx) const { return; }
+  DI void init_key(KVP& out, LabelT idx) const { out.key = idx; }
+
+  DI DataT get_value(KVP& out) const { return out.value; }
+  DI DataT get_value(DataT& out) const { return out; }
+};
+
+template <typename LabelT, typename DataT>
+struct MinReduceOpImpl {
+  typedef typename raft::KeyValuePair<LabelT, DataT> KVP;
+  DI void operator()(LabelT rid, DataT* out, const KVP& other)
+  {
+    if (other.value < *out) { *out = other.value; }
+  }
+
+  DI void init(DataT* out, DataT maxVal) { *out = maxVal; }
+};
+
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT>
+RAFT_KERNEL initKernel(OutT* min, IdxT m, DataT maxVal, ReduceOpT redOp)
+{
+  auto tid = IdxT(blockIdx.x) * blockDim.x + threadIdx.x;
+  if (tid < m) { redOp.init(min + tid, maxVal); }
+}
+
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT>
+void initialize(OutT* min, IdxT m, DataT maxVal, ReduceOpT redOp, cudaStream_t stream)
+{
+  auto blks = raft::ceildiv(m, 256);
+  initKernel<DataT, OutT, IdxT><<<blks, 256, 0, stream>>>(min, m, maxVal, redOp);
+}
+
+// cg::reduce functor for FusedDistanceNN used in its cutlass version
+// to output the min distance value & key(loc id).
+// This is used in fused_distance_nn/predicated_tile_iterator_reduced_vec.h
+// store_with_byte_offset() passed to cg::reduce() & select_reduce.
+template <typename AccType, typename Index, typename OutType>
+struct kvp_cg_min_reduce_op {
+  typedef typename raft::KeyValuePair<Index, AccType> KVP;
+
+  __host__ __device__ kvp_cg_min_reduce_op() noexcept {};
+
+  using AccTypeT = AccType;
+  using IndexT   = Index;
+  // functor signature.
+  __host__ __device__ KVP operator()(KVP a, KVP b) const { return a.value < b.value ? a : b; }
+
+  __host__ __device__ AccType operator()(AccType a, AccType b) const { return min(a, b); }
+
+  __host__ __device__ bool isAmin(AccType a, AccType b) const { return a < b ? true : false; }
+};
+
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h b/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
new file mode 100644
index 0000000000..f1a7c728e9
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
@@ -0,0 +1,512 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*! \file
+    \brief Problem visitor for grouped GEMMs
+This file contains heavily customized version of GemmGrouped from CUTLASS 2.10.0
+(https://github.com/NVIDIA/cutlass/blob/v2.10.0/include/cutlass/gemm/kernel/gemm_grouped.h)
+
+Changes:
+- adds support for only single problem size to be launched persistently
+  where each threablock processes more than one tile of the same problem.
+*/
+
+#pragma once
+
+#include <cutlass/complex.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/fast_math.h>
+#include <cutlass/gemm/gemm.h>
+#include <cutlass/gemm/kernel/gemm_grouped_problem_visitor.h>
+#include <cutlass/gemm/kernel/gemm_transpose_operands.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/matrix_coord.h>
+#include <cutlass/semaphore.h>
+#include <cutlass/trace.h>
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace gemm {
+namespace kernel {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+template <typename Mma_,                         ///! Threadblock-scoped matrix multiply-accumulate
+          typename Epilogue_,                    ///! Epilogue
+          typename ThreadblockSwizzle_,          ///! Threadblock swizzling function
+          GroupScheduleMode GroupScheduleMode_,  ///! Type of scheduling to perform
+          bool Transposed = false>
+struct FusedDistanceNNPersistent {
+ public:
+  using Mma                                         = Mma_;
+  using Epilogue                                    = Epilogue_;
+  using EpilogueOutputOp                            = typename Epilogue::OutputOp;
+  using ThreadblockSwizzle                          = ThreadblockSwizzle_;
+  static GroupScheduleMode const kGroupScheduleMode = GroupScheduleMode_;
+  static bool const kTransposed                     = Transposed;
+
+  // Optional transpose
+  using MapArguments = kernel::detail::MapArguments<typename Mma::IteratorA::Element,
+                                                    typename Mma::IteratorA::Layout,
+                                                    Mma::kTransformA,
+                                                    Mma::IteratorA::AccessType::kElements,
+                                                    typename Mma::IteratorB::Element,
+                                                    typename Mma::IteratorB::Layout,
+                                                    Mma::kTransformB,
+                                                    Mma::IteratorB::AccessType::kElements,
+                                                    typename Mma::LayoutC,
+                                                    kTransposed>;
+
+  // Public-facing type definitions related to operand element type, layout, and complex conjugate
+  // operation. Must interact with the 'kTransposed' notion.
+  using ElementA = typename MapArguments::ElementA;
+  using LayoutA  = typename MapArguments::LayoutA;
+  using ElementB = typename MapArguments::ElementB;
+  using LayoutB  = typename MapArguments::LayoutB;
+  using ElementC = typename Epilogue::OutputTileIterator::Element;
+  using LayoutC  = typename MapArguments::LayoutC;
+
+  static ComplexTransform const kTransformA = MapArguments::kTransformA;
+  static ComplexTransform const kTransformB = MapArguments::kTransformB;
+
+  // Type definitions about the mainloop.
+  using Operator         = typename Mma::Operator;
+  using OperatorClass    = typename Mma::Operator::OperatorClass;
+  using ThreadblockShape = typename Mma::Shape;
+  using WarpShape        = typename Mma::Operator::Shape;
+  using InstructionShape = typename Mma::Policy::Operator::InstructionShape;
+  using ArchTag          = typename Mma::ArchTag;
+
+  static int const kStages     = Mma::kStages;
+  static int const kAlignmentA = MapArguments::kAlignmentA;
+  static int const kAlignmentB = MapArguments::kAlignmentB;
+  static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
+
+  /// Warp count (concept: GemmShape)
+  using WarpCount               = typename Mma::WarpCount;
+  static int const kThreadCount = 32 * WarpCount::kCount;
+
+  using ProblemVisitor = GemmGroupedProblemVisitor<ThreadblockShape,
+                                                   kGroupScheduleMode,
+                                                   kThreadCount,
+                                                   kThreadCount,
+                                                   kTransposed>;
+
+  //
+  // Structures
+  //
+
+  struct temp_problem_visitor {
+    int problem_count;
+
+    CUTLASS_HOST_DEVICE temp_problem_visitor() : problem_count(0){};
+    CUTLASS_HOST_DEVICE temp_problem_visitor(int problem_count_) : problem_count(problem_count_){};
+  };
+
+  /// Argument structure
+  struct Arguments {
+    //
+    // Data members
+    //
+    GemmCoord problem_sizes;
+    temp_problem_visitor problem_visitor;
+    int problem_count;
+    int threadblock_count;
+
+    typename EpilogueOutputOp::Params output_op;
+
+    void const* ptr_A;
+    void const* ptr_B;
+    void const* ptr_C;
+    void* ptr_Vector;
+    void* ptr_Tensor;
+
+    typename LayoutA::Stride::Index lda;
+    typename LayoutB::Stride::Index ldb;
+    typename LayoutC::Stride::Index ldc;
+    typename LayoutC::Stride::Index ldt;
+
+    // Only used by device-level operator
+    GemmCoord* host_problem_sizes;
+
+    //
+    // Methods
+    //
+
+    /// Default ctor
+    CUTLASS_HOST_DEVICE
+    Arguments()
+      : threadblock_count(0),
+        ptr_A(nullptr),
+        ptr_B(nullptr),
+        ptr_C(nullptr),
+        ptr_Vector(nullptr),
+        ptr_Tensor(nullptr),
+        lda(0),
+        ldb(0),
+        ldc(0),
+        ldt(0),
+        host_problem_sizes(nullptr)
+    {
+    }
+
+    /// Ctor
+    CUTLASS_HOST_DEVICE
+    Arguments(GemmCoord problem_sizes,
+              int problem_count,
+              int threadblock_count,
+              typename EpilogueOutputOp::Params output_op,
+              void const* ptr_A,
+              void const* ptr_B,
+              void const* ptr_C,
+              void* ptr_Vector,
+              void* ptr_Tensor,
+              typename LayoutA::Stride::Index lda,
+              typename LayoutB::Stride::Index ldb,
+              typename LayoutC::Stride::Index ldc,
+              typename LayoutC::Stride::Index ldt,
+              GemmCoord* host_problem_sizes = nullptr)
+      : problem_sizes(problem_sizes),
+        threadblock_count(threadblock_count),
+        output_op(output_op),
+        ptr_A(ptr_A),
+        ptr_B(ptr_B),
+        ptr_C(ptr_C),
+        ptr_Vector(ptr_Vector),
+        ptr_Tensor(ptr_Tensor),
+        lda(lda),
+        ldb(ldb),
+        ldc(ldc),
+        ldt(ldt),
+        host_problem_sizes(host_problem_sizes)
+    {
+      problem_visitor.problem_count = problem_count;
+    }
+  };
+
+  //
+  // Structure for precomputing values in host memory and passing to kernels
+  //
+
+  /// Parameters structure
+  struct Params {
+    temp_problem_visitor problem_visitor;
+    int threadblock_count;
+
+    typename Mma::IteratorA::Params params_A;
+    typename Mma::IteratorB::Params params_B;
+    typename Epilogue::OutputTileIterator::Params params_C;
+    typename Epilogue::TensorTileIterator::Params params_Tensor;
+
+    typename EpilogueOutputOp::Params output_op;
+
+    void* ptr_A;
+    void* ptr_B;
+    void* ptr_C;
+    void* ptr_Vector;
+    void* ptr_Tensor;
+
+    GemmCoord problem_size;
+    typename LayoutA::Stride::Index lda;
+    typename LayoutB::Stride::Index ldb;
+    typename LayoutC::Stride::Index ldc;
+    typename LayoutC::Stride::Index ldt;
+
+    //
+    // Methods
+    //
+
+    CUTLASS_HOST_DEVICE
+    Params()
+      : params_A(0),
+        params_B(0),
+        params_C(0),
+        ptr_A(nullptr),
+        ptr_B(nullptr),
+        ptr_C(nullptr),
+        ptr_Vector(nullptr),
+        ptr_Tensor(nullptr),
+        lda(0),
+        ldb(0),
+        ldc(0),
+        ldt(0)
+    {
+    }
+
+    CUTLASS_HOST_DEVICE
+    Params(Arguments const& args, void* workspace = nullptr, int tile_count = 0)
+      : problem_size(args.problem_sizes),
+        threadblock_count(args.threadblock_count),
+        output_op(args.output_op),
+        params_A(args.lda),
+        params_B(args.ldb),
+        params_C(args.ldc),
+        // Here we pass additional user args via args.output_op
+        // to the reduction output tile iterator
+        params_Tensor(args.ldt, args.output_op),
+        ptr_A(const_cast<void*>(args.ptr_A)),
+        ptr_B(const_cast<void*>(args.ptr_B)),
+        ptr_C(const_cast<void*>(args.ptr_C)),
+        ptr_Vector(args.ptr_Vector),
+        ptr_Tensor(args.ptr_Tensor),
+        lda(args.lda),
+        ldb(args.ldb),
+        ldc(args.ldc),
+        ldt(args.ldt)
+    {
+      problem_visitor.problem_count = args.problem_visitor.problem_count;
+    }
+
+    CUTLASS_HOST_DEVICE
+    void update(Arguments const& args, void* workspace = nullptr, int tile_count = 0)
+    {
+      threadblock_count = args.threadblock_count;
+      output_op         = args.output_op;
+      ptr_A             = const_cast<void*>(args.ptr_A);
+      ptr_B             = const_cast<void*>(args.ptr_B);
+      ptr_C             = const_cast<void*>(args.ptr_C);
+      ptr_Vector        = args.ptr_Vector;
+      ptr_Tensor        = args.ptr_Tensor;
+      lda               = args.lda;
+      ldb               = args.ldb;
+      ldc               = args.ldc;
+      ldt               = args.ldt;
+
+      problem_size = args.problem_sizes;
+    }
+  };
+
+  /// Shared memory storage structure
+  struct SharedStorage {
+    union {
+      typename Mma::SharedStorage main_loop;
+      typename Epilogue::SharedStorage epilogue;
+    } kernel;
+
+    typename Epilogue::TensorTileIterator::SharedStorage reduced_store;
+    typename Epilogue::OutputTileIterator::SharedStorage rownorm_store;
+  };
+
+ public:
+  //
+  // Methods
+  //
+
+  CUTLASS_DEVICE
+  FusedDistanceNNPersistent() {}
+
+  /// Determines whether kernel satisfies alignment
+  static Status can_implement(cutlass::gemm::GemmCoord const& problem_size)
+  {
+    return Status::kSuccess;
+  }
+
+  static Status can_implement(Arguments const& args) { return Status::kSuccess; }
+
+  static size_t get_extra_workspace_size(Arguments const& args,
+                                         cutlass::gemm::GemmCoord const& grid_tiled_shape)
+  {
+    return 0;
+  }
+
+  CUTLASS_DEVICE
+  static uint32_t tile_count(const cutlass::MatrixCoord& grid)
+  {
+    return grid.row() * grid.column();
+  }
+
+  /// Get the grid shape
+  CUTLASS_DEVICE
+  static cutlass::MatrixCoord grid_shape(const cutlass::gemm::GemmCoord& problem)
+  {
+    return cutlass::MatrixCoord(((problem.m() - 1 + ThreadblockShape::kM) / ThreadblockShape::kM),
+                                ((problem.n() - 1 + ThreadblockShape::kN) / ThreadblockShape::kN));
+  }
+
+  /// Executes one GEMM
+  CUTLASS_DEVICE
+  void operator()(Params const& params, SharedStorage& shared_storage)
+  {
+#if __CUDA_ARCH__ >= 800
+    //
+    // These types shadow the type-level definitions and support the ability to implement
+    // a 'transposed' GEMM that computes the transposed problems.
+    //
+    using ElementA = typename Mma::IteratorA::Element;
+    using LayoutA  = typename Mma::IteratorA::Layout;
+    using ElementB = typename Mma::IteratorB::Element;
+    using LayoutB  = typename Mma::IteratorB::Layout;
+    using ElementC = typename Epilogue::OutputTileIterator::Element;
+    using LayoutC  = typename Epilogue::OutputTileIterator::Layout;
+
+    const GemmCoord& problem_size    = params.problem_size;
+    const auto grid_shape_           = grid_shape(problem_size);
+    const uint32_t problem_chunk     = (tile_count(grid_shape_) - 1 + gridDim.x) / gridDim.x;
+    const uint32_t problem_chunk_end = blockIdx.x * problem_chunk + problem_chunk;
+    typename LayoutB::Index column =
+      ((blockIdx.x * problem_chunk) % grid_shape_.column()) * Mma::Shape::kN;
+
+    typename LayoutB::Index row =
+      ((blockIdx.x * problem_chunk) / grid_shape_.column()) * Mma::Shape::kM;
+    if (column) {
+      shared_storage.reduced_store.initSmem(params.output_op);
+      shared_storage.rownorm_store.initSmem(params.ptr_C, problem_size.m(), row, sizeof(ElementC));
+    }
+
+    // Outer 'persistent' loop to iterate over tiles
+    for (uint32_t tile_idx = blockIdx.x * problem_chunk; tile_idx < problem_chunk_end; tile_idx++) {
+      const auto grid_shape_ = grid_shape(problem_size);
+      cutlass::MatrixCoord threadblock_offset(
+        int(tile_idx / grid_shape_.column()) * Mma::Shape::kM,
+        int(tile_idx % grid_shape_.column()) * Mma::Shape::kN);
+
+      const bool isNextTile = ((tile_idx + 1) < problem_chunk_end);
+      const bool doesRowChange =
+        ((threadblock_offset.column() + Mma::Shape::kN) >= problem_size.n());
+      const bool do_gmem_reduce = (doesRowChange || !isNextTile) ? true : false;
+
+      ElementA* ptr_A = static_cast<ElementA*>(params.ptr_A);
+      ElementB* ptr_B = static_cast<ElementB*>(params.ptr_B);
+
+      // Compute initial location in logical coordinates
+      cutlass::MatrixCoord tb_offset_A{threadblock_offset.row(), 0};
+      cutlass::MatrixCoord tb_offset_B{0, threadblock_offset.column()};
+
+      // Compute position within threadblock
+      int thread_idx = threadIdx.x;
+
+      // Construct iterators to A and B operands
+      typename Mma::IteratorA iterator_A(
+        params.params_A, ptr_A, {problem_size.m(), problem_size.k()}, thread_idx, tb_offset_A);
+
+      typename Mma::IteratorB iterator_B(
+        params.params_B, ptr_B, {problem_size.k(), problem_size.n()}, thread_idx, tb_offset_B);
+
+      // Broadcast the warp_id computed by lane 0 to ensure dependent code
+      // is compiled as warp-uniform.
+      int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
+
+      int lane_idx = threadIdx.x % 32;
+
+      //
+      // Matrix multiply phase
+      //
+
+      // Construct thread-scoped matrix multiply
+      Mma mma(shared_storage.kernel.main_loop, thread_idx, warp_idx, lane_idx);
+
+      typename Mma::FragmentC accumulators;
+
+      accumulators.clear();
+      // Compute threadblock-scoped matrix multiply-add
+      int gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
+
+      // Wait for all threads to finish their epilogue phases from the previous tile.
+      //__syncthreads();
+
+      // Compute threadblock-scoped matrix multiply-add
+      mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators);
+
+      //
+      // Epilogue
+      //
+
+      EpilogueOutputOp output_op(params.output_op);
+
+      ElementC* ptr_C = static_cast<ElementC*>(params.ptr_C);
+      typename Epilogue::ElementTensor* ptr_Tensor =
+        static_cast<typename Epilogue::ElementTensor*>(params.ptr_Tensor);
+
+      // Define the reduction output pointer and move to the appropriate place
+      typename Epilogue::ElementVector* ptr_Vector =
+        static_cast<typename Epilogue::ElementVector*>(params.ptr_Vector);
+
+      // Tile iterator loading from source tensor.
+      typename Epilogue::OutputTileIterator iterator_rownorm(shared_storage.rownorm_store,
+                                                             params.params_C,
+                                                             ptr_C,
+                                                             problem_size.mn(),
+                                                             thread_idx,
+                                                             threadblock_offset);
+
+      // Additional tensor to load from
+      typename Epilogue::TensorTileIterator tensor_iterator(shared_storage.reduced_store,
+                                                            params.params_Tensor,
+                                                            // Only the final block outputs Tensor
+                                                            ptr_Tensor,
+                                                            problem_size.mn(),
+                                                            thread_idx,
+                                                            do_gmem_reduce,
+                                                            threadblock_offset);
+
+      Epilogue epilogue(shared_storage.kernel.epilogue, thread_idx, warp_idx, lane_idx);
+
+      // Execute the epilogue operator to update the destination tensor.
+      // Move to appropriate location for this output tile
+      if (ptr_Vector) { ptr_Vector += threadblock_offset.column(); }
+
+      // Execute the epilogue operator to update the destination tensor.
+      epilogue(output_op,
+               ptr_Vector,
+               // iterator_D,
+               accumulators,
+               iterator_rownorm,
+               tensor_iterator,
+               problem_size.mn(),
+               threadblock_offset);
+    }
+#endif
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace kernel
+}  // namespace gemm
+}  // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h
new file mode 100644
index 0000000000..794cd5eb63
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h
@@ -0,0 +1,448 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*! \file
+  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
+
+This file contains a customized version of PredicatedTileIterator from CUTLASS 2.9.0
+(https://github.com/NVIDIA/cutlass/blob/v2.9.0/include/cutlass/epilogue/threadblock/predicated_tile_iterator.h#L75)
+
+Changes:
+- added `Layout_` template param
+- Only the row index is used to load the data in load_with_byte_offset().
+  This way the same normalization data is used across all columns in a row.
+
+*/
+#pragma once
+
+#include <raft/util/device_loads_stores.cuh>
+
+#include <cutlass/arch/arch.h>
+#include <cutlass/arch/memory.h>
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/threadblock/output_tile_thread_map.h>
+#include <cutlass/epilogue/threadblock/predicated_tile_iterator_params.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/layout/tensor.h>
+#include <cutlass/matrix_shape.h>
+#include <cutlass/numeric_types.h>
+#include <cutlass/tensor_ref.h>
+#include <cutlass/transform/pitch_linear_thread_map.h>
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace epilogue {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Tile iterator used to load and store output tile from global memory in epilogue.
+///
+/// Satisfies: ReadableTileIterator | PredicatedTileIterator | ForwardTileIterator
+///
+template <typename ThreadMap_,  ///< Thread map (conept: OutputTileThreadMap)
+          typename Element_,    ///< Element data type
+          typename Layout_,
+          bool ScatterD     = false,  ///< Scatter D operand or not
+          bool UseCUDAStore = false>
+class PredicatedTileIteratorNormVecSmem {
+ public:
+  using ThreadMap = ThreadMap_;
+  using Shape     = typename ThreadMap::Shape;
+
+  using Element = Element_;
+
+  using Layout         = Layout_;
+  using TensorRef      = TensorRef<Element, Layout>;
+  using ConstTensorRef = typename TensorRef::ConstTensorRef;
+
+  using Index       = typename Layout::Index;
+  using LongIndex   = typename Layout::LongIndex;
+  using TensorCoord = MatrixCoord;
+
+  static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
+  static int const kThreads           = ThreadMap::kThreads;
+  static int const kIterations        = ThreadMap::Count::kTile;
+
+  static int const total_rows = ThreadMap::kWarpCount * ThreadMap::Iterations::kRow *
+                                ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster *
+                                ThreadMap::Count::kTile * ThreadMap::Delta::kRow;
+
+  static_assert(ThreadMap::Iterations::kRow > 0, "ThreadMap::Iterations::kRow must be > 0");
+  static_assert(ThreadMap::Iterations::kGroup > 0, "ThreadMap::Iterations::kGroup must be > 0");
+  static_assert(ThreadMap::Iterations::kCluster > 0, "ThreadMap::Iterations::kCluster must be > 0");
+  static_assert(ThreadMap::Iterations::kColumn > 0, "ThreadMap::Iterations::kColumn must be > 0");
+
+  using Fragment = Array<Element,
+                         ThreadMap::Iterations::kRow * ThreadMap::Iterations::kGroup *
+                           ThreadMap::Iterations::kCluster * ThreadMap::kElementsPerAccess>;
+
+  /// Memory access size
+  using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
+
+  //
+  // Parameters struct
+  //
+
+  /// Uses a non-template class
+  struct Params : PredicatedTileIteratorParams {
+    using Base = PredicatedTileIteratorParams;
+
+    CUTLASS_HOST_DEVICE
+    Params() {}
+
+    CUTLASS_HOST_DEVICE
+    Params(Layout const& layout)
+      : PredicatedTileIteratorParams(
+          layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
+          make_OutputTileThreadMapDesc<ThreadMap>())
+    {
+    }
+
+    CUTLASS_HOST_DEVICE
+    Params(Base const& base) : Base(base) {}
+  };
+
+  /// Mask object
+  struct Mask {
+    static int const kCount = ThreadMap::Iterations::kColumn;
+
+    /// Predicate state
+    bool predicates[kCount];
+
+    //
+    // Mask
+    //
+    CUTLASS_HOST_DEVICE
+    Mask() { enable(); }
+
+    ///< Efficiently disables all accesses guarded by mask
+    CUTLASS_HOST_DEVICE void clear()
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kCount; ++i) {
+        predicates[i] = false;
+      }
+    }
+
+    ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
+    CUTLASS_DEVICE void enable()
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kCount; ++i) {
+        predicates[i] = true;
+      }
+    }
+  };
+
+  /// Shared storage allocation needed by the predicated tile
+  //  iterator for storing rowNorm chunk.
+  struct SharedStorage {
+    //
+    // Type definitions
+    //
+    using Shape = MatrixShape<total_rows, 1>;
+
+    /// Shape of the shared memory allocation
+    using StorageShape = MatrixShape<Shape::kRow, Shape::kColumn>;
+
+    //
+    // Data members
+    //
+    // Methods
+    //
+    AlignedBuffer<Element, StorageShape::kCount> storage;
+
+    CUTLASS_DEVICE
+    Element* data() { return storage.data(); }
+
+    SharedStorage() {}
+
+    CUTLASS_DEVICE
+    void initSmem(void* pointer,
+                  const Index& num_rows,
+                  const Index& tb_row_offset,
+                  const LongIndex& stride)
+    {
+      Element* shared_elem_arr = data();
+      uint8_t* first_tile_byte_pointer_ =
+        reinterpret_cast<uint8_t*>(pointer) + LongIndex(tb_row_offset) * LongIndex(stride);
+      const auto gmem_ptr = reinterpret_cast<Element*>(first_tile_byte_pointer_);
+
+      for (int row = threadIdx.x; row < total_rows; row += blockDim.x) {
+        bool guard = (tb_row_offset + row) < num_rows;
+        cutlass::arch::cp_async<sizeof(Element)>(shared_elem_arr + row, gmem_ptr + row, guard);
+        cutlass::arch::cp_async_wait<0>();
+      }
+    }
+  };
+
+ private:
+  //
+  // Data members
+  //
+
+  /// Parameters structure containing reference and precomputed state.
+  PredicatedTileIteratorParams params_;
+
+  /// Byte-level pointer
+  uint8_t* byte_pointer_;
+
+  /// Array of boolean values to contain steady-state predicates
+  Mask mask_;
+
+  /// Extent of the matrix tile in rows
+  Index extent_row_;
+
+  /// Extent of the matrix tile in rows
+  Index extent_column_;
+
+  /// A thread's starting row position (assuming steady-state predicates have been computed)
+  Index thread_start_row_;
+
+  /// A thread's starting column
+  Index thread_start_column_;
+
+  /// Internal state counter
+  int state_[3];
+
+  /// Scatter indices
+  int const* indices_;
+
+  //
+  // Static asserts about internal strides
+  //
+
+  static_assert(sizeof(extent_row_) == 4, "Expected 32b extents");
+  static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents");
+  static_assert(sizeof(PredicatedTileIteratorParams::stride) == 8, "Expected 64b strides");
+
+ private:
+  //
+  // Methods
+  //
+
+ protected:
+  SharedStorage& shared_storage_;
+
+ public:
+  //
+  // Methods
+  //
+
+  /// Constructor
+  CUTLASS_DEVICE
+  PredicatedTileIteratorNormVecSmem(SharedStorage& shared_storage,
+                                    PredicatedTileIteratorParams const& params,
+                                    Element* pointer,
+                                    TensorCoord extent,
+                                    int thread_idx,
+                                    TensorCoord& threadblock_offset,
+                                    int const* indices = nullptr)
+    : params_(params), indices_(indices), shared_storage_(shared_storage)
+  {
+    TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
+
+    extent_row_    = extent.row();
+    extent_column_ = extent.column();
+
+    thread_start_row_    = thread_offset.row();
+    thread_start_column_ = thread_offset.column();
+
+    // Initialize predicates
+    CUTLASS_PRAGMA_UNROLL
+    for (int c = 0; c < ThreadMap::Iterations::kColumn; ++c) {
+      mask_.predicates[c] =
+        ((thread_offset.column() + ThreadMap::Delta::kColumn * c) < extent.column());
+    }
+
+    // Null pointer performs no accesses
+    if (!pointer) {
+      mask_.clear();
+      return;
+    }
+
+    if (ScatterD && !indices) { mask_.clear(); }
+
+    // Initialize pointer
+    byte_pointer_ = reinterpret_cast<uint8_t*>(pointer) +
+                    LongIndex(thread_offset.row()) * LongIndex(params_.stride);
+
+    if (ScatterD) {
+      byte_pointer_ = reinterpret_cast<uint8_t*>(pointer) +
+                      LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
+    }
+
+    if (threadblock_offset.column() == 0) {
+      shared_storage_.initSmem(pointer, extent_row_, threadblock_offset.row(), params_.stride);
+    }
+
+    // Initialize internal state counter
+    state_[0] = state_[1] = state_[2] = 0;
+  }
+
+  /// Adds a pointer offset in units of Element
+  CUTLASS_HOST_DEVICE
+  void add_pointer_offset(LongIndex pointer_offset)
+  {
+    byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
+  }
+
+  /// Loads a fragment from memory
+  CUTLASS_DEVICE
+  void load_with_byte_offset(Fragment& frag, int64_t byte_offset) const
+  {
+    AccessType* frag_ptr = reinterpret_cast<AccessType*>(&frag);
+
+    Element* shared_elem_arr = shared_storage_.data();
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
+      CUTLASS_PRAGMA_UNROLL
+      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
+        CUTLASS_PRAGMA_UNROLL
+        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
+          int frag_row_idx =
+            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
+
+          int row_offset = row * ThreadMap::Delta::kRow + group * ThreadMap::Delta::kGroup +
+                           cluster * ThreadMap::Delta::kCluster;
+          int iter_row = ((row_offset + thread_start_row_) % total_rows);
+          Element val  = shared_elem_arr[iter_row];
+
+          CUTLASS_PRAGMA_UNROLL
+          for (int i = 0; i < kElementsPerAccess; ++i) {
+            (*frag_ptr)[frag_row_idx + i] = val;
+          }
+        }
+      }
+    }
+  }
+
+  /// Loads a fragment from memory
+  CUTLASS_DEVICE
+  void load(Fragment& frag) const { load_with_byte_offset(frag, 0); }
+
+  CUTLASS_DEVICE
+  MatrixCoord thread_start() const { return MatrixCoord(thread_start_row_, thread_start_column_); }
+
+  /// Need to get the thread start row from the tile iterator
+  CUTLASS_DEVICE
+  int32_t thread_start_row() const { return thread_start_row_; }
+
+  /// Need to get the thread start row from the tile iterator
+  CUTLASS_DEVICE
+  int32_t thread_start_column() const { return thread_start_column_; }
+
+  /// Extent of the matrix in rows
+  CUTLASS_DEVICE
+  Index extent_row() const { return extent_row_; }
+
+  /// Extent of the matrix in columns
+  CUTLASS_DEVICE
+  Index extent_column() const { return extent_column_; }
+
+  /// Advances to the next position to load or store
+  CUTLASS_HOST_DEVICE
+  PredicatedTileIteratorNormVecSmem& operator++()
+  {
+    ++state_[0];
+
+    if (!ScatterD) { byte_pointer_ += params_.advance_row; }
+
+    thread_start_row_ += ThreadMap::Shape::kRow;
+
+    if (state_[0] == ThreadMap::Count::kRow) {
+      state_[0] = 0;
+      ++state_[1];
+      byte_pointer_ += params_.advance_group;
+
+      thread_start_row_ +=
+        (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
+
+      if (state_[1] == ThreadMap::Count::kGroup) {
+        state_[1] = 0;
+        ++state_[2];
+        byte_pointer_ += params_.advance_cluster;
+
+        thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup *
+                             ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
+
+        if (state_[2] == ThreadMap::Count::kCluster) {
+          state_[2] = 0;
+          byte_pointer_ += params_.advance_tile;
+        }
+      }
+    }
+
+    return *this;
+  }
+
+  ///< Efficiently disables all accesses guarded by mask
+  CUTLASS_DEVICE void clear_mask() { mask_.clear(); }
+
+  ///< Efficiently enables all accesses guarded by mask
+  CUTLASS_DEVICE void enable_mask() { mask_.enable(); }
+
+  ///< Sets the mask
+  CUTLASS_DEVICE void get_mask(Mask& mask) const { mask = mask_; }
+
+  ///< Sets the mask
+  CUTLASS_DEVICE void set_mask(Mask const& mask) { mask_ = mask; }
+};
+
+///////////////////////////////////////////////////////////////////////////////
+
+}  // namespace threadblock
+}  // namespace epilogue
+}  // namespace cutlass
+
+////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h
new file mode 100644
index 0000000000..5ffb74e9cd
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h
@@ -0,0 +1,610 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*! \file
+  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
+
+This file contains a customized version of PredicatedTileIterator from CUTLASS 2.9.0
+(https://github.com/NVIDIA/cutlass/blob/v2.9.0/include/cutlass/epilogue/threadblock/predicated_tile_iterator.h#L75)
+
+Changes:
+- added `Layout_` template param
+- PredicatedTileIteratorParams() is customized to not stride by layout.stride(0).
+- makes use of `SharedStorage` to store reduced values across warps to gmem in coalesced manner.
+- customized the store_with_byte_offset() to perform reduction per row and write final value to
+gmem.
+- customized the Params() struct to take user inputs from epilogueOp params.
+
+*/
+
+#pragma once
+
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+
+#include <raft/core/kvp.hpp>
+
+#include <cutlass/arch/arch.h>
+#include <cutlass/arch/memory.h>
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/threadblock/output_tile_thread_map.h>
+#include <cutlass/epilogue/threadblock/predicated_tile_iterator_params.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/layout/tensor.h>
+#include <cutlass/matrix_shape.h>
+#include <cutlass/numeric_types.h>
+#include <cutlass/tensor_ref.h>
+#include <cutlass/transform/pitch_linear_thread_map.h>
+
+namespace cg = cooperative_groups;
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace epilogue {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Tile iterator used to load and store output tile from global memory in epilogue.
+///
+/// Satisfies: ReadableTileIterator | PredicatedTileIterator | ForwardTileIterator
+///
+template <typename ThreadMap_,  ///< Thread map (conept: OutputTileThreadMap)
+          typename Element_,    ///< Element data type
+          typename Layout_,
+          typename EpilogueOpParams_,
+          bool ScatterD     = false,  ///< Scatter D operand or not
+          bool UseCUDAStore = false>
+class PredicatedTileIteratorReducedVec {
+ public:
+  using ThreadMap = ThreadMap_;
+  using Shape     = typename ThreadMap::Shape;
+
+  using Element = Element_;
+
+  using Layout         = Layout_;
+  using TensorRef      = TensorRef<Element, Layout>;
+  using ConstTensorRef = typename TensorRef::ConstTensorRef;
+
+  using Index            = typename Layout::Index;
+  using LongIndex        = typename Layout::LongIndex;
+  using TensorCoord      = MatrixCoord;
+  using EpilogueOpParams = EpilogueOpParams_;
+  using OutIdxT          = typename EpilogueOpParams::CGReduceT::IndexT;
+  using OutValT          = typename EpilogueOpParams::CGReduceT::AccTypeT;
+
+  static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
+  static int const kThreads           = ThreadMap::kThreads;
+  static int const kIterations        = ThreadMap::Count::kTile;
+
+  static_assert(ThreadMap::Iterations::kRow > 0, "ThreadMap::Iterations::kRow must be > 0");
+  static_assert(ThreadMap::Iterations::kGroup > 0, "ThreadMap::Iterations::kGroup must be > 0");
+  static_assert(ThreadMap::Iterations::kCluster > 0, "ThreadMap::Iterations::kCluster must be > 0");
+  static_assert(ThreadMap::Iterations::kColumn > 0, "ThreadMap::Iterations::kColumn must be > 0");
+  static_assert(!UseCUDAStore, "UseCUDAStore path is not supported");
+
+  static int const total_rows = ThreadMap::kWarpCount * ThreadMap::Iterations::kRow *
+                                ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster *
+                                ThreadMap::Count::kTile * ThreadMap::Delta::kRow;
+  /// Fragment object
+  using Fragment =
+    Array<OutValT,
+          ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow *
+            ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster * kElementsPerAccess>;
+
+  // Memory access size
+  using AccessType     = AlignedArray<Element, kElementsPerAccess>;
+  using AccessTypeValT = AlignedArray<OutValT, kElementsPerAccess>;
+
+  //
+  // Parameters struct
+  //
+
+  /// Uses a non-template class
+  struct Params : PredicatedTileIteratorParams {
+    using Base = PredicatedTileIteratorParams;
+
+    EpilogueOpParams user_param;
+    CUTLASS_HOST_DEVICE
+    Params() {}
+
+    CUTLASS_HOST_DEVICE
+    Params(Layout const& layout)
+      : PredicatedTileIteratorParams(
+          layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
+          make_OutputTileThreadMapDesc<ThreadMap>())
+    {
+    }
+
+    CUTLASS_HOST_DEVICE
+    Params(Layout const& layout, EpilogueOpParams const& user_param_)
+      : PredicatedTileIteratorParams(int(sizeof(AccessType)) / kElementsPerAccess,
+                                     make_OutputTileThreadMapDesc<ThreadMap>()),
+        user_param(user_param_)
+    {
+    }
+
+    CUTLASS_HOST_DEVICE
+    Params(Base const& base) : Base(base) {}
+  };
+
+  /// Mask object
+  struct Mask {
+    // static int const kCount = ThreadMap::Iterations::kColumn;
+    static int const kCount = ThreadMap::Iterations::kColumn * kElementsPerAccess;
+
+    /// Predicate state
+    bool predicates[kCount];
+
+    //
+    // Mask
+    //
+    CUTLASS_HOST_DEVICE
+    Mask() { enable(); }
+
+    ///< Efficiently disables all accesses guarded by mask
+    CUTLASS_HOST_DEVICE void clear()
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kCount; ++i) {
+        predicates[i] = false;
+      }
+    }
+
+    ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
+    CUTLASS_DEVICE void enable()
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kCount; ++i) {
+        predicates[i] = true;
+      }
+    }
+  };
+
+  /// Shared storage allocation needed by the predicated tile
+  //  iterator for reduction.
+  struct SharedStorage {
+    //
+    // Type definitions
+    //
+    using Shape = MatrixShape<total_rows, 1>;
+
+    /// Shape of the shared memory allocation for the reduced values store
+    using StorageShape = MatrixShape<Shape::kRow, Shape::kColumn>;
+
+    //
+    // Data members
+
+    //
+    // Methods
+    //
+    AlignedBuffer<Element, StorageShape::kCount> storage;
+
+    CUTLASS_DEVICE
+    Element* data() { return storage.data(); }
+
+    SharedStorage() {}
+
+    CUTLASS_DEVICE
+    void initSmem(EpilogueOpParams const& user_params)
+    {
+      Element* shared_elem_arr = data();
+      constexpr auto maxVal    = std::numeric_limits<OutValT>::max();
+
+      for (int row = threadIdx.x; row < total_rows; row += blockDim.x) {
+        user_params.red_op_.init(&shared_elem_arr[row], maxVal);
+      }
+    }
+  };
+
+  template <typename cg_reduce_op_t,
+            typename cg_group_t,
+            typename IdxT,
+            typename ValT,
+            typename OutT>
+  struct select_reduce {
+    /// Performs warp level reduction and stores a reduced output to memory
+    CUTLASS_DEVICE
+    select_reduce(OutT value,
+                  ValT prev_red_val,
+                  cg_reduce_op_t reduce_op,
+                  cg_group_t cg_warp_group,
+                  OutT& shmem_ptr)
+    {
+      if (cg_warp_group.any(reduce_op.isAmin(value, prev_red_val))) {
+        OutT reduced_val = cg::reduce(cg_warp_group, value, reduce_op);
+        if (cg_warp_group.thread_rank() == 0) { shmem_ptr = reduced_val; }
+      }
+    }
+  };
+
+  template <typename cg_reduce_op_t, typename cg_group_t, typename IdxT>
+  struct select_reduce<cg_reduce_op_t, cg_group_t, IdxT, float, raft::KeyValuePair<IdxT, float>> {
+    using ValT = float;
+    using Ty   = raft::KeyValuePair<IdxT, ValT>;
+    /// Performs warp level reduction of key value pair and stores a reduced output to memory
+    CUTLASS_DEVICE
+    select_reduce(Ty val_to_red,
+                  float prev_red_val,
+                  cg_reduce_op_t cg_reduce_op,
+                  cg_group_t cg_warp_group,
+                  Ty& shmem_ptr)
+    {
+      ValT val = val_to_red.value;
+
+      if (cg_warp_group.any(cg_reduce_op.isAmin(val, prev_red_val))) {
+        ValT reduced_val = cg::reduce(cg_warp_group, val, cg_reduce_op);
+        bool pred        = (reduced_val == val);
+        auto subTile     = cg::binary_partition(cg_warp_group, pred);
+        if (pred) {
+          if (subTile.thread_rank() == 0) { shmem_ptr = val_to_red; }
+        }
+      }
+    }
+  };
+
+  template <typename cg_reduce_op_t, typename cg_group_t, typename IdxT>
+  struct select_reduce<cg_reduce_op_t, cg_group_t, IdxT, double, raft::KeyValuePair<IdxT, double>> {
+    using ValT = double;
+    using Ty   = raft::KeyValuePair<IdxT, ValT>;
+    /// Performs warp level reduction of key value pair and stores a reduced output to memory
+    CUTLASS_DEVICE
+    select_reduce(Ty val_to_red,
+                  double prev_red_val,
+                  cg_reduce_op_t cg_reduce_op,
+                  cg_group_t cg_warp_group,
+                  Ty& shmem_ptr)
+    {
+      ValT val = val_to_red.value;
+
+      if (cg_warp_group.any(cg_reduce_op.isAmin(val, prev_red_val))) {
+        ValT reduced_val = cg::reduce(cg_warp_group, val, cg_reduce_op);
+        bool pred        = (reduced_val == val);
+        auto subTile     = cg::binary_partition(cg_warp_group, pred);
+        if (pred) {
+          if (subTile.thread_rank() == 0) { shmem_ptr = val_to_red; }
+        }
+      }
+    }
+  };
+
+ private:
+  //
+  // Data members
+  //
+
+  /// Parameters structure containing reference and precomputed state.
+  Params params_;
+
+  /// Byte-level pointer first tile offset of this threadblock.
+  volatile uint8_t* first_tile_byte_pointer_;
+
+  /// Array of boolean values to contain steady-state predicates
+  Mask mask_;
+
+  /// Extent of the matrix tile in rows
+  Index extent_row_;
+
+  /// Extent of the matrix tile in rows
+  Index extent_column_;
+
+  /// A thread's starting row position (assuming steady-state predicates have been computed)
+  Index thread_start_row_;
+  Index block_start_row_first_tile_;
+
+  /// A thread's starting column
+  Index thread_start_column_;
+
+  /// Internal state counter
+  int state_[3];
+  // mutable int shared_tile_id;
+
+  /// Scatter indices
+  int const* indices_;
+
+  const int do_gmem_reduction_;
+
+  //
+  // Static asserts about internal strides
+  //
+
+  static_assert(sizeof(extent_row_) == 4, "Expected 32b extents");
+  static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents");
+  static_assert(sizeof(Params::stride) == 8, "Expected 64b strides");
+
+ protected:
+  SharedStorage& shared_storage_;
+
+ private:
+  //
+  // Methods
+  //
+ public:
+  //
+  // Methods
+  //
+  /// Constructor
+  CUTLASS_DEVICE
+  PredicatedTileIteratorReducedVec(SharedStorage& shared_storage,
+                                   Params const& params,
+                                   volatile Element* pointer,
+                                   TensorCoord extent,
+                                   int thread_idx,
+                                   const bool do_gmem_reduction,
+                                   TensorCoord threadblock_offset = TensorCoord(),
+                                   int const* indices             = nullptr)
+    : params_(params),
+      indices_(indices),
+      shared_storage_(shared_storage),
+      do_gmem_reduction_(do_gmem_reduction)
+  {
+    TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
+
+    extent_row_    = extent.row();
+    extent_column_ = extent.column();
+
+    thread_start_row_    = thread_offset.row();
+    thread_start_column_ = thread_offset.column();
+
+    TensorCoord block_offset    = ThreadMap::initial_offset(0) + threadblock_offset;
+    block_start_row_first_tile_ = block_offset.row();
+
+    // Initialize predicates
+    CUTLASS_PRAGMA_UNROLL
+    for (int c = 0; c < ThreadMap::Iterations::kColumn * kElementsPerAccess; ++c) {
+      int columnPerAccess       = (c / kElementsPerAccess);
+      int columnWithinPerAccess = c % kElementsPerAccess;
+      mask_.predicates[c] = ((thread_offset.column() + ThreadMap::Delta::kColumn * columnPerAccess +
+                              columnWithinPerAccess) < extent.column());
+    }
+
+    if (threadblock_offset.column() == 0) {
+      EpilogueOpParams const& user_params = params_.user_param;
+      shared_storage_.initSmem(user_params);
+    }
+    __syncthreads();
+
+    // Null pointer performs no accesses
+    if (!pointer) { mask_.clear(); }
+
+    if (ScatterD && !indices) { mask_.clear(); }
+
+    // Initialize pointer
+    first_tile_byte_pointer_ = reinterpret_cast<volatile uint8_t*>(pointer) +
+                               LongIndex(block_offset.row()) * LongIndex(params_.stride);
+
+    // Initialize internal state counter
+    state_[0] = state_[1] = state_[2] = 0;
+  }
+
+  CUTLASS_DEVICE void dumpToGmem()
+  {
+    if (block_start_row_first_tile_ >= extent_row_) { return; }
+
+    if (do_gmem_reduction_) {
+      EpilogueOpParams const& user_params = params_.user_param;
+      const uint32_t mutex_id             = (block_start_row_first_tile_ / total_rows);
+      const bool useGmemMutex  = (gridDim.x != ((extent_row_ - 1 + total_rows) / total_rows));
+      int row                  = threadIdx.x;
+      Element* shared_elem_arr = shared_storage_.data();
+      Element row_local_min;
+      if (row < total_rows) { row_local_min = shared_elem_arr[row]; }
+
+      // single lock per block for multiple rows
+      if (useGmemMutex && threadIdx.x == 0) { user_params.bin_mutex_[mutex_id].acquire(); }
+      __syncthreads();
+
+      if (row < total_rows) {
+        volatile Element* gmem_ptr = reinterpret_cast<volatile Element*>(first_tile_byte_pointer_);
+
+        if ((block_start_row_first_tile_ + row) < extent_row_) {
+          user_params.red_op_(block_start_row_first_tile_ + row, (gmem_ptr + row), row_local_min);
+        }
+      }
+
+      __syncthreads();
+      __threadfence();
+
+      if (useGmemMutex && (threadIdx.x == 0)) {
+        // release mutex lock.
+        user_params.bin_mutex_[mutex_id].release();
+      }
+      shared_storage_.initSmem(user_params);
+      __syncthreads();
+    }
+  }
+
+  /// Destructor
+  CUTLASS_DEVICE
+  ~PredicatedTileIteratorReducedVec() {}
+
+  /// Performs reduction and Stores a reduced output to memory
+  CUTLASS_DEVICE
+  void store_with_byte_offset(Fragment& frag, int64_t byte_offset) const
+  {
+    AccessTypeValT* frag_ptr = reinterpret_cast<AccessTypeValT*>(&frag);
+
+    cg::thread_block cta = cg::this_thread_block();
+    // tile_width 16 is required if kElementPerAccess > 1
+    constexpr int tile_width                 = (32 / ThreadMap::Delta::kColumn) ? 32 : 16;
+    cg::thread_block_tile<tile_width> tile32 = cg::tiled_partition<tile_width>(cta);
+    EpilogueOpParams const& user_params      = params_.user_param;
+
+    using cg_reduce_t = decltype(user_params.cg_reduce_op);
+    using tile32_t    = decltype(tile32);
+
+    Element* shared_elem_arr = shared_storage_.data();
+    constexpr auto maxVal    = std::numeric_limits<OutValT>::max();
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
+      CUTLASS_PRAGMA_UNROLL
+      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
+        CUTLASS_PRAGMA_UNROLL
+        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
+          int frag_row_idx =
+            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
+
+          int row_offset = row * ThreadMap::Delta::kRow + group * ThreadMap::Delta::kGroup +
+                           cluster * ThreadMap::Delta::kCluster;
+
+          const OutIdxT row_id = row_offset + thread_start_row_;
+          bool row_guard       = (row_id < extent_row_);
+
+          const int frag_idx = frag_row_idx * ThreadMap::Iterations::kColumn * kElementsPerAccess;
+          Element red_val;
+          user_params.red_op_.init(&red_val, maxVal);
+
+          if (row_guard) {
+            CUTLASS_PRAGMA_UNROLL
+            for (int column = 0; column < ThreadMap::Iterations::kColumn * kElementsPerAccess;
+                 ++column) {
+              int columnPerAccess     = column / kElementsPerAccess;
+              int columnWithPerAccess = column % kElementsPerAccess;
+              bool guard              = mask_.predicates[column];
+              if (guard) {
+                const OutIdxT key_id = thread_start_column_ +
+                                       ThreadMap::Delta::kColumn * columnPerAccess +
+                                       columnWithPerAccess;
+                const int frag_col_idx = frag_idx + column;
+
+                Element this_val;
+                user_params.red_op_.init(&this_val, (*frag_ptr)[frag_col_idx]);
+                user_params.red_op_.init_key(this_val, key_id);
+                user_params.red_op_(row_id, &red_val, this_val);
+              }
+            }
+          }
+          const int iter_row      = (row_id % total_rows);
+          const auto prev_red_val = user_params.red_op_.get_value(shared_elem_arr[iter_row]);
+          if (row_guard) {
+            // select_reduce doesn't need to use `red_op_` as at the warp level we use cg_reduce_op,
+            // this satisfies the requirement of mst/single linkage of checking colors buffer.
+            select_reduce<cg_reduce_t, tile32_t, OutIdxT, OutValT, Element> red_obj(
+              red_val, prev_red_val, user_params.cg_reduce_op, tile32, shared_elem_arr[iter_row]);
+          }
+        }
+      }
+    }
+    __syncthreads();
+  }
+
+  /// Stores a fragment to memory
+  CUTLASS_DEVICE
+  void store(Fragment& frag) const { store_with_byte_offset(frag, 0); }
+
+  CUTLASS_DEVICE
+  MatrixCoord thread_start() const { return MatrixCoord(thread_start_row_, thread_start_column_); }
+
+  /// Need to get the thread start row from the tile iterator
+  CUTLASS_DEVICE
+  int32_t thread_start_row() const { return thread_start_row_; }
+
+  /// Need to get the thread start row from the tile iterator
+  CUTLASS_DEVICE
+  int32_t thread_start_column() const { return thread_start_column_; }
+
+  /// Extent of the matrix in rows
+  CUTLASS_DEVICE
+  Index extent_row() const { return extent_row_; }
+
+  /// Extent of the matrix in columns
+  CUTLASS_DEVICE
+  Index extent_column() const { return extent_column_; }
+
+  /// Advances to the next position to load or store
+  CUTLASS_HOST_DEVICE
+  PredicatedTileIteratorReducedVec& operator++()
+  {
+    ++state_[0];
+
+    thread_start_row_ += ThreadMap::Shape::kRow;
+
+    if (state_[0] == ThreadMap::Count::kRow) {
+      state_[0] = 0;
+      ++state_[1];
+
+      thread_start_row_ +=
+        (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
+
+      if (state_[1] == ThreadMap::Count::kGroup) {
+        state_[1] = 0;
+        ++state_[2];
+
+        thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup *
+                             ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
+
+        if (state_[2] == ThreadMap::Count::kCluster) { state_[2] = 0; }
+      }
+    }
+    return *this;
+  }
+
+  ///< Efficiently disables all accesses guarded by mask
+  CUTLASS_DEVICE void clear_mask() { mask_.clear(); }
+
+  ///< Efficiently enables all accesses guarded by mask
+  CUTLASS_DEVICE void enable_mask() { mask_.enable(); }
+
+  ///< Sets the mask
+  CUTLASS_DEVICE void get_mask(Mask& mask) const { mask = mask_; }
+
+  ///< Sets the mask
+  CUTLASS_DEVICE void set_mask(Mask const& mask) { mask_ = mask; }
+};
+
+///////////////////////////////////////////////////////////////////////////////
+
+}  // namespace threadblock
+}  // namespace epilogue
+}  // namespace cutlass
+
+////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/simt_kernel.cuh b/cpp/src/distance/detail/fused_distance_nn/simt_kernel.cuh
new file mode 100644
index 0000000000..184063c8ba
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn/simt_kernel.cuh
@@ -0,0 +1,187 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../distance_ops/l2_exp.cuh"     // ops::l2_exp_distance_op
+#include "../pairwise_distance_base.cuh"  // PairwiseDistances
+#include <raft/core/kvp.hpp>              // raft::KeyValuePair
+#include <raft/linalg/contractions.cuh>   // Policy
+
+#include <cstddef>  // size_t
+#include <limits>   // std::numeric_limits
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+
+// TODO: specialize this function for MinAndDistanceReduceOp<int, float>
+// with atomicCAS of 64 bit which will eliminate mutex and shfls
+template <typename P, typename OutT, typename IdxT, typename KVPair, typename ReduceOpT>
+DI void updateReducedVal(
+  int* mutex, OutT* min, KVPair* val, ReduceOpT red_op, IdxT m, IdxT gridStrideY)
+{
+  const auto lid      = threadIdx.x % raft::WarpSize;
+  const auto accrowid = threadIdx.x / P::AccThCols;
+
+  // Update each output row in order within a warp. This will resolve hang
+  // issues with pre-Volta architectures
+#pragma unroll
+  for (int j = 0; j < (raft::WarpSize / P::AccThCols); j++) {
+    if (lid == j * P::AccThCols) {
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+        auto rid = gridStrideY + accrowid + i * P::AccThRows;
+        if (rid < m) {
+          auto value = val[i];
+          while (atomicCAS(mutex + rid, 0, 1) == 1)
+            ;
+          __threadfence();
+          red_op(rid, min + rid, value);
+          __threadfence();
+          atomicCAS(mutex + rid, 1, 0);
+        }
+      }
+    }
+  }
+}
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename P,
+          typename ReduceOpT,
+          typename KVPReduceOpT,
+          typename OpT,
+          typename FinalLambda>
+__launch_bounds__(P::Nthreads, 2) RAFT_KERNEL fusedDistanceNNkernel(OutT* min,
+                                                                    const DataT* x,
+                                                                    const DataT* y,
+                                                                    const DataT* xn,
+                                                                    const DataT* yn,
+                                                                    IdxT m,
+                                                                    IdxT n,
+                                                                    IdxT k,
+                                                                    DataT maxVal,
+                                                                    int* mutex,
+                                                                    ReduceOpT redOp,
+                                                                    KVPReduceOpT pairRedOp,
+                                                                    OpT distance_op,
+                                                                    FinalLambda fin_op)
+{
+// compile only if below non-ampere arch.
+#if __CUDA_ARCH__ < 800
+  extern __shared__ char smem[];
+
+  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
+  KVPair val[P::AccRowsPerTh];
+#pragma unroll
+  for (int i = 0; i < P::AccRowsPerTh; ++i) {
+    val[i] = {0, maxVal};
+  }
+
+  // epilogue operation lambda for final value calculation
+  auto epilog_lambda = [n, pairRedOp, &val, maxVal] __device__(
+                         DataT acc[P::AccRowsPerTh][P::AccColsPerTh],
+                         DataT * regxn,
+                         DataT * regyn,
+                         IdxT gridStrideX,
+                         IdxT gridStrideY) {
+    KVPReduceOpT pairRed_op(pairRedOp);
+
+    // intra thread reduce
+    const auto acccolid = threadIdx.x % P::AccThCols;
+    const auto accrowid = threadIdx.x / P::AccThCols;
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        auto tmpkey = acccolid + j * P::AccThCols + gridStrideX;
+        KVPair tmp  = {tmpkey, acc[i][j]};
+        if (tmpkey < n) {
+          val[i] = pairRed_op(accrowid + i * P::AccThRows + gridStrideY, tmp, val[i]);
+        }
+      }
+    }
+  };
+
+  auto rowEpilog_lambda =
+    [m, mutex, min, pairRedOp, redOp, &val, maxVal] __device__(IdxT gridStrideY) {
+      KVPReduceOpT pairRed_op(pairRedOp);
+      ReduceOpT red_op(redOp);
+
+      const auto accrowid = threadIdx.x / P::AccThCols;
+      const auto lid      = raft::laneId();
+
+    // reduce
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = P::AccThCols / 2; j > 0; j >>= 1) {
+          // Actually, the srcLane (lid +j) should be (lid +j) % P:AccThCols,
+          // but the shfl op applies the modulo internally.
+          auto tmpkey   = raft::shfl(val[i].key, lid + j, P::AccThCols);
+          auto tmpvalue = raft::shfl(val[i].value, lid + j, P::AccThCols);
+          KVPair tmp    = {tmpkey, tmpvalue};
+          val[i]        = pairRed_op(accrowid + i * P::AccThRows + gridStrideY, tmp, val[i]);
+        }
+      }
+
+      updateReducedVal<P, OutT, IdxT, KVPair, ReduceOpT>(mutex, min, val, red_op, m, gridStrideY);
+
+    // reset the val array.
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+        val[i] = {0, maxVal};
+      }
+    };
+
+  IdxT lda = k, ldb = k, ldd = n;
+  constexpr bool row_major = true;
+  constexpr bool write_out = false;
+  PairwiseDistances<DataT,
+                    DataT,  // OutT (unused in PairwiseDistances)
+                    IdxT,
+                    P,
+                    decltype(distance_op),
+                    decltype(epilog_lambda),
+                    FinalLambda,
+                    decltype(rowEpilog_lambda),
+                    row_major,
+                    write_out>
+    obj(x,
+        y,
+        m,
+        n,
+        k,
+        lda,
+        ldb,
+        ldd,
+        xn,
+        yn,
+        nullptr,  // Output pointer
+        smem,
+        distance_op,
+        epilog_lambda,
+        fin_op,
+        rowEpilog_lambda);
+  obj.run();
+#endif
+}
+
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/fused_l2_nn.cuh b/cpp/src/distance/detail/fused_l2_nn.cuh
new file mode 100644
index 0000000000..3b8c426ea7
--- /dev/null
+++ b/cpp/src/distance/detail/fused_l2_nn.cuh
@@ -0,0 +1,386 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../distance_ops/l2_exp.cuh"  // ops::l2_exp_distance_op
+#include "../fused_distance_nn/cutlass_base.cuh"
+#include "../pairwise_distance_base.cuh"  // PairwiseDistances
+#include <raft/core/kvp.hpp>              // raft::KeyValuePair
+#include <raft/core/operators.hpp>        // raft::identity_op
+#include <raft/linalg/contractions.cuh>   // Policy
+#include <raft/util/arch.cuh>             // raft::util::arch::SM_*
+#include <raft/util/cuda_utils.cuh>       // raft::ceildiv, raft::shfl
+
+#include <cstddef>  // size_t
+#include <limits>   // std::numeric_limits
+
+namespace cuvs {
+namespace distance {
+
+namespace detail {
+
+template <typename LabelT, typename DataT>
+struct KVPMinReduceImpl {
+  typedef raft::KeyValuePair<LabelT, DataT> KVP;
+  DI KVP operator()(LabelT rit, const KVP& a, const KVP& b) { return b.value < a.value ? b : a; }
+  DI KVP operator()(const KVP& a, const KVP& b) { return b.value < a.value ? b : a; }
+
+};  // KVPMinReduce
+
+template <typename LabelT, typename DataT>
+struct MinAndDistanceReduceOpImpl {
+  typedef typename raft::KeyValuePair<LabelT, DataT> KVP;
+  DI void operator()(LabelT rid, KVP* out, const KVP& other) const
+  {
+    if (other.value < out->value) {
+      out->key   = other.key;
+      out->value = other.value;
+    }
+  }
+
+  DI void operator()(LabelT rid, DataT* out, const KVP& other) const
+  {
+    if (other.value < *out) { *out = other.value; }
+  }
+
+  DI void operator()(LabelT rid, DataT* out, const DataT& other) const
+  {
+    if (other < *out) { *out = other; }
+  }
+
+  DI void init(DataT* out, DataT maxVal) const { *out = maxVal; }
+  DI void init(KVP* out, DataT maxVal) const { out->value = maxVal; }
+
+  DI void init_key(DataT& out, LabelT idx) const { return; }
+  DI void init_key(KVP& out, LabelT idx) const { out.key = idx; }
+
+  DI DataT get_value(KVP& out) const
+  {
+    return out.value;
+    ;
+  }
+  DI DataT get_value(DataT& out) const { return out; }
+};
+
+template <typename LabelT, typename DataT>
+struct MinReduceOpImpl {
+  typedef typename raft::KeyValuePair<LabelT, DataT> KVP;
+  DI void operator()(LabelT rid, DataT* out, const KVP& other)
+  {
+    if (other.value < *out) { *out = other.value; }
+  }
+
+  DI void init(DataT* out, DataT maxVal) { *out = maxVal; }
+};
+
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT>
+RAFT_KERNEL initKernel(OutT* min, IdxT m, DataT maxVal, ReduceOpT redOp)
+{
+  auto tid = IdxT(blockIdx.x) * blockDim.x + threadIdx.x;
+  if (tid < m) { redOp.init(min + tid, maxVal); }
+}
+
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT>
+void initialize(OutT* min, IdxT m, DataT maxVal, ReduceOpT redOp, cudaStream_t stream)
+{
+  auto blks = raft::ceildiv(m, 256);
+  initKernel<DataT, OutT, IdxT><<<blks, 256, 0, stream>>>(min, m, maxVal, redOp);
+}
+
+// TODO: specialize this function for MinAndDistanceReduceOp<int, float>
+// with atomicCAS of 64 bit which will eliminate mutex and shfls
+template <typename P, typename OutT, typename IdxT, typename KVPair, typename ReduceOpT>
+DI void updateReducedVal(
+  int* mutex, OutT* min, KVPair* val, ReduceOpT red_op, IdxT m, IdxT gridStrideY)
+{
+  const auto lid      = threadIdx.x % raft::WarpSize;
+  const auto accrowid = threadIdx.x / P::AccThCols;
+
+  // Update each output row in order within a warp. This will resolve hang
+  // issues with pre-Volta architectures
+#pragma unroll
+  for (int j = 0; j < (raft::WarpSize / P::AccThCols); j++) {
+    if (lid == j * P::AccThCols) {
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+        auto rid = gridStrideY + accrowid + i * P::AccThRows;
+        if (rid < m) {
+          auto value = val[i];
+          while (atomicCAS(mutex + rid, 0, 1) == 1)
+            ;
+          __threadfence();
+          red_op(rid, min + rid, value);
+          __threadfence();
+          atomicCAS(mutex + rid, 1, 0);
+        }
+      }
+    }
+  }
+}
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename P,
+          typename ReduceOpT,
+          typename KVPReduceOpT,
+          typename OpT,
+          typename FinalLambda>
+__launch_bounds__(P::Nthreads, 2) RAFT_KERNEL fusedL2NNkernel(OutT* min,
+                                                              const DataT* x,
+                                                              const DataT* y,
+                                                              const DataT* xn,
+                                                              const DataT* yn,
+                                                              IdxT m,
+                                                              IdxT n,
+                                                              IdxT k,
+                                                              DataT maxVal,
+                                                              int* mutex,
+                                                              ReduceOpT redOp,
+                                                              KVPReduceOpT pairRedOp,
+                                                              OpT distance_op,
+                                                              FinalLambda fin_op)
+{
+// compile only if below non-ampere arch.
+#if __CUDA_ARCH__ < 800
+  extern __shared__ char smem[];
+
+  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
+  KVPair val[P::AccRowsPerTh];
+#pragma unroll
+  for (int i = 0; i < P::AccRowsPerTh; ++i) {
+    val[i] = {0, maxVal};
+  }
+
+  // epilogue operation lambda for final value calculation
+  auto epilog_lambda = [n, pairRedOp, &val, maxVal] __device__(
+                         DataT acc[P::AccRowsPerTh][P::AccColsPerTh],
+                         DataT * regxn,
+                         DataT * regyn,
+                         IdxT gridStrideX,
+                         IdxT gridStrideY) {
+    KVPReduceOpT pairRed_op(pairRedOp);
+
+    // intra thread reduce
+    const auto acccolid = threadIdx.x % P::AccThCols;
+    const auto accrowid = threadIdx.x / P::AccThCols;
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        auto tmpkey = acccolid + j * P::AccThCols + gridStrideX;
+        KVPair tmp  = {tmpkey, acc[i][j]};
+        if (tmpkey < n) {
+          val[i] = pairRed_op(accrowid + i * P::AccThRows + gridStrideY, tmp, val[i]);
+        }
+      }
+    }
+  };
+
+  auto rowEpilog_lambda =
+    [m, mutex, min, pairRedOp, redOp, &val, maxVal] __device__(IdxT gridStrideY) {
+      KVPReduceOpT pairRed_op(pairRedOp);
+      ReduceOpT red_op(redOp);
+
+      const auto accrowid = threadIdx.x / P::AccThCols;
+      const auto lid      = raft::laneId();
+
+    // reduce
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = P::AccThCols / 2; j > 0; j >>= 1) {
+          // Actually, the srcLane (lid +j) should be (lid +j) % P:AccThCols,
+          // but the shfl op applies the modulo internally.
+          auto tmpkey   = raft::shfl(val[i].key, lid + j, P::AccThCols);
+          auto tmpvalue = raft::shfl(val[i].value, lid + j, P::AccThCols);
+          KVPair tmp    = {tmpkey, tmpvalue};
+          val[i]        = pairRed_op(accrowid + i * P::AccThRows + gridStrideY, tmp, val[i]);
+        }
+      }
+
+      updateReducedVal<P, OutT, IdxT, KVPair, ReduceOpT>(mutex, min, val, red_op, m, gridStrideY);
+
+    // reset the val array.
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+        val[i] = {0, maxVal};
+      }
+    };
+
+  IdxT lda = k, ldb = k, ldd = n;
+  constexpr bool row_major = true;
+  constexpr bool write_out = false;
+  PairwiseDistances<DataT,
+                    DataT,  // OutT (unused in PairwiseDistances)
+                    IdxT,
+                    P,
+                    decltype(distance_op),
+                    decltype(epilog_lambda),
+                    FinalLambda,
+                    decltype(rowEpilog_lambda),
+                    row_major,
+                    write_out>
+    obj(x,
+        y,
+        m,
+        n,
+        k,
+        lda,
+        ldb,
+        ldd,
+        xn,
+        yn,
+        nullptr,  // Output pointer
+        smem,
+        distance_op,
+        epilog_lambda,
+        fin_op,
+        rowEpilog_lambda);
+  obj.run();
+#endif
+}
+
+// cg::reduce functor for FusedDistanceNN used in its cutlass version
+// to output the min distance value & key(loc id).
+// This is used in fused_distance_nn/predicated_tile_iterator_reduced_vec.h
+// store_with_byte_offset() passed to cg::reduce() & select_reduce.
+template <typename AccType, typename Index, typename OutType>
+struct kvp_cg_min_reduce_op {
+  typedef typename raft::KeyValuePair<Index, AccType> KVP;
+
+  __host__ __device__ kvp_cg_min_reduce_op() noexcept {};
+
+  using AccTypeT = AccType;
+  using IndexT   = Index;
+  // functor signature.
+  __host__ __device__ KVP operator()(KVP a, KVP b) const { return a.value < b.value ? a : b; }
+
+  __host__ __device__ AccType operator()(AccType a, AccType b) const { return min(a, b); }
+
+  __host__ __device__ bool isAmin(AccType a, AccType b) const { return a < b ? true : false; }
+};
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename Policy,
+          typename ReduceOpT,
+          typename KVPReduceOpT>
+void fusedL2NNImpl(OutT* min,
+                   const DataT* x,
+                   const DataT* y,
+                   const DataT* xn,
+                   const DataT* yn,
+                   IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   int* workspace,
+                   ReduceOpT redOp,
+                   KVPReduceOpT pairRedOp,
+                   bool sqrt,
+                   bool initOutBuffer,
+                   cudaStream_t stream)
+{
+  // The kernel policy is determined by fusedL2NN.
+  typedef Policy P;
+
+  dim3 blk(P::Nthreads);
+  auto nblks            = raft::ceildiv<int>(m, P::Nthreads);
+  constexpr auto maxVal = std::numeric_limits<DataT>::max();
+  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(workspace, 0, sizeof(int) * m, stream));
+  if (initOutBuffer) {
+    initKernel<DataT, OutT, IdxT, ReduceOpT>
+      <<<nblks, P::Nthreads, 0, stream>>>(min, m, maxVal, redOp);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+
+  namespace arch = raft::util::arch;
+  using AccT     = DataT;
+  ops::l2_exp_distance_op<DataT, AccT, IdxT> distance_op{sqrt};
+
+  raft::identity_op fin_op{};
+
+  auto kernel = fusedL2NNkernel<DataT,
+                                OutT,
+                                IdxT,
+                                P,
+                                ReduceOpT,
+                                KVPReduceOpT,
+                                decltype(distance_op),
+                                decltype(fin_op)>;
+
+  // Get pointer to fp32 SIMT kernel to determine the best compute architecture
+  // out of all for which the kernel was compiled for that matches closely
+  // to the current device. Other methods to determine the architecture (that do not
+  // require a pointer) can be error prone. See:
+  // https://github.com/NVIDIA/cub/issues/545
+  void* kernel_ptr   = reinterpret_cast<void*>(kernel);
+  auto runtime_arch  = arch::kernel_virtual_arch(kernel_ptr);
+  auto cutlass_range = arch::SM_range(arch::SM_80(), arch::SM_future());
+
+  if (cutlass_range.contains(runtime_arch)) {
+    // If device is SM_80 or later, use CUTLASS-based kernel.
+    using L2Op                  = cuvs::distance::detail::ops::l2_exp_cutlass_op<DataT, DataT>;
+    using kvp_cg_min_reduce_op_ = kvp_cg_min_reduce_op<DataT, IdxT, OutT>;
+    kvp_cg_min_reduce_op_ cg_reduce_op;
+    L2Op L2_dist_op(sqrt);
+
+    IdxT lda, ldb, ldd;
+    lda = k, ldb = k, ldd = n;
+
+    cutlassFusedDistanceNN<DataT,
+                           DataT,
+                           OutT,
+                           IdxT,
+                           P::Veclen,
+                           kvp_cg_min_reduce_op_,
+                           L2Op,
+                           ReduceOpT,
+                           KVPReduceOpT>(x,
+                                         y,
+                                         xn,
+                                         yn,
+                                         m,
+                                         n,
+                                         k,
+                                         lda,
+                                         ldb,
+                                         ldd,
+                                         min,
+                                         workspace,
+                                         cg_reduce_op,
+                                         L2_dist_op,
+                                         redOp,
+                                         pairRedOp,
+                                         stream);
+  } else {
+    // If device less than SM_80, use fp32 SIMT kernel.
+    constexpr size_t shmemSize = P::SmemSize + ((P::Mblk + P::Nblk) * sizeof(DataT));
+    dim3 grid                  = launchConfigGenerator<P>(m, n, shmemSize, kernel);
+
+    kernel<<<grid, blk, shmemSize, stream>>>(
+      min, x, y, xn, yn, m, n, k, maxVal, workspace, redOp, pairRedOp, distance_op, fin_op);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+}
+
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/kernels/gram_matrix.cuh b/cpp/src/distance/detail/kernels/gram_matrix.cuh
new file mode 100644
index 0000000000..009941fa7b
--- /dev/null
+++ b/cpp/src/distance/detail/kernels/gram_matrix.cuh
@@ -0,0 +1,488 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../distance.cuh"
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/device_csr_matrix.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+// #include <raft/sparse/detail/cusparse_wrappers.h>
+#include <raft/linalg/detail/cublas_wrappers.hpp>
+#include <raft/linalg/gemm.cuh>
+#include <raft/sparse/distance/distance.cuh>
+#include <raft/sparse/linalg/spmm.hpp>
+
+namespace cuvs::distance::kernels::detail {
+
+template <typename math_t>
+using dense_input_matrix_view_t = raft::device_matrix_view<const math_t, int, layout_stride>;
+template <typename math_t>
+using dense_output_matrix_view_t = raft::device_matrix_view<math_t, int, layout_stride>;
+template <typename math_t>
+using csr_input_matrix_view_t = raft::device_csr_matrix_view<const math_t, int, int, int>;
+
+/**
+ * Base class for general Gram matrices
+ * A Gram matrix is the Hermitian matrix of inner probucts G_ik = <x_i, x_k>
+ * Here, the  inner product is evaluated for all elements from vectors sets X1,
+ * and X2.
+ *
+ * To be more precise, on exit the output buffer will store:
+ * - if is_row_major == true: out[j+k*n1] = <x1_j, x2_k>,
+ * - if is_row_major == false: out[j*n2 + k] = <x1_j, x2_k>,
+ * where x1_j is the j-th vector from the x1 set and x2_k is the k-th vector
+ * from the x2 set.
+ */
+template <typename math_t>
+class GramMatrixBase {
+ protected:
+  cublasHandle_t cublas_handle;
+  bool legacy_interface;
+
+ public:
+  GramMatrixBase() : legacy_interface(false){};
+  [[deprecated]] GramMatrixBase(cublasHandle_t cublas_handle)
+    : cublas_handle(cublas_handle), legacy_interface(true){};
+
+  virtual ~GramMatrixBase(){};
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *  Vector sets are provided in Matrix format
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void operator()(raft::resources const& handle,
+                  dense_input_matrix_view_t<math_t> x1,
+                  dense_input_matrix_view_t<math_t> x2,
+                  dense_output_matrix_view_t<math_t> out,
+                  math_t* norm_x1 = nullptr,
+                  math_t* norm_x2 = nullptr)
+  {
+    evaluate(handle, x1, x2, out, norm_x1, norm_x2);
+  }
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *  Vector sets are provided in Matrix format
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void operator()(raft::resources const& handle,
+                  csr_input_matrix_view_t<math_t> x1,
+                  dense_input_matrix_view_t<math_t> x2,
+                  dense_output_matrix_view_t<math_t> out,
+                  math_t* norm_x1 = nullptr,
+                  math_t* norm_x2 = nullptr)
+  {
+    evaluate(handle, x1, x2, out, norm_x1, norm_x2);
+  }
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *  Vector sets are provided in Matrix format
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void operator()(raft::resources const& handle,
+                  csr_input_matrix_view_t<math_t> x1,
+                  csr_input_matrix_view_t<math_t> x2,
+                  dense_output_matrix_view_t<math_t> out,
+                  math_t* norm_x1 = nullptr,
+                  math_t* norm_x2 = nullptr)
+  {
+    evaluate(handle, x1, x2, out, norm_x1, norm_x2);
+  }
+
+  // unfortunately, 'evaluate' cannot be templatized as it needs to be virtual
+
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  virtual void evaluate(raft::resources const& handle,
+                        dense_input_matrix_view_t<math_t> x1,
+                        dense_input_matrix_view_t<math_t> x2,
+                        dense_output_matrix_view_t<math_t> out,
+                        math_t* norm_x1,
+                        math_t* norm_x2)
+  {
+    linear(handle, x1, x2, out);
+  }
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  virtual void evaluate(raft::resources const& handle,
+                        csr_input_matrix_view_t<math_t> x1,
+                        dense_input_matrix_view_t<math_t> x2,
+                        dense_output_matrix_view_t<math_t> out,
+                        math_t* norm_x1,
+                        math_t* norm_x2)
+  {
+    linear(handle, x1, x2, out);
+  }
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  virtual void evaluate(raft::resources const& handle,
+                        csr_input_matrix_view_t<math_t> x1,
+                        csr_input_matrix_view_t<math_t> x2,
+                        dense_output_matrix_view_t<math_t> out,
+                        math_t* norm_x1,
+                        math_t* norm_x2)
+  {
+    linear(handle, x1, x2, out);
+  }
+
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] virtual void evaluate(const math_t* x1,
+                                       int n1,
+                                       int n_cols,
+                                       const math_t* x2,
+                                       int n2,
+                                       math_t* out,
+                                       bool is_row_major,
+                                       cudaStream_t stream,
+                                       int ld1,
+                                       int ld2,
+                                       int ld_out)
+  {
+    linear(x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+  }
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1
+   * @param ld2 leading dimension of x2
+   * @param ld_out leading dimension of out
+   */
+  [[deprecated]] void operator()(const math_t* x1,
+                                 int n1,
+                                 int n_cols,
+                                 const math_t* x2,
+                                 int n2,
+                                 math_t* out,
+                                 bool is_row_major,
+                                 cudaStream_t stream,
+                                 int ld1    = 0,
+                                 int ld2    = 0,
+                                 int ld_out = 0)
+  {
+    ASSERT(legacy_interface, "Legacy interface can only be used with legacy ctor.");
+    if (ld1 <= 0) { ld1 = is_row_major ? n_cols : n1; }
+    if (ld2 <= 0) { ld2 = is_row_major ? n_cols : n2; }
+    if (ld_out <= 0) { ld_out = is_row_major ? n2 : n1; }
+    evaluate(x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+  }
+
+ protected:
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1
+   * @param ld2 leading dimension of x2
+   * @param ld_out leading dimension of out
+   */
+  [[deprecated]] void linear(const math_t* x1,
+                             int n1,
+                             int n_cols,
+                             const math_t* x2,
+                             int n2,
+                             math_t* out,
+                             bool is_row_major,
+                             cudaStream_t stream,
+                             int ld1,
+                             int ld2,
+                             int ld_out)
+  {
+    math_t alpha = 1.0;
+    math_t beta  = 0.0;
+    if (is_row_major) {
+      // #TODO: Call from public API when ready
+      RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(cublas_handle,
+                                                       CUBLAS_OP_T,
+                                                       CUBLAS_OP_N,
+                                                       n2,
+                                                       n1,
+                                                       n_cols,
+                                                       &alpha,
+                                                       x2,
+                                                       ld2,
+                                                       x1,
+                                                       ld1,
+                                                       &beta,
+                                                       out,
+                                                       ld_out,
+                                                       stream));
+    } else {
+      // #TODO: Call from public API when ready
+      RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(cublas_handle,
+                                                       CUBLAS_OP_N,
+                                                       CUBLAS_OP_T,
+                                                       n1,
+                                                       n2,
+                                                       n_cols,
+                                                       &alpha,
+                                                       x1,
+                                                       ld1,
+                                                       x2,
+                                                       ld2,
+                                                       &beta,
+                                                       out,
+                                                       ld_out,
+                                                       stream));
+    }
+  }
+
+ protected:
+  bool get_is_row_major(dense_output_matrix_view_t<math_t> matrix)
+  {
+    return (matrix.stride(1) == 1);
+  }
+
+  bool get_is_row_major(dense_input_matrix_view_t<math_t> matrix)
+  {
+    return (matrix.stride(1) == 1);
+  }
+
+  bool get_is_col_major(dense_output_matrix_view_t<math_t> matrix)
+  {
+    return (matrix.stride(0) == 1);
+  }
+
+  bool get_is_col_major(dense_input_matrix_view_t<math_t> matrix)
+  {
+    return (matrix.stride(0) == 1);
+  }
+
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   */
+  void linear(raft::resources const& handle,
+              dense_input_matrix_view_t<math_t> x1,
+              dense_input_matrix_view_t<math_t> x2,
+              dense_output_matrix_view_t<math_t> out)
+  {
+    // check is_row_major consistency
+    bool is_row_major = get_is_row_major(x1) && get_is_row_major(x2) && get_is_row_major(out);
+    bool is_col_major = get_is_col_major(x1) && get_is_col_major(x2) && get_is_col_major(out);
+    ASSERT(is_row_major || is_col_major,
+           "GramMatrix leading dimensions for x1, x2 and out do not match");
+
+    // check dimensions
+    int n1     = out.extent(0);
+    int n2     = out.extent(1);
+    int n_cols = x1.extent(1);
+    ASSERT(x1.extent(0) == n1, "GramMatrix input matrix dimensions for x1 and out do not match");
+    ASSERT(x2.extent(0) == n2, "GramMatrix input matrix dimensions for x2 and out do not match");
+    ASSERT(x2.extent(1) == n_cols, "GramMatrix input matrix dimensions for x1 and x2 do not match");
+
+    // extract major stride
+    int ld1    = is_row_major ? x1.stride(0) : x1.stride(1);
+    int ld2    = is_row_major ? x2.stride(0) : x2.stride(1);
+    int ld_out = is_row_major ? out.stride(0) : out.stride(1);
+
+    math_t alpha = 1.0;
+    math_t beta  = 0.0;
+    if (is_row_major) {
+      // #TODO: Use mdspan-based API when stride-capable
+      // https://github.com/rapidsai/raft/issues/875
+      raft::linalg::gemm(handle,
+                         true,
+                         false,
+                         n2,
+                         n1,
+                         n_cols,
+                         &alpha,
+                         x2.data_handle(),
+                         ld2,
+                         x1.data_handle(),
+                         ld1,
+                         &beta,
+                         out.data_handle(),
+                         ld_out,
+                         resource::get_cuda_stream(handle));
+    } else {
+      // #TODO: Use mdspan-based API when stride-capable
+      // https://github.com/rapidsai/raft/issues/875
+      raft::linalg::gemm(handle,
+                         false,
+                         true,
+                         n1,
+                         n2,
+                         n_cols,
+                         &alpha,
+                         x1.data_handle(),
+                         ld1,
+                         x2.data_handle(),
+                         ld2,
+                         &beta,
+                         out.data_handle(),
+                         ld_out,
+                         resource::get_cuda_stream(handle));
+    }
+  }
+
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   */
+  void linear(raft::resources const& handle,
+              csr_input_matrix_view_t<math_t> x1,
+              dense_input_matrix_view_t<math_t> x2,
+              dense_output_matrix_view_t<math_t> out)
+  {
+    // check is_row_major consistency
+    bool is_row_major = get_is_row_major(x2) && get_is_row_major(out);
+    bool is_col_major = get_is_col_major(x2) && get_is_col_major(out);
+    ASSERT(is_row_major || is_col_major,
+           "GramMatrix leading dimensions for x2 and out do not match");
+
+    // check dimensions
+    auto x1_structure = x1.structure_view();
+    ASSERT(x1_structure.get_n_rows() == out.extent(0),
+           "GramMatrix input matrix dimensions for x1 and out do not match");
+    ASSERT(x2.extent(0) == out.extent(1),
+           "GramMatrix input matrix dimensions for x2 and out do not match");
+    ASSERT(x2.extent(1) == x1_structure.get_n_cols(),
+           "GramMatrix input matrix dimensions for x1 and x2 do not match");
+
+    math_t alpha = 1.0;
+    math_t beta  = 0.0;
+
+    raft::sparse::linalg::spmm(handle, false, true, &alpha, x1, x2, &beta, out);
+  }
+
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   */
+  void linear(raft::resources const& handle,
+              csr_input_matrix_view_t<math_t> x1,
+              csr_input_matrix_view_t<math_t> x2,
+              dense_output_matrix_view_t<math_t> out)
+  {
+    // check layout consistency (w.r.t. strides a matrix might be both row & col major)
+    bool is_row_major_nopad = get_is_row_major(out) && out.stride(0) == out.extent(1);
+    bool is_col_major_nopad = get_is_col_major(out) && out.stride(1) == out.extent(0);
+
+    ASSERT(is_row_major_nopad || is_col_major_nopad,
+           "Sparse linear Kernel distance does not support ld_out parameter");
+
+    // switch a,b based on is_row_major
+    if (is_col_major_nopad) {
+      auto out_row_major = raft::make_device_matrix_view<math_t, int, raft::row_major>(
+        out.data_handle(), out.extent(1), out.extent(0));
+      raft::sparse::distance::pairwise_distance(
+        handle, x2, x1, out_row_major, cuvs::distance::DistanceType::InnerProduct, 0.0);
+    } else {
+      auto out_row_major = raft::make_device_matrix_view<math_t, int, raft::row_major>(
+        out.data_handle(), out.extent(0), out.extent(1));
+      raft::sparse::distance::pairwise_distance(
+        handle, x1, x2, out_row_major, cuvs::distance::DistanceType::InnerProduct, 0.0);
+    }
+  }
+};
+
+};  // end namespace cuvs::distance::kernels::detail
diff --git a/cpp/src/distance/detail/kernels/kernel_factory.cuh b/cpp/src/distance/detail/kernels/kernel_factory.cuh
new file mode 100644
index 0000000000..534339a153
--- /dev/null
+++ b/cpp/src/distance/detail/kernels/kernel_factory.cuh
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "gram_matrix.cuh"
+#include "kernel_matrices.cuh"
+
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+namespace cuvs::distance::kernels::detail {
+
+template <typename math_t>
+class KernelFactory {
+ public:
+  static GramMatrixBase<math_t>* create(KernelParams params)
+  {
+    GramMatrixBase<math_t>* res;
+    // KernelParams is not templated, we convert the parameters to math_t here:
+    math_t coef0 = params.coef0;
+    math_t gamma = params.gamma;
+    switch (params.kernel) {
+      case LINEAR: res = new GramMatrixBase<math_t>(); break;
+      case POLYNOMIAL: res = new PolynomialKernel<math_t, int>(params.degree, gamma, coef0); break;
+      case TANH: res = new TanhKernel<math_t>(gamma, coef0); break;
+      case RBF: res = new RBFKernel<math_t>(gamma); break;
+      default: throw raft::exception("Kernel not implemented");
+    }
+    return res;
+  }
+
+  [[deprecated]] static GramMatrixBase<math_t>* create(KernelParams params, cublasHandle_t handle)
+  {
+    GramMatrixBase<math_t>* res;
+    // KernelParams is not templated, we convert the parameters to math_t here:
+    math_t coef0 = params.coef0;
+    math_t gamma = params.gamma;
+    switch (params.kernel) {
+      case LINEAR: res = new GramMatrixBase<math_t>(handle); break;
+      case POLYNOMIAL:
+        res = new PolynomialKernel<math_t, int>(params.degree, gamma, coef0, handle);
+        break;
+      case TANH: res = new TanhKernel<math_t>(gamma, coef0, handle); break;
+      case RBF: res = new RBFKernel<math_t>(gamma, handle); break;
+      default: throw raft::exception("Kernel not implemented");
+    }
+    return res;
+  }
+};
+
+};  // end namespace cuvs::distance::kernels::detail
diff --git a/cpp/src/distance/detail/kernels/kernel_matrices.cuh b/cpp/src/distance/detail/kernels/kernel_matrices.cuh
new file mode 100644
index 0000000000..bff5bda923
--- /dev/null
+++ b/cpp/src/distance/detail/kernels/kernel_matrices.cuh
@@ -0,0 +1,777 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "gram_matrix.cuh"
+
+#include "../detail/kernels/rbf_fin_op.cuh"
+#include <cuvs/distance/distance.cuh>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/gemm.cuh>
+#include <raft/sparse/linalg/norm.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+namespace cuvs::distance::kernels::detail {
+
+/** Epiloge function for polynomial kernel without padding.
+ * Calculates output = (gain*in + offset)^exponent
+ * @param inout device vector in column major format, size [len]
+ * @param len array length
+ * @param exponent
+ * @param gain
+ * @param offset
+ */
+template <typename math_t, typename exp_t>
+RAFT_KERNEL polynomial_kernel_nopad(
+  math_t* inout, size_t len, exp_t exponent, math_t gain, math_t offset)
+{
+  for (size_t tid = threadIdx.x + blockIdx.x * blockDim.x; tid < len;
+       tid += blockDim.x * gridDim.x) {
+    inout[tid] = pow(gain * inout[tid] + offset, exponent);
+  }
+}
+
+/** Epiloge function for polynomial kernel with padding.
+ * Calculates output = (gain*input + offset)^exponent
+ * @param inout device vector in column major format, size [ld * cols]
+ * @param ld leading dimension of the inout buffer
+ * @param rows number of rows (rows <= ld)
+ * @param cols number of columns
+ * @param exponent
+ * @param gain
+ * @param offset
+ */
+template <typename math_t, typename exp_t>
+RAFT_KERNEL polynomial_kernel(
+  math_t* inout, int ld, int rows, int cols, exp_t exponent, math_t gain, math_t offset)
+{
+  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
+       tidy += blockDim.y * gridDim.y)
+    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
+         tidx += blockDim.x * gridDim.x) {
+      inout[tidx + tidy * ld] = pow(gain * inout[tidx + tidy * ld] + offset, exponent);
+    }
+}
+
+/** Epiloge function for tanh kernel without padding.
+ * Calculates output = tanh(gain*input + offset)
+ * @param inout device vector, size [len]
+ * @param len length of the input vector
+ * @param gain
+ * @param offset
+ */
+template <typename math_t>
+RAFT_KERNEL tanh_kernel_nopad(math_t* inout, size_t len, math_t gain, math_t offset)
+{
+  for (size_t tid = threadIdx.x + blockIdx.x * blockDim.x; tid < len;
+       tid += blockDim.x * gridDim.x) {
+    inout[tid] = tanh(gain * inout[tid] + offset);
+  }
+}
+
+/** Epiloge function for tanh kernel without padding.
+ * Calculates output = tanh(gain*input + offset)
+ * @param inout device vector in column major format, size [ld * cols]
+ * @param ld leading dimension of the inout buffer
+ * @param rows number of rows (rows <= ld)
+ * @param cols number of columns
+ * @param gain
+ * @param offset
+ */
+template <typename math_t>
+RAFT_KERNEL tanh_kernel(math_t* inout, int ld, int rows, int cols, math_t gain, math_t offset)
+{
+  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
+       tidy += blockDim.y * gridDim.y)
+    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
+         tidx += blockDim.x * gridDim.x) {
+      inout[tidx + tidy * ld] = tanh(gain * inout[tidx + tidy * ld] + offset);
+    }
+}
+
+/** Epiloge function for rbf kernel using expansion.
+ *
+ * Calculates output_ij = exp(-gain * (norm_x_i + norm_y_j - 2*input_ij));
+ *
+ * Intended usage
+ *   - input is the product of two matrices X and Y input_ij = sum_k X_ik * Y_jk
+ *   - norm_x_i = l2_norm(x_i), where x_i is the i-th row of matrix X
+ *   - norm_y_j = l2_norm(y_j), where y_j is the j-th row of matrix Y
+ *
+ * @param inout device vector in column major format, size [ld * cols]
+ * @param ld leading dimension of the inout buffer
+ * @param rows number of rows (rows <= ld)
+ * @param cols number of columns
+ * @param norm_x l2-norm of X's rows
+ * @param norm_y l2-norm of Y's rows
+ * @param gain
+ */
+template <typename math_t>
+RAFT_KERNEL rbf_kernel_expanded(
+  math_t* inout, int ld, int rows, int cols, math_t* norm_x, math_t* norm_y, math_t gain)
+{
+  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
+       tidy += blockDim.y * gridDim.y) {
+    math_t norm_y_val = norm_y[tidy];
+    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
+         tidx += blockDim.x * gridDim.x) {
+      inout[tidx + tidy * ld] =
+        exp(-1.0 * gain * (norm_x[tidx] + norm_y_val - inout[tidx + tidy * ld] * 2));
+    }
+  }
+}
+
+namespace {
+std::tuple<dim3, dim3> generateLaunchConfig2dElementwiseOp(int n1, int n2)
+{
+  dim3 block_shape       = dim3(32, 4);
+  const int num_blocks_x = raft::ceildiv(n1, 32);
+  const int num_blocks_y = std::min(raft::ceildiv(n2, 32), (1 << 16) - 1);
+  dim3 grid_shape        = dim3(num_blocks_x, num_blocks_y);
+  return std::make_tuple(grid_shape, block_shape);
+}
+}  // namespace
+
+/**
+ * Create a kernel matrix using polynomial kernel function.
+ */
+template <typename math_t, typename exp_t>
+class PolynomialKernel : public GramMatrixBase<math_t> {
+  exp_t exponent;
+  math_t gain;
+  math_t offset;
+
+  void applyKernel(
+    math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream)
+  {
+    const int n_minor = is_row_major ? cols : rows;
+    if (ld == n_minor) {
+      polynomial_kernel_nopad<<<raft::ceildiv<size_t>((size_t)rows * cols, 128), 128, 0, stream>>>(
+        inout, rows * cols, exponent, gain, offset);
+    } else {
+      int n1                         = is_row_major ? cols : rows;
+      int n2                         = is_row_major ? rows : cols;
+      auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
+      polynomial_kernel<<<grid_shape, block_shape, 0, stream>>>(
+        inout, ld, n1, n2, exponent, gain, offset);
+    }
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+  }
+
+ public:
+  /**
+   * Constructs a polynomial kernel object.
+   * It evaluates the kernel matrix using the following formula:
+   * K_ij = (gain*<x1_i, x2_k> + offset)^exponent
+   *
+   * @tparam math_t floating point type
+   * @tparam exp_t type of exponent
+   * @param exponent
+   * @param gain
+   * @param offset
+   */
+  PolynomialKernel(exp_t exponent, math_t gain, math_t offset)
+    : GramMatrixBase<math_t>(), exponent(exponent), gain(gain), offset(offset)
+  {
+  }
+
+  [[deprecated]] PolynomialKernel(exp_t exponent, math_t gain, math_t offset, cublasHandle_t handle)
+    : GramMatrixBase<math_t>(handle), exponent(exponent), gain(gain), offset(offset)
+  {
+  }
+
+  /** Evaluate kernel matrix using polynomial kernel.
+   *
+   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                dense_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate kernel matrix using polynomial kernel.
+   *
+   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate kernel matrix using polynomial kernel.
+   *
+   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                csr_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate the Gram matrix using the legacy interface.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] void evaluate(const math_t* x1,
+                               int n1,
+                               int n_cols,
+                               const math_t* x2,
+                               int n2,
+                               math_t* out,
+                               bool is_row_major,
+                               cudaStream_t stream,
+                               int ld1,
+                               int ld2,
+                               int ld_out)
+  {
+    ASSERT(GramMatrixBase<math_t>::legacy_interface,
+           "Legacy interface can only be used with legacy ctor.");
+    GramMatrixBase<math_t>::linear(
+      x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+    applyKernel(out, ld_out, n1, n2, is_row_major, stream);
+  }
+};
+
+/**
+ * Create a kernel matrix using tanh kernel function.
+ */
+template <typename math_t>
+class TanhKernel : public GramMatrixBase<math_t> {
+  math_t gain, offset;
+
+  void applyKernel(
+    math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream)
+  {
+    const int n_minor = is_row_major ? cols : rows;
+    if (ld == n_minor) {
+      tanh_kernel_nopad<<<raft::ceildiv<size_t>((size_t)rows * cols, 128), 128, 0, stream>>>(
+        inout, rows * cols, gain, offset);
+    } else {
+      int n1                         = is_row_major ? cols : rows;
+      int n2                         = is_row_major ? rows : cols;
+      auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
+      tanh_kernel<<<grid_shape, block_shape, 0, stream>>>(inout, ld, n1, n2, gain, offset);
+    }
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+  }
+
+ public:
+  /**
+   * Constructs a tanh kernel object.
+   * It evaluates the kernel matrix using the following formula:
+   * K_ij = tanh(gain*<x1_i, x2_k> + offset)
+   *
+   * @tparam math_t floating point type
+   * @param gain
+   * @param offset
+   */
+  TanhKernel(math_t gain, math_t offset) : GramMatrixBase<math_t>(), gain(gain), offset(offset) {}
+
+  [[deprecated]] TanhKernel(math_t gain, math_t offset, cublasHandle_t handle)
+    : GramMatrixBase<math_t>(handle), gain(gain), offset(offset)
+  {
+  }
+
+  /** Evaluate kernel matrix using tanh kernel.
+   *
+   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                dense_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate kernel matrix using tanh kernel.
+   *
+   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate kernel matrix using tanh kernel.
+   *
+   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                csr_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate the Gram matrix using the legacy interface.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] void evaluate(const math_t* x1,
+                               int n1,
+                               int n_cols,
+                               const math_t* x2,
+                               int n2,
+                               math_t* out,
+                               bool is_row_major,
+                               cudaStream_t stream,
+                               int ld1,
+                               int ld2,
+                               int ld_out)
+  {
+    ASSERT(GramMatrixBase<math_t>::legacy_interface,
+           "Legacy interface can only be used with legacy ctor.");
+    GramMatrixBase<math_t>::linear(
+      x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+    applyKernel(out, ld_out, n1, n2, is_row_major, stream);
+  }
+};
+
+/**
+ * Create a kernel matrix using RBF kernel function.
+ */
+template <typename math_t>
+class RBFKernel : public GramMatrixBase<math_t> {
+  math_t gain;
+
+  void applyKernel(math_t* inout,
+                   int ld,
+                   int rows,
+                   int cols,
+                   math_t* norm_x1,
+                   math_t* norm_x2,
+                   bool is_row_major,
+                   cudaStream_t stream)
+  {
+    int n1                         = is_row_major ? cols : rows;
+    int n2                         = is_row_major ? rows : cols;
+    math_t* norm_n1                = is_row_major ? norm_x2 : norm_x1;
+    math_t* norm_n2                = is_row_major ? norm_x1 : norm_x2;
+    auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
+    rbf_kernel_expanded<<<grid_shape, block_shape, 0, stream>>>(
+      inout, ld, n1, n2, norm_n1, norm_n2, gain);
+  }
+
+ public:
+  /**
+   * Constructs a RBF kernel object.
+   * It evaluates the kernel matrix using the following formula:
+   * K_ij = exp(-gain*|x1_i- x2_k|^2)
+   *
+   * @tparam math_t floating point type
+   * @param gain
+   */
+  RBFKernel(math_t gain) : GramMatrixBase<math_t>(), gain(gain) {}
+
+  [[deprecated]] RBFKernel(math_t gain, cublasHandle_t handle)
+    : GramMatrixBase<math_t>(handle), gain(gain)
+  {
+  }
+
+  void matrixRowNormL2(raft::resources const& handle,
+                       dense_input_matrix_view_t<math_t> matrix,
+                       math_t* target)
+  {
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(matrix);
+    int minor         = is_row_major ? matrix.extent(1) : matrix.extent(0);
+    int ld            = is_row_major ? matrix.stride(0) : matrix.stride(1);
+    ASSERT(ld == minor, "RBF Kernel lazy rowNorm compute does not support ld parameter");
+    raft::linalg::rowNorm(target,
+                          matrix.data_handle(),
+                          matrix.extent(1),
+                          matrix.extent(0),
+                          raft::linalg::NormType::L2Norm,
+                          is_row_major,
+                          resource::get_cuda_stream(handle));
+  }
+
+  void matrixRowNormL2(raft::resources const& handle,
+                       csr_input_matrix_view_t<math_t> matrix,
+                       math_t* target)
+  {
+    auto matrix_structure = matrix.structure_view();
+    raft::sparse::linalg::rowNormCsr(handle,
+                                     matrix_structure.get_indptr().data(),
+                                     matrix.get_elements().data(),
+                                     matrix_structure.get_nnz(),
+                                     matrix_structure.get_n_rows(),
+                                     target,
+                                     raft::linalg::NormType::L2Norm);
+  }
+
+  /** Evaluate kernel matrix using RBF kernel.
+   *
+   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and | | euclidean distance.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void evaluate(raft::resources const& handle,
+                dense_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    cudaStream_t stream = resource::get_cuda_stream(handle);
+    // lazy compute norms if not given
+    rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
+    rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
+    if (norm_x1 == nullptr) {
+      tmp_norm_x1.reserve(x1.extent(0), stream);
+      norm_x1 = tmp_norm_x1.data();
+      matrixRowNormL2(handle, x1, norm_x1);
+    }
+    if (norm_x2 == nullptr) {
+      tmp_norm_x2.reserve(x2.extent(0), stream);
+      norm_x2 = tmp_norm_x2.data();
+      matrixRowNormL2(handle, x2, norm_x2);
+    }
+
+    // compute L2expanded
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                norm_x1,
+                norm_x2,
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate kernel matrix using RBF kernel.
+   *
+   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and | | euclidean distance.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    cudaStream_t stream = resource::get_cuda_stream(handle);
+
+    // lazy compute norms if not given
+    rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
+    rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
+    if (norm_x1 == nullptr) {
+      tmp_norm_x1.reserve(x1.structure_view().get_n_rows(), stream);
+      norm_x1 = tmp_norm_x1.data();
+      matrixRowNormL2(handle, x1, norm_x1);
+    }
+    if (norm_x2 == nullptr) {
+      tmp_norm_x2.reserve(x2.extent(0), stream);
+      norm_x2 = tmp_norm_x2.data();
+      matrixRowNormL2(handle, x2, norm_x2);
+    }
+
+    // compute L2expanded
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                norm_x1,
+                norm_x2,
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate kernel matrix using RBF kernel.
+   *
+   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and | | euclidean distance.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                csr_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2)
+  {
+    cudaStream_t stream = resource::get_cuda_stream(handle);
+
+    // lazy compute norms if not given
+    rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
+    rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
+    if (norm_x1 == nullptr) {
+      tmp_norm_x1.reserve(x1.structure_view().get_n_rows(), stream);
+      norm_x1 = tmp_norm_x1.data();
+      matrixRowNormL2(handle, x1, norm_x1);
+    }
+    if (norm_x2 == nullptr) {
+      tmp_norm_x2.reserve(x2.structure_view().get_n_rows(), stream);
+      norm_x2 = tmp_norm_x2.data();
+      matrixRowNormL2(handle, x2, norm_x2);
+    }
+
+    // compute L2expanded
+    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+    applyKernel(out.data_handle(),
+                ld_out,
+                out.extent(0),
+                out.extent(1),
+                norm_x1,
+                norm_x2,
+                is_row_major,
+                resource::get_cuda_stream(handle));
+  }
+
+  /** Evaluate the Gram matrix using the legacy interface.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] void evaluate(const math_t* x1,
+                               int n1,
+                               int n_cols,
+                               const math_t* x2,
+                               int n2,
+                               math_t* out,
+                               bool is_row_major,
+                               cudaStream_t stream,
+                               int ld1,
+                               int ld2,
+                               int ld_out)
+  {
+    ASSERT(GramMatrixBase<math_t>::legacy_interface,
+           "Legacy interface can only be used with legacy ctor.");
+    int minor1    = is_row_major ? n_cols : n1;
+    int minor2    = is_row_major ? n_cols : n2;
+    int minor_out = is_row_major ? n2 : n1;
+    ASSERT(ld1 == minor1, "RBF Kernel distance does not support ld1 parameter");
+    ASSERT(ld2 == minor2, "RBF Kernel distance does not support ld2 parameter");
+    ASSERT(ld_out == minor_out, "RBF Kernel distance does not support ld_out parameter");
+
+    math_t gain   = this->gain;
+    using index_t = int64_t;
+
+    rbf_fin_op fin_op{gain};
+
+    raft::resources handle;
+    resource::set_cuda_stream(handle, stream);
+
+    cuvs::distance::distance<cuvs::distance::DistanceType::L2Unexpanded,
+                             math_t,
+                             math_t,
+                             math_t,
+                             decltype(fin_op),
+                             index_t>(handle,
+                                      const_cast<math_t*>(x1),
+                                      const_cast<math_t*>(x2),
+                                      out,
+                                      n1,
+                                      n2,
+                                      n_cols,
+                                      NULL,
+                                      0,
+                                      fin_op,
+                                      is_row_major);
+  }
+};
+
+};  // end namespace cuvs::distance::kernels::detail
diff --git a/cpp/src/distance/detail/kernels/rbf_fin_op.cuh b/cpp/src/distance/detail/kernels/rbf_fin_op.cuh
new file mode 100644
index 0000000000..73588baeae
--- /dev/null
+++ b/cpp/src/distance/detail/kernels/rbf_fin_op.cuh
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+/*
+ * This file defines rbf_fin_op, which is used in GramMatrixBase.
+ *
+ * This struct has been moved to a separate file, so that it is cheap to include
+ * in distance/distance-ext.cuh, where an instance of cuvs::distance::distance
+ * with the rbf_fin_op is instantiated.
+ *
+ */
+
+#include <raft/core/math.hpp>                 // raft::exp
+#include <raft/util/cuda_dev_essentials.cuh>  // HD
+
+namespace cuvs::distance::kernels::detail {
+
+/** @brief: Final op for Gram matrix with RBF kernel.
+ *
+ * Calculates output = e^(-gain * in)
+ *
+ */
+template <typename OutT>
+struct rbf_fin_op {
+  OutT gain;
+
+  explicit HD rbf_fin_op(OutT gain_) noexcept : gain(gain_) {}
+
+  template <typename... Args>
+  HDI OutT operator()(OutT d_val, Args... unused_args)
+  {
+    return raft::exp(-gain * d_val);
+  }
+};  // struct rbf_fin_op
+
+}  // namespace cuvs::distance::kernels::detail
diff --git a/cpp/src/distance/detail/masked_distance_base.cuh b/cpp/src/distance/detail/masked_distance_base.cuh
new file mode 100644
index 0000000000..d92052c84c
--- /dev/null
+++ b/cpp/src/distance/detail/masked_distance_base.cuh
@@ -0,0 +1,326 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+#include "../pairwise_distance_base.cuh"
+#include <raft/linalg/contractions.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <cstddef>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+
+/**
+ * @brief Device class for masked nearest neighbor computations.
+ *
+ * @tparam useNorms       whether norms are needed
+ * @tparam DataT          input data-type (for x and y matrices)
+ * @tparam AccT           accumulation data-type
+  * @tparam IdxT           index data-type
+ * @tparam Policy         struct which tunes the Contraction kernel
+ * @tparam CoreLambda     tells how to accumulate an x and y into
+                          acc. its signature:
+    template <typename AccT, typename DataT> void core_lambda(AccT& acc,
+      const DataT& x, const DataT& y)
+ * @tparam EpilogueLambda applies an elementwise function to compute final
+    values. Its signature is:
+    template <typename AccT, typename DataT> void epilogue_lambda
+    (AccT acc[][], DataT* regxn, DataT* regyn);
+ * @tparam FinalLambda the final lambda called on final distance value
+ * @tparam rowEpilogueLambda epilog lambda that executes when a full row has
+ * been processed.
+ *
+ * @param[in] x input matrix
+ * @param[in] y input matrix
+ * @param[in] m number of rows of x
+ * @param[in] n number of columns of y
+ * @param[in] k number of cols of x and y
+ * @param[in] lda leading dimension of x
+ * @param[in] ldb leading dimension of y
+ * @param[in] ldd parameter to keep Contractions_NT happy..
+ * @param[in] xn row norms of input matrix A. Required for expanded L2, cosine
+ * @param[in] yn row norms of input matrix B. Required for expanded L2, cosine
+ * @param[in]  adj           An adjacency matrix encoded as a bitfield indicating for each
+ *                           row of `x` and each group in `y` whether to compute the
+ *                           distance. Dim = `(m / 64) x num_groups`.
+ * @param[in]  group_idxs    An array containing the *end* indices of each group
+ *                           in `y`. The value of group_idxs[j] indicates the
+ *                           start of group j + 1, i.e., it is the inclusive
+ *                           scan of the group lengths. The first group is
+ *                           always assumed to start at index 0 and the last
+ *                           group typically ends at index `n`. Length =
+ *                           `num_groups`.
+ * @param[in] num_groups     The number of groups in group_idxs.
+ * @param[in] smem shared mem buffer for intermediate storage of x, y, xn & yn.
+ * @param core_op the core accumulation operation lambda
+ * @param epilog_op the epilog operation lambda
+ * @param fin_op the final gemm epilogue lambda
+ * @param rowEpilog_op epilog lambda that executes when a full row has been processed.
+ */
+template <bool useNorms,
+          typename DataT,
+          typename AccT,
+          typename IdxT,
+          typename Policy,
+          typename CoreLambda,
+          typename EpilogueLambda,
+          typename FinalLambda,
+          typename rowEpilogueLambda,
+          bool isRowMajor    = true,
+          typename BaseClass = raft::linalg::Contractions_NT<DataT, IdxT, Policy, isRowMajor>>
+struct MaskedDistances : public BaseClass {
+ private:
+  typedef Policy P;
+  const DataT* xn;
+  const DataT* yn;
+  const DataT* const yBase;
+  const uint64_t* adj;
+  const IdxT* group_idxs;
+  IdxT num_groups;
+  char* smem;
+  CoreLambda core_op;
+  EpilogueLambda epilog_op;
+  FinalLambda fin_op;
+  rowEpilogueLambda rowEpilog_op;
+
+  AccT acc[P::AccRowsPerTh][P::AccColsPerTh];
+
+ public:
+  // Constructor
+  DI MaskedDistances(const DataT* _x,
+                     const DataT* _y,
+                     IdxT _m,
+                     IdxT _n,
+                     IdxT _k,
+                     IdxT _lda,
+                     IdxT _ldb,
+                     IdxT _ldd,
+                     const DataT* _xn,
+                     const DataT* _yn,
+                     const uint64_t* _adj,
+                     const IdxT* _group_idxs,
+                     IdxT _num_groups,
+                     char* _smem,
+                     CoreLambda _core_op,
+                     EpilogueLambda _epilog_op,
+                     FinalLambda _fin_op,
+                     rowEpilogueLambda _rowEpilog_op)
+    : BaseClass(_x, _y, _m, _n, _k, _lda, _ldb, _ldd, _smem),
+      xn(_xn),
+      yn(_yn),
+      yBase(_y),
+      adj(_adj),
+      group_idxs(_group_idxs),
+      num_groups(_num_groups),
+      smem(_smem),
+      core_op(_core_op),
+      epilog_op(_epilog_op),
+      fin_op(_fin_op),
+      rowEpilog_op(_rowEpilog_op)
+  {
+  }
+
+  DI void run()
+  {
+    const auto grid_stride_m = (P::Mblk * gridDim.y);
+    const auto grid_offset_m = (P::Mblk * blockIdx.y);
+
+    const auto grid_stride_g = gridDim.x;
+    const auto grid_offset_g = blockIdx.x;
+
+    for (auto tile_idx_m = grid_offset_m; tile_idx_m < this->m; tile_idx_m += grid_stride_m) {
+      // Start loop over groups
+      for (auto idx_g = grid_offset_g; idx_g < this->num_groups; idx_g += grid_stride_g) {
+        const uint64_t block_adj = get_block_adjacency(adj, tile_idx_m, idx_g);
+        // block_adj is a bitfield that contains a 1 if a row is adjacent to the
+        // current group. All zero means we can skip this group.
+        if (block_adj == 0) { continue; }
+
+        // thread_adj is a bitfield that contains a 1 at location i iff we must
+        // compute row i of acc (the accumulator register tile). That is,
+        // for i = 0,.., AccRowsPerTh and j = 0,.., AccColsPerTh:
+        //
+        //   ((1 << i) & thread_adj) > 0 <=> acc[i][j] must be computed.
+        //
+        // We precompute this information because it is used in various
+        // locations to skip thread-local computations, specifically:
+        //
+        // 1. To skip computations if thread_adj == 0, i.e., none of the values
+        //    of `acc` have to be computed.
+        //
+        // 2. In epilog_op, to consider only values of `acc` to be reduced that
+        //    are not masked of.
+        //
+        // Note 1: Even when the computation can be skipped for a specific thread,
+        // the thread still participates in synchronization operations.
+        //
+        // Note 2: In theory, it should be possible to skip computations for
+        // specific rows of `acc`. In practice, however, this does not improve
+        // performance.
+        int thread_adj = compute_thread_adjacency(block_adj);
+
+        auto tile_idx_n        = idx_g == 0 ? 0 : group_idxs[idx_g - 1];
+        const auto group_end_n = group_idxs[idx_g];
+        for (; tile_idx_n < group_end_n; tile_idx_n += P::Nblk) {
+          // We provide group_end_n to limit the number of unnecessary data
+          // points that are loaded from y.
+          this->ldgXY(tile_idx_m, tile_idx_n, 0, group_end_n);
+
+          reset_accumulator();
+          this->stsXY();
+          __syncthreads();
+          this->switch_write_buffer();
+
+          for (int kidx = P::Kblk; kidx < this->k; kidx += P::Kblk) {
+            this->ldgXY(tile_idx_m, tile_idx_n, kidx, group_end_n);
+            // Process all data in shared memory (previous k-block) and
+            // accumulate in registers.
+            if (thread_adj != 0) { accumulate(); }
+            this->stsXY();
+            __syncthreads();
+            this->switch_write_buffer();
+            this->switch_read_buffer();
+          }
+          if (thread_adj != 0) {
+            accumulate();  // last iteration
+          }
+          // The pre-condition for the loop over tile_idx_n is that write_buffer
+          // and read_buffer point to the same buffer. This flips read_buffer
+          // back so that it satisfies the pre-condition of this loop.
+          this->switch_read_buffer();
+
+          if (useNorms) {
+            DataT regxn[P::AccRowsPerTh], regyn[P::AccColsPerTh];
+            load_norms(tile_idx_m, tile_idx_n, group_end_n, regxn, regyn);
+            if (thread_adj != 0) {
+              epilog_op(acc, thread_adj, regxn, regyn, tile_idx_n, tile_idx_m, group_end_n);
+            }
+          } else {
+            if (thread_adj != 0) {
+              epilog_op(acc, thread_adj, nullptr, nullptr, tile_idx_n, tile_idx_m, group_end_n);
+            }
+          }
+        }  // tile_idx_n
+      }    // idx_g
+      rowEpilog_op(tile_idx_m);
+    }  // tile_idx_m
+  }
+
+ private:
+  DI uint64_t get_block_adjacency(const uint64_t* adj, IdxT tile_idx_m, IdxT idx_group)
+  {
+    // A single element of `adj` contains exactly enough bits to indicate which
+    // rows in the current tile to skip and which to compute.
+    static_assert(P::Mblk == 8 * sizeof(adj[0]),
+                  "masked_l2_nn only supports a policy with 64 rows per block.");
+    IdxT block_flag_idx = tile_idx_m / P::Mblk;
+    // Index into adj at row tile_idx_m / 64 and column idx_group.
+    return adj[block_flag_idx * this->num_groups + idx_group];
+  }
+
+  DI uint32_t compute_thread_adjacency(const uint64_t block_adj)
+  {
+    // thread_adj is a bitfield that contains a 1 at location i iff we must
+    // compute row i of acc (the accumulator register tile). It is described in
+    // more detail in the run() method.
+    uint32_t thread_adj = 0;
+#pragma unroll
+    for (int thread_row_idx = 0; thread_row_idx < P::AccRowsPerTh; ++thread_row_idx) {
+      // Index `thread_row_idx` refers to a row of the current threads' register
+      // tile `acc`, i.e., acc[i][:]. Index `block_row_idx` refers to the
+      // corresponding row of the current block tile in shared memory.
+      const int block_row_idx = this->accrowid + thread_row_idx * P::AccThRows;
+
+      // block_row_is_adjacent is true if the current block_row_idx is adjacent
+      // to the current group.
+      const uint64_t block_mask        = 1ull << block_row_idx;
+      const bool block_row_is_adjacent = (block_adj & block_mask) != 0;
+      if (block_row_is_adjacent) {
+        // If block row is adjacent, write a 1 bit to thread_adj at location
+        // `thread_row_idx`.
+        const uint32_t thread_mask = 1 << thread_row_idx;
+        thread_adj |= thread_mask;
+      }
+    }
+    return thread_adj;
+  }
+
+  DI void reset_accumulator()
+  {
+    // Reset accumulator registers to zero.
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        acc[i][j] = BaseClass::Zero;
+      }
+    }
+  }
+
+  DI void accumulate()
+  {
+#pragma unroll
+    for (int ki = 0; ki < P::Kblk; ki += P::Veclen) {
+      this->ldsXY(ki);
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = 0; j < P::AccColsPerTh; ++j) {
+#pragma unroll
+          for (int v = 0; v < P::Veclen; ++v) {
+            core_op(acc[i][j], this->regx[i][v], this->regy[j][v]);
+          }
+        }
+      }
+    }
+  }
+
+  DI void load_norms(IdxT tile_idx_m,
+                     IdxT tile_idx_n,
+                     IdxT end_n,
+                     DataT (&regxn)[P::AccRowsPerTh],
+                     DataT (&regyn)[P::AccColsPerTh])
+  {
+    DataT* sxNorm = (DataT*)(&smem[P::SmemSize]);
+    DataT* syNorm = (&sxNorm[P::Mblk]);
+
+    // Load x & y norms required by this threadblock in shmem buffer
+    for (int i = threadIdx.x; i < P::Mblk; i += P::Nthreads) {
+      auto idx  = tile_idx_m + i;
+      sxNorm[i] = idx < this->m ? xn[idx] : 0;
+    }
+
+    for (int i = threadIdx.x; i < P::Nblk; i += P::Nthreads) {
+      auto idx  = tile_idx_n + i;
+      syNorm[i] = idx < end_n ? yn[idx] : 0;
+    }
+    __syncthreads();
+
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+      regxn[i] = sxNorm[i * P::AccThRows + (threadIdx.x / P::AccThCols)];
+    }
+#pragma unroll
+    for (int i = 0; i < P::AccColsPerTh; ++i) {
+      regyn[i] = syNorm[i * P::AccThCols + (threadIdx.x % P::AccThCols)];
+    }
+  }
+};  // struct MaskedDistances
+
+};  // namespace detail
+};  // namespace distance
+};  // namespace cuvs
diff --git a/cpp/src/distance/detail/masked_nn.cuh b/cpp/src/distance/detail/masked_nn.cuh
new file mode 100644
index 0000000000..6520b1e2e9
--- /dev/null
+++ b/cpp/src/distance/detail/masked_nn.cuh
@@ -0,0 +1,329 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../compress_to_bits.cuh"
+#include "../fused_distance_nn/fused_l2_nn.cuh"
+#include "../masked_distance_base.cuh"
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/linalg/contractions.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <stdint.h>
+
+#include <limits>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename P,
+          typename ReduceOpT,
+          typename KVPReduceOpT,
+          typename CoreLambda,
+          typename FinalLambda>
+__launch_bounds__(P::Nthreads, 2) RAFT_KERNEL masked_l2_nn_kernel(OutT* min,
+                                                                  const DataT* x,
+                                                                  const DataT* y,
+                                                                  const DataT* xn,
+                                                                  const DataT* yn,
+                                                                  const uint64_t* adj,
+                                                                  const IdxT* group_idxs,
+                                                                  IdxT num_groups,
+                                                                  IdxT m,
+                                                                  IdxT n,
+                                                                  IdxT k,
+                                                                  bool sqrt,
+                                                                  DataT maxVal,
+                                                                  int* mutex,
+                                                                  ReduceOpT redOp,
+                                                                  KVPReduceOpT pairRedOp,
+                                                                  CoreLambda core_op,
+                                                                  FinalLambda fin_op)
+{
+  extern __shared__ char smem[];
+
+  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
+  KVPair val[P::AccRowsPerTh];
+#pragma unroll
+  for (int i = 0; i < P::AccRowsPerTh; ++i) {
+    val[i] = {-1, maxVal};
+  }
+
+  // epilogue operation lambda for final value calculation
+  auto epilog_lambda = [pairRedOp, &val, maxVal, sqrt] __device__(
+                         DataT acc[P::AccRowsPerTh][P::AccColsPerTh],
+                         int thread_adj,
+                         DataT* regxn,
+                         DataT* regyn,
+                         IdxT tile_idx_n,
+                         IdxT tile_idx_m,
+                         IdxT tile_end_n) {
+    KVPReduceOpT pairRed_op(pairRedOp);
+
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        acc[i][j] = regxn[i] + regyn[j] - (DataT)2.0 * acc[i][j];
+      }
+    }
+    if (sqrt) {
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = 0; j < P::AccColsPerTh; ++j) {
+          acc[i][j] = raft::sqrt(acc[i][j]);
+        }
+      }
+    }
+
+    // intra thread reduce
+    const auto acccolid = threadIdx.x % P::AccThCols;
+    const auto accrowid = threadIdx.x / P::AccThCols;
+
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+      // thread_adj is a bitfield that contains a 1 at location i iff we must
+      // compute row i of acc (the accumulator register tile). It is described in
+      // more detail in the maskedDistances.run() method.
+      const bool ignore = (thread_adj & (1 << i)) == 0;
+      if (ignore) { continue; }
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        auto tmpkey = acccolid + j * P::AccThCols + tile_idx_n;
+        if (tile_end_n <= tmpkey) {
+          // Do not process beyond end of tile.
+          continue;
+        }
+        KVPair tmp = {tmpkey, acc[i][j]};
+        if (tmpkey < tile_end_n) {
+          val[i] = pairRed_op(accrowid + i * P::AccThRows + tile_idx_m, tmp, val[i]);
+        }
+      }
+    }
+  };
+
+  auto rowEpilog_lambda =
+    [m, mutex, min, pairRedOp, redOp, &val, maxVal] __device__(IdxT tile_idx_m) {
+      KVPReduceOpT pairRed_op(pairRedOp);
+      ReduceOpT red_op(redOp);
+
+      const auto accrowid = threadIdx.x / P::AccThCols;
+      const auto lid      = raft::laneId();
+    // reduce
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = P::AccThCols / 2; j > 0; j >>= 1) {
+          auto tmpkey   = raft::shfl(val[i].key, lid + j);
+          auto tmpvalue = raft::shfl(val[i].value, lid + j);
+          KVPair tmp    = {tmpkey, tmpvalue};
+          val[i]        = pairRed_op(accrowid + i * P::AccThRows + tile_idx_m, tmp, val[i]);
+        }
+      }
+
+      updateReducedVal<P, OutT, IdxT, KVPair, ReduceOpT>(mutex, min, val, red_op, m, tile_idx_m);
+
+    // reset the val array.
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+        val[i] = {-1, maxVal};
+      }
+    };
+
+  IdxT lda = k, ldb = k, ldd = n;
+  MaskedDistances<true,
+                  DataT,
+                  DataT,
+                  IdxT,
+                  P,
+                  CoreLambda,
+                  decltype(epilog_lambda),
+                  FinalLambda,
+                  decltype(rowEpilog_lambda),
+                  true>
+    obj(x,
+        y,
+        m,
+        n,
+        k,
+        lda,
+        ldb,
+        ldd,
+        xn,
+        yn,
+        adj,
+        group_idxs,
+        num_groups,
+        smem,
+        core_op,
+        epilog_lambda,
+        fin_op,
+        rowEpilog_lambda);
+  obj.run();
+}
+
+/**
+ * @brief Wrapper for masked_l2_nn_kernel
+ *
+ * Responsibilities:
+ * - Allocate (and initialize) workspace memory for:
+ *   - mutexes used in nearest neighbor update step
+ *   - adjacency matrix bitfield
+ * - Compress adjacency matrix to bitfield
+ * - Initialize output buffer (conditional on `initOutBuffer`)
+ * - Specify core and final operations for the L2 norm
+ * - Determine optimal launch configuration for kernel.
+ * - Launch kernel and check for errors.
+ *
+ * @tparam DataT         Input data-type (for x and y matrices).
+ * @tparam OutT          Output data-type (for key-value pairs).
+ * @tparam IdxT          Index data-type.
+ * @tparam ReduceOpT     A struct to perform the final needed reduction
+ *                       operation and also to initialize the output array
+ *                       elements with the appropriate initial value needed for
+ *                       reduction.
+ * @tparam KVPReduceOpT  Type of Reduction operation on key value pairs.
+ *
+ * @param      handle            RAFT handle for managing expensive resources
+ * @param[out] out               Will contain reduced output (nn key-value pairs)
+ * @param[in]  x                 First matrix. Row major. Dim = `m x k`. (on device)
+ * @param[in]  y                 Second matrix. Row major. Dim = `n x k`. (on device)
+ * @param[in]  xn                L2 squared norm of `x`. Length = `m`.
+ * @param[in]  yn                L2 squared norm of `y`. Length = `n`.
+ * @param[in]  adj           A boolean adjacency matrix indicating for each
+ *                           row of `x` and each group in `y` whether to compute the
+ *                           distance. Dim = `m x num_groups`.
+ * @param[in]  group_idxs    An array containing the *end* indices of each group
+ *                           in `y`. The value of group_idxs[j] indicates the
+ *                           start of group j + 1, i.e., it is the inclusive
+ *                           scan of the group lengths. The first group is
+ *                           always assumed to start at index 0 and the last
+ *                           group typically ends at index `n`. Length =
+ *                           `num_groups`.
+ * @param[in]  num_groups    Length of `group_idxs`.
+ * @param      m             Rows of `x`.
+ * @param      n             Rows of `y`.
+ * @param      k             Cols of `x` and `y`.
+ * @param      redOp         Reduction operator in the epilogue
+ * @param      pairRedOp     Reduction operation on key value pairs
+ * @param      sqrt          Whether to compute the squared or actual (i.e. sqrt) L2 norm.
+ * @param      initOutBuffer Whether to initialize the output buffer
+ *
+ *
+ */
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT, typename KVPReduceOpT>
+void masked_l2_nn_impl(raft::resources const& handle,
+                       OutT* out,
+                       const DataT* x,
+                       const DataT* y,
+                       const DataT* xn,
+                       const DataT* yn,
+                       const bool* adj,
+                       const IdxT* group_idxs,
+                       IdxT num_groups,
+                       IdxT m,
+                       IdxT n,
+                       IdxT k,
+                       ReduceOpT redOp,
+                       KVPReduceOpT pairRedOp,
+                       bool sqrt,
+                       bool initOutBuffer)
+{
+  typedef typename linalg::Policy4x4<DataT, 1>::Policy P;
+
+  static_assert(P::Mblk == 64, "masked_l2_nn_impl only supports a policy with 64 rows per block.");
+
+  // Get stream and workspace memory resource
+  rmm::mr::device_memory_resource* ws_mr =
+    dynamic_cast<rmm::mr::device_memory_resource*>(resource::get_workspace_resource(handle));
+  auto stream = resource::get_cuda_stream(handle);
+
+  // Acquire temporary buffers and initialize to zero:
+  // 1) Adjacency matrix bitfield
+  // 2) Workspace for fused nearest neighbor operation
+  size_t m_div_64 = raft::ceildiv(m, IdxT(64));
+  rmm::device_uvector<uint64_t> ws_adj64{m_div_64 * num_groups, stream, ws_mr};
+  rmm::device_uvector<int> ws_fused_nn{size_t(m), stream, ws_mr};
+  RAFT_CUDA_TRY(cudaMemsetAsync(ws_adj64.data(), 0, ws_adj64.size() * sizeof(uint64_t), stream));
+  RAFT_CUDA_TRY(cudaMemsetAsync(ws_fused_nn.data(), 0, ws_fused_nn.size() * sizeof(int), stream));
+
+  // Compress boolean adjacency matrix to bitfield.
+  auto adj_view = raft::make_device_matrix_view<const bool, int>(adj, m, num_groups);
+  auto adj64_view =
+    raft::make_device_matrix_view<uint64_t, int>(ws_adj64.data(), m_div_64, num_groups);
+  compress_to_bits(handle, adj_view, adj64_view);
+
+  // Initialize output buffer with keyvalue pairs as determined by the reduction
+  // operator (it will be called with maxVal).
+  constexpr auto maxVal = std::numeric_limits<DataT>::max();
+  if (initOutBuffer) {
+    dim3 grid(raft::ceildiv<int>(m, P::Nthreads));
+    dim3 block(P::Nthreads);
+
+    initKernel<DataT, OutT, IdxT, ReduceOpT><<<grid, block, 0, stream>>>(out, m, maxVal, redOp);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+
+  // Accumulation operation lambda
+  auto core_lambda = [] __device__(DataT & acc, DataT & x, DataT & y) { acc += x * y; };
+  auto fin_op      = raft::identity_op{};
+
+  auto kernel               = masked_l2_nn_kernel<DataT,
+                                    OutT,
+                                    IdxT,
+                                    P,
+                                    ReduceOpT,
+                                    KVPReduceOpT,
+                                    decltype(core_lambda),
+                                    decltype(fin_op)>;
+  constexpr size_t smemSize = P::SmemSize + ((P::Mblk + P::Nblk) * sizeof(DataT));
+  dim3 block(P::Nthreads);
+  dim3 grid = launchConfigGenerator<P>(m, n, smemSize, kernel);
+
+  kernel<<<grid, block, smemSize, stream>>>(out,
+                                            x,
+                                            y,
+                                            xn,
+                                            yn,
+                                            ws_adj64.data(),
+                                            group_idxs,
+                                            num_groups,
+                                            m,
+                                            n,
+                                            k,
+                                            sqrt,
+                                            maxVal,
+                                            ws_fused_nn.data(),
+                                            redOp,
+                                            pairRedOp,
+                                            core_lambda,
+                                            fin_op);
+
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/pairwise_distance_base.cuh b/cpp/src/distance/detail/pairwise_distance_base.cuh
new file mode 100644
index 0000000000..990f845fd4
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_distance_base.cuh
@@ -0,0 +1,326 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+#include <raft/linalg/contractions.cuh>       // raft::linalg::Contractions_NT
+#include <raft/util/cuda_dev_essentials.cuh>  // ceildiv
+#include <raft/util/cuda_rt_essentials.hpp>   // RAFT_CUDA_TRY
+
+#include <cstddef>  // size_t
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+
+/**
+ * @brief Device class for L1, L2 and cosine distance metrics.
+ * @tparam DataT          input data-type (for A and B matrices)
+ * @tparam AccT           accumulation data-type
+ * @tparam OutT           output data-type (for C and D matrices)
+ * @tparam IdxT           index data-type
+ * @tparam Policy         struct which tunes the Contraction kernel
+ * @tparam OpT            A distance operation, e.g., cosine_distance_op.
+ * @tparam EpilogueLambda applies an elementwise function to compute final
+    values. Its signature is:
+    template <typename AccT, typename DataT> void epilogue_lambda
+    (AccT acc[][], DataT* regxn, DataT* regyn);
+ * @tparam FinalLambda the final lambda called on final distance value
+ * @param[in] x input matrix
+ * @param[in] y input matrix
+ * @param[in] m number of rows of A and C/D
+ * @param[in] n number of columns of B and C/D
+ * @param[in] k number of cols of A and rows of B
+ * @param[in] lda leading dimension of A
+ * @param[in] ldb leading dimension of B
+ * @param[in] ldd leading dimension of C/D
+ * @param[in] xn row norms of input matrix A. Required for expanded L2, cosine
+ * @param[in] yn row norms of input matrix B. Required for expanded L2, cosine
+ * @param[output] pD output matrix
+ * @param[in] smem shared mem buffer for intermediate storage of A, B, xn & yn.
+ * @param distance_op the distance operation, e.g. cosine_distance_op
+ * @param epilog_op the epilog operation lambda
+ * @param fin_op the final gemm epilogue lambda
+ * @param rowEpilog_op epilog lambda that executes when a full row has been processed
+ */
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename Policy,
+          typename OpT,
+          typename EpilogueLambda,
+          typename FinalLambda,
+          typename rowEpilogueLambda,
+          bool isRowMajor    = true,
+          bool writeOut      = true,
+          typename BaseClass = raft::linalg::Contractions_NT<DataT, IdxT, Policy, isRowMajor>>
+struct PairwiseDistances : public BaseClass {
+  // Get accumulation type from distance_op
+  using AccT = typename OpT::AccT;
+
+ private:
+  typedef Policy P;
+  const DataT* xn;
+  const DataT* yn;
+  const DataT* const yBase;
+  OutT* dOutput;
+  char* smem;
+  OpT distance_op;
+  EpilogueLambda epilog_op;
+  FinalLambda fin_op;
+  rowEpilogueLambda rowEpilog_op;
+
+  const IdxT grid_stride_m;
+  const IdxT grid_stride_n;
+  const IdxT grid_offset_m;
+  const IdxT grid_offset_n;
+
+  AccT acc[P::AccRowsPerTh][P::AccColsPerTh];
+
+ public:
+  // Constructor
+  DI PairwiseDistances(const DataT* _x,
+                       const DataT* _y,
+                       IdxT _m,
+                       IdxT _n,
+                       IdxT _k,
+                       IdxT _lda,
+                       IdxT _ldb,
+                       IdxT _ldd,
+                       const DataT* _xn,
+                       const DataT* _yn,
+                       OutT* _dOutput,
+                       char* _smem,
+                       OpT _distance_op,
+                       EpilogueLambda _epilog_op,
+                       FinalLambda _fin_op,
+                       rowEpilogueLambda _rowEpilog_op)
+    : BaseClass(_x, _y, _m, _n, _k, _lda, _ldb, _ldd, _smem),
+      xn(_xn),
+      yn(_yn),
+      yBase(_y),
+      dOutput(_dOutput),
+      smem(_smem),
+      distance_op(_distance_op),
+      epilog_op(_epilog_op),
+      fin_op(_fin_op),
+      rowEpilog_op(_rowEpilog_op),
+      grid_stride_m(P::Mblk * gridDim.y),
+      grid_stride_n(P::Nblk * gridDim.x),
+      grid_offset_m(P::Mblk * blockIdx.y),
+      grid_offset_n(P::Nblk * blockIdx.x)
+  {
+  }
+
+  DI void run()
+  {
+    for (auto tile_idx_m = grid_offset_m; tile_idx_m < this->m; tile_idx_m += grid_stride_m) {
+      this->ldgXY(tile_idx_m, grid_offset_n, 0);
+      for (auto tile_idx_n = grid_offset_n; tile_idx_n < this->n; tile_idx_n += grid_stride_n) {
+        // Prolog:
+        reset_accumulator();
+        this->stsXY();
+        __syncthreads();
+        this->switch_write_buffer();
+
+        // Main loop:
+        for (int kidx = P::Kblk; kidx < this->k; kidx += P::Kblk) {
+          this->ldgXY(tile_idx_m, tile_idx_n, kidx);
+          // Process all data in shared memory (previous k-block) and
+          // accumulate in registers.
+          accumulate();
+          this->stsXY();
+          __syncthreads();
+          this->switch_write_buffer();
+          this->switch_read_buffer();
+        }
+        accumulate();  // last iteration
+        // The pre-condition for the loop over tile_idx_n is that write_buffer
+        // and read_buffer point to the same buffer. This flips read_buffer back
+        // so that it satisfies the pre-condition of this loop.
+        this->switch_read_buffer();
+
+        // Epilog:
+        if (distance_op.use_norms) {
+          DataT regxn[P::AccRowsPerTh], regyn[P::AccColsPerTh];
+          load_norms(tile_idx_m, tile_idx_n, regxn, regyn);
+          // Overlap ldg with epilog computation
+          ldgNextGridStride(tile_idx_m, tile_idx_n);
+          // Calculate distance_op epilog.
+          // Use .template to disambiguate (See:
+          // https://en.cppreference.com/w/cpp/language/dependent_name)
+          distance_op.template epilog<Policy>(acc, regxn, regyn, tile_idx_n, tile_idx_m);
+          // And any possible additional epilogs
+          epilog_op(acc, regxn, regyn, tile_idx_n, tile_idx_m);
+        } else {
+          // Overlap ldg with epilog computation
+          ldgNextGridStride(tile_idx_m, tile_idx_n);
+          // Calculate distance_op epilog.
+          // Use .template to disambiguate (See:
+          // https://en.cppreference.com/w/cpp/language/dependent_name)
+          distance_op.template epilog<Policy>(acc, nullptr, nullptr, tile_idx_n, tile_idx_m);
+          // And any possible additional epilogs
+          epilog_op(acc, nullptr, nullptr, tile_idx_n, tile_idx_m);
+        }
+        if (writeOut) { store_output(tile_idx_m, tile_idx_n); }
+      }
+      rowEpilog_op(tile_idx_m);
+    }
+  }
+
+ private:
+  DI void ldgNextGridStride(IdxT tile_idx_m, IdxT tile_idx_n)
+  {
+    // Fetch next grid stride ldg if within range
+    const auto next_tile_tile_idx_n = tile_idx_n + grid_stride_n;
+    const auto next_tile_tile_idx_m = tile_idx_m + grid_stride_m;
+    if ((next_tile_tile_idx_n) < this->n) {
+      this->ldgXY(tile_idx_m, next_tile_tile_idx_n, 0);
+    } else if ((next_tile_tile_idx_m) < this->m) {
+      this->ldgXY(next_tile_tile_idx_m, grid_offset_n, 0);
+    }
+  }
+
+  DI void reset_accumulator()
+  {
+    // Reset accumulator registers to zero.
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        acc[i][j] = BaseClass::Zero;
+      }
+    }
+  }
+
+  DI void accumulate_reg_tile(DataT (&reg_x)[P::AccRowsPerTh][P::Veclen],
+                              DataT (&reg_y)[P::AccColsPerTh][P::Veclen])
+  {
+#pragma unroll
+    for (int v = 0; v < P::Veclen; ++v) {
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = 0; j < P::AccColsPerTh; ++j) {
+          distance_op.core(acc[i][j], reg_x[i][v], reg_y[j][v]);
+        }
+      }
+    }
+  }
+
+  DI void accumulate()
+  {
+    // We have a separate ldsXY and accumulate_reg_tile outside the loop body,
+    // so that these separated calls can be interspersed with preceding and
+    // following instructions, thereby hiding latency.
+    this->ldsXY(0);
+
+    // If expensive inner loop, do not unroll loop.
+    constexpr int num_iterations = P::Kblk / P::Veclen - 1;
+    constexpr int unroll_count   = decltype(distance_op)::expensive_inner_loop ? 1 : num_iterations;
+#pragma unroll unroll_count
+    for (int ki = P::Veclen; ki < P::Kblk; ki += P::Veclen) {
+      accumulate_reg_tile(this->regx, this->regy);
+      this->ldsXY(ki);
+    }
+
+    // Accumulate last loaded tile.
+    accumulate_reg_tile(this->regx, this->regy);
+  }
+
+  DI void load_norms(IdxT tile_idx_m,
+                     IdxT tile_idx_n,
+                     DataT (&regxn)[P::AccRowsPerTh],
+                     DataT (&regyn)[P::AccColsPerTh])
+  {
+    DataT* sxNorm = (DataT*)(&smem[P::SmemSize]);
+    DataT* syNorm = (&sxNorm[P::Mblk]);
+
+    // Load x & y norms required by this threadblock in shmem buffer
+    if (tile_idx_n == blockIdx.x * P::Nblk) {
+      for (int i = threadIdx.x; i < P::Mblk; i += P::Nthreads) {
+        auto idx  = tile_idx_m + i;
+        sxNorm[i] = idx < this->m ? xn[idx] : 0;
+      }
+    }
+
+    for (int i = threadIdx.x; i < P::Nblk; i += P::Nthreads) {
+      auto idx  = tile_idx_n + i;
+      syNorm[i] = idx < this->n ? yn[idx] : 0;
+    }
+    __syncthreads();
+
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+      regxn[i] = sxNorm[i * P::AccThRows + (threadIdx.x / P::AccThCols)];
+    }
+#pragma unroll
+    for (int i = 0; i < P::AccColsPerTh; ++i) {
+      regyn[i] = syNorm[i * P::AccThCols + (threadIdx.x % P::AccThCols)];
+    }
+  }
+
+  DI void store_output(IdxT tile_idx_m, IdxT tile_idx_n)
+  {
+    IdxT starty = tile_idx_m + this->accrowid;
+    IdxT startx = tile_idx_n + this->acccolid;
+
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+      auto rowId = starty + i * P::AccThRows;
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        auto colId = startx + j * P::AccThCols;
+        if (rowId < this->m && colId < this->n) {
+          // Promote to 64 bit index for final write, as output array can be > 2^31
+          dOutput[std::size_t(rowId) * this->n + colId] = fin_op(acc[i][j], 0);
+        }
+      }
+    }
+  }
+};  // struct PairwiseDistances
+
+template <typename P, typename IdxT, typename T>
+dim3 launchConfigGenerator(IdxT m, IdxT n, std::size_t sMemSize, T func)
+{
+  int devId;
+  RAFT_CUDA_TRY(cudaGetDevice(&devId));
+  int numSMs;
+  RAFT_CUDA_TRY(cudaDeviceGetAttribute(&numSMs, cudaDevAttrMultiProcessorCount, devId));
+
+  int numBlocksPerSm = 0;
+  dim3 grid;
+
+  RAFT_CUDA_TRY(
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(&numBlocksPerSm, func, P::Nthreads, sMemSize));
+  std::size_t minGridSize = numSMs * numBlocksPerSm;
+  std::size_t yChunks     = raft::ceildiv<int>(m, P::Mblk);
+  std::size_t xChunks     = raft::ceildiv<int>(n, P::Nblk);
+  grid.y                  = yChunks > minGridSize ? minGridSize : yChunks;
+  grid.x                  = (minGridSize - grid.y) <= 0 ? 1 : xChunks;
+  if (grid.x != 1) {
+    std::size_t i = 1;
+    while (grid.y * i < minGridSize) {
+      i++;
+    }
+    grid.x = i >= xChunks ? xChunks : i;
+  }
+
+  return grid;
+}
+
+};  // namespace detail
+};  // namespace distance
+};  // namespace cuvs
diff --git a/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh b/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
new file mode 100644
index 0000000000..da9a1ac4eb
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
@@ -0,0 +1,185 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wstrict-aliasing"
+#pragma GCC diagnostic ignored "-Wtautological-compare"
+
+// We define CUTLASS_NAMESPACE in case
+// RAFT cmake is not used
+#ifndef CUTLASS_NAMESPACE
+#define cutlass raft_cutlass
+#endif
+
+#include "pairwise_distance_epilogue_elementwise.h"
+#include "pairwise_distance_gemm.h"
+
+#include "distance_ops/cutlass.cuh"
+#include <raft/util/cutlass_utils.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cutlass/cutlass.h>
+#include <cutlass/gemm/device/gemm.h>
+#include <cutlass/gemm/device/gemm_universal_adapter.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/layout/tensor.h>
+#include <cutlass/matrix_coord.h>
+#include <cutlass/tensor_view.h>
+
+#include <type_traits>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+
+template <typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT,
+          int VecLen,
+          typename FinalLambda,
+          typename OpT,
+          bool isRowMajor>
+std::enable_if_t<ops::has_cutlass_op<OpT>::value> cutlassDistanceKernel(const DataT* x,
+                                                                        const DataT* y,
+                                                                        const DataT* xn,
+                                                                        const DataT* yn,
+                                                                        IdxT m,
+                                                                        IdxT n,
+                                                                        IdxT k,
+                                                                        IdxT lda,
+                                                                        IdxT ldb,
+                                                                        IdxT ldd,
+                                                                        OutT* dOutput,
+                                                                        FinalLambda fin_op,
+                                                                        OpT distance_op,
+                                                                        cudaStream_t stream)
+{
+  static_assert(!(std::is_same<OutT, bool>::value),
+                "OutType bool is not supported use uint8_t instead");
+
+  auto dist_op     = distance_op.get_cutlass_op();
+  using DistanceFn = decltype(dist_op);
+  using EpilogueOutputOp =
+    cutlass::epilogue::thread::PairwiseDistanceEpilogueElementwise<DataT,  // ElementC_
+                                                                   AccT,   // ElementAccumulator_
+                                                                   DataT,  // ElementCompute_
+                                                                   AccT,   // ElementZ_
+                                                                   OutT,   // ElementT_
+                                                                   1,      // Elements per access 1
+                                                                   DistanceFn,
+                                                                   FinalLambda>;
+  constexpr int batch_count = 1;
+
+  constexpr auto mode = cutlass::gemm::GemmUniversalMode::kGemm;
+
+  typename EpilogueOutputOp::Params epilog_op_param(dist_op, fin_op);
+
+  // Number of pipelines you want to use
+  constexpr int NumStages = 3;
+  // Alignment
+  constexpr int Alignment = VecLen;
+
+  using cutlassDistKernel =
+    typename cutlass::gemm::kernel::PairwiseDistanceGemm<DataT,
+                                                         Alignment,
+                                                         DataT,
+                                                         Alignment,
+                                                         AccT,
+                                                         AccT,
+                                                         EpilogueOutputOp,
+                                                         NumStages,  // Number of pipeline stages
+                                                         isRowMajor>::GemmKernel;
+
+  using cutlassDist = cutlass::gemm::device::GemmUniversalAdapter<cutlassDistKernel>;
+
+  constexpr uint32_t gridYZMax      = ((1 << (sizeof(uint16_t) * 8)) - 1);
+  constexpr uint32_t max_batch_size = gridYZMax * cutlassDistKernel::ThreadblockShape::kN;
+  IdxT numNbatches                  = (n - 1 + max_batch_size) / max_batch_size;
+
+  for (IdxT i = 0; i < numNbatches; i++) {
+    const DataT *a, *b;
+    IdxT gemm_lda, gemm_ldb;
+    size_t offsetN = i * max_batch_size;
+
+    if constexpr (isRowMajor) {
+      gemm_lda = ldb;
+      gemm_ldb = lda;
+      a        = y + offsetN * gemm_lda;
+      b        = x;
+    } else {
+      gemm_lda = lda;
+      gemm_ldb = ldb;
+      a        = x;
+      b        = y + offsetN;
+    }
+    IdxT chunkN   = (i + 1) * max_batch_size;
+    IdxT currentN = (chunkN < n) ? max_batch_size : (n - offsetN);
+
+    // default initialize problem size with row major inputs
+    auto problem_size = isRowMajor ? cutlass::gemm::GemmCoord(currentN, m, k)
+                                   : cutlass::gemm::GemmCoord(m, currentN, k);
+
+    typename cutlassDist::Arguments arguments{
+      mode,
+      problem_size,
+      batch_count,
+      epilog_op_param,
+      a,
+      b,
+      xn,                    // C matrix eq vector param, which here is A norm
+      nullptr,               // tensor_Z,
+      (DataT*)yn + offsetN,  // this is broadcast vec, which is required to be non-const param
+      dOutput + offsetN,     // Output distance matrix
+      (int64_t)0,            // batch stride A
+      (int64_t)0,            // batch stride B
+      (int64_t)0,            // batch stride Norm A
+      (int64_t)0,
+      (int64_t)0,  // batch stride Norm B
+      (int64_t)0,  // batch stride Output
+      gemm_lda,    // stride A
+      gemm_ldb,    // stride B
+      1,           // stride A norm
+      0,           // this is no-op for Z
+      0,           // This must be zero
+      ldd          // stride Output matrix
+    };
+
+    // Using the arguments, query for extra workspace required for matrix multiplication computation
+    size_t workspace_size = cutlassDist::get_workspace_size(arguments);
+    // Allocate workspace memory
+    rmm::device_uvector<uint8_t> workspace(workspace_size, stream);
+    // Instantiate CUTLASS kernel depending on templates
+    cutlassDist cutlassDist_op;
+    // Check the problem size is supported or not
+    RAFT_CUTLASS_TRY(cutlassDist_op.can_implement(arguments));
+
+    // Initialize CUTLASS kernel with arguments and workspace pointer
+    RAFT_CUTLASS_TRY(cutlassDist_op.initialize(arguments, workspace.data(), stream));
+
+    // Launch initialized CUTLASS kernel
+    RAFT_CUTLASS_TRY(cutlassDist_op(stream));
+  }
+}
+
+};  // namespace detail
+};  // namespace distance
+};  // namespace cuvs
+
+#pragma GCC diagnostic pop
diff --git a/cpp/src/distance/detail/pairwise_distance_epilogue.h b/cpp/src/distance/detail/pairwise_distance_epilogue.h
new file mode 100644
index 0000000000..6ead09ed16
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_distance_epilogue.h
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*! \file
+  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
+
+This is adapted from DefaultEpilogueWithBroadcastTensorOp from CUTLASS 2.9.0
+(https://github.com/NVIDIA/cutlass/blob/master/include/cutlass/epilogue/threadblock/default_epilogue_with_broadcast.h#L75)
+
+This epilogue allows us to load norm buffers using PredicatedTileIteratorNormVec
+and EpilogueWithBroadcast used for distances L2/cosine as well as applies user-define elementwise
+operation.
+-- A norm load is provided PredicatedTileIteratorNormVec
+-- B norm load is provided by EpilogueWithBroadcast
+-- elementwise operation is provided by OutputOp
+*/
+
+#pragma once
+
+#include "./predicated_tile_iterator_normvec.h"
+
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/threadblock/default_epilogue_tensor_op.h>
+#include <cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h>
+#include <cutlass/epilogue/threadblock/epilogue.h>
+#include <cutlass/epilogue/threadblock/epilogue_with_broadcast.h>
+#include <cutlass/gemm/gemm.h>
+#include <cutlass/numeric_types.h>
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace epilogue {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Defines sensible defaults for epilogues for TensorOps.
+template <typename Shape,
+          typename WarpMmaTensorOp,
+          int PartitionsK,
+          typename ElementOutput,
+          typename ElementTensor,
+          typename ElementVector,
+          typename OutputOp,
+          typename LayoutT,
+          int ElementsPerAccess,
+          bool ScatterD = false>
+struct PairwiseDistanceEpilogue {
+  /// Use defaults related to the existing epilogue
+  using Base =
+    DefaultEpilogueTensorOp<Shape, WarpMmaTensorOp, PartitionsK, OutputOp, ElementsPerAccess>;
+
+  //
+  // Stores the result z = (y = GEMM(A, B, C), broadcast)
+  //
+  using OutputTileIterator = cutlass::epilogue::threadblock::
+    PredicatedTileIteratorNormVec<typename Base::OutputTileThreadMap, ElementOutput, LayoutT>;
+
+  //
+  // Additional tensor tile iterator - stores t = Elementwise(z)
+  //
+  using TensorTileIterator =
+    cutlass::epilogue::threadblock::PredicatedTileIterator<typename Base::OutputTileThreadMap,
+                                                           ElementTensor>;
+
+  /// Define the epilogue
+  using Epilogue = EpilogueWithBroadcast<Shape,
+                                         WarpMmaTensorOp,
+                                         PartitionsK,
+                                         OutputTileIterator,
+                                         TensorTileIterator,
+                                         ElementVector,
+                                         typename Base::AccumulatorFragmentIterator,
+                                         typename Base::WarpTileIterator,
+                                         typename Base::SharedLoadIterator,
+                                         OutputOp,
+                                         typename Base::Padding,
+                                         Base::kFragmentsPerIteration>;
+};
+
+}  // namespace threadblock
+}  // namespace epilogue
+}  // namespace cutlass
+
+////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h b/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
new file mode 100644
index 0000000000..2b2c04b9d3
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//
+/*! \file
+  \brief Functor performing distance operations used by epilogues of pairwise distance
+  * kernels.
+* This is adapted from LinearCombinationBiasElementwise from CUTLASS 2.9.0
+* customized for applying elementwise distance formula on accumulated GEMM value
+* and applying user-defined final custom operation on the distance value.
+*/
+
+#pragma once
+
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/thread/activation.h>
+#include <cutlass/functional.h>
+#include <cutlass/layout/pitch_linear.h>
+#include <cutlass/numeric_conversion.h>
+#include <cutlass/numeric_types.h>
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace epilogue {
+namespace thread {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// This base class is meant to define the concept required of the
+/// EpilogueWithBroadcast::OutputOp
+template <typename ElementC_,
+          typename ElementAccumulator_,
+          typename ElementCompute_,
+          typename ElementZ_,
+          typename ElementT_,
+          int ElementsPerAccess,
+          typename DistanceOp_,
+          typename FinalOp_>
+class PairwiseDistanceEpilogueElementwise {
+ public:
+  using ElementOutput                 = ElementC_;
+  using ElementC                      = ElementC_;
+  using ElementAccumulator            = ElementAccumulator_;
+  using ElementCompute                = ElementCompute_;
+  using ElementZ                      = ElementZ_;
+  using ElementT                      = ElementT_;
+  static int const kElementsPerAccess = ElementsPerAccess;
+  static int const kCount             = kElementsPerAccess;
+
+  using DistanceOp = DistanceOp_;
+  using FinalOp    = FinalOp_;
+
+  using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
+  using FragmentCompute     = Array<ElementCompute, kElementsPerAccess>;
+  using FragmentC           = Array<ElementOutput, kElementsPerAccess>;
+  using FragmentZ           = Array<ElementZ, kElementsPerAccess>;
+  using FragmentT           = Array<ElementT, kElementsPerAccess>;
+
+  using FragmentOutput = FragmentZ;
+
+  static bool const kIsHeavy = false;  // ElementwiseOp::kIsHeavy;
+
+  /// If true, the 'Z' tensor is stored
+  static bool const kStoreZ = false;  // We don't store anything in Z,
+
+  /// If true, the 'T' tensor is stored
+  static bool const kStoreT = true;  // this is our final output storage.
+
+  /// Host-constructable parameters structure
+  struct Params {
+    FinalOp_ final_op_;
+    DistanceOp_ dist_op_;
+
+    //
+    // Methods
+    //
+    CUTLASS_HOST_DEVICE
+    Params(DistanceOp_ dist_op, FinalOp final_op) : final_op_(final_op), dist_op_(dist_op) {}
+
+    CUTLASS_HOST_DEVICE
+    Params() {}
+  };
+
+ private:
+  //
+  // Data members
+  //
+  FinalOp_ final_op;
+  DistanceOp_ elementwise_op;
+
+ public:
+  //
+  // Methods
+  //
+
+  /// Constructor from Params
+  CUTLASS_HOST_DEVICE
+  PairwiseDistanceEpilogueElementwise(Params const& params)
+    : final_op(params.final_op_), elementwise_op(params.dist_op_)
+  {
+  }
+
+  /// Returns true if source is needed
+  CUTLASS_HOST_DEVICE
+  bool is_source_needed() const
+  {
+    // we use for making sure C matrix path is used for A mat norm.
+    return true;
+  }
+
+  /// Functionally required for serial reduction in the epilogue
+  CUTLASS_HOST_DEVICE
+  void set_k_partition(int k_partition, int k_partition_count) {}
+
+  /// Applies the operation when is_source_needed() is true
+  CUTLASS_HOST_DEVICE
+  void operator()(FragmentZ& frag_Z,
+                  FragmentT& frag_T,
+                  FragmentAccumulator const& AB,
+                  FragmentC const& frag_C,
+                  FragmentCompute const& V) const
+  {
+    FragmentCompute tmp_Accum =
+      NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
+    FragmentCompute tmp_C =
+      NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(frag_C);
+    FragmentCompute result_Z;
+    FragmentCompute result_T;
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int i = 0; i < kElementsPerAccess; ++i) {
+      result_Z[i] = elementwise_op(tmp_C[i], V[i], tmp_Accum[i]);
+      result_T[i] = final_op(result_Z[i], 0);
+    }
+
+    NumericArrayConverter<ElementT, ElementCompute, kElementsPerAccess> convert_t;
+    frag_T = convert_t(result_T);
+  }
+
+  /// Applies the operation when is_source_needed() is false
+  CUTLASS_HOST_DEVICE
+  void operator()(FragmentZ& frag_Z,
+                  FragmentT& frag_T,
+                  FragmentAccumulator const& AB,
+                  FragmentCompute const& V) const
+  {
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace thread
+}  // namespace epilogue
+}  // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/pairwise_distance_gemm.h b/cpp/src/distance/detail/pairwise_distance_gemm.h
new file mode 100644
index 0000000000..aaf2689dab
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_distance_gemm.h
@@ -0,0 +1,238 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "./pairwise_distance_epilogue.h"
+
+#include <cutlass/cutlass.h>
+#include <cutlass/gemm/kernel/default_gemm_universal.h>
+#include <cutlass/gemm/kernel/gemm_with_fused_epilogue.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/layout/tensor.h>
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace gemm {
+namespace kernel {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+template <
+  /// Element type for A matrix operand
+  typename ElementA_,
+  /// Layout type for A matrix operand
+  int kAlignmentA,
+  /// Element type for B matrix operand
+  typename ElementB_,
+  /// Layout type for B matrix operand
+  int kAlignmentB,
+  /// Element type for C and D matrix operands
+  typename ElementC_,
+  /// Element type for internal accumulation
+  typename ElementAccumulator,
+  /// Element type for final output
+  // typename ElementOutT,
+  /// Epilogue output operator      - must satisfy concept of 'EpilogueWithBroadcastOp'
+  typename EpilogueOutputOp,
+  /// Number of stages used in the pipelined mainloop
+  int Stages,
+  /// data layout row/column major of inputs
+  bool isRowMajor>
+struct PairwiseDistanceGemm {
+  // This struct is specialized for fp32/3xTF32
+
+  /// Threadblock-level tile size (concept: GemmShape)
+  using ThreadblockShape =
+    cutlass::gemm::GemmShape<128, 128, 16>;  // <- threadblock tile M = 128, N = 128, K = 16
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes tile size a warp will compute
+  using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;  // <- warp tile M = 64, N = 64, K = 16
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes the size of MMA op
+  using InstructionShape =
+    cutlass::gemm::GemmShape<16, 8, 4>;  // <- MMA Op tile M = 16, N = 8, K = 4
+
+  /// Operation performed by GEMM
+  using Operator = cutlass::arch::OpMultiplyAddFastF32;
+
+  // This code section describes whether you want to use tensor cores or regular SIMT cores on GPU
+  // SM
+  using OperatorClass = cutlass::arch::OpClassTensorOp;
+
+  // This code section describes CUDA SM architecture number
+  using ArchTag = cutlass::arch::Sm80;
+
+  // This code section describes how threadblocks are scheduled on GPU
+  /// Threadblock-level swizzling operator
+  using ThreadblockSwizzle = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>;
+
+  /// data layout for final output matrix.
+  // we keep this same layout even for column major inputs
+  using LayoutOutput = cutlass::layout::RowMajor;
+
+  typedef typename std::conditional<isRowMajor,
+                                    cutlass::layout::RowMajor,
+                                    cutlass::layout::ColumnMajor>::type NormXLayout;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::RowMajor, cutlass::layout::ColumnMajor>::type LayoutA_;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
+
+  using GemmBase = typename DefaultGemmUniversal<ElementA_,
+                                                 LayoutA_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 kAlignmentA,
+                                                 ElementB_,
+                                                 LayoutB_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 kAlignmentB,
+                                                 ElementC_,
+                                                 LayoutOutput,
+                                                 ElementAccumulator,
+                                                 OperatorClass,
+                                                 ArchTag,
+                                                 ThreadblockShape,
+                                                 WarpShape,
+                                                 InstructionShape,
+                                                 EpilogueOutputOp,
+                                                 ThreadblockSwizzle,
+                                                 Stages,
+                                                 Operator>::GemmKernel;
+
+  // Replace epilogue
+  using Epilogue = typename cutlass::epilogue::threadblock::PairwiseDistanceEpilogue<
+    typename GemmBase::Epilogue::Shape,
+    typename GemmBase::Epilogue::WarpMmaOperator,
+    GemmBase::Epilogue::kPartitionsK,
+    ElementAccumulator,
+    typename EpilogueOutputOp::ElementT,
+    ElementAccumulator,
+    EpilogueOutputOp,
+    NormXLayout,
+    GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
+
+  // Compose the GEMM kernel
+  using GemmKernel = GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
+};
+
+template <
+  /// Layout type for A matrix operand
+  int kAlignmentA,
+  /// Layout type for B matrix operand
+  int kAlignmentB,
+  /// Element type for C and D matrix operands
+  typename ElementC_,
+  /// Element type for internal accumulation
+  typename ElementAccumulator,
+  /// Epilogue output operator      - must satisfy concept of 'EpilogueWithBroadcastOp'
+  typename EpilogueOutputOp,
+  /// Number of stages used in the pipelined mainloop
+  int Stages,
+  /// data layout row/column major of inputs
+  bool isRowMajor>
+struct PairwiseDistanceGemm<double,
+                            kAlignmentA,
+                            double,
+                            kAlignmentB,
+                            ElementC_,
+                            ElementAccumulator,
+                            EpilogueOutputOp,
+                            Stages,
+                            isRowMajor> {
+  // using Transform = cutlass::ComplexTransform::kNone;
+  // Threadblock-level tile size (concept: GemmShape)
+  using ThreadblockShape =
+    cutlass::gemm::GemmShape<64, 64, 16>;  // <- threadblock tile M = 64, N = 64, K = 16
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes tile size a warp will compute
+  using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;  // <- warp tile M = 32, N = 32, K = 16
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes the size of MMA op
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
+
+  // Operation performed by GEMM
+  using Operator = cutlass::arch::OpMultiplyAdd;
+  // This code section describes whether you want to use tensor cores or regular SIMT cores on GPU
+  // SM
+  using OperatorClass = cutlass::arch::OpClassTensorOp;
+
+  // This code section describes CUDA SM architecture number
+  using ArchTag = cutlass::arch::Sm80;
+
+  // This code section describes how threadblocks are scheduled on GPU
+  /// Threadblock-level swizzling operator
+  using ThreadblockSwizzle = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>;
+
+  /// data layout for final output matrix.
+  // we keep this same layout even for column major inputs
+  using LayoutOutput = cutlass::layout::RowMajor;
+
+  typedef typename std::conditional<isRowMajor,
+                                    cutlass::layout::RowMajor,
+                                    cutlass::layout::ColumnMajor>::type NormXLayout;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::RowMajor, cutlass::layout::ColumnMajor>::type LayoutA_;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
+
+  using GemmBase = typename DefaultGemmUniversal<double,
+                                                 LayoutA_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 1,
+                                                 double,
+                                                 LayoutB_,
+                                                 cutlass::ComplexTransform::kNone,
+                                                 1,
+                                                 ElementC_,
+                                                 LayoutOutput,
+                                                 ElementAccumulator,
+                                                 OperatorClass,
+                                                 ArchTag,
+                                                 ThreadblockShape,
+                                                 WarpShape,
+                                                 InstructionShape,
+                                                 EpilogueOutputOp,
+                                                 ThreadblockSwizzle,
+                                                 Stages,
+                                                 Operator>::GemmKernel;
+
+  // Replace epilogue
+  using Epilogue = typename cutlass::epilogue::threadblock::PairwiseDistanceEpilogue<
+    typename GemmBase::Epilogue::Shape,
+    typename GemmBase::Epilogue::WarpMmaOperator,
+    GemmBase::Epilogue::kPartitionsK,
+    ElementC_,
+    typename EpilogueOutputOp::ElementT,
+    ElementC_,
+    EpilogueOutputOp,
+    NormXLayout,
+    GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
+
+  // Compose the GEMM kernel
+  using GemmKernel = GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace kernel
+}  // namespace gemm
+}  // namespace cutlass
\ No newline at end of file
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
new file mode 100644
index 0000000000..3e8402f1f9
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
@@ -0,0 +1,194 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../distance_ops/all_ops.cuh"    // ops::*
+#include "../distance_ops/cutlass.cuh"    // ops::has_cutlass_op
+#include "../kernels/rbf_fin_op.cuh"      // rbf_fin_op
+#include "../pairwise_matrix/params.cuh"  // pairwise_matrix_params
+#include <raft/core/operators.hpp>        // raft::identity_op
+#include <raft/util/raft_explicit.hpp>    // RAFT_EXPLICIT
+
+#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+namespace cuvs::distance::detail {
+
+template <typename OpT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename FinOpT,
+          typename IdxT = int>
+void pairwise_matrix_dispatch(OpT distance_op,
+                              IdxT m,
+                              IdxT n,
+                              IdxT k,
+                              const DataT* x,
+                              const DataT* y,
+                              const DataT* x_norm,
+                              const DataT* y_norm,
+                              OutT* out,
+                              FinOpT fin_op,
+                              cudaStream_t stream,
+                              bool is_row_major) RAFT_EXPLICIT;
+
+};  // namespace cuvs::distance::detail
+
+#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  extern template void cuvs::distance::detail::                                        \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+/*
+ * Hierarchy of instantiations:
+ *
+ * This file defines extern template instantiations of the distance kernels. The
+ * instantiation of the public API is handled in raft/distance/distance-ext.cuh.
+ *
+ * After adding an instance here, make sure to also add the instance there.
+ */
+
+// The following two instances are used in the RBF kernel object. Note the use of int64_t for the
+// index type.
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op,
+  float,
+  float,
+  float,
+  cuvs::distance::kernels::detail::rbf_fin_op<float>,
+  int64_t);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op,
+  double,
+  double,
+  double,
+  cuvs::distance::kernels::detail::rbf_fin_op<double>,
+  int64_t);
+
+// Rest of instances
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::canberra_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::canberra_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::correlation_distance_op,
+  float,
+  float,
+  float,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::correlation_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::cosine_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::cosine_distance_op, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hamming_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hamming_distance_op, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hellinger_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hellinger_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::jensen_shannon_distance_op,
+  float,
+  float,
+  float,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::jensen_shannon_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::kl_divergence_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::kl_divergence_op, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l1_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l1_distance_op, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_exp_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_exp_distance_op, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l_inf_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l_inf_distance_op, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::lp_unexp_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::lp_unexp_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::russel_rao_distance_op, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::russel_rao_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
new file mode 100644
index 0000000000..e64e9e5d7f
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+/* This file has two responsibilities:
+ *
+ * 1. Dispatch to the correct implementation of a kernel based on the
+ *    architecture of the device on which the kernel will be launched. For
+ *    instance, the cosine distance has a CUTLASS-based implementation that can
+ *    be used on SM80+ and the normal implementation that is used on older
+ *    architectures.
+ *
+ * 2. Provide concise function templates that can be instantiated in
+ *    src/distance/detail/pairwise_matrix/. Previously,
+ *    cuvs::distance::detail::distance was instantiated. The function
+ *    necessarily required a large set of include files, which slowed down the
+ *    build. The cuvs::distance::detail::pairwise_matrix_arch_dispatch functions
+ *    do not require as large an include files set, which speeds up the build.
+ */
+
+#include "../distance_ops/cutlass.cuh"           // ops::has_cutlass_op
+#include "../pairwise_matrix/dispatch_sm60.cuh"  // dispatch_sm60
+#include "../pairwise_matrix/params.cuh"         // pairwise_matrix_params
+#include <raft/util/arch.cuh>                    // raft::util::arch::SM_*
+
+// NOTE: to minimize compile times, we do not include dispatch_sm80.cuh.
+// Including dispatch_sm80.cuh can slow down compile times (due to CUTLASS).
+// Therefore, it is the including file's responsibility to include the correct
+// dispatch_smXX.cuh headers, as is done in raft/distance/detail/distance.cuh
+// and src/distance/detail/pairwise_matrix/dispatch_*.cu.
+
+namespace cuvs::distance::detail {
+
+// This forward-declaration ensures that we do not need to include
+// dispatch_sm80.cuh if we are not calling it in practice. This makes compiling
+// all the non-CUTLASS based distance instantiations faster. For CUTLASS-based
+// distances, dispatch_sm80.cuh has to be included by the file including this
+// file.
+template <typename OpT,
+          typename IdxT,
+          typename DataT,
+          typename OutT,
+          typename FinOpT,
+          typename SM_compat_t>
+void pairwise_matrix_sm80_dispatch(OpT,
+                                   pairwise_matrix_params<IdxT, DataT, OutT, FinOpT>,
+                                   SM_compat_t,
+                                   cudaStream_t);
+
+template <typename OpT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename FinOpT,
+          typename IdxT = int>
+void pairwise_matrix_dispatch(OpT distance_op,
+                              IdxT m,
+                              IdxT n,
+                              IdxT k,
+                              const DataT* x,
+                              const DataT* y,
+                              const DataT* x_norm,
+                              const DataT* y_norm,
+                              OutT* out,
+                              FinOpT fin_op,
+                              cudaStream_t stream,
+                              bool is_row_major)
+{
+  // Create kernel parameter struct. Flip x and y if column major.
+  IdxT ldx    = is_row_major ? k : m;
+  IdxT ldy    = is_row_major ? k : n;
+  IdxT ld_out = is_row_major ? n : m;
+
+  pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params{
+    m, n, k, ldx, ldy, ld_out, x, y, x_norm, y_norm, out, fin_op, is_row_major};
+
+  if (!params.is_row_major) { params.flip_x_and_y(); }
+
+  // Dispatch rule:
+  // - execute CUTLASS-based kernel on SM_80 and above
+  // - execute normal kernel below SM_80
+  namespace arch = raft::util::arch;
+
+  constexpr bool cutlass_op_unavailable = !ops::has_cutlass_op<OpT>();
+
+  if constexpr (cutlass_op_unavailable) {
+    // Always execute legacy kernels when no cutlass op is available
+    auto any_range = arch::SM_range(arch::SM_min(), arch::SM_future());
+    pairwise_matrix_sm60_dispatch(distance_op, params, any_range, stream);
+  } else {
+    auto cutlass_range = arch::SM_range(arch::SM_80(), arch::SM_future());
+    auto legacy_range  = arch::SM_range(arch::SM_min(), arch::SM_80());
+
+    // Get pointer to SM60 kernel to determine the best compute architecture
+    // out of all for which the kernel was compiled for that matches closely
+    // to the current device. Other methods to determine the architecture (that do not
+    // require a pointer) can be error prone. See:
+    // https://github.com/NVIDIA/cub/issues/545
+    auto sm60_wrapper = pairwise_matrix_sm60_get_wrapper(distance_op, params, legacy_range);
+    void* kernel_ptr  = reinterpret_cast<void*>(sm60_wrapper.kernel_ptr);
+    auto runtime_arch = arch::kernel_virtual_arch(kernel_ptr);
+
+    if (cutlass_range.contains(runtime_arch)) {
+      // If device is SM_80 or later, use CUTLASS-based kernel.
+      pairwise_matrix_sm80_dispatch(distance_op, params, cutlass_range, stream);
+    } else {
+      // Reuse kernel wrapper that we obtained above. This avoids performing the
+      // dispatch twice.
+      sm60_wrapper.launch(distance_op, params, stream);
+    }
+  }
+}
+
+};  // namespace cuvs::distance::detail
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
new file mode 100644
index 0000000000..4a52b7ebe7
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#include "dispatch-inl.cuh"
+#endif
+
+#ifdef RAFT_COMPILED
+#include "dispatch-ext.cuh"
+#endif
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
new file mode 100644
index 0000000000..accd8de9bb
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
@@ -0,0 +1,194 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# NOTE: this template is not perfectly formatted. Use pre-commit to get
+# everything in shape again.
+header = """/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include <raft/core/operators.hpp> // raft::identity_op
+#include <raft/distance/detail/distance_ops/all_ops.cuh>  // ops::*
+#include <raft/distance/detail/pairwise_matrix/dispatch-inl.cuh> // dispatch
+"""
+
+
+macro = """
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \\
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \\
+  template void cuvs::distance::detail::                                               \\
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \\
+      OpT<DataT, AccT, IdxT> distance_op,                                              \\
+      IdxT m,                                                                          \\
+      IdxT n,                                                                          \\
+      IdxT k,                                                                          \\
+      const DataT* x,                                                                  \\
+      const DataT* y,                                                                  \\
+      const DataT* x_norm,                                                             \\
+      const DataT* y_norm,                                                             \\
+      OutT* out,                                                                       \\
+      FinOpT fin_op,                                                                   \\
+      cudaStream_t stream,                                                             \\
+      bool is_row_major)
+"""
+
+data_type_instances = [
+    dict(
+        DataT="float",
+        AccT="float",
+        OutT="float",
+        IdxT="int",
+    ),
+    dict(
+        DataT="double",
+        AccT="double",
+        OutT="double",
+        IdxT="int",
+    ),
+]
+
+op_instances = [
+    dict(
+        path_prefix="canberra",
+        OpT="cuvs::distance::detail::ops::canberra_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="correlation",
+        OpT="cuvs::distance::detail::ops::correlation_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="cosine",
+        OpT="cuvs::distance::detail::ops::cosine_distance_op",
+        archs = [60, 80],
+    ),
+    dict(
+        path_prefix="hamming_unexpanded",
+        OpT="cuvs::distance::detail::ops::hamming_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="hellinger_expanded",
+        OpT="cuvs::distance::detail::ops::hellinger_distance_op",
+        archs = [60],
+    ),
+    # inner product is handled by cublas.
+    dict(
+        path_prefix="jensen_shannon",
+        OpT="cuvs::distance::detail::ops::jensen_shannon_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="kl_divergence",
+        OpT="cuvs::distance::detail::ops::kl_divergence_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="l1",
+        OpT="cuvs::distance::detail::ops::l1_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="l2_expanded",
+        OpT="cuvs::distance::detail::ops::l2_exp_distance_op",
+        archs = [60, 80],
+    ),
+    dict(
+        path_prefix="l2_unexpanded",
+        OpT="cuvs::distance::detail::ops::l2_unexp_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="l_inf",
+        OpT="cuvs::distance::detail::ops::l_inf_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="lp_unexpanded",
+        OpT="cuvs::distance::detail::ops::lp_unexp_distance_op",
+        archs = [60],
+    ),
+    dict(
+        path_prefix="russel_rao",
+        OpT="cuvs::distance::detail::ops::russel_rao_distance_op",
+        archs = [60],
+     ),
+]
+
+def arch_headers(archs):
+    include_headers ="\n".join([
+        f"#include <raft/distance/detail/pairwise_matrix/dispatch_sm{arch}.cuh>"
+        for arch in archs
+    ])
+    return include_headers
+
+
+
+for op in op_instances:
+    for dt in data_type_instances:
+        DataT, AccT, OutT, IdxT = (dt[k] for k in ["DataT", "AccT", "OutT", "IdxT"]);
+        path = f"dispatch_{op['path_prefix']}_{DataT}_{AccT}_{OutT}_{IdxT}.cu"
+        with open(path, "w") as f:
+            f.write(header)
+            f.write(arch_headers(op["archs"]))
+            f.write(macro)
+
+            OpT = op['OpT']
+            FinOpT = "raft::identity_op"
+            f.write(f"\ninstantiate_raft_distance_detail_pairwise_matrix_dispatch({OpT}, {DataT}, {AccT}, {OutT}, {FinOpT}, {IdxT});\n")
+            f.write("\n#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch\n")
+        print(f"src/distance/detail/pairwise_matrix/{path}")
+
+# Dispatch kernels for with the RBF fin op.
+with open("dispatch_rbf.cu", "w") as f:
+        OpT="cuvs::distance::detail::ops::l2_unexp_distance_op"
+        archs = [60]
+
+        f.write(header)
+        f.write("#include <raft/distance/detail/kernels/rbf_fin_op.cuh> // rbf_fin_op\n")
+        f.write(arch_headers(archs))
+        f.write(macro)
+
+        for dt in data_type_instances:
+            DataT, AccT, OutT, IdxT = (dt[k] for k in ["DataT", "AccT", "OutT", "IdxT"]);
+            IdxT = "int64_t"    # overwrite IdxT
+
+            FinOpT = f"cuvs::distance::kernels::detail::rbf_fin_op<{DataT}>"
+            f.write(f"\ninstantiate_raft_distance_detail_pairwise_matrix_dispatch({OpT}, {DataT}, {AccT}, {OutT}, {FinOpT}, {IdxT});\n")
+
+        f.write("\n#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch\n")
+
+print("src/distance/detail/pairwise_matrix/dispatch_rbf.cu")
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
new file mode 100644
index 0000000000..f82df6cc03
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::canberra_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
new file mode 100644
index 0000000000..a20ca5f472
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::canberra_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
new file mode 100644
index 0000000000..7bb7e4a96e
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::correlation_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
new file mode 100644
index 0000000000..34fcc4be42
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::correlation_distance_op,
+  float,
+  float,
+  float,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
new file mode 100644
index 0000000000..cb23743c1f
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::cosine_distance_op, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
new file mode 100644
index 0000000000..ad71ff2957
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::cosine_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
new file mode 100644
index 0000000000..e81d544112
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hamming_distance_op, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
new file mode 100644
index 0000000000..ddbdab6021
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hamming_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
new file mode 100644
index 0000000000..d2acecaf0e
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hellinger_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
new file mode 100644
index 0000000000..034d76679e
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hellinger_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
new file mode 100644
index 0000000000..030faeecd2
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::jensen_shannon_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
new file mode 100644
index 0000000000..f7551a5665
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::jensen_shannon_distance_op,
+  float,
+  float,
+  float,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
new file mode 100644
index 0000000000..6640d39492
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::kl_divergence_op, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
new file mode 100644
index 0000000000..60cafa4742
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::kl_divergence_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
new file mode 100644
index 0000000000..b5e8a2f687
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l1_distance_op, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
new file mode 100644
index 0000000000..73868a486b
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l1_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
new file mode 100644
index 0000000000..8ac80b77d2
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_exp_distance_op, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
new file mode 100644
index 0000000000..abebb91219
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_exp_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
new file mode 100644
index 0000000000..ffa6bf02ba
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
new file mode 100644
index 0000000000..acef42a4e0
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
new file mode 100644
index 0000000000..c2bbbf06b5
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l_inf_distance_op, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
new file mode 100644
index 0000000000..163b9f37ba
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l_inf_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_layout.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch_layout.cuh
new file mode 100644
index 0000000000..1f95e8e41e
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_layout.cuh
@@ -0,0 +1,117 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "params.cuh"           // pairwise_matrix_params
+#include <raft/core/error.hpp>  // RAFT_EXPECTS
+
+#include <algorithm>    // std::min
+#include <cstdint>      // size_t
+#include <type_traits>  // std::integral_constant
+namespace cuvs::distance::detail {
+
+/**
+ * @brief: Computes minimal common alignment of the rows in a 2D array in bytes
+ *
+ * The 2D matrix `x` is assumed to be row-major. This function computes the
+ * minimal alignment in bytes of the first elements of each row.
+ * Output can be 16, 8, 4, 2, 1.
+ *
+ * @param x        Base pointer of row-major input matrix
+ * @param stride   Stride in number of element between consecutive rows.
+ */
+template <typename DataT>
+size_t alignment_of_2d_array(const DataT* x, size_t stride)
+{
+  auto base           = reinterpret_cast<uintptr_t>(x);
+  size_t stride_bytes = sizeof(DataT) * stride;
+
+  for (int align = 16; align >= 0; align /= 2) {
+    bool base_aligned   = base % align == 0;
+    bool stride_aligned = stride_bytes % align == 0;
+    if (base_aligned && stride_aligned) { return align; }
+  }
+  return 1;
+}
+
+/**
+ * @brief: Computes the vec_len parameter kernel policy parameter
+ *
+ * @param params  Kernel parameters
+ */
+template <typename IdxT, typename DataT, typename OutT, typename FinOpT>
+int determine_vec_len(pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params)
+{
+  size_t align_x        = alignment_of_2d_array(params.x, params.ldx);
+  size_t align_y        = alignment_of_2d_array(params.y, params.ldy);
+  size_t byte_alignment = min(align_x, align_y);
+
+  // Since alignment is in bytes, it could be smaller than sizeof(DataT).
+  // Handle this (unlikely) case here.
+  RAFT_EXPECTS(sizeof(DataT) <= byte_alignment,
+               "Input matrix must be aligned to size of elements.");
+
+  // Compute number of elements that can be loaded in one instruction
+  // without causing misalignent errors.
+  int vec_len_aligned = (byte_alignment % sizeof(DataT) == 0) ? byte_alignment / sizeof(DataT) : 1;
+
+  // In the future, pairwise_matrix might support `int8_t` input. In that case,
+  // byte_alignment / sizeof(DataT) might exceed 4. We maximize at 4 here, to
+  // prevent adding more cases in dispatch_layout below (which are expensive to
+  // compile).
+  vec_len_aligned = std::min(vec_len_aligned, 4);
+
+  return vec_len_aligned;
+}
+
+template <int n>
+using vec_len_constant = std::integral_constant<int, n>;
+
+/**
+ * @brief: Converts run-time arguments to compile-time arguments
+ *
+ * Converts run-time arguments row_major and vec_len to compile-time arguments
+ * and dispatches a lambda f with these compile-time arguments.
+ *
+ * This is equivalent to copying and pasting the lambda function `f` in each of
+ * the switch case statements.
+ *
+ * @tparam F         Type of lambda f.
+ * @param row_major  Boolean indicating whether input arrays have row-major layout.
+ * @param vec_len    Integer value 1, 2, or 4 specifying the Veclen template parameter of
+ *                   the KernelPolicy.
+ * @param f          Lambda that takes two std::integral_constant parameters representing
+ *                   row_major and vec_len.
+ */
+template <typename F>
+auto dispatch_layout(bool row_major, int vec_len, F&& f)
+{
+  if (row_major) {
+    switch (vec_len) {
+      case 4: return f(std::true_type(), vec_len_constant<4>());
+      case 2: return f(std::true_type(), vec_len_constant<2>());
+      default: return f(std::true_type(), vec_len_constant<1>());
+    }
+  } else {
+    switch (vec_len) {
+      case 4: return f(std::false_type(), vec_len_constant<4>());
+      case 2: return f(std::false_type(), vec_len_constant<2>());
+      default: return f(std::false_type(), vec_len_constant<1>());
+    }
+  }
+}
+
+};  // namespace cuvs::distance::detail
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
new file mode 100644
index 0000000000..d13532ac60
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::lp_unexp_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
new file mode 100644
index 0000000000..65e0163d78
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::lp_unexp_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
new file mode 100644
index 0000000000..23f2b34e8c
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "../kernels/rbf_fin_op.cuh"    // rbf_fin_op
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op,
+  float,
+  float,
+  float,
+  cuvs::distance::kernels::detail::rbf_fin_op<float>,
+  int64_t);
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op,
+  double,
+  double,
+  double,
+  cuvs::distance::kernels::detail::rbf_fin_op<double>,
+  int64_t);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
new file mode 100644
index 0000000000..1a5e5cf981
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::russel_rao_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
new file mode 100644
index 0000000000..a9b1f6bb43
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const DataT* x_norm,                                                             \
+      const DataT* y_norm,                                                             \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::russel_rao_distance_op, float, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_sm60.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch_sm60.cuh
new file mode 100644
index 0000000000..2b0ed01ef5
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_sm60.cuh
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "dispatch_layout.cuh"           // dispatch_layout
+#include "kernel_sm60.cuh"               // pairwise_matrix_sm60_wrapper
+#include <raft/linalg/contractions.cuh>  // raft::linalg::Policy4x4
+
+#include <algorithm>  // std::min
+
+namespace cuvs::distance::detail {
+
+template <typename OpT,
+          typename IdxT,
+          typename DataT,
+          typename OutT,
+          typename FinOpT,
+          typename SM_compat_t>
+pairwise_matrix_sm60_wrapper<OpT, IdxT, DataT, OutT, FinOpT> pairwise_matrix_sm60_get_wrapper(
+  OpT distance_op,
+  pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params,
+  SM_compat_t sm_compat_range)
+{
+  int vec_len = determine_vec_len(params);
+
+  // f takes compile-time constants row_major and vec_len aligned and returns
+  // the corresponding kernel wrapper. The wrapper contains the launch
+  // parameters of the kernel: a pointer to the kernel function, grid size,
+  // block size, and shared memory size.
+  auto f = [&](auto row_major, auto vec_len_aligned) {
+    // row_major and vec_len are std::integral_constants of type bool and int
+    // respectively.
+
+    // To keep compile times in check, we only specialize on veclen > 1 when
+    // the inner loop is relatively cheap (< 5 flops).
+    constexpr int vec_len_op = distance_op.expensive_inner_loop ? 1 : vec_len_aligned();
+
+    // Prevent double, vec_len=4 combination (this is not supported)
+    constexpr int vec_len = std::min(vec_len_op, static_cast<int>(16 / sizeof(DataT)));
+
+    using RowPolicy = typename raft::linalg::Policy4x4<DataT, vec_len>::Policy;
+    using ColPolicy = typename raft::linalg::Policy4x4<DataT, vec_len>::ColPolicy;
+    using Policy    = typename std::conditional<row_major(), RowPolicy, ColPolicy>::type;
+
+    auto wrapper =
+      make_pairwise_matrix_sm60_wrapper<Policy, row_major()>(distance_op, params, sm_compat_range);
+
+    return wrapper;
+  };
+
+  // Dispatch_layout calls f with appropriate compile time constants based on
+  // the runtime values of params.is_row_major and vec_len.
+  return dispatch_layout(params.is_row_major, vec_len, f);
+}
+
+template <typename OpT,
+          typename IdxT,
+          typename DataT,
+          typename OutT,
+          typename FinOpT,
+          typename SM_compat_t>
+void pairwise_matrix_sm60_dispatch(OpT distance_op,
+                                   pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params,
+                                   SM_compat_t sm_compat_range,
+                                   cudaStream_t stream)
+{
+  auto wrapper = pairwise_matrix_sm60_get_wrapper(distance_op, params, sm_compat_range);
+
+  wrapper.launch(distance_op, params, stream);
+}
+
+}  // namespace cuvs::distance::detail
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_sm80.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch_sm80.cuh
new file mode 100644
index 0000000000..d9761545e3
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_sm80.cuh
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../pairwise_distance_cutlass_base.cuh"  // cutlassDistanceKernel
+#include "dispatch_layout.cuh"                    // dispatch_layout
+
+#include <algorithm>  // std::min
+
+namespace cuvs::distance::detail {
+
+template <typename OpT,
+          typename IdxT,
+          typename DataT,
+          typename OutT,
+          typename FinOpT,
+          typename SM_compat_t>
+void pairwise_matrix_sm80_dispatch(OpT distance_op,
+                                   pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params,
+                                   SM_compat_t sm_compat_range,
+                                   cudaStream_t stream)
+{
+  int vec_len = determine_vec_len(params);
+
+  // f takes compile-time constants row_major and vec_len aligned and runs the
+  // corresponding cutlass launch code.
+  auto f = [&](auto row_major, auto vec_len_aligned) {
+    // row_major and vec_len are std::integral_constants of type bool and int
+    // respectively.
+
+    // Prevent double, vec_len=4 combination (this is not supported)
+    constexpr int vec_len = std::min(vec_len_aligned(), static_cast<int>(16 / sizeof(DataT)));
+
+    using AccT = typename OpT::AccT;
+    cutlassDistanceKernel<DataT, AccT, OutT, IdxT, vec_len, FinOpT, OpT, row_major()>(params.x,
+                                                                                      params.y,
+                                                                                      params.x_norm,
+                                                                                      params.y_norm,
+                                                                                      params.m,
+                                                                                      params.n,
+                                                                                      params.k,
+                                                                                      params.ldx,
+                                                                                      params.ldy,
+                                                                                      params.ld_out,
+                                                                                      params.out,
+                                                                                      params.fin_op,
+                                                                                      distance_op,
+                                                                                      stream);
+  };
+
+  // Dispatch_layout calls f with appropriate compile time constants based on
+  // the runtime values of params.is_row_major and vec_len.
+  dispatch_layout(params.is_row_major, vec_len, f);
+}
+
+};  // namespace cuvs::distance::detail
diff --git a/cpp/src/distance/detail/pairwise_matrix/kernel_sm60.cuh b/cpp/src/distance/detail/pairwise_matrix/kernel_sm60.cuh
new file mode 100644
index 0000000000..b63955422d
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/kernel_sm60.cuh
@@ -0,0 +1,156 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../pairwise_distance_base.cuh"  // PairwiseDistances
+#include "params.cuh"                     // pairwise_matrix_params
+#include <raft/core/operators.hpp>        // raft::void_op
+#include <raft/util/arch.cuh>             // raft::util::arch::SM_compute_arch
+
+#include <cassert>  // assert
+
+namespace cuvs::distance::detail {
+
+template <typename Policy,
+          bool row_major,
+          typename SM_compat_t,
+          typename OpT,
+          typename IdxT,
+          typename DataT,
+          typename OutT,
+          typename FinOpT>
+__launch_bounds__(Policy::Nthreads, 2) RAFT_KERNEL
+  pairwise_matrix_kernel(OpT distance_op, pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params)
+{
+  // Early exit to minimize the size of the kernel when it is not supposed to be compiled.
+  constexpr SM_compat_t sm_compat_range{};
+  if constexpr (!sm_compat_range.contains(raft::util::arch::SM_compute_arch())) {
+    assert(false);
+    return;
+  }
+
+  extern __shared__ char smem[];
+
+  // The epilog is already provided by distance_op. Do not provide additional
+  // epilogs.
+  auto epilog_op = raft::void_op();
+  // No support for row_epilog_op.
+  auto row_epilog_op = raft::void_op();
+
+  // Always write output
+  constexpr bool write_out = true;
+  constexpr bool use_norms = distance_op.use_norms;
+  PairwiseDistances<DataT,
+                    OutT,
+                    IdxT,
+                    Policy,
+                    decltype(distance_op),
+                    decltype(epilog_op),
+                    decltype(params.fin_op),
+                    decltype(row_epilog_op),
+                    row_major,
+                    write_out>
+    obj(params.x,
+        params.y,
+        params.m,
+        params.n,
+        params.k,
+        params.ldx,
+        params.ldy,
+        params.ld_out,
+        params.x_norm,
+        params.y_norm,
+        params.out,
+        smem,
+        distance_op,
+        epilog_op,
+        params.fin_op,
+        row_epilog_op);
+  obj.run();
+}
+
+// The type of a pointer to the pairwise matrix kernel. The following template
+// arguments are type-erased:
+//
+// - The kernel policy
+// - row_major
+// - SM_compat_t
+template <typename OpT, typename IdxT, typename DataT, typename OutT, typename FinOpT>
+using pairwise_matrix_kernel_t = void (*)(OpT, pairwise_matrix_params<IdxT, DataT, OutT, FinOpT>);
+
+// A wrapper for the pairwise matrix kernel launch. Includes kernel launch
+// parameters.
+template <typename OpT, typename IdxT, typename DataT, typename OutT, typename FinOpT>
+struct pairwise_matrix_sm60_wrapper {
+  dim3 grid;
+  dim3 block;
+  int smem_size;
+  pairwise_matrix_kernel_t<OpT, IdxT, DataT, OutT, FinOpT> kernel_ptr;
+
+  void launch(OpT distance_op,
+              pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params,
+              cudaStream_t stream)
+  {
+    kernel_ptr<<<grid, block, smem_size, stream>>>(distance_op, params);
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+};
+
+/** @brief: Create kernel launch wrapper for pairwise matrix kernel
+ *
+ * This can be used to type-erase the kernel execution policy, row_major, and SM
+ * compatibility range.
+ *
+ * @tparam Policy: Kernel execution policy
+ * @tparam row_major: Indicates whether input matrices are row major
+ * @tparam OpT: Type of distance operation
+ * @tparam IdxT: Index type
+ * @tparam DataT: Data type
+ * @tparam OutT: Output data type
+ * @tparam FinOpT: Final operation type
+ * @tparam SM_compat_t: Type of the SM architecture compatibility
+ *
+ * @param distance_op: Distance operation
+ * @param params: Parameters
+ * @param sm_compat_range: Which SM architectures to compile for.
+ */
+template <typename Policy,
+          bool row_major,
+          typename OpT,
+          typename IdxT,
+          typename DataT,
+          typename OutT,
+          typename FinOpT,
+          typename SM_compat_t>
+pairwise_matrix_sm60_wrapper<OpT, IdxT, DataT, OutT, FinOpT> make_pairwise_matrix_sm60_wrapper(
+  OpT distance_op,
+  pairwise_matrix_params<IdxT, DataT, OutT, FinOpT> params,
+  SM_compat_t sm_compat_range)
+{
+  dim3 block(Policy::Nthreads);
+  // Use ::template to disambiguate (See:
+  // https://en.cppreference.com/w/cpp/language/dependent_name)
+  int smem_size = OpT::template shared_mem_size<Policy>();
+  // Obtain function pointer to kernel
+  auto kernel =
+    pairwise_matrix_kernel<Policy, row_major, SM_compat_t, OpT, IdxT, DataT, OutT, FinOpT>;
+  dim3 grid = launchConfigGenerator<Policy>(params.m, params.n, smem_size, kernel);
+
+  return pairwise_matrix_sm60_wrapper<OpT, IdxT, DataT, OutT, FinOpT>{
+    grid, block, smem_size, kernel};
+}
+
+};  // namespace cuvs::distance::detail
diff --git a/cpp/src/distance/detail/pairwise_matrix/params.cuh b/cpp/src/distance/detail/pairwise_matrix/params.cuh
new file mode 100644
index 0000000000..aa419aca0d
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/params.cuh
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+namespace cuvs::distance::detail {
+
+template <typename IdxT, typename DataT, typename OutT, typename FinOpT>
+struct pairwise_matrix_params {
+  IdxT m;
+  IdxT n;
+  IdxT k;
+  IdxT ldx;
+  IdxT ldy;
+  IdxT ld_out;
+  const DataT* x;
+  const DataT* y;
+  const DataT* x_norm;
+  const DataT* y_norm;
+  OutT* out;
+  FinOpT fin_op;
+  bool is_row_major;
+
+  /// @brief: Flips the x and y input and corresponding sizes
+  void flip_x_and_y()
+  {
+    // Flip m, n; ldx, ldy; x, y; x_norm, y_norm.
+    std::swap(m, n);
+    std::swap(ldx, ldy);
+    std::swap(x, y);
+    std::swap(x_norm, y_norm);
+  }
+};
+
+}  // namespace cuvs::distance::detail
diff --git a/cpp/src/distance/detail/predicated_tile_iterator_normvec.h b/cpp/src/distance/detail/predicated_tile_iterator_normvec.h
new file mode 100644
index 0000000000..951f8a0132
--- /dev/null
+++ b/cpp/src/distance/detail/predicated_tile_iterator_normvec.h
@@ -0,0 +1,585 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*! \file
+  \brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
+
+This file contains a customized version of PredicatedTileIterator from CUTLASS 2.9.0
+(https://github.com/NVIDIA/cutlass/blob/v2.9.0/include/cutlass/epilogue/threadblock/predicated_tile_iterator.h#L75)
+
+Changes:
+- added `Layout_` template param
+- Only the row index is used to load the data in load_with_byte_offset().
+  This way the same normalization data is used across all columns in a row.
+
+*/
+
+#pragma once
+
+#include <cutlass/arch/arch.h>
+#include <cutlass/arch/memory.h>
+#include <cutlass/array.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/epilogue/threadblock/output_tile_thread_map.h>
+#include <cutlass/epilogue/threadblock/predicated_tile_iterator_params.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/layout/tensor.h>
+#include <cutlass/matrix_shape.h>
+#include <cutlass/numeric_types.h>
+#include <cutlass/tensor_ref.h>
+#include <cutlass/transform/pitch_linear_thread_map.h>
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace epilogue {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Tile iterator used to load and store output tile from global memory in epilogue.
+///
+/// Satisfies: ReadableTileIterator | PredicatedTileIterator | ForwardTileIterator
+///
+template <typename ThreadMap_,  ///< Thread map (conept: OutputTileThreadMap)
+          typename Element_,    ///< Element data type
+          typename Layout_,
+          bool ScatterD     = false,  ///< Scatter D operand or not
+          bool UseCUDAStore = false>
+class PredicatedTileIteratorNormVec {
+ public:
+  using ThreadMap = ThreadMap_;
+  using Shape     = typename ThreadMap::Shape;
+
+  using Element = Element_;
+
+  using Layout         = Layout_;
+  using TensorRef      = TensorRef<Element, Layout>;
+  using ConstTensorRef = typename TensorRef::ConstTensorRef;
+
+  using Index       = typename Layout::Index;
+  using LongIndex   = typename Layout::LongIndex;
+  using TensorCoord = MatrixCoord;
+
+  static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
+  static int const kThreads           = ThreadMap::kThreads;
+  static int const kIterations        = ThreadMap::Count::kTile;
+
+  static_assert(ThreadMap::Iterations::kRow > 0, "ThreadMap::Iterations::kRow must be > 0");
+  static_assert(ThreadMap::Iterations::kGroup > 0, "ThreadMap::Iterations::kGroup must be > 0");
+  static_assert(ThreadMap::Iterations::kCluster > 0, "ThreadMap::Iterations::kCluster must be > 0");
+  static_assert(ThreadMap::Iterations::kColumn > 0, "ThreadMap::Iterations::kColumn must be > 0");
+
+  /// Fragment object
+  using Fragment = Array<Element,
+                         ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow *
+                           ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster *
+                           ThreadMap::kElementsPerAccess>;
+
+  /// Memory access size
+  using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
+
+  //
+  // Parameters struct
+  //
+
+  /// Uses a non-template class
+  struct Params : PredicatedTileIteratorParams {
+    using Base = PredicatedTileIteratorParams;
+
+    CUTLASS_HOST_DEVICE
+    Params() {}
+
+    CUTLASS_HOST_DEVICE
+    Params(Layout const& layout)
+      : PredicatedTileIteratorParams(
+          layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
+          make_OutputTileThreadMapDesc<ThreadMap>())
+    {
+    }
+
+    CUTLASS_HOST_DEVICE
+    Params(Base const& base) : Base(base) {}
+  };
+
+  /// Mask object
+  struct Mask {
+    static int const kCount = ThreadMap::Iterations::kColumn;
+
+    /// Predicate state
+    bool predicates[kCount];
+
+    //
+    // Mask
+    //
+    CUTLASS_HOST_DEVICE
+    Mask() { enable(); }
+
+    ///< Efficiently disables all accesses guarded by mask
+    CUTLASS_HOST_DEVICE void clear()
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kCount; ++i) {
+        predicates[i] = false;
+      }
+    }
+
+    ///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
+    CUTLASS_DEVICE void enable()
+    {
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kCount; ++i) {
+        predicates[i] = true;
+      }
+    }
+  };
+
+ private:
+  //
+  // Data members
+  //
+
+  /// Parameters structure containing reference and precomputed state.
+  PredicatedTileIteratorParams params_;
+
+  /// Byte-level pointer
+  uint8_t* byte_pointer_;
+
+  /// Array of boolean values to contain steady-state predicates
+  Mask mask_;
+
+  /// Extent of the matrix tile in rows
+  Index extent_row_;
+
+  /// Extent of the matrix tile in rows
+  Index extent_column_;
+
+  /// A thread's starting row position (assuming steady-state predicates have been computed)
+  Index thread_start_row_;
+
+  /// A thread's starting column
+  Index thread_start_column_;
+
+  /// Internal state counter
+  int state_[3];
+
+  /// Scatter indices
+  int const* indices_;
+
+  //
+  // Static asserts about internal strides
+  //
+
+  static_assert(sizeof(extent_row_) == 4, "Expected 32b extents");
+  static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents");
+  static_assert(sizeof(PredicatedTileIteratorParams::stride) == 8, "Expected 64b strides");
+
+ private:
+  //
+  // Methods
+  //
+
+ public:
+  //
+  // Methods
+  //
+
+  /// Constructor
+  CUTLASS_DEVICE
+  PredicatedTileIteratorNormVec(PredicatedTileIteratorParams const& params,
+                                Element* pointer,
+                                TensorCoord extent,
+                                int thread_idx,
+                                TensorCoord threadblock_offset = TensorCoord(),
+                                int const* indices             = nullptr)
+    : params_(params), indices_(indices)
+  {
+    TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
+
+    extent_row_    = extent.row();
+    extent_column_ = extent.column();
+
+    thread_start_row_    = thread_offset.row();
+    thread_start_column_ = thread_offset.column();
+
+    // Initialize predicates
+    CUTLASS_PRAGMA_UNROLL
+    for (int c = 0; c < ThreadMap::Iterations::kColumn; ++c) {
+      mask_.predicates[c] =
+        ((thread_offset.column() + ThreadMap::Delta::kColumn * c) < extent.column());
+    }
+
+    // Null pointer performs no accesses
+    if (!pointer) { mask_.clear(); }
+
+    if (ScatterD && !indices) { mask_.clear(); }
+
+    // Initialize pointer
+    byte_pointer_ = reinterpret_cast<uint8_t*>(pointer) +
+                    LongIndex(thread_offset.row()) * LongIndex(params_.stride);
+
+    if (ScatterD) {
+      byte_pointer_ = reinterpret_cast<uint8_t*>(pointer) +
+                      LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
+    }
+
+    // Initialize internal state counter
+    state_[0] = state_[1] = state_[2] = 0;
+  }
+
+  /// Adds a pointer offset in units of Element
+  CUTLASS_HOST_DEVICE
+  void add_pointer_offset(LongIndex pointer_offset)
+  {
+    byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
+  }
+
+  /// Loads a fragment from memory
+  CUTLASS_DEVICE
+  void load_with_byte_offset(Fragment& frag, int64_t byte_offset) const
+  {
+    uint8_t* byte_pointer = byte_pointer_;
+    AccessType* frag_ptr  = reinterpret_cast<AccessType*>(&frag);
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
+      CUTLASS_PRAGMA_UNROLL
+      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
+        CUTLASS_PRAGMA_UNROLL
+        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
+          int frag_row_idx =
+            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
+
+          int row_offset = row * ThreadMap::Delta::kRow + group * ThreadMap::Delta::kGroup +
+                           cluster * ThreadMap::Delta::kCluster;
+
+          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
+
+          AccessType* memory_pointer = reinterpret_cast<AccessType*>(byte_pointer + byte_offset);
+
+          if (ScatterD && row_guard) {
+            assert(indices_);
+
+            memory_pointer = reinterpret_cast<AccessType*>(
+              byte_pointer + byte_offset +
+              LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
+          }
+
+          CUTLASS_PRAGMA_UNROLL
+          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
+            bool guard = row_guard && mask_.predicates[column];
+            if (column == 0) {
+              cutlass::arch::global_load<AccessType, sizeof(AccessType)>(
+                frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
+                (void*)&memory_pointer[0],
+                guard);
+            } else {
+              frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column] =
+                frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn];
+            }
+          }
+
+          if (row + 1 < ThreadMap::Iterations::kRow) {
+            if (!ScatterD) { byte_pointer += params_.increment_row; }
+          }
+        }
+
+        if (group + 1 < ThreadMap::Iterations::kGroup) { byte_pointer += params_.increment_group; }
+      }
+
+      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
+        byte_pointer += params_.increment_cluster;
+      }
+    }
+  }
+
+  /// Loads a fragment from memory
+  CUTLASS_DEVICE
+  void load(Fragment& frag) const { load_with_byte_offset(frag, 0); }
+
+  /// Stores a fragment to memory
+  CUTLASS_DEVICE
+  void store_with_byte_offset(Fragment const& frag, int64_t byte_offset) const
+  {
+    uint8_t* byte_pointer      = byte_pointer_;
+    AccessType const* frag_ptr = reinterpret_cast<AccessType const*>(&frag);
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
+      CUTLASS_PRAGMA_UNROLL
+      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
+        CUTLASS_PRAGMA_UNROLL
+        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
+          int frag_row_idx =
+            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
+
+          int row_offset = row * ThreadMap::Delta::kRow + group * ThreadMap::Delta::kGroup +
+                           cluster * ThreadMap::Delta::kCluster;
+
+          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
+
+          AccessType* memory_pointer = reinterpret_cast<AccessType*>(byte_pointer + byte_offset);
+
+          if (ScatterD && row_guard) {
+            assert(indices_);
+
+            memory_pointer = reinterpret_cast<AccessType*>(
+              byte_pointer + byte_offset +
+              LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
+          }
+
+          CUTLASS_PRAGMA_UNROLL
+          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
+            bool guard = row_guard && mask_.predicates[column];
+
+            if (UseCUDAStore) {
+              if (guard) {
+                memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess] =
+                  frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column];
+              }
+            } else {
+              cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
+                frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
+                (void*)&memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess],
+                guard);
+            }
+          }
+
+          if (row + 1 < ThreadMap::Iterations::kRow) {
+            if (!ScatterD) { byte_pointer += params_.increment_row; }
+          }
+        }
+
+        if (group + 1 < ThreadMap::Iterations::kGroup) { byte_pointer += params_.increment_group; }
+      }
+
+      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
+        byte_pointer += params_.increment_cluster;
+      }
+    }
+  }
+
+  /// Stores a fragment to memory
+  CUTLASS_DEVICE
+  void store(Fragment const& frag) const { store_with_byte_offset(frag, 0); }
+
+  /// Loads a fragment from memory
+  CUTLASS_DEVICE
+  void downsample_load_with_byte_offset(Fragment& frag,
+                                        int64_t byte_offset,
+                                        int convolution_P,
+                                        int convolution_Q,
+                                        int add_P,
+                                        int add_Q,
+                                        int problem_N) const
+  {
+    uint8_t* byte_pointer = byte_pointer_;
+    AccessType* frag_ptr  = reinterpret_cast<AccessType*>(&frag);
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
+      CUTLASS_PRAGMA_UNROLL
+      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
+        CUTLASS_PRAGMA_UNROLL
+        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
+          int frag_row_idx =
+            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
+
+          int row_offset = row * ThreadMap::Delta::kRow + group * ThreadMap::Delta::kGroup +
+                           cluster * ThreadMap::Delta::kCluster;
+
+          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
+
+          int output_row = row_offset + thread_start_row_;
+          int output_N   = output_row / (convolution_P * convolution_Q);
+          int output_PQ  = output_row % (convolution_P * convolution_Q);
+          int output_P   = output_PQ / convolution_Q;
+          int output_Q   = output_PQ % convolution_Q;
+
+          int input_row = output_N * 2 * convolution_P * 2 * convolution_Q +
+                          (2 * output_P + add_P) * 2 * convolution_Q + 2 * output_Q + add_Q;
+
+          int64_t byte_offset = (input_row - output_row) * problem_N * sizeof(float);
+
+          AccessType* memory_pointer = reinterpret_cast<AccessType*>(byte_pointer + byte_offset);
+
+          CUTLASS_PRAGMA_UNROLL
+          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
+            bool guard = row_guard && mask_.predicates[column];
+
+            cutlass::arch::global_load<AccessType, sizeof(AccessType)>(
+              frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
+              (void*)&memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess],
+              guard);
+          }
+
+          if (row + 1 < ThreadMap::Iterations::kRow) { byte_pointer += params_.increment_row; }
+        }
+
+        if (group + 1 < ThreadMap::Iterations::kGroup) { byte_pointer += params_.increment_group; }
+      }
+
+      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
+        byte_pointer += params_.increment_cluster;
+      }
+    }
+  }
+
+  /// Loads a fragment from memory
+  CUTLASS_DEVICE
+  void upsample_load_with_byte_offset(Fragment& frag,
+                                      int64_t byte_offset,
+                                      int convolution_P,
+                                      int convolution_Q,
+                                      int add_P,
+                                      int add_Q,
+                                      int problem_N) const
+  {
+    uint8_t* byte_pointer = byte_pointer_;
+    AccessType* frag_ptr  = reinterpret_cast<AccessType*>(&frag);
+
+    CUTLASS_PRAGMA_UNROLL
+    for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
+      CUTLASS_PRAGMA_UNROLL
+      for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
+        CUTLASS_PRAGMA_UNROLL
+        for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
+          int frag_row_idx =
+            (row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
+
+          int row_offset = row * ThreadMap::Delta::kRow + group * ThreadMap::Delta::kGroup +
+                           cluster * ThreadMap::Delta::kCluster;
+
+          bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
+
+          int output_row = row_offset + thread_start_row_;
+          int output_N   = output_row / (convolution_P * convolution_Q);
+          int output_PQ  = output_row % (convolution_P * convolution_Q);
+          int output_P   = output_PQ / convolution_Q;
+          int output_Q   = output_PQ % convolution_Q;
+          int row_add_P  = add_P;
+          int row_add_Q  = add_Q;
+          if (output_P > convolution_P - 2) row_add_P = 0;
+          if (output_Q > convolution_Q - 2) row_add_Q = 0;
+
+          int input_row = output_N * (convolution_P / 2) * (convolution_Q / 2) +
+                          ((output_P + row_add_P) / 2) * (convolution_Q / 2) +
+                          (output_Q + row_add_Q) / 2;
+
+          int64_t byte_offset = (input_row - output_row) * problem_N * sizeof(float);
+
+          AccessType* memory_pointer = reinterpret_cast<AccessType*>(byte_pointer + byte_offset);
+
+          CUTLASS_PRAGMA_UNROLL
+          for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
+            bool guard = row_guard && mask_.predicates[column];
+
+            cutlass::arch::global_load<AccessType, sizeof(AccessType)>(
+              frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
+              (void*)&memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess],
+              guard);
+          }
+
+          if (row + 1 < ThreadMap::Iterations::kRow) { byte_pointer += params_.increment_row; }
+        }
+
+        if (group + 1 < ThreadMap::Iterations::kGroup) { byte_pointer += params_.increment_group; }
+      }
+
+      if (cluster + 1 < ThreadMap::Iterations::kCluster) {
+        byte_pointer += params_.increment_cluster;
+      }
+    }
+  }
+
+  CUTLASS_DEVICE
+  MatrixCoord thread_start() const { return MatrixCoord(thread_start_row_, thread_start_column_); }
+
+  /// Need to get the thread start row from the tile iterator
+  CUTLASS_DEVICE
+  int32_t thread_start_row() const { return thread_start_row_; }
+
+  /// Need to get the thread start row from the tile iterator
+  CUTLASS_DEVICE
+  int32_t thread_start_column() const { return thread_start_column_; }
+
+  /// Extent of the matrix in rows
+  CUTLASS_DEVICE
+  Index extent_row() const { return extent_row_; }
+
+  /// Extent of the matrix in columns
+  CUTLASS_DEVICE
+  Index extent_column() const { return extent_column_; }
+
+  /// Advances to the next position to load or store
+  CUTLASS_HOST_DEVICE
+  PredicatedTileIteratorNormVec& operator++()
+  {
+    ++state_[0];
+
+    if (!ScatterD) { byte_pointer_ += params_.advance_row; }
+
+    thread_start_row_ += ThreadMap::Shape::kRow;
+
+    if (state_[0] == ThreadMap::Count::kRow) {
+      state_[0] = 0;
+      ++state_[1];
+      byte_pointer_ += params_.advance_group;
+
+      thread_start_row_ +=
+        (ThreadMap::Shape::kGroup - 1) * ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
+
+      if (state_[1] == ThreadMap::Count::kGroup) {
+        state_[1] = 0;
+        ++state_[2];
+        byte_pointer_ += params_.advance_cluster;
+
+        thread_start_row_ += ThreadMap::Count::kGroup * ThreadMap::Shape::kGroup *
+                             ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
+
+        if (state_[2] == ThreadMap::Count::kCluster) {
+          state_[2] = 0;
+          byte_pointer_ += params_.advance_tile;
+        }
+      }
+    }
+
+    return *this;
+  }
+
+  ///< Efficiently disables all accesses guarded by mask
+  CUTLASS_DEVICE void clear_mask() { mask_.clear(); }
+
+  ///< Efficiently enables all accesses guarded by mask
+  CUTLASS_DEVICE void enable_mask() { mask_.enable(); }
+
+  ///< Sets the mask
+  CUTLASS_DEVICE void get_mask(Mask& mask) const { mask = mask_; }
+
+  ///< Sets the mask
+  CUTLASS_DEVICE void set_mask(Mask const& mask) { mask_ = mask; }
+};
+
+///////////////////////////////////////////////////////////////////////////////
+
+}  // namespace threadblock
+}  // namespace epilogue
+}  // namespace cutlass
+
+////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/distance-ext.cuh b/cpp/src/distance/distance-ext.cuh
new file mode 100644
index 0000000000..ad45e84059
--- /dev/null
+++ b/cpp/src/distance/distance-ext.cuh
@@ -0,0 +1,1066 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "detail/kernels/rbf_fin_op.cuh"     // rbf_fin_op
+#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
+#include <raft/core/device_mdspan.hpp>       // raft::device_matrix_view
+#include <raft/core/operators.hpp>           // raft::identity_op
+#include <raft/core/resources.hpp>           // raft::resources
+#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+
+#include <rmm/device_uvector.hpp>  // rmm::device_uvector
+
+#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+namespace cuvs {
+namespace distance {
+
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename FinalLambda,
+          typename IdxT = int>
+void distance(raft::resources const& handle,
+              const DataT* x,
+              const DataT* y,
+              OutT* dist,
+              IdxT m,
+              IdxT n,
+              IdxT k,
+              void* workspace,
+              size_t worksize,
+              FinalLambda fin_op,
+              bool isRowMajor  = true,
+              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int>
+void distance(raft::resources const& handle,
+              const DataT* x,
+              const DataT* y,
+              OutT* dist,
+              IdxT m,
+              IdxT n,
+              IdxT k,
+              void* workspace,
+              size_t worksize,
+              bool isRowMajor  = true,
+              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int>
+size_t getWorkspaceSize(const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k) RAFT_EXPLICIT;
+
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int,
+          typename layout>
+size_t getWorkspaceSize(raft::device_matrix_view<DataT, IdxT, layout> const& x,
+                        raft::device_matrix_view<DataT, IdxT, layout> const& y) RAFT_EXPLICIT;
+
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int>
+void distance(raft::resources const& handle,
+              const DataT* x,
+              const DataT* y,
+              OutT* dist,
+              IdxT m,
+              IdxT n,
+              IdxT k,
+              bool isRowMajor  = true,
+              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+
+template <typename Type, typename IdxT = int>
+void pairwise_distance(raft::resources const& handle,
+                       const Type* x,
+                       const Type* y,
+                       Type* dist,
+                       IdxT m,
+                       IdxT n,
+                       IdxT k,
+                       rmm::device_uvector<char>& workspace,
+                       cuvs::distance::DistanceType metric,
+                       bool isRowMajor = true,
+                       Type metric_arg = 2.0f) RAFT_EXPLICIT;
+
+template <typename Type, typename IdxT = int>
+void pairwise_distance(raft::resources const& handle,
+                       const Type* x,
+                       const Type* y,
+                       Type* dist,
+                       IdxT m,
+                       IdxT n,
+                       IdxT k,
+                       cuvs::distance::DistanceType metric,
+                       bool isRowMajor = true,
+                       Type metric_arg = 2.0f) RAFT_EXPLICIT;
+
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename layout = raft::layout_c_contiguous,
+          typename IdxT   = int>
+void distance(raft::resources const& handle,
+              raft::device_matrix_view<DataT, IdxT, layout> const x,
+              raft::device_matrix_view<DataT, IdxT, layout> const y,
+              raft::device_matrix_view<OutT, IdxT, layout> dist,
+              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+
+template <typename Type, typename layout = layout_c_contiguous, typename IdxT = int>
+void pairwise_distance(raft::resources const& handle,
+                       device_matrix_view<Type, IdxT, layout> const x,
+                       device_matrix_view<Type, IdxT, layout> const y,
+                       device_matrix_view<Type, IdxT, layout> dist,
+                       cuvs::distance::DistanceType metric,
+                       Type metric_arg = 2.0f) RAFT_EXPLICIT;
+
+};  // namespace distance
+};  // namespace cuvs
+
+#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+/*
+ * Hierarchy of instantiations:
+ *
+ * This file defines the extern template instantiations for the public API of
+ * cuvs::distance. To improve compile times, the extern template instantiation
+ * of the distance kernels is handled in
+ * distance/detail/pairwise_matrix/dispatch-ext.cuh.
+ *
+ * After adding an instance here, make sure to also add the instance to
+ * dispatch-ext.cuh and the corresponding .cu files.
+ */
+
+#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, FinalLambda, IdxT)       \
+  extern template void cuvs::distance::distance<DT, DataT, AccT, OutT, FinalLambda, IdxT>( \
+    raft::resources const& handle,                                                         \
+    const DataT* x,                                                                        \
+    const DataT* y,                                                                        \
+    OutT* dist,                                                                            \
+    IdxT m,                                                                                \
+    IdxT n,                                                                                \
+    IdxT k,                                                                                \
+    void* workspace,                                                                       \
+    size_t worksize,                                                                       \
+    FinalLambda fin_op,                                                                    \
+    bool isRowMajor,                                                                       \
+    DataT metric_arg)
+
+// The following two instances are used in test/distance/gram.cu. Note the use
+// of int64_t for the index type.
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   cuvs::distance::kernels::detail::rbf_fin_op<float>,
+                                   int64_t);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   cuvs::distance::kernels::detail::rbf_fin_op<double>,
+                                   int64_t);
+
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::identity_op,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_distance
+
+// Same, but without raft::identity_op
+#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT)       \
+  extern template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>( \
+    raft::resources const& handle,                                            \
+    const DataT* x,                                                           \
+    const DataT* y,                                                           \
+    OutT* dist,                                                               \
+    IdxT m,                                                                   \
+    IdxT n,                                                                   \
+    IdxT k,                                                                   \
+    void* workspace,                                                          \
+    size_t worksize,                                                          \
+    bool isRowMajor,                                                          \
+    DataT metric_arg)
+
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
+
+#undef instantiate_raft_distance_distance
+
+// Same, but without workspace
+#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT)       \
+  extern template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>( \
+    raft::resources const& handle,                                            \
+    const DataT* x,                                                           \
+    const DataT* y,                                                           \
+    OutT* dist,                                                               \
+    IdxT m,                                                                   \
+    IdxT n,                                                                   \
+    IdxT k,                                                                   \
+    bool isRowMajor,                                                          \
+    DataT metric_arg)
+
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
+
+#undef instantiate_raft_distance_distance
+
+#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT)         \
+  extern template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>( \
+    const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k)
+
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Linf, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Linf, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
+
+#undef instantiate_raft_distance_getWorkspaceSize
+
+#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT, layout)         \
+  extern template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, layout>( \
+    raft::device_matrix_view<DataT, IdxT, layout> const& x,                                        \
+    raft::device_matrix_view<DataT, IdxT, layout> const& y)
+
+// We could consider not taking template parameters for this function. The
+// number of instantiations seems a bit excessive..
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2Unexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_f_contiguous);
+
+#undef instantiate_raft_distance_getWorkspaceSize
+
+#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                               \
+  extern template void cuvs::distance::pairwise_distance(raft::resources const& handle,        \
+                                                         const DataT* x,                       \
+                                                         const DataT* y,                       \
+                                                         DataT* dist,                          \
+                                                         IdxT m,                               \
+                                                         IdxT n,                               \
+                                                         IdxT k,                               \
+                                                         rmm::device_uvector<char>& workspace, \
+                                                         cuvs::distance::DistanceType metric,  \
+                                                         bool isRowMajor,                      \
+                                                         DataT metric_arg)
+
+instantiate_raft_distance_pairwise_distance(float, int);
+instantiate_raft_distance_pairwise_distance(double, int);
+
+#undef instantiate_raft_distance_pairwise_distance
+
+// Same, but without workspace
+#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                              \
+  extern template void cuvs::distance::pairwise_distance(raft::resources const& handle,       \
+                                                         const DataT* x,                      \
+                                                         const DataT* y,                      \
+                                                         DataT* dist,                         \
+                                                         IdxT m,                              \
+                                                         IdxT n,                              \
+                                                         IdxT k,                              \
+                                                         cuvs::distance::DistanceType metric, \
+                                                         bool isRowMajor,                     \
+                                                         DataT metric_arg)
+
+instantiate_raft_distance_pairwise_distance(float, int);
+instantiate_raft_distance_pairwise_distance(double, int);
+
+#undef instantiate_raft_distance_pairwise_distance
+
+// Version with mdspan
+#define instantiate_raft_distance_distance(DistT, DataT, AccT, OutT, layout, IdxT)       \
+  extern template void cuvs::distance::distance<DistT, DataT, AccT, OutT, layout, IdxT>( \
+    raft::resources const& handle,                                                       \
+    raft::device_matrix_view<const DataT, IdxT, layout> const x,                         \
+    raft::device_matrix_view<const DataT, IdxT, layout> const y,                         \
+    raft::device_matrix_view<OutT, IdxT, layout> dist,                                   \
+    DataT metric_arg)
+
+// Again, we might want to consider reigning in the number of instantiations...
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+
+#undef instantiate_raft_distance_distance
+
+#define instantiate_raft_distance_pairwise_distance(DataT, layout, IdxT) \
+  extern template void cuvs::distance::pairwise_distance(                \
+    raft::resources const& handle,                                       \
+    raft::device_matrix_view<const DataT, IdxT, layout> const x,         \
+    raft::device_matrix_view<const DataT, IdxT, layout> const y,         \
+    raft::device_matrix_view<DataT, IdxT, layout> dist,                  \
+    cuvs::distance::DistanceType metric,                                 \
+    DataT metric_arg)
+
+instantiate_raft_distance_pairwise_distance(float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_pairwise_distance(float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_pairwise_distance(double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_pairwise_distance(double, raft::layout_f_contiguous, int);
+
+#undef instantiate_raft_distance_pairwise_distance
diff --git a/cpp/src/distance/distance-inl.cuh b/cpp/src/distance/distance-inl.cuh
new file mode 100644
index 0000000000..5b82f5438c
--- /dev/null
+++ b/cpp/src/distance/distance-inl.cuh
@@ -0,0 +1,478 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "detail/distance.cuh"
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <type_traits>
+
+namespace cuvs {
+namespace distance {
+
+/**
+ * @defgroup pairwise_distance pointer-based pairwise distance prims
+ * @{
+ */
+
+/**
+ * @brief Evaluate pairwise distances with the user epilogue lamba allowed
+ * @tparam DistanceType which distance to evaluate
+ * @tparam DataT input argument type
+ * @tparam AccT accumulation type
+ * @tparam OutT output type
+ * @tparam FinalLambda user-defined epilogue lamba
+ * @tparam IdxT Index type
+ * @param handle raft handle for managing expensive resources
+ * @param x first set of points
+ * @param y second set of points
+ * @param dist output distance matrix
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ * @param workspace temporary workspace needed for computations
+ * @param worksize number of bytes of the workspace
+ * @param fin_op the final gemm epilogue lambda
+ * @param isRowMajor whether the matrices are row-major or col-major
+ * @param metric_arg metric argument (used for Minkowski distance)
+ *
+ * @note fin_op: This is a device lambda which is supposed to operate upon the
+ * input which is AccT and returns the output in OutT. It's signature is
+ * as follows:  <pre>OutT fin_op(AccT in, int g_idx);</pre>. If one needs
+ * any other parameters, feel free to pass them via closure.
+ */
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename FinalLambda,
+          typename IdxT = int>
+void distance(raft::resources const& handle,
+              const DataT* x,
+              const DataT* y,
+              OutT* dist,
+              IdxT m,
+              IdxT n,
+              IdxT k,
+              void* workspace,
+              size_t worksize,
+              FinalLambda fin_op,
+              bool isRowMajor  = true,
+              DataT metric_arg = 2.0f)
+{
+  detail::distance<DistT, DataT, AccT, OutT, FinalLambda, IdxT>(
+    handle, x, y, dist, m, n, k, workspace, worksize, fin_op, isRowMajor, metric_arg);
+}
+
+/**
+ * @brief Evaluate pairwise distances for the simple use case
+ * @tparam DistanceType which distance to evaluate
+ * @tparam DataT input argument type
+ * @tparam AccT accumulation type
+ * @tparam OutT output type
+ * @tparam IdxT Index type
+ * @param handle raft handle for managing expensive resources
+ * @param x first set of points
+ * @param y second set of points
+ * @param dist output distance matrix
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ * @param workspace temporary workspace needed for computations
+ * @param worksize number of bytes of the workspace
+ * @param isRowMajor whether the matrices are row-major or col-major
+ * @param metric_arg metric argument (used for Minkowski distance)
+ */
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int>
+void distance(raft::resources const& handle,
+              const DataT* x,
+              const DataT* y,
+              OutT* dist,
+              IdxT m,
+              IdxT n,
+              IdxT k,
+              void* workspace,
+              size_t worksize,
+              bool isRowMajor  = true,
+              DataT metric_arg = 2.0f)
+{
+  detail::distance<DistT, DataT, AccT, OutT, IdxT>(
+    handle, x, y, dist, m, n, k, workspace, worksize, isRowMajor, metric_arg);
+}
+
+/**
+ * @brief Return the exact workspace size to compute the distance
+ * @tparam DistanceType which distance to evaluate
+ * @tparam DataT input argument type
+ * @tparam AccT accumulation type
+ * @tparam OutT output type
+ * @tparam IdxT Index type
+ * @param x first set of points
+ * @param y second set of points
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ *
+ * @note If the specified DistT doesn't need the workspace at all, it
+ * returns 0.
+ */
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int>
+size_t getWorkspaceSize(const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k)
+{
+  return detail::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>(x, y, m, n, k);
+}
+
+/**
+ * @brief Return the exact workspace size to compute the distance
+ * @tparam DistanceType which distance to evaluate
+ * @tparam DataT input argument type
+ * @tparam AccT accumulation type
+ * @tparam OutT output type
+ * @tparam IdxT Index type
+ * @param x first set of points (size m*k)
+ * @param y second set of points (size n*k)
+ * @return number of bytes needed in workspace
+ *
+ * @note If the specified DistT doesn't need the workspace at all, it
+ * returns 0.
+ */
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int,
+          typename layout>
+size_t getWorkspaceSize(raft::device_matrix_view<DataT, IdxT, layout> const& x,
+                        raft::device_matrix_view<DataT, IdxT, layout> const& y)
+{
+  RAFT_EXPECTS(x.extent(1) == y.extent(1), "Number of columns must be equal.");
+
+  return getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>(
+    x.data_handle(), y.data_handle(), x.extent(0), y.extent(0), x.extent(1));
+}
+
+/**
+ * @brief Evaluate pairwise distances for the simple use case
+ * @tparam DistanceType which distance to evaluate
+ * @tparam DataT input argument type
+ * @tparam AccT accumulation type
+ * @tparam OutT output type
+ * @tparam IdxT Index type
+ * @param handle raft handle for managing expensive resources
+ * @param x first set of points
+ * @param y second set of points
+ * @param dist output distance matrix
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ * @param isRowMajor whether the matrices are row-major or col-major
+ * @param metric_arg metric argument (used for Minkowski distance)
+ */
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT = int>
+void distance(raft::resources const& handle,
+              const DataT* x,
+              const DataT* y,
+              OutT* dist,
+              IdxT m,
+              IdxT n,
+              IdxT k,
+              bool isRowMajor  = true,
+              DataT metric_arg = 2.0f)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<char> workspace(0, stream);
+  auto worksize = getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>(x, y, m, n, k);
+  workspace.resize(worksize, stream);
+  detail::distance<DistT, DataT, AccT, OutT, IdxT>(
+    handle, x, y, dist, m, n, k, workspace.data(), worksize, isRowMajor, metric_arg);
+}
+
+/**
+ * @brief Convenience wrapper around 'distance' prim to convert runtime metric
+ * into compile time for the purpose of dispatch
+ * @tparam Type input/accumulation/output data-type
+ * @tparam IdxT indexing type
+ * @param handle raft handle for managing expensive resources
+ * @param x first set of points
+ * @param y second set of points
+ * @param dist output distance matrix
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ * @param workspace temporary workspace buffer which can get resized as per the
+ * needed workspace size
+ * @param metric distance metric
+ * @param isRowMajor whether the matrices are row-major or col-major
+ * @param metric_arg metric argument (used for Minkowski distance)
+ */
+template <typename Type, typename IdxT = int>
+void pairwise_distance(raft::resources const& handle,
+                       const Type* x,
+                       const Type* y,
+                       Type* dist,
+                       IdxT m,
+                       IdxT n,
+                       IdxT k,
+                       rmm::device_uvector<char>& workspace,
+                       cuvs::distance::DistanceType metric,
+                       bool isRowMajor = true,
+                       Type metric_arg = 2.0f)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  auto dispatch = [&](auto distance_type) {
+    auto worksize = getWorkspaceSize<distance_type(), Type, Type, Type, IdxT>(x, y, m, n, k);
+    workspace.resize(worksize, stream);
+    detail::distance<distance_type(), Type, Type, Type, IdxT>(
+      handle, x, y, dist, m, n, k, workspace.data(), worksize, isRowMajor, metric_arg);
+  };
+
+  switch (metric) {
+    case DistanceType::Canberra:
+      dispatch(std::integral_constant<DistanceType, DistanceType::Canberra>{});
+      break;
+    case DistanceType::CorrelationExpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::CorrelationExpanded>{});
+      break;
+    case DistanceType::CosineExpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::CosineExpanded>{});
+      break;
+    case DistanceType::HammingUnexpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::HammingUnexpanded>{});
+      break;
+    case DistanceType::HellingerExpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::HellingerExpanded>{});
+      break;
+    case cuvs::distance::DistanceType::InnerProduct:
+      dispatch(std::integral_constant<DistanceType, DistanceType::InnerProduct>{});
+      break;
+    case DistanceType::JensenShannon:
+      dispatch(std::integral_constant<DistanceType, DistanceType::JensenShannon>{});
+      break;
+    case DistanceType::KLDivergence:
+      dispatch(std::integral_constant<DistanceType, DistanceType::KLDivergence>{});
+      break;
+    case DistanceType::L1:
+      dispatch(std::integral_constant<DistanceType, DistanceType::L1>{});
+      break;
+    case DistanceType::L2Expanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::L2Expanded>{});
+      break;
+    case DistanceType::L2SqrtExpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::L2SqrtExpanded>{});
+      break;
+    case DistanceType::L2SqrtUnexpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::L2SqrtUnexpanded>{});
+      break;
+    case DistanceType::L2Unexpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::L2Unexpanded>{});
+      break;
+    case DistanceType::Linf:
+      dispatch(std::integral_constant<DistanceType, DistanceType::Linf>{});
+      break;
+    case DistanceType::LpUnexpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::LpUnexpanded>{});
+      break;
+    case DistanceType::RusselRaoExpanded:
+      dispatch(std::integral_constant<DistanceType, DistanceType::RusselRaoExpanded>{});
+      break;
+    default: THROW("Unknown or unsupported distance metric '%d'!", (int)metric);
+  };
+}
+
+/**
+ * @brief Convenience wrapper around 'distance' prim to convert runtime metric
+ * into compile time for the purpose of dispatch
+ * @tparam Type input/accumulation/output data-type
+ * @tparam IdxT indexing type
+ * @param handle raft handle for managing expensive resources
+ * @param x first set of points
+ * @param y second set of points
+ * @param dist output distance matrix
+ * @param m number of points in x
+ * @param n number of points in y
+ * @param k dimensionality
+ * @param metric distance metric
+ * @param isRowMajor whether the matrices are row-major or col-major
+ * @param metric_arg metric argument (used for Minkowski distance)
+ */
+template <typename Type, typename IdxT = int>
+void pairwise_distance(raft::resources const& handle,
+                       const Type* x,
+                       const Type* y,
+                       Type* dist,
+                       IdxT m,
+                       IdxT n,
+                       IdxT k,
+                       cuvs::distance::DistanceType metric,
+                       bool isRowMajor = true,
+                       Type metric_arg = 2.0f)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<char> workspace(0, stream);
+  pairwise_distance<Type, IdxT>(
+    handle, x, y, dist, m, n, k, workspace, metric, isRowMajor, metric_arg);
+}
+
+/** @} */
+
+/**
+ * \defgroup distance_mdspan Pairwise distance functions
+ * @{
+ */
+
+/**
+ * @brief Evaluate pairwise distances for the simple use case.
+ *
+ * Note: Only contiguous row- or column-major layouts supported currently.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/core/device_mdarray.hpp>
+ * #include <raft/random/make_blobs.cuh>
+ * #include <raft/distance/distance.cuh>
+ *
+ * raft::raft::resources handle;
+ * int n_samples = 5000;
+ * int n_features = 50;
+ *
+ * auto input = raft::make_device_matrix<float>(handle, n_samples, n_features);
+ * auto labels = raft::make_device_vector<int>(handle, n_samples);
+ * auto output = raft::make_device_matrix<float>(handle, n_samples, n_samples);
+ *
+ * raft::random::make_blobs(handle, input.view(), labels.view());
+ * auto metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+ * cuvs::distance::pairwise_distance(handle, input.view(), input.view(), output.view(), metric);
+ * @endcode
+ *
+ * @tparam DistanceType which distance to evaluate
+ * @tparam DataT input argument type
+ * @tparam AccT accumulation type
+ * @tparam OutT output type
+ * @tparam IdxT Index type
+ * @param handle raft handle for managing expensive resources
+ * @param x first set of points (size n*k)
+ * @param y second set of points (size m*k)
+ * @param dist output distance matrix (size n*m)
+ * @param metric_arg metric argument (used for Minkowski distance)
+ */
+template <cuvs::distance::DistanceType DistT,
+          typename DataT,
+          typename AccT,
+          typename OutT,
+          typename layout = raft::layout_c_contiguous,
+          typename IdxT   = int>
+void distance(raft::resources const& handle,
+              raft::device_matrix_view<const DataT, IdxT, layout> const x,
+              raft::device_matrix_view<const DataT, IdxT, layout> const y,
+              raft::device_matrix_view<OutT, IdxT, layout> dist,
+              DataT metric_arg = 2.0f)
+{
+  RAFT_EXPECTS(x.extent(1) == y.extent(1), "Number of columns must be equal.");
+  RAFT_EXPECTS(dist.extent(0) == x.extent(0),
+               "Number of rows in output must be equal to "
+               "number of rows in X");
+  RAFT_EXPECTS(dist.extent(1) == y.extent(0),
+               "Number of columns in output must be equal to "
+               "number of rows in Y");
+
+  RAFT_EXPECTS(x.is_exhaustive(), "Input x must be contiguous.");
+  RAFT_EXPECTS(y.is_exhaustive(), "Input y must be contiguous.");
+
+  constexpr auto is_rowmajor = std::is_same_v<layout, raft::layout_c_contiguous>;
+
+  distance<DistT, DataT, AccT, OutT, IdxT>(handle,
+                                           x.data_handle(),
+                                           y.data_handle(),
+                                           dist.data_handle(),
+                                           x.extent(0),
+                                           y.extent(0),
+                                           x.extent(1),
+                                           is_rowmajor,
+                                           metric_arg);
+}
+
+/**
+ * @brief Convenience wrapper around 'distance' prim to convert runtime metric
+ * into compile time for the purpose of dispatch
+ * @tparam Type input/accumulation/output data-type
+ * @tparam IdxT indexing type
+ * @param handle raft handle for managing expensive resources
+ * @param x first matrix of points (size mxk)
+ * @param y second matrix of points (size nxk)
+ * @param dist output distance matrix (size mxn)
+ * @param metric distance metric
+ * @param metric_arg metric argument (used for Minkowski distance)
+ */
+template <typename Type, typename layout = raft::layout_c_contiguous, typename IdxT = int>
+void pairwise_distance(raft::resources const& handle,
+                       raft::device_matrix_view<const Type, IdxT, layout> const x,
+                       raft::device_matrix_view<const Type, IdxT, layout> const y,
+                       raft::device_matrix_view<Type, IdxT, layout> dist,
+                       cuvs::distance::DistanceType metric,
+                       Type metric_arg = 2.0f)
+{
+  RAFT_EXPECTS(x.extent(1) == y.extent(1), "Number of columns must be equal.");
+  RAFT_EXPECTS(dist.extent(0) == x.extent(0),
+               "Number of rows in output must be equal to "
+               "number of rows in X");
+  RAFT_EXPECTS(dist.extent(1) == y.extent(0),
+               "Number of columns in output must be equal to "
+               "number of rows in Y");
+
+  RAFT_EXPECTS(x.is_exhaustive(), "Input x must be contiguous.");
+  RAFT_EXPECTS(y.is_exhaustive(), "Input y must be contiguous.");
+  RAFT_EXPECTS(dist.is_exhaustive(), "Output must be contiguous.");
+
+  constexpr auto rowmajor = std::is_same_v<layout, raft::layout_c_contiguous>;
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<char> workspace(0, stream);
+
+  pairwise_distance(handle,
+                    x.data_handle(),
+                    y.data_handle(),
+                    dist.data_handle(),
+                    x.extent(0),
+                    y.extent(0),
+                    x.extent(1),
+                    metric,
+                    rowmajor,
+                    metric_arg);
+}
+
+/** @} */
+
+};  // namespace distance
+};  // namespace cuvs
diff --git a/cpp/src/distance/distance.cu b/cpp/src/distance/distance.cu
new file mode 100644
index 0000000000..02c071d13c
--- /dev/null
+++ b/cpp/src/distance/distance.cu
@@ -0,0 +1,934 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/kernels/rbf_fin_op.cuh"  // rbf_fin_op
+#include "distance-inl.cuh"
+
+/*
+ * Hierarchy of instantiations:
+ *
+ * This file defines the template instantiations for the public API of
+ * cuvs::distance. To improve compile times, the compilation of the distance
+ * kernels is handled in distance/detail/pairwise_matrix/dispatch_*.cu.
+ *
+ */
+
+#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, FinalLambda, IdxT) \
+  template void cuvs::distance::distance<DT, DataT, AccT, OutT, FinalLambda, IdxT>(  \
+    raft::resources const& handle,                                                   \
+    const DataT* x,                                                                  \
+    const DataT* y,                                                                  \
+    OutT* dist,                                                                      \
+    IdxT m,                                                                          \
+    IdxT n,                                                                          \
+    IdxT k,                                                                          \
+    void* workspace,                                                                 \
+    size_t worksize,                                                                 \
+    FinalLambda fin_op,                                                              \
+    bool isRowMajor,                                                                 \
+    DataT metric_arg)
+
+// The following two instances are used in test/distance/gram.cu. Note the use
+// of int64_t for the index type.
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   cuvs::distance::kernels::detail::rbf_fin_op<float>,
+                                   int64_t);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   cuvs::distance::kernels::detail::rbf_fin_op<double>,
+                                   int64_t);
+
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::identity_op,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, raft::identity_op, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, raft::identity_op, int);
+
+#undef instantiate_raft_distance_distance
+
+// Same, but without raft::identity_op
+#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT) \
+  template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>(  \
+    raft::resources const& handle,                                      \
+    const DataT* x,                                                     \
+    const DataT* y,                                                     \
+    OutT* dist,                                                         \
+    IdxT m,                                                             \
+    IdxT n,                                                             \
+    IdxT k,                                                             \
+    void* workspace,                                                    \
+    size_t worksize,                                                    \
+    bool isRowMajor,                                                    \
+    DataT metric_arg)
+
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
+
+#undef instantiate_raft_distance_distance
+
+// Same, but without workspace
+#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT) \
+  template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>(  \
+    raft::resources const& handle,                                      \
+    const DataT* x,                                                     \
+    const DataT* y,                                                     \
+    OutT* dist,                                                         \
+    IdxT m,                                                             \
+    IdxT n,                                                             \
+    IdxT k,                                                             \
+    bool isRowMajor,                                                    \
+    DataT metric_arg)
+
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
+
+#undef instantiate_raft_distance_distance
+
+#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT)  \
+  template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>( \
+    const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k)
+
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Linf, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Linf, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
+
+#undef instantiate_raft_distance_getWorkspaceSize
+
+#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT, layout)  \
+  template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, layout>( \
+    raft::device_matrix_view<DataT, IdxT, layout> const& x,                                 \
+    raft::device_matrix_view<DataT, IdxT, layout> const& y)
+
+// We could consider not taking template parameters for this function. The
+// number of instantiations seems a bit excessive..
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           float,
+                                           float,
+                                           float,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_f_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2Unexpanded,
+                                           double,
+                                           double,
+                                           double,
+                                           int,
+                                           raft::layout_c_contiguous);
+instantiate_raft_distance_getWorkspaceSize(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_f_contiguous);
+
+#undef instantiate_raft_distance_getWorkspaceSize
+
+#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                        \
+  template void cuvs::distance::pairwise_distance(raft::resources const& handle,        \
+                                                  const DataT* x,                       \
+                                                  const DataT* y,                       \
+                                                  DataT* dist,                          \
+                                                  IdxT m,                               \
+                                                  IdxT n,                               \
+                                                  IdxT k,                               \
+                                                  rmm::device_uvector<char>& workspace, \
+                                                  cuvs::distance::DistanceType metric,  \
+                                                  bool isRowMajor,                      \
+                                                  DataT metric_arg)
+
+instantiate_raft_distance_pairwise_distance(float, int);
+instantiate_raft_distance_pairwise_distance(double, int);
+
+#undef instantiate_raft_distance_pairwise_distance
+
+// Same, but without workspace
+#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                       \
+  template void cuvs::distance::pairwise_distance(raft::resources const& handle,       \
+                                                  const DataT* x,                      \
+                                                  const DataT* y,                      \
+                                                  DataT* dist,                         \
+                                                  IdxT m,                              \
+                                                  IdxT n,                              \
+                                                  IdxT k,                              \
+                                                  cuvs::distance::DistanceType metric, \
+                                                  bool isRowMajor,                     \
+                                                  DataT metric_arg)
+
+instantiate_raft_distance_pairwise_distance(float, int);
+instantiate_raft_distance_pairwise_distance(double, int);
+
+#undef instantiate_raft_distance_pairwise_distance
+
+// Version with mdspan
+#define instantiate_raft_distance_distance(DistT, DataT, AccT, OutT, layout, IdxT) \
+  template void cuvs::distance::distance<DistT, DataT, AccT, OutT, layout, IdxT>(  \
+    raft::resources const& handle,                                                 \
+    raft::device_matrix_view<const DataT, IdxT, layout> const x,                   \
+    raft::device_matrix_view<const DataT, IdxT, layout> const y,                   \
+    raft::device_matrix_view<OutT, IdxT, layout> dist,                             \
+    DataT metric_arg)
+
+// Again, we might want to consider reigning in the number of instantiations...
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(
+  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_c_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   float,
+                                   float,
+                                   float,
+                                   raft::layout_f_contiguous,
+                                   int);
+instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
+                                   double,
+                                   double,
+                                   double,
+                                   raft::layout_f_contiguous,
+                                   int);
+
+#undef instantiate_raft_distance_distance
+
+#define instantiate_raft_distance_pairwise_distance(DataT, layout, IdxT) \
+  template void cuvs::distance::pairwise_distance(                       \
+    raft::resources const& handle,                                       \
+    raft::device_matrix_view<const DataT, IdxT, layout> const x,         \
+    raft::device_matrix_view<const DataT, IdxT, layout> const y,         \
+    raft::device_matrix_view<DataT, IdxT, layout> dist,                  \
+    cuvs::distance::DistanceType metric,                                 \
+    DataT metric_arg)
+
+instantiate_raft_distance_pairwise_distance(float, raft::layout_c_contiguous, int);
+instantiate_raft_distance_pairwise_distance(float, raft::layout_f_contiguous, int);
+instantiate_raft_distance_pairwise_distance(double, raft::layout_c_contiguous, int);
+instantiate_raft_distance_pairwise_distance(double, raft::layout_f_contiguous, int);
+
+#undef instantiate_raft_distance_pairwise_distance
diff --git a/cpp/src/distance/distance.cuh b/cpp/src/distance/distance.cuh
new file mode 100644
index 0000000000..de70cd4691
--- /dev/null
+++ b/cpp/src/distance/distance.cuh
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#include "distance-inl.cuh"
+#endif
+
+#ifdef RAFT_COMPILED
+#include "distance-ext.cuh"
+#endif
diff --git a/cpp/src/distance/pairwise_distance.cu b/cpp/src/distance/pairwise_distance.cu
new file mode 100644
index 0000000000..bf4b21669b
--- /dev/null
+++ b/cpp/src/distance/pairwise_distance.cu
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "distance.cuh"
+#include <cstdint>
+#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/pairwise_distance.hpp>
+#include <raft/core/device_mdspan.hpp>
+
+namespace cuvs::distance {
+
+/**
+ * @defgroup pairwise_distance_runtime Pairwise Distances Runtime API
+ * @{
+ */
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_c_contiguous> const x,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_c_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_c_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg)
+{
+  auto x_v = raft::make_device_matrix_view<const float, int, raft::layout_c_contiguous>(
+    x.data_handle(), x.extent(0), x.extent(1));
+  auto y_v = raft::make_device_matrix_view<const float, int, raft::layout_c_contiguous>(
+    y.data_handle(), y.extent(0), y.extent(1));
+  auto d_v = raft::make_device_matrix_view<float, int, raft::layout_c_contiguous>(
+    dist.data_handle(), dist.extent(0), dist.extent(1));
+  pairwise_distance<float, raft::layout_c_contiguous, int>(
+    handle, x_v, y_v, d_v, metric, metric_arg);
+}
+
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_c_contiguous> const x,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_c_contiguous> const y,
+  raft::device_matrix_view<double, std::int64_t, raft::layout_c_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  double metric_arg)
+{
+  auto x_v = raft::make_device_matrix_view<const double, int, raft::layout_c_contiguous>(
+    x.data_handle(), x.extent(0), x.extent(1));
+  auto y_v = raft::make_device_matrix_view<const double, int, raft::layout_c_contiguous>(
+    y.data_handle(), y.extent(0), y.extent(1));
+  auto d_v = raft::make_device_matrix_view<double, int, raft::layout_c_contiguous>(
+    dist.data_handle(), dist.extent(0), dist.extent(1));
+  pairwise_distance<double, raft::layout_c_contiguous, int>(
+    handle, x_v, y_v, d_v, metric, metric_arg);
+}
+
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_f_contiguous> const x,
+  raft::device_matrix_view<const float, std::int64_t, raft::layout_f_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_f_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg)
+{
+  auto x_v = raft::make_device_matrix_view<const float, int, raft::layout_f_contiguous>(
+    x.data_handle(), x.extent(0), x.extent(1));
+  auto y_v = raft::make_device_matrix_view<const float, int, raft::layout_f_contiguous>(
+    y.data_handle(), y.extent(0), y.extent(1));
+  auto d_v = raft::make_device_matrix_view<float, int, raft::layout_f_contiguous>(
+    dist.data_handle(), dist.extent(0), dist.extent(1));
+  pairwise_distance<float, raft::layout_f_contiguous, int>(
+    handle, x_v, y_v, d_v, metric, metric_arg);
+}
+
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_f_contiguous> const x,
+  raft::device_matrix_view<const double, std::int64_t, raft::layout_f_contiguous> const y,
+  raft::device_matrix_view<double, std::int64_t, raft::layout_f_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  double metric_arg)
+{
+  auto x_v = raft::make_device_matrix_view<const double, int, raft::layout_f_contiguous>(
+    x.data_handle(), x.extent(0), x.extent(1));
+  auto y_v = raft::make_device_matrix_view<const double, int, raft::layout_f_contiguous>(
+    y.data_handle(), y.extent(0), y.extent(1));
+  auto d_v = raft::make_device_matrix_view<double, int, raft::layout_f_contiguous>(
+    dist.data_handle(), dist.extent(0), dist.extent(1));
+  pairwise_distance<double, raft::layout_f_contiguous, int>(
+    handle, x_v, y_v, d_v, metric, metric_arg);
+}
+
+/** @} */  // end group pairwise_distance_runtime
+
+}  // namespace cuvs::distance
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index 88349e089e..e988ac2f0c 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -33,7 +33,7 @@ namespace {
 template <typename T>
 void* _build(cuvsResources_t res,
              DLManagedTensor* dataset_tensor,
-             enum DistanceType metric,
+             cuvsDistanceType metric,
              T metric_arg)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
@@ -97,7 +97,7 @@ extern "C" cuvsError_t cuvsBruteForceIndexDestroy(cuvsBruteForceIndex_t index_c_
 
 extern "C" cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
                                            DLManagedTensor* dataset_tensor,
-                                           enum DistanceType metric,
+                                           cuvsDistanceType metric,
                                            float metric_arg,
                                            cuvsBruteForceIndex_t index)
 {
diff --git a/cpp/src/neighbors/cagra_build_float.cpp b/cpp/src/neighbors/cagra_build_float.cpp
index f66ae5cfb9..5ff1da7f21 100644
--- a/cpp/src/neighbors/cagra_build_float.cpp
+++ b/cpp/src/neighbors/cagra_build_float.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 841058f60f..131737039a 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -128,35 +128,47 @@ if(BUILD_TESTS)
     PERCENT
     100
   )
+
+  ConfigureTest(
+    NAME
+    DISTANCE_TEST
+    PATH
+    test/distance/dist_canberra.cu
+    test/distance/dist_correlation.cu
+    test/distance/dist_cos.cu
+    test/distance/dist_hamming.cu
+    test/distance/dist_hellinger.cu
+    test/distance/dist_inner_product.cu
+    test/distance/dist_jensen_shannon.cu
+    test/distance/dist_kl_divergence.cu
+    test/distance/dist_l1.cu
+    test/distance/dist_l2_exp.cu
+    test/distance/dist_l2_sqrt_exp.cu
+    test/distance/dist_l_inf.cu
+    test/distance/dist_lp_unexp.cu
+    test/distance/dist_russell_rao.cu
+    GPUS
+    1
+    PERCENT
+    100
+  )
 endif()
 
 if(BUILD_C_TESTS)
   ConfigureTest(NAME INTEROP_TEST PATH test/core/interop.cu C_LIB)
 
   ConfigureTest(
-    NAME
-    BRUTEFORCE_C_TEST
-    PATH
-    test/neighbors/run_brute_force_c.c
-    test/neighbors/brute_force_c.cu
+    NAME BRUTEFORCE_C_TEST PATH test/neighbors/run_brute_force_c.c test/neighbors/brute_force_c.cu
     C_LIB
   )
 
   ConfigureTest(
-    NAME
-    IVF_FLAT_C_TEST
-    PATH
-    test/neighbors/run_ivf_flat_c.c
-    test/neighbors/ann_ivf_flat_c.cu
+    NAME IVF_FLAT_C_TEST PATH test/neighbors/run_ivf_flat_c.c test/neighbors/ann_ivf_flat_c.cu
     C_LIB
   )
 
   ConfigureTest(
-    NAME
-    IVF_PQ_C_TEST
-    PATH
-    test/neighbors/run_ivf_pq_c.c
-    test/neighbors/ann_ivf_pq_c.cu C_LIB
+    NAME IVF_PQ_C_TEST PATH test/neighbors/run_ivf_pq_c.c test/neighbors/ann_ivf_pq_c.cu C_LIB
   )
 
   ConfigureTest(NAME CAGRA_C_TEST PATH test/neighbors/ann_cagra_c.cu C_LIB)
diff --git a/cpp/test/distance/dist_canberra.cu b/cpp/test/distance/dist_canberra.cu
new file mode 100644
index 0000000000..2bf5906013
--- /dev/null
+++ b/cpp/test/distance/dist_canberra.cu
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceCanberra : public DistanceTest<cuvs::distance::DistanceType::Canberra, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceCanberra<float> DistanceCanberraF;
+TEST_P(DistanceCanberraF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCanberraF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceCanberra<double> DistanceCanberraD;
+TEST_P(DistanceCanberraD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCanberraD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixCanberra : public BigMatrixDistanceTest<cuvs::distance::DistanceType::Canberra> {};
+TEST_F(BigMatrixCanberra, Result) {}
+
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_correlation.cu b/cpp/test/distance/dist_correlation.cu
new file mode 100644
index 0000000000..9e061bebc3
--- /dev/null
+++ b/cpp/test/distance/dist_correlation.cu
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceCorrelation
+  : public DistanceTest<cuvs::distance::DistanceType::CorrelationExpanded, DataType> {};
+
+template <typename DataType>
+class DistanceCorrelationXequalY
+  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::CorrelationExpanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceCorrelation<float> DistanceCorrelationF;
+TEST_P(DistanceCorrelationF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCorrelationF, ::testing::ValuesIn(inputsf));
+
+typedef DistanceCorrelationXequalY<float> DistanceCorrelationXequalYF;
+TEST_P(DistanceCorrelationXequalYF, Result)
+{
+  int m = params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[0].data(),
+                                dist[0].data(),
+                                m,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[1].data(),
+                                dist[1].data(),
+                                m / 2,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCorrelationXequalYF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceCorrelation<double> DistanceCorrelationD;
+TEST_P(DistanceCorrelationD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCorrelationD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixCorrelation
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::CorrelationExpanded> {};
+TEST_F(BigMatrixCorrelation, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_cos.cu b/cpp/test/distance/dist_cos.cu
new file mode 100644
index 0000000000..e134f045fc
--- /dev/null
+++ b/cpp/test/distance/dist_cos.cu
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceExpCos : public DistanceTest<cuvs::distance::DistanceType::CosineExpanded, DataType> {
+};
+
+template <typename DataType>
+class DistanceExpCosXequalY
+  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::CosineExpanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 128, (65536 + 128) * 128, 8, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, (65536 + 128) * 128, 128, 8, false, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+
+const std::vector<DistanceInputs<float>> inputsXeqYf = {
+  {0.01f, 1024, 1024, 32, true, 1234ULL},
+  {0.01f, 1024, 32, 1024, true, 1234ULL},
+  {0.01f, 32, 1024, 1024, true, 1234ULL},
+  {0.03f, 1024, 1024, 1024, true, 1234ULL},
+  {0.01f, 1024, 1024, 32, false, 1234ULL},
+  {0.01f, 1024, 32, 1024, false, 1234ULL},
+  {0.01f, 32, 1024, 1024, false, 1234ULL},
+  {0.03f, 1024, 1024, 1024, false, 1234ULL},
+};
+
+typedef DistanceExpCos<float> DistanceExpCosF;
+TEST_P(DistanceExpCosF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceExpCosF, ::testing::ValuesIn(inputsf));
+
+typedef DistanceExpCosXequalY<float> DistanceExpCosXequalYF;
+TEST_P(DistanceExpCosXequalYF, Result)
+{
+  int m = params.m;
+  int n = params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[0].data(),
+                                dist[0].data(),
+                                m,
+                                n,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+  n = params.isRowMajor ? m : m / 2;
+  m = params.isRowMajor ? m / 2 : m;
+
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[1].data(),
+                                dist[1].data(),
+                                m,
+                                n,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceExpCosXequalYF, ::testing::ValuesIn(inputsXeqYf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceExpCos<double> DistanceExpCosD;
+TEST_P(DistanceExpCosD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceExpCosD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixCos : public BigMatrixDistanceTest<cuvs::distance::DistanceType::CosineExpanded> {};
+TEST_F(BigMatrixCos, Result) {}
+
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_hamming.cu b/cpp/test/distance/dist_hamming.cu
new file mode 100644
index 0000000000..0cf753ecae
--- /dev/null
+++ b/cpp/test/distance/dist_hamming.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceHamming
+  : public DistanceTest<cuvs::distance::DistanceType::HammingUnexpanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceHamming<float> DistanceHammingF;
+TEST_P(DistanceHammingF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHammingF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceHamming<double> DistanceHammingD;
+TEST_P(DistanceHammingD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHammingD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixHamming
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::HammingUnexpanded> {};
+TEST_F(BigMatrixHamming, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_hellinger.cu b/cpp/test/distance/dist_hellinger.cu
new file mode 100644
index 0000000000..3998a60ab1
--- /dev/null
+++ b/cpp/test/distance/dist_hellinger.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceHellingerExp
+  : public DistanceTest<cuvs::distance::DistanceType::HellingerExpanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceHellingerExp<float> DistanceHellingerExpF;
+TEST_P(DistanceHellingerExpF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHellingerExpF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceHellingerExp<double> DistanceHellingerExpD;
+TEST_P(DistanceHellingerExpD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHellingerExpD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixHellingerExp
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::HellingerExpanded> {};
+TEST_F(BigMatrixHellingerExp, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_inner_product.cu b/cpp/test/distance/dist_inner_product.cu
new file mode 100644
index 0000000000..1d6709d52b
--- /dev/null
+++ b/cpp/test/distance/dist_inner_product.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceInnerProduct
+  : public DistanceTest<cuvs::distance::DistanceType::InnerProduct, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 10, 5, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceInnerProduct<float> DistanceInnerProductF;
+TEST_P(DistanceInnerProductF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceInnerProductF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceInnerProduct<double> DistanceInnerProductD;
+TEST_P(DistanceInnerProductD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceInnerProductD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixInnerProduct
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::InnerProduct> {};
+TEST_F(BigMatrixInnerProduct, Result) {}
+
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_jensen_shannon.cu b/cpp/test/distance/dist_jensen_shannon.cu
new file mode 100644
index 0000000000..43b7b361d1
--- /dev/null
+++ b/cpp/test/distance/dist_jensen_shannon.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceJensenShannon
+  : public DistanceTest<cuvs::distance::DistanceType::JensenShannon, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceJensenShannon<float> DistanceJensenShannonF;
+TEST_P(DistanceJensenShannonF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceJensenShannonF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceJensenShannon<double> DistanceJensenShannonD;
+TEST_P(DistanceJensenShannonD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceJensenShannonD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixJensenShannon
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::JensenShannon> {};
+TEST_F(BigMatrixJensenShannon, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_kl_divergence.cu b/cpp/test/distance/dist_kl_divergence.cu
new file mode 100644
index 0000000000..5e56928411
--- /dev/null
+++ b/cpp/test/distance/dist_kl_divergence.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceKLDivergence
+  : public DistanceTest<cuvs::distance::DistanceType::KLDivergence, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceKLDivergence<float> DistanceKLDivergenceF;
+TEST_P(DistanceKLDivergenceF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceKLDivergenceF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceKLDivergence<double> DistanceKLDivergenceD;
+TEST_P(DistanceKLDivergenceD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceKLDivergenceD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixKLDivergence
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::KLDivergence> {};
+TEST_F(BigMatrixKLDivergence, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_l1.cu b/cpp/test/distance/dist_l1.cu
new file mode 100644
index 0000000000..a3ecd21fe0
--- /dev/null
+++ b/cpp/test/distance/dist_l1.cu
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceUnexpL1 : public DistanceTest<cuvs::distance::DistanceType::L1, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceUnexpL1<float> DistanceUnexpL1F;
+TEST_P(DistanceUnexpL1F, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceUnexpL1F, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceUnexpL1<double> DistanceUnexpL1D;
+TEST_P(DistanceUnexpL1D, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceUnexpL1D, ::testing::ValuesIn(inputsd));
+
+class BigMatrixUnexpL1 : public BigMatrixDistanceTest<cuvs::distance::DistanceType::L1> {};
+TEST_F(BigMatrixUnexpL1, Result) {}
+
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_l2_exp.cu b/cpp/test/distance/dist_l2_exp.cu
new file mode 100644
index 0000000000..f3d038cbc9
--- /dev/null
+++ b/cpp/test/distance/dist_l2_exp.cu
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceEucExpTest : public DistanceTest<cuvs::distance::DistanceType::L2Expanded, DataType> {
+};
+
+template <typename DataType>
+class DistanceEucExpTestXequalY
+  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::L2Expanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 128, (65536 + 128) * 128, 8, true, 1234ULL},
+  {0.001f, 2048, 4096, 128, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.003f, 1021, 1021, 1021, true, 1234ULL},
+  {0.001f, (65536 + 128) * 128, 128, 8, false, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+  {0.003f, 1021, 1021, 1021, false, 1234ULL},
+};
+
+const std::vector<DistanceInputs<float>> inputsXeqYf = {
+  {0.01f, 2048, 4096, 128, true, 1234ULL},
+  {0.01f, 1024, 1024, 32, true, 1234ULL},
+  {0.01f, 1024, 32, 1024, true, 1234ULL},
+  {0.01f, 32, 1024, 1024, true, 1234ULL},
+  {0.03f, 1024, 1024, 1024, true, 1234ULL},
+  {0.03f, 1021, 1021, 1021, true, 1234ULL},
+  {0.01f, 1024, 1024, 32, false, 1234ULL},
+  {0.01f, 1024, 32, 1024, false, 1234ULL},
+  {0.01f, 32, 1024, 1024, false, 1234ULL},
+  {0.03f, 1024, 1024, 1024, false, 1234ULL},
+  {0.03f, 1021, 1021, 1021, false, 1234ULL},
+};
+
+typedef DistanceEucExpTest<float> DistanceEucExpTestF;
+TEST_P(DistanceEucExpTestF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucExpTestF, ::testing::ValuesIn(inputsf));
+
+typedef DistanceEucExpTestXequalY<float> DistanceEucExpTestXequalYF;
+TEST_P(DistanceEucExpTestXequalYF, Result)
+{
+  int m = params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[0].data(),
+                                dist[0].data(),
+                                m,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[1].data(),
+                                dist[1].data(),
+                                m / 2,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests,
+                        DistanceEucExpTestXequalYF,
+                        ::testing::ValuesIn(inputsXeqYf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceEucExpTest<double> DistanceEucExpTestD;
+TEST_P(DistanceEucExpTestD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucExpTestD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixEucExp : public BigMatrixDistanceTest<cuvs::distance::DistanceType::L2Expanded> {};
+TEST_F(BigMatrixEucExp, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_l2_sqrt_exp.cu b/cpp/test/distance/dist_l2_sqrt_exp.cu
new file mode 100644
index 0000000000..b24384be8a
--- /dev/null
+++ b/cpp/test/distance/dist_l2_sqrt_exp.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceEucSqrtExpTest
+  : public DistanceTest<cuvs::distance::DistanceType::L2SqrtExpanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 2048, 4096, 128, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.003f, 1021, 1021, 1021, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+  {0.003f, 1021, 1021, 1021, false, 1234ULL},
+};
+typedef DistanceEucSqrtExpTest<float> DistanceEucSqrtExpTestF;
+TEST_P(DistanceEucSqrtExpTestF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucSqrtExpTestF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceEucSqrtExpTest<double> DistanceEucSqrtExpTestD;
+TEST_P(DistanceEucSqrtExpTestD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucSqrtExpTestD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixEucSqrtExp
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::L2SqrtExpanded> {};
+TEST_F(BigMatrixEucSqrtExp, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_l2_unexp.cu b/cpp/test/distance/dist_l2_unexp.cu
new file mode 100644
index 0000000000..c057434fae
--- /dev/null
+++ b/cpp/test/distance/dist_l2_unexp.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceEucUnexpTest
+  : public DistanceTest<cuvs::distance::DistanceType::L2Unexpanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceEucUnexpTest<float> DistanceEucUnexpTestF;
+TEST_P(DistanceEucUnexpTestF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucUnexpTestF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceEucUnexpTest<double> DistanceEucUnexpTestD;
+TEST_P(DistanceEucUnexpTestD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucUnexpTestD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixEucUnexp : public BigMatrixDistanceTest<cuvs::distance::DistanceType::L2Unexpanded> {
+};
+TEST_F(BigMatrixEucUnexp, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_l_inf.cu b/cpp/test/distance/dist_l_inf.cu
new file mode 100644
index 0000000000..b9ced68f34
--- /dev/null
+++ b/cpp/test/distance/dist_l_inf.cu
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceLinf : public DistanceTest<cuvs::distance::DistanceType::Linf, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceLinf<float> DistanceLinfF;
+TEST_P(DistanceLinfF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLinfF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceLinf<double> DistanceLinfD;
+TEST_P(DistanceLinfD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLinfD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixLinf : public BigMatrixDistanceTest<cuvs::distance::DistanceType::Linf> {};
+TEST_F(BigMatrixLinf, Result) {}
+
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_lp_unexp.cu b/cpp/test/distance/dist_lp_unexp.cu
new file mode 100644
index 0000000000..26620b44b3
--- /dev/null
+++ b/cpp/test/distance/dist_lp_unexp.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceLpUnexp : public DistanceTest<cuvs::distance::DistanceType::LpUnexpanded, DataType> {
+};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL, 4.0f},
+  {0.001f, 1024, 32, 1024, true, 1234ULL, 3.0f},
+  {0.001f, 32, 1024, 1024, true, 1234ULL, 4.0f},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL, 3.0f},
+  {0.001f, 1024, 1024, 32, false, 1234ULL, 4.0f},
+  {0.001f, 1024, 32, 1024, false, 1234ULL, 3.0f},
+  {0.001f, 32, 1024, 1024, false, 1234ULL, 4.0f},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL, 3.0f},
+};
+typedef DistanceLpUnexp<float> DistanceLpUnexpF;
+TEST_P(DistanceLpUnexpF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLpUnexpF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL, 4.0},
+  {0.001, 1024, 32, 1024, true, 1234ULL, 3.0},
+  {0.001, 32, 1024, 1024, true, 1234ULL, 4.0},
+  {0.003, 1024, 1024, 1024, true, 1234ULL, 3.0},
+  {0.001, 1024, 1024, 32, false, 1234ULL, 4.0},
+  {0.001, 1024, 32, 1024, false, 1234ULL, 3.0},
+  {0.001, 32, 1024, 1024, false, 1234ULL, 4.0},
+  {0.003, 1024, 1024, 1024, false, 1234ULL, 3.0},
+};
+typedef DistanceLpUnexp<double> DistanceLpUnexpD;
+TEST_P(DistanceLpUnexpD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLpUnexpD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixLpUnexp : public BigMatrixDistanceTest<cuvs::distance::DistanceType::LpUnexpanded> {
+};
+TEST_F(BigMatrixLpUnexp, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/dist_russell_rao.cu b/cpp/test/distance/dist_russell_rao.cu
new file mode 100644
index 0000000000..46da7f9cd0
--- /dev/null
+++ b/cpp/test/distance/dist_russell_rao.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "distance_base.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+class DistanceRussellRao
+  : public DistanceTest<cuvs::distance::DistanceType::RusselRaoExpanded, DataType> {};
+
+const std::vector<DistanceInputs<float>> inputsf = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceRussellRao<float> DistanceRussellRaoF;
+TEST_P(DistanceRussellRaoF, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceRussellRaoF, ::testing::ValuesIn(inputsf));
+
+const std::vector<DistanceInputs<double>> inputsd = {
+  {0.001, 1024, 1024, 32, true, 1234ULL},
+  {0.001, 1024, 32, 1024, true, 1234ULL},
+  {0.001, 32, 1024, 1024, true, 1234ULL},
+  {0.003, 1024, 1024, 1024, true, 1234ULL},
+  {0.001, 1024, 1024, 32, false, 1234ULL},
+  {0.001, 1024, 32, 1024, false, 1234ULL},
+  {0.001, 32, 1024, 1024, false, 1234ULL},
+  {0.003, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceRussellRao<double> DistanceRussellRaoD;
+TEST_P(DistanceRussellRaoD, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<double>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceRussellRaoD, ::testing::ValuesIn(inputsd));
+
+class BigMatrixRussellRao
+  : public BigMatrixDistanceTest<cuvs::distance::DistanceType::RusselRaoExpanded> {};
+TEST_F(BigMatrixRussellRao, Result) {}
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/distance/distance_base.cuh b/cpp/test/distance/distance_base.cuh
new file mode 100644
index 0000000000..4a8191ae64
--- /dev/null
+++ b/cpp/test/distance/distance_base.cuh
@@ -0,0 +1,710 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+
+#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
+#include <cuvs/distance/pairwise_distance.hpp>
+#include <raft/common/nvtx.hpp>         // raft::common::nvtx::range
+#include <raft/core/device_mdspan.hpp>  //raft::make_device_matrix_view
+#include <raft/core/operators.hpp>      // raft::sqrt
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>  // raft::resources
+#include <raft/random/rng.cuh>
+
+#include <rmm/device_uvector.hpp>  // rmm::device_uvector
+
+#include <gtest/gtest.h>
+
+namespace cuvs {
+namespace distance {
+
+template <typename DataType>
+RAFT_KERNEL naiveDistanceKernel(DataType* dist,
+                                const DataType* x,
+                                const DataType* y,
+                                std::int64_t m,
+                                std::int64_t n,
+                                std::int64_t k,
+                                cuvs::distance::DistanceType type,
+                                bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) return;
+  DataType acc = DataType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto diff         = x[xidx] - y[yidx];
+    acc += diff * diff;
+  }
+  if (type == cuvs::distance::DistanceType::L2SqrtExpanded ||
+      type == cuvs::distance::DistanceType::L2SqrtUnexpanded)
+    acc = raft::sqrt(acc);
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType>
+RAFT_KERNEL naiveL1_Linf_CanberraDistanceKernel(DataType* dist,
+                                                const DataType* x,
+                                                const DataType* y,
+                                                std::int64_t m,
+                                                std::int64_t n,
+                                                std::int64_t k,
+                                                cuvs::distance::DistanceType type,
+                                                bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) { return; }
+
+  DataType acc = DataType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    auto diff         = (a > b) ? (a - b) : (b - a);
+    if (type == cuvs::distance::DistanceType::Linf) {
+      acc = raft::max(acc, diff);
+    } else if (type == cuvs::distance::DistanceType::Canberra) {
+      const auto add = raft::abs(a) + raft::abs(b);
+      // deal with potential for 0 in denominator by
+      // forcing 1/0 instead
+      acc += ((add != 0) * diff / (add + (add == 0)));
+    } else {
+      acc += diff;
+    }
+  }
+
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType>
+RAFT_KERNEL naiveCosineDistanceKernel(DataType* dist,
+                                      const DataType* x,
+                                      const DataType* y,
+                                      std::int64_t m,
+                                      std::int64_t n,
+                                      std::int64_t k,
+                                      bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) { return; }
+
+  DataType acc_a  = DataType(0);
+  DataType acc_b  = DataType(0);
+  DataType acc_ab = DataType(0);
+
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    acc_a += a * a;
+    acc_b += b * b;
+    acc_ab += a * b;
+  }
+
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+
+  // Use 1.0 - (cosine similarity) to calc the distance
+  dist[outidx] = (DataType)1.0 - acc_ab / (raft::sqrt(acc_a) * raft::sqrt(acc_b));
+}
+
+template <typename DataType>
+RAFT_KERNEL naiveInnerProductKernel(DataType* dist,
+                                    const DataType* x,
+                                    const DataType* y,
+                                    std::int64_t m,
+                                    std::int64_t n,
+                                    std::int64_t k,
+                                    bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) { return; }
+
+  DataType acc_ab = DataType(0);
+
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    acc_ab += a * b;
+  }
+
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc_ab;
+}
+
+template <typename DataType>
+RAFT_KERNEL naiveHellingerDistanceKernel(DataType* dist,
+                                         const DataType* x,
+                                         const DataType* y,
+                                         std::int64_t m,
+                                         std::int64_t n,
+                                         std::int64_t k,
+                                         bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) { return; }
+
+  DataType acc_ab = DataType(0);
+
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    acc_ab += raft::sqrt(a) * raft::sqrt(b);
+  }
+
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+
+  // Adjust to replace NaN in sqrt with 0 if input to sqrt is negative
+  acc_ab         = 1 - acc_ab;
+  auto rectifier = (!signbit(acc_ab));
+  dist[outidx]   = raft::sqrt(rectifier * acc_ab);
+}
+
+template <typename DataType>
+RAFT_KERNEL naiveLpUnexpDistanceKernel(DataType* dist,
+                                       const DataType* x,
+                                       const DataType* y,
+                                       std::int64_t m,
+                                       std::int64_t n,
+                                       std::int64_t k,
+                                       bool isRowMajor,
+                                       DataType p)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) return;
+  DataType acc = DataType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    auto diff         = raft::abs(a - b);
+    acc += raft::pow(diff, p);
+  }
+  auto one_over_p     = 1 / p;
+  acc                 = raft::pow(acc, one_over_p);
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType>
+RAFT_KERNEL naiveHammingDistanceKernel(DataType* dist,
+                                       const DataType* x,
+                                       const DataType* y,
+                                       std::int64_t m,
+                                       std::int64_t n,
+                                       std::int64_t k,
+                                       bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) return;
+  DataType acc = DataType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    acc += (a != b);
+  }
+  acc                 = acc / k;
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType>
+RAFT_KERNEL naiveJensenShannonDistanceKernel(DataType* dist,
+                                             const DataType* x,
+                                             const DataType* y,
+                                             std::int64_t m,
+                                             std::int64_t n,
+                                             std::int64_t k,
+                                             bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) return;
+  DataType acc = DataType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+
+    DataType m  = 0.5f * (a + b);
+    bool a_zero = a == 0;
+    bool b_zero = b == 0;
+
+    DataType p = (!a_zero * m) / (a_zero + a);
+    DataType q = (!b_zero * m) / (b_zero + b);
+
+    bool p_zero = p == 0;
+    bool q_zero = q == 0;
+
+    acc += (-a * (!p_zero * log(p + p_zero))) + (-b * (!q_zero * log(q + q_zero)));
+  }
+  acc                 = raft::sqrt(0.5f * acc);
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType, typename OutType>
+RAFT_KERNEL naiveRussellRaoDistanceKernel(OutType* dist,
+                                          const DataType* x,
+                                          const DataType* y,
+                                          std::int64_t m,
+                                          std::int64_t n,
+                                          std::int64_t k,
+                                          bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) return;
+  OutType acc = OutType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    acc += (a * b);
+  }
+  acc                 = (k - acc) / k;
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType, typename OutType>
+RAFT_KERNEL naiveKLDivergenceDistanceKernel(OutType* dist,
+                                            const DataType* x,
+                                            const DataType* y,
+                                            std::int64_t m,
+                                            std::int64_t n,
+                                            std::int64_t k,
+                                            bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) return;
+  OutType acc = OutType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    bool b_zero       = (b == 0);
+    bool a_zero       = (a == 0);
+    acc += a * (log(a + a_zero) - log(b + b_zero));
+  }
+  acc                 = 0.5f * acc;
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType, typename OutType>
+RAFT_KERNEL naiveCorrelationDistanceKernel(OutType* dist,
+                                           const DataType* x,
+                                           const DataType* y,
+                                           std::int64_t m,
+                                           std::int64_t n,
+                                           std::int64_t k,
+                                           bool isRowMajor)
+{
+  std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
+  std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
+  if (midx >= m || nidx >= n) return;
+  OutType acc    = OutType(0);
+  auto a_norm    = DataType(0);
+  auto b_norm    = DataType(0);
+  auto a_sq_norm = DataType(0);
+  auto b_sq_norm = DataType(0);
+  for (std::int64_t i = 0; i < k; ++i) {
+    std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
+    std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
+    auto a            = x[xidx];
+    auto b            = y[yidx];
+    a_norm += a;
+    b_norm += b;
+    a_sq_norm += (a * a);
+    b_sq_norm += (b * b);
+    acc += (a * b);
+  }
+
+  auto numer   = k * acc - (a_norm * b_norm);
+  auto Q_denom = k * a_sq_norm - (a_norm * a_norm);
+  auto R_denom = k * b_sq_norm - (b_norm * b_norm);
+
+  acc = 1 - (numer / raft::sqrt(Q_denom * R_denom));
+
+  std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
+  dist[outidx]        = acc;
+}
+
+template <typename DataType>
+void naiveDistance(DataType* dist,
+                   const DataType* x,
+                   const DataType* y,
+                   std::int64_t m,
+                   std::int64_t n,
+                   std::int64_t k,
+                   cuvs::distance::DistanceType type,
+                   bool isRowMajor,
+                   DataType metric_arg = 2.0f,
+                   cudaStream_t stream = 0)
+{
+  static const dim3 TPB(4, 256, 1);
+  dim3 nblks(raft::ceildiv(m, (std::int64_t)TPB.x), raft::ceildiv(n, (std::int64_t)TPB.y), 1);
+
+  switch (type) {
+    case cuvs::distance::DistanceType::Canberra:
+    case cuvs::distance::DistanceType::Linf:
+    case cuvs::distance::DistanceType::L1:
+      naiveL1_Linf_CanberraDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, type, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::L2SqrtUnexpanded:
+    case cuvs::distance::DistanceType::L2Unexpanded:
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded:
+      naiveDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, type, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::CosineExpanded:
+      naiveCosineDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::HellingerExpanded:
+      naiveHellingerDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::LpUnexpanded:
+      naiveLpUnexpDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor, metric_arg);
+      break;
+    case cuvs::distance::DistanceType::HammingUnexpanded:
+      naiveHammingDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::InnerProduct:
+      naiveInnerProductKernel<DataType><<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::JensenShannon:
+      naiveJensenShannonDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::RusselRaoExpanded:
+      naiveRussellRaoDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::KLDivergence:
+      naiveKLDivergenceDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    case cuvs::distance::DistanceType::CorrelationExpanded:
+      naiveCorrelationDistanceKernel<DataType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      break;
+    default: FAIL() << "should be here\n";
+  }
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+template <typename DataType>
+struct DistanceInputs {
+  DataType tolerance;
+  std::int64_t m, n, k;
+  bool isRowMajor;
+  unsigned long long int seed;
+  DataType metric_arg = 2.0f;
+};
+
+template <typename DataType>
+::std::ostream& operator<<(::std::ostream& os, const DistanceInputs<DataType>& dims)
+{
+  return os;
+}
+
+// TODO: Remove when mdspan-based raft::runtime::distance::pairwise_distance is
+// implemented.
+//
+// Context:
+// https://github.com/rapidsai/raft/issues/1338
+template <typename layout>
+constexpr bool layout_to_row_major();
+
+template <>
+constexpr bool layout_to_row_major<raft::layout_c_contiguous>()
+{
+  return true;
+}
+template <>
+constexpr bool layout_to_row_major<raft::layout_f_contiguous>()
+{
+  return false;
+}
+
+template <cuvs::distance::DistanceType distanceType, typename DataType, typename layout>
+void distanceLauncher(raft::resources const& handle,
+                      DataType* x,
+                      DataType* y,
+                      DataType* dist,
+                      DataType* dist2,
+                      std::int64_t m,
+                      std::int64_t n,
+                      std::int64_t k,
+                      DistanceInputs<DataType>& params,
+                      DataType threshold,
+                      DataType metric_arg = 2.0f)
+{
+  auto x_v    = raft::make_device_matrix_view<DataType, std::int64_t, layout>(x, m, k);
+  auto y_v    = raft::make_device_matrix_view<DataType, std::int64_t, layout>(y, n, k);
+  auto dist_v = raft::make_device_matrix_view<DataType, std::int64_t, layout>(dist, m, n);
+
+  cuvs::distance::pairwise_distance(handle, x_v, y_v, dist_v, distanceType, metric_arg);
+}
+
+template <cuvs::distance::DistanceType distanceType, typename DataType>
+class DistanceTest : public ::testing::TestWithParam<DistanceInputs<DataType>> {
+ public:
+  DistanceTest()
+    : params(::testing::TestWithParam<DistanceInputs<DataType>>::GetParam()),
+      stream(raft::resource::get_cuda_stream(handle)),
+      x(params.m * params.k, stream),
+      y(params.n * params.k, stream),
+      dist_ref(params.m * params.n, stream),
+      dist(params.m * params.n, stream),
+      dist2(params.m * params.n, stream)
+  {
+  }
+
+  void SetUp() override
+  {
+    auto testInfo = testing::UnitTest::GetInstance()->current_test_info();
+    raft::common::nvtx::range fun_scope(
+      "test::%s/%s", testInfo->test_suite_name(), testInfo->name());
+
+    raft::random::RngState r(params.seed);
+    std::int64_t m      = params.m;
+    std::int64_t n      = params.n;
+    std::int64_t k      = params.k;
+    DataType metric_arg = params.metric_arg;
+    bool isRowMajor     = params.isRowMajor;
+    if (distanceType == cuvs::distance::DistanceType::HellingerExpanded ||
+        distanceType == cuvs::distance::DistanceType::JensenShannon ||
+        distanceType == cuvs::distance::DistanceType::KLDivergence) {
+      // Hellinger works only on positive numbers
+      uniform(handle, r, x.data(), m * k, DataType(0.0), DataType(1.0));
+      uniform(handle, r, y.data(), n * k, DataType(0.0), DataType(1.0));
+    } else if (distanceType == cuvs::distance::DistanceType::RusselRaoExpanded) {
+      uniform(handle, r, x.data(), m * k, DataType(0.0), DataType(1.0));
+      uniform(handle, r, y.data(), n * k, DataType(0.0), DataType(1.0));
+      // Russel rao works on boolean values.
+      bernoulli(handle, r, x.data(), m * k, 0.5f);
+      bernoulli(handle, r, y.data(), n * k, 0.5f);
+    } else {
+      uniform(handle, r, x.data(), m * k, DataType(-1.0), DataType(1.0));
+      uniform(handle, r, y.data(), n * k, DataType(-1.0), DataType(1.0));
+    }
+    naiveDistance(
+      dist_ref.data(), x.data(), y.data(), m, n, k, distanceType, isRowMajor, metric_arg, stream);
+
+    DataType threshold = -10000.f;
+
+    if (isRowMajor) {
+      distanceLauncher<distanceType, DataType, raft::layout_c_contiguous>(handle,
+                                                                          x.data(),
+                                                                          y.data(),
+                                                                          dist.data(),
+                                                                          dist2.data(),
+                                                                          m,
+                                                                          n,
+                                                                          k,
+                                                                          params,
+                                                                          threshold,
+                                                                          metric_arg);
+
+    } else {
+      distanceLauncher<distanceType, DataType, raft::layout_f_contiguous>(handle,
+                                                                          x.data(),
+                                                                          y.data(),
+                                                                          dist.data(),
+                                                                          dist2.data(),
+                                                                          m,
+                                                                          n,
+                                                                          k,
+                                                                          params,
+                                                                          threshold,
+                                                                          metric_arg);
+    }
+    raft::resource::sync_stream(handle, stream);
+  }
+
+ protected:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  DistanceInputs<DataType> params;
+  rmm::device_uvector<DataType> x, y, dist_ref, dist, dist2;
+};
+
+/*
+ * This test suite verifies the path when X and Y are same buffer,
+ * distance metrics which requires norms like L2 expanded/cosine/correlation
+ * takes a more optimal path in such case to skip norm calculation for Y buffer.
+ * It may happen that though both X and Y are same buffer but user passes
+ * different dimensions for them like in case of tiled_brute_force_knn.
+ */
+template <cuvs::distance::DistanceType distanceType, typename DataType>
+class DistanceTestSameBuffer : public ::testing::TestWithParam<DistanceInputs<DataType>> {
+ public:
+  using dev_vector = rmm::device_uvector<DataType>;
+  DistanceTestSameBuffer()
+    : params(::testing::TestWithParam<DistanceInputs<DataType>>::GetParam()),
+      stream(raft::resource::get_cuda_stream(handle)),
+      x(params.m * params.k, stream),
+      dist_ref({dev_vector(params.m * params.m, stream), dev_vector(params.m * params.m, stream)}),
+      dist({dev_vector(params.m * params.m, stream), dev_vector(params.m * params.m, stream)}),
+      dist2({dev_vector(params.m * params.m, stream), dev_vector(params.m * params.m, stream)})
+  {
+  }
+
+  void SetUp() override
+  {
+    auto testInfo = testing::UnitTest::GetInstance()->current_test_info();
+    raft::common::nvtx::range fun_scope(
+      "test::%s/%s", testInfo->test_suite_name(), testInfo->name());
+
+    raft::random::RngState r(params.seed);
+    std::int64_t m      = params.m;
+    std::int64_t n      = params.m;
+    std::int64_t k      = params.k;
+    DataType metric_arg = params.metric_arg;
+    bool isRowMajor     = params.isRowMajor;
+    if (distanceType == cuvs::distance::DistanceType::HellingerExpanded ||
+        distanceType == cuvs::distance::DistanceType::JensenShannon ||
+        distanceType == cuvs::distance::DistanceType::KLDivergence) {
+      // Hellinger works only on positive numbers
+      uniform(handle, r, x.data(), m * k, DataType(0.0), DataType(1.0));
+    } else if (distanceType == cuvs::distance::DistanceType::RusselRaoExpanded) {
+      uniform(handle, r, x.data(), m * k, DataType(0.0), DataType(1.0));
+      // Russel rao works on boolean values.
+      bernoulli(handle, r, x.data(), m * k, 0.5f);
+    } else {
+      uniform(handle, r, x.data(), m * k, DataType(-1.0), DataType(1.0));
+    }
+
+    for (std::int64_t i = 0; i < 2; i++) {
+      // both X and Y are same buffer but when i = 1
+      // different dimensions for x & y is passed.
+      m = m / (i + 1);
+      naiveDistance(dist_ref[i].data(),
+                    x.data(),
+                    x.data(),
+                    m,
+                    n,
+                    k,
+                    distanceType,
+                    isRowMajor,
+                    metric_arg,
+                    stream);
+
+      DataType threshold = -10000.f;
+
+      if (isRowMajor) {
+        distanceLauncher<distanceType, DataType, raft::layout_c_contiguous>(handle,
+                                                                            x.data(),
+                                                                            x.data(),
+                                                                            dist[i].data(),
+                                                                            dist2[i].data(),
+                                                                            m,
+                                                                            n,
+                                                                            k,
+                                                                            params,
+                                                                            threshold,
+                                                                            metric_arg);
+
+      } else {
+        distanceLauncher<distanceType, DataType, raft::layout_f_contiguous>(handle,
+                                                                            x.data(),
+                                                                            x.data(),
+                                                                            dist[i].data(),
+                                                                            dist2[i].data(),
+                                                                            m,
+                                                                            n,
+                                                                            k,
+                                                                            params,
+                                                                            threshold,
+                                                                            metric_arg);
+      }
+    }
+    raft::resource::sync_stream(handle, stream);
+  }
+
+ protected:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  DistanceInputs<DataType> params;
+  dev_vector x;
+  static const std::int64_t N = 2;
+  std::array<dev_vector, N> dist_ref, dist, dist2;
+};
+
+template <cuvs::distance::DistanceType distanceType>
+class BigMatrixDistanceTest : public ::testing::Test {
+ public:
+  BigMatrixDistanceTest()
+    : x(m * k, raft::resource::get_cuda_stream(handle)),
+      dist(std::size_t(m) * m, raft::resource::get_cuda_stream(handle)){};
+  void SetUp() override
+  {
+    auto testInfo = testing::UnitTest::GetInstance()->current_test_info();
+    raft::common::nvtx::range fun_scope(
+      "test::%s/%s", testInfo->test_suite_name(), testInfo->name());
+
+    constexpr float metric_arg = 0.0f;
+    auto x_v =
+      raft::make_device_matrix_view<float, std::int64_t, raft::layout_c_contiguous>(x.data(), m, k);
+    auto dist_v = raft::make_device_matrix_view<float, std::int64_t, raft::layout_c_contiguous>(
+      dist.data(), m, n);
+
+    cuvs::distance::pairwise_distance(handle, x_v, x_v, dist_v, distanceType, metric_arg);
+    raft::resource::sync_stream(handle);
+  }
+
+ protected:
+  raft::resources handle;
+  std::int64_t m = 48000;
+  std::int64_t n = 48000;
+  std::int64_t k = 1;
+  rmm::device_uvector<float> x, dist;
+};
+}  // end namespace distance
+}  // namespace cuvs
diff --git a/cpp/test/neighbors/ann_ivf_flat_c.cu b/cpp/test/neighbors/ann_ivf_flat_c.cu
index e854504948..7844188603 100644
--- a/cpp/test/neighbors/ann_ivf_flat_c.cu
+++ b/cpp/test/neighbors/ann_ivf_flat_c.cu
@@ -30,7 +30,7 @@ extern "C" void run_ivf_flat(int64_t n_rows,
                              float* query_data,
                              float* distances_data,
                              int64_t* neighbors_data,
-                             enum DistanceType metric,
+                             cuvsDistanceType metric,
                              size_t n_probes,
                              size_t n_lists);
 
@@ -51,7 +51,7 @@ void recall_eval(T* query_data,
                  size_t n_rows,
                  size_t n_dim,
                  size_t n_neighbors,
-                 DistanceType metric,
+                 cuvsDistanceType metric,
                  size_t n_probes,
                  size_t n_lists)
 {
@@ -101,9 +101,9 @@ TEST(IvfFlatC, BuildSearch)
   int64_t n_dim        = 32;
   uint32_t n_neighbors = 8;
 
-  enum DistanceType metric = L2Expanded;
-  size_t n_probes          = 20;
-  size_t n_lists           = 1024;
+  cuvsDistanceType metric = L2Expanded;
+  size_t n_probes         = 20;
+  size_t n_lists          = 1024;
 
   float *index_data, *query_data, *distances_data;
   int64_t* neighbors_data;
diff --git a/cpp/test/neighbors/ann_ivf_pq_c.cu b/cpp/test/neighbors/ann_ivf_pq_c.cu
index 94d121ce2f..88cd1bd93a 100644
--- a/cpp/test/neighbors/ann_ivf_pq_c.cu
+++ b/cpp/test/neighbors/ann_ivf_pq_c.cu
@@ -30,7 +30,7 @@ extern "C" void run_ivf_pq(int64_t n_rows,
                            float* query_data,
                            float* distances_data,
                            int64_t* neighbors_data,
-                           enum DistanceType metric,
+                           cuvsDistanceType metric,
                            size_t n_probes,
                            size_t n_lists);
 
@@ -51,7 +51,7 @@ void recall_eval(T* query_data,
                  size_t n_rows,
                  size_t n_dim,
                  size_t n_neighbors,
-                 DistanceType metric,
+                 cuvsDistanceType metric,
                  size_t n_probes,
                  size_t n_lists)
 {
@@ -101,9 +101,9 @@ TEST(IvfPqC, BuildSearch)
   int64_t n_dim        = 32;
   uint32_t n_neighbors = 8;
 
-  enum DistanceType metric = L2Expanded;
-  size_t n_probes          = 20;
-  size_t n_lists           = 1024;
+  cuvsDistanceType metric = L2Expanded;
+  size_t n_probes         = 20;
+  size_t n_lists          = 1024;
 
   float *index_data, *query_data, *distances_data;
   int64_t* neighbors_data;
diff --git a/cpp/test/neighbors/ann_utils.cuh b/cpp/test/neighbors/ann_utils.cuh
index 079740945f..27a0fff7fd 100644
--- a/cpp/test/neighbors/ann_utils.cuh
+++ b/cpp/test/neighbors/ann_utils.cuh
@@ -83,27 +83,35 @@ struct print_metric {
 inline auto operator<<(std::ostream& os, const print_metric& p) -> std::ostream&
 {
   switch (p.value) {
-    case cuvs::distance::L2Expanded: os << "distance::L2Expanded"; break;
-    case cuvs::distance::L2SqrtExpanded: os << "distance::L2SqrtExpanded"; break;
-    case cuvs::distance::CosineExpanded: os << "distance::CosineExpanded"; break;
-    case cuvs::distance::L1: os << "distance::L1"; break;
-    case cuvs::distance::L2Unexpanded: os << "distance::L2Unexpanded"; break;
-    case cuvs::distance::L2SqrtUnexpanded: os << "distance::L2SqrtUnexpanded"; break;
-    case cuvs::distance::InnerProduct: os << "distance::InnerProduct"; break;
-    case cuvs::distance::Linf: os << "distance::Linf"; break;
-    case cuvs::distance::Canberra: os << "distance::Canberra"; break;
-    case cuvs::distance::LpUnexpanded: os << "distance::LpUnexpanded"; break;
-    case cuvs::distance::CorrelationExpanded: os << "distance::CorrelationExpanded"; break;
-    case cuvs::distance::JaccardExpanded: os << "distance::JaccardExpanded"; break;
-    case cuvs::distance::HellingerExpanded: os << "distance::HellingerExpanded"; break;
-    case cuvs::distance::Haversine: os << "distance::Haversine"; break;
-    case cuvs::distance::BrayCurtis: os << "distance::BrayCurtis"; break;
-    case cuvs::distance::JensenShannon: os << "distance::JensenShannon"; break;
-    case cuvs::distance::HammingUnexpanded: os << "distance::HammingUnexpanded"; break;
-    case cuvs::distance::KLDivergence: os << "distance::KLDivergence"; break;
-    case cuvs::distance::RusselRaoExpanded: os << "distance::RusselRaoExpanded"; break;
-    case cuvs::distance::DiceExpanded: os << "distance::DiceExpanded"; break;
-    case cuvs::distance::Precomputed: os << "distance::Precomputed"; break;
+    case cuvs::distance::DistanceType::L2Expanded: os << "distance::L2Expanded"; break;
+    case cuvs::distance::DistanceType::L2SqrtExpanded: os << "distance::L2SqrtExpanded"; break;
+    case cuvs::distance::DistanceType::CosineExpanded: os << "distance::CosineExpanded"; break;
+    case cuvs::distance::DistanceType::L1: os << "distance::L1"; break;
+    case cuvs::distance::DistanceType::L2Unexpanded: os << "distance::L2Unexpanded"; break;
+    case cuvs::distance::DistanceType::L2SqrtUnexpanded: os << "distance::L2SqrtUnexpanded"; break;
+    case cuvs::distance::DistanceType::InnerProduct: os << "distance::InnerProduct"; break;
+    case cuvs::distance::DistanceType::Linf: os << "distance::Linf"; break;
+    case cuvs::distance::DistanceType::Canberra: os << "distance::Canberra"; break;
+    case cuvs::distance::DistanceType::LpUnexpanded: os << "distance::LpUnexpanded"; break;
+    case cuvs::distance::DistanceType::CorrelationExpanded:
+      os << "distance::CorrelationExpanded";
+      break;
+    case cuvs::distance::DistanceType::JaccardExpanded: os << "distance::JaccardExpanded"; break;
+    case cuvs::distance::DistanceType::HellingerExpanded:
+      os << "distance::HellingerExpanded";
+      break;
+    case cuvs::distance::DistanceType::Haversine: os << "distance::Haversine"; break;
+    case cuvs::distance::DistanceType::BrayCurtis: os << "distance::BrayCurtis"; break;
+    case cuvs::distance::DistanceType::JensenShannon: os << "distance::JensenShannon"; break;
+    case cuvs::distance::DistanceType::HammingUnexpanded:
+      os << "distance::HammingUnexpanded";
+      break;
+    case cuvs::distance::DistanceType::KLDivergence: os << "distance::KLDivergence"; break;
+    case cuvs::distance::DistanceType::RusselRaoExpanded:
+      os << "distance::RusselRaoExpanded";
+      break;
+    case cuvs::distance::DistanceType::DiceExpanded: os << "distance::DiceExpanded"; break;
+    case cuvs::distance::DistanceType::Precomputed: os << "distance::Precomputed"; break;
     default: RAFT_FAIL("unreachable code");
   }
   return os;
diff --git a/cpp/test/neighbors/brute_force_c.cu b/cpp/test/neighbors/brute_force_c.cu
index 7730a98c63..8caf1c9d1c 100644
--- a/cpp/test/neighbors/brute_force_c.cu
+++ b/cpp/test/neighbors/brute_force_c.cu
@@ -30,7 +30,7 @@ extern "C" void run_brute_force(int64_t n_rows,
                                 float* query_data,
                                 float* distances_data,
                                 int64_t* neighbors_data,
-                                enum DistanceType metric);
+                                cuvsDistanceType metric);
 
 template <typename T>
 void generate_random_data(T* devPtr, size_t size)
@@ -49,7 +49,7 @@ void recall_eval(T* query_data,
                  size_t n_rows,
                  size_t n_dim,
                  size_t n_neighbors,
-                 DistanceType metric)
+                 cuvsDistanceType metric)
 {
   raft::handle_t handle;
   auto distances_ref = raft::make_device_matrix<T, IdxT>(handle, n_queries, n_neighbors);
@@ -97,7 +97,7 @@ TEST(BruteForceC, BuildSearch)
   int64_t n_dim        = 32;
   uint32_t n_neighbors = 8;
 
-  enum DistanceType metric = L2Expanded;
+  cuvsDistanceType metric = L2Expanded;
 
   float *index_data, *query_data, *distances_data;
   int64_t* neighbors_data;
diff --git a/cpp/test/neighbors/run_brute_force_c.c b/cpp/test/neighbors/run_brute_force_c.c
index ed775a2d6c..ed9e999709 100644
--- a/cpp/test/neighbors/run_brute_force_c.c
+++ b/cpp/test/neighbors/run_brute_force_c.c
@@ -24,7 +24,7 @@ void run_brute_force(int64_t n_rows,
                      float* query_data,
                      float* distances_data,
                      int64_t* neighbors_data,
-                     enum DistanceType metric)
+                     cuvsDistanceType metric)
 {
   // create cuvsResources_t
   cuvsResources_t res;
diff --git a/cpp/test/neighbors/run_ivf_flat_c.c b/cpp/test/neighbors/run_ivf_flat_c.c
index badb507a50..9ecbd18eb2 100644
--- a/cpp/test/neighbors/run_ivf_flat_c.c
+++ b/cpp/test/neighbors/run_ivf_flat_c.c
@@ -24,7 +24,7 @@ void run_ivf_flat(int64_t n_rows,
                   float* query_data,
                   float* distances_data,
                   int64_t* neighbors_data,
-                  enum DistanceType metric,
+                  cuvsDistanceType metric,
                   size_t n_probes,
                   size_t n_lists)
 {
diff --git a/cpp/test/neighbors/run_ivf_pq_c.c b/cpp/test/neighbors/run_ivf_pq_c.c
index fece4a644c..332c8a0f88 100644
--- a/cpp/test/neighbors/run_ivf_pq_c.c
+++ b/cpp/test/neighbors/run_ivf_pq_c.c
@@ -24,7 +24,7 @@ void run_ivf_pq(int64_t n_rows,
                 float* query_data,
                 float* distances_data,
                 int64_t* neighbors_data,
-                enum DistanceType metric,
+                cuvsDistanceType metric,
                 size_t n_probes,
                 size_t n_lists)
 {
diff --git a/dependencies.yaml b/dependencies.yaml
index eb1f99d4a1..0c72b9b9ac 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -220,6 +220,7 @@ dependencies:
               - cuda-nvtx-dev
               - cuda-cudart-dev
               - cuda-profiler-api
+              - libnvjitlink
               - libcublas-dev
               - libcurand-dev
               - libcusolver-dev
diff --git a/docs/source/cpp_api/distance.rst b/docs/source/cpp_api/distance.rst
index 21f4558ecd..c1b8c619d1 100644
--- a/docs/source/cpp_api/distance.rst
+++ b/docs/source/cpp_api/distance.rst
@@ -17,3 +17,16 @@ namespace *cuvs::distance*
 
 .. doxygenenum:: cuvs::distance::DistanceType
    :project: cuvs
+
+
+Pairwise Distances
+------------------
+
+``include <cuvs/distance/pairwise_distance.hpp>``
+
+namespace *cuvs::distance*
+
+.. doxygengroup:: pairwise_distance
+    :project: cuvs
+    :members:
+    :content-only:
\ No newline at end of file
diff --git a/python/cuvs/cuvs/distance_type.pxd b/python/cuvs/cuvs/distance_type.pxd
index a1f0366a56..b85ee4b366 100644
--- a/python/cuvs/cuvs/distance_type.pxd
+++ b/python/cuvs/cuvs/distance_type.pxd
@@ -17,7 +17,7 @@
 
 
 cdef extern from "cuvs/distance/distance_types.h" nogil:
-    ctypedef enum DistanceType:
+    ctypedef enum cuvsDistanceType:
         L2Expanded
         L2SqrtExpanded
         CosineExpanded
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
index 77e484fba2..c57fa9e8da 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
@@ -19,7 +19,7 @@ from libc.stdint cimport uintptr_t
 
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
-from cuvs.distance_type cimport DistanceType
+from cuvs.distance_type cimport cuvsDistanceType
 
 
 cdef extern from "cuvs/neighbors/brute_force.h" nogil:
@@ -36,7 +36,7 @@ cdef extern from "cuvs/neighbors/brute_force.h" nogil:
 
     cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
                                     DLManagedTensor* dataset,
-                                    DistanceType metric,
+                                    cuvsDistanceType metric,
                                     float metric_arg,
                                     cuvsBruteForceIndex_t index) except +
 
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index ccb10e3055..6af3c920cf 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -26,7 +26,7 @@ from libc.stdint cimport uint32_t
 from libcpp cimport bool
 
 from cuvs.common cimport cydlpack
-from cuvs.distance_type cimport DistanceType
+from cuvs.distance_type cimport cuvsDistanceType
 
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
@@ -105,7 +105,7 @@ def build(dataset, metric="sqeuclidean", metric_arg=2.0, resources=None):
 
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
-    cdef DistanceType c_metric = <DistanceType>DISTANCE_TYPES[metric]
+    cdef cuvsDistanceType c_metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
     cdef Index idx = Index()
     cdef cydlpack.DLManagedTensor* dataset_dlpack = \
         cydlpack.dlpack_c(dataset_ai)
diff --git a/rust/cuvs/src/distance_type.rs b/rust/cuvs/src/distance_type.rs
index 4ac3e9164b..a0cbcc86e3 100644
--- a/rust/cuvs/src/distance_type.rs
+++ b/rust/cuvs/src/distance_type.rs
@@ -14,4 +14,4 @@
  * limitations under the License.
  */
 
-pub type DistanceType = ffi::DistanceType;
+pub type DistanceType = ffi::cuvsDistanceType;

From 6399d05e836337796ad1547a760ba16ddbcd0d48 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Mon, 29 Apr 2024 23:14:04 +0200
Subject: [PATCH 043/623] Remove libnvjitlink dependency. (#97)

PR #85 added a dependency on `libnvjitlink`. This was needed only temporarily, due to an issue with a particular build of `cupy` that caused an undesired conda environment. This should be safe to remove.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/97
---
 conda/environments/all_cuda-122_arch-aarch64.yaml | 1 -
 conda/environments/all_cuda-122_arch-x86_64.yaml  | 1 -
 dependencies.yaml                                 | 1 -
 3 files changed, 3 deletions(-)

diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index 3c53191605..7b50ceb0a0 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -32,7 +32,6 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libnvjitlink
 - make
 - nccl>=2.9.9
 - ninja
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 4e84731c72..6c933e193d 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -32,7 +32,6 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libnvjitlink
 - make
 - nccl>=2.9.9
 - ninja
diff --git a/dependencies.yaml b/dependencies.yaml
index 0c72b9b9ac..eb1f99d4a1 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -220,7 +220,6 @@ dependencies:
               - cuda-nvtx-dev
               - cuda-cudart-dev
               - cuda-profiler-api
-              - libnvjitlink
               - libcublas-dev
               - libcurand-dev
               - libcusolver-dev

From 82de6ef1982b59637b36af5617ffdcafc47ccb0d Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 30 Apr 2024 13:51:53 -0700
Subject: [PATCH 044/623] Add python and rust bindings for Ivf-Pq (#90)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/90
---
 cpp/include/cuvs/neighbors/ivf_flat.h         |   2 +-
 cpp/include/cuvs/neighbors/ivf_pq.h           |  50 +-
 cpp/src/neighbors/ivf_pq_c.cpp                |  62 +--
 cpp/test/neighbors/run_ivf_pq_c.c             |   8 +-
 docs/source/python_api/neighbors.rst          |   4 +-
 .../python_api/neighbors_brute_force.rst      |  22 +
 docs/source/python_api/neighbors_ivf_pq.rst   |  34 ++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 python/cuvs/cuvs/neighbors/__init__.py        |   4 +-
 .../neighbors/brute_force/brute_force.pyx     |   4 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |   2 +-
 .../cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt |  24 +
 .../cuvs/cuvs/neighbors/ivf_pq/__init__.pxd   |   0
 python/cuvs/cuvs/neighbors/ivf_pq/__init__.py |  18 +
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd  |  93 ++++
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  | 433 ++++++++++++++++++
 python/cuvs/cuvs/test/test_ivf_pq.py          | 168 +++++++
 rust/cuvs-sys/build.rs                        |   2 +-
 rust/cuvs-sys/cuvs_c_wrapper.h                |   1 +
 rust/cuvs/src/brute_force.rs                  |   3 +-
 rust/cuvs/src/ivf_pq/index.rs                 | 165 +++++++
 rust/cuvs/src/ivf_pq/index_params.rs          | 176 +++++++
 rust/cuvs/src/ivf_pq/mod.rs                   |  84 ++++
 rust/cuvs/src/ivf_pq/search_params.rs         | 102 +++++
 rust/cuvs/src/lib.rs                          |   1 +
 25 files changed, 1393 insertions(+), 70 deletions(-)
 create mode 100644 docs/source/python_api/neighbors_brute_force.rst
 create mode 100644 docs/source/python_api/neighbors_ivf_pq.rst
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_pq/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
 create mode 100644 python/cuvs/cuvs/test/test_ivf_pq.py
 create mode 100644 rust/cuvs/src/ivf_pq/index.rs
 create mode 100644 rust/cuvs/src/ivf_pq/index_params.rs
 create mode 100644 rust/cuvs/src/ivf_pq/mod.rs
 create mode 100644 rust/cuvs/src/ivf_pq/search_params.rs

diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 22c4d361ca..07877bc75d 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -153,7 +153,7 @@ typedef ivfFlatIndex* cuvsIvfFlatIndex_t;
  * @brief Allocate IVF-Flat index
  *
  * @param[in] index cuvsIvfFlatIndex_t to allocate
- * @return ivfFlatError_t
+ * @return cuvsError_t
  */
 cuvsError_t ivfFlatIndexCreate(cuvsIvfFlatIndex_t* index);
 
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index 2a8269eb07..c4ebc31f4a 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -43,7 +43,7 @@ enum codebook_gen {  // NOLINT
  * @brief Supplemental parameters to build IVF-PQ Index
  *
  */
-struct ivfPqIndexParams {
+struct cuvsIvfPqIndexParams {
   /** Distance type. */
   cuvsDistanceType metric;
   /** The argument used by some distance metrics. */
@@ -118,7 +118,7 @@ struct ivfPqIndexParams {
   bool conservative_memory_allocation;
 };
 
-typedef struct ivfPqIndexParams* cuvsIvfPqIndexParams_t;
+typedef struct cuvsIvfPqIndexParams* cuvsIvfPqIndexParams_t;
 
 /**
  * @brief Allocate IVF-PQ Index params, and populate with default values
@@ -147,7 +147,7 @@ cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params);
  * @brief Supplemental parameters to search IVF-PQ index
  *
  */
-struct ivfPqSearchParams {
+struct cuvsIvfPqSearchParams {
   /** The number of clusters to search. */
   uint32_t n_probes;
   /**
@@ -186,7 +186,7 @@ struct ivfPqSearchParams {
   double preferred_shmem_carveout;
 };
 
-typedef struct ivfPqSearchParams* cuvsIvfPqSearchParams_t;
+typedef struct cuvsIvfPqSearchParams* cuvsIvfPqSearchParams_t;
 
 /**
  * @brief Allocate IVF-PQ search params, and populate with default values
@@ -218,24 +218,24 @@ cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params);
 typedef struct {
   uintptr_t addr;
   DLDataType dtype;
-} ivfPqIndex;
+} cuvsIvfPqIndex;
 
-typedef ivfPqIndex* cuvsIvfPqIndex_t;
+typedef cuvsIvfPqIndex* cuvsIvfPqIndex_t;
 
 /**
  * @brief Allocate IVF-PQ index
  *
  * @param[in] index cuvsIvfPqIndex_t to allocate
- * @return ivfPqError_t
+ * @return cuvsError_t
  */
-cuvsError_t ivfPqIndexCreate(cuvsIvfPqIndex_t* index);
+cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index);
 
 /**
  * @brief De-allocate IVF-PQ index
  *
  * @param[in] index cuvsIvfPqIndex_t to de-allocate
  */
-cuvsError_t ivfPqIndexDestroy(cuvsIvfPqIndex_t index);
+cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index);
 /**
  * @}
  */
@@ -269,14 +269,14 @@ cuvsError_t ivfPqIndexDestroy(cuvsIvfPqIndex_t index);
  *
  * // Create IVF-PQ index
  * cuvsIvfPqIndex_t index;
- * cuvsError_t index_create_status = ivfPqIndexCreate(&index);
+ * cuvsError_t index_create_status = cuvsIvfPqIndexCreate(&index);
  *
  * // Build the IVF-PQ Index
- * cuvsError_t build_status = ivfPqBuild(res, index_params, &dataset, index);
+ * cuvsError_t build_status = cuvsIvfPqBuild(res, index_params, &dataset, index);
  *
  * // de-allocate `index_params`, `index` and `res`
  * cuvsError_t params_destroy_status = cuvsIvfPqIndexParamsDestroy(index_params);
- * cuvsError_t index_destroy_status = ivfPqIndexDestroy(index);
+ * cuvsError_t index_destroy_status = cuvsIvfPqIndexDestroy(index);
  * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
  * @endcode
  *
@@ -286,10 +286,10 @@ cuvsError_t ivfPqIndexDestroy(cuvsIvfPqIndex_t index);
  * @param[out] index cuvsIvfPqIndex_t Newly built IVF-PQ index
  * @return cuvsError_t
  */
-cuvsError_t ivfPqBuild(cuvsResources_t res,
-                       cuvsIvfPqIndexParams_t params,
-                       DLManagedTensor* dataset,
-                       cuvsIvfPqIndex_t index);
+cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
+                           cuvsIvfPqIndexParams_t params,
+                           DLManagedTensor* dataset,
+                           cuvsIvfPqIndex_t index);
 /**
  * @}
  */
@@ -325,8 +325,8 @@ cuvsError_t ivfPqBuild(cuvsResources_t res,
  * cuvsIvfPqSearchParams_t search_params;
  * cuvsError_t params_create_status = cuvsIvfPqSearchParamsCreate(&search_params);
  *
- * // Search the `index` built using `ivfPqBuild`
- * cuvsError_t search_status = ivfPqSearch(res, search_params, index, &queries, &neighbors,
+ * // Search the `index` built using `cuvsIvfPqBuild`
+ * cuvsError_t search_status = cuvsIvfPqSearch(res, search_params, index, &queries, &neighbors,
  * &distances);
  *
  * // de-allocate `search_params` and `res`
@@ -336,17 +336,17 @@ cuvsError_t ivfPqBuild(cuvsResources_t res,
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] search_params cuvsIvfPqSearchParams_t used to search IVF-PQ index
- * @param[in] index ivfPqIndex which has been returned by `ivfPqBuild`
+ * @param[in] index cuvsIvfPqIndex which has been returned by `cuvsIvfPqBuild`
  * @param[in] queries DLManagedTensor* queries dataset to search
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
  */
-cuvsError_t ivfPqSearch(cuvsResources_t res,
-                        cuvsIvfPqSearchParams_t search_params,
-                        cuvsIvfPqIndex_t index,
-                        DLManagedTensor* queries,
-                        DLManagedTensor* neighbors,
-                        DLManagedTensor* distances);
+cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
+                            cuvsIvfPqSearchParams_t search_params,
+                            cuvsIvfPqIndex_t index,
+                            DLManagedTensor* queries,
+                            DLManagedTensor* neighbors,
+                            DLManagedTensor* distances);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 27076cbc1e..14a8791233 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -29,7 +29,7 @@
 namespace {
 
 template <typename IdxT>
-void* _build(cuvsResources_t res, ivfPqIndexParams params, DLManagedTensor* dataset_tensor)
+void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor* dataset_tensor)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
 
@@ -62,8 +62,8 @@ void* _build(cuvsResources_t res, ivfPqIndexParams params, DLManagedTensor* data
 
 template <typename IdxT>
 void _search(cuvsResources_t res,
-             ivfPqSearchParams params,
-             ivfPqIndex index,
+             cuvsIvfPqSearchParams params,
+             cuvsIvfPqIndex index,
              DLManagedTensor* queries_tensor,
              DLManagedTensor* neighbors_tensor,
              DLManagedTensor* distances_tensor)
@@ -90,17 +90,17 @@ void _search(cuvsResources_t res,
 
 }  // namespace
 
-extern "C" cuvsError_t ivfPqIndexCreate(cuvsIvfPqIndex_t* index)
+extern "C" cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index)
 {
   try {
-    *index = new ivfPqIndex{};
+    *index = new cuvsIvfPqIndex{};
     return CUVS_SUCCESS;
   } catch (...) {
     return CUVS_ERROR;
   }
 }
 
-extern "C" cuvsError_t ivfPqIndexDestroy(cuvsIvfPqIndex_t index_c_ptr)
+extern "C" cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index_c_ptr)
 {
   try {
     auto index = *index_c_ptr;
@@ -114,10 +114,10 @@ extern "C" cuvsError_t ivfPqIndexDestroy(cuvsIvfPqIndex_t index_c_ptr)
   }
 }
 
-extern "C" cuvsError_t ivfPqBuild(cuvsResources_t res,
-                                  cuvsIvfPqIndexParams_t params,
-                                  DLManagedTensor* dataset_tensor,
-                                  cuvsIvfPqIndex_t index)
+extern "C" cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
+                                      cuvsIvfPqIndexParams_t params,
+                                      DLManagedTensor* dataset_tensor,
+                                      cuvsIvfPqIndex_t index)
 {
   try {
     auto dataset = dataset_tensor->dl_tensor;
@@ -138,12 +138,12 @@ extern "C" cuvsError_t ivfPqBuild(cuvsResources_t res,
   }
 }
 
-extern "C" cuvsError_t ivfPqSearch(cuvsResources_t res,
-                                   cuvsIvfPqSearchParams_t params,
-                                   cuvsIvfPqIndex_t index_c_ptr,
-                                   DLManagedTensor* queries_tensor,
-                                   DLManagedTensor* neighbors_tensor,
-                                   DLManagedTensor* distances_tensor)
+extern "C" cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
+                                       cuvsIvfPqSearchParams_t params,
+                                       cuvsIvfPqIndex_t index_c_ptr,
+                                       DLManagedTensor* queries_tensor,
+                                       DLManagedTensor* neighbors_tensor,
+                                       DLManagedTensor* distances_tensor)
 {
   try {
     auto queries   = queries_tensor->dl_tensor;
@@ -184,17 +184,17 @@ extern "C" cuvsError_t ivfPqSearch(cuvsResources_t res,
 extern "C" cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t* params)
 {
   try {
-    *params = new ivfPqIndexParams{.metric                         = L2Expanded,
-                                   .metric_arg                     = 2.0f,
-                                   .add_data_on_build              = true,
-                                   .n_lists                        = 1024,
-                                   .kmeans_n_iters                 = 20,
-                                   .kmeans_trainset_fraction       = 0.5,
-                                   .pq_bits                        = 8,
-                                   .pq_dim                         = 0,
-                                   .codebook_kind                  = codebook_gen::PER_SUBSPACE,
-                                   .force_random_rotation          = false,
-                                   .conservative_memory_allocation = false};
+    *params = new cuvsIvfPqIndexParams{.metric                         = L2Expanded,
+                                       .metric_arg                     = 2.0f,
+                                       .add_data_on_build              = true,
+                                       .n_lists                        = 1024,
+                                       .kmeans_n_iters                 = 20,
+                                       .kmeans_trainset_fraction       = 0.5,
+                                       .pq_bits                        = 8,
+                                       .pq_dim                         = 0,
+                                       .codebook_kind                  = codebook_gen::PER_SUBSPACE,
+                                       .force_random_rotation          = false,
+                                       .conservative_memory_allocation = false};
     return CUVS_SUCCESS;
   } catch (...) {
     return CUVS_ERROR;
@@ -214,10 +214,10 @@ extern "C" cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t params
 extern "C" cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t* params)
 {
   try {
-    *params = new ivfPqSearchParams{.n_probes                 = 20,
-                                    .lut_dtype                = CUDA_R_32F,
-                                    .internal_distance_dtype  = CUDA_R_32F,
-                                    .preferred_shmem_carveout = 1.0};
+    *params = new cuvsIvfPqSearchParams{.n_probes                 = 20,
+                                        .lut_dtype                = CUDA_R_32F,
+                                        .internal_distance_dtype  = CUDA_R_32F,
+                                        .preferred_shmem_carveout = 1.0};
     return CUVS_SUCCESS;
   } catch (...) {
     return CUVS_ERROR;
diff --git a/cpp/test/neighbors/run_ivf_pq_c.c b/cpp/test/neighbors/run_ivf_pq_c.c
index 332c8a0f88..b7f1444a82 100644
--- a/cpp/test/neighbors/run_ivf_pq_c.c
+++ b/cpp/test/neighbors/run_ivf_pq_c.c
@@ -46,14 +46,14 @@ void run_ivf_pq(int64_t n_rows,
 
   // create index
   cuvsIvfPqIndex_t index;
-  ivfPqIndexCreate(&index);
+  cuvsIvfPqIndexCreate(&index);
 
   // build index
   cuvsIvfPqIndexParams_t build_params;
   cuvsIvfPqIndexParamsCreate(&build_params);
   build_params->metric  = metric;
   build_params->n_lists = n_lists;
-  ivfPqBuild(res, build_params, &dataset_tensor, index);
+  cuvsIvfPqBuild(res, build_params, &dataset_tensor, index);
 
   // create queries DLTensor
   DLManagedTensor queries_tensor;
@@ -95,11 +95,11 @@ void run_ivf_pq(int64_t n_rows,
   cuvsIvfPqSearchParams_t search_params;
   cuvsIvfPqSearchParamsCreate(&search_params);
   search_params->n_probes = n_probes;
-  ivfPqSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  cuvsIvfPqSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
 
   // de-allocate index and res
   cuvsIvfPqSearchParamsDestroy(search_params);
   cuvsIvfPqIndexParamsDestroy(build_params);
-  ivfPqIndexDestroy(index);
+  cuvsIvfPqIndexDestroy(index);
   cuvsResourcesDestroy(res);
 }
diff --git a/docs/source/python_api/neighbors.rst b/docs/source/python_api/neighbors.rst
index 14e907002e..3cbbd52ea9 100644
--- a/docs/source/python_api/neighbors.rst
+++ b/docs/source/python_api/neighbors.rst
@@ -9,4 +9,6 @@ Nearest Neighbors
    :maxdepth: 2
    :caption: Contents:
 
-   neighbors_cagra.rst
\ No newline at end of file
+   neighbors_brute_force.rst
+   neighbors_cagra.rst
+   neighbors_ivf_pq.rst
diff --git a/docs/source/python_api/neighbors_brute_force.rst b/docs/source/python_api/neighbors_brute_force.rst
new file mode 100644
index 0000000000..5fdc3658fe
--- /dev/null
+++ b/docs/source/python_api/neighbors_brute_force.rst
@@ -0,0 +1,22 @@
+Brute Force KNN
+===============
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.brute_force.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.brute_force.build
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.brute_force.search
diff --git a/docs/source/python_api/neighbors_ivf_pq.rst b/docs/source/python_api/neighbors_ivf_pq.rst
new file mode 100644
index 0000000000..e3625ba676
--- /dev/null
+++ b/docs/source/python_api/neighbors_ivf_pq.rst
@@ -0,0 +1,34 @@
+IVF-PQ
+======
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Index build parameters
+######################
+
+.. autoclass:: cuvs.neighbors.ivf_pq.IndexParams
+    :members:
+
+Index search parameters
+#######################
+
+.. autoclass:: cuvs.neighbors.ivf_pq.SearchParams
+    :members:
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.ivf_pq.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.ivf_pq.build
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.ivf_pq.search
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 481ec9915f..1218c3ef06 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -14,3 +14,4 @@
 
 add_subdirectory(brute_force)
 add_subdirectory(cagra)
+add_subdirectory(ivf_pq)
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index 1f8f956d9c..c6801973cb 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -13,6 +13,6 @@
 # limitations under the License.
 
 
-from cuvs.neighbors import cagra
+from cuvs.neighbors import brute_force, cagra, ivf_pq
 
-__all__ = ["common", "cagra"]
+__all__ = ["brute_force", "cagra", "ivf_pq"]
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 6af3c920cf..226ee23d66 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -68,8 +68,6 @@ cdef class Index:
 def build(dataset, metric="sqeuclidean", metric_arg=2.0, resources=None):
     """
     Build the Brute Force index from the dataset for efficient search.
-    The following distance metrics are supported:
-        - L2
 
     Parameters
     ----------
@@ -167,7 +165,7 @@ def search(Index index,
     >>> k = 10
     >>> # Using a pooling allocator reduces overhead of temporary array
     >>> # creation during search. This is useful if multiple searches
-    >>> # are performad with same query size.
+    >>> # are performed with same query size.
     >>> distances, neighbors = brute_force.search(index, queries, k)
     >>> neighbors = cp.asarray(neighbors)
     >>> distances = cp.asarray(distances)
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index f5f94aba5e..56439dd924 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -512,7 +512,7 @@ def search(SearchParams search_params,
     ... )
     >>> # Using a pooling allocator reduces overhead of temporary array
     >>> # creation during search. This is useful if multiple searches
-    >>> # are performad with same query size.
+    >>> # are performed with same query size.
     >>> distances, neighbors = cagra.search(search_params, index, queries,
     ...                                     k)
     >>> neighbors = cp.asarray(neighbors)
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
new file mode 100644
index 0000000000..97c3a18249
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources ivf_pq.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_pq_
+)
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/__init__.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py b/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
new file mode 100644
index 0000000000..3c939aa270
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .ivf_pq import Index, IndexParams, SearchParams, build, search
+
+__all__ = ["Index", "IndexParams", "SearchParams", "build", "search"]
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
new file mode 100644
index 0000000000..f036c2c611
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -0,0 +1,93 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uint32_t, uintptr_t
+from libcpp cimport bool
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "library_types.h":
+    ctypedef enum cudaDataType_t:
+        CUDA_R_32F "CUDA_R_32F"  # float
+        CUDA_R_16F "CUDA_R_16F"  # half
+
+        # uint8 - used to refer to IVF-PQ's fp8 storage type
+        CUDA_R_8U "CUDA_R_8U"
+
+
+cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
+
+    ctypedef enum codebook_gen:
+        PER_SUBSPACE
+        PER_CLUSTER
+
+    ctypedef struct cuvsIvfPqIndexParams:
+        cuvsDistanceType metric
+        float metric_arg
+        bool add_data_on_build
+        uint32_t n_lists
+        uint32_t kmeans_n_iters
+        double kmeans_trainset_fraction
+        uint32_t pq_bits
+        uint32_t pq_dim
+        codebook_gen codebook_kind
+        bool force_random_rotation
+        bool conservative_memory_allocation
+
+    ctypedef cuvsIvfPqIndexParams* cuvsIvfPqIndexParams_t
+
+    ctypedef struct cuvsIvfPqSearchParams:
+        uint32_t n_probes
+        cudaDataType_t lut_dtype
+        cudaDataType_t internal_distance_dtype
+        double preferred_shmem_carveout
+
+    ctypedef cuvsIvfPqSearchParams* cuvsIvfPqSearchParams_t
+
+    ctypedef struct cuvsIvfPqIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsIvfPqIndex* cuvsIvfPqIndex_t
+
+    cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t* params)
+
+    cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index)
+
+    cuvsError_t cuvsIvfPqSearchParamsCreate(
+        cuvsIvfPqSearchParams_t* params)
+
+    cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t index)
+
+    cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index)
+
+    cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
+
+    cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
+                               cuvsIvfPqIndexParams* params,
+                               DLManagedTensor* dataset,
+                               cuvsIvfPqIndex_t index) except +
+
+    cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
+                                cuvsIvfPqSearchParams* params,
+                                cuvsIvfPqIndex_t index,
+                                DLManagedTensor* queries,
+                                DLManagedTensor* neighbors,
+                                DLManagedTensor* distances) except +
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
new file mode 100644
index 0000000000..a1ca6768a9
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -0,0 +1,433 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libcpp cimport bool, cast
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+from pylibraft.distance.pairwise_distance import DISTANCE_TYPES
+from pylibraft.neighbors.common import _check_input_array
+
+from libc.stdint cimport (
+    int8_t,
+    int64_t,
+    uint8_t,
+    uint32_t,
+    uint64_t,
+    uintptr_t,
+)
+
+from cuvs.common.exceptions import check_cuvs
+
+
+cdef class IndexParams:
+    """
+    Parameters to build index for IvfPq nearest neighbor search
+
+    Parameters
+    ----------
+    n_lists : int, default = 1024
+        The number of clusters used in the coarse quantizer.
+    metric : str, default="sqeuclidean"
+        String denoting the metric type.
+        Valid values for metric: ["sqeuclidean", "inner_product", "euclidean"],
+        where:
+            - sqeuclidean is the euclidean distance without the square root
+              operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2,
+            - euclidean is the euclidean distance
+            - inner product distance is defined as
+              distance(a, b) = \\sum_i a_i * b_i.
+    kmeans_n_iters : int, default = 20
+        The number of iterations searching for kmeans centers during index
+        building.
+    kmeans_trainset_fraction : int, default = 0.5
+        If kmeans_trainset_fraction is less than 1, then the dataset is
+        subsampled, and only n_samples * kmeans_trainset_fraction rows
+        are used for training.
+    pq_bits : int, default = 8
+        The bit length of the vector element after quantization.
+    pq_dim : int, default = 0
+        The dimensionality of a the vector after product quantization.
+        When zero, an optimal value is selected using a heuristic. Note
+        pq_dim * pq_bits must be a multiple of 8. Hint: a smaller 'pq_dim'
+        results in a smaller index size and better search performance, but
+        lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any number,
+        but multiple of 8 are desirable for good performance. If 'pq_bits'
+        is not 8, 'pq_dim' should be a multiple of 8. For good performance,
+        it is desirable that 'pq_dim' is a multiple of 32. Ideally,
+        'pq_dim' should be also a divisor of the dataset dim.
+    codebook_kind : string, default = "subspace"
+        Valid values ["subspace", "cluster"]
+    force_random_rotation : bool, default = False
+        Apply a random rotation matrix on the input data and queries even
+        if `dim % pq_dim == 0`. Note: if `dim` is not multiple of `pq_dim`,
+        a random rotation is always applied to the input data and queries
+        to transform the working space from `dim` to `rot_dim`, which may
+        be slightly larger than the original space and and is a multiple
+        of `pq_dim` (`rot_dim % pq_dim == 0`). However, this transform is
+        not necessary when `dim` is multiple of `pq_dim` (`dim == rot_dim`,
+        hence no need in adding "extra" data columns / features). By
+        default, if `dim == rot_dim`, the rotation transform is
+        initialized with the identity matrix. When
+        `force_random_rotation == True`, a random orthogonal transform
+    add_data_on_build : bool, default = True
+        After training the coarse and fine quantizers, we will populate
+        the index with the dataset if add_data_on_build == True, otherwise
+        the index is left empty, and the extend method can be used
+        to add new vectors to the index.
+    """
+
+    cdef cuvsIvfPqIndexParams* params
+    cdef object _metric
+
+    def __cinit__(self):
+        cuvsIvfPqIndexParamsCreate(&self.params)
+
+    def __dealloc__(self):
+        check_cuvs(cuvsIvfPqIndexParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 n_lists=1024,
+                 metric="sqeuclidean",
+                 metric_arg=2.0,
+                 kmeans_n_iters=20,
+                 kmeans_trainset_fraction=0.5,
+                 pq_bits=8,
+                 pq_dim=0,
+                 codebook_kind="subspace",
+                 force_random_rotation=False,
+                 add_data_on_build=True,
+                 conservative_memory_allocation=False):
+        self.params.n_lists = n_lists
+        self._metric = metric
+        self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+        self.params.metric_arg = metric_arg
+        self.params.kmeans_n_iters = kmeans_n_iters
+        self.params.kmeans_trainset_fraction = kmeans_trainset_fraction
+        self.params.pq_bits = pq_bits
+        self.params.pq_dim = pq_dim
+        if codebook_kind == "subspace":
+            self.params.codebook_kind = codebook_gen.PER_SUBSPACE
+        elif codebook_kind == "cluster":
+            self.params.codebook_kind = codebook_gen.PER_CLUSTER
+        else:
+            raise ValueError("Incorrect codebook kind %s" % codebook_kind)
+        self.params.force_random_rotation = force_random_rotation
+        self.params.add_data_on_build = add_data_on_build
+        self.params.conservative_memory_allocation = \
+            conservative_memory_allocation
+
+    @property
+    def metric(self):
+        return self._metric
+
+    @property
+    def metric_arg(self):
+        return self.params.metric_arg
+
+    @property
+    def add_data_on_build(self):
+        return self.params.add_data_on_build
+
+    @property
+    def n_lists(self):
+        return self.params.n_lists
+
+    @property
+    def kmeans_n_iters(self):
+        return self.params.kmeans_n_iters
+
+    @property
+    def kmeans_trainset_fraction(self):
+        return self.params.kmeans_trainset_fraction
+
+    @property
+    def pq_bits(self):
+        return self.params.pq_bits
+
+    @property
+    def pq_dim(self):
+        return self.params.pq_dim
+
+    @property
+    def codebook_kind(self):
+        return self.params.codebook_kind
+
+    @property
+    def force_random_rotation(self):
+        return self.params.force_random_rotation
+
+    @property
+    def add_data_on_build(self):
+        return self.params.add_data_on_build
+
+    @property
+    def conservative_memory_allocation(self):
+        return self.params.conservative_memory_allocation
+
+
+cdef class Index:
+    """
+    IvfPq index object. This object stores the trained IvfPq index state
+    which can be used to perform nearest neighbors searches.
+    """
+
+    cdef cuvsIvfPqIndex_t index
+    cdef bool trained
+
+    def __cinit__(self):
+        self.trained = False
+        check_cuvs(cuvsIvfPqIndexCreate(&self.index))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsIvfPqIndexDestroy(self.index))
+
+    @property
+    def trained(self):
+        return self.trained
+
+    def __repr__(self):
+        return "Index(type=IvfPq)"
+
+
+@auto_sync_resources
+def build(IndexParams index_params, dataset, resources=None):
+    """
+    Build the IvfPq index from the dataset for efficient search.
+
+    Parameters
+    ----------
+    index_params : :py:class:`cuvs.neighbors.ivf_pq.IndexParams`
+        Parameters on how to build the index
+    dataset : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: :py:class:`cuvs.neighbors.ivf_pq.Index`
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> build_params = ivf_pq.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_pq.build(build_params, dataset)
+    >>> distances, neighbors = ivf_pq.search(ivf_pq.SearchParams(),
+    ...                                        index, dataset,
+    ...                                        k)
+    >>> distances = cp.asarray(distances)
+    >>> neighbors = cp.asarray(neighbors)
+    """
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
+                                    np.dtype('ubyte')])
+
+    cdef Index idx = Index()
+    cdef cuvsError_t build_status
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cuvsIvfPqIndexParams* params = index_params.params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsIvfPqBuild(
+            res,
+            params,
+            dataset_dlpack,
+            idx.index
+        ))
+        idx.trained = True
+
+    return idx
+
+
+cdef _map_dtype_np_to_cuda(dtype, supported_dtypes=None):
+    if supported_dtypes is not None and dtype not in supported_dtypes:
+        raise TypeError("Type %s is not supported" % str(dtype))
+    return {np.float32: cudaDataType_t.CUDA_R_32F,
+            np.float16: cudaDataType_t.CUDA_R_16F,
+            np.uint8: cudaDataType_t.CUDA_R_8U}[dtype]
+
+
+cdef class SearchParams:
+    """
+    Supplemental parameters to search IVF-Pq index
+
+    Parameters
+    ----------
+    n_probes: int
+        The number of clusters to search.
+    lut_dtype: default = np.float32
+        Data type of look up table to be created dynamically at search
+        time. The use of low-precision types reduces the amount of shared
+        memory required at search time, so fast shared memory kernels can
+        be used even for datasets with large dimansionality. Note that
+        the recall is slightly degraded when low-precision type is
+        selected. Possible values [np.float32, np.float16, np.uint8]
+    internal_distance_dtype: default = np.float32
+        Storage data type for distance/similarity computation.
+        Possible values [np.float32, np.float16]
+    """
+
+    cdef cuvsIvfPqSearchParams* params
+
+    def __cinit__(self):
+        cuvsIvfPqSearchParamsCreate(&self.params)
+
+    def __dealloc__(self):
+        check_cuvs(cuvsIvfPqSearchParamsDestroy(self.params))
+
+    def __init__(self, *, n_probes=20, lut_dtype=np.float32,
+                 internal_distance_dtype=np.float32):
+        self.params.n_probes = n_probes
+        self.params.lut_dtype = _map_dtype_np_to_cuda(lut_dtype)
+        self.params.internal_distance_dtype = \
+            _map_dtype_np_to_cuda(internal_distance_dtype)
+
+    @property
+    def n_probes(self):
+        return self.params.n_probes
+
+    @property
+    def n_probes(self):
+        return self.params.n_probes
+
+    @property
+    def lut_dtype(self):
+        return self.params.lut_dtype
+
+    @property
+    def internal_distance_dtype(self):
+        return self.params.internal_distance_dtype
+
+
+@auto_sync_resources
+@auto_convert_output
+def search(SearchParams search_params,
+           Index index,
+           queries,
+           k,
+           neighbors=None,
+           distances=None,
+           resources=None):
+    """
+    Find the k nearest neighbors for each query.
+
+    Parameters
+    ----------
+    search_params : :py:class:`cuvs.neighbors.ivf_pq.SearchParams`
+        Parameters on how to search the index
+    index : :py:class:`cuvs.neighbors.ivf_pq.Index`
+        Trained IvfPq index.
+    queries : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    k : int
+        The number of neighbors.
+    neighbors : Optional CUDA array interface compliant matrix shape
+                (n_queries, k), dtype int64_t. If supplied, neighbor
+                indices will be written here in-place. (default None)
+    distances : Optional CUDA array interface compliant matrix shape
+                (n_queries, k) If supplied, the distances to the
+                neighbors will be written here in-place. (default None)
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build the index
+    >>> index = ivf_pq.build(ivf_pq.IndexParams(), dataset)
+    >>>
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> k = 10
+    >>> search_params = ivf_pq.SearchParams(n_probes=20)
+    >>>
+    >>> distances, neighbors = ivf_pq.search(search_params, index, queries,
+    ...                                     k)
+    """
+    if not index.trained:
+        raise ValueError("Index needs to be built before calling search.")
+
+    queries_cai = wrap_array(queries)
+    _check_input_array(queries_cai, [np.dtype('float32'), np.dtype('byte'),
+                                     np.dtype('ubyte')])
+
+    cdef uint32_t n_queries = queries_cai.shape[0]
+
+    if neighbors is None:
+        neighbors = device_ndarray.empty((n_queries, k), dtype='int64')
+
+    neighbors_cai = wrap_array(neighbors)
+    _check_input_array(neighbors_cai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    if distances is None:
+        distances = device_ndarray.empty((n_queries, k), dtype='float32')
+
+    distances_cai = wrap_array(distances)
+    _check_input_array(distances_cai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    cdef cuvsIvfPqSearchParams* params = search_params.params
+    cdef cuvsError_t search_status
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_cai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_cai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_cai)
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsIvfPqSearch(
+            res,
+            params,
+            index.index,
+            queries_dlpack,
+            neighbors_dlpack,
+            distances_dlpack
+        ))
+
+    return (distances, neighbors)
diff --git a/python/cuvs/cuvs/test/test_ivf_pq.py b/python/cuvs/cuvs/test/test_ivf_pq.py
new file mode 100644
index 0000000000..86fdf1a23f
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_ivf_pq.py
@@ -0,0 +1,168 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+
+from cuvs.neighbors import ivf_pq
+from cuvs.test.ann_utils import calc_recall, generate_data
+
+
+def run_ivf_pq_build_search_test(
+    n_rows=10000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    n_lists=100,
+    metric="euclidean",
+    pq_bits=8,
+    pq_dim=0,
+    codebook_kind="subspace",
+    add_data_on_build="True",
+    n_probes=100,
+    lut_dtype=np.float32,
+    internal_distance_dtype=np.float32,
+    force_random_rotation=False,
+    kmeans_trainset_fraction=1,
+    kmeans_n_iters=20,
+    compare=True,
+    inplace=True,
+):
+    dataset = generate_data((n_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+    dataset_device = device_ndarray(dataset)
+
+    build_params = ivf_pq.IndexParams(
+        n_lists=n_lists,
+        metric=metric,
+        kmeans_n_iters=kmeans_n_iters,
+        kmeans_trainset_fraction=kmeans_trainset_fraction,
+        pq_bits=pq_bits,
+        pq_dim=pq_dim,
+        codebook_kind=codebook_kind,
+        force_random_rotation=force_random_rotation,
+        add_data_on_build=add_data_on_build,
+    )
+
+    index = ivf_pq.build(build_params, dataset_device)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    out_idx = np.zeros((n_queries, k), dtype=np.int64)
+    out_dist = np.zeros((n_queries, k), dtype=np.float32)
+
+    queries_device = device_ndarray(queries)
+    out_idx_device = device_ndarray(out_idx) if inplace else None
+    out_dist_device = device_ndarray(out_dist) if inplace else None
+
+    search_params = ivf_pq.SearchParams(
+        n_probes=n_probes,
+        lut_dtype=lut_dtype,
+        internal_distance_dtype=internal_distance_dtype,
+    )
+
+    ret_output = ivf_pq.search(
+        search_params,
+        index,
+        queries_device,
+        k,
+        neighbors=out_idx_device,
+        distances=out_dist_device,
+    )
+
+    if not inplace:
+        out_dist_device, out_idx_device = ret_output
+
+    if not compare:
+        return
+
+    out_idx = out_idx_device.copy_to_host()
+    out_dist = out_dist_device.copy_to_host()
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    recall = calc_recall(out_idx, skl_idx)
+    assert recall > 0.7
+
+
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize(
+    "metric", ["sqeuclidean", "inner_product", "euclidean"]
+)
+def test_ivf_pq(inplace, dtype, metric):
+    run_ivf_pq_build_search_test(
+        dtype=dtype,
+        inplace=inplace,
+        metric=metric,
+    )
+
+
+@pytest.mark.parametrize(
+    "params",
+    [
+        {
+            "k": 10,
+            "n_probes": 100,
+            "lut": np.float16,
+            "idd": np.float32,
+        },
+        {
+            "k": 10,
+            "n_probes": 99,
+            "lut": np.uint8,
+            "idd": np.float32,
+        },
+        {
+            "k": 10,
+            "n_probes": 100,
+            "lut": np.float16,
+            "idd": np.float16,
+        },
+        {
+            "k": 129,
+            "n_probes": 100,
+            "lut": np.float32,
+            "idd": np.float32,
+        },
+    ],
+)
+def test_ivf_pq_search_params(params):
+    run_ivf_pq_build_search_test(
+        n_rows=10000,
+        n_cols=16,
+        n_queries=1000,
+        k=params["k"],
+        n_lists=100,
+        n_probes=params["n_probes"],
+        metric="sqeuclidean",
+        dtype=np.float32,
+        lut_dtype=params["lut"],
+        internal_distance_dtype=params["idd"],
+    )
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
index f521f8af3f..3dcf185de2 100644
--- a/rust/cuvs-sys/build.rs
+++ b/rust/cuvs-sys/build.rs
@@ -100,7 +100,7 @@ fn main() {
         // Only generate bindings for cuvs/cagra types and functions
         .allowlist_type("(cuvs|bruteForce|cagra|DL).*")
         .allowlist_function("(cuvs|bruteForce|cagra).*")
-        .rustified_enum("(cuvs|cagra|DL|DistanceType).*")
+        .rustified_enum("(cuvs|cagra|DL|DistanceType|codebook_gen|cudaDataType_t).*")
         // also need some basic cuda mem functions for copying data
         .allowlist_function("(cudaMemcpyAsync|cudaMemcpy)")
         .rustified_enum("cudaError")
diff --git a/rust/cuvs-sys/cuvs_c_wrapper.h b/rust/cuvs-sys/cuvs_c_wrapper.h
index e2c35f12dc..d2771ed524 100644
--- a/rust/cuvs-sys/cuvs_c_wrapper.h
+++ b/rust/cuvs-sys/cuvs_c_wrapper.h
@@ -19,3 +19,4 @@
 #include <cuvs/core/c_api.h>
 #include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/ivf_pq.h>
diff --git a/rust/cuvs/src/brute_force.rs b/rust/cuvs/src/brute_force.rs
index e2e03c2367..975a4ddc38 100644
--- a/rust/cuvs/src/brute_force.rs
+++ b/rust/cuvs/src/brute_force.rs
@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+//! Brute Force KNN
 
 use std::io::{stderr, Write};
 
@@ -126,7 +127,7 @@ mod tests {
             Index::build(&res, metric, None, dataset).expect("failed to create brute force index");
 
         res.sync_stream().unwrap();
-        
+
         // use the first 4 points from the dataset as queries : will test that we get them back
         // as their own nearest neighbor
         let n_queries = 4;
diff --git a/rust/cuvs/src/ivf_pq/index.rs b/rust/cuvs/src/ivf_pq/index.rs
new file mode 100644
index 0000000000..db214bb8eb
--- /dev/null
+++ b/rust/cuvs/src/ivf_pq/index.rs
@@ -0,0 +1,165 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::io::{stderr, Write};
+
+use crate::dlpack::ManagedTensor;
+use crate::error::{check_cuvs, Result};
+use crate::ivf_pq::{IndexParams, SearchParams};
+use crate::resources::Resources;
+
+/// Ivf-Pq ANN Index
+#[derive(Debug)]
+pub struct Index(ffi::cuvsIvfPqIndex_t);
+
+impl Index {
+    /// Builds a new Index from the dataset for efficient search.
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `params` - Parameters for building the index
+    /// * `dataset` - A row-major matrix on either the host or device to index
+    pub fn build<T: Into<ManagedTensor>>(
+        res: &Resources,
+        params: &IndexParams,
+        dataset: T,
+    ) -> Result<Index> {
+        let dataset: ManagedTensor = dataset.into();
+        let index = Index::new()?;
+        unsafe {
+            check_cuvs(ffi::cuvsIvfPqBuild(
+                res.0,
+                params.0,
+                dataset.as_ptr(),
+                index.0,
+            ))?;
+        }
+        Ok(index)
+    }
+
+    /// Creates a new empty index
+    pub fn new() -> Result<Index> {
+        unsafe {
+            let mut index = std::mem::MaybeUninit::<ffi::cuvsIvfPqIndex_t>::uninit();
+            check_cuvs(ffi::cuvsIvfPqIndexCreate(index.as_mut_ptr()))?;
+            Ok(Index(index.assume_init()))
+        }
+    }
+
+    /// Perform a Approximate Nearest Neighbors search on the Index
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `params` - Parameters to use in searching the index
+    /// * `queries` - A matrix in device memory to query for
+    /// * `neighbors` - Matrix in device memory that receives the indices of the nearest neighbors
+    /// * `distances` - Matrix in device memory that receives the distances of the nearest neighbors
+    pub fn search(
+        self,
+        res: &Resources,
+        params: &SearchParams,
+        queries: &ManagedTensor,
+        neighbors: &ManagedTensor,
+        distances: &ManagedTensor,
+    ) -> Result<()> {
+        unsafe {
+            check_cuvs(ffi::cuvsIvfPqSearch(
+                res.0,
+                params.0,
+                self.0,
+                queries.as_ptr(),
+                neighbors.as_ptr(),
+                distances.as_ptr(),
+            ))
+        }
+    }
+}
+
+impl Drop for Index {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsIvfPqIndexDestroy(self.0) }) {
+            write!(stderr(), "failed to call cuvsIvfPqIndexDestroy {:?}", e)
+                .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::s;
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    #[test]
+    fn test_ivf_pq() {
+        let build_params = IndexParams::new().unwrap().set_n_lists(64);
+
+        let res = Resources::new().unwrap();
+
+        // Create a new random dataset to index
+        let n_datapoints = 1024;
+        let n_features = 16;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        let dataset_device = ManagedTensor::from(&dataset).to_device(&res).unwrap();
+
+        // build the ivf-pq index
+        let index = Index::build(&res, &build_params, dataset_device)
+            .expect("failed to create ivf-pq index");
+
+        // use the first 4 points from the dataset as queries : will test that we get them back
+        // as their own nearest neighbor
+        let n_queries = 4;
+        let queries = dataset.slice(s![0..n_queries, ..]);
+
+        let k = 10;
+
+        // Ivf-Pq search API requires queries and outputs to be on device memory
+        // copy query data over, and allocate new device memory for the distances/ neighbors
+        // outputs
+        let queries = ManagedTensor::from(&queries).to_device(&res).unwrap();
+        let mut neighbors_host = ndarray::Array::<i64, _>::zeros((n_queries, k));
+        let neighbors = ManagedTensor::from(&neighbors_host)
+            .to_device(&res)
+            .unwrap();
+
+        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+        let distances = ManagedTensor::from(&distances_host)
+            .to_device(&res)
+            .unwrap();
+
+        let search_params = SearchParams::new().unwrap();
+
+        index
+            .search(&res, &search_params, &queries, &neighbors, &distances)
+            .unwrap();
+
+        // Copy back to host memory
+        distances.to_host(&res, &mut distances_host).unwrap();
+        neighbors.to_host(&res, &mut neighbors_host).unwrap();
+
+        // nearest neighbors should be themselves, since queries are from the
+        // dataset
+        assert_eq!(neighbors_host[[0, 0]], 0);
+        assert_eq!(neighbors_host[[1, 0]], 1);
+        assert_eq!(neighbors_host[[2, 0]], 2);
+        assert_eq!(neighbors_host[[3, 0]], 3);
+    }
+}
diff --git a/rust/cuvs/src/ivf_pq/index_params.rs b/rust/cuvs/src/ivf_pq/index_params.rs
new file mode 100644
index 0000000000..e38c0945fa
--- /dev/null
+++ b/rust/cuvs/src/ivf_pq/index_params.rs
@@ -0,0 +1,176 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::distance_type::DistanceType;
+use crate::error::{check_cuvs, Result};
+use std::fmt;
+use std::io::{stderr, Write};
+
+pub use ffi::codebook_gen;
+
+pub struct IndexParams(pub ffi::cuvsIvfPqIndexParams_t);
+
+impl IndexParams {
+    /// Returns a new IndexParams
+    pub fn new() -> Result<IndexParams> {
+        unsafe {
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsIvfPqIndexParams_t>::uninit();
+            check_cuvs(ffi::cuvsIvfPqIndexParamsCreate(params.as_mut_ptr()))?;
+            Ok(IndexParams(params.assume_init()))
+        }
+    }
+
+    /// The number of clusters used in the coarse quantizer.
+    pub fn set_n_lists(self, n_lists: u32) -> IndexParams {
+        unsafe {
+            (*self.0).n_lists = n_lists;
+        }
+        self
+    }
+
+    /// DistanceType to use for building the index
+    pub fn set_metric(self, metric: DistanceType) -> IndexParams {
+        unsafe {
+            (*self.0).metric = metric;
+        }
+        self
+    }
+
+    /// The number of iterations searching for kmeans centers during index building.
+    pub fn set_metric_arg(self, metric_arg: f32) -> IndexParams {
+        unsafe {
+            (*self.0).metric_arg = metric_arg;
+        }
+        self
+    }
+
+    /// The number of iterations searching for kmeans centers during index building.
+    pub fn set_kmeans_n_iters(self, kmeans_n_iters: u32) -> IndexParams {
+        unsafe {
+            (*self.0).kmeans_n_iters = kmeans_n_iters;
+        }
+        self
+    }
+
+    /// If kmeans_trainset_fraction is less than 1, then the dataset is
+    /// subsampled, and only n_samples * kmeans_trainset_fraction rows
+    /// are used for training.
+    pub fn set_kmeans_trainset_fraction(self, kmeans_trainset_fraction: f64) -> IndexParams {
+        unsafe {
+            (*self.0).kmeans_trainset_fraction = kmeans_trainset_fraction;
+        }
+        self
+    }
+
+    /// The bit length of the vector element after quantization.
+    pub fn set_pq_bits(self, pq_bits: u32) -> IndexParams {
+        unsafe {
+            (*self.0).pq_bits = pq_bits;
+        }
+        self
+    }
+
+    /// The dimensionality of a the vector after product quantization.
+    /// When zero, an optimal value is selected using a heuristic. Note
+    /// pq_dim * pq_bits must be a multiple of 8. Hint: a smaller 'pq_dim'
+    /// results in a smaller index size and better search performance, but
+    /// lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any number,
+    /// but multiple of 8 are desirable for good performance. If 'pq_bits'
+    /// is not 8, 'pq_dim' should be a multiple of 8. For good performance,
+    /// it is desirable that 'pq_dim' is a multiple of 32. Ideally,
+    /// 'pq_dim' should be also a divisor of the dataset dim.
+    pub fn set_pq_dim(self, pq_dim: u32) -> IndexParams {
+        unsafe {
+            (*self.0).pq_dim = pq_dim;
+        }
+        self
+    }
+
+    pub fn set_codebook_kind(self, codebook_kind: codebook_gen) -> IndexParams {
+        unsafe {
+            (*self.0).codebook_kind = codebook_kind;
+        }
+        self
+    }
+
+    /// Apply a random rotation matrix on the input data and queries even
+    /// if `dim % pq_dim == 0`. Note: if `dim` is not multiple of `pq_dim`,
+    /// a random rotation is always applied to the input data and queries
+    /// to transform the working space from `dim` to `rot_dim`, which may
+    /// be slightly larger than the original space and and is a multiple
+    /// of `pq_dim` (`rot_dim % pq_dim == 0`). However, this transform is
+    /// not necessary when `dim` is multiple of `pq_dim` (`dim == rot_dim`,
+    /// hence no need in adding "extra" data columns / features). By
+    /// default, if `dim == rot_dim`, the rotation transform is
+    /// initialized with the identity matrix. When
+    /// `force_random_rotation == True`, a random orthogonal transform
+    pub fn set_force_random_rotation(self, force_random_rotation: bool) -> IndexParams {
+        unsafe {
+            (*self.0).force_random_rotation = force_random_rotation;
+        }
+        self
+    }
+
+    /// After training the coarse and fine quantizers, we will populate
+    /// the index with the dataset if add_data_on_build == true, otherwise
+    /// the index is left empty, and the extend method can be used
+    /// to add new vectors to the index.
+    pub fn set_add_data_on_build(self, add_data_on_build: bool) -> IndexParams {
+        unsafe {
+            (*self.0).add_data_on_build = add_data_on_build;
+        }
+        self
+    }
+}
+
+impl fmt::Debug for IndexParams {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        // custom debug trait here, default value will show the pointer address
+        // for the inner params object which isn't that useful.
+        write!(f, "IndexParams({:?})", unsafe { *self.0 })
+    }
+}
+
+impl Drop for IndexParams {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsIvfPqIndexParamsDestroy(self.0) }) {
+            write!(
+                stderr(),
+                "failed to call cuvsIvfPqIndexParamsDestroy {:?}",
+                e
+            )
+            .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_index_params() {
+        let params = IndexParams::new()
+            .unwrap()
+            .set_n_lists(128)
+            .set_add_data_on_build(false);
+
+        unsafe {
+            assert_eq!((*params.0).n_lists, 128);
+            assert_eq!((*params.0).add_data_on_build, false);
+        }
+    }
+}
diff --git a/rust/cuvs/src/ivf_pq/mod.rs b/rust/cuvs/src/ivf_pq/mod.rs
new file mode 100644
index 0000000000..e101439dff
--- /dev/null
+++ b/rust/cuvs/src/ivf_pq/mod.rs
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+//! Inverted File Product Quantization
+//!
+//! Example:
+//! ```
+//!
+//! use cuvs::ivf_pq::{Index, IndexParams, SearchParams};
+//! use cuvs::{ManagedTensor, Resources, Result};
+//!
+//! use ndarray::s;
+//! use ndarray_rand::rand_distr::Uniform;
+//! use ndarray_rand::RandomExt;
+//!
+//! fn ivf_pq_example() -> Result<()> {
+//!     let res = Resources::new()?;
+//!
+//!     // Create a new random dataset to index
+//!     let n_datapoints = 65536;
+//!     let n_features = 512;
+//!     let dataset =
+//!         ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+//!
+//!     // build the ivf-pq index
+//!     let build_params = IndexParams::new()?;
+//!     let index = Index::build(&res, &build_params, &dataset)?;
+//!     println!(
+//!         "Indexed {}x{} datapoints into ivf-pq index",
+//!         n_datapoints, n_features
+//!     );
+//!
+//!     // use the first 4 points from the dataset as queries : will test that we get them back
+//!     // as their own nearest neighbor
+//!     let n_queries = 4;
+//!     let queries = dataset.slice(s![0..n_queries, ..]);
+//!
+//!     let k = 10;
+//!
+//!     // Ivf-Pq search API requires queries and outputs to be on device memory
+//!     // copy query data over, and allocate new device memory for the distances/ neighbors
+//!     // outputs
+//!     let queries = ManagedTensor::from(&queries).to_device(&res)?;
+//!     let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+//!     let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;
+//!
+//!     let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+//!     let distances = ManagedTensor::from(&distances_host).to_device(&res)?;
+//!
+//!     let search_params = SearchParams::new()?;
+//!
+//!     index.search(&res, &search_params, &queries, &neighbors, &distances)?;
+//!
+//!     // Copy back to host memory
+//!     distances.to_host(&res, &mut distances_host)?;
+//!     neighbors.to_host(&res, &mut neighbors_host)?;
+//!
+//!     // nearest neighbors should be themselves, since queries are from the
+//!     // dataset
+//!     println!("Neighbors {:?}", neighbors_host);
+//!     println!("Distances {:?}", distances_host);
+//!     Ok(())
+//! }
+//! ```
+
+mod index;
+mod index_params;
+mod search_params;
+
+pub use index::Index;
+pub use index_params::IndexParams;
+pub use search_params::SearchParams;
diff --git a/rust/cuvs/src/ivf_pq/search_params.rs b/rust/cuvs/src/ivf_pq/search_params.rs
new file mode 100644
index 0000000000..b23ac490c1
--- /dev/null
+++ b/rust/cuvs/src/ivf_pq/search_params.rs
@@ -0,0 +1,102 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::error::{check_cuvs, Result};
+use std::fmt;
+use std::io::{stderr, Write};
+
+pub use ffi::cudaDataType_t;
+
+/// Supplemental parameters to search IvfPq index
+pub struct SearchParams(pub ffi::cuvsIvfPqSearchParams_t);
+
+impl SearchParams {
+    /// Returns a new SearchParams object
+    pub fn new() -> Result<SearchParams> {
+        unsafe {
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsIvfPqSearchParams_t>::uninit();
+            check_cuvs(ffi::cuvsIvfPqSearchParamsCreate(params.as_mut_ptr()))?;
+            Ok(SearchParams(params.assume_init()))
+        }
+    }
+
+    /// The number of clusters to search.
+    pub fn set_n_probes(self, n_probes: u32) -> SearchParams {
+        unsafe {
+            (*self.0).n_probes = n_probes;
+        }
+        self
+    }
+
+    /// Data type of look up table to be created dynamically at search
+    /// time. The use of low-precision types reduces the amount of shared
+    /// memory required at search time, so fast shared memory kernels can
+    /// be used even for datasets with large dimansionality. Note that
+    /// the recall is slightly degraded when low-precision type is
+    /// selected.
+    pub fn set_lut_dtype(self, lut_dtype: cudaDataType_t) -> SearchParams {
+        unsafe {
+            (*self.0).lut_dtype = lut_dtype;
+        }
+        self
+    }
+
+    /// Storage data type for distance/similarity computation.
+    pub fn set_internal_distance_dtype(
+        self,
+        internal_distance_dtype: cudaDataType_t,
+    ) -> SearchParams {
+        unsafe {
+            (*self.0).internal_distance_dtype = internal_distance_dtype;
+        }
+        self
+    }
+}
+
+impl fmt::Debug for SearchParams {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        // custom debug trait here, default value will show the pointer address
+        // for the inner params object which isn't that useful.
+        write!(f, "SearchParams {{ params: {:?} }}", unsafe { *self.0 })
+    }
+}
+
+impl Drop for SearchParams {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsIvfPqSearchParamsDestroy(self.0) }) {
+            write!(
+                stderr(),
+                "failed to call cuvsIvfPqSearchParamsDestroy {:?}",
+                e
+            )
+            .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_search_params() {
+        let params = SearchParams::new().unwrap().set_n_probes(128);
+
+        unsafe {
+            assert_eq!((*params.0).n_probes, 128);
+        }
+    }
+}
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
index de974dad7a..933b5a3828 100644
--- a/rust/cuvs/src/lib.rs
+++ b/rust/cuvs/src/lib.rs
@@ -23,6 +23,7 @@ pub mod cagra;
 pub mod distance_type;
 mod dlpack;
 mod error;
+pub mod ivf_pq;
 mod resources;
 
 pub use dlpack::ManagedTensor;

From 00616f999232a3b103361a052b7c11e233f8fdeb Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 30 Apr 2024 16:47:25 -0700
Subject: [PATCH 045/623] Add python and rust bindings for Ivf-Flat (#82)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/82
---
 cpp/include/cuvs/neighbors/ivf_flat.h         |  44 +--
 cpp/src/neighbors/ivf_flat_c.cpp              |  50 +--
 cpp/test/neighbors/run_ivf_flat_c.c           |   9 +-
 docs/source/python_api/neighbors.rst          |   1 +
 docs/source/python_api/neighbors_ivf_flat.rst |  34 ++
 examples/cpp/src/cagra_example.cu             |   2 +-
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 python/cuvs/cuvs/neighbors/__init__.py        |   4 +-
 .../cuvs/neighbors/ivf_flat/CMakeLists.txt    |  24 ++
 .../cuvs/cuvs/neighbors/ivf_flat/__init__.pxd |   0
 .../cuvs/cuvs/neighbors/ivf_flat/__init__.py  |  18 +
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |  74 ++++
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx | 359 ++++++++++++++++++
 python/cuvs/cuvs/test/test_doctests.py        |   2 +
 python/cuvs/cuvs/test/test_ivf_flat.py        | 102 +++++
 rust/cuvs-sys/cuvs_c_wrapper.h                |   1 +
 rust/cuvs/src/ivf_flat/index.rs               | 165 ++++++++
 rust/cuvs/src/ivf_flat/index_params.rs        | 124 ++++++
 rust/cuvs/src/ivf_flat/mod.rs                 |  87 +++++
 rust/cuvs/src/ivf_flat/search_params.rs       |  76 ++++
 rust/cuvs/src/lib.rs                          |   1 +
 21 files changed, 1124 insertions(+), 54 deletions(-)
 create mode 100644 docs/source/python_api/neighbors_ivf_flat.rst
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_flat/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
 create mode 100644 python/cuvs/cuvs/test/test_ivf_flat.py
 create mode 100644 rust/cuvs/src/ivf_flat/index.rs
 create mode 100644 rust/cuvs/src/ivf_flat/index_params.rs
 create mode 100644 rust/cuvs/src/ivf_flat/mod.rs
 create mode 100644 rust/cuvs/src/ivf_flat/search_params.rs

diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 07877bc75d..a6616e73e5 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -34,7 +34,7 @@ extern "C" {
  * @brief Supplemental parameters to build IVF-Flat Index
  *
  */
-struct ivfFlatIndexParams {
+struct cuvsIvfFlatIndexParams {
   /** Distance type. */
   cuvsDistanceType metric;
   /** The argument used by some distance metrics. */
@@ -79,7 +79,7 @@ struct ivfFlatIndexParams {
   bool conservative_memory_allocation;
 };
 
-typedef struct ivfFlatIndexParams* cuvsIvfFlatIndexParams_t;
+typedef struct cuvsIvfFlatIndexParams* cuvsIvfFlatIndexParams_t;
 
 /**
  * @brief Allocate IVF-Flat Index params, and populate with default values
@@ -108,12 +108,12 @@ cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
  * @brief Supplemental parameters to search IVF-Flat index
  *
  */
-struct ivfFlatSearchParams {
+struct cuvsIvfFlatSearchParams {
   /** The number of clusters to search. */
   uint32_t n_probes;
 };
 
-typedef struct ivfFlatSearchParams* cuvsIvfFlatSearchParams_t;
+typedef struct cuvsIvfFlatSearchParams* cuvsIvfFlatSearchParams_t;
 
 /**
  * @brief Allocate IVF-Flat search params, and populate with default values
@@ -145,9 +145,9 @@ cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params);
 typedef struct {
   uintptr_t addr;
   DLDataType dtype;
-} ivfFlatIndex;
+} cuvsIvfFlatIndex;
 
-typedef ivfFlatIndex* cuvsIvfFlatIndex_t;
+typedef cuvsIvfFlatIndex* cuvsIvfFlatIndex_t;
 
 /**
  * @brief Allocate IVF-Flat index
@@ -155,14 +155,14 @@ typedef ivfFlatIndex* cuvsIvfFlatIndex_t;
  * @param[in] index cuvsIvfFlatIndex_t to allocate
  * @return cuvsError_t
  */
-cuvsError_t ivfFlatIndexCreate(cuvsIvfFlatIndex_t* index);
+cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index);
 
 /**
  * @brief De-allocate IVF-Flat index
  *
  * @param[in] index cuvsIvfFlatIndex_t to de-allocate
  */
-cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index);
+cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index);
 /**
  * @}
  */
@@ -196,14 +196,14 @@ cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index);
  *
  * // Create IVF-Flat index
  * cuvsIvfFlatIndex_t index;
- * cuvsError_t index_create_status = ivfFlatIndexCreate(&index);
+ * cuvsError_t index_create_status = cuvsIvfFlatIndexCreate(&index);
  *
  * // Build the IVF-Flat Index
- * cuvsError_t build_status = ivfFlatBuild(res, index_params, &dataset, index);
+ * cuvsError_t build_status = cuvsIvfFlatBuild(res, index_params, &dataset, index);
  *
  * // de-allocate `index_params`, `index` and `res`
  * cuvsError_t params_destroy_status = cuvsIvfFlatIndexParamsDestroy(index_params);
- * cuvsError_t index_destroy_status = ivfFlatIndexDestroy(index);
+ * cuvsError_t index_destroy_status = cuvsIvfFlatIndexDestroy(index);
  * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
  * @endcode
  *
@@ -213,10 +213,10 @@ cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index);
  * @param[out] index cuvsIvfFlatIndex_t Newly built IVF-Flat index
  * @return cuvsError_t
  */
-cuvsError_t ivfFlatBuild(cuvsResources_t res,
-                         cuvsIvfFlatIndexParams_t index_params,
-                         DLManagedTensor* dataset,
-                         cuvsIvfFlatIndex_t index);
+cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
+                             cuvsIvfFlatIndexParams_t index_params,
+                             DLManagedTensor* dataset,
+                             cuvsIvfFlatIndex_t index);
 /**
  * @}
  */
@@ -253,7 +253,7 @@ cuvsError_t ivfFlatBuild(cuvsResources_t res,
  * cuvsError_t params_create_status = cuvsIvfFlatSearchParamsCreate(&search_params);
  *
  * // Search the `index` built using `ivfFlatBuild`
- * cuvsError_t search_status = ivfFlatSearch(res, search_params, index, &queries, &neighbors,
+ * cuvsError_t search_status = cuvsIvfFlatSearch(res, search_params, index, &queries, &neighbors,
  * &distances);
  *
  * // de-allocate `search_params` and `res`
@@ -268,12 +268,12 @@ cuvsError_t ivfFlatBuild(cuvsResources_t res,
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
  */
-cuvsError_t ivfFlatSearch(cuvsResources_t res,
-                          cuvsIvfFlatSearchParams_t search_params,
-                          cuvsIvfFlatIndex_t index,
-                          DLManagedTensor* queries,
-                          DLManagedTensor* neighbors,
-                          DLManagedTensor* distances);
+cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
+                              cuvsIvfFlatSearchParams_t search_params,
+                              cuvsIvfFlatIndex_t index,
+                              DLManagedTensor* queries,
+                              DLManagedTensor* neighbors,
+                              DLManagedTensor* distances);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index d8185c4f77..c79ca8df96 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -31,7 +31,7 @@
 namespace {
 
 template <typename T, typename IdxT>
-void* _build(cuvsResources_t res, ivfFlatIndexParams params, DLManagedTensor* dataset_tensor)
+void* _build(cuvsResources_t res, cuvsIvfFlatIndexParams params, DLManagedTensor* dataset_tensor)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
 
@@ -60,8 +60,8 @@ void* _build(cuvsResources_t res, ivfFlatIndexParams params, DLManagedTensor* da
 
 template <typename T, typename IdxT>
 void _search(cuvsResources_t res,
-             ivfFlatSearchParams params,
-             ivfFlatIndex index,
+             cuvsIvfFlatSearchParams params,
+             cuvsIvfFlatIndex index,
              DLManagedTensor* queries_tensor,
              DLManagedTensor* neighbors_tensor,
              DLManagedTensor* distances_tensor)
@@ -85,12 +85,12 @@ void _search(cuvsResources_t res,
 
 }  // namespace
 
-extern "C" cuvsError_t ivfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
+extern "C" cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
 {
-  return cuvs::core::translate_exceptions([=] { *index = new ivfFlatIndex{}; });
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsIvfFlatIndex{}; });
 }
 
-extern "C" cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index_c_ptr)
+extern "C" cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index_c_ptr)
 {
   return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
@@ -112,10 +112,10 @@ extern "C" cuvsError_t ivfFlatIndexDestroy(cuvsIvfFlatIndex_t index_c_ptr)
   });
 }
 
-extern "C" cuvsError_t ivfFlatBuild(cuvsResources_t res,
-                                    cuvsIvfFlatIndexParams_t params,
-                                    DLManagedTensor* dataset_tensor,
-                                    cuvsIvfFlatIndex_t index)
+extern "C" cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
+                                        cuvsIvfFlatIndexParams_t params,
+                                        DLManagedTensor* dataset_tensor,
+                                        cuvsIvfFlatIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
@@ -140,12 +140,12 @@ extern "C" cuvsError_t ivfFlatBuild(cuvsResources_t res,
   });
 }
 
-extern "C" cuvsError_t ivfFlatSearch(cuvsResources_t res,
-                                     cuvsIvfFlatSearchParams_t params,
-                                     cuvsIvfFlatIndex_t index_c_ptr,
-                                     DLManagedTensor* queries_tensor,
-                                     DLManagedTensor* neighbors_tensor,
-                                     DLManagedTensor* distances_tensor)
+extern "C" cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
+                                         cuvsIvfFlatSearchParams_t params,
+                                         cuvsIvfFlatIndex_t index_c_ptr,
+                                         DLManagedTensor* queries_tensor,
+                                         DLManagedTensor* neighbors_tensor,
+                                         DLManagedTensor* distances_tensor)
 {
   return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
@@ -187,14 +187,14 @@ extern "C" cuvsError_t ivfFlatSearch(cuvsResources_t res,
 extern "C" cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
-    *params = new ivfFlatIndexParams{.metric                         = L2Expanded,
-                                     .metric_arg                     = 2.0f,
-                                     .add_data_on_build              = true,
-                                     .n_lists                        = 1024,
-                                     .kmeans_n_iters                 = 20,
-                                     .kmeans_trainset_fraction       = 0.5,
-                                     .adaptive_centers               = false,
-                                     .conservative_memory_allocation = false};
+    *params = new cuvsIvfFlatIndexParams{.metric                         = L2Expanded,
+                                         .metric_arg                     = 2.0f,
+                                         .add_data_on_build              = true,
+                                         .n_lists                        = 1024,
+                                         .kmeans_n_iters                 = 20,
+                                         .kmeans_trainset_fraction       = 0.5,
+                                         .adaptive_centers               = false,
+                                         .conservative_memory_allocation = false};
   });
 }
 
@@ -206,7 +206,7 @@ extern "C" cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t pa
 extern "C" cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t* params)
 {
   return cuvs::core::translate_exceptions(
-    [=] { *params = new ivfFlatSearchParams{.n_probes = 20}; });
+    [=] { *params = new cuvsIvfFlatSearchParams{.n_probes = 20}; });
 }
 
 extern "C" cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
diff --git a/cpp/test/neighbors/run_ivf_flat_c.c b/cpp/test/neighbors/run_ivf_flat_c.c
index 9ecbd18eb2..8cd79c91fa 100644
--- a/cpp/test/neighbors/run_ivf_flat_c.c
+++ b/cpp/test/neighbors/run_ivf_flat_c.c
@@ -46,14 +46,14 @@ void run_ivf_flat(int64_t n_rows,
 
   // create index
   cuvsIvfFlatIndex_t index;
-  ivfFlatIndexCreate(&index);
+  cuvsIvfFlatIndexCreate(&index);
 
   // build index
   cuvsIvfFlatIndexParams_t build_params;
   cuvsIvfFlatIndexParamsCreate(&build_params);
   build_params->metric  = metric;
   build_params->n_lists = n_lists;
-  ivfFlatBuild(res, build_params, &dataset_tensor, index);
+  cuvsIvfFlatBuild(res, build_params, &dataset_tensor, index);
 
   // create queries DLTensor
   DLManagedTensor queries_tensor;
@@ -95,11 +95,12 @@ void run_ivf_flat(int64_t n_rows,
   cuvsIvfFlatSearchParams_t search_params;
   cuvsIvfFlatSearchParamsCreate(&search_params);
   search_params->n_probes = n_probes;
-  ivfFlatSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  cuvsIvfFlatSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
 
   // de-allocate index and res
   cuvsIvfFlatSearchParamsDestroy(search_params);
   cuvsIvfFlatIndexParamsDestroy(build_params);
-  ivfFlatIndexDestroy(index);
+  cuvsIvfFlatIndexDestroy(index);
   cuvsResourcesDestroy(res);
 }
diff --git a/docs/source/python_api/neighbors.rst b/docs/source/python_api/neighbors.rst
index 3cbbd52ea9..022c50de34 100644
--- a/docs/source/python_api/neighbors.rst
+++ b/docs/source/python_api/neighbors.rst
@@ -11,4 +11,5 @@ Nearest Neighbors
 
    neighbors_brute_force.rst
    neighbors_cagra.rst
+   neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
diff --git a/docs/source/python_api/neighbors_ivf_flat.rst b/docs/source/python_api/neighbors_ivf_flat.rst
new file mode 100644
index 0000000000..5514e5e43f
--- /dev/null
+++ b/docs/source/python_api/neighbors_ivf_flat.rst
@@ -0,0 +1,34 @@
+IVF-Flat
+========
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Index build parameters
+######################
+
+.. autoclass:: cuvs.neighbors.ivf_flat.IndexParams
+    :members:
+
+Index search parameters
+#######################
+
+.. autoclass:: cuvs.neighbors.ivf_flat.SearchParams
+    :members:
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.ivf_flat.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.ivf_flat.build
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.ivf_flat.search
diff --git a/examples/cpp/src/cagra_example.cu b/examples/cpp/src/cagra_example.cu
index 6fab4291b0..630fcd4ecd 100644
--- a/examples/cpp/src/cagra_example.cu
+++ b/examples/cpp/src/cagra_example.cu
@@ -54,7 +54,7 @@ void cagra_build_search_simple(raft::device_resources const& dev_resources,
   // search K nearest neighbors
   cagra::search(dev_resources, search_params, index, queries, neighbors.view(), distances.view());
 
-  // The call to ivf_flat::search is asynchronous. Before accessing the data, sync by calling
+  // The call to cagra::search is asynchronous. Before accessing the data, sync by calling
   // raft::resource::sync_stream(dev_resources);
 
   print_results(dev_resources, neighbors.view(), distances.view());
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 1218c3ef06..e0041243ab 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -14,4 +14,5 @@
 
 add_subdirectory(brute_force)
 add_subdirectory(cagra)
+add_subdirectory(ivf_flat)
 add_subdirectory(ivf_pq)
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index c6801973cb..c431ffde7b 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -13,6 +13,6 @@
 # limitations under the License.
 
 
-from cuvs.neighbors import brute_force, cagra, ivf_pq
+from cuvs.neighbors import brute_force, cagra, ivf_flat, ivf_pq
 
-__all__ = ["brute_force", "cagra", "ivf_pq"]
+__all__ = ["brute_force", "cagra", "ivf_flat", "ivf_pq"]
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
new file mode 100644
index 0000000000..09bd8f4227
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources ivf_flat.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_ivf_flat_
+)
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/__init__.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py b/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
new file mode 100644
index 0000000000..a9c3e9b2db
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .ivf_flat import Index, IndexParams, SearchParams, build, search
+
+__all__ = ["Index", "IndexParams", "SearchParams", "build", "search"]
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
new file mode 100644
index 0000000000..8859d5d969
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -0,0 +1,74 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uint32_t, uintptr_t
+from libcpp cimport bool
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
+
+    ctypedef struct cuvsIvfFlatIndexParams:
+        cuvsDistanceType metric
+        float metric_arg
+        bool add_data_on_build
+        uint32_t n_lists
+        uint32_t kmeans_n_iters
+        double kmeans_trainset_fraction
+        bool adaptive_centers
+        bool conservative_memory_allocation
+
+    ctypedef cuvsIvfFlatIndexParams* cuvsIvfFlatIndexParams_t
+
+    ctypedef struct cuvsIvfFlatSearchParams:
+        uint32_t n_probes
+
+    ctypedef cuvsIvfFlatSearchParams* cuvsIvfFlatSearchParams_t
+
+    ctypedef struct cuvsIvfFlatIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsIvfFlatIndex* cuvsIvfFlatIndex_t
+
+    cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t* params)
+
+    cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index)
+
+    cuvsError_t cuvsIvfFlatSearchParamsCreate(
+        cuvsIvfFlatSearchParams_t* params)
+
+    cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t index)
+
+    cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
+
+    cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
+
+    cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
+                                 cuvsIvfFlatIndexParams* params,
+                                 DLManagedTensor* dataset,
+                                 cuvsIvfFlatIndex_t index) except +
+
+    cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
+                                  cuvsIvfFlatSearchParams* params,
+                                  cuvsIvfFlatIndex_t index,
+                                  DLManagedTensor* queries,
+                                  DLManagedTensor* neighbors,
+                                  DLManagedTensor* distances) except +
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
new file mode 100644
index 0000000000..870e9412a8
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -0,0 +1,359 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libcpp cimport bool, cast
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+from pylibraft.distance.pairwise_distance import DISTANCE_TYPES
+from pylibraft.neighbors.common import _check_input_array
+
+from libc.stdint cimport (
+    int8_t,
+    int64_t,
+    uint8_t,
+    uint32_t,
+    uint64_t,
+    uintptr_t,
+)
+
+from cuvs.common.exceptions import check_cuvs
+
+
+cdef class IndexParams:
+    """
+    Parameters to build index for IvfFlat nearest neighbor search
+
+    Parameters
+    ----------
+    n_lists : int, default = 1024
+        The number of clusters used in the coarse quantizer.
+    metric : str, default = "sqeuclidean"
+        String denoting the metric type.
+        Valid values for metric: ["sqeuclidean", "inner_product",
+        "euclidean"], where
+            - sqeuclidean is the euclidean distance without the square root
+              operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2,
+            - euclidean is the euclidean distance
+            - inner product distance is defined as
+              distance(a, b) = \\sum_i a_i * b_i.
+    kmeans_n_iters : int, default = 20
+        The number of iterations searching for kmeans centers during index
+        building.
+        The default setting is often fine, but this parameter can be decreased
+        to improve training time wih larger trainset fractions (10M+ vectors)
+        or increased for smaller trainset fractions (very small number of
+        vectors) to improve recall.
+    kmeans_trainset_fraction : int, default = 0.5
+        If kmeans_trainset_fraction is less than 1, then the dataset is
+        subsampled, and only n_samples * kmeans_trainset_fraction rows
+        are used for training.
+    add_data_on_build : bool, default = True
+        After training the coarse and fine quantizers, we will populate
+        the index with the dataset if add_data_on_build == True, otherwise
+        the index is left empty, and the extend method can be used
+        to add new vectors to the index.
+    adaptive_centers : bool, default = False
+        By default (adaptive_centers = False), the cluster centers are
+        trained in `ivf_flat.build`, and and never modified in
+        `ivf_flat.extend`. The alternative behavior (adaptive_centers
+        = true) is to update the cluster centers for new data when it is
+        added. In this case, `index.centers()` are always exactly the
+        centroids of the data in the corresponding clusters. The drawback
+        of this behavior is that the centroids depend on the order of
+        adding new data (through the classification of the added data);
+        that is, `index.centers()` "drift" together with the changing
+        distribution of the newly added data.
+    """
+
+    cdef cuvsIvfFlatIndexParams* params
+    cdef object _metric
+
+    def __cinit__(self):
+        cuvsIvfFlatIndexParamsCreate(&self.params)
+
+    def __dealloc__(self):
+        check_cuvs(cuvsIvfFlatIndexParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 n_lists=1024,
+                 metric="sqeuclidean",
+                 metric_arg=2.0,
+                 kmeans_n_iters=20,
+                 kmeans_trainset_fraction=0.5,
+                 adaptive_centers=False,
+                 add_data_on_build=True,
+                 conservative_memory_allocation=False):
+        self._metric = metric
+        self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+        self.params.metric_arg = metric_arg
+        self.params.add_data_on_build = add_data_on_build
+        self.params.n_lists = n_lists
+        self.params.kmeans_n_iters = kmeans_n_iters
+        self.params.kmeans_trainset_fraction = kmeans_trainset_fraction
+        self.params.adaptive_centers = adaptive_centers
+        self.params.conservative_memory_allocation = \
+            conservative_memory_allocation
+
+    @property
+    def metric(self):
+        return self._metric
+
+    @property
+    def metric_arg(self):
+        return self.params.metric_arg
+
+    @property
+    def add_data_on_build(self):
+        return self.params.add_data_on_build
+
+    @property
+    def n_lists(self):
+        return self.params.n_lists
+
+    @property
+    def kmeans_n_iters(self):
+        return self.params.kmeans_n_iters
+
+    @property
+    def kmeans_trainset_fraction(self):
+        return self.params.kmeans_trainset_fraction
+
+    @property
+    def adaptive_centers(self):
+        return self.params.adaptive_centers
+
+    @property
+    def conservative_memory_allocation(self):
+        return self.params.conservative_memory_allocation
+
+
+cdef class Index:
+    """
+    IvfFlat index object. This object stores the trained IvfFlat index state
+    which can be used to perform nearest neighbors searches.
+    """
+
+    cdef cuvsIvfFlatIndex_t index
+    cdef bool trained
+
+    def __cinit__(self):
+        self.trained = False
+        check_cuvs(cuvsIvfFlatIndexCreate(&self.index))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsIvfFlatIndexDestroy(self.index))
+
+    @property
+    def trained(self):
+        return self.trained
+
+    def __repr__(self):
+        return "Index(type=IvfFlat)"
+
+
+@auto_sync_resources
+def build(IndexParams index_params, dataset, resources=None):
+    """
+    Build the IvfFlat index from the dataset for efficient search.
+
+    Parameters
+    ----------
+    index_params : :py:class:`cuvs.neighbors.ivf_flat.IndexParams`
+    dataset : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.ivf_flat.Index`
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> build_params = ivf_flat.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_flat.build(build_params, dataset)
+    >>> distances, neighbors = ivf_flat.search(ivf_flat.SearchParams(),
+    ...                                        index, dataset,
+    ...                                        k)
+    >>> distances = cp.asarray(distances)
+    >>> neighbors = cp.asarray(neighbors)
+    """
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
+                                    np.dtype('ubyte')])
+
+    cdef Index idx = Index()
+    cdef cuvsError_t build_status
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cuvsIvfFlatIndexParams* params = index_params.params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsIvfFlatBuild(
+            res,
+            params,
+            dataset_dlpack,
+            idx.index
+        ))
+        idx.trained = True
+
+    return idx
+
+
+cdef class SearchParams:
+    """
+    Supplemental parameters to search IVF-Flat index
+
+    Parameters
+    ----------
+    n_probes: int
+        The number of clusters to search.
+    """
+
+    cdef cuvsIvfFlatSearchParams* params
+
+    def __cinit__(self):
+        cuvsIvfFlatSearchParamsCreate(&self.params)
+
+    def __dealloc__(self):
+        check_cuvs(cuvsIvfFlatSearchParamsDestroy(self.params))
+
+    def __init__(self, *, n_probes=20):
+        self.params.n_probes = n_probes
+
+    @property
+    def n_probes(self):
+        return self.params.n_probes
+
+
+@auto_sync_resources
+@auto_convert_output
+def search(SearchParams search_params,
+           Index index,
+           queries,
+           k,
+           neighbors=None,
+           distances=None,
+           resources=None):
+    """
+    Find the k nearest neighbors for each query.
+
+    Parameters
+    ----------
+    search_params : py:class:`cuvs.neighbors.ivf_flat.SearchParams`
+    index : py:class:`cuvs.neighbors.ivf_flat.Index`
+        Trained IvfFlat index.
+    queries : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    k : int
+        The number of neighbors.
+    neighbors : Optional CUDA array interface compliant matrix shape
+                (n_queries, k), dtype int64_t. If supplied, neighbor
+                indices will be written here in-place. (default None)
+    distances : Optional CUDA array interface compliant matrix shape
+                (n_queries, k) If supplied, the distances to the
+                neighbors will be written here in-place. (default None)
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build the index
+    >>> index = ivf_flat.build(ivf_flat.IndexParams(), dataset)
+    >>>
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> k = 10
+    >>> search_params = ivf_flat.SearchParams(n_probes=20)
+    >>>
+    >>> distances, neighbors = ivf_flat.search(search_params, index, queries,
+    ...                                     k)
+    """
+    if not index.trained:
+        raise ValueError("Index needs to be built before calling search.")
+
+    queries_cai = wrap_array(queries)
+    _check_input_array(queries_cai, [np.dtype('float32'), np.dtype('byte'),
+                                     np.dtype('ubyte')])
+
+    cdef uint32_t n_queries = queries_cai.shape[0]
+
+    if neighbors is None:
+        neighbors = device_ndarray.empty((n_queries, k), dtype='int64')
+
+    neighbors_cai = wrap_array(neighbors)
+    _check_input_array(neighbors_cai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    if distances is None:
+        distances = device_ndarray.empty((n_queries, k), dtype='float32')
+
+    distances_cai = wrap_array(distances)
+    _check_input_array(distances_cai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    cdef cuvsIvfFlatSearchParams* params = search_params.params
+    cdef cuvsError_t search_status
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_cai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_cai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_cai)
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsIvfFlatSearch(
+            res,
+            params,
+            index.index,
+            queries_dlpack,
+            neighbors_dlpack,
+            distances_dlpack
+        ))
+
+    return (distances, neighbors)
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
index d334aa3228..68b2861dff 100644
--- a/python/cuvs/cuvs/test/test_doctests.py
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -90,6 +90,8 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 # doctests for here
 DOC_STRINGS = list(_find_doctests_in_obj(cuvs.neighbors))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.cagra))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.brute_force))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.ivf_flat))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.common))
 
 
diff --git a/python/cuvs/cuvs/test/test_ivf_flat.py b/python/cuvs/cuvs/test/test_ivf_flat.py
new file mode 100644
index 0000000000..81917b0e3c
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_ivf_flat.py
@@ -0,0 +1,102 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+
+from cuvs.neighbors import ivf_flat
+from cuvs.test.ann_utils import calc_recall, generate_data
+
+
+def run_ivf_flat_build_search_test(
+    n_rows=10000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    metric="euclidean",
+    compare=True,
+    inplace=True,
+    search_params={},
+):
+    dataset = generate_data((n_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+    dataset_device = device_ndarray(dataset)
+
+    build_params = ivf_flat.IndexParams(
+        metric=metric,
+    )
+
+    index = ivf_flat.build(build_params, dataset_device)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    out_idx = np.zeros((n_queries, k), dtype=np.int64)
+    out_dist = np.zeros((n_queries, k), dtype=np.float32)
+
+    queries_device = device_ndarray(queries)
+    out_idx_device = device_ndarray(out_idx) if inplace else None
+    out_dist_device = device_ndarray(out_dist) if inplace else None
+
+    search_params = ivf_flat.SearchParams(**search_params)
+
+    ret_output = ivf_flat.search(
+        search_params,
+        index,
+        queries_device,
+        k,
+        neighbors=out_idx_device,
+        distances=out_dist_device,
+    )
+
+    if not inplace:
+        out_dist_device, out_idx_device = ret_output
+
+    if not compare:
+        return
+
+    out_idx = out_idx_device.copy_to_host()
+    out_dist = out_dist_device.copy_to_host()
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    recall = calc_recall(out_idx, skl_idx)
+    assert recall > 0.7
+
+
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize(
+    "metric", ["sqeuclidean", "inner_product", "euclidean"]
+)
+def test_ivf_flat(inplace, dtype, metric):
+    run_ivf_flat_build_search_test(
+        dtype=dtype,
+        inplace=inplace,
+        metric=metric,
+    )
diff --git a/rust/cuvs-sys/cuvs_c_wrapper.h b/rust/cuvs-sys/cuvs_c_wrapper.h
index d2771ed524..c6ab02da03 100644
--- a/rust/cuvs-sys/cuvs_c_wrapper.h
+++ b/rust/cuvs-sys/cuvs_c_wrapper.h
@@ -18,5 +18,6 @@
 // bindings for
 #include <cuvs/core/c_api.h>
 #include <cuvs/neighbors/brute_force.h>
+#include <cuvs/neighbors/ivf_flat.h>
 #include <cuvs/neighbors/cagra.h>
 #include <cuvs/neighbors/ivf_pq.h>
diff --git a/rust/cuvs/src/ivf_flat/index.rs b/rust/cuvs/src/ivf_flat/index.rs
new file mode 100644
index 0000000000..b1462a0e69
--- /dev/null
+++ b/rust/cuvs/src/ivf_flat/index.rs
@@ -0,0 +1,165 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use std::io::{stderr, Write};
+
+use crate::ivf_flat::{IndexParams, SearchParams};
+use crate::dlpack::ManagedTensor;
+use crate::error::{check_cuvs, Result};
+use crate::resources::Resources;
+
+/// Ivf-Flat ANN Index
+#[derive(Debug)]
+pub struct Index(ffi::cuvsIvfFlatIndex_t);
+
+impl Index {
+    /// Builds a new Index from the dataset for efficient search.
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `params` - Parameters for building the index
+    /// * `dataset` - A row-major matrix on either the host or device to index
+    pub fn build<T: Into<ManagedTensor>>(
+        res: &Resources,
+        params: &IndexParams,
+        dataset: T,
+    ) -> Result<Index> {
+        let dataset: ManagedTensor = dataset.into();
+        let index = Index::new()?;
+        unsafe {
+            check_cuvs(ffi::cuvsIvfFlatBuild(
+                res.0,
+                params.0,
+                dataset.as_ptr(),
+                index.0,
+            ))?;
+        }
+        Ok(index)
+    }
+
+    /// Creates a new empty index
+    pub fn new() -> Result<Index> {
+        unsafe {
+            let mut index = std::mem::MaybeUninit::<ffi::cuvsIvfFlatIndex_t>::uninit();
+            check_cuvs(ffi::cuvsIvfFlatIndexCreate(index.as_mut_ptr()))?;
+            Ok(Index(index.assume_init()))
+        }
+    }
+
+    /// Perform a Approximate Nearest Neighbors search on the Index
+    ///
+    /// # Arguments
+    ///
+    /// * `res` - Resources to use
+    /// * `params` - Parameters to use in searching the index
+    /// * `queries` - A matrix in device memory to query for
+    /// * `neighbors` - Matrix in device memory that receives the indices of the nearest neighbors
+    /// * `distances` - Matrix in device memory that receives the distances of the nearest neighbors
+    pub fn search(
+        self,
+        res: &Resources,
+        params: &SearchParams,
+        queries: &ManagedTensor,
+        neighbors: &ManagedTensor,
+        distances: &ManagedTensor,
+    ) -> Result<()> {
+        unsafe {
+            check_cuvs(ffi::cuvsIvfFlatSearch(
+                res.0,
+                params.0,
+                self.0,
+                queries.as_ptr(),
+                neighbors.as_ptr(),
+                distances.as_ptr(),
+            ))
+        }
+    }
+}
+
+impl Drop for Index {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsIvfFlatIndexDestroy(self.0) }) {
+            write!(stderr(), "failed to call cuvsIvfFlatIndexDestroy {:?}", e)
+                .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::s;
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    #[test]
+    fn test_ivf_flat() {
+        let build_params = IndexParams::new().unwrap().set_n_lists(64);
+
+        let res = Resources::new().unwrap();
+
+        // Create a new random dataset to index
+        let n_datapoints = 1024;
+        let n_features = 16;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+        let dataset_device = ManagedTensor::from(&dataset).to_device(&res).unwrap();
+
+        // build the ivf-flat index
+        let index =
+            Index::build(&res, &build_params, dataset_device).expect("failed to create ivf-flat index");
+
+        // use the first 4 points from the dataset as queries : will test that we get them back
+        // as their own nearest neighbor
+        let n_queries = 4;
+        let queries = dataset.slice(s![0..n_queries, ..]);
+
+        let k = 10;
+
+        // IvfFlat search API requires queries and outputs to be on device memory
+        // copy query data over, and allocate new device memory for the distances/ neighbors
+        // outputs
+        let queries = ManagedTensor::from(&queries).to_device(&res).unwrap();
+        let mut neighbors_host = ndarray::Array::<i64, _>::zeros((n_queries, k));
+        let neighbors = ManagedTensor::from(&neighbors_host)
+            .to_device(&res)
+            .unwrap();
+
+        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+        let distances = ManagedTensor::from(&distances_host)
+            .to_device(&res)
+            .unwrap();
+
+        let search_params = SearchParams::new().unwrap();
+
+        index
+            .search(&res, &search_params, &queries, &neighbors, &distances)
+            .unwrap();
+
+        // Copy back to host memory
+        distances.to_host(&res, &mut distances_host).unwrap();
+        neighbors.to_host(&res, &mut neighbors_host).unwrap();
+
+        // nearest neighbors should be themselves, since queries are from the
+        // dataset
+        assert_eq!(neighbors_host[[0, 0]], 0);
+        assert_eq!(neighbors_host[[1, 0]], 1);
+        assert_eq!(neighbors_host[[2, 0]], 2);
+        assert_eq!(neighbors_host[[3, 0]], 3);
+    }
+}
diff --git a/rust/cuvs/src/ivf_flat/index_params.rs b/rust/cuvs/src/ivf_flat/index_params.rs
new file mode 100644
index 0000000000..6c86ef527f
--- /dev/null
+++ b/rust/cuvs/src/ivf_flat/index_params.rs
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::error::{check_cuvs, Result};
+use crate::distance_type::DistanceType;
+use std::fmt;
+use std::io::{stderr, Write};
+
+pub struct IndexParams(pub ffi::cuvsIvfFlatIndexParams_t);
+
+impl IndexParams {
+    /// Returns a new IndexParams
+    pub fn new() -> Result<IndexParams> {
+        unsafe {
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsIvfFlatIndexParams_t>::uninit();
+            check_cuvs(ffi::cuvsIvfFlatIndexParamsCreate(params.as_mut_ptr()))?;
+            Ok(IndexParams(params.assume_init()))
+        }
+    }
+
+    /// The number of clusters used in the coarse quantizer.
+    pub fn set_n_lists(self, n_lists: u32) -> IndexParams {
+        unsafe {
+            (*self.0).n_lists = n_lists;
+        }
+        self
+    }
+
+    /// DistanceType to use for building the index
+    pub fn set_metric(self, metric: DistanceType) -> IndexParams {
+        unsafe {
+            (*self.0).metric = metric;
+        }
+        self
+    }
+
+    /// The number of iterations searching for kmeans centers during index building.
+    pub fn set_metric_arg(self, metric_arg: f32) -> IndexParams {
+        unsafe {
+            (*self.0).metric_arg = metric_arg;
+        }
+        self
+    }
+    /// The number of iterations searching for kmeans centers during index building.
+    pub fn set_kmeans_n_iters(self, kmeans_n_iters: u32) -> IndexParams {
+        unsafe {
+            (*self.0).kmeans_n_iters = kmeans_n_iters;
+        }
+        self
+    }
+
+    /// If kmeans_trainset_fraction is less than 1, then the dataset is
+    /// subsampled, and only n_samples * kmeans_trainset_fraction rows
+    /// are used for training.
+    pub fn set_kmeans_trainset_fraction(self, kmeans_trainset_fraction: f64) -> IndexParams {
+        unsafe {
+            (*self.0).kmeans_trainset_fraction = kmeans_trainset_fraction;
+        }
+        self
+    }
+
+    /// After training the coarse and fine quantizers, we will populate
+    /// the index with the dataset if add_data_on_build == true, otherwise
+    /// the index is left empty, and the extend method can be used
+    /// to add new vectors to the index.
+    pub fn set_add_data_on_build(self, add_data_on_build: bool) -> IndexParams {
+        unsafe {
+            (*self.0).add_data_on_build = add_data_on_build;
+        }
+        self
+    }
+}
+
+impl fmt::Debug for IndexParams {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        // custom debug trait here, default value will show the pointer address
+        // for the inner params object which isn't that useful.
+        write!(f, "IndexParams({:?})", unsafe { *self.0 })
+    }
+}
+
+impl Drop for IndexParams {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsIvfFlatIndexParamsDestroy(self.0) }) {
+            write!(
+                stderr(),
+                "failed to call cuvsIvfFlatIndexParamsDestroy {:?}",
+                e
+            )
+            .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_index_params() {
+        let params = IndexParams::new()
+            .unwrap()
+            .set_n_lists(128)
+            .set_add_data_on_build(false);
+
+        unsafe {
+            assert_eq!((*params.0).n_lists, 128);
+            assert_eq!((*params.0).add_data_on_build, false);
+        }
+    }
+}
diff --git a/rust/cuvs/src/ivf_flat/mod.rs b/rust/cuvs/src/ivf_flat/mod.rs
new file mode 100644
index 0000000000..32aa707845
--- /dev/null
+++ b/rust/cuvs/src/ivf_flat/mod.rs
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//! The IVF-Flat method is an ANN algorithm. It uses an inverted file index (IVF) with 
+//! unmodified (that is, flat) vectors. This algorithm provides simple knobs to reduce 
+//! the overall search space and to trade-off accuracy for speed.
+//!
+//! Example:
+//! ```
+//!
+//! use cuvs::ivf_flat::{Index, IndexParams, SearchParams};
+//! use cuvs::{ManagedTensor, Resources, Result};
+//!
+//! use ndarray::s;
+//! use ndarray_rand::rand_distr::Uniform;
+//! use ndarray_rand::RandomExt;
+//!
+//! fn ivf_flat_example() -> Result<()> {
+//!     let res = Resources::new()?;
+//!
+//!     // Create a new random dataset to index
+//!     let n_datapoints = 65536;
+//!     let n_features = 512;
+//!     let dataset =
+//!         ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+//!
+//!     // build the ivf-flat index
+//!     let build_params = IndexParams::new()?;
+//!     let index = Index::build(&res, &build_params, &dataset)?;
+//!     println!(
+//!         "Indexed {}x{} datapoints into ivf-flat index",
+//!         n_datapoints, n_features
+//!     );
+//!
+//!     // use the first 4 points from the dataset as queries : will test that we get them back
+//!     // as their own nearest neighbor
+//!     let n_queries = 4;
+//!     let queries = dataset.slice(s![0..n_queries, ..]);
+//!
+//!     let k = 10;
+//!
+//!     // Ivf-Flat search API requires queries and outputs to be on device memory
+//!     // copy query data over, and allocate new device memory for the distances/ neighbors
+//!     // outputs
+//!     let queries = ManagedTensor::from(&queries).to_device(&res)?;
+//!     let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+//!     let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;
+//!
+//!     let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+//!     let distances = ManagedTensor::from(&distances_host).to_device(&res)?;
+//!
+//!     let search_params = SearchParams::new()?;
+//!
+//!     index.search(&res, &search_params, &queries, &neighbors, &distances)?;
+//!
+//!     // Copy back to host memory
+//!     distances.to_host(&res, &mut distances_host)?;
+//!     neighbors.to_host(&res, &mut neighbors_host)?;
+//!
+//!     // nearest neighbors should be themselves, since queries are from the
+//!     // dataset
+//!     println!("Neighbors {:?}", neighbors_host);
+//!     println!("Distances {:?}", distances_host);
+//!     Ok(())
+//! }
+//! ```
+
+mod index;
+mod index_params;
+mod search_params;
+
+pub use index::Index;
+pub use index_params::IndexParams;
+pub use search_params::SearchParams;
diff --git a/rust/cuvs/src/ivf_flat/search_params.rs b/rust/cuvs/src/ivf_flat/search_params.rs
new file mode 100644
index 0000000000..9da2b22e73
--- /dev/null
+++ b/rust/cuvs/src/ivf_flat/search_params.rs
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::error::{check_cuvs, Result};
+use std::fmt;
+use std::io::{stderr, Write};
+
+/// Supplemental parameters to search IvfFlat index
+pub struct SearchParams(pub ffi::cuvsIvfFlatSearchParams_t);
+
+impl SearchParams {
+    /// Returns a new SearchParams object
+    pub fn new() -> Result<SearchParams> {
+        unsafe {
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsIvfFlatSearchParams_t>::uninit();
+            check_cuvs(ffi::cuvsIvfFlatSearchParamsCreate(params.as_mut_ptr()))?;
+            Ok(SearchParams(params.assume_init()))
+        }
+    }
+
+    /// Supplemental parameters to search IVF-Flat index
+    pub fn set_n_probes(self, n_probes: u32) -> SearchParams {
+        unsafe {
+            (*self.0).n_probes = n_probes;
+        }
+        self
+    }
+}
+
+impl fmt::Debug for SearchParams {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        // custom debug trait here, default value will show the pointer address
+        // for the inner params object which isn't that useful.
+        write!(f, "SearchParams {{ params: {:?} }}", unsafe { *self.0 })
+    }
+}
+
+impl Drop for SearchParams {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsIvfFlatSearchParamsDestroy(self.0) }) {
+            write!(
+                stderr(),
+                "failed to call cuvsIvfFlatSearchParamsDestroy {:?}",
+                e
+            )
+            .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_search_params() {
+        let params = SearchParams::new().unwrap().set_n_probes(128);
+
+        unsafe {
+            assert_eq!((*params.0).n_probes, 128);
+        }
+    }
+}
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
index 933b5a3828..6ed450c03e 100644
--- a/rust/cuvs/src/lib.rs
+++ b/rust/cuvs/src/lib.rs
@@ -21,6 +21,7 @@
 pub mod brute_force;
 pub mod cagra;
 pub mod distance_type;
+pub mod ivf_flat;
 mod dlpack;
 mod error;
 pub mod ivf_pq;

From d44aa39f9b75dee3d3f5fdc038188333c1d177ac Mon Sep 17 00:00:00 2001
From: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>
Date: Thu, 2 May 2024 09:50:51 -1000
Subject: [PATCH 046/623] Enable Warnings as errors in Python tests (#102)

Part of https://github.com/rapidsai/build-planning/issues/26, warnings in Python tests will now be treated as errors

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: https://github.com/rapidsai/cuvs/pull/102
---
 python/cuvs/pyproject.toml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index d3eae51ea5..c100511d58 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -120,3 +120,8 @@ wheel.packages = ["cuvs"]
 provider = "scikit_build_core.metadata.regex"
 input = "cuvs/VERSION"
 regex = "(?P<value>.*)"
+
+[tool.pytest.ini_options]
+filterwarnings = [
+    "error",
+]

From 07791dfc23b46a211aac9df525c50885db372f9c Mon Sep 17 00:00:00 2001
From: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
Date: Mon, 6 May 2024 10:40:17 -0700
Subject: [PATCH 047/623] Migrate to `{{ stdlib("c") }}` (#93)

The `sysroot*` syntax is getting phased out (conda-forge/conda-forge.github.io#2102).
The recommendation is to move to `{{ stdlib("c") }}`.

Ref https://github.com/rapidsai/build-planning/issues/39

Authors:
  - Philip Hyunsu Cho (https://github.com/hcho3)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - https://github.com/jakirkham
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/93
---
 conda/recipes/cuvs/conda_build_config.yaml    | 5 ++++-
 conda/recipes/cuvs/meta.yaml                  | 2 +-
 conda/recipes/libcuvs/conda_build_config.yaml | 5 ++++-
 conda/recipes/libcuvs/meta.yaml               | 8 ++++----
 4 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/conda/recipes/cuvs/conda_build_config.yaml b/conda/recipes/cuvs/conda_build_config.yaml
index e28b98da7f..e3ca633eb9 100644
--- a/conda/recipes/cuvs/conda_build_config.yaml
+++ b/conda/recipes/cuvs/conda_build_config.yaml
@@ -10,7 +10,10 @@ cuda_compiler:
 cuda11_compiler:
   - nvcc
 
-sysroot_version:
+c_stdlib:
+  - sysroot
+
+c_stdlib_version:
   - "2.17"
 
 cmake_version:
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index b2a8074007..349923bf0e 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -39,7 +39,7 @@ requirements:
     - cuda-version ={{ cuda_version }}
     - cmake {{ cmake_version }}
     - ninja
-    - sysroot_{{ target_platform }} {{ sysroot_version }}
+    - {{ stdlib("c") }}
   host:
     {% if cuda_major == "11" %}
     - cuda-python >=11.7.1,<12.0a0
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index c833f68ba7..8141af1543 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -10,7 +10,10 @@ cuda_compiler:
 cuda11_compiler:
   - nvcc
 
-sysroot_version:
+c_stdlib:
+  - sysroot
+
+c_stdlib_version:
   - "2.17"
 
 cmake_version:
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index 7a09015ccc..d1e8506c87 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -61,7 +61,7 @@ outputs:
         - cuda-version ={{ cuda_version }}
         - cmake {{ cmake_version }}
         - ninja
-        - sysroot_{{ target_platform }} {{ sysroot_version }}
+        - {{ stdlib("c") }}
       host:
         - libraft ={{ minor_version }}
         - cuda-version ={{ cuda_version }}
@@ -127,7 +127,7 @@ outputs:
         - cuda-version ={{ cuda_version }}
         - cmake {{ cmake_version }}
         - ninja
-        - sysroot_{{ target_platform }} {{ sysroot_version }}
+        - {{ stdlib("c") }}
       host:
         - libraft ={{ minor_version }}
         - cuda-version ={{ cuda_version }}
@@ -193,7 +193,7 @@ outputs:
         - cuda-version ={{ cuda_version }}
         - cmake {{ cmake_version }}
         - ninja
-        - sysroot_{{ target_platform }} {{ sysroot_version }}
+        - {{ stdlib("c") }}
       host:
         - libraft ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
@@ -260,7 +260,7 @@ outputs:
         - cuda-version ={{ cuda_version }}
         - cmake {{ cmake_version }}
         - ninja
-        - sysroot_{{ target_platform }} {{ sysroot_version }}
+        - {{ stdlib("c") }}
       host:
         - libraft ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}

From 22f107cea465c2f6a59913c38e51dea988f430f0 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Sat, 11 May 2024 00:20:41 +0200
Subject: [PATCH 048/623] Migrate IVF-PQ from RAFT to cuVS (#86)

List of changes I made during the migration:
- The unit tests are testing IVFPQ codepacker and build functions. So these functions had to be exposed (through `cuvs/neighbors/details`)
- The bitset filter is now located under `cuvs/neighbors/bitset_filter.cuh"
- search_with_filter is added to the public API, with bitset_filter
- Bitset is exposed in public API in a `.hpp` header, for inclusion in CUDA-free code. `bitset::test()` function has been temporarily disabled due to the switch from `.cuh` to `.hpp`.

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/86
---
 cpp/CMakeLists.txt                            |  209 +-
 cpp/include/cuvs/core/bitset.hpp              |   30 +
 cpp/include/cuvs/neighbors/ann_types.hpp      |   40 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |  213 +-
 cpp/include/cuvs/neighbors/dataset.hpp        |  298 +++
 cpp/include/cuvs/neighbors/ivf_list.hpp       |  123 ++
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  279 ++-
 cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp |  274 +++
 cpp/include/cuvs/neighbors/nn_descent.hpp     |  342 +++
 cpp/include/cuvs/neighbors/sample_filter.hpp  |  171 ++
 cpp/src/core/nvtx.hpp                         |   24 +
 cpp/src/neighbors/cagra.cuh                   |  399 ++++
 ...ra_build_int8.cpp => cagra_build_float.cu} |   70 +-
 ...ra_build_uint8.cpp => cagra_build_int8.cu} |   70 +-
 ...a_build_float.cpp => cagra_build_uint8.cu} |   72 +-
 cpp/src/neighbors/cagra_c.cpp                 |    4 +-
 .../{cagra_optimize.cpp => cagra_optimize.cu} |   18 +-
 ...search_float.cpp => cagra_search_float.cu} |   21 +-
 ...a_search_int8.cpp => cagra_search_int8.cu} |   22 +-
 ...search_uint8.cpp => cagra_search_uint8.cu} |   21 +-
 cpp/src/neighbors/cagra_serialize.cuh         |  230 ++
 ...lize_int8.cpp => cagra_serialize_float.cu} |   47 +-
 ...lize_uint8.cpp => cagra_serialize_int8.cu} |   47 +-
 ...ize_float.cpp => cagra_serialize_uint8.cu} |   47 +-
 cpp/src/neighbors/detail/ann_utils.cuh        |  577 +++++
 cpp/src/neighbors/detail/cagra/bitonic.hpp    |  228 ++
 .../neighbors/detail/cagra/cagra_build.cuh    |  384 ++++
 .../neighbors/detail/cagra/cagra_search.cuh   |  338 +++
 .../detail/cagra/cagra_serialize.cuh          |  279 +++
 .../detail/cagra/compute_distance.hpp         |  323 +++
 .../detail/cagra/compute_distance_vpq.cuh     |  231 ++
 .../neighbors/detail/cagra/device_common.hpp  |   60 +
 cpp/src/neighbors/detail/cagra/factory.cuh    |   95 +
 cpp/src/neighbors/detail/cagra/graph_core.cuh |  582 +++++
 cpp/src/neighbors/detail/cagra/hashmap.hpp    |   82 +
 .../cagra/q_search_multi_cta_00_generate.py   |   83 +
 ...float_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ...float_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...a_float_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...a_float_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ..._float_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ..._float_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ..._float_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ..._float_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 ...float_uint64_dim1024_t32_8pq_2subd_half.cu |   36 +
 ...float_uint64_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...a_float_uint64_dim128_t8_8pq_2subd_half.cu |   36 +
 ...a_float_uint64_dim128_t8_8pq_4subd_half.cu |   36 +
 ..._float_uint64_dim256_t16_8pq_2subd_half.cu |   36 +
 ..._float_uint64_dim256_t16_8pq_4subd_half.cu |   36 +
 ..._float_uint64_dim512_t32_8pq_2subd_half.cu |   36 +
 ..._float_uint64_dim512_t32_8pq_4subd_half.cu |   36 +
 ..._half_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ..._half_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...ta_half_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...ta_half_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ...a_half_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ...a_half_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ...a_half_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ...a_half_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 ..._half_uint64_dim1024_t32_8pq_2subd_half.cu |   36 +
 ..._half_uint64_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...ta_half_uint64_dim128_t8_8pq_2subd_half.cu |   36 +
 ...ta_half_uint64_dim128_t8_8pq_4subd_half.cu |   36 +
 ...a_half_uint64_dim256_t16_8pq_2subd_half.cu |   36 +
 ...a_half_uint64_dim256_t16_8pq_4subd_half.cu |   36 +
 ...a_half_uint64_dim512_t32_8pq_2subd_half.cu |   36 +
 ...a_half_uint64_dim512_t32_8pq_4subd_half.cu |   36 +
 ..._int8_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ..._int8_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...ta_int8_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...ta_int8_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ...a_int8_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ...a_int8_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ...a_int8_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ...a_int8_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 ...uint8_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ...uint8_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...a_uint8_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...a_uint8_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ..._uint8_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ..._uint8_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ..._uint8_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ..._uint8_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 .../cagra/q_search_single_cta_00_generate.py  |   88 +
 ...float_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ...float_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...a_float_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...a_float_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ..._float_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ..._float_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ..._float_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ..._float_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 ...float_uint64_dim1024_t32_8pq_2subd_half.cu |   36 +
 ...float_uint64_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...a_float_uint64_dim128_t8_8pq_2subd_half.cu |   36 +
 ...a_float_uint64_dim128_t8_8pq_4subd_half.cu |   36 +
 ..._float_uint64_dim256_t16_8pq_2subd_half.cu |   36 +
 ..._float_uint64_dim256_t16_8pq_4subd_half.cu |   36 +
 ..._float_uint64_dim512_t32_8pq_2subd_half.cu |   36 +
 ..._float_uint64_dim512_t32_8pq_4subd_half.cu |   36 +
 ..._half_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ..._half_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...ta_half_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...ta_half_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ...a_half_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ...a_half_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ...a_half_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ...a_half_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 ..._half_uint64_dim1024_t32_8pq_2subd_half.cu |   36 +
 ..._half_uint64_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...ta_half_uint64_dim128_t8_8pq_2subd_half.cu |   36 +
 ...ta_half_uint64_dim128_t8_8pq_4subd_half.cu |   36 +
 ...a_half_uint64_dim256_t16_8pq_2subd_half.cu |   36 +
 ...a_half_uint64_dim256_t16_8pq_4subd_half.cu |   36 +
 ...a_half_uint64_dim512_t32_8pq_2subd_half.cu |   36 +
 ...a_half_uint64_dim512_t32_8pq_4subd_half.cu |   36 +
 ..._int8_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ..._int8_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...ta_int8_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...ta_int8_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ...a_int8_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ...a_int8_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ...a_int8_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ...a_int8_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 ...uint8_uint32_dim1024_t32_8pq_2subd_half.cu |   36 +
 ...uint8_uint32_dim1024_t32_8pq_4subd_half.cu |   36 +
 ...a_uint8_uint32_dim128_t8_8pq_2subd_half.cu |   36 +
 ...a_uint8_uint32_dim128_t8_8pq_4subd_half.cu |   36 +
 ..._uint8_uint32_dim256_t16_8pq_2subd_half.cu |   36 +
 ..._uint8_uint32_dim256_t16_8pq_4subd_half.cu |   36 +
 ..._uint8_uint32_dim512_t32_8pq_2subd_half.cu |   36 +
 ..._uint8_uint32_dim512_t32_8pq_4subd_half.cu |   36 +
 .../detail/cagra/search_multi_cta.cuh         |  273 +++
 .../cagra/search_multi_cta_00_generate.py     |   78 +
 ...arch_multi_cta_float_uint32_dim1024_t32.cu |   37 +
 ...search_multi_cta_float_uint32_dim128_t8.cu |   37 +
 ...earch_multi_cta_float_uint32_dim256_t16.cu |   37 +
 ...earch_multi_cta_float_uint32_dim512_t32.cu |   37 +
 ...arch_multi_cta_float_uint64_dim1024_t32.cu |   37 +
 ...search_multi_cta_float_uint64_dim128_t8.cu |   37 +
 ...earch_multi_cta_float_uint64_dim256_t16.cu |   37 +
 ...earch_multi_cta_float_uint64_dim512_t32.cu |   37 +
 ...earch_multi_cta_half_uint32_dim1024_t32.cu |   37 +
 .../search_multi_cta_half_uint32_dim128_t8.cu |   37 +
 ...search_multi_cta_half_uint32_dim256_t16.cu |   37 +
 ...search_multi_cta_half_uint32_dim512_t32.cu |   37 +
 ...earch_multi_cta_half_uint64_dim1024_t32.cu |   37 +
 .../search_multi_cta_half_uint64_dim128_t8.cu |   37 +
 ...search_multi_cta_half_uint64_dim256_t16.cu |   37 +
 ...search_multi_cta_half_uint64_dim512_t32.cu |   37 +
 .../detail/cagra/search_multi_cta_inst.cuh    |   53 +
 ...earch_multi_cta_int8_uint32_dim1024_t32.cu |   37 +
 .../search_multi_cta_int8_uint32_dim128_t8.cu |   37 +
 ...search_multi_cta_int8_uint32_dim256_t16.cu |   37 +
 ...search_multi_cta_int8_uint32_dim512_t32.cu |   37 +
 .../cagra/search_multi_cta_kernel-ext.cuh     |  419 ++++
 .../cagra/search_multi_cta_kernel-inl.cuh     |  522 +++++
 .../detail/cagra/search_multi_cta_kernel.cuh  |   24 +
 ...arch_multi_cta_uint8_uint32_dim1024_t32.cu |   37 +
 ...search_multi_cta_uint8_uint32_dim128_t8.cu |   37 +
 ...earch_multi_cta_uint8_uint32_dim256_t16.cu |   37 +
 ...earch_multi_cta_uint8_uint32_dim512_t32.cu |   37 +
 .../detail/cagra/search_multi_kernel.cuh      | 1095 ++++++++++
 .../neighbors/detail/cagra/search_plan.cuh    |  349 +++
 .../detail/cagra/search_single_cta.cuh        |  258 +++
 .../cagra/search_single_cta_00_generate.py    |   82 +
 ...rch_single_cta_float_uint32_dim1024_t32.cu |   37 +
 ...earch_single_cta_float_uint32_dim128_t8.cu |   37 +
 ...arch_single_cta_float_uint32_dim256_t16.cu |   37 +
 ...arch_single_cta_float_uint32_dim512_t32.cu |   37 +
 ...rch_single_cta_float_uint64_dim1024_t32.cu |   37 +
 ...earch_single_cta_float_uint64_dim128_t8.cu |   37 +
 ...arch_single_cta_float_uint64_dim256_t16.cu |   37 +
 ...arch_single_cta_float_uint64_dim512_t32.cu |   37 +
 ...arch_single_cta_half_uint32_dim1024_t32.cu |   37 +
 ...search_single_cta_half_uint32_dim128_t8.cu |   37 +
 ...earch_single_cta_half_uint32_dim256_t16.cu |   37 +
 ...earch_single_cta_half_uint32_dim512_t32.cu |   37 +
 ...arch_single_cta_half_uint64_dim1024_t32.cu |   37 +
 ...search_single_cta_half_uint64_dim128_t8.cu |   37 +
 ...earch_single_cta_half_uint64_dim256_t16.cu |   37 +
 ...earch_single_cta_half_uint64_dim512_t32.cu |   37 +
 .../detail/cagra/search_single_cta_inst.cuh   |   54 +
 ...arch_single_cta_int8_uint32_dim1024_t32.cu |   37 +
 ...search_single_cta_int8_uint32_dim128_t8.cu |   37 +
 ...earch_single_cta_int8_uint32_dim256_t16.cu |   37 +
 ...earch_single_cta_int8_uint32_dim512_t32.cu |   37 +
 .../cagra/search_single_cta_kernel-ext.cuh    |  603 ++++++
 .../cagra/search_single_cta_kernel-inl.cuh    |  980 +++++++++
 .../detail/cagra/search_single_cta_kernel.cuh |   24 +
 ...rch_single_cta_uint8_uint32_dim1024_t32.cu |   37 +
 ...earch_single_cta_uint8_uint32_dim128_t8.cu |   37 +
 ...arch_single_cta_uint8_uint32_dim256_t16.cu |   37 +
 ...arch_single_cta_uint8_uint32_dim512_t32.cu |   37 +
 .../neighbors/detail/cagra/topk_by_radix.cuh  |   91 +
 .../detail/cagra/topk_for_cagra/topk.h        |   59 +
 .../detail/cagra/topk_for_cagra/topk_core.cuh | 1040 +++++++++
 cpp/src/neighbors/detail/cagra/utils.hpp      |  297 +++
 .../neighbors/detail/dataset_serialize.hpp    |  197 ++
 cpp/src/neighbors/detail/nn_descent.cuh       | 1457 +++++++++++++
 cpp/src/neighbors/detail/refine.cuh           |   19 +
 cpp/src/neighbors/detail/refine_common.hpp    |   57 +
 cpp/src/neighbors/detail/refine_device.cuh    |  145 ++
 cpp/src/neighbors/detail/refine_host-ext.hpp  |   66 +
 cpp/src/neighbors/detail/refine_host-inl.hpp  |  193 ++
 cpp/src/neighbors/detail/refine_host.hpp      |   19 +
 .../detail/refine_host_float_float.cpp        |   30 +
 .../detail/refine_host_half_float.cpp         |   31 +
 .../detail/refine_host_int8_t_float.cpp       |   29 +
 .../detail/refine_host_uint8_t_float.cpp      |   30 +
 cpp/src/neighbors/detail/vpq_dataset.cuh      |  431 ++++
 cpp/src/neighbors/ivf_common.cuh              |  327 +++
 cpp/src/neighbors/ivf_list.cuh                |  191 ++
 .../ivf_pq/{ => detail}/generate_ivf_pq.py    |  100 +-
 .../generate_ivf_pq_compute_similarity.py     |  116 +
 .../ivf_pq_build_extend_float_int64_t.cu      |   68 +
 .../ivf_pq_build_extend_int8_t_int64_t.cu     |   68 +
 .../ivf_pq_build_extend_uint8_t_int64_t.cu    |   68 +
 .../ivf_pq_compute_similarity_float_float.cu  |   74 +
 ...compute_similarity_float_float_bitset64.cu |   74 +
 ...f_pq_compute_similarity_float_fp8_false.cu |   74 +
 ...ute_similarity_float_fp8_false_bitset64.cu |   74 +
 ...vf_pq_compute_similarity_float_fp8_true.cu |   74 +
 ...pute_similarity_float_fp8_true_bitset64.cu |   74 +
 .../ivf_pq_compute_similarity_float_half.cu   |   74 +
 ..._compute_similarity_float_half_bitset64.cu |   74 +
 ...vf_pq_compute_similarity_half_fp8_false.cu |   74 +
 ...pute_similarity_half_fp8_false_bitset64.cu |   74 +
 ...ivf_pq_compute_similarity_half_fp8_true.cu |   74 +
 ...mpute_similarity_half_fp8_true_bitset64.cu |   74 +
 .../ivf_pq_compute_similarity_half_half.cu    |   74 +
 ...q_compute_similarity_half_half_bitset64.cu |   74 +
 .../ivf_pq_search_float_int64_t.cu}           |   22 +-
 .../ivf_pq_search_int8_t_int64_t.cu}          |   22 +-
 .../ivf_pq_search_uint8_t_int64_t.cu}         |   22 +-
 ...ivf_pq_search_with_filter_float_int64_t.cu |   49 +
 ...vf_pq_search_with_filter_int8_t_int64_t.cu |   49 +
 ...f_pq_search_with_filter_uint8_t_int64_t.cu |   49 +
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     | 1884 +++++++++++++++++
 .../neighbors/ivf_pq/ivf_pq_build_common.cu   |  167 ++
 .../ivf_pq/ivf_pq_build_float_int64_t.cpp     |   52 -
 .../ivf_pq/ivf_pq_build_int8_t_int64_t.cpp    |   52 -
 .../ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp   |   52 -
 .../neighbors/ivf_pq/ivf_pq_codepacking.cuh   |  219 ++
 .../ivf_pq/ivf_pq_compute_similarity.cuh      |  249 +++
 .../ivf_pq/ivf_pq_compute_similarity_impl.cuh |  942 +++++++++
 .../neighbors/ivf_pq/ivf_pq_deserialize.cu    |   30 +
 .../ivf_pq/ivf_pq_extend_float_int64_t.cpp    |   55 -
 .../ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp   |   55 -
 .../ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp  |   55 -
 cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh   |  114 +
 cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh      |   55 +
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |  797 +++++++
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu  |   29 +
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh |  193 ++
 cpp/src/neighbors/ivf_pq_index.cpp            |  182 +-
 cpp/src/neighbors/ivf_pq_serialize.cpp        |   40 -
 cpp/src/neighbors/nn_descent.cuh              |  182 ++
 cpp/src/neighbors/nn_descent_float.cu         |   43 +
 cpp/src/neighbors/nn_descent_int8.cu          |   43 +
 cpp/src/neighbors/nn_descent_uint8.cu         |   43 +
 cpp/src/neighbors/refine-ext.cuh              |   85 +
 cpp/src/neighbors/refine-inl.cuh              |  104 +
 cpp/src/neighbors/refine.cuh                  |   24 +
 cpp/src/neighbors/refine_float_float.cu       |   54 +
 cpp/src/neighbors/refine_half_float.cu        |   50 +
 cpp/src/neighbors/refine_int8_t_float.cu      |   50 +
 cpp/src/neighbors/refine_uint8_t_float.cu     |   50 +
 cpp/src/neighbors/sample_filter.cu            |   26 +
 cpp/src/neighbors/sample_filter.cuh           |  110 +
 cpp/src/neighbors/vpq_dataset.cuh             |   52 +
 cpp/test/CMakeLists.txt                       |   13 +
 cpp/test/neighbors/ann_cagra.cuh              |   24 +-
 cpp/test/neighbors/ann_ivf_flat.cuh           |    2 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |  162 +-
 .../ann_ivf_pq/test_float_int64_t.cu          |    6 +-
 .../ann_ivf_pq/test_int8_t_int64_t.cu         |    5 +-
 .../ann_ivf_pq/test_uint8_t_int64_t.cu        |    5 +-
 cpp/test/neighbors/ann_nn_descent.cuh         |  157 ++
 .../ann_nn_descent/test_float_uint32_t.cu     |   28 +
 .../ann_nn_descent/test_int8_t_uint32_t.cu    |   28 +
 .../ann_nn_descent/test_uint8_t_uint32_t.cu   |   28 +
 cpp/test/neighbors/ann_utils.cuh              |   55 +-
 cpp/test/neighbors/ivf_pq_helpers.cuh         |  428 ----
 285 files changed, 28539 insertions(+), 1385 deletions(-)
 create mode 100644 cpp/include/cuvs/core/bitset.hpp
 create mode 100644 cpp/include/cuvs/neighbors/dataset.hpp
 create mode 100644 cpp/include/cuvs/neighbors/ivf_list.hpp
 create mode 100644 cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
 create mode 100644 cpp/include/cuvs/neighbors/nn_descent.hpp
 create mode 100644 cpp/include/cuvs/neighbors/sample_filter.hpp
 create mode 100644 cpp/src/core/nvtx.hpp
 create mode 100644 cpp/src/neighbors/cagra.cuh
 rename cpp/src/neighbors/{cagra_build_int8.cpp => cagra_build_float.cu} (57%)
 rename cpp/src/neighbors/{cagra_build_uint8.cpp => cagra_build_int8.cu} (57%)
 rename cpp/src/neighbors/{cagra_build_float.cpp => cagra_build_uint8.cu} (56%)
 rename cpp/src/neighbors/{cagra_optimize.cpp => cagra_optimize.cu} (52%)
 rename cpp/src/neighbors/{cagra_search_float.cpp => cagra_search_float.cu} (75%)
 rename cpp/src/neighbors/{cagra_search_int8.cpp => cagra_search_int8.cu} (75%)
 rename cpp/src/neighbors/{cagra_search_uint8.cpp => cagra_search_uint8.cu} (75%)
 create mode 100644 cpp/src/neighbors/cagra_serialize.cuh
 rename cpp/src/neighbors/{cagra_serialize_int8.cpp => cagra_serialize_float.cu} (56%)
 rename cpp/src/neighbors/{cagra_serialize_uint8.cpp => cagra_serialize_int8.cu} (56%)
 rename cpp/src/neighbors/{cagra_serialize_float.cpp => cagra_serialize_uint8.cu} (56%)
 create mode 100644 cpp/src/neighbors/detail/ann_utils.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/bitonic.hpp
 create mode 100644 cpp/src/neighbors/detail/cagra/cagra_build.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/cagra_search.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance.hpp
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/device_common.hpp
 create mode 100644 cpp/src/neighbors/detail/cagra/factory.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/graph_core.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/hashmap.hpp
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_plan.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/topk_by_radix.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.h
 create mode 100644 cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/utils.hpp
 create mode 100644 cpp/src/neighbors/detail/dataset_serialize.hpp
 create mode 100644 cpp/src/neighbors/detail/nn_descent.cuh
 create mode 100644 cpp/src/neighbors/detail/refine.cuh
 create mode 100644 cpp/src/neighbors/detail/refine_common.hpp
 create mode 100644 cpp/src/neighbors/detail/refine_device.cuh
 create mode 100644 cpp/src/neighbors/detail/refine_host-ext.hpp
 create mode 100644 cpp/src/neighbors/detail/refine_host-inl.hpp
 create mode 100644 cpp/src/neighbors/detail/refine_host.hpp
 create mode 100644 cpp/src/neighbors/detail/refine_host_float_float.cpp
 create mode 100644 cpp/src/neighbors/detail/refine_host_half_float.cpp
 create mode 100644 cpp/src/neighbors/detail/refine_host_int8_t_float.cpp
 create mode 100644 cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp
 create mode 100644 cpp/src/neighbors/detail/vpq_dataset.cuh
 create mode 100644 cpp/src/neighbors/ivf_common.cuh
 create mode 100644 cpp/src/neighbors/ivf_list.cuh
 rename cpp/src/neighbors/ivf_pq/{ => detail}/generate_ivf_pq.py (62%)
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
 rename cpp/src/neighbors/ivf_pq/{ivf_pq_search_float_int64_t.cpp => detail/ivf_pq_search_float_int64_t.cu} (77%)
 rename cpp/src/neighbors/ivf_pq/{ivf_pq_search_int8_t_int64_t.cpp => detail/ivf_pq_search_int8_t_int64_t.cu} (77%)
 rename cpp/src/neighbors/ivf_pq/{ivf_pq_search_uint8_t_int64_t.cpp => detail/ivf_pq_search_uint8_t_int64_t.cu} (77%)
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
 delete mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
 delete mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
 delete mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
 delete mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
 delete mode 100644 cpp/src/neighbors/ivf_pq_serialize.cpp
 create mode 100644 cpp/src/neighbors/nn_descent.cuh
 create mode 100644 cpp/src/neighbors/nn_descent_float.cu
 create mode 100644 cpp/src/neighbors/nn_descent_int8.cu
 create mode 100644 cpp/src/neighbors/nn_descent_uint8.cu
 create mode 100644 cpp/src/neighbors/refine-ext.cuh
 create mode 100644 cpp/src/neighbors/refine-inl.cuh
 create mode 100644 cpp/src/neighbors/refine.cuh
 create mode 100644 cpp/src/neighbors/refine_float_float.cu
 create mode 100644 cpp/src/neighbors/refine_half_float.cu
 create mode 100644 cpp/src/neighbors/refine_int8_t_float.cu
 create mode 100644 cpp/src/neighbors/refine_uint8_t_float.cu
 create mode 100644 cpp/src/neighbors/sample_filter.cu
 create mode 100644 cpp/src/neighbors/sample_filter.cuh
 create mode 100644 cpp/src/neighbors/vpq_dataset.cuh
 create mode 100644 cpp/test/neighbors/ann_nn_descent.cuh
 create mode 100644 cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu
 create mode 100644 cpp/test/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
 create mode 100644 cpp/test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
 delete mode 100644 cpp/test/neighbors/ivf_pq_helpers.cuh

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 8556e2941e..2b1fe8aa09 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -34,6 +34,7 @@ project(
   VERSION "${RAPIDS_VERSION}"
   LANGUAGES ${lang_list}
 )
+set(CMAKE_INSTALL_MESSAGE LAZY)
 
 # Write the version header
 rapids_cmake_write_version_file(include/cuvs/version_config.hpp)
@@ -219,16 +220,164 @@ add_library(
   src/distance/pairwise_distance.cu
   src/neighbors/brute_force_index.cu
   src/neighbors/brute_force.cu
-  src/neighbors/cagra_build_float.cpp
-  src/neighbors/cagra_build_int8.cpp
-  src/neighbors/cagra_build_uint8.cpp
-  src/neighbors/cagra_optimize.cpp
-  src/neighbors/cagra_search_float.cpp
-  src/neighbors/cagra_search_int8.cpp
-  src/neighbors/cagra_search_uint8.cpp
-  src/neighbors/cagra_serialize_float.cpp
-  src/neighbors/cagra_serialize_int8.cpp
-  src/neighbors/cagra_serialize_uint8.cpp
+  src/neighbors/cagra_build_float.cu
+  src/neighbors/cagra_build_int8.cu
+  src/neighbors/cagra_build_uint8.cu
+  src/neighbors/cagra_optimize.cu
+  src/neighbors/cagra_search_float.cu
+  src/neighbors/cagra_search_int8.cu
+  src/neighbors/cagra_search_uint8.cu
+  src/neighbors/cagra_serialize_float.cu
+  src/neighbors/cagra_serialize_int8.cu
+  src/neighbors/cagra_serialize_uint8.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
+  src/neighbors/detail/refine_host_float_float.cpp
+  src/neighbors/detail/refine_host_half_float.cpp
+  src/neighbors/detail/refine_host_int8_t_float.cpp
+  src/neighbors/detail/refine_host_uint8_t_float.cpp
   src/neighbors/ivf_flat_index.cpp
   src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
   src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
@@ -243,16 +392,36 @@ add_library(
   src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
   src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
   src/neighbors/ivf_pq_index.cpp
-  src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp
-  src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp
-  src/neighbors/ivf_pq_serialize.cpp
+  src/neighbors/ivf_pq/ivf_pq_build_common.cu
+  src/neighbors/ivf_pq/ivf_pq_serialize.cu
+  src/neighbors/ivf_pq/ivf_pq_deserialize.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
+  src/neighbors/nn_descent_float.cu
+  src/neighbors/nn_descent_int8.cu
+  src/neighbors/nn_descent_uint8.cu
+  src/neighbors/sample_filter.cu
 )
 
 target_compile_options(
diff --git a/cpp/include/cuvs/core/bitset.hpp b/cpp/include/cuvs/core/bitset.hpp
new file mode 100644
index 0000000000..99942e21c4
--- /dev/null
+++ b/cpp/include/cuvs/core/bitset.hpp
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/bitset.hpp>
+
+namespace cuvs::core {
+/* To use bitset functions containing CUDA code, include <raft/core/bitset.cuh> */
+
+template <typename bitset_t, typename index_t>
+using bitset_view = raft::core::bitset_view<bitset_t, index_t>;
+
+template <typename bitset_t, typename index_t>
+using bitset = raft::core::bitset<bitset_t, index_t>;
+
+}  // end namespace cuvs::core
diff --git a/cpp/include/cuvs/neighbors/ann_types.hpp b/cpp/include/cuvs/neighbors/ann_types.hpp
index 36b545ebd0..e9b7bd903f 100644
--- a/cpp/include/cuvs/neighbors/ann_types.hpp
+++ b/cpp/include/cuvs/neighbors/ann_types.hpp
@@ -16,10 +16,48 @@
 
 #pragma once
 
+#include <cstdint>
 #include <cuvs/distance/distance_types.hpp>
 
 namespace cuvs::neighbors::ann {
 
+/** Parameters for VPQ compression. */
+struct vpq_params {
+  /**
+   * The bit length of the vector element after compression by PQ.
+   *
+   * Possible values: [4, 5, 6, 7, 8].
+   *
+   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
+   * performance, but the lower the recall.
+   */
+  uint32_t pq_bits = 8;
+  /**
+   * The dimensionality of the vector after compression by PQ.
+   * When zero, an optimal value is selected using a heuristic.
+   *
+   * TODO: at the moment `dim` must be a multiple `pq_dim`.
+   */
+  uint32_t pq_dim = 0;
+  /**
+   * Vector Quantization (VQ) codebook size - number of "coarse cluster centers".
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  uint32_t vq_n_centers = 0;
+  /** The number of iterations searching for kmeans centers (both VQ & PQ phases). */
+  uint32_t kmeans_n_iters = 25;
+  /**
+   * The fraction of data to use during iterative kmeans building (VQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double vq_kmeans_trainset_fraction = 0;
+  /**
+   * The fraction of data to use during iterative kmeans building (PQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double pq_kmeans_trainset_fraction = 0;
+};
+
 /**
  * @defgroup ann_types Approximate Nearest Neighbors Types
  * @{
@@ -31,7 +69,7 @@ struct index {};
 /** The base for KNN index parameters. */
 struct index_params {
   /** Distance type. */
-  cuvs::distance::DistanceType metric = distance::DistanceType::L2Expanded;
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
   /** The argument used by some distance metrics. */
   float metric_arg = 2.0f;
   /**
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index d6f5fef496..e52c15937a 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -18,11 +18,14 @@
 
 #include "ann_types.hpp"
 #include <cuvs/distance/distance_types.hpp>
+#include <cuvs/neighbors/dataset.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdspan.hpp>
 #include <raft/core/mdspan.hpp>
+#include <raft/core/resource/stream_view.hpp>
 #include <raft/core/resources.hpp>
-#include <raft/neighbors/cagra_types.hpp>
+#include <raft/util/integer_utils.hpp>
 #include <rmm/cuda_stream_view.hpp>
 
 #include <optional>
@@ -44,54 +47,6 @@ enum class graph_build_algo {
   NN_DESCENT
 };
 
-/** Parameters for VPQ compression. */
-struct vpq_params {
-  /**
-   * The bit length of the vector element after compression by PQ.
-   *
-   * Possible values: [4, 5, 6, 7, 8].
-   *
-   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
-   * performance, but the lower the recall.
-   */
-  uint32_t pq_bits = 8;
-  /**
-   * The dimensionality of the vector after compression by PQ.
-   * When zero, an optimal value is selected using a heuristic.
-   *
-   * TODO: at the moment `dim` must be a multiple `pq_dim`.
-   */
-  uint32_t pq_dim = 0;
-  /**
-   * Vector Quantization (VQ) codebook size - number of "coarse cluster centers".
-   * When zero, an optimal value is selected using a heuristic.
-   */
-  uint32_t vq_n_centers = 0;
-  /** The number of iterations searching for kmeans centers (both VQ & PQ phases). */
-  uint32_t kmeans_n_iters = 25;
-  /**
-   * The fraction of data to use during iterative kmeans building (VQ phase).
-   * When zero, an optimal value is selected using a heuristic.
-   */
-  double vq_kmeans_trainset_fraction = 0;
-  /**
-   * The fraction of data to use during iterative kmeans building (PQ phase).
-   * When zero, an optimal value is selected using a heuristic.
-   */
-  double pq_kmeans_trainset_fraction = 0;
-
-  /** Build a raft CAGRA index params from an existing cuvs CAGRA index params. */
-  operator raft::neighbors::vpq_params() const
-  {
-    return {.pq_bits                     = pq_bits,
-            .pq_dim                      = pq_dim,
-            .vq_n_centers                = vq_n_centers,
-            .kmeans_n_iters              = kmeans_n_iters,
-            .vq_kmeans_trainset_fraction = vq_kmeans_trainset_fraction,
-            .pq_kmeans_trainset_fraction = pq_kmeans_trainset_fraction};
-  }
-};
-
 struct index_params : ann::index_params {
   /** Degree of input graph for pruning. */
   size_t intermediate_graph_degree = 128;
@@ -106,23 +61,7 @@ struct index_params : ann::index_params {
    *
    * NOTE: this is experimental new API, consider it unsafe.
    */
-  std::optional<vpq_params> compression = std::nullopt;
-
-  /** Build a raft CAGRA index params from an existing cuvs CAGRA index params. */
-  operator raft::neighbors::cagra::index_params() const
-  {
-    return raft::neighbors::cagra::index_params{
-      {
-        .metric            = static_cast<raft::distance::DistanceType>((int)this->metric),
-        .metric_arg        = this->metric_arg,
-        .add_data_on_build = this->add_data_on_build,
-      },
-      .intermediate_graph_degree = intermediate_graph_degree,
-      .graph_degree              = graph_degree,
-      .build_algo       = static_cast<raft::neighbors::cagra::graph_build_algo>((int)build_algo),
-      .nn_descent_niter = nn_descent_niter,
-      .compression      = compression};
-  }
+  std::optional<cuvs::neighbors::ann::vpq_params> compression = std::nullopt;
 };
 
 /**
@@ -187,27 +126,6 @@ struct search_params : ann::search_params {
   uint32_t num_random_samplings = 1;
   /** Bit mask used for initial random seed node selection. */
   uint64_t rand_xor_mask = 0x128394;
-
-  /** Build a raft CAGRA search params from an existing cuvs CAGRA search params. */
-  operator raft::neighbors::cagra::search_params() const
-  {
-    raft::neighbors::cagra::search_params result = {
-      {},
-      max_queries,
-      itopk_size,
-      max_iterations,
-      static_cast<raft::neighbors::cagra::search_algo>((int)algo),
-      team_size,
-      search_width,
-      min_iterations,
-      thread_block_size,
-      static_cast<raft::neighbors::cagra::hash_mode>((int)hashmap_mode),
-      hashmap_min_bitlen,
-      hashmap_max_fill_rate,
-      num_random_samplings,
-      rand_xor_mask};
-    return result;
-  }
 };
 
 /**
@@ -232,12 +150,6 @@ static_assert(std::is_aggregate_v<search_params>);
  */
 template <typename T, typename IdxT>
 struct index : ann::index {
-  /** Build a cuvs CAGRA index from an existing RAFT CAGRA index. */
-  index(raft::neighbors::cagra::index<T, IdxT>&& raft_idx)
-    : ann::index(),
-      raft_index_{std::make_unique<raft::neighbors::cagra::index<T, IdxT>>(std::move(raft_idx))}
-  {
-  }
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
@@ -245,35 +157,50 @@ struct index : ann::index {
   /** Distance metric used for clustering. */
   [[nodiscard]] constexpr inline auto metric() const noexcept -> cuvs::distance::DistanceType
   {
-    return static_cast<cuvs::distance::DistanceType>((int)raft_index_->metric());
+    return metric_;
   }
 
   /** Total length of the index (number of vectors). */
-  [[nodiscard]] constexpr inline auto size() const noexcept -> IdxT { return raft_index_->size(); }
-
-  /** Dimensionality of the data. */
-  [[nodiscard]] constexpr inline auto dim() const noexcept -> uint32_t
+  [[nodiscard]] constexpr inline auto size() const noexcept -> IdxT
   {
-    return raft_index_->dim();
+    auto data_rows = dataset_->n_rows();
+    return data_rows > 0 ? data_rows : graph_view_.extent(0);
   }
+
+  /** Dimensionality of the data. */
+  [[nodiscard]] constexpr inline auto dim() const noexcept -> uint32_t { return dataset_->dim(); }
   /** Graph degree */
   [[nodiscard]] constexpr inline auto graph_degree() const noexcept -> uint32_t
   {
-    return raft_index_->graph_degree();
+    return graph_view_.extent(1);
   }
 
-  /** Dataset [size, dim] */
-  [[nodiscard]] inline auto dataset() const noexcept
+  /**
+   * DEPRECATED: please use data() instead.
+   *   If you need to query dataset dimensions, use the dim() and size() of the cagra index.
+   *   The data_handle() is not always available: you need to do a dynamic_cast to the expected
+   *   dataset type at runtime.
+   */
+  [[nodiscard]] [[deprecated("Use data()")]] inline auto dataset() const noexcept
     -> raft::device_matrix_view<const T, int64_t, raft::layout_stride>
   {
-    return raft_index_->dataset();
+    auto p = dynamic_cast<strided_dataset<T, int64_t>*>(dataset_.get());
+    if (p != nullptr) { return p->view(); }
+    auto d = dataset_->dim();
+    return raft::make_device_strided_matrix_view<const T, int64_t>(nullptr, 0, d, d);
+  }
+
+  /** Dataset [size, dim] */
+  [[nodiscard]] inline auto data() const noexcept -> const cuvs::neighbors::dataset<int64_t>&
+  {
+    return *dataset_;
   }
 
   /** neighborhood graph [size, graph-degree] */
   [[nodiscard]] inline auto graph() const noexcept
     -> raft::device_matrix_view<const IdxT, int64_t, raft::row_major>
   {
-    return raft_index_->graph();
+    return graph_view_;
   }
 
   // Don't allow copying the index for performance reasons (try avoiding copying data)
@@ -287,10 +214,12 @@ struct index : ann::index {
   index(raft::resources const& res,
         cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
     : ann::index(),
-      raft_index_(std::make_unique<raft::neighbors::cagra::index<T, IdxT>>(
-        res, static_cast<raft::distance::DistanceType>((int)metric)))
+      metric_(metric),
+      graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
+      dataset_(new cuvs::neighbors::empty_dataset<int64_t>(0))
   {
   }
+
   /** Construct an index from dataset and knn_graph arrays
    *
    * If the dataset and graph is already in GPU memory, then the index is just a thin wrapper around
@@ -306,7 +235,7 @@ struct index : ann::index {
    *
    * - Cagra index is normally created by the cagra::build
    * @code{.cpp}
-   *   using namespace cuvs::neighbors::experimental;
+   *   using namespace raft::neighbors::experimental;
    *   auto dataset = raft::make_host_matrix<float, int64_t>(n_rows, n_cols);
    *   load_dataset(dataset.view());
    *   // use default index parameters
@@ -322,11 +251,11 @@ struct index : ann::index {
    * @endcode
    *   In the above example, we have passed a host dataset to build. The returned index will own a
    * device copy of the dataset and the knn_graph. In contrast, if we pass the dataset as a
-   * raft::device_mdspan to build, then it will only store a reference to it.
+   * device_mdspan to build, then it will only store a reference to it.
    *
    * - Constructing index using existing knn-graph
    * @code{.cpp}
-   *   using namespace cuvs::neighbors::experimental;
+   *   using namespace raft::neighbors::experimental;
    *
    *   auto dataset = raft::make_device_matrix<float, int64_t>(res, n_rows, n_cols);
    *   auto knn_graph = raft::make_device_matrix<uint32_n, int64_t>(res, n_rows, graph_degree);
@@ -352,14 +281,17 @@ struct index : ann::index {
         raft::mdspan<const IdxT, raft::matrix_extent<int64_t>, raft::row_major, graph_accessor>
           knn_graph)
     : ann::index(),
-      raft_index_(std::make_unique<raft::neighbors::cagra::index<T, IdxT>>(
-        res, static_cast<raft::distance::DistanceType>((int)metric), dataset, knn_graph))
+      metric_(metric),
+      graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
+      dataset_(make_aligned_dataset(res, dataset, 16))
   {
     RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
                  "Dataset and knn_graph must have equal number of rows");
-    update_dataset(res, dataset);
     update_graph(res, knn_graph);
+
+    printf("Called update_graph\n");
     raft::resource::sync_stream(res);
+    printf("Done syncing\n");
   }
 
   /**
@@ -372,8 +304,16 @@ struct index : ann::index {
   void update_dataset(raft::resources const& res,
                       raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
   {
-    raft_index_->update_dataset(res, dataset);
+    dataset_ = make_aligned_dataset(res, dataset, 16);
   }
+
+  /** Set the dataset reference explicitly to a device matrix view with padding. */
+  void update_dataset(raft::resources const& res,
+                      raft::device_matrix_view<const T, int64_t, raft::layout_stride> dataset)
+  {
+    dataset_ = make_aligned_dataset(res, dataset, 16);
+  }
+
   /**
    * Replace the dataset with a new dataset.
    *
@@ -382,7 +322,22 @@ struct index : ann::index {
   void update_dataset(raft::resources const& res,
                       raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)
   {
-    raft_index_->update_dataset(res, dataset);
+    dataset_ = make_aligned_dataset(res, dataset, 16);
+  }
+
+  /** Replace the dataset with a new dataset. */
+  template <typename DatasetT>
+  auto update_dataset(raft::resources const& res, DatasetT&& dataset)
+    -> std::enable_if_t<std::is_base_of_v<cuvs::neighbors::dataset<int64_t>, DatasetT>>
+  {
+    dataset_ = std::make_unique<DatasetT>(std::move(dataset));
+  }
+
+  template <typename DatasetT>
+  auto update_dataset(raft::resources const& res, std::unique_ptr<DatasetT>&& dataset)
+    -> std::enable_if_t<std::is_base_of_v<neighbors::dataset<int64_t>, DatasetT>>
+  {
+    dataset_ = std::move(dataset);
   }
 
   /**
@@ -394,7 +349,7 @@ struct index : ann::index {
   void update_graph(raft::resources const& res,
                     raft::device_matrix_view<const IdxT, int64_t, raft::row_major> knn_graph)
   {
-    raft_index_->update_graph(res, knn_graph);
+    graph_view_ = knn_graph;
   }
 
   /**
@@ -405,19 +360,29 @@ struct index : ann::index {
   void update_graph(raft::resources const& res,
                     raft::host_matrix_view<const IdxT, int64_t, raft::row_major> knn_graph)
   {
-    raft_index_->update_graph(res, knn_graph);
-  }
-
-  auto get_raft_index() const -> const raft::neighbors::cagra::index<T, IdxT>*
-  {
-    return raft_index_.get();
+    RAFT_LOG_DEBUG("Copying CAGRA knn graph from host to device");
+
+    printf("Copying graph...\n");
+    if ((graph_.extent(0) != knn_graph.extent(0)) || (graph_.extent(1) != knn_graph.extent(1))) {
+      // clear existing memory before allocating to prevent OOM errors on large graphs
+      if (graph_.size()) { graph_ = raft::make_device_matrix<IdxT, int64_t>(res, 0, 0); }
+      graph_ =
+        raft::make_device_matrix<IdxT, int64_t>(res, knn_graph.extent(0), knn_graph.extent(1));
+    }
+    raft::copy(graph_.data_handle(),
+               knn_graph.data_handle(),
+               knn_graph.size(),
+               raft::resource::get_cuda_stream(res));
+    graph_view_ = graph_.view();
+    printf("Done...\n");
   }
-  auto get_raft_index() -> raft::neighbors::cagra::index<T, IdxT>* { return raft_index_.get(); }
 
  private:
-  std::unique_ptr<raft::neighbors::cagra::index<T, IdxT>> raft_index_;
+  cuvs::distance::DistanceType metric_;
+  raft::device_matrix<IdxT, int64_t, raft::row_major> graph_;
+  raft::device_matrix_view<const IdxT, int64_t, raft::row_major> graph_view_;
+  std::unique_ptr<neighbors::dataset<int64_t>> dataset_;
 };
-
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/dataset.hpp b/cpp/include/cuvs/neighbors/dataset.hpp
new file mode 100644
index 0000000000..79c3f644f9
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/dataset.hpp
@@ -0,0 +1,298 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/neighbors/ann_types.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/cudart_utils.hpp>   // get_device_for_address
+#include <raft/util/integer_utils.hpp>  // rounding up
+
+#include <memory>
+#include <numeric>
+#include <type_traits>
+
+#ifdef __cpp_lib_bitops
+#include <bit>
+#endif
+
+namespace cuvs::neighbors {
+
+/** Two-dimensional dataset; maybe owning, maybe compressed, maybe strided. */
+template <typename IdxT>
+struct dataset {
+  using index_type = IdxT;
+  /**  Size of the dataset. */
+  [[nodiscard]] virtual auto n_rows() const noexcept -> index_type = 0;
+  /** Dimensionality of the dataset. */
+  [[nodiscard]] virtual auto dim() const noexcept -> uint32_t = 0;
+  /** Whether the object owns the data. */
+  [[nodiscard]] virtual auto is_owning() const noexcept -> bool = 0;
+  virtual ~dataset() noexcept                                   = default;
+};
+
+template <typename IdxT>
+struct empty_dataset : public dataset<IdxT> {
+  using index_type = IdxT;
+  uint32_t suggested_dim;
+  explicit empty_dataset(uint32_t dim) noexcept : suggested_dim(dim) {}
+  [[nodiscard]] auto n_rows() const noexcept -> index_type final { return 0; }
+  [[nodiscard]] auto dim() const noexcept -> uint32_t final { return suggested_dim; }
+  [[nodiscard]] auto is_owning() const noexcept -> bool final { return true; }
+};
+
+template <typename DataT, typename IdxT>
+struct strided_dataset : public dataset<IdxT> {
+  using index_type = IdxT;
+  using value_type = DataT;
+  using view_type  = raft::device_matrix_view<const value_type, index_type, raft::layout_stride>;
+  [[nodiscard]] auto n_rows() const noexcept -> index_type final { return view().extent(0); }
+  [[nodiscard]] auto dim() const noexcept -> uint32_t final
+  {
+    return static_cast<uint32_t>(view().extent(1));
+  }
+  /** Leading dimension of the dataset. */
+  [[nodiscard]] constexpr auto stride() const noexcept -> uint32_t
+  {
+    auto v = view();
+    return static_cast<uint32_t>(v.stride(0) > 0 ? v.stride(0) : v.extent(1));
+  }
+  /** Get the view of the data. */
+  [[nodiscard]] virtual auto view() const noexcept -> view_type = 0;
+};
+
+template <typename DataT, typename IdxT>
+struct non_owning_dataset : public strided_dataset<DataT, IdxT> {
+  using index_type = IdxT;
+  using value_type = DataT;
+  using typename strided_dataset<value_type, index_type>::view_type;
+  view_type data;
+  explicit non_owning_dataset(view_type v) noexcept : data(v) {}
+  [[nodiscard]] auto is_owning() const noexcept -> bool final { return false; }
+  [[nodiscard]] auto view() const noexcept -> view_type final { return data; };
+};
+
+template <typename DataT, typename IdxT, typename LayoutPolicy, typename ContainerPolicy>
+struct owning_dataset : public strided_dataset<DataT, IdxT> {
+  using index_type = IdxT;
+  using value_type = DataT;
+  using typename strided_dataset<value_type, index_type>::view_type;
+  using storage_type =
+    raft::mdarray<value_type, raft::matrix_extent<index_type>, LayoutPolicy, ContainerPolicy>;
+  using mapping_type = typename view_type::mapping_type;
+  storage_type data;
+  mapping_type view_mapping;
+  owning_dataset(storage_type&& store, mapping_type view_mapping) noexcept
+    : data{std::move(store)}, view_mapping{view_mapping}
+  {
+  }
+
+  [[nodiscard]] auto is_owning() const noexcept -> bool final { return true; }
+  [[nodiscard]] auto view() const noexcept -> view_type final
+  {
+    return view_type{data.data_handle(), view_mapping};
+  };
+};
+
+/**
+ * @brief Contstruct a strided matrix from any mdarray or mdspan.
+ *
+ * This function constructs a non-owning view if the input satisfied two conditions:
+ *
+ *   1) The data is accessible from the current device
+ *   2) The memory layout is the same as expected (row-major matrix with the required stride)
+ *
+ * Otherwise, this function constructs an owning device matrix and copies the data.
+ * When the data is copied, padding elements are filled with zeroes.
+ *
+ * @tparam SrcT the source mdarray or mdspan
+ *
+ * @param[in] res raft resources handle
+ * @param[in] src the source mdarray or mdspan
+ * @param[in] required_stride the leading dimension (in elements)
+ * @return maybe owning current-device-accessible strided matrix
+ */
+template <typename SrcT>
+auto make_strided_dataset(const raft::resources& res, const SrcT& src, uint32_t required_stride)
+  -> std::unique_ptr<strided_dataset<typename SrcT::value_type, typename SrcT::index_type>>
+{
+  using extents_type = typename SrcT::extents_type;
+  using value_type   = typename SrcT::value_type;
+  using index_type   = typename SrcT::index_type;
+  using layout_type  = typename SrcT::layout_type;
+  static_assert(extents_type::rank() == 2, "The input must be a matrix.");
+  static_assert(std::is_same_v<layout_type, raft::layout_right> ||
+                  std::is_same_v<layout_type, raft::layout_right_padded<value_type>> ||
+                  std::is_same_v<layout_type, raft::layout_stride>,
+                "The input must be row-major");
+  RAFT_EXPECTS(src.extent(1) <= required_stride,
+               "The input row length must be not larger than the desired stride.");
+  cudaPointerAttributes ptr_attrs;
+  RAFT_CUDA_TRY(cudaPointerGetAttributes(&ptr_attrs, src.data_handle()));
+  auto* device_ptr             = reinterpret_cast<value_type*>(ptr_attrs.devicePointer);
+  const uint32_t src_stride    = src.stride(0) > 0 ? src.stride(0) : src.extent(1);
+  const bool device_accessible = device_ptr != nullptr;
+  const bool row_major         = src.stride(1) <= 1;
+  const bool stride_matches    = required_stride == src_stride;
+
+  if (device_accessible && row_major && stride_matches) {
+    // Everything matches: make a non-owning dataset
+    return std::make_unique<non_owning_dataset<value_type, index_type>>(
+      raft::make_device_strided_matrix_view<const value_type, index_type>(
+        device_ptr, src.extent(0), src.extent(1), required_stride));
+  }
+  // Something is wrong: have to make a copy and produce an owning dataset
+  auto out_layout =
+    raft::make_strided_layout(src.extents(), std::array<index_type, 2>{required_stride, 1});
+  auto out_array =
+    raft::make_device_matrix<value_type, index_type>(res, src.extent(0), required_stride);
+
+  using out_mdarray_type          = decltype(out_array);
+  using out_layout_type           = typename out_mdarray_type::layout_type;
+  using out_container_policy_type = typename out_mdarray_type::container_policy_type;
+  using out_owning_type =
+    owning_dataset<value_type, index_type, out_layout_type, out_container_policy_type>;
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(out_array.data_handle(),
+                                0,
+                                out_array.size() * sizeof(value_type),
+                                raft::resource::get_cuda_stream(res)));
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(out_array.data_handle(),
+                                  sizeof(value_type) * required_stride,
+                                  src.data_handle(),
+                                  sizeof(value_type) * src_stride,
+                                  sizeof(value_type) * src.extent(1),
+                                  src.extent(0),
+                                  cudaMemcpyDefault,
+                                  raft::resource::get_cuda_stream(res)));
+
+  return std::make_unique<out_owning_type>(std::move(out_array), out_layout);
+}
+
+/**
+ * @brief Contstruct a strided matrix from any mdarray or mdspan.
+ *
+ * A variant `make_strided_dataset` that allows specifying the byte alignment instead of the
+ * explicit stride length.
+ *
+ * @tparam SrcT the source mdarray or mdspan
+ *
+ * @param[in] res raft resources handle
+ * @param[in] src the source mdarray or mdspan
+ * @param[in] align_bytes the required byte alignment for the dataset rows.
+ * @return maybe owning current-device-accessible strided matrix
+ */
+template <typename SrcT>
+auto make_aligned_dataset(const raft::resources& res, const SrcT& src, uint32_t align_bytes = 16)
+  -> std::unique_ptr<strided_dataset<typename SrcT::value_type, typename SrcT::index_type>>
+{
+  using value_type       = typename SrcT::value_type;
+  constexpr size_t kSize = sizeof(value_type);
+  uint32_t required_stride =
+    raft::round_up_safe<size_t>(src.extent(1) * kSize, std::lcm(align_bytes, kSize)) / kSize;
+  return make_strided_dataset(res, src, required_stride);
+}
+
+/** Parameters for VPQ compression. */
+using vpq_params = cuvs::neighbors::ann::vpq_params;
+
+/**
+ * @brief VPQ compressed dataset.
+ *
+ * The dataset is compressed using two level quantization
+ *
+ *   1. Vector Quantization
+ *   2. Product Quantization of residuals
+ *
+ * @tparam MathT the type of elements in the codebooks
+ * @tparam IdxT type of the vector indices (represent dataset.extent(0))
+ *
+ */
+template <typename MathT, typename IdxT>
+struct vpq_dataset : public dataset<IdxT> {
+  /** Vector Quantization codebook - "coarse cluster centers". */
+  raft::device_matrix<MathT, uint32_t, raft::row_major> vq_code_book;
+  /** Product Quantization codebook - "fine cluster centers".  */
+  raft::device_matrix<MathT, uint32_t, raft::row_major> pq_code_book;
+  /** Compressed dataset.  */
+  raft::device_matrix<uint8_t, IdxT, raft::row_major> data;
+
+  vpq_dataset(raft::device_matrix<MathT, uint32_t, raft::row_major>&& vq_code_book,
+              raft::device_matrix<MathT, uint32_t, raft::row_major>&& pq_code_book,
+              raft::device_matrix<uint8_t, IdxT, raft::row_major>&& data)
+    : vq_code_book{std::move(vq_code_book)},
+      pq_code_book{std::move(pq_code_book)},
+      data{std::move(data)}
+  {
+  }
+
+  [[nodiscard]] auto n_rows() const noexcept -> IdxT final { return data.extent(0); }
+  [[nodiscard]] auto dim() const noexcept -> uint32_t final { return vq_code_book.extent(1); }
+  [[nodiscard]] auto is_owning() const noexcept -> bool final { return true; }
+
+  /** Row length of the encoded data in bytes. */
+  [[nodiscard]] constexpr inline auto encoded_row_length() const noexcept -> uint32_t
+  {
+    return data.extent(1);
+  }
+  /** The number of "coarse cluster centers" */
+  [[nodiscard]] constexpr inline auto vq_n_centers() const noexcept -> uint32_t
+  {
+    return vq_code_book.extent(0);
+  }
+  /** The bit length of an encoded vector element after compression by PQ. */
+  [[nodiscard]] constexpr inline auto pq_bits() const noexcept -> uint32_t
+  {
+    /*
+    NOTE: pq_bits and the book size
+
+    Normally, we'd store `pq_bits` as a part of the index.
+    However, we know there's an invariant `pq_n_centers = 1 << pq_bits`, i.e. the codebook size is
+    the same as the number of possible code values. Hence, we don't store the pq_bits and derive it
+    from the array dimensions instead.
+     */
+    auto pq_width = pq_n_centers();
+#ifdef __cpp_lib_bitops
+    return std::countr_zero(pq_width);
+#else
+    uint32_t pq_bits = 0;
+    while (pq_width > 1) {
+      pq_bits++;
+      pq_width >>= 1;
+    }
+    return pq_bits;
+#endif
+  }
+  /** The dimensionality of an encoded vector after compression by PQ. */
+  [[nodiscard]] constexpr inline auto pq_dim() const noexcept -> uint32_t
+  {
+    return raft::div_rounding_up_unsafe(dim(), pq_len());
+  }
+  /** Dimensionality of a subspaces, i.e. the number of vector components mapped to a subspace */
+  [[nodiscard]] constexpr inline auto pq_len() const noexcept -> uint32_t
+  {
+    return pq_code_book.extent(1);
+  }
+  /** The number of vectors in a PQ codebook (`1 << pq_bits`). */
+  [[nodiscard]] constexpr inline auto pq_n_centers() const noexcept -> uint32_t
+  {
+    return pq_code_book.extent(0);
+  }
+};
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/include/cuvs/neighbors/ivf_list.hpp b/cpp/include/cuvs/neighbors/ivf_list.hpp
new file mode 100644
index 0000000000..a12be19638
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/ivf_list.hpp
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+#include <raft/util/integer_utils.hpp>
+
+#include <thrust/fill.h>
+
+#include <atomic>
+#include <fstream>
+#include <limits>
+#include <memory>
+#include <type_traits>
+
+namespace cuvs::neighbors::ivf {
+
+/**
+ * Default value filled in the `indices` array.
+ * One may encounter it trying to access a record within a list that is outside of the
+ * `size` bound or whenever the list is allocated but not filled-in yet.
+ */
+template <typename IdxT>
+constexpr static IdxT kInvalidRecord =
+  (std::is_signed_v<IdxT> ? IdxT{0} : std::numeric_limits<IdxT>::max()) - 1;
+
+/** The data for a single IVF list. */
+template <template <typename, typename...> typename SpecT,
+          typename SizeT,
+          typename... SpecExtraArgs>
+struct list {
+  using size_type    = SizeT;
+  using spec_type    = SpecT<size_type, SpecExtraArgs...>;
+  using value_type   = typename spec_type::value_type;
+  using index_type   = typename spec_type::index_type;
+  using list_extents = typename spec_type::list_extents;
+
+  /** Possibly encoded data; it's layout is defined by `SpecT`. */
+  raft::device_mdarray<value_type, list_extents, raft::row_major> data;
+  /** Source indices. */
+  raft::device_mdarray<index_type, raft::extent_1d<size_type>, raft::row_major> indices;
+  /** The actual size of the content. */
+  std::atomic<size_type> size;
+
+  /** Allocate a new list capable of holding at least `n_rows` data records and indices. */
+  list(raft::resources const& res, const spec_type& spec, size_type n_rows);
+};
+
+template <typename ListT, class T = void>
+struct enable_if_valid_list {};
+
+template <class T,
+          template <typename, typename...>
+          typename SpecT,
+          typename SizeT,
+          typename... SpecExtraArgs>
+struct enable_if_valid_list<list<SpecT, SizeT, SpecExtraArgs...>, T> {
+  using type = T;
+};
+
+/**
+ * Designed after `std::enable_if_t`, this trait is helpful in the instance resolution;
+ * plug this in the return type of a function that has an instance of `ivf::list` as
+ * a template parameter.
+ */
+template <typename ListT, class T = void>
+using enable_if_valid_list_t = typename enable_if_valid_list<ListT, T>::type;
+
+/**
+ * Resize a list by the given id, so that it can contain the given number of records;
+ * copy the data if necessary.
+ */
+template <typename ListT>
+void resize_list(raft::resources const& res,
+                 std::shared_ptr<ListT>& orig_list,  // NOLINT
+                 const typename ListT::spec_type& spec,
+                 typename ListT::size_type new_used_size,
+                 typename ListT::size_type old_used_size);
+
+template <typename ListT>
+enable_if_valid_list_t<ListT> serialize_list(
+  const raft::resources& handle,
+  std::ostream& os,
+  const ListT& ld,
+  const typename ListT::spec_type& store_spec,
+  std::optional<typename ListT::size_type> size_override = std::nullopt);
+template <typename ListT>
+enable_if_valid_list_t<ListT> serialize_list(
+  const raft::resources& handle,
+  std::ostream& os,
+  const std::shared_ptr<ListT>& ld,
+  const typename ListT::spec_type& store_spec,
+  std::optional<typename ListT::size_type> size_override = std::nullopt);
+
+template <typename ListT>
+enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
+                                               std::istream& is,
+                                               std::shared_ptr<ListT>& ld,
+                                               const typename ListT::spec_type& store_spec,
+                                               const typename ListT::spec_type& device_spec);
+
+}  // namespace cuvs::neighbors::ivf
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index b2fc9a3668..f013615ded 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -17,7 +17,15 @@
 #pragma once
 
 #include "ann_types.hpp"
-#include <raft/neighbors/ivf_pq_types.hpp>
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/sample_filter.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/integer_utils.hpp>
 
 namespace cuvs::neighbors::ivf_pq {
 
@@ -92,23 +100,34 @@ struct index_params : ann::index_params {
    */
   bool conservative_memory_allocation = false;
 
-  /** Build a raft IVF_PQ index params from an existing cuvs IVF_PQ index params. */
-  operator raft::neighbors::ivf_pq::index_params() const
+  /**
+   * Creates index_params based on shape of the input dataset.
+   * Usage example:
+   * @code{.cpp}
+   *   using namespace raft::neighbors;
+   *   raft::resources res;
+   *   // create index_params for a [N. D] dataset and have InnerProduct as the distance metric
+   *   auto dataset = raft::make_device_matrix<float, int64_t>(res, N, D);
+   *   ivf_pq::index_params index_params =
+   *     ivf_pq::index_params::from_dataset(dataset.view(), raft::distance::InnerProduct);
+   *   // modify/update index_params as needed
+   *   index_params.add_data_on_build = true;
+   * @endcode
+   */
+  template <typename DataT, typename Accessor>
+  static index_params from_dataset(
+    raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+    cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
   {
-    return raft::neighbors::ivf_pq::index_params{
-      {
-        .metric            = static_cast<raft::distance::DistanceType>((int)this->metric),
-        .metric_arg        = this->metric_arg,
-        .add_data_on_build = this->add_data_on_build,
-      },
-      .n_lists                  = n_lists,
-      .kmeans_n_iters           = kmeans_n_iters,
-      .kmeans_trainset_fraction = kmeans_trainset_fraction,
-      .pq_bits                  = pq_bits,
-      .pq_dim                   = pq_dim,
-      .codebook_kind = static_cast<raft::neighbors::ivf_pq::codebook_gen>((int)this->codebook_kind),
-      .force_random_rotation          = force_random_rotation,
-      .conservative_memory_allocation = conservative_memory_allocation};
+    index_params params;
+    params.n_lists =
+      dataset.extent(0) < 4 * 2500 ? 4 : static_cast<uint32_t>(std::sqrt(dataset.extent(0)));
+    params.pq_dim =
+      raft::round_up_safe(static_cast<uint32_t>(dataset.extent(1) / 4), static_cast<uint32_t>(8));
+    params.pq_bits                  = 8;
+    params.kmeans_trainset_fraction = dataset.extent(0) < 10000 ? 1 : 0.1;
+    params.metric                   = metric;
+    return params;
   }
 };
 /**
@@ -156,21 +175,59 @@ struct search_params : ann::search_params {
    * performance if tweaked incorrectly.
    */
   double preferred_shmem_carveout = 1.0;
-
-  /** Build a raft IVF_PQ search params from an existing cuvs IVF_PQ search params. */
-  operator raft::neighbors::ivf_pq::search_params() const
-  {
-    raft::neighbors::ivf_pq::search_params result = {
-      {}, n_probes, lut_dtype, internal_distance_dtype, preferred_shmem_carveout};
-    return result;
-  }
 };
 /**
  * @}
  */
 
+static_assert(std::is_aggregate_v<index_params>);
+static_assert(std::is_aggregate_v<search_params>);
+
+/** Size of the interleaved group. */
+constexpr static uint32_t kIndexGroupSize = 32;
+/** Stride of the interleaved group for vectorized loads. */
+constexpr static uint32_t kIndexGroupVecLen = 16;
+
+/**
+ * Default value returned by `search` when the `n_probes` is too small and top-k is too large.
+ * One may encounter it if the combined size of probed clusters is smaller than the requested
+ * number of results per query.
+ */
+template <typename IdxT>
+constexpr static IdxT kOutOfBoundsRecord = std::numeric_limits<IdxT>::max();
+
+template <typename SizeT, typename IdxT>
+struct list_spec {
+  using value_type = uint8_t;
+  using index_type = IdxT;
+  /** PQ-encoded data stored in the interleaved format:
+   *
+   *    [ ceildiv(list_size, kIndexGroupSize)
+   *    , ceildiv(pq_dim, (kIndexGroupVecLen * 8u) / pq_bits)
+   *    , kIndexGroupSize
+   *    , kIndexGroupVecLen
+   *    ].
+   */
+  using list_extents = raft::
+    extents<SizeT, raft::dynamic_extent, raft::dynamic_extent, kIndexGroupSize, kIndexGroupVecLen>;
+
+  SizeT align_max;
+  SizeT align_min;
+  uint32_t pq_bits;
+  uint32_t pq_dim;
+
+  constexpr list_spec(uint32_t pq_bits, uint32_t pq_dim, bool conservative_memory_allocation);
+
+  // Allow casting between different size-types (for safer size and offset calculations)
+  template <typename OtherSizeT>
+  constexpr explicit list_spec(const list_spec<OtherSizeT, IdxT>& other_spec);
+
+  /** Determine the extents of an array enough to hold a given amount of data. */
+  constexpr list_extents make_list_extents(SizeT n_rows) const;
+};
+
 template <typename IdxT, typename SizeT = uint32_t>
-using list_data = raft::neighbors::ivf_pq::list_data<IdxT, SizeT>;
+using list_data = ivf::list<list_spec, SizeT, IdxT>;
 
 /**
  * @defgroup ivf_pq_cpp_index IVF-PQ index
@@ -226,7 +283,8 @@ struct index : ann::index {
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
-  using pq_centers_extents = typename raft::neighbors::ivf_pq::index<IdxT>::pq_centers_extents;
+  using pq_centers_extents = std::experimental::
+    extents<uint32_t, raft::dynamic_extent, raft::dynamic_extent, raft::dynamic_extent>;
 
  public:
   index(const index&)                    = delete;
@@ -235,9 +293,18 @@ struct index : ann::index {
   auto operator=(index&&) -> index&      = default;
   ~index()                               = default;
 
+  /** Construct an empty index. It needs to be trained and then populated. */
+  index(raft::resources const& handle,
+        cuvs::distance::DistanceType metric,
+        codebook_gen codebook_kind,
+        uint32_t n_lists,
+        uint32_t dim,
+        uint32_t pq_bits                    = 8,
+        uint32_t pq_dim                     = 0,
+        bool conservative_memory_allocation = false);
+
   /** Construct an empty index. It needs to be trained and then populated. */
   index(raft::resources const& handle, const index_params& params, uint32_t dim);
-  index(raft::neighbors::ivf_pq::index<IdxT>&& raft_idx);
 
   /** Total length of the index. */
   IdxT size() const noexcept;
@@ -290,8 +357,8 @@ struct index : ann::index {
    *   - codebook_gen::PER_SUBSPACE: [pq_dim , pq_len, pq_book_size]
    *   - codebook_gen::PER_CLUSTER:  [n_lists, pq_len, pq_book_size]
    */
-  raft::mdspan<float, pq_centers_extents, raft::row_major> pq_centers() noexcept;
-  raft::mdspan<const float, pq_centers_extents, raft::row_major> pq_centers() const noexcept;
+  raft::device_mdspan<float, pq_centers_extents, raft::row_major> pq_centers() noexcept;
+  raft::device_mdspan<const float, pq_centers_extents, raft::row_major> pq_centers() const noexcept;
 
   /** Lists' data and indices. */
   std::vector<std::shared_ptr<list_data<IdxT>>>& lists() noexcept;
@@ -334,18 +401,51 @@ struct index : ann::index {
   raft::device_matrix_view<float, uint32_t, raft::row_major> centers_rot() noexcept;
   raft::device_matrix_view<const float, uint32_t, raft::row_major> centers_rot() const noexcept;
 
-  // Get pointer to underlying RAFT index, not meant to be used outside of cuVS
-  inline raft::neighbors::ivf_pq::index<IdxT>* get_raft_index() noexcept
-  {
-    return raft_index_.get();
-  }
-  inline const raft::neighbors::ivf_pq::index<IdxT>* get_raft_index() const noexcept
-  {
-    return raft_index_.get();
-  }
+  /** fetch size of a particular IVF list in bytes using the list extents.
+   * Usage example:
+   * @code{.cpp}
+   *   raft::resources res;
+   *   // use default index params
+   *   ivf_pq::index_params index_params;
+   *   // extend the IVF lists while building the index
+   *   index_params.add_data_on_build = true;
+   *   // create and fill the index from a [N, D] dataset
+   *   auto index = cuvs::neighbors::ivf_pq::build<int64_t>(res, index_params, dataset, N, D);
+   *   // Fetch the size of the fourth list
+   *   uint32_t size = index.get_list_size_in_bytes(3);
+   * @endcode
+   *
+   * @param[in] label list ID
+   */
+  uint32_t get_list_size_in_bytes(uint32_t label);
 
  private:
-  std::unique_ptr<raft::neighbors::ivf_pq::index<IdxT>> raft_index_;
+  cuvs::distance::DistanceType metric_;
+  codebook_gen codebook_kind_;
+  uint32_t dim_;
+  uint32_t pq_bits_;
+  uint32_t pq_dim_;
+  bool conservative_memory_allocation_;
+
+  // Primary data members
+  std::vector<std::shared_ptr<list_data<IdxT>>> lists_;
+  raft::device_vector<uint32_t, uint32_t, raft::row_major> list_sizes_;
+  raft::device_mdarray<float, pq_centers_extents, raft::row_major> pq_centers_;
+  raft::device_matrix<float, uint32_t, raft::row_major> centers_;
+  raft::device_matrix<float, uint32_t, raft::row_major> centers_rot_;
+  raft::device_matrix<float, uint32_t, raft::row_major> rotation_matrix_;
+
+  // Computed members for accelerating search.
+  raft::device_vector<uint8_t*, uint32_t, raft::row_major> data_ptrs_;
+  raft::device_vector<IdxT*, uint32_t, raft::row_major> inds_ptrs_;
+  raft::host_vector<IdxT, uint32_t, raft::row_major> accum_sorted_sizes_;
+
+  /** Throw an error if the index content is inconsistent. */
+  void check_consistency();
+
+  pq_centers_extents make_pq_centers_extents();
+
+  static uint32_t calculate_pq_dim(uint32_t dim);
 };
 /**
  * @}
@@ -803,6 +903,105 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
+ * query.
+ */
+void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  index<int64_t>& idx,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
+
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
+ * query.
+ */
+void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  index<int64_t>& idx,
+  raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
+
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
+ * query.
+ */
+void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  index<int64_t>& idx,
+  raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp b/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
new file mode 100644
index 0000000000..b1da7301c4
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
@@ -0,0 +1,274 @@
+/*
+ * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a raft::copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/random/rng_state.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+
+#include <memory>
+#include <variant>
+
+namespace cuvs::neighbors::ivf_pq::helpers {
+
+namespace codepacker {
+/**
+ * Unpack flat PQ codes from an existing list by the given offset.
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[out] out_codes flat PQ codes, one code per byte [n_rows, pq_dim]
+ * @param[in] label
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ */
+void unpack_list_data(raft::resources const& res,
+                      const index<int64_t>& index,
+                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+                      uint32_t label,
+                      std::variant<uint32_t, const uint32_t*> offset_or_indices);
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_rows).
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[out] new_codes
+ * @param[in] label
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ */
+void pack_list_data(raft::resources const& res,
+                    index<int64_t>* index,
+                    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+                    uint32_t label,
+                    std::variant<uint32_t, const uint32_t*> offset_or_indices);
+
+};  // namespace codepacker
+
+/**
+ * @brief Fill-in a random orthogonal transformation matrix.
+ *
+ * @param handle
+ * @param force_random_rotation
+ * @param n_rows
+ * @param n_cols
+ * @param[out] rotation_matrix device pointer to a row-major matrix of size [n_rows, n_cols].
+ * @param rng random number generator state
+ */
+void make_rotation_matrix(raft::resources const& handle,
+                          bool force_random_rotation,
+                          uint32_t n_rows,
+                          uint32_t n_cols,
+                          float* rotation_matrix,
+                          raft::random::RngState rng = raft::random::RngState(7ULL));
+
+/**
+ * @brief Set cluster centers on an index
+ *
+ * @param handle
+ * @param index
+ * @param cluster_centers
+ */
+void set_centers(raft::resources const& handle,
+                 index<int64_t>* index,
+                 const float* cluster_centers);
+
+/**
+ * @brief Decode `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will reconstruct the fourth cluster
+ *   uint32_t label = 3;
+ *   // Get the list size
+ *   uint32_t list_size = 0;
+ *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
+ *   resource::get_cuda_stream(res)); resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto decoded_vectors = raft::make_device_matrix<float>(res, list_size, index.dim());
+ *   // decode the whole list
+ *   ivf_pq::helpers::reconstruct_list_data(res, index, decoded_vectors.view(), label, 0);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[out] out_vectors
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to reconstruct,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ * @param[in] offset_or_indices
+ *   How many records in the list to skip.
+ */
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           std::variant<uint32_t, const uint32_t*> offset_or_indices);
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           std::variant<uint32_t, const uint32_t*> offset_or_indices);
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           std::variant<uint32_t, const uint32_t*> offset_or_indices);
+
+/**
+ * Write flat PQ codes into an existing list by the given offset. The input codes of a single vector
+ * are contiguous (not expanded to one code per byte).
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_rows records).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<uint8_t>(
+ *     res, n_rows, raft::ceildiv(index.pq_dim() * index.pq_bits(), 8));
+ *   ... prepare compressed vectors to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position. If the current size of the list
+ *   // is greater than 42, this will overwrite the codes starting at this offset.
+ *   ivf_pq::helpers::codepacker::pack_contiguous(
+ *     res, codes.data_handle(), n_rows, index.pq_dim(), index.pq_bits(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index
+ * @param[in] new_codes flat PQ codes, [n_vec, ceildiv(pq_dim * pq_bits, 8)]
+ * @param[in] n_rows number of records
+ * @param[in] label The id of the list (cluster) to decode.
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ */
+void pack_contiguous_list_data(raft::resources const& res,
+                               index<int64_t>* index,
+                               const uint8_t* new_codes,
+                               uint32_t n_rows,
+                               uint32_t label,
+                               std::variant<uint32_t, const uint32_t*> offset_or_indices);
+
+/**
+ * @brief Extend one list of the index in-place, by the list label, skipping the classification and
+ * encoding steps.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will extend the fourth cluster
+ *   uint32_t label = 3;
+ *   // We will fill 4 new vectors
+ *   uint32_t n_vec = 4;
+ *   // Indices of the new vectors
+ *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
+ *   ... fill the indices ...
+ *   auto new_codes = raft::make_device_matrix<uint8_t, uint32_t, row_major> new_codes(
+ *       res, n_vec, index.pq_dim());
+ *   ... fill codes ...
+ *   // extend list with new codes
+ *   ivf_pq::helpers::extend_list_with_codes(
+ *       res, &index, codes.view(), indices.view(), label);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] new_codes flat PQ codes, one code per byte [n_rows, index.pq_dim()]
+ * @param[in] new_indices source indices [n_rows]
+ * @param[in] label the id of the target list (cluster).
+ */
+void extend_list_with_codes(
+  raft::resources const& res,
+  index<int64_t>* index,
+  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+  raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+  uint32_t label);
+
+/**
+ * @brief Extend one list of the index in-place, by the list label, skipping the classification
+ * step.
+ *
+ *  Usage example:
+ * @code{.cpp}
+ *   // We will extend the fourth cluster
+ *   uint32_t label = 3;
+ *   // We will extend with 4 new vectors
+ *   uint32_t n_vec = 4;
+ *   // Indices of the new vectors
+ *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
+ *   ... fill the indices ...
+ *   auto new_vectors = raft::make_device_matrix<float, uint32_t, row_major> new_codes(
+ *       res, n_vec, index.dim());
+ *   ... fill vectors ...
+ *   // extend list with new vectors
+ *   ivf_pq::helpers::extend_list(
+ *       res, &index, new_vectors.view(), indices.view(), label);
+ * @endcode
+ *
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] new_vectors data to encode [n_rows, index.dim()]
+ * @param[in] new_indices source indices [n_rows]
+ * @param[in] label the id of the target list (cluster).
+ */
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const float, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label);
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label);
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label);
+/**
+ * @brief Remove all data from a single list (cluster) in the index.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will erase the fourth cluster (label = 3)
+ *   ivf_pq::helpers::erase_list(res, &index, 3);
+ * @endcode
+ *
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] label the id of the target list (cluster).
+ */
+void erase_list(raft::resources const& res, index<int64_t>* index, uint32_t label);
+
+}  // namespace cuvs::neighbors::ivf_pq::helpers
diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
new file mode 100644
index 0000000000..2d0d2ca306
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -0,0 +1,342 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/ann_types.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+/**
+ * @defgroup nn-descent The nn-descent algorithm.
+ * @{
+ */
+
+/**
+ * @brief Parameters used to build an nn-descent index
+ *
+ * `graph_degree`: For an input dataset of dimensions (N, D),
+ * determines the final dimensions of the all-neighbors knn graph
+ * which turns out to be of dimensions (N, graph_degree)
+ * `intermediate_graph_degree`: Internally, nn-descent builds an
+ * all-neighbors knn graph of dimensions (N, intermediate_graph_degree)
+ * before selecting the final `graph_degree` neighbors. It's recommended
+ * that `intermediate_graph_degree` >= 1.5 * graph_degree
+ * `max_iterations`: The number of iterations that nn-descent will refine
+ * the graph for. More iterations produce a better quality graph at cost of performance
+ * `termination_threshold`: The delta at which nn-descent will terminate its iterations
+ *
+ */
+struct index_params : ann::index_params {
+  size_t graph_degree              = 64;      // Degree of output graph.
+  size_t intermediate_graph_degree = 128;     // Degree of input graph for pruning.
+  size_t max_iterations            = 20;      // Number of nn-descent iterations.
+  float termination_threshold      = 0.0001;  // Termination threshold of nn-descent.
+};
+
+/**
+ * @brief nn-descent Build an nn-descent index
+ * The index contains an all-neighbors graph of the input dataset
+ * stored in host memory of dimensions (n_rows, n_cols)
+ *
+ * Reference:
+ * Hui Wang, Wan-Lei Zhao, Xiangxiang Zeng, and Jianye Yang. 2021.
+ * Fast k-NN Graph Construction by GPU based NN-Descent. In Proceedings of the 30th ACM
+ * International Conference on Information & Knowledge Management (CIKM '21). Association for
+ * Computing Machinery, New York, NY, USA, 1929–1938. https://doi.org/10.1145/3459637.3482344
+ *
+ * @tparam IdxT dtype to be used for constructing knn-graph
+ */
+template <typename IdxT>
+struct index : ann::index {
+ public:
+  /**
+   * @brief Construct a new index object
+   *
+   * This constructor creates an nn-descent index which is a knn-graph in host memory.
+   * The type of the knn-graph is a dense raft::host_matrix and dimensions are
+   * (n_rows, n_cols).
+   *
+   * @param res raft::resources is an object mangaging resources
+   * @param n_rows number of rows in knn-graph
+   * @param n_cols number of cols in knn-graph
+   */
+  index(raft::resources const& res, int64_t n_rows, int64_t n_cols)
+    : ann::index(),
+      res_{res},
+      metric_{cuvs::distance::DistanceType::L2Expanded},
+      graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(n_rows, n_cols)},
+      graph_view_{graph_.view()}
+  {
+  }
+
+  /**
+   * @brief Construct a new index object
+   *
+   * This constructor creates an nn-descent index using a user allocated host memory knn-graph.
+   * The type of the knn-graph is a dense raft::host_matrix and dimensions are
+   * (n_rows, n_cols).
+   *
+   * @param res raft::resources is an object mangaging resources
+   * @param graph_view raft::host_matrix_view<IdxT, int64_t, raft::row_major> for storing knn-graph
+   */
+  index(raft::resources const& res,
+        raft::host_matrix_view<IdxT, int64_t, raft::row_major> graph_view)
+    : ann::index(),
+      res_{res},
+      metric_{cuvs::distance::DistanceType::L2Expanded},
+      graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(0, 0)},
+      graph_view_{graph_view}
+  {
+  }
+
+  /** Distance metric used for clustering. */
+  [[nodiscard]] constexpr inline auto metric() const noexcept -> cuvs::distance::DistanceType
+  {
+    return metric_;
+  }
+
+  // /** Total length of the index (number of vectors). */
+  [[nodiscard]] constexpr inline auto size() const noexcept -> IdxT
+  {
+    return graph_view_.extent(0);
+  }
+
+  /** Graph degree */
+  [[nodiscard]] constexpr inline auto graph_degree() const noexcept -> uint32_t
+  {
+    return graph_view_.extent(1);
+  }
+
+  /** neighborhood graph [size, graph-degree] */
+  [[nodiscard]] inline auto graph() noexcept
+    -> raft::host_matrix_view<IdxT, int64_t, raft::row_major>
+  {
+    return graph_view_;
+  }
+
+  // Don't allow copying the index for performance reasons (try avoiding copying data)
+  index(const index&)                    = delete;
+  index(index&&)                         = default;
+  auto operator=(const index&) -> index& = delete;
+  auto operator=(index&&) -> index&      = default;
+  ~index()                               = default;
+
+ private:
+  raft::resources const& res_;
+  cuvs::distance::DistanceType metric_;
+  raft::host_matrix<IdxT, int64_t, raft::row_major> graph_;  // graph to return for non-int IdxT
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major>
+    graph_view_;  // view of graph for user provided matrix
+};
+
+/** @} */
+
+/**
+ * @brief Build nn-descent Index with dataset in device memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
+ *   auto index = nn_descent::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @param[in] res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::device_matrix_view input dataset expected to be located
+ *                in device memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+/**
+ * @brief Build nn-descent Index with dataset in host memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::host_matrix_view input dataset expected to be located
+ *                in host memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+/**
+ * @brief Build nn-descent Index with dataset in device memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
+ *   auto index = nn_descent::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @param[in] res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::device_matrix_view input dataset expected to be located
+ *                in device memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+/**
+ * @brief Build nn-descent Index with dataset in host memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::host_matrix_view input dataset expected to be located
+ *                in host memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+/**
+ * @brief Build nn-descent Index with dataset in device memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
+ *   auto index = nn_descent::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @param[in] res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::device_matrix_view input dataset expected to be located
+ *                in device memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+/**
+ * @brief Build nn-descent Index with dataset in host memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::host_matrix_view input dataset expected to be located
+ *                in host memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+}  // namespace cuvs::neighbors::nn_descent
\ No newline at end of file
diff --git a/cpp/include/cuvs/neighbors/sample_filter.hpp b/cpp/include/cuvs/neighbors/sample_filter.hpp
new file mode 100644
index 0000000000..01ac284a3d
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/sample_filter.hpp
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/bitset.hpp>
+#include <raft/core/detail/macros.hpp>
+
+#include <cstddef>
+#include <cstdint>
+
+namespace cuvs::neighbors::filtering {
+
+/* A filter that filters nothing. This is the default behavior. */
+struct none_ivf_sample_filter {
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the current inverted list index
+    const uint32_t cluster_ix,
+    // the index of the current sample inside the current inverted list
+    const uint32_t sample_ix) const;
+};
+
+/* A filter that filters nothing. This is the default behavior. */
+struct none_cagra_sample_filter {
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the index of the current sample
+    const uint32_t sample_ix) const;
+};
+
+/**
+ * @brief Filter used to convert the cluster index and sample index
+ * of an IVF search into a sample index. This can be used as an
+ * intermediate filter.
+ *
+ * @tparam index_t Indexing type
+ * @tparam filter_t
+ */
+template <typename index_t, typename filter_t>
+struct ivf_to_sample_filter {
+  const index_t* const* inds_ptrs_;
+  const filter_t next_filter_;
+
+  ivf_to_sample_filter(const index_t* const* inds_ptrs, const filter_t next_filter);
+
+  /** If the original filter takes three arguments, then don't modify the arguments.
+   * If the original filter takes two arguments, then we are using `inds_ptr_` to obtain the sample
+   * index.
+   */
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the current inverted list index
+    const uint32_t cluster_ix,
+    // the index of the current sample inside the current inverted list
+    const uint32_t sample_ix) const;
+};
+
+/**
+ * @brief Filter an index with a bitset
+ *
+ * @tparam index_t Indexing type
+ */
+template <typename bitset_t, typename index_t>
+struct bitset_filter {
+  // View of the bitset to use as a filter
+  const cuvs::core::bitset_view<bitset_t, index_t> bitset_view_;
+
+  bitset_filter(const cuvs::core::bitset_view<bitset_t, index_t> bitset_for_filtering);
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the index of the current sample
+    const uint32_t sample_ix) const;
+};
+
+/**
+ * If the filtering depends on the index of a sample, then the following
+ * filter template can be used:
+ *
+ * template <typename IdxT>
+ * struct index_ivf_sample_filter {
+ *   using index_type = IdxT;
+ *
+ *   const index_type* const* inds_ptr = nullptr;
+ *
+ *   index_ivf_sample_filter() {}
+ *   index_ivf_sample_filter(const index_type* const* _inds_ptr)
+ *       : inds_ptr{_inds_ptr} {}
+ *   index_ivf_sample_filter(const index_ivf_sample_filter&) = default;
+ *   index_ivf_sample_filter(index_ivf_sample_filter&&) = default;
+ *   index_ivf_sample_filter& operator=(const index_ivf_sample_filter&) = default;
+ *   index_ivf_sample_filter& operator=(index_ivf_sample_filter&&) = default;
+ *
+ *   inline _RAFT_HOST_DEVICE bool operator()(
+ *       const uint32_t query_ix,
+ *       const uint32_t cluster_ix,
+ *       const uint32_t sample_ix) const {
+ *     index_type database_idx = inds_ptr[cluster_ix][sample_ix];
+ *
+ *     // return true or false, depending on the database_idx
+ *     return true;
+ *   }
+ * };
+ *
+ * Initialize it as:
+ *   using filter_type = index_ivf_sample_filter<idx_t>;
+ *   filter_type filter(cuvs_ivfpq_index.inds_ptrs().data_handle());
+ *
+ * Use it as:
+ *   cuvs::neighbors::ivf_pq::search_with_filtering<data_t, idx_t, filter_type>(
+ *     ...regular parameters here...,
+ *     filter
+ *   );
+ *
+ * Another example would be the following filter that greenlights samples according
+ * to a contiguous bit mask vector.
+ *
+ * template <typename IdxT>
+ * struct bitmask_ivf_sample_filter {
+ *   using index_type = IdxT;
+ *
+ *   const index_type* const* inds_ptr = nullptr;
+ *   const uint64_t* const bit_mask_ptr = nullptr;
+ *   const int64_t bit_mask_stride_64 = 0;
+ *
+ *   bitmask_ivf_sample_filter() {}
+ *   bitmask_ivf_sample_filter(
+ *       const index_type* const* _inds_ptr,
+ *       const uint64_t* const _bit_mask_ptr,
+ *       const int64_t _bit_mask_stride_64)
+ *       : inds_ptr{_inds_ptr},
+ *         bit_mask_ptr{_bit_mask_ptr},
+ *         bit_mask_stride_64{_bit_mask_stride_64} {}
+ *   bitmask_ivf_sample_filter(const bitmask_ivf_sample_filter&) = default;
+ *   bitmask_ivf_sample_filter(bitmask_ivf_sample_filter&&) = default;
+ *   bitmask_ivf_sample_filter& operator=(const bitmask_ivf_sample_filter&) = default;
+ *   bitmask_ivf_sample_filter& operator=(bitmask_ivf_sample_filter&&) = default;
+ *
+ *   inline _RAFT_HOST_DEVICE bool operator()(
+ *       const uint32_t query_ix,
+ *       const uint32_t cluster_ix,
+ *       const uint32_t sample_ix) const {
+ *     const index_type database_idx = inds_ptr[cluster_ix][sample_ix];
+ *     const uint64_t bit_mask_element =
+ *         bit_mask_ptr[query_ix * bit_mask_stride_64 + database_idx / 64];
+ *     const uint64_t masked_bool =
+ *         bit_mask_element & (1ULL << (uint64_t)(database_idx % 64));
+ *     const bool is_bit_set = (masked_bool != 0);
+ *
+ *     return is_bit_set;
+ *   }
+ * };
+ */
+}  // namespace cuvs::neighbors::filtering
diff --git a/cpp/src/core/nvtx.hpp b/cpp/src/core/nvtx.hpp
new file mode 100644
index 0000000000..79382bbd0e
--- /dev/null
+++ b/cpp/src/core/nvtx.hpp
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+namespace cuvs::common::nvtx::domain {
+/** @brief This NVTX domain is supposed to be used within cuvs.  */
+struct cuvs {
+  static constexpr const char* name = "cuvs";
+};
+};  // namespace cuvs::common::nvtx::domain
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
new file mode 100644
index 0000000000..840ac29364
--- /dev/null
+++ b/cpp/src/neighbors/cagra.cuh
@@ -0,0 +1,399 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/cagra/cagra_build.cuh"
+#include "detail/cagra/cagra_search.cuh"
+#include "detail/cagra/graph_core.cuh"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <cuvs/neighbors/dataset.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+/**
+ * @defgroup cagra CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
+/**
+ * @brief Build a kNN graph using IVF-PQ.
+ *
+ * The kNN graph is the first building block for CAGRA index.
+ *
+ * The output is a dense matrix that stores the neighbor indices for each point in the dataset.
+ * Each point has the same number of neighbors.
+ *
+ * See [cagra::build](#cagra::build) for an alternative method.
+ *
+ * The following distance metrics are supported:
+ * - L2Expanded
+ * - InnerProduct
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters based on shape of the dataset
+ *   ivf_pq::index_params build_params = ivf_pq::index_params::from_dataset(dataset);
+ *   ivf_pq::search_params search_params;
+ *   auto knn_graph      = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 128);
+ *   // create knn graph
+ *   cagra::build_knn_graph(res, dataset, knn_graph.view(), 2, build_params, search_params);
+ *   auto optimized_gaph = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 64);
+ *   cagra::optimize(res, dataset, knn_graph.view(), optimized_graph.view());
+ *   // Construct an index from dataset and optimized knn_graph
+ *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset,
+ *                                      optimized_graph.view());
+ * @endcode
+ *
+ * @tparam DataT data element type
+ * @tparam IdxT type of the dataset vector indices
+ *
+ * @param[in] res raft resources
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ * @param[out] knn_graph a host matrix view to store the output knn graph [n_rows, graph_degree]
+ * @param[in] refine_rate (optional) refinement rate for ivf-pq search
+ * @param[in] build_params (optional) ivf_pq index building parameters for knn graph
+ * @param[in] search_params (optional) ivf_pq search parameters
+ */
+template <typename DataT, typename IdxT, typename accessor>
+void build_knn_graph(
+  raft::resources const& res,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
+  std::optional<float> refine_rate                   = std::nullopt,
+  std::optional<ivf_pq::index_params> build_params   = std::nullopt,
+  std::optional<ivf_pq::search_params> search_params = std::nullopt)
+{
+  using internal_IdxT = typename std::make_unsigned<IdxT>::type;
+
+  auto knn_graph_internal = raft::make_host_matrix_view<internal_IdxT, int64_t>(
+    reinterpret_cast<internal_IdxT*>(knn_graph.data_handle()),
+    knn_graph.extent(0),
+    knn_graph.extent(1));
+  auto dataset_internal =
+    raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor>(
+      dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+
+  cagra::detail::build_knn_graph(
+    res, dataset_internal, knn_graph_internal, refine_rate, build_params, search_params);
+}
+
+/**
+ * @brief Build a kNN graph using NN-descent.
+ *
+ * The kNN graph is the first building block for CAGRA index.
+ *
+ * The output is a dense matrix that stores the neighbor indices for each point in the dataset.
+ * Each point has the same number of neighbors.
+ *
+ * See [cagra::build](#cagra::build) for an alternative method.
+ *
+ * The following distance metrics are supported:
+ * - L2Expanded
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params build_params;
+ *   build_params.graph_degree = 128;
+ *   auto knn_graph      = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 128);
+ *   // create knn graph
+ *   cagra::build_knn_graph(res, dataset, knn_graph.view(), build_params);
+ *   auto optimized_gaph      = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), 64);
+ *   cagra::optimize(res, dataset, nn_descent_index.graph.view(), optimized_graph.view());
+ *   // Construct an index from dataset and optimized knn_graph
+ *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset,
+ * optimized_graph.view());
+ * @endcode
+ *
+ * @tparam DataT data element type
+ * @tparam IdxT type of the dataset vector indices
+ * @tparam accessor host or device accessor_type for the dataset
+ * @param[in] res raft::resources is an object mangaging resources
+ * @param[in] dataset input raft::host/device_matrix_view that can be located in
+ *                in host or device memory
+ * @param[out] knn_graph a host matrix view to store the output knn graph [n_rows, graph_degree]
+ * @param[in] build_params an instance of experimental::nn_descent::index_params that are parameters
+ *                     to run the nn-descent algorithm
+ */
+template <typename DataT,
+          typename IdxT     = uint32_t,
+          typename accessor = raft::host_device_accessor<std::experimental::default_accessor<DataT>,
+                                                         raft::memory_type::device>>
+void build_knn_graph(
+  raft::resources const& res,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
+  cuvs::neighbors::nn_descent::index_params build_params)
+{
+  detail::build_knn_graph<DataT, IdxT>(res, dataset, knn_graph, build_params);
+}
+
+/**
+ * @brief Sort a KNN graph index.
+ * Preprocessing step for `cagra::optimize`: If a KNN graph is not built using
+ * `cagra::build_knn_graph`, then it is necessary to call this function before calling
+ * `cagra::optimize`. If the graph is built by `cagra::build_knn_graph`, it is already sorted and
+ * you do not need to call this function.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   cagra::index_params build_params;
+ *   auto knn_graph = raft::make_host_matrix<IdxT, IdxT>(dataset.extent(0), 128);
+ *   // build KNN graph not using `cagra::build_knn_graph`
+ *   // build(knn_graph, dataset, ...);
+ *   // sort graph index
+ *   sort_knn_graph(res, dataset.view(), knn_graph.view());
+ *   // optimize graph
+ *   cagra::optimize(res, dataset, knn_graph.view(), optimized_graph.view());
+ *   // Construct an index from dataset and optimized knn_graph
+ *   auto index = cagra::index<T, IdxT>(res, build_params.metric(), dataset,
+ *                                      optimized_graph.view());
+ * @endcode
+ *
+ * @tparam DataT type of the data in the source dataset
+ * @tparam IdxT type of the dataset vector indices
+ *
+ * @param[in] res raft resources
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ * @param[in,out] knn_graph a matrix view (host or device) of the input knn graph [n_rows,
+ * knn_graph_degree]
+ */
+template <
+  typename DataT,
+  typename IdxT       = uint32_t,
+  typename d_accessor = raft::host_device_accessor<std::experimental::default_accessor<DataT>,
+                                                   raft::memory_type::device>,
+  typename g_accessor =
+    raft::host_device_accessor<std::experimental::default_accessor<IdxT>, raft::memory_type::host>>
+void sort_knn_graph(
+  raft::resources const& res,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, d_accessor> dataset,
+  raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph)
+{
+  using internal_IdxT = typename std::make_unsigned<IdxT>::type;
+
+  using g_accessor_internal =
+    raft::host_device_accessor<std::experimental::default_accessor<internal_IdxT>,
+                               g_accessor::mem_type>;
+  auto knn_graph_internal =
+    raft::mdspan<internal_IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor_internal>(
+      reinterpret_cast<internal_IdxT*>(knn_graph.data_handle()),
+      knn_graph.extent(0),
+      knn_graph.extent(1));
+
+  auto dataset_internal =
+    raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, d_accessor>(
+      dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+
+  cagra::detail::graph::sort_knn_graph(res, dataset_internal, knn_graph_internal);
+}
+
+/**
+ * @brief Prune a KNN graph.
+ *
+ * Decrease the number of neighbors for each node.
+ *
+ * See [cagra::build_knn_graph](#cagra::build_knn_graph) for usage example
+ *
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res raft resources
+ * @param[in] knn_graph a matrix view (host or device) of the input knn graph [n_rows,
+ * knn_graph_degree]
+ * @param[out] new_graph a host matrix view of the optimized knn graph [n_rows, graph_degree]
+ */
+template <
+  typename IdxT = uint32_t,
+  typename g_accessor =
+    raft::host_device_accessor<std::experimental::default_accessor<IdxT>, raft::memory_type::host>>
+void optimize(
+  raft::resources const& res,
+  raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph)
+{
+  detail::optimize(res, knn_graph, new_graph);
+}
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * It is required that dataset and the optimized graph fit the GPU memory.
+ *
+ * To customize the parameters for knn-graph building and pruning, and to reuse the
+ * intermediate results, you could build the index in two steps using
+ * [cagra::build_knn_graph](#cagra::build_knn_graph) and [cagra::optimize](#cagra::optimize).
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+index<T, IdxT> build(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
+{
+  return cuvs::neighbors::cagra::detail::build<T, IdxT, Accessor>(res, params, dataset);
+}
+
+/**
+ * @brief Search ANN using the constructed index with the given sample filter.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // create a bitset to filter the search
+ *   auto removed_indices = raft::make_device_vector<IdxT>(res, n_removed_indices);
+ *   raft::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
+ *     res, removed_indices.view(), dataset.extent(0));
+ *   // search K nearest neighbours according to a bitset
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search_with_filtering(res, search_params, index, queries, neighbors, distances,
+ *     filtering::bitset_filter(removed_indices_bitset.view()));
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ * @tparam CagraSampleFilterT Device filter function, with the signature
+ *         `(uint32_t query ix, uint32_t sample_ix) -> bool`
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] idx cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device filter function that greenlights samples for a given query
+ */
+template <typename T, typename IdxT, typename CagraSampleFilterT>
+void search_with_filtering(raft::resources const& res,
+                           const search_params& params,
+                           const index<T, IdxT>& idx,
+                           raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+                           raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
+                           raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+                           CagraSampleFilterT sample_filter = CagraSampleFilterT())
+{
+  RAFT_EXPECTS(
+    queries.extent(0) == neighbors.extent(0) && queries.extent(0) == distances.extent(0),
+    "Number of rows in output neighbors and distances matrices must equal the number of queries.");
+
+  RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1),
+               "Number of columns in output neighbors and distances matrices must equal k");
+  RAFT_EXPECTS(queries.extent(1) == idx.dim(),
+               "Number of query dimensions should equal number of dimensions in the index.");
+
+  using internal_IdxT   = typename std::make_unsigned<IdxT>::type;
+  auto queries_internal = raft::make_device_matrix_view<const T, int64_t, raft::row_major>(
+    queries.data_handle(), queries.extent(0), queries.extent(1));
+  auto neighbors_internal = raft::make_device_matrix_view<internal_IdxT, int64_t, raft::row_major>(
+    reinterpret_cast<internal_IdxT*>(neighbors.data_handle()),
+    neighbors.extent(0),
+    neighbors.extent(1));
+  auto distances_internal = raft::make_device_matrix_view<float, int64_t, raft::row_major>(
+    distances.data_handle(), distances.extent(0), distances.extent(1));
+
+  return cagra::detail::search_main<T, internal_IdxT, CagraSampleFilterT, IdxT>(
+    res, params, idx, queries_internal, neighbors_internal, distances_internal, sample_filter);
+}
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] idx cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+template <typename T, typename IdxT>
+void search(raft::resources const& res,
+            const search_params& params,
+            const index<T, IdxT>& idx,
+            raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances)
+{
+  using none_filter_type = cuvs::neighbors::filtering::none_cagra_sample_filter;
+  return cagra::search_with_filtering<T, IdxT, none_filter_type>(
+    res, params, idx, queries, neighbors, distances, none_filter_type{});
+}
+
+/** @} */  // end group cagra
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_build_int8.cpp b/cpp/src/neighbors/cagra_build_float.cu
similarity index 57%
rename from cpp/src/neighbors/cagra_build_int8.cpp
rename to cpp/src/neighbors/cagra_build_float.cu
index 4123a4293c..c5e331925b 100644
--- a/cpp/src/neighbors/cagra_build_int8.cpp
+++ b/cpp/src/neighbors/cagra_build_float.cu
@@ -14,48 +14,46 @@
  * limitations under the License.
  */
 
+#include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_BUILD(T, IdxT)                                                             \
-  auto build(raft::resources const& handle,                                                        \
-             const cuvs::neighbors::cagra::index_params& params,                                   \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)                  \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                                       \
-  {                                                                                                \
-    return cuvs::neighbors::cagra::index<T, IdxT>(                                                 \
-      std::move(raft::runtime::neighbors::cagra::build(handle, params, dataset)));                 \
-  }                                                                                                \
-                                                                                                   \
-  auto build(raft::resources const& handle,                                                        \
-             const cuvs::neighbors::cagra::index_params& params,                                   \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)                    \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                                       \
-  {                                                                                                \
-    return cuvs::neighbors::cagra::index<T, IdxT>(                                                 \
-      std::move(raft::runtime::neighbors::cagra::build(handle, params, dataset)));                 \
-  }                                                                                                \
-                                                                                                   \
-  void build_device(raft::resources const& handle,                                                 \
-                    const cuvs::neighbors::cagra::index_params& params,                            \
-                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,           \
-                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                                   \
-  {                                                                                                \
-    raft::runtime::neighbors::cagra::build_device(handle, params, dataset, *idx.get_raft_index()); \
-  }                                                                                                \
-                                                                                                   \
-  void build_host(raft::resources const& handle,                                                   \
-                  const cuvs::neighbors::cagra::index_params& params,                              \
-                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
-                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                                     \
-  {                                                                                                \
-    raft::runtime::neighbors::cagra::build_host(handle, params, dataset, *idx.get_raft_index());   \
+#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                                   \
+  auto build(raft::resources const& handle,                                              \
+             const cuvs::neighbors::cagra::index_params& params,                         \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)        \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
+  {                                                                                      \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
+  }                                                                                      \
+                                                                                         \
+  auto build(raft::resources const& handle,                                              \
+             const cuvs::neighbors::cagra::index_params& params,                         \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)          \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
+  {                                                                                      \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
+  }                                                                                      \
+                                                                                         \
+  void build_device(raft::resources const& handle,                                       \
+                    const cuvs::neighbors::cagra::index_params& params,                  \
+                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset, \
+                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                         \
+  {                                                                                      \
+    idx = build(handle, params, dataset);                                                \
+  }                                                                                      \
+                                                                                         \
+  void build_host(raft::resources const& handle,                                         \
+                  const cuvs::neighbors::cagra::index_params& params,                    \
+                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,     \
+                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                           \
+  {                                                                                      \
+    idx = build(handle, params, dataset);                                                \
   }
 
-CUVS_INST_CAGRA_BUILD(int8_t, uint32_t);
+RAFT_INST_CAGRA_BUILD(float, uint32_t);
 
-#undef CUVS_INST_CAGRA_BUILD
+#undef RAFT_INST_CAGRA_BUILD
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_build_uint8.cpp b/cpp/src/neighbors/cagra_build_int8.cu
similarity index 57%
rename from cpp/src/neighbors/cagra_build_uint8.cpp
rename to cpp/src/neighbors/cagra_build_int8.cu
index fcdcaa0b62..e48286b0f5 100644
--- a/cpp/src/neighbors/cagra_build_uint8.cpp
+++ b/cpp/src/neighbors/cagra_build_int8.cu
@@ -14,48 +14,46 @@
  * limitations under the License.
  */
 
+#include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_BUILD(T, IdxT)                                                             \
-  auto build(raft::resources const& handle,                                                        \
-             const cuvs::neighbors::cagra::index_params& params,                                   \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)                  \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                                       \
-  {                                                                                                \
-    return cuvs::neighbors::cagra::index<T, IdxT>(                                                 \
-      std::move(raft::runtime::neighbors::cagra::build(handle, params, dataset)));                 \
-  }                                                                                                \
-                                                                                                   \
-  auto build(raft::resources const& handle,                                                        \
-             const cuvs::neighbors::cagra::index_params& params,                                   \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)                    \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                                       \
-  {                                                                                                \
-    return cuvs::neighbors::cagra::index<T, IdxT>(                                                 \
-      std::move(raft::runtime::neighbors::cagra::build(handle, params, dataset)));                 \
-  }                                                                                                \
-                                                                                                   \
-  void build_device(raft::resources const& handle,                                                 \
-                    const cuvs::neighbors::cagra::index_params& params,                            \
-                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,           \
-                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                                   \
-  {                                                                                                \
-    raft::runtime::neighbors::cagra::build_device(handle, params, dataset, *idx.get_raft_index()); \
-  }                                                                                                \
-                                                                                                   \
-  void build_host(raft::resources const& handle,                                                   \
-                  const cuvs::neighbors::cagra::index_params& params,                              \
-                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
-                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                                     \
-  {                                                                                                \
-    raft::runtime::neighbors::cagra::build_host(handle, params, dataset, *idx.get_raft_index());   \
+#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                                   \
+  auto build(raft::resources const& handle,                                              \
+             const cuvs::neighbors::cagra::index_params& params,                         \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)        \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
+  {                                                                                      \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
+  }                                                                                      \
+                                                                                         \
+  auto build(raft::resources const& handle,                                              \
+             const cuvs::neighbors::cagra::index_params& params,                         \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)          \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
+  {                                                                                      \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
+  }                                                                                      \
+                                                                                         \
+  void build_device(raft::resources const& handle,                                       \
+                    const cuvs::neighbors::cagra::index_params& params,                  \
+                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset, \
+                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                         \
+  {                                                                                      \
+    idx = build(handle, params, dataset);                                                \
+  }                                                                                      \
+                                                                                         \
+  void build_host(raft::resources const& handle,                                         \
+                  const cuvs::neighbors::cagra::index_params& params,                    \
+                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,     \
+                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                           \
+  {                                                                                      \
+    idx = build(handle, params, dataset);                                                \
   }
 
-CUVS_INST_CAGRA_BUILD(uint8_t, uint32_t);
+RAFT_INST_CAGRA_BUILD(int8_t, uint32_t);
 
-#undef CUVS_INST_CAGRA_BUILD
+#undef RAFT_INST_CAGRA_BUILD
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_build_float.cpp b/cpp/src/neighbors/cagra_build_uint8.cu
similarity index 56%
rename from cpp/src/neighbors/cagra_build_float.cpp
rename to cpp/src/neighbors/cagra_build_uint8.cu
index 5ff1da7f21..796fdda745 100644
--- a/cpp/src/neighbors/cagra_build_float.cpp
+++ b/cpp/src/neighbors/cagra_build_uint8.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,48 +14,46 @@
  * limitations under the License.
  */
 
+#include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_BUILD(T, IdxT)                                                             \
-  auto build(raft::resources const& handle,                                                        \
-             const cuvs::neighbors::cagra::index_params& params,                                   \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)                  \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                                       \
-  {                                                                                                \
-    return cuvs::neighbors::cagra::index<T, IdxT>(                                                 \
-      std::move(raft::runtime::neighbors::cagra::build(handle, params, dataset)));                 \
-  }                                                                                                \
-                                                                                                   \
-  auto build(raft::resources const& handle,                                                        \
-             const cuvs::neighbors::cagra::index_params& params,                                   \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)                    \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                                       \
-  {                                                                                                \
-    return cuvs::neighbors::cagra::index<T, IdxT>(                                                 \
-      std::move(raft::runtime::neighbors::cagra::build(handle, params, dataset)));                 \
-  }                                                                                                \
-                                                                                                   \
-  void build_device(raft::resources const& handle,                                                 \
-                    const cuvs::neighbors::cagra::index_params& params,                            \
-                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,           \
-                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                                   \
-  {                                                                                                \
-    raft::runtime::neighbors::cagra::build_device(handle, params, dataset, *idx.get_raft_index()); \
-  }                                                                                                \
-                                                                                                   \
-  void build_host(raft::resources const& handle,                                                   \
-                  const cuvs::neighbors::cagra::index_params& params,                              \
-                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
-                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                                     \
-  {                                                                                                \
-    raft::runtime::neighbors::cagra::build_host(handle, params, dataset, *idx.get_raft_index());   \
+#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                                   \
+  auto build(raft::resources const& handle,                                              \
+             const cuvs::neighbors::cagra::index_params& params,                         \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)        \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
+  {                                                                                      \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
+  }                                                                                      \
+                                                                                         \
+  auto build(raft::resources const& handle,                                              \
+             const cuvs::neighbors::cagra::index_params& params,                         \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)          \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
+  {                                                                                      \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
+  }                                                                                      \
+                                                                                         \
+  void build_device(raft::resources const& handle,                                       \
+                    const cuvs::neighbors::cagra::index_params& params,                  \
+                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset, \
+                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                         \
+  {                                                                                      \
+    idx = build(handle, params, dataset);                                                \
+  }                                                                                      \
+                                                                                         \
+  void build_host(raft::resources const& handle,                                         \
+                  const cuvs::neighbors::cagra::index_params& params,                    \
+                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,     \
+                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                           \
+  {                                                                                      \
+    idx = build(handle, params, dataset);                                                \
   }
 
-CUVS_INST_CAGRA_BUILD(float, uint32_t);
+RAFT_INST_CAGRA_BUILD(uint8_t, uint32_t);
 
-#undef CUVS_INST_CAGRA_BUILD
+#undef RAFT_INST_CAGRA_BUILD
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 2e96e8db68..6a7b2f0a6a 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -46,7 +46,7 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   build_params.nn_descent_niter = params.nn_descent_niter;
 
   if (auto* cparams = params.compression; cparams != nullptr) {
-    auto compression_params                        = cuvs::neighbors::cagra::vpq_params();
+    auto compression_params                        = cuvs::neighbors::ann::vpq_params();
     compression_params.pq_bits                     = cparams->pq_bits;
     compression_params.pq_dim                      = cparams->pq_dim;
     compression_params.vq_n_centers                = cparams->vq_n_centers;
@@ -216,7 +216,7 @@ extern "C" cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params
 extern "C" cuvsError_t cuvsCagraCompressionParamsCreate(cuvsCagraCompressionParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto ps = cuvs::neighbors::cagra::vpq_params();
+    auto ps = cuvs::neighbors::ann::vpq_params();
     *params =
       new cuvsCagraCompressionParams{.pq_bits                     = ps.pq_bits,
                                      .pq_dim                      = ps.pq_dim,
diff --git a/cpp/src/neighbors/cagra_optimize.cpp b/cpp/src/neighbors/cagra_optimize.cu
similarity index 52%
rename from cpp/src/neighbors/cagra_optimize.cpp
rename to cpp/src/neighbors/cagra_optimize.cu
index 5ec53c043d..4c152cf121 100644
--- a/cpp/src/neighbors/cagra_optimize.cpp
+++ b/cpp/src/neighbors/cagra_optimize.cu
@@ -14,22 +14,22 @@
  * limitations under the License.
  */
 
+#include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
 
 namespace cuvs::neighbors::cagra {
 
-void optimize_device(raft::resources const& handle,
-                     raft::device_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
-                     raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
+void optimize(raft::resources const& handle,
+              raft::device_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
+              raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
 {
-  raft::runtime::neighbors::cagra::optimize_device(handle, knn_graph, new_graph);
+  cuvs::neighbors::cagra::optimize(handle, knn_graph, new_graph);
 }
-void optimize_host(raft::resources const& handle,
-                   raft::host_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
-                   raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
+void optimize(raft::resources const& handle,
+              raft::host_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
+              raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
 {
-  raft::runtime::neighbors::cagra::optimize_host(handle, knn_graph, new_graph);
+  cuvs::neighbors::cagra::optimize(handle, knn_graph, new_graph);
 }
 
 }  // namespace cuvs::neighbors::cagra
\ No newline at end of file
diff --git a/cpp/src/neighbors/cagra_search_float.cpp b/cpp/src/neighbors/cagra_search_float.cu
similarity index 75%
rename from cpp/src/neighbors/cagra_search_float.cpp
rename to cpp/src/neighbors/cagra_search_float.cu
index f05f1e6905..e981d91274 100644
--- a/cpp/src/neighbors/cagra_search_float.cpp
+++ b/cpp/src/neighbors/cagra_search_float.cu
@@ -14,21 +14,20 @@
  * limitations under the License.
  */
 
+#include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
-  void search(raft::resources const& handle,                                       \
-              cuvs::neighbors::cagra::search_params const& params,                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances) \
-  {                                                                                \
-    raft::runtime::neighbors::cagra::search(                                       \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);     \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
+  void search(raft::resources const& handle,                                                       \
+              cuvs::neighbors::cagra::search_params const& params,                                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
+  {                                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
   }
 
 CUVS_INST_CAGRA_SEARCH(float, uint32_t);
diff --git a/cpp/src/neighbors/cagra_search_int8.cpp b/cpp/src/neighbors/cagra_search_int8.cu
similarity index 75%
rename from cpp/src/neighbors/cagra_search_int8.cpp
rename to cpp/src/neighbors/cagra_search_int8.cu
index 56b722f7bc..b44a7507d1 100644
--- a/cpp/src/neighbors/cagra_search_int8.cpp
+++ b/cpp/src/neighbors/cagra_search_int8.cu
@@ -14,21 +14,19 @@
  * limitations under the License.
  */
 
+#include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
-
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
-  void search(raft::resources const& handle,                                       \
-              cuvs::neighbors::cagra::search_params const& params,                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances) \
-  {                                                                                \
-    raft::runtime::neighbors::cagra::search(                                       \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);     \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
+  void search(raft::resources const& handle,                                                       \
+              cuvs::neighbors::cagra::search_params const& params,                                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
+  {                                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
   }
 
 CUVS_INST_CAGRA_SEARCH(int8_t, uint32_t);
diff --git a/cpp/src/neighbors/cagra_search_uint8.cpp b/cpp/src/neighbors/cagra_search_uint8.cu
similarity index 75%
rename from cpp/src/neighbors/cagra_search_uint8.cpp
rename to cpp/src/neighbors/cagra_search_uint8.cu
index 3b980cf472..cbb7d6652c 100644
--- a/cpp/src/neighbors/cagra_search_uint8.cpp
+++ b/cpp/src/neighbors/cagra_search_uint8.cu
@@ -14,21 +14,20 @@
  * limitations under the License.
  */
 
+#include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
-  void search(raft::resources const& handle,                                       \
-              cuvs::neighbors::cagra::search_params const& params,                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances) \
-  {                                                                                \
-    raft::runtime::neighbors::cagra::search(                                       \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);     \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
+  void search(raft::resources const& handle,                                                       \
+              cuvs::neighbors::cagra::search_params const& params,                                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
+  {                                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
   }
 
 CUVS_INST_CAGRA_SEARCH(uint8_t, uint32_t);
diff --git a/cpp/src/neighbors/cagra_serialize.cuh b/cpp/src/neighbors/cagra_serialize.cuh
new file mode 100644
index 0000000000..f5a2748eac
--- /dev/null
+++ b/cpp/src/neighbors/cagra_serialize.cuh
@@ -0,0 +1,230 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/cagra/cagra_serialize.cuh"
+
+namespace cuvs::neighbors::cagra {
+
+/**
+ * \defgroup cagra_serialize CAGRA Serialize
+ * @{
+ */
+
+/**
+ * Write the index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * raft::cagra::serialize(handle, os, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ *
+ */
+template <typename T, typename IdxT>
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const index<T, IdxT>& index,
+               bool include_dataset = true)
+{
+  detail::serialize(handle, os, index, include_dataset);
+}
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * raft::cagra::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ *
+ */
+template <typename T, typename IdxT>
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const index<T, IdxT>& index,
+               bool include_dataset = true)
+{
+  detail::serialize(handle, filename, index, include_dataset);
+}
+
+/**
+ * Write the CAGRA built index as a base layer HNSW index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * raft::cagra::serialize_to_hnswlib(handle, os, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ *
+ */
+template <typename T, typename IdxT>
+void serialize_to_hnswlib(raft::resources const& handle,
+                          std::ostream& os,
+                          const cuvs::neighbors::cagra::index<T, IdxT>& index)
+{
+  detail::serialize_to_hnswlib<T, IdxT>(handle, os, index);
+}
+
+/**
+ * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * raft::cagra::serialize_to_hnswlib(handle, filename, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ *
+ */
+template <typename T, typename IdxT>
+void serialize_to_hnswlib(raft::resources const& handle,
+                          const std::string& filename,
+                          const cuvs::neighbors::cagra::index<T, IdxT>& index)
+{
+  detail::serialize_to_hnswlib<T, IdxT>(handle, filename, index);
+}
+
+/**
+ * Load index from input stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * using T    = float; // data element type
+ * using IdxT = int; // type of the index
+ * auto index = raft::cagra::deserialize<T, IdxT>(handle, is);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ *
+ * @return cuvs::neighbors::experimental::cagra::index<T, IdxT>
+ */
+template <typename T, typename IdxT>
+index<T, IdxT> deserialize(raft::resources const& handle, std::istream& is)
+{
+  return detail::deserialize<T, IdxT>(handle, is);
+}
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using T    = float; // data element type
+ * using IdxT = int; // type of the index
+ * auto index = raft::cagra::deserialize<T, IdxT>(handle, filename);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ *
+ * @return cuvs::neighbors::experimental::cagra::index<T, IdxT>
+ */
+template <typename T, typename IdxT>
+index<T, IdxT> deserialize(raft::resources const& handle, const std::string& filename)
+{
+  return detail::deserialize<T, IdxT>(handle, filename);
+}
+
+/**@}*/
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_serialize_int8.cpp b/cpp/src/neighbors/cagra_serialize_float.cu
similarity index 56%
rename from cpp/src/neighbors/cagra_serialize_int8.cpp
rename to cpp/src/neighbors/cagra_serialize_float.cu
index adfa8cc064..edaae0a0a9 100644
--- a/cpp/src/neighbors/cagra_serialize_int8.cpp
+++ b/cpp/src/neighbors/cagra_serialize_float.cu
@@ -14,48 +14,69 @@
  * limitations under the License.
  */
 
-#include <sstream>
-#include <string>
+#include "cagra_serialize.cuh"
+#include <raft/core/device_resources.hpp>
 
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft/core/device_resources.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
+
+#include <cuda_fp16.h>
+
+#include <sstream>
+#include <string>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
+#define RAFT_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
   void serialize_file(raft::resources const& handle,                                              \
                       const std::string& filename,                                                \
                       const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                \
                       bool include_dataset)                                                       \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::serialize_file(                                              \
-      handle, filename, *index.get_raft_index(), include_dataset);                                \
+    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, filename, index, include_dataset); \
   };                                                                                              \
                                                                                                   \
   void deserialize_file(raft::resources const& handle,                                            \
                         const std::string& filename,                                              \
                         cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                    \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::deserialize_file(handle, filename, index->get_raft_index()); \
+    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
+    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, filename);              \
   };                                                                                              \
   void serialize(raft::resources const& handle,                                                   \
                  std::string& str,                                                                \
                  const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                     \
                  bool include_dataset)                                                            \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::serialize(                                                   \
-      handle, str, *index.get_raft_index(), include_dataset);                                     \
+    std::stringstream os;                                                                         \
+    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, os, index, include_dataset);       \
+    str = os.str();                                                                               \
+  }                                                                                               \
+                                                                                                  \
+  void serialize_to_hnswlib_file(raft::resources const& handle,                                   \
+                                 const std::string& filename,                                     \
+                                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)     \
+  {                                                                                               \
+    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, filename, index);       \
+  };                                                                                              \
+  void serialize_to_hnswlib(raft::resources const& handle,                                        \
+                            std::string& str,                                                     \
+                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)          \
+  {                                                                                               \
+    std::stringstream os;                                                                         \
+    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index);             \
+    str = os.str();                                                                               \
   }                                                                                               \
                                                                                                   \
   void deserialize(raft::resources const& handle,                                                 \
                    const std::string& str,                                                        \
                    cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                         \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::deserialize(handle, str, index->get_raft_index());           \
+    std::istringstream is(str);                                                                   \
+    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
+    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, is);                    \
   }
 
-CUVS_INST_CAGRA_SERIALIZE(int8_t);
+RAFT_INST_CAGRA_SERIALIZE(float);
 
-#undef CUVS_INST_CAGRA_SERIALIZE
+#undef RAFT_INST_CAGRA_SERIALIZE
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_serialize_uint8.cpp b/cpp/src/neighbors/cagra_serialize_int8.cu
similarity index 56%
rename from cpp/src/neighbors/cagra_serialize_uint8.cpp
rename to cpp/src/neighbors/cagra_serialize_int8.cu
index a88517602c..bb23e0ae55 100644
--- a/cpp/src/neighbors/cagra_serialize_uint8.cpp
+++ b/cpp/src/neighbors/cagra_serialize_int8.cu
@@ -14,48 +14,69 @@
  * limitations under the License.
  */
 
-#include <sstream>
-#include <string>
+#include "cagra_serialize.cuh"
+#include <raft/core/device_resources.hpp>
 
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft/core/device_resources.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
+
+#include <cuda_fp16.h>
+
+#include <sstream>
+#include <string>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
+#define RAFT_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
   void serialize_file(raft::resources const& handle,                                              \
                       const std::string& filename,                                                \
                       const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                \
                       bool include_dataset)                                                       \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::serialize_file(                                              \
-      handle, filename, *index.get_raft_index(), include_dataset);                                \
+    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, filename, index, include_dataset); \
   };                                                                                              \
                                                                                                   \
   void deserialize_file(raft::resources const& handle,                                            \
                         const std::string& filename,                                              \
                         cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                    \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::deserialize_file(handle, filename, index->get_raft_index()); \
+    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
+    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, filename);              \
   };                                                                                              \
   void serialize(raft::resources const& handle,                                                   \
                  std::string& str,                                                                \
                  const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                     \
                  bool include_dataset)                                                            \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::serialize(                                                   \
-      handle, str, *index.get_raft_index(), include_dataset);                                     \
+    std::stringstream os;                                                                         \
+    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, os, index, include_dataset);       \
+    str = os.str();                                                                               \
+  }                                                                                               \
+                                                                                                  \
+  void serialize_to_hnswlib_file(raft::resources const& handle,                                   \
+                                 const std::string& filename,                                     \
+                                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)     \
+  {                                                                                               \
+    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, filename, index);       \
+  };                                                                                              \
+  void serialize_to_hnswlib(raft::resources const& handle,                                        \
+                            std::string& str,                                                     \
+                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)          \
+  {                                                                                               \
+    std::stringstream os;                                                                         \
+    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index);             \
+    str = os.str();                                                                               \
   }                                                                                               \
                                                                                                   \
   void deserialize(raft::resources const& handle,                                                 \
                    const std::string& str,                                                        \
                    cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                         \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::deserialize(handle, str, index->get_raft_index());           \
+    std::istringstream is(str);                                                                   \
+    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
+    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, is);                    \
   }
 
-CUVS_INST_CAGRA_SERIALIZE(uint8_t);
+RAFT_INST_CAGRA_SERIALIZE(int8_t);
 
-#undef CUVS_INST_CAGRA_SERIALIZE
+#undef RAFT_INST_CAGRA_SERIALIZE
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_serialize_float.cpp b/cpp/src/neighbors/cagra_serialize_uint8.cu
similarity index 56%
rename from cpp/src/neighbors/cagra_serialize_float.cpp
rename to cpp/src/neighbors/cagra_serialize_uint8.cu
index 2f27a3e4b7..b59b2cbe88 100644
--- a/cpp/src/neighbors/cagra_serialize_float.cpp
+++ b/cpp/src/neighbors/cagra_serialize_uint8.cu
@@ -14,48 +14,69 @@
  * limitations under the License.
  */
 
-#include <sstream>
-#include <string>
+#include "cagra_serialize.cuh"
+#include <raft/core/device_resources.hpp>
 
 #include <cuvs/neighbors/cagra.hpp>
-#include <raft/core/device_resources.hpp>
-#include <raft_runtime/neighbors/cagra.hpp>
+
+#include <cuda_fp16.h>
+
+#include <sstream>
+#include <string>
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
+#define RAFT_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
   void serialize_file(raft::resources const& handle,                                              \
                       const std::string& filename,                                                \
                       const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                \
                       bool include_dataset)                                                       \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::serialize_file(                                              \
-      handle, filename, *index.get_raft_index(), include_dataset);                                \
+    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, filename, index, include_dataset); \
   };                                                                                              \
                                                                                                   \
   void deserialize_file(raft::resources const& handle,                                            \
                         const std::string& filename,                                              \
                         cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                    \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::deserialize_file(handle, filename, index->get_raft_index()); \
+    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
+    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, filename);              \
   };                                                                                              \
   void serialize(raft::resources const& handle,                                                   \
                  std::string& str,                                                                \
                  const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                     \
                  bool include_dataset)                                                            \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::serialize(                                                   \
-      handle, str, *index.get_raft_index(), include_dataset);                                     \
+    std::stringstream os;                                                                         \
+    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, os, index, include_dataset);       \
+    str = os.str();                                                                               \
+  }                                                                                               \
+                                                                                                  \
+  void serialize_to_hnswlib_file(raft::resources const& handle,                                   \
+                                 const std::string& filename,                                     \
+                                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)     \
+  {                                                                                               \
+    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, filename, index);       \
+  };                                                                                              \
+  void serialize_to_hnswlib(raft::resources const& handle,                                        \
+                            std::string& str,                                                     \
+                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)          \
+  {                                                                                               \
+    std::stringstream os;                                                                         \
+    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index);             \
+    str = os.str();                                                                               \
   }                                                                                               \
                                                                                                   \
   void deserialize(raft::resources const& handle,                                                 \
                    const std::string& str,                                                        \
                    cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                         \
   {                                                                                               \
-    raft::runtime::neighbors::cagra::deserialize(handle, str, index->get_raft_index());           \
+    std::istringstream is(str);                                                                   \
+    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
+    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, is);                    \
   }
 
-CUVS_INST_CAGRA_SERIALIZE(float);
+RAFT_INST_CAGRA_SERIALIZE(uint8_t);
 
-#undef CUVS_INST_CAGRA_SERIALIZE
+#undef RAFT_INST_CAGRA_SERIALIZE
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
new file mode 100644
index 0000000000..d73b2e3a18
--- /dev/null
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -0,0 +1,577 @@
+/*
+ * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a raft::copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/logger.hpp>
+#include <raft/distance/distance_types.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/integer_utils.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/resource_ref.hpp>
+
+#include <cuda_fp16.hpp>
+
+#include <memory>
+#include <optional>
+
+namespace cuvs::spatial::knn::detail::utils {
+
+/** Whether pointers are accessible on the device or on the host. */
+enum class pointer_residency {
+  /** Some of the pointers are on the device, some on the host. */
+  mixed,
+  /** All pointers accessible from both the device and the host. */
+  host_and_device,
+  /** All pointers are host accessible. */
+  host_only,
+  /** All poitners are device accessible. */
+  device_only
+};
+
+template <typename... Types>
+struct pointer_residency_count {};
+
+template <>
+struct pointer_residency_count<> {
+  static inline auto run() -> std::tuple<int, int> { return std::make_tuple(0, 0); }
+};
+
+template <typename Type, typename... Types>
+struct pointer_residency_count<Type, Types...> {
+  static inline auto run(const Type* ptr, const Types*... ptrs) -> std::tuple<int, int>
+  {
+    auto [on_device, on_host] = pointer_residency_count<Types...>::run(ptrs...);
+    cudaPointerAttributes attr;
+    RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, ptr));
+    switch (attr.type) {
+      case cudaMemoryTypeUnregistered: return std::make_tuple(on_device, on_host + 1);
+      case cudaMemoryTypeHost:
+        return std::make_tuple(on_device + int(attr.devicePointer == ptr), on_host + 1);
+      case cudaMemoryTypeDevice: return std::make_tuple(on_device + 1, on_host);
+      case cudaMemoryTypeManaged: return std::make_tuple(on_device + 1, on_host + 1);
+      default: return std::make_tuple(on_device, on_host);
+    }
+  }
+};
+
+/** Check if all argument pointers reside on the host or on the device. */
+template <typename... Types>
+auto check_pointer_residency(const Types*... ptrs) -> pointer_residency
+{
+  auto [on_device, on_host] = pointer_residency_count<Types...>::run(ptrs...);
+  int n_args                = sizeof...(Types);
+  if (on_device == n_args && on_host == n_args) { return pointer_residency::host_and_device; }
+  if (on_device == n_args) { return pointer_residency::device_only; }
+  if (on_host == n_args) { return pointer_residency::host_only; }
+  return pointer_residency::mixed;
+}
+
+/** RAII helper to access the host data from gpu when necessary. */
+template <typename PtrT, typename Action>
+struct with_mapped_memory_t {
+  with_mapped_memory_t(PtrT ptr, size_t size, Action action) : action_(action)
+  {
+    if (ptr == nullptr) { return; }
+    switch (utils::check_pointer_residency(ptr)) {
+      case utils::pointer_residency::device_only:
+      case utils::pointer_residency::host_and_device: {
+        dev_ptr_ = (void*)ptr;  // NOLINT
+      } break;
+      default: {
+        host_ptr_ = (void*)ptr;  // NOLINT
+        RAFT_CUDA_TRY(cudaHostRegister(host_ptr_, size, choose_flags(ptr)));
+        RAFT_CUDA_TRY(cudaHostGetDevicePointer(&dev_ptr_, host_ptr_, 0));
+      } break;
+    }
+  }
+
+  ~with_mapped_memory_t()
+  {
+    if (host_ptr_ != nullptr) { cudaHostUnregister(host_ptr_); }
+  }
+
+  auto operator()() { return action_((PtrT)dev_ptr_); }  // NOLINT
+
+ private:
+  Action action_;
+  void* host_ptr_ = nullptr;
+  void* dev_ptr_  = nullptr;
+
+  template <typename T>
+  static auto choose_flags(const T*) -> unsigned int
+  {
+    int dev_id, readonly_supported;
+    RAFT_CUDA_TRY(cudaGetDevice(&dev_id));
+    RAFT_CUDA_TRY(cudaDeviceGetAttribute(
+      &readonly_supported, cudaDevAttrHostRegisterReadOnlySupported, dev_id));
+    if (readonly_supported) {
+      return cudaHostRegisterMapped | cudaHostRegisterReadOnly;
+    } else {
+      return cudaHostRegisterMapped;
+    }
+  }
+
+  template <typename T>
+  static auto choose_flags(T*) -> unsigned int
+  {
+    return cudaHostRegisterMapped;
+  }
+};
+
+template <typename T>
+struct config {};
+
+template <>
+struct config<double> {
+  using value_t                    = double;
+  static constexpr double kDivisor = 1.0;
+};
+template <>
+struct config<float> {
+  using value_t                    = float;
+  static constexpr double kDivisor = 1.0;
+};
+template <>
+struct config<half> {
+  using value_t                    = half;
+  static constexpr double kDivisor = 1.0;
+};
+template <>
+struct config<uint8_t> {
+  using value_t                    = uint32_t;
+  static constexpr double kDivisor = 256.0;
+};
+template <>
+struct config<int8_t> {
+  using value_t                    = int32_t;
+  static constexpr double kDivisor = 128.0;
+};
+
+/**
+ * @brief Converting values between the types taking into account scaling factors
+ * for the integral types.
+ *
+ * @tparam T target type of the mapping.
+ */
+template <typename T>
+struct mapping {
+  /**
+   * @defgroup
+   * @brief Cast and possibly scale a value of the source type `S` to the target type `T`.
+   *
+   * @tparam S source type
+   * @param x source value
+   * @{
+   */
+  template <typename S>
+  HDI constexpr auto operator()(const S& x) const -> std::enable_if_t<std::is_same_v<S, T>, T>
+  {
+    return x;
+  };
+
+  template <typename S>
+  HDI constexpr auto operator()(const S& x) const -> std::enable_if_t<!std::is_same_v<S, T>, T>
+  {
+    constexpr double kMult = config<T>::kDivisor / config<S>::kDivisor;
+    if constexpr (std::is_floating_point_v<S>) { return static_cast<T>(x * static_cast<S>(kMult)); }
+    if constexpr (std::is_floating_point_v<T>) { return static_cast<T>(x) * static_cast<T>(kMult); }
+    return static_cast<T>(static_cast<float>(x) * static_cast<float>(kMult));
+  };
+  /** @} */
+};
+
+/**
+ * @brief Sets the first num bytes of the block of memory pointed by ptr to the specified value.
+ *
+ * @param[out] ptr host or device pointer
+ * @param[in] value
+ * @param[in] n_bytes
+ */
+template <typename T, typename IdxT>
+inline void memzero(T* ptr, IdxT n_elems, rmm::cuda_stream_view stream)
+{
+  switch (check_pointer_residency(ptr)) {
+    case pointer_residency::host_and_device:
+    case pointer_residency::device_only: {
+      RAFT_CUDA_TRY(cudaMemsetAsync(ptr, 0, n_elems * sizeof(T), stream));
+    } break;
+    case pointer_residency::host_only: {
+      stream.synchronize();
+      ::memset(ptr, 0, n_elems * sizeof(T));
+    } break;
+    default: RAFT_FAIL("memset: unreachable code");
+  }
+}
+
+template <typename T, typename IdxT>
+RAFT_KERNEL outer_add_kernel(const T* a, IdxT len_a, const T* b, IdxT len_b, T* c)
+{
+  IdxT gid = threadIdx.x + blockDim.x * static_cast<IdxT>(blockIdx.x);
+  IdxT i   = gid / len_b;
+  IdxT j   = gid % len_b;
+  if (i >= len_a) return;
+  c[gid] = (a == nullptr ? T(0) : a[i]) + (b == nullptr ? T(0) : b[j]);
+}
+
+template <typename T, typename IdxT>
+RAFT_KERNEL block_copy_kernel(const IdxT* in_offsets,
+                              const IdxT* out_offsets,
+                              IdxT n_blocks,
+                              const T* in_data,
+                              T* out_data,
+                              IdxT n_mult)
+{
+  IdxT i = static_cast<IdxT>(blockDim.x) * static_cast<IdxT>(blockIdx.x) + threadIdx.x;
+  // find the source offset using the binary search.
+  uint32_t l     = 0;
+  uint32_t r     = n_blocks;
+  IdxT in_offset = 0;
+  if (in_offsets[r] * n_mult <= i) return;
+  while (l + 1 < r) {
+    uint32_t c = (l + r) >> 1;
+    IdxT o     = in_offsets[c] * n_mult;
+    if (o <= i) {
+      l         = c;
+      in_offset = o;
+    } else {
+      r = c;
+    }
+  }
+  // raft::copy the data
+  out_data[out_offsets[l] * n_mult - in_offset + i] = in_data[i];
+}
+
+/**
+ * raft::copy chunks of data from one array to another at given offsets.
+ *
+ * @tparam T element type
+ * @tparam IdxT index type
+ *
+ * @param[in] in_offsets
+ * @param[in] out_offsets
+ * @param n_blocks size of the offset arrays minus one.
+ * @param[in] in_data
+ * @param[out] out_data
+ * @param n_mult constant multiplier for offset values (such as e.g. `dim`)
+ * @param stream
+ */
+template <typename T, typename IdxT>
+void block_copy(const IdxT* in_offsets,
+                const IdxT* out_offsets,
+                IdxT n_blocks,
+                const T* in_data,
+                T* out_data,
+                IdxT n_mult,
+                rmm::cuda_stream_view stream)
+{
+  IdxT in_size;
+  update_host(&in_size, in_offsets + n_blocks, 1, stream);
+  stream.synchronize();
+  dim3 threads(128, 1, 1);
+  dim3 blocks(raft::ceildiv<IdxT>(in_size * n_mult, threads.x), 1, 1);
+  block_copy_kernel<<<blocks, threads, 0, stream>>>(
+    in_offsets, out_offsets, n_blocks, in_data, out_data, n_mult);
+}
+
+/**
+ * @brief Fill matrix `c` with all combinations of sums of vectors `a` and `b`.
+ *
+ * NB: device-only function
+ *
+ * @tparam T    element type
+ * @tparam IdxT index type
+ *
+ * @param[in] a device pointer to a vector [len_a]
+ * @param len_a number of elements in `a`
+ * @param[in] b device pointer to a vector [len_b]
+ * @param len_b number of elements in `b`
+ * @param[out] c row-major matrix [len_a, len_b]
+ * @param stream
+ */
+template <typename T, typename IdxT>
+void outer_add(const T* a, IdxT len_a, const T* b, IdxT len_b, T* c, rmm::cuda_stream_view stream)
+{
+  dim3 threads(128, 1, 1);
+  dim3 blocks(raft::ceildiv<IdxT>(len_a * len_b, threads.x), 1, 1);
+  outer_add_kernel<<<blocks, threads, 0, stream>>>(a, len_a, b, len_b, c);
+}
+
+template <typename T, typename S, typename IdxT, typename LabelT>
+RAFT_KERNEL copy_selected_kernel(
+  IdxT n_rows, IdxT n_cols, const S* src, const LabelT* row_ids, IdxT ld_src, T* dst, IdxT ld_dst)
+{
+  IdxT gid   = threadIdx.x + blockDim.x * static_cast<IdxT>(blockIdx.x);
+  IdxT j     = gid % n_cols;
+  IdxT i_dst = gid / n_cols;
+  if (i_dst >= n_rows) return;
+  auto i_src              = static_cast<IdxT>(row_ids[i_dst]);
+  dst[ld_dst * i_dst + j] = mapping<T>{}(src[ld_src * i_src + j]);
+}
+
+/**
+ * @brief raft::copy selected rows of a matrix while mapping the data from the source to the target
+ * type.
+ *
+ * @tparam T      target type
+ * @tparam S      source type
+ * @tparam IdxT   index type
+ * @tparam LabelT label type
+ *
+ * @param n_rows
+ * @param n_cols
+ * @param[in] src input matrix [..., ld_src]
+ * @param[in] row_ids selection of rows to be copied [n_rows]
+ * @param ld_src number of cols in the input (ld_src >= n_cols)
+ * @param[out] dst output matrix [n_rows, ld_dst]
+ * @param ld_dst number of cols in the output (ld_dst >= n_cols)
+ * @param stream
+ */
+template <typename T, typename S, typename IdxT, typename LabelT>
+void copy_selected(IdxT n_rows,
+                   IdxT n_cols,
+                   const S* src,
+                   const LabelT* row_ids,
+                   IdxT ld_src,
+                   T* dst,
+                   IdxT ld_dst,
+                   rmm::cuda_stream_view stream)
+{
+  switch (check_pointer_residency(src, dst, row_ids)) {
+    case pointer_residency::host_and_device:
+    case pointer_residency::device_only: {
+      IdxT block_dim = 128;
+      IdxT grid_dim  = raft::ceildiv(n_rows * n_cols, block_dim);
+      copy_selected_kernel<T, S>
+        <<<grid_dim, block_dim, 0, stream>>>(n_rows, n_cols, src, row_ids, ld_src, dst, ld_dst);
+    } break;
+    case pointer_residency::host_only: {
+      stream.synchronize();
+      for (IdxT i_dst = 0; i_dst < n_rows; i_dst++) {
+        auto i_src = static_cast<IdxT>(row_ids[i_dst]);
+        for (IdxT j = 0; j < n_cols; j++) {
+          dst[ld_dst * i_dst + j] = mapping<T>{}(src[ld_src * i_src + j]);
+        }
+      }
+      stream.synchronize();
+    } break;
+    default: RAFT_FAIL("All pointers must reside on the same side, host or device.");
+  }
+}
+
+/**
+ * A batch input iterator over the data source.
+ * Given an input pointer, it decides whether the current device has the access to the data and
+ * gives it back to the user in batches. Three scenarios are possible:
+ *
+ *  1. if `source == nullptr`: then `batch.data() == nullptr`
+ *  2. if `source` is accessible from the device, `batch.data()` points directly at the source at
+ *     the proper offsets on each iteration.
+ *  3. if `source` is not accessible from the device, `batch.data()` points to an intermediate
+ *     buffer; the corresponding data is copied in the given `stream` on every iterator dereference
+ *     (i.e. batches can be skipped). Dereferencing the same batch two times in a row does not force
+ *     the raft::copy.
+ *
+ * In all three scenarios, the number of iterations, batch offsets and sizes are the same.
+ *
+ * The iterator can be reused. If the number of iterations is one, at most one raft::copy will ever
+ * be invoked (i.e. small datasets are not reloaded multiple times).
+ */
+template <typename T>
+struct batch_load_iterator {
+  using size_type = size_t;
+
+  /** A single batch of data residing in device memory. */
+  struct batch {
+    /** Logical width of a single row in a batch, in elements of type `T`. */
+    [[nodiscard]] auto row_width() const -> size_type { return row_width_; }
+    /** Logical offset of the batch, in rows (`row_width()`) */
+    [[nodiscard]] auto offset() const -> size_type { return pos_.value_or(0) * batch_size_; }
+    /** Logical size of the batch, in rows (`row_width()`) */
+    [[nodiscard]] auto size() const -> size_type { return batch_len_; }
+    /** Logical size of the batch, in rows (`row_width()`) */
+    [[nodiscard]] auto data() const -> const T* { return const_cast<const T*>(dev_ptr_); }
+    /** Whether this batch copies the data (i.e. the source is inaccessible from the device). */
+    [[nodiscard]] auto does_copy() const -> bool { return needs_copy_; }
+
+   private:
+    batch(const T* source,
+          size_type n_rows,
+          size_type row_width,
+          size_type batch_size,
+          rmm::cuda_stream_view stream,
+          rmm::device_async_resource_ref mr)
+      : stream_(stream),
+        buf_(0, stream, mr),
+        source_(source),
+        dev_ptr_(nullptr),
+        n_rows_(n_rows),
+        row_width_(row_width),
+        batch_size_(std::min(batch_size, n_rows)),
+        pos_(std::nullopt),
+        n_iters_(raft::div_rounding_up_safe(n_rows, batch_size)),
+        needs_copy_(false)
+    {
+      if (source_ == nullptr) { return; }
+      cudaPointerAttributes attr;
+      RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, source_));
+      dev_ptr_ = reinterpret_cast<T*>(attr.devicePointer);
+      if (dev_ptr_ == nullptr) {
+        buf_.resize(row_width_ * batch_size_, stream);
+        dev_ptr_    = buf_.data();
+        needs_copy_ = true;
+      }
+    }
+    rmm::cuda_stream_view stream_;
+    rmm::device_uvector<T> buf_;
+    const T* source_;
+    size_type n_rows_;
+    size_type row_width_;
+    size_type batch_size_;
+    size_type n_iters_;
+    bool needs_copy_;
+
+    std::optional<size_type> pos_;
+    size_type batch_len_;
+    T* dev_ptr_;
+
+    friend class batch_load_iterator<T>;
+
+    /**
+     * Changes the state of the batch to point at the `pos` index.
+     * If necessary, copies the data from the source in the registered stream.
+     */
+    void load(const size_type& pos)
+    {
+      // No-op if the data is already loaded, or it's the end of the input.
+      if (pos == pos_ || pos >= n_iters_) { return; }
+      pos_.emplace(pos);
+      batch_len_ = std::min(batch_size_, n_rows_ - std::min(offset(), n_rows_));
+      if (source_ == nullptr) { return; }
+      if (needs_copy_) {
+        if (size() > 0) {
+          RAFT_LOG_TRACE("batch_load_iterator::copy(offset = %zu, size = %zu, row_width = %zu)",
+                         size_t(offset()),
+                         size_t(size()),
+                         size_t(row_width()));
+          raft::copy(dev_ptr_, source_ + offset() * row_width(), size() * row_width(), stream_);
+        }
+      } else {
+        dev_ptr_ = const_cast<T*>(source_) + offset() * row_width();
+      }
+    }
+  };
+
+  using value_type = batch;
+  using reference  = const value_type&;
+  using pointer    = const value_type*;
+
+  /**
+   * Create a batch iterator over the data `source`.
+   *
+   * For convenience, the data `source` is read in logical units of size `row_width`; batch sizes
+   * and offsets are calculated in logical rows. Hence, can interpret the data as a contiguous
+   * row-major matrix of size [n_rows, row_width], and the batches are the sub-matrices of size
+   * [x<=batch_size, n_rows].
+   *
+   * @param source the input data -- host, device, or nullptr.
+   * @param n_rows the size of the input in logical rows.
+   * @param row_width the size of the logical row in the elements of type `T`.
+   * @param batch_size the desired size of the batch.
+   * @param stream the ordering for the host->device copies, if applicable.
+   * @param mr a custom memory resource for the intermediate buffer, if applicable.
+   */
+  batch_load_iterator(const T* source,
+                      size_type n_rows,
+                      size_type row_width,
+                      size_type batch_size,
+                      rmm::cuda_stream_view stream,
+                      rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())
+    : cur_batch_(new batch(source, n_rows, row_width, batch_size, stream, mr)), cur_pos_(0)
+  {
+  }
+  /**
+   * Whether this iterator copies the data on every iteration
+   * (i.e. the source is inaccessible from the device).
+   */
+  [[nodiscard]] auto does_copy() const -> bool { return cur_batch_->does_copy(); }
+  /** Reset the iterator position to `begin()` */
+  void reset() { cur_pos_ = 0; }
+  /** Reset the iterator position to `end()` */
+  void reset_to_end() { cur_pos_ = cur_batch_->n_iters_; }
+  [[nodiscard]] auto begin() const -> const batch_load_iterator<T>
+  {
+    batch_load_iterator<T> x(*this);
+    x.reset();
+    return x;
+  }
+  [[nodiscard]] auto end() const -> const batch_load_iterator<T>
+  {
+    batch_load_iterator<T> x(*this);
+    x.reset_to_end();
+    return x;
+  }
+  [[nodiscard]] auto operator*() const -> reference
+  {
+    cur_batch_->load(cur_pos_);
+    return *cur_batch_;
+  }
+  [[nodiscard]] auto operator->() const -> pointer
+  {
+    cur_batch_->load(cur_pos_);
+    return cur_batch_.get();
+  }
+  friend auto operator==(const batch_load_iterator<T>& x, const batch_load_iterator<T>& y) -> bool
+  {
+    return x.cur_batch_ == y.cur_batch_ && x.cur_pos_ == y.cur_pos_;
+  };
+  friend auto operator!=(const batch_load_iterator<T>& x, const batch_load_iterator<T>& y) -> bool
+  {
+    return x.cur_batch_ != y.cur_batch_ || x.cur_pos_ != y.cur_pos_;
+  };
+  auto operator++() -> batch_load_iterator<T>&
+  {
+    ++cur_pos_;
+    return *this;
+  }
+  auto operator++(int) -> batch_load_iterator<T>
+  {
+    batch_load_iterator<T> x(*this);
+    ++cur_pos_;
+    return x;
+  }
+  auto operator--() -> batch_load_iterator<T>&
+  {
+    --cur_pos_;
+    return *this;
+  }
+  auto operator--(int) -> batch_load_iterator<T>
+  {
+    batch_load_iterator<T> x(*this);
+    --cur_pos_;
+    return x;
+  }
+
+ private:
+  std::shared_ptr<value_type> cur_batch_;
+  size_type cur_pos_;
+};
+
+}  // namespace cuvs::spatial::knn::detail::utils
diff --git a/cpp/src/neighbors/detail/cagra/bitonic.hpp b/cpp/src/neighbors/detail/cagra/bitonic.hpp
new file mode 100644
index 0000000000..26195bd9c9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/bitonic.hpp
@@ -0,0 +1,228 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+// TODO: This shouldn't be calling RAFT detail APIs
+#include <raft/core/detail/macros.hpp>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace bitonic {
+
+namespace detail {
+
+template <class K, class V>
+_RAFT_DEVICE inline void swap_if_needed(K& k0, V& v0, K& k1, V& v1, const bool asc)
+{
+  if ((k0 != k1) && ((k0 < k1) != asc)) {
+    const auto tmp_k = k0;
+    k0               = k1;
+    k1               = tmp_k;
+    const auto tmp_v = v0;
+    v0               = v1;
+    v1               = tmp_v;
+  }
+}
+
+template <class K, class V>
+_RAFT_DEVICE inline void swap_if_needed(K& k0, V& v0, const unsigned lane_offset, const bool asc)
+{
+  auto k1 = __shfl_xor_sync(~0u, k0, lane_offset);
+  auto v1 = __shfl_xor_sync(~0u, v0, lane_offset);
+  if ((k0 != k1) && ((k0 < k1) != asc)) {
+    k0 = k1;
+    v0 = v1;
+  }
+}
+
+template <class K, class V, unsigned N, unsigned warp_size = 32>
+struct warp_merge_core {
+  _RAFT_DEVICE inline void operator()(K k[N], V v[N], const std::uint32_t range, const bool asc)
+  {
+    const auto lane_id = threadIdx.x % warp_size;
+
+    if (range == 1) {
+      for (std::uint32_t b = 2; b <= N; b <<= 1) {
+        for (std::uint32_t c = b / 2; c >= 1; c >>= 1) {
+#pragma unroll
+          for (std::uint32_t i = 0; i < N; i++) {
+            std::uint32_t j = i ^ c;
+            if (i >= j) continue;
+            const auto line_id = i + (N * lane_id);
+            const auto p       = static_cast<bool>(line_id & b) == static_cast<bool>(line_id & c);
+            swap_if_needed(k[i], v[i], k[j], v[j], p);
+          }
+        }
+      }
+      return;
+    }
+
+    const std::uint32_t b = range;
+    for (std::uint32_t c = b / 2; c >= 1; c >>= 1) {
+      const auto p = static_cast<bool>(lane_id & b) == static_cast<bool>(lane_id & c);
+#pragma unroll
+      for (std::uint32_t i = 0; i < N; i++) {
+        swap_if_needed(k[i], v[i], c, p);
+      }
+    }
+    const auto p = ((lane_id & b) == 0);
+    for (std::uint32_t c = N / 2; c >= 1; c >>= 1) {
+#pragma unroll
+      for (std::uint32_t i = 0; i < N; i++) {
+        std::uint32_t j = i ^ c;
+        if (i >= j) continue;
+        swap_if_needed(k[i], v[i], k[j], v[j], p);
+      }
+    }
+  }
+};
+
+template <class K, class V, unsigned warp_size>
+struct warp_merge_core<K, V, 6, warp_size> {
+  _RAFT_DEVICE inline void operator()(K k[6], V v[6], const std::uint32_t range, const bool asc)
+  {
+    constexpr unsigned N = 6;
+    const auto lane_id   = threadIdx.x % warp_size;
+
+    if (range == 1) {
+      for (std::uint32_t i = 0; i < N; i += 3) {
+        const auto p = (i == 0);
+        swap_if_needed(k[0 + i], v[0 + i], k[1 + i], v[1 + i], p);
+        swap_if_needed(k[1 + i], v[1 + i], k[2 + i], v[2 + i], p);
+        swap_if_needed(k[0 + i], v[0 + i], k[1 + i], v[1 + i], p);
+      }
+      const auto p = ((lane_id & 1) == 0);
+      for (std::uint32_t i = 0; i < 3; i++) {
+        std::uint32_t j = i + 3;
+        swap_if_needed(k[i], v[i], k[j], v[j], p);
+      }
+      for (std::uint32_t i = 0; i < N; i += 3) {
+        swap_if_needed(k[0 + i], v[0 + i], k[1 + i], v[1 + i], p);
+        swap_if_needed(k[1 + i], v[1 + i], k[2 + i], v[2 + i], p);
+        swap_if_needed(k[0 + i], v[0 + i], k[1 + i], v[1 + i], p);
+      }
+      return;
+    }
+
+    const std::uint32_t b = range;
+    for (std::uint32_t c = b / 2; c >= 1; c >>= 1) {
+      const auto p = static_cast<bool>(lane_id & b) == static_cast<bool>(lane_id & c);
+#pragma unroll
+      for (std::uint32_t i = 0; i < N; i++) {
+        swap_if_needed(k[i], v[i], c, p);
+      }
+    }
+    const auto p = ((lane_id & b) == 0);
+    for (std::uint32_t i = 0; i < 3; i++) {
+      std::uint32_t j = i + 3;
+      swap_if_needed(k[i], v[i], k[j], v[j], p);
+    }
+    for (std::uint32_t i = 0; i < N; i += N / 2) {
+      swap_if_needed(k[0 + i], v[0 + i], k[1 + i], v[1 + i], p);
+      swap_if_needed(k[1 + i], v[1 + i], k[2 + i], v[2 + i], p);
+      swap_if_needed(k[0 + i], v[0 + i], k[1 + i], v[1 + i], p);
+    }
+  }
+};
+
+template <class K, class V, unsigned warp_size>
+struct warp_merge_core<K, V, 3, warp_size> {
+  _RAFT_DEVICE inline void operator()(K k[3], V v[3], const std::uint32_t range, const bool asc)
+  {
+    constexpr unsigned N = 3;
+    const auto lane_id   = threadIdx.x % warp_size;
+
+    if (range == 1) {
+      const auto p = ((lane_id & 1) == 0);
+      swap_if_needed(k[0], v[0], k[1], v[1], p);
+      swap_if_needed(k[1], v[1], k[2], v[2], p);
+      swap_if_needed(k[0], v[0], k[1], v[1], p);
+      return;
+    }
+
+    const std::uint32_t b = range;
+    for (std::uint32_t c = b / 2; c >= 1; c >>= 1) {
+      const auto p = static_cast<bool>(lane_id & b) == static_cast<bool>(lane_id & c);
+#pragma unroll
+      for (std::uint32_t i = 0; i < N; i++) {
+        swap_if_needed(k[i], v[i], c, p);
+      }
+    }
+    const auto p = ((lane_id & b) == 0);
+    swap_if_needed(k[0], v[0], k[1], v[1], p);
+    swap_if_needed(k[1], v[1], k[2], v[2], p);
+    swap_if_needed(k[0], v[0], k[1], v[1], p);
+  }
+};
+
+template <class K, class V, unsigned warp_size>
+struct warp_merge_core<K, V, 2, warp_size> {
+  _RAFT_DEVICE inline void operator()(K k[2], V v[2], const std::uint32_t range, const bool asc)
+  {
+    constexpr unsigned N = 2;
+    const auto lane_id   = threadIdx.x % warp_size;
+
+    if (range == 1) {
+      const auto p = ((lane_id & 1) == 0);
+      swap_if_needed(k[0], v[0], k[1], v[1], p);
+      return;
+    }
+
+    const std::uint32_t b = range;
+    for (std::uint32_t c = b / 2; c >= 1; c >>= 1) {
+      const auto p = static_cast<bool>(lane_id & b) == static_cast<bool>(lane_id & c);
+#pragma unroll
+      for (std::uint32_t i = 0; i < N; i++) {
+        swap_if_needed(k[i], v[i], c, p);
+      }
+    }
+    const auto p = ((lane_id & b) == 0);
+    swap_if_needed(k[0], v[0], k[1], v[1], p);
+  }
+};
+
+template <class K, class V, unsigned warp_size>
+struct warp_merge_core<K, V, 1, warp_size> {
+  _RAFT_DEVICE inline void operator()(K k[1], V v[1], const std::uint32_t range, const bool asc)
+  {
+    const auto lane_id    = threadIdx.x % warp_size;
+    const std::uint32_t b = range;
+    for (std::uint32_t c = b / 2; c >= 1; c >>= 1) {
+      const auto p = static_cast<bool>(lane_id & b) == static_cast<bool>(lane_id & c);
+      swap_if_needed(k[0], v[0], c, p);
+    }
+  }
+};
+
+}  // namespace detail
+
+template <class K, class V, unsigned N, unsigned warp_size = 32>
+__device__ void warp_merge(K k[N], V v[N], unsigned range, const bool asc = true)
+{
+  detail::warp_merge_core<K, V, N, warp_size>{}(k, v, range, asc);
+}
+
+template <class K, class V, unsigned N, unsigned warp_size = 32>
+__device__ void warp_sort(K k[N], V v[N], const bool asc = true)
+{
+  for (std::uint32_t range = 1; range <= warp_size; range <<= 1) {
+    warp_merge<K, V, N, warp_size>(k, v, range, asc);
+  }
+}
+
+}  // namespace bitonic
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
new file mode 100644
index 0000000000..9d31fc6f07
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -0,0 +1,384 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../../vpq_dataset.cuh"
+#include "graph_core.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+// TODO: Fixme- this needs to be migrated
+#include "../../ivf_pq/ivf_pq_build.cuh"
+#include "../../nn_descent.cuh"
+#include "../../refine.cuh"
+
+// TODO: This shouldn't be calling spatial/knn APIs
+#include "../ann_utils.cuh"
+
+#include <rmm/resource_ref.hpp>
+
+#include <chrono>
+#include <cstdio>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail {
+
+static const std::string RAFT_NAME = "raft";
+
+template <typename DataT, typename IdxT, typename accessor>
+void build_knn_graph(
+  raft::resources const& res,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
+  std::optional<float> refine_rate                                    = std::nullopt,
+  std::optional<cuvs::neighbors::ivf_pq::index_params> build_params   = std::nullopt,
+  std::optional<cuvs::neighbors::ivf_pq::search_params> search_params = std::nullopt)
+{
+  RAFT_EXPECTS(!build_params || build_params->metric == cuvs::distance::DistanceType::L2Expanded ||
+                 build_params->metric == cuvs::distance::DistanceType::InnerProduct,
+               "Currently only L2Expanded or InnerProduct metric are supported");
+
+  uint32_t node_degree = knn_graph.extent(1);
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "cagra::build_graph(%zu, %zu, %u)",
+    size_t(dataset.extent(0)),
+    size_t(dataset.extent(1)),
+    node_degree);
+
+  if (!build_params) {
+    build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset);
+  }
+
+  // Make model name
+  const std::string model_name = [&]() {
+    char model_name[1024];
+    sprintf(model_name,
+            "%s-%lux%lu.cluster_%u.pq_%u.%ubit.itr_%u.metric_%u.pqcenter_%u",
+            "IVF-PQ",
+            static_cast<size_t>(dataset.extent(0)),
+            static_cast<size_t>(dataset.extent(1)),
+            build_params->n_lists,
+            build_params->pq_dim,
+            build_params->pq_bits,
+            build_params->kmeans_n_iters,
+            build_params->metric,
+            static_cast<uint32_t>(build_params->codebook_kind));
+    return std::string(model_name);
+  }();
+
+  RAFT_LOG_DEBUG("# Building IVF-PQ index %s", model_name.c_str());
+  auto index = cuvs::neighbors::ivf_pq::detail::build<DataT, int64_t>(
+    res, *build_params, dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+
+  //
+  // search top (k + 1) neighbors
+  //
+  if (!search_params) {
+    search_params            = cuvs::neighbors::ivf_pq::search_params{};
+    search_params->n_probes  = std::min<IdxT>(dataset.extent(1) * 2, build_params->n_lists);
+    search_params->lut_dtype = CUDA_R_8U;
+    search_params->internal_distance_dtype = CUDA_R_32F;
+  }
+  const auto top_k          = node_degree + 1;
+  uint32_t gpu_top_k        = node_degree * refine_rate.value_or(2.0f);
+  gpu_top_k                 = std::min<IdxT>(std::max(gpu_top_k, top_k), dataset.extent(0));
+  const auto num_queries    = dataset.extent(0);
+  const auto max_batch_size = 1024;
+  RAFT_LOG_DEBUG(
+    "IVF-PQ search node_degree: %d, top_k: %d,  gpu_top_k: %d,  max_batch_size:: %d, n_probes: %u",
+    node_degree,
+    top_k,
+    gpu_top_k,
+    max_batch_size,
+    search_params->n_probes);
+
+  auto distances = raft::make_device_matrix<float, int64_t>(res, max_batch_size, gpu_top_k);
+  auto neighbors = raft::make_device_matrix<int64_t, int64_t>(res, max_batch_size, gpu_top_k);
+  auto refined_distances = raft::make_device_matrix<float, int64_t>(res, max_batch_size, top_k);
+  auto refined_neighbors = raft::make_device_matrix<int64_t, int64_t>(res, max_batch_size, top_k);
+  auto neighbors_host    = raft::make_host_matrix<int64_t, int64_t>(max_batch_size, gpu_top_k);
+  auto queries_host = raft::make_host_matrix<DataT, int64_t>(max_batch_size, dataset.extent(1));
+  auto refined_neighbors_host = raft::make_host_matrix<int64_t, int64_t>(max_batch_size, top_k);
+  auto refined_distances_host = raft::make_host_matrix<float, int64_t>(max_batch_size, top_k);
+
+  // TODO(tfeher): batched search with multiple GPUs
+  std::size_t num_self_included = 0;
+  bool first                    = true;
+  const auto start_clock        = std::chrono::system_clock::now();
+
+  rmm::device_async_resource_ref device_memory = raft::resource::get_workspace_resource(res);
+
+  cuvs::spatial::knn::detail::utils::batch_load_iterator<DataT> vec_batches(
+    dataset.data_handle(),
+    dataset.extent(0),
+    dataset.extent(1),
+    (int64_t)max_batch_size,
+    raft::resource::get_cuda_stream(res),
+    device_memory);
+
+  size_t next_report_offset = 0;
+  size_t d_report_offset    = dataset.extent(0) / 100;  // Report progress in 1% steps.
+
+  for (const auto& batch : vec_batches) {
+    // Map int64_t to uint32_t because ivf_pq requires the latter.
+    // TODO(tfeher): remove this mapping once ivf_pq accepts mdspan with int64_t index type
+    auto queries_view = raft::make_device_matrix_view<const DataT, uint32_t>(
+      batch.data(), batch.size(), batch.row_width());
+    auto neighbors_view = raft::make_device_matrix_view<int64_t, uint32_t>(
+      neighbors.data_handle(), batch.size(), neighbors.extent(1));
+    auto distances_view = raft::make_device_matrix_view<float, uint32_t>(
+      distances.data_handle(), batch.size(), distances.extent(1));
+
+    cuvs::neighbors::ivf_pq::search(
+      res, *search_params, index, queries_view, neighbors_view, distances_view);
+    if constexpr (raft::is_host_mdspan_v<decltype(dataset)>) {
+      raft::copy(neighbors_host.data_handle(),
+                 neighbors.data_handle(),
+                 neighbors_view.size(),
+                 raft::resource::get_cuda_stream(res));
+      raft::copy(queries_host.data_handle(),
+                 batch.data(),
+                 queries_view.size(),
+                 raft::resource::get_cuda_stream(res));
+      auto queries_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+        queries_host.data_handle(), batch.size(), batch.row_width());
+      auto neighbors_host_view = raft::make_host_matrix_view<const int64_t, int64_t>(
+        neighbors_host.data_handle(), batch.size(), neighbors.extent(1));
+      auto refined_neighbors_host_view = raft::make_host_matrix_view<int64_t, int64_t>(
+        refined_neighbors_host.data_handle(), batch.size(), top_k);
+      auto refined_distances_host_view = raft::make_host_matrix_view<float, int64_t>(
+        refined_distances_host.data_handle(), batch.size(), top_k);
+      raft::resource::sync_stream(res);
+
+      cuvs::neighbors::detail::refine_host<int64_t, DataT, float, int64_t>(
+        dataset,
+        queries_host_view,
+        neighbors_host_view,
+        refined_neighbors_host_view,
+        refined_distances_host_view,
+        build_params->metric);
+    } else {
+      auto neighbor_candidates_view = raft::make_device_matrix_view<const int64_t, uint64_t>(
+        neighbors.data_handle(), batch.size(), gpu_top_k);
+      auto refined_neighbors_view = raft::make_device_matrix_view<int64_t, int64_t>(
+        refined_neighbors.data_handle(), batch.size(), top_k);
+      auto refined_distances_view = raft::make_device_matrix_view<float, int64_t>(
+        refined_distances.data_handle(), batch.size(), top_k);
+
+      auto dataset_view = raft::make_device_matrix_view<const DataT, int64_t>(
+        dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+      cuvs::neighbors::detail::refine_device<int64_t, DataT, float, int64_t>(
+        res,
+        dataset_view,
+        queries_view,
+        neighbor_candidates_view,
+        refined_neighbors_view,
+        refined_distances_view,
+        build_params->metric);
+      raft::copy(refined_neighbors_host.data_handle(),
+                 refined_neighbors_view.data_handle(),
+                 refined_neighbors_view.size(),
+                 raft::resource::get_cuda_stream(res));
+      raft::resource::sync_stream(res);
+    }
+    // omit itself & write out
+    // TODO(tfeher): do this in parallel with GPU processing of next batch
+    for (std::size_t i = 0; i < batch.size(); i++) {
+      size_t vec_idx = i + batch.offset();
+      for (std::size_t j = 0, num_added = 0; j < top_k && num_added < node_degree; j++) {
+        const auto v = refined_neighbors_host(i, j);
+        if (static_cast<size_t>(v) == vec_idx) {
+          num_self_included++;
+          continue;
+        }
+        knn_graph(vec_idx, num_added) = v;
+        num_added++;
+      }
+    }
+
+    size_t num_queries_done = batch.offset() + batch.size();
+    const auto end_clock    = std::chrono::system_clock::now();
+    if (batch.offset() > next_report_offset) {
+      next_report_offset += d_report_offset;
+      const auto time =
+        std::chrono::duration_cast<std::chrono::microseconds>(end_clock - start_clock).count() *
+        1e-6;
+      const auto throughput = num_queries_done / time;
+
+      RAFT_LOG_DEBUG(
+        "# Search %12lu / %12lu (%3.2f %%), %e queries/sec, %.2f minutes ETA, self included = "
+        "%3.2f %%    \r",
+        num_queries_done,
+        dataset.extent(0),
+        num_queries_done / static_cast<double>(dataset.extent(0)) * 100,
+        throughput,
+        (num_queries - num_queries_done) / throughput / 60,
+        static_cast<double>(num_self_included) / num_queries_done * 100.);
+    }
+    first = false;
+  }
+
+  if (!first) RAFT_LOG_DEBUG("# Finished building kNN graph");
+}
+
+template <typename DataT, typename IdxT, typename accessor>
+void build_knn_graph(
+  raft::resources const& res,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
+  cuvs::neighbors::nn_descent::index_params build_params)
+{
+  auto nn_descent_idx = cuvs::neighbors::nn_descent::index<IdxT>(res, knn_graph);
+  cuvs::neighbors::nn_descent::build<DataT, IdxT>(res, build_params, dataset, nn_descent_idx);
+
+  using internal_IdxT = typename std::make_unsigned<IdxT>::type;
+  using g_accessor    = typename decltype(nn_descent_idx.graph())::accessor_type;
+  using g_accessor_internal =
+    raft::host_device_accessor<std::experimental::default_accessor<internal_IdxT>,
+                               g_accessor::mem_type>;
+
+  auto knn_graph_internal =
+    raft::mdspan<internal_IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor_internal>(
+      reinterpret_cast<internal_IdxT*>(nn_descent_idx.graph().data_handle()),
+      nn_descent_idx.graph().extent(0),
+      nn_descent_idx.graph().extent(1));
+
+  cuvs::neighbors::cagra::detail::graph::sort_knn_graph(res, dataset, knn_graph_internal);
+}
+
+template <
+  typename IdxT = uint32_t,
+  typename g_accessor =
+    raft::host_device_accessor<std::experimental::default_accessor<IdxT>, raft::memory_type::host>>
+void optimize(
+  raft::resources const& res,
+  raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph)
+{
+  using internal_IdxT = typename std::make_unsigned<IdxT>::type;
+
+  auto new_graph_internal = raft::make_host_matrix_view<internal_IdxT, int64_t>(
+    reinterpret_cast<internal_IdxT*>(new_graph.data_handle()),
+    new_graph.extent(0),
+    new_graph.extent(1));
+
+  using g_accessor_internal =
+    raft::host_device_accessor<std::experimental::default_accessor<internal_IdxT>,
+                               raft::memory_type::host>;
+  auto knn_graph_internal =
+    raft::mdspan<internal_IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor_internal>(
+      reinterpret_cast<internal_IdxT*>(knn_graph.data_handle()),
+      knn_graph.extent(0),
+      knn_graph.extent(1));
+
+  cagra::detail::graph::optimize(res, knn_graph_internal, new_graph_internal);
+}
+
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+index<T, IdxT> build(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  std::optional<cuvs::neighbors::nn_descent::index_params> nn_descent_params = std::nullopt,
+  std::optional<float> refine_rate                                           = std::nullopt,
+  std::optional<cuvs::neighbors::ivf_pq::index_params> pq_build_params       = std::nullopt,
+  std::optional<cuvs::neighbors::ivf_pq::search_params> search_params        = std::nullopt,
+  bool construct_index_with_dataset                                          = true)
+{
+  size_t intermediate_degree = params.intermediate_graph_degree;
+  size_t graph_degree        = params.graph_degree;
+  if (intermediate_degree >= static_cast<size_t>(dataset.extent(0))) {
+    RAFT_LOG_WARN(
+      "Intermediate graph degree cannot be larger than dataset size, reducing it to %lu",
+      dataset.extent(0));
+    intermediate_degree = dataset.extent(0) - 1;
+  }
+  if (intermediate_degree < graph_degree) {
+    RAFT_LOG_WARN(
+      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
+      "graph_degree.",
+      graph_degree,
+      intermediate_degree);
+    graph_degree = intermediate_degree;
+  }
+
+  std::optional<raft::host_matrix<IdxT, int64_t>> knn_graph(
+    raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), intermediate_degree));
+
+  if (params.build_algo == graph_build_algo::IVF_PQ) {
+    build_knn_graph(res, dataset, knn_graph->view(), refine_rate, pq_build_params, search_params);
+  } else {
+    RAFT_EXPECTS(
+      params.metric == cuvs::distance::DistanceType::L2Expanded,
+      "L2Expanded is the only distance metrics supported for CAGRA build with nn_descent");
+    // Use nn-descent to build CAGRA knn graph
+    if (!nn_descent_params) {
+      nn_descent_params                            = cuvs::neighbors::nn_descent::index_params();
+      nn_descent_params->graph_degree              = intermediate_degree;
+      nn_descent_params->intermediate_graph_degree = 1.5 * intermediate_degree;
+      nn_descent_params->max_iterations            = params.nn_descent_niter;
+    }
+    build_knn_graph<T, IdxT>(res, dataset, knn_graph->view(), *nn_descent_params);
+  }
+
+  auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
+
+  RAFT_LOG_INFO("optimizing graph");
+  optimize<IdxT>(res, knn_graph->view(), cagra_graph.view());
+
+  // free intermediate graph before trying to create the index
+  knn_graph.reset();
+
+  RAFT_LOG_INFO("Graph optimized, creating index");
+  // Construct an index from dataset and optimized knn graph.
+  if (construct_index_with_dataset) {
+    if (params.compression.has_value()) {
+      RAFT_EXPECTS(params.metric == cuvs::distance::DistanceType::L2Expanded,
+                   "VPQ compression is only supported with L2Expanded distance mertric");
+      index<T, IdxT> idx(res, params.metric);
+      idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
+      idx.update_dataset(
+        res,
+        // TODO: hardcoding codebook math to `half`, we can do runtime dispatching later
+        cuvs::neighbors::vpq_build<decltype(dataset), half, int64_t>(
+          res, *params.compression, dataset));
+
+      return idx;
+    }
+    return index<T, IdxT>(res, params.metric, dataset, raft::make_const_mdspan(cagra_graph.view()));
+  } else {
+    // We just add the graph. User is expected to update dataset separately. This branch is used
+    // if user needs special control of memory allocations for the dataset.
+    index<T, IdxT> idx(res, params.metric);
+    idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
+    return idx;
+  }
+}
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
new file mode 100644
index 0000000000..543595f4a9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -0,0 +1,338 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "compute_distance_vpq.cuh"
+#include "factory.cuh"
+#include "search_plan.cuh"
+#include "search_single_cta_inst.cuh"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/nvtx.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+
+#include <cuvs/neighbors/cagra.hpp>
+
+// TODO: Fix these when ivf methods are moved over
+#include "../../ivf_common.cuh"
+#include "../../ivf_pq/ivf_pq_search.cuh"
+#include <cuvs/neighbors/sample_filter.hpp>
+
+// TODO: This shouldn't be calling spatial/knn apis
+#include "../ann_utils.cuh"
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cuvs::neighbors::cagra::detail {
+
+template <class CagraSampleFilterT>
+struct CagraSampleFilterWithQueryIdOffset {
+  const uint32_t offset;
+  CagraSampleFilterT filter;
+
+  CagraSampleFilterWithQueryIdOffset(const uint32_t offset, const CagraSampleFilterT filter)
+    : offset(offset), filter(filter)
+  {
+  }
+
+  _RAFT_DEVICE auto operator()(const uint32_t query_id, const uint32_t sample_id)
+  {
+    return filter(query_id + offset, sample_id);
+  }
+};
+
+template <class CagraSampleFilterT>
+struct CagraSampleFilterT_Selector {
+  using type = CagraSampleFilterWithQueryIdOffset<CagraSampleFilterT>;
+};
+template <>
+struct CagraSampleFilterT_Selector<cuvs::neighbors::filtering::none_cagra_sample_filter> {
+  using type = cuvs::neighbors::filtering::none_cagra_sample_filter;
+};
+
+// A helper function to set a query id offset
+template <class CagraSampleFilterT>
+inline typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type set_offset(
+  CagraSampleFilterT filter, const uint32_t offset)
+{
+  typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type new_filter(offset, filter);
+  return new_filter;
+}
+template <>
+inline
+  typename CagraSampleFilterT_Selector<cuvs::neighbors::filtering::none_cagra_sample_filter>::type
+  set_offset<cuvs::neighbors::filtering::none_cagra_sample_filter>(
+    cuvs::neighbors::filtering::none_cagra_sample_filter filter, const uint32_t)
+{
+  return filter;
+}
+
+template <typename DatasetDescriptorT, typename CagraSampleFilterT>
+void search_main_core(
+  raft::resources const& res,
+  search_params params,
+  DatasetDescriptorT dataset_desc,
+  raft::device_matrix_view<const typename DatasetDescriptorT::INDEX_T, int64_t, raft::row_major>
+    graph,
+  raft::device_matrix_view<const typename DatasetDescriptorT::DATA_T, int64_t, raft::row_major>
+    queries,
+  raft::device_matrix_view<typename DatasetDescriptorT::INDEX_T, int64_t, raft::row_major>
+    neighbors,
+  raft::device_matrix_view<typename DatasetDescriptorT::DISTANCE_T, int64_t, raft::row_major>
+    distances,
+  CagraSampleFilterT sample_filter    = CagraSampleFilterT(),
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
+{
+  RAFT_LOG_DEBUG("# dataset size = %lu, dim = %lu\n",
+                 static_cast<size_t>(dataset_desc.size),
+                 static_cast<size_t>(dataset_desc.dim));
+  RAFT_LOG_DEBUG("# query size = %lu, dim = %lu\n",
+                 static_cast<size_t>(queries.extent(0)),
+                 static_cast<size_t>(queries.extent(1)));
+  RAFT_EXPECTS(queries.extent(1) == dataset_desc.dim, "Queries and index dim must match");
+  const uint32_t topk = neighbors.extent(1);
+
+  cudaDeviceProp deviceProp = raft::resource::get_device_properties(res);
+  if (params.max_queries == 0) {
+    params.max_queries = std::min<size_t>(queries.extent(0), deviceProp.maxGridSize[1]);
+  }
+
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "cagra::search(max_queries = %u, k = %u, dim = %zu)",
+    params.max_queries,
+    topk,
+    dataset_desc.dim);
+
+  using CagraSampleFilterT_s = typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type;
+  std::unique_ptr<search_plan_impl<DatasetDescriptorT, CagraSampleFilterT_s>> plan =
+    factory<DatasetDescriptorT, CagraSampleFilterT_s>::create(
+      res, params, dataset_desc.dim, graph.extent(1), topk, metric);
+
+  plan->check(topk);
+
+  RAFT_LOG_DEBUG("Cagra search");
+  const uint32_t max_queries = plan->max_queries;
+  const uint32_t query_dim   = queries.extent(1);
+
+  for (unsigned qid = 0; qid < queries.extent(0); qid += max_queries) {
+    const uint32_t n_queries = std::min<std::size_t>(max_queries, queries.extent(0) - qid);
+    auto _topk_indices_ptr =
+      reinterpret_cast<typename DatasetDescriptorT::INDEX_T*>(neighbors.data_handle()) +
+      (topk * qid);
+    auto _topk_distances_ptr = distances.data_handle() + (topk * qid);
+    // todo(tfeher): one could keep distances optional and pass nullptr
+    const auto* _query_ptr = queries.data_handle() + (query_dim * qid);
+    const auto* _seed_ptr =
+      plan->num_seeds > 0
+        ? reinterpret_cast<const typename DatasetDescriptorT::INDEX_T*>(plan->dev_seed.data()) +
+            (plan->num_seeds * qid)
+        : nullptr;
+    uint32_t* _num_executed_iterations = nullptr;
+
+    (*plan)(res,
+            dataset_desc,
+            graph,
+            _topk_indices_ptr,
+            _topk_distances_ptr,
+            _query_ptr,
+            n_queries,
+            _seed_ptr,
+            _num_executed_iterations,
+            topk,
+            set_offset(sample_filter, qid));
+  }
+}
+
+template <class T,
+          class DatasetT,
+          class DatasetIdxT,
+          class InternalIdxT,
+          class DistanceT,
+          class CagraSampleFilterT>
+void launch_vpq_search_main_core(
+  raft::resources const& res,
+  const vpq_dataset<DatasetT, DatasetIdxT>* vpq_dset,
+  search_params params,
+  raft::device_matrix_view<const InternalIdxT, int64_t, raft::row_major> graph,
+  raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<InternalIdxT, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
+  CagraSampleFilterT sample_filter,
+  const cuvs::distance::DistanceType metric)
+{
+  RAFT_EXPECTS(vpq_dset->pq_bits() == 8, "Only pq_bits = 8 is supported for now");
+  RAFT_EXPECTS(vpq_dset->pq_len() == 2 || vpq_dset->pq_len() == 4,
+               "Only pq_len 2 or 4 is supported for now");
+  RAFT_EXPECTS(vpq_dset->dim() % vpq_dset->pq_dim() == 0,
+               "dim must be a multiple of pq_dim at the moment");
+
+  const float vq_scale = 1.0f;
+  const float pq_scale = 1.0f;
+
+  if (vpq_dset->pq_bits() == 8) {
+    if (vpq_dset->pq_len() == 2) {
+      using dataset_desc_t = cagra_q_dataset_descriptor_t<T,
+                                                          DatasetT,
+                                                          8 /*PQ bit*/,
+                                                          2 /* Subspace dimension*/,
+                                                          DistanceT,
+                                                          InternalIdxT>;
+      dataset_desc_t dataset_desc(vpq_dset->data.data_handle(),
+                                  vpq_dset->encoded_row_length(),
+                                  vpq_dset->pq_dim(),
+                                  vpq_dset->vq_code_book.data_handle(),
+                                  vq_scale,
+                                  vpq_dset->pq_code_book.data_handle(),
+                                  pq_scale,
+                                  size_t(vpq_dset->n_rows()),
+                                  vpq_dset->dim());
+      search_main_core(
+        res, params, dataset_desc, graph, queries, neighbors, distances, sample_filter, metric);
+    } else if (vpq_dset->pq_len() == 4) {
+      using dataset_desc_t = cagra_q_dataset_descriptor_t<T,
+                                                          DatasetT,
+                                                          8 /*PQ bit*/,
+                                                          4 /* Subspace dimension*/,
+                                                          DistanceT,
+                                                          InternalIdxT>;
+      dataset_desc_t dataset_desc(vpq_dset->data.data_handle(),
+                                  vpq_dset->encoded_row_length(),
+                                  vpq_dset->pq_dim(),
+                                  vpq_dset->vq_code_book.data_handle(),
+                                  vq_scale,
+                                  vpq_dset->pq_code_book.data_handle(),
+                                  pq_scale,
+                                  size_t(vpq_dset->n_rows()),
+                                  vpq_dset->dim());
+      search_main_core(
+        res, params, dataset_desc, graph, queries, neighbors, distances, sample_filter, metric);
+    } else {
+      RAFT_FAIL("Subspace dimension must be 2 or 4");
+    }
+  } else {
+    RAFT_FAIL("Only 8-bit PQ is supported now");
+  }
+}
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [build](#build) documentation for a usage example.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of database vector indices
+ * @tparam internal_IdxT during search we map IdxT to internal_IdxT, this way we do not need
+ * separate kernels for int/uint.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+template <typename T,
+          typename InternalIdxT,
+          typename CagraSampleFilterT,
+          typename IdxT      = uint32_t,
+          typename DistanceT = float>
+void search_main(raft::resources const& res,
+                 search_params params,
+                 const index<T, IdxT>& index,
+                 raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+                 raft::device_matrix_view<InternalIdxT, int64_t, raft::row_major> neighbors,
+                 raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
+                 CagraSampleFilterT sample_filter = CagraSampleFilterT())
+{
+  const auto& graph   = index.graph();
+  auto graph_internal = raft::make_device_matrix_view<const InternalIdxT, int64_t, raft::row_major>(
+    reinterpret_cast<const InternalIdxT*>(graph.data_handle()), graph.extent(0), graph.extent(1));
+
+  // n_rows has the same type as the dataset index (the array extents type)
+  using ds_idx_type = decltype(index.data().n_rows());
+  // Dispatch search parameters based on the dataset kind.
+  if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index.data());
+      strided_dset != nullptr) {
+    // Set TEAM_SIZE and DATASET_BLOCK_SIZE to zero tentatively since these parameters cannot be
+    // determined here. They are set just before kernel launch.
+    using dataset_desc_t = standard_dataset_descriptor_t<T, InternalIdxT, DistanceT>;
+    // Search using a plain (strided) row-major dataset
+    const dataset_desc_t dataset_desc(strided_dset->view().data_handle(),
+                                      strided_dset->n_rows(),
+                                      strided_dset->dim(),
+                                      strided_dset->stride());
+    search_main_core<dataset_desc_t, CagraSampleFilterT>(res,
+                                                         params,
+                                                         dataset_desc,
+                                                         graph_internal,
+                                                         queries,
+                                                         neighbors,
+                                                         distances,
+                                                         sample_filter,
+                                                         index.metric());
+  } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<float, ds_idx_type>*>(&index.data());
+             vpq_dset != nullptr) {
+    // Search using a compressed dataset
+    RAFT_FAIL("FP32 VPQ dataset support is coming soon");
+  } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<half, ds_idx_type>*>(&index.data());
+             vpq_dset != nullptr) {
+    launch_vpq_search_main_core<T, half, ds_idx_type, InternalIdxT, DistanceT, CagraSampleFilterT>(
+      res,
+      vpq_dset,
+      params,
+      graph_internal,
+      queries,
+      neighbors,
+      distances,
+      sample_filter,
+      index.metric());
+  } else if (auto* empty_dset = dynamic_cast<const empty_dataset<ds_idx_type>*>(&index.data());
+             empty_dset != nullptr) {
+    // Forgot to add a dataset.
+    RAFT_FAIL(
+      "Attempted to search without a dataset. Please call index.update_dataset(...) first.");
+  } else {
+    // This is a logic error.
+    RAFT_FAIL("Unrecognized dataset format");
+  }
+
+  static_assert(std::is_same_v<DistanceT, float>,
+                "only float distances are supported at the moment");
+  float* dist_out          = distances.data_handle();
+  const DistanceT* dist_in = distances.data_handle();
+  // We're converting the data from T to DistanceT during distance computation
+  // and divide the values by kDivisor. Here we restore the original scale.
+  constexpr float kScale = cuvs::spatial::knn::detail::utils::config<T>::kDivisor /
+                           cuvs::spatial::knn::detail::utils::config<DistanceT>::kDivisor;
+  cuvs::neighbors::ivf::detail::postprocess_distances(dist_out,
+                                                      dist_in,
+                                                      index.metric(),
+                                                      distances.extent(0),
+                                                      distances.extent(1),
+                                                      kScale,
+                                                      true,
+                                                      raft::resource::get_cuda_stream(res));
+}
+/** @} */  // end group cagra
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
new file mode 100644
index 0000000000..44c952caca
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -0,0 +1,279 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/nvtx.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/serialize.hpp>
+
+#include "../dataset_serialize.hpp"
+
+#include <raft/neighbors/detail/dataset_serialize.hpp>
+
+#include <cstddef>
+#include <cstdint>
+#include <fstream>
+#include <type_traits>
+
+static const std::string RAFT_NAME = "raft";
+namespace cuvs::neighbors::cagra::detail {
+
+constexpr int serialization_version = 4;
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res the raft resource handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index_ CAGRA index
+ *
+ */
+template <typename T, typename IdxT>
+void serialize(raft::resources const& res,
+               std::ostream& os,
+               const index<T, IdxT>& index_,
+               bool include_dataset)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("cagra::serialize");
+
+  RAFT_LOG_DEBUG(
+    "Saving CAGRA index, size %zu, dim %u", static_cast<size_t>(index_.size()), index_.dim());
+
+  std::string dtype_string = raft::detail::numpy_serializer::get_numpy_dtype<T>().to_string();
+  dtype_string.resize(4);
+  os << dtype_string;
+
+  printf("Inside serialize...\n");
+  raft::serialize_scalar(res, os, serialization_version);
+  raft::serialize_scalar(res, os, index_.size());
+  raft::serialize_scalar(res, os, index_.dim());
+  raft::serialize_scalar(res, os, index_.graph_degree());
+  raft::serialize_scalar(res, os, index_.metric());
+
+  printf("Serializing mdspan\n");
+  raft::serialize_mdspan(res, os, index_.graph());
+
+  include_dataset &= (index_.data().n_rows() > 0);
+
+  printf("Serializing include dataset\n");
+  raft::serialize_scalar(res, os, include_dataset);
+  if (include_dataset) {
+    RAFT_LOG_INFO("Saving CAGRA index with dataset");
+    neighbors::detail::serialize(res, os, index_.data());
+  } else {
+    RAFT_LOG_DEBUG("Saving CAGRA index WITHOUT dataset");
+  }
+}
+
+template <typename T, typename IdxT>
+void serialize(raft::resources const& res,
+               const std::string& filename,
+               const index<T, IdxT>& index_,
+               bool include_dataset)
+{
+  std::ofstream of(filename, std::ios::out | std::ios::binary);
+  if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  detail::serialize(res, of, index_, include_dataset);
+
+  of.close();
+  if (!of) { RAFT_FAIL("Error writing output %s", filename.c_str()); }
+}
+
+template <typename T, typename IdxT>
+void serialize_to_hnswlib(raft::resources const& res,
+                          std::ostream& os,
+                          const cuvs::neighbors::cagra::index<T, IdxT>& index_)
+{
+  // static_assert(std::is_same_v<IdxT, int> or std::is_same_v<IdxT, uint32_t>,
+  //               "An hnswlib index can only be trained with int32 or uint32 IdxT");
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("cagra::serialize");
+  RAFT_LOG_DEBUG("Saving CAGRA index to hnswlib format, size %zu, dim %u",
+                 static_cast<size_t>(index_.size()),
+                 index_.dim());
+
+  // offset_level_0
+  std::size_t offset_level_0 = 0;
+  os.write(reinterpret_cast<char*>(&offset_level_0), sizeof(std::size_t));
+  // max_element
+  std::size_t max_element = index_.size();
+  os.write(reinterpret_cast<char*>(&max_element), sizeof(std::size_t));
+  // curr_element_count
+  std::size_t curr_element_count = index_.size();
+  os.write(reinterpret_cast<char*>(&curr_element_count), sizeof(std::size_t));
+  // Example:M: 16, dim = 128, data_t = float, index_t = uint32_t, list_size_type = uint32_t,
+  // labeltype: size_t size_data_per_element_ = M * 2 * sizeof(index_t) + sizeof(list_size_type) +
+  // dim * sizeof(data_t) + sizeof(labeltype)
+  auto size_data_per_element = static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4 +
+                                                        index_.dim() * sizeof(T) + 8);
+  os.write(reinterpret_cast<char*>(&size_data_per_element), sizeof(std::size_t));
+  // label_offset
+  std::size_t label_offset = size_data_per_element - 8;
+  os.write(reinterpret_cast<char*>(&label_offset), sizeof(std::size_t));
+  // offset_data
+  auto offset_data = static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4);
+  os.write(reinterpret_cast<char*>(&offset_data), sizeof(std::size_t));
+  // max_level
+  int max_level = 1;
+  os.write(reinterpret_cast<char*>(&max_level), sizeof(int));
+  // entrypoint_node
+  auto entrypoint_node = static_cast<int>(index_.size() / 2);
+  os.write(reinterpret_cast<char*>(&entrypoint_node), sizeof(int));
+  // max_M
+  auto max_M = static_cast<std::size_t>(index_.graph_degree() / 2);
+  os.write(reinterpret_cast<char*>(&max_M), sizeof(std::size_t));
+  // max_M0
+  std::size_t max_M0 = index_.graph_degree();
+  os.write(reinterpret_cast<char*>(&max_M0), sizeof(std::size_t));
+  // M
+  auto M = static_cast<std::size_t>(index_.graph_degree() / 2);
+  os.write(reinterpret_cast<char*>(&M), sizeof(std::size_t));
+  // mult, can be anything
+  double mult = 0.42424242;
+  os.write(reinterpret_cast<char*>(&mult), sizeof(double));
+  // efConstruction, can be anything
+  std::size_t efConstruction = 500;
+  os.write(reinterpret_cast<char*>(&efConstruction), sizeof(std::size_t));
+
+  auto dataset = index_.dataset();
+  // Remove padding before saving the dataset
+  auto host_dataset = raft::make_host_matrix<T, int64_t>(dataset.extent(0), dataset.extent(1));
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_dataset.data_handle(),
+                                  sizeof(T) * host_dataset.extent(1),
+                                  dataset.data_handle(),
+                                  sizeof(T) * dataset.stride(0),
+                                  sizeof(T) * host_dataset.extent(1),
+                                  dataset.extent(0),
+                                  cudaMemcpyDefault,
+                                  raft::resource::get_cuda_stream(res)));
+  raft::resource::sync_stream(res);
+
+  auto graph = index_.graph();
+  auto host_graph =
+    raft::make_host_matrix<IdxT, int64_t, raft::row_major>(graph.extent(0), graph.extent(1));
+  raft::copy(host_graph.data_handle(),
+             graph.data_handle(),
+             graph.size(),
+             raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+
+  // Write one dataset and graph row at a time
+  for (std::size_t i = 0; i < index_.size(); i++) {
+    auto graph_degree = static_cast<int>(index_.graph_degree());
+    os.write(reinterpret_cast<char*>(&graph_degree), sizeof(int));
+
+    for (std::size_t j = 0; j < index_.graph_degree(); ++j) {
+      auto graph_elem = host_graph(i, j);
+      os.write(reinterpret_cast<char*>(&graph_elem), sizeof(IdxT));
+    }
+
+    auto data_row = host_dataset.data_handle() + (index_.dim() * i);
+    // if constexpr (std::is_same_v<T, float>) {
+    for (std::size_t j = 0; j < index_.dim(); ++j) {
+      auto data_elem = host_dataset(i, j);
+      os.write(reinterpret_cast<char*>(&data_elem), sizeof(T));
+    }
+    // } else if constexpr (std::is_same_v<T, std::int8_t> or std::is_same_v<T, std::uint8_t>) {
+    //   for (std::size_t j = 0; j < index_.dim(); ++j) {
+    //     auto data_elem = static_cast<int>(host_dataset(i, j));
+    //     os.write(reinterpret_cast<char*>(&data_elem), sizeof(int));
+    //   }
+    // }
+
+    os.write(reinterpret_cast<char*>(&i), sizeof(std::size_t));
+  }
+
+  for (std::size_t i = 0; i < index_.size(); i++) {
+    // zeroes
+    auto zero = 0;
+    os.write(reinterpret_cast<char*>(&zero), sizeof(int));
+  }
+}
+
+template <typename T, typename IdxT>
+void serialize_to_hnswlib(raft::resources const& res,
+                          const std::string& filename,
+                          const cuvs::neighbors::cagra::index<T, IdxT>& index_)
+{
+  std::ofstream of(filename, std::ios::out | std::ios::binary);
+  if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  detail::serialize_to_hnswlib<T, IdxT>(res, of, index_);
+
+  of.close();
+  if (!of) { RAFT_FAIL("Error writing output %s", filename.c_str()); }
+}
+
+/** Load an index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res the raft resource handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[in] index_ CAGRA index
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize(raft::resources const& res, std::istream& is) -> index<T, IdxT>
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("cagra::deserialize");
+
+  char dtype_string[4];
+  is.read(dtype_string, 4);
+
+  auto ver = raft::deserialize_scalar<int>(res, is);
+  if (ver != serialization_version) {
+    RAFT_FAIL("serialization version mismatch, expected %d, got %d ", serialization_version, ver);
+  }
+  auto n_rows       = raft::deserialize_scalar<IdxT>(res, is);
+  auto dim          = raft::deserialize_scalar<std::uint32_t>(res, is);
+  auto graph_degree = raft::deserialize_scalar<std::uint32_t>(res, is);
+  auto metric       = raft::deserialize_scalar<cuvs::distance::DistanceType>(res, is);
+
+  auto graph = raft::make_host_matrix<IdxT, int64_t>(n_rows, graph_degree);
+  deserialize_mdspan(res, is, graph.view());
+
+  index<T, IdxT> idx(res, metric);
+  idx.update_graph(res, raft::make_const_mdspan(graph.view()));
+  bool has_dataset = raft::deserialize_scalar<bool>(res, is);
+  if (has_dataset) {
+    idx.update_dataset(res, cuvs::neighbors::detail::deserialize_dataset<int64_t>(res, is));
+  }
+  return idx;
+}
+
+template <typename T, typename IdxT>
+auto deserialize(raft::resources const& res, const std::string& filename) -> index<T, IdxT>
+{
+  std::ifstream is(filename, std::ios::in | std::ios::binary);
+
+  if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  auto index = detail::deserialize<T, IdxT>(res, is);
+
+  is.close();
+
+  return index;
+}
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.hpp b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
new file mode 100644
index 0000000000..155fc24360
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
@@ -0,0 +1,323 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "device_common.hpp"
+#include "hashmap.hpp"
+#include "utils.hpp"
+
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/operators.hpp>
+
+// TODO: This shouldn't be invoking spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/util/vectorized.cuh>
+
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace device {
+
+// using LOAD_256BIT_T = ulonglong4;
+using LOAD_128BIT_T = uint4;
+using LOAD_64BIT_T  = uint64_t;
+
+template <class LOAD_T, class DATA_T>
+_RAFT_DEVICE constexpr unsigned get_vlen()
+{
+  return utils::size_of<LOAD_T>() / utils::size_of<DATA_T>();
+}
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          class DATASET_DESCRIPTOR_T,
+          class DISTANCE_T,
+          class INDEX_T>
+_RAFT_DEVICE void compute_distance_to_random_nodes(
+  INDEX_T* const result_indices_ptr,       // [num_pickup]
+  DISTANCE_T* const result_distances_ptr,  // [num_pickup]
+  const typename DATASET_DESCRIPTOR_T::QUERY_T* const query_buffer,
+  const DATASET_DESCRIPTOR_T& dataset_desc,
+  const std::size_t num_pickup,
+  const unsigned num_distilation,
+  const uint64_t rand_xor_mask,
+  const INDEX_T* const seed_ptr,  // [num_seeds]
+  const uint32_t num_seeds,
+  INDEX_T* const visited_hash_ptr,
+  const uint32_t hash_bitlen,
+  const cuvs::distance::DistanceType metric,
+  const uint32_t block_id   = 0,
+  const uint32_t num_blocks = 1)
+{
+  uint32_t max_i = num_pickup;
+  if (max_i % (32 / TEAM_SIZE)) { max_i += (32 / TEAM_SIZE) - (max_i % (32 / TEAM_SIZE)); }
+
+  for (uint32_t i = threadIdx.x / TEAM_SIZE; i < max_i; i += blockDim.x / TEAM_SIZE) {
+    const bool valid_i = (i < num_pickup);
+
+    INDEX_T best_index_team_local;
+    DISTANCE_T best_norm2_team_local = utils::get_max_value<DISTANCE_T>();
+    for (uint32_t j = 0; j < num_distilation; j++) {
+      // Select a node randomly and compute the distance to it
+      INDEX_T seed_index;
+      if (valid_i) {
+        // uint32_t gid = i + (num_pickup * (j + (num_distilation * block_id)));
+        uint32_t gid = block_id + (num_blocks * (i + (num_pickup * j)));
+        if (seed_ptr && (gid < num_seeds)) {
+          seed_index = seed_ptr[gid];
+        } else {
+          seed_index = device::xorshift64(gid ^ rand_xor_mask) % dataset_desc.size;
+        }
+      }
+
+      DISTANCE_T norm2;
+      switch (metric) {
+        case cuvs::distance::DistanceType::L2Expanded:
+          norm2 =
+            dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                     TEAM_SIZE,
+                                                     cuvs::distance::DistanceType::L2Expanded>(
+              query_buffer, seed_index, valid_i);
+          break;
+        case cuvs::distance::DistanceType::InnerProduct:
+          norm2 =
+            dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                     TEAM_SIZE,
+                                                     cuvs::distance::DistanceType::InnerProduct>(
+              query_buffer, seed_index, valid_i);
+          break;
+        default: break;
+      }
+
+      if (valid_i && (norm2 < best_norm2_team_local)) {
+        best_norm2_team_local = norm2;
+        best_index_team_local = seed_index;
+      }
+    }
+
+    const unsigned lane_id = threadIdx.x % TEAM_SIZE;
+    if (valid_i && lane_id == 0) {
+      if (hashmap::insert(visited_hash_ptr, hash_bitlen, best_index_team_local)) {
+        result_distances_ptr[i] = best_norm2_team_local;
+        result_indices_ptr[i]   = best_index_team_local;
+      } else {
+        result_distances_ptr[i] = utils::get_max_value<DISTANCE_T>();
+        result_indices_ptr[i]   = utils::get_max_value<INDEX_T>();
+      }
+    }
+  }
+}
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          unsigned MAX_N_FRAGS,
+          class DATASET_DESCRIPTOR_T,
+          class DISTANCE_T,
+          class INDEX_T>
+_RAFT_DEVICE void compute_distance_to_child_nodes(
+  INDEX_T* const result_child_indices_ptr,
+  DISTANCE_T* const result_child_distances_ptr,
+  // query
+  const typename DATASET_DESCRIPTOR_T::QUERY_T* const query_buffer,
+  // [dataset_dim, dataset_size]
+  const DATASET_DESCRIPTOR_T& dataset_desc,
+  // [knn_k, dataset_size]
+  const INDEX_T* const knn_graph,
+  const std::uint32_t knn_k,
+  // hashmap
+  INDEX_T* const visited_hashmap_ptr,
+  const std::uint32_t hash_bitlen,
+  const INDEX_T* const parent_indices,
+  const INDEX_T* const internal_topk_list,
+  const std::uint32_t search_width,
+  const cuvs::distance::DistanceType metric)
+{
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+  const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
+
+  // Read child indices of parents from knn graph and check if the distance
+  // computaiton is necessary.
+  for (uint32_t i = threadIdx.x; i < knn_k * search_width; i += blockDim.x) {
+    const INDEX_T smem_parent_id = parent_indices[i / knn_k];
+    INDEX_T child_id             = invalid_index;
+    if (smem_parent_id != invalid_index) {
+      const auto parent_id = internal_topk_list[smem_parent_id] & ~index_msb_1_mask;
+      child_id             = knn_graph[(i % knn_k) + (static_cast<int64_t>(knn_k) * parent_id)];
+    }
+    if (child_id != invalid_index) {
+      if (hashmap::insert(visited_hashmap_ptr, hash_bitlen, child_id) == 0) {
+        child_id = invalid_index;
+      }
+    }
+    result_child_indices_ptr[i] = child_id;
+  }
+  __syncthreads();
+
+  // Compute the distance to child nodes
+  std::uint32_t max_i = knn_k * search_width;
+  if (max_i % (32 / TEAM_SIZE)) { max_i += (32 / TEAM_SIZE) - (max_i % (32 / TEAM_SIZE)); }
+  for (std::uint32_t tid = threadIdx.x; tid < max_i * TEAM_SIZE; tid += blockDim.x) {
+    const auto i       = tid / TEAM_SIZE;
+    const bool valid_i = (i < (knn_k * search_width));
+    INDEX_T child_id   = invalid_index;
+    if (valid_i) { child_id = result_child_indices_ptr[i]; }
+
+    DISTANCE_T norm2;
+    switch (metric) {
+      case cuvs::distance::DistanceType::L2Expanded:
+        norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                         TEAM_SIZE,
+                                                         cuvs::distance::DistanceType::L2Expanded>(
+          query_buffer, child_id, child_id != invalid_index);
+        break;
+      case cuvs::distance::DistanceType::InnerProduct:
+        norm2 =
+          dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                   TEAM_SIZE,
+                                                   cuvs::distance::DistanceType::InnerProduct>(
+            query_buffer, child_id, child_id != invalid_index);
+        break;
+      default: break;
+    }
+
+    // Store the distance
+    const unsigned lane_id = threadIdx.x % TEAM_SIZE;
+    if (valid_i && lane_id == 0) {
+      if (child_id != invalid_index) {
+        result_child_distances_ptr[i] = norm2;
+      } else {
+        result_child_distances_ptr[i] = utils::get_max_value<DISTANCE_T>();
+      }
+    }
+  }
+}
+
+}  // namespace device
+
+template <class QUERY_T_, class DISTANCE_T_, class INDEX_T_>
+struct dataset_descriptor_base_t {
+  using INDEX_T    = INDEX_T_;
+  using QUERY_T    = QUERY_T_;
+  using DISTANCE_T = DISTANCE_T_;
+
+  const INDEX_T size;
+  const std::uint32_t dim;
+
+  dataset_descriptor_base_t(const INDEX_T size, const std::uint32_t dim) : size(size), dim(dim) {}
+};
+
+template <class DATA_T_, class INDEX_T, class DISTANCE_T = float>
+struct standard_dataset_descriptor_t
+  : public dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T> {
+  using LOAD_T  = device::LOAD_128BIT_T;
+  using DATA_T  = DATA_T_;
+  using QUERY_T = typename dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>::QUERY_T;
+
+  const DATA_T* const ptr;
+  const std::size_t ld;
+  using dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>::size;
+  using dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>::dim;
+
+  standard_dataset_descriptor_t(const DATA_T* const ptr,
+                                const std::size_t size,
+                                const std::uint32_t dim,
+                                const std::size_t ld)
+    : dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>(size, dim), ptr(ptr), ld(ld)
+  {
+  }
+
+  static const std::uint32_t smem_buffer_size_in_byte = 0;
+  __device__ void set_smem_ptr(void* const){};
+
+  template <uint32_t DATASET_BLOCK_DIM>
+  __device__ void copy_query(const DATA_T* const dmem_query_ptr,
+                             QUERY_T* const smem_query_ptr,
+                             const std::uint32_t query_smem_buffer_length)
+  {
+    for (unsigned i = threadIdx.x; i < query_smem_buffer_length; i += blockDim.x) {
+      unsigned j = device::swizzling(i);
+      if (i < dim) {
+        smem_query_ptr[j] =
+          cuvs::spatial::knn::detail::utils::mapping<QUERY_T>{}(dmem_query_ptr[i]);
+      } else {
+        smem_query_ptr[j] = 0.0;
+      }
+    }
+  }
+
+  template <typename T, cuvs::distance::DistanceType METRIC>
+  std::enable_if_t<METRIC == cuvs::distance::DistanceType::L2Expanded, T> __device__
+  dist_op(T a, T b) const
+  {
+    T diff = a - b;
+    return diff * diff;
+  }
+
+  template <typename T, cuvs::distance::DistanceType METRIC>
+  std::enable_if_t<METRIC == cuvs::distance::DistanceType::InnerProduct, T> __device__
+  dist_op(T a, T b) const
+  {
+    return -a * b;
+  }
+
+  template <uint32_t DATASET_BLOCK_DIM, uint32_t TEAM_SIZE, cuvs::distance::DistanceType METRIC>
+  __device__ DISTANCE_T compute_similarity(const QUERY_T* const query_ptr,
+                                           const INDEX_T dataset_i,
+                                           const bool valid) const
+  {
+    const auto dataset_ptr  = ptr + dataset_i * ld;
+    const unsigned lane_id  = threadIdx.x % TEAM_SIZE;
+    constexpr unsigned vlen = device::get_vlen<LOAD_T, DATA_T>();
+    // #include <raft/util/cuda_dev_essentials.cuh
+    constexpr unsigned reg_nelem = raft::ceildiv<unsigned>(DATASET_BLOCK_DIM, TEAM_SIZE * vlen);
+    raft::TxN_t<DATA_T, vlen> dl_buff[reg_nelem];
+
+    DISTANCE_T norm2 = 0;
+    if (valid) {
+      for (uint32_t elem_offset = 0; elem_offset < dim; elem_offset += DATASET_BLOCK_DIM) {
+#pragma unroll
+        for (uint32_t e = 0; e < reg_nelem; e++) {
+          const uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset;
+          if (k >= dim) break;
+          dl_buff[e].load(dataset_ptr, k);
+        }
+#pragma unroll
+        for (uint32_t e = 0; e < reg_nelem; e++) {
+          const uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset;
+          if (k >= dim) break;
+#pragma unroll
+          for (uint32_t v = 0; v < vlen; v++) {
+            const uint32_t kv = k + v;
+            // Note this loop can go above the dataset_dim for padded arrays. This is not a problem
+            // because:
+            // - Above the last element (dataset_dim-1), the query array is filled with zeros.
+            // - The data buffer has to be also padded with zeros.
+            DISTANCE_T d = query_ptr[device::swizzling(kv)];
+            norm2 += dist_op<DISTANCE_T, METRIC>(
+              d, cuvs::spatial::knn::detail::utils::mapping<float>{}(dl_buff[e].val.data[v]));
+          }
+        }
+      }
+    }
+    for (uint32_t offset = TEAM_SIZE / 2; offset > 0; offset >>= 1) {
+      norm2 += __shfl_xor_sync(0xffffffff, norm2, offset);
+    }
+    return norm2;
+  }
+};
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
new file mode 100644
index 0000000000..9ed1d5edd1
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
@@ -0,0 +1,231 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "compute_distance.hpp"
+
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/util/integer_utils.hpp>
+
+namespace cuvs::neighbors::cagra::detail {
+template <class DATA_T_,
+          class CODE_BOOK_T_,
+          unsigned PQ_BITS,
+          unsigned PQ_LEN,
+          class DISTANCE_T,
+          class INDEX_T>
+struct cagra_q_dataset_descriptor_t : public dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T> {
+  using LOAD_T      = device::LOAD_128BIT_T;
+  using DATA_T      = DATA_T_;
+  using CODE_BOOK_T = CODE_BOOK_T_;
+  using QUERY_T     = typename dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>::QUERY_T;
+
+  static_assert(std::is_same_v<CODE_BOOK_T, half>, "Only CODE_BOOK_T = `half` is supported now");
+
+  const std::uint8_t* encoded_dataset_ptr;
+  const std::uint32_t encoded_dataset_dim;
+  const std::uint32_t n_subspace;
+  const CODE_BOOK_T* vq_code_book_ptr;
+  const float vq_scale;
+  const CODE_BOOK_T* pq_code_book_ptr;
+  const float pq_scale;
+  using dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>::size;
+  using dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>::dim;
+
+  // Set on device
+  CODE_BOOK_T* smem_pq_code_book_ptr;
+  static const std::uint32_t smem_buffer_size_in_byte =
+    (1 << PQ_BITS) * PQ_LEN * utils::size_of<CODE_BOOK_T>();
+
+  __device__ void set_smem_ptr(void* const smem_ptr)
+  {
+    smem_pq_code_book_ptr = reinterpret_cast<CODE_BOOK_T*>(smem_ptr);
+
+    // Copy PQ table
+    for (unsigned i = threadIdx.x * 2; i < (1 << PQ_BITS) * PQ_LEN; i += blockDim.x * 2) {
+      half2 buf2;
+      buf2.x = pq_code_book_ptr[i];
+      buf2.y = pq_code_book_ptr[i + 1];
+
+      // Change the order of PQ code book array to reduce the
+      // frequency of bank conflicts.
+      constexpr auto num_elements_per_bank  = 4 / utils::size_of<CODE_BOOK_T>();
+      constexpr auto num_banks_per_subspace = PQ_LEN / num_elements_per_bank;
+      const auto j                          = i / num_elements_per_bank;
+      const auto smem_index =
+        (j / num_banks_per_subspace) + (j % num_banks_per_subspace) * (1 << PQ_BITS);
+      reinterpret_cast<half2*>(smem_pq_code_book_ptr)[smem_index] = buf2;
+    }
+  }
+
+  cagra_q_dataset_descriptor_t(const std::uint8_t* encoded_dataset_ptr,
+                               const std::uint32_t encoded_dataset_dim,
+                               const std::uint32_t n_subspace,
+                               const CODE_BOOK_T* const vq_code_book_ptr,
+                               const float vq_scale,
+                               const CODE_BOOK_T* const pq_code_book_ptr,
+                               const float pq_scale,
+                               const std::size_t size,
+                               const std::uint32_t dim)
+    : dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>(size, dim),
+      encoded_dataset_ptr(encoded_dataset_ptr),
+      encoded_dataset_dim(encoded_dataset_dim),
+      n_subspace(n_subspace),
+      vq_code_book_ptr(vq_code_book_ptr),
+      vq_scale(vq_scale),
+      pq_code_book_ptr(pq_code_book_ptr),
+      pq_scale(pq_scale)
+  {
+  }
+
+  template <uint32_t DATASET_BLOCK_DIM>
+  __device__ void copy_query(const DATA_T* const dmem_query_ptr,
+                             QUERY_T* const smem_query_ptr,
+                             const std::uint32_t query_smem_buffer_length)
+  {
+    constexpr cuvs::spatial::knn::detail::utils::mapping<half> mapping{};
+    for (unsigned i = threadIdx.x * 2; i < dim; i += blockDim.x * 2) {
+      half2 buf2{0, 0};
+      if (i < dim) { buf2.x = mapping(dmem_query_ptr[i]); }
+      if (i + 1 < dim) { buf2.y = mapping(dmem_query_ptr[i + 1]); }
+      if ((PQ_BITS == 8) && (PQ_LEN % 2 == 0)) {
+        // Use swizzling in the condition to reduce bank conflicts in shared
+        // memory, which are likely to occur when pq_code_book_dim is large.
+        ((half2*)smem_query_ptr)[device::swizzling<std::uint32_t, DATASET_BLOCK_DIM / 2>(i / 2)] =
+          buf2;
+      } else {
+        (reinterpret_cast<half2*>(smem_query_ptr + i))[0] = buf2;
+      }
+    }
+  }
+
+  template <uint32_t DATASET_BLOCK_DIM, uint32_t TEAM_SIZE, cuvs::distance::DistanceType METRIC>
+  __device__ DISTANCE_T compute_similarity(const QUERY_T* const query_ptr,
+                                           const INDEX_T node_id,
+                                           const bool valid) const
+  {
+    float norm = 0;
+    if (valid) {
+      const unsigned lane_id = threadIdx.x % TEAM_SIZE;
+      const uint32_t vq_code = *(reinterpret_cast<const std::uint32_t*>(
+        encoded_dataset_ptr + (static_cast<std::uint64_t>(encoded_dataset_dim) * node_id)));
+      if (PQ_BITS == 8) {
+        for (uint32_t elem_offset = 0; elem_offset < dim; elem_offset += DATASET_BLOCK_DIM) {
+          constexpr unsigned vlen = 4;  // **** DO NOT CHANGE ****
+          constexpr unsigned nelem =
+            raft::div_rounding_up_unsafe<unsigned>(DATASET_BLOCK_DIM / PQ_LEN, TEAM_SIZE * vlen);
+          // Loading PQ codes
+          uint32_t pq_codes[nelem];
+#pragma unroll
+          for (std::uint32_t e = 0; e < nelem; e++) {
+            const std::uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset / PQ_LEN;
+            if (k >= n_subspace) break;
+            // Loading 4 x 8-bit PQ-codes using 32-bit load ops (from device memory)
+            pq_codes[e] = *(reinterpret_cast<const std::uint32_t*>(
+              encoded_dataset_ptr + (static_cast<std::uint64_t>(encoded_dataset_dim) * node_id) +
+              4 + k));
+          }
+          //
+          if constexpr (PQ_LEN % 2 == 0) {
+            // **** Use half2 for distance computation ****
+            half2 norm2{0, 0};
+#pragma unroll
+            for (std::uint32_t e = 0; e < nelem; e++) {
+              const std::uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset / PQ_LEN;
+              if (k >= n_subspace) break;
+              // Loading VQ code-book
+              raft::TxN_t<half2, vlen / 2> vq_vals[PQ_LEN];
+#pragma unroll
+              for (std::uint32_t m = 0; m < PQ_LEN; m += 1) {
+                const uint32_t d = (vlen * m) + (PQ_LEN * k);
+                if (d >= dim) break;
+                vq_vals[m].load(
+                  reinterpret_cast<const half2*>(vq_code_book_ptr + d + (dim * vq_code)), 0);
+              }
+              // Compute distance
+              std::uint32_t pq_code = pq_codes[e];
+#pragma unroll
+              for (std::uint32_t v = 0; v < vlen; v++) {
+                if (PQ_LEN * (v + k) >= dim) break;
+#pragma unroll
+                for (std::uint32_t m = 0; m < PQ_LEN; m += 2) {
+                  const std::uint32_t d1 = m + (PQ_LEN * v);
+                  const std::uint32_t d  = d1 + (PQ_LEN * k);
+                  // Loading query vector in smem
+                  half2 diff2 = (reinterpret_cast<const half2*>(
+                    query_ptr))[device::swizzling<std::uint32_t, DATASET_BLOCK_DIM / 2>(d / 2)];
+                  // Loading PQ code book in smem
+                  diff2 -= *(reinterpret_cast<half2*>(
+                    smem_pq_code_book_ptr + (1 << PQ_BITS) * 2 * (m / 2) + (2 * (pq_code & 0xff))));
+                  diff2 -= vq_vals[d1 / vlen].val.data[(d1 % vlen) / 2];
+                  norm2 += diff2 * diff2;
+                }
+                pq_code >>= 8;
+              }
+            }
+            norm += static_cast<float>(norm2.x + norm2.y);
+          } else {
+            // **** Use float for distance computation ****
+#pragma unroll
+            for (std::uint32_t e = 0; e < nelem; e++) {
+              const std::uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset / PQ_LEN;
+              if (k >= n_subspace) break;
+              // Loading VQ code-book
+              raft::TxN_t<CODE_BOOK_T, vlen> vq_vals[PQ_LEN];
+#pragma unroll
+              for (std::uint32_t m = 0; m < PQ_LEN; m++) {
+                const std::uint32_t d = (vlen * m) + (PQ_LEN * k);
+                if (d >= dim) break;
+                // Loading 4 x 8/16-bit VQ-values using 32/64-bit load ops (from L2$ or device
+                // memory)
+                vq_vals[m].load(
+                  reinterpret_cast<const half2*>(vq_code_book_ptr + d + (dim * vq_code)), 0);
+              }
+              // Compute distance
+              std::uint32_t pq_code = pq_codes[e];
+#pragma unroll
+              for (std::uint32_t v = 0; v < vlen; v++) {
+                if (PQ_LEN * (v + k) >= dim) break;
+                raft::TxN_t<CODE_BOOK_T, PQ_LEN> pq_vals;
+                pq_vals.load(
+                  reinterpret_cast<const half2*>(smem_pq_code_book_ptr + PQ_LEN * (pq_code & 0xff)),
+                  0);  // (from L1$ or smem)
+#pragma unroll
+                for (std::uint32_t m = 0; m < PQ_LEN; m++) {
+                  const std::uint32_t d1 = m + (PQ_LEN * v);
+                  const std::uint32_t d  = d1 + (PQ_LEN * k);
+                  // if (d >= dataset_dim) break;
+                  DISTANCE_T diff = query_ptr[d];  // (from smem)
+                  diff -= pq_scale * static_cast<float>(pq_vals.data[m]);
+                  diff -= vq_scale * static_cast<float>(vq_vals[d1 / vlen].val.data[d1 % vlen]);
+                  norm += diff * diff;
+                }
+                pq_code >>= 8;
+              }
+            }
+          }
+        }
+      }
+    }
+    for (uint32_t offset = TEAM_SIZE / 2; offset > 0; offset >>= 1) {
+      norm += __shfl_xor_sync(0xffffffff, norm, offset);
+    }
+    return norm;
+  }
+};
+
+}  // namespace cuvs::neighbors::cagra::detail
\ No newline at end of file
diff --git a/cpp/src/neighbors/detail/cagra/device_common.hpp b/cpp/src/neighbors/detail/cagra/device_common.hpp
new file mode 100644
index 0000000000..192d81aa85
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/device_common.hpp
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "utils.hpp"
+
+// TODO: This shouldn't be invoking anything in detail APIs outside of cuvs/neighbors
+#include <raft/core/detail/macros.hpp>
+
+#include <cuda_fp16.h>
+
+#include <cfloat>
+#include <cstdint>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace device {
+
+// warpSize for compile time calculation
+constexpr unsigned warp_size = 32;
+
+/** Xorshift rondem number generator.
+ *
+ * See https://en.wikipedia.org/wiki/Xorshift#xorshift for reference.
+ */
+_RAFT_HOST_DEVICE inline uint64_t xorshift64(uint64_t u)
+{
+  u ^= u >> 12;
+  u ^= u << 25;
+  u ^= u >> 27;
+  return u * 0x2545F4914F6CDD1DULL;
+}
+
+template <class T, unsigned X_MAX = 1024>
+_RAFT_DEVICE inline T swizzling(T x)
+{
+  // Address swizzling reduces bank conflicts in shared memory, but increases
+  // the amount of operation instead.
+  // return x;
+  if constexpr (X_MAX <= 1024) {
+    return (x) ^ ((x) >> 5);
+  } else {
+    return (x) ^ (((x) >> 5) & 0x1f);
+  }
+}
+
+}  // namespace device
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
new file mode 100644
index 0000000000..3822422a6e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "search_multi_cta.cuh"
+#include "search_multi_kernel.cuh"
+#include "search_plan.cuh"
+#include "search_single_cta.cuh"
+
+#include <raft/neighbors/sample_filter_types.hpp>
+
+namespace cuvs::neighbors::cagra::detail {
+
+template <typename DATASET_DESCRIPTOR_T,
+          typename CagraSampleFilterT = cuvs::neighbors::filtering::none_cagra_sample_filter>
+class factory {
+  using T         = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using IdxT      = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DistanceT = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+
+ public:
+  /**
+   * Create a search structure for dataset with dim features.
+   */
+  static std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>> create(
+    raft::resources const& res,
+    search_params const& params,
+    int64_t dim,
+    int64_t graph_degree,
+    uint32_t topk,
+    const cuvs::distance::DistanceType metric)
+  {
+    search_plan_impl_base plan(params, dim, graph_degree, topk, metric);
+    switch (plan.dataset_block_dim) {
+      case 128:
+        switch (plan.team_size) {
+          case 8: return dispatch_kernel<128, 8>(res, plan); break;
+          default: THROW("Incorrect team size %lu", plan.team_size);
+        }
+        break;
+      case 256:
+        switch (plan.team_size) {
+          case 16: return dispatch_kernel<256, 16>(res, plan); break;
+          default: THROW("Incorrect team size %lu", plan.team_size);
+        }
+        break;
+      case 512:
+        switch (plan.team_size) {
+          case 32: return dispatch_kernel<512, 32>(res, plan); break;
+          default: THROW("Incorrect team size %lu", plan.team_size);
+        }
+        break;
+      default: THROW("Incorrect dataset_block_dim (%lu)\n", plan.dataset_block_dim);
+    }
+    return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>();
+  }
+
+ private:
+  template <unsigned DATASET_BLOCK_DIM, unsigned TEAM_SIZE>
+  static std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>
+  dispatch_kernel(raft::resources const& res, search_plan_impl_base& plan)
+  {
+    if (plan.algo == search_algo::SINGLE_CTA) {
+      return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>(
+        new single_cta_search::
+          search<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, CagraSampleFilterT>(
+            res, plan, plan.dim, plan.graph_degree, plan.topk, plan.metric));
+    } else if (plan.algo == search_algo::MULTI_CTA) {
+      return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>(
+        new multi_cta_search::
+          search<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, CagraSampleFilterT>(
+            res, plan, plan.dim, plan.graph_degree, plan.topk, plan.metric));
+    } else {
+      return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>(
+        new multi_kernel_search::
+          search<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, CagraSampleFilterT>(
+            res, plan, plan.dim, plan.graph_degree, plan.topk, plan.metric));
+    }
+  }
+};
+};  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
new file mode 100644
index 0000000000..2e90eed64a
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -0,0 +1,582 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "utils.hpp"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+// TODO: This shouldn't be invoking anything from spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/util/bitonic_sort.cuh>
+#include <raft/util/cuda_rt_essentials.hpp>
+
+#include <cuda_fp16.h>
+
+#include <float.h>
+#include <omp.h>
+#include <sys/time.h>
+
+#include <cassert>
+#include <climits>
+#include <iostream>
+#include <memory>
+#include <random>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace graph {
+
+// unnamed namespace to avoid multiple definition error
+namespace {
+inline double cur_time(void)
+{
+  struct timeval tv;
+  gettimeofday(&tv, NULL);
+  return ((double)tv.tv_sec + (double)tv.tv_usec * 1e-6);
+}
+
+template <typename T>
+__device__ inline void swap(T& val1, T& val2)
+{
+  T val0 = val1;
+  val1   = val2;
+  val2   = val0;
+}
+
+template <typename K, typename V>
+__device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2, bool ascending)
+{
+  if (key1 == key2) { return false; }
+  if ((key1 > key2) == ascending) {
+    swap<K>(key1, key2);
+    swap<V>(val1, val2);
+    return true;
+  }
+  return false;
+}
+
+template <class DATA_T, class IdxT, int numElementsPerThread>
+RAFT_KERNEL kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size, dataset_dim]
+                      const IdxT dataset_size,
+                      const uint32_t dataset_dim,
+                      IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
+                      const uint32_t graph_size,
+                      const uint32_t graph_degree)
+{
+  const IdxT srcNode = (blockDim.x * blockIdx.x + threadIdx.x) / raft::WarpSize;
+  if (srcNode >= graph_size) { return; }
+
+  const uint32_t lane_id = threadIdx.x % raft::WarpSize;
+
+  float my_keys[numElementsPerThread];
+  IdxT my_vals[numElementsPerThread];
+
+  // Compute distance from a src node to its neighbors
+  for (int k = 0; k < graph_degree; k++) {
+    const IdxT dstNode = knn_graph[k + static_cast<uint64_t>(graph_degree) * srcNode];
+    float dist         = 0.0;
+    for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+      float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                     dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) -
+                   cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                     dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
+      dist += diff * diff;
+    }
+    dist += __shfl_xor_sync(0xffffffff, dist, 1);
+    dist += __shfl_xor_sync(0xffffffff, dist, 2);
+    dist += __shfl_xor_sync(0xffffffff, dist, 4);
+    dist += __shfl_xor_sync(0xffffffff, dist, 8);
+    dist += __shfl_xor_sync(0xffffffff, dist, 16);
+    if (lane_id == (k % raft::WarpSize)) {
+      my_keys[k / raft::WarpSize] = dist;
+      my_vals[k / raft::WarpSize] = dstNode;
+    }
+  }
+  for (int k = graph_degree; k < raft::WarpSize * numElementsPerThread; k++) {
+    if (lane_id == k % raft::WarpSize) {
+      my_keys[k / raft::WarpSize] = utils::get_max_value<float>();
+      my_vals[k / raft::WarpSize] = utils::get_max_value<IdxT>();
+    }
+  }
+
+  // Sort by RAFT bitonic sort
+  raft::util::bitonic<numElementsPerThread>(true).sort(my_keys, my_vals);
+
+  // Update knn_graph
+  for (int i = 0; i < numElementsPerThread; i++) {
+    const int k = i * raft::WarpSize + lane_id;
+    if (k < graph_degree) {
+      knn_graph[k + (static_cast<uint64_t>(graph_degree) * srcNode)] = my_vals[i];
+    }
+  }
+}
+
+template <int MAX_DEGREE, class IdxT>
+RAFT_KERNEL kern_prune(const IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
+                       const uint32_t graph_size,
+                       const uint32_t graph_degree,
+                       const uint32_t degree,
+                       const uint32_t batch_size,
+                       const uint32_t batch_id,
+                       uint8_t* const detour_count,          // [graph_chunk_size, graph_degree]
+                       uint32_t* const num_no_detour_edges,  // [graph_size]
+                       uint64_t* const stats)
+{
+  __shared__ uint32_t smem_num_detour[MAX_DEGREE];
+  uint64_t* const num_retain = stats;
+  uint64_t* const num_full   = stats + 1;
+
+  const uint64_t nid = blockIdx.x + (batch_size * batch_id);
+  if (nid >= graph_size) { return; }
+  for (uint32_t k = threadIdx.x; k < graph_degree; k += blockDim.x) {
+    smem_num_detour[k] = 0;
+  }
+  __syncthreads();
+
+  const uint64_t iA = nid;
+  if (iA >= graph_size) { return; }
+
+  // count number of detours (A->D->B)
+  for (uint32_t kAD = 0; kAD < graph_degree - 1; kAD++) {
+    const uint64_t iD = knn_graph[kAD + (graph_degree * iA)];
+    for (uint32_t kDB = threadIdx.x; kDB < graph_degree; kDB += blockDim.x) {
+      const uint64_t iB_candidate = knn_graph[kDB + ((uint64_t)graph_degree * iD)];
+      for (uint32_t kAB = kAD + 1; kAB < graph_degree; kAB++) {
+        // if ( kDB < kAB )
+        {
+          const uint64_t iB = knn_graph[kAB + (graph_degree * iA)];
+          if (iB == iB_candidate) {
+            atomicAdd(smem_num_detour + kAB, 1);
+            break;
+          }
+        }
+      }
+    }
+    __syncthreads();
+  }
+
+  uint32_t num_edges_no_detour = 0;
+  for (uint32_t k = threadIdx.x; k < graph_degree; k += blockDim.x) {
+    detour_count[k + (graph_degree * iA)] = min(smem_num_detour[k], (uint32_t)255);
+    if (smem_num_detour[k] == 0) { num_edges_no_detour++; }
+  }
+  num_edges_no_detour += __shfl_xor_sync(0xffffffff, num_edges_no_detour, 1);
+  num_edges_no_detour += __shfl_xor_sync(0xffffffff, num_edges_no_detour, 2);
+  num_edges_no_detour += __shfl_xor_sync(0xffffffff, num_edges_no_detour, 4);
+  num_edges_no_detour += __shfl_xor_sync(0xffffffff, num_edges_no_detour, 8);
+  num_edges_no_detour += __shfl_xor_sync(0xffffffff, num_edges_no_detour, 16);
+  num_edges_no_detour = min(num_edges_no_detour, degree);
+
+  if (threadIdx.x == 0) {
+    num_no_detour_edges[iA] = num_edges_no_detour;
+    atomicAdd((unsigned long long int*)num_retain, (unsigned long long int)num_edges_no_detour);
+    if (num_edges_no_detour >= degree) { atomicAdd((unsigned long long int*)num_full, 1); }
+  }
+}
+
+template <class IdxT>
+RAFT_KERNEL kern_make_rev_graph(const IdxT* const dest_nodes,     // [graph_size]
+                                IdxT* const rev_graph,            // [size, degree]
+                                uint32_t* const rev_graph_count,  // [graph_size]
+                                const uint32_t graph_size,
+                                const uint32_t degree)
+{
+  const uint32_t tid  = threadIdx.x + (blockDim.x * blockIdx.x);
+  const uint32_t tnum = blockDim.x * gridDim.x;
+
+  for (uint32_t src_id = tid; src_id < graph_size; src_id += tnum) {
+    const IdxT dest_id = dest_nodes[src_id];
+    if (dest_id >= graph_size) continue;
+
+    const uint32_t pos = atomicAdd(rev_graph_count + dest_id, 1);
+    if (pos < degree) { rev_graph[pos + ((uint64_t)degree * dest_id)] = src_id; }
+  }
+}
+
+template <class T>
+uint64_t pos_in_array(T val, const T* array, uint64_t num)
+{
+  for (uint64_t i = 0; i < num; i++) {
+    if (val == array[i]) { return i; }
+  }
+  return num;
+}
+
+template <class T>
+void shift_array(T* array, uint64_t num)
+{
+  for (uint64_t i = num; i > 0; i--) {
+    array[i] = array[i - 1];
+  }
+}
+}  // namespace
+
+template <
+  typename DataT,
+  typename IdxT       = uint32_t,
+  typename d_accessor = raft::host_device_accessor<std::experimental::default_accessor<DataT>,
+                                                   raft::memory_type::device>,
+  typename g_accessor =
+    raft::host_device_accessor<std::experimental::default_accessor<IdxT>, raft::memory_type::host>>
+void sort_knn_graph(
+  raft::resources const& res,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, d_accessor> dataset,
+  raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph)
+{
+  RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
+               "dataset size is expected to have the same number of graph index size");
+  const uint32_t dataset_size = dataset.extent(0);
+  const uint32_t dataset_dim  = dataset.extent(1);
+  const DataT* dataset_ptr    = dataset.data_handle();
+
+  const IdxT graph_size             = dataset_size;
+  const uint32_t input_graph_degree = knn_graph.extent(1);
+  IdxT* const input_graph_ptr       = knn_graph.data_handle();
+
+  auto d_input_graph = raft::make_device_matrix<IdxT, int64_t>(res, graph_size, input_graph_degree);
+
+  //
+  // Sorting kNN graph
+  //
+  const double time_sort_start = cur_time();
+  RAFT_LOG_DEBUG("# Sorting kNN Graph on GPUs ");
+
+  auto d_dataset = raft::make_device_matrix<DataT, int64_t>(res, dataset_size, dataset_dim);
+  raft::copy(d_dataset.data_handle(),
+             dataset_ptr,
+             dataset_size * dataset_dim,
+             raft::resource::get_cuda_stream(res));
+
+  raft::copy(d_input_graph.data_handle(),
+             input_graph_ptr,
+             graph_size * input_graph_degree,
+             raft::resource::get_cuda_stream(res));
+
+  void (*kernel_sort)(
+    const DataT* const, const IdxT, const uint32_t, IdxT* const, const uint32_t, const uint32_t);
+  if (input_graph_degree <= 32) {
+    constexpr int numElementsPerThread = 1;
+    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
+  } else if (input_graph_degree <= 64) {
+    constexpr int numElementsPerThread = 2;
+    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
+  } else if (input_graph_degree <= 128) {
+    constexpr int numElementsPerThread = 4;
+    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
+  } else if (input_graph_degree <= 256) {
+    constexpr int numElementsPerThread = 8;
+    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
+  } else if (input_graph_degree <= 512) {
+    constexpr int numElementsPerThread = 16;
+    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
+  } else if (input_graph_degree <= 1024) {
+    constexpr int numElementsPerThread = 32;
+    kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
+  } else {
+    RAFT_FAIL(
+      "The degree of input knn graph is too large (%u). "
+      "It must be equal to or smaller than %d.",
+      input_graph_degree,
+      1024);
+  }
+  const auto block_size          = 256;
+  const auto num_warps_per_block = block_size / raft::WarpSize;
+  const auto grid_size           = (graph_size + num_warps_per_block - 1) / num_warps_per_block;
+
+  RAFT_LOG_DEBUG(".");
+  kernel_sort<<<grid_size, block_size, 0, raft::resource::get_cuda_stream(res)>>>(
+    d_dataset.data_handle(),
+    dataset_size,
+    dataset_dim,
+    d_input_graph.data_handle(),
+    graph_size,
+    input_graph_degree);
+  raft::resource::sync_stream(res);
+  RAFT_LOG_DEBUG(".");
+  raft::copy(input_graph_ptr,
+             d_input_graph.data_handle(),
+             graph_size * input_graph_degree,
+             raft::resource::get_cuda_stream(res));
+  RAFT_LOG_DEBUG("\n");
+
+  const double time_sort_end = cur_time();
+  RAFT_LOG_DEBUG("# Sorting kNN graph time: %.1lf sec\n", time_sort_end - time_sort_start);
+}
+
+template <
+  typename IdxT = uint32_t,
+  typename g_accessor =
+    raft::host_device_accessor<std::experimental::default_accessor<IdxT>, raft::memory_type::host>>
+void optimize(
+  raft::resources const& res,
+  raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph)
+{
+  RAFT_LOG_DEBUG(
+    "# Pruning kNN graph (size=%lu, degree=%lu)\n", knn_graph.extent(0), knn_graph.extent(1));
+
+  RAFT_EXPECTS(knn_graph.extent(0) == new_graph.extent(0),
+               "Each input array is expected to have the same number of rows");
+  RAFT_EXPECTS(new_graph.extent(1) <= knn_graph.extent(1),
+               "output graph cannot have more columns than input graph");
+  const uint32_t input_graph_degree  = knn_graph.extent(1);
+  const uint32_t output_graph_degree = new_graph.extent(1);
+  auto input_graph_ptr               = knn_graph.data_handle();
+  auto output_graph_ptr              = new_graph.data_handle();
+  const IdxT graph_size              = new_graph.extent(0);
+
+  {
+    //
+    // Prune kNN graph
+    //
+    auto d_detour_count =
+      raft::make_device_matrix<uint8_t, int64_t>(res, graph_size, input_graph_degree);
+
+    RAFT_CUDA_TRY(cudaMemsetAsync(d_detour_count.data_handle(),
+                                  0xff,
+                                  graph_size * input_graph_degree * sizeof(uint8_t),
+                                  raft::resource::get_cuda_stream(res)));
+
+    auto d_num_no_detour_edges = raft::make_device_vector<uint32_t, int64_t>(res, graph_size);
+    RAFT_CUDA_TRY(cudaMemsetAsync(d_num_no_detour_edges.data_handle(),
+                                  0x00,
+                                  graph_size * sizeof(uint32_t),
+                                  raft::resource::get_cuda_stream(res)));
+
+    auto dev_stats  = raft::make_device_vector<uint64_t>(res, 2);
+    auto host_stats = raft::make_host_vector<uint64_t>(2);
+
+    //
+    // Prune unimportant edges.
+    //
+    // The edge to be retained is determined without explicitly considering
+    // distance or angle. Suppose the edge is the k-th edge of some node-A to
+    // node-B (A->B). Among the edges originating at node-A, there are k-1 edges
+    // shorter than the edge A->B. Each of these k-1 edges are connected to a
+    // different k-1 nodes. Among these k-1 nodes, count the number of nodes with
+    // edges to node-B, which is the number of 2-hop detours for the edge A->B.
+    // Once the number of 2-hop detours has been counted for all edges, the
+    // specified number of edges are picked up for each node, starting with the
+    // edge with the lowest number of 2-hop detours.
+    //
+    const double time_prune_start = cur_time();
+    RAFT_LOG_DEBUG("# Pruning kNN Graph on GPUs\r");
+
+    // Copy input_graph_ptr over to device if necessary
+    device_matrix_view_from_host d_input_graph(
+      res,
+      raft::make_host_matrix_view<IdxT, int64_t>(input_graph_ptr, graph_size, input_graph_degree));
+
+    constexpr int MAX_DEGREE = 1024;
+    if (input_graph_degree > MAX_DEGREE) {
+      RAFT_FAIL(
+        "The degree of input knn graph is too large (%u). "
+        "It must be equal to or smaller than %d.",
+        input_graph_degree,
+        1024);
+    }
+    const uint32_t batch_size =
+      std::min(static_cast<uint32_t>(graph_size), static_cast<uint32_t>(256 * 1024));
+    const uint32_t num_batch = (graph_size + batch_size - 1) / batch_size;
+    const dim3 threads_prune(32, 1, 1);
+    const dim3 blocks_prune(batch_size, 1, 1);
+
+    RAFT_CUDA_TRY(cudaMemsetAsync(
+      dev_stats.data_handle(), 0, sizeof(uint64_t) * 2, raft::resource::get_cuda_stream(res)));
+
+    for (uint32_t i_batch = 0; i_batch < num_batch; i_batch++) {
+      kern_prune<MAX_DEGREE, IdxT>
+        <<<blocks_prune, threads_prune, 0, raft::resource::get_cuda_stream(res)>>>(
+          d_input_graph.data_handle(),
+          graph_size,
+          input_graph_degree,
+          output_graph_degree,
+          batch_size,
+          i_batch,
+          d_detour_count.data_handle(),
+          d_num_no_detour_edges.data_handle(),
+          dev_stats.data_handle());
+      raft::resource::sync_stream(res);
+      RAFT_LOG_DEBUG(
+        "# Pruning kNN Graph on GPUs (%.1lf %%)\r",
+        (double)std::min<IdxT>((i_batch + 1) * batch_size, graph_size) / graph_size * 100);
+    }
+    raft::resource::sync_stream(res);
+    RAFT_LOG_DEBUG("\n");
+
+    host_matrix_view_from_device<uint8_t, int64_t> detour_count(res, d_detour_count.view());
+
+    raft::copy(
+      host_stats.data_handle(), dev_stats.data_handle(), 2, raft::resource::get_cuda_stream(res));
+    const auto num_keep = host_stats.data_handle()[0];
+    const auto num_full = host_stats.data_handle()[1];
+
+    // Create pruned kNN graph
+    uint32_t max_detour = 0;
+#pragma omp parallel for reduction(max : max_detour)
+    for (uint64_t i = 0; i < graph_size; i++) {
+      uint64_t pk = 0;
+      for (uint32_t num_detour = 0; num_detour < output_graph_degree; num_detour++) {
+        if (max_detour < num_detour) { max_detour = num_detour; /* stats */ }
+        for (uint64_t k = 0; k < input_graph_degree; k++) {
+          if (detour_count.data_handle()[k + (input_graph_degree * i)] != num_detour) { continue; }
+          output_graph_ptr[pk + (output_graph_degree * i)] =
+            input_graph_ptr[k + (input_graph_degree * i)];
+          pk += 1;
+          if (pk >= output_graph_degree) break;
+        }
+        if (pk >= output_graph_degree) break;
+      }
+      assert(pk == output_graph_degree);
+    }
+    // RAFT_LOG_DEBUG("# max_detour: %u\n", max_detour);
+
+    const double time_prune_end = cur_time();
+    RAFT_LOG_DEBUG(
+      "# Pruning time: %.1lf sec, "
+      "avg_no_detour_edges_per_node: %.2lf/%u, "
+      "nodes_with_no_detour_at_all_edges: %.1lf%%\n",
+      time_prune_end - time_prune_start,
+      (double)num_keep / graph_size,
+      output_graph_degree,
+      (double)num_full / graph_size * 100);
+  }
+
+  auto rev_graph       = raft::make_host_matrix<IdxT, int64_t>(graph_size, output_graph_degree);
+  auto rev_graph_count = raft::make_host_vector<uint32_t, int64_t>(graph_size);
+
+  {
+    //
+    // Make reverse graph
+    //
+    const double time_make_start = cur_time();
+
+    device_matrix_view_from_host<IdxT, int64_t> d_rev_graph(res, rev_graph.view());
+    RAFT_CUDA_TRY(cudaMemsetAsync(d_rev_graph.data_handle(),
+                                  0xff,
+                                  graph_size * output_graph_degree * sizeof(IdxT),
+                                  raft::resource::get_cuda_stream(res)));
+
+    auto d_rev_graph_count = raft::make_device_vector<uint32_t, int64_t>(res, graph_size);
+    RAFT_CUDA_TRY(cudaMemsetAsync(d_rev_graph_count.data_handle(),
+                                  0x00,
+                                  graph_size * sizeof(uint32_t),
+                                  raft::resource::get_cuda_stream(res)));
+
+    auto dest_nodes   = raft::make_host_vector<IdxT, int64_t>(graph_size);
+    auto d_dest_nodes = raft::make_device_vector<IdxT, int64_t>(res, graph_size);
+
+    for (uint64_t k = 0; k < output_graph_degree; k++) {
+#pragma omp parallel for
+      for (uint64_t i = 0; i < graph_size; i++) {
+        dest_nodes.data_handle()[i] = output_graph_ptr[k + (output_graph_degree * i)];
+      }
+      raft::resource::sync_stream(res);
+
+      raft::copy(d_dest_nodes.data_handle(),
+                 dest_nodes.data_handle(),
+                 graph_size,
+                 raft::resource::get_cuda_stream(res));
+
+      dim3 threads(256, 1, 1);
+      dim3 blocks(1024, 1, 1);
+      kern_make_rev_graph<<<blocks, threads, 0, raft::resource::get_cuda_stream(res)>>>(
+        d_dest_nodes.data_handle(),
+        d_rev_graph.data_handle(),
+        d_rev_graph_count.data_handle(),
+        graph_size,
+        output_graph_degree);
+      RAFT_LOG_DEBUG("# Making reverse graph on GPUs: %lu / %u    \r", k, output_graph_degree);
+    }
+
+    raft::resource::sync_stream(res);
+    RAFT_LOG_DEBUG("\n");
+
+    if (d_rev_graph.allocated_memory()) {
+      raft::copy(rev_graph.data_handle(),
+                 d_rev_graph.data_handle(),
+                 graph_size * output_graph_degree,
+                 raft::resource::get_cuda_stream(res));
+    }
+    raft::copy(rev_graph_count.data_handle(),
+               d_rev_graph_count.data_handle(),
+               graph_size,
+               raft::resource::get_cuda_stream(res));
+
+    const double time_make_end = cur_time();
+    RAFT_LOG_DEBUG("# Making reverse graph time: %.1lf sec", time_make_end - time_make_start);
+  }
+
+  {
+    //
+    // Replace some edges with reverse edges
+    //
+    const double time_replace_start = cur_time();
+
+    const uint64_t num_protected_edges = output_graph_degree / 2;
+    RAFT_LOG_DEBUG("# num_protected_edges: %lu", num_protected_edges);
+
+    constexpr int _omp_chunk = 1024;
+#pragma omp parallel for schedule(dynamic, _omp_chunk)
+    for (uint64_t j = 0; j < graph_size; j++) {
+      uint64_t k = std::min(rev_graph_count.data_handle()[j], output_graph_degree);
+      while (k) {
+        k--;
+        uint64_t i = rev_graph.data_handle()[k + (output_graph_degree * j)];
+
+        uint64_t pos =
+          pos_in_array<IdxT>(i, output_graph_ptr + (output_graph_degree * j), output_graph_degree);
+        if (pos < num_protected_edges) { continue; }
+        uint64_t num_shift = pos - num_protected_edges;
+        if (pos == output_graph_degree) {
+          num_shift = output_graph_degree - num_protected_edges - 1;
+        }
+        shift_array<IdxT>(output_graph_ptr + num_protected_edges + (output_graph_degree * j),
+                          num_shift);
+        output_graph_ptr[num_protected_edges + (output_graph_degree * j)] = i;
+      }
+      if ((omp_get_thread_num() == 0) && ((j % _omp_chunk) == 0)) {
+        RAFT_LOG_DEBUG("# Replacing reverse edges: %lu / %lu    ", j, graph_size);
+      }
+    }
+    RAFT_LOG_DEBUG("\n");
+
+    const double time_replace_end = cur_time();
+    RAFT_LOG_DEBUG("# Replacing edges time: %.1lf sec", time_replace_end - time_replace_start);
+
+    /* stats */
+    uint64_t num_replaced_edges = 0;
+#pragma omp parallel for reduction(+ : num_replaced_edges)
+    for (uint64_t i = 0; i < graph_size; i++) {
+      for (uint64_t k = 0; k < output_graph_degree; k++) {
+        const uint64_t j = output_graph_ptr[k + (output_graph_degree * i)];
+        const uint64_t pos =
+          pos_in_array<IdxT>(j, output_graph_ptr + (output_graph_degree * i), output_graph_degree);
+        if (pos == output_graph_degree) { num_replaced_edges += 1; }
+      }
+    }
+    RAFT_LOG_DEBUG("# Average number of replaced edges per node: %.2f",
+                   (double)num_replaced_edges / graph_size);
+  }
+}
+
+}  // namespace graph
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/hashmap.hpp b/cpp/src/neighbors/detail/cagra/hashmap.hpp
new file mode 100644
index 0000000000..dd6c6c844e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/hashmap.hpp
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "utils.hpp"
+
+// TODO: This shouldn't be invoking anything from detail outside of neighbors/
+#include <raft/core/detail/macros.hpp>
+#include <raft/util/device_atomics.cuh>
+
+#include <cstdint>
+
+// #pragma GCC diagnostic push
+// #pragma GCC diagnostic ignored
+// #pragma GCC diagnostic pop
+namespace cuvs::neighbors::cagra::detail {
+namespace hashmap {
+
+_RAFT_HOST_DEVICE inline uint32_t get_size(const uint32_t bitlen) { return 1U << bitlen; }
+
+template <class IdxT>
+_RAFT_DEVICE inline void init(IdxT* const table, const unsigned bitlen, unsigned FIRST_TID = 0)
+{
+  if (threadIdx.x < FIRST_TID) return;
+  for (unsigned i = threadIdx.x - FIRST_TID; i < get_size(bitlen); i += blockDim.x - FIRST_TID) {
+    table[i] = utils::get_max_value<IdxT>();
+  }
+}
+
+template <class IdxT>
+_RAFT_DEVICE inline uint32_t insert(IdxT* const table, const uint32_t bitlen, const IdxT key)
+{
+  // Open addressing is used for collision resolution
+  const uint32_t size     = get_size(bitlen);
+  const uint32_t bit_mask = size - 1;
+#if 1
+  // Linear probing
+  IdxT index                = (key ^ (key >> bitlen)) & bit_mask;
+  constexpr uint32_t stride = 1;
+#else
+  // Double hashing
+  uint32_t index        = key & bit_mask;
+  const uint32_t stride = (key >> bitlen) * 2 + 1;
+#endif
+  for (unsigned i = 0; i < size; i++) {
+    const IdxT old = atomicCAS(&table[index], ~static_cast<IdxT>(0), key);
+    if (old == ~static_cast<IdxT>(0)) {
+      return 1;
+    } else if (old == key) {
+      return 0;
+    }
+    index = (index + stride) & bit_mask;
+  }
+  return 0;
+}
+
+template <unsigned TEAM_SIZE, class IdxT>
+_RAFT_DEVICE inline uint32_t insert(IdxT* const table, const uint32_t bitlen, const IdxT key)
+{
+  IdxT ret = 0;
+  if (threadIdx.x % TEAM_SIZE == 0) { ret = insert(table, bitlen, key); }
+  for (unsigned offset = 1; offset < TEAM_SIZE; offset *= 2) {
+    ret |= __shfl_xor_sync(0xffffffff, ret, offset);
+  }
+  return ret;
+}
+
+}  // namespace hashmap
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py
new file mode 100644
index 0000000000..63171373f7
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py
@@ -0,0 +1,83 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+#include "compute_distance_vpq.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+"""
+
+trailer = """
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+"""
+
+mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
+pq_bits = [8]
+subspace_dims = [2, 4]
+# block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
+# mxelem = [64, 128, 256]
+load_types = ["uint4"]
+code_book_types = ["half"]
+search_types = dict(
+    float_uint32=(
+        "float",
+        "uint32_t",
+        "float",
+    ),  # data_t, vec_idx_t, distance_t
+    half_uint32=("half", "uint32_t", "float"),
+    int8_uint32=("int8_t", "uint32_t", "float"),
+    uint8_uint32=("uint8_t", "uint32_t", "float"),
+    float_uint64=("float", "uint64_t", "float"),
+    half_uint64=("half", "uint64_t", "float"),
+)
+# knn
+for type_path, (data_t, idx_t, distance_t) in search_types.items():
+    for (mxdim, team) in mxdim_team:
+        for code_book_t in code_book_types:
+            for subspace_dim in subspace_dims:
+                for pq_bit in pq_bits:
+                    path = f"q_search_multi_cta_{type_path}_dim{mxdim}_t{team}_{pq_bit}pq_{subspace_dim}subd_{code_book_t}.cu"
+                    with open(path, "w") as f:
+                        f.write(header)
+                        f.write(
+                                f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<{data_t} COMMA {code_book_t} COMMA {pq_bit} COMMA {subspace_dim} COMMA {distance_t} COMMA {idx_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+                        )
+                        f.write(trailer)
+                        # For pasting into CMakeLists.txt
+                    print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..9ec7ce3dd0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..292a1429ad
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..1a5ad50e31
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..0ab23d7eb2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..5d94a501aa
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..56534dc05e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..7ff962058b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..3387a32a3f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..2d3f2cb1da
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..73dd8cd4b6
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..b5e33602d6
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..32fe0d6289
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..e2726ea26c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..b4ebd49c44
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..72f198c92b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..dfb667a7fd
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..c583569f6f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..fedfb51464
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..1116eaaa49
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..7e3ec363d9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..2b6e8e3da8
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..4a97fb7525
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..675cd3c937
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..b42b3289c0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..0db4296f1e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..4a2610dc7a
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..af60c776a2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..5dd79a79b4
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..b1c15662ef
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..201f68fb53
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..26744ed766
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..1bce71bef6
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..694304f3c4
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..e6a563731e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..5c554af3f0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..965b43c076
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..97a4f80921
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..bdd1719b36
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..e39bc1e2d2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..599cf327aa
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..621c5a249d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..cbed3ef8a0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..7428bfd9eb
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..70efefdb0d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..4039b85822
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..022eb0e051
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..e48b2ed714
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..64f08530f4
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_multi_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py
new file mode 100644
index 0000000000..bc5f506ac2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py
@@ -0,0 +1,88 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+#include "compute_distance_vpq.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+"""
+
+trailer = """
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
+"""
+
+mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
+# block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
+# itopk_candidates = [64, 128, 256]
+# itopk_size = [64, 128, 256, 512]
+# mxelem = [64, 128, 256]
+
+pq_bits = [8]
+subspace_dims = [2, 4]
+
+# rblock = [(256, 4), (512, 2), (1024, 1)]
+# rcandidates = [32]
+# rsize = [256, 512]
+code_book_types = ["half"]
+
+search_types = dict(
+    float_uint32=("float", "uint32_t", "float"),  # data_t, idx_t, distance_t
+    half_uint32=("half", "uint32_t", "float"),
+    int8_uint32=("int8_t", "uint32_t", "float"),
+    uint8_uint32=("uint8_t", "uint32_t", "float"),
+    float_uint64=("float", "uint64_t", "float"),
+    half_uint64=("half", "uint64_t", "float"),
+)
+
+# knn
+for type_path, (data_t, idx_t, distance_t) in search_types.items():
+    for (mxdim, team) in mxdim_team:
+        for code_book_t in code_book_types:
+            for subspace_dim in subspace_dims:
+                for pq_bit in pq_bits:
+                    path = f"q_search_single_cta_{type_path}_dim{mxdim}_t{team}_{pq_bit}pq_{subspace_dim}subd_{code_book_t}.cu"
+                    with open(path, "w") as f:
+                        f.write(header)
+                        f.write(
+                                f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<{data_t} COMMA {code_book_t} COMMA {pq_bit} COMMA {subspace_dim} COMMA {distance_t} COMMA {idx_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+                        )
+
+                        f.write(trailer)
+                        # For pasting into CMakeLists.txt
+                        print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..b403227418
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..36273d0d42
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..ef483437ad
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..d9ebb1b851
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..e86524ee0d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..9f2b7fbc77
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..1ce4f5520e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..2d6f93ef0b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..5f3267410e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..631ac7938b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..ea8faee1c9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..061b1a04ef
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..15610d853d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..f984b46f0a
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..45299f2727
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..fcb91be8c8
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..b594fedab5
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..a82be6b554
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..d80fef52c8
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..e2c3ef4f76
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..98889811db
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..f5e9d12c9c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..4f14910b4e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..67d52f8d5c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..1420918a1b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..eb0a72da3e
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..7a98b59a9f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..7e07033c73
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..857f327122
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..3c00c52234
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..e5c4c7b69f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..22359d71b1
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..37c783f192
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..0a4049d793
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..773f567c46
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..dfc176abd9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..680c326557
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..e57881e828
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..525004f2e2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..7af2ef124c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..0fd36c31b5
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..d4cc5f4490
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             1024,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..aa58ac2b7b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..189c3ed9c7
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(8,
+                             128,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..9dc9aaae33
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..1001103138
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(16,
+                             256,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..8d4e0aeeed
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..4c73187351
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by q_search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python q_search_single_cta_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq.cuh"
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(32,
+                             512,
+                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
+                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
new file mode 100644
index 0000000000..11161628b8
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -0,0 +1,273 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "bitonic.hpp"
+#include "compute_distance.hpp"
+#include "device_common.hpp"
+#include "hashmap.hpp"
+#include "search_multi_cta_kernel.cuh"
+#include "search_plan.cuh"
+#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk if possible
+#include "utils.hpp"
+
+#include <raft/core/detail/macros.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_properties.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+
+#include <raft/linalg/map.cuh>
+
+// TODO: This shouldn't be invoking anything in spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/util/cuda_rt_essentials.hpp>
+#include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
+
+#include <algorithm>
+#include <cassert>
+#include <iostream>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace multi_cta_search {
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+
+struct search : public search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
+  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_queries;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::itopk_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::algo;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::team_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::search_width;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::min_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::thread_block_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_mode;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_min_bitlen;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_max_fill_rate;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_random_samplings;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::rand_xor_mask;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dim;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::graph_degree;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::topk;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hash_bitlen;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_bitlen;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_reset_interval;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dataset_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::result_buffer_size;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::smem_size;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_executed_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dev_seed;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_seeds;
+
+  uint32_t num_cta_per_query;
+  rmm::device_uvector<INDEX_T> intermediate_indices;
+  rmm::device_uvector<float> intermediate_distances;
+  size_t topk_workspace_size;
+  rmm::device_uvector<uint32_t> topk_workspace;
+
+  search(raft::resources const& res,
+         search_params params,
+         int64_t dim,
+         int64_t graph_degree,
+         uint32_t topk,
+         cuvs::distance::DistanceType metric)
+    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
+        res, params, dim, graph_degree, topk, metric),
+      intermediate_indices(0, raft::resource::get_cuda_stream(res)),
+      intermediate_distances(0, raft::resource::get_cuda_stream(res)),
+      topk_workspace(0, raft::resource::get_cuda_stream(res))
+
+  {
+    set_params(res, params);
+  }
+
+  void set_params(raft::resources const& res, const search_params& params)
+  {
+    constexpr unsigned muti_cta_itopk_size = 32;
+    this->itopk_size                       = muti_cta_itopk_size;
+    search_width                           = 1;
+    num_cta_per_query =
+      max(params.search_width, raft::ceildiv(params.itopk_size, (size_t)muti_cta_itopk_size));
+    result_buffer_size = itopk_size + search_width * graph_degree;
+    typedef raft::Pow2<32> AlignBytes;
+    unsigned result_buffer_size_32 = AlignBytes::roundUp(result_buffer_size);
+    // constexpr unsigned max_result_buffer_size = 256;
+    RAFT_EXPECTS(result_buffer_size_32 <= 256, "Result buffer size cannot exceed 256");
+
+    const auto query_smem_buffer_length =
+      raft::ceildiv<uint32_t>(dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+
+    smem_size = sizeof(float) * query_smem_buffer_length +
+                (sizeof(INDEX_T) + sizeof(DISTANCE_T)) * result_buffer_size_32 +
+                sizeof(uint32_t) * search_width + sizeof(uint32_t) +
+                DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte;
+    RAFT_LOG_DEBUG("# smem_size: %u", smem_size);
+
+    //
+    // Determine the thread block size
+    //
+    constexpr unsigned min_block_size = 64;
+    constexpr unsigned max_block_size = 1024;
+    uint32_t block_size               = thread_block_size;
+    if (block_size == 0) {
+      block_size = min_block_size;
+
+      // Increase block size according to shared memory requirements.
+      // If block size is 32, upper limit of shared memory size per
+      // thread block is set to 4096. This is GPU generation dependent.
+      constexpr unsigned ulimit_smem_size_cta32 = 4096;
+      while (smem_size > ulimit_smem_size_cta32 / 32 * block_size) {
+        block_size *= 2;
+      }
+
+      // Increase block size to improve GPU occupancy when total number of
+      // CTAs (= num_cta_per_query * max_queries) is small.
+      cudaDeviceProp deviceProp = raft::resource::get_device_properties(res);
+      RAFT_LOG_DEBUG("# multiProcessorCount: %d", deviceProp.multiProcessorCount);
+      while ((block_size < max_block_size) &&
+             (graph_degree * search_width * team_size >= block_size * 2) &&
+             (num_cta_per_query * max_queries <=
+              (1024 / (block_size * 2)) * deviceProp.multiProcessorCount)) {
+        block_size *= 2;
+      }
+    }
+    RAFT_LOG_DEBUG("# thread_block_size: %u", block_size);
+    RAFT_EXPECTS(block_size >= min_block_size,
+                 "block_size cannot be smaller than min_block size, %u",
+                 min_block_size);
+    RAFT_EXPECTS(block_size <= max_block_size,
+                 "block_size cannot be larger than max_block size %u",
+                 max_block_size);
+    thread_block_size = block_size;
+
+    //
+    // Allocate memory for intermediate buffer and workspace.
+    //
+    uint32_t num_intermediate_results = num_cta_per_query * itopk_size;
+    intermediate_indices.resize(num_intermediate_results * max_queries,
+                                raft::resource::get_cuda_stream(res));
+    intermediate_distances.resize(num_intermediate_results * max_queries,
+                                  raft::resource::get_cuda_stream(res));
+
+    hashmap.resize(hashmap_size, raft::resource::get_cuda_stream(res));
+
+    topk_workspace_size = _cuann_find_topk_bufferSize(
+      topk, max_queries, num_intermediate_results, utils::get_cuda_data_type<DATA_T>());
+    RAFT_LOG_DEBUG("# topk_workspace_size: %lu", topk_workspace_size);
+    topk_workspace.resize(topk_workspace_size, raft::resource::get_cuda_stream(res));
+  }
+
+  void check(const uint32_t topk) override
+  {
+    RAFT_EXPECTS(num_cta_per_query * 32 >= topk,
+                 "`num_cta_per_query` (%u) * 32 must be equal to or greater than "
+                 "`topk` (%u) when 'search_mode' is \"multi-cta\". "
+                 "(`num_cta_per_query`=max(`search_width`, ceildiv(`itopk_size`, 32)))",
+                 num_cta_per_query,
+                 topk);
+  }
+
+  ~search() {}
+
+  void operator()(
+    raft::resources const& res,
+    // raft::device_matrix_view<const DATA_T, int64_t, layout_stride> dataset,
+    DATASET_DESCRIPTOR_T dataset_desc,
+    raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
+      graph,
+    typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+    typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+    const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+    const uint32_t num_queries,
+    const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
+    uint32_t* const num_executed_iterations,                     // [num_queries,]
+    uint32_t topk,
+    SAMPLE_FILTER_T sample_filter)
+  {
+    cudaStream_t stream = raft::resource::get_cuda_stream(res);
+
+    select_and_run<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
+      dataset_desc,
+      graph,
+      intermediate_indices.data(),
+      intermediate_distances.data(),
+      queries_ptr,
+      num_queries,
+      dev_seed_ptr,
+      num_executed_iterations,
+      topk,
+      thread_block_size,
+      result_buffer_size,
+      smem_size,
+      hash_bitlen,
+      hashmap.data(),
+      num_cta_per_query,
+      num_random_samplings,
+      rand_xor_mask,
+      num_seeds,
+      itopk_size,
+      search_width,
+      min_iterations,
+      max_iterations,
+      sample_filter,
+      this->metric,
+      stream);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+    // Select the top-k results from the intermediate results
+    const uint32_t num_intermediate_results = num_cta_per_query * itopk_size;
+    _cuann_find_topk(topk,
+                     num_queries,
+                     num_intermediate_results,
+                     intermediate_distances.data(),
+                     num_intermediate_results,
+                     intermediate_indices.data(),
+                     num_intermediate_results,
+                     topk_distances_ptr,
+                     topk,
+                     topk_indices_ptr,
+                     topk,
+                     topk_workspace.data(),
+                     true,
+                     NULL,
+                     stream);
+  }
+};
+
+}  // namespace multi_cta_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
new file mode 100644
index 0000000000..cb63c0e03b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
@@ -0,0 +1,78 @@
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+"""
+
+trailer = """
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+"""
+
+mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
+# block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
+# mxelem = [64, 128, 256]
+load_types = ["uint4"]
+search_types = dict(
+    float_uint32=(
+        "float",
+        "uint32_t",
+        "float",
+    ),  # data_t, vec_idx_t, distance_t
+    half_uint32=("half", "uint32_t", "float"),
+    int8_uint32=("int8_t", "uint32_t", "float"),
+    uint8_uint32=("uint8_t", "uint32_t", "float"),
+    float_uint64=("float", "uint64_t", "float"),
+    half_uint64=("half", "uint64_t", "float"),
+)
+# knn
+for type_path, (data_t, idx_t, distance_t) in search_types.items():
+    for (mxdim, team) in mxdim_team:
+        path = f"search_multi_cta_{type_path}_dim{mxdim}_t{team}.cu"
+        with open(path, "w") as f:
+            f.write(header)
+            f.write(
+                    f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<{data_t} COMMA {idx_t} COMMA {distance_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+            )
+            f.write(trailer)
+            # For pasting into CMakeLists.txt
+        print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..0bf4a192ff
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..ea38b60c0b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..a77859b7d2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..ab49fa9f22
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
new file mode 100644
index 0000000000..c38eeb009c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
new file mode 100644
index 0000000000..3094ddaeb4
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
new file mode 100644
index 0000000000..91725d1851
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
new file mode 100644
index 0000000000..0f452a6faf
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..cfe7a7aef1
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..157942dc52
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..ee24000379
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..292859382b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
new file mode 100644
index 0000000000..13044f12d8
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu
new file mode 100644
index 0000000000..2a14699f45
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
new file mode 100644
index 0000000000..2ce6f292db
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
new file mode 100644
index 0000000000..2d607eb8db
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
new file mode 100644
index 0000000000..b5be1b772f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "search_multi_cta_kernel-inl.cuh"
+#include <cuvs/neighbors/sample_filter.hpp>
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+
+#define instantiate_kernel_selection(TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T) \
+  template void select_and_run<TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T>(      \
+    DATASET_DESC_T dataset_desc,                                                                  \
+    raft::device_matrix_view<const typename DATASET_DESC_T::INDEX_T, int64_t, raft::row_major>    \
+      graph,                                                                                      \
+    typename DATASET_DESC_T::INDEX_T* const topk_indices_ptr,                                     \
+    typename DATASET_DESC_T::DISTANCE_T* const topk_distances_ptr,                                \
+    const typename DATASET_DESC_T::DATA_T* const queries_ptr,                                     \
+    const uint32_t num_queries,                                                                   \
+    const typename DATASET_DESC_T::INDEX_T* dev_seed_ptr,                                         \
+    uint32_t* const num_executed_iterations,                                                      \
+    uint32_t topk,                                                                                \
+    uint32_t block_size,                                                                          \
+    uint32_t result_buffer_size,                                                                  \
+    uint32_t smem_size,                                                                           \
+    int64_t hash_bitlen,                                                                          \
+    typename DATASET_DESC_T::INDEX_T* hashmap_ptr,                                                \
+    uint32_t num_cta_per_query,                                                                   \
+    uint32_t num_random_samplings,                                                                \
+    uint64_t rand_xor_mask,                                                                       \
+    uint32_t num_seeds,                                                                           \
+    size_t itopk_size,                                                                            \
+    size_t search_width,                                                                          \
+    size_t min_iterations,                                                                        \
+    size_t max_iterations,                                                                        \
+    SAMPLE_FILTER_T sample_filter,                                                                \
+    cuvs::distance::DistanceType metric,                                                          \
+    cudaStream_t stream);
+
+#define COMMA ,
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..c28adbf808
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..af5f133974
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..bcc7b9b8c1
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..916196c356
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
new file mode 100644
index 0000000000..bfac43d5ec
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
@@ -0,0 +1,419 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "compute_distance_vpq.cuh"
+#include <cuvs/neighbors/sample_filter.hpp>  // none_cagra_sample_filter
+#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+
+#include <cuda_fp16.h>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace multi_cta_search {
+
+#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+template <unsigned TEAM_SIZE,
+          unsigned MAX_DATASET_DIM,
+          class DATASET_DESCRIPTOR_T,
+          class SAMPLE_FILTER_T>
+void select_and_run(
+  DATASET_DESCRIPTOR_T dataset_desc,
+  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
+    graph,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,
+  const uint32_t num_queries,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,
+  uint32_t* const num_executed_iterations,
+  uint32_t topk,
+  uint32_t block_size,
+  uint32_t result_buffer_size,
+  uint32_t smem_size,
+  int64_t hash_bitlen,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
+  uint32_t num_cta_per_query,
+  uint32_t num_random_samplings,
+  uint64_t rand_xor_mask,
+  uint32_t num_seeds,
+  size_t itopk_size,
+  size_t search_width,
+  size_t min_iterations,
+  size_t max_iterations,
+  SAMPLE_FILTER_T sample_filter,
+  cuvs::distance::DistanceType metric,
+  cudaStream_t stream) RAFT_EXPLICIT;
+#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+#define instantiate_kernel_selection(                                                           \
+  TEAM_SIZE, MAX_DATASET_DIM, DATA_T, INDEX_T, DISTANCE_T, SAMPLE_FILTER_T)                     \
+  extern template void select_and_run<                                                          \
+    TEAM_SIZE,                                                                                  \
+    MAX_DATASET_DIM,                                                                            \
+    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>, \
+    SAMPLE_FILTER_T>(                                                                           \
+    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>  \
+      dataset_desc,                                                                             \
+    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
+    INDEX_T* const topk_indices_ptr,                                                            \
+    DISTANCE_T* const topk_distances_ptr,                                                       \
+    const DATA_T* const queries_ptr,                                                            \
+    const uint32_t num_queries,                                                                 \
+    const INDEX_T* dev_seed_ptr,                                                                \
+    uint32_t* const num_executed_iterations,                                                    \
+    uint32_t topk,                                                                              \
+    uint32_t block_size,                                                                        \
+    uint32_t result_buffer_size,                                                                \
+    uint32_t smem_size,                                                                         \
+    int64_t hash_bitlen,                                                                        \
+    INDEX_T* hashmap_ptr,                                                                       \
+    uint32_t num_cta_per_query,                                                                 \
+    uint32_t num_random_samplings,                                                              \
+    uint64_t rand_xor_mask,                                                                     \
+    uint32_t num_seeds,                                                                         \
+    size_t itopk_size,                                                                          \
+    size_t search_width,                                                                        \
+    size_t min_iterations,                                                                      \
+    size_t max_iterations,                                                                      \
+    SAMPLE_FILTER_T sample_filter,                                                              \
+    cuvs::distance::DistanceType metric,                                                        \
+    cudaStream_t stream);
+
+instantiate_kernel_selection(
+  32, 1024, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  8, 128, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  16, 256, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  32, 512, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  32, 1024, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  8, 128, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  16, 256, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  32, 512, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  32, 1024, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  8, 128, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  16, 256, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  32, 512, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  32, 1024, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  8, 128, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  16, 256, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(
+  32, 512, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+#undef instantiate_kernel_selection
+
+#define instantiate_q_kernel_selection(TEAM_SIZE,                                               \
+                                       MAX_DATASET_DIM,                                         \
+                                       CODE_BOOK_T,                                             \
+                                       PQ_BITS,                                                 \
+                                       PQ_CODE_BOOK_DIM,                                        \
+                                       DATA_T,                                                  \
+                                       INDEX_T,                                                 \
+                                       DISTANCE_T,                                              \
+                                       SAMPLE_FILTER_T)                                         \
+  extern template void                                                                          \
+  select_and_run<TEAM_SIZE,                                                                     \
+                 MAX_DATASET_DIM,                                                               \
+                 cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,           \
+                                                                              CODE_BOOK_T,      \
+                                                                              PQ_BITS,          \
+                                                                              PQ_CODE_BOOK_DIM, \
+                                                                              DISTANCE_T,       \
+                                                                              INDEX_T>,         \
+                 SAMPLE_FILTER_T>(                                                              \
+    cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,                        \
+                                                                 CODE_BOOK_T,                   \
+                                                                 PQ_BITS,                       \
+                                                                 PQ_CODE_BOOK_DIM,              \
+                                                                 DISTANCE_T,                    \
+                                                                 INDEX_T> dataset_desc,         \
+    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
+    INDEX_T* const topk_indices_ptr,                                                            \
+    DISTANCE_T* const topk_distances_ptr,                                                       \
+    const DATA_T* const queries_ptr,                                                            \
+    const uint32_t num_queries,                                                                 \
+    const INDEX_T* dev_seed_ptr,                                                                \
+    uint32_t* const num_executed_iterations,                                                    \
+    uint32_t topk,                                                                              \
+    uint32_t block_size,                                                                        \
+    uint32_t result_buffer_size,                                                                \
+    uint32_t smem_size,                                                                         \
+    int64_t hash_bitlen,                                                                        \
+    INDEX_T* hashmap_ptr,                                                                       \
+    uint32_t num_cta_per_query,                                                                 \
+    uint32_t num_random_samplings,                                                              \
+    uint64_t rand_xor_mask,                                                                     \
+    uint32_t num_seeds,                                                                         \
+    size_t itopk_size,                                                                          \
+    size_t search_width,                                                                        \
+    size_t min_iterations,                                                                      \
+    size_t max_iterations,                                                                      \
+    SAMPLE_FILTER_T sample_filter,                                                              \
+    cuvs::distance::DistanceType metric,                                                        \
+    cudaStream_t stream);
+
+instantiate_q_kernel_selection(
+  8, 128, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(
+  16, 256, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(
+  32, 512, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               2,
+                               half,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(
+  8, 128, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(
+  16, 256, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(
+  32, 512, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               4,
+                               half,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_kernel_selection(
+  8, 128, half, 8, 2, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(16,
+                               256,
+                               half,
+                               8,
+                               2,
+                               float,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               512,
+                               half,
+                               8,
+                               2,
+                               float,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               2,
+                               float,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(
+  8, 128, half, 8, 4, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(16,
+                               256,
+                               half,
+                               8,
+                               4,
+                               float,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               512,
+                               half,
+                               8,
+                               4,
+                               float,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               4,
+                               float,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_kernel_selection(8,
+                               128,
+                               half,
+                               8,
+                               2,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(16,
+                               256,
+                               half,
+                               8,
+                               2,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               512,
+                               half,
+                               8,
+                               2,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               2,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(8,
+                               128,
+                               half,
+                               8,
+                               4,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(16,
+                               256,
+                               half,
+                               8,
+                               4,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               512,
+                               half,
+                               8,
+                               4,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               4,
+                               uint8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_kernel_selection(8,
+                               128,
+                               half,
+                               8,
+                               2,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(16,
+                               256,
+                               half,
+                               8,
+                               2,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               512,
+                               half,
+                               8,
+                               2,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               2,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(8,
+                               128,
+                               half,
+                               8,
+                               4,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(16,
+                               256,
+                               half,
+                               8,
+                               4,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               512,
+                               half,
+                               8,
+                               4,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_kernel_selection(32,
+                               1024,
+                               half,
+                               8,
+                               4,
+                               int8_t,
+                               uint32_t,
+                               float,
+                               cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+#undef instantiate_q_kernel_selection
+}  // namespace multi_cta_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
new file mode 100644
index 0000000000..3647b84321
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -0,0 +1,522 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "bitonic.hpp"
+#include "compute_distance.hpp"
+#include "device_common.hpp"
+#include "hashmap.hpp"
+#include "search_plan.cuh"
+#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk if possible
+#include "utils.hpp"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_properties.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+
+#include <cuvs/neighbors/sample_filter.hpp>
+
+// TODO: This shouldn't be invoking anything from spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/util/cuda_rt_essentials.hpp>
+#include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
+
+#include <algorithm>
+#include <cassert>
+#include <iostream>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace multi_cta_search {
+
+// #define _CLK_BREAKDOWN
+
+template <class INDEX_T>
+__device__ void pickup_next_parents(INDEX_T* const next_parent_indices,  // [search_width]
+                                    const uint32_t search_width,
+                                    INDEX_T* const itopk_indices,  // [num_itopk]
+                                    const size_t num_itopk,
+                                    uint32_t* const terminate_flag)
+{
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+  const unsigned warp_id             = threadIdx.x / 32;
+  if (warp_id > 0) { return; }
+  const unsigned lane_id = threadIdx.x % 32;
+  for (uint32_t i = lane_id; i < search_width; i += 32) {
+    next_parent_indices[i] = utils::get_max_value<INDEX_T>();
+  }
+  uint32_t max_itopk = num_itopk;
+  if (max_itopk % 32) { max_itopk += 32 - (max_itopk % 32); }
+  uint32_t num_new_parents = 0;
+  for (uint32_t j = lane_id; j < max_itopk; j += 32) {
+    INDEX_T index;
+    int new_parent = 0;
+    if (j < num_itopk) {
+      index = itopk_indices[j];
+      if ((index & index_msb_1_mask) == 0) {  // check if most significant bit is set
+        new_parent = 1;
+      }
+    }
+    const uint32_t ballot_mask = __ballot_sync(0xffffffff, new_parent);
+    if (new_parent) {
+      const auto i = __popc(ballot_mask & ((1 << lane_id) - 1)) + num_new_parents;
+      if (i < search_width) {
+        next_parent_indices[i] = j;
+        itopk_indices[j] |= index_msb_1_mask;  // set most significant bit as used node
+      }
+    }
+    num_new_parents += __popc(ballot_mask);
+    if (num_new_parents >= search_width) { break; }
+  }
+  if (threadIdx.x == 0 && (num_new_parents == 0)) { *terminate_flag = 1; }
+}
+
+template <unsigned MAX_ELEMENTS, class INDEX_T>
+__device__ inline void topk_by_bitonic_sort(float* distances,  // [num_elements]
+                                            INDEX_T* indices,  // [num_elements]
+                                            const uint32_t num_elements,
+                                            const uint32_t num_itopk  // num_itopk <= num_elements
+)
+{
+  const unsigned warp_id = threadIdx.x / 32;
+  if (warp_id > 0) { return; }
+  const unsigned lane_id = threadIdx.x % 32;
+  constexpr unsigned N   = (MAX_ELEMENTS + 31) / 32;
+  float key[N];
+  INDEX_T val[N];
+  for (unsigned i = 0; i < N; i++) {
+    unsigned j = lane_id + (32 * i);
+    if (j < num_elements) {
+      key[i] = distances[j];
+      val[i] = indices[j];
+    } else {
+      key[i] = utils::get_max_value<float>();
+      val[i] = utils::get_max_value<INDEX_T>();
+    }
+  }
+  /* Warp Sort */
+  bitonic::warp_sort<float, INDEX_T, N>(key, val);
+  /* Store itopk sorted results */
+  for (unsigned i = 0; i < N; i++) {
+    unsigned j = (N * lane_id) + i;
+    if (j < num_itopk) {
+      distances[j] = key[i];
+      indices[j]   = val[i];
+    }
+  }
+}
+
+//
+// multiple CTAs per single query
+//
+template <int32_t TEAM_SIZE,
+          uint32_t DATASET_BLOCK_DIM,
+          std::uint32_t MAX_ELEMENTS,
+          class DATASET_DESCRIPTOR_T,
+          class SAMPLE_FILTER_T>
+__launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    result_indices_ptr,  // [num_queries, num_cta_per_query, itopk_size]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const
+    result_distances_ptr,  // [num_queries, num_cta_per_query, itopk_size]
+  DATASET_DESCRIPTOR_T dataset_desc,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const knn_graph,   // [dataset_size, graph_degree]
+  const uint32_t graph_degree,
+  const unsigned num_distilation,
+  const uint64_t rand_xor_mask,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
+  const uint32_t num_seeds,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+  const uint32_t hash_bitlen,
+  const uint32_t itopk_size,
+  const uint32_t search_width,
+  const uint32_t min_iteration,
+  const uint32_t max_iteration,
+  uint32_t* const num_executed_iterations, /* stats */
+  SAMPLE_FILTER_T sample_filter,
+  const cuvs::distance::DistanceType metric)
+{
+  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+  using QUERY_T    = typename DATASET_DESCRIPTOR_T::QUERY_T;
+
+  const auto num_queries       = gridDim.y;
+  const auto query_id          = blockIdx.y;
+  const auto num_cta_per_query = gridDim.x;
+  const auto cta_id            = blockIdx.x;  // local CTA ID
+
+#ifdef _CLK_BREAKDOWN
+  uint64_t clk_init                 = 0;
+  uint64_t clk_compute_1st_distance = 0;
+  uint64_t clk_topk                 = 0;
+  uint64_t clk_pickup_parents       = 0;
+  uint64_t clk_compute_distance     = 0;
+  uint64_t clk_start;
+#define _CLK_START() clk_start = clock64()
+#define _CLK_REC(V)  V += clock64() - clk_start;
+#else
+#define _CLK_START()
+#define _CLK_REC(V)
+#endif
+  _CLK_START();
+
+  extern __shared__ uint32_t smem[];
+
+  // Layout of result_buffer
+  // +----------------+------------------------------+---------+
+  // | internal_top_k | neighbors of parent nodes    | padding |
+  // | <itopk_size>   | <search_width * graph_degree> | upto 32 |
+  // +----------------+------------------------------+---------+
+  // |<---          result_buffer_size           --->|
+  uint32_t result_buffer_size    = itopk_size + (search_width * graph_degree);
+  uint32_t result_buffer_size_32 = result_buffer_size;
+  if (result_buffer_size % 32) { result_buffer_size_32 += 32 - (result_buffer_size % 32); }
+  assert(result_buffer_size_32 <= MAX_ELEMENTS);
+
+  const auto query_smem_buffer_length =
+    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+  auto query_buffer          = reinterpret_cast<QUERY_T*>(smem);
+  auto result_indices_buffer = reinterpret_cast<INDEX_T*>(query_buffer + query_smem_buffer_length);
+  auto result_distances_buffer =
+    reinterpret_cast<DISTANCE_T*>(result_indices_buffer + result_buffer_size_32);
+  auto parent_indices_buffer =
+    reinterpret_cast<INDEX_T*>(result_distances_buffer + result_buffer_size_32);
+  auto distance_work_buffer_ptr =
+    reinterpret_cast<std::uint8_t*>(parent_indices_buffer + search_width);
+  auto terminate_flag = reinterpret_cast<uint32_t*>(distance_work_buffer_ptr +
+                                                    DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte);
+
+  // Set smem working buffer for the distance calculation
+  dataset_desc.set_smem_ptr(distance_work_buffer_ptr);
+
+#if 0
+    /* debug */
+    for (unsigned i = threadIdx.x; i < result_buffer_size_32; i += BLOCK_SIZE) {
+        result_indices_buffer[i] = utils::get_max_value<INDEX_T>();
+        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+    }
+#endif
+  const DATA_T* const query_ptr = queries_ptr + (dataset_desc.dim * query_id);
+  dataset_desc.template copy_query<DATASET_BLOCK_DIM>(
+    query_ptr, query_buffer, query_smem_buffer_length);
+
+  if (threadIdx.x == 0) { terminate_flag[0] = 0; }
+  INDEX_T* const local_visited_hashmap_ptr =
+    visited_hashmap_ptr + (hashmap::get_size(hash_bitlen) * query_id);
+  __syncthreads();
+  _CLK_REC(clk_init);
+
+  // compute distance to randomly selecting nodes
+  _CLK_START();
+  const INDEX_T* const local_seed_ptr = seed_ptr ? seed_ptr + (num_seeds * query_id) : nullptr;
+  uint32_t block_id                   = cta_id + (num_cta_per_query * query_id);
+  uint32_t num_blocks                 = num_cta_per_query * num_queries;
+
+  device::compute_distance_to_random_nodes<TEAM_SIZE, DATASET_BLOCK_DIM>(result_indices_buffer,
+                                                                         result_distances_buffer,
+                                                                         query_buffer,
+                                                                         dataset_desc,
+                                                                         result_buffer_size,
+                                                                         num_distilation,
+                                                                         rand_xor_mask,
+                                                                         local_seed_ptr,
+                                                                         num_seeds,
+                                                                         local_visited_hashmap_ptr,
+                                                                         hash_bitlen,
+                                                                         metric,
+                                                                         block_id,
+                                                                         num_blocks);
+  __syncthreads();
+  _CLK_REC(clk_compute_1st_distance);
+
+  uint32_t iter = 0;
+  while (1) {
+    // topk with bitonic sort
+    _CLK_START();
+    topk_by_bitonic_sort<MAX_ELEMENTS, INDEX_T>(result_distances_buffer,
+                                                result_indices_buffer,
+                                                itopk_size + (search_width * graph_degree),
+                                                itopk_size);
+    _CLK_REC(clk_topk);
+
+    if (iter + 1 == max_iteration) {
+      __syncthreads();
+      break;
+    }
+
+    // pick up next parents
+    _CLK_START();
+    pickup_next_parents<INDEX_T>(
+      parent_indices_buffer, search_width, result_indices_buffer, itopk_size, terminate_flag);
+    _CLK_REC(clk_pickup_parents);
+
+    __syncthreads();
+    if (*terminate_flag && iter >= min_iteration) { break; }
+
+    // compute the norms between child nodes and query node
+    _CLK_START();
+    // constexpr unsigned max_n_frags = 16;
+    constexpr unsigned max_n_frags = 0;
+    device::compute_distance_to_child_nodes<TEAM_SIZE, DATASET_BLOCK_DIM, max_n_frags>(
+      result_indices_buffer + itopk_size,
+      result_distances_buffer + itopk_size,
+      query_buffer,
+      dataset_desc,
+      knn_graph,
+      graph_degree,
+      local_visited_hashmap_ptr,
+      hash_bitlen,
+      parent_indices_buffer,
+      result_indices_buffer,
+      search_width,
+      metric);
+    _CLK_REC(clk_compute_distance);
+    __syncthreads();
+
+    // Filtering
+    if constexpr (!std::is_same<SAMPLE_FILTER_T,
+                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+      constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+      const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
+
+      for (unsigned p = threadIdx.x; p < search_width; p += blockDim.x) {
+        if (parent_indices_buffer[p] != invalid_index) {
+          const auto parent_id =
+            result_indices_buffer[parent_indices_buffer[p]] & ~index_msb_1_mask;
+          if (!sample_filter(query_id, parent_id)) {
+            // If the parent must not be in the resulting top-k list, remove from the parent list
+            result_distances_buffer[parent_indices_buffer[p]] = utils::get_max_value<DISTANCE_T>();
+            result_indices_buffer[parent_indices_buffer[p]]   = invalid_index;
+          }
+        }
+      }
+      __syncthreads();
+    }
+
+    iter++;
+  }
+
+  // Post process for filtering
+  if constexpr (!std::is_same<SAMPLE_FILTER_T,
+                              cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+    constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+    const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
+
+    for (unsigned i = threadIdx.x; i < itopk_size + search_width * graph_degree; i += blockDim.x) {
+      const auto node_id = result_indices_buffer[i] & ~index_msb_1_mask;
+      if (node_id != (invalid_index & ~index_msb_1_mask) && !sample_filter(query_id, node_id)) {
+        // If the parent must not be in the resulting top-k list, remove from the parent list
+        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+        result_indices_buffer[i]   = invalid_index;
+      }
+    }
+
+    __syncthreads();
+    topk_by_bitonic_sort<MAX_ELEMENTS, INDEX_T>(result_distances_buffer,
+                                                result_indices_buffer,
+                                                itopk_size + (search_width * graph_degree),
+                                                itopk_size);
+    __syncthreads();
+  }
+
+  for (uint32_t i = threadIdx.x; i < itopk_size; i += blockDim.x) {
+    uint32_t j = i + (itopk_size * (cta_id + (num_cta_per_query * query_id)));
+    if (result_distances_ptr != nullptr) { result_distances_ptr[j] = result_distances_buffer[i]; }
+    constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+
+    result_indices_ptr[j] =
+      result_indices_buffer[i] & ~index_msb_1_mask;  // clear most significant bit
+  }
+
+  if (threadIdx.x == 0 && cta_id == 0 && num_executed_iterations != nullptr) {
+    num_executed_iterations[query_id] = iter + 1;
+  }
+
+#ifdef _CLK_BREAKDOWN
+  if ((threadIdx.x == 0 || threadIdx.x == BLOCK_SIZE - 1) && (blockIdx.x == 0) &&
+      ((query_id * 3) % gridDim.y < 3)) {
+    RAFT_LOG_DEBUG(
+      "query, %d, thread, %d"
+      ", init, %d"
+      ", 1st_distance, %lu"
+      ", topk, %lu"
+      ", pickup_parents, %lu"
+      ", distance, %lu"
+      "\n",
+      query_id,
+      threadIdx.x,
+      clk_init,
+      clk_compute_1st_distance,
+      clk_topk,
+      clk_pickup_parents,
+      clk_compute_distance);
+  }
+#endif
+}
+
+template <class T>
+RAFT_KERNEL set_value_batch_kernel(T* const dev_ptr,
+                                   const std::size_t ld,
+                                   const T val,
+                                   const std::size_t count,
+                                   const std::size_t batch_size)
+{
+  const auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid >= count * batch_size) { return; }
+  const auto batch_id              = tid / count;
+  const auto elem_id               = tid % count;
+  dev_ptr[elem_id + ld * batch_id] = val;
+}
+
+template <class T>
+void set_value_batch(T* const dev_ptr,
+                     const std::size_t ld,
+                     const T val,
+                     const std::size_t count,
+                     const std::size_t batch_size,
+                     cudaStream_t cuda_stream)
+{
+  constexpr std::uint32_t block_size = 256;
+  const auto grid_size               = (count * batch_size + block_size - 1) / block_size;
+  set_value_batch_kernel<T>
+    <<<grid_size, block_size, 0, cuda_stream>>>(dev_ptr, ld, val, count, batch_size);
+}
+
+template <uint32_t TEAM_SIZE,
+          uint32_t DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+struct search_kernel_config {
+  // Search kernel function type. Note that the actual values for the template value
+  // parameters do not matter, because they are not part of the function signature. The
+  // second to fourth value parameters will be selected by the choose_* functions below.
+  using kernel_t = decltype(&search_kernel<TEAM_SIZE,
+                                           DATASET_BLOCK_DIM,
+                                           128,
+                                           DATASET_DESCRIPTOR_T,
+                                           SAMPLE_FILTER_T>);
+
+  static auto choose_buffer_size(unsigned result_buffer_size, unsigned block_size) -> kernel_t
+  {
+    if (result_buffer_size <= 64) {
+      return search_kernel<TEAM_SIZE, DATASET_BLOCK_DIM, 64, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
+    } else if (result_buffer_size <= 128) {
+      return search_kernel<TEAM_SIZE,
+                           DATASET_BLOCK_DIM,
+                           128,
+                           DATASET_DESCRIPTOR_T,
+                           SAMPLE_FILTER_T>;
+    } else if (result_buffer_size <= 256) {
+      return search_kernel<TEAM_SIZE,
+                           DATASET_BLOCK_DIM,
+                           256,
+                           DATASET_DESCRIPTOR_T,
+                           SAMPLE_FILTER_T>;
+    }
+    THROW("Result buffer size %u larger than max buffer size %u", result_buffer_size, 256);
+  }
+};
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+void select_and_run(
+  DATASET_DESCRIPTOR_T dataset_desc,
+  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
+    graph,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const uint32_t num_queries,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
+  uint32_t* const num_executed_iterations,                     // [num_queries,]
+  uint32_t topk,
+  // multi_cta_search (params struct)
+  uint32_t block_size,  //
+  uint32_t result_buffer_size,
+  uint32_t smem_size,
+  int64_t hash_bitlen,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
+  uint32_t num_cta_per_query,
+  uint32_t num_random_samplings,
+  uint64_t rand_xor_mask,
+  uint32_t num_seeds,
+  size_t itopk_size,
+  size_t search_width,
+  size_t min_iterations,
+  size_t max_iterations,
+  SAMPLE_FILTER_T sample_filter,
+  cuvs::distance::DistanceType metric,
+  cudaStream_t stream)
+{
+  auto kernel =
+    search_kernel_config<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::
+      choose_buffer_size(result_buffer_size, block_size);
+
+  RAFT_CUDA_TRY(cudaFuncSetAttribute(kernel,
+                                     cudaFuncAttributeMaxDynamicSharedMemorySize,
+                                     smem_size + DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte));
+  // Initialize hash table
+  const uint32_t hash_size = hashmap::get_size(hash_bitlen);
+  set_value_batch(hashmap_ptr,
+                  hash_size,
+                  utils::get_max_value<typename DATASET_DESCRIPTOR_T::INDEX_T>(),
+                  hash_size,
+                  num_queries,
+                  stream);
+
+  dim3 block_dims(block_size, 1, 1);
+  dim3 grid_dims(num_cta_per_query, num_queries, 1);
+  RAFT_LOG_DEBUG("Launching kernel with %u threads, (%u, %u) blocks %u smem",
+                 block_size,
+                 num_cta_per_query,
+                 num_queries,
+                 smem_size);
+
+  kernel<<<grid_dims, block_dims, smem_size, stream>>>(topk_indices_ptr,
+                                                       topk_distances_ptr,
+                                                       dataset_desc,
+                                                       queries_ptr,
+                                                       graph.data_handle(),
+                                                       graph.extent(1),
+                                                       num_random_samplings,
+                                                       rand_xor_mask,
+                                                       dev_seed_ptr,
+                                                       num_seeds,
+                                                       hashmap_ptr,
+                                                       hash_bitlen,
+                                                       itopk_size,
+                                                       search_width,
+                                                       min_iterations,
+                                                       max_iterations,
+                                                       num_executed_iterations,
+                                                       sample_filter,
+                                                       metric);
+}
+
+}  // namespace multi_cta_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
new file mode 100644
index 0000000000..e003907292
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#include "search_multi_cta_kernel-inl.cuh"
+#endif
+
+#ifdef RAFT_COMPILED
+#include "search_multi_cta_kernel-ext.cuh"
+#endif
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..3fa12d9338
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..e2f25a1c24
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..4cd206d8ce
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..56989a1d57
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
new file mode 100644
index 0000000000..8c13b61c0b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -0,0 +1,1095 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "compute_distance.hpp"
+#include "compute_distance_vpq.cuh"
+#include "device_common.hpp"
+#include "hashmap.hpp"
+#include "search_plan.cuh"
+#include "topk_for_cagra/topk_core.cuh"  //todo replace with raft kernel
+#include "utils.hpp"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+
+#include <raft/matrix/select_k.cuh>
+
+#include <cuvs/neighbors/sample_filter.hpp>
+
+// TODO: This shouldn't be invoking anything from spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/util/cuda_rt_essentials.hpp>
+#include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
+
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <algorithm>
+#include <cassert>
+#include <iostream>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace multi_kernel_search {
+
+template <class T>
+RAFT_KERNEL set_value_kernel(T* const dev_ptr, const T val)
+{
+  *dev_ptr = val;
+}
+
+template <class T>
+RAFT_KERNEL set_value_kernel(T* const dev_ptr, const T val, const std::size_t count)
+{
+  const auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid >= count) { return; }
+  dev_ptr[tid] = val;
+}
+
+template <class T>
+void set_value(T* const dev_ptr, const T val, cudaStream_t cuda_stream)
+{
+  set_value_kernel<T><<<1, 1, 0, cuda_stream>>>(dev_ptr, val);
+}
+
+template <class T>
+void set_value(T* const dev_ptr, const T val, const std::size_t count, cudaStream_t cuda_stream)
+{
+  constexpr std::uint32_t block_size = 256;
+  const auto grid_size               = (count + block_size - 1) / block_size;
+  set_value_kernel<T><<<grid_size, block_size, 0, cuda_stream>>>(dev_ptr, val, count);
+}
+
+template <class T>
+RAFT_KERNEL get_value_kernel(T* const host_ptr, const T* const dev_ptr)
+{
+  *host_ptr = *dev_ptr;
+}
+
+template <class T>
+void get_value(T* const host_ptr, const T* const dev_ptr, cudaStream_t cuda_stream)
+{
+  get_value_kernel<T><<<1, 1, 0, cuda_stream>>>(host_ptr, dev_ptr);
+}
+
+// MAX_DATASET_DIM : must equal to or greater than dataset_dim
+template <unsigned TEAM_SIZE, unsigned DATASET_BLOCK_DIM, class DATASET_DESCRIPTOR_T>
+RAFT_KERNEL random_pickup_kernel(
+  const DATASET_DESCRIPTOR_T dataset_desc,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const std::size_t num_pickup,
+  const unsigned num_distilation,
+  const uint64_t rand_xor_mask,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
+  const uint32_t num_seeds,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, ldr]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldr]
+  const std::uint32_t ldr,                                                // (*) ldr >= num_pickup
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const visited_hashmap_ptr,  // [num_queries, 1 << bitlen]
+  const std::uint32_t hash_bitlen,
+  const cuvs::distance::DistanceType metric)
+{
+  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+
+  const auto ldb               = hashmap::get_size(hash_bitlen);
+  const auto global_team_index = (blockIdx.x * blockDim.x + threadIdx.x) / TEAM_SIZE;
+  const uint32_t query_id      = blockIdx.y;
+  if (global_team_index >= num_pickup) { return; }
+  // Load a query
+  extern __shared__ float query_buffer[];
+  const auto query_smem_buffer_length =
+    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+  for (uint32_t i = threadIdx.x; i < query_smem_buffer_length; i += blockDim.x) {
+    unsigned j = device::swizzling(i);
+    if (i < dataset_desc.dim) {
+      query_buffer[j] = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+        (queries_ptr + query_id * dataset_desc.dim)[i]);
+    } else {
+      query_buffer[j] = 0.0;
+    }
+  }
+  __syncthreads();
+
+  INDEX_T best_index_team_local;
+  DISTANCE_T best_norm2_team_local = utils::get_max_value<DISTANCE_T>();
+  for (unsigned i = 0; i < num_distilation; i++) {
+    INDEX_T seed_index;
+    if (seed_ptr && (global_team_index < num_seeds)) {
+      seed_index = seed_ptr[global_team_index + (num_seeds * query_id)];
+    } else {
+      // Chose a seed node randomly
+      seed_index =
+        device::xorshift64((global_team_index ^ rand_xor_mask) * (i + 1)) % dataset_desc.size;
+    }
+
+    DISTANCE_T norm2;
+    switch (metric) {
+      case cuvs::distance::DistanceType::L2Expanded:
+        norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                         TEAM_SIZE,
+                                                         cuvs::distance::DistanceType::L2Expanded>(
+          query_buffer, seed_index, true);
+        break;
+      case cuvs::distance::DistanceType::InnerProduct:
+        norm2 =
+          dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                   TEAM_SIZE,
+                                                   cuvs::distance::DistanceType::InnerProduct>(
+            query_buffer, seed_index, true);
+        break;
+      default: break;
+    }
+
+    if (norm2 < best_norm2_team_local) {
+      best_norm2_team_local = norm2;
+      best_index_team_local = seed_index;
+    }
+  }
+
+  const auto store_gmem_index = global_team_index + (ldr * query_id);
+  if (threadIdx.x % TEAM_SIZE == 0) {
+    if (hashmap::insert(
+          visited_hashmap_ptr + (ldb * query_id), hash_bitlen, best_index_team_local)) {
+      result_distances_ptr[store_gmem_index] = best_norm2_team_local;
+      result_indices_ptr[store_gmem_index]   = best_index_team_local;
+    } else {
+      result_distances_ptr[store_gmem_index] = utils::get_max_value<DISTANCE_T>();
+      result_indices_ptr[store_gmem_index]   = utils::get_max_value<INDEX_T>();
+    }
+  }
+}
+
+// MAX_DATASET_DIM : must be equal to or greater than dataset_dim
+template <unsigned TEAM_SIZE, unsigned DATASET_BLOCK_DIM, class DATASET_DESCRIPTOR_T>
+void random_pickup(
+  const DATASET_DESCRIPTOR_T dataset_desc,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const std::size_t num_queries,
+  const std::size_t num_pickup,
+  const unsigned num_distilation,
+  const uint64_t rand_xor_mask,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
+  const uint32_t num_seeds,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, ldr]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldr]
+  const std::size_t ldr,                                                  // (*) ldr >= num_pickup
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const visited_hashmap_ptr,  // [num_queries, 1 << bitlen]
+  const std::uint32_t hash_bitlen,
+  const cuvs::distance::DistanceType metric,
+  cudaStream_t const cuda_stream = 0)
+{
+  const auto block_size                = 256u;
+  const auto num_teams_per_threadblock = block_size / TEAM_SIZE;
+  const dim3 grid_size((num_pickup + num_teams_per_threadblock - 1) / num_teams_per_threadblock,
+                       num_queries);
+
+  const auto query_smem_buffer_length =
+    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+  const auto smem_size = query_smem_buffer_length * sizeof(float);
+
+  random_pickup_kernel<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T>
+    <<<grid_size, block_size, smem_size, cuda_stream>>>(dataset_desc,
+                                                        queries_ptr,
+                                                        num_pickup,
+                                                        num_distilation,
+                                                        rand_xor_mask,
+                                                        seed_ptr,
+                                                        num_seeds,
+                                                        result_indices_ptr,
+                                                        result_distances_ptr,
+                                                        ldr,
+                                                        visited_hashmap_ptr,
+                                                        hash_bitlen,
+                                                        metric);
+}
+
+template <class INDEX_T>
+RAFT_KERNEL pickup_next_parents_kernel(
+  INDEX_T* const parent_candidates_ptr,        // [num_queries, lds]
+  const std::size_t lds,                       // (*) lds >= parent_candidates_size
+  const std::uint32_t parent_candidates_size,  //
+  INDEX_T* const visited_hashmap_ptr,          // [num_queries, 1 << hash_bitlen]
+  const std::size_t hash_bitlen,
+  const std::uint32_t small_hash_bitlen,
+  INDEX_T* const parent_list_ptr,      // [num_queries, ldd]
+  const std::size_t ldd,               // (*) ldd >= parent_list_size
+  const std::size_t parent_list_size,  //
+  std::uint32_t* const terminate_flag)
+{
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+
+  const std::size_t ldb   = hashmap::get_size(hash_bitlen);
+  const uint32_t query_id = blockIdx.x;
+  if (threadIdx.x < 32) {
+    // pickup next parents with single warp
+    for (std::uint32_t i = threadIdx.x; i < parent_list_size; i += 32) {
+      parent_list_ptr[i + (ldd * query_id)] = utils::get_max_value<INDEX_T>();
+    }
+    std::uint32_t parent_candidates_size_max = parent_candidates_size;
+    if (parent_candidates_size % 32) {
+      parent_candidates_size_max += 32 - (parent_candidates_size % 32);
+    }
+    std::uint32_t num_new_parents = 0;
+    for (std::uint32_t j = threadIdx.x; j < parent_candidates_size_max; j += 32) {
+      INDEX_T index;
+      int new_parent = 0;
+      if (j < parent_candidates_size) {
+        index = parent_candidates_ptr[j + (lds * query_id)];
+        if ((index & index_msb_1_mask) == 0) {  // check most significant bit
+          new_parent = 1;
+        }
+      }
+      const std::uint32_t ballot_mask = __ballot_sync(0xffffffff, new_parent);
+      if (new_parent) {
+        const auto i = __popc(ballot_mask & ((1 << threadIdx.x) - 1)) + num_new_parents;
+        if (i < parent_list_size) {
+          parent_list_ptr[i + (ldd * query_id)] = j;
+          parent_candidates_ptr[j + (lds * query_id)] |=
+            index_msb_1_mask;  // set most significant bit as used node
+        }
+      }
+      num_new_parents += __popc(ballot_mask);
+      if (num_new_parents >= parent_list_size) { break; }
+    }
+    if ((num_new_parents > 0) && (threadIdx.x == 0)) { *terminate_flag = 0; }
+  } else if (small_hash_bitlen) {
+    // reset small-hash
+    hashmap::init(visited_hashmap_ptr + (ldb * query_id), hash_bitlen, 32);
+  }
+
+  if (small_hash_bitlen) {
+    __syncthreads();
+    // insert internal-topk indices into small-hash
+    for (unsigned i = threadIdx.x; i < parent_candidates_size; i += blockDim.x) {
+      auto key = parent_candidates_ptr[i + (lds * query_id)] &
+                 ~index_msb_1_mask;  // clear most significant bit
+      hashmap::insert(visited_hashmap_ptr + (ldb * query_id), hash_bitlen, key);
+    }
+  }
+}
+
+template <class INDEX_T>
+void pickup_next_parents(INDEX_T* const parent_candidates_ptr,  // [num_queries, lds]
+                         const std::size_t lds,                 // (*) lds >= parent_candidates_size
+                         const std::size_t parent_candidates_size,  //
+                         const std::size_t num_queries,
+                         INDEX_T* const visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+                         const std::size_t hash_bitlen,
+                         const std::size_t small_hash_bitlen,
+                         INDEX_T* const parent_list_ptr,      // [num_queries, ldd]
+                         const std::size_t ldd,               // (*) ldd >= parent_list_size
+                         const std::size_t parent_list_size,  //
+                         std::uint32_t* const terminate_flag,
+                         cudaStream_t cuda_stream = 0)
+{
+  std::uint32_t block_size = 32;
+  if (small_hash_bitlen) {
+    block_size = 128;
+    while (parent_candidates_size > block_size) {
+      block_size *= 2;
+    }
+    block_size = min(block_size, (uint32_t)512);
+  }
+  pickup_next_parents_kernel<INDEX_T>
+    <<<num_queries, block_size, 0, cuda_stream>>>(parent_candidates_ptr,
+                                                  lds,
+                                                  parent_candidates_size,
+                                                  visited_hashmap_ptr,
+                                                  hash_bitlen,
+                                                  small_hash_bitlen,
+                                                  parent_list_ptr,
+                                                  ldd,
+                                                  parent_list_size,
+                                                  terminate_flag);
+}
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          class DATASET_DESCRIPTOR_T,
+          class SAMPLE_FILTER_T>
+RAFT_KERNEL compute_distance_to_child_nodes_kernel(
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    parent_node_list,  // [num_queries, search_width]
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    parent_candidates_ptr,  // [num_queries, search_width]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const
+    parent_distance_ptr,  // [num_queries, search_width]
+  const std::size_t lds,
+  const std::uint32_t search_width,
+  const DATASET_DESCRIPTOR_T dataset_desc,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    neighbor_graph_ptr,  // [dataset_size, graph_degree]
+  const std::uint32_t graph_degree,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* query_ptr,  // [num_queries, data_dim]
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+  const std::uint32_t hash_bitlen,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, ldd]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldd]
+  const std::uint32_t ldd,  // (*) ldd >= search_width * graph_degree
+  SAMPLE_FILTER_T sample_filter,
+  const cuvs::distance::DistanceType metric)
+{
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+
+  const uint32_t ldb        = hashmap::get_size(hash_bitlen);
+  const auto tid            = threadIdx.x + blockDim.x * blockIdx.x;
+  const auto global_team_id = tid / TEAM_SIZE;
+  const auto query_id       = blockIdx.y;
+
+  extern __shared__ float query_buffer[];
+  const auto query_smem_buffer_length =
+    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+  for (uint32_t i = threadIdx.x; i < query_smem_buffer_length; i += blockDim.x) {
+    unsigned j = device::swizzling(i);
+    if (i < dataset_desc.dim) {
+      query_buffer[j] = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+        (query_ptr + query_id * dataset_desc.dim)[i]);
+    } else {
+      query_buffer[j] = 0.0;
+    }
+  }
+  __syncthreads();
+  if (global_team_id >= search_width * graph_degree) { return; }
+
+  const std::size_t parent_list_index =
+    parent_node_list[global_team_id / graph_degree + (search_width * blockIdx.y)];
+
+  if (parent_list_index == utils::get_max_value<INDEX_T>()) { return; }
+
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+  const auto raw_parent_index        = parent_candidates_ptr[parent_list_index + (lds * query_id)];
+
+  if (raw_parent_index == utils::get_max_value<INDEX_T>()) {
+    result_distances_ptr[ldd * blockIdx.y + global_team_id] = utils::get_max_value<DISTANCE_T>();
+    return;
+  }
+  const auto parent_index = raw_parent_index & ~index_msb_1_mask;
+
+  const auto neighbor_list_head_ptr = neighbor_graph_ptr + (graph_degree * parent_index);
+
+  const std::size_t child_id = neighbor_list_head_ptr[global_team_id % graph_degree];
+
+  const auto compute_distance_flag = hashmap::insert<TEAM_SIZE, INDEX_T>(
+    visited_hashmap_ptr + (ldb * blockIdx.y), hash_bitlen, child_id);
+
+  DISTANCE_T norm2;
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2Expanded:
+      norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                       TEAM_SIZE,
+                                                       cuvs::distance::DistanceType::L2Expanded>(
+        query_buffer, child_id, compute_distance_flag);
+      break;
+    case cuvs::distance::DistanceType::InnerProduct:
+      norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
+                                                       TEAM_SIZE,
+                                                       cuvs::distance::DistanceType::InnerProduct>(
+        query_buffer, child_id, compute_distance_flag);
+      break;
+    default: break;
+  }
+
+  if (compute_distance_flag) {
+    if (threadIdx.x % TEAM_SIZE == 0) {
+      result_indices_ptr[ldd * blockIdx.y + global_team_id]   = child_id;
+      result_distances_ptr[ldd * blockIdx.y + global_team_id] = norm2;
+    }
+  } else {
+    if (threadIdx.x % TEAM_SIZE == 0) {
+      result_distances_ptr[ldd * blockIdx.y + global_team_id] = utils::get_max_value<DISTANCE_T>();
+    }
+  }
+
+  if constexpr (!std::is_same<SAMPLE_FILTER_T,
+                              cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+    if (!sample_filter(query_id, parent_index)) {
+      parent_candidates_ptr[parent_list_index + (lds * query_id)] = utils::get_max_value<INDEX_T>();
+      parent_distance_ptr[parent_list_index + (lds * query_id)] =
+        utils::get_max_value<DISTANCE_T>();
+    }
+  }
+}
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          class SAMPLE_FILTER_T,
+          class DATASET_DESCRIPTOR_T>
+void compute_distance_to_child_nodes(
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    parent_node_list,  // [num_queries, search_width]
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    parent_candidates_ptr,  // [num_queries, search_width]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const
+    parent_distance_ptr,  // [num_queries, search_width]
+  const std::size_t lds,
+  const uint32_t search_width,
+  const DATASET_DESCRIPTOR_T dataset_desc,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    neighbor_graph_ptr,  // [dataset_size, graph_degree]
+  const std::uint32_t graph_degree,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* query_ptr,  // [num_queries, data_dim]
+  const std::uint32_t num_queries,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+  const std::uint32_t hash_bitlen,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, ldd]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldd]
+  const std::uint32_t ldd,  // (*) ldd >= search_width * graph_degree
+  SAMPLE_FILTER_T sample_filter,
+  const cuvs::distance::DistanceType metric,
+  cudaStream_t cuda_stream = 0)
+{
+  const auto block_size = 128;
+  const dim3 grid_size(
+    (search_width * graph_degree + (block_size / TEAM_SIZE) - 1) / (block_size / TEAM_SIZE),
+    num_queries);
+
+  const auto query_smem_buffer_length =
+    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+
+  const auto smem_size =
+    query_smem_buffer_length * sizeof(float) + DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte;
+
+  compute_distance_to_child_nodes_kernel<TEAM_SIZE,
+                                         DATASET_BLOCK_DIM,
+                                         DATASET_DESCRIPTOR_T,
+                                         SAMPLE_FILTER_T>
+    <<<grid_size, block_size, smem_size, cuda_stream>>>(parent_node_list,
+                                                        parent_candidates_ptr,
+                                                        parent_distance_ptr,
+                                                        lds,
+                                                        search_width,
+                                                        dataset_desc,
+                                                        neighbor_graph_ptr,
+                                                        graph_degree,
+                                                        query_ptr,
+                                                        visited_hashmap_ptr,
+                                                        hash_bitlen,
+                                                        result_indices_ptr,
+                                                        result_distances_ptr,
+                                                        ldd,
+                                                        sample_filter,
+                                                        metric);
+}
+
+template <class INDEX_T>
+RAFT_KERNEL remove_parent_bit_kernel(const std::uint32_t num_queries,
+                                     const std::uint32_t num_topk,
+                                     INDEX_T* const topk_indices_ptr,  // [ld, num_queries]
+                                     const std::uint32_t ld)
+{
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+
+  uint32_t i_query = blockIdx.x;
+  if (i_query >= num_queries) return;
+
+  for (unsigned i = threadIdx.x; i < num_topk; i += blockDim.x) {
+    topk_indices_ptr[i + (ld * i_query)] &= ~index_msb_1_mask;  // clear most significant bit
+  }
+}
+
+template <class INDEX_T>
+void remove_parent_bit(const std::uint32_t num_queries,
+                       const std::uint32_t num_topk,
+                       INDEX_T* const topk_indices_ptr,  // [ld, num_queries]
+                       const std::uint32_t ld,
+                       cudaStream_t cuda_stream = 0)
+{
+  const std::size_t grid_size  = num_queries;
+  const std::size_t block_size = 256;
+  remove_parent_bit_kernel<<<grid_size, block_size, 0, cuda_stream>>>(
+    num_queries, num_topk, topk_indices_ptr, ld);
+}
+
+// This function called after the `remove_parent_bit` function
+template <class INDEX_T, class DISTANCE_T, class SAMPLE_FILTER_T>
+RAFT_KERNEL apply_filter_kernel(INDEX_T* const result_indices_ptr,
+                                DISTANCE_T* const result_distances_ptr,
+                                const std::size_t lds,
+                                const std::uint32_t result_buffer_size,
+                                const std::uint32_t num_queries,
+                                const INDEX_T query_id_offset,
+                                SAMPLE_FILTER_T sample_filter)
+{
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+  const auto tid                     = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid >= result_buffer_size * num_queries) { return; }
+  const auto i     = tid % result_buffer_size;
+  const auto j     = tid / result_buffer_size;
+  const auto index = i + j * lds;
+
+  if (result_indices_ptr[index] != ~index_msb_1_mask &&
+      !sample_filter(query_id_offset + j, result_indices_ptr[index])) {
+    result_indices_ptr[index]   = utils::get_max_value<INDEX_T>();
+    result_distances_ptr[index] = utils::get_max_value<DISTANCE_T>();
+  }
+}
+
+template <class INDEX_T, class DISTANCE_T, class SAMPLE_FILTER_T>
+void apply_filter(INDEX_T* const result_indices_ptr,
+                  DISTANCE_T* const result_distances_ptr,
+                  const std::size_t lds,
+                  const std::uint32_t result_buffer_size,
+                  const std::uint32_t num_queries,
+                  const INDEX_T query_id_offset,
+                  SAMPLE_FILTER_T sample_filter,
+                  cudaStream_t cuda_stream)
+{
+  const std::uint32_t block_size = 256;
+  const std::uint32_t grid_size  = raft::ceildiv(num_queries * result_buffer_size, block_size);
+
+  apply_filter_kernel<<<grid_size, block_size, 0, cuda_stream>>>(result_indices_ptr,
+                                                                 result_distances_ptr,
+                                                                 lds,
+                                                                 result_buffer_size,
+                                                                 num_queries,
+                                                                 query_id_offset,
+                                                                 sample_filter);
+}
+
+template <class T>
+RAFT_KERNEL batched_memcpy_kernel(T* const dst,  // [batch_size, ld_dst]
+                                  const uint64_t ld_dst,
+                                  const T* const src,  // [batch_size, ld_src]
+                                  const uint64_t ld_src,
+                                  const uint64_t count,
+                                  const uint64_t batch_size)
+{
+  const auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid >= count * batch_size) { return; }
+  const auto i          = tid % count;
+  const auto j          = tid / count;
+  dst[i + (ld_dst * j)] = src[i + (ld_src * j)];
+}
+
+template <class T>
+void batched_memcpy(T* const dst,  // [batch_size, ld_dst]
+                    const uint64_t ld_dst,
+                    const T* const src,  // [batch_size, ld_src]
+                    const uint64_t ld_src,
+                    const uint64_t count,
+                    const uint64_t batch_size,
+                    cudaStream_t cuda_stream)
+{
+  assert(ld_dst >= count);
+  assert(ld_src >= count);
+  constexpr uint32_t block_size = 256;
+  const auto grid_size          = (batch_size * count + block_size - 1) / block_size;
+  batched_memcpy_kernel<T>
+    <<<grid_size, block_size, 0, cuda_stream>>>(dst, ld_dst, src, ld_src, count, batch_size);
+}
+
+template <class T>
+RAFT_KERNEL set_value_batch_kernel(T* const dev_ptr,
+                                   const std::size_t ld,
+                                   const T val,
+                                   const std::size_t count,
+                                   const std::size_t batch_size)
+{
+  const auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid >= count * batch_size) { return; }
+  const auto batch_id              = tid / count;
+  const auto elem_id               = tid % count;
+  dev_ptr[elem_id + ld * batch_id] = val;
+}
+
+template <class T>
+void set_value_batch(T* const dev_ptr,
+                     const std::size_t ld,
+                     const T val,
+                     const std::size_t count,
+                     const std::size_t batch_size,
+                     cudaStream_t cuda_stream)
+{
+  constexpr std::uint32_t block_size = 256;
+  const auto grid_size               = (count * batch_size + block_size - 1) / block_size;
+  set_value_batch_kernel<T>
+    <<<grid_size, block_size, 0, cuda_stream>>>(dev_ptr, ld, val, count, batch_size);
+}
+
+// result_buffer (work buffer) for "multi-kernel"
+// +--------------------+------------------------------+-------------------+
+// | internal_top_k (A) | neighbors of internal_top_k  | internal_topk (B) |
+// | <itopk_size>       | <search_width * graph_degree> | <itopk_size>      |
+// +--------------------+------------------------------+-------------------+
+// |<---                 result_buffer_allocation_size                 --->|
+// |<---                       result_buffer_size  --->|                     // Double buffer (A)
+//                      |<---  result_buffer_size                      --->| // Double buffer (B)
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
+  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+
+  static_assert(std::is_same_v<DISTANCE_T, float>, "Only float is supported as resulting distance");
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_queries;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::itopk_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::algo;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::team_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::search_width;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::min_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::thread_block_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_mode;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_min_bitlen;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_max_fill_rate;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_random_samplings;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::rand_xor_mask;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dim;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::graph_degree;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::topk;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hash_bitlen;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_bitlen;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_reset_interval;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dataset_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::result_buffer_size;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::smem_size;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_executed_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dev_seed;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_seeds;
+
+  size_t result_buffer_allocation_size;
+  rmm::device_uvector<INDEX_T> result_indices;       // results_indices_buffer
+  rmm::device_uvector<DISTANCE_T> result_distances;  // result_distances_buffer
+  rmm::device_uvector<INDEX_T> parent_node_list;
+  rmm::device_uvector<uint32_t> topk_hint;
+  rmm::device_scalar<uint32_t> terminate_flag;  // dev_terminate_flag, host_terminate_flag.;
+  rmm::device_uvector<uint32_t> topk_workspace;
+
+  // temporary storage for _find_topk
+  rmm::device_uvector<float> input_keys_storage;
+  rmm::device_uvector<float> output_keys_storage;
+  rmm::device_uvector<INDEX_T> input_values_storage;
+  rmm::device_uvector<INDEX_T> output_values_storage;
+
+  search(raft::resources const& res,
+         search_params params,
+         int64_t dim,
+         int64_t graph_degree,
+         uint32_t topk,
+         cuvs::distance::DistanceType metric)
+    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
+        res, params, dim, graph_degree, topk, metric),
+      result_indices(0, raft::resource::get_cuda_stream(res)),
+      result_distances(0, raft::resource::get_cuda_stream(res)),
+      parent_node_list(0, raft::resource::get_cuda_stream(res)),
+      topk_hint(0, raft::resource::get_cuda_stream(res)),
+      topk_workspace(0, raft::resource::get_cuda_stream(res)),
+      terminate_flag(raft::resource::get_cuda_stream(res)),
+      input_keys_storage(0, raft::resource::get_cuda_stream(res)),
+      output_keys_storage(0, raft::resource::get_cuda_stream(res)),
+      input_values_storage(0, raft::resource::get_cuda_stream(res)),
+      output_values_storage(0, raft::resource::get_cuda_stream(res))
+  {
+    set_params(res);
+  }
+
+  void set_params(raft::resources const& res)
+  {
+    //
+    // Allocate memory for intermediate buffer and workspace.
+    //
+    result_buffer_size            = itopk_size + (search_width * graph_degree);
+    result_buffer_allocation_size = result_buffer_size + itopk_size;
+    result_indices.resize(result_buffer_allocation_size * max_queries,
+                          raft::resource::get_cuda_stream(res));
+    result_distances.resize(result_buffer_allocation_size * max_queries,
+                            raft::resource::get_cuda_stream(res));
+
+    parent_node_list.resize(max_queries * search_width, raft::resource::get_cuda_stream(res));
+    topk_hint.resize(max_queries, raft::resource::get_cuda_stream(res));
+
+    size_t topk_workspace_size = _cuann_find_topk_bufferSize(
+      itopk_size, max_queries, result_buffer_size, utils::get_cuda_data_type<DATA_T>());
+    RAFT_LOG_DEBUG("# topk_workspace_size: %lu", topk_workspace_size);
+    topk_workspace.resize(topk_workspace_size, raft::resource::get_cuda_stream(res));
+
+    hashmap.resize(hashmap_size, raft::resource::get_cuda_stream(res));
+  }
+
+  ~search() {}
+
+  inline void _find_topk(raft::resources const& handle,
+                         uint32_t topK,
+                         uint32_t sizeBatch,
+                         uint32_t numElements,
+                         const float* inputKeys,    // [sizeBatch, ldIK,]
+                         uint32_t ldIK,             // (*) ldIK >= numElements
+                         const INDEX_T* inputVals,  // [sizeBatch, ldIV,]
+                         uint32_t ldIV,             // (*) ldIV >= numElements
+                         float* outputKeys,         // [sizeBatch, ldOK,]
+                         uint32_t ldOK,             // (*) ldOK >= topK
+                         INDEX_T* outputVals,       // [sizeBatch, ldOV,]
+                         uint32_t ldOV,             // (*) ldOV >= topK
+                         void* workspace,
+                         bool sort,
+                         uint32_t* hints)
+  {
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    // _cuann_find_topk right now is limited to a max-k of 1024.
+    // RAFT has a matrix::select_k function - which handles arbitrary sized values of k,
+    // but doesn't accept strided inputs unlike _cuann_find_topk
+    // The multi-kernel search path requires strided access - since its cleverly allocating memory
+    // (layout described in the search_plan_impl function below), such that both the
+    // neighbors and the internal_topk are adjacent - in a double buffered format.
+    // Since this layout doesn't work with the matrix::select_k code - we have to copy
+    // over to a contiguous (non-strided) access to handle topk larger than 1024, and
+    // potentially also copy back to a strided layout afterwards
+    if (topK <= 1024) {
+      return _cuann_find_topk(topK,
+                              sizeBatch,
+                              numElements,
+                              inputKeys,
+                              ldIK,
+                              inputVals,
+                              ldIV,
+                              outputKeys,
+                              ldOK,
+                              outputVals,
+                              ldOV,
+                              workspace,
+                              sort,
+                              hints,
+                              stream);
+    }
+
+    if (ldIK > numElements) {
+      if (input_keys_storage.size() != sizeBatch * numElements) {
+        input_keys_storage.resize(sizeBatch * numElements, stream);
+      }
+      batched_memcpy(
+        input_keys_storage.data(), numElements, inputKeys, ldIK, numElements, sizeBatch, stream);
+      inputKeys = input_keys_storage.data();
+    }
+
+    if (ldIV > numElements) {
+      if (input_values_storage.size() != sizeBatch * numElements) {
+        input_values_storage.resize(sizeBatch * numElements, stream);
+      }
+
+      batched_memcpy(
+        input_values_storage.data(), numElements, inputVals, ldIV, numElements, sizeBatch, stream);
+      inputVals = input_values_storage.data();
+    }
+
+    if ((ldOK > topK) && (output_keys_storage.size() != sizeBatch * topK)) {
+      output_keys_storage.resize(sizeBatch * topK, stream);
+    }
+
+    if ((ldOV > topK) && (output_values_storage.size() != sizeBatch * topK)) {
+      output_values_storage.resize(sizeBatch * topK, stream);
+    }
+
+    raft::matrix::select_k<float, INDEX_T>(
+      handle,
+      raft::make_device_matrix_view<const float, int64_t>(inputKeys, sizeBatch, numElements),
+      raft::make_device_matrix_view<const INDEX_T, int64_t>(inputVals, sizeBatch, numElements),
+      raft::make_device_matrix_view<float, int64_t>(
+        ldOK > topK ? output_keys_storage.data() : outputKeys, sizeBatch, topK),
+      raft::make_device_matrix_view<INDEX_T, int64_t>(
+        ldOV > topK ? output_values_storage.data() : outputVals, sizeBatch, topK),
+      true,  // select_min
+      sort);
+
+    if (ldOK > topK) {
+      batched_memcpy(outputKeys, ldOK, output_keys_storage.data(), topK, topK, sizeBatch, stream);
+    }
+
+    if (ldOV > topK) {
+      batched_memcpy(outputVals, ldOV, output_values_storage.data(), topK, topK, sizeBatch, stream);
+    }
+  }
+
+  void operator()(raft::resources const& res,
+                  DATASET_DESCRIPTOR_T dataset_desc,
+                  raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
+                  INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+                  DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+                  const DATA_T* const queries_ptr,       // [num_queries, dataset_dim]
+                  const uint32_t num_queries,
+                  const INDEX_T* dev_seed_ptr,              // [num_queries, num_seeds]
+                  uint32_t* const num_executed_iterations,  // [num_queries,]
+                  uint32_t topk,
+                  SAMPLE_FILTER_T sample_filter)
+  {
+    // Init hashmap
+    cudaStream_t stream      = raft::resource::get_cuda_stream(res);
+    const uint32_t hash_size = hashmap::get_size(hash_bitlen);
+    set_value_batch(
+      hashmap.data(), hash_size, utils::get_max_value<INDEX_T>(), hash_size, num_queries, stream);
+
+    // Topk hint can not be used when applying a filter
+    uint32_t* const top_hint_ptr =
+      std::is_same<SAMPLE_FILTER_T, cuvs::neighbors::filtering::none_cagra_sample_filter>::value
+        ? topk_hint.data()
+        : nullptr;
+    // Init topk_hint
+    if (top_hint_ptr != nullptr && topk_hint.size() > 0) {
+      set_value(top_hint_ptr, 0xffffffffu, num_queries, stream);
+    }
+
+    // Choose initial entry point candidates at random
+    random_pickup<TEAM_SIZE, DATASET_BLOCK_DIM>(dataset_desc,
+                                                queries_ptr,
+                                                num_queries,
+                                                result_buffer_size,
+                                                num_random_samplings,
+                                                rand_xor_mask,
+                                                dev_seed_ptr,
+                                                num_seeds,
+                                                result_indices.data(),
+                                                result_distances.data(),
+                                                result_buffer_allocation_size,
+                                                hashmap.data(),
+                                                hash_bitlen,
+                                                this->metric,
+                                                stream);
+
+    unsigned iter = 0;
+    while (1) {
+      // Make an index list of internal top-k nodes
+      _find_topk(res,
+                 itopk_size,
+                 num_queries,
+                 result_buffer_size,
+                 result_distances.data() + (iter & 0x1) * itopk_size,
+                 result_buffer_allocation_size,
+                 result_indices.data() + (iter & 0x1) * itopk_size,
+                 result_buffer_allocation_size,
+                 result_distances.data() + (1 - (iter & 0x1)) * result_buffer_size,
+                 result_buffer_allocation_size,
+                 result_indices.data() + (1 - (iter & 0x1)) * result_buffer_size,
+                 result_buffer_allocation_size,
+                 topk_workspace.data(),
+                 true,
+                 top_hint_ptr);
+
+      // termination (1)
+      if ((iter + 1 == max_iterations)) {
+        iter++;
+        break;
+      }
+
+      if (iter + 1 >= min_iterations) { set_value<uint32_t>(terminate_flag.data(), 1, stream); }
+
+      // pickup parent nodes
+      uint32_t _small_hash_bitlen = 0;
+      if ((iter + 1) % small_hash_reset_interval == 0) { _small_hash_bitlen = small_hash_bitlen; }
+      pickup_next_parents(result_indices.data() + (1 - (iter & 0x1)) * result_buffer_size,
+                          result_buffer_allocation_size,
+                          itopk_size,
+                          num_queries,
+                          hashmap.data(),
+                          hash_bitlen,
+                          _small_hash_bitlen,
+                          parent_node_list.data(),
+                          search_width,
+                          search_width,
+                          terminate_flag.data(),
+                          stream);
+
+      // termination (2)
+      if (iter + 1 >= min_iterations && terminate_flag.value(stream)) {
+        iter++;
+        break;
+      }
+
+      // Compute distance to child nodes that are adjacent to the parent node
+      compute_distance_to_child_nodes<TEAM_SIZE, DATASET_BLOCK_DIM, SAMPLE_FILTER_T>(
+        parent_node_list.data(),
+        result_indices.data() + (1 - (iter & 0x1)) * result_buffer_size,
+        result_distances.data() + (1 - (iter & 0x1)) * result_buffer_size,
+        result_buffer_allocation_size,
+        search_width,
+        dataset_desc,
+        graph.data_handle(),
+        graph.extent(1),
+        queries_ptr,
+        num_queries,
+        hashmap.data(),
+        hash_bitlen,
+        result_indices.data() + itopk_size,
+        result_distances.data() + itopk_size,
+        result_buffer_allocation_size,
+        sample_filter,
+        this->metric,
+        stream);
+
+      iter++;
+    }  // while ( 1 )
+    auto result_indices_ptr   = result_indices.data() + (iter & 0x1) * result_buffer_size;
+    auto result_distances_ptr = result_distances.data() + (iter & 0x1) * result_buffer_size;
+
+    if constexpr (!std::is_same<SAMPLE_FILTER_T,
+                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+      // Remove parent bit in search results
+      remove_parent_bit(num_queries,
+                        result_buffer_size,
+                        result_indices.data() + (iter & 0x1) * itopk_size,
+                        result_buffer_allocation_size,
+                        stream);
+
+      apply_filter<INDEX_T, DISTANCE_T, SAMPLE_FILTER_T>(
+        result_indices.data() + (iter & 0x1) * itopk_size,
+        result_distances.data() + (iter & 0x1) * itopk_size,
+        result_buffer_allocation_size,
+        result_buffer_size,
+        num_queries,
+        0,
+        sample_filter,
+        stream);
+
+      result_indices_ptr   = result_indices.data() + (1 - (iter & 0x1)) * result_buffer_size;
+      result_distances_ptr = result_distances.data() + (1 - (iter & 0x1)) * result_buffer_size;
+      _find_topk(res,
+                 itopk_size,
+                 num_queries,
+                 result_buffer_size,
+                 result_distances.data() + (iter & 0x1) * itopk_size,
+                 result_buffer_allocation_size,
+                 result_indices.data() + (iter & 0x1) * itopk_size,
+                 result_buffer_allocation_size,
+                 result_distances_ptr,
+                 result_buffer_allocation_size,
+                 result_indices_ptr,
+                 result_buffer_allocation_size,
+                 topk_workspace.data(),
+                 true,
+                 top_hint_ptr);
+    } else {
+      // Remove parent bit in search results
+      remove_parent_bit(
+        num_queries, itopk_size, result_indices_ptr, result_buffer_allocation_size, stream);
+    }
+
+    // Copy results from working buffer to final buffer
+    batched_memcpy(topk_indices_ptr,
+                   topk,
+                   result_indices_ptr,
+                   result_buffer_allocation_size,
+                   topk,
+                   num_queries,
+                   stream);
+    if (topk_distances_ptr) {
+      batched_memcpy(topk_distances_ptr,
+                     topk,
+                     result_distances_ptr,
+                     result_buffer_allocation_size,
+                     topk,
+                     num_queries,
+                     stream);
+    }
+
+    if (num_executed_iterations) {
+      for (std::uint32_t i = 0; i < num_queries; i++) {
+        num_executed_iterations[i] = iter;
+      }
+    }
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+  }
+};
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          class CODE_BOOK_T,
+          unsigned PQ_BITS,
+          unsigned PQ_CODE_BOOK_DIM,
+          class DATA_T,
+          class DISTANCE_T_,
+          class INDEX_T_,
+          typename SAMPLE_FILTER_T>
+struct search<TEAM_SIZE,
+              DATASET_BLOCK_DIM,
+              cagra_q_dataset_descriptor_t<DATA_T,
+                                           CODE_BOOK_T,
+                                           PQ_BITS,
+                                           PQ_CODE_BOOK_DIM,
+                                           DISTANCE_T_,
+                                           INDEX_T_>,
+              SAMPLE_FILTER_T>
+  : public search_plan_impl<cagra_q_dataset_descriptor_t<DATA_T,
+                                                         CODE_BOOK_T,
+                                                         PQ_BITS,
+                                                         PQ_CODE_BOOK_DIM,
+
+                                                         DISTANCE_T_,
+                                                         INDEX_T_>,
+                            SAMPLE_FILTER_T> {
+  using DATASET_DESCRIPTOR_T = cagra_q_dataset_descriptor_t<DATA_T,
+                                                            CODE_BOOK_T,
+                                                            PQ_BITS,
+                                                            PQ_CODE_BOOK_DIM,
+
+                                                            DISTANCE_T_,
+                                                            INDEX_T_>;
+  using INDEX_T              = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T           = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+
+  search(raft::resources const& res,
+         search_params params,
+         int64_t dim,
+         int64_t graph_degree,
+         uint32_t topk,
+         cuvs::distance::DistanceType metric)
+    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
+        res, params, dim, graph_degree, topk, metric)
+  {
+    THROW("The multi-kernel mode does not support VPQ");
+  }
+
+  void set_params(raft::resources const& res) {}
+
+  void operator()(raft::resources const& res,
+                  DATASET_DESCRIPTOR_T dataset_desc,
+                  raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
+                  INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+                  DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+                  const DATA_T* const queries_ptr,       // [num_queries, dataset_dim]
+                  const uint32_t num_queries,
+                  const INDEX_T* dev_seed_ptr,              // [num_queries, num_seeds]
+                  uint32_t* const num_executed_iterations,  // [num_queries,]
+                  uint32_t topk,
+                  SAMPLE_FILTER_T sample_filter)
+  {
+  }
+};
+
+}  // namespace multi_kernel_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
new file mode 100644
index 0000000000..3c44194faa
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -0,0 +1,349 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "hashmap.hpp"
+
+#include <cuvs/neighbors/sample_filter.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+// #include "search_single_cta_inst.cuh"
+// #include "topk_for_cagra/topk_core.cuh"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance_types.hpp>
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+namespace cuvs::neighbors::cagra::detail {
+
+struct search_plan_impl_base : public search_params {
+  int64_t dataset_block_dim;
+  int64_t dim;
+  int64_t graph_degree;
+  uint32_t topk;
+  cuvs::distance::DistanceType metric;
+  search_plan_impl_base(search_params params,
+                        int64_t dim,
+                        int64_t graph_degree,
+                        uint32_t topk,
+                        cuvs::distance::DistanceType metric)
+    : search_params(params), dim(dim), graph_degree(graph_degree), topk(topk), metric(metric)
+  {
+    set_dataset_block_and_team_size(dim);
+    if (algo == search_algo::AUTO) {
+      const size_t num_sm = raft::getMultiProcessorCount();
+      if (itopk_size <= 512 && search_params::max_queries >= num_sm * 2lu) {
+        algo = search_algo::SINGLE_CTA;
+        RAFT_LOG_DEBUG("Auto strategy: selecting single-cta");
+      } else if (topk <= 1024) {
+        algo = search_algo::MULTI_CTA;
+        RAFT_LOG_DEBUG("Auto strategy: selecting multi-cta");
+      } else {
+        algo = search_algo::MULTI_KERNEL;
+        RAFT_LOG_DEBUG("Auto strategy: selecting multi kernel");
+      }
+    }
+  }
+
+  void set_dataset_block_and_team_size(int64_t dim)
+  {
+    constexpr int64_t max_dataset_block_dim = 512;
+    dataset_block_dim                       = 128;
+    while (dataset_block_dim < dim && dataset_block_dim < max_dataset_block_dim) {
+      dataset_block_dim *= 2;
+    }
+    // To keep binary size in check we limit only one team size specialization for each max_dim.
+    // TODO(tfeher): revise this decision.
+    switch (dataset_block_dim) {
+      case 128: team_size = 8; break;
+      case 256: team_size = 16; break;
+      default: team_size = 32; break;
+    }
+  }
+};
+
+template <class DATASET_DESCRIPTOR_T, class SAMPLE_FILTER_T>
+struct search_plan_impl : public search_plan_impl_base {
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+
+  int64_t hash_bitlen;
+
+  size_t small_hash_bitlen;
+  size_t small_hash_reset_interval;
+  size_t hashmap_size;
+  uint32_t dataset_size;
+  uint32_t result_buffer_size;
+
+  uint32_t smem_size;
+  uint32_t topk;
+  uint32_t num_seeds;
+
+  rmm::device_uvector<INDEX_T> hashmap;
+  rmm::device_uvector<uint32_t> num_executed_iterations;  // device or managed?
+  rmm::device_uvector<INDEX_T> dev_seed;
+
+  search_plan_impl(raft::resources const& res,
+                   search_params params,
+                   int64_t dim,
+                   int64_t graph_degree,
+                   uint32_t topk,
+                   cuvs::distance::DistanceType metric)
+    : search_plan_impl_base(params, dim, graph_degree, topk, metric),
+      hashmap(0, raft::resource::get_cuda_stream(res)),
+      num_executed_iterations(0, raft::resource::get_cuda_stream(res)),
+      dev_seed(0, raft::resource::get_cuda_stream(res)),
+      num_seeds(0)
+  {
+    adjust_search_params();
+    check_params();
+    calc_hashmap_params(res);
+    set_dataset_block_and_team_size(dim);
+    num_executed_iterations.resize(max_queries, raft::resource::get_cuda_stream(res));
+    RAFT_LOG_DEBUG("# algo = %d", static_cast<int>(algo));
+  }
+
+  virtual ~search_plan_impl() {}
+
+  virtual void operator()(raft::resources const& res,
+                          DATASET_DESCRIPTOR_T dataset_desc,
+                          raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
+                          INDEX_T* const result_indices_ptr,       // [num_queries, topk]
+                          DISTANCE_T* const result_distances_ptr,  // [num_queries, topk]
+                          const DATA_T* const queries_ptr,         // [num_queries, dataset_dim]
+                          const std::uint32_t num_queries,
+                          const INDEX_T* dev_seed_ptr,                   // [num_queries, num_seeds]
+                          std::uint32_t* const num_executed_iterations,  // [num_queries]
+                          uint32_t topk,
+                          SAMPLE_FILTER_T sample_filter){};
+
+  void adjust_search_params()
+  {
+    uint32_t _max_iterations = max_iterations;
+    if (max_iterations == 0) {
+      if (algo == search_algo::MULTI_CTA) {
+        _max_iterations = 1 + std::min(32 * 1.1, 32 + 10.0);  // TODO(anaruse)
+      } else {
+        _max_iterations =
+          1 + std::min((itopk_size / search_width) * 1.1, (itopk_size / search_width) + 10.0);
+      }
+    }
+    if (max_iterations < min_iterations) { _max_iterations = min_iterations; }
+    if (max_iterations < _max_iterations) {
+      RAFT_LOG_DEBUG(
+        "# max_iterations is increased from %lu to %u.", max_iterations, _max_iterations);
+      max_iterations = _max_iterations;
+    }
+    if (itopk_size % 32) {
+      uint32_t itopk32 = itopk_size;
+      itopk32 += 32 - (itopk_size % 32);
+      RAFT_LOG_DEBUG("# internal_topk is increased from %lu to %u, as it must be multiple of 32.",
+                     itopk_size,
+                     itopk32);
+      itopk_size = itopk32;
+    }
+  }
+
+  // defines hash_bitlen, small_hash_bitlen, small_hash_reset interval, hash_size
+  inline void calc_hashmap_params(raft::resources const& res)
+  {
+    // for multiple CTA search
+    uint32_t mc_num_cta_per_query = 0;
+    uint32_t mc_search_width      = 0;
+    uint32_t mc_itopk_size        = 0;
+    if (algo == search_algo::MULTI_CTA) {
+      mc_itopk_size        = 32;
+      mc_search_width      = 1;
+      mc_num_cta_per_query = max(search_width, raft::ceildiv(itopk_size, (size_t)32));
+      RAFT_LOG_DEBUG("# mc_itopk_size: %u", mc_itopk_size);
+      RAFT_LOG_DEBUG("# mc_search_width: %u", mc_search_width);
+      RAFT_LOG_DEBUG("# mc_num_cta_per_query: %u", mc_num_cta_per_query);
+    }
+
+    // Determine hash size (bit length)
+    hashmap_size              = 0;
+    hash_bitlen               = 0;
+    small_hash_bitlen         = 0;
+    small_hash_reset_interval = 1024 * 1024;
+    float max_fill_rate       = hashmap_max_fill_rate;
+    while (hashmap_mode == hash_mode::AUTO || hashmap_mode == hash_mode::SMALL) {
+      //
+      // The small-hash reduces hash table size by initializing the hash table
+      // for each iteraton and re-registering only the nodes that should not be
+      // re-visited in that iteration. Therefore, the size of small-hash should
+      // be determined based on the internal topk size and the number of nodes
+      // visited per iteration.
+      //
+      const auto max_visited_nodes = itopk_size + (search_width * graph_degree * 1);
+      unsigned min_bitlen          = 8;   // 256
+      unsigned max_bitlen          = 13;  // 8K
+      if (min_bitlen < hashmap_min_bitlen) { min_bitlen = hashmap_min_bitlen; }
+      hash_bitlen = min_bitlen;
+      while (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) {
+        hash_bitlen += 1;
+      }
+      if (hash_bitlen > max_bitlen) {
+        // Switch to normal hash if hashmap_mode is AUTO, otherwise exit.
+        if (hashmap_mode == hash_mode::AUTO) {
+          hash_bitlen = 0;
+          break;
+        } else {
+          RAFT_FAIL(
+            "small-hash cannot be used because the required hash size exceeds the limit (%u)",
+            hashmap::get_size(max_bitlen));
+        }
+      }
+      small_hash_bitlen = hash_bitlen;
+      //
+      // Sincc the hash table size is limited to a power of 2, the requirement,
+      // the maximum fill rate, may be satisfied even if the frequency of hash
+      // table reset is reduced to once every 2 or more iterations without
+      // changing the hash table size. In that case, reduce the reset frequency.
+      //
+      small_hash_reset_interval = 1;
+      while (1) {
+        const auto max_visited_nodes =
+          itopk_size + (search_width * graph_degree * (small_hash_reset_interval + 1));
+        if (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) { break; }
+        small_hash_reset_interval += 1;
+      }
+      break;
+    }
+    if (hash_bitlen == 0) {
+      //
+      // The size of hash table is determined based on the maximum number of
+      // nodes that may be visited before the search is completed and the
+      // maximum fill rate of the hash table.
+      //
+      uint32_t max_visited_nodes = itopk_size + (search_width * graph_degree * max_iterations);
+      if (algo == search_algo::MULTI_CTA) {
+        max_visited_nodes = mc_itopk_size + (mc_search_width * graph_degree * max_iterations);
+        max_visited_nodes *= mc_num_cta_per_query;
+      }
+      unsigned min_bitlen = 11;  // 2K
+      if (min_bitlen < hashmap_min_bitlen) { min_bitlen = hashmap_min_bitlen; }
+      hash_bitlen = min_bitlen;
+      while (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) {
+        hash_bitlen += 1;
+      }
+      RAFT_EXPECTS(hash_bitlen <= 20, "hash_bitlen cannot be largen than 20 (1M)");
+    }
+
+    RAFT_LOG_DEBUG("# internal topK = %lu", itopk_size);
+    RAFT_LOG_DEBUG("# parent size = %lu", search_width);
+    RAFT_LOG_DEBUG("# min_iterations = %lu", min_iterations);
+    RAFT_LOG_DEBUG("# max_iterations = %lu", max_iterations);
+    RAFT_LOG_DEBUG("# max_queries = %lu", max_queries);
+    RAFT_LOG_DEBUG("# hashmap mode = %s%s-%u",
+                   (small_hash_bitlen > 0 ? "small-" : ""),
+                   "hash",
+                   hashmap::get_size(hash_bitlen));
+    if (small_hash_bitlen > 0) {
+      RAFT_LOG_DEBUG("# small_hash_reset_interval = %lu", small_hash_reset_interval);
+    }
+    hashmap_size = sizeof(INDEX_T) * max_queries * hashmap::get_size(hash_bitlen);
+    RAFT_LOG_DEBUG("# hashmap size: %lu", hashmap_size);
+    if (hashmap_size >= 1024 * 1024 * 1024) {
+      RAFT_LOG_DEBUG(" (%.2f GiB)", (double)hashmap_size / (1024 * 1024 * 1024));
+    } else if (hashmap_size >= 1024 * 1024) {
+      RAFT_LOG_DEBUG(" (%.2f MiB)", (double)hashmap_size / (1024 * 1024));
+    } else if (hashmap_size >= 1024) {
+      RAFT_LOG_DEBUG(" (%.2f KiB)", (double)hashmap_size / (1024));
+    }
+  }
+
+  virtual void check(const uint32_t topk)
+  {
+    // For single-CTA and multi kernel
+    RAFT_EXPECTS(
+      topk <= itopk_size, "topk = %u must be smaller than itopk_size = %lu", topk, itopk_size);
+  }
+
+  inline void check_params()
+  {
+    std::string error_message = "";
+
+    if (itopk_size > 1024) {
+      if ((algo == search_algo::MULTI_CTA) || (algo == search_algo::MULTI_KERNEL)) {
+      } else {
+        error_message += std::string("- `internal_topk` (" + std::to_string(itopk_size) +
+                                     ") must be smaller or equal to 1024");
+      }
+    }
+    if (algo != search_algo::SINGLE_CTA && algo != search_algo::MULTI_CTA &&
+        algo != search_algo::MULTI_KERNEL) {
+      error_message += "An invalid kernel mode has been given: " + std::to_string((int)algo) + "";
+    }
+    if (team_size != 0 && team_size != 4 && team_size != 8 && team_size != 16 && team_size != 32) {
+      error_message +=
+        "`team_size` must be 0, 4, 8, 16 or 32. " + std::to_string(team_size) + " has been given.";
+    }
+    if (thread_block_size != 0 && thread_block_size != 64 && thread_block_size != 128 &&
+        thread_block_size != 256 && thread_block_size != 512 && thread_block_size != 1024) {
+      error_message += "`thread_block_size` must be 0, 64, 128, 256 or 512. " +
+                       std::to_string(thread_block_size) + " has been given.";
+    }
+    if (hashmap_min_bitlen > 20) {
+      error_message += "`hashmap_min_bitlen` must be equal to or smaller than 20. " +
+                       std::to_string(hashmap_min_bitlen) + " has been given.";
+    }
+    if (hashmap_max_fill_rate < 0.1 || hashmap_max_fill_rate >= 0.9) {
+      error_message +=
+        "`hashmap_max_fill_rate` must be equal to or greater than 0.1 and smaller than 0.9. " +
+        std::to_string(hashmap_max_fill_rate) + " has been given.";
+    }
+    if constexpr (!std::is_same<SAMPLE_FILTER_T,
+                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+      if (hashmap_mode == hash_mode::SMALL) {
+        error_message += "`SMALL` hash is not available when filtering";
+      } else {
+        hashmap_mode = hash_mode::HASH;
+      }
+    }
+    if (algo == search_algo::MULTI_CTA) {
+      if (hashmap_mode == hash_mode::SMALL) {
+        error_message += "`small_hash` is not available when 'search_mode' is \"multi-cta\"";
+      } else {
+        hashmap_mode = hash_mode::HASH;
+      }
+    }
+
+    if (error_message.length() != 0) { THROW("[CAGRA Error] %s", error_message.c_str()); }
+  }
+};
+
+// template <class DATA_T, class DISTANCE_T, class INDEX_T>
+// struct search_plan {
+//   search_plan(raft::resources const& res,
+//               search_params param,
+//               int64_t dim,
+//               int64_t graph_degree)
+//     : plan(res, param, dim, graph_degree)
+//   {
+//   }
+//   void check(uint32_t topk) { plan.check(topk); }
+
+//   // private:
+//   detail::search_plan_impl<DATA_T, DISTANCE_T, INDEX_T> plan;
+// };
+/** @} */  // end group cagra
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
new file mode 100644
index 0000000000..011f183869
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -0,0 +1,258 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "bitonic.hpp"
+#include "compute_distance.hpp"
+#include "device_common.hpp"
+#include "hashmap.hpp"
+#include "search_plan.cuh"
+#include "search_single_cta_kernel.cuh"
+#include "topk_by_radix.cuh"
+#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk
+#include "utils.hpp"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_properties.hpp>
+#include <raft/core/resources.hpp>
+
+// TODO: This shouldn't be invoking anything from spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/util/cuda_rt_essentials.hpp>
+#include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
+
+#include <rmm/device_uvector.hpp>
+
+#include <algorithm>
+#include <cassert>
+#include <iostream>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace single_cta_search {
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
+  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_queries;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::itopk_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::algo;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::team_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::search_width;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::min_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::thread_block_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_mode;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_min_bitlen;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_max_fill_rate;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_random_samplings;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::rand_xor_mask;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dim;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::graph_degree;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::topk;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hash_bitlen;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_bitlen;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_reset_interval;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dataset_size;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::result_buffer_size;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::smem_size;
+
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_executed_iterations;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dev_seed;
+  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_seeds;
+
+  uint32_t num_itopk_candidates;
+
+  search(raft::resources const& res,
+         search_params params,
+         int64_t dim,
+         int64_t graph_degree,
+         uint32_t topk,
+         cuvs::distance::DistanceType metric)
+    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
+        res, params, dim, graph_degree, topk, metric)
+  {
+    set_params(res);
+  }
+
+  ~search() {}
+
+  inline void set_params(raft::resources const& res)
+  {
+    num_itopk_candidates = search_width * graph_degree;
+    result_buffer_size   = itopk_size + num_itopk_candidates;
+
+    typedef raft::Pow2<32> AlignBytes;
+    unsigned result_buffer_size_32 = AlignBytes::roundUp(result_buffer_size);
+
+    constexpr unsigned max_itopk = 512;
+    RAFT_EXPECTS(itopk_size <= max_itopk, "itopk_size cannot be larger than %u", max_itopk);
+
+    RAFT_LOG_DEBUG("# num_itopk_candidates: %u", num_itopk_candidates);
+    RAFT_LOG_DEBUG("# num_itopk: %lu", itopk_size);
+    //
+    // Determine the thread block size
+    //
+    constexpr unsigned min_block_size       = 64;  // 32 or 64
+    constexpr unsigned min_block_size_radix = 256;
+    constexpr unsigned max_block_size       = 1024;
+    //
+    const std::uint32_t topk_ws_size = 3;
+    const auto query_smem_buffer_length =
+      raft::ceildiv<uint32_t>(dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+    const std::uint32_t base_smem_size =
+      sizeof(float) * query_smem_buffer_length +
+      (sizeof(INDEX_T) + sizeof(DISTANCE_T)) * result_buffer_size_32 +
+      sizeof(INDEX_T) * hashmap::get_size(small_hash_bitlen) + sizeof(INDEX_T) * search_width +
+      sizeof(std::uint32_t) * topk_ws_size + sizeof(std::uint32_t) +
+      DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte;
+    smem_size = base_smem_size;
+    if (num_itopk_candidates > 256) {
+      // Tentatively calculate the required share memory size when radix
+      // sort based topk is used, assuming the block size is the maximum.
+      if (itopk_size <= 256) {
+        smem_size += topk_by_radix_sort<256, INDEX_T>::smem_size * sizeof(std::uint32_t);
+      } else {
+        smem_size += topk_by_radix_sort<512, INDEX_T>::smem_size * sizeof(std::uint32_t);
+      }
+    }
+
+    uint32_t block_size = thread_block_size;
+    if (block_size == 0) {
+      block_size = min_block_size;
+
+      if (num_itopk_candidates > 256) {
+        // radix-based topk is used.
+        block_size = min_block_size_radix;
+
+        // Internal topk values per thread must be equlal to or less than 4
+        // when radix-sort block_topk is used.
+        while ((block_size < max_block_size) && (max_itopk / block_size > 4)) {
+          block_size *= 2;
+        }
+      }
+
+      // Increase block size according to shared memory requirements.
+      // If block size is 32, upper limit of shared memory size per
+      // thread block is set to 4096. This is GPU generation dependent.
+      constexpr unsigned ulimit_smem_size_cta32 = 4096;
+      while (smem_size > ulimit_smem_size_cta32 / 32 * block_size) {
+        block_size *= 2;
+      }
+
+      // Increase block size to improve GPU occupancy when batch size
+      // is small, that is, number of queries is low.
+      cudaDeviceProp deviceProp = raft::resource::get_device_properties(res);
+      RAFT_LOG_DEBUG("# multiProcessorCount: %d", deviceProp.multiProcessorCount);
+      while ((block_size < max_block_size) &&
+             (graph_degree * search_width * team_size >= block_size * 2) &&
+             (max_queries <= (1024 / (block_size * 2)) * deviceProp.multiProcessorCount)) {
+        block_size *= 2;
+      }
+    }
+    RAFT_LOG_DEBUG("# thread_block_size: %u", block_size);
+    RAFT_EXPECTS(block_size >= min_block_size,
+                 "block_size cannot be smaller than min_block size, %u",
+                 min_block_size);
+    RAFT_EXPECTS(block_size <= max_block_size,
+                 "block_size cannot be larger than max_block size %u",
+                 max_block_size);
+    thread_block_size = block_size;
+
+    if (num_itopk_candidates <= 256) {
+      RAFT_LOG_DEBUG("# bitonic-sort based topk routine is used");
+    } else {
+      RAFT_LOG_DEBUG("# radix-sort based topk routine is used");
+      smem_size = base_smem_size;
+      if (itopk_size <= 256) {
+        constexpr unsigned MAX_ITOPK = 256;
+        smem_size += topk_by_radix_sort<MAX_ITOPK, INDEX_T>::smem_size * sizeof(std::uint32_t);
+      } else {
+        constexpr unsigned MAX_ITOPK = 512;
+        smem_size += topk_by_radix_sort<MAX_ITOPK, INDEX_T>::smem_size * sizeof(std::uint32_t);
+      }
+    }
+    RAFT_LOG_DEBUG("# smem_size: %u", smem_size);
+    hashmap_size = 0;
+    if (small_hash_bitlen == 0) {
+      hashmap_size = sizeof(INDEX_T) * max_queries * hashmap::get_size(hash_bitlen);
+      hashmap.resize(hashmap_size, raft::resource::get_cuda_stream(res));
+    }
+    RAFT_LOG_DEBUG("# hashmap_size: %lu", hashmap_size);
+  }
+
+  void operator()(raft::resources const& res,
+                  DATASET_DESCRIPTOR_T dataset_desc,
+                  raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
+                  INDEX_T* const result_indices_ptr,       // [num_queries, topk]
+                  DISTANCE_T* const result_distances_ptr,  // [num_queries, topk]
+                  const DATA_T* const queries_ptr,         // [num_queries, dataset_dim]
+                  const std::uint32_t num_queries,
+                  const INDEX_T* dev_seed_ptr,                   // [num_queries, num_seeds]
+                  std::uint32_t* const num_executed_iterations,  // [num_queries]
+                  uint32_t topk,
+                  SAMPLE_FILTER_T sample_filter)
+  {
+    cudaStream_t stream = raft::resource::get_cuda_stream(res);
+    select_and_run<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T>(
+      dataset_desc,
+      graph,
+      result_indices_ptr,
+      result_distances_ptr,
+      queries_ptr,
+      num_queries,
+      dev_seed_ptr,
+      num_executed_iterations,
+      topk,
+      num_itopk_candidates,
+      static_cast<uint32_t>(thread_block_size),
+      smem_size,
+      hash_bitlen,
+      hashmap.data(),
+      small_hash_bitlen,
+      small_hash_reset_interval,
+      num_random_samplings,
+      rand_xor_mask,
+      num_seeds,
+      itopk_size,
+      search_width,
+      min_iterations,
+      max_iterations,
+      sample_filter,
+      this->metric,
+      stream);
+  }
+};
+
+}  // namespace single_cta_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
new file mode 100644
index 0000000000..a361269a6f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
@@ -0,0 +1,82 @@
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+"""
+
+trailer = """
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
+"""
+
+mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
+# block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
+# itopk_candidates = [64, 128, 256]
+# itopk_size = [64, 128, 256, 512]
+# mxelem = [64, 128, 256]
+
+# rblock = [(256, 4), (512, 2), (1024, 1)]
+# rcandidates = [32]
+# rsize = [256, 512]
+
+search_types = dict(
+    float_uint32=("float", "uint32_t", "float"),  # data_t, idx_t, distance_t
+    half_uint32=("half", "uint32_t", "float"),
+    int8_uint32=("int8_t", "uint32_t", "float"),
+    uint8_uint32=("uint8_t", "uint32_t", "float"),
+    float_uint64=("float", "uint64_t", "float"),
+    half_uint64=("half", "uint64_t", "float"),
+)
+
+# knn
+for type_path, (data_t, idx_t, distance_t) in search_types.items():
+    for (mxdim, team) in mxdim_team:
+        path = f"search_single_cta_{type_path}_dim{mxdim}_t{team}.cu"
+        with open(path, "w") as f:
+            f.write(header)
+            f.write(
+                    f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<{data_t} COMMA {idx_t} COMMA  {distance_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+            )
+
+            f.write(trailer)
+            # For pasting into CMakeLists.txt
+            print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..4cf4a26f74
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..7d3e86f383
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..6927104762
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..ed3a900ff9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
new file mode 100644
index 0000000000..2c4da00db3
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
new file mode 100644
index 0000000000..8b26a595f0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
new file mode 100644
index 0000000000..a93f893d4d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
new file mode 100644
index 0000000000..4a7502e3ed
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..6c13df91a7
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..1e2b834924
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..cfae9e367d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..12aa72a248
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
new file mode 100644
index 0000000000..84a173d6d0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu
new file mode 100644
index 0000000000..c2cfb13c43
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
new file mode 100644
index 0000000000..d9c5198ebe
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
new file mode 100644
index 0000000000..3ba8f4e4d1
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
new file mode 100644
index 0000000000..7229ac6042
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "search_single_cta_kernel-inl.cuh"
+#include <cuvs/neighbors/sample_filter.hpp>
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+
+#define instantiate_kernel_selection(TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T) \
+  template void select_and_run<TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T>(      \
+    DATASET_DESC_T dataset_desc,                                                                  \
+    raft::device_matrix_view<const typename DATASET_DESC_T::INDEX_T, int64_t, raft::row_major>    \
+      graph,                                                                                      \
+    typename DATASET_DESC_T::INDEX_T* const topk_indices_ptr,                                     \
+    typename DATASET_DESC_T::DISTANCE_T* const topk_distances_ptr,                                \
+    const typename DATASET_DESC_T::DATA_T* const queries_ptr,                                     \
+    const uint32_t num_queries,                                                                   \
+    const typename DATASET_DESC_T::INDEX_T* dev_seed_ptr,                                         \
+    uint32_t* const num_executed_iterations,                                                      \
+    uint32_t topk,                                                                                \
+    uint32_t num_itopk_candidates,                                                                \
+    uint32_t block_size,                                                                          \
+    uint32_t smem_size,                                                                           \
+    int64_t hash_bitlen,                                                                          \
+    typename DATASET_DESC_T::INDEX_T* hashmap_ptr,                                                \
+    size_t small_hash_bitlen,                                                                     \
+    size_t small_hash_reset_interval,                                                             \
+    uint32_t num_random_samplings,                                                                \
+    uint64_t rand_xor_mask,                                                                       \
+    uint32_t num_seeds,                                                                           \
+    size_t itopk_size,                                                                            \
+    size_t search_width,                                                                          \
+    size_t min_iterations,                                                                        \
+    size_t max_iterations,                                                                        \
+    SAMPLE_FILTER_T sample_filter,                                                                \
+    cuvs::distance::DistanceType metric,                                                          \
+    cudaStream_t stream);
+
+#define COMMA ,
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..ad2ca16fc0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..6130a84bcb
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..1e7bee57c7
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..7f789e3d0c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
new file mode 100644
index 0000000000..549b692bc2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
@@ -0,0 +1,603 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/neighbors/sample_filter.hpp>
+#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
+
+#include <cuda_fp16.h>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace single_cta_search {
+
+#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+template <unsigned TEAM_SIZE,
+          unsigned MAX_DATASET_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+void select_and_run(  // raft::resources const& res,
+  DATASET_DESCRIPTOR_T dataset_desc,
+  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
+    graph,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const uint32_t num_queries,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
+  uint32_t* const num_executed_iterations,                     // [num_queries,]
+  uint32_t topk,
+  uint32_t num_itopk_candidates,
+  uint32_t block_size,
+  uint32_t smem_size,
+  int64_t hash_bitlen,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
+  size_t small_hash_bitlen,
+  size_t small_hash_reset_interval,
+  uint32_t num_random_samplings,
+  uint64_t rand_xor_mask,
+  uint32_t num_seeds,
+  size_t itopk_size,
+  size_t search_width,
+  size_t min_iterations,
+  size_t max_iterations,
+  SAMPLE_FILTER_T sample_filter,
+  cuvs::distance::DistanceType metric,
+  cudaStream_t stream) RAFT_EXPLICIT;
+
+#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+#define instantiate_single_cta_select_and_run(                                                  \
+  TEAM_SIZE, MAX_DATASET_DIM, DATA_T, INDEX_T, DISTANCE_T, SAMPLE_FILTER_T)                     \
+  extern template void select_and_run<                                                          \
+    TEAM_SIZE,                                                                                  \
+    MAX_DATASET_DIM,                                                                            \
+    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>, \
+    SAMPLE_FILTER_T>(                                                                           \
+    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>  \
+      dataset,                                                                                  \
+    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
+    INDEX_T* const topk_indices_ptr,                                                            \
+    DISTANCE_T* const topk_distances_ptr,                                                       \
+    const DATA_T* const queries_ptr,                                                            \
+    const uint32_t num_queries,                                                                 \
+    const INDEX_T* dev_seed_ptr,                                                                \
+    uint32_t* const num_executed_iterations,                                                    \
+    uint32_t topk,                                                                              \
+    uint32_t num_itopk_candidates,                                                              \
+    uint32_t block_size,                                                                        \
+    uint32_t smem_size,                                                                         \
+    int64_t hash_bitlen,                                                                        \
+    INDEX_T* hashmap_ptr,                                                                       \
+    size_t small_hash_bitlen,                                                                   \
+    size_t small_hash_reset_interval,                                                           \
+    uint32_t num_random_samplings,                                                              \
+    uint64_t rand_xor_mask,                                                                     \
+    uint32_t num_seeds,                                                                         \
+    size_t itopk_size,                                                                          \
+    size_t search_width,                                                                        \
+    size_t min_iterations,                                                                      \
+    size_t max_iterations,                                                                      \
+    SAMPLE_FILTER_T sample_filter,                                                              \
+    cuvs::distance::DistanceType metric,                                                        \
+    cudaStream_t stream);
+
+instantiate_single_cta_select_and_run(
+  32, 1024, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  8, 128, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  16, 256, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  32, 512, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  32, 1024, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  8, 128, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  16, 256, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  32, 512, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  32, 1024, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  8, 128, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  16, 256, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  32, 512, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  32, 1024, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  8, 128, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  16, 256, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_single_cta_select_and_run(
+  32, 512, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+#undef instantiate_single_cta_select_and_run
+
+#define instantiate_q_single_cta_select_and_run(TEAM_SIZE,                                      \
+                                                MAX_DATASET_DIM,                                \
+                                                CODE_BOOK_T,                                    \
+                                                PQ_BITS,                                        \
+                                                PQ_CODE_BOOK_DIM,                               \
+                                                DATA_T,                                         \
+                                                INDEX_T,                                        \
+                                                DISTANCE_T,                                     \
+                                                SAMPLE_FILTER_T)                                \
+  extern template void                                                                          \
+  select_and_run<TEAM_SIZE,                                                                     \
+                 MAX_DATASET_DIM,                                                               \
+                 cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,           \
+                                                                              CODE_BOOK_T,      \
+                                                                              PQ_BITS,          \
+                                                                              PQ_CODE_BOOK_DIM, \
+                                                                              DISTANCE_T,       \
+                                                                              INDEX_T>,         \
+                 SAMPLE_FILTER_T>(                                                              \
+    cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,                        \
+                                                                 CODE_BOOK_T,                   \
+                                                                 PQ_BITS,                       \
+                                                                 PQ_CODE_BOOK_DIM,              \
+                                                                 DISTANCE_T,                    \
+                                                                 INDEX_T> dataset,              \
+    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
+    INDEX_T* const topk_indices_ptr,                                                            \
+    DISTANCE_T* const topk_distances_ptr,                                                       \
+    const DATA_T* const queries_ptr,                                                            \
+    const uint32_t num_queries,                                                                 \
+    const INDEX_T* dev_seed_ptr,                                                                \
+    uint32_t* const num_executed_iterations,                                                    \
+    uint32_t topk,                                                                              \
+    uint32_t num_itopk_candidates,                                                              \
+    uint32_t block_size,                                                                        \
+    uint32_t smem_size,                                                                         \
+    int64_t hash_bitlen,                                                                        \
+    INDEX_T* hashmap_ptr,                                                                       \
+    size_t small_hash_bitlen,                                                                   \
+    size_t small_hash_reset_interval,                                                           \
+    uint32_t num_random_samplings,                                                              \
+    uint64_t rand_xor_mask,                                                                     \
+    uint32_t num_seeds,                                                                         \
+    size_t itopk_size,                                                                          \
+    size_t search_width,                                                                        \
+    size_t min_iterations,                                                                      \
+    size_t max_iterations,                                                                      \
+    SAMPLE_FILTER_T sample_filter,                                                              \
+    cuvs::distance::DistanceType metric,                                                        \
+    cudaStream_t stream);
+
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  16, 256, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 512, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        2,
+                                        half,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  16, 256, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 512, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        4,
+                                        half,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 2, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        2,
+                                        float,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        2,
+                                        float,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        2,
+                                        float,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 4, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        4,
+                                        float,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        4,
+                                        float,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        4,
+                                        float,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  16, 256, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 512, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 1024, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  16, 256, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 512, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 1024, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 2, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  16, 256, half, 8, 2, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 512, half, 8, 2, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        2,
+                                        float,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 4, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  16, 256, half, 8, 4, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  32, 512, half, 8, 4, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        4,
+                                        float,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_single_cta_select_and_run(8,
+                                        128,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(8,
+                                        128,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_single_cta_select_and_run(8,
+                                        128,
+                                        half,
+                                        8,
+                                        2,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        2,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        2,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        2,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(8,
+                                        128,
+                                        half,
+                                        8,
+                                        4,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        4,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        4,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        4,
+                                        int8_t,
+                                        uint32_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_single_cta_select_and_run(8,
+                                        128,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        2,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(8,
+                                        128,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        4,
+                                        uint8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 2, int8_t, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        2,
+                                        int8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        2,
+                                        int8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        2,
+                                        int8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(
+  8, 128, half, 8, 4, int8_t, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(16,
+                                        256,
+                                        half,
+                                        8,
+                                        4,
+                                        int8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        512,
+                                        half,
+                                        8,
+                                        4,
+                                        int8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_q_single_cta_select_and_run(32,
+                                        1024,
+                                        half,
+                                        8,
+                                        4,
+                                        int8_t,
+                                        int64_t,
+                                        float,
+                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+#undef instantiate_q_single_cta_select_and_run
+
+}  // namespace single_cta_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
new file mode 100644
index 0000000000..2c3b16556b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -0,0 +1,980 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "bitonic.hpp"
+#include "compute_distance.hpp"
+#include "device_common.hpp"
+#include "hashmap.hpp"
+#include "search_plan.cuh"
+#include "topk_by_radix.cuh"
+#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk
+#include "utils.hpp"
+
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_properties.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/neighbors/sample_filter.hpp>
+
+// TODO: This shouldn't be invoking anything from spatial/knn
+#include "../ann_utils.cuh"
+
+#include <raft/util/cuda_rt_essentials.hpp>
+#include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
+
+#include <rmm/device_uvector.hpp>
+
+#include <algorithm>
+#include <cassert>
+#include <cstdint>
+#include <iostream>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace single_cta_search {
+
+// #define _CLK_BREAKDOWN
+
+template <unsigned TOPK_BY_BITONIC_SORT, class INDEX_T>
+__device__ void pickup_next_parents(std::uint32_t* const terminate_flag,
+                                    INDEX_T* const next_parent_indices,
+                                    INDEX_T* const internal_topk_indices,
+                                    const std::size_t internal_topk_size,
+                                    const std::size_t dataset_size,
+                                    const std::uint32_t search_width)
+{
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+  // if (threadIdx.x >= 32) return;
+
+  for (std::uint32_t i = threadIdx.x; i < search_width; i += 32) {
+    next_parent_indices[i] = utils::get_max_value<INDEX_T>();
+  }
+  std::uint32_t itopk_max = internal_topk_size;
+  if (itopk_max % 32) { itopk_max += 32 - (itopk_max % 32); }
+  std::uint32_t num_new_parents = 0;
+  for (std::uint32_t j = threadIdx.x; j < itopk_max; j += 32) {
+    std::uint32_t jj = j;
+    if (TOPK_BY_BITONIC_SORT) { jj = device::swizzling(j); }
+    INDEX_T index;
+    int new_parent = 0;
+    if (j < internal_topk_size) {
+      index = internal_topk_indices[jj];
+      if ((index & index_msb_1_mask) == 0) {  // check if most significant bit is set
+        new_parent = 1;
+      }
+    }
+    const std::uint32_t ballot_mask = __ballot_sync(0xffffffff, new_parent);
+    if (new_parent) {
+      const auto i = __popc(ballot_mask & ((1 << threadIdx.x) - 1)) + num_new_parents;
+      if (i < search_width) {
+        next_parent_indices[i] = jj;
+        // set most significant bit as used node
+        internal_topk_indices[jj] |= index_msb_1_mask;
+      }
+    }
+    num_new_parents += __popc(ballot_mask);
+    if (num_new_parents >= search_width) { break; }
+  }
+  if (threadIdx.x == 0 && (num_new_parents == 0)) { *terminate_flag = 1; }
+}
+
+template <unsigned MAX_CANDIDATES, class IdxT = void>
+__device__ inline void topk_by_bitonic_sort_1st(float* candidate_distances,  // [num_candidates]
+                                                IdxT* candidate_indices,     // [num_candidates]
+                                                const std::uint32_t num_candidates,
+                                                const std::uint32_t num_itopk,
+                                                unsigned MULTI_WARPS = 0)
+{
+  const unsigned lane_id = threadIdx.x % 32;
+  const unsigned warp_id = threadIdx.x / 32;
+  if (MULTI_WARPS == 0) {
+    if (warp_id > 0) { return; }
+    constexpr unsigned N = (MAX_CANDIDATES + 31) / 32;
+    float key[N];
+    IdxT val[N];
+    /* Candidates -> Reg */
+    for (unsigned i = 0; i < N; i++) {
+      unsigned j = lane_id + (32 * i);
+      if (j < num_candidates) {
+        key[i] = candidate_distances[j];
+        val[i] = candidate_indices[j];
+      } else {
+        key[i] = utils::get_max_value<float>();
+        val[i] = utils::get_max_value<IdxT>();
+      }
+    }
+    /* Sort */
+    bitonic::warp_sort<float, IdxT, N>(key, val);
+    /* Reg -> Temp_itopk */
+    for (unsigned i = 0; i < N; i++) {
+      unsigned j = (N * lane_id) + i;
+      if (j < num_candidates && j < num_itopk) {
+        candidate_distances[device::swizzling(j)] = key[i];
+        candidate_indices[device::swizzling(j)]   = val[i];
+      }
+    }
+  } else {
+    // Use two warps (64 threads)
+    constexpr unsigned max_candidates_per_warp = (MAX_CANDIDATES + 1) / 2;
+    constexpr unsigned N                       = (max_candidates_per_warp + 31) / 32;
+    float key[N];
+    IdxT val[N];
+    if (warp_id < 2) {
+      /* Candidates -> Reg */
+      for (unsigned i = 0; i < N; i++) {
+        unsigned jl = lane_id + (32 * i);
+        unsigned j  = jl + (max_candidates_per_warp * warp_id);
+        if (j < num_candidates) {
+          key[i] = candidate_distances[j];
+          val[i] = candidate_indices[j];
+        } else {
+          key[i] = utils::get_max_value<float>();
+          val[i] = utils::get_max_value<IdxT>();
+        }
+      }
+      /* Sort */
+      bitonic::warp_sort<float, IdxT, N>(key, val);
+      /* Reg -> Temp_candidates */
+      for (unsigned i = 0; i < N; i++) {
+        unsigned jl = (N * lane_id) + i;
+        unsigned j  = jl + (max_candidates_per_warp * warp_id);
+        if (j < num_candidates && jl < num_itopk) {
+          candidate_distances[device::swizzling(j)] = key[i];
+          candidate_indices[device::swizzling(j)]   = val[i];
+        }
+      }
+    }
+    __syncthreads();
+
+    unsigned num_warps_used = (num_itopk + max_candidates_per_warp - 1) / max_candidates_per_warp;
+    if (warp_id < num_warps_used) {
+      /* Temp_candidates -> Reg */
+      for (unsigned i = 0; i < N; i++) {
+        unsigned jl = (N * lane_id) + i;
+        unsigned kl = max_candidates_per_warp - 1 - jl;
+        unsigned j  = jl + (max_candidates_per_warp * warp_id);
+        unsigned k  = MAX_CANDIDATES - 1 - j;
+        if (j >= num_candidates || k >= num_candidates || kl >= num_itopk) continue;
+        float temp_key = candidate_distances[device::swizzling(k)];
+        if (key[i] == temp_key) continue;
+        if ((warp_id == 0) == (key[i] > temp_key)) {
+          key[i] = temp_key;
+          val[i] = candidate_indices[device::swizzling(k)];
+        }
+      }
+    }
+    if (num_warps_used > 1) { __syncthreads(); }
+    if (warp_id < num_warps_used) {
+      /* Merge */
+      bitonic::warp_merge<float, IdxT, N>(key, val, 32);
+      /* Reg -> Temp_itopk */
+      for (unsigned i = 0; i < N; i++) {
+        unsigned jl = (N * lane_id) + i;
+        unsigned j  = jl + (max_candidates_per_warp * warp_id);
+        if (j < num_candidates && j < num_itopk) {
+          candidate_distances[device::swizzling(j)] = key[i];
+          candidate_indices[device::swizzling(j)]   = val[i];
+        }
+      }
+    }
+    if (num_warps_used > 1) { __syncthreads(); }
+  }
+}
+
+template <unsigned MAX_ITOPK, class IdxT = void>
+__device__ inline void topk_by_bitonic_sort_2nd(float* itopk_distances,  // [num_itopk]
+                                                IdxT* itopk_indices,     // [num_itopk]
+                                                const std::uint32_t num_itopk,
+                                                float* candidate_distances,  // [num_candidates]
+                                                IdxT* candidate_indices,     // [num_candidates]
+                                                const std::uint32_t num_candidates,
+                                                std::uint32_t* work_buf,
+                                                const bool first,
+                                                unsigned MULTI_WARPS = 0)
+{
+  const unsigned lane_id = threadIdx.x % 32;
+  const unsigned warp_id = threadIdx.x / 32;
+  if (MULTI_WARPS == 0) {
+    if (warp_id > 0) { return; }
+    constexpr unsigned N = (MAX_ITOPK + 31) / 32;
+    float key[N];
+    IdxT val[N];
+    if (first) {
+      /* Load itopk results */
+      for (unsigned i = 0; i < N; i++) {
+        unsigned j = lane_id + (32 * i);
+        if (j < num_itopk) {
+          key[i] = itopk_distances[j];
+          val[i] = itopk_indices[j];
+        } else {
+          key[i] = utils::get_max_value<float>();
+          val[i] = utils::get_max_value<IdxT>();
+        }
+      }
+      /* Warp Sort */
+      bitonic::warp_sort<float, IdxT, N>(key, val);
+    } else {
+      /* Load itopk results */
+      for (unsigned i = 0; i < N; i++) {
+        unsigned j = (N * lane_id) + i;
+        if (j < num_itopk) {
+          key[i] = itopk_distances[device::swizzling(j)];
+          val[i] = itopk_indices[device::swizzling(j)];
+        } else {
+          key[i] = utils::get_max_value<float>();
+          val[i] = utils::get_max_value<IdxT>();
+        }
+      }
+    }
+    /* Merge candidates */
+    for (unsigned i = 0; i < N; i++) {
+      unsigned j = (N * lane_id) + i;  // [0:MAX_ITOPK-1]
+      unsigned k = MAX_ITOPK - 1 - j;
+      if (k >= num_itopk || k >= num_candidates) continue;
+      float candidate_key = candidate_distances[device::swizzling(k)];
+      if (key[i] > candidate_key) {
+        key[i] = candidate_key;
+        val[i] = candidate_indices[device::swizzling(k)];
+      }
+    }
+    /* Warp Merge */
+    bitonic::warp_merge<float, IdxT, N>(key, val, 32);
+    /* Store new itopk results */
+    for (unsigned i = 0; i < N; i++) {
+      unsigned j = (N * lane_id) + i;
+      if (j < num_itopk) {
+        itopk_distances[device::swizzling(j)] = key[i];
+        itopk_indices[device::swizzling(j)]   = val[i];
+      }
+    }
+  } else {
+    // Use two warps (64 threads) or more
+    constexpr unsigned max_itopk_per_warp = (MAX_ITOPK + 1) / 2;
+    constexpr unsigned N                  = (max_itopk_per_warp + 31) / 32;
+    float key[N];
+    IdxT val[N];
+    if (first) {
+      /* Load itop results (not sorted) */
+      if (warp_id < 2) {
+        for (unsigned i = 0; i < N; i++) {
+          unsigned j = lane_id + (32 * i) + (max_itopk_per_warp * warp_id);
+          if (j < num_itopk) {
+            key[i] = itopk_distances[j];
+            val[i] = itopk_indices[j];
+          } else {
+            key[i] = utils::get_max_value<float>();
+            val[i] = utils::get_max_value<IdxT>();
+          }
+        }
+        /* Warp Sort */
+        bitonic::warp_sort<float, IdxT, N>(key, val);
+        /* Store intermedidate results */
+        for (unsigned i = 0; i < N; i++) {
+          unsigned j = (N * threadIdx.x) + i;
+          if (j >= num_itopk) continue;
+          itopk_distances[device::swizzling(j)] = key[i];
+          itopk_indices[device::swizzling(j)]   = val[i];
+        }
+      }
+      __syncthreads();
+      if (warp_id < 2) {
+        /* Load intermedidate results */
+        for (unsigned i = 0; i < N; i++) {
+          unsigned j = (N * threadIdx.x) + i;
+          unsigned k = MAX_ITOPK - 1 - j;
+          if (k >= num_itopk) continue;
+          float temp_key = itopk_distances[device::swizzling(k)];
+          if (key[i] == temp_key) continue;
+          if ((warp_id == 0) == (key[i] > temp_key)) {
+            key[i] = temp_key;
+            val[i] = itopk_indices[device::swizzling(k)];
+          }
+        }
+        /* Warp Merge */
+        bitonic::warp_merge<float, IdxT, N>(key, val, 32);
+      }
+      __syncthreads();
+      /* Store itopk results (sorted) */
+      if (warp_id < 2) {
+        for (unsigned i = 0; i < N; i++) {
+          unsigned j = (N * threadIdx.x) + i;
+          if (j >= num_itopk) continue;
+          itopk_distances[device::swizzling(j)] = key[i];
+          itopk_indices[device::swizzling(j)]   = val[i];
+        }
+      }
+    }
+    const uint32_t num_itopk_div2 = num_itopk / 2;
+    if (threadIdx.x < 3) {
+      // work_buf is used to obtain turning points in 1st and 2nd half of itopk afer merge.
+      work_buf[threadIdx.x] = num_itopk_div2;
+    }
+    __syncthreads();
+
+    // Merge candidates (using whole threads)
+    for (unsigned k = threadIdx.x; k < min(num_candidates, num_itopk); k += blockDim.x) {
+      const unsigned j          = num_itopk - 1 - k;
+      const float itopk_key     = itopk_distances[device::swizzling(j)];
+      const float candidate_key = candidate_distances[device::swizzling(k)];
+      if (itopk_key > candidate_key) {
+        itopk_distances[device::swizzling(j)] = candidate_key;
+        itopk_indices[device::swizzling(j)]   = candidate_indices[device::swizzling(k)];
+        if (j < num_itopk_div2) {
+          atomicMin(work_buf + 2, j);
+        } else {
+          atomicMin(work_buf + 1, j - num_itopk_div2);
+        }
+      }
+    }
+    __syncthreads();
+
+    // Merge 1st and 2nd half of itopk (using whole threads)
+    for (unsigned j = threadIdx.x; j < num_itopk_div2; j += blockDim.x) {
+      const unsigned k = j + num_itopk_div2;
+      float key_0      = itopk_distances[device::swizzling(j)];
+      float key_1      = itopk_distances[device::swizzling(k)];
+      if (key_0 > key_1) {
+        itopk_distances[device::swizzling(j)] = key_1;
+        itopk_distances[device::swizzling(k)] = key_0;
+        IdxT val_0                            = itopk_indices[device::swizzling(j)];
+        IdxT val_1                            = itopk_indices[device::swizzling(k)];
+        itopk_indices[device::swizzling(j)]   = val_1;
+        itopk_indices[device::swizzling(k)]   = val_0;
+        atomicMin(work_buf + 0, j);
+      }
+    }
+    if (threadIdx.x == blockDim.x - 1) {
+      if (work_buf[2] < num_itopk_div2) { work_buf[1] = work_buf[2]; }
+    }
+    __syncthreads();
+    // if ((blockIdx.x == 0) && (threadIdx.x == 0)) {
+    //     RAFT_LOG_DEBUG( "work_buf: %u, %u, %u\n", work_buf[0], work_buf[1], work_buf[2] );
+    // }
+
+    // Warp-0 merges 1st half of itopk, warp-1 does 2nd half.
+    if (warp_id < 2) {
+      // Load intermedidate itopk results
+      const uint32_t turning_point = work_buf[warp_id];  // turning_point <= num_itopk_div2
+      for (unsigned i = 0; i < N; i++) {
+        unsigned k = num_itopk;
+        unsigned j = (N * lane_id) + i;
+        if (j < turning_point) {
+          k = j + (num_itopk_div2 * warp_id);
+        } else if (j >= (MAX_ITOPK / 2 - num_itopk_div2)) {
+          j -= (MAX_ITOPK / 2 - num_itopk_div2);
+          if ((turning_point <= j) && (j < num_itopk_div2)) { k = j + (num_itopk_div2 * warp_id); }
+        }
+        if (k < num_itopk) {
+          key[i] = itopk_distances[device::swizzling(k)];
+          val[i] = itopk_indices[device::swizzling(k)];
+        } else {
+          key[i] = utils::get_max_value<float>();
+          val[i] = utils::get_max_value<IdxT>();
+        }
+      }
+      /* Warp Merge */
+      bitonic::warp_merge<float, IdxT, N>(key, val, 32);
+      /* Store new itopk results */
+      for (unsigned i = 0; i < N; i++) {
+        const unsigned j = (N * lane_id) + i;
+        if (j < num_itopk_div2) {
+          unsigned k                            = j + (num_itopk_div2 * warp_id);
+          itopk_distances[device::swizzling(k)] = key[i];
+          itopk_indices[device::swizzling(k)]   = val[i];
+        }
+      }
+    }
+  }
+}
+
+template <unsigned MAX_ITOPK,
+          unsigned MAX_CANDIDATES,
+          class IdxT>
+__device__ void topk_by_bitonic_sort(float* itopk_distances,  // [num_itopk]
+                                     IdxT* itopk_indices,     // [num_itopk]
+                                     const std::uint32_t num_itopk,
+                                     float* candidate_distances,  // [num_candidates]
+                                     IdxT* candidate_indices,     // [num_candidates]
+                                     const std::uint32_t num_candidates,
+                                     std::uint32_t* work_buf,
+                                     const bool first,
+                                     const unsigned MULTI_WARPS_1,
+                                     const unsigned MULTI_WARPS_2)
+{
+  // The results in candidate_distances/indices are sorted by bitonic sort.
+  topk_by_bitonic_sort_1st<MAX_CANDIDATES, IdxT>(
+    candidate_distances, candidate_indices, num_candidates, num_itopk, MULTI_WARPS_1);
+
+  // The results sorted above are merged with the internal intermediate top-k
+  // results so far using bitonic merge.
+  topk_by_bitonic_sort_2nd<MAX_ITOPK, IdxT>(itopk_distances,
+                                            itopk_indices,
+                                            num_itopk,
+                                            candidate_distances,
+                                            candidate_indices,
+                                            num_candidates,
+                                            work_buf,
+                                            first,
+                                            MULTI_WARPS_2);
+}
+
+template <class INDEX_T>
+__device__ inline void hashmap_restore(INDEX_T* const hashmap_ptr,
+                                       const size_t hashmap_bitlen,
+                                       const INDEX_T* itopk_indices,
+                                       const uint32_t itopk_size,
+                                       const uint32_t first_tid = 0)
+{
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+  if (threadIdx.x < first_tid) return;
+  for (unsigned i = threadIdx.x - first_tid; i < itopk_size; i += blockDim.x - first_tid) {
+    auto key = itopk_indices[i] & ~index_msb_1_mask;  // clear most significant bit
+    hashmap::insert(hashmap_ptr, hashmap_bitlen, key);
+  }
+}
+
+template <class T, unsigned BLOCK_SIZE>
+__device__ inline void set_value_device(T* const ptr, const T fill, const std::uint32_t count)
+{
+  for (std::uint32_t i = threadIdx.x; i < count; i += BLOCK_SIZE) {
+    ptr[i] = fill;
+  }
+}
+
+// One query one thread block
+template <uint32_t TEAM_SIZE,
+          uint32_t DATASET_BLOCK_DIM,
+          unsigned MAX_ITOPK,
+          unsigned MAX_CANDIDATES,
+          unsigned TOPK_BY_BITONIC_SORT,
+          class DATASET_DESCRIPTOR_T,
+          class SAMPLE_FILTER_T>
+__launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, top_k]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, top_k]
+  const std::uint32_t top_k,
+  DATASET_DESCRIPTOR_T dataset_desc,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const knn_graph,   // [dataset_size, graph_degree]
+  const std::uint32_t graph_degree,
+  const unsigned num_distilation,
+  const uint64_t rand_xor_mask,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
+  const uint32_t num_seeds,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+  const std::uint32_t internal_topk,
+  const std::uint32_t search_width,
+  const std::uint32_t min_iteration,
+  const std::uint32_t max_iteration,
+  std::uint32_t* const num_executed_iterations,  // [num_queries]
+  const std::uint32_t hash_bitlen,
+  const std::uint32_t small_hash_bitlen,
+  const std::uint32_t small_hash_reset_interval,
+  SAMPLE_FILTER_T sample_filter,
+  cuvs::distance::DistanceType metric)
+{
+  using LOAD_T = device::LOAD_128BIT_T;
+
+  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+  using QUERY_T    = typename DATASET_DESCRIPTOR_T::QUERY_T;
+
+  const auto query_id = blockIdx.y;
+
+#ifdef _CLK_BREAKDOWN
+  std::uint64_t clk_init                 = 0;
+  std::uint64_t clk_compute_1st_distance = 0;
+  std::uint64_t clk_topk                 = 0;
+  std::uint64_t clk_reset_hash           = 0;
+  std::uint64_t clk_pickup_parents       = 0;
+  std::uint64_t clk_restore_hash         = 0;
+  std::uint64_t clk_compute_distance     = 0;
+  std::uint64_t clk_start;
+#define _CLK_START() clk_start = clock64()
+#define _CLK_REC(V)  V += clock64() - clk_start;
+#else
+#define _CLK_START()
+#define _CLK_REC(V)
+#endif
+  _CLK_START();
+
+  extern __shared__ std::uint32_t smem[];
+
+  // Layout of result_buffer
+  // +----------------------+------------------------------+---------+
+  // | internal_top_k       | neighbors of internal_top_k  | padding |
+  // | <internal_topk_size> | <search_width * graph_degree> | upto 32 |
+  // +----------------------+------------------------------+---------+
+  // |<---             result_buffer_size              --->|
+  std::uint32_t result_buffer_size    = internal_topk + (search_width * graph_degree);
+  std::uint32_t result_buffer_size_32 = result_buffer_size;
+  if (result_buffer_size % 32) { result_buffer_size_32 += 32 - (result_buffer_size % 32); }
+  const auto small_hash_size = hashmap::get_size(small_hash_bitlen);
+
+  const auto query_smem_buffer_length =
+    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
+  auto query_buffer          = reinterpret_cast<QUERY_T*>(smem);
+  auto result_indices_buffer = reinterpret_cast<INDEX_T*>(query_buffer + query_smem_buffer_length);
+  auto result_distances_buffer =
+    reinterpret_cast<DISTANCE_T*>(result_indices_buffer + result_buffer_size_32);
+  auto visited_hash_buffer =
+    reinterpret_cast<INDEX_T*>(result_distances_buffer + result_buffer_size_32);
+  auto parent_list_buffer = reinterpret_cast<INDEX_T*>(visited_hash_buffer + small_hash_size);
+  auto distance_work_buffer_ptr =
+    reinterpret_cast<std::uint8_t*>(parent_list_buffer + search_width);
+  auto topk_ws        = reinterpret_cast<std::uint32_t*>(distance_work_buffer_ptr +
+                                                  DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte);
+  auto terminate_flag = reinterpret_cast<std::uint32_t*>(topk_ws + 3);
+  auto smem_work_ptr  = reinterpret_cast<std::uint32_t*>(terminate_flag + 1);
+
+  // Set smem working buffer for the distance calculation
+  dataset_desc.set_smem_ptr(distance_work_buffer_ptr);
+
+  // A flag for filtering.
+  auto filter_flag = terminate_flag;
+
+  const DATA_T* const query_ptr = queries_ptr + query_id * dataset_desc.dim;
+  dataset_desc.template copy_query<DATASET_BLOCK_DIM>(
+    query_ptr, query_buffer, query_smem_buffer_length);
+
+  if (threadIdx.x == 0) {
+    terminate_flag[0] = 0;
+    topk_ws[0]        = ~0u;
+  }
+
+  // Init hashmap
+  INDEX_T* local_visited_hashmap_ptr;
+  if (small_hash_bitlen) {
+    local_visited_hashmap_ptr = visited_hash_buffer;
+  } else {
+    local_visited_hashmap_ptr = visited_hashmap_ptr + (hashmap::get_size(hash_bitlen) * query_id);
+  }
+  hashmap::init(local_visited_hashmap_ptr, hash_bitlen, 0);
+  __syncthreads();
+  _CLK_REC(clk_init);
+
+  // compute distance to randomly selecting nodes
+  _CLK_START();
+  const INDEX_T* const local_seed_ptr = seed_ptr ? seed_ptr + (num_seeds * query_id) : nullptr;
+  device::compute_distance_to_random_nodes<TEAM_SIZE, DATASET_BLOCK_DIM>(result_indices_buffer,
+                                                                         result_distances_buffer,
+                                                                         query_buffer,
+                                                                         dataset_desc,
+                                                                         result_buffer_size,
+                                                                         num_distilation,
+                                                                         rand_xor_mask,
+                                                                         local_seed_ptr,
+                                                                         num_seeds,
+                                                                         local_visited_hashmap_ptr,
+                                                                         hash_bitlen,
+                                                                         metric);
+  __syncthreads();
+  _CLK_REC(clk_compute_1st_distance);
+
+  std::uint32_t iter = 0;
+  while (1) {
+    // sort
+    if constexpr (TOPK_BY_BITONIC_SORT) {
+      // [Notice]
+      // It is good to use multiple warps in topk_by_bitonic_sort() when
+      // batch size is small (short-latency), but it might not be always good
+      // when batch size is large (high-throughput).
+      // topk_by_bitonic_sort() consists of two operations:
+      // if MAX_CANDIDATES is greater than 128, the first operation uses two warps;
+      // if MAX_ITOPK is greater than 256, the second operation used two warps.
+      const unsigned multi_warps_1 = ((blockDim.x >= 64) && (MAX_CANDIDATES > 128)) ? 1 : 0;
+      const unsigned multi_warps_2 = ((blockDim.x >= 64) && (MAX_ITOPK > 256)) ? 1 : 0;
+
+      // reset small-hash table.
+      if ((iter + 1) % small_hash_reset_interval == 0) {
+        // Depending on the block size and the number of warps used in
+        // topk_by_bitonic_sort(), determine which warps are used to reset
+        // the small hash and whether they are performed in overlap with
+        // topk_by_bitonic_sort().
+        _CLK_START();
+        unsigned hash_start_tid;
+        if (blockDim.x == 32) {
+          hash_start_tid = 0;
+        } else if (blockDim.x == 64) {
+          if (multi_warps_1 || multi_warps_2) {
+            hash_start_tid = 0;
+          } else {
+            hash_start_tid = 32;
+          }
+        } else {
+          if (multi_warps_1 || multi_warps_2) {
+            hash_start_tid = 64;
+          } else {
+            hash_start_tid = 32;
+          }
+        }
+        hashmap::init(local_visited_hashmap_ptr, hash_bitlen, hash_start_tid);
+        _CLK_REC(clk_reset_hash);
+      }
+
+      // topk with bitonic sort
+      _CLK_START();
+      if (std::is_same<SAMPLE_FILTER_T,
+                       cuvs::neighbors::filtering::none_cagra_sample_filter>::value ||
+          *filter_flag == 0) {
+        topk_by_bitonic_sort<MAX_ITOPK, MAX_CANDIDATES>(result_distances_buffer,
+                                                        result_indices_buffer,
+                                                        internal_topk,
+                                                        result_distances_buffer + internal_topk,
+                                                        result_indices_buffer + internal_topk,
+                                                        search_width * graph_degree,
+                                                        topk_ws,
+                                                        (iter == 0),
+                                                        multi_warps_1,
+                                                        multi_warps_2);
+        __syncthreads();
+      } else {
+        topk_by_bitonic_sort_1st<MAX_ITOPK + MAX_CANDIDATES>(
+          result_distances_buffer,
+          result_indices_buffer,
+          internal_topk + search_width * graph_degree,
+          internal_topk,
+          false);
+        if (threadIdx.x == 0) { *terminate_flag = 0; }
+      }
+      _CLK_REC(clk_topk);
+    } else {
+      _CLK_START();
+      // topk with radix block sort
+      topk_by_radix_sort<MAX_ITOPK, INDEX_T>{}(
+        internal_topk,
+        gridDim.x,
+        result_buffer_size,
+        reinterpret_cast<std::uint32_t*>(result_distances_buffer),
+        result_indices_buffer,
+        reinterpret_cast<std::uint32_t*>(result_distances_buffer),
+        result_indices_buffer,
+        nullptr,
+        topk_ws,
+        true,
+        reinterpret_cast<std::uint32_t*>(smem_work_ptr));
+      _CLK_REC(clk_topk);
+
+      // reset small-hash table
+      if ((iter + 1) % small_hash_reset_interval == 0) {
+        _CLK_START();
+        hashmap::init(local_visited_hashmap_ptr, hash_bitlen);
+        _CLK_REC(clk_reset_hash);
+      }
+    }
+    __syncthreads();
+
+    if (iter + 1 == max_iteration) { break; }
+
+    // pick up next parents
+    if (threadIdx.x < 32) {
+      _CLK_START();
+      pickup_next_parents<TOPK_BY_BITONIC_SORT, INDEX_T>(terminate_flag,
+                                                         parent_list_buffer,
+                                                         result_indices_buffer,
+                                                         internal_topk,
+                                                         dataset_desc.size,
+                                                         search_width);
+      _CLK_REC(clk_pickup_parents);
+    }
+
+    // restore small-hash table by putting internal-topk indices in it
+    if ((iter + 1) % small_hash_reset_interval == 0) {
+      const unsigned first_tid = ((blockDim.x <= 32) ? 0 : 32);
+      _CLK_START();
+      hashmap_restore(
+        local_visited_hashmap_ptr, hash_bitlen, result_indices_buffer, internal_topk, first_tid);
+      _CLK_REC(clk_restore_hash);
+    }
+    __syncthreads();
+
+    if (*terminate_flag && iter >= min_iteration) { break; }
+
+    // compute the norms between child nodes and query node
+    _CLK_START();
+    constexpr unsigned max_n_frags = 8;
+    device::compute_distance_to_child_nodes<TEAM_SIZE, DATASET_BLOCK_DIM, max_n_frags>(
+      result_indices_buffer + internal_topk,
+      result_distances_buffer + internal_topk,
+      query_buffer,
+      dataset_desc,
+      knn_graph,
+      graph_degree,
+      local_visited_hashmap_ptr,
+      hash_bitlen,
+      parent_list_buffer,
+      result_indices_buffer,
+      search_width,
+      metric);
+    __syncthreads();
+    _CLK_REC(clk_compute_distance);
+
+    // Filtering
+    if constexpr (!std::is_same<SAMPLE_FILTER_T,
+                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+      if (threadIdx.x == 0) { *filter_flag = 0; }
+      __syncthreads();
+
+      constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+      const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
+
+      for (unsigned p = threadIdx.x; p < search_width; p += blockDim.x) {
+        if (parent_list_buffer[p] != invalid_index) {
+          const auto parent_id = result_indices_buffer[parent_list_buffer[p]] & ~index_msb_1_mask;
+          if (!sample_filter(query_id, parent_id)) {
+            // If the parent must not be in the resulting top-k list, remove from the parent list
+            result_distances_buffer[parent_list_buffer[p]] = utils::get_max_value<DISTANCE_T>();
+            result_indices_buffer[parent_list_buffer[p]]   = invalid_index;
+            *filter_flag                                   = 1;
+          }
+        }
+      }
+      __syncthreads();
+    }
+
+    iter++;
+  }
+
+  // Post process for filtering
+  if constexpr (!std::is_same<SAMPLE_FILTER_T,
+                              cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+    constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+    const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
+
+    for (unsigned i = threadIdx.x; i < internal_topk + search_width * graph_degree;
+         i += blockDim.x) {
+      const auto node_id = result_indices_buffer[i] & ~index_msb_1_mask;
+      if (node_id != (invalid_index & ~index_msb_1_mask) && !sample_filter(query_id, node_id)) {
+        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+        result_indices_buffer[i]   = invalid_index;
+      }
+    }
+
+    __syncthreads();
+    topk_by_bitonic_sort_1st<MAX_ITOPK + MAX_CANDIDATES>(
+      result_distances_buffer,
+      result_indices_buffer,
+      internal_topk + search_width * graph_degree,
+      top_k,
+      false);
+    __syncthreads();
+  }
+
+  for (std::uint32_t i = threadIdx.x; i < top_k; i += blockDim.x) {
+    unsigned j  = i + (top_k * query_id);
+    unsigned ii = i;
+    if (TOPK_BY_BITONIC_SORT) { ii = device::swizzling(i); }
+    if (result_distances_ptr != nullptr) { result_distances_ptr[j] = result_distances_buffer[ii]; }
+    constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+
+    result_indices_ptr[j] =
+      result_indices_buffer[ii] & ~index_msb_1_mask;  // clear most significant bit
+  }
+  if (threadIdx.x == 0 && num_executed_iterations != nullptr) {
+    num_executed_iterations[query_id] = iter + 1;
+  }
+#ifdef _CLK_BREAKDOWN
+  if ((threadIdx.x == 0 || threadIdx.x == BLOCK_SIZE - 1) && ((query_id * 3) % gridDim.y < 3)) {
+    RAFT_LOG_DEBUG(
+      "query, %d, thread, %d"
+      ", init, %d"
+      ", 1st_distance, %lu"
+      ", topk, %lu"
+      ", reset_hash, %lu"
+      ", pickup_parents, %lu"
+      ", restore_hash, %lu"
+      ", distance, %lu"
+      "\n",
+      query_id,
+      threadIdx.x,
+      clk_init,
+      clk_compute_1st_distance,
+      clk_topk,
+      clk_reset_hash,
+      clk_pickup_parents,
+      clk_restore_hash,
+      clk_compute_distance);
+  }
+#endif
+}
+
+template <uint32_t TEAM_SIZE,
+          uint32_t DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+struct search_kernel_config {
+  using kernel_t = decltype(&search_kernel<TEAM_SIZE,
+                                           DATASET_BLOCK_DIM,
+                                           64,
+                                           64,
+                                           0,
+                                           DATASET_DESCRIPTOR_T,
+                                           SAMPLE_FILTER_T>);
+
+  template <unsigned MAX_CANDIDATES, unsigned USE_BITONIC_SORT>
+  static auto choose_search_kernel(unsigned itopk_size) -> kernel_t
+  {
+    if (itopk_size <= 64) {
+      return search_kernel<TEAM_SIZE,
+                           DATASET_BLOCK_DIM,
+                           64,
+                           MAX_CANDIDATES,
+                           USE_BITONIC_SORT,
+                           DATASET_DESCRIPTOR_T,
+                           SAMPLE_FILTER_T>;
+    } else if (itopk_size <= 128) {
+      return search_kernel<TEAM_SIZE,
+                           DATASET_BLOCK_DIM,
+                           128,
+                           MAX_CANDIDATES,
+                           USE_BITONIC_SORT,
+                           DATASET_DESCRIPTOR_T,
+                           SAMPLE_FILTER_T>;
+    } else if (itopk_size <= 256) {
+      return search_kernel<TEAM_SIZE,
+                           DATASET_BLOCK_DIM,
+                           256,
+                           MAX_CANDIDATES,
+                           USE_BITONIC_SORT,
+                           DATASET_DESCRIPTOR_T,
+                           SAMPLE_FILTER_T>;
+    } else if (itopk_size <= 512) {
+      return search_kernel<TEAM_SIZE,
+                           DATASET_BLOCK_DIM,
+                           512,
+                           MAX_CANDIDATES,
+                           USE_BITONIC_SORT,
+                           DATASET_DESCRIPTOR_T,
+                           SAMPLE_FILTER_T>;
+    }
+    THROW("No kernel for parametels itopk_size %u, max_candidates %u", itopk_size, MAX_CANDIDATES);
+  }
+
+  static auto choose_itopk_and_mx_candidates(unsigned itopk_size,
+                                             unsigned num_itopk_candidates,
+                                             unsigned block_size) -> kernel_t
+  {
+    if (num_itopk_candidates <= 64) {
+      // use bitonic sort based topk
+      return choose_search_kernel<64, 1>(itopk_size);
+    } else if (num_itopk_candidates <= 128) {
+      return choose_search_kernel<128, 1>(itopk_size);
+    } else if (num_itopk_candidates <= 256) {
+      return choose_search_kernel<256, 1>(itopk_size);
+    } else {
+      // Radix-based topk is used
+      constexpr unsigned max_candidates = 32;  // to avoid build failure
+      if (itopk_size <= 256) {
+        return search_kernel<TEAM_SIZE,
+                             DATASET_BLOCK_DIM,
+                             256,
+                             max_candidates,
+                             0,
+                             DATASET_DESCRIPTOR_T,
+                             SAMPLE_FILTER_T>;
+      } else if (itopk_size <= 512) {
+        return search_kernel<TEAM_SIZE,
+                             DATASET_BLOCK_DIM,
+                             512,
+                             max_candidates,
+                             0,
+                             DATASET_DESCRIPTOR_T,
+                             SAMPLE_FILTER_T>;
+      }
+    }
+    THROW("No kernel for parametels itopk_size %u, num_itopk_candidates %u",
+          itopk_size,
+          num_itopk_candidates);
+  }
+};
+
+template <unsigned TEAM_SIZE,
+          unsigned DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
+void select_and_run(
+  DATASET_DESCRIPTOR_T dataset_desc,
+  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
+    graph,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const uint32_t num_queries,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
+  uint32_t* const num_executed_iterations,                     // [num_queries,]
+  uint32_t topk,
+  uint32_t num_itopk_candidates,
+  uint32_t block_size,  //
+  uint32_t smem_size,
+  int64_t hash_bitlen,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
+  size_t small_hash_bitlen,
+  size_t small_hash_reset_interval,
+  uint32_t num_random_samplings,
+  uint64_t rand_xor_mask,
+  uint32_t num_seeds,
+  size_t itopk_size,
+  size_t search_width,
+  size_t min_iterations,
+  size_t max_iterations,
+  SAMPLE_FILTER_T sample_filter,
+  cuvs::distance::DistanceType metric,
+  cudaStream_t stream)
+{
+  auto kernel =
+    search_kernel_config<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::
+      choose_itopk_and_mx_candidates(itopk_size, num_itopk_candidates, block_size);
+  RAFT_CUDA_TRY(cudaFuncSetAttribute(kernel,
+                                     cudaFuncAttributeMaxDynamicSharedMemorySize,
+                                     smem_size + DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte));
+  dim3 thread_dims(block_size, 1, 1);
+  dim3 block_dims(1, num_queries, 1);
+  RAFT_LOG_DEBUG(
+    "Launching kernel with %u threads, %u block %u smem", block_size, num_queries, smem_size);
+  kernel<<<block_dims, thread_dims, smem_size, stream>>>(topk_indices_ptr,
+                                                         topk_distances_ptr,
+                                                         topk,
+                                                         dataset_desc,
+                                                         queries_ptr,
+                                                         graph.data_handle(),
+                                                         graph.extent(1),
+                                                         num_random_samplings,
+                                                         rand_xor_mask,
+                                                         dev_seed_ptr,
+                                                         num_seeds,
+                                                         hashmap_ptr,
+                                                         itopk_size,
+                                                         search_width,
+                                                         min_iterations,
+                                                         max_iterations,
+                                                         num_executed_iterations,
+                                                         hash_bitlen,
+                                                         small_hash_bitlen,
+                                                         small_hash_reset_interval,
+                                                         sample_filter,
+                                                         metric);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+}  // namespace single_cta_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
new file mode 100644
index 0000000000..1d8fd8e30a
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#include "search_single_cta_kernel-inl.cuh"
+#endif
+
+#ifdef RAFT_COMPILED
+#include "search_single_cta_kernel-ext.cuh"
+#endif
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
new file mode 100644
index 0000000000..35e04ea6a0
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  1024,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..614e6ca014
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  8,
+  128,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..005afb5669
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  16,
+  256,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..af30b2e244
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+#include "compute_distance.hpp"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(
+  32,
+  512,
+  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
+  cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/topk_by_radix.cuh b/cpp/src/neighbors/detail/cagra/topk_by_radix.cuh
new file mode 100644
index 0000000000..67173026b3
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/topk_by_radix.cuh
@@ -0,0 +1,91 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "topk_for_cagra/topk_core.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+namespace single_cta_search {
+
+template <unsigned MAX_INTERNAL_TOPK>
+struct topk_by_radix_sort_base {
+  static constexpr std::uint32_t smem_size        = MAX_INTERNAL_TOPK * 2 + 2048 + 8;
+  static constexpr std::uint32_t state_bit_lenght = 0;
+  static constexpr std::uint32_t vecLen           = 2;  // TODO
+};
+template <unsigned MAX_INTERNAL_TOPK, class IdxT, class = void>
+struct topk_by_radix_sort : topk_by_radix_sort_base<MAX_INTERNAL_TOPK> {};
+
+template <unsigned MAX_INTERNAL_TOPK, class IdxT>
+struct topk_by_radix_sort<MAX_INTERNAL_TOPK, IdxT, std::enable_if_t<((MAX_INTERNAL_TOPK <= 64))>>
+  : topk_by_radix_sort_base<MAX_INTERNAL_TOPK> {
+  __device__ void operator()(uint32_t topk,
+                             uint32_t batch_size,
+                             uint32_t len_x,
+                             const uint32_t* _x,
+                             const IdxT* _in_vals,
+                             uint32_t* _y,
+                             IdxT* _out_vals,
+                             uint32_t* work,
+                             uint32_t* _hints,
+                             bool sort,
+                             uint32_t* _smem)
+  {
+    std::uint8_t* const state = reinterpret_cast<std::uint8_t*>(work);
+    topk_cta_11_core<topk_by_radix_sort_base<MAX_INTERNAL_TOPK>::state_bit_lenght,
+                     topk_by_radix_sort_base<MAX_INTERNAL_TOPK>::vecLen,
+                     64,
+                     32,
+                     IdxT>(topk, len_x, _x, _in_vals, _y, _out_vals, state, _hints, sort, _smem);
+  }
+};
+
+#define TOP_FUNC_PARTIAL_SPECIALIZATION(V)                                           \
+  template <unsigned MAX_INTERNAL_TOPK, class IdxT>                                  \
+  struct topk_by_radix_sort<                                                         \
+    MAX_INTERNAL_TOPK,                                                               \
+    IdxT,                                                                            \
+    std::enable_if_t<((MAX_INTERNAL_TOPK <= V) && (2 * MAX_INTERNAL_TOPK > V))>>     \
+    : topk_by_radix_sort_base<MAX_INTERNAL_TOPK> {                                   \
+    __device__ void operator()(uint32_t topk,                                        \
+                               uint32_t batch_size,                                  \
+                               uint32_t len_x,                                       \
+                               const uint32_t* _x,                                   \
+                               const IdxT* _in_vals,                                 \
+                               uint32_t* _y,                                         \
+                               IdxT* _out_vals,                                      \
+                               uint32_t* work,                                       \
+                               uint32_t* _hints,                                     \
+                               bool sort,                                            \
+                               uint32_t* _smem)                                      \
+    {                                                                                \
+      assert(blockDim.x >= V / 4);                                                   \
+      std::uint8_t* state = (std::uint8_t*)work;                                     \
+      topk_cta_11_core<topk_by_radix_sort_base<MAX_INTERNAL_TOPK>::state_bit_lenght, \
+                       topk_by_radix_sort_base<MAX_INTERNAL_TOPK>::vecLen,           \
+                       V,                                                            \
+                       V / 4,                                                        \
+                       IdxT>(                                                        \
+        topk, len_x, _x, _in_vals, _y, _out_vals, state, _hints, sort, _smem);       \
+    }                                                                                \
+  };
+TOP_FUNC_PARTIAL_SPECIALIZATION(128);
+TOP_FUNC_PARTIAL_SPECIALIZATION(256);
+TOP_FUNC_PARTIAL_SPECIALIZATION(512);
+TOP_FUNC_PARTIAL_SPECIALIZATION(1024);
+
+}  // namespace single_cta_search
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.h b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.h
new file mode 100644
index 0000000000..6bc490df2c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.h
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuda_fp16.h>
+
+#include <stdint.h>
+
+namespace cuvs::neighbors::cagra::detail {
+
+//
+size_t _cuann_find_topk_bufferSize(uint32_t topK,
+                                   uint32_t sizeBatch,
+                                   uint32_t numElements,
+                                   cudaDataType_t sampleDtype = CUDA_R_32F);
+
+//
+template <class ValT>
+void _cuann_find_topk(uint32_t topK,
+                      uint32_t sizeBatch,
+                      uint32_t numElements,
+                      const float* inputKeys,  // [sizeBatch, ldIK,]
+                      uint32_t ldIK,           // (*) ldIK >= numElements
+                      const ValT* inputVals,   // [sizeBatch, ldIV,]
+                      uint32_t ldIV,           // (*) ldIV >= numElements
+                      float* outputKeys,       // [sizeBatch, ldOK,]
+                      uint32_t ldOK,           // (*) ldOK >= topK
+                      ValT* outputVals,        // [sizeBatch, ldOV,]
+                      uint32_t ldOV,           // (*) ldOV >= topK
+                      void* workspace,
+                      bool sort           = false,
+                      uint32_t* hint      = NULL,
+                      cudaStream_t stream = 0);
+
+#ifdef __CUDA_ARCH__
+#define CUDA_DEVICE_HOST_FUNC __device__
+#else
+#define CUDA_DEVICE_HOST_FUNC
+#endif
+//
+CUDA_DEVICE_HOST_FUNC inline size_t _cuann_aligned(size_t size, size_t unit = 128)
+{
+  if (size % unit) { size += unit - (size % unit); }
+  return size;
+}
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh
new file mode 100644
index 0000000000..cbf99a5565
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh
@@ -0,0 +1,1040 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+#include "topk.h"
+
+#include <cub/cub.cuh>
+
+#include <assert.h>
+#include <float.h>
+#include <stdint.h>
+#include <stdio.h>
+
+namespace cuvs::neighbors::cagra::detail {
+//
+__device__ inline uint32_t convert(uint32_t x)
+{
+  if (x & 0x80000000) {
+    return x ^ 0xffffffff;
+  } else {
+    return x ^ 0x80000000;
+  }
+}
+
+//
+__device__ inline uint16_t convert(uint16_t x)
+{
+  if (x & 0x8000) {
+    return x ^ 0xffff;
+  } else {
+    return x ^ 0x8000;
+  }
+}
+
+//
+struct u32_vector {
+  uint1 x1;
+  uint2 x2;
+  uint4 x4;
+  ulonglong4 x8;
+};
+
+//
+struct u16_vector {
+  ushort1 x1;
+  ushort2 x2;
+  ushort4 x4;
+  uint4 x8;
+};
+
+//
+template <int vecLen>
+__device__ inline void load_u32_vector(struct u32_vector& vec, const uint32_t* x, int i)
+{
+  if (vecLen == 1) {
+    vec.x1 = ((uint1*)(x + i))[0];
+  } else if (vecLen == 2) {
+    vec.x2 = ((uint2*)(x + i))[0];
+  } else if (vecLen == 4) {
+    vec.x4 = ((uint4*)(x + i))[0];
+  } else if (vecLen == 8) {
+    vec.x8 = ((ulonglong4*)(x + i))[0];
+  }
+}
+
+//
+template <int vecLen>
+__device__ inline void load_u16_vector(struct u16_vector& vec, const uint16_t* x, int i)
+{
+  if (vecLen == 1) {
+    vec.x1 = ((ushort1*)(x + i))[0];
+  } else if (vecLen == 2) {
+    vec.x2 = ((ushort2*)(x + i))[0];
+  } else if (vecLen == 4) {
+    vec.x4 = ((ushort4*)(x + i))[0];
+  } else if (vecLen == 8) {
+    vec.x8 = ((uint4*)(x + i))[0];
+  }
+}
+
+//
+template <int vecLen>
+__device__ inline uint32_t get_element_from_u32_vector(struct u32_vector& vec, int i)
+{
+  uint32_t xi;
+  if (vecLen == 1) {
+    xi = convert(vec.x1.x);
+  } else if (vecLen == 2) {
+    if (i == 0)
+      xi = convert(vec.x2.x);
+    else
+      xi = convert(vec.x2.y);
+  } else if (vecLen == 4) {
+    if (i == 0)
+      xi = convert(vec.x4.x);
+    else if (i == 1)
+      xi = convert(vec.x4.y);
+    else if (i == 2)
+      xi = convert(vec.x4.z);
+    else
+      xi = convert(vec.x4.w);
+  } else if (vecLen == 8) {
+    if (i == 0)
+      xi = convert((uint32_t)(vec.x8.x & 0xffffffff));
+    else if (i == 1)
+      xi = convert((uint32_t)(vec.x8.x >> 32));
+    else if (i == 2)
+      xi = convert((uint32_t)(vec.x8.y & 0xffffffff));
+    else if (i == 3)
+      xi = convert((uint32_t)(vec.x8.y >> 32));
+    else if (i == 4)
+      xi = convert((uint32_t)(vec.x8.z & 0xffffffff));
+    else if (i == 5)
+      xi = convert((uint32_t)(vec.x8.z >> 32));
+    else if (i == 6)
+      xi = convert((uint32_t)(vec.x8.w & 0xffffffff));
+    else
+      xi = convert((uint32_t)(vec.x8.w >> 32));
+  }
+  return xi;
+}
+
+//
+template <int vecLen>
+__device__ inline uint16_t get_element_from_u16_vector(struct u16_vector& vec, int i)
+{
+  uint16_t xi;
+  if (vecLen == 1) {
+    xi = convert(vec.x1.x);
+  } else if (vecLen == 2) {
+    if (i == 0)
+      xi = convert(vec.x2.x);
+    else
+      xi = convert(vec.x2.y);
+  } else if (vecLen == 4) {
+    if (i == 0)
+      xi = convert(vec.x4.x);
+    else if (i == 1)
+      xi = convert(vec.x4.y);
+    else if (i == 2)
+      xi = convert(vec.x4.z);
+    else
+      xi = convert(vec.x4.w);
+  } else if (vecLen == 8) {
+    if (i == 0)
+      xi = convert((uint16_t)(vec.x8.x & 0xffff));
+    else if (i == 1)
+      xi = convert((uint16_t)(vec.x8.x >> 16));
+    else if (i == 2)
+      xi = convert((uint16_t)(vec.x8.y & 0xffff));
+    else if (i == 3)
+      xi = convert((uint16_t)(vec.x8.y >> 16));
+    else if (i == 4)
+      xi = convert((uint16_t)(vec.x8.z & 0xffff));
+    else if (i == 5)
+      xi = convert((uint16_t)(vec.x8.z >> 16));
+    else if (i == 6)
+      xi = convert((uint16_t)(vec.x8.w & 0xffff));
+    else
+      xi = convert((uint16_t)(vec.x8.w >> 16));
+  }
+  return xi;
+}
+
+template <typename T>
+__device__ inline void block_scan(const T input, T& output)
+{
+  switch (blockDim.x) {
+    case 32: {
+      typedef cub::BlockScan<T, 32> BlockScanT;
+      __shared__ typename BlockScanT::TempStorage temp_storage;
+      BlockScanT(temp_storage).InclusiveSum(input, output);
+    } break;
+    case 64: {
+      typedef cub::BlockScan<T, 64> BlockScanT;
+      __shared__ typename BlockScanT::TempStorage temp_storage;
+      BlockScanT(temp_storage).InclusiveSum(input, output);
+    } break;
+    case 128: {
+      typedef cub::BlockScan<T, 128> BlockScanT;
+      __shared__ typename BlockScanT::TempStorage temp_storage;
+      BlockScanT(temp_storage).InclusiveSum(input, output);
+    } break;
+    case 256: {
+      typedef cub::BlockScan<T, 256> BlockScanT;
+      __shared__ typename BlockScanT::TempStorage temp_storage;
+      BlockScanT(temp_storage).InclusiveSum(input, output);
+    } break;
+    case 512: {
+      typedef cub::BlockScan<T, 512> BlockScanT;
+      __shared__ typename BlockScanT::TempStorage temp_storage;
+      BlockScanT(temp_storage).InclusiveSum(input, output);
+    } break;
+    case 1024: {
+      typedef cub::BlockScan<T, 1024> BlockScanT;
+      __shared__ typename BlockScanT::TempStorage temp_storage;
+      BlockScanT(temp_storage).InclusiveSum(input, output);
+    } break;
+    default: break;
+  }
+}
+
+//
+template <typename T, int stateBitLen, int vecLen>
+__device__ inline void update_histogram(int itr,
+                                        uint32_t thread_id,
+                                        uint32_t num_threads,
+                                        uint32_t hint,
+                                        uint32_t threshold,
+                                        uint32_t& num_bins,
+                                        uint32_t& shift,
+                                        const T* x,  // [nx,]
+                                        uint32_t nx,
+                                        uint32_t* hist,  // [num_bins]
+                                        uint8_t* state,
+                                        uint32_t* output,  // [topk]
+                                        uint32_t* output_count)
+{
+  if (sizeof(T) == 4) {
+    // 32-bit (uint32_t)
+    // itr:0, calculate histogram with 11 bits from bit-21 to bit-31
+    // itr:1, calculate histogram with 11 bits from bit-10 to bit-20
+    // itr:2, calculate histogram with 10 bits from bit-0 to bit-9
+    if (itr == 0) {
+      shift    = 21;
+      num_bins = 2048;
+    } else if (itr == 1) {
+      shift    = 10;
+      num_bins = 2048;
+    } else {
+      shift    = 0;
+      num_bins = 1024;
+    }
+  } else if (sizeof(T) == 2) {
+    // 16-bit (uint16_t)
+    // itr:0, calculate histogram with 8 bits from bit-8 to bit-15
+    // itr:1, calculate histogram with 8 bits from bit-0 to bit-7
+    if (itr == 0) {
+      shift    = 8;
+      num_bins = 256;
+    } else {
+      shift    = 0;
+      num_bins = 256;
+    }
+  } else {
+    return;
+  }
+  if (itr > 0) {
+    for (int i = threadIdx.x; i < num_bins; i += blockDim.x) {
+      hist[i] = 0;
+    }
+    __syncthreads();
+  }
+
+  // (*) Note that 'thread_id' may be different from 'threadIdx.x',
+  // and 'num_threads' may be different from 'blockDim.x'
+  int ii = 0;
+  for (int i = thread_id * vecLen; i < nx; i += num_threads * max(vecLen, stateBitLen), ii++) {
+    uint8_t iState = 0;
+    if ((stateBitLen == 8) && (itr > 0)) {
+      iState = state[thread_id + (num_threads * ii)];
+      if (iState == (uint8_t)0xff) continue;
+    }
+#pragma unroll
+    for (int v = 0; v < max(vecLen, stateBitLen); v += vecLen) {
+      const int iv = i + (num_threads * v);
+      if (iv >= nx) break;
+
+      struct u32_vector x_u32_vec;
+      struct u16_vector x_u16_vec;
+      if (sizeof(T) == 4) {
+        load_u32_vector<vecLen>(x_u32_vec, (const uint32_t*)x, iv);
+      } else {
+        load_u16_vector<vecLen>(x_u16_vec, (const uint16_t*)x, iv);
+      }
+#pragma unroll
+      for (int u = 0; u < vecLen; u++) {
+        const int ivu = iv + u;
+        if (ivu >= nx) break;
+
+        uint8_t mask = (uint8_t)0x1 << (v + u);
+        if ((stateBitLen == 8) && (iState & mask)) continue;
+
+        uint32_t xi;
+        if (sizeof(T) == 4) {
+          xi = get_element_from_u32_vector<vecLen>(x_u32_vec, u);
+        } else {
+          xi = get_element_from_u16_vector<vecLen>(x_u16_vec, u);
+        }
+        if ((xi > hint) && (itr == 0)) {
+          if (stateBitLen == 8) { iState |= mask; }
+        } else if (xi < threshold) {
+          if (stateBitLen == 8) {
+            // If the condition is already met, record the index.
+            output[atomicAdd(output_count, 1)] = ivu;
+            iState |= mask;
+          }
+        } else {
+          const uint32_t k = (xi - threshold) >> shift;  // 0 <= k
+          if (k >= num_bins) {
+            if (stateBitLen == 8) { iState |= mask; }
+          } else if (k + 1 < num_bins) {
+            // Update histogram
+            atomicAdd(&(hist[k + 1]), 1);
+          }
+        }
+      }
+    }
+    if (stateBitLen == 8) { state[thread_id + (num_threads * ii)] = iState; }
+  }
+  __syncthreads();
+}
+
+template <unsigned blockDim_x>
+__device__ inline void select_best_index_for_next_threshold_core(uint32_t& my_index,
+                                                                 uint32_t& my_csum,
+                                                                 const unsigned num_bins,
+                                                                 const uint32_t* const hist,
+                                                                 const uint32_t nx_below_threshold,
+                                                                 const uint32_t max_threshold,
+                                                                 const uint32_t threshold,
+                                                                 const uint32_t shift,
+                                                                 const uint32_t topk)
+{
+  typedef cub::BlockScan<uint32_t, blockDim_x> BlockScanT;
+  __shared__ typename BlockScanT::TempStorage temp_storage;
+  if (num_bins == 2048) {
+    constexpr int n_data = 2048 / blockDim_x;
+    uint32_t csum[n_data];
+    for (int i = 0; i < n_data; i++) {
+      csum[i] = hist[i + (n_data * threadIdx.x)];
+    }
+    BlockScanT(temp_storage).InclusiveSum(csum, csum);
+    for (int i = n_data - 1; i >= 0; i--) {
+      if (nx_below_threshold + csum[i] > topk) continue;
+      const uint32_t index = i + (n_data * threadIdx.x);
+      if (threshold + (index << shift) > max_threshold) continue;
+      my_index = index;
+      my_csum  = csum[i];
+      break;
+    }
+  } else if (num_bins == 1024) {
+    constexpr int n_data = 1024 / blockDim_x;
+    uint32_t csum[n_data];
+    for (int i = 0; i < n_data; i++) {
+      csum[i] = hist[i + (n_data * threadIdx.x)];
+    }
+    BlockScanT(temp_storage).InclusiveSum(csum, csum);
+    for (int i = n_data - 1; i >= 0; i--) {
+      if (nx_below_threshold + csum[i] > topk) continue;
+      const uint32_t index = i + (n_data * threadIdx.x);
+      if (threshold + (index << shift) > max_threshold) continue;
+      my_index = index;
+      my_csum  = csum[i];
+      break;
+    }
+  }
+}
+
+//
+__device__ inline void select_best_index_for_next_threshold(
+  const uint32_t topk,
+  const uint32_t threshold,
+  const uint32_t max_threshold,
+  const uint32_t nx_below_threshold,
+  const uint32_t num_bins,
+  const uint32_t shift,
+  const uint32_t* const hist,  // [num_bins]
+  uint32_t* const best_index,
+  uint32_t* const best_csum)
+{
+  // Scan the histogram ('hist') and compute csum. Then, find the largest
+  // index under the condition that the sum of the number of elements found
+  // so far ('nx_below_threshold') and the csum value does not exceed the
+  // topk value.
+  uint32_t my_index = 0xffffffff;
+  uint32_t my_csum  = 0;
+  if (num_bins <= blockDim.x) {
+    uint32_t csum = 0;
+    if (threadIdx.x < num_bins) { csum = hist[threadIdx.x]; }
+    detail::block_scan(csum, csum);
+    if (threadIdx.x < num_bins) {
+      const uint32_t index = threadIdx.x;
+      if ((nx_below_threshold + csum <= topk) && (threshold + (index << shift) <= max_threshold)) {
+        my_index = index;
+        my_csum  = csum;
+      }
+    }
+  } else {
+    switch (blockDim.x) {
+      case 64:
+        select_best_index_for_next_threshold_core<64>(my_index,
+                                                      my_csum,
+                                                      num_bins,
+                                                      hist,
+                                                      nx_below_threshold,
+                                                      max_threshold,
+                                                      threshold,
+                                                      shift,
+                                                      topk);
+        break;
+      case 128:
+        select_best_index_for_next_threshold_core<128>(my_index,
+                                                       my_csum,
+                                                       num_bins,
+                                                       hist,
+                                                       nx_below_threshold,
+                                                       max_threshold,
+                                                       threshold,
+                                                       shift,
+                                                       topk);
+        break;
+      case 256:
+        select_best_index_for_next_threshold_core<256>(my_index,
+                                                       my_csum,
+                                                       num_bins,
+                                                       hist,
+                                                       nx_below_threshold,
+                                                       max_threshold,
+                                                       threshold,
+                                                       shift,
+                                                       topk);
+        break;
+      case 512:
+        select_best_index_for_next_threshold_core<512>(my_index,
+                                                       my_csum,
+                                                       num_bins,
+                                                       hist,
+                                                       nx_below_threshold,
+                                                       max_threshold,
+                                                       threshold,
+                                                       shift,
+                                                       topk);
+        break;
+      case 1024:
+        select_best_index_for_next_threshold_core<1024>(my_index,
+                                                        my_csum,
+                                                        num_bins,
+                                                        hist,
+                                                        nx_below_threshold,
+                                                        max_threshold,
+                                                        threshold,
+                                                        shift,
+                                                        topk);
+        break;
+    }
+  }
+  if (threadIdx.x < num_bins) {
+    const int laneid = 31 - __clz(__ballot_sync(0xffffffff, (my_index != 0xffffffff)));
+    if ((threadIdx.x & 0x1f) == laneid) {
+      const uint32_t old_index = atomicMax(best_index, my_index);
+      if (old_index < my_index) { atomicMax(best_csum, my_csum); }
+    }
+  }
+  __syncthreads();
+}
+
+//
+template <typename T, int stateBitLen, int vecLen>
+__device__ inline void output_index_below_threshold(const uint32_t topk,
+                                                    const uint32_t thread_id,
+                                                    const uint32_t num_threads,
+                                                    const uint32_t threshold,
+                                                    const uint32_t nx_below_threshold,
+                                                    const T* const x,  // [nx,]
+                                                    const uint32_t nx,
+                                                    const uint8_t* state,
+                                                    uint32_t* const output,  // [topk]
+                                                    uint32_t* const output_count,
+                                                    uint32_t* const output_count_eq)
+{
+  int ii = 0;
+  for (int i = thread_id * vecLen; i < nx; i += num_threads * max(vecLen, stateBitLen), ii++) {
+    uint8_t iState = 0;
+    if (stateBitLen == 8) {
+      iState = state[thread_id + (num_threads * ii)];
+      if (iState == (uint8_t)0xff) continue;
+    }
+#pragma unroll
+    for (int v = 0; v < max(vecLen, stateBitLen); v += vecLen) {
+      const int iv = i + (num_threads * v);
+      if (iv >= nx) break;
+
+      struct u32_vector u32_vec;
+      struct u16_vector u16_vec;
+      if (sizeof(T) == 4) {
+        load_u32_vector<vecLen>(u32_vec, (const uint32_t*)x, iv);
+      } else {
+        load_u16_vector<vecLen>(u16_vec, (const uint16_t*)x, iv);
+      }
+#pragma unroll
+      for (int u = 0; u < vecLen; u++) {
+        const int ivu = iv + u;
+        if (ivu >= nx) break;
+
+        const uint8_t mask = (uint8_t)0x1 << (v + u);
+        if ((stateBitLen == 8) && (iState & mask)) continue;
+
+        uint32_t xi;
+        if (sizeof(T) == 4) {
+          xi = get_element_from_u32_vector<vecLen>(u32_vec, u);
+        } else {
+          xi = get_element_from_u16_vector<vecLen>(u16_vec, u);
+        }
+        if (xi < threshold) {
+          output[atomicAdd(output_count, 1)] = ivu;
+        } else if (xi == threshold) {
+          // (*) If the value is equal to the threshold, the index
+          // processed first is recorded. Cause of non-determinism.
+          if (nx_below_threshold + atomicAdd(output_count_eq, 1) < topk) {
+            output[atomicAdd(output_count, 1)] = ivu;
+          }
+        }
+      }
+    }
+  }
+}
+
+//
+template <typename T>
+__device__ inline void swap(T& val1, T& val2)
+{
+  const T val0 = val1;
+  val1         = val2;
+  val2         = val0;
+}
+
+//
+template <typename K>
+__device__ inline bool swap_if_needed(K& key1, K& key2)
+{
+  if (key1 > key2) {
+    swap<K>(key1, key2);
+    return true;
+  }
+  return false;
+}
+
+//
+template <typename K, typename V>
+__device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2)
+{
+  if (key1 > key2) {
+    swap<K>(key1, key2);
+    swap<V>(val1, val2);
+    return true;
+  }
+  return false;
+}
+
+//
+template <typename K, typename V>
+__device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2, bool ascending)
+{
+  if (key1 == key2) { return false; }
+  if ((key1 > key2) == ascending) {
+    swap<K>(key1, key2);
+    swap<V>(val1, val2);
+    return true;
+  }
+  return false;
+}
+
+//
+template <typename T>
+__device__ inline T max_value_of();
+template <>
+__device__ inline float max_value_of<float>()
+{
+  return FLT_MAX;
+}
+template <>
+__device__ inline uint32_t max_value_of<uint32_t>()
+{
+  return ~0u;
+}
+
+template <int stateBitLen, unsigned BLOCK_SIZE = 0>
+__device__ __host__ inline uint32_t get_state_size(uint32_t len_x)
+{
+#ifdef __CUDA_ARCH__
+  const uint32_t num_threads = blockDim.x;
+#else
+  const uint32_t num_threads = BLOCK_SIZE;
+#endif
+  if (stateBitLen == 8) {
+    uint32_t numElements_perThread = (len_x + num_threads - 1) / num_threads;
+    uint32_t numState_perThread    = (numElements_perThread + stateBitLen - 1) / stateBitLen;
+    return numState_perThread * num_threads;
+  }
+  return 0;
+}
+
+//
+template <int stateBitLen, int vecLen, int maxTopk, int numSortThreads, class ValT>
+__device__ inline void topk_cta_11_core(uint32_t topk,
+                                        uint32_t len_x,
+                                        const uint32_t* _x,    // [size_batch, ld_x,]
+                                        const ValT* _in_vals,  // [size_batch, ld_iv,]
+                                        uint32_t* _y,          // [size_batch, ld_y,]
+                                        ValT* _out_vals,       // [size_batch, ld_ov,]
+                                        uint8_t* _state,       // [size_batch, ...,]
+                                        uint32_t* _hint,
+                                        bool sort,
+                                        uint32_t* _smem)
+{
+  uint32_t* const smem_out_vals = _smem;
+  uint32_t* const hist          = &(_smem[2 * maxTopk]);
+  uint32_t* const best_index    = &(_smem[2 * maxTopk + 2048]);
+  uint32_t* const best_csum     = &(_smem[2 * maxTopk + 2048 + 3]);
+
+  const uint32_t num_threads = blockDim.x;
+  const uint32_t thread_id   = threadIdx.x;
+  uint32_t nx                = len_x;
+  const uint32_t* const x    = _x;
+  const ValT* in_vals        = NULL;
+  if (_in_vals) { in_vals = _in_vals; }
+  uint32_t* y = NULL;
+  if (_y) { y = _y; }
+  ValT* out_vals = NULL;
+  if (_out_vals) { out_vals = _out_vals; }
+  uint8_t* state      = _state;
+  const uint32_t hint = (_hint == NULL ? ~0u : *_hint);
+
+  // Initialize shared memory
+  for (int i = 2 * maxTopk + thread_id; i < 2 * maxTopk + 2048 + 8; i += num_threads) {
+    _smem[i] = 0;
+  }
+  uint32_t* const output_count    = &(_smem[2 * maxTopk + 2048 + 6]);
+  uint32_t* const output_count_eq = &(_smem[2 * maxTopk + 2048 + 7]);
+  uint32_t threshold              = 0;
+  uint32_t nx_below_threshold     = 0;
+  __syncthreads();
+
+  //
+  // Search for the maximum threshold that satisfies "(x < threshold).sum() <= topk".
+  //
+#pragma unroll
+  for (int j = 0; j < 3; j += 1) {
+    uint32_t num_bins;
+    uint32_t shift;
+
+    update_histogram<uint32_t, stateBitLen, vecLen>(j,
+                                                    thread_id,
+                                                    num_threads,
+                                                    hint,
+                                                    threshold,
+                                                    num_bins,
+                                                    shift,
+                                                    x,
+                                                    nx,
+                                                    hist,
+                                                    state,
+                                                    smem_out_vals,
+                                                    output_count);
+    select_best_index_for_next_threshold(topk,
+                                         threshold,
+                                         hint,
+                                         nx_below_threshold,
+                                         num_bins,
+                                         shift,
+                                         hist,
+                                         best_index + j,
+                                         best_csum + j);
+
+    threshold += (best_index[j] << shift);
+    nx_below_threshold += best_csum[j];
+    if (nx_below_threshold == topk) break;
+  }
+
+  if ((_hint != NULL) && (thread_id == 0)) { *_hint = min(threshold, hint); }
+
+  //
+  // Output index that satisfies "x[i] < threshold".
+  //
+  output_index_below_threshold<uint32_t, stateBitLen, vecLen>(topk,
+                                                              thread_id,
+                                                              num_threads,
+                                                              threshold,
+                                                              nx_below_threshold,
+                                                              x,
+                                                              nx,
+                                                              state,
+                                                              smem_out_vals,
+                                                              output_count,
+                                                              output_count_eq);
+  __syncthreads();
+
+#ifdef CUANN_DEBUG
+  if (thread_id == 0 && output_count[0] < topk) {
+    RAFT_LOG_DEBUG(
+      "# i_batch:%d, topk:%d, output_count:%d, nx_below_threshold:%d, threshold:%08x\n",
+      i_batch,
+      topk,
+      output_count[0],
+      nx_below_threshold,
+      threshold);
+  }
+#endif
+
+  if (!sort) {
+    for (int k = thread_id; k < topk; k += blockDim.x) {
+      const uint32_t i = smem_out_vals[k];
+      if (y) { y[k] = x[i]; }
+      if (out_vals) {
+        if (in_vals) {
+          out_vals[k] = in_vals[i];
+        } else {
+          out_vals[k] = i;
+        }
+      }
+    }
+    return;
+  }
+
+  constexpr int numTopkPerThread = maxTopk / numSortThreads;
+  float my_keys[numTopkPerThread];
+  ValT my_vals[numTopkPerThread];
+
+  // Read keys and values to registers
+  if (thread_id < numSortThreads) {
+    for (int i = 0; i < numTopkPerThread; i++) {
+      const int k = thread_id + (numSortThreads * i);
+      if (k < topk) {
+        const int j = smem_out_vals[k];
+        my_keys[i]  = ((float*)x)[j];
+        if (in_vals) {
+          my_vals[i] = in_vals[j];
+        } else {
+          my_vals[i] = j;
+        }
+      } else {
+        my_keys[i] = FLT_MAX;
+        my_vals[i] = ~static_cast<ValT>(0);
+      }
+    }
+  }
+
+  uint32_t mask = 1;
+
+  // Sorting by thread
+  if (thread_id < numSortThreads) {
+    const bool ascending = ((thread_id & mask) == 0);
+    if (numTopkPerThread == 3) {
+      swap_if_needed<float, ValT>(my_keys[0], my_keys[1], my_vals[0], my_vals[1], ascending);
+      swap_if_needed<float, ValT>(my_keys[0], my_keys[2], my_vals[0], my_vals[2], ascending);
+      swap_if_needed<float, ValT>(my_keys[1], my_keys[2], my_vals[1], my_vals[2], ascending);
+    } else {
+      for (int j = 0; j < numTopkPerThread / 2; j += 1) {
+#pragma unroll
+        for (int i = 0; i < numTopkPerThread; i += 2) {
+          swap_if_needed<float, ValT>(
+            my_keys[i], my_keys[i + 1], my_vals[i], my_vals[i + 1], ascending);
+        }
+#pragma unroll
+        for (int i = 1; i < numTopkPerThread - 1; i += 2) {
+          swap_if_needed<float, ValT>(
+            my_keys[i], my_keys[i + 1], my_vals[i], my_vals[i + 1], ascending);
+        }
+      }
+    }
+  }
+
+  // Bitonic Sorting
+  while (mask < numSortThreads) {
+    uint32_t next_mask = mask << 1;
+
+    for (uint32_t curr_mask = mask; curr_mask > 0; curr_mask >>= 1) {
+      const bool ascending = ((thread_id & curr_mask) == 0) == ((thread_id & next_mask) == 0);
+      if (curr_mask >= 32) {
+        // inter warp
+        ValT* const smem_vals = reinterpret_cast<ValT*>(_smem);  // [maxTopk]
+        float* const smem_keys =
+          reinterpret_cast<float*>(smem_vals + maxTopk);  // [numTopkPerThread, numSortThreads]
+        __syncthreads();
+        if (thread_id < numSortThreads) {
+#pragma unroll
+          for (int i = 0; i < numTopkPerThread; i++) {
+            smem_keys[thread_id + (numSortThreads * i)] = my_keys[i];
+            smem_vals[thread_id + (numSortThreads * i)] = my_vals[i];
+          }
+        }
+        __syncthreads();
+        if (thread_id < numSortThreads) {
+#pragma unroll
+          for (int i = 0; i < numTopkPerThread; i++) {
+            float opp_key = smem_keys[(thread_id ^ curr_mask) + (numSortThreads * i)];
+            ValT opp_val  = smem_vals[(thread_id ^ curr_mask) + (numSortThreads * i)];
+            swap_if_needed<float, ValT>(my_keys[i], opp_key, my_vals[i], opp_val, ascending);
+          }
+        }
+      } else {
+        // intra warp
+        if (thread_id < numSortThreads) {
+#pragma unroll
+          for (int i = 0; i < numTopkPerThread; i++) {
+            float opp_key = __shfl_xor_sync(0xffffffff, my_keys[i], curr_mask);
+            ValT opp_val  = __shfl_xor_sync(0xffffffff, my_vals[i], curr_mask);
+            swap_if_needed<float, ValT>(my_keys[i], opp_key, my_vals[i], opp_val, ascending);
+          }
+        }
+      }
+    }
+
+    if (thread_id < numSortThreads) {
+      const bool ascending = ((thread_id & next_mask) == 0);
+      if (numTopkPerThread == 3) {
+        swap_if_needed<float, ValT>(my_keys[0], my_keys[1], my_vals[0], my_vals[1], ascending);
+        swap_if_needed<float, ValT>(my_keys[0], my_keys[2], my_vals[0], my_vals[2], ascending);
+        swap_if_needed<float, ValT>(my_keys[1], my_keys[2], my_vals[1], my_vals[2], ascending);
+      } else {
+#pragma unroll
+        for (uint32_t curr_mask = numTopkPerThread / 2; curr_mask > 0; curr_mask >>= 1) {
+#pragma unroll
+          for (int i = 0; i < numTopkPerThread; i++) {
+            const int j = i ^ curr_mask;
+            if (i > j) continue;
+            swap_if_needed<float, ValT>(my_keys[i], my_keys[j], my_vals[i], my_vals[j], ascending);
+          }
+        }
+      }
+    }
+    mask = next_mask;
+  }
+
+  // Write sorted keys and values
+  if (thread_id < numSortThreads) {
+    for (int i = 0; i < numTopkPerThread; i++) {
+      const int k = i + (numTopkPerThread * thread_id);
+      if (k < topk) {
+        if (y) { y[k] = reinterpret_cast<uint32_t*>(my_keys)[i]; }
+        if (out_vals) { out_vals[k] = my_vals[i]; }
+      }
+    }
+  }
+}
+
+namespace {
+
+//
+constexpr std::uint32_t NUM_THREADS      = 1024;  // DO NOT CHANGE
+constexpr std::uint32_t STATE_BIT_LENGTH = 8;     // 0: state not used,  8: state used
+constexpr std::uint32_t MAX_VEC_LENGTH   = 4;     // 1, 2, 4 or 8
+
+//
+//
+int _get_vecLen(uint32_t maxSamples, int maxVecLen = MAX_VEC_LENGTH)
+{
+  int vecLen = min(maxVecLen, (int)MAX_VEC_LENGTH);
+  while ((maxSamples % vecLen) != 0) {
+    vecLen /= 2;
+  }
+  return vecLen;
+}
+}  // unnamed namespace
+
+template <int stateBitLen, int vecLen, int maxTopk, int numSortThreads, class ValT>
+__launch_bounds__(1024, 1) RAFT_KERNEL
+  kern_topk_cta_11(uint32_t topk,
+                   uint32_t size_batch,
+                   uint32_t len_x,
+                   const uint32_t* _x,  // [size_batch, ld_x,]
+                   uint32_t ld_x,
+                   const ValT* _in_vals,  // [size_batch, ld_iv,]
+                   uint32_t ld_iv,
+                   uint32_t* _y,  // [size_batch, ld_y,]
+                   uint32_t ld_y,
+                   ValT* _out_vals,  // [size_batch, ld_ov,]
+                   uint32_t ld_ov,
+                   uint8_t* _state,   // [size_batch, ...,]
+                   uint32_t* _hints,  // [size_batch,]
+                   bool sort)
+{
+  const uint32_t i_batch = blockIdx.x;
+  if (i_batch >= size_batch) return;
+
+  constexpr uint32_t smem_len = 2 * maxTopk + 2048 + 8;
+  static_assert(maxTopk * (1 + utils::size_of<ValT>() / utils::size_of<uint32_t>()) <= smem_len,
+                "maxTopk * sizeof(ValT) must be smaller or equal to 8192 byte");
+  __shared__ uint32_t _smem[smem_len];
+
+  topk_cta_11_core<stateBitLen, vecLen, maxTopk, numSortThreads, ValT>(
+    topk,
+    len_x,
+    (_x == NULL ? NULL : _x + i_batch * ld_x),
+    (_in_vals == NULL ? NULL : _in_vals + i_batch * ld_iv),
+    (_y == NULL ? NULL : _y + i_batch * ld_y),
+    (_out_vals == NULL ? NULL : _out_vals + i_batch * ld_ov),
+    (_state == NULL ? NULL : _state + i_batch * get_state_size<stateBitLen>(len_x)),
+    (_hints == NULL ? NULL : _hints + i_batch),
+    sort,
+    _smem);
+}
+
+//
+size_t inline _cuann_find_topk_bufferSize(uint32_t topK,
+                                          uint32_t sizeBatch,
+                                          uint32_t numElements,
+                                          cudaDataType_t sampleDtype)
+{
+  constexpr int numThreads  = NUM_THREADS;
+  constexpr int stateBitLen = STATE_BIT_LENGTH;
+  assert(stateBitLen == 0 || stateBitLen == 8);
+
+  size_t workspaceSize = 1;
+  // state
+  if (stateBitLen == 8) {
+    workspaceSize = _cuann_aligned(
+      sizeof(uint8_t) * get_state_size<stateBitLen, numThreads>(numElements) * sizeBatch);
+  }
+
+  return workspaceSize;
+}
+
+template <class ValT>
+inline void _cuann_find_topk(uint32_t topK,
+                             uint32_t sizeBatch,
+                             uint32_t numElements,
+                             const float* inputKeys,  // [sizeBatch, ldIK,]
+                             uint32_t ldIK,           // (*) ldIK >= numElements
+                             const ValT* inputVals,   // [sizeBatch, ldIV,]
+                             uint32_t ldIV,           // (*) ldIV >= numElements
+                             float* outputKeys,       // [sizeBatch, ldOK,]
+                             uint32_t ldOK,           // (*) ldOK >= topK
+                             ValT* outputVals,        // [sizeBatch, ldOV,]
+                             uint32_t ldOV,           // (*) ldOV >= topK
+                             void* workspace,
+                             bool sort,
+                             uint32_t* hints,
+                             cudaStream_t stream)
+{
+  assert(ldIK >= numElements);
+  assert(ldIV >= numElements);
+  assert(ldOK >= topK);
+  assert(ldOV >= topK);
+
+  constexpr int numThreads  = NUM_THREADS;
+  constexpr int stateBitLen = STATE_BIT_LENGTH;
+  assert(stateBitLen == 0 || stateBitLen == 8);
+
+  uint8_t* state = NULL;
+  if (stateBitLen == 8) { state = (uint8_t*)workspace; }
+
+  dim3 threads(numThreads, 1, 1);
+  dim3 blocks(sizeBatch, 1, 1);
+
+  void (*cta_kernel)(uint32_t,
+                     uint32_t,
+                     uint32_t,
+                     const uint32_t*,
+                     uint32_t,
+                     const ValT*,
+                     uint32_t,
+                     uint32_t*,
+                     uint32_t,
+                     ValT*,
+                     uint32_t,
+                     uint8_t*,
+                     uint32_t*,
+                     bool) = nullptr;
+
+  // V:vecLen, K:maxTopk, T:numSortThreads
+#define SET_KERNEL_VKT(V, K, T, ValT)                          \
+  do {                                                         \
+    assert(numThreads >= T);                                   \
+    assert((K % T) == 0);                                      \
+    assert((K / T) <= 4);                                      \
+    cta_kernel = kern_topk_cta_11<stateBitLen, V, K, T, ValT>; \
+  } while (0)
+
+  // V: vecLen
+#define SET_KERNEL_V(V, ValT)                                \
+  do {                                                       \
+    if (topK <= 32) {                                        \
+      SET_KERNEL_VKT(V, 32, 32, ValT);                       \
+    } else if (topK <= 64) {                                 \
+      SET_KERNEL_VKT(V, 64, 32, ValT);                       \
+    } else if (topK <= 96) {                                 \
+      SET_KERNEL_VKT(V, 96, 32, ValT);                       \
+    } else if (topK <= 128) {                                \
+      SET_KERNEL_VKT(V, 128, 32, ValT);                      \
+    } else if (topK <= 192) {                                \
+      SET_KERNEL_VKT(V, 192, 64, ValT);                      \
+    } else if (topK <= 256) {                                \
+      SET_KERNEL_VKT(V, 256, 64, ValT);                      \
+    } else if (topK <= 384) {                                \
+      SET_KERNEL_VKT(V, 384, 128, ValT);                     \
+    } else if (topK <= 512) {                                \
+      SET_KERNEL_VKT(V, 512, 128, ValT);                     \
+    } else if (topK <= 768) {                                \
+      SET_KERNEL_VKT(V, 768, 256, ValT);                     \
+    } else if (topK <= 1024) {                               \
+      SET_KERNEL_VKT(V, 1024, 256, ValT);                    \
+    } \
+        /* else if (topK <= 1536) { SET_KERNEL_VKT(V, 1536, 512); } */ \
+        /* else if (topK <= 2048) { SET_KERNEL_VKT(V, 2048, 512); } */ \
+        /* else if (topK <= 3072) { SET_KERNEL_VKT(V, 3072, 1024); } */ \
+        /* else if (topK <= 4096) { SET_KERNEL_VKT(V, 4096, 1024); } */ \
+        else {                                                      \
+      RAFT_FAIL("topk must be lower than or equal to 1024"); \
+    }                                                        \
+  } while (0)
+
+  int _vecLen = _get_vecLen(ldIK, 2);
+  if (_vecLen == 2) {
+    SET_KERNEL_V(2, ValT);
+  } else if (_vecLen == 1) {
+    SET_KERNEL_V(1, ValT);
+  }
+
+  cta_kernel<<<blocks, threads, 0, stream>>>(topK,
+                                             sizeBatch,
+                                             numElements,
+                                             (const uint32_t*)inputKeys,
+                                             ldIK,
+                                             inputVals,
+                                             ldIV,
+                                             (uint32_t*)outputKeys,
+                                             ldOK,
+                                             outputVals,
+                                             ldOV,
+                                             state,
+                                             hints,
+                                             sort);
+
+  return;
+}
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/utils.hpp b/cpp/src/neighbors/detail/cagra/utils.hpp
new file mode 100644
index 0000000000..395ec15229
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/utils.hpp
@@ -0,0 +1,297 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+// TODO: This shouldn't be invoking anything from detail outside of neighbors namespace
+#include <raft/core/detail/macros.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/util/integer_utils.hpp>
+
+#include <rmm/resource_ref.hpp>
+
+#include <cuda.h>
+#include <cuda_fp16.h>
+
+#include <cfloat>
+#include <cstdint>
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace utils {
+template <class DATA_T>
+inline cudaDataType_t get_cuda_data_type();
+template <>
+inline cudaDataType_t get_cuda_data_type<float>()
+{
+  return CUDA_R_32F;
+}
+template <>
+inline cudaDataType_t get_cuda_data_type<half>()
+{
+  return CUDA_R_16F;
+}
+template <>
+inline cudaDataType_t get_cuda_data_type<int8_t>()
+{
+  return CUDA_R_8I;
+}
+template <>
+inline cudaDataType_t get_cuda_data_type<uint8_t>()
+{
+  return CUDA_R_8U;
+}
+template <>
+inline cudaDataType_t get_cuda_data_type<uint32_t>()
+{
+  return CUDA_R_32U;
+}
+template <>
+inline cudaDataType_t get_cuda_data_type<uint64_t>()
+{
+  return CUDA_R_64U;
+}
+
+template <class T>
+constexpr unsigned size_of();
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<std::int8_t>()
+{
+  return 1;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<std::uint8_t>()
+{
+  return 1;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<std::uint16_t>()
+{
+  return 2;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<std::uint32_t>()
+{
+  return 4;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<std::uint64_t>()
+{
+  return 8;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<uint4>()
+{
+  return 16;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<ulonglong4>()
+{
+  return 32;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<float>()
+{
+  return 4;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<half>()
+{
+  return 2;
+}
+template <>
+_RAFT_HOST_DEVICE constexpr unsigned size_of<half2>()
+{
+  return 4;
+}
+
+// max values for data types
+template <class BS_T, class FP_T>
+union fp_conv {
+  BS_T bs;
+  FP_T fp;
+};
+template <class T>
+_RAFT_HOST_DEVICE inline T get_max_value();
+template <>
+_RAFT_HOST_DEVICE inline float get_max_value<float>()
+{
+  return FLT_MAX;
+};
+template <>
+_RAFT_HOST_DEVICE inline half get_max_value<half>()
+{
+  return fp_conv<std::uint16_t, half>{.bs = 0x7aff}.fp;
+};
+template <>
+_RAFT_HOST_DEVICE inline std::uint32_t get_max_value<std::uint32_t>()
+{
+  return 0xffffffffu;
+};
+template <>
+_RAFT_HOST_DEVICE inline std::uint64_t get_max_value<std::uint64_t>()
+{
+  return 0xfffffffffffffffflu;
+};
+
+template <int A, int B, class = void>
+struct constexpr_max {
+  static const int value = A;
+};
+
+template <int A, int B>
+struct constexpr_max<A, B, std::enable_if_t<(B > A), bool>> {
+  static const int value = B;
+};
+
+template <class IdxT>
+struct gen_index_msb_1_mask {
+  static constexpr IdxT value = static_cast<IdxT>(1) << (utils::size_of<IdxT>() * 8 - 1);
+};
+}  // namespace utils
+
+/**
+ * Utility to sync memory from a host_matrix_view to a device_matrix_view
+ *
+ * In certain situations (UVM/HMM/ATS) host memory might be directly accessible on the
+ * device, and no extra allocations need to be performed. This class checks
+ * if the host_matrix_view is already accessible on the device, and only creates device
+ * memory and copies over if necessary. In memory limited situations this is preferable
+ * to having both a host and device copy
+ * TODO: once the mdbuffer changes here https://github.com/wphicks/raft/blob/fea-mdbuffer
+ * have been merged, we should remove this class and switch over to using mdbuffer for this
+ */
+template <typename T, typename IdxT>
+class device_matrix_view_from_host {
+ public:
+  device_matrix_view_from_host(raft::resources const& res,
+                               raft::host_matrix_view<T, IdxT> host_view)
+    : host_view_(host_view)
+  {
+    cudaPointerAttributes attr;
+    RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, host_view.data_handle()));
+    device_ptr = reinterpret_cast<T*>(attr.devicePointer);
+    if (device_ptr == NULL) {
+      // allocate memory and copy over
+      device_mem_.emplace(
+        raft::make_device_matrix<T, IdxT>(res, host_view.extent(0), host_view.extent(1)));
+      raft::copy(device_mem_->data_handle(),
+                 host_view.data_handle(),
+                 host_view.extent(0) * host_view.extent(1),
+                 raft::resource::get_cuda_stream(res));
+      device_ptr = device_mem_->data_handle();
+    }
+  }
+
+  raft::device_matrix_view<T, IdxT> view()
+  {
+    return raft::make_device_matrix_view<T, IdxT>(
+      device_ptr, host_view_.extent(0), host_view_.extent(1));
+  }
+
+  T* data_handle() { return device_ptr; }
+
+  bool allocated_memory() const { return device_mem_.has_value(); }
+
+ private:
+  std::optional<raft::device_matrix<T, IdxT>> device_mem_;
+  raft::host_matrix_view<T, IdxT> host_view_;
+  T* device_ptr;
+};
+
+/**
+ * Utility to sync memory from a device_matrix_view to a host_matrix_view
+ *
+ * In certain situations (UVM/HMM/ATS) device memory might be directly accessible on the
+ * host, and no extra allocations need to be performed. This class checks
+ * if the device_matrix_view is already accessible on the host, and only creates host
+ * memory and copies over if necessary. In memory limited situations this is preferable
+ * to having both a host and device copy
+ * TODO: once the mdbuffer changes here https://github.com/wphicks/raft/blob/fea-mdbuffer
+ * have been merged, we should remove this class and switch over to using mdbuffer for this
+ */
+template <typename T, typename IdxT>
+class host_matrix_view_from_device {
+ public:
+  host_matrix_view_from_device(raft::resources const& res,
+                               raft::device_matrix_view<T, IdxT> device_view)
+    : device_view_(device_view)
+  {
+    cudaPointerAttributes attr;
+    RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, device_view.data_handle()));
+    host_ptr = reinterpret_cast<T*>(attr.hostPointer);
+    if (host_ptr == NULL) {
+      // allocate memory and copy over
+      host_mem_.emplace(
+        raft::make_host_matrix<T, IdxT>(device_view.extent(0), device_view.extent(1)));
+      raft::copy(host_mem_->data_handle(),
+                 device_view.data_handle(),
+                 device_view.extent(0) * device_view.extent(1),
+                 raft::resource::get_cuda_stream(res));
+      host_ptr = host_mem_->data_handle();
+    }
+  }
+
+  raft::host_matrix_view<T, IdxT> view()
+  {
+    return raft::make_host_matrix_view<T, IdxT>(
+      host_ptr, device_view_.extent(0), device_view_.extent(1));
+  }
+
+  T* data_handle() { return host_ptr; }
+
+  bool allocated_memory() const { return host_mem_.has_value(); }
+
+ private:
+  std::optional<raft::host_matrix<T, IdxT>> host_mem_;
+  raft::device_matrix_view<T, IdxT> device_view_;
+  T* host_ptr;
+};
+
+// Copy matrix src to dst. pad rows with 0 if necessary to make them 16 byte aligned.
+template <typename T, typename data_accessor>
+void copy_with_padding(
+  raft::resources const& res,
+  raft::device_matrix<T, int64_t, raft::row_major>& dst,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, data_accessor> src,
+  rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())
+{
+  size_t padded_dim = raft::round_up_safe<size_t>(src.extent(1) * sizeof(T), 16) / sizeof(T);
+
+  if ((dst.extent(0) != src.extent(0)) || (static_cast<size_t>(dst.extent(1)) != padded_dim)) {
+    // clear existing memory before allocating to prevent OOM errors on large datasets
+    if (dst.size()) { dst = raft::make_device_matrix<T, int64_t>(res, 0, 0); }
+    dst =
+      raft::make_device_mdarray<T>(res, mr, raft::make_extents<int64_t>(src.extent(0), padded_dim));
+  }
+  if (dst.extent(1) == src.extent(1)) {
+    raft::copy(
+      dst.data_handle(), src.data_handle(), src.size(), raft::resource::get_cuda_stream(res));
+  } else {
+    // copy with padding
+    RAFT_CUDA_TRY(cudaMemsetAsync(
+      dst.data_handle(), 0, dst.size() * sizeof(T), raft::resource::get_cuda_stream(res)));
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(dst.data_handle(),
+                                    sizeof(T) * dst.extent(1),
+                                    src.data_handle(),
+                                    sizeof(T) * src.extent(1),
+                                    sizeof(T) * src.extent(1),
+                                    src.extent(0),
+                                    cudaMemcpyDefault,
+                                    raft::resource::get_cuda_stream(res)));
+  }
+}
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/dataset_serialize.hpp b/cpp/src/neighbors/detail/dataset_serialize.hpp
new file mode 100644
index 0000000000..e2fddc2620
--- /dev/null
+++ b/cpp/src/neighbors/detail/dataset_serialize.hpp
@@ -0,0 +1,197 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/neighbors/dataset.hpp>
+
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <raft/core/logger-ext.hpp>
+
+#include <cuda_fp16.h>
+
+#include <fstream>
+#include <memory>
+
+namespace cuvs::neighbors::detail {
+
+using dataset_instance_tag                              = uint32_t;
+constexpr dataset_instance_tag kSerializeEmptyDataset   = 1;
+constexpr dataset_instance_tag kSerializeStridedDataset = 2;
+constexpr dataset_instance_tag kSerializeVPQDataset     = 3;
+
+template <typename IdxT>
+void serialize(const raft::resources& res, std::ostream& os, const empty_dataset<IdxT>& dataset)
+{
+  raft::serialize_scalar(res, os, dataset.suggested_dim);
+}
+
+template <typename DataT, typename IdxT>
+void serialize(const raft::resources& res,
+               std::ostream& os,
+               const strided_dataset<DataT, IdxT>& dataset)
+{
+  auto n_rows = dataset.n_rows();
+  auto dim    = dataset.dim();
+  auto stride = dataset.stride();
+  raft::serialize_scalar(res, os, n_rows);
+  raft::serialize_scalar(res, os, dim);
+  raft::serialize_scalar(res, os, stride);
+  // Remove padding before saving the dataset
+  auto src = dataset.view();
+  auto dst = raft::make_host_matrix<DataT, IdxT>(n_rows, dim);
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(dst.data_handle(),
+                                  sizeof(DataT) * dim,
+                                  src.data_handle(),
+                                  sizeof(DataT) * stride,
+                                  sizeof(DataT) * dim,
+                                  n_rows,
+                                  cudaMemcpyDefault,
+                                  raft::resource::get_cuda_stream(res)));
+  raft::resource::sync_stream(res);
+  raft::serialize_mdspan(res, os, dst.view());
+}
+
+template <typename MathT, typename IdxT>
+void serialize(const raft::resources& res,
+               std::ostream& os,
+               const vpq_dataset<MathT, IdxT>& dataset)
+{
+  raft::serialize_scalar(res, os, dataset.n_rows());
+  raft::serialize_scalar(res, os, dataset.dim());
+  raft::serialize_scalar(res, os, dataset.vq_n_centers());
+  raft::serialize_scalar(res, os, dataset.pq_n_centers());
+  raft::serialize_scalar(res, os, dataset.pq_len());
+  raft::serialize_scalar(res, os, dataset.encoded_row_length());
+  raft::serialize_mdspan(res, os, make_const_mdspan(dataset.vq_code_book.view()));
+  raft::serialize_mdspan(res, os, make_const_mdspan(dataset.pq_code_book.view()));
+  raft::serialize_mdspan(res, os, make_const_mdspan(dataset.data.view()));
+}
+
+template <typename IdxT>
+void serialize(const raft::resources& res, std::ostream& os, const dataset<IdxT>& dataset)
+{
+  if (auto x = dynamic_cast<const empty_dataset<IdxT>*>(&dataset); x != nullptr) {
+    raft::serialize_scalar(res, os, kSerializeEmptyDataset);
+    return serialize(res, os, *x);
+  }
+  if (auto x = dynamic_cast<const strided_dataset<float, IdxT>*>(&dataset); x != nullptr) {
+    raft::serialize_scalar(res, os, kSerializeStridedDataset);
+    raft::serialize_scalar(res, os, CUDA_R_32F);
+    return serialize(res, os, *x);
+  }
+  if (auto x = dynamic_cast<const strided_dataset<half, IdxT>*>(&dataset); x != nullptr) {
+    raft::serialize_scalar(res, os, kSerializeStridedDataset);
+    raft::serialize_scalar(res, os, CUDA_R_16F);
+    return serialize(res, os, *x);
+  }
+  if (auto x = dynamic_cast<const strided_dataset<int8_t, IdxT>*>(&dataset); x != nullptr) {
+    raft::serialize_scalar(res, os, kSerializeStridedDataset);
+    raft::serialize_scalar(res, os, CUDA_R_8I);
+    return serialize(res, os, *x);
+  }
+  if (auto x = dynamic_cast<const strided_dataset<uint8_t, IdxT>*>(&dataset); x != nullptr) {
+    raft::serialize_scalar(res, os, kSerializeStridedDataset);
+    raft::serialize_scalar(res, os, CUDA_R_8U);
+    return serialize(res, os, *x);
+  }
+  if (auto x = dynamic_cast<const vpq_dataset<float, IdxT>*>(&dataset); x != nullptr) {
+    raft::serialize_scalar(res, os, kSerializeVPQDataset);
+    raft::serialize_scalar(res, os, CUDA_R_32F);
+    return serialize(res, os, *x);
+  }
+  if (auto x = dynamic_cast<const vpq_dataset<half, IdxT>*>(&dataset); x != nullptr) {
+    raft::serialize_scalar(res, os, kSerializeVPQDataset);
+    raft::serialize_scalar(res, os, CUDA_R_16F);
+    return serialize(res, os, *x);
+  }
+  RAFT_FAIL("unsupported dataset type.");
+}
+
+template <typename IdxT>
+auto deserialize_empty(raft::resources const& res, std::istream& is)
+  -> std::unique_ptr<empty_dataset<IdxT>>
+{
+  auto suggested_dim = raft::deserialize_scalar<uint32_t>(res, is);
+  return std::make_unique<empty_dataset<IdxT>>(suggested_dim);
+}
+
+template <typename DataT, typename IdxT>
+auto deserialize_strided(raft::resources const& res, std::istream& is)
+  -> std::unique_ptr<strided_dataset<DataT, IdxT>>
+{
+  auto n_rows     = raft::deserialize_scalar<IdxT>(res, is);
+  auto dim        = raft::deserialize_scalar<uint32_t>(res, is);
+  auto stride     = raft::deserialize_scalar<uint32_t>(res, is);
+  auto host_array = raft::make_host_matrix<DataT, IdxT>(n_rows, dim);
+  raft::deserialize_mdspan(res, is, host_array.view());
+  return make_strided_dataset(res, host_array, stride);
+}
+
+template <typename MathT, typename IdxT>
+auto deserialize_vpq(raft::resources const& res, std::istream& is)
+  -> std::unique_ptr<vpq_dataset<MathT, IdxT>>
+{
+  auto n_rows             = raft::deserialize_scalar<IdxT>(res, is);
+  auto dim                = raft::deserialize_scalar<uint32_t>(res, is);
+  auto vq_n_centers       = raft::deserialize_scalar<uint32_t>(res, is);
+  auto pq_n_centers       = raft::deserialize_scalar<uint32_t>(res, is);
+  auto pq_len             = raft::deserialize_scalar<uint32_t>(res, is);
+  auto encoded_row_length = raft::deserialize_scalar<uint32_t>(res, is);
+
+  auto vq_code_book =
+    raft::make_device_matrix<MathT, uint32_t, raft::row_major>(res, vq_n_centers, dim);
+  auto pq_code_book =
+    raft::make_device_matrix<MathT, uint32_t, raft::row_major>(res, pq_n_centers, pq_len);
+  auto data =
+    raft::make_device_matrix<uint8_t, IdxT, raft::row_major>(res, n_rows, encoded_row_length);
+
+  raft::deserialize_mdspan(res, is, vq_code_book.view());
+  raft::deserialize_mdspan(res, is, pq_code_book.view());
+  raft::deserialize_mdspan(res, is, data.view());
+
+  return std::make_unique<vpq_dataset<MathT, IdxT>>(
+    std::move(vq_code_book), std::move(pq_code_book), std::move(data));
+}
+
+template <typename IdxT>
+auto deserialize_dataset(raft::resources const& res, std::istream& is)
+  -> std::unique_ptr<dataset<IdxT>>
+{
+  switch (raft::deserialize_scalar<dataset_instance_tag>(res, is)) {
+    case kSerializeEmptyDataset: return deserialize_empty<IdxT>(res, is);
+    case kSerializeStridedDataset:
+      switch (raft::deserialize_scalar<cudaDataType_t>(res, is)) {
+        case CUDA_R_32F: return deserialize_strided<float, IdxT>(res, is);
+        case CUDA_R_16F: return deserialize_strided<half, IdxT>(res, is);
+        case CUDA_R_8I: return deserialize_strided<int8_t, IdxT>(res, is);
+        case CUDA_R_8U: return deserialize_strided<uint8_t, IdxT>(res, is);
+        default: break;
+      }
+    case kSerializeVPQDataset:
+      switch (raft::deserialize_scalar<cudaDataType_t>(res, is)) {
+        case CUDA_R_32F: return deserialize_vpq<float, IdxT>(res, is);
+        case CUDA_R_16F: return deserialize_vpq<half, IdxT>(res, is);
+        default: break;
+      }
+    default: break;
+  }
+  RAFT_FAIL("Failed to deserialize dataset: unsupported combination of instance tags.");
+}
+
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
new file mode 100644
index 0000000000..31a26aba4c
--- /dev/null
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -0,0 +1,1457 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/nn_descent.hpp>
+
+#include "ann_utils.cuh"
+#include "cagra/device_common.hpp"
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#include <raft/util/arch.cuh>  // raft::util::arch::SM_*
+#include <raft/util/cuda_dev_essentials.cuh>
+#include <raft/util/cuda_rt_essentials.hpp>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cub/cub.cuh>
+#include <cuda_runtime.h>
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/host_vector.h>
+#include <thrust/mr/allocator.h>
+#include <thrust/mr/device_memory_resource.h>
+
+#include <mma.h>
+#include <omp.h>
+
+#include <limits>
+#include <queue>
+#include <random>
+
+namespace cuvs::neighbors::nn_descent::detail {
+static const std::string RAFT_NAME = "raft";
+using pinned_memory_resource       = thrust::universal_host_pinned_memory_resource;
+template <typename T>
+using pinned_memory_allocator = thrust::mr::stateless_resource_allocator<T, pinned_memory_resource>;
+
+using DistData_t = float;
+constexpr int DEGREE_ON_DEVICE{32};
+constexpr int SEGMENT_SIZE{32};
+constexpr int counter_interval{100};
+template <typename Index_t>
+struct InternalID_t;
+
+// InternalID_t uses 1 bit for marking (new or old).
+template <>
+class InternalID_t<int> {
+ private:
+  using Index_t = int;
+  Index_t id_{std::numeric_limits<Index_t>::max()};
+
+ public:
+  __host__ __device__ bool is_new() const { return id_ >= 0; }
+  __host__ __device__ Index_t& id_with_flag() { return id_; }
+  __host__ __device__ Index_t id() const
+  {
+    if (is_new()) return id_;
+    return -id_ - 1;
+  }
+  __host__ __device__ void mark_old()
+  {
+    if (id_ >= 0) id_ = -id_ - 1;
+  }
+  __host__ __device__ bool operator==(const InternalID_t<int>& other) const
+  {
+    return id() == other.id();
+  }
+};
+
+template <typename Index_t>
+struct ResultItem;
+
+template <>
+class ResultItem<int> {
+ private:
+  using Index_t = int;
+  Index_t id_;
+  DistData_t dist_;
+
+ public:
+  __host__ __device__ ResultItem()
+    : id_(std::numeric_limits<Index_t>::max()), dist_(std::numeric_limits<DistData_t>::max()){};
+  __host__ __device__ ResultItem(const Index_t id_with_flag, const DistData_t dist)
+    : id_(id_with_flag), dist_(dist){};
+  __host__ __device__ bool is_new() const { return id_ >= 0; }
+  __host__ __device__ Index_t& id_with_flag() { return id_; }
+  __host__ __device__ Index_t id() const
+  {
+    if (is_new()) return id_;
+    return -id_ - 1;
+  }
+  __host__ __device__ DistData_t& dist() { return dist_; }
+
+  __host__ __device__ void mark_old()
+  {
+    if (id_ >= 0) id_ = -id_ - 1;
+  }
+
+  __host__ __device__ bool operator<(const ResultItem<Index_t>& other) const
+  {
+    if (dist_ == other.dist_) return id() < other.id();
+    return dist_ < other.dist_;
+  }
+  __host__ __device__ bool operator==(const ResultItem<Index_t>& other) const
+  {
+    return id() == other.id();
+  }
+  __host__ __device__ bool operator>=(const ResultItem<Index_t>& other) const
+  {
+    return !(*this < other);
+  }
+  __host__ __device__ bool operator<=(const ResultItem<Index_t>& other) const
+  {
+    return (*this == other) || (*this < other);
+  }
+  __host__ __device__ bool operator>(const ResultItem<Index_t>& other) const
+  {
+    return !(*this <= other);
+  }
+  __host__ __device__ bool operator!=(const ResultItem<Index_t>& other) const
+  {
+    return !(*this == other);
+  }
+};
+
+using align32 = raft::Pow2<32>;
+
+template <typename T>
+int get_batch_size(const int it_now, const T nrow, const int batch_size)
+{
+  int it_total = raft::ceildiv(nrow, batch_size);
+  return (it_now == it_total - 1) ? nrow - it_now * batch_size : batch_size;
+}
+
+// for avoiding bank conflict
+template <typename T>
+constexpr __host__ __device__ __forceinline__ int skew_dim(int ndim)
+{
+  // all "4"s are for alignment
+  if constexpr (std::is_same<T, float>::value) {
+    ndim = raft::ceildiv(ndim, 4) * 4;
+    return ndim + (ndim % 32 == 0) * 4;
+  }
+}
+
+template <typename T>
+__device__ __forceinline__ ResultItem<T> xor_swap(ResultItem<T> x, int mask, int dir)
+{
+  ResultItem<T> y;
+  y.dist() = __shfl_xor_sync(raft::warp_full_mask(), x.dist(), mask, raft::warp_size());
+  y.id_with_flag() =
+    __shfl_xor_sync(raft::warp_full_mask(), x.id_with_flag(), mask, raft::warp_size());
+  return x < y == dir ? y : x;
+}
+
+__device__ __forceinline__ int xor_swap(int x, int mask, int dir)
+{
+  int y = __shfl_xor_sync(raft::warp_full_mask(), x, mask, raft::warp_size());
+  return x < y == dir ? y : x;
+}
+
+// TODO: Move to RAFT utils https://github.com/rapidsai/raft/issues/1827
+__device__ __forceinline__ uint bfe(uint lane_id, uint pos)
+{
+  uint res;
+  asm("bfe.u32 %0,%1,%2,%3;" : "=r"(res) : "r"(lane_id), "r"(pos), "r"(1));
+  return res;
+}
+
+template <typename T>
+__device__ __forceinline__ void warp_bitonic_sort(T* element_ptr, const int lane_id)
+{
+  static_assert(raft::warp_size() == 32);
+  auto& element = *element_ptr;
+  element       = xor_swap(element, 0x01, bfe(lane_id, 1) ^ bfe(lane_id, 0));
+  element       = xor_swap(element, 0x02, bfe(lane_id, 2) ^ bfe(lane_id, 1));
+  element       = xor_swap(element, 0x01, bfe(lane_id, 2) ^ bfe(lane_id, 0));
+  element       = xor_swap(element, 0x04, bfe(lane_id, 3) ^ bfe(lane_id, 2));
+  element       = xor_swap(element, 0x02, bfe(lane_id, 3) ^ bfe(lane_id, 1));
+  element       = xor_swap(element, 0x01, bfe(lane_id, 3) ^ bfe(lane_id, 0));
+  element       = xor_swap(element, 0x08, bfe(lane_id, 4) ^ bfe(lane_id, 3));
+  element       = xor_swap(element, 0x04, bfe(lane_id, 4) ^ bfe(lane_id, 2));
+  element       = xor_swap(element, 0x02, bfe(lane_id, 4) ^ bfe(lane_id, 1));
+  element       = xor_swap(element, 0x01, bfe(lane_id, 4) ^ bfe(lane_id, 0));
+  element       = xor_swap(element, 0x10, bfe(lane_id, 4));
+  element       = xor_swap(element, 0x08, bfe(lane_id, 3));
+  element       = xor_swap(element, 0x04, bfe(lane_id, 2));
+  element       = xor_swap(element, 0x02, bfe(lane_id, 1));
+  element       = xor_swap(element, 0x01, bfe(lane_id, 0));
+  return;
+}
+
+struct BuildConfig {
+  size_t max_dataset_size;
+  size_t dataset_dim;
+  size_t node_degree{64};
+  size_t internal_node_degree{0};
+  // If internal_node_degree == 0, the value of node_degree will be assigned to it
+  size_t max_iterations{50};
+  float termination_threshold{0.0001};
+};
+
+template <typename Index_t>
+class BloomFilter {
+ public:
+  BloomFilter(size_t nrow, size_t num_sets_per_list, size_t num_hashs)
+    : nrow_(nrow),
+      num_sets_per_list_(num_sets_per_list),
+      num_hashs_(num_hashs),
+      bitsets_(nrow * num_bits_per_set_ * num_sets_per_list)
+  {
+  }
+
+  void add(size_t list_id, Index_t key)
+  {
+    if (is_cleared) { is_cleared = false; }
+    uint32_t hash         = hash_0(key);
+    size_t global_set_idx = list_id * num_bits_per_set_ * num_sets_per_list_ +
+                            key % num_sets_per_list_ * num_bits_per_set_;
+    bitsets_[global_set_idx + hash % num_bits_per_set_] = 1;
+    for (size_t i = 1; i < num_hashs_; i++) {
+      hash                                                = hash + hash_1(key);
+      bitsets_[global_set_idx + hash % num_bits_per_set_] = 1;
+    }
+  }
+
+  bool check(size_t list_id, Index_t key)
+  {
+    bool is_present       = true;
+    uint32_t hash         = hash_0(key);
+    size_t global_set_idx = list_id * num_bits_per_set_ * num_sets_per_list_ +
+                            key % num_sets_per_list_ * num_bits_per_set_;
+    is_present &= bitsets_[global_set_idx + hash % num_bits_per_set_];
+
+    if (!is_present) return false;
+    for (size_t i = 1; i < num_hashs_; i++) {
+      hash = hash + hash_1(key);
+      is_present &= bitsets_[global_set_idx + hash % num_bits_per_set_];
+      if (!is_present) return false;
+    }
+    return true;
+  }
+
+  void clear()
+  {
+    if (is_cleared) return;
+#pragma omp parallel for
+    for (size_t i = 0; i < nrow_ * num_bits_per_set_ * num_sets_per_list_; i++) {
+      bitsets_[i] = 0;
+    }
+    is_cleared = true;
+  }
+
+ private:
+  uint32_t hash_0(uint32_t value)
+  {
+    value *= 1103515245;
+    value += 12345;
+    value ^= value << 13;
+    value ^= value >> 17;
+    value ^= value << 5;
+    return value;
+  }
+
+  uint32_t hash_1(uint32_t value)
+  {
+    value *= 1664525;
+    value += 1013904223;
+    value ^= value << 13;
+    value ^= value >> 17;
+    value ^= value << 5;
+    return value;
+  }
+
+  static constexpr int num_bits_per_set_ = 512;
+  bool is_cleared{true};
+  std::vector<bool> bitsets_;
+  size_t nrow_;
+  size_t num_sets_per_list_;
+  size_t num_hashs_;
+};
+
+template <typename Index_t>
+struct GnndGraph {
+  static constexpr int segment_size = 32;
+  InternalID_t<Index_t>* h_graph;
+
+  size_t nrow;
+  size_t node_degree;
+  int num_samples;
+  int num_segments;
+
+  raft::host_matrix<DistData_t, size_t, raft::row_major> h_dists;
+
+  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_graph_new;
+  thrust::host_vector<int2, pinned_memory_allocator<int2>> h_list_sizes_new;
+
+  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_graph_old;
+  thrust::host_vector<int2, pinned_memory_allocator<int2>> h_list_sizes_old;
+  BloomFilter<Index_t> bloom_filter;
+
+  GnndGraph(const GnndGraph&)            = delete;
+  GnndGraph& operator=(const GnndGraph&) = delete;
+  GnndGraph(const size_t nrow,
+            const size_t node_degree,
+            const size_t internal_node_degree,
+            const size_t num_samples);
+  void init_random_graph();
+  // TODO: Create a generic bloom filter utility https://github.com/rapidsai/raft/issues/1827
+  // Use Bloom filter to sample "new" neighbors for local joining
+  void sample_graph_new(InternalID_t<Index_t>* new_neighbors, const size_t width);
+  void sample_graph(bool sample_new);
+  void update_graph(const InternalID_t<Index_t>* new_neighbors,
+                    const DistData_t* new_dists,
+                    const size_t width,
+                    std::atomic<int64_t>& update_counter);
+  void sort_lists();
+  void clear();
+  ~GnndGraph();
+};
+
+template <typename Data_t = float, typename Index_t = int>
+class GNND {
+ public:
+  GNND(raft::resources const& res, const BuildConfig& build_config);
+  GNND(const GNND&)            = delete;
+  GNND& operator=(const GNND&) = delete;
+
+  void build(Data_t* data, const Index_t nrow, Index_t* output_graph);
+  ~GNND()    = default;
+  using ID_t = InternalID_t<Index_t>;
+
+ private:
+  void add_reverse_edges(Index_t* graph_ptr,
+                         Index_t* h_rev_graph_ptr,
+                         Index_t* d_rev_graph_ptr,
+                         int2* list_sizes,
+                         cudaStream_t stream = 0);
+  void local_join(cudaStream_t stream = 0);
+
+  raft::resources const& res;
+
+  BuildConfig build_config_;
+  GnndGraph<Index_t> graph_;
+  std::atomic<int64_t> update_counter_;
+
+  size_t nrow_;
+  size_t ndim_;
+
+  raft::device_matrix<__half, size_t, raft::row_major> d_data_;
+  raft::device_vector<DistData_t, size_t> l2_norms_;
+
+  raft::device_matrix<ID_t, size_t, raft::row_major> graph_buffer_;
+  raft::device_matrix<DistData_t, size_t, raft::row_major> dists_buffer_;
+
+  // TODO: Investigate using RMM/RAFT types https://github.com/rapidsai/raft/issues/1827
+  thrust::host_vector<ID_t, pinned_memory_allocator<ID_t>> graph_host_buffer_;
+  thrust::host_vector<DistData_t, pinned_memory_allocator<DistData_t>> dists_host_buffer_;
+
+  raft::device_vector<int, size_t> d_locks_;
+
+  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_rev_graph_new_;
+  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_graph_old_;
+  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_rev_graph_old_;
+  // int2.x is the number of forward edges, int2.y is the number of reverse edges
+
+  raft::device_vector<int2, size_t> d_list_sizes_new_;
+  raft::device_vector<int2, size_t> d_list_sizes_old_;
+};
+
+constexpr int TILE_ROW_WIDTH = 64;
+constexpr int TILE_COL_WIDTH = 128;
+
+constexpr int NUM_SAMPLES = 32;
+// For now, the max. number of samples is 32, so the sample cache size is fixed
+// to 64 (32 * 2).
+constexpr int MAX_NUM_BI_SAMPLES        = 64;
+constexpr int SKEWED_MAX_NUM_BI_SAMPLES = skew_dim<float>(MAX_NUM_BI_SAMPLES);
+constexpr int BLOCK_SIZE                = 512;
+constexpr int WMMA_M                    = 16;
+constexpr int WMMA_N                    = 16;
+constexpr int WMMA_K                    = 16;
+
+template <typename Data_t>
+__device__ __forceinline__ void load_vec(Data_t* vec_buffer,
+                                         const Data_t* d_vec,
+                                         const int load_dims,
+                                         const int padding_dims,
+                                         const int lane_id)
+{
+  if constexpr (std::is_same_v<Data_t, float> or std::is_same_v<Data_t, uint8_t> or
+                std::is_same_v<Data_t, int8_t>) {
+    constexpr int num_load_elems_per_warp = raft::warp_size();
+    for (int step = 0; step < raft::ceildiv(padding_dims, num_load_elems_per_warp); step++) {
+      int idx = step * num_load_elems_per_warp + lane_id;
+      if (idx < load_dims) {
+        vec_buffer[idx] = d_vec[idx];
+      } else if (idx < padding_dims) {
+        vec_buffer[idx] = 0.0f;
+      }
+    }
+  }
+  if constexpr (std::is_same_v<Data_t, __half>) {
+    if ((size_t)d_vec % sizeof(float2) == 0 && (size_t)vec_buffer % sizeof(float2) == 0 &&
+        load_dims % 4 == 0 && padding_dims % 4 == 0) {
+      constexpr int num_load_elems_per_warp = raft::warp_size() * 4;
+#pragma unroll
+      for (int step = 0; step < raft::ceildiv(padding_dims, num_load_elems_per_warp); step++) {
+        int idx_in_vec = step * num_load_elems_per_warp + lane_id * 4;
+        if (idx_in_vec + 4 <= load_dims) {
+          *(float2*)(vec_buffer + idx_in_vec) = *(float2*)(d_vec + idx_in_vec);
+        } else if (idx_in_vec + 4 <= padding_dims) {
+          *(float2*)(vec_buffer + idx_in_vec) = float2({0.0f, 0.0f});
+        }
+      }
+    } else {
+      constexpr int num_load_elems_per_warp = raft::warp_size();
+      for (int step = 0; step < raft::ceildiv(padding_dims, num_load_elems_per_warp); step++) {
+        int idx = step * num_load_elems_per_warp + lane_id;
+        if (idx < load_dims) {
+          vec_buffer[idx] = d_vec[idx];
+        } else if (idx < padding_dims) {
+          vec_buffer[idx] = 0.0f;
+        }
+      }
+    }
+  }
+}
+
+// TODO: Replace with RAFT utilities https://github.com/rapidsai/raft/issues/1827
+/** Calculate L2 norm, and cast data to __half */
+template <typename Data_t>
+RAFT_KERNEL preprocess_data_kernel(const Data_t* input_data,
+                                   __half* output_data,
+                                   int dim,
+                                   DistData_t* l2_norms,
+                                   size_t list_offset = 0)
+{
+  extern __shared__ char buffer[];
+  __shared__ float l2_norm;
+  Data_t* s_vec  = (Data_t*)buffer;
+  size_t list_id = list_offset + blockIdx.x;
+
+  load_vec(s_vec, input_data + blockIdx.x * dim, dim, dim, threadIdx.x % raft::warp_size());
+  if (threadIdx.x == 0) { l2_norm = 0; }
+  __syncthreads();
+  int lane_id = threadIdx.x % raft::warp_size();
+  for (int step = 0; step < raft::ceildiv(dim, raft::warp_size()); step++) {
+    int idx         = step * raft::warp_size() + lane_id;
+    float part_dist = 0;
+    if (idx < dim) {
+      part_dist = s_vec[idx];
+      part_dist = part_dist * part_dist;
+    }
+    __syncwarp();
+    for (int offset = raft::warp_size() >> 1; offset >= 1; offset >>= 1) {
+      part_dist += __shfl_down_sync(raft::warp_full_mask(), part_dist, offset);
+    }
+    if (lane_id == 0) { l2_norm += part_dist; }
+    __syncwarp();
+  }
+
+  for (int step = 0; step < raft::ceildiv(dim, raft::warp_size()); step++) {
+    int idx = step * raft::warp_size() + threadIdx.x;
+    if (idx < dim) {
+      if (l2_norms == nullptr) {
+        output_data[list_id * dim + idx] =
+          (float)input_data[(size_t)blockIdx.x * dim + idx] / sqrt(l2_norm);
+      } else {
+        output_data[list_id * dim + idx] = input_data[(size_t)blockIdx.x * dim + idx];
+        if (idx == 0) { l2_norms[list_id] = l2_norm; }
+      }
+    }
+  }
+}
+
+template <typename Index_t>
+RAFT_KERNEL add_rev_edges_kernel(const Index_t* graph,
+                                 Index_t* rev_graph,
+                                 int num_samples,
+                                 int2* list_sizes)
+{
+  size_t list_id = blockIdx.x;
+  int2 list_size = list_sizes[list_id];
+
+  for (int idx = threadIdx.x; idx < list_size.x; idx += blockDim.x) {
+    // each node has same number (num_samples) of forward and reverse edges
+    size_t rev_list_id = graph[list_id * num_samples + idx];
+    // there are already num_samples forward edges
+    int idx_in_rev_list = atomicAdd(&list_sizes[rev_list_id].y, 1);
+    if (idx_in_rev_list >= num_samples) {
+      atomicExch(&list_sizes[rev_list_id].y, num_samples);
+    } else {
+      rev_graph[rev_list_id * num_samples + idx_in_rev_list] = list_id;
+    }
+  }
+}
+
+template <typename Index_t, typename ID_t = InternalID_t<Index_t>>
+__device__ void insert_to_global_graph(ResultItem<Index_t> elem,
+                                       size_t list_id,
+                                       ID_t* graph,
+                                       DistData_t* dists,
+                                       int node_degree,
+                                       int* locks)
+{
+  int tx                 = threadIdx.x;
+  int lane_id            = tx % raft::warp_size();
+  size_t global_idx_base = list_id * node_degree;
+  if (elem.id() == list_id) return;
+
+  const int num_segments = raft::ceildiv(node_degree, raft::warp_size());
+
+  int loop_flag = 0;
+  do {
+    int segment_id = elem.id() % num_segments;
+    if (lane_id == 0) {
+      loop_flag = atomicCAS(&locks[list_id * num_segments + segment_id], 0, 1) == 0;
+    }
+
+    loop_flag = __shfl_sync(raft::warp_full_mask(), loop_flag, 0);
+
+    if (loop_flag == 1) {
+      ResultItem<Index_t> knn_list_frag;
+      int local_idx     = segment_id * raft::warp_size() + lane_id;
+      size_t global_idx = global_idx_base + local_idx;
+      if (local_idx < node_degree) {
+        knn_list_frag.id_with_flag() = graph[global_idx].id_with_flag();
+        knn_list_frag.dist()         = dists[global_idx];
+      }
+
+      int pos_to_insert = -1;
+      ResultItem<Index_t> prev_elem;
+
+      prev_elem.id_with_flag() =
+        __shfl_up_sync(raft::warp_full_mask(), knn_list_frag.id_with_flag(), 1);
+      prev_elem.dist() = __shfl_up_sync(raft::warp_full_mask(), knn_list_frag.dist(), 1);
+
+      if (lane_id == 0) {
+        prev_elem = ResultItem<Index_t>{std::numeric_limits<Index_t>::min(),
+                                        std::numeric_limits<DistData_t>::lowest()};
+      }
+      if (elem > prev_elem && elem < knn_list_frag) {
+        pos_to_insert = segment_id * raft::warp_size() + lane_id;
+      } else if (elem == prev_elem || elem == knn_list_frag) {
+        pos_to_insert = -2;
+      }
+      uint mask = __ballot_sync(raft::warp_full_mask(), pos_to_insert >= 0);
+      if (mask) {
+        uint set_lane_id = __fns(mask, 0, 1);
+        pos_to_insert    = __shfl_sync(raft::warp_full_mask(), pos_to_insert, set_lane_id);
+      }
+
+      if (pos_to_insert >= 0) {
+        int local_idx = segment_id * raft::warp_size() + lane_id;
+        if (local_idx > pos_to_insert) {
+          local_idx++;
+        } else if (local_idx == pos_to_insert) {
+          graph[global_idx_base + local_idx].id_with_flag() = elem.id_with_flag();
+          dists[global_idx_base + local_idx]                = elem.dist();
+          local_idx++;
+        }
+        size_t global_pos = global_idx_base + local_idx;
+        if (local_idx < (segment_id + 1) * raft::warp_size() && local_idx < node_degree) {
+          graph[global_pos].id_with_flag() = knn_list_frag.id_with_flag();
+          dists[global_pos]                = knn_list_frag.dist();
+        }
+      }
+      __threadfence();
+      if (loop_flag && lane_id == 0) { atomicExch(&locks[list_id * num_segments + segment_id], 0); }
+    }
+  } while (!loop_flag);
+}
+
+template <typename Index_t>
+__device__ ResultItem<Index_t> get_min_item(const Index_t id,
+                                            const int idx_in_list,
+                                            const Index_t* neighbs,
+                                            const DistData_t* distances,
+                                            const bool find_in_row = true)
+{
+  int lane_id = threadIdx.x % raft::warp_size();
+
+  static_assert(MAX_NUM_BI_SAMPLES == 64);
+  int idx[MAX_NUM_BI_SAMPLES / raft::warp_size()];
+  float dist[MAX_NUM_BI_SAMPLES / raft::warp_size()] = {std::numeric_limits<DistData_t>::max(),
+                                                        std::numeric_limits<DistData_t>::max()};
+  idx[0]                                             = lane_id;
+  idx[1]                                             = raft::warp_size() + lane_id;
+
+  if (neighbs[idx[0]] != id) {
+    dist[0] = find_in_row ? distances[idx_in_list * SKEWED_MAX_NUM_BI_SAMPLES + lane_id]
+                          : distances[idx_in_list + lane_id * SKEWED_MAX_NUM_BI_SAMPLES];
+  }
+
+  if (neighbs[idx[1]] != id) {
+    dist[1] =
+      find_in_row
+        ? distances[idx_in_list * SKEWED_MAX_NUM_BI_SAMPLES + raft::warp_size() + lane_id]
+        : distances[idx_in_list + (raft::warp_size() + lane_id) * SKEWED_MAX_NUM_BI_SAMPLES];
+  }
+
+  if (dist[1] < dist[0]) {
+    dist[0] = dist[1];
+    idx[0]  = idx[1];
+  }
+  __syncwarp();
+  for (int offset = raft::warp_size() >> 1; offset >= 1; offset >>= 1) {
+    float other_idx  = __shfl_down_sync(raft::warp_full_mask(), idx[0], offset);
+    float other_dist = __shfl_down_sync(raft::warp_full_mask(), dist[0], offset);
+    if (other_dist < dist[0]) {
+      dist[0] = other_dist;
+      idx[0]  = other_idx;
+    }
+  }
+
+  ResultItem<Index_t> result;
+  result.dist()         = __shfl_sync(raft::warp_full_mask(), dist[0], 0);
+  result.id_with_flag() = neighbs[__shfl_sync(raft::warp_full_mask(), idx[0], 0)];
+  return result;
+}
+
+template <typename T>
+__device__ __forceinline__ void remove_duplicates(
+  T* list_a, int list_a_size, T* list_b, int list_b_size, int& unique_counter, int execute_warp_id)
+{
+  static_assert(raft::warp_size() == 32);
+  if (!(threadIdx.x >= execute_warp_id * raft::warp_size() &&
+        threadIdx.x < execute_warp_id * raft::warp_size() + raft::warp_size())) {
+    return;
+  }
+  int lane_id = threadIdx.x % raft::warp_size();
+  T elem      = std::numeric_limits<T>::max();
+  if (lane_id < list_a_size) { elem = list_a[lane_id]; }
+  warp_bitonic_sort(&elem, lane_id);
+
+  if (elem != std::numeric_limits<T>::max()) { list_a[lane_id] = elem; }
+
+  T elem_b = std::numeric_limits<T>::max();
+
+  if (lane_id < list_b_size) { elem_b = list_b[lane_id]; }
+  __syncwarp();
+
+  int idx_l    = 0;
+  int idx_r    = list_a_size;
+  bool existed = false;
+  while (idx_l < idx_r) {
+    int idx  = (idx_l + idx_r) / 2;
+    int elem = list_a[idx];
+    if (elem == elem_b) {
+      existed = true;
+      break;
+    }
+    if (elem_b > elem) {
+      idx_l = idx + 1;
+    } else {
+      idx_r = idx;
+    }
+  }
+  if (!existed && elem_b != std::numeric_limits<T>::max()) {
+    int idx                   = atomicAdd(&unique_counter, 1);
+    list_a[list_a_size + idx] = elem_b;
+  }
+}
+
+// launch_bounds here denote BLOCK_SIZE = 512 and MIN_BLOCKS_PER_SM = 4
+// Per
+// https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#features-and-technical-specifications,
+// MAX_RESIDENT_THREAD_PER_SM = BLOCK_SIZE * BLOCKS_PER_SM = 2048
+// For architectures 750 and 860 (890), the values for MAX_RESIDENT_THREAD_PER_SM
+// is 1024 and 1536 respectively, which means the bounds don't work anymore
+template <typename Index_t, typename ID_t = InternalID_t<Index_t>>
+RAFT_KERNEL
+#ifdef __CUDA_ARCH__
+#if (__CUDA_ARCH__) == 750 || ((__CUDA_ARCH__) >= 860 && (__CUDA_ARCH__) <= 890)
+__launch_bounds__(BLOCK_SIZE)
+#else
+__launch_bounds__(BLOCK_SIZE, 4)
+#endif
+#endif
+  local_join_kernel(const Index_t* graph_new,
+                    const Index_t* rev_graph_new,
+                    const int2* sizes_new,
+                    const Index_t* graph_old,
+                    const Index_t* rev_graph_old,
+                    const int2* sizes_old,
+                    const int width,
+                    const __half* data,
+                    const int data_dim,
+                    ID_t* graph,
+                    DistData_t* dists,
+                    int graph_width,
+                    int* locks,
+                    DistData_t* l2_norms)
+{
+#if (__CUDA_ARCH__ >= 700)
+  using namespace nvcuda;
+  __shared__ int s_list[MAX_NUM_BI_SAMPLES * 2];
+
+  constexpr int APAD = 8;
+  constexpr int BPAD = 8;
+  __shared__ __half s_nv[MAX_NUM_BI_SAMPLES][TILE_COL_WIDTH + APAD];  // New vectors
+  __shared__ __half s_ov[MAX_NUM_BI_SAMPLES][TILE_COL_WIDTH + BPAD];  // Old vectors
+  static_assert(sizeof(float) * MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES <=
+                sizeof(__half) * MAX_NUM_BI_SAMPLES * (TILE_COL_WIDTH + BPAD));
+  // s_distances: MAX_NUM_BI_SAMPLES x SKEWED_MAX_NUM_BI_SAMPLES, reuse the space of s_ov
+  float* s_distances    = (float*)&s_ov[0][0];
+  int* s_unique_counter = (int*)&s_ov[0][0];
+
+  if (threadIdx.x == 0) {
+    s_unique_counter[0] = 0;
+    s_unique_counter[1] = 0;
+  }
+
+  Index_t* new_neighbors = s_list;
+  Index_t* old_neighbors = s_list + MAX_NUM_BI_SAMPLES;
+
+  size_t list_id      = blockIdx.x;
+  int2 list_new_size2 = sizes_new[list_id];
+  int list_new_size   = list_new_size2.x + list_new_size2.y;
+  int2 list_old_size2 = sizes_old[list_id];
+  int list_old_size   = list_old_size2.x + list_old_size2.y;
+
+  if (!list_new_size) return;
+  int tx = threadIdx.x;
+
+  if (tx < list_new_size2.x) {
+    new_neighbors[tx] = graph_new[list_id * width + tx];
+  } else if (tx >= list_new_size2.x && tx < list_new_size) {
+    new_neighbors[tx] = rev_graph_new[list_id * width + tx - list_new_size2.x];
+  }
+
+  if (tx < list_old_size2.x) {
+    old_neighbors[tx] = graph_old[list_id * width + tx];
+  } else if (tx >= list_old_size2.x && tx < list_old_size) {
+    old_neighbors[tx] = rev_graph_old[list_id * width + tx - list_old_size2.x];
+  }
+
+  __syncthreads();
+
+  remove_duplicates(new_neighbors,
+                    list_new_size2.x,
+                    new_neighbors + list_new_size2.x,
+                    list_new_size2.y,
+                    s_unique_counter[0],
+                    0);
+
+  remove_duplicates(old_neighbors,
+                    list_old_size2.x,
+                    old_neighbors + list_old_size2.x,
+                    list_old_size2.y,
+                    s_unique_counter[1],
+                    1);
+  __syncthreads();
+  list_new_size = list_new_size2.x + s_unique_counter[0];
+  list_old_size = list_old_size2.x + s_unique_counter[1];
+
+  int warp_id             = threadIdx.x / raft::warp_size();
+  int lane_id             = threadIdx.x % raft::warp_size();
+  constexpr int num_warps = BLOCK_SIZE / raft::warp_size();
+
+  int warp_id_y = warp_id / 4;
+  int warp_id_x = warp_id % 4;
+
+  wmma::fragment<wmma::matrix_a, WMMA_M, WMMA_N, WMMA_K, half, wmma::row_major> a_frag;
+  wmma::fragment<wmma::matrix_b, WMMA_M, WMMA_N, WMMA_K, half, wmma::col_major> b_frag;
+  wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, float> c_frag;
+  wmma::fill_fragment(c_frag, 0.0);
+  for (int step = 0; step < raft::ceildiv(data_dim, TILE_COL_WIDTH); step++) {
+    int num_load_elems = (step == raft::ceildiv(data_dim, TILE_COL_WIDTH) - 1)
+                           ? data_dim - step * TILE_COL_WIDTH
+                           : TILE_COL_WIDTH;
+#pragma unroll
+    for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
+      int idx = i * num_warps + warp_id;
+      if (idx < list_new_size) {
+        size_t neighbor_id = new_neighbors[idx];
+        size_t idx_in_data = neighbor_id * data_dim;
+        load_vec(s_nv[idx],
+                 data + idx_in_data + step * TILE_COL_WIDTH,
+                 num_load_elems,
+                 TILE_COL_WIDTH,
+                 lane_id);
+      }
+    }
+    __syncthreads();
+
+    for (int i = 0; i < TILE_COL_WIDTH / WMMA_K; i++) {
+      wmma::load_matrix_sync(a_frag, s_nv[warp_id_y * WMMA_M] + i * WMMA_K, TILE_COL_WIDTH + APAD);
+      wmma::load_matrix_sync(b_frag, s_nv[warp_id_x * WMMA_N] + i * WMMA_K, TILE_COL_WIDTH + BPAD);
+      wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
+      __syncthreads();
+    }
+  }
+
+  wmma::store_matrix_sync(
+    s_distances + warp_id_y * WMMA_M * SKEWED_MAX_NUM_BI_SAMPLES + warp_id_x * WMMA_N,
+    c_frag,
+    SKEWED_MAX_NUM_BI_SAMPLES,
+    wmma::mem_row_major);
+  __syncthreads();
+
+  for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
+    if (i % SKEWED_MAX_NUM_BI_SAMPLES < list_new_size &&
+        i / SKEWED_MAX_NUM_BI_SAMPLES < list_new_size) {
+      if (l2_norms == nullptr) {
+        s_distances[i] = -s_distances[i];
+      } else {
+        s_distances[i] = l2_norms[new_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
+                         l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
+                         2.0 * s_distances[i];
+      }
+    } else {
+      s_distances[i] = std::numeric_limits<float>::max();
+    }
+  }
+  __syncthreads();
+
+  for (int step = 0; step < raft::ceildiv(list_new_size, num_warps); step++) {
+    int idx_in_list = step * num_warps + tx / raft::warp_size();
+    if (idx_in_list >= list_new_size) continue;
+    auto min_elem = get_min_item(s_list[idx_in_list], idx_in_list, new_neighbors, s_distances);
+    if (min_elem.id() < gridDim.x) {
+      insert_to_global_graph(min_elem, s_list[idx_in_list], graph, dists, graph_width, locks);
+    }
+  }
+
+  if (!list_old_size) return;
+
+  __syncthreads();
+
+  wmma::fill_fragment(c_frag, 0.0);
+  for (int step = 0; step < raft::ceildiv(data_dim, TILE_COL_WIDTH); step++) {
+    int num_load_elems = (step == raft::ceildiv(data_dim, TILE_COL_WIDTH) - 1)
+                           ? data_dim - step * TILE_COL_WIDTH
+                           : TILE_COL_WIDTH;
+    if (TILE_COL_WIDTH < data_dim) {
+#pragma unroll
+      for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
+        int idx = i * num_warps + warp_id;
+        if (idx < list_new_size) {
+          size_t neighbor_id = new_neighbors[idx];
+          size_t idx_in_data = neighbor_id * data_dim;
+          load_vec(s_nv[idx],
+                   data + idx_in_data + step * TILE_COL_WIDTH,
+                   num_load_elems,
+                   TILE_COL_WIDTH,
+                   lane_id);
+        }
+      }
+    }
+#pragma unroll
+    for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
+      int idx = i * num_warps + warp_id;
+      if (idx < list_old_size) {
+        size_t neighbor_id = old_neighbors[idx];
+        size_t idx_in_data = neighbor_id * data_dim;
+        load_vec(s_ov[idx],
+                 data + idx_in_data + step * TILE_COL_WIDTH,
+                 num_load_elems,
+                 TILE_COL_WIDTH,
+                 lane_id);
+      }
+    }
+    __syncthreads();
+
+    for (int i = 0; i < TILE_COL_WIDTH / WMMA_K; i++) {
+      wmma::load_matrix_sync(a_frag, s_nv[warp_id_y * WMMA_M] + i * WMMA_K, TILE_COL_WIDTH + APAD);
+      wmma::load_matrix_sync(b_frag, s_ov[warp_id_x * WMMA_N] + i * WMMA_K, TILE_COL_WIDTH + BPAD);
+      wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
+      __syncthreads();
+    }
+  }
+
+  wmma::store_matrix_sync(
+    s_distances + warp_id_y * WMMA_M * SKEWED_MAX_NUM_BI_SAMPLES + warp_id_x * WMMA_N,
+    c_frag,
+    SKEWED_MAX_NUM_BI_SAMPLES,
+    wmma::mem_row_major);
+  __syncthreads();
+
+  for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
+    if (i % SKEWED_MAX_NUM_BI_SAMPLES < list_old_size &&
+        i / SKEWED_MAX_NUM_BI_SAMPLES < list_new_size) {
+      if (l2_norms == nullptr) {
+        s_distances[i] = -s_distances[i];
+      } else {
+        s_distances[i] = l2_norms[old_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
+                         l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
+                         2.0 * s_distances[i];
+      }
+    } else {
+      s_distances[i] = std::numeric_limits<float>::max();
+    }
+  }
+  __syncthreads();
+
+  for (int step = 0; step < raft::ceildiv(MAX_NUM_BI_SAMPLES * 2, num_warps); step++) {
+    int idx_in_list = step * num_warps + tx / raft::warp_size();
+    if (idx_in_list >= list_new_size && idx_in_list < MAX_NUM_BI_SAMPLES) continue;
+    if (idx_in_list >= MAX_NUM_BI_SAMPLES + list_old_size && idx_in_list < MAX_NUM_BI_SAMPLES * 2)
+      continue;
+    ResultItem<Index_t> min_elem{std::numeric_limits<Index_t>::max(),
+                                 std::numeric_limits<DistData_t>::max()};
+    if (idx_in_list < MAX_NUM_BI_SAMPLES) {
+      auto temp_min_item =
+        get_min_item(s_list[idx_in_list], idx_in_list, old_neighbors, s_distances);
+      if (temp_min_item.dist() < min_elem.dist()) { min_elem = temp_min_item; }
+    } else {
+      auto temp_min_item = get_min_item(
+        s_list[idx_in_list], idx_in_list - MAX_NUM_BI_SAMPLES, new_neighbors, s_distances, false);
+      if (temp_min_item.dist() < min_elem.dist()) { min_elem = temp_min_item; }
+    }
+
+    if (min_elem.id() < gridDim.x) {
+      insert_to_global_graph(min_elem, s_list[idx_in_list], graph, dists, graph_width, locks);
+    }
+  }
+#endif
+}
+
+namespace {
+template <typename Index_t>
+int insert_to_ordered_list(InternalID_t<Index_t>* list,
+                           DistData_t* dist_list,
+                           const int width,
+                           const InternalID_t<Index_t> neighb_id,
+                           const DistData_t dist)
+{
+  if (dist > dist_list[width - 1]) { return width; }
+
+  int idx_insert      = width;
+  bool position_found = false;
+  for (int i = 0; i < width; i++) {
+    if (list[i].id() == neighb_id.id()) { return width; }
+    if (!position_found && dist_list[i] > dist) {
+      idx_insert     = i;
+      position_found = true;
+    }
+  }
+  if (idx_insert == width) return idx_insert;
+
+  memmove(list + idx_insert + 1, list + idx_insert, sizeof(*list) * (width - idx_insert - 1));
+  memmove(dist_list + idx_insert + 1,
+          dist_list + idx_insert,
+          sizeof(*dist_list) * (width - idx_insert - 1));
+
+  list[idx_insert]      = neighb_id;
+  dist_list[idx_insert] = dist;
+  return idx_insert;
+};
+
+}  // namespace
+
+template <typename Index_t>
+GnndGraph<Index_t>::GnndGraph(const size_t nrow,
+                              const size_t node_degree,
+                              const size_t internal_node_degree,
+                              const size_t num_samples)
+  : nrow(nrow),
+    node_degree(node_degree),
+    num_samples(num_samples),
+    bloom_filter(nrow, internal_node_degree / segment_size, 3),
+    h_dists{raft::make_host_matrix<DistData_t, size_t, raft::row_major>(nrow, node_degree)},
+    h_graph_new(nrow * num_samples),
+    h_list_sizes_new(nrow),
+    h_graph_old(nrow * num_samples),
+    h_list_sizes_old{nrow}
+{
+  // node_degree must be a multiple of segment_size;
+  assert(node_degree % segment_size == 0);
+  assert(internal_node_degree % segment_size == 0);
+
+  num_segments = node_degree / segment_size;
+  // To save the CPU memory, graph should be allocated by external function
+  h_graph = nullptr;
+}
+
+// This is the only operation on the CPU that cannot be overlapped.
+// So it should be as fast as possible.
+template <typename Index_t>
+void GnndGraph<Index_t>::sample_graph_new(InternalID_t<Index_t>* new_neighbors, const size_t width)
+{
+#pragma omp parallel for
+  for (size_t i = 0; i < nrow; i++) {
+    auto list_new         = h_graph_new.data() + i * num_samples;
+    h_list_sizes_new[i].x = 0;
+    h_list_sizes_new[i].y = 0;
+
+    for (size_t j = 0; j < width; j++) {
+      auto new_neighb_id = new_neighbors[i * width + j].id();
+      if ((size_t)new_neighb_id >= nrow) break;
+      if (bloom_filter.check(i, new_neighb_id)) { continue; }
+      bloom_filter.add(i, new_neighb_id);
+      new_neighbors[i * width + j].mark_old();
+      list_new[h_list_sizes_new[i].x++] = new_neighb_id;
+      if (h_list_sizes_new[i].x == num_samples) break;
+    }
+  }
+}
+
+template <typename Index_t>
+void GnndGraph<Index_t>::init_random_graph()
+{
+  for (size_t seg_idx = 0; seg_idx < static_cast<size_t>(num_segments); seg_idx++) {
+    // random sequence (range: 0~nrow)
+    // segment_x stores neighbors which id % num_segments == x
+    std::vector<Index_t> rand_seq(nrow / num_segments);
+    std::iota(rand_seq.begin(), rand_seq.end(), 0);
+    auto gen = std::default_random_engine{seg_idx};
+    std::shuffle(rand_seq.begin(), rand_seq.end(), gen);
+
+#pragma omp parallel for
+    for (size_t i = 0; i < nrow; i++) {
+      size_t base_idx      = i * node_degree + seg_idx * segment_size;
+      auto h_neighbor_list = h_graph + base_idx;
+      auto h_dist_list     = h_dists.data_handle() + base_idx;
+      for (size_t j = 0; j < static_cast<size_t>(segment_size); j++) {
+        size_t idx = base_idx + j;
+        Index_t id = rand_seq[idx % rand_seq.size()] * num_segments + seg_idx;
+        if ((size_t)id == i) {
+          id = rand_seq[(idx + segment_size) % rand_seq.size()] * num_segments + seg_idx;
+        }
+        h_neighbor_list[j].id_with_flag() = id;
+        h_dist_list[j]                    = std::numeric_limits<DistData_t>::max();
+      }
+    }
+  }
+}
+
+template <typename Index_t>
+void GnndGraph<Index_t>::sample_graph(bool sample_new)
+{
+#pragma omp parallel for
+  for (size_t i = 0; i < nrow; i++) {
+    h_list_sizes_old[i].x = 0;
+    h_list_sizes_old[i].y = 0;
+    h_list_sizes_new[i].x = 0;
+    h_list_sizes_new[i].y = 0;
+
+    auto list     = h_graph + i * node_degree;
+    auto list_old = h_graph_old.data() + i * num_samples;
+    auto list_new = h_graph_new.data() + i * num_samples;
+    for (int j = 0; j < segment_size; j++) {
+      for (int k = 0; k < num_segments; k++) {
+        auto neighbor = list[k * segment_size + j];
+        if ((size_t)neighbor.id() >= nrow) continue;
+        if (!neighbor.is_new()) {
+          if (h_list_sizes_old[i].x < num_samples) {
+            list_old[h_list_sizes_old[i].x++] = neighbor.id();
+          }
+        } else if (sample_new) {
+          if (h_list_sizes_new[i].x < num_samples) {
+            list[k * segment_size + j].mark_old();
+            list_new[h_list_sizes_new[i].x++] = neighbor.id();
+          }
+        }
+        if (h_list_sizes_old[i].x == num_samples && h_list_sizes_new[i].x == num_samples) { break; }
+      }
+      if (h_list_sizes_old[i].x == num_samples && h_list_sizes_new[i].x == num_samples) { break; }
+    }
+  }
+}
+
+template <typename Index_t>
+void GnndGraph<Index_t>::update_graph(const InternalID_t<Index_t>* new_neighbors,
+                                      const DistData_t* new_dists,
+                                      const size_t width,
+                                      std::atomic<int64_t>& update_counter)
+{
+#pragma omp parallel for
+  for (size_t i = 0; i < nrow; i++) {
+    for (size_t j = 0; j < width; j++) {
+      auto new_neighb_id = new_neighbors[i * width + j];
+      auto new_dist      = new_dists[i * width + j];
+      if (new_dist == std::numeric_limits<DistData_t>::max()) break;
+      if ((size_t)new_neighb_id.id() == i) continue;
+      int seg_idx    = new_neighb_id.id() % num_segments;
+      auto list      = h_graph + i * node_degree + seg_idx * segment_size;
+      auto dist_list = h_dists.data_handle() + i * node_degree + seg_idx * segment_size;
+      int insert_pos =
+        insert_to_ordered_list(list, dist_list, segment_size, new_neighb_id, new_dist);
+      if (i % counter_interval == 0 && insert_pos != segment_size) { update_counter++; }
+    }
+  }
+}
+
+template <typename Index_t>
+void GnndGraph<Index_t>::sort_lists()
+{
+#pragma omp parallel for
+  for (size_t i = 0; i < nrow; i++) {
+    std::vector<std::pair<DistData_t, Index_t>> new_list;
+    for (size_t j = 0; j < node_degree; j++) {
+      new_list.emplace_back(h_dists.data_handle()[i * node_degree + j],
+                            h_graph[i * node_degree + j].id());
+    }
+    std::sort(new_list.begin(), new_list.end());
+    for (size_t j = 0; j < node_degree; j++) {
+      h_graph[i * node_degree + j].id_with_flag() = new_list[j].second;
+      h_dists.data_handle()[i * node_degree + j]  = new_list[j].first;
+    }
+  }
+}
+
+template <typename Index_t>
+void GnndGraph<Index_t>::clear()
+{
+  bloom_filter.clear();
+}
+
+template <typename Index_t>
+GnndGraph<Index_t>::~GnndGraph()
+{
+  assert(h_graph == nullptr);
+}
+
+template <typename Data_t, typename Index_t>
+GNND<Data_t, Index_t>::GNND(raft::resources const& res, const BuildConfig& build_config)
+  : res(res),
+    build_config_(build_config),
+    graph_(build_config.max_dataset_size,
+           align32::roundUp(build_config.node_degree),
+           align32::roundUp(build_config.internal_node_degree ? build_config.internal_node_degree
+                                                              : build_config.node_degree),
+           NUM_SAMPLES),
+    nrow_(build_config.max_dataset_size),
+    ndim_(build_config.dataset_dim),
+    d_data_{raft::make_device_matrix<__half, size_t, raft::row_major>(
+      res, nrow_, build_config.dataset_dim)},
+    l2_norms_{raft::make_device_vector<DistData_t, size_t>(res, nrow_)},
+    graph_buffer_{
+      raft::make_device_matrix<ID_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
+    dists_buffer_{
+      raft::make_device_matrix<DistData_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
+    graph_host_buffer_(nrow_ * DEGREE_ON_DEVICE),
+    dists_host_buffer_(nrow_ * DEGREE_ON_DEVICE),
+    d_locks_{raft::make_device_vector<int, size_t>(res, nrow_)},
+    h_rev_graph_new_(nrow_ * NUM_SAMPLES),
+    h_graph_old_(nrow_ * NUM_SAMPLES),
+    h_rev_graph_old_(nrow_ * NUM_SAMPLES),
+    d_list_sizes_new_{raft::make_device_vector<int2, size_t>(res, nrow_)},
+    d_list_sizes_old_{raft::make_device_vector<int2, size_t>(res, nrow_)}
+{
+  static_assert(NUM_SAMPLES <= 32);
+
+  thrust::fill(thrust::device,
+               dists_buffer_.data_handle(),
+               dists_buffer_.data_handle() + dists_buffer_.size(),
+               std::numeric_limits<float>::max());
+  thrust::fill(thrust::device,
+               reinterpret_cast<Index_t*>(graph_buffer_.data_handle()),
+               reinterpret_cast<Index_t*>(graph_buffer_.data_handle()) + graph_buffer_.size(),
+               std::numeric_limits<Index_t>::max());
+  thrust::fill(thrust::device, d_locks_.data_handle(), d_locks_.data_handle() + d_locks_.size(), 0);
+};
+
+template <typename Data_t, typename Index_t>
+void GNND<Data_t, Index_t>::add_reverse_edges(Index_t* graph_ptr,
+                                              Index_t* h_rev_graph_ptr,
+                                              Index_t* d_rev_graph_ptr,
+                                              int2* list_sizes,
+                                              cudaStream_t stream)
+{
+  add_rev_edges_kernel<<<nrow_, raft::warp_size(), 0, stream>>>(
+    graph_ptr, d_rev_graph_ptr, NUM_SAMPLES, list_sizes);
+  raft::copy(
+    h_rev_graph_ptr, d_rev_graph_ptr, nrow_ * NUM_SAMPLES, raft::resource::get_cuda_stream(res));
+}
+
+template <typename Data_t, typename Index_t>
+void GNND<Data_t, Index_t>::local_join(cudaStream_t stream)
+{
+  thrust::fill(thrust::device.on(stream),
+               dists_buffer_.data_handle(),
+               dists_buffer_.data_handle() + dists_buffer_.size(),
+               std::numeric_limits<float>::max());
+  local_join_kernel<<<nrow_, BLOCK_SIZE, 0, stream>>>(
+    thrust::raw_pointer_cast(graph_.h_graph_new.data()),
+    thrust::raw_pointer_cast(h_rev_graph_new_.data()),
+    d_list_sizes_new_.data_handle(),
+    thrust::raw_pointer_cast(h_graph_old_.data()),
+    thrust::raw_pointer_cast(h_rev_graph_old_.data()),
+    d_list_sizes_old_.data_handle(),
+    NUM_SAMPLES,
+    d_data_.data_handle(),
+    ndim_,
+    graph_buffer_.data_handle(),
+    dists_buffer_.data_handle(),
+    DEGREE_ON_DEVICE,
+    d_locks_.data_handle(),
+    l2_norms_.data_handle());
+}
+
+template <typename Data_t, typename Index_t>
+void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* output_graph)
+{
+  using input_t = typename std::remove_const<Data_t>::type;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(res);
+  nrow_               = nrow;
+  graph_.h_graph      = (InternalID_t<Index_t>*)output_graph;
+
+  cudaPointerAttributes data_ptr_attr;
+  RAFT_CUDA_TRY(cudaPointerGetAttributes(&data_ptr_attr, data));
+  size_t batch_size = (data_ptr_attr.devicePointer == nullptr) ? 100000 : nrow_;
+
+  cuvs::spatial::knn::detail::utils::batch_load_iterator vec_batches{
+    data, static_cast<size_t>(nrow_), build_config_.dataset_dim, batch_size, stream};
+  for (auto const& batch : vec_batches) {
+    preprocess_data_kernel<<<batch.size(),
+                             raft::warp_size(),
+                             sizeof(Data_t) *
+                               raft::ceildiv(build_config_.dataset_dim,
+                                             static_cast<size_t>(raft::warp_size())) *
+                               raft::warp_size(),
+                             stream>>>(batch.data(),
+                                       d_data_.data_handle(),
+                                       build_config_.dataset_dim,
+                                       l2_norms_.data_handle(),
+                                       batch.offset());
+  }
+
+  thrust::fill(thrust::device.on(stream),
+               (Index_t*)graph_buffer_.data_handle(),
+               (Index_t*)graph_buffer_.data_handle() + graph_buffer_.size(),
+               std::numeric_limits<Index_t>::max());
+
+  graph_.clear();
+  graph_.init_random_graph();
+  graph_.sample_graph(true);
+
+  auto update_and_sample = [&](bool update_graph) {
+    if (update_graph) {
+      update_counter_ = 0;
+      graph_.update_graph(thrust::raw_pointer_cast(graph_host_buffer_.data()),
+                          thrust::raw_pointer_cast(dists_host_buffer_.data()),
+                          DEGREE_ON_DEVICE,
+                          update_counter_);
+      if (update_counter_ < build_config_.termination_threshold * nrow_ *
+                              build_config_.dataset_dim / counter_interval) {
+        update_counter_ = -1;
+      }
+    }
+    graph_.sample_graph(false);
+  };
+
+  for (size_t it = 0; it < build_config_.max_iterations; it++) {
+    raft::copy(d_list_sizes_new_.data_handle(),
+               thrust::raw_pointer_cast(graph_.h_list_sizes_new.data()),
+               nrow_,
+               raft::resource::get_cuda_stream(res));
+    raft::copy(thrust::raw_pointer_cast(h_graph_old_.data()),
+               thrust::raw_pointer_cast(graph_.h_graph_old.data()),
+               nrow_ * NUM_SAMPLES,
+               raft::resource::get_cuda_stream(res));
+    raft::copy(d_list_sizes_old_.data_handle(),
+               thrust::raw_pointer_cast(graph_.h_list_sizes_old.data()),
+               nrow_,
+               raft::resource::get_cuda_stream(res));
+    raft::resource::sync_stream(res);
+
+    std::thread update_and_sample_thread(update_and_sample, it);
+
+    RAFT_LOG_DEBUG("# GNND iteraton: %lu / %lu", it + 1, build_config_.max_iterations);
+
+    // Reuse dists_buffer_ to save GPU memory. graph_buffer_ cannot be reused, because it
+    // contains some information for local_join.
+    static_assert(DEGREE_ON_DEVICE * sizeof(*(dists_buffer_.data_handle())) >=
+                  NUM_SAMPLES * sizeof(*(graph_buffer_.data_handle())));
+    add_reverse_edges(thrust::raw_pointer_cast(graph_.h_graph_new.data()),
+                      thrust::raw_pointer_cast(h_rev_graph_new_.data()),
+                      (Index_t*)dists_buffer_.data_handle(),
+                      d_list_sizes_new_.data_handle(),
+                      stream);
+    add_reverse_edges(thrust::raw_pointer_cast(h_graph_old_.data()),
+                      thrust::raw_pointer_cast(h_rev_graph_old_.data()),
+                      (Index_t*)dists_buffer_.data_handle(),
+                      d_list_sizes_old_.data_handle(),
+                      stream);
+
+    // Tensor operations from `mma.h` are guarded with archicteture
+    // __CUDA_ARCH__ >= 700. Since RAFT supports compilation for ARCH 600,
+    // we need to ensure that `local_join_kernel` (which uses tensor) operations
+    // is not only not compiled, but also a runtime error is presented to the user
+    auto kernel       = preprocess_data_kernel<input_t>;
+    void* kernel_ptr  = reinterpret_cast<void*>(kernel);
+    auto runtime_arch = raft::util::arch::kernel_virtual_arch(kernel_ptr);
+    auto wmma_range =
+      raft::util::arch::SM_range(raft::util::arch::SM_70(), raft::util::arch::SM_future());
+
+    if (wmma_range.contains(runtime_arch)) {
+      local_join(stream);
+    } else {
+      THROW("NN_DESCENT cannot be run for __CUDA_ARCH__ < 700");
+    }
+
+    update_and_sample_thread.join();
+
+    if (update_counter_ == -1) { break; }
+    raft::copy(thrust::raw_pointer_cast(graph_host_buffer_.data()),
+               graph_buffer_.data_handle(),
+               nrow_ * DEGREE_ON_DEVICE,
+               raft::resource::get_cuda_stream(res));
+    raft::resource::sync_stream(res);
+    raft::copy(thrust::raw_pointer_cast(dists_host_buffer_.data()),
+               dists_buffer_.data_handle(),
+               nrow_ * DEGREE_ON_DEVICE,
+               raft::resource::get_cuda_stream(res));
+
+    graph_.sample_graph_new(thrust::raw_pointer_cast(graph_host_buffer_.data()), DEGREE_ON_DEVICE);
+  }
+
+  graph_.update_graph(thrust::raw_pointer_cast(graph_host_buffer_.data()),
+                      thrust::raw_pointer_cast(dists_host_buffer_.data()),
+                      DEGREE_ON_DEVICE,
+                      update_counter_);
+  raft::resource::sync_stream(res);
+  graph_.sort_lists();
+
+  // Reuse graph_.h_dists as the buffer for shrink the lists in graph
+  static_assert(sizeof(decltype(*(graph_.h_dists.data_handle()))) >= sizeof(Index_t));
+  Index_t* graph_shrink_buffer = (Index_t*)graph_.h_dists.data_handle();
+
+#pragma omp parallel for
+  for (size_t i = 0; i < (size_t)nrow_; i++) {
+    for (size_t j = 0; j < build_config_.node_degree; j++) {
+      size_t idx = i * graph_.node_degree + j;
+      int id     = graph_.h_graph[idx].id();
+      if (id < static_cast<int>(nrow_)) {
+        graph_shrink_buffer[i * build_config_.node_degree + j] = id;
+      } else {
+        graph_shrink_buffer[i * build_config_.node_degree + j] =
+          cuvs::neighbors::cagra::detail::device::xorshift64(idx) % nrow_;
+      }
+    }
+  }
+  graph_.h_graph = nullptr;
+
+#pragma omp parallel for
+  for (size_t i = 0; i < (size_t)nrow_; i++) {
+    for (size_t j = 0; j < build_config_.node_degree; j++) {
+      output_graph[i * build_config_.node_degree + j] =
+        graph_shrink_buffer[i * build_config_.node_degree + j];
+    }
+  }
+}
+
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+void build(raft::resources const& res,
+           const index_params& params,
+           raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+           index<IdxT>& idx)
+{
+  RAFT_EXPECTS(dataset.extent(0) < std::numeric_limits<int>::max() - 1,
+               "The dataset size for GNND should be less than %d",
+               std::numeric_limits<int>::max() - 1);
+  size_t intermediate_degree = params.intermediate_graph_degree;
+  size_t graph_degree        = params.graph_degree;
+
+  if (intermediate_degree >= static_cast<size_t>(dataset.extent(0))) {
+    RAFT_LOG_WARN(
+      "Intermediate graph degree cannot be larger than dataset size, reducing it to %lu",
+      dataset.extent(0));
+    intermediate_degree = dataset.extent(0) - 1;
+  }
+  if (intermediate_degree < graph_degree) {
+    RAFT_LOG_WARN(
+      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
+      "graph_degree.",
+      graph_degree,
+      intermediate_degree);
+    graph_degree = intermediate_degree;
+  }
+
+  // The elements in each knn-list are partitioned into different buckets, and we need more buckets
+  // to mitigate bucket collisions. `intermediate_degree` is OK to larger than
+  // extended_graph_degree.
+  size_t extended_graph_degree =
+    align32::roundUp(static_cast<size_t>(graph_degree * (graph_degree <= 32 ? 1.0 : 1.3)));
+  size_t extended_intermediate_degree = align32::roundUp(
+    static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
+
+  auto int_graph = raft::make_host_matrix<int, int64_t, raft::row_major>(
+    dataset.extent(0), static_cast<int64_t>(extended_graph_degree));
+
+  BuildConfig build_config{.max_dataset_size      = static_cast<size_t>(dataset.extent(0)),
+                           .dataset_dim           = static_cast<size_t>(dataset.extent(1)),
+                           .node_degree           = extended_graph_degree,
+                           .internal_node_degree  = extended_intermediate_degree,
+                           .max_iterations        = params.max_iterations,
+                           .termination_threshold = params.termination_threshold};
+
+  GNND<const T, int> nnd(res, build_config);
+  nnd.build(dataset.data_handle(), dataset.extent(0), int_graph.data_handle());
+
+#pragma omp parallel for
+  for (size_t i = 0; i < static_cast<size_t>(dataset.extent(0)); i++) {
+    for (size_t j = 0; j < graph_degree; j++) {
+      auto graph                  = idx.graph().data_handle();
+      graph[i * graph_degree + j] = int_graph.data_handle()[i * extended_graph_degree + j];
+    }
+  }
+}
+
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+index<IdxT> build(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
+{
+  size_t intermediate_degree = params.intermediate_graph_degree;
+  size_t graph_degree        = params.graph_degree;
+
+  if (intermediate_degree < graph_degree) {
+    RAFT_LOG_WARN(
+      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
+      "graph_degree.",
+      graph_degree,
+      intermediate_degree);
+    graph_degree = intermediate_degree;
+  }
+
+  index<IdxT> idx{res, dataset.extent(0), static_cast<int64_t>(graph_degree)};
+
+  build(res, params, dataset, idx);
+
+  return idx;
+}
+
+}  // namespace  cuvs::neighbors::nn_descent::detail
diff --git a/cpp/src/neighbors/detail/refine.cuh b/cpp/src/neighbors/detail/refine.cuh
new file mode 100644
index 0000000000..170f973984
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine.cuh
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "refine_device.cuh"
+#include "refine_host.hpp"
diff --git a/cpp/src/neighbors/detail/refine_common.hpp b/cpp/src/neighbors/detail/refine_common.hpp
new file mode 100644
index 0000000000..8f3bb01444
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_common.hpp
@@ -0,0 +1,57 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/mdspan.hpp>
+
+namespace cuvs::neighbors::detail {
+
+/** Checks whether the input data extents are compatible. */
+template <typename ExtentsT>
+void refine_check_input(ExtentsT dataset,
+                        ExtentsT queries,
+                        ExtentsT candidates,
+                        ExtentsT indices,
+                        ExtentsT distances,
+                        cuvs::distance::DistanceType metric)
+{
+  auto n_queries = queries.extent(0);
+  auto k         = distances.extent(1);
+
+  RAFT_EXPECTS(indices.extent(0) == n_queries && distances.extent(0) == n_queries &&
+                 candidates.extent(0) == n_queries,
+               "Number of rows in output indices, distances and candidates matrices must be equal"
+               " with the number of rows in search matrix. Expected %d, got %d, %d, and %d",
+               static_cast<int>(n_queries),
+               static_cast<int>(indices.extent(0)),
+               static_cast<int>(distances.extent(0)),
+               static_cast<int>(candidates.extent(0)));
+
+  RAFT_EXPECTS(indices.extent(1) == k,
+               "Number of columns in output indices and distances matrices must be equal to k");
+
+  RAFT_EXPECTS(queries.extent(1) == dataset.extent(1),
+               "Number of columns must be equal for dataset and queries");
+
+  RAFT_EXPECTS(candidates.extent(1) >= k,
+               "Number of neighbor candidates must not be smaller than k (%d vs %d)",
+               static_cast<int>(candidates.extent(1)),
+               static_cast<int>(k));
+}
+
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/refine_device.cuh b/cpp/src/neighbors/detail/refine_device.cuh
new file mode 100644
index 0000000000..b81f7d77fa
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_device.cuh
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "ann_utils.cuh"
+#include "refine_common.hpp"
+#include <cuvs/neighbors/sample_filter.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/nvtx.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/matrix/detail/select_warpsort.cuh>
+#include <raft/neighbors/detail/ivf_flat_build.cuh>
+#include <raft/neighbors/detail/ivf_flat_interleaved_scan.cuh>
+
+#include <thrust/sequence.h>
+
+namespace cuvs::neighbors::detail {
+
+/**
+ * See cuvs::neighbors::refine for docs.
+ */
+template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
+void refine_device(
+  raft::resources const& handle,
+  raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
+  raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
+  raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
+  raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
+  raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  matrix_idx n_candidates = neighbor_candidates.extent(1);
+  matrix_idx n_queries    = queries.extent(0);
+  matrix_idx dim          = dataset.extent(1);
+  uint32_t k              = static_cast<uint32_t>(indices.extent(1));
+
+  // TODO: this restriction could be lifted with some effort
+  RAFT_EXPECTS(k <= raft::matrix::detail::select::warpsort::kMaxCapacity,
+               "k must be less than topk::kMaxCapacity (%d).",
+               raft::matrix::detail::select::warpsort::kMaxCapacity);
+
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "neighbors::refine(%zu, %u)", size_t(n_queries), uint32_t(n_candidates));
+
+  refine_check_input(dataset.extents(),
+                     queries.extents(),
+                     neighbor_candidates.extents(),
+                     indices.extents(),
+                     distances.extents(),
+                     metric);
+
+  // The refinement search can be mapped to an IVF flat search:
+  // - We consider that the candidate vectors form a cluster, separately for each query.
+  // - In other words, the n_queries * n_candidates vectors form n_queries clusters, each with
+  //   n_candidates elements.
+  // - We consider that the coarse level search is already performed and assigned a single cluster
+  //   to search for each query (the cluster formed from the corresponding candidates).
+  // - We run IVF flat search with n_probes=1 to select the best k elements of the candidates.
+  rmm::device_uvector<uint32_t> fake_coarse_idx(n_queries, raft::resource::get_cuda_stream(handle));
+
+  thrust::sequence(raft::resource::get_thrust_policy(handle),
+                   fake_coarse_idx.data(),
+                   fake_coarse_idx.data() + n_queries);
+
+  raft::neighbors::ivf_flat::index<data_t, idx_t> refinement_index(
+    handle, raft::distance::DistanceType(metric), n_queries, false, true, dim);
+
+  raft::neighbors::ivf_flat::detail::fill_refinement_index(handle,
+                                                           &refinement_index,
+                                                           dataset.data_handle(),
+                                                           neighbor_candidates.data_handle(),
+                                                           n_queries,
+                                                           n_candidates);
+  uint32_t grid_dim_x = 1;
+
+  // the neighbor ids will be computed in uint32_t as offset
+  rmm::device_uvector<uint32_t> neighbors_uint32_buf(0, raft::resource::get_cuda_stream(handle));
+  // Offsets per probe for each query [n_queries] as n_probes = 1
+  rmm::device_uvector<uint32_t> chunk_index(n_queries, raft::resource::get_cuda_stream(handle));
+
+  // we know that each cluster has exactly n_candidates entries
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               chunk_index.data(),
+               chunk_index.data() + n_queries,
+               uint32_t(n_candidates));
+
+  uint32_t* neighbors_uint32 = nullptr;
+  if constexpr (sizeof(idx_t) == sizeof(uint32_t)) {
+    neighbors_uint32 = reinterpret_cast<uint32_t*>(indices.data_handle());
+  } else {
+    neighbors_uint32_buf.resize(std::size_t(n_queries) * std::size_t(k),
+                                raft::resource::get_cuda_stream(handle));
+    neighbors_uint32 = neighbors_uint32_buf.data();
+  }
+
+  raft::neighbors::ivf_flat::detail::ivfflat_interleaved_scan<
+    data_t,
+    typename cuvs::spatial::knn::detail::utils::config<data_t>::value_t,
+    idx_t>(refinement_index,
+           queries.data_handle(),
+           fake_coarse_idx.data(),
+           static_cast<uint32_t>(n_queries),
+           0,
+           raft::distance::DistanceType(refinement_index.metric()),
+           1,
+           k,
+           0,
+           chunk_index.data(),
+           raft::distance::is_min_close(raft::distance::DistanceType(metric)),
+           raft::neighbors::filtering::none_ivf_sample_filter(),
+           neighbors_uint32,
+           distances.data_handle(),
+           grid_dim_x,
+           raft::resource::get_cuda_stream(handle));
+
+  // postprocessing -- neighbors from position to actual id
+  raft::neighbors::ivf::detail::postprocess_neighbors(indices.data_handle(),
+                                                      neighbors_uint32,
+                                                      refinement_index.inds_ptrs().data_handle(),
+                                                      fake_coarse_idx.data(),
+                                                      chunk_index.data(),
+                                                      n_queries,
+                                                      1,
+                                                      k,
+                                                      raft::resource::get_cuda_stream(handle));
+}
+
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/refine_host-ext.hpp b/cpp/src/neighbors/detail/refine_host-ext.hpp
new file mode 100644
index 0000000000..e4ea9bb90a
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_host-ext.hpp
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "refine_host-inl.hpp"
+#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
+#include <raft/core/detail/macros.hpp>       // _RAFT_HAS_CUDA
+#include <raft/core/host_mdspan.hpp>         // raft::host_matrix_view
+#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+
+#include <cstdint>  // int64_t
+
+#if defined(_RAFT_HAS_CUDA)
+#include <cuda_fp16.h>
+#endif
+
+#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+namespace cuvs::neighbors::detail {
+
+template <typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
+[[gnu::optimize(3), gnu::optimize("tree-vectorize")]] void refine_host(
+  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,
+  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,
+  raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,
+  raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,
+  raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded) RAFT_EXPLICIT;
+
+}
+
+#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+#define instantiate_raft_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)                    \
+  extern template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,                    \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,                    \
+    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,         \
+    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                           \
+    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,                    \
+    cuvs::distance::DistanceType metric);
+
+instantiate_raft_neighbors_refine(int64_t, float, float, int64_t);
+instantiate_raft_neighbors_refine(uint32_t, float, float, int64_t);
+instantiate_raft_neighbors_refine(int64_t, int8_t, float, int64_t);
+instantiate_raft_neighbors_refine(int64_t, uint8_t, float, int64_t);
+
+#if defined(_RAFT_HAS_CUDA)
+instantiate_raft_neighbors_refine(int64_t, half, float, int64_t);
+#endif
+
+#undef instantiate_raft_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host-inl.hpp b/cpp/src/neighbors/detail/refine_host-inl.hpp
new file mode 100644
index 0000000000..eddcc13e9b
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_host-inl.hpp
@@ -0,0 +1,193 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "refine_common.hpp"
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/nvtx.hpp>
+#include <raft/util/integer_utils.hpp>
+
+#include <omp.h>
+
+#include <algorithm>
+
+namespace cuvs::neighbors::detail {
+
+template <typename DC, typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
+[[gnu::optimize(3), gnu::optimize("tree-vectorize")]] void refine_host_impl(
+  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,
+  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,
+  raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,
+  raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,
+  raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances)
+{
+  size_t n_queries = queries.extent(0);
+  size_t n_rows    = dataset.extent(0);
+  size_t dim       = dataset.extent(1);
+  size_t orig_k    = neighbor_candidates.extent(1);
+  size_t refined_k = indices.extent(1);
+
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "neighbors::refine_host(%zu, %zu -> %zu)", n_queries, orig_k, refined_k);
+
+  auto suggested_n_threads = std::max(1, std::min(omp_get_num_procs(), omp_get_max_threads()));
+
+  // If the number of queries is small, separate the distance calculation and
+  // the top-k calculation into separate loops, and apply finer-grained thread
+  // parallelism to the distance calculation loop.
+  if (n_queries < size_t(suggested_n_threads)) {
+    std::vector<std::vector<std::tuple<DistanceT, IdxT>>> refined_pairs(
+      n_queries, std::vector<std::tuple<DistanceT, IdxT>>(orig_k));
+
+    // For efficiency, each thread should read a certain amount of array
+    // elements. The number of threads for distance computation is determined
+    // taking this into account.
+    auto n_elements    = std::max(size_t(512), dim);
+    auto max_n_threads = raft::div_rounding_up_safe<size_t>(n_queries * orig_k * dim, n_elements);
+    auto suggested_n_threads_for_distance = std::min(size_t(suggested_n_threads), max_n_threads);
+
+    // The max number of threads for topk computation is the number of queries.
+    auto suggested_n_threads_for_topk = std::min(size_t(suggested_n_threads), n_queries);
+
+    // Compute the refined distance using original dataset vectors
+#pragma omp parallel for collapse(2) num_threads(suggested_n_threads_for_distance)
+    for (size_t i = 0; i < n_queries; i++) {
+      for (size_t j = 0; j < orig_k; j++) {
+        const DataT* query = queries.data_handle() + dim * i;
+        IdxT id            = neighbor_candidates(i, j);
+        DistanceT distance = 0.0;
+        if (static_cast<size_t>(id) >= n_rows) {
+          distance = std::numeric_limits<DistanceT>::max();
+        } else {
+          const DataT* row = dataset.data_handle() + dim * id;
+          for (size_t k = 0; k < dim; k++) {
+            distance += DC::template eval<DistanceT>(query[k], row[k]);
+          }
+        }
+        refined_pairs[i][j] = std::make_tuple(distance, id);
+      }
+    }
+
+    // Sort the query neighbors by their refined distances
+#pragma omp parallel for num_threads(suggested_n_threads_for_topk)
+    for (size_t i = 0; i < n_queries; i++) {
+      std::sort(refined_pairs[i].begin(), refined_pairs[i].end());
+      // Store first refined_k neighbors
+      for (size_t j = 0; j < refined_k; j++) {
+        indices(i, j) = std::get<1>(refined_pairs[i][j]);
+        if (distances.data_handle() != nullptr) {
+          distances(i, j) = DC::template postprocess(std::get<0>(refined_pairs[i][j]));
+        }
+      }
+    }
+    return;
+  }
+
+  if (size_t(suggested_n_threads) > n_queries) { suggested_n_threads = n_queries; }
+
+#pragma omp parallel num_threads(suggested_n_threads)
+  {
+    std::vector<std::tuple<DistanceT, IdxT>> refined_pairs(orig_k);
+    for (size_t i = omp_get_thread_num(); i < n_queries; i += omp_get_num_threads()) {
+      // Compute the refined distance using original dataset vectors
+      const DataT* query = queries.data_handle() + dim * i;
+      for (size_t j = 0; j < orig_k; j++) {
+        IdxT id            = neighbor_candidates(i, j);
+        DistanceT distance = 0.0;
+        if (static_cast<size_t>(id) >= n_rows) {
+          distance = std::numeric_limits<DistanceT>::max();
+        } else {
+          const DataT* row = dataset.data_handle() + dim * id;
+          for (size_t k = 0; k < dim; k++) {
+            distance += DC::template eval<DistanceT>(query[k], row[k]);
+          }
+        }
+        refined_pairs[j] = std::make_tuple(distance, id);
+      }
+      // Sort the query neighbors by their refined distances
+      std::sort(refined_pairs.begin(), refined_pairs.end());
+      // Store first refined_k neighbors
+      for (size_t j = 0; j < refined_k; j++) {
+        indices(i, j) = std::get<1>(refined_pairs[j]);
+        if (distances.data_handle() != nullptr) {
+          distances(i, j) = DC::template postprocess(std::get<0>(refined_pairs[j]));
+        }
+      }
+    }
+  }
+}
+
+struct distance_comp_l2 {
+  template <typename DistanceT>
+  static inline auto eval(const DistanceT& a, const DistanceT& b) -> DistanceT
+  {
+    auto d = a - b;
+    return d * d;
+  }
+  template <typename DistanceT>
+  static inline auto postprocess(const DistanceT& a) -> DistanceT
+  {
+    return a;
+  }
+};
+
+struct distance_comp_inner {
+  template <typename DistanceT>
+  static inline auto eval(const DistanceT& a, const DistanceT& b) -> DistanceT
+  {
+    return -a * b;
+  }
+  template <typename DistanceT>
+  static inline auto postprocess(const DistanceT& a) -> DistanceT
+  {
+    return -a;
+  }
+};
+
+/**
+ * Naive CPU implementation of refine operation
+ *
+ * All pointers are expected to be accessible on the host.
+ */
+template <typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
+[[gnu::optimize(3), gnu::optimize("tree-vectorize")]] void refine_host(
+  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,
+  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,
+  raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,
+  raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,
+  raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  refine_check_input(dataset.extents(),
+                     queries.extents(),
+                     neighbor_candidates.extents(),
+                     indices.extents(),
+                     distances.extents(),
+                     metric);
+
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2Expanded:
+      return refine_host_impl<distance_comp_l2>(
+        dataset, queries, neighbor_candidates, indices, distances);
+    case cuvs::distance::DistanceType::InnerProduct:
+      return refine_host_impl<distance_comp_inner>(
+        dataset, queries, neighbor_candidates, indices, distances);
+    default: throw raft::logic_error("Unsupported metric");
+  }
+}
+
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/refine_host.hpp b/cpp/src/neighbors/detail/refine_host.hpp
new file mode 100644
index 0000000000..d20bea110f
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_host.hpp
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "refine_host-ext.hpp"
+#include "refine_host-inl.hpp"
diff --git a/cpp/src/neighbors/detail/refine_host_float_float.cpp b/cpp/src/neighbors/detail/refine_host_float_float.cpp
new file mode 100644
index 0000000000..c9acfc51f1
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_host_float_float.cpp
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "refine_host-inl.hpp"
+
+#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
+  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
+    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
+    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
+    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
+    cuvs::distance::DistanceType metric);
+
+instantiate_cuvs_neighbors_refine(int64_t, float, float, int64_t);
+instantiate_cuvs_neighbors_refine(uint32_t, float, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host_half_float.cpp b/cpp/src/neighbors/detail/refine_host_half_float.cpp
new file mode 100644
index 0000000000..3dbc1b1794
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_host_half_float.cpp
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "refine_host-inl.hpp"
+
+#include <cuda_fp16.h>
+
+#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
+  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
+    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
+    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
+    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
+    cuvs::distance::DistanceType metric);
+
+instantiate_cuvs_neighbors_refine(int64_t, half, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host_int8_t_float.cpp b/cpp/src/neighbors/detail/refine_host_int8_t_float.cpp
new file mode 100644
index 0000000000..c46cb3efba
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_host_int8_t_float.cpp
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "refine_host-inl.hpp"
+
+#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
+  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
+    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
+    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
+    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
+    cuvs::distance::DistanceType metric);
+instantiate_cuvs_neighbors_refine(int64_t, int8_t, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp b/cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp
new file mode 100644
index 0000000000..532fbaac05
--- /dev/null
+++ b/cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "refine_host-inl.hpp"
+
+#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
+  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
+    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
+    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
+    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
+    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
+    cuvs::distance::DistanceType metric);
+
+instantiate_cuvs_neighbors_refine(int64_t, uint8_t, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/vpq_dataset.cuh b/cpp/src/neighbors/detail/vpq_dataset.cuh
new file mode 100644
index 0000000000..b0a565cb10
--- /dev/null
+++ b/cpp/src/neighbors/detail/vpq_dataset.cuh
@@ -0,0 +1,431 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/neighbors/dataset.hpp>
+
+#include "../ivf_pq/ivf_pq_build.cuh"  // pq_bits-bitfield
+#include "ann_utils.cuh"               // utils::mapping etc
+#include <raft/cluster/kmeans_balanced.cuh>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/map.cuh>
+#include <raft/util/integer_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+// A temporary stub till https://github.com/rapidsai/raft/pull/2077 is re-merged
+namespace cuvs::util {
+
+/**
+ * Subsample the dataset to create a training set.
+ *
+ * @tparam DatasetT a row-major mdspan or mdarray (device or host)
+ *
+ * @param res raft handle
+ * @param dataset input row-major mdspan or mdarray (device or host)
+ * @param n_samples the size of the output mdarray
+ *
+ * @return a newly allocated subset of the dataset.
+ */
+template <typename DatasetT>
+auto subsample(raft::resources const& res,
+               const DatasetT& dataset,
+               typename DatasetT::index_type n_samples)
+  -> raft::device_matrix<typename DatasetT::value_type, typename DatasetT::index_type>
+{
+  using value_type = typename DatasetT::value_type;
+  using index_type = typename DatasetT::index_type;
+  static_assert(std::is_same_v<typename DatasetT::layout_type, raft::row_major>,
+                "Only row-major layout is supported at the moment");
+  RAFT_EXPECTS(n_samples <= dataset.extent(0),
+               "The number of samples must be smaller than the number of input rows in the current "
+               "implementation.");
+  size_t dim            = dataset.extent(1);
+  size_t trainset_ratio = dataset.extent(0) / n_samples;
+  auto result = raft::make_device_matrix<value_type, index_type>(res, n_samples, dataset.extent(1));
+
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(result.data_handle(),
+                                  sizeof(value_type) * dim,
+                                  dataset.data_handle(),
+                                  sizeof(value_type) * dim * trainset_ratio,
+                                  sizeof(value_type) * dim,
+                                  n_samples,
+                                  cudaMemcpyDefault,
+                                  raft::resource::get_cuda_stream(res)));
+  return result;
+}
+
+}  // namespace cuvs::util
+
+namespace cuvs::neighbors::detail {
+
+template <typename DatasetT>
+auto fill_missing_params_heuristics(const vpq_params& params, const DatasetT& dataset) -> vpq_params
+{
+  vpq_params r  = params;
+  double n_rows = dataset.extent(0);
+  size_t dim    = dataset.extent(1);
+  if (r.pq_dim == 0) { r.pq_dim = raft::div_rounding_up_safe(dim, size_t{4}); }
+  if (r.pq_bits == 0) { r.pq_bits = 8; }
+  if (r.vq_n_centers == 0) { r.vq_n_centers = raft::round_up_safe<uint32_t>(std::sqrt(n_rows), 8); }
+  if (r.vq_kmeans_trainset_fraction == 0) {
+    double vq_trainset_size       = 100.0 * r.vq_n_centers;
+    r.vq_kmeans_trainset_fraction = std::min(1.0, vq_trainset_size / n_rows);
+  }
+  if (r.pq_kmeans_trainset_fraction == 0) {
+    // NB: we'll have actually `pq_dim` times more samples than this
+    //     (because the dataset is reinterpreted as `[n_rows * pq_dim, pq_len]`)
+    double pq_trainset_size       = 1000.0 * (1u << r.pq_bits);
+    r.pq_kmeans_trainset_fraction = std::min(1.0, pq_trainset_size / n_rows);
+  }
+  return r;
+}
+
+template <typename T, typename DatasetT>
+auto transform_data(const raft::resources& res, DatasetT dataset)
+  -> raft::device_mdarray<T, typename DatasetT::extents_type, typename DatasetT::layout_type>
+{
+  using index_type       = typename DatasetT::index_type;
+  using extents_type     = typename DatasetT::extents_type;
+  using layout_type      = typename DatasetT::layout_type;
+  using out_mdarray_type = raft::device_mdarray<T, extents_type, layout_type>;
+  if constexpr (std::is_same_v<out_mdarray_type, std::decay<DatasetT>>) { return dataset; }
+
+  auto result = raft::make_device_mdarray<T, index_type, layout_type>(res, dataset.extents());
+
+  raft::linalg::map(res,
+                    result.view(),
+                    cuvs::spatial::knn::detail::utils::mapping<T>{},
+                    raft::make_const_mdspan(dataset.view()));
+
+  return result;
+}
+
+/** Fix the internal indexing type to avoid integer underflows/overflows */
+using ix_t = int64_t;
+
+template <typename MathT, typename DatasetT>
+auto train_vq(const raft::resources& res, const vpq_params& params, const DatasetT& dataset)
+  -> raft::device_matrix<MathT, uint32_t, raft::row_major>
+{
+  const ix_t n_rows       = dataset.extent(0);
+  const ix_t vq_n_centers = params.vq_n_centers;
+  const ix_t dim          = dataset.extent(1);
+  const ix_t n_rows_train = n_rows * params.vq_kmeans_trainset_fraction;
+
+  // Subsample the dataset and transform into the required type if necessary
+  auto vq_trainset = util::subsample(res, dataset, n_rows_train);
+  auto vq_centers =
+    raft::make_device_matrix<MathT, uint32_t, raft::row_major>(res, vq_n_centers, dim);
+
+  using kmeans_in_type = typename DatasetT::value_type;
+  raft::cluster::kmeans_balanced_params kmeans_params;
+  kmeans_params.n_iters = params.kmeans_n_iters;
+  kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
+  auto vq_centers_view =
+    raft::make_device_matrix_view<MathT, ix_t>(vq_centers.data_handle(), vq_n_centers, dim);
+  auto vq_trainset_view = raft::make_device_matrix_view<const kmeans_in_type, ix_t>(
+    vq_trainset.data_handle(), n_rows_train, dim);
+  raft::cluster::kmeans_balanced::fit<kmeans_in_type, MathT, ix_t>(
+    res,
+    kmeans_params,
+    vq_trainset_view,
+    vq_centers_view,
+    cuvs::spatial::knn::detail::utils::mapping<MathT>{});
+
+  return vq_centers;
+}
+
+template <typename LabelT, typename DatasetT, typename VqCentersT>
+auto predict_vq(const raft::resources& res, const DatasetT& dataset, const VqCentersT& vq_centers)
+  -> raft::device_vector<LabelT, typename DatasetT::index_type>
+{
+  using kmeans_data_type = typename DatasetT::value_type;
+  using kmeans_math_type = typename VqCentersT::value_type;
+  using index_type       = typename DatasetT::index_type;
+  using label_type       = LabelT;
+
+  auto vq_labels = raft::make_device_vector<label_type, index_type>(res, dataset.extent(0));
+
+  raft::cluster::kmeans_balanced_params kmeans_params;
+  kmeans_params.metric = raft::distance::DistanceType::L2Expanded;
+
+  auto vq_centers_view = raft::make_device_matrix_view<const kmeans_math_type, index_type>(
+    vq_centers.data_handle(), vq_centers.extent(0), vq_centers.extent(1));
+
+  auto vq_dataset_view = raft::make_device_matrix_view<const kmeans_data_type, index_type>(
+    dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+
+  raft::cluster::kmeans_balanced::
+    predict<kmeans_data_type, kmeans_math_type, index_type, label_type>(
+      res,
+      kmeans_params,
+      vq_dataset_view,
+      vq_centers_view,
+      vq_labels.view(),
+      cuvs::spatial::knn::detail::utils::mapping<kmeans_math_type>{});
+
+  return vq_labels;
+}
+
+template <typename MathT, typename DatasetT>
+auto train_pq(const raft::resources& res,
+              const vpq_params& params,
+              const DatasetT& dataset,
+              const raft::device_matrix_view<const MathT, uint32_t, raft::row_major>& vq_centers)
+  -> raft::device_matrix<MathT, uint32_t, raft::row_major>
+{
+  const ix_t n_rows       = dataset.extent(0);
+  const ix_t dim          = dataset.extent(1);
+  const ix_t pq_dim       = params.pq_dim;
+  const ix_t pq_bits      = params.pq_bits;
+  const ix_t pq_n_centers = ix_t{1} << pq_bits;
+  const ix_t pq_len       = raft::div_rounding_up_safe(dim, pq_dim);
+  const ix_t n_rows_train = n_rows * params.pq_kmeans_trainset_fraction;
+
+  // Subsample the dataset and transform into the required type if necessary
+  auto pq_trainset = transform_data<MathT>(res, util::subsample(res, dataset, n_rows_train));
+
+  // Subtract VQ centers
+  {
+    auto vq_labels   = predict_vq<uint32_t>(res, pq_trainset, vq_centers);
+    using index_type = typename DatasetT::index_type;
+    raft::linalg::map_offset(
+      res,
+      pq_trainset.view(),
+      [labels = vq_labels.view(), centers = vq_centers, dim] __device__(index_type off, MathT x) {
+        index_type i = off / dim;
+        index_type j = off % dim;
+        return x - centers(labels(i), j);
+      },
+      raft::make_const_mdspan(pq_trainset.view()));
+  }
+
+  auto pq_centers =
+    raft::make_device_matrix<MathT, uint32_t, raft::row_major>(res, pq_n_centers, pq_len);
+
+  // Train PQ centers
+  {
+    raft::cluster::kmeans_balanced_params kmeans_params;
+    kmeans_params.n_iters = params.kmeans_n_iters;
+    kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
+
+    auto pq_centers_view =
+      raft::make_device_matrix_view<MathT, ix_t>(pq_centers.data_handle(), pq_n_centers, pq_len);
+
+    auto pq_trainset_view = raft::make_device_matrix_view<const MathT, ix_t>(
+      pq_trainset.data_handle(), n_rows_train * pq_dim, pq_len);
+
+    raft::cluster::kmeans_balanced::fit<MathT, MathT, ix_t>(
+      res, kmeans_params, pq_trainset_view, pq_centers_view);
+  }
+
+  return pq_centers;
+}
+
+template <uint32_t SubWarpSize, typename DataT, typename MathT, typename IdxT, typename LabelT>
+__device__ auto compute_code(
+  raft::device_matrix_view<const DataT, IdxT, raft::row_major> dataset,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> vq_centers,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> pq_centers,
+  IdxT i,
+  uint32_t j,
+  LabelT vq_label) -> uint8_t
+{
+  auto data_mapping = cuvs::spatial::knn::detail::utils::mapping<MathT>{};
+  uint32_t lane_id  = raft::Pow2<SubWarpSize>::mod(raft::laneId());
+
+  const uint32_t pq_book_size = pq_centers.extent(0);
+  const uint32_t pq_len       = pq_centers.extent(1);
+  float min_dist              = std::numeric_limits<float>::infinity();
+  uint8_t code                = 0;
+  // calculate the distance for each PQ cluster, find the minimum for each thread
+  for (uint32_t l = lane_id; l < pq_book_size; l += SubWarpSize) {
+    // NB: the L2 quantifiers on residuals are always trained on L2 metric.
+    float d = 0.0f;
+    for (uint32_t k = 0; k < pq_len; k++) {
+      auto jk = j * pq_len + k;
+      auto x  = data_mapping(dataset(i, jk)) - vq_centers(vq_label, jk);
+      auto t  = x - pq_centers(l, k);
+      d += t * t;
+    }
+    if (d < min_dist) {
+      min_dist = d;
+      code     = uint8_t(l);
+    }
+  }
+  // reduce among threads
+#pragma unroll
+  for (uint32_t stride = SubWarpSize >> 1; stride > 0; stride >>= 1) {
+    const auto other_dist = raft::shfl_xor(min_dist, stride, SubWarpSize);
+    const auto other_code = raft::shfl_xor(code, stride, SubWarpSize);
+    if (other_dist < min_dist) {
+      min_dist = other_dist;
+      code     = other_code;
+    }
+  }
+  return code;
+}
+
+template <uint32_t BlockSize,
+          uint32_t PqBits,
+          typename DataT,
+          typename MathT,
+          typename IdxT,
+          typename LabelT>
+__launch_bounds__(BlockSize) RAFT_KERNEL process_and_fill_codes_kernel(
+  raft::device_matrix_view<uint8_t, IdxT, raft::row_major> out_codes,
+  raft::device_matrix_view<const DataT, IdxT, raft::row_major> dataset,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> vq_centers,
+  raft::device_vector_view<const LabelT, IdxT, raft::row_major> vq_labels,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> pq_centers)
+{
+  constexpr uint32_t kSubWarpSize = std::min<uint32_t>(raft::WarpSize, 1u << PqBits);
+  using subwarp_align             = raft::Pow2<kSubWarpSize>;
+  const IdxT row_ix = subwarp_align::div(IdxT{threadIdx.x} + IdxT{BlockSize} * IdxT{blockIdx.x});
+  if (row_ix >= out_codes.extent(0)) { return; }
+
+  const uint32_t pq_dim = raft::div_rounding_up_unsafe(vq_centers.extent(1), pq_centers.extent(1));
+
+  const uint32_t lane_id = raft::Pow2<kSubWarpSize>::mod(threadIdx.x);
+  const LabelT vq_label  = vq_labels(row_ix);
+
+  // write label
+  auto* out_label_ptr = reinterpret_cast<LabelT*>(&out_codes(row_ix, 0));
+  if (lane_id == 0) { *out_label_ptr = vq_label; }
+
+  auto* out_codes_ptr = reinterpret_cast<uint8_t*>(out_label_ptr + 1);
+  cuvs::neighbors::ivf_pq::detail::bitfield_view_t<PqBits> code_view{out_codes_ptr};
+  for (uint32_t j = 0; j < pq_dim; j++) {
+    // find PQ label
+    uint8_t code = compute_code<kSubWarpSize>(dataset, vq_centers, pq_centers, row_ix, j, vq_label);
+    // TODO: this writes in global memory one byte per warp, which is very slow.
+    //  It's better to keep the codes in the shared memory or registers and dump them at once.
+    if (lane_id == 0) { code_view[j] = code; }
+  }
+}
+
+template <typename MathT, typename IdxT, typename DatasetT>
+auto process_and_fill_codes(
+  const raft::resources& res,
+  const vpq_params& params,
+  const DatasetT& dataset,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> vq_centers,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> pq_centers)
+  -> raft::device_matrix<uint8_t, IdxT, raft::row_major>
+{
+  using data_t     = typename DatasetT::value_type;
+  using cdataset_t = vpq_dataset<MathT, IdxT>;
+  using label_t    = uint32_t;
+
+  const ix_t n_rows       = dataset.extent(0);
+  const ix_t dim          = dataset.extent(1);
+  const ix_t pq_dim       = params.pq_dim;
+  const ix_t pq_bits      = params.pq_bits;
+  const ix_t pq_n_centers = ix_t{1} << pq_bits;
+  // NB: codes must be aligned at least to sizeof(label_t) to be able to read labels.
+  const ix_t codes_rowlen =
+    sizeof(label_t) * (1 + raft::div_rounding_up_safe<ix_t>(pq_dim * pq_bits, 8 * sizeof(label_t)));
+
+  auto codes = raft::make_device_matrix<uint8_t, IdxT, raft::row_major>(res, n_rows, codes_rowlen);
+
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  // TODO: with scaling workspace we could choose the batch size dynamically
+  constexpr ix_t kReasonableMaxBatchSize = 65536;
+  constexpr ix_t kBlockSize              = 256;
+  const ix_t threads_per_vec             = std::min<ix_t>(raft::WarpSize, pq_n_centers);
+  dim3 threads(kBlockSize, 1, 1);
+  ix_t max_batch_size = std::min<ix_t>(n_rows, kReasonableMaxBatchSize);
+  auto kernel         = [](uint32_t pq_bits) {
+    switch (pq_bits) {
+      case 4: return process_and_fill_codes_kernel<kBlockSize, 4, data_t, MathT, IdxT, label_t>;
+      case 5: return process_and_fill_codes_kernel<kBlockSize, 5, data_t, MathT, IdxT, label_t>;
+      case 6: return process_and_fill_codes_kernel<kBlockSize, 6, data_t, MathT, IdxT, label_t>;
+      case 7: return process_and_fill_codes_kernel<kBlockSize, 7, data_t, MathT, IdxT, label_t>;
+      case 8: return process_and_fill_codes_kernel<kBlockSize, 8, data_t, MathT, IdxT, label_t>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }(pq_bits);
+  for (const auto& batch : cuvs::spatial::knn::detail::utils::batch_load_iterator(
+         dataset.data_handle(),
+         n_rows,
+         dim,
+         max_batch_size,
+         stream,
+         rmm::mr::get_current_device_resource())) {
+    auto batch_view = raft::make_device_matrix_view(batch.data(), ix_t(batch.size()), dim);
+    auto labels     = predict_vq<label_t>(res, batch_view, vq_centers);
+    dim3 blocks(raft::div_rounding_up_safe<ix_t>(n_rows, kBlockSize / threads_per_vec), 1, 1);
+    kernel<<<blocks, threads, 0, stream>>>(
+      raft::make_device_matrix_view<uint8_t, IdxT>(
+        codes.data_handle() + batch.offset() * codes_rowlen, batch.size(), codes_rowlen),
+      batch_view,
+      vq_centers,
+      raft::make_const_mdspan(labels.view()),
+      pq_centers);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+  }
+
+  return codes;
+}
+
+template <typename NewMathT, typename OldMathT, typename IdxT>
+auto vpq_convert_math_type(const raft::resources& res, vpq_dataset<OldMathT, IdxT>&& src)
+  -> vpq_dataset<NewMathT, IdxT>
+{
+  auto vq_code_book = raft::make_device_mdarray<NewMathT>(res, src.vq_code_book.extents());
+  auto pq_code_book = raft::make_device_mdarray<NewMathT>(res, src.pq_code_book.extents());
+
+  raft::linalg::map(res,
+                    vq_code_book.view(),
+                    cuvs::spatial::knn::detail::utils::mapping<NewMathT>{},
+                    raft::make_const_mdspan(src.vq_code_book.view()));
+  raft::linalg::map(res,
+                    pq_code_book.view(),
+                    cuvs::spatial::knn::detail::utils::mapping<NewMathT>{},
+                    raft::make_const_mdspan(src.pq_code_book.view()));
+  return vpq_dataset<NewMathT, IdxT>{
+    std::move(vq_code_book), std::move(pq_code_book), std::move(src.data)};
+}
+
+template <typename DatasetT, typename MathT, typename IdxT>
+auto vpq_build(const raft::resources& res, const vpq_params& params, const DatasetT& dataset)
+  -> vpq_dataset<MathT, IdxT>
+{
+  // Use a heuristic to impute missing parameters.
+  auto ps = fill_missing_params_heuristics(params, dataset);
+
+  // Train codes
+  auto vq_code_book = train_vq<MathT>(res, ps, dataset);
+  auto pq_code_book =
+    train_pq<MathT>(res, ps, dataset, raft::make_const_mdspan(vq_code_book.view()));
+
+  // Encode dataset
+  auto codes = process_and_fill_codes<MathT, IdxT>(res,
+                                                   ps,
+                                                   dataset,
+                                                   raft::make_const_mdspan(vq_code_book.view()),
+                                                   raft::make_const_mdspan(pq_code_book.view()));
+
+  return vpq_dataset<MathT, IdxT>{
+    std::move(vq_code_book), std::move(pq_code_book), std::move(codes)};
+}
+
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/ivf_common.cuh b/cpp/src/neighbors/ivf_common.cuh
new file mode 100644
index 0000000000..061d18a579
--- /dev/null
+++ b/cpp/src/neighbors/ivf_common.cuh
@@ -0,0 +1,327 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/detail/select_warpsort.cuh>  // matrix::detail::select::warpsort::warp_sort_distributed
+
+#include <cub/cub.cuh>
+
+namespace cuvs::neighbors::ivf::detail {
+
+/**
+ * Default value returned by `search` when the `n_probes` is too small and top-k is too large.
+ * One may encounter it if the combined size of probed clusters is smaller than the requested
+ * number of results per query.
+ */
+template <typename IdxT>
+constexpr static IdxT kOutOfBoundsRecord = std::numeric_limits<IdxT>::max();
+
+template <typename T, typename IdxT, bool Ascending = true>
+struct dummy_block_sort_t {
+  using queue_t = raft::matrix::detail::select::warpsort::
+    warp_sort_distributed<raft::WarpSize, Ascending, T, IdxT>;
+  template <typename... Args>
+  __device__ dummy_block_sort_t(int k, Args...){};
+};
+
+/**
+ * For each query, we calculate a cumulative sum of the cluster sizes that we probe, and return that
+ * in chunk_indices. Essentially this is a segmented inclusive scan of the cluster sizes. The total
+ * number of samples per query (sum of the cluster sizes that we probe) is returned in n_samples.
+ */
+template <int BlockDim>
+__launch_bounds__(BlockDim) RAFT_KERNEL
+  calc_chunk_indices_kernel(uint32_t n_probes,
+                            const uint32_t* cluster_sizes,      // [n_clusters]
+                            const uint32_t* clusters_to_probe,  // [n_queries, n_probes]
+                            uint32_t* chunk_indices,            // [n_queries, n_probes]
+                            uint32_t* n_samples                 // [n_queries]
+  )
+{
+  using block_scan = cub::BlockScan<uint32_t, BlockDim>;
+  __shared__ typename block_scan::TempStorage shm;
+
+  // locate the query data
+  clusters_to_probe += n_probes * blockIdx.x;
+  chunk_indices += n_probes * blockIdx.x;
+
+  // block scan
+  const uint32_t n_probes_aligned = raft::Pow2<BlockDim>::roundUp(n_probes);
+  uint32_t total                  = 0;
+  for (uint32_t probe_ix = threadIdx.x; probe_ix < n_probes_aligned; probe_ix += BlockDim) {
+    auto label = probe_ix < n_probes ? clusters_to_probe[probe_ix] : 0u;
+    auto chunk = probe_ix < n_probes ? cluster_sizes[label] : 0u;
+    if (threadIdx.x == 0) { chunk += total; }
+    block_scan(shm).InclusiveSum(chunk, chunk, total);
+    __syncthreads();
+    if (probe_ix < n_probes) { chunk_indices[probe_ix] = chunk; }
+  }
+  // save the total size
+  if (threadIdx.x == 0) { n_samples[blockIdx.x] = total; }
+}
+
+struct calc_chunk_indices {
+ public:
+  struct configured {
+    void* kernel;
+    dim3 block_dim;
+    dim3 grid_dim;
+    uint32_t n_probes;
+
+    inline void operator()(const uint32_t* cluster_sizes,
+                           const uint32_t* clusters_to_probe,
+                           uint32_t* chunk_indices,
+                           uint32_t* n_samples,
+                           rmm::cuda_stream_view stream)
+    {
+      void* args[] =  // NOLINT
+        {&n_probes, &cluster_sizes, &clusters_to_probe, &chunk_indices, &n_samples};
+      RAFT_CUDA_TRY(cudaLaunchKernel(kernel, grid_dim, block_dim, args, 0, stream));
+    }
+  };
+
+  static inline auto configure(uint32_t n_probes, uint32_t n_queries) -> configured
+  {
+    return try_block_dim<1024>(n_probes, n_queries);
+  }
+
+ private:
+  template <int BlockDim>
+  static auto try_block_dim(uint32_t n_probes, uint32_t n_queries) -> configured
+  {
+    if constexpr (BlockDim >= raft::WarpSize * 2) {
+      if (BlockDim >= n_probes * 2) { return try_block_dim<(BlockDim / 2)>(n_probes, n_queries); }
+    }
+    return {reinterpret_cast<void*>(calc_chunk_indices_kernel<BlockDim>),
+            dim3(BlockDim, 1, 1),
+            dim3(n_queries, 1, 1),
+            n_probes};
+  }
+};
+
+/**
+ * Look up the chunk id corresponding to the sample index.
+ *
+ * Each query vector was compared to all the vectors from n_probes clusters, and sample_ix is an
+ * ordered number of one of such vectors. This function looks up to which chunk it belongs,
+ * and returns the index within the chunk (which is also an index within a cluster).
+ *
+ * @param[inout] sample_ix
+ *   input: the offset of the sample in the batch;
+ *   output: the offset inside the chunk (probe) / selected cluster.
+ * @param[in] n_probes number of probes
+ * @param[in] chunk_indices offsets of the chunks within the batch [n_probes]
+ * @return chunk index (== n_probes when the input index is not in the valid range,
+ *    which can happen if there is not enough data to output in the selected clusters).
+ */
+__device__ inline auto find_chunk_ix(uint32_t& sample_ix,  // NOLINT
+                                     uint32_t n_probes,
+                                     const uint32_t* chunk_indices) -> uint32_t
+{
+  uint32_t ix_min = 0;
+  uint32_t ix_max = n_probes;
+  do {
+    uint32_t i = (ix_min + ix_max) / 2;
+    if (chunk_indices[i] <= sample_ix) {
+      ix_min = i + 1;
+    } else {
+      ix_max = i;
+    }
+  } while (ix_min < ix_max);
+  if (ix_min > 0) { sample_ix -= chunk_indices[ix_min - 1]; }
+  return ix_min;
+}
+
+template <int BlockDim, typename IdxT>
+__launch_bounds__(BlockDim) RAFT_KERNEL
+  postprocess_neighbors_kernel(IdxT* neighbors_out,                // [n_queries, topk]
+                               const uint32_t* neighbors_in,       // [n_queries, topk]
+                               const IdxT* const* db_indices,      // [n_clusters][..]
+                               const uint32_t* clusters_to_probe,  // [n_queries, n_probes]
+                               const uint32_t* chunk_indices,      // [n_queries, n_probes]
+                               uint32_t n_queries,
+                               uint32_t n_probes,
+                               uint32_t topk)
+{
+  const uint64_t i        = threadIdx.x + BlockDim * uint64_t(blockIdx.x);
+  const uint32_t query_ix = i / uint64_t(topk);
+  if (query_ix >= n_queries) { return; }
+  const uint32_t k = i % uint64_t(topk);
+  neighbors_in += query_ix * topk;
+  neighbors_out += query_ix * topk;
+  chunk_indices += query_ix * n_probes;
+  clusters_to_probe += query_ix * n_probes;
+  uint32_t data_ix        = neighbors_in[k];
+  const uint32_t chunk_ix = find_chunk_ix(data_ix, n_probes, chunk_indices);
+  const bool valid        = chunk_ix < n_probes;
+  neighbors_out[k] =
+    valid ? db_indices[clusters_to_probe[chunk_ix]][data_ix] : kOutOfBoundsRecord<IdxT>;
+}
+
+/**
+ * Transform found sample indices into the corresponding database indices
+ * (as stored in index.indices()).
+ * The sample indices are the record indices as they appear in the database view formed by the
+ * probed clusters / defined by the `chunk_indices`.
+ * We assume the searched sample sizes (for a single query) fit into `uint32_t`.
+ */
+template <typename IdxT>
+void postprocess_neighbors(IdxT* neighbors_out,                // [n_queries, topk]
+                           const uint32_t* neighbors_in,       // [n_queries, topk]
+                           const IdxT* const* db_indices,      // [n_clusters][..]
+                           const uint32_t* clusters_to_probe,  // [n_queries, n_probes]
+                           const uint32_t* chunk_indices,      // [n_queries, n_probes]
+                           uint32_t n_queries,
+                           uint32_t n_probes,
+                           uint32_t topk,
+                           rmm::cuda_stream_view stream)
+{
+  constexpr int kPNThreads = 256;
+  const int pn_blocks      = raft::div_rounding_up_unsafe<size_t>(n_queries * topk, kPNThreads);
+  postprocess_neighbors_kernel<kPNThreads, IdxT>
+    <<<pn_blocks, kPNThreads, 0, stream>>>(neighbors_out,
+                                           neighbors_in,
+                                           db_indices,
+                                           clusters_to_probe,
+                                           chunk_indices,
+                                           n_queries,
+                                           n_probes,
+                                           topk);
+}
+
+/**
+ * Post-process the scores depending on the metric type;
+ * translate the element type if necessary.
+ */
+template <typename ScoreInT, typename ScoreOutT = float>
+void postprocess_distances(ScoreOutT* out,      // [n_queries, topk]
+                           const ScoreInT* in,  // [n_queries, topk]
+                           distance::DistanceType metric,
+                           uint32_t n_queries,
+                           uint32_t topk,
+                           float scaling_factor,
+                           bool account_for_max_close,
+                           rmm::cuda_stream_view stream)
+{
+  constexpr bool needs_cast = !std::is_same<ScoreInT, ScoreOutT>::value;
+  const bool needs_copy     = ((void*)in) != ((void*)out);
+  size_t len                = size_t(n_queries) * size_t(topk);
+  switch (metric) {
+    case distance::DistanceType::L2Unexpanded:
+    case distance::DistanceType::L2Expanded: {
+      if (scaling_factor != 1.0) {
+        raft::linalg::unaryOp(
+          out,
+          in,
+          len,
+          raft::compose_op(raft::mul_const_op<ScoreOutT>{scaling_factor * scaling_factor},
+                           raft::cast_op<ScoreOutT>{}),
+          stream);
+      } else if (needs_cast || needs_copy) {
+        raft::linalg::unaryOp(out, in, len, raft::cast_op<ScoreOutT>{}, stream);
+      }
+    } break;
+    case distance::DistanceType::L2SqrtUnexpanded:
+    case distance::DistanceType::L2SqrtExpanded: {
+      if (scaling_factor != 1.0) {
+        raft::linalg::unaryOp(out,
+                              in,
+                              len,
+                              raft::compose_op{raft::mul_const_op<ScoreOutT>{scaling_factor},
+                                               raft::sqrt_op{},
+                                               raft::cast_op<ScoreOutT>{}},
+                              stream);
+      } else if (needs_cast) {
+        raft::linalg::unaryOp(
+          out, in, len, raft::compose_op{raft::sqrt_op{}, raft::cast_op<ScoreOutT>{}}, stream);
+      } else {
+        raft::linalg::unaryOp(out, in, len, raft::sqrt_op{}, stream);
+      }
+    } break;
+    case distance::DistanceType::InnerProduct: {
+      float factor = (account_for_max_close ? -1.0 : 1.0) * scaling_factor * scaling_factor;
+      if (factor != 1.0) {
+        raft::linalg::unaryOp(
+          out,
+          in,
+          len,
+          raft::compose_op(raft::mul_const_op<ScoreOutT>{factor}, raft::cast_op<ScoreOutT>{}),
+          stream);
+      } else if (needs_cast || needs_copy) {
+        raft::linalg::unaryOp(out, in, len, raft::cast_op<ScoreOutT>{}, stream);
+      }
+    } break;
+    default: RAFT_FAIL("Unexpected metric.");
+  }
+}
+
+/** Update the state of the dependent index members. */
+template <typename Index>
+void recompute_internal_state(const raft::resources& res, Index& index)
+{
+  auto stream  = raft::resource::get_cuda_stream(res);
+  auto tmp_res = raft::resource::get_workspace_resource(res);
+  rmm::device_uvector<uint32_t> sorted_sizes(index.n_lists(), stream, tmp_res);
+
+  // Actualize the list pointers
+  auto data_ptrs = index.data_ptrs();
+  auto inds_ptrs = index.inds_ptrs();
+  for (uint32_t label = 0; label < index.n_lists(); label++) {
+    auto& list          = index.lists()[label];
+    const auto data_ptr = list ? list->data.data_handle() : nullptr;
+    const auto inds_ptr = list ? list->indices.data_handle() : nullptr;
+    raft::copy(&data_ptrs(label), &data_ptr, 1, stream);
+    raft::copy(&inds_ptrs(label), &inds_ptr, 1, stream);
+  }
+
+  // Sort the cluster sizes in the descending order.
+  int begin_bit             = 0;
+  int end_bit               = sizeof(uint32_t) * 8;
+  size_t cub_workspace_size = 0;
+  cub::DeviceRadixSort::SortKeysDescending(nullptr,
+                                           cub_workspace_size,
+                                           index.list_sizes().data_handle(),
+                                           sorted_sizes.data(),
+                                           index.n_lists(),
+                                           begin_bit,
+                                           end_bit,
+                                           stream);
+  rmm::device_buffer cub_workspace(cub_workspace_size, stream, tmp_res);
+  cub::DeviceRadixSort::SortKeysDescending(cub_workspace.data(),
+                                           cub_workspace_size,
+                                           index.list_sizes().data_handle(),
+                                           sorted_sizes.data(),
+                                           index.n_lists(),
+                                           begin_bit,
+                                           end_bit,
+                                           stream);
+  // copy the results to CPU
+  std::vector<uint32_t> sorted_sizes_host(index.n_lists());
+  raft::copy(sorted_sizes_host.data(), sorted_sizes.data(), index.n_lists(), stream);
+  raft::resource::sync_stream(res);
+
+  // accumulate the sorted cluster sizes
+  auto accum_sorted_sizes = index.accum_sorted_sizes();
+  accum_sorted_sizes(0)   = 0;
+  for (uint32_t label = 0; label < sorted_sizes_host.size(); label++) {
+    accum_sorted_sizes(label + 1) = accum_sorted_sizes(label) + sorted_sizes_host[label];
+  }
+}
+
+}  // namespace cuvs::neighbors::ivf::detail
diff --git a/cpp/src/neighbors/ivf_list.cuh b/cpp/src/neighbors/ivf_list.cuh
new file mode 100644
index 0000000000..d99a500f05
--- /dev/null
+++ b/cpp/src/neighbors/ivf_list.cuh
@@ -0,0 +1,191 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+#include <raft/util/integer_utils.hpp>
+
+#include <thrust/fill.h>
+
+#include <atomic>
+#include <fstream>
+#include <limits>
+#include <memory>
+#include <type_traits>
+
+namespace cuvs::neighbors::ivf {
+
+template <template <typename, typename...> typename SpecT,
+          typename SizeT,
+          typename... SpecExtraArgs>
+list<SpecT, SizeT, SpecExtraArgs...>::list(raft::resources const& res,
+                                           const spec_type& spec,
+                                           size_type n_rows)
+  : size{n_rows}, data{res}, indices{res}
+{
+  auto capacity = raft::round_up_safe<SizeT>(n_rows, spec.align_max);
+  if (n_rows < spec.align_max) {
+    capacity = raft::bound_by_power_of_two<SizeT>(std::max<SizeT>(n_rows, spec.align_min));
+    capacity = std::min<SizeT>(capacity, spec.align_max);
+  }
+  try {
+    data    = raft::make_device_mdarray<value_type>(res, spec.make_list_extents(capacity));
+    indices = raft::make_device_vector<index_type, SizeT>(res, capacity);
+  } catch (std::bad_alloc& e) {
+    RAFT_FAIL(
+      "ivf::list: failed to allocate a big enough list to hold all data "
+      "(requested size: %zu records, selected capacity: %zu records). "
+      "Allocator exception: %s",
+      size_t(size),
+      size_t(capacity),
+      e.what());
+  }
+  // Fill the index buffer with a pre-defined marker for easier debugging
+  thrust::fill_n(raft::resource::get_thrust_policy(res),
+                 indices.data_handle(),
+                 indices.size(),
+                 ivf::kInvalidRecord<index_type>);
+}
+
+template <typename ListT>
+void resize_list(raft::resources const& res,
+                 std::shared_ptr<ListT>& orig_list,  // NOLINT
+                 const typename ListT::spec_type& spec,
+                 typename ListT::size_type new_used_size,
+                 typename ListT::size_type old_used_size)
+{
+  bool skip_resize = false;
+  if (orig_list) {
+    if (new_used_size <= orig_list->indices.extent(0)) {
+      auto shared_list_size = old_used_size;
+      if (new_used_size <= old_used_size ||
+          orig_list->size.compare_exchange_strong(shared_list_size, new_used_size)) {
+        // We don't need to resize the list if:
+        //  1. The list exists
+        //  2. The new size fits in the list
+        //  3. The list doesn't grow or no-one else has grown it yet
+        skip_resize = true;
+      }
+    }
+  } else {
+    old_used_size = 0;
+  }
+  if (skip_resize) { return; }
+  auto new_list = std::make_shared<ListT>(res, spec, new_used_size);
+  if (old_used_size > 0) {
+    auto copied_data_extents = spec.make_list_extents(old_used_size);
+    auto copied_view         = raft::make_mdspan<typename ListT::value_type,
+                                         typename ListT::size_type,
+                                         raft::row_major,
+                                         false,
+                                         true>(new_list->data.data_handle(), copied_data_extents);
+    raft::copy(copied_view.data_handle(),
+               orig_list->data.data_handle(),
+               copied_view.size(),
+               raft::resource::get_cuda_stream(res));
+    raft::copy(new_list->indices.data_handle(),
+               orig_list->indices.data_handle(),
+               old_used_size,
+               raft::resource::get_cuda_stream(res));
+  }
+  // swap the shared pointer content with the new list
+  new_list.swap(orig_list);
+}
+
+template <typename ListT>
+enable_if_valid_list_t<ListT> serialize_list(const raft::resources& handle,
+                                             std::ostream& os,
+                                             const ListT& ld,
+                                             const typename ListT::spec_type& store_spec,
+                                             std::optional<typename ListT::size_type> size_override)
+{
+  using size_type = typename ListT::size_type;
+  auto size       = size_override.value_or(ld.size.load());
+  raft::serialize_scalar(handle, os, size);
+  if (size == 0) { return; }
+
+  auto data_extents = store_spec.make_list_extents(size);
+  auto data_array =
+    raft::make_host_mdarray<typename ListT::value_type, size_type, raft::row_major>(data_extents);
+  auto inds_array = raft::make_host_mdarray<typename ListT::index_type, size_type, raft::row_major>(
+    raft::make_extents<size_type>(size));
+  raft::copy(data_array.data_handle(),
+             ld.data.data_handle(),
+             data_array.size(),
+             raft::resource::get_cuda_stream(handle));
+  raft::copy(inds_array.data_handle(),
+             ld.indices.data_handle(),
+             inds_array.size(),
+             raft::resource::get_cuda_stream(handle));
+  raft::resource::sync_stream(handle);
+  raft::serialize_mdspan(handle, os, data_array.view());
+  raft::serialize_mdspan(handle, os, inds_array.view());
+}
+
+template <typename ListT>
+enable_if_valid_list_t<ListT> serialize_list(const raft::resources& handle,
+                                             std::ostream& os,
+                                             const std::shared_ptr<ListT>& ld,
+                                             const typename ListT::spec_type& store_spec,
+                                             std::optional<typename ListT::size_type> size_override)
+{
+  if (ld) {
+    return serialize_list<ListT>(handle, os, *ld, store_spec, size_override);
+  } else {
+    return raft::serialize_scalar(handle, os, typename ListT::size_type{0});
+  }
+}
+
+template <typename ListT>
+enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
+                                               std::istream& is,
+                                               std::shared_ptr<ListT>& ld,
+                                               const typename ListT::spec_type& store_spec,
+                                               const typename ListT::spec_type& device_spec)
+{
+  using size_type = typename ListT::size_type;
+  auto size       = raft::deserialize_scalar<size_type>(handle, is);
+  if (size == 0) { return ld.reset(); }
+  std::make_shared<ListT>(handle, device_spec, size).swap(ld);
+  auto data_extents = store_spec.make_list_extents(size);
+  auto data_array =
+    raft::make_host_mdarray<typename ListT::value_type, size_type, raft::row_major>(data_extents);
+  auto inds_array = raft::make_host_mdarray<typename ListT::index_type, size_type, raft::row_major>(
+    raft::make_extents<size_type>(size));
+  raft::deserialize_mdspan(handle, is, data_array.view());
+  raft::deserialize_mdspan(handle, is, inds_array.view());
+  raft::copy(ld->data.data_handle(),
+             data_array.data_handle(),
+             data_array.size(),
+             raft::resource::get_cuda_stream(handle));
+  // NB: copying exactly 'size' indices to leave the rest 'kInvalidRecord' intact.
+  raft::copy(ld->indices.data_handle(),
+             inds_array.data_handle(),
+             size,
+             raft::resource::get_cuda_stream(handle));
+  // Make sure the data is copied from host to device before the host arrays get out of the scope.
+  raft::resource::sync_stream(handle);
+}
+}  // namespace cuvs::neighbors::ivf
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_pq/generate_ivf_pq.py b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
similarity index 62%
rename from cpp/src/neighbors/ivf_pq/generate_ivf_pq.py
rename to cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
index d0a5d2b192..2ba513579c 100644
--- a/cpp/src/neighbors/ivf_pq/generate_ivf_pq.py
+++ b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
@@ -38,8 +38,16 @@
  */
 
 #include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
+"""
+
+build_include_macro = """
+#include "../ivf_pq_build.cuh"
+"""
+search_include_macro = """
+#include "../ivf_pq_search.cuh"
+"""
 
+namespace_macro = """
 namespace cuvs::neighbors::ivf_pq {
 """
 
@@ -53,15 +61,14 @@
     uint8_t_int64_t=("uint8_t", "int64_t"),
 )
 
-build_macro = """
-#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \\
+build_extend_macro = """
+#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                      \\
   auto build(raft::resources const& handle,                                                  \\
              const cuvs::neighbors::ivf_pq::index_params& params,                            \\
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \\
     ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \\
   {                                                                                          \\
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \\
-      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \\
+    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                  \\
   }                                                                                          \\
                                                                                              \\
   void build(raft::resources const& handle,                                                  \\
@@ -69,30 +76,25 @@
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \\
              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \\
   {                                                                                          \\
-    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \\
-  }
-"""
-
-extend_macro = """
-#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \\
-  auto extend(raft::resources const& handle,                                         \\
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \\
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \\
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \\
-  {                                                                                  \\
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \\
-      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \\
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \\
-  }                                                                                  \\
-                                                                                     \\
-  void extend(raft::resources const& handle,                                         \\
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \\
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \\
-  {                                                                                  \\
-    raft::runtime::neighbors::ivf_pq::extend(                                        \\
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \\
+    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                    \\
+  }                                                                                          \\
+  auto extend(raft::resources const& handle,                                                 \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,          \\
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,         \\
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                        \\
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \\
+  {                                                                                          \\
+    return cuvs::neighbors::ivf_pq::detail::extend(                                          \\
+      handle, new_vectors, new_indices, orig_index);                                         \\
+  }                                                                                          \\
+                                                                                             \\
+  void extend(raft::resources const& handle,                                                 \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,          \\
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,         \\
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                     \\
+  {                                                                                          \\
+    cuvs::neighbors::ivf_pq::detail::extend(                                                 \\
+      handle, new_vectors, new_indices, idx);                                                \\
   }             
 """
 
@@ -105,31 +107,51 @@
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
               raft::device_matrix_view<float, IdxT, raft::row_major> distances) \\
   {                                                                             \\
-    raft::runtime::neighbors::ivf_pq::search(                                   \\
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \\
+    cuvs::neighbors::ivf_pq::detail::search(                                   \\
+      handle, params, index, queries, neighbors, distances);  \\
+  }
+"""
+search_with_filter_macro = """
+#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                                 \\
+  void search_with_filtering(raft::resources const& handle,                     \\
+              const cuvs::neighbors::ivf_pq::search_params& params,             \\
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \\
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \\
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \\
+              cuvs::neighbors::filtering::bitset_filter<                        \\
+                  uint32_t, IdxT> sample_filter)                                \\
+  {                                                                             \\
+    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                     \\
+      handle, params, index, queries, neighbors, distances, sample_filter);     \\
   }
 """
 
 macros = dict(
-    build=dict(
-        definition=build_macro,
-        name="CUVS_INST_IVF_PQ_BUILD",
-    ),
-    extend=dict(
-        definition=extend_macro,
-        name="CUVS_INST_IVF_PQ_EXTEND",
+    build_extend=dict(
+        include=build_include_macro,
+        definition=build_extend_macro,
+        name="CUVS_INST_IVF_PQ_BUILD_EXTEND",
     ),
     search=dict(
+        include=search_include_macro,
         definition=search_macro,
         name="CUVS_INST_IVF_PQ_SEARCH",
     ),
+    search_with_filter=dict(
+        include=search_include_macro,
+        definition=search_with_filter_macro,
+        name="CUVS_INST_IVF_PQ_SEARCH_FILTER",
+    ),
 )
 
 for type_path, (T, IdxT) in types.items():
     for macro_path, macro in macros.items():
-        path = f"ivf_pq_{macro_path}_{type_path}.cpp"
+        path = f"ivf_pq_{macro_path}_{type_path}.cu"
         with open(path, "w") as f:
             f.write(header)
+            f.write(macro['include'])
+            f.write(namespace_macro)
             f.write(macro["definition"])
             f.write(f"{macro['name']}({T}, {IdxT});\n\n")
             f.write(f"#undef {macro['name']}\n")
diff --git a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py
new file mode 100644
index 0000000000..4c35b28361
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py
@@ -0,0 +1,116 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+#include "../../sample_filter.cuh"
+"""
+
+declaration_macro = """
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \\
+  OutT, LutT, IvfSampleFilterT)                                                             \\
+  template auto                                                                             \\
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \\
+    const cudaDeviceProp& dev_props,                                                        \\
+    bool manage_local_topk,                                                                 \\
+    int locality_hint,                                                                      \\
+    double preferred_shmem_carveout,                                                        \\
+    uint32_t pq_bits,                                                                       \\
+    uint32_t pq_dim,                                                                        \\
+    uint32_t precomp_data_count,                                                            \\
+    uint32_t n_queries,                                                                     \\
+    uint32_t n_probes,                                                                      \\
+    uint32_t topk)                                                                          \\
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \\
+                                                                                            \\
+  template void                                                                             \\
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \\
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \\
+    rmm::cuda_stream_view stream,                                                           \\
+    uint32_t dim,                                                                           \\
+    uint32_t n_probes,                                                                      \\
+    uint32_t pq_dim,                                                                        \\
+    uint32_t n_queries,                                                                     \\
+    uint32_t queries_offset,                                                                \\
+    cuvs::distance::DistanceType metric,                                                    \\
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \\
+    uint32_t topk,                                                                          \\
+    uint32_t max_samples,                                                                   \\
+    const float* cluster_centers,                                                           \\
+    const float* pq_centers,                                                                \\
+    const uint8_t* const* pq_dataset,                                                       \\
+    const uint32_t* cluster_labels,                                                         \\
+    const uint32_t* _chunk_indices,                                                         \\
+    const float* queries,                                                                   \\
+    const uint32_t* index_list,                                                             \\
+    float* query_kths,                                                                      \\
+    IvfSampleFilterT sample_filter,                                                         \\
+    LutT* lut_scores,                                                                       \\
+    OutT* _out_scores,                                                                      \\
+    uint32_t* _out_indices);
+
+#define COMMA ,
+"""
+
+none_filter_int64 = "cuvs::neighbors::filtering::ivf_to_sample_filter" \
+                    "<int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>"
+bitset_filter64 = "cuvs::neighbors::filtering::ivf_to_sample_filter" \
+                  "<int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>"
+
+types = dict(
+    half_fp8_false=("half", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>", none_filter_int64),
+    half_fp8_true=("half", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>", none_filter_int64),
+    half_half=("half", "half", none_filter_int64),
+    float_half=("float", "half", none_filter_int64),
+    float_float= ("float", "float", none_filter_int64),
+    float_fp8_false=("float", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>", none_filter_int64),
+    float_fp8_true=("float", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>", none_filter_int64),
+    half_fp8_false_bitset64=("half", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>", bitset_filter64),
+    half_fp8_true_bitset64=("half", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>", bitset_filter64),
+    half_half_bitset64=("half", "half", bitset_filter64),
+    float_half_bitset64=("float", "half", bitset_filter64),
+    float_float_bitset64= ("float", "float", bitset_filter64),
+    float_fp8_false_bitset64=("float", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>", bitset_filter64),
+    float_fp8_true_bitset64=("float", "cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>", bitset_filter64)
+)
+
+for path_key, (OutT, LutT, FilterT) in types.items():
+    path = f"ivf_pq_compute_similarity_{path_key}.cu"
+    with open(path, "w") as f:
+        f.write(header)
+        f.write(declaration_macro)
+        f.write(f"instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select({OutT}, {LutT}, {FilterT});\n")
+    print(f"src/neighbors/ivf_pq/{path}")
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
new file mode 100644
index 0000000000..ee7f26381f
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_build.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                    \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
+  }                                                                                               \
+                                                                                                  \
+  void build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                    \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
+  }                                                                                               \
+  auto extend(raft::resources const& handle,                                                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
+  }                                                                                               \
+                                                                                                  \
+  void extend(raft::resources const& handle,                                                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
+  }
+CUVS_INST_IVF_PQ_BUILD_EXTEND(float, int64_t);
+
+#undef CUVS_INST_IVF_PQ_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
new file mode 100644
index 0000000000..8b612439d6
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_build.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                    \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
+  }                                                                                               \
+                                                                                                  \
+  void build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                    \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
+  }                                                                                               \
+  auto extend(raft::resources const& handle,                                                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
+  }                                                                                               \
+                                                                                                  \
+  void extend(raft::resources const& handle,                                                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
+  }
+CUVS_INST_IVF_PQ_BUILD_EXTEND(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..df6e12b7e3
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_build.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                    \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
+  }                                                                                               \
+                                                                                                  \
+  void build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                    \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
+  }                                                                                               \
+  auto extend(raft::resources const& handle,                                                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
+  }                                                                                               \
+                                                                                                  \
+  void extend(raft::resources const& handle,                                                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
+  }
+CUVS_INST_IVF_PQ_BUILD_EXTEND(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
new file mode 100644
index 0000000000..26312a4ae1
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  float,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
new file mode 100644
index 0000000000..6e9e0bcf77
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  float,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
new file mode 100644
index 0000000000..f08f1700c0
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
new file mode 100644
index 0000000000..4a57add148
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
new file mode 100644
index 0000000000..588c89604d
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
new file mode 100644
index 0000000000..1ad240b5bb
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
new file mode 100644
index 0000000000..6c2f77412d
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
new file mode 100644
index 0000000000..fcc883c92f
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
new file mode 100644
index 0000000000..7170e49db0
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
new file mode 100644
index 0000000000..5b34cb3536
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
new file mode 100644
index 0000000000..c552065ab2
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
new file mode 100644
index 0000000000..dee55e22b7
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
new file mode 100644
index 0000000000..8d9399da3a
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
new file mode 100644
index 0000000000..4340956157
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
+ * Make changes there and run in this directory:
+ * > python generate_ivf_pq_compute_similarity.py
+ */
+
+#include "../../sample_filter.cuh"
+#include "../ivf_pq_compute_similarity_impl.cuh"
+#include "../ivf_pq_fp_8bit.cuh"
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  template auto                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  template void                                                                             \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
similarity index 77%
rename from cpp/src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp
rename to cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
index 69db44d9be..0f54eede77 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search_float_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
@@ -24,20 +24,20 @@
  */
 
 #include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
 
 namespace cuvs::neighbors::ivf_pq {
 
-#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
-  void search(raft::resources const& handle,                                    \
-              const cuvs::neighbors::ivf_pq::search_params& params,             \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
-  {                                                                             \
-    raft::runtime::neighbors::ivf_pq::search(                                   \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
+  void search(raft::resources const& handle,                                                       \
+              const cuvs::neighbors::ivf_pq::search_params& params,                                \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
+  {                                                                                                \
+    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
   }
 CUVS_INST_IVF_PQ_SEARCH(float, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
similarity index 77%
rename from cpp/src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp
rename to cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
index e2e96b9a78..297e615d20 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search_int8_t_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
@@ -24,20 +24,20 @@
  */
 
 #include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
 
 namespace cuvs::neighbors::ivf_pq {
 
-#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
-  void search(raft::resources const& handle,                                    \
-              const cuvs::neighbors::ivf_pq::search_params& params,             \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
-  {                                                                             \
-    raft::runtime::neighbors::ivf_pq::search(                                   \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
+  void search(raft::resources const& handle,                                                       \
+              const cuvs::neighbors::ivf_pq::search_params& params,                                \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
+  {                                                                                                \
+    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
   }
 CUVS_INST_IVF_PQ_SEARCH(int8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
similarity index 77%
rename from cpp/src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp
rename to cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
index 18e7f46183..3cf8bfaff0 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search_uint8_t_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
@@ -24,20 +24,20 @@
  */
 
 #include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
 
 namespace cuvs::neighbors::ivf_pq {
 
-#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
-  void search(raft::resources const& handle,                                    \
-              const cuvs::neighbors::ivf_pq::search_params& params,             \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
-  {                                                                             \
-    raft::runtime::neighbors::ivf_pq::search(                                   \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
+  void search(raft::resources const& handle,                                                       \
+              const cuvs::neighbors::ivf_pq::search_params& params,                                \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
+  {                                                                                                \
+    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
   }
 CUVS_INST_IVF_PQ_SEARCH(uint8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
new file mode 100644
index 0000000000..4e75418827
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
+  void search_with_filtering(                                                \
+    raft::resources const& handle,                                           \
+    const cuvs::neighbors::ivf_pq::search_params& params,                    \
+    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
+  {                                                                          \
+    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
+      handle, params, index, queries, neighbors, distances, sample_filter);  \
+  }
+CUVS_INST_IVF_PQ_SEARCH_FILTER(float, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
new file mode 100644
index 0000000000..52b1c68e7a
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
+  void search_with_filtering(                                                \
+    raft::resources const& handle,                                           \
+    const cuvs::neighbors::ivf_pq::search_params& params,                    \
+    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
+  {                                                                          \
+    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
+      handle, params, index, queries, neighbors, distances, sample_filter);  \
+  }
+CUVS_INST_IVF_PQ_SEARCH_FILTER(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..e3d9361559
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
+  void search_with_filtering(                                                \
+    raft::resources const& handle,                                           \
+    const cuvs::neighbors::ivf_pq::search_params& params,                    \
+    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
+  {                                                                          \
+    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
+      handle, params, index, queries, neighbors, distances, sample_filter);  \
+  }
+CUVS_INST_IVF_PQ_SEARCH_FILTER(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
new file mode 100644
index 0000000000..3f2f145b09
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -0,0 +1,1884 @@
+/*
+ * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a raft::copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../core/nvtx.hpp"
+#include "../ivf_common.cuh"
+#include "../ivf_list.cuh"
+#include "ivf_pq_codepacking.cuh"
+#include "ivf_pq_list.cuh"
+#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/ivf_pq_helpers.hpp>
+
+#include "../detail/ann_utils.cuh"  // utils::mapping
+#include <raft/cluster/kmeans_balanced.cuh>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/distance/distance_types.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/detail/qr.cuh>
+#include <raft/linalg/gemm.cuh>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/matrix/linewise_op.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/stats/histogram.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/device_atomics.cuh>
+#include <raft/util/integer_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+#include <raft/util/vectorized.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+
+#include <cuda_fp16.h>
+#include <thrust/extrema.h>
+#include <thrust/scan.h>
+
+#include <memory>
+#include <variant>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+using namespace cuvs::spatial::knn::detail;  // NOLINT
+
+using internal_extents_t = int64_t;  // The default mdspan extent type used internally.
+
+template <uint32_t BlockDim, typename T, typename S>
+__launch_bounds__(BlockDim) static __global__ void copy_warped_kernel(
+  T* out, uint32_t ld_out, const S* in, uint32_t ld_in, uint32_t n_cols, size_t n_rows)
+{
+  using warp    = raft::Pow2<raft::WarpSize>;
+  size_t row_ix = warp::div(size_t(threadIdx.x) + size_t(BlockDim) * size_t(blockIdx.x));
+  uint32_t i    = warp::mod(threadIdx.x);
+  if (row_ix >= n_rows) return;
+  out += row_ix * ld_out;
+  in += row_ix * ld_in;
+  auto f = utils::mapping<T>{};
+  for (uint32_t col_ix = i; col_ix < n_cols; col_ix += warp::Value) {
+    auto x = f(in[col_ix]);
+    __syncwarp();
+    out[col_ix] = x;
+  }
+}
+
+/**
+ * raft::copy the data one warp-per-row:
+ *
+ *  1. load the data per-warp
+ *  2. apply the `utils::mapping<T>{}`
+ *  3. sync within warp
+ *  4. store the data.
+ *
+ * Assuming sizeof(T) >= sizeof(S) and the data is properly aligned (see the usage in `build`), this
+ * allows to re-structure the data within rows in-place.
+ */
+template <typename T, typename S>
+void copy_warped(T* out,
+                 uint32_t ld_out,
+                 const S* in,
+                 uint32_t ld_in,
+                 uint32_t n_cols,
+                 size_t n_rows,
+                 rmm::cuda_stream_view stream)
+{
+  constexpr uint32_t kBlockDim = 128;
+  dim3 threads(kBlockDim, 1, 1);
+  dim3 blocks(raft::div_rounding_up_safe<size_t>(n_rows, kBlockDim / raft::WarpSize), 1, 1);
+  copy_warped_kernel<kBlockDim, T, S>
+    <<<blocks, threads, 0, stream>>>(out, ld_out, in, ld_in, n_cols, n_rows);
+}
+
+/**
+ * @brief Compute residual vectors from the source dataset given by selected indices.
+ *
+ * The residual has the form `rotation_matrix %* (dataset[row_ids, :] - center)`
+ *
+ */
+template <typename T, typename IdxT>
+void select_residuals(raft::resources const& handle,
+                      float* residuals,
+                      IdxT n_rows,
+                      uint32_t dim,
+                      uint32_t rot_dim,
+                      const float* rotation_matrix,  // [rot_dim, dim]
+                      const float* center,           // [dim]
+                      const T* dataset,              // [.., dim]
+                      const IdxT* row_ids,           // [n_rows]
+                      rmm::mr::device_memory_resource* device_memory
+
+)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<float> tmp(size_t(n_rows) * size_t(dim), stream, device_memory);
+  // Note: the number of rows of the input dataset isn't actually n_rows, but matrix::gather doesn't
+  // need to know it, any strictly positive number would work.
+  cub::TransformInputIterator<float, utils::mapping<float>, const T*> mapping_itr(
+    dataset, utils::mapping<float>{});
+  raft::matrix::gather(mapping_itr, (IdxT)dim, n_rows, row_ids, n_rows, tmp.data(), stream);
+
+  raft::matrix::linewise_op(handle,
+                            raft::make_device_matrix_view<const T, IdxT>(tmp.data(), n_rows, dim),
+                            raft::make_device_matrix_view<T, IdxT>(tmp.data(), n_rows, dim),
+                            true,
+                            raft::sub_op{},
+                            raft::make_device_vector_view<const T, IdxT>(center, dim));
+
+  float alpha = 1.0;
+  float beta  = 0.0;
+  raft::linalg::gemm(handle,
+                     true,
+                     false,
+                     rot_dim,
+                     n_rows,
+                     dim,
+                     &alpha,
+                     rotation_matrix,
+                     dim,
+                     tmp.data(),
+                     dim,
+                     &beta,
+                     residuals,
+                     rot_dim,
+                     stream);
+}
+
+/**
+ * @brief Compute residual vectors from the source dataset given by selected indices.
+ *
+ * The residual has the form
+ *  `rotation_matrix %* (dataset[:, :] - centers[labels[:], 0:dim])`
+ *
+ */
+template <typename T, typename IdxT>
+void flat_compute_residuals(
+  raft::resources const& handle,
+  float* residuals,  // [n_rows, rot_dim]
+  IdxT n_rows,
+  raft::device_matrix_view<const float, uint32_t, raft::row_major>
+    rotation_matrix,                                                         // [rot_dim, dim]
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> centers,  // [n_lists, dim_ext]
+  const T* dataset,                                                          // [n_rows, dim]
+  std::variant<uint32_t, const uint32_t*> labels,                            // [n_rows]
+  rmm::mr::device_memory_resource* device_memory)
+{
+  auto stream  = raft::resource::get_cuda_stream(handle);
+  auto dim     = rotation_matrix.extent(1);
+  auto rot_dim = rotation_matrix.extent(0);
+  rmm::device_uvector<float> tmp(n_rows * dim, stream, device_memory);
+  auto tmp_view = raft::make_device_vector_view<float, IdxT>(tmp.data(), tmp.size());
+  raft::linalg::map_offset(handle, tmp_view, [centers, dataset, labels, dim] __device__(size_t i) {
+    auto row_ix = i / dim;
+    auto el_ix  = i % dim;
+    auto label  = std::holds_alternative<uint32_t>(labels)
+                    ? std::get<uint32_t>(labels)
+                    : std::get<const uint32_t*>(labels)[row_ix];
+    return utils::mapping<float>{}(dataset[i]) - centers(label, el_ix);
+  });
+
+  float alpha = 1.0f;
+  float beta  = 0.0f;
+  raft::linalg::gemm(handle,
+                     true,
+                     false,
+                     rot_dim,
+                     n_rows,
+                     dim,
+                     &alpha,
+                     rotation_matrix.data_handle(),
+                     dim,
+                     tmp.data(),
+                     dim,
+                     &beta,
+                     residuals,
+                     rot_dim,
+                     stream);
+}
+
+template <uint32_t BlockDim, typename IdxT>
+__launch_bounds__(BlockDim) static __global__ void fill_indices_kernel(IdxT n_rows,
+                                                                       IdxT* data_indices,
+                                                                       IdxT* data_offsets,
+                                                                       const uint32_t* labels)
+{
+  const auto i = IdxT(BlockDim) * IdxT(blockIdx.x) + IdxT(threadIdx.x);
+  if (i >= n_rows) { return; }
+  data_indices[atomicAdd<IdxT>(data_offsets + labels[i], 1)] = i;
+}
+
+/**
+ * @brief Calculate cluster offsets and arrange data indices into clusters.
+ *
+ * @param n_rows
+ * @param n_lists
+ * @param[in] labels output of k-means prediction [n_rows]
+ * @param[in] cluster_sizes [n_lists]
+ * @param[out] cluster_offsets [n_lists+1]
+ * @param[out] data_indices [n_rows]
+ *
+ * @return size of the largest cluster
+ */
+template <typename IdxT>
+auto calculate_offsets_and_indices(IdxT n_rows,
+                                   uint32_t n_lists,
+                                   const uint32_t* labels,
+                                   const uint32_t* cluster_sizes,
+                                   IdxT* cluster_offsets,
+                                   IdxT* data_indices,
+                                   rmm::cuda_stream_view stream) -> uint32_t
+{
+  auto exec_policy = rmm::exec_policy(stream);
+  // Calculate the offsets
+  IdxT cumsum = 0;
+  raft::update_device(cluster_offsets, &cumsum, 1, stream);
+  thrust::inclusive_scan(
+    exec_policy, cluster_sizes, cluster_sizes + n_lists, cluster_offsets + 1, raft::add_op{});
+  raft::update_host(&cumsum, cluster_offsets + n_lists, 1, stream);
+  uint32_t max_cluster_size =
+    *thrust::max_element(exec_policy, cluster_sizes, cluster_sizes + n_lists);
+  stream.synchronize();
+  RAFT_EXPECTS(cumsum == n_rows, "cluster sizes do not add up.");
+  RAFT_LOG_DEBUG("Max cluster size %d", max_cluster_size);
+  rmm::device_uvector<IdxT> data_offsets_buf(n_lists, stream);
+  auto data_offsets = data_offsets_buf.data();
+  raft::copy(data_offsets, cluster_offsets, n_lists, stream);
+  constexpr uint32_t n_threads = 128;  // NOLINT
+  const IdxT n_blocks          = raft::div_rounding_up_unsafe(n_rows, n_threads);
+  fill_indices_kernel<n_threads>
+    <<<n_blocks, n_threads, 0, stream>>>(n_rows, data_indices, data_offsets, labels);
+  return max_cluster_size;
+}
+
+template <typename IdxT>
+void set_centers(raft::resources const& handle, index<IdxT>* index, const float* cluster_centers)
+{
+  auto stream         = raft::resource::get_cuda_stream(handle);
+  auto* device_memory = raft::resource::get_workspace_resource(handle);
+
+  // combine cluster_centers and their norms
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(index->centers().data_handle(),
+                                  sizeof(float) * index->dim_ext(),
+                                  cluster_centers,
+                                  sizeof(float) * index->dim(),
+                                  sizeof(float) * index->dim(),
+                                  index->n_lists(),
+                                  cudaMemcpyDefault,
+                                  stream));
+
+  rmm::device_uvector<float> center_norms(index->n_lists(), stream, device_memory);
+  raft::linalg::rowNorm(center_norms.data(),
+                        cluster_centers,
+                        index->dim(),
+                        index->n_lists(),
+                        raft::linalg::L2Norm,
+                        true,
+                        stream);
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(index->centers().data_handle() + index->dim(),
+                                  sizeof(float) * index->dim_ext(),
+                                  center_norms.data(),
+                                  sizeof(float),
+                                  sizeof(float),
+                                  index->n_lists(),
+                                  cudaMemcpyDefault,
+                                  stream));
+
+  //     Rotate cluster_centers
+  float alpha = 1.0;
+  float beta  = 0.0;
+  raft::linalg::gemm(handle,
+                     true,
+                     false,
+                     index->rot_dim(),
+                     index->n_lists(),
+                     index->dim(),
+                     &alpha,
+                     index->rotation_matrix().data_handle(),
+                     index->dim(),
+                     cluster_centers,
+                     index->dim(),
+                     &beta,
+                     index->centers_rot().data_handle(),
+                     index->rot_dim(),
+                     raft::resource::get_cuda_stream(handle));
+}
+
+template <typename IdxT>
+void transpose_pq_centers(const raft::resources& handle,
+                          index<IdxT>& index,
+                          const float* pq_centers_source)
+{
+  auto stream  = raft::resource::get_cuda_stream(handle);
+  auto extents = index.pq_centers().extents();
+  static_assert(extents.rank() == 3);
+  auto extents_source =
+    raft::make_extents<uint32_t>(extents.extent(0), extents.extent(2), extents.extent(1));
+  auto span_source = raft::make_mdspan<const float, uint32_t, raft::row_major, false, true>(
+    pq_centers_source, extents_source);
+  auto pq_centers_view = raft::make_device_vector_view<float, IdxT>(
+    index.pq_centers().data_handle(), index.pq_centers().size());
+  raft::linalg::map_offset(handle, pq_centers_view, [span_source, extents] __device__(size_t i) {
+    uint32_t ii[3];
+    for (int r = 2; r > 0; r--) {
+      ii[r] = i % extents.extent(r);
+      i /= extents.extent(r);
+    }
+    ii[0] = i;
+    return span_source(ii[0], ii[2], ii[1]);
+  });
+}
+
+template <typename IdxT>
+void train_per_subset(raft::resources const& handle,
+                      index<IdxT>& index,
+                      size_t n_rows,
+                      const float* trainset,   // [n_rows, dim]
+                      const uint32_t* labels,  // [n_rows]
+                      uint32_t kmeans_n_iters,
+                      rmm::mr::device_memory_resource* managed_memory)
+{
+  auto stream        = raft::resource::get_cuda_stream(handle);
+  auto device_memory = raft::resource::get_workspace_resource(handle);
+
+  rmm::device_uvector<float> pq_centers_tmp(index.pq_centers().size(), stream, device_memory);
+  rmm::device_uvector<float> sub_trainset(n_rows * size_t(index.pq_len()), stream, device_memory);
+  rmm::device_uvector<uint32_t> sub_labels(n_rows, stream, device_memory);
+
+  rmm::device_uvector<uint32_t> pq_cluster_sizes(index.pq_book_size(), stream, device_memory);
+
+  for (uint32_t j = 0; j < index.pq_dim(); j++) {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> pq_per_subspace_scope(
+      "ivf_pq::build::per_subspace[%u]", j);
+
+    // Get the rotated cluster centers for each training vector.
+    // This will be subtracted from the input vectors afterwards.
+    utils::copy_selected<float, float, size_t, uint32_t>(
+      n_rows,
+      index.pq_len(),
+      index.centers_rot().data_handle() + index.pq_len() * j,
+      labels,
+      index.rot_dim(),
+      sub_trainset.data(),
+      index.pq_len(),
+      stream);
+
+    // sub_trainset is the slice of: rotate(trainset) - centers_rot
+    float alpha = 1.0;
+    float beta  = -1.0;
+    raft::linalg::gemm(handle,
+                       true,
+                       false,
+                       index.pq_len(),
+                       n_rows,
+                       index.dim(),
+                       &alpha,
+                       index.rotation_matrix().data_handle() + index.dim() * index.pq_len() * j,
+                       index.dim(),
+                       trainset,
+                       index.dim(),
+                       &beta,
+                       sub_trainset.data(),
+                       index.pq_len(),
+                       stream);
+
+    // train PQ codebook for this subspace
+    auto sub_trainset_view = raft::make_device_matrix_view<const float, internal_extents_t>(
+      sub_trainset.data(), n_rows, index.pq_len());
+    auto centers_tmp_view = raft::make_device_matrix_view<float, internal_extents_t>(
+      pq_centers_tmp.data() + index.pq_book_size() * index.pq_len() * j,
+      index.pq_book_size(),
+      index.pq_len());
+    auto sub_labels_view =
+      raft::make_device_vector_view<uint32_t, internal_extents_t>(sub_labels.data(), n_rows);
+    auto cluster_sizes_view = raft::make_device_vector_view<uint32_t, internal_extents_t>(
+      pq_cluster_sizes.data(), index.pq_book_size());
+    raft::cluster::kmeans_balanced_params kmeans_params;
+    kmeans_params.n_iters = kmeans_n_iters;
+    kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
+    raft::cluster::kmeans_balanced::helpers::build_clusters(handle,
+                                                            kmeans_params,
+                                                            sub_trainset_view,
+                                                            centers_tmp_view,
+                                                            sub_labels_view,
+                                                            cluster_sizes_view,
+                                                            utils::mapping<float>{});
+  }
+  transpose_pq_centers(handle, index, pq_centers_tmp.data());
+}
+
+template <typename IdxT>
+void train_per_cluster(raft::resources const& handle,
+                       index<IdxT>& index,
+                       size_t n_rows,
+                       const float* trainset,   // [n_rows, dim]
+                       const uint32_t* labels,  // [n_rows]
+                       uint32_t kmeans_n_iters,
+                       rmm::mr::device_memory_resource* managed_memory)
+{
+  auto stream        = raft::resource::get_cuda_stream(handle);
+  auto device_memory = raft::resource::get_workspace_resource(handle);
+
+  rmm::device_uvector<float> pq_centers_tmp(index.pq_centers().size(), stream, device_memory);
+  rmm::device_uvector<uint32_t> cluster_sizes(index.n_lists(), stream, managed_memory);
+  rmm::device_uvector<IdxT> indices_buf(n_rows, stream, device_memory);
+  rmm::device_uvector<IdxT> offsets_buf(index.n_lists() + 1, stream, managed_memory);
+
+  raft::stats::histogram<uint32_t, size_t>(raft::stats::HistTypeAuto,
+                                           reinterpret_cast<int32_t*>(cluster_sizes.data()),
+                                           index.n_lists(),
+                                           labels,
+                                           n_rows,
+                                           1,
+                                           stream);
+
+  auto cluster_offsets      = offsets_buf.data();
+  auto indices              = indices_buf.data();
+  uint32_t max_cluster_size = calculate_offsets_and_indices(
+    IdxT(n_rows), index.n_lists(), labels, cluster_sizes.data(), cluster_offsets, indices, stream);
+
+  rmm::device_uvector<uint32_t> pq_labels(
+    size_t(max_cluster_size) * size_t(index.pq_dim()), stream, device_memory);
+  rmm::device_uvector<uint32_t> pq_cluster_sizes(index.pq_book_size(), stream, device_memory);
+  rmm::device_uvector<float> rot_vectors(
+    size_t(max_cluster_size) * size_t(index.rot_dim()), stream, device_memory);
+
+  raft::resource::sync_stream(handle);  // make sure cluster offsets are up-to-date
+  for (uint32_t l = 0; l < index.n_lists(); l++) {
+    auto cluster_size = cluster_sizes.data()[l];
+    if (cluster_size == 0) continue;
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> pq_per_cluster_scope(
+      "ivf_pq::build::per_cluster[%u](size = %u)", l, cluster_size);
+
+    select_residuals(handle,
+                     rot_vectors.data(),
+                     IdxT(cluster_size),
+                     index.dim(),
+                     index.rot_dim(),
+                     index.rotation_matrix().data_handle(),
+                     index.centers().data_handle() + size_t(l) * size_t(index.dim_ext()),
+                     trainset,
+                     indices + cluster_offsets[l],
+                     device_memory);
+
+    // limit the cluster size to bound the training time.
+    // [sic] we interpret the data as pq_len-dimensional
+    size_t big_enough     = 256ul * std::max<size_t>(index.pq_book_size(), index.pq_dim());
+    size_t available_rows = size_t(cluster_size) * size_t(index.pq_dim());
+    auto pq_n_rows        = uint32_t(std::min(big_enough, available_rows));
+    // train PQ codebook for this cluster
+    auto rot_vectors_view = raft::make_device_matrix_view<const float, internal_extents_t>(
+      rot_vectors.data(), pq_n_rows, index.pq_len());
+    auto centers_tmp_view = raft::make_device_matrix_view<float, internal_extents_t>(
+      pq_centers_tmp.data() + static_cast<size_t>(index.pq_book_size()) *
+                                static_cast<size_t>(index.pq_len()) * static_cast<size_t>(l),
+      index.pq_book_size(),
+      index.pq_len());
+    auto pq_labels_view =
+      raft::make_device_vector_view<uint32_t, internal_extents_t>(pq_labels.data(), pq_n_rows);
+    auto pq_cluster_sizes_view = raft::make_device_vector_view<uint32_t, internal_extents_t>(
+      pq_cluster_sizes.data(), index.pq_book_size());
+    raft::cluster::kmeans_balanced_params kmeans_params;
+    kmeans_params.n_iters = kmeans_n_iters;
+    kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
+    raft::cluster::kmeans_balanced::helpers::build_clusters(handle,
+                                                            kmeans_params,
+                                                            rot_vectors_view,
+                                                            centers_tmp_view,
+                                                            pq_labels_view,
+                                                            pq_cluster_sizes_view,
+                                                            utils::mapping<float>{});
+  }
+  transpose_pq_centers(handle, index, pq_centers_tmp.data());
+}
+
+/**
+ * A helper function: given the dataset in the rotated space
+ *  [n_rows, rot_dim] = [n_rows, pq_dim * pq_len],
+ * reinterpret the last dimension as two: [n_rows, pq_dim, pq_len]
+ *
+ * @tparam T
+ * @tparam IdxT
+ *
+ * @param vectors input data [n_rows, rot_dim]
+ * @param pq_centers codebook (used to infer the structure - pq_len)
+ * @return reinterpreted vectors [n_rows, pq_dim, pq_len]
+ */
+template <typename T, typename IdxT>
+static __device__ auto reinterpret_vectors(
+  raft::device_matrix_view<T, IdxT, raft::row_major> vectors,
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers)
+  -> raft::device_mdspan<T, raft::extent_3d<IdxT>, raft::row_major>
+{
+  const uint32_t pq_len = pq_centers.extent(1);
+  const uint32_t pq_dim = vectors.extent(1) / pq_len;
+  using layout_t        = typename decltype(vectors)::layout_type;
+  using accessor_t      = typename decltype(vectors)::accessor_type;
+  return raft::mdspan<T, raft::extent_3d<IdxT>, layout_t, accessor_t>(
+    vectors.data_handle(), raft::extent_3d<IdxT>{vectors.extent(0), pq_dim, pq_len});
+}
+
+/**
+ * A consumer for the `run_on_list` and `run_on_vector` that just flattens PQ codes
+ * one-per-byte. That is, independent of the code width (pq_bits), one code uses
+ * the whole byte, hence one vectors uses pq_dim bytes.
+ */
+struct unpack_codes {
+  raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes;
+
+  /**
+   * Create a callable to be passed to `run_on_list`.
+   *
+   * @param[out] out_codes the destination for the read codes.
+   */
+  __device__ inline unpack_codes(
+    raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes)
+    : out_codes{out_codes}
+  {
+  }
+
+  /**  Write j-th component (code) of the i-th vector into the output array. */
+  __device__ inline void operator()(uint8_t code, uint32_t i, uint32_t j)
+  {
+    out_codes(i, j) = code;
+  }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void unpack_list_data_kernel(
+  raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    in_list_data,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  const uint32_t pq_dim = out_codes.extent(1);
+  auto unpack_action    = unpack_codes{out_codes};
+  run_on_list<PqBits>(in_list_data, offset_or_indices, out_codes.extent(0), pq_dim, unpack_action);
+}
+
+/**
+ * Unpack flat PQ codes from an existing list by the given offset.
+ *
+ * @param[out] codes flat PQ codes, one code per byte [n_rows, pq_dim]
+ * @param[in] list_data the packed ivf::list data.
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ * @param[in] pq_bits codebook size (1 << pq_bits)
+ * @param[in] stream
+ */
+inline void unpack_list_data(
+  raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  auto n_rows = codes.extent(0);
+  if (n_rows == 0) { return; }
+
+  constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [pq_bits]() {
+    switch (pq_bits) {
+      case 4: return unpack_list_data_kernel<kBlockSize, 4>;
+      case 5: return unpack_list_data_kernel<kBlockSize, 5>;
+      case 6: return unpack_list_data_kernel<kBlockSize, 6>;
+      case 7: return unpack_list_data_kernel<kBlockSize, 7>;
+      case 8: return unpack_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }();
+  kernel<<<blocks, threads, 0, stream>>>(codes, list_data, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/** Unpack the list data; see the public interface for the api and usage. */
+template <typename IdxT>
+void unpack_list_data(raft::resources const& res,
+                      const index<IdxT>& index,
+                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+                      uint32_t label,
+                      std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  unpack_list_data(out_codes,
+                   index.lists()[label]->data.view(),
+                   offset_or_indices,
+                   index.pq_bits(),
+                   raft::resource::get_cuda_stream(res));
+}
+
+/**
+ * A consumer for the `run_on_vector` that just flattens PQ codes
+ * into a tightly packed matrix. That is, the codes are not expanded to one code-per-byte.
+ */
+template <uint32_t PqBits>
+struct unpack_contiguous {
+  uint8_t* codes;
+  uint32_t code_size;
+
+  /**
+   * Create a callable to be passed to `run_on_vector`.
+   *
+   * @param[in] codes flat compressed PQ codes
+   */
+  __host__ __device__ inline unpack_contiguous(uint8_t* codes, uint32_t pq_dim)
+    : codes{codes}, code_size{raft::ceildiv<uint32_t>(pq_dim * PqBits, 8)}
+  {
+  }
+
+  /**  Write j-th component (code) of the i-th vector into the output array. */
+  __host__ __device__ inline void operator()(uint8_t code, uint32_t i, uint32_t j)
+  {
+    bitfield_view_t<PqBits> code_view{codes + i * code_size};
+    code_view[j] = code;
+  }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void unpack_contiguous_list_data_kernel(
+  uint8_t* out_codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    in_list_data,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  run_on_list<PqBits>(
+    in_list_data, offset_or_indices, n_rows, pq_dim, unpack_contiguous<PqBits>(out_codes, pq_dim));
+}
+
+/**
+ * Unpack flat PQ codes from an existing list by the given offset.
+ *
+ * @param[out] codes flat compressed PQ codes [n_rows, ceildiv(pq_dim * pq_bits, 8)]
+ * @param[in] list_data the packed ivf::list data.
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ * @param[in] pq_bits codebook size (1 << pq_bits)
+ * @param[in] stream
+ */
+inline void unpack_contiguous_list_data(
+  uint8_t* codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  if (n_rows == 0) { return; }
+
+  constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [pq_bits]() {
+    switch (pq_bits) {
+      case 4: return unpack_contiguous_list_data_kernel<kBlockSize, 4>;
+      case 5: return unpack_contiguous_list_data_kernel<kBlockSize, 5>;
+      case 6: return unpack_contiguous_list_data_kernel<kBlockSize, 6>;
+      case 7: return unpack_contiguous_list_data_kernel<kBlockSize, 7>;
+      case 8: return unpack_contiguous_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }();
+  kernel<<<blocks, threads, 0, stream>>>(codes, list_data, n_rows, pq_dim, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/** Unpack the list data; see the public interface for the api and usage. */
+template <typename IdxT>
+void unpack_contiguous_list_data(raft::resources const& res,
+                                 const index<IdxT>& index,
+                                 uint8_t* out_codes,
+                                 uint32_t n_rows,
+                                 uint32_t label,
+                                 std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  unpack_contiguous_list_data(out_codes,
+                              index.lists()[label]->data.view(),
+                              n_rows,
+                              index.pq_dim(),
+                              offset_or_indices,
+                              index.pq_bits(),
+                              raft::resource::get_cuda_stream(res));
+}
+
+/** A consumer for the `run_on_list` and `run_on_vector` that approximates the original input data.
+ */
+struct reconstruct_vectors {
+  codebook_gen codebook_kind;
+  uint32_t cluster_ix;
+  uint32_t pq_len;
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers;
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> centers_rot;
+  raft::device_mdspan<float, raft::extent_3d<uint32_t>, raft::row_major> out_vectors;
+
+  /**
+   * Create a callable to be passed to `run_on_list`.
+   *
+   * @param[out] out_vectors the destination for the decoded vectors.
+   * @param[in] pq_centers the codebook
+   * @param[in] centers_rot
+   * @param[in] codebook_kind
+   * @param[in] cluster_ix label/id of the cluster.
+   */
+  __device__ inline reconstruct_vectors(
+    raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
+    raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
+    raft::device_matrix_view<const float, uint32_t, raft::row_major> centers_rot,
+    codebook_gen codebook_kind,
+    uint32_t cluster_ix)
+    : codebook_kind{codebook_kind},
+      cluster_ix{cluster_ix},
+      pq_len{pq_centers.extent(1)},
+      pq_centers{pq_centers},
+      centers_rot{reinterpret_vectors(centers_rot, pq_centers)},
+      out_vectors{reinterpret_vectors(out_vectors, pq_centers)}
+  {
+  }
+
+  /**
+   * Decode j-th component of the i-th vector by its code and write it into a chunk of the output
+   * vectors (pq_len elements).
+   */
+  __device__ inline void operator()(uint8_t code, uint32_t i, uint32_t j)
+  {
+    uint32_t partition_ix;
+    switch (codebook_kind) {
+      case codebook_gen::PER_CLUSTER: {
+        partition_ix = cluster_ix;
+      } break;
+      case codebook_gen::PER_SUBSPACE: {
+        partition_ix = j;
+      } break;
+      default: __builtin_unreachable();
+    }
+    for (uint32_t k = 0; k < pq_len; k++) {
+      out_vectors(i, j, k) = pq_centers(partition_ix, k, code) + centers_rot(cluster_ix, j, k);
+    }
+  }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void reconstruct_list_data_kernel(
+  raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    in_list_data,
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> centers_rot,
+  codebook_gen codebook_kind,
+  uint32_t cluster_ix,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  const uint32_t pq_dim = out_vectors.extent(1) / pq_centers.extent(1);
+  auto reconstruct_action =
+    reconstruct_vectors{out_vectors, pq_centers, centers_rot, codebook_kind, cluster_ix};
+  run_on_list<PqBits>(
+    in_list_data, offset_or_indices, out_vectors.extent(0), pq_dim, reconstruct_action);
+}
+
+/** Decode the list data; see the public interface for the api and usage. */
+template <typename T, typename IdxT>
+void reconstruct_list_data(raft::resources const& res,
+                           const index<IdxT>& index,
+                           raft::device_matrix_view<T, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  auto n_rows = out_vectors.extent(0);
+  if (n_rows == 0) { return; }
+  auto& list = index.lists()[label];
+  if (std::holds_alternative<uint32_t>(offset_or_indices)) {
+    auto n_skip = std::get<uint32_t>(offset_or_indices);
+    // sic! I'm using the upper bound `list.size` instead of exact `list_sizes(label)`
+    // to avoid an extra device-host data raft::copy and the stream sync.
+    RAFT_EXPECTS(n_skip + n_rows <= list->size.load(),
+                 "offset + output size must be not bigger than the cluster size.");
+  }
+
+  auto tmp =
+    raft::make_device_mdarray<float>(res,
+                                     raft::resource::get_workspace_resource(res),
+                                     raft::make_extents<uint32_t>(n_rows, index.rot_dim()));
+
+  constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [](uint32_t pq_bits) {
+    switch (pq_bits) {
+      case 4: return reconstruct_list_data_kernel<kBlockSize, 4>;
+      case 5: return reconstruct_list_data_kernel<kBlockSize, 5>;
+      case 6: return reconstruct_list_data_kernel<kBlockSize, 6>;
+      case 7: return reconstruct_list_data_kernel<kBlockSize, 7>;
+      case 8: return reconstruct_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }(index.pq_bits());
+  kernel<<<blocks, threads, 0, raft::resource::get_cuda_stream(res)>>>(tmp.view(),
+                                                                       list->data.view(),
+                                                                       index.pq_centers(),
+                                                                       index.centers_rot(),
+                                                                       index.codebook_kind(),
+                                                                       label,
+                                                                       offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+  float* out_float_ptr = nullptr;
+  rmm::device_uvector<float> out_float_buf(
+    0, raft::resource::get_cuda_stream(res), raft::resource::get_workspace_resource(res));
+  if constexpr (std::is_same_v<T, float>) {
+    out_float_ptr = out_vectors.data_handle();
+  } else {
+    out_float_buf.resize(size_t{n_rows} * size_t{index.dim()},
+                         raft::resource::get_cuda_stream(res));
+    out_float_ptr = out_float_buf.data();
+  }
+  // Rotate the results back to the original space
+  float alpha = 1.0;
+  float beta  = 0.0;
+  raft::linalg::gemm(res,
+                     false,
+                     false,
+                     index.dim(),
+                     n_rows,
+                     index.rot_dim(),
+                     &alpha,
+                     index.rotation_matrix().data_handle(),
+                     index.dim(),
+                     tmp.data_handle(),
+                     index.rot_dim(),
+                     &beta,
+                     out_float_ptr,
+                     index.dim(),
+                     raft::resource::get_cuda_stream(res));
+  // Transform the data to the original type, if necessary
+  if constexpr (!std::is_same_v<T, float>) {
+    raft::linalg::map(
+      res,
+      out_vectors,
+      utils::mapping<T>{},
+      raft::make_device_matrix_view<const float>(out_float_ptr, n_rows, index.dim()));
+  }
+}
+
+/**
+ * A producer for the `write_list` and `write_vector` reads the codes byte-by-byte. That is,
+ * independent of the code width (pq_bits), one code uses the whole byte, hence one vectors uses
+ * pq_dim bytes.
+ */
+struct pass_codes {
+  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes;
+
+  /**
+   * Create a callable to be passed to `run_on_list`.
+   *
+   * @param[in] codes the source codes.
+   */
+  __device__ inline pass_codes(
+    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes)
+    : codes{codes}
+  {
+  }
+
+  /** Read j-th component (code) of the i-th vector from the source. */
+  __device__ inline auto operator()(uint32_t i, uint32_t j) const -> uint8_t { return codes(i, j); }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void pack_list_data_kernel(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  write_list<PqBits, 1>(
+    list_data, offset_or_indices, codes.extent(0), codes.extent(1), pass_codes{codes});
+}
+
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_rows).
+ *
+ * @param[out] list_data the packed ivf::list data.
+ * @param[in] codes flat PQ codes, one code per byte [n_rows, pq_dim]
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ * @param[in] pq_bits codebook size (1 << pq_bits)
+ * @param[in] stream
+ */
+inline void pack_list_data(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  auto n_rows = codes.extent(0);
+  if (n_rows == 0) { return; }
+
+  constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [pq_bits]() {
+    switch (pq_bits) {
+      case 4: return pack_list_data_kernel<kBlockSize, 4>;
+      case 5: return pack_list_data_kernel<kBlockSize, 5>;
+      case 6: return pack_list_data_kernel<kBlockSize, 6>;
+      case 7: return pack_list_data_kernel<kBlockSize, 7>;
+      case 8: return pack_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }();
+  kernel<<<blocks, threads, 0, stream>>>(list_data, codes, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+template <typename IdxT>
+void pack_list_data(raft::resources const& res,
+                    index<IdxT>* index,
+                    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+                    uint32_t label,
+                    std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  pack_list_data(index->lists()[label]->data.view(),
+                 new_codes,
+                 offset_or_indices,
+                 index->pq_bits(),
+                 raft::resource::get_cuda_stream(res));
+}
+
+/**
+ * A producer for the `write_vector` reads tightly packed flat codes. That is,
+ * the codes are not expanded to one code-per-byte.
+ */
+template <uint32_t PqBits>
+struct pack_contiguous {
+  const uint8_t* codes;
+  uint32_t code_size;
+
+  /**
+   * Create a callable to be passed to `write_vector`.
+   *
+   * @param[in] codes flat compressed PQ codes
+   */
+  __host__ __device__ inline pack_contiguous(const uint8_t* codes, uint32_t pq_dim)
+    : codes{codes}, code_size{raft::ceildiv<uint32_t>(pq_dim * PqBits, 8)}
+  {
+  }
+
+  /** Read j-th component (code) of the i-th vector from the source. */
+  __host__ __device__ inline auto operator()(uint32_t i, uint32_t j) -> uint8_t
+  {
+    bitfield_view_t<PqBits> code_view{const_cast<uint8_t*>(codes + i * code_size)};
+    return uint8_t(code_view[j]);
+  }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void pack_contiguous_list_data_kernel(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  write_list<PqBits, 1>(
+    list_data, offset_or_indices, n_rows, pq_dim, pack_contiguous<PqBits>(codes, pq_dim));
+}
+
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_rows).
+ *
+ * @param[out] list_data the packed ivf::list data.
+ * @param[in] codes flat compressed PQ codes [n_rows, ceildiv(pq_dim * pq_bits, 8)]
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ * @param[in] pq_bits codebook size (1 << pq_bits)
+ * @param[in] stream
+ */
+inline void pack_contiguous_list_data(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  if (n_rows == 0) { return; }
+
+  constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [pq_bits]() {
+    switch (pq_bits) {
+      case 4: return pack_contiguous_list_data_kernel<kBlockSize, 4>;
+      case 5: return pack_contiguous_list_data_kernel<kBlockSize, 5>;
+      case 6: return pack_contiguous_list_data_kernel<kBlockSize, 6>;
+      case 7: return pack_contiguous_list_data_kernel<kBlockSize, 7>;
+      case 8: return pack_contiguous_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }();
+  kernel<<<blocks, threads, 0, stream>>>(list_data, codes, n_rows, pq_dim, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+template <typename IdxT>
+void pack_contiguous_list_data(raft::resources const& res,
+                               index<IdxT>* index,
+                               const uint8_t* new_codes,
+                               uint32_t n_rows,
+                               uint32_t label,
+                               std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  pack_contiguous_list_data(index->lists()[label]->data.view(),
+                            new_codes,
+                            n_rows,
+                            index->pq_dim(),
+                            offset_or_indices,
+                            index->pq_bits(),
+                            raft::resource::get_cuda_stream(res));
+}
+
+/**
+ *
+ * A producer for the `write_list` and `write_vector` that encodes level-1 input vector residuals
+ * into lvl-2 PQ codes.
+ * Computing a PQ code means finding the closest cluster in a pq_dim-subspace.
+ *
+ * @tparam SubWarpSize
+ *   how many threads work on a single vector;
+ *   bounded by either WarpSize or pq_book_size.
+ *
+ * @param pq_centers
+ *   - codebook_gen::PER_SUBSPACE: [pq_dim , pq_len, pq_book_size]
+ *   - codebook_gen::PER_CLUSTER:  [n_lists, pq_len, pq_book_size]
+ * @param new_vector a single input of length rot_dim, reinterpreted as [pq_dim, pq_len].
+ *   the input must be already transformed to floats, rotated, and the level 1 cluster
+ *   center must be already substructed (i.e. this is the residual of a single input vector).
+ * @param codebook_kind
+ * @param j index along pq_dim "dimension"
+ * @param cluster_ix is used for PER_CLUSTER codebooks.
+ */
+/**
+ */
+template <uint32_t SubWarpSize, typename IdxT>
+struct encode_vectors {
+  codebook_gen codebook_kind;
+  uint32_t cluster_ix;
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers;
+  raft::device_mdspan<const float, raft::extent_3d<IdxT>, raft::row_major> in_vectors;
+
+  __device__ inline encode_vectors(
+    raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
+    raft::device_matrix_view<const float, IdxT, raft::row_major> in_vectors,
+    codebook_gen codebook_kind,
+    uint32_t cluster_ix)
+    : codebook_kind{codebook_kind},
+      cluster_ix{cluster_ix},
+      pq_centers{pq_centers},
+      in_vectors{reinterpret_vectors(in_vectors, pq_centers)}
+  {
+  }
+
+  /**
+   * Decode j-th component of the i-th vector by its code and write it into a chunk of the output
+   * vectors (pq_len elements).
+   */
+  __device__ inline auto operator()(IdxT i, uint32_t j) -> uint8_t
+  {
+    uint32_t lane_id = raft::Pow2<SubWarpSize>::mod(raft::laneId());
+    uint32_t partition_ix;
+    switch (codebook_kind) {
+      case codebook_gen::PER_CLUSTER: {
+        partition_ix = cluster_ix;
+      } break;
+      case codebook_gen::PER_SUBSPACE: {
+        partition_ix = j;
+      } break;
+      default: __builtin_unreachable();
+    }
+
+    const uint32_t pq_book_size = pq_centers.extent(2);
+    const uint32_t pq_len       = pq_centers.extent(1);
+    float min_dist              = std::numeric_limits<float>::infinity();
+    uint8_t code                = 0;
+    // calculate the distance for each PQ cluster, find the minimum for each thread
+    for (uint32_t l = lane_id; l < pq_book_size; l += SubWarpSize) {
+      // NB: the L2 quantifiers on residuals are always trained on L2 metric.
+      float d = 0.0f;
+      for (uint32_t k = 0; k < pq_len; k++) {
+        auto t = in_vectors(i, j, k) - pq_centers(partition_ix, k, l);
+        d += t * t;
+      }
+      if (d < min_dist) {
+        min_dist = d;
+        code     = uint8_t(l);
+      }
+    }
+    // reduce among threads
+#pragma unroll
+    for (uint32_t stride = SubWarpSize >> 1; stride > 0; stride >>= 1) {
+      const auto other_dist = raft::shfl_xor(min_dist, stride, SubWarpSize);
+      const auto other_code = raft::shfl_xor(code, stride, SubWarpSize);
+      if (other_dist < min_dist) {
+        min_dist = other_dist;
+        code     = other_code;
+      }
+    }
+    return code;
+  }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits, typename IdxT>
+__launch_bounds__(BlockSize) static __global__ void process_and_fill_codes_kernel(
+  raft::device_matrix_view<const float, IdxT, raft::row_major> new_vectors,
+  std::variant<IdxT, const IdxT*> src_offset_or_indices,
+  const uint32_t* new_labels,
+  raft::device_vector_view<uint32_t, uint32_t, raft::row_major> list_sizes,
+  raft::device_vector_view<IdxT*, uint32_t, raft::row_major> inds_ptrs,
+  raft::device_vector_view<uint8_t*, uint32_t, raft::row_major> data_ptrs,
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
+  codebook_gen codebook_kind)
+{
+  constexpr uint32_t kSubWarpSize = std::min<uint32_t>(raft::WarpSize, 1u << PqBits);
+  using subwarp_align             = raft::Pow2<kSubWarpSize>;
+  const uint32_t lane_id          = subwarp_align::mod(threadIdx.x);
+  const IdxT row_ix = subwarp_align::div(IdxT{threadIdx.x} + IdxT{BlockSize} * IdxT{blockIdx.x});
+  if (row_ix >= new_vectors.extent(0)) { return; }
+
+  const uint32_t cluster_ix = new_labels[row_ix];
+  uint32_t out_ix;
+  if (lane_id == 0) { out_ix = atomicAdd(&list_sizes(cluster_ix), 1); }
+  out_ix = raft::shfl(out_ix, 0, kSubWarpSize);
+
+  // write the label  (one record per subwarp)
+  auto pq_indices = inds_ptrs(cluster_ix);
+  if (lane_id == 0) {
+    if (std::holds_alternative<IdxT>(src_offset_or_indices)) {
+      pq_indices[out_ix] = std::get<IdxT>(src_offset_or_indices) + row_ix;
+    } else {
+      pq_indices[out_ix] = std::get<const IdxT*>(src_offset_or_indices)[row_ix];
+    }
+  }
+
+  // write the codes (one record per subwarp):
+  const uint32_t pq_dim = new_vectors.extent(1) / pq_centers.extent(1);
+  auto pq_extents = list_spec<uint32_t, IdxT>{PqBits, pq_dim, true}.make_list_extents(out_ix + 1);
+  auto pq_dataset = raft::make_mdspan<uint8_t, uint32_t, raft::row_major, false, true>(
+    data_ptrs[cluster_ix], pq_extents);
+  write_vector<PqBits, kSubWarpSize>(
+    pq_dataset,
+    out_ix,
+    row_ix,
+    pq_dim,
+    encode_vectors<kSubWarpSize, IdxT>{pq_centers, new_vectors, codebook_kind, cluster_ix});
+}
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void encode_list_data_kernel(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> new_vectors,
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
+  codebook_gen codebook_kind,
+  uint32_t cluster_ix,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  constexpr uint32_t kSubWarpSize = std::min<uint32_t>(raft::WarpSize, 1u << PqBits);
+  const uint32_t pq_dim           = new_vectors.extent(1) / pq_centers.extent(1);
+  auto encode_action =
+    encode_vectors<kSubWarpSize, uint32_t>{pq_centers, new_vectors, codebook_kind, cluster_ix};
+  write_list<PqBits, kSubWarpSize>(
+    list_data, offset_or_indices, new_vectors.extent(0), pq_dim, encode_action);
+}
+
+template <typename T, typename IdxT>
+void encode_list_data(raft::resources const& res,
+                      index<IdxT>* index,
+                      raft::device_matrix_view<const T, uint32_t, raft::row_major> new_vectors,
+                      uint32_t label,
+                      std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  auto n_rows = new_vectors.extent(0);
+  if (n_rows == 0) { return; }
+
+  auto mr = raft::resource::get_workspace_resource(res);
+
+  auto new_vectors_residual = raft::make_device_mdarray<float>(
+    res, mr, raft::make_extents<uint32_t>(n_rows, index->rot_dim()));
+
+  flat_compute_residuals<T, uint32_t>(res,
+                                      new_vectors_residual.data_handle(),
+                                      n_rows,
+                                      index->rotation_matrix(),
+                                      index->centers(),
+                                      new_vectors.data_handle(),
+                                      label,
+                                      mr);
+
+  constexpr uint32_t kBlockSize  = 256;
+  const uint32_t threads_per_vec = std::min<uint32_t>(raft::WarpSize, index->pq_book_size());
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize / threads_per_vec), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [](uint32_t pq_bits) {
+    switch (pq_bits) {
+      case 4: return encode_list_data_kernel<kBlockSize, 4>;
+      case 5: return encode_list_data_kernel<kBlockSize, 5>;
+      case 6: return encode_list_data_kernel<kBlockSize, 6>;
+      case 7: return encode_list_data_kernel<kBlockSize, 7>;
+      case 8: return encode_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }(index->pq_bits());
+  kernel<<<blocks, threads, 0, raft::resource::get_cuda_stream(res)>>>(
+    index->lists()[label]->data.view(),
+    new_vectors_residual.view(),
+    index->pq_centers(),
+    index->codebook_kind(),
+    label,
+    offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/**
+ * Assuming the index already has some data and allocated the space for more, write more data in it.
+ * There must be enough free space in `pq_dataset()` and `indices()`, as computed using
+ * `list_offsets()` and `list_sizes()`.
+ *
+ * NB: Since the pq_dataset is stored in the interleaved blocked format (see ivf_pq_types.hpp), one
+ * cannot just concatenate the old and the new codes; the positions for the codes are determined the
+ * same way as in the ivfpq_compute_similarity_kernel (see ivf_pq_search.cuh).
+ *
+ * @tparam T
+ * @tparam IdxT
+ *
+ * @param handle
+ * @param index
+ * @param[in] new_vectors
+ *    a pointer to a row-major device array [index.dim(), n_rows];
+ * @param[in] src_offset_or_indices
+ *    references for the new data:
+ *      either a starting index for the auto-indexing
+ *      or a pointer to a device array of explicit indices [n_rows];
+ * @param[in] new_labels
+ *    cluster ids (first-level quantization) - a device array [n_rows];
+ * @param n_rows
+ *    the number of records to write in.
+ * @param mr
+ *    a memory resource to use for device allocations
+ */
+template <typename T, typename IdxT>
+void process_and_fill_codes(raft::resources const& handle,
+                            index<IdxT>& index,
+                            const T* new_vectors,
+                            std::variant<IdxT, const IdxT*> src_offset_or_indices,
+                            const uint32_t* new_labels,
+                            IdxT n_rows,
+                            rmm::mr::device_memory_resource* mr)
+{
+  auto new_vectors_residual =
+    raft::make_device_mdarray<float>(handle, mr, raft::make_extents<IdxT>(n_rows, index.rot_dim()));
+
+  flat_compute_residuals<T, IdxT>(handle,
+                                  new_vectors_residual.data_handle(),
+                                  n_rows,
+                                  index.rotation_matrix(),
+                                  index.centers(),
+                                  new_vectors,
+                                  new_labels,
+                                  mr);
+
+  constexpr uint32_t kBlockSize  = 256;
+  const uint32_t threads_per_vec = std::min<uint32_t>(raft::WarpSize, index.pq_book_size());
+  dim3 blocks(raft::div_rounding_up_safe<IdxT>(n_rows, kBlockSize / threads_per_vec), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [](uint32_t pq_bits) {
+    switch (pq_bits) {
+      case 4: return process_and_fill_codes_kernel<kBlockSize, 4, IdxT>;
+      case 5: return process_and_fill_codes_kernel<kBlockSize, 5, IdxT>;
+      case 6: return process_and_fill_codes_kernel<kBlockSize, 6, IdxT>;
+      case 7: return process_and_fill_codes_kernel<kBlockSize, 7, IdxT>;
+      case 8: return process_and_fill_codes_kernel<kBlockSize, 8, IdxT>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }(index.pq_bits());
+  kernel<<<blocks, threads, 0, raft::resource::get_cuda_stream(handle)>>>(
+    new_vectors_residual.view(),
+    src_offset_or_indices,
+    new_labels,
+    index.list_sizes(),
+    index.inds_ptrs(),
+    index.data_ptrs(),
+    index.pq_centers(),
+    index.codebook_kind());
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/**
+ * Helper function: allocate enough space in the list, compute the offset, at which to start
+ * writing, and fill-in indices.
+ *
+ * @return offset for writing the data
+ */
+template <typename IdxT>
+auto extend_list_prepare(
+  raft::resources const& res,
+  index<IdxT>* index,
+  raft::device_vector_view<const IdxT, uint32_t, raft::row_major> new_indices,
+  uint32_t label) -> uint32_t
+{
+  uint32_t n_rows = new_indices.extent(0);
+  uint32_t offset;
+  // Allocate the lists to fit the new data
+  raft::copy(
+    &offset, index->list_sizes().data_handle() + label, 1, raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+  uint32_t new_size = offset + n_rows;
+  raft::copy(
+    index->list_sizes().data_handle() + label, &new_size, 1, raft::resource::get_cuda_stream(res));
+  auto spec = list_spec<uint32_t, IdxT>{
+    index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+  auto& list = index->lists()[label];
+  ivf::resize_list(res, list, spec, new_size, offset);
+  raft::copy(list->indices.data_handle() + offset,
+             new_indices.data_handle(),
+             n_rows,
+             raft::resource::get_cuda_stream(res));
+  return offset;
+}
+
+/**
+ * Extend one list of the index in-place, by the list label, skipping the classification and
+ * encoding steps.
+ * See the public interface for the api and usage.
+ */
+template <typename IdxT>
+void extend_list_with_codes(
+  raft::resources const& res,
+  index<IdxT>* index,
+  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+  raft::device_vector_view<const IdxT, uint32_t, raft::row_major> new_indices,
+  uint32_t label)
+{
+  // Allocate memory and write indices
+  auto offset = extend_list_prepare(res, index, new_indices, label);
+  // Pack the data
+  helpers::codepacker::pack_list_data(res, index, new_codes, label, offset);
+  // Update the pointers and the sizes
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
+/**
+ * Extend one list of the index in-place, by the list label, skipping the classification step.
+ * See the public interface for the api and usage.
+ */
+template <typename T, typename IdxT>
+void extend_list(raft::resources const& res,
+                 index<IdxT>* index,
+                 raft::device_matrix_view<const T, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const IdxT, uint32_t, raft::row_major> new_indices,
+                 uint32_t label)
+{
+  // Allocate memory and write indices
+  auto offset = extend_list_prepare(res, index, new_indices, label);
+  // Encode the data
+  encode_list_data<T, IdxT>(res, index, new_vectors, label, offset);
+  // Update the pointers and the sizes
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
+/**
+ * Remove all data from a single list.
+ * See the public interface for the api and usage.
+ */
+template <typename IdxT>
+void erase_list(raft::resources const& res, index<IdxT>* index, uint32_t label)
+{
+  uint32_t zero = 0;
+  raft::copy(
+    index->list_sizes().data_handle() + label, &zero, 1, raft::resource::get_cuda_stream(res));
+  index->lists()[label].reset();
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
+/** raft::copy the state of an index into a new index, but share the list data among the two. */
+template <typename IdxT>
+auto clone(const raft::resources& res, const index<IdxT>& source) -> index<IdxT>
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  // Allocate the new index
+  index<IdxT> target(res,
+                     source.metric(),
+                     source.codebook_kind(),
+                     source.n_lists(),
+                     source.dim(),
+                     source.pq_bits(),
+                     source.pq_dim());
+
+  // raft::copy the independent parts
+  raft::copy(target.list_sizes().data_handle(),
+             source.list_sizes().data_handle(),
+             source.list_sizes().size(),
+             stream);
+  raft::copy(target.rotation_matrix().data_handle(),
+             source.rotation_matrix().data_handle(),
+             source.rotation_matrix().size(),
+             stream);
+  raft::copy(target.pq_centers().data_handle(),
+             source.pq_centers().data_handle(),
+             source.pq_centers().size(),
+             stream);
+  raft::copy(target.centers().data_handle(),
+             source.centers().data_handle(),
+             source.centers().size(),
+             stream);
+  raft::copy(target.centers_rot().data_handle(),
+             source.centers_rot().data_handle(),
+             source.centers_rot().size(),
+             stream);
+
+  // raft::copy shared pointers
+  target.lists() = source.lists();
+
+  // Make sure the device pointers point to the new lists
+  ivf::detail::recompute_internal_state(res, target);
+
+  return target;
+}
+
+/**
+ * Extend the index in-place.
+ * See raft::spatial::knn::ivf_pq::extend docs.
+ */
+template <typename T, typename IdxT>
+void extend(raft::resources const& handle,
+            index<IdxT>* index,
+            const T* new_vectors,
+            const IdxT* new_indices,
+            IdxT n_rows)
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::extend(%zu, %u)", size_t(n_rows), index->dim());
+
+  auto stream           = raft::resource::get_cuda_stream(handle);
+  const auto n_clusters = index->n_lists();
+
+  RAFT_EXPECTS(new_indices != nullptr || index->size() == 0,
+               "You must pass data indices when the index is non-empty.");
+
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, uint8_t> ||
+                  std::is_same_v<T, int8_t>,
+                "Unsupported data type");
+
+  rmm::mr::device_memory_resource* device_memory = raft::resource::get_workspace_resource(handle);
+
+  // The spec defines how the clusters look like
+  auto spec = list_spec<uint32_t, IdxT>{
+    index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+  // Try to allocate an index with the same parameters and the projected new size
+  // (which can be slightly larger than index->size() + n_rows, due to padding).
+  // If this fails, the index would be too big to fit in the device anyway.
+  std::optional<list_data<IdxT, size_t>> placeholder_list(
+    std::in_place_t{},
+    handle,
+    list_spec<size_t, IdxT>{spec},
+    n_rows + (kIndexGroupSize - 1) * std::min<IdxT>(n_clusters, n_rows));
+
+  // Available device memory
+  size_t free_mem, total_mem;
+  RAFT_CUDA_TRY(cudaMemGetInfo(&free_mem, &total_mem));
+
+  // Allocate a buffer for the new labels (classifying the new data)
+  rmm::device_uvector<uint32_t> new_data_labels(n_rows, stream, device_memory);
+  free_mem -= sizeof(uint32_t) * n_rows;
+
+  // Calculate the batch size for the input data if it's not accessible directly from the device
+  constexpr size_t kReasonableMaxBatchSize = 65536;
+  size_t max_batch_size                    = std::min<size_t>(n_rows, kReasonableMaxBatchSize);
+  {
+    size_t size_factor = 0;
+    // we'll use two temporary buffers for converted inputs when computing the codes.
+    size_factor += (index->dim() + index->rot_dim()) * sizeof(float);
+    // ...and another buffer for indices
+    size_factor += sizeof(IdxT);
+    // if the input data is not accessible on device, we'd need a buffer for it.
+    switch (utils::check_pointer_residency(new_vectors)) {
+      case utils::pointer_residency::device_only:
+      case utils::pointer_residency::host_and_device: break;
+      default: size_factor += index->dim() * sizeof(T);
+    }
+    // the same with indices
+    if (new_indices != nullptr) {
+      switch (utils::check_pointer_residency(new_indices)) {
+        case utils::pointer_residency::device_only:
+        case utils::pointer_residency::host_and_device: break;
+        default: size_factor += sizeof(IdxT);
+      }
+    }
+    // make the batch size fit into the remaining memory
+    while (size_factor * max_batch_size > free_mem && max_batch_size > 128) {
+      max_batch_size >>= 1;
+    }
+    // If we're keeping the batches in device memory, update the available mem tracker.
+    free_mem -= size_factor * max_batch_size;
+  }
+
+  // Predict the cluster labels for the new data, in batches if necessary
+  utils::batch_load_iterator<T> vec_batches(
+    new_vectors, n_rows, index->dim(), max_batch_size, stream, device_memory);
+  // Release the placeholder memory, because we don't intend to allocate any more long-living
+  // temporary buffers before we allocate the index data.
+  // This memory could potentially speed up UVM accesses, if any.
+  placeholder_list.reset();
+  {
+    // The cluster centers in the index are stored padded, which is not acceptable by
+    // the kmeans_balanced::predict. Thus, we need the restructuring raft::copy.
+    rmm::device_uvector<float> cluster_centers(
+      size_t(n_clusters) * size_t(index->dim()), stream, device_memory);
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(cluster_centers.data(),
+                                    sizeof(float) * index->dim(),
+                                    index->centers().data_handle(),
+                                    sizeof(float) * index->dim_ext(),
+                                    sizeof(float) * index->dim(),
+                                    n_clusters,
+                                    cudaMemcpyDefault,
+                                    stream));
+    for (const auto& batch : vec_batches) {
+      auto batch_data_view = raft::make_device_matrix_view<const T, internal_extents_t>(
+        batch.data(), batch.size(), index->dim());
+      auto batch_labels_view = raft::make_device_vector_view<uint32_t, internal_extents_t>(
+        new_data_labels.data() + batch.offset(), batch.size());
+      auto centers_view = raft::make_device_matrix_view<const float, internal_extents_t>(
+        cluster_centers.data(), n_clusters, index->dim());
+      raft::cluster::kmeans_balanced_params kmeans_params;
+      kmeans_params.metric = static_cast<raft::distance::DistanceType>((int)index->metric());
+      raft::cluster::kmeans_balanced::predict(handle,
+                                              kmeans_params,
+                                              batch_data_view,
+                                              centers_view,
+                                              batch_labels_view,
+                                              utils::mapping<float>{});
+    }
+  }
+
+  auto list_sizes = index->list_sizes().data_handle();
+  // store the current cluster sizes, because we'll need them later
+  rmm::device_uvector<uint32_t> orig_list_sizes(n_clusters, stream, device_memory);
+  raft::copy(orig_list_sizes.data(), list_sizes, n_clusters, stream);
+
+  // Get the combined cluster sizes
+  raft::stats::histogram<uint32_t, IdxT>(raft::stats::HistTypeAuto,
+                                         reinterpret_cast<int32_t*>(list_sizes),
+                                         IdxT(n_clusters),
+                                         new_data_labels.data(),
+                                         n_rows,
+                                         1,
+                                         stream);
+  raft::linalg::add(list_sizes, list_sizes, orig_list_sizes.data(), n_clusters, stream);
+
+  // Allocate the lists to fit the new data
+  {
+    std::vector<uint32_t> new_cluster_sizes(n_clusters);
+    std::vector<uint32_t> old_cluster_sizes(n_clusters);
+    raft::copy(new_cluster_sizes.data(), list_sizes, n_clusters, stream);
+    raft::copy(old_cluster_sizes.data(), orig_list_sizes.data(), n_clusters, stream);
+    raft::resource::sync_stream(handle);
+    for (uint32_t label = 0; label < n_clusters; label++) {
+      ivf::resize_list(
+        handle, index->lists()[label], spec, new_cluster_sizes[label], old_cluster_sizes[label]);
+    }
+  }
+
+  // Update the pointers and the sizes
+  ivf::detail::recompute_internal_state(handle, *index);
+
+  // Recover old cluster sizes: they are used as counters in the fill-codes kernel
+  raft::copy(list_sizes, orig_list_sizes.data(), n_clusters, stream);
+
+  // By this point, the index state is updated and valid except it doesn't contain the new data
+  // Fill the extended index with the new data (possibly, in batches)
+  utils::batch_load_iterator<IdxT> idx_batches(
+    new_indices, n_rows, 1, max_batch_size, stream, device_memory);
+  for (const auto& vec_batch : vec_batches) {
+    const auto& idx_batch = *idx_batches++;
+    process_and_fill_codes(handle,
+                           *index,
+                           vec_batch.data(),
+                           new_indices != nullptr
+                             ? std::variant<IdxT, const IdxT*>(idx_batch.data())
+                             : std::variant<IdxT, const IdxT*>(IdxT(idx_batch.offset())),
+                           new_data_labels.data() + vec_batch.offset(),
+                           IdxT(vec_batch.size()),
+                           device_memory);
+  }
+}
+
+/**
+ * Create a new index that contains more data.
+ * See raft::spatial::knn::ivf_pq::extend docs.
+ */
+template <typename T, typename IdxT>
+auto extend(raft::resources const& handle,
+            const index<IdxT>& orig_index,
+            const T* new_vectors,
+            const IdxT* new_indices,
+            IdxT n_rows) -> index<IdxT>
+{
+  auto ext_index = clone(handle, orig_index);
+  detail::extend(handle, &ext_index, new_vectors, new_indices, n_rows);
+  return ext_index;
+}
+
+template <typename T, typename IdxT>
+auto build(raft::resources const& handle,
+           const index_params& params,
+           const T* dataset,
+           IdxT n_rows,
+           uint32_t dim) -> index<IdxT>
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::build(%zu, %u)", size_t(n_rows), dim);
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, uint8_t> ||
+                  std::is_same_v<T, int8_t>,
+                "Unsupported data type");
+
+  RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
+  RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  index<IdxT> index(handle, params, dim);
+  utils::memzero(
+    index.accum_sorted_sizes().data_handle(), index.accum_sorted_sizes().size(), stream);
+  utils::memzero(index.list_sizes().data_handle(), index.list_sizes().size(), stream);
+  utils::memzero(index.data_ptrs().data_handle(), index.data_ptrs().size(), stream);
+  utils::memzero(index.inds_ptrs().data_handle(), index.inds_ptrs().size(), stream);
+
+  {
+    auto trainset_ratio = std::max<size_t>(
+      1,
+      size_t(n_rows) / std::max<size_t>(params.kmeans_trainset_fraction * n_rows, index.n_lists()));
+    size_t n_rows_train = n_rows / trainset_ratio;
+
+    auto* device_memory = raft::resource::get_workspace_resource(handle);
+    rmm::mr::managed_memory_resource managed_memory_upstream;
+
+    // Besides just sampling, we transform the input dataset into floats to make it easier
+    // to use gemm operations from cublas.
+    rmm::device_uvector<float> trainset(n_rows_train * index.dim(), stream, device_memory);
+    // TODO: a proper sampling
+    if constexpr (std::is_same_v<T, float>) {
+      RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset.data(),
+                                      sizeof(T) * index.dim(),
+                                      dataset,
+                                      sizeof(T) * index.dim() * trainset_ratio,
+                                      sizeof(T) * index.dim(),
+                                      n_rows_train,
+                                      cudaMemcpyDefault,
+                                      stream));
+    } else {
+      size_t dim = index.dim();
+      cudaPointerAttributes dataset_attr;
+      RAFT_CUDA_TRY(cudaPointerGetAttributes(&dataset_attr, dataset));
+      if (dataset_attr.devicePointer != nullptr) {
+        // data is available on device: just run the kernel to raft::copy and map the data
+        auto p = reinterpret_cast<T*>(dataset_attr.devicePointer);
+        auto trainset_view =
+          raft::make_device_vector_view<float, IdxT>(trainset.data(), dim * n_rows_train);
+        raft::linalg::map_offset(
+          handle, trainset_view, [p, trainset_ratio, dim] __device__(size_t i) {
+            auto col = i % dim;
+            return utils::mapping<float>{}(p[(i - col) * size_t(trainset_ratio) + col]);
+          });
+      } else {
+        // data is not available: first raft::copy, then map inplace
+        auto trainset_tmp = reinterpret_cast<T*>(reinterpret_cast<uint8_t*>(trainset.data()) +
+                                                 (sizeof(float) - sizeof(T)) * index.dim());
+        // We raft::copy the data in strides, one row at a time, and place the smaller rows of type
+        // T at the end of float rows.
+        RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset_tmp,
+                                        sizeof(float) * index.dim(),
+                                        dataset,
+                                        sizeof(T) * index.dim() * trainset_ratio,
+                                        sizeof(T) * index.dim(),
+                                        n_rows_train,
+                                        cudaMemcpyDefault,
+                                        stream));
+        // Transform the input `{T -> float}`, one row per warp.
+        // The threads in each warp raft::copy the data synchronously; this and the layout of the
+        // data (content is aligned to the end of the rows) together allow doing the transform
+        // in-place.
+        copy_warped(trainset.data(),
+                    index.dim(),
+                    trainset_tmp,
+                    index.dim() * sizeof(float) / sizeof(T),
+                    index.dim(),
+                    n_rows_train,
+                    stream);
+      }
+    }
+
+    // NB: here cluster_centers is used as if it is [n_clusters, data_dim] not [n_clusters,
+    // dim_ext]!
+    rmm::device_uvector<float> cluster_centers_buf(
+      index.n_lists() * index.dim(), stream, device_memory);
+    auto cluster_centers = cluster_centers_buf.data();
+
+    // Train balanced hierarchical kmeans clustering
+    auto trainset_const_view = raft::make_device_matrix_view<const float, internal_extents_t>(
+      trainset.data(), n_rows_train, index.dim());
+    auto centers_view = raft::make_device_matrix_view<float, internal_extents_t>(
+      cluster_centers, index.n_lists(), index.dim());
+    raft::cluster::kmeans_balanced_params kmeans_params;
+    kmeans_params.n_iters = params.kmeans_n_iters;
+    kmeans_params.metric  = static_cast<raft::distance::DistanceType>((int)index.metric());
+    raft::cluster::kmeans_balanced::fit(
+      handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
+
+    // Trainset labels are needed for training PQ codebooks
+    rmm::device_uvector<uint32_t> labels(n_rows_train, stream, device_memory);
+    auto centers_const_view = raft::make_device_matrix_view<const float, internal_extents_t>(
+      cluster_centers, index.n_lists(), index.dim());
+    auto labels_view =
+      raft::make_device_vector_view<uint32_t, internal_extents_t>(labels.data(), n_rows_train);
+    raft::cluster::kmeans_balanced::predict(handle,
+                                            kmeans_params,
+                                            trainset_const_view,
+                                            centers_const_view,
+                                            labels_view,
+                                            utils::mapping<float>());
+
+    // Make rotation matrix
+    helpers::make_rotation_matrix(handle,
+                                  params.force_random_rotation,
+                                  index.rot_dim(),
+                                  index.dim(),
+                                  index.rotation_matrix().data_handle());
+
+    helpers::set_centers(handle, &index, cluster_centers);
+
+    // Train PQ codebooks
+    switch (index.codebook_kind()) {
+      case codebook_gen::PER_SUBSPACE:
+        train_per_subset(handle,
+                         index,
+                         n_rows_train,
+                         trainset.data(),
+                         labels.data(),
+                         params.kmeans_n_iters,
+                         &managed_memory_upstream);
+        break;
+      case codebook_gen::PER_CLUSTER:
+        train_per_cluster(handle,
+                          index,
+                          n_rows_train,
+                          trainset.data(),
+                          labels.data(),
+                          params.kmeans_n_iters,
+                          &managed_memory_upstream);
+        break;
+      default: RAFT_FAIL("Unreachable code");
+    }
+  }
+
+  // add the data if necessary
+  if (params.add_data_on_build) {
+    detail::extend<T, IdxT>(handle, &index, dataset, nullptr, n_rows);
+  }
+  return index;
+}
+
+template <typename T, typename IdxT>
+auto build(raft::resources const& handle,
+           const index_params& params,
+           raft::device_matrix_view<const T, IdxT, raft::row_major> dataset) -> index<IdxT>
+{
+  IdxT n_rows = dataset.extent(0);
+  IdxT dim    = dataset.extent(1);
+  return build(handle, params, dataset.data_handle(), n_rows, dim);
+}
+
+template <typename T, typename IdxT>
+void build(raft::resources const& handle,
+           const index_params& params,
+           raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,
+           index<IdxT>* index)
+{
+  IdxT n_rows = dataset.extent(0);
+  IdxT dim    = dataset.extent(1);
+  *index      = build(handle, params, dataset.data_handle(), n_rows, dim);
+}
+
+template <typename T, typename IdxT>
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index) -> index<IdxT>
+{
+  ASSERT(new_vectors.extent(1) == orig_index.dim(),
+         "new_vectors should have the same dimension as the index");
+
+  IdxT n_rows = new_vectors.extent(0);
+  if (new_indices.has_value()) {
+    ASSERT(n_rows == new_indices.value().extent(0),
+           "new_vectors and new_indices have different number of rows");
+  }
+
+  return extend(handle,
+                orig_index,
+                new_vectors.data_handle(),
+                new_indices.has_value() ? new_indices.value().data_handle() : nullptr,
+                n_rows);
+}
+
+template <typename T, typename IdxT>
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,
+            index<IdxT>* index)
+{
+  ASSERT(new_vectors.extent(1) == index->dim(),
+         "new_vectors should have the same dimension as the index");
+
+  IdxT n_rows = new_vectors.extent(0);
+  if (new_indices.has_value()) {
+    ASSERT(n_rows == new_indices.value().extent(0),
+           "new_vectors and new_indices have different number of rows");
+  }
+
+  *index = extend(handle,
+                  *index,
+                  new_vectors.data_handle(),
+                  new_indices.has_value() ? new_indices.value().data_handle() : nullptr,
+                  n_rows);
+}
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
new file mode 100644
index 0000000000..15c965ec91
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
@@ -0,0 +1,167 @@
+/*
+ * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a raft::copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../../core/nvtx.hpp"
+#include "ivf_pq_build.cuh"
+#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq::helpers {
+
+namespace codepacker {
+
+void unpack_list_data(raft::resources const& res,
+                      const index<int64_t>& index,
+                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+                      uint32_t label,
+                      std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  detail::unpack_list_data(res, index, out_codes, label, offset_or_indices);
+}
+
+void pack_list_data(raft::resources const& res,
+                    index<int64_t>* index,
+                    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+                    uint32_t label,
+                    std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  detail::pack_list_data(res, index, new_codes, label, offset_or_indices);
+}
+
+};  // namespace codepacker
+
+void make_rotation_matrix(raft::resources const& handle,
+                          bool force_random_rotation,
+                          uint32_t n_rows,
+                          uint32_t n_cols,
+                          float* rotation_matrix,
+                          raft::random::RngState rng)
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::make_rotation_matrix(%u * %u)", n_rows, n_cols);
+  auto stream  = raft::resource::get_cuda_stream(handle);
+  bool inplace = n_rows == n_cols;
+  uint32_t n   = std::max(n_rows, n_cols);
+  if (force_random_rotation || !inplace) {
+    rmm::device_uvector<float> buf(inplace ? 0 : n * n, stream);
+    float* mat = inplace ? rotation_matrix : buf.data();
+    raft::random::normal(handle, rng, mat, n * n, 0.0f, 1.0f);
+    raft::linalg::detail::qrGetQ_inplace(handle, mat, n, n, stream);
+    if (!inplace) {
+      RAFT_CUDA_TRY(cudaMemcpy2DAsync(rotation_matrix,
+                                      sizeof(float) * n_cols,
+                                      mat,
+                                      sizeof(float) * n,
+                                      sizeof(float) * n_cols,
+                                      n_rows,
+                                      cudaMemcpyDefault,
+                                      stream));
+    }
+  } else {
+    uint32_t stride = n + 1;
+    auto rotation_matrix_view =
+      raft::make_device_vector_view<float, uint32_t>(rotation_matrix, n * n);
+    raft::linalg::map_offset(handle, rotation_matrix_view, [stride] __device__(uint32_t i) {
+      return static_cast<float>(i % stride == 0u);
+    });
+  }
+}
+
+void set_centers(raft::resources const& handle, index<int64_t>* index, const float* cluster_centers)
+{
+  detail::set_centers(handle, index, cluster_centers);
+}
+
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  detail::reconstruct_list_data<float, int64_t>(res, index, out_vectors, label, offset_or_indices);
+}
+
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  detail::reconstruct_list_data<int8_t, int64_t>(res, index, out_vectors, label, offset_or_indices);
+}
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  detail::reconstruct_list_data<uint8_t, int64_t>(
+    res, index, out_vectors, label, offset_or_indices);
+}
+
+void pack_contiguous_list_data(raft::resources const& res,
+                               index<int64_t>* index,
+                               const uint8_t* new_codes,
+                               uint32_t n_rows,
+                               uint32_t label,
+                               std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  detail::pack_contiguous_list_data<int64_t>(
+    res, index, new_codes, n_rows, label, offset_or_indices);
+}
+
+void extend_list_with_codes(
+  raft::resources const& res,
+  index<int64_t>* index,
+  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+  raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+  uint32_t label)
+{
+  detail::extend_list_with_codes<int64_t>(res, index, new_codes, new_indices, label);
+}
+
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const float, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label)
+{
+  detail::extend_list<float, int64_t>(res, index, new_vectors, new_indices, label);
+}
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label)
+{
+  detail::extend_list<int8_t, int64_t>(res, index, new_vectors, new_indices, label);
+}
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label)
+{
+  detail::extend_list<uint8_t, int64_t>(res, index, new_vectors, new_indices, label);
+}
+
+void erase_list(raft::resources const& res, index<int64_t>* index, uint32_t label)
+{
+  detail::erase_list<int64_t>(res, index, label);
+}
+
+}  // namespace cuvs::neighbors::ivf_pq::helpers
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
deleted file mode 100644
index 78c4a0f67d..0000000000
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_float_int64_t.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \
-  auto build(raft::resources const& handle,                                                  \
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \
-  {                                                                                          \
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \
-      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \
-  }                                                                                          \
-                                                                                             \
-  void build(raft::resources const& handle,                                                  \
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
-             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \
-  {                                                                                          \
-    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \
-  }
-CUVS_INST_IVF_PQ_BUILD(float, int64_t);
-
-#undef CUVS_INST_IVF_PQ_BUILD
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
deleted file mode 100644
index c9d7fc4c9c..0000000000
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_int8_t_int64_t.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \
-  auto build(raft::resources const& handle,                                                  \
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \
-  {                                                                                          \
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \
-      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \
-  }                                                                                          \
-                                                                                             \
-  void build(raft::resources const& handle,                                                  \
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
-             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \
-  {                                                                                          \
-    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \
-  }
-CUVS_INST_IVF_PQ_BUILD(int8_t, int64_t);
-
-#undef CUVS_INST_IVF_PQ_BUILD
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
deleted file mode 100644
index 24e56592df..0000000000
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_uint8_t_int64_t.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_BUILD(T, IdxT)                                                      \
-  auto build(raft::resources const& handle,                                                  \
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \
-  {                                                                                          \
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                             \
-      std::move(raft::runtime::neighbors::ivf_pq::build(handle, params, dataset)));          \
-  }                                                                                          \
-                                                                                             \
-  void build(raft::resources const& handle,                                                  \
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
-             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \
-  {                                                                                          \
-    raft::runtime::neighbors::ivf_pq::build(handle, params, dataset, idx->get_raft_index()); \
-  }
-CUVS_INST_IVF_PQ_BUILD(uint8_t, int64_t);
-
-#undef CUVS_INST_IVF_PQ_BUILD
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
new file mode 100644
index 0000000000..fc739a7d8a
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
@@ -0,0 +1,219 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "ivf_pq_list.cuh"
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/device_atomics.cuh>
+#include <raft/util/integer_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+#include <raft/util/vectorized.cuh>
+
+#include <variant>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+/** A chunk of PQ-encoded vector managed by one CUDA thread. */
+using pq_vec_t = raft::TxN_t<uint8_t, kIndexGroupVecLen>::io_t;
+
+/**
+ * This type mimics the `uint8_t&` for the indexing operator of `bitfield_view_t`.
+ *
+ * @tparam Bits number of bits comprising the value.
+ */
+template <uint32_t Bits>
+struct bitfield_ref_t {
+  static_assert(Bits <= 8 && Bits > 0, "Bit code must fit one byte");
+  constexpr static uint8_t kMask = static_cast<uint8_t>((1u << Bits) - 1u);
+  uint8_t* ptr;
+  uint32_t offset;
+
+  constexpr operator uint8_t()  // NOLINT
+  {
+    auto pair = static_cast<uint16_t>(ptr[0]);
+    if (offset + Bits > 8) { pair |= static_cast<uint16_t>(ptr[1]) << 8; }
+    return static_cast<uint8_t>((pair >> offset) & kMask);
+  }
+
+  constexpr auto operator=(uint8_t code) -> bitfield_ref_t&
+  {
+    if (offset + Bits > 8) {
+      auto pair = static_cast<uint16_t>(ptr[0]);
+      pair |= static_cast<uint16_t>(ptr[1]) << 8;
+      pair &= ~(static_cast<uint16_t>(kMask) << offset);
+      pair |= static_cast<uint16_t>(code) << offset;
+      ptr[0] = static_cast<uint8_t>(raft::Pow2<256>::mod(pair));
+      ptr[1] = static_cast<uint8_t>(raft::Pow2<256>::div(pair));
+    } else {
+      ptr[0] = (ptr[0] & ~(kMask << offset)) | (code << offset);
+    }
+    return *this;
+  }
+};
+
+/**
+ * View a byte array as an array of unsigned integers of custom small bit size.
+ *
+ * @tparam Bits number of bits comprising a single element of the array.
+ */
+template <uint32_t Bits>
+struct bitfield_view_t {
+  static_assert(Bits <= 8 && Bits > 0, "Bit code must fit one byte");
+  uint8_t* raw;
+
+  constexpr auto operator[](uint32_t i) -> bitfield_ref_t<Bits>
+  {
+    uint32_t bit_offset = i * Bits;
+    return bitfield_ref_t<Bits>{raw + raft::Pow2<8>::div(bit_offset),
+                                raft::Pow2<8>::mod(bit_offset)};
+  }
+};
+
+/**
+ * Process a single vector in a list.
+ *
+ * @tparam PqBits
+ * @tparam Action tells how to process a single vector (e.g. reconstruct or just unpack)
+ *
+ * @param[in] in_list_data the encoded cluster data.
+ * @param[in] in_ix in-cluster index of the vector to be decoded (one-per-thread).
+ * @param[in] out_ix the output index passed to the action
+ * @param[in] pq_dim
+ * @param action a callable action to be invoked on each PQ code (component of the encoding)
+ *    type: void (uint8_t code, uint32_t out_ix, uint32_t j), where j = [0..pq_dim).
+ */
+template <uint32_t PqBits, typename Action>
+__device__ void run_on_vector(
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    in_list_data,
+  uint32_t in_ix,
+  uint32_t out_ix,
+  uint32_t pq_dim,
+  Action action)
+{
+  using group_align         = raft::Pow2<kIndexGroupSize>;
+  const uint32_t group_ix   = group_align::div(in_ix);
+  const uint32_t ingroup_ix = group_align::mod(in_ix);
+
+  pq_vec_t code_chunk;
+  bitfield_view_t<PqBits> code_view{reinterpret_cast<uint8_t*>(&code_chunk)};
+  constexpr uint32_t kChunkSize = (sizeof(pq_vec_t) * 8u) / PqBits;
+  for (uint32_t j = 0, i = 0; j < pq_dim; i++) {
+    // read the chunk
+    code_chunk = *reinterpret_cast<const pq_vec_t*>(&in_list_data(group_ix, i, ingroup_ix, 0));
+    // read the codes, one/pq_dim at a time
+#pragma unroll
+    for (uint32_t k = 0; k < kChunkSize && j < pq_dim; k++, j++) {
+      // read a piece of the reconstructed vector
+      action(code_view[k], out_ix, j);
+    }
+  }
+}
+
+/**
+ * Process a single vector in a list.
+ *
+ * @tparam PqBits
+ * @tparam SubWarpSize how many threads work on the same ix (only the first thread writes data).
+ * @tparam IdxT type of the index passed to the action
+ * @tparam Action tells how to process a single vector (e.g. encode or just pack)
+ *
+ * @param[in] out_list_data the encoded cluster data.
+ * @param[in] out_ix in-cluster index of the vector to be processed (one-per-SubWarpSize threads).
+ * @param[in] in_ix the input index passed to the action (one-per-SubWarpSize threads).
+ * @param[in] pq_dim
+ * @param action a callable action to be invoked on each PQ code (component of the encoding)
+ *    type: (uint32_t in_ix, uint32_t j) -> uint8_t, where j = [0..pq_dim).
+ */
+template <uint32_t PqBits, uint32_t SubWarpSize, typename IdxT, typename Action>
+__device__ void write_vector(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    out_list_data,
+  uint32_t out_ix,
+  IdxT in_ix,
+  uint32_t pq_dim,
+  Action action)
+{
+  const uint32_t lane_id = raft::Pow2<SubWarpSize>::mod(threadIdx.x);
+
+  using group_align         = raft::Pow2<kIndexGroupSize>;
+  const uint32_t group_ix   = group_align::div(out_ix);
+  const uint32_t ingroup_ix = group_align::mod(out_ix);
+
+  pq_vec_t code_chunk;
+  bitfield_view_t<PqBits> code_view{reinterpret_cast<uint8_t*>(&code_chunk)};
+  constexpr uint32_t kChunkSize = (sizeof(pq_vec_t) * 8u) / PqBits;
+  for (uint32_t j = 0, i = 0; j < pq_dim; i++) {
+    // clear the chunk
+    if (lane_id == 0) { code_chunk = pq_vec_t{}; }
+    // write the codes, one/pq_dim at a time
+#pragma unroll
+    for (uint32_t k = 0; k < kChunkSize && j < pq_dim; k++, j++) {
+      // write a single code
+      uint8_t code = action(in_ix, j);
+      if (lane_id == 0) { code_view[k] = code; }
+    }
+    // write the chunk to the list
+    if (lane_id == 0) {
+      *reinterpret_cast<pq_vec_t*>(&out_list_data(group_ix, i, ingroup_ix, 0)) = code_chunk;
+    }
+  }
+}
+
+/** Process the given indices or a block of a single list (cluster). */
+template <uint32_t PqBits, typename Action>
+__device__ void run_on_list(
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    in_list_data,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t len,
+  uint32_t pq_dim,
+  Action action)
+{
+  for (uint32_t ix = threadIdx.x + blockDim.x * blockIdx.x; ix < len; ix += blockDim.x) {
+    const uint32_t src_ix = std::holds_alternative<uint32_t>(offset_or_indices)
+                              ? std::get<uint32_t>(offset_or_indices) + ix
+                              : std::get<const uint32_t*>(offset_or_indices)[ix];
+    run_on_vector<PqBits>(in_list_data, src_ix, ix, pq_dim, action);
+  }
+}
+
+/** Process the given indices or a block of a single list (cluster). */
+template <uint32_t PqBits, uint32_t SubWarpSize, typename Action>
+__device__ void write_list(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    out_list_data,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t len,
+  uint32_t pq_dim,
+  Action action)
+{
+  using subwarp_align = raft::Pow2<SubWarpSize>;
+  uint32_t stride     = subwarp_align::div(blockDim.x);
+  uint32_t ix         = subwarp_align::div(threadIdx.x + blockDim.x * blockIdx.x);
+  for (; ix < len; ix += stride) {
+    const uint32_t dst_ix = std::holds_alternative<uint32_t>(offset_or_indices)
+                              ? std::get<uint32_t>(offset_or_indices) + ix
+                              : std::get<const uint32_t*>(offset_or_indices)[ix];
+    write_vector<PqBits, SubWarpSize>(out_list_data, dst_ix, ix, pq_dim, action);
+  }
+}
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
new file mode 100644
index 0000000000..16fdcef8a1
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
@@ -0,0 +1,249 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../sample_filter.cuh"  // none_ivf_sample_filter
+#include "ivf_pq_fp_8bit.cuh"    // cuvs::neighbors::ivf_pq::detail::fp_8bit
+
+#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
+#include <cuvs/neighbors/ivf_pq.hpp>         // cuvs::neighbors::ivf_pq::codebook_gen
+#include <raft/core/detail/macros.hpp>       // RAFT_WEAK_FUNCTION
+#include <rmm/cuda_stream_view.hpp>          // rmm::cuda_stream_view
+
+#include <cuda_fp16.h>  // __half
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+// is_local_topk_feasible is not inline here, because we would have to define it
+// here as well. That would run the risk of the definitions here and in the
+// -inl.cuh header diverging.
+auto RAFT_WEAK_FUNCTION is_local_topk_feasible(uint32_t k, uint32_t n_probes, uint32_t n_queries)
+  -> bool;
+
+template <typename OutT,
+          typename LutT,
+          typename IvfSampleFilterT,
+          uint32_t PqBits,
+          int Capacity,
+          bool PrecompBaseDiff,
+          bool EnableSMemLut>
+RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
+                                      uint32_t n_probes,
+                                      uint32_t pq_dim,
+                                      uint32_t n_queries,
+                                      uint32_t queries_offset,
+                                      distance::DistanceType metric,
+                                      codebook_gen codebook_kind,
+                                      uint32_t topk,
+                                      uint32_t max_samples,
+                                      const float* cluster_centers,
+                                      const float* pq_centers,
+                                      const uint8_t* const* pq_dataset,
+                                      const uint32_t* cluster_labels,
+                                      const uint32_t* _chunk_indices,
+                                      const float* queries,
+                                      const uint32_t* index_list,
+                                      float* query_kths,
+                                      IvfSampleFilterT sample_filter,
+                                      LutT* lut_scores,
+                                      OutT* _out_scores,
+                                      uint32_t* _out_indices);
+
+// The signature of the kernel defined by a minimal set of template parameters
+template <typename OutT, typename LutT, typename IvfSampleFilterT>
+using compute_similarity_kernel_t =
+  decltype(&compute_similarity_kernel<OutT, LutT, IvfSampleFilterT, 8, 0, true, true>);
+
+template <typename OutT, typename LutT, typename IvfSampleFilterT>
+struct selected {
+  compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT> kernel;
+  dim3 grid_dim;
+  dim3 block_dim;
+  size_t smem_size;
+  size_t device_lut_size;
+};
+
+template <typename OutT, typename LutT, typename IvfSampleFilterT>
+void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
+                            rmm::cuda_stream_view stream,
+                            uint32_t dim,
+                            uint32_t n_probes,
+                            uint32_t pq_dim,
+                            uint32_t n_queries,
+                            uint32_t queries_offset,
+                            distance::DistanceType metric,
+                            codebook_gen codebook_kind,
+                            uint32_t topk,
+                            uint32_t max_samples,
+                            const float* cluster_centers,
+                            const float* pq_centers,
+                            const uint8_t* const* pq_dataset,
+                            const uint32_t* cluster_labels,
+                            const uint32_t* _chunk_indices,
+                            const float* queries,
+                            const uint32_t* index_list,
+                            float* query_kths,
+                            IvfSampleFilterT sample_filter,
+                            LutT* lut_scores,
+                            OutT* _out_scores,
+                            uint32_t* _out_indices);
+
+/**
+ * Use heuristics to choose an optimal instance of the search kernel.
+ * It selects among a few kernel variants (with/out using shared mem for
+ * lookup tables / precomputed distances) and tries to choose the block size
+ * to maximize kernel occupancy.
+ *
+ * @param manage_local_topk
+ *    whether use the fused calculate+select or just calculate the distances for each
+ *    query and probed cluster.
+ *
+ * @param locality_hint
+ *    beyond this limit do not consider increasing the number of active blocks per SM
+ *    would improve locality anymore.
+ */
+template <typename OutT, typename LutT, typename IvfSampleFilterT>
+auto compute_similarity_select(const cudaDeviceProp& dev_props,
+                               bool manage_local_topk,
+                               int locality_hint,
+                               double preferred_shmem_carveout,
+                               uint32_t pq_bits,
+                               uint32_t pq_dim,
+                               uint32_t precomp_data_count,
+                               uint32_t n_queries,
+                               uint32_t n_probes,
+                               uint32_t topk) -> selected<OutT, LutT, IvfSampleFilterT>;
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
+  OutT, LutT, IvfSampleFilterT)                                                             \
+  extern template auto                                                                      \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
+    const cudaDeviceProp& dev_props,                                                        \
+    bool manage_local_topk,                                                                 \
+    int locality_hint,                                                                      \
+    double preferred_shmem_carveout,                                                        \
+    uint32_t pq_bits,                                                                       \
+    uint32_t pq_dim,                                                                        \
+    uint32_t precomp_data_count,                                                            \
+    uint32_t n_queries,                                                                     \
+    uint32_t n_probes,                                                                      \
+    uint32_t topk)                                                                          \
+    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
+                                                                                            \
+  extern template void                                                                      \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
+    rmm::cuda_stream_view stream,                                                           \
+    uint32_t dim,                                                                           \
+    uint32_t n_probes,                                                                      \
+    uint32_t pq_dim,                                                                        \
+    uint32_t n_queries,                                                                     \
+    uint32_t queries_offset,                                                                \
+    cuvs::distance::DistanceType metric,                                                    \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
+    uint32_t topk,                                                                          \
+    uint32_t max_samples,                                                                   \
+    const float* cluster_centers,                                                           \
+    const float* pq_centers,                                                                \
+    const uint8_t* const* pq_dataset,                                                       \
+    const uint32_t* cluster_labels,                                                         \
+    const uint32_t* _chunk_indices,                                                         \
+    const float* queries,                                                                   \
+    const uint32_t* index_list,                                                             \
+    float* query_kths,                                                                      \
+    IvfSampleFilterT sample_filter,                                                         \
+    LutT* lut_scores,                                                                       \
+    OutT* _out_scores,                                                                      \
+    uint32_t* _out_indices);
+
+#define COMMA ,
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  float,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  half,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  half,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  float,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
+  float,
+  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
+  cuvs::neighbors::filtering::ivf_to_sample_filter<
+    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+
+#undef COMMA
+
+#undef instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
new file mode 100644
index 0000000000..463527bb8f
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
@@ -0,0 +1,942 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../ivf_common.cuh"                 // dummy_block_sort_t
+#include "../sample_filter.cuh"              // none_ivf_sample_filter
+#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
+#include <cuvs/neighbors/ivf_pq.hpp>         // codebook_gen
+#include <raft/matrix/detail/select_warpsort.cuh>  // matrix::detail::select::warpsort::warp_sort_distributed
+#include <raft/util/cuda_rt_essentials.hpp>  // RAFT_CUDA_TRY
+#include <raft/util/device_atomics.cuh>      // raft::atomicMin
+#include <raft/util/pow2_utils.cuh>          // raft::Pow2
+#include <raft/util/vectorized.cuh>          // raft::TxN_t
+
+#include <rmm/cuda_stream_view.hpp>  // rmm::cuda_stream_view
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+/**
+ * Maximum value of k for the fused calculate & select in ivfpq.
+ *
+ * If runtime value of k is larger than this, the main search operation
+ * is split into two kernels (per batch, first calculate distance, then select top-k).
+ */
+static constexpr int kMaxCapacity = 128;
+static_assert((kMaxCapacity >= 32) && !(kMaxCapacity & (kMaxCapacity - 1)),
+              "kMaxCapacity must be a power of two, not smaller than the WarpSize.");
+
+// using weak attribute here, because it may be compiled multiple times.
+auto RAFT_WEAK_FUNCTION is_local_topk_feasible(uint32_t k, uint32_t n_probes, uint32_t n_queries)
+  -> bool
+{
+  if (k > kMaxCapacity) { return false; }            // warp_sort not possible
+  if (n_queries * n_probes <= 16) { return false; }  // overall amount of work is too small
+  return true;
+}
+
+template <int Capacity, typename T, typename IdxT>
+struct pq_block_sort {
+  using type = raft::matrix::detail::select::warpsort::block_sort<
+    raft::matrix::detail::select::warpsort::warp_sort_distributed_ext,
+    Capacity,
+    true,
+    T,
+    IdxT>;
+
+  static auto get_mem_required(uint32_t k_max)
+  {
+    if (k_max == 0 || k_max > Capacity) {
+      return pq_block_sort<0, T, IdxT>::get_mem_required(k_max);
+    }
+    if constexpr (Capacity > 1) {
+      if (k_max * 2 <= Capacity) {
+        return pq_block_sort<(Capacity / 2), T, IdxT>::get_mem_required(k_max);
+      }
+    }
+    return type::queue_t::mem_required;
+  }
+};
+
+template <typename T, typename IdxT>
+struct pq_block_sort<0, T, IdxT> : ivf::detail::dummy_block_sort_t<T, IdxT> {
+  using type = ivf::detail::dummy_block_sort_t<T, IdxT>;
+  static auto mem_required(uint32_t) -> size_t { return 0; }
+  static auto get_mem_required(uint32_t) { return mem_required; }
+};
+
+template <int Capacity, typename T, typename IdxT>
+using block_sort_t = typename pq_block_sort<Capacity, T, IdxT>::type;
+
+/**
+ * Estimate a carveout value as expected by `cudaFuncAttributePreferredSharedMemoryCarveout`
+ * (which does not take into account `reservedSharedMemPerBlock`),
+ * given by a desired schmem-L1 split and a per-block memory requirement in bytes.
+ *
+ * NB: As per the programming guide, the memory carveout setting is just a hint for the driver; it's
+ * free to choose any shmem-L1 configuration it deems appropriate. For example, if you set the
+ * carveout to zero, it will choose a non-zero config that will allow to run at least one active
+ * block per SM.
+ *
+ * @param shmem_fraction
+ *   a fraction representing a desired split (shmem / (shmem + L1)) [0, 1].
+ * @param shmem_per_block
+ *   a shared memory usage per block (dynamic + static shared memory sizes), in bytes.
+ * @param dev_props
+ *   device properties.
+ * @return
+ *   a carveout value in percents [0, 100].
+ */
+constexpr inline auto estimate_carveout(double shmem_fraction,
+                                        size_t shmem_per_block,
+                                        const cudaDeviceProp& dev_props) -> int
+{
+  using shmem_unit = raft::Pow2<128>;
+  size_t m         = shmem_unit::roundUp(shmem_per_block);
+  size_t r         = dev_props.reservedSharedMemPerBlock;
+  size_t s         = dev_props.sharedMemPerMultiprocessor;
+  return (size_t(100 * s * m * shmem_fraction) - (m - 1) * r) / (s * (m + r));
+}
+
+/* Manually unrolled loop over a chunk of pq_dataset that fits into one VecT. */
+template <typename OutT,
+          typename LutT,
+          typename VecT,
+          bool CheckBounds,
+          uint32_t PqBits,
+          uint32_t BitsLeft = 0,
+          uint32_t Ix       = 0>
+__device__ __forceinline__ void ivfpq_compute_chunk(OutT& score /* NOLINT */,
+                                                    typename VecT::math_t& pq_code,
+                                                    const VecT& pq_codes,
+                                                    const LutT*& lut_head,
+                                                    const LutT*& lut_end)
+{
+  if constexpr (CheckBounds) {
+    if (lut_head >= lut_end) { return; }
+  }
+  constexpr uint32_t kTotalBits = 8 * sizeof(typename VecT::math_t);
+  constexpr uint32_t kPqShift   = 1u << PqBits;
+  constexpr uint32_t kPqMask    = kPqShift - 1u;
+  if constexpr (BitsLeft >= PqBits) {
+    uint8_t code = pq_code & kPqMask;
+    pq_code >>= PqBits;
+    score += OutT(lut_head[code]);
+    lut_head += kPqShift;
+    return ivfpq_compute_chunk<OutT, LutT, VecT, CheckBounds, PqBits, BitsLeft - PqBits, Ix>(
+      score, pq_code, pq_codes, lut_head, lut_end);
+  } else if constexpr (Ix < VecT::Ratio) {
+    uint8_t code                = pq_code;
+    pq_code                     = pq_codes.val.data[Ix];
+    constexpr uint32_t kRemBits = PqBits - BitsLeft;
+    constexpr uint32_t kRemMask = (1u << kRemBits) - 1u;
+    code |= (pq_code & kRemMask) << BitsLeft;
+    pq_code >>= kRemBits;
+    score += OutT(lut_head[code]);
+    lut_head += kPqShift;
+    return ivfpq_compute_chunk<OutT,
+                               LutT,
+                               VecT,
+                               CheckBounds,
+                               PqBits,
+                               kTotalBits - kRemBits,
+                               Ix + 1>(score, pq_code, pq_codes, lut_head, lut_end);
+  }
+}
+
+/* Compute the similarity for one vector in the pq_dataset */
+template <typename OutT, typename LutT, typename VecT, uint32_t PqBits>
+__device__ auto ivfpq_compute_score(uint32_t pq_dim,
+                                    const typename VecT::io_t* pq_head,
+                                    const LutT* lut_scores,
+                                    OutT early_stop_limit) -> OutT
+{
+  constexpr uint32_t kChunkSize = sizeof(VecT) * 8u / PqBits;
+  auto lut_head                 = lut_scores;
+  auto lut_end                  = lut_scores + (pq_dim << PqBits);
+  VecT pq_codes;
+  OutT score{0};
+  for (; pq_dim >= kChunkSize; pq_dim -= kChunkSize) {
+    *pq_codes.vectorized_data() = *pq_head;
+    pq_head += kIndexGroupSize;
+    typename VecT::math_t pq_code = 0;
+    ivfpq_compute_chunk<OutT, LutT, VecT, false, PqBits>(
+      score, pq_code, pq_codes, lut_head, lut_end);
+    // Early stop when it makes sense (otherwise early_stop_limit is kDummy/infinity).
+    if (score >= early_stop_limit) { return score; }
+  }
+  if (pq_dim > 0) {
+    *pq_codes.vectorized_data()   = *pq_head;
+    typename VecT::math_t pq_code = 0;
+    ivfpq_compute_chunk<OutT, LutT, VecT, true, PqBits>(
+      score, pq_code, pq_codes, lut_head, lut_end);
+  }
+  return score;
+}
+
+/**
+ * The main kernel that computes similarity scores across multiple queries and probes.
+ * When `Capacity > 0`, it also selects top K candidates for each query and probe
+ * (which need to be merged across probes afterwards).
+ *
+ * Each block processes a (query, probe) pair: it calculates the distance between the single query
+ * vector and all the dataset vector in the cluster that we are probing.
+ *
+ * @tparam OutT
+ *   The output type - distances.
+ * @tparam LutT
+ *   The lookup table element type (lut_scores).
+ * @tparam PqBits
+ *   The bit length of an encoded vector element after compression by PQ
+ *   (NB: pq_book_size = 1 << PqBits).
+ * @tparam Capacity
+ *   Power-of-two; the maximum possible `k` in top-k. Value zero disables fused top-k search.
+ * @tparam PrecompBaseDiff
+ *   Defines whether we should precompute part of the distance and keep it in shared memory
+ *   before the main part (score calculation) to increase memory usage efficiency in the latter.
+ *   For L2, this is the distance between the query and the cluster center.
+ * @tparam EnableSMemLut
+ *   Defines whether to use the shared memory for the lookup table (`lut_scores`).
+ *   Setting this to `false` allows to reduce the shared memory usage (and maximum data dim)
+ *   at the cost of reducing global memory reading throughput.
+ *
+ * @param dim the dimensionality of the data (NB: after rotation transform, i.e. `index.rot_dim()`).
+ * @param n_probes the number of clusters to search for each query
+ * @param pq_dim
+ *   The dimensionality of an encoded vector after compression by PQ.
+ * @param n_queries the number of queries.
+ * @param queries_offset
+ *   An offset of the current query batch. It is used for feeding sample_filter with the
+ *   correct query index.
+ * @param metric the distance type.
+ * @param codebook_kind Defines the way PQ codebooks have been trained.
+ * @param topk the `k` in the select top-k.
+ * @param max_samples the size of the output for a single query.
+ * @param cluster_centers
+ *   The device pointer to the cluster centers in the original space (NB: after rotation)
+ *   [n_clusters, dim].
+ * @param pq_centers
+ *   The device pointer to the cluster centers in the PQ space
+ *   [pq_dim, pq_book_size, pq_len] or [n_clusters, pq_book_size, pq_len].
+ * @param pq_dataset
+ *   The device pointer to the PQ index (data) [n_rows, ...].
+ * @param cluster_labels
+ *   The device pointer to the labels (clusters) for each query and probe [n_queries, n_probes].
+ * @param _chunk_indices
+ *   The device pointer to the data offsets for each query and probe [n_queries, n_probes].
+ * @param queries
+ *   The device pointer to the queries (NB: after rotation) [n_queries, dim].
+ * @param index_list
+ *   An optional device pointer to the enforced order of search [n_queries, n_probes].
+ *   One can pass reordered indices here to try to improve data reading locality.
+ * @param query_kth
+ *   query_kths keep the current state of the filtering - atomically updated distances to the
+ *   k-th closest neighbors for each query [n_queries].
+ * @param sample_filter
+ *   A filter that selects samples for a given query. Use an instance of none_ivf_sample_filter to
+ *   provide a green light for every sample.
+ * @param lut_scores
+ *   The device pointer for storing the lookup table globally [gridDim.x, pq_dim << PqBits].
+ *   Ignored when `EnableSMemLut == true`.
+ * @param _out_scores
+ *   The device pointer to the output scores
+ *   [n_queries, max_samples] or [n_queries, n_probes, topk].
+ * @param _out_indices
+ *   The device pointer to the output indices [n_queries, n_probes, topk].
+ *   These are the indices of the records as they appear in the database view formed by the probed
+ *   clusters / defined by the `_chunk_indices`.
+ *   The indices can have values within the range [0, max_samples).
+ *   Ignored  when `Capacity == 0`.
+ */
+template <typename OutT,
+          typename LutT,
+          typename IvfSampleFilterT,
+          uint32_t PqBits,
+          int Capacity,
+          bool PrecompBaseDiff,
+          bool EnableSMemLut>
+RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
+                                      uint32_t n_probes,
+                                      uint32_t pq_dim,
+                                      uint32_t n_queries,
+                                      uint32_t queries_offset,
+                                      cuvs::distance::DistanceType metric,
+                                      codebook_gen codebook_kind,
+                                      uint32_t topk,
+                                      uint32_t max_samples,
+                                      const float* cluster_centers,
+                                      const float* pq_centers,
+                                      const uint8_t* const* pq_dataset,
+                                      const uint32_t* cluster_labels,
+                                      const uint32_t* _chunk_indices,
+                                      const float* queries,
+                                      const uint32_t* index_list,
+                                      float* query_kths,
+                                      IvfSampleFilterT sample_filter,
+                                      LutT* lut_scores,
+                                      OutT* _out_scores,
+                                      uint32_t* _out_indices)
+{
+  /* Shared memory:
+
+    * lut_scores: lookup table (LUT) of size = `pq_dim << PqBits`  (when EnableSMemLut)
+    * lut_end+:
+       * base_diff: size = dim (which is equal to `pq_dim * pq_len`)  or dim*2
+       * topk::warp_sort::mem_required - local topk temporary buffer (if necessary)
+    * topk::block_sort: some amount of shared memory, but overlaps with the rest:
+        block_sort only needs shared memory for `.done()` operation, which can come very last.
+  */
+  extern __shared__ __align__(256) uint8_t smem_buf[];  // NOLINT
+  constexpr bool kManageLocalTopK = Capacity > 0;
+
+  constexpr uint32_t PqShift = 1u << PqBits;  // NOLINT
+  constexpr uint32_t PqMask  = PqShift - 1u;  // NOLINT
+
+  const uint32_t pq_len   = dim / pq_dim;
+  const uint32_t lut_size = pq_dim * PqShift;
+
+  if constexpr (EnableSMemLut) {
+    lut_scores = reinterpret_cast<LutT*>(smem_buf);
+  } else {
+    lut_scores += lut_size * blockIdx.x;
+  }
+
+  uint8_t* lut_end = nullptr;
+  if constexpr (EnableSMemLut) {
+    lut_end = reinterpret_cast<uint8_t*>(lut_scores + lut_size);
+  } else {
+    lut_end = smem_buf;
+  }
+
+  for (int ib = blockIdx.x; ib < n_queries * n_probes; ib += gridDim.x) {
+    if (ib >= gridDim.x) {
+      // sync shared memory accesses on the second and further iterations
+      __syncthreads();
+    }
+    uint32_t query_ix;
+    uint32_t probe_ix;
+    if (index_list == nullptr) {
+      query_ix = ib % n_queries;
+      probe_ix = ib / n_queries;
+    } else {
+      auto ordered_ix = index_list[ib];
+      query_ix        = ordered_ix / n_probes;
+      probe_ix        = ordered_ix % n_probes;
+    }
+
+    const uint32_t* chunk_indices = _chunk_indices + (n_probes * query_ix);
+    const float* query            = queries + (dim * query_ix);
+    OutT* out_scores;
+    uint32_t* out_indices = nullptr;
+    if constexpr (kManageLocalTopK) {
+      // Store topk calculated distances to out_scores (and its indices to out_indices)
+      const uint64_t out_offset = probe_ix + n_probes * query_ix;
+      out_scores                = _out_scores + out_offset * topk;
+      out_indices               = _out_indices + out_offset * topk;
+    } else {
+      // Store all calculated distances to out_scores
+      out_scores = _out_scores + uint64_t(max_samples) * query_ix;
+    }
+    uint32_t label              = cluster_labels[n_probes * query_ix + probe_ix];
+    const float* cluster_center = cluster_centers + dim * label;
+    const float* pq_center;
+    if (codebook_kind == codebook_gen::PER_SUBSPACE) {
+      pq_center = pq_centers;
+    } else {
+      pq_center = pq_centers + (pq_len << PqBits) * label;
+    }
+
+    if constexpr (PrecompBaseDiff) {
+      // Reduce number of memory reads later by pre-computing parts of the score
+      switch (metric) {
+        case distance::DistanceType::L2SqrtExpanded:
+        case distance::DistanceType::L2Expanded: {
+          for (uint32_t i = threadIdx.x; i < dim; i += blockDim.x) {
+            reinterpret_cast<float*>(lut_end)[i] = query[i] - cluster_center[i];
+          }
+        } break;
+        case distance::DistanceType::InnerProduct: {
+          float2 pvals;
+          for (uint32_t i = threadIdx.x; i < dim; i += blockDim.x) {
+            pvals.x                               = query[i];
+            pvals.y                               = cluster_center[i] * pvals.x;
+            reinterpret_cast<float2*>(lut_end)[i] = pvals;
+          }
+        } break;
+        default: __builtin_unreachable();
+      }
+      __syncthreads();
+    }
+
+    {
+      // Create a lookup table
+      // For each subspace, the lookup table stores the distance between the actual query vector
+      // (projected into the subspace) and all possible pq vectors in that subspace.
+      for (uint32_t i = threadIdx.x; i < lut_size; i += blockDim.x) {
+        const uint32_t i_pq  = i >> PqBits;
+        uint32_t j           = i_pq * pq_len;
+        const uint32_t j_end = pq_len + j;
+        auto cur_pq_center   = pq_center + (i & PqMask) +
+                             (codebook_kind == codebook_gen::PER_SUBSPACE ? j * PqShift : 0u);
+        float score = 0.0;
+        do {
+          float pq_c = *cur_pq_center;
+          cur_pq_center += PqShift;
+          switch (metric) {
+            case distance::DistanceType::L2SqrtExpanded:
+            case distance::DistanceType::L2Expanded: {
+              float diff;
+              if constexpr (PrecompBaseDiff) {
+                diff = reinterpret_cast<float*>(lut_end)[j];
+              } else {
+                diff = query[j] - cluster_center[j];
+              }
+              diff -= pq_c;
+              score += diff * diff;
+            } break;
+            case distance::DistanceType::InnerProduct: {
+              // NB: we negate the scores as we hardcoded select-topk to always compute the minimum
+              float q;
+              if constexpr (PrecompBaseDiff) {
+                float2 pvals = reinterpret_cast<float2*>(lut_end)[j];
+                q            = pvals.x;
+                score -= pvals.y;
+              } else {
+                q = query[j];
+                score -= q * cluster_center[j];
+              }
+              score -= q * pq_c;
+            } break;
+            default: __builtin_unreachable();
+          }
+        } while (++j < j_end);
+        lut_scores[i] = LutT(score);
+      }
+    }
+
+    // Define helper types for efficient access to the pq_dataset, which is stored in an interleaved
+    // format. The chunks of PQ data are stored in kIndexGroupVecLen-bytes-long chunks, interleaved
+    // in groups of kIndexGroupSize elems (which is normally equal to the warp size) for the fastest
+    // possible access by thread warps.
+    //
+    // Consider one record in the pq_dataset is `pq_dim * pq_bits`-bit-long.
+    // Assuming `kIndexGroupVecLen = 16`, one chunk of data read by a thread at once is 128-bits.
+    // Then, such a chunk contains `chunk_size = 128 / pq_bits` record elements, and the record
+    // consists of `ceildiv(pq_dim, chunk_size)` chunks. The chunks are interleaved in groups of 32,
+    // so that the warp can achieve the best coalesced read throughput.
+    using group_align  = raft::Pow2<kIndexGroupSize>;
+    using vec_align    = raft::Pow2<kIndexGroupVecLen>;
+    using local_topk_t = block_sort_t<Capacity, OutT, uint32_t>;
+    using op_t         = uint32_t;
+    using vec_t        = raft::TxN_t<op_t, kIndexGroupVecLen / sizeof(op_t)>;
+
+    uint32_t sample_offset = 0;
+    if (probe_ix > 0) { sample_offset = chunk_indices[probe_ix - 1]; }
+    uint32_t n_samples            = chunk_indices[probe_ix] - sample_offset;
+    uint32_t n_samples_aligned    = group_align::roundUp(n_samples);
+    constexpr uint32_t kChunkSize = (kIndexGroupVecLen * 8u) / PqBits;
+    uint32_t pq_line_width = raft::div_rounding_up_unsafe(pq_dim, kChunkSize) * kIndexGroupVecLen;
+    auto pq_thread_data = pq_dataset[label] + group_align::roundDown(threadIdx.x) * pq_line_width +
+                          group_align::mod(threadIdx.x) * vec_align::Value;
+    pq_line_width *= blockDim.x;
+
+    constexpr OutT kDummy = raft::upper_bound<OutT>();
+    OutT query_kth        = kDummy;
+    if constexpr (kManageLocalTopK) { query_kth = OutT(query_kths[query_ix]); }
+    OutT early_stop_limit = kDummy;
+    switch (metric) {
+      // If the metric is non-negative, we can use the query_kth approximation as an early stop
+      // threshold to skip some iterations when computing the score. Add such metrics here.
+      case distance::DistanceType::L2SqrtExpanded:
+      case distance::DistanceType::L2Expanded: {
+        early_stop_limit = query_kth;
+      } break;
+      default: break;
+    }
+
+    // Ensure lut_scores is written by all threads before using it in ivfpq-compute-score
+    __threadfence_block();
+    __syncthreads();
+    local_topk_t block_topk(topk, lut_end, query_kth);
+
+    // Compute a distance for each sample
+    for (uint32_t i = threadIdx.x; i < n_samples_aligned;
+         i += blockDim.x, pq_thread_data += pq_line_width) {
+      OutT score = kDummy;
+      bool valid = i < n_samples;
+      // Check bounds and that the sample is acceptable for the query
+      if (valid && sample_filter(queries_offset + query_ix, label, i)) {
+        score = ivfpq_compute_score<OutT, LutT, vec_t, PqBits>(
+          pq_dim,
+          reinterpret_cast<const vec_t::io_t*>(pq_thread_data),
+          lut_scores,
+          early_stop_limit);
+      }
+      if constexpr (kManageLocalTopK) {
+        block_topk.add(score, sample_offset + i);
+      } else {
+        if (valid) { out_scores[sample_offset + i] = score; }
+      }
+    }
+    if constexpr (kManageLocalTopK) {
+      // sync threads before the topk merging operation, because we reuse smem_buf
+      __syncthreads();
+      block_topk.done(smem_buf);
+      block_topk.store(out_scores, out_indices);
+      if (threadIdx.x == 0) { atomicMin(query_kths + query_ix, float(out_scores[topk - 1])); }
+    } else {
+      // fill in the rest of the out_scores with dummy values
+      if (probe_ix + 1 == n_probes) {
+        for (uint32_t i = threadIdx.x + sample_offset + n_samples; i < max_samples;
+             i += blockDim.x) {
+          out_scores[i] = kDummy;
+        }
+      }
+    }
+  }
+}
+
+// The signature of the kernel defined by a minimal set of template parameters
+template <typename OutT,
+          typename LutT,
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+using compute_similarity_kernel_t =
+  decltype(&compute_similarity_kernel<OutT, LutT, IvfSampleFilterT, 8, 0, true, true>);
+
+// The config struct lifts the runtime parameters to the template parameters
+template <typename OutT,
+          typename LutT,
+          bool PrecompBaseDiff,
+          bool EnableSMemLut,
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+struct compute_similarity_kernel_config {
+ public:
+  static auto get(uint32_t pq_bits, uint32_t k_max)
+    -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+  {
+    return kernel_choose_bits(pq_bits, k_max);
+  }
+
+ private:
+  static auto kernel_choose_bits(uint32_t pq_bits, uint32_t k_max)
+    -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+  {
+    switch (pq_bits) {
+      case 4: return kernel_try_capacity<4, kMaxCapacity>(k_max);
+      case 5: return kernel_try_capacity<5, kMaxCapacity>(k_max);
+      case 6: return kernel_try_capacity<6, kMaxCapacity>(k_max);
+      case 7: return kernel_try_capacity<7, kMaxCapacity>(k_max);
+      case 8: return kernel_try_capacity<8, kMaxCapacity>(k_max);
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }
+
+  template <uint32_t PqBits, int Capacity>
+  static auto kernel_try_capacity(uint32_t k_max)
+    -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+  {
+    if constexpr (Capacity > 0) {
+      if (k_max == 0 || k_max > Capacity) { return kernel_try_capacity<PqBits, 0>(k_max); }
+    }
+    if constexpr (Capacity > 1) {
+      if (k_max * 2 <= Capacity) { return kernel_try_capacity<PqBits, (Capacity / 2)>(k_max); }
+    }
+    return compute_similarity_kernel<OutT,
+                                     LutT,
+                                     IvfSampleFilterT,
+                                     PqBits,
+                                     Capacity,
+                                     PrecompBaseDiff,
+                                     EnableSMemLut>;
+  }
+};
+
+// A standalone accessor function was necessary to make sure template
+// instantiation work correctly. This accessor function is not used anymore and
+// may be removed.
+template <typename OutT,
+          typename LutT,
+          bool PrecompBaseDiff,
+          bool EnableSMemLut,
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+auto get_compute_similarity_kernel(uint32_t pq_bits, uint32_t k_max)
+  -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+{
+  return compute_similarity_kernel_config<OutT,
+                                          LutT,
+                                          PrecompBaseDiff,
+                                          EnableSMemLut,
+                                          IvfSampleFilterT>::get(pq_bits, k_max);
+}
+
+/** Estimate the occupancy for the given kernel on the given device. */
+template <typename OutT, typename LutT, typename IvfSampleFilterT>
+struct occupancy_t {
+  using shmem_unit = raft::Pow2<128>;
+
+  int blocks_per_sm = 0;
+  double occupancy  = 0.0;
+  double shmem_use  = 1.0;
+
+  inline occupancy_t() = default;
+  inline occupancy_t(size_t smem,
+                     uint32_t n_threads,
+                     compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT> kernel,
+                     const cudaDeviceProp& dev_props)
+  {
+    RAFT_CUDA_TRY(
+      cudaOccupancyMaxActiveBlocksPerMultiprocessor(&blocks_per_sm, kernel, n_threads, smem));
+    occupancy = double(blocks_per_sm * n_threads) / double(dev_props.maxThreadsPerMultiProcessor);
+    shmem_use = double(shmem_unit::roundUp(smem) * blocks_per_sm) /
+                double(dev_props.sharedMemPerMultiprocessor);
+  }
+};
+
+template <typename OutT, typename LutT, typename IvfSampleFilterT>
+struct selected {
+  compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT> kernel;
+  dim3 grid_dim;
+  dim3 block_dim;
+  size_t smem_size;
+  size_t device_lut_size;
+};
+
+template <typename OutT,
+          typename LutT,
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
+                            rmm::cuda_stream_view stream,
+                            uint32_t dim,
+                            uint32_t n_probes,
+                            uint32_t pq_dim,
+                            uint32_t n_queries,
+                            uint32_t queries_offset,
+                            cuvs::distance::DistanceType metric,
+                            codebook_gen codebook_kind,
+                            uint32_t topk,
+                            uint32_t max_samples,
+                            const float* cluster_centers,
+                            const float* pq_centers,
+                            const uint8_t* const* pq_dataset,
+                            const uint32_t* cluster_labels,
+                            const uint32_t* _chunk_indices,
+                            const float* queries,
+                            const uint32_t* index_list,
+                            float* query_kths,
+                            IvfSampleFilterT sample_filter,
+                            LutT* lut_scores,
+                            OutT* _out_scores,
+                            uint32_t* _out_indices)
+{
+  s.kernel<<<s.grid_dim, s.block_dim, s.smem_size, stream>>>(dim,
+                                                             n_probes,
+                                                             pq_dim,
+                                                             n_queries,
+                                                             queries_offset,
+                                                             metric,
+                                                             codebook_kind,
+                                                             topk,
+                                                             max_samples,
+                                                             cluster_centers,
+                                                             pq_centers,
+                                                             pq_dataset,
+                                                             cluster_labels,
+                                                             _chunk_indices,
+                                                             queries,
+                                                             index_list,
+                                                             query_kths,
+                                                             sample_filter,
+                                                             lut_scores,
+                                                             _out_scores,
+                                                             _out_indices);
+  RAFT_CHECK_CUDA(stream);
+}
+
+/**
+ * Use heuristics to choose an optimal instance of the search kernel.
+ * It selects among a few kernel variants (with/out using shared mem for
+ * lookup tables / precomputed distances) and tries to choose the block size
+ * to maximize kernel occupancy.
+ *
+ * @param manage_local_topk
+ *    whether use the fused calculate+select or just calculate the distances for each
+ *    query and probed cluster.
+ *
+ * @param locality_hint
+ *    beyond this limit do not consider increasing the number of active blocks per SM
+ *    would improve locality anymore.
+ */
+template <typename OutT,
+          typename LutT,
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+auto compute_similarity_select(const cudaDeviceProp& dev_props,
+                               bool manage_local_topk,
+                               int locality_hint,
+                               double preferred_shmem_carveout,
+                               uint32_t pq_bits,
+                               uint32_t pq_dim,
+                               uint32_t precomp_data_count,
+                               uint32_t n_queries,
+                               uint32_t n_probes,
+                               uint32_t topk) -> selected<OutT, LutT, IvfSampleFilterT>
+{
+  // Shared memory for storing the lookup table
+  size_t lut_mem = sizeof(LutT) * (pq_dim << pq_bits);
+  // Shared memory for storing pre-computed pieces to speedup the lookup table construction
+  // (e.g. the distance between a cluster center and the query for L2).
+  size_t bdf_mem = sizeof(float) * precomp_data_count;
+
+  // Shared memory used by the fused top-k during cluster scanning;
+  // may overlap with the precomputed distance array
+  struct ltk_add_mem_t {
+    size_t (*mem_required)(uint32_t);
+
+    ltk_add_mem_t(bool manage_local_topk, uint32_t topk)
+      : mem_required(pq_block_sort<kMaxCapacity, OutT, uint32_t>::get_mem_required(
+          manage_local_topk ? topk : 0))
+    {
+    }
+
+    [[nodiscard]] auto operator()(uint32_t n_threads) const -> size_t
+    {
+      return mem_required(n_threads);
+    }
+  } ltk_add_mem{manage_local_topk, topk};
+
+  // Shared memory for the fused top-k component;
+  // may overlap with all other uses of shared memory
+  struct ltk_reduce_mem_t {
+    uint32_t subwarp_size;
+    uint32_t topk;
+    bool manage_local_topk;
+    ltk_reduce_mem_t(bool manage_local_topk, uint32_t topk)
+      : manage_local_topk(manage_local_topk), topk(topk)
+    {
+      subwarp_size = raft::WarpSize;
+      while (topk * 2 <= subwarp_size) {
+        subwarp_size /= 2;
+      }
+    }
+
+    [[nodiscard]] auto operator()(uint32_t n_threads) const -> size_t
+    {
+      return manage_local_topk ? raft::matrix::detail::select::warpsort::
+                                   template calc_smem_size_for_block_wide<OutT, uint32_t>(
+                                     n_threads / subwarp_size, topk)
+                               : 0;
+    }
+  } ltk_reduce_mem{manage_local_topk, topk};
+
+  struct total_shared_mem_t {
+    ltk_add_mem_t& ltk_add_mem;
+    ltk_reduce_mem_t& ltk_reduce_mem;
+    size_t lut_mem;
+    size_t bdf_mem;
+    [[nodiscard]] auto operator()(uint32_t n_threads) const -> size_t
+    {
+      return std::max(ltk_reduce_mem(n_threads),
+                      lut_mem + std::max(bdf_mem, ltk_add_mem(n_threads)));
+    }
+  };
+
+  // Total amount of work; should be enough to occupy the GPU.
+  uint32_t n_blocks = n_queries * n_probes;
+
+  // The minimum block size we may want:
+  //   1. It's a power-of-two for efficient L1 caching of pq_centers values
+  //      (multiples of `1 << pq_bits`).
+  //   2. It should be large enough to fully utilize an SM.
+  uint32_t n_threads_min = raft::WarpSize;
+  while (dev_props.maxBlocksPerMultiProcessor * int(n_threads_min) <
+         dev_props.maxThreadsPerMultiProcessor) {
+    n_threads_min *= 2;
+  }
+  // Further increase the minimum block size to make sure full device occupancy
+  // (NB: this may lead to `n_threads_min` being larger than the kernel's maximum)
+  while (int(n_blocks * n_threads_min) <
+           dev_props.multiProcessorCount * dev_props.maxThreadsPerMultiProcessor &&
+         int(n_threads_min) < dev_props.maxThreadsPerBlock) {
+    n_threads_min *= 2;
+  }
+  // Even further, increase it to allow less blocks per SM if there not enough queries.
+  // With this, we reduce the chance of different clusters being processed by two blocks
+  // on the same SM and thus improve the data locality for L1 caching.
+  while (int(n_queries * n_threads_min) < dev_props.maxThreadsPerMultiProcessor &&
+         int(n_threads_min) < dev_props.maxThreadsPerBlock) {
+    n_threads_min *= 2;
+  }
+
+  // Granularity of changing the number of threads when computing the maximum block size.
+  // It's good to have it multiple of the PQ book width.
+  uint32_t n_threads_gty = raft::round_up_safe<uint32_t>(1u << pq_bits, raft::WarpSize);
+
+  /*
+   Shared memory / L1 cache balance is the main limiter of this kernel.
+   The more blocks per SM we launch, the more shared memory we need. Besides that, we have
+   three versions of the kernel varying in performance and shmem usage.
+
+   We try the most demanding and the fastest kernel first, trying to maximize occupancy with
+   the minimum number of blocks (just one, really). Then, we tweak the `n_threads` to further
+   optimize occupancy and data locality for the L1 cache.
+   */
+  auto conf_fast        = get_compute_similarity_kernel<OutT, LutT, true, true, IvfSampleFilterT>;
+  auto conf_no_basediff = get_compute_similarity_kernel<OutT, LutT, false, true, IvfSampleFilterT>;
+  auto conf_no_smem_lut = get_compute_similarity_kernel<OutT, LutT, true, false, IvfSampleFilterT>;
+  auto topk_or_zero     = manage_local_topk ? topk : 0u;
+  std::array candidates{
+    std::make_tuple(conf_fast(pq_bits, topk_or_zero),
+                    total_shared_mem_t{ltk_add_mem, ltk_reduce_mem, lut_mem, bdf_mem},
+                    true),
+    std::make_tuple(conf_no_basediff(pq_bits, topk_or_zero),
+                    total_shared_mem_t{ltk_add_mem, ltk_reduce_mem, lut_mem, 0},
+                    true),
+    std::make_tuple(conf_no_smem_lut(pq_bits, topk_or_zero),
+                    total_shared_mem_t{ltk_add_mem, ltk_reduce_mem, 0, bdf_mem},
+                    false)};
+
+  // we may allow slightly lower than 100% occupancy;
+  constexpr double kTargetOccupancy = 0.75;
+  // This struct is used to select the better candidate
+  occupancy_t<OutT, LutT, IvfSampleFilterT> selected_perf{};
+  selected<OutT, LutT, IvfSampleFilterT> selected_config;
+  for (auto [kernel, smem_size_f, lut_is_in_shmem] : candidates) {
+    if (smem_size_f(raft::WarpSize) > dev_props.sharedMemPerBlockOptin) {
+      // Even a single block cannot fit into an SM due to shmem requirements. Skip the candidate.
+      continue;
+    }
+
+    // First, we set the carveout hint to the preferred value. The driver will increase this if
+    // needed to run at least one block per SM. At the same time, if more blocks fit into one SM,
+    // this carveout value will limit the calculated occupancy. When we're done selecting the best
+    // launch configuration, we will tighten the carveout once more, based on the final memory
+    // usage and occupancy.
+    const int max_carveout =
+      estimate_carveout(preferred_shmem_carveout, smem_size_f(raft::WarpSize), dev_props);
+    RAFT_CUDA_TRY(
+      cudaFuncSetAttribute(kernel, cudaFuncAttributePreferredSharedMemoryCarveout, max_carveout));
+
+    // Get the theoretical maximum possible number of threads per block
+    cudaFuncAttributes kernel_attrs;
+    RAFT_CUDA_TRY(cudaFuncGetAttributes(&kernel_attrs, kernel));
+    uint32_t n_threads =
+      raft::round_down_safe<uint32_t>(kernel_attrs.maxThreadsPerBlock, n_threads_gty);
+
+    // Actual required shmem depens on the number of threads
+    size_t smem_size = smem_size_f(n_threads);
+
+    // Make sure the kernel can get enough shmem.
+    cudaError_t cuda_status =
+      cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
+    if (cuda_status != cudaSuccess) {
+      RAFT_EXPECTS(
+        cuda_status == cudaGetLastError(),
+        "Tried to reset the expected cuda error code, but it didn't match the expectation");
+      // Failed to request enough shmem for the kernel. Skip the candidate.
+      continue;
+    }
+
+    occupancy_t<OutT, LutT, IvfSampleFilterT> cur(smem_size, n_threads, kernel, dev_props);
+    if (cur.blocks_per_sm <= 0) {
+      // For some reason, we still cannot make this kernel run. Skip the candidate.
+      continue;
+    }
+
+    {
+      // Try to reduce the number of threads to increase occupancy and data locality
+      auto n_threads_tmp = n_threads_min;
+      while (n_threads_tmp * 2 < n_threads) {
+        n_threads_tmp *= 2;
+      }
+      if (n_threads_tmp < n_threads) {
+        while (n_threads_tmp >= n_threads_min) {
+          auto smem_size_tmp = smem_size_f(n_threads_tmp);
+          occupancy_t<OutT, LutT, IvfSampleFilterT> tmp(
+            smem_size_tmp, n_threads_tmp, kernel, dev_props);
+          bool select_it = false;
+          if (lut_is_in_shmem && locality_hint >= tmp.blocks_per_sm) {
+            // Normally, the smaller the block the better for L1 cache hit rate.
+            // Hence, the occupancy should be "just good enough"
+            select_it = tmp.occupancy >= min(kTargetOccupancy, cur.occupancy);
+          } else if (lut_is_in_shmem) {
+            // If we don't have enough repeating probes (locality_hint < tmp.blocks_per_sm),
+            // the locality is not going to improve with increasing the number of blocks per SM.
+            // Hence, the only metric here is the occupancy.
+            bool improves_occupancy = tmp.occupancy > cur.occupancy;
+            // Otherwise, the performance still improves with a smaller block size,
+            // given there is enough work to do
+            bool improves_parallelism =
+              tmp.occupancy == cur.occupancy &&
+              7u * tmp.blocks_per_sm * dev_props.multiProcessorCount <= n_blocks;
+            select_it = improves_occupancy || improves_parallelism;
+          } else {
+            // If we don't use shared memory for the lookup table, increasing the number of blocks
+            // is very taxing on the global memory usage.
+            // In this case, the occupancy must increase a lot to make it worth the cost.
+            select_it = tmp.occupancy >= min(1.0, cur.occupancy / kTargetOccupancy);
+          }
+          if (select_it) {
+            n_threads = n_threads_tmp;
+            smem_size = smem_size_tmp;
+            cur       = tmp;
+          }
+          n_threads_tmp /= 2;
+        }
+      }
+    }
+
+    {
+      if (selected_perf.occupancy <= 0.0  // no candidate yet
+          || (selected_perf.occupancy < cur.occupancy * kTargetOccupancy &&
+              selected_perf.shmem_use >= cur.shmem_use)  // much improved occupancy
+      ) {
+        selected_perf = cur;
+        if (lut_is_in_shmem) {
+          selected_config = {
+            kernel, dim3(n_blocks, 1, 1), dim3(n_threads, 1, 1), smem_size, size_t(0)};
+        } else {
+          // When the global memory is used for the lookup table, we need to minimize the grid
+          // size; otherwise, the kernel may quickly run out of memory.
+          auto n_blocks_min =
+            std::min<uint32_t>(n_blocks, cur.blocks_per_sm * dev_props.multiProcessorCount);
+          selected_config = {kernel,
+                             dim3(n_blocks_min, 1, 1),
+                             dim3(n_threads, 1, 1),
+                             smem_size,
+                             size_t(n_blocks_min) * size_t(pq_dim << pq_bits)};
+        }
+        // Actual shmem/L1 split wildly rounds up the specified preferred carveout, so we set here
+        // a rather conservative bar; most likely, the kernel gets more shared memory than this,
+        // and the occupancy doesn't get hurt.
+        auto carveout = std::min<int>(max_carveout, std::ceil(100.0 * cur.shmem_use));
+        RAFT_CUDA_TRY(
+          cudaFuncSetAttribute(kernel, cudaFuncAttributePreferredSharedMemoryCarveout, carveout));
+        if (cur.occupancy >= kTargetOccupancy) { break; }
+      } else if (selected_perf.occupancy > 0.0) {
+        // If we found a reasonable candidate on a previous iteration, and this one is not better,
+        // then don't try any more candidates because they are much slower anyway.
+        break;
+      }
+    }
+  }
+
+  RAFT_EXPECTS(selected_perf.occupancy > 0.0,
+               "Couldn't determine a working kernel launch configuration.");
+
+  return selected_config;
+}
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
new file mode 100644
index 0000000000..9515cd2d58
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_pq_serialize.cuh"
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_pq::index<int64_t>* index)
+{
+  if (!index) { RAFT_FAIL("Invalid index pointer"); }
+  *index = cuvs::neighbors::ivf_pq::detail::deserialize<int64_t>(handle, filename);
+}
+
+}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
deleted file mode 100644
index ec189ca9a4..0000000000
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_float_int64_t.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \
-  auto extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \
-      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
-  }                                                                                  \
-                                                                                     \
-  void extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \
-  {                                                                                  \
-    raft::runtime::neighbors::ivf_pq::extend(                                        \
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \
-  }
-CUVS_INST_IVF_PQ_EXTEND(float, int64_t);
-
-#undef CUVS_INST_IVF_PQ_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
deleted file mode 100644
index 27eadec727..0000000000
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_int8_t_int64_t.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \
-  auto extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \
-      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
-  }                                                                                  \
-                                                                                     \
-  void extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \
-  {                                                                                  \
-    raft::runtime::neighbors::ivf_pq::extend(                                        \
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \
-  }
-CUVS_INST_IVF_PQ_EXTEND(int8_t, int64_t);
-
-#undef CUVS_INST_IVF_PQ_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
deleted file mode 100644
index 072b30bb00..0000000000
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_extend_uint8_t_int64_t.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_EXTEND(T, IdxT)                                             \
-  auto extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                           \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_pq::index<IdxT>(                                     \
-      std::move(raft::runtime::neighbors::ivf_pq::extend(                            \
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
-  }                                                                                  \
-                                                                                     \
-  void extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                             \
-  {                                                                                  \
-    raft::runtime::neighbors::ivf_pq::extend(                                        \
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \
-  }
-CUVS_INST_IVF_PQ_EXTEND(uint8_t, int64_t);
-
-#undef CUVS_INST_IVF_PQ_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
new file mode 100644
index 0000000000..1b098ac5c1
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/nvtx.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/vectorized.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <cub/cub.cuh>
+#include <cuda_fp16.h>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+/** 8-bit floating-point storage type.
+ *
+ * This is a custom type for the current IVF-PQ implementation. No arithmetic operations defined
+ * only conversion to and from fp32. This type is unrelated to the proposed FP8 specification.
+ */
+template <uint32_t ExpBits, bool Signed>
+struct fp_8bit {
+  static_assert(ExpBits + uint8_t{Signed} <= 8, "The type does not fit in 8 bits.");
+  constexpr static uint32_t ExpMask = (1u << (ExpBits - 1u)) - 1u;  // NOLINT
+  constexpr static uint32_t ValBits = 8u - ExpBits;                 // NOLINT
+
+ public:
+  uint8_t bitstring;
+
+  HDI explicit fp_8bit(uint8_t bs) : bitstring(bs) {}
+  HDI explicit fp_8bit(float fp) : fp_8bit(float2fp_8bit(fp).bitstring) {}
+  HDI auto operator=(float fp) -> fp_8bit<ExpBits, Signed>&
+  {
+    bitstring = float2fp_8bit(fp).bitstring;
+    return *this;
+  }
+  HDI explicit operator float() const { return fp_8bit2float(*this); }
+  HDI explicit operator half() const { return fp_8bit2half(*this); }
+
+ private:
+  static constexpr float kMin = 1.0f / float(1u << ExpMask);
+  static constexpr float kMax = float(1u << (ExpMask + 1)) * (2.0f - 1.0f / float(1u << ValBits));
+
+  static HDI auto float2fp_8bit(float v) -> fp_8bit<ExpBits, Signed>
+  {
+    if constexpr (Signed) {
+      auto u = fp_8bit<ExpBits, false>(std::abs(v)).bitstring;
+      u      = (u & 0xfeu) | uint8_t{v < 0};  // set the sign bit
+      return fp_8bit<ExpBits, true>(u);
+    } else {
+      // sic! all small and negative numbers are truncated to zero.
+      if (v < kMin) { return fp_8bit<ExpBits, false>{static_cast<uint8_t>(0)}; }
+      // protect from overflow
+      if (v >= kMax) { return fp_8bit<ExpBits, false>{static_cast<uint8_t>(0xffu)}; }
+      // the rest of possible float values should be within the normalized range
+      return fp_8bit<ExpBits, false>{static_cast<uint8_t>(
+        (*reinterpret_cast<uint32_t*>(&v) + (ExpMask << 23u) - 0x3f800000u) >> (15u + ExpBits))};
+    }
+  }
+
+  static HDI auto fp_8bit2float(const fp_8bit<ExpBits, Signed>& v) -> float
+  {
+    uint32_t u = v.bitstring;
+    if constexpr (Signed) {
+      u &= ~1;  // zero the sign bit
+    }
+    float r;
+    constexpr uint32_t kBase32       = (0x3f800000u | (0x00400000u >> ValBits)) - (ExpMask << 23);
+    *reinterpret_cast<uint32_t*>(&r) = kBase32 + (u << (15u + ExpBits));
+    if constexpr (Signed) {  // recover the sign bit
+      if (v.bitstring & 1) { r = -r; }
+    }
+    return r;
+  }
+
+  static HDI auto fp_8bit2half(const fp_8bit<ExpBits, Signed>& v) -> half
+  {
+    uint16_t u = v.bitstring;
+    if constexpr (Signed) {
+      u &= ~1;  // zero the sign bit
+    }
+    half r;
+    constexpr uint16_t kBase16       = (0x3c00u | (0x0200u >> ValBits)) - (ExpMask << 10);
+    *reinterpret_cast<uint16_t*>(&r) = kBase16 + (u << (2u + ExpBits));
+    if constexpr (Signed) {  // recover the sign bit
+      if (v.bitstring & 1) { r = -r; }
+    }
+    return r;
+  }
+};
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
new file mode 100644
index 0000000000..9a4263c26c
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/util/integer_utils.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+template <typename SizeT, typename IdxT>
+constexpr list_spec<SizeT, IdxT>::list_spec(uint32_t pq_bits,
+                                            uint32_t pq_dim,
+                                            bool conservative_memory_allocation)
+  : pq_bits(pq_bits),
+    pq_dim(pq_dim),
+    align_min(kIndexGroupSize),
+    align_max(conservative_memory_allocation ? kIndexGroupSize : 1024)
+{
+}
+
+template <typename SizeT, typename IdxT>
+template <typename OtherSizeT>
+constexpr list_spec<SizeT, IdxT>::list_spec(const list_spec<OtherSizeT, IdxT>& other_spec)
+  : pq_bits{other_spec.pq_bits},
+    pq_dim{other_spec.pq_dim},
+    align_min{other_spec.align_min},
+    align_max{other_spec.align_max}
+{
+}
+
+template <typename SizeT, typename IdxT>
+constexpr typename list_spec<SizeT, IdxT>::list_extents list_spec<SizeT, IdxT>::make_list_extents(
+  SizeT n_rows) const
+{
+  // how many elems of pq_dim fit into one kIndexGroupVecLen-byte chunk
+  auto pq_chunk = (kIndexGroupVecLen * 8u) / pq_bits;
+  return raft::make_extents<SizeT>(raft::div_rounding_up_safe<SizeT>(n_rows, kIndexGroupSize),
+                                   raft::div_rounding_up_safe<SizeT>(pq_dim, pq_chunk),
+                                   kIndexGroupSize,
+                                   kIndexGroupVecLen);
+}
+}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
new file mode 100644
index 0000000000..b3b8f281a4
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -0,0 +1,797 @@
+/*
+ * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a raft::copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../core/nvtx.hpp"
+#include "../detail/ann_utils.cuh"
+#include "../ivf_common.cuh"
+#include "../sample_filter.cuh"  // none_ivf_sample_filter
+#include "ivf_pq_compute_similarity.cuh"
+#include "ivf_pq_fp_8bit.cuh"
+
+#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/custom_resource.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/core/resource/device_properties.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/gemm.cuh>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/detail/select_k.cuh>
+#include <raft/matrix/detail/select_warpsort.cuh>
+#include <raft/util/cache.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/device_atomics.cuh>
+#include <raft/util/device_loads_stores.cuh>
+#include <raft/util/pow2_utils.cuh>
+#include <raft/util/vectorized.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <cub/cub.cuh>
+#include <cuda_fp16.h>
+
+#include <optional>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+using namespace cuvs::spatial::knn::detail;  // NOLINT
+
+/**
+ * Select the clusters to probe and, as a side-effect, translate the queries type `T -> float`
+ *
+ * Assuming the number of clusters is not that big (a few thousands), we do a plain GEMM
+ * followed by select_k to select the clusters to probe. There's no need to return the similarity
+ * scores here.
+ */
+template <typename T>
+void select_clusters(raft::resources const& handle,
+                     uint32_t* clusters_to_probe,  // [n_queries, n_probes]
+                     float* float_queries,         // [n_queries, dim_ext]
+                     uint32_t n_queries,
+                     uint32_t n_probes,
+                     uint32_t n_lists,
+                     uint32_t dim,
+                     uint32_t dim_ext,
+                     cuvs::distance::DistanceType metric,
+                     const T* queries,              // [n_queries, dim]
+                     const float* cluster_centers,  // [n_lists, dim_ext]
+                     rmm::mr::device_memory_resource* mr)
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::search::select_clusters(n_probes = %u, n_queries = %u, n_lists = %u, dim = %u)",
+    n_probes,
+    n_queries,
+    n_lists,
+    dim);
+  auto stream = raft::resource::get_cuda_stream(handle);
+  /* NOTE[qc_distances]
+
+  We compute query-center distances to choose the clusters to probe.
+  We accomplish that with just one GEMM operation thanks to some preprocessing:
+
+    L2 distance:
+      cluster_centers[i, dim()] contains the squared norm of the center vector i;
+      we extend the dimension K of the GEMM to compute it together with all the dot products:
+
+      `qc_distances[i, j] = |cluster_centers[j]|^2 - 2 * (queries[i], cluster_centers[j])`
+
+      This is a monotonous mapping of the proper L2 distance.
+
+    IP distance:
+      `qc_distances[i, j] = - (queries[i], cluster_centers[j])`
+
+      This is a negative inner-product distance. We minimize it to find the similar clusters.
+
+      NB: qc_distances is NOT used further in ivfpq_search.
+ */
+  float norm_factor;
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded: norm_factor = 1.0 / -2.0; break;
+    case cuvs::distance::DistanceType::InnerProduct: norm_factor = 0.0; break;
+    default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
+  }
+  auto float_queries_view =
+    raft::make_device_vector_view<float, uint32_t>(float_queries, dim_ext * n_queries);
+  raft::linalg::map_offset(
+    handle, float_queries_view, [queries, dim, dim_ext, norm_factor] __device__(uint32_t ix) {
+      uint32_t col = ix % dim_ext;
+      uint32_t row = ix / dim_ext;
+      return col < dim ? utils::mapping<float>{}(queries[col + dim * row]) : norm_factor;
+    });
+
+  float alpha;
+  float beta;
+  uint32_t gemm_k = dim;
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded: {
+      alpha  = -2.0;
+      beta   = 0.0;
+      gemm_k = dim + 1;
+      RAFT_EXPECTS(gemm_k <= dim_ext, "unexpected gemm_k or dim_ext");
+    } break;
+    case cuvs::distance::DistanceType::InnerProduct: {
+      alpha = -1.0;
+      beta  = 0.0;
+    } break;
+    default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
+  }
+  rmm::device_uvector<float> qc_distances(n_queries * n_lists, stream, mr);
+  raft::linalg::gemm(handle,
+                     true,
+                     false,
+                     n_lists,
+                     n_queries,
+                     gemm_k,
+                     &alpha,
+                     cluster_centers,
+                     dim_ext,
+                     float_queries,
+                     dim_ext,
+                     &beta,
+                     qc_distances.data(),
+                     n_lists,
+                     stream);
+
+  // Select neighbor clusters for each query.
+  rmm::device_uvector<float> cluster_dists(n_queries * n_probes, stream, mr);
+  raft::matrix::detail::select_k<float, uint32_t>(handle,
+                                                  qc_distances.data(),
+                                                  nullptr,
+                                                  n_queries,
+                                                  n_lists,
+                                                  n_probes,
+                                                  cluster_dists.data(),
+                                                  clusters_to_probe,
+                                                  true);
+}
+
+/**
+ * An approximation to the number of times each cluster appears in a batched sample.
+ *
+ * If the pairs (probe_ix, query_ix) are sorted by the probe_ix, there is a good chance that
+ * the same probe_ix (cluster) is processed by several blocks on a single SM. This greatly
+ * increases the L1 cache hit rate (i.e. increases the data locality).
+ *
+ * This function gives an estimate of how many times a specific cluster may appear in the
+ * batch. Thus, it gives a practical limit to how many blocks should be active on the same SM
+ * to improve the L1 cache hit rate.
+ */
+constexpr inline auto expected_probe_coresidency(uint32_t n_clusters,
+                                                 uint32_t n_probes,
+                                                 uint32_t n_queries) -> uint32_t
+{
+  /*
+    Let say:
+      n = n_clusters
+      k = n_probes
+      m = n_queries
+      r = # of times a specific block appears in the batched sample.
+
+    Then, r has the Binomial distribution (p = k / n):
+      P(r) = C(m,r) * k^r * (n - k)^(m - r) / n^m
+      E[r] = m * k / n
+      E[r | r > 0] = m * k / n / (1 - (1 - k/n)^m)
+
+    The latter can be approximated by a much simpler formula, assuming (k / n) -> 0:
+      E[r | r > 0] = 1 + (m - 1) * k / (2 * n) + O( (k/n)^2 )
+   */
+  return 1 + (n_queries - 1) * n_probes / (2 * n_clusters);
+}
+
+struct search_kernel_key {
+  bool manage_local_topk;
+  uint32_t locality_hint;
+  double preferred_shmem_carveout;
+  uint32_t pq_bits;
+  uint32_t pq_dim;
+  uint32_t precomp_data_count;
+  uint32_t n_queries;
+  uint32_t n_probes;
+  uint32_t topk;
+};
+
+inline auto operator==(const search_kernel_key& a, const search_kernel_key& b) -> bool
+{
+  return a.manage_local_topk == b.manage_local_topk && a.locality_hint == b.locality_hint &&
+         a.preferred_shmem_carveout == b.preferred_shmem_carveout && a.pq_bits == b.pq_bits &&
+         a.pq_dim == b.pq_dim && a.precomp_data_count == b.precomp_data_count &&
+         a.n_queries == b.n_queries && a.n_probes == b.n_probes && a.topk == b.topk;
+}
+
+struct search_kernel_key_hash {
+  inline auto operator()(const search_kernel_key& x) const noexcept -> std::size_t
+  {
+    return (size_t{x.manage_local_topk} << 63) +
+           size_t{x.topk} * size_t{x.n_probes} * size_t{x.n_queries} +
+           size_t{x.precomp_data_count} * size_t{x.pq_dim} * size_t{x.pq_bits};
+  }
+};
+
+template <typename OutT, typename LutT, typename IvfSampleFilterT>
+struct search_kernel_cache {
+  /** Number of matmul invocations to cache. */
+  static constexpr size_t kDefaultSize = 100;
+  raft::cache::lru<search_kernel_key,
+                   search_kernel_key_hash,
+                   std::equal_to<>,
+                   selected<OutT, LutT, IvfSampleFilterT>>
+    value{kDefaultSize};
+};
+
+/**
+ * The "main part" of the search, which assumes that outer-level `search` has already:
+ *
+ *   1. computed the closest clusters to probe (`clusters_to_probe`);
+ *   2. transformed input queries into the rotated space (rot_dim);
+ *   3. split the query batch into smaller chunks, so that the device workspace
+ *      is guaranteed to fit into GPU memory.
+ */
+template <typename ScoreT, typename LutT, typename IvfSampleFilterT, typename IdxT>
+void ivfpq_search_worker(raft::resources const& handle,
+                         const index<IdxT>& index,
+                         uint32_t max_samples,
+                         uint32_t n_probes,
+                         uint32_t topK,
+                         uint32_t n_queries,
+                         uint32_t queries_offset,            // needed for filtering
+                         const uint32_t* clusters_to_probe,  // [n_queries, n_probes]
+                         const float* query,                 // [n_queries, rot_dim]
+                         IdxT* neighbors,                    // [n_queries, topK]
+                         float* distances,                   // [n_queries, topK]
+                         float scaling_factor,
+                         double preferred_shmem_carveout,
+                         IvfSampleFilterT sample_filter)
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::search-worker(n_queries = %u, n_probes = %u, k = %u, dim = %zu)",
+    n_queries,
+    n_probes,
+    topK,
+    index.dim());
+  auto stream = raft::resource::get_cuda_stream(handle);
+  auto mr     = raft::resource::get_workspace_resource(handle);
+
+  bool manage_local_topk         = is_local_topk_feasible(topK, n_probes, n_queries);
+  auto topk_len                  = manage_local_topk ? n_probes * topK : max_samples;
+  std::size_t n_queries_probes   = std::size_t(n_queries) * std::size_t(n_probes);
+  std::size_t n_queries_topk_len = std::size_t(n_queries) * std::size_t(topk_len);
+  if (manage_local_topk) {
+    RAFT_LOG_DEBUG("Fused version of the search kernel is selected (manage_local_topk == true)");
+  } else {
+    RAFT_LOG_DEBUG(
+      "Non-fused version of the search kernel is selected (manage_local_topk == false)");
+  }
+
+  rmm::device_uvector<uint32_t> index_list_sorted_buf(0, stream, mr);
+  uint32_t* index_list_sorted = nullptr;
+  rmm::device_uvector<uint32_t> num_samples(n_queries, stream, mr);
+  rmm::device_uvector<uint32_t> chunk_index(n_queries_probes, stream, mr);
+  // [maxBatchSize, max_samples] or  [maxBatchSize, n_probes, topk]
+  rmm::device_uvector<ScoreT> distances_buf(n_queries_topk_len, stream, mr);
+  rmm::device_uvector<uint32_t> neighbors_buf(0, stream, mr);
+  uint32_t* neighbors_ptr = nullptr;
+  if (manage_local_topk) {
+    neighbors_buf.resize(n_queries_topk_len, stream);
+    neighbors_ptr = neighbors_buf.data();
+  }
+  rmm::device_uvector<uint32_t> neighbors_uint32_buf(0, stream, mr);
+  uint32_t* neighbors_uint32 = nullptr;
+  if constexpr (sizeof(IdxT) == sizeof(uint32_t)) {
+    neighbors_uint32 = reinterpret_cast<uint32_t*>(neighbors);
+  } else {
+    neighbors_uint32_buf.resize(n_queries * topK, stream);
+    neighbors_uint32 = neighbors_uint32_buf.data();
+  }
+
+  ivf::detail::calc_chunk_indices::configure(n_probes, n_queries)(index.list_sizes().data_handle(),
+                                                                  clusters_to_probe,
+                                                                  chunk_index.data(),
+                                                                  num_samples.data(),
+                                                                  stream);
+
+  auto coresidency = expected_probe_coresidency(index.n_lists(), n_probes, n_queries);
+
+  if (coresidency > 1) {
+    // Sorting index by cluster number (label).
+    // The goal is to incrase the L2 cache hit rate to read the vectors
+    // of a cluster by processing the cluster at the same time as much as
+    // possible.
+    index_list_sorted_buf.resize(n_queries_probes, stream);
+    auto index_list_buf = raft::make_device_mdarray<uint32_t>(
+      handle, mr, raft::make_extents<uint32_t>(n_queries_probes));
+    rmm::device_uvector<uint32_t> cluster_labels_out(n_queries_probes, stream, mr);
+    auto index_list   = index_list_buf.data_handle();
+    index_list_sorted = index_list_sorted_buf.data();
+
+    raft::linalg::map_offset(handle, index_list_buf.view(), raft::identity_op{});
+
+    int begin_bit             = 0;
+    int end_bit               = sizeof(uint32_t) * 8;
+    size_t cub_workspace_size = 0;
+    cub::DeviceRadixSort::SortPairs(nullptr,
+                                    cub_workspace_size,
+                                    clusters_to_probe,
+                                    cluster_labels_out.data(),
+                                    index_list,
+                                    index_list_sorted,
+                                    n_queries_probes,
+                                    begin_bit,
+                                    end_bit,
+                                    stream);
+    rmm::device_buffer cub_workspace(cub_workspace_size, stream, mr);
+    cub::DeviceRadixSort::SortPairs(cub_workspace.data(),
+                                    cub_workspace_size,
+                                    clusters_to_probe,
+                                    cluster_labels_out.data(),
+                                    index_list,
+                                    index_list_sorted,
+                                    n_queries_probes,
+                                    begin_bit,
+                                    end_bit,
+                                    stream);
+  }
+
+  // select and run the main search kernel
+  uint32_t precomp_data_count = 0;
+  switch (index.metric()) {
+    case distance::DistanceType::L2SqrtExpanded:
+    case distance::DistanceType::L2SqrtUnexpanded:
+    case distance::DistanceType::L2Unexpanded:
+    case distance::DistanceType::L2Expanded: {
+      // stores basediff (query[i] - center[i])
+      precomp_data_count = index.rot_dim();
+    } break;
+    case distance::DistanceType::InnerProduct: {
+      // stores two components (query[i] * center[i], query[i] * center[i])
+      precomp_data_count = index.rot_dim() * 2;
+    } break;
+    default: {
+      RAFT_FAIL("Unsupported metric");
+    } break;
+  }
+
+  selected<ScoreT, LutT, IvfSampleFilterT> search_instance;
+  search_kernel_key search_key{manage_local_topk,
+                               coresidency,
+                               preferred_shmem_carveout,
+                               index.pq_bits(),
+                               index.pq_dim(),
+                               precomp_data_count,
+                               n_queries,
+                               n_probes,
+                               topK};
+  auto& cache =
+    raft::resource::get_custom_resource<search_kernel_cache<ScoreT, LutT, IvfSampleFilterT>>(handle)
+      ->value;
+  if (!cache.get(search_key, &search_instance)) {
+    search_instance = compute_similarity_select<ScoreT, LutT, IvfSampleFilterT>(
+      raft::resource::get_device_properties(handle),
+      manage_local_topk,
+      coresidency,
+      preferred_shmem_carveout,
+      index.pq_bits(),
+      index.pq_dim(),
+      precomp_data_count,
+      n_queries,
+      n_probes,
+      topK);
+    cache.set(search_key, search_instance);
+  }
+
+  rmm::device_uvector<LutT> device_lut(search_instance.device_lut_size, stream, mr);
+  std::optional<raft::device_vector<float>> query_kths_buf{std::nullopt};
+  float* query_kths = nullptr;
+  if (manage_local_topk) {
+    query_kths_buf.emplace(
+      raft::make_device_mdarray<float>(handle, mr, raft::make_extents<uint32_t>(n_queries)));
+    raft::linalg::map(
+      handle,
+      query_kths_buf->view(),
+      raft::const_op<float>{ivf::detail::dummy_block_sort_t<ScoreT, IdxT>::queue_t::kDummy});
+    query_kths = query_kths_buf->data_handle();
+  }
+  compute_similarity_run(search_instance,
+                         stream,
+                         index.rot_dim(),
+                         n_probes,
+                         index.pq_dim(),
+                         n_queries,
+                         queries_offset,
+                         index.metric(),
+                         index.codebook_kind(),
+                         topK,
+                         max_samples,
+                         index.centers_rot().data_handle(),
+                         index.pq_centers().data_handle(),
+                         index.data_ptrs().data_handle(),
+                         clusters_to_probe,
+                         chunk_index.data(),
+                         query,
+                         index_list_sorted,
+                         query_kths,
+                         sample_filter,
+                         device_lut.data(),
+                         distances_buf.data(),
+                         neighbors_ptr);
+
+  // Select topk vectors for each query
+  rmm::device_uvector<ScoreT> topk_dists(n_queries * topK, stream, mr);
+  raft::matrix::detail::select_k<ScoreT, uint32_t>(
+    handle,
+    distances_buf.data(),
+    neighbors_ptr,
+    n_queries,
+    topk_len,
+    topK,
+    topk_dists.data(),
+    neighbors_uint32,
+    true,
+    false,
+    raft::matrix::SelectAlgo::kAuto,
+    manage_local_topk ? nullptr : num_samples.data());
+
+  // Postprocessing
+  ivf::detail::postprocess_distances(
+    distances, topk_dists.data(), index.metric(), n_queries, topK, scaling_factor, true, stream);
+  ivf::detail::postprocess_neighbors(neighbors,
+                                     neighbors_uint32,
+                                     index.inds_ptrs().data_handle(),
+                                     clusters_to_probe,
+                                     chunk_index.data(),
+                                     n_queries,
+                                     n_probes,
+                                     topK,
+                                     stream);
+}
+
+/**
+ * This structure helps selecting a proper instance of the worker search function,
+ * which contains a few template parameters.
+ */
+template <typename IdxT, typename IvfSampleFilterT>
+struct ivfpq_search {
+ public:
+  using fun_t = decltype(&ivfpq_search_worker<float, float, IvfSampleFilterT, IdxT>);
+
+  /**
+   * Select an instance of the ivf-pq search function based on search tuning parameters,
+   * such as the look-up data type or the internal score type.
+   */
+  static auto fun(const search_params& params, distance::DistanceType metric) -> fun_t
+  {
+    return fun_try_score_t(params, metric);
+  }
+
+ private:
+  template <typename ScoreT, typename LutT>
+  static auto filter_reasonable_instances(const search_params& params) -> fun_t
+  {
+    if constexpr (sizeof(ScoreT) >= sizeof(LutT)) {
+      return ivfpq_search_worker<ScoreT, LutT, IvfSampleFilterT, IdxT>;
+    } else {
+      RAFT_FAIL(
+        "Unexpected lut_dtype / internal_distance_dtype combination (%d, %d). "
+        "Size of the internal_distance_dtype should be not smaller than the size of the lut_dtype.",
+        int(params.lut_dtype),
+        int(params.internal_distance_dtype));
+    }
+  }
+
+  template <typename ScoreT>
+  static auto fun_try_lut_t(const search_params& params, distance::DistanceType metric) -> fun_t
+  {
+    bool signed_metric = false;
+    switch (metric) {
+      case cuvs::distance::DistanceType::InnerProduct: signed_metric = true; break;
+      default: break;
+    }
+
+    switch (params.lut_dtype) {
+      case CUDA_R_32F: return filter_reasonable_instances<ScoreT, float>(params);
+      case CUDA_R_16F: return filter_reasonable_instances<ScoreT, half>(params);
+      case CUDA_R_8U:
+      case CUDA_R_8I:
+        if (signed_metric) {
+          return filter_reasonable_instances<ScoreT, fp_8bit<5, true>>(params);
+        } else {
+          return filter_reasonable_instances<ScoreT, fp_8bit<5, false>>(params);
+        }
+      default: RAFT_FAIL("Unexpected lut_dtype (%d)", int(params.lut_dtype));
+    }
+  }
+
+  static auto fun_try_score_t(const search_params& params, distance::DistanceType metric) -> fun_t
+  {
+    switch (params.internal_distance_dtype) {
+      case CUDA_R_32F: return fun_try_lut_t<float>(params, metric);
+      case CUDA_R_16F: return fun_try_lut_t<half>(params, metric);
+      default:
+        RAFT_FAIL("Unexpected internal_distance_dtype (%d)", int(params.internal_distance_dtype));
+    }
+  }
+};
+
+/**
+ * A heuristic for bounding the number of queries per batch, to improve GPU utilization.
+ * (based on the number of SMs and the work size).
+ *
+ * @param res is used to query the workspace size
+ * @param k top-k
+ * @param n_probes number of selected clusters per query
+ * @param n_queries number of queries hoped to be processed at once.
+ *                  (maximum value for the returned batch size)
+ * @param max_samples maximum possible number of samples to be processed for the given `n_probes`
+ *
+ * @return maximum recommended batch size.
+ */
+inline auto get_max_batch_size(raft::resources const& res,
+                               uint32_t k,
+                               uint32_t n_probes,
+                               uint32_t n_queries,
+                               uint32_t max_samples) -> uint32_t
+{
+  uint32_t max_batch_size = n_queries;
+  uint32_t n_ctas_total   = raft::resource::get_device_properties(res).multiProcessorCount * 2;
+  uint32_t n_ctas_total_per_batch = n_ctas_total / max_batch_size;
+  float utilization               = float(n_ctas_total_per_batch * max_batch_size) / n_ctas_total;
+  if (n_ctas_total_per_batch > 1 || (n_ctas_total_per_batch == 1 && utilization < 0.6)) {
+    uint32_t n_ctas_total_per_batch_1 = n_ctas_total_per_batch + 1;
+    uint32_t max_batch_size_1         = n_ctas_total / n_ctas_total_per_batch_1;
+    float utilization_1 = float(n_ctas_total_per_batch_1 * max_batch_size_1) / n_ctas_total;
+    if (utilization < utilization_1) { max_batch_size = max_batch_size_1; }
+  }
+  // Check in the tmp distance buffer is not too big
+  auto ws_size = [k, n_probes, max_samples](uint32_t bs) -> uint64_t {
+    const uint64_t buffers_fused     = 12ull * k * n_probes;
+    const uint64_t buffers_non_fused = 4ull * max_samples;
+    const uint64_t other             = 32ull * n_probes;
+    return static_cast<uint64_t>(bs) *
+           (other + (is_local_topk_feasible(k, n_probes, bs) ? buffers_fused : buffers_non_fused));
+  };
+  auto max_ws_size = raft::resource::get_workspace_free_bytes(res);
+  if (ws_size(max_batch_size) > max_ws_size) {
+    uint32_t smaller_batch_size = raft::bound_by_power_of_two(max_batch_size);
+    // gradually reduce the batch size until we fit into the max size limit.
+    while (smaller_batch_size > 1 && ws_size(smaller_batch_size) > max_ws_size) {
+      smaller_batch_size >>= 1;
+    }
+    return smaller_batch_size;
+  }
+  return max_batch_size;
+}
+
+/** See raft::spatial::knn::ivf_pq::search docs */
+template <typename T,
+          typename IdxT,
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+inline void search(raft::resources const& handle,
+                   const search_params& params,
+                   const index<IdxT>& index,
+                   const T* queries,
+                   uint32_t n_queries,
+                   uint32_t k,
+                   IdxT* neighbors,
+                   float* distances,
+                   IvfSampleFilterT sample_filter = IvfSampleFilterT())
+{
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, uint8_t> ||
+                  std::is_same_v<T, int8_t>,
+                "Unsupported element type.");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::search(n_queries = %u, n_probes = %u, k = %u, dim = %zu)",
+    n_queries,
+    params.n_probes,
+    k,
+    index.dim());
+
+  RAFT_EXPECTS(
+    params.internal_distance_dtype == CUDA_R_16F || params.internal_distance_dtype == CUDA_R_32F,
+    "internal_distance_dtype must be either CUDA_R_16F or CUDA_R_32F");
+  RAFT_EXPECTS(params.lut_dtype == CUDA_R_16F || params.lut_dtype == CUDA_R_32F ||
+                 params.lut_dtype == CUDA_R_8U,
+               "lut_dtype must be CUDA_R_16F, CUDA_R_32F or CUDA_R_8U");
+  RAFT_EXPECTS(k > 0, "parameter `k` in top-k must be positive.");
+  RAFT_EXPECTS(
+    k <= index.size(),
+    "parameter `k` (%u) in top-k must not be larger that the total size of the index (%zu)",
+    k,
+    static_cast<uint64_t>(index.size()));
+  RAFT_EXPECTS(params.n_probes > 0,
+               "n_probes (number of clusters to probe in the search) must be positive.");
+
+  switch (utils::check_pointer_residency(queries, neighbors, distances)) {
+    case utils::pointer_residency::device_only:
+    case utils::pointer_residency::host_and_device: break;
+    default: RAFT_FAIL("all pointers must be accessible from the device.");
+  }
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  auto dim      = index.dim();
+  auto dim_ext  = index.dim_ext();
+  auto n_probes = std::min<uint32_t>(params.n_probes, index.n_lists());
+
+  uint32_t max_samples = 0;
+  {
+    IdxT ms = raft::Pow2<128>::roundUp(index.accum_sorted_sizes()(n_probes));
+    RAFT_EXPECTS(ms <= IdxT(std::numeric_limits<uint32_t>::max()),
+                 "The maximum sample size is too big.");
+    max_samples = ms;
+  }
+
+  auto mr = raft::resource::get_workspace_resource(handle);
+
+  // Maximum number of query vectors to search at the same time.
+  const auto max_queries = std::min<uint32_t>(std::max<uint32_t>(n_queries, 1), 4096);
+  auto max_batch_size    = get_max_batch_size(handle, k, n_probes, max_queries, max_samples);
+
+  rmm::device_uvector<float> float_queries(max_queries * dim_ext, stream, mr);
+  rmm::device_uvector<float> rot_queries(max_queries * index.rot_dim(), stream, mr);
+  rmm::device_uvector<uint32_t> clusters_to_probe(max_queries * n_probes, stream, mr);
+
+  auto filter_adapter = cuvs::neighbors::filtering::ivf_to_sample_filter(
+    index.inds_ptrs().data_handle(), sample_filter);
+  auto search_instance = ivfpq_search<IdxT, decltype(filter_adapter)>::fun(params, index.metric());
+
+  for (uint32_t offset_q = 0; offset_q < n_queries; offset_q += max_queries) {
+    uint32_t queries_batch = min(max_queries, n_queries - offset_q);
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> batch_scope(
+      "ivf_pq::search-batch(queries: %u - %u)", offset_q, offset_q + queries_batch);
+
+    select_clusters(handle,
+                    clusters_to_probe.data(),
+                    float_queries.data(),
+                    queries_batch,
+                    n_probes,
+                    index.n_lists(),
+                    dim,
+                    dim_ext,
+                    index.metric(),
+                    queries + static_cast<size_t>(dim) * offset_q,
+                    index.centers().data_handle(),
+                    mr);
+
+    // Rotate queries
+    float alpha = 1.0;
+    float beta  = 0.0;
+    raft::linalg::gemm(handle,
+                       true,
+                       false,
+                       index.rot_dim(),
+                       queries_batch,
+                       dim,
+                       &alpha,
+                       index.rotation_matrix().data_handle(),
+                       dim,
+                       float_queries.data(),
+                       dim_ext,
+                       &beta,
+                       rot_queries.data(),
+                       index.rot_dim(),
+                       stream);
+
+    for (uint32_t offset_b = 0; offset_b < queries_batch; offset_b += max_batch_size) {
+      uint32_t batch_size = min(max_batch_size, queries_batch - offset_b);
+      /* The distance calculation is done in the rotated/transformed space;
+         as long as `index.rotation_matrix()` is orthogonal, the distances and thus results are
+         preserved.
+       */
+      search_instance(handle,
+                      index,
+                      max_samples,
+                      n_probes,
+                      k,
+                      batch_size,
+                      offset_q + offset_b,
+                      clusters_to_probe.data() + uint64_t(n_probes) * offset_b,
+                      rot_queries.data() + uint64_t(index.rot_dim()) * offset_b,
+                      neighbors + uint64_t(k) * (offset_q + offset_b),
+                      distances + uint64_t(k) * (offset_q + offset_b),
+                      utils::config<T>::kDivisor / utils::config<float>::kDivisor,
+                      params.preferred_shmem_carveout,
+                      filter_adapter);
+    }
+  }
+}
+
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ * @tparam IvfSampleFilterT Device filter function, with the signature
+ *         `(uint32_t query_ix, uint32 cluster_ix, uint32_t sample_ix) -> bool` or
+ *         `(uint32_t query_ix, uint32 sample_ix) -> bool`
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device filter function that greenlights samples for a given query.
+ */
+template <typename T, typename IdxT, typename IvfSampleFilterT>
+void search_with_filtering(raft::resources const& handle,
+                           const search_params& params,
+                           const index<IdxT>& idx,
+                           raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
+                           raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
+                           raft::device_matrix_view<float, IdxT, raft::row_major> distances,
+                           IvfSampleFilterT sample_filter = IvfSampleFilterT{})
+{
+  RAFT_EXPECTS(
+    queries.extent(0) == neighbors.extent(0) && queries.extent(0) == distances.extent(0),
+    "Number of rows in output neighbors and distances matrices must equal the number of queries.");
+
+  RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1),
+               "Number of columns in output neighbors and distances matrices must equal k");
+
+  RAFT_EXPECTS(queries.extent(1) == idx.dim(),
+               "Number of query dimensions should equal number of dimensions in the index.");
+
+  std::uint32_t k = neighbors.extent(1);
+  search(handle,
+         params,
+         idx,
+         queries.data_handle(),
+         queries.extent(0),
+         k,
+         neighbors.data_handle(),
+         distances.data_handle(),
+         sample_filter);
+}
+
+template <typename T, typename IdxT>
+void search(raft::resources const& handle,
+            const search_params& params,
+            const index<IdxT>& idx,
+            raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
+            raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
+            raft::device_matrix_view<float, IdxT, raft::row_major> distances)
+{
+  search_with_filtering(handle,
+                        params,
+                        idx,
+                        queries,
+                        neighbors,
+                        distances,
+                        cuvs::neighbors::filtering::none_ivf_sample_filter{});
+}
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
new file mode 100644
index 0000000000..0fb71d0317
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_pq_serialize.cuh"
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+
+void serialize(raft::resources const& handle,
+               std::string& filename,
+               const cuvs::neighbors::ivf_pq::index<int64_t>& index)
+{
+  cuvs::neighbors::ivf_pq::detail::serialize(handle, filename, index);
+}
+
+}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
new file mode 100644
index 0000000000..37c902e7ed
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
@@ -0,0 +1,193 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../ivf_common.cuh"
+#include "../ivf_list.cuh"
+#include "ivf_pq_list.cuh"
+#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <fstream>
+#include <memory>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+// Serialization version
+// No backward compatibility yet; that is, can't add additional fields without breaking
+// backward compatibility.
+// TODO(hcho3) Implement next-gen serializer for IVF that allows for expansion in a backward
+//             compatible fashion.
+constexpr int kSerializationVersion = 3;
+
+/**
+ * Write the index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index IVF-PQ index
+ *
+ */
+template <typename IdxT>
+void serialize(raft::resources const& handle_, std::ostream& os, const index<IdxT>& index)
+{
+  RAFT_LOG_DEBUG("Size %zu, dim %d, pq_dim %d, pq_bits %d",
+                 static_cast<size_t>(index.size()),
+                 static_cast<int>(index.dim()),
+                 static_cast<int>(index.pq_dim()),
+                 static_cast<int>(index.pq_bits()));
+
+  raft::serialize_scalar(handle_, os, kSerializationVersion);
+  raft::serialize_scalar(handle_, os, index.size());
+  raft::serialize_scalar(handle_, os, index.dim());
+  raft::serialize_scalar(handle_, os, index.pq_bits());
+  raft::serialize_scalar(handle_, os, index.pq_dim());
+  raft::serialize_scalar(handle_, os, index.conservative_memory_allocation());
+
+  raft::serialize_scalar(handle_, os, index.metric());
+  raft::serialize_scalar(handle_, os, index.codebook_kind());
+  raft::serialize_scalar(handle_, os, index.n_lists());
+
+  raft::serialize_mdspan(handle_, os, index.pq_centers());
+  raft::serialize_mdspan(handle_, os, index.centers());
+  raft::serialize_mdspan(handle_, os, index.centers_rot());
+  raft::serialize_mdspan(handle_, os, index.rotation_matrix());
+
+  auto sizes_host =
+    raft::make_host_mdarray<uint32_t, uint32_t, raft::row_major>(index.list_sizes().extents());
+  raft::copy(sizes_host.data_handle(),
+             index.list_sizes().data_handle(),
+             sizes_host.size(),
+             raft::resource::get_cuda_stream(handle_));
+  raft::resource::sync_stream(handle_);
+  raft::serialize_mdspan(handle_, os, sizes_host.view());
+  auto list_store_spec = list_spec<uint32_t, IdxT>{index.pq_bits(), index.pq_dim(), true};
+  for (uint32_t label = 0; label < index.n_lists(); label++) {
+    ivf::serialize_list(handle_, os, index.lists()[label], list_store_spec, sizes_host(label));
+  }
+}
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-PQ index
+ *
+ */
+template <typename IdxT>
+void serialize(raft::resources const& handle_,
+               const std::string& filename,
+               const index<IdxT>& index)
+{
+  std::ofstream of(filename, std::ios::out | std::ios::binary);
+  if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  detail::serialize(handle_, of, index);
+
+  of.close();
+  if (!of) { RAFT_FAIL("Error writing output %s", filename.c_str()); }
+  return;
+}
+
+/**
+ * Load index from input stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ *
+ */
+template <typename IdxT>
+auto deserialize(raft::resources const& handle_, std::istream& is) -> index<IdxT>
+{
+  auto ver = raft::deserialize_scalar<int>(handle_, is);
+  if (ver != kSerializationVersion) {
+    RAFT_FAIL("serialization version mismatch %d vs. %d", ver, kSerializationVersion);
+  }
+  auto n_rows  = raft::deserialize_scalar<IdxT>(handle_, is);
+  auto dim     = raft::deserialize_scalar<std::uint32_t>(handle_, is);
+  auto pq_bits = raft::deserialize_scalar<std::uint32_t>(handle_, is);
+  auto pq_dim  = raft::deserialize_scalar<std::uint32_t>(handle_, is);
+  auto cma     = raft::deserialize_scalar<bool>(handle_, is);
+
+  auto metric        = raft::deserialize_scalar<cuvs::distance::DistanceType>(handle_, is);
+  auto codebook_kind = raft::deserialize_scalar<cuvs::neighbors::ivf_pq::codebook_gen>(handle_, is);
+  auto n_lists       = raft::deserialize_scalar<std::uint32_t>(handle_, is);
+
+  RAFT_LOG_DEBUG("n_rows %zu, dim %d, pq_dim %d, pq_bits %d, n_lists %d",
+                 static_cast<std::size_t>(n_rows),
+                 static_cast<int>(dim),
+                 static_cast<int>(pq_dim),
+                 static_cast<int>(pq_bits),
+                 static_cast<int>(n_lists));
+
+  auto index = cuvs::neighbors::ivf_pq::index<IdxT>(
+    handle_, metric, codebook_kind, n_lists, dim, pq_bits, pq_dim, cma);
+
+  raft::deserialize_mdspan(handle_, is, index.pq_centers());
+  raft::deserialize_mdspan(handle_, is, index.centers());
+  raft::deserialize_mdspan(handle_, is, index.centers_rot());
+  raft::deserialize_mdspan(handle_, is, index.rotation_matrix());
+  raft::deserialize_mdspan(handle_, is, index.list_sizes());
+  auto list_device_spec = list_spec<uint32_t, IdxT>{pq_bits, pq_dim, cma};
+  auto list_store_spec  = list_spec<uint32_t, IdxT>{pq_bits, pq_dim, true};
+  for (auto& list : index.lists()) {
+    ivf::deserialize_list(handle_, is, list, list_store_spec, list_device_spec);
+  }
+
+  raft::resource::sync_stream(handle_);
+
+  ivf::detail::recompute_internal_state(handle_, index);
+
+  return index;
+}
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ *
+ */
+template <typename IdxT>
+auto deserialize(raft::resources const& handle_, const std::string& filename) -> index<IdxT>
+{
+  std::ifstream infile(filename, std::ios::in | std::ios::binary);
+
+  if (!infile) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  auto index = detail::deserialize<IdxT>(handle_, infile);
+
+  infile.close();
+
+  return index;
+}
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq_index.cpp b/cpp/src/neighbors/ivf_pq_index.cpp
index b464da6706..1e19198671 100644
--- a/cpp/src/neighbors/ivf_pq_index.cpp
+++ b/cpp/src/neighbors/ivf_pq_index.cpp
@@ -20,216 +20,290 @@ namespace cuvs::neighbors::ivf_pq {
 
 template <typename IdxT>
 index<IdxT>::index(raft::resources const& handle, const index_params& params, uint32_t dim)
+  : index(handle,
+          params.metric,
+          params.codebook_kind,
+          params.n_lists,
+          dim,
+          params.pq_bits,
+          params.pq_dim,
+          params.conservative_memory_allocation)
+{
+}
+
+template <typename IdxT>
+index<IdxT>::index(raft::resources const& handle,
+                   cuvs::distance::DistanceType metric,
+                   codebook_gen codebook_kind,
+                   uint32_t n_lists,
+                   uint32_t dim,
+                   uint32_t pq_bits,
+                   uint32_t pq_dim,
+                   bool conservative_memory_allocation)
   : ann::index(),
-    raft_index_(std::make_unique<raft::neighbors::ivf_pq::index<IdxT>>(
-      handle,
-      static_cast<raft::distance::DistanceType>((int)params.metric),
-      static_cast<raft::neighbors::ivf_pq::codebook_gen>((int)params.codebook_kind),
-      params.n_lists,
-      dim,
-      params.pq_bits,
-      params.pq_dim,
-      params.conservative_memory_allocation))
-{
-}
-
-template <typename IdxT>
-index<IdxT>::index(raft::neighbors::ivf_pq::index<IdxT>&& raft_idx)
-  : ann::index(),
-    raft_index_(std::make_unique<raft::neighbors::ivf_pq::index<IdxT>>(std::move(raft_idx)))
-{
+    metric_(metric),
+    codebook_kind_(codebook_kind),
+    dim_(dim),
+    pq_bits_(pq_bits),
+    pq_dim_(pq_dim == 0 ? calculate_pq_dim(dim) : pq_dim),
+    conservative_memory_allocation_(conservative_memory_allocation),
+    pq_centers_{raft::make_device_mdarray<float>(handle, make_pq_centers_extents())},
+    lists_{n_lists},
+    rotation_matrix_{
+      raft::make_device_matrix<float, uint32_t>(handle, this->rot_dim(), this->dim())},
+    list_sizes_{raft::make_device_vector<uint32_t, uint32_t>(handle, n_lists)},
+    centers_{raft::make_device_matrix<float, uint32_t>(handle, n_lists, this->dim_ext())},
+    centers_rot_{raft::make_device_matrix<float, uint32_t>(handle, n_lists, this->rot_dim())},
+    data_ptrs_{raft::make_device_vector<uint8_t*, uint32_t>(handle, n_lists)},
+    inds_ptrs_{raft::make_device_vector<IdxT*, uint32_t>(handle, n_lists)},
+    accum_sorted_sizes_{raft::make_host_vector<IdxT, uint32_t>(n_lists + 1)}
+{
+  check_consistency();
+  accum_sorted_sizes_(n_lists) = 0;
 }
-
 template <typename IdxT>
 IdxT index<IdxT>::size() const noexcept
 {
-  return raft_index_->size();
+  return accum_sorted_sizes_(n_lists());
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::dim() const noexcept
 {
-  return raft_index_->dim();
+  return dim_;
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::dim_ext() const noexcept
 {
-  return raft_index_->dim_ext();
+  return raft::round_up_safe(dim() + 1, 8u);
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::rot_dim() const noexcept
 {
-  return raft_index_->rot_dim();
+  return pq_len() * pq_dim();
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::pq_bits() const noexcept
 {
-  return raft_index_->pq_bits();
+  return pq_bits_;
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::pq_dim() const noexcept
 {
-  return raft_index_->pq_dim();
+  return pq_dim_;
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::pq_len() const noexcept
 {
-  return raft_index_->pq_len();
+  return raft::div_rounding_up_unsafe(dim(), pq_dim());
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::pq_book_size() const noexcept
 {
-  return raft_index_->pq_book_size();
+  return 1 << pq_bits();
 }
 
 template <typename IdxT>
 cuvs::distance::DistanceType index<IdxT>::metric() const noexcept
 {
-  return static_cast<cuvs::distance::DistanceType>((int)raft_index_->metric());
+  return metric_;
 }
 
 template <typename IdxT>
 codebook_gen index<IdxT>::codebook_kind() const noexcept
 {
-  return static_cast<codebook_gen>((int)raft_index_->codebook_kind());
+  return codebook_kind_;
 }
 
 template <typename IdxT>
 uint32_t index<IdxT>::n_lists() const noexcept
 {
-  return raft_index_->n_lists();
+  return lists_.size();
 }
 
 template <typename IdxT>
 bool index<IdxT>::conservative_memory_allocation() const noexcept
 {
-  return raft_index_->conservative_memory_allocation();
+  return conservative_memory_allocation_;
 }
 
 template <typename IdxT>
-raft::
-  mdspan<float, typename cuvs::neighbors::ivf_pq::index<IdxT>::pq_centers_extents, raft::row_major>
-  index<IdxT>::pq_centers() noexcept
+raft::device_mdspan<float,
+                    typename cuvs::neighbors::ivf_pq::index<IdxT>::pq_centers_extents,
+                    raft::row_major>
+index<IdxT>::pq_centers() noexcept
 {
-  return raft_index_->pq_centers();
+  return pq_centers_.view();
 }
 
 template <typename IdxT>
-raft::mdspan<const float,
-             typename cuvs::neighbors::ivf_pq::index<IdxT>::pq_centers_extents,
-             raft::row_major>
+raft::device_mdspan<const float,
+                    typename cuvs::neighbors::ivf_pq::index<IdxT>::pq_centers_extents,
+                    raft::row_major>
 index<IdxT>::pq_centers() const noexcept
 {
-  return raft_index_->pq_centers();
+  return pq_centers_.view();
 }
 
 template <typename IdxT>
 std::vector<std::shared_ptr<list_data<IdxT>>>& index<IdxT>::lists() noexcept
 {
-  return raft_index_->lists();
+  return lists_;
 }
 
 template <typename IdxT>
 const std::vector<std::shared_ptr<list_data<IdxT>>>& index<IdxT>::lists() const noexcept
 {
-  return raft_index_->lists();
+  return lists_;
 }
 
 template <typename IdxT>
 raft::device_vector_view<uint8_t*, uint32_t, raft::row_major> index<IdxT>::data_ptrs() noexcept
 {
-  return raft_index_->data_ptrs();
+  return data_ptrs_.view();
 }
 
 template <typename IdxT>
 raft::device_vector_view<const uint8_t* const, uint32_t, raft::row_major> index<IdxT>::data_ptrs()
   const noexcept
 {
-  return raft_index_->data_ptrs();
+  return raft::make_mdspan<const uint8_t* const, uint32_t, raft::row_major, false, true>(
+    data_ptrs_.data_handle(), data_ptrs_.extents());
 }
 
 template <typename IdxT>
 raft::device_vector_view<IdxT*, uint32_t, raft::row_major> index<IdxT>::inds_ptrs() noexcept
 {
-  return raft_index_->inds_ptrs();
+  return inds_ptrs_.view();
 }
 
 template <typename IdxT>
 raft::device_vector_view<const IdxT* const, uint32_t, raft::row_major> index<IdxT>::inds_ptrs()
   const noexcept
 {
-  return raft_index_->inds_ptrs();
+  return raft::make_mdspan<const IdxT* const, uint32_t, raft::row_major, false, true>(
+    inds_ptrs_.data_handle(), inds_ptrs_.extents());
 }
 
 template <typename IdxT>
 raft::device_matrix_view<float, uint32_t, raft::row_major> index<IdxT>::rotation_matrix() noexcept
 {
-  return raft_index_->rotation_matrix();
+  return rotation_matrix_.view();
 }
 
 template <typename IdxT>
 raft::device_matrix_view<const float, uint32_t, raft::row_major> index<IdxT>::rotation_matrix()
   const noexcept
 {
-  return raft_index_->rotation_matrix();
+  return rotation_matrix_.view();
 }
 
 template <typename IdxT>
 raft::host_vector_view<IdxT, uint32_t, raft::row_major> index<IdxT>::accum_sorted_sizes() noexcept
 {
-  return raft_index_->accum_sorted_sizes();
+  return accum_sorted_sizes_.view();
 }
 
 template <typename IdxT>
 raft::host_vector_view<const IdxT, uint32_t, raft::row_major> index<IdxT>::accum_sorted_sizes()
   const noexcept
 {
-  return raft_index_->accum_sorted_sizes();
+  return accum_sorted_sizes_.view();
 }
 
 template <typename IdxT>
 raft::device_vector_view<uint32_t, uint32_t, raft::row_major> index<IdxT>::list_sizes() noexcept
 {
-  return raft_index_->list_sizes();
+  return list_sizes_.view();
 }
 
 template <typename IdxT>
 raft::device_vector_view<const uint32_t, uint32_t, raft::row_major> index<IdxT>::list_sizes()
   const noexcept
 {
-  return raft_index_->list_sizes();
+  return list_sizes_.view();
 }
 
 template <typename IdxT>
 raft::device_matrix_view<float, uint32_t, raft::row_major> index<IdxT>::centers() noexcept
 {
-  return raft_index_->centers();
+  return centers_.view();
 }
 
 template <typename IdxT>
 raft::device_matrix_view<const float, uint32_t, raft::row_major> index<IdxT>::centers()
   const noexcept
 {
-  return raft_index_->centers();
+  return centers_.view();
 }
 
 template <typename IdxT>
 raft::device_matrix_view<float, uint32_t, raft::row_major> index<IdxT>::centers_rot() noexcept
 {
-  return raft_index_->centers_rot();
+  return centers_rot_.view();
 }
 
 template <typename IdxT>
 raft::device_matrix_view<const float, uint32_t, raft::row_major> index<IdxT>::centers_rot()
   const noexcept
 {
-  return raft_index_->centers_rot();
+  return centers_rot_.view();
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::get_list_size_in_bytes(uint32_t label)
+{
+  RAFT_EXPECTS(label < this->n_lists(),
+               "Expected label to be less than number of lists in the index");
+  auto list_data = this->lists()[label]->data;
+  return list_data.size();
+}
+
+template <typename IdxT>
+void index<IdxT>::check_consistency()
+{
+  RAFT_EXPECTS(pq_bits() >= 4 && pq_bits() <= 8,
+               "`pq_bits` must be within closed range [4,8], but got %u.",
+               pq_bits());
+  RAFT_EXPECTS((pq_bits() * pq_dim()) % 8 == 0,
+               "`pq_bits * pq_dim` must be a multiple of 8, but got %u * %u = %u.",
+               pq_bits(),
+               pq_dim(),
+               pq_bits() * pq_dim());
+}
+
+template <typename IdxT>
+typename index<IdxT>::pq_centers_extents index<IdxT>::make_pq_centers_extents()
+{
+  switch (codebook_kind()) {
+    case codebook_gen::PER_SUBSPACE:
+      return raft::make_extents<uint32_t>(pq_dim(), pq_len(), pq_book_size());
+    case codebook_gen::PER_CLUSTER:
+      return raft::make_extents<uint32_t>(n_lists(), pq_len(), pq_book_size());
+    default: RAFT_FAIL("Unreachable code");
+  }
+}
+
+template <typename IdxT>
+uint32_t index<IdxT>::calculate_pq_dim(uint32_t dim)
+{
+  // If the dimensionality is large enough, we can reduce it to improve performance
+  if (dim >= 128) { dim /= 2; }
+  // Round it down to 32 to improve performance.
+  auto r = raft::round_down_safe<uint32_t>(dim, 32);
+  if (r > 0) return r;
+  // If the dimensionality is really low, round it to the closest power-of-two
+  r = 1;
+  while ((r << 1) <= dim) {
+    r = r << 1;
+  }
+  return r;
 }
 
 template struct index<int64_t>;
diff --git a/cpp/src/neighbors/ivf_pq_serialize.cpp b/cpp/src/neighbors/ivf_pq_serialize.cpp
deleted file mode 100644
index 4bdd3b04bc..0000000000
--- a/cpp/src/neighbors/ivf_pq_serialize.cpp
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft_runtime/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_SERIALIZE(IdxT)                                                      \
-  void serialize(raft::resources const& handle,                                               \
-                 std::string& filename,                                                       \
-                 const cuvs::neighbors::ivf_pq::index<IdxT>& index)                           \
-  {                                                                                           \
-    raft::runtime::neighbors::ivf_pq::serialize(handle, filename, *index.get_raft_index());   \
-  }                                                                                           \
-  void deserialize(raft::resources const& handle,                                             \
-                   const std::string& filename,                                               \
-                   cuvs::neighbors::ivf_pq::index<IdxT>* index)                               \
-  {                                                                                           \
-    raft::runtime::neighbors::ivf_pq::deserialize(handle, filename, index->get_raft_index()); \
-  }
-
-CUVS_INST_IVF_PQ_SERIALIZE(int64_t);
-
-#undef CUVS_INST_IVF_PQ_SERIALIZE
-
-}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/src/neighbors/nn_descent.cuh b/cpp/src/neighbors/nn_descent.cuh
new file mode 100644
index 0000000000..582da72c12
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent.cuh
@@ -0,0 +1,182 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/nn_descent.cuh"
+#include <cuvs/neighbors/nn_descent.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+
+/**
+ * @defgroup nn-descent CUDA gradient descent nearest neighbor
+ * @{
+ */
+
+/**
+ * @brief Build nn-descent Index with dataset in device memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param[in] res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::device_matrix_view input dataset expected to be located
+ *                in device memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+template <typename T, typename IdxT = uint32_t>
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
+{
+  return detail::build<T, IdxT>(res, params, dataset);
+}
+
+/**
+ * @brief Build nn-descent Index with dataset in device memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
+ *   auto knn_graph = raft::make_host_matrix<uint32_t, int64_t>(N, D);
+ *   auto index = nn_descent::index{res, knn_graph.view()};
+ *   cagra::build(res, index_params, dataset, index);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::device_matrix_view input dataset expected to be located
+ *                in device memory
+ * @param[out] idx  cuvs::neighbors::nn_descentindex containing all-neighbors knn graph
+ * in host memory
+ */
+template <typename T, typename IdxT = uint32_t>
+void build(raft::resources const& res,
+           index_params const& params,
+           raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+           index<IdxT>& idx)
+{
+  detail::build<T, IdxT>(res, params, dataset, idx);
+}
+
+/**
+ * @brief Build nn-descent Index with dataset in host memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::host_matrix_view input dataset expected to be located
+ *                in host memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+template <typename T, typename IdxT = uint32_t>
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::host_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
+{
+  return detail::build<T, IdxT>(res, params, dataset);
+}
+
+/**
+ * @brief Build nn-descent Index with dataset in host memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
+ *   auto knn_graph = raft::make_host_matrix<uint32_t, int64_t>(N, D);
+ *   auto index = nn_descent::index{res, knn_graph.view()};
+ *   cagra::build(res, index_params, dataset, index);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param[in] res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::host_matrix_view input dataset expected to be located
+ *                in host memory
+ * @param[out] idx  cuvs::neighbors::nn_descentindex containing all-neighbors knn graph
+ * in host memory
+ */
+template <typename T, typename IdxT = uint32_t>
+void build(raft::resources const& res,
+           index_params const& params,
+           raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
+           index<IdxT>& idx)
+{
+  detail::build<T, IdxT>(res, params, dataset, idx);
+}
+
+/** @} */  // end group nn-descent
+
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/src/neighbors/nn_descent_float.cu b/cpp/src/neighbors/nn_descent_float.cu
new file mode 100644
index 0000000000..c6d3566713
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent_float.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nn_descent.cuh"
+#include <cuvs/neighbors/nn_descent.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };                                                                              \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };
+
+CUVS_INST_NN_DESCENT_BUILD(float, uint32_t);
+
+#undef CUVS_INST_NN_DESCENT_BUILD
+
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/src/neighbors/nn_descent_int8.cu b/cpp/src/neighbors/nn_descent_int8.cu
new file mode 100644
index 0000000000..813a01746b
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent_int8.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nn_descent.cuh"
+#include <cuvs/neighbors/nn_descent.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };                                                                              \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };
+
+CUVS_INST_NN_DESCENT_BUILD(int8_t, uint32_t);
+
+#undef CUVS_INST_NN_DESCENT_BUILD
+
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/src/neighbors/nn_descent_uint8.cu b/cpp/src/neighbors/nn_descent_uint8.cu
new file mode 100644
index 0000000000..9d73dd90ff
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent_uint8.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nn_descent.cuh"
+#include <cuvs/neighbors/nn_descent.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };                                                                              \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };
+
+CUVS_INST_NN_DESCENT_BUILD(uint8_t, uint32_t);
+
+#undef CUVS_INST_NN_DESCENT_BUILD
+
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/src/neighbors/refine-ext.cuh b/cpp/src/neighbors/refine-ext.cuh
new file mode 100644
index 0000000000..53e7394a91
--- /dev/null
+++ b/cpp/src/neighbors/refine-ext.cuh
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
+#include <raft/core/device_mdspan.hpp>       // raft::device_matrix_view
+#include <raft/core/host_mdspan.hpp>         // // raft::host_matrix_view
+#include <raft/core/resources.hpp>           // raft::resources
+#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+
+#include <cstdint>  // int64_t
+
+#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+namespace cuvs::neighbors {
+
+template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
+            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
+            raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
+            raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+  RAFT_EXPLICIT;
+
+template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
+void refine(raft::resources const& handle,
+            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
+            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
+            raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
+            raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
+            raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+  RAFT_EXPLICIT;
+
+}  // namespace cuvs::neighbors
+
+#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+#define instantiate_raft_neighbors_refine_d(idx_t, data_t, distance_t, matrix_idx)          \
+  extern template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(      \
+    raft::resources const& handle,                                                          \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric);
+
+#define instantiate_raft_neighbors_refine_h(idx_t, data_t, distance_t, matrix_idx)        \
+  extern template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(    \
+    raft::resources const& handle,                                                        \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric);
+
+instantiate_raft_neighbors_refine_d(int64_t, float, float, int64_t);
+instantiate_raft_neighbors_refine_d(int64_t, int8_t, float, int64_t);
+instantiate_raft_neighbors_refine_d(int64_t, uint8_t, float, int64_t);
+
+instantiate_raft_neighbors_refine_h(int64_t, float, float, int64_t);
+instantiate_raft_neighbors_refine_h(uint32_t, float, float, int64_t);
+instantiate_raft_neighbors_refine_h(int64_t, int8_t, float, int64_t);
+instantiate_raft_neighbors_refine_h(int64_t, uint8_t, float, int64_t);
+
+#undef instantiate_raft_neighbors_refine_d
+#undef instantiate_raft_neighbors_refine_h
diff --git a/cpp/src/neighbors/refine-inl.cuh b/cpp/src/neighbors/refine-inl.cuh
new file mode 100644
index 0000000000..6203290273
--- /dev/null
+++ b/cpp/src/neighbors/refine-inl.cuh
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/ann_utils.cuh"
+#include "detail/refine.cuh"
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::neighbors {
+
+/**
+ * @defgroup ann_refine Approximate Nearest Neighbors Refinement
+ * @{
+ */
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset, N, D);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, N, 4 * k, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
+            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
+            raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
+            raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  detail::refine_device(handle, dataset, queries, neighbor_candidates, indices, distances, metric);
+}
+
+/** Same as above, but all input and out data is in host memory.
+ * @param[in] handle the raft handle
+ * @param[in] dataset host matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries host matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates host matrix with indices of candidate vectors [n_queries,
+ *   n_candidates], where n_candidates >= k
+ * @param[out] indices host matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances host matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
+void refine(raft::resources const& handle,
+            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
+            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
+            raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
+            raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
+            raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  detail::refine_host(dataset, queries, neighbor_candidates, indices, distances, metric);
+}
+
+/** @} */  // end group ann_refine
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/refine.cuh b/cpp/src/neighbors/refine.cuh
new file mode 100644
index 0000000000..15f2b02928
--- /dev/null
+++ b/cpp/src/neighbors/refine.cuh
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#include "refine-inl.cuh"
+#endif
+
+#ifdef RAFT_COMPILED
+#include "refine-ext.cuh"
+#endif
diff --git a/cpp/src/neighbors/refine_float_float.cu b/cpp/src/neighbors/refine_float_float.cu
new file mode 100644
index 0000000000..648d2e02a2
--- /dev/null
+++ b/cpp/src/neighbors/refine_float_float.cu
@@ -0,0 +1,54 @@
+
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by refine_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python refine_00_generate.py
+ *
+ */
+
+#include "refine-inl.cuh"
+
+#define instantiate_raft_neighbors_refine_d(idx_t, data_t, distance_t, matrix_idx)          \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+    raft::resources const& handle,                                                          \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric);
+
+#define instantiate_raft_neighbors_refine_h(idx_t, data_t, distance_t, matrix_idx)        \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(           \
+    raft::resources const& handle,                                                        \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric);
+
+instantiate_raft_neighbors_refine_d(int64_t, float, float, int64_t);
+instantiate_raft_neighbors_refine_h(int64_t, float, float, int64_t);
+instantiate_raft_neighbors_refine_h(uint32_t, float, float, int64_t);
+
+#undef instantiate_raft_neighbors_refine_d
+#undef instantiate_raft_neighbors_refine_h
diff --git a/cpp/src/neighbors/refine_half_float.cu b/cpp/src/neighbors/refine_half_float.cu
new file mode 100644
index 0000000000..3967c15abb
--- /dev/null
+++ b/cpp/src/neighbors/refine_half_float.cu
@@ -0,0 +1,50 @@
+
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by refine_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python refine_00_generate.py
+ *
+ */
+
+#include "refine-inl.cuh"
+
+#define instantiate_raft_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+    raft::resources const& handle,                                                          \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric);                                                   \
+                                                                                            \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+    raft::resources const& handle,                                                          \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,              \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,              \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,   \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                     \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,              \
+    cuvs::distance::DistanceType metric);
+
+instantiate_raft_neighbors_refine(int64_t, half, float, int64_t);
+
+#undef instantiate_raft_neighbors_refine
diff --git a/cpp/src/neighbors/refine_int8_t_float.cu b/cpp/src/neighbors/refine_int8_t_float.cu
new file mode 100644
index 0000000000..8115c71044
--- /dev/null
+++ b/cpp/src/neighbors/refine_int8_t_float.cu
@@ -0,0 +1,50 @@
+
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by refine_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python refine_00_generate.py
+ *
+ */
+
+#include "refine-inl.cuh"
+
+#define instantiate_raft_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+    raft::resources const& handle,                                                          \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric);                                                   \
+                                                                                            \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+    raft::resources const& handle,                                                          \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,              \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,              \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,   \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                     \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,              \
+    cuvs::distance::DistanceType metric);
+
+instantiate_raft_neighbors_refine(int64_t, int8_t, float, int64_t);
+
+#undef instantiate_raft_neighbors_refine
diff --git a/cpp/src/neighbors/refine_uint8_t_float.cu b/cpp/src/neighbors/refine_uint8_t_float.cu
new file mode 100644
index 0000000000..ccee2e6788
--- /dev/null
+++ b/cpp/src/neighbors/refine_uint8_t_float.cu
@@ -0,0 +1,50 @@
+
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by refine_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python refine_00_generate.py
+ *
+ */
+
+#include "refine-inl.cuh"
+
+#define instantiate_raft_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+    raft::resources const& handle,                                                          \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric);                                                   \
+                                                                                            \
+  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+    raft::resources const& handle,                                                          \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,              \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,              \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,   \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                     \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,              \
+    cuvs::distance::DistanceType metric);
+
+instantiate_raft_neighbors_refine(int64_t, uint8_t, float, int64_t);
+
+#undef instantiate_raft_neighbors_refine
diff --git a/cpp/src/neighbors/sample_filter.cu b/cpp/src/neighbors/sample_filter.cu
new file mode 100644
index 0000000000..32a0d3bfb3
--- /dev/null
+++ b/cpp/src/neighbors/sample_filter.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "sample_filter.cuh"
+
+namespace cuvs::neighbors::filtering {
+
+template struct bitset_filter<uint8_t, uint32_t>;
+template struct bitset_filter<uint16_t, uint32_t>;
+template struct bitset_filter<uint32_t, uint32_t>;
+template struct bitset_filter<uint32_t, int64_t>;
+template struct bitset_filter<uint64_t, int64_t>;
+}  // namespace cuvs::neighbors::filtering
diff --git a/cpp/src/neighbors/sample_filter.cuh b/cpp/src/neighbors/sample_filter.cuh
new file mode 100644
index 0000000000..c550e4a4de
--- /dev/null
+++ b/cpp/src/neighbors/sample_filter.cuh
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/sample_filter.hpp>
+#include <raft/core/bitset.cuh>
+#include <raft/core/detail/macros.hpp>
+
+#include <cstddef>
+#include <cstdint>
+
+namespace cuvs::neighbors::filtering {
+
+/* A filter that filters nothing. This is the default behavior. */
+inline _RAFT_HOST_DEVICE bool none_ivf_sample_filter::operator()(
+  // query index
+  const uint32_t query_ix,
+  // the current inverted list index
+  const uint32_t cluster_ix,
+  // the index of the current sample inside the current inverted list
+  const uint32_t sample_ix) const
+{
+  return true;
+}
+
+/* A filter that filters nothing. This is the default behavior. */
+inline _RAFT_HOST_DEVICE bool none_cagra_sample_filter::operator()(
+  // query index
+  const uint32_t query_ix,
+  // the index of the current sample
+  const uint32_t sample_ix) const
+{
+  return true;
+}
+
+template <typename filter_t, typename = void>
+struct takes_three_args : std::false_type {};
+template <typename filter_t>
+struct takes_three_args<
+  filter_t,
+  std::void_t<decltype(std::declval<filter_t>()(uint32_t{}, uint32_t{}, uint32_t{}))>>
+  : std::true_type {};
+
+/**
+ * @brief Filter used to convert the cluster index and sample index
+ * of an IVF search into a sample index. This can be used as an
+ * intermediate filter.
+ *
+ * @tparam index_t Indexing type
+ * @tparam filter_t
+ */
+template <typename index_t, typename filter_t>
+ivf_to_sample_filter<index_t, filter_t>::ivf_to_sample_filter(const index_t* const* inds_ptrs,
+                                                              const filter_t next_filter)
+  : inds_ptrs_{inds_ptrs}, next_filter_{next_filter}
+{
+}
+
+/** If the original filter takes three arguments, then don't modify the arguments.
+ * If the original filter takes two arguments, then we are using `inds_ptr_` to obtain the sample
+ * index.
+ */
+template <typename index_t, typename filter_t>
+inline _RAFT_HOST_DEVICE bool ivf_to_sample_filter<index_t, filter_t>::operator()(
+  // query index
+  const uint32_t query_ix,
+  // the current inverted list index
+  const uint32_t cluster_ix,
+  // the index of the current sample inside the current inverted list
+  const uint32_t sample_ix) const
+{
+  if constexpr (takes_three_args<filter_t>::value) {
+    return next_filter_(query_ix, cluster_ix, sample_ix);
+  } else {
+    return next_filter_(query_ix, inds_ptrs_[cluster_ix][sample_ix]);
+  }
+}
+
+template <typename bitset_t, typename index_t>
+bitset_filter<bitset_t, index_t>::bitset_filter(
+  const cuvs::core::bitset_view<bitset_t, index_t> bitset_for_filtering)
+  : bitset_view_{bitset_for_filtering}
+{
+}
+
+template <typename bitset_t, typename index_t>
+inline _RAFT_HOST_DEVICE bool bitset_filter<bitset_t, index_t>::operator()(
+  // query index
+  const uint32_t query_ix,
+  // the index of the current sample
+  const uint32_t sample_ix) const
+{
+  return bitset_view_.test(sample_ix);
+}
+
+}  // namespace cuvs::neighbors::filtering
diff --git a/cpp/src/neighbors/vpq_dataset.cuh b/cpp/src/neighbors/vpq_dataset.cuh
new file mode 100644
index 0000000000..1d80bea3fb
--- /dev/null
+++ b/cpp/src/neighbors/vpq_dataset.cuh
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/vpq_dataset.cuh"
+#include <cuvs/neighbors/dataset.hpp>
+
+#include <raft/core/resources.hpp>
+
+namespace cuvs::neighbors {
+
+/**
+ * @brief Compress a dataset for use in CAGRA-Q search in place of the original data.
+ *
+ * @tparam DatasetT a row-major mdspan or mdarray (device or host).
+ * @tparam MathT a type of the codebook elements and internal math ops.
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] params VQ and PQ parameters for compressing the data
+ * @param[in] dataset a row-major mdspan or mdarray (device or host) [n_rows, dim].
+ */
+template <typename DatasetT,
+          typename MathT = typename DatasetT::value_type,
+          typename IdxT  = typename DatasetT::index_type>
+auto vpq_build(const raft::resources& res, const vpq_params& params, const DatasetT& dataset)
+  -> vpq_dataset<MathT, IdxT>
+
+{
+  if constexpr (std::is_same_v<MathT, half>) {
+    return detail::vpq_convert_math_type<half, float, IdxT>(
+      res, detail::vpq_build<DatasetT, float, IdxT>(res, params, dataset));
+  } else {
+    return detail::vpq_build<DatasetT, MathT, IdxT>(res, params, dataset);
+  }
+}
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 131737039a..8455d0bac1 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -129,6 +129,19 @@ if(BUILD_TESTS)
     100
   )
 
+  ConfigureTest(
+    NAME
+    NEIGHBORS_ANN_NN_DESCENT_TEST
+    PATH
+    test/neighbors/ann_nn_descent/test_float_uint32_t.cu
+    test/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
+    test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
+    GPUS
+    1
+    PERCENT
+    100
+  )
+
   ConfigureTest(
     NAME
     DISTANCE_TEST
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index c9bc0808d2..3d3c06631c 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -25,9 +25,10 @@
 #include <cuvs/neighbors/cagra.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/device_resources.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/linalg/add.cuh>
-#include <raft/neighbors/cagra.cuh>
 #include <raft/random/rng.cuh>
 #include <raft/util/itertools.hpp>
 
@@ -46,6 +47,18 @@
 namespace cuvs::neighbors::cagra {
 namespace {
 
+/** Xorshift rondem number generator.
+ *
+ * See https://en.wikipedia.org/wiki/Xorshift#xorshift for reference.
+ */
+_RAFT_HOST_DEVICE inline uint64_t xorshift64(uint64_t u)
+{
+  u ^= u >> 12;
+  u ^= u << 25;
+  u ^= u >> 27;
+  return u * 0x2545F4914F6CDD1DULL;
+}
+
 // For sort_knn_graph test
 template <typename IdxT>
 void RandomSuffle(raft::host_matrix_view<IdxT, int64_t> index)
@@ -55,9 +68,9 @@ void RandomSuffle(raft::host_matrix_view<IdxT, int64_t> index)
     IdxT* const row_ptr = index.data_handle() + i * index.extent(1);
     for (unsigned j = 0; j < index.extent(1); j++) {
       // Swap two indices at random
-      rand          = raft::neighbors::cagra::detail::device::xorshift64(rand);
+      rand          = xorshift64(rand);
       const auto i0 = rand % index.extent(1);
-      rand          = raft::neighbors::cagra::detail::device::xorshift64(rand);
+      rand          = xorshift64(rand);
       const auto i1 = rand % index.extent(1);
 
       const auto tmp = row_ptr[i0];
@@ -189,6 +202,7 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
     {
       rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
       rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+
       cuvs::neighbors::naive_knn<DistanceT, DataT, IdxT>(handle_,
                                                          distances_naive_dev.data(),
                                                          indices_naive_dev.data(),
@@ -229,15 +243,18 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
             raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
             auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
               (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+
             index = cagra::build(handle_, index_params, database_host_view);
           } else {
             index = cagra::build(handle_, index_params, database_view);
           };
+
           cagra::serialize_file(handle_, "cagra_index", index, ps.include_serialized_dataset);
         }
 
         cagra::index<DataT, IdxT> index(handle_);
         cagra::deserialize_file(handle_, "cagra_index", &index);
+
         if (!ps.include_serialized_dataset) { index.update_dataset(handle_, database_view); }
 
         auto search_queries_view = raft::make_device_matrix_view<const DataT, int64_t>(
@@ -251,6 +268,7 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
           handle_, search_params, index, search_queries_view, indices_out_view, dists_out_view);
         raft::update_host(distances_Cagra.data(), distances_dev.data(), queries_size, stream_);
         raft::update_host(indices_Cagra.data(), indices_dev.data(), queries_size, stream_);
+
         raft::resource::sync_stream(handle_);
       }
 
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index 90b70ba112..9b70ee250c 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -507,7 +507,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
           search_queries_view,
           indices_ivfflat_dev.view(),
           distances_ivfflat_dev.view(),
-          raft::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
+           cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
 
         raft::update_host(
           distances_ivfflat.data(), distances_ivfflat_dev.data_handle(), queries_size, stream_);
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index 5e276cbf1c..d5fc0d0b72 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -17,17 +17,19 @@
 
 #include "../test_utils.cuh"
 #include "ann_utils.cuh"
-#include "ivf_pq_helpers.cuh"
 #include "naive_knn.cuh"
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/ivf_pq_helpers.hpp>
+#include <cuvs/neighbors/sample_filter.hpp>
 
-#include <raft/neighbors/ivf_pq-inl.cuh>
-#include <raft/neighbors/sample_filter.cuh>
+#include <raft/core/bitset.cuh>
+#include <raft/linalg/add.cuh>
+#include <raft/matrix/gather.cuh>
 #include <thrust/sequence.h>
 
 namespace cuvs::neighbors::ivf_pq {
 
-using namespace raft;
+using raft::RAFT_NAME;  // For logging
 
 struct test_ivf_sample_filter {
   static constexpr unsigned offset = 300;
@@ -108,9 +110,10 @@ void compare_vectors_l2(
   auto n_rows = a.extent(0);
   auto dim    = a.extent(1);
   rmm::mr::managed_memory_resource managed_memory;
-  auto dist = make_device_mdarray<double>(res, &managed_memory, make_extents<uint32_t>(n_rows));
-  linalg::map_offset(res, dist.view(), [a, b, dim] __device__(uint32_t i) {
-    spatial::knn::detail::utils::mapping<float> f{};
+  auto dist =
+    raft::make_device_mdarray<double>(res, &managed_memory, raft::make_extents<uint32_t>(n_rows));
+  raft::linalg::map_offset(res, dist.view(), [a, b, dim] __device__(uint32_t i) {
+    raft::spatial::knn::detail::utils::mapping<float> f{};
     double d = 0.0f;
     for (uint32_t j = 0; j < dim; j++) {
       double t = f(a(i, j)) - f(b(i, j));
@@ -118,7 +121,7 @@ void compare_vectors_l2(
     }
     return sqrt(d / double(dim));
   });
-  resource::sync_stream(res);
+  raft::resource::sync_stream(res);
   for (uint32_t i = 0; i < n_rows; i++) {
     double d = dist(i);
     // The theoretical estimate of the error is hard to come up with,
@@ -145,7 +148,7 @@ template <typename EvalT, typename DataT, typename IdxT>
 class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
  public:
   ivf_pq_test()
-    : stream_(resource::get_cuda_stream(handle_)),
+    : stream_(raft::resource::get_cuda_stream(handle_)),
       ps(::testing::TestWithParam<ivf_pq_inputs>::GetParam()),
       database(0, stream_),
       search_queries(0, stream_)
@@ -169,7 +172,7 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
       raft::random::uniformInt(
         handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(1), DataT(20));
     }
-    resource::sync_stream(handle_);
+    raft::resource::sync_stream(handle_);
   }
 
   void calc_ref()
@@ -189,10 +192,10 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
       ps.k,
       static_cast<cuvs::distance::DistanceType>((int)ps.index_params.metric));
     distances_ref.resize(queries_size);
-    update_host(distances_ref.data(), distances_naive_dev.data(), queries_size, stream_);
+    raft::update_host(distances_ref.data(), distances_naive_dev.data(), queries_size, stream_);
     indices_ref.resize(queries_size);
-    update_host(indices_ref.data(), indices_naive_dev.data(), queries_size, stream_);
-    resource::sync_stream(handle_);
+    raft::update_host(indices_ref.data(), indices_naive_dev.data(), queries_size, stream_);
+    raft::resource::sync_stream(handle_);
   }
 
   auto build_only()
@@ -207,9 +210,9 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
 
   auto build_2_extends()
   {
-    auto db_indices = make_device_vector<IdxT>(handle_, ps.num_db_vecs);
-    linalg::map_offset(handle_, db_indices.view(), identity_op{});
-    resource::sync_stream(handle_);
+    auto db_indices = raft::make_device_vector<IdxT>(handle_, ps.num_db_vecs);
+    raft::linalg::map_offset(handle_, db_indices.view(), raft::identity_op{});
+    raft::resource::sync_stream(handle_);
     auto size_1 = IdxT(ps.num_db_vecs) / 2;
     auto size_2 = IdxT(ps.num_db_vecs) - size_1;
     auto vecs_1 = database.data();
@@ -229,7 +232,7 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     cuvs::neighbors::ivf_pq::extend(handle_, vecs_2_view, inds_2_view, &idx);
 
     auto vecs_1_view =
-      raft::make_device_matrix_view<DataT, IdxT, row_major>(vecs_1, size_1, ps.dim);
+      raft::make_device_matrix_view<DataT, IdxT, raft::row_major>(vecs_1, size_1, ps.dim);
     auto inds_1_view = raft::make_device_vector_view<const IdxT, IdxT>(inds_1, size_1);
     cuvs::neighbors::ivf_pq::extend(handle_, vecs_1_view, inds_1_view, &idx);
     return idx;
@@ -257,19 +260,18 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
 
     if (n_take == 0) { return; }
 
-    auto rec_data  = make_device_matrix<DataT>(handle_, n_take, dim);
-    auto orig_data = make_device_matrix<DataT>(handle_, n_take, dim);
+    auto rec_data  = raft::make_device_matrix<DataT>(handle_, n_take, dim);
+    auto orig_data = raft::make_device_matrix<DataT>(handle_, n_take, dim);
 
-    cuvs::neighbors::ivf_pq::helpers::reconstruct_list_data(
-      handle_, index, rec_data.view(), label, n_skip);
+    ivf_pq::helpers::reconstruct_list_data(handle_, index, rec_data.view(), label, n_skip);
 
-    matrix::gather(database.data(),
-                   IdxT{dim},
-                   IdxT{n_take},
-                   rec_list->indices.data_handle() + n_skip,
-                   IdxT{n_take},
-                   orig_data.data_handle(),
-                   stream_);
+    raft::matrix::gather(database.data(),
+                         IdxT{dim},
+                         IdxT{n_take},
+                         rec_list->indices.data_handle() + n_skip,
+                         IdxT{n_take},
+                         orig_data.data_handle(),
+                         stream_);
 
     compare_vectors_l2(handle_, rec_data.view(), orig_data.view(), label, compression_ratio, 0.06);
   }
@@ -282,26 +284,23 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     auto n_rows   = old_list->size.load();
     if (n_rows == 0) { return; }
 
-    auto vectors_1 = make_device_matrix<EvalT>(handle_, n_rows, index->dim());
-    auto indices   = make_device_vector<IdxT>(handle_, n_rows);
-    copy(indices.data_handle(), old_list->indices.data_handle(), n_rows, stream_);
+    auto vectors_1 = raft::make_device_matrix<EvalT>(handle_, n_rows, index->dim());
+    auto indices   = raft::make_device_vector<IdxT>(handle_, n_rows);
+    raft::copy(indices.data_handle(), old_list->indices.data_handle(), n_rows, stream_);
 
-    cuvs::neighbors::ivf_pq::helpers::reconstruct_list_data(
-      handle_, *index, vectors_1.view(), label, 0);
-    cuvs::neighbors::ivf_pq::helpers::erase_list(handle_, index, label);
+    ivf_pq::helpers::reconstruct_list_data(handle_, *index, vectors_1.view(), label, uint32_t(0));
+    ivf_pq::helpers::erase_list(handle_, index, label);
     // NB: passing the type parameter because const->non-const implicit conversion of the mdspans
     // breaks type inference
-    cuvs::neighbors::ivf_pq::helpers::extend_list<EvalT, IdxT>(
-      handle_, index, vectors_1.view(), indices.view(), label);
+    ivf_pq::helpers::extend_list(handle_, index, vectors_1.view(), indices.view(), label);
 
     auto& new_list = index->lists()[label];
     ASSERT_NE(old_list.get(), new_list.get())
       << "The old list should have been shared and retained after ivf_pq index has erased the "
          "corresponding cluster.";
 
-    auto vectors_2 = make_device_matrix<EvalT>(handle_, n_rows, index->dim());
-    cuvs::neighbors::ivf_pq::helpers::reconstruct_list_data(
-      handle_, *index, vectors_2.view(), label, 0);
+    auto vectors_2 = raft::make_device_matrix<EvalT>(handle_, n_rows, index->dim());
+    ivf_pq::helpers::reconstruct_list_data(handle_, *index, vectors_2.view(), label, uint32_t(0));
     // The code search is unstable, and there's high chance of repeating values of the lvl-2 codes.
     // Hence, encoding-decoding chain often leads to altering both the PQ codes and the
     // reconstructed data.
@@ -316,20 +315,20 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
 
     if (n_rows == 0) { return; }
 
-    auto codes   = make_device_matrix<uint8_t>(handle_, n_rows, index->pq_dim());
-    auto indices = make_device_vector<IdxT>(handle_, n_rows);
-    copy(indices.data_handle(), old_list->indices.data_handle(), n_rows, stream_);
+    auto codes   = raft::make_device_matrix<uint8_t>(handle_, n_rows, index->pq_dim());
+    auto indices = raft::make_device_vector<IdxT>(handle_, n_rows);
+    raft::copy(indices.data_handle(), old_list->indices.data_handle(), n_rows, stream_);
 
-    cuvs::neighbors::ivf_pq::helpers::unpack_list_data(handle_, *index, codes.view(), label, 0);
-    cuvs::neighbors::ivf_pq::helpers::erase_list(handle_, index, label);
-    cuvs::neighbors::ivf_pq::helpers::extend_list_with_codes<IdxT>(
-      handle_, index, codes.view(), indices.view(), label);
+    ivf_pq::helpers::codepacker::unpack_list_data(
+      handle_, *index, codes.view(), label, uint32_t(0));
+    ivf_pq::helpers::erase_list(handle_, index, label);
+    ivf_pq::helpers::extend_list_with_codes(handle_, index, codes.view(), indices.view(), label);
 
     auto& new_list = index->lists()[label];
     ASSERT_NE(old_list.get(), new_list.get())
       << "The old list should have been shared and retained after ivf_pq index has erased the "
          "corresponding cluster.";
-    auto list_data_size = (n_rows / raft::neighbors::ivf_pq::kIndexGroupSize) *
+    auto list_data_size = (n_rows / cuvs::neighbors::ivf_pq::kIndexGroupSize) *
                           new_list->data.extent(1) * new_list->data.extent(2) *
                           new_list->data.extent(3);
 
@@ -345,9 +344,10 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     int n_vec      = 3;
     ASSERT_TRUE(row_offset + n_vec < n_rows);
     size_t offset      = row_offset * index->pq_dim();
-    auto codes_to_pack = make_device_matrix_view<const uint8_t, uint32_t>(
+    auto codes_to_pack = raft::make_device_matrix_view<const uint8_t, uint32_t>(
       codes.data_handle() + offset, n_vec, index->pq_dim());
-    ivf_pq::helpers::pack_list_data(handle_, index, codes_to_pack, label, row_offset);
+    ivf_pq::helpers::codepacker::pack_list_data(
+      handle_, index, codes_to_pack, label, uint32_t(row_offset));
     ASSERT_TRUE(cuvs::devArrMatch(old_list->data.data_handle(),
                                   new_list->data.data_handle(),
                                   list_data_size,
@@ -358,12 +358,22 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     uint32_t n_take = 4;
     ASSERT_TRUE(row_offset + n_take < n_rows);
     auto codes2 = raft::make_device_matrix<uint8_t>(handle_, n_take, index->pq_dim());
-    ivf_pq::helpers::codepacker::unpack(
-      handle_, list_data, index->pq_bits(), row_offset, codes2.view());
+    ivf_pq::helpers::codepacker::unpack_list_data(
+      // handle_, list_data, index->pq_bits(), row_offset, codes2.view());
+      handle_,
+      *index,
+      codes2.view(),
+      label,
+      uint32_t(row_offset));
 
     // Write it back
-    ivf_pq::helpers::codepacker::pack(
-      handle_, make_const_mdspan(codes2.view()), index->pq_bits(), row_offset, list_data);
+    ivf_pq::helpers::codepacker::pack_list_data(
+      // handle_, make_const_mdspan(codes2.view()), index->pq_bits(), row_offset, list_data);
+      handle_,
+      index,
+      make_const_mdspan(codes2.view()),
+      label,
+      uint32_t(row_offset));
     ASSERT_TRUE(cuvs::devArrMatch(old_list->data.data_handle(),
                                   new_list->data.data_handle(),
                                   list_data_size,
@@ -413,9 +423,9 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     cuvs::neighbors::ivf_pq::search(
       handle_, ps.search_params, index, query_view, inds_view, dists_view);
 
-    update_host(distances_ivf_pq.data(), distances_ivf_pq_dev.data(), queries_size, stream_);
-    update_host(indices_ivf_pq.data(), indices_ivf_pq_dev.data(), queries_size, stream_);
-    resource::sync_stream(handle_);
+    raft::update_host(distances_ivf_pq.data(), distances_ivf_pq_dev.data(), queries_size, stream_);
+    raft::update_host(indices_ivf_pq.data(), indices_ivf_pq_dev.data(), queries_size, stream_);
+    raft::resource::sync_stream(handle_);
 
     // A very conservative lower bound on recall
     double min_recall =
@@ -444,11 +454,11 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
       for (uint32_t k = 0; k < ps.k; k++) {
         auto flat_i   = query_ix * ps.k + k;
         auto found_ix = indices_ivf_pq[flat_i];
-        if (found_ix == raft::neighbors::ivf_pq::kOutOfBoundsRecord<IdxT>) {
+        if (found_ix == cuvs::neighbors::ivf_pq::kOutOfBoundsRecord<IdxT>) {
           found_oob++;
           continue;
         }
-        ASSERT_NE(found_ix, raft::neighbors::ivf::kInvalidRecord<IdxT>)
+        ASSERT_NE(found_ix, cuvs::neighbors::ivf::kInvalidRecord<IdxT>)
           << "got an invalid record at query_ix = " << query_ix << ", k = " << k
           << " (distance = " << distances_ivf_pq[flat_i] << ")";
         ASSERT_LT(found_ix, ps.num_db_vecs)
@@ -479,7 +489,7 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
   void TearDown() override  // NOLINT
   {
     cudaGetLastError();
-    resource::sync_stream(handle_);
+    raft::resource::sync_stream(handle_);
     database.resize(0, stream_);
     search_queries.resize(0, stream_);
   }
@@ -498,7 +508,7 @@ template <typename EvalT, typename DataT, typename IdxT>
 class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
  public:
   ivf_pq_filter_test()
-    : stream_(resource::get_cuda_stream(handle_)),
+    : stream_(raft::resource::get_cuda_stream(handle_)),
       ps(::testing::TestWithParam<ivf_pq_inputs>::GetParam()),
       database(0, stream_),
       search_queries(0, stream_)
@@ -522,7 +532,7 @@ class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
       raft::random::uniformInt(
         handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(1), DataT(20));
     }
-    resource::sync_stream(handle_);
+    raft::resource::sync_stream(handle_);
   }
 
   void calc_ref()
@@ -547,10 +557,10 @@ class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
                             queries_size,
                             stream_);
     distances_ref.resize(queries_size);
-    update_host(distances_ref.data(), distances_naive_dev.data(), queries_size, stream_);
+    raft::update_host(distances_ref.data(), distances_naive_dev.data(), queries_size, stream_);
     indices_ref.resize(queries_size);
-    update_host(indices_ref.data(), indices_naive_dev.data(), queries_size, stream_);
-    resource::sync_stream(handle_);
+    raft::update_host(indices_ref.data(), indices_naive_dev.data(), queries_size, stream_);
+    raft::resource::sync_stream(handle_);
   }
 
   auto build_only()
@@ -588,25 +598,25 @@ class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     auto removed_indices =
       raft::make_device_vector<IdxT, int64_t>(handle_, test_ivf_sample_filter::offset);
     thrust::sequence(
-      resource::get_thrust_policy(handle_),
+      raft::resource::get_thrust_policy(handle_),
       thrust::device_pointer_cast(removed_indices.data_handle()),
       thrust::device_pointer_cast(removed_indices.data_handle() + test_ivf_sample_filter::offset));
-    resource::sync_stream(handle_);
+    raft::resource::sync_stream(handle_);
 
-    raft::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
+    cuvs::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
       handle_, removed_indices.view(), ps.num_db_vecs);
-    raft::neighbors::ivf_pq::search_with_filtering<DataT, IdxT>(
+    cuvs::neighbors::ivf_pq::search_with_filtering(
       handle_,
       ps.search_params,
       index,
       query_view,
       inds_view,
       dists_view,
-      raft::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
+      cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
 
-    update_host(distances_ivf_pq.data(), distances_ivf_pq_dev.data(), queries_size, stream_);
-    update_host(indices_ivf_pq.data(), indices_ivf_pq_dev.data(), queries_size, stream_);
-    resource::sync_stream(handle_);
+    raft::update_host(distances_ivf_pq.data(), distances_ivf_pq_dev.data(), queries_size, stream_);
+    raft::update_host(indices_ivf_pq.data(), indices_ivf_pq_dev.data(), queries_size, stream_);
+    raft::resource::sync_stream(handle_);
 
     // A very conservative lower bound on recall
     double min_recall =
@@ -637,7 +647,7 @@ class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
   void TearDown() override  // NOLINT
   {
     cudaGetLastError();
-    resource::sync_stream(handle_);
+    raft::resource::sync_stream(handle_);
     database.resize(0, stream_);
     search_queries.resize(0, stream_);
   }
@@ -702,7 +712,7 @@ inline auto big_dims() -> test_cases_t
   return map<ivf_pq_inputs>(xs, [](const ivf_pq_inputs& x) {
     ivf_pq_inputs y(x);
     uint32_t pq_len       = 2;
-    y.index_params.pq_dim = div_rounding_up_safe(x.dim, pq_len);
+    y.index_params.pq_dim = raft::div_rounding_up_safe(x.dim, pq_len);
     // This comes from pure experimentation, also the recall depens a lot on pq_len.
     y.min_recall = 0.48 + 0.028 * std::log2(x.dim);
     return y;
@@ -715,7 +725,7 @@ inline auto big_dims_moderate_lut() -> test_cases_t
   return map<ivf_pq_inputs>(big_dims(), [](const ivf_pq_inputs& x) {
     ivf_pq_inputs y(x);
     uint32_t pq_len           = 2;
-    y.index_params.pq_dim     = round_up_safe(div_rounding_up_safe(x.dim, pq_len), 4u);
+    y.index_params.pq_dim     = raft::round_up_safe(raft::div_rounding_up_safe(x.dim, pq_len), 4u);
     y.index_params.pq_bits    = 6;
     y.search_params.lut_dtype = CUDA_R_16F;
     y.min_recall              = 0.69;
@@ -729,7 +739,7 @@ inline auto big_dims_small_lut() -> test_cases_t
   return map<ivf_pq_inputs>(big_dims(), [](const ivf_pq_inputs& x) {
     ivf_pq_inputs y(x);
     uint32_t pq_len           = 8;
-    y.index_params.pq_dim     = round_up_safe(div_rounding_up_safe(x.dim, pq_len), 4u);
+    y.index_params.pq_dim     = raft::round_up_safe(raft::div_rounding_up_safe(x.dim, pq_len), 4u);
     y.index_params.pq_bits    = 6;
     y.search_params.lut_dtype = CUDA_R_8U;
     y.min_recall              = 0.21;
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
index 5ca4cde68d..4c55c7b396 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
@@ -18,10 +18,14 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-using f32_f32_i64 = ivf_pq_test<float, float, int64_t>;
+using f32_f32_i64        = ivf_pq_test<float, float, int64_t>;
+using f32_f32_i64_filter = ivf_pq_filter_test<float, float, int64_t>;
 
 TEST_BUILD_EXTEND_SEARCH(f32_f32_i64)
 TEST_BUILD_SERIALIZE_SEARCH(f32_f32_i64)
 INSTANTIATE(f32_f32_i64, defaults() + small_dims() + big_dims_moderate_lut());
 
+TEST_BUILD_SEARCH(f32_f32_i64_filter)
+INSTANTIATE(f32_f32_i64_filter, defaults() + small_dims() + big_dims_moderate_lut());
+
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
index 0763b661e7..1e2c0ebd21 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
@@ -18,10 +18,13 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-using f32_i08_i64 = ivf_pq_test<float, int8_t, int64_t>;
+using f32_i08_i64        = ivf_pq_test<float, int8_t, int64_t>;
+using f32_i08_i64_filter = ivf_pq_filter_test<float, int8_t, int64_t>;
 
 TEST_BUILD_SEARCH(f32_i08_i64)
 TEST_BUILD_SERIALIZE_SEARCH(f32_i08_i64)
 INSTANTIATE(f32_i08_i64, defaults() + big_dims() + var_k());
 
+TEST_BUILD_SEARCH(f32_i08_i64_filter)
+INSTANTIATE(f32_i08_i64_filter, defaults() + big_dims() + var_k());
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
index 93e7d5336b..6b78c73b88 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
@@ -18,10 +18,13 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-using f32_u08_i64 = ivf_pq_test<float, uint8_t, int64_t>;
+using f32_u08_i64        = ivf_pq_test<float, uint8_t, int64_t>;
+using f32_u08_i64_filter = ivf_pq_filter_test<float, uint8_t, int64_t>;
 
 TEST_BUILD_SEARCH(f32_u08_i64)
 TEST_BUILD_EXTEND_SEARCH(f32_u08_i64)
 INSTANTIATE(f32_u08_i64, small_dims_per_cluster() + enum_variety());
 
+TEST_BUILD_SEARCH(f32_u08_i64_filter)
+INSTANTIATE(f32_u08_i64_filter, small_dims_per_cluster() + enum_variety());
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_nn_descent.cuh b/cpp/test/neighbors/ann_nn_descent.cuh
new file mode 100644
index 0000000000..bce0f98996
--- /dev/null
+++ b/cpp/test/neighbors/ann_nn_descent.cuh
@@ -0,0 +1,157 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+
+#include <cuvs/neighbors/nn_descent.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/itertools.hpp>
+
+#include "naive_knn.cuh"
+
+#include <gtest/gtest.h>
+
+#include <cstddef>
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace cuvs::neighbors::nn_descent {
+
+struct AnnNNDescentInputs {
+  int n_rows;
+  int dim;
+  int graph_degree;
+  cuvs::distance::DistanceType metric;
+  bool host_dataset;
+  double min_recall;
+};
+
+inline ::std::ostream& operator<<(::std::ostream& os, const AnnNNDescentInputs& p)
+{
+  os << "dataset shape=" << p.n_rows << "x" << p.dim << ", graph_degree=" << p.graph_degree
+     << ", metric=" << static_cast<int>(p.metric) << (p.host_dataset ? ", host" : ", device")
+     << std::endl;
+  return os;
+}
+
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
+ public:
+  AnnNNDescentTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnNNDescentInputs>::GetParam()),
+      database(0, stream_)
+  {
+  }
+
+ protected:
+  void testNNDescent()
+  {
+    size_t queries_size = ps.n_rows * ps.graph_degree;
+    std::vector<IdxT> indices_NNDescent(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+
+    {
+      rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+      naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                        distances_naive_dev.data(),
+                                        indices_naive_dev.data(),
+                                        database.data(),
+                                        database.data(),
+                                        ps.n_rows,
+                                        ps.n_rows,
+                                        ps.dim,
+                                        ps.graph_degree,
+                                        ps.metric);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    {
+      {
+        cuvs::neighbors::nn_descent::index_params index_params;
+        index_params.metric                    = ps.metric;
+        index_params.graph_degree              = ps.graph_degree;
+        index_params.intermediate_graph_degree = 2 * ps.graph_degree;
+        index_params.max_iterations            = 100;
+
+        auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          (const DataT*)database.data(), ps.n_rows, ps.dim);
+
+        {
+          if (ps.host_dataset) {
+            auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+            raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+            auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+              (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+            auto index =
+              cuvs::neighbors::nn_descent::build(handle_, index_params, database_host_view);
+            raft::update_host(
+              indices_NNDescent.data(), index.graph().data_handle(), queries_size, stream_);
+          } else {
+            auto index = cuvs::neighbors::nn_descent::build(handle_, index_params, database_view);
+            raft::update_host(
+              indices_NNDescent.data(), index.graph().data_handle(), queries_size, stream_);
+          };
+        }
+        raft::resource::sync_stream(handle_);
+      }
+
+      double min_recall = ps.min_recall;
+      EXPECT_TRUE(eval_recall(
+        indices_naive, indices_NNDescent, ps.n_rows, ps.graph_degree, 0.001, min_recall));
+    }
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(20));
+    }
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnNNDescentInputs ps;
+  rmm::device_uvector<DataT> database;
+};
+
+const std::vector<AnnNNDescentInputs> inputs = raft::util::itertools::product<AnnNNDescentInputs>(
+  {1000, 2000},                                              // n_rows
+  {3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // dim
+  {32, 64},                                                  // graph_degree
+  {cuvs::distance::DistanceType::L2Expanded},
+  {false, true},
+  {0.90});
+
+}  // namespace  cuvs::neighbors::nn_descent
diff --git a/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu b/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu
new file mode 100644
index 0000000000..64c0e0291a
--- /dev/null
+++ b/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_nn_descent.cuh"
+
+#include <gtest/gtest.h>
+
+namespace cuvs::neighbors::nn_descent {
+
+typedef AnnNNDescentTest<float, float, std::uint32_t> AnnNNDescentTestF_U32;
+TEST_P(AnnNNDescentTestF_U32, AnnNNDescent) { this->testNNDescent(); }
+
+INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestF_U32, ::testing::ValuesIn(inputs));
+
+}  // namespace   cuvs::neighbors::nn_descent
diff --git a/cpp/test/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu b/cpp/test/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
new file mode 100644
index 0000000000..80d31921d3
--- /dev/null
+++ b/cpp/test/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_nn_descent.cuh"
+
+#include <gtest/gtest.h>
+
+namespace cuvs::neighbors::nn_descent {
+
+typedef AnnNNDescentTest<float, int8_t, std::uint32_t> AnnNNDescentTestI8_U32;
+TEST_P(AnnNNDescentTestI8_U32, AnnNNDescent) { this->testNNDescent(); }
+
+INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestI8_U32, ::testing::ValuesIn(inputs));
+
+}  // namespace   cuvs::neighbors::nn_descent
diff --git a/cpp/test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu b/cpp/test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
new file mode 100644
index 0000000000..2348573466
--- /dev/null
+++ b/cpp/test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_nn_descent.cuh"
+
+#include <gtest/gtest.h>
+
+namespace cuvs::neighbors::nn_descent {
+
+typedef AnnNNDescentTest<float, uint8_t, std::uint32_t> AnnNNDescentTestUI8_U32;
+TEST_P(AnnNNDescentTestUI8_U32, AnnNNDescent) { this->testNNDescent(); }
+
+INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestUI8_U32, ::testing::ValuesIn(inputs));
+
+}  // namespace   cuvs::neighbors::nn_descent
diff --git a/cpp/test/neighbors/ann_utils.cuh b/cpp/test/neighbors/ann_utils.cuh
index 27a0fff7fd..9bc0099dd5 100644
--- a/cpp/test/neighbors/ann_utils.cuh
+++ b/cpp/test/neighbors/ann_utils.cuh
@@ -33,9 +33,12 @@
 #include "../test_utils.cuh"
 #include <gtest/gtest.h>
 #include <iostream>
+#include <limits>
 
 namespace cuvs::neighbors {
 
+using raft::RAFT_NAME;  // For logging
+
 struct print_dtype {
   cudaDataType_t value;
 };
@@ -159,30 +162,60 @@ auto calc_recall(const std::vector<T>& expected_idx,
     static_cast<double>(match_count) / static_cast<double>(total_count), match_count, total_count);
 }
 
+/** check uniqueness of indices
+ */
+template <typename T>
+auto check_unique_indices(const std::vector<T>& actual_idx, size_t rows, size_t cols)
+{
+  size_t max_count;
+  std::set<T> unique_indices;
+  for (size_t i = 0; i < rows; ++i) {
+    unique_indices.clear();
+    max_count = 0;
+    for (size_t k = 0; k < cols; ++k) {
+      size_t idx_k = i * cols + k;  // row major assumption!
+      auto act_idx = actual_idx[idx_k];
+      if (act_idx == std::numeric_limits<T>::max()) {
+        max_count++;
+      } else if (unique_indices.find(act_idx) == unique_indices.end()) {
+        unique_indices.insert(act_idx);
+      } else {
+        return testing::AssertionFailure()
+               << "Duplicated index " << act_idx << " at k " << k << " for query " << i << "! ";
+      }
+    }
+  }
+  return testing::AssertionSuccess();
+}
+
 template <typename T>
 auto eval_recall(const std::vector<T>& expected_idx,
                  const std::vector<T>& actual_idx,
                  size_t rows,
                  size_t cols,
                  double eps,
-                 double min_recall) -> testing::AssertionResult
+                 double min_recall,
+                 bool test_unique = true) -> testing::AssertionResult
 {
   auto [actual_recall, match_count, total_count] =
     calc_recall(expected_idx, actual_idx, rows, cols);
   double error_margin = (actual_recall - min_recall) / std::max(1.0 - min_recall, eps);
-  /*RAFT_LOG_INFO("Recall = %f (%zu/%zu), the error is %2.1f%% %s the threshold (eps = %f).",
+  RAFT_LOG_INFO("Recall = %f (%zu/%zu), the error is %2.1f%% %s the threshold (eps = %f).",
                 actual_recall,
                 match_count,
                 total_count,
                 std::abs(error_margin * 100.0),
                 error_margin < 0 ? "above" : "below",
-                eps);*/
+                eps);
   if (actual_recall < min_recall - eps) {
     return testing::AssertionFailure()
            << "actual recall (" << actual_recall << ") is lower than the minimum expected recall ("
            << min_recall << "); eps = " << eps << ". ";
   }
-  return testing::AssertionSuccess();
+  if (test_unique)
+    return check_unique_indices(actual_idx, rows, cols);
+  else
+    return testing::AssertionSuccess();
 }
 
 /** Overload of calc_recall to account for distances
@@ -230,12 +263,13 @@ auto eval_neighbours(const std::vector<T>& expected_idx,
                      size_t rows,
                      size_t cols,
                      double eps,
-                     double min_recall) -> testing::AssertionResult
+                     double min_recall,
+                     bool test_unique = true) -> testing::AssertionResult
 {
   auto [actual_recall, match_count, total_count] =
     calc_recall(expected_idx, actual_idx, expected_dist, actual_dist, rows, cols, eps);
   double error_margin = (actual_recall - min_recall) / std::max(1.0 - min_recall, eps);
-  /*
+
   RAFT_LOG_INFO("Recall = %f (%zu/%zu), the error is %2.1f%% %s the threshold (eps = %f).",
                 actual_recall,
                 match_count,
@@ -243,13 +277,16 @@ auto eval_neighbours(const std::vector<T>& expected_idx,
                 std::abs(error_margin * 100.0),
                 error_margin < 0 ? "above" : "below",
                 eps);
-  */
+
   if (actual_recall < min_recall - eps) {
     return testing::AssertionFailure()
            << "actual recall (" << actual_recall << ") is lower than the minimum expected recall ("
            << min_recall << "); eps = " << eps << ". ";
   }
-  return testing::AssertionSuccess();
+  if (test_unique)
+    return check_unique_indices(actual_idx, rows, cols);
+  else
+    return testing::AssertionSuccess();
 }
 
 template <typename T, typename DistT, typename IdxT>
@@ -273,7 +310,7 @@ auto eval_distances(raft::resources const& handle,
 
     raft::matrix::copy_rows<T, IdxT>(
       handle,
-      raft::make_device_matrix_view<const T, IdxT>(x, k, n_cols),
+      raft::make_device_matrix_view<const T, IdxT>(x, n_rows, n_cols),
       y.view(),
       raft::make_device_vector_view<const IdxT, IdxT>(neighbors + i * k, k));
 
diff --git a/cpp/test/neighbors/ivf_pq_helpers.cuh b/cpp/test/neighbors/ivf_pq_helpers.cuh
deleted file mode 100644
index 79a310f07b..0000000000
--- a/cpp/test/neighbors/ivf_pq_helpers.cuh
+++ /dev/null
@@ -1,428 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <raft/core/resource/cuda_stream.hpp>
-#include <raft/neighbors/detail/ivf_pq_build.cuh>
-#include <raft/neighbors/ivf_pq_types.hpp>
-
-#include <raft/core/device_mdspan.hpp>
-#include <raft/core/resources.hpp>
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-
-namespace cuvs::neighbors::ivf_pq::helpers {
-
-using namespace raft;
-using namespace raft::neighbors;
-using namespace raft::neighbors::ivf_pq;
-
-/**
- * @defgroup ivf_pq_helpers Helper functions for manipulationg IVF PQ Index
- * @{
- */
-
-namespace codepacker {
-/**
- * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
- * starting at given `offset`.
- *
- * Bit compression is removed, which means output will have pq_dim dimensional vectors (one code per
- * byte, instead of ceildiv(pq_dim * pq_bits, 8) bytes of pq codes).
- *
- * Usage example:
- * @code{.cpp}
- *   auto list_data = index.lists()[label]->data.view();
- *   // allocate the buffer for the output
- *   uint32_t n_take = 4;
- *   auto codes = raft::make_device_matrix<uint8_t>(res, n_take, index.pq_dim());
- *   uint32_t offset = 0;
- *   // unpack n_take elements from the list
- *   ivf_pq::helpers::codepacker::unpack(res, list_data, index.pq_bits(), offset, codes.view());
- * @endcode
- *
- * @tparam IdxT type of the indices in the source dataset
- *
- * @param[in] res raft resource
- * @param[in] list_data block to read from
- * @param[in] pq_bits bit length of encoded vector elements
- * @param[in] offset
- *   How many records in the list to skip.
- * @param[out] codes
- *   the destination buffer [n_take, index.pq_dim()].
- *   The length `n_take` defines how many records to unpack,
- *   it must be smaller than the list size.
- */
-inline void unpack(
-  raft::resources const& res,
-  device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, row_major> list_data,
-  uint32_t pq_bits,
-  uint32_t offset,
-  device_matrix_view<uint8_t, uint32_t, row_major> codes)
-{
-  raft::neighbors::ivf_pq::detail::unpack_list_data(
-    codes, list_data, offset, pq_bits, resource::get_cuda_stream(res));
-}
-
-/**
- * Write flat PQ codes into an existing list by the given offset.
- *
- * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
- *
- * Usage example:
- * @code{.cpp}
- *   auto list_data  = index.lists()[label]->data.view();
- *   // allocate the buffer for the input codes
- *   auto codes = raft::make_device_matrix<uint8_t>(res, n_vec, index.pq_dim());
- *   ... prepare n_vecs to pack into the list in codes ...
- *   // write codes into the list starting from the 42nd position
- *   ivf_pq::helpers::codepacker::pack(
- *       res, make_const_mdspan(codes.view()), index.pq_bits(), 42, list_data);
- * @endcode
- *
- * @param[in] res
- * @param[in] codes flat PQ codes, one code per byte [n_vec, pq_dim]
- * @param[in] pq_bits bit length of encoded vector elements
- * @param[in] offset how many records to skip before writing the data into the list
- * @param[in] list_data block to write into
- */
-inline void pack(
-  raft::resources const& res,
-  device_matrix_view<const uint8_t, uint32_t, row_major> codes,
-  uint32_t pq_bits,
-  uint32_t offset,
-  device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, row_major> list_data)
-{
-  raft::neighbors::ivf_pq::detail::pack_list_data(
-    list_data, codes, offset, pq_bits, resource::get_cuda_stream(res));
-}
-}  // namespace codepacker
-
-/**
- * Write flat PQ codes into an existing list by the given offset.
- *
- * The list is identified by its label.
- *
- * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
- *
- * Usage example:
- * @code{.cpp}
- *   // We will write into the 137th cluster
- *   uint32_t label = 137;
- *   // allocate the buffer for the input codes
- *   auto codes = raft::make_device_matrix<const uint8_t>(res, n_vec, index.pq_dim());
- *   ... prepare n_vecs to pack into the list in codes ...
- *   // write codes into the list starting from the 42nd position
- *   ivf_pq::helpers::pack_list_data(res, &index, codes_to_pack, label, 42);
- * @endcode
- *
- * @param[in] res
- * @param[inout] index IVF-PQ index.
- * @param[in] codes flat PQ codes, one code per byte [n_rows, pq_dim]
- * @param[in] label The id of the list (cluster) into which we write.
- * @param[in] offset how many records to skip before writing the data into the list
- */
-template <typename IdxT>
-void pack_list_data(raft::resources const& res,
-                    cuvs::neighbors::ivf_pq::index<IdxT>* index,
-                    device_matrix_view<const uint8_t, uint32_t, row_major> codes,
-                    uint32_t label,
-                    uint32_t offset)
-{
-  raft::neighbors::ivf_pq::detail::pack_list_data(
-    res, index->get_raft_index(), codes, label, offset);
-}
-
-/**
- * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
- * starting at given `offset`, one code per byte (independently of pq_bits).
- *
- * Usage example:
- * @code{.cpp}
- *   // We will unpack the fourth cluster
- *   uint32_t label = 3;
- *   // Get the list size
- *   uint32_t list_size = 0;
- *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
- * resource::get_cuda_stream(res)); resource::sync_stream(res);
- *   // allocate the buffer for the output
- *   auto codes = raft::make_device_matrix<float>(res, list_size, index.pq_dim());
- *   // unpack the whole list
- *   ivf_pq::helpers::unpack_list_data(res, index, codes.view(), label, 0);
- * @endcode
- *
- * @tparam IdxT type of the indices in the source dataset
- *
- * @param[in] res
- * @param[in] index
- * @param[out] out_codes
- *   the destination buffer [n_take, index.pq_dim()].
- *   The length `n_take` defines how many records to unpack,
- *   it must be smaller than the list size.
- * @param[in] label
- *   The id of the list (cluster) to decode.
- * @param[in] offset
- *   How many records in the list to skip.
- */
-template <typename IdxT>
-void unpack_list_data(raft::resources const& res,
-                      const cuvs::neighbors::ivf_pq::index<IdxT>& index,
-                      device_matrix_view<uint8_t, uint32_t, row_major> out_codes,
-                      uint32_t label,
-                      uint32_t offset)
-{
-  return raft::neighbors::ivf_pq::detail::unpack_list_data<IdxT>(
-    res, *index.get_raft_index(), out_codes, label, offset);
-}
-
-/**
- * @brief Unpack a series of records of a single list (cluster) in the compressed index
- * by their in-list offsets, one code per byte (independently of pq_bits).
- *
- * Usage example:
- * @code{.cpp}
- *   // We will unpack the fourth cluster
- *   uint32_t label = 3;
- *   // Create the selection vector
- *   auto selected_indices = raft::make_device_vector<uint32_t>(res, 4);
- *   ... fill the indices ...
- *   resource::sync_stream(res);
- *   // allocate the buffer for the output
- *   auto codes = raft::make_device_matrix<float>(res, selected_indices.size(), index.pq_dim());
- *   // decode the whole list
- *   ivf_pq::helpers::unpack_list_data(
- *       res, index, selected_indices.view(), codes.view(), label);
- * @endcode
- *
- * @tparam IdxT type of the indices in the source dataset
- *
- * @param[in] res
- * @param[in] index
- * @param[in] in_cluster_indices
- *   The offsets of the selected indices within the cluster.
- * @param[out] out_codes
- *   the destination buffer [n_take, index.pq_dim()].
- *   The length `n_take` defines how many records to unpack,
- *   it must be smaller than the list size.
- * @param[in] label
- *   The id of the list (cluster) to decode.
- */
-template <typename IdxT>
-void unpack_list_data(raft::resources const& res,
-                      const cuvs::neighbors::ivf_pq::index<IdxT>& index,
-                      device_vector_view<const uint32_t> in_cluster_indices,
-                      device_matrix_view<uint8_t, uint32_t, row_major> out_codes,
-                      uint32_t label)
-{
-  return raft::neighbors::ivf_pq::detail::unpack_list_data<IdxT>(
-    res, index, out_codes, label, in_cluster_indices);
-}
-
-/**
- * @brief Decode `n_take` consecutive records of a single list (cluster) in the compressed index
- * starting at given `offset`.
- *
- * Usage example:
- * @code{.cpp}
- *   // We will reconstruct the fourth cluster
- *   uint32_t label = 3;
- *   // Get the list size
- *   uint32_t list_size = 0;
- *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
- * resource::get_cuda_stream(res)); resource::sync_stream(res);
- *   // allocate the buffer for the output
- *   auto decoded_vectors = raft::make_device_matrix<float>(res, list_size, index.dim());
- *   // decode the whole list
- *   ivf_pq::helpers::reconstruct_list_data(res, index, decoded_vectors.view(), label, 0);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices in the source dataset
- *
- * @param[in] res
- * @param[in] index
- * @param[out] out_vectors
- *   the destination buffer [n_take, index.dim()].
- *   The length `n_take` defines how many records to reconstruct,
- *   it must be smaller than the list size.
- * @param[in] label
- *   The id of the list (cluster) to decode.
- * @param[in] offset
- *   How many records in the list to skip.
- */
-template <typename T, typename IdxT>
-void reconstruct_list_data(raft::resources const& res,
-                           const cuvs::neighbors::ivf_pq::index<IdxT>& index,
-                           device_matrix_view<T, uint32_t, row_major> out_vectors,
-                           uint32_t label,
-                           uint32_t offset)
-{
-  return raft::neighbors::ivf_pq::detail::reconstruct_list_data(
-    res, *index.get_raft_index(), out_vectors, label, offset);
-}
-
-/**
- * @brief Decode a series of records of a single list (cluster) in the compressed index
- * by their in-list offsets.
- *
- * Usage example:
- * @code{.cpp}
- *   // We will reconstruct the fourth cluster
- *   uint32_t label = 3;
- *   // Create the selection vector
- *   auto selected_indices = raft::make_device_vector<uint32_t>(res, 4);
- *   ... fill the indices ...
- *   resource::sync_stream(res);
- *   // allocate the buffer for the output
- *   auto decoded_vectors = raft::make_device_matrix<float>(
- *                             res, selected_indices.size(), index.dim());
- *   // decode the whole list
- *   ivf_pq::helpers::reconstruct_list_data(
- *       res, index, selected_indices.view(), decoded_vectors.view(), label);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices in the source dataset
- *
- * @param[in] res
- * @param[in] index
- * @param[in] in_cluster_indices
- *   The offsets of the selected indices within the cluster.
- * @param[out] out_vectors
- *   the destination buffer [n_take, index.dim()].
- *   The length `n_take` defines how many records to reconstruct,
- *   it must be smaller than the list size.
- * @param[in] label
- *   The id of the list (cluster) to decode.
- */
-template <typename T, typename IdxT>
-void reconstruct_list_data(raft::resources const& res,
-                           const cuvs::neighbors::ivf_pq::index<IdxT>& index,
-                           device_vector_view<const uint32_t> in_cluster_indices,
-                           device_matrix_view<T, uint32_t, row_major> out_vectors,
-                           uint32_t label)
-{
-  return raft::neighbors::ivf_pq::detail::reconstruct_list_data(
-    res, index, out_vectors, label, in_cluster_indices);
-}
-
-/**
- * @brief Extend one list of the index in-place, by the list label, skipping the classification and
- * encoding steps.
- *
- * Usage example:
- * @code{.cpp}
- *   // We will extend the fourth cluster
- *   uint32_t label = 3;
- *   // We will fill 4 new vectors
- *   uint32_t n_vec = 4;
- *   // Indices of the new vectors
- *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
- *   ... fill the indices ...
- *   auto new_codes = raft::make_device_matrix<uint8_t, uint32_t, row_major> new_codes(
- *       res, n_vec, index.pq_dim());
- *   ... fill codes ...
- *   // extend list with new codes
- *   ivf_pq::helpers::extend_list_with_codes(
- *       res, &index, codes.view(), indices.view(), label);
- * @endcode
- *
- * @tparam IdxT
- *
- * @param[in] res
- * @param[inout] index
- * @param[in] new_codes flat PQ codes, one code per byte [n_rows, index.pq_dim()]
- * @param[in] new_indices source indices [n_rows]
- * @param[in] label the id of the target list (cluster).
- */
-template <typename IdxT>
-void extend_list_with_codes(raft::resources const& res,
-                            cuvs::neighbors::ivf_pq::index<IdxT>* index,
-                            device_matrix_view<const uint8_t, uint32_t, row_major> new_codes,
-                            device_vector_view<const IdxT, uint32_t, row_major> new_indices,
-                            uint32_t label)
-{
-  raft::neighbors::ivf_pq::detail::extend_list_with_codes(
-    res, index->get_raft_index(), new_codes, new_indices, label);
-}
-
-/**
- * @brief Extend one list of the index in-place, by the list label, skipping the classification
- * step.
- *
- *  Usage example:
- * @code{.cpp}
- *   // We will extend the fourth cluster
- *   uint32_t label = 3;
- *   // We will extend with 4 new vectors
- *   uint32_t n_vec = 4;
- *   // Indices of the new vectors
- *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
- *   ... fill the indices ...
- *   auto new_vectors = raft::make_device_matrix<float, uint32_t, row_major> new_codes(
- *       res, n_vec, index.dim());
- *   ... fill vectors ...
- *   // extend list with new vectors
- *   ivf_pq::helpers::extend_list(
- *       res, &index, new_vectors.view(), indices.view(), label);
- * @endcode
- *
- * @tparam T
- * @tparam IdxT
- *
- * @param[in] res
- * @param[inout] index
- * @param[in] new_vectors data to encode [n_rows, index.dim()]
- * @param[in] new_indices source indices [n_rows]
- * @param[in] label the id of the target list (cluster).
- *
- */
-template <typename T, typename IdxT>
-void extend_list(raft::resources const& res,
-                 cuvs::neighbors::ivf_pq::index<IdxT>* index,
-                 device_matrix_view<const T, uint32_t, row_major> new_vectors,
-                 device_vector_view<const IdxT, uint32_t, row_major> new_indices,
-                 uint32_t label)
-{
-  raft::neighbors::ivf_pq::detail::extend_list(
-    res, index->get_raft_index(), new_vectors, new_indices, label);
-}
-
-/**
- * @brief Remove all data from a single list (cluster) in the index.
- *
- * Usage example:
- * @code{.cpp}
- *   // We will erase the fourth cluster (label = 3)
- *   ivf_pq::helpers::erase_list(res, &index, 3);
- * @endcode
- *
- * @tparam IdxT
- * @param[in] res
- * @param[inout] index
- * @param[in] label the id of the target list (cluster).
- */
-template <typename IdxT>
-void erase_list(raft::resources const& res,
-                cuvs::neighbors::ivf_pq::index<IdxT>* index,
-                uint32_t label)
-{
-  raft::neighbors::ivf_pq::detail::erase_list(res, index->get_raft_index(), label);
-}
-
-/** @} */
-}  // namespace cuvs::neighbors::ivf_pq::helpers

From 9f02d70437bf125feecd9c990dedb94ad3d2f147 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyas.ramasubramani@gmail.com>
Date: Fri, 10 May 2024 15:21:10 -0700
Subject: [PATCH 049/623] Pass through raft static CMake var (#111)

cuvs wheel builds were deviating from the behavior of the rest of RAPIDS before this PR by not linking statically to libraft.so but instead building the full dynamic and static lib (the default behavior) and then presumably bundling libraft.so. This change should fix that linking.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: https://github.com/rapidsai/cuvs/pull/111
---
 cpp/cmake/thirdparty/get_raft.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 3675136ee8..e3c2de3200 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -17,7 +17,7 @@ set(RAFT_FORK "rapidsai")
 set(RAFT_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
 
 function(find_and_configure_raft)
-    set(oneValueArgs VERSION FORK PINNED_TAG COMPILE_LIBRARY ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES)
+    set(oneValueArgs VERSION FORK PINNED_TAG COMPILE_LIBRARY USE_RAFT_STATIC ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
@@ -75,4 +75,5 @@ find_and_configure_raft(VERSION  ${RAFT_VERSION}.00
         COMPILE_LIBRARY          ON
         ENABLE_MNMG_DEPENDENCIES OFF
         ENABLE_NVTX              OFF
+        USE_RAFT_STATIC ${CUVS_USE_RAFT_STATIC}
 )

From 55e60ffca341bc3ce1ddb715763219f2a2a5192f Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 14 May 2024 20:53:21 -0700
Subject: [PATCH 050/623] Fix rust api docs (#119)

The rust api docs on docs.rapids.ai are deployed to https://docs.rapids.ai/api/cuvs/nightly/rust_api/index.html but the relative link in the iframe was assuming that these were at  https://docs.rapids.ai/api/cuvs/nightly/rust_api.html. (There seems to be some difference in the url structure depending on whether you build locally or not)

Fix by forcing the url structure to always be `rust_api/index.html`

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/119
---
 docs/source/api_docs.rst                         | 2 +-
 docs/source/{rust_api.rst => rust_api/index.rst} | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)
 rename docs/source/{rust_api.rst => rust_api/index.rst} (78%)

diff --git a/docs/source/api_docs.rst b/docs/source/api_docs.rst
index c911539b95..fe71ff3131 100644
--- a/docs/source/api_docs.rst
+++ b/docs/source/api_docs.rst
@@ -8,4 +8,4 @@ API Reference
    c_api.rst
    cpp_api.rst
    python_api.rst
-   rust_api.rst
+   rust_api/index.rst
diff --git a/docs/source/rust_api.rst b/docs/source/rust_api/index.rst
similarity index 78%
rename from docs/source/rust_api.rst
rename to docs/source/rust_api/index.rst
index 8297dbef94..f79d04fdf8 100644
--- a/docs/source/rust_api.rst
+++ b/docs/source/rust_api/index.rst
@@ -4,7 +4,7 @@ Rust API Documentation
 
 .. raw:: html
 
-    <iframe src="./_static/rust/cuvs/index.html" height="720px" width="100%"></iframe>
+    <iframe src="../_static/rust/cuvs/index.html" height="720px" width="100%"></iframe>
 
     <!-- hide the 'view source' section here, since it doesn't work with the iframe
     and we want the iframe to use the space -->

From bd7df645ae12a7f00b950fd5051cb9628734863d Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Thu, 16 May 2024 15:53:15 -0400
Subject: [PATCH 051/623] Migrate IVF-Flat from RAFT (#94)

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/94
---
 cpp/CMakeLists.txt                            |   22 +-
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |  924 ++++++++++++-
 cpp/src/core/nvtx.hpp                         |   85 +-
 cpp/src/neighbors/detail/div_utils.cuh        |   50 +
 .../neighbors/ivf_flat/generate_ivf_flat.py   |  135 +-
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |  601 +++++++++
 .../ivf_flat_build_extend_float_int64_t.cu    |  103 ++
 .../ivf_flat_build_extend_int8_t_int64_t.cu   |  103 ++
 .../ivf_flat_build_extend_uint8_t_int64_t.cu  |  103 ++
 .../ivf_flat/ivf_flat_build_float_int64_t.cpp |   52 -
 .../ivf_flat/ivf_flat_build_float_int64_t.cu  |   69 +
 .../ivf_flat_build_int8_t_int64_t.cpp         |   52 -
 .../ivf_flat/ivf_flat_build_int8_t_int64_t.cu |   69 +
 .../ivf_flat_build_uint8_t_int64_t.cpp        |   52 -
 .../ivf_flat_build_uint8_t_int64_t.cu         |   69 +
 .../ivf_flat_extend_float_int64_t.cpp         |   55 -
 .../ivf_flat/ivf_flat_extend_float_int64_t.cu |   71 +
 .../ivf_flat_extend_int8_t_int64_t.cpp        |   55 -
 .../ivf_flat_extend_int8_t_int64_t.cu         |   71 +
 .../ivf_flat_extend_uint8_t_int64_t.cpp       |   55 -
 .../ivf_flat_extend_uint8_t_int64_t.cu        |   71 +
 .../neighbors/ivf_flat/ivf_flat_helpers.cu    |  260 ++++
 .../neighbors/ivf_flat/ivf_flat_helpers.cuh   |  141 ++
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    | 1201 +++++++++++++++++
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |  383 ++++++
 ...t.cpp => ivf_flat_search_float_int64_t.cu} |   19 +-
 ....cpp => ivf_flat_search_int8_t_int64_t.cu} |   19 +-
 ...cpp => ivf_flat_search_uint8_t_int64_t.cu} |   19 +-
 .../neighbors/ivf_flat/ivf_flat_serialize.cuh |  176 +++
 ...pp => ivf_flat_serialize_float_int64_t.cu} |   60 +-
 ...p => ivf_flat_serialize_int8_t_int64_t.cu} |   60 +-
 ... => ivf_flat_serialize_uint8_t_int64_t.cu} |   60 +-
 cpp/src/neighbors/ivf_flat_index.cpp          |  129 +-
 .../neighbors/ivf_pq/ivf_pq_build_common.cu   |    2 +-
 cpp/test/neighbors/ann_ivf_flat.cuh           |  338 ++---
 examples/cpp/CMakeLists.txt                   |    4 +-
 rapids_config.cmake                           |    6 +-
 37 files changed, 5004 insertions(+), 740 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/div_utils.cuh
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
 rename cpp/src/neighbors/ivf_flat/{ivf_flat_search_float_int64_t.cpp => ivf_flat_search_float_int64_t.cu} (58%)
 rename cpp/src/neighbors/ivf_flat/{ivf_flat_search_int8_t_int64_t.cpp => ivf_flat_search_int8_t_int64_t.cu} (58%)
 rename cpp/src/neighbors/ivf_flat/{ivf_flat_search_uint8_t_int64_t.cpp => ivf_flat_search_uint8_t_int64_t.cu} (58%)
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
 rename cpp/src/neighbors/ivf_flat/{ivf_flat_serialize_float_int64_t.cpp => ivf_flat_serialize_float_int64_t.cu} (61%)
 rename cpp/src/neighbors/ivf_flat/{ivf_flat_serialize_int8_t_int64_t.cpp => ivf_flat_serialize_int8_t_int64_t.cu} (61%)
 rename cpp/src/neighbors/ivf_flat/{ivf_flat_serialize_uint8_t_int64_t.cpp => ivf_flat_serialize_uint8_t_int64_t.cu} (61%)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 2b1fe8aa09..9b2ae0ce05 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -379,18 +379,16 @@ add_library(
   src/neighbors/detail/refine_host_int8_t_float.cpp
   src/neighbors/detail/refine_host_uint8_t_float.cpp
   src/neighbors/ivf_flat_index.cpp
-  src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
-  src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
+  src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_helpers.cu
+  src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
+  src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
   src/neighbors/ivf_pq_index.cpp
   src/neighbors/ivf_pq/ivf_pq_build_common.cu
   src/neighbors/ivf_pq/ivf_pq_serialize.cu
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index efb32e024d..33daedc788 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -17,13 +17,19 @@
 #pragma once
 
 #include "ann_types.hpp"
-#include <raft/neighbors/ivf_flat_types.hpp>
+#include "ivf_list.hpp"
+#include <cstdint>
+#include <cuvs/neighbors/sample_filter.hpp>
 
 namespace cuvs::neighbors::ivf_flat {
 /**
  * @defgroup ivf_flat_cpp_index_params IVF-Flat index build parameters
  * @{
  */
+
+/** Size of the interleaved group (see `index::data` description). */
+constexpr static uint32_t kIndexGroupSize = 32;
+
 struct index_params : ann::index_params {
   /** The number of inverted lists (clusters) */
   uint32_t n_lists = 1024;
@@ -54,22 +60,6 @@ struct index_params : ann::index_params {
    * flag to `true` if you prefer to use as little GPU memory for the database as possible.
    */
   bool conservative_memory_allocation = false;
-
-  /** Build a raft IVF_FLAT index params from an existing cuvs IVF_FLAT index params. */
-  operator raft::neighbors::ivf_flat::index_params() const
-  {
-    return raft::neighbors::ivf_flat::index_params{
-      {
-        .metric            = static_cast<raft::distance::DistanceType>((int)this->metric),
-        .metric_arg        = this->metric_arg,
-        .add_data_on_build = this->add_data_on_build,
-      },
-      .n_lists                        = n_lists,
-      .kmeans_n_iters                 = kmeans_n_iters,
-      .kmeans_trainset_fraction       = kmeans_trainset_fraction,
-      .adaptive_centers               = adaptive_centers,
-      .conservative_memory_allocation = conservative_memory_allocation};
-  }
 };
 /**
  * @}
@@ -82,14 +72,45 @@ struct index_params : ann::index_params {
 struct search_params : ann::search_params {
   /** The number of clusters to search. */
   uint32_t n_probes = 20;
+};
+
+static_assert(std::is_aggregate_v<index_params>);
+static_assert(std::is_aggregate_v<search_params>);
+
+template <typename SizeT, typename ValueT, typename IdxT>
+struct list_spec {
+  using value_type   = ValueT;
+  using list_extents = raft::matrix_extent<SizeT>;
+  using index_type   = IdxT;
+
+  SizeT align_max;
+  SizeT align_min;
+  uint32_t dim;
+
+  constexpr list_spec(uint32_t dim, bool conservative_memory_allocation)
+    : dim(dim),
+      align_min(kIndexGroupSize),
+      align_max(conservative_memory_allocation ? kIndexGroupSize : 1024)
+  {
+  }
+
+  // Allow casting between different size-types (for safer size and offset calculations)
+  template <typename OtherSizeT>
+  constexpr explicit list_spec(const list_spec<OtherSizeT, ValueT, IdxT>& other_spec)
+    : dim{other_spec.dim}, align_min{other_spec.align_min}, align_max{other_spec.align_max}
+  {
+  }
 
-  /** Build a raft IVF_FLAT search params from an existing cuvs IVF_FLAT search params. */
-  operator raft::neighbors::ivf_flat::search_params() const
+  /** Determine the extents of an array enough to hold a given amount of data. */
+  constexpr auto make_list_extents(SizeT n_rows) const -> list_extents
   {
-    raft::neighbors::ivf_flat::search_params result = {{}, n_probes};
-    return result;
+    return raft::make_extents<SizeT>(n_rows, dim);
   }
 };
+
+template <typename ValueT, typename IdxT, typename SizeT = uint32_t>
+using list_data = ivf::list<list_spec, SizeT, ValueT, IdxT>;
+
 /**
  * @}
  */
@@ -125,7 +146,6 @@ struct index : ann::index {
         bool adaptive_centers,
         bool conservative_memory_allocation,
         uint32_t dim);
-  index(raft::neighbors::ivf_flat::index<T, IdxT>&& raft_idx);
 
   /**
    * Vectorized load/store size in elements, determines the size of interleaved data chunks.
@@ -181,6 +201,19 @@ struct index : ann::index {
   std::optional<raft::device_vector_view<float, uint32_t>> center_norms() noexcept;
   std::optional<raft::device_vector_view<const float, uint32_t>> center_norms() const noexcept;
 
+  /**
+   * Accumulated list sizes, sorted in descending order [n_lists + 1].
+   * The last value contains the total length of the index.
+   * The value at index zero is always zero.
+   *
+   * That is, the content of this span is as if the `list_sizes` was sorted and then accumulated.
+   *
+   * This span is used during search to estimate the maximum size of the workspace.
+   */
+  auto accum_sorted_sizes() noexcept -> raft::host_vector_view<IdxT, uint32_t>;
+  [[nodiscard]] auto accum_sorted_sizes() const noexcept
+    -> raft::host_vector_view<const IdxT, uint32_t>;
+
   /** Total length of the index. */
   IdxT size() const noexcept;
 
@@ -202,23 +235,44 @@ struct index : ann::index {
    */
   bool conservative_memory_allocation() const noexcept;
 
+  void allocate_center_norms(raft::resources const& res);
+
   /** Lists' data and indices. */
-  std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>& lists() noexcept;
-  const std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>& lists()
-    const noexcept;
+  std::vector<std::shared_ptr<list_data<T, IdxT>>>& lists() noexcept;
+  const std::vector<std::shared_ptr<list_data<T, IdxT>>>& lists() const noexcept;
 
-  // Get pointer to underlying RAFT index, not meant to be used outside of cuVS
-  inline raft::neighbors::ivf_flat::index<T, IdxT>* get_raft_index() noexcept
-  {
-    return raft_index_.get();
-  }
-  inline const raft::neighbors::ivf_flat::index<T, IdxT>* get_raft_index() const noexcept
-  {
-    return raft_index_.get();
-  }
+  void check_consistency();
 
  private:
-  std::unique_ptr<raft::neighbors::ivf_flat::index<T, IdxT>> raft_index_;
+  /**
+   * TODO: in theory, we can lift this to the template parameter and keep it at hardware maximum
+   * possible value by padding the `dim` of the data https://github.com/rapidsai/raft/issues/711
+   */
+  uint32_t veclen_;
+  cuvs::distance::DistanceType metric_;
+  bool adaptive_centers_;
+  bool conservative_memory_allocation_;
+  std::vector<std::shared_ptr<list_data<T, IdxT>>> lists_;
+  raft::device_vector<uint32_t, uint32_t> list_sizes_;
+  raft::device_matrix<float, uint32_t, raft::row_major> centers_;
+  std::optional<raft::device_vector<float, uint32_t>> center_norms_;
+
+  // Computed members
+  raft::device_vector<T*, uint32_t> data_ptrs_;
+  raft::device_vector<IdxT*, uint32_t> inds_ptrs_;
+  raft::host_vector<IdxT, uint32_t> accum_sorted_sizes_;
+
+  static auto calculate_veclen(uint32_t dim) -> uint32_t
+  {
+    // TODO: consider padding the dimensions and fixing veclen to its maximum possible value as a
+    // template parameter (https://github.com/rapidsai/raft/issues/711)
+
+    // NOTE: keep this consistent with the select_interleaved_scan_kernel logic
+    // in detail/ivf_flat_interleaved_scan-inl.cuh.
+    uint32_t veclen = std::max<uint32_t>(1, 16 / sizeof(T));
+    if (dim % veclen != 0) { veclen = 1; }
+    return veclen;
+  }
 };
 /**
  * @}
@@ -368,6 +422,147 @@ void build(raft::resources const& handle,
            const cuvs::neighbors::ivf_flat::index_params& index_params,
            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
            cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<float, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<float, int64_t>& idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a host pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<int8_t, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a host pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& idx);
 /**
  * @}
  */
@@ -568,6 +763,198 @@ void extend(raft::resources const& handle,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
             std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
             cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* idx);
+
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<float, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<float, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<float, int64_t>* idx);
+
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, dataset, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<int8_t, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<int8_t, int64_t>* idx);
+
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, dataset, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* idx);
 /**
  * @}
  */
@@ -687,6 +1074,105 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-flat constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
+ * query.
+ */
+void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  index<float, int64_t>& idx,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
+
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-flat constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
+ * query.
+ */
+void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  index<int8_t, int64_t>& idx,
+  raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
+
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-flat constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
+ * query.
+ */
+void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  index<uint8_t, int64_t>& idx,
+  raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
 /**
  * @}
  */
@@ -737,7 +1223,7 @@ void serialize_file(raft::resources const& handle,
  * std::string filename("/path/to/index");
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
- * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
  * cuvs::deserialize_file(handle, filename, &index);
  * @endcode
  *
@@ -791,7 +1277,7 @@ void serialize(raft::resources const& handle,
  * std::string str;
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
- * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
  * auto index = cuvs::deserialize(handle, str, &index);
  * @endcode
  *
@@ -845,7 +1331,7 @@ void serialize_file(raft::resources const& handle,
  * std::string filename("/path/to/index");
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
- * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
  * cuvs::deserialize_file(handle, filename, &index);
  * @endcode
  *
@@ -899,7 +1385,7 @@ void serialize(raft::resources const& handle,
  * std::string str;
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
- * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
  * auto index = cuvs::deserialize(handle, str, &index);
  * @endcode
  *
@@ -953,7 +1439,7 @@ void serialize_file(raft::resources const& handle,
  * std::string filename("/path/to/index");
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
- * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * // create an empty index with ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
  * cuvs::deserialize_file(handle, filename, &index);
  * @endcode
  *
@@ -1007,7 +1493,7 @@ void serialize(raft::resources const& handle,
  * std::string str;
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
- * // create an empty index with `ivf_pq::index<T, IdxT> index(handle, index_params, dim);`
+ * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
  * auto index = cuvs::deserialize(handle, str, &index);
  * @endcode
  *
@@ -1023,4 +1509,360 @@ void deserialize(raft::resources const& handle,
 /**
  * @}
  */
+
+namespace helpers {
+
+/**
+ * @defgroup ivf_flat_helpers Helper functions for IVF Flat
+ * @{
+ */
+
+namespace codepacker {
+
+/**
+ * Write flat codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<float>(res, n_vec, index.dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_flat::helpers::codepacker::pack(
+ *       res, make_const_mdspan(codes.view()), index.veclen(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] codes flat codes [n_vec, dim]
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ * @param[inout] list_data block to write into
+ */
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const float, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<float,
+                              typename list_spec<uint32_t, float, int64_t>::list_extents,
+                              raft::row_major> list_data);
+
+/**
+ * Write flat codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<int8_t>(res, n_vec, index.dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_flat::helpers::codepacker::pack(
+ *       res, make_const_mdspan(codes.view()), index.veclen(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] codes flat codes [n_vec, dim]
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ * @param[inout] list_data block to write into
+ */
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<int8_t,
+                              typename list_spec<uint32_t, int8_t, int64_t>::list_extents,
+                              raft::row_major> list_data);
+
+/**
+ * Write flat codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<uint8_t>(res, n_vec, index.dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_flat::helpers::codepacker::pack(
+ *       res, make_const_mdspan(codes.view()), index.veclen(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] codes flat codes [n_vec, dim]
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ * @param[inout] list_data block to write into
+ */
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<uint8_t,
+                              typename list_spec<uint32_t, uint8_t, int64_t>::list_extents,
+                              raft::row_major> list_data);
+
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data = index.lists()[label]->data.view();
+ *   // allocate the buffer for the output
+ *   uint32_t n_take = 4;
+ *   auto codes = raft::make_device_matrix<float>(res, n_take, index.dim());
+ *   uint32_t offset = 0;
+ *   // unpack n_take elements from the list
+ *   ivf_fat::helpers::codepacker::unpack(res, list_data, index.veclen(), offset, codes.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] list_data block to read from
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset
+ *   How many records in the list to skip.
+ * @param[inout] codes
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be <= the list size.
+ */
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const float,
+                                typename list_spec<uint32_t, float, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<float, uint32_t, raft::row_major> codes);
+
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data = index.lists()[label]->data.view();
+ *   // allocate the buffer for the output
+ *   uint32_t n_take = 4;
+ *   auto codes = raft::make_device_matrix<int8_t>(res, n_take, index.dim());
+ *   uint32_t offset = 0;
+ *   // unpack n_take elements from the list
+ *   ivf_fat::helpers::codepacker::unpack(res, list_data, index.veclen(), offset, codes.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] list_data block to read from
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset
+ *   How many records in the list to skip.
+ * @param[inout] codes
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be <= the list size.
+ */
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const int8_t,
+                                typename list_spec<uint32_t, int8_t, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<int8_t, uint32_t, raft::row_major> codes);
+
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data = index.lists()[label]->data.view();
+ *   // allocate the buffer for the output
+ *   uint32_t n_take = 4;
+ *   auto codes = raft::make_device_matrix<uint8_t>(res, n_take, index.dim());
+ *   uint32_t offset = 0;
+ *   // unpack n_take elements from the list
+ *   ivf_fat::helpers::codepacker::unpack(res, list_data, index.veclen(), offset, codes.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] list_data block to read from
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset
+ *   How many records in the list to skip.
+ * @param[inout] codes
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be <= the list size.
+ */
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const uint8_t,
+                                typename list_spec<uint32_t, uint8_t, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> codes);
+
+/**
+ * Write one flat code into a block by the given offset. The offset indicates the id of the record
+ * in the list. This function interleaves the code and is intended to later copy the interleaved
+ * codes over to the IVF list on device. NB: no memory allocation happens here; the block must fit
+ * the record (offset + 1).
+ *
+ * @param[in] flat_code input flat code
+ * @param[out] block block of memory to write interleaved codes to
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ */
+void pack_1(const float* flat_code, float* block, uint32_t dim, uint32_t veclen, uint32_t offset);
+
+/**
+ * Write one flat code into a block by the given offset. The offset indicates the id of the record
+ * in the list. This function interleaves the code and is intended to later copy the interleaved
+ * codes over to the IVF list on device. NB: no memory allocation happens here; the block must fit
+ * the record (offset + 1).
+ *
+ * @param[in] flat_code input flat code
+ * @param[out] block block of memory to write interleaved codes to
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ */
+void pack_1(const int8_t* flat_code, int8_t* block, uint32_t dim, uint32_t veclen, uint32_t offset);
+
+/**
+ * Write one flat code into a block by the given offset. The offset indicates the id of the record
+ * in the list. This function interleaves the code and is intended to later copy the interleaved
+ * codes over to the IVF list on device. NB: no memory allocation happens here; the block must fit
+ * the record (offset + 1).
+ *
+ * @param[in] flat_code input flat code
+ * @param[out] block block of memory to write interleaved codes to
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ */
+void pack_1(
+  const uint8_t* flat_code, uint8_t* block, uint32_t dim, uint32_t veclen, uint32_t offset);
+
+/**
+ * Unpack 1 record of a single list (cluster) in the index to fetch the flat code. The offset
+ * indicates the id of the record. This function fetches one flat code from an interleaved code.
+ *
+ * @param[in] block interleaved block. The block can be thought of as the whole inverted list in
+ * interleaved format.
+ * @param[out] flat_code output flat code
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset fetch the flat code by the given offset
+ */
+void unpack_1(const float* block, float* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset);
+
+/**
+ * Unpack 1 record of a single list (cluster) in the index to fetch the flat code. The offset
+ * indicates the id of the record. This function fetches one flat code from an interleaved code.
+ *
+ * @param[in] block interleaved block. The block can be thought of as the whole inverted list in
+ * interleaved format.
+ * @param[out] flat_code output flat code
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset fetch the flat code by the given offset
+ */
+void unpack_1(
+  const int8_t* block, int8_t* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset);
+
+/**
+ * Unpack 1 record of a single list (cluster) in the index to fetch the flat code. The offset
+ * indicates the id of the record. This function fetches one flat code from an interleaved code.
+ *
+ * @param[in] block interleaved block. The block can be thought of as the whole inverted list in
+ * interleaved format.
+ * @param[out] flat_code output flat code
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset fetch the flat code by the given offset
+ */
+void unpack_1(
+  const uint8_t* block, uint8_t* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset);
+
+}  // namespace codepacker
+
+/**
+ * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
+ * externally modifying the index without going through the build stage. The data and indices of the
+ * IVF lists will be lost.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_flat::index<float, int64_t> index(res, index_params, D);
+ *   // reset the index's state and list sizes
+ *   ivf_flat::helpers::reset_index(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-Flat index
+ */
+void reset_index(const raft::resources& res, index<float, int64_t>* index);
+
+/**
+ * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
+ * externally modifying the index without going through the build stage. The data and indices of the
+ * IVF lists will be lost.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_flat::index<int8_t, int64_t> index(res, index_params, D);
+ *   // reset the index's state and list sizes
+ *   ivf_flat::helpers::reset_index(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-Flat index
+ */
+void reset_index(const raft::resources& res, index<int8_t, int64_t>* index);
+
+/**
+ * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
+ * externally modifying the index without going through the build stage. The data and indices of the
+ * IVF lists will be lost.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_flat::index<uint8_t, int64_t> index(res, index_params, D);
+ *   // reset the index's state and list sizes
+ *   ivf_flat::helpers::reset_index(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-Flat index
+ */
+void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index);
+
+/**
+ * @}
+ */
+
+}  // namespace helpers
 }  // namespace cuvs::neighbors::ivf_flat
\ No newline at end of file
diff --git a/cpp/src/core/nvtx.hpp b/cpp/src/core/nvtx.hpp
index 79382bbd0e..2580b4964f 100644
--- a/cpp/src/core/nvtx.hpp
+++ b/cpp/src/core/nvtx.hpp
@@ -16,9 +16,90 @@
 
 #pragma once
 
-namespace cuvs::common::nvtx::domain {
+#include <raft/common/nvtx.hpp>
+
+namespace cuvs::common::nvtx {
+namespace domain {
+/** @brief The default NVTX domain. */
+struct app {
+  static constexpr char const* name{"application"};
+};
+
 /** @brief This NVTX domain is supposed to be used within cuvs.  */
 struct cuvs {
   static constexpr const char* name = "cuvs";
 };
-};  // namespace cuvs::common::nvtx::domain
+}  // namespace domain
+
+/**
+ * @brief Push a named NVTX range.
+ *
+ * @tparam Domain optional struct that defines the NVTX domain message;
+ *   You can create a new domain with a custom message as follows:
+ *   \code{.cpp}
+ *      struct custom_domain { static constexpr char const* name{"custom message"}; }
+ *   \endcode
+ *   NB: make sure to use the same domain for `push_range` and `pop_range`.
+ * @param format range name format (accepts printf-style arguments)
+ * @param args the arguments for the printf-style formatting
+ */
+template <typename Domain = domain::app, typename... Args>
+inline void push_range(const char* format, Args... args)
+{
+  raft::common::nvtx::detail::push_range<Domain, Args...>(format, args...);
+}
+
+/**
+ * @brief Pop the latest range.
+ *
+ * @tparam Domain optional struct that defines the NVTX domain message;
+ *   You can create a new domain with a custom message as follows:
+ *   \code{.cpp}
+ *      struct custom_domain { static constexpr char const* name{"custom message"}; }
+ *   \endcode
+ *   NB: make sure to use the same domain for `push_range` and `pop_range`.
+ */
+template <typename Domain = domain::app>
+inline void pop_range()
+{
+  raft::common::nvtx::detail::pop_range<Domain>();
+}
+
+/**
+ * @brief Push a named NVTX range that would be popped at the end of the object lifetime.
+ *
+ * Refer to \ref Usage for the usage examples.
+ *
+ * @tparam Domain optional struct that defines the NVTX domain message;
+ *   You can create a new domain with a custom message as follows:
+ *   \code{.cpp}
+ *      struct custom_domain { static constexpr char const* name{"custom message"}; }
+ *   \endcode
+ */
+template <typename Domain = domain::app>
+class range {
+ public:
+  /**
+   * Push a named NVTX range.
+   * At the end of the object lifetime, pop the range back.
+   *
+   * @param format range name format (accepts printf-style arguments)
+   * @param args the arguments for the printf-style formatting
+   */
+  template <typename... Args>
+  explicit range(const char* format, Args... args)
+  {
+    push_range<Domain, Args...>(format, args...);
+  }
+
+  ~range() { pop_range<Domain>(); }
+
+  /* This object is not meant to be touched. */
+  range(const range&)                              = delete;
+  range(range&&)                                   = delete;
+  auto operator=(const range&) -> range&           = delete;
+  auto operator=(range&&) -> range&                = delete;
+  static auto operator new(std::size_t) -> void*   = delete;
+  static auto operator new[](std::size_t) -> void* = delete;
+};
+};  // namespace cuvs::common::nvtx
diff --git a/cpp/src/neighbors/detail/div_utils.cuh b/cpp/src/neighbors/detail/div_utils.cuh
new file mode 100644
index 0000000000..b54b8400ea
--- /dev/null
+++ b/cpp/src/neighbors/detail/div_utils.cuh
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/util/pow2_utils.cuh>
+
+/**
+ * @brief A simple wrapper for raft::Pow2 which uses Pow2 utils only when available and regular
+ * integer division otherwise. This is done to allow a common interface for division arithmetic for
+ * non CUDA headers.
+ *
+ * @tparam Value_ a compile-time value representable as a power-of-two.
+ */
+namespace cuvs::neighbors::detail {
+template <auto Value_>
+struct div_utils {
+  typedef decltype(Value_) Type;
+  static constexpr Type Value = Value_;
+
+  template <typename T>
+  static constexpr _RAFT_HOST_DEVICE inline auto roundDown(T x)
+  {
+    return raft::Pow2<Value_>::roundDown(x);
+  }
+
+  template <typename T>
+  static constexpr _RAFT_HOST_DEVICE inline auto mod(T x)
+  {
+    return raft::Pow2<Value_>::mod(x);
+  }
+
+  template <typename T>
+  static constexpr _RAFT_HOST_DEVICE inline auto div(T x)
+  {
+    return raft::Pow2<Value_>::div(x);
+  }
+};
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
index bf0cad6d4a..1733ca8b25 100644
--- a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
+++ b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
@@ -37,9 +37,21 @@
  *
  */
 
-#include <raft_runtime/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
+"""
+
+build_include_macro = """
+#include "ivf_flat_build.cuh"
+"""
+search_include_macro = """
+#include "ivf_flat_search.cuh"
+"""
+
+serialize_include_macro = """
+#include "ivf_flat_serialize.cuh"
+"""
 
+namespace_macro = """
 namespace cuvs::neighbors::ivf_flat {
 """
 
@@ -54,14 +66,14 @@
 )
 
 build_macro = """
-#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \\
+#define CUVS_INST_IVF_FLAT_BUILD_EXTEND(T, IdxT)                                               \\
   auto build(raft::resources const& handle,                                                    \\
              const cuvs::neighbors::ivf_flat::index_params& params,                            \\
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \\
     ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \\
   {                                                                                            \\
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \\
-      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \\
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));           \\
   }                                                                                            \\
                                                                                                \\
   void build(raft::resources const& handle,                                                    \\
@@ -69,12 +81,24 @@
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \\
              cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \\
   {                                                                                            \\
-    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \\
-  }
-"""
-
-extend_macro = """
-#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \\
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                    \\
+  }                                                                                            \\
+  auto build(raft::resources const& handle,                                                    \\
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \\
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                   \\
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \\
+  {                                                                                            \\
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \\
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));           \\
+  }                                                                                            \\
+                                                                                               \\
+  void build(raft::resources const& handle,                                                    \\
+             const cuvs::neighbors::ivf_flat::index_params& params,                            \\
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,                   \\
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \\
+  {                                                                                            \\
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                    \\
+  }                                                                                            \\
   auto extend(raft::resources const& handle,                                         \\
               raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \\
               std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
@@ -82,8 +106,8 @@
     ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \\
   {                                                                                  \\
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \\
-      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \\
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \\
+      std::move(cuvs::neighbors::ivf_flat::detail::extend(                           \\
+        handle, new_vectors, new_indices, orig_index)));                             \\
   }                                                                                  \\
                                                                                      \\
   void extend(raft::resources const& handle,                                         \\
@@ -91,9 +115,28 @@
               std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \\
               cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \\
   {                                                                                  \\
-    raft::runtime::neighbors::ivf_flat::extend(                                      \\
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \\
-  }                 
+    cuvs::neighbors::ivf_flat::detail::extend(                                       \\
+      handle, new_vectors, new_indices, idx);                                        \\
+  }                                                                                  \\
+  auto extend(raft::resources const& handle,                                         \\
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,    \\
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,   \\
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \\
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \\
+  {                                                                                  \\
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \\
+      std::move(cuvs::neighbors::ivf_flat::detail::extend(                           \\
+        handle, new_vectors, new_indices, orig_index)));                             \\
+  }                                                                                  \\
+                                                                                     \\
+  void extend(raft::resources const& handle,                                         \\
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,    \\
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,   \\
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \\
+  {                                                                                  \\
+    cuvs::neighbors::ivf_flat::detail::extend(                                       \\
+      handle, new_vectors, new_indices, idx);                                        \\
+  }                    
 """
 
 search_macro = """
@@ -105,8 +148,20 @@
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
               raft::device_matrix_view<float, IdxT, raft::row_major> distances) \\
   {                                                                             \\
-    raft::runtime::neighbors::ivf_flat::search(                                 \\
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \\
+    cuvs::neighbors::ivf_flat::detail::search(                                  \\
+      handle, params, index, queries, neighbors, distances);                    \\
+  }                                                                             \\
+  void search_with_filtering(                                                   \\
+    raft::resources const& handle,                                              \\
+    const search_params& params,                                                \\
+    index<T, IdxT>& idx,                                                        \\
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \\
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \\
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \\
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \\
+  {                                                                             \\
+    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \\
+      handle, params, idx, queries, neighbors, distances, sample_filter);       \\
   }
 """
 
@@ -116,46 +171,48 @@
                       const std::string& filename,                                                 \\
                       const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \\
   {                                                                                                \\
-    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \\
-  }                                                                                                \\
-                                                                                                   \\
-  void deserialize_file(raft::resources const& handle,                                             \\
-                        const std::string& filename,                                               \\
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \\
-  {                                                                                                \\
-    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \\
-      handle, filename, index->get_raft_index());                                                  \\
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);                         \\
   }                                                                                                \\
                                                                                                    \\
   void serialize(raft::resources const& handle,                                                    \\
                  std::string& str,                                                                 \\
                  const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \\
   {                                                                                                \\
-    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \\
+    std::ostringstream os;                                                                         \\
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                               \\
+    str = os.str();                                                                                \\
   }                                                                                                \\
                                                                                                    \\
-  void deserialize(raft::resources const& handle,                                                  \\
-                   const std::string& str,                                                         \\
-                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \\
+  void deserialize_file(raft::resources const& handle,                                             \\
+                        const std::string& filename,                                               \\
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \\
   {                                                                                                \\
-    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \\
-  }
+    * index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(                             \\
+      handle, filename);                                                                           \\
+  }                                                                                                \\
+    void deserialize(raft::resources const& handle,                                                \\
+                     const std::string& str,                                                       \\
+                     cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                             \\
+  {                                                                                                \\
+    std::istringstream is(str);                                                                    \\
+    * index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(                             \\
+      handle, is);                                                                                 \\
+  }                                                                                                 
 """
 
 macros = dict(
-    build=dict(
+    build_extend=dict(
+        include=build_include_macro,
         definition=build_macro,
-        name="CUVS_INST_IVF_FLAT_BUILD",
-    ),
-    extend=dict(
-        definition=extend_macro,
-        name="CUVS_INST_IVF_FLAT_EXTEND",
+        name="CUVS_INST_IVF_FLAT_BUILD_EXTEND",
     ),
     search=dict(
+        include=search_include_macro,
         definition=search_macro,
         name="CUVS_INST_IVF_FLAT_SEARCH",
     ),
     serialize=dict(
+        include=serialize_include_macro,
         definition=serialize_macro,
         name="CUVS_INST_IVF_FLAT_SERIALIZE",
     ),
@@ -163,9 +220,11 @@
 
 for type_path, (T, IdxT) in types.items():
     for macro_path, macro in macros.items():
-        path = f"ivf_flat_{macro_path}_{type_path}.cpp"
+        path = f"ivf_flat_{macro_path}_{type_path}.cu"
         with open(path, "w") as f:
             f.write(header)
+            f.write(macro['include'])
+            f.write(namespace_macro)
             f.write(macro["definition"])
             f.write(f"{macro['name']}({T}, {IdxT});\n\n")
             f.write(f"#undef {macro['name']}\n")
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
new file mode 100644
index 0000000000..89cd5ec57b
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -0,0 +1,601 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../core/nvtx.hpp"
+#include "../ivf_common.cuh"
+#include "../ivf_list.cuh"
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_list.hpp>
+
+#include "../detail/ann_utils.cuh"
+#include <raft/cluster/kmeans_balanced.cuh>
+#include <raft/core/logger.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/distance/distance_types.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/stats/histogram.cuh>
+#include <raft/util/pow2_utils.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::ivf_flat {
+using namespace cuvs::spatial::knn::detail;  // NOLINT
+
+namespace detail {
+
+template <typename T, typename IdxT>
+auto clone(const raft::resources& res, const index<T, IdxT>& source) -> index<T, IdxT>
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  // Allocate the new index
+  index<T, IdxT> target(res,
+                        source.metric(),
+                        source.n_lists(),
+                        source.adaptive_centers(),
+                        source.conservative_memory_allocation(),
+                        source.dim());
+
+  // Copy the independent parts
+  raft::copy(target.list_sizes().data_handle(),
+             source.list_sizes().data_handle(),
+             source.list_sizes().size(),
+             stream);
+  raft::copy(target.centers().data_handle(),
+             source.centers().data_handle(),
+             source.centers().size(),
+             stream);
+  if (source.center_norms().has_value()) {
+    target.allocate_center_norms(res);
+    raft::copy(target.center_norms()->data_handle(),
+               source.center_norms()->data_handle(),
+               source.center_norms()->size(),
+               stream);
+  }
+  // Copy shared pointers
+  target.lists() = source.lists();
+
+  // Make sure the device pointers point to the new lists
+  ivf::detail::recompute_internal_state(res, target);
+
+  return target;
+}
+
+/**
+ * @brief Record the dataset into the index, one source row at a time.
+ *
+ * The index consists of the dataset rows, grouped by their labels (into clusters/lists).
+ * Within each cluster (list), the data is grouped into blocks of `WarpSize` interleaved
+ * vectors. Note, the total index length is slightly larger than the dataset length, because
+ * each cluster is padded by `WarpSize` elements
+ *
+ * CUDA launch grid:
+ *   X dimension must cover the dataset (n_rows), YZ are not used;
+ *   there are no dependencies between threads, hence no constraints on the block size.
+ *
+ * @tparam T      element type.
+ * @tparam IdxT   type of the indices in the source source_vecs
+ * @tparam LabelT label type
+ * @tparam gather_src if false, then we build the index from vectors source_vecs[i,:], otherwise
+ *     we use source_vecs[source_ixs[i],:]. In both cases i=0..n_rows-1.
+ *
+ * @param[in] labels device pointer to the cluster ids for each row [n_rows]
+ * @param[in] source_vecs device pointer to the input data [n_rows, dim]
+ * @param[in] source_ixs device pointer to the input indices [n_rows]
+ * @param[out] list_data_ptrs device pointer to the index data of size [n_lists][index_size, dim]
+ * @param[out] list_index_ptrs device pointer to the source ids corr. to the output [n_lists]
+ * [index_size]
+ * @param[out] list_sizes_ptr device pointer to the cluster sizes [n_lists];
+ *                          it's used as an atomic counter, and must be initialized with zeros.
+ * @param n_rows source length
+ * @param dim the dimensionality of the data
+ * @param veclen size of vectorized loads/stores; must satisfy `dim % veclen == 0`.
+ *
+ */
+template <typename T, typename IdxT, typename LabelT, bool gather_src = false>
+RAFT_KERNEL build_index_kernel(const LabelT* labels,
+                               const T* source_vecs,
+                               const IdxT* source_ixs,
+                               T** list_data_ptrs,
+                               IdxT** list_index_ptrs,
+                               uint32_t* list_sizes_ptr,
+                               IdxT n_rows,
+                               uint32_t dim,
+                               uint32_t veclen,
+                               IdxT batch_offset = 0)
+{
+  const IdxT i = IdxT(blockDim.x) * IdxT(blockIdx.x) + threadIdx.x;
+  if (i >= n_rows) { return; }
+
+  auto list_id     = labels[i];
+  auto inlist_id   = atomicAdd(list_sizes_ptr + list_id, 1);
+  auto* list_index = list_index_ptrs[list_id];
+  auto* list_data  = list_data_ptrs[list_id];
+
+  // Record the source vector id in the index
+  list_index[inlist_id] = source_ixs == nullptr ? i + batch_offset : source_ixs[i];
+
+  // The data is written in interleaved groups of `index::kGroupSize` vectors
+  using interleaved_group = raft::Pow2<kIndexGroupSize>;
+  auto group_offset       = interleaved_group::roundDown(inlist_id);
+  auto ingroup_id         = interleaved_group::mod(inlist_id) * veclen;
+
+  // Point to the location of the interleaved group of vectors
+  list_data += group_offset * dim;
+
+  // Point to the source vector
+  if constexpr (gather_src) {
+    source_vecs += source_ixs[i] * dim;
+  } else {
+    source_vecs += i * dim;
+  }
+  // Interleave dimensions of the source vector while recording it.
+  // NB: such `veclen` is selected, that `dim % veclen == 0`
+  for (uint32_t l = 0; l < dim; l += veclen) {
+    for (uint32_t j = 0; j < veclen; j++) {
+      list_data[l * kIndexGroupSize + ingroup_id + j] = source_vecs[l + j];
+    }
+  }
+}
+
+/** See raft::neighbors::ivf_flat::extend docs */
+template <typename T, typename IdxT>
+void extend(raft::resources const& handle,
+            index<T, IdxT>* index,
+            const T* new_vectors,
+            const IdxT* new_indices,
+            IdxT n_rows)
+{
+  using LabelT = uint32_t;
+  RAFT_EXPECTS(index != nullptr, "index cannot be empty.");
+
+  auto stream  = raft::resource::get_cuda_stream(handle);
+  auto n_lists = index->n_lists();
+  auto dim     = index->dim();
+  list_spec<uint32_t, T, IdxT> list_device_spec{index->dim(),
+                                                index->conservative_memory_allocation()};
+  cuvs::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_flat::extend(%zu, %u)", size_t(n_rows), dim);
+
+  RAFT_EXPECTS(new_indices != nullptr || index->size() == 0,
+               "You must pass data indices when the index is non-empty.");
+
+  auto new_labels = raft::make_device_vector<LabelT, IdxT>(handle, n_rows);
+  raft::cluster::kmeans_balanced_params kmeans_params;
+  kmeans_params.metric = static_cast<raft::distance::DistanceType>(index->metric());
+  auto orig_centroids_view =
+    raft::make_device_matrix_view<const float, IdxT>(index->centers().data_handle(), n_lists, dim);
+  // Calculate the batch size for the input data if it's not accessible directly from the device
+  constexpr size_t kReasonableMaxBatchSize = 65536;
+  size_t max_batch_size                    = std::min<size_t>(n_rows, kReasonableMaxBatchSize);
+
+  // Predict the cluster labels for the new data, in batches if necessary
+  utils::batch_load_iterator<T> vec_batches(new_vectors,
+                                            n_rows,
+                                            index->dim(),
+                                            max_batch_size,
+                                            stream,
+                                            raft::resource::get_workspace_resource(handle));
+
+  for (const auto& batch : vec_batches) {
+    auto batch_data_view =
+      raft::make_device_matrix_view<const T, IdxT>(batch.data(), batch.size(), index->dim());
+    auto batch_labels_view = raft::make_device_vector_view<LabelT, IdxT>(
+      new_labels.data_handle() + batch.offset(), batch.size());
+    raft::cluster::kmeans_balanced::predict(handle,
+                                            kmeans_params,
+                                            batch_data_view,
+                                            orig_centroids_view,
+                                            batch_labels_view,
+                                            utils::mapping<float>{});
+  }
+
+  auto* list_sizes_ptr    = index->list_sizes().data_handle();
+  auto old_list_sizes_dev = raft::make_device_vector<uint32_t, IdxT>(handle, n_lists);
+  raft::copy(old_list_sizes_dev.data_handle(), list_sizes_ptr, n_lists, stream);
+
+  // Calculate the centers and sizes on the new data, starting from the original values
+  if (index->adaptive_centers()) {
+    auto centroids_view = raft::make_device_matrix_view<float, IdxT>(
+      index->centers().data_handle(), index->centers().extent(0), index->centers().extent(1));
+    auto list_sizes_view =
+      raft::make_device_vector_view<std::remove_pointer_t<decltype(list_sizes_ptr)>, IdxT>(
+        list_sizes_ptr, n_lists);
+    for (const auto& batch : vec_batches) {
+      auto batch_data_view =
+        raft::make_device_matrix_view<const T, IdxT>(batch.data(), batch.size(), index->dim());
+      auto batch_labels_view = raft::make_device_vector_view<const LabelT, IdxT>(
+        new_labels.data_handle() + batch.offset(), batch.size());
+      raft::cluster::kmeans_balanced::helpers::calc_centers_and_sizes(handle,
+                                                                      batch_data_view,
+                                                                      batch_labels_view,
+                                                                      centroids_view,
+                                                                      list_sizes_view,
+                                                                      false,
+                                                                      utils::mapping<float>{});
+    }
+  } else {
+    raft::stats::histogram<uint32_t, IdxT>(raft::stats::HistTypeAuto,
+                                           reinterpret_cast<int32_t*>(list_sizes_ptr),
+                                           IdxT(n_lists),
+                                           new_labels.data_handle(),
+                                           n_rows,
+                                           1,
+                                           stream);
+    raft::linalg::add(
+      list_sizes_ptr, list_sizes_ptr, old_list_sizes_dev.data_handle(), n_lists, stream);
+  }
+
+  // Calculate and allocate new list data
+  std::vector<uint32_t> new_list_sizes(n_lists);
+  std::vector<uint32_t> old_list_sizes(n_lists);
+  {
+    raft::copy(old_list_sizes.data(), old_list_sizes_dev.data_handle(), n_lists, stream);
+    raft::copy(new_list_sizes.data(), list_sizes_ptr, n_lists, stream);
+    raft::resource::sync_stream(handle);
+    auto& lists = index->lists();
+    for (uint32_t label = 0; label < n_lists; label++) {
+      ivf::resize_list(handle,
+                       lists[label],
+                       list_device_spec,
+                       new_list_sizes[label],
+                       raft::Pow2<kIndexGroupSize>::roundUp(old_list_sizes[label]));
+    }
+  }
+  // Update the pointers and the sizes
+  ivf::detail::recompute_internal_state(handle, *index);
+  // Copy the old sizes, so we can start from the current state of the index;
+  // we'll rebuild the `list_sizes_ptr` in the following kernel, using it as an atomic counter.
+  raft::copy(list_sizes_ptr, old_list_sizes_dev.data_handle(), n_lists, stream);
+
+  utils::batch_load_iterator<IdxT> vec_indices(
+    new_indices, n_rows, 1, max_batch_size, stream, raft::resource::get_workspace_resource(handle));
+  utils::batch_load_iterator<IdxT> idx_batch = vec_indices.begin();
+  size_t next_report_offset                  = 0;
+  size_t d_report_offset                     = n_rows * 5 / 100;
+  for (const auto& batch : vec_batches) {
+    auto batch_data_view =
+      raft::make_device_matrix_view<const T, IdxT>(batch.data(), batch.size(), index->dim());
+    // Kernel to insert the new vectors
+    const dim3 block_dim(256);
+    const dim3 grid_dim(raft::ceildiv<IdxT>(batch.size(), block_dim.x));
+    build_index_kernel<T, IdxT, LabelT>
+      <<<grid_dim, block_dim, 0, stream>>>(new_labels.data_handle() + batch.offset(),
+                                           batch_data_view.data_handle(),
+                                           idx_batch->data(),
+                                           index->data_ptrs().data_handle(),
+                                           index->inds_ptrs().data_handle(),
+                                           list_sizes_ptr,
+                                           batch.size(),
+                                           dim,
+                                           index->veclen(),
+                                           batch.offset());
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+    if (batch.offset() > next_report_offset) {
+      float progress = batch.offset() * 100.0f / n_rows;
+      RAFT_LOG_DEBUG("ivf_flat::extend added vectors %zu, %6.1f%% complete",
+                     static_cast<size_t>(batch.offset()),
+                     progress);
+      next_report_offset += d_report_offset;
+    }
+    ++idx_batch;
+  }
+  // Precompute the centers vector norms for L2Expanded distance
+  if (!index->center_norms().has_value()) {
+    index->allocate_center_norms(handle);
+    if (index->center_norms().has_value()) {
+      raft::linalg::rowNorm(index->center_norms()->data_handle(),
+                            index->centers().data_handle(),
+                            dim,
+                            n_lists,
+                            raft::linalg::L2Norm,
+                            true,
+                            stream);
+      RAFT_LOG_TRACE_VEC(index->center_norms()->data_handle(), std::min<uint32_t>(dim, 20));
+    }
+  } else if (index->center_norms().has_value() && index->adaptive_centers()) {
+    raft::linalg::rowNorm(index->center_norms()->data_handle(),
+                          index->centers().data_handle(),
+                          dim,
+                          n_lists,
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+    RAFT_LOG_TRACE_VEC(index->center_norms()->data_handle(), std::min<uint32_t>(dim, 20));
+  }
+}
+
+/** See raft::neighbors::ivf_flat::extend docs */
+template <typename T, typename IdxT>
+auto extend(raft::resources const& handle,
+            const index<T, IdxT>& orig_index,
+            const T* new_vectors,
+            const IdxT* new_indices,
+            IdxT n_rows) -> index<T, IdxT>
+{
+  auto ext_index = clone(handle, orig_index);
+  detail::extend(handle, &ext_index, new_vectors, new_indices, n_rows);
+  return ext_index;
+}
+
+/** See raft::neighbors::ivf_flat::build docs */
+template <typename T, typename IdxT>
+inline auto build(raft::resources const& handle,
+                  const index_params& params,
+                  const T* dataset,
+                  IdxT n_rows,
+                  uint32_t dim) -> index<T, IdxT>
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  cuvs::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_flat::build(%zu, %u)", size_t(n_rows), dim);
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>,
+                "unsupported data type");
+  RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
+  RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
+
+  index<T, IdxT> index(handle, params, dim);
+  utils::memzero(
+    index.accum_sorted_sizes().data_handle(), index.accum_sorted_sizes().size(), stream);
+  utils::memzero(index.list_sizes().data_handle(), index.list_sizes().size(), stream);
+  utils::memzero(index.data_ptrs().data_handle(), index.data_ptrs().size(), stream);
+  utils::memzero(index.inds_ptrs().data_handle(), index.inds_ptrs().size(), stream);
+
+  // Train the kmeans clustering
+  {
+    auto trainset_ratio = std::max<size_t>(
+      1, n_rows / std::max<size_t>(params.kmeans_trainset_fraction * n_rows, index.n_lists()));
+    auto n_rows_train = n_rows / trainset_ratio;
+    rmm::device_uvector<T> trainset(n_rows_train * index.dim(), stream);
+    // TODO: a proper sampling
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset.data(),
+                                    sizeof(T) * index.dim(),
+                                    dataset,
+                                    sizeof(T) * index.dim() * trainset_ratio,
+                                    sizeof(T) * index.dim(),
+                                    n_rows_train,
+                                    cudaMemcpyDefault,
+                                    stream));
+    auto trainset_const_view =
+      raft::make_device_matrix_view<const T, IdxT>(trainset.data(), n_rows_train, index.dim());
+    auto centers_view = raft::make_device_matrix_view<float, IdxT>(
+      index.centers().data_handle(), index.n_lists(), index.dim());
+    raft::cluster::kmeans_balanced_params kmeans_params;
+    kmeans_params.n_iters = params.kmeans_n_iters;
+    kmeans_params.metric  = static_cast<raft::distance::DistanceType>(index.metric());
+    raft::cluster::kmeans_balanced::fit(
+      handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
+  }
+
+  // add the data if necessary
+  if (params.add_data_on_build) {
+    detail::extend<T, IdxT>(handle, &index, dataset, nullptr, n_rows);
+  }
+  return index;
+}
+
+/**
+ * Build an index that can be used in refinement operation.
+ *
+ * See raft::neighbors::refine for details on the refinement operation.
+ *
+ * The returned index cannot be used for a regular ivf_flat::search. The index misses information
+ * about coarse clusters. Instead, the neighbor candidates are assumed to form clusters, one for
+ * each query. The candidate vectors are gathered into the index dataset, that can be later used
+ * in ivfflat_interleaved_scan.
+ *
+ * @param[in] handle the raft handle
+ * @param[inout] refinement_index
+ * @param[in] dataset device pointer to dataset vectors, size [n_rows, dim]. Note that n_rows is
+ *   not known to this function, but each candidate_idx has to be smaller than n_rows.
+ * @param[in] candidate_idx device pointer to neighbor candidates, size [n_queries, n_candidates]
+ * @param[in] n_candidates  of neighbor_candidates
+ */
+template <typename T, typename IdxT>
+inline void fill_refinement_index(raft::resources const& handle,
+                                  index<T, IdxT>* refinement_index,
+                                  const T* dataset,
+                                  const IdxT* candidate_idx,
+                                  IdxT n_queries,
+                                  uint32_t n_candidates)
+{
+  using LabelT = uint32_t;
+
+  auto stream      = raft::resource::get_cuda_stream(handle);
+  uint32_t n_lists = n_queries;
+  common::nvtx::range<common::nvtx::domain::cuvs> fun_scope(
+    "ivf_flat::fill_refinement_index(%zu, %u)", size_t(n_queries));
+
+  rmm::device_uvector<LabelT> new_labels(n_queries * n_candidates, stream);
+  auto new_labels_view =
+    raft::make_device_vector_view<LabelT, IdxT>(new_labels.data(), n_queries * n_candidates);
+  raft::linalg::map_offset(
+    handle,
+    new_labels_view,
+    raft::compose_op(raft::cast_op<LabelT>(), raft::div_const_op<IdxT>(n_candidates)));
+
+  auto list_sizes_ptr = refinement_index->list_sizes().data_handle();
+  // We do not fill centers and center norms, since we will not run coarse search.
+
+  // Allocate new memory
+  auto& lists = refinement_index->lists();
+  list_spec<uint32_t, T, IdxT> list_device_spec{refinement_index->dim(), false};
+  for (uint32_t label = 0; label < n_lists; label++) {
+    ivf::resize_list(handle, lists[label], list_device_spec, n_candidates, uint32_t(0));
+  }
+  // Update the pointers and the sizes
+  ivf::detail::recompute_internal_state(handle, *refinement_index);
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(list_sizes_ptr, 0, n_lists * sizeof(uint32_t), stream));
+
+  const dim3 block_dim(256);
+  const dim3 grid_dim(raft::ceildiv<IdxT>(n_queries * n_candidates, block_dim.x));
+  build_index_kernel<T, IdxT, LabelT, true>
+    <<<grid_dim, block_dim, 0, stream>>>(new_labels.data(),
+                                         dataset,
+                                         candidate_idx,
+                                         refinement_index->data_ptrs().data_handle(),
+                                         refinement_index->inds_ptrs().data_handle(),
+                                         list_sizes_ptr,
+                                         n_queries * n_candidates,
+                                         refinement_index->dim(),
+                                         refinement_index->veclen());
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+template <typename T, typename IdxT>
+auto build(raft::resources const& handle,
+           const index_params& params,
+           raft::device_matrix_view<const T, IdxT, raft::row_major> dataset) -> index<T, IdxT>
+{
+  IdxT n_rows = dataset.extent(0);
+  IdxT dim    = dataset.extent(1);
+  return build(handle, params, dataset.data_handle(), n_rows, dim);
+}
+
+template <typename T, typename IdxT>
+auto build(raft::resources const& handle,
+           const index_params& params,
+           raft::host_matrix_view<const T, IdxT, raft::row_major> dataset) -> index<T, IdxT>
+{
+  IdxT n_rows = dataset.extent(0);
+  IdxT dim    = dataset.extent(1);
+  return build(handle, params, dataset.data_handle(), n_rows, dim);
+}
+
+template <typename T, typename IdxT>
+void build(raft::resources const& handle,
+           const index_params& params,
+           raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,
+           index<T, IdxT>& index)
+{
+  IdxT n_rows = dataset.extent(0);
+  IdxT dim    = dataset.extent(1);
+  index       = build(handle, params, dataset.data_handle(), n_rows, dim);
+}
+
+template <typename T, typename IdxT>
+void build(raft::resources const& handle,
+           const index_params& params,
+           raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,
+           index<T, IdxT>& index)
+{
+  IdxT n_rows = dataset.extent(0);
+  IdxT dim    = dataset.extent(1);
+  index       = build(handle, params, dataset.data_handle(), n_rows, dim);
+}
+
+template <typename T, typename IdxT>
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index) -> index<T, IdxT>
+{
+  ASSERT(new_vectors.extent(1) == orig_index.dim(),
+         "new_vectors should have the same dimension as the index");
+
+  IdxT n_rows = new_vectors.extent(0);
+  if (new_indices.has_value()) {
+    ASSERT(n_rows == new_indices.value().extent(0),
+           "new_vectors and new_indices have different number of rows");
+  }
+
+  return extend(handle,
+                orig_index,
+                new_vectors.data_handle(),
+                new_indices.has_value() ? new_indices.value().data_handle() : nullptr,
+                n_rows);
+}
+
+template <typename T, typename IdxT>
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index) -> index<T, IdxT>
+{
+  ASSERT(new_vectors.extent(1) == orig_index.dim(),
+         "new_vectors should have the same dimension as the index");
+
+  IdxT n_rows = new_vectors.extent(0);
+  if (new_indices.has_value()) {
+    ASSERT(n_rows == new_indices.value().extent(0),
+           "new_vectors and new_indices have different number of rows");
+  }
+
+  return extend(handle,
+                orig_index,
+                new_vectors.data_handle(),
+                new_indices.has_value() ? new_indices.value().data_handle() : nullptr,
+                n_rows);
+}
+
+template <typename T, typename IdxT>
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,
+            index<T, IdxT>* index)
+{
+  ASSERT(new_vectors.extent(1) == index->dim(),
+         "new_vectors should have the same dimension as the index");
+
+  IdxT n_rows = new_vectors.extent(0);
+  if (new_indices.has_value()) {
+    ASSERT(n_rows == new_indices.value().extent(0),
+           "new_vectors and new_indices have different number of rows");
+  }
+
+  *index = extend(handle,
+                  *index,
+                  new_vectors.data_handle(),
+                  new_indices.has_value() ? new_indices.value().data_handle() : nullptr,
+                  n_rows);
+}
+
+template <typename T, typename IdxT>
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,
+            index<T, IdxT>* index)
+{
+  ASSERT(new_vectors.extent(1) == index->dim(),
+         "new_vectors should have the same dimension as the index");
+
+  IdxT n_rows = new_vectors.extent(0);
+  if (new_indices.has_value()) {
+    ASSERT(n_rows == new_indices.value().extent(0),
+           "new_vectors and new_indices have different number of rows");
+  }
+
+  *index = extend(handle,
+                  *index,
+                  new_vectors.data_handle(),
+                  new_indices.has_value() ? new_indices.value().data_handle() : nullptr,
+                  n_rows);
+}
+
+}  // namespace detail
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
new file mode 100644
index 0000000000..52026172c7
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD_EXTEND(T, IdxT)                                                 \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                   \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,                     \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }
+CUVS_INST_IVF_FLAT_BUILD_EXTEND(float, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
new file mode 100644
index 0000000000..ef326f2415
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD_EXTEND(T, IdxT)                                                 \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                   \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,                     \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }
+CUVS_INST_IVF_FLAT_BUILD_EXTEND(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..8a1f3e42fa
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD_EXTEND(T, IdxT)                                                 \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                   \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,                     \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }
+CUVS_INST_IVF_FLAT_BUILD_EXTEND(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
deleted file mode 100644
index 177aaac11f..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \
-  auto build(raft::resources const& handle,                                                    \
-             const cuvs::neighbors::ivf_flat::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \
-  {                                                                                            \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \
-      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \
-  }                                                                                            \
-                                                                                               \
-  void build(raft::resources const& handle,                                                    \
-             const cuvs::neighbors::ivf_flat::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \
-  {                                                                                            \
-    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \
-  }
-CUVS_INST_IVF_FLAT_BUILD(float, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_BUILD
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu
new file mode 100644
index 0000000000..56bb71094f
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                            \
+  auto build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
+  }                                                                                  \
+                                                                                     \
+  void build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,       \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
+  {                                                                                  \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
+  }                                                                                  \
+  auto build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)         \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
+  }                                                                                  \
+                                                                                     \
+  void build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,         \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
+  {                                                                                  \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
+  }
+CUVS_INST_IVF_FLAT_BUILD(float, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
deleted file mode 100644
index 6fe6e2b8d6..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \
-  auto build(raft::resources const& handle,                                                    \
-             const cuvs::neighbors::ivf_flat::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \
-  {                                                                                            \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \
-      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \
-  }                                                                                            \
-                                                                                               \
-  void build(raft::resources const& handle,                                                    \
-             const cuvs::neighbors::ivf_flat::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \
-  {                                                                                            \
-    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \
-  }
-CUVS_INST_IVF_FLAT_BUILD(int8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_BUILD
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu
new file mode 100644
index 0000000000..4803868c0c
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                            \
+  auto build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
+  }                                                                                  \
+                                                                                     \
+  void build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,       \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
+  {                                                                                  \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
+  }                                                                                  \
+  auto build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)         \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
+  }                                                                                  \
+                                                                                     \
+  void build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,         \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
+  {                                                                                  \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
+  }
+CUVS_INST_IVF_FLAT_BUILD(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
deleted file mode 100644
index 01098ed450..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                                      \
-  auto build(raft::resources const& handle,                                                    \
-             const cuvs::neighbors::ivf_flat::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                 \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                \
-  {                                                                                            \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                          \
-      std::move(raft::runtime::neighbors::ivf_flat::build(handle, params, dataset)));          \
-  }                                                                                            \
-                                                                                               \
-  void build(raft::resources const& handle,                                                    \
-             const cuvs::neighbors::ivf_flat::index_params& params,                            \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                 \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                   \
-  {                                                                                            \
-    raft::runtime::neighbors::ivf_flat::build(handle, params, dataset, *idx.get_raft_index()); \
-  }
-CUVS_INST_IVF_FLAT_BUILD(uint8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_BUILD
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..e087f94c4b
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                            \
+  auto build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
+  }                                                                                  \
+                                                                                     \
+  void build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,       \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
+  {                                                                                  \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
+  }                                                                                  \
+  auto build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)         \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
+  {                                                                                  \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
+  }                                                                                  \
+                                                                                     \
+  void build(raft::resources const& handle,                                          \
+             const cuvs::neighbors::ivf_flat::index_params& params,                  \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,         \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
+  {                                                                                  \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
+  }
+CUVS_INST_IVF_FLAT_BUILD(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
deleted file mode 100644
index 04ca3a50ff..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \
-  auto extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
-  }                                                                                  \
-                                                                                     \
-  void extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \
-  {                                                                                  \
-    raft::runtime::neighbors::ivf_flat::extend(                                      \
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \
-  }
-CUVS_INST_IVF_FLAT_EXTEND(float, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu
new file mode 100644
index 0000000000..2636067bf8
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                                       \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }
+CUVS_INST_IVF_FLAT_EXTEND(float, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
deleted file mode 100644
index accc53e04d..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \
-  auto extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
-  }                                                                                  \
-                                                                                     \
-  void extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \
-  {                                                                                  \
-    raft::runtime::neighbors::ivf_flat::extend(                                      \
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \
-  }
-CUVS_INST_IVF_FLAT_EXTEND(int8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu
new file mode 100644
index 0000000000..191cb9f391
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                                       \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }
+CUVS_INST_IVF_FLAT_EXTEND(int8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
deleted file mode 100644
index e44ae51b12..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                           \
-  auto extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)           \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(raft::runtime::neighbors::ivf_flat::extend(                          \
-        handle, new_vectors, new_indices, *orig_index.get_raft_index())));           \
-  }                                                                                  \
-                                                                                     \
-  void extend(raft::resources const& handle,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,  \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices, \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                        \
-  {                                                                                  \
-    raft::runtime::neighbors::ivf_flat::extend(                                      \
-      handle, new_vectors, new_indices, idx->get_raft_index());                      \
-  }
-CUVS_INST_IVF_FLAT_EXTEND(uint8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..29b7e7b69d
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                                       \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }
+CUVS_INST_IVF_FLAT_EXTEND(uint8_t, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
new file mode 100644
index 0000000000..be1eb87a01
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
@@ -0,0 +1,260 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+
+#include "ivf_flat_helpers.cuh"
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat::helpers {
+namespace codepacker {
+namespace detail {
+template <typename T, typename IdxT>
+void pack(
+  raft::resources const& res,
+  raft::device_matrix_view<const T, uint32_t, raft::row_major> codes,
+  uint32_t veclen,
+  uint32_t offset,
+  raft::device_mdspan<T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
+    list_data)
+{
+  pack_list_data<T, IdxT>(res, codes, veclen, offset, list_data);
+}
+
+template <typename T, typename IdxT>
+void unpack(
+  raft::resources const& res,
+  raft::device_mdspan<const T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
+    list_data,
+  uint32_t veclen,
+  uint32_t offset,
+  raft::device_matrix_view<T, uint32_t, raft::row_major> codes)
+{
+  unpack_list_data<T, IdxT>(res, list_data, veclen, offset, codes);
+}
+
+template <typename T>
+void pack_1(const T* flat_code, T* block, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  // The data is written in interleaved groups of `index::kGroupSize` vectors
+  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+
+  // Interleave dimensions of the source vector while recording it.
+  // NB: such `veclen` is selected, that `dim % veclen == 0`
+  auto group_offset = interleaved_group::roundDown(offset);
+  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
+
+  for (uint32_t l = 0; l < dim; l += veclen) {
+    for (uint32_t j = 0; j < veclen; j++) {
+      block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j] = flat_code[l + j];
+    }
+  }
+}
+
+template <typename T>
+void unpack_1(const T* block, T* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  // The data is written in interleaved groups of `index::kGroupSize` vectors
+  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+
+  // NB: such `veclen` is selected, that `dim % veclen == 0`
+  auto group_offset = interleaved_group::roundDown(offset);
+  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
+
+  for (uint32_t l = 0; l < dim; l += veclen) {
+    for (uint32_t j = 0; j < veclen; j++) {
+      flat_code[l + j] = block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j];
+    }
+  }
+}
+}  // namespace detail
+
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const float, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<float,
+                              typename list_spec<uint32_t, float, int64_t>::list_extents,
+                              raft::row_major> list_data)
+{
+  detail::pack<float, int64_t>(res, codes, veclen, offset, list_data);
+}
+
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<int8_t,
+                              typename list_spec<uint32_t, int8_t, int64_t>::list_extents,
+                              raft::row_major> list_data)
+{
+  detail::pack<int8_t, int64_t>(res, codes, veclen, offset, list_data);
+}
+
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<uint8_t,
+                              typename list_spec<uint32_t, uint8_t, int64_t>::list_extents,
+                              raft::row_major> list_data)
+{
+  detail::pack<uint8_t, int64_t>(res, codes, veclen, offset, list_data);
+}
+
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const float,
+                                typename list_spec<uint32_t, float, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<float, uint32_t, raft::row_major> codes)
+{
+  detail::unpack<float, int64_t>(res, list_data, veclen, offset, codes);
+}
+
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const int8_t,
+                                typename list_spec<uint32_t, int8_t, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<int8_t, uint32_t, raft::row_major> codes)
+{
+  detail::unpack<int8_t, int64_t>(res, list_data, veclen, offset, codes);
+}
+
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const uint8_t,
+                                typename list_spec<uint32_t, uint8_t, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> codes)
+{
+  detail::unpack<uint8_t, int64_t>(res, list_data, veclen, offset, codes);
+}
+
+void pack_1(const float* flat_code, float* block, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::pack_1<float>(flat_code, block, dim, veclen, offset);
+}
+
+void pack_1(const int8_t* flat_code, int8_t* block, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::pack_1<int8_t>(flat_code, block, dim, veclen, offset);
+}
+
+void pack_1(
+  const uint8_t* flat_code, uint8_t* block, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::pack_1<uint8_t>(flat_code, block, dim, veclen, offset);
+}
+
+void unpack_1(const float* block, float* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::unpack_1<float>(block, flat_code, dim, veclen, offset);
+}
+
+void unpack_1(
+  const int8_t* block, int8_t* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::unpack_1<int8_t>(block, flat_code, dim, veclen, offset);
+}
+
+void unpack_1(
+  const uint8_t* block, uint8_t* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::unpack_1<uint8_t>(block, flat_code, dim, veclen, offset);
+}
+
+}  // namespace codepacker
+
+namespace detail {
+
+template <typename T, typename IdxT>
+void reset_index(const raft::resources& res, index<T, IdxT>* idx)
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  cuvs::spatial::knn::detail::utils::memzero(
+    idx->accum_sorted_sizes().data_handle(), idx->accum_sorted_sizes().size(), stream);
+  cuvs::spatial::knn::detail::utils::memzero(
+    idx->list_sizes().data_handle(), idx->list_sizes().size(), stream);
+  cuvs::spatial::knn::detail::utils::memzero(
+    idx->data_ptrs().data_handle(), idx->data_ptrs().size(), stream);
+  cuvs::spatial::knn::detail::utils::memzero(
+    idx->inds_ptrs().data_handle(), idx->inds_ptrs().size(), stream);
+}
+
+}  // namespace detail
+
+void reset_index(const raft::resources& res, index<float, int64_t>* index)
+{
+  detail::reset_index<float, int64_t>(res, index);
+}
+
+/**
+ * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
+ * externally modifying the index without going through the build stage. The data and indices of the
+ * IVF lists will be lost.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_flat::index<int8_t, int64_t> index(res, index_params, D);
+ *   // reset the index's state and list sizes
+ *   ivf_flat::helpers::reset_index(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-Flat index
+ */
+void reset_index(const raft::resources& res, index<int8_t, int64_t>* index)
+{
+  detail::reset_index<int8_t, int64_t>(res, index);
+}
+
+/**
+ * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
+ * externally modifying the index without going through the build stage. The data and indices of the
+ * IVF lists will be lost.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_flat::index<uint8_t, int64_t> index(res, index_params, D);
+ *   // reset the index's state and list sizes
+ *   ivf_flat::helpers::reset_index(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-Flat index
+ */
+void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index)
+{
+  detail::reset_index<uint8_t, int64_t>(res, index);
+}
+
+}  // namespace cuvs::neighbors::ivf_flat::helpers
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
new file mode 100644
index 0000000000..f76b5e95f6
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "../detail/ann_utils.cuh"
+
+#include "../detail/div_utils.cuh"
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <variant>
+
+namespace cuvs::neighbors::ivf_flat::helpers::codepacker {
+
+namespace {
+
+template <typename T>
+__device__ void pack_1(const T* flat_code, T* block, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  // The data is written in interleaved groups of `index::kGroupSize` vectors
+  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+
+  // Interleave dimensions of the source vector while recording it.
+  // NB: such `veclen` is selected, that `dim % veclen == 0`
+  auto group_offset = interleaved_group::roundDown(offset);
+  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
+
+  for (uint32_t l = 0; l < dim; l += veclen) {
+    for (uint32_t j = 0; j < veclen; j++) {
+      block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j] = flat_code[l + j];
+    }
+  }
+}
+
+template <typename T>
+__device__ void unpack_1(
+  const T* block, T* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  // The data is written in interleaved groups of `index::kGroupSize` vectors
+  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+
+  // NB: such `veclen` is selected, that `dim % veclen == 0`
+  auto group_offset = interleaved_group::roundDown(offset);
+  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
+
+  for (uint32_t l = 0; l < dim; l += veclen) {
+    for (uint32_t j = 0; j < veclen; j++) {
+      flat_code[l + j] = block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j];
+    }
+  }
+}
+
+template <typename T>
+RAFT_KERNEL pack_interleaved_list_kernel(const T* codes,
+                                         T* list_data,
+                                         uint32_t n_rows,
+                                         uint32_t dim,
+                                         uint32_t veclen,
+                                         std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  uint32_t tid          = blockIdx.x * blockDim.x + threadIdx.x;
+  const uint32_t dst_ix = std::holds_alternative<uint32_t>(offset_or_indices)
+                            ? std::get<uint32_t>(offset_or_indices) + tid
+                            : std::get<const uint32_t*>(offset_or_indices)[tid];
+  if (tid < n_rows) { pack_1(codes + tid * dim, list_data, dim, veclen, dst_ix); }
+}
+
+template <typename T>
+RAFT_KERNEL unpack_interleaved_list_kernel(
+  const T* list_data,
+  T* codes,
+  uint32_t n_rows,
+  uint32_t dim,
+  uint32_t veclen,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  uint32_t tid          = blockIdx.x * blockDim.x + threadIdx.x;
+  const uint32_t src_ix = std::holds_alternative<uint32_t>(offset_or_indices)
+                            ? std::get<uint32_t>(offset_or_indices) + tid
+                            : std::get<const uint32_t*>(offset_or_indices)[tid];
+  if (tid < n_rows) { unpack_1(list_data, codes + tid * dim, dim, veclen, src_ix); }
+}
+
+template <typename T, typename IdxT>
+void pack_list_data(
+  raft::resources const& res,
+  raft::device_matrix_view<const T, uint32_t, raft::row_major> codes,
+  uint32_t veclen,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  raft::device_mdspan<T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
+    list_data)
+{
+  uint32_t n_rows = codes.extent(0);
+  uint32_t dim    = codes.extent(1);
+  if (n_rows == 0 || dim == 0) return;
+  static constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto stream = raft::resource::get_cuda_stream(res);
+  pack_interleaved_list_kernel<<<blocks, threads, 0, stream>>>(
+    codes.data_handle(), list_data.data_handle(), n_rows, dim, veclen, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+template <typename T, typename IdxT>
+void unpack_list_data(
+  raft::resources const& res,
+  raft::device_mdspan<const T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
+    list_data,
+  uint32_t veclen,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  raft::device_matrix_view<T, uint32_t, raft::row_major> codes)
+{
+  uint32_t n_rows = codes.extent(0);
+  uint32_t dim    = codes.extent(1);
+  if (n_rows == 0 || dim == 0) return;
+  static constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto stream = raft::resource::get_cuda_stream(res);
+  unpack_interleaved_list_kernel<<<blocks, threads, 0, stream>>>(
+    list_data.data_handle(), codes.data_handle(), n_rows, dim, veclen, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+}  // namespace
+}  // namespace cuvs::neighbors::ivf_flat::helpers::codepacker
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
new file mode 100644
index 0000000000..d63059457b
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -0,0 +1,1201 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../ivf_common.cuh"
+#include "../sample_filter.cuh"
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/sample_filter.hpp>
+
+#include <raft/core/logger.hpp>  // RAFT_LOG_TRACE
+#include <raft/core/operators.hpp>
+#include <raft/distance/distance_types.hpp>
+#include <raft/matrix/detail/select_warpsort.cuh>
+#include <raft/spatial/knn/detail/ann_utils.cuh>
+#include <raft/util/cuda_rt_essentials.hpp>  // RAFT_CUDA_TRY
+#include <raft/util/device_loads_stores.cuh>
+#include <raft/util/integer_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+#include <raft/util/vectorized.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+using namespace raft::spatial::knn::detail;  // NOLINT
+
+constexpr int kThreadsPerBlock = 128;
+
+auto RAFT_WEAK_FUNCTION is_local_topk_feasible(uint32_t k) -> bool
+{
+  return k <= raft::matrix::detail::select::warpsort::kMaxCapacity;
+}
+
+/**
+ * @brief Copy `n` elements per block from one place to another.
+ *
+ * @param[out] out target pointer (unique per block)
+ * @param[in] in source pointer
+ * @param n number of elements to copy
+ */
+template <int VecBytes = 16, typename T>
+__device__ inline void copy_vectorized(T* out, const T* in, uint32_t n)
+{
+  constexpr int VecElems = VecBytes / sizeof(T);  // NOLINT
+  using align_bytes      = raft::Pow2<(size_t)VecBytes>;
+  if constexpr (VecElems > 1) {
+    using align_elems = raft::Pow2<VecElems>;
+    if (!align_bytes::areSameAlignOffsets(out, in)) {
+      return copy_vectorized<(VecBytes >> 1), T>(out, in, n);
+    }
+    {  // process unaligned head
+      uint32_t head = align_bytes::roundUp(in) - in;
+      if (head > 0) {
+        copy_vectorized<sizeof(T), T>(out, in, head);
+        n -= head;
+        in += head;
+        out += head;
+      }
+    }
+    {  // process main part vectorized
+      using vec_t = typename raft::IOType<T, VecElems>::Type;
+      copy_vectorized<sizeof(vec_t), vec_t>(
+        reinterpret_cast<vec_t*>(out), reinterpret_cast<const vec_t*>(in), align_elems::div(n));
+    }
+    {  // process unaligned tail
+      uint32_t tail = align_elems::mod(n);
+      if (tail > 0) {
+        n -= tail;
+        copy_vectorized<sizeof(T), T>(out + n, in + n, tail);
+      }
+    }
+  }
+  if constexpr (VecElems <= 1) {
+    for (int i = threadIdx.x; i < n; i += blockDim.x) {
+      out[i] = in[i];
+    }
+  }
+}
+
+/**
+ * @brief Load a part of a vector from the index and from query, compute the (part of the) distance
+ * between them, and aggregate it using the provided Lambda; one structure per thread, per query,
+ * and per index item.
+ *
+ * @tparam kUnroll elements per loop (normally, kUnroll = WarpSize / Veclen)
+ * @tparam Lambda computing the part of the distance for one dimension and aggregating it:
+ *                void (AccT& acc, AccT x, AccT y)
+ * @tparam Veclen size of the vectorized load
+ * @tparam T type of the data in the query and the index
+ * @tparam AccT type of the accumulated value (an optimization for 8bit values to be loaded as 32bit
+ * values)
+ */
+template <int kUnroll, typename Lambda, int Veclen, typename T, typename AccT>
+struct loadAndComputeDist {
+  Lambda compute_dist;
+  AccT& dist;
+
+  __device__ __forceinline__ loadAndComputeDist(AccT& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+
+  /**
+   * Load parts of vectors from the index and query and accumulates the partial distance.
+   * This version assumes the query is stored in shared memory.
+   * Every thread here processes exactly kUnroll * Veclen elements independently of others.
+   */
+  template <typename IdxT>
+  __device__ __forceinline__ void runLoadShmemCompute(const T* const& data,
+                                                      const T* query_shared,
+                                                      IdxT loadIndex,
+                                                      IdxT shmemIndex)
+  {
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      T encV[Veclen];
+      raft::ldg(encV, data + (loadIndex + j * kIndexGroupSize) * Veclen);
+      T queryRegs[Veclen];
+      raft::lds(queryRegs, &query_shared[shmemIndex + j * Veclen]);
+#pragma unroll
+      for (int k = 0; k < Veclen; ++k) {
+        compute_dist(dist, queryRegs[k], encV[k]);
+      }
+    }
+  }
+
+  /**
+   * Load parts of vectors from the index and query and accumulates the partial distance.
+   * This version assumes the query is stored in the global memory and is different for every
+   * thread. One warp loads exactly WarpSize query elements at once and then reshuffles them into
+   * corresponding threads (`WarpSize / (kUnroll * Veclen)` elements per thread at once).
+   */
+  template <typename IdxT>
+  __device__ __forceinline__ void runLoadShflAndCompute(const T*& data,
+                                                        const T* query,
+                                                        IdxT baseLoadIndex,
+                                                        const int lane_id)
+  {
+    T queryReg               = query[baseLoadIndex + lane_id];
+    constexpr int stride     = kUnroll * Veclen;
+    constexpr int totalIter  = raft::WarpSize / stride;
+    constexpr int gmemStride = stride * kIndexGroupSize;
+#pragma unroll
+    for (int i = 0; i < totalIter; ++i, data += gmemStride) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        T encV[Veclen];
+        raft::ldg(encV, data + (lane_id + j * kIndexGroupSize) * Veclen);
+        const int d = (i * kUnroll + j) * Veclen;
+#pragma unroll
+        for (int k = 0; k < Veclen; ++k) {
+          compute_dist(dist, raft::shfl(queryReg, d + k, raft::WarpSize), encV[k]);
+        }
+      }
+    }
+  }
+
+  /**
+   * Load parts of vectors from the index and query and accumulates the partial distance.
+   * This version augments `runLoadShflAndCompute` when `dim` is not a multiple of `WarpSize`.
+   */
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(
+    const T*& data, const T* query, const int lane_id, const int dim, const int dimBlocks)
+  {
+    const int loadDim     = dimBlocks + lane_id;
+    T queryReg            = loadDim < dim ? query[loadDim] : T{0};
+    const int loadDataIdx = lane_id * Veclen;
+    for (int d = 0; d < dim - dimBlocks; d += Veclen, data += kIndexGroupSize * Veclen) {
+      T enc[Veclen];
+      raft::ldg(enc, data + loadDataIdx);
+#pragma unroll
+      for (int k = 0; k < Veclen; k++) {
+        compute_dist(dist, raft::shfl(queryReg, d + k, raft::WarpSize), enc[k]);
+      }
+    }
+  }
+};
+
+// This handles uint8_t 8, 16 Veclens
+template <int kUnroll, typename Lambda, int uint8_veclen>
+struct loadAndComputeDist<kUnroll, Lambda, uint8_veclen, uint8_t, uint32_t> {
+  Lambda compute_dist;
+  uint32_t& dist;
+
+  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+
+  __device__ __forceinline__ void runLoadShmemCompute(const uint8_t* const& data,
+                                                      const uint8_t* query_shared,
+                                                      int loadIndex,
+                                                      int shmemIndex)
+  {
+    constexpr int veclen_int = uint8_veclen / 4;  // converting uint8_t veclens to int
+    loadIndex                = loadIndex * veclen_int;
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      uint32_t encV[veclen_int];
+      raft::ldg(
+        encV,
+        reinterpret_cast<unsigned const*>(data) + loadIndex + j * kIndexGroupSize * veclen_int);
+      uint32_t queryRegs[veclen_int];
+      raft::lds(queryRegs,
+                reinterpret_cast<unsigned const*>(query_shared + shmemIndex) + j * veclen_int);
+#pragma unroll
+      for (int k = 0; k < veclen_int; k++) {
+        compute_dist(dist, queryRegs[k], encV[k]);
+      }
+    }
+  }
+  __device__ __forceinline__ void runLoadShflAndCompute(const uint8_t*& data,
+                                                        const uint8_t* query,
+                                                        int baseLoadIndex,
+                                                        const int lane_id)
+  {
+    constexpr int veclen_int = uint8_veclen / 4;  // converting uint8_t veclens to int
+    uint32_t queryReg =
+      (lane_id < 8) ? reinterpret_cast<unsigned const*>(query + baseLoadIndex)[lane_id] : 0;
+    constexpr int stride = kUnroll * uint8_veclen;
+
+#pragma unroll
+    for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        uint32_t encV[veclen_int];
+        raft::ldg(
+          encV,
+          reinterpret_cast<unsigned const*>(data) + (lane_id + j * kIndexGroupSize) * veclen_int);
+        const int d = (i * kUnroll + j) * veclen_int;
+#pragma unroll
+        for (int k = 0; k < veclen_int; ++k) {
+          compute_dist(dist, raft::shfl(queryReg, d + k, raft::WarpSize), encV[k]);
+        }
+      }
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(const uint8_t*& data,
+                                                                 const uint8_t* query,
+                                                                 const int lane_id,
+                                                                 const int dim,
+                                                                 const int dimBlocks)
+  {
+    constexpr int veclen_int = uint8_veclen / 4;
+    const int loadDim        = dimBlocks + lane_id * 4;  // Here 4 is for 1 - int
+    uint32_t queryReg = loadDim < dim ? reinterpret_cast<uint32_t const*>(query + loadDim)[0] : 0;
+    for (int d = 0; d < dim - dimBlocks;
+         d += uint8_veclen, data += kIndexGroupSize * uint8_veclen) {
+      uint32_t enc[veclen_int];
+      raft::ldg(enc, reinterpret_cast<uint32_t const*>(data) + lane_id * veclen_int);
+#pragma unroll
+      for (int k = 0; k < veclen_int; k++) {
+        uint32_t q = raft::shfl(queryReg, (d / 4) + k, raft::WarpSize);
+        compute_dist(dist, q, enc[k]);
+      }
+    }
+  }
+};
+
+// Keep this specialized uint8 Veclen = 4, because compiler is generating suboptimal code while
+// using above common template of int2/int4
+template <int kUnroll, typename Lambda>
+struct loadAndComputeDist<kUnroll, Lambda, 4, uint8_t, uint32_t> {
+  Lambda compute_dist;
+  uint32_t& dist;
+
+  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+
+  __device__ __forceinline__ void runLoadShmemCompute(const uint8_t* const& data,
+                                                      const uint8_t* query_shared,
+                                                      int loadIndex,
+                                                      int shmemIndex)
+  {
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      uint32_t encV      = reinterpret_cast<unsigned const*>(data)[loadIndex + j * kIndexGroupSize];
+      uint32_t queryRegs = reinterpret_cast<unsigned const*>(query_shared + shmemIndex)[j];
+      compute_dist(dist, queryRegs, encV);
+    }
+  }
+  __device__ __forceinline__ void runLoadShflAndCompute(const uint8_t*& data,
+                                                        const uint8_t* query,
+                                                        int baseLoadIndex,
+                                                        const int lane_id)
+  {
+    uint32_t queryReg =
+      (lane_id < 8) ? reinterpret_cast<unsigned const*>(query + baseLoadIndex)[lane_id] : 0;
+    constexpr int veclen = 4;
+    constexpr int stride = kUnroll * veclen;
+
+#pragma unroll
+    for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        uint32_t encV = reinterpret_cast<unsigned const*>(data)[lane_id + j * kIndexGroupSize];
+        uint32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
+        compute_dist(dist, q, encV);
+      }
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(const uint8_t*& data,
+                                                                 const uint8_t* query,
+                                                                 const int lane_id,
+                                                                 const int dim,
+                                                                 const int dimBlocks)
+  {
+    constexpr int veclen = 4;
+    const int loadDim    = dimBlocks + lane_id;
+    uint32_t queryReg    = loadDim < dim ? reinterpret_cast<unsigned const*>(query)[loadDim] : 0;
+    for (int d = 0; d < dim - dimBlocks; d += veclen, data += kIndexGroupSize * veclen) {
+      uint32_t enc = reinterpret_cast<unsigned const*>(data)[lane_id];
+      uint32_t q   = raft::shfl(queryReg, d / veclen, raft::WarpSize);
+      compute_dist(dist, q, enc);
+    }
+  }
+};
+
+template <int kUnroll, typename Lambda>
+struct loadAndComputeDist<kUnroll, Lambda, 2, uint8_t, uint32_t> {
+  Lambda compute_dist;
+  uint32_t& dist;
+
+  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+
+  __device__ __forceinline__ void runLoadShmemCompute(const uint8_t* const& data,
+                                                      const uint8_t* query_shared,
+                                                      int loadIndex,
+                                                      int shmemIndex)
+  {
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      uint32_t encV      = reinterpret_cast<uint16_t const*>(data)[loadIndex + j * kIndexGroupSize];
+      uint32_t queryRegs = reinterpret_cast<uint16_t const*>(query_shared + shmemIndex)[j];
+      compute_dist(dist, queryRegs, encV);
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndCompute(const uint8_t*& data,
+                                                        const uint8_t* query,
+                                                        int baseLoadIndex,
+                                                        const int lane_id)
+  {
+    uint32_t queryReg =
+      (lane_id < 16) ? reinterpret_cast<uint16_t const*>(query + baseLoadIndex)[lane_id] : 0;
+    constexpr int veclen = 2;
+    constexpr int stride = kUnroll * veclen;
+
+#pragma unroll
+    for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        uint32_t encV = reinterpret_cast<uint16_t const*>(data)[lane_id + j * kIndexGroupSize];
+        uint32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
+        compute_dist(dist, q, encV);
+      }
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(const uint8_t*& data,
+                                                                 const uint8_t* query,
+                                                                 const int lane_id,
+                                                                 const int dim,
+                                                                 const int dimBlocks)
+  {
+    constexpr int veclen = 2;
+    int loadDim          = dimBlocks + lane_id * veclen;
+    uint32_t queryReg = loadDim < dim ? reinterpret_cast<uint16_t const*>(query + loadDim)[0] : 0;
+    for (int d = 0; d < dim - dimBlocks; d += veclen, data += kIndexGroupSize * veclen) {
+      uint32_t enc = reinterpret_cast<uint16_t const*>(data)[lane_id];
+      uint32_t q   = raft::shfl(queryReg, d / veclen, raft::WarpSize);
+      compute_dist(dist, q, enc);
+    }
+  }
+};
+
+template <int kUnroll, typename Lambda>
+struct loadAndComputeDist<kUnroll, Lambda, 1, uint8_t, uint32_t> {
+  Lambda compute_dist;
+  uint32_t& dist;
+
+  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+
+  __device__ __forceinline__ void runLoadShmemCompute(const uint8_t* const& data,
+                                                      const uint8_t* query_shared,
+                                                      int loadIndex,
+                                                      int shmemIndex)
+  {
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      uint32_t encV      = data[loadIndex + j * kIndexGroupSize];
+      uint32_t queryRegs = query_shared[shmemIndex + j];
+      compute_dist(dist, queryRegs, encV);
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndCompute(const uint8_t*& data,
+                                                        const uint8_t* query,
+                                                        int baseLoadIndex,
+                                                        const int lane_id)
+  {
+    uint32_t queryReg    = query[baseLoadIndex + lane_id];
+    constexpr int veclen = 1;
+    constexpr int stride = kUnroll * veclen;
+
+#pragma unroll
+    for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        uint32_t encV = data[lane_id + j * kIndexGroupSize];
+        uint32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
+        compute_dist(dist, q, encV);
+      }
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(const uint8_t*& data,
+                                                                 const uint8_t* query,
+                                                                 const int lane_id,
+                                                                 const int dim,
+                                                                 const int dimBlocks)
+  {
+    constexpr int veclen = 1;
+    int loadDim          = dimBlocks + lane_id;
+    uint32_t queryReg    = loadDim < dim ? query[loadDim] : 0;
+    for (int d = 0; d < dim - dimBlocks; d += veclen, data += kIndexGroupSize * veclen) {
+      uint32_t enc = data[lane_id];
+      uint32_t q   = raft::shfl(queryReg, d, raft::WarpSize);
+      compute_dist(dist, q, enc);
+    }
+  }
+};
+
+// This device function is for int8 veclens 4, 8 and 16
+template <int kUnroll, typename Lambda, int int8_veclen>
+struct loadAndComputeDist<kUnroll, Lambda, int8_veclen, int8_t, int32_t> {
+  Lambda compute_dist;
+  int32_t& dist;
+
+  __device__ __forceinline__ loadAndComputeDist(int32_t& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+
+  __device__ __forceinline__ void runLoadShmemCompute(const int8_t* const& data,
+                                                      const int8_t* query_shared,
+                                                      int loadIndex,
+                                                      int shmemIndex)
+  {
+    constexpr int veclen_int = int8_veclen / 4;  // converting int8_t veclens to int
+
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      int32_t encV[veclen_int];
+      raft::ldg(
+        encV,
+        reinterpret_cast<int32_t const*>(data) + (loadIndex + j * kIndexGroupSize) * veclen_int);
+      int32_t queryRegs[veclen_int];
+      raft::lds(queryRegs,
+                reinterpret_cast<int32_t const*>(query_shared + shmemIndex) + j * veclen_int);
+#pragma unroll
+      for (int k = 0; k < veclen_int; k++) {
+        compute_dist(dist, queryRegs[k], encV[k]);
+      }
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndCompute(const int8_t*& data,
+                                                        const int8_t* query,
+                                                        int baseLoadIndex,
+                                                        const int lane_id)
+  {
+    constexpr int veclen_int = int8_veclen / 4;  // converting int8_t veclens to int
+
+    int32_t queryReg =
+      (lane_id < 8) ? reinterpret_cast<int32_t const*>(query + baseLoadIndex)[lane_id] : 0;
+    constexpr int stride = kUnroll * int8_veclen;
+
+#pragma unroll
+    for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        int32_t encV[veclen_int];
+        raft::ldg(
+          encV,
+          reinterpret_cast<int32_t const*>(data) + (lane_id + j * kIndexGroupSize) * veclen_int);
+        const int d = (i * kUnroll + j) * veclen_int;
+#pragma unroll
+        for (int k = 0; k < veclen_int; ++k) {
+          int32_t q = raft::shfl(queryReg, d + k, raft::WarpSize);
+          compute_dist(dist, q, encV[k]);
+        }
+      }
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(
+    const int8_t*& data, const int8_t* query, const int lane_id, const int dim, const int dimBlocks)
+  {
+    constexpr int veclen_int = int8_veclen / 4;
+    const int loadDim        = dimBlocks + lane_id * 4;  // Here 4 is for 1 - int;
+    int32_t queryReg = loadDim < dim ? reinterpret_cast<int32_t const*>(query + loadDim)[0] : 0;
+    for (int d = 0; d < dim - dimBlocks; d += int8_veclen, data += kIndexGroupSize * int8_veclen) {
+      int32_t enc[veclen_int];
+      raft::ldg(enc, reinterpret_cast<int32_t const*>(data) + lane_id * veclen_int);
+#pragma unroll
+      for (int k = 0; k < veclen_int; k++) {
+        int32_t q = raft::shfl(queryReg, (d / 4) + k, raft::WarpSize);  // Here 4 is for 1 - int;
+        compute_dist(dist, q, enc[k]);
+      }
+    }
+  }
+};
+
+template <int kUnroll, typename Lambda>
+struct loadAndComputeDist<kUnroll, Lambda, 2, int8_t, int32_t> {
+  Lambda compute_dist;
+  int32_t& dist;
+  __device__ __forceinline__ loadAndComputeDist(int32_t& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+  __device__ __forceinline__ void runLoadShmemCompute(const int8_t* const& data,
+                                                      const int8_t* query_shared,
+                                                      int loadIndex,
+                                                      int shmemIndex)
+  {
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      int32_t encV      = reinterpret_cast<uint16_t const*>(data)[loadIndex + j * kIndexGroupSize];
+      int32_t queryRegs = reinterpret_cast<uint16_t const*>(query_shared + shmemIndex)[j];
+      compute_dist(dist, queryRegs, encV);
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndCompute(const int8_t*& data,
+                                                        const int8_t* query,
+                                                        int baseLoadIndex,
+                                                        const int lane_id)
+  {
+    int32_t queryReg =
+      (lane_id < 16) ? reinterpret_cast<uint16_t const*>(query + baseLoadIndex)[lane_id] : 0;
+    constexpr int veclen = 2;
+    constexpr int stride = kUnroll * veclen;
+
+#pragma unroll
+    for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        int32_t encV = reinterpret_cast<uint16_t const*>(data)[lane_id + j * kIndexGroupSize];
+        int32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
+        compute_dist(dist, q, encV);
+      }
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(
+    const int8_t*& data, const int8_t* query, const int lane_id, const int dim, const int dimBlocks)
+  {
+    constexpr int veclen = 2;
+    int loadDim          = dimBlocks + lane_id * veclen;
+    int32_t queryReg = loadDim < dim ? reinterpret_cast<uint16_t const*>(query + loadDim)[0] : 0;
+    for (int d = 0; d < dim - dimBlocks; d += veclen, data += kIndexGroupSize * veclen) {
+      int32_t enc = reinterpret_cast<uint16_t const*>(data + lane_id * veclen)[0];
+      int32_t q   = raft::shfl(queryReg, d / veclen, raft::WarpSize);
+      compute_dist(dist, q, enc);
+    }
+  }
+};
+
+template <int kUnroll, typename Lambda>
+struct loadAndComputeDist<kUnroll, Lambda, 1, int8_t, int32_t> {
+  Lambda compute_dist;
+  int32_t& dist;
+  __device__ __forceinline__ loadAndComputeDist(int32_t& dist, Lambda op)
+    : dist(dist), compute_dist(op)
+  {
+  }
+
+  __device__ __forceinline__ void runLoadShmemCompute(const int8_t* const& data,
+                                                      const int8_t* query_shared,
+                                                      int loadIndex,
+                                                      int shmemIndex)
+  {
+#pragma unroll
+    for (int j = 0; j < kUnroll; ++j) {
+      compute_dist(dist, query_shared[shmemIndex + j], data[loadIndex + j * kIndexGroupSize]);
+    }
+  }
+
+  __device__ __forceinline__ void runLoadShflAndCompute(const int8_t*& data,
+                                                        const int8_t* query,
+                                                        int baseLoadIndex,
+                                                        const int lane_id)
+  {
+    constexpr int veclen = 1;
+    constexpr int stride = kUnroll * veclen;
+    int32_t queryReg     = query[baseLoadIndex + lane_id];
+
+#pragma unroll
+    for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
+#pragma unroll
+      for (int j = 0; j < kUnroll; ++j) {
+        compute_dist(dist,
+                     raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize),
+                     data[lane_id + j * kIndexGroupSize]);
+      }
+    }
+  }
+  __device__ __forceinline__ void runLoadShflAndComputeRemainder(
+    const int8_t*& data, const int8_t* query, const int lane_id, const int dim, const int dimBlocks)
+  {
+    constexpr int veclen = 1;
+    const int loadDim    = dimBlocks + lane_id;
+    int32_t queryReg     = loadDim < dim ? query[loadDim] : 0;
+    for (int d = 0; d < dim - dimBlocks; d += veclen, data += kIndexGroupSize * veclen) {
+      compute_dist(dist, raft::shfl(queryReg, d, raft::WarpSize), data[lane_id]);
+    }
+  }
+};
+
+// switch to dummy blocksort when Capacity is 0 this explicit dummy is chosen
+// to support access to warpsort constants like ::queue_t::kDummy
+template <int Capacity, bool Ascending, typename T, typename IdxT>
+struct flat_block_sort {
+  using type = raft::matrix::detail::select::warpsort::block_sort<
+    raft::matrix::detail::select::warpsort::warp_sort_filtered,
+    Capacity,
+    Ascending,
+    T,
+    IdxT>;
+};
+
+template <typename T, bool Ascending, typename IdxT>
+struct flat_block_sort<0, Ascending, T, IdxT>
+  : ivf::detail::dummy_block_sort_t<T, IdxT, Ascending> {
+  using type = ivf::detail::dummy_block_sort_t<T, IdxT, Ascending>;
+};
+
+template <int Capacity, bool Ascending, typename T, typename IdxT>
+using block_sort_t = typename flat_block_sort<Capacity, Ascending, T, IdxT>::type;
+
+/**
+ * Scan clusters for nearest neighbors of the query vectors.
+ * See `ivfflat_interleaved_scan` for more information.
+ *
+ * The clusters are stored in the interleaved index format described in ivf_flat_types.hpp.
+ * For each query vector, a set of clusters is probed: the distance to each vector in the cluster is
+ * calculated, and the top-k nearest neighbors are selected.
+ *
+ * @param compute_dist distance function
+ * @param query_smem_elems number of dimensions of the query vector to fit in a shared memory of a
+ * block; this number must be a multiple of `WarpSize * Veclen`.
+ * @param[in] query a pointer to all queries in a row-major contiguous format [gridDim.y, dim]
+ * @param[in] coarse_index a pointer to the cluster indices to search through [n_probes]
+ * @param[in] list_indices index<T, IdxT>.indices
+ * @param[in] list_data index<T, IdxT>.data
+ * @param[in] list_sizes index<T, IdxT>.list_sizes
+ * @param[in] list_offsets index<T, IdxT>.list_offsets
+ * @param n_probes
+ * @param k
+ * @param dim
+ * @param sample_filter
+ * @param[out] neighbors
+ * @param[out] distances
+ */
+template <int Capacity,
+          int Veclen,
+          bool Ascending,
+          typename T,
+          typename AccT,
+          typename IdxT,
+          typename IvfSampleFilterT,
+          typename Lambda,
+          typename PostLambda>
+RAFT_KERNEL __launch_bounds__(kThreadsPerBlock)
+  interleaved_scan_kernel(Lambda compute_dist,
+                          PostLambda post_process,
+                          const uint32_t query_smem_elems,
+                          const T* query,
+                          const uint32_t* coarse_index,
+                          const T* const* list_data_ptrs,
+                          const uint32_t* list_sizes,
+                          const uint32_t queries_offset,
+                          const uint32_t n_probes,
+                          const uint32_t k,
+                          const uint32_t max_samples,
+                          const uint32_t* chunk_indices,
+                          const uint32_t dim,
+                          IvfSampleFilterT sample_filter,
+                          uint32_t* neighbors,
+                          float* distances)
+{
+  extern __shared__ __align__(256) uint8_t interleaved_scan_kernel_smem[];
+  constexpr bool kManageLocalTopK = Capacity > 0;
+  // Using shared memory for the (part of the) query;
+  // This allows to save on global memory bandwidth when reading index and query
+  // data at the same time.
+  // Its size is `query_smem_elems`.
+  T* query_shared = reinterpret_cast<T*>(interleaved_scan_kernel_smem);
+  // Make the query input and output point to this block's shared query
+  {
+    const int query_id = blockIdx.y;
+    query += query_id * dim;
+    if constexpr (kManageLocalTopK) {
+      neighbors += query_id * k * gridDim.x + blockIdx.x * k;
+      distances += query_id * k * gridDim.x + blockIdx.x * k;
+    } else {
+      distances += query_id * uint64_t(max_samples);
+    }
+    chunk_indices += (n_probes * query_id);
+    coarse_index += query_id * n_probes;
+  }
+
+  // Copy a part of the query into shared memory for faster processing
+  copy_vectorized(query_shared, query, std::min(dim, query_smem_elems));
+  __syncthreads();
+
+  using local_topk_t = block_sort_t<Capacity, Ascending, float, uint32_t>;
+  local_topk_t queue(k);
+  {
+    using align_warp  = raft::Pow2<raft::WarpSize>;
+    const int lane_id = align_warp::mod(threadIdx.x);
+
+    // How many full warps needed to compute the distance (without remainder)
+    const uint32_t full_warps_along_dim = align_warp::roundDown(dim);
+
+    const uint32_t shm_assisted_dim =
+      (dim > query_smem_elems) ? query_smem_elems : full_warps_along_dim;
+
+    // Every CUDA block scans one cluster at a time.
+    for (int probe_id = blockIdx.x; probe_id < n_probes; probe_id += gridDim.x) {
+      const uint32_t list_id = coarse_index[probe_id];  // The id of cluster(list)
+
+      // The number of vectors in each cluster(list); [nlist]
+      const uint32_t list_length = list_sizes[list_id];
+
+      // The number of interleaved groups to be processed
+      const uint32_t num_groups =
+        align_warp::div(list_length + align_warp::Mask);  // ceildiv by power of 2
+
+      uint32_t sample_offset = 0;
+      if (probe_id > 0) { sample_offset = chunk_indices[probe_id - 1]; }
+      assert(list_length == chunk_indices[probe_id] - sample_offset);
+      assert(sample_offset + list_length <= max_samples);
+
+      constexpr int kUnroll        = raft::WarpSize / Veclen;
+      constexpr uint32_t kNumWarps = kThreadsPerBlock / raft::WarpSize;
+      // Every warp reads WarpSize vectors and computes the distances to them.
+      // Then, the distances and corresponding ids are distributed among the threads,
+      // and each thread adds one (id, dist) pair to the filtering queue.
+      for (uint32_t group_id = align_warp::div(threadIdx.x); group_id < num_groups;
+           group_id += kNumWarps) {
+        AccT dist = 0;
+        // This is where this warp begins reading data (start position of an interleaved group)
+        const T* data = list_data_ptrs[list_id] + (group_id * kIndexGroupSize) * dim;
+
+        // This is the vector a given lane/thread handles
+        const uint32_t vec_id = group_id * raft::WarpSize + lane_id;
+        const bool valid =
+          vec_id < list_length && sample_filter(queries_offset + blockIdx.y, list_id, vec_id);
+
+        // Process first shm_assisted_dim dimensions (always using shared memory)
+        if (valid) {
+          loadAndComputeDist<kUnroll, decltype(compute_dist), Veclen, T, AccT> lc(dist,
+                                                                                  compute_dist);
+          for (int pos = 0; pos < shm_assisted_dim;
+               pos += raft::WarpSize, data += kIndexGroupSize * raft::WarpSize) {
+            lc.runLoadShmemCompute(data, query_shared, lane_id, pos);
+          }
+        }
+
+        if (dim > query_smem_elems) {
+          // The default path - using shfl ops - for dimensions beyond query_smem_elems
+          loadAndComputeDist<kUnroll, decltype(compute_dist), Veclen, T, AccT> lc(dist,
+                                                                                  compute_dist);
+          for (int pos = shm_assisted_dim; pos < full_warps_along_dim; pos += raft::WarpSize) {
+            lc.runLoadShflAndCompute(data, query, pos, lane_id);
+          }
+          lc.runLoadShflAndComputeRemainder(data, query, lane_id, dim, full_warps_along_dim);
+        } else {
+          // when  shm_assisted_dim == full_warps_along_dim < dim
+          if (valid) {
+            loadAndComputeDist<1, decltype(compute_dist), Veclen, T, AccT> lc(dist, compute_dist);
+            for (int pos = full_warps_along_dim; pos < dim;
+                 pos += Veclen, data += kIndexGroupSize * Veclen) {
+              lc.runLoadShmemCompute(data, query_shared, lane_id, pos);
+            }
+          }
+        }
+
+        // Enqueue one element per thread
+        const float val = valid ? static_cast<float>(dist) : local_topk_t::queue_t::kDummy;
+        if constexpr (kManageLocalTopK) {
+          queue.add(val, sample_offset + vec_id);
+        } else {
+          if (vec_id < list_length) distances[sample_offset + vec_id] = val;
+        }
+      }
+
+      // fill up unused slots for current query
+      if constexpr (!kManageLocalTopK) {
+        if (probe_id + 1 == n_probes) {
+          for (uint32_t i = threadIdx.x + sample_offset + list_length; i < max_samples;
+               i += blockDim.x) {
+            distances[i] = local_topk_t::queue_t::kDummy;
+          }
+        }
+      }
+    }
+  }
+
+  // finalize and store selected neighbours
+  if constexpr (kManageLocalTopK) {
+    __syncthreads();
+    queue.done(interleaved_scan_kernel_smem);
+    queue.store(distances, neighbors, post_process);
+  }
+}
+
+/**
+ *  Configure the gridDim.x to maximize GPU occupancy, but reduce the output size
+ */
+template <typename T>
+uint32_t configure_launch_x(uint32_t numQueries, uint32_t n_probes, int32_t sMemSize, T func)
+{
+  int dev_id;
+  RAFT_CUDA_TRY(cudaGetDevice(&dev_id));
+  int num_sms;
+  RAFT_CUDA_TRY(cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id));
+  int num_blocks_per_sm = 0;
+  RAFT_CUDA_TRY(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &num_blocks_per_sm, func, kThreadsPerBlock, sMemSize));
+
+  size_t min_grid_size = num_sms * num_blocks_per_sm;
+  size_t min_grid_x    = raft::ceildiv<size_t>(min_grid_size, numQueries);
+  return min_grid_x > n_probes ? n_probes : static_cast<uint32_t>(min_grid_x);
+}
+
+template <int Capacity,
+          int Veclen,
+          bool Ascending,
+          typename T,
+          typename AccT,
+          typename IdxT,
+          typename IvfSampleFilterT,
+          typename Lambda,
+          typename PostLambda>
+void launch_kernel(Lambda lambda,
+                   PostLambda post_process,
+                   const index<T, IdxT>& index,
+                   const T* queries,
+                   const uint32_t* coarse_index,
+                   const uint32_t num_queries,
+                   const uint32_t queries_offset,
+                   const uint32_t n_probes,
+                   const uint32_t k,
+                   const uint32_t max_samples,
+                   const uint32_t* chunk_indices,
+                   IvfSampleFilterT sample_filter,
+                   uint32_t* neighbors,
+                   float* distances,
+                   uint32_t& grid_dim_x,
+                   rmm::cuda_stream_view stream)
+{
+  RAFT_EXPECTS(Veclen == index.veclen(),
+               "Configured Veclen does not match the index interleaving pattern.");
+  constexpr auto kKernel   = interleaved_scan_kernel<Capacity,
+                                                   Veclen,
+                                                   Ascending,
+                                                   T,
+                                                   AccT,
+                                                   IdxT,
+                                                   IvfSampleFilterT,
+                                                   Lambda,
+                                                   PostLambda>;
+  const int max_query_smem = 16384;
+  int query_smem_elems     = std::min<int>(max_query_smem / sizeof(T),
+                                       raft::Pow2<Veclen * raft::WarpSize>::roundUp(index.dim()));
+  int smem_size            = query_smem_elems * sizeof(T);
+
+  if constexpr (Capacity > 0) {
+    constexpr int kSubwarpSize = std::min<int>(Capacity, raft::WarpSize);
+    auto block_merge_mem =
+      raft::matrix::detail::select::warpsort::calc_smem_size_for_block_wide<float, IdxT>(
+        kThreadsPerBlock / kSubwarpSize, k);
+    smem_size += std::max<int>(smem_size, block_merge_mem);
+  }
+
+  // power-of-two less than cuda limit (for better addr alignment)
+  constexpr uint32_t kMaxGridY = 32768;
+
+  if (grid_dim_x == 0) {
+    grid_dim_x = configure_launch_x(std::min(kMaxGridY, num_queries), n_probes, smem_size, kKernel);
+    return;
+  }
+
+  for (uint32_t query_offset = 0; query_offset < num_queries; query_offset += kMaxGridY) {
+    uint32_t grid_dim_y = std::min<uint32_t>(kMaxGridY, num_queries - query_offset);
+    dim3 grid_dim(grid_dim_x, grid_dim_y, 1);
+    dim3 block_dim(kThreadsPerBlock);
+    RAFT_LOG_TRACE(
+      "Launching the ivf-flat interleaved_scan_kernel (%d, %d, 1) x (%d, 1, 1), n_probes = %d, "
+      "smem_size = %d",
+      grid_dim.x,
+      grid_dim.y,
+      block_dim.x,
+      n_probes,
+      smem_size);
+    kKernel<<<grid_dim, block_dim, smem_size, stream>>>(lambda,
+                                                        post_process,
+                                                        query_smem_elems,
+                                                        queries,
+                                                        coarse_index,
+                                                        index.data_ptrs().data_handle(),
+                                                        index.list_sizes().data_handle(),
+                                                        queries_offset + query_offset,
+                                                        n_probes,
+                                                        k,
+                                                        max_samples,
+                                                        chunk_indices,
+                                                        index.dim(),
+                                                        sample_filter,
+                                                        neighbors,
+                                                        distances);
+    queries += grid_dim_y * index.dim();
+    if constexpr (Capacity > 0) {
+      neighbors += grid_dim_y * grid_dim_x * k;
+      distances += grid_dim_y * grid_dim_x * k;
+    } else {
+      distances += grid_dim_y * max_samples;
+    }
+    chunk_indices += grid_dim_y * n_probes;
+    coarse_index += grid_dim_y * n_probes;
+  }
+}
+
+template <int Veclen, typename T, typename AccT>
+struct euclidean_dist {
+  __device__ __forceinline__ void operator()(AccT& acc, AccT x, AccT y)
+  {
+    const auto diff = x - y;
+    acc += diff * diff;
+  }
+};
+
+template <int Veclen>
+struct euclidean_dist<Veclen, uint8_t, uint32_t> {
+  __device__ __forceinline__ void operator()(uint32_t& acc, uint32_t x, uint32_t y)
+  {
+    if constexpr (Veclen > 1) {
+      const auto diff = __vabsdiffu4(x, y);
+      acc             = raft::dp4a(diff, diff, acc);
+    } else {
+      const auto diff = __usad(x, y, 0u);
+      acc += diff * diff;
+    }
+  }
+};
+
+template <int Veclen>
+struct euclidean_dist<Veclen, int8_t, int32_t> {
+  __device__ __forceinline__ void operator()(int32_t& acc, int32_t x, int32_t y)
+  {
+    if constexpr (Veclen > 1) {
+      // Note that we enforce here that the unsigned version of dp4a is used, because the difference
+      // between two int8 numbers can be greater than 127 and therefore represented as a negative
+      // number in int8. Casting from int8 to int32 would yield incorrect results, while casting
+      // from uint8 to uint32 is correct.
+      const auto diff = __vabsdiffs4(x, y);
+      acc             = raft::dp4a(diff, diff, static_cast<uint32_t>(acc));
+    } else {
+      const auto diff = x - y;
+      acc += diff * diff;
+    }
+  }
+};
+
+template <int Veclen, typename T, typename AccT>
+struct inner_prod_dist {
+  __device__ __forceinline__ void operator()(AccT& acc, AccT x, AccT y)
+  {
+    if constexpr (Veclen > 1 && (std::is_same_v<T, int8_t> || std::is_same_v<T, uint8_t>)) {
+      acc = raft::dp4a(x, y, acc);
+    } else {
+      acc += x * y;
+    }
+  }
+};
+
+/** Select the distance computation function and forward the rest of the arguments. */
+template <int Capacity,
+          int Veclen,
+          bool Ascending,
+          typename T,
+          typename AccT,
+          typename IdxT,
+          typename IvfSampleFilterT,
+          typename... Args>
+void launch_with_fixed_consts(cuvs::distance::DistanceType metric, Args&&... args)
+{
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2Expanded:
+    case cuvs::distance::DistanceType::L2Unexpanded:
+      return launch_kernel<Capacity,
+                           Veclen,
+                           Ascending,
+                           T,
+                           AccT,
+                           IdxT,
+                           IvfSampleFilterT,
+                           euclidean_dist<Veclen, T, AccT>,
+                           raft::identity_op>({}, {}, std::forward<Args>(args)...);
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2SqrtUnexpanded:
+      return launch_kernel<Capacity,
+                           Veclen,
+                           Ascending,
+                           T,
+                           AccT,
+                           IdxT,
+                           IvfSampleFilterT,
+                           euclidean_dist<Veclen, T, AccT>,
+                           raft::sqrt_op>({}, {}, std::forward<Args>(args)...);
+    case cuvs::distance::DistanceType::InnerProduct:
+      return launch_kernel<Capacity,
+                           Veclen,
+                           Ascending,
+                           T,
+                           AccT,
+                           IdxT,
+                           IvfSampleFilterT,
+                           inner_prod_dist<Veclen, T, AccT>,
+                           raft::identity_op>({}, {}, std::forward<Args>(args)...);
+    // NB: update the description of `knn::ivf_flat::build` when adding here a new metric.
+    default: RAFT_FAIL("The chosen distance metric is not supported (%d)", int(metric));
+  }
+}
+
+/**
+ * Lift the `capacity` and `veclen` parameters to the template level,
+ * forward the rest of the arguments unmodified to `launch_interleaved_scan_kernel`.
+ */
+template <typename T,
+          typename AccT,
+          typename IdxT,
+          typename IvfSampleFilterT,
+          int Capacity = raft::matrix::detail::select::warpsort::kMaxCapacity,
+          int Veclen   = std::max<int>(1, 16 / sizeof(T))>
+struct select_interleaved_scan_kernel {
+  /**
+   * Recursively reduce the `Capacity` and `Veclen` parameters until they match the
+   * corresponding runtime arguments.
+   * By default, this recursive process starts with maximum possible values of the
+   * two parameters and ends with both values equal to 1.
+   */
+  template <typename... Args>
+  static inline void run(int k_max, int veclen, bool select_min, Args&&... args)
+  {
+    if constexpr (Capacity > 0) {
+      if (k_max == 0 || k_max > Capacity) {
+        return select_interleaved_scan_kernel<T, AccT, IdxT, IvfSampleFilterT, 0, Veclen>::run(
+          k_max, veclen, select_min, std::forward<Args>(args)...);
+      }
+    }
+    if constexpr (Capacity > 1) {
+      if (k_max * 2 <= Capacity) {
+        return select_interleaved_scan_kernel<T,
+                                              AccT,
+                                              IdxT,
+                                              IvfSampleFilterT,
+                                              Capacity / 2,
+                                              Veclen>::run(k_max,
+                                                           veclen,
+                                                           select_min,
+                                                           std::forward<Args>(args)...);
+      }
+    }
+    if constexpr (Veclen > 1) {
+      if (veclen % Veclen != 0) {
+        return select_interleaved_scan_kernel<T, AccT, IdxT, IvfSampleFilterT, Capacity, 1>::run(
+          k_max, 1, select_min, std::forward<Args>(args)...);
+      }
+    }
+    // NB: this is the limitation of the warpsort structures that use a huge number of
+    //     registers (used in the main kernel here).
+    RAFT_EXPECTS(Capacity == 0 || k_max == Capacity,
+                 "Capacity must be either 0 or a power-of-two not bigger than the maximum "
+                 "allowed size matrix::detail::select::warpsort::kMaxCapacity (%d).",
+                 raft::matrix::detail::select::warpsort::kMaxCapacity);
+    RAFT_EXPECTS(
+      veclen == Veclen,
+      "Veclen must be power-of-two not bigger than the maximum allowed size for this data type.");
+    if (select_min) {
+      launch_with_fixed_consts<Capacity, Veclen, true, T, AccT, IdxT, IvfSampleFilterT>(
+        std::forward<Args>(args)...);
+    } else {
+      launch_with_fixed_consts<Capacity, Veclen, false, T, AccT, IdxT, IvfSampleFilterT>(
+        std::forward<Args>(args)...);
+    }
+  }
+};
+
+/**
+ * @brief Configure and launch an appropriate template instance of the interleaved scan kernel.
+ *
+ * @tparam T value type
+ * @tparam AccT accumulated type
+ * @tparam IdxT type of the indices
+ *
+ * @param index previously built ivf-flat index
+ * @param[in] queries device pointer to the query vectors [batch_size, dim]
+ * @param[in] coarse_query_results device pointer to the cluster (list) ids [batch_size, n_probes]
+ * @param n_queries batch size
+ * @param[in] queries_offset
+ *   An offset of the current query batch. It is used for feeding sample_filter with the
+ *   correct query index.
+ * @param metric type of the measured distance
+ * @param n_probes number of nearest clusters to query
+ * @param k number of nearest neighbors.
+ *            NB: the maximum value of `k` is limited statically by `kMaxCapacity`.
+ * @param select_min whether to select nearest (true) or furthest (false) points w.r.t. the given
+ * metric.
+ * @param[out] neighbors device pointer to the result indices for each query and cluster
+ * [batch_size, grid_dim_x, k]
+ * @param[out] distances device pointer to the result distances for each query and cluster
+ * [batch_size, grid_dim_x, k]
+ * @param[inout] grid_dim_x number of blocks launched across all n_probes clusters;
+ *               (one block processes one or more probes, hence: 1 <= grid_dim_x <= n_probes)
+ * @param stream
+ * @param sample_filter
+ *   A filter that selects samples for a given query. Use an instance of none_ivf_sample_filter to
+ *   provide a green light for every sample.
+ */
+template <typename T, typename AccT, typename IdxT, typename IvfSampleFilterT>
+void ivfflat_interleaved_scan(const index<T, IdxT>& index,
+                              const T* queries,
+                              const uint32_t* coarse_query_results,
+                              const uint32_t n_queries,
+                              const uint32_t queries_offset,
+                              const cuvs::distance::DistanceType metric,
+                              const uint32_t n_probes,
+                              const uint32_t k,
+                              const uint32_t max_samples,
+                              const uint32_t* chunk_indices,
+                              const bool select_min,
+                              IvfSampleFilterT sample_filter,
+                              uint32_t* neighbors,
+                              float* distances,
+                              uint32_t& grid_dim_x,
+                              rmm::cuda_stream_view stream)
+{
+  const int capacity = raft::bound_by_power_of_two(k);
+
+  auto filter_adapter = cuvs::neighbors::filtering::ivf_to_sample_filter(
+    index.inds_ptrs().data_handle(), sample_filter);
+  select_interleaved_scan_kernel<T, AccT, IdxT, decltype(filter_adapter)>::run(capacity,
+                                                                               index.veclen(),
+                                                                               select_min,
+                                                                               metric,
+                                                                               index,
+                                                                               queries,
+                                                                               coarse_query_results,
+                                                                               n_queries,
+                                                                               queries_offset,
+                                                                               n_probes,
+                                                                               k,
+                                                                               max_samples,
+                                                                               chunk_indices,
+                                                                               filter_adapter,
+                                                                               neighbors,
+                                                                               distances,
+                                                                               grid_dim_x,
+                                                                               stream);
+}
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
new file mode 100644
index 0000000000..bb106ba2f0
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -0,0 +1,383 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../core/nvtx.hpp"
+#include "../detail/ann_utils.cuh"
+#include "../ivf_common.cuh"                 // cuvs::neighbors::detail::ivf
+#include "ivf_flat_interleaved_scan.cuh"     // interleaved_scan
+#include <cuvs/neighbors/ivf_flat.hpp>       // raft::neighbors::ivf_flat::index
+#include <cuvs/neighbors/sample_filter.hpp>  // none_ivf_sample_filter
+
+#include <cuvs/distance/distance_types.hpp>  // is_min_close, DistanceType
+#include <raft/core/logger.hpp>              // RAFT_LOG_TRACE
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>                // raft::resources
+#include <raft/linalg/gemm.cuh>                   // raft::linalg::gemm
+#include <raft/linalg/norm.cuh>                   // raft::linalg::norm
+#include <raft/linalg/unary_op.cuh>               // raft::linalg::unary_op
+#include <raft/matrix/detail/select_k.cuh>        // matrix::detail::select_k
+#include <raft/spatial/knn/detail/ann_utils.cuh>  // utils::mapping
+
+#include <rmm/resource_ref.hpp>
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+using namespace cuvs::spatial::knn::detail;  // NOLINT
+
+template <typename T, typename AccT, typename IdxT, typename IvfSampleFilterT>
+void search_impl(raft::resources const& handle,
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index,
+                 const T* queries,
+                 uint32_t n_queries,
+                 uint32_t queries_offset,
+                 uint32_t k,
+                 uint32_t n_probes,
+                 uint32_t max_samples,
+                 bool select_min,
+                 IdxT* neighbors,
+                 AccT* distances,
+                 rmm::device_async_resource_ref search_mr,
+                 IvfSampleFilterT sample_filter)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  std::size_t n_queries_probes = std::size_t(n_queries) * std::size_t(n_probes);
+
+  // The norm of query
+  rmm::device_uvector<float> query_norm_dev(n_queries, stream, search_mr);
+  // The distance value of cluster(list) and queries
+  rmm::device_uvector<float> distance_buffer_dev(n_queries * index.n_lists(), stream, search_mr);
+  // The topk distance value of cluster(list) and queries
+  rmm::device_uvector<float> coarse_distances_dev(n_queries_probes, stream, search_mr);
+  // The topk  index of cluster(list) and queries
+  rmm::device_uvector<uint32_t> coarse_indices_dev(n_queries_probes, stream, search_mr);
+
+  // Optional structures if postprocessing is required
+  // The topk distance value of candidate vectors from each cluster(list)
+  rmm::device_uvector<AccT> distances_tmp_dev(0, stream, search_mr);
+  // Number of samples for each query
+  rmm::device_uvector<uint32_t> num_samples(0, stream, search_mr);
+  // Offsets per probe for each query
+  rmm::device_uvector<uint32_t> chunk_index(0, stream, search_mr);
+
+  // The topk index of candidate vectors from each cluster(list), local index offset
+  // also we might need additional storage for select_k
+  rmm::device_uvector<uint32_t> indices_tmp_dev(0, stream, search_mr);
+  rmm::device_uvector<uint32_t> neighbors_uint32_buf(0, stream, search_mr);
+
+  size_t float_query_size;
+  if constexpr (std::is_integral_v<T>) {
+    float_query_size = n_queries * index.dim();
+  } else {
+    float_query_size = 0;
+  }
+  rmm::device_uvector<float> converted_queries_dev(float_query_size, stream, search_mr);
+  float* converted_queries_ptr = converted_queries_dev.data();
+
+  if constexpr (std::is_same_v<T, float>) {
+    converted_queries_ptr = const_cast<float*>(queries);
+  } else {
+    raft::linalg::unaryOp(
+      converted_queries_ptr, queries, n_queries * index.dim(), utils::mapping<float>{}, stream);
+  }
+
+  float alpha = 1.0f;
+  float beta  = 0.0f;
+
+  // todo(lsugy): raft distance? (if performance is similar/better than gemm)
+  switch (index.metric()) {
+    case cuvs::distance::DistanceType::L2Expanded:
+    case cuvs::distance::DistanceType::L2SqrtExpanded: {
+      alpha = -2.0f;
+      beta  = 1.0f;
+      raft::linalg::rowNorm(query_norm_dev.data(),
+                            converted_queries_ptr,
+                            static_cast<IdxT>(index.dim()),
+                            static_cast<IdxT>(n_queries),
+                            raft::linalg::L2Norm,
+                            true,
+                            stream);
+      utils::outer_add(query_norm_dev.data(),
+                       (IdxT)n_queries,
+                       index.center_norms()->data_handle(),
+                       (IdxT)index.n_lists(),
+                       distance_buffer_dev.data(),
+                       stream);
+      RAFT_LOG_TRACE_VEC(index.center_norms()->data_handle(), std::min<uint32_t>(20, index.dim()));
+      RAFT_LOG_TRACE_VEC(distance_buffer_dev.data(), std::min<uint32_t>(20, index.n_lists()));
+      break;
+    }
+    default: {
+      alpha = 1.0f;
+      beta  = 0.0f;
+    }
+  }
+
+  raft::linalg::gemm(handle,
+                     true,
+                     false,
+                     index.n_lists(),
+                     n_queries,
+                     index.dim(),
+                     &alpha,
+                     index.centers().data_handle(),
+                     index.dim(),
+                     converted_queries_ptr,
+                     index.dim(),
+                     &beta,
+                     distance_buffer_dev.data(),
+                     index.n_lists(),
+                     stream);
+
+  RAFT_LOG_TRACE_VEC(distance_buffer_dev.data(), std::min<uint32_t>(20, index.n_lists()));
+  raft::matrix::detail::select_k<AccT, uint32_t>(handle,
+                                                 distance_buffer_dev.data(),
+                                                 nullptr,
+                                                 n_queries,
+                                                 index.n_lists(),
+                                                 n_probes,
+                                                 coarse_distances_dev.data(),
+                                                 coarse_indices_dev.data(),
+                                                 select_min);
+  RAFT_LOG_TRACE_VEC(coarse_indices_dev.data(), n_probes);
+  RAFT_LOG_TRACE_VEC(coarse_distances_dev.data(), n_probes);
+
+  uint32_t grid_dim_x = 0;
+  if (n_probes > 1) {
+    // query the gridDimX size to store probes topK output
+    ivfflat_interleaved_scan<T, typename utils::config<T>::value_t, IdxT, IvfSampleFilterT>(
+      index,
+      nullptr,
+      nullptr,
+      n_queries,
+      queries_offset,
+      index.metric(),
+      n_probes,
+      k,
+      0,
+      nullptr,
+      select_min,
+      sample_filter,
+      nullptr,
+      nullptr,
+      grid_dim_x,
+      stream);
+  } else {
+    grid_dim_x = 1;
+  }
+
+  num_samples.resize(n_queries, stream);
+  chunk_index.resize(n_queries_probes, stream);
+
+  ivf::detail::calc_chunk_indices::configure(n_probes, n_queries)(index.list_sizes().data_handle(),
+                                                                  coarse_indices_dev.data(),
+                                                                  chunk_index.data(),
+                                                                  num_samples.data(),
+                                                                  stream);
+
+  auto distances_dev_ptr = distances;
+
+  uint32_t* neighbors_uint32 = nullptr;
+  if constexpr (sizeof(IdxT) == sizeof(uint32_t)) {
+    neighbors_uint32 = reinterpret_cast<uint32_t*>(neighbors);
+  } else {
+    neighbors_uint32_buf.resize(std::size_t(n_queries) * std::size_t(k), stream);
+    neighbors_uint32 = neighbors_uint32_buf.data();
+  }
+
+  uint32_t* indices_dev_ptr = nullptr;
+
+  bool manage_local_topk = is_local_topk_feasible(k);
+  if (!manage_local_topk || grid_dim_x > 1) {
+    auto target_size = std::size_t(n_queries) * (manage_local_topk ? grid_dim_x * k : max_samples);
+
+    distances_tmp_dev.resize(target_size, stream);
+    if (manage_local_topk) indices_tmp_dev.resize(target_size, stream);
+
+    distances_dev_ptr = distances_tmp_dev.data();
+    indices_dev_ptr   = indices_tmp_dev.data();
+  } else {
+    indices_dev_ptr = neighbors_uint32;
+  }
+
+  ivfflat_interleaved_scan<T, typename utils::config<T>::value_t, IdxT, IvfSampleFilterT>(
+    index,
+    queries,
+    coarse_indices_dev.data(),
+    n_queries,
+    queries_offset,
+    index.metric(),
+    n_probes,
+    k,
+    max_samples,
+    chunk_index.data(),
+    select_min,
+    sample_filter,
+    indices_dev_ptr,
+    distances_dev_ptr,
+    grid_dim_x,
+    stream);
+
+  RAFT_LOG_TRACE_VEC(distances_dev_ptr, 2 * k);
+  if (indices_dev_ptr != nullptr) { RAFT_LOG_TRACE_VEC(indices_dev_ptr, 2 * k); }
+
+  // Merge topk values from different blocks
+  if (!manage_local_topk || grid_dim_x > 1) {
+    raft::matrix::detail::select_k<AccT, uint32_t>(
+      handle,
+      distances_tmp_dev.data(),
+      indices_tmp_dev.data(),
+      n_queries,
+      manage_local_topk ? (k * grid_dim_x) : max_samples,
+      k,
+      distances,
+      neighbors_uint32,
+      select_min,
+      false,
+      raft::matrix::SelectAlgo::kAuto,
+      manage_local_topk ? nullptr : num_samples.data());
+  }
+  if (!manage_local_topk) {
+    // post process distances && neighbor IDs
+    ivf::detail::postprocess_distances(
+      distances, distances, index.metric(), n_queries, k, 1.0, false, stream);
+  }
+  ivf::detail::postprocess_neighbors(neighbors,
+                                     neighbors_uint32,
+                                     index.inds_ptrs().data_handle(),
+                                     coarse_indices_dev.data(),
+                                     chunk_index.data(),
+                                     n_queries,
+                                     n_probes,
+                                     k,
+                                     stream);
+}
+
+/** See raft::neighbors::ivf_flat::search docs */
+template <typename T,
+          typename IdxT,
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+inline void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  const index<T, IdxT>& index,
+  const T* queries,
+  uint32_t n_queries,
+  uint32_t k,
+  IdxT* neighbors,
+  float* distances,
+  rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource(),
+  IvfSampleFilterT sample_filter    = IvfSampleFilterT())
+{
+  common::nvtx::range<common::nvtx::domain::cuvs> fun_scope(
+    "ivf_flat::search(k = %u, n_queries = %u, dim = %zu)", k, n_queries, index.dim());
+
+  RAFT_EXPECTS(params.n_probes > 0,
+               "n_probes (number of clusters to probe in the search) must be positive.");
+  auto n_probes          = std::min<uint32_t>(params.n_probes, index.n_lists());
+  bool manage_local_topk = is_local_topk_feasible(k);
+
+  uint32_t max_samples = 0;
+  if (!manage_local_topk) {
+    IdxT ms = raft::Pow2<128 / sizeof(float)>::roundUp(
+      std::max<IdxT>(index.accum_sorted_sizes()(n_probes), k));
+    RAFT_EXPECTS(ms <= IdxT(std::numeric_limits<uint32_t>::max()),
+                 "The maximum sample size is too big.");
+    max_samples = ms;
+  }
+
+  // a batch size heuristic: try to keep the workspace within the specified size
+  constexpr uint64_t kExpectedWsSize = 1024 * 1024 * 1024;
+  uint64_t max_ws_size =
+    std::min(raft::resource::get_workspace_free_bytes(handle), kExpectedWsSize);
+
+  uint64_t ws_size_per_query = 4ull * (2 * n_probes + index.n_lists() + index.dim() + 1) +
+                               (manage_local_topk ? ((sizeof(IdxT) + 4) * n_probes * k)
+                                                  : (4ull * (max_samples + n_probes + 1)));
+
+  const uint32_t max_queries =
+    std::min<uint32_t>(n_queries, raft::div_rounding_up_safe(max_ws_size, ws_size_per_query));
+
+  for (uint32_t offset_q = 0; offset_q < n_queries; offset_q += max_queries) {
+    uint32_t queries_batch = raft::min(max_queries, n_queries - offset_q);
+
+    search_impl<T, float, IdxT, IvfSampleFilterT>(handle,
+                                                  index,
+                                                  queries + offset_q * index.dim(),
+                                                  queries_batch,
+                                                  offset_q,
+                                                  k,
+                                                  n_probes,
+                                                  max_samples,
+                                                  cuvs::distance::is_min_close(index.metric()),
+                                                  neighbors + offset_q * k,
+                                                  distances + offset_q * k,
+                                                  mr,
+                                                  sample_filter);
+  }
+}
+
+template <typename T, typename IdxT, typename IvfSampleFilterT>
+void search_with_filtering(raft::resources const& handle,
+                           const search_params& params,
+                           const index<T, IdxT>& index,
+                           raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
+                           raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
+                           raft::device_matrix_view<float, IdxT, raft::row_major> distances,
+                           IvfSampleFilterT sample_filter = IvfSampleFilterT())
+{
+  RAFT_EXPECTS(
+    queries.extent(0) == neighbors.extent(0) && queries.extent(0) == distances.extent(0),
+    "Number of rows in output neighbors and distances matrices must equal the number of queries.");
+
+  RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1),
+               "Number of columns in output neighbors and distances matrices must be equal");
+
+  RAFT_EXPECTS(queries.extent(1) == index.dim(),
+               "Number of query dimensions should equal number of dimensions in the index.");
+
+  search_with_filtering(handle,
+                        params,
+                        index,
+                        queries.data_handle(),
+                        static_cast<std::uint32_t>(queries.extent(0)),
+                        static_cast<std::uint32_t>(neighbors.extent(1)),
+                        neighbors.data_handle(),
+                        distances.data_handle(),
+                        raft::resource::get_workspace_resource(handle),
+                        sample_filter);
+}
+
+template <typename T, typename IdxT>
+void search(raft::resources const& handle,
+            const search_params& params,
+            const index<T, IdxT>& idx,
+            raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
+            raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
+            raft::device_matrix_view<float, IdxT, raft::row_major> distances)
+{
+  search_with_filtering(handle,
+                        params,
+                        idx,
+                        queries,
+                        neighbors,
+                        distances,
+                        cuvs::neighbors::filtering::none_ivf_sample_filter());
+}
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
similarity index 58%
rename from cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp
rename to cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
index 48a584e9ec..93e46cbefa 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
@@ -24,7 +24,8 @@
  */
 
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_search.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
 
@@ -36,8 +37,20 @@ namespace cuvs::neighbors::ivf_flat {
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
   {                                                                             \
-    raft::runtime::neighbors::ivf_flat::search(                                 \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+    cuvs::neighbors::ivf_flat::detail::search(                                  \
+      handle, params, index, queries, neighbors, distances);                    \
+  }                                                                             \
+  void search_with_filtering(                                                   \
+    raft::resources const& handle,                                              \
+    const search_params& params,                                                \
+    index<T, IdxT>& idx,                                                        \
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \
+  {                                                                             \
+    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \
+      handle, params, idx, queries, neighbors, distances, sample_filter);       \
   }
 CUVS_INST_IVF_FLAT_SEARCH(float, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
similarity index 58%
rename from cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp
rename to cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
index 5645c18e0f..5f75d3d481 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
@@ -24,7 +24,8 @@
  */
 
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_search.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
 
@@ -36,8 +37,20 @@ namespace cuvs::neighbors::ivf_flat {
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
   {                                                                             \
-    raft::runtime::neighbors::ivf_flat::search(                                 \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+    cuvs::neighbors::ivf_flat::detail::search(                                  \
+      handle, params, index, queries, neighbors, distances);                    \
+  }                                                                             \
+  void search_with_filtering(                                                   \
+    raft::resources const& handle,                                              \
+    const search_params& params,                                                \
+    index<T, IdxT>& idx,                                                        \
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \
+  {                                                                             \
+    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \
+      handle, params, idx, queries, neighbors, distances, sample_filter);       \
   }
 CUVS_INST_IVF_FLAT_SEARCH(int8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
similarity index 58%
rename from cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp
rename to cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
index ab1bee8b60..a2696dc842 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
@@ -24,7 +24,8 @@
  */
 
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_search.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
 
@@ -36,8 +37,20 @@ namespace cuvs::neighbors::ivf_flat {
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
   {                                                                             \
-    raft::runtime::neighbors::ivf_flat::search(                                 \
-      handle, params, *index.get_raft_index(), queries, neighbors, distances);  \
+    cuvs::neighbors::ivf_flat::detail::search(                                  \
+      handle, params, index, queries, neighbors, distances);                    \
+  }                                                                             \
+  void search_with_filtering(                                                   \
+    raft::resources const& handle,                                              \
+    const search_params& params,                                                \
+    index<T, IdxT>& idx,                                                        \
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \
+  {                                                                             \
+    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \
+      handle, params, idx, queries, neighbors, distances, sample_filter);       \
   }
 CUVS_INST_IVF_FLAT_SEARCH(uint8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
new file mode 100644
index 0000000000..3d3ff49d0c
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
@@ -0,0 +1,176 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../ivf_common.cuh"
+#include "../ivf_list.cuh"
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_list.hpp>
+
+#include <raft/core/detail/mdspan_numpy_serializer.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/serialize.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+#include <fstream>
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+// Serialization version
+// No backward compatibility yet; that is, can't add additional fields without breaking
+// backward compatibility.
+// TODO(hcho3) Implement next-gen serializer for IVF that allows for expansion in a backward
+//             compatible fashion.
+constexpr int serialization_version = 4;
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index_ IVF-Flat index
+ *
+ */
+template <typename T, typename IdxT>
+void serialize(raft::resources const& handle, std::ostream& os, const index<T, IdxT>& index_)
+{
+  RAFT_LOG_DEBUG(
+    "Saving IVF-Flat index, size %zu, dim %u", static_cast<size_t>(index_.size()), index_.dim());
+
+  std::string dtype_string = raft::detail::numpy_serializer::get_numpy_dtype<T>().to_string();
+  dtype_string.resize(4);
+  os << dtype_string;
+
+  serialize_scalar(handle, os, serialization_version);
+  serialize_scalar(handle, os, index_.size());
+  serialize_scalar(handle, os, index_.dim());
+  serialize_scalar(handle, os, index_.n_lists());
+  serialize_scalar(handle, os, index_.metric());
+  serialize_scalar(handle, os, index_.adaptive_centers());
+  serialize_scalar(handle, os, index_.conservative_memory_allocation());
+  serialize_mdspan(handle, os, index_.centers());
+  if (index_.center_norms()) {
+    bool has_norms = true;
+    serialize_scalar(handle, os, has_norms);
+    serialize_mdspan(handle, os, *index_.center_norms());
+  } else {
+    bool has_norms = false;
+    serialize_scalar(handle, os, has_norms);
+  }
+  auto sizes_host = raft::make_host_vector<uint32_t, uint32_t>(index_.list_sizes().extent(0));
+  raft::copy(sizes_host.data_handle(),
+             index_.list_sizes().data_handle(),
+             sizes_host.size(),
+             raft::resource::get_cuda_stream(handle));
+  raft::resource::sync_stream(handle);
+  serialize_mdspan(handle, os, sizes_host.view());
+
+  list_spec<uint32_t, T, IdxT> list_store_spec{index_.dim(), true};
+  for (uint32_t label = 0; label < index_.n_lists(); label++) {
+    ivf::serialize_list(handle,
+                        os,
+                        index_.lists()[label],
+                        list_store_spec,
+                        raft::Pow2<kIndexGroupSize>::roundUp(sizes_host(label)));
+  }
+  raft::resource::sync_stream(handle);
+}
+
+template <typename T, typename IdxT>
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const index<T, IdxT>& index_)
+{
+  std::ofstream of(filename, std::ios::out | std::ios::binary);
+  if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  detail::serialize(handle, of, index_);
+
+  of.close();
+  if (!of) { RAFT_FAIL("Error writing output %s", filename.c_str()); }
+}
+
+/** Load an index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[in] index_ IVF-Flat index
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize(raft::resources const& handle, std::istream& is) -> index<T, IdxT>
+{
+  char dtype_string[4];
+  is.read(dtype_string, 4);
+
+  auto ver = raft::deserialize_scalar<int>(handle, is);
+  if (ver != serialization_version) {
+    RAFT_FAIL("serialization version mismatch, expected %d, got %d ", serialization_version, ver);
+  }
+  auto n_rows           = raft::deserialize_scalar<IdxT>(handle, is);
+  auto dim              = raft::deserialize_scalar<std::uint32_t>(handle, is);
+  auto n_lists          = raft::deserialize_scalar<std::uint32_t>(handle, is);
+  auto metric           = raft::deserialize_scalar<cuvs::distance::DistanceType>(handle, is);
+  bool adaptive_centers = raft::deserialize_scalar<bool>(handle, is);
+  bool cma              = raft::deserialize_scalar<bool>(handle, is);
+
+  index<T, IdxT> index_ = index<T, IdxT>(handle, metric, n_lists, adaptive_centers, cma, dim);
+
+  deserialize_mdspan(handle, is, index_.centers());
+  bool has_norms = raft::deserialize_scalar<bool>(handle, is);
+  if (has_norms) {
+    index_.allocate_center_norms(handle);
+    if (!index_.center_norms()) {
+      RAFT_FAIL("Error inconsistent center norms");
+    } else {
+      auto center_norms = index_.center_norms().value();
+      deserialize_mdspan(handle, is, center_norms);
+    }
+  }
+  deserialize_mdspan(handle, is, index_.list_sizes());
+
+  list_spec<uint32_t, T, IdxT> list_device_spec{index_.dim(), cma};
+  list_spec<uint32_t, T, IdxT> list_store_spec{index_.dim(), true};
+  for (uint32_t label = 0; label < index_.n_lists(); label++) {
+    ivf::deserialize_list(handle, is, index_.lists()[label], list_store_spec, list_device_spec);
+  }
+  raft::resource::sync_stream(handle);
+
+  ivf::detail::recompute_internal_state(handle, index_);
+
+  return index_;
+}
+
+template <typename T, typename IdxT>
+auto deserialize(raft::resources const& handle, const std::string& filename) -> index<T, IdxT>
+{
+  std::ifstream is(filename, std::ios::in | std::ios::binary);
+
+  if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  auto index = detail::deserialize<T, IdxT>(handle, is);
+
+  is.close();
+
+  return index;
+}
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
similarity index 61%
rename from cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp
rename to cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
index 19a3d72d98..9ab00623ad 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
@@ -24,38 +24,40 @@
  */
 
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_serialize.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
 
-#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \
-  void serialize_file(raft::resources const& handle,                                               \
-                      const std::string& filename,                                                 \
-                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \
-  }                                                                                                \
-                                                                                                   \
-  void deserialize_file(raft::resources const& handle,                                             \
-                        const std::string& filename,                                               \
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \
-      handle, filename, index->get_raft_index());                                                  \
-  }                                                                                                \
-                                                                                                   \
-  void serialize(raft::resources const& handle,                                                    \
-                 std::string& str,                                                                 \
-                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \
-  }                                                                                                \
-                                                                                                   \
-  void deserialize(raft::resources const& handle,                                                  \
-                   const std::string& str,                                                         \
-                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                           \
+  void serialize_file(raft::resources const& handle,                                    \
+                      const std::string& filename,                                      \
+                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)           \
+  {                                                                                     \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);              \
+  }                                                                                     \
+                                                                                        \
+  void serialize(raft::resources const& handle,                                         \
+                 std::string& str,                                                      \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
+  {                                                                                     \
+    std::ostringstream os;                                                              \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                    \
+    str = os.str();                                                                     \
+  }                                                                                     \
+                                                                                        \
+  void deserialize_file(raft::resources const& handle,                                  \
+                        const std::string& filename,                                    \
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)               \
+  {                                                                                     \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, filename); \
+  }                                                                                     \
+  void deserialize(raft::resources const& handle,                                       \
+                   const std::string& str,                                              \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
+  {                                                                                     \
+    std::istringstream is(str);                                                         \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, is);       \
   }
 CUVS_INST_IVF_FLAT_SERIALIZE(float, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
similarity index 61%
rename from cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
rename to cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
index f65fe221d4..18d6a02873 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
@@ -24,38 +24,40 @@
  */
 
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_serialize.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
 
-#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \
-  void serialize_file(raft::resources const& handle,                                               \
-                      const std::string& filename,                                                 \
-                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \
-  }                                                                                                \
-                                                                                                   \
-  void deserialize_file(raft::resources const& handle,                                             \
-                        const std::string& filename,                                               \
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \
-      handle, filename, index->get_raft_index());                                                  \
-  }                                                                                                \
-                                                                                                   \
-  void serialize(raft::resources const& handle,                                                    \
-                 std::string& str,                                                                 \
-                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \
-  }                                                                                                \
-                                                                                                   \
-  void deserialize(raft::resources const& handle,                                                  \
-                   const std::string& str,                                                         \
-                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                           \
+  void serialize_file(raft::resources const& handle,                                    \
+                      const std::string& filename,                                      \
+                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)           \
+  {                                                                                     \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);              \
+  }                                                                                     \
+                                                                                        \
+  void serialize(raft::resources const& handle,                                         \
+                 std::string& str,                                                      \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
+  {                                                                                     \
+    std::ostringstream os;                                                              \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                    \
+    str = os.str();                                                                     \
+  }                                                                                     \
+                                                                                        \
+  void deserialize_file(raft::resources const& handle,                                  \
+                        const std::string& filename,                                    \
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)               \
+  {                                                                                     \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, filename); \
+  }                                                                                     \
+  void deserialize(raft::resources const& handle,                                       \
+                   const std::string& str,                                              \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
+  {                                                                                     \
+    std::istringstream is(str);                                                         \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, is);       \
   }
 CUVS_INST_IVF_FLAT_SERIALIZE(int8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
similarity index 61%
rename from cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
rename to cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
index 5f312dc0a4..c5ab7d5c12 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cpp
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
@@ -24,38 +24,40 @@
  */
 
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <raft_runtime/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_serialize.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
 
-#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \
-  void serialize_file(raft::resources const& handle,                                               \
-                      const std::string& filename,                                                 \
-                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::serialize_file(handle, filename, *index.get_raft_index()); \
-  }                                                                                                \
-                                                                                                   \
-  void deserialize_file(raft::resources const& handle,                                             \
-                        const std::string& filename,                                               \
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::deserialize_file(                                          \
-      handle, filename, index->get_raft_index());                                                  \
-  }                                                                                                \
-                                                                                                   \
-  void serialize(raft::resources const& handle,                                                    \
-                 std::string& str,                                                                 \
-                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::serialize(handle, str, *index.get_raft_index());           \
-  }                                                                                                \
-                                                                                                   \
-  void deserialize(raft::resources const& handle,                                                  \
-                   const std::string& str,                                                         \
-                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                               \
-  {                                                                                                \
-    raft::runtime::neighbors::ivf_flat::deserialize(handle, str, index->get_raft_index());         \
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                           \
+  void serialize_file(raft::resources const& handle,                                    \
+                      const std::string& filename,                                      \
+                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)           \
+  {                                                                                     \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);              \
+  }                                                                                     \
+                                                                                        \
+  void serialize(raft::resources const& handle,                                         \
+                 std::string& str,                                                      \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
+  {                                                                                     \
+    std::ostringstream os;                                                              \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                    \
+    str = os.str();                                                                     \
+  }                                                                                     \
+                                                                                        \
+  void deserialize_file(raft::resources const& handle,                                  \
+                        const std::string& filename,                                    \
+                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)               \
+  {                                                                                     \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, filename); \
+  }                                                                                     \
+  void deserialize(raft::resources const& handle,                                       \
+                   const std::string& str,                                              \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
+  {                                                                                     \
+    std::istringstream is(str);                                                         \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, is);       \
   }
 CUVS_INST_IVF_FLAT_SERIALIZE(uint8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
index 678bec32a5..b2fbbfc12c 100644
--- a/cpp/src/neighbors/ivf_flat_index.cpp
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -20,140 +20,203 @@ namespace cuvs::neighbors::ivf_flat {
 
 template <typename T, typename IdxT>
 index<T, IdxT>::index(raft::resources const& res, const index_params& params, uint32_t dim)
-  : ann::index(),
-    raft_index_(std::make_unique<raft::neighbors::ivf_flat::index<T, IdxT>>(
-      res,
-      static_cast<raft::distance::DistanceType>((int)params.metric),
-      params.n_lists,
-      params.adaptive_centers,
-      params.conservative_memory_allocation,
-      dim))
+  : index(res,
+          params.metric,
+          params.n_lists,
+          params.adaptive_centers,
+          params.conservative_memory_allocation,
+          dim)
 {
 }
 
 template <typename T, typename IdxT>
-index<T, IdxT>::index(raft::neighbors::ivf_flat::index<T, IdxT>&& raft_idx)
+index<T, IdxT>::index(raft::resources const& res,
+                      cuvs::distance::DistanceType metric,
+                      uint32_t n_lists,
+                      bool adaptive_centers,
+                      bool conservative_memory_allocation,
+                      uint32_t dim)
   : ann::index(),
-    raft_index_(std::make_unique<raft::neighbors::ivf_flat::index<T, IdxT>>(std::move(raft_idx)))
+    veclen_(calculate_veclen(dim)),
+    metric_(metric),
+    adaptive_centers_(adaptive_centers),
+    conservative_memory_allocation_{conservative_memory_allocation},
+    centers_(raft::make_device_matrix<float, uint32_t>(res, n_lists, dim)),
+    center_norms_(std::nullopt),
+    lists_{n_lists},
+    list_sizes_{raft::make_device_vector<uint32_t, uint32_t>(res, n_lists)},
+    data_ptrs_{raft::make_device_vector<T*, uint32_t>(res, n_lists)},
+    inds_ptrs_{raft::make_device_vector<IdxT*, uint32_t>(res, n_lists)},
+    accum_sorted_sizes_{raft::make_host_vector<IdxT, uint32_t>(n_lists + 1)}
 {
+  check_consistency();
+  accum_sorted_sizes_(n_lists) = 0;
 }
 
 template <typename T, typename IdxT>
 uint32_t index<T, IdxT>::veclen() const noexcept
 {
-  return raft_index_->veclen();
+  return veclen_;
 }
 
 template <typename T, typename IdxT>
 cuvs::distance::DistanceType index<T, IdxT>::metric() const noexcept
 {
-  return static_cast<cuvs::distance::DistanceType>((int)raft_index_->metric());
+  return metric_;
 }
 
 template <typename T, typename IdxT>
 bool index<T, IdxT>::adaptive_centers() const noexcept
 {
-  return raft_index_->adaptive_centers();
+  return adaptive_centers_;
 }
 
 template <typename T, typename IdxT>
 raft::device_vector_view<uint32_t, uint32_t> index<T, IdxT>::list_sizes() noexcept
 {
-  return raft_index_->list_sizes();
+  return list_sizes_.view();
 }
 
 template <typename T, typename IdxT>
 raft::device_vector_view<const uint32_t, uint32_t> index<T, IdxT>::list_sizes() const noexcept
 {
-  return raft_index_->list_sizes();
+  return list_sizes_.view();
 }
 
 template <typename T, typename IdxT>
 raft::device_matrix_view<float, uint32_t, raft::row_major> index<T, IdxT>::centers() noexcept
 {
-  return raft_index_->centers();
+  return centers_.view();
 }
 
 template <typename T, typename IdxT>
 raft::device_matrix_view<const float, uint32_t, raft::row_major> index<T, IdxT>::centers()
   const noexcept
 {
-  return raft_index_->centers();
+  return centers_.view();
 }
 
 template <typename T, typename IdxT>
 std::optional<raft::device_vector_view<float, uint32_t>> index<T, IdxT>::center_norms() noexcept
 {
-  return raft_index_->center_norms();
+  if (center_norms_.has_value()) {
+    return std::make_optional<raft::device_vector_view<float, uint32_t>>(center_norms_->view());
+  } else {
+    return std::nullopt;
+  }
 }
 
 template <typename T, typename IdxT>
 std::optional<raft::device_vector_view<const float, uint32_t>> index<T, IdxT>::center_norms()
   const noexcept
 {
-  return raft_index_->center_norms();
+  if (center_norms_.has_value()) {
+    return std::make_optional<raft::device_vector_view<const float, uint32_t>>(
+      center_norms_->view());
+  } else {
+    return std::nullopt;
+  }
+}
+
+template <typename T, typename IdxT>
+auto index<T, IdxT>::accum_sorted_sizes() noexcept -> raft::host_vector_view<IdxT, uint32_t>
+{
+  return accum_sorted_sizes_.view();
+}
+
+template <typename T, typename IdxT>
+[[nodiscard]] auto index<T, IdxT>::accum_sorted_sizes() const noexcept
+  -> raft::host_vector_view<const IdxT, uint32_t>
+{
+  return accum_sorted_sizes_.view();
 }
 
 template <typename T, typename IdxT>
 IdxT index<T, IdxT>::size() const noexcept
 {
-  return raft_index_->size();
+  return accum_sorted_sizes()(n_lists());
 }
 
 template <typename T, typename IdxT>
 uint32_t index<T, IdxT>::dim() const noexcept
 {
-  return raft_index_->dim();
+  return centers_.extent(1);
 }
 
 template <typename T, typename IdxT>
 uint32_t index<T, IdxT>::n_lists() const noexcept
 {
-  return raft_index_->n_lists();
+  return lists_.size();
 }
 
 template <typename T, typename IdxT>
 raft::device_vector_view<T*, uint32_t> index<T, IdxT>::data_ptrs() noexcept
 {
-  return raft_index_->data_ptrs();
+  return data_ptrs_.view();
 }
 
 template <typename T, typename IdxT>
 raft::device_vector_view<T* const, uint32_t> index<T, IdxT>::data_ptrs() const noexcept
 {
-  return raft_index_->data_ptrs();
+  return data_ptrs_.view();
 }
 
 template <typename T, typename IdxT>
 raft::device_vector_view<IdxT*, uint32_t> index<T, IdxT>::inds_ptrs() noexcept
 {
-  return raft_index_->inds_ptrs();
+  return inds_ptrs_.view();
 }
 
 template <typename T, typename IdxT>
 raft::device_vector_view<IdxT* const, uint32_t> index<T, IdxT>::inds_ptrs() const noexcept
 {
-  return raft_index_->inds_ptrs();
+  return inds_ptrs_.view();
 }
 
 template <typename T, typename IdxT>
 bool index<T, IdxT>::conservative_memory_allocation() const noexcept
 {
-  return raft_index_->conservative_memory_allocation();
+  return conservative_memory_allocation_;
+}
+
+template <typename T, typename IdxT>
+void index<T, IdxT>::allocate_center_norms(raft::resources const& res)
+{
+  switch (metric_) {
+    case cuvs::distance::DistanceType::L2Expanded:
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Unexpanded:
+    case cuvs::distance::DistanceType::L2SqrtUnexpanded:
+      center_norms_ = raft::make_device_vector<float, uint32_t>(res, n_lists());
+      break;
+    default: center_norms_ = std::nullopt;
+  }
+}
+
+template <typename T, typename IdxT>
+std::vector<std::shared_ptr<list_data<T, IdxT>>>& index<T, IdxT>::lists() noexcept
+{
+  return lists_;
 }
 
 template <typename T, typename IdxT>
-std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>&
-index<T, IdxT>::lists() noexcept
+const std::vector<std::shared_ptr<list_data<T, IdxT>>>& index<T, IdxT>::lists() const noexcept
 {
-  return raft_index_->lists();
+  return lists_;
 }
 
 template <typename T, typename IdxT>
-const std::vector<std::shared_ptr<raft::neighbors::ivf_flat::list_data<T, IdxT>>>&
-index<T, IdxT>::lists() const noexcept
+void index<T, IdxT>::check_consistency()
 {
-  return raft_index_->lists();
+  auto n_lists = lists_.size();
+  RAFT_EXPECTS(dim() % veclen_ == 0, "dimensionality is not a multiple of the veclen");
+  RAFT_EXPECTS(list_sizes_.extent(0) == n_lists, "inconsistent list size");
+  RAFT_EXPECTS(data_ptrs_.extent(0) == n_lists, "inconsistent list size");
+  RAFT_EXPECTS(inds_ptrs_.extent(0) == n_lists, "inconsistent list size");
+  RAFT_EXPECTS(                                       //
+    (centers_.extent(0) == list_sizes_.extent(0)) &&  //
+      (!center_norms_.has_value() || centers_.extent(0) == center_norms_->extent(0)),
+    "inconsistent number of lists (clusters)");
 }
 
 template struct index<float, int64_t>;
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
index 15c965ec91..b08ebb17e2 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
@@ -164,4 +164,4 @@ void erase_list(raft::resources const& res, index<int64_t>* index, uint32_t labe
   detail::erase_list<int64_t>(res, index, label);
 }
 
-}  // namespace cuvs::neighbors::ivf_pq::helpers
\ No newline at end of file
+}  // namespace cuvs::neighbors::ivf_pq::helpers
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index 9b70ee250c..dadd450b7d 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -19,11 +19,14 @@
 #include "ann_utils.cuh"
 #include "naive_knn.cuh"
 
+#include <cuvs/core/bitset.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <raft/stats/mean.cuh>
 #include <thrust/sequence.h>
 
-#include <raft/spatial/knn/ann.cuh>
+#include <raft/linalg/add.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/util/fast_int_div.cuh>
 
 namespace cuvs::neighbors::ivf_flat {
 
@@ -41,7 +44,7 @@ struct AnnIvfFlatInputs {
   IdxT nlist;
   cuvs::distance::DistanceType metric;
   bool adaptive_centers;
-  // bool host_dataset;
+  bool host_dataset;
 };
 
 template <typename IdxT>
@@ -98,47 +101,6 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
       rmm::device_uvector<T> distances_ivfflat_dev(queries_size, stream_);
       rmm::device_uvector<IdxT> indices_ivfflat_dev(queries_size, stream_);
 
-      {
-        // legacy interface
-        raft::spatial::knn::IVFFlatParam ivfParams;
-        ivfParams.nprobe = ps.nprobe;
-        ivfParams.nlist  = ps.nlist;
-        raft::spatial::knn::knnIndex index;
-
-        raft::spatial::knn::approx_knn_build_index(
-          handle_,
-          &index,
-          dynamic_cast<raft::spatial::knn::knnIndexParam*>(&ivfParams),
-          static_cast<raft::distance::DistanceType>((int)ps.metric),
-          (IdxT)0,
-          database.data(),
-          ps.num_db_vecs,
-          ps.dim);
-
-        raft::resource::sync_stream(handle_);
-        raft::spatial::knn::approx_knn_search(handle_,
-                                              distances_ivfflat_dev.data(),
-                                              indices_ivfflat_dev.data(),
-                                              &index,
-                                              ps.k,
-                                              search_queries.data(),
-                                              ps.num_queries);
-
-        raft::update_host(
-          distances_ivfflat.data(), distances_ivfflat_dev.data(), queries_size, stream_);
-        raft::update_host(
-          indices_ivfflat.data(), indices_ivfflat_dev.data(), queries_size, stream_);
-        raft::resource::sync_stream(handle_);
-      }
-
-      ASSERT_TRUE(eval_neighbours(indices_naive,
-                                  indices_ivfflat,
-                                  distances_naive,
-                                  distances_ivfflat,
-                                  ps.num_queries,
-                                  ps.k,
-                                  0.001,
-                                  min_recall));
       {
         cuvs::neighbors::ivf_flat::index_params index_params;
         cuvs::neighbors::ivf_flat::search_params search_params;
@@ -154,45 +116,42 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
         cuvs::neighbors::ivf_flat::index<DataT, IdxT> idx(handle_, index_params, ps.dim);
         cuvs::neighbors::ivf_flat::index<DataT, IdxT> index_2(handle_, index_params, ps.dim);
 
-        // if (!ps.host_dataset) {
-
-        auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
-          (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
-        idx = cuvs::neighbors::ivf_flat::build(handle_, index_params, database_view);
-        rmm::device_uvector<IdxT> vector_indices(ps.num_db_vecs, stream_);
-        thrust::sequence(raft::resource::get_thrust_policy(handle_),
-                         thrust::device_pointer_cast(vector_indices.data()),
-                         thrust::device_pointer_cast(vector_indices.data() + ps.num_db_vecs));
-        raft::resource::sync_stream(handle_);
-
-        IdxT half_of_data = ps.num_db_vecs / 2;
+        if (!ps.host_dataset) {
+          auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
+            (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
+          idx = cuvs::neighbors::ivf_flat::build(handle_, index_params, database_view);
+          rmm::device_uvector<IdxT> vector_indices(ps.num_db_vecs, stream_);
+          thrust::sequence(raft::resource::get_thrust_policy(handle_),
+                           thrust::device_pointer_cast(vector_indices.data()),
+                           thrust::device_pointer_cast(vector_indices.data() + ps.num_db_vecs));
+          raft::resource::sync_stream(handle_);
 
-        auto half_of_data_view = raft::make_device_matrix_view<const DataT, IdxT>(
-          (const DataT*)database.data(), half_of_data, ps.dim);
+          IdxT half_of_data = ps.num_db_vecs / 2;
 
-        const std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
-        index_2 = cuvs::neighbors::ivf_flat::extend(handle_, half_of_data_view, no_opt, idx);
+          auto half_of_data_view = raft::make_device_matrix_view<const DataT, IdxT>(
+            (const DataT*)database.data(), half_of_data, ps.dim);
 
-        auto new_half_of_data_view = raft::make_device_matrix_view<const DataT, IdxT>(
-          database.data() + half_of_data * ps.dim, IdxT(ps.num_db_vecs) - half_of_data, ps.dim);
+          const std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
+          index_2 = cuvs::neighbors::ivf_flat::extend(handle_, half_of_data_view, no_opt, idx);
 
-        auto new_half_of_data_indices_view = raft::make_device_vector_view<const IdxT, IdxT>(
-          vector_indices.data() + half_of_data, IdxT(ps.num_db_vecs) - half_of_data);
+          auto new_half_of_data_view = raft::make_device_matrix_view<const DataT, IdxT>(
+            database.data() + half_of_data * ps.dim, IdxT(ps.num_db_vecs) - half_of_data, ps.dim);
 
-        cuvs::neighbors::ivf_flat::extend(
-          handle_,
-          new_half_of_data_view,
-          std::make_optional<raft::device_vector_view<const IdxT, IdxT>>(
-            new_half_of_data_indices_view),
-          &index_2);
+          auto new_half_of_data_indices_view = raft::make_device_vector_view<const IdxT, IdxT>(
+            vector_indices.data() + half_of_data, IdxT(ps.num_db_vecs) - half_of_data);
 
-        /*
+          cuvs::neighbors::ivf_flat::extend(
+            handle_,
+            new_half_of_data_view,
+            std::make_optional<raft::device_vector_view<const IdxT, IdxT>>(
+              new_half_of_data_indices_view),
+            &index_2);
         } else {
           auto host_database = raft::make_host_matrix<DataT, IdxT>(ps.num_db_vecs, ps.dim);
           raft::copy(
             host_database.data_handle(), database.data(), ps.num_db_vecs * ps.dim, stream_);
-          idx = ivf_flat::build(handle_, index_params,
-        raft::make_const_mdspan(host_database.view()));
+          idx =
+            ivf_flat::build(handle_, index_params, raft::make_const_mdspan(host_database.view()));
 
           auto vector_indices = raft::make_host_vector<IdxT>(handle_, ps.num_db_vecs);
           std::iota(vector_indices.data_handle(), vector_indices.data_handle() + ps.num_db_vecs, 0);
@@ -217,7 +176,6 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
                              new_half_of_data_indices_view),
                            &index_2);
         }
-        */
 
         auto search_queries_view = raft::make_device_matrix_view<const DataT, IdxT>(
           search_queries.data(), ps.num_queries, ps.dim);
@@ -294,137 +252,137 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
     }
   }
 
-  /*
-    void testPacker()
-    {
-      ivf_flat::index_params index_params;
-      ivf_flat::search_params search_params;
-      index_params.n_lists          = ps.nlist;
-      index_params.metric           = ps.metric;
-      index_params.adaptive_centers = false;
-      search_params.n_probes        = ps.nprobe;
-
-      index_params.add_data_on_build        = false;
-      index_params.kmeans_trainset_fraction = 1.0;
-      index_params.metric_arg               = 0;
-
-      auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
-        (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
-
-      auto idx = ivf_flat::build(handle_, index_params, database_view);
-
-      const std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
-      index<DataT, IdxT> extend_index = ivf_flat::extend(handle_, database_view, no_opt, idx);
-
-      auto list_sizes = raft::make_host_vector<uint32_t>(idx.n_lists());
-      raft::update_host(list_sizes.data_handle(),
-                  extend_index.list_sizes().data_handle(),
-                  extend_index.n_lists(),
-                  stream_);
-      raft::resource::sync_stream(handle_);
+  void testPacker()
+  {
+    ivf_flat::index_params index_params;
+    ivf_flat::search_params search_params;
+    index_params.n_lists          = ps.nlist;
+    index_params.metric           = ps.metric;
+    index_params.adaptive_centers = false;
+    search_params.n_probes        = ps.nprobe;
+
+    index_params.add_data_on_build        = false;
+    index_params.kmeans_trainset_fraction = 1.0;
+    index_params.metric_arg               = 0;
+
+    auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
+      (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
+
+    auto idx = ivf_flat::build(handle_, index_params, database_view);
+
+    const std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
+    index<DataT, IdxT> extend_index = ivf_flat::extend(handle_, database_view, no_opt, idx);
+
+    auto list_sizes = raft::make_host_vector<uint32_t>(idx.n_lists());
+    raft::update_host(list_sizes.data_handle(),
+                      extend_index.list_sizes().data_handle(),
+                      extend_index.n_lists(),
+                      stream_);
+    raft::resource::sync_stream(handle_);
 
-      auto& lists = idx.lists();
+    auto& lists = idx.lists();
 
-      // conservative memory allocation for codepacking
-      auto list_device_spec = list_spec<uint32_t, DataT, IdxT>{idx.dim(), false};
+    // conservative memory allocation for codepacking
+    auto list_device_spec = list_spec<uint32_t, DataT, IdxT>{idx.dim(), false};
 
-      for (uint32_t label = 0; label < idx.n_lists(); label++) {
-        uint32_t list_size = list_sizes.data_handle()[label];
+    for (uint32_t label = 0; label < idx.n_lists(); label++) {
+      uint32_t list_size = list_sizes.data_handle()[label];
 
-        ivf::resize_list(handle_, lists[label], list_device_spec, list_size, 0);
-      }
+      ivf::resize_list(handle_, lists[label], list_device_spec, list_size, 0);
+    }
 
-      idx.recompute_internal_state(handle_);
+    idx.recompute_internal_state(handle_);
 
-      using interleaved_group = Pow2<kIndexGroupSize>;
+    using interleaved_group = raft::Pow2<kIndexGroupSize>;
 
-      for (uint32_t label = 0; label < idx.n_lists(); label++) {
-        uint32_t list_size = list_sizes.data_handle()[label];
+    for (uint32_t label = 0; label < idx.n_lists(); label++) {
+      uint32_t list_size = list_sizes.data_handle()[label];
 
-        if (list_size > 0) {
-          uint32_t padded_list_size = interleaved_group::roundUp(list_size);
-          uint32_t n_elems          = padded_list_size * idx.dim();
-          auto list_data            = lists[label]->data;
-          auto list_inds            = extend_index.lists()[label]->indices;
+      if (list_size > 0) {
+        uint32_t padded_list_size = interleaved_group::roundUp(list_size);
+        uint32_t n_elems          = padded_list_size * idx.dim();
+        auto list_data            = lists[label]->data;
+        auto list_inds            = extend_index.lists()[label]->indices;
 
-          // fetch the flat codes
-          auto flat_codes = make_device_matrix<DataT, uint32_t>(handle_, list_size, idx.dim());
+        // fetch the flat codes
+        auto flat_codes = raft::make_device_matrix<DataT, uint32_t>(handle_, list_size, idx.dim());
 
-          matrix::gather(
-            handle_,
-            make_device_matrix_view<const DataT, uint32_t>(
-              (const DataT*)database.data(), static_cast<uint32_t>(ps.num_db_vecs), idx.dim()),
-            make_device_vector_view<const IdxT, uint32_t>((const IdxT*)list_inds.data_handle(),
-                                                          list_size),
-            flat_codes.view());
-
-          helpers::codepacker::pack<DataT, IdxT>(
-            handle_, make_const_mdspan(flat_codes.view()), idx.veclen(), 0, list_data.view());
-
-          {
-            auto mask = make_device_vector<bool>(handle_, n_elems);
-
-            linalg::map_offset(handle_,
-                               mask.view(),
-                               [dim = idx.dim(),
-                                list_size,
-                                padded_list_size,
-                                chunk_size = util::FastIntDiv(idx.veclen())] __device__(auto i) {
-                                 uint32_t max_group_offset =
-    interleaved_group::roundDown(list_size); if (i < max_group_offset * dim) { return true; }
-                                 uint32_t surplus    = (i - max_group_offset * dim);
-                                 uint32_t ingroup_id = interleaved_group::mod(surplus / chunk_size);
-                                 return ingroup_id < (list_size - max_group_offset);
-                               });
-
-            // ensure that the correct number of indices are masked out
-            ASSERT_TRUE(thrust::reduce(raft::resource::get_thrust_policy(handle_),
-                                       mask.data_handle(),
-                                       mask.data_handle() + n_elems,
-                                       0) == list_size * ps.dim);
-
-            auto packed_list_data = make_device_vector<DataT, uint32_t>(handle_, n_elems);
-
-            linalg::map_offset(handle_,
-                               packed_list_data.view(),
-                               [mask      = mask.data_handle(),
-                                list_data = list_data.data_handle()] __device__(uint32_t i) {
-                                 if (mask[i]) return list_data[i];
-                                 return DataT{0};
-                               });
-
-            auto extend_data          = extend_index.lists()[label]->data;
-            auto extend_data_filtered = make_device_vector<DataT, uint32_t>(handle_, n_elems);
-            linalg::map_offset(handle_,
-                               extend_data_filtered.view(),
-                               [mask        = mask.data_handle(),
-                                extend_data = extend_data.data_handle()] __device__(uint32_t i) {
-                                 if (mask[i]) return extend_data[i];
-                                 return DataT{0};
-                               });
-
-            ASSERT_TRUE(cuvs::devArrMatch(packed_list_data.data_handle(),
-                                          extend_data_filtered.data_handle(),
-                                          n_elems,
-                                          cuvs::Compare<DataT>(),
-                                          stream_));
-          }
+        raft::matrix::gather(
+          handle_,
+          raft::make_device_matrix_view<const DataT, uint32_t>(
+            (const DataT*)database.data(), static_cast<uint32_t>(ps.num_db_vecs), idx.dim()),
+          raft::make_device_vector_view<const IdxT, uint32_t>((const IdxT*)list_inds.data_handle(),
+                                                              list_size),
+          flat_codes.view());
 
-          auto unpacked_flat_codes =
-            make_device_matrix<DataT, uint32_t>(handle_, list_size, idx.dim());
+        helpers::codepacker::pack(
+          handle_, make_const_mdspan(flat_codes.view()), idx.veclen(), 0, list_data.view());
 
-          helpers::codepacker::unpack<DataT, IdxT>(
-            handle_, list_data.view(), idx.veclen(), 0, unpacked_flat_codes.view());
+        {
+          auto mask = raft::make_device_vector<bool>(handle_, n_elems);
 
-          ASSERT_TRUE(cuvs::devArrMatch(flat_codes.data_handle(),
-                                        unpacked_flat_codes.data_handle(),
-                                        list_size * ps.dim,
+          raft::linalg::map_offset(
+            handle_,
+            mask.view(),
+            [dim = idx.dim(),
+             list_size,
+             padded_list_size,
+             chunk_size = raft::util::FastIntDiv(idx.veclen())] __device__(auto i) {
+              uint32_t max_group_offset = interleaved_group::roundDown(list_size);
+              if (i < max_group_offset * dim) { return true; }
+              uint32_t surplus    = (i - max_group_offset * dim);
+              uint32_t ingroup_id = interleaved_group::mod(surplus / chunk_size);
+              return ingroup_id < (list_size - max_group_offset);
+            });
+
+          // ensure that the correct number of indices are masked out
+          ASSERT_TRUE(thrust::reduce(raft::resource::get_thrust_policy(handle_),
+                                     mask.data_handle(),
+                                     mask.data_handle() + n_elems,
+                                     0) == list_size * ps.dim);
+
+          auto packed_list_data = raft::make_device_vector<DataT, uint32_t>(handle_, n_elems);
+
+          raft::linalg::map_offset(handle_,
+                                   packed_list_data.view(),
+                                   [mask      = mask.data_handle(),
+                                    list_data = list_data.data_handle()] __device__(uint32_t i) {
+                                     if (mask[i]) return list_data[i];
+                                     return DataT{0};
+                                   });
+
+          auto extend_data          = extend_index.lists()[label]->data;
+          auto extend_data_filtered = raft::make_device_vector<DataT, uint32_t>(handle_, n_elems);
+          raft::linalg::map_offset(
+            handle_,
+            extend_data_filtered.view(),
+            [mask        = mask.data_handle(),
+             extend_data = extend_data.data_handle()] __device__(uint32_t i) {
+              if (mask[i]) return extend_data[i];
+              return DataT{0};
+            });
+
+          ASSERT_TRUE(cuvs::devArrMatch(packed_list_data.data_handle(),
+                                        extend_data_filtered.data_handle(),
+                                        n_elems,
                                         cuvs::Compare<DataT>(),
                                         stream_));
         }
+
+        auto unpacked_flat_codes =
+          raft::make_device_matrix<DataT, uint32_t>(handle_, list_size, idx.dim());
+
+        helpers::codepacker::unpack(
+          handle_, list_data.view(), idx.veclen(), 0, unpacked_flat_codes.view());
+
+        ASSERT_TRUE(cuvs::devArrMatch(flat_codes.data_handle(),
+                                      unpacked_flat_codes.data_handle(),
+                                      list_size * ps.dim,
+                                      cuvs::Compare<DataT>(),
+                                      stream_));
       }
     }
-  */
+  }
 
   void testFilter()
   {
@@ -458,7 +416,6 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
       raft::resource::sync_stream(handle_);
     }
 
-    /*
     {
       // unless something is really wrong with clustering, this could serve as a lower bound on
       // recall
@@ -494,7 +451,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
                                                      test_ivf_sample_filter::offset));
         raft::resource::sync_stream(handle_);
 
-        raft::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
+        cuvs::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
           handle_, removed_indices.view(), ps.num_db_vecs);
 
         // Search with the filter
@@ -507,7 +464,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
           search_queries_view,
           indices_ivfflat_dev.view(),
           distances_ivfflat_dev.view(),
-           cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
+          cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
 
         raft::update_host(
           distances_ivfflat.data(), distances_ivfflat_dev.data_handle(), queries_size, stream_);
@@ -524,7 +481,6 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
                                   0.001,
                                   min_recall));
     }
-    */
   }
 
   void SetUp() override
@@ -591,7 +547,6 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, true},
   {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false},
 
-  /*
   // host input data
   {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
   {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
@@ -600,7 +555,6 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
   {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
   {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
-  */
 
   {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
   {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::InnerProduct, true},
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 5ff7bef612..15b12b1182 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -37,6 +37,6 @@ include(../cmake/thirdparty/get_cuvs.cmake)
 # -------------- compile tasks ----------------- #
 add_executable(CAGRA_EXAMPLE src/cagra_example.cu)
 
-# `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets
-# are installed in a conda environment, if one exists
+# `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets are
+# installed in a conda environment, if one exists
 target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/rapids_config.cmake b/rapids_config.cmake
index 19906dbc62..4ceefb9916 100644
--- a/rapids_config.cmake
+++ b/rapids_config.cmake
@@ -22,13 +22,15 @@ else()
   string(REPLACE "\n" "\n  " _rapids_version_formatted "  ${_rapids_version}")
   message(
     FATAL_ERROR
-      "Could not determine RAPIDS version. Contents of VERSION file:\n${_rapids_version_formatted}")
+      "Could not determine RAPIDS version. Contents of VERSION file:\n${_rapids_version_formatted}"
+  )
 endif()
 
 if(NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")
   file(
     DOWNLOAD
     "https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION_MAJOR_MINOR}/RAPIDS.cmake"
-    "${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")
+    "${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake"
+  )
 endif()
 include("${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")

From bb193ab4cb79825bb8aa568120759173a89abc38 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 16 May 2024 23:47:44 -0400
Subject: [PATCH 052/623] Migrate `raft::cluster` to `cuvs::cluster` (#115)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Micka (https://github.com/lowener)
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/115
---
 cpp/CMakeLists.txt                            |   42 +-
 cpp/include/cuvs/cluster/agglomerative.hpp    |  108 ++
 cpp/include/cuvs/cluster/kmeans.hpp           |  332 +++++
 cpp/include/cuvs/distance/distance_types.hpp  |    3 +-
 .../cuvs/distance/pairwise_distance.hpp       |   10 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |    8 +-
 cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp |    1 -
 cpp/src/cluster/detail/agglomerative.cuh      |  328 +++++
 cpp/src/cluster/detail/connectivities.cuh     |  237 +++
 cpp/src/cluster/detail/kmeans.cuh             | 1271 +++++++++++++++++
 cpp/src/cluster/detail/kmeans_auto_find_k.cuh |  233 +++
 cpp/src/cluster/detail/kmeans_balanced.cuh    | 1096 ++++++++++++++
 cpp/src/cluster/detail/kmeans_common.cuh      |  668 +++++++++
 cpp/src/cluster/detail/mst.cuh                |  207 +++
 cpp/src/cluster/detail/single_linkage.cuh     |  125 ++
 cpp/src/cluster/kmeans.cuh                    | 1122 +++++++++++++++
 cpp/src/cluster/kmeans_auto_find_k_float.cu   |   37 +
 cpp/src/cluster/kmeans_balanced.cuh           |  369 +++++
 cpp/src/cluster/kmeans_balanced_fit_float.cu  |   33 +
 cpp/src/cluster/kmeans_balanced_fit_int8.cu   |   33 +
 .../kmeans_balanced_fit_predict_float.cu      |   34 +
 .../kmeans_balanced_fit_predict_int8.cu       |   34 +
 .../cluster/kmeans_balanced_predict_float.cu  |   34 +
 .../cluster/kmeans_balanced_predict_int8.cu   |   34 +
 cpp/src/cluster/kmeans_fit_float.cu           |   33 +
 cpp/src/cluster/kmeans_fit_predict_float.cu   |   35 +
 cpp/src/cluster/kmeans_predict_float.cu       |   35 +
 cpp/src/cluster/kmeans_transform_float.cu     |   31 +
 cpp/src/cluster/single_linkage.cuh            |  104 ++
 cpp/src/cluster/single_linkage_float.cu       |   42 +
 .../distance/detail/distance_ops/all_ops.cuh  |    2 +-
 cpp/src/distance/detail/fused_distance_nn.cu  |   53 +
 .../custom_epilogue_with_broadcast.h          |   64 +-
 .../detail/fused_distance_nn/cutlass_base.cuh |   22 +-
 .../detail/fused_distance_nn/epilogue.cuh     |   13 +-
 .../epilogue_elementwise.cuh                  |   21 +-
 .../distance/detail/fused_distance_nn/gemm.h  |   13 +-
 .../fused_distance_nn/persistent_gemm.h       |   52 +-
 .../predicated_tile_iterator_normvec_smem.h   |   35 +-
 .../predicated_tile_iterator_reduced_vec.h    |   36 +-
 cpp/src/distance/detail/fused_l2_nn.cuh       |  386 -----
 .../detail/pairwise_distance_cutlass_base.cuh |   36 +-
 .../detail/pairwise_distance_epilogue.h       |   33 +-
 .../pairwise_distance_epilogue_elementwise.h  |   20 +-
 .../distance/detail/pairwise_distance_gemm.h  |   96 +-
 .../detail/pairwise_matrix/dispatch-inl.cuh   |    2 +-
 .../pairwise_matrix/dispatch_00_generate.py   |    8 +-
 .../dispatch_l1_double_double_double_int.cu   |    1 -
 .../detail/predicated_tile_iterator_normvec.h |   52 +-
 cpp/src/distance/distance-inl.cuh             |    4 +-
 cpp/src/distance/fused_distance_nn-inl.cuh    |  330 +++++
 cpp/src/distance/fused_distance_nn.cuh        |   18 +
 .../distance/fused_distance_nn_helpers.cuh    |   50 +
 cpp/src/neighbors/cagra_serialize.cuh         |   12 +-
 cpp/src/neighbors/detail/ann_utils.cuh        |    4 +-
 .../detail/cagra/cagra_serialize.cuh          |    2 -
 cpp/src/neighbors/detail/cagra/factory.cuh    |    2 +-
 .../detail/cagra/search_multi_cta.cuh         |    2 +-
 .../detail/cagra/search_multi_cta_inst.cuh    |    2 +
 .../cagra/search_multi_cta_kernel-inl.cuh     |    2 +-
 .../detail/cagra/search_multi_kernel.cuh      |    2 +-
 .../detail/cagra/search_single_cta.cuh        |    2 +-
 .../detail/cagra/search_single_cta_inst.cuh   |    2 +
 .../cagra/search_single_cta_kernel-inl.cuh    |    2 +-
 cpp/src/neighbors/detail/div_utils.cuh        |    2 +
 cpp/src/neighbors/detail/refine_device.cuh    |   21 +-
 cpp/src/neighbors/detail/vpq_dataset.cuh      |   25 +-
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |   21 +-
 .../neighbors/ivf_flat/ivf_flat_helpers.cu    |   60 -
 .../neighbors/ivf_flat/ivf_flat_helpers.cuh   |   62 +
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    |    8 +-
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |   14 +-
 cpp/src/neighbors/ivf_list.cuh                |    2 +
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |   34 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh   |    2 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |    2 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh |    2 +-
 cpp/test/CMakeLists.txt                       |    5 +
 cpp/test/cluster/kmeans.cu                    |  360 +++++
 cpp/test/cluster/kmeans_balanced.cu           |  238 +++
 cpp/test/cluster/kmeans_find_k.cu             |  122 ++
 cpp/test/cluster/linkage.cu                   |  672 +++++++++
 cpp/test/distance/distance_base.cuh           |    2 +
 cpp/test/neighbors/ann_ivf_flat.cuh           |    4 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |    2 +-
 cpp/test/neighbors/naive_knn.cuh              |    4 +-
 86 files changed, 8934 insertions(+), 790 deletions(-)
 create mode 100644 cpp/include/cuvs/cluster/agglomerative.hpp
 create mode 100644 cpp/include/cuvs/cluster/kmeans.hpp
 create mode 100644 cpp/src/cluster/detail/agglomerative.cuh
 create mode 100644 cpp/src/cluster/detail/connectivities.cuh
 create mode 100644 cpp/src/cluster/detail/kmeans.cuh
 create mode 100644 cpp/src/cluster/detail/kmeans_auto_find_k.cuh
 create mode 100644 cpp/src/cluster/detail/kmeans_balanced.cuh
 create mode 100644 cpp/src/cluster/detail/kmeans_common.cuh
 create mode 100644 cpp/src/cluster/detail/mst.cuh
 create mode 100644 cpp/src/cluster/detail/single_linkage.cuh
 create mode 100644 cpp/src/cluster/kmeans.cuh
 create mode 100644 cpp/src/cluster/kmeans_auto_find_k_float.cu
 create mode 100644 cpp/src/cluster/kmeans_balanced.cuh
 create mode 100644 cpp/src/cluster/kmeans_balanced_fit_float.cu
 create mode 100644 cpp/src/cluster/kmeans_balanced_fit_int8.cu
 create mode 100644 cpp/src/cluster/kmeans_balanced_fit_predict_float.cu
 create mode 100644 cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu
 create mode 100644 cpp/src/cluster/kmeans_balanced_predict_float.cu
 create mode 100644 cpp/src/cluster/kmeans_balanced_predict_int8.cu
 create mode 100644 cpp/src/cluster/kmeans_fit_float.cu
 create mode 100644 cpp/src/cluster/kmeans_fit_predict_float.cu
 create mode 100644 cpp/src/cluster/kmeans_predict_float.cu
 create mode 100644 cpp/src/cluster/kmeans_transform_float.cu
 create mode 100644 cpp/src/cluster/single_linkage.cuh
 create mode 100644 cpp/src/cluster/single_linkage_float.cu
 create mode 100644 cpp/src/distance/detail/fused_distance_nn.cu
 delete mode 100644 cpp/src/distance/detail/fused_l2_nn.cuh
 create mode 100644 cpp/src/distance/fused_distance_nn-inl.cuh
 create mode 100755 cpp/src/distance/fused_distance_nn.cuh
 create mode 100644 cpp/src/distance/fused_distance_nn_helpers.cuh
 create mode 100644 cpp/test/cluster/kmeans.cu
 create mode 100644 cpp/test/cluster/kmeans_balanced.cu
 create mode 100644 cpp/test/cluster/kmeans_find_k.cu
 create mode 100644 cpp/test/cluster/linkage.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 9b2ae0ce05..991200637c 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -189,33 +189,47 @@ include(cmake/thirdparty/get_cutlass.cmake)
 
 add_library(
   cuvs SHARED
-  src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
+  src/cluster/kmeans_balanced_fit_float.cu
+  src/cluster/kmeans_fit_float.cu
+  src/cluster/kmeans_auto_find_k_float.cu
+  src/cluster/kmeans_fit_predict_float.cu
+  src/cluster/kmeans_predict_float.cu
+  src/cluster/kmeans_balanced_fit_float.cu
+  src/cluster/kmeans_balanced_fit_predict_float.cu
+  src/cluster/kmeans_balanced_predict_float.cu
+  src/cluster/kmeans_balanced_fit_int8.cu
+  src/cluster/kmeans_balanced_fit_predict_int8.cu
+  src/cluster/kmeans_balanced_predict_int8.cu
+  src/cluster/kmeans_transform_float.cu
+  src/cluster/single_linkage_float.cu
   src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_rbf.cu
-  src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_rbf.cu
+  src/distance/detail/fused_distance_nn.cu
   src/distance/distance.cu
   src/distance/pairwise_distance.cu
   src/neighbors/brute_force_index.cu
diff --git a/cpp/include/cuvs/cluster/agglomerative.hpp b/cpp/include/cuvs/cluster/agglomerative.hpp
new file mode 100644
index 0000000000..5da03c4d1f
--- /dev/null
+++ b/cpp/include/cuvs/cluster/agglomerative.hpp
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance_types.hpp>
+#include <optional>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::agglomerative {
+
+// constant to indirectly control the number of neighbors. k = sqrt(n) + c. default to 15
+constexpr int DEFAULT_CONST_C = 15;
+
+/**
+ * Determines the method for computing the minimum spanning tree (MST)
+ */
+enum Linkage {
+
+  /**
+   * Use a pairwise distance matrix as input to the mst. This
+   * is very fast and the best option for fairly small datasets (~50k data points)
+   */
+  PAIRWISE = 0,
+
+  /**
+   * Construct a KNN graph as input to the mst and provide additional
+   * edges if the mst does not converge. This is slower but scales
+   * to very large datasets.
+   */
+  KNN_GRAPH = 1
+};
+
+/**
+ * Simple container object for consolidating linkage results. This closely
+ * mirrors the trained instance variables populated in
+ * Scikit-learn's AgglomerativeClustering estimator.
+ * @tparam idx_t
+ */
+template <typename idx_t>
+class single_linkage_output {
+ public:
+  idx_t m;
+  idx_t n_clusters;
+
+  idx_t n_leaves;
+  idx_t n_connected_components;
+
+  // TODO: These will be made private in a future release
+  idx_t* labels;    // size: m
+  idx_t* children;  // size: (m-1, 2)
+
+  raft::device_vector_view<idx_t> get_labels()
+  {
+    return raft::make_device_vector_view<idx_t>(labels, m);
+  }
+
+  raft::device_matrix_view<idx_t> get_children()
+  {
+    return raft::make_device_matrix_view<idx_t>(children, m - 1, 2);
+  }
+};
+
+/**
+ * Single-linkage clustering, capable of constructing a KNN graph to
+ * scale the algorithm beyond the n^2 memory consumption of implementations
+ * that use the fully-connected graph of pairwise distances by connecting
+ * a knn graph when k is not large enough to connect it.
+
+ * @param[in] handle raft handle
+ * @param[in] X dense input matrix in row-major layout
+ * @param[out] dendrogram output dendrogram (size [n_rows - 1] * 2)
+ * @param[out] labels output labels vector (size n_rows)
+ * @param[in] metric distance metrix to use when constructing connectivities graph
+ * @param[in] n_clusters number of clusters to assign data samples
+ * @param[in] linkage strategy for constructing the linkage. PAIRWISE uses more memory but can be
+ faster for
+ *                    smaller datasets. KNN_GRAPH allows the memory usage to be controlled (using
+ parameter c)
+ *                    at the expense of potentially additional minimum spanning tree iterations.
+ * @param[in] c a constant used when constructing linkage from knn graph. Allows the indirect
+ control of k. The algorithm will set `k = log(n) + c`
+ */
+void single_linkage(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int, raft::row_major> X,
+  raft::device_matrix_view<int, int, raft::row_major> dendrogram,
+  raft::device_vector_view<int, int> labels,
+  cuvs::distance::DistanceType metric,
+  size_t n_clusters,
+  cuvs::cluster::agglomerative::Linkage linkage = cuvs::cluster::agglomerative::Linkage::KNN_GRAPH,
+  std::optional<int> c                          = std::make_optional<int>(DEFAULT_CONST_C));
+
+};  // end namespace  cuvs::cluster::agglomerative
diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
new file mode 100644
index 0000000000..471a4d12df
--- /dev/null
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -0,0 +1,332 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/random/rng_state.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+/** Base structure for parameters that are common to all k-means algorithms */
+struct base_params {
+  /**
+   * Metric to use for distance computation. The supported metrics can vary per algorithm.
+   */
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
+};
+
+/**
+ * Simple object to specify hyper-parameters to the kmeans algorithm.
+ */
+struct params : base_params {
+  enum InitMethod {
+
+    /**
+     * Sample the centroids using the kmeans++ strategy
+     */
+    KMeansPlusPlus,
+
+    /**
+     * Sample the centroids uniformly at random
+     */
+    Random,
+
+    /**
+     * User provides the array of initial centroids
+     */
+    Array
+  };
+
+  /**
+   * The number of clusters to form as well as the number of centroids to generate (default:8).
+   */
+  int n_clusters = 8;
+
+  /**
+   * Method for initialization, defaults to k-means++:
+   *  - InitMethod::KMeansPlusPlus (k-means++): Use scalable k-means++ algorithm
+   * to select the initial cluster centers.
+   *  - InitMethod::Random (random): Choose 'n_clusters' observations (rows) at
+   * random from the input data for the initial centroids.
+   *  - InitMethod::Array (ndarray): Use 'centroids' as initial cluster centers.
+   */
+  InitMethod init = KMeansPlusPlus;
+
+  /**
+   * Maximum number of iterations of the k-means algorithm for a single run.
+   */
+  int max_iter = 300;
+
+  /**
+   * Relative tolerance with regards to inertia to declare convergence.
+   */
+  double tol = 1e-4;
+
+  /**
+   * verbosity level.
+   */
+  int verbosity = RAFT_LEVEL_INFO;
+
+  /**
+   * Seed to the random number generator.
+   */
+  raft::random::RngState rng_state{0};
+
+  /**
+   * Number of instance k-means algorithm will be run with different seeds.
+   */
+  int n_init = 1;
+
+  /**
+   * Oversampling factor for use in the k-means|| algorithm
+   */
+  double oversampling_factor = 2.0;
+
+  // batch_samples and batch_centroids are used to tile 1NN computation which is
+  // useful to optimize/control the memory footprint
+  // Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0
+  // then don't tile the centroids
+  int batch_samples = 1 << 15;
+
+  /**
+   * if 0 then batch_centroids = n_clusters
+   */
+  int batch_centroids = 0;  //
+
+  bool inertia_check = false;
+};
+
+/**
+ * Simple object to specify hyper-parameters to the balanced k-means algorithm.
+ *
+ * The following metrics are currently supported in k-means balanced:
+ *  - InnerProduct
+ *  - L2Expanded
+ *  - L2SqrtExpanded
+ */
+struct balanced_params : base_params {
+  /**
+   * Number of training iterations
+   */
+  uint32_t n_iters = 20;
+};
+
+/**
+ * Regular k-means
+ */
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int> X,
+         std::optional<raft::device_vector_view<const float, int>> sample_weight,
+         raft::device_matrix_view<float, int> centroids,
+         raft::host_scalar_view<float, int> inertia,
+         raft::host_scalar_view<int, int> n_iter);
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const int8_t, int> X,
+         std::optional<raft::device_vector_view<const int8_t, int>> sample_weight,
+         raft::device_matrix_view<int8_t, int> centroids,
+         raft::host_scalar_view<int8_t, int> inertia,
+         raft::host_scalar_view<int, int> n_iter);
+
+/**
+ * Balanced k-means
+ */
+
+void fit(const raft::resources& handle,
+         cuvs::cluster::kmeans::balanced_params const& params,
+         raft::device_matrix_view<const float, int> X,
+         raft::device_matrix_view<float, int> centroids);
+
+void fit(const raft::resources& handle,
+         cuvs::cluster::kmeans::balanced_params const& params,
+         raft::device_matrix_view<const int8_t, int> X,
+         raft::device_matrix_view<int8_t, int> centroids);
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids.view(),
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ *   ...
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   std::nullopt,
+ *                   centroids.view(),
+ *                   false,
+ *                   labels.view(),
+ *                   raft::make_scalar_view(&ineratia));
+ * @endcode
+ *
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     sample_weight    Optional weights for each observation in X.
+ *                                 [len = n_samples]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[in]     normalize_weight True if the weights should be normalized
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ * @param[out]    inertia          Sum of squared distances of samples to
+ *                                 their closest cluster center.
+ */
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const float, int> X,
+             std::optional<raft::device_vector_view<const float, int>> sample_weight,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<uint32_t, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<float> inertia);
+
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const int8_t, int> X,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<uint32_t, int> labels);
+
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const float, int> X,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<uint32_t, int> labels);
+
+/**
+ * @brief Compute k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::fit_predict(handle,
+ *                       params,
+ *                       X,
+ *                       std::nullopt,
+ *                       centroids.view(),
+ *                       labels.view(),
+ *                       raft::make_scalar_view(&inertia),
+ *                       raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const float, int> X,
+                 std::optional<raft::device_vector_view<const float, int>> sample_weight,
+                 std::optional<raft::device_matrix_view<float, int>> centroids,
+                 raft::device_vector_view<int, int> labels,
+                 raft::host_scalar_view<float> inertia,
+                 raft::host_scalar_view<int> n_iter);
+
+void fit_predict(const raft::resources& handle,
+                 cuvs::cluster::kmeans::balanced_params const& params,
+                 raft::device_matrix_view<const float, int> X,
+                 raft::device_matrix_view<float, int> centroids,
+                 raft::device_vector_view<uint32_t, int> labels);
+
+void fit_predict(const raft::resources& handle,
+                 cuvs::cluster::kmeans::balanced_params const& params,
+                 raft::device_matrix_view<const int8_t, int> X,
+                 raft::device_matrix_view<float, int> centroids,
+                 raft::device_vector_view<uint32_t, int> labels);
+
+/**
+ * @brief Transform X to a cluster-distance space.
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format
+ *                              [dim = n_samples x n_features]
+ * @param[in]     centroids     Cluster centroids. The data must be in row-major format.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    X_new         X transformed in the new space.
+ *                              [dim = n_samples x n_features]
+ */
+void transform(raft::resources const& handle,
+               const kmeans::params& params,
+               raft::device_matrix_view<const float, int> X,
+               raft::device_matrix_view<const float, int> centroids,
+               raft::device_matrix_view<float, int> X_new);
+
+namespace helpers {
+
+void find_k(raft::resources const& handle,
+            raft::device_matrix_view<const float, int> X,
+            raft::host_scalar_view<int> best_k,
+            raft::host_scalar_view<float> inertia,
+            raft::host_scalar_view<int> n_iter,
+            int kmax,
+            int kmin    = 1,
+            int maxiter = 100,
+            float tol   = 1e-3);
+}
+
+}  // namespace  cuvs::cluster::kmeans
diff --git a/cpp/include/cuvs/distance/distance_types.hpp b/cpp/include/cuvs/distance/distance_types.hpp
index 7b18646471..8d6eb71377 100644
--- a/cpp/include/cuvs/distance/distance_types.hpp
+++ b/cpp/include/cuvs/distance/distance_types.hpp
@@ -14,9 +14,10 @@
  * limitations under the License.
  */
 
-#include "distance_types.h"
 #pragma once
 
+#include "distance_types.h"
+
 namespace cuvs::distance {
 
 using DistanceType = cuvsDistanceType;
diff --git a/cpp/include/cuvs/distance/pairwise_distance.hpp b/cpp/include/cuvs/distance/pairwise_distance.hpp
index 27d9af6c15..5f774e3bc2 100644
--- a/cpp/include/cuvs/distance/pairwise_distance.hpp
+++ b/cpp/include/cuvs/distance/pairwise_distance.hpp
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#pragma once
+
 #include <cstdint>
 #include <cuvs/distance/distance_types.hpp>
 #include <raft/core/device_mdspan.hpp>
@@ -37,7 +39,7 @@ namespace cuvs::distance {
  * #include <raft/core/device_mdarray.hpp>
  * #include <cuvs/distance/pairwise_distance.hpp>
  *
- * raft::raft::resources handle;
+ * raft::resources handle;
  * int n_samples = 5000;
  * int n_features = 50;
  *
@@ -81,7 +83,7 @@ void pairwise_distance(
  * #include <raft/core/device_mdarray.hpp>
  * #include <cuvs/distance/pairwise_distance.hpp>
  *
- * raft::raft::resources handle;
+ * raft::resources handle;
  * int n_samples = 5000;
  * int n_features = 50;
  *
@@ -125,7 +127,7 @@ void pairwise_distance(
  * #include <raft/core/device_mdarray.hpp>
  * #include <cuvs/distance/pairwise_distance.hpp>
  *
- * raft::raft::resources handle;
+ * raft::resources handle;
  * int n_samples = 5000;
  * int n_features = 50;
  *
@@ -168,7 +170,7 @@ void pairwise_distance(
  * #include <raft/core/device_mdarray.hpp>
  * #include <cuvs/distance/pairwise_distance.hpp>
  *
- * raft::raft::resources handle;
+ * raft::resources handle;
  * int n_samples = 5000;
  * int n_features = 50;
  *
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index e52c15937a..d094e6afab 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -175,13 +175,7 @@ struct index : ann::index {
     return graph_view_.extent(1);
   }
 
-  /**
-   * DEPRECATED: please use data() instead.
-   *   If you need to query dataset dimensions, use the dim() and size() of the cagra index.
-   *   The data_handle() is not always available: you need to do a dynamic_cast to the expected
-   *   dataset type at runtime.
-   */
-  [[nodiscard]] [[deprecated("Use data()")]] inline auto dataset() const noexcept
+  [[nodiscard]] inline auto dataset() const noexcept
     -> raft::device_matrix_view<const T, int64_t, raft::layout_stride>
   {
     auto p = dynamic_cast<strided_dataset<T, int64_t>*>(dataset_.get());
diff --git a/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp b/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
index b1da7301c4..39e6eb54fd 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
@@ -21,7 +21,6 @@
 #include <cuvs/neighbors/ivf_pq.hpp>
 
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/logger.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
diff --git a/cpp/src/cluster/detail/agglomerative.cuh b/cpp/src/cluster/detail/agglomerative.cuh
new file mode 100644
index 0000000000..829e4be0c5
--- /dev/null
+++ b/cpp/src/cluster/detail/agglomerative.cuh
@@ -0,0 +1,328 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/execution_policy.h>
+#include <thrust/extrema.h>
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/sort.h>
+#include <thrust/tuple.h>
+
+#include <cstddef>
+
+namespace cuvs::cluster::agglomerative::detail {
+template <typename value_idx, typename value_t>
+class UnionFind {
+ public:
+  value_idx next_label;
+  std::vector<value_idx> parent;
+  std::vector<value_idx> size;
+
+  value_idx n_indices;
+
+  UnionFind(value_idx N_)
+    : n_indices(2 * N_ - 1), parent(2 * N_ - 1, -1), size(2 * N_ - 1, 1), next_label(N_)
+  {
+    memset(size.data() + N_, 0, (size.size() - N_) * sizeof(value_idx));
+  }
+
+  value_idx find(value_idx n)
+  {
+    value_idx p;
+    p = n;
+
+    while (parent[n] != -1)
+      n = parent[n];
+
+    // path compression
+    while (parent[p] != n) {
+      p                                   = parent[p == -1 ? n_indices - 1 : p];
+      parent[p == -1 ? n_indices - 1 : p] = n;
+    }
+    return n;
+  }
+
+  void perform_union(value_idx m, value_idx n)
+  {
+    size[next_label] = size[m] + size[n];
+    parent[m]        = next_label;
+    parent[n]        = next_label;
+
+    next_label += 1;
+  }
+};
+
+/**
+ * Agglomerative labeling on host. This has not been found to be a bottleneck
+ * in the algorithm. A parallel version of this can be done using a parallel
+ * variant of Kruskal's MST algorithm
+ * (ref http://cucis.ece.northwestern.edu/publications/pdf/HenPat12.pdf),
+ * which breaks apart the sorted MST results into overlapping subsets and
+ * independently runs Kruskal's algorithm on each subset, merging them back
+ * together into a single hierarchy when complete. Unfortunately,
+ * this is nontrivial and the speedup wouldn't be useful until this
+ * becomes a bottleneck.
+ *
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[in] handle the raft handle
+ * @param[in] rows src edges of the sorted MST
+ * @param[in] cols dst edges of the sorted MST
+ * @param[in] nnz the number of edges in the sorted MST
+ * @param[out] out_src parents of output
+ * @param[out] out_dst children of output
+ * @param[out] out_delta distances of output
+ * @param[out] out_size cluster sizes of output
+ */
+template <typename value_idx, typename value_t>
+void build_dendrogram_host(raft::resources const& handle,
+                           const value_idx* rows,
+                           const value_idx* cols,
+                           const value_t* data,
+                           size_t nnz,
+                           value_idx* children,
+                           value_t* out_delta,
+                           value_idx* out_size)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  value_idx n_edges = nnz;
+
+  std::vector<value_idx> mst_src_h(n_edges);
+  std::vector<value_idx> mst_dst_h(n_edges);
+  std::vector<value_t> mst_weights_h(n_edges);
+
+  raft::update_host(mst_src_h.data(), rows, n_edges, stream);
+  raft::update_host(mst_dst_h.data(), cols, n_edges, stream);
+  raft::update_host(mst_weights_h.data(), data, n_edges, stream);
+
+  raft::resource::sync_stream(handle, stream);
+
+  std::vector<value_idx> children_h(n_edges * 2);
+  std::vector<value_idx> out_size_h(n_edges);
+  std::vector<value_t> out_delta_h(n_edges);
+
+  UnionFind<value_idx, value_t> U(nnz + 1);
+
+  for (std::size_t i = 0; i < nnz; i++) {
+    value_idx a   = mst_src_h[i];
+    value_idx b   = mst_dst_h[i];
+    value_t delta = mst_weights_h[i];
+
+    value_idx aa = U.find(a);
+    value_idx bb = U.find(b);
+
+    value_idx children_idx = i * 2;
+
+    children_h[children_idx]     = aa;
+    children_h[children_idx + 1] = bb;
+    out_delta_h[i]               = delta;
+    out_size_h[i]                = U.size[aa] + U.size[bb];
+
+    U.perform_union(aa, bb);
+  }
+
+  raft::update_device(children, children_h.data(), n_edges * 2, stream);
+  raft::update_device(out_size, out_size_h.data(), n_edges, stream);
+  raft::update_device(out_delta, out_delta_h.data(), n_edges, stream);
+}
+
+template <typename value_idx>
+RAFT_KERNEL write_levels_kernel(const value_idx* children, value_idx* parents, value_idx n_vertices)
+{
+  value_idx tid = blockDim.x * blockIdx.x + threadIdx.x;
+  if (tid < n_vertices) {
+    value_idx level = tid / 2;
+    value_idx child = children[tid];
+    parents[child]  = level;
+  }
+}
+
+/**
+ * Instead of propagating a label from roots to children,
+ * the children each iterate up the tree until they find
+ * the label of their parent. This increases the potential
+ * parallelism.
+ * @tparam value_idx
+ * @param children
+ * @param parents
+ * @param n_leaves
+ * @param labels
+ */
+template <typename value_idx>
+RAFT_KERNEL inherit_labels(const value_idx* children,
+                           const value_idx* levels,
+                           std::size_t n_leaves,
+                           value_idx* labels,
+                           int cut_level,
+                           value_idx n_vertices)
+{
+  value_idx tid = blockDim.x * blockIdx.x + threadIdx.x;
+
+  if (tid < n_vertices) {
+    value_idx node      = children[tid];
+    value_idx cur_level = tid / 2;
+
+    /**
+     * Any roots above the cut level should be ignored.
+     * Any leaves at the cut level should already be labeled
+     */
+    if (cur_level > cut_level) return;
+
+    value_idx cur_parent = node;
+    value_idx label      = labels[cur_parent];
+
+    while (label == -1) {
+      cur_parent = cur_level + n_leaves;
+      cur_level  = levels[cur_parent];
+      label      = labels[cur_parent];
+    }
+
+    labels[node] = label;
+  }
+}
+
+template <typename value_idx>
+struct init_label_roots {
+  init_label_roots(value_idx* labels_) : labels(labels_) {}
+
+  template <typename Tuple>
+  __host__ __device__ void operator()(Tuple t)
+  {
+    labels[thrust::get<1>(t)] = thrust::get<0>(t);
+  }
+
+ private:
+  value_idx* labels;
+};
+
+/**
+ * Cuts the dendrogram at a particular level where the number of nodes
+ * is equal to n_clusters, then propagates the resulting labels
+ * to all the children.
+ *
+ * @tparam value_idx
+ * @param handle
+ * @param labels
+ * @param children
+ * @param n_clusters
+ * @param n_leaves
+ */
+template <typename value_idx, int tpb = 256>
+void extract_flattened_clusters(raft::resources const& handle,
+                                value_idx* labels,
+                                const value_idx* children,
+                                size_t n_clusters,
+                                size_t n_leaves)
+{
+  auto stream        = raft::resource::get_cuda_stream(handle);
+  auto thrust_policy = raft::resource::get_thrust_policy(handle);
+
+  // Handle special case where n_clusters == 1
+  if (n_clusters == 1) {
+    thrust::fill(thrust_policy, labels, labels + n_leaves, 0);
+  } else {
+    /**
+     * Compute levels for each node
+     *
+     *     1. Initialize "levels" array of size n_leaves * 2
+     *
+     *     2. For each entry in children, write parent
+     *        out for each of the children
+     */
+
+    auto n_edges = (n_leaves - 1) * 2;
+
+    thrust::device_ptr<const value_idx> d_ptr = thrust::device_pointer_cast(children);
+    value_idx n_vertices = *(thrust::max_element(thrust_policy, d_ptr, d_ptr + n_edges)) + 1;
+
+    // Prevent potential infinite loop from labeling disconnected
+    // connectivities graph.
+    RAFT_EXPECTS(n_leaves > 0, "n_leaves must be positive");
+    RAFT_EXPECTS(
+      static_cast<std::size_t>(n_vertices) == static_cast<std::size_t>((n_leaves - 1) * 2),
+      "Multiple components found in MST or MST is invalid. "
+      "Cannot find single-linkage solution.");
+
+    rmm::device_uvector<value_idx> levels(n_vertices, stream);
+
+    value_idx n_blocks = raft::ceildiv(n_vertices, (value_idx)tpb);
+    write_levels_kernel<<<n_blocks, tpb, 0, stream>>>(children, levels.data(), n_vertices);
+    /**
+     * Step 1: Find label roots:
+     *
+     *     1. Copying children[children.size()-(n_clusters-1):] entries to
+     *        separate arrayo
+     *     2. sort array
+     *     3. take first n_clusters entries
+     */
+
+    value_idx child_size = (n_clusters - 1) * 2;
+    rmm::device_uvector<value_idx> label_roots(child_size, stream);
+
+    value_idx children_cpy_start = n_edges - child_size;
+    raft::copy_async(label_roots.data(), children + children_cpy_start, child_size, stream);
+
+    thrust::sort(thrust_policy,
+                 label_roots.data(),
+                 label_roots.data() + (child_size),
+                 thrust::greater<value_idx>());
+
+    rmm::device_uvector<value_idx> tmp_labels(n_vertices, stream);
+
+    // Init labels to -1
+    thrust::fill(thrust_policy, tmp_labels.data(), tmp_labels.data() + n_vertices, -1);
+
+    // Write labels for cluster roots to "labels"
+    thrust::counting_iterator<uint> first(0);
+
+    auto z_iter = thrust::make_zip_iterator(
+      thrust::make_tuple(first, label_roots.data() + (label_roots.size() - n_clusters)));
+
+    thrust::for_each(
+      thrust_policy, z_iter, z_iter + n_clusters, init_label_roots<value_idx>(tmp_labels.data()));
+
+    /**
+     * Step 2: Propagate labels by having children iterate through their parents
+     *     1. Initialize labels to -1
+     *     2. For each element in levels array, propagate until parent's
+     *        label is !=-1
+     */
+    value_idx cut_level = (n_edges / 2) - (n_clusters - 1);
+
+    inherit_labels<<<n_blocks, tpb, 0, stream>>>(
+      children, levels.data(), n_leaves, tmp_labels.data(), cut_level, n_vertices);
+
+    // copy tmp labels to actual labels
+    raft::copy_async(labels, tmp_labels.data(), n_leaves, stream);
+  }
+}
+
+};  // namespace  cuvs::cluster::agglomerative::detail
diff --git a/cpp/src/cluster/detail/connectivities.cuh b/cpp/src/cluster/detail/connectivities.cuh
new file mode 100644
index 0000000000..a02ad2cc0d
--- /dev/null
+++ b/cpp/src/cluster/detail/connectivities.cuh
@@ -0,0 +1,237 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../distance/distance.cuh"
+#include <cuvs/cluster/agglomerative.hpp>
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/distance/distance_types.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/sparse/neighbors/knn_graph.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <limits>
+
+namespace cuvs::cluster::agglomerative::detail {
+
+template <Linkage dist_type, typename value_idx, typename value_t>
+struct distance_graph_impl {
+  void run(raft::resources const& handle,
+           const value_t* X,
+           size_t m,
+           size_t n,
+           cuvs::distance::DistanceType metric,
+           rmm::device_uvector<value_idx>& indptr,
+           rmm::device_uvector<value_idx>& indices,
+           rmm::device_uvector<value_t>& data,
+           int c);
+};
+
+/**
+ * Connectivities specialization to build a knn graph
+ * @tparam value_idx
+ * @tparam value_t
+ */
+template <typename value_idx, typename value_t>
+struct distance_graph_impl<Linkage::KNN_GRAPH, value_idx, value_t> {
+  void run(raft::resources const& handle,
+           const value_t* X,
+           size_t m,
+           size_t n,
+           cuvs::distance::DistanceType metric,
+           rmm::device_uvector<value_idx>& indptr,
+           rmm::device_uvector<value_idx>& indices,
+           rmm::device_uvector<value_t>& data,
+           int c)
+  {
+    auto stream        = raft::resource::get_cuda_stream(handle);
+    auto thrust_policy = raft::resource::get_thrust_policy(handle);
+
+    // Need to symmetrize knn into undirected graph
+    raft::sparse::COO<value_t, value_idx> knn_graph_coo(stream);
+
+    raft::sparse::neighbors::knn_graph(
+      handle, X, m, n, static_cast<raft::distance::DistanceType>(metric), knn_graph_coo, c);
+
+    indices.resize(knn_graph_coo.nnz, stream);
+    data.resize(knn_graph_coo.nnz, stream);
+
+    // self-loops get max distance
+    auto transform_in = thrust::make_zip_iterator(
+      thrust::make_tuple(knn_graph_coo.rows(), knn_graph_coo.cols(), knn_graph_coo.vals()));
+
+    thrust::transform(thrust_policy,
+                      transform_in,
+                      transform_in + knn_graph_coo.nnz,
+                      knn_graph_coo.vals(),
+                      [=] __device__(const thrust::tuple<value_idx, value_idx, value_t>& tup) {
+                        bool self_loop = thrust::get<0>(tup) == thrust::get<1>(tup);
+                        return (self_loop * std::numeric_limits<value_t>::max()) +
+                               (!self_loop * thrust::get<2>(tup));
+                      });
+
+    raft::sparse::convert::sorted_coo_to_csr(
+      knn_graph_coo.rows(), knn_graph_coo.nnz, indptr.data(), m + 1, stream);
+
+    // TODO: Wouldn't need to copy here if we could compute knn
+    // graph directly on the device uvectors
+    // ref: https://github.com/rapidsai/raft/issues/227
+    raft::copy_async(indices.data(), knn_graph_coo.cols(), knn_graph_coo.nnz, stream);
+    raft::copy_async(data.data(), knn_graph_coo.vals(), knn_graph_coo.nnz, stream);
+  }
+};
+
+template <typename value_idx>
+RAFT_KERNEL fill_indices2(value_idx* indices, size_t m, size_t nnz)
+{
+  value_idx tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+  if (tid >= nnz) return;
+  value_idx v  = tid % m;
+  indices[tid] = v;
+}
+
+/**
+ * Compute connected CSR of pairwise distances
+ * @tparam value_idx
+ * @tparam value_t
+ * @param handle
+ * @param X
+ * @param m
+ * @param n
+ * @param metric
+ * @param[out] indptr
+ * @param[out] indices
+ * @param[out] data
+ */
+template <typename value_idx, typename value_t>
+void pairwise_distances(const raft::resources& handle,
+                        const value_t* X,
+                        size_t m,
+                        size_t n,
+                        cuvs::distance::DistanceType metric,
+                        value_idx* indptr,
+                        value_idx* indices,
+                        value_t* data)
+{
+  auto stream      = raft::resource::get_cuda_stream(handle);
+  auto exec_policy = raft::resource::get_thrust_policy(handle);
+
+  value_idx nnz = m * m;
+
+  value_idx blocks = raft::ceildiv(nnz, (value_idx)256);
+  fill_indices2<value_idx><<<blocks, 256, 0, stream>>>(indices, m, nnz);
+
+  thrust::sequence(exec_policy, indptr, indptr + m, 0, (int)m);
+
+  raft::update_device(indptr + m, &nnz, 1, stream);
+
+  // TODO: It would ultimately be nice if the MST could accept
+  // dense inputs directly so we don't need to double the memory
+  // usage to hand it a sparse array here.
+  distance::pairwise_distance<value_t, value_idx>(handle, X, X, data, m, m, n, metric);
+  // self-loops get max distance
+  auto transform_in =
+    thrust::make_zip_iterator(thrust::make_tuple(thrust::make_counting_iterator(0), data));
+
+  thrust::transform(exec_policy,
+                    transform_in,
+                    transform_in + nnz,
+                    data,
+                    [=] __device__(const thrust::tuple<value_idx, value_t>& tup) {
+                      value_idx idx  = thrust::get<0>(tup);
+                      bool self_loop = idx % m == idx / m;
+                      return (self_loop * std::numeric_limits<value_t>::max()) +
+                             (!self_loop * thrust::get<1>(tup));
+                    });
+}
+
+/**
+ * Connectivities specialization for pairwise distances
+ * @tparam value_idx
+ * @tparam value_t
+ */
+template <typename value_idx, typename value_t>
+struct distance_graph_impl<Linkage::PAIRWISE, value_idx, value_t> {
+  void run(const raft::resources& handle,
+           const value_t* X,
+           size_t m,
+           size_t n,
+           cuvs::distance::DistanceType metric,
+           rmm::device_uvector<value_idx>& indptr,
+           rmm::device_uvector<value_idx>& indices,
+           rmm::device_uvector<value_t>& data,
+           int c)
+  {
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    size_t nnz = m * m;
+
+    indices.resize(nnz, stream);
+    data.resize(nnz, stream);
+
+    pairwise_distances(handle, X, m, n, metric, indptr.data(), indices.data(), data.data());
+  }
+};
+
+/**
+ * Returns a CSR connectivities graph based on the given linkage distance.
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam dist_type
+ * @param[in] handle raft handle
+ * @param[in] X dense data for which to construct connectivites
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[in] metric distance metric to use
+ * @param[out] indptr indptr array of connectivities graph
+ * @param[out] indices column indices array of connectivities graph
+ * @param[out] data distances array of connectivities graph
+ * @param[out] c constant 'c' used for nearest neighbors-based distances
+ *             which will guarantee k <= log(n) + c
+ */
+template <typename value_idx, typename value_t, Linkage dist_type>
+void get_distance_graph(raft::resources const& handle,
+                        const value_t* X,
+                        size_t m,
+                        size_t n,
+                        cuvs::distance::DistanceType metric,
+                        rmm::device_uvector<value_idx>& indptr,
+                        rmm::device_uvector<value_idx>& indices,
+                        rmm::device_uvector<value_t>& data,
+                        int c)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  indptr.resize(m + 1, stream);
+
+  distance_graph_impl<dist_type, value_idx, value_t> dist_graph;
+  dist_graph.run(handle, X, m, n, metric, indptr, indices, data, c);
+}
+
+};  // namespace  cuvs::cluster::agglomerative::detail
diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
new file mode 100644
index 0000000000..0f2ac39f0a
--- /dev/null
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -0,0 +1,1271 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "kmeans_common.cuh"
+
+#include <cuvs/cluster/kmeans.hpp>
+#include <cuvs/distance/distance_types.hpp>
+
+#include <raft/common/nvtx.hpp>
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/kvp.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/map_then_reduce.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/reduce_cols_by_key.cuh>
+#include <raft/linalg/reduce_rows_by_key.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cuda.h>
+#include <thrust/fill.h>
+#include <thrust/transform.h>
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <ctime>
+#include <optional>
+#include <random>
+
+namespace cuvs::cluster::kmeans::detail {
+
+// TODO(cjnolet): RAFT_NAME needs to be removed and the raft::logger fixed to not require it
+static const std::string RAFT_NAME = "raft";
+static const std::string CUVS_NAME = "cuvs";
+
+// =========================================================
+// Init functions
+// =========================================================
+
+// Selects 'n_clusters' samples randomly from X
+template <typename DataT, typename IndexT>
+void initRandom(raft::resources const& handle,
+                const cuvs::cluster::kmeans::params& params,
+                raft::device_matrix_view<const DataT, IndexT> X,
+                raft::device_matrix_view<DataT, IndexT> centroids)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("initRandom");
+  auto n_clusters = params.n_clusters;
+  cuvs::cluster::kmeans::detail::shuffleAndGather<DataT, IndexT>(
+    handle, X, centroids, n_clusters, params.rng_state.seed);
+}
+
+/*
+ * @brief Selects 'n_clusters' samples from the input X using kmeans++ algorithm.
+
+ * @note  This is the algorithm described in
+ *        "k-means++: the advantages of careful seeding". 2007, Arthur, D. and Vassilvitskii, S.
+ *        ACM-SIAM symposium on Discrete algorithms.
+ *
+ * Scalable kmeans++ pseudocode
+ * 1: C = sample a point uniformly at random from X
+ * 2: while |C| < k
+ * 3:   Sample x in X with probability p_x = d^2(x, C) / phi_X (C)
+ * 4:   C = C U {x}
+ * 5: end for
+ */
+template <typename DataT, typename IndexT>
+void kmeansPlusPlus(raft::resources const& handle,
+                    const cuvs::cluster::kmeans::params& params,
+                    raft::device_matrix_view<const DataT, IndexT> X,
+                    raft::device_matrix_view<DataT, IndexT> centroidsRawData,
+                    rmm::device_uvector<char>& workspace)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeansPlusPlus");
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = params.n_clusters;
+  auto metric         = params.metric;
+
+  // number of seeding trials for each center (except the first)
+  auto n_trials = 2 + static_cast<int>(std::ceil(log(n_clusters)));
+
+  RAFT_LOG_DEBUG(
+    "Run sequential k-means++ to select %d centroids from %d input samples "
+    "(%d seeding trials per iterations)",
+    n_clusters,
+    n_samples,
+    n_trials);
+
+  auto dataBatchSize = getDataBatchSize(params.batch_samples, n_samples);
+
+  // temporary buffers
+  auto indices            = raft::make_device_vector<IndexT, IndexT>(handle, n_trials);
+  auto centroidCandidates = raft::make_device_matrix<DataT, IndexT>(handle, n_trials, n_features);
+  auto costPerCandidate   = raft::make_device_vector<DataT, IndexT>(handle, n_trials);
+  auto minClusterDistance = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  auto distBuffer         = raft::make_device_matrix<DataT, IndexT>(handle, n_trials, n_samples);
+
+  rmm::device_uvector<DataT> L2NormBuf_OR_DistBuf(0, stream);
+  rmm::device_scalar<DataT> clusterCost(stream);
+  rmm::device_scalar<cub::KeyValuePair<int, DataT>> minClusterIndexAndDistance(stream);
+
+  // Device and matrix views
+  raft::device_vector_view<IndexT, IndexT> indices_view(indices.data_handle(), n_trials);
+  auto const_weights_view =
+    raft::make_device_vector_view<const DataT, IndexT>(minClusterDistance.data_handle(), n_samples);
+  auto const_indices_view =
+    raft::make_device_vector_view<const IndexT, IndexT>(indices.data_handle(), n_trials);
+  auto const_X_view =
+    raft::make_device_matrix_view<const DataT, IndexT>(X.data_handle(), n_samples, n_features);
+  raft::device_matrix_view<DataT, IndexT> candidates_view(
+    centroidCandidates.data_handle(), n_trials, n_features);
+
+  // L2 norm of X: ||c||^2
+  auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    raft::linalg::rowNorm(L2NormX.data_handle(),
+                          X.data_handle(),
+                          X.extent(1),
+                          X.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  }
+
+  raft::random::RngState rng(params.rng_state.seed, params.rng_state.type);
+  std::mt19937 gen(params.rng_state.seed);
+  std::uniform_int_distribution<> dis(0, n_samples - 1);
+
+  // <<< Step-1 >>>: C <-- sample a point uniformly at random from X
+  auto initialCentroid = raft::make_device_matrix_view<const DataT, IndexT>(
+    X.data_handle() + dis(gen) * n_features, 1, n_features);
+  int n_clusters_picked = 1;
+
+  // store the chosen centroid in the buffer
+  raft::copy(
+    centroidsRawData.data_handle(), initialCentroid.data_handle(), initialCentroid.size(), stream);
+
+  //  C = initial set of centroids
+  auto centroids = raft::make_device_matrix_view<DataT, IndexT>(
+    centroidsRawData.data_handle(), initialCentroid.extent(0), initialCentroid.extent(1));
+  // <<< End of Step-1 >>>
+
+  // Calculate cluster distance, d^2(x, C), for all the points x in X to the nearest centroid
+  cuvs::cluster::kmeans::detail::minClusterDistanceCompute<DataT, IndexT>(handle,
+                                                                          X,
+                                                                          centroids,
+                                                                          minClusterDistance.view(),
+                                                                          L2NormX.view(),
+                                                                          L2NormBuf_OR_DistBuf,
+                                                                          params.metric,
+                                                                          params.batch_samples,
+                                                                          params.batch_centroids,
+                                                                          workspace);
+
+  RAFT_LOG_DEBUG(" k-means++ - Sampled %d/%d centroids", n_clusters_picked, n_clusters);
+
+  // <<<< Step-2 >>> : while |C| < k
+  while (n_clusters_picked < n_clusters) {
+    // <<< Step-3 >>> : Sample x in X with probability p_x = d^2(x, C) / phi_X (C)
+    // Choose 'n_trials' centroid candidates from X with probability proportional to the squared
+    // distance to the nearest existing cluster
+
+    raft::random::discrete(handle, rng, indices_view, const_weights_view);
+    raft::matrix::gather(handle, const_X_view, const_indices_view, candidates_view);
+
+    // Calculate pairwise distance between X and the centroid candidates
+    // Output - pwd [n_trials x n_samples]
+    auto pwd = distBuffer.view();
+    cuvs::cluster::kmeans::detail::pairwise_distance_kmeans<DataT, IndexT>(
+      handle, centroidCandidates.view(), X, pwd, workspace, metric);
+
+    // Update nearest cluster distance for each centroid candidate
+    // Note pwd and minDistBuf points to same buffer which currently holds pairwise distance values.
+    // Outputs minDistanceBuf[n_trials x n_samples] where minDistance[i, :] contains updated
+    // minClusterDistance that includes candidate-i
+    auto minDistBuf = distBuffer.view();
+    raft::linalg::matrixVectorOp(minDistBuf.data_handle(),
+                                 pwd.data_handle(),
+                                 minClusterDistance.data_handle(),
+                                 pwd.extent(1),
+                                 pwd.extent(0),
+                                 true,
+                                 true,
+                                 raft::min_op{},
+                                 stream);
+
+    // Calculate costPerCandidate[n_trials] where costPerCandidate[i] is the cluster cost when using
+    // centroid candidate-i
+    raft::linalg::reduce(costPerCandidate.data_handle(),
+                         minDistBuf.data_handle(),
+                         minDistBuf.extent(1),
+                         minDistBuf.extent(0),
+                         static_cast<DataT>(0),
+                         true,
+                         true,
+                         stream);
+
+    // Greedy Choice - Choose the candidate that has minimum cluster cost
+    // ArgMin operation below identifies the index of minimum cost in costPerCandidate
+    {
+      // Determine temporary device storage requirements
+      size_t temp_storage_bytes = 0;
+      cub::DeviceReduce::ArgMin(nullptr,
+                                temp_storage_bytes,
+                                costPerCandidate.data_handle(),
+                                minClusterIndexAndDistance.data(),
+                                costPerCandidate.extent(0),
+                                stream);
+
+      // Allocate temporary storage
+      workspace.resize(temp_storage_bytes, stream);
+
+      // Run argmin-reduction
+      cub::DeviceReduce::ArgMin(workspace.data(),
+                                temp_storage_bytes,
+                                costPerCandidate.data_handle(),
+                                minClusterIndexAndDistance.data(),
+                                costPerCandidate.extent(0),
+                                stream);
+
+      int bestCandidateIdx = -1;
+      raft::copy(&bestCandidateIdx, &minClusterIndexAndDistance.data()->key, 1, stream);
+      raft::resource::sync_stream(handle);
+      /// <<< End of Step-3 >>>
+
+      /// <<< Step-4 >>>: C = C U {x}
+      // Update minimum cluster distance corresponding to the chosen centroid candidate
+      raft::copy(minClusterDistance.data_handle(),
+                 minDistBuf.data_handle() + bestCandidateIdx * n_samples,
+                 n_samples,
+                 stream);
+
+      raft::copy(centroidsRawData.data_handle() + n_clusters_picked * n_features,
+                 centroidCandidates.data_handle() + bestCandidateIdx * n_features,
+                 n_features,
+                 stream);
+
+      ++n_clusters_picked;
+      /// <<< End of Step-4 >>>
+    }
+
+    RAFT_LOG_DEBUG(" k-means++ - Sampled %d/%d centroids", n_clusters_picked, n_clusters);
+  }  /// <<<< Step-5 >>>
+}
+
+/**
+ *
+ * @tparam DataT
+ * @tparam IndexT
+ * @param handle
+ * @param[in] X input matrix (size n_samples, n_features)
+ * @param[in] weight number of samples currently assigned to each centroid
+ * @param[in] cur_centroids matrix of current centroids (size n_clusters, n_features)
+ * @param[in] l2norm_x
+ * @param[out] min_cluster_and_dist
+ * @param[out] new_centroids
+ * @param[out] new_weight
+ * @param[inout] workspace
+ */
+template <typename DataT, typename IndexT, typename LabelsIterator>
+void update_centroids(raft::resources const& handle,
+                      raft::device_matrix_view<const DataT, IndexT, raft::row_major> X,
+                      raft::device_vector_view<const DataT, IndexT> sample_weights,
+                      raft::device_matrix_view<const DataT, IndexT, raft::row_major> centroids,
+
+                      // TODO: Figure out how to best wrap iterator types in mdspan
+                      LabelsIterator cluster_labels,
+                      raft::device_vector_view<DataT, IndexT> weight_per_cluster,
+                      raft::device_matrix_view<DataT, IndexT, raft::row_major> new_centroids,
+                      rmm::device_uvector<char>& workspace)
+{
+  auto n_clusters = centroids.extent(0);
+  auto n_samples  = X.extent(0);
+
+  workspace.resize(n_samples, raft::resource::get_cuda_stream(handle));
+
+  // Calculates weighted sum of all the samples assigned to cluster-i and stores the
+  // result in new_centroids[i]
+  raft::linalg::reduce_rows_by_key((DataT*)X.data_handle(),
+                                   X.extent(1),
+                                   cluster_labels,
+                                   sample_weights.data_handle(),
+                                   workspace.data(),
+                                   X.extent(0),
+                                   X.extent(1),
+                                   n_clusters,
+                                   new_centroids.data_handle(),
+                                   raft::resource::get_cuda_stream(handle));
+
+  // Reduce weights by key to compute weight in each cluster
+  raft::linalg::reduce_cols_by_key(sample_weights.data_handle(),
+                                   cluster_labels,
+                                   weight_per_cluster.data_handle(),
+                                   (IndexT)1,
+                                   (IndexT)sample_weights.extent(0),
+                                   (IndexT)n_clusters,
+                                   raft::resource::get_cuda_stream(handle));
+
+  // Computes new_centroids[i] = new_centroids[i]/weight_per_cluster[i] where
+  //   new_centroids[n_clusters x n_features] - 2D array, new_centroids[i] has sum of all the
+  //   samples assigned to cluster-i
+  //   weight_per_cluster[n_clusters] - 1D array, weight_per_cluster[i] contains sum of weights in
+  //   cluster-i.
+  // Note - when weight_per_cluster[i] is 0, new_centroids[i] is reset to 0
+  raft::linalg::matrixVectorOp(new_centroids.data_handle(),
+                               new_centroids.data_handle(),
+                               weight_per_cluster.data_handle(),
+                               new_centroids.extent(1),
+                               new_centroids.extent(0),
+                               true,
+                               false,
+                               raft::div_checkzero_op{},
+                               raft::resource::get_cuda_stream(handle));
+
+  // copy centroids[i] to new_centroids[i] when weight_per_cluster[i] is 0
+  cub::ArgIndexInputIterator<DataT*> itr_wt(weight_per_cluster.data_handle());
+  raft::matrix::gather_if(
+    const_cast<DataT*>(centroids.data_handle()),
+    static_cast<int>(centroids.extent(1)),
+    static_cast<int>(centroids.extent(0)),
+    itr_wt,
+    itr_wt,
+    static_cast<int>(weight_per_cluster.size()),
+    new_centroids.data_handle(),
+    [=] __device__(raft::KeyValuePair<ptrdiff_t, DataT> map) {  // predicate
+      // copy when the sum of weights in the cluster is 0
+      return map.value == 0;
+    },
+    raft::key_op{},
+    raft::resource::get_cuda_stream(handle));
+}
+
+// TODO: Resizing is needed to use mdarray instead of rmm::device_uvector
+template <typename DataT, typename IndexT>
+void kmeans_fit_main(raft::resources const& handle,
+                     const cuvs::cluster::kmeans::params& params,
+                     raft::device_matrix_view<const DataT, IndexT> X,
+                     raft::device_vector_view<const DataT, IndexT> weight,
+                     raft::device_matrix_view<DataT, IndexT> centroidsRawData,
+                     raft::host_scalar_view<DataT> inertia,
+                     raft::host_scalar_view<IndexT> n_iter,
+                     rmm::device_uvector<char>& workspace)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_fit_main");
+  raft::logger::get(RAFT_NAME).set_level(params.verbosity);
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = params.n_clusters;
+  auto metric         = params.metric;
+
+  // stores (key, value) pair corresponding to each sample where
+  //   - key is the index of nearest cluster
+  //   - value is the distance to the nearest cluster
+  auto minClusterAndDistance =
+    raft::make_device_vector<raft::KeyValuePair<IndexT, DataT>, IndexT>(handle, n_samples);
+
+  // temporary buffer to store L2 norm of centroids or distance matrix,
+  // destructor releases the resource
+  rmm::device_uvector<DataT> L2NormBuf_OR_DistBuf(0, stream);
+
+  // temporary buffer to store intermediate centroids, destructor releases the
+  // resource
+  auto newCentroids = raft::make_device_matrix<DataT, IndexT>(handle, n_clusters, n_features);
+
+  // temporary buffer to store weights per cluster, destructor releases the
+  // resource
+  auto wtInCluster = raft::make_device_vector<DataT, IndexT>(handle, n_clusters);
+
+  rmm::device_scalar<DataT> clusterCostD(stream);
+
+  // L2 norm of X: ||x||^2
+  auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  auto l2normx_view =
+    raft::make_device_vector_view<const DataT, IndexT>(L2NormX.data_handle(), n_samples);
+
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    raft::linalg::rowNorm(L2NormX.data_handle(),
+                          X.data_handle(),
+                          X.extent(1),
+                          X.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  }
+
+  RAFT_LOG_DEBUG(
+    "Calling KMeans.fit with %d samples of input data and the initialized "
+    "cluster centers",
+    n_samples);
+
+  DataT priorClusteringCost = 0;
+  for (n_iter[0] = 1; n_iter[0] <= params.max_iter; ++n_iter[0]) {
+    RAFT_LOG_DEBUG(
+      "KMeans.fit: Iteration-%d: fitting the model using the initialized "
+      "cluster centers",
+      n_iter[0]);
+
+    auto centroids = raft::make_device_matrix_view<DataT, IndexT>(
+      centroidsRawData.data_handle(), n_clusters, n_features);
+
+    // computes minClusterAndDistance[0:n_samples) where
+    // minClusterAndDistance[i] is a <key, value> pair where
+    //   'key' is index to a sample in 'centroids' (index of the nearest
+    //   centroid) and 'value' is the distance between the sample 'X[i]' and the
+    //   'centroid[key]'
+    cuvs::cluster::kmeans::detail::minClusterAndDistanceCompute<DataT, IndexT>(
+      handle,
+      X,
+      centroids,
+      minClusterAndDistance.view(),
+      l2normx_view,
+      L2NormBuf_OR_DistBuf,
+      params.metric,
+      params.batch_samples,
+      params.batch_centroids,
+      workspace);
+
+    // Using TransformInputIteratorT to dereference an array of
+    // raft::KeyValuePair and converting them to just return the Key to be used
+    // in reduce_rows_by_key prims
+    cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT> conversion_op;
+    cub::TransformInputIterator<IndexT,
+                                cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT>,
+                                raft::KeyValuePair<IndexT, DataT>*>
+      itr(minClusterAndDistance.data_handle(), conversion_op);
+
+    update_centroids(handle,
+                     X,
+                     weight,
+                     raft::make_device_matrix_view<const DataT, IndexT>(
+                       centroidsRawData.data_handle(), n_clusters, n_features),
+                     itr,
+                     wtInCluster.view(),
+                     newCentroids.view(),
+                     workspace);
+
+    // compute the squared norm between the newCentroids and the original
+    // centroids, destructor releases the resource
+    auto sqrdNorm = raft::make_device_scalar(handle, DataT(0));
+    raft::linalg::mapThenSumReduce(sqrdNorm.data_handle(),
+                                   newCentroids.size(),
+                                   raft::sqdiff_op{},
+                                   stream,
+                                   centroids.data_handle(),
+                                   newCentroids.data_handle());
+
+    DataT sqrdNormError = 0;
+    raft::copy(&sqrdNormError, sqrdNorm.data_handle(), sqrdNorm.size(), stream);
+
+    raft::copy(
+      centroidsRawData.data_handle(), newCentroids.data_handle(), newCentroids.size(), stream);
+
+    bool done = false;
+    if (params.inertia_check) {
+      // calculate cluster cost phi_x(C)
+      cuvs::cluster::kmeans::detail::computeClusterCost(
+        handle,
+        minClusterAndDistance.view(),
+        workspace,
+        raft::make_device_scalar_view(clusterCostD.data()),
+        raft::value_op{},
+        raft::add_op{});
+
+      DataT curClusteringCost = clusterCostD.value(stream);
+
+      ASSERT(curClusteringCost != (DataT)0.0,
+             "Too few points and centroids being found is getting 0 cost from "
+             "centers");
+
+      if (n_iter[0] > 1) {
+        DataT delta = curClusteringCost / priorClusteringCost;
+        if (delta > 1 - params.tol) done = true;
+      }
+      priorClusteringCost = curClusteringCost;
+    }
+
+    raft::resource::sync_stream(handle, stream);
+    if (sqrdNormError < params.tol) done = true;
+
+    if (done) {
+      RAFT_LOG_DEBUG("Threshold triggered after %d iterations. Terminating early.", n_iter[0]);
+      break;
+    }
+  }
+
+  auto centroids = raft::make_device_matrix_view<DataT, IndexT>(
+    centroidsRawData.data_handle(), n_clusters, n_features);
+
+  cuvs::cluster::kmeans::detail::minClusterAndDistanceCompute<DataT, IndexT>(
+    handle,
+    X,
+    centroids,
+    minClusterAndDistance.view(),
+    l2normx_view,
+    L2NormBuf_OR_DistBuf,
+    params.metric,
+    params.batch_samples,
+    params.batch_centroids,
+    workspace);
+
+  // TODO: add different templates for InType of binaryOp to avoid thrust transform
+  thrust::transform(raft::resource::get_thrust_policy(handle),
+                    minClusterAndDistance.data_handle(),
+                    minClusterAndDistance.data_handle() + minClusterAndDistance.size(),
+                    weight.data_handle(),
+                    minClusterAndDistance.data_handle(),
+                    [=] __device__(const raft::KeyValuePair<IndexT, DataT> kvp, DataT wt) {
+                      raft::KeyValuePair<IndexT, DataT> res;
+                      res.value = kvp.value * wt;
+                      res.key   = kvp.key;
+                      return res;
+                    });
+
+  // calculate cluster cost phi_x(C)
+  cuvs::cluster::kmeans::detail::computeClusterCost(
+    handle,
+    minClusterAndDistance.view(),
+    workspace,
+    raft::make_device_scalar_view(clusterCostD.data()),
+    raft::value_op{},
+    raft::add_op{});
+
+  inertia[0] = clusterCostD.value(stream);
+
+  RAFT_LOG_DEBUG("KMeans.fit: completed after %d iterations with %f inertia[0] ",
+                 n_iter[0] > params.max_iter ? n_iter[0] - 1 : n_iter[0],
+                 inertia[0]);
+}
+
+/*
+ * @brief Selects 'n_clusters' samples from X using scalable kmeans++ algorithm.
+
+ * @note  This is the algorithm described in
+ *        "Scalable K-Means++", 2012, Bahman Bahmani, Benjamin Moseley,
+ *         Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii,
+ *         https://arxiv.org/abs/1203.6402
+
+ * Scalable kmeans++ pseudocode
+ * 1: C = sample a point uniformly at random from X
+ * 2: psi = phi_X (C)
+ * 3: for O( log(psi) ) times do
+ * 4:   C' = sample each point x in X independently with probability
+ *           p_x = l * (d^2(x, C) / phi_X (C) )
+ * 5:   C = C U C'
+ * 6: end for
+ * 7: For x in C, set w_x to be the number of points in X closer to x than any
+ * other point in C
+ * 8: Recluster the weighted points in C into k clusters
+
+ * TODO: Resizing is needed to use mdarray instead of rmm::device_uvector
+
+ */
+template <typename DataT, typename IndexT>
+void initScalableKMeansPlusPlus(raft::resources const& handle,
+                                const cuvs::cluster::kmeans::params& params,
+                                raft::device_matrix_view<const DataT, IndexT> X,
+                                raft::device_matrix_view<DataT, IndexT> centroidsRawData,
+                                rmm::device_uvector<char>& workspace)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "initScalableKMeansPlusPlus");
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = params.n_clusters;
+  auto metric         = params.metric;
+
+  raft::random::RngState rng(params.rng_state.seed, params.rng_state.type);
+
+  // <<<< Step-1 >>> : C <- sample a point uniformly at random from X
+  std::mt19937 gen(params.rng_state.seed);
+  std::uniform_int_distribution<> dis(0, n_samples - 1);
+
+  auto cIdx            = dis(gen);
+  auto initialCentroid = raft::make_device_matrix_view<const DataT, IndexT>(
+    X.data_handle() + cIdx * n_features, 1, n_features);
+
+  // flag the sample that is chosen as initial centroid
+  std::vector<uint8_t> h_isSampleCentroid(n_samples);
+  std::fill(h_isSampleCentroid.begin(), h_isSampleCentroid.end(), 0);
+  h_isSampleCentroid[cIdx] = 1;
+
+  // device buffer to flag the sample that is chosen as initial centroid
+  auto isSampleCentroid = raft::make_device_vector<uint8_t, IndexT>(handle, n_samples);
+
+  raft::copy(
+    isSampleCentroid.data_handle(), h_isSampleCentroid.data(), isSampleCentroid.size(), stream);
+
+  rmm::device_uvector<DataT> centroidsBuf(initialCentroid.size(), stream);
+
+  // reset buffer to store the chosen centroid
+  raft::copy(centroidsBuf.data(), initialCentroid.data_handle(), initialCentroid.size(), stream);
+
+  auto potentialCentroids = raft::make_device_matrix_view<DataT, IndexT>(
+    centroidsBuf.data(), initialCentroid.extent(0), initialCentroid.extent(1));
+  // <<< End of Step-1 >>>
+
+  // temporary buffer to store L2 norm of centroids or distance matrix,
+  // destructor releases the resource
+  rmm::device_uvector<DataT> L2NormBuf_OR_DistBuf(0, stream);
+
+  // L2 norm of X: ||x||^2
+  auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    raft::linalg::rowNorm(L2NormX.data_handle(),
+                          X.data_handle(),
+                          X.extent(1),
+                          X.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  }
+
+  auto minClusterDistanceVec = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  auto uniformRands          = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  rmm::device_scalar<DataT> clusterCost(stream);
+
+  // <<< Step-2 >>>: psi <- phi_X (C)
+  cuvs::cluster::kmeans::detail::minClusterDistanceCompute<DataT, IndexT>(
+    handle,
+    X,
+    potentialCentroids,
+    minClusterDistanceVec.view(),
+    L2NormX.view(),
+    L2NormBuf_OR_DistBuf,
+    params.metric,
+    params.batch_samples,
+    params.batch_centroids,
+    workspace);
+
+  // compute partial cluster cost from the samples in rank
+  cuvs::cluster::kmeans::detail::computeClusterCost(
+    handle,
+    minClusterDistanceVec.view(),
+    workspace,
+    raft::make_device_scalar_view(clusterCost.data()),
+    raft::identity_op{},
+    raft::add_op{});
+
+  auto psi = clusterCost.value(stream);
+
+  // <<< End of Step-2 >>>
+
+  // Scalable kmeans++ paper claims 8 rounds is sufficient
+  raft::resource::sync_stream(handle, stream);
+  int niter = std::min(8, (int)ceil(log(psi)));
+  RAFT_LOG_DEBUG("KMeans||: psi = %g, log(psi) = %g, niter = %d ", psi, log(psi), niter);
+
+  // <<<< Step-3 >>> : for O( log(psi) ) times do
+  for (int iter = 0; iter < niter; ++iter) {
+    RAFT_LOG_DEBUG("KMeans|| - Iteration %d: # potential centroids sampled - %d",
+                   iter,
+                   potentialCentroids.extent(0));
+
+    cuvs::cluster::kmeans::detail::minClusterDistanceCompute<DataT, IndexT>(
+      handle,
+      X,
+      potentialCentroids,
+      minClusterDistanceVec.view(),
+      L2NormX.view(),
+      L2NormBuf_OR_DistBuf,
+      params.metric,
+      params.batch_samples,
+      params.batch_centroids,
+      workspace);
+
+    cuvs::cluster::kmeans::detail::computeClusterCost(
+      handle,
+      minClusterDistanceVec.view(),
+      workspace,
+      raft::make_device_scalar_view<DataT>(clusterCost.data()),
+      raft::identity_op{},
+      raft::add_op{});
+
+    psi = clusterCost.value(stream);
+
+    // <<<< Step-4 >>> : Sample each point x in X independently and identify new
+    // potentialCentroids
+    raft::random::uniform(
+      handle, rng, uniformRands.data_handle(), uniformRands.extent(0), (DataT)0, (DataT)1);
+
+    cuvs::cluster::kmeans::detail::SamplingOp<DataT, IndexT> select_op(
+      psi,
+      params.oversampling_factor,
+      n_clusters,
+      uniformRands.data_handle(),
+      isSampleCentroid.data_handle());
+
+    rmm::device_uvector<DataT> CpRaw(0, stream);
+    cuvs::cluster::kmeans::detail::sampleCentroids<DataT, IndexT>(handle,
+                                                                  X,
+                                                                  minClusterDistanceVec.view(),
+                                                                  isSampleCentroid.view(),
+                                                                  select_op,
+                                                                  CpRaw,
+                                                                  workspace);
+    auto Cp = raft::make_device_matrix_view<DataT, IndexT>(
+      CpRaw.data(), CpRaw.size() / n_features, n_features);
+    /// <<<< End of Step-4 >>>>
+
+    /// <<<< Step-5 >>> : C = C U C'
+    // append the data in Cp to the buffer holding the potentialCentroids
+    centroidsBuf.resize(centroidsBuf.size() + Cp.size(), stream);
+    raft::copy(
+      centroidsBuf.data() + centroidsBuf.size() - Cp.size(), Cp.data_handle(), Cp.size(), stream);
+
+    IndexT tot_centroids = potentialCentroids.extent(0) + Cp.extent(0);
+    potentialCentroids =
+      raft::make_device_matrix_view<DataT, IndexT>(centroidsBuf.data(), tot_centroids, n_features);
+    /// <<<< End of Step-5 >>>
+  }  /// <<<< Step-6 >>>
+
+  RAFT_LOG_DEBUG("KMeans||: total # potential centroids sampled - %d",
+                 potentialCentroids.extent(0));
+
+  if ((int)potentialCentroids.extent(0) > n_clusters) {
+    // <<< Step-7 >>>: For x in C, set w_x to be the number of pts closest to X
+    // temporary buffer to store the sample count per cluster, destructor
+    // releases the resource
+    auto weight = raft::make_device_vector<DataT, IndexT>(handle, potentialCentroids.extent(0));
+
+    cuvs::cluster::kmeans::detail::countSamplesInCluster<DataT, IndexT>(
+      handle, params, X, L2NormX.view(), potentialCentroids, workspace, weight.view());
+
+    // <<< end of Step-7 >>>
+
+    // Step-8: Recluster the weighted points in C into k clusters
+    cuvs::cluster::kmeans::detail::kmeansPlusPlus<DataT, IndexT>(
+      handle, params, potentialCentroids, centroidsRawData, workspace);
+
+    auto inertia = raft::make_host_scalar<DataT>(0);
+    auto n_iter  = raft::make_host_scalar<IndexT>(0);
+    cuvs::cluster::kmeans::params default_params;
+    default_params.n_clusters = params.n_clusters;
+
+    cuvs::cluster::kmeans::detail::kmeans_fit_main<DataT, IndexT>(handle,
+                                                                  default_params,
+                                                                  potentialCentroids,
+                                                                  weight.view(),
+                                                                  centroidsRawData,
+                                                                  inertia.view(),
+                                                                  n_iter.view(),
+                                                                  workspace);
+
+  } else if ((int)potentialCentroids.extent(0) < n_clusters) {
+    // supplement with random
+    auto n_random_clusters = n_clusters - potentialCentroids.extent(0);
+
+    RAFT_LOG_DEBUG(
+      "[Warning!] KMeans||: found fewer than %d centroids during "
+      "initialization (found %d centroids, remaining %d centroids will be "
+      "chosen randomly from input samples)",
+      n_clusters,
+      potentialCentroids.extent(0),
+      n_random_clusters);
+
+    // generate `n_random_clusters` centroids
+    cuvs::cluster::kmeans::params rand_params;
+    rand_params.init       = cuvs::cluster::kmeans::params::InitMethod::Random;
+    rand_params.n_clusters = n_random_clusters;
+    initRandom<DataT, IndexT>(handle, rand_params, X, centroidsRawData);
+
+    // copy centroids generated during kmeans|| iteration to the buffer
+    raft::copy(centroidsRawData.data_handle() + n_random_clusters * n_features,
+               potentialCentroids.data_handle(),
+               potentialCentroids.size(),
+               stream);
+  } else {
+    // found the required n_clusters
+    raft::copy(centroidsRawData.data_handle(),
+               potentialCentroids.data_handle(),
+               potentialCentroids.size(),
+               stream);
+  }
+}
+
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. It must be noted
+ * that the data must be in row-major format and stored in device accessible
+ * location.
+ * @param[in]     n_samples     Number of samples in the input X.
+ * @param[in]     n_features    Number of features or the dimensions of each
+ * sample.
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ * centroids as the initial cluster centers
+ *                              [out] Otherwise, generated centroids from the
+ * kmeans algorithm is stored at the address pointed by 'centroids'.
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ * closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+template <typename DataT, typename IndexT>
+void kmeans_fit(raft::resources const& handle,
+                const cuvs::cluster::kmeans::params& pams,
+                raft::device_matrix_view<const DataT, IndexT> X,
+                std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+                raft::device_matrix_view<DataT, IndexT> centroids,
+                raft::host_scalar_view<DataT> inertia,
+                raft::host_scalar_view<IndexT> n_iter)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_fit");
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = pams.n_clusters;
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  // Check that parameters are valid
+  if (sample_weight.has_value())
+    RAFT_EXPECTS(sample_weight.value().extent(0) == n_samples,
+                 "invalid parameter (sample_weight!=n_samples)");
+  RAFT_EXPECTS(n_clusters > 0, "invalid parameter (n_clusters<=0)");
+  RAFT_EXPECTS(pams.tol > 0, "invalid parameter (tol<=0)");
+  RAFT_EXPECTS(pams.oversampling_factor >= 0, "invalid parameter (oversampling_factor<0)");
+  RAFT_EXPECTS((int)centroids.extent(0) == pams.n_clusters,
+               "invalid parameter (centroids.extent(0) != n_clusters)");
+  RAFT_EXPECTS(centroids.extent(1) == n_features,
+               "invalid parameter (centroids.extent(1) != n_features)");
+
+  // Display a message if the batch size is smaller than n_samples but will be ignored
+  if (pams.batch_samples < (int)n_samples &&
+      (pams.metric == cuvs::distance::DistanceType::L2Expanded ||
+       pams.metric == cuvs::distance::DistanceType::L2SqrtExpanded)) {
+    RAFT_LOG_DEBUG(
+      "batch_samples=%d was passed, but batch_samples=%d will be used (reason: "
+      "batch_samples has no impact on the memory footprint when FusedL2NN can be used)",
+      pams.batch_samples,
+      (int)n_samples);
+  }
+  // Display a message if batch_centroids is set and a fusedL2NN-compatible metric is used
+  if (pams.batch_centroids != 0 && pams.batch_centroids != pams.n_clusters &&
+      (pams.metric == cuvs::distance::DistanceType::L2Expanded ||
+       pams.metric == cuvs::distance::DistanceType::L2SqrtExpanded)) {
+    RAFT_LOG_DEBUG(
+      "batch_centroids=%d was passed, but batch_centroids=%d will be used (reason: "
+      "batch_centroids has no impact on the memory footprint when FusedL2NN can be used)",
+      pams.batch_centroids,
+      pams.n_clusters);
+  }
+
+  raft::logger::get(RAFT_NAME).set_level(pams.verbosity);
+
+  // Allocate memory
+  rmm::device_uvector<char> workspace(0, stream);
+  auto weight = raft::make_device_vector<DataT>(handle, n_samples);
+  if (sample_weight.has_value())
+    raft::copy(weight.data_handle(), sample_weight.value().data_handle(), n_samples, stream);
+  else
+    thrust::fill(raft::resource::get_thrust_policy(handle),
+                 weight.data_handle(),
+                 weight.data_handle() + weight.size(),
+                 1);
+
+  // check if weights sum up to n_samples
+  checkWeight<DataT>(handle, weight.view(), workspace);
+
+  auto centroidsRawData = raft::make_device_matrix<DataT, IndexT>(handle, n_clusters, n_features);
+
+  auto n_init = pams.n_init;
+  if (pams.init == cuvs::cluster::kmeans::params::InitMethod::Array && n_init != 1) {
+    RAFT_LOG_DEBUG(
+      "Explicit initial center position passed: performing only one init in "
+      "k-means instead of n_init=%d",
+      n_init);
+    n_init = 1;
+  }
+
+  std::mt19937 gen(pams.rng_state.seed);
+  inertia[0] = std::numeric_limits<DataT>::max();
+
+  for (auto seed_iter = 0; seed_iter < n_init; ++seed_iter) {
+    cuvs::cluster::kmeans::params iter_params = pams;
+    iter_params.rng_state.seed                = gen();
+
+    DataT iter_inertia    = std::numeric_limits<DataT>::max();
+    IndexT n_current_iter = 0;
+    if (iter_params.init == cuvs::cluster::kmeans::params::InitMethod::Random) {
+      // initializing with random samples from input dataset
+      RAFT_LOG_DEBUG(
+        "KMeans.fit (Iteration-%d/%d): initialize cluster centers by "
+        "randomly choosing from the "
+        "input data.",
+        seed_iter + 1,
+        n_init);
+      initRandom<DataT, IndexT>(handle, iter_params, X, centroidsRawData.view());
+    } else if (iter_params.init == cuvs::cluster::kmeans::params::InitMethod::KMeansPlusPlus) {
+      // default method to initialize is kmeans++
+      RAFT_LOG_DEBUG(
+        "KMeans.fit (Iteration-%d/%d): initialize cluster centers using "
+        "k-means++ algorithm.",
+        seed_iter + 1,
+        n_init);
+      if (iter_params.oversampling_factor == 0)
+        cuvs::cluster::kmeans::detail::kmeansPlusPlus<DataT, IndexT>(
+          handle, iter_params, X, centroidsRawData.view(), workspace);
+      else
+        cuvs::cluster::kmeans::detail::initScalableKMeansPlusPlus<DataT, IndexT>(
+          handle, iter_params, X, centroidsRawData.view(), workspace);
+    } else if (iter_params.init == cuvs::cluster::kmeans::params::InitMethod::Array) {
+      RAFT_LOG_DEBUG(
+        "KMeans.fit (Iteration-%d/%d): initialize cluster centers from "
+        "the ndarray array input "
+        "passed to init argument.",
+        seed_iter + 1,
+        n_init);
+      raft::copy(
+        centroidsRawData.data_handle(), centroids.data_handle(), n_clusters * n_features, stream);
+    } else {
+      THROW("unknown initialization method to select initial centers");
+    }
+
+    cuvs::cluster::kmeans::detail::kmeans_fit_main<DataT, IndexT>(
+      handle,
+      iter_params,
+      X,
+      weight.view(),
+      centroidsRawData.view(),
+      raft::make_host_scalar_view<DataT>(&iter_inertia),
+      raft::make_host_scalar_view<IndexT>(&n_current_iter),
+      workspace);
+    if (iter_inertia < inertia[0]) {
+      inertia[0] = iter_inertia;
+      n_iter[0]  = n_current_iter;
+      raft::copy(
+        centroids.data_handle(), centroidsRawData.data_handle(), n_clusters * n_features, stream);
+    }
+    RAFT_LOG_DEBUG("KMeans.fit after iteration-%d/%d: inertia - %f, n_iter[0] - %d",
+                   seed_iter + 1,
+                   n_init,
+                   inertia[0],
+                   n_iter[0]);
+  }
+  RAFT_LOG_DEBUG("KMeans.fit: async call returned (fit could still be running on the device)");
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_fit(raft::resources const& handle,
+                const cuvs::cluster::kmeans::params& pams,
+                const DataT* X,
+                const DataT* sample_weight,
+                DataT* centroids,
+                IndexT n_samples,
+                IndexT n_features,
+                DataT& inertia,
+                IndexT& n_iter)
+{
+  auto XView = raft::make_device_matrix_view<const DataT, IndexT>(X, n_samples, n_features);
+  auto centroidsView =
+    raft::make_device_matrix_view<DataT, IndexT>(centroids, pams.n_clusters, n_features);
+  std::optional<raft::device_vector_view<const DataT>> sample_weightView = std::nullopt;
+  if (sample_weight)
+    sample_weightView =
+      raft::make_device_vector_view<const DataT, IndexT>(sample_weight, n_samples);
+  auto inertiaView = raft::make_host_scalar_view(&inertia);
+  auto n_iterView  = raft::make_host_scalar_view(&n_iter);
+
+  cuvs::cluster::kmeans::detail::kmeans_fit<DataT, IndexT>(
+    handle, pams, XView, sample_weightView, centroidsView, inertiaView, n_iterView);
+}
+
+template <typename DataT, typename IndexT>
+void kmeans_predict(raft::resources const& handle,
+                    const cuvs::cluster::kmeans::params& pams,
+                    raft::device_matrix_view<const DataT, IndexT> X,
+                    std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+                    raft::device_matrix_view<const DataT, IndexT> centroids,
+                    raft::device_vector_view<IndexT, IndexT> labels,
+                    bool normalize_weight,
+                    raft::host_scalar_view<DataT> inertia)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_predict");
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  // Check that parameters are valid
+  if (sample_weight.has_value())
+    RAFT_EXPECTS(sample_weight.value().extent(0) == n_samples,
+                 "invalid parameter (sample_weight!=n_samples)");
+  RAFT_EXPECTS(pams.n_clusters > 0, "invalid parameter (n_clusters<=0)");
+  RAFT_EXPECTS(pams.tol > 0, "invalid parameter (tol<=0)");
+  RAFT_EXPECTS(pams.oversampling_factor >= 0, "invalid parameter (oversampling_factor<0)");
+  RAFT_EXPECTS((int)centroids.extent(0) == pams.n_clusters,
+               "invalid parameter (centroids.extent(0) != n_clusters)");
+  RAFT_EXPECTS(centroids.extent(1) == n_features,
+               "invalid parameter (centroids.extent(1) != n_features)");
+
+  raft::logger::get(RAFT_NAME).set_level(pams.verbosity);
+  auto metric = pams.metric;
+
+  // Allocate memory
+  // Device-accessible allocation of expandable storage used as temporary buffers
+  rmm::device_uvector<char> workspace(0, stream);
+  auto weight = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  if (sample_weight.has_value())
+    raft::copy(weight.data_handle(), sample_weight.value().data_handle(), n_samples, stream);
+  else
+    thrust::fill(raft::resource::get_thrust_policy(handle),
+                 weight.data_handle(),
+                 weight.data_handle() + weight.size(),
+                 1);
+
+  // check if weights sum up to n_samples
+  if (normalize_weight) checkWeight(handle, weight.view(), workspace);
+
+  auto minClusterAndDistance =
+    raft::make_device_vector<raft::KeyValuePair<IndexT, DataT>, IndexT>(handle, n_samples);
+  rmm::device_uvector<DataT> L2NormBuf_OR_DistBuf(0, stream);
+
+  // L2 norm of X: ||x||^2
+  auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    raft::linalg::rowNorm(L2NormX.data_handle(),
+                          X.data_handle(),
+                          X.extent(1),
+                          X.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  }
+
+  // computes minClusterAndDistance[0:n_samples) where  minClusterAndDistance[i]
+  // is a <key, value> pair where
+  //   'key' is index to a sample in 'centroids' (index of the nearest
+  //   centroid) and 'value' is the distance between the sample 'X[i]' and the
+  //   'centroid[key]'
+  auto l2normx_view =
+    raft::make_device_vector_view<const DataT, IndexT>(L2NormX.data_handle(), n_samples);
+  cuvs::cluster::kmeans::detail::minClusterAndDistanceCompute<DataT, IndexT>(
+    handle,
+    X,
+    centroids,
+    minClusterAndDistance.view(),
+    l2normx_view,
+    L2NormBuf_OR_DistBuf,
+    pams.metric,
+    pams.batch_samples,
+    pams.batch_centroids,
+    workspace);
+
+  // calculate cluster cost phi_x(C)
+  rmm::device_scalar<DataT> clusterCostD(stream);
+  // TODO: add different templates for InType of binaryOp to avoid thrust transform
+  thrust::transform(raft::resource::get_thrust_policy(handle),
+                    minClusterAndDistance.data_handle(),
+                    minClusterAndDistance.data_handle() + minClusterAndDistance.size(),
+                    weight.data_handle(),
+                    minClusterAndDistance.data_handle(),
+                    [=] __device__(const raft::KeyValuePair<IndexT, DataT> kvp, DataT wt) {
+                      raft::KeyValuePair<IndexT, DataT> res;
+                      res.value = kvp.value * wt;
+                      res.key   = kvp.key;
+                      return res;
+                    });
+
+  cuvs::cluster::kmeans::detail::computeClusterCost(
+    handle,
+    minClusterAndDistance.view(),
+    workspace,
+    raft::make_device_scalar_view(clusterCostD.data()),
+    raft::value_op{},
+    raft::add_op{});
+
+  thrust::transform(raft::resource::get_thrust_policy(handle),
+                    minClusterAndDistance.data_handle(),
+                    minClusterAndDistance.data_handle() + minClusterAndDistance.size(),
+                    labels.data_handle(),
+                    raft::key_op{});
+
+  inertia[0] = clusterCostD.value(stream);
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_predict(raft::resources const& handle,
+                    const cuvs::cluster::kmeans::params& pams,
+                    const DataT* X,
+                    const DataT* sample_weight,
+                    const DataT* centroids,
+                    IndexT n_samples,
+                    IndexT n_features,
+                    IndexT* labels,
+                    bool normalize_weight,
+                    DataT& inertia)
+{
+  auto XView = raft::make_device_matrix_view<const DataT, IndexT>(X, n_samples, n_features);
+  auto centroidsView =
+    raft::make_device_matrix_view<const DataT, IndexT>(centroids, pams.n_clusters, n_features);
+  std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weightView{std::nullopt};
+  if (sample_weight)
+    sample_weightView.emplace(
+      raft::make_device_vector_view<const DataT, IndexT>(sample_weight, n_samples));
+  auto labelsView  = raft::make_device_vector_view<IndexT, IndexT>(labels, n_samples);
+  auto inertiaView = raft::make_host_scalar_view(&inertia);
+
+  cuvs::cluster::kmeans::detail::kmeans_predict<DataT, IndexT>(handle,
+                                                               pams,
+                                                               XView,
+                                                               sample_weightView,
+                                                               centroidsView,
+                                                               labelsView,
+                                                               normalize_weight,
+                                                               inertiaView);
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_fit_predict(raft::resources const& handle,
+                        const cuvs::cluster::kmeans::params& pams,
+                        raft::device_matrix_view<const DataT, IndexT> X,
+                        std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+                        std::optional<raft::device_matrix_view<DataT, IndexT>> centroids,
+                        raft::device_vector_view<IndexT, IndexT> labels,
+                        raft::host_scalar_view<DataT> inertia,
+                        raft::host_scalar_view<IndexT> n_iter)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_fit_predict");
+  if (!centroids.has_value()) {
+    auto n_features = X.extent(1);
+    auto centroids_matrix =
+      raft::make_device_matrix<DataT, IndexT>(handle, pams.n_clusters, n_features);
+    cuvs::cluster::kmeans::detail::kmeans_fit<DataT, IndexT>(
+      handle, pams, X, sample_weight, centroids_matrix.view(), inertia, n_iter);
+    cuvs::cluster::kmeans::detail::kmeans_predict<DataT, IndexT>(
+      handle, pams, X, sample_weight, centroids_matrix.view(), labels, true, inertia);
+  } else {
+    cuvs::cluster::kmeans::detail::kmeans_fit<DataT, IndexT>(
+      handle, pams, X, sample_weight, centroids.value(), inertia, n_iter);
+    cuvs::cluster::kmeans::detail::kmeans_predict<DataT, IndexT>(
+      handle, pams, X, sample_weight, centroids.value(), labels, true, inertia);
+  }
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_fit_predict(raft::resources const& handle,
+                        const cuvs::cluster::kmeans::params& pams,
+                        const DataT* X,
+                        const DataT* sample_weight,
+                        DataT* centroids,
+                        IndexT n_samples,
+                        IndexT n_features,
+                        IndexT* labels,
+                        DataT& inertia,
+                        IndexT& n_iter)
+{
+  auto XView = raft::make_device_matrix_view<const DataT, IndexT>(X, n_samples, n_features);
+  std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weightView{std::nullopt};
+  if (sample_weight)
+    sample_weightView.emplace(
+      raft::make_device_vector_view<const DataT, IndexT>(sample_weight, n_samples));
+  std::optional<raft::device_matrix_view<DataT, IndexT>> centroidsView{std::nullopt};
+  if (centroids)
+    centroidsView.emplace(
+      raft::make_device_matrix_view<DataT, IndexT>(centroids, pams.n_clusters, n_features));
+  auto labelsView  = raft::make_device_vector_view<IndexT, IndexT>(labels, n_samples);
+  auto inertiaView = raft::make_host_scalar_view(&inertia);
+  auto n_iterView  = raft::make_host_scalar_view(&n_iter);
+
+  cuvs::cluster::kmeans::detail::kmeans_fit_predict<DataT, IndexT>(
+    handle, pams, XView, sample_weightView, centroidsView, labelsView, inertiaView, n_iterView);
+}
+
+/**
+ * @brief Transform X to a cluster-distance space.
+ *
+ * @param[in]     handle        The handle to the cuML library context that
+ * manages the CUDA resources.
+ * @param[in]     pams        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ * be in row-major format
+ * @param[in]     centroids     Cluster centroids. The data must be in row-major format.
+ * @param[out]    X_new         X transformed in the new space..
+ */
+template <typename DataT, typename IndexT = int>
+void kmeans_transform(raft::resources const& handle,
+                      const cuvs::cluster::kmeans::params& pams,
+                      raft::device_matrix_view<const DataT> X,
+                      raft::device_matrix_view<const DataT> centroids,
+                      raft::device_matrix_view<DataT> X_new)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_transform");
+  raft::logger::get(RAFT_NAME).set_level(pams.verbosity);
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = pams.n_clusters;
+  auto metric         = pams.metric;
+
+  // Device-accessible allocation of expandable storage used as temporary buffers
+  rmm::device_uvector<char> workspace(0, stream);
+  auto dataBatchSize = getDataBatchSize(pams.batch_samples, n_samples);
+
+  // tile over the input data and calculate distance matrix [n_samples x
+  // n_clusters]
+  for (IndexT dIdx = 0; dIdx < (IndexT)n_samples; dIdx += dataBatchSize) {
+    // # of samples for the current batch
+    auto ns = std::min(static_cast<IndexT>(dataBatchSize), static_cast<IndexT>(n_samples - dIdx));
+
+    // datasetView [ns x n_features] - view representing the current batch of
+    // input dataset
+    auto datasetView = raft::make_device_matrix_view<const DataT, IndexT>(
+      X.data_handle() + n_features * dIdx, ns, n_features);
+
+    // pairwiseDistanceView [ns x n_clusters]
+    auto pairwiseDistanceView = raft::make_device_matrix_view<DataT, IndexT>(
+      X_new.data_handle() + n_clusters * dIdx, ns, n_clusters);
+
+    // calculate pairwise distance between cluster centroids and current batch
+    // of input dataset
+    pairwise_distance_kmeans<DataT, IndexT>(
+      handle, datasetView, centroids, pairwiseDistanceView, workspace, metric);
+  }
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_transform(raft::resources const& handle,
+                      const cuvs::cluster::kmeans::params& pams,
+                      const DataT* X,
+                      const DataT* centroids,
+                      IndexT n_samples,
+                      IndexT n_features,
+                      DataT* X_new)
+{
+  auto XView = raft::make_device_matrix_view<const DataT, IndexT>(X, n_samples, n_features);
+  auto centroidsView =
+    raft::make_device_matrix_view<const DataT, IndexT>(centroids, pams.n_clusters, n_features);
+  auto X_newView = raft::make_device_matrix_view<DataT, IndexT>(X_new, n_samples, n_features);
+
+  cuvs::cluster::kmeans::detail::kmeans_transform<DataT, IndexT>(
+    handle, pams, XView, centroidsView, X_newView);
+}
+}  // namespace cuvs::cluster::kmeans::detail
diff --git a/cpp/src/cluster/detail/kmeans_auto_find_k.cuh b/cpp/src/cluster/detail/kmeans_auto_find_k.cuh
new file mode 100644
index 0000000000..6441f7ad5b
--- /dev/null
+++ b/cpp/src/cluster/detail/kmeans_auto_find_k.cuh
@@ -0,0 +1,233 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "kmeans.cuh"
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/stats/dispersion.cuh>
+
+#include <thrust/host_vector.h>
+
+namespace cuvs::cluster::kmeans::detail {
+
+template <typename value_t, typename idx_t>
+void compute_dispersion(raft::resources const& handle,
+                        raft::device_matrix_view<const value_t, idx_t> X,
+                        cuvs::cluster::kmeans::params& params,
+                        raft::device_matrix_view<value_t, idx_t> centroids_view,
+                        raft::device_vector_view<idx_t> labels,
+                        raft::device_vector_view<idx_t> clusterSizes,
+                        rmm::device_uvector<char>& workspace,
+                        raft::host_vector_view<value_t> clusterDispertionView,
+                        raft::host_vector_view<value_t> resultsView,
+                        raft::host_scalar_view<value_t> residual,
+                        raft::host_scalar_view<idx_t> n_iter,
+                        int val,
+                        idx_t n,
+                        idx_t d)
+{
+  auto centroids_const_view =
+    raft::make_device_matrix_view<const value_t, idx_t>(centroids_view.data_handle(), val, d);
+
+  idx_t* clusterSizes_ptr = clusterSizes.data_handle();
+  auto cluster_sizes_view =
+    raft::make_device_vector_view<const idx_t, idx_t>(clusterSizes_ptr, val);
+
+  params.n_clusters = val;
+
+  cuvs::cluster::kmeans::detail::kmeans_fit_predict<value_t, idx_t>(
+    handle, params, X, std::nullopt, std::make_optional(centroids_view), labels, residual, n_iter);
+
+  detail::countLabels(handle, labels.data_handle(), clusterSizes.data_handle(), n, val, workspace);
+
+  resultsView[val]           = residual[0];
+  clusterDispertionView[val] = raft::stats::cluster_dispersion(
+    handle, centroids_const_view, cluster_sizes_view, std::nullopt, n);
+}
+
+template <typename idx_t, typename value_t>
+void find_k(raft::resources const& handle,
+            raft::device_matrix_view<const value_t, idx_t> X,
+            raft::host_scalar_view<idx_t> best_k,
+            raft::host_scalar_view<value_t> residual,
+            raft::host_scalar_view<idx_t> n_iter,
+            idx_t kmax,
+            idx_t kmin    = 1,
+            idx_t maxiter = 100,
+            value_t tol   = 1e-2)
+{
+  idx_t n = X.extent(0);
+  idx_t d = X.extent(1);
+
+  RAFT_EXPECTS(n >= 1, "n must be >= 1");
+  RAFT_EXPECTS(d >= 1, "d must be >= 1");
+  RAFT_EXPECTS(kmin >= 1, "kmin must be >= 1");
+  RAFT_EXPECTS(kmax <= n, "kmax must be <= number of data samples in X");
+  RAFT_EXPECTS(tol >= 0, "tolerance must be >= 0");
+  RAFT_EXPECTS(maxiter >= 0, "maxiter must be >= 0");
+  // Allocate memory
+  // Device memory
+
+  auto centroids    = raft::make_device_matrix<value_t, idx_t>(handle, kmax, X.extent(1));
+  auto clusterSizes = raft::make_device_vector<idx_t>(handle, kmax);
+  auto labels       = raft::make_device_vector<idx_t>(handle, n);
+
+  rmm::device_uvector<char> workspace(0, raft::resource::get_cuda_stream(handle));
+
+  idx_t* clusterSizes_ptr = clusterSizes.data_handle();
+
+  // Host memory
+  auto results           = raft::make_host_vector<value_t>(kmax + 1);
+  auto clusterDispersion = raft::make_host_vector<value_t>(kmax + 1);
+
+  auto clusterDispertionView = clusterDispersion.view();
+  auto resultsView           = results.view();
+
+  // Loop to find *best* k
+  // Perform k-means in binary search
+  int left   = kmin;  // must be at least 2
+  int right  = kmax;  // int(floor(len(data)/2)) #assumption of clusters of size 2 at least
+  int mid    = ((unsigned int)left + (unsigned int)right) >> 1;
+  int oldmid = mid;
+  int tests  = 0;
+  double objective[3];      // 0= left of mid, 1= right of mid
+  if (left == 1) left = 2;  // at least do 2 clusters
+
+  cuvs::cluster::kmeans::params params;
+  params.max_iter = maxiter;
+  params.tol      = tol;
+
+  auto centroids_view =
+    raft::make_device_matrix_view<value_t, idx_t>(centroids.data_handle(), left, d);
+  compute_dispersion<value_t, idx_t>(handle,
+                                     X,
+                                     params,
+                                     centroids_view,
+                                     labels.view(),
+                                     clusterSizes.view(),
+                                     workspace,
+                                     clusterDispertionView,
+                                     resultsView,
+                                     residual,
+                                     n_iter,
+                                     left,
+                                     n,
+                                     d);
+
+  // eval right edge0
+  resultsView[right] = 1e20;
+  while (resultsView[right] > resultsView[left] && tests < 3) {
+    centroids_view =
+      raft::make_device_matrix_view<value_t, idx_t>(centroids.data_handle(), right, d);
+    compute_dispersion<value_t, idx_t>(handle,
+                                       X,
+                                       params,
+                                       centroids_view,
+                                       labels.view(),
+                                       clusterSizes.view(),
+                                       workspace,
+                                       clusterDispertionView,
+                                       resultsView,
+                                       residual,
+                                       n_iter,
+                                       right,
+                                       n,
+                                       d);
+
+    tests += 1;
+  }
+
+  objective[0] = (n - left) / (left - 1) * clusterDispertionView[left] / resultsView[left];
+  objective[1] = (n - right) / (right - 1) * clusterDispertionView[right] / resultsView[right];
+  while (left < right - 1) {
+    resultsView[mid] = 1e20;
+    tests            = 0;
+    while (resultsView[mid] > resultsView[left] && tests < 3) {
+      centroids_view =
+        raft::make_device_matrix_view<value_t, idx_t>(centroids.data_handle(), mid, d);
+      compute_dispersion<value_t, idx_t>(handle,
+                                         X,
+                                         params,
+                                         centroids_view,
+                                         labels.view(),
+                                         clusterSizes.view(),
+                                         workspace,
+                                         clusterDispertionView,
+                                         resultsView,
+                                         residual,
+                                         n_iter,
+                                         mid,
+                                         n,
+                                         d);
+
+      if (resultsView[mid] > resultsView[left] && (mid + 1) < right) {
+        mid += 1;
+        resultsView[mid] = 1e20;
+      } else if (resultsView[mid] > resultsView[left] && (mid - 1) > left) {
+        mid -= 1;
+        resultsView[mid] = 1e20;
+      }
+      tests += 1;
+    }
+
+    // maximize Calinski-Harabasz Index, minimize resid/ cluster
+    objective[0] = (n - left) / (left - 1) * clusterDispertionView[left] / resultsView[left];
+    objective[1] = (n - right) / (right - 1) * clusterDispertionView[right] / resultsView[right];
+    objective[2] = (n - mid) / (mid - 1) * clusterDispertionView[mid] / resultsView[mid];
+    objective[0] = (objective[2] - objective[0]) / (mid - left);
+    objective[1] = (objective[1] - objective[2]) / (right - mid);
+
+    if (objective[0] > 0 && objective[1] < 0) {
+      // our point is in the left-of-mid side
+      right = mid;
+    } else {
+      left = mid;
+    }
+    oldmid = mid;
+    mid    = ((unsigned int)right + (unsigned int)left) >> 1;
+  }
+
+  best_k[0]    = right;
+  objective[0] = (n - left) / (left - 1) * clusterDispertionView[left] / resultsView[left];
+  objective[1] = (n - oldmid) / (oldmid - 1) * clusterDispertionView[oldmid] / resultsView[oldmid];
+  if (objective[1] < objective[0]) { best_k[0] = left; }
+
+  // if best_k isn't what we just ran, re-run to get correct centroids and dist data on return->
+  // this saves memory
+  if (best_k[0] != oldmid) {
+    auto centroids_view =
+      raft::make_device_matrix_view<value_t, idx_t>(centroids.data_handle(), best_k[0], d);
+
+    params.n_clusters = best_k[0];
+    cuvs::cluster::kmeans::detail::kmeans_fit_predict<value_t, idx_t>(
+      handle,
+      params,
+      X,
+      std::nullopt,
+      std::make_optional(centroids_view),
+      labels.view(),
+      residual,
+      n_iter);
+  }
+}
+}  // namespace  cuvs::cluster::kmeans::detail
\ No newline at end of file
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
new file mode 100644
index 0000000000..aea07c1aa4
--- /dev/null
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -0,0 +1,1096 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../distance/fused_distance_nn.cuh"
+#include "kmeans_common.cuh"
+#include <cuvs/cluster/kmeans.hpp>
+
+#include "../../distance/distance.cuh"
+
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/common/nvtx.hpp>
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/gemm.cuh>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/normalize.cuh>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/argmin.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/device_atomics.cuh>
+#include <raft/util/integer_utils.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+#include <rmm/resource_ref.hpp>
+
+#include <thrust/gather.h>
+#include <thrust/transform.h>
+
+#include <limits>
+#include <optional>
+#include <tuple>
+#include <type_traits>
+
+namespace cuvs::cluster::kmeans::detail {
+
+static const std::string RAFT_NAME                 = "raft";
+constexpr static inline float kAdjustCentersWeight = 7.0f;
+
+/**
+ * @brief Predict labels for the dataset; floating-point types only.
+ *
+ * NB: no minibatch splitting is done here, it may require large amount of temporary memory (n_rows
+ * * n_cluster * sizeof(MathT)).
+ *
+ * @tparam MathT  type of the centroids and mapped data
+ * @tparam IdxT   index type
+ * @tparam LabelT label type
+ *
+ * @param[in] handle The raft handle.
+ * @param[in] params Structure containing the hyper-parameters
+ * @param[in] centers Pointer to the row-major matrix of cluster centers [n_clusters, dim]
+ * @param[in] n_clusters Number of clusters/centers
+ * @param[in] dim Dimensionality of the data
+ * @param[in] dataset Pointer to the data [n_rows, dim]
+ * @param[in] dataset_norm Pointer to the precomputed norm (for L2 metrics only) [n_rows]
+ * @param[in] n_rows Number samples in the `dataset`
+ * @param[out] labels Output predictions [n_rows]
+ * @param[inout] mr (optional) Memory resource to use for temporary allocations
+ */
+template <typename MathT, typename IdxT, typename LabelT>
+inline std::enable_if_t<std::is_floating_point_v<MathT>> predict_core(
+  const raft::resources& handle,
+  const cuvs::cluster::kmeans::balanced_params& params,
+  const MathT* centers,
+  IdxT n_clusters,
+  IdxT dim,
+  const MathT* dataset,
+  const MathT* dataset_norm,
+  IdxT n_rows,
+  LabelT* labels,
+  rmm::device_async_resource_ref mr)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  switch (params.metric) {
+    case cuvs::distance::DistanceType::L2Expanded:
+    case cuvs::distance::DistanceType::L2SqrtExpanded: {
+      auto workspace = raft::make_device_mdarray<char, IdxT>(
+        handle, mr, raft::make_extents<IdxT>((sizeof(int)) * n_rows));
+
+      auto minClusterAndDistance = raft::make_device_mdarray<raft::KeyValuePair<IdxT, MathT>, IdxT>(
+        handle, mr, raft::make_extents<IdxT>(n_rows));
+      raft::KeyValuePair<IdxT, MathT> initial_value(0, std::numeric_limits<MathT>::max());
+      thrust::fill(raft::resource::get_thrust_policy(handle),
+                   minClusterAndDistance.data_handle(),
+                   minClusterAndDistance.data_handle() + minClusterAndDistance.size(),
+                   initial_value);
+
+      auto centroidsNorm =
+        raft::make_device_mdarray<MathT, IdxT>(handle, mr, raft::make_extents<IdxT>(n_clusters));
+      raft::linalg::rowNorm<MathT, IdxT>(
+        centroidsNorm.data_handle(), centers, dim, n_clusters, raft::linalg::L2Norm, true, stream);
+
+      cuvs::distance::fusedDistanceNNMinReduce<MathT, raft::KeyValuePair<IdxT, MathT>, IdxT>(
+        minClusterAndDistance.data_handle(),
+        dataset,
+        centers,
+        dataset_norm,
+        centroidsNorm.data_handle(),
+        n_rows,
+        n_clusters,
+        dim,
+        (void*)workspace.data_handle(),
+        (params.metric == cuvs::distance::DistanceType::L2Expanded) ? false : true,
+        false,
+        true,
+        params.metric,
+        0.0f,
+        stream);
+
+      // todo(lsugy): use KVP + iterator in caller.
+      // Copy keys to output labels
+      thrust::transform(raft::resource::get_thrust_policy(handle),
+                        minClusterAndDistance.data_handle(),
+                        minClusterAndDistance.data_handle() + n_rows,
+                        labels,
+                        raft::compose_op<raft::cast_op<LabelT>, raft::key_op>());
+      break;
+    }
+    case cuvs::distance::DistanceType::InnerProduct: {
+      // TODO: pass buffer
+      rmm::device_uvector<MathT> distances(n_rows * n_clusters, stream, mr);
+
+      MathT alpha = -1.0;
+      MathT beta  = 0.0;
+
+      raft::linalg::gemm(handle,
+                         true,
+                         false,
+                         n_clusters,
+                         n_rows,
+                         dim,
+                         &alpha,
+                         centers,
+                         dim,
+                         dataset,
+                         dim,
+                         &beta,
+                         distances.data(),
+                         n_clusters,
+                         stream);
+
+      auto distances_const_view = raft::make_device_matrix_view<const MathT, IdxT, raft::row_major>(
+        distances.data(), n_rows, n_clusters);
+      auto labels_view = raft::make_device_vector_view<LabelT, IdxT>(labels, n_rows);
+      raft::matrix::argmin(handle, distances_const_view, labels_view);
+      break;
+    }
+    default: {
+      RAFT_FAIL("The chosen distance metric is not supported (%d)", int(params.metric));
+    }
+  }
+}
+
+/**
+ * @brief Suggest a minibatch size for kmeans prediction.
+ *
+ * This function is used as a heuristic to split the work over a large dataset
+ * to reduce the size of temporary memory allocations.
+ *
+ * @tparam MathT type of the centroids and mapped data
+ * @tparam IdxT  index type
+ *
+ * @param[in] n_clusters number of clusters in kmeans clustering
+ * @param[in] n_rows Number of samples in the dataset
+ * @param[in] dim Number of features in the dataset
+ * @param[in] metric Distance metric
+ * @param[in] needs_conversion Whether the data needs to be converted to MathT
+ * @return A suggested minibatch size and the expected memory cost per-row (in bytes)
+ */
+template <typename MathT, typename IdxT>
+constexpr auto calc_minibatch_size(IdxT n_clusters,
+                                   IdxT n_rows,
+                                   IdxT dim,
+                                   cuvs::distance::DistanceType metric,
+                                   bool needs_conversion) -> std::tuple<IdxT, size_t>
+{
+  n_clusters = std::max<IdxT>(1, n_clusters);
+
+  // Estimate memory needs per row (i.e element of the batch).
+  size_t mem_per_row = 0;
+  switch (metric) {
+    // fusedL2NN needs a mutex and a key-value pair for each row.
+    case distance::DistanceType::L2Expanded:
+    case distance::DistanceType::L2SqrtExpanded: {
+      mem_per_row += sizeof(int);
+      mem_per_row += sizeof(raft::KeyValuePair<IdxT, MathT>);
+    } break;
+    // Other metrics require storing a distance matrix.
+    default: {
+      mem_per_row += sizeof(MathT) * n_clusters;
+    }
+  }
+
+  // If we need to convert to MathT, space required for the converted batch.
+  if (!needs_conversion) { mem_per_row += sizeof(MathT) * dim; }
+
+  // Heuristic: calculate the minibatch size in order to use at most 1GB of memory.
+  IdxT minibatch_size = (1 << 30) / mem_per_row;
+  minibatch_size      = 64 * raft::div_rounding_up_safe(minibatch_size, IdxT{64});
+  minibatch_size      = std::min<IdxT>(minibatch_size, n_rows);
+  return std::make_tuple(minibatch_size, mem_per_row);
+}
+
+/**
+ * @brief Given the data and labels, calculate cluster centers and sizes in one sweep.
+ *
+ * @note all pointers must be accessible on the device.
+ *
+ * @tparam T          element type
+ * @tparam MathT      type of the centroids and mapped data
+ * @tparam IdxT       index type
+ * @tparam LabelT     label type
+ * @tparam CounterT   counter type supported by CUDA's native atomicAdd
+ * @tparam MappingOpT type of the mapping operation
+ *
+ * @param[in] handle The raft handle.
+ * @param[inout] centers Pointer to the output [n_clusters, dim]
+ * @param[inout] cluster_sizes Number of rows in each cluster [n_clusters]
+ * @param[in] n_clusters Number of clusters/centers
+ * @param[in] dim Dimensionality of the data
+ * @param[in] dataset Pointer to the data [n_rows, dim]
+ * @param[in] n_rows Number of samples in the `dataset`
+ * @param[in] labels Output predictions [n_rows]
+ * @param[in] reset_counters Whether to clear the output arrays before calculating.
+ *    When set to `false`, this function may be used to update existing centers and sizes using
+ *    the weighted average principle.
+ * @param[in] mapping_op Mapping operation from T to MathT
+ * @param[inout] mr (optional) Memory resource to use for temporary allocations on the device
+ */
+template <typename T,
+          typename MathT,
+          typename IdxT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT>
+void calc_centers_and_sizes(const raft::resources& handle,
+                            MathT* centers,
+                            CounterT* cluster_sizes,
+                            IdxT n_clusters,
+                            IdxT dim,
+                            const T* dataset,
+                            IdxT n_rows,
+                            const LabelT* labels,
+                            bool reset_counters,
+                            MappingOpT mapping_op,
+                            rmm::device_async_resource_ref mr)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  if (!reset_counters) {
+    raft::linalg::matrixVectorOp(
+      centers, centers, cluster_sizes, dim, n_clusters, true, false, raft::mul_op(), stream);
+  }
+
+  rmm::device_uvector<char> workspace(0, stream, mr);
+
+  // If we reset the counters, we can compute directly the new sizes in cluster_sizes.
+  // If we don't reset, we compute in a temporary buffer and add in a separate step.
+  rmm::device_uvector<CounterT> temp_cluster_sizes(0, stream, mr);
+  CounterT* temp_sizes = cluster_sizes;
+  if (!reset_counters) {
+    temp_cluster_sizes.resize(n_clusters, stream);
+    temp_sizes = temp_cluster_sizes.data();
+  }
+
+  // Apply mapping only when the data and math types are different.
+  if constexpr (std::is_same_v<T, MathT>) {
+    raft::linalg::reduce_rows_by_key(
+      dataset, dim, labels, nullptr, n_rows, dim, n_clusters, centers, stream, reset_counters);
+  } else {
+    // todo(lsugy): use iterator from KV output of fusedL2NN
+    cub::TransformInputIterator<MathT, MappingOpT, const T*> mapping_itr(dataset, mapping_op);
+    raft::linalg::reduce_rows_by_key(
+      mapping_itr, dim, labels, nullptr, n_rows, dim, n_clusters, centers, stream, reset_counters);
+  }
+
+  // Compute weight of each cluster
+  cuvs::cluster::kmeans::detail::countLabels(
+    handle, labels, temp_sizes, n_rows, n_clusters, workspace);
+
+  // Add previous sizes if necessary
+  if (!reset_counters) {
+    raft::linalg::add(cluster_sizes, cluster_sizes, temp_sizes, n_clusters, stream);
+  }
+
+  raft::linalg::matrixVectorOp(centers,
+                               centers,
+                               cluster_sizes,
+                               dim,
+                               n_clusters,
+                               true,
+                               false,
+                               raft::div_checkzero_op(),
+                               stream);
+}
+
+/** Computes the L2 norm of the dataset, converting to MathT if necessary */
+template <typename T, typename MathT, typename IdxT, typename MappingOpT>
+void compute_norm(const raft::resources& handle,
+                  MathT* dataset_norm,
+                  const T* dataset,
+                  IdxT dim,
+                  IdxT n_rows,
+                  MappingOpT mapping_op,
+                  std::optional<rmm::device_async_resource_ref> mr = std::nullopt)
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("compute_norm");
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<MathT> mapped_dataset(
+    0, stream, mr.value_or(raft::resource::get_workspace_resource(handle)));
+
+  const MathT* dataset_ptr = nullptr;
+
+  if (std::is_same_v<MathT, T>) {
+    dataset_ptr = reinterpret_cast<const MathT*>(dataset);
+  } else {
+    mapped_dataset.resize(n_rows * dim, stream);
+
+    raft::linalg::unaryOp(mapped_dataset.data(), dataset, n_rows * dim, mapping_op, stream);
+
+    dataset_ptr = static_cast<const MathT*>(mapped_dataset.data());
+  }
+
+  raft::linalg::rowNorm<MathT, IdxT>(
+    dataset_norm, dataset_ptr, dim, n_rows, raft::linalg::L2Norm, true, stream);
+}
+
+/**
+ * @brief Predict labels for the dataset.
+ *
+ * @tparam T element type
+ * @tparam MathT type of the centroids and mapped data
+ * @tparam IdxT index type
+ * @tparam LabelT label type
+ * @tparam MappingOpT type of the mapping operation
+ *
+ * @param[in] handle The raft handle
+ * @param[in] params Structure containing the hyper-parameters
+ * @param[in] centers Pointer to the row-major matrix of cluster centers [n_clusters, dim]
+ * @param[in] n_clusters Number of clusters/centers
+ * @param[in] dim Dimensionality of the data
+ * @param[in] dataset Pointer to the data [n_rows, dim]
+ * @param[in] n_rows Number samples in the `dataset`
+ * @param[out] labels Output predictions [n_rows]
+ * @param[in] mapping_op Mapping operation from T to MathT
+ * @param[inout] mr (optional) memory resource to use for temporary allocations
+ * @param[in] dataset_norm (optional) Pre-computed norms of each row in the dataset [n_rows]
+ */
+template <typename T, typename MathT, typename IdxT, typename LabelT, typename MappingOpT>
+void predict(const raft::resources& handle,
+             const cuvs::cluster::kmeans::balanced_params& params,
+             const MathT* centers,
+             IdxT n_clusters,
+             IdxT dim,
+             const T* dataset,
+             IdxT n_rows,
+             LabelT* labels,
+             MappingOpT mapping_op,
+             std::optional<rmm::device_async_resource_ref> mr = std::nullopt,
+             const MathT* dataset_norm                        = nullptr)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "predict(%zu, %u)", static_cast<size_t>(n_rows), n_clusters);
+  auto mem_res = mr.value_or(raft::resource::get_workspace_resource(handle));
+  auto [max_minibatch_size, _mem_per_row] =
+    calc_minibatch_size<MathT>(n_clusters, n_rows, dim, params.metric, std::is_same_v<T, MathT>);
+  rmm::device_uvector<MathT> cur_dataset(
+    std::is_same_v<T, MathT> ? 0 : max_minibatch_size * dim, stream, mem_res);
+  bool need_compute_norm =
+    dataset_norm == nullptr && (params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                                params.metric == cuvs::distance::DistanceType::L2SqrtExpanded);
+  rmm::device_uvector<MathT> cur_dataset_norm(
+    need_compute_norm ? max_minibatch_size : 0, stream, mem_res);
+  const MathT* dataset_norm_ptr = nullptr;
+  auto cur_dataset_ptr          = cur_dataset.data();
+  for (IdxT offset = 0; offset < n_rows; offset += max_minibatch_size) {
+    IdxT minibatch_size = std::min<IdxT>(max_minibatch_size, n_rows - offset);
+
+    if constexpr (std::is_same_v<T, MathT>) {
+      cur_dataset_ptr = const_cast<MathT*>(dataset + offset * dim);
+    } else {
+      raft::linalg::unaryOp(
+        cur_dataset_ptr, dataset + offset * dim, minibatch_size * dim, mapping_op, stream);
+    }
+
+    // Compute the norm now if it hasn't been pre-computed.
+    if (need_compute_norm) {
+      compute_norm(
+        handle, cur_dataset_norm.data(), cur_dataset_ptr, dim, minibatch_size, mapping_op, mem_res);
+      dataset_norm_ptr = cur_dataset_norm.data();
+    } else if (dataset_norm != nullptr) {
+      dataset_norm_ptr = dataset_norm + offset;
+    }
+
+    predict_core(handle,
+                 params,
+                 centers,
+                 n_clusters,
+                 dim,
+                 cur_dataset_ptr,
+                 dataset_norm_ptr,
+                 minibatch_size,
+                 labels + offset,
+                 mem_res);
+  }
+}
+
+template <uint32_t BlockDimY,
+          typename T,
+          typename MathT,
+          typename IdxT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT>
+__launch_bounds__((raft::WarpSize * BlockDimY)) RAFT_KERNEL
+  adjust_centers_kernel(MathT* centers,  // [n_clusters, dim]
+                        IdxT n_clusters,
+                        IdxT dim,
+                        const T* dataset,  // [n_rows, dim]
+                        IdxT n_rows,
+                        const LabelT* labels,           // [n_rows]
+                        const CounterT* cluster_sizes,  // [n_clusters]
+                        MathT threshold,
+                        IdxT average,
+                        IdxT seed,
+                        IdxT* count,
+                        MappingOpT mapping_op)
+{
+  IdxT l = threadIdx.y + BlockDimY * static_cast<IdxT>(blockIdx.y);
+  if (l >= n_clusters) return;
+  auto csize = static_cast<IdxT>(cluster_sizes[l]);
+  // skip big clusters
+  if (csize > static_cast<IdxT>(average * threshold)) return;
+
+  // choose a "random" i that belongs to a rather large cluster
+  IdxT i;
+  IdxT j = raft::laneId();
+  if (j == 0) {
+    do {
+      auto old = atomicAdd(count, IdxT{1});
+      i        = (seed * (old + 1)) % n_rows;
+    } while (static_cast<IdxT>(cluster_sizes[labels[i]]) < average);
+  }
+  i = raft::shfl(i, 0);
+
+  // Adjust the center of the selected smaller cluster to gravitate towards
+  // a sample from the selected larger cluster.
+  const IdxT li = static_cast<IdxT>(labels[i]);
+  // Weight of the current center for the weighted average.
+  // We dump it for anomalously small clusters, but keep constant otherwise.
+  const MathT wc = min(static_cast<MathT>(csize), static_cast<MathT>(kAdjustCentersWeight));
+  // Weight for the datapoint used to shift the center.
+  const MathT wd = 1.0;
+  for (; j < dim; j += raft::WarpSize) {
+    MathT val = 0;
+    val += wc * centers[j + dim * li];
+    val += wd * mapping_op(dataset[j + dim * i]);
+    val /= wc + wd;
+    centers[j + dim * l] = val;
+  }
+}
+
+/**
+ * @brief Adjust centers for clusters that have small number of entries.
+ *
+ * For each cluster, where the cluster size is not bigger than a threshold, the center is moved
+ * towards a data point that belongs to a large cluster.
+ *
+ * NB: if this function returns `true`, you should update the labels.
+ *
+ * NB: all pointers must be on the device side.
+ *
+ * @tparam T element type
+ * @tparam MathT type of the centroids and mapped data
+ * @tparam IdxT index type
+ * @tparam LabelT label type
+ * @tparam CounterT counter type supported by CUDA's native atomicAdd
+ * @tparam MappingOpT type of the mapping operation
+ *
+ * @param[inout] centers cluster centers [n_clusters, dim]
+ * @param[in] n_clusters number of rows in `centers`
+ * @param[in] dim number of columns in `centers` and `dataset`
+ * @param[in] dataset a host pointer to the row-major data matrix [n_rows, dim]
+ * @param[in] n_rows number of rows in `dataset`
+ * @param[in] labels a host pointer to the cluster indices [n_rows]
+ * @param[in] cluster_sizes number of rows in each cluster [n_clusters]
+ * @param[in] threshold defines a criterion for adjusting a cluster
+ *                   (cluster_sizes <= average_size * threshold)
+ *                   0 <= threshold < 1
+ * @param[in] mapping_op Mapping operation from T to MathT
+ * @param[in] stream CUDA stream
+ * @param[inout] device_memory  memory resource to use for temporary allocations
+ *
+ * @return whether any of the centers has been updated (and thus, `labels` need to be recalculated).
+ */
+template <typename T,
+          typename MathT,
+          typename IdxT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT>
+auto adjust_centers(MathT* centers,
+                    IdxT n_clusters,
+                    IdxT dim,
+                    const T* dataset,
+                    IdxT n_rows,
+                    const LabelT* labels,
+                    const CounterT* cluster_sizes,
+                    MathT threshold,
+                    MappingOpT mapping_op,
+                    rmm::cuda_stream_view stream,
+                    rmm::device_async_resource_ref device_memory) -> bool
+{
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "adjust_centers(%zu, %u)", static_cast<size_t>(n_rows), n_clusters);
+  if (n_clusters == 0) { return false; }
+  constexpr static std::array kPrimes{29,   71,   113,  173,  229,  281,  349,  409,  463,  541,
+                                      601,  659,  733,  809,  863,  941,  1013, 1069, 1151, 1223,
+                                      1291, 1373, 1451, 1511, 1583, 1657, 1733, 1811, 1889, 1987,
+                                      2053, 2129, 2213, 2287, 2357, 2423, 2531, 2617, 2687, 2741};
+  static IdxT i        = 0;
+  static IdxT i_primes = 0;
+
+  bool adjusted = false;
+  IdxT average  = n_rows / n_clusters;
+  IdxT ofst;
+  do {
+    i_primes = (i_primes + 1) % kPrimes.size();
+    ofst     = kPrimes[i_primes];
+  } while (n_rows % ofst == 0);
+
+  constexpr uint32_t kBlockDimY = 4;
+  const dim3 block_dim(raft::WarpSize, kBlockDimY, 1);
+  const dim3 grid_dim(1, raft::ceildiv(n_clusters, static_cast<IdxT>(kBlockDimY)), 1);
+  rmm::device_scalar<IdxT> update_count(0, stream, device_memory);
+  adjust_centers_kernel<kBlockDimY><<<grid_dim, block_dim, 0, stream>>>(centers,
+                                                                        n_clusters,
+                                                                        dim,
+                                                                        dataset,
+                                                                        n_rows,
+                                                                        labels,
+                                                                        cluster_sizes,
+                                                                        threshold,
+                                                                        average,
+                                                                        ofst,
+                                                                        update_count.data(),
+                                                                        mapping_op);
+  adjusted = update_count.value(stream) > 0;  // NB: rmm scalar performs the sync
+
+  return adjusted;
+}
+
+/**
+ * @brief Expectation-maximization-balancing combined in an iterative process.
+ *
+ * Note, the `cluster_centers` is assumed to be already initialized here.
+ * Thus, this function can be used for fine-tuning existing clusters;
+ * to train from scratch, use `build_clusters` function below.
+ *
+ * @tparam T      element type
+ * @tparam MathT  type of the centroids and mapped data
+ * @tparam IdxT   index type
+ * @tparam LabelT label type
+ * @tparam CounterT counter type supported by CUDA's native atomicAdd
+ * @tparam MappingOpT type of the mapping operation
+ *
+ * @param[in] handle The raft handle
+ * @param[in] params Structure containing the hyper-parameters
+ * @param[in] n_iters Requested number of iterations (can differ from params.n_iter!)
+ * @param[in] dim Dimensionality of the dataset
+ * @param[in] dataset Pointer to a managed row-major array [n_rows, dim]
+ * @param[in] dataset_norm Pointer to the precomputed norm (for L2 metrics only) [n_rows]
+ * @param[in] n_rows Number of rows in the dataset
+ * @param[in] n_cluster Requested number of clusters
+ * @param[inout] cluster_centers Pointer to a managed row-major array [n_clusters, dim]
+ * @param[out] cluster_labels Pointer to a managed row-major array [n_rows]
+ * @param[out] cluster_sizes Pointer to a managed row-major array [n_clusters]
+ * @param[in] balancing_pullback
+ *   if the cluster centers are rebalanced on this number of iterations,
+ *   one extra iteration is performed (this could happen several times) (default should be `2`).
+ *   In other words, the first and then every `ballancing_pullback`-th rebalancing operation adds
+ *   one more iteration to the main cycle.
+ * @param[in] balancing_threshold
+ *   the rebalancing takes place if any cluster is smaller than `avg_size * balancing_threshold`
+ *   on a given iteration (default should be `~ 0.25`).
+ * @param[in] mapping_op Mapping operation from T to MathT
+ * @param[inout] device_memory
+ *   A memory resource for device allocations (makes sense to provide a memory pool here)
+ */
+template <typename T,
+          typename MathT,
+          typename IdxT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT>
+void balancing_em_iters(const raft::resources& handle,
+                        const cuvs::cluster::kmeans::balanced_params& params,
+                        uint32_t n_iters,
+                        IdxT dim,
+                        const T* dataset,
+                        const MathT* dataset_norm,
+                        IdxT n_rows,
+                        IdxT n_clusters,
+                        MathT* cluster_centers,
+                        LabelT* cluster_labels,
+                        CounterT* cluster_sizes,
+                        uint32_t balancing_pullback,
+                        MathT balancing_threshold,
+                        MappingOpT mapping_op,
+                        rmm::device_async_resource_ref device_memory)
+{
+  auto stream                = raft::resource::get_cuda_stream(handle);
+  uint32_t balancing_counter = balancing_pullback;
+  for (uint32_t iter = 0; iter < n_iters; iter++) {
+    // Balancing step - move the centers around to equalize cluster sizes
+    // (but not on the first iteration)
+    if (iter > 0 && adjust_centers(cluster_centers,
+                                   n_clusters,
+                                   dim,
+                                   dataset,
+                                   n_rows,
+                                   cluster_labels,
+                                   cluster_sizes,
+                                   balancing_threshold,
+                                   mapping_op,
+                                   stream,
+                                   device_memory)) {
+      if (balancing_counter++ >= balancing_pullback) {
+        balancing_counter -= balancing_pullback;
+        n_iters++;
+      }
+    }
+    switch (params.metric) {
+      // For some metrics, cluster calculation and adjustment tends to favor zero center vectors.
+      // To avoid converging to zero, we normalize the center vectors on every iteration.
+      case cuvs::distance::DistanceType::InnerProduct:
+      case cuvs::distance::DistanceType::CosineExpanded:
+      case cuvs::distance::DistanceType::CorrelationExpanded: {
+        auto clusters_in_view = raft::make_device_matrix_view<const MathT, IdxT, raft::row_major>(
+          cluster_centers, n_clusters, dim);
+        auto clusters_out_view = raft::make_device_matrix_view<MathT, IdxT, raft::row_major>(
+          cluster_centers, n_clusters, dim);
+        raft::linalg::row_normalize(
+          handle, clusters_in_view, clusters_out_view, raft::linalg::L2Norm);
+        break;
+      }
+      default: break;
+    }
+    // E: Expectation step - predict labels
+    predict(handle,
+            params,
+            cluster_centers,
+            n_clusters,
+            dim,
+            dataset,
+            n_rows,
+            cluster_labels,
+            mapping_op,
+            device_memory,
+            dataset_norm);
+    // M: Maximization step - calculate optimal cluster centers
+    calc_centers_and_sizes(handle,
+                           cluster_centers,
+                           cluster_sizes,
+                           n_clusters,
+                           dim,
+                           dataset,
+                           n_rows,
+                           cluster_labels,
+                           true,
+                           mapping_op,
+                           device_memory);
+  }
+}
+
+/** Randomly initialize cluster centers and then call `balancing_em_iters`. */
+template <typename T,
+          typename MathT,
+          typename IdxT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT>
+void build_clusters(const raft::resources& handle,
+                    const cuvs::cluster::kmeans::balanced_params& params,
+                    IdxT dim,
+                    const T* dataset,
+                    IdxT n_rows,
+                    IdxT n_clusters,
+                    MathT* cluster_centers,
+                    LabelT* cluster_labels,
+                    CounterT* cluster_sizes,
+                    MappingOpT mapping_op,
+                    rmm::device_async_resource_ref device_memory,
+                    const MathT* dataset_norm = nullptr)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  // "randomly" initialize labels
+  auto labels_view = raft::make_device_vector_view<LabelT, IdxT>(cluster_labels, n_rows);
+  raft::linalg::map_offset(
+    handle,
+    labels_view,
+    raft::compose_op(raft::cast_op<LabelT>(), raft::mod_const_op<IdxT>(n_clusters)));
+
+  // update centers to match the initialized labels.
+  calc_centers_and_sizes(handle,
+                         cluster_centers,
+                         cluster_sizes,
+                         n_clusters,
+                         dim,
+                         dataset,
+                         n_rows,
+                         cluster_labels,
+                         true,
+                         mapping_op,
+                         device_memory);
+
+  // run EM
+  balancing_em_iters(handle,
+                     params,
+                     params.n_iters,
+                     dim,
+                     dataset,
+                     dataset_norm,
+                     n_rows,
+                     n_clusters,
+                     cluster_centers,
+                     cluster_labels,
+                     cluster_sizes,
+                     2,
+                     MathT{0.25},
+                     mapping_op,
+                     device_memory);
+}
+
+/** Calculate how many fine clusters should belong to each mesocluster. */
+template <typename IdxT, typename CounterT>
+inline auto arrange_fine_clusters(IdxT n_clusters,
+                                  IdxT n_mesoclusters,
+                                  IdxT n_rows,
+                                  const CounterT* mesocluster_sizes)
+{
+  std::vector<IdxT> fine_clusters_nums(n_mesoclusters);
+  std::vector<IdxT> fine_clusters_csum(n_mesoclusters + 1);
+  fine_clusters_csum[0] = 0;
+
+  IdxT n_lists_rem       = n_clusters;
+  IdxT n_nonempty_ms_rem = 0;
+  for (IdxT i = 0; i < n_mesoclusters; i++) {
+    n_nonempty_ms_rem += mesocluster_sizes[i] > CounterT{0} ? 1 : 0;
+  }
+  IdxT n_rows_rem               = n_rows;
+  CounterT mesocluster_size_sum = 0;
+  CounterT mesocluster_size_max = 0;
+  IdxT fine_clusters_nums_max   = 0;
+  for (IdxT i = 0; i < n_mesoclusters; i++) {
+    if (i < n_mesoclusters - 1) {
+      // Although the algorithm is meant to produce balanced clusters, when something
+      // goes wrong, we may get empty clusters (e.g. during development/debugging).
+      // The code below ensures a proportional arrangement of fine cluster numbers
+      // per mesocluster, even if some clusters are empty.
+      if (mesocluster_sizes[i] == 0) {
+        fine_clusters_nums[i] = 0;
+      } else {
+        n_nonempty_ms_rem--;
+        auto s = static_cast<IdxT>(
+          static_cast<double>(n_lists_rem * mesocluster_sizes[i]) / n_rows_rem + .5);
+        s                     = std::min<IdxT>(s, n_lists_rem - n_nonempty_ms_rem);
+        fine_clusters_nums[i] = std::max(s, IdxT{1});
+      }
+    } else {
+      fine_clusters_nums[i] = n_lists_rem;
+    }
+    n_lists_rem -= fine_clusters_nums[i];
+    n_rows_rem -= mesocluster_sizes[i];
+    mesocluster_size_max = max(mesocluster_size_max, mesocluster_sizes[i]);
+    mesocluster_size_sum += mesocluster_sizes[i];
+    fine_clusters_nums_max    = max(fine_clusters_nums_max, fine_clusters_nums[i]);
+    fine_clusters_csum[i + 1] = fine_clusters_csum[i] + fine_clusters_nums[i];
+  }
+
+  RAFT_EXPECTS(static_cast<IdxT>(mesocluster_size_sum) == n_rows,
+               "mesocluster sizes do not add up (%zu) to the total trainset size (%zu)",
+               static_cast<size_t>(mesocluster_size_sum),
+               static_cast<size_t>(n_rows));
+  RAFT_EXPECTS(fine_clusters_csum[n_mesoclusters] == n_clusters,
+               "fine cluster numbers do not add up (%zu) to the total number of clusters (%zu)",
+               static_cast<size_t>(fine_clusters_csum[n_mesoclusters]),
+               static_cast<size_t>(n_clusters));
+
+  return std::make_tuple(static_cast<IdxT>(mesocluster_size_max),
+                         fine_clusters_nums_max,
+                         std::move(fine_clusters_nums),
+                         std::move(fine_clusters_csum));
+}
+
+/**
+ *  Given the (coarse) mesoclusters and the distribution of fine clusters within them,
+ *  build the fine clusters.
+ *
+ *  Processing one mesocluster at a time:
+ *   1. Copy mesocluster data into a separate buffer
+ *   2. Predict fine cluster
+ *   3. Refince the fine cluster centers
+ *
+ *  As a result, the fine clusters are what is returned by `build_hierarchical`;
+ *  this function returns the total number of fine clusters, which can be checked to be
+ *  the same as the requested number of clusters.
+ *
+ *  Note: this function uses at most `fine_clusters_nums_max` points per mesocluster for training;
+ *  if one of the clusters is larger than that (as given by `mesocluster_sizes`), the extra data
+ *  is ignored.
+ */
+template <typename T,
+          typename MathT,
+          typename IdxT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT>
+auto build_fine_clusters(const raft::resources& handle,
+                         const cuvs::cluster::kmeans::balanced_params& params,
+                         IdxT dim,
+                         const T* dataset_mptr,
+                         const MathT* dataset_norm_mptr,
+                         const LabelT* labels_mptr,
+                         IdxT n_rows,
+                         const IdxT* fine_clusters_nums,
+                         const IdxT* fine_clusters_csum,
+                         const CounterT* mesocluster_sizes,
+                         IdxT n_mesoclusters,
+                         IdxT mesocluster_size_max,
+                         IdxT fine_clusters_nums_max,
+                         MathT* cluster_centers,
+                         MappingOpT mapping_op,
+                         rmm::device_async_resource_ref managed_memory,
+                         rmm::device_async_resource_ref device_memory) -> IdxT
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<IdxT> mc_trainset_ids_buf(mesocluster_size_max, stream, managed_memory);
+  rmm::device_uvector<MathT> mc_trainset_buf(mesocluster_size_max * dim, stream, device_memory);
+  rmm::device_uvector<MathT> mc_trainset_norm_buf(mesocluster_size_max, stream, device_memory);
+  auto mc_trainset_ids  = mc_trainset_ids_buf.data();
+  auto mc_trainset      = mc_trainset_buf.data();
+  auto mc_trainset_norm = mc_trainset_norm_buf.data();
+
+  // label (cluster ID) of each vector
+  rmm::device_uvector<LabelT> mc_trainset_labels(mesocluster_size_max, stream, device_memory);
+
+  rmm::device_uvector<MathT> mc_trainset_ccenters(
+    fine_clusters_nums_max * dim, stream, device_memory);
+  // number of vectors in each cluster
+  rmm::device_uvector<CounterT> mc_trainset_csizes_tmp(
+    fine_clusters_nums_max, stream, device_memory);
+
+  // Training clusters in each meso-cluster
+  IdxT n_clusters_done = 0;
+  for (IdxT i = 0; i < n_mesoclusters; i++) {
+    IdxT k = 0;
+    for (IdxT j = 0; j < n_rows && k < mesocluster_size_max; j++) {
+      if (labels_mptr[j] == LabelT(i)) { mc_trainset_ids[k++] = j; }
+    }
+    if (k != static_cast<IdxT>(mesocluster_sizes[i]))
+      RAFT_LOG_DEBUG("Incorrect mesocluster size at %d. %zu vs %zu",
+                     static_cast<int>(i),
+                     static_cast<size_t>(k),
+                     static_cast<size_t>(mesocluster_sizes[i]));
+    if (k == 0) {
+      RAFT_LOG_DEBUG("Empty cluster %d", i);
+      RAFT_EXPECTS(fine_clusters_nums[i] == 0,
+                   "Number of fine clusters must be zero for the empty mesocluster (got %d)",
+                   static_cast<int>(fine_clusters_nums[i]));
+      continue;
+    } else {
+      RAFT_EXPECTS(fine_clusters_nums[i] > 0,
+                   "Number of fine clusters must be non-zero for a non-empty mesocluster");
+    }
+
+    cub::TransformInputIterator<MathT, MappingOpT, const T*> mapping_itr(dataset_mptr, mapping_op);
+    raft::matrix::gather(mapping_itr, dim, n_rows, mc_trainset_ids, k, mc_trainset, stream);
+    if (params.metric == cuvs::distance::DistanceType::L2Expanded ||
+        params.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+      thrust::gather(raft::resource::get_thrust_policy(handle),
+                     mc_trainset_ids,
+                     mc_trainset_ids + k,
+                     dataset_norm_mptr,
+                     mc_trainset_norm);
+    }
+
+    build_clusters(handle,
+                   params,
+                   dim,
+                   mc_trainset,
+                   k,
+                   fine_clusters_nums[i],
+                   mc_trainset_ccenters.data(),
+                   mc_trainset_labels.data(),
+                   mc_trainset_csizes_tmp.data(),
+                   mapping_op,
+                   device_memory,
+                   mc_trainset_norm);
+
+    raft::copy(cluster_centers + (dim * fine_clusters_csum[i]),
+               mc_trainset_ccenters.data(),
+               fine_clusters_nums[i] * dim,
+               stream);
+    raft::resource::sync_stream(handle, stream);
+    n_clusters_done += fine_clusters_nums[i];
+  }
+  return n_clusters_done;
+}
+
+/**
+ * @brief Hierarchical balanced k-means
+ *
+ * @tparam T      element type
+ * @tparam MathT  type of the centroids and mapped data
+ * @tparam IdxT   index type
+ * @tparam LabelT label type
+ * @tparam MappingOpT type of the mapping operation
+ *
+ * @param[in] handle The raft handle.
+ * @param[in] params Structure containing the hyper-parameters
+ * @param dim number of columns in `centers` and `dataset`
+ * @param[in] dataset a device pointer to the source dataset [n_rows, dim]
+ * @param n_rows number of rows in the input
+ * @param[out] cluster_centers a device pointer to the found cluster centers [n_cluster, dim]
+ * @param n_cluster
+ * @param metric the distance type
+ * @param mapping_op Mapping operation from T to MathT
+ * @param stream
+ */
+template <typename T, typename MathT, typename IdxT, typename MappingOpT>
+void build_hierarchical(const raft::resources& handle,
+                        const cuvs::cluster::kmeans::balanced_params& params,
+                        IdxT dim,
+                        const T* dataset,
+                        IdxT n_rows,
+                        MathT* cluster_centers,
+                        IdxT n_clusters,
+                        MappingOpT mapping_op)
+{
+  auto stream  = raft::resource::get_cuda_stream(handle);
+  using LabelT = uint32_t;
+
+  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+    "build_hierarchical(%zu, %u)", static_cast<size_t>(n_rows), n_clusters);
+
+  IdxT n_mesoclusters = std::min(n_clusters, static_cast<IdxT>(std::sqrt(n_clusters) + 0.5));
+  RAFT_LOG_DEBUG("build_hierarchical: n_mesoclusters: %u", n_mesoclusters);
+
+  // TODO: Remove the explicit managed memory- we shouldn't be creating this on the user's behalf.
+  rmm::mr::managed_memory_resource managed_memory;
+  rmm::device_async_resource_ref device_memory = raft::resource::get_workspace_resource(handle);
+  auto [max_minibatch_size, mem_per_row] =
+    calc_minibatch_size<MathT>(n_clusters, n_rows, dim, params.metric, std::is_same_v<T, MathT>);
+
+  // Precompute the L2 norm of the dataset if relevant.
+  const MathT* dataset_norm = nullptr;
+  rmm::device_uvector<MathT> dataset_norm_buf(0, stream, device_memory);
+  if (params.metric == cuvs::distance::DistanceType::L2Expanded ||
+      params.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    dataset_norm_buf.resize(n_rows, stream);
+    for (IdxT offset = 0; offset < n_rows; offset += max_minibatch_size) {
+      IdxT minibatch_size = std::min<IdxT>(max_minibatch_size, n_rows - offset);
+      compute_norm(handle,
+                   dataset_norm_buf.data() + offset,
+                   dataset + dim * offset,
+                   dim,
+                   minibatch_size,
+                   mapping_op,
+                   device_memory);
+    }
+    dataset_norm = (const MathT*)dataset_norm_buf.data();
+  }
+
+  /* Temporary workaround to cub::DeviceHistogram not supporting any type that isn't natively
+   * supported by atomicAdd: find a supported CounterT based on the IdxT. */
+  typedef typename std::conditional_t<sizeof(IdxT) == 8, unsigned long long int, unsigned int>
+    CounterT;
+
+  // build coarse clusters (mesoclusters)
+  rmm::device_uvector<LabelT> mesocluster_labels_buf(n_rows, stream, &managed_memory);
+  rmm::device_uvector<CounterT> mesocluster_sizes_buf(n_mesoclusters, stream, &managed_memory);
+  {
+    rmm::device_uvector<MathT> mesocluster_centers_buf(n_mesoclusters * dim, stream, device_memory);
+    build_clusters(handle,
+                   params,
+                   dim,
+                   dataset,
+                   n_rows,
+                   n_mesoclusters,
+                   mesocluster_centers_buf.data(),
+                   mesocluster_labels_buf.data(),
+                   mesocluster_sizes_buf.data(),
+                   mapping_op,
+                   device_memory,
+                   dataset_norm);
+  }
+
+  auto mesocluster_sizes  = mesocluster_sizes_buf.data();
+  auto mesocluster_labels = mesocluster_labels_buf.data();
+
+  raft::resource::sync_stream(handle, stream);
+
+  // build fine clusters
+  auto [mesocluster_size_max, fine_clusters_nums_max, fine_clusters_nums, fine_clusters_csum] =
+    arrange_fine_clusters(n_clusters, n_mesoclusters, n_rows, mesocluster_sizes);
+
+  const IdxT mesocluster_size_max_balanced = raft::div_rounding_up_safe<size_t>(
+    2lu * size_t(n_rows), std::max<size_t>(size_t(n_mesoclusters), 1lu));
+  if (mesocluster_size_max > mesocluster_size_max_balanced) {
+    RAFT_LOG_DEBUG(
+      "build_hierarchical: built unbalanced mesoclusters (max_mesocluster_size == %u > %u). "
+      "At most %u points will be used for training within each mesocluster. "
+      "Consider increasing the number of training iterations `n_iters`.",
+      mesocluster_size_max,
+      mesocluster_size_max_balanced,
+      mesocluster_size_max_balanced);
+    RAFT_LOG_TRACE_VEC(mesocluster_sizes, n_mesoclusters);
+    RAFT_LOG_TRACE_VEC(fine_clusters_nums.data(), n_mesoclusters);
+    mesocluster_size_max = mesocluster_size_max_balanced;
+  }
+
+  auto n_clusters_done = build_fine_clusters(handle,
+                                             params,
+                                             dim,
+                                             dataset,
+                                             dataset_norm,
+                                             mesocluster_labels,
+                                             n_rows,
+                                             fine_clusters_nums.data(),
+                                             fine_clusters_csum.data(),
+                                             mesocluster_sizes,
+                                             n_mesoclusters,
+                                             mesocluster_size_max,
+                                             fine_clusters_nums_max,
+                                             cluster_centers,
+                                             mapping_op,
+                                             &managed_memory,
+                                             device_memory);
+  RAFT_EXPECTS(n_clusters_done == n_clusters, "Didn't process all clusters.");
+
+  rmm::device_uvector<CounterT> cluster_sizes(n_clusters, stream, device_memory);
+  rmm::device_uvector<LabelT> labels(n_rows, stream, device_memory);
+
+  // Fine-tuning k-means for all clusters
+  //
+  // (*) Since the likely cluster centroids have been calculated hierarchically already, the number
+  // of iterations for fine-tuning kmeans for whole clusters should be reduced. However, there is a
+  // possibility that the clusters could be unbalanced here, in which case the actual number of
+  // iterations would be increased.
+  //
+  balancing_em_iters(handle,
+                     params,
+                     std::max<uint32_t>(params.n_iters / 10, 2),
+                     dim,
+                     dataset,
+                     dataset_norm,
+                     n_rows,
+                     n_clusters,
+                     cluster_centers,
+                     labels.data(),
+                     cluster_sizes.data(),
+                     5,
+                     MathT{0.2},
+                     mapping_op,
+                     device_memory);
+}
+
+}  // namespace  cuvs::cluster::kmeans::detail
diff --git a/cpp/src/cluster/detail/kmeans_common.cuh b/cpp/src/cluster/detail/kmeans_common.cuh
new file mode 100644
index 0000000000..0f84421a9b
--- /dev/null
+++ b/cpp/src/cluster/detail/kmeans_common.cuh
@@ -0,0 +1,668 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../../distance/distance.cuh"
+#include "../../distance/fused_distance_nn.cuh"
+#include <cuvs/cluster/kmeans.hpp>
+#include <cuvs/distance/distance_types.hpp>
+
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/kvp.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/reduce_rows_by_key.cuh>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/random/permute.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cub/cub.cuh>
+#include <cuda.h>
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <ctime>
+#include <optional>
+#include <random>
+
+namespace cuvs::cluster::kmeans::detail {
+
+template <typename DataT, typename IndexT>
+struct SamplingOp {
+  DataT* rnd;
+  uint8_t* flag;
+  DataT cluster_cost;
+  double oversampling_factor;
+  IndexT n_clusters;
+
+  CUB_RUNTIME_FUNCTION __forceinline__
+  SamplingOp(DataT c, double l, IndexT k, DataT* rand, uint8_t* ptr)
+    : cluster_cost(c), oversampling_factor(l), n_clusters(k), rnd(rand), flag(ptr)
+  {
+  }
+
+  __host__ __device__ __forceinline__ bool operator()(
+    const raft::KeyValuePair<ptrdiff_t, DataT>& a) const
+  {
+    DataT prob_threshold = (DataT)rnd[a.key];
+
+    DataT prob_x = ((oversampling_factor * n_clusters * a.value) / cluster_cost);
+
+    return !flag[a.key] && (prob_x > prob_threshold);
+  }
+};
+
+template <typename IndexT, typename DataT>
+struct KeyValueIndexOp {
+  __host__ __device__ __forceinline__ IndexT
+  operator()(const raft::KeyValuePair<IndexT, DataT>& a) const
+  {
+    return a.key;
+  }
+};
+
+// Computes the intensity histogram from a sequence of labels
+template <typename SampleIteratorT, typename CounterT, typename IndexT>
+void countLabels(raft::resources const& handle,
+                 SampleIteratorT labels,
+                 CounterT* count,
+                 IndexT n_samples,
+                 IndexT n_clusters,
+                 rmm::device_uvector<char>& workspace)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  // CUB::DeviceHistogram requires a signed index type
+  typedef typename std::make_signed_t<IndexT> CubIndexT;
+
+  CubIndexT num_levels  = n_clusters + 1;
+  CubIndexT lower_level = 0;
+  CubIndexT upper_level = n_clusters;
+
+  size_t temp_storage_bytes = 0;
+  RAFT_CUDA_TRY(cub::DeviceHistogram::HistogramEven(nullptr,
+                                                    temp_storage_bytes,
+                                                    labels,
+                                                    count,
+                                                    num_levels,
+                                                    lower_level,
+                                                    upper_level,
+                                                    static_cast<CubIndexT>(n_samples),
+                                                    stream));
+
+  workspace.resize(temp_storage_bytes, stream);
+
+  RAFT_CUDA_TRY(cub::DeviceHistogram::HistogramEven(workspace.data(),
+                                                    temp_storage_bytes,
+                                                    labels,
+                                                    count,
+                                                    num_levels,
+                                                    lower_level,
+                                                    upper_level,
+                                                    static_cast<CubIndexT>(n_samples),
+                                                    stream));
+}
+
+template <typename DataT, typename IndexT>
+void checkWeight(raft::resources const& handle,
+                 raft::device_vector_view<DataT, IndexT> weight,
+                 rmm::device_uvector<char>& workspace)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto wt_aggr        = raft::make_device_scalar<DataT>(handle, 0);
+  auto n_samples      = weight.extent(0);
+
+  size_t temp_storage_bytes = 0;
+  RAFT_CUDA_TRY(cub::DeviceReduce::Sum(
+    nullptr, temp_storage_bytes, weight.data_handle(), wt_aggr.data_handle(), n_samples, stream));
+
+  workspace.resize(temp_storage_bytes, stream);
+
+  RAFT_CUDA_TRY(cub::DeviceReduce::Sum(workspace.data(),
+                                       temp_storage_bytes,
+                                       weight.data_handle(),
+                                       wt_aggr.data_handle(),
+                                       n_samples,
+                                       stream));
+  DataT wt_sum = 0;
+  raft::copy(&wt_sum, wt_aggr.data_handle(), 1, stream);
+  raft::resource::sync_stream(handle, stream);
+
+  if (wt_sum != n_samples) {
+    RAFT_LOG_DEBUG(
+      "[Warning!] KMeans: normalizing the user provided sample weight to "
+      "sum up to %d samples",
+      n_samples);
+
+    auto scale = static_cast<DataT>(n_samples) / wt_sum;
+    raft::linalg::unaryOp(weight.data_handle(),
+                          weight.data_handle(),
+                          n_samples,
+                          raft::mul_const_op<DataT>{scale},
+                          stream);
+  }
+}
+
+template <typename IndexT>
+IndexT getDataBatchSize(int batch_samples, IndexT n_samples)
+{
+  auto minVal = std::min(static_cast<IndexT>(batch_samples), n_samples);
+  return (minVal == 0) ? n_samples : minVal;
+}
+
+template <typename IndexT>
+IndexT getCentroidsBatchSize(int batch_centroids, IndexT n_local_clusters)
+{
+  auto minVal = std::min(static_cast<IndexT>(batch_centroids), n_local_clusters);
+  return (minVal == 0) ? n_local_clusters : minVal;
+}
+
+template <typename InputT,
+          typename OutputT,
+          typename MainOpT,
+          typename ReductionOpT,
+          typename IndexT = int>
+void computeClusterCost(raft::resources const& handle,
+                        raft::device_vector_view<InputT, IndexT> minClusterDistance,
+                        rmm::device_uvector<char>& workspace,
+                        raft::device_scalar_view<OutputT> clusterCost,
+                        MainOpT main_op,
+                        ReductionOpT reduction_op)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  cub::TransformInputIterator<OutputT, MainOpT, InputT*> itr(minClusterDistance.data_handle(),
+                                                             main_op);
+
+  size_t temp_storage_bytes = 0;
+  RAFT_CUDA_TRY(cub::DeviceReduce::Reduce(nullptr,
+                                          temp_storage_bytes,
+                                          itr,
+                                          clusterCost.data_handle(),
+                                          minClusterDistance.size(),
+                                          reduction_op,
+                                          OutputT(),
+                                          stream));
+
+  workspace.resize(temp_storage_bytes, stream);
+
+  RAFT_CUDA_TRY(cub::DeviceReduce::Reduce(workspace.data(),
+                                          temp_storage_bytes,
+                                          itr,
+                                          clusterCost.data_handle(),
+                                          minClusterDistance.size(),
+                                          reduction_op,
+                                          OutputT(),
+                                          stream));
+}
+
+template <typename DataT, typename IndexT>
+void sampleCentroids(raft::resources const& handle,
+                     raft::device_matrix_view<const DataT, IndexT> X,
+                     raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                     raft::device_vector_view<uint8_t, IndexT> isSampleCentroid,
+                     SamplingOp<DataT, IndexT>& select_op,
+                     rmm::device_uvector<DataT>& inRankCp,
+                     rmm::device_uvector<char>& workspace)
+{
+  cudaStream_t stream  = raft::resource::get_cuda_stream(handle);
+  auto n_local_samples = X.extent(0);
+  auto n_features      = X.extent(1);
+
+  auto nSelected = raft::make_device_scalar<IndexT>(handle, 0);
+  cub::ArgIndexInputIterator<DataT*> ip_itr(minClusterDistance.data_handle());
+  auto sampledMinClusterDistance =
+    raft::make_device_vector<raft::KeyValuePair<ptrdiff_t, DataT>, IndexT>(handle, n_local_samples);
+  size_t temp_storage_bytes = 0;
+  RAFT_CUDA_TRY(cub::DeviceSelect::If(nullptr,
+                                      temp_storage_bytes,
+                                      ip_itr,
+                                      sampledMinClusterDistance.data_handle(),
+                                      nSelected.data_handle(),
+                                      n_local_samples,
+                                      select_op,
+                                      stream));
+
+  workspace.resize(temp_storage_bytes, stream);
+
+  RAFT_CUDA_TRY(cub::DeviceSelect::If(workspace.data(),
+                                      temp_storage_bytes,
+                                      ip_itr,
+                                      sampledMinClusterDistance.data_handle(),
+                                      nSelected.data_handle(),
+                                      n_local_samples,
+                                      select_op,
+                                      stream));
+
+  IndexT nPtsSampledInRank = 0;
+  raft::copy(&nPtsSampledInRank, nSelected.data_handle(), 1, stream);
+  raft::resource::sync_stream(handle, stream);
+
+  uint8_t* rawPtr_isSampleCentroid = isSampleCentroid.data_handle();
+  thrust::for_each_n(raft::resource::get_thrust_policy(handle),
+                     sampledMinClusterDistance.data_handle(),
+                     nPtsSampledInRank,
+                     [=] __device__(raft::KeyValuePair<ptrdiff_t, DataT> val) {
+                       rawPtr_isSampleCentroid[val.key] = 1;
+                     });
+
+  inRankCp.resize(nPtsSampledInRank * n_features, stream);
+
+  raft::matrix::gather((DataT*)X.data_handle(),
+                       X.extent(1),
+                       X.extent(0),
+                       sampledMinClusterDistance.data_handle(),
+                       nPtsSampledInRank,
+                       inRankCp.data(),
+                       raft::key_op{},
+                       stream);
+}
+
+// calculate pairwise distance between 'dataset[n x d]' and 'centroids[k x d]',
+// result will be stored in 'pairwiseDistance[n x k]'
+template <typename DataT, typename IndexT>
+void pairwise_distance_kmeans(raft::resources const& handle,
+                              raft::device_matrix_view<const DataT, IndexT> X,
+                              raft::device_matrix_view<const DataT, IndexT> centroids,
+                              raft::device_matrix_view<DataT, IndexT> pairwiseDistance,
+                              rmm::device_uvector<char>& workspace,
+                              cuvs::distance::DistanceType metric)
+{
+  auto n_samples  = X.extent(0);
+  auto n_features = X.extent(1);
+  auto n_clusters = centroids.extent(0);
+
+  ASSERT(X.extent(1) == centroids.extent(1),
+         "# features in dataset and centroids are different (must be same)");
+
+  cuvs::distance::pairwise_distance(handle,
+                                    X.data_handle(),
+                                    centroids.data_handle(),
+                                    pairwiseDistance.data_handle(),
+                                    n_samples,
+                                    n_clusters,
+                                    n_features,
+                                    workspace,
+                                    metric);
+}
+
+// shuffle and randomly select 'n_samples_to_gather' from input 'in' and stores
+// in 'out' does not modify the input
+template <typename DataT, typename IndexT>
+void shuffleAndGather(raft::resources const& handle,
+                      raft::device_matrix_view<const DataT, IndexT> in,
+                      raft::device_matrix_view<DataT, IndexT> out,
+                      uint32_t n_samples_to_gather,
+                      uint64_t seed)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = in.extent(0);
+  auto n_features     = in.extent(1);
+
+  auto indices = raft::make_device_vector<IndexT, IndexT>(handle, n_samples);
+
+  // shuffle indices on device
+  raft::random::permute<DataT, IndexT, IndexT>(indices.data_handle(),
+                                               nullptr,
+                                               nullptr,
+                                               (IndexT)in.extent(1),
+                                               (IndexT)in.extent(0),
+                                               true,
+                                               stream);
+
+  raft::matrix::gather((DataT*)in.data_handle(),
+                       in.extent(1),
+                       in.extent(0),
+                       indices.data_handle(),
+                       static_cast<IndexT>(n_samples_to_gather),
+                       out.data_handle(),
+                       stream);
+}
+
+// Calculates a <key, value> pair for every sample in input 'X' where key is an
+// index to an sample in 'centroids' (index of the nearest centroid) and 'value'
+// is the distance between the sample and the 'centroid[key]'
+template <typename DataT, typename IndexT>
+void minClusterAndDistanceCompute(
+  raft::resources const& handle,
+  raft::device_matrix_view<const DataT, IndexT> X,
+  raft::device_matrix_view<const DataT, IndexT> centroids,
+  raft::device_vector_view<raft::KeyValuePair<IndexT, DataT>, IndexT> minClusterAndDistance,
+  raft::device_vector_view<const DataT, IndexT> L2NormX,
+  rmm::device_uvector<DataT>& L2NormBuf_OR_DistBuf,
+  cuvs::distance::DistanceType metric,
+  int batch_samples,
+  int batch_centroids,
+  rmm::device_uvector<char>& workspace)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = centroids.extent(0);
+  // todo(lsugy): change batch size computation when using fusedL2NN!
+  bool is_fused = metric == cuvs::distance::DistanceType::L2Expanded ||
+                  metric == cuvs::distance::DistanceType::L2SqrtExpanded;
+  auto dataBatchSize = is_fused ? (IndexT)n_samples : getDataBatchSize(batch_samples, n_samples);
+  auto centroidsBatchSize = getCentroidsBatchSize(batch_centroids, n_clusters);
+
+  if (is_fused) {
+    L2NormBuf_OR_DistBuf.resize(n_clusters, stream);
+    raft::linalg::rowNorm(L2NormBuf_OR_DistBuf.data(),
+                          centroids.data_handle(),
+                          centroids.extent(1),
+                          centroids.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  } else {
+    // TODO: Unless pool allocator is used, passing in a workspace for this
+    // isn't really increasing performance because this needs to do a re-allocation
+    // anyways. ref https://github.com/rapidsai/raft/issues/930
+    L2NormBuf_OR_DistBuf.resize(dataBatchSize * centroidsBatchSize, stream);
+  }
+
+  // Note - pairwiseDistance and centroidsNorm share the same buffer
+  // centroidsNorm [n_clusters] - tensor wrapper around centroids L2 Norm
+  auto centroidsNorm =
+    raft::make_device_vector_view<DataT, IndexT>(L2NormBuf_OR_DistBuf.data(), n_clusters);
+  // pairwiseDistance[ns x nc] - tensor wrapper around the distance buffer
+  auto pairwiseDistance = raft::make_device_matrix_view<DataT, IndexT>(
+    L2NormBuf_OR_DistBuf.data(), dataBatchSize, centroidsBatchSize);
+
+  raft::KeyValuePair<IndexT, DataT> initial_value(0, std::numeric_limits<DataT>::max());
+
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               minClusterAndDistance.data_handle(),
+               minClusterAndDistance.data_handle() + minClusterAndDistance.size(),
+               initial_value);
+
+  // tile over the input dataset
+  for (IndexT dIdx = 0; dIdx < n_samples; dIdx += dataBatchSize) {
+    // # of samples for the current batch
+    auto ns = std::min((IndexT)dataBatchSize, n_samples - dIdx);
+
+    // datasetView [ns x n_features] - view representing the current batch of
+    // input dataset
+    auto datasetView = raft::make_device_matrix_view<const DataT, IndexT>(
+      X.data_handle() + (dIdx * n_features), ns, n_features);
+
+    // minClusterAndDistanceView [ns x n_clusters]
+    auto minClusterAndDistanceView =
+      raft::make_device_vector_view<raft::KeyValuePair<IndexT, DataT>, IndexT>(
+        minClusterAndDistance.data_handle() + dIdx, ns);
+
+    auto L2NormXView =
+      raft::make_device_vector_view<const DataT, IndexT>(L2NormX.data_handle() + dIdx, ns);
+
+    if (is_fused) {
+      workspace.resize((sizeof(int)) * ns, stream);
+
+      // todo(lsugy): remove cIdx
+      cuvs::distance::fusedDistanceNNMinReduce<DataT, raft::KeyValuePair<IndexT, DataT>, IndexT>(
+        minClusterAndDistanceView.data_handle(),
+        datasetView.data_handle(),
+        centroids.data_handle(),
+        L2NormXView.data_handle(),
+        centroidsNorm.data_handle(),
+        ns,
+        n_clusters,
+        n_features,
+        (void*)workspace.data(),
+        metric != cuvs::distance::DistanceType::L2Expanded,
+        false,
+        true,
+        metric,
+        0.0f,
+        stream);
+    } else {
+      // tile over the centroids
+      for (IndexT cIdx = 0; cIdx < n_clusters; cIdx += centroidsBatchSize) {
+        // # of centroids for the current batch
+        auto nc = std::min((IndexT)centroidsBatchSize, n_clusters - cIdx);
+
+        // centroidsView [nc x n_features] - view representing the current batch
+        // of centroids
+        auto centroidsView = raft::make_device_matrix_view<const DataT, IndexT>(
+          centroids.data_handle() + (cIdx * n_features), nc, n_features);
+
+        // pairwiseDistanceView [ns x nc] - view representing the pairwise
+        // distance for current batch
+        auto pairwiseDistanceView =
+          raft::make_device_matrix_view<DataT, IndexT>(pairwiseDistance.data_handle(), ns, nc);
+
+        // calculate pairwise distance between current tile of cluster centroids
+        // and input dataset
+        pairwise_distance_kmeans<DataT, IndexT>(
+          handle, datasetView, centroidsView, pairwiseDistanceView, workspace, metric);
+
+        // argmin reduction returning <index, value> pair
+        // calculates the closest centroid and the distance to the closest
+        // centroid
+        raft::linalg::coalescedReduction(
+          minClusterAndDistanceView.data_handle(),
+          pairwiseDistanceView.data_handle(),
+          pairwiseDistanceView.extent(1),
+          pairwiseDistanceView.extent(0),
+          initial_value,
+          stream,
+          true,
+          [=] __device__(const DataT val, const IndexT i) {
+            raft::KeyValuePair<IndexT, DataT> pair;
+            pair.key   = cIdx + i;
+            pair.value = val;
+            return pair;
+          },
+          raft::argmin_op{},
+          raft::identity_op{});
+      }
+    }
+  }
+}
+
+template <typename DataT, typename IndexT>
+void minClusterDistanceCompute(raft::resources const& handle,
+                               raft::device_matrix_view<const DataT, IndexT> X,
+                               raft::device_matrix_view<DataT, IndexT> centroids,
+                               raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                               raft::device_vector_view<DataT, IndexT> L2NormX,
+                               rmm::device_uvector<DataT>& L2NormBuf_OR_DistBuf,
+                               cuvs::distance::DistanceType metric,
+                               int batch_samples,
+                               int batch_centroids,
+                               rmm::device_uvector<char>& workspace)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = centroids.extent(0);
+
+  bool is_fused = metric == cuvs::distance::DistanceType::L2Expanded ||
+                  metric == cuvs::distance::DistanceType::L2SqrtExpanded;
+  auto dataBatchSize = is_fused ? (IndexT)n_samples : getDataBatchSize(batch_samples, n_samples);
+  auto centroidsBatchSize = getCentroidsBatchSize(batch_centroids, n_clusters);
+
+  if (is_fused) {
+    L2NormBuf_OR_DistBuf.resize(n_clusters, stream);
+    raft::linalg::rowNorm(L2NormBuf_OR_DistBuf.data(),
+                          centroids.data_handle(),
+                          centroids.extent(1),
+                          centroids.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  } else {
+    L2NormBuf_OR_DistBuf.resize(dataBatchSize * centroidsBatchSize, stream);
+  }
+
+  // Note - pairwiseDistance and centroidsNorm share the same buffer
+  // centroidsNorm [n_clusters] - tensor wrapper around centroids L2 Norm
+  auto centroidsNorm =
+    raft::make_device_vector_view<DataT, IndexT>(L2NormBuf_OR_DistBuf.data(), n_clusters);
+  // pairwiseDistance[ns x nc] - tensor wrapper around the distance buffer
+  auto pairwiseDistance = raft::make_device_matrix_view<DataT, IndexT>(
+    L2NormBuf_OR_DistBuf.data(), dataBatchSize, centroidsBatchSize);
+
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               minClusterDistance.data_handle(),
+               minClusterDistance.data_handle() + minClusterDistance.size(),
+               std::numeric_limits<DataT>::max());
+
+  // tile over the input data and calculate distance matrix [n_samples x
+  // n_clusters]
+  for (IndexT dIdx = 0; dIdx < n_samples; dIdx += dataBatchSize) {
+    // # of samples for the current batch
+    auto ns = std::min((IndexT)dataBatchSize, n_samples - dIdx);
+
+    // datasetView [ns x n_features] - view representing the current batch of
+    // input dataset
+    auto datasetView = raft::make_device_matrix_view<const DataT, IndexT>(
+      X.data_handle() + dIdx * n_features, ns, n_features);
+
+    // minClusterDistanceView [ns x n_clusters]
+    auto minClusterDistanceView =
+      raft::make_device_vector_view<DataT, IndexT>(minClusterDistance.data_handle() + dIdx, ns);
+
+    auto L2NormXView =
+      raft::make_device_vector_view<DataT, IndexT>(L2NormX.data_handle() + dIdx, ns);
+
+    if (is_fused) {
+      workspace.resize((sizeof(IndexT)) * ns, stream);
+
+      cuvs::distance::fusedDistanceNNMinReduce<DataT, DataT, IndexT>(
+        minClusterDistanceView.data_handle(),
+        datasetView.data_handle(),
+        centroids.data_handle(),
+        L2NormXView.data_handle(),
+        centroidsNorm.data_handle(),
+        ns,
+        n_clusters,
+        n_features,
+        (void*)workspace.data(),
+        metric != cuvs::distance::DistanceType::L2Expanded,
+        false,
+        true,
+        metric,
+        0.0f,
+        stream);
+    } else {
+      // tile over the centroids
+      for (IndexT cIdx = 0; cIdx < n_clusters; cIdx += centroidsBatchSize) {
+        // # of centroids for the current batch
+        auto nc = std::min((IndexT)centroidsBatchSize, n_clusters - cIdx);
+
+        // centroidsView [nc x n_features] - view representing the current batch
+        // of centroids
+        auto centroidsView = raft::make_device_matrix_view<DataT, IndexT>(
+          centroids.data_handle() + cIdx * n_features, nc, n_features);
+
+        // pairwiseDistanceView [ns x nc] - view representing the pairwise
+        // distance for current batch
+        auto pairwiseDistanceView =
+          raft::make_device_matrix_view<DataT, IndexT>(pairwiseDistance.data_handle(), ns, nc);
+
+        // calculate pairwise distance between current tile of cluster centroids
+        // and input dataset
+        pairwise_distance_kmeans<DataT, IndexT>(
+          handle, datasetView, centroidsView, pairwiseDistanceView, workspace, metric);
+
+        raft::linalg::coalescedReduction(minClusterDistanceView.data_handle(),
+                                         pairwiseDistanceView.data_handle(),
+                                         pairwiseDistanceView.extent(1),
+                                         pairwiseDistanceView.extent(0),
+                                         std::numeric_limits<DataT>::max(),
+                                         stream,
+                                         true,
+                                         raft::identity_op{},
+                                         raft::min_op{},
+                                         raft::identity_op{});
+      }
+    }
+  }
+}
+
+template <typename DataT, typename IndexT>
+void countSamplesInCluster(raft::resources const& handle,
+                           const cuvs::cluster::kmeans::params& params,
+                           raft::device_matrix_view<const DataT, IndexT> X,
+                           raft::device_vector_view<const DataT, IndexT> L2NormX,
+                           raft::device_matrix_view<DataT, IndexT> centroids,
+                           rmm::device_uvector<char>& workspace,
+                           raft::device_vector_view<DataT, IndexT> sampleCountInCluster)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = centroids.extent(0);
+
+  // stores (key, value) pair corresponding to each sample where
+  //   - key is the index of nearest cluster
+  //   - value is the distance to the nearest cluster
+  auto minClusterAndDistance =
+    raft::make_device_vector<raft::KeyValuePair<IndexT, DataT>, IndexT>(handle, n_samples);
+
+  // temporary buffer to store distance matrix, destructor releases the resource
+  rmm::device_uvector<DataT> L2NormBuf_OR_DistBuf(0, stream);
+
+  // computes minClusterAndDistance[0:n_samples) where  minClusterAndDistance[i]
+  // is a <key, value> pair where
+  //   'key' is index to an sample in 'centroids' (index of the nearest
+  //   centroid) and 'value' is the distance between the sample 'X[i]' and the
+  //   'centroid[key]'
+  cuvs::cluster::kmeans::detail::minClusterAndDistanceCompute(
+    handle,
+    X,
+    (raft::device_matrix_view<const DataT, IndexT>)centroids,
+    minClusterAndDistance.view(),
+    L2NormX,
+    L2NormBuf_OR_DistBuf,
+    params.metric,
+    params.batch_samples,
+    params.batch_centroids,
+    workspace);
+
+  // Using TransformInputIteratorT to dereference an array of raft::KeyValuePair
+  // and converting them to just return the Key to be used in reduce_rows_by_key
+  // prims
+  cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT> conversion_op;
+  cub::TransformInputIterator<IndexT,
+                              cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT>,
+                              raft::KeyValuePair<IndexT, DataT>*>
+    itr(minClusterAndDistance.data_handle(), conversion_op);
+
+  // count # of samples in each cluster
+  countLabels(handle,
+              itr,
+              sampleCountInCluster.data_handle(),
+              (IndexT)n_samples,
+              (IndexT)n_clusters,
+              workspace);
+}
+}  // namespace cuvs::cluster::kmeans::detail
diff --git a/cpp/src/cluster/detail/mst.cuh b/cpp/src/cluster/detail/mst.cuh
new file mode 100644
index 0000000000..de88b954e2
--- /dev/null
+++ b/cpp/src/cluster/detail/mst.cuh
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/sparse/neighbors/cross_component_nn.cuh>
+#include <raft/sparse/op/sort.cuh>
+#include <raft/sparse/solver/mst.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+
+namespace cuvs::cluster::agglomerative::detail {
+
+template <typename value_idx, typename value_t>
+void merge_msts(raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>& coo1,
+                raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>& coo2,
+                cudaStream_t stream)
+{
+  /** Add edges to existing mst **/
+  int final_nnz = coo2.n_edges + coo1.n_edges;
+
+  coo1.src.resize(final_nnz, stream);
+  coo1.dst.resize(final_nnz, stream);
+  coo1.weights.resize(final_nnz, stream);
+
+  /**
+   * Construct final edge list
+   */
+  raft::copy_async(coo1.src.data() + coo1.n_edges, coo2.src.data(), coo2.n_edges, stream);
+  raft::copy_async(coo1.dst.data() + coo1.n_edges, coo2.dst.data(), coo2.n_edges, stream);
+  raft::copy_async(coo1.weights.data() + coo1.n_edges, coo2.weights.data(), coo2.n_edges, stream);
+
+  coo1.n_edges = final_nnz;
+}
+
+/**
+ * Connect an unconnected knn graph (one in which mst returns an msf). The
+ * device buffers underlying the Graph_COO object are modified in-place.
+ * @tparam value_idx index type
+ * @tparam value_t floating-point value type
+ * @param[in] handle raft handle
+ * @param[in] X original dense data from which knn grpah was constructed
+ * @param[inout] msf edge list containing the mst result
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[inout] color the color labels array returned from the mst invocation
+ * @return updated MST edge list
+ */
+template <typename value_idx, typename value_t, typename red_op>
+void connect_knn_graph(
+  raft::resources const& handle,
+  const value_t* X,
+  raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>& msf,
+  size_t m,
+  size_t n,
+  value_idx* color,
+  red_op reduction_op,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  raft::sparse::COO<value_t, value_idx> connected_edges(stream);
+
+  // default row and column batch sizes are chosen for computing cross component nearest neighbors.
+  // Reference: PR #1445
+  static constexpr size_t default_row_batch_size = 4096;
+  static constexpr size_t default_col_batch_size = 16;
+
+  raft::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
+                                                                  connected_edges,
+                                                                  X,
+                                                                  color,
+                                                                  m,
+                                                                  n,
+                                                                  reduction_op,
+                                                                  min(m, default_row_batch_size),
+                                                                  min(n, default_col_batch_size));
+
+  rmm::device_uvector<value_idx> indptr2(m + 1, stream);
+  raft::sparse::convert::sorted_coo_to_csr(
+    connected_edges.rows(), connected_edges.nnz, indptr2.data(), m + 1, stream);
+
+  // On the second call, we hand the MST the original colors
+  // and the new set of edges and let it restart the optimization process
+  auto new_mst =
+    raft::sparse::solver::mst<value_idx, value_idx, value_t, double>(handle,
+                                                                     indptr2.data(),
+                                                                     connected_edges.cols(),
+                                                                     connected_edges.vals(),
+                                                                     m,
+                                                                     connected_edges.nnz,
+                                                                     color,
+                                                                     stream,
+                                                                     false,
+                                                                     false);
+
+  merge_msts<value_idx, value_t>(msf, new_mst, stream);
+}
+
+/**
+ * Constructs an MST and sorts the resulting edges in ascending
+ * order by their weight.
+ *
+ * Hierarchical clustering heavily relies upon the ordering
+ * and vertices returned in the MST. If the result of the
+ * MST was actually a minimum-spanning forest, the CSR
+ * being passed into the MST is not connected. In such a
+ * case, this graph will be connected by performing a
+ * KNN across the components.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[in] handle raft handle
+ * @param[in] indptr CSR indptr of connectivities graph
+ * @param[in] indices CSR indices array of connectivities graph
+ * @param[in] pw_dists CSR weights array of connectivities graph
+ * @param[in] m number of rows in X / src vertices in connectivities graph
+ * @param[in] n number of columns in X
+ * @param[out] mst_src output src edges
+ * @param[out] mst_dst output dst edges
+ * @param[out] mst_weight output weights (distances)
+ * @param[in] max_iter maximum iterations to run knn graph connection. This
+ *  argument is really just a safeguard against the potential for infinite loops.
+ */
+template <typename value_idx, typename value_t, typename red_op>
+void build_sorted_mst(
+  raft::resources const& handle,
+  const value_t* X,
+  const value_idx* indptr,
+  const value_idx* indices,
+  const value_t* pw_dists,
+  size_t m,
+  size_t n,
+  value_idx* mst_src,
+  value_idx* mst_dst,
+  value_t* mst_weight,
+  value_idx* color,
+  size_t nnz,
+  red_op reduction_op,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded,
+  int max_iter                        = 10)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  // We want to have MST initialize colors on first call.
+  auto mst_coo = raft::sparse::solver::mst<value_idx, value_idx, value_t, double>(
+    handle, indptr, indices, pw_dists, (value_idx)m, nnz, color, stream, false, true);
+
+  int iters        = 1;
+  int n_components = raft::sparse::neighbors::get_n_components(color, m, stream);
+
+  while (n_components > 1 && iters < max_iter) {
+    connect_knn_graph<value_idx, value_t>(handle, X, mst_coo, m, n, color, reduction_op);
+
+    iters++;
+
+    n_components = raft::sparse::neighbors::get_n_components(color, m, stream);
+  }
+
+  /**
+   * The `max_iter` argument was introduced only to prevent the potential for an infinite loop.
+   * Ideally the log2(n) guarantees of the MST should be enough to connect KNN graphs with a
+   * massive number of data samples in very few iterations. If it does not, there are 3 likely
+   * reasons why (in order of their likelihood):
+   * 1. There is a bug in this code somewhere
+   * 2. Either the given KNN graph wasn't generated from X or the same metric is not being used
+   *    to generate the 1-nn (currently only L2SqrtExpanded is supported).
+   * 3. max_iter was not large enough to connect the graph (less likely).
+   *
+   * Note that a KNN graph generated from 50 random isotropic balls (with significant overlap)
+   * was able to be connected in a single iteration.
+   */
+  RAFT_EXPECTS(n_components == 1,
+               "KNN graph could not be connected in %d iterations. "
+               "Please verify that the input knn graph is generated from X "
+               "(and the same distance metric used),"
+               " or increase 'max_iter'",
+               max_iter);
+
+  raft::sparse::op::coo_sort_by_weight(
+    mst_coo.src.data(), mst_coo.dst.data(), mst_coo.weights.data(), mst_coo.n_edges, stream);
+
+  raft::copy_async(mst_src, mst_coo.src.data(), mst_coo.n_edges, stream);
+  raft::copy_async(mst_dst, mst_coo.dst.data(), mst_coo.n_edges, stream);
+  raft::copy_async(mst_weight, mst_coo.weights.data(), mst_coo.n_edges, stream);
+}
+
+};  // namespace  cuvs::cluster::agglomerative::detail
\ No newline at end of file
diff --git a/cpp/src/cluster/detail/single_linkage.cuh b/cpp/src/cluster/detail/single_linkage.cuh
new file mode 100644
index 0000000000..837acf797e
--- /dev/null
+++ b/cpp/src/cluster/detail/single_linkage.cuh
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "agglomerative.cuh"
+#include "connectivities.cuh"
+#include "mst.cuh"
+#include <cuvs/cluster/agglomerative.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+namespace cuvs::cluster::agglomerative::detail {
+
+static const size_t EMPTY = 0;
+
+/**
+ * Single-linkage clustering, capable of constructing a KNN graph to
+ * scale the algorithm beyond the n^2 memory consumption of implementations
+ * that use the fully-connected graph of pairwise distances by connecting
+ * a knn graph when k is not large enough to connect it.
+
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam dist_type method to use for constructing connectivities graph
+ * @param[in] handle raft handle
+ * @param[in] X dense input matrix in row-major layout
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[in] metric distance metrix to use when constructing connectivities graph
+ * @param[out] out struct containing output dendrogram and cluster assignments
+ * @param[in] c a constant used when constructing connectivities from knn graph. Allows the indirect
+ control
+ *            of k. The algorithm will set `k = log(n) + c`
+ * @param[in] n_clusters number of clusters to assign data samples
+ */
+template <typename value_idx, typename value_t, Linkage dist_type>
+void single_linkage(raft::resources const& handle,
+                    const value_t* X,
+                    size_t m,
+                    size_t n,
+                    cuvs::distance::DistanceType metric,
+                    single_linkage_output<value_idx>* out,
+                    int c,
+                    size_t n_clusters)
+{
+  ASSERT(n_clusters <= m, "n_clusters must be less than or equal to the number of data points");
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  rmm::device_uvector<value_idx> indptr(EMPTY, stream);
+  rmm::device_uvector<value_idx> indices(EMPTY, stream);
+  rmm::device_uvector<value_t> pw_dists(EMPTY, stream);
+
+  /**
+   * 1. Construct distance graph
+   */
+  detail::get_distance_graph<value_idx, value_t, dist_type>(
+    handle, X, m, n, metric, indptr, indices, pw_dists, c);
+
+  rmm::device_uvector<value_idx> mst_rows(m - 1, stream);
+  rmm::device_uvector<value_idx> mst_cols(m - 1, stream);
+  rmm::device_uvector<value_t> mst_data(m - 1, stream);
+
+  /**
+   * 2. Construct MST, sorted by weights
+   */
+  rmm::device_uvector<value_idx> color(m, stream);
+  raft::sparse::neighbors::FixConnectivitiesRedOp<value_idx, value_t> op(m);
+  detail::build_sorted_mst<value_idx, value_t>(handle,
+                                               X,
+                                               indptr.data(),
+                                               indices.data(),
+                                               pw_dists.data(),
+                                               m,
+                                               n,
+                                               mst_rows.data(),
+                                               mst_cols.data(),
+                                               mst_data.data(),
+                                               color.data(),
+                                               indices.size(),
+                                               op,
+                                               metric);
+
+  pw_dists.release();
+
+  /**
+   * Perform hierarchical labeling
+   */
+  size_t n_edges = mst_rows.size();
+
+  rmm::device_uvector<value_t> out_delta(n_edges, stream);
+  rmm::device_uvector<value_idx> out_size(n_edges, stream);
+  // Create dendrogram
+  detail::build_dendrogram_host<value_idx, value_t>(handle,
+                                                    mst_rows.data(),
+                                                    mst_cols.data(),
+                                                    mst_data.data(),
+                                                    n_edges,
+                                                    out->children,
+                                                    out_delta.data(),
+                                                    out_size.data());
+  detail::extract_flattened_clusters(handle, out->labels, out->children, n_clusters, m);
+
+  out->m                      = m;
+  out->n_clusters             = n_clusters;
+  out->n_leaves               = m;
+  out->n_connected_components = 1;
+}
+};  // namespace  cuvs::cluster::agglomerative::detail
\ No newline at end of file
diff --git a/cpp/src/cluster/kmeans.cuh b/cpp/src/cluster/kmeans.cuh
new file mode 100644
index 0000000000..1d12142da6
--- /dev/null
+++ b/cpp/src/cluster/kmeans.cuh
@@ -0,0 +1,1122 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "detail/kmeans.cuh"
+#include "detail/kmeans_auto_find_k.cuh"
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/kvp.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+#include <optional>
+
+namespace cuvs::cluster::kmeans {
+
+/**
+ * Functor used for sampling centroids
+ */
+template <typename DataT, typename IndexT>
+using SamplingOp = cuvs::cluster::kmeans::detail::SamplingOp<DataT, IndexT>;
+
+/**
+ * Functor used to extract the index from a KeyValue pair
+ * storing both index and a distance.
+ */
+template <typename IndexT, typename DataT>
+using KeyValueIndexOp = cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT>;
+
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.cuh>
+ *   #include <cuvs/cluster/kmeans_types.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids,
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+template <typename DataT, typename IndexT>
+void fit(raft::resources const& handle,
+         const kmeans::params& params,
+         raft::device_matrix_view<const DataT, IndexT> X,
+         std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+         raft::device_matrix_view<DataT, IndexT> centroids,
+         raft::host_scalar_view<DataT> inertia,
+         raft::host_scalar_view<IndexT> n_iter)
+{
+  cuvs::cluster::kmeans::detail::kmeans_fit<DataT, IndexT>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter);
+}
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.cuh>
+ *   #include <cuvs/cluster/kmeans_types.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids.view(),
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ *   ...
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   std::nullopt,
+ *                   centroids.view(),
+ *                   false,
+ *                   labels.view(),
+ *                   raft::make_scalar_view(&ineratia));
+ * @endcode
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     sample_weight    Optional weights for each observation in X.
+ *                                 [len = n_samples]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[in]     normalize_weight True if the weights should be normalized
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ * @param[out]    inertia          Sum of squared distances of samples to
+ *                                 their closest cluster center.
+ */
+template <typename DataT, typename IndexT>
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const DataT, IndexT> X,
+             std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+             raft::device_matrix_view<const DataT, IndexT> centroids,
+             raft::device_vector_view<IndexT, IndexT> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<DataT> inertia)
+{
+  cuvs::cluster::kmeans::detail::kmeans_predict<DataT, IndexT>(
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+}
+
+/**
+ * @brief Compute k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.cuh>
+ *   #include <cuvs/cluster/kmeans_types.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::fit_predict(handle,
+ *                       params,
+ *                       X,
+ *                       std::nullopt,
+ *                       centroids.view(),
+ *                       labels.view(),
+ *                       raft::make_scalar_view(&inertia),
+ *                       raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+template <typename DataT, typename IndexT>
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const DataT, IndexT> X,
+                 std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+                 std::optional<raft::device_matrix_view<DataT, IndexT>> centroids,
+                 raft::device_vector_view<IndexT, IndexT> labels,
+                 raft::host_scalar_view<DataT> inertia,
+                 raft::host_scalar_view<IndexT> n_iter)
+{
+  cuvs::cluster::kmeans::detail::kmeans_fit_predict<DataT, IndexT>(
+    handle, params, X, sample_weight, centroids, labels, inertia, n_iter);
+}
+
+/**
+ * @brief Transform X to a cluster-distance space.
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format
+ *                              [dim = n_samples x n_features]
+ * @param[in]     centroids     Cluster centroids. The data must be in row-major format.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    X_new         X transformed in the new space.
+ *                              [dim = n_samples x n_features]
+ */
+template <typename DataT, typename IndexT>
+void transform(raft::resources const& handle,
+               const kmeans::params& params,
+               raft::device_matrix_view<const DataT, IndexT> X,
+               raft::device_matrix_view<const DataT, IndexT> centroids,
+               raft::device_matrix_view<DataT, IndexT> X_new)
+{
+  cuvs::cluster::kmeans::detail::kmeans_transform<DataT, IndexT>(
+    handle, params, X, centroids, X_new);
+}
+
+template <typename DataT, typename IndexT>
+void transform(raft::resources const& handle,
+               const kmeans::params& params,
+               const DataT* X,
+               const DataT* centroids,
+               IndexT n_samples,
+               IndexT n_features,
+               DataT* X_new)
+{
+  cuvs::cluster::kmeans::detail::kmeans_transform<DataT, IndexT>(
+    handle, params, X, centroids, n_samples, n_features, X_new);
+}
+
+/**
+ * Automatically find the optimal value of k using a binary search.
+ * This method maximizes the Calinski-Harabasz Index while minimizing the per-cluster inertia.
+ *
+ *  @code{.cpp}
+ *   #include <raft/core/handle.hpp>
+ *   #include <cuvs/cluster/kmeans.cuh>
+ *   #include <cuvs/cluster/kmeans_types.hpp>
+ *
+ *   #include <raft/random/make_blobs.cuh>
+ *
+ *   using namespace  cuvs::cluster;
+ *
+ *   raft::handle_t handle;
+ *   int n_samples = 100, n_features = 15, n_clusters = 10;
+ *   auto X = raft::make_device_matrix<float, int>(handle, n_samples, n_features);
+ *   auto labels = raft::make_device_vector<float, int>(handle, n_samples);
+ *
+ *   raft::random::make_blobs(handle, X, labels, n_clusters);
+ *
+ *   auto best_k = raft::make_host_scalar<int>(0);
+ *   auto n_iter = raft::make_host_scalar<int>(0);
+ *   auto inertia = raft::make_host_scalar<int>(0);
+ *
+ *   kmeans::find_k(handle, X, best_k.view(), inertia.view(), n_iter.view(), n_clusters+1);
+ *
+ * @endcode
+ *
+ * @tparam idx_t indexing type (should be integral)
+ * @tparam value_t value type (should be floating point)
+ * @param handle raft handle
+ * @param X input observations (shape n_samples, n_dims)
+ * @param best_k best k found from binary search
+ * @param inertia inertia of best k found
+ * @param n_iter number of iterations used to find best k
+ * @param kmax maximum k to try in search
+ * @param kmin minimum k to try in search (should be >= 1)
+ * @param maxiter maximum number of iterations to run
+ * @param tol tolerance for early stopping convergence
+ */
+template <typename idx_t, typename value_t>
+void find_k(raft::resources const& handle,
+            raft::device_matrix_view<const value_t, idx_t> X,
+            raft::host_scalar_view<idx_t> best_k,
+            raft::host_scalar_view<value_t> inertia,
+            raft::host_scalar_view<idx_t> n_iter,
+            idx_t kmax,
+            idx_t kmin    = 1,
+            idx_t maxiter = 100,
+            value_t tol   = 1e-3)
+{
+  cuvs::cluster::kmeans::detail::find_k(
+    handle, X, best_k, inertia, n_iter, kmax, kmin, maxiter, tol);
+}
+
+/**
+ * @brief Select centroids according to a sampling operation
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle             The raft handle
+ * @param[in]  X                  The data in row-major format
+ *                                [dim = n_samples x n_features]
+ * @param[in]  minClusterDistance Distance for every sample to it's nearest centroid
+ *                                [dim = n_samples]
+ * @param[in]  isSampleCentroid   Flag the sample chosen as initial centroid
+ *                                [dim = n_samples]
+ * @param[in]  select_op          The sampling operation used to select the centroids
+ * @param[out] inRankCp           The sampled centroids
+ *                                [dim = n_selected_centroids x n_features]
+ * @param[in]  workspace          Temporary workspace buffer which can get resized
+ *
+ */
+template <typename DataT, typename IndexT>
+void sample_centroids(raft::resources const& handle,
+                      raft::device_matrix_view<const DataT, IndexT> X,
+                      raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                      raft::device_vector_view<std::uint8_t, IndexT> isSampleCentroid,
+                      SamplingOp<DataT, IndexT>& select_op,
+                      rmm::device_uvector<DataT>& inRankCp,
+                      rmm::device_uvector<char>& workspace)
+{
+  cuvs::cluster::kmeans::detail::sampleCentroids<DataT, IndexT>(
+    handle, X, minClusterDistance, isSampleCentroid, select_op, inRankCp, workspace);
+}
+
+/**
+ * @brief Compute cluster cost
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam ReductionOpT the type of data used for the reduction operation.
+ *
+ * @param[in]  handle             The raft handle
+ * @param[in]  minClusterDistance Distance for every sample to it's nearest centroid
+ *                                [dim = n_samples]
+ * @param[in]  workspace          Temporary workspace buffer which can get resized
+ * @param[out] clusterCost        Resulting cluster cost
+ * @param[in]  reduction_op       The reduction operation used for the cost
+ *
+ */
+template <typename DataT, typename IndexT, typename ReductionOpT>
+void cluster_cost(raft::resources const& handle,
+                  raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                  rmm::device_uvector<char>& workspace,
+                  raft::device_scalar_view<DataT> clusterCost,
+                  ReductionOpT reduction_op)
+{
+  cuvs::cluster::kmeans::detail::computeClusterCost(
+    handle, minClusterDistance, workspace, clusterCost, raft::identity_op{}, reduction_op);
+}
+
+/**
+ * @brief Update centroids given current centroids and number of points assigned to each centroid.
+ *  This function also produces a vector of RAFT key/value pairs containing the cluster assignment
+ *  for each point and its distance.
+ *
+ * @tparam DataT
+ * @tparam IndexT
+ * @param[in] handle: Raft handle to use for managing library resources
+ * @param[in] X: input matrix (size n_samples, n_features)
+ * @param[in] sample_weights: number of samples currently assigned to each centroid (size n_samples)
+ * @param[in] centroids: matrix of current centroids (size n_clusters, n_features)
+ * @param[in] labels: Iterator of labels (can also be a raw pointer)
+ * @param[out] weight_per_cluster: sum of sample weights per cluster (size n_clusters)
+ * @param[out] new_centroids: output matrix of updated centroids (size n_clusters, n_features)
+ */
+template <typename DataT, typename IndexT, typename LabelsIterator>
+void update_centroids(raft::resources const& handle,
+                      raft::device_matrix_view<const DataT, IndexT, raft::row_major> X,
+                      raft::device_vector_view<const DataT, IndexT> sample_weights,
+                      raft::device_matrix_view<const DataT, IndexT, raft::row_major> centroids,
+                      LabelsIterator labels,
+                      raft::device_vector_view<DataT, IndexT> weight_per_cluster,
+                      raft::device_matrix_view<DataT, IndexT, raft::row_major> new_centroids)
+{
+  // TODO: Passing these into the algorithm doesn't really present much of a benefit
+  // because they are being resized anyways.
+  // ref https://github.com/rapidsai/raft/issues/930
+  rmm::device_uvector<char> workspace(0, raft::resource::get_cuda_stream(handle));
+
+  cuvs::cluster::kmeans::detail::update_centroids<DataT, IndexT>(
+    handle, X, sample_weights, centroids, labels, weight_per_cluster, new_centroids, workspace);
+}
+
+/**
+ * @brief Compute distance for every sample to it's nearest centroid
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle               The raft handle
+ * @param[in]  X                    The data in row-major format
+ *                                  [dim = n_samples x n_features]
+ * @param[in]  centroids            Centroids data
+ *                                  [dim = n_cluster x n_features]
+ * @param[out] minClusterDistance   Distance for every sample to it's nearest centroid
+ *                                  [dim = n_samples]
+ * @param[in]  L2NormX              L2 norm of X : ||x||^2
+ *                                  [dim = n_samples]
+ * @param[out] L2NormBuf_OR_DistBuf Resizable buffer to store L2 norm of centroids or distance
+ *                                  matrix
+ * @param[in]  metric               Distance metric to use
+ * @param[in]  batch_samples        batch size for input data samples
+ * @param[in]  batch_centroids      batch size for input centroids
+ * @param[in]  workspace            Temporary workspace buffer which can get resized
+ *
+ */
+template <typename DataT, typename IndexT>
+void min_cluster_distance(raft::resources const& handle,
+                          raft::device_matrix_view<const DataT, IndexT> X,
+                          raft::device_matrix_view<DataT, IndexT> centroids,
+                          raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                          raft::device_vector_view<DataT, IndexT> L2NormX,
+                          rmm::device_uvector<DataT>& L2NormBuf_OR_DistBuf,
+                          cuvs::distance::DistanceType metric,
+                          int batch_samples,
+                          int batch_centroids,
+                          rmm::device_uvector<char>& workspace)
+{
+  cuvs::cluster::kmeans::detail::minClusterDistanceCompute<DataT, IndexT>(handle,
+                                                                          X,
+                                                                          centroids,
+                                                                          minClusterDistance,
+                                                                          L2NormX,
+                                                                          L2NormBuf_OR_DistBuf,
+                                                                          metric,
+                                                                          batch_samples,
+                                                                          batch_centroids,
+                                                                          workspace);
+}
+
+/**
+ * @brief Calculates a <key, value> pair for every sample in input 'X' where key is an
+ * index of one of the 'centroids' (index of the nearest centroid) and 'value'
+ * is the distance between the sample and the 'centroid[key]'
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle                The raft handle
+ * @param[in]  X                     The data in row-major format
+ *                                   [dim = n_samples x n_features]
+ * @param[in]  centroids             Centroids data
+ *                                   [dim = n_cluster x n_features]
+ * @param[out] minClusterAndDistance Distance vector that contains for every sample, the nearest
+ *                                   centroid and it's distance
+ *                                   [dim = n_samples]
+ * @param[in]  L2NormX               L2 norm of X : ||x||^2
+ *                                   [dim = n_samples]
+ * @param[out] L2NormBuf_OR_DistBuf  Resizable buffer to store L2 norm of centroids or distance
+ *                                   matrix
+ * @param[in] metric                 distance metric
+ * @param[in] batch_samples          batch size of data samples
+ * @param[in] batch_centroids        batch size of centroids
+ * @param[in] workspace              Temporary workspace buffer which can get resized
+ *
+ */
+template <typename DataT, typename IndexT>
+void min_cluster_and_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const DataT, IndexT> X,
+  raft::device_matrix_view<const DataT, IndexT> centroids,
+  raft::device_vector_view<raft::KeyValuePair<IndexT, DataT>, IndexT> minClusterAndDistance,
+  raft::device_vector_view<DataT, IndexT> L2NormX,
+  rmm::device_uvector<DataT>& L2NormBuf_OR_DistBuf,
+  cuvs::distance::DistanceType metric,
+  int batch_samples,
+  int batch_centroids,
+  rmm::device_uvector<char>& workspace)
+{
+  cuvs::cluster::kmeans::detail::minClusterAndDistanceCompute<DataT, IndexT>(handle,
+                                                                             X,
+                                                                             centroids,
+                                                                             minClusterAndDistance,
+                                                                             L2NormX,
+                                                                             L2NormBuf_OR_DistBuf,
+                                                                             metric,
+                                                                             batch_samples,
+                                                                             batch_centroids,
+                                                                             workspace);
+}
+
+/**
+ * @brief Shuffle and randomly select 'n_samples_to_gather' from input 'in' and stores
+ * in 'out' does not modify the input
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle              The raft handle
+ * @param[in]  in                  The data to shuffle and gather
+ *                                 [dim = n_samples x n_features]
+ * @param[out] out                 The sampled data
+ *                                 [dim = n_samples_to_gather x n_features]
+ * @param[in]  n_samples_to_gather Number of sample to gather
+ * @param[in]  seed                Seed for the shuffle
+ *
+ */
+template <typename DataT, typename IndexT>
+void shuffle_and_gather(raft::resources const& handle,
+                        raft::device_matrix_view<const DataT, IndexT> in,
+                        raft::device_matrix_view<DataT, IndexT> out,
+                        uint32_t n_samples_to_gather,
+                        uint64_t seed)
+{
+  cuvs::cluster::kmeans::detail::shuffleAndGather<DataT, IndexT>(
+    handle, in, out, n_samples_to_gather, seed);
+}
+
+/**
+ * @brief Count the number of samples in each cluster
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle               The raft handle
+ * @param[in]  params               The parameters for KMeans
+ * @param[in]  X                    The data in row-major format
+ *                                  [dim = n_samples x n_features]
+ * @param[in]  L2NormX              L2 norm of X : ||x||^2
+ *                                  [dim = n_samples]
+ * @param[in]  centroids            Centroids data
+ *                                  [dim = n_cluster x n_features]
+ * @param[in]  workspace            Temporary workspace buffer which can get resized
+ * @param[out] sampleCountInCluster The count for each centroid
+ *                                  [dim = n_cluster]
+ *
+ */
+template <typename DataT, typename IndexT>
+void count_samples_in_cluster(raft::resources const& handle,
+                              const kmeans::params& params,
+                              raft::device_matrix_view<const DataT, IndexT> X,
+                              raft::device_vector_view<DataT, IndexT> L2NormX,
+                              raft::device_matrix_view<DataT, IndexT> centroids,
+                              rmm::device_uvector<char>& workspace,
+                              raft::device_vector_view<DataT, IndexT> sampleCountInCluster)
+{
+  cuvs::cluster::kmeans::detail::countSamplesInCluster<DataT, IndexT>(
+    handle, params, X, L2NormX, centroids, workspace, sampleCountInCluster);
+}
+
+/**
+ * @brief Selects 'n_clusters' samples from the input X using kmeans++ algorithm.
+ *
+ * @see "k-means++: the advantages of careful seeding". 2007, Arthur, D. and Vassilvitskii, S.
+ *        ACM-SIAM symposium on Discrete algorithms.
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle                The raft handle
+ * @param[in]  params                The parameters for KMeans
+ * @param[in]  X                     The data in row-major format
+ *                                   [dim = n_samples x n_features]
+ * @param[out] centroids             Centroids data
+ *                                   [dim = n_cluster x n_features]
+ * @param[in]  workspace             Temporary workspace buffer which can get resized
+ */
+template <typename DataT, typename IndexT>
+void init_plus_plus(raft::resources const& handle,
+                    const kmeans::params& params,
+                    raft::device_matrix_view<const DataT, IndexT> X,
+                    raft::device_matrix_view<DataT, IndexT> centroids,
+                    rmm::device_uvector<char>& workspace)
+{
+  cuvs::cluster::kmeans::detail::kmeansPlusPlus<DataT, IndexT>(
+    handle, params, X, centroids, workspace);
+}
+
+/*
+ * @brief Main function used to fit KMeans (after cluster initialization)
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] Initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ * @param[in]     workspace     Temporary workspace buffer which can get resized
+ */
+template <typename DataT, typename IndexT>
+void fit_main(raft::resources const& handle,
+              const kmeans::params& params,
+              raft::device_matrix_view<const DataT, IndexT> X,
+              raft::device_vector_view<const DataT, IndexT> sample_weights,
+              raft::device_matrix_view<DataT, IndexT> centroids,
+              raft::host_scalar_view<DataT> inertia,
+              raft::host_scalar_view<IndexT> n_iter,
+              rmm::device_uvector<char>& workspace)
+{
+  cuvs::cluster::kmeans::detail::kmeans_fit_main<DataT, IndexT>(
+    handle, params, X, sample_weights, centroids, inertia, n_iter, workspace);
+}
+
+};  // end namespace  cuvs::cluster::kmeans
+
+namespace cuvs::cluster {
+
+/**
+ * Note: All of the functions below in  cuvs::cluster are deprecated and will
+ * be removed in a future release. Please use  cuvs::cluster::kmeans instead.
+ */
+
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+template <typename DataT, typename IndexT = int>
+void kmeans_fit(raft::resources const& handle,
+                const kmeans::params& params,
+                raft::device_matrix_view<const DataT, IndexT> X,
+                std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+                raft::device_matrix_view<DataT, IndexT> centroids,
+                raft::host_scalar_view<DataT> inertia,
+                raft::host_scalar_view<IndexT> n_iter)
+{
+  kmeans::fit<DataT, IndexT>(handle, params, X, sample_weight, centroids, inertia, n_iter);
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_fit(raft::resources const& handle,
+                const kmeans::params& params,
+                const DataT* X,
+                const DataT* sample_weight,
+                DataT* centroids,
+                IndexT n_samples,
+                IndexT n_features,
+                DataT& inertia,
+                IndexT& n_iter)
+{
+  kmeans::fit<DataT, IndexT>(
+    handle, params, X, sample_weight, centroids, n_samples, n_features, inertia, n_iter);
+}
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     sample_weight    Optional weights for each observation in X.
+ *                                 [len = n_samples]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[in]     normalize_weight True if the weights should be normalized
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ * @param[out]    inertia          Sum of squared distances of samples to
+ *                                 their closest cluster center.
+ */
+template <typename DataT, typename IndexT = int>
+void kmeans_predict(raft::resources const& handle,
+                    const kmeans::params& params,
+                    raft::device_matrix_view<const DataT, IndexT> X,
+                    std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+                    raft::device_matrix_view<const DataT, IndexT> centroids,
+                    raft::device_vector_view<IndexT, IndexT> labels,
+                    bool normalize_weight,
+                    raft::host_scalar_view<DataT> inertia)
+{
+  kmeans::predict<DataT, IndexT>(
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_predict(raft::resources const& handle,
+                    const kmeans::params& params,
+                    const DataT* X,
+                    const DataT* sample_weight,
+                    const DataT* centroids,
+                    IndexT n_samples,
+                    IndexT n_features,
+                    IndexT* labels,
+                    bool normalize_weight,
+                    DataT& inertia)
+{
+  kmeans::predict<DataT, IndexT>(handle,
+                                 params,
+                                 X,
+                                 sample_weight,
+                                 centroids,
+                                 n_samples,
+                                 n_features,
+                                 labels,
+                                 normalize_weight,
+                                 inertia);
+}
+
+/**
+ * @brief Compute k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+template <typename DataT, typename IndexT = int>
+void kmeans_fit_predict(raft::resources const& handle,
+                        const kmeans::params& params,
+                        raft::device_matrix_view<const DataT, IndexT> X,
+                        std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+                        std::optional<raft::device_matrix_view<DataT, IndexT>> centroids,
+                        raft::device_vector_view<IndexT, IndexT> labels,
+                        raft::host_scalar_view<DataT> inertia,
+                        raft::host_scalar_view<IndexT> n_iter)
+{
+  kmeans::fit_predict<DataT, IndexT>(
+    handle, params, X, sample_weight, centroids, labels, inertia, n_iter);
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_fit_predict(raft::resources const& handle,
+                        const kmeans::params& params,
+                        const DataT* X,
+                        const DataT* sample_weight,
+                        DataT* centroids,
+                        IndexT n_samples,
+                        IndexT n_features,
+                        IndexT* labels,
+                        DataT& inertia,
+                        IndexT& n_iter)
+{
+  kmeans::fit_predict<DataT, IndexT>(
+    handle, params, X, sample_weight, centroids, n_samples, n_features, labels, inertia, n_iter);
+}
+
+/**
+ * @brief Transform X to a cluster-distance space.
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format
+ *                              [dim = n_samples x n_features]
+ * @param[in]     centroids     Cluster centroids. The data must be in row-major format.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    X_new         X transformed in the new space.
+ *                              [dim = n_samples x n_features]
+ */
+template <typename DataT, typename IndexT = int>
+void kmeans_transform(raft::resources const& handle,
+                      const kmeans::params& params,
+                      raft::device_matrix_view<const DataT, IndexT> X,
+                      raft::device_matrix_view<const DataT, IndexT> centroids,
+                      raft::device_matrix_view<DataT, IndexT> X_new)
+{
+  kmeans::transform<DataT, IndexT>(handle, params, X, centroids, X_new);
+}
+
+template <typename DataT, typename IndexT = int>
+void kmeans_transform(raft::resources const& handle,
+                      const kmeans::params& params,
+                      const DataT* X,
+                      const DataT* centroids,
+                      IndexT n_samples,
+                      IndexT n_features,
+                      DataT* X_new)
+{
+  kmeans::transform<DataT, IndexT>(handle, params, X, centroids, n_samples, n_features, X_new);
+}
+
+template <typename DataT, typename IndexT>
+using SamplingOp = kmeans::SamplingOp<DataT, IndexT>;
+
+template <typename IndexT, typename DataT>
+using KeyValueIndexOp = kmeans::KeyValueIndexOp<IndexT, DataT>;
+
+/**
+ * @brief Select centroids according to a sampling operation
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle             The raft handle
+ * @param[in]  X                  The data in row-major format
+ *                                [dim = n_samples x n_features]
+ * @param[in]  minClusterDistance Distance for every sample to it's nearest centroid
+ *                                [dim = n_samples]
+ * @param[in]  isSampleCentroid   Flag the sample chosen as initial centroid
+ *                                [dim = n_samples]
+ * @param[in]  select_op          The sampling operation used to select the centroids
+ * @param[out] inRankCp           The sampled centroids
+ *                                [dim = n_selected_centroids x n_features]
+ * @param[in]  workspace          Temporary workspace buffer which can get resized
+ *
+ */
+template <typename DataT, typename IndexT>
+void sampleCentroids(raft::resources const& handle,
+                     raft::device_matrix_view<const DataT, IndexT> X,
+                     raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                     raft::device_vector_view<std::uint8_t, IndexT> isSampleCentroid,
+                     SamplingOp<DataT, IndexT>& select_op,
+                     rmm::device_uvector<DataT>& inRankCp,
+                     rmm::device_uvector<char>& workspace)
+{
+  kmeans::sample_centroids<DataT, IndexT>(
+    handle, X, minClusterDistance, isSampleCentroid, select_op, inRankCp, workspace);
+}
+
+/**
+ * @brief Compute cluster cost
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam ReductionOpT the type of data used for the reduction operation.
+ *
+ * @param[in]  handle             The raft handle
+ * @param[in]  minClusterDistance Distance for every sample to it's nearest centroid
+ *                                [dim = n_samples]
+ * @param[in]  workspace          Temporary workspace buffer which can get resized
+ * @param[out] clusterCost        Resulting cluster cost
+ * @param[in]  reduction_op       The reduction operation used for the cost
+ *
+ */
+template <typename DataT, typename IndexT, typename ReductionOpT>
+void computeClusterCost(raft::resources const& handle,
+                        raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                        rmm::device_uvector<char>& workspace,
+                        raft::device_scalar_view<DataT> clusterCost,
+                        ReductionOpT reduction_op)
+{
+  kmeans::cluster_cost(handle, minClusterDistance, workspace, clusterCost, reduction_op);
+}
+
+/**
+ * @brief Compute distance for every sample to it's nearest centroid
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle               The raft handle
+ * @param[in]  params               The parameters for KMeans
+ * @param[in]  X                    The data in row-major format
+ *                                  [dim = n_samples x n_features]
+ * @param[in]  centroids            Centroids data
+ *                                  [dim = n_cluster x n_features]
+ * @param[out] minClusterDistance   Distance for every sample to it's nearest centroid
+ *                                  [dim = n_samples]
+ * @param[in]  L2NormX              L2 norm of X : ||x||^2
+ *                                  [dim = n_samples]
+ * @param[out] L2NormBuf_OR_DistBuf Resizable buffer to store L2 norm of centroids or distance
+ *                                  matrix
+ * @param[in]  workspace            Temporary workspace buffer which can get resized
+ *
+ */
+template <typename DataT, typename IndexT>
+void minClusterDistanceCompute(raft::resources const& handle,
+                               const kmeans::params& params,
+                               raft::device_matrix_view<const DataT, IndexT> X,
+                               raft::device_matrix_view<DataT, IndexT> centroids,
+                               raft::device_vector_view<DataT, IndexT> minClusterDistance,
+                               raft::device_vector_view<DataT, IndexT> L2NormX,
+                               rmm::device_uvector<DataT>& L2NormBuf_OR_DistBuf,
+                               rmm::device_uvector<char>& workspace)
+{
+  kmeans::min_cluster_distance<DataT, IndexT>(handle,
+                                              X,
+                                              centroids,
+                                              minClusterDistance,
+                                              L2NormX,
+                                              L2NormBuf_OR_DistBuf,
+                                              params.metric,
+                                              params.batch_samples,
+                                              params.batch_centroids,
+                                              workspace);
+}
+
+/**
+ * @brief Calculates a <key, value> pair for every sample in input 'X' where key is an
+ * index of one of the 'centroids' (index of the nearest centroid) and 'value'
+ * is the distance between the sample and the 'centroid[key]'
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle                The raft handle
+ * @param[in]  params                The parameters for KMeans
+ * @param[in]  X                     The data in row-major format
+ *                                   [dim = n_samples x n_features]
+ * @param[in]  centroids             Centroids data
+ *                                   [dim = n_cluster x n_features]
+ * @param[out] minClusterAndDistance Distance vector that contains for every sample, the nearest
+ *                                   centroid and it's distance
+ *                                   [dim = n_samples]
+ * @param[in]  L2NormX               L2 norm of X : ||x||^2
+ *                                   [dim = n_samples]
+ * @param[out] L2NormBuf_OR_DistBuf  Resizable buffer to store L2 norm of centroids or distance
+ *                                   matrix
+ * @param[in]  workspace             Temporary workspace buffer which can get resized
+ *
+ */
+template <typename DataT, typename IndexT>
+void minClusterAndDistanceCompute(
+  raft::resources const& handle,
+  const kmeans::params& params,
+  raft::device_matrix_view<const DataT, IndexT> X,
+  raft::device_matrix_view<const DataT, IndexT> centroids,
+  raft::device_vector_view<raft::KeyValuePair<IndexT, DataT>, IndexT> minClusterAndDistance,
+  raft::device_vector_view<DataT, IndexT> L2NormX,
+  rmm::device_uvector<DataT>& L2NormBuf_OR_DistBuf,
+  rmm::device_uvector<char>& workspace)
+{
+  kmeans::min_cluster_and_distance<DataT, IndexT>(handle,
+                                                  X,
+                                                  centroids,
+                                                  minClusterAndDistance,
+                                                  L2NormX,
+                                                  L2NormBuf_OR_DistBuf,
+                                                  params.metric,
+                                                  params.batch_samples,
+                                                  params.batch_centroids,
+                                                  workspace);
+}
+
+/**
+ * @brief Shuffle and randomly select 'n_samples_to_gather' from input 'in' and stores
+ * in 'out' does not modify the input
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle              The raft handle
+ * @param[in]  in                  The data to shuffle and gather
+ *                                 [dim = n_samples x n_features]
+ * @param[out] out                 The sampled data
+ *                                 [dim = n_samples_to_gather x n_features]
+ * @param[in]  n_samples_to_gather Number of sample to gather
+ * @param[in]  seed                Seed for the shuffle
+ *
+ */
+template <typename DataT, typename IndexT>
+void shuffleAndGather(raft::resources const& handle,
+                      raft::device_matrix_view<const DataT, IndexT> in,
+                      raft::device_matrix_view<DataT, IndexT> out,
+                      uint32_t n_samples_to_gather,
+                      uint64_t seed)
+{
+  kmeans::shuffle_and_gather<DataT, IndexT>(handle, in, out, n_samples_to_gather, seed);
+}
+
+/**
+ * @brief Count the number of samples in each cluster
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle               The raft handle
+ * @param[in]  params               The parameters for KMeans
+ * @param[in]  X                    The data in row-major format
+ *                                  [dim = n_samples x n_features]
+ * @param[in]  L2NormX              L2 norm of X : ||x||^2
+ *                                  [dim = n_samples]
+ * @param[in]  centroids            Centroids data
+ *                                  [dim = n_cluster x n_features]
+ * @param[in]  workspace            Temporary workspace buffer which can get resized
+ * @param[out] sampleCountInCluster The count for each centroid
+ *                                  [dim = n_cluster]
+ *
+ */
+template <typename DataT, typename IndexT>
+void countSamplesInCluster(raft::resources const& handle,
+                           const kmeans::params& params,
+                           raft::device_matrix_view<const DataT, IndexT> X,
+                           raft::device_vector_view<DataT, IndexT> L2NormX,
+                           raft::device_matrix_view<DataT, IndexT> centroids,
+                           rmm::device_uvector<char>& workspace,
+                           raft::device_vector_view<DataT, IndexT> sampleCountInCluster)
+{
+  kmeans::count_samples_in_cluster<DataT, IndexT>(
+    handle, params, X, L2NormX, centroids, workspace, sampleCountInCluster);
+}
+
+/*
+ * @brief Selects 'n_clusters' samples from the input X using kmeans++ algorithm.
+
+ * @note  This is the algorithm described in
+ *        "k-means++: the advantages of careful seeding". 2007, Arthur, D. and Vassilvitskii, S.
+ *        ACM-SIAM symposium on Discrete algorithms.
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]  handle                The raft handle
+ * @param[in]  params                The parameters for KMeans
+ * @param[in]  X                     The data in row-major format
+ *                                   [dim = n_samples x n_features]
+ * @param[out] centroids             Centroids data
+ *                                   [dim = n_cluster x n_features]
+ * @param[in]  workspace             Temporary workspace buffer which can get resized
+ */
+template <typename DataT, typename IndexT>
+void kmeansPlusPlus(raft::resources const& handle,
+                    const kmeans::params& params,
+                    raft::device_matrix_view<const DataT, IndexT> X,
+                    raft::device_matrix_view<DataT, IndexT> centroidsRawData,
+                    rmm::device_uvector<char>& workspace)
+{
+  kmeans::init_plus_plus<DataT, IndexT>(handle, params, X, centroidsRawData, workspace);
+}
+
+/*
+ * @brief Main function used to fit KMeans (after cluster initialization)
+ *
+ * @tparam DataT the type of data used for weights, distances.
+ * @tparam IndexT the type of data used for indexing.
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] Initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ * @param[in]     workspace     Temporary workspace buffer which can get resized
+ */
+template <typename DataT, typename IndexT>
+void kmeans_fit_main(raft::resources const& handle,
+                     const kmeans::params& params,
+                     raft::device_matrix_view<const DataT, IndexT> X,
+                     raft::device_vector_view<const DataT, IndexT> weight,
+                     raft::device_matrix_view<DataT, IndexT> centroidsRawData,
+                     raft::host_scalar_view<DataT> inertia,
+                     raft::host_scalar_view<IndexT> n_iter,
+                     rmm::device_uvector<char>& workspace)
+{
+  kmeans::fit_main<DataT, IndexT>(
+    handle, params, X, weight, centroidsRawData, inertia, n_iter, workspace);
+}
+};  // namespace  cuvs::cluster
diff --git a/cpp/src/cluster/kmeans_auto_find_k_float.cu b/cpp/src/cluster/kmeans_auto_find_k_float.cu
new file mode 100644
index 0000000000..44d18f0a11
--- /dev/null
+++ b/cpp/src/cluster/kmeans_auto_find_k_float.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans::helpers {
+
+void find_k(raft::resources const& handle,
+            raft::device_matrix_view<const float, int> X,
+            raft::host_scalar_view<int> best_k,
+            raft::host_scalar_view<float> inertia,
+            raft::host_scalar_view<int> n_iter,
+            int kmax,
+            int kmin,
+            int maxiter,
+            float tol)
+{
+  cuvs::cluster::kmeans::find_k<int, float>(
+    handle, X, best_k, inertia, n_iter, kmax, kmin, maxiter, tol);
+}
+}  // namespace cuvs::cluster::kmeans::helpers
diff --git a/cpp/src/cluster/kmeans_balanced.cuh b/cpp/src/cluster/kmeans_balanced.cuh
new file mode 100644
index 0000000000..040d17b369
--- /dev/null
+++ b/cpp/src/cluster/kmeans_balanced.cuh
@@ -0,0 +1,369 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/kmeans_balanced.cuh"
+#include <raft/core/mdarray.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/util/cuda_utils.cuh>
+
+#include <utility>
+
+namespace cuvs::cluster::kmeans_balanced {
+
+/**
+ * @brief Find clusters of balanced sizes with a hierarchical k-means algorithm.
+ *
+ * This variant of the k-means algorithm first clusters the dataset in mesoclusters, then clusters
+ * the subsets associated to each mesocluster into fine clusters, and finally runs a few k-means
+ * iterations over the whole dataset and with all the centroids to obtain the final clusters.
+ *
+ * Each k-means iteration applies expectation-maximization-balancing:
+ *  - Balancing: adjust centers for clusters that have a small number of entries. If the size of a
+ *    cluster is below a threshold, the center is moved towards a bigger cluster.
+ *  - Expectation: predict the labels (i.e find closest cluster centroid to each point)
+ *  - Maximization: calculate optimal centroids (i.e find the center of gravity of each cluster)
+ *
+ * The number of mesoclusters is chosen by rounding the square root of the number of clusters. E.g
+ * for 512 clusters, we would have 23 mesoclusters. The number of fine clusters per mesocluster is
+ * chosen proportionally to the number of points in each mesocluster.
+ *
+ * This variant of k-means uses random initialization and a fixed number of iterations, though
+ * iterations can be repeated if the balancing step moved the centroids.
+ *
+ * Additionally, this algorithm supports quantized datasets in arbitrary types but the core part of
+ * the algorithm will work with a floating-point type, hence a conversion function can be provided
+ * to map the data type to the math type.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/handle.hpp>
+ *   #include <cuvs/cluster/kmeans_balanced.cuh>
+ *   #include <cuvs/cluster/kmeans_balanced_types.hpp>
+ *   ...
+ *   raft::handle_t handle;
+ *    cuvs::cluster::balanced_params params;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *    cuvs::cluster::kmeans_balanced::fit(handle, params, X, centroids.view());
+ * @endcode
+ *
+ * @tparam DataT Type of the input data.
+ * @tparam MathT Type of the centroids and mapped data.
+ * @tparam IndexT Type used for indexing.
+ * @tparam MappingOpT Type of the mapping function.
+ * @param[in]  handle     The raft resources
+ * @param[in]  params     Structure containing the hyper-parameters
+ * @param[in]  X          Training instances to cluster. The data must be in row-major format.
+ *                        [dim = n_samples x n_features]
+ * @param[out] centroids  The generated centroids [dim = n_clusters x n_features]
+ * @param[in]  mapping_op (optional) Functor to convert from the input datatype to the arithmetic
+ *                        datatype. If DataT == MathT, this must be the identity.
+ */
+template <typename DataT, typename MathT, typename IndexT, typename MappingOpT = raft::identity_op>
+void fit(const raft::resources& handle,
+         cuvs::cluster::kmeans::balanced_params const& params,
+         raft::device_matrix_view<const DataT, IndexT> X,
+         raft::device_matrix_view<MathT, IndexT> centroids,
+         MappingOpT mapping_op = raft::identity_op())
+{
+  RAFT_EXPECTS(X.extent(1) == centroids.extent(1),
+               "Number of features in dataset and centroids are different");
+  RAFT_EXPECTS(static_cast<uint64_t>(X.extent(0)) * static_cast<uint64_t>(X.extent(1)) <=
+                 static_cast<uint64_t>(std::numeric_limits<IndexT>::max()),
+               "The chosen index type cannot represent all indices for the given dataset");
+  RAFT_EXPECTS(centroids.extent(0) > IndexT{0} && centroids.extent(0) <= X.extent(0),
+               "The number of centroids must be strictly positive and cannot exceed the number of "
+               "points in the training dataset.");
+
+  cuvs::cluster::kmeans::detail::build_hierarchical(handle,
+                                                    params,
+                                                    X.extent(1),
+                                                    X.data_handle(),
+                                                    X.extent(0),
+                                                    centroids.data_handle(),
+                                                    centroids.extent(0),
+                                                    mapping_op);
+}
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/handle.hpp>
+ *   #include <cuvs/cluster/kmeans_balanced.cuh>
+ *   #include <cuvs/cluster/kmeans_balanced_types.hpp>
+ *   ...
+ *   raft::handle_t handle;
+ *    cuvs::cluster::balanced_params params;
+ *   auto labels = raft::make_device_vector<float, int>(handle, n_rows);
+ *    cuvs::cluster::kmeans_balanced::predict(handle, params, X, centroids, labels);
+ * @endcode
+ *
+ * @tparam DataT Type of the input data.
+ * @tparam MathT Type of the centroids and mapped data.
+ * @tparam IndexT Type used for indexing.
+ * @tparam LabelT Type of the output labels.
+ * @tparam MappingOpT Type of the mapping function.
+ * @param[in]  handle     The raft resources
+ * @param[in]  params     Structure containing the hyper-parameters
+ * @param[in]  X          Dataset for which to infer the closest clusters.
+ *                        [dim = n_samples x n_features]
+ * @param[in]  centroids  The input centroids [dim = n_clusters x n_features]
+ * @param[out] labels     The output labels [dim = n_samples]
+ * @param[in]  mapping_op (optional) Functor to convert from the input datatype to the arithmetic
+ *                        datatype. If DataT == MathT, this must be the identity.
+ */
+template <typename DataT,
+          typename MathT,
+          typename IndexT,
+          typename LabelT,
+          typename MappingOpT = raft::identity_op>
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const DataT, IndexT> X,
+             raft::device_matrix_view<const MathT, IndexT> centroids,
+             raft::device_vector_view<LabelT, IndexT> labels,
+             MappingOpT mapping_op = raft::identity_op())
+{
+  RAFT_EXPECTS(X.extent(0) == labels.extent(0),
+               "Number of rows in dataset and labels are different");
+  RAFT_EXPECTS(X.extent(1) == centroids.extent(1),
+               "Number of features in dataset and centroids are different");
+  RAFT_EXPECTS(static_cast<uint64_t>(X.extent(0)) * static_cast<uint64_t>(X.extent(1)) <=
+                 static_cast<uint64_t>(std::numeric_limits<IndexT>::max()),
+               "The chosen index type cannot represent all indices for the given dataset");
+  RAFT_EXPECTS(static_cast<uint64_t>(centroids.extent(0)) <=
+                 static_cast<uint64_t>(std::numeric_limits<LabelT>::max()),
+               "The chosen label type cannot represent all cluster labels");
+
+  cuvs::cluster::kmeans::detail::predict(handle,
+                                         params,
+                                         centroids.data_handle(),
+                                         centroids.extent(0),
+                                         X.extent(1),
+                                         X.data_handle(),
+                                         X.extent(0),
+                                         labels.data_handle(),
+                                         mapping_op);
+}
+
+/**
+ * @brief Compute hierarchical balanced k-means clustering and predict cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/handle.hpp>
+ *   #include <cuvs/cluster/kmeans_balanced.cuh>
+ *   #include <cuvs/cluster/kmeans_balanced_types.hpp>
+ *   ...
+ *   raft::handle_t handle;
+ *    cuvs::cluster::balanced_params params;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<float, int>(handle, n_rows);
+ *    cuvs::cluster::kmeans_balanced::fit_predict(
+ *       handle, params, X, centroids.view(), labels.view());
+ * @endcode
+ *
+ * @tparam DataT Type of the input data.
+ * @tparam MathT Type of the centroids and mapped data.
+ * @tparam IndexT Type used for indexing.
+ * @tparam LabelT Type of the output labels.
+ * @tparam MappingOpT Type of the mapping function.
+ * @param[in]  handle     The raft resources
+ * @param[in]  params     Structure containing the hyper-parameters
+ * @param[in]  X          Training instances to cluster. The data must be in row-major format.
+ *                        [dim = n_samples x n_features]
+ * @param[out] centroids  The output centroids [dim = n_clusters x n_features]
+ * @param[out] labels     The output labels [dim = n_samples]
+ * @param[in]  mapping_op (optional) Functor to convert from the input datatype to the arithmetic
+ *                        datatype. If DataT and MathT are the same, this must be the identity.
+ */
+template <typename DataT,
+          typename MathT,
+          typename IndexT,
+          typename LabelT,
+          typename MappingOpT = raft::identity_op>
+void fit_predict(const raft::resources& handle,
+                 cuvs::cluster::kmeans::balanced_params const& params,
+                 raft::device_matrix_view<const DataT, IndexT> X,
+                 raft::device_matrix_view<MathT, IndexT> centroids,
+                 raft::device_vector_view<LabelT, IndexT> labels,
+                 MappingOpT mapping_op = raft::identity_op())
+{
+  auto centroids_const = raft::make_device_matrix_view<const MathT, IndexT>(
+    centroids.data_handle(), centroids.extent(0), centroids.extent(1));
+  cuvs::cluster::kmeans_balanced::fit(handle, params, X, centroids, mapping_op);
+  cuvs::cluster::kmeans_balanced::predict(handle, params, X, centroids_const, labels, mapping_op);
+}
+
+namespace helpers {
+
+/**
+ * @brief Randomly initialize centers and apply expectation-maximization-balancing iterations
+ *
+ * This is essentially the non-hierarchical balanced k-means algorithm which is used by the
+ * hierarchical algorithm once to build the mesoclusters and once per mesocluster to build the fine
+ * clusters.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/handle.hpp>
+ *   #include <cuvs/cluster/kmeans_balanced.cuh>
+ *   #include <cuvs/cluster/kmeans_balanced_types.hpp>
+ *   ...
+ *   raft::handle_t handle;
+ *    cuvs::cluster::balanced_params params;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int>(handle, n_samples);
+ *   auto sizes = raft::make_device_vector<int, int>(handle, n_clusters);
+ *    cuvs::cluster::kmeans_balanced::build_clusters(
+ *       handle, params, X, centroids.view(), labels.view(), sizes.view());
+ * @endcode
+ *
+ * @tparam DataT Type of the input data.
+ * @tparam MathT Type of the centroids and mapped data.
+ * @tparam IndexT Type used for indexing.
+ * @tparam LabelT Type of the output labels.
+ * @tparam CounterT Counter type supported by CUDA's native atomicAdd.
+ * @tparam MappingOpT Type of the mapping function.
+ * @param[in]  handle        The raft resources
+ * @param[in]  params        Structure containing the hyper-parameters
+ * @param[in]  X             Training instances to cluster. The data must be in row-major format.
+ *                           [dim = n_samples x n_features]
+ * @param[out] centroids     The output centroids [dim = n_clusters x n_features]
+ * @param[out] labels        The output labels [dim = n_samples]
+ * @param[out] cluster_sizes Size of each cluster [dim = n_clusters]
+ * @param[in]  mapping_op    (optional) Functor to convert from the input datatype to the
+ *                           arithmetic datatype. If DataT == MathT, this must be the identity.
+ * @param[in]  X_norm        (optional) Dataset's row norms [dim = n_samples]
+ */
+template <typename DataT,
+          typename MathT,
+          typename IndexT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT>
+void build_clusters(const raft::resources& handle,
+                    const cuvs::cluster::kmeans::balanced_params& params,
+                    raft::device_matrix_view<const DataT, IndexT> X,
+                    raft::device_matrix_view<MathT, IndexT> centroids,
+                    raft::device_vector_view<LabelT, IndexT> labels,
+                    raft::device_vector_view<CounterT, IndexT> cluster_sizes,
+                    MappingOpT mapping_op = raft::identity_op(),
+                    std::optional<raft::device_vector_view<const MathT>> X_norm = std::nullopt)
+{
+  RAFT_EXPECTS(X.extent(0) == labels.extent(0),
+               "Number of rows in dataset and labels are different");
+  RAFT_EXPECTS(X.extent(1) == centroids.extent(1),
+               "Number of features in dataset and centroids are different");
+  RAFT_EXPECTS(centroids.extent(0) == cluster_sizes.extent(0),
+               "Number of rows in centroids and clusyer_sizes are different");
+
+  cuvs::cluster::kmeans::detail::build_clusters(
+    handle,
+    params,
+    X.extent(1),
+    X.data_handle(),
+    X.extent(0),
+    centroids.extent(0),
+    centroids.data_handle(),
+    labels.data_handle(),
+    cluster_sizes.data_handle(),
+    mapping_op,
+    raft::resource::get_workspace_resource(handle),
+    X_norm.has_value() ? X_norm.value().data_handle() : nullptr);
+}
+
+/**
+ * @brief Given the data and labels, calculate cluster centers and sizes in one sweep.
+ *
+ * Let `S_i = {x_k | x_k \in X & labels[k] == i}` be the vectors in the dataset with label i.
+ *
+ * On exit,
+ *   `centers_i = (\sum_{x \in S_i} x + w_i * center_i) / (|S_i| + w_i)`,
+ *     where  `w_i = reset_counters ?  0 : cluster_size[i]`.
+ *
+ * In other words, the updated cluster centers are a weighted average of the existing cluster
+ * center, and the coordinates of the points labeled with i. _This allows calling this function
+ * multiple times with different datasets with the same effect as if calling this function once
+ * on the combined dataset_.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/handle.hpp>
+ *   #include <cuvs/cluster/kmeans_balanced.cuh>
+ *   ...
+ *   raft::handle_t handle;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *   auto sizes = raft::make_device_vector<int, int>(handle, n_clusters);
+ *    cuvs::cluster::kmeans_balanced::calc_centers_and_sizes(
+ *       handle, X, labels, centroids.view(), sizes.view(), true);
+ * @endcode
+ *
+ * @tparam DataT Type of the input data.
+ * @tparam MathT Type of the centroids and mapped data.
+ * @tparam IndexT Type used for indexing.
+ * @tparam LabelT Type of the output labels.
+ * @tparam CounterT Counter type supported by CUDA's native atomicAdd.
+ * @tparam MappingOpT Type of the mapping function.
+ * @param[in]  handle         The raft resources
+ * @param[in]  X              Dataset for which to calculate cluster centers. The data must be in
+ *                            row-major format. [dim = n_samples x n_features]
+ * @param[in]  labels         The input labels [dim = n_samples]
+ * @param[out] centroids      The output centroids [dim = n_clusters x n_features]
+ * @param[out] cluster_sizes  Size of each cluster [dim = n_clusters]
+ * @param[in]  reset_counters Whether to clear the output arrays before calculating.
+ *                            When set to `false`, this function may be used to update existing
+ *                            centers and sizes using the weighted average principle.
+ * @param[in]  mapping_op     (optional) Functor to convert from the input datatype to the
+ *                            arithmetic datatype. If DataT == MathT, this must be the identity.
+ */
+template <typename DataT,
+          typename MathT,
+          typename IndexT,
+          typename LabelT,
+          typename CounterT,
+          typename MappingOpT = raft::identity_op>
+void calc_centers_and_sizes(const raft::resources& handle,
+                            raft::device_matrix_view<const DataT, IndexT> X,
+                            raft::device_vector_view<const LabelT, IndexT> labels,
+                            raft::device_matrix_view<MathT, IndexT> centroids,
+                            raft::device_vector_view<CounterT, IndexT> cluster_sizes,
+                            bool reset_counters   = true,
+                            MappingOpT mapping_op = raft::identity_op())
+{
+  RAFT_EXPECTS(X.extent(0) == labels.extent(0),
+               "Number of rows in dataset and labels are different");
+  RAFT_EXPECTS(X.extent(1) == centroids.extent(1),
+               "Number of features in dataset and centroids are different");
+  RAFT_EXPECTS(centroids.extent(0) == cluster_sizes.extent(0),
+               "Number of rows in centroids and clusyer_sizes are different");
+
+  cuvs::cluster::kmeans::detail::calc_centers_and_sizes(
+    handle,
+    centroids.data_handle(),
+    cluster_sizes.data_handle(),
+    centroids.extent(0),
+    X.extent(1),
+    X.data_handle(),
+    X.extent(0),
+    labels.data_handle(),
+    reset_counters,
+    mapping_op,
+    raft::resource::get_workspace_resource(handle));
+}
+
+}  // namespace helpers
+
+}  // namespace  cuvs::cluster::kmeans_balanced
diff --git a/cpp/src/cluster/kmeans_balanced_fit_float.cu b/cpp/src/cluster/kmeans_balanced_fit_float.cu
new file mode 100644
index 0000000000..ac5a664929
--- /dev/null
+++ b/cpp/src/cluster/kmeans_balanced_fit_float.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// clang-format off
+#include "kmeans_balanced.cuh"
+#include "../neighbors/detail/ann_utils.cuh"
+#include <raft/core/resources.hpp>
+// clang-format on
+
+namespace cuvs::cluster::kmeans {
+
+void fit(const raft::resources& handle,
+         cuvs::cluster::kmeans::balanced_params const& params,
+         raft::device_matrix_view<const float, int> X,
+         raft::device_matrix_view<float, int> centroids)
+{
+  cuvs::cluster::kmeans_balanced::fit(
+    handle, params, X, centroids, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_balanced_fit_int8.cu b/cpp/src/cluster/kmeans_balanced_fit_int8.cu
new file mode 100644
index 0000000000..ad9c118684
--- /dev/null
+++ b/cpp/src/cluster/kmeans_balanced_fit_int8.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// clang-format off
+#include "kmeans_balanced.cuh"
+#include "../neighbors/detail/ann_utils.cuh"
+#include <raft/core/resources.hpp>
+// clang-format on
+
+namespace cuvs::cluster::kmeans {
+
+void fit(const raft::resources& handle,
+         cuvs::cluster::kmeans::balanced_params const& params,
+         raft::device_matrix_view<const int8_t, int> X,
+         raft::device_matrix_view<float, int> centroids)
+{
+  cuvs::cluster::kmeans_balanced::fit(
+    handle, params, X, centroids, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_balanced_fit_predict_float.cu b/cpp/src/cluster/kmeans_balanced_fit_predict_float.cu
new file mode 100644
index 0000000000..f6f177121f
--- /dev/null
+++ b/cpp/src/cluster/kmeans_balanced_fit_predict_float.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// clang-format off
+#include "kmeans_balanced.cuh"
+#include "../neighbors/detail/ann_utils.cuh"
+#include <raft/core/resources.hpp>
+// clang-format on
+
+namespace cuvs::cluster::kmeans {
+
+void fit_predict(const raft::resources& handle,
+                 cuvs::cluster::kmeans::balanced_params const& params,
+                 raft::device_matrix_view<const float, int> X,
+                 raft::device_matrix_view<float, int> centroids,
+                 raft::device_vector_view<uint32_t, int> labels)
+{
+  cuvs::cluster::kmeans_balanced::fit_predict(
+    handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu b/cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu
new file mode 100644
index 0000000000..b6171ece5a
--- /dev/null
+++ b/cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// clang-format off
+#include "kmeans_balanced.cuh"
+#include "../neighbors/detail/ann_utils.cuh"
+#include <raft/core/resources.hpp>
+// clang-format on
+
+namespace cuvs::cluster::kmeans {
+
+void fit_predict(const raft::resources& handle,
+                 cuvs::cluster::kmeans::balanced_params const& params,
+                 raft::device_matrix_view<const int8_t, int> X,
+                 raft::device_matrix_view<float, int> centroids,
+                 raft::device_vector_view<uint32_t, int> labels)
+{
+  cuvs::cluster::kmeans_balanced::fit_predict(
+    handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_balanced_predict_float.cu b/cpp/src/cluster/kmeans_balanced_predict_float.cu
new file mode 100644
index 0000000000..e8cd1ffeb0
--- /dev/null
+++ b/cpp/src/cluster/kmeans_balanced_predict_float.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// clang-format off
+#include "kmeans_balanced.cuh"
+#include "../neighbors/detail/ann_utils.cuh"
+#include <raft/core/resources.hpp>
+// clang-format on
+
+namespace cuvs::cluster::kmeans {
+
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const float, int> X,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<uint32_t, int> labels)
+{
+  cuvs::cluster::kmeans_balanced::predict(
+    handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_balanced_predict_int8.cu b/cpp/src/cluster/kmeans_balanced_predict_int8.cu
new file mode 100644
index 0000000000..e08d8fc87e
--- /dev/null
+++ b/cpp/src/cluster/kmeans_balanced_predict_int8.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// clang-format off
+#include "kmeans_balanced.cuh"
+#include "../neighbors/detail/ann_utils.cuh"
+#include <raft/core/resources.hpp>
+// clang-format on
+
+namespace cuvs::cluster::kmeans {
+
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const int8_t, int> X,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<uint32_t, int> labels)
+{
+  cuvs::cluster::kmeans_balanced::predict(
+    handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_fit_float.cu b/cpp/src/cluster/kmeans_fit_float.cu
new file mode 100644
index 0000000000..89862a46c7
--- /dev/null
+++ b/cpp/src/cluster/kmeans_fit_float.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int> X,
+         std::optional<raft::device_vector_view<const float, int>> sample_weight,
+         raft::device_matrix_view<float, int> centroids,
+         raft::host_scalar_view<float, int> inertia,
+         raft::host_scalar_view<int, int> n_iter)
+{
+  cuvs::cluster::kmeans::fit<float, int>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_fit_predict_float.cu b/cpp/src/cluster/kmeans_fit_predict_float.cu
new file mode 100644
index 0000000000..f043f76244
--- /dev/null
+++ b/cpp/src/cluster/kmeans_fit_predict_float.cu
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const float, int> X,
+                 std::optional<raft::device_vector_view<const float, int>> sample_weight,
+                 std::optional<raft::device_matrix_view<float, int>> centroids,
+                 raft::device_vector_view<int, int> labels,
+                 raft::host_scalar_view<float> inertia,
+                 raft::host_scalar_view<int> n_iter)
+
+{
+  cuvs::cluster::kmeans::fit_predict<float, int>(
+    handle, params, X, sample_weight, centroids, labels, inertia, n_iter);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_predict_float.cu b/cpp/src/cluster/kmeans_predict_float.cu
new file mode 100644
index 0000000000..d092152f14
--- /dev/null
+++ b/cpp/src/cluster/kmeans_predict_float.cu
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const float, int> X,
+             std::optional<raft::device_vector_view<const float, int>> sample_weight,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<int, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<float> inertia)
+
+{
+  cuvs::cluster::kmeans::predict<float, int>(
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_transform_float.cu b/cpp/src/cluster/kmeans_transform_float.cu
new file mode 100644
index 0000000000..d9df366286
--- /dev/null
+++ b/cpp/src/cluster/kmeans_transform_float.cu
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void transform(raft::resources const& handle,
+               const kmeans::params& params,
+               raft::device_matrix_view<const float, int> X,
+               raft::device_matrix_view<const float, int> centroids,
+               raft::device_matrix_view<float, int> X_new)
+
+{
+  cuvs::cluster::kmeans::transform<float, int>(handle, params, X, centroids, X_new);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/single_linkage.cuh b/cpp/src/cluster/single_linkage.cuh
new file mode 100644
index 0000000000..364482d789
--- /dev/null
+++ b/cpp/src/cluster/single_linkage.cuh
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "detail/single_linkage.cuh"
+#include <cuvs/cluster/agglomerative.hpp>
+#include <raft/core/device_mdspan.hpp>
+
+namespace cuvs::cluster::agglomerative {
+
+/**
+ * Note: All of the functions below in the  cuvs::cluster namespace are deprecated
+ * and will be removed in a future release. Please use   cuvs::cluster::agglomerative
+ * instead.
+ */
+
+/**
+ * Single-linkage clustering, capable of constructing a KNN graph to
+ * scale the algorithm beyond the n^2 memory consumption of implementations
+ * that use the fully-connected graph of pairwise distances by connecting
+ * a knn graph when k is not large enough to connect it.
+
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam dist_type method to use for constructing connectivities graph
+ * @param[in] handle raft handle
+ * @param[in] X dense input matrix in row-major layout
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[in] metric distance metrix to use when constructing connectivities graph
+ * @param[out] out struct containing output dendrogram and cluster assignments
+ * @param[in] c a constant used when constructing connectivities from knn graph. Allows the indirect
+ control
+ *            of k. The algorithm will set `k = log(n) + c`
+ * @param[in] n_clusters number of clusters to assign data samples
+ */
+template <typename value_idx, typename value_t, Linkage dist_type = Linkage::KNN_GRAPH>
+void single_linkage(raft::resources const& handle,
+                    const value_t* X,
+                    size_t m,
+                    size_t n,
+                    cuvs::distance::DistanceType metric,
+                    single_linkage_output<value_idx>* out,
+                    int c,
+                    size_t n_clusters)
+{
+  detail::single_linkage<value_idx, value_t, dist_type>(
+    handle, X, m, n, metric, out, c, n_clusters);
+}
+
+/**
+ * Single-linkage clustering, capable of constructing a KNN graph to
+ * scale the algorithm beyond the n^2 memory consumption of implementations
+ * that use the fully-connected graph of pairwise distances by connecting
+ * a knn graph when k is not large enough to connect it.
+
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam dist_type method to use for constructing connectivities graph
+ * @param[in] handle raft handle
+ * @param[in] X dense input matrix in row-major layout
+ * @param[out] dendrogram output dendrogram (size [n_rows - 1] * 2)
+ * @param[out] labels output labels vector (size n_rows)
+ * @param[in] metric distance metrix to use when constructing connectivities graph
+ * @param[in] n_clusters number of clusters to assign data samples
+ * @param[in] c a constant used when constructing connectivities from knn graph. Allows the indirect
+ control of k. The algorithm will set `k = log(n) + c`
+ */
+template <typename value_t, typename idx_t, Linkage dist_type = Linkage::KNN_GRAPH>
+void single_linkage(raft::resources const& handle,
+                    raft::device_matrix_view<const value_t, idx_t, raft::row_major> X,
+                    raft::device_matrix_view<idx_t, idx_t, raft::row_major> dendrogram,
+                    raft::device_vector_view<idx_t, idx_t> labels,
+                    cuvs::distance::DistanceType metric,
+                    size_t n_clusters,
+                    std::optional<int> c = std::make_optional<int>(DEFAULT_CONST_C))
+{
+  single_linkage_output<idx_t> out_arrs;
+  out_arrs.children = dendrogram.data_handle();
+  out_arrs.labels   = labels.data_handle();
+
+  single_linkage<idx_t, value_t, dist_type>(handle,
+                                            X.data_handle(),
+                                            static_cast<std::size_t>(X.extent(0)),
+                                            static_cast<std::size_t>(X.extent(1)),
+                                            metric,
+                                            &out_arrs,
+                                            c.has_value() ? c.value() : DEFAULT_CONST_C,
+                                            n_clusters);
+}
+};  // namespace   cuvs::cluster::agglomerative
diff --git a/cpp/src/cluster/single_linkage_float.cu b/cpp/src/cluster/single_linkage_float.cu
new file mode 100644
index 0000000000..1614c7eeef
--- /dev/null
+++ b/cpp/src/cluster/single_linkage_float.cu
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "single_linkage.cuh"
+#include <cuvs/cluster/agglomerative.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::agglomerative {
+
+void single_linkage(raft::resources const& handle,
+                    raft::device_matrix_view<const float, int, raft::row_major> X,
+                    raft::device_matrix_view<int, int, raft::row_major> dendrogram,
+                    raft::device_vector_view<int, int> labels,
+                    cuvs::distance::DistanceType metric,
+                    size_t n_clusters,
+                    cuvs::cluster::agglomerative::Linkage linkage,
+                    std::optional<int> c)
+{
+  if (linkage == Linkage::KNN_GRAPH) {
+    single_linkage<float, int, Linkage::KNN_GRAPH>(
+      handle, X, dendrogram, labels, metric, n_clusters, c);
+  } else {
+    single_linkage<float, int, Linkage::PAIRWISE>(
+      handle, X, dendrogram, labels, metric, n_clusters, c);
+  }
+}
+}  // namespace cuvs::cluster::agglomerative
diff --git a/cpp/src/distance/detail/distance_ops/all_ops.cuh b/cpp/src/distance/detail/distance_ops/all_ops.cuh
index 55989697f7..534aa16fa1 100644
--- a/cpp/src/distance/detail/distance_ops/all_ops.cuh
+++ b/cpp/src/distance/detail/distance_ops/all_ops.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 // Defines a named requirement "has_cutlass_op"
-#include "../distance_ops/cutlass.cuh"
+#include "cutlass.cuh"
 
 // The distance operations:
 #include "../distance_ops/canberra.cuh"
diff --git a/cpp/src/distance/detail/fused_distance_nn.cu b/cpp/src/distance/detail/fused_distance_nn.cu
new file mode 100644
index 0000000000..6d2e0f3cdf
--- /dev/null
+++ b/cpp/src/distance/detail/fused_distance_nn.cu
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../fused_distance_nn.cuh"
+#include <raft/core/kvp.hpp>  // raft::KeyValuePair
+
+#include <cstdint>  // int64_t
+
+#define instantiate_cuvs_distance_fusedDistanceNNMinReduce(DataT, OutT, IdxT) \
+  template void cuvs::distance::fusedDistanceNNMinReduce<DataT, OutT, IdxT>(  \
+    OutT * min,                                                               \
+    const DataT* x,                                                           \
+    const DataT* y,                                                           \
+    const DataT* xn,                                                          \
+    const DataT* yn,                                                          \
+    IdxT m,                                                                   \
+    IdxT n,                                                                   \
+    IdxT k,                                                                   \
+    void* workspace,                                                          \
+    bool sqrt,                                                                \
+    bool initOutBuffer,                                                       \
+    bool isRowMajor,                                                          \
+    cuvs::distance::DistanceType metric,                                      \
+    float metric_arg,                                                         \
+    cudaStream_t stream)
+
+instantiate_cuvs_distance_fusedDistanceNNMinReduce(float, float, int);
+instantiate_cuvs_distance_fusedDistanceNNMinReduce(float, float, int64_t);
+
+// We can't have comma's in the macro expansion, so we use the COMMA macro:
+#define COMMA ,
+
+instantiate_cuvs_distance_fusedDistanceNNMinReduce(float, raft::KeyValuePair<int COMMA float>, int);
+instantiate_cuvs_distance_fusedDistanceNNMinReduce(float,
+                                                   raft::KeyValuePair<int64_t COMMA float>,
+                                                   int64_t);
+
+#undef COMMA
+
+#undef instantiate_cuvs_distance_fusedDistanceNNMinReduce
diff --git a/cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h b/cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h
index 186715851b..98e503e7a5 100644
--- a/cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h
+++ b/cpp/src/distance/detail/fused_distance_nn/custom_epilogue_with_broadcast.h
@@ -89,10 +89,13 @@ reduction
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace epilogue {
 namespace threadblock {
 
+// TODO (cjnolet): We shouldn't be doing `using namespace` in this file.
+using namespace cutlass::epilogue::threadblock;
+
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 /// This base class is meant to define the concept required of the
@@ -113,11 +116,11 @@ struct EpilogueWithBroadcastOpBaseCustom {
   using ElementT                      = ElementT_;
   static int const kElementsPerAccess = ElementsPerAccess;
 
-  using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
-  using FragmentCompute     = Array<ElementCompute, kElementsPerAccess>;
-  using FragmentC           = Array<ElementOutput, kElementsPerAccess>;
-  using FragmentZ           = Array<ElementZ, kElementsPerAccess>;
-  using FragmentT           = Array<ElementT, kElementsPerAccess>;
+  using FragmentAccumulator = cutlass::Array<ElementAccumulator, kElementsPerAccess>;
+  using FragmentCompute     = cutlass::Array<ElementCompute, kElementsPerAccess>;
+  using FragmentC           = cutlass::Array<ElementOutput, kElementsPerAccess>;
+  using FragmentZ           = cutlass::Array<ElementZ, kElementsPerAccess>;
+  using FragmentT           = cutlass::Array<ElementT, kElementsPerAccess>;
 
   /// If true, the 'Z' tensor is stored
   static bool const kStoreZ = StoreZ;
@@ -222,7 +225,7 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
   using OutputOp                    = OutputOp_;
   using Padding                     = Padding_;
 
-  using Layout    = layout::RowMajor;
+  using Layout    = cutlass::layout::RowMajor;
   using LongIndex = typename Layout::LongIndex;
 
   /// The complete warp-level accumulator tile
@@ -235,14 +238,14 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
   using ElementCompute = typename OutputOp::ElementCompute;
 
   /// Compute fragment
-  using FragmentCompute = Array<ElementCompute, OutputTileIterator::Fragment::kElements>;
+  using FragmentCompute = cutlass::Array<ElementCompute, OutputTileIterator::Fragment::kElements>;
 
   /// Thread map used by output tile iterators
   using ThreadMap = typename OutputTileIterator::ThreadMap;
 
   /// Fragment object used to store the broadcast values
   using BroadcastFragment =
-    Array<ElementCompute, ThreadMap::Iterations::kColumn * ThreadMap::kElementsPerAccess>;
+    cutlass::Array<ElementCompute, ThreadMap::Iterations::kColumn * ThreadMap::kElementsPerAccess>;
 
   /// Output element
   using ElementOutput = typename OutputTileIterator::Element;
@@ -262,19 +265,19 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
   /// Const tensor reference to source tensor
   using ConstTensorRef = typename OutputTileIterator::ConstTensorRef;
 
-  /// Array type used to output
+  /// cutlass::Array type used to output
   using OutputAccessType =
-    Array<typename OutputTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
+    cutlass::Array<typename OutputTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
 
-  /// Array type used by output functor
+  /// cutlass::Array type used by output functor
   using AccumulatorAccessType =
-    Array<typename WarpTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
+    cutlass::Array<typename WarpTileIterator::Element, OutputTileIterator::kElementsPerAccess>;
 
-  /// Array type used by output functor
-  using ComputeAccessType = Array<ElementCompute, OutputTileIterator::kElementsPerAccess>;
+  /// cutlass::Array type used by output functor
+  using ComputeAccessType = cutlass::Array<ElementCompute, OutputTileIterator::kElementsPerAccess>;
 
   /// Tensor access type
-  using TensorAccessType = Array<ElementTensor, OutputTileIterator::kElementsPerAccess>;
+  using TensorAccessType = cutlass::Array<ElementTensor, OutputTileIterator::kElementsPerAccess>;
 
   /// Number of warps
   using WarpCount = typename Base::WarpCount;
@@ -316,7 +319,7 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
       const_max(1, (Shape::kN + kThreadCount - 1) / kThreadCount);
 
     /// Shape of the shared memory allocation for the epilogue
-    using StorageShape = MatrixShape<kThreadRows, Shape::kN>;
+    using StorageShape = cutlass::MatrixShape<kThreadRows, Shape::kN>;
 
     /// Debug printing
     CUTLASS_DEVICE
@@ -392,12 +395,12 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
     OutputTileIterator source_iterator,   ///< Tile iterator for source accumulator matrix
     TensorTileIterator
       tensor_iterator,  ///< Threadblock tile iterator for additional tensor operand
-    MatrixCoord const&
+    cutlass::MatrixCoord const&
       problem_size =  ///< Problem size needed to guard against out-of-bounds accesses
-    MatrixCoord(Shape::kM, Shape::kN),
-    MatrixCoord const&
+    cutlass::MatrixCoord(Shape::kM, Shape::kN),
+    cutlass::MatrixCoord const&
       threadblock_offset =  ///< Threadblock's initial offset within the problem size space
-    MatrixCoord())
+    cutlass::MatrixCoord())
   {
     BroadcastFragment broadcast_fragment;
 
@@ -413,9 +416,9 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
     BroadcastFragment&
       broadcast_fragment,  ///< Fragment containing the accumulated partial reduction over columns
     ElementVector const* broadcast_ptr,  ///< Broadcast vector
-    MatrixCoord const&
+    cutlass::MatrixCoord const&
       problem_size,  ///< Problem size needed to guard against out-of-bounds accesses
-    MatrixCoord const&
+    cutlass::MatrixCoord const&
       threadblock_offset  ///< Threadblock's initial offset within the problem size space
   )
   {
@@ -429,10 +432,11 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
     int thread_column_idx = threadblock_offset.column() + thread_initial_column;
     broadcast_ptr += thread_initial_column;
 
-    NumericArrayConverter<ElementCompute, ElementVector, BroadcastDetail::kElementsPerAccess>
-      converter;
-    using AccessType          = AlignedArray<ElementVector, BroadcastDetail::kElementsPerAccess>;
-    using ComputeFragmentType = Array<ElementCompute, BroadcastDetail::kElementsPerAccess>;
+    cutlass::
+      NumericArrayConverter<ElementCompute, ElementVector, BroadcastDetail::kElementsPerAccess>
+        converter;
+    using AccessType = cutlass::AlignedArray<ElementVector, BroadcastDetail::kElementsPerAccess>;
+    using ComputeFragmentType = cutlass::Array<ElementCompute, BroadcastDetail::kElementsPerAccess>;
 
     ComputeFragmentType* frag_ptr = reinterpret_cast<ComputeFragmentType*>(&broadcast_fragment);
 
@@ -622,8 +626,8 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
                               typename OutputTileIterator::Fragment const& frag_C,
                               BroadcastFragment const& frag_Broadcast)
   {
-    using AccessTypeT         = Array<typename TensorTileIterator::OutValT, kElementsPerAccess>;
-    using AccessTypeBroadcast = Array<ElementCompute, kElementsPerAccess>;
+    using AccessTypeT = cutlass::Array<typename TensorTileIterator::OutValT, kElementsPerAccess>;
+    using AccessTypeBroadcast = cutlass::Array<ElementCompute, kElementsPerAccess>;
 
     AccessTypeT* frag_T_ptr = reinterpret_cast<AccessTypeT*>(&frag_T);
 
@@ -663,6 +667,6 @@ class EpilogueWithBroadcastCustom : public EpilogueBase<Shape_,
 
 }  // namespace threadblock
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh b/cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh
index f58c48aff3..84357017a5 100644
--- a/cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh
+++ b/cpp/src/distance/detail/fused_distance_nn/cutlass_base.cuh
@@ -23,7 +23,7 @@
 // We define CUTLASS_NAMESPACE in case
 // RAFT cmake is not used
 #ifndef CUTLASS_NAMESPACE
-#define cutlass raft_cutlass
+#define cutlass cuvs_cutlass
 #endif
 
 #include "epilogue_elementwise.cuh"     // FusedDistanceNNEpilogueElementwise
@@ -83,7 +83,7 @@ void cutlassFusedDistanceNN(const DataT* x,
                             KVPReduceOpT pairRedOp,
                             cudaStream_t stream)
 {
-  using EpilogueOutputOp = cutlass::epilogue::thread::FusedDistanceNNEpilogueElementwise<
+  using EpilogueOutputOp = cuvs::epilogue::thread::FusedDistanceNNEpilogueElementwise<
     DataT,  // ElementC_
     AccT,   // ElementAccumulator_
     DataT,  // ElementCompute_
@@ -117,15 +117,15 @@ void cutlassFusedDistanceNN(const DataT* x,
   constexpr bool isRowMajor = true;
 
   using fusedDistanceNNKernel =
-    typename cutlass::gemm::kernel::FusedDistanceNNGemm<DataT,
-                                                        Alignment,
-                                                        DataT,
-                                                        Alignment,
-                                                        AccT,
-                                                        AccT,
-                                                        EpilogueOutputOp,
-                                                        NumStages,  // Number of pipeline stages
-                                                        isRowMajor>::GemmKernel;
+    typename cuvs::gemm::kernel::FusedDistanceNNGemm<DataT,
+                                                     Alignment,
+                                                     DataT,
+                                                     Alignment,
+                                                     AccT,
+                                                     AccT,
+                                                     EpilogueOutputOp,
+                                                     NumStages,  // Number of pipeline stages
+                                                     isRowMajor>::GemmKernel;
 
   using fusedDistanceNN = cutlass::gemm::device::GemmGrouped<fusedDistanceNNKernel>;
 
diff --git a/cpp/src/distance/detail/fused_distance_nn/epilogue.cuh b/cpp/src/distance/detail/fused_distance_nn/epilogue.cuh
index 06939e2bfd..b2aad65f13 100644
--- a/cpp/src/distance/detail/fused_distance_nn/epilogue.cuh
+++ b/cpp/src/distance/detail/fused_distance_nn/epilogue.cuh
@@ -74,10 +74,13 @@ operation.
 
 ////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace epilogue {
 namespace threadblock {
 
+// TODO (cjnolet): We shouldn't be doing `using namespace` in this file.
+using namespace cutlass::epilogue::threadblock;
+
 ////////////////////////////////////////////////////////////////////////////////
 
 /// Defines sensible defaults for epilogues for TensorOps.
@@ -99,20 +102,20 @@ struct FusedDistanceNNEpilogue {
   //
   // Stores the result z = (y = GEMM(A, B, C), broadcast)
   //
-  using RowNormTileIterator = cutlass::epilogue::threadblock::
+  using RowNormTileIterator = cuvs::epilogue::threadblock::
     PredicatedTileIteratorNormVecSmem<typename Base::OutputTileThreadMap, ElementOutput, LayoutT>;
 
   //
   // Additional tensor tile iterator - stores t = Elementwise(z)
   //
-  using OutputTileIterator = cutlass::epilogue::threadblock::PredicatedTileIteratorReducedVec<
+  using OutputTileIterator = cuvs::epilogue::threadblock::PredicatedTileIteratorReducedVec<
     typename Base::OutputTileThreadMap,
     ElementTensor,
     LayoutT,
     typename OutputOp::Params>;
 
   /// Define the epilogue
-  using Epilogue = cutlass::epilogue::threadblock::EpilogueWithBroadcastCustom<
+  using Epilogue = cuvs::epilogue::threadblock::EpilogueWithBroadcastCustom<
     Shape,
     WarpMmaTensorOp,
     PartitionsK,
@@ -129,6 +132,6 @@ struct FusedDistanceNNEpilogue {
 
 }  // namespace threadblock
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh b/cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh
index e69b2486df..6095f2eef7 100644
--- a/cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh
+++ b/cpp/src/distance/detail/fused_distance_nn/epilogue_elementwise.cuh
@@ -67,10 +67,13 @@
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace epilogue {
 namespace thread {
 
+// TODO (cjnolet): We shouldn't be doing `using namespace` in this file.
+using namespace cutlass::epilogue::thread;
+
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 /// This base class is meant to define the concept required of the
@@ -99,12 +102,12 @@ class FusedDistanceNNEpilogueElementwise {
   using DistanceOp = DistanceOp_;
   using CGReduceOp = CGReduceOp_;
 
-  using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
-  using FragmentCompute     = Array<ElementCompute, kElementsPerAccess>;
-  using FragmentC           = Array<ElementOutput, kElementsPerAccess>;
-  using FragmentZ           = Array<ElementZ, kElementsPerAccess>;
+  using FragmentAccumulator = cutlass::Array<ElementAccumulator, kElementsPerAccess>;
+  using FragmentCompute     = cutlass::Array<ElementCompute, kElementsPerAccess>;
+  using FragmentC           = cutlass::Array<ElementOutput, kElementsPerAccess>;
+  using FragmentZ           = cutlass::Array<ElementZ, kElementsPerAccess>;
   using OutValT             = typename CGReduceOp::AccTypeT;
-  using FragmentT           = Array<OutValT, kElementsPerAccess>;
+  using FragmentT           = cutlass::Array<OutValT, kElementsPerAccess>;
 
   using FragmentOutput = FragmentZ;
 
@@ -189,9 +192,9 @@ class FusedDistanceNNEpilogueElementwise {
                   FragmentCompute const& V) const
   {
     FragmentCompute tmp_Accum =
-      NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
+      cutlass::NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
     FragmentCompute tmp_C =
-      NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(frag_C);
+      cutlass::NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(frag_C);
     FragmentCompute result_Z;
 
     CUTLASS_PRAGMA_UNROLL
@@ -215,6 +218,6 @@ class FusedDistanceNNEpilogueElementwise {
 
 }  // namespace thread
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/gemm.h b/cpp/src/distance/detail/fused_distance_nn/gemm.h
index 0385b95cd1..6c8d718d0e 100644
--- a/cpp/src/distance/detail/fused_distance_nn/gemm.h
+++ b/cpp/src/distance/detail/fused_distance_nn/gemm.h
@@ -56,10 +56,13 @@
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace gemm {
 namespace kernel {
 
+// TODO (cjnolet): We shouldn't be doing `using namespace` in this file.
+using namespace cutlass::gemm::kernel;
+
 /////////////////////////////////////////////////////////////////////////////////////////////////
 /*
  * This configuration is used for float inputs with veclen(kAlignmentA/B) = 2 or 4,
@@ -166,7 +169,7 @@ struct FusedDistanceNNGemm {
                                                  Operator>::GemmKernel;
 
   // Replace epilogue
-  using Epilogue = typename cutlass::epilogue::threadblock::FusedDistanceNNEpilogue<
+  using Epilogue = typename cuvs::epilogue::threadblock::FusedDistanceNNEpilogue<
     typename GemmBase::Epilogue::Shape,
     typename GemmBase::Epilogue::WarpMmaOperator,
     GemmBase::Epilogue::kPartitionsK,
@@ -280,7 +283,7 @@ struct FusedDistanceNNGemm<float,  /// Element type for A matrix operand
                                                  Operator>::GemmKernel;
 
   // Replace epilogue
-  using Epilogue = typename cutlass::epilogue::threadblock::FusedDistanceNNEpilogue<
+  using Epilogue = typename cuvs::epilogue::threadblock::FusedDistanceNNEpilogue<
     typename GemmBase::Epilogue::Shape,
     typename GemmBase::Epilogue::WarpMmaOperator,
     GemmBase::Epilogue::kPartitionsK,
@@ -384,7 +387,7 @@ struct FusedDistanceNNGemm<double,
                                                  Operator>::GemmKernel;
 
   // Replace epilogue
-  using Epilogue = typename cutlass::epilogue::threadblock::FusedDistanceNNEpilogue<
+  using Epilogue = typename cuvs::epilogue::threadblock::FusedDistanceNNEpilogue<
     typename GemmBase::Epilogue::Shape,
     typename GemmBase::Epilogue::WarpMmaOperator,
     GemmBase::Epilogue::kPartitionsK,
@@ -406,4 +409,4 @@ struct FusedDistanceNNGemm<double,
 
 }  // namespace kernel
 }  // namespace gemm
-}  // namespace cutlass
\ No newline at end of file
+}  // namespace cuvs
\ No newline at end of file
diff --git a/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h b/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
index f1a7c728e9..c506da4ff2 100644
--- a/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
+++ b/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
@@ -69,10 +69,12 @@ This file contains heavily customized version of GemmGrouped from CUTLASS 2.10.0
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace gemm {
 namespace kernel {
 
+// TODO (cjnolet): We shouldn't be doing `using namespace` in this file.
+using namespace cutlass::gemm::kernel;
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 template <typename Mma_,                         ///! Threadblock-scoped matrix multiply-accumulate
@@ -90,16 +92,18 @@ struct FusedDistanceNNPersistent {
   static bool const kTransposed                     = Transposed;
 
   // Optional transpose
-  using MapArguments = kernel::detail::MapArguments<typename Mma::IteratorA::Element,
-                                                    typename Mma::IteratorA::Layout,
-                                                    Mma::kTransformA,
-                                                    Mma::IteratorA::AccessType::kElements,
-                                                    typename Mma::IteratorB::Element,
-                                                    typename Mma::IteratorB::Layout,
-                                                    Mma::kTransformB,
-                                                    Mma::IteratorB::AccessType::kElements,
-                                                    typename Mma::LayoutC,
-                                                    kTransposed>;
+  // TODO: This is dangerous- we shouldn't be using `detail` APIs from cutlass
+  using MapArguments =
+    cutlass::gemm::kernel::detail::MapArguments<typename Mma::IteratorA::Element,
+                                                typename Mma::IteratorA::Layout,
+                                                Mma::kTransformA,
+                                                Mma::IteratorA::AccessType::kElements,
+                                                typename Mma::IteratorB::Element,
+                                                typename Mma::IteratorB::Layout,
+                                                Mma::kTransformB,
+                                                Mma::IteratorB::AccessType::kElements,
+                                                typename Mma::LayoutC,
+                                                kTransposed>;
 
   // Public-facing type definitions related to operand element type, layout, and complex conjugate
   // operation. Must interact with the 'kTransposed' notion.
@@ -110,8 +114,8 @@ struct FusedDistanceNNPersistent {
   using ElementC = typename Epilogue::OutputTileIterator::Element;
   using LayoutC  = typename MapArguments::LayoutC;
 
-  static ComplexTransform const kTransformA = MapArguments::kTransformA;
-  static ComplexTransform const kTransformB = MapArguments::kTransformB;
+  static cutlass::ComplexTransform const kTransformA = MapArguments::kTransformA;
+  static cutlass::ComplexTransform const kTransformB = MapArguments::kTransformB;
 
   // Type definitions about the mainloop.
   using Operator         = typename Mma::Operator;
@@ -152,7 +156,7 @@ struct FusedDistanceNNPersistent {
     //
     // Data members
     //
-    GemmCoord problem_sizes;
+    cutlass::gemm::GemmCoord problem_sizes;
     temp_problem_visitor problem_visitor;
     int problem_count;
     int threadblock_count;
@@ -171,7 +175,7 @@ struct FusedDistanceNNPersistent {
     typename LayoutC::Stride::Index ldt;
 
     // Only used by device-level operator
-    GemmCoord* host_problem_sizes;
+    cutlass::gemm::GemmCoord* host_problem_sizes;
 
     //
     // Methods
@@ -196,7 +200,7 @@ struct FusedDistanceNNPersistent {
 
     /// Ctor
     CUTLASS_HOST_DEVICE
-    Arguments(GemmCoord problem_sizes,
+    Arguments(cutlass::gemm::GemmCoord problem_sizes,
               int problem_count,
               int threadblock_count,
               typename EpilogueOutputOp::Params output_op,
@@ -209,7 +213,7 @@ struct FusedDistanceNNPersistent {
               typename LayoutB::Stride::Index ldb,
               typename LayoutC::Stride::Index ldc,
               typename LayoutC::Stride::Index ldt,
-              GemmCoord* host_problem_sizes = nullptr)
+              cutlass::gemm::GemmCoord* host_problem_sizes = nullptr)
       : problem_sizes(problem_sizes),
         threadblock_count(threadblock_count),
         output_op(output_op),
@@ -250,7 +254,7 @@ struct FusedDistanceNNPersistent {
     void* ptr_Vector;
     void* ptr_Tensor;
 
-    GemmCoord problem_size;
+    cutlass::gemm::GemmCoord problem_size;
     typename LayoutA::Stride::Index lda;
     typename LayoutB::Stride::Index ldb;
     typename LayoutC::Stride::Index ldc;
@@ -340,12 +344,12 @@ struct FusedDistanceNNPersistent {
   FusedDistanceNNPersistent() {}
 
   /// Determines whether kernel satisfies alignment
-  static Status can_implement(cutlass::gemm::GemmCoord const& problem_size)
+  static cutlass::Status can_implement(cutlass::gemm::GemmCoord const& problem_size)
   {
-    return Status::kSuccess;
+    return cutlass::Status::kSuccess;
   }
 
-  static Status can_implement(Arguments const& args) { return Status::kSuccess; }
+  static cutlass::Status can_implement(Arguments const& args) { return cutlass::Status::kSuccess; }
 
   static size_t get_extra_workspace_size(Arguments const& args,
                                          cutlass::gemm::GemmCoord const& grid_tiled_shape)
@@ -383,8 +387,8 @@ struct FusedDistanceNNPersistent {
     using ElementC = typename Epilogue::OutputTileIterator::Element;
     using LayoutC  = typename Epilogue::OutputTileIterator::Layout;
 
-    const GemmCoord& problem_size    = params.problem_size;
-    const auto grid_shape_           = grid_shape(problem_size);
+    const cutlass::gemm::GemmCoord& problem_size = params.problem_size;
+    const auto grid_shape_                       = grid_shape(problem_size);
     const uint32_t problem_chunk     = (tile_count(grid_shape_) - 1 + gridDim.x) / gridDim.x;
     const uint32_t problem_chunk_end = blockIdx.x * problem_chunk + problem_chunk;
     typename LayoutB::Index column =
@@ -507,6 +511,6 @@ struct FusedDistanceNNPersistent {
 
 }  // namespace kernel
 }  // namespace gemm
-}  // namespace cutlass
+}  // namespace cuvs
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h
index 794cd5eb63..fd7d4aab50 100644
--- a/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h
+++ b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_normvec_smem.h
@@ -75,13 +75,16 @@ This file contains a customized version of PredicatedTileIterator from CUTLASS 2
 
 ////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 
 ////////////////////////////////////////////////////////////////////////////////
 
 namespace epilogue {
 namespace threadblock {
 
+// TODO (cjnolet): We shouldn't be doing `using namespace` in this file.
+using namespace cutlass::epilogue::threadblock;
+
 ////////////////////////////////////////////////////////////////////////////////
 
 /// Tile iterator used to load and store output tile from global memory in epilogue.
@@ -101,12 +104,12 @@ class PredicatedTileIteratorNormVecSmem {
   using Element = Element_;
 
   using Layout         = Layout_;
-  using TensorRef      = TensorRef<Element, Layout>;
+  using TensorRef      = cutlass::TensorRef<Element, Layout>;
   using ConstTensorRef = typename TensorRef::ConstTensorRef;
 
   using Index       = typename Layout::Index;
   using LongIndex   = typename Layout::LongIndex;
-  using TensorCoord = MatrixCoord;
+  using TensorCoord = cutlass::MatrixCoord;
 
   static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
   static int const kThreads           = ThreadMap::kThreads;
@@ -121,12 +124,13 @@ class PredicatedTileIteratorNormVecSmem {
   static_assert(ThreadMap::Iterations::kCluster > 0, "ThreadMap::Iterations::kCluster must be > 0");
   static_assert(ThreadMap::Iterations::kColumn > 0, "ThreadMap::Iterations::kColumn must be > 0");
 
-  using Fragment = Array<Element,
-                         ThreadMap::Iterations::kRow * ThreadMap::Iterations::kGroup *
-                           ThreadMap::Iterations::kCluster * ThreadMap::kElementsPerAccess>;
+  using Fragment =
+    cutlass::Array<Element,
+                   ThreadMap::Iterations::kRow * ThreadMap::Iterations::kGroup *
+                     ThreadMap::Iterations::kCluster * ThreadMap::kElementsPerAccess>;
 
   /// Memory access size
-  using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
+  using AccessType = cutlass::AlignedArray<Element, ThreadMap::kElementsPerAccess>;
 
   //
   // Parameters struct
@@ -189,17 +193,17 @@ class PredicatedTileIteratorNormVecSmem {
     //
     // Type definitions
     //
-    using Shape = MatrixShape<total_rows, 1>;
+    using Shape = cutlass::MatrixShape<total_rows, 1>;
 
     /// Shape of the shared memory allocation
-    using StorageShape = MatrixShape<Shape::kRow, Shape::kColumn>;
+    using StorageShape = cutlass::MatrixShape<Shape::kRow, Shape::kColumn>;
 
     //
     // Data members
     //
     // Methods
     //
-    AlignedBuffer<Element, StorageShape::kCount> storage;
+    cutlass::AlignedBuffer<Element, StorageShape::kCount> storage;
 
     CUTLASS_DEVICE
     Element* data() { return storage.data(); }
@@ -236,7 +240,7 @@ class PredicatedTileIteratorNormVecSmem {
   /// Byte-level pointer
   uint8_t* byte_pointer_;
 
-  /// Array of boolean values to contain steady-state predicates
+  /// cutlass::Array of boolean values to contain steady-state predicates
   Mask mask_;
 
   /// Extent of the matrix tile in rows
@@ -333,7 +337,7 @@ class PredicatedTileIteratorNormVecSmem {
   CUTLASS_HOST_DEVICE
   void add_pointer_offset(LongIndex pointer_offset)
   {
-    byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
+    byte_pointer_ += pointer_offset * cutlass::sizeof_bits<Element>::value / 8;
   }
 
   /// Loads a fragment from memory
@@ -372,7 +376,10 @@ class PredicatedTileIteratorNormVecSmem {
   void load(Fragment& frag) const { load_with_byte_offset(frag, 0); }
 
   CUTLASS_DEVICE
-  MatrixCoord thread_start() const { return MatrixCoord(thread_start_row_, thread_start_column_); }
+  cutlass::MatrixCoord thread_start() const
+  {
+    return cutlass::MatrixCoord(thread_start_row_, thread_start_column_);
+  }
 
   /// Need to get the thread start row from the tile iterator
   CUTLASS_DEVICE
@@ -443,6 +450,6 @@ class PredicatedTileIteratorNormVecSmem {
 
 }  // namespace threadblock
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h
index 5ffb74e9cd..6c26a82aba 100644
--- a/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h
+++ b/cpp/src/distance/detail/fused_distance_nn/predicated_tile_iterator_reduced_vec.h
@@ -84,13 +84,16 @@ namespace cg = cooperative_groups;
 
 ////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 
 ////////////////////////////////////////////////////////////////////////////////
 
 namespace epilogue {
 namespace threadblock {
 
+// TODO (cjnolet): We shouldn't be doing `using namespace` in this file.
+using namespace cutlass::epilogue::threadblock;
+
 ////////////////////////////////////////////////////////////////////////////////
 
 /// Tile iterator used to load and store output tile from global memory in epilogue.
@@ -111,12 +114,12 @@ class PredicatedTileIteratorReducedVec {
   using Element = Element_;
 
   using Layout         = Layout_;
-  using TensorRef      = TensorRef<Element, Layout>;
+  using TensorRef      = cutlass::TensorRef<Element, Layout>;
   using ConstTensorRef = typename TensorRef::ConstTensorRef;
 
   using Index            = typename Layout::Index;
   using LongIndex        = typename Layout::LongIndex;
-  using TensorCoord      = MatrixCoord;
+  using TensorCoord      = cutlass::MatrixCoord;
   using EpilogueOpParams = EpilogueOpParams_;
   using OutIdxT          = typename EpilogueOpParams::CGReduceT::IndexT;
   using OutValT          = typename EpilogueOpParams::CGReduceT::AccTypeT;
@@ -135,14 +138,14 @@ class PredicatedTileIteratorReducedVec {
                                 ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster *
                                 ThreadMap::Count::kTile * ThreadMap::Delta::kRow;
   /// Fragment object
-  using Fragment =
-    Array<OutValT,
-          ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow *
-            ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster * kElementsPerAccess>;
+  using Fragment = cutlass::Array<OutValT,
+                                  ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow *
+                                    ThreadMap::Iterations::kGroup *
+                                    ThreadMap::Iterations::kCluster * kElementsPerAccess>;
 
   // Memory access size
-  using AccessType     = AlignedArray<Element, kElementsPerAccess>;
-  using AccessTypeValT = AlignedArray<OutValT, kElementsPerAccess>;
+  using AccessType     = cutlass::AlignedArray<Element, kElementsPerAccess>;
+  using AccessTypeValT = cutlass::AlignedArray<OutValT, kElementsPerAccess>;
 
   //
   // Parameters struct
@@ -215,10 +218,10 @@ class PredicatedTileIteratorReducedVec {
     //
     // Type definitions
     //
-    using Shape = MatrixShape<total_rows, 1>;
+    using Shape = cutlass::MatrixShape<total_rows, 1>;
 
     /// Shape of the shared memory allocation for the reduced values store
-    using StorageShape = MatrixShape<Shape::kRow, Shape::kColumn>;
+    using StorageShape = cutlass::MatrixShape<Shape::kRow, Shape::kColumn>;
 
     //
     // Data members
@@ -226,7 +229,7 @@ class PredicatedTileIteratorReducedVec {
     //
     // Methods
     //
-    AlignedBuffer<Element, StorageShape::kCount> storage;
+    cutlass::AlignedBuffer<Element, StorageShape::kCount> storage;
 
     CUTLASS_DEVICE
     Element* data() { return storage.data(); }
@@ -327,7 +330,7 @@ class PredicatedTileIteratorReducedVec {
   /// Byte-level pointer first tile offset of this threadblock.
   volatile uint8_t* first_tile_byte_pointer_;
 
-  /// Array of boolean values to contain steady-state predicates
+  /// cutlass::Array of boolean values to contain steady-state predicates
   Mask mask_;
 
   /// Extent of the matrix tile in rows
@@ -542,7 +545,10 @@ class PredicatedTileIteratorReducedVec {
   void store(Fragment& frag) const { store_with_byte_offset(frag, 0); }
 
   CUTLASS_DEVICE
-  MatrixCoord thread_start() const { return MatrixCoord(thread_start_row_, thread_start_column_); }
+  cutlass::MatrixCoord thread_start() const
+  {
+    return cutlass::MatrixCoord(thread_start_row_, thread_start_column_);
+  }
 
   /// Need to get the thread start row from the tile iterator
   CUTLASS_DEVICE
@@ -605,6 +611,6 @@ class PredicatedTileIteratorReducedVec {
 
 }  // namespace threadblock
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/fused_l2_nn.cuh b/cpp/src/distance/detail/fused_l2_nn.cuh
deleted file mode 100644
index 3b8c426ea7..0000000000
--- a/cpp/src/distance/detail/fused_l2_nn.cuh
+++ /dev/null
@@ -1,386 +0,0 @@
-/*
- * Copyright (c) 2021-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "../distance_ops/l2_exp.cuh"  // ops::l2_exp_distance_op
-#include "../fused_distance_nn/cutlass_base.cuh"
-#include "../pairwise_distance_base.cuh"  // PairwiseDistances
-#include <raft/core/kvp.hpp>              // raft::KeyValuePair
-#include <raft/core/operators.hpp>        // raft::identity_op
-#include <raft/linalg/contractions.cuh>   // Policy
-#include <raft/util/arch.cuh>             // raft::util::arch::SM_*
-#include <raft/util/cuda_utils.cuh>       // raft::ceildiv, raft::shfl
-
-#include <cstddef>  // size_t
-#include <limits>   // std::numeric_limits
-
-namespace cuvs {
-namespace distance {
-
-namespace detail {
-
-template <typename LabelT, typename DataT>
-struct KVPMinReduceImpl {
-  typedef raft::KeyValuePair<LabelT, DataT> KVP;
-  DI KVP operator()(LabelT rit, const KVP& a, const KVP& b) { return b.value < a.value ? b : a; }
-  DI KVP operator()(const KVP& a, const KVP& b) { return b.value < a.value ? b : a; }
-
-};  // KVPMinReduce
-
-template <typename LabelT, typename DataT>
-struct MinAndDistanceReduceOpImpl {
-  typedef typename raft::KeyValuePair<LabelT, DataT> KVP;
-  DI void operator()(LabelT rid, KVP* out, const KVP& other) const
-  {
-    if (other.value < out->value) {
-      out->key   = other.key;
-      out->value = other.value;
-    }
-  }
-
-  DI void operator()(LabelT rid, DataT* out, const KVP& other) const
-  {
-    if (other.value < *out) { *out = other.value; }
-  }
-
-  DI void operator()(LabelT rid, DataT* out, const DataT& other) const
-  {
-    if (other < *out) { *out = other; }
-  }
-
-  DI void init(DataT* out, DataT maxVal) const { *out = maxVal; }
-  DI void init(KVP* out, DataT maxVal) const { out->value = maxVal; }
-
-  DI void init_key(DataT& out, LabelT idx) const { return; }
-  DI void init_key(KVP& out, LabelT idx) const { out.key = idx; }
-
-  DI DataT get_value(KVP& out) const
-  {
-    return out.value;
-    ;
-  }
-  DI DataT get_value(DataT& out) const { return out; }
-};
-
-template <typename LabelT, typename DataT>
-struct MinReduceOpImpl {
-  typedef typename raft::KeyValuePair<LabelT, DataT> KVP;
-  DI void operator()(LabelT rid, DataT* out, const KVP& other)
-  {
-    if (other.value < *out) { *out = other.value; }
-  }
-
-  DI void init(DataT* out, DataT maxVal) { *out = maxVal; }
-};
-
-template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT>
-RAFT_KERNEL initKernel(OutT* min, IdxT m, DataT maxVal, ReduceOpT redOp)
-{
-  auto tid = IdxT(blockIdx.x) * blockDim.x + threadIdx.x;
-  if (tid < m) { redOp.init(min + tid, maxVal); }
-}
-
-template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT>
-void initialize(OutT* min, IdxT m, DataT maxVal, ReduceOpT redOp, cudaStream_t stream)
-{
-  auto blks = raft::ceildiv(m, 256);
-  initKernel<DataT, OutT, IdxT><<<blks, 256, 0, stream>>>(min, m, maxVal, redOp);
-}
-
-// TODO: specialize this function for MinAndDistanceReduceOp<int, float>
-// with atomicCAS of 64 bit which will eliminate mutex and shfls
-template <typename P, typename OutT, typename IdxT, typename KVPair, typename ReduceOpT>
-DI void updateReducedVal(
-  int* mutex, OutT* min, KVPair* val, ReduceOpT red_op, IdxT m, IdxT gridStrideY)
-{
-  const auto lid      = threadIdx.x % raft::WarpSize;
-  const auto accrowid = threadIdx.x / P::AccThCols;
-
-  // Update each output row in order within a warp. This will resolve hang
-  // issues with pre-Volta architectures
-#pragma unroll
-  for (int j = 0; j < (raft::WarpSize / P::AccThCols); j++) {
-    if (lid == j * P::AccThCols) {
-#pragma unroll
-      for (int i = 0; i < P::AccRowsPerTh; ++i) {
-        auto rid = gridStrideY + accrowid + i * P::AccThRows;
-        if (rid < m) {
-          auto value = val[i];
-          while (atomicCAS(mutex + rid, 0, 1) == 1)
-            ;
-          __threadfence();
-          red_op(rid, min + rid, value);
-          __threadfence();
-          atomicCAS(mutex + rid, 1, 0);
-        }
-      }
-    }
-  }
-}
-
-template <typename DataT,
-          typename OutT,
-          typename IdxT,
-          typename P,
-          typename ReduceOpT,
-          typename KVPReduceOpT,
-          typename OpT,
-          typename FinalLambda>
-__launch_bounds__(P::Nthreads, 2) RAFT_KERNEL fusedL2NNkernel(OutT* min,
-                                                              const DataT* x,
-                                                              const DataT* y,
-                                                              const DataT* xn,
-                                                              const DataT* yn,
-                                                              IdxT m,
-                                                              IdxT n,
-                                                              IdxT k,
-                                                              DataT maxVal,
-                                                              int* mutex,
-                                                              ReduceOpT redOp,
-                                                              KVPReduceOpT pairRedOp,
-                                                              OpT distance_op,
-                                                              FinalLambda fin_op)
-{
-// compile only if below non-ampere arch.
-#if __CUDA_ARCH__ < 800
-  extern __shared__ char smem[];
-
-  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
-  KVPair val[P::AccRowsPerTh];
-#pragma unroll
-  for (int i = 0; i < P::AccRowsPerTh; ++i) {
-    val[i] = {0, maxVal};
-  }
-
-  // epilogue operation lambda for final value calculation
-  auto epilog_lambda = [n, pairRedOp, &val, maxVal] __device__(
-                         DataT acc[P::AccRowsPerTh][P::AccColsPerTh],
-                         DataT * regxn,
-                         DataT * regyn,
-                         IdxT gridStrideX,
-                         IdxT gridStrideY) {
-    KVPReduceOpT pairRed_op(pairRedOp);
-
-    // intra thread reduce
-    const auto acccolid = threadIdx.x % P::AccThCols;
-    const auto accrowid = threadIdx.x / P::AccThCols;
-#pragma unroll
-    for (int i = 0; i < P::AccRowsPerTh; ++i) {
-#pragma unroll
-      for (int j = 0; j < P::AccColsPerTh; ++j) {
-        auto tmpkey = acccolid + j * P::AccThCols + gridStrideX;
-        KVPair tmp  = {tmpkey, acc[i][j]};
-        if (tmpkey < n) {
-          val[i] = pairRed_op(accrowid + i * P::AccThRows + gridStrideY, tmp, val[i]);
-        }
-      }
-    }
-  };
-
-  auto rowEpilog_lambda =
-    [m, mutex, min, pairRedOp, redOp, &val, maxVal] __device__(IdxT gridStrideY) {
-      KVPReduceOpT pairRed_op(pairRedOp);
-      ReduceOpT red_op(redOp);
-
-      const auto accrowid = threadIdx.x / P::AccThCols;
-      const auto lid      = raft::laneId();
-
-    // reduce
-#pragma unroll
-      for (int i = 0; i < P::AccRowsPerTh; ++i) {
-#pragma unroll
-        for (int j = P::AccThCols / 2; j > 0; j >>= 1) {
-          // Actually, the srcLane (lid +j) should be (lid +j) % P:AccThCols,
-          // but the shfl op applies the modulo internally.
-          auto tmpkey   = raft::shfl(val[i].key, lid + j, P::AccThCols);
-          auto tmpvalue = raft::shfl(val[i].value, lid + j, P::AccThCols);
-          KVPair tmp    = {tmpkey, tmpvalue};
-          val[i]        = pairRed_op(accrowid + i * P::AccThRows + gridStrideY, tmp, val[i]);
-        }
-      }
-
-      updateReducedVal<P, OutT, IdxT, KVPair, ReduceOpT>(mutex, min, val, red_op, m, gridStrideY);
-
-    // reset the val array.
-#pragma unroll
-      for (int i = 0; i < P::AccRowsPerTh; ++i) {
-        val[i] = {0, maxVal};
-      }
-    };
-
-  IdxT lda = k, ldb = k, ldd = n;
-  constexpr bool row_major = true;
-  constexpr bool write_out = false;
-  PairwiseDistances<DataT,
-                    DataT,  // OutT (unused in PairwiseDistances)
-                    IdxT,
-                    P,
-                    decltype(distance_op),
-                    decltype(epilog_lambda),
-                    FinalLambda,
-                    decltype(rowEpilog_lambda),
-                    row_major,
-                    write_out>
-    obj(x,
-        y,
-        m,
-        n,
-        k,
-        lda,
-        ldb,
-        ldd,
-        xn,
-        yn,
-        nullptr,  // Output pointer
-        smem,
-        distance_op,
-        epilog_lambda,
-        fin_op,
-        rowEpilog_lambda);
-  obj.run();
-#endif
-}
-
-// cg::reduce functor for FusedDistanceNN used in its cutlass version
-// to output the min distance value & key(loc id).
-// This is used in fused_distance_nn/predicated_tile_iterator_reduced_vec.h
-// store_with_byte_offset() passed to cg::reduce() & select_reduce.
-template <typename AccType, typename Index, typename OutType>
-struct kvp_cg_min_reduce_op {
-  typedef typename raft::KeyValuePair<Index, AccType> KVP;
-
-  __host__ __device__ kvp_cg_min_reduce_op() noexcept {};
-
-  using AccTypeT = AccType;
-  using IndexT   = Index;
-  // functor signature.
-  __host__ __device__ KVP operator()(KVP a, KVP b) const { return a.value < b.value ? a : b; }
-
-  __host__ __device__ AccType operator()(AccType a, AccType b) const { return min(a, b); }
-
-  __host__ __device__ bool isAmin(AccType a, AccType b) const { return a < b ? true : false; }
-};
-
-template <typename DataT,
-          typename OutT,
-          typename IdxT,
-          typename Policy,
-          typename ReduceOpT,
-          typename KVPReduceOpT>
-void fusedL2NNImpl(OutT* min,
-                   const DataT* x,
-                   const DataT* y,
-                   const DataT* xn,
-                   const DataT* yn,
-                   IdxT m,
-                   IdxT n,
-                   IdxT k,
-                   int* workspace,
-                   ReduceOpT redOp,
-                   KVPReduceOpT pairRedOp,
-                   bool sqrt,
-                   bool initOutBuffer,
-                   cudaStream_t stream)
-{
-  // The kernel policy is determined by fusedL2NN.
-  typedef Policy P;
-
-  dim3 blk(P::Nthreads);
-  auto nblks            = raft::ceildiv<int>(m, P::Nthreads);
-  constexpr auto maxVal = std::numeric_limits<DataT>::max();
-  typedef raft::KeyValuePair<IdxT, DataT> KVPair;
-
-  RAFT_CUDA_TRY(cudaMemsetAsync(workspace, 0, sizeof(int) * m, stream));
-  if (initOutBuffer) {
-    initKernel<DataT, OutT, IdxT, ReduceOpT>
-      <<<nblks, P::Nthreads, 0, stream>>>(min, m, maxVal, redOp);
-    RAFT_CUDA_TRY(cudaGetLastError());
-  }
-
-  namespace arch = raft::util::arch;
-  using AccT     = DataT;
-  ops::l2_exp_distance_op<DataT, AccT, IdxT> distance_op{sqrt};
-
-  raft::identity_op fin_op{};
-
-  auto kernel = fusedL2NNkernel<DataT,
-                                OutT,
-                                IdxT,
-                                P,
-                                ReduceOpT,
-                                KVPReduceOpT,
-                                decltype(distance_op),
-                                decltype(fin_op)>;
-
-  // Get pointer to fp32 SIMT kernel to determine the best compute architecture
-  // out of all for which the kernel was compiled for that matches closely
-  // to the current device. Other methods to determine the architecture (that do not
-  // require a pointer) can be error prone. See:
-  // https://github.com/NVIDIA/cub/issues/545
-  void* kernel_ptr   = reinterpret_cast<void*>(kernel);
-  auto runtime_arch  = arch::kernel_virtual_arch(kernel_ptr);
-  auto cutlass_range = arch::SM_range(arch::SM_80(), arch::SM_future());
-
-  if (cutlass_range.contains(runtime_arch)) {
-    // If device is SM_80 or later, use CUTLASS-based kernel.
-    using L2Op                  = cuvs::distance::detail::ops::l2_exp_cutlass_op<DataT, DataT>;
-    using kvp_cg_min_reduce_op_ = kvp_cg_min_reduce_op<DataT, IdxT, OutT>;
-    kvp_cg_min_reduce_op_ cg_reduce_op;
-    L2Op L2_dist_op(sqrt);
-
-    IdxT lda, ldb, ldd;
-    lda = k, ldb = k, ldd = n;
-
-    cutlassFusedDistanceNN<DataT,
-                           DataT,
-                           OutT,
-                           IdxT,
-                           P::Veclen,
-                           kvp_cg_min_reduce_op_,
-                           L2Op,
-                           ReduceOpT,
-                           KVPReduceOpT>(x,
-                                         y,
-                                         xn,
-                                         yn,
-                                         m,
-                                         n,
-                                         k,
-                                         lda,
-                                         ldb,
-                                         ldd,
-                                         min,
-                                         workspace,
-                                         cg_reduce_op,
-                                         L2_dist_op,
-                                         redOp,
-                                         pairRedOp,
-                                         stream);
-  } else {
-    // If device less than SM_80, use fp32 SIMT kernel.
-    constexpr size_t shmemSize = P::SmemSize + ((P::Mblk + P::Nblk) * sizeof(DataT));
-    dim3 grid                  = launchConfigGenerator<P>(m, n, shmemSize, kernel);
-
-    kernel<<<grid, blk, shmemSize, stream>>>(
-      min, x, y, xn, yn, m, n, k, maxVal, workspace, redOp, pairRedOp, distance_op, fin_op);
-    RAFT_CUDA_TRY(cudaGetLastError());
-  }
-}
-
-}  // namespace detail
-}  // namespace distance
-}  // namespace cuvs
diff --git a/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh b/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
index da9a1ac4eb..d41c5d30cd 100644
--- a/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
+++ b/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
@@ -23,7 +23,7 @@
 // We define CUTLASS_NAMESPACE in case
 // RAFT cmake is not used
 #ifndef CUTLASS_NAMESPACE
-#define cutlass raft_cutlass
+#define cutlass cuvs_cutlass
 #endif
 
 #include "pairwise_distance_epilogue_elementwise.h"
@@ -77,14 +77,14 @@ std::enable_if_t<ops::has_cutlass_op<OpT>::value> cutlassDistanceKernel(const Da
   auto dist_op     = distance_op.get_cutlass_op();
   using DistanceFn = decltype(dist_op);
   using EpilogueOutputOp =
-    cutlass::epilogue::thread::PairwiseDistanceEpilogueElementwise<DataT,  // ElementC_
-                                                                   AccT,   // ElementAccumulator_
-                                                                   DataT,  // ElementCompute_
-                                                                   AccT,   // ElementZ_
-                                                                   OutT,   // ElementT_
-                                                                   1,      // Elements per access 1
-                                                                   DistanceFn,
-                                                                   FinalLambda>;
+    epilogue::thread::PairwiseDistanceEpilogueElementwise<DataT,  // ElementC_
+                                                          AccT,   // ElementAccumulator_
+                                                          DataT,  // ElementCompute_
+                                                          AccT,   // ElementZ_
+                                                          OutT,   // ElementT_
+                                                          1,      // Elements per access 1
+                                                          DistanceFn,
+                                                          FinalLambda>;
   constexpr int batch_count = 1;
 
   constexpr auto mode = cutlass::gemm::GemmUniversalMode::kGemm;
@@ -97,15 +97,15 @@ std::enable_if_t<ops::has_cutlass_op<OpT>::value> cutlassDistanceKernel(const Da
   constexpr int Alignment = VecLen;
 
   using cutlassDistKernel =
-    typename cutlass::gemm::kernel::PairwiseDistanceGemm<DataT,
-                                                         Alignment,
-                                                         DataT,
-                                                         Alignment,
-                                                         AccT,
-                                                         AccT,
-                                                         EpilogueOutputOp,
-                                                         NumStages,  // Number of pipeline stages
-                                                         isRowMajor>::GemmKernel;
+    typename gemm::kernel::PairwiseDistanceGemm<DataT,
+                                                Alignment,
+                                                DataT,
+                                                Alignment,
+                                                AccT,
+                                                AccT,
+                                                EpilogueOutputOp,
+                                                NumStages,  // Number of pipeline stages
+                                                isRowMajor>::GemmKernel;
 
   using cutlassDist = cutlass::gemm::device::GemmUniversalAdapter<cutlassDistKernel>;
 
diff --git a/cpp/src/distance/detail/pairwise_distance_epilogue.h b/cpp/src/distance/detail/pairwise_distance_epilogue.h
index 6ead09ed16..48787da55b 100644
--- a/cpp/src/distance/detail/pairwise_distance_epilogue.h
+++ b/cpp/src/distance/detail/pairwise_distance_epilogue.h
@@ -43,7 +43,7 @@ operation.
 
 ////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace epilogue {
 namespace threadblock {
 
@@ -62,13 +62,13 @@ template <typename Shape,
           bool ScatterD = false>
 struct PairwiseDistanceEpilogue {
   /// Use defaults related to the existing epilogue
-  using Base =
+  using Base = cutlass::epilogue::threadblock::
     DefaultEpilogueTensorOp<Shape, WarpMmaTensorOp, PartitionsK, OutputOp, ElementsPerAccess>;
 
   //
   // Stores the result z = (y = GEMM(A, B, C), broadcast)
   //
-  using OutputTileIterator = cutlass::epilogue::threadblock::
+  using OutputTileIterator = cuvs::epilogue::threadblock::
     PredicatedTileIteratorNormVec<typename Base::OutputTileThreadMap, ElementOutput, LayoutT>;
 
   //
@@ -79,22 +79,23 @@ struct PairwiseDistanceEpilogue {
                                                            ElementTensor>;
 
   /// Define the epilogue
-  using Epilogue = EpilogueWithBroadcast<Shape,
-                                         WarpMmaTensorOp,
-                                         PartitionsK,
-                                         OutputTileIterator,
-                                         TensorTileIterator,
-                                         ElementVector,
-                                         typename Base::AccumulatorFragmentIterator,
-                                         typename Base::WarpTileIterator,
-                                         typename Base::SharedLoadIterator,
-                                         OutputOp,
-                                         typename Base::Padding,
-                                         Base::kFragmentsPerIteration>;
+  using Epilogue = cutlass::epilogue::threadblock::EpilogueWithBroadcast<
+    Shape,
+    WarpMmaTensorOp,
+    PartitionsK,
+    OutputTileIterator,
+    TensorTileIterator,
+    ElementVector,
+    typename Base::AccumulatorFragmentIterator,
+    typename Base::WarpTileIterator,
+    typename Base::SharedLoadIterator,
+    OutputOp,
+    typename Base::Padding,
+    Base::kFragmentsPerIteration>;
 };
 
 }  // namespace threadblock
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h b/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
index 2b2c04b9d3..f9955334de 100644
--- a/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
+++ b/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
@@ -35,7 +35,7 @@
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace epilogue {
 namespace thread {
 
@@ -65,11 +65,11 @@ class PairwiseDistanceEpilogueElementwise {
   using DistanceOp = DistanceOp_;
   using FinalOp    = FinalOp_;
 
-  using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
-  using FragmentCompute     = Array<ElementCompute, kElementsPerAccess>;
-  using FragmentC           = Array<ElementOutput, kElementsPerAccess>;
-  using FragmentZ           = Array<ElementZ, kElementsPerAccess>;
-  using FragmentT           = Array<ElementT, kElementsPerAccess>;
+  using FragmentAccumulator = cutlass::Array<ElementAccumulator, kElementsPerAccess>;
+  using FragmentCompute     = cutlass::Array<ElementCompute, kElementsPerAccess>;
+  using FragmentC           = cutlass::Array<ElementOutput, kElementsPerAccess>;
+  using FragmentZ           = cutlass::Array<ElementZ, kElementsPerAccess>;
+  using FragmentT           = cutlass::Array<ElementT, kElementsPerAccess>;
 
   using FragmentOutput = FragmentZ;
 
@@ -136,9 +136,9 @@ class PairwiseDistanceEpilogueElementwise {
                   FragmentCompute const& V) const
   {
     FragmentCompute tmp_Accum =
-      NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
+      cutlass::NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
     FragmentCompute tmp_C =
-      NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(frag_C);
+      cutlass::NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(frag_C);
     FragmentCompute result_Z;
     FragmentCompute result_T;
 
@@ -148,7 +148,7 @@ class PairwiseDistanceEpilogueElementwise {
       result_T[i] = final_op(result_Z[i], 0);
     }
 
-    NumericArrayConverter<ElementT, ElementCompute, kElementsPerAccess> convert_t;
+    cutlass::NumericArrayConverter<ElementT, ElementCompute, kElementsPerAccess> convert_t;
     frag_T = convert_t(result_T);
   }
 
@@ -166,6 +166,6 @@ class PairwiseDistanceEpilogueElementwise {
 
 }  // namespace thread
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/detail/pairwise_distance_gemm.h b/cpp/src/distance/detail/pairwise_distance_gemm.h
index aaf2689dab..6ac13f27b6 100644
--- a/cpp/src/distance/detail/pairwise_distance_gemm.h
+++ b/cpp/src/distance/detail/pairwise_distance_gemm.h
@@ -26,7 +26,7 @@
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 namespace gemm {
 namespace kernel {
 
@@ -95,29 +95,30 @@ struct PairwiseDistanceGemm {
   typedef typename std::
     conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
 
-  using GemmBase = typename DefaultGemmUniversal<ElementA_,
-                                                 LayoutA_,
-                                                 cutlass::ComplexTransform::kNone,
-                                                 kAlignmentA,
-                                                 ElementB_,
-                                                 LayoutB_,
-                                                 cutlass::ComplexTransform::kNone,
-                                                 kAlignmentB,
-                                                 ElementC_,
-                                                 LayoutOutput,
-                                                 ElementAccumulator,
-                                                 OperatorClass,
-                                                 ArchTag,
-                                                 ThreadblockShape,
-                                                 WarpShape,
-                                                 InstructionShape,
-                                                 EpilogueOutputOp,
-                                                 ThreadblockSwizzle,
-                                                 Stages,
-                                                 Operator>::GemmKernel;
+  using GemmBase =
+    typename cutlass::gemm::kernel::DefaultGemmUniversal<ElementA_,
+                                                         LayoutA_,
+                                                         cutlass::ComplexTransform::kNone,
+                                                         kAlignmentA,
+                                                         ElementB_,
+                                                         LayoutB_,
+                                                         cutlass::ComplexTransform::kNone,
+                                                         kAlignmentB,
+                                                         ElementC_,
+                                                         LayoutOutput,
+                                                         ElementAccumulator,
+                                                         OperatorClass,
+                                                         ArchTag,
+                                                         ThreadblockShape,
+                                                         WarpShape,
+                                                         InstructionShape,
+                                                         EpilogueOutputOp,
+                                                         ThreadblockSwizzle,
+                                                         Stages,
+                                                         Operator>::GemmKernel;
 
   // Replace epilogue
-  using Epilogue = typename cutlass::epilogue::threadblock::PairwiseDistanceEpilogue<
+  using Epilogue = typename cuvs::epilogue::threadblock::PairwiseDistanceEpilogue<
     typename GemmBase::Epilogue::Shape,
     typename GemmBase::Epilogue::WarpMmaOperator,
     GemmBase::Epilogue::kPartitionsK,
@@ -129,7 +130,8 @@ struct PairwiseDistanceGemm {
     GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
 
   // Compose the GEMM kernel
-  using GemmKernel = GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
+  using GemmKernel = cutlass::gemm::kernel::
+    GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
 };
 
 template <
@@ -194,29 +196,30 @@ struct PairwiseDistanceGemm<double,
   typedef typename std::
     conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
 
-  using GemmBase = typename DefaultGemmUniversal<double,
-                                                 LayoutA_,
-                                                 cutlass::ComplexTransform::kNone,
-                                                 1,
-                                                 double,
-                                                 LayoutB_,
-                                                 cutlass::ComplexTransform::kNone,
-                                                 1,
-                                                 ElementC_,
-                                                 LayoutOutput,
-                                                 ElementAccumulator,
-                                                 OperatorClass,
-                                                 ArchTag,
-                                                 ThreadblockShape,
-                                                 WarpShape,
-                                                 InstructionShape,
-                                                 EpilogueOutputOp,
-                                                 ThreadblockSwizzle,
-                                                 Stages,
-                                                 Operator>::GemmKernel;
+  using GemmBase =
+    typename cutlass::gemm::kernel::DefaultGemmUniversal<double,
+                                                         LayoutA_,
+                                                         cutlass::ComplexTransform::kNone,
+                                                         1,
+                                                         double,
+                                                         LayoutB_,
+                                                         cutlass::ComplexTransform::kNone,
+                                                         1,
+                                                         ElementC_,
+                                                         LayoutOutput,
+                                                         ElementAccumulator,
+                                                         OperatorClass,
+                                                         ArchTag,
+                                                         ThreadblockShape,
+                                                         WarpShape,
+                                                         InstructionShape,
+                                                         EpilogueOutputOp,
+                                                         ThreadblockSwizzle,
+                                                         Stages,
+                                                         Operator>::GemmKernel;
 
   // Replace epilogue
-  using Epilogue = typename cutlass::epilogue::threadblock::PairwiseDistanceEpilogue<
+  using Epilogue = typename cuvs::epilogue::threadblock::PairwiseDistanceEpilogue<
     typename GemmBase::Epilogue::Shape,
     typename GemmBase::Epilogue::WarpMmaOperator,
     GemmBase::Epilogue::kPartitionsK,
@@ -228,11 +231,12 @@ struct PairwiseDistanceGemm<double,
     GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
 
   // Compose the GEMM kernel
-  using GemmKernel = GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
+  using GemmKernel = cutlass::gemm::kernel::
+    GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
 };
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 }  // namespace kernel
 }  // namespace gemm
-}  // namespace cutlass
\ No newline at end of file
+}  // namespace cuvs
\ No newline at end of file
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
index e64e9e5d7f..fc3849febe 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
@@ -39,7 +39,7 @@
 // NOTE: to minimize compile times, we do not include dispatch_sm80.cuh.
 // Including dispatch_sm80.cuh can slow down compile times (due to CUTLASS).
 // Therefore, it is the including file's responsibility to include the correct
-// dispatch_smXX.cuh headers, as is done in raft/distance/detail/distance.cuh
+// dispatch_smXX.cuh headers, as is done in cuvs/distance/detail/distance.cuh
 // and src/distance/detail/pairwise_matrix/dispatch_*.cu.
 
 namespace cuvs::distance::detail {
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
index accd8de9bb..e013db1e18 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
@@ -40,8 +40,8 @@
  */
 
 #include <raft/core/operators.hpp> // raft::identity_op
-#include <raft/distance/detail/distance_ops/all_ops.cuh>  // ops::*
-#include <raft/distance/detail/pairwise_matrix/dispatch-inl.cuh> // dispatch
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh" // dispatch
 """
 
 
@@ -150,7 +150,7 @@
 
 def arch_headers(archs):
     include_headers ="\n".join([
-        f"#include <raft/distance/detail/pairwise_matrix/dispatch_sm{arch}.cuh>"
+        f"#include \"dispatch_sm{arch}.cuh\""
         for arch in archs
     ])
     return include_headers
@@ -178,7 +178,7 @@ def arch_headers(archs):
         archs = [60]
 
         f.write(header)
-        f.write("#include <raft/distance/detail/kernels/rbf_fin_op.cuh> // rbf_fin_op\n")
+        f.write("#include \"../kernels/rbf_fin_op.cuh\" // rbf_fin_op\n")
         f.write(arch_headers(archs))
         f.write(macro)
 
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
index b5e8a2f687..8f6e3a35d2 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
@@ -27,7 +27,6 @@
 #include "dispatch-inl.cuh"             // dispatch
 #include "dispatch_sm60.cuh"
 #include <raft/core/operators.hpp>  // raft::identity_op
-
 #define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
   OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
   template void cuvs::distance::detail::                                               \
diff --git a/cpp/src/distance/detail/predicated_tile_iterator_normvec.h b/cpp/src/distance/detail/predicated_tile_iterator_normvec.h
index 951f8a0132..26ef44b676 100644
--- a/cpp/src/distance/detail/predicated_tile_iterator_normvec.h
+++ b/cpp/src/distance/detail/predicated_tile_iterator_normvec.h
@@ -44,7 +44,7 @@ This file contains a customized version of PredicatedTileIterator from CUTLASS 2
 
 ////////////////////////////////////////////////////////////////////////////////
 
-namespace cutlass {
+namespace cuvs {
 
 ////////////////////////////////////////////////////////////////////////////////
 
@@ -70,12 +70,12 @@ class PredicatedTileIteratorNormVec {
   using Element = Element_;
 
   using Layout         = Layout_;
-  using TensorRef      = TensorRef<Element, Layout>;
+  using TensorRef      = cutlass::TensorRef<Element, Layout>;
   using ConstTensorRef = typename TensorRef::ConstTensorRef;
 
   using Index       = typename Layout::Index;
   using LongIndex   = typename Layout::LongIndex;
-  using TensorCoord = MatrixCoord;
+  using TensorCoord = cutlass::MatrixCoord;
 
   static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
   static int const kThreads           = ThreadMap::kThreads;
@@ -87,21 +87,22 @@ class PredicatedTileIteratorNormVec {
   static_assert(ThreadMap::Iterations::kColumn > 0, "ThreadMap::Iterations::kColumn must be > 0");
 
   /// Fragment object
-  using Fragment = Array<Element,
-                         ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow *
-                           ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster *
-                           ThreadMap::kElementsPerAccess>;
+  using Fragment =
+    cutlass::Array<Element,
+                   ThreadMap::Iterations::kColumn * ThreadMap::Iterations::kRow *
+                     ThreadMap::Iterations::kGroup * ThreadMap::Iterations::kCluster *
+                     ThreadMap::kElementsPerAccess>;
 
   /// Memory access size
-  using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
+  using AccessType = cutlass::AlignedArray<Element, ThreadMap::kElementsPerAccess>;
 
   //
   // Parameters struct
   //
 
   /// Uses a non-template class
-  struct Params : PredicatedTileIteratorParams {
-    using Base = PredicatedTileIteratorParams;
+  struct Params : cutlass::epilogue::threadblock::PredicatedTileIteratorParams {
+    using Base = cutlass::epilogue::threadblock::PredicatedTileIteratorParams;
 
     CUTLASS_HOST_DEVICE
     Params() {}
@@ -110,7 +111,7 @@ class PredicatedTileIteratorNormVec {
     Params(Layout const& layout)
       : PredicatedTileIteratorParams(
           layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
-          make_OutputTileThreadMapDesc<ThreadMap>())
+          cutlass::epilogue::threadblock::make_OutputTileThreadMapDesc<ThreadMap>())
     {
     }
 
@@ -156,12 +157,12 @@ class PredicatedTileIteratorNormVec {
   //
 
   /// Parameters structure containing reference and precomputed state.
-  PredicatedTileIteratorParams params_;
+  cutlass::epilogue::threadblock::PredicatedTileIteratorParams params_;
 
   /// Byte-level pointer
   uint8_t* byte_pointer_;
 
-  /// Array of boolean values to contain steady-state predicates
+  /// cutlass::Array of boolean values to contain steady-state predicates
   Mask mask_;
 
   /// Extent of the matrix tile in rows
@@ -188,7 +189,8 @@ class PredicatedTileIteratorNormVec {
 
   static_assert(sizeof(extent_row_) == 4, "Expected 32b extents");
   static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents");
-  static_assert(sizeof(PredicatedTileIteratorParams::stride) == 8, "Expected 64b strides");
+  static_assert(sizeof(cutlass::epilogue::threadblock::PredicatedTileIteratorParams::stride) == 8,
+                "Expected 64b strides");
 
  private:
   //
@@ -202,12 +204,13 @@ class PredicatedTileIteratorNormVec {
 
   /// Constructor
   CUTLASS_DEVICE
-  PredicatedTileIteratorNormVec(PredicatedTileIteratorParams const& params,
-                                Element* pointer,
-                                TensorCoord extent,
-                                int thread_idx,
-                                TensorCoord threadblock_offset = TensorCoord(),
-                                int const* indices             = nullptr)
+  PredicatedTileIteratorNormVec(
+    cutlass::epilogue::threadblock::PredicatedTileIteratorParams const& params,
+    Element* pointer,
+    TensorCoord extent,
+    int thread_idx,
+    TensorCoord threadblock_offset = TensorCoord(),
+    int const* indices             = nullptr)
     : params_(params), indices_(indices)
   {
     TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
@@ -247,7 +250,7 @@ class PredicatedTileIteratorNormVec {
   CUTLASS_HOST_DEVICE
   void add_pointer_offset(LongIndex pointer_offset)
   {
-    byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
+    byte_pointer_ += pointer_offset * cutlass::sizeof_bits<Element>::value / 8;
   }
 
   /// Loads a fragment from memory
@@ -509,7 +512,10 @@ class PredicatedTileIteratorNormVec {
   }
 
   CUTLASS_DEVICE
-  MatrixCoord thread_start() const { return MatrixCoord(thread_start_row_, thread_start_column_); }
+  cutlass::MatrixCoord thread_start() const
+  {
+    return MatrixCoord(thread_start_row_, thread_start_column_);
+  }
 
   /// Need to get the thread start row from the tile iterator
   CUTLASS_DEVICE
@@ -580,6 +586,6 @@ class PredicatedTileIteratorNormVec {
 
 }  // namespace threadblock
 }  // namespace epilogue
-}  // namespace cutlass
+}  // namespace cuvs
 
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/cpp/src/distance/distance-inl.cuh b/cpp/src/distance/distance-inl.cuh
index 5b82f5438c..2c4138c3ff 100644
--- a/cpp/src/distance/distance-inl.cuh
+++ b/cpp/src/distance/distance-inl.cuh
@@ -361,9 +361,9 @@ void pairwise_distance(raft::resources const& handle,
  * #include <raft/core/resources.hpp>
  * #include <raft/core/device_mdarray.hpp>
  * #include <raft/random/make_blobs.cuh>
- * #include <raft/distance/distance.cuh>
+ * #include <cuvs/distance/distance.cuh>
  *
- * raft::raft::resources handle;
+ * raft::resources handle;
  * int n_samples = 5000;
  * int n_features = 50;
  *
diff --git a/cpp/src/distance/fused_distance_nn-inl.cuh b/cpp/src/distance/fused_distance_nn-inl.cuh
new file mode 100644
index 0000000000..b2df7a70e7
--- /dev/null
+++ b/cpp/src/distance/fused_distance_nn-inl.cuh
@@ -0,0 +1,330 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __FUSED_DISTANCE_NN_H
+#define __FUSED_DISTANCE_NN_H
+
+#pragma once
+
+#include "detail/fused_distance_nn.cuh"
+#include "fused_distance_nn_helpers.cuh"
+#include <raft/core/resources.hpp>
+#include <raft/linalg/contractions.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <cub/cub.cuh>
+
+#include <stdint.h>
+
+#include <limits>
+#include <type_traits>
+
+namespace cuvs {
+namespace distance {
+
+/**
+ * \ingroup fused_l2_nn
+ * @{
+ */
+/**
+ * @brief Fused L2 distance and 1-nearest-neighbor computation in a single call.
+ *
+ * The benefits of such a call are 2-fold: 1) eliminate the need for an
+ * intermediate buffer to store the output of gemm 2) reduce the memory read
+ * traffic on this intermediate buffer, otherwise needed during the reduction
+ * phase for 1-NN.
+ *
+ * @tparam DataT      data type
+ * @tparam OutT       output type to either store 1-NN indices and their minimum
+ *                    distances or store only the min distances. Accordingly, one
+ *                    has to pass an appropriate `ReduceOpT`
+ * @tparam IdxT       indexing arithmetic type
+ * @tparam ReduceOpT  A struct to perform the final needed reduction operation
+ *                    and also to initialize the output array elements with the
+ *                    appropriate initial value needed for reduction.
+ * @tparam KVPReduceOpT A struct providing functions for key-value pair comparison.
+ *
+ * @param[out] min           will contain the reduced output (Length = `m`)
+ *                           (on device)
+ * @param[in]  x             first matrix. Row major. Dim = `m x k`.
+ *                           (on device).
+ * @param[in]  y             second matrix. Row major. Dim = `n x k`.
+ *                           (on device).
+ * @param[in]  xn            L2 squared norm of `x`. Length = `m`. (on device).
+ * @param[in]  yn            L2 squared norm of `y`. Length = `n`. (on device)
+ * @param[in]  m             gemm m
+ * @param[in]  n             gemm n
+ * @param[in]  k             gemm k
+ * @param[in]  workspace     temp workspace. Size = sizeof(int)*m. (on device)
+ * @param[in]  redOp         reduction operator in the epilogue
+ * @param[in]  pairRedOp     reduction operation on key value pairs
+ * @param[in]  sqrt          Whether the output `minDist` should contain L2-sqrt
+ * @param[in]  initOutBuffer whether to initialize the output buffer before the
+ *                           main kernel launch
+ * @param[in]  isRowMajor    whether the input/output is row or column major.
+ * @param[in]  metric        Distance metric to be used (supports L2, cosine)
+ * @param[in]  metric_arg    power argument for distances like Minkowski (not supported for now)
+ * @param[in]  stream        cuda stream
+ */
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT, typename KVPReduceOpT>
+void fusedDistanceNN(OutT* min,
+                     const DataT* x,
+                     const DataT* y,
+                     const DataT* xn,
+                     const DataT* yn,
+                     IdxT m,
+                     IdxT n,
+                     IdxT k,
+                     void* workspace,
+                     ReduceOpT redOp,
+                     KVPReduceOpT pairRedOp,
+                     bool sqrt,
+                     bool initOutBuffer,
+                     bool isRowMajor,
+                     cuvs::distance::DistanceType metric,
+                     float metric_arg,
+                     cudaStream_t stream)
+{
+  ASSERT(isRowMajor, "fusedDistanceNN only supports row major inputs");
+  // When k is smaller than 32, the Policy4x4 results in redundant calculations
+  // as it uses tiles that have k=32. Therefore, use a "skinny" policy instead
+  // that uses tiles with a smaller value of k.
+  bool is_skinny = k < 32;
+
+  size_t bytes = sizeof(DataT) * k;
+  auto px      = reinterpret_cast<uintptr_t>(x);
+  auto py      = reinterpret_cast<uintptr_t>(y);
+  if (16 % sizeof(DataT) == 0 && bytes % 16 == 0 && px % 16 == 0 && py % 16 == 0) {
+    if (is_skinny) {
+      detail::fusedDistanceNNImpl<
+        DataT,
+        OutT,
+        IdxT,
+        typename raft::linalg::Policy4x4Skinny<DataT, 16 / sizeof(DataT)>::Policy,
+        ReduceOpT>(min,
+                   x,
+                   y,
+                   xn,
+                   yn,
+                   m,
+                   n,
+                   k,
+                   (int*)workspace,
+                   redOp,
+                   pairRedOp,
+                   sqrt,
+                   initOutBuffer,
+                   isRowMajor,
+                   metric,
+                   metric_arg,
+                   stream);
+    } else {
+      detail::fusedDistanceNNImpl<
+        DataT,
+        OutT,
+        IdxT,
+        typename raft::linalg::Policy4x4<DataT, 16 / sizeof(DataT)>::Policy,
+        ReduceOpT>(min,
+                   x,
+                   y,
+                   xn,
+                   yn,
+                   m,
+                   n,
+                   k,
+                   (int*)workspace,
+                   redOp,
+                   pairRedOp,
+                   sqrt,
+                   initOutBuffer,
+                   isRowMajor,
+                   metric,
+                   metric_arg,
+                   stream);
+    }
+  } else if (8 % sizeof(DataT) == 0 && bytes % 8 == 0 && px % 8 == 0 && py % 8 == 0) {
+    if (is_skinny) {
+      detail::fusedDistanceNNImpl<
+        DataT,
+        OutT,
+        IdxT,
+        typename raft::linalg::Policy4x4Skinny<DataT, 8 / sizeof(DataT)>::Policy,
+        ReduceOpT>(min,
+                   x,
+                   y,
+                   xn,
+                   yn,
+                   m,
+                   n,
+                   k,
+                   (int*)workspace,
+                   redOp,
+                   pairRedOp,
+                   sqrt,
+                   initOutBuffer,
+                   isRowMajor,
+                   metric,
+                   metric_arg,
+                   stream);
+    } else {
+      detail::fusedDistanceNNImpl<
+        DataT,
+        OutT,
+        IdxT,
+        typename raft::linalg::Policy4x4<DataT, 8 / sizeof(DataT)>::Policy,
+        ReduceOpT>(min,
+                   x,
+                   y,
+                   xn,
+                   yn,
+                   m,
+                   n,
+                   k,
+                   (int*)workspace,
+                   redOp,
+                   pairRedOp,
+                   sqrt,
+                   initOutBuffer,
+                   isRowMajor,
+                   metric,
+                   metric_arg,
+                   stream);
+    }
+  } else {
+    if (is_skinny) {
+      detail::fusedDistanceNNImpl<DataT,
+                                  OutT,
+                                  IdxT,
+                                  typename raft::linalg::Policy4x4Skinny<DataT, 1>::Policy,
+                                  ReduceOpT>(min,
+                                             x,
+                                             y,
+                                             xn,
+                                             yn,
+                                             m,
+                                             n,
+                                             k,
+                                             (int*)workspace,
+                                             redOp,
+                                             pairRedOp,
+                                             sqrt,
+                                             initOutBuffer,
+                                             isRowMajor,
+                                             metric,
+                                             metric_arg,
+                                             stream);
+    } else {
+      detail::fusedDistanceNNImpl<DataT,
+                                  OutT,
+                                  IdxT,
+                                  typename raft::linalg::Policy4x4<DataT, 1>::Policy,
+                                  ReduceOpT>(min,
+                                             x,
+                                             y,
+                                             xn,
+                                             yn,
+                                             m,
+                                             n,
+                                             k,
+                                             (int*)workspace,
+                                             redOp,
+                                             pairRedOp,
+                                             sqrt,
+                                             initOutBuffer,
+                                             isRowMajor,
+                                             metric,
+                                             metric_arg,
+                                             stream);
+    }
+  }
+}
+
+/**
+ * @brief Wrapper around fusedDistanceNN with minimum reduction operators.
+ *
+ * fusedDistanceNN cannot be compiled in the distance library due to the lambda
+ * operators, so this wrapper covers the most common case (minimum).
+ *
+ * @tparam DataT     data type
+ * @tparam OutT      output type to either store 1-NN indices and their minimum
+ *                   distances (e.g. raft::KeyValuePair<int, float>) or store only the min
+ * distances.
+ * @tparam IdxT      indexing arithmetic type
+ * @param[out] min           will contain the reduced output (Length = `m`)
+ *                           (on device)
+ * @param[in]  x             first matrix. Row major. Dim = `m x k`.
+ *                           (on device).
+ * @param[in]  y             second matrix. Row major. Dim = `n x k`.
+ *                           (on device).
+ * @param[in]  xn            L2 squared norm of `x`. Length = `m`. (on device).
+ * @param[in]  yn            L2 squared norm of `y`. Length = `n`. (on device)
+ * @param[in]  m             gemm m
+ * @param[in]  n             gemm n
+ * @param[in]  k             gemm k
+ * @param[in]  workspace     temp workspace. Size = sizeof(int)*m. (on device)
+ * @param[in]  sqrt          Whether the output `minDist` should contain L2-sqrt
+ * @param[in]  initOutBuffer whether to initialize the output buffer before the
+ *                           main kernel launch
+ * @param[in]  isRowMajor    whether the input/output is row or column major.
+ * @param[in]  metric        Distance metric to be used (supports L2, cosine)
+ * @param[in]  metric_arg    power argument for distances like Minkowski (not supported for now)
+ * @param[in]  stream        cuda stream
+ */
+template <typename DataT, typename OutT, typename IdxT>
+void fusedDistanceNNMinReduce(OutT* min,
+                              const DataT* x,
+                              const DataT* y,
+                              const DataT* xn,
+                              const DataT* yn,
+                              IdxT m,
+                              IdxT n,
+                              IdxT k,
+                              void* workspace,
+                              bool sqrt,
+                              bool initOutBuffer,
+                              bool isRowMajor,
+                              cuvs::distance::DistanceType metric,
+                              float metric_arg,
+                              cudaStream_t stream)
+{
+  MinAndDistanceReduceOp<IdxT, DataT> redOp;
+  KVPMinReduce<IdxT, DataT> pairRedOp;
+
+  fusedDistanceNN<DataT, OutT, IdxT>(min,
+                                     x,
+                                     y,
+                                     xn,
+                                     yn,
+                                     m,
+                                     n,
+                                     k,
+                                     workspace,
+                                     redOp,
+                                     pairRedOp,
+                                     sqrt,
+                                     initOutBuffer,
+                                     isRowMajor,
+                                     metric,
+                                     metric_arg,
+                                     stream);
+}
+
+/** @} */
+
+}  // namespace distance
+}  // namespace cuvs
+
+#endif
diff --git a/cpp/src/distance/fused_distance_nn.cuh b/cpp/src/distance/fused_distance_nn.cuh
new file mode 100755
index 0000000000..aa20bfeaf1
--- /dev/null
+++ b/cpp/src/distance/fused_distance_nn.cuh
@@ -0,0 +1,18 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "fused_distance_nn-inl.cuh"
diff --git a/cpp/src/distance/fused_distance_nn_helpers.cuh b/cpp/src/distance/fused_distance_nn_helpers.cuh
new file mode 100644
index 0000000000..7dd370fa0d
--- /dev/null
+++ b/cpp/src/distance/fused_distance_nn_helpers.cuh
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/fused_distance_nn/helper_structs.cuh"
+#include <raft/core/resource/cuda_stream.hpp>
+
+namespace cuvs::distance {
+
+/**
+ * \defgroup fused_l2_nn Fused 1-nearest neighbors
+ * @{
+ */
+
+template <typename LabelT, typename DataT>
+using KVPMinReduce = detail::KVPMinReduceImpl<LabelT, DataT>;
+
+template <typename LabelT, typename DataT>
+using MinAndDistanceReduceOp = detail::MinAndDistanceReduceOpImpl<LabelT, DataT>;
+
+template <typename LabelT, typename DataT>
+using MinReduceOp = detail::MinReduceOpImpl<LabelT, DataT>;
+
+/** @} */
+
+/**
+ * Initialize array using init value from reduction op
+ */
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT>
+void initialize(raft::resources const& handle, OutT* min, IdxT m, DataT maxVal, ReduceOpT redOp)
+{
+  detail::initialize<DataT, OutT, IdxT, ReduceOpT>(
+    min, m, maxVal, redOp, raft::resource::get_cuda_stream(handle));
+}
+
+}  // namespace cuvs::distance
diff --git a/cpp/src/neighbors/cagra_serialize.cuh b/cpp/src/neighbors/cagra_serialize.cuh
index f5a2748eac..100453b203 100644
--- a/cpp/src/neighbors/cagra_serialize.cuh
+++ b/cpp/src/neighbors/cagra_serialize.cuh
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::cagra {
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <raft/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
  *
  * raft::resources handle;
  *
@@ -67,7 +67,7 @@ void serialize(raft::resources const& handle,
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <raft/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
  *
  * raft::resources handle;
  *
@@ -102,7 +102,7 @@ void serialize(raft::resources const& handle,
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <raft/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
  *
  * raft::resources handle;
  *
@@ -135,7 +135,7 @@ void serialize_to_hnswlib(raft::resources const& handle,
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <raft/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
  *
  * raft::resources handle;
  *
@@ -168,7 +168,7 @@ void serialize_to_hnswlib(raft::resources const& handle,
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <raft/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
  *
  * raft::resources handle;
  *
@@ -200,7 +200,7 @@ index<T, IdxT> deserialize(raft::resources const& handle, std::istream& is)
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <raft/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
  *
  * raft::resources handle;
  *
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index d73b2e3a18..222b4c6a76 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -16,8 +16,8 @@
 
 #pragma once
 
-#include <raft/core/logger.hpp>
-#include <raft/distance/distance_types.hpp>
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
 #include <raft/util/integer_utils.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index 44c952caca..df703a04e4 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -27,8 +27,6 @@
 
 #include "../dataset_serialize.hpp"
 
-#include <raft/neighbors/detail/dataset_serialize.hpp>
-
 #include <cstddef>
 #include <cstdint>
 #include <fstream>
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index 3822422a6e..dceee68425 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -21,7 +21,7 @@
 #include "search_plan.cuh"
 #include "search_single_cta.cuh"
 
-#include <raft/neighbors/sample_filter_types.hpp>
+#include <cuvs/neighbors/sample_filter.hpp>
 
 namespace cuvs::neighbors::cagra::detail {
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index 11161628b8..11a8a99685 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -26,7 +26,7 @@
 
 #include <raft/core/detail/macros.hpp>
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
index b5be1b772f..ce47d7ec5e 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#pragma once
+
 #include "search_multi_cta_kernel-inl.cuh"
 #include <cuvs/neighbors/sample_filter.hpp>
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 3647b84321..877d559722 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -24,7 +24,7 @@
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index 8c13b61c0b..0296dc9d42 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -24,7 +24,7 @@
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index 011f183869..b8e1726e78 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -26,7 +26,7 @@
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
index 7229ac6042..51328d488f 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#pragma once
+
 #include "search_single_cta_kernel-inl.cuh"
 #include <cuvs/neighbors/sample_filter.hpp>
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index 2c3b16556b..c6f42d9d69 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -26,7 +26,7 @@
 
 #include <cuvs/distance/distance_types.hpp>
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/div_utils.cuh b/cpp/src/neighbors/detail/div_utils.cuh
index b54b8400ea..eb10b728a4 100644
--- a/cpp/src/neighbors/detail/div_utils.cuh
+++ b/cpp/src/neighbors/detail/div_utils.cuh
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#pragma once
+
 #include <raft/util/pow2_utils.cuh>
 
 /**
diff --git a/cpp/src/neighbors/detail/refine_device.cuh b/cpp/src/neighbors/detail/refine_device.cuh
index b81f7d77fa..c1bef6dbaf 100644
--- a/cpp/src/neighbors/detail/refine_device.cuh
+++ b/cpp/src/neighbors/detail/refine_device.cuh
@@ -16,8 +16,11 @@
 
 #pragma once
 
+#include "../ivf_flat/ivf_flat_build.cuh"
+#include "../ivf_flat/ivf_flat_interleaved_scan.cuh"
 #include "ann_utils.cuh"
 #include "refine_common.hpp"
+#include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/sample_filter.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/host_mdspan.hpp>
@@ -26,8 +29,6 @@
 #include <raft/core/resource/thrust_policy.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/matrix/detail/select_warpsort.cuh>
-#include <raft/neighbors/detail/ivf_flat_build.cuh>
-#include <raft/neighbors/detail/ivf_flat_interleaved_scan.cuh>
 
 #include <thrust/sequence.h>
 
@@ -79,10 +80,10 @@ void refine_device(
                    fake_coarse_idx.data(),
                    fake_coarse_idx.data() + n_queries);
 
-  raft::neighbors::ivf_flat::index<data_t, idx_t> refinement_index(
-    handle, raft::distance::DistanceType(metric), n_queries, false, true, dim);
+  cuvs::neighbors::ivf_flat::index<data_t, idx_t> refinement_index(
+    handle, cuvs::distance::DistanceType(metric), n_queries, false, true, dim);
 
-  raft::neighbors::ivf_flat::detail::fill_refinement_index(handle,
+  cuvs::neighbors::ivf_flat::detail::fill_refinement_index(handle,
                                                            &refinement_index,
                                                            dataset.data_handle(),
                                                            neighbor_candidates.data_handle(),
@@ -110,7 +111,7 @@ void refine_device(
     neighbors_uint32 = neighbors_uint32_buf.data();
   }
 
-  raft::neighbors::ivf_flat::detail::ivfflat_interleaved_scan<
+  cuvs::neighbors::ivf_flat::detail::ivfflat_interleaved_scan<
     data_t,
     typename cuvs::spatial::knn::detail::utils::config<data_t>::value_t,
     idx_t>(refinement_index,
@@ -118,20 +119,20 @@ void refine_device(
            fake_coarse_idx.data(),
            static_cast<uint32_t>(n_queries),
            0,
-           raft::distance::DistanceType(refinement_index.metric()),
+           cuvs::distance::DistanceType(refinement_index.metric()),
            1,
            k,
            0,
            chunk_index.data(),
-           raft::distance::is_min_close(raft::distance::DistanceType(metric)),
-           raft::neighbors::filtering::none_ivf_sample_filter(),
+           cuvs::distance::is_min_close(cuvs::distance::DistanceType(metric)),
+           cuvs::neighbors::filtering::none_ivf_sample_filter(),
            neighbors_uint32,
            distances.data_handle(),
            grid_dim_x,
            raft::resource::get_cuda_stream(handle));
 
   // postprocessing -- neighbors from position to actual id
-  raft::neighbors::ivf::detail::postprocess_neighbors(indices.data_handle(),
+  cuvs::neighbors::ivf::detail::postprocess_neighbors(indices.data_handle(),
                                                       neighbors_uint32,
                                                       refinement_index.inds_ptrs().data_handle(),
                                                       fake_coarse_idx.data(),
diff --git a/cpp/src/neighbors/detail/vpq_dataset.cuh b/cpp/src/neighbors/detail/vpq_dataset.cuh
index b0a565cb10..c0bf24f500 100644
--- a/cpp/src/neighbors/detail/vpq_dataset.cuh
+++ b/cpp/src/neighbors/detail/vpq_dataset.cuh
@@ -19,10 +19,13 @@
 
 #include "../ivf_pq/ivf_pq_build.cuh"  // pq_bits-bitfield
 #include "ann_utils.cuh"               // utils::mapping etc
-#include <raft/cluster/kmeans_balanced.cuh>
+
+// TODO(cjnolet): This should be using exposed APIs.
+#include "../../cluster/kmeans_balanced.cuh"
+
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/linalg/map.cuh>
@@ -135,14 +138,14 @@ auto train_vq(const raft::resources& res, const vpq_params& params, const Datase
     raft::make_device_matrix<MathT, uint32_t, raft::row_major>(res, vq_n_centers, dim);
 
   using kmeans_in_type = typename DatasetT::value_type;
-  raft::cluster::kmeans_balanced_params kmeans_params;
+  cuvs::cluster::kmeans::balanced_params kmeans_params;
   kmeans_params.n_iters = params.kmeans_n_iters;
-  kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
+  kmeans_params.metric  = cuvs::distance::DistanceType::L2Expanded;
   auto vq_centers_view =
     raft::make_device_matrix_view<MathT, ix_t>(vq_centers.data_handle(), vq_n_centers, dim);
   auto vq_trainset_view = raft::make_device_matrix_view<const kmeans_in_type, ix_t>(
     vq_trainset.data_handle(), n_rows_train, dim);
-  raft::cluster::kmeans_balanced::fit<kmeans_in_type, MathT, ix_t>(
+  cuvs::cluster::kmeans_balanced::fit<kmeans_in_type, MathT, ix_t>(
     res,
     kmeans_params,
     vq_trainset_view,
@@ -163,8 +166,8 @@ auto predict_vq(const raft::resources& res, const DatasetT& dataset, const VqCen
 
   auto vq_labels = raft::make_device_vector<label_type, index_type>(res, dataset.extent(0));
 
-  raft::cluster::kmeans_balanced_params kmeans_params;
-  kmeans_params.metric = raft::distance::DistanceType::L2Expanded;
+  cuvs::cluster::kmeans::balanced_params kmeans_params;
+  kmeans_params.metric = cuvs::distance::DistanceType::L2Expanded;
 
   auto vq_centers_view = raft::make_device_matrix_view<const kmeans_math_type, index_type>(
     vq_centers.data_handle(), vq_centers.extent(0), vq_centers.extent(1));
@@ -172,7 +175,7 @@ auto predict_vq(const raft::resources& res, const DatasetT& dataset, const VqCen
   auto vq_dataset_view = raft::make_device_matrix_view<const kmeans_data_type, index_type>(
     dataset.data_handle(), dataset.extent(0), dataset.extent(1));
 
-  raft::cluster::kmeans_balanced::
+  cuvs::cluster::kmeans_balanced::
     predict<kmeans_data_type, kmeans_math_type, index_type, label_type>(
       res,
       kmeans_params,
@@ -222,9 +225,9 @@ auto train_pq(const raft::resources& res,
 
   // Train PQ centers
   {
-    raft::cluster::kmeans_balanced_params kmeans_params;
+    cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = params.kmeans_n_iters;
-    kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
+    kmeans_params.metric  = cuvs::distance::DistanceType::L2Expanded;
 
     auto pq_centers_view =
       raft::make_device_matrix_view<MathT, ix_t>(pq_centers.data_handle(), pq_n_centers, pq_len);
@@ -232,7 +235,7 @@ auto train_pq(const raft::resources& res,
     auto pq_trainset_view = raft::make_device_matrix_view<const MathT, ix_t>(
       pq_trainset.data_handle(), n_rows_train * pq_dim, pq_len);
 
-    raft::cluster::kmeans_balanced::fit<MathT, MathT, ix_t>(
+    cuvs::cluster::kmeans_balanced::fit<MathT, MathT, ix_t>(
       res, kmeans_params, pq_trainset_view, pq_centers_view);
   }
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index 89cd5ec57b..65c7203815 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -20,17 +20,18 @@
 #include "../ivf_common.cuh"
 #include "../ivf_list.cuh"
 
+#include <cuvs/cluster/kmeans.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/ivf_list.hpp>
 
+#include "../../cluster/kmeans_balanced.cuh"
 #include "../detail/ann_utils.cuh"
-#include <raft/cluster/kmeans_balanced.cuh>
-#include <raft/core/logger.hpp>
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
-#include <raft/distance/distance_types.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/linalg/map.cuh>
 #include <raft/linalg/norm.cuh>
@@ -184,8 +185,8 @@ void extend(raft::resources const& handle,
                "You must pass data indices when the index is non-empty.");
 
   auto new_labels = raft::make_device_vector<LabelT, IdxT>(handle, n_rows);
-  raft::cluster::kmeans_balanced_params kmeans_params;
-  kmeans_params.metric = static_cast<raft::distance::DistanceType>(index->metric());
+  cuvs::cluster::kmeans::balanced_params kmeans_params;
+  kmeans_params.metric = index->metric();
   auto orig_centroids_view =
     raft::make_device_matrix_view<const float, IdxT>(index->centers().data_handle(), n_lists, dim);
   // Calculate the batch size for the input data if it's not accessible directly from the device
@@ -205,7 +206,7 @@ void extend(raft::resources const& handle,
       raft::make_device_matrix_view<const T, IdxT>(batch.data(), batch.size(), index->dim());
     auto batch_labels_view = raft::make_device_vector_view<LabelT, IdxT>(
       new_labels.data_handle() + batch.offset(), batch.size());
-    raft::cluster::kmeans_balanced::predict(handle,
+    cuvs::cluster::kmeans_balanced::predict(handle,
                                             kmeans_params,
                                             batch_data_view,
                                             orig_centroids_view,
@@ -229,7 +230,7 @@ void extend(raft::resources const& handle,
         raft::make_device_matrix_view<const T, IdxT>(batch.data(), batch.size(), index->dim());
       auto batch_labels_view = raft::make_device_vector_view<const LabelT, IdxT>(
         new_labels.data_handle() + batch.offset(), batch.size());
-      raft::cluster::kmeans_balanced::helpers::calc_centers_and_sizes(handle,
+      cuvs::cluster::kmeans_balanced::helpers::calc_centers_and_sizes(handle,
                                                                       batch_data_view,
                                                                       batch_labels_view,
                                                                       centroids_view,
@@ -384,10 +385,10 @@ inline auto build(raft::resources const& handle,
       raft::make_device_matrix_view<const T, IdxT>(trainset.data(), n_rows_train, index.dim());
     auto centers_view = raft::make_device_matrix_view<float, IdxT>(
       index.centers().data_handle(), index.n_lists(), index.dim());
-    raft::cluster::kmeans_balanced_params kmeans_params;
+    cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = params.kmeans_n_iters;
-    kmeans_params.metric  = static_cast<raft::distance::DistanceType>(index.metric());
-    raft::cluster::kmeans_balanced::fit(
+    kmeans_params.metric  = static_cast<cuvs::distance::DistanceType>(index.metric());
+    cuvs::cluster::kmeans_balanced::fit(
       handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
   }
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
index be1eb87a01..03243221d1 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
@@ -21,66 +21,6 @@
 
 namespace cuvs::neighbors::ivf_flat::helpers {
 namespace codepacker {
-namespace detail {
-template <typename T, typename IdxT>
-void pack(
-  raft::resources const& res,
-  raft::device_matrix_view<const T, uint32_t, raft::row_major> codes,
-  uint32_t veclen,
-  uint32_t offset,
-  raft::device_mdspan<T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
-    list_data)
-{
-  pack_list_data<T, IdxT>(res, codes, veclen, offset, list_data);
-}
-
-template <typename T, typename IdxT>
-void unpack(
-  raft::resources const& res,
-  raft::device_mdspan<const T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
-    list_data,
-  uint32_t veclen,
-  uint32_t offset,
-  raft::device_matrix_view<T, uint32_t, raft::row_major> codes)
-{
-  unpack_list_data<T, IdxT>(res, list_data, veclen, offset, codes);
-}
-
-template <typename T>
-void pack_1(const T* flat_code, T* block, uint32_t dim, uint32_t veclen, uint32_t offset)
-{
-  // The data is written in interleaved groups of `index::kGroupSize` vectors
-  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
-
-  // Interleave dimensions of the source vector while recording it.
-  // NB: such `veclen` is selected, that `dim % veclen == 0`
-  auto group_offset = interleaved_group::roundDown(offset);
-  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
-
-  for (uint32_t l = 0; l < dim; l += veclen) {
-    for (uint32_t j = 0; j < veclen; j++) {
-      block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j] = flat_code[l + j];
-    }
-  }
-}
-
-template <typename T>
-void unpack_1(const T* block, T* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
-{
-  // The data is written in interleaved groups of `index::kGroupSize` vectors
-  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
-
-  // NB: such `veclen` is selected, that `dim % veclen == 0`
-  auto group_offset = interleaved_group::roundDown(offset);
-  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
-
-  for (uint32_t l = 0; l < dim; l += veclen) {
-    for (uint32_t j = 0; j < veclen; j++) {
-      flat_code[l + j] = block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j];
-    }
-  }
-}
-}  // namespace detail
 
 void pack(raft::resources const& res,
           raft::device_matrix_view<const float, uint32_t, raft::row_major> codes,
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
index f76b5e95f6..936c78fc5e 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
@@ -138,4 +138,66 @@ void unpack_list_data(
 }
 
 }  // namespace
+
+namespace detail {
+template <typename T, typename IdxT>
+void pack(
+  raft::resources const& res,
+  raft::device_matrix_view<const T, uint32_t, raft::row_major> codes,
+  uint32_t veclen,
+  uint32_t offset,
+  raft::device_mdspan<T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
+    list_data)
+{
+  pack_list_data<T, IdxT>(res, codes, veclen, offset, list_data);
+}
+
+template <typename T, typename IdxT>
+void unpack(
+  raft::resources const& res,
+  raft::device_mdspan<const T, typename list_spec<uint32_t, T, IdxT>::list_extents, raft::row_major>
+    list_data,
+  uint32_t veclen,
+  uint32_t offset,
+  raft::device_matrix_view<T, uint32_t, raft::row_major> codes)
+{
+  unpack_list_data<T, IdxT>(res, list_data, veclen, offset, codes);
+}
+
+template <typename T>
+void pack_1(const T* flat_code, T* block, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  // The data is written in interleaved groups of `index::kGroupSize` vectors
+  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+
+  // Interleave dimensions of the source vector while recording it.
+  // NB: such `veclen` is selected, that `dim % veclen == 0`
+  auto group_offset = interleaved_group::roundDown(offset);
+  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
+
+  for (uint32_t l = 0; l < dim; l += veclen) {
+    for (uint32_t j = 0; j < veclen; j++) {
+      block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j] = flat_code[l + j];
+    }
+  }
+}
+
+template <typename T>
+void unpack_1(const T* block, T* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  // The data is written in interleaved groups of `index::kGroupSize` vectors
+  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+
+  // NB: such `veclen` is selected, that `dim % veclen == 0`
+  auto group_offset = interleaved_group::roundDown(offset);
+  auto ingroup_id   = interleaved_group::mod(offset) * veclen;
+
+  for (uint32_t l = 0; l < dim; l += veclen) {
+    for (uint32_t j = 0; j < veclen; j++) {
+      flat_code[l + j] = block[group_offset * dim + l * kIndexGroupSize + ingroup_id + j];
+    }
+  }
+}
+
+}  // namespace detail
 }  // namespace cuvs::neighbors::ivf_flat::helpers::codepacker
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index d63059457b..86346f1e8e 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -21,11 +21,11 @@
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/sample_filter.hpp>
 
-#include <raft/core/logger.hpp>  // RAFT_LOG_TRACE
+#include "../detail/ann_utils.cuh"
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/logger-ext.hpp>  // RAFT_LOG_TRACE
 #include <raft/core/operators.hpp>
-#include <raft/distance/distance_types.hpp>
 #include <raft/matrix/detail/select_warpsort.cuh>
-#include <raft/spatial/knn/detail/ann_utils.cuh>
 #include <raft/util/cuda_rt_essentials.hpp>  // RAFT_CUDA_TRY
 #include <raft/util/device_loads_stores.cuh>
 #include <raft/util/integer_utils.hpp>
@@ -36,7 +36,7 @@
 
 namespace cuvs::neighbors::ivf_flat::detail {
 
-using namespace raft::spatial::knn::detail;  // NOLINT
+using namespace cuvs::spatial::knn::detail;  // NOLINT
 
 constexpr int kThreadsPerBlock = 128;
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index bb106ba2f0..36861fbf09 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -23,15 +23,15 @@
 #include <cuvs/neighbors/ivf_flat.hpp>       // raft::neighbors::ivf_flat::index
 #include <cuvs/neighbors/sample_filter.hpp>  // none_ivf_sample_filter
 
+#include "../detail/ann_utils.cuh"           // utils::mapping
 #include <cuvs/distance/distance_types.hpp>  // is_min_close, DistanceType
-#include <raft/core/logger.hpp>              // RAFT_LOG_TRACE
+#include <raft/core/logger-ext.hpp>          // RAFT_LOG_TRACE
 #include <raft/core/resource/cuda_stream.hpp>
-#include <raft/core/resources.hpp>                // raft::resources
-#include <raft/linalg/gemm.cuh>                   // raft::linalg::gemm
-#include <raft/linalg/norm.cuh>                   // raft::linalg::norm
-#include <raft/linalg/unary_op.cuh>               // raft::linalg::unary_op
-#include <raft/matrix/detail/select_k.cuh>        // matrix::detail::select_k
-#include <raft/spatial/knn/detail/ann_utils.cuh>  // utils::mapping
+#include <raft/core/resources.hpp>          // raft::resources
+#include <raft/linalg/gemm.cuh>             // raft::linalg::gemm
+#include <raft/linalg/norm.cuh>             // raft::linalg::norm
+#include <raft/linalg/unary_op.cuh>         // raft::linalg::unary_op
+#include <raft/matrix/detail/select_k.cuh>  // matrix::detail::select_k
 
 #include <rmm/resource_ref.hpp>
 
diff --git a/cpp/src/neighbors/ivf_list.cuh b/cpp/src/neighbors/ivf_list.cuh
index d99a500f05..d80f9d9a0c 100644
--- a/cpp/src/neighbors/ivf_list.cuh
+++ b/cpp/src/neighbors/ivf_list.cuh
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#pragma once
+
 #include <cuvs/neighbors/ivf_list.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 3f2f145b09..b843ea067a 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -27,14 +27,16 @@
 #include <cuvs/neighbors/ivf_pq_helpers.hpp>
 
 #include "../detail/ann_utils.cuh"  // utils::mapping
-#include <raft/cluster/kmeans_balanced.cuh>
+
+// TODO (cjnolet): This should be using an exposed API instead of circumventing the public APIs.
+#include "../../cluster/kmeans_balanced.cuh"
+
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/core/resources.hpp>
-#include <raft/distance/distance_types.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/linalg/detail/qr.cuh>
 #include <raft/linalg/gemm.cuh>
@@ -414,10 +416,10 @@ void train_per_subset(raft::resources const& handle,
       raft::make_device_vector_view<uint32_t, internal_extents_t>(sub_labels.data(), n_rows);
     auto cluster_sizes_view = raft::make_device_vector_view<uint32_t, internal_extents_t>(
       pq_cluster_sizes.data(), index.pq_book_size());
-    raft::cluster::kmeans_balanced_params kmeans_params;
+    cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = kmeans_n_iters;
-    kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
-    raft::cluster::kmeans_balanced::helpers::build_clusters(handle,
+    kmeans_params.metric  = cuvs::distance::DistanceType::L2Expanded;
+    cuvs::cluster::kmeans_balanced::helpers::build_clusters(handle,
                                                             kmeans_params,
                                                             sub_trainset_view,
                                                             centers_tmp_view,
@@ -499,10 +501,10 @@ void train_per_cluster(raft::resources const& handle,
       raft::make_device_vector_view<uint32_t, internal_extents_t>(pq_labels.data(), pq_n_rows);
     auto pq_cluster_sizes_view = raft::make_device_vector_view<uint32_t, internal_extents_t>(
       pq_cluster_sizes.data(), index.pq_book_size());
-    raft::cluster::kmeans_balanced_params kmeans_params;
+    cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = kmeans_n_iters;
-    kmeans_params.metric  = raft::distance::DistanceType::L2Expanded;
-    raft::cluster::kmeans_balanced::helpers::build_clusters(handle,
+    kmeans_params.metric  = cuvs::distance::DistanceType::L2Expanded;
+    cuvs::cluster::kmeans_balanced::helpers::build_clusters(handle,
                                                             kmeans_params,
                                                             rot_vectors_view,
                                                             centers_tmp_view,
@@ -1579,9 +1581,9 @@ void extend(raft::resources const& handle,
         new_data_labels.data() + batch.offset(), batch.size());
       auto centers_view = raft::make_device_matrix_view<const float, internal_extents_t>(
         cluster_centers.data(), n_clusters, index->dim());
-      raft::cluster::kmeans_balanced_params kmeans_params;
-      kmeans_params.metric = static_cast<raft::distance::DistanceType>((int)index->metric());
-      raft::cluster::kmeans_balanced::predict(handle,
+      cuvs::cluster::kmeans::balanced_params kmeans_params;
+      kmeans_params.metric = static_cast<cuvs::distance::DistanceType>((int)index->metric());
+      cuvs::cluster::kmeans_balanced::predict(handle,
                                               kmeans_params,
                                               batch_data_view,
                                               centers_view,
@@ -1758,10 +1760,10 @@ auto build(raft::resources const& handle,
       trainset.data(), n_rows_train, index.dim());
     auto centers_view = raft::make_device_matrix_view<float, internal_extents_t>(
       cluster_centers, index.n_lists(), index.dim());
-    raft::cluster::kmeans_balanced_params kmeans_params;
+    cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = params.kmeans_n_iters;
-    kmeans_params.metric  = static_cast<raft::distance::DistanceType>((int)index.metric());
-    raft::cluster::kmeans_balanced::fit(
+    kmeans_params.metric  = static_cast<cuvs::distance::DistanceType>((int)index.metric());
+    cuvs::cluster::kmeans_balanced::fit(
       handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
 
     // Trainset labels are needed for training PQ codebooks
@@ -1770,7 +1772,7 @@ auto build(raft::resources const& handle,
       cluster_centers, index.n_lists(), index.dim());
     auto labels_view =
       raft::make_device_vector_view<uint32_t, internal_extents_t>(labels.data(), n_rows_train);
-    raft::cluster::kmeans_balanced::predict(handle,
+    cuvs::cluster::kmeans_balanced::predict(handle,
                                             kmeans_params,
                                             trainset_const_view,
                                             centers_const_view,
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
index 1b098ac5c1..5b41e5f3d0 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
@@ -20,7 +20,7 @@
 
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/nvtx.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index b3b8f281a4..5c77e00089 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -27,7 +27,7 @@
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/custom_resource.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
index 37c902e7ed..f70c423735 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
@@ -22,7 +22,7 @@
 #include <cuvs/neighbors/ivf_list.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/host_mdarray.hpp>
-#include <raft/core/logger.hpp>
+#include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/core/serialize.hpp>
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 8455d0bac1..fe9b8ae4f9 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -90,6 +90,11 @@ endfunction()
 if(BUILD_TESTS)
   ConfigureTest(NAME NEIGHBORS_TEST PATH test/neighbors/brute_force.cu GPUS 1 PERCENT 100)
 
+  ConfigureTest(
+    NAME CLUSTER_TEST PATH test/cluster/kmeans.cu test/cluster/kmeans_balanced.cu
+    test/cluster/kmeans_find_k.cu test/cluster/linkage.cu GPUS 1 PERCENT 100
+  )
+
   ConfigureTest(
     NAME
     NEIGHBORS_ANN_IVF_FLAT_TEST
diff --git a/cpp/test/cluster/kmeans.cu b/cpp/test/cluster/kmeans.cu
new file mode 100644
index 0000000000..3caf00c41e
--- /dev/null
+++ b/cpp/test/cluster/kmeans.cu
@@ -0,0 +1,360 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/stats/adjusted_rand_index.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/fill.h>
+
+#include <gtest/gtest.h>
+
+#include <optional>
+#include <vector>
+
+namespace cuvs {
+
+template <typename T>
+struct KmeansInputs {
+  int n_row;
+  int n_col;
+  int n_clusters;
+  T tol;
+  bool weighted;
+};
+
+// template <typename DataT, typename IndexT>
+// void run_cluster_cost(const raft::resources& handle,
+//                       raft::device_vector_view<DataT, IndexT> minClusterDistance,
+//                       rmm::device_uvector<char>& workspace,
+//                       raft::device_scalar_view<DataT> clusterCost)
+//{
+//   cuvs::cluster::kmeans::cluster_cost(
+//     handle, minClusterDistance, workspace, clusterCost, raft::add_op{});
+// }
+
+template <typename T>
+class KmeansTest : public ::testing::TestWithParam<KmeansInputs<T>> {
+ protected:
+  KmeansTest()
+    : d_labels(0, raft::resource::get_cuda_stream(handle)),
+      d_labels_ref(0, raft::resource::get_cuda_stream(handle)),
+      d_centroids(0, raft::resource::get_cuda_stream(handle)),
+      d_sample_weight(0, raft::resource::get_cuda_stream(handle))
+  {
+  }
+
+  //  void apiTest()
+  //  {
+  //    testparams = ::testing::TestWithParam<KmeansInputs<T>>::GetParam();
+  //
+  //    auto stream                = raft::resource::get_cuda_stream(handle);
+  //    int n_samples              = testparams.n_row;
+  //    int n_features             = testparams.n_col;
+  //    params.n_clusters          = testparams.n_clusters;
+  //    params.tol                 = testparams.tol;
+  //    params.n_init              = 1;
+  //    params.rng_state.seed      = 1;
+  //    params.oversampling_factor = 0;
+  //
+  //    raft::random::RngState rng(params.rng_state.seed, params.rng_state.type);
+  //
+  //    auto X      = raft::make_device_matrix<T, int>(handle, n_samples, n_features);
+  //    auto labels = raft::make_device_vector<int, int>(handle, n_samples);
+  //
+  //    raft::random::make_blobs<T, int>(X.data_handle(),
+  //                                     labels.data_handle(),
+  //                                     n_samples,
+  //                                     n_features,
+  //                                     params.n_clusters,
+  //                                     stream,
+  //                                     true,
+  //                                     nullptr,
+  //                                     nullptr,
+  //                                     T(1.0),
+  //                                     false,
+  //                                     (T)-10.0f,
+  //                                     (T)10.0f,
+  //                                     (uint64_t)1234);
+  //    d_labels.resize(n_samples, stream);
+  //    d_labels_ref.resize(n_samples, stream);
+  //    d_centroids.resize(params.n_clusters * n_features, stream);
+  //    raft::copy(d_labels_ref.data(), labels.data_handle(), n_samples, stream);
+  //    rmm::device_uvector<T> d_sample_weight(n_samples, stream);
+  //    thrust::fill(
+  //      thrust::cuda::par.on(stream), d_sample_weight.data(), d_sample_weight.data() + n_samples,
+  //      1);
+  //    auto weight_view =
+  //      raft::make_device_vector_view<const T, int>(d_sample_weight.data(), n_samples);
+  //
+  //    T inertia  = 0;
+  //    int n_iter = 0;
+  //    rmm::device_uvector<char> workspace(0, stream);
+  //    rmm::device_uvector<T> L2NormBuf_OR_DistBuf(0, stream);
+  //    rmm::device_uvector<T> inRankCp(0, stream);
+  //    auto X_view = raft::make_const_mdspan(X.view());
+  //    auto centroids_view =
+  //      raft::make_device_matrix_view<T, int>(d_centroids.data(), params.n_clusters, n_features);
+  //    auto miniX = raft::make_device_matrix<T, int>(handle, n_samples / 4, n_features);
+  //
+  //    // Initialize kmeans on a portion of X
+  //    raft::cluster::kmeans::shuffle_and_gather(
+  //      handle,
+  //      X_view,
+  //      raft::make_device_matrix_view<T, int>(miniX.data_handle(), miniX.extent(0),
+  //      miniX.extent(1)), miniX.extent(0), params.rng_state.seed);
+  //
+  //    raft::cluster::kmeans::init_plus_plus(
+  //      handle, params, raft::make_const_mdspan(miniX.view()), centroids_view, workspace);
+  //
+  //    auto minClusterDistance = raft::make_device_vector<T, int>(handle, n_samples);
+  //    auto minClusterAndDistance =
+  //      raft::make_device_vector<raft::KeyValuePair<int, T>, int>(handle, n_samples);
+  //    auto L2NormX           = raft::make_device_vector<T, int>(handle, n_samples);
+  //    auto clusterCostBefore = raft::make_device_scalar<T>(handle, 0);
+  //    auto clusterCostAfter  = raft::make_device_scalar<T>(handle, 0);
+  //
+  //    raft::linalg::rowNorm(L2NormX.data_handle(),
+  //                          X.data_handle(),
+  //                          X.extent(1),
+  //                          X.extent(0),
+  //                          raft::linalg::L2Norm,
+  //                          true,
+  //                          stream);
+  //
+  //    raft::cluster::kmeans::min_cluster_distance(handle,
+  //                                                X_view,
+  //                                                centroids_view,
+  //                                                minClusterDistance.view(),
+  //                                                L2NormX.view(),
+  //                                                L2NormBuf_OR_DistBuf,
+  //                                                params.metric,
+  //                                                params.batch_samples,
+  //                                                params.batch_centroids,
+  //                                                workspace);
+  //
+  //    run_cluster_cost(handle, minClusterDistance.view(), workspace, clusterCostBefore.view());
+  //
+  //    // Run a fit of kmeans
+  //    raft::cluster::kmeans::fit_main(handle,
+  //                                    params,
+  //                                    X_view,
+  //                                    weight_view,
+  //                                    centroids_view,
+  //                                    raft::make_host_scalar_view(&inertia),
+  //                                    raft::make_host_scalar_view(&n_iter),
+  //                                    workspace);
+  //
+  //    // Check that the cluster cost decreased
+  //    raft::cluster::kmeans::min_cluster_distance(handle,
+  //                                                X_view,
+  //                                                centroids_view,
+  //                                                minClusterDistance.view(),
+  //                                                L2NormX.view(),
+  //                                                L2NormBuf_OR_DistBuf,
+  //                                                params.metric,
+  //                                                params.batch_samples,
+  //                                                params.batch_centroids,
+  //                                                workspace);
+  //
+  //    run_cluster_cost(handle, minClusterDistance.view(), workspace, clusterCostAfter.view());
+  //    T h_clusterCostBefore = T(0);
+  //    T h_clusterCostAfter  = T(0);
+  //    raft::update_host(&h_clusterCostBefore, clusterCostBefore.data_handle(), 1, stream);
+  //    raft::update_host(&h_clusterCostAfter, clusterCostAfter.data_handle(), 1, stream);
+  //    ASSERT_TRUE(h_clusterCostAfter < h_clusterCostBefore);
+  //
+  //    // Count samples in clusters using 2 methods and compare them
+  //    // Fill minClusterAndDistance
+  //    raft::cluster::kmeans::min_cluster_and_distance(
+  //      handle,
+  //      X_view,
+  //      raft::make_device_matrix_view<const T, int>(
+  //        d_centroids.data(), params.n_clusters, n_features),
+  //      minClusterAndDistance.view(),
+  //      L2NormX.view(),
+  //      L2NormBuf_OR_DistBuf,
+  //      params.metric,
+  //      params.batch_samples,
+  //      params.batch_centroids,
+  //      workspace);
+  //    raft::cluster::kmeans::KeyValueIndexOp<int, T> conversion_op;
+  //    cub::TransformInputIterator<int,
+  //                                raft::cluster::kmeans::KeyValueIndexOp<int, T>,
+  //                                raft::KeyValuePair<int, T>*>
+  //      itr(minClusterAndDistance.data_handle(), conversion_op);
+  //
+  //    auto sampleCountInCluster = raft::make_device_vector<T, int>(handle, params.n_clusters);
+  //    auto weigthInCluster      = raft::make_device_vector<T, int>(handle, params.n_clusters);
+  //    auto newCentroids = raft::make_device_matrix<T, int>(handle, params.n_clusters, n_features);
+  //    raft::cluster::kmeans::update_centroids(handle,
+  //                                            X_view,
+  //                                            weight_view,
+  //                                            raft::make_device_matrix_view<const T, int>(
+  //                                              d_centroids.data(), params.n_clusters,
+  //                                              n_features),
+  //                                            itr,
+  //                                            weigthInCluster.view(),
+  //                                            newCentroids.view());
+  //    raft::cluster::kmeans::count_samples_in_cluster(handle,
+  //                                                    params,
+  //                                                    X_view,
+  //                                                    L2NormX.view(),
+  //                                                    newCentroids.view(),
+  //                                                    workspace,
+  //                                                    sampleCountInCluster.view());
+  //
+  //    ASSERT_TRUE(devArrMatch(sampleCountInCluster.data_handle(),
+  //                            weigthInCluster.data_handle(),
+  //                            params.n_clusters,
+  //                            CompareApprox<T>(params.tol)));
+  //  }
+
+  void basicTest()
+  {
+    testparams = ::testing::TestWithParam<KmeansInputs<T>>::GetParam();
+
+    int n_samples              = testparams.n_row;
+    int n_features             = testparams.n_col;
+    params.n_clusters          = testparams.n_clusters;
+    params.tol                 = testparams.tol;
+    params.n_init              = 5;
+    params.rng_state.seed      = 1;
+    params.oversampling_factor = 0;
+
+    auto X      = raft::make_device_matrix<T, int>(handle, n_samples, n_features);
+    auto labels = raft::make_device_vector<int, int>(handle, n_samples);
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    raft::random::make_blobs<T, int>(X.data_handle(),
+                                     labels.data_handle(),
+                                     n_samples,
+                                     n_features,
+                                     params.n_clusters,
+                                     stream,
+                                     true,
+                                     nullptr,
+                                     nullptr,
+                                     T(1.0),
+                                     false,
+                                     (T)-10.0f,
+                                     (T)10.0f,
+                                     (uint64_t)1234);
+
+    d_labels.resize(n_samples, stream);
+    d_labels_ref.resize(n_samples, stream);
+    d_centroids.resize(params.n_clusters * n_features, stream);
+
+    std::optional<raft::device_vector_view<const T>> d_sw = std::nullopt;
+    auto d_centroids_view =
+      raft::make_device_matrix_view<T, int>(d_centroids.data(), params.n_clusters, n_features);
+    if (testparams.weighted) {
+      d_sample_weight.resize(n_samples, stream);
+      d_sw = std::make_optional(
+        raft::make_device_vector_view<const T, int>(d_sample_weight.data(), n_samples));
+      thrust::fill(thrust::cuda::par.on(stream),
+                   d_sample_weight.data(),
+                   d_sample_weight.data() + n_samples,
+                   1);
+    }
+
+    raft::copy(d_labels_ref.data(), labels.data_handle(), n_samples, stream);
+
+    T inertia   = 0;
+    int n_iter  = 0;
+    auto X_view = raft::make_const_mdspan(X.view());
+
+    cuvs::cluster::kmeans::fit_predict(
+      handle,
+      params,
+      X_view,
+      d_sw,
+      d_centroids_view,
+      raft::make_device_vector_view<int, int>(d_labels.data(), n_samples),
+      raft::make_host_scalar_view<T>(&inertia),
+      raft::make_host_scalar_view<int>(&n_iter));
+
+    raft::resource::sync_stream(handle, stream);
+
+    score = raft::stats::adjusted_rand_index(
+      d_labels_ref.data(), d_labels.data(), n_samples, raft::resource::get_cuda_stream(handle));
+
+    if (score < 1.0) {
+      std::stringstream ss;
+      ss << "Expected: " << raft::arr2Str(d_labels_ref.data(), 25, "d_labels_ref", stream);
+      std::cout << (ss.str().c_str()) << '\n';
+      ss.str(std::string());
+      ss << "Actual: " << raft::arr2Str(d_labels.data(), 25, "d_labels", stream);
+      std::cout << (ss.str().c_str()) << '\n';
+      std::cout << "Score = " << score << '\n';
+    }
+  }
+
+  void SetUp() override
+  {
+    basicTest();
+    //    apiTest();
+  }
+
+ protected:
+  raft::resources handle;
+  KmeansInputs<T> testparams;
+  rmm::device_uvector<int> d_labels;
+  rmm::device_uvector<int> d_labels_ref;
+  rmm::device_uvector<T> d_centroids;
+  rmm::device_uvector<T> d_sample_weight;
+  double score;
+  cuvs::cluster::kmeans::params params;
+};
+
+const std::vector<KmeansInputs<float>> inputsf2 = {{1000, 32, 5, 0.0001f, true},
+                                                   {1000, 32, 5, 0.0001f, false},
+                                                   {1000, 100, 20, 0.0001f, true},
+                                                   {1000, 100, 20, 0.0001f, false},
+                                                   {10000, 32, 10, 0.0001f, true},
+                                                   {10000, 32, 10, 0.0001f, false},
+                                                   {10000, 100, 50, 0.0001f, true},
+                                                   {10000, 100, 50, 0.0001f, false},
+                                                   {10000, 500, 100, 0.0001f, true},
+                                                   {10000, 500, 100, 0.0001f, false}};
+
+const std::vector<KmeansInputs<double>> inputsd2 = {{1000, 32, 5, 0.0001, true},
+                                                    {1000, 32, 5, 0.0001, false},
+                                                    {1000, 100, 20, 0.0001, true},
+                                                    {1000, 100, 20, 0.0001, false},
+                                                    {10000, 32, 10, 0.0001, true},
+                                                    {10000, 32, 10, 0.0001, false},
+                                                    {10000, 100, 50, 0.0001, true},
+                                                    {10000, 100, 50, 0.0001, false},
+                                                    {10000, 500, 100, 0.0001, true},
+                                                    {10000, 500, 100, 0.0001, false}};
+
+typedef KmeansTest<float> KmeansTestF;
+
+TEST_P(KmeansTestF, Result) { ASSERT_TRUE(score == 1.0); }
+
+INSTANTIATE_TEST_CASE_P(KmeansTests, KmeansTestF, ::testing::ValuesIn(inputsf2));
+
+}  // namespace cuvs
diff --git a/cpp/test/cluster/kmeans_balanced.cu b/cpp/test/cluster/kmeans_balanced.cu
new file mode 100644
index 0000000000..f273dd92fa
--- /dev/null
+++ b/cpp/test/cluster/kmeans_balanced.cu
@@ -0,0 +1,238 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.h"
+
+#include <cuvs/cluster/kmeans.hpp>
+
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/random/make_blobs.cuh>
+#include <raft/stats/adjusted_rand_index.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/fill.h>
+
+#include <gtest/gtest.h>
+
+#include <optional>
+#include <vector>
+
+/* This test takes advantage of the fact that make_blobs generates balanced clusters.
+ * It doesn't currently test whether the algorithm can make balanced clusters with an imbalanced
+ * dataset.
+ */
+
+namespace cuvs {
+
+template <typename MathT, typename IdxT>
+struct KmeansBalancedInputs {
+  IdxT n_rows;
+  IdxT n_cols;
+  IdxT n_clusters;
+  cuvs::cluster::kmeans::balanced_params kb_params;
+  MathT tol;
+};
+
+template <typename MathT, typename IdxT>
+::std::ostream& operator<<(::std::ostream& os, const KmeansBalancedInputs<MathT, IdxT>& p)
+{
+  os << "{ " << p.n_rows << ", " << p.n_cols << ", " << p.n_clusters << ", " << p.kb_params.n_iters
+     << static_cast<int>(p.kb_params.metric) << '}' << std::endl;
+  return os;
+}
+
+template <typename DataT, typename MathT, typename LabelT, typename IdxT, typename MappingOpT>
+class KmeansBalancedTest : public ::testing::TestWithParam<KmeansBalancedInputs<MathT, IdxT>> {
+ protected:
+  KmeansBalancedTest()
+    : stream(raft::resource::get_cuda_stream(handle)),
+      d_labels(0, stream),
+      d_labels_ref(0, stream),
+      d_centroids(0, stream)
+  {
+  }
+
+  void basicTest()
+  {
+    MappingOpT op{};
+
+    auto p = ::testing::TestWithParam<KmeansBalancedInputs<MathT, IdxT>>::GetParam();
+
+    auto X           = raft::make_device_matrix<DataT, IdxT>(handle, p.n_rows, p.n_cols);
+    auto blob_labels = raft::make_device_vector<IdxT, IdxT>(handle, p.n_rows);
+
+    MathT* blobs_ptr;
+    rmm::device_uvector<MathT> blobs(0, stream);
+    if constexpr (!std::is_same_v<DataT, MathT>) {
+      blobs.resize(p.n_rows * p.n_cols, stream);
+      blobs_ptr = blobs.data();
+    } else {
+      blobs_ptr = X.data_handle();
+    }
+
+    raft::random::make_blobs<MathT, IdxT>(blobs_ptr,
+                                          blob_labels.data_handle(),
+                                          p.n_rows,
+                                          p.n_cols,
+                                          p.n_clusters,
+                                          stream,
+                                          true,
+                                          nullptr,
+                                          nullptr,
+                                          MathT{0.1},
+                                          true,
+                                          MathT{-1},
+                                          MathT{1},
+                                          (uint64_t)1234);
+
+    // Convert blobs dataset to DataT if necessary
+    if constexpr (!std::is_same_v<DataT, MathT>) {
+      raft::linalg::unaryOp(
+        X.data_handle(), blobs.data(), p.n_rows * p.n_cols, op.reverse_op, stream);
+    }
+
+    d_labels.resize(p.n_rows, stream);
+    d_labels_ref.resize(p.n_rows, stream);
+    d_centroids.resize(p.n_clusters * p.n_cols, stream);
+
+    raft::linalg::unaryOp(
+      d_labels_ref.data(), blob_labels.data_handle(), p.n_rows, raft::cast_op<LabelT>(), stream);
+
+    auto X_view =
+      raft::make_device_matrix_view<const DataT, IdxT>(X.data_handle(), X.extent(0), X.extent(1));
+    auto d_centroids_view =
+      raft::make_device_matrix_view<MathT, IdxT>(d_centroids.data(), p.n_clusters, p.n_cols);
+    auto d_labels_view = raft::make_device_vector_view<LabelT, IdxT>(d_labels.data(), p.n_rows);
+
+    cuvs::cluster::kmeans::fit_predict(
+      handle, p.kb_params, X_view, d_centroids_view, d_labels_view);
+
+    raft::resource::sync_stream(handle, stream);
+
+    score = raft::stats::adjusted_rand_index(
+      d_labels_ref.data(), d_labels.data(), p.n_rows, raft::resource::get_cuda_stream(handle));
+
+    if (score < 1.0) {
+      std::stringstream ss;
+      ss << "Expected: " << raft::arr2Str(d_labels_ref.data(), 25, "d_labels_ref", stream);
+      std::cout << (ss.str().c_str()) << '\n';
+      ss.str(std::string());
+      ss << "Actual: " << raft::arr2Str(d_labels.data(), 25, "d_labels", stream);
+      std::cout << (ss.str().c_str()) << '\n';
+      std::cout << "Score = " << score << '\n';
+    }
+  }
+
+  void SetUp() override { basicTest(); }
+
+ protected:
+  raft::handle_t handle;
+  cudaStream_t stream;
+  rmm::device_uvector<LabelT> d_labels;
+  rmm::device_uvector<LabelT> d_labels_ref;
+  rmm::device_uvector<MathT> d_centroids;
+  double score;
+};
+
+template <typename MathT, typename IdxT>
+std::vector<KmeansBalancedInputs<MathT, IdxT>> get_kmeans_balanced_inputs()
+{
+  std::vector<KmeansBalancedInputs<MathT, IdxT>> out;
+  KmeansBalancedInputs<MathT, IdxT> p;
+  p.kb_params.n_iters = 20;
+  p.kb_params.metric  = cuvs::distance::DistanceType::L2Expanded;
+  p.tol               = MathT{0.0001};
+  std::vector<std::tuple<size_t, size_t, size_t>> row_cols_k = {{1000, 32, 5},
+                                                                {1000, 100, 20},
+                                                                {10000, 32, 10},
+                                                                {10000, 100, 50},
+                                                                {10000, 500, 100},
+                                                                {1000000, 128, 10}};
+  for (auto& rck : row_cols_k) {
+    p.n_rows     = static_cast<IdxT>(std::get<0>(rck));
+    p.n_cols     = static_cast<IdxT>(std::get<1>(rck));
+    p.n_clusters = static_cast<IdxT>(std::get<2>(rck));
+    out.push_back(p);
+  }
+  return out;
+}
+
+const auto inputsf_i32 = get_kmeans_balanced_inputs<float, int>();
+// const auto inputsd_i32 = get_kmeans_balanced_inputs<double, int>();
+const auto inputsf_i64 = get_kmeans_balanced_inputs<float, int64_t>();
+// const auto inputsd_i64 = get_kmeans_balanced_inputs<double, int64_t>();
+
+#define KB_TEST(test_type, test_name, test_inputs)         \
+  typedef RAFT_DEPAREN(test_type) test_name;               \
+  TEST_P(test_name, Result) { ASSERT_TRUE(score == 1.0); } \
+  INSTANTIATE_TEST_CASE_P(KmeansBalancedTests, test_name, ::testing::ValuesIn(test_inputs))
+
+/*
+ * First set of tests: no conversion
+ */
+
+KB_TEST((KmeansBalancedTest<float, float, uint32_t, int, raft::identity_op>),
+        KmeansBalancedTestFFU32I32,
+        inputsf_i32);
+// KB_TEST((KmeansBalancedTest<double, double, uint32_t, int, raft::identity_op>),
+//         KmeansBalancedTestDDU32I32,
+//         inputsd_i32);
+// KB_TEST((KmeansBalancedTest<float, float, uint32_t, int64_t, raft::identity_op>),
+//         KmeansBalancedTestFFU32I64,
+//         inputsf_i64);
+// KB_TEST((KmeansBalancedTest<double, double, uint32_t, int64_t, raft::identity_op>),
+//         KmeansBalancedTestDDU32I64,
+//         inputsd_i64);
+// KB_TEST((KmeansBalancedTest<float, float, int, int, raft::identity_op>),
+//         KmeansBalancedTestFFI32I32,
+//         inputsf_i32);
+// KB_TEST((KmeansBalancedTest<float, float, int, int64_t, raft::identity_op>),
+//         KmeansBalancedTestFFI32I64,
+//         inputsf_i64);
+// KB_TEST((KmeansBalancedTest<float, float, int64_t, int, raft::identity_op>),
+//         KmeansBalancedTestFFI64I32,
+//         inputsf_i32);
+// KB_TEST((KmeansBalancedTest<float, float, int64_t, int64_t, raft::identity_op>),
+//         KmeansBalancedTestFFI64I64,
+//         inputsf_i64);
+
+/*
+ * Second set of tests: integer dataset with conversion
+ */
+
+template <typename DataT, typename MathT>
+struct i2f_scaler {
+  // Note: with a scaling factor of 42, and generating blobs with centers between -1 and 1 with a
+  // standard deviation of 0.1, it's statistically very unlikely that we'd overflow
+  const raft::compose_op<raft::div_const_op<MathT>, raft::cast_op<MathT>> op{
+    raft::div_const_op<MathT>{42}, raft::cast_op<MathT>{}};
+  const raft::compose_op<raft::cast_op<DataT>, raft::mul_const_op<MathT>> reverse_op{
+    raft::cast_op<DataT>{}, raft::mul_const_op<MathT>{42}};
+
+  RAFT_INLINE_FUNCTION auto operator()(const DataT& x) const { return op(x); };
+};
+
+KB_TEST((KmeansBalancedTest<int8_t, float, uint32_t, int, i2f_scaler<int8_t, float>>),
+        KmeansBalancedTestFI8U32I32,
+        inputsf_i32);
+
+}  // namespace cuvs
diff --git a/cpp/test/cluster/kmeans_find_k.cu b/cpp/test/cluster/kmeans_find_k.cu
new file mode 100644
index 0000000000..dd35e1bfba
--- /dev/null
+++ b/cpp/test/cluster/kmeans_find_k.cu
@@ -0,0 +1,122 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.h"
+
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/cudart_utils.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_span.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <gtest/gtest.h>
+
+#include <optional>
+#include <vector>
+
+namespace cuvs::cluster::kmeans {
+
+template <typename T>
+struct KmeansFindKInputs {
+  int n_row;
+  int n_col;
+  int n_clusters;
+  T tol;
+  bool weighted;
+};
+
+template <typename T>
+class KmeansFindKTest : public ::testing::TestWithParam<KmeansFindKInputs<T>> {
+ protected:
+  KmeansFindKTest()
+    : stream(raft::resource::get_cuda_stream(handle)), best_k(raft::make_host_scalar<int>(0))
+  {
+  }
+
+  void basicTest()
+  {
+    testparams = ::testing::TestWithParam<KmeansFindKInputs<T>>::GetParam();
+
+    int n_samples  = testparams.n_row;
+    int n_features = testparams.n_col;
+    int n_clusters = testparams.n_clusters;
+
+    auto X      = raft::make_device_matrix<T, int>(handle, n_samples, n_features);
+    auto labels = raft::make_device_vector<int, int>(handle, n_samples);
+
+    raft::random::make_blobs<T, int>(X.data_handle(),
+                                     labels.data_handle(),
+                                     n_samples,
+                                     n_features,
+                                     n_clusters,
+                                     stream,
+                                     true,
+                                     nullptr,
+                                     nullptr,
+                                     T(.001),
+                                     false,
+                                     (T)-10.0f,
+                                     (T)10.0f,
+                                     (uint64_t)1234);
+
+    auto inertia = raft::make_host_scalar<T>(0);
+    auto n_iter  = raft::make_host_scalar<int>(0);
+
+    auto X_view =
+      raft::make_device_matrix_view<const T, int>(X.data_handle(), X.extent(0), X.extent(1));
+
+    cuvs::cluster::kmeans::helpers::find_k(
+      handle, X_view, best_k.view(), inertia.view(), n_iter.view(), n_clusters);
+
+    raft::resource::sync_stream(handle, stream);
+  }
+
+  void SetUp() override { basicTest(); }
+
+ protected:
+  raft::resources handle;
+  cudaStream_t stream;
+  KmeansFindKInputs<T> testparams;
+  raft::host_scalar<int> best_k;
+};
+
+const std::vector<KmeansFindKInputs<float>> inputsf2 = {{1000, 32, 8, 0.001f, true},
+                                                        {1000, 32, 8, 0.001f, false},
+                                                        {1000, 100, 20, 0.001f, true},
+                                                        {1000, 100, 20, 0.001f, false},
+                                                        {10000, 32, 10, 0.001f, true},
+                                                        {10000, 32, 10, 0.001f, false},
+                                                        {10000, 100, 50, 0.001f, true},
+                                                        {10000, 100, 50, 0.001f, false},
+                                                        {10000, 500, 100, 0.001f, true},
+                                                        {10000, 500, 100, 0.001f, false}};
+
+typedef KmeansFindKTest<float> KmeansFindKTestF;
+TEST_P(KmeansFindKTestF, Result)
+{
+  if (best_k.view()[0] != testparams.n_clusters) {
+    std::cout << best_k.view()[0] << " " << testparams.n_clusters << std::endl;
+  }
+  ASSERT_TRUE(best_k.view()[0] == testparams.n_clusters);
+}
+
+INSTANTIATE_TEST_CASE_P(KmeansFindKTests, KmeansFindKTestF, ::testing::ValuesIn(inputsf2));
+
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/test/cluster/linkage.cu b/cpp/test/cluster/linkage.cu
new file mode 100644
index 0000000000..0768ce80bf
--- /dev/null
+++ b/cpp/test/cluster/linkage.cu
@@ -0,0 +1,672 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// XXX: We allow the instantiation of masked_l2_nn here:
+// raft::linkage::FixConnectivitiesRedOp<value_idx, value_t> red_op(params.n_row);
+// raft::linkage::cross_component_nn<value_idx, value_t>(
+//   handle, out_edges, data.data(), colors.data(), params.n_row, params.n_col, red_op);
+//
+// TODO: consider adding this to libraft.so or creating an instance in a
+// separate translation unit for this test.
+#undef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+#include "../test_utils.cuh"
+
+#include <cuvs/cluster/agglomerative.hpp>
+#include <cuvs/distance/distance_types.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/transpose.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <gtest/gtest.h>
+
+#include <vector>
+
+namespace cuvs::cluster::agglomerative {
+
+using namespace std;
+
+template <typename T, typename IdxT>
+struct LinkageInputs {
+  IdxT n_row;
+  IdxT n_col;
+
+  std::vector<T> data;
+
+  std::vector<IdxT> expected_labels;
+
+  int n_clusters;
+
+  bool use_knn;
+
+  int c;
+};
+
+/**
+ * @brief kernel to calculate the values of a and b
+ * @param firstClusterArray: the array of classes of type T
+ * @param secondClusterArray: the array of classes of type T
+ * @param size: the size of the data points
+ * @param a: number of pairs of points that both the clusters have classified the same
+ * @param b: number of pairs of points that both the clusters have classified differently
+ */
+template <typename T, int BLOCK_DIM_X, int BLOCK_DIM_Y>
+RAFT_KERNEL computeTheNumerator(
+  const T* firstClusterArray, const T* secondClusterArray, uint64_t size, uint64_t* a, uint64_t* b)
+{
+  // calculating the indices of pairs of datapoints compared by the current thread
+  uint64_t j = threadIdx.x + blockIdx.x * blockDim.x;
+  uint64_t i = threadIdx.y + blockIdx.y * blockDim.y;
+
+  // thread-local variables to count a and b
+  uint64_t myA = 0, myB = 0;
+
+  if (i < size && j < size && j < i) {
+    // checking if the pair have been classified the same by both the clusters
+    if (firstClusterArray[i] == firstClusterArray[j] &&
+        secondClusterArray[i] == secondClusterArray[j]) {
+      ++myA;
+    }
+
+    // checking if the pair have been classified differently by both the clusters
+    else if (firstClusterArray[i] != firstClusterArray[j] &&
+             secondClusterArray[i] != secondClusterArray[j]) {
+      ++myB;
+    }
+  }
+
+  // specialize blockReduce for a 2D block of 1024 threads of type uint64_t
+  typedef cub::BlockReduce<uint64_t, BLOCK_DIM_X, cub::BLOCK_REDUCE_WARP_REDUCTIONS, BLOCK_DIM_Y>
+    BlockReduce;
+
+  // Allocate shared memory for blockReduce
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+
+  // summing up thread-local counts specific to a block
+  myA = BlockReduce(temp_storage).Sum(myA);
+  __syncthreads();
+  myB = BlockReduce(temp_storage).Sum(myB);
+  __syncthreads();
+
+  // executed once per block
+  if (threadIdx.x == 0 && threadIdx.y == 0) {
+    raft::myAtomicAdd<unsigned long long int>((unsigned long long int*)a, myA);
+    raft::myAtomicAdd<unsigned long long int>((unsigned long long int*)b, myB);
+  }
+}
+
+/**
+ * @brief Function to calculate RandIndex
+ * <a href="https://en.wikipedia.org/wiki/Rand_index">more info on rand index</a>
+ * @param firstClusterArray: the array of classes of type T
+ * @param secondClusterArray: the array of classes of type T
+ * @param size: the size of the data points of type uint64_t
+ * @param stream: the cudaStream object
+ */
+template <typename T>
+double compute_rand_index(T* firstClusterArray,
+                          T* secondClusterArray,
+                          uint64_t size,
+                          cudaStream_t stream)
+{
+  // rand index for size less than 2 is not defined
+  ASSERT(size >= 2, "Rand Index for size less than 2 not defined!");
+
+  // allocating and initializing memory for a and b in the GPU
+  rmm::device_uvector<uint64_t> arr_buf(2, stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(arr_buf.data(), 0, 2 * sizeof(uint64_t), stream));
+
+  // kernel configuration
+  static const int BLOCK_DIM_Y = 16, BLOCK_DIM_X = 16;
+  dim3 numThreadsPerBlock(BLOCK_DIM_X, BLOCK_DIM_Y);
+  dim3 numBlocks(raft::ceildiv<int>(size, numThreadsPerBlock.x),
+                 raft::ceildiv<int>(size, numThreadsPerBlock.y));
+
+  // calling the kernel
+  computeTheNumerator<T, BLOCK_DIM_X, BLOCK_DIM_Y><<<numBlocks, numThreadsPerBlock, 0, stream>>>(
+    firstClusterArray, secondClusterArray, size, arr_buf.data(), arr_buf.data() + 1);
+
+  // synchronizing and updating the calculated values of a and b from device to host
+  uint64_t ab_host[2] = {0};
+  raft::update_host(ab_host, arr_buf.data(), 2, stream);
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+
+  // error handling
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  // denominator
+  uint64_t nChooseTwo = size * (size - 1) / 2;
+
+  // calculating the rand_index
+  return (double)(((double)(ab_host[0] + ab_host[1])) / (double)nChooseTwo);
+}
+
+template <typename T, typename IdxT>
+::std::ostream& operator<<(::std::ostream& os, const LinkageInputs<T, IdxT>& dims)
+{
+  return os;
+}
+
+template <typename T, typename IdxT>
+class LinkageTest : public ::testing::TestWithParam<LinkageInputs<T, IdxT>> {
+ public:
+  LinkageTest()
+    : params(::testing::TestWithParam<LinkageInputs<T, IdxT>>::GetParam()),
+      labels(0, raft::resource::get_cuda_stream(handle)),
+      labels_ref(0, raft::resource::get_cuda_stream(handle))
+  {
+  }
+
+ protected:
+  void basicTest()
+  {
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    labels.resize(params.n_row, stream);
+    labels_ref.resize(params.n_row, stream);
+    rmm::device_uvector<T> data(params.n_row * params.n_col, stream);
+
+    raft::copy(data.data(), params.data.data(), data.size(), stream);
+    raft::copy(labels_ref.data(), params.expected_labels.data(), params.n_row, stream);
+
+    rmm::device_uvector<IdxT> out_children(params.n_row * 2, stream);
+
+    auto data_view = raft::make_device_matrix_view<const T, IdxT, raft::row_major>(
+      data.data(), params.n_row, params.n_col);
+    auto dendrogram_view = raft::make_device_matrix_view<IdxT, IdxT, raft::row_major>(
+      out_children.data(), params.n_row, 2);
+    auto labels_view = raft::make_device_vector_view<IdxT, IdxT>(labels.data(), params.n_row);
+
+    if (params.use_knn) {
+      cuvs::cluster::agglomerative::single_linkage(handle,
+                                                   data_view,
+                                                   dendrogram_view,
+                                                   labels_view,
+                                                   cuvs::distance::DistanceType::L2SqrtExpanded,
+                                                   params.n_clusters,
+                                                   Linkage::KNN_GRAPH,
+                                                   std::make_optional<int>(params.c));
+
+    } else {
+      cuvs::cluster::agglomerative::single_linkage(handle,
+                                                   data_view,
+                                                   dendrogram_view,
+                                                   labels_view,
+                                                   cuvs::distance::DistanceType::L2SqrtExpanded,
+                                                   params.n_clusters,
+                                                   Linkage::PAIRWISE,
+                                                   std::make_optional<int>(params.c));
+    }
+
+    raft::resource::sync_stream(handle, stream);
+
+    score = compute_rand_index(labels.data(), labels_ref.data(), params.n_row, stream);
+  }
+
+  void SetUp() override { basicTest(); }
+
+ protected:
+  raft::resources handle;
+
+  LinkageInputs<T, IdxT> params;
+  rmm::device_uvector<IdxT> labels, labels_ref;
+  double score;
+};
+
+const std::vector<LinkageInputs<float, int>> linkage_inputsf2 = {
+  // Test n_clusters == n_points
+  {10,
+   5,
+   {0.21390334, 0.50261639, 0.91036676, 0.59166485, 0.71162682, 0.10248392, 0.77782677, 0.43772379,
+    0.4035871,  0.3282796,  0.47544681, 0.59862974, 0.12319357, 0.06239463, 0.28200272, 0.1345717,
+    0.50498218, 0.5113505,  0.16233086, 0.62165332, 0.42281548, 0.933117,   0.41386077, 0.23264562,
+    0.73325968, 0.37537541, 0.70719873, 0.14522645, 0.73279625, 0.9126674,  0.84854131, 0.28890216,
+    0.85267903, 0.74703138, 0.83842071, 0.34942792, 0.27864171, 0.70911132, 0.21338564, 0.32035554,
+    0.73788331, 0.46926692, 0.57570162, 0.42559178, 0.87120209, 0.22734951, 0.01847905, 0.75549396,
+    0.76166195, 0.66613745},
+   {9, 8, 7, 6, 5, 4, 3, 2, 1, 0},
+   10,
+   true,
+   -1},
+  //  // Test outlier points
+  {9,
+   2,
+   {-1, -50, 3, 4, 5000, 10000, 1, 3, 4, 5, 0.000005, 0.00002, 2000000, 500000, 10, 50, 30, 5},
+   {6, 0, 5, 0, 0, 4, 3, 2, 1},
+   7,
+   true,
+   -1},
+
+  // Test n_clusters == (n_points / 2)
+  {10,
+   5,
+   {0.21390334, 0.50261639, 0.91036676, 0.59166485, 0.71162682, 0.10248392, 0.77782677, 0.43772379,
+    0.4035871,  0.3282796,  0.47544681, 0.59862974, 0.12319357, 0.06239463, 0.28200272, 0.1345717,
+    0.50498218, 0.5113505,  0.16233086, 0.62165332, 0.42281548, 0.933117,   0.41386077, 0.23264562,
+    0.73325968, 0.37537541, 0.70719873, 0.14522645, 0.73279625, 0.9126674,  0.84854131, 0.28890216,
+    0.85267903, 0.74703138, 0.83842071, 0.34942792, 0.27864171, 0.70911132, 0.21338564, 0.32035554,
+    0.73788331, 0.46926692, 0.57570162, 0.42559178, 0.87120209, 0.22734951, 0.01847905, 0.75549396,
+    0.76166195, 0.66613745},
+   {1, 0, 4, 0, 0, 3, 2, 0, 2, 1},
+   5,
+   true,
+   -1},
+
+  // Test n_points == 100
+  {100,
+   10,
+   {6.26168372e-01, 9.30437651e-01, 6.02450208e-01, 2.73025296e-01, 9.53050619e-01, 3.32164396e-01,
+    6.88942598e-01, 5.79163537e-01, 6.70341547e-01, 2.70140602e-02, 9.30429671e-01, 7.17721157e-01,
+    9.89948537e-01, 7.75253347e-01, 1.34491522e-02, 2.48522428e-02, 3.51413378e-01, 7.64405834e-01,
+    7.86373507e-01, 7.18748577e-01, 8.66998621e-01, 6.80316582e-01, 2.51288712e-01, 4.91078420e-01,
+    3.76246281e-01, 4.86828710e-01, 5.67464772e-01, 5.30734742e-01, 8.99478296e-01, 7.66699088e-01,
+    9.49339111e-01, 3.55248484e-01, 9.06046929e-01, 4.48407772e-01, 6.96395305e-01, 2.44277335e-01,
+    7.74840000e-01, 5.21046603e-01, 4.66423971e-02, 5.12019638e-02, 8.95019614e-01, 5.28956953e-01,
+    4.31536306e-01, 5.83857744e-01, 4.41787364e-01, 4.68656523e-01, 5.73971433e-01, 6.79989654e-01,
+    3.19650588e-01, 6.12579596e-01, 6.49126442e-02, 8.39131142e-01, 2.85252117e-01, 5.84848929e-01,
+    9.46507115e-01, 8.58440748e-01, 3.61528940e-01, 2.44215959e-01, 3.80101125e-01, 4.57128957e-02,
+    8.82216988e-01, 8.31498633e-01, 7.23474381e-01, 7.75788607e-01, 1.40864146e-01, 6.62092382e-01,
+    5.13985168e-01, 3.00686418e-01, 8.70109949e-01, 2.43187753e-01, 2.89391938e-01, 2.84214238e-01,
+    8.70985521e-01, 8.77491176e-01, 6.72537226e-01, 3.30929686e-01, 1.85934324e-01, 9.16222614e-01,
+    6.18239142e-01, 2.64768597e-01, 5.76145451e-01, 8.62961369e-01, 6.84757925e-01, 7.60549082e-01,
+    1.27645356e-01, 4.51004673e-01, 3.92292980e-01, 4.63170803e-01, 4.35449330e-02, 2.17583404e-01,
+    5.71832605e-02, 2.06763039e-01, 3.70116249e-01, 2.09750028e-01, 6.17283019e-01, 8.62549231e-01,
+    9.84156240e-02, 2.66249156e-01, 3.87635103e-01, 2.85591012e-02, 4.24826068e-01, 4.45795088e-01,
+    6.86227676e-01, 1.08848960e-01, 5.96731841e-02, 3.71770228e-01, 1.91548833e-01, 6.95136078e-01,
+    9.00700636e-01, 8.76363105e-01, 2.67334632e-01, 1.80619709e-01, 7.94060419e-01, 1.42854171e-02,
+    1.09372387e-01, 8.74028108e-01, 6.46403232e-01, 4.86588834e-01, 5.93446175e-02, 6.11886291e-01,
+    8.83865057e-01, 3.15879821e-01, 2.27043992e-01, 9.76764951e-01, 6.15620336e-01, 9.76199360e-01,
+    2.40548962e-01, 3.21795663e-01, 8.75087904e-02, 8.11234663e-01, 6.96070480e-01, 8.12062321e-01,
+    1.21958818e-01, 3.44348628e-02, 8.72630414e-01, 3.06162776e-01, 1.76043529e-02, 9.45894971e-01,
+    5.33896401e-01, 6.21642973e-01, 4.93062535e-01, 4.48984262e-01, 2.24560379e-01, 4.24052195e-02,
+    4.43447610e-01, 8.95646149e-01, 6.05220676e-01, 1.81840491e-01, 9.70831206e-01, 2.12563586e-02,
+    6.92582693e-01, 7.55946922e-01, 7.95086143e-01, 6.05328941e-01, 3.99350764e-01, 4.32846636e-01,
+    9.81114529e-01, 4.98266428e-01, 6.37127930e-03, 1.59085889e-01, 6.34682067e-05, 5.59429440e-01,
+    7.38827633e-01, 8.93214770e-01, 2.16494306e-01, 9.35430573e-02, 4.75665868e-02, 7.80503518e-01,
+    7.86240041e-01, 7.06854594e-01, 2.13725879e-02, 7.68246091e-01, 4.50234808e-01, 5.21231104e-01,
+    5.01989826e-03, 4.22081572e-02, 1.65337732e-01, 8.54134740e-01, 4.99430262e-01, 8.94525601e-01,
+    1.14028379e-01, 3.69739861e-01, 1.32955599e-01, 2.65563824e-01, 2.52811151e-01, 1.44792843e-01,
+    6.88449594e-01, 4.44921417e-01, 8.23296587e-01, 1.93266317e-01, 1.19033309e-01, 1.36368966e-01,
+    3.42600285e-01, 5.64505195e-01, 5.57594559e-01, 7.44257892e-01, 8.38231569e-02, 4.11548847e-01,
+    3.21010077e-01, 8.55081359e-01, 4.30105779e-01, 1.16229135e-01, 9.87731964e-02, 3.14712335e-01,
+    4.50880592e-01, 2.72289598e-01, 6.31615256e-01, 8.97432958e-01, 4.44764250e-01, 8.03776440e-01,
+    2.68767748e-02, 2.43374608e-01, 4.02141103e-01, 4.98881209e-01, 5.33173003e-01, 8.82890436e-01,
+    7.16149148e-01, 4.19664401e-01, 2.29335357e-01, 2.88637806e-01, 3.44696803e-01, 6.78171906e-01,
+    5.69849716e-01, 5.86454477e-01, 3.54474989e-01, 9.03876540e-01, 6.45980000e-01, 6.34887593e-01,
+    7.88039746e-02, 2.04814126e-01, 7.82251754e-01, 2.43147074e-01, 7.50951808e-01, 1.72799092e-02,
+    2.95349590e-01, 6.57991826e-01, 8.81214312e-01, 5.73970708e-01, 2.77610881e-01, 1.82155097e-01,
+    7.69797417e-02, 6.44792402e-01, 9.46950998e-01, 7.73064845e-01, 6.04733624e-01, 5.80094567e-01,
+    1.67498426e-01, 2.66514296e-01, 6.50140368e-01, 1.91170299e-01, 2.08752199e-01, 3.01664091e-01,
+    9.85033484e-01, 2.92909152e-01, 8.65816607e-01, 1.85222119e-01, 2.28814559e-01, 1.34286382e-02,
+    2.89234322e-01, 8.18668708e-01, 4.71706924e-01, 9.23199803e-01, 2.80879188e-01, 1.47319284e-01,
+    4.13915748e-01, 9.31274932e-02, 6.66322195e-01, 9.66953974e-01, 3.19405786e-01, 6.69486551e-01,
+    5.03096313e-02, 6.95225201e-01, 5.78469859e-01, 6.29481655e-01, 1.39252534e-01, 1.22564968e-01,
+    6.80663678e-01, 6.34607157e-01, 6.42765834e-01, 1.57127410e-02, 2.92132086e-01, 5.24423878e-01,
+    4.68676824e-01, 2.86003928e-01, 7.18608322e-01, 8.95617933e-01, 5.48844309e-01, 1.74517278e-01,
+    5.24379196e-01, 2.13526524e-01, 5.88375435e-01, 9.88560185e-01, 4.17435771e-01, 6.14438688e-01,
+    9.53760881e-01, 5.27151288e-01, 7.03017278e-01, 3.44448559e-01, 4.47059676e-01, 2.83414901e-01,
+    1.98979011e-01, 4.24917361e-01, 5.73172761e-01, 2.32398853e-02, 1.65887230e-01, 4.05552785e-01,
+    9.29665524e-01, 2.26135696e-01, 9.20563384e-01, 7.65259963e-01, 4.54820075e-01, 8.97710267e-01,
+    3.78559302e-03, 9.15219382e-01, 3.55705698e-01, 6.94905124e-01, 8.58540202e-01, 3.89790666e-01,
+    2.49478206e-01, 7.93679304e-01, 4.75830027e-01, 4.40425353e-01, 3.70579459e-01, 1.40578049e-01,
+    1.70386675e-01, 7.04056121e-01, 4.85963102e-01, 9.68450060e-01, 6.77178001e-01, 2.65934654e-01,
+    2.58915007e-01, 6.70052890e-01, 2.61945109e-01, 8.46207759e-01, 1.01928951e-01, 2.85611334e-01,
+    2.45776933e-01, 2.66658783e-01, 3.71724077e-01, 4.34319025e-01, 4.24407347e-01, 7.15417683e-01,
+    8.07997684e-01, 1.64296275e-01, 6.01638065e-01, 8.60606804e-02, 2.68719187e-01, 5.11764101e-01,
+    9.75844338e-01, 7.81226782e-01, 2.20925515e-01, 7.18135040e-01, 9.82395577e-01, 8.39160243e-01,
+    9.08058083e-01, 6.88010677e-01, 8.14271847e-01, 5.12460821e-01, 1.17311345e-01, 5.96075228e-01,
+    9.17455497e-01, 2.12052706e-01, 7.04074603e-01, 8.72872565e-02, 8.76047818e-01, 6.96235046e-01,
+    8.54801557e-01, 2.49729159e-01, 9.76594604e-01, 2.87386363e-01, 2.36461559e-02, 9.94075254e-01,
+    4.25193986e-01, 7.61869994e-01, 5.13334255e-01, 6.44711165e-02, 8.92156689e-01, 3.55235167e-01,
+    1.08154647e-01, 8.78446825e-01, 2.43833016e-01, 9.23071293e-01, 2.72724115e-01, 9.46631338e-01,
+    3.74510294e-01, 4.08451278e-02, 9.78392777e-01, 3.65079221e-01, 6.37199516e-01, 5.51144906e-01,
+    5.25978080e-01, 1.42803678e-01, 4.05451674e-01, 7.79788219e-01, 6.26009784e-01, 3.35249497e-01,
+    1.43159543e-02, 1.80363779e-01, 5.05096904e-01, 2.82619947e-01, 5.83561392e-01, 3.10951324e-01,
+    8.73223968e-01, 4.38545619e-01, 4.81348800e-01, 6.68497085e-01, 3.79345401e-01, 9.58832501e-01,
+    1.89869550e-01, 2.34083070e-01, 2.94066207e-01, 5.74892667e-02, 6.92106828e-02, 9.61127686e-02,
+    6.72650672e-02, 8.47345378e-01, 2.80916761e-01, 7.32177357e-03, 9.80785961e-01, 5.73192225e-02,
+    8.48781331e-01, 8.83225408e-01, 7.34398275e-01, 7.70381941e-01, 6.20778343e-01, 8.96822048e-01,
+    5.40732486e-01, 3.69704071e-01, 5.77305837e-01, 2.08221827e-01, 7.34275341e-01, 1.06110900e-01,
+    3.49496706e-01, 8.34948910e-01, 1.56403291e-02, 6.78576376e-01, 8.96141268e-01, 5.94835119e-01,
+    1.43943153e-01, 3.49618530e-01, 2.10440392e-01, 3.46585620e-01, 1.05153093e-01, 3.45446174e-01,
+    2.72177079e-01, 7.07946300e-01, 4.33717726e-02, 3.31232203e-01, 3.91874320e-01, 4.76338141e-01,
+    6.22777789e-01, 2.95989228e-02, 4.32855769e-01, 7.61049310e-01, 3.63279149e-01, 9.47210350e-01,
+    6.43721247e-01, 6.58025802e-01, 1.05247633e-02, 5.29974442e-01, 7.30675767e-01, 4.30041079e-01,
+    6.62634841e-01, 8.25936616e-01, 9.91253704e-01, 6.79399281e-01, 5.44177006e-01, 7.52876048e-01,
+    3.32139049e-01, 7.98732398e-01, 7.38865223e-01, 9.16055132e-01, 6.11736493e-01, 9.63672879e-01,
+    1.83778839e-01, 7.27558919e-02, 5.91602822e-01, 3.25235484e-01, 2.34741217e-01, 9.52346277e-01,
+    9.18556407e-01, 9.35373324e-01, 6.89209070e-01, 2.56049054e-01, 6.17975395e-01, 7.82285691e-01,
+    9.84983432e-01, 6.62322741e-01, 2.04144457e-01, 3.98446577e-01, 1.38918297e-01, 3.05919921e-01,
+    3.14043787e-01, 5.91072666e-01, 7.44703771e-01, 8.92272567e-01, 9.78017873e-01, 9.01203161e-01,
+    1.41526372e-01, 4.14878484e-01, 6.80683651e-01, 5.01733152e-02, 8.14635389e-01, 2.27926375e-01,
+    9.03269815e-01, 8.68443745e-01, 9.86939190e-01, 7.40779486e-01, 2.61005311e-01, 3.19276232e-01,
+    9.69509248e-01, 1.11908818e-01, 4.49198556e-01, 1.27056715e-01, 3.84064823e-01, 5.14591811e-01,
+    2.10747488e-01, 9.53884090e-01, 8.43167950e-01, 4.51187972e-01, 3.75331782e-01, 6.23566461e-01,
+    3.55290379e-01, 2.95705968e-01, 1.69622690e-01, 1.42981830e-01, 2.72180991e-01, 9.46468040e-01,
+    3.70932500e-01, 9.94292830e-01, 4.62587505e-01, 7.14817405e-01, 2.45370540e-02, 3.00906377e-01,
+    5.75768304e-01, 9.71448393e-01, 6.95574827e-02, 3.93693854e-01, 5.29306116e-01, 5.04694554e-01,
+    6.73797120e-02, 6.76596969e-01, 5.50948898e-01, 3.24909641e-01, 7.70337719e-01, 6.51842631e-03,
+    3.03264879e-01, 7.61037886e-03, 2.72289601e-01, 1.50502041e-01, 6.71103888e-02, 7.41503703e-01,
+    1.92088941e-01, 2.19043977e-01, 9.09320161e-01, 2.37993569e-01, 6.18107973e-02, 8.31447852e-01,
+    2.23355609e-01, 1.84789435e-01, 4.16104518e-01, 4.21573859e-01, 8.72446305e-02, 2.97294197e-01,
+    4.50328256e-01, 8.72199917e-01, 2.51279916e-01, 4.86219272e-01, 7.57071329e-01, 4.85655942e-01,
+    1.06187277e-01, 4.92341327e-01, 1.46017513e-01, 5.25421017e-01, 4.22637906e-01, 2.24685018e-01,
+    8.72648431e-01, 5.54051490e-01, 1.80745062e-01, 2.12756336e-01, 5.20883169e-01, 7.60363654e-01,
+    8.30254678e-01, 5.00003328e-01, 4.69017439e-01, 6.38105527e-01, 3.50638261e-02, 5.22217353e-02,
+    9.06516882e-02, 8.52975842e-01, 1.19985883e-01, 3.74926753e-01, 6.50302066e-01, 1.98875727e-01,
+    6.28362507e-02, 4.32693501e-01, 3.10500685e-01, 6.20732833e-01, 4.58503272e-01, 3.20790034e-01,
+    7.91284868e-01, 7.93054570e-01, 2.93406765e-01, 8.95399023e-01, 1.06441034e-01, 7.53085241e-02,
+    8.67523104e-01, 1.47963482e-01, 1.25584706e-01, 3.81545040e-02, 6.34338619e-01, 1.76368938e-02,
+    5.75553531e-02, 5.31607516e-01, 2.63869588e-01, 9.41945823e-01, 9.24028838e-02, 5.21496463e-01,
+    7.74866558e-01, 5.65210610e-01, 7.28015327e-02, 6.51963790e-01, 8.94727453e-01, 4.49571590e-01,
+    1.29932405e-01, 8.64026259e-01, 9.92599934e-01, 7.43721560e-01, 8.87300215e-01, 1.06369925e-01,
+    8.11335531e-01, 7.87734900e-01, 9.87344678e-01, 5.32502820e-01, 4.42612382e-01, 9.64041183e-01,
+    1.66085871e-01, 1.12937664e-01, 5.24423470e-01, 6.54689333e-01, 4.59119726e-01, 5.22774091e-01,
+    3.08722276e-02, 6.26979315e-01, 4.49754105e-01, 8.07495757e-01, 2.34199499e-01, 1.67765675e-01,
+    9.22168418e-01, 3.73210378e-01, 8.04432575e-01, 5.61890354e-01, 4.47025593e-01, 6.43155678e-01,
+    2.40407640e-01, 5.91631279e-01, 1.59369206e-01, 7.75799090e-01, 8.32067212e-01, 5.59791576e-02,
+    6.39105224e-01, 4.85274738e-01, 2.12630838e-01, 2.81431312e-02, 7.16205363e-01, 6.83885011e-01,
+    5.23869697e-01, 9.99418314e-01, 8.35331599e-01, 4.69877463e-02, 6.74712562e-01, 7.99273684e-01,
+    2.77001890e-02, 5.75809742e-01, 2.78513031e-01, 8.36209905e-01, 7.25472379e-01, 4.87173943e-01,
+    7.88311357e-01, 9.64676177e-01, 1.75752651e-01, 4.98112580e-01, 8.08850418e-02, 6.40981131e-01,
+    4.06647450e-01, 8.46539387e-01, 2.12620694e-01, 9.11012851e-01, 8.25041445e-01, 8.90065575e-01,
+    9.63626055e-01, 5.96689242e-01, 1.63372670e-01, 4.51640148e-01, 3.43026542e-01, 5.80658851e-01,
+    2.82327625e-01, 4.75535418e-01, 6.27760926e-01, 8.46314115e-01, 9.61961932e-01, 3.19806094e-01,
+    5.05508062e-01, 5.28102944e-01, 6.13045057e-01, 7.44714938e-01, 1.50586073e-01, 7.91878033e-01,
+    4.89839179e-01, 3.10496849e-01, 8.82309038e-01, 2.86922314e-01, 4.84687559e-01, 5.20838630e-01,
+    4.62955493e-01, 2.38185305e-01, 5.47259907e-02, 7.10916137e-01, 7.31887202e-01, 6.25602317e-01,
+    8.77741168e-01, 4.19881322e-01, 4.81222328e-01, 1.28224501e-01, 2.46034010e-01, 3.34971854e-01,
+    7.37216484e-01, 5.62134821e-02, 7.14089724e-01, 9.85549393e-01, 4.66295827e-01, 3.08722434e-03,
+    4.70237690e-01, 2.66524167e-01, 7.93875484e-01, 4.54795911e-02, 8.09702944e-01, 1.47709735e-02,
+    1.70082405e-01, 6.35905179e-01, 3.75379109e-01, 4.30315011e-01, 3.15788760e-01, 5.58065230e-01,
+    2.24643800e-01, 2.42142981e-01, 6.57283636e-01, 3.34921891e-01, 1.26588975e-01, 7.68064155e-01,
+    9.43856291e-01, 4.47518596e-01, 5.44453573e-01, 9.95764932e-01, 7.16444391e-01, 8.51019765e-01,
+    1.01179183e-01, 4.45473958e-01, 4.60327322e-01, 4.96895844e-02, 4.72907738e-01, 5.58987444e-01,
+    3.41027487e-01, 1.56175026e-01, 7.58283148e-01, 6.83600909e-01, 2.14623396e-01, 3.27348880e-01,
+    3.92517893e-01, 6.70418431e-01, 5.16440832e-01, 8.63140348e-01, 5.73277464e-01, 3.46608058e-01,
+    7.39396341e-01, 7.20852434e-01, 2.35653246e-02, 3.89935659e-01, 7.53783745e-01, 6.34563528e-01,
+    8.79339335e-01, 7.41599159e-02, 5.62433904e-01, 6.15553852e-01, 4.56956324e-01, 5.20047447e-01,
+    5.26845015e-02, 5.58471266e-01, 1.63632233e-01, 5.38936665e-02, 6.49593683e-01, 2.56838748e-01,
+    8.99035326e-01, 7.20847756e-01, 5.68954684e-01, 7.43684755e-01, 5.70924238e-01, 3.82318724e-01,
+    4.89328290e-01, 5.62208561e-01, 4.97540804e-02, 4.18011085e-01, 6.88041565e-01, 2.16234653e-01,
+    7.89548214e-01, 8.46136387e-01, 8.46816189e-01, 1.73842353e-01, 6.11627842e-02, 8.44440559e-01,
+    4.50646654e-01, 3.74785037e-01, 4.87196697e-01, 4.56276448e-01, 9.13284391e-01, 4.15715464e-01,
+    7.13597697e-01, 1.23641270e-02, 5.10031271e-01, 4.74601930e-02, 2.55731159e-01, 3.22090006e-01,
+    1.91165703e-01, 4.51170940e-01, 7.50843157e-01, 4.42420576e-01, 4.25380660e-01, 4.50667257e-01,
+    6.55689206e-01, 9.68257670e-02, 1.96528793e-01, 8.97343028e-01, 4.99940904e-01, 6.65504083e-01,
+    9.41828079e-01, 4.54397338e-01, 5.61893331e-01, 5.09839880e-01, 4.53117514e-01, 8.96804127e-02,
+    1.74888861e-01, 6.65641378e-01, 2.81668336e-01, 1.89532742e-01, 5.61668382e-01, 8.68330157e-02,
+    8.25092797e-01, 5.18106324e-01, 1.71904024e-01, 3.68385523e-01, 1.62005436e-01, 7.48507399e-01,
+    9.30274827e-01, 2.38198517e-01, 9.52222901e-01, 5.23587800e-01, 6.94384557e-01, 1.09338652e-01,
+    4.83356794e-01, 2.73050402e-01, 3.68027050e-01, 5.92366466e-01, 1.83192289e-01, 8.60376029e-01,
+    7.13926203e-01, 8.16750052e-01, 1.57890291e-01, 6.25691951e-01, 5.24831646e-01, 1.73873797e-01,
+    1.02429784e-01, 9.17488471e-01, 4.03584434e-01, 9.31170884e-01, 2.79386137e-01, 8.77745206e-01,
+    2.45200576e-01, 1.28896951e-01, 3.15713052e-01, 5.27874291e-01, 2.16444335e-01, 7.03883817e-01,
+    7.74738919e-02, 8.42422142e-01, 3.75598924e-01, 3.51002411e-01, 6.22752776e-01, 4.82407943e-01,
+    7.43107867e-01, 9.46182666e-01, 9.44344819e-01, 3.28124763e-01, 1.06147431e-01, 1.65102684e-01,
+    3.84060507e-01, 2.91057722e-01, 7.68173662e-02, 1.03543651e-01, 6.76698940e-01, 1.43141994e-01,
+    7.21342202e-01, 6.69471294e-03, 9.07298311e-01, 5.57080171e-01, 8.10954489e-01, 4.11120526e-01,
+    2.06407453e-01, 2.59590556e-01, 7.58512718e-01, 5.79873897e-01, 2.92875650e-01, 2.83686529e-01,
+    2.42829343e-01, 9.19323719e-01, 3.46832864e-01, 3.58238858e-01, 7.42827585e-01, 2.05760059e-01,
+    9.58438860e-01, 5.66326411e-01, 6.60292846e-01, 5.61095078e-02, 6.79465531e-01, 7.05118513e-01,
+    4.44713264e-01, 2.09732933e-01, 5.22732436e-01, 1.74396512e-01, 5.29356748e-01, 4.38475687e-01,
+    4.94036404e-01, 4.09785794e-01, 6.40025507e-01, 5.79371821e-01, 1.57726118e-01, 6.04572263e-01,
+    5.41072639e-01, 5.18847173e-01, 1.97093284e-01, 8.91767002e-01, 4.29050835e-01, 8.25490570e-01,
+    3.87699807e-01, 4.50705808e-01, 2.49371643e-01, 3.36074898e-01, 9.29925118e-01, 6.65393649e-01,
+    9.07275994e-01, 3.73075859e-01, 4.14044139e-03, 2.37463702e-01, 2.25893784e-01, 2.46900245e-01,
+    4.50350196e-01, 3.48618117e-01, 5.07193932e-01, 5.23435142e-01, 8.13611417e-01, 8.92715622e-01,
+    1.02623450e-01, 3.06088345e-01, 7.80461650e-01, 2.21453645e-01, 2.01419652e-01, 2.84254457e-01,
+    3.68286735e-01, 7.39358243e-01, 8.97879394e-01, 9.81599566e-01, 7.56526442e-01, 7.37645545e-01,
+    4.23976657e-02, 8.25922012e-01, 2.60956996e-01, 2.90702065e-01, 8.98388344e-01, 3.03733299e-01,
+    8.49071471e-01, 3.45835425e-01, 7.65458276e-01, 5.68094872e-01, 8.93770930e-01, 9.93161641e-01,
+    5.63368667e-02, 4.26548945e-01, 5.46745780e-01, 5.75674571e-01, 7.94599487e-01, 7.18935553e-02,
+    4.46492976e-01, 6.40240123e-01, 2.73246969e-01, 2.00465968e-01, 1.30718835e-01, 1.92492005e-01,
+    1.96617189e-01, 6.61271644e-01, 8.12687657e-01, 8.66342445e-01
+
+   },
+   {0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+   10,
+   true,
+   -4},
+  {10,
+   5,
+   {0.21390334, 0.50261639, 0.91036676, 0.59166485, 0.71162682, 0.10248392, 0.77782677, 0.43772379,
+    0.4035871,  0.3282796,  0.47544681, 0.59862974, 0.12319357, 0.06239463, 0.28200272, 0.1345717,
+    0.50498218, 0.5113505,  0.16233086, 0.62165332, 0.42281548, 0.933117,   0.41386077, 0.23264562,
+    0.73325968, 0.37537541, 0.70719873, 0.14522645, 0.73279625, 0.9126674,  0.84854131, 0.28890216,
+    0.85267903, 0.74703138, 0.83842071, 0.34942792, 0.27864171, 0.70911132, 0.21338564, 0.32035554,
+    0.73788331, 0.46926692, 0.57570162, 0.42559178, 0.87120209, 0.22734951, 0.01847905, 0.75549396,
+    0.76166195, 0.66613745},
+   {9, 8, 7, 6, 5, 4, 3, 2, 1, 0},
+   10,
+   false,
+   5},
+  // Test outlier points
+  {9,
+   2,
+   {-1, -50, 3, 4, 5000, 10000, 1, 3, 4, 5, 0.000005, 0.00002, 2000000, 500000, 10, 50, 30, 5},
+   {6, 0, 5, 0, 0, 4, 3, 2, 1},
+   7,
+   false,
+   5},
+
+  // Test n_clusters == (n_points / 2)
+  {10,
+   5,
+   {0.21390334, 0.50261639, 0.91036676, 0.59166485, 0.71162682, 0.10248392, 0.77782677, 0.43772379,
+    0.4035871,  0.3282796,  0.47544681, 0.59862974, 0.12319357, 0.06239463, 0.28200272, 0.1345717,
+    0.50498218, 0.5113505,  0.16233086, 0.62165332, 0.42281548, 0.933117,   0.41386077, 0.23264562,
+    0.73325968, 0.37537541, 0.70719873, 0.14522645, 0.73279625, 0.9126674,  0.84854131, 0.28890216,
+    0.85267903, 0.74703138, 0.83842071, 0.34942792, 0.27864171, 0.70911132, 0.21338564, 0.32035554,
+    0.73788331, 0.46926692, 0.57570162, 0.42559178, 0.87120209, 0.22734951, 0.01847905, 0.75549396,
+    0.76166195, 0.66613745},
+   {1, 0, 4, 0, 0, 3, 2, 0, 2, 1},
+   5,
+   false,
+   10},
+
+  // Test n_points == 100
+  {100,
+   10,
+   {6.26168372e-01, 9.30437651e-01, 6.02450208e-01, 2.73025296e-01, 9.53050619e-01, 3.32164396e-01,
+    6.88942598e-01, 5.79163537e-01, 6.70341547e-01, 2.70140602e-02, 9.30429671e-01, 7.17721157e-01,
+    9.89948537e-01, 7.75253347e-01, 1.34491522e-02, 2.48522428e-02, 3.51413378e-01, 7.64405834e-01,
+    7.86373507e-01, 7.18748577e-01, 8.66998621e-01, 6.80316582e-01, 2.51288712e-01, 4.91078420e-01,
+    3.76246281e-01, 4.86828710e-01, 5.67464772e-01, 5.30734742e-01, 8.99478296e-01, 7.66699088e-01,
+    9.49339111e-01, 3.55248484e-01, 9.06046929e-01, 4.48407772e-01, 6.96395305e-01, 2.44277335e-01,
+    7.74840000e-01, 5.21046603e-01, 4.66423971e-02, 5.12019638e-02, 8.95019614e-01, 5.28956953e-01,
+    4.31536306e-01, 5.83857744e-01, 4.41787364e-01, 4.68656523e-01, 5.73971433e-01, 6.79989654e-01,
+    3.19650588e-01, 6.12579596e-01, 6.49126442e-02, 8.39131142e-01, 2.85252117e-01, 5.84848929e-01,
+    9.46507115e-01, 8.58440748e-01, 3.61528940e-01, 2.44215959e-01, 3.80101125e-01, 4.57128957e-02,
+    8.82216988e-01, 8.31498633e-01, 7.23474381e-01, 7.75788607e-01, 1.40864146e-01, 6.62092382e-01,
+    5.13985168e-01, 3.00686418e-01, 8.70109949e-01, 2.43187753e-01, 2.89391938e-01, 2.84214238e-01,
+    8.70985521e-01, 8.77491176e-01, 6.72537226e-01, 3.30929686e-01, 1.85934324e-01, 9.16222614e-01,
+    6.18239142e-01, 2.64768597e-01, 5.76145451e-01, 8.62961369e-01, 6.84757925e-01, 7.60549082e-01,
+    1.27645356e-01, 4.51004673e-01, 3.92292980e-01, 4.63170803e-01, 4.35449330e-02, 2.17583404e-01,
+    5.71832605e-02, 2.06763039e-01, 3.70116249e-01, 2.09750028e-01, 6.17283019e-01, 8.62549231e-01,
+    9.84156240e-02, 2.66249156e-01, 3.87635103e-01, 2.85591012e-02, 4.24826068e-01, 4.45795088e-01,
+    6.86227676e-01, 1.08848960e-01, 5.96731841e-02, 3.71770228e-01, 1.91548833e-01, 6.95136078e-01,
+    9.00700636e-01, 8.76363105e-01, 2.67334632e-01, 1.80619709e-01, 7.94060419e-01, 1.42854171e-02,
+    1.09372387e-01, 8.74028108e-01, 6.46403232e-01, 4.86588834e-01, 5.93446175e-02, 6.11886291e-01,
+    8.83865057e-01, 3.15879821e-01, 2.27043992e-01, 9.76764951e-01, 6.15620336e-01, 9.76199360e-01,
+    2.40548962e-01, 3.21795663e-01, 8.75087904e-02, 8.11234663e-01, 6.96070480e-01, 8.12062321e-01,
+    1.21958818e-01, 3.44348628e-02, 8.72630414e-01, 3.06162776e-01, 1.76043529e-02, 9.45894971e-01,
+    5.33896401e-01, 6.21642973e-01, 4.93062535e-01, 4.48984262e-01, 2.24560379e-01, 4.24052195e-02,
+    4.43447610e-01, 8.95646149e-01, 6.05220676e-01, 1.81840491e-01, 9.70831206e-01, 2.12563586e-02,
+    6.92582693e-01, 7.55946922e-01, 7.95086143e-01, 6.05328941e-01, 3.99350764e-01, 4.32846636e-01,
+    9.81114529e-01, 4.98266428e-01, 6.37127930e-03, 1.59085889e-01, 6.34682067e-05, 5.59429440e-01,
+    7.38827633e-01, 8.93214770e-01, 2.16494306e-01, 9.35430573e-02, 4.75665868e-02, 7.80503518e-01,
+    7.86240041e-01, 7.06854594e-01, 2.13725879e-02, 7.68246091e-01, 4.50234808e-01, 5.21231104e-01,
+    5.01989826e-03, 4.22081572e-02, 1.65337732e-01, 8.54134740e-01, 4.99430262e-01, 8.94525601e-01,
+    1.14028379e-01, 3.69739861e-01, 1.32955599e-01, 2.65563824e-01, 2.52811151e-01, 1.44792843e-01,
+    6.88449594e-01, 4.44921417e-01, 8.23296587e-01, 1.93266317e-01, 1.19033309e-01, 1.36368966e-01,
+    3.42600285e-01, 5.64505195e-01, 5.57594559e-01, 7.44257892e-01, 8.38231569e-02, 4.11548847e-01,
+    3.21010077e-01, 8.55081359e-01, 4.30105779e-01, 1.16229135e-01, 9.87731964e-02, 3.14712335e-01,
+    4.50880592e-01, 2.72289598e-01, 6.31615256e-01, 8.97432958e-01, 4.44764250e-01, 8.03776440e-01,
+    2.68767748e-02, 2.43374608e-01, 4.02141103e-01, 4.98881209e-01, 5.33173003e-01, 8.82890436e-01,
+    7.16149148e-01, 4.19664401e-01, 2.29335357e-01, 2.88637806e-01, 3.44696803e-01, 6.78171906e-01,
+    5.69849716e-01, 5.86454477e-01, 3.54474989e-01, 9.03876540e-01, 6.45980000e-01, 6.34887593e-01,
+    7.88039746e-02, 2.04814126e-01, 7.82251754e-01, 2.43147074e-01, 7.50951808e-01, 1.72799092e-02,
+    2.95349590e-01, 6.57991826e-01, 8.81214312e-01, 5.73970708e-01, 2.77610881e-01, 1.82155097e-01,
+    7.69797417e-02, 6.44792402e-01, 9.46950998e-01, 7.73064845e-01, 6.04733624e-01, 5.80094567e-01,
+    1.67498426e-01, 2.66514296e-01, 6.50140368e-01, 1.91170299e-01, 2.08752199e-01, 3.01664091e-01,
+    9.85033484e-01, 2.92909152e-01, 8.65816607e-01, 1.85222119e-01, 2.28814559e-01, 1.34286382e-02,
+    2.89234322e-01, 8.18668708e-01, 4.71706924e-01, 9.23199803e-01, 2.80879188e-01, 1.47319284e-01,
+    4.13915748e-01, 9.31274932e-02, 6.66322195e-01, 9.66953974e-01, 3.19405786e-01, 6.69486551e-01,
+    5.03096313e-02, 6.95225201e-01, 5.78469859e-01, 6.29481655e-01, 1.39252534e-01, 1.22564968e-01,
+    6.80663678e-01, 6.34607157e-01, 6.42765834e-01, 1.57127410e-02, 2.92132086e-01, 5.24423878e-01,
+    4.68676824e-01, 2.86003928e-01, 7.18608322e-01, 8.95617933e-01, 5.48844309e-01, 1.74517278e-01,
+    5.24379196e-01, 2.13526524e-01, 5.88375435e-01, 9.88560185e-01, 4.17435771e-01, 6.14438688e-01,
+    9.53760881e-01, 5.27151288e-01, 7.03017278e-01, 3.44448559e-01, 4.47059676e-01, 2.83414901e-01,
+    1.98979011e-01, 4.24917361e-01, 5.73172761e-01, 2.32398853e-02, 1.65887230e-01, 4.05552785e-01,
+    9.29665524e-01, 2.26135696e-01, 9.20563384e-01, 7.65259963e-01, 4.54820075e-01, 8.97710267e-01,
+    3.78559302e-03, 9.15219382e-01, 3.55705698e-01, 6.94905124e-01, 8.58540202e-01, 3.89790666e-01,
+    2.49478206e-01, 7.93679304e-01, 4.75830027e-01, 4.40425353e-01, 3.70579459e-01, 1.40578049e-01,
+    1.70386675e-01, 7.04056121e-01, 4.85963102e-01, 9.68450060e-01, 6.77178001e-01, 2.65934654e-01,
+    2.58915007e-01, 6.70052890e-01, 2.61945109e-01, 8.46207759e-01, 1.01928951e-01, 2.85611334e-01,
+    2.45776933e-01, 2.66658783e-01, 3.71724077e-01, 4.34319025e-01, 4.24407347e-01, 7.15417683e-01,
+    8.07997684e-01, 1.64296275e-01, 6.01638065e-01, 8.60606804e-02, 2.68719187e-01, 5.11764101e-01,
+    9.75844338e-01, 7.81226782e-01, 2.20925515e-01, 7.18135040e-01, 9.82395577e-01, 8.39160243e-01,
+    9.08058083e-01, 6.88010677e-01, 8.14271847e-01, 5.12460821e-01, 1.17311345e-01, 5.96075228e-01,
+    9.17455497e-01, 2.12052706e-01, 7.04074603e-01, 8.72872565e-02, 8.76047818e-01, 6.96235046e-01,
+    8.54801557e-01, 2.49729159e-01, 9.76594604e-01, 2.87386363e-01, 2.36461559e-02, 9.94075254e-01,
+    4.25193986e-01, 7.61869994e-01, 5.13334255e-01, 6.44711165e-02, 8.92156689e-01, 3.55235167e-01,
+    1.08154647e-01, 8.78446825e-01, 2.43833016e-01, 9.23071293e-01, 2.72724115e-01, 9.46631338e-01,
+    3.74510294e-01, 4.08451278e-02, 9.78392777e-01, 3.65079221e-01, 6.37199516e-01, 5.51144906e-01,
+    5.25978080e-01, 1.42803678e-01, 4.05451674e-01, 7.79788219e-01, 6.26009784e-01, 3.35249497e-01,
+    1.43159543e-02, 1.80363779e-01, 5.05096904e-01, 2.82619947e-01, 5.83561392e-01, 3.10951324e-01,
+    8.73223968e-01, 4.38545619e-01, 4.81348800e-01, 6.68497085e-01, 3.79345401e-01, 9.58832501e-01,
+    1.89869550e-01, 2.34083070e-01, 2.94066207e-01, 5.74892667e-02, 6.92106828e-02, 9.61127686e-02,
+    6.72650672e-02, 8.47345378e-01, 2.80916761e-01, 7.32177357e-03, 9.80785961e-01, 5.73192225e-02,
+    8.48781331e-01, 8.83225408e-01, 7.34398275e-01, 7.70381941e-01, 6.20778343e-01, 8.96822048e-01,
+    5.40732486e-01, 3.69704071e-01, 5.77305837e-01, 2.08221827e-01, 7.34275341e-01, 1.06110900e-01,
+    3.49496706e-01, 8.34948910e-01, 1.56403291e-02, 6.78576376e-01, 8.96141268e-01, 5.94835119e-01,
+    1.43943153e-01, 3.49618530e-01, 2.10440392e-01, 3.46585620e-01, 1.05153093e-01, 3.45446174e-01,
+    2.72177079e-01, 7.07946300e-01, 4.33717726e-02, 3.31232203e-01, 3.91874320e-01, 4.76338141e-01,
+    6.22777789e-01, 2.95989228e-02, 4.32855769e-01, 7.61049310e-01, 3.63279149e-01, 9.47210350e-01,
+    6.43721247e-01, 6.58025802e-01, 1.05247633e-02, 5.29974442e-01, 7.30675767e-01, 4.30041079e-01,
+    6.62634841e-01, 8.25936616e-01, 9.91253704e-01, 6.79399281e-01, 5.44177006e-01, 7.52876048e-01,
+    3.32139049e-01, 7.98732398e-01, 7.38865223e-01, 9.16055132e-01, 6.11736493e-01, 9.63672879e-01,
+    1.83778839e-01, 7.27558919e-02, 5.91602822e-01, 3.25235484e-01, 2.34741217e-01, 9.52346277e-01,
+    9.18556407e-01, 9.35373324e-01, 6.89209070e-01, 2.56049054e-01, 6.17975395e-01, 7.82285691e-01,
+    9.84983432e-01, 6.62322741e-01, 2.04144457e-01, 3.98446577e-01, 1.38918297e-01, 3.05919921e-01,
+    3.14043787e-01, 5.91072666e-01, 7.44703771e-01, 8.92272567e-01, 9.78017873e-01, 9.01203161e-01,
+    1.41526372e-01, 4.14878484e-01, 6.80683651e-01, 5.01733152e-02, 8.14635389e-01, 2.27926375e-01,
+    9.03269815e-01, 8.68443745e-01, 9.86939190e-01, 7.40779486e-01, 2.61005311e-01, 3.19276232e-01,
+    9.69509248e-01, 1.11908818e-01, 4.49198556e-01, 1.27056715e-01, 3.84064823e-01, 5.14591811e-01,
+    2.10747488e-01, 9.53884090e-01, 8.43167950e-01, 4.51187972e-01, 3.75331782e-01, 6.23566461e-01,
+    3.55290379e-01, 2.95705968e-01, 1.69622690e-01, 1.42981830e-01, 2.72180991e-01, 9.46468040e-01,
+    3.70932500e-01, 9.94292830e-01, 4.62587505e-01, 7.14817405e-01, 2.45370540e-02, 3.00906377e-01,
+    5.75768304e-01, 9.71448393e-01, 6.95574827e-02, 3.93693854e-01, 5.29306116e-01, 5.04694554e-01,
+    6.73797120e-02, 6.76596969e-01, 5.50948898e-01, 3.24909641e-01, 7.70337719e-01, 6.51842631e-03,
+    3.03264879e-01, 7.61037886e-03, 2.72289601e-01, 1.50502041e-01, 6.71103888e-02, 7.41503703e-01,
+    1.92088941e-01, 2.19043977e-01, 9.09320161e-01, 2.37993569e-01, 6.18107973e-02, 8.31447852e-01,
+    2.23355609e-01, 1.84789435e-01, 4.16104518e-01, 4.21573859e-01, 8.72446305e-02, 2.97294197e-01,
+    4.50328256e-01, 8.72199917e-01, 2.51279916e-01, 4.86219272e-01, 7.57071329e-01, 4.85655942e-01,
+    1.06187277e-01, 4.92341327e-01, 1.46017513e-01, 5.25421017e-01, 4.22637906e-01, 2.24685018e-01,
+    8.72648431e-01, 5.54051490e-01, 1.80745062e-01, 2.12756336e-01, 5.20883169e-01, 7.60363654e-01,
+    8.30254678e-01, 5.00003328e-01, 4.69017439e-01, 6.38105527e-01, 3.50638261e-02, 5.22217353e-02,
+    9.06516882e-02, 8.52975842e-01, 1.19985883e-01, 3.74926753e-01, 6.50302066e-01, 1.98875727e-01,
+    6.28362507e-02, 4.32693501e-01, 3.10500685e-01, 6.20732833e-01, 4.58503272e-01, 3.20790034e-01,
+    7.91284868e-01, 7.93054570e-01, 2.93406765e-01, 8.95399023e-01, 1.06441034e-01, 7.53085241e-02,
+    8.67523104e-01, 1.47963482e-01, 1.25584706e-01, 3.81545040e-02, 6.34338619e-01, 1.76368938e-02,
+    5.75553531e-02, 5.31607516e-01, 2.63869588e-01, 9.41945823e-01, 9.24028838e-02, 5.21496463e-01,
+    7.74866558e-01, 5.65210610e-01, 7.28015327e-02, 6.51963790e-01, 8.94727453e-01, 4.49571590e-01,
+    1.29932405e-01, 8.64026259e-01, 9.92599934e-01, 7.43721560e-01, 8.87300215e-01, 1.06369925e-01,
+    8.11335531e-01, 7.87734900e-01, 9.87344678e-01, 5.32502820e-01, 4.42612382e-01, 9.64041183e-01,
+    1.66085871e-01, 1.12937664e-01, 5.24423470e-01, 6.54689333e-01, 4.59119726e-01, 5.22774091e-01,
+    3.08722276e-02, 6.26979315e-01, 4.49754105e-01, 8.07495757e-01, 2.34199499e-01, 1.67765675e-01,
+    9.22168418e-01, 3.73210378e-01, 8.04432575e-01, 5.61890354e-01, 4.47025593e-01, 6.43155678e-01,
+    2.40407640e-01, 5.91631279e-01, 1.59369206e-01, 7.75799090e-01, 8.32067212e-01, 5.59791576e-02,
+    6.39105224e-01, 4.85274738e-01, 2.12630838e-01, 2.81431312e-02, 7.16205363e-01, 6.83885011e-01,
+    5.23869697e-01, 9.99418314e-01, 8.35331599e-01, 4.69877463e-02, 6.74712562e-01, 7.99273684e-01,
+    2.77001890e-02, 5.75809742e-01, 2.78513031e-01, 8.36209905e-01, 7.25472379e-01, 4.87173943e-01,
+    7.88311357e-01, 9.64676177e-01, 1.75752651e-01, 4.98112580e-01, 8.08850418e-02, 6.40981131e-01,
+    4.06647450e-01, 8.46539387e-01, 2.12620694e-01, 9.11012851e-01, 8.25041445e-01, 8.90065575e-01,
+    9.63626055e-01, 5.96689242e-01, 1.63372670e-01, 4.51640148e-01, 3.43026542e-01, 5.80658851e-01,
+    2.82327625e-01, 4.75535418e-01, 6.27760926e-01, 8.46314115e-01, 9.61961932e-01, 3.19806094e-01,
+    5.05508062e-01, 5.28102944e-01, 6.13045057e-01, 7.44714938e-01, 1.50586073e-01, 7.91878033e-01,
+    4.89839179e-01, 3.10496849e-01, 8.82309038e-01, 2.86922314e-01, 4.84687559e-01, 5.20838630e-01,
+    4.62955493e-01, 2.38185305e-01, 5.47259907e-02, 7.10916137e-01, 7.31887202e-01, 6.25602317e-01,
+    8.77741168e-01, 4.19881322e-01, 4.81222328e-01, 1.28224501e-01, 2.46034010e-01, 3.34971854e-01,
+    7.37216484e-01, 5.62134821e-02, 7.14089724e-01, 9.85549393e-01, 4.66295827e-01, 3.08722434e-03,
+    4.70237690e-01, 2.66524167e-01, 7.93875484e-01, 4.54795911e-02, 8.09702944e-01, 1.47709735e-02,
+    1.70082405e-01, 6.35905179e-01, 3.75379109e-01, 4.30315011e-01, 3.15788760e-01, 5.58065230e-01,
+    2.24643800e-01, 2.42142981e-01, 6.57283636e-01, 3.34921891e-01, 1.26588975e-01, 7.68064155e-01,
+    9.43856291e-01, 4.47518596e-01, 5.44453573e-01, 9.95764932e-01, 7.16444391e-01, 8.51019765e-01,
+    1.01179183e-01, 4.45473958e-01, 4.60327322e-01, 4.96895844e-02, 4.72907738e-01, 5.58987444e-01,
+    3.41027487e-01, 1.56175026e-01, 7.58283148e-01, 6.83600909e-01, 2.14623396e-01, 3.27348880e-01,
+    3.92517893e-01, 6.70418431e-01, 5.16440832e-01, 8.63140348e-01, 5.73277464e-01, 3.46608058e-01,
+    7.39396341e-01, 7.20852434e-01, 2.35653246e-02, 3.89935659e-01, 7.53783745e-01, 6.34563528e-01,
+    8.79339335e-01, 7.41599159e-02, 5.62433904e-01, 6.15553852e-01, 4.56956324e-01, 5.20047447e-01,
+    5.26845015e-02, 5.58471266e-01, 1.63632233e-01, 5.38936665e-02, 6.49593683e-01, 2.56838748e-01,
+    8.99035326e-01, 7.20847756e-01, 5.68954684e-01, 7.43684755e-01, 5.70924238e-01, 3.82318724e-01,
+    4.89328290e-01, 5.62208561e-01, 4.97540804e-02, 4.18011085e-01, 6.88041565e-01, 2.16234653e-01,
+    7.89548214e-01, 8.46136387e-01, 8.46816189e-01, 1.73842353e-01, 6.11627842e-02, 8.44440559e-01,
+    4.50646654e-01, 3.74785037e-01, 4.87196697e-01, 4.56276448e-01, 9.13284391e-01, 4.15715464e-01,
+    7.13597697e-01, 1.23641270e-02, 5.10031271e-01, 4.74601930e-02, 2.55731159e-01, 3.22090006e-01,
+    1.91165703e-01, 4.51170940e-01, 7.50843157e-01, 4.42420576e-01, 4.25380660e-01, 4.50667257e-01,
+    6.55689206e-01, 9.68257670e-02, 1.96528793e-01, 8.97343028e-01, 4.99940904e-01, 6.65504083e-01,
+    9.41828079e-01, 4.54397338e-01, 5.61893331e-01, 5.09839880e-01, 4.53117514e-01, 8.96804127e-02,
+    1.74888861e-01, 6.65641378e-01, 2.81668336e-01, 1.89532742e-01, 5.61668382e-01, 8.68330157e-02,
+    8.25092797e-01, 5.18106324e-01, 1.71904024e-01, 3.68385523e-01, 1.62005436e-01, 7.48507399e-01,
+    9.30274827e-01, 2.38198517e-01, 9.52222901e-01, 5.23587800e-01, 6.94384557e-01, 1.09338652e-01,
+    4.83356794e-01, 2.73050402e-01, 3.68027050e-01, 5.92366466e-01, 1.83192289e-01, 8.60376029e-01,
+    7.13926203e-01, 8.16750052e-01, 1.57890291e-01, 6.25691951e-01, 5.24831646e-01, 1.73873797e-01,
+    1.02429784e-01, 9.17488471e-01, 4.03584434e-01, 9.31170884e-01, 2.79386137e-01, 8.77745206e-01,
+    2.45200576e-01, 1.28896951e-01, 3.15713052e-01, 5.27874291e-01, 2.16444335e-01, 7.03883817e-01,
+    7.74738919e-02, 8.42422142e-01, 3.75598924e-01, 3.51002411e-01, 6.22752776e-01, 4.82407943e-01,
+    7.43107867e-01, 9.46182666e-01, 9.44344819e-01, 3.28124763e-01, 1.06147431e-01, 1.65102684e-01,
+    3.84060507e-01, 2.91057722e-01, 7.68173662e-02, 1.03543651e-01, 6.76698940e-01, 1.43141994e-01,
+    7.21342202e-01, 6.69471294e-03, 9.07298311e-01, 5.57080171e-01, 8.10954489e-01, 4.11120526e-01,
+    2.06407453e-01, 2.59590556e-01, 7.58512718e-01, 5.79873897e-01, 2.92875650e-01, 2.83686529e-01,
+    2.42829343e-01, 9.19323719e-01, 3.46832864e-01, 3.58238858e-01, 7.42827585e-01, 2.05760059e-01,
+    9.58438860e-01, 5.66326411e-01, 6.60292846e-01, 5.61095078e-02, 6.79465531e-01, 7.05118513e-01,
+    4.44713264e-01, 2.09732933e-01, 5.22732436e-01, 1.74396512e-01, 5.29356748e-01, 4.38475687e-01,
+    4.94036404e-01, 4.09785794e-01, 6.40025507e-01, 5.79371821e-01, 1.57726118e-01, 6.04572263e-01,
+    5.41072639e-01, 5.18847173e-01, 1.97093284e-01, 8.91767002e-01, 4.29050835e-01, 8.25490570e-01,
+    3.87699807e-01, 4.50705808e-01, 2.49371643e-01, 3.36074898e-01, 9.29925118e-01, 6.65393649e-01,
+    9.07275994e-01, 3.73075859e-01, 4.14044139e-03, 2.37463702e-01, 2.25893784e-01, 2.46900245e-01,
+    4.50350196e-01, 3.48618117e-01, 5.07193932e-01, 5.23435142e-01, 8.13611417e-01, 8.92715622e-01,
+    1.02623450e-01, 3.06088345e-01, 7.80461650e-01, 2.21453645e-01, 2.01419652e-01, 2.84254457e-01,
+    3.68286735e-01, 7.39358243e-01, 8.97879394e-01, 9.81599566e-01, 7.56526442e-01, 7.37645545e-01,
+    4.23976657e-02, 8.25922012e-01, 2.60956996e-01, 2.90702065e-01, 8.98388344e-01, 3.03733299e-01,
+    8.49071471e-01, 3.45835425e-01, 7.65458276e-01, 5.68094872e-01, 8.93770930e-01, 9.93161641e-01,
+    5.63368667e-02, 4.26548945e-01, 5.46745780e-01, 5.75674571e-01, 7.94599487e-01, 7.18935553e-02,
+    4.46492976e-01, 6.40240123e-01, 2.73246969e-01, 2.00465968e-01, 1.30718835e-01, 1.92492005e-01,
+    1.96617189e-01, 6.61271644e-01, 8.12687657e-01, 8.66342445e-01
+
+   },
+   {0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+   10,
+   false,
+   5}};
+
+typedef LinkageTest<float, int> LinkageTestF_Int;
+TEST_P(LinkageTestF_Int, Result) { EXPECT_TRUE(score == 1.0); }
+
+INSTANTIATE_TEST_CASE_P(LinkageTest, LinkageTestF_Int, ::testing::ValuesIn(linkage_inputsf2));
+}  // namespace cuvs::cluster::agglomerative
diff --git a/cpp/test/distance/distance_base.cuh b/cpp/test/distance/distance_base.cuh
index 4a8191ae64..d77fcf407b 100644
--- a/cpp/test/distance/distance_base.cuh
+++ b/cpp/test/distance/distance_base.cuh
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#pragma once
+
 #include "../test_utils.cuh"
 
 #include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index dadd450b7d..9ce23cc7df 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -216,7 +216,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
           for (uint32_t l = 0; l < index_2.n_lists(); l++) {
             if (list_sizes[l] == 0) continue;
             rmm::device_uvector<float> cluster_data(list_sizes[l] * ps.dim, stream_);
-            raft::spatial::knn::detail::utils::copy_selected<float>((IdxT)list_sizes[l],
+            cuvs::spatial::knn::detail::utils::copy_selected<float>((IdxT)list_sizes[l],
                                                                     (IdxT)ps.dim,
                                                                     database.data(),
                                                                     list_indices[l],
@@ -594,4 +594,4 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   {20000, 8712, 3, 10, 51, 66, cuvs::distance::DistanceType::L2Expanded, false},
   {100000, 8712, 3, 10, 51, 66, cuvs::distance::DistanceType::L2Expanded, false}};
 
-}  // namespace cuvs::neighbors::ivf_flat
\ No newline at end of file
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index d5fc0d0b72..19442e0b52 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -113,7 +113,7 @@ void compare_vectors_l2(
   auto dist =
     raft::make_device_mdarray<double>(res, &managed_memory, raft::make_extents<uint32_t>(n_rows));
   raft::linalg::map_offset(res, dist.view(), [a, b, dim] __device__(uint32_t i) {
-    raft::spatial::knn::detail::utils::mapping<float> f{};
+    cuvs::spatial::knn::detail::utils::mapping<float> f{};
     double d = 0.0f;
     for (uint32_t j = 0; j < dim; j++) {
       double t = f(a(i, j)) - f(b(i, j));
diff --git a/cpp/test/neighbors/naive_knn.cuh b/cpp/test/neighbors/naive_knn.cuh
index 9657df892e..d9ea4ddac3 100644
--- a/cpp/test/neighbors/naive_knn.cuh
+++ b/cpp/test/neighbors/naive_knn.cuh
@@ -16,9 +16,9 @@
 
 #pragma once
 
-#include <raft/distance/distance_types.hpp>
+#include "../../src/neighbors/detail/ann_utils.cuh"
+#include <cuvs/distance/distance_types.hpp>
 #include <raft/matrix/detail/select_k.cuh>
-#include <raft/spatial/knn/detail/ann_utils.cuh>
 #include <raft/util/cuda_utils.cuh>
 
 #include <raft/core/resource/cuda_stream.hpp>

From 36ce0ff082e046dee709ee726a8807195727b6d0 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 16 May 2024 20:49:36 -0700
Subject: [PATCH 053/623] Migrate BFKNN from raft (#118)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/118
---
 cpp/CMakeLists.txt                            |    1 -
 cpp/include/cuvs/neighbors/brute_force.hpp    |   82 +-
 cpp/src/neighbors/brute_force.cu              |  115 +-
 cpp/src/neighbors/brute_force_index.cu        |   86 --
 .../neighbors/detail/faiss_distance_utils.h   |   52 +
 cpp/src/neighbors/detail/fused_l2_knn.cuh     | 1061 +++++++++++++++++
 .../neighbors/detail/haversine_distance.cuh   |  136 +++
 cpp/src/neighbors/detail/knn_brute_force.cuh  |  567 +++++++++
 cpp/src/neighbors/detail/knn_merge_parts.cuh  |  182 +++
 9 files changed, 2154 insertions(+), 128 deletions(-)
 delete mode 100644 cpp/src/neighbors/brute_force_index.cu
 create mode 100644 cpp/src/neighbors/detail/faiss_distance_utils.h
 create mode 100644 cpp/src/neighbors/detail/fused_l2_knn.cuh
 create mode 100644 cpp/src/neighbors/detail/haversine_distance.cuh
 create mode 100644 cpp/src/neighbors/detail/knn_brute_force.cuh
 create mode 100644 cpp/src/neighbors/detail/knn_merge_parts.cuh

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 991200637c..5715ee8a29 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -232,7 +232,6 @@ add_library(
   src/distance/detail/fused_distance_nn.cu
   src/distance/distance.cu
   src/distance/pairwise_distance.cu
-  src/neighbors/brute_force_index.cu
   src/neighbors/brute_force.cu
   src/neighbors/cagra_build_float.cu
   src/neighbors/cagra_build_int8.cu
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 26951e1ec3..755a941228 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -18,8 +18,10 @@
 
 #include "ann_types.hpp"
 #include <cuvs/neighbors/ann_types.hpp>
+#include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/handle.hpp>
+#include <raft/core/host_mdspan.hpp>
 
 namespace cuvs::neighbors::brute_force {
 
@@ -42,34 +44,90 @@ struct index : cuvs::neighbors::ann::index {
   index& operator=(const index&) = delete;
   index& operator=(index&&)      = default;
   ~index()                       = default;
-  index(void* raft_index);
+
+  /** Construct a brute force index from dataset
+   *
+   * Constructs a brute force index from a dataset. This lets us precompute norms for
+   * the dataset, providing a speed benefit over doing this at query time.
+   * This index will store a non-owning reference to the dataset.
+   */
+  index(raft::resources const& res,
+        raft::host_matrix_view<const T, int64_t, raft::row_major> dataset_view,
+        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        cuvs::distance::DistanceType metric,
+        T metric_arg = 0.0);
+
+  /** Construct a brute force index from dataset
+   *
+   * Constructs a brute force index from a dataset. This lets us precompute norms for
+   * the dataset, providing a speed benefit over doing this at query time.
+   * The dataset will be copied to the device and the index will own the device memory.
+   */
+  index(raft::resources const& res,
+        raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
+        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        cuvs::distance::DistanceType metric,
+        T metric_arg = 0.0);
+
+  /** Construct a brute force index from dataset
+   *
+   * This class stores a non-owning reference to the dataset and norms here.
+   * Having precomputed norms gives us a performance advantage at query time.
+   */
+  index(raft::resources const& res,
+        raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
+        std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
+        cuvs::distance::DistanceType metric,
+        T metric_arg = 0.0);
+
+  /**
+   * Replace the dataset with a new dataset.
+   */
+  void update_dataset(raft::resources const& res,
+                      raft::device_matrix_view<const T, int64_t, raft::row_major> dataset);
+
+  /**
+   * Replace the dataset with a new dataset.
+   *
+   * We create a copy of the dataset on the device. The index manages the lifetime of this copy.
+   */
+  void update_dataset(raft::resources const& res,
+                      raft::host_matrix_view<const T, int64_t, raft::row_major> dataset);
 
   /** Distance metric used for retrieval */
-  cuvs::distance::DistanceType metric() const noexcept;
+  cuvs::distance::DistanceType metric() const noexcept { return metric_; }
 
   /** Metric argument */
-  T metric_arg() const noexcept;
+  T metric_arg() const noexcept { return metric_arg_; }
 
   /** Total length of the index (number of vectors). */
-  size_t size() const noexcept;
+  size_t size() const noexcept { return dataset_view_.extent(0); }
 
   /** Dimensionality of the data. */
-  size_t dim() const noexcept;
+  size_t dim() const noexcept { return dataset_view_.extent(1); }
 
   /** Dataset [size, dim] */
-  raft::device_matrix_view<const T, int64_t, raft::row_major> dataset() const noexcept;
+  raft::device_matrix_view<const T, int64_t, raft::row_major> dataset() const noexcept
+  {
+    return dataset_view_;
+  }
 
   /** Dataset norms */
-  raft::device_vector_view<const T, int64_t, raft::row_major> norms() const;
+  raft::device_vector_view<const T, int64_t, raft::row_major> norms() const
+  {
+    return norms_view_.value();
+  }
 
   /** Whether ot not this index has dataset norms */
-  bool has_norms() const noexcept;
-
-  // Get pointer to underlying RAFT index, not meant to be used outside of cuVS
-  inline const void* get_raft_index() const noexcept { return raft_index_.get(); }
+  inline bool has_norms() const noexcept { return norms_view_.has_value(); }
 
  private:
-  std::unique_ptr<void*> raft_index_;
+  cuvs::distance::DistanceType metric_;
+  raft::device_matrix<T, int64_t, raft::row_major> dataset_;
+  std::optional<raft::device_vector<T, int64_t>> norms_;
+  std::optional<raft::device_vector_view<const T, int64_t>> norms_view_;
+  raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view_;
+  T metric_arg_;
 };
 /**
  * @}
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index 33dc2088ca..b9b74a3f25 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -14,41 +14,98 @@
  * limitations under the License.
  */
 
+#include "./detail/knn_brute_force.cuh"
 #include <cuvs/neighbors/brute_force.hpp>
-#include <raft/neighbors/brute_force-inl.cuh>
+
+#include <raft/core/copy.hpp>
 
 namespace cuvs::neighbors::brute_force {
+template <typename T>
+index<T>::index(raft::resources const& res,
+                raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
+                std::optional<raft::device_vector<T, int64_t>>&& norms,
+                cuvs::distance::DistanceType metric,
+                T metric_arg)
+  : ann::index(),
+    metric_(metric),
+    dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
+    norms_(std::move(norms)),
+    metric_arg_(metric_arg)
+{
+  if (norms_) { norms_view_ = raft::make_const_mdspan(norms_.value().view()); }
+  update_dataset(res, dataset);
+  raft::resource::sync_stream(res);
+}
+
+template <typename T>
+index<T>::index(raft::resources const& res,
+                raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+                std::optional<raft::device_vector<T, int64_t>>&& norms,
+                cuvs::distance::DistanceType metric,
+                T metric_arg)
+  : ann::index(),
+    metric_(metric),
+    dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
+    norms_(std::move(norms)),
+    metric_arg_(metric_arg)
+{
+  if (norms_) { norms_view_ = raft::make_const_mdspan(norms_.value().view()); }
+  update_dataset(res, dataset);
+}
+
+template <typename T>
+index<T>::index(raft::resources const& res,
+                raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
+                std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
+                cuvs::distance::DistanceType metric,
+                T metric_arg)
+  : ann::index(),
+    metric_(metric),
+    dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
+    dataset_view_(dataset_view),
+    norms_view_(norms_view),
+    metric_arg_(metric_arg)
+{
+}
+
+template <typename T>
+void index<T>::update_dataset(raft::resources const& res,
+                              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
+{
+  dataset_view_ = dataset;
+}
+
+template <typename T>
+void index<T>::update_dataset(raft::resources const& res,
+                              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)
+{
+  dataset_ = raft::make_device_matrix<T, int64_t>(res, dataset.extent(0), dataset.extent(1));
+  raft::copy(res, dataset_.view(), dataset);
+  dataset_view_ = raft::make_const_mdspan(dataset_.view());
+}
 
-#define CUVS_INST_BFKNN(T, IdxT)                                                             \
-  auto build(raft::resources const& res,                                                     \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \
-             cuvs::distance::DistanceType metric,                                            \
-             T metric_arg)                                                                   \
-    ->cuvs::neighbors::brute_force::index<T>                                                 \
-  {                                                                                          \
-    auto index_on_stack = raft::neighbors::brute_force::build(                               \
-      res, dataset, static_cast<raft::distance::DistanceType>(metric), metric_arg);          \
-    auto index_on_heap =                                                                     \
-      new raft::neighbors::brute_force::index<float>(std::move(index_on_stack));             \
-    return cuvs::neighbors::brute_force::index<float>(index_on_heap);                        \
-  }                                                                                          \
-                                                                                             \
-  void search(raft::resources const& res,                                                    \
-              const cuvs::neighbors::brute_force::index<T>& idx,                             \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,              \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,               \
-              raft::device_matrix_view<T, IdxT, raft::row_major> distances)                  \
-  {                                                                                          \
-    auto raft_idx =                                                                          \
-      reinterpret_cast<const raft::neighbors::brute_force::index<T>*>(idx.get_raft_index()); \
-    raft::neighbors::brute_force::search(res, *raft_idx, queries, neighbors, distances);     \
-  }                                                                                          \
-                                                                                             \
+#define CUVS_INST_BFKNN(T)                                                           \
+  auto build(raft::resources const& res,                                             \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,    \
+             cuvs::distance::DistanceType metric,                                    \
+             T metric_arg)                                                           \
+    ->cuvs::neighbors::brute_force::index<T>                                         \
+  {                                                                                  \
+    return detail::build<T>(res, dataset, metric, metric_arg);                       \
+  }                                                                                  \
+                                                                                     \
+  void search(raft::resources const& res,                                            \
+              const cuvs::neighbors::brute_force::index<T>& idx,                     \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,   \
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors, \
+              raft::device_matrix_view<T, int64_t, raft::row_major> distances)       \
+  {                                                                                  \
+    detail::brute_force_search<T, int64_t>(res, idx, queries, neighbors, distances); \
+  }                                                                                  \
+                                                                                     \
   template struct cuvs::neighbors::brute_force::index<T>;
 
-CUVS_INST_BFKNN(float, int64_t);
-// CUVS_INST_BFKNN(int8_t, int64_t);
-// CUVS_INST_BFKNN(uint8_t, int64_t);
+CUVS_INST_BFKNN(float);
 
 #undef CUVS_INST_BFKNN
 
diff --git a/cpp/src/neighbors/brute_force_index.cu b/cpp/src/neighbors/brute_force_index.cu
deleted file mode 100644
index b05fa7ced9..0000000000
--- a/cpp/src/neighbors/brute_force_index.cu
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cuvs/neighbors/brute_force.hpp>
-#include <raft/neighbors/brute_force-inl.cuh>
-
-namespace cuvs::neighbors::brute_force {
-
-template <typename T>
-inline const raft::neighbors::brute_force::index<T>* get_underlying_index(
-  const cuvs::neighbors::brute_force::index<T>* idx)
-{
-  return reinterpret_cast<const raft::neighbors::brute_force::index<T>*>(idx->get_raft_index());
-}
-
-template <typename T>
-index<T>::index(void* raft_index)
-  : cuvs::neighbors::ann::index(), raft_index_(reinterpret_cast<void**>(raft_index))
-{
-}
-
-template <typename T>
-cuvs::distance::DistanceType index<T>::metric() const noexcept
-{
-  auto raft_index = cuvs::neighbors::brute_force::get_underlying_index(this);
-  return static_cast<cuvs::distance::DistanceType>((int)raft_index->metric());
-}
-
-template <typename T>
-size_t index<T>::size() const noexcept
-{
-  auto raft_index = get_underlying_index(this);
-  return raft_index->size();
-}
-
-template <typename T>
-size_t index<T>::dim() const noexcept
-{
-  auto raft_index = get_underlying_index(this);
-  return raft_index->dim();
-}
-
-template <typename T>
-raft::device_matrix_view<const T, int64_t, raft::row_major> index<T>::dataset() const noexcept
-{
-  auto raft_index = get_underlying_index(this);
-  return raft_index->dataset();
-}
-
-template <typename T>
-raft::device_vector_view<const T, int64_t, raft::row_major> index<T>::norms() const
-{
-  auto raft_index = get_underlying_index(this);
-  return raft_index->norms();
-}
-
-template <typename T>
-bool index<T>::has_norms() const noexcept
-{
-  auto raft_index = get_underlying_index(this);
-  return raft_index->has_norms();
-}
-
-template <typename T>
-T index<T>::metric_arg() const noexcept
-{
-  auto raft_index = get_underlying_index(this);
-  return raft_index->metric_arg();
-}
-
-template struct index<float>;
-
-}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/src/neighbors/detail/faiss_distance_utils.h b/cpp/src/neighbors/detail/faiss_distance_utils.h
new file mode 100644
index 0000000000..e8a41c1aa6
--- /dev/null
+++ b/cpp/src/neighbors/detail/faiss_distance_utils.h
@@ -0,0 +1,52 @@
+/**
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file thirdparty/LICENSES/LICENSE.faiss
+ */
+
+#pragma once
+
+namespace cuvs::neighbors::detail::faiss_select {
+// If the inner size (dim) of the vectors is small, we want a larger query tile
+// size, like 1024
+inline void chooseTileSize(size_t numQueries,
+                           size_t numCentroids,
+                           size_t dim,
+                           size_t elementSize,
+                           size_t totalMem,
+                           size_t& tileRows,
+                           size_t& tileCols)
+{
+  // The matrix multiplication should be large enough to be efficient, but if
+  // it is too large, we seem to lose efficiency as opposed to
+  // double-streaming. Each tile size here defines 1/2 of the memory use due
+  // to double streaming. We ignore available temporary memory, as that is
+  // adjusted independently by the user and can thus meet these requirements
+  // (or not). For <= 4 GB GPUs, prefer 512 MB of usage. For <= 8 GB GPUs,
+  // prefer 768 MB of usage. Otherwise, prefer 1 GB of usage.
+  size_t targetUsage = 0;
+
+  if (totalMem <= ((size_t)4) * 1024 * 1024 * 1024) {
+    targetUsage = 512 * 1024 * 1024;
+  } else if (totalMem <= ((size_t)8) * 1024 * 1024 * 1024) {
+    targetUsage = 768 * 1024 * 1024;
+  } else {
+    targetUsage = 1024 * 1024 * 1024;
+  }
+
+  targetUsage /= 2 * elementSize;
+
+  // 512 seems to be a batch size sweetspot for float32.
+  // If we are on float16, increase to 512.
+  // If the k size (vec dim) of the matrix multiplication is small (<= 32),
+  // increase to 1024.
+  size_t preferredTileRows = 512;
+  if (dim <= 32) { preferredTileRows = 1024; }
+
+  tileRows = std::min(preferredTileRows, numQueries);
+
+  // tileCols is the remainder size
+  tileCols = std::min(targetUsage / preferredTileRows, numCentroids);
+}
+}  // namespace cuvs::neighbors::detail::faiss_select
diff --git a/cpp/src/neighbors/detail/fused_l2_knn.cuh b/cpp/src/neighbors/detail/fused_l2_knn.cuh
new file mode 100644
index 0000000000..13ea4d4189
--- /dev/null
+++ b/cpp/src/neighbors/detail/fused_l2_knn.cuh
@@ -0,0 +1,1061 @@
+/*
+ * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+#include <raft/core/operators.hpp>
+#include <raft/linalg/norm.cuh>
+#include <raft/neighbors/detail/faiss_select/Select.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <cub/cub.cuh>
+
+#include <limits>
+
+#include "../../distance/detail/distance.cuh"
+#include "../../distance/detail/distance_ops/l2_exp.cuh"
+#include "../../distance/detail/distance_ops/l2_unexp.cuh"
+#include "../../distance/detail/pairwise_distance_base.cuh"
+#include "../../distance/distance.cuh"
+
+namespace cuvs::neighbors::detail {
+
+template <typename Policy, typename Pair, typename myWarpSelect, typename IdxT>
+DI void loadAllWarpQShmem(myWarpSelect** heapArr,
+                          Pair* shDumpKV,
+                          const IdxT m,
+                          const unsigned int numOfNN)
+{
+  const int lid = raft::laneId();
+#pragma unroll
+  for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+    const auto rowId = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+    if (rowId < m) {
+#pragma unroll
+      for (int j = 0; j < myWarpSelect::kNumWarpQRegisters; ++j) {
+        const int idx = j * warpSize + lid;
+        if (idx < numOfNN) {
+          Pair KVPair          = shDumpKV[rowId * numOfNN + idx];
+          heapArr[i]->warpV[j] = KVPair.key;
+          heapArr[i]->warpK[j] = KVPair.value;
+        }
+      }
+    }
+  }
+}
+
+template <typename Policy, typename Pair, typename myWarpSelect>
+DI void loadWarpQShmem(myWarpSelect* heapArr,
+                       Pair* shDumpKV,
+                       const int rowId,
+                       const unsigned int numOfNN)
+{
+  const int lid = raft::laneId();
+#pragma unroll
+  for (int j = 0; j < myWarpSelect::kNumWarpQRegisters; ++j) {
+    const int idx = j * warpSize + lid;
+    if (idx < numOfNN) {
+      Pair KVPair       = shDumpKV[rowId * numOfNN + idx];
+      heapArr->warpV[j] = KVPair.key;
+      heapArr->warpK[j] = KVPair.value;
+    }
+  }
+}
+
+template <typename Policy, typename Pair, typename myWarpSelect, typename IdxT>
+DI void storeWarpQShmem(myWarpSelect* heapArr,
+                        Pair* shDumpKV,
+                        const IdxT rowId,
+                        const unsigned int numOfNN)
+{
+  const int lid = raft::laneId();
+
+#pragma unroll
+  for (int j = 0; j < myWarpSelect::kNumWarpQRegisters; ++j) {
+    const int idx = j * warpSize + lid;
+    if (idx < numOfNN) {
+      Pair otherKV                    = Pair(heapArr->warpV[j], heapArr->warpK[j]);
+      shDumpKV[rowId * numOfNN + idx] = otherKV;
+    }
+  }
+}
+
+template <typename Policy, typename Pair, typename myWarpSelect, typename IdxT, typename OutT>
+DI void storeWarpQGmem(myWarpSelect** heapArr,
+                       volatile OutT* out_dists,
+                       volatile IdxT* out_inds,
+                       const IdxT m,
+                       const unsigned int numOfNN,
+                       const IdxT starty)
+{
+  const int lid = raft::laneId();
+#pragma unroll
+  for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+    const auto gmemRowId = starty + i * Policy::AccThRows;
+    if (gmemRowId < m) {
+#pragma unroll
+      for (int j = 0; j < myWarpSelect::kNumWarpQRegisters; ++j) {
+        const auto idx = j * warpSize + lid;
+        if (idx < numOfNN) {
+          out_dists[std::size_t(gmemRowId) * numOfNN + idx] = heapArr[i]->warpK[j];
+          out_inds[std::size_t(gmemRowId) * numOfNN + idx]  = (IdxT)heapArr[i]->warpV[j];
+        }
+      }
+    }
+  }
+}
+
+template <typename Policy, typename Pair, typename myWarpSelect, typename IdxT, typename OutT>
+DI void loadPrevTopKsGmemWarpQ(myWarpSelect** heapArr,
+                               volatile OutT* out_dists,
+                               volatile IdxT* out_inds,
+                               const IdxT m,
+                               const unsigned int numOfNN,
+                               const IdxT starty)
+{
+  const int lid = raft::laneId();
+#pragma unroll
+  for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+    const auto gmemRowId = starty + i * Policy::AccThRows;
+    if (gmemRowId < m) {
+#pragma unroll
+      for (int j = 0; j < myWarpSelect::kNumWarpQRegisters; ++j) {
+        const auto idx = j * warpSize + lid;
+        if (idx < numOfNN) {
+          heapArr[i]->warpK[j] = out_dists[std::size_t(gmemRowId) * numOfNN + idx];
+          heapArr[i]->warpV[j] = (uint32_t)out_inds[std::size_t(gmemRowId) * numOfNN + idx];
+        }
+      }
+      static constexpr auto kLaneWarpKTop = myWarpSelect::kNumWarpQRegisters - 1;
+      heapArr[i]->warpKTop = raft::shfl(heapArr[i]->warpK[kLaneWarpKTop], heapArr[i]->kLane);
+    }
+  }
+}
+
+template <typename Pair, int NumWarpQRegs, typename myWarpSelect>
+DI void updateSortedWarpQ(
+  myWarpSelect& heapArr, Pair* allWarpTopKs, int rowId, int finalNumVals, int startId = 0)
+{
+  constexpr uint32_t mask = 0xffffffffu;
+  const int lid           = raft::laneId();
+  // calculate srcLane such that tid 0 -> 31, 1 -> 0,... 31 -> 30.
+  // warp around 0 to 31 required for NN > 32
+  const auto srcLane = (warpSize + (lid - 1)) & (warpSize - 1);
+
+  for (int k = startId; k < finalNumVals; k++) {
+    Pair KVPair = allWarpTopKs[rowId * (256) + k];
+#pragma unroll
+    for (int i = 0; i < NumWarpQRegs; i++) {
+      unsigned activeLanes = __ballot_sync(mask, KVPair.value < heapArr->warpK[i]);
+      if (activeLanes) {
+        Pair tempKV;
+        tempKV.value               = raft::shfl(heapArr->warpK[i], srcLane);
+        tempKV.key                 = raft::shfl(heapArr->warpV[i], srcLane);
+        const auto firstActiveLane = __ffs(activeLanes) - 1;
+        if (firstActiveLane == lid) {
+          heapArr->warpK[i] = KVPair.value;
+          heapArr->warpV[i] = KVPair.key;
+        } else if (lid > firstActiveLane) {
+          heapArr->warpK[i] = tempKV.value;
+          heapArr->warpV[i] = tempKV.key;
+        }
+        if (i == 0 && NumWarpQRegs > 1) {
+          heapArr->warpK[1] = __shfl_up_sync(mask, heapArr->warpK[1], 1);
+          heapArr->warpV[1] = __shfl_up_sync(mask, heapArr->warpV[1], 1);
+          if (lid == 0) {
+            heapArr->warpK[1] = tempKV.value;
+            heapArr->warpV[1] = tempKV.key;
+          }
+          break;
+        }
+      }
+    }
+  }
+}
+
+template <typename DataT,
+          typename OutT,
+          typename IdxT,
+          typename Policy,
+          typename OpT,
+          typename FinalLambda,
+          int NumWarpQ,
+          int NumThreadQ,
+          bool usePrevTopKs = false,
+          bool isRowMajor   = true>
+__launch_bounds__(Policy::Nthreads, 2) RAFT_KERNEL fusedL2kNN(const DataT* x,
+                                                              const DataT* y,
+                                                              const DataT* _xn,
+                                                              const DataT* _yn,
+                                                              const IdxT m,
+                                                              const IdxT n,
+                                                              const IdxT k,
+                                                              const IdxT lda,
+                                                              const IdxT ldb,
+                                                              const IdxT ldd,
+                                                              OpT distance_op,
+                                                              FinalLambda fin_op,
+                                                              unsigned int numOfNN,
+                                                              volatile int* mutexes,
+                                                              volatile OutT* out_dists,
+                                                              volatile IdxT* out_inds)
+{
+  using AccT = typename OpT::AccT;
+  extern __shared__ char smem[];
+
+  typedef cub::KeyValuePair<uint32_t, AccT> Pair;
+  constexpr auto identity = std::numeric_limits<AccT>::max();
+  constexpr auto keyMax   = std::numeric_limits<uint32_t>::max();
+  constexpr auto Dir      = false;
+  using namespace raft::neighbors::detail::faiss_select;
+  typedef WarpSelect<AccT, uint32_t, Dir, Comparator<AccT>, NumWarpQ, NumThreadQ, 32> myWarpSelect;
+
+  auto rowEpilog_lambda =
+    [m, n, &distance_op, numOfNN, out_dists, out_inds, mutexes] __device__(IdxT gridStrideY) {
+      if (gridDim.x == 1) { return; }
+
+      // Use ::template to disambiguate (See:
+      // https://en.cppreference.com/w/cpp/language/dependent_name)
+      int smem_offset = OpT::template shared_mem_size<Policy>();
+      Pair* shDumpKV  = (Pair*)(&smem[smem_offset]);
+
+      const int lid     = threadIdx.x % warpSize;
+      const IdxT starty = gridStrideY + (threadIdx.x / Policy::AccThCols);
+
+      //  0 -> consumer done consuming the buffer.
+      // -1 -> consumer started consuming the buffer
+      // -2 -> producer done filling the buffer
+      //  1 -> prod acquired to fill the buffer
+      if (blockIdx.x == 0) {
+        auto cta_processed = 0;
+        myWarpSelect heapArr1(identity, keyMax, numOfNN);
+        myWarpSelect heapArr2(identity, keyMax, numOfNN);
+        myWarpSelect* heapArr[] = {&heapArr1, &heapArr2};
+        __syncwarp();
+
+        loadAllWarpQShmem<Policy, Pair>(heapArr, &shDumpKV[0], m, numOfNN);
+
+        while (cta_processed < gridDim.x - 1) {
+          if (threadIdx.x == 0) {
+            while (atomicCAS((int*)&mutexes[gridStrideY / Policy::Mblk], -2, -1) != -2)
+              ;
+          }
+          __threadfence();
+          __syncthreads();
+
+#pragma unroll
+          for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+            const auto rowId = starty + i * Policy::AccThRows;
+            if (rowId < m) {
+#pragma unroll
+              for (int j = 0; j < myWarpSelect::kNumWarpQRegisters; ++j) {
+                Pair otherKV;
+                otherKV.value  = identity;
+                otherKV.key    = keyMax;
+                const auto idx = j * warpSize + lid;
+                if (idx < numOfNN) {
+                  otherKV.value         = out_dists[rowId * numOfNN + idx];
+                  otherKV.key           = (uint32_t)out_inds[rowId * numOfNN + idx];
+                  const auto shMemRowId = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+                  shDumpKV[shMemRowId * numOfNN + idx] = otherKV;
+                }
+              }
+            }
+          }
+          __threadfence();
+          __syncthreads();
+
+          if (threadIdx.x == 0) { atomicExch((int*)&mutexes[gridStrideY / Policy::Mblk], 0); }
+          __threadfence();
+
+        // Perform merging of otherKV with topk's across warp.
+#pragma unroll
+          for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+            const auto rowId = starty + i * Policy::AccThRows;
+            if (rowId < m) {
+#pragma unroll
+              for (int j = 0; j < myWarpSelect::kNumWarpQRegisters; ++j) {
+                Pair otherKV;
+                otherKV.value  = identity;
+                otherKV.key    = keyMax;
+                const auto idx = j * warpSize + lid;
+                if (idx < numOfNN) {
+                  const auto shMemRowId = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+                  otherKV               = shDumpKV[shMemRowId * numOfNN + idx];
+                }
+                heapArr[i]->add(otherKV.value, otherKV.key);
+              }
+            }
+          }
+          cta_processed++;
+        }
+#pragma unroll
+        for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+          const auto rowId = starty + i * Policy::AccThRows;
+          if (rowId < m) {
+            bool needSort = (heapArr[i]->numVals > 0);
+            needSort      = __any_sync(0xffffffff, needSort);
+            if (needSort) { heapArr[i]->reduce(); }
+          }
+        }
+        storeWarpQGmem<Policy, Pair>(heapArr, out_dists, out_inds, m, numOfNN, starty);
+      } else {
+        if (threadIdx.x == 0) {
+          while (atomicCAS((int*)&mutexes[gridStrideY / Policy::Mblk], 0, 1) != 0)
+            ;
+        }
+        __threadfence();
+        __syncthreads();
+
+#pragma unroll
+        for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+          const auto rowId = starty + i * Policy::AccThRows;
+          if (rowId < m) {
+            for (int idx = lid; idx < numOfNN; idx += warpSize) {
+              const auto shMemRowId = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+              Pair KVPair           = shDumpKV[shMemRowId * numOfNN + idx];
+              out_dists[rowId * numOfNN + idx] = KVPair.value;
+              out_inds[rowId * numOfNN + idx]  = (IdxT)KVPair.key;
+            }
+          }
+        }
+        __threadfence();
+        __syncthreads();
+
+        if (threadIdx.x == 0) { atomicExch((int*)&mutexes[gridStrideY / Policy::Mblk], -2); }
+        __threadfence();
+      }
+    };
+
+  // epilogue operation lambda for final value calculation
+  auto epilog_lambda =
+    [&distance_op, numOfNN, m, n, ldd, out_dists, out_inds, keyMax, identity] __device__(
+      AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
+      DataT * regxn,
+      DataT * regyn,
+      IdxT gridStrideX,
+      IdxT gridStrideY) {
+      // Use ::template to disambiguate (See:
+      // https://en.cppreference.com/w/cpp/language/dependent_name)
+      int smem_offset = OpT::template shared_mem_size<Policy>();
+      Pair* shDumpKV  = (Pair*)(&smem[smem_offset]);
+
+      constexpr uint32_t mask = 0xffffffffu;
+      const IdxT starty       = gridStrideY + (threadIdx.x / Policy::AccThCols);
+      const IdxT startx       = gridStrideX + (threadIdx.x % Policy::AccThCols);
+      const int lid           = raft::laneId();
+
+      myWarpSelect heapArr1(identity, keyMax, numOfNN);
+      myWarpSelect heapArr2(identity, keyMax, numOfNN);
+      myWarpSelect* heapArr[] = {&heapArr1, &heapArr2};
+      if (usePrevTopKs) {
+        if (gridStrideX == blockIdx.x * Policy::Nblk) {
+          loadPrevTopKsGmemWarpQ<Policy, Pair>(heapArr, out_dists, out_inds, m, numOfNN, starty);
+        }
+      }
+
+      if (gridStrideX > blockIdx.x * Policy::Nblk) {
+#pragma unroll
+        for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+          const auto rowId     = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+          Pair tempKV          = shDumpKV[(rowId * numOfNN) + numOfNN - 1];
+          heapArr[i]->warpKTop = tempKV.value;
+        }
+
+        // total vals can atmost be 256, (32*8)
+        int numValsWarpTopK[Policy::AccRowsPerTh];
+        int anyWarpTopKs = 0;
+#pragma unroll
+        for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+          const auto rowId   = starty + i * Policy::AccThRows;
+          numValsWarpTopK[i] = 0;
+          if (rowId < m) {
+#pragma unroll
+            for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+              const auto colId = startx + j * Policy::AccThCols;
+              if (colId < ldd) {
+                if (acc[i][j] < heapArr[i]->warpKTop) { numValsWarpTopK[i]++; }
+              }
+            }
+            anyWarpTopKs += numValsWarpTopK[i];
+          }
+        }
+        anyWarpTopKs = __syncthreads_or(anyWarpTopKs > 0);
+        if (anyWarpTopKs) {
+          Pair* allWarpTopKs = (Pair*)(&smem[0]);
+          uint32_t needScanSort[Policy::AccRowsPerTh];
+
+#pragma unroll
+          for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+            const auto gmemRowId = starty + i * Policy::AccThRows;
+            needScanSort[i]      = 0;
+            if (gmemRowId < m) {
+              int myVals      = numValsWarpTopK[i];
+              needScanSort[i] = __ballot_sync(mask, myVals > 0);
+              if (needScanSort[i]) {
+#pragma unroll
+                for (unsigned int k = 1; k <= 16; k *= 2) {
+                  const unsigned int n = __shfl_up_sync(mask, numValsWarpTopK[i], k);
+                  if (lid >= k) { numValsWarpTopK[i] += n; }
+                }
+              }
+              // As each thread will know its total vals to write.
+              // we only store its starting location.
+              numValsWarpTopK[i] -= myVals;
+            }
+
+            if (needScanSort[i]) {
+              const auto rowId = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+              if (gmemRowId < m) {
+                if (needScanSort[i] & ((uint32_t)1 << lid)) {
+#pragma unroll
+                  for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+                    const auto colId = startx + j * Policy::AccThCols;
+                    if (colId < ldd) {
+                      if (acc[i][j] < heapArr[i]->warpKTop) {
+                        Pair otherKV                                     = {colId, acc[i][j]};
+                        allWarpTopKs[rowId * (256) + numValsWarpTopK[i]] = otherKV;
+                        numValsWarpTopK[i]++;
+                      }
+                    }
+                  }
+                }
+                __syncwarp();
+                const int finalNumVals = raft::shfl(numValsWarpTopK[i], 31);
+                loadWarpQShmem<Policy, Pair>(heapArr[i], &shDumpKV[0], rowId, numOfNN);
+                updateSortedWarpQ<Pair, myWarpSelect::kNumWarpQRegisters>(
+                  heapArr[i], &allWarpTopKs[0], rowId, finalNumVals);
+              }
+            }
+          }
+          __syncthreads();
+#pragma unroll
+          for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+            if (needScanSort[i]) {
+              const auto rowId     = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+              const auto gmemRowId = starty + i * Policy::AccThRows;
+              if (gmemRowId < m) {
+                storeWarpQShmem<Policy, Pair>(heapArr[i], shDumpKV, rowId, numOfNN);
+              }
+            }
+          }
+        }
+      } else {
+#pragma unroll
+        for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
+          const auto gmemRowId  = starty + i * Policy::AccThRows;
+          const auto shMemRowId = (threadIdx.x / Policy::AccThCols) + i * Policy::AccThRows;
+          if (gmemRowId < m) {
+#pragma unroll
+            for (int j = 0; j < Policy::AccColsPerTh; ++j) {
+              const auto colId = startx + j * Policy::AccThCols;
+              Pair otherKV     = {keyMax, identity};
+              if (colId < ldd) {
+                otherKV.value = acc[i][j];
+                otherKV.key   = colId;
+              }
+              heapArr[i]->add(otherKV.value, otherKV.key);
+            }
+
+            bool needSort = (heapArr[i]->numVals > 0);
+            needSort      = __any_sync(mask, needSort);
+            if (needSort) { heapArr[i]->reduce(); }
+            storeWarpQShmem<Policy, Pair>(heapArr[i], shDumpKV, shMemRowId, numOfNN);
+          }
+        }
+      }
+
+      if (((gridStrideX + Policy::Nblk * gridDim.x) >= n) && gridDim.x == 1) {
+        // This is last iteration of grid stride X
+        loadAllWarpQShmem<Policy, Pair>(heapArr, &shDumpKV[0], m, numOfNN);
+        storeWarpQGmem<Policy, Pair>(heapArr, out_dists, out_inds, m, numOfNN, starty);
+      }
+    };
+
+  constexpr bool write_out = false;
+  cuvs::distance::detail::PairwiseDistances<DataT,
+                                            OutT,
+                                            IdxT,
+                                            Policy,
+                                            OpT,
+                                            decltype(epilog_lambda),
+                                            FinalLambda,
+                                            decltype(rowEpilog_lambda),
+                                            isRowMajor,
+                                            write_out>
+    obj(x,
+        y,
+        m,
+        n,
+        k,
+        lda,
+        ldb,
+        ldd,
+        _xn,
+        _yn,
+        nullptr,  // output ptr, can be null as write_out == false.
+        smem,
+        distance_op,
+        epilog_lambda,
+        fin_op,
+        rowEpilog_lambda);
+  obj.run();
+}
+
+template <typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT,
+          int VecLen,
+          bool usePrevTopKs,
+          bool isRowMajor>
+void fusedL2UnexpKnnImpl(const DataT* x,
+                         const DataT* y,
+                         IdxT m,
+                         IdxT n,
+                         IdxT k,
+                         IdxT lda,
+                         IdxT ldb,
+                         IdxT ldd,
+                         bool sqrt,
+                         OutT* out_dists,
+                         IdxT* out_inds,
+                         IdxT numOfNN,
+                         cudaStream_t stream,
+                         void* workspace,
+                         size_t& worksize)
+{
+  typedef typename raft::linalg::Policy2x8<DataT, 1>::Policy RowPolicy;
+  typedef typename raft::linalg::Policy4x4<DataT, VecLen>::ColPolicy ColPolicy;
+
+  typedef typename std::conditional<true, RowPolicy, ColPolicy>::type KPolicy;
+
+  ASSERT(isRowMajor, "Only Row major inputs are allowed");
+
+  dim3 blk(KPolicy::Nthreads);
+  // Accumulation operation lambda
+  typedef cub::KeyValuePair<uint32_t, AccT> Pair;
+
+  cuvs::distance::detail::ops::l2_unexp_distance_op<DataT, AccT, IdxT> distance_op{sqrt};
+  raft::identity_op fin_op{};
+
+  if constexpr (isRowMajor) {
+    constexpr auto fusedL2UnexpKnn32RowMajor = fusedL2kNN<DataT,
+                                                          OutT,
+                                                          IdxT,
+                                                          KPolicy,
+                                                          decltype(distance_op),
+                                                          decltype(fin_op),
+                                                          32,
+                                                          2,
+                                                          usePrevTopKs,
+                                                          isRowMajor>;
+    constexpr auto fusedL2UnexpKnn64RowMajor = fusedL2kNN<DataT,
+                                                          OutT,
+                                                          IdxT,
+                                                          KPolicy,
+                                                          decltype(distance_op),
+                                                          decltype(fin_op),
+                                                          64,
+                                                          3,
+                                                          usePrevTopKs,
+                                                          isRowMajor>;
+
+    auto fusedL2UnexpKnnRowMajor = fusedL2UnexpKnn32RowMajor;
+    if (numOfNN <= 32) {
+      fusedL2UnexpKnnRowMajor = fusedL2UnexpKnn32RowMajor;
+    } else if (numOfNN <= 64) {
+      fusedL2UnexpKnnRowMajor = fusedL2UnexpKnn64RowMajor;
+    } else {
+      ASSERT(numOfNN <= 64, "fusedL2kNN: num of nearest neighbors must be <= 64");
+    }
+
+    const auto sharedMemSize =
+      distance_op.template shared_mem_size<KPolicy>() + KPolicy::Mblk * numOfNN * sizeof(Pair);
+
+    dim3 grid = cuvs::distance::detail::launchConfigGenerator<KPolicy>(
+      m, n, sharedMemSize, fusedL2UnexpKnnRowMajor);
+
+    if (grid.x > 1) {
+      const auto numMutexes = raft::ceildiv<int>(m, KPolicy::Mblk);
+      if (workspace == nullptr || worksize < (sizeof(int32_t) * numMutexes)) {
+        worksize = sizeof(int32_t) * numMutexes;
+        return;
+      } else {
+        RAFT_CUDA_TRY(cudaMemsetAsync(workspace, 0, sizeof(int32_t) * numMutexes, stream));
+      }
+    }
+
+    fusedL2UnexpKnnRowMajor<<<grid, blk, sharedMemSize, stream>>>(x,
+                                                                  y,
+                                                                  nullptr,
+                                                                  nullptr,
+                                                                  m,
+                                                                  n,
+                                                                  k,
+                                                                  lda,
+                                                                  ldb,
+                                                                  ldd,
+                                                                  distance_op,
+                                                                  fin_op,
+                                                                  (uint32_t)numOfNN,
+                                                                  (int*)workspace,
+                                                                  out_dists,
+                                                                  out_inds);
+  } else {
+  }
+
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+template <typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT,
+          bool usePrevTopKs,
+          bool isRowMajor>
+void fusedL2UnexpKnn(IdxT m,
+                     IdxT n,
+                     IdxT k,
+                     IdxT lda,
+                     IdxT ldb,
+                     IdxT ldd,
+                     const DataT* x,
+                     const DataT* y,
+                     bool sqrt,
+                     OutT* out_dists,
+                     IdxT* out_inds,
+                     IdxT numOfNN,
+                     cudaStream_t stream,
+                     void* workspace,
+                     size_t& worksize)
+{
+  size_t bytesA = sizeof(DataT) * lda;
+  size_t bytesB = sizeof(DataT) * ldb;
+  if (16 % sizeof(DataT) == 0 && bytesA % 16 == 0 && bytesB % 16 == 0) {
+    fusedL2UnexpKnnImpl<DataT, AccT, OutT, IdxT, 16 / sizeof(DataT), usePrevTopKs, isRowMajor>(
+      x,
+      y,
+      m,
+      n,
+      k,
+      lda,
+      ldb,
+      ldd,
+      sqrt,
+      out_dists,
+      out_inds,
+      numOfNN,
+      stream,
+      workspace,
+      worksize);
+  } else if (8 % sizeof(DataT) == 0 && bytesA % 8 == 0 && bytesB % 8 == 0) {
+    fusedL2UnexpKnnImpl<DataT, AccT, OutT, IdxT, 8 / sizeof(DataT), usePrevTopKs, isRowMajor>(
+      x,
+      y,
+      m,
+      n,
+      k,
+      lda,
+      ldb,
+      ldd,
+      sqrt,
+      out_dists,
+      out_inds,
+      numOfNN,
+      stream,
+      workspace,
+      worksize);
+  } else {
+    fusedL2UnexpKnnImpl<DataT, AccT, OutT, IdxT, 1, usePrevTopKs, isRowMajor>(x,
+                                                                              y,
+                                                                              m,
+                                                                              n,
+                                                                              k,
+                                                                              lda,
+                                                                              ldb,
+                                                                              ldd,
+                                                                              sqrt,
+                                                                              out_dists,
+                                                                              out_inds,
+                                                                              numOfNN,
+                                                                              stream,
+                                                                              workspace,
+                                                                              worksize);
+  }
+}
+
+template <typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT,
+          int VecLen,
+          bool usePrevTopKs,
+          bool isRowMajor>
+void fusedL2ExpKnnImpl(const DataT* x,
+                       const DataT* y,
+                       const DataT* xn,
+                       const DataT* yn,
+                       IdxT m,
+                       IdxT n,
+                       IdxT k,
+                       IdxT lda,
+                       IdxT ldb,
+                       IdxT ldd,
+                       bool sqrt,
+                       OutT* out_dists,
+                       IdxT* out_inds,
+                       IdxT numOfNN,
+                       cudaStream_t stream,
+                       void* workspace,
+                       size_t& worksize)
+{
+  typedef typename raft::linalg::Policy2x8<DataT, 1>::Policy RowPolicy;
+  typedef typename raft::linalg::Policy4x4<DataT, VecLen>::ColPolicy ColPolicy;
+
+  typedef typename std::conditional<true, RowPolicy, ColPolicy>::type KPolicy;
+
+  ASSERT(isRowMajor, "Only Row major inputs are allowed");
+
+  ASSERT(!(((x != y) && (worksize < (m + n) * sizeof(AccT))) || (worksize < m * sizeof(AccT))),
+         "workspace size error");
+  ASSERT(workspace != nullptr, "workspace is null");
+
+  dim3 blk(KPolicy::Nthreads);
+
+  typedef cub::KeyValuePair<uint32_t, AccT> Pair;
+
+  cuvs::distance::detail::ops::l2_exp_distance_op<DataT, AccT, IdxT> distance_op{sqrt};
+  raft::identity_op fin_op{};
+
+  if constexpr (isRowMajor) {
+    constexpr auto fusedL2ExpKnn32RowMajor = fusedL2kNN<DataT,
+                                                        OutT,
+                                                        IdxT,
+                                                        KPolicy,
+                                                        decltype(distance_op),
+                                                        decltype(fin_op),
+                                                        32,
+                                                        2,
+                                                        usePrevTopKs,
+                                                        isRowMajor>;
+    constexpr auto fusedL2ExpKnn64RowMajor = fusedL2kNN<DataT,
+                                                        OutT,
+                                                        IdxT,
+                                                        KPolicy,
+                                                        decltype(distance_op),
+                                                        decltype(fin_op),
+                                                        64,
+                                                        3,
+                                                        usePrevTopKs,
+                                                        isRowMajor>;
+
+    auto fusedL2ExpKnnRowMajor = fusedL2ExpKnn32RowMajor;
+    if (numOfNN <= 32) {
+      fusedL2ExpKnnRowMajor = fusedL2ExpKnn32RowMajor;
+    } else if (numOfNN <= 64) {
+      fusedL2ExpKnnRowMajor = fusedL2ExpKnn64RowMajor;
+    } else {
+      ASSERT(numOfNN <= 64, "fusedL2kNN: num of nearest neighbors must be <= 64");
+    }
+
+    const auto sharedMemSize =
+      distance_op.template shared_mem_size<KPolicy>() + (KPolicy::Mblk * numOfNN * sizeof(Pair));
+    dim3 grid = cuvs::distance::detail::launchConfigGenerator<KPolicy>(
+      m, n, sharedMemSize, fusedL2ExpKnnRowMajor);
+    int32_t* mutexes = nullptr;
+    if (grid.x > 1) {
+      const auto numMutexes   = raft::ceildiv<int>(m, KPolicy::Mblk);
+      const auto normsSize    = (x != y) ? (m + n) * sizeof(DataT) : n * sizeof(DataT);
+      const auto requiredSize = sizeof(int32_t) * numMutexes + normsSize;
+      if (worksize < requiredSize) {
+        worksize = requiredSize;
+        return;
+      } else {
+        mutexes = (int32_t*)((char*)workspace + normsSize);
+        RAFT_CUDA_TRY(cudaMemsetAsync(mutexes, 0, sizeof(int32_t) * numMutexes, stream));
+      }
+    }
+
+    // calculate norms if they haven't been passed in
+    if (!xn) {
+      DataT* xn_ = (DataT*)workspace;
+      workspace  = xn_ + m;
+      raft::linalg::rowNorm(
+        xn_, x, k, m, raft::linalg::L2Norm, isRowMajor, stream, raft::identity_op{});
+      xn = xn_;
+    }
+    if (!yn) {
+      if (x == y) {
+        yn = xn;
+      } else {
+        DataT* yn_ = (DataT*)(workspace);
+        raft::linalg::rowNorm(
+          yn_, y, k, n, raft::linalg::L2Norm, isRowMajor, stream, raft::identity_op{});
+        yn = yn_;
+      }
+    }
+
+    fusedL2ExpKnnRowMajor<<<grid, blk, sharedMemSize, stream>>>(x,
+                                                                y,
+                                                                xn,
+                                                                yn,
+                                                                m,
+                                                                n,
+                                                                k,
+                                                                lda,
+                                                                ldb,
+                                                                ldd,
+                                                                distance_op,
+                                                                fin_op,
+                                                                (uint32_t)numOfNN,
+                                                                mutexes,
+                                                                out_dists,
+                                                                out_inds);
+  } else {
+  }
+
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+template <typename DataT,
+          typename AccT,
+          typename OutT,
+          typename IdxT,
+          bool usePrevTopKs,
+          bool isRowMajor>
+void fusedL2ExpKnn(IdxT m,
+                   IdxT n,
+                   IdxT k,
+                   IdxT lda,
+                   IdxT ldb,
+                   IdxT ldd,
+                   const DataT* x,
+                   const DataT* y,
+                   const DataT* xn,
+                   const DataT* yn,
+                   bool sqrt,
+                   OutT* out_dists,
+                   IdxT* out_inds,
+                   IdxT numOfNN,
+                   cudaStream_t stream,
+                   void* workspace,
+                   size_t& worksize)
+{
+  size_t bytesA = sizeof(DataT) * lda;
+  size_t bytesB = sizeof(DataT) * ldb;
+  if (16 % sizeof(DataT) == 0 && bytesA % 16 == 0 && bytesB % 16 == 0) {
+    fusedL2ExpKnnImpl<DataT, AccT, OutT, IdxT, 16 / sizeof(DataT), usePrevTopKs, isRowMajor>(
+      x,
+      y,
+      xn,
+      yn,
+      m,
+      n,
+      k,
+      lda,
+      ldb,
+      ldd,
+      sqrt,
+      out_dists,
+      out_inds,
+      numOfNN,
+      stream,
+      workspace,
+      worksize);
+  } else if (8 % sizeof(DataT) == 0 && bytesA % 8 == 0 && bytesB % 8 == 0) {
+    fusedL2ExpKnnImpl<DataT, AccT, OutT, IdxT, 8 / sizeof(DataT), usePrevTopKs, isRowMajor>(
+      x,
+      y,
+      xn,
+      yn,
+      m,
+      n,
+      k,
+      lda,
+      ldb,
+      ldd,
+      sqrt,
+      out_dists,
+      out_inds,
+      numOfNN,
+      stream,
+      workspace,
+      worksize);
+  } else {
+    fusedL2ExpKnnImpl<DataT, AccT, OutT, IdxT, 1, usePrevTopKs, isRowMajor>(x,
+                                                                            y,
+                                                                            xn,
+                                                                            yn,
+                                                                            m,
+                                                                            n,
+                                                                            k,
+                                                                            lda,
+                                                                            ldb,
+                                                                            ldd,
+                                                                            sqrt,
+                                                                            out_dists,
+                                                                            out_inds,
+                                                                            numOfNN,
+                                                                            stream,
+                                                                            workspace,
+                                                                            worksize);
+  }
+}
+
+/**
+ * Compute the k-nearest neighbors using L2 expanded/unexpanded distance.
+
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[out] out_inds output indices array on device (size n_query_rows * k)
+ * @param[out] out_dists output dists array on device (size n_query_rows * k)
+ * @param[in] index input index array on device (size n_index_rows * D)
+ * @param[in] query input query array on device (size n_query_rows * D)
+ * @param[in] n_index_rows number of rows in index array
+ * @param[in] n_query_rows number of rows in query array
+ * @param[in] k number of closest neighbors to return
+ * @param[in] rowMajorIndex are the index arrays in row-major layout?
+ * @param[in] rowMajorQuery are the query array in row-major layout?
+ * @param[in] stream stream to order kernel launch
+ */
+template <typename value_idx, typename value_t, bool usePrevTopKs = false>
+void fusedL2Knn(size_t D,
+                value_idx* out_inds,
+                value_t* out_dists,
+                const value_t* index,
+                const value_t* query,
+                size_t n_index_rows,
+                size_t n_query_rows,
+                int k,
+                bool rowMajorIndex,
+                bool rowMajorQuery,
+                cudaStream_t stream,
+                cuvs::distance::DistanceType metric,
+                const value_t* index_norms = NULL,
+                const value_t* query_norms = NULL)
+{
+  // Validate the input data
+  ASSERT(k > 0, "l2Knn: k must be > 0");
+  ASSERT(D > 0, "l2Knn: D must be > 0");
+  ASSERT(n_index_rows > 0, "l2Knn: n_index_rows must be > 0");
+  ASSERT(index, "l2Knn: index must be provided (passed null)");
+  ASSERT(n_query_rows > 0, "l2Knn: n_query_rows must be > 0");
+  ASSERT(query, "l2Knn: query must be provided (passed null)");
+  ASSERT(out_dists, "l2Knn: out_dists must be provided (passed null)");
+  ASSERT(out_inds, "l2Knn: out_inds must be provided (passed null)");
+  // Currently we only support same layout for x & y inputs.
+  ASSERT(rowMajorIndex == rowMajorQuery,
+         "l2Knn: rowMajorIndex and rowMajorQuery should have same layout");
+  // TODO: Add support for column major layout
+  ASSERT(rowMajorIndex == true, "l2Knn: only rowMajor inputs are supported for now.");
+
+  // Even for L2 Sqrt distance case we use non-sqrt version as FAISS bfKNN only support
+  // non-sqrt metric & some tests in RAFT/cuML (like Linkage) fails if we use L2 sqrt.
+  constexpr bool sqrt = false;
+
+  size_t worksize = 0, tempWorksize = 0;
+  rmm::device_uvector<char> workspace(worksize, stream);
+  value_idx lda = D, ldb = D, ldd = n_index_rows;
+  // <cuvs::distance::DistanceType::L2Expanded, float, float, float, value_idx>
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded:
+      tempWorksize =
+        cuvs::distance::getWorkspaceSize<cuvs::distance::DistanceType::L2Expanded,
+                                         value_t,
+                                         value_t,
+                                         value_t,
+                                         value_idx>(query, index, n_query_rows, n_index_rows, D);
+      worksize = tempWorksize;
+      workspace.resize(worksize, stream);
+      fusedL2ExpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
+                                                                              n_index_rows,
+                                                                              D,
+                                                                              lda,
+                                                                              ldb,
+                                                                              ldd,
+                                                                              query,
+                                                                              index,
+                                                                              query_norms,
+                                                                              index_norms,
+                                                                              sqrt,
+                                                                              out_dists,
+                                                                              out_inds,
+                                                                              k,
+                                                                              stream,
+                                                                              workspace.data(),
+                                                                              worksize);
+      if (worksize > tempWorksize) {
+        workspace.resize(worksize, stream);
+        fusedL2ExpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
+                                                                                n_index_rows,
+                                                                                D,
+                                                                                lda,
+                                                                                ldb,
+                                                                                ldd,
+                                                                                query,
+                                                                                index,
+                                                                                query_norms,
+                                                                                index_norms,
+                                                                                sqrt,
+                                                                                out_dists,
+                                                                                out_inds,
+                                                                                k,
+                                                                                stream,
+                                                                                workspace.data(),
+                                                                                worksize);
+      }
+      break;
+    case cuvs::distance::DistanceType::L2Unexpanded:
+    case cuvs::distance::DistanceType::L2SqrtUnexpanded:
+      fusedL2UnexpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
+                                                                                n_index_rows,
+                                                                                D,
+                                                                                lda,
+                                                                                ldb,
+                                                                                ldd,
+                                                                                query,
+                                                                                index,
+                                                                                sqrt,
+                                                                                out_dists,
+                                                                                out_inds,
+                                                                                k,
+                                                                                stream,
+                                                                                workspace.data(),
+                                                                                worksize);
+      if (worksize) {
+        workspace.resize(worksize, stream);
+        fusedL2UnexpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
+                                                                                  n_index_rows,
+                                                                                  D,
+                                                                                  lda,
+                                                                                  ldb,
+                                                                                  ldd,
+                                                                                  query,
+                                                                                  index,
+                                                                                  sqrt,
+                                                                                  out_dists,
+                                                                                  out_inds,
+                                                                                  k,
+                                                                                  stream,
+                                                                                  workspace.data(),
+                                                                                  worksize);
+      }
+      break;
+    default: printf("only L2 distance metric is supported\n"); break;
+  };
+}
+
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/haversine_distance.cuh b/cpp/src/neighbors/detail/haversine_distance.cuh
new file mode 100644
index 0000000000..fc6aa477d1
--- /dev/null
+++ b/cpp/src/neighbors/detail/haversine_distance.cuh
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/resources.hpp>
+#include <raft/neighbors/detail/faiss_select/Select.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+namespace cuvs::neighbors::detail {
+template <typename value_t>
+DI value_t compute_haversine(value_t x1, value_t y1, value_t x2, value_t y2)
+{
+  value_t sin_0 = raft::sin(0.5 * (x1 - y1));
+  value_t sin_1 = raft::sin(0.5 * (x2 - y2));
+  value_t rdist = sin_0 * sin_0 + raft::cos(x1) * raft::cos(y1) * sin_1 * sin_1;
+
+  return 2 * raft::asin(raft::sqrt(rdist));
+}
+
+/**
+ * @tparam value_idx data type of indices
+ * @tparam value_t data type of values and distances
+ * @tparam warp_q
+ * @tparam thread_q
+ * @tparam tpb
+ * @param[out] out_inds output indices
+ * @param[out] out_dists output distances
+ * @param[in] index index array
+ * @param[in] query query array
+ * @param[in] n_index_rows number of rows in index array
+ * @param[in] k number of closest neighbors to return
+ */
+template <typename value_idx, typename value_t, int warp_q = 1024, int thread_q = 8, int tpb = 128>
+RAFT_KERNEL haversine_knn_kernel(value_idx* out_inds,
+                                 value_t* out_dists,
+                                 const value_t* index,
+                                 const value_t* query,
+                                 size_t n_index_rows,
+                                 int k)
+{
+  constexpr int kNumWarps = tpb / raft::WarpSize;
+
+  __shared__ value_t smemK[kNumWarps * warp_q];
+  __shared__ value_idx smemV[kNumWarps * warp_q];
+
+  using namespace raft::neighbors::detail::faiss_select;
+  BlockSelect<value_t, value_idx, false, Comparator<value_t>, warp_q, thread_q, tpb> heap(
+    std::numeric_limits<value_t>::max(), std::numeric_limits<value_idx>::max(), smemK, smemV, k);
+
+  // Grid is exactly sized to rows available
+  int limit = raft::Pow2<raft::WarpSize>::roundDown(n_index_rows);
+
+  const value_t* query_ptr = query + (blockIdx.x * 2);
+  value_t x1               = query_ptr[0];
+  value_t x2               = query_ptr[1];
+
+  int i = threadIdx.x;
+
+  for (; i < limit; i += tpb) {
+    const value_t* idx_ptr = index + (i * 2);
+    value_t y1             = idx_ptr[0];
+    value_t y2             = idx_ptr[1];
+
+    value_t dist = compute_haversine(x1, y1, x2, y2);
+
+    heap.add(dist, i);
+  }
+
+  // Handle last remainder fraction of a warp of elements
+  if (i < n_index_rows) {
+    const value_t* idx_ptr = index + (i * 2);
+    value_t y1             = idx_ptr[0];
+    value_t y2             = idx_ptr[1];
+
+    value_t dist = compute_haversine(x1, y1, x2, y2);
+
+    heap.addThreadQ(dist, i);
+  }
+
+  heap.reduce();
+
+  for (int i = threadIdx.x; i < k; i += tpb) {
+    out_dists[blockIdx.x * k + i] = smemK[i];
+    out_inds[blockIdx.x * k + i]  = smemV[i];
+  }
+}
+
+/**
+ * Conmpute the k-nearest neighbors using the Haversine
+ * (great circle arc) distance. Input is assumed to have
+ * 2 dimensions (latitude, longitude) in radians.
+
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[out] out_inds output indices array on device (size n_query_rows * k)
+ * @param[out] out_dists output dists array on device (size n_query_rows * k)
+ * @param[in] index input index array on device (size n_index_rows * 2)
+ * @param[in] query input query array on device (size n_query_rows * 2)
+ * @param[in] n_index_rows number of rows in index array
+ * @param[in] n_query_rows number of rows in query array
+ * @param[in] k number of closest neighbors to return
+ * @param[in] stream stream to order kernel launch
+ */
+template <typename value_idx, typename value_t>
+void haversine_knn(value_idx* out_inds,
+                   value_t* out_dists,
+                   const value_t* index,
+                   const value_t* query,
+                   size_t n_index_rows,
+                   size_t n_query_rows,
+                   int k,
+                   cudaStream_t stream)
+{
+  // ensure kernel does not breach shared memory limits
+  constexpr int kWarpQ = sizeof(value_t) > 4 ? 512 : 1024;
+  haversine_knn_kernel<value_idx, value_t, kWarpQ>
+    <<<n_query_rows, 128, 0, stream>>>(out_inds, out_dists, index, query, n_index_rows, k);
+}
+
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
new file mode 100644
index 0000000000..2f5aa176d8
--- /dev/null
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -0,0 +1,567 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <cuvs/distance/pairwise_distance.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include "../../distance/detail/distance_ops/l2_exp.cuh"
+#include "./faiss_distance_utils.h"
+#include "./fused_l2_knn.cuh"
+#include "./haversine_distance.cuh"
+#include "./knn_merge_parts.cuh"
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/cuda_stream_pool.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/transpose.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/matrix/select_k.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/cuda_device.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <cstdint>
+#include <iostream>
+#include <set>
+
+namespace cuvs::neighbors::detail {
+/**
+ * Calculates brute force knn, using a fixed memory budget
+ * by tiling over both the rows and columns of pairwise_distances
+ */
+template <typename ElementType = float, typename IndexType = int64_t>
+void tiled_brute_force_knn(const raft::resources& handle,
+                           const ElementType* search,  // size (m ,d)
+                           const ElementType* index,   // size (n ,d)
+                           size_t m,
+                           size_t n,
+                           size_t d,
+                           size_t k,
+                           ElementType* distances,  // size (m, k)
+                           IndexType* indices,      // size (m, k)
+                           cuvs::distance::DistanceType metric,
+                           float metric_arg                            = 2.0,
+                           size_t max_row_tile_size                    = 0,
+                           size_t max_col_tile_size                    = 0,
+                           const ElementType* precomputed_index_norms  = nullptr,
+                           const ElementType* precomputed_search_norms = nullptr)
+{
+  // Figure out the number of rows/cols to tile for
+  size_t tile_rows   = 0;
+  size_t tile_cols   = 0;
+  auto stream        = raft::resource::get_cuda_stream(handle);
+  auto device_memory = raft::resource::get_workspace_resource(handle);
+  auto total_mem     = rmm::available_device_memory().second;
+
+  cuvs::neighbors::detail::faiss_select::chooseTileSize(
+    m, n, d, sizeof(ElementType), total_mem, tile_rows, tile_cols);
+
+  // for unittesting, its convenient to be able to put a max size on the tiles
+  // so we can test the tiling logic without having to use huge inputs.
+  if (max_row_tile_size && (tile_rows > max_row_tile_size)) { tile_rows = max_row_tile_size; }
+  if (max_col_tile_size && (tile_cols > max_col_tile_size)) { tile_cols = max_col_tile_size; }
+
+  // tile_cols must be at least k items
+  tile_cols = std::max(tile_cols, k);
+
+  // stores pairwise distances for the current tile
+  rmm::device_uvector<ElementType> temp_distances(tile_rows * tile_cols, stream);
+
+  // calculate norms for L2 expanded distances - this lets us avoid calculating
+  // norms repeatedly per-tile, and just do once for the entire input
+  auto pairwise_metric = metric;
+  rmm::device_uvector<ElementType> search_norms(0, stream);
+  rmm::device_uvector<ElementType> index_norms(0, stream);
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+      metric == cuvs::distance::DistanceType::CosineExpanded) {
+    if (!precomputed_search_norms) { search_norms.resize(m, stream); }
+    if (!precomputed_index_norms) { index_norms.resize(n, stream); }
+    // cosine needs the l2norm, where as l2 distances needs the squared norm
+    if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+      if (!precomputed_search_norms) {
+        raft::linalg::rowNorm(search_norms.data(),
+                              search,
+                              d,
+                              m,
+                              raft::linalg::NormType::L2Norm,
+                              true,
+                              stream,
+                              raft::sqrt_op{});
+      }
+      if (!precomputed_index_norms) {
+        raft::linalg::rowNorm(index_norms.data(),
+                              index,
+                              d,
+                              n,
+                              raft::linalg::NormType::L2Norm,
+                              true,
+                              stream,
+                              raft::sqrt_op{});
+      }
+    } else {
+      if (!precomputed_search_norms) {
+        raft::linalg::rowNorm(
+          search_norms.data(), search, d, m, raft::linalg::NormType::L2Norm, true, stream);
+      }
+      if (!precomputed_index_norms) {
+        raft::linalg::rowNorm(
+          index_norms.data(), index, d, n, raft::linalg::NormType::L2Norm, true, stream);
+      }
+    }
+    pairwise_metric = cuvs::distance::DistanceType::InnerProduct;
+  }
+
+  // if we're tiling over columns, we need additional buffers for temporary output
+  // distances/indices
+  size_t num_col_tiles = raft::ceildiv(n, tile_cols);
+  size_t temp_out_cols = k * num_col_tiles;
+
+  // the final column tile could have less than 'k' items in it
+  // in which case the number of columns here is too high in the temp output.
+  // adjust if necessary
+  auto last_col_tile_size = n % tile_cols;
+  if (last_col_tile_size && (last_col_tile_size < k)) { temp_out_cols -= k - last_col_tile_size; }
+
+  // if we have less than k items in the index, we should fill out the result
+  // to indicate that we are missing items (and match behaviour in faiss)
+  if (n < k) {
+    raft::matrix::fill(handle,
+                       raft::make_device_matrix_view(distances, m, k),
+                       std::numeric_limits<ElementType>::lowest());
+
+    if constexpr (std::is_signed_v<IndexType>) {
+      raft::matrix::fill(handle, raft::make_device_matrix_view(indices, m, k), IndexType{-1});
+    }
+  }
+
+  rmm::device_uvector<ElementType> temp_out_distances(tile_rows * temp_out_cols, stream);
+  rmm::device_uvector<IndexType> temp_out_indices(tile_rows * temp_out_cols, stream);
+
+  bool select_min = cuvs::distance::is_min_close(metric);
+
+  for (size_t i = 0; i < m; i += tile_rows) {
+    size_t current_query_size = std::min(tile_rows, m - i);
+
+    for (size_t j = 0; j < n; j += tile_cols) {
+      size_t current_centroid_size = std::min(tile_cols, n - j);
+      size_t current_k             = std::min(current_centroid_size, k);
+
+      // calculate the top-k elements for the current tile, by calculating the
+      // full pairwise distance for the tile - and then selecting the top-k from that
+      cuvs::distance::pairwise_distance(
+        handle,
+        raft::make_device_matrix_view<const ElementType, int64_t>(
+          search + i * d, current_query_size, d),
+        raft::make_device_matrix_view<const ElementType, int64_t>(
+          index + j * d, current_centroid_size, d),
+        raft::make_device_matrix_view<ElementType, int64_t>(
+          temp_distances.data(), current_query_size, current_centroid_size),
+        pairwise_metric,
+        metric_arg);
+
+      if (metric == cuvs::distance::DistanceType::L2Expanded ||
+          metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+        auto row_norms = precomputed_search_norms ? precomputed_search_norms : search_norms.data();
+        auto col_norms = precomputed_index_norms ? precomputed_index_norms : index_norms.data();
+        auto dist      = temp_distances.data();
+        bool sqrt      = metric == cuvs::distance::DistanceType::L2SqrtExpanded;
+
+        raft::linalg::map_offset(
+          handle,
+          raft::make_device_vector_view(dist, current_query_size * current_centroid_size),
+          [=] __device__(IndexType idx) {
+            IndexType row = i + (idx / current_centroid_size);
+            IndexType col = j + (idx % current_centroid_size);
+
+            cuvs::distance::detail::ops::l2_exp_cutlass_op<ElementType, ElementType> l2_op(sqrt);
+            return l2_op(row_norms[row], col_norms[col], dist[idx]);
+          });
+      } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+        auto row_norms = precomputed_search_norms ? precomputed_search_norms : search_norms.data();
+        auto col_norms = precomputed_index_norms ? precomputed_index_norms : index_norms.data();
+        auto dist      = temp_distances.data();
+
+        raft::linalg::map_offset(
+          handle,
+          raft::make_device_vector_view(dist, current_query_size * current_centroid_size),
+          [=] __device__(IndexType idx) {
+            IndexType row = i + (idx / current_centroid_size);
+            IndexType col = j + (idx % current_centroid_size);
+            auto val      = 1.0 - dist[idx] / (row_norms[row] * col_norms[col]);
+            return val;
+          });
+      }
+
+      raft::matrix::select_k<ElementType, IndexType>(
+        handle,
+        raft::make_device_matrix_view<const ElementType, int64_t, raft::row_major>(
+          temp_distances.data(), current_query_size, current_centroid_size),
+        std::nullopt,
+        raft::make_device_matrix_view<ElementType, int64_t, raft::row_major>(
+          distances + i * k, current_query_size, current_k),
+        raft::make_device_matrix_view<IndexType, int64_t, raft::row_major>(
+          indices + i * k, current_query_size, current_k),
+        select_min,
+        true);
+
+      // if we're tiling over columns, we need to do a couple things to fix up
+      // the output of select_k
+      // 1. The column id's in the output are relative to the tile, so we need
+      // to adjust the column ids by adding the column the tile starts at (j)
+      // 2. select_k writes out output in a row-major format, which means we
+      // can't just concat the output of all the tiles and do a select_k on the
+      // concatenation.
+      // Fix both of these problems in a single pass here
+      if (tile_cols != n) {
+        const ElementType* in_distances = distances + i * k;
+        const IndexType* in_indices     = indices + i * k;
+        ElementType* out_distances      = temp_out_distances.data();
+        IndexType* out_indices          = temp_out_indices.data();
+
+        auto count = thrust::make_counting_iterator<IndexType>(0);
+        thrust::for_each(raft::resource::get_thrust_policy(handle),
+                         count,
+                         count + current_query_size * current_k,
+                         [=] __device__(IndexType i) {
+                           IndexType row = i / current_k, col = i % current_k;
+                           IndexType out_index = row * temp_out_cols + j * k / tile_cols + col;
+
+                           out_distances[out_index] = in_distances[i];
+                           out_indices[out_index]   = in_indices[i] + j;
+                         });
+      }
+    }
+
+    if (tile_cols != n) {
+      // select the actual top-k items here from the temporary output
+      raft::matrix::select_k<ElementType, IndexType>(
+        handle,
+        raft::make_device_matrix_view<const ElementType, int64_t, raft::row_major>(
+          temp_out_distances.data(), current_query_size, temp_out_cols),
+        raft::make_device_matrix_view<const IndexType, int64_t, raft::row_major>(
+          temp_out_indices.data(), current_query_size, temp_out_cols),
+        raft::make_device_matrix_view<ElementType, int64_t, raft::row_major>(
+          distances + i * k, current_query_size, k),
+        raft::make_device_matrix_view<IndexType, int64_t, raft::row_major>(
+          indices + i * k, current_query_size, k),
+        select_min,
+        true);
+    }
+  }
+}
+
+/**
+ * Search the kNN for the k-nearest neighbors of a set of query vectors
+ * @param[in] input vector of device device memory array pointers to search
+ * @param[in] sizes vector of memory sizes for each device array pointer in input
+ * @param[in] D number of cols in input and search_items
+ * @param[in] search_items set of vectors to query for neighbors
+ * @param[in] n        number of items in search_items
+ * @param[out] res_I    pointer to device memory for returning k nearest indices
+ * @param[out] res_D    pointer to device memory for returning k nearest distances
+ * @param[in] k        number of neighbors to query
+ * @param[in] userStream the main cuda stream to use
+ * @param[in] internalStreams optional when n_params > 0, the index partitions can be
+ *        queried in parallel using these streams. Note that n_int_streams also
+ *        has to be > 0 for these to be used and their cardinality does not need
+ *        to correspond to n_parts.
+ * @param[in] n_int_streams size of internalStreams. When this is <= 0, only the
+ *        user stream will be used.
+ * @param[in] rowMajorIndex are the index arrays in row-major layout?
+ * @param[in] rowMajorQuery are the query array in row-major layout?
+ * @param[in] translations translation ids for indices when index rows represent
+ *        non-contiguous partitions
+ * @param[in] metric corresponds to the cuvs::distance::DistanceType enum (default is L2Expanded)
+ * @param[in] metricArg metric argument to use. Corresponds to the p arg for lp norm
+ */
+template <typename IntType = int, typename IdxType = std::int64_t, typename value_t = float>
+void brute_force_knn_impl(
+  raft::resources const& handle,
+  std::vector<value_t*>& input,
+  std::vector<IntType>& sizes,
+  IntType D,
+  value_t* search_items,
+  IntType n,
+  IdxType* res_I,
+  value_t* res_D,
+  IntType k,
+  bool rowMajorIndex                  = true,
+  bool rowMajorQuery                  = true,
+  std::vector<IdxType>* translations  = nullptr,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded,
+  float metricArg                     = 0,
+  std::vector<value_t*>* input_norms  = nullptr,
+  const value_t* search_norms         = nullptr)
+{
+  auto userStream = raft::resource::get_cuda_stream(handle);
+
+  ASSERT(input.size() == sizes.size(), "input and sizes vectors should be the same size");
+
+  std::vector<IdxType>* id_ranges;
+  if (translations == nullptr) {
+    // If we don't have explicit translations
+    // for offsets of the indices, build them
+    // from the local partitions
+    id_ranges       = new std::vector<IdxType>();
+    IdxType total_n = 0;
+    for (size_t i = 0; i < input.size(); i++) {
+      id_ranges->push_back(total_n);
+      total_n += sizes[i];
+    }
+  } else {
+    // otherwise, use the given translations
+    id_ranges = translations;
+  }
+
+  int device;
+  RAFT_CUDA_TRY(cudaGetDevice(&device));
+
+  rmm::device_uvector<IdxType> trans(id_ranges->size(), userStream);
+  raft::update_device(trans.data(), id_ranges->data(), id_ranges->size(), userStream);
+
+  rmm::device_uvector<value_t> all_D(0, userStream);
+  rmm::device_uvector<IdxType> all_I(0, userStream);
+
+  value_t* out_D = res_D;
+  IdxType* out_I = res_I;
+
+  if (input.size() > 1) {
+    all_D.resize(input.size() * k * n, userStream);
+    all_I.resize(input.size() * k * n, userStream);
+
+    out_D = all_D.data();
+    out_I = all_I.data();
+  }
+
+  // currently we don't support col_major inside tiled_brute_force_knn, because
+  // of limitations of the pairwise_distance API:
+  // 1) paiwise_distance takes a single 'isRowMajor' parameter - and we have
+  // multiple options here (like rowMajorQuery/rowMajorIndex)
+  // 2) because of tiling, we need to be able to set a custom stride in the PW
+  // api, which isn't supported
+  // Instead, transpose the input matrices if they are passed as col-major.
+  auto search = search_items;
+  rmm::device_uvector<value_t> search_row_major(0, userStream);
+  if (!rowMajorQuery) {
+    search_row_major.resize(n * D, userStream);
+    raft::linalg::transpose(handle, search, search_row_major.data(), n, D, userStream);
+    search = search_row_major.data();
+  }
+
+  // transpose into a temporary buffer if necessary
+  rmm::device_uvector<value_t> index_row_major(0, userStream);
+  if (!rowMajorIndex) {
+    size_t total_size = 0;
+    for (auto size : sizes) {
+      total_size += size;
+    }
+    index_row_major.resize(total_size * D, userStream);
+  }
+
+  // Make other streams from pool wait on main stream
+  raft::resource::wait_stream_pool_on_stream(handle);
+
+  size_t total_rows_processed = 0;
+  for (size_t i = 0; i < input.size(); i++) {
+    value_t* out_d_ptr = out_D + (i * k * n);
+    IdxType* out_i_ptr = out_I + (i * k * n);
+
+    auto stream = raft::resource::get_next_usable_stream(handle, i);
+
+    if (k <= 64 && rowMajorQuery == rowMajorIndex && rowMajorQuery == true &&
+        (metric == cuvs::distance::DistanceType::L2Unexpanded ||
+         metric == cuvs::distance::DistanceType::L2SqrtUnexpanded ||
+         metric == cuvs::distance::DistanceType::L2Expanded ||
+         metric == cuvs::distance::DistanceType::L2SqrtExpanded)) {
+      fusedL2Knn(D,
+                 out_i_ptr,
+                 out_d_ptr,
+                 input[i],
+                 search_items,
+                 sizes[i],
+                 n,
+                 k,
+                 rowMajorIndex,
+                 rowMajorQuery,
+                 stream,
+                 metric,
+                 input_norms ? (*input_norms)[i] : nullptr,
+                 search_norms);
+
+      // Perform necessary post-processing
+      if (metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+          metric == cuvs::distance::DistanceType::L2SqrtUnexpanded ||
+          metric == cuvs::distance::DistanceType::LpUnexpanded) {
+        value_t p = 0.5;  // standard l2
+        if (metric == cuvs::distance::DistanceType::LpUnexpanded) p = 1.0 / metricArg;
+        raft::linalg::unaryOp<value_t>(
+          res_D,
+          res_D,
+          n * k,
+          [p] __device__(value_t input) { return powf(fabsf(input), p); },
+          stream);
+      }
+    } else {
+      switch (metric) {
+        case cuvs::distance::DistanceType::Haversine:
+          ASSERT(D == 2,
+                 "Haversine distance requires 2 dimensions "
+                 "(latitude / longitude).");
+
+          haversine_knn(out_i_ptr, out_d_ptr, input[i], search_items, sizes[i], n, k, stream);
+          break;
+        default:
+          // Create a new handle with the current stream from the stream pool
+          raft::resources stream_pool_handle(handle);
+          raft::resource::set_cuda_stream(stream_pool_handle, stream);
+
+          auto index = input[i];
+          if (!rowMajorIndex) {
+            index = index_row_major.data() + total_rows_processed * D;
+            total_rows_processed += sizes[i];
+            raft::linalg::transpose(handle, input[i], index, sizes[i], D, stream);
+          }
+
+          tiled_brute_force_knn<value_t, IdxType>(stream_pool_handle,
+                                                  search,
+                                                  index,
+                                                  n,
+                                                  sizes[i],
+                                                  D,
+                                                  k,
+                                                  out_d_ptr,
+                                                  out_i_ptr,
+                                                  metric,
+                                                  metricArg,
+                                                  0,
+                                                  0,
+                                                  input_norms ? (*input_norms)[i] : nullptr,
+                                                  search_norms);
+          break;
+      }
+    }
+
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+  }
+
+  // Sync internal streams if used. We don't need to
+  // sync the user stream because we'll already have
+  // fully serial execution.
+  raft::resource::sync_stream_pool(handle);
+
+  if (input.size() > 1 || translations != nullptr) {
+    // This is necessary for proper index translations. If there are
+    // no translations or partitions to combine, it can be skipped.
+    knn_merge_parts(out_D, out_I, res_D, res_I, n, input.size(), k, userStream, trans.data());
+  }
+
+  if (translations == nullptr) delete id_ranges;
+};
+
+template <typename T, typename IdxT>
+void brute_force_search(
+  raft::resources const& res,
+  const cuvs::neighbors::brute_force::index<T>& idx,
+  raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<T, int64_t, raft::row_major> distances,
+  std::optional<raft::device_vector_view<const T, int64_t>> query_norms = std::nullopt)
+{
+  RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1), "Value of k must match for outputs");
+  RAFT_EXPECTS(idx.dataset().extent(1) == queries.extent(1),
+               "Number of columns in queries must match brute force index");
+
+  auto k = neighbors.extent(1);
+  auto d = idx.dataset().extent(1);
+
+  std::vector<T*> dataset    = {const_cast<T*>(idx.dataset().data_handle())};
+  std::vector<int64_t> sizes = {idx.dataset().extent(0)};
+  std::vector<T*> norms;
+  if (idx.has_norms()) { norms.push_back(const_cast<T*>(idx.norms().data_handle())); }
+
+  brute_force_knn_impl<int64_t, IdxT, T>(res,
+                                         dataset,
+                                         sizes,
+                                         d,
+                                         const_cast<T*>(queries.data_handle()),
+                                         queries.extent(0),
+                                         neighbors.data_handle(),
+                                         distances.data_handle(),
+                                         k,
+                                         true,
+                                         true,
+                                         nullptr,
+                                         idx.metric(),
+                                         idx.metric_arg(),
+                                         norms.size() ? &norms : nullptr,
+                                         query_norms ? query_norms->data_handle() : nullptr);
+}
+
+template <typename T>
+cuvs::neighbors::brute_force::index<T> build(
+  raft::resources const& res,
+  raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+  cuvs::distance::DistanceType metric,
+  T metric_arg)
+{
+  // certain distance metrics can benefit by pre-calculating the norms for the index dataset
+  // which lets us avoid calculating these at query time
+  std::optional<raft::device_vector<T, int64_t>> norms;
+  auto dataset_storage = std::optional<raft::device_matrix<T, int64_t>>{};
+  auto dataset_view    = [&res, &dataset_storage, dataset]() {
+    if constexpr (std::is_same_v<decltype(dataset),
+                                 raft::device_matrix_view<const T, int64_t, raft::row_major>>) {
+      return dataset;
+    } else {
+      dataset_storage =
+        raft::make_device_matrix<T, int64_t>(res, dataset.extent(0), dataset.extent(1));
+      raft::copy(res, dataset_storage->view(), dataset);
+      return raft::make_const_mdspan(dataset_storage->view());
+    }
+  }();
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+      metric == cuvs::distance::DistanceType::CosineExpanded) {
+    norms = raft::make_device_vector<T, int64_t>(res, dataset.extent(0));
+    // cosine needs the l2norm, where as l2 distances needs the squared norm
+    if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+      raft::linalg::norm(res,
+                         dataset_view,
+                         norms->view(),
+                         raft::linalg::NormType::L2Norm,
+                         raft::linalg::Apply::ALONG_ROWS,
+                         raft::sqrt_op{});
+    } else {
+      raft::linalg::norm(res,
+                         dataset_view,
+                         norms->view(),
+                         raft::linalg::NormType::L2Norm,
+                         raft::linalg::Apply::ALONG_ROWS);
+    }
+  }
+
+  return cuvs::neighbors::brute_force::index<T>(res, dataset, std::move(norms), metric, metric_arg);
+}
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/knn_merge_parts.cuh b/cpp/src/neighbors/detail/knn_merge_parts.cuh
new file mode 100644
index 0000000000..0e9410c7b4
--- /dev/null
+++ b/cpp/src/neighbors/detail/knn_merge_parts.cuh
@@ -0,0 +1,182 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/error.hpp>
+#include <raft/neighbors/detail/faiss_select/Select.cuh>
+#include <raft/util/cuda_dev_essentials.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::detail {
+
+template <typename value_idx = std::int64_t,
+          typename value_t   = float,
+          int warp_q,
+          int thread_q,
+          int tpb>
+RAFT_KERNEL knn_merge_parts_kernel(const value_t* inK,
+                                   const value_idx* inV,
+                                   value_t* outK,
+                                   value_idx* outV,
+                                   size_t n_samples,
+                                   int n_parts,
+                                   value_t initK,
+                                   value_idx initV,
+                                   int k,
+                                   value_idx* translations)
+{
+  constexpr int kNumWarps = tpb / raft::WarpSize;
+
+  __shared__ value_t smemK[kNumWarps * warp_q];
+  __shared__ value_idx smemV[kNumWarps * warp_q];
+
+  /**
+   * Uses shared memory
+   */
+  raft::neighbors::detail::faiss_select::BlockSelect<
+    value_t,
+    value_idx,
+    false,
+    raft::neighbors::detail::faiss_select::Comparator<value_t>,
+    warp_q,
+    thread_q,
+    tpb>
+    heap(initK, initV, smemK, smemV, k);
+
+  // Grid is exactly sized to rows available
+  int row     = blockIdx.x;
+  int total_k = k * n_parts;
+
+  int i = threadIdx.x;
+
+  // Get starting pointers for cols in current thread
+  int part       = i / k;
+  size_t row_idx = (row * k) + (part * n_samples * k);
+
+  int col = i % k;
+
+  const value_t* inKStart   = inK + (row_idx + col);
+  const value_idx* inVStart = inV + (row_idx + col);
+
+  int limit             = raft::Pow2<raft::WarpSize>::roundDown(total_k);
+  value_idx translation = 0;
+
+  for (; i < limit; i += tpb) {
+    translation = translations[part];
+    heap.add(*inKStart, (*inVStart) + translation);
+
+    part    = (i + tpb) / k;
+    row_idx = (row * k) + (part * n_samples * k);
+
+    col = (i + tpb) % k;
+
+    inKStart = inK + (row_idx + col);
+    inVStart = inV + (row_idx + col);
+  }
+
+  // Handle last remainder fraction of a warp of elements
+  if (i < total_k) {
+    translation = translations[part];
+    heap.addThreadQ(*inKStart, (*inVStart) + translation);
+  }
+
+  heap.reduce();
+
+  for (int i = threadIdx.x; i < k; i += tpb) {
+    outK[row * k + i] = smemK[i];
+    outV[row * k + i] = smemV[i];
+  }
+}
+
+template <typename value_idx = std::int64_t, typename value_t = float, int warp_q, int thread_q>
+inline void knn_merge_parts_impl(const value_t* inK,
+                                 const value_idx* inV,
+                                 value_t* outK,
+                                 value_idx* outV,
+                                 size_t n_samples,
+                                 int n_parts,
+                                 int k,
+                                 cudaStream_t stream,
+                                 value_idx* translations)
+{
+  auto grid = dim3(n_samples);
+
+  constexpr int n_threads = (warp_q < 1024) ? 128 : 64;
+  auto block              = dim3(n_threads);
+
+  auto kInit = std::numeric_limits<value_t>::max();
+  auto vInit = -1;
+  knn_merge_parts_kernel<value_idx, value_t, warp_q, thread_q, n_threads>
+    <<<grid, block, 0, stream>>>(
+      inK, inV, outK, outV, n_samples, n_parts, kInit, vInit, k, translations);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/**
+ * @brief Merge knn distances and index matrix, which have been partitioned
+ * by row, into a single matrix with only the k-nearest neighbors.
+ *
+ * @param inK partitioned knn distance matrix
+ * @param inV partitioned knn index matrix
+ * @param outK merged knn distance matrix
+ * @param outV merged knn index matrix
+ * @param n_samples number of samples per partition
+ * @param n_parts number of partitions
+ * @param k number of neighbors per partition (also number of merged neighbors)
+ * @param stream CUDA stream to use
+ * @param translations mapping of index offsets for each partition
+ */
+template <typename value_idx = std::int64_t, typename value_t = float>
+inline void knn_merge_parts(const value_t* inK,
+                            const value_idx* inV,
+                            value_t* outK,
+                            value_idx* outV,
+                            size_t n_samples,
+                            int n_parts,
+                            int k,
+                            cudaStream_t stream,
+                            value_idx* translations)
+{
+  if (k == 1)
+    knn_merge_parts_impl<value_idx, value_t, 1, 1>(
+      inK, inV, outK, outV, n_samples, n_parts, k, stream, translations);
+  else if (k <= 32)
+    knn_merge_parts_impl<value_idx, value_t, 32, 2>(
+      inK, inV, outK, outV, n_samples, n_parts, k, stream, translations);
+  else if (k <= 64)
+    knn_merge_parts_impl<value_idx, value_t, 64, 3>(
+      inK, inV, outK, outV, n_samples, n_parts, k, stream, translations);
+  else if (k <= 128)
+    knn_merge_parts_impl<value_idx, value_t, 128, 3>(
+      inK, inV, outK, outV, n_samples, n_parts, k, stream, translations);
+  else if (k <= 256)
+    knn_merge_parts_impl<value_idx, value_t, 256, 4>(
+      inK, inV, outK, outV, n_samples, n_parts, k, stream, translations);
+  else if (k <= 512)
+    knn_merge_parts_impl<value_idx, value_t, 512, 8>(
+      inK, inV, outK, outV, n_samples, n_parts, k, stream, translations);
+  else if (k <= 1024)
+    knn_merge_parts_impl<value_idx, value_t, 1024, 8>(
+      inK, inV, outK, outV, n_samples, n_parts, k, stream, translations);
+  else
+    THROW("Unimplemented for k=%d, knn_merge_parts works for k<=1024", k);
+}
+}  // namespace cuvs::neighbors::detail

From 11b3ecf4c89364709dc7a8ee12ca190450421a49 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Thu, 16 May 2024 23:59:46 -0400
Subject: [PATCH 054/623] ConfigureCUDA.cmake now sets CUVS_ prefixed variables
 (#66)

This makes sure that CUVS uses the compile flags that are required for RAPIDS C++ projects.

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/66
---
 cpp/cmake/modules/ConfigureCUDA.cmake | 32 +++++++++++++--------------
 cpp/src/neighbors/ivf_pq_index.cpp    |  6 ++---
 cpp/test/neighbors/ann_ivf_pq.cuh     |  4 ++--
 3 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index ea8a077b0c..2134482917 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+# Copyright (c) 2018-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -13,45 +13,45 @@
 # =============================================================================
 
 if(DISABLE_DEPRECATION_WARNINGS)
-  list(APPEND RAFT_CXX_FLAGS -Wno-deprecated-declarations)
-  list(APPEND RAFT_CUDA_FLAGS -Xcompiler=-Wno-deprecated-declarations)
+  list(APPEND CUVS_CXX_FLAGS -Wno-deprecated-declarations)
+  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wno-deprecated-declarations)
 endif()
 
 # Be very strict when compiling with GCC as host compiler (and thus more lenient when compiling with
 # clang)
 if(CMAKE_COMPILER_IS_GNUCXX)
-  list(APPEND RAFT_CXX_FLAGS -Wall -Werror -Wno-unknown-pragmas -Wno-error=deprecated-declarations)
-  list(APPEND RAFT_CUDA_FLAGS -Xcompiler=-Wall,-Werror,-Wno-error=deprecated-declarations)
+  list(APPEND CUVS_CXX_FLAGS -Wall -Werror -Wno-unknown-pragmas -Wno-error=deprecated-declarations)
+  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wall,-Werror,-Wno-error=deprecated-declarations)
 
   # set warnings as errors
   if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 11.2.0)
-    list(APPEND RAFT_CUDA_FLAGS -Werror=all-warnings)
+    list(APPEND CUVS_CUDA_FLAGS -Werror=all-warnings)
   endif()
 endif()
 
 if(CUDA_LOG_COMPILE_TIME)
-  list(APPEND RAFT_CUDA_FLAGS "--time=nvcc_compile_log.csv")
+  list(APPEND CUVS_CUDA_FLAGS "--time=nvcc_compile_log.csv")
 endif()
 
-list(APPEND RAFT_CUDA_FLAGS --expt-extended-lambda --expt-relaxed-constexpr)
-list(APPEND RAFT_CXX_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
-list(APPEND RAFT_CUDA_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
+list(APPEND CUVS_CUDA_FLAGS --expt-extended-lambda --expt-relaxed-constexpr)
+list(APPEND CUVS_CXX_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
+list(APPEND CUVS_CUDA_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
 # make sure we produce smallest binary size
-list(APPEND RAFT_CUDA_FLAGS -Xfatbin=-compress-all)
+list(APPEND CUVS_CUDA_FLAGS -Xfatbin=-compress-all)
 
 # Option to enable line info in CUDA device compilation to allow introspection when profiling /
 # memchecking
 if(CUDA_ENABLE_LINEINFO)
-  list(APPEND RAFT_CUDA_FLAGS -lineinfo)
+  list(APPEND CUVS_CUDA_FLAGS -lineinfo)
 endif()
 
 if(OpenMP_FOUND)
-  list(APPEND RAFT_CUDA_FLAGS -Xcompiler=${OpenMP_CXX_FLAGS})
+  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=${OpenMP_CXX_FLAGS})
 endif()
 
 # Debug options
 if(CMAKE_BUILD_TYPE MATCHES Debug)
-  message(VERBOSE "RAFT: Building with debugging flags")
-  list(APPEND RAFT_CUDA_FLAGS -G -Xcompiler=-rdynamic)
-  list(APPEND RAFT_CUDA_FLAGS -Xptxas --suppress-stack-size-warning)
+  message(VERBOSE "cuVS: Building with debugging flags")
+  list(APPEND CUVS_CUDA_FLAGS -G -Xcompiler=-rdynamic)
+  list(APPEND CUVS_CUDA_FLAGS -Xptxas --suppress-stack-size-warning)
 endif()
diff --git a/cpp/src/neighbors/ivf_pq_index.cpp b/cpp/src/neighbors/ivf_pq_index.cpp
index 1e19198671..fcb2c976dd 100644
--- a/cpp/src/neighbors/ivf_pq_index.cpp
+++ b/cpp/src/neighbors/ivf_pq_index.cpp
@@ -47,13 +47,13 @@ index<IdxT>::index(raft::resources const& handle,
     pq_bits_(pq_bits),
     pq_dim_(pq_dim == 0 ? calculate_pq_dim(dim) : pq_dim),
     conservative_memory_allocation_(conservative_memory_allocation),
-    pq_centers_{raft::make_device_mdarray<float>(handle, make_pq_centers_extents())},
     lists_{n_lists},
-    rotation_matrix_{
-      raft::make_device_matrix<float, uint32_t>(handle, this->rot_dim(), this->dim())},
     list_sizes_{raft::make_device_vector<uint32_t, uint32_t>(handle, n_lists)},
+    pq_centers_{raft::make_device_mdarray<float>(handle, make_pq_centers_extents())},
     centers_{raft::make_device_matrix<float, uint32_t>(handle, n_lists, this->dim_ext())},
     centers_rot_{raft::make_device_matrix<float, uint32_t>(handle, n_lists, this->rot_dim())},
+    rotation_matrix_{
+      raft::make_device_matrix<float, uint32_t>(handle, this->rot_dim(), this->dim())},
     data_ptrs_{raft::make_device_vector<uint8_t*, uint32_t>(handle, n_lists)},
     inds_ptrs_{raft::make_device_vector<IdxT*, uint32_t>(handle, n_lists)},
     accum_sorted_sizes_{raft::make_host_vector<IdxT, uint32_t>(n_lists + 1)}
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index 19442e0b52..0a17aec120 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -354,8 +354,8 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
                                   cuvs::Compare<uint8_t>{}));
 
     // Another test with the API that take list_data directly
-    auto list_data  = index->lists()[label]->data.view();
-    uint32_t n_take = 4;
+    [[maybe_unused]] auto list_data = index->lists()[label]->data.view();
+    uint32_t n_take                 = 4;
     ASSERT_TRUE(row_offset + n_take < n_rows);
     auto codes2 = raft::make_device_matrix<uint8_t>(handle_, n_take, index->pq_dim());
     ivf_pq::helpers::codepacker::unpack_list_data(

From 7c1054227d3e64e73da6509562e238b2a4e1686c Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Fri, 17 May 2024 19:35:16 +0200
Subject: [PATCH 055/623] Fix IVF-PQ helper functions (#116)

Some IVF-PQ helper functions needed for FAISS were missing.

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/116
---
 .gitignore                                    |   2 +
 cpp/include/cuvs/neighbors/ivf_pq.hpp         | 672 ++++++++++++++++++
 cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp | 273 -------
 cpp/src/neighbors/detail/ann_utils.cuh        |   4 +-
 cpp/src/neighbors/ivf_flat_index.cpp          |   4 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |  15 +-
 .../neighbors/ivf_pq/ivf_pq_build_common.cu   | 251 +++++--
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |   4 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |  35 +-
 docs/source/cpp_api/neighbors_ivf_pq.rst      |  12 +
 10 files changed, 900 insertions(+), 372 deletions(-)
 delete mode 100644 cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp

diff --git a/.gitignore b/.gitignore
index 68996dbdf0..4b6f46320f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,6 +26,8 @@ dask-worker-space/
 *.bin
 bench/ann/data
 temporary_*.json
+rust/target/
+rust/Cargo.lock
 
 ## scikit-build
 _skbuild
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index f013615ded..c6b1b2ee74 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -1064,4 +1064,676 @@ void deserialize(raft::resources const& handle,
  * @}
  */
 
+namespace helpers {
+/**
+ * @defgroup ivf_pq_cpp_helpers IVF-PQ helper methods
+ * @{
+ */
+namespace codepacker {
+/**
+ * @addtogroup ivf_pq_cpp_helpers
+ * @{
+ */
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Bit compression is removed, which means output will have pq_dim dimensional vectors (one code per
+ * byte, instead of ceildiv(pq_dim * pq_bits, 8) bytes of pq codes).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data = index.lists()[label]->data.view();
+ *   // allocate the buffer for the output
+ *   uint32_t n_take = 4;
+ *   auto codes = raft::make_device_matrix<uint8_t>(res, n_take, index.pq_dim());
+ *   uint32_t offset = 0;
+ *   // unpack n_take elements from the list
+ *   ivf_pq::helpers::codepacker::unpack(res, list_data, index.pq_bits(), offset, codes.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] list_data block to read from
+ * @param[in] pq_bits bit length of encoded vector elements
+ * @param[in] offset
+ *   How many records in the list to skip.
+ * @param[out] codes
+ *   the destination buffer [n_take, index.pq_dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be smaller than the list size.
+ */
+void unpack(
+  raft::resources const& res,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t pq_bits,
+  uint32_t offset,
+  raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> codes);
+
+/**
+ * @brief Unpack `n_rows` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`. The output codes of a single vector are contiguous, not expanded to
+ * one code per byte, which means the output has ceildiv(pq_dim * pq_bits, 8) bytes per PQ encoded
+ * vector.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   auto list_data = index.lists()[label]->data.view();
+ *   // allocate the buffer for the output
+ *   uint32_t n_rows = 4;
+ *   auto codes = raft::make_device_matrix<uint8_t>(
+ *     res, n_rows, raft::ceildiv(index.pq_dim() * index.pq_bits(), 8));
+ *   uint32_t offset = 0;
+ *   // unpack n_rows elements from the list
+ *   ivf_pq::helpers::codepacker::unpack_contiguous(
+ *     res, list_data, index.pq_bits(), offset, n_rows, index.pq_dim(), codes.data_handle());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] list_data block to read from
+ * @param[in] pq_bits bit length of encoded vector elements
+ * @param[in] offset
+ *   How many records in the list to skip.
+ * @param[in] n_rows How many records to unpack
+ * @param[in] pq_dim The dimensionality of the PQ compressed records
+ * @param[out] codes
+ *   the destination buffer [n_rows, ceildiv(pq_dim * pq_bits, 8)].
+ *   The length `n_rows` defines how many records to unpack,
+ *   it must be smaller than the list size.
+ */
+void unpack_contiguous(
+  raft::resources const& res,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t pq_bits,
+  uint32_t offset,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  uint8_t* codes);
+
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<uint8_t>(res, n_vec, index.pq_dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_pq::helpers::codepacker::pack(
+ *       res, make_const_mdspan(codes.view()), index.pq_bits(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] codes flat PQ codes, one code per byte [n_vec, pq_dim]
+ * @param[in] pq_bits bit length of encoded vector elements
+ * @param[in] offset how many records to skip before writing the data into the list
+ * @param[in] list_data block to write into
+ */
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
+          uint32_t pq_bits,
+          uint32_t offset,
+          raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+            list_data);
+
+/**
+ * Write flat PQ codes into an existing list by the given offset. The input codes of a single vector
+ * are contiguous (not expanded to one code per byte).
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_rows records).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<uint8_t>(
+ *     res, n_rows, raft::ceildiv(index.pq_dim() * index.pq_bits(), 8));
+ *   ... prepare compressed vectors to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position. If the current size of the list
+ *   // is greater than 42, this will overwrite the codes starting at this offset.
+ *   ivf_pq::helpers::codepacker::pack_contiguous(
+ *     res, codes.data_handle(), n_rows, index.pq_dim(), index.pq_bits(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] codes flat PQ codes, [n_vec, ceildiv(pq_dim * pq_bits, 8)]
+ * @param[in] n_rows number of records
+ * @param[in] pq_dim
+ * @param[in] pq_bits bit length of encoded vector elements
+ * @param[in] offset how many records to skip before writing the data into the list
+ * @param[in] list_data block to write into
+ */
+void pack_contiguous(
+  raft::resources const& res,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  uint32_t pq_bits,
+  uint32_t offset,
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data);
+
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * The list is identified by its label.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will write into the 137th cluster
+ *   uint32_t label = 137;
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<const uint8_t>(res, n_vec, index.pq_dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_pq::helpers::codepacker::pack_list_data(res, &index, codes_to_pack, label, 42);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index IVF-PQ index.
+ * @param[in] codes flat PQ codes, one code per byte [n_rows, pq_dim]
+ * @param[in] label The id of the list (cluster) into which we write.
+ * @param[in] offset how many records to skip before writing the data into the list
+ */
+void pack_list_data(raft::resources const& res,
+                    index<int64_t>* index,
+                    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
+                    uint32_t label,
+                    uint32_t offset);
+
+/**
+ * Write flat PQ codes into an existing list by the given offset. Use this when the input
+ * vectors are PQ encoded and not expanded to one code per byte.
+ *
+ * The list is identified by its label.
+ *
+ * NB: no memory allocation happens here; the list into which the vectors are packed must fit offset
+ * + n_rows rows.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   raft::resources res;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(res, index_params, dataset, N, D);
+ *   // allocate the buffer for n_rows input codes. Each vector occupies
+ *   // raft::ceildiv(index.pq_dim() * index.pq_bits(), 8) bytes because
+ *   // codes are compressed and without gaps.
+ *   auto codes = raft::make_device_matrix<const uint8_t>(
+ *     res, n_rows, raft::ceildiv(index.pq_dim() * index.pq_bits(), 8));
+ *   ... prepare the compressed vectors to pack into the list in codes ...
+ *   // the first n_rows codes in the fourth IVF list are to be overwritten.
+ *   uint32_t label = 3;
+ *   // write codes into the list starting from the 0th position
+ *   ivf_pq::helpers::codepacker::pack_contiguous_list_data(
+ *     res, &index, codes.data_handle(), n_rows, label, 0);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ * @param[in] codes flat contiguous PQ codes [n_rows, ceildiv(pq_dim * pq_bits, 8)]
+ * @param[in] n_rows how many records to pack
+ * @param[in] label The id of the list (cluster) into which we write.
+ * @param[in] offset how many records to skip before writing the data into the list
+ */
+void pack_contiguous_list_data(raft::resources const& res,
+                               index<int64_t>* index,
+                               uint8_t* codes,
+                               uint32_t n_rows,
+                               uint32_t label,
+                               uint32_t offset);
+
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`, one code per byte (independently of pq_bits).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will unpack the fourth cluster
+ *   uint32_t label = 3;
+ *   // Get the list size
+ *   uint32_t list_size = 0;
+ *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
+ * resource::get_cuda_stream(res)); resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto codes = raft::make_device_matrix<float>(res, list_size, index.pq_dim());
+ *   // unpack the whole list
+ *   ivf_pq::helpers::codepacker::unpack_list_data(res, index, codes.view(), label, 0);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[out] out_codes
+ *   the destination buffer [n_take, index.pq_dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ * @param[in] offset
+ *   How many records in the list to skip.
+ */
+void unpack_list_data(raft::resources const& res,
+                      const index<int64_t>& index,
+                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+                      uint32_t label,
+                      uint32_t offset);
+
+/**
+ * @brief Unpack a series of records of a single list (cluster) in the compressed index
+ * by their in-list offsets, one code per byte (independently of pq_bits).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will unpack the fourth cluster
+ *   uint32_t label = 3;
+ *   // Create the selection vector
+ *   auto selected_indices = raft::make_device_vector<uint32_t>(res, 4);
+ *   ... fill the indices ...
+ *   resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto codes = raft::make_device_matrix<float>(res, selected_indices.size(), index.pq_dim());
+ *   // decode the whole list
+ *   ivf_pq::helpers::codepacker::unpack_list_data(
+ *       res, index, selected_indices.view(), codes.view(), label);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] index IVF-PQ index (passed by reference)
+ * @param[in] in_cluster_indices
+ *   The offsets of the selected indices within the cluster.
+ * @param[out] out_codes
+ *   the destination buffer [n_take, index.pq_dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ */
+void unpack_list_data(raft::resources const& res,
+                      const index<int64_t>& index,
+                      raft::device_vector_view<const uint32_t> in_cluster_indices,
+                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+                      uint32_t label);
+
+/**
+ * @brief Unpack `n_rows` consecutive PQ encoded vectors of a single list (cluster) in the
+ * compressed index starting at given `offset`, not expanded to one code per byte. Each code in the
+ * output buffer occupies ceildiv(index.pq_dim() * index.pq_bits(), 8) bytes.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   // We will unpack the whole fourth cluster
+ *   uint32_t label = 3;
+ *   // Get the list size
+ *   uint32_t list_size = 0;
+ *   raft::update_host(&list_size, index.list_sizes().data_handle() + label, 1,
+ *     raft::resource::get_cuda_stream(res));
+ *   raft::resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto codes = raft::make_device_matrix<float>(res, list_size, raft::ceildiv(index.pq_dim() *
+ *     index.pq_bits(), 8));
+ *   // unpack the whole list
+ *   ivf_pq::helpers::codepacker::unpack_list_data(res, index, codes.data_handle(), list_size,
+ * label, 0);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] index IVF-PQ index (passed by reference)
+ * @param[out] out_codes
+ *   the destination buffer [n_rows, ceildiv(index.pq_dim() * index.pq_bits(), 8)].
+ *   The length `n_rows` defines how many records to unpack,
+ *   offset + n_rows must be smaller than or equal to the list size.
+ * @param[in] n_rows how many codes to unpack
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ * @param[in] offset
+ *   How many records in the list to skip.
+ */
+void unpack_contiguous_list_data(raft::resources const& res,
+                                 const index<int64_t>& index,
+                                 uint8_t* out_codes,
+                                 uint32_t n_rows,
+                                 uint32_t label,
+                                 uint32_t offset);
+
+/**
+ * @brief Decode `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will reconstruct the fourth cluster
+ *   uint32_t label = 3;
+ *   // Get the list size
+ *   uint32_t list_size = 0;
+ *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
+ *   resource::get_cuda_stream(res)); resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto decoded_vectors = raft::make_device_matrix<float>(res, list_size, index.dim());
+ *   // decode the whole list
+ *   ivf_pq::helpers::codepacker::reconstruct_list_data(res, index, decoded_vectors.view(), label,
+ * 0);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[out] out_vectors
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to reconstruct,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ * @param[in] offset
+ *   How many records in the list to skip.
+ */
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           uint32_t offset);
+
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           uint32_t offset);
+
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           uint32_t offset);
+
+/**
+ * @brief Decode a series of records of a single list (cluster) in the compressed index
+ * by their in-list offsets.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will reconstruct the fourth cluster
+ *   uint32_t label = 3;
+ *   // Create the selection vector
+ *   auto selected_indices = raft::make_device_vector<uint32_t>(res, 4);
+ *   ... fill the indices ...
+ *   resource::sync_stream(res);
+ *   // allocate the buffer for the output
+ *   auto decoded_vectors = raft::make_device_matrix<float>(
+ *                             res, selected_indices.size(), index.dim());
+ *   // decode the whole list
+ *   ivf_pq::helpers::codepacker::reconstruct_list_data(
+ *       res, index, selected_indices.view(), decoded_vectors.view(), label);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] index
+ * @param[in] in_cluster_indices
+ *   The offsets of the selected indices within the cluster.
+ * @param[out] out_vectors
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to reconstruct,
+ *   it must be smaller than the list size.
+ * @param[in] label
+ *   The id of the list (cluster) to decode.
+ */
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_vector_view<const uint32_t> in_cluster_indices,
+                           raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label);
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_vector_view<const uint32_t> in_cluster_indices,
+                           raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label);
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_vector_view<const uint32_t> in_cluster_indices,
+                           raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label);
+
+/**
+ * @brief Extend one list of the index in-place, by the list label, skipping the classification and
+ * encoding steps.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will extend the fourth cluster
+ *   uint32_t label = 3;
+ *   // We will fill 4 new vectors
+ *   uint32_t n_vec = 4;
+ *   // Indices of the new vectors
+ *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
+ *   ... fill the indices ...
+ *   auto new_codes = raft::make_device_matrix<uint8_t, uint32_t, row_major> new_codes(
+ *       res, n_vec, index.pq_dim());
+ *   ... fill codes ...
+ *   // extend list with new codes
+ *   ivf_pq::helpers::codepacker::extend_list_with_codes(
+ *       res, &index, codes.view(), indices.view(), label);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] new_codes flat PQ codes, one code per byte [n_rows, index.pq_dim()]
+ * @param[in] new_indices source indices [n_rows]
+ * @param[in] label the id of the target list (cluster).
+ */
+void extend_list_with_codes(
+  raft::resources const& res,
+  index<int64_t>* index,
+  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+  raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+  uint32_t label);
+
+/**
+ * @brief Extend one list of the index in-place, by the list label, skipping the classification
+ * step.
+ *
+ *  Usage example:
+ * @code{.cpp}
+ *   // We will extend the fourth cluster
+ *   uint32_t label = 3;
+ *   // We will extend with 4 new vectors
+ *   uint32_t n_vec = 4;
+ *   // Indices of the new vectors
+ *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
+ *   ... fill the indices ...
+ *   auto new_vectors = raft::make_device_matrix<float, uint32_t, row_major> new_codes(
+ *       res, n_vec, index.dim());
+ *   ... fill vectors ...
+ *   // extend list with new vectors
+ *   ivf_pq::helpers::codepacker::extend_list(
+ *       res, &index, new_vectors.view(), indices.view(), label);
+ * @endcode
+ *
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] new_vectors data to encode [n_rows, index.dim()]
+ * @param[in] new_indices source indices [n_rows]
+ * @param[in] label the id of the target list (cluster).
+ */
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const float, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label);
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label);
+void extend_list(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_vectors,
+                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
+                 uint32_t label);
+
+/**
+ * @}
+ */
+};  // namespace codepacker
+
+/**
+ * @brief Remove all data from a single list (cluster) in the index.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // We will erase the fourth cluster (label = 3)
+ *   ivf_pq::helpers::erase_list(res, &index, 3);
+ * @endcode
+ *
+ *
+ * @param[in] res
+ * @param[inout] index
+ * @param[in] label the id of the target list (cluster).
+ */
+void erase_list(raft::resources const& res, index<int64_t>* index, uint32_t label);
+
+/**
+ * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
+ * externally modifying the index without going through the build stage. The data and indices of the
+ * IVF lists will be lost.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_pq::index<int64_t> index(res, index_params, D);
+ *   // reset the index's state and list sizes
+ *   ivf_pq::helpers::reset_index(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ */
+void reset_index(const raft::resources& res, index<int64_t>* index);
+
+/**
+ * @brief Public helper API exposing the computation of the index's rotation matrix.
+ * NB: This is to be used only when the rotation matrix is not already computed through
+ * cuvs::neighbors::ivf_pq::build.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // force random rotation
+ *   index_params.force_random_rotation = true;
+ *   // initialize an empty index
+ *   cuvs::neighbors::ivf_pq::index<int64_t> index(res, index_params, D);
+ *   // reset the index
+ *   reset_index(res, &index);
+ *   // compute the rotation matrix with random_rotation
+ *   cuvs::neighbors::ivf_pq::helpers::make_rotation_matrix(
+ *     res, &index, index_params.force_random_rotation);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ * @param[in] force_random_rotation whether to apply a random rotation matrix on the input data. See
+ * cuvs::neighbors::ivf_pq::index_params for more details.
+ */
+void make_rotation_matrix(raft::resources const& res,
+                          index<int64_t>* index,
+                          bool force_random_rotation);
+
+/**
+ * @brief Public helper API for externally modifying the index's IVF centroids.
+ * NB: The index must be reset before this. Use raft::neighbors::ivf_pq::extend to construct IVF
+ lists according to new centroids.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   // allocate the buffer for the input centers
+ *   auto cluster_centers = raft::make_device_matrix<float, uint32_t>(res, index.n_lists(),
+ index.dim());
+ *   ... prepare ivf centroids in cluster_centers ...
+ *   // reset the index
+ *   reset_index(res, &index);
+ *   // recompute the state of the index
+ *   cuvs::neighbors::ivf_pq::helpers::recompute_internal_state(res, index);
+ *   // Write the IVF centroids
+ *   cuvs::neighbors::ivf_pq::helpers::set_centers(
+                    res,
+                    &index,
+                    cluster_centers);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ * @param[in] cluster_centers new cluster centers [index.n_lists(), index.dim()]
+ */
+void set_centers(raft::resources const& res,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const float, uint32_t> cluster_centers);
+/**
+ * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
+ * modified.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   raft::resources res;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_pq::index<int64_t> index(res, index_params, D);
+ *   ivf_pq::helpers::reset_index(res, &index);
+ *   // resize the first IVF list to hold 5 records
+ *   auto spec = list_spec<uint32_t, int64_t>{
+ *     index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+ *   uint32_t new_size = 5;
+ *   ivf::resize_list(res, list, spec, new_size, 0);
+ *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
+ *   // recompute the internal state of the index
+ *   ivf_pq::helpers::recompute_internal_state(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ */
+void recompute_internal_state(const raft::resources& res, index<int64_t>* index);
+
+/**
+ * @brief Public helper API for fetching a trained index's IVF centroids into a buffer that may be
+ * allocated on either host or device.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   // allocate the buffer for the output centers
+ *   auto cluster_centers = raft::make_device_matrix<float, uint32_t>(
+ *     res, index.n_lists(), index.dim());
+ *   // Extract the IVF centroids into the buffer
+ *   cuvs::neighbors::ivf_pq::helpers::extract_centers(res, index, cluster_centers.data_handle());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] index IVF-PQ index (passed by reference)
+ * @param[out] cluster_centers IVF cluster centers [index.n_lists(), index.dim]
+ */
+void extract_centers(raft::resources const& res,
+                     const index<int64_t>& index,
+                     raft::device_matrix_view<float> cluster_centers);
+/**
+ * @}
+ */
+}  // namespace helpers
+
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp b/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
deleted file mode 100644
index 39e6eb54fd..0000000000
--- a/cpp/include/cuvs/neighbors/ivf_pq_helpers.hpp
+++ /dev/null
@@ -1,273 +0,0 @@
-/*
- * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a raft::copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <cuvs/distance/distance_types.hpp>
-#include <cuvs/neighbors/ivf_list.hpp>
-#include <cuvs/neighbors/ivf_pq.hpp>
-
-#include <raft/core/device_mdarray.hpp>
-#include <raft/core/operators.hpp>
-#include <raft/core/resource/cuda_stream.hpp>
-#include <raft/core/resource/device_memory_resource.hpp>
-#include <raft/core/resources.hpp>
-#include <raft/random/rng_state.hpp>
-
-#include <rmm/cuda_stream_view.hpp>
-#include <rmm/device_uvector.hpp>
-#include <rmm/mr/device/managed_memory_resource.hpp>
-
-#include <memory>
-#include <variant>
-
-namespace cuvs::neighbors::ivf_pq::helpers {
-
-namespace codepacker {
-/**
- * Unpack flat PQ codes from an existing list by the given offset.
- *
- * @param[in] res
- * @param[in] index
- * @param[out] out_codes flat PQ codes, one code per byte [n_rows, pq_dim]
- * @param[in] label
- * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
- */
-void unpack_list_data(raft::resources const& res,
-                      const index<int64_t>& index,
-                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
-                      uint32_t label,
-                      std::variant<uint32_t, const uint32_t*> offset_or_indices);
-/**
- * Write flat PQ codes into an existing list by the given offset.
- *
- * NB: no memory allocation happens here; the list must fit the data (offset + n_rows).
- *
- * @param[in] res
- * @param[in] index
- * @param[out] new_codes
- * @param[in] label
- * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
- */
-void pack_list_data(raft::resources const& res,
-                    index<int64_t>* index,
-                    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
-                    uint32_t label,
-                    std::variant<uint32_t, const uint32_t*> offset_or_indices);
-
-};  // namespace codepacker
-
-/**
- * @brief Fill-in a random orthogonal transformation matrix.
- *
- * @param handle
- * @param force_random_rotation
- * @param n_rows
- * @param n_cols
- * @param[out] rotation_matrix device pointer to a row-major matrix of size [n_rows, n_cols].
- * @param rng random number generator state
- */
-void make_rotation_matrix(raft::resources const& handle,
-                          bool force_random_rotation,
-                          uint32_t n_rows,
-                          uint32_t n_cols,
-                          float* rotation_matrix,
-                          raft::random::RngState rng = raft::random::RngState(7ULL));
-
-/**
- * @brief Set cluster centers on an index
- *
- * @param handle
- * @param index
- * @param cluster_centers
- */
-void set_centers(raft::resources const& handle,
-                 index<int64_t>* index,
-                 const float* cluster_centers);
-
-/**
- * @brief Decode `n_take` consecutive records of a single list (cluster) in the compressed index
- * starting at given `offset`.
- *
- * Usage example:
- * @code{.cpp}
- *   // We will reconstruct the fourth cluster
- *   uint32_t label = 3;
- *   // Get the list size
- *   uint32_t list_size = 0;
- *   raft::copy(&list_size, index.list_sizes().data_handle() + label, 1,
- *   resource::get_cuda_stream(res)); resource::sync_stream(res);
- *   // allocate the buffer for the output
- *   auto decoded_vectors = raft::make_device_matrix<float>(res, list_size, index.dim());
- *   // decode the whole list
- *   ivf_pq::helpers::reconstruct_list_data(res, index, decoded_vectors.view(), label, 0);
- * @endcode
- *
- * @param[in] res
- * @param[in] index
- * @param[out] out_vectors
- *   the destination buffer [n_take, index.dim()].
- *   The length `n_take` defines how many records to reconstruct,
- *   it must be smaller than the list size.
- * @param[in] label
- *   The id of the list (cluster) to decode.
- * @param[in] offset_or_indices
- *   How many records in the list to skip.
- */
-void reconstruct_list_data(raft::resources const& res,
-                           const index<int64_t>& index,
-                           raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
-                           uint32_t label,
-                           std::variant<uint32_t, const uint32_t*> offset_or_indices);
-void reconstruct_list_data(raft::resources const& res,
-                           const index<int64_t>& index,
-                           raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
-                           uint32_t label,
-                           std::variant<uint32_t, const uint32_t*> offset_or_indices);
-void reconstruct_list_data(raft::resources const& res,
-                           const index<int64_t>& index,
-                           raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_vectors,
-                           uint32_t label,
-                           std::variant<uint32_t, const uint32_t*> offset_or_indices);
-
-/**
- * Write flat PQ codes into an existing list by the given offset. The input codes of a single vector
- * are contiguous (not expanded to one code per byte).
- *
- * NB: no memory allocation happens here; the list must fit the data (offset + n_rows records).
- *
- * Usage example:
- * @code{.cpp}
- *   raft::resources res;
- *   auto list_data  = index.lists()[label]->data.view();
- *   // allocate the buffer for the input codes
- *   auto codes = raft::make_device_matrix<uint8_t>(
- *     res, n_rows, raft::ceildiv(index.pq_dim() * index.pq_bits(), 8));
- *   ... prepare compressed vectors to pack into the list in codes ...
- *   // write codes into the list starting from the 42nd position. If the current size of the list
- *   // is greater than 42, this will overwrite the codes starting at this offset.
- *   ivf_pq::helpers::codepacker::pack_contiguous(
- *     res, codes.data_handle(), n_rows, index.pq_dim(), index.pq_bits(), 42, list_data);
- * @endcode
- *
- * @param[in] res raft resource
- * @param[inout] index
- * @param[in] new_codes flat PQ codes, [n_vec, ceildiv(pq_dim * pq_bits, 8)]
- * @param[in] n_rows number of records
- * @param[in] label The id of the list (cluster) to decode.
- * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
- */
-void pack_contiguous_list_data(raft::resources const& res,
-                               index<int64_t>* index,
-                               const uint8_t* new_codes,
-                               uint32_t n_rows,
-                               uint32_t label,
-                               std::variant<uint32_t, const uint32_t*> offset_or_indices);
-
-/**
- * @brief Extend one list of the index in-place, by the list label, skipping the classification and
- * encoding steps.
- *
- * Usage example:
- * @code{.cpp}
- *   // We will extend the fourth cluster
- *   uint32_t label = 3;
- *   // We will fill 4 new vectors
- *   uint32_t n_vec = 4;
- *   // Indices of the new vectors
- *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
- *   ... fill the indices ...
- *   auto new_codes = raft::make_device_matrix<uint8_t, uint32_t, row_major> new_codes(
- *       res, n_vec, index.pq_dim());
- *   ... fill codes ...
- *   // extend list with new codes
- *   ivf_pq::helpers::extend_list_with_codes(
- *       res, &index, codes.view(), indices.view(), label);
- * @endcode
- *
- * @param[in] res
- * @param[inout] index
- * @param[in] new_codes flat PQ codes, one code per byte [n_rows, index.pq_dim()]
- * @param[in] new_indices source indices [n_rows]
- * @param[in] label the id of the target list (cluster).
- */
-void extend_list_with_codes(
-  raft::resources const& res,
-  index<int64_t>* index,
-  raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
-  raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
-  uint32_t label);
-
-/**
- * @brief Extend one list of the index in-place, by the list label, skipping the classification
- * step.
- *
- *  Usage example:
- * @code{.cpp}
- *   // We will extend the fourth cluster
- *   uint32_t label = 3;
- *   // We will extend with 4 new vectors
- *   uint32_t n_vec = 4;
- *   // Indices of the new vectors
- *   auto indices = raft::make_device_vector<uint32_t>(res, n_vec);
- *   ... fill the indices ...
- *   auto new_vectors = raft::make_device_matrix<float, uint32_t, row_major> new_codes(
- *       res, n_vec, index.dim());
- *   ... fill vectors ...
- *   // extend list with new vectors
- *   ivf_pq::helpers::extend_list(
- *       res, &index, new_vectors.view(), indices.view(), label);
- * @endcode
- *
- *
- * @param[in] res
- * @param[inout] index
- * @param[in] new_vectors data to encode [n_rows, index.dim()]
- * @param[in] new_indices source indices [n_rows]
- * @param[in] label the id of the target list (cluster).
- */
-void extend_list(raft::resources const& res,
-                 index<int64_t>* index,
-                 raft::device_matrix_view<const float, uint32_t, raft::row_major> new_vectors,
-                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
-                 uint32_t label);
-void extend_list(raft::resources const& res,
-                 index<int64_t>* index,
-                 raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> new_vectors,
-                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
-                 uint32_t label);
-void extend_list(raft::resources const& res,
-                 index<int64_t>* index,
-                 raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_vectors,
-                 raft::device_vector_view<const int64_t, uint32_t, raft::row_major> new_indices,
-                 uint32_t label);
-/**
- * @brief Remove all data from a single list (cluster) in the index.
- *
- * Usage example:
- * @code{.cpp}
- *   // We will erase the fourth cluster (label = 3)
- *   ivf_pq::helpers::erase_list(res, &index, 3);
- * @endcode
- *
- *
- * @param[in] res
- * @param[inout] index
- * @param[in] label the id of the target list (cluster).
- */
-void erase_list(raft::resources const& res, index<int64_t>* index, uint32_t label);
-
-}  // namespace cuvs::neighbors::ivf_pq::helpers
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index 222b4c6a76..c634fb146c 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -1,9 +1,9 @@
 /*
- * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
- * You may obtain a raft::copy of the License at
+ * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
index b2fbbfc12c..b25bb051a1 100644
--- a/cpp/src/neighbors/ivf_flat_index.cpp
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -41,10 +41,10 @@ index<T, IdxT>::index(raft::resources const& res,
     metric_(metric),
     adaptive_centers_(adaptive_centers),
     conservative_memory_allocation_{conservative_memory_allocation},
-    centers_(raft::make_device_matrix<float, uint32_t>(res, n_lists, dim)),
-    center_norms_(std::nullopt),
     lists_{n_lists},
     list_sizes_{raft::make_device_vector<uint32_t, uint32_t>(res, n_lists)},
+    centers_(raft::make_device_matrix<float, uint32_t>(res, n_lists, dim)),
+    center_norms_(std::nullopt),
     data_ptrs_{raft::make_device_vector<T*, uint32_t>(res, n_lists)},
     inds_ptrs_{raft::make_device_vector<IdxT*, uint32_t>(res, n_lists)},
     accum_sorted_sizes_{raft::make_host_vector<IdxT, uint32_t>(n_lists + 1)}
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index b843ea067a..07e3c497c7 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1,9 +1,9 @@
 /*
- * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
- * You may obtain a raft::copy of the License at
+ * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
@@ -24,7 +24,6 @@
 #include <cuvs/distance/distance_types.hpp>
 #include <cuvs/neighbors/ivf_list.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
-#include <cuvs/neighbors/ivf_pq_helpers.hpp>
 
 #include "../detail/ann_utils.cuh"  // utils::mapping
 
@@ -1393,7 +1392,7 @@ void extend_list_with_codes(
   // Allocate memory and write indices
   auto offset = extend_list_prepare(res, index, new_indices, label);
   // Pack the data
-  helpers::codepacker::pack_list_data(res, index, new_codes, label, offset);
+  pack_list_data(res, index, new_codes, label, offset);
   // Update the pointers and the sizes
   ivf::detail::recompute_internal_state(res, *index);
 }
@@ -1780,13 +1779,9 @@ auto build(raft::resources const& handle,
                                             utils::mapping<float>());
 
     // Make rotation matrix
-    helpers::make_rotation_matrix(handle,
-                                  params.force_random_rotation,
-                                  index.rot_dim(),
-                                  index.dim(),
-                                  index.rotation_matrix().data_handle());
+    helpers::make_rotation_matrix(handle, &index, params.force_random_rotation);
 
-    helpers::set_centers(handle, &index, cluster_centers);
+    helpers::set_centers(handle, &index, raft::make_const_mdspan(centers_view));
 
     // Train PQ codebooks
     switch (index.codebook_kind()) {
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
index b08ebb17e2..db488b9c08 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
@@ -1,9 +1,9 @@
 /*
- * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
- * You may obtain a raft::copy of the License at
+ * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
@@ -24,104 +24,153 @@ namespace cuvs::neighbors::ivf_pq::helpers {
 
 namespace codepacker {
 
-void unpack_list_data(raft::resources const& res,
-                      const index<int64_t>& index,
-                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
-                      uint32_t label,
-                      std::variant<uint32_t, const uint32_t*> offset_or_indices)
+void unpack(
+  raft::resources const& res,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t pq_bits,
+  uint32_t offset,
+  raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> codes)
 {
-  detail::unpack_list_data(res, index, out_codes, label, offset_or_indices);
+  detail::unpack_list_data(codes, list_data, offset, pq_bits, raft::resource::get_cuda_stream(res));
+}
+
+void unpack_contiguous(
+  raft::resources const& res,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t pq_bits,
+  uint32_t offset,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  uint8_t* codes)
+{
+  detail::unpack_contiguous_list_data(
+    codes, list_data, n_rows, pq_dim, offset, pq_bits, raft::resource::get_cuda_stream(res));
+}
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
+          uint32_t pq_bits,
+          uint32_t offset,
+          raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+            list_data)
+{
+  detail::pack_list_data(list_data, codes, offset, pq_bits, raft::resource::get_cuda_stream(res));
+}
+
+void pack_contiguous(
+  raft::resources const& res,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  uint32_t pq_bits,
+  uint32_t offset,
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data)
+{
+  detail::pack_contiguous_list_data(
+    list_data, codes, n_rows, pq_dim, offset, pq_bits, raft::resource::get_cuda_stream(res));
 }
 
 void pack_list_data(raft::resources const& res,
                     index<int64_t>* index,
-                    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> new_codes,
+                    raft::device_matrix_view<const uint8_t, uint32_t, raft::row_major> codes,
                     uint32_t label,
-                    std::variant<uint32_t, const uint32_t*> offset_or_indices)
+                    uint32_t offset)
 {
-  detail::pack_list_data(res, index, new_codes, label, offset_or_indices);
+  detail::pack_list_data(res, index, codes, label, offset);
 }
 
-};  // namespace codepacker
+void pack_contiguous_list_data(raft::resources const& res,
+                               index<int64_t>* index,
+                               uint8_t* codes,
+                               uint32_t n_rows,
+                               uint32_t label,
+                               uint32_t offset)
+{
+  detail::pack_contiguous_list_data(res, index, codes, n_rows, label, offset);
+}
 
-void make_rotation_matrix(raft::resources const& handle,
-                          bool force_random_rotation,
-                          uint32_t n_rows,
-                          uint32_t n_cols,
-                          float* rotation_matrix,
-                          raft::random::RngState rng)
+void unpack_list_data(raft::resources const& res,
+                      const index<int64_t>& index,
+                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+                      uint32_t label,
+                      uint32_t offset)
 {
-  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
-    "ivf_pq::make_rotation_matrix(%u * %u)", n_rows, n_cols);
-  auto stream  = raft::resource::get_cuda_stream(handle);
-  bool inplace = n_rows == n_cols;
-  uint32_t n   = std::max(n_rows, n_cols);
-  if (force_random_rotation || !inplace) {
-    rmm::device_uvector<float> buf(inplace ? 0 : n * n, stream);
-    float* mat = inplace ? rotation_matrix : buf.data();
-    raft::random::normal(handle, rng, mat, n * n, 0.0f, 1.0f);
-    raft::linalg::detail::qrGetQ_inplace(handle, mat, n, n, stream);
-    if (!inplace) {
-      RAFT_CUDA_TRY(cudaMemcpy2DAsync(rotation_matrix,
-                                      sizeof(float) * n_cols,
-                                      mat,
-                                      sizeof(float) * n,
-                                      sizeof(float) * n_cols,
-                                      n_rows,
-                                      cudaMemcpyDefault,
-                                      stream));
-    }
-  } else {
-    uint32_t stride = n + 1;
-    auto rotation_matrix_view =
-      raft::make_device_vector_view<float, uint32_t>(rotation_matrix, n * n);
-    raft::linalg::map_offset(handle, rotation_matrix_view, [stride] __device__(uint32_t i) {
-      return static_cast<float>(i % stride == 0u);
-    });
-  }
+  detail::unpack_list_data(res, index, out_codes, label, offset);
 }
 
-void set_centers(raft::resources const& handle, index<int64_t>* index, const float* cluster_centers)
+void unpack_list_data(raft::resources const& res,
+                      const index<int64_t>& index,
+                      raft::device_vector_view<const uint32_t> in_cluster_indices,
+                      raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_codes,
+                      uint32_t label)
+{
+  detail::unpack_list_data<int64_t>(res, index, out_codes, label, in_cluster_indices.data_handle());
+}
+
+void unpack_contiguous_list_data(raft::resources const& res,
+                                 const index<int64_t>& index,
+                                 uint8_t* out_codes,
+                                 uint32_t n_rows,
+                                 uint32_t label,
+                                 uint32_t offset)
 {
-  detail::set_centers(handle, index, cluster_centers);
+  detail::unpack_contiguous_list_data(res, index, out_codes, n_rows, label, offset);
 }
 
 void reconstruct_list_data(raft::resources const& res,
                            const index<int64_t>& index,
                            raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
                            uint32_t label,
-                           std::variant<uint32_t, const uint32_t*> offset_or_indices)
+                           uint32_t offset)
 {
-  detail::reconstruct_list_data<float, int64_t>(res, index, out_vectors, label, offset_or_indices);
+  detail::reconstruct_list_data<float, int64_t>(res, index, out_vectors, label, offset);
 }
 
 void reconstruct_list_data(raft::resources const& res,
                            const index<int64_t>& index,
                            raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
                            uint32_t label,
-                           std::variant<uint32_t, const uint32_t*> offset_or_indices)
+                           uint32_t offset)
 {
-  detail::reconstruct_list_data<int8_t, int64_t>(res, index, out_vectors, label, offset_or_indices);
+  detail::reconstruct_list_data<int8_t, int64_t>(res, index, out_vectors, label, offset);
 }
 void reconstruct_list_data(raft::resources const& res,
                            const index<int64_t>& index,
                            raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_vectors,
                            uint32_t label,
-                           std::variant<uint32_t, const uint32_t*> offset_or_indices)
+                           uint32_t offset)
 {
-  detail::reconstruct_list_data<uint8_t, int64_t>(
-    res, index, out_vectors, label, offset_or_indices);
+  detail::reconstruct_list_data<uint8_t, int64_t>(res, index, out_vectors, label, offset);
 }
 
-void pack_contiguous_list_data(raft::resources const& res,
-                               index<int64_t>* index,
-                               const uint8_t* new_codes,
-                               uint32_t n_rows,
-                               uint32_t label,
-                               std::variant<uint32_t, const uint32_t*> offset_or_indices)
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_vector_view<const uint32_t> in_cluster_indices,
+                           raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label)
 {
-  detail::pack_contiguous_list_data<int64_t>(
-    res, index, new_codes, n_rows, label, offset_or_indices);
+  detail::reconstruct_list_data<float, int64_t>(
+    res, index, out_vectors, label, in_cluster_indices.data_handle());
+}
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_vector_view<const uint32_t> in_cluster_indices,
+                           raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label)
+{
+  detail::reconstruct_list_data<int8_t, int64_t>(
+    res, index, out_vectors, label, in_cluster_indices.data_handle());
+}
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_vector_view<const uint32_t> in_cluster_indices,
+                           raft::device_matrix_view<uint8_t, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label)
+{
+  detail::reconstruct_list_data<uint8_t, int64_t>(
+    res, index, out_vectors, label, in_cluster_indices.data_handle());
 }
 
 void extend_list_with_codes(
@@ -159,9 +208,85 @@ void extend_list(raft::resources const& res,
   detail::extend_list<uint8_t, int64_t>(res, index, new_vectors, new_indices, label);
 }
 
+};  // namespace codepacker
+
 void erase_list(raft::resources const& res, index<int64_t>* index, uint32_t label)
 {
   detail::erase_list<int64_t>(res, index, label);
 }
 
+void reset_index(const raft::resources& res, index<int64_t>* index)
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  cuvs::spatial::knn::detail::utils::memzero(
+    index->accum_sorted_sizes().data_handle(), index->accum_sorted_sizes().size(), stream);
+  cuvs::spatial::knn::detail::utils::memzero(
+    index->list_sizes().data_handle(), index->list_sizes().size(), stream);
+  cuvs::spatial::knn::detail::utils::memzero(
+    index->data_ptrs().data_handle(), index->data_ptrs().size(), stream);
+  cuvs::spatial::knn::detail::utils::memzero(
+    index->inds_ptrs().data_handle(), index->inds_ptrs().size(), stream);
+}
+
+void make_rotation_matrix(raft::resources const& handle,
+                          bool force_random_rotation,
+                          uint32_t n_rows,
+                          uint32_t n_cols,
+                          float* rotation_matrix,
+                          raft::random::RngState rng = raft::random::RngState(7ULL))
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::make_rotation_matrix(%u * %u)", n_rows, n_cols);
+  auto stream  = raft::resource::get_cuda_stream(handle);
+  bool inplace = n_rows == n_cols;
+  uint32_t n   = std::max(n_rows, n_cols);
+  if (force_random_rotation || !inplace) {
+    rmm::device_uvector<float> buf(inplace ? 0 : n * n, stream);
+    float* mat = inplace ? rotation_matrix : buf.data();
+    raft::random::normal(handle, rng, mat, n * n, 0.0f, 1.0f);
+    raft::linalg::detail::qrGetQ_inplace(handle, mat, n, n, stream);
+    if (!inplace) {
+      RAFT_CUDA_TRY(cudaMemcpy2DAsync(rotation_matrix,
+                                      sizeof(float) * n_cols,
+                                      mat,
+                                      sizeof(float) * n,
+                                      sizeof(float) * n_cols,
+                                      n_rows,
+                                      cudaMemcpyDefault,
+                                      stream));
+    }
+  } else {
+    uint32_t stride = n + 1;
+    auto rotation_matrix_view =
+      raft::make_device_vector_view<float, uint32_t>(rotation_matrix, n * n);
+    raft::linalg::map_offset(handle, rotation_matrix_view, [stride] __device__(uint32_t i) {
+      return static_cast<float>(i % stride == 0u);
+    });
+  }
+}
+
+void make_rotation_matrix(raft::resources const& res,
+                          index<int64_t>* index,
+                          bool force_random_rotation)
+{
+  make_rotation_matrix(res,
+                       force_random_rotation,
+                       index->rot_dim(),
+                       index->dim(),
+                       index->rotation_matrix().data_handle());
+}
+
+void set_centers(raft::resources const& handle,
+                 index<int64_t>* index,
+                 raft::device_matrix_view<const float, uint32_t> cluster_centers)
+{
+  RAFT_EXPECTS(cluster_centers.extent(0) == index->n_lists(),
+               "Number of rows in the new centers must be equal to the number of IVF lists");
+  RAFT_EXPECTS(cluster_centers.extent(1) == index->dim(),
+               "Number of columns in the new cluster centers and index dim are different");
+  RAFT_EXPECTS(index->size() == 0, "Index must be empty");
+  detail::set_centers(handle, index, cluster_centers.data_handle());
+}
+
 }  // namespace cuvs::neighbors::ivf_pq::helpers
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index 5c77e00089..ed28969a59 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -1,9 +1,9 @@
 /*
- * raft::copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
- * You may obtain a raft::copy of the License at
+ * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index 0a17aec120..985134221c 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -19,12 +19,12 @@
 #include "ann_utils.cuh"
 #include "naive_knn.cuh"
 #include <cuvs/neighbors/ivf_pq.hpp>
-#include <cuvs/neighbors/ivf_pq_helpers.hpp>
 #include <cuvs/neighbors/sample_filter.hpp>
 
 #include <raft/core/bitset.cuh>
 #include <raft/linalg/add.cuh>
 #include <raft/matrix/gather.cuh>
+#include <rmm/mr/device/managed_memory_resource.hpp>
 #include <thrust/sequence.h>
 
 namespace cuvs::neighbors::ivf_pq {
@@ -263,7 +263,8 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     auto rec_data  = raft::make_device_matrix<DataT>(handle_, n_take, dim);
     auto orig_data = raft::make_device_matrix<DataT>(handle_, n_take, dim);
 
-    ivf_pq::helpers::reconstruct_list_data(handle_, index, rec_data.view(), label, n_skip);
+    ivf_pq::helpers::codepacker::reconstruct_list_data(
+      handle_, index, rec_data.view(), label, n_skip);
 
     raft::matrix::gather(database.data(),
                          IdxT{dim},
@@ -288,11 +289,13 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     auto indices   = raft::make_device_vector<IdxT>(handle_, n_rows);
     raft::copy(indices.data_handle(), old_list->indices.data_handle(), n_rows, stream_);
 
-    ivf_pq::helpers::reconstruct_list_data(handle_, *index, vectors_1.view(), label, uint32_t(0));
+    ivf_pq::helpers::codepacker::reconstruct_list_data(
+      handle_, *index, vectors_1.view(), label, uint32_t(0));
     ivf_pq::helpers::erase_list(handle_, index, label);
     // NB: passing the type parameter because const->non-const implicit conversion of the mdspans
     // breaks type inference
-    ivf_pq::helpers::extend_list(handle_, index, vectors_1.view(), indices.view(), label);
+    ivf_pq::helpers::codepacker::extend_list(
+      handle_, index, vectors_1.view(), indices.view(), label);
 
     auto& new_list = index->lists()[label];
     ASSERT_NE(old_list.get(), new_list.get())
@@ -300,7 +303,8 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
          "corresponding cluster.";
 
     auto vectors_2 = raft::make_device_matrix<EvalT>(handle_, n_rows, index->dim());
-    ivf_pq::helpers::reconstruct_list_data(handle_, *index, vectors_2.view(), label, uint32_t(0));
+    ivf_pq::helpers::codepacker::reconstruct_list_data(
+      handle_, *index, vectors_2.view(), label, uint32_t(0));
     // The code search is unstable, and there's high chance of repeating values of the lvl-2 codes.
     // Hence, encoding-decoding chain often leads to altering both the PQ codes and the
     // reconstructed data.
@@ -322,7 +326,8 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     ivf_pq::helpers::codepacker::unpack_list_data(
       handle_, *index, codes.view(), label, uint32_t(0));
     ivf_pq::helpers::erase_list(handle_, index, label);
-    ivf_pq::helpers::extend_list_with_codes(handle_, index, codes.view(), indices.view(), label);
+    ivf_pq::helpers::codepacker::extend_list_with_codes(
+      handle_, index, codes.view(), indices.view(), label);
 
     auto& new_list = index->lists()[label];
     ASSERT_NE(old_list.get(), new_list.get())
@@ -358,22 +363,12 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     uint32_t n_take                 = 4;
     ASSERT_TRUE(row_offset + n_take < n_rows);
     auto codes2 = raft::make_device_matrix<uint8_t>(handle_, n_take, index->pq_dim());
-    ivf_pq::helpers::codepacker::unpack_list_data(
-      // handle_, list_data, index->pq_bits(), row_offset, codes2.view());
-      handle_,
-      *index,
-      codes2.view(),
-      label,
-      uint32_t(row_offset));
+    ivf_pq::helpers::codepacker::unpack(
+      handle_, list_data, index->pq_bits(), row_offset, codes2.view());
 
     // Write it back
-    ivf_pq::helpers::codepacker::pack_list_data(
-      // handle_, make_const_mdspan(codes2.view()), index->pq_bits(), row_offset, list_data);
-      handle_,
-      index,
-      make_const_mdspan(codes2.view()),
-      label,
-      uint32_t(row_offset));
+    ivf_pq::helpers::codepacker::pack(
+      handle_, make_const_mdspan(codes2.view()), index->pq_bits(), row_offset, list_data);
     ASSERT_TRUE(cuvs::devArrMatch(old_list->data.data_handle(),
                                   new_list->data.data_handle(),
                                   list_data_size,
diff --git a/docs/source/cpp_api/neighbors_ivf_pq.rst b/docs/source/cpp_api/neighbors_ivf_pq.rst
index 0d4d7061a6..cc515682b9 100644
--- a/docs/source/cpp_api/neighbors_ivf_pq.rst
+++ b/docs/source/cpp_api/neighbors_ivf_pq.rst
@@ -66,3 +66,15 @@ Index serialize
     :project: cuvs
     :members:
     :content-only:
+
+Helper Methods
+---------------
+
+Additional helper functions for manipulating the underlying data of an IVF-PQ index, unpacking records, and writing PQ codes into an existing IVF list.
+
+namespace *cuvs::neighbors::ivf_pq::helpers*
+
+.. doxygengroup:: ivf_pq_cpp_helpers
+    :project: cuvs
+    :members:
+    :content-only:

From 85aa026ed65f929f983d7dd4a3974ddb17e9226d Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 17 May 2024 15:28:42 -0700
Subject: [PATCH 056/623] Speed-up rust build (#138)

We are using cmake-rs to link the c++ cmake build and the rust build. This had the side effect of causing us to always recompile all of libcuvs with the rust bindings. With the migration of the libraft ann code to cuvs, this has led to the rust bindings being the bottleneck in building.

Get around this by creating a new dummy cmake target that depends on cuvs, and having this target only build libcuvs if needed.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/138
---
 rust/cuvs-sys/CMakeLists.txt | 65 ++++++++++++++++++++++++++++++++++++
 rust/cuvs-sys/build.rs       | 13 +-------
 2 files changed, 66 insertions(+), 12 deletions(-)
 create mode 100644 rust/cuvs-sys/CMakeLists.txt

diff --git a/rust/cuvs-sys/CMakeLists.txt b/rust/cuvs-sys/CMakeLists.txt
new file mode 100644
index 0000000000..e2ca3a444f
--- /dev/null
+++ b/rust/cuvs-sys/CMakeLists.txt
@@ -0,0 +1,65 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+include(../../rapids_config.cmake)
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-export)
+include(rapids-find)
+rapids_cpm_init()
+
+# we want to use the already built libcuvs if its available, but the rust cmake-rs project doesn't
+# support anything like find_package https://github.com/rust-lang/cmake-rs/issues/111 instead we're
+# adding an extra level of indirection here - cmake-rs will attempt to build this project, and we'll
+# using the existing libcuvs if its already built, and only fall back to building libcuvs if it
+# isn't
+
+project(
+  cuvs-rs
+  VERSION "${RAPIDS_VERSION}"
+  LANGUAGES CXX CUDA
+)
+
+option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulting to local files"
+       ON
+)
+
+# If the user requested it we attempt to find CUVS.
+if(FIND_CUVS_CPP)
+  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS compiled)
+  if(NOT TARGET cuvs::cuvs)
+    message(
+      FATAL_ERROR
+        "Building against a preexisting libcuvs library requires the compiled libcuvs to have been built!"
+    )
+
+  endif()
+else()
+  set(cuvs_FOUND OFF)
+endif()
+
+if(NOT cuvs_FOUND)
+  set(BUILD_TESTS OFF)
+  set(BUILD_C_LIBRARY ON)
+  add_subdirectory(../../cpp cuvs-cpp EXCLUDE_FROM_ALL)
+endif()
+
+include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
+
+# add a dummy target here,
+add_library(cuvs-rust INTERFACE)
+target_link_libraries(cuvs-rust INTERFACE cuvs::cuvs)
+install(TARGETS cuvs-rust)
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
index 3dcf185de2..bcb0a88c7d 100644
--- a/rust/cuvs-sys/build.rs
+++ b/rust/cuvs-sys/build.rs
@@ -18,20 +18,9 @@ use std::env;
 use std::io::BufRead;
 use std::path::PathBuf;
 
-/*
-    TODO:
-    * would be nice to use already built versions of libcuvs_c / libcuvs
-        if they already existed, but this might not be possible here using cmake-rs
-        (https://github.com/rust-lang/cmake-rs/issues/111)
-    * figure out how this works with rust packaging: does the c++ code
-        need to be in a subdirectory? If so would a symlink work here
-        should we be using static linking ?
-*/
 fn main() {
     // build the cuvs c-api library with cmake, and link it into this crate
-    let cuvs_build = cmake::Config::new("../../cpp")
-        .configure_arg("-DBUILD_TESTS:BOOL=OFF")
-        .configure_arg("-DBUILD_C_LIBRARY:BOOL=ON")
+    let cuvs_build = cmake::Config::new(".")
         .build();
 
     println!(

From cb581ac648f4d829ffd85385b8161edde13dcbaf Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Mon, 20 May 2024 09:21:59 -0500
Subject: [PATCH 057/623] Adds missing files to `update-version.sh` (#69)

Co-authored-by: Corey J. Nolet <cjnolet@gmail.com>
Co-authored-by: Ray Douglass <3107146+raydouglass@users.noreply.github.com>
---
 ci/release/update-version.sh | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index b8f15aba78..0082461c59 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -37,7 +37,7 @@ function sed_runner() {
     sed -i.bak ''"$1"'' $2 && rm -f ${2}.bak
 }
 
-sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" cpp/examples/cmake/thirdparty/fetch_rapids.cmake
+sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" examples/cmake/thirdparty/fetch_rapids.cmake
 
 # Centralized version file update
 echo "${NEXT_FULL_TAG}" > VERSION
@@ -47,6 +47,7 @@ DEPENDENCIES=(
   cuvs
   cuvs-cu11
   cuvs-cu12
+  pylibraft
   pylibraft-cu11
   pylibraft-cu12
   rmm
@@ -68,19 +69,13 @@ for FILE in python/*/pyproject.toml; do
   sed_runner "/\"ucx-py==/ s/==.*\"/==${NEXT_UCX_PY_SHORT_TAG_PEP440}.*\"/g" ${FILE}
 done
 
-sed_runner "/^ucx_py_version:$/ {n;s/.*/  - \"${NEXT_UCX_PY_VERSION}\"/}" conda/recipes/raft-dask/conda_build_config.yaml
-
 for FILE in .github/workflows/*.yaml; do
   sed_runner "/shared-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}"
 done
 
-sed_runner "/^set(CUVS_VERSION/ s|\".*\"|\"${NEXT_SHORT_TAG}\"|g" docs/source/build.md
-sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/build.md
 sed_runner "/rapidsai\/raft/ s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/developer_guide.md
 
-sed_runner "s|:[0-9][0-9].[0-9][0-9]|:${NEXT_SHORT_TAG}|g" docs/source/raft_ann_benchmarks.md
-
-sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" README.md
+sed_runner "s|=[0-9][0-9].[0-9][0-9]|=${NEXT_SHORT_TAG}|g" README.md
 
 # rust can't handle leading 0's in the major/minor/patch version - remove
 NEXT_FULL_RUST_TAG=$(printf "%d.%d.%d" $((10#$NEXT_MAJOR)) $((10#$NEXT_MINOR)) $((10#$NEXT_PATCH)))
@@ -90,6 +85,7 @@ sed_runner "s/version = \".*\"/version = \"${NEXT_FULL_RUST_TAG}\"/g" rust/Cargo
 find .devcontainer/ -type f -name devcontainer.json -print0 | while IFS= read -r -d '' filename; do
     sed_runner "s@rapidsai/devcontainers:[0-9.]*@rapidsai/devcontainers:${NEXT_SHORT_TAG}@g" "${filename}"
     sed_runner "s@rapidsai/devcontainers/features/ucx:[0-9.]*@rapidsai/devcontainers/features/ucx:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
+    sed_runner "s@rapidsai/devcontainers/features/cuda:[0-9.]*@rapidsai/devcontainers/features/cuda:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
     sed_runner "s@rapidsai/devcontainers/features/rapids-build-utils:[0-9.]*@rapidsai/devcontainers/features/rapids-build-utils:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
     sed_runner "s@rapids-\${localWorkspaceFolderBasename}-${CURRENT_SHORT_TAG}@rapids-\${localWorkspaceFolderBasename}-${NEXT_SHORT_TAG}@g" "${filename}"
 done

From 105e47e1303533cbb5ef452df1d6f01df1c47983 Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Mon, 20 May 2024 17:41:23 -0400
Subject: [PATCH 058/623] DOC v24.08 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda11.8-pip/devcontainer.json  | 10 ++++----
 .../cuda12.2-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda12.2-pip/devcontainer.json  | 10 ++++----
 .github/workflows/build.yaml                  | 14 +++++------
 .github/workflows/pr.yaml                     | 24 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++----
 README.md                                     |  2 +-
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-118_arch-x86_64.yaml             |  4 ++--
 .../all_cuda-122_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-122_arch-x86_64.yaml             |  4 ++--
 dependencies.yaml                             | 12 +++++-----
 docs/source/developer_guide.md                |  6 ++---
 examples/cmake/thirdparty/fetch_rapids.cmake  |  2 +-
 python/cuvs/pyproject.toml                    |  6 ++---
 rust/Cargo.toml                               |  2 +-
 18 files changed, 63 insertions(+), 63 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index bfba756fbe..902e134fb4 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.06-cpp-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-conda"
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.08-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index bb25e2ba31..1a83940a5e 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,27 +5,27 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.06-cpp-cuda11.8-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda11.8-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-pip"
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.08-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.8": {
       "version": "1.15.0"
     },
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.6": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.8": {
       "version": "11.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.2-conda/devcontainer.json b/.devcontainer/cuda12.2-conda/devcontainer.json
index bc0548622b..a07472ab1b 100644
--- a/.devcontainer/cuda12.2-conda/devcontainer.json
+++ b/.devcontainer/cuda12.2-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.2",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.06-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.08-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-conda"
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.2-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.2-pip/devcontainer.json b/.devcontainer/cuda12.2-pip/devcontainer.json
index 19ab3e145e..76a0f139ec 100644
--- a/.devcontainer/cuda12.2-pip/devcontainer.json
+++ b/.devcontainer/cuda12.2-pip/devcontainer.json
@@ -5,27 +5,27 @@
     "args": {
       "CUDA": "12.2",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.06-cpp-cuda12.2-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda12.2-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-pip"
+    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.2-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.6": {
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.8": {
       "version": "1.15.0"
     },
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.6": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.8": {
       "version": "12.2",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 66945cb2ae..b71b4ff045 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index ffac028052..647cf6df85 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.08
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.08
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,20 +86,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.08
     with:
       arch: '["amd64"]'
       cuda: '["12.2"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 4f49e87cfe..55262b9433 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/README.md b/README.md
index 6829c80f72..08a734cb89 100755
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@ mamba install -c conda-forge -c nvidia -c rapidsai pycuvs
 ### Nightlies
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai-nightly pycuvs=24.06
+mamba install -c conda-forge -c nvidia -c rapidsai-nightly pycuvs=24.08
 ```
 
 Please see the [Build and Install Guide](docs/source/build.md) for more information on installing cuVS and building from source.
diff --git a/VERSION b/VERSION
index 0bff6981a3..ec8489fda9 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.06.00
+24.08.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index d1a6fa011e..3540039dcd 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -43,11 +43,11 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.6.*
+- pylibraft==24.8.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.6.*
+- rmm==24.8.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index e604ed8f31..51170b40d5 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -43,11 +43,11 @@ dependencies:
 - nvcc_linux-64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.6.*
+- pylibraft==24.8.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.6.*
+- rmm==24.8.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index 7b50ceb0a0..b430fa837b 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -39,11 +39,11 @@ dependencies:
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.6.*
+- pylibraft==24.8.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.6.*
+- rmm==24.8.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 6c933e193d..10913378e4 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -39,11 +39,11 @@ dependencies:
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.6.*
+- pylibraft==24.8.*
 - pytest-cov
 - pytest==7.*
 - recommonmark
-- rmm==24.6.*
+- rmm==24.8.*
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/dependencies.yaml b/dependencies.yaml
index eb1f99d4a1..97dae890e0 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -141,8 +141,8 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
-          - &rmm_conda rmm==24.6.*
-          - &pylibraft_conda pylibraft==24.6.*
+          - &rmm_conda rmm==24.8.*
+          - &pylibraft_conda pylibraft==24.8.*
           - dlpack>=0.8,<1.0
       - output_types: requirements
         packages:
@@ -164,12 +164,12 @@ dependencies:
         matrices:
           - matrix: {cuda: "12.*"}
             packages:
-              - &rmm_cu12 rmm-cu12==24.6.*
-              - &pylibraft_cu12 pylibraft-cu12==24.6.*
+              - &rmm_cu12 rmm-cu12==24.8.*
+              - &pylibraft_cu12 pylibraft-cu12==24.8.*
           - matrix: {cuda: "11.*"}
             packages:
-              - &rmm_cu11 rmm-cu11==24.6.*
-              - &pylibraft_cu11 pylibraft-cu11==24.6.*
+              - &rmm_cu11 rmm-cu11==24.8.*
+              - &pylibraft_cu11 pylibraft-cu11==24.8.*
           - {matrix: null, packages: [*rmm_conda, *pylibraft_conda] }
   checks:
     common:
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 5e288e9f2f..36588f3450 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.06/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.08/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.06/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.08/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
@@ -230,7 +230,7 @@ Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY`
 ## Logging
 
 ### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.06/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
+Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.08/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
 
 ### Usage
 ```cpp
diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
index ec0e0816db..0f1d5ff020 100644
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and RAFT versions
-set(RAPIDS_VERSION "24.06")
+set(RAPIDS_VERSION "24.08")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index c100511d58..a9660a8ca0 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -19,8 +19,8 @@ requires = [
     "cuda-python>=11.7.1,<12.0a0",
     "cython>=3.0.0",
     "ninja",
-    "pylibraft==24.6.*",
-    "rmm==24.6.*",
+    "pylibraft==24.8.*",
+    "rmm==24.8.*",
     "scikit-build-core[pyproject]>=0.7.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
@@ -38,7 +38,7 @@ requires-python = ">=3.9"
 dependencies = [
     "cuda-python>=11.7.1,<12.0a0",
     "numpy>=1.23,<2.0a0",
-    "pylibraft==24.6.*",
+    "pylibraft==24.8.*",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index 700b65b40e..d35ab66ad0 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "24.6.0"
+version = "24.8.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"

From b757c19b3a02eb8c4e5cebbbb148cbfc09825cb9 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Tue, 21 May 2024 13:29:45 -0400
Subject: [PATCH 059/623] Removing `libraft.so` from libcuvs dependencies
 (#132)

Unfortunately, libraft.so is still a dependency of pylibraft and so it's needed in order to use pylibraft. We'll work on that during the 24.08 release cycle. For now, libraft.so is no longer a required dependency to build `libcuvs` or any of its wrappers.

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Micka (https://github.com/lowener)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/132
---
 .pre-commit-config.yaml                       |   1 -
 cpp/CMakeLists.txt                            |  25 +-
 cpp/cmake/thirdparty/get_raft.cmake           |  15 +-
 cpp/include/cuvs/cluster/agglomerative.hpp    |   2 +-
 cpp/include/cuvs/cluster/kmeans.hpp           |   2 +-
 .../distance/{distance_types.h => distance.h} |   0
 .../{pairwise_distance.hpp => distance.hpp}   |  55 ++-
 cpp/include/cuvs/distance/distance_types.hpp  |  63 ----
 cpp/include/cuvs/neighbors/ann_types.hpp      |  90 -----
 cpp/include/cuvs/neighbors/brute_force.h      |   2 +-
 cpp/include/cuvs/neighbors/brute_force.hpp    |   6 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |  18 +-
 .../neighbors/{dataset.hpp => common.hpp}     | 315 +++++++++++++++++-
 cpp/include/cuvs/neighbors/ivf_flat.h         |   2 +-
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |  13 +-
 cpp/include/cuvs/neighbors/ivf_list.hpp       | 123 -------
 cpp/include/cuvs/neighbors/ivf_pq.h           |   2 +-
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  11 +-
 cpp/include/cuvs/neighbors/nn_descent.hpp     |  12 +-
 cpp/include/cuvs/neighbors/sample_filter.hpp  | 171 ----------
 .../cuvs_internal/neighbors/naive_knn.cuh     |   2 +-
 .../cuvs_internal/neighbors/refine_helper.cuh |   2 +-
 cpp/src/cluster/detail/connectivities.cuh     |   3 +-
 cpp/src/cluster/detail/kmeans.cuh             |   2 +-
 cpp/src/cluster/detail/kmeans_balanced.cuh    |   2 +-
 cpp/src/cluster/detail/kmeans_common.cuh      |   2 +-
 cpp/src/distance/detail/distance.cuh          |   2 +-
 cpp/src/distance/detail/fused_distance_nn.cuh |   2 +-
 .../distance/detail/kernels/gram_matrix.cuh   |   2 +-
 .../detail/kernels/kernel_factory.cuh         |   2 +-
 cpp/src/distance/distance-ext.cuh             |  12 +-
 cpp/src/distance/distance-inl.cuh             |   2 +-
 cpp/src/distance/pairwise_distance.cu         |   3 +-
 cpp/src/neighbors/brute_force.cu              |   6 +-
 cpp/src/neighbors/cagra.cuh                   |   4 +-
 cpp/src/neighbors/cagra_c.cpp                 |   4 +-
 cpp/src/neighbors/detail/ann_utils.cuh        |   2 +-
 .../neighbors/detail/cagra/cagra_build.cuh    |   2 +-
 .../neighbors/detail/cagra/cagra_search.cuh   |   4 +-
 .../detail/cagra/compute_distance.hpp         |   2 +-
 .../detail/cagra/compute_distance_vpq.cuh     |   2 +-
 cpp/src/neighbors/detail/cagra/factory.cuh    |   2 +-
 .../detail/cagra/search_multi_cta.cuh         |   2 +-
 .../detail/cagra/search_multi_cta_inst.cuh    |   2 +-
 .../cagra/search_multi_cta_kernel-ext.cuh     |   4 +-
 .../cagra/search_multi_cta_kernel-inl.cuh     |   4 +-
 .../detail/cagra/search_multi_kernel.cuh      |   4 +-
 .../neighbors/detail/cagra/search_plan.cuh    |   4 +-
 .../detail/cagra/search_single_cta_inst.cuh   |   2 +-
 .../cagra/search_single_cta_kernel-ext.cuh    |   2 +-
 .../cagra/search_single_cta_kernel-inl.cuh    |   4 +-
 .../neighbors/detail/dataset_serialize.hpp    |   2 +-
 cpp/src/neighbors/detail/knn_brute_force.cuh  |   2 +-
 cpp/src/neighbors/detail/refine_common.hpp    |   2 +-
 cpp/src/neighbors/detail/refine_device.cuh    |   2 +-
 cpp/src/neighbors/detail/refine_host-ext.hpp  |   8 +-
 cpp/src/neighbors/detail/vpq_dataset.cuh      |   2 +-
 cpp/src/neighbors/ivf_common.cuh              |   2 +-
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |   4 +-
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    |   4 +-
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |  16 +-
 .../neighbors/ivf_flat/ivf_flat_serialize.cuh |   2 +-
 cpp/src/neighbors/ivf_flat_index.cpp          |   2 +-
 cpp/src/neighbors/ivf_list.cuh                |   2 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |   4 +-
 .../neighbors/ivf_pq/ivf_pq_build_common.cu   |   4 +-
 .../neighbors/ivf_pq/ivf_pq_codepacking.cuh   |   2 +-
 .../ivf_pq/ivf_pq_compute_similarity.cuh      |   8 +-
 .../ivf_pq/ivf_pq_compute_similarity_impl.cuh |   8 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh      |   2 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |   2 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh |   2 +-
 cpp/src/neighbors/ivf_pq_index.cpp            |   2 +-
 cpp/src/neighbors/refine-ext.cuh              |  10 +-
 cpp/src/neighbors/sample_filter.cuh           |   2 +-
 cpp/src/neighbors/vpq_dataset.cuh             |   2 +-
 cpp/test/CMakeLists.txt                       |   1 +
 cpp/test/cluster/linkage.cu                   |   2 +-
 cpp/test/distance/distance_base.cuh           |   4 +-
 cpp/test/neighbors/ann_cagra.cuh              |   2 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |   2 +-
 cpp/test/neighbors/ann_utils.cuh              |   2 +-
 cpp/test/neighbors/brute_force.cu             |   2 +-
 cpp/test/neighbors/naive_knn.cuh              |   2 +-
 python/cuvs/cuvs/distance_type.pxd            |   2 +-
 85 files changed, 519 insertions(+), 620 deletions(-)
 rename cpp/include/cuvs/distance/{distance_types.h => distance.h} (100%)
 rename cpp/include/cuvs/distance/{pairwise_distance.hpp => distance.hpp} (81%)
 delete mode 100644 cpp/include/cuvs/distance/distance_types.hpp
 delete mode 100644 cpp/include/cuvs/neighbors/ann_types.hpp
 rename cpp/include/cuvs/neighbors/{dataset.hpp => common.hpp} (53%)
 delete mode 100644 cpp/include/cuvs/neighbors/ivf_list.hpp
 delete mode 100644 cpp/include/cuvs/neighbors/sample_filter.hpp

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 576b53907a..b1de648b02 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -114,7 +114,6 @@ repos:
                   setup[.]cfg$
             exclude: |
               (?x)
-                  cpp/include/cuvs/neighbors/detail/faiss_select/|
                   docs/source/sphinxext/github_link\.py|
                   cpp/cmake/modules/FindAVX\.cmake|
 
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 5715ee8a29..b972fc1226 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -447,14 +447,29 @@ target_include_directories(
               "$<INSTALL_INTERFACE:include>"
 )
 
+rapids_find_package(
+  OpenMP REQUIRED
+  BUILD_EXPORT_SET cuvs-exports
+  INSTALL_EXPORT_SET cuvs-exports
+)
+
 if(NOT BUILD_CPU_ONLY)
+
+  set(CUVS_CUSOLVER_DEPENDENCY CUDA::cusolver${_ctk_static_suffix})
+  set(CUVS_CUBLAS_DEPENDENCY CUDA::cublas${_ctk_static_suffix})
+  set(CUVS_CURAND_DEPENDENCY CUDA::curand${_ctk_static_suffix})
+  set(CUVS_CUSPARSE_DEPENDENCY CUDA::cusparse${_ctk_static_suffix})
+
+  set(CUVS_CTK_MATH_DEPENDENCIES ${CUVS_CUBLAS_DEPENDENCY} ${CUVS_CUSOLVER_DEPENDENCY}
+                                 ${CUVS_CUSPARSE_DEPENDENCY} ${CUVS_CURAND_DEPENDENCY}
+  )
+
   # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
   target_link_libraries(
     cuvs
-    PUBLIC rmm::rmm $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::raft>
-           $<$<NOT:$<BOOL:${CUDA_STATIC_RUNTIME}>>:raft::compiled>
-    PRIVATE $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
-            $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::compiled_static> nvidia::cutlass::cutlass
+    PUBLIC rmm::rmm raft::raft
+    PRIVATE nvidia::cutlass::cutlass ${CUVS_CTK_MATH_DEPENDENCIES}
+            $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
   )
 endif()
 
@@ -559,7 +574,7 @@ if(BUILD_C_LIBRARY)
   target_link_libraries(
     cuvs_c
     PUBLIC cuvs::cuvs
-    PRIVATE $<$<BOOL:${CUDA_STATIC_RUNTIME}>:raft::raft>
+    PRIVATE raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
   )
 
   # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index e3c2de3200..44825f91cb 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -17,7 +17,7 @@ set(RAFT_FORK "rapidsai")
 set(RAFT_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
 
 function(find_and_configure_raft)
-    set(oneValueArgs VERSION FORK PINNED_TAG COMPILE_LIBRARY USE_RAFT_STATIC ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES)
+    set(oneValueArgs VERSION FORK PINNED_TAG USE_RAFT_STATIC ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
@@ -31,16 +31,6 @@ function(find_and_configure_raft)
 
     set(RAFT_COMPONENTS "")
 
-    if(PKG_COMPILE_LIBRARY)
-      if(NOT PKG_USE_RAFT_STATIC)
-        string(APPEND RAFT_COMPONENTS " compiled")
-        set(RAFT_COMPILED_LIB raft::compiled PARENT_SCOPE)
-      else()
-        string(APPEND RAFT_COMPONENTS " compiled_static")
-        set(RAFT_COMPILED_LIB raft::compiled_static PARENT_SCOPE)
-      endif()
-    endif()
-
     if(PKG_ENABLE_MNMG_DEPENDENCIES)
         string(APPEND RAFT_COMPONENTS " distributed")
     endif()
@@ -62,7 +52,7 @@ function(find_and_configure_raft)
               "BUILD_PRIMS_BENCH OFF"
               "BUILD_ANN_BENCH OFF"
               "RAFT_NVTX ${PKG_ENABLE_NVTX}"
-              "RAFT_COMPILE_LIBRARY ${PKG_COMPILE_LIBRARY}"
+              "RAFT_COMPILE_LIBRARY OFF"
             )
 endfunction()
 
@@ -72,7 +62,6 @@ endfunction()
 find_and_configure_raft(VERSION  ${RAFT_VERSION}.00
         FORK                     ${RAFT_FORK}
         PINNED_TAG               ${RAFT_PINNED_TAG}
-        COMPILE_LIBRARY          ON
         ENABLE_MNMG_DEPENDENCIES OFF
         ENABLE_NVTX              OFF
         USE_RAFT_STATIC ${CUVS_USE_RAFT_STATIC}
diff --git a/cpp/include/cuvs/cluster/agglomerative.hpp b/cpp/include/cuvs/cluster/agglomerative.hpp
index 5da03c4d1f..658f5628cf 100644
--- a/cpp/include/cuvs/cluster/agglomerative.hpp
+++ b/cpp/include/cuvs/cluster/agglomerative.hpp
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <optional>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index 471a4d12df..7a372b1bc3 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #pragma once
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/include/cuvs/distance/distance_types.h b/cpp/include/cuvs/distance/distance.h
similarity index 100%
rename from cpp/include/cuvs/distance/distance_types.h
rename to cpp/include/cuvs/distance/distance.h
diff --git a/cpp/include/cuvs/distance/pairwise_distance.hpp b/cpp/include/cuvs/distance/distance.hpp
similarity index 81%
rename from cpp/include/cuvs/distance/pairwise_distance.hpp
rename to cpp/include/cuvs/distance/distance.hpp
index 5f774e3bc2..5786b0a32b 100644
--- a/cpp/include/cuvs/distance/pairwise_distance.hpp
+++ b/cpp/include/cuvs/distance/distance.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,13 +16,54 @@
 
 #pragma once
 
+#include "distance.h"
+
 #include <cstdint>
-#include <cuvs/distance/distance_types.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
 
 namespace cuvs::distance {
 
+using DistanceType = cuvsDistanceType;
+
+/**
+ * Whether minimal distance corresponds to similar elements (using the given metric).
+ */
+inline bool is_min_close(DistanceType metric)
+{
+  bool select_min;
+  switch (metric) {
+    case DistanceType::InnerProduct:
+      // Similarity metrics have the opposite meaning, i.e. nearest neighbors are those with larger
+      // similarity (See the same logic at cpp/include/raft/sparse/spatial/detail/knn.cuh:362
+      // {perform_k_selection})
+      select_min = false;
+      break;
+    default: select_min = true;
+  }
+  return select_min;
+}
+
+namespace kernels {
+enum KernelType { LINEAR, POLYNOMIAL, RBF, TANH };
+
+/**
+ * Parameters for kernel matrices.
+ * The following kernels are implemented:
+ * - LINEAR \f[ K(x_1,x_2) = <x_1,x_2>, \f] where \f$< , >\f$ is the dot product
+ * - POLYNOMIAL \f[ K(x_1, x_2) = (\gamma <x_1,x_2> + \mathrm{coef0})^\mathrm{degree} \f]
+ * - RBF \f[ K(x_1, x_2) = \exp(- \gamma |x_1-x_2|^2) \f]
+ * - TANH \f[ K(x_1, x_2) = \tanh(\gamma <x_1,x_2> + \mathrm{coef0}) \f]
+ */
+struct KernelParams {
+  // Kernel function parameters
+  KernelType kernel;  //!< Type of the kernel function
+  int degree;         //!< Degree of polynomial kernel (ignored by others)
+  double gamma;       //!< multiplier in the
+  double coef0;       //!< additive constant in poly and tanh kernels
+};
+}  // end namespace kernels
+
 /**
  * @defgroup pairwise_distance Pairwise Distances API
  * @{
@@ -37,7 +78,7 @@ namespace cuvs::distance {
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
  * #include <raft/core/device_mdarray.hpp>
- * #include <cuvs/distance/pairwise_distance.hpp>
+ * #include <cuvs/distance/distance.hpp>
  *
  * raft::resources handle;
  * int n_samples = 5000;
@@ -81,7 +122,7 @@ void pairwise_distance(
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
  * #include <raft/core/device_mdarray.hpp>
- * #include <cuvs/distance/pairwise_distance.hpp>
+ * #include <cuvs/distance/distance.hpp>
  *
  * raft::resources handle;
  * int n_samples = 5000;
@@ -125,7 +166,7 @@ void pairwise_distance(
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
  * #include <raft/core/device_mdarray.hpp>
- * #include <cuvs/distance/pairwise_distance.hpp>
+ * #include <cuvs/distance/distance.hpp>
  *
  * raft::resources handle;
  * int n_samples = 5000;
@@ -168,7 +209,7 @@ void pairwise_distance(
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
  * #include <raft/core/device_mdarray.hpp>
- * #include <cuvs/distance/pairwise_distance.hpp>
+ * #include <cuvs/distance/distance.hpp>
  *
  * raft::resources handle;
  * int n_samples = 5000;
@@ -205,4 +246,4 @@ void pairwise_distance(
 
 /** @} */  // end group pairwise_distance_runtime
 
-}  // namespace cuvs::distance
+};  // namespace cuvs::distance
diff --git a/cpp/include/cuvs/distance/distance_types.hpp b/cpp/include/cuvs/distance/distance_types.hpp
deleted file mode 100644
index 8d6eb71377..0000000000
--- a/cpp/include/cuvs/distance/distance_types.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2021-2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "distance_types.h"
-
-namespace cuvs::distance {
-
-using DistanceType = cuvsDistanceType;
-
-/**
- * Whether minimal distance corresponds to similar elements (using the given metric).
- */
-inline bool is_min_close(DistanceType metric)
-{
-  bool select_min;
-  switch (metric) {
-    case DistanceType::InnerProduct:
-      // Similarity metrics have the opposite meaning, i.e. nearest neighbors are those with larger
-      // similarity (See the same logic at cpp/include/raft/sparse/spatial/detail/knn.cuh:362
-      // {perform_k_selection})
-      select_min = false;
-      break;
-    default: select_min = true;
-  }
-  return select_min;
-}
-
-namespace kernels {
-enum KernelType { LINEAR, POLYNOMIAL, RBF, TANH };
-
-/**
- * Parameters for kernel matrices.
- * The following kernels are implemented:
- * - LINEAR \f[ K(x_1,x_2) = <x_1,x_2>, \f] where \f$< , >\f$ is the dot product
- * - POLYNOMIAL \f[ K(x_1, x_2) = (\gamma <x_1,x_2> + \mathrm{coef0})^\mathrm{degree} \f]
- * - RBF \f[ K(x_1, x_2) = \exp(- \gamma |x_1-x_2|^2) \f]
- * - TANH \f[ K(x_1, x_2) = \tanh(\gamma <x_1,x_2> + \mathrm{coef0}) \f]
- */
-struct KernelParams {
-  // Kernel function parameters
-  KernelType kernel;  //!< Type of the kernel function
-  int degree;         //!< Degree of polynomial kernel (ignored by others)
-  double gamma;       //!< multiplier in the
-  double coef0;       //!< additive constant in poly and tanh kernels
-};
-}  // end namespace kernels
-
-};  // namespace cuvs::distance
diff --git a/cpp/include/cuvs/neighbors/ann_types.hpp b/cpp/include/cuvs/neighbors/ann_types.hpp
deleted file mode 100644
index e9b7bd903f..0000000000
--- a/cpp/include/cuvs/neighbors/ann_types.hpp
+++ /dev/null
@@ -1,90 +0,0 @@
-/*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <cstdint>
-#include <cuvs/distance/distance_types.hpp>
-
-namespace cuvs::neighbors::ann {
-
-/** Parameters for VPQ compression. */
-struct vpq_params {
-  /**
-   * The bit length of the vector element after compression by PQ.
-   *
-   * Possible values: [4, 5, 6, 7, 8].
-   *
-   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
-   * performance, but the lower the recall.
-   */
-  uint32_t pq_bits = 8;
-  /**
-   * The dimensionality of the vector after compression by PQ.
-   * When zero, an optimal value is selected using a heuristic.
-   *
-   * TODO: at the moment `dim` must be a multiple `pq_dim`.
-   */
-  uint32_t pq_dim = 0;
-  /**
-   * Vector Quantization (VQ) codebook size - number of "coarse cluster centers".
-   * When zero, an optimal value is selected using a heuristic.
-   */
-  uint32_t vq_n_centers = 0;
-  /** The number of iterations searching for kmeans centers (both VQ & PQ phases). */
-  uint32_t kmeans_n_iters = 25;
-  /**
-   * The fraction of data to use during iterative kmeans building (VQ phase).
-   * When zero, an optimal value is selected using a heuristic.
-   */
-  double vq_kmeans_trainset_fraction = 0;
-  /**
-   * The fraction of data to use during iterative kmeans building (PQ phase).
-   * When zero, an optimal value is selected using a heuristic.
-   */
-  double pq_kmeans_trainset_fraction = 0;
-};
-
-/**
- * @defgroup ann_types Approximate Nearest Neighbors Types
- * @{
- */
-
-/** The base for approximate KNN index structures. */
-struct index {};
-
-/** The base for KNN index parameters. */
-struct index_params {
-  /** Distance type. */
-  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
-  /** The argument used by some distance metrics. */
-  float metric_arg = 2.0f;
-  /**
-   * Whether to add the dataset content to the index, i.e.:
-   *
-   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
-   * `build`.
-   *  - `false` means `build` only trains the underlying model (e.g. quantizer or clustering), but
-   * the index is left empty; you'd need to call `extend` on the index afterwards to populate it.
-   */
-  bool add_data_on_build = true;
-};
-
-struct search_params {};
-
-/** @} */  // end group ann_types
-
-};  // namespace cuvs::neighbors::ann
diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
index 145bb55550..e285eae37e 100644
--- a/cpp/include/cuvs/neighbors/brute_force.h
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -17,7 +17,7 @@
 #pragma once
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/distance/distance_types.h>
+#include <cuvs/distance/distance.h>
 #include <dlpack/dlpack.h>
 #include <stdint.h>
 
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 755a941228..755c8cfdb3 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -16,8 +16,8 @@
 
 #pragma once
 
-#include "ann_types.hpp"
-#include <cuvs/neighbors/ann_types.hpp>
+#include "common.hpp"
+#include <cuvs/neighbors/common.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/handle.hpp>
@@ -37,7 +37,7 @@ namespace cuvs::neighbors::brute_force {
  * @tparam T data element type
  */
 template <typename T>
-struct index : cuvs::neighbors::ann::index {
+struct index : cuvs::neighbors::index {
  public:
   index(const index&)            = delete;
   index(index&&)                 = default;
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index d094e6afab..9d976d28e0 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -16,9 +16,9 @@
 
 #pragma once
 
-#include "ann_types.hpp"
-#include <cuvs/distance/distance_types.hpp>
-#include <cuvs/neighbors/dataset.hpp>
+#include "common.hpp"
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_device_accessor.hpp>
 #include <raft/core/host_mdspan.hpp>
@@ -47,7 +47,7 @@ enum class graph_build_algo {
   NN_DESCENT
 };
 
-struct index_params : ann::index_params {
+struct index_params : cuvs::neighbors::index_params {
   /** Degree of input graph for pruning. */
   size_t intermediate_graph_degree = 128;
   /** Degree of output graph. */
@@ -61,7 +61,7 @@ struct index_params : ann::index_params {
    *
    * NOTE: this is experimental new API, consider it unsafe.
    */
-  std::optional<cuvs::neighbors::ann::vpq_params> compression = std::nullopt;
+  std::optional<cuvs::neighbors::vpq_params> compression = std::nullopt;
 };
 
 /**
@@ -84,7 +84,7 @@ enum class search_algo {
 
 enum class hash_mode { HASH, SMALL, AUTO };
 
-struct search_params : ann::search_params {
+struct search_params : cuvs::neighbors::search_params {
   /** Maximum number of queries to search at the same time (batch size). Auto select when 0.*/
   size_t max_queries = 0;
 
@@ -149,7 +149,7 @@ static_assert(std::is_aggregate_v<search_params>);
  *
  */
 template <typename T, typename IdxT>
-struct index : ann::index {
+struct index : cuvs::neighbors::index {
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
@@ -207,7 +207,7 @@ struct index : ann::index {
   /** Construct an empty index. */
   index(raft::resources const& res,
         cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
-    : ann::index(),
+    : cuvs::neighbors::index(),
       metric_(metric),
       graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
       dataset_(new cuvs::neighbors::empty_dataset<int64_t>(0))
@@ -274,7 +274,7 @@ struct index : ann::index {
         raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, data_accessor> dataset,
         raft::mdspan<const IdxT, raft::matrix_extent<int64_t>, raft::row_major, graph_accessor>
           knn_graph)
-    : ann::index(),
+    : cuvs::neighbors::index(),
       metric_(metric),
       graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
       dataset_(make_aligned_dataset(res, dataset, 16))
diff --git a/cpp/include/cuvs/neighbors/dataset.hpp b/cpp/include/cuvs/neighbors/common.hpp
similarity index 53%
rename from cpp/include/cuvs/neighbors/dataset.hpp
rename to cpp/include/cuvs/neighbors/common.hpp
index 79c3f644f9..45fa1a107f 100644
--- a/cpp/include/cuvs/neighbors/dataset.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -13,9 +13,11 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+
 #pragma once
 
-#include <cuvs/neighbors/ann_types.hpp>
+#include <cstdint>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
@@ -23,6 +25,9 @@
 #include <raft/util/cudart_utils.hpp>   // get_device_for_address
 #include <raft/util/integer_utils.hpp>  // rounding up
 
+#include <cuvs/core/bitset.hpp>
+#include <raft/core/detail/macros.hpp>
+
 #include <memory>
 #include <numeric>
 #include <type_traits>
@@ -33,6 +38,72 @@
 
 namespace cuvs::neighbors {
 
+/** Parameters for VPQ compression. */
+struct vpq_params {
+  /**
+   * The bit length of the vector element after compression by PQ.
+   *
+   * Possible values: [4, 5, 6, 7, 8].
+   *
+   * Hint: the smaller the 'pq_bits', the smaller the index size and the better the search
+   * performance, but the lower the recall.
+   */
+  uint32_t pq_bits = 8;
+  /**
+   * The dimensionality of the vector after compression by PQ.
+   * When zero, an optimal value is selected using a heuristic.
+   *
+   * TODO: at the moment `dim` must be a multiple `pq_dim`.
+   */
+  uint32_t pq_dim = 0;
+  /**
+   * Vector Quantization (VQ) codebook size - number of "coarse cluster centers".
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  uint32_t vq_n_centers = 0;
+  /** The number of iterations searching for kmeans centers (both VQ & PQ phases). */
+  uint32_t kmeans_n_iters = 25;
+  /**
+   * The fraction of data to use during iterative kmeans building (VQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double vq_kmeans_trainset_fraction = 0;
+  /**
+   * The fraction of data to use during iterative kmeans building (PQ phase).
+   * When zero, an optimal value is selected using a heuristic.
+   */
+  double pq_kmeans_trainset_fraction = 0;
+};
+
+/**
+ * @defgroup neighbors_index Approximate Nearest Neighbors Types
+ * @{
+ */
+
+/** The base for approximate KNN index structures. */
+struct index {};
+
+/** The base for KNN index parameters. */
+struct index_params {
+  /** Distance type. */
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
+  /** The argument used by some distance metrics. */
+  float metric_arg = 2.0f;
+  /**
+   * Whether to add the dataset content to the index, i.e.:
+   *
+   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
+   * `build`.
+   *  - `false` means `build` only trains the underlying model (e.g. quantizer or clustering), but
+   * the index is left empty; you'd need to call `extend` on the index afterwards to populate it.
+   */
+  bool add_data_on_build = true;
+};
+
+struct search_params {};
+
+/** @} */  // end group neighbors_index
+
 /** Two-dimensional dataset; maybe owning, maybe compressed, maybe strided. */
 template <typename IdxT>
 struct dataset {
@@ -207,10 +278,6 @@ auto make_aligned_dataset(const raft::resources& res, const SrcT& src, uint32_t
     raft::round_up_safe<size_t>(src.extent(1) * kSize, std::lcm(align_bytes, kSize)) / kSize;
   return make_strided_dataset(res, src, required_stride);
 }
-
-/** Parameters for VPQ compression. */
-using vpq_params = cuvs::neighbors::ann::vpq_params;
-
 /**
  * @brief VPQ compressed dataset.
  *
@@ -295,4 +362,240 @@ struct vpq_dataset : public dataset<IdxT> {
   }
 };
 
-}  // namespace cuvs::neighbors
+namespace filtering {
+
+/* A filter that filters nothing. This is the default behavior. */
+struct none_ivf_sample_filter {
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the current inverted list index
+    const uint32_t cluster_ix,
+    // the index of the current sample inside the current inverted list
+    const uint32_t sample_ix) const;
+};
+
+/* A filter that filters nothing. This is the default behavior. */
+struct none_cagra_sample_filter {
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the index of the current sample
+    const uint32_t sample_ix) const;
+};
+
+/**
+ * @brief Filter used to convert the cluster index and sample index
+ * of an IVF search into a sample index. This can be used as an
+ * intermediate filter.
+ *
+ * @tparam index_t Indexing type
+ * @tparam filter_t
+ */
+template <typename index_t, typename filter_t>
+struct ivf_to_sample_filter {
+  const index_t* const* inds_ptrs_;
+  const filter_t next_filter_;
+
+  ivf_to_sample_filter(const index_t* const* inds_ptrs, const filter_t next_filter);
+
+  /** If the original filter takes three arguments, then don't modify the arguments.
+   * If the original filter takes two arguments, then we are using `inds_ptr_` to obtain the sample
+   * index.
+   */
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the current inverted list index
+    const uint32_t cluster_ix,
+    // the index of the current sample inside the current inverted list
+    const uint32_t sample_ix) const;
+};
+
+/**
+ * @brief Filter an index with a bitset
+ *
+ * @tparam index_t Indexing type
+ */
+template <typename bitset_t, typename index_t>
+struct bitset_filter {
+  // View of the bitset to use as a filter
+  const cuvs::core::bitset_view<bitset_t, index_t> bitset_view_;
+
+  bitset_filter(const cuvs::core::bitset_view<bitset_t, index_t> bitset_for_filtering);
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the index of the current sample
+    const uint32_t sample_ix) const;
+};
+
+/**
+ * If the filtering depends on the index of a sample, then the following
+ * filter template can be used:
+ *
+ * template <typename IdxT>
+ * struct index_ivf_sample_filter {
+ *   using index_type = IdxT;
+ *
+ *   const index_type* const* inds_ptr = nullptr;
+ *
+ *   index_ivf_sample_filter() {}
+ *   index_ivf_sample_filter(const index_type* const* _inds_ptr)
+ *       : inds_ptr{_inds_ptr} {}
+ *   index_ivf_sample_filter(const index_ivf_sample_filter&) = default;
+ *   index_ivf_sample_filter(index_ivf_sample_filter&&) = default;
+ *   index_ivf_sample_filter& operator=(const index_ivf_sample_filter&) = default;
+ *   index_ivf_sample_filter& operator=(index_ivf_sample_filter&&) = default;
+ *
+ *   inline _RAFT_HOST_DEVICE bool operator()(
+ *       const uint32_t query_ix,
+ *       const uint32_t cluster_ix,
+ *       const uint32_t sample_ix) const {
+ *     index_type database_idx = inds_ptr[cluster_ix][sample_ix];
+ *
+ *     // return true or false, depending on the database_idx
+ *     return true;
+ *   }
+ * };
+ *
+ * Initialize it as:
+ *   using filter_type = index_ivf_sample_filter<idx_t>;
+ *   filter_type filter(cuvs_ivfpq_index.inds_ptrs().data_handle());
+ *
+ * Use it as:
+ *   cuvs::neighbors::ivf_pq::search_with_filtering<data_t, idx_t, filter_type>(
+ *     ...regular parameters here...,
+ *     filter
+ *   );
+ *
+ * Another example would be the following filter that greenlights samples according
+ * to a contiguous bit mask vector.
+ *
+ * template <typename IdxT>
+ * struct bitmask_ivf_sample_filter {
+ *   using index_type = IdxT;
+ *
+ *   const index_type* const* inds_ptr = nullptr;
+ *   const uint64_t* const bit_mask_ptr = nullptr;
+ *   const int64_t bit_mask_stride_64 = 0;
+ *
+ *   bitmask_ivf_sample_filter() {}
+ *   bitmask_ivf_sample_filter(
+ *       const index_type* const* _inds_ptr,
+ *       const uint64_t* const _bit_mask_ptr,
+ *       const int64_t _bit_mask_stride_64)
+ *       : inds_ptr{_inds_ptr},
+ *         bit_mask_ptr{_bit_mask_ptr},
+ *         bit_mask_stride_64{_bit_mask_stride_64} {}
+ *   bitmask_ivf_sample_filter(const bitmask_ivf_sample_filter&) = default;
+ *   bitmask_ivf_sample_filter(bitmask_ivf_sample_filter&&) = default;
+ *   bitmask_ivf_sample_filter& operator=(const bitmask_ivf_sample_filter&) = default;
+ *   bitmask_ivf_sample_filter& operator=(bitmask_ivf_sample_filter&&) = default;
+ *
+ *   inline _RAFT_HOST_DEVICE bool operator()(
+ *       const uint32_t query_ix,
+ *       const uint32_t cluster_ix,
+ *       const uint32_t sample_ix) const {
+ *     const index_type database_idx = inds_ptr[cluster_ix][sample_ix];
+ *     const uint64_t bit_mask_element =
+ *         bit_mask_ptr[query_ix * bit_mask_stride_64 + database_idx / 64];
+ *     const uint64_t masked_bool =
+ *         bit_mask_element & (1ULL << (uint64_t)(database_idx % 64));
+ *     const bool is_bit_set = (masked_bool != 0);
+ *
+ *     return is_bit_set;
+ *   }
+ * };
+ */
+}  // namespace filtering
+
+namespace ivf {
+
+/**
+ * Default value filled in the `indices` array.
+ * One may encounter it trying to access a record within a list that is outside of the
+ * `size` bound or whenever the list is allocated but not filled-in yet.
+ */
+template <typename IdxT>
+constexpr static IdxT kInvalidRecord =
+  (std::is_signed_v<IdxT> ? IdxT{0} : std::numeric_limits<IdxT>::max()) - 1;
+
+/** The data for a single IVF list. */
+template <template <typename, typename...> typename SpecT,
+          typename SizeT,
+          typename... SpecExtraArgs>
+struct list {
+  using size_type    = SizeT;
+  using spec_type    = SpecT<size_type, SpecExtraArgs...>;
+  using value_type   = typename spec_type::value_type;
+  using index_type   = typename spec_type::index_type;
+  using list_extents = typename spec_type::list_extents;
+
+  /** Possibly encoded data; it's layout is defined by `SpecT`. */
+  raft::device_mdarray<value_type, list_extents, raft::row_major> data;
+  /** Source indices. */
+  raft::device_mdarray<index_type, raft::extent_1d<size_type>, raft::row_major> indices;
+  /** The actual size of the content. */
+  std::atomic<size_type> size;
+
+  /** Allocate a new list capable of holding at least `n_rows` data records and indices. */
+  list(raft::resources const& res, const spec_type& spec, size_type n_rows);
+};
+
+template <typename ListT, class T = void>
+struct enable_if_valid_list {};
+
+template <class T,
+          template <typename, typename...>
+          typename SpecT,
+          typename SizeT,
+          typename... SpecExtraArgs>
+struct enable_if_valid_list<list<SpecT, SizeT, SpecExtraArgs...>, T> {
+  using type = T;
+};
+
+/**
+ * Designed after `std::enable_if_t`, this trait is helpful in the instance resolution;
+ * plug this in the return type of a function that has an instance of `ivf::list` as
+ * a template parameter.
+ */
+template <typename ListT, class T = void>
+using enable_if_valid_list_t = typename enable_if_valid_list<ListT, T>::type;
+
+/**
+ * Resize a list by the given id, so that it can contain the given number of records;
+ * copy the data if necessary.
+ */
+template <typename ListT>
+void resize_list(raft::resources const& res,
+                 std::shared_ptr<ListT>& orig_list,  // NOLINT
+                 const typename ListT::spec_type& spec,
+                 typename ListT::size_type new_used_size,
+                 typename ListT::size_type old_used_size);
+
+template <typename ListT>
+enable_if_valid_list_t<ListT> serialize_list(
+  const raft::resources& handle,
+  std::ostream& os,
+  const ListT& ld,
+  const typename ListT::spec_type& store_spec,
+  std::optional<typename ListT::size_type> size_override = std::nullopt);
+template <typename ListT>
+enable_if_valid_list_t<ListT> serialize_list(
+  const raft::resources& handle,
+  std::ostream& os,
+  const std::shared_ptr<ListT>& ld,
+  const typename ListT::spec_type& store_spec,
+  std::optional<typename ListT::size_type> size_override = std::nullopt);
+
+template <typename ListT>
+enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
+                                               std::istream& is,
+                                               std::shared_ptr<ListT>& ld,
+                                               const typename ListT::spec_type& store_spec,
+                                               const typename ListT::spec_type& device_spec);
+
+}  // namespace ivf
+
+};  // namespace cuvs::neighbors
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index a6616e73e5..2e8c551e48 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -17,7 +17,7 @@
 #pragma once
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/distance/distance_types.h>
+#include <cuvs/distance/distance.h>
 #include <dlpack/dlpack.h>
 #include <stdbool.h>
 #include <stdint.h>
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index 33daedc788..38c7be612d 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -16,10 +16,11 @@
 
 #pragma once
 
-#include "ann_types.hpp"
-#include "ivf_list.hpp"
+#include "common.hpp"
 #include <cstdint>
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
 
 namespace cuvs::neighbors::ivf_flat {
 /**
@@ -30,7 +31,7 @@ namespace cuvs::neighbors::ivf_flat {
 /** Size of the interleaved group (see `index::data` description). */
 constexpr static uint32_t kIndexGroupSize = 32;
 
-struct index_params : ann::index_params {
+struct index_params : cuvs::neighbors::index_params {
   /** The number of inverted lists (clusters) */
   uint32_t n_lists = 1024;
   /** The number of iterations searching for kmeans centers (index building). */
@@ -69,7 +70,7 @@ struct index_params : ann::index_params {
  * @defgroup ivf_flat_cpp_search_params IVF-Flat index search parameters
  * @{
  */
-struct search_params : ann::search_params {
+struct search_params : cuvs::neighbors::search_params {
   /** The number of clusters to search. */
   uint32_t n_probes = 20;
 };
@@ -127,7 +128,7 @@ using list_data = ivf::list<list_spec, SizeT, ValueT, IdxT>;
  *
  */
 template <typename T, typename IdxT>
-struct index : ann::index {
+struct index : cuvs::neighbors::index {
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
diff --git a/cpp/include/cuvs/neighbors/ivf_list.hpp b/cpp/include/cuvs/neighbors/ivf_list.hpp
deleted file mode 100644
index a12be19638..0000000000
--- a/cpp/include/cuvs/neighbors/ivf_list.hpp
+++ /dev/null
@@ -1,123 +0,0 @@
-/*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <raft/core/device_mdarray.hpp>
-#include <raft/core/error.hpp>
-#include <raft/core/host_mdarray.hpp>
-#include <raft/core/mdspan.hpp>
-#include <raft/core/resource/cuda_stream.hpp>
-#include <raft/core/resource/thrust_policy.hpp>
-#include <raft/core/resources.hpp>
-#include <raft/core/serialize.hpp>
-#include <raft/util/integer_utils.hpp>
-
-#include <thrust/fill.h>
-
-#include <atomic>
-#include <fstream>
-#include <limits>
-#include <memory>
-#include <type_traits>
-
-namespace cuvs::neighbors::ivf {
-
-/**
- * Default value filled in the `indices` array.
- * One may encounter it trying to access a record within a list that is outside of the
- * `size` bound or whenever the list is allocated but not filled-in yet.
- */
-template <typename IdxT>
-constexpr static IdxT kInvalidRecord =
-  (std::is_signed_v<IdxT> ? IdxT{0} : std::numeric_limits<IdxT>::max()) - 1;
-
-/** The data for a single IVF list. */
-template <template <typename, typename...> typename SpecT,
-          typename SizeT,
-          typename... SpecExtraArgs>
-struct list {
-  using size_type    = SizeT;
-  using spec_type    = SpecT<size_type, SpecExtraArgs...>;
-  using value_type   = typename spec_type::value_type;
-  using index_type   = typename spec_type::index_type;
-  using list_extents = typename spec_type::list_extents;
-
-  /** Possibly encoded data; it's layout is defined by `SpecT`. */
-  raft::device_mdarray<value_type, list_extents, raft::row_major> data;
-  /** Source indices. */
-  raft::device_mdarray<index_type, raft::extent_1d<size_type>, raft::row_major> indices;
-  /** The actual size of the content. */
-  std::atomic<size_type> size;
-
-  /** Allocate a new list capable of holding at least `n_rows` data records and indices. */
-  list(raft::resources const& res, const spec_type& spec, size_type n_rows);
-};
-
-template <typename ListT, class T = void>
-struct enable_if_valid_list {};
-
-template <class T,
-          template <typename, typename...>
-          typename SpecT,
-          typename SizeT,
-          typename... SpecExtraArgs>
-struct enable_if_valid_list<list<SpecT, SizeT, SpecExtraArgs...>, T> {
-  using type = T;
-};
-
-/**
- * Designed after `std::enable_if_t`, this trait is helpful in the instance resolution;
- * plug this in the return type of a function that has an instance of `ivf::list` as
- * a template parameter.
- */
-template <typename ListT, class T = void>
-using enable_if_valid_list_t = typename enable_if_valid_list<ListT, T>::type;
-
-/**
- * Resize a list by the given id, so that it can contain the given number of records;
- * copy the data if necessary.
- */
-template <typename ListT>
-void resize_list(raft::resources const& res,
-                 std::shared_ptr<ListT>& orig_list,  // NOLINT
-                 const typename ListT::spec_type& spec,
-                 typename ListT::size_type new_used_size,
-                 typename ListT::size_type old_used_size);
-
-template <typename ListT>
-enable_if_valid_list_t<ListT> serialize_list(
-  const raft::resources& handle,
-  std::ostream& os,
-  const ListT& ld,
-  const typename ListT::spec_type& store_spec,
-  std::optional<typename ListT::size_type> size_override = std::nullopt);
-template <typename ListT>
-enable_if_valid_list_t<ListT> serialize_list(
-  const raft::resources& handle,
-  std::ostream& os,
-  const std::shared_ptr<ListT>& ld,
-  const typename ListT::spec_type& store_spec,
-  std::optional<typename ListT::size_type> size_override = std::nullopt);
-
-template <typename ListT>
-enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
-                                               std::istream& is,
-                                               std::shared_ptr<ListT>& ld,
-                                               const typename ListT::spec_type& store_spec,
-                                               const typename ListT::spec_type& device_spec);
-
-}  // namespace cuvs::neighbors::ivf
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index c4ebc31f4a..9424729ff6 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -17,7 +17,7 @@
 #pragma once
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/distance/distance_types.h>
+#include <cuvs/distance/distance.h>
 #include <dlpack/dlpack.h>
 #include <stdbool.h>
 #include <stdint.h>
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index c6b1b2ee74..d2d6ca9bb9 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -16,9 +16,8 @@
 
 #pragma once
 
-#include "ann_types.hpp"
-#include <cuvs/neighbors/ivf_list.hpp>
-#include <cuvs/neighbors/sample_filter.hpp>
+#include "common.hpp"
+#include <cuvs/neighbors/common.hpp>
 
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/error.hpp>
@@ -39,7 +38,7 @@ enum class codebook_gen {  // NOLINT
   PER_CLUSTER  = 1,        // NOLINT
 };
 
-struct index_params : ann::index_params {
+struct index_params : cuvs::neighbors::index_params {
   /**
    * The number of inverted lists (clusters)
    *
@@ -138,7 +137,7 @@ struct index_params : ann::index_params {
  * @defgroup ivf_pq_cpp_search_params IVF-PQ index search parameters
  * @{
  */
-struct search_params : ann::search_params {
+struct search_params : cuvs::neighbors::search_params {
   /** The number of clusters to search. */
   uint32_t n_probes = 20;
   /**
@@ -279,7 +278,7 @@ using list_data = ivf::list<list_spec, SizeT, IdxT>;
  *
  */
 template <typename IdxT>
-struct index : ann::index {
+struct index : cuvs::neighbors::index {
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index 2d0d2ca306..d697f4a3f0 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/neighbors/ann_types.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_mdarray.hpp>
@@ -25,7 +25,7 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
 namespace cuvs::neighbors::nn_descent {
 /**
@@ -48,7 +48,7 @@ namespace cuvs::neighbors::nn_descent {
  * `termination_threshold`: The delta at which nn-descent will terminate its iterations
  *
  */
-struct index_params : ann::index_params {
+struct index_params : cuvs::neighbors::index_params {
   size_t graph_degree              = 64;      // Degree of output graph.
   size_t intermediate_graph_degree = 128;     // Degree of input graph for pruning.
   size_t max_iterations            = 20;      // Number of nn-descent iterations.
@@ -69,7 +69,7 @@ struct index_params : ann::index_params {
  * @tparam IdxT dtype to be used for constructing knn-graph
  */
 template <typename IdxT>
-struct index : ann::index {
+struct index : cuvs::neighbors::index {
  public:
   /**
    * @brief Construct a new index object
@@ -83,7 +83,7 @@ struct index : ann::index {
    * @param n_cols number of cols in knn-graph
    */
   index(raft::resources const& res, int64_t n_rows, int64_t n_cols)
-    : ann::index(),
+    : cuvs::neighbors::index(),
       res_{res},
       metric_{cuvs::distance::DistanceType::L2Expanded},
       graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(n_rows, n_cols)},
@@ -103,7 +103,7 @@ struct index : ann::index {
    */
   index(raft::resources const& res,
         raft::host_matrix_view<IdxT, int64_t, raft::row_major> graph_view)
-    : ann::index(),
+    : cuvs::neighbors::index(),
       res_{res},
       metric_{cuvs::distance::DistanceType::L2Expanded},
       graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(0, 0)},
diff --git a/cpp/include/cuvs/neighbors/sample_filter.hpp b/cpp/include/cuvs/neighbors/sample_filter.hpp
deleted file mode 100644
index 01ac284a3d..0000000000
--- a/cpp/include/cuvs/neighbors/sample_filter.hpp
+++ /dev/null
@@ -1,171 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <cuvs/core/bitset.hpp>
-#include <raft/core/detail/macros.hpp>
-
-#include <cstddef>
-#include <cstdint>
-
-namespace cuvs::neighbors::filtering {
-
-/* A filter that filters nothing. This is the default behavior. */
-struct none_ivf_sample_filter {
-  inline _RAFT_HOST_DEVICE bool operator()(
-    // query index
-    const uint32_t query_ix,
-    // the current inverted list index
-    const uint32_t cluster_ix,
-    // the index of the current sample inside the current inverted list
-    const uint32_t sample_ix) const;
-};
-
-/* A filter that filters nothing. This is the default behavior. */
-struct none_cagra_sample_filter {
-  inline _RAFT_HOST_DEVICE bool operator()(
-    // query index
-    const uint32_t query_ix,
-    // the index of the current sample
-    const uint32_t sample_ix) const;
-};
-
-/**
- * @brief Filter used to convert the cluster index and sample index
- * of an IVF search into a sample index. This can be used as an
- * intermediate filter.
- *
- * @tparam index_t Indexing type
- * @tparam filter_t
- */
-template <typename index_t, typename filter_t>
-struct ivf_to_sample_filter {
-  const index_t* const* inds_ptrs_;
-  const filter_t next_filter_;
-
-  ivf_to_sample_filter(const index_t* const* inds_ptrs, const filter_t next_filter);
-
-  /** If the original filter takes three arguments, then don't modify the arguments.
-   * If the original filter takes two arguments, then we are using `inds_ptr_` to obtain the sample
-   * index.
-   */
-  inline _RAFT_HOST_DEVICE bool operator()(
-    // query index
-    const uint32_t query_ix,
-    // the current inverted list index
-    const uint32_t cluster_ix,
-    // the index of the current sample inside the current inverted list
-    const uint32_t sample_ix) const;
-};
-
-/**
- * @brief Filter an index with a bitset
- *
- * @tparam index_t Indexing type
- */
-template <typename bitset_t, typename index_t>
-struct bitset_filter {
-  // View of the bitset to use as a filter
-  const cuvs::core::bitset_view<bitset_t, index_t> bitset_view_;
-
-  bitset_filter(const cuvs::core::bitset_view<bitset_t, index_t> bitset_for_filtering);
-  inline _RAFT_HOST_DEVICE bool operator()(
-    // query index
-    const uint32_t query_ix,
-    // the index of the current sample
-    const uint32_t sample_ix) const;
-};
-
-/**
- * If the filtering depends on the index of a sample, then the following
- * filter template can be used:
- *
- * template <typename IdxT>
- * struct index_ivf_sample_filter {
- *   using index_type = IdxT;
- *
- *   const index_type* const* inds_ptr = nullptr;
- *
- *   index_ivf_sample_filter() {}
- *   index_ivf_sample_filter(const index_type* const* _inds_ptr)
- *       : inds_ptr{_inds_ptr} {}
- *   index_ivf_sample_filter(const index_ivf_sample_filter&) = default;
- *   index_ivf_sample_filter(index_ivf_sample_filter&&) = default;
- *   index_ivf_sample_filter& operator=(const index_ivf_sample_filter&) = default;
- *   index_ivf_sample_filter& operator=(index_ivf_sample_filter&&) = default;
- *
- *   inline _RAFT_HOST_DEVICE bool operator()(
- *       const uint32_t query_ix,
- *       const uint32_t cluster_ix,
- *       const uint32_t sample_ix) const {
- *     index_type database_idx = inds_ptr[cluster_ix][sample_ix];
- *
- *     // return true or false, depending on the database_idx
- *     return true;
- *   }
- * };
- *
- * Initialize it as:
- *   using filter_type = index_ivf_sample_filter<idx_t>;
- *   filter_type filter(cuvs_ivfpq_index.inds_ptrs().data_handle());
- *
- * Use it as:
- *   cuvs::neighbors::ivf_pq::search_with_filtering<data_t, idx_t, filter_type>(
- *     ...regular parameters here...,
- *     filter
- *   );
- *
- * Another example would be the following filter that greenlights samples according
- * to a contiguous bit mask vector.
- *
- * template <typename IdxT>
- * struct bitmask_ivf_sample_filter {
- *   using index_type = IdxT;
- *
- *   const index_type* const* inds_ptr = nullptr;
- *   const uint64_t* const bit_mask_ptr = nullptr;
- *   const int64_t bit_mask_stride_64 = 0;
- *
- *   bitmask_ivf_sample_filter() {}
- *   bitmask_ivf_sample_filter(
- *       const index_type* const* _inds_ptr,
- *       const uint64_t* const _bit_mask_ptr,
- *       const int64_t _bit_mask_stride_64)
- *       : inds_ptr{_inds_ptr},
- *         bit_mask_ptr{_bit_mask_ptr},
- *         bit_mask_stride_64{_bit_mask_stride_64} {}
- *   bitmask_ivf_sample_filter(const bitmask_ivf_sample_filter&) = default;
- *   bitmask_ivf_sample_filter(bitmask_ivf_sample_filter&&) = default;
- *   bitmask_ivf_sample_filter& operator=(const bitmask_ivf_sample_filter&) = default;
- *   bitmask_ivf_sample_filter& operator=(bitmask_ivf_sample_filter&&) = default;
- *
- *   inline _RAFT_HOST_DEVICE bool operator()(
- *       const uint32_t query_ix,
- *       const uint32_t cluster_ix,
- *       const uint32_t sample_ix) const {
- *     const index_type database_idx = inds_ptr[cluster_ix][sample_ix];
- *     const uint64_t bit_mask_element =
- *         bit_mask_ptr[query_ix * bit_mask_stride_64 + database_idx / 64];
- *     const uint64_t masked_bool =
- *         bit_mask_element & (1ULL << (uint64_t)(database_idx % 64));
- *     const bool is_bit_set = (masked_bool != 0);
- *
- *     return is_bit_set;
- *   }
- * };
- */
-}  // namespace cuvs::neighbors::filtering
diff --git a/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh b/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh
index c105c716ff..1bde550ab2 100644
--- a/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh
+++ b/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/spatial/knn/detail/ann_utils.cuh>
 #include <raft/matrix/detail/select_k.cuh>
 #include <raft/util/cuda_utils.cuh>
diff --git a/cpp/internal/cuvs_internal/neighbors/refine_helper.cuh b/cpp/internal/cuvs_internal/neighbors/refine_helper.cuh
index b47f331785..def009b375 100644
--- a/cpp/internal/cuvs_internal/neighbors/refine_helper.cuh
+++ b/cpp/internal/cuvs_internal/neighbors/refine_helper.cuh
@@ -18,7 +18,7 @@
 #include <cuvs_internal/neighbors/naive_knn.cuh>
 #include <raft/core/resource/cuda_stream.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_mdarray.hpp>
diff --git a/cpp/src/cluster/detail/connectivities.cuh b/cpp/src/cluster/detail/connectivities.cuh
index a02ad2cc0d..ada4241924 100644
--- a/cpp/src/cluster/detail/connectivities.cuh
+++ b/cpp/src/cluster/detail/connectivities.cuh
@@ -18,11 +18,10 @@
 
 #include "../../distance/distance.cuh"
 #include <cuvs/cluster/agglomerative.hpp>
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
 #include <raft/core/resources.hpp>
-#include <raft/distance/distance_types.hpp>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/sparse/convert/csr.cuh>
 #include <raft/sparse/coo.hpp>
diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
index 0f2ac39f0a..e7d4bdf76a 100644
--- a/cpp/src/cluster/detail/kmeans.cuh
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -18,7 +18,7 @@
 #include "kmeans_common.cuh"
 
 #include <cuvs/cluster/kmeans.hpp>
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
 #include <raft/common/nvtx.hpp>
 #include <raft/core/cudart_utils.hpp>
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index aea07c1aa4..a09b17532b 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -22,7 +22,7 @@
 
 #include "../../distance/distance.cuh"
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/common/nvtx.hpp>
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/logger-ext.hpp>
diff --git a/cpp/src/cluster/detail/kmeans_common.cuh b/cpp/src/cluster/detail/kmeans_common.cuh
index 0f84421a9b..04c1a68020 100644
--- a/cpp/src/cluster/detail/kmeans_common.cuh
+++ b/cpp/src/cluster/detail/kmeans_common.cuh
@@ -18,7 +18,7 @@
 #include "../../distance/distance.cuh"
 #include "../../distance/fused_distance_nn.cuh"
 #include <cuvs/cluster/kmeans.hpp>
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
diff --git a/cpp/src/distance/detail/distance.cuh b/cpp/src/distance/detail/distance.cuh
index 7765bc672a..c8dde4ea4f 100644
--- a/cpp/src/distance/detail/distance.cuh
+++ b/cpp/src/distance/detail/distance.cuh
@@ -20,7 +20,7 @@
 #include "pairwise_matrix/dispatch.cuh"
 #include "pairwise_matrix/dispatch_sm60.cuh"
 #include "pairwise_matrix/dispatch_sm80.cuh"
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/linalg/gemm.cuh>
diff --git a/cpp/src/distance/detail/fused_distance_nn.cuh b/cpp/src/distance/detail/fused_distance_nn.cuh
index 1bf8793fdc..b786fbc99c 100644
--- a/cpp/src/distance/detail/fused_distance_nn.cuh
+++ b/cpp/src/distance/detail/fused_distance_nn.cuh
@@ -23,7 +23,7 @@
 #include "fused_distance_nn/helper_structs.cuh"
 #include "fused_distance_nn/simt_kernel.cuh"
 #include "pairwise_distance_base.cuh"  // PairwiseDistances
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/kvp.hpp>             // raft::KeyValuePair
 #include <raft/core/operators.hpp>       // raft::identity_op
 #include <raft/linalg/contractions.cuh>  // Policy
diff --git a/cpp/src/distance/detail/kernels/gram_matrix.cuh b/cpp/src/distance/detail/kernels/gram_matrix.cuh
index 009941fa7b..d435fb4d1d 100644
--- a/cpp/src/distance/detail/kernels/gram_matrix.cuh
+++ b/cpp/src/distance/detail/kernels/gram_matrix.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 #include "../../distance.cuh"
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/distance/detail/kernels/kernel_factory.cuh b/cpp/src/distance/detail/kernels/kernel_factory.cuh
index 534339a153..5c50a95a33 100644
--- a/cpp/src/distance/detail/kernels/kernel_factory.cuh
+++ b/cpp/src/distance/detail/kernels/kernel_factory.cuh
@@ -19,7 +19,7 @@
 #include "gram_matrix.cuh"
 #include "kernel_matrices.cuh"
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/util/cudart_utils.hpp>
 
 namespace cuvs::distance::kernels::detail {
diff --git a/cpp/src/distance/distance-ext.cuh b/cpp/src/distance/distance-ext.cuh
index ad45e84059..e574c11b0c 100644
--- a/cpp/src/distance/distance-ext.cuh
+++ b/cpp/src/distance/distance-ext.cuh
@@ -15,12 +15,12 @@
  */
 #pragma once
 
-#include "detail/kernels/rbf_fin_op.cuh"     // rbf_fin_op
-#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
-#include <raft/core/device_mdspan.hpp>       // raft::device_matrix_view
-#include <raft/core/operators.hpp>           // raft::identity_op
-#include <raft/core/resources.hpp>           // raft::resources
-#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+#include "detail/kernels/rbf_fin_op.cuh"  // rbf_fin_op
+#include <cuvs/distance/distance.hpp>     // cuvs::distance::DistanceType
+#include <raft/core/device_mdspan.hpp>    // raft::device_matrix_view
+#include <raft/core/operators.hpp>        // raft::identity_op
+#include <raft/core/resources.hpp>        // raft::resources
+#include <raft/util/raft_explicit.hpp>    // RAFT_EXPLICIT
 
 #include <rmm/device_uvector.hpp>  // rmm::device_uvector
 
diff --git a/cpp/src/distance/distance-inl.cuh b/cpp/src/distance/distance-inl.cuh
index 2c4138c3ff..6236901c3d 100644
--- a/cpp/src/distance/distance-inl.cuh
+++ b/cpp/src/distance/distance-inl.cuh
@@ -16,7 +16,7 @@
 #pragma once
 
 #include "detail/distance.cuh"
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/distance/pairwise_distance.cu b/cpp/src/distance/pairwise_distance.cu
index bf4b21669b..10f096b966 100644
--- a/cpp/src/distance/pairwise_distance.cu
+++ b/cpp/src/distance/pairwise_distance.cu
@@ -16,8 +16,7 @@
 
 #include "distance.cuh"
 #include <cstdint>
-#include <cuvs/distance/distance_types.hpp>
-#include <cuvs/distance/pairwise_distance.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdspan.hpp>
 
 namespace cuvs::distance {
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index b9b74a3f25..45d4be4a77 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -26,7 +26,7 @@ index<T>::index(raft::resources const& res,
                 std::optional<raft::device_vector<T, int64_t>>&& norms,
                 cuvs::distance::DistanceType metric,
                 T metric_arg)
-  : ann::index(),
+  : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
     norms_(std::move(norms)),
@@ -43,7 +43,7 @@ index<T>::index(raft::resources const& res,
                 std::optional<raft::device_vector<T, int64_t>>&& norms,
                 cuvs::distance::DistanceType metric,
                 T metric_arg)
-  : ann::index(),
+  : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
     norms_(std::move(norms)),
@@ -59,7 +59,7 @@ index<T>::index(raft::resources const& res,
                 std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
                 cuvs::distance::DistanceType metric,
                 T metric_arg)
-  : ann::index(),
+  : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
     dataset_view_(dataset_view),
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 840ac29364..997f369a95 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -25,10 +25,10 @@
 #include <raft/core/mdspan.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
 
-#include <cuvs/neighbors/dataset.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 #include <rmm/cuda_stream_view.hpp>
 
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 6a7b2f0a6a..cadda19d5d 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -46,7 +46,7 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   build_params.nn_descent_niter = params.nn_descent_niter;
 
   if (auto* cparams = params.compression; cparams != nullptr) {
-    auto compression_params                        = cuvs::neighbors::ann::vpq_params();
+    auto compression_params                        = cuvs::neighbors::vpq_params();
     compression_params.pq_bits                     = cparams->pq_bits;
     compression_params.pq_dim                      = cparams->pq_dim;
     compression_params.vq_n_centers                = cparams->vq_n_centers;
@@ -216,7 +216,7 @@ extern "C" cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params
 extern "C" cuvsError_t cuvsCagraCompressionParamsCreate(cuvsCagraCompressionParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto ps = cuvs::neighbors::ann::vpq_params();
+    auto ps = cuvs::neighbors::vpq_params();
     *params =
       new cuvsCagraCompressionParams{.pq_bits                     = ps.pq_bits,
                                      .pq_dim                      = ps.pq_dim,
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index c634fb146c..f73524b7aa 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/logger-ext.hpp>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 9d31fc6f07..9a92a53502 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -28,7 +28,7 @@
 #include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
 // TODO: Fixme- this needs to be migrated
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index 543595f4a9..cfb5f7919c 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -27,14 +27,14 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
 #include <cuvs/neighbors/cagra.hpp>
 
 // TODO: Fix these when ivf methods are moved over
 #include "../../ivf_common.cuh"
 #include "../../ivf_pq/ivf_pq_search.cuh"
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 // TODO: This shouldn't be calling spatial/knn apis
 #include "../ann_utils.cuh"
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.hpp b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
index 155fc24360..2b0c750ffa 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
@@ -19,7 +19,7 @@
 #include "hashmap.hpp"
 #include "utils.hpp"
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/operators.hpp>
 
 // TODO: This shouldn't be invoking spatial/knn
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
index 9ed1d5edd1..68973662fd 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
@@ -18,7 +18,7 @@
 
 #include "compute_distance.hpp"
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/util/integer_utils.hpp>
 
 namespace cuvs::neighbors::cagra::detail {
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index dceee68425..183d6051fc 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -21,7 +21,7 @@
 #include "search_plan.cuh"
 #include "search_single_cta.cuh"
 
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 namespace cuvs::neighbors::cagra::detail {
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index 11a8a99685..a6a20ca49d 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -31,7 +31,7 @@
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
 #include <raft/linalg/map.cuh>
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
index ce47d7ec5e..e28389f388 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 #include "search_multi_cta_kernel-inl.cuh"
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
index bfac43d5ec..63f3838d09 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
@@ -16,8 +16,8 @@
 #pragma once
 
 #include "compute_distance_vpq.cuh"
-#include <cuvs/neighbors/sample_filter.hpp>  // none_cagra_sample_filter
-#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+#include <cuvs/neighbors/common.hpp>    // none_cagra_sample_filter
+#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
 
 #include <cuda_fp16.h>
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 877d559722..3c5c07a84f 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -29,9 +29,9 @@
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 // TODO: This shouldn't be invoking anything from spatial/knn
 #include "../ann_utils.cuh"
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index 0296dc9d42..4dd53600ce 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -28,11 +28,11 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
 #include <raft/matrix/select_k.cuh>
 
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 // TODO: This shouldn't be invoking anything from spatial/knn
 #include "../ann_utils.cuh"
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index 3c44194faa..d46814e6cb 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -18,7 +18,7 @@
 
 #include "hashmap.hpp"
 
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 // #include "search_single_cta_inst.cuh"
 // #include "topk_for_cagra/topk_core.cuh"
@@ -26,7 +26,7 @@
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 
 #include <cuvs/neighbors/cagra.hpp>
 #include <raft/util/pow2_utils.cuh>
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
index 51328d488f..b3d75e9234 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 #include "search_single_cta_kernel-inl.cuh"
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
index 549b692bc2..c4ed705684 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
 
 #include <cuda_fp16.h>
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index c6f42d9d69..99543b9d35 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -24,14 +24,14 @@
 #include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk
 #include "utils.hpp"
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/logger-ext.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 // TODO: This shouldn't be invoking anything from spatial/knn
 #include "../ann_utils.cuh"
diff --git a/cpp/src/neighbors/detail/dataset_serialize.hpp b/cpp/src/neighbors/detail/dataset_serialize.hpp
index e2fddc2620..40d9df930f 100644
--- a/cpp/src/neighbors/detail/dataset_serialize.hpp
+++ b/cpp/src/neighbors/detail/dataset_serialize.hpp
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#include <cuvs/neighbors/dataset.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 2f5aa176d8..29cd26d9f3 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -15,7 +15,7 @@
  */
 
 #pragma once
-#include <cuvs/distance/pairwise_distance.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
 
 #include "../../distance/detail/distance_ops/l2_exp.cuh"
diff --git a/cpp/src/neighbors/detail/refine_common.hpp b/cpp/src/neighbors/detail/refine_common.hpp
index 8f3bb01444..d7c6e340ca 100644
--- a/cpp/src/neighbors/detail/refine_common.hpp
+++ b/cpp/src/neighbors/detail/refine_common.hpp
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/mdspan.hpp>
 
 namespace cuvs::neighbors::detail {
diff --git a/cpp/src/neighbors/detail/refine_device.cuh b/cpp/src/neighbors/detail/refine_device.cuh
index c1bef6dbaf..7148733f1c 100644
--- a/cpp/src/neighbors/detail/refine_device.cuh
+++ b/cpp/src/neighbors/detail/refine_device.cuh
@@ -20,8 +20,8 @@
 #include "../ivf_flat/ivf_flat_interleaved_scan.cuh"
 #include "ann_utils.cuh"
 #include "refine_common.hpp"
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <cuvs/neighbors/sample_filter.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/nvtx.hpp>
diff --git a/cpp/src/neighbors/detail/refine_host-ext.hpp b/cpp/src/neighbors/detail/refine_host-ext.hpp
index e4ea9bb90a..74ba2a679b 100644
--- a/cpp/src/neighbors/detail/refine_host-ext.hpp
+++ b/cpp/src/neighbors/detail/refine_host-ext.hpp
@@ -17,10 +17,10 @@
 #pragma once
 
 #include "refine_host-inl.hpp"
-#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
-#include <raft/core/detail/macros.hpp>       // _RAFT_HAS_CUDA
-#include <raft/core/host_mdspan.hpp>         // raft::host_matrix_view
-#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+#include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
+#include <raft/core/detail/macros.hpp>  // _RAFT_HAS_CUDA
+#include <raft/core/host_mdspan.hpp>    // raft::host_matrix_view
+#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
 
 #include <cstdint>  // int64_t
 
diff --git a/cpp/src/neighbors/detail/vpq_dataset.cuh b/cpp/src/neighbors/detail/vpq_dataset.cuh
index c0bf24f500..d85bad9205 100644
--- a/cpp/src/neighbors/detail/vpq_dataset.cuh
+++ b/cpp/src/neighbors/detail/vpq_dataset.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#include <cuvs/neighbors/dataset.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 #include "../ivf_pq/ivf_pq_build.cuh"  // pq_bits-bitfield
 #include "ann_utils.cuh"               // utils::mapping etc
diff --git a/cpp/src/neighbors/ivf_common.cuh b/cpp/src/neighbors/ivf_common.cuh
index 061d18a579..60d43bed6c 100644
--- a/cpp/src/neighbors/ivf_common.cuh
+++ b/cpp/src/neighbors/ivf_common.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/matrix/detail/select_warpsort.cuh>  // matrix::detail::select::warpsort::warp_sort_distributed
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index 65c7203815..811f9c231d 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -21,12 +21,12 @@
 #include "../ivf_list.cuh"
 
 #include <cuvs/cluster/kmeans.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <cuvs/neighbors/ivf_list.hpp>
 
 #include "../../cluster/kmeans_balanced.cuh"
 #include "../detail/ann_utils.cuh"
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/logger-ext.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index 86346f1e8e..ce29a7e7c9 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -18,11 +18,11 @@
 
 #include "../ivf_common.cuh"
 #include "../sample_filter.cuh"
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <cuvs/neighbors/sample_filter.hpp>
 
 #include "../detail/ann_utils.cuh"
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/logger-ext.hpp>  // RAFT_LOG_TRACE
 #include <raft/core/operators.hpp>
 #include <raft/matrix/detail/select_warpsort.cuh>
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index 36861fbf09..f55e99c185 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -18,14 +18,14 @@
 
 #include "../../core/nvtx.hpp"
 #include "../detail/ann_utils.cuh"
-#include "../ivf_common.cuh"                 // cuvs::neighbors::detail::ivf
-#include "ivf_flat_interleaved_scan.cuh"     // interleaved_scan
-#include <cuvs/neighbors/ivf_flat.hpp>       // raft::neighbors::ivf_flat::index
-#include <cuvs/neighbors/sample_filter.hpp>  // none_ivf_sample_filter
-
-#include "../detail/ann_utils.cuh"           // utils::mapping
-#include <cuvs/distance/distance_types.hpp>  // is_min_close, DistanceType
-#include <raft/core/logger-ext.hpp>          // RAFT_LOG_TRACE
+#include "../ivf_common.cuh"              // cuvs::neighbors::detail::ivf
+#include "ivf_flat_interleaved_scan.cuh"  // interleaved_scan
+#include <cuvs/neighbors/common.hpp>      // none_ivf_sample_filter
+#include <cuvs/neighbors/ivf_flat.hpp>    // raft::neighbors::ivf_flat::index
+
+#include "../detail/ann_utils.cuh"     // utils::mapping
+#include <cuvs/distance/distance.hpp>  // is_min_close, DistanceType
+#include <raft/core/logger-ext.hpp>    // RAFT_LOG_TRACE
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>          // raft::resources
 #include <raft/linalg/gemm.cuh>             // raft::linalg::gemm
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
index 3d3ff49d0c..d88e173230 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
@@ -18,8 +18,8 @@
 
 #include "../ivf_common.cuh"
 #include "../ivf_list.cuh"
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
-#include <cuvs/neighbors/ivf_list.hpp>
 
 #include <raft/core/detail/mdspan_numpy_serializer.hpp>
 #include <raft/core/mdarray.hpp>
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
index b25bb051a1..25cf36d329 100644
--- a/cpp/src/neighbors/ivf_flat_index.cpp
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -36,7 +36,7 @@ index<T, IdxT>::index(raft::resources const& res,
                       bool adaptive_centers,
                       bool conservative_memory_allocation,
                       uint32_t dim)
-  : ann::index(),
+  : cuvs::neighbors::index(),
     veclen_(calculate_veclen(dim)),
     metric_(metric),
     adaptive_centers_(adaptive_centers),
diff --git a/cpp/src/neighbors/ivf_list.cuh b/cpp/src/neighbors/ivf_list.cuh
index d80f9d9a0c..604fafe6bc 100644
--- a/cpp/src/neighbors/ivf_list.cuh
+++ b/cpp/src/neighbors/ivf_list.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
 #include <raft/core/device_mdarray.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 07e3c497c7..5536d6edb1 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -21,8 +21,8 @@
 #include "../ivf_list.cuh"
 #include "ivf_pq_codepacking.cuh"
 #include "ivf_pq_list.cuh"
-#include <cuvs/distance/distance_types.hpp>
-#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
 #include "../detail/ann_utils.cuh"  // utils::mapping
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
index db488b9c08..6a8d07a0f0 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
@@ -16,8 +16,8 @@
 
 #include "../../core/nvtx.hpp"
 #include "ivf_pq_build.cuh"
-#include <cuvs/distance/distance_types.hpp>
-#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
 namespace cuvs::neighbors::ivf_pq::helpers {
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
index fc739a7d8a..f15ec293a0 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 #include "ivf_pq_list.cuh"
-#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/util/cuda_utils.cuh>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
index 16fdcef8a1..48e2bf2221 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
@@ -19,10 +19,10 @@
 #include "../sample_filter.cuh"  // none_ivf_sample_filter
 #include "ivf_pq_fp_8bit.cuh"    // cuvs::neighbors::ivf_pq::detail::fp_8bit
 
-#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
-#include <cuvs/neighbors/ivf_pq.hpp>         // cuvs::neighbors::ivf_pq::codebook_gen
-#include <raft/core/detail/macros.hpp>       // RAFT_WEAK_FUNCTION
-#include <rmm/cuda_stream_view.hpp>          // rmm::cuda_stream_view
+#include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
+#include <cuvs/neighbors/ivf_pq.hpp>    // cuvs::neighbors::ivf_pq::codebook_gen
+#include <raft/core/detail/macros.hpp>  // RAFT_WEAK_FUNCTION
+#include <rmm/cuda_stream_view.hpp>     // rmm::cuda_stream_view
 
 #include <cuda_fp16.h>  // __half
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
index 463527bb8f..5fccbb3857 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
@@ -16,10 +16,10 @@
 
 #pragma once
 
-#include "../ivf_common.cuh"                 // dummy_block_sort_t
-#include "../sample_filter.cuh"              // none_ivf_sample_filter
-#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
-#include <cuvs/neighbors/ivf_pq.hpp>         // codebook_gen
+#include "../ivf_common.cuh"                       // dummy_block_sort_t
+#include "../sample_filter.cuh"                    // none_ivf_sample_filter
+#include <cuvs/distance/distance.hpp>              // cuvs::distance::DistanceType
+#include <cuvs/neighbors/ivf_pq.hpp>               // codebook_gen
 #include <raft/matrix/detail/select_warpsort.cuh>  // matrix::detail::select::warpsort::warp_sort_distributed
 #include <raft/util/cuda_rt_essentials.hpp>  // RAFT_CUDA_TRY
 #include <raft/util/device_atomics.cuh>      // raft::atomicMin
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
index 9a4263c26c..57f074328e 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/util/integer_utils.hpp>
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index ed28969a59..16cf7e7437 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -23,7 +23,7 @@
 #include "ivf_pq_compute_similarity.cuh"
 #include "ivf_pq_fp_8bit.cuh"
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
index f70c423735..9f0c085d15 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
@@ -19,7 +19,7 @@
 #include "../ivf_common.cuh"
 #include "../ivf_list.cuh"
 #include "ivf_pq_list.cuh"
-#include <cuvs/neighbors/ivf_list.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/logger-ext.hpp>
diff --git a/cpp/src/neighbors/ivf_pq_index.cpp b/cpp/src/neighbors/ivf_pq_index.cpp
index fcb2c976dd..286838387c 100644
--- a/cpp/src/neighbors/ivf_pq_index.cpp
+++ b/cpp/src/neighbors/ivf_pq_index.cpp
@@ -40,7 +40,7 @@ index<IdxT>::index(raft::resources const& handle,
                    uint32_t pq_bits,
                    uint32_t pq_dim,
                    bool conservative_memory_allocation)
-  : ann::index(),
+  : cuvs::neighbors::index(),
     metric_(metric),
     codebook_kind_(codebook_kind),
     dim_(dim),
diff --git a/cpp/src/neighbors/refine-ext.cuh b/cpp/src/neighbors/refine-ext.cuh
index 53e7394a91..5b686e1abf 100644
--- a/cpp/src/neighbors/refine-ext.cuh
+++ b/cpp/src/neighbors/refine-ext.cuh
@@ -16,11 +16,11 @@
 
 #pragma once
 
-#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
-#include <raft/core/device_mdspan.hpp>       // raft::device_matrix_view
-#include <raft/core/host_mdspan.hpp>         // // raft::host_matrix_view
-#include <raft/core/resources.hpp>           // raft::resources
-#include <raft/util/raft_explicit.hpp>       // RAFT_EXPLICIT
+#include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
+#include <raft/core/device_mdspan.hpp>  // raft::device_matrix_view
+#include <raft/core/host_mdspan.hpp>    // // raft::host_matrix_view
+#include <raft/core/resources.hpp>      // raft::resources
+#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
 
 #include <cstdint>  // int64_t
 
diff --git a/cpp/src/neighbors/sample_filter.cuh b/cpp/src/neighbors/sample_filter.cuh
index c550e4a4de..e49d54920a 100644
--- a/cpp/src/neighbors/sample_filter.cuh
+++ b/cpp/src/neighbors/sample_filter.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/neighbors/sample_filter.hpp>
+#include <cuvs/neighbors/common.hpp>
 #include <raft/core/bitset.cuh>
 #include <raft/core/detail/macros.hpp>
 
diff --git a/cpp/src/neighbors/vpq_dataset.cuh b/cpp/src/neighbors/vpq_dataset.cuh
index 1d80bea3fb..25ee14074a 100644
--- a/cpp/src/neighbors/vpq_dataset.cuh
+++ b/cpp/src/neighbors/vpq_dataset.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 #include "detail/vpq_dataset.cuh"
-#include <cuvs/neighbors/dataset.hpp>
+#include <cuvs/neighbors/common.hpp>
 
 #include <raft/core/resources.hpp>
 
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index fe9b8ae4f9..903c28f8d8 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -48,6 +48,7 @@ function(ConfigureTest)
     ${TEST_NAME}
     PRIVATE cuvs
             cuvs::cuvs
+            raft::raft
             GTest::gtest
             GTest::gtest_main
             Threads::Threads
diff --git a/cpp/test/cluster/linkage.cu b/cpp/test/cluster/linkage.cu
index 0768ce80bf..6ea6bd61b9 100644
--- a/cpp/test/cluster/linkage.cu
+++ b/cpp/test/cluster/linkage.cu
@@ -26,7 +26,7 @@
 #include "../test_utils.cuh"
 
 #include <cuvs/cluster/agglomerative.hpp>
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/linalg/transpose.cuh>
diff --git a/cpp/test/distance/distance_base.cuh b/cpp/test/distance/distance_base.cuh
index d77fcf407b..2213db87eb 100644
--- a/cpp/test/distance/distance_base.cuh
+++ b/cpp/test/distance/distance_base.cuh
@@ -18,8 +18,8 @@
 
 #include "../test_utils.cuh"
 
-#include <cuvs/distance/distance_types.hpp>  // cuvs::distance::DistanceType
-#include <cuvs/distance/pairwise_distance.hpp>
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
 #include <raft/common/nvtx.hpp>         // raft::common::nvtx::range
 #include <raft/core/device_mdspan.hpp>  //raft::make_device_matrix_view
 #include <raft/core/operators.hpp>      // raft::sqrt
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 3d3c06631c..017defe390 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -21,7 +21,7 @@
 
 #include "naive_knn.cuh"
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/device_resources.hpp>
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index 985134221c..fda32c6df2 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -18,8 +18,8 @@
 #include "../test_utils.cuh"
 #include "ann_utils.cuh"
 #include "naive_knn.cuh"
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
-#include <cuvs/neighbors/sample_filter.hpp>
 
 #include <raft/core/bitset.cuh>
 #include <raft/linalg/add.cuh>
diff --git a/cpp/test/neighbors/ann_utils.cuh b/cpp/test/neighbors/ann_utils.cuh
index 9bc0099dd5..b08e1d7256 100644
--- a/cpp/test/neighbors/ann_utils.cuh
+++ b/cpp/test/neighbors/ann_utils.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdarray.hpp>  // raft::make_device_matrix
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/matrix/copy.cuh>
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index d058bfacef..fdb1801861 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -15,8 +15,8 @@
  */
 
 #include "../test_utils.cuh"
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
-#include <raft/distance/distance_types.hpp>
 
 namespace cuvs::neighbors::brute_force {
 struct KNNInputs {
diff --git a/cpp/test/neighbors/naive_knn.cuh b/cpp/test/neighbors/naive_knn.cuh
index d9ea4ddac3..90547150f2 100644
--- a/cpp/test/neighbors/naive_knn.cuh
+++ b/cpp/test/neighbors/naive_knn.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 #include "../../src/neighbors/detail/ann_utils.cuh"
-#include <cuvs/distance/distance_types.hpp>
+#include <cuvs/distance/distance.hpp>
 #include <raft/matrix/detail/select_k.cuh>
 #include <raft/util/cuda_utils.cuh>
 
diff --git a/python/cuvs/cuvs/distance_type.pxd b/python/cuvs/cuvs/distance_type.pxd
index b85ee4b366..e3cc545687 100644
--- a/python/cuvs/cuvs/distance_type.pxd
+++ b/python/cuvs/cuvs/distance_type.pxd
@@ -16,7 +16,7 @@
 # cython: language_level=3
 
 
-cdef extern from "cuvs/distance/distance_types.h" nogil:
+cdef extern from "cuvs/distance/distance.h" nogil:
     ctypedef enum cuvsDistanceType:
         L2Expanded
         L2SqrtExpanded

From ac85fa69ae10fac2c12f482380142bd9f99822f2 Mon Sep 17 00:00:00 2001
From: Paul Taylor <178183+trxcllnt@users.noreply.github.com>
Date: Thu, 23 May 2024 04:12:37 -0700
Subject: [PATCH 060/623] hide RAFT #pragma deprecation warnings (#114)

Define `-DRAFT_HIDE_DEPRECATION_WARNINGS` added in https://github.com/rapidsai/raft/pull/2271.

Authors:
  - Paul Taylor (https://github.com/trxcllnt)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/114
---
 cpp/cmake/modules/ConfigureCUDA.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index 2134482917..e2dd829da2 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -13,8 +13,8 @@
 # =============================================================================
 
 if(DISABLE_DEPRECATION_WARNINGS)
-  list(APPEND CUVS_CXX_FLAGS -Wno-deprecated-declarations)
-  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wno-deprecated-declarations)
+  list(APPEND CUVS_CXX_FLAGS -Wno-deprecated-declarations -DRAFT_HIDE_DEPRECATION_WARNINGS)
+  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wno-deprecated-declarations -DRAFT_HIDE_DEPRECATION_WARNINGS)
 endif()
 
 # Be very strict when compiling with GCC as host compiler (and thus more lenient when compiling with

From 3515f44284020f016b324cba383e035cf394c5cc Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 23 May 2024 15:47:15 -0400
Subject: [PATCH 061/623] Changing RAFT_EXPLICT_* to CUVS_EXPLITI_* (#141)

Changing this for now. We'll work on removing this macro in a future release.

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/141
---
 cpp/CMakeLists.txt                            |    9 +-
 cpp/src/cluster/detail/mst.cuh                |    8 +-
 cpp/src/cluster/detail/single_linkage.cuh     |    2 +-
 cpp/src/distance/detail/compress_to_bits.cuh  |    3 +-
 .../distance/detail/masked_distance_base.cuh  |    2 +-
 cpp/src/distance/detail/masked_nn.cuh         |   12 +-
 .../detail/pairwise_matrix/dispatch-ext.cuh   |    2 +-
 .../detail/pairwise_matrix/dispatch.cuh       |    2 +-
 cpp/src/distance/distance-ext.cuh             |    2 +-
 cpp/src/distance/distance.cuh                 |    2 +-
 cpp/src/distance/masked_nn.cuh                |  201 ++++
 .../cagra/search_multi_cta_kernel-ext.cuh     |    2 +-
 .../detail/cagra/search_multi_cta_kernel.cuh  |    2 +-
 .../cagra/search_single_cta_kernel-ext.cuh    |    2 +-
 .../detail/cagra/search_single_cta_kernel.cuh |    2 +-
 cpp/src/neighbors/detail/refine_host-ext.hpp  |    2 +-
 cpp/src/neighbors/refine-ext.cuh              |    2 +-
 cpp/src/neighbors/refine.cuh                  |    2 +-
 .../sparse/neighbors/cross_component_nn.cuh   |   99 ++
 .../neighbors/detail/cross_component_nn.cuh   |  542 +++++++++
 cpp/test/CMakeLists.txt                       |    3 +
 cpp/test/cluster/linkage.cu                   |    2 +-
 cpp/test/distance/masked_nn.cu                |  438 +++++++
 .../sparse/neighbors/cross_component_nn.cu    | 1039 +++++++++++++++++
 docs/source/integrations.rst                  |    2 +-
 25 files changed, 2353 insertions(+), 31 deletions(-)
 create mode 100644 cpp/src/distance/masked_nn.cuh
 create mode 100644 cpp/src/sparse/neighbors/cross_component_nn.cuh
 create mode 100644 cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
 create mode 100644 cpp/test/distance/masked_nn.cu
 create mode 100644 cpp/test/sparse/neighbors/cross_component_nn.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index b972fc1226..8ede967064 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -467,9 +467,8 @@ if(NOT BUILD_CPU_ONLY)
   # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
   target_link_libraries(
     cuvs
-    PUBLIC rmm::rmm raft::raft
-    PRIVATE nvidia::cutlass::cutlass ${CUVS_CTK_MATH_DEPENDENCIES}
-            $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+    PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
+    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
   )
 endif()
 
@@ -573,8 +572,8 @@ if(BUILD_C_LIBRARY)
 
   target_link_libraries(
     cuvs_c
-    PUBLIC cuvs::cuvs
-    PRIVATE raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
+    PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
+    PRIVATE raft::raft
   )
 
   # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
diff --git a/cpp/src/cluster/detail/mst.cuh b/cpp/src/cluster/detail/mst.cuh
index de88b954e2..5804b8b5d9 100644
--- a/cpp/src/cluster/detail/mst.cuh
+++ b/cpp/src/cluster/detail/mst.cuh
@@ -16,8 +16,8 @@
 
 #pragma once
 
+#include "../../sparse/neighbors/cross_component_nn.cuh"
 #include <raft/core/resource/cuda_stream.hpp>
-#include <raft/sparse/neighbors/cross_component_nn.cuh>
 #include <raft/sparse/op/sort.cuh>
 #include <raft/sparse/solver/mst.cuh>
 #include <raft/util/cuda_utils.cuh>
@@ -86,7 +86,7 @@ void connect_knn_graph(
   static constexpr size_t default_row_batch_size = 4096;
   static constexpr size_t default_col_batch_size = 16;
 
-  raft::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
+  cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
                                                                   connected_edges,
                                                                   X,
                                                                   color,
@@ -166,14 +166,14 @@ void build_sorted_mst(
     handle, indptr, indices, pw_dists, (value_idx)m, nnz, color, stream, false, true);
 
   int iters        = 1;
-  int n_components = raft::sparse::neighbors::get_n_components(color, m, stream);
+  int n_components = cuvs::sparse::neighbors::get_n_components(color, m, stream);
 
   while (n_components > 1 && iters < max_iter) {
     connect_knn_graph<value_idx, value_t>(handle, X, mst_coo, m, n, color, reduction_op);
 
     iters++;
 
-    n_components = raft::sparse::neighbors::get_n_components(color, m, stream);
+    n_components = cuvs::sparse::neighbors::get_n_components(color, m, stream);
   }
 
   /**
diff --git a/cpp/src/cluster/detail/single_linkage.cuh b/cpp/src/cluster/detail/single_linkage.cuh
index 837acf797e..8b90336b71 100644
--- a/cpp/src/cluster/detail/single_linkage.cuh
+++ b/cpp/src/cluster/detail/single_linkage.cuh
@@ -81,7 +81,7 @@ void single_linkage(raft::resources const& handle,
    * 2. Construct MST, sorted by weights
    */
   rmm::device_uvector<value_idx> color(m, stream);
-  raft::sparse::neighbors::FixConnectivitiesRedOp<value_idx, value_t> op(m);
+  cuvs::sparse::neighbors::FixConnectivitiesRedOp<value_idx, value_t> op(m);
   detail::build_sorted_mst<value_idx, value_t>(handle,
                                                X,
                                                indptr.data(),
diff --git a/cpp/src/distance/detail/compress_to_bits.cuh b/cpp/src/distance/detail/compress_to_bits.cuh
index 9ce47774a6..0f02631169 100644
--- a/cpp/src/distance/detail/compress_to_bits.cuh
+++ b/cpp/src/distance/detail/compress_to_bits.cuh
@@ -15,6 +15,7 @@
  */
 #pragma once
 
+#include <raft/core/device_mdspan.hpp>
 #include <raft/core/handle.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/util/cuda_utils.cuh>
@@ -100,7 +101,7 @@ void compress_to_bits(raft::resources const& handle,
                       raft::device_matrix_view<const bool, int, raft::layout_c_contiguous> in,
                       raft::device_matrix_view<T, int, raft::layout_c_contiguous> out)
 {
-  auto stream                    = resource::get_cuda_stream(handle);
+  auto stream                    = raft::resource::get_cuda_stream(handle);
   constexpr int bits_per_element = 8 * sizeof(T);
 
   RAFT_EXPECTS(raft::ceildiv(in.extent(0), bits_per_element) == out.extent(0),
diff --git a/cpp/src/distance/detail/masked_distance_base.cuh b/cpp/src/distance/detail/masked_distance_base.cuh
index d92052c84c..2c41ee3be0 100644
--- a/cpp/src/distance/detail/masked_distance_base.cuh
+++ b/cpp/src/distance/detail/masked_distance_base.cuh
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #pragma once
-#include "../pairwise_distance_base.cuh"
+#include "pairwise_distance_base.cuh"
 #include <raft/linalg/contractions.cuh>
 #include <raft/util/cuda_utils.cuh>
 
diff --git a/cpp/src/distance/detail/masked_nn.cuh b/cpp/src/distance/detail/masked_nn.cuh
index 6520b1e2e9..e10d2b7c69 100644
--- a/cpp/src/distance/detail/masked_nn.cuh
+++ b/cpp/src/distance/detail/masked_nn.cuh
@@ -16,9 +16,9 @@
 
 #pragma once
 
-#include "../compress_to_bits.cuh"
-#include "../fused_distance_nn/fused_l2_nn.cuh"
-#include "../masked_distance_base.cuh"
+#include "compress_to_bits.cuh"
+#include "fused_distance_nn/fused_l2_nn.cuh"
+#include "masked_distance_base.cuh"
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/linalg/contractions.cuh>
@@ -251,14 +251,14 @@ void masked_l2_nn_impl(raft::resources const& handle,
                        bool sqrt,
                        bool initOutBuffer)
 {
-  typedef typename linalg::Policy4x4<DataT, 1>::Policy P;
+  typedef typename raft::linalg::Policy4x4<DataT, 1>::Policy P;
 
   static_assert(P::Mblk == 64, "masked_l2_nn_impl only supports a policy with 64 rows per block.");
 
   // Get stream and workspace memory resource
   rmm::mr::device_memory_resource* ws_mr =
-    dynamic_cast<rmm::mr::device_memory_resource*>(resource::get_workspace_resource(handle));
-  auto stream = resource::get_cuda_stream(handle);
+    dynamic_cast<rmm::mr::device_memory_resource*>(raft::resource::get_workspace_resource(handle));
+  auto stream = raft::resource::get_cuda_stream(handle);
 
   // Acquire temporary buffers and initialize to zero:
   // 1) Adjacency matrix bitfield
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
index 3e8402f1f9..bc8189c709 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
@@ -22,7 +22,7 @@
 #include <raft/core/operators.hpp>        // raft::identity_op
 #include <raft/util/raft_explicit.hpp>    // RAFT_EXPLICIT
 
-#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 namespace cuvs::distance::detail {
 
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
index 4a52b7ebe7..06b039c3a1 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
 #include "dispatch-inl.cuh"
 #endif
 
diff --git a/cpp/src/distance/distance-ext.cuh b/cpp/src/distance/distance-ext.cuh
index e574c11b0c..148951afad 100644
--- a/cpp/src/distance/distance-ext.cuh
+++ b/cpp/src/distance/distance-ext.cuh
@@ -24,7 +24,7 @@
 
 #include <rmm/device_uvector.hpp>  // rmm::device_uvector
 
-#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 namespace cuvs {
 namespace distance {
diff --git a/cpp/src/distance/distance.cuh b/cpp/src/distance/distance.cuh
index de70cd4691..b5bfc07cb2 100644
--- a/cpp/src/distance/distance.cuh
+++ b/cpp/src/distance/distance.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
 #include "distance-inl.cuh"
 #endif
 
diff --git a/cpp/src/distance/masked_nn.cuh b/cpp/src/distance/masked_nn.cuh
new file mode 100644
index 0000000000..82dd189196
--- /dev/null
+++ b/cpp/src/distance/masked_nn.cuh
@@ -0,0 +1,201 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __MASKED_L2_NN_H
+#define __MASKED_L2_NN_H
+
+#pragma once
+
+#include "detail/masked_nn.cuh"
+#include "fused_distance_nn_helpers.cuh"
+#include <raft/core/handle.hpp>
+#include <raft/util/cuda_utils.cuh>
+
+#include <stdint.h>
+
+#include <limits>
+
+namespace cuvs {
+namespace distance {
+/**
+ * \defgroup masked_nn Masked 1-nearest neighbors
+ * @{
+ */
+
+/**
+ * @brief Parameter struct for masked_l2_nn function
+ *
+ * @tparam ReduceOpT    Type of reduction operator in the epilogue.
+ * @tparam KVPReduceOpT Type of Reduction operation on key value pairs.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <cuvs/distance/masked_nn.cuh>
+ *
+ * using IdxT        = int;
+ * using DataT       = float;
+ * using RedOpT      = cuvs::distance::MinAndDistanceReduceOp<IdxT, DataT>;
+ * using PairRedOpT  = cuvs::distance::KVPMinReduce<IdxT, DataT>;
+ * using ParamT      = cuvs::distance::masked_l2_nn_params<RedOpT, PairRedOpT>;
+ *
+ * bool init_out = true;
+ * bool sqrt     = false;
+ *
+ * ParamT masked_l2_params{RedOpT{}, PairRedOpT{}, sqrt, init_out};
+ * @endcode
+ *
+ * Prescribes how to reduce a distance to an intermediate type (`redOp`), and
+ * how to reduce two intermediate types (`pairRedOp`). Typically, a distance is
+ * mapped to an (index, value) pair and (index, value) pair with the lowest
+ * value (distance) is selected.
+ *
+ * In addition, prescribes whether to compute the square root of the distance
+ * (`sqrt`) and whether to initialize the output buffer (`initOutBuffer`).
+ */
+template <typename ReduceOpT, typename KVPReduceOpT>
+struct masked_l2_nn_params {
+  /** Reduction operator in the epilogue */
+  ReduceOpT redOp;
+  /** Reduction operation on key value pairs */
+  KVPReduceOpT pairRedOp;
+  /** Whether the output `minDist` should contain L2-sqrt */
+  bool sqrt;
+  /** Whether to initialize the output buffer before the main kernel launch */
+  bool initOutBuffer;
+};
+
+/**
+ * @brief Masked L2 distance and 1-nearest-neighbor computation in a single call.
+ *
+ * This function enables faster computation of nearest neighbors if the
+ * computation of distances between certain point pairs can be skipped.
+ *
+ * We use an adjacency matrix that describes which distances to calculate. The
+ * points in `y` are divided into groups, and the adjacency matrix indicates
+ * whether to compute distances between points in `x` and groups in `y`. In other
+ * words, if `adj[i,k]` is true then distance between point `x_i`, and points in
+ * `group_k` will be calculated.
+ *
+ * **Performance considerations**
+ *
+ * The points in `x` are processed in tiles of `M` points (`M` is currently 64,
+ * but may change in the future). As a result, the largest compute time
+ * reduction occurs if all `M` points can skip a group. If only part of the `M`
+ * points can skip a group, then at most a minor compute time reduction and a
+ * modest energy use reduction can be expected.
+ *
+ * The points in `y` are also grouped into tiles of `N` points (`N` is currently
+ * 64, but may change in the future). As a result, group sizes should be larger
+ * than `N` to avoid wasting computational resources. If the group sizes are
+ * evenly divisible by `N`, then the computation is most efficient, although for
+ * larger group sizes this effect is minor.
+ *
+ *
+ * **Comparison to SDDM**
+ *
+ * [SDDMM](https://ieeexplore.ieee.org/document/8638042) (sampled dense-dense
+ * matrix multiplication) is a matrix-matrix multiplication where only part of
+ * the output is computed. Compared to masked_l2_nn, there are a few differences:
+ *
+ * - The output of masked_l2_nn is a single vector (of nearest neighbors) and not
+ *   a sparse matrix.
+ *
+ * - The sampling in masked_l2_nn is expressed through intermediate "groups"
+     rather than a CSR format.
+ *
+ * @tparam DataT     data type
+ * @tparam OutT      output type to either store 1-NN indices and their minimum
+ *                   distances or store only the min distances. Accordingly, one
+ *                   has to pass an appropriate `ReduceOpT`
+ * @tparam IdxT      indexing arithmetic type
+ * @tparam ReduceOpT A struct to perform the final needed reduction operation
+ *                   and also to initialize the output array elements with the
+ *                   appropriate initial value needed for reduction.
+ *
+ * @param handle             RAFT handle for managing expensive resources
+ * @param params             Parameter struct specifying the reduction operations.
+ * @param[in]  x             First matrix. Row major. Dim = `m x k`.
+ *                           (on device).
+ * @param[in]  y             Second matrix. Row major. Dim = `n x k`.
+ *                           (on device).
+ * @param[in]  x_norm        L2 squared norm of `x`. Length = `m`. (on device).
+ * @param[in]  y_norm        L2 squared norm of `y`. Length = `n`. (on device)
+ * @param[in]  adj           A boolean adjacency matrix indicating for each
+ *                           row of `x` and each group in `y` whether to compute the
+ *                           distance. Dim = `m x num_groups`.
+ * @param[in]  group_idxs    An array containing the *end* indices of each group
+ *                           in `y`. The value of group_idxs[j] indicates the
+ *                           start of group j + 1, i.e., it is the inclusive
+ *                           scan of the group lengths. The first group is
+ *                           always assumed to start at index 0 and the last
+ *                           group typically ends at index `n`. Length =
+ *                           `num_groups`.
+ * @param[out] out           will contain the reduced output (Length = `m`)
+ *                           (on device)
+ */
+template <typename DataT, typename OutT, typename IdxT, typename ReduceOpT, typename KVPReduceOpT>
+void masked_l2_nn(raft::resources const& handle,
+                  cuvs::distance::masked_l2_nn_params<ReduceOpT, KVPReduceOpT> params,
+                  raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> x,
+                  raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> y,
+                  raft::device_vector_view<const DataT, IdxT, raft::layout_c_contiguous> x_norm,
+                  raft::device_vector_view<const DataT, IdxT, raft::layout_c_contiguous> y_norm,
+                  raft::device_matrix_view<const bool, IdxT, raft::layout_c_contiguous> adj,
+                  raft::device_vector_view<const IdxT, IdxT, raft::layout_c_contiguous> group_idxs,
+                  raft::device_vector_view<OutT, IdxT, raft::layout_c_contiguous> out)
+{
+  IdxT m          = x.extent(0);
+  IdxT n          = y.extent(0);
+  IdxT k          = x.extent(1);
+  IdxT num_groups = group_idxs.extent(0);
+
+  // Match k dimension of x, y
+  RAFT_EXPECTS(x.extent(1) == y.extent(1), "Dimension of vectors in x and y must be equal.");
+  // Match x, x_norm and y, y_norm
+  RAFT_EXPECTS(m == x_norm.extent(0), "Length of `x_norm` must match input `x`.");
+  RAFT_EXPECTS(n == y_norm.extent(0), "Length of `y_norm` must match input `y` ");
+  // Match adj to x and group_idxs
+  RAFT_EXPECTS(m == adj.extent(0), "#rows in `adj` must match input `x`.");
+  RAFT_EXPECTS(num_groups == adj.extent(1), "#cols in `adj` must match length of `group_idxs`.");
+  // NOTE: We do not check if all indices in group_idxs actually points *inside* y.
+
+  // If there is no work to be done, return immediately.
+  if (m == 0 || n == 0 || k == 0 || num_groups == 0) { return; }
+
+  detail::masked_l2_nn_impl<DataT, OutT, IdxT, ReduceOpT>(handle,
+                                                          out.data_handle(),
+                                                          x.data_handle(),
+                                                          y.data_handle(),
+                                                          x_norm.data_handle(),
+                                                          y_norm.data_handle(),
+                                                          adj.data_handle(),
+                                                          group_idxs.data_handle(),
+                                                          num_groups,
+                                                          m,
+                                                          n,
+                                                          k,
+                                                          params.redOp,
+                                                          params.pairRedOp,
+                                                          params.sqrt,
+                                                          params.initOutBuffer);
+}
+
+/** @} */
+
+}  // namespace distance
+}  // namespace cuvs
+
+#endif
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
index 63f3838d09..495ec6a4d8 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
@@ -24,7 +24,7 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace multi_cta_search {
 
-#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 template <unsigned TEAM_SIZE,
           unsigned MAX_DATASET_DIM,
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
index e003907292..2cbb758f91 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
 #include "search_multi_cta_kernel-inl.cuh"
 #endif
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
index c4ed705684..dfcdec28fe 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
@@ -23,7 +23,7 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace single_cta_search {
 
-#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 template <unsigned TEAM_SIZE,
           unsigned MAX_DATASET_DIM,
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
index 1d8fd8e30a..40de22f2e3 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
 #include "search_single_cta_kernel-inl.cuh"
 #endif
 
diff --git a/cpp/src/neighbors/detail/refine_host-ext.hpp b/cpp/src/neighbors/detail/refine_host-ext.hpp
index 74ba2a679b..88f5a2a646 100644
--- a/cpp/src/neighbors/detail/refine_host-ext.hpp
+++ b/cpp/src/neighbors/detail/refine_host-ext.hpp
@@ -28,7 +28,7 @@
 #include <cuda_fp16.h>
 #endif
 
-#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 namespace cuvs::neighbors::detail {
 
diff --git a/cpp/src/neighbors/refine-ext.cuh b/cpp/src/neighbors/refine-ext.cuh
index 5b686e1abf..2f40842871 100644
--- a/cpp/src/neighbors/refine-ext.cuh
+++ b/cpp/src/neighbors/refine-ext.cuh
@@ -24,7 +24,7 @@
 
 #include <cstdint>  // int64_t
 
-#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 namespace cuvs::neighbors {
 
diff --git a/cpp/src/neighbors/refine.cuh b/cpp/src/neighbors/refine.cuh
index 15f2b02928..b3a3cdb4eb 100644
--- a/cpp/src/neighbors/refine.cuh
+++ b/cpp/src/neighbors/refine.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
 #include "refine-inl.cuh"
 #endif
 
diff --git a/cpp/src/sparse/neighbors/cross_component_nn.cuh b/cpp/src/sparse/neighbors/cross_component_nn.cuh
new file mode 100644
index 0000000000..36a0c79f59
--- /dev/null
+++ b/cpp/src/sparse/neighbors/cross_component_nn.cuh
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/cross_component_nn.cuh"
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/sparse/coo.hpp>
+
+namespace cuvs::sparse::neighbors {
+
+template <typename value_idx, typename value_t>
+using FixConnectivitiesRedOp = detail::FixConnectivitiesRedOp<value_idx, value_t>;
+
+/**
+ * Gets the number of unique components from array of
+ * colors or labels. This does not assume the components are
+ * drawn from a monotonically increasing set.
+ * @tparam value_idx
+ * @param[in] colors array of components
+ * @param[in] n_rows size of components array
+ * @param[in] stream cuda stream for which to order cuda operations
+ * @return total number of components
+ */
+template <typename value_idx>
+value_idx get_n_components(value_idx* colors, size_t n_rows, cudaStream_t stream)
+{
+  return detail::get_n_components(colors, n_rows, stream);
+}
+
+/**
+ * Connects the components of an otherwise unconnected knn graph
+ * by computing a 1-nn to neighboring components of each data point
+ * (e.g. component(nn) != component(self)) and reducing the results to
+ * include the set of smallest destination components for each source
+ * component. The result will not necessarily contain
+ * n_components^2 - n_components number of elements because many components
+ * will likely not be contained in the neighborhoods of 1-nns.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[in] handle raft handle
+ * @param[out] out output edge list containing nearest cross-component
+ *             edges.
+ * @param[in] X original (row-major) dense matrix for which knn graph should be constructed.
+ * @param[in] orig_colors array containing component number for each row of X
+ * @param[in] n_rows number of rows in X
+ * @param[in] n_cols number of cols in X
+ * @param[in] reduction_op reduction operation for computing nearest neighbors. The reduction
+ * operation must have `gather` and `scatter` functions defined
+ * @param[in] row_batch_size the batch size for computing nearest neighbors. This parameter controls
+ * the number of samples for which the nearest neighbors are computed at once. Therefore, it affects
+ * the memory consumption mainly by reducing the size of the adjacency matrix for masked nearest
+ * neighbors computation
+ * @param[in] col_batch_size the input data is sorted and 'unsorted' based on color. An additional
+ * scratch space buffer of shape (n_rows, col_batch_size) is created for this. Usually, this
+ * parameter affects the memory consumption more drastically than the row_batch_size with a marginal
+ * increase in compute time as the col_batch_size is reduced
+ * @param[in] metric distance metric
+ */
+template <typename value_idx, typename value_t, typename red_op>
+void cross_component_nn(
+  raft::resources const& handle,
+  raft::sparse::COO<value_t, value_idx>& out,
+  const value_t* X,
+  const value_idx* orig_colors,
+  size_t n_rows,
+  size_t n_cols,
+  red_op reduction_op,
+  size_t row_batch_size               = 0,
+  size_t col_batch_size               = 0,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded)
+{
+  detail::cross_component_nn(handle,
+                             out,
+                             X,
+                             orig_colors,
+                             n_rows,
+                             n_cols,
+                             reduction_op,
+                             row_batch_size,
+                             col_batch_size,
+                             metric);
+}
+
+};  // end namespace cuvs::sparse::neighbors
\ No newline at end of file
diff --git a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
new file mode 100644
index 0000000000..43bb6d8a81
--- /dev/null
+++ b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
@@ -0,0 +1,542 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../../../distance/masked_nn.cuh"
+#include <cuvs/distance/distance.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/kvp.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/label/classlabels.cuh>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/matrix/scatter.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/sparse/linalg/symmetrize.cuh>
+#include <raft/sparse/op/reduce.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/fast_int_div.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cub/cub.cuh>
+#include <thrust/copy.h>
+#include <thrust/device_ptr.h>
+#include <thrust/gather.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/scatter.h>
+#include <thrust/sort.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <cstdint>
+#include <limits>
+
+namespace cuvs::sparse::neighbors::detail {
+
+/**
+ * Base functor with reduction ops for performing masked 1-nn
+ * computation.
+ * @tparam value_idx
+ * @tparam value_t
+ */
+template <typename value_idx, typename value_t>
+struct FixConnectivitiesRedOp {
+  value_idx m;
+
+  // default constructor for cutlass
+  DI FixConnectivitiesRedOp() : m(0) {}
+
+  FixConnectivitiesRedOp(value_idx m_) : m(m_){};
+
+  typedef typename raft::KeyValuePair<value_idx, value_t> KVP;
+  DI void operator()(value_idx rit, KVP* out, const KVP& other) const
+  {
+    if (rit < m && other.value < out->value) {
+      out->key   = other.key;
+      out->value = other.value;
+    }
+  }
+
+  DI KVP operator()(value_idx rit, const KVP& a, const KVP& b) const
+  {
+    if (rit < m && a.value < b.value) {
+      return a;
+    } else
+      return b;
+  }
+
+  DI void init(value_t* out, value_t maxVal) const { *out = maxVal; }
+  DI void init(KVP* out, value_t maxVal) const
+  {
+    out->key   = -1;
+    out->value = maxVal;
+  }
+
+  DI void init_key(value_t& out, value_idx idx) const { return; }
+  DI void init_key(KVP& out, value_idx idx) const { out.key = idx; }
+
+  DI value_t get_value(KVP& out) const { return out.value; }
+
+  DI value_t get_value(value_t& out) const { return out; }
+
+  /** The gather and scatter ensure that operator() is still consistent after rearranging the data.
+   * TODO (tarang-jain): refactor cross_component_nn API to separate out the gather and scatter
+   * functions from the reduction op. Reference: https://github.com/rapidsai/raft/issues/1614 */
+  void gather(const raft::resources& handle, value_idx* map) {}
+
+  void scatter(const raft::resources& handle, value_idx* map) {}
+};
+
+/**
+ * Assumes 3-iterator tuple containing COO rows, cols, and
+ * a cub keyvalue pair object. Sorts the 3 arrays in
+ * ascending order: row->col->keyvaluepair
+ */
+struct TupleComp {
+  template <typename one, typename two>
+  __host__ __device__ bool operator()(const one& t1, const two& t2)
+  {
+    // sort first by each sample's color,
+    if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
+    if (thrust::get<0>(t1) > thrust::get<0>(t2)) return false;
+
+    // then by the color of each sample's closest neighbor,
+    if (thrust::get<1>(t1) < thrust::get<1>(t2)) return true;
+    if (thrust::get<1>(t1) > thrust::get<1>(t2)) return false;
+
+    // then sort by value in descending order
+    return thrust::get<2>(t1).value < thrust::get<2>(t2).value;
+  }
+};
+
+template <typename LabelT, typename DataT>
+struct CubKVPMinReduce {
+  typedef raft::KeyValuePair<LabelT, DataT> KVP;
+
+  DI KVP
+
+  operator()(LabelT rit, const KVP& a, const KVP& b)
+  {
+    return b.value < a.value ? b : a;
+  }
+
+  DI KVP
+
+  operator()(const KVP& a, const KVP& b)
+  {
+    return b.value < a.value ? b : a;
+  }
+
+};  // KVPMinReduce
+
+/**
+ * Gets the number of unique components from array of
+ * colors or labels. This does not assume the components are
+ * drawn from a monotonically increasing set.
+ * @tparam value_idx
+ * @param[in] colors array of components
+ * @param[in] n_rows size of components array
+ * @param[in] stream cuda stream for which to order cuda operations
+ * @return total number of components
+ */
+template <typename value_idx>
+value_idx get_n_components(value_idx* colors, size_t n_rows, cudaStream_t stream)
+{
+  rmm::device_uvector<value_idx> map_ids(0, stream);
+  int num_clusters = raft::label::getUniquelabels(map_ids, colors, n_rows, stream);
+  return num_clusters;
+}
+
+/**
+ * Functor to look up a component for a vertex
+ * @tparam value_idx
+ * @tparam value_t
+ */
+template <typename value_idx, typename value_t>
+struct LookupColorOp {
+  value_idx* colors;
+
+  LookupColorOp(value_idx* colors_) : colors(colors_) {}
+
+  DI value_idx
+
+  operator()(const raft::KeyValuePair<value_idx, value_t>& kvp)
+  {
+    return colors[kvp.key];
+  }
+};
+
+/**
+ * Compute the cross-component 1-nearest neighbors for each row in X using
+ * the given array of components
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[in] handle raft handle
+ * @param[out] kvp mapping of closest neighbor vertex and distance for each vertex in the given
+ * array of components
+ * @param[out] nn_colors components of nearest neighbors for each vertex
+ * @param[in] colors components of each vertex
+ * @param[in] X original dense data
+ * @param[in] n_rows number of rows in original dense data
+ * @param[in] n_cols number of columns in original dense data
+ * @param[in] row_batch_size row batch size for computing nearest neighbors
+ * @param[in] col_batch_size column batch size for sorting and 'unsorting'
+ * @param[in] reduction_op reduction operation for computing nearest neighbors
+ */
+template <typename value_idx, typename value_t, typename red_op>
+void perform_1nn(raft::resources const& handle,
+                 raft::KeyValuePair<value_idx, value_t>* kvp,
+                 value_idx* nn_colors,
+                 value_idx* colors,
+                 const value_t* X,
+                 size_t n_rows,
+                 size_t n_cols,
+                 size_t row_batch_size,
+                 size_t col_batch_size,
+                 red_op reduction_op)
+{
+  auto stream      = raft::resource::get_cuda_stream(handle);
+  auto exec_policy = raft::resource::get_thrust_policy(handle);
+
+  auto sort_plan = raft::make_device_vector<value_idx>(handle, (value_idx)n_rows);
+  raft::linalg::map_offset(handle, sort_plan.view(), [] __device__(value_idx idx) { return idx; });
+
+  thrust::sort_by_key(
+    raft::resource::get_thrust_policy(handle), colors, colors + n_rows, sort_plan.data_handle());
+
+  // Modify the reduction operation based on the sort plan.
+  reduction_op.gather(handle, sort_plan.data_handle());
+
+  auto X_mutable_view =
+    raft::make_device_matrix_view<value_t, value_idx>(const_cast<value_t*>(X), n_rows, n_cols);
+  auto sort_plan_const_view =
+    raft::make_device_vector_view<const value_idx, value_idx>(sort_plan.data_handle(), n_rows);
+  raft::matrix::gather(handle, X_mutable_view, sort_plan_const_view, (value_idx)col_batch_size);
+
+  // Get the number of unique components from the array of colors
+  value_idx n_components = get_n_components(colors, n_rows, stream);
+
+  // colors_group_idxs is an array containing the *end* indices of each color
+  // component in colors. That is, the value of colors_group_idxs[j] indicates
+  // the start of color j + 1, i.e., it is the inclusive scan of the sizes of
+  // the color components.
+  auto colors_group_idxs = raft::make_device_vector<value_idx, value_idx>(handle, n_components + 1);
+  raft::sparse::convert::sorted_coo_to_csr(
+    colors, n_rows, colors_group_idxs.data_handle(), n_components + 1, stream);
+
+  auto group_idxs_view = raft::make_device_vector_view<const value_idx, value_idx>(
+    colors_group_idxs.data_handle() + 1, n_components);
+
+  auto x_norm = raft::make_device_vector<value_t, value_idx>(handle, (value_idx)n_rows);
+  raft::linalg::rowNorm(
+    x_norm.data_handle(), X, n_cols, n_rows, raft::linalg::L2Norm, true, stream);
+
+  auto adj     = raft::make_device_matrix<bool, value_idx>(handle, row_batch_size, n_components);
+  using OutT   = raft::KeyValuePair<value_idx, value_t>;
+  using ParamT = cuvs::distance::masked_l2_nn_params<red_op, red_op>;
+
+  bool apply_sqrt      = true;
+  bool init_out_buffer = true;
+  ParamT params{reduction_op, reduction_op, apply_sqrt, init_out_buffer};
+
+  auto X_full_view = raft::make_device_matrix_view<const value_t, value_idx>(X, n_rows, n_cols);
+
+  size_t n_batches = raft::ceildiv(n_rows, row_batch_size);
+
+  for (size_t bid = 0; bid < n_batches; bid++) {
+    size_t batch_offset   = bid * row_batch_size;
+    size_t rows_per_batch = min(row_batch_size, n_rows - batch_offset);
+
+    auto X_batch_view = raft::make_device_matrix_view<const value_t, value_idx>(
+      X + batch_offset * n_cols, rows_per_batch, n_cols);
+
+    auto x_norm_batch_view = raft::make_device_vector_view<const value_t, value_idx>(
+      x_norm.data_handle() + batch_offset, rows_per_batch);
+
+    auto mask_op = [colors,
+                    n_components = raft::util::FastIntDiv(n_components),
+                    batch_offset] __device__(value_idx idx) {
+      value_idx row = idx / n_components;
+      value_idx col = idx % n_components;
+      return colors[batch_offset + row] != col;
+    };
+
+    auto adj_vector_view = raft::make_device_vector_view<bool, value_idx>(
+      adj.data_handle(), rows_per_batch * n_components);
+
+    raft::linalg::map_offset(handle, adj_vector_view, mask_op);
+
+    auto adj_view = raft::make_device_matrix_view<const bool, value_idx>(
+      adj.data_handle(), rows_per_batch, n_components);
+
+    auto kvp_view =
+      raft::make_device_vector_view<raft::KeyValuePair<value_idx, value_t>, value_idx>(
+        kvp + batch_offset, rows_per_batch);
+
+    cuvs::distance::masked_l2_nn<value_t, OutT, value_idx, red_op, red_op>(handle,
+                                                                           params,
+                                                                           X_batch_view,
+                                                                           X_full_view,
+                                                                           x_norm_batch_view,
+                                                                           x_norm.view(),
+                                                                           adj_view,
+                                                                           group_idxs_view,
+                                                                           kvp_view);
+  }
+
+  // Transform the keys so that they correctly point to the unpermuted indices.
+  thrust::transform(exec_policy,
+                    kvp,
+                    kvp + n_rows,
+                    kvp,
+                    [sort_plan = sort_plan.data_handle()] __device__(OutT KVP) {
+                      OutT res;
+                      res.value = KVP.value;
+                      res.key   = sort_plan[KVP.key];
+                      return res;
+                    });
+
+  // Undo permutation of the rows of X by scattering in place.
+  raft::matrix::scatter(handle, X_mutable_view, sort_plan_const_view, (value_idx)col_batch_size);
+
+  // Undo permutation of the key-value pair and color vectors. This is not done
+  // inplace, so using two temporary vectors.
+  auto tmp_colors = raft::make_device_vector<value_idx>(handle, n_rows);
+  auto tmp_kvp    = raft::make_device_vector<OutT>(handle, n_rows);
+
+  thrust::scatter(exec_policy, kvp, kvp + n_rows, sort_plan.data_handle(), tmp_kvp.data_handle());
+  thrust::scatter(
+    exec_policy, colors, colors + n_rows, sort_plan.data_handle(), tmp_colors.data_handle());
+  reduction_op.scatter(handle, sort_plan.data_handle());
+
+  raft::copy_async(colors, tmp_colors.data_handle(), n_rows, stream);
+  raft::copy_async(kvp, tmp_kvp.data_handle(), n_rows, stream);
+
+  LookupColorOp<value_idx, value_t> extract_colors_op(colors);
+  thrust::transform(exec_policy, kvp, kvp + n_rows, nn_colors, extract_colors_op);
+}
+
+/**
+ * Sort nearest neighboring components wrt component of source vertices
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[inout] colors components array of source vertices
+ * @param[inout] nn_colors nearest neighbors components array
+ * @param[inout] kvp nearest neighbor source vertex / distance array
+ * @param[inout] src_indices array of source vertex indices which will become arg_sort
+ *               indices
+ * @param n_rows number of components in `colors`
+ * @param stream stream for which to order CUDA operations
+ */
+template <typename value_idx, typename value_t>
+void sort_by_color(raft::resources const& handle,
+                   value_idx* colors,
+                   value_idx* nn_colors,
+                   raft::KeyValuePair<value_idx, value_t>* kvp,
+                   value_idx* src_indices,
+                   size_t n_rows)
+{
+  auto exec_policy = raft::resource::get_thrust_policy(handle);
+  thrust::counting_iterator<value_idx> arg_sort_iter(0);
+  thrust::copy(exec_policy, arg_sort_iter, arg_sort_iter + n_rows, src_indices);
+
+  auto keys = thrust::make_zip_iterator(
+    thrust::make_tuple(colors, nn_colors, (raft::KeyValuePair<value_idx, value_t>*)kvp));
+  auto vals = thrust::make_zip_iterator(thrust::make_tuple(src_indices));
+  // get all the colors in contiguous locations so we can map them to warps.
+  thrust::sort_by_key(exec_policy, keys, keys + n_rows, vals, TupleComp());
+}
+
+template <typename value_idx, typename value_t>
+RAFT_KERNEL min_components_by_color_kernel(value_idx* out_rows,
+                                           value_idx* out_cols,
+                                           value_t* out_vals,
+                                           const value_idx* out_index,
+                                           const value_idx* indices,
+                                           const raft::KeyValuePair<value_idx, value_t>* kvp,
+                                           size_t nnz)
+{
+  size_t tid = blockDim.x * blockIdx.x + threadIdx.x;
+
+  if (tid >= nnz) return;
+
+  int idx = out_index[tid];
+
+  if ((tid == 0 || (out_index[tid - 1] != idx))) {
+    out_rows[idx] = indices[tid];
+    out_cols[idx] = kvp[tid].key;
+    out_vals[idx] = kvp[tid].value;
+  }
+}
+
+/**
+ * Computes the min set of unique components that neighbor the
+ * components of each source vertex.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[out] coo output edge list
+ * @param[in] out_index output indptr for ordering edge list
+ * @param[in] indices indices of source vertices for each component
+ * @param[in] kvp indices and distances of each destination vertex for each component
+ * @param[in] n_colors number of components
+ * @param[in] stream cuda stream for which to order cuda operations
+ */
+template <typename value_idx, typename value_t>
+void min_components_by_color(raft::sparse::COO<value_t, value_idx>& coo,
+                             const value_idx* out_index,
+                             const value_idx* indices,
+                             const raft::KeyValuePair<value_idx, value_t>* kvp,
+                             size_t nnz,
+                             cudaStream_t stream)
+{
+  /**
+   * Arrays should be ordered by: colors_indptr->colors_n->kvp.value
+   * so the last element of each column in the input CSR should be
+   * the min.
+   */
+  min_components_by_color_kernel<<<raft::ceildiv(nnz, (size_t)256), 256, 0, stream>>>(
+    coo.rows(), coo.cols(), coo.vals(), out_index, indices, kvp, nnz);
+}
+
+/**
+ * Connects the components of an otherwise unconnected knn graph
+ * by computing a 1-nn to neighboring components of each data point
+ * (e.g. component(nn) != component(self)) and reducing the results to
+ * include the set of smallest destination components for each source
+ * component. The result will not necessarily contain
+ * n_components^2 - n_components number of elements because many components
+ * will likely not be contained in the neighborhoods of 1-nns.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[in] handle raft handle
+ * @param[out] out output edge list containing nearest cross-component
+ *             edges.
+ * @param[in] X original (row-major) dense matrix for which knn graph should be constructed.
+ * @param[in] orig_colors array containing component number for each row of X
+ * @param[in] n_rows number of rows in X
+ * @param[in] n_cols number of cols in X
+ * @param[in] reduction_op reduction operation for computing nearest neighbors. The reduction
+ * operation must have `gather` and `scatter` functions defined
+ * @param[in] row_batch_size the batch size for computing nearest neighbors. This parameter controls
+ * the number of samples for which the nearest neighbors are computed at once. Therefore, it affects
+ * the memory consumption mainly by reducing the size of the adjacency matrix for masked nearest
+ * neighbors computation. default 0 indicates that no batching is done
+ * @param[in] col_batch_size the input data is sorted and 'unsorted' based on color. An additional
+ * scratch space buffer of shape (n_rows, col_batch_size) is created for this. Usually, this
+ * parameter affects the memory consumption more drastically than the col_batch_size with a marginal
+ * increase in compute time as the col_batch_size is reduced. default 0 indicates that no batching
+ * is done
+ * @param[in] metric distance metric
+ */
+template <typename value_idx, typename value_t, typename red_op>
+void cross_component_nn(
+  raft::resources const& handle,
+  raft::sparse::COO<value_t, value_idx>& out,
+  const value_t* X,
+  const value_idx* orig_colors,
+  size_t n_rows,
+  size_t n_cols,
+  red_op reduction_op,
+  size_t row_batch_size,
+  size_t col_batch_size,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  RAFT_EXPECTS(metric == cuvs::distance::DistanceType::L2SqrtExpanded,
+               "Fixing connectivities for an unconnected k-NN graph only "
+               "supports L2SqrtExpanded currently.");
+
+  if (row_batch_size == 0 || row_batch_size > n_rows) { row_batch_size = n_rows; }
+
+  if (col_batch_size == 0 || col_batch_size > n_cols) { col_batch_size = n_cols; }
+
+  rmm::device_uvector<value_idx> colors(n_rows, stream);
+
+  // Normalize colors so they are drawn from a monotonically increasing set
+  constexpr bool zero_based = true;
+  raft::label::make_monotonic(
+    colors.data(), const_cast<value_idx*>(orig_colors), n_rows, stream, zero_based);
+
+  /**
+   * First compute 1-nn for all colors where the color of each data point
+   * is guaranteed to be != color of its nearest neighbor.
+   */
+  rmm::device_uvector<value_idx> nn_colors(n_rows, stream);
+  rmm::device_uvector<raft::KeyValuePair<value_idx, value_t>> temp_inds_dists(n_rows, stream);
+  rmm::device_uvector<value_idx> src_indices(n_rows, stream);
+
+  perform_1nn(handle,
+              temp_inds_dists.data(),
+              nn_colors.data(),
+              colors.data(),
+              X,
+              n_rows,
+              n_cols,
+              row_batch_size,
+              col_batch_size,
+              reduction_op);
+
+  /**
+   * Sort data points by color (neighbors are not sorted)
+   */
+  // max_color + 1 = number of connected components
+  // sort nn_colors by key w/ original colors
+  sort_by_color(
+    handle, colors.data(), nn_colors.data(), temp_inds_dists.data(), src_indices.data(), n_rows);
+
+  /**
+   * Take the min for any duplicate colors
+   */
+  // Compute mask of duplicates
+  rmm::device_uvector<value_idx> out_index(n_rows + 1, stream);
+  raft::sparse::op::compute_duplicates_mask(
+    out_index.data(), colors.data(), nn_colors.data(), n_rows, stream);
+
+  thrust::exclusive_scan(raft::resource::get_thrust_policy(handle),
+                         out_index.data(),
+                         out_index.data() + out_index.size(),
+                         out_index.data());
+
+  // compute final size
+  value_idx size = 0;
+  raft::update_host(&size, out_index.data() + (out_index.size() - 1), 1, stream);
+  raft::resource::sync_stream(handle, stream);
+
+  size++;
+
+  raft::sparse::COO<value_t, value_idx> min_edges(stream);
+  min_edges.allocate(size, n_rows, n_rows, true, stream);
+
+  min_components_by_color(
+    min_edges, out_index.data(), src_indices.data(), temp_inds_dists.data(), n_rows, stream);
+
+  /**
+   * Symmetrize resulting edge list
+   */
+  raft::sparse::linalg::symmetrize(
+    handle, min_edges.rows(), min_edges.cols(), min_edges.vals(), n_rows, n_rows, size, out);
+}
+
+};  // end namespace cuvs::sparse::neighbors::detail
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 903c28f8d8..fac4c62444 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -52,6 +52,7 @@ function(ConfigureTest)
             GTest::gtest
             GTest::gtest_main
             Threads::Threads
+            ${CUVS_CTK_MATH_DEPENDENCIES}
             $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
             $<TARGET_NAME_IF_EXISTS:conda_env>
             $<$<BOOL:${_CUVS_TEST_C_LIB}>:cuvs::c_api>
@@ -166,6 +167,8 @@ if(BUILD_TESTS)
     test/distance/dist_l_inf.cu
     test/distance/dist_lp_unexp.cu
     test/distance/dist_russell_rao.cu
+    test/distance/masked_nn.cu
+    test/sparse/neighbors/cross_component_nn.cu
     GPUS
     1
     PERCENT
diff --git a/cpp/test/cluster/linkage.cu b/cpp/test/cluster/linkage.cu
index 6ea6bd61b9..0f2461fa72 100644
--- a/cpp/test/cluster/linkage.cu
+++ b/cpp/test/cluster/linkage.cu
@@ -21,7 +21,7 @@
 //
 // TODO: consider adding this to libraft.so or creating an instance in a
 // separate translation unit for this test.
-#undef RAFT_EXPLICIT_INSTANTIATE_ONLY
+#undef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 #include "../test_utils.cuh"
 
diff --git a/cpp/test/distance/masked_nn.cu b/cpp/test/distance/masked_nn.cu
new file mode 100644
index 0000000000..a8f2f5163f
--- /dev/null
+++ b/cpp/test/distance/masked_nn.cu
@@ -0,0 +1,438 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/core/kvp.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/norm.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/itertools.hpp>
+
+#include "../../src/distance/masked_nn.cuh"
+#include "../test_utils.h"
+
+#include <gtest/gtest.h>
+
+#include <iostream>
+
+namespace cuvs::distance::masked_nn {
+
+// The adjacency pattern determines what distances get computed.
+enum AdjacencyPattern {
+  checkerboard = 0,  // adjacency matrix looks like a checkerboard (half the distances are computed)
+  checkerboard_4  = 1,  // checkerboard with tiles of size 4x4
+  checkerboard_64 = 2,  // checkerboard with tiles of size 64x64
+  all_true        = 3,  // no distance computations can be skipped
+  all_false       = 4   // all distance computations can be skipped
+};
+
+// Kernels:
+// - init_adj: to initialize the adjacency kernel with a specific adjacency pattern
+// - referenceKernel: to produce the ground-truth output
+
+RAFT_KERNEL init_adj(AdjacencyPattern pattern,
+                     int n,
+                     raft::device_matrix_view<bool, int, raft::layout_c_contiguous> adj,
+                     raft::device_vector_view<int, int, raft::layout_c_contiguous> group_idxs)
+{
+  int m          = adj.extent(0);
+  int num_groups = adj.extent(1);
+
+  for (int idx_m = blockIdx.y * blockDim.y + threadIdx.y; idx_m < m;
+       idx_m += blockDim.y * gridDim.y) {
+    for (int idx_g = blockIdx.x * blockDim.x + threadIdx.x; idx_g < num_groups;
+         idx_g += blockDim.x * gridDim.x) {
+      switch (pattern) {
+        case checkerboard: adj(idx_m, idx_g) = (idx_m + idx_g) % 2; break;
+        case checkerboard_4: adj(idx_m, idx_g) = (idx_m / 4 + idx_g) % 2; break;
+        case checkerboard_64: adj(idx_m, idx_g) = (idx_m / 64 + idx_g) % 2; break;
+        case all_true: adj(idx_m, idx_g) = true; break;
+        case all_false: adj(idx_m, idx_g) = false; break;
+        default: assert(false && "unknown pattern");
+      }
+    }
+  }
+  // Each group is of size n / num_groups.
+  //
+  // - group_idxs[j] indicates the start of group j + 1 (i.e. is the inclusive
+  // scan of the group lengths)
+  //
+  // - The first group always starts at index zero, so we do not store it.
+  //
+  // - The group_idxs[num_groups - 1] should always equal n.
+
+  if (blockIdx.y == 0 && threadIdx.y == 0) {
+    const int g_stride = blockDim.x * gridDim.x;
+    for (int idx_g = blockIdx.x * blockDim.x + threadIdx.x; idx_g < num_groups; idx_g += g_stride) {
+      group_idxs(idx_g) = (idx_g + 1) * (n / num_groups);
+    }
+    group_idxs(num_groups - 1) = n;
+  }
+}
+
+template <typename DataT, typename ReduceOpT, int NWARPS>
+__launch_bounds__(32 * NWARPS, 2) RAFT_KERNEL referenceKernel(raft::KeyValuePair<int, DataT>* min,
+                                                              DataT* x,
+                                                              DataT* y,
+                                                              bool* adj,
+                                                              int* group_idxs,
+                                                              int m,
+                                                              int n,
+                                                              int k,
+                                                              int num_groups,
+                                                              bool sqrt,
+                                                              int* workspace,
+                                                              DataT maxVal)
+{
+  const int m_stride = blockDim.y * gridDim.y;
+  const int m_offset = threadIdx.y + blockIdx.y * blockDim.y;
+  const int n_stride = blockDim.x * gridDim.x;
+  const int n_offset = threadIdx.x + blockIdx.x * blockDim.x;
+
+  for (int m_grid = 0; m_grid < m; m_grid += m_stride) {
+    for (int n_grid = 0; n_grid < n; n_grid += n_stride) {
+      int midx = m_grid + m_offset;
+      int nidx = n_grid + n_offset;
+
+      // Do a reverse linear search to determine the group index.
+      int group_idx = 0;
+      for (int i = num_groups; 0 <= i; --i) {
+        if (nidx < group_idxs[i]) { group_idx = i; }
+      }
+      const bool include_dist = adj[midx * num_groups + group_idx] && midx < m && nidx < n;
+
+      // Compute L2 metric.
+      DataT acc = DataT(0);
+      for (int i = 0; i < k; ++i) {
+        int xidx  = i + midx * k;
+        int yidx  = i + nidx * k;
+        auto diff = x[xidx] - y[yidx];
+        acc += diff * diff;
+      }
+      if (sqrt) { acc = raft::sqrt(acc); }
+      ReduceOpT redOp;
+      typedef cub::WarpReduce<raft::KeyValuePair<int, DataT>> WarpReduce;
+      __shared__ typename WarpReduce::TempStorage temp[NWARPS];
+      int warpId = threadIdx.x / raft::WarpSize;
+      raft::KeyValuePair<int, DataT> tmp;
+      tmp.key   = include_dist ? nidx : -1;
+      tmp.value = include_dist ? acc : maxVal;
+      tmp       = WarpReduce(temp[warpId]).Reduce(tmp, cuvs::distance::KVPMinReduce<int, DataT>{});
+      if (threadIdx.x % raft::WarpSize == 0 && midx < m) {
+        while (atomicCAS(workspace + midx, 0, 1) == 1)
+          ;
+        __threadfence();
+        redOp(midx, min + midx, tmp);
+        __threadfence();
+        atomicCAS(workspace + midx, 1, 0);
+      }
+      __syncthreads();
+    }
+  }
+}
+
+// Structs
+// - Params: holds parameters for test case
+// - Inputs: holds the inputs to the functions under test (x, y, adj, group_idxs). Is generated from
+//   the inputs.
+struct Params {
+  double tolerance;
+  int m, n, k, num_groups;
+  bool sqrt;
+  unsigned long long int seed;
+  AdjacencyPattern pattern;
+};
+
+inline auto operator<<(std::ostream& os, const Params& p) -> std::ostream&
+{
+  os << "m: " << p.m << ", n: " << p.n << ", k: " << p.k << ", num_groups: " << p.num_groups
+     << ", sqrt: " << p.sqrt << ", seed: " << p.seed << ", tol: " << p.tolerance;
+  return os;
+}
+
+template <typename DataT>
+struct Inputs {
+  using IdxT = int;
+
+  raft::device_matrix<DataT, IdxT> x, y;
+  raft::device_matrix<bool, IdxT> adj;
+  raft::device_vector<IdxT, IdxT> group_idxs;
+
+  Inputs(const raft::handle_t& handle, const Params& p)
+    : x{raft::make_device_matrix<DataT, IdxT>(handle, p.m, p.k)},
+      y{raft::make_device_matrix<DataT, IdxT>(handle, p.n, p.k)},
+      adj{raft::make_device_matrix<bool, IdxT>(handle, p.m, p.num_groups)},
+      group_idxs{raft::make_device_vector<IdxT, IdxT>(handle, p.num_groups)}
+  {
+    // Initialize x, y
+    raft::random::RngState r(p.seed);
+    uniform(handle, r, x.data_handle(), p.m * p.k, DataT(-1.0), DataT(1.0));
+    uniform(handle, r, y.data_handle(), p.n * p.k, DataT(-1.0), DataT(1.0));
+
+    // Initialize adj, group_idxs.
+    dim3 block(32, 32);
+    dim3 grid(10, 10);
+    init_adj<<<grid, block, 0, raft::resource::get_cuda_stream(handle)>>>(
+      p.pattern, p.n, adj.view(), group_idxs.view());
+    RAFT_CUDA_TRY(cudaGetLastError());
+  }
+};
+
+template <typename DataT, typename OutT = raft::KeyValuePair<int, DataT>>
+auto reference(const raft::handle_t& handle, Inputs<DataT> inp, const Params& p)
+  -> raft::device_vector<OutT, int>
+{
+  int m          = inp.x.extent(0);
+  int n          = inp.y.extent(0);
+  int k          = inp.x.extent(1);
+  int num_groups = inp.group_idxs.extent(0);
+
+  if (m == 0 || n == 0 || k == 0 || num_groups == 0) {
+    return raft::make_device_vector<OutT, int>(handle, 0);
+  }
+
+  // Initialize workspace
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<char> workspace(p.m * sizeof(int), stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(workspace.data(), 0, sizeof(int) * m, stream));
+
+  // Initialize output
+  auto out  = raft::make_device_vector<OutT, int>(handle, m);
+  auto blks = raft::ceildiv(m, 256);
+  cuvs::distance::MinAndDistanceReduceOp<int, DataT> op;
+  cuvs::distance::detail::initKernel<DataT, raft::KeyValuePair<int, DataT>, int>
+    <<<blks, 256, 0, stream>>>(out.data_handle(), m, std::numeric_limits<DataT>::max(), op);
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  // Launch reference kernel
+  const int nwarps = 16;
+  static const dim3 TPB(32, nwarps, 1);
+  dim3 nblks(1, 200, 1);
+  referenceKernel<DataT, decltype(op), nwarps>
+    <<<nblks, TPB, 0, stream>>>(out.data_handle(),
+                                inp.x.data_handle(),
+                                inp.y.data_handle(),
+                                inp.adj.data_handle(),
+                                inp.group_idxs.data_handle(),
+                                m,
+                                n,
+                                k,
+                                num_groups,
+                                p.sqrt,
+                                (int*)workspace.data(),
+                                std::numeric_limits<DataT>::max());
+  RAFT_CUDA_TRY(cudaGetLastError());
+
+  return out;
+}
+
+template <typename DataT, typename OutT = raft::KeyValuePair<int, DataT>>
+auto run_masked_nn(const raft::handle_t& handle, Inputs<DataT> inp, const Params& p)
+  -> raft::device_vector<OutT, int>
+{
+  // Compute norms:
+  auto x_norm = raft::make_device_vector<DataT, int>(handle, p.m);
+  auto y_norm = raft::make_device_vector<DataT, int>(handle, p.n);
+
+  raft::linalg::norm(handle,
+                     std::as_const(inp.x).view(),
+                     x_norm.view(),
+                     raft::linalg::L2Norm,
+                     raft::linalg::Apply::ALONG_ROWS);
+  raft::linalg::norm(handle,
+                     std::as_const(inp.y).view(),
+                     y_norm.view(),
+                     raft::linalg::L2Norm,
+                     raft::linalg::Apply::ALONG_ROWS);
+
+  // Create parameters for masked_l2_nn
+  using IdxT       = int;
+  using RedOpT     = cuvs::distance::MinAndDistanceReduceOp<int, DataT>;
+  using PairRedOpT = cuvs::distance::KVPMinReduce<int, DataT>;
+  using ParamT     = cuvs::distance::masked_l2_nn_params<RedOpT, PairRedOpT>;
+
+  bool init_out = true;
+  ParamT masked_l2_params{RedOpT{}, PairRedOpT{}, p.sqrt, init_out};
+
+  // Create output
+  auto out = raft::make_device_vector<OutT, IdxT, raft::layout_c_contiguous>(handle, p.m);
+
+  // Launch kernel
+  cuvs::distance::masked_l2_nn<DataT, OutT, IdxT>(handle,
+                                                  masked_l2_params,
+                                                  inp.x.view(),
+                                                  inp.y.view(),
+                                                  x_norm.view(),
+                                                  y_norm.view(),
+                                                  inp.adj.view(),
+                                                  inp.group_idxs.view(),
+                                                  out.view());
+
+  raft::resource::sync_stream(handle);
+
+  return out;
+}
+
+template <typename T>
+struct CompareApproxAbsKVP {
+  typedef typename raft::KeyValuePair<int, T> KVP;
+  CompareApproxAbsKVP(T eps_) : eps(eps_) {}
+  bool operator()(const KVP& a, const KVP& b) const
+  {
+    T diff  = raft::abs(raft::abs(a.value) - raft::abs(b.value));
+    T m     = std::max(raft::abs(a.value), raft::abs(b.value));
+    T ratio = m >= eps ? diff / m : diff;
+    return (ratio <= eps);
+  }
+
+ private:
+  T eps;
+};
+
+template <typename K, typename V, typename L>
+::testing::AssertionResult devArrMatch(const raft::KeyValuePair<K, V>* expected,
+                                       const raft::KeyValuePair<K, V>* actual,
+                                       size_t size,
+                                       L eq_compare,
+                                       cudaStream_t stream = 0)
+{
+  typedef typename raft::KeyValuePair<K, V> KVP;
+  std::shared_ptr<KVP> exp_h(new KVP[size]);
+  std::shared_ptr<KVP> act_h(new KVP[size]);
+  raft::update_host<KVP>(exp_h.get(), expected, size, stream);
+  raft::update_host<KVP>(act_h.get(), actual, size, stream);
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+  for (size_t i(0); i < size; ++i) {
+    auto exp = exp_h.get()[i];
+    auto act = act_h.get()[i];
+    if (!eq_compare(exp, act)) {
+      return ::testing::AssertionFailure()
+             << "actual=" << act.key << "," << act.value << " != expected=" << exp.key << ","
+             << exp.value << " @" << i;
+    }
+  }
+  return ::testing::AssertionSuccess();
+}
+
+inline auto gen_params() -> std::vector<Params>
+{
+  // Regular powers of two
+  auto regular = raft::util::itertools::product<Params>({0.001f},       // tolerance
+                                                        {32, 64, 512},  // m
+                                                        {32, 64, 512},  // n
+                                                        {8, 32},        // k
+                                                        {2, 32},        // num_groups
+                                                        {true, false},  // sqrt
+                                                        {1234ULL},      // seed
+                                                        {AdjacencyPattern::all_true,
+                                                         AdjacencyPattern::checkerboard,
+                                                         AdjacencyPattern::checkerboard_64,
+                                                         AdjacencyPattern::all_false});
+
+  // Irregular sizes to check tiling and bounds checking
+  auto irregular = raft::util::itertools::product<Params>({0.001f},         // tolerance
+                                                          {511, 512, 513},  // m
+                                                          {127, 128, 129},  // n
+                                                          {5},              // k
+                                                          {3, 9},           // num_groups
+                                                          {true, false},    // sqrt
+                                                          {1234ULL},        // seed
+                                                          {AdjacencyPattern::all_true,
+                                                           AdjacencyPattern::checkerboard,
+                                                           AdjacencyPattern::checkerboard_64});
+
+  regular.insert(regular.end(), irregular.begin(), irregular.end());
+
+  return regular;
+}
+
+class MaskedL2NNTest : public ::testing::TestWithParam<Params> {
+  // Empty.
+};
+
+//
+TEST_P(MaskedL2NNTest, ReferenceCheckFloat)
+{
+  using DataT = float;
+
+  // Get parameters; create handle and input data.
+  Params p = GetParam();
+  raft::handle_t handle{};
+  Inputs<DataT> inputs{handle, p};
+
+  // Calculate reference and test output
+  auto out_reference = reference(handle, inputs, p);
+  auto out_fast      = run_masked_nn(handle, inputs, p);
+
+  // Check for differences.
+  ASSERT_TRUE(devArrMatch(out_reference.data_handle(),
+                          out_fast.data_handle(),
+                          p.m,
+                          CompareApproxAbsKVP<DataT>(p.tolerance),
+                          raft::resource::get_cuda_stream(handle)));
+}
+
+// This test checks whether running the masked_l2_nn twice returns the same
+// output.
+TEST_P(MaskedL2NNTest, DeterminismCheck)
+{
+  using DataT = float;
+
+  // Get parameters; create handle and input data.
+  Params p = GetParam();
+  raft::handle_t handle{};
+  Inputs<DataT> inputs{handle, p};
+
+  // Calculate reference and test output
+  auto out1 = run_masked_nn(handle, inputs, p);
+  auto out2 = run_masked_nn(handle, inputs, p);
+
+  // Check for differences.
+  ASSERT_TRUE(devArrMatch(out1.data_handle(),
+                          out2.data_handle(),
+                          p.m,
+                          CompareApproxAbsKVP<DataT>(p.tolerance),
+                          raft::resource::get_cuda_stream(handle)));
+}
+
+TEST_P(MaskedL2NNTest, ReferenceCheckDouble)
+{
+  using DataT = double;
+
+  // Get parameters; create handle and input data.
+  Params p = GetParam();
+  raft::handle_t handle{};
+  Inputs<DataT> inputs{handle, p};
+
+  // Calculate reference and test output
+  auto out_reference = reference(handle, inputs, p);
+  auto out_fast      = run_masked_nn(handle, inputs, p);
+
+  // Check for differences.
+  ASSERT_TRUE(devArrMatch(out_reference.data_handle(),
+                          out_fast.data_handle(),
+                          p.m,
+                          CompareApproxAbsKVP<DataT>(p.tolerance),
+                          raft::resource::get_cuda_stream(handle)));
+}
+
+INSTANTIATE_TEST_CASE_P(MaskedL2NNTests, MaskedL2NNTest, ::testing::ValuesIn(gen_params()));
+
+}  // end namespace cuvs::distance::masked_nn
diff --git a/cpp/test/sparse/neighbors/cross_component_nn.cu b/cpp/test/sparse/neighbors/cross_component_nn.cu
new file mode 100644
index 0000000000..d931fedc46
--- /dev/null
+++ b/cpp/test/sparse/neighbors/cross_component_nn.cu
@@ -0,0 +1,1039 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// XXX: We allow the instantiation of masked_l2_nn here:
+// raft::sparse::neighbors::FixConnectivitiesRedOp<value_idx, value_t> red_op(params.n_row);
+// cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(
+//   handle, out_edges, data.data(), colors.data(), params.n_row, params.n_col, red_op);
+//
+// TODO: consider adding this to libraft.so or creating an instance in a
+// separate translation unit for this test.
+//
+// TODO: edge case testing. Reference: https://github.com/rapidsai/raft/issues/1669
+
+#include "../../test_utils.cuh"
+
+#include "../../../src/cluster/single_linkage.cuh"
+#include "../../../src/sparse/neighbors/cross_component_nn.cuh"
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/transpose.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/sparse/linalg/symmetrize.cuh>
+#include <raft/sparse/mst/mst.cuh>
+#include <raft/sparse/neighbors/knn_graph.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cub/cub.cuh>
+
+#include <gtest/gtest.h>
+
+#include <vector>
+
+namespace cuvs {
+namespace sparse {
+
+using namespace std;
+
+template <typename value_t, typename value_idx>
+struct ConnectComponentsInputs {
+  value_idx n_row;
+  value_idx n_col;
+  std::vector<value_t> data;
+
+  int c;
+};
+
+template <typename value_idx, typename value_t>
+class ConnectComponentsTest
+  : public ::testing::TestWithParam<ConnectComponentsInputs<value_t, value_idx>> {
+ protected:
+  void basicTest()
+  {
+    raft::resources handle;
+
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    params = ::testing::TestWithParam<ConnectComponentsInputs<value_t, value_idx>>::GetParam();
+
+    raft::sparse::COO<value_t, value_idx> out_edges(stream);
+    raft::sparse::COO<value_t, value_idx> out_edges_batched(stream);
+
+    rmm::device_uvector<value_t> data(params.n_row * params.n_col, stream);
+
+    raft::copy(
+      data.data(), params.data.data(), data.size(), raft::resource::get_cuda_stream(handle));
+
+    rmm::device_uvector<value_idx> indptr(params.n_row + 1, stream);
+
+    /**
+     * 1. Construct knn graph
+     */
+    raft::sparse::COO<value_t, value_idx> knn_graph_coo(stream);
+
+    raft::sparse::neighbors::knn_graph(handle,
+                                       data.data(),
+                                       params.n_row,
+                                       params.n_col,
+                                       raft::distance::DistanceType::L2SqrtExpanded,
+                                       knn_graph_coo,
+                                       params.c);
+
+    raft::sparse::convert::sorted_coo_to_csr(
+      knn_graph_coo.rows(), knn_graph_coo.nnz, indptr.data(), params.n_row + 1, stream);
+
+    /**
+     * 2. Construct MST, sorted by weights
+     */
+    rmm::device_uvector<value_idx> colors(params.n_row, stream);
+
+    auto mst_coo = raft::mst::mst<value_idx, value_idx, value_t, double>(handle,
+                                                                         indptr.data(),
+                                                                         knn_graph_coo.cols(),
+                                                                         knn_graph_coo.vals(),
+                                                                         params.n_row,
+                                                                         knn_graph_coo.nnz,
+                                                                         colors.data(),
+                                                                         stream,
+                                                                         false,
+                                                                         true);
+
+    /**
+     * 3. cross_component_nn to fix connectivities
+     */
+    cuvs::sparse::neighbors::FixConnectivitiesRedOp<value_idx, value_t> red_op(params.n_row);
+    cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
+                                                                    out_edges,
+                                                                    data.data(),
+                                                                    colors.data(),
+                                                                    params.n_row,
+                                                                    params.n_col,
+                                                                    red_op,
+                                                                    params.n_row,
+                                                                    params.n_col);
+
+    cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
+                                                                    out_edges_batched,
+                                                                    data.data(),
+                                                                    colors.data(),
+                                                                    params.n_row,
+                                                                    params.n_col,
+                                                                    red_op,
+                                                                    params.n_row / 2,
+                                                                    params.n_col / 2);
+
+    ASSERT_TRUE(out_edges.nnz == out_edges_batched.nnz);
+
+    ASSERT_TRUE(
+      devArrMatch(out_edges.rows(), out_edges_batched.rows(), out_edges.nnz, Compare<int>()));
+
+    ASSERT_TRUE(
+      devArrMatch(out_edges.cols(), out_edges_batched.cols(), out_edges.nnz, Compare<int>()));
+
+    ASSERT_TRUE(devArrMatch(
+      out_edges.vals(), out_edges_batched.vals(), out_edges.nnz, CompareApprox<float>(1e-4)));
+
+    /**
+     * Construct final edge list
+     */
+    rmm::device_uvector<value_idx> indptr2(params.n_row + 1, stream);
+
+    raft::sparse::convert::sorted_coo_to_csr(
+      out_edges.rows(), out_edges.nnz, indptr2.data(), params.n_row + 1, stream);
+
+    auto output_mst = raft::mst::mst<value_idx, value_idx, value_t>(handle,
+                                                                    indptr2.data(),
+                                                                    out_edges.cols(),
+                                                                    out_edges.vals(),
+                                                                    params.n_row,
+                                                                    out_edges.nnz,
+                                                                    colors.data(),
+                                                                    stream,
+                                                                    false,
+                                                                    false);
+
+    raft::resource::sync_stream(handle, stream);
+
+    // The sum of edges for both MST runs should be n_rows - 1
+    final_edges = output_mst.n_edges + mst_coo.n_edges;
+  }
+
+  void SetUp() override { basicTest(); }
+
+  void TearDown() override {}
+
+ protected:
+  ConnectComponentsInputs<value_t, value_idx> params;
+
+  value_idx final_edges;
+};
+
+const std::vector<ConnectComponentsInputs<float, int>> fix_conn_inputsf2 = {
+  // Test n_clusters == n_points
+  {10,
+   5,
+   {0.21390334, 0.50261639, 0.91036676, 0.59166485, 0.71162682, 0.10248392, 0.77782677, 0.43772379,
+    0.4035871,  0.3282796,  0.47544681, 0.59862974, 0.12319357, 0.06239463, 0.28200272, 0.1345717,
+    0.50498218, 0.5113505,  0.16233086, 0.62165332, 0.42281548, 0.933117,   0.41386077, 0.23264562,
+    0.73325968, 0.37537541, 0.70719873, 0.14522645, 0.73279625, 0.9126674,  0.84854131, 0.28890216,
+    0.85267903, 0.74703138, 0.83842071, 0.34942792, 0.27864171, 0.70911132, 0.21338564, 0.32035554,
+    0.73788331, 0.46926692, 0.57570162, 0.42559178, 0.87120209, 0.22734951, 0.01847905, 0.75549396,
+    0.76166195, 0.66613745},
+   -1},
+  // Test n_points == 100
+  {100,
+   10,
+   {6.26168372e-01, 9.30437651e-01, 6.02450208e-01, 2.73025296e-01, 9.53050619e-01, 3.32164396e-01,
+    6.88942598e-01, 5.79163537e-01, 6.70341547e-01, 2.70140602e-02, 9.30429671e-01, 7.17721157e-01,
+    9.89948537e-01, 7.75253347e-01, 1.34491522e-02, 2.48522428e-02, 3.51413378e-01, 7.64405834e-01,
+    7.86373507e-01, 7.18748577e-01, 8.66998621e-01, 6.80316582e-01, 2.51288712e-01, 4.91078420e-01,
+    3.76246281e-01, 4.86828710e-01, 5.67464772e-01, 5.30734742e-01, 8.99478296e-01, 7.66699088e-01,
+    9.49339111e-01, 3.55248484e-01, 9.06046929e-01, 4.48407772e-01, 6.96395305e-01, 2.44277335e-01,
+    7.74840000e-01, 5.21046603e-01, 4.66423971e-02, 5.12019638e-02, 8.95019614e-01, 5.28956953e-01,
+    4.31536306e-01, 5.83857744e-01, 4.41787364e-01, 4.68656523e-01, 5.73971433e-01, 6.79989654e-01,
+    3.19650588e-01, 6.12579596e-01, 6.49126442e-02, 8.39131142e-01, 2.85252117e-01, 5.84848929e-01,
+    9.46507115e-01, 8.58440748e-01, 3.61528940e-01, 2.44215959e-01, 3.80101125e-01, 4.57128957e-02,
+    8.82216988e-01, 8.31498633e-01, 7.23474381e-01, 7.75788607e-01, 1.40864146e-01, 6.62092382e-01,
+    5.13985168e-01, 3.00686418e-01, 8.70109949e-01, 2.43187753e-01, 2.89391938e-01, 2.84214238e-01,
+    8.70985521e-01, 8.77491176e-01, 6.72537226e-01, 3.30929686e-01, 1.85934324e-01, 9.16222614e-01,
+    6.18239142e-01, 2.64768597e-01, 5.76145451e-01, 8.62961369e-01, 6.84757925e-01, 7.60549082e-01,
+    1.27645356e-01, 4.51004673e-01, 3.92292980e-01, 4.63170803e-01, 4.35449330e-02, 2.17583404e-01,
+    5.71832605e-02, 2.06763039e-01, 3.70116249e-01, 2.09750028e-01, 6.17283019e-01, 8.62549231e-01,
+    9.84156240e-02, 2.66249156e-01, 3.87635103e-01, 2.85591012e-02, 4.24826068e-01, 4.45795088e-01,
+    6.86227676e-01, 1.08848960e-01, 5.96731841e-02, 3.71770228e-01, 1.91548833e-01, 6.95136078e-01,
+    9.00700636e-01, 8.76363105e-01, 2.67334632e-01, 1.80619709e-01, 7.94060419e-01, 1.42854171e-02,
+    1.09372387e-01, 8.74028108e-01, 6.46403232e-01, 4.86588834e-01, 5.93446175e-02, 6.11886291e-01,
+    8.83865057e-01, 3.15879821e-01, 2.27043992e-01, 9.76764951e-01, 6.15620336e-01, 9.76199360e-01,
+    2.40548962e-01, 3.21795663e-01, 8.75087904e-02, 8.11234663e-01, 6.96070480e-01, 8.12062321e-01,
+    1.21958818e-01, 3.44348628e-02, 8.72630414e-01, 3.06162776e-01, 1.76043529e-02, 9.45894971e-01,
+    5.33896401e-01, 6.21642973e-01, 4.93062535e-01, 4.48984262e-01, 2.24560379e-01, 4.24052195e-02,
+    4.43447610e-01, 8.95646149e-01, 6.05220676e-01, 1.81840491e-01, 9.70831206e-01, 2.12563586e-02,
+    6.92582693e-01, 7.55946922e-01, 7.95086143e-01, 6.05328941e-01, 3.99350764e-01, 4.32846636e-01,
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+    3.87699807e-01, 4.50705808e-01, 2.49371643e-01, 3.36074898e-01, 9.29925118e-01, 6.65393649e-01,
+    9.07275994e-01, 3.73075859e-01, 4.14044139e-03, 2.37463702e-01, 2.25893784e-01, 2.46900245e-01,
+    4.50350196e-01, 3.48618117e-01, 5.07193932e-01, 5.23435142e-01, 8.13611417e-01, 8.92715622e-01,
+    1.02623450e-01, 3.06088345e-01, 7.80461650e-01, 2.21453645e-01, 2.01419652e-01, 2.84254457e-01,
+    3.68286735e-01, 7.39358243e-01, 8.97879394e-01, 9.81599566e-01, 7.56526442e-01, 7.37645545e-01,
+    4.23976657e-02, 8.25922012e-01, 2.60956996e-01, 2.90702065e-01, 8.98388344e-01, 3.03733299e-01,
+    8.49071471e-01, 3.45835425e-01, 7.65458276e-01, 5.68094872e-01, 8.93770930e-01, 9.93161641e-01,
+    5.63368667e-02, 4.26548945e-01, 5.46745780e-01, 5.75674571e-01, 7.94599487e-01, 7.18935553e-02,
+    4.46492976e-01, 6.40240123e-01, 2.73246969e-01, 2.00465968e-01, 1.30718835e-01, 1.92492005e-01,
+    1.96617189e-01, 6.61271644e-01, 8.12687657e-01, 8.66342445e-01
+
+   },
+   -4}};
+
+typedef ConnectComponentsTest<int, float> ConnectComponentsTestF_Int;
+TEST_P(ConnectComponentsTestF_Int, Result)
+{
+  /**
+   * Verify the src & dst vertices on each edge have different colors
+   */
+  EXPECT_TRUE(final_edges == params.n_row - 1);
+}
+
+INSTANTIATE_TEST_CASE_P(ConnectComponentsTest,
+                        ConnectComponentsTestF_Int,
+                        ::testing::ValuesIn(fix_conn_inputsf2));
+
+template <typename value_idx, typename value_t>
+struct MutualReachabilityFixConnectivitiesRedOp {
+  value_t* core_dists;
+  value_idx m;
+
+  DI MutualReachabilityFixConnectivitiesRedOp() : m(0) {}
+
+  MutualReachabilityFixConnectivitiesRedOp(value_t* core_dists_, value_idx m_)
+    : core_dists(core_dists_), m(m_){};
+
+  typedef typename raft::KeyValuePair<value_idx, value_t> KVP;
+  DI void operator()(value_idx rit, KVP* out, const KVP& other) const
+  {
+    if (rit < m && other.value < std::numeric_limits<value_t>::max()) {
+      value_t core_dist_rit   = core_dists[rit];
+      value_t core_dist_other = max(core_dist_rit, max(core_dists[other.key], other.value));
+
+      value_t core_dist_out;
+      if (out->key > -1) {
+        core_dist_out = max(core_dist_rit, max(core_dists[out->key], out->value));
+      } else {
+        core_dist_out = out->value;
+      }
+
+      bool smaller = core_dist_other < core_dist_out;
+      out->key     = smaller ? other.key : out->key;
+      out->value   = smaller ? core_dist_other : core_dist_out;
+    }
+  }
+
+  DI KVP operator()(value_idx rit, const KVP& a, const KVP& b) const
+  {
+    if (rit < m && a.key > -1) {
+      value_t core_dist_rit = core_dists[rit];
+      value_t core_dist_a   = max(core_dist_rit, max(core_dists[a.key], a.value));
+
+      value_t core_dist_b;
+      if (b.key > -1) {
+        core_dist_b = max(core_dist_rit, max(core_dists[b.key], b.value));
+      } else {
+        core_dist_b = b.value;
+      }
+
+      return core_dist_a < core_dist_b ? KVP(a.key, core_dist_a) : KVP(b.key, core_dist_b);
+    }
+
+    return b;
+  }
+
+  DI void init(value_t* out, value_t maxVal) const { *out = maxVal; }
+  DI void init(KVP* out, value_t maxVal) const
+  {
+    out->key   = -1;
+    out->value = maxVal;
+  }
+
+  DI void init_key(value_t& out, value_idx idx) const { return; }
+  DI void init_key(KVP& out, value_idx idx) const { out.key = idx; }
+
+  DI value_t get_value(KVP& out) const { return out.value; }
+  DI value_t get_value(value_t& out) const { return out; }
+
+  void gather(const raft::resources& handle, value_idx* map)
+  {
+    auto tmp_core_dists = raft::make_device_vector<value_t>(handle, m);
+    thrust::gather(raft::resource::get_thrust_policy(handle),
+                   map,
+                   map + m,
+                   core_dists,
+                   tmp_core_dists.data_handle());
+    raft::copy_async(
+      core_dists, tmp_core_dists.data_handle(), m, raft::resource::get_cuda_stream(handle));
+  }
+
+  void scatter(const raft::resources& handle, value_idx* map)
+  {
+    auto tmp_core_dists = raft::make_device_vector<value_t>(handle, m);
+    thrust::scatter(raft::resource::get_thrust_policy(handle),
+                    core_dists,
+                    core_dists + m,
+                    map,
+                    tmp_core_dists.data_handle());
+    raft::copy_async(
+      core_dists, tmp_core_dists.data_handle(), m, raft::resource::get_cuda_stream(handle));
+  }
+};
+
+template <typename value_t, typename value_idx>
+struct ConnectComponentsMutualReachabilityInputs {
+  value_idx n_row;
+  value_idx n_col;
+  std::vector<value_t> data;
+  std::vector<value_t> core_dists;
+  std::vector<value_idx> colors;
+  std::vector<value_idx> expected_rows;
+  std::vector<value_idx> expected_cols;
+  std::vector<value_t> expected_vals;
+};
+
+template <typename value_idx, typename value_t>
+class ConnectComponentsEdgesTest
+  : public ::testing::TestWithParam<ConnectComponentsMutualReachabilityInputs<value_t, value_idx>> {
+ protected:
+  void basicTest()
+  {
+    raft::resources handle;
+
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    params = ::testing::TestWithParam<
+      ConnectComponentsMutualReachabilityInputs<value_t, value_idx>>::GetParam();
+
+    raft::sparse::COO<value_t, value_idx> out_edges_unbatched(
+      raft::resource::get_cuda_stream(handle));
+    raft::sparse::COO<value_t, value_idx> out_edges_batched(
+      raft::resource::get_cuda_stream(handle));
+
+    rmm::device_uvector<value_t> data(params.n_row * params.n_col,
+                                      raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_t> core_dists(params.n_row, raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_idx> colors(params.n_row, raft::resource::get_cuda_stream(handle));
+
+    raft::copy(
+      data.data(), params.data.data(), data.size(), raft::resource::get_cuda_stream(handle));
+    raft::copy(core_dists.data(),
+               params.core_dists.data(),
+               core_dists.size(),
+               raft::resource::get_cuda_stream(handle));
+    raft::copy(
+      colors.data(), params.colors.data(), colors.size(), raft::resource::get_cuda_stream(handle));
+
+    /**
+     * 3. cross_component_nn to fix connectivities
+     */
+    MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t> red_op(core_dists.data(),
+                                                                        params.n_row);
+
+    cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
+                                                                    out_edges_unbatched,
+                                                                    data.data(),
+                                                                    colors.data(),
+                                                                    params.n_row,
+                                                                    params.n_col,
+                                                                    red_op,
+                                                                    params.n_row,
+                                                                    params.n_col);
+
+    cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
+                                                                    out_edges_batched,
+                                                                    data.data(),
+                                                                    colors.data(),
+                                                                    params.n_row,
+                                                                    params.n_col,
+                                                                    red_op,
+                                                                    11,
+                                                                    1);
+
+    ASSERT_TRUE(out_edges_unbatched.nnz == out_edges_batched.nnz &&
+                out_edges_unbatched.nnz == params.expected_rows.size());
+
+    ASSERT_TRUE(devArrMatch(out_edges_unbatched.rows(),
+                            params.expected_rows.data(),
+                            out_edges_unbatched.nnz,
+                            Compare<int>()));
+
+    ASSERT_TRUE(devArrMatch(out_edges_unbatched.cols(),
+                            params.expected_cols.data(),
+                            out_edges_unbatched.nnz,
+                            Compare<int>()));
+
+    ASSERT_TRUE(devArrMatch(out_edges_unbatched.vals(),
+                            params.expected_vals.data(),
+                            out_edges_unbatched.nnz,
+                            CompareApprox<float>(1e-4)));
+
+    ASSERT_TRUE(devArrMatch(out_edges_batched.rows(),
+                            params.expected_rows.data(),
+                            out_edges_batched.nnz,
+                            Compare<int>()));
+
+    ASSERT_TRUE(devArrMatch(out_edges_batched.cols(),
+                            params.expected_cols.data(),
+                            out_edges_batched.nnz,
+                            Compare<int>()));
+
+    ASSERT_TRUE(devArrMatch(out_edges_batched.vals(),
+                            params.expected_vals.data(),
+                            out_edges_batched.nnz,
+                            CompareApprox<float>(1e-4)));
+  }
+
+  void SetUp() override { basicTest(); }
+
+  void TearDown() override {}
+
+ protected:
+  ConnectComponentsMutualReachabilityInputs<value_t, value_idx> params;
+};
+
+const std::vector<ConnectComponentsMutualReachabilityInputs<float, int>> mr_fix_conn_inputsf2 = {
+  {100,
+   2,
+   {-7.72642, -8.39496, 5.4534,   0.742305, -2.97867, 9.55685,   6.04267,  0.571319,   -6.52184,
+    -6.31932, 3.64934,  1.40687,  -2.17793, 9.98983,  4.42021,   2.33028,  4.73696,    2.94181,
+    -3.66019, 9.38998,  -3.05358, 9.12521,  -6.65217, -5.57297,  -6.35769, -6.58313,   -3.61553,
+    7.81808,  -1.77073, 9.18565,  -7.95052, -6.39764, -6.60294,  -6.05293, -2.58121,   10.0178,
+    -7.76348, -6.72638, -6.40639, -6.95294, -2.97262, 8.54856,   -6.95673, -6.53896,   -7.32614,
+    -6.02371, -2.1478,  10.5523,  -2.54502, 10.5789,  -2.96984,  10.0714,  3.22451,    1.55252,
+    -6.25396, -7.73727, -7.85431, -6.09303, -8.11658, -8.20057,  -7.55965, -6.64786,   4.936,
+    2.23423,  4.44752,  2.27472,  -5.72103, -7.70079, -0.929985, 9.78172,  -3.10984,   8.72259,
+    -2.44167, 7.58954,  -2.18511, 8.6292,   5.55528,  2.30192,   4.73164,  -0.0143992, -8.2573,
+    -7.81793, -2.98837, 8.82863,  4.60517,  0.804492, -3.83738,  9.21115,  -2.62485,   8.71318,
+    3.57758,  2.44676,  -8.48711, -6.69548, -6.70645, -6.49479,  -6.86663, -5.42658,   3.83139,
+    1.47141,  2.02013,  2.79507,  4.64499,  1.73858,  -1.69667,  10.3705,  -6.61974,   -6.09829,
+    -6.05757, -4.98332, -7.10309, -6.16611, -3.52203, 9.32853,   -2.26724, 7.10101,    6.11777,
+    1.4549,   -4.23412, 8.452,    -6.58655, -7.59446, 3.93783,   1.64551,  -7.12502,   -7.63385,
+    2.72111,  1.94666,  -7.14428, -4.15994, -6.66553, -8.12585,  4.70011,  4.43641,    -7.76914,
+    -7.69592, 4.11012,  2.48644,  4.89743,  1.89872,  4.29716,   1.17089,  -6.62913,   -6.53366,
+    -8.07093, -6.22356, -2.16558, 7.25125,  4.73953,  1.46969,   -5.91625, -6.46733,   5.43091,
+    1.06378,  -6.82142, -8.02308, 6.52606,  2.14775,  3.08922,   2.04173,  -2.14756,   8.36917,
+    3.85663,  1.65111,  -1.68665, 7.79344,  -5.01385, -6.40628,  -2.52269, 7.95658,    -2.30033,
+    7.05462,  -1.04355, 8.78851,  3.72045,  3.5231,   -3.98772,  8.29444,  4.24777,    0.509655,
+    4.72693,  1.67416,  5.7827,   2.7251,   -3.41722, 7.60198,   5.22674,  4.16363,    -3.1109,
+    10.8666,  -3.18612, 9.62596,  -1.4782,  9.94557,  4.47859,   2.37722,  -5.79658,   -5.82631,
+    -3.34842, 8.70507},
+   {0.978428, 1.01917,  0.608673, 1.45629,  0.310713, 0.689461, 0.701126, 0.63296,  0.774788,
+    0.701648, 0.513282, 0.757651, 0.45638,  0.973111, 0.901396, 0.613692, 0.482497, 0.688143,
+    0.72428,  0.666345, 0.58232,  0.554756, 0.710315, 0.903611, 0.694115, 0.796099, 0.639759,
+    0.798998, 0.639839, 1.30727,  0.663729, 0.57476,  0.571348, 1.14662,  1.26518,  0.485068,
+    0.78207,  0.791621, 1.01678,  1.28509,  1.14715,  0.381395, 0.850507, 0.788511, 0.588341,
+    0.878516, 0.928669, 0.405874, 0.776421, 0.612274, 1.84963,  0.57476,  0.95226,  0.488078,
+    1.24868,  0.515136, 0.589378, 0.903632, 1.01678,  1.09964,  0.666345, 0.713265, 0.877168,
+    1.10053,  1.96887,  1.03574,  2.03728,  0.969553, 0.774788, 0.586338, 0.65168,  0.435472,
+    0.664396, 0.790584, 0.678637, 0.715964, 0.865494, 0.978428, 1.59242,  0.861109, 0.833259,
+    0.65168,  0.903632, 1.49599,  0.76347,  0.960453, 1.1848,   1.37398,  0.928957, 1.07848,
+    0.661798, 1.21104,  1.04579,  1.89047,  1.24288,  0.529553, 0.903611, 0.620897, 0.882467,
+    0.647189},
+   {0, 1, 2, 1, 0, 1, 2, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 2, 0, 0, 2, 2,
+    2, 1, 0, 0, 0, 0, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 1, 2, 2, 1, 0, 0, 0, 1,
+    1, 1, 2, 0, 0, 0, 2, 2, 1, 2, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 2, 1,
+    0, 1, 0, 1, 1, 2, 1, 2, 0, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 0, 2},
+   {50, 54, 57, 63, 82, 87},
+   {57, 63, 50, 54, 87, 82},
+   {6.0764, 11.1843, 6.0764, 11.1843, 6.89004, 6.89004}},
+  {1000,
+   2,
+   {-6.59634,  -7.13901,  -6.13753,  -6.58082, 5.19821,    2.04918,     -2.96856,  8.16444,
+    -2.76879,  7.51114,   -6.82261,  -6.61152, 5.02008,    2.58376,     5.55621,   2.31966,
+    4.86379,   3.33731,   5.84639,   1.15623,  -2.17159,   8.60241,     -4.97844,  -6.94077,
+    -2.31014,  8.41407,   5.5582,    0.402669, 5.25265,    0.919754,    5.85298,   2.11489,
+    -3.29245,  8.69222,   -1.9621,   8.81209,  -1.53408,   8.86723,     -2.18227,  8.79519,
+    4.60519,   2.20738,   -6.4759,   -6.9043,  -7.18766,   -6.10045,    -9.00148,  -7.48793,
+    4.01674,   1.41769,   -2.45347,  10.1085,  -3.20892,   9.22827,     -3.18612,  9.62596,
+    4.81977,   3.36517,   4.90693,   2.8628,   -6.44269,   -5.68946,    -8.30144,  -5.37878,
+    4.61485,   2.79094,   -1.98726,  9.31127,  -3.66019,   9.38998,     -6.58607,  -8.23669,
+    -7.46015,  -6.29153,  4.08468,   3.85433,  -6.36842,   -5.50645,    -6.83602,  -5.18506,
+    -0.627173, 10.3597,   3.98846,   1.48928,  -2.9968,    8.58173,     -7.2144,   -7.28376,
+    -0.660242, 10.1409,   -4.23528,  -8.38308, -3.15984,   8.52716,     -2.40987,  9.76567,
+    -8.7548,   -6.76508,  4.56971,   0.312209, -7.5487,    -5.8402,     -1.6096,   9.32159,
+    5.04813,   0.270586,  -7.6525,   -6.47306, -1.79758,   7.88964,     -9.0153,   -3.74236,
+    -3.5715,   9.48788,   -1.65154,  8.85435,  -3.47412,   9.70034,     6.31245,   2.39219,
+    4.03851,   2.29295,   -3.17098,  9.86672,  -6.90693,   -7.81338,    -6.22373,  -6.68537,
+    -3.22204,  9.12072,   -0.365254, 9.6482,   -7.76712,   -7.31757,    4.15669,   3.54716,
+    4.1937,    0.083629,  -3.03896,  9.52755,  -6.29293,   -7.35501,    -2.95926,  9.63714,
+    4.02709,   1.58547,   4.56828,   1.93595,  5.6242,     1.75918,     -7.36237,  -7.83344,
+    5.32177,   3.81988,   -2.43183,  8.153,    -1.97939,   10.4559,     -3.49492,  9.51833,
+    3.39602,   1.28026,   -2.42215,  8.71528,  -3.57682,   8.87191,     -2.77385,  11.7345,
+    5.71351,   0.946654,  -6.50253,  -6.90937, 4.08239,    0.603367,    -5.64134,  -6.85884,
+    -2.76177,  7.7665,    -2.25165,  8.93984,  -3.49071,   9.47639,     -1.06792,  7.57842,
+    5.15754,   1.24743,   3.63574,   1.20537,  -6.07969,   -8.49642,    4.12227,   2.19696,
+    -7.17144,  -8.4433,   -1.92234,  11.2047,  3.23237,    1.19535,     3.85389,   0.641937,
+    4.82665,   1.21779,   -7.68923,  -6.45605, -7.00816,   -8.76196,    -5.12894,  9.83619,
+    -5.66247,  -5.35879,  3.05598,   2.73358,  6.06038,    1.40242,     -1.69568,  7.78342,
+    5.13391,   2.23384,   -2.96984,  10.0714,  -5.36618,   -6.2493,     5.55896,   1.6829,
+    3.55882,   2.58911,   5.36155,   0.844118, -0.0634456, 9.14351,     4.88368,   1.40909,
+    -7.04675,  -6.59753,  -7.78333,  -6.55575, 5.39881,    2.25436,     -2.85189,  8.64285,
+    -2.22821,  8.39159,   3.88591,   1.69249,  -7.55481,   -7.02463,    4.60032,   2.65467,
+    -6.90615,  -7.76198,  -6.76005,  -7.85318, 4.15044,    3.01733,     -7.18884,  -7.63227,
+    4.68874,   2.01376,   3.51716,   2.35558,  -3.81367,   9.68396,     4.42644,   3.4639,
+    4.81758,   0.637825,  -6.20705,  -4.98023, -1.68603,   9.0876,      -4.99504,  -5.33687,
+    -1.77073,  9.18565,   4.86433,   3.02027,  4.20538,    1.664,       4.59042,   2.64799,
+    -3.09856,  9.86389,   -3.02306,  7.95507,  -6.32402,   -6.79053,    -7.67205,  -7.18807,
+    -8.10918,  -6.38341,  -1.67979,  6.80315,  4.00249,    3.16219,     -2.54391,  7.84561,
+    -3.22764,  8.80084,   -2.63712,  8.05875,  -2.41744,   7.02672,     -6.71117,  -5.56251,
+    5.18348,   1.60256,   -7.40824,  -6.29375, -4.22233,   10.3682,     4.8509,    1.87646,
+    -2.99456,  9.09616,   5.1332,    2.15801,  -2.27358,   9.78515,     -6.73874,  -8.64855,
+    4.96124,   2.39509,   -3.70949,  8.67978,  -4.13674,   9.06237,     2.80367,   2.48116,
+    -0.876786, 7.58414,   -3.7005,   9.67084,  6.48652,    0.903085,    6.28189,   2.98299,
+    -6.07922,  -6.12582,  -5.67921,  -7.537,   4.55014,    3.41329,     -1.63688,  9.19763,
+    -4.02439,  10.3812,   5.23053,   3.08187,  -2.2951,    7.76855,     -6.24491,  -5.77041,
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+    0.215128,  0.604169, 0.330165, 0.494229,  0.40775,  0.167377,  0.192648, 0.234635,  0.275578,
+    0.253094,  0.420063, 0.228299, 0.206478,  0.20395,  0.377656,  0.317393, 0.478623,  0.159009,
+    0.217034,  0.300933, 0.139754, 0.153901,  0.261077, 0.22834,   0.449609, 0.157672,  0.176474,
+    0.285704,  0.180186, 0.212738, 0.266428,  0.388313, 0.0954637, 0.298093, 0.251643,  0.330696,
+    0.159572,  0.210666, 0.149411, 0.139618,  0.338472, 0.450304,  0.208793, 0.583609,  0.185865,
+    0.400576,  0.21626,  0.174867, 0.239144,  0.249113, 0.200402,  0.275065, 0.238793,  0.205784,
+    0.4475,    0.231262, 0.259082, 0.20934,   0.16806,  0.193616,  0.213811, 0.395632,  0.482465,
+    0.274649,  0.307405, 0.165866, 0.334275,  0.683337, 0.368825,  0.14625,  0.780742,  0.163457,
+    0.226596,  0.138713, 1.79155,  0.400443,  0.233658, 0.426399,  0.623024, 0.670955,  0.123588,
+    0.110899,  0.173751, 0.651068, 0.199983,  0.190887, 0.541435,  0.21324,  0.266571,  0.134638,
+    0.179348,  0.145636, 0.170929, 0.623252,  0.587738, 0.109688,  0.515314, 0.217666,  0.213311,
+    0.249144,  0.187947, 0.270999, 0.268311,  0.469782, 0.763609,  0.32124,  0.146315,  0.265223,
+    0.298694,  0.197623, 0.21349,  0.845778,  0.175466, 0.123588,  0.17223,  0.258603,  1.17119,
+    0.538142,  0.407675, 0.120288, 0.587238,  0.244664, 0.333956,  0.132812, 0.21399,   0.302375,
+    0.275882,  0.134284, 0.377555, 0.228541,  0.187307, 0.143804,  0.180545, 0.222451,  0.239638,
+    0.188028,  0.46334,  0.175868, 0.242392,  0.314762, 0.44473,   0.21962,  0.175966,  1.12364,
+    0.138837,  0.400576, 0.18184,  0.137706,  0.409763, 0.216894,  0.466662, 0.376604,  0.487155,
+    0.283143,  0.118547, 0.221591, 0.122783,  0.179007, 0.16628,   0.180999, 0.239845,  0.169607,
+    0.578402,  0.396537, 0.222288, 0.563237,  0.371238, 0.138658,  0.324336, 0.191526,  0.168603,
+    0.357715,  0.640905, 0.460706, 0.220902,  0.240797, 0.164062,  0.157853, 0.34457,   0.196092,
+    0.289353,  0.104597, 0.259641, 0.126878,  0.175781, 0.441458,  0.820108, 0.261864,  0.23431,
+    0.254506,  0.271955, 0.227529, 0.22834,   0.196753, 0.224906,  0.193783, 0.419481,  0.236933,
+    0.229706,  0.29785,  0.222947, 0.177606,  0.216911, 0.305188,  0.933438, 0.116666,  0.278483,
+    0.0973824, 0.271224, 0.127717, 1.28139,   0.276283, 0.180704,  0.234554, 0.285984,  0.290172,
+    0.49594,   0.135879, 0.436784, 0.206219,  0.342215, 0.374165,  0.182217, 0.274864,  0.625,
+    0.356925,  0.194324, 0.342215, 0.113012,  0.155123, 0.254207,  0.438919, 0.262548,  0.302299,
+    0.179528,  0.312744, 0.168513, 0.142618,  0.150543, 0.231361,  0.166004, 0.186725,  0.38848,
+    0.179857,  0.182301, 0.629476, 0.44113,   0.289669, 0.328543,  0.279938, 0.14625,   0.187174,
+    0.157635,  0.396749, 0.798931, 0.201541,  0.778619, 0.265883,  0.258027, 0.218576,  0.266571,
+    0.160168,  0.230303, 0.273633, 0.233298,  0.30175,  0.217069,  0.345145, 0.397901,  0.224499,
+    0.248101,  0.241335, 0.222947, 0.237094,  0.176518, 0.380032,  0.634775, 0.426193,  0.16362,
+    0.231097,  0.219898, 0.343789, 0.275578,  0.282022, 0.628542,  0.232184, 0.848367,  0.200754,
+    0.179177},
+   {0, 0, 2, 3, 3, 0, 2, 2, 2, 2, 3, 0, 3, 2, 2, 2, 3, 3, 3, 3, 2, 0, 0, 0, 2, 3, 3, 3, 2, 2, 0, 0,
+    2, 3, 3, 0, 0, 2, 0, 0, 3, 2, 3, 0, 3, 0, 3, 3, 0, 2, 0, 3, 2, 0, 3, 0, 3, 3, 3, 2, 2, 3, 0, 0,
+    3, 3, 0, 2, 2, 3, 0, 3, 2, 2, 2, 0, 2, 3, 3, 3, 2, 3, 3, 3, 2, 0, 2, 0, 3, 3, 3, 3, 2, 2, 0, 2,
+    0, 3, 2, 2, 2, 0, 0, 3, 0, 2, 2, 3, 2, 3, 0, 2, 2, 2, 3, 2, 0, 0, 2, 3, 3, 2, 0, 2, 0, 0, 2, 0,
+    2, 2, 3, 2, 2, 0, 3, 0, 3, 2, 2, 2, 3, 3, 0, 0, 0, 3, 2, 3, 3, 3, 3, 0, 2, 0, 3, 2, 3, 2, 3, 0,
+    2, 3, 3, 2, 3, 3, 2, 2, 0, 0, 2, 3, 3, 2, 3, 0, 2, 0, 2, 0, 3, 2, 3, 2, 3, 0, 3, 0, 3, 0, 2, 3,
+    2, 2, 3, 0, 2, 2, 2, 0, 3, 2, 3, 3, 2, 3, 2, 3, 3, 2, 2, 0, 0, 2, 2, 3, 0, 3, 0, 2, 0, 0, 2, 3,
+    0, 3, 3, 2, 0, 3, 3, 0, 3, 0, 2, 2, 0, 2, 0, 2, 0, 0, 0, 2, 0, 3, 2, 3, 2, 3, 2, 2, 0, 2, 3, 2,
+    3, 2, 2, 2, 2, 3, 0, 2, 0, 0, 2, 3, 3, 0, 2, 3, 2, 2, 3, 0, 3, 0, 0, 2, 0, 2, 0, 2, 2, 3, 3, 2,
+    3, 0, 0, 3, 2, 2, 0, 3, 2, 0, 0, 3, 0, 0, 2, 0, 3, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 3, 0, 0,
+    2, 0, 0, 2, 0, 2, 3, 2, 3, 3, 2, 2, 0, 0, 0, 3, 0, 2, 0, 2, 0, 2, 2, 2, 3, 3, 0, 0, 3, 3, 3, 3,
+    3, 2, 3, 3, 2, 3, 3, 0, 2, 2, 2, 2, 0, 2, 0, 0, 0, 2, 2, 3, 3, 2, 3, 2, 3, 0, 2, 3, 0, 2, 0, 2,
+    2, 0, 3, 0, 2, 0, 2, 3, 0, 3, 0, 0, 0, 3, 2, 3, 3, 0, 3, 2, 3, 0, 2, 3, 3, 0, 2, 3, 0, 0, 0, 2,
+    0, 3, 0, 2, 3, 3, 3, 3, 3, 0, 2, 0, 2, 2, 3, 3, 0, 3, 0, 2, 0, 2, 0, 3, 0, 0, 0, 2, 3, 3, 2, 3,
+    0, 0, 0, 0, 3, 3, 0, 3, 2, 0, 2, 3, 2, 2, 3, 3, 2, 2, 2, 0, 2, 3, 0, 3, 3, 0, 0, 2, 0, 3, 2, 3,
+    0, 2, 0, 2, 2, 3, 2, 0, 3, 3, 3, 2, 3, 0, 3, 0, 2, 2, 0, 0, 0, 3, 0, 3, 3, 2, 3, 2, 3, 2, 3, 0,
+    2, 3, 0, 2, 0, 3, 3, 3, 3, 3, 3, 2, 0, 3, 2, 2, 2, 3, 3, 2, 3, 0, 2, 3, 3, 2, 2, 0, 0, 0, 0, 3,
+    0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 2, 3, 3, 3, 3, 3, 3, 0, 0, 2, 2, 3, 3, 2, 2, 0, 0, 3, 0,
+    0, 0, 2, 3, 0, 0, 0, 3, 0, 3, 0, 2, 2, 0, 0, 0, 0, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 0, 0, 2, 3,
+    0, 3, 3, 0, 3, 0, 0, 2, 0, 3, 3, 0, 2, 2, 3, 3, 0, 0, 2, 0, 2, 3, 2, 0, 0, 3, 3, 0, 3, 2, 0, 2,
+    0, 2, 3, 2, 0, 3, 3, 2, 0, 0, 2, 2, 0, 0, 2, 0, 3, 3, 2, 3, 2, 0, 3, 0, 2, 2, 3, 3, 0, 3, 2, 2,
+    0, 3, 0, 0, 0, 2, 0, 3, 2, 0, 2, 3, 2, 3, 2, 2, 3, 3, 0, 2, 3, 2, 3, 2, 2, 0, 3, 0, 3, 0, 2, 2,
+    2, 0, 2, 0, 2, 2, 0, 0, 3, 3, 0, 0, 3, 2, 0, 2, 3, 2, 2, 0, 3, 3, 0, 2, 0, 3, 3, 0, 2, 3, 2, 3,
+    2, 0, 2, 2, 0, 0, 0, 2, 2, 3, 3, 2, 2, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 2, 0, 3, 3,
+    3, 0, 2, 0, 2, 3, 2, 0, 3, 3, 2, 0, 2, 0, 3, 2, 0, 3, 0, 0, 2, 2, 0, 3, 0, 2, 3, 3, 3, 0, 2, 0,
+    0, 3, 0, 2, 3, 2, 2, 0, 3, 3, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 2, 0, 0, 2, 3, 3, 2, 2, 0, 3, 2, 0,
+    3, 0, 2, 3, 3, 0, 2, 2, 3, 2, 2, 2, 3, 2, 0, 0, 3, 2, 0, 0, 0, 2, 0, 2, 0, 0, 2, 2, 3, 0, 3, 0,
+    0, 3, 0, 0, 0, 3, 0, 0, 2, 2, 0, 2, 2, 3, 3, 3, 3, 0, 0, 2, 2, 2, 0, 3, 2, 2, 2, 2, 2, 0, 3, 0,
+    0, 3, 2, 0, 0, 3, 2, 3, 3, 0, 3, 0, 3, 0, 3, 2, 2, 2, 0, 0, 3, 2, 2, 0, 0, 0, 2, 3, 2, 0, 2, 3,
+    3, 3, 0, 3, 3, 0, 2, 0, 0, 2, 3, 3, 0, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 3, 2, 3, 0, 3, 3, 0, 3,
+    2, 2, 0, 2, 0, 3, 0, 3, 0, 2, 3, 0, 2, 3, 2, 0, 2, 0, 3, 0, 2, 3, 3, 2, 0, 3, 3, 3, 2, 2, 3, 3,
+    2, 2, 2, 0, 3, 2, 2, 0},
+   {271, 271, 329, 343, 387, 426, 426, 601},
+   {426, 601, 426, 387, 343, 271, 329, 271},
+   {3.70991, 4.43491, 3.76334, 9.43944, 9.43944, 3.70991, 3.76334, 4.43491}}};
+
+typedef ConnectComponentsEdgesTest<int, float> ConnectComponentsEdgesTestF_Int;
+TEST_P(ConnectComponentsEdgesTestF_Int, Result) { EXPECT_TRUE(true); }
+
+INSTANTIATE_TEST_CASE_P(ConnectComponentsEdgesTest,
+                        ConnectComponentsEdgesTestF_Int,
+                        ::testing::ValuesIn(mr_fix_conn_inputsf2));
+
+};  // namespace sparse
+};  // end namespace cuvs
diff --git a/docs/source/integrations.rst b/docs/source/integrations.rst
index 46cbaa52c9..19d72fd90e 100644
--- a/docs/source/integrations.rst
+++ b/docs/source/integrations.rst
@@ -5,7 +5,7 @@ Aside from using cuVS directly, it can be consumed through a number of sdk and v
 
 - `FAISS`_
 - `Milvus`_
-- `Lucene` _
+- `Lucene`_
 - `Kinetica`_
 
 

From 4b260aa6297abb13f19789c4f100229030c3f06b Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 23 May 2024 14:22:51 -0700
Subject: [PATCH 062/623] Add pairwise_distance api's for C, Python and Rust
 (#142)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/142
---
 cpp/CMakeLists.txt                            |   5 +-
 cpp/include/cuvs/distance/pairwise_distance.h |  62 ++++++++
 cpp/include/cuvs/neighbors/cagra.h            |   3 +-
 cpp/src/distance/pairwise_distance_c.cpp      |  81 ++++++++++
 cpp/test/CMakeLists.txt                       |   4 +
 cpp/test/distance/pairwise_distance_c.cu      |  64 ++++++++
 cpp/test/distance/run_pairwise_distance_c.c   |  72 +++++++++
 docs/source/cpp_api/distance.rst              |   6 +-
 python/cuvs/CMakeLists.txt                    |   1 +
 python/cuvs/cuvs/distance/CMakeLists.txt      |  24 +++
 python/cuvs/cuvs/distance/__init__.pxd        |   0
 python/cuvs/cuvs/distance/__init__.py         |  17 +++
 python/cuvs/cuvs/distance/distance.pxd        |  29 ++++
 python/cuvs/cuvs/distance/distance.pyx        | 139 ++++++++++++++++++
 .../neighbors/brute_force/brute_force.pyx     |   4 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |   3 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  |   3 +-
 python/cuvs/cuvs/test/test_distance.py        |  79 ++++++++++
 python/cuvs/cuvs/test/test_doctests.py        |   2 +
 rust/cuvs-sys/cuvs_c_wrapper.h                |   1 +
 rust/cuvs/src/distance/mod.rs                 |  87 +++++++++++
 rust/cuvs/src/lib.rs                          |   1 +
 22 files changed, 677 insertions(+), 10 deletions(-)
 create mode 100644 cpp/include/cuvs/distance/pairwise_distance.h
 create mode 100644 cpp/src/distance/pairwise_distance_c.cpp
 create mode 100644 cpp/test/distance/pairwise_distance_c.cu
 create mode 100644 cpp/test/distance/run_pairwise_distance_c.c
 create mode 100644 python/cuvs/cuvs/distance/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/distance/__init__.pxd
 create mode 100644 python/cuvs/cuvs/distance/__init__.py
 create mode 100644 python/cuvs/cuvs/distance/distance.pxd
 create mode 100644 python/cuvs/cuvs/distance/distance.pyx
 create mode 100644 python/cuvs/cuvs/test/test_distance.py
 create mode 100644 rust/cuvs/src/distance/mod.rs

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 8ede967064..229eb5b934 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -545,8 +545,9 @@ target_link_options(cuvs PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
 # * cuvs_c -------------------------------------------------------------------------------
 if(BUILD_C_LIBRARY)
   add_library(
-    cuvs_c SHARED src/core/c_api.cpp src/neighbors/brute_force_c.cpp src/neighbors/ivf_flat_c.cpp
-                  src/neighbors/ivf_pq_c.cpp src/neighbors/cagra_c.cpp
+    cuvs_c SHARED
+    src/core/c_api.cpp src/neighbors/brute_force_c.cpp src/neighbors/ivf_flat_c.cpp
+    src/neighbors/ivf_pq_c.cpp src/neighbors/cagra_c.cpp src/distance/pairwise_distance_c.cpp
   )
 
   add_library(cuvs::c_api ALIAS cuvs_c)
diff --git a/cpp/include/cuvs/distance/pairwise_distance.h b/cpp/include/cuvs/distance/pairwise_distance.h
new file mode 100644
index 0000000000..a9e49b359b
--- /dev/null
+++ b/cpp/include/cuvs/distance/pairwise_distance.h
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+#include <dlpack/dlpack.h>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Compute pairwise distances for two matrices
+ *
+ *
+ * Usage example:
+ * @code{.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/distance/pairwise_distance.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor x;
+ * DLManagedTensor y;
+ * DLManagedTensor dist;
+ *
+ * cuvsPairwiseDistance(res, &x, &y, &dist, L2SqrtUnexpanded, 2.0);
+ * @endcode
+ *
+ * @param[in] res cuvs resources object for managing expensive resources
+ * @param[in] x first set of points (size n*k)
+ * @param[in] y second set of points (size m*k)
+ * @param[out] dist output distance matrix (size n*m)
+ * @param[in] metric distance to evaluate
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+cuvsError_t cuvsPairwiseDistance(cuvsResources_t res,
+                                 DLManagedTensor* x,
+                                 DLManagedTensor* y,
+                                 DLManagedTensor* dist,
+                                 cuvsDistanceType metric,
+                                 float metric_arg);
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 3a1a8c9fec..727c39c6ef 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -356,7 +356,8 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  * cuvsError_t params_create_status = cuvsCagraSearchParamsCreate(&params);
  *
  * // Search the `index` built using `cuvsCagraBuild`
- * cuvsError_t search_status = cuvsCagraSearch(res, params, index, queries, neighbors, distances);
+ * cuvsError_t search_status = cuvsCagraSearch(res, params, index, &queries, &neighbors,
+ * &distances);
  *
  * // de-allocate `params` and `res`
  * cuvsError_t params_destroy_status = cuvsCagraSearchParamsDestroy(params);
diff --git a/cpp/src/distance/pairwise_distance_c.cpp b/cpp/src/distance/pairwise_distance_c.cpp
new file mode 100644
index 0000000000..d457198a29
--- /dev/null
+++ b/cpp/src/distance/pairwise_distance_c.cpp
@@ -0,0 +1,81 @@
+
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/distance/distance.hpp>
+
+namespace {
+
+template <typename T>
+void _pairwise_distance(cuvsResources_t res,
+                        DLManagedTensor* x_tensor,
+                        DLManagedTensor* y_tensor,
+                        DLManagedTensor* distances_tensor,
+                        cuvsDistanceType metric,
+                        float metric_arg)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  using mdspan_type           = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::device_matrix_view<T, int64_t, raft::row_major>;
+
+  auto x_mds         = cuvs::core::from_dlpack<mdspan_type>(x_tensor);
+  auto y_mds         = cuvs::core::from_dlpack<mdspan_type>(y_tensor);
+  auto distances_mds = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  cuvs::distance::pairwise_distance(*res_ptr, x_mds, y_mds, distances_mds, metric, metric_arg);
+}
+}  // namespace
+
+extern "C" cuvsError_t cuvsPairwiseDistance(cuvsResources_t res,
+                                            DLManagedTensor* x_tensor,
+                                            DLManagedTensor* y_tensor,
+                                            DLManagedTensor* distances_tensor,
+                                            cuvsDistanceType metric,
+                                            float metric_arg)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto x_dt    = x_tensor->dl_tensor.dtype;
+    auto y_dt    = x_tensor->dl_tensor.dtype;
+    auto dist_dt = x_tensor->dl_tensor.dtype;
+
+    if ((x_dt.code != kDLFloat) || (y_dt.code != kDLFloat) || (dist_dt.code != kDLFloat)) {
+      RAFT_FAIL("Inputs to cuvsPairwiseDistance must all be floating point tensors");
+    }
+
+    if ((x_dt.bits != y_dt.bits) || (x_dt.bits != dist_dt.bits)) {
+      RAFT_FAIL("Inputs to cuvsPairwiseDistance must all have the same dtype");
+    }
+
+    if (x_dt.bits == 32) {
+      _pairwise_distance<float>(res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+    } else if (x_dt.bits == 64) {
+      _pairwise_distance<double>(res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+    } else {
+      RAFT_FAIL("Unsupported DLtensor dtype: %d and bits: %d", x_dt.code, x_dt.bits);
+    }
+  });
+}
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index fac4c62444..8d532336e5 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -178,6 +178,10 @@ endif()
 
 if(BUILD_C_TESTS)
   ConfigureTest(NAME INTEROP_TEST PATH test/core/interop.cu C_LIB)
+  ConfigureTest(
+    NAME DISTANCE_C_TEST PATH test/distance/run_pairwise_distance_c.c
+    test/distance/pairwise_distance_c.cu C_LIB
+  )
 
   ConfigureTest(
     NAME BRUTEFORCE_C_TEST PATH test/neighbors/run_brute_force_c.c test/neighbors/brute_force_c.cu
diff --git a/cpp/test/distance/pairwise_distance_c.cu b/cpp/test/distance/pairwise_distance_c.cu
new file mode 100644
index 0000000000..c79dca4af2
--- /dev/null
+++ b/cpp/test/distance/pairwise_distance_c.cu
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+
+#include <gtest/gtest.h>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/random/rng.cuh>
+
+#include <cuvs/distance/distance.h>
+
+extern "C" void run_pairwise_distance(int64_t n_rows,
+                                      int64_t n_queries,
+                                      int64_t n_dim,
+                                      float* index_data,
+                                      float* query_data,
+                                      float* distances_data,
+                                      cuvsDistanceType metric);
+
+template <typename T>
+void generate_random_data(T* devPtr, size_t size)
+{
+  raft::handle_t handle;
+  raft::random::RngState r(1234ULL);
+  raft::random::uniform(handle, r, devPtr, size, T(0.1), T(2.0));
+};
+
+TEST(PairwiseDistanceC, Distance)
+{
+  int64_t n_rows    = 8096;
+  int64_t n_queries = 128;
+  int64_t n_dim     = 32;
+
+  cuvsDistanceType metric = L2Expanded;
+
+  float *index_data, *query_data, *distances_data;
+  cudaMalloc(&index_data, sizeof(float) * n_rows * n_dim);
+  cudaMalloc(&query_data, sizeof(float) * n_queries * n_dim);
+  cudaMalloc(&distances_data, sizeof(float) * n_queries * n_rows);
+
+  generate_random_data(index_data, n_rows * n_dim);
+  generate_random_data(query_data, n_queries * n_dim);
+
+  run_pairwise_distance(n_rows, n_queries, n_dim, index_data, query_data, distances_data, metric);
+
+  // delete device memory
+  cudaFree(index_data);
+  cudaFree(query_data);
+  cudaFree(distances_data);
+}
diff --git a/cpp/test/distance/run_pairwise_distance_c.c b/cpp/test/distance/run_pairwise_distance_c.c
new file mode 100644
index 0000000000..bbcf0d7b71
--- /dev/null
+++ b/cpp/test/distance/run_pairwise_distance_c.c
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cuvs/distance/pairwise_distance.h>
+#include <stdint.h>
+
+void run_pairwise_distance(int64_t n_rows,
+                           int64_t n_queries,
+                           int64_t n_dim,
+                           float* index_data,
+                           float* query_data,
+                           float* distances_data,
+                           int64_t* neighbors_data,
+                           cuvsDistanceType metric)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // create dataset DLTensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {n_rows, n_dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // create queries DLTensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = (void*)query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {n_queries, n_dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // create distances DLTensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = (void*)distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {n_rows, n_queries};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // run pairwise distances
+  cuvsPairwiseDistance(res, &dataset_tensor, &queries_tensor, &distances_tensor, metric, 2.0);
+
+  cuvsResourcesDestroy(res);
+}
diff --git a/docs/source/cpp_api/distance.rst b/docs/source/cpp_api/distance.rst
index c1b8c619d1..2e83da7b35 100644
--- a/docs/source/cpp_api/distance.rst
+++ b/docs/source/cpp_api/distance.rst
@@ -11,7 +11,7 @@ distances have been highly optimized and support a wide assortment of different
 Distance Types
 --------------
 
-``#include <cuvs/distance/distance_types.hpp>``
+``#include <cuvs/distance/distance.hpp>``
 
 namespace *cuvs::distance*
 
@@ -22,11 +22,11 @@ namespace *cuvs::distance*
 Pairwise Distances
 ------------------
 
-``include <cuvs/distance/pairwise_distance.hpp>``
+``include <cuvs/distance/distance.hpp>``
 
 namespace *cuvs::distance*
 
 .. doxygengroup:: pairwise_distance
     :project: cuvs
     :members:
-    :content-only:
\ No newline at end of file
+    :content-only:
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index db6f256751..d48cc7405a 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -81,6 +81,7 @@ endif()
 rapids_cython_init()
 
 add_subdirectory(cuvs/common)
+add_subdirectory(cuvs/distance)
 add_subdirectory(cuvs/neighbors)
 
 if(DEFINED cython_lib_dir)
diff --git a/python/cuvs/cuvs/distance/CMakeLists.txt b/python/cuvs/cuvs/distance/CMakeLists.txt
new file mode 100644
index 0000000000..363778a9ca
--- /dev/null
+++ b/python/cuvs/cuvs/distance/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources distance.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX distance_
+)
diff --git a/python/cuvs/cuvs/distance/__init__.pxd b/python/cuvs/cuvs/distance/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/distance/__init__.py b/python/cuvs/cuvs/distance/__init__.py
new file mode 100644
index 0000000000..5c985e7b19
--- /dev/null
+++ b/python/cuvs/cuvs/distance/__init__.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .distance import DISTANCE_TYPES, pairwise_distance
+
+__all__ = ["DISTANCE_TYPES", "pairwise_distance"]
diff --git a/python/cuvs/cuvs/distance/distance.pxd b/python/cuvs/cuvs/distance/distance.pxd
new file mode 100644
index 0000000000..e40d2c4250
--- /dev/null
+++ b/python/cuvs/cuvs/distance/distance.pxd
@@ -0,0 +1,29 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "cuvs/distance/pairwise_distance.h" nogil:
+    cuvsError_t cuvsPairwiseDistance(cuvsResources_t res,
+                                     DLManagedTensor* x,
+                                     DLManagedTensor* y,
+                                     DLManagedTensor* distances,
+                                     cuvsDistanceType metric,
+                                     float metric_arg) except +
diff --git a/python/cuvs/cuvs/distance/distance.pyx b/python/cuvs/cuvs/distance/distance.pyx
new file mode 100644
index 0000000000..eb34366e43
--- /dev/null
+++ b/python/cuvs/cuvs/distance/distance.pyx
@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.resources import auto_sync_resources
+
+from cuvs.common cimport cydlpack
+
+from pylibraft.common import auto_convert_output, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+
+DISTANCE_TYPES = {
+    "l2": cuvsDistanceType.L2SqrtExpanded,
+    "sqeuclidean": cuvsDistanceType.L2Expanded,
+    "euclidean": cuvsDistanceType.L2SqrtExpanded,
+    "l1": cuvsDistanceType.L1,
+    "cityblock": cuvsDistanceType.L1,
+    "inner_product": cuvsDistanceType.InnerProduct,
+    "chebyshev": cuvsDistanceType.Linf,
+    "canberra": cuvsDistanceType.Canberra,
+    "cosine": cuvsDistanceType.CosineExpanded,
+    "lp": cuvsDistanceType.LpUnexpanded,
+    "correlation": cuvsDistanceType.CorrelationExpanded,
+    "jaccard": cuvsDistanceType.JaccardExpanded,
+    "hellinger": cuvsDistanceType.HellingerExpanded,
+    "braycurtis": cuvsDistanceType.BrayCurtis,
+    "jensenshannon": cuvsDistanceType.JensenShannon,
+    "hamming": cuvsDistanceType.HammingUnexpanded,
+    "kl_divergence": cuvsDistanceType.KLDivergence,
+    "minkowski": cuvsDistanceType.LpUnexpanded,
+    "russellrao": cuvsDistanceType.RusselRaoExpanded,
+    "dice": cuvsDistanceType.DiceExpanded,
+}
+
+SUPPORTED_DISTANCES = ["euclidean", "l1", "cityblock", "l2", "inner_product",
+                       "chebyshev", "minkowski", "canberra", "kl_divergence",
+                       "correlation", "russellrao", "hellinger", "lp",
+                       "hamming", "jensenshannon", "cosine", "sqeuclidean"]
+
+
+@auto_sync_resources
+@auto_convert_output
+def pairwise_distance(X, Y, out=None, metric="euclidean", metric_arg=2.0,
+                      resources=None):
+    """
+    Compute pairwise distances between X and Y
+
+    Valid values for metric:
+        ["euclidean", "l2", "l1", "cityblock", "inner_product",
+         "chebyshev", "canberra", "lp", "hellinger", "jensenshannon",
+         "kl_divergence", "russellrao", "minkowski", "correlation",
+         "cosine"]
+
+    Parameters
+    ----------
+
+    X : CUDA array interface compliant matrix shape (m, k)
+    Y : CUDA array interface compliant matrix shape (n, k)
+    out : Optional writable CUDA array interface matrix shape (m, n)
+    metric : string denoting the metric type (default="euclidean")
+    metric_arg : metric parameter (currently used only for "minkowski")
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.distance import pairwise_distance
+    >>> n_samples = 5000
+    >>> n_features = 50
+    >>> in1 = cp.random.random_sample((n_samples, n_features),
+    ...                               dtype=cp.float32)
+    >>> in2 = cp.random.random_sample((n_samples, n_features),
+    ...                               dtype=cp.float32)
+    >>> output = pairwise_distance(in1, in2, metric="euclidean")
+    """
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    x_cai = wrap_array(X)
+    y_cai = wrap_array(Y)
+
+    m = x_cai.shape[0]
+    n = y_cai.shape[0]
+
+    if out is None:
+        out = device_ndarray.empty((m, n), dtype=y_cai.dtype)
+    out_cai = wrap_array(out)
+
+    x_k = x_cai.shape[1]
+    y_k = y_cai.shape[1]
+
+    if x_k != y_k:
+        raise ValueError("Inputs must have same number of columns. "
+                         "a=%s, b=%s" % (x_k, y_k))
+
+    if metric not in SUPPORTED_DISTANCES:
+        raise ValueError("metric %s is not supported" % metric)
+
+    cdef cuvsDistanceType distance_type = DISTANCE_TYPES[metric]
+
+    x_dt = x_cai.dtype
+    y_dt = y_cai.dtype
+    d_dt = out_cai.dtype
+
+    if x_dt != y_dt or x_dt != d_dt:
+        raise ValueError("Inputs must have the same dtypes")
+
+    cdef cydlpack.DLManagedTensor* x_dlpack = \
+        cydlpack.dlpack_c(x_cai)
+    cdef cydlpack.DLManagedTensor* y_dlpack = \
+        cydlpack.dlpack_c(y_cai)
+    cdef cydlpack.DLManagedTensor* out_dlpack = \
+        cydlpack.dlpack_c(out_cai)
+
+    check_cuvs(cuvsPairwiseDistance(res,
+                                    x_dlpack,
+                                    y_dlpack,
+                                    out_dlpack,
+                                    distance_type,
+                                    metric_arg))
+
+    return out
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 226ee23d66..490f1d3ac2 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -31,9 +31,10 @@ from cuvs.distance_type cimport cuvsDistanceType
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.distance.pairwise_distance import DISTANCE_TYPES
 from pylibraft.neighbors.common import _check_input_array
 
+from cuvs.distance import DISTANCE_TYPES
+
 from cuvs.common.c_api cimport cuvsResources_t
 
 from cuvs.common.exceptions import check_cuvs
@@ -194,7 +195,6 @@ def search(Index index,
     _check_input_array(distances_cai, [np.dtype('float32')],
                        exp_rows=n_queries, exp_cols=k)
 
-    cdef cuvsError_t search_status
     cdef cydlpack.DLManagedTensor* queries_dlpack = \
         cydlpack.dlpack_c(queries_cai)
     cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 870e9412a8..daa723099f 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -30,9 +30,10 @@ from cuvs.distance_type cimport cuvsDistanceType
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.distance.pairwise_distance import DISTANCE_TYPES
 from pylibraft.neighbors.common import _check_input_array
 
+from cuvs.distance import DISTANCE_TYPES
+
 from libc.stdint cimport (
     int8_t,
     int64_t,
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index a1ca6768a9..81baed1f0e 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -30,9 +30,10 @@ from cuvs.distance_type cimport cuvsDistanceType
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.distance.pairwise_distance import DISTANCE_TYPES
 from pylibraft.neighbors.common import _check_input_array
 
+from cuvs.distance import DISTANCE_TYPES
+
 from libc.stdint cimport (
     int8_t,
     int64_t,
diff --git a/python/cuvs/cuvs/test/test_distance.py b/python/cuvs/cuvs/test/test_distance.py
new file mode 100644
index 0000000000..681217fc81
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_distance.py
@@ -0,0 +1,79 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+from scipy.spatial.distance import cdist
+
+from cuvs.distance import pairwise_distance
+
+
+@pytest.mark.parametrize("n_rows", [50, 100])
+@pytest.mark.parametrize("n_cols", [10, 50])
+@pytest.mark.parametrize(
+    "metric",
+    [
+        "euclidean",
+        "cityblock",
+        "chebyshev",
+        "canberra",
+        "correlation",
+        "hamming",
+        "jensenshannon",
+        "russellrao",
+        "cosine",
+        "sqeuclidean",
+        "inner_product",
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+def test_distance(n_rows, n_cols, inplace, metric, dtype):
+    input1 = np.random.random_sample((n_rows, n_cols))
+    input1 = np.asarray(input1).astype(dtype)
+
+    # RussellRao expects boolean arrays
+    if metric == "russellrao":
+        input1[input1 < 0.5] = 0
+        input1[input1 >= 0.5] = 1
+
+    # JensenShannon expects probability arrays
+    elif metric == "jensenshannon":
+        norm = np.sum(input1, axis=1)
+        input1 = (input1.T / norm).T
+
+    output = np.zeros((n_rows, n_rows), dtype=dtype)
+
+    if metric == "inner_product":
+        expected = np.matmul(input1, input1.T)
+    else:
+        expected = cdist(input1, input1, metric)
+
+    input1_device = device_ndarray(input1)
+    output_device = device_ndarray(output) if inplace else None
+
+    ret_output = pairwise_distance(
+        input1_device,
+        input1_device,
+        output_device,
+        metric,
+    )
+
+    output_device = ret_output if not inplace else output_device
+
+    actual = output_device.copy_to_host()
+
+    assert np.allclose(expected, actual, atol=1e-3, rtol=1e-3)
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
index 68b2861dff..64b0c5da6d 100644
--- a/python/cuvs/cuvs/test/test_doctests.py
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -20,6 +20,7 @@
 
 import pytest
 
+import cuvs.distance
 import cuvs.neighbors
 
 # Code adapted from https://github.com/rapidsai/cudf/blob/branch-23.02/python/cudf/cudf/tests/test_doctests.py  # noqa
@@ -93,6 +94,7 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.brute_force))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.ivf_flat))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.common))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.distance))
 
 
 @pytest.mark.parametrize(
diff --git a/rust/cuvs-sys/cuvs_c_wrapper.h b/rust/cuvs-sys/cuvs_c_wrapper.h
index c6ab02da03..0c66928bcf 100644
--- a/rust/cuvs-sys/cuvs_c_wrapper.h
+++ b/rust/cuvs-sys/cuvs_c_wrapper.h
@@ -17,6 +17,7 @@
 // wrapper file containing all the C-API's we should automatically be creating rust
 // bindings for
 #include <cuvs/core/c_api.h>
+#include <cuvs/distance/pairwise_distance.h>
 #include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/ivf_flat.h>
 #include <cuvs/neighbors/cagra.h>
diff --git a/rust/cuvs/src/distance/mod.rs b/rust/cuvs/src/distance/mod.rs
new file mode 100644
index 0000000000..0377f3e323
--- /dev/null
+++ b/rust/cuvs/src/distance/mod.rs
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+use std::io::{stderr, Write};
+
+use crate::distance_type::DistanceType;
+use crate::dlpack::ManagedTensor;
+use crate::error::{check_cuvs, Result};
+use crate::resources::Resources;
+
+/// Compute pairwise distances between X and Y
+///
+/// # Arguments
+///
+/// * `res` - Resources to use
+/// * `x` - A matrix in device memory - shape (m, k)
+/// * `y` - A matrix in device memory - shape (n, k)
+/// * `distances` - A matrix in device memory that receives the output distances - shape (m, n)
+/// * `metric` - DistanceType to use for building the index
+/// * `metric_arg` - Optional value of `p` for Minkowski distances
+pub fn pairwise_distance(
+    res: &Resources,
+    x: &ManagedTensor,
+    y: &ManagedTensor,
+    distances: &ManagedTensor,
+    metric: DistanceType,
+    metric_arg: Option<f32>,
+) -> Result<()> {
+    unsafe {
+        check_cuvs(ffi::cuvsPairwiseDistance(
+            res.0,
+            x.as_ptr(),
+            y.as_ptr(),
+            distances.as_ptr(),
+            metric,
+            metric_arg.unwrap_or(2.0),
+        ))
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::s;
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    #[test]
+    fn test_pairwise_distance() {
+        let res = Resources::new().unwrap();
+
+        // Create a new random dataset to index
+        let n_datapoints = 256;
+        let n_features = 16;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+        let dataset_device = ManagedTensor::from(&dataset).to_device(&res).unwrap();
+
+        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_datapoints, n_datapoints));
+        let distances = ManagedTensor::from(&distances_host)
+            .to_device(&res)
+            .unwrap();
+    
+        pairwise_distance(&res, &dataset_device, &dataset_device, &distances, DistanceType::L2Expanded,
+        None).unwrap();
+
+        // Copy back to host memory
+        distances.to_host(&res, &mut distances_host).unwrap();
+
+        // Self distance should be 0
+        assert_eq!(distances_host[[0, 0]], 0.0);
+    }
+}
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
index 6ed450c03e..71a387b2c6 100644
--- a/rust/cuvs/src/lib.rs
+++ b/rust/cuvs/src/lib.rs
@@ -20,6 +20,7 @@
 //! approximate nearest neighbors search on the GPU.
 pub mod brute_force;
 pub mod cagra;
+pub mod distance;
 pub mod distance_type;
 pub mod ivf_flat;
 mod dlpack;

From b1f17edaacd01875eb579592ee4c5a4cbb2afa87 Mon Sep 17 00:00:00 2001
From: Paul Taylor <178183+trxcllnt@users.noreply.github.com>
Date: Fri, 24 May 2024 14:18:57 -0700
Subject: [PATCH 063/623] fix devcontainer name for codespaces (#153)

This PR fixes launching the devcontainers in GitHub Codespaces:
![image](https://github.com/rapidsai/cuvs/assets/178183/71d4bab2-82b1-4671-b382-c5f21503dfce)

Authors:
  - Paul Taylor (https://github.com/trxcllnt)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/153
---
 .devcontainer/cuda11.8-conda/devcontainer.json | 2 +-
 .devcontainer/cuda11.8-pip/devcontainer.json   | 2 +-
 .devcontainer/cuda12.2-conda/devcontainer.json | 2 +-
 .devcontainer/cuda12.2-pip/devcontainer.json   | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index bfba756fbe..1fcd67a012 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index bb25e2ba31..534e50effb 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.06-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
diff --git a/.devcontainer/cuda12.2-conda/devcontainer.json b/.devcontainer/cuda12.2-conda/devcontainer.json
index bc0548622b..4ff6263c2e 100644
--- a/.devcontainer/cuda12.2-conda/devcontainer.json
+++ b/.devcontainer/cuda12.2-conda/devcontainer.json
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
diff --git a/.devcontainer/cuda12.2-pip/devcontainer.json b/.devcontainer/cuda12.2-pip/devcontainer.json
index 19ab3e145e..6979fd5d92 100644
--- a/.devcontainer/cuda12.2-pip/devcontainer.json
+++ b/.devcontainer/cuda12.2-pip/devcontainer.json
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.06-cuda12.2-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {

From b9e8a4baaae91e9c3f50c9d91a4bca019a2ce931 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Tue, 28 May 2024 20:17:56 +0200
Subject: [PATCH 064/623] Add `refine` to public API (#154)

Add `cuvs::neighbors::refine` to public API, with it's test

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/154
---
 cpp/CMakeLists.txt                            |  12 +-
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |   1 -
 cpp/include/cuvs/neighbors/refine.hpp         | 439 ++++++++++++++++++
 .../neighbors/detail/cagra/cagra_build.cuh    |  31 +-
 cpp/src/neighbors/detail/refine.cuh           |  19 -
 cpp/src/neighbors/detail/refine_host-ext.hpp  |  66 ---
 cpp/src/neighbors/detail/refine_host.hpp      |  19 -
 .../detail/refine_host_float_float.cpp        |  30 --
 .../detail/refine_host_half_float.cpp         |  31 --
 .../detail/refine_host_int8_t_float.cpp       |  29 --
 .../detail/refine_host_uint8_t_float.cpp      |  30 --
 cpp/src/neighbors/ivf_flat_index.cpp          |   1 +
 cpp/src/neighbors/refine-ext.cuh              |  85 ----
 cpp/src/neighbors/refine-inl.cuh              | 104 -----
 cpp/src/neighbors/refine.cuh                  |  24 -
 .../detail/refine_device_float_float.cu}      |  27 +-
 .../refine/detail/refine_device_half_float.cu |  49 ++
 .../detail/refine_device_int8_t_float.cu}     |  27 +-
 .../detail/refine_device_uint8_t_float.cu}    |  27 +-
 .../refine/detail/refine_host_float_float.cpp |  38 ++
 .../refine/detail/refine_host_half_float.cpp  |  37 ++
 .../detail/refine_host_int8_t_float.cpp       |  37 ++
 .../detail/refine_host_uint8_t_float.cpp      |  37 ++
 .../{detail => refine}/refine_common.hpp      |   0
 .../{detail => refine}/refine_device.cuh      |  25 +-
 .../refine_host.hpp}                          |  25 +-
 cpp/src/neighbors/refine_float_float.cu       |  54 ---
 cpp/test/CMakeLists.txt                       |   5 +-
 cpp/test/neighbors/refine.cu                  | 127 +++++
 cpp/test/neighbors/refine_helper.cuh          | 158 +++++++
 docs/source/cpp_api/neighbors.rst             |   1 +
 docs/source/cpp_api/neighbors_refine.rst      |  20 +
 32 files changed, 1048 insertions(+), 567 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/refine.hpp
 delete mode 100644 cpp/src/neighbors/detail/refine.cuh
 delete mode 100644 cpp/src/neighbors/detail/refine_host-ext.hpp
 delete mode 100644 cpp/src/neighbors/detail/refine_host.hpp
 delete mode 100644 cpp/src/neighbors/detail/refine_host_float_float.cpp
 delete mode 100644 cpp/src/neighbors/detail/refine_host_half_float.cpp
 delete mode 100644 cpp/src/neighbors/detail/refine_host_int8_t_float.cpp
 delete mode 100644 cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp
 delete mode 100644 cpp/src/neighbors/refine-ext.cuh
 delete mode 100644 cpp/src/neighbors/refine-inl.cuh
 delete mode 100644 cpp/src/neighbors/refine.cuh
 rename cpp/src/neighbors/{refine_half_float.cu => refine/detail/refine_device_float_float.cu} (56%)
 create mode 100644 cpp/src/neighbors/refine/detail/refine_device_half_float.cu
 rename cpp/src/neighbors/{refine_int8_t_float.cu => refine/detail/refine_device_int8_t_float.cu} (57%)
 rename cpp/src/neighbors/{refine_uint8_t_float.cu => refine/detail/refine_device_uint8_t_float.cu} (57%)
 create mode 100644 cpp/src/neighbors/refine/detail/refine_host_float_float.cpp
 create mode 100644 cpp/src/neighbors/refine/detail/refine_host_half_float.cpp
 create mode 100644 cpp/src/neighbors/refine/detail/refine_host_int8_t_float.cpp
 create mode 100644 cpp/src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
 rename cpp/src/neighbors/{detail => refine}/refine_common.hpp (100%)
 rename cpp/src/neighbors/{detail => refine}/refine_device.cuh (87%)
 rename cpp/src/neighbors/{detail/refine_host-inl.hpp => refine/refine_host.hpp} (89%)
 delete mode 100644 cpp/src/neighbors/refine_float_float.cu
 create mode 100644 cpp/test/neighbors/refine.cu
 create mode 100644 cpp/test/neighbors/refine_helper.cuh
 create mode 100644 docs/source/cpp_api/neighbors_refine.rst

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 229eb5b934..d0c4f85489 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -387,10 +387,6 @@ add_library(
   src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
   src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
   src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
-  src/neighbors/detail/refine_host_float_float.cpp
-  src/neighbors/detail/refine_host_half_float.cpp
-  src/neighbors/detail/refine_host_int8_t_float.cpp
-  src/neighbors/detail/refine_host_uint8_t_float.cpp
   src/neighbors/ivf_flat_index.cpp
   src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
   src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
@@ -432,6 +428,14 @@ add_library(
   src/neighbors/nn_descent_float.cu
   src/neighbors/nn_descent_int8.cu
   src/neighbors/nn_descent_uint8.cu
+  src/neighbors/refine/detail/refine_device_float_float.cu
+  src/neighbors/refine/detail/refine_device_half_float.cu
+  src/neighbors/refine/detail/refine_device_int8_t_float.cu
+  src/neighbors/refine/detail/refine_device_uint8_t_float.cu
+  src/neighbors/refine/detail/refine_host_float_float.cpp
+  src/neighbors/refine/detail/refine_host_half_float.cpp
+  src/neighbors/refine/detail/refine_host_int8_t_float.cpp
+  src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
   src/neighbors/sample_filter.cu
 )
 
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index d2d6ca9bb9..d1e6b37c2f 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -16,7 +16,6 @@
 
 #pragma once
 
-#include "common.hpp"
 #include <cuvs/neighbors/common.hpp>
 
 #include <raft/core/device_mdarray.hpp>
diff --git a/cpp/include/cuvs/neighbors/refine.hpp b/cpp/include/cuvs/neighbors/refine.hpp
new file mode 100644
index 0000000000..19fbd30bb7
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/refine.hpp
@@ -0,0 +1,439 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/common.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/integer_utils.hpp>
+
+namespace cuvs::neighbors {
+/**
+ * @defgroup ann_refine Approximate Nearest Neighbors Refinement
+ * @{
+ */
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset host matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries host matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates host matrix with indices of candidate vectors [n_queries,
+ *   n_candidates], where n_candidates >= k
+ * @param[out] indices host matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances host matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::host_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset host matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries host matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates host matrix with indices of candidate vectors [n_queries,
+ *   n_candidates], where n_candidates >= k
+ * @param[out] indices host matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances host matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const uint32_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::host_matrix_view<uint32_t, int64_t, raft::row_major> indices,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset host matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries host matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates host matrix with indices of candidate vectors [n_queries,
+ *   n_candidates], where n_candidates >= k
+ * @param[out] indices host matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances host matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> dataset,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::host_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset host matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries host matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates host matrix with indices of candidate vectors [n_queries,
+ *   n_candidates], where n_candidates >= k
+ * @param[out] indices host matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances host matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::host_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset host matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries host matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates host matrix with indices of candidate vectors [n_queries,
+ *   n_candidates], where n_candidates >= k
+ * @param[out] indices host matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances host matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const int64_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::host_matrix_view<int64_t, int64_t, raft::row_major> indices,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+}  // namespace cuvs::neighbors
\ No newline at end of file
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 9a92a53502..2f6cb17e36 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -30,11 +30,11 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/refine.hpp>
 
 // TODO: Fixme- this needs to be migrated
 #include "../../ivf_pq/ivf_pq_build.cuh"
 #include "../../nn_descent.cuh"
-#include "../../refine.cuh"
 
 // TODO: This shouldn't be calling spatial/knn APIs
 #include "../ann_utils.cuh"
@@ -174,13 +174,13 @@ void build_knn_graph(
         refined_distances_host.data_handle(), batch.size(), top_k);
       raft::resource::sync_stream(res);
 
-      cuvs::neighbors::detail::refine_host<int64_t, DataT, float, int64_t>(
-        dataset,
-        queries_host_view,
-        neighbors_host_view,
-        refined_neighbors_host_view,
-        refined_distances_host_view,
-        build_params->metric);
+      cuvs::neighbors::refine(res,
+                              dataset,
+                              queries_host_view,
+                              neighbors_host_view,
+                              refined_neighbors_host_view,
+                              refined_distances_host_view,
+                              build_params->metric);
     } else {
       auto neighbor_candidates_view = raft::make_device_matrix_view<const int64_t, uint64_t>(
         neighbors.data_handle(), batch.size(), gpu_top_k);
@@ -191,14 +191,13 @@ void build_knn_graph(
 
       auto dataset_view = raft::make_device_matrix_view<const DataT, int64_t>(
         dataset.data_handle(), dataset.extent(0), dataset.extent(1));
-      cuvs::neighbors::detail::refine_device<int64_t, DataT, float, int64_t>(
-        res,
-        dataset_view,
-        queries_view,
-        neighbor_candidates_view,
-        refined_neighbors_view,
-        refined_distances_view,
-        build_params->metric);
+      cuvs::neighbors::refine(res,
+                              dataset_view,
+                              queries_view,
+                              neighbor_candidates_view,
+                              refined_neighbors_view,
+                              refined_distances_view,
+                              build_params->metric);
       raft::copy(refined_neighbors_host.data_handle(),
                  refined_neighbors_view.data_handle(),
                  refined_neighbors_view.size(),
diff --git a/cpp/src/neighbors/detail/refine.cuh b/cpp/src/neighbors/detail/refine.cuh
deleted file mode 100644
index 170f973984..0000000000
--- a/cpp/src/neighbors/detail/refine.cuh
+++ /dev/null
@@ -1,19 +0,0 @@
-/*
- * Copyright (c) 2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#include "refine_device.cuh"
-#include "refine_host.hpp"
diff --git a/cpp/src/neighbors/detail/refine_host-ext.hpp b/cpp/src/neighbors/detail/refine_host-ext.hpp
deleted file mode 100644
index 88f5a2a646..0000000000
--- a/cpp/src/neighbors/detail/refine_host-ext.hpp
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "refine_host-inl.hpp"
-#include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
-#include <raft/core/detail/macros.hpp>  // _RAFT_HAS_CUDA
-#include <raft/core/host_mdspan.hpp>    // raft::host_matrix_view
-#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
-
-#include <cstdint>  // int64_t
-
-#if defined(_RAFT_HAS_CUDA)
-#include <cuda_fp16.h>
-#endif
-
-#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
-
-namespace cuvs::neighbors::detail {
-
-template <typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
-[[gnu::optimize(3), gnu::optimize("tree-vectorize")]] void refine_host(
-  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,
-  raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,
-  raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,
-  raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,
-  raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,
-  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded) RAFT_EXPLICIT;
-
-}
-
-#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
-
-#define instantiate_raft_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)                    \
-  extern template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,                    \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,                    \
-    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,         \
-    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                           \
-    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,                    \
-    cuvs::distance::DistanceType metric);
-
-instantiate_raft_neighbors_refine(int64_t, float, float, int64_t);
-instantiate_raft_neighbors_refine(uint32_t, float, float, int64_t);
-instantiate_raft_neighbors_refine(int64_t, int8_t, float, int64_t);
-instantiate_raft_neighbors_refine(int64_t, uint8_t, float, int64_t);
-
-#if defined(_RAFT_HAS_CUDA)
-instantiate_raft_neighbors_refine(int64_t, half, float, int64_t);
-#endif
-
-#undef instantiate_raft_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host.hpp b/cpp/src/neighbors/detail/refine_host.hpp
deleted file mode 100644
index d20bea110f..0000000000
--- a/cpp/src/neighbors/detail/refine_host.hpp
+++ /dev/null
@@ -1,19 +0,0 @@
-/*
- * Copyright (c) 2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#include "refine_host-ext.hpp"
-#include "refine_host-inl.hpp"
diff --git a/cpp/src/neighbors/detail/refine_host_float_float.cpp b/cpp/src/neighbors/detail/refine_host_float_float.cpp
deleted file mode 100644
index c9acfc51f1..0000000000
--- a/cpp/src/neighbors/detail/refine_host_float_float.cpp
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "refine_host-inl.hpp"
-
-#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
-  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
-    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
-    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
-    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
-    cuvs::distance::DistanceType metric);
-
-instantiate_cuvs_neighbors_refine(int64_t, float, float, int64_t);
-instantiate_cuvs_neighbors_refine(uint32_t, float, float, int64_t);
-
-#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host_half_float.cpp b/cpp/src/neighbors/detail/refine_host_half_float.cpp
deleted file mode 100644
index 3dbc1b1794..0000000000
--- a/cpp/src/neighbors/detail/refine_host_half_float.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "refine_host-inl.hpp"
-
-#include <cuda_fp16.h>
-
-#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
-  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
-    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
-    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
-    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
-    cuvs::distance::DistanceType metric);
-
-instantiate_cuvs_neighbors_refine(int64_t, half, float, int64_t);
-
-#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host_int8_t_float.cpp b/cpp/src/neighbors/detail/refine_host_int8_t_float.cpp
deleted file mode 100644
index c46cb3efba..0000000000
--- a/cpp/src/neighbors/detail/refine_host_int8_t_float.cpp
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (c) 2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "refine_host-inl.hpp"
-
-#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
-  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
-    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
-    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
-    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
-    cuvs::distance::DistanceType metric);
-instantiate_cuvs_neighbors_refine(int64_t, int8_t, float, int64_t);
-
-#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp b/cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp
deleted file mode 100644
index 532fbaac05..0000000000
--- a/cpp/src/neighbors/detail/refine_host_uint8_t_float.cpp
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) 2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "refine_host-inl.hpp"
-
-#define instantiate_cuvs_neighbors_refine(IdxT, DataT, DistanceT, ExtentsT)             \
-  template void cuvs::neighbors::detail::refine_host<IdxT, DataT, DistanceT, ExtentsT>( \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,             \
-    raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> queries,             \
-    raft::host_matrix_view<const IdxT, ExtentsT, raft::row_major> neighbor_candidates,  \
-    raft::host_matrix_view<IdxT, ExtentsT, raft::row_major> indices,                    \
-    raft::host_matrix_view<DistanceT, ExtentsT, raft::row_major> distances,             \
-    cuvs::distance::DistanceType metric);
-
-instantiate_cuvs_neighbors_refine(int64_t, uint8_t, float, int64_t);
-
-#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
index 25cf36d329..61d720665f 100644
--- a/cpp/src/neighbors/ivf_flat_index.cpp
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -220,6 +220,7 @@ void index<T, IdxT>::check_consistency()
 }
 
 template struct index<float, int64_t>;
+template struct index<half, int64_t>;
 template struct index<int8_t, int64_t>;
 template struct index<uint8_t, int64_t>;
 
diff --git a/cpp/src/neighbors/refine-ext.cuh b/cpp/src/neighbors/refine-ext.cuh
deleted file mode 100644
index 2f40842871..0000000000
--- a/cpp/src/neighbors/refine-ext.cuh
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
-#include <raft/core/device_mdspan.hpp>  // raft::device_matrix_view
-#include <raft/core/host_mdspan.hpp>    // // raft::host_matrix_view
-#include <raft/core/resources.hpp>      // raft::resources
-#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
-
-#include <cstdint>  // int64_t
-
-#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
-
-namespace cuvs::neighbors {
-
-template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
-void refine(raft::resources const& handle,
-            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
-            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
-            raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
-            raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
-            raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
-            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
-  RAFT_EXPLICIT;
-
-template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
-void refine(raft::resources const& handle,
-            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
-            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
-            raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
-            raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
-            raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
-            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
-  RAFT_EXPLICIT;
-
-}  // namespace cuvs::neighbors
-
-#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
-
-#define instantiate_raft_neighbors_refine_d(idx_t, data_t, distance_t, matrix_idx)          \
-  extern template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(      \
-    raft::resources const& handle,                                                          \
-    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
-    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
-    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
-    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
-    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
-    cuvs::distance::DistanceType metric);
-
-#define instantiate_raft_neighbors_refine_h(idx_t, data_t, distance_t, matrix_idx)        \
-  extern template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(    \
-    raft::resources const& handle,                                                        \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
-    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
-    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
-    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
-    cuvs::distance::DistanceType metric);
-
-instantiate_raft_neighbors_refine_d(int64_t, float, float, int64_t);
-instantiate_raft_neighbors_refine_d(int64_t, int8_t, float, int64_t);
-instantiate_raft_neighbors_refine_d(int64_t, uint8_t, float, int64_t);
-
-instantiate_raft_neighbors_refine_h(int64_t, float, float, int64_t);
-instantiate_raft_neighbors_refine_h(uint32_t, float, float, int64_t);
-instantiate_raft_neighbors_refine_h(int64_t, int8_t, float, int64_t);
-instantiate_raft_neighbors_refine_h(int64_t, uint8_t, float, int64_t);
-
-#undef instantiate_raft_neighbors_refine_d
-#undef instantiate_raft_neighbors_refine_h
diff --git a/cpp/src/neighbors/refine-inl.cuh b/cpp/src/neighbors/refine-inl.cuh
deleted file mode 100644
index 6203290273..0000000000
--- a/cpp/src/neighbors/refine-inl.cuh
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "detail/ann_utils.cuh"
-#include "detail/refine.cuh"
-#include <raft/core/device_mdspan.hpp>
-#include <raft/core/host_mdspan.hpp>
-#include <raft/core/resources.hpp>
-
-namespace cuvs::neighbors {
-
-/**
- * @defgroup ann_refine Approximate Nearest Neighbors Refinement
- * @{
- */
-
-/**
- * @brief Refine nearest neighbor search.
- *
- * Refinement is an operation that follows an approximate NN search. The approximate search has
- * already selected n_candidates neighbor candidates for each query. We narrow it down to k
- * neighbors. For each query, we calculate the exact distance between the query and its
- * n_candidates neighbor candidate, and select the k nearest ones.
- *
- * The k nearest neighbors and distances are returned.
- *
- * Example usage
- * @code{.cpp}
- *   using namespace raft::neighbors;
- *   // use default index parameters
- *   ivf_pq::index_params index_params;
- *   // create and fill the index from a [N, D] dataset
- *   auto index = ivf_pq::build(handle, index_params, dataset, N, D);
- *   // use default search parameters
- *   ivf_pq::search_params search_params;
- *   // search m = 4 * k nearest neighbours for each of the N queries
- *   ivf_pq::search(handle, search_params, index, queries, N, 4 * k, neighbor_candidates,
- *                  out_dists_tmp);
- *   // refine it to the k nearest one
- *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
- *           index.metric());
- * @endcode
- *
- *
- * @param[in] handle the raft handle
- * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
- * @param[in] queries device matrix of the queries [n_queris, dims]
- * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
- *   n_candidates >= k
- * @param[out] indices device matrix that stores the refined indices [n_queries, k]
- * @param[out] distances device matrix that stores the refined distances [n_queries, k]
- * @param[in] metric distance metric to use. Euclidean (L2) is used by default
- */
-template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
-void refine(raft::resources const& handle,
-            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
-            raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
-            raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
-            raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
-            raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
-            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
-{
-  detail::refine_device(handle, dataset, queries, neighbor_candidates, indices, distances, metric);
-}
-
-/** Same as above, but all input and out data is in host memory.
- * @param[in] handle the raft handle
- * @param[in] dataset host matrix that stores the dataset [n_rows, dims]
- * @param[in] queries host matrix of the queries [n_queris, dims]
- * @param[in] neighbor_candidates host matrix with indices of candidate vectors [n_queries,
- *   n_candidates], where n_candidates >= k
- * @param[out] indices host matrix that stores the refined indices [n_queries, k]
- * @param[out] distances host matrix that stores the refined distances [n_queries, k]
- * @param[in] metric distance metric to use. Euclidean (L2) is used by default
- */
-template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
-void refine(raft::resources const& handle,
-            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
-            raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
-            raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
-            raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
-            raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
-            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
-{
-  detail::refine_host(dataset, queries, neighbor_candidates, indices, distances, metric);
-}
-
-/** @} */  // end group ann_refine
-}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/refine.cuh b/cpp/src/neighbors/refine.cuh
deleted file mode 100644
index b3a3cdb4eb..0000000000
--- a/cpp/src/neighbors/refine.cuh
+++ /dev/null
@@ -1,24 +0,0 @@
-/*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
-#include "refine-inl.cuh"
-#endif
-
-#ifdef RAFT_COMPILED
-#include "refine-ext.cuh"
-#endif
diff --git a/cpp/src/neighbors/refine_half_float.cu b/cpp/src/neighbors/refine/detail/refine_device_float_float.cu
similarity index 56%
rename from cpp/src/neighbors/refine_half_float.cu
rename to cpp/src/neighbors/refine/detail/refine_device_float_float.cu
index 3967c15abb..25bad201b1 100644
--- a/cpp/src/neighbors/refine_half_float.cu
+++ b/cpp/src/neighbors/refine/detail/refine_device_float_float.cu
@@ -24,27 +24,24 @@
  *
  */
 
-#include "refine-inl.cuh"
+#include <cuvs/neighbors/refine.hpp>
 
-#define instantiate_raft_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+#include "../refine_device.cuh"
+
+#define instantiate_cuvs_neighbors_refine_d(idx_t, data_t, distance_t, matrix_idx)          \
+  void cuvs::neighbors::refine(                                                             \
     raft::resources const& handle,                                                          \
     raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
     raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
     raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
     raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
     raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
-    cuvs::distance::DistanceType metric);                                                   \
-                                                                                            \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
-    raft::resources const& handle,                                                          \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,              \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,              \
-    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,   \
-    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                     \
-    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,              \
-    cuvs::distance::DistanceType metric);
+    cuvs::distance::DistanceType metric)                                                    \
+  {                                                                                         \
+    refine_impl<idx_t, data_t, distance_t, matrix_idx>(                                     \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);           \
+  }
 
-instantiate_raft_neighbors_refine(int64_t, half, float, int64_t);
+instantiate_cuvs_neighbors_refine_d(int64_t, float, float, int64_t);
 
-#undef instantiate_raft_neighbors_refine
+#undef instantiate_cuvs_neighbors_refine_d
diff --git a/cpp/src/neighbors/refine/detail/refine_device_half_float.cu b/cpp/src/neighbors/refine/detail/refine_device_half_float.cu
new file mode 100644
index 0000000000..1113025487
--- /dev/null
+++ b/cpp/src/neighbors/refine/detail/refine_device_half_float.cu
@@ -0,0 +1,49 @@
+
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by refine_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python refine_00_generate.py
+ *
+ */
+
+#include <cuvs/neighbors/refine.hpp>
+
+#include <cuda_fp16.h>
+
+#include "../refine_device.cuh"
+
+#define instantiate_cuvs_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
+  void cuvs::neighbors::refine(                                                             \
+    raft::resources const& handle,                                                          \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric)                                                    \
+  {                                                                                         \
+    refine_impl<idx_t, data_t, distance_t, matrix_idx>(                                     \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);           \
+  }
+
+instantiate_cuvs_neighbors_refine(int64_t, half, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/refine_int8_t_float.cu b/cpp/src/neighbors/refine/detail/refine_device_int8_t_float.cu
similarity index 57%
rename from cpp/src/neighbors/refine_int8_t_float.cu
rename to cpp/src/neighbors/refine/detail/refine_device_int8_t_float.cu
index 8115c71044..5917cf459f 100644
--- a/cpp/src/neighbors/refine_int8_t_float.cu
+++ b/cpp/src/neighbors/refine/detail/refine_device_int8_t_float.cu
@@ -24,27 +24,24 @@
  *
  */
 
-#include "refine-inl.cuh"
+#include <cuvs/neighbors/refine.hpp>
 
-#define instantiate_raft_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+#include "../refine_device.cuh"
+
+#define instantiate_cuvs_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
+  void cuvs::neighbors::refine(                                                             \
     raft::resources const& handle,                                                          \
     raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
     raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
     raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
     raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
     raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
-    cuvs::distance::DistanceType metric);                                                   \
-                                                                                            \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
-    raft::resources const& handle,                                                          \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,              \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,              \
-    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,   \
-    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                     \
-    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,              \
-    cuvs::distance::DistanceType metric);
+    cuvs::distance::DistanceType metric)                                                    \
+  {                                                                                         \
+    refine_impl<idx_t, data_t, distance_t, matrix_idx>(                                     \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);           \
+  }
 
-instantiate_raft_neighbors_refine(int64_t, int8_t, float, int64_t);
+instantiate_cuvs_neighbors_refine(int64_t, int8_t, float, int64_t);
 
-#undef instantiate_raft_neighbors_refine
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/refine_uint8_t_float.cu b/cpp/src/neighbors/refine/detail/refine_device_uint8_t_float.cu
similarity index 57%
rename from cpp/src/neighbors/refine_uint8_t_float.cu
rename to cpp/src/neighbors/refine/detail/refine_device_uint8_t_float.cu
index ccee2e6788..03c38f1c56 100644
--- a/cpp/src/neighbors/refine_uint8_t_float.cu
+++ b/cpp/src/neighbors/refine/detail/refine_device_uint8_t_float.cu
@@ -24,27 +24,24 @@
  *
  */
 
-#include "refine-inl.cuh"
+#include <cuvs/neighbors/refine.hpp>
 
-#define instantiate_raft_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
+#include "../refine_device.cuh"
+
+#define instantiate_cuvs_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)            \
+  void cuvs::neighbors::refine(                                                             \
     raft::resources const& handle,                                                          \
     raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
     raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
     raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
     raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
     raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
-    cuvs::distance::DistanceType metric);                                                   \
-                                                                                            \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
-    raft::resources const& handle,                                                          \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,              \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,              \
-    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,   \
-    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                     \
-    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,              \
-    cuvs::distance::DistanceType metric);
+    cuvs::distance::DistanceType metric)                                                    \
+  {                                                                                         \
+    cuvs::neighbors::refine_impl<idx_t, data_t, distance_t, matrix_idx>(                    \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);           \
+  }
 
-instantiate_raft_neighbors_refine(int64_t, uint8_t, float, int64_t);
+instantiate_cuvs_neighbors_refine(int64_t, uint8_t, float, int64_t);
 
-#undef instantiate_raft_neighbors_refine
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/refine/detail/refine_host_float_float.cpp b/cpp/src/neighbors/refine/detail/refine_host_float_float.cpp
new file mode 100644
index 0000000000..dbbe05f84e
--- /dev/null
+++ b/cpp/src/neighbors/refine/detail/refine_host_float_float.cpp
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/refine.hpp>
+
+#include "../refine_host.hpp"
+
+#define instantiate_cuvs_neighbors_refine_h(idx_t, data_t, distance_t, matrix_idx)        \
+  void cuvs::neighbors::refine(                                                           \
+    raft::resources const& handle,                                                        \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric)                                                  \
+  {                                                                                       \
+    refine_impl<idx_t, data_t, distance_t, matrix_idx>(                                   \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);         \
+  }
+
+instantiate_cuvs_neighbors_refine_h(int64_t, float, float, int64_t);
+instantiate_cuvs_neighbors_refine_h(uint32_t, float, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine_h
diff --git a/cpp/src/neighbors/refine/detail/refine_host_half_float.cpp b/cpp/src/neighbors/refine/detail/refine_host_half_float.cpp
new file mode 100644
index 0000000000..ed9c412089
--- /dev/null
+++ b/cpp/src/neighbors/refine/detail/refine_host_half_float.cpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "../refine_host.hpp"
+#include <cuvs/neighbors/refine.hpp>
+
+#include <cuda_fp16.h>
+
+#define instantiate_cuvs_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)          \
+  void cuvs::neighbors::refine(                                                           \
+    raft::resources const& handle,                                                        \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric)                                                  \
+  {                                                                                       \
+    refine_impl<idx_t, data_t, distance_t, matrix_idx>(                                   \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);         \
+  }
+
+instantiate_cuvs_neighbors_refine(int64_t, half, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
\ No newline at end of file
diff --git a/cpp/src/neighbors/refine/detail/refine_host_int8_t_float.cpp b/cpp/src/neighbors/refine/detail/refine_host_int8_t_float.cpp
new file mode 100644
index 0000000000..47b6fc356b
--- /dev/null
+++ b/cpp/src/neighbors/refine/detail/refine_host_int8_t_float.cpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/refine.hpp>
+
+#include "../refine_host.hpp"
+
+#define instantiate_cuvs_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)          \
+  void cuvs::neighbors::refine(                                                           \
+    raft::resources const& handle,                                                        \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric)                                                  \
+  {                                                                                       \
+    refine_impl<idx_t, data_t, distance_t, matrix_idx>(                                   \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);         \
+  }
+
+instantiate_cuvs_neighbors_refine(int64_t, int8_t, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/refine/detail/refine_host_uint8_t_float.cpp b/cpp/src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
new file mode 100644
index 0000000000..1698cd3ee9
--- /dev/null
+++ b/cpp/src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/refine.hpp>
+
+#include "../refine_host.hpp"
+
+#define instantiate_cuvs_neighbors_refine(idx_t, data_t, distance_t, matrix_idx)          \
+  void cuvs::neighbors::refine(                                                           \
+    raft::resources const& handle,                                                        \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
+    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
+    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
+    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
+    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
+    cuvs::distance::DistanceType metric)                                                  \
+  {                                                                                       \
+    cuvs::neighbors::refine_impl<idx_t, data_t, distance_t, matrix_idx>(                  \
+      handle, dataset, queries, neighbor_candidates, indices, distances, metric);         \
+  }
+
+instantiate_cuvs_neighbors_refine(int64_t, uint8_t, float, int64_t);
+
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/detail/refine_common.hpp b/cpp/src/neighbors/refine/refine_common.hpp
similarity index 100%
rename from cpp/src/neighbors/detail/refine_common.hpp
rename to cpp/src/neighbors/refine/refine_common.hpp
diff --git a/cpp/src/neighbors/detail/refine_device.cuh b/cpp/src/neighbors/refine/refine_device.cuh
similarity index 87%
rename from cpp/src/neighbors/detail/refine_device.cuh
rename to cpp/src/neighbors/refine/refine_device.cuh
index 7148733f1c..5bf315ae5b 100644
--- a/cpp/src/neighbors/detail/refine_device.cuh
+++ b/cpp/src/neighbors/refine/refine_device.cuh
@@ -16,15 +16,15 @@
 
 #pragma once
 
+#include "../../core/nvtx.hpp"
+#include "../detail/ann_utils.cuh"
 #include "../ivf_flat/ivf_flat_build.cuh"
 #include "../ivf_flat/ivf_flat_interleaved_scan.cuh"
-#include "ann_utils.cuh"
 #include "refine_common.hpp"
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/host_mdspan.hpp>
-#include <raft/core/nvtx.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
 #include <raft/core/resources.hpp>
@@ -32,8 +32,9 @@
 
 #include <thrust/sequence.h>
 
-namespace cuvs::neighbors::detail {
+namespace cuvs::neighbors {
 
+namespace detail {
 /**
  * See cuvs::neighbors::refine for docs.
  */
@@ -57,7 +58,7 @@ void refine_device(
                "k must be less than topk::kMaxCapacity (%d).",
                raft::matrix::detail::select::warpsort::kMaxCapacity);
 
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  cuvs::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "neighbors::refine(%zu, %u)", size_t(n_queries), uint32_t(n_candidates));
 
   refine_check_input(dataset.extents(),
@@ -143,4 +144,18 @@ void refine_device(
                                                       raft::resource::get_cuda_stream(handle));
 }
 
-}  // namespace cuvs::neighbors::detail
+}  // namespace detail
+
+template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
+void refine_impl(
+  raft::resources const& handle,
+  raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
+  raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
+  raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
+  raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
+  raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  detail::refine_device(handle, dataset, queries, neighbor_candidates, indices, distances, metric);
+}
+}  // namespace cuvs::neighbors
\ No newline at end of file
diff --git a/cpp/src/neighbors/detail/refine_host-inl.hpp b/cpp/src/neighbors/refine/refine_host.hpp
similarity index 89%
rename from cpp/src/neighbors/detail/refine_host-inl.hpp
rename to cpp/src/neighbors/refine/refine_host.hpp
index eddcc13e9b..4f293e5b81 100644
--- a/cpp/src/neighbors/detail/refine_host-inl.hpp
+++ b/cpp/src/neighbors/refine/refine_host.hpp
@@ -16,16 +16,18 @@
 
 #pragma once
 
+#include "../../core/nvtx.hpp"
 #include "refine_common.hpp"
 #include <raft/core/host_mdspan.hpp>
-#include <raft/core/nvtx.hpp>
 #include <raft/util/integer_utils.hpp>
 
 #include <omp.h>
 
 #include <algorithm>
 
-namespace cuvs::neighbors::detail {
+namespace cuvs::neighbors {
+
+namespace detail {
 
 template <typename DC, typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
 [[gnu::optimize(3), gnu::optimize("tree-vectorize")]] void refine_host_impl(
@@ -41,7 +43,7 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
   size_t orig_k    = neighbor_candidates.extent(1);
   size_t refined_k = indices.extent(1);
 
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  cuvs::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "neighbors::refine_host(%zu, %zu -> %zu)", n_queries, orig_k, refined_k);
 
   auto suggested_n_threads = std::max(1, std::min(omp_get_num_procs(), omp_get_max_threads()));
@@ -190,4 +192,19 @@ template <typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
   }
 }
 
-}  // namespace cuvs::neighbors::detail
+}  // namespace detail
+
+template <typename idx_t, typename data_t, typename distance_t, typename matrix_idx>
+void refine_impl(
+  raft::resources const& handle,
+  raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,
+  raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,
+  raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates,
+  raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,
+  raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  detail::refine_host(dataset, queries, neighbor_candidates, indices, distances, metric);
+}
+
+}  // namespace cuvs::neighbors
\ No newline at end of file
diff --git a/cpp/src/neighbors/refine_float_float.cu b/cpp/src/neighbors/refine_float_float.cu
deleted file mode 100644
index 648d2e02a2..0000000000
--- a/cpp/src/neighbors/refine_float_float.cu
+++ /dev/null
@@ -1,54 +0,0 @@
-
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by refine_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python refine_00_generate.py
- *
- */
-
-#include "refine-inl.cuh"
-
-#define instantiate_raft_neighbors_refine_d(idx_t, data_t, distance_t, matrix_idx)          \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(             \
-    raft::resources const& handle,                                                          \
-    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
-    raft::device_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
-    raft::device_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
-    raft::device_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
-    raft::device_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
-    cuvs::distance::DistanceType metric);
-
-#define instantiate_raft_neighbors_refine_h(idx_t, data_t, distance_t, matrix_idx)        \
-  template void cuvs::neighbors::refine<idx_t, data_t, distance_t, matrix_idx>(           \
-    raft::resources const& handle,                                                        \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> dataset,            \
-    raft::host_matrix_view<const data_t, matrix_idx, raft::row_major> queries,            \
-    raft::host_matrix_view<const idx_t, matrix_idx, raft::row_major> neighbor_candidates, \
-    raft::host_matrix_view<idx_t, matrix_idx, raft::row_major> indices,                   \
-    raft::host_matrix_view<distance_t, matrix_idx, raft::row_major> distances,            \
-    cuvs::distance::DistanceType metric);
-
-instantiate_raft_neighbors_refine_d(int64_t, float, float, int64_t);
-instantiate_raft_neighbors_refine_h(int64_t, float, float, int64_t);
-instantiate_raft_neighbors_refine_h(uint32_t, float, float, int64_t);
-
-#undef instantiate_raft_neighbors_refine_d
-#undef instantiate_raft_neighbors_refine_h
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 8d532336e5..3fbf62cdba 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -90,7 +90,10 @@ endfunction()
 # ##################################################################################################
 
 if(BUILD_TESTS)
-  ConfigureTest(NAME NEIGHBORS_TEST PATH test/neighbors/brute_force.cu GPUS 1 PERCENT 100)
+  ConfigureTest(
+    NAME NEIGHBORS_TEST PATH test/neighbors/brute_force.cu test/neighbors/refine.cu GPUS 1 PERCENT
+    100
+  )
 
   ConfigureTest(
     NAME CLUSTER_TEST PATH test/cluster/kmeans.cu test/cluster/kmeans_balanced.cu
diff --git a/cpp/test/neighbors/refine.cu b/cpp/test/neighbors/refine.cu
new file mode 100644
index 0000000000..10575e8eb8
--- /dev/null
+++ b/cpp/test/neighbors/refine.cu
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+#include "refine_helper.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/refine.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/neighbors/detail/refine.cuh>
+#include <raft/util/itertools.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <gtest/gtest.h>
+
+#include <vector>
+
+namespace cuvs::neighbors {
+
+template <typename DataT, typename DistanceT, typename IdxT>
+class RefineTest : public ::testing::TestWithParam<RefineInputs<IdxT>> {
+ public:
+  RefineTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      data(handle_, ::testing::TestWithParam<RefineInputs<IdxT>>::GetParam())
+  {
+  }
+
+ protected:
+ public:  // tamas remove
+  void testRefine()
+  {
+    std::vector<IdxT> indices(data.p.n_queries * data.p.k);
+    std::vector<DistanceT> distances(data.p.n_queries * data.p.k);
+
+    if (data.p.host_data) {
+      cuvs::neighbors::refine(handle_,
+                              data.dataset_host.view(),
+                              data.queries_host.view(),
+                              data.candidates_host.view(),
+                              data.refined_indices_host.view(),
+                              data.refined_distances_host.view(),
+                              data.p.metric);
+      raft::copy(indices.data(),
+                 data.refined_indices_host.data_handle(),
+                 data.refined_indices_host.size(),
+                 stream_);
+      raft::copy(distances.data(),
+                 data.refined_distances_host.data_handle(),
+                 data.refined_distances_host.size(),
+                 stream_);
+
+    } else {
+      cuvs::neighbors::refine(handle_,
+                              data.dataset.view(),
+                              data.queries.view(),
+                              data.candidates.view(),
+                              data.refined_indices.view(),
+                              data.refined_distances.view(),
+                              data.p.metric);
+      raft::update_host(distances.data(),
+                        data.refined_distances.data_handle(),
+                        data.refined_distances.size(),
+                        stream_);
+      raft::update_host(
+        indices.data(), data.refined_indices.data_handle(), data.refined_indices.size(), stream_);
+    }
+    raft::resource::sync_stream(handle_);
+
+    double min_recall = 1;
+
+    ASSERT_TRUE(cuvs::neighbors::eval_neighbours(data.true_refined_indices_host,
+                                                 indices,
+                                                 data.true_refined_distances_host,
+                                                 distances,
+                                                 data.p.n_queries,
+                                                 data.p.k,
+                                                 0.001,
+                                                 min_recall));
+  }
+
+ public:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  RefineHelper<DataT, DistanceT, IdxT> data;
+};
+
+const std::vector<RefineInputs<int64_t>> inputs =
+  raft::util::itertools::product<RefineInputs<int64_t>>(
+    {static_cast<int64_t>(137)},
+    {static_cast<int64_t>(1000)},
+    {static_cast<int64_t>(16)},
+    {static_cast<int64_t>(1), static_cast<int64_t>(10), static_cast<int64_t>(33)},
+    {static_cast<int64_t>(33)},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false, true});
+
+typedef RefineTest<float, float, std::int64_t> RefineTestF;
+TEST_P(RefineTestF, AnnRefine) { this->testRefine(); }
+
+INSTANTIATE_TEST_CASE_P(RefineTest, RefineTestF, ::testing::ValuesIn(inputs));
+
+typedef RefineTest<uint8_t, float, std::int64_t> RefineTestF_uint8;
+TEST_P(RefineTestF_uint8, AnnRefine) { this->testRefine(); }
+INSTANTIATE_TEST_CASE_P(RefineTest, RefineTestF_uint8, ::testing::ValuesIn(inputs));
+
+typedef RefineTest<int8_t, float, std::int64_t> RefineTestF_int8;
+TEST_P(RefineTestF_int8, AnnRefine) { this->testRefine(); }
+INSTANTIATE_TEST_CASE_P(RefineTest, RefineTestF_int8, ::testing::ValuesIn(inputs));
+}  // namespace cuvs::neighbors
diff --git a/cpp/test/neighbors/refine_helper.cuh b/cpp/test/neighbors/refine_helper.cuh
new file mode 100644
index 0000000000..18af47a10b
--- /dev/null
+++ b/cpp/test/neighbors/refine_helper.cuh
@@ -0,0 +1,158 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/random/rng.cuh>
+
+#include "naive_knn.cuh"
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cuvs::neighbors {
+
+template <typename IdxT>
+struct RefineInputs {
+  IdxT n_queries;
+  IdxT n_rows;
+  IdxT dim;
+  IdxT k;   // after refinement
+  IdxT k0;  // initial k before refinement (k0 >= k).
+  cuvs::distance::DistanceType metric;
+  bool host_data;
+};
+
+/** Helper class to allocate arrays and generate input data for refinement test and benchmark. */
+template <typename DataT, typename DistanceT, typename IdxT>
+class RefineHelper {
+ public:
+  RefineHelper(const raft::resources& handle, RefineInputs<IdxT> params)
+    : handle_(handle),
+      stream_(raft::resource::get_cuda_stream(handle)),
+      p(params),
+      dataset(handle),
+      queries(handle),
+      refined_distances(handle),
+      refined_indices(handle),
+      candidates(handle),
+      dataset_host(handle),
+      queries_host(handle),
+      candidates_host(handle),
+      refined_distances_host(handle),
+      refined_indices_host(handle)
+  {
+    raft::random::RngState rng(1234ULL);
+
+    dataset = raft::make_device_matrix<DataT, IdxT>(handle_, p.n_rows, p.dim);
+    queries = raft::make_device_matrix<DataT, IdxT>(handle_, p.n_queries, p.dim);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::uniform(
+        handle, rng, dataset.data_handle(), dataset.size(), DataT(-10.0), DataT(10.0));
+      raft::random::uniform(
+        handle, rng, queries.data_handle(), queries.size(), DataT(-10.0), DataT(10.0));
+    } else {
+      raft::random::uniformInt(
+        handle, rng, dataset.data_handle(), dataset.size(), DataT(1), DataT(20));
+      raft::random::uniformInt(
+        handle, rng, queries.data_handle(), queries.size(), DataT(1), DataT(20));
+    }
+
+    refined_distances = raft::make_device_matrix<DistanceT, IdxT>(handle_, p.n_queries, p.k);
+    refined_indices   = raft::make_device_matrix<IdxT, IdxT>(handle_, p.n_queries, p.k);
+
+    // Generate candidate vectors
+    {
+      candidates = raft::make_device_matrix<IdxT, IdxT>(handle_, p.n_queries, p.k0);
+      rmm::device_uvector<DistanceT> distances_tmp(p.n_queries * p.k0, stream_);
+      naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                        distances_tmp.data(),
+                                        candidates.data_handle(),
+                                        queries.data_handle(),
+                                        dataset.data_handle(),
+                                        p.n_queries,
+                                        p.n_rows,
+                                        p.dim,
+                                        p.k0,
+                                        p.metric);
+      raft::resource::sync_stream(handle_, stream_);
+    }
+
+    if (p.host_data) {
+      dataset_host    = raft::make_host_matrix<DataT, IdxT>(p.n_rows, p.dim);
+      queries_host    = raft::make_host_matrix<DataT, IdxT>(p.n_queries, p.dim);
+      candidates_host = raft::make_host_matrix<IdxT, IdxT>(p.n_queries, p.k0);
+
+      raft::copy(dataset_host.data_handle(), dataset.data_handle(), dataset.size(), stream_);
+      raft::copy(queries_host.data_handle(), queries.data_handle(), queries.size(), stream_);
+      raft::copy(
+        candidates_host.data_handle(), candidates.data_handle(), candidates.size(), stream_);
+
+      refined_distances_host = raft::make_host_matrix<DistanceT, IdxT>(p.n_queries, p.k);
+      refined_indices_host   = raft::make_host_matrix<IdxT, IdxT>(p.n_queries, p.k);
+      raft::resource::sync_stream(handle_, stream_);
+    }
+
+    // Generate ground thruth for testing.
+    {
+      rmm::device_uvector<DistanceT> distances_dev(p.n_queries * p.k, stream_);
+      rmm::device_uvector<IdxT> indices_dev(p.n_queries * p.k, stream_);
+      naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                        distances_dev.data(),
+                                        indices_dev.data(),
+                                        queries.data_handle(),
+                                        dataset.data_handle(),
+                                        p.n_queries,
+                                        p.n_rows,
+                                        p.dim,
+                                        p.k,
+                                        p.metric);
+      true_refined_distances_host.resize(p.n_queries * p.k);
+      true_refined_indices_host.resize(p.n_queries * p.k);
+      raft::copy(true_refined_indices_host.data(), indices_dev.data(), indices_dev.size(), stream_);
+      raft::copy(
+        true_refined_distances_host.data(), distances_dev.data(), distances_dev.size(), stream_);
+      raft::resource::sync_stream(handle_, stream_);
+    }
+  }
+
+ public:
+  RefineInputs<IdxT> p;
+  const raft::resources& handle_;
+  rmm::cuda_stream_view stream_;
+
+  raft::device_matrix<DataT, IdxT, raft::row_major> dataset;
+  raft::device_matrix<DataT, IdxT, raft::row_major> queries;
+  raft::device_matrix<IdxT, IdxT, raft::row_major> candidates;  // Neighbor candidate indices
+  raft::device_matrix<IdxT, IdxT, raft::row_major> refined_indices;
+  raft::device_matrix<DistanceT, IdxT, raft::row_major> refined_distances;
+
+  raft::host_matrix<DataT, IdxT, raft::row_major> dataset_host;
+  raft::host_matrix<DataT, IdxT, raft::row_major> queries_host;
+  raft::host_matrix<IdxT, IdxT, raft::row_major> candidates_host;
+  raft::host_matrix<IdxT, IdxT, raft::row_major> refined_indices_host;
+  raft::host_matrix<DistanceT, IdxT, raft::row_major> refined_distances_host;
+
+  std::vector<IdxT> true_refined_indices_host;
+  std::vector<DistanceT> true_refined_distances_host;
+};
+}  // namespace cuvs::neighbors
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index f9006412c9..15b4ff0516 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -13,3 +13,4 @@ Nearest Neighbors
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
    neighbors_cagra.rst
+   neighbors_refine.rst
diff --git a/docs/source/cpp_api/neighbors_refine.rst b/docs/source/cpp_api/neighbors_refine.rst
new file mode 100644
index 0000000000..d53c8087cc
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_refine.rst
@@ -0,0 +1,20 @@
+Refinement
+==========
+
+Candidate refinement methods for nearest neighbors search
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/refine.hpp>``
+
+namespace *cuvs::neighbors*
+
+Index
+-----
+
+.. doxygengroup:: ann_refine
+    :project: cuvs
+    :members:
+    :content-only:
\ No newline at end of file

From 191235517c23e4ce4b8e69f2ff08a95c75456d69 Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Tue, 28 May 2024 20:21:56 +0200
Subject: [PATCH 065/623] Accept host_mdspan for IVF-PQ build and extend (#148)

This PR enables host input arrays for `ivf_pq::build` and `ivf_pq::extend`.

closes #120
closes #143

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/148
---
 cpp/include/cuvs/neighbors/ivf_pq.hpp         | 380 +++++++++++++++++-
 .../neighbors/detail/cagra/cagra_build.cuh    |   3 +-
 .../ivf_pq/detail/generate_ivf_pq.py          |  39 +-
 .../ivf_pq_build_extend_float_int64_t.cu      |  35 +-
 .../detail/ivf_pq_build_extend_inst.cuh       |  93 +++++
 .../ivf_pq_build_extend_int8_t_int64_t.cu     |  35 +-
 .../ivf_pq_build_extend_uint8_t_int64_t.cu    |  35 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |  59 ++-
 .../neighbors/ivf_pq/ivf_pq_deserialize.cu    |  15 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu  |  15 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |  18 +-
 11 files changed, 527 insertions(+), 200 deletions(-)
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh

diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index d1e6b37c2f..8493882b43 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -593,6 +593,146 @@ void build(raft::resources const& handle,
            const cuvs::neighbors::ivf_pq::index_params& index_params,
            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a host_matrix_view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a host_matrix_view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a host_matrix_view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
 /**
  * @}
  */
@@ -771,6 +911,177 @@ void extend(raft::resources const& handle,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
             std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
             cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
 /**
  * @}
  */
@@ -1009,9 +1320,30 @@ void search_with_filtering(
  * @{
  */
 /**
- * Save the index to file.
+ * Serialize the index to an output string.
  *
- * Experimental, both the API and the serialization format are subject to change.
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input string
+ * std::string str
+ * // create an index with `auto index = ivf_pq::build(...);`
+ * cuvs::serialize(handle, str, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[out] str output string
+ * @param[in] index IVF-PQ index
+ *
+ */
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::ivf_pq::index<int64_t>& index);
+
+/**
+ * Save the index to file.
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
@@ -1029,14 +1361,38 @@ void search_with_filtering(
  * @param[in] index IVF-PQ index
  *
  */
-void serialize(raft::resources const& handle,
-               std::string& filename,
-               const cuvs::neighbors::ivf_pq::index<int64_t>& index);
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::ivf_pq::index<int64_t>& index);
 
 /**
- * Load index from file.
+ * Load index from input string.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
  *
- * Experimental, both the API and the serialization format are subject to change.
+ * raft::resources handle;
+ *
+ * std::string str = ...
+ *
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index
+ * cuvs::neighbors::ivf_pq::index<IdxT> index(handl, index_params, dim);
+ *
+ * cuvs::deserialize(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] str the name of the file that stores the index
+ * @param[out] index IVF-PQ index
+ *
+ */
+
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::ivf_pq::index<int64_t>* index);
+/**
+ * Load index from file.
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
@@ -1046,7 +1402,9 @@ void serialize(raft::resources const& handle,
  * // create a string with a filepath
  * std::string filename("/path/to/index");
  * using IdxT = int64_t; // type of the index
- * // create an empty index with `ivf_pq::index<IdxT> index(handle, index_params, dim);`
+ * // create an empty index with
+ * ivf_pq::index<IdxT> index(handle, index_params, dim);
+ *
  * cuvs::deserialize(handle, filename, &index);
  * @endcode
  *
@@ -1055,9 +1413,9 @@ void serialize(raft::resources const& handle,
  * @param[out] index IVF-PQ index
  *
  */
-void deserialize(raft::resources const& handle,
-                 const std::string& filename,
-                 cuvs::neighbors::ivf_pq::index<int64_t>* index);
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::ivf_pq::index<int64_t>* index);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 2f6cb17e36..0ca97b9ca9 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -91,8 +91,7 @@ void build_knn_graph(
   }();
 
   RAFT_LOG_DEBUG("# Building IVF-PQ index %s", model_name.c_str());
-  auto index = cuvs::neighbors::ivf_pq::detail::build<DataT, int64_t>(
-    res, *build_params, dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+  auto index = cuvs::neighbors::ivf_pq::detail::build<DataT, int64_t>(res, *build_params, dataset);
 
   //
   // search top (k + 1) neighbors
diff --git a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
index 2ba513579c..878c7ee214 100644
--- a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
+++ b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
@@ -41,7 +41,7 @@
 """
 
 build_include_macro = """
-#include "../ivf_pq_build.cuh"
+#include "ivf_pq_build_extend_inst.cuh"
 """
 search_include_macro = """
 #include "../ivf_pq_search.cuh"
@@ -61,42 +61,7 @@
     uint8_t_int64_t=("uint8_t", "int64_t"),
 )
 
-build_extend_macro = """
-#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                      \\
-  auto build(raft::resources const& handle,                                                  \\
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \\
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)               \\
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \\
-  {                                                                                          \\
-    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                  \\
-  }                                                                                          \\
-                                                                                             \\
-  void build(raft::resources const& handle,                                                  \\
-             const cuvs::neighbors::ivf_pq::index_params& params,                            \\
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,               \\
-             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                      \\
-  {                                                                                          \\
-    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                    \\
-  }                                                                                          \\
-  auto extend(raft::resources const& handle,                                                 \\
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,          \\
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,         \\
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                        \\
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                   \\
-  {                                                                                          \\
-    return cuvs::neighbors::ivf_pq::detail::extend(                                          \\
-      handle, new_vectors, new_indices, orig_index);                                         \\
-  }                                                                                          \\
-                                                                                             \\
-  void extend(raft::resources const& handle,                                                 \\
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,          \\
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,         \\
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                     \\
-  {                                                                                          \\
-    cuvs::neighbors::ivf_pq::detail::extend(                                                 \\
-      handle, new_vectors, new_indices, idx);                                                \\
-  }             
-"""
+build_extend_macro = ""  # moved to header ivf_pq_build_extend_inst.cuh
 
 search_macro = """
 #define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \\
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
index ee7f26381f..65c443d883 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
@@ -25,42 +25,9 @@
 
 #include <cuvs/neighbors/ivf_pq.hpp>
 
-#include "../ivf_pq_build.cuh"
+#include "ivf_pq_build_extend_inst.cuh"
 
 namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                    \
-  auto build(raft::resources const& handle,                                                       \
-             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                    \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
-  {                                                                                               \
-    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
-  }                                                                                               \
-                                                                                                  \
-  void build(raft::resources const& handle,                                                       \
-             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                    \
-             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
-  {                                                                                               \
-    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
-  }                                                                                               \
-  auto extend(raft::resources const& handle,                                                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
-  {                                                                                               \
-    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
-  }                                                                                               \
-                                                                                                  \
-  void extend(raft::resources const& handle,                                                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
-  {                                                                                               \
-    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
-  }
 CUVS_INST_IVF_PQ_BUILD_EXTEND(float, int64_t);
 
 #undef CUVS_INST_IVF_PQ_BUILD_EXTEND
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh
new file mode 100644
index 0000000000..4b963b0b37
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is used by generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_build.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)                 \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
+  }                                                                                               \
+                                                                                                  \
+  void build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,                 \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
+  }                                                                                               \
+                                                                                                  \
+  auto build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)                   \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
+  }                                                                                               \
+                                                                                                  \
+  void build(raft::resources const& handle,                                                       \
+             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,                   \
+             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
+  }                                                                                               \
+  auto extend(                                                                                    \
+    raft::resources const& handle,                                                                \
+    raft::device_matrix_view<const T, int64_t, raft::row_major> new_vectors,                      \
+    std::optional<raft::device_vector_view<const IdxT, int64_t, raft::row_major>> new_indices,    \
+    const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                                       \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
+  }                                                                                               \
+  void extend(raft::resources const& handle,                                                      \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> new_vectors,            \
+              std::optional<raft::device_vector_view<const IdxT, int64_t>> new_indices,           \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
+  }                                                                                               \
+  auto extend(raft::resources const& handle,                                                      \
+              raft::host_matrix_view<const T, int64_t, raft::row_major> new_vectors,              \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices,             \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
+    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+  {                                                                                               \
+    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
+  }                                                                                               \
+                                                                                                  \
+  void extend(raft::resources const& handle,                                                      \
+              raft::host_matrix_view<const T, int64_t, raft::row_major> new_vectors,              \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
+  {                                                                                               \
+    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
+  }
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
index 8b612439d6..048560b5a4 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
@@ -25,42 +25,9 @@
 
 #include <cuvs/neighbors/ivf_pq.hpp>
 
-#include "../ivf_pq_build.cuh"
+#include "ivf_pq_build_extend_inst.cuh"
 
 namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                    \
-  auto build(raft::resources const& handle,                                                       \
-             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                    \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
-  {                                                                                               \
-    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
-  }                                                                                               \
-                                                                                                  \
-  void build(raft::resources const& handle,                                                       \
-             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                    \
-             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
-  {                                                                                               \
-    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
-  }                                                                                               \
-  auto extend(raft::resources const& handle,                                                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
-  {                                                                                               \
-    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
-  }                                                                                               \
-                                                                                                  \
-  void extend(raft::resources const& handle,                                                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
-  {                                                                                               \
-    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
-  }
 CUVS_INST_IVF_PQ_BUILD_EXTEND(int8_t, int64_t);
 
 #undef CUVS_INST_IVF_PQ_BUILD_EXTEND
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
index df6e12b7e3..755fb9f8d7 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
@@ -25,42 +25,9 @@
 
 #include <cuvs/neighbors/ivf_pq.hpp>
 
-#include "../ivf_pq_build.cuh"
+#include "ivf_pq_build_extend_inst.cuh"
 
 namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_BUILD_EXTEND(T, IdxT)                                                    \
-  auto build(raft::resources const& handle,                                                       \
-             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                    \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
-  {                                                                                               \
-    return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
-  }                                                                                               \
-                                                                                                  \
-  void build(raft::resources const& handle,                                                       \
-             const cuvs::neighbors::ivf_pq::index_params& params,                                 \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                    \
-             cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                           \
-  {                                                                                               \
-    cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset, idx);                         \
-  }                                                                                               \
-  auto extend(raft::resources const& handle,                                                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
-              const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
-  {                                                                                               \
-    return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
-  }                                                                                               \
-                                                                                                  \
-  void extend(raft::resources const& handle,                                                      \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,               \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,              \
-              cuvs::neighbors::ivf_pq::index<IdxT>* idx)                                          \
-  {                                                                                               \
-    cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, idx);               \
-  }
 CUVS_INST_IVF_PQ_BUILD_EXTEND(uint8_t, int64_t);
 
 #undef CUVS_INST_IVF_PQ_BUILD_EXTEND
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 5536d6edb1..3b0e475f4b 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1659,13 +1659,14 @@ auto extend(raft::resources const& handle,
   return ext_index;
 }
 
-template <typename T, typename IdxT>
+template <typename T, typename IdxT, typename accessor>
 auto build(raft::resources const& handle,
            const index_params& params,
-           const T* dataset,
-           IdxT n_rows,
-           uint32_t dim) -> index<IdxT>
+           raft::mdspan<const T, raft::matrix_extent<IdxT>, raft::row_major, accessor> dataset)
+  -> index<IdxT>
 {
+  IdxT n_rows = dataset.extent(0);
+  IdxT dim    = dataset.extent(1);
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "ivf_pq::build(%zu, %u)", size_t(n_rows), dim);
   static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, uint8_t> ||
@@ -1700,7 +1701,7 @@ auto build(raft::resources const& handle,
     if constexpr (std::is_same_v<T, float>) {
       RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset.data(),
                                       sizeof(T) * index.dim(),
-                                      dataset,
+                                      dataset.data_handle(),
                                       sizeof(T) * index.dim() * trainset_ratio,
                                       sizeof(T) * index.dim(),
                                       n_rows_train,
@@ -1709,7 +1710,7 @@ auto build(raft::resources const& handle,
     } else {
       size_t dim = index.dim();
       cudaPointerAttributes dataset_attr;
-      RAFT_CUDA_TRY(cudaPointerGetAttributes(&dataset_attr, dataset));
+      RAFT_CUDA_TRY(cudaPointerGetAttributes(&dataset_attr, dataset.data_handle()));
       if (dataset_attr.devicePointer != nullptr) {
         // data is available on device: just run the kernel to raft::copy and map the data
         auto p = reinterpret_cast<T*>(dataset_attr.devicePointer);
@@ -1728,7 +1729,7 @@ auto build(raft::resources const& handle,
         // T at the end of float rows.
         RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset_tmp,
                                         sizeof(float) * index.dim(),
-                                        dataset,
+                                        dataset.data_handle(),
                                         sizeof(T) * index.dim() * trainset_ratio,
                                         sizeof(T) * index.dim(),
                                         n_rows_train,
@@ -1809,37 +1810,27 @@ auto build(raft::resources const& handle,
 
   // add the data if necessary
   if (params.add_data_on_build) {
-    detail::extend<T, IdxT>(handle, &index, dataset, nullptr, n_rows);
+    detail::extend<T, IdxT>(handle, &index, dataset.data_handle(), nullptr, n_rows);
   }
   return index;
 }
 
-template <typename T, typename IdxT>
-auto build(raft::resources const& handle,
-           const index_params& params,
-           raft::device_matrix_view<const T, IdxT, raft::row_major> dataset) -> index<IdxT>
-{
-  IdxT n_rows = dataset.extent(0);
-  IdxT dim    = dataset.extent(1);
-  return build(handle, params, dataset.data_handle(), n_rows, dim);
-}
-
-template <typename T, typename IdxT>
+template <typename T, typename IdxT, typename accessor>
 void build(raft::resources const& handle,
            const index_params& params,
-           raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,
+           raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
            index<IdxT>* index)
 {
-  IdxT n_rows = dataset.extent(0);
-  IdxT dim    = dataset.extent(1);
-  *index      = build(handle, params, dataset.data_handle(), n_rows, dim);
+  *index = build(handle, params, dataset);
 }
 
-template <typename T, typename IdxT>
-auto extend(raft::resources const& handle,
-            raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,
-            std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,
-            const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index) -> index<IdxT>
+template <typename T, typename IdxT, typename accessor, typename accessor2>
+auto extend(
+  raft::resources const& handle,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, accessor> new_vectors,
+  std::optional<raft::mdspan<const IdxT, raft::vector_extent<int64_t>, raft::row_major, accessor2>>
+    new_indices,
+  const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index) -> index<IdxT>
 {
   ASSERT(new_vectors.extent(1) == orig_index.dim(),
          "new_vectors should have the same dimension as the index");
@@ -1857,11 +1848,13 @@ auto extend(raft::resources const& handle,
                 n_rows);
 }
 
-template <typename T, typename IdxT>
-void extend(raft::resources const& handle,
-            raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,
-            std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,
-            index<IdxT>* index)
+template <typename T, typename IdxT, typename accessor, typename accessor2>
+void extend(
+  raft::resources const& handle,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, accessor> new_vectors,
+  std::optional<raft::mdspan<const IdxT, raft::vector_extent<int64_t>, raft::row_major, accessor2>>
+    new_indices,
+  index<IdxT>* index)
 {
   ASSERT(new_vectors.extent(1) == index->dim(),
          "new_vectors should have the same dimension as the index");
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
index 9515cd2d58..7827e7892a 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
@@ -16,15 +16,24 @@
 
 #include "ivf_pq_serialize.cuh"
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <sstream>
 
 namespace cuvs::neighbors::ivf_pq {
 
-void deserialize(raft::resources const& handle,
-                 const std::string& filename,
-                 cuvs::neighbors::ivf_pq::index<int64_t>* index)
+void deserialize_file(raft::resources const& handle,
+                      const std::string& filename,
+                      cuvs::neighbors::ivf_pq::index<int64_t>* index)
 {
   if (!index) { RAFT_FAIL("Invalid index pointer"); }
   *index = cuvs::neighbors::ivf_pq::detail::deserialize<int64_t>(handle, filename);
 }
 
+void deserialize(raft::resources const& handle,
+                 const std::string& str,
+                 cuvs::neighbors::ivf_pq::index<int64_t>* index)
+{
+  if (!index) { RAFT_FAIL("Invalid index pointer"); }
+  std::istringstream is(str);
+  *index = cuvs::neighbors::ivf_pq::detail::deserialize<int64_t>(handle, is);
+}
 }  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
index 0fb71d0317..f0214f4bb3 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
@@ -16,14 +16,23 @@
 
 #include "ivf_pq_serialize.cuh"
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <sstream>
 
 namespace cuvs::neighbors::ivf_pq {
 
-void serialize(raft::resources const& handle,
-               std::string& filename,
-               const cuvs::neighbors::ivf_pq::index<int64_t>& index)
+void serialize_file(raft::resources const& handle,
+                    const std::string& filename,
+                    const cuvs::neighbors::ivf_pq::index<int64_t>& index)
 {
   cuvs::neighbors::ivf_pq::detail::serialize(handle, filename, index);
 }
 
+void serialize(raft::resources const& handle,
+               std::string& str,
+               const cuvs::neighbors::ivf_pq::index<int64_t>& index)
+{
+  std::ostringstream os;
+  cuvs::neighbors::ivf_pq::detail::serialize(handle, os, index);
+  str = os.str();
+}
 }  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index fda32c6df2..f716a8efed 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -204,7 +204,7 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     ipams.add_data_on_build = true;
 
     auto index_view =
-      raft::make_device_matrix_view<DataT, IdxT>(database.data(), ps.num_db_vecs, ps.dim);
+      raft::make_device_matrix_view<const DataT, int64_t>(database.data(), ps.num_db_vecs, ps.dim);
     return cuvs::neighbors::ivf_pq::build(handle_, ipams, index_view);
   }
 
@@ -224,16 +224,16 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     ipams.add_data_on_build = false;
 
     auto database_view =
-      raft::make_device_matrix_view<DataT, IdxT>(database.data(), ps.num_db_vecs, ps.dim);
+      raft::make_device_matrix_view<const DataT, int64_t>(database.data(), ps.num_db_vecs, ps.dim);
     auto idx = cuvs::neighbors::ivf_pq::build(handle_, ipams, database_view);
 
-    auto vecs_2_view = raft::make_device_matrix_view<DataT, IdxT>(vecs_2, size_2, ps.dim);
-    auto inds_2_view = raft::make_device_vector_view<IdxT, IdxT>(inds_2, size_2);
+    auto vecs_2_view = raft::make_device_matrix_view<const DataT, int64_t>(vecs_2, size_2, ps.dim);
+    auto inds_2_view = raft::make_device_vector_view<const IdxT, int64_t>(inds_2, size_2);
     cuvs::neighbors::ivf_pq::extend(handle_, vecs_2_view, inds_2_view, &idx);
 
     auto vecs_1_view =
-      raft::make_device_matrix_view<DataT, IdxT, raft::row_major>(vecs_1, size_1, ps.dim);
-    auto inds_1_view = raft::make_device_vector_view<const IdxT, IdxT>(inds_1, size_1);
+      raft::make_device_matrix_view<const DataT, int64_t, raft::row_major>(vecs_1, size_1, ps.dim);
+    auto inds_1_view = raft::make_device_vector_view<const IdxT, int64_t>(inds_1, size_1);
     cuvs::neighbors::ivf_pq::extend(handle_, vecs_1_view, inds_1_view, &idx);
     return idx;
   }
@@ -241,9 +241,9 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
   auto build_serialize()
   {
     std::string filename = "ivf_pq_index";
-    cuvs::neighbors::ivf_pq::serialize(handle_, filename, build_only());
+    cuvs::neighbors::ivf_pq::serialize_file(handle_, filename, build_only());
     cuvs::neighbors::ivf_pq::index<IdxT> index(handle_, ps.index_params, ps.dim);
-    cuvs::neighbors::ivf_pq::deserialize(handle_, filename, &index);
+    cuvs::neighbors::ivf_pq::deserialize_file(handle_, filename, &index);
     return index;
   }
 
@@ -564,7 +564,7 @@ class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     ipams.add_data_on_build = true;
 
     auto index_view =
-      raft::make_device_matrix_view<DataT, IdxT>(database.data(), ps.num_db_vecs, ps.dim);
+      raft::make_device_matrix_view<const DataT, IdxT>(database.data(), ps.num_db_vecs, ps.dim);
     return cuvs::neighbors::ivf_pq::build(handle_, ipams, index_view);
   }
 

From c533fe3624db3784e8838c38c737ff858fdffe5d Mon Sep 17 00:00:00 2001
From: rhdong <hrong@nvidia.com>
Date: Wed, 29 May 2024 16:00:49 -0700
Subject: [PATCH 066/623] [FEA] support of prefiltered brute force (#146)

- The PR is one part of prefiltered brute force and should work with the PR of raft: https://github.com/rapidsai/raft/pull/2294

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/146
---
 cpp/include/cuvs/core/bitmap.hpp              |  27 +
 cpp/include/cuvs/neighbors/brute_force.hpp    |   5 +-
 cpp/include/cuvs/neighbors/common.hpp         |   1 +
 cpp/src/neighbors/brute_force.cu              |  46 +-
 cpp/src/neighbors/brute_force_c.cpp           |   2 +-
 cpp/src/neighbors/detail/knn_brute_force.cuh  | 202 ++++++-
 cpp/src/neighbors/detail/knn_utils.cuh        |  96 ++++
 cpp/test/CMakeLists.txt                       |  10 +-
 cpp/test/neighbors/brute_force.cu             |   2 +-
 cpp/test/neighbors/brute_force_prefiltered.cu | 524 ++++++++++++++++++
 cpp/test/neighbors/knn_utils.cuh              |  95 ++++
 11 files changed, 986 insertions(+), 24 deletions(-)
 create mode 100644 cpp/include/cuvs/core/bitmap.hpp
 create mode 100644 cpp/src/neighbors/detail/knn_utils.cuh
 create mode 100644 cpp/test/neighbors/brute_force_prefiltered.cu
 create mode 100644 cpp/test/neighbors/knn_utils.cuh

diff --git a/cpp/include/cuvs/core/bitmap.hpp b/cpp/include/cuvs/core/bitmap.hpp
new file mode 100644
index 0000000000..80ae25cd28
--- /dev/null
+++ b/cpp/include/cuvs/core/bitmap.hpp
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/bitmap.hpp>
+
+namespace cuvs::core {
+/* To use bitmap functions containing CUDA code, include <raft/core/bitmap.cuh> */
+
+template <typename bitmap_t, typename index_t>
+using bitmap_view = raft::core::bitmap_view<bitmap_t, index_t>;
+
+}  // end namespace cuvs::core
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 755c8cfdb3..13a5ea0cbf 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -191,12 +191,15 @@ auto build(raft::resources const& handle,
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
+ * @param[in] sample_filter a optional device bitmap filter function that greenlights samples for a
+ * given
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::brute_force::index<float>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 45fa1a107f..72d35961f0 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -25,6 +25,7 @@
 #include <raft/util/cudart_utils.hpp>   // get_device_for_address
 #include <raft/util/integer_utils.hpp>  // rounding up
 
+#include <cuvs/core/bitmap.hpp>
 #include <cuvs/core/bitset.hpp>
 #include <raft/core/detail/macros.hpp>
 
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index 45d4be4a77..13554c0b5d 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -15,6 +15,7 @@
  */
 
 #include "./detail/knn_brute_force.cuh"
+
 #include <cuvs/neighbors/brute_force.hpp>
 
 #include <raft/core/copy.hpp>
@@ -84,25 +85,32 @@ void index<T>::update_dataset(raft::resources const& res,
   dataset_view_ = raft::make_const_mdspan(dataset_.view());
 }
 
-#define CUVS_INST_BFKNN(T)                                                           \
-  auto build(raft::resources const& res,                                             \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,    \
-             cuvs::distance::DistanceType metric,                                    \
-             T metric_arg)                                                           \
-    ->cuvs::neighbors::brute_force::index<T>                                         \
-  {                                                                                  \
-    return detail::build<T>(res, dataset, metric, metric_arg);                       \
-  }                                                                                  \
-                                                                                     \
-  void search(raft::resources const& res,                                            \
-              const cuvs::neighbors::brute_force::index<T>& idx,                     \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,   \
-              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors, \
-              raft::device_matrix_view<T, int64_t, raft::row_major> distances)       \
-  {                                                                                  \
-    detail::brute_force_search<T, int64_t>(res, idx, queries, neighbors, distances); \
-  }                                                                                  \
-                                                                                     \
+#define CUVS_INST_BFKNN(T)                                                                        \
+  auto build(raft::resources const& res,                                                          \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,                 \
+             cuvs::distance::DistanceType metric,                                                 \
+             T metric_arg)                                                                        \
+    ->cuvs::neighbors::brute_force::index<T>                                                      \
+  {                                                                                               \
+    return detail::build<T>(res, dataset, metric, metric_arg);                                    \
+  }                                                                                               \
+                                                                                                  \
+  void search(                                                                                    \
+    raft::resources const& res,                                                                   \
+    const cuvs::neighbors::brute_force::index<T>& idx,                                            \
+    raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                          \
+    raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,                        \
+    raft::device_matrix_view<T, int64_t, raft::row_major> distances,                              \
+    std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter = std::nullopt) \
+  {                                                                                               \
+    if (!sample_filter.has_value()) {                                                             \
+      detail::brute_force_search<T, int64_t>(res, idx, queries, neighbors, distances);            \
+    } else {                                                                                      \
+      detail::brute_force_search_filtered<T, int64_t>(                                            \
+        res, idx, queries, *sample_filter, neighbors, distances);                                 \
+    }                                                                                             \
+  }                                                                                               \
+                                                                                                  \
   template struct cuvs::neighbors::brute_force::index<T>;
 
 CUVS_INST_BFKNN(float);
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index e988ac2f0c..5f04ffa340 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -66,7 +66,7 @@ void _search(cuvsResources_t res,
   auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
 
   cuvs::neighbors::brute_force::search(
-    *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+    *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds, std::nullopt);
 }
 
 }  // namespace
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 29cd26d9f3..4865ade77d 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -15,6 +15,7 @@
  */
 
 #pragma once
+
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
 
@@ -23,16 +24,26 @@
 #include "./fused_l2_knn.cuh"
 #include "./haversine_distance.cuh"
 #include "./knn_merge_parts.cuh"
+#include "./knn_utils.cuh"
 
+#include <raft/core/bitmap.cuh>
+#include <raft/core/detail/popc.cuh>
+#include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/linalg/map.cuh>
+#include <raft/linalg/norm.cuh>
 #include <raft/linalg/transpose.cuh>
 #include <raft/matrix/init.cuh>
 #include <raft/matrix/select_k.cuh>
+#include <raft/sparse/convert/coo.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/sparse/distance/detail/utils.cuh>
+#include <raft/sparse/linalg/sddmm.hpp>
+#include <raft/sparse/matrix/select_k.cuh>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
 
@@ -65,7 +76,8 @@ void tiled_brute_force_knn(const raft::resources& handle,
                            size_t max_row_tile_size                    = 0,
                            size_t max_col_tile_size                    = 0,
                            const ElementType* precomputed_index_norms  = nullptr,
-                           const ElementType* precomputed_search_norms = nullptr)
+                           const ElementType* precomputed_search_norms = nullptr,
+                           const uint32_t* filter_bitmap               = nullptr)
 {
   // Figure out the number of rows/cols to tile for
   size_t tile_rows   = 0;
@@ -214,6 +226,27 @@ void tiled_brute_force_knn(const raft::resources& handle,
           });
       }
 
+      if (filter_bitmap != nullptr) {
+        auto distances_ptr          = temp_distances.data();
+        auto count                  = thrust::make_counting_iterator<IndexType>(0);
+        ElementType masked_distance = select_min ? std::numeric_limits<ElementType>::infinity()
+                                                 : std::numeric_limits<ElementType>::lowest();
+        thrust::for_each(raft::resource::get_thrust_policy(handle),
+                         count,
+                         count + current_query_size * current_centroid_size,
+                         [=] __device__(IndexType idx) {
+                           IndexType row      = i + (idx / current_centroid_size);
+                           IndexType col      = j + (idx % current_centroid_size);
+                           IndexType g_idx    = row * n + col;
+                           IndexType item_idx = (g_idx) >> 5;
+                           uint32_t bit_idx   = (g_idx)&31;
+                           uint32_t filter    = filter_bitmap[item_idx];
+                           if ((filter & (uint32_t(1) << bit_idx)) == 0) {
+                             distances_ptr[idx] = masked_distance;
+                           }
+                         });
+      }
+
       raft::matrix::select_k<ElementType, IndexType>(
         handle,
         raft::make_device_matrix_view<const ElementType, int64_t, raft::row_major>(
@@ -519,6 +552,173 @@ void brute_force_search(
                                          query_norms ? query_norms->data_handle() : nullptr);
 }
 
+template <typename T, typename IdxT, typename BitmapT>
+void brute_force_search_filtered(
+  raft::resources const& res,
+  const cuvs::neighbors::brute_force::index<T>& idx,
+  raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
+  cuvs::core::bitmap_view<const BitmapT, IdxT> filter,
+  raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
+  raft::device_matrix_view<T, IdxT, raft::row_major> distances,
+  std::optional<raft::device_vector_view<const T, IdxT>> query_norms = std::nullopt)
+{
+  auto metric = idx.metric();
+
+  RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1), "Value of k must match for outputs");
+  RAFT_EXPECTS(idx.dataset().extent(1) == queries.extent(1),
+               "Number of columns in queries must match brute force index");
+  RAFT_EXPECTS(metric == cuvs::distance::DistanceType::InnerProduct ||
+                 metric == cuvs::distance::DistanceType::L2Expanded ||
+                 metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                 metric == cuvs::distance::DistanceType::CosineExpanded,
+               "Only Euclidean, IP, and Cosine are supported!");
+
+  RAFT_EXPECTS(idx.has_norms() || !(metric == cuvs::distance::DistanceType::L2Expanded ||
+                                    metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                                    metric == cuvs::distance::DistanceType::CosineExpanded),
+               "Index must has norms when using Euclidean, IP, and Cosine!");
+
+  IdxT n_queries = queries.extent(0);
+  IdxT n_dataset = idx.dataset().extent(0);
+  IdxT dim       = idx.dataset().extent(1);
+  IdxT k         = neighbors.extent(1);
+
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  // calc nnz
+  IdxT nnz_h = 0;
+  rmm::device_scalar<IdxT> nnz(0, stream);
+  auto nnz_view = raft::make_device_scalar_view<IdxT>(nnz.data());
+  auto filter_view =
+    raft::make_device_vector_view<const BitmapT, IdxT>(filter.data(), filter.n_elements());
+
+  // TODO(rhdong): Need to switch to the public API,
+  // with the issue: https://github.com/rapidsai/cuvs/issues/158
+  raft::detail::popc(res, filter_view, n_queries * n_dataset, nnz_view);
+  raft::copy(&nnz_h, nnz.data(), 1, stream);
+
+  raft::resource::sync_stream(res, stream);
+  float sparsity = (1.0f * nnz_h / (1.0f * n_queries * n_dataset));
+
+  if (sparsity > 0.01f) {
+    raft::resources stream_pool_handle(res);
+    raft::resource::set_cuda_stream(stream_pool_handle, stream);
+    auto idx_norm = idx.has_norms() ? const_cast<T*>(idx.norms().data_handle()) : nullptr;
+
+    tiled_brute_force_knn<T, IdxT>(stream_pool_handle,
+                                   queries.data_handle(),
+                                   idx.dataset().data_handle(),
+                                   n_queries,
+                                   n_dataset,
+                                   dim,
+                                   k,
+                                   distances.data_handle(),
+                                   neighbors.data_handle(),
+                                   metric,
+                                   2.0,
+                                   0,
+                                   0,
+                                   idx_norm,
+                                   nullptr,
+                                   filter.data());
+  } else {
+    auto csr = raft::make_device_csr_matrix<T, IdxT>(res, n_queries, n_dataset, nnz_h);
+
+    // fill csr
+    raft::sparse::convert::bitmap_to_csr(res, filter, csr);
+
+    // create filter csr view
+    auto compressed_csr_view = csr.structure_view();
+    rmm::device_uvector<IdxT> rows(compressed_csr_view.get_nnz(), stream);
+    raft::sparse::convert::csr_to_coo(compressed_csr_view.get_indptr().data(),
+                                      compressed_csr_view.get_n_rows(),
+                                      rows.data(),
+                                      compressed_csr_view.get_nnz(),
+                                      stream);
+    if (n_queries > 10) {
+      auto csr_view = raft::make_device_csr_matrix_view<T, IdxT, IdxT, IdxT>(
+        csr.get_elements().data(), compressed_csr_view);
+
+      // create dataset view
+      auto dataset_view = raft::make_device_matrix_view<const T, IdxT, raft::col_major>(
+        idx.dataset().data_handle(), dim, n_dataset);
+
+      // calc dot
+      T alpha = static_cast<T>(1.0f);
+      T beta  = static_cast<T>(0.0f);
+      raft::sparse::linalg::sddmm(res,
+                                  queries,
+                                  dataset_view,
+                                  csr_view,
+                                  raft::linalg::Operation::NON_TRANSPOSE,
+                                  raft::linalg::Operation::NON_TRANSPOSE,
+                                  raft::make_host_scalar_view<T>(&alpha),
+                                  raft::make_host_scalar_view<T>(&beta));
+    } else {
+      raft::sparse::distance::detail::faster_dot_on_csr(res,
+                                                        csr.get_elements().data(),
+                                                        compressed_csr_view.get_nnz(),
+                                                        compressed_csr_view.get_indptr().data(),
+                                                        compressed_csr_view.get_indices().data(),
+                                                        queries.data_handle(),
+                                                        idx.dataset().data_handle(),
+                                                        compressed_csr_view.get_n_rows(),
+                                                        dim);
+    }
+
+    // post process
+    std::optional<raft::device_vector<T, IdxT>> query_norms_;
+    if (metric == cuvs::distance::DistanceType::L2Expanded ||
+        metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+        metric == cuvs::distance::DistanceType::CosineExpanded) {
+      if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+        if (!query_norms) {
+          query_norms_ = raft::make_device_vector<T, IdxT>(res, n_queries);
+          raft::linalg::rowNorm((T*)(query_norms_->data_handle()),
+                                queries.data_handle(),
+                                dim,
+                                n_queries,
+                                raft::linalg::L2Norm,
+                                true,
+                                stream,
+                                raft::sqrt_op{});
+        }
+      } else {
+        if (!query_norms) {
+          query_norms_ = raft::make_device_vector<T, IdxT>(res, n_queries);
+          raft::linalg::rowNorm((T*)(query_norms_->data_handle()),
+                                queries.data_handle(),
+                                dim,
+                                n_queries,
+                                raft::linalg::L2Norm,
+                                true,
+                                stream,
+                                raft::identity_op{});
+        }
+      }
+      cuvs::neighbors::detail::epilogue_on_csr(
+        res,
+        csr.get_elements().data(),
+        compressed_csr_view.get_nnz(),
+        rows.data(),
+        compressed_csr_view.get_indices().data(),
+        query_norms ? query_norms->data_handle() : query_norms_->data_handle(),
+        idx.norms().data_handle(),
+        metric);
+    }
+
+    // select k
+    auto const_csr_view = raft::make_device_csr_matrix_view<const T, IdxT, IdxT, IdxT>(
+      csr.get_elements().data(), compressed_csr_view);
+    std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
+    bool select_min = cuvs::distance::is_min_close(metric);
+    raft::sparse::matrix::select_k(
+      res, const_csr_view, no_opt, distances, neighbors, select_min, true);
+  }
+
+  return;
+}
+
 template <typename T>
 cuvs::neighbors::brute_force::index<T> build(
   raft::resources const& res,
diff --git a/cpp/src/neighbors/detail/knn_utils.cuh b/cpp/src/neighbors/detail/knn_utils.cuh
new file mode 100644
index 0000000000..1cc709fa40
--- /dev/null
+++ b/cpp/src/neighbors/detail/knn_utils.cuh
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/math.hpp>
+
+#include <cuvs/distance/distance.hpp>
+
+#include <cub/cub.cuh>
+#include <cuda_pipeline.h>
+
+namespace cuvs::neighbors::detail {
+
+template <typename value_idx, typename value_t, typename expansion_f>
+RAFT_KERNEL epilogue_on_csr_kernel(value_t* __restrict__ compressed_C,
+                                   const value_idx* __restrict__ rows,
+                                   const value_idx* __restrict__ cols,
+                                   const value_t* __restrict__ Q_sq_norms,
+                                   const value_t* __restrict__ R_sq_norms,
+                                   value_idx nnz,
+                                   expansion_f expansion_func)
+{
+  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+
+  if (tid >= nnz) return;
+  const value_idx i = rows[tid];
+  const value_idx j = cols[tid];
+
+  compressed_C[tid] = expansion_func(compressed_C[tid], Q_sq_norms[i], R_sq_norms[j]);
+}
+
+template <typename value_idx, typename value_t, int tpb = 256>
+void epilogue_on_csr(raft::resources const& handle,
+                     value_t* compressed_C,
+                     const value_idx nnz,
+                     const value_idx* rows,
+                     const value_idx* cols,
+                     const value_t* Q_sq_norms,
+                     const value_t* R_sq_norms,
+                     cuvs::distance::DistanceType metric)
+{
+  if (nnz == 0) return;
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  int blocks = raft::ceildiv<size_t>((size_t)nnz, tpb);
+  if (metric == cuvs::distance::DistanceType::L2Expanded) {
+    epilogue_on_csr_kernel<<<blocks, tpb, 0, stream>>>(
+      compressed_C,
+      rows,
+      cols,
+      Q_sq_norms,
+      R_sq_norms,
+      nnz,
+      [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) -> value_t {
+        return value_t(-2.0) * dot + q_norm + r_norm;
+      });
+  } else if (metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    epilogue_on_csr_kernel<<<blocks, tpb, 0, stream>>>(
+      compressed_C,
+      rows,
+      cols,
+      Q_sq_norms,
+      R_sq_norms,
+      nnz,
+      [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) -> value_t {
+        return raft::sqrt(value_t(-2.0) * dot + q_norm + r_norm);
+      });
+  } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+    epilogue_on_csr_kernel<<<blocks, tpb, 0, stream>>>(
+      compressed_C,
+      rows,
+      cols,
+      Q_sq_norms,
+      R_sq_norms,
+      nnz,
+      [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) -> value_t {
+        return value_t(1.0) - dot / (q_norm * r_norm);
+      });
+  }
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+}  // namespace cuvs::neighbors::detail
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 3fbf62cdba..1fae2f70ba 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -91,7 +91,15 @@ endfunction()
 
 if(BUILD_TESTS)
   ConfigureTest(
-    NAME NEIGHBORS_TEST PATH test/neighbors/brute_force.cu test/neighbors/refine.cu GPUS 1 PERCENT
+    NAME
+    NEIGHBORS_TEST
+    PATH
+    test/neighbors/brute_force.cu
+    test/neighbors/brute_force_prefiltered.cu
+    test/neighbors/refine.cu
+    GPUS
+    1
+    PERCENT
     100
   )
 
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index fdb1801861..081a2966ee 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -82,7 +82,7 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs> {
 
     auto metric = cuvs::distance::DistanceType::L2Unexpanded;
     auto idx    = cuvs::neighbors::brute_force::build(handle, index, metric);
-    cuvs::neighbors::brute_force::search(handle, idx, search, indices, distances);
+    cuvs::neighbors::brute_force::search(handle, idx, search, indices, distances, std::nullopt);
 
     build_actual_output<<<raft::ceildiv(rows_ * k_, 32), 32, 0, stream>>>(
       actual_labels_.data(), rows_, k_, search_labels_.data(), indices_.data());
diff --git a/cpp/test/neighbors/brute_force_prefiltered.cu b/cpp/test/neighbors/brute_force_prefiltered.cu
new file mode 100644
index 0000000000..17166fd7a5
--- /dev/null
+++ b/cpp/test/neighbors/brute_force_prefiltered.cu
@@ -0,0 +1,524 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "knn_utils.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include <raft/core/detail/popc.cuh>
+#include <raft/matrix/copy.cuh>
+#include <raft/random/make_blobs.cuh>
+#include <raft/random/rmat_rectangular_generator.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/random/rng_state.hpp>
+
+#include <gtest/gtest.h>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <optional>
+#include <queue>
+#include <random>
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+namespace cuvs::neighbors::brute_force {
+
+template <typename index_t>
+struct PrefilteredBruteForceInputs {
+  index_t n_queries;
+  index_t n_dataset;
+  index_t dim;
+  index_t top_k;
+  float sparsity;
+  cuvs::distance::DistanceType metric;
+  bool select_min = true;
+};
+
+template <typename T>
+struct CompareApproxWithInf {
+  CompareApproxWithInf(T eps_) : eps(eps_) {}
+  bool operator()(const T& a, const T& b) const
+  {
+    if (std::isinf(a) && std::isinf(b)) return true;
+    T diff  = std::abs(a - b);
+    T m     = std::max(std::abs(a), std::abs(b));
+    T ratio = diff > eps ? diff / m : diff;
+
+    return (ratio <= eps);
+  }
+
+ private:
+  T eps;
+};
+
+template <typename OutT, typename InT>
+RAFT_KERNEL normalize_kernel(
+  OutT* theta, const InT* in_vals, size_t max_scale, size_t r_scale, size_t c_scale)
+{
+  size_t idx = threadIdx.x;
+  if (idx < max_scale) {
+    auto a   = OutT(in_vals[4 * idx]);
+    auto b   = OutT(in_vals[4 * idx + 1]);
+    auto c   = OutT(in_vals[4 * idx + 2]);
+    auto d   = OutT(in_vals[4 * idx + 3]);
+    auto sum = a + b + c + d;
+    a /= sum;
+    b /= sum;
+    c /= sum;
+    d /= sum;
+    theta[4 * idx]     = a;
+    theta[4 * idx + 1] = b;
+    theta[4 * idx + 2] = c;
+    theta[4 * idx + 3] = d;
+  }
+}
+
+template <typename OutT, typename InT>
+void normalize(OutT* theta,
+               const InT* in_vals,
+               size_t max_scale,
+               size_t r_scale,
+               size_t c_scale,
+               bool handle_rect,
+               bool theta_array,
+               cudaStream_t stream)
+{
+  normalize_kernel<OutT, InT><<<1, 256, 0, stream>>>(theta, in_vals, max_scale, r_scale, c_scale);
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+template <typename index_t, typename bitmap_t = uint32_t>
+RAFT_KERNEL set_bitmap_kernel(
+  const index_t* src, const index_t* dst, bitmap_t* bitmap, index_t n_edges, index_t n_cols)
+{
+  size_t idx = threadIdx.x + blockDim.x * blockIdx.x;
+  if (idx < n_edges) {
+    index_t row      = src[idx];
+    index_t col      = dst[idx];
+    index_t g_idx    = row * n_cols + col;
+    index_t item_idx = (g_idx) >> 5;
+    uint32_t bit_idx = (g_idx)&31;
+    atomicOr(bitmap + item_idx, (uint32_t(1) << bit_idx));
+  }
+}
+
+template <typename index_t, typename bitmap_t = uint32_t>
+void set_bitmap(const index_t* src,
+                const index_t* dst,
+                bitmap_t* bitmap,
+                index_t n_edges,
+                index_t n_cols,
+                cudaStream_t stream)
+{
+  int block_size = 256;
+  int blocks     = raft::ceildiv<index_t>(n_edges, block_size);
+  set_bitmap_kernel<index_t, bitmap_t>
+    <<<blocks, block_size, 0, stream>>>(src, dst, bitmap, n_edges, n_cols);
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+template <typename value_t, typename index_t, typename bitmap_t = uint32_t>
+class PrefilteredBruteForceTest
+  : public ::testing::TestWithParam<PrefilteredBruteForceInputs<index_t>> {
+ public:
+  PrefilteredBruteForceTest()
+    : stream(raft::resource::get_cuda_stream(handle)),
+      params(::testing::TestWithParam<PrefilteredBruteForceInputs<index_t>>::GetParam()),
+      filter_d(0, stream),
+      dataset_d(0, stream),
+      queries_d(0, stream),
+      out_val_d(0, stream),
+      out_val_expected_d(0, stream),
+      out_idx_d(0, stream),
+      out_idx_expected_d(0, stream)
+  {
+  }
+
+ protected:
+  index_t create_sparse_matrix_with_rmat(index_t m,
+                                         index_t n,
+                                         value_t sparsity,
+                                         rmm::device_uvector<bitmap_t>& filter_d)
+  {
+    index_t r_scale   = (index_t)std::log2(m);
+    index_t c_scale   = (index_t)std::log2(n);
+    index_t n_edges   = (index_t)(m * n * 1.0 * sparsity);
+    index_t max_scale = std::max(r_scale, c_scale);
+
+    rmm::device_uvector<index_t> out_src{(unsigned long)n_edges, stream};
+    rmm::device_uvector<index_t> out_dst{(unsigned long)n_edges, stream};
+    rmm::device_uvector<value_t> theta{(unsigned long)(4 * max_scale), stream};
+
+    raft::random::RngState state{2024ULL, raft::random::GeneratorType::GenPC};
+
+    raft::random::uniform<value_t>(handle, state, theta.data(), theta.size(), 0.0f, 1.0f);
+    normalize<value_t, value_t>(
+      theta.data(), theta.data(), max_scale, r_scale, c_scale, r_scale != c_scale, true, stream);
+    raft::random::rmat_rectangular_gen((index_t*)nullptr,
+                                       out_src.data(),
+                                       out_dst.data(),
+                                       theta.data(),
+                                       r_scale,
+                                       c_scale,
+                                       n_edges,
+                                       stream,
+                                       state);
+
+    index_t nnz_h = 0;
+    {
+      auto src    = out_src.data();
+      auto dst    = out_dst.data();
+      auto bitmap = filter_d.data();
+      rmm::device_scalar<index_t> nnz(0, stream);
+      auto nnz_view = raft::make_device_scalar_view<index_t>(nnz.data());
+      auto filter_view =
+        raft::make_device_vector_view<const uint32_t, index_t>(filter_d.data(), filter_d.size());
+
+      set_bitmap(src, dst, bitmap, n_edges, n, stream);
+
+      // TODO(rhdong): Need to switch to the public API,
+      // with the issue: https://github.com/rapidsai/cuvs/issues/158
+      raft::detail::popc(handle, filter_view, m * n, nnz_view);
+      raft::copy(&nnz_h, nnz.data(), 1, stream);
+
+      raft::resource::sync_stream(handle, stream);
+    }
+
+    return nnz_h;
+  }
+
+  void cpu_convert_to_csr(std::vector<bitmap_t>& bitmap,
+                          index_t rows,
+                          index_t cols,
+                          std::vector<index_t>& indices,
+                          std::vector<index_t>& indptr)
+  {
+    index_t offset_indptr   = 0;
+    index_t offset_values   = 0;
+    indptr[offset_indptr++] = 0;
+
+    index_t index        = 0;
+    bitmap_t element     = 0;
+    index_t bit_position = 0;
+
+    for (index_t i = 0; i < rows; ++i) {
+      for (index_t j = 0; j < cols; ++j) {
+        index        = i * cols + j;
+        element      = bitmap[index / (8 * sizeof(bitmap_t))];
+        bit_position = index % (8 * sizeof(bitmap_t));
+
+        if (((element >> bit_position) & 1)) {
+          indices[offset_values] = static_cast<index_t>(j);
+          offset_values++;
+        }
+      }
+      indptr[offset_indptr++] = static_cast<index_t>(offset_values);
+    }
+  }
+
+  void cpu_sddmm(const std::vector<value_t>& A,
+                 const std::vector<value_t>& B,
+                 std::vector<value_t>& vals,
+                 const std::vector<index_t>& cols,
+                 const std::vector<index_t>& row_ptrs,
+                 bool is_row_major_A,
+                 bool is_row_major_B,
+                 value_t alpha = 1.0,
+                 value_t beta  = 0.0)
+  {
+    if (params.n_queries * params.dim != static_cast<index_t>(A.size()) ||
+        params.dim * params.n_dataset != static_cast<index_t>(B.size())) {
+      std::cerr << "Matrix dimensions and vector size do not match!" << std::endl;
+      return;
+    }
+
+    bool trans_a = is_row_major_A;
+    bool trans_b = is_row_major_B;
+
+    for (index_t i = 0; i < params.n_queries; ++i) {
+      for (index_t j = row_ptrs[i]; j < row_ptrs[i + 1]; ++j) {
+        value_t sum     = 0;
+        value_t norms_A = 0;
+        value_t norms_B = 0;
+        for (index_t l = 0; l < params.dim; ++l) {
+          index_t a_index = trans_a ? i * params.dim + l : l * params.n_queries + i;
+          index_t b_index = trans_b ? l * params.n_dataset + cols[j] : cols[j] * params.dim + l;
+          sum += A[a_index] * B[b_index];
+
+          norms_A += A[a_index] * A[a_index];
+          norms_B += B[b_index] * B[b_index];
+        }
+        vals[j] = alpha * sum + beta * vals[j];
+        if (params.metric == cuvs::distance::DistanceType::L2Expanded) {
+          vals[j] = value_t(-2.0) * vals[j] + norms_A + norms_B;
+        } else if (params.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+          vals[j] = std::sqrt(value_t(-2.0) * vals[j] + norms_A + norms_B);
+        } else if (params.metric == cuvs::distance::DistanceType::CosineExpanded) {
+          vals[j] = value_t(1.0) - vals[j] / std::sqrt(norms_A * norms_B);
+        }
+      }
+    }
+  }
+
+  void cpu_select_k(const std::vector<index_t>& indptr_h,
+                    const std::vector<index_t>& indices_h,
+                    const std::vector<value_t>& values_h,
+                    std::optional<std::vector<index_t>>& in_idx_h,
+                    index_t n_queries,
+                    index_t n_dataset,
+                    index_t top_k,
+                    std::vector<value_t>& out_values_h,
+                    std::vector<index_t>& out_indices_h,
+                    bool select_min = true)
+  {
+    auto comp = [select_min](const std::pair<value_t, index_t>& a,
+                             const std::pair<value_t, index_t>& b) {
+      return select_min ? a.first < b.first : a.first >= b.first;
+    };
+
+    for (index_t row = 0; row < n_queries; ++row) {
+      std::priority_queue<std::pair<value_t, index_t>,
+                          std::vector<std::pair<value_t, index_t>>,
+                          decltype(comp)>
+        pq(comp);
+
+      for (index_t idx = indptr_h[row]; idx < indptr_h[row + 1]; ++idx) {
+        pq.push({values_h[idx], (in_idx_h.has_value()) ? (*in_idx_h)[idx] : indices_h[idx]});
+        if (pq.size() > size_t(top_k)) { pq.pop(); }
+      }
+
+      std::vector<std::pair<value_t, index_t>> row_pairs;
+      while (!pq.empty()) {
+        row_pairs.push_back(pq.top());
+        pq.pop();
+      }
+
+      if (select_min) {
+        std::sort(row_pairs.begin(), row_pairs.end(), [](const auto& a, const auto& b) {
+          return a.first <= b.first;
+        });
+      } else {
+        std::sort(row_pairs.begin(), row_pairs.end(), [](const auto& a, const auto& b) {
+          return a.first >= b.first;
+        });
+      }
+      for (index_t col = 0; col < top_k; col++) {
+        if (col < index_t(row_pairs.size())) {
+          out_values_h[row * top_k + col]  = row_pairs[col].first;
+          out_indices_h[row * top_k + col] = row_pairs[col].second;
+        }
+      }
+    }
+  }
+
+  void SetUp() override
+  {
+    index_t element =
+      raft::ceildiv(params.n_queries * params.n_dataset, index_t(sizeof(bitmap_t) * 8));
+    std::vector<bitmap_t> filter_h(element);
+    filter_d.resize(element, stream);
+
+    nnz =
+      create_sparse_matrix_with_rmat(params.n_queries, params.n_dataset, params.sparsity, filter_d);
+
+    raft::update_host(filter_h.data(), filter_d.data(), filter_d.size(), stream);
+    raft::resource::sync_stream(handle, stream);
+
+    index_t dataset_size = params.n_dataset * params.dim;
+    index_t queries_size = params.n_queries * params.dim;
+
+    std::vector<value_t> dataset_h(dataset_size);
+    std::vector<value_t> queries_h(queries_size);
+
+    dataset_d.resize(dataset_size, stream);
+    queries_d.resize(queries_size, stream);
+
+    auto blobs_in_val =
+      raft::make_device_matrix<value_t, index_t>(handle, 1, dataset_size + queries_size);
+    auto labels = raft::make_device_vector<index_t, index_t>(handle, 1);
+
+    raft::random::make_blobs<value_t, index_t>(blobs_in_val.data_handle(),
+                                               labels.data_handle(),
+                                               1,
+                                               dataset_size + queries_size,
+                                               1,
+                                               stream,
+                                               false,
+                                               nullptr,
+                                               nullptr,
+                                               value_t(1.0),
+                                               false,
+                                               value_t(-1.0f),
+                                               value_t(1.0f),
+                                               uint64_t(2024));
+
+    raft::copy(dataset_h.data(), blobs_in_val.data_handle(), dataset_size, stream);
+    raft::copy(dataset_d.data(), blobs_in_val.data_handle(), dataset_size, stream);
+
+    raft::copy(queries_h.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
+    raft::copy(queries_d.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
+
+    raft::resource::sync_stream(handle);
+
+    std::vector<value_t> values_h(nnz);
+    std::vector<index_t> indices_h(nnz);
+    std::vector<index_t> indptr_h(params.n_queries + 1);
+
+    cpu_convert_to_csr(filter_h, params.n_queries, params.n_dataset, indices_h, indptr_h);
+
+    cpu_sddmm(queries_h, dataset_h, values_h, indices_h, indptr_h, true, false);
+
+    bool select_min = cuvs::distance::is_min_close(params.metric);
+
+    std::vector<value_t> out_val_h(params.n_queries * params.top_k,
+                                   select_min ? std::numeric_limits<value_t>::infinity()
+                                              : std::numeric_limits<value_t>::lowest());
+    std::vector<index_t> out_idx_h(params.n_queries * params.top_k, static_cast<index_t>(0));
+
+    out_val_d.resize(params.n_queries * params.top_k, stream);
+    out_idx_d.resize(params.n_queries * params.top_k, stream);
+
+    raft::update_device(out_val_d.data(), out_val_h.data(), out_val_h.size(), stream);
+    raft::update_device(out_idx_d.data(), out_idx_h.data(), out_idx_h.size(), stream);
+
+    raft::resource::sync_stream(handle);
+
+    std::optional<std::vector<index_t>> optional_indices_h = std::nullopt;
+
+    cpu_select_k(indptr_h,
+                 indices_h,
+                 values_h,
+                 optional_indices_h,
+                 params.n_queries,
+                 params.n_dataset,
+                 params.top_k,
+                 out_val_h,
+                 out_idx_h,
+                 select_min);
+
+    out_val_expected_d.resize(params.n_queries * params.top_k, stream);
+    out_idx_expected_d.resize(params.n_queries * params.top_k, stream);
+
+    raft::update_device(out_val_expected_d.data(), out_val_h.data(), out_val_h.size(), stream);
+    raft::update_device(out_idx_expected_d.data(), out_idx_h.data(), out_idx_h.size(), stream);
+
+    raft::resource::sync_stream(handle);
+  }
+
+  void Run()
+  {
+    auto dataset_raw = raft::make_device_matrix_view<const value_t, index_t, raft::row_major>(
+      (const value_t*)dataset_d.data(), params.n_dataset, params.dim);
+
+    auto queries = raft::make_device_matrix_view<const value_t, index_t, raft::row_major>(
+      (const value_t*)queries_d.data(), params.n_queries, params.dim);
+
+    auto dataset = brute_force::build(handle, dataset_raw, params.metric);
+
+    auto filter = cuvs::core::bitmap_view<const bitmap_t, index_t>(
+      (const bitmap_t*)filter_d.data(), params.n_queries, params.n_dataset);
+
+    auto out_val = raft::make_device_matrix_view<value_t, index_t, raft::row_major>(
+      out_val_d.data(), params.n_queries, params.top_k);
+    auto out_idx = raft::make_device_matrix_view<index_t, index_t, raft::row_major>(
+      out_idx_d.data(), params.n_queries, params.top_k);
+
+    brute_force::search(handle, dataset, queries, out_idx, out_val, std::make_optional(filter));
+
+    ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(out_idx_expected_d.data(),
+                                                    out_idx.data_handle(),
+                                                    out_val_expected_d.data(),
+                                                    out_val.data_handle(),
+                                                    params.n_queries,
+                                                    params.top_k,
+                                                    0.001f,
+                                                    stream,
+                                                    true));
+  }
+
+ protected:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  PrefilteredBruteForceInputs<index_t> params;
+
+  index_t nnz;
+
+  rmm::device_uvector<value_t> dataset_d;
+  rmm::device_uvector<value_t> queries_d;
+  rmm::device_uvector<bitmap_t> filter_d;
+
+  rmm::device_uvector<value_t> out_val_d;
+  rmm::device_uvector<value_t> out_val_expected_d;
+
+  rmm::device_uvector<index_t> out_idx_d;
+  rmm::device_uvector<index_t> out_idx_expected_d;
+};
+
+using PrefilteredBruteForceTest_float_int64 = PrefilteredBruteForceTest<float, int64_t>;
+TEST_P(PrefilteredBruteForceTest_float_int64, Result) { Run(); }
+
+template <typename index_t>
+const std::vector<PrefilteredBruteForceInputs<index_t>> selectk_inputs = {
+  {2, 131072, 255, 255, 0.4, cuvs::distance::DistanceType::L2Expanded},
+  {8, 131072, 512, 16, 0.5, cuvs::distance::DistanceType::L2Expanded},
+  {16, 131072, 2052, 16, 0.2, cuvs::distance::DistanceType::L2Expanded},
+  {2, 8192, 255, 16, 0.4, cuvs::distance::DistanceType::InnerProduct},
+  {16, 8192, 512, 16, 0.5, cuvs::distance::DistanceType::InnerProduct},
+  {128, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 1, 0, 0.1, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 3, 0, 0.1, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 5, 0, 0.1, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1024, 8192, 8, 0, 0.1, cuvs::distance::DistanceType::CosineExpanded},
+
+  {1024, 8192, 1, 1, 0.1, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 3, 1, 0.1, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 5, 1, 0.1, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1024, 8192, 8, 1, 0.1, cuvs::distance::DistanceType::CosineExpanded},
+
+  {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::CosineExpanded},
+  {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::CosineExpanded},
+  {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::CosineExpanded},
+
+  {1024, 8192, 1, 16, 0.5, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 2, 16, 0.2, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 3, 16, 0.4, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 4, 16, 0.5, cuvs::distance::DistanceType::InnerProduct},
+  {1024, 8192, 5, 16, 0.2, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1024, 8192, 8, 16, 0.4, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1024, 8192, 5, 16, 0.5, cuvs::distance::DistanceType::CosineExpanded},
+  {1024, 8192, 8, 16, 0.2, cuvs::distance::DistanceType::CosineExpanded}};
+
+INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceTest,
+                        PrefilteredBruteForceTest_float_int64,
+                        ::testing::ValuesIn(selectk_inputs<int64_t>));
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/neighbors/knn_utils.cuh b/cpp/test/neighbors/knn_utils.cuh
new file mode 100644
index 0000000000..d95174ef65
--- /dev/null
+++ b/cpp/test/neighbors/knn_utils.cuh
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../test_utils.cuh"
+
+#include <raft/util/cudart_utils.hpp>
+
+#include <gtest/gtest.h>
+
+#include <memory>
+
+namespace cuvs::neighbors {
+template <typename IdxT, typename DistT, typename compareDist>
+struct idx_dist_pair {
+  IdxT idx;
+  DistT dist;
+  compareDist eq_compare;
+  bool operator==(const idx_dist_pair<IdxT, DistT, compareDist>& a) const
+  {
+    if (idx == a.idx) return true;
+    if (eq_compare(dist, a.dist)) return true;
+    return false;
+  }
+  idx_dist_pair(IdxT x, DistT y, compareDist op) : idx(x), dist(y), eq_compare(op) {}
+};
+
+template <typename T, typename DistT>
+testing::AssertionResult devArrMatchKnnPair(const T* expected_idx,
+                                            const T* actual_idx,
+                                            const DistT* expected_dist,
+                                            const DistT* actual_dist,
+                                            size_t rows,
+                                            size_t cols,
+                                            const DistT eps,
+                                            cudaStream_t stream = 0,
+                                            bool sort_inputs    = false)
+{
+  size_t size = rows * cols;
+  std::unique_ptr<T[]> exp_idx_h(new T[size]);
+  std::unique_ptr<T[]> act_idx_h(new T[size]);
+  std::unique_ptr<DistT[]> exp_dist_h(new DistT[size]);
+  std::unique_ptr<DistT[]> act_dist_h(new DistT[size]);
+  raft::update_host<T>(exp_idx_h.get(), expected_idx, size, stream);
+  raft::update_host<T>(act_idx_h.get(), actual_idx, size, stream);
+  raft::update_host<DistT>(exp_dist_h.get(), expected_dist, size, stream);
+  raft::update_host<DistT>(act_dist_h.get(), actual_dist, size, stream);
+
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+  for (size_t i(0); i < rows; ++i) {
+    std::vector<std::pair<DistT, T>> actual;
+    std::vector<std::pair<DistT, T>> expected;
+    for (size_t j(0); j < cols; ++j) {
+      auto idx      = i * cols + j;  // row major assumption!
+      auto exp_idx  = exp_idx_h.get()[idx];
+      auto act_idx  = act_idx_h.get()[idx];
+      auto exp_dist = exp_dist_h.get()[idx];
+      auto act_dist = act_dist_h.get()[idx];
+      actual.push_back(std::make_pair(act_dist, act_idx));
+      expected.push_back(std::make_pair(exp_dist, exp_idx));
+    }
+    if (sort_inputs) {
+      // inputs could be unsorted here, sort for comparison
+      std::sort(actual.begin(), actual.end());
+      std::sort(expected.begin(), expected.end());
+    }
+    for (size_t j(0); j < cols; ++j) {
+      auto act = actual[j];
+      auto exp = expected[j];
+      idx_dist_pair exp_kvp(exp.second, exp.first, cuvs::CompareApprox<DistT>(eps));
+      idx_dist_pair act_kvp(act.second, act.first, cuvs::CompareApprox<DistT>(eps));
+      if (!(exp_kvp == act_kvp)) {
+        return testing::AssertionFailure()
+               << "actual=" << act_kvp.idx << "," << act_kvp.dist << "!="
+               << "expected" << exp_kvp.idx << "," << exp_kvp.dist << " @" << i << "," << j;
+      }
+    }
+  }
+  return testing::AssertionSuccess();
+}
+}  // namespace cuvs::neighbors

From 5937d543af276123d57837650309c913b1ca196d Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Thu, 30 May 2024 08:01:41 +0900
Subject: [PATCH 067/623] chore: update Doxyfile (#162)

efficively -> effectively

Authors:
  - Ikko Eltociear Ashimine (https://github.com/eltociear)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/162
---
 cpp/doxygen/Doxyfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index 614cf43d99..2459d521d5 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -454,7 +454,7 @@ LOOKUP_CACHE_SIZE      = 0
 # than 0 to get more control over the balance between CPU load and processing
 # speed. At this moment only the input processing can be done using multiple
 # threads. Since this is still an experimental feature the default is set to 1,
-# which efficively disables parallel processing. Please report any issues you
+# which effectively disables parallel processing. Please report any issues you
 # encounter. Generating dot graphs in parallel is controlled by the
 # DOT_NUM_THREADS setting.
 # Minimum value: 0, maximum value: 32, default value: 1.

From 758d147b50e8f25b15b892a015d3f090f30bd5b7 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 29 May 2024 18:11:21 -0700
Subject: [PATCH 068/623] Select k instantiations (#159)

Since we're now using raft in header only mode, we don't have the compiled select_k instantiations in raft available to us anymore. Instead instantiate inside cuvs so we don't recompile in multiple spots.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/159
---
 cpp/CMakeLists.txt                            |   3 +
 cpp/include/cuvs/selection/select_k.hpp       | 201 ++++++++++++++++++
 .../detail/cagra/search_multi_kernel.cuh      |   5 +-
 cpp/src/neighbors/detail/knn_brute_force.cuh  |   6 +-
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |  62 +++---
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |  42 ++--
 cpp/src/selection/select_k.cuh                |  80 +++++++
 cpp/src/selection/select_k_float_int64_t.cu   |  19 ++
 cpp/src/selection/select_k_float_uint32_t.cu  |  19 ++
 cpp/src/selection/select_k_half_uint32_t.cu   |  19 ++
 docs/source/cpp_api.rst                       |   1 +
 docs/source/cpp_api/selection.rst             |  19 ++
 12 files changed, 423 insertions(+), 53 deletions(-)
 create mode 100644 cpp/include/cuvs/selection/select_k.hpp
 create mode 100644 cpp/src/selection/select_k.cuh
 create mode 100644 cpp/src/selection/select_k_float_int64_t.cu
 create mode 100644 cpp/src/selection/select_k_float_uint32_t.cu
 create mode 100644 cpp/src/selection/select_k_half_uint32_t.cu
 create mode 100644 docs/source/cpp_api/selection.rst

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d0c4f85489..d4ab1e62bb 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -437,6 +437,9 @@ add_library(
   src/neighbors/refine/detail/refine_host_int8_t_float.cpp
   src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
   src/neighbors/sample_filter.cu
+  src/selection/select_k_float_int64_t.cu
+  src/selection/select_k_float_uint32_t.cu
+  src/selection/select_k_half_uint32_t.cu
 )
 
 target_compile_options(
diff --git a/cpp/include/cuvs/selection/select_k.hpp b/cpp/include/cuvs/selection/select_k.hpp
new file mode 100644
index 0000000000..dc34caf419
--- /dev/null
+++ b/cpp/include/cuvs/selection/select_k.hpp
@@ -0,0 +1,201 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuda_fp16.h>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/matrix/select_k_types.hpp>
+
+#include <optional>
+
+namespace cuvs::selection {
+using SelectAlgo = raft::matrix::SelectAlgo;
+
+/**
+ * @defgroup select_k Batched-select k smallest or largest key/values
+ * @{
+ */
+
+/**
+ * Select k smallest or largest key/values from each row in the input data.
+ *
+ * If you think of the input data `in_val` as a row-major matrix with `len` columns and
+ * `batch_size` rows, then this function selects `k` smallest/largest values in each row and fills
+ * in the row-major matrix `out_val` of size (batch_size, k).
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace raft;
+ *   // get a 2D row-major array of values to search through
+ *   auto in_values = {... input device_matrix_view<const float, int64_t, row_major> ...}
+ *   // prepare output arrays
+ *   auto out_extents = make_extents<int64_t>(in_values.extent(0), k);
+ *   auto out_values  = make_device_mdarray<float>(handle, out_extents);
+ *   auto out_indices = make_device_mdarray<int64_t>(handle, out_extents);
+ *   // search `k` smallest values in each row
+ *   cuvs::selection::select_k(
+ *     handle, in_values, std::nullopt, out_values.view(), out_indices.view(), true);
+ * @endcode
+ *
+ * @param[in] handle container of reusable resources
+ * @param[in] in_val
+ *   inputs values [batch_size, len];
+ *   these are compared and selected.
+ * @param[in] in_idx
+ *   optional input payload [batch_size, len];
+ *   typically, these are indices of the corresponding `in_val`.
+ *   If `in_idx` is `std::nullopt`, a contiguous array `0...len-1` is implied.
+ * @param[out] out_val
+ *   output values [batch_size, k];
+ *   the k smallest/largest values from each row of the `in_val`.
+ * @param[out] out_idx
+ *   output payload (e.g. indices) [batch_size, k];
+ *   the payload selected together with `out_val`.
+ * @param[in] select_min
+ *   whether to select k smallest (true) or largest (false) keys.
+ * @param[in] sorted
+ *   whether to make sure selected pairs are sorted by value
+ * @param[in] algo
+ *   the selection algorithm to use
+ * @param[in] len_i
+ *  optional array of size (batch_size) providing lengths for each individual row
+ */
+void select_k(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> in_val,
+  std::optional<raft::device_matrix_view<const int64_t, int64_t, raft::row_major>> in_idx,
+  raft::device_matrix_view<float, int64_t, raft::row_major> out_val,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> out_idx,
+  bool select_min,
+  bool sorted                                                           = false,
+  SelectAlgo algo                                                       = SelectAlgo::kAuto,
+  std::optional<raft::device_vector_view<const int64_t, int64_t>> len_i = std::nullopt);
+
+/**
+ * Select k smallest or largest key/values from each row in the input data.
+ *
+ * If you think of the input data `in_val` as a row-major matrix with `len` columns and
+ * `batch_size` rows, then this function selects `k` smallest/largest values in each row and fills
+ * in the row-major matrix `out_val` of size (batch_size, k).
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace raft;
+ *   // get a 2D row-major array of values to search through
+ *   auto in_values = {... input device_matrix_view<const float, int64_t, row_major> ...}
+ *   // prepare output arrays
+ *   auto out_extents = make_extents<int64_t>(in_values.extent(0), k);
+ *   auto out_values  = make_device_mdarray<float>(handle, out_extents);
+ *   auto out_indices = make_device_mdarray<uint32_t>(handle, out_extents);
+ *   // search `k` smallest values in each row
+ *   cuvs::selection::select_k(
+ *     handle, in_values, std::nullopt, out_values.view(), out_indices.view(), true);
+ * @endcode
+ *
+ * @param[in] handle container of reusable resources
+ * @param[in] in_val
+ *   inputs values [batch_size, len];
+ *   these are compared and selected.
+ * @param[in] in_idx
+ *   optional input payload [batch_size, len];
+ *   typically, these are indices of the corresponding `in_val`.
+ *   If `in_idx` is `std::nullopt`, a contiguous array `0...len-1` is implied.
+ * @param[out] out_val
+ *   output values [batch_size, k];
+ *   the k smallest/largest values from each row of the `in_val`.
+ * @param[out] out_idx
+ *   output payload (e.g. indices) [batch_size, k];
+ *   the payload selected together with `out_val`.
+ * @param[in] select_min
+ *   whether to select k smallest (true) or largest (false) keys.
+ * @param[in] sorted
+ *   whether to make sure selected pairs are sorted by value
+ * @param[in] algo
+ *   the selection algorithm to use
+ * @param[in] len_i
+ *  optional array of size (batch_size) providing lengths for each individual row
+ */
+void select_k(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> in_val,
+  std::optional<raft::device_matrix_view<const uint32_t, int64_t, raft::row_major>> in_idx,
+  raft::device_matrix_view<float, int64_t, raft::row_major> out_val,
+  raft::device_matrix_view<uint32_t, int64_t, raft::row_major> out_idx,
+  bool select_min,
+  bool sorted                                                            = false,
+  SelectAlgo algo                                                        = SelectAlgo::kAuto,
+  std::optional<raft::device_vector_view<const uint32_t, int64_t>> len_i = std::nullopt);
+
+/**
+ * Select k smallest or largest key/values from each row in the input data.
+ *
+ * If you think of the input data `in_val` as a row-major matrix with `len` columns and
+ * `batch_size` rows, then this function selects `k` smallest/largest values in each row and fills
+ * in the row-major matrix `out_val` of size (batch_size, k).
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace raft;
+ *   // get a 2D row-major array of values to search through
+ *   auto in_values = {... input device_matrix_view<const half, int64_t, row_major> ...}
+ *   // prepare output arrays
+ *   auto out_extents = make_extents<int64_t>(in_values.extent(0), k);
+ *   auto out_values  = make_device_mdarray<half>(handle, out_extents);
+ *   auto out_indices = make_device_mdarray<uint32_t>(handle, out_extents);
+ *   // search `k` smallest values in each row
+ *   cuvs::selection::select_k(
+ *     handle, in_values, std::nullopt, out_values.view(), out_indices.view(), true);
+ * @endcode
+ *
+ * @param[in] handle container of reusable resources
+ * @param[in] in_val
+ *   inputs values [batch_size, len];
+ *   these are compared and selected.
+ * @param[in] in_idx
+ *   optional input payload [batch_size, len];
+ *   typically, these are indices of the corresponding `in_val`.
+ *   If `in_idx` is `std::nullopt`, a contiguous array `0...len-1` is implied.
+ * @param[out] out_val
+ *   output values [batch_size, k];
+ *   the k smallest/largest values from each row of the `in_val`.
+ * @param[out] out_idx
+ *   output payload (e.g. indices) [batch_size, k];
+ *   the payload selected together with `out_val`.
+ * @param[in] select_min
+ *   whether to select k smallest (true) or largest (false) keys.
+ * @param[in] sorted
+ *   whether to make sure selected pairs are sorted by value
+ * @param[in] algo
+ *   the selection algorithm to use
+ * @param[in] len_i
+ *  optional array of size (batch_size) providing lengths for each individual row
+ */
+void select_k(
+  raft::resources const& handle,
+  raft::device_matrix_view<const half, int64_t, raft::row_major> in_val,
+  std::optional<raft::device_matrix_view<const uint32_t, int64_t, raft::row_major>> in_idx,
+  raft::device_matrix_view<half, int64_t, raft::row_major> out_val,
+  raft::device_matrix_view<uint32_t, int64_t, raft::row_major> out_idx,
+  bool select_min,
+  bool sorted                                                            = false,
+  SelectAlgo algo                                                        = SelectAlgo::kAuto,
+  std::optional<raft::device_vector_view<const uint32_t, int64_t>> len_i = std::nullopt);
+/** @} */  // end of group select_k
+
+}  // namespace cuvs::selection
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index 4dd53600ce..bc1266fb4b 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -30,9 +30,8 @@
 
 #include <cuvs/distance/distance.hpp>
 
-#include <raft/matrix/select_k.cuh>
-
 #include <cuvs/neighbors/common.hpp>
+#include <cuvs/selection/select_k.hpp>
 
 // TODO: This shouldn't be invoking anything from spatial/knn
 #include "../ann_utils.cuh"
@@ -817,7 +816,7 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
       output_values_storage.resize(sizeBatch * topK, stream);
     }
 
-    raft::matrix::select_k<float, INDEX_T>(
+    cuvs::selection::select_k(
       handle,
       raft::make_device_matrix_view<const float, int64_t>(inputKeys, sizeBatch, numElements),
       raft::make_device_matrix_view<const INDEX_T, int64_t>(inputVals, sizeBatch, numElements),
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 4865ade77d..97f7fba75d 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -18,6 +18,7 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/selection/select_k.hpp>
 
 #include "../../distance/detail/distance_ops/l2_exp.cuh"
 #include "./faiss_distance_utils.h"
@@ -38,7 +39,6 @@
 #include <raft/linalg/norm.cuh>
 #include <raft/linalg/transpose.cuh>
 #include <raft/matrix/init.cuh>
-#include <raft/matrix/select_k.cuh>
 #include <raft/sparse/convert/coo.cuh>
 #include <raft/sparse/convert/csr.cuh>
 #include <raft/sparse/distance/detail/utils.cuh>
@@ -247,7 +247,7 @@ void tiled_brute_force_knn(const raft::resources& handle,
                          });
       }
 
-      raft::matrix::select_k<ElementType, IndexType>(
+      cuvs::selection::select_k(
         handle,
         raft::make_device_matrix_view<const ElementType, int64_t, raft::row_major>(
           temp_distances.data(), current_query_size, current_centroid_size),
@@ -289,7 +289,7 @@ void tiled_brute_force_knn(const raft::resources& handle,
 
     if (tile_cols != n) {
       // select the actual top-k items here from the temporary output
-      raft::matrix::select_k<ElementType, IndexType>(
+      cuvs::selection::select_k(
         handle,
         raft::make_device_matrix_view<const ElementType, int64_t, raft::row_major>(
           temp_out_distances.data(), current_query_size, temp_out_cols),
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index f55e99c185..d5efdeb2dc 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -23,15 +23,15 @@
 #include <cuvs/neighbors/common.hpp>      // none_ivf_sample_filter
 #include <cuvs/neighbors/ivf_flat.hpp>    // raft::neighbors::ivf_flat::index
 
-#include "../detail/ann_utils.cuh"     // utils::mapping
-#include <cuvs/distance/distance.hpp>  // is_min_close, DistanceType
-#include <raft/core/logger-ext.hpp>    // RAFT_LOG_TRACE
+#include "../detail/ann_utils.cuh"      // utils::mapping
+#include <cuvs/distance/distance.hpp>   // is_min_close, DistanceType
+#include <cuvs/selection/select_k.hpp>  // cuvs::selection::select_k
+#include <raft/core/logger-ext.hpp>     // RAFT_LOG_TRACE
 #include <raft/core/resource/cuda_stream.hpp>
-#include <raft/core/resources.hpp>          // raft::resources
-#include <raft/linalg/gemm.cuh>             // raft::linalg::gemm
-#include <raft/linalg/norm.cuh>             // raft::linalg::norm
-#include <raft/linalg/unary_op.cuh>         // raft::linalg::unary_op
-#include <raft/matrix/detail/select_k.cuh>  // matrix::detail::select_k
+#include <raft/core/resources.hpp>   // raft::resources
+#include <raft/linalg/gemm.cuh>      // raft::linalg::gemm
+#include <raft/linalg/norm.cuh>      // raft::linalg::norm
+#include <raft/linalg/unary_op.cuh>  // raft::linalg::unary_op
 
 #include <rmm/resource_ref.hpp>
 
@@ -145,15 +145,17 @@ void search_impl(raft::resources const& handle,
                      stream);
 
   RAFT_LOG_TRACE_VEC(distance_buffer_dev.data(), std::min<uint32_t>(20, index.n_lists()));
-  raft::matrix::detail::select_k<AccT, uint32_t>(handle,
-                                                 distance_buffer_dev.data(),
-                                                 nullptr,
-                                                 n_queries,
-                                                 index.n_lists(),
-                                                 n_probes,
-                                                 coarse_distances_dev.data(),
-                                                 coarse_indices_dev.data(),
-                                                 select_min);
+
+  cuvs::selection::select_k(
+    handle,
+    raft::make_device_matrix_view<const AccT, int64_t>(
+      distance_buffer_dev.data(), n_queries, index.n_lists()),
+    std::nullopt,
+    raft::make_device_matrix_view<AccT, int64_t>(coarse_distances_dev.data(), n_queries, n_probes),
+    raft::make_device_matrix_view<uint32_t, int64_t>(
+      coarse_indices_dev.data(), n_queries, n_probes),
+    select_min);
+
   RAFT_LOG_TRACE_VEC(coarse_indices_dev.data(), n_probes);
   RAFT_LOG_TRACE_VEC(coarse_distances_dev.data(), n_probes);
 
@@ -238,19 +240,25 @@ void search_impl(raft::resources const& handle,
 
   // Merge topk values from different blocks
   if (!manage_local_topk || grid_dim_x > 1) {
-    raft::matrix::detail::select_k<AccT, uint32_t>(
+    std::optional<raft::device_vector_view<const uint32_t>> num_samples_vector;
+    if (!manage_local_topk) {
+      num_samples_vector =
+        raft::make_device_vector_view<const uint32_t>(num_samples.data(), n_queries);
+    }
+
+    auto cols = manage_local_topk ? (k * grid_dim_x) : max_samples;
+
+    cuvs::selection::select_k(
       handle,
-      distances_tmp_dev.data(),
-      indices_tmp_dev.data(),
-      n_queries,
-      manage_local_topk ? (k * grid_dim_x) : max_samples,
-      k,
-      distances,
-      neighbors_uint32,
+      raft::make_device_matrix_view<const AccT, int64_t>(distances_tmp_dev.data(), n_queries, cols),
+      raft::make_device_matrix_view<const uint32_t, int64_t>(
+        indices_tmp_dev.data(), n_queries, cols),
+      raft::make_device_matrix_view<AccT, int64_t>(distances, n_queries, k),
+      raft::make_device_matrix_view<uint32_t, int64_t>(neighbors_uint32, n_queries, k),
       select_min,
       false,
-      raft::matrix::SelectAlgo::kAuto,
-      manage_local_topk ? nullptr : num_samples.data());
+      cuvs::selection::SelectAlgo::kAuto,
+      num_samples_vector);
   }
   if (!manage_local_topk) {
     // post process distances && neighbor IDs
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index 16cf7e7437..e131b8fb30 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -25,6 +25,7 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/selection/select_k.hpp>
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/logger-ext.hpp>
@@ -37,7 +38,6 @@
 #include <raft/linalg/gemm.cuh>
 #include <raft/linalg/map.cuh>
 #include <raft/linalg/unary_op.cuh>
-#include <raft/matrix/detail/select_k.cuh>
 #include <raft/matrix/detail/select_warpsort.cuh>
 #include <raft/util/cache.hpp>
 #include <raft/util/cuda_utils.cuh>
@@ -158,15 +158,13 @@ void select_clusters(raft::resources const& handle,
 
   // Select neighbor clusters for each query.
   rmm::device_uvector<float> cluster_dists(n_queries * n_probes, stream, mr);
-  raft::matrix::detail::select_k<float, uint32_t>(handle,
-                                                  qc_distances.data(),
-                                                  nullptr,
-                                                  n_queries,
-                                                  n_lists,
-                                                  n_probes,
-                                                  cluster_dists.data(),
-                                                  clusters_to_probe,
-                                                  true);
+  cuvs::selection::select_k(
+    handle,
+    raft::make_device_matrix_view<const float, int64_t>(qc_distances.data(), n_queries, n_lists),
+    std::nullopt,
+    raft::make_device_matrix_view<float, int64_t>(cluster_dists.data(), n_queries, n_probes),
+    raft::make_device_matrix_view<uint32_t, int64_t>(clusters_to_probe, n_queries, n_probes),
+    true);
 }
 
 /**
@@ -440,19 +438,23 @@ void ivfpq_search_worker(raft::resources const& handle,
 
   // Select topk vectors for each query
   rmm::device_uvector<ScoreT> topk_dists(n_queries * topK, stream, mr);
-  raft::matrix::detail::select_k<ScoreT, uint32_t>(
+
+  std::optional<raft::device_vector_view<const uint32_t>> num_samples_vector;
+  if (!manage_local_topk) {
+    num_samples_vector =
+      raft::make_device_vector_view<const uint32_t>(num_samples.data(), n_queries);
+  }
+
+  cuvs::selection::select_k(
     handle,
-    distances_buf.data(),
-    neighbors_ptr,
-    n_queries,
-    topk_len,
-    topK,
-    topk_dists.data(),
-    neighbors_uint32,
+    raft::make_device_matrix_view<const ScoreT, int64_t>(distances_buf.data(), n_queries, topk_len),
+    raft::make_device_matrix_view<const uint32_t, int64_t>(neighbors_ptr, n_queries, topk_len),
+    raft::make_device_matrix_view<ScoreT, int64_t>(topk_dists.data(), n_queries, topK),
+    raft::make_device_matrix_view<uint32_t, int64_t>(neighbors_uint32, n_queries, topK),
     true,
     false,
-    raft::matrix::SelectAlgo::kAuto,
-    manage_local_topk ? nullptr : num_samples.data());
+    cuvs::selection::SelectAlgo::kAuto,
+    num_samples_vector);
 
   // Postprocessing
   ivf::detail::postprocess_distances(
diff --git a/cpp/src/selection/select_k.cuh b/cpp/src/selection/select_k.cuh
new file mode 100644
index 0000000000..78c30f98a5
--- /dev/null
+++ b/cpp/src/selection/select_k.cuh
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/selection/select_k.hpp>
+#include <raft/matrix/detail/select_k.cuh>
+
+namespace cuvs::selection::detail {
+
+template <typename T, typename IdxT>
+void select_k(raft::resources const& handle,
+              raft::device_matrix_view<const T, int64_t, raft::row_major> in_val,
+              std::optional<raft::device_matrix_view<const IdxT, int64_t, raft::row_major>> in_idx,
+              raft::device_matrix_view<T, int64_t, raft::row_major> out_val,
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> out_idx,
+              bool select_min,
+              bool sorted,
+              SelectAlgo algo,
+              std::optional<raft::device_vector_view<const IdxT, int64_t>> len_i)
+{
+  RAFT_EXPECTS(out_val.extent(1) <= int64_t(std::numeric_limits<int>::max()),
+               "output k must fit the int type.");
+  auto batch_size = in_val.extent(0);
+  auto len        = in_val.extent(1);
+  auto k          = int(out_val.extent(1));
+  RAFT_EXPECTS(batch_size == out_val.extent(0), "batch sizes must be equal");
+  RAFT_EXPECTS(batch_size == out_idx.extent(0), "batch sizes must be equal");
+  if (in_idx.has_value()) {
+    RAFT_EXPECTS(batch_size == in_idx->extent(0), "batch sizes must be equal");
+    RAFT_EXPECTS(len == in_idx->extent(1), "value and index input lengths must be equal");
+  }
+  RAFT_EXPECTS(int64_t(k) == out_idx.extent(1), "value and index output lengths must be equal");
+
+  // just delegate implementation to raft - the primary benefit here is to have
+  // instantiations only compiled once in cuvs
+  return raft::matrix::detail::select_k<T, IdxT>(
+    handle,
+    in_val.data_handle(),
+    in_idx.has_value() ? in_idx->data_handle() : nullptr,
+    batch_size,
+    len,
+    k,
+    out_val.data_handle(),
+    out_idx.data_handle(),
+    select_min,
+    sorted,
+    algo,
+    len_i.has_value() ? len_i->data_handle() : nullptr);
+}
+}  // namespace cuvs::selection::detail
+
+#define instantiate_cuvs_selection_select_k(T, IdxT)                                      \
+  void cuvs::selection::select_k(                                                         \
+    raft::resources const& handle,                                                        \
+    raft::device_matrix_view<const T, int64_t, raft::row_major> in_val,                   \
+    std::optional<raft::device_matrix_view<const IdxT, int64_t, raft::row_major>> in_idx, \
+    raft::device_matrix_view<T, int64_t, raft::row_major> out_val,                        \
+    raft::device_matrix_view<IdxT, int64_t, raft::row_major> out_idx,                     \
+    bool select_min,                                                                      \
+    bool sorted,                                                                          \
+    SelectAlgo algo,                                                                      \
+    std::optional<raft::device_vector_view<const IdxT, int64_t>> len_i)                   \
+  {                                                                                       \
+    detail::select_k<T, IdxT>(                                                            \
+      handle, in_val, in_idx, out_val, out_idx, select_min, sorted, algo, len_i);         \
+  }
diff --git a/cpp/src/selection/select_k_float_int64_t.cu b/cpp/src/selection/select_k_float_int64_t.cu
new file mode 100644
index 0000000000..707d6add2d
--- /dev/null
+++ b/cpp/src/selection/select_k_float_int64_t.cu
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./select_k.cuh"
+
+instantiate_cuvs_selection_select_k(float, int64_t);
diff --git a/cpp/src/selection/select_k_float_uint32_t.cu b/cpp/src/selection/select_k_float_uint32_t.cu
new file mode 100644
index 0000000000..05202f4812
--- /dev/null
+++ b/cpp/src/selection/select_k_float_uint32_t.cu
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./select_k.cuh"
+
+instantiate_cuvs_selection_select_k(float, uint32_t);
diff --git a/cpp/src/selection/select_k_half_uint32_t.cu b/cpp/src/selection/select_k_half_uint32_t.cu
new file mode 100644
index 0000000000..b05cb2299a
--- /dev/null
+++ b/cpp/src/selection/select_k_half_uint32_t.cu
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./select_k.cuh"
+
+instantiate_cuvs_selection_select_k(half, uint32_t);
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index e04fff0b87..270d5c06ed 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -9,3 +9,4 @@ C++ API Documentation
 
    cpp_api/distance.rst
    cpp_api/neighbors.rst
+   cpp_api/selection.rst
diff --git a/docs/source/cpp_api/selection.rst b/docs/source/cpp_api/selection.rst
new file mode 100644
index 0000000000..5abe81662f
--- /dev/null
+++ b/docs/source/cpp_api/selection.rst
@@ -0,0 +1,19 @@
+Selection
+=========
+
+This page provides C++ class references for the publicly-exposed elements of the `cuvs/selection`
+package.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+Select-K
+--------
+
+``#include <cuvs/selection/select_k.hpp>``
+
+namespace *cuvs::selection*
+
+.. doxygengroup:: select_k
+   :project: cuvs

From e0d169e3415dc34b893b4b8cad05b4392911a0fc Mon Sep 17 00:00:00 2001
From: Shunya Ueta <hurutoriya@users.noreply.github.com>
Date: Thu, 30 May 2024 21:30:40 +0900
Subject: [PATCH 069/623] update: replace to cuvs from RAFT in
 PULL_REQUEST_TEMPLATE (#92)

## What

SSIA

Authors:
  - Shunya Ueta (https://github.com/hurutoriya)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/92
---
 .github/PULL_REQUEST_TEMPLATE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 9c42cda720..23d3a11feb 100755
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,6 +1,6 @@
 <!--
 
-Thank you for contributing to RAFT :)
+Thank you for contributing to cuvs :)
 
 Here are some guidelines to help the review process go smoothly.
 

From a4c3a6f38e0213d1dca0f361293bc0da03940c69 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 30 May 2024 10:32:20 -0400
Subject: [PATCH 070/623] cuVS docs updates for release (#161)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/161
---
 cpp/include/cuvs/cluster/agglomerative.hpp    |  16 +
 cpp/include/cuvs/cluster/kmeans.hpp           | 364 +++++++++++-
 cpp/include/cuvs/neighbors/nn_descent.hpp     |  19 +-
 docs/source/cpp_api.rst                       |   1 +
 docs/source/cpp_api/cluster.rst               |  14 +
 docs/source/cpp_api/cluster_agglomerative.rst |  31 +
 docs/source/cpp_api/cluster_kmeans.rst        |  44 ++
 docs/source/cpp_api/neighbors.rst             |   3 +-
 docs/source/cpp_api/neighbors_nn_descent.rst  |  37 ++
 docs/source/python_api.rst                    |   1 +
 docs/source/python_api/distance.rst           |  12 +
 docs/source/working_with_ann_indexes_c.rst    |  29 +
 docs/source/working_with_ann_indexes_cpp.rst  |  24 +-
 .../working_with_ann_indexes_python.rst       |  14 +-
 .../VectorSearch_QuestionRetrieval.ipynb      | 378 ++----------
 notebooks/ivf_flat_example.ipynb              | 208 ++-----
 notebooks/tutorial_ivf_pq.ipynb               | 546 ++----------------
 17 files changed, 751 insertions(+), 990 deletions(-)
 create mode 100644 docs/source/cpp_api/cluster.rst
 create mode 100644 docs/source/cpp_api/cluster_agglomerative.rst
 create mode 100644 docs/source/cpp_api/cluster_kmeans.rst
 create mode 100644 docs/source/cpp_api/neighbors_nn_descent.rst
 create mode 100644 docs/source/python_api/distance.rst

diff --git a/cpp/include/cuvs/cluster/agglomerative.hpp b/cpp/include/cuvs/cluster/agglomerative.hpp
index 658f5628cf..e1da040854 100644
--- a/cpp/include/cuvs/cluster/agglomerative.hpp
+++ b/cpp/include/cuvs/cluster/agglomerative.hpp
@@ -26,6 +26,11 @@ namespace cuvs::cluster::agglomerative {
 // constant to indirectly control the number of neighbors. k = sqrt(n) + c. default to 15
 constexpr int DEFAULT_CONST_C = 15;
 
+/**
+ * @defgroup agglomerative_params agglomerative clustering hyperparameters
+ * @{
+ */
+
 /**
  * Determines the method for computing the minimum spanning tree (MST)
  */
@@ -45,6 +50,10 @@ enum Linkage {
   KNN_GRAPH = 1
 };
 
+/**
+ * @}
+ */
+
 /**
  * Simple container object for consolidating linkage results. This closely
  * mirrors the trained instance variables populated in
@@ -75,6 +84,10 @@ class single_linkage_output {
   }
 };
 
+/**
+ * @defgroup single_linkage single-linkage clustering APIs
+ * @{
+ */
 /**
  * Single-linkage clustering, capable of constructing a KNN graph to
  * scale the algorithm beyond the n^2 memory consumption of implementations
@@ -105,4 +118,7 @@ void single_linkage(
   cuvs::cluster::agglomerative::Linkage linkage = cuvs::cluster::agglomerative::Linkage::KNN_GRAPH,
   std::optional<int> c                          = std::make_optional<int>(DEFAULT_CONST_C));
 
+/**
+ * @}
+ */
 };  // end namespace  cuvs::cluster::agglomerative
diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index 7a372b1bc3..c6fe4d0465 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -30,6 +30,11 @@ struct base_params {
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
 };
 
+/**
+ * @defgroup kmeans_params k-means hyperparameters
+ * @{
+ */
+
 /**
  * Simple object to specify hyper-parameters to the kmeans algorithm.
  */
@@ -127,9 +132,56 @@ struct balanced_params : base_params {
 };
 
 /**
- * Regular k-means
+ * @}
+ */
+
+/**
+ * @defgroup kmeans k-means clustering APIs
+ * @{
  */
 
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids,
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
 void fit(raft::resources const& handle,
          const cuvs::cluster::kmeans::params& params,
          raft::device_matrix_view<const float, int> X,
@@ -138,6 +190,48 @@ void fit(raft::resources const& handle,
          raft::host_scalar_view<float, int> inertia,
          raft::host_scalar_view<int, int> n_iter);
 
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids,
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
 void fit(raft::resources const& handle,
          const cuvs::cluster::kmeans::params& params,
          raft::device_matrix_view<const int8_t, int> X,
@@ -147,14 +241,68 @@ void fit(raft::resources const& handle,
          raft::host_scalar_view<int, int> n_iter);
 
 /**
- * Balanced k-means
+ * @brief Find balanced clusters with k-means algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::balanced_params params;
+ *   int n_features = 15;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               centroids);
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[out]  centroids       [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
  */
-
 void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
          raft::device_matrix_view<const float, int> X,
          raft::device_matrix_view<float, int> centroids);
 
+/**
+ * @brief Find balanced clusters with k-means algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::balanced_params params;
+ *   int n_features = 15;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               centroids);
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[inout]  centroids     [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ */
 void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
          raft::device_matrix_view<const int8_t, int> X,
@@ -218,12 +366,94 @@ void predict(raft::resources const& handle,
              bool normalize_weight,
              raft::host_scalar_view<float> inertia);
 
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids.view(),
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ *   ...
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   std::nullopt,
+ *                   centroids.view(),
+ *                   false,
+ *                   labels.view(),
+ *                   raft::make_scalar_view(&ineratia));
+ * @endcode
+ *
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ */
 void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
              raft::device_matrix_view<const int8_t, int> X,
              raft::device_matrix_view<const float, int> centroids,
              raft::device_vector_view<uint32_t, int> labels);
 
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::balanced_params params;
+ *   int n_features = 15;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               centroids.view());
+ *   ...
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   centroids.view(),
+ *                   labels.view());
+ * @endcode
+ *
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ */
 void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
              raft::device_matrix_view<const float, int> X,
@@ -285,12 +515,88 @@ void fit_predict(raft::resources const& handle,
                  raft::host_scalar_view<float> inertia,
                  raft::host_scalar_view<int> n_iter);
 
+/**
+ * @brief Compute balanced k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::balanced_params params;
+ *   int n_features = 15;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::fit_predict(handle,
+ *                       params,
+ *                       X,
+ *                       centroids.view(),
+ *                       labels.view());
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ */
 void fit_predict(const raft::resources& handle,
                  cuvs::cluster::kmeans::balanced_params const& params,
                  raft::device_matrix_view<const float, int> X,
                  raft::device_matrix_view<float, int> centroids,
                  raft::device_vector_view<uint32_t, int> labels);
 
+/**
+ * @brief Compute balanced k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *    cuvs::cluster::kmeans::balanced_params params;
+ *   int n_features = 15;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::fit_predict(handle,
+ *                       params,
+ *                       X,
+ *                       centroids.view(),
+ *                       labels.view());
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ */
 void fit_predict(const raft::resources& handle,
                  cuvs::cluster::kmeans::balanced_params const& params,
                  raft::device_matrix_view<const int8_t, int> X,
@@ -316,8 +622,54 @@ void transform(raft::resources const& handle,
                raft::device_matrix_view<const float, int> centroids,
                raft::device_matrix_view<float, int> X_new);
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup kmeans_helpers k-means API helpers
+ * @{
+ */
+
 namespace helpers {
 
+/**
+ * Automatically find the optimal value of k using a binary search.
+ * This method maximizes the Calinski-Harabasz Index while minimizing the per-cluster inertia.
+ *
+ *  @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *
+ *   #include <raft/random/make_blobs.cuh>
+ *
+ *   using namespace  cuvs::cluster;
+ *
+ *   raft::handle_t handle;
+ *   int n_samples = 100, n_features = 15, n_clusters = 10;
+ *   auto X = raft::make_device_matrix<float, int>(handle, n_samples, n_features);
+ *   auto labels = raft::make_device_vector<float, int>(handle, n_samples);
+ *
+ *   raft::random::make_blobs(handle, X, labels, n_clusters);
+ *
+ *   auto best_k = raft::make_host_scalar<int>(0);
+ *   auto n_iter = raft::make_host_scalar<int>(0);
+ *   auto inertia = raft::make_host_scalar<int>(0);
+ *
+ *   kmeans::find_k(handle, X, best_k.view(), inertia.view(), n_iter.view(), n_clusters+1);
+ *
+ * @endcode
+ *
+ * @param handle raft handle
+ * @param X input observations (shape n_samples, n_dims)
+ * @param best_k best k found from binary search
+ * @param inertia inertia of best k found
+ * @param n_iter number of iterations used to find best k
+ * @param kmax maximum k to try in search
+ * @param kmin minimum k to try in search (should be >= 1)
+ * @param maxiter maximum number of iterations to run
+ * @param tol tolerance for early stopping convergence
+ */
 void find_k(raft::resources const& handle,
             raft::device_matrix_view<const float, int> X,
             raft::host_scalar_view<int> best_k,
@@ -327,6 +679,10 @@ void find_k(raft::resources const& handle,
             int kmin    = 1,
             int maxiter = 100,
             float tol   = 1e-3);
-}
+}  // namespace helpers
+
+/**
+ * @}
+ */
 
 }  // namespace  cuvs::cluster::kmeans
diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index d697f4a3f0..1f216d68f4 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -29,7 +29,7 @@
 
 namespace cuvs::neighbors::nn_descent {
 /**
- * @defgroup nn-descent The nn-descent algorithm.
+ * @defgroup nn_descent_cpp_index_params The nn-descent algorithm parameters.
  * @{
  */
 
@@ -55,6 +55,14 @@ struct index_params : cuvs::neighbors::index_params {
   float termination_threshold      = 0.0001;  // Termination threshold of nn-descent.
 };
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup nn_descent_cpp_index nn-descent index
+ * @{
+ */
 /**
  * @brief nn-descent Build an nn-descent index
  * The index contains an all-neighbors graph of the input dataset
@@ -153,6 +161,11 @@ struct index : cuvs::neighbors::index {
 
 /** @} */
 
+/**
+ * @defgroup nn_descent_cpp_index_build nn-descent index build
+ * @{
+ */
+
 /**
  * @brief Build nn-descent Index with dataset in device memory
  *
@@ -339,4 +352,8 @@ auto build(raft::resources const& res,
            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
+/**
+ * @}
+ */
+
 }  // namespace cuvs::neighbors::nn_descent
\ No newline at end of file
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index 270d5c06ed..5b8b4841af 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -7,6 +7,7 @@ C++ API Documentation
 .. toctree::
    :maxdepth: 4
 
+   cpp_api/cluster.rst
    cpp_api/distance.rst
    cpp_api/neighbors.rst
    cpp_api/selection.rst
diff --git a/docs/source/cpp_api/cluster.rst b/docs/source/cpp_api/cluster.rst
new file mode 100644
index 0000000000..3e3e627f75
--- /dev/null
+++ b/docs/source/cpp_api/cluster.rst
@@ -0,0 +1,14 @@
+Cluster
+=======
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   cluster_kmeans.rst
+   cluster_agglomerative.rst
+
diff --git a/docs/source/cpp_api/cluster_agglomerative.rst b/docs/source/cpp_api/cluster_agglomerative.rst
new file mode 100644
index 0000000000..08a811780a
--- /dev/null
+++ b/docs/source/cpp_api/cluster_agglomerative.rst
@@ -0,0 +1,31 @@
+Cluster
+=======
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+Params
+------
+
+``#include <cuvs/cluster/agglomerative.hpp>``
+
+namespace *cuvs::cluster::agglomerative*
+
+.. doxygengroup:: agglomerative_params
+   :project: cuvs
+   :members:
+   :content-only:
+
+
+Single-linkage
+--------------
+
+``include <cuvs/cluster/agglomerative.hpp>``
+
+namespace *cuvs::cluster::agglomerative*
+
+.. doxygengroup:: single_linkage
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cpp_api/cluster_kmeans.rst b/docs/source/cpp_api/cluster_kmeans.rst
new file mode 100644
index 0000000000..3ff8a03e5a
--- /dev/null
+++ b/docs/source/cpp_api/cluster_kmeans.rst
@@ -0,0 +1,44 @@
+Cluster
+=======
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+Params
+------
+
+``#include <cuvs/cluster/kmeans.hpp>``
+
+namespace *cuvs::cluster::kmeans*
+
+.. doxygengroup:: kmeans_params
+   :project: cuvs
+   :members:
+   :content-only:
+
+
+K-means
+-------
+
+``include <cuvs/cluster/kmeans.hpp>``
+
+namespace *cuvs::cluster::kmeans*
+
+.. doxygengroup:: kmeans
+    :project: cuvs
+    :members:
+    :content-only:
+
+
+K-means Helpers
+---------------
+
+``include <cuvs/cluster/kmeans.hpp>``
+
+namespace *cuvs::cluster::kmeans::helpers*
+
+.. doxygengroup:: kmeans_helpers
+    :project: cuvs
+    :members:
+    :content-only:
\ No newline at end of file
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index 15b4ff0516..e5a9fc9773 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -10,7 +10,8 @@ Nearest Neighbors
    :caption: Contents:
 
    neighbors_bruteforce.rst
+   neighbors_cagra.rst
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
-   neighbors_cagra.rst
+   neighbors_nn_descent.rst
    neighbors_refine.rst
diff --git a/docs/source/cpp_api/neighbors_nn_descent.rst b/docs/source/cpp_api/neighbors_nn_descent.rst
new file mode 100644
index 0000000000..edecdfa8df
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_nn_descent.rst
@@ -0,0 +1,37 @@
+NN-Descent
+==========
+
+The NN-descent method is an ANN algorithm that directly approximates a k-nearest neighbors graph by randomly sampling points to compute distances and using neighbors of neighbors distances to reduce distance computations.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/nn_descent.hpp>``
+
+namespace *cuvs::neighbors::nn_descent*
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: nn_descent_cpp_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+
+Index
+-----
+
+.. doxygengroup:: nn_descent_cpp_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: nn_descent_cpp_index_build
+    :project: cuvs
+    :members:
+    :content-only:
\ No newline at end of file
diff --git a/docs/source/python_api.rst b/docs/source/python_api.rst
index 999c40fd37..69bcd300b6 100644
--- a/docs/source/python_api.rst
+++ b/docs/source/python_api.rst
@@ -7,4 +7,5 @@ Python API Documentation
 .. toctree::
    :maxdepth: 4
 
+   python_api/distance.rst
    python_api/neighbors.rst
\ No newline at end of file
diff --git a/docs/source/python_api/distance.rst b/docs/source/python_api/distance.rst
new file mode 100644
index 0000000000..fd9efa853e
--- /dev/null
+++ b/docs/source/python_api/distance.rst
@@ -0,0 +1,12 @@
+Distance
+========
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+
+Pairwise Distance
+##################
+
+.. autofunction:: cuvs.distance.pairwise_distance
diff --git a/docs/source/working_with_ann_indexes_c.rst b/docs/source/working_with_ann_indexes_c.rst
index 6632e1c96e..89f368aaac 100644
--- a/docs/source/working_with_ann_indexes_c.rst
+++ b/docs/source/working_with_ann_indexes_c.rst
@@ -18,6 +18,8 @@ Building an index
     cuvsCagraIndex_t index;
 
     DLManagedTensor *dataset;
+
+    // populate tensor with data
     load_dataset(dataset);
 
     cuvsResourcesCreate(&res);
@@ -34,6 +36,33 @@ Building an index
 Searching an index
 ------------------
 
+.. code-block:: c
+
+    #include <cuvs/neighbors/cagra.h>
+
+    cuvsResources_t res;
+    cuvsCagraSearchParams_t search_params;
+    cuvsCagraIndex_t index;
+
+    // ... build index ...
+
+    DLManagedTensor *queries;
+
+    DLManagedTensor *neighbors;
+    DLManagedTensor *distances;
+
+    // populate tensor with data
+    load_queries(queries);
+
+    cuvsResourcesCreate(&res);
+    cuvsCagraSearchParamsCreate(&index_params);
+
+    cuvsCagraSearch(res, search_params, index, queries, neighbors, distances);
+
+    cuvsCagraIndexDestroy(index);
+    cuvsCagraIndexParamsDestroy(index_params);
+    cuvsResourcesDestroy(res);
+
 
 Serializing an index
 --------------------
\ No newline at end of file
diff --git a/docs/source/working_with_ann_indexes_cpp.rst b/docs/source/working_with_ann_indexes_cpp.rst
index a74b4c88c1..1d3225cb85 100644
--- a/docs/source/working_with_ann_indexes_cpp.rst
+++ b/docs/source/working_with_ann_indexes_cpp.rst
@@ -23,13 +23,29 @@ Building an index
     auto index = cagra::build(res, index_params, dataset);
 
 
-
 Searching an index
 ------------------
 
+.. code-block:: c++
+
+    #include <cuvs/neighbors/cagra.hpp>
+
+    using namespace cuvs::neighbors;
+    cagra::index index;
+
+    // ... build index ...
+
+    raft::device_matrix_view<float> queries = load_queries();
+    raft::device_matrix_view<uint32_t> neighbors = make_device_matrix_view<uint32_t>(n_queries, k);
+    raft::device_matrix_view<float> distances = make_device_matrix_view<float>(n_queries, k);
+    raft::device_resources res;
+
+    cagra::search_params search_params;
+
+    cagra::search(res, search_params, index, queries, neighbors, distances);
 
-CPU/GPU interoperability
-------------------------
 
 Serializing an index
---------------------
\ No newline at end of file
+--------------------
+
+
diff --git a/docs/source/working_with_ann_indexes_python.rst b/docs/source/working_with_ann_indexes_python.rst
index bc439bbd5b..e9aba18019 100644
--- a/docs/source/working_with_ann_indexes_python.rst
+++ b/docs/source/working_with_ann_indexes_python.rst
@@ -22,9 +22,19 @@ Building an index
 Searching an index
 ------------------
 
+.. code-block:: python
+
+    from cuvs.neighbors import cagra
+
+    queries = load_queries()
+
+    search_params = cagra.SearchParams()
+
+    index = // ... build index ...
+
+    neighbors, distances = cagra.search(search_params, index, queries, k)
+
 
-CPU/GPU interoperability
-------------------------
 
 Serializing an index
 --------------------
\ No newline at end of file
diff --git a/notebooks/VectorSearch_QuestionRetrieval.ipynb b/notebooks/VectorSearch_QuestionRetrieval.ipynb
index b3a15d3a08..4023a1821b 100644
--- a/notebooks/VectorSearch_QuestionRetrieval.ipynb
+++ b/notebooks/VectorSearch_QuestionRetrieval.ipynb
@@ -8,77 +8,21 @@
     "\n",
     "# Similar Questions Retrieval\n",
     "\n",
-    "This notebook is inspired by the [similar search example of Sentence-Transformers](https://www.sbert.net/examples/applications/semantic-search/README.html#similar-questions-retrieval), and adapted to support [RAFT ANN](https://github.com/rapidsai/raft) algorithm.\n",
+    "This notebook is inspired by the [similar search example of Sentence-Transformers](https://www.sbert.net/examples/applications/semantic-search/README.html#similar-questions-retrieval), and adapted to support [cuVS ANN](https://github.com/rapidsai/cuvs) algorithm.\n",
     "\n",
     "The model was pre-trained on the [Natural Questions dataset](https://ai.google.com/research/NaturalQuestions). It consists of about 100k real Google search queries, together with an annotated passage from Wikipedia that provides the answer. It is an example of an asymmetric search task. As corpus, we use the smaller [Simple English Wikipedia](http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz) so that it fits easily into memory.\n",
     "\n",
-    "The steps to install the latest stable `pylibraft` package are available in the [documentation](https://docs.rapids.ai/api/raft/stable/build)."
+    "The steps to install the latest stable `cuvs` python package are available in the [documentation](https://docs.rapids.ai/api/cuvs/nightly/build/)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "e8d55ede",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: sentence_transformers in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (2.2.2)\n",
-      "Requirement already satisfied: torch in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (2.0.1)\n",
-      "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (4.31.0)\n",
-      "Requirement already satisfied: tqdm in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (4.65.0)\n",
-      "Requirement already satisfied: torchvision in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (0.15.2)\n",
-      "Requirement already satisfied: numpy in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (1.24.4)\n",
-      "Requirement already satisfied: scikit-learn in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (1.3.0)\n",
-      "Requirement already satisfied: scipy in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (1.11.1)\n",
-      "Requirement already satisfied: nltk in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (3.8.1)\n",
-      "Requirement already satisfied: sentencepiece in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (0.1.99)\n",
-      "Requirement already satisfied: huggingface-hub>=0.4.0 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sentence_transformers) (0.16.4)\n",
-      "Requirement already satisfied: filelock in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (3.12.2)\n",
-      "Requirement already satisfied: typing-extensions in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (4.7.1)\n",
-      "Requirement already satisfied: sympy in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (1.12)\n",
-      "Requirement already satisfied: networkx in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (3.1)\n",
-      "Requirement already satisfied: jinja2 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (3.1.2)\n",
-      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (11.7.99)\n",
-      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (11.7.99)\n",
-      "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (11.7.101)\n",
-      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (8.5.0.96)\n",
-      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (11.10.3.66)\n",
-      "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (10.9.0.58)\n",
-      "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (10.2.10.91)\n",
-      "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (11.4.0.1)\n",
-      "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (11.7.4.91)\n",
-      "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (2.14.3)\n",
-      "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (11.7.91)\n",
-      "Requirement already satisfied: triton==2.0.0 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torch) (2.0.0)\n",
-      "Requirement already satisfied: setuptools in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch) (68.0.0)\n",
-      "Requirement already satisfied: wheel in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch) (0.41.0)\n",
-      "Requirement already satisfied: cmake in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from triton==2.0.0->torch) (3.27.0)\n",
-      "Requirement already satisfied: lit in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from triton==2.0.0->torch) (16.0.6)\n",
-      "Requirement already satisfied: fsspec in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (2023.6.0)\n",
-      "Requirement already satisfied: requests in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (2.31.0)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (6.0)\n",
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-      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.13.3)\n",
-      "Requirement already satisfied: safetensors>=0.3.1 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.3.1)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from jinja2->torch) (2.1.3)\n",
-      "Requirement already satisfied: click in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from nltk->sentence_transformers) (8.1.6)\n",
-      "Requirement already satisfied: joblib in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from nltk->sentence_transformers) (1.3.0)\n",
-      "Requirement already satisfied: threadpoolctl>=2.0.0 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from scikit-learn->sentence_transformers) (3.2.0)\n",
-      "Requirement already satisfied: mpmath>=0.19 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\n",
-      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from torchvision->sentence_transformers) (10.0.0)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (3.2.0)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (3.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2.0.4)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /raid/danteg/miniconda3/envs/raft-ann/lib/python3.10/site-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2023.7.22)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install sentence_transformers torch\n",
     "\n",
@@ -89,84 +33,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "eb1e81c3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mon Jul 31 14:35:31 2023       \n",
-      "+-----------------------------------------------------------------------------+\n",
-      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
-      "|-------------------------------+----------------------+----------------------+\n",
-      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
-      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
-      "|                               |                      |               MIG M. |\n",
-      "|===============================+======================+======================|\n",
-      "|   0  NVIDIA H100 80G...  On   | 00000000:1B:00.0 Off |                    0 |\n",
-      "| N/A   30C    P0    75W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "|   1  NVIDIA H100 80G...  On   | 00000000:43:00.0 Off |                    0 |\n",
-      "| N/A   31C    P0    72W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "|   2  NVIDIA H100 80G...  On   | 00000000:52:00.0 Off |                    0 |\n",
-      "| N/A   34C    P0    70W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "|   3  NVIDIA H100 80G...  On   | 00000000:61:00.0 Off |                    0 |\n",
-      "| N/A   33C    P0    70W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "|   4  NVIDIA H100 80G...  On   | 00000000:9D:00.0 Off |                    0 |\n",
-      "| N/A   32C    P0    74W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "|   5  NVIDIA H100 80G...  On   | 00000000:C3:00.0 Off |                    0 |\n",
-      "| N/A   30C    P0    73W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "|   6  NVIDIA H100 80G...  On   | 00000000:D1:00.0 Off |                    0 |\n",
-      "| N/A   33C    P0    73W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "|   7  NVIDIA H100 80G...  On   | 00000000:DF:00.0 Off |                    0 |\n",
-      "| N/A   35C    P0    73W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
-      "|                               |                      |             Disabled |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "                                                                               \n",
-      "+-----------------------------------------------------------------------------+\n",
-      "| Processes:                                                                  |\n",
-      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
-      "|        ID   ID                                                   Usage      |\n",
-      "|=============================================================================|\n",
-      "|  No running processes found                                                 |\n",
-      "+-----------------------------------------------------------------------------+\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!nvidia-smi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "ee4c5cc0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/raid/danteg/miniconda3/envs/raftann/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import json\n",
     "from sentence_transformers import SentenceTransformer, CrossEncoder, util\n",
@@ -175,7 +55,7 @@
     "import os\n",
     "import torch\n",
     "import pylibraft\n",
-    "from pylibraft.neighbors import ivf_flat, ivf_pq\n",
+    "from cuvs.neighbors import ivf_flat, ivf_pq\n",
     "pylibraft.config.set_output_as(lambda device_ndarray: device_ndarray.copy_to_host())\n",
     "\n",
     "if not torch.cuda.is_available():\n",
@@ -184,18 +64,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "0a1a6307",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Passages: 509663\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We use the Bi-Encoder to encode all passages, so that we can use it with semantic search\n",
     "model_name = 'nq-distilbert-base-v1'\n",
@@ -240,37 +112,26 @@
    "id": "1f4e9b9d",
    "metadata": {},
    "source": [
-    "# Vector Search using RAPIDS RAFT\n",
-    "Now that our embeddings are ready to be indexed and that the model has been loaded, we can use RAPIDS RAFT to do our vector search.\n",
+    "# Vector Search using RAPIDS cuVS\n",
+    "Now that our embeddings are ready to be indexed and that the model has been loaded, we can use RAPIDS cuVS to do our vector search.\n",
     "\n",
     "This is done in two step: First we build the index, then we search it.\n",
-    "With `pylibraft` all you need is those four Python lines:"
+    "With `cuvs` all you need is those four Python lines:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "ad90b4be",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[W] [14:35:48.810785] [raft::ivf_pq::build] the default cuda resource is used for the raft workspace allocations. This may lead to a significant slowdown for this algorithm. Consider using the default pool resource (`raft::resource::set_workspace_to_pool_resource`) or set your own resource explicitly (`raft::resource::set_workspace_resource`).\n",
-      "[W] [14:35:53.831753] [raft::ivf_pq::extend] the default cuda resource is used for the raft workspace allocations. This may lead to a significant slowdown for this algorithm. Consider using the default pool resource (`raft::resource::set_workspace_to_pool_resource`) or set your own resource explicitly (`raft::resource::set_workspace_resource`).\n",
-      "CPU times: user 2.21 s, sys: 2.49 s, total: 4.7 s\n",
-      "Wall time: 5.13 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "params = ivf_pq.IndexParams(n_lists=150, pq_dim=96)\n",
     "pq_index = ivf_pq.build(params, corpus_embeddings)\n",
     "search_params = ivf_pq.SearchParams()\n",
     "\n",
-    "def search_raft_pq(query, top_k = 5):\n",
+    "def search_cuvs_pq(query, top_k = 5):\n",
     "    # Encode the query using the bi-encoder and find potentially relevant passages\n",
     "    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n",
     "\n",
@@ -292,22 +153,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "724dcacb",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IVF-PQ memory footprint: 373.3 MB\n",
-      "Original dataset: 1493.2 MB\n",
-      "Memory saved: 75.0%\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pq_index_mem = pq_index.pq_dim * pq_index.size * pq_index.pq_bits\n",
     "print(\"IVF-PQ memory footprint: {:.1f} MB\".format(pq_index_mem / 2**20))\n",
@@ -320,105 +171,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "c27d4715",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[W] [14:36:07.640223] [raft::ivf_pq::search] the default cuda resource is used for the raft workspace allocations. This may lead to a significant slowdown for this algorithm. Consider using the default pool resource (`raft::resource::set_workspace_to_pool_resource`) or set your own resource explicitly (`raft::resource::set_workspace_resource`).\n",
-      "Input question: Who was Grace Hopper?\n",
-      "\t190.855\t['Leona Helmsley', 'Leona Helmsley (July 4, 1920 – August 20, 2007) was an American businesswoman. She was known for having a flamboyant personality. She had a reputation for tyrannical behavior; she was nicknamed the Queen of Mean.']\n",
-      "\t195.364\t['Grace Hopper', 'Hopper was born in New York, USA. Hopper graduated from Vassar College in 1928 and Yale University in 1934 with a Ph.D degree in mathematics. She joined the US Navy during the World War II in 1943. She worked on computers in the Navy for 43 years. She then worked in other private industry companies after 1949. She retired from the Navy in 1986 and died on January 1, 1992.']\n",
-      "\t202.536\t['Anita Borg', 'Anita Borg (January 17, 1949 – April 6, 2003) was an American computer scientist. She founded the Institute for Women and Technology and the Grace Hopper Celebration of Women in Computing.']\n",
-      "\t203.717\t['Brett Butler', 'Brett Butler (born January 30, 1958) is an American actress and stand-up comedian. She is best known for playing Grace in the sitcom \"Grace Under Fire\". She has also done other television programs and comedy acts.']\n",
-      "\t203.991\t['Nellie Bly', 'Elizabeth Cochrane Seaman (born Elizabeth Jane Cochran; May 5, 1864 – January 27, 1922), better known by her pen name Nellie Bly, was an American journalist, novelist and inventor. She was a newspaper reporter, who worked at various jobs for exposing poor working conditions. Nellie Bly, also, fought for women\\'s right and was known for investigative reporting. She best known for her record-breaking trip around the world in 72 days, inspired by the adventure novel \"Around the World in Eighty Days\" by Jules Verne. In the 1880s, she went undercover as a mentally ill patient in a psychiatric hospital for ten days, with the report being made public in a book called \"\"Ten Days in a Mad-House\"\". She was added to the National Women\\'s Hall of Fame in 1998.']\n",
-      "CPU times: user 98.3 ms, sys: 81.2 ms, total: 180 ms\n",
-      "Wall time: 120 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "search_raft_pq(query=\"Who was Grace Hopper?\")"
+    "search_cuvs_pq(query=\"Who was Grace Hopper?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "bc375518",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input question: Who was Alan Turing?\n",
-      "\t139.827\t['Alan Turing', 'Alan Mathison Turing OBE FRS (London, 23 June 1912 – Wilmslow, Cheshire, 7 June 1954) was an English mathematician and computer scientist. He was born in Maida Vale, London.']\n",
-      "\t169.849\t['William Kahan', 'William Morton Kahan (born June 5, 1933) is a Canadian mathematician and computer scientist. He received the Turing Award in 1989 for \"\"his fundamental contributions to numerical analysis\".\" He was named an ACM Fellow in 1994, and added to the National Academy of Engineering in 2005.']\n",
-      "\t177.520\t['Rolf Noskwith', 'Rolf Noskwith (19 June 1919 – 3 January 2017) was a British businessman. During the Second World War, he worked under Alan Turing as a cryptographer at the British military base Bletchley Park in Milton Keynes, Buckinghamshire.']\n",
-      "\t179.202\t['Marvin Minsky', \"Marvin Lee Minsky (August 9, 1927 – January 24, 2016) was an American cognitive scientist in the field of artificial intelligence (AI). He was the co-founder of the Massachusetts Institute of Technology's AI laboratory, and author of several texts on AI and philosophy. He won the Turing Award in 1969.\"]\n",
-      "\t179.819\t['Edsger W. Dijkstra', 'Edsger Wybe Dijkstra (May 11, 1930 – August 6, 2002; ) was a Dutch computer scientist. He received the 1972 Turing Award for fundamental contributions to developing programming languages, and was the Schlumberger Centennial Chair of Computer Sciences at The University of Texas at Austin from 1984 until 2000.']\n",
-      "CPU times: user 4.89 ms, sys: 7.52 ms, total: 12.4 ms\n",
-      "Wall time: 12 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "search_raft_pq(query=\"Who was Alan Turing?\")"
+    "search_cuvs_pq(query=\"Who was Alan Turing?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "ab154181",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input question: What is creating tides?\n",
-      "\t125.037\t['Tide', \"A tide is the periodic rising and falling of Earth's ocean surface caused mainly by the gravitational pull of the Moon acting on the oceans. Tides cause changes in the depth of marine and estuarine (river mouth) waters. Tides also make oscillating currents known as tidal streams (~'rip tides'). This means that being able to predict the tide is important for coastal navigation. The strip of seashore that is under water at high tide and exposed at low tide, called the intertidal zone, is an important ecological product of ocean tides.\"]\n",
-      "\t163.835\t['Tidal energy', \"Many things affect tides. The pull of the Moon is the largest effect, and most of the energy comes from the slowing of the Earth's spin.\"]\n",
-      "\t167.368\t['Storm surge', 'A storm surge is a sudden rise of water hitting areas close to the coast. Storm surges are usually created by a hurricane or other tropical cyclone. The surge happens because a storm has fast winds and low atmospheric pressure. Water is pushed on shore, and the water level rises. Strong storm surges can flood coastal towns and destroy homes. A storm surge is considered the deadliest part of a hurricane. They kill many people each year.']\n",
-      "\t177.143\t['Tidal force', 'Tidal force is caused by gravity and makes tides happen. This is because the gravitational field changes across the middle of a body (the diameter).']\n",
-      "\t186.108\t['Tsunami', \"A tsunami is a natural disaster which is a series of fast-moving waves in the ocean caused by powerful earthquakes, volcanic eruptions, landslides, or simply an asteroid or a meteor crash inside the ocean. A tsunami has a very long wavelength. It can be hundreds of kilometers long. Usually, a tsunami starts suddenly. The waves travel at a great speed across an ocean with little energy loss. They can remove sand from beaches, destroy trees, toss and drag vehicles, houses and even destroy whole towns. Tsunamis can even be caused when a meteorite strikes the earth's surface, though it is very rare. A tsunami normally occurs in the Pacific Ocean, especially in what is called the ring of fire, but can occur in any large body of water.\"]\n",
-      "CPU times: user 4.44 ms, sys: 4.65 ms, total: 9.09 ms\n",
-      "Wall time: 12.4 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "search_raft_pq(query = \"What is creating tides?\")"
+    "search_cuvs_pq(query = \"What is creating tides?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "2d6017ed",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 208 ms, sys: 63.8 ms, total: 271 ms\n",
-      "Wall time: 286 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "params = ivf_flat.IndexParams(n_lists=150)\n",
     "flat_index = ivf_flat.build(params, corpus_embeddings)\n",
     "search_params = ivf_flat.SearchParams()\n",
     "\n",
-    "def search_raft_flat(query, top_k = 5):\n",
+    "def search_cuvs_flat(query, top_k = 5):\n",
     "    # Encode the query using the bi-encoder and find potentially relevant passages\n",
     "    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n",
     "    \n",
@@ -435,83 +231,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "f5cfb644",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input question: Who was Grace Hopper?\n",
-      "Results (after 0.002 seconds):\n",
-      "\t181.650\t['Grace Hopper', 'Hopper was born in New York, USA. Hopper graduated from Vassar College in 1928 and Yale University in 1934 with a Ph.D degree in mathematics. She joined the US Navy during the World War II in 1943. She worked on computers in the Navy for 43 years. She then worked in other private industry companies after 1949. She retired from the Navy in 1986 and died on January 1, 1992.']\n",
-      "\t192.946\t['Leona Helmsley', 'Leona Helmsley (July 4, 1920 – August 20, 2007) was an American businesswoman. She was known for having a flamboyant personality. She had a reputation for tyrannical behavior; she was nicknamed the Queen of Mean.']\n",
-      "\t194.951\t['Grace Hopper', 'Grace Murray Hopper (December 9 1906 – January 1 1992) was an American computer scientist and United States Navy officer.']\n",
-      "\t202.192\t['Nellie Bly', 'Elizabeth Cochrane Seaman (born Elizabeth Jane Cochran; May 5, 1864 – January 27, 1922), better known by her pen name Nellie Bly, was an American journalist, novelist and inventor. She was a newspaper reporter, who worked at various jobs for exposing poor working conditions. Nellie Bly, also, fought for women\\'s right and was known for investigative reporting. She best known for her record-breaking trip around the world in 72 days, inspired by the adventure novel \"Around the World in Eighty Days\" by Jules Verne. In the 1880s, she went undercover as a mentally ill patient in a psychiatric hospital for ten days, with the report being made public in a book called \"\"Ten Days in a Mad-House\"\". She was added to the National Women\\'s Hall of Fame in 1998.']\n",
-      "\t205.038\t['Abbie Hoffman', 'Abbot Howard \"Abbie\" Hoffman (November 30, 1936 – April 12, 1989) was an American social and political activist.']\n",
-      "CPU times: user 6.48 ms, sys: 0 ns, total: 6.48 ms\n",
-      "Wall time: 6.22 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "search_raft_flat(query=\"Who was Grace Hopper?\")"
+    "search_cuvs_flat(query=\"Who was Grace Hopper?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "b5694d00",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input question: Who was Alan Turing?\n",
-      "Results (after 0.002 seconds):\n",
-      "\t106.131\t['Alan Turing', 'Alan Mathison Turing OBE FRS (London, 23 June 1912 – Wilmslow, Cheshire, 7 June 1954) was an English mathematician and computer scientist. He was born in Maida Vale, London.']\n",
-      "\t158.646\t['William Kahan', 'William Morton Kahan (born June 5, 1933) is a Canadian mathematician and computer scientist. He received the Turing Award in 1989 for \"\"his fundamental contributions to numerical analysis\".\" He was named an ACM Fellow in 1994, and added to the National Academy of Engineering in 2005.']\n",
-      "\t165.094\t['Alan Turing', 'A brilliant mathematician and cryptographer Alan was to become the founder of modern-day computer science and artificial intelligence; designing a machine at Bletchley Park to break secret Enigma encrypted messages used by the Nazi German war machine to protect sensitive commercial, diplomatic and military communications during World War 2. Thus, Turing made the single biggest contribution to the Allied victory in the war against Nazi Germany, possibly saving the lives of an estimated 2 million people, through his effort in shortening World War II.']\n",
-      "\t167.321\t['Rolf Noskwith', 'Rolf Noskwith (19 June 1919 – 3 January 2017) was a British businessman. During the Second World War, he worked under Alan Turing as a cryptographer at the British military base Bletchley Park in Milton Keynes, Buckinghamshire.']\n",
-      "\t176.480\t['Marvin Minsky', \"Marvin Lee Minsky (August 9, 1927 – January 24, 2016) was an American cognitive scientist in the field of artificial intelligence (AI). He was the co-founder of the Massachusetts Institute of Technology's AI laboratory, and author of several texts on AI and philosophy. He won the Turing Award in 1969.\"]\n",
-      "CPU times: user 4.81 ms, sys: 1.19 ms, total: 6 ms\n",
-      "Wall time: 6.06 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "search_raft_flat(query=\"Who was Alan Turing?\")"
+    "search_cuvs_flat(query=\"Who was Alan Turing?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "fcfc3c5b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input question: What is creating tides?\n",
-      "Results (after 0.002 seconds):\n",
-      "\t94.909\t['Tide', \"A tide is the periodic rising and falling of Earth's ocean surface caused mainly by the gravitational pull of the Moon acting on the oceans. Tides cause changes in the depth of marine and estuarine (river mouth) waters. Tides also make oscillating currents known as tidal streams (~'rip tides'). This means that being able to predict the tide is important for coastal navigation. The strip of seashore that is under water at high tide and exposed at low tide, called the intertidal zone, is an important ecological product of ocean tides.\"]\n",
-      "\t159.539\t['Tidal energy', \"Many things affect tides. The pull of the Moon is the largest effect, and most of the energy comes from the slowing of the Earth's spin.\"]\n",
-      "\t159.740\t['Storm surge', 'A storm surge is a sudden rise of water hitting areas close to the coast. Storm surges are usually created by a hurricane or other tropical cyclone. The surge happens because a storm has fast winds and low atmospheric pressure. Water is pushed on shore, and the water level rises. Strong storm surges can flood coastal towns and destroy homes. A storm surge is considered the deadliest part of a hurricane. They kill many people each year.']\n",
-      "\t178.283\t['Sea', 'Wind blowing over the surface of a body of water forms waves. The friction between air and water caused by a gentle breeze on a pond causes ripples to form. A strong blow over the ocean causes larger waves as the moving air pushes against the raised ridges of water. The waves reach their greatest height when the rate at which they travel nearly matches the speed of the wind. The waves form at right angles to the direction from which the wind blows. In open water, if the wind continues to blow, as happens in the Roaring Forties in the southern hemisphere, long, organized masses of water called swell roll across the ocean. If the wind dies down, the wave formation is reduced but waves already formed continue to travel in their original direction until they meet land. Small waves form in small areas of water with islands and other landmasses but large waves form in open stretches of sea where the wind blows steadily and strongly. When waves meet other waves coming from different directions, interference between the two can produce broken, irregular seas.']\n",
-      "\t181.498\t['Tidal force', 'Tidal force is caused by gravity and makes tides happen. This is because the gravitational field changes across the middle of a body (the diameter).']\n",
-      "CPU times: user 5.91 ms, sys: 0 ns, total: 5.91 ms\n",
-      "Wall time: 5.65 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "search_raft_flat(query = \"What is creating tides?\")"
+    "search_cuvs_flat(query = \"What is creating tides?\")"
    ]
   },
   {
@@ -523,34 +271,25 @@
     "\n",
     "CAGRA is a graph-based nearest neighbors implementation with state-of-the art query performance for both small- and large-batch sized vector searches. \n",
     "\n",
-    "CAGRA follows the same two-step APIs as IVF-FLAT and IVF-PQ in RAFT. First we build the index:"
+    "CAGRA follows the same two-step APIs as IVF-FLAT and IVF-PQ in cuVS. First we build the index:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "50df1f43-c580-4019-949a-06bdc7185536",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pylibraft.neighbors import cagra"
+    "from cuvs.neighbors import cagra"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "091cde52-4652-4230-af2b-75c35357f833",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1min 23s, sys: 2min 7s, total: 3min 31s\n",
-      "Wall time: 4min 43s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "params = cagra.IndexParams(intermediate_graph_degree=128, graph_degree=64)\n",
@@ -560,12 +299,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "df229e21-f6b6-4d6c-ad54-2724f8738934",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def search_raft_cagra(query, top_k = 5):\n",
+    "def search_cuvs_cagra(query, top_k = 5):\n",
     "    # Encode the query using the bi-encoder and find potentially relevant passages\n",
     "    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n",
     "\n",
@@ -579,28 +318,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "b5e862fd-b7e5-4423-8fbf-36918f02c8f3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 16 µs, sys: 25 µs, total: 41 µs\n",
-      "Wall time: 83.7 µs\n",
-      "Input question: Who was Grace Hopper?\n",
-      "\t181.649\t['Grace Hopper', 'Hopper was born in New York, USA. Hopper graduated from Vassar College in 1928 and Yale University in 1934 with a Ph.D degree in mathematics. She joined the US Navy during the World War II in 1943. She worked on computers in the Navy for 43 years. She then worked in other private industry companies after 1949. She retired from the Navy in 1986 and died on January 1, 1992.']\n",
-      "\t192.946\t['Leona Helmsley', 'Leona Helmsley (July 4, 1920 – August 20, 2007) was an American businesswoman. She was known for having a flamboyant personality. She had a reputation for tyrannical behavior; she was nicknamed the Queen of Mean.']\n",
-      "\t194.951\t['Grace Hopper', 'Grace Murray Hopper (December 9 1906 – January 1 1992) was an American computer scientist and United States Navy officer.']\n",
-      "\t202.192\t['Nellie Bly', 'Elizabeth Cochrane Seaman (born Elizabeth Jane Cochran; May 5, 1864 – January 27, 1922), better known by her pen name Nellie Bly, was an American journalist, novelist and inventor. She was a newspaper reporter, who worked at various jobs for exposing poor working conditions. Nellie Bly, also, fought for women\\'s right and was known for investigative reporting. She best known for her record-breaking trip around the world in 72 days, inspired by the adventure novel \"Around the World in Eighty Days\" by Jules Verne. In the 1880s, she went undercover as a mentally ill patient in a psychiatric hospital for ten days, with the report being made public in a book called \"\"Ten Days in a Mad-House\"\". She was added to the National Women\\'s Hall of Fame in 1998.']\n",
-      "\t205.038\t['Abbie Hoffman', 'Abbot Howard \"Abbie\" Hoffman (November 30, 1936 – April 12, 1989) was an American social and political activist.']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%time \n",
-    "search_raft_cagra(query=\"Who was Grace Hopper?\")"
+    "search_cuvs_cagra(query=\"Who was Grace Hopper?\")"
    ]
   }
  ],
@@ -620,7 +344,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/ivf_flat_example.ipynb b/notebooks/ivf_flat_example.ipynb
index 08b9d78169..38bacb8a74 100644
--- a/notebooks/ivf_flat_example.ipynb
+++ b/notebooks/ivf_flat_example.ipynb
@@ -5,7 +5,7 @@
    "id": "4f49c5c4-1170-42a7-9d6a-b90acd00c3c3",
    "metadata": {},
    "source": [
-    "# RAFT IVF Flat Example Notebook"
+    "# cuVS IVF Flat Example Notebook"
    ]
   },
   {
@@ -15,13 +15,13 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "This notebook demonstrates how to run approximate nearest neighbor search using RAFT IVF-Flat algorithm.\n",
+    "This notebook demonstrates how to run approximate nearest neighbor search using cuVS IVF-Flat algorithm.\n",
     "It builds and searches an index using a dataset from the ann-benchmarks million-scale datasets, saves/loads the index to disk, and explores important parameters for fine-tuning the search performance and accuracy of the index."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "fe73ada7-7b7f-4005-9440-85428194311b",
    "metadata": {},
    "outputs": [],
@@ -29,8 +29,8 @@
     "import os\n",
     "import cupy as cp\n",
     "import numpy as np\n",
-    "from pylibraft.common import DeviceResources\n",
-    "from pylibraft.neighbors import ivf_flat\n",
+    "from cuvs.common import Resources\n",
+    "from cuvs.neighbors import ivf_flat\n",
     "import matplotlib.pyplot as plt\n",
     "import tempfile\n",
     "from utils import BenchmarkTimer, calc_recall, load_dataset"
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "5350e4d9-0993-406a-80af-29538b5677c2",
    "metadata": {},
    "outputs": [],
@@ -71,37 +71,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "a5daa4b4-96de-4e74-bfd6-505b13595f62",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Thu Sep 21 02:30:53 2023       \n",
-      "+---------------------------------------------------------------------------------------+\n",
-      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
-      "|-----------------------------------------+----------------------+----------------------+\n",
-      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
-      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
-      "|                                         |                      |               MIG M. |\n",
-      "|=========================================+======================+======================|\n",
-      "|   0  NVIDIA H100 PCIe               On  | 00000000:41:00.0 Off |                    0 |\n",
-      "| N/A   35C    P0              69W / 350W |   1487MiB / 81559MiB |      0%      Default |\n",
-      "|                                         |                      |             Disabled |\n",
-      "+-----------------------------------------+----------------------+----------------------+\n",
-      "                                                                                         \n",
-      "+---------------------------------------------------------------------------------------+\n",
-      "| Processes:                                                                            |\n",
-      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
-      "|        ID   ID                                                             Usage      |\n",
-      "|=======================================================================================|\n",
-      "|    0   N/A  N/A      3940      C   /opt/conda/envs/rapids/bin/python          1474MiB |\n",
-      "+---------------------------------------------------------------------------------------+\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Report the GPU in use\n",
     "!nvidia-smi"
@@ -121,45 +94,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "5f529ad6-b0bd-495c-bf7c-43f10fb6aa14",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The index and data will be saved in /tmp/raft_example\n"
-     ]
-    }
-   ],
-   "source": [
-    "WORK_FOLDER = os.path.join(tempfile.gettempdir(), \"raft_example\")\n",
+   "outputs": [],
+   "source": [
+    "WORK_FOLDER = os.path.join(tempfile.gettempdir(), \"cuvs_example\")\n",
     "f = load_dataset(\"http://ann-benchmarks.com/sift-128-euclidean.hdf5\", work_folder=WORK_FOLDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "3d68a7db-bcf4-449c-96c3-1e8ab146c84d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded dataset of size (1000000, 128),  0.5 GiB; metric: 'euclidean'.\n",
-      "Number of test queries: 10000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "metric = f.attrs['distance']\n",
     "\n",
-    "dataset = cp.array(f['train'])\n",
-    "queries = cp.array(f['test'])\n",
-    "gt_neighbors = cp.array(f['neighbors'])\n",
-    "gt_distances = cp.array(f['distances'])\n",
+    "dataset = cp.asarray(f['train'])\n",
+    "queries = cp.asarray(f['test'])\n",
+    "gt_neighbors = cp.asarray(f['neighbors'][:])\n",
+    "gt_distances = cp.asarray(f['distances'][:])\n",
     "\n",
     "itemsize = dataset.dtype.itemsize \n",
     "\n",
@@ -173,24 +129,15 @@
    "metadata": {},
    "source": [
     "## Build index\n",
-    "We set [IndexParams](https://docs.rapids.ai/api/raft/nightly/pylibraft_api/neighbors/#pylibraft.neighbors.ivf_flat.IndexParams) and build the index. The index parameters will be discussed in more detail in later sections of this notebook."
+    "We set [IndexParams](https://docs.rapids.ai/api/cuvs/nightly/python_api/neighbors/#cuvs.neighbors.ivf_flat.IndexParams) and build the index. The index parameters will be discussed in more detail in later sections of this notebook."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "737f8841-93f9-4c8e-b2e1-787d4474ef94",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 120 ms, sys: 5.33 ms, total: 125 ms\n",
-      "Wall time: 124 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "build_params = ivf_flat.IndexParams(\n",
@@ -214,18 +161,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "1aec7024-6e5d-4d2c-82e6-7b5734aec958",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(type=IVF-FLAT, metric=euclidean, size=1000000, dim=128, n_lists=1024, adaptive_centers=False)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(index)"
    ]
@@ -243,17 +182,17 @@
    "id": "89ba2eaa-4c85-4e1c-b07c-920394e55dce",
    "metadata": {},
    "source": [
-    "It is recommended to reuse [device recosources](https://docs.rapids.ai/api/raft/nightly/pylibraft_api/common/#pylibraft.common.DeviceResources) across multiple invocations of search, since constructing these can be time consuming. We will reuse the resources by passing the same handle to each  RAFT API call."
+    "It is recommended to reuse [Resources](https://docs.rapids.ai/api/cuvs/nightly/python_api/common/#cuvs.common.Resources) across multiple invocations of search, since constructing these can be time consuming. We will reuse the resources by passing the same handle to each  cuVS API call."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "46e0421b-9335-47a2-8451-a91f56c2f086",
    "metadata": {},
    "outputs": [],
    "source": [
-    "handle = DeviceResources()"
+    "handle = Resources()"
    ]
   },
   {
@@ -261,24 +200,15 @@
    "id": "a6365229-18fd-468f-af30-e24b950cbd6e",
    "metadata": {},
    "source": [
-    "After setting [SearchParams](https://docs.rapids.ai/api/raft/nightly/pylibraft_api/neighbors/#pylibraft.neighbors.ivf_flat.SearchParams) we search for for `k=10` neighbors."
+    "After setting [SearchParams](https://docs.rapids.ai/api/cuvs/nightly/python_api/neighbors/#cuvs.neighbors.ivf_flat.SearchParams) we search for for `k=10` neighbors."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "595454e1-7240-4b43-9a73-963d5670b00c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 171 ms, sys: 52.6 ms, total: 224 ms\n",
-      "Wall time: 236 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "n_queries=10000\n",
@@ -288,7 +218,7 @@
     "# Search 10 nearest neighbors.\n",
     "distances, indices = ivf_flat.search(search_params, index, cp.asarray(queries[:n_queries,:]), k=10, handle=handle)\n",
     "    \n",
-    "# RAFT calls are asynchronous (when handle arg is provided), we need to sync before accessing the results.\n",
+    "# cuVS calls are asynchronous (when handle arg is provided), we need to sync before accessing the results.\n",
     "handle.sync()\n",
     "distances, neighbors = cp.asnumpy(distances), cp.asnumpy(indices)"
    ]
@@ -303,21 +233,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "8cd9cd20-ca00-4a35-a0a0-86636521b31a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97406"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "calc_recall(neighbors, gt_neighbors)"
    ]
@@ -328,12 +247,12 @@
    "metadata": {},
    "source": [
     "## Save and load the index\n",
-    "You can serialize the index to file using [save](https://docs.rapids.ai/api/raft/nightly/pylibraft_api/neighbors/#pylibraft.neighbors.ivf_flat.save), and [load](https://docs.rapids.ai/api/raft/nightly/pylibraft_api/neighbors/#pylibraft.neighbors.ivf_flat.load) it later."
+    "You can serialize the index to file using [save](https://docs.rapids.ai/api/cuvs/nightly/python_api/neighbors/#cuvs.neighbors.ivf_flat.save), and [load](https://docs.rapids.ai/api/cuvs/nightly/python_api/neighbors/#cuvs.neighbors.ivf_flat.load) it later."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "bf94e45c-e7fb-4aa3-a611-ddaee7ac41ae",
    "metadata": {},
    "outputs": [],
@@ -344,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "1622d9be-be41-4d25-be99-d348c5e54957",
    "metadata": {},
    "outputs": [],
@@ -365,57 +284,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "ace0c31f-af75-4352-a438-123a9a03612c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Benchmarking search with n_probes = 10\n",
-      "recall 0.86625\n",
-      "Average search time:   0.026 +/- 0.000259 s\n",
-      "Queries per second (QPS):   384968\n",
-      "\n",
-      "Benchmarking search with n_probes = 20\n",
-      "recall 0.94705\n",
-      "Average search time:   0.050 +/- 5.43e-05 s\n",
-      "Queries per second (QPS):   198880\n",
-      "\n",
-      "Benchmarking search with n_probes = 30\n",
-      "recall 0.97406\n",
-      "Average search time:   0.075 +/- 8.59e-05 s\n",
-      "Queries per second (QPS):   133954\n",
-      "\n",
-      "Benchmarking search with n_probes = 50\n",
-      "recall 0.99169\n",
-      "Average search time:   0.123 +/- 4.78e-05 s\n",
-      "Queries per second (QPS):    80997\n",
-      "\n",
-      "Benchmarking search with n_probes = 100\n",
-      "recall 0.99844\n",
-      "Average search time:   0.244 +/- 0.000249 s\n",
-      "Queries per second (QPS):    40934\n",
-      "\n",
-      "Benchmarking search with n_probes = 200\n",
-      "recall 0.99932\n",
-      "Average search time:   0.468 +/- 0.000367 s\n",
-      "Queries per second (QPS):    21382\n",
-      "\n",
-      "Benchmarking search with n_probes = 500\n",
-      "recall 0.99933\n",
-      "Average search time:   1.039 +/- 0.000209 s\n",
-      "Queries per second (QPS):     9625\n",
-      "\n",
-      "Benchmarking search with n_probes = 1024\n",
-      "recall 0.99935\n",
-      "Average search time:   0.701 +/- 0.00579 s\n",
-      "Queries per second (QPS):    14273\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_probes = np.asarray([10, 20, 30, 50, 100, 200, 500, 1024]);\n",
     "qps = np.zeros(n_probes.shape);\n",
@@ -606,11 +478,11 @@
    "metadata": {},
    "source": [
     "### Add vectors on build\n",
-    "Currently you cannot configure how RAFT sub-samples the input. If you want to have a fine control on how the training set is selected, then create the index in two steps:\n",
+    "Currently you cannot configure how cuVS sub-samples the input. If you want to have a fine control on how the training set is selected, then create the index in two steps:\n",
     "1. Define cluster centers on a training set, but do not add any vector to the index\n",
     "2. Add vectors to the index (extend)\n",
     "\n",
-    "This workflow shall be familiar to FAISS users. Note that raft does not require adding the data in batches, internal batching is used when necessary.\n"
+    "This workflow shall be familiar to FAISS users. Note that cuvs does not require adding the data in batches, internal batching is used when necessary.\n"
    ]
   },
   {
@@ -666,7 +538,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/tutorial_ivf_pq.ipynb b/notebooks/tutorial_ivf_pq.ipynb
index 397e39bfba..cc0fe41428 100644
--- a/notebooks/tutorial_ivf_pq.ipynb
+++ b/notebooks/tutorial_ivf_pq.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# RAFT IVF-PQ tutorial\n",
+    "# cuVS IVF-PQ tutorial\n",
     "In this tutorial you will learn to build IVF-PQ index and use it to search approximate nearest neighbors (ANN).\n",
     "We will start with a brief overview of the functionality, but then dive into details to gain the understanding of the model parameters.\n",
     "Along the way, we will benchmark the model and give some practical recommendations on how to maximize its performance for various use cases.\n",
@@ -14,55 +14,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting adjustText\n",
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-      "Collecting importlib-resources>=3.2.0 (from matplotlib)\n",
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-      "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/envs/cuml_dev/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)\n",
-      "Requirement already satisfied: six>=1.5 in /opt/conda/envs/cuml_dev/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
-      "Installing collected packages: pyparsing, kiwisolver, importlib-resources, h5py, fonttools, cycler, contourpy, matplotlib, adjustText\n",
-      "Successfully installed adjustText-0.8 contourpy-1.1.0 cycler-0.11.0 fonttools-4.41.1 h5py-3.9.0 importlib-resources-6.0.0 kiwisolver-1.4.4 matplotlib-3.7.2 pyparsing-3.0.9\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install adjustText h5py matplotlib"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,8 +37,8 @@
     "import h5py\n",
     "\n",
     "from rmm.allocators.cupy import rmm_cupy_allocator\n",
-    "from pylibraft.common import DeviceResources\n",
-    "from pylibraft.neighbors import ivf_pq, refine\n",
+    "from cuvs.common import Resources\n",
+    "from cuvs.neighbors import ivf_pq, refine\n",
     "from adjustText import adjust_text\n",
     "from utils import calc_recall, load_dataset\n",
     "\n",
@@ -86,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,20 +62,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The index and data will be saved in /tmp/raft_ivf_pq_tutorial\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We'll need to load store some data in this tutorial\n",
-    "WORK_FOLDER = os.path.join(tempfile.gettempdir(), 'raft_ivf_pq_tutorial')\n",
+    "WORK_FOLDER = os.path.join(tempfile.gettempdir(), 'cuvs_ivf_pq_tutorial')\n",
     "\n",
     "if not os.path.exists(WORK_FOLDER):\n",
     "   os.makedirs(WORK_FOLDER)\n",
@@ -123,36 +76,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fri Jul 28 08:21:25 2023       \n",
-      "+---------------------------------------------------------------------------------------+\n",
-      "| NVIDIA-SMI 535.49                 Driver Version: 535.49       CUDA Version: 12.2     |\n",
-      "|-----------------------------------------+----------------------+----------------------+\n",
-      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
-      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
-      "|                                         |                      |               MIG M. |\n",
-      "|=========================================+======================+======================|\n",
-      "|   0  NVIDIA H100 PCIe               On  | 00000000:41:00.0 Off |                    0 |\n",
-      "| N/A   34C    P0              46W / 350W |      4MiB / 81559MiB |      0%      Default |\n",
-      "|                                         |                      |             Disabled |\n",
-      "+-----------------------------------------+----------------------+----------------------+\n",
-      "                                                                                         \n",
-      "+---------------------------------------------------------------------------------------+\n",
-      "| Processes:                                                                            |\n",
-      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
-      "|        ID   ID                                                             Usage      |\n",
-      "|=======================================================================================|\n",
-      "|  No running processes found                                                           |\n",
-      "+---------------------------------------------------------------------------------------+\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Report the GPU in use to put the measurements into perspective\n",
     "!nvidia-smi"
@@ -163,13 +89,13 @@
    "metadata": {},
    "source": [
     "## Use the pool memory resource\n",
-    "RAFT uses RMM allocator widely across its algorithms, including the performance-sensitive parts like IVF-PQ search.\n",
+    "cuVS uses RMM allocator widely across its algorithms, including the performance-sensitive parts like IVF-PQ search.\n",
     "It's strongly advised to set up the RMM pool memory resource to minimize the overheads of repeated CUDA allocations.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,17 +119,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The index and data will be saved in /tmp/raft_example\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "DATASET_URL = \"http://ann-benchmarks.com/sift-128-euclidean.hdf5\"\n",
     "f = load_dataset(DATASET_URL)"
@@ -218,18 +136,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded dataset of size (1000000, 128); metric: 'euclidean'.\n",
-      "Number of test queries: 10000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "metric = f.attrs['distance']\n",
     "\n",
@@ -256,40 +165,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# RAFT's DeviceResources controls the GPU, cuda stream, memory policies etc.\n",
+    "# cuVS' Resources controls the GPU, cuda stream, memory policies etc.\n",
     "# For now, we just create a default instance.\n",
-    "resources = DeviceResources()"
+    "resources = Resources()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'add_data_on_build': True,\n",
-       " 'codebook_kind': 0,\n",
-       " 'conservative_memory_allocation': False,\n",
-       " 'force_random_rotation': False,\n",
-       " 'kmeans_n_iters': 20,\n",
-       " 'kmeans_trainset_fraction': 0.5,\n",
-       " 'metric': 1,\n",
-       " 'n_lists': 1024,\n",
-       " 'pq_bits': 8,\n",
-       " 'pq_dim': 64}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# First, we need to initialize the build/indexing parameters.\n",
     "# One of the more important parameters is the product quantisation (PQ) dim.\n",
@@ -308,28 +197,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.71 s, sys: 16.2 ms, total: 1.72 s\n",
-      "Wall time: 1.71 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Index(type=IVF-PQ, metric=euclidean, codebook=subspace, size=1000000, dim=128, pq_dim=64, pq_bits=8, n_lists=1024, rot_dim=128)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "## Build the index\n",
@@ -352,28 +222,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 89.7 ms, sys: 56 ms, total: 146 ms\n",
-      "Wall time: 145 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Index(type=IVF-PQ, metric=euclidean, codebook=subspace, size=1000000, dim=128, pq_dim=64, pq_bits=8, n_lists=1024, rot_dim=128)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "index_filepath = os.path.join(WORK_FOLDER, \"ivf_pq.bin\")\n",
@@ -395,20 +246,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'internal_distance_dtype': 0, 'lut_dtype': 0, 'n_probes': 20}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "k = 10\n",
     "search_params = ivf_pq.SearchParams()\n",
@@ -417,18 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 19.9 ms, sys: 12.3 ms, total: 32.2 ms\n",
-      "Wall time: 31.5 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "distances, neighbors = ivf_pq.search(search_params, index, queries, k, handle=resources)\n",
@@ -446,17 +277,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Got recall = 0.85409 with the default parameters (k = 10).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "recall_first_try = calc_recall(neighbors, gt_neighbors)\n",
     "print(f\"Got recall = {recall_first_try} with the default parameters (k = {k}).\")"
@@ -474,18 +297,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 193 ms, sys: 142 µs, total: 193 ms\n",
-      "Wall time: 191 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -496,17 +310,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Got recall = 0.94953 with 2x refinement (k = 10).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "recall_refine2x = calc_recall(neighbors, gt_neighbors)\n",
     "print(f\"Got recall = {recall_refine2x} with 2x refinement (k = {k}).\")"
@@ -535,36 +341,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "16.5 ms ± 13.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "17 ms ± 2.12 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "17.5 ms ± 2.92 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "18 ms ± 3.05 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "18.7 ms ± 4.25 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "23.4 ms ± 45.5 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "25.9 ms ± 5.49 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "40.2 ms ± 12.7 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "23.6 ms ± 26.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "28.7 ms ± 18.5 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "bench_k = np.exp2(np.arange(10)).astype(np.int32)\n",
     "bench_avg = np.zeros_like(bench_k, dtype=np.float32)\n",
@@ -598,27 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.67 ms ± 3.91 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "4.78 ms ± 1.74 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "6.65 ms ± 3.72 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "10.2 ms ± 4.86 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "17.2 ms ± 14.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "60.2 ms ± 16.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "115 ms ± 41.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "222 ms ± 184 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "430 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "829 ms ± 162 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "1.6 s ± 354 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "bench_probes = np.exp2(np.arange(11)).astype(np.int32)\n",
     "bench_qps = np.zeros_like(bench_probes, dtype=np.float32)\n",
@@ -646,20 +407,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x400 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1, 3, figsize=plt.figaspect(1/4))\n",
     "\n",
@@ -725,21 +475,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "209 ms ± 151 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "178 ms ± 485 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "182 ms ± 297 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "176 ms ± 220 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "181 ms ± 439 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "bench_qps_s1 = np.zeros((5,), dtype=np.float32)\n",
     "bench_recall_s1 = np.zeros((5,), dtype=np.float32)\n",
@@ -761,20 +499,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=plt.figaspect(1/2))\n",
     "fig.suptitle(\n",
@@ -820,31 +547,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "210 ms ± 129 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "181 ms ± 331 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "184 ms ± 536 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "179 ms ± 331 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "182 ms ± 329 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "410 ms ± 203 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "344 ms ± 304 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "338 ms ± 632 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "320 ms ± 269 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "323 ms ± 194 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "425 ms ± 743 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "389 ms ± 688 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "381 ms ± 519 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "325 ms ± 552 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "340 ms ± 876 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def search_refine(ps, ratio):\n",
     "    k_search = k * ratio\n",
@@ -864,20 +569,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=plt.figaspect(1/2))\n",
     "fig.suptitle(\n",
@@ -925,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -960,11 +654,11 @@
    "metadata": {},
    "source": [
     "Deciding on the indexing parameters is a bit more involved than on the search parameters. This is obviously because `ivf_pq.IndexParams` has more members than `ivf_pq.SearchParams`, but also because the try-test loop takes longer time when it includes training.\n",
-    "Since RAFT's IVF-PQ algorithm uses balanced-hierarchical k-means clustering and efficient logic for encoding, we find significantly improved index build times.\n",
+    "Since cuVS' IVF-PQ algorithm uses balanced-hierarchical k-means clustering and efficient logic for encoding, we find significantly improved index build times.\n",
     "\n",
     "First of all, let's pick the parameters we __don't need__ to tweak:\n",
     "\n",
-    "  - `metric` - the distance metric often depens on the problem and thus fixed (currently RAFT supports variations of eucliean and inner product distances).\n",
+    "  - `metric` - the distance metric often depens on the problem and thus fixed (currently cuVS supports variations of eucliean and inner product distances).\n",
     "  - `conservative_memory_allocation` only affects how data is allocated - does not affect the search performance.\n",
     "  - `add_data_on_build` is a convenience flag. When activated, it automatically adds the training data to the index during `ivf_pq.build`. Otherwise, no data is added during `ivf_pq.build` and vectors need to be explicitly added to the index using `ivf_pq.extend`.\n",
     "  - `force_random_rotation` may slightly affect performance when the data dimensionality is a power of two (see the module docs), but normally you don't need to change the defaults. \n",
@@ -994,46 +688,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.36 ms ± 2.38 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "4.36 ms ± 1.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "4.37 ms ± 2.47 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "7.74 ms ± 19.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "33.8 ms ± 733 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "44.1 ms ± 714 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "1.83 ms ± 1.66 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
-      "3.1 ms ± 14.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "6.43 ms ± 16.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "11.9 ms ± 33 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "45.2 ms ± 622 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "87.3 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "2.55 ms ± 452 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "5.1 ms ± 11.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "9.32 ms ± 15.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "16.1 ms ± 34.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "74 ms ± 254 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "145 ms ± 295 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "3.92 ms ± 5.94 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "8.12 ms ± 6.62 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "14.7 ms ± 23.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "27.8 ms ± 131 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "132 ms ± 289 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "259 ms ± 3.04 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "7.49 ms ± 4.68 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "17.2 ms ± 48.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "32.4 ms ± 111 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "63 ms ± 149 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "303 ms ± 2.32 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "603 ms ± 1.78 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_list_variants = [100, 500, 1000, 2000, 5000]\n",
     "pl_ratio_variants = [500, 200, 100, 50, 10, 5]\n",
@@ -1056,20 +713,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=plt.figaspect(1/2))\n",
     "fig.suptitle(\n",
@@ -1207,64 +853,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8.25 ms ± 10.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "15.5 ms ± 24.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "36.7 ms ± 468 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "71.8 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "9.4 ms ± 16.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "16.2 ms ± 32.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "38.2 ms ± 520 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "74.4 ms ± 291 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "160 ms ± 48.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "168 ms ± 393 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "191 ms ± 139 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "228 ms ± 590 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "12.2 ms ± 24.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "25.2 ms ± 73.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "59.8 ms ± 167 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "117 ms ± 84.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "14.3 ms ± 19.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "25.2 ms ± 2.93 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "59.6 ms ± 29.3 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "116 ms ± 17.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "165 ms ± 757 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "176 ms ± 168 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "212 ms ± 245 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "270 ms ± 283 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "6.47 ms ± 20.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "11 ms ± 13.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "24.5 ms ± 285 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "46.2 ms ± 460 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "8.25 ms ± 19.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "13.2 ms ± 3.08 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "28.7 ms ± 3.21 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "53.4 ms ± 6.59 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "158 ms ± 135 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "164 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "180 ms ± 114 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "206 ms ± 322 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-      "6.29 ms ± 3.05 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "10.7 ms ± 10.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "23.8 ms ± 5.83 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "44.6 ms ± 126 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "8.17 ms ± 6.97 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "13 ms ± 35.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "28.4 ms ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "52.6 ms ± 69.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "159 ms ± 205 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "164 ms ± 121 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "181 ms ± 256 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
-      "207 ms ± 2.57 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Let's try a few build configurations.\n",
     "#   Warning: this will take some time\n",
@@ -1294,20 +885,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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+f78uL/xckmTr4yPniHBzr0+nkBDZODkV56lYNRuTjSp5VVIlr0p6oGrON6nx6fHmoe7RF6K1++JuJWYkatPZTdp0dpMkySSTqnhXUbhvuML9chY6Ku9enhAaAAAAKC6GIWWk/PPHzc7OOfYdFB8fL5PJJC8vL0mSj4+PgoKC9Omnn6p27dpydHTUhx9+KD8/P9WpU6fAeurUqSMbGxvNmTNH/fr1U1JSkubPn682bdoUGHJmZmaqW7duGjhwoD7//HNdvXpVW7duLfJnn3nz5mnAgAHaunWrtm/frieeeELly5fXwIEDtX37dg0bNkzz589Xo0aNdOnSJa1fv77Q+0s5Q/4nTJigoKAgXbhwQcOHD1e/fv20ZMkSSdKZM2fUrFkztWjRQqtXr5aHh4c2btyozMxMSdKCBQs0ZswYvffee4qIiNCuXbs0cOBAubq6KjIyskjnWhQEnQDuSiZ7ezmH1pRzaE2pbx9JUsYff+T09syd63PfPmXFxSlp1S9KWpWzqrjs7eVUo4Zlr88Cvr1D4Xg6eqpZuWZqVq6ZJCkzO1Mxl2PMK7xHXYjSmaQzirkco5jLMfrq0FeSJG9Hb/NQ9zDfMIWUDJGznXNxngoAAADw35GRIk0sc/Nyt5mNJA3ZL8nzjtSflpam559/Xr169ZLHnwvamkwmrVq1St26dZO7u7tsbGzk5+enZcuWydu74PUGKlasqBUrVqhnz54aNGiQsrKy1LBhQ3MYmJ+EhATFx8erc+fO5p6PNWrUKPJ5BAQEaOrUqTKZTAoKCtKePXs0depUDRw4UCdPnpSrq6s6d+4sd3d3BQYGKiIiotD7S9Jjjz1mLlupUiW9++67qlevnpKSkuTm5qb3339fnp6e+uKLL8yhbrVq1cz7jB07VlOmTDFPJVCxYkXt27dPH374IUEnANwO9qVKyb5De3l0aC9Jyk5PV9rv+yx6fWbFXlTa7t1K271bmvepJMmudGnLXp/Vq8vk4FCcp2LV7GzsVMOnhmr41NDD1XOmHriYelHRF/4KPvfF7dPl9Mtae2qt1p5am7OfyU7VS1Q3h5/hfuEq7UoIDQAAAKBwMjIy1LNnTxmGoVmzZpm3G4ahIUOGyM/PT+vXr5ezs7M+/vhjdenSRdu2bZO/v79CQkJ04sQJSVLTpk21dOlSnT9/XgMHDlRkZKR69eqlxMREjRkzRt27d9fKlSt16tQpBQcHm48zevRojR49Wv369VP79u3Vtm1btWnTRj179pS/v3+RzuWee+6x6AXasGFDTZkyRVlZWWrbtq0CAwNVqVIldejQQR06dND9998vFxeXQu1va2urHTt2aNy4cYqOjtbly5eVnZ0tSTp58qSCg4MVFRWlpk2b5ttzNTk5WUeOHNGAAQPMwamU05vV0/POBNi5CDoB/GfZODrKpXaEXGr/tap4xpkzFr0+0w4eVOb580pcukyJS5dJkkyOjnKqWfOvXp/h4bIrWbI4T8XqlXQuqdaBrdU6sLUk6WrWVe2/tN88z2fUhSjFpsZqb9xe7Y3bqwX7F0iS/Fz8LIa71yhRQ/a2+Q8RAQAAAFAE9i7S6LP/+GGzs7Ol1MzbXm9uyHnixAnzUOtcq1ev1k8//aTLly+bt8+cOVMrV67UvHnz9MILL2jJkiXKyMiQJDk754w0y+3VOGnSJHNdn332mQICArRlyxbVrVtXUVFR5sdKlCghSZozZ46GDRumZcuW6csvv9TLL7+slStX6p577pGNjY2M64bu5x63sNzd3bVz506tXbtWK1as0JgxYzRu3Dht27bNPFz/RpKTk9W+fXu1b99eCxYskK+vr06ePKn27dvr6tWrFtcgP0lJSZKk2bNnq0GDBhaP2draFulcioqgEwD+ZDKZ5FCunBzKlZNnl86SpOyUFKXu2fvXcPddu5QVH6/UHTuUumOHeV/7gAA5/xl8ukREyLFqVZnseIm9VQ62DjmrtPuGScoJoc8lnzOHnlGxUTp46aAupFzQihMrtOLECkmSo62jQnxCFOYXZt6/pDMhNAAAAFBkJpPk4PrPHzc7W0pLuK1V5oacMTExWrNmjXx8fCweT0nJmYs0d0GhXDY2NuaejIGBgXnqTUlJybNPbpCXnZ0tOzs7ValSJd82RUREKCIiQi+++KIaNmyohQsX6p577pGvr6/27t1rUTYqKipPz8ktW7ZY/Lx582ZVrVrVfHw7Ozu1adNGbdq00dixY+Xl5aXVq1ebh5LfaP8DBw4oLi5Ob775pgICAiTJYoV6SapVq5bmzZuX76JLpUqVUpkyZXT06FE98sgj+Z7/ncKncAC4ARsXF7k2qC/XBvUl5QRuV48fz+n1+WfwmX74sDJOnVLGqVNK+OFHSZLJxUXOtWrJOTxMLhERcg4Lk20hvjlD/kwmk8q4lVEZtzLqWDFnNcWUjBT9Hve7OfyMjo3WlfQr2nlhp3Ze2GneN8A9wDzPZ7hfuKp4VZGtzZ39FhEAAADAPycpKUmHDx82/3zs2DFFRUWpRIkS8vf3V/fu3bVz50799NNPysrK0vnz5yXl9LB0cHBQw4YN5e3trcjISI0ZM0bOzs6aPXu2jh07pk6dOhV43E6dOmnq1Kl69dVXzUPXR48ene+cmNe27aOPPlLXrl1VpkwZHTx4UDExMerbt68kqVWrVpo8ebI+/fRTNWzYUJ999pn27t2bp76TJ09q+PDhGjRokHbu3KkZM2ZoypQpkqSffvpJR48eVbNmzeTt7a0lS5YoOztbQUFBhdq/fPnycnBw0IwZM/Tkk09q7969mjBhgsXxhw4dqhkzZujhhx/Wiy++KE9PT23evFn169dXUFCQxo8fr2HDhsnT01MdOnRQenq6tm/frsuXL2v48OGFfWqLjKATAIrAZDLJsWJFOVasKK8H7pckZSUmKjV691+9PqOjlZ2UpJTNm5WyebPi/tzXoVIli16fDpUqyXTdt38oPBd7F9UrXU/1SteTlBNCn0g4YZ7nMzo2WkeuHNGpxFM6lXhKPx7NCaFd7FwU6htqHvJey7eWPBw8bnQoAAAAAP9i27dvV8uWLc0/5wZpkZGRGjdunH744QdJUnh4uMV+a9asUYsWLVSyZEktW7ZML730klq1aqWMjAyFhITo+++/V1hYWIHHbdWqlRYuXKhJkyZp0qRJcnFxUcOGDbVs2bICh3a7uLjowIEDmjdvnuLi4uTv768hQ4Zo0KBBkqT27dvrlVde0ahRo5SWlqbHHntMffv21Z49eyzq6du3r1JTU1W/fn3Z2trqmWee0RNPPCFJ8vLy0rfffqtx48YpLS1NVatW1eeff66QkJBC7e/r66u5c+dq9OjRevfdd1W7dm29/fbb6tq1q3l/Hx8frV69WiNHjlTz5s1la2ur8PBwNW7cWJL0+OOPy8XFRZMnT9bIkSPl6uqq0NBQPfvsswVez9vBZFw/8B+3TUJCgjw9PRUfH28x98OdlJGRoSVLlujee+/Nd0JYwFpY871sZGUp/cgRi7k+rx4/nqecjYeHnMPCzAsdOdWqJVs3t3++wXexhKsJ2hO7x9zrc/fF3UrOSM5TrrJnZfM8n2F+YaroUdFiYu6/y5rvZ+B63M+4m3A/427BvYzbIS0tTceOHVPFihXl5ORUbO3Izs5WQkKCPDw88gwLx+3RokULhYeHa9q0acXdFLMb3X9Fydfo0QkAt5nJ1lZO1arJqVo1eT/UU5KUefnyn0Pd/xzyvmePshMSlLx+vZLXr8/Z0cZGjlWrWvT6tC9f/rYGbv81Hg4ealy2sRqXzflWMSs7S0fij1gscnQy8aSOxB/Rkfgj+ibmG0mSp6OneY7PcN9w1SxZUy72Ljc6FAAAAACgmBF0AsA/wM7bW+4tW8r9z+EURmam0g4etJjrM+PMGaUfPKj0gwd15YsvJUm2JUqYV3Z3iQiXU82asrnB6na4MVsbW1XzrqZq3tXUMygnhI5LjdPu2N2Kis0JP/de3Kv49HitO71O606vy9nPlLNf7jyf4X7hKuNahhAaAAAAAP5FCDoBoBiY7OzkHBIi55AQ6dGcVegyLlyw6PWZtnevsi5dUtLq1UpavTpnRzs7OVWvLueICPNCR3b+/gRuf4OPs49alm+pluVzQuiMrAwdvHzwr16fsVE6n3xe+y/t1/5L+/XFwS8kSSWdS5rn+QzzDVMNnxpytHUszlMBAAAAgJtau3ZtcTfhjiHoBIB/CXs/P9m3ayePdu0kSdlXryp93z6lXNPrM/PCBaXt3au0vXt1ef58SZKdn9+fwWdOr0/H4GDZODgU56lYNXtbe9UsWVM1S9bUo3pUknQ++bzF6u77L+3XxdSLWnVylVadXJWzn429gn2Cc1Z498sZ8u7r4lucpwIAAAAA/ykEnQDwL2Xj4GAeti7lrCqeefasUq7t9bl/vzIvXFDi8uVKXL5ckmRycJBTSIi516dzeLjs/fyK8UysX2nX0irtWlrtK7SXJKVlpmlf3D6LFd4vpV1SdGy0omOjpX05+5V1K6tavrUUWiJUSZlJyszOlL1YIAAAAAAA7gSCTgCwEiaTSfZly8qzbFl5duokScpOTVXa3r05vT7/XOE96/LlnP/ftcu8r33ZsuZen84R4XIKCpLJjreAW+Vk56TapWqrdqnaknJC6NOJp83zfEZdiFLMlRidSTqjM0lntPTYUknS/776n0J9Q81D3muVrCUvJ69iPBMAAAAAuHvwKRcArJiNs7Nc6tWTS716knICt4wTJyx6faYfOqSMM2eUceaMEn76SZJkcnaWc2ioOfh0Dg+Xnbd3cZ6KVTOZTArwCFCAR4C6VO4iSUrOSNaei3sUdSFKu/7YpZ3ndiotK03bzm/TtvPbzPtW8Khgnucz3DdclbwqycZkU1ynAgAAAABWi6ATAO4iJpNJDhUqyKFCBXl16yZJykpKUtru3UrZtUupUdFKjYpSdmKiUrZuVcrWreZ9HSpUsOj16Vilikw2BG63ytXeVff436N7/O9RRkaGfvr5JwU3CdbeS3vNPT+PxR/T8YTjOp5wXN8d/k6S5G7vrlp+tczBZ2jJULk5uBXvyQAAAACAFSDoBIC7nK2bm1wbNZJro0aSJCM7W1ePHs0JPv/s9Xn16FFdPX5cV48fV/zixZIkGzc3OYeF/RV+htWSrbt7cZ6KVbMx2aiSZyUFlQzSg9UelCRdSbui3Rd3m+f53HNxjxIzErXxzEZtPLPRvF8VryoWK7wHuAfIZDIV5+kAAAAAwL8OQScA/MeYbGzkWKWKHKtUkXePHpKkrCtXlBod/Vevz927lZ2UpOSNG5W8ceOfO5rkWKWKRa9PhwoVCNz+Bi8nLzUr10zNyjWTJGVmZ+rQ5UMWK7yfSTqjQ5cP6dDlQ1p0aJEkqYRTiZwen38GnyE+IXKycyrOUwEAAADwD2nRooXCw8M1bdq04m7Kvw5BJwBAtl5ecmveXG7Nm0uSjMxMpR86ZDHXZ8apU0qPiVF6TIyuLFpk3i93ZXjniAg5h9aUjYtLcZ6KVbOzsVOwT7CCfYLVq3ovSVJsSqw5+IyKjdK+uH26lHZJa06t0ZpTa3L2M9mphk8NhfmGKcwvZ8h7adfSxXkqAAAAwD9m3bp1mjx5snbs2KFz585p8eLF6vbnVF4ZGRl6+eWXtWTJEh09elSenp5q06aN3nzzTZUpU8Zcx6FDhzRy5Eht3LhRV69eVa1atTRhwgS1bNnyhsc2DENTpkzRRx99pBMnTqhkyZIaPHiwXnrppTt5yigAQScAIA+TnZ2cgoPlFBws9e4tScq8eFGpUVHmXp9pe/Yo68oVJa1dq6S1a3N2tLWVU1DQNb0+I2Rftgy9Pv8GXxdftQlsozaBbSRJV7Oual/cPovw82LqRe25uEd7Lu7RZ/s/kySVdi1tnucz3C9cQSWCZG9jX5ynAgAAANwRycnJCgsL02OPPaYHHnjA4rGUlBTt3LlTr7zyisLCwnT58mU988wz6tq1q7Zv324u17lzZ1WtWlWrV6+Ws7Ozpk2bps6dO+vIkSMqXbrgTgTPPPOMVqxYobfffluhoaG6dOmSLl26dMfOFTdG0AkAKBS7kiXl3qaN3NvkBG7G1atK27//z/AzSqm7dinzjz+Utm+f0vbt0+UFCyRJtr4l5RL+V/DpFBIsG0fH4jwVq+Zg66Bwv5zwMjIkUoZh6GzyWfNQ96gLUTp0+ZDOJ5/X+eTzWn58uSTJ0dZRIT4h5uHuYb5h8nH2KeazAQAAAP6+jh07qmPHjvk+5unpqZUrV1pse++991S/fn2dPHlS5cuX18WLFxUTE6NPPvlEtWrVkiS9+eabmjlzpvbu3Vtg0Ll//37NmjVLe/fuVVBQkCSpYsWKN23v2rVrNWrUKP3++++yt7dXSEiIFi5cqMDAQPXr109XrlzRd999Zy7/7LPPKioqSmtzO5hIyszM1NChQzV//nzZ29vrqaee0quvvmruZDJz5kxNnTpVp06dkqenp5o2baqvv/5aUs7Q95o1a0pSgfvPnz9f06dP18GDB+Xq6qpWrVpp2rRp8vPzM7fh999/1/PPP69169bJMAyFh4dr7ty5qly5siTp448/1pQpU3Ts2DFVqFBBw4YN0+DBg296ff4Ogk4AwC0xOTjkLFYUFqYSkZGSpIxz5yx7fe7bp6zYi0pcuVKJf/5xYbK3l1NwsGWvz1J+NzoUbsBkMqmsW1mVdSurTpU6SZJSMlL0e9zv5h6f0bHRik+P184LO7Xzwk7zvuXdy1sEn1W8qsjWxra4TgUAAAD/IoZhKDUz9R8/bnZ2tgzDuKPHiI+Pl8lkkpeXlyTJx8dHQUFB+vTTT1W7dm05Ojrqww8/lJ+fn+rUqVNgPT/++KMqVaqkn376SR06dJBhGGrTpo0mTZqkEiVK5LtPZmamunXrpoEDB+rzzz/X1atXtXXr1iKPgps3b54GDBigrVu3avv27XriiSdUvnx5DRw4UNu3b9ewYcM0f/58NWrUSJcuXdL69esLvb+UM+R/woQJCgoK0oULFzR8+HD169dPS5YskSSdOXNGzZo1U4sWLbR69Wp5eHho48aNyszMlCQtWLBAY8aM0XvvvaeIiAjt2rVLAwcOlKurqyL//Px4JxB0AgBuG3t/f9n7+8vjz29Ts9PSlPb770rdtcs832dWXJxSo6OVGh1t3s+ujL9lr8/qQTLZM8z6VrnYu6he6XqqV7qepJw/Uo8nHDf3+oyOjdbhK4d1MvGkTiae1A9HfpAkudq7KrRkaE6PUd9whfqGysPBozhPBQAAAMUkNTNVDRY2KJZjr+i0Qp7yvCN1p6Wl6fnnn1evXr3k4ZHzt67JZNKqVavUrVs3ubu7y8bGRn5+flq2bJm8vb0LrOvo0aM6ceKEvvrqK3366afKysrSc889p+7du2v16tX57pOQkKD4+Hh17tzZ3POxRo0aRT6PgIAATZ06VSaTSUFBQdqzZ4+mTp2qgQMH6uTJk3J1dVXnzp3l7u6uwMBARUREFHp/SXrsscfMZStVqqR3331X9erVU1JSktzc3PT+++/L09NTX3zxhez//OxWrVo18z5jx47VlClTzFMJVKxYUfv27dOHH35I0AkAsE42Tk5yqVNHLnXqyEc5gVvGqVMWvT7TDx5U5tlzSjh7Tgl/fjtocnKSc82aOb0+I3IWO7Ir4BtR3JzJZFJFz4qq6FlR91e9X5IUnx6vPRf3mIe7747dreSMZG0+t1mbz23O2U8mVfaqbLHCewWPCsy5CgAAAKuUkZGhnj17yjAMzZo1y7zdMAwNGTJEfn5+Wr9+vZydnfXxxx+rS5cu2rZtm/z9/RUSEqITJ05Ikpo2baqlS5cqOztb6enp+vTTT80h3yeffKI6dero4MGDcnZ2VnBwsPk4o0eP1ujRo9WvXz+1b99ebdu2VZs2bdSzZ0/5+/sX6Vzuuecei7/LGzZsqClTpigrK0tt27ZVYGCgKlWqpA4dOqhDhw66//775XLNwrE32t/W1lY7duzQuHHjFB0drcuXLys7O1uSdPLkSQUHBysqKkpNmzY1h5zXSk5O1pEjRzRgwABzcCrl9Gb19LwzAXYugk4AwD/GZDLJoXx5OZQvL8+uXSVJWUnJStu7J6fX565dSo3erez4eKVs366UayYHtw8sn9PrMyKn16djlSoy2TLM+lZ5OnqqSdkmalK2iSQpKztLh68ctljk6FTiKR2+cliHrxzWNzHfSJK8HL3MQ93D/cIV4hMiF3uXGx0KAAAAVsjZzllbem/5x4+bnZ2tjJSM215vbsh54sQJ81DrXKtXr9ZPP/2ky5cvm7fPnDlTK1eu1Lx58/TCCy9oyZIlysjIaZezs7Mkyd/fX3Z2dhY9GXN7Z548eVItW7ZUVFSU+bHc4exz5szRsGHDtGzZMn355Zd6+eWXtXLlSt1zzz2ysbHJM3Q/97iF5e7urp07d2rt2rVasWKFxowZo3Hjxmnbtm3m4fo3kpycrPbt26t9+/ZasGCBfH19dfLkSbVv315Xr161uAb5SUpKkiTNnj1bDRpY9gq2vcOf4Qg6AQDFytbNVa733CPXe+6RJBnZ2bp67Ng1vT6jdPXwEWWcOKn4EycV//33kiQbV1c5h9UyD3d3DguTrQfDrG+VrY2tgkoEKahEkHoG9ZQkxaXG5QSfsVGKvhCt3+N+15X0K/r19K/69fSvOfuZcva7doV3f1d/en0CAABYOZPJVCxfaGdnZyvBlHBb68wNOWNiYrRmzRr5+FguypmSkiJJsrGxsdhuY2Nj7skYGBiYp97GjRsrMzNTR44cMQ9DP3TokLm8nZ2dqlSpkm+bIiIiFBERoRdffFENGzbUwoULdc8998jX11d79+61KBsVFZWn5+SWLZYh9ObNm1W1alVzkGhnZ6c2bdqoTZs2Gjt2rLy8vLR69WrzUPIb7X/gwAHFxcXpzTffVEBAgCRZrFAvSbVq1dK8efOUkZGRp22lSpVSmTJldPToUT3yyCP5nv+dQtAJAPhXMdnYyLFyZTlWriyvBx+UJGXFxyt1925zr8+06N3KTk5W8qbflLzpN/O+DlUqy+WaRY4cKlSQ6bo/VlB4Ps4+alW+lVqVbyVJysjK0IFLB8wLHO26sEsXUi5oX9w+7Yvbp88PfC5J8nX2tVjkKNgnWA62DsV5KgAAALiLJSUl6fDhw+afjx07pqioKJUoUUL+/v7q3r27du7cqZ9++klZWVk6f/68pJwelg4ODmrYsKG8vb0VGRmpMWPGyNnZWbNnz9axY8fUqVOnAo/bpk0b1a5dW4899pimTZum7OxsDRkyRG3btrXo5XmtY8eO6aOPPlLXrl1VpkwZHTx4UDExMerbt68kqVWrVpo8ebI+/fRTNWzYUJ999pn27t2bZ47NkydPavjw4Ro0aJB27typGTNmaMqUKZKkn376SUePHlWzZs3k7e2tJUuWKDs727wy/M32L1++vBwcHDRjxgw9+eST2rt3ryZMmGBx/KFDh2rGjBl6+OGH9eKLL8rT01ObN29W/fr1FRQUpPHjx2vYsGHy9PRUhw4dlJ6eru3bt+vy5csaPnx4YZ/aIiPoBAD869l6esqtaVO5NW0qSTKyspQeE6PUqCjzQkcZJ07q6uEjunr4iK589bUkycbTU87hYXLJ7fUZGiobV9fiPBWrZm9rr1DfUIX6hqqP+kiSziefN/f4jLoQpQOXDig2NVYrT6zUyhMrc/azsVeIT4g5/Az3C1dJ55LFeSoAAAC4i2zfvl0tW7Y0/5wbpEVGRmrcuHH64YecxTfDw8Mt9luzZo1atGihkiVLatmyZXrppZfUqlUrZWRkKCQkRN9//73CwsIKPK6NjY1+/PFHPf3002rWrJlcXV3VsWNHc2CYHxcXFx04cEDz5s1TXFyc/P39NWTIEA0aNEiS1L59e73yyisaNWqU0tLS9Nhjj6lv377as2ePRT19+/ZVamqq6tevL1tbWz3zzDN64oknJEleXl769ttvNW7cOKWlpalq1ar6/PPPFRISUqj9fX19NXfuXI0ePVrvvvuuateurbfffltd/5x+TMpZqX716tUaOXKkmjdvLltbW4WHh6tx48aSpMcff1wuLi6aPHmyRo4cKVdXV4WGhurZZ58t8NrcDibj+oH/uG0SEhLk6emp+Ph4i7kf7qSMjAwtWbJE9957b74TwgLWgnsZRZWZu5r7rl1K3RWl1D17ZKSnWxaysZFjUJBc/lzgyDkiQvblyt3xYdb/pfs5NTNV++L2mef53B27W5fSLuUpV9at7F/Bp2+4qnpXlZ0N379ag//S/Yy7H/cz7hbcy7gd0tLSdOzYMVWsWFFOTk7F1o7s7GwlJCTIw8Mjz1By3B4tWrRQeHi4pk2bVtxNMbvR/VeUfI1PFACAu4Kdj4/cW7WSe6ucYdbG1atKO3gwJ/SMyun1mXn2nNL371f6/v26vDBnmLWtj4+cI8LNvT6dQkJkU4x/2Fk7Zztn1SlVR3VK1ZGUs4LlqcRTf/X6jI1SzOUYnUk6ozNJZ/Tz0Z/N+4WWDLVY4d3T8c6uyAgAAADg7kLQCQC4K5kcHOQcGirn0FCpb84w64w//sgJPnftUkrULqXt26+suDglrfpFSat+ydnR3l5ONWpY9vosXboYz8S6mUwmlfcor/Ie5dW1cs5Ql6SrSdp9cbeiY6MVfSFa0bHRSspI0tbzW7X1/FbzvhU9Kyrc96/h7hU9K8rGxLf6AAAAAPJH0AkA+M+wL1VK9h3ay6NDe0lSdnq60n7//a9en7uilHXxotJ271ba7t3SvE8lSXalS1v2+qxeXSYHFte5VW4ObmpUppEalWkkSco2snX0ylFFxUYp6kLOQkfHE47rWPwxHYs/psWHF0uS3B3czQschfuFK7RkqFztmXMVAAAAKIq1a9cWdxPuGIJO3LLoU1f08nd71TLIVy2q+ymsnJdsbe7sPHcAcDvZODrKpXZtudSuLSlnmHXGmTPmXp+pUVFKO3hQmefPK3HpMiUuXSZJMjk6yqlmzb96fYaHy64ki+vcKhuTjap4V1EV7yrqXq27JOly2mXtjt1tXuF978W9SryaqA1nNmjDmQ3m/ap6VbVY5Kic252fcxUAAADAvxNBJ27ZmoMXtOdMvPacide7qw/L28Vezav5qkWQn5pV81UJV3o7AbAuJpNJDuXKyaFcOXl26SxJyk5JUeqevX8ucpQTfmbFxyt1xw6l7thh3tc+IEDOfwafLhERcqxaVSY73mZvlbeTt5oHNFfzgOaSpIzsDB26fMjc4zP6QrTOJp/VwcsHdfDyQX158EtJUgmnEgr3DTeHn8E+wXKyY85VAAAA4L+AT2C4ZY80CFRZL2etPRirdTGxupySoe+izuq7qLMymaTwAC+1DPJTyyA/hZTxkA29PQFYIRsXF7k2qC/XBvUl5fT6vHrsuFKj/ur1mX74sDJOnVLGqVNK+OFHSZLJxUXOtWrJOTxMDqG1ZJOSUpynYfXsbewV4hOiEJ8QPVLjEUnShZQLio6NNq/wvj9uvy6lXdLqU6u1+tRqSZKdjZ2CSwSrlm8thfuFK9w3XKVcSxXnqQAAAAC4Qwg6cct83R3Vo26AetQNUEZWtnaeuKy1h2K15sAFHTifqF0nr2jXySt6Z+UhlXRzVIsgX7UM8lOTqiXl6Wxf3M0HgFtiMpnkWKmiHCtVlNcD90uSshISlBq9+6/wc/duZSclKWXzZqVs3ixJqiLpxKfz5VI7wtzr06FSJZlsWFznVvm5+KltYFu1DWwrSUrPStf+uP3mXp+7LuxSXFqcdl/crd0Xd+uz/Z9Jkvxd/c1D3cN9w1WtRDXZ2/C+BAAAAFg7gk7cFva2NmpQyUcNKvno+Q7VdS4+VWsP5oSeGw5f1MWkdH2947S+3nFatjYm1Qn0VssgP7UI8lX10u7MpwbAqtl6eMitaRO5NW0iSTKyspR++Ig5+EyJ2qWM4yeUceyY4o8dU/w330qSbDw85BwWJufwMLlERMipVi3ZurkV56lYNUdbx5zw0i9cUk7v2zNJZ3Lm+fxzdfeDlw/qXPI5nUs+p2XHc+ZcdbJ1UkjJEIsh795O3sV4JgAAAABuBUEn7gh/T2f1ql9eveqXV3pmlrYfv6w1By5ozcELOhKbrK3HLmnrsUt6a9kBlfZwUsvqOXN7Nq5SUm6O3JYArJvJ1lZOQdXkFFRN3g/1VEZGhpYvWqQmfn66mjvf5969yk5IUPL69Upevz5nRxsbOVatajHXp3358nwZdItMJpPKuZdTOfdy6lwpZ87VlIwU7b2412KF94SrCdrxxw7t+OOvOVcDPQItVniv7FlZtja2xXUqAAAAAAqBRAl3nKOdrRpXKanGVUrq5c7BOnUpRWsPXtCag7HadOSiziek6fOtp/T51lOytzWpfsUSf/b29FNlX1c+4AO4K2S5/T979x1W9Xn+cfz9PYs9ZCkCgoKCE9wxjkj2Mjsam2GSZptp9mxWs2Nsotm7WZo0TX4x00Rwxa2IiyXTyd4bzu+PL2Js00z1MD6v63quFj0H7qecIny4n+f2xmvyZPxPMI9ZO5uaqE/POOiuz6Zdu2hIT6chPZ3yj8zhOtaAgPbJ7p7DE3AfMgSLh4crt9Kpedo9GRM6hjGh5p2rrc5Wcitz2VS4qb3zc0fFDvIq88irzOP/dvwfAN52b4YGDW0/7j40eCg+Dh9XbkVEREREuijDMPj3v//NWWed5epSOh0FnXLERQR4cvG4KC4eF0V9UwursktITi9icVoh+aW1rMgqYUVWCY9+uZ3wHh7mQKO4YMb1C8LDoW4aEekaDLsdjyGD8RgyGC4yh+s0FRa2BZ9m+Fm/dSstpaVUL15M9WJzuA42G+5xcXgMH95+5N0WGqpfCv1BFsNCP79+9PPrx9n9zTtXKxoqSC1KNQcdFaWwuWgz1U3VrNyzkpV7VgJgYBDtH90efMYHxxPpG6nPg4iIiIgLLF26lKeffpr169ezZ8+eg0LCpqYm7rvvPr766iuys7Px8/Pj+OOP54knnqB3797t7yMjI4Pbb7+dFStW0NjYyLBhw3jkkUdITEz8xY/tdDp59tlnefXVV8nLyyMoKIjrrruOe++993Bu+TdLTk4mMTGRsrIy/P39XV3OYaegU1zK3W5lclv35t+mDCKnuIak9CKS0wtZnV3KzrI6/rkqj3+uysNhszCuXyCJscEkxoUQGejl6vJFRA4pe0gI9hNPxPfEEwFobWykfutW6lI2mV2fGzfSXFRE/ZYt1G/ZQtk//wmALSSkLfg0uz7dBg3C4nC4ciudmp+bHxPDJzIxfCIAza3NZJVntXd9phSmsLN6J1nlWWSVZ/FJxicA9HDrYR53DzGPvA8JGoKHTd23IiIiIodbTU0N8fHxXH755ZxzzjkH/V1tbS0bNmzg/vvvJz4+nrKyMm666SbOOOMM1q1b1/64008/nf79+7N48WI8PDyYM2cOp59+Ojt27KBXr17/82PfdNNNfPfddzzzzDMMHTqU0tJSSktLD9teXcXpdNLS0oLN1rGjxI5dnXQrhmHQL9ibfsHe/HVCX2oamlm5o4Sk9EKS0grZXVHPkowilmQU8eAX2+gX5MXktm7PMX0DcLOp21NEuhaLw4Hn8OF4Dh8Ol12K0+mkefduajemtB95r09Lo7mwkKpvv6Xq228BMBwO3AcPbu/69EhIwB4S4uLddF42i424gDjiAuKYFjcNgOK6YjYVbWofcrSleAtlDWUk70wmeWey+TzDRmxA7EET3nt59VLXp4iIiMghdsopp3DKKaf87N/5+fmxaNGig/5s7ty5jBkzhvz8fPr06UNxcTGZmZm88cYbDBs2DIAnnniCF198kS1btvzPoHP79u289NJLbNmyhdjYWAD69u37m2p+8803efbZZ8nKyiIgIIBzzz2XuXPn/tfjfq4jMyUlheHDh5OTk0NUVBR5eXlcf/31LF++nMbGRqKionj66acZNGhQe0dqjx7msM0ZM2bw9ttv09raypNPPsmrr77K3r17GTBgAPfffz/nnXfeQR/3q6++4r777mPz5s189913TJ48+Tftz1UUdEqH5eVm4/hBPTl+UE+cTieZhdXtA43W5ZaRXVxDdnEOb67IwcNu3gM6ua3bM8xfHTQi0vUYhoE9LAy/sDD8Tj8NgNbaWuq2bDnQ9ZmSQktZWXsH6H72sLD2rk+P4Qm4x8ZidPDfxnZkQR5BHNfnOI7rcxwATS1NbC/d3j7gKKUwhcK6QraWbGVryVY+SPsAgBDPEDP4bJvwPjBgIHar3ZVbEREREflZTqcTZ13dEf+4ra2tOJ3Ow/oxKioqMAyjPTgMDAwkNjaWd999lxEjRuDm5sYrr7xCSEgII0eO/J/v54svvqBfv34sXLiQk08+GafTyfHHH89TTz1FQEDA/3zeSy+9xKxZs3jiiSc45ZRTqKioYMWKFX94PzNnzqSxsZGlS5fi5eXFtm3b8Pb2JiIign/961+ce+65pKen4+vri0fbff+PP/447733Hi+//DL9+/dn6dKlXHTRRQQHB3PMMce0v++77rqLZ555hn79+rWHpR2ZfsKRTsEwDAb09GFATx+uPiaayvomVmQWk5ReSHJ6EYVVDXy/fR/fb98HwICe3u0DjUZF9cButbh4ByIih4fF0xOvMWPwGmMO13E6nTTl5VH7k7s+GzIzadq1i6Zdu6hcuBAAw8MDj6FD24NPj4QEbJ3gG5eOym61Myx4GMOCzQ4Ap9PJ3pq95oCjtuAzrTSNwtpCFuUtYlGe2VXgsDgYHDS4/Z7P+JB4gjyCXLkVEREREQCcdXWkj/jfId/h1DNpMfj5HZb3XV9fz5133sn06dPx9fUFzMzh+++/56yzzsLHxweLxUJISAjffPPNL4Z72dnZ5OXl8fHHH/Puu+/S0tLCLbfcwnnnncfi/Xfs/4xHH32UW2+9lZtuuqn9z0aPHv2H95Sfn8+5557L0KFDAejXr1/73+0PXENCQtqD3YaGBh577DG+//57xo0b1/6c5cuX88orrxwUdD788MOc0DZQtTNQ0Cmdkq+7nVOGhnLK0FCcTifb9lSSnF5EUlohG/LLyNhXTca+al5Zmo2Pm40J/YNIjA3hmNhgevq6u7p8EZHDxjAMHFFROKKi8G+7gL2lupq6TZsODDratInWqipq16yhds2a9uc6oqIO6vp0i4nBsOgXRX+EYRiEeocS6h3KKX3NY1R1zXVsLd7aPt19U9EmyhrK2Fi4kY2FB7pvw73DSQhJaD/yHuMfg82ib9lERERE/qympiamTp2K0+nkpZdeav9zp9PJzJkzCQkJYdmyZXh4ePD6668zZcoU1q5dS2hoKIMHDyYvLw+AiRMn8vXXX9Pa2kpDQwPvvvsuAwYMAOCNN95g5MiRpKen4+HhwaBBg9o/zj333MMVV1zB7t27Oe644w7Zvm688UauvfZavvvuO44//njOPffc9iP4PycrK4va2tr/CjAbGxsZPnz4QX82atSoQ1bnkaDvmqXTMwyDwb39GNzbj5mJMZTXNrI0s5jktEKSM4oorWnk6y17+XrLXgAGhfqSGBdMYmwICRH+2NTtKSJdnNXbG+/x4/EePx4AZ2srjTt2HOj6TEmhMTubxtxcGnNzqfj3vwGweHvjER9/IPyMH4bVx8eVW+nUPGwejOo1ilG9zG8WnU4n+VX5B467F6WQVZbFzuqd7KzeycJss/vW0+bJ0KChxIeYR96HBQ/Dz+3wdDiIiIiI7Gd4eBC7Yf0R/7itra1UNTUd8ve7P+TMy8tj8eLF7d2cAIsXL2bhwoWUlZW1//mLL77IokWLeOedd7jrrrv46quvaGqra//x79DQUGw2W3vICTBw4EDA7LJMTEwkJSWl/e8CAgKw23/ftUWWtsaDnx7nb/qP/32uuOIKTjrpJL788ku+++47Hn/8cZ599lluuOGGn32f1dXVAHz55ZeEhYUd9Hdubm4Hve3l1bkGQSvolC7H39PBGfG9OSO+N62tTlJ3VZCcXkhSehGpO8vZtqeSbXsqmZe0Az8PO5MGBJMYG8ykAcEEebv9+gcQEenkDIsFt/79cevfnx7nnw9Ac1nZwV2fmzfTWl1NzYoV1Oy/L8gwcIuJOajr0xEVpeE6f5BhGET6RhLpG8mZMWcCUNVYxeaize3BZ2pRKtVN1azeu5rVe1e3P7efX7/2AUfxwfFE+UVhMfSLOxERETl0DMPA8PQ88h+4tRWjsvKQvsv9IWdmZiZJSUkEBgYe9Pe1tbXAgVBxP4vFQmtrKwCRkZH/9X7Hjx9Pc3MzO3bsIDo6GoCMjIz2x9tsNmJiYv7reVFRUfzwww/tg4J+SXBwMAB79uxpP0b/0/B0v4iICK655hquueYa7r77bl577TVuuOEGHA4HAC0tLe2PHTRoEG5ubuTn5x90TL0rUNApXZrFYpAQ4U9ChD83Hz+A4uoGlmYUkZRexNKMIirqmvhi026+2LQbw4Bh4f4kxprdnkPD/LBY9MO7iHQPth498Jk8GZ+2KYrO5mYaMjIOuuuzaedOGjIzacjMpHzBAgCs/v5m6JmQYAagQ4dgccU3xF2Ej8OHo8OO5uiwowFoaW1hR8WO9ns+NxVtIq8yj+yKbLIrsvk081MAfB2+5h2fbcfdhwYNxdOuz4OIiIh0D9XV1WRlZbW/nZOTQ0pKCgEBAYSGhnLeeeexYcMGFi5cSEtLC3v3mic+AwICcDgcjBs3jh49ejBjxgweeOABPDw8eO2118jJyeG00077nx/3+OOPZ8SIEVx++eXMmTOH1tZWZs6cyQknnHBQl+d/evDBB7nmmmsICQnhlFNOoaqqihUrVvxsB2ZMTAwRERE8+OCD/P3vfycjI4Nnn332oMfcfPPNnHLKKQwYMICysjKSkpLaO0sjIyMxDIOFCxdy6qmn4uHhgY+PD7fddhu33HILra2tTJgwoX0gkq+vLzNmzPhd//t3JAo6pVsJ8nbjnBHhnDMinOaWVlIKytsHGm3dXcmmgnI2FZQz5/tMAr0cHDMgmMlxIUzqH4S/p8PV5YuIHDGGzYb7oEG4DxoEf/kLAM1FRWbw2RZ+1m/ZQkt5OdXJyVQnJ5tPtFpxj439SdfncOxhvdX1+QdZLVYG9BjAgB4DOH+A2X1bWl9KalFqe/C5pXgLlY2VLNu1jGW7lgFgMSwM6DGgPfhMCE4gzDtMnwcRERHpktatW3dQd+SsWbMAmDFjBg8++CD/93//B0BCQsJBz0tKSmLy5MkEBQXxzTffcO+993LsscfS1NTE4MGD+fzzz4mPj/+fH9disfDFF19www03MGnSJLy8vDjllFP+K4j8TzNmzKC+vp7nnnuO2267jaCgIM4777yffazdbufDDz/k2muvZdiwYYwePZpHH32U89tOZoHZrTlz5kx27tyJr68vJ598Ms899xwAYWFhPPTQQ9x1111cdtllXHLJJbz99ts88sgjBAcH8/jjj5OdnY2/vz8jRozgnnvu+cXaOzrD+dND/nJIVVZW4ufnR0VFxUF3PxxOTU1NfPXVV5x66qm/+96H7m5fZT1L0otISi9kWWYx1Q3N7X9nMWBEnx4kxoUwOTaYQaG++mHxMNNrWbqSrvp6djY2Ur99O3UpKdS2dX0279v3X4+zBgfhmXAg+HQfPAiLm64KOVSaWpvIKM1oH3KUUpTCnpo9//W4QPfAA8fdQ+IZFDgIN+vv/zx01dezdE96PUtXodeyHAr19fXk5OTQt29f3N1dN8S3tbWVyspKfH19/+souXRdv/T6+z35mjo6Rdr09HVn6ugIpo6OoLG5lfV5ZW13exaSsa+adXllrMsr4+lv0wnxcSMx1gw9x/cPwtdd30yISPdjOBzmsKL4eALajrc07dlD3caN7Ufe67dvp6WomKpFi6hatMh8nt2O+6BBB3d99gxx5VY6NbvFzuCgwQwOGsyFAy8EYF/NvvZ7PjcVbmJb6TZK6kv4If8Hfsj/AQCbxcagwEHt93wmhCQQ4qnPg4iIiIh0Xgo6RX6Gw2ZhXHQg46IDufvUgewsqyU5vYjk9EJWZJVQWNXA/HUFzF9XgM1iMCqqB4mxISTGhdA/xFvdniLSbdlDQ7GHhuJ76qkAtNbXU79ly4Guz5QUWkpKzMFHmza1P8/WO/Tgrs+4WAx1pPxhPb16cqLXiZwYdSIADS0NbCvZRkphSvuR95L6ElKLUkktSm1/Xm+v3uZdnyFm8DmgxwDsFn0eRERERKRzUNAp8huE9/DkoqMiueioSOqbWlibW0pSmhl8ZhfXsCq7lFXZpTz+dRph/h5MbhtodHRMIJ4O/d9MRLovi7s7nqNG4TlqFIGA0+mkqaDgoK7PhowMmnfvoXL3Hiq/+goAw90djyFDzK7P4eawI1tAgGs304m5Wd0YHjKc4SHDAfPzsLN6Z3voualoExllGeyu2c3umt18nfs1AB42DwYHDm4/8j4seBjeVm9XbkVERERE5H9SAiPyO7nbrUzsH8zE/sE8MGUQucU1bUfci1iZXcKu8jreX53P+6vzcVgtjO0X0N7t2TfIy9Xli4i4lGEYOPr0wdGnD35nnglAS3UN9ZtT27o+N1KXsonWykpq162jdt269ufaI/uYXZ/Dza5Pt5gYDKvVVVvp1AzDIMInggifCKZETwGgpqmGLcVbzK7PIjMArWqsYt2+dazbd+DzEOkTSUBDAHVZdYzsNZJo/2gshu7PEhERERHXU9Ap8idFBXlxaVBfLh3fl7rGFlZmF5OcXsTitEJ2ltWxLLOYZZnFPLxwG5GBnu13ex7VLxB3u35AFxGxenvhNW4cXuPGAeBsbaUxJ+egrs/GHTtoysunIi+fis8/B8Di5YVH/LD24+4e8fFYj9Dwv67Iy+7F2NCxjA0dC0Crs5XcilxSig4cd8+uyCavKo888ti4ZiMAPnYfhgUPaz/yPixoGN4OdX2KiIiIyJGnoFPkEPJwWDk2rifHxvXkoTOc7CiqaR9otCanlLySWt7+MZe3f8zF3W7h6OggEmODmRwbQkSAp6vLFxHpEAyLBbfoaNyio/E/7zwAWioqzHs927o+6zel0lpTQ82PK6n5cWX7cx0x0Xj+ZMiRIyoKQ9M6/xCLYaGffz/6+ffjnP7nAFDRUMGGPRv4dNWn1PjXsKVkC1VNVazYvYIVu1cAYGAQ0yOGhOCE9iPvET4Rur9aRESkk3A6na4uQbqhQ/W6U9ApcpgYhkFMiDcxId5cMbEf1Q3NrMgqNoPPtCL2VtazOK2QxWmFwFZiQryZPCCYxLgQRkcF4LDpB3MRkf2sfn54T5qE96RJADhbWmjIzKRu48b2QUdN+fk0Zu2gMWsH5R9/AoDFzw+PhHg893d9Dh2KxUvXiPxRfm5+TAibQKVHJacedyqG1SCzLLN9wntKYQq7qneRWZZJZlkmH2d8DEAPtx7mgKO2Ce+DgwbjYfNw8W5ERETkp6xtVwI1Njbi4aF/p+XIamxsBA68Dv8oBZ0iR4i3m42TBvfipMG9cDqdpO+rIimtiKT0QtbnlZFVWE1WYTWvL8/By2FlfEwQiXHmMfdQP/0jIyLyU4bVintcHO5xcfSYPh2A5pIS6lJSDnR9bt5Ca0UFNUuWUrNkqflEiwW32Fg82wYceQwfjj08XN2Gf5DNYmNg4EAGBg7kgrgLACiuK2ZT4YHgc1vJNsoaykguSCa5INl8nmEjLiCuPfxMCEmgl1cvl+1DREREwGaz4enpSVFREXa7HYuLTsW0trbS2NhIfX29y2qQI6u1tZWioiI8PT2x2f5cVKmgU8QFDMMgrpcvcb18uXZyNBV1TSzPLCYpvZDk9CKKqxv4bts+vtu2D4C4Xj4kxoWQGBvCiD7+2Kz6Yi8i8p9sgYH4HHccPscdB4CzsZH69PSDuj6b9+yhYft2GrZvp+yDDwGwBgbiMTyhvevTffBgLO7urtxKpxbkEcRxkcdxXKT5eWhsaWR76fb2ez5TClMoqitiS8kWtpRs4f3t7wMQ4hly0HH3uIA47Fa7K7ciIiLSrRiGQWhoKDk5OeTl5bmsDqfTSV1dHR4eHvpldDdisVjo06fPn/6cK+gU6QD8POycNiyU04aF0trqZOvuSpLa7vZMKSgnbW8VaXureCl5Bz7uNib1D2ZybDDHxAYT4qMfxkVEfo7hcOAxdCgeQ4fCJZcA0LR3r9n1uTGF2pSN1G/bTktJCdXf/0D19z+YT7TbcR848OCuz17qNvyjHFaHOagoOB4wf3jZU7OnPfRMKUohvTSdwtpCvsv7ju/yvgPAzerG4MDBxIfEtz8/yCPIlVsRERHp8hwOB/37928/RuwKTU1NLF26lEmTJmG365ee3YXD4TgkHbwKOn9FTk4Ol19+Ofv27cNqtbJq1Sq8dLeXHEYWi8HQcD+Ghvtx43H9Ka1pZFlmEUlphSzJKKKstokvN+/hy817ABga5mcONIoLIT7cH6tFv/ESEflf7L16YT/5ZHxPPhmA1oYG6rdupW5jCnUpG6ndmEJLcTH1qanUp6bCO+8CYOvV6+Cuz7g4DIfDlVvptAzDoLd3b3p79+aUvqcAUNtUy9aSre3h56aiTZQ3lLOhcAMbCje0PzfCJ6L9ns+EkARi/GOwWv7cPU4iIiJyMIvFgrsLT7dYrVaam5txd3dX0Cm/m4LOX3HppZfy6KOPMnHiREpLS3Fzc3N1SdLNBHg5ODMhjDMTwmhpdbJpZznJaYUkpRexeVdF+3p+cRY9PO0c0zbQaFL/YHp46YdwEZFfYnFzw3PECDxHjADMbsOmXbvM4+5tXZ8N6Rk0791L1dffUPX1NwAYbm64DxlyoOszIQFbkLoN/yhPuyeje41mdK/RgPl5yKvMa7/nc1PRJnaU76CgqoCCqgK+yP7CfJ7Nk6HBQ9uPvA8LHoavw9eVWxERERERF1LQ+Qu2bt2K3W5n4sSJAAQEBLi4IunurBaDEX16MKJPD2adGEthVT1L0otITi9iaabZ7flZym4+S9mNYUBChD+JsebdnoN7+2JRt6eIyC8yDANHeDiO8HD8pkwBoLWmhrrNW9qOvJv3fbZUVFC3fj1169e3P9ceEYFHW/DpOXw4bv37Y/zJy9S7K8MwiPKLIsovirNizgKgsrGSzUWb27s+U4tTqWmqYfWe1azes7r9udF+0SSEmF2f8SHx9PXtq/u9RERERLqJLv3d99KlS3n66adZv349e/bs4d///jdnnXXWQY+ZN28eTz/9NHv37iU+Pp4XXniBMWPGAJCZmYm3tzdTpkxh165dnHfeedxzzz0u2InIzwvxcef8URGcPyqCppZWNuaXm3d7phWStreKjfnlbMwvZ/aiDIK83ZgcG0xibAgT+gfh56EjACIiv4XFywuvo8biddRYwOw2bMzJbQ8961I20pC1g6aCApoKCqj8P7Pb0PD0xGPYMDwS4vEcPhyP+His/v4u3Enn5uvwZXzYeMaHjQegpbWFHRU7DhpylF+Vz46KHeyo2MG/Mv8FgJ+bX/sdnwnBCQwJGoKn3dOVWxERERGRw6RLB501NTXEx8dz+eWXc8455/zX38+fP59Zs2bx8ssvM3bsWObMmcNJJ51Eeno6ISEhNDc3s2zZMlJSUggJCeHkk09m9OjRnHDCCS7Yjcgvs1stjOkbwJi+Adx5chx7KupITjfv9lyRVUxxdQOfrN/JJ+t3YrUYjIzsQWJsCJNjg4nr5aNuFxGR38gwDNz69cWtX1/8zzW/v2iprKRuU+qB8HPTJlpraqhdtYraVasoaXuuo1+/g7o+Hf36YRyCS9e7I6vFyoAeAxjQYwBTY6cCUFJXQmpRKilFZvi5pXgLFQ0VLN25lKU7l5rPM8zn7b/nMyEkgd5evfXvoIiIiEgX0KWDzlNOOYVTTjnlf/797NmzufLKK7nssssAePnll/nyyy958803ueuuuwgLC2PUqFFEREQAcOqpp5KSkvI/g86GhgYaGhra366srATMiWFNTU2Halu/aP/HOVIfTzquIE8b5w0P5bzhoTQ0t7I+r4wlGcUsySxmR1ENa3JKWZNTypPfpNHT143JA4I4pn8w46ID8HZz/ZcGvZalK9HruRvw8MDtqLG4HTUWf8DZ0kLjjh3Up2yifpO5mvLyaMzOpjE7m4p/fQqAxccH92HDcI+Pxz0hAfehQ7B4e7t0K7+mI7+efW2+TAidwITQCQA0tTSRUZ7BpqJNpBanklqcyt7avWwv3c720u18lP4RAEHuQQwLHkZ8UDzDgoYxMGAgDqvuue4OOvLrWeT30GtZuhK9nuU//Z7XguF0Op2HsZYOwzCMg46uNzY24unpySeffHLQcfYZM2ZQXl7O559/TnNzM6NHj2bx4sX4+flx5plncvXVV3P66af/7Md48MEHeeihh/7rzz/44AM8PXVESjqOknrYVm6wrcwgs9KgqfVAF4vVcBLt62SQv5NBPZyEuIOaXERE/jxrdTXu+fm45+fjkZeHe8FOLP/xTZvTMGjs1ZO6yEjq+vShPjKSpsBAfSE+hCpaKyhoLiC/JZ/85nz2tOyhhZaDHmPFSm9rb/rY+tDH2oc+tj74WHxcVLGIiIhI91ZbW8tf/vIXKioq8PX95cGTrm/bcpHi4mJaWlro2bPnQX/es2dP0tLSALDZbDz22GNMmjQJp9PJiSee+D9DToC7776bWbNmtb9dWVlJREQEJ5544q9+Ig6VpqYmFi1axAknnIDdrjsY5dfVN7WwJreM5IxiktOLKCirI6PCIKMCPsuD8B4eZrfngCDGRgXg4bAekbr0WpauRK9n+TnOpiYaMjKpT0lp7/ps3r0btz17cduzF/9V5oAda0AP3IfFt3V9xuM2eDAWDw+X1d3VXs/1zfVsL93OpuIDXZ+l9aUUtBRQ0FLAClYA0NurN8OChjEsaBjxwfH09++PzdJtv5XuMrra61m6L72WpSvR61n+0/4T07+Fvjv7Fb92/P2n3NzccHNz+68/t9vtR/z/nK74mNI52e12jhsUynGDQnE6neQU15CUXkRyeiGrs0vZWVbHe6sLeG91AW42C0f1CyQxNpjEuBAiA72OSH16LUtXodezHMRux5EQj09CfPsfNe0rbBtwZE54r9+6lZbSMmqSk6lJTjYfZLPhHheHx/Dh7YOObKGhR/yOya7yerbb7YwJG8OYMHMYpdPpZGfVzvZ7PlMKU8gsz2R3zW521+zmm7xvAPCweTAkaAgJweY9n8OChuHv7u/Cncif0VVezyJ6LUtXotez7Pd7XgfdNugMCgrCarWyb9++g/5837599OrVy0VVibiWYRj0C/amX7A3f53Ql5qGZlbuKCEpvZDk9CJ2ldexJKOIJRlFPPjFNvoFeTE5NoTEuGDG9A3AzXZkuj1FRLoqe88Q7CediO9JJwLQ2thI/dat1G08EH42FxVRv2UL9Vu2UPbPfwJgCwlpCz4T8ByegNugQVgcumPyjzAMgwjfCCJ8I5gSPQWAmqYaNhdvJqUwhZSiFFILU6lqqmLt3rWs3bu2/blRvlHmgKPgBOKD4+nn3w+LoWFTIiIiIkdKtw06HQ4HI0eO5Icffmi/o7O1tZUffviB66+/3rXFiXQQXm42jh/Uk+MH9cTpdJJZWE1SWiFJ6YWsyy0ju7iG7OIc3lyRg4fdyviYICa3dXuG+bvuWKWISFdhcTjwHD4cz+HDAbPbsHn3bmp/EnzWp6XRXFhI1bffUvXttwAYDgfugwe3d316JCRgDwlx5VY6NS+7F0eFHsVRoUcB0OpsJacipz343FS0iZyKHHIrc8mtzOWzrM8A8LH7MCzEPOqeEJzA0KCheDs69rApERERkc6sSwed1dXVZGVltb+dk5NDSkoKAQEB9OnTh1mzZjFjxgxGjRrFmDFjmDNnDjU1Ne1T2EXkAMMwGNDThwE9fbj6mGiq6ptYkVVMUloRSemFFFY18P32fXy/3eySHtDTm8TYECbHhjAqqgd2qzpaRET+LMMwsIeF4RcWht/ppwHQWltL3ZYtB3V9tpSXU7dxI3UbN7Y/1x4W1t716TE8AffYWAxbl/5W8LCxGBai/aOJ9o/m3AHnAlBeX05qcSophWbwubl4M1VNVazYtYIVu1a0Py/GP6b9uHtCcALhPuFH/NoBERERka6qS393u27dOhITE9vf3j8oaMaMGbz99ttMmzaNoqIiHnjgAfbu3UtCQgLffPPNfw0oEpH/5uNu5+QhoZw8xLzbc9ueSpLTi0hKK2RDfhkZ+6rJ2FfNK0uz8XGzMaF/EImxIRwTG0xPX3dXly8i0mVYPD3xGjMGrzEH7phsyss7qOuzITOTpl27aNq1i8qFCwEwPDzwGDq0Pfj0SEjA1qOHK7fSqfm7+zMpfBKTwicB0NzaTEZZRvs9n5uKNrGrehcZZRlklGWwIGMBAAHuAWbHZ4h53H1w4GDcbfp3UkREROSP6NJB5+TJk3E6nb/4mOuvv15H1UX+JMMwGNzbj8G9/ZiZGEN5bSNLM4tJTiskOaOI0ppGvt6yl6+37AVgcG/ftm7PYBIi/LGp21NE5JAxDANHVBSOqCj8zz4LgJaqKupSUw90faak0FpdTe2aNdSuWdP+XEdU1EFdn24xMRgWfY3+I2wWG4MCBzEocBDT46YDUFRb1B58phSlsK1kG6X1pSQVJJFUkGQ+z7AxMHAg8cHxxIeYR957een+eBEREZHfoksHnSLiGv6eDs6I780Z8b1pbXWSuquC5PRCktKLSN1ZztbdlWzdXcncpCz8POxMGhBMYmwwxwwIJtDbzdXli4h0OVYfH7zHj8d7/HgAnK2tNO7YQe3GjdSlbKJu40Yac3JozM2lMTeXin//GwCLtzce8fEHws/4YeCubsM/KtgzmOMjj+f4yOMBaGxpZFvJtoPCz+K6YjYXb2Zz8Wbe2/4eAL28erXf85kQkkBsQCx2i6bQioiIiPwnBZ0iclhZLAYJEf4kRPhz8/EDKK5uYGlGEUnpRSzNKKKirokvNu3mi027MQwYFu5PYmwwE6MDaP3lhmwREfmDDIsFt/79cevfnx5TpwLQXFZG3aZNB7o+U1Npra6mZsUKalasaHuigSM6mpCAACobm/AeNRJHVJTumPyDHFaHeVdnSAIzBs/A6XSyu2Z3+1H3lMIUMsoy2Fuzl701e/k21xw25W51Z1DgoAMT3kPiCXAPcPFuRERERFxPQaeIHFFB3m6cMyKcc0aE09zSSkpBOUnphSSnF7F1dyWbCsrZVFDOnO/B22ZlSd1mjh3Ui0n9g/D3dLi6fBGRLsvWowc+kyfjM3kyAM7mZhoyMg7q+mzauZPGrCz8gcI1aygErP7+eCQk0GP6BXgfc4wLd9D5GYZBmHcYYd5hnNbPHDZV21TL1pKtB014r2ioYEPhBjYUbmh/bh+fPu33fMYHxxPjH4PVYnXVVkRERERcQkGniLiMzWphVFQAo6ICuP2kOPZV1rMk3ZzivjSziOqGFj7btIfPNu3BYsCIPj1IjDPv9hwU6qsOIhGRw8iw2XAfNAj3QYPgwgsBaC4qomr9erZ9+m96V1XRsHUrLeXlVCcnU52cTPDNNxF49dX6+nwIedo9Gd1rNKN7jQbMYVO5lbntXZ+bijaRVZ5FflU++VX5/N+O/wPAy+7F0KCh7V2fQ4OH4uvwdeVWRERERA47BZ0i0mH09HVn6ugIpo6OoKaugZc+/pb6gGiWZhaTsa+adXllrMsr4+lv0wnxcWsfaDS+fxC+7rqrTETkcLMFB+N93HEUNzQw5tRTsTqdNGzfTvmn/6Z8/nyK5vyDhqwdhD76CBbd5XlYGIZBX7++9PXry9n9zwagoqGCzcWb24+7pxalUtNUw6o9q1i1Z5X5PAyi/aMPmvAe5atrB0RERKRrUdApIh2Sw2ahv5+TU08awH2nD2ZXeZ050CitiBVZxRRWNTB/XQHz1xVgsxiMiupBYmwIiXEh9A/x1g9uIiJHgMXhMIcVxcfjPnAgex99lMqFC2nMzyd87gvYQ0JcXWK34Ofmx4SwCUwImwBAS2sLWeVZBw05KqgqIKs8i6zyLP6V+S8A/N38Dwo+BwcOxtPu6cqtiIiIiPwpCjpFpFMI8/fgwrGRXDg2kvqmFtbmlpKUVkRyeiHZxTWsyi5lVXYpj3+dRpi/B5Njg0mMDeHomEA8HfpSJyJyuPW4YBqOqCh23nQT9amp5E6dRvi8uXgMHuzq0rodq8VKbEAssQGxTI01h02V1JWYwWdRCpsKN7G1ZCvlDeUs2bmEJTuXmM8zzOfFB8dzRvQZDAka4sptiIiIiPxu+ulfRDodd7uVif2Dmdg/mAemDCK3uMbs9kwvYmV2CbvK63h/dT7vr87HYbUwtl9Ae7dn3yAvV5cvItJleR01lr4L5lNw7XU0ZmeTd+FF9H7ySXxPOtHVpXV7gR6BHNvnWI7tcywATS1NpJWmtQ842li4kcLaQraVbGNbyTY+Tv+YW0fdyoUDL9QpCREREek0FHSKSKcXFeTFpUF9uXR8X+oaW1iZXUxyehGL0wrZWVbHssxilmUW8/DCbUQGerbf7XlUv0Dc7ZpIKyJyKDkiI4n66EN2zbqVmuXL2XXTTTTceANB116rwKwDsVvtDA0eytDgoVzMxQDsrdlLSlEK3+R8ww/5P/Dk2idJLU7lwXEP6ki7iIiIdAoKOkWkS/FwWDk2rifHxvXkoTOc7Cja3+1ZyJqcUvJKann7x1ze/jEXd7uFo6ODSIwNZnJsCBEB+iFORORQsPr6EvHyS+x76inK3v0nxc+/QGPWDkIf+7uGFHVgvbx6cbLXyZwUeRIfpH3AM2uf4eucr8ksy2RO4hwifSNdXaKIiIjIL1LQeRjMmzePefPm0dLS4upSRLo1wzCICfEmJsSbKyb2o7qhmRVZxe1DjfZW1rM4rZDFaYXAVmJCvJk8IJjEuBBGRwXgsFlcvQURkU7LsNnodc89uEXHsPeRR6j86isaCwoInzsXe08NKerIDMPgwoEXMjBgILcuuZWs8iwuWHgBj014jMQ+ia4uT0REROR/UtB5GMycOZOZM2dSWVmJn5+fq8sRkTbebjZOGtyLkwb3wul0kr6viqS0IpLSC1mfV0ZWYTVZhdW8vjwHL4eV8TFBJMaZx9xD/TxcXb6ISKfUY9pUHFFR7LrxRuo3byZ36lTCX5ynIUWdwIieI1hw+gJuW3IbGwo3cGPSjVw59EpmJszEatHVLyIiItLxKOgUkW7JMAzievkS18uXaydHU1HXxPLMYpLSC0lOL6K4uoHvtu3ju237AIjr5UNiXAiJsSGM6OOPzapuTxGR38pr7BiiFsyn4LqZNO7YYQ4peuIJfE8+ydWlya8I9gzm9ZNe59l1z/L+9vd5bfNrbC3ZypMTn8Tf3d/V5YmIiIgcREGniAjg52HntGGhnDYslNZWJ1t3V7aFnoVsLCgnbW8VaXureCl5Bz7uNiYNCGbygGCOiQ0mxEf3zYmI/Jr2IUW33krN0mXsuvlmGm64nqDrrtOQog7ObrFz15i7GBo0lIdWPsSPu39k2sJpzE6czeBAdeaKiIhIx6GgU0TkP1gsBkPD/Rga7seNx/WntKaRZZlFJKUVsiSjiLLaJr5M3cOXqXsAGBrmZw40igshPtwfq0U/sIuI/Byrjw8RL71E4VNPU/rOOxS/MJfGHTsIfewxDSnqBE7rdxr9e/Tn5qSbKagq4JKvLuG+o+7j7P5nu7o0EREREUBBp4jIrwrwcnBmQhhnJoTR0upk085yktMKSUovYvOuivb1/OIsenjaOaZtoNGk/sH08HK4unwRkQ7FsFrpefddOGKi2fvQw1R+9TWN+QWEz5unIUWdwIAeA/jo9I+4d9m9JO9M5oEfHyC1OJW7x9yNw6p/80RERMS1FHSKiPwOVovBiD49GNGnB7NOjKWwqp4l6UUkpxexNNPs9vwsZTefpezGMCAhwp/EWPNuz8G9fbGo21NEBIAe55+PIzKSXTfeRP2WLeSefz7h8+bhMXSIq0uTX+Hr8OUfx/6D11JfY17KPD7J+IT00nRmT55NL69eri5PREREujFN0xAR+RNCfNw5f1QE8y4cwYb7T2DB1eO4dnI0cb18cDphY345sxdlMGXucsY89gO3fbyJL1P3UFHX5OrSRURczmvMGKI+XoAjJprmwkLyLr6Yyq+/dnVZ8htYDAtXx1/Ni8e/iK/Dl83Fm5n6xVRW71nt6tJERESkG1PQKSJyiNitFsb0DeDOk+P45uZJrLz7WB4/ZygnDuqJl8NKcXUDn6zfycwPNjDikUVMfWUlLyXvIG1vJU6n09Xli4i4hCMigqiPPsJr0kSc9fXsumUWRXPn6etiJzEhbALzT5/PwICBlDWUcdWiq3hzy5v6/ImIiIhLKOgUETlMQv08mD6mD69eMooND5zA+1eM5cqJfYkJ8aal1cmanFKe/CaNk+cs4+gnFnP3p6l8u3Uv1Q3Nri5dROSIsnp7E/HSSwRceikAxXPnsmvWLFrr6lxbmPwm4T7hvHvKu5wZfSatzlaeW/8cs5JnUd1Y7erSREREpJvRHZ0iIkeAm83K+JggxscEce9pUFBaS3K6OdDoxx3F7Kmo58M1BXy4pgC71WBM3wASY0OYHBtCdLAXhqG7PUWkazOsVnredSduMdHseehhqr7+hryCnYTPm4u9Z09Xlye/wt3mziPjH2FY8DAeX/M43+d/z46KHcyZPId+/v1cXZ6IiIh0Ewo6RURcICLAk4vHRXHxuCjqm1pYlV1CcnoRSemF5JXUsiKrhBVZJTz65XYiAjzaBxod1S8QD4fV1eWLiBw2/uedhyMykp033GgOKTrvfMJfnIfH0KGuLk1+hWEYTI2dSlxAHLck30JORQ7Tv5zOI+Mf4cSoE11dnoiIiHQDOrouIuJi7nYrk2NDePCMwSy5PZGk2ybzwOmDmNg/CIfVQkFpHe+uzOOyt9eS8PB3zHhzDW+vyCGvpMbVpYuIHBaeo0cfGFJUVETeRRdT+dVXri5LfqNhwcNYcPoCxvQaQ21zLbcuuZXZ62bT3KqrWUREROTwUkeniEgH0zfIi74T+nL5hL7UNjbzY1YJSemFJKcXsau8jiUZRSzJKOLBL7bRL8iLybEhJMYFM6ZvAG42dXuKSNewf0jRrltvpWbJUnbNupWGrB0EXT8Tw6Lf1Xd0gR6BvHLCKzy/4Xne2voWb219iy0lW3h60tMEegS6ujwRERHpohR0ioh0YJ4OG8cP6snxg3ridDrJLKwmKa2QpPRC1uWWkV1cQ3ZxDm+uyMHDbt4DmhgXzOTYEML8PVxdvshvs28rBMeBRUG9HMzq7U3Eiy9S+MyzlL71FsUvvkjDjh30fuJxLB76GtfR2Sw2Zo2axZCgIdy/4n7W7l3L1IVTmT15NvHB8a4uT0RERLogBZ0iIp2EYRgM6OnDgJ4+XH1MNFX1TazIKiYpzbzbs7Cqge+37+P77fsAGNDTu32g0aioHtit6oCSDqhqH7x0NLj5QeTREDUB+k6EnkNBXXtC25CiO+8whxQ9+BBV335LXkEB4S/Ow96rl6vLk9/gxKgTifGP4ebkm8mpyOHSby7l7jF3c/6A8zVsT0RERA4pBZ0iIp2Uj7udk4eEcvKQUJxOJ9v2VJoDjdIK2ZBfRsa+ajL2VfPK0mx83GxM6B9EYmwIx8QG09PX3dXli5hKssyQs6ECMr42F4C7vxl6Rk2AqIkQMkjBZzfnf+65B4YUbdtG7vlTCZ83F49hw1xdmvwG/fz78eFpH3L/ivtZlLeIR1Y9wqaiTdx/1P242/RvkoiIiBwaCjpFRLoAwzAY3NuPwb39mJkYQ3ltI0szi0lOL2RJehElNY18vWUvX2/ZC8Dg3r5t3Z7BJET4Y1O3p7hK1Hi4Mwf2bILc5ZC7DPJWQn05pC00F4BnIESOh76TzPAzOA7UCdbteI4aRdTHC9h57XU0ZGaSd/ElhD72d/xOO83Vpclv4GX34tljnuXtrW8zZ8Mc/m/H/5FZlsnsybMJ9wl3dXkiIiLSBSjoFBHpgvw9HZwR35sz4nvT2upk864KktILSUovInVnOVt3V7J1dyVzk7Lw87AzaUAwibHBHDMgmEBvN1eXL92NxQphI8w1/kZoaYY9KZCz1Aw/81dBbQls/z9zAXgFH+j2jJoIQf0VfHYTjvBwIj/8gN233U51cjK7b72Nxh07CLr+eg0p6gQMw+CyIZcxKHAQty+5ne2l25m2cBpPTnqSCWETXF2eiIiIdHIKOkVEujiLxSA+wp/4CH9uPn4AxdUNLM0oIim9iKUZRVTUNfHFpt18sWk3hgHDwv1JjA0mMTaEoWF+WCwKj+QIs9ogfJS5Js6ClibYtQFyl0LOMihYDTVFsPXf5gLw7nXgfs+oiRDQT8FnF2b19iZ83lwKZ8+m9I03KX7xJRqy2oYUeXq6ujz5DcaGjmXBlAXMSp7F5uLNXPf9dVyXcB1XDbsKi6HAWkRERP4YBZ2Hwbx585g3bx4tLS2uLkVE5L8EebtxzohwzhkRTnNLK5t2lrcPNNq6u5JNBeVsKihnzveZBHo5OGZAMJPjQpjUPwh/T4ery5fuyGqHPmPNNel2aG6AXevN0DN3GRSsgeq9sOUTcwH49D4QevadCD2iXLoFOfQMq5Wet9+OW3QMe/72N6q++47cnQVEvPiihhR1Er28evH2yW/zxJon+DjjY+alzGNL8RYem/gYvg5fV5cnIiIinZCCzsNg5syZzJw5k8rKSvz8/FxdjojI/2SzWhgZGcDIyABuOymWfZX1LEk3Q89lmcWU1DTy6cZdfLpxFxYDRvTpQWKcebfnoFBfTcsV17C5mRPaI48G7oSmeti51gw9c5aZ/71qN6TONxeAX5+DOz79I1y6BTl0/M85G0dkH3ZefwMN27aTc/75RMydi0d8vKtLk9/AYXXwwLgHGBo0lEdXPcqSnUu4YOEFPDf5OWIDYl1dnoiIiHQyCjpFRKRdT193po6OYOroCBqbW1mfV0ZyeiFJ6YVk7KtmXV4Z6/LKePrbdHr6ujF5QAiJccGMjwnCx93u6vKlu7K7mwFm34mQCDTWws41Bzo+d62HinzY9IG5wOzwjJoAUZPM5/n2duUO5E/yHDmSqI8/Zue11/5kSNFj+J2uIUWdxdn9zyY2IJZbkm6hoKqAi766iL8d/TdO73e6q0sTERGRTkRBp4iI/CyHzcK46EDGRQdy96kD2VVeZ4aeaUWsyCpmX2UD89cVMH9dATaLweioACbHBpMYF0L/EG91e4rrODyh32RzATTWmAON9nd87t4IZbnm2vie+ZiA6LaOz0lmx6dPT9fULn+YIzyMyA8/ZPftt1OdlMTu226jYUcWwTfcoCFFncSgwEHMP30+dy67kx93/8jdy+5mc9Fmbht1G3arfpkmIiIiv05Bp4iI/CZh/h5cODaSC8dGUt/UwtrcUpLSikhOLyS7uIaV2SWszC7h8a/TCPP3MEPP2BCOjgnE06F/bsSFHF4Qc5y5AOorzYFGOUvN8HPPJijdYa4N75iPCRrQNtG9bbK7d7Dr6pffzOrtRfjcFyh67jlKXn+DkpdepjFrB72ffEJDijoJf3d/XjzuRV7c9CKvpr7KB2kfsL10O88e8yzBnvr/oYiIiPwy/eQpIiK/m7vdysT+wUzsH8wDUwaRW1zTdsS9iFXZJewqr+P91fm8vzofh9XC2H4BJMaGkBgXQt8gL1eXL92duy/0P8FcAHXlkL8Scpeb4efezVCcYa51b5iPCR7Ydr/nBIicAF6BLitffplhtRJy2204omPY+8ADVC1aRO6unUTMm4c9NNTV5clvYLVYuWH4DQwJHMI9y+9hY+FGpi6cyjPHPMPIniNdXZ6IiIh0YAo6RUTkT4sK8uLSoL5cOr4vdY0trMouISm9kMVphewsq2NZZjHLMot5eOE2IgM9SYw1Bxod1S8Qd7vV1eVLd+fhD7GnmAugthTyfjSDz9xlsG8LFG0315pXzcf0HPKTjs/x4NHDZeXLz/M/+6z/GFI0lYh5GlLUmST2SeSj0z/i5qSbySrP4opvr+DWUbdy4cALdT2KiIiI/CwFnSIickh5OKwkxpndmw+d4WRHUU37QKM1OaXkldTy9o+5vP1jLu52C0dHB5EYG8zk2BAiAnS0VDoAzwAYeLq5AGpKIG9523Cj5WbguW+LuVa/BBjQa+iB+z0jx4G7n0u3ICbPESPo+/ECCq69joaMDHNI0d//jt8UDbjpLCJ9I3n/1Pd5cOWDfJ3zNU+ufZLU4lQeHPcgnnb9myEiIiIHU9ApIiKHjWEYxIR4ExPizRUT+1Hd0MyKrGKS0827PfdU1LM4zez8hK3EhHiT2Ha356ioABw2DRCRDsArEAadaS6A6iKz0zO3LfgszoC9qeZaORcMC4TGm6Fn30nQ5yhw83HtHroxe1gYkR98wO477qB68WJ23347DVlZBN90o4YUdRKedk+enPgkw4KG8ey6Z/k652syyzKZkziHSN9IV5cnIiIiHYiCThEROWK83WycNLgXJw3uhdPpJH1fFUlpRSSlF7I+r4yswmqyCqt5bVkOXg4r42OCSIwzj7mH+nm4unwRk3cwDDnHXABVew/c75m73BxqtHujuX58Hgwr9B7edsfnRDP4dOiu2iPpwJCiOZS89holr7xCY/YOej/xBBYvfS46A8MwuGjQRQwMHMhtS24jqzyLCxZewOMTH2dyxGRXlyciIiIdhIJOERFxCcMwiOvlS1wvX66dHE1FXRPLM4tJSi8kOb2I4uoGvtu2j++27QMgrpePeSQ+NoQRffyxWdWJJR2ETy8Yep65ACp2td3v2RZ8luXCrnXmWv4cWGwQNrKt43MiRIwFu4L8w82wWAi5dRZuMdHsue9+qhZ9T27BRUS8OA97796uLk9+o5E9R7Lg9AXcuuRWNhZu5IbFN3DVsKu4Lv46rBbd+SwiItLdKegUEZEOwc/DzmnDQjltWCitrU627q5sCz0L2VhQTtreKtL2VvFS8g583G1MGhDM5AHBHBMbTIiPu6vLFznALwzip5kLoDy/reOz7bh7RQEUrDbXsmfA6oCwUQc6PsNHg12v6cPF78wzsUf0YecNN9CQlkbO1GmEv/A8nsOHu7o0+Y2CPYN548Q3eHb9s7y//X1eTX2VLcVbeHLik/i7+7u6PBEREXEhBZ0iItLhWCwGQ8P9GBrux43H9ae0ppFlmUUkpRWyJKOIstomvkzdw5epewAYGuZnDjSKCyE+3B+rRdN4pQPx7wMJfzGX02l2eO6f6J6zDKp2Q/6P5lryJFjdIGLMgY7PsFFgc7h6F12K54jh9F0wn4LrZtKQnk7+jEsJffQR/M44w9WlyW9kt9q5a8xdDAkawkM/PsSPu39k2sJpPJf4HIMCB7m6PBEREXERBZ0iItLhBXg5ODMhjDMTwmhpdbJpZznJaYUkpRexeVdF+3p+cRY9PO0cMyCYxLgQJvUPpoeXAiLpQAwDAvqaa8TFZvBZmn0g9MxdBtX7Dgw7SgZsHtBnLERNgKhJEDYCrHZX76TTs4eFEfXB++y6806qv/+B3XfcSUPWDoJvvklDijqR0/udTn///tySfAsFVQVc/NXF3HfUfZzd/2xXlyYiIiIuoKBTREQ6FavFYESfHozo04NZJ8ZSWFXP0gzzbs+lbd2en6Xs5rOU3VgMSIjwJzE2hMS4EAaF+mJRt6d0JIYBgdHmGnmpGXwWZx481b2mCLKTzQVg9zIHGkVNMKe6hyaAVd/S/REWLy/Cn3+eojn/oOTVVyl59VUasncQ9uSTGlLUicQGxPLR6R9xz7J7WLJzCQ/8+ACpxancPeZuHFb9sktERKQ70XfFIiLSqYX4uHPeyHDOGxlOc0srG/LLSUovJCmtkLS9VWzIL2dDfjnPLsogyNuNybHBJMaGMKF/EH4e6oqTDsYwIHiAuUb/1Qw+i9IOdHvmLoe6Utjxg7kAHD4QOc486h41AULjQUNZfjPDYiFk1i3tQ4qqv/+B3As1pKiz8XX48vyxz/Nq6qu8mPIin2R8QnppOrMnz6aXVy9XlyciIiJHiIJOERHpMmxWC2P6BjCmbwB3nhzHnoo6ktOLSE4vZHlmMcXVDXyyfiefrN+J1WIwMrJHW7dnMLE9fTAMdXtKB2MYEDLQXGOvgtZWKNx2IPTMXQ715ZD5nbkA3Pwg8ugDw416DgEdxf5VfmecgT0igp3Xtw0pOn8q4XNf0JCiTsRiWLgm/hqGBA3hzqV3srl4M1O/mMrTxzzN2NCxri5PREREjgAFnSIi0mWF+nkwfUwfpo/pQ2NzK+tyS81uz/QisgqrWZNTypqcUp78Jo1QP3cmxwYzOTaE8TFBeLvpn0jpgCwW6DXEXEddC60tsG9LW8fncshbAQ0VkPG1uQDc/dvu92wbbhQ8UMHn/+A5fDh9P15gDilKSyP/khnmkKIzz3R1afI7TAibwPzT5zMreRbbS7dz1aKruGnETVw2+DL9QktERKSL009xIiLSLThsFo6OCeLomCDuPQ0KSmtJbgs9f9xRzJ6Kej5cU8CHawqwWw3G9A0gMTaEybEhRAd76Ydj6ZgsVvOoemg8HH29GXzu2XRguFH+SrPjM22huQA8AyFyvHm/Z9RECI41O0cFAHvv3kS9/96BIUV33kVDVhbBt9yiIUWdSLhPOO+e8i6PrnqUz3d8znPrn2Nz0WYeGf8I3g5vV5cnIiIih4mCThER6ZYiAjy5eFwUF4+Lor6phVXZJSSnF5GUXkheSS0rskpYkVXCo19uJyLAwzziHhvCUf0C8XDo/kPpoCxWcyp72AgYfxO0NJnBZ85SM/zMXwW1JbD9/8wF4BX8k47PSRAY0+2Dz/YhRf94npJXXqHktddpyM4h7CkNKepM3G3uPDL+EYYFD+PxNY/zff737KjYwZzJc+jn38/V5YmIiMhhoKBTRES6PXe7lclt3ZsPMpic4hqS0gpJSi9kdXYpBaV1vLsyj3dX5uFms3BUv0ASY4NJjAshMlChh3RgVjuEjzLXxFnQ3Ai7N0LuUrPjs2C1OdV967/NBeDdq22ie9sdnwH9umXwaVgshNxyszmk6N77qP7hB3L/cqE5pCgszNXlyW9kGAZTY6cSGxDLrORZ5FTkMP3L6Twy/hFOjDrR1eWJiIjIIaagU0RE5D/0DfKi74S+XD6hL7WNzfyYVUJSeiHJ6UXsKq9jSUYRSzKKePCLbfQL8mJy20CjMX0DcLOp21M6MJsD+ow116TbobkBdq0/MNW9YA1U74Utn5gLwDfs4Ds+vbtXyOc3ZQqOiAgKrr+BhvR0cqZOI/yFF/AcoSFFnUl8cDwLTl/A7UtvZ+3etdy65FYuK76Ma4de6+rSRERE5BBS0HkYzJs3j3nz5tHS0uLqUkRE5E/ydNg4flBPjh/UE6fTSWZhtXm3Z1oRa3NLyS6uIbs4hzdX5ODpsHJ0dBCJceZQozB/D1eXL/LLbG7mhPbIo4E7oakedq49cMfnzrVQuQtS55sLsPlFMNwahZFaCdGTwT/CpVs4EjwSEg4MKdq+nfwZM+j1yMP4n3WWq0uT3yHQI5BXT3iV5zc8z1tb3+KtrW+xuXgzx7ce7+rSRERE5BBR0HkYzJw5k5kzZ1JZWYmfn5+ryxERkUPEMAwG9PRhQE8frpoUTVV9EyuyiklKM+/2LKxq4Pvt+/h++z4ABvT0JjE2hIkxAbS0urh4kd/C7m52bfadCIlAY615vD13uRl+7lqPUVFAHwrgi2Xmc3pEHbjfM2oC+PZ25Q4OG3toKFHvv8fuO++iatEi9tx1N437hxRZ1cndWdgsNmaNmsWQoCHcv+J+1u1bR4aRwcDigYwIHeHq8kRERORPUtApIiLyB/m42zl5SCgnDwnF6XSybU+lOdAorZAN+WVk7KsmY181ryzNxt1q5duqFI4b2ItjYoPp6evu6vJFfp3DE6ITzQXQUE1zzgqyk94lxroHy54UKMs118Z/mo8JiD5wv2fURPDp6aLiDz2Lpydh/5hD0QsvUPLSy5S8/gYNO7Lp/fTTWL11X29ncmLUicT4x3BT0k3kVuby1+//yt1j7ub8AedjdMM7aUVERLoKBZ0iIiKHgGEYDO7tx+DefsxMjKG8tpFlmcVtd3sWUlrTxLfbCvl2WyEAg3v7khgbwuTYYBIi/LFZLS7egchv4OaNM/pYtqfX0/fUU7G01JmT3HOXml2fezZB6Q5zrX/bfE7QgAP3e0ZOAO9gl27hzzIsFkJuugm3ftHsufdeqpOSyPvLXwh/8UUc4d3r/tLOrp9/P9496V2u+ewatjVt45FVj5BalMp9R92Hu02/jBIREemMFHSKiIgcBv6eDqbE92ZKfG8aGhp55ZOvaQ6KZUlWCak7y9m6u5KtuyuZm5SFn4edSQOCSYwN5pgBwQR6u7m6fJHfxt0XBpxoLoC6cshf2TbcaCns3QLFGeZa94b5mOCBP+n4nACeAS4r/8/wm3I6jj4RFFx/PQ0ZGeROnUr4C8/jOXKkq0uT38Hb7s10z+kU9S1i7qa5fL7jczLKMpg9eTbhPuGuLk9ERER+JwWdIiIih5nFYhDpDaceG82sk+Iorm5gaUYRSelFLM0ooqKuiS827eaLTbsxDBgW7k9ibDCJsSEMDfPDYtExSukkPPwh9hRzAdSWQt6PB4YbFW6Fou3mWvOq+ZieQ37S8Xk0ePRwWfm/l0d8PH0//piC666jYdt28i69jNCHHsL/nLNdXZr8DoZhcOmgSxkaMpQ7ltzB9tLtTFs4jacmPcX4sPGuLk9ERER+BwWdIiIiR1iQtxvnjAjnnBHhNLe0smlneftAo627K9lUUM6mgnLmfJ9JoJeDY9pCz0n9g/HztLu6fJHfzjMABp5uLoCaEshb3tbxuQyK0mDfFnOtfgkwoNfQtsFGEyFyHLh37MGO9l69iHrvPXbfdTdV333HnnvuoWFHFiGzZmlIUSdzVOhRzD99PrOSZ7GlZAvXfn8tMxNmcuWwK7EYul5ERESkM1DQKSIi4kI2q4WRkQGMjAzgtpNi2VdZz5J0M/RclllMSU0jn27YxacbdmExYESfHiTGmXd7Dgr11dAM6Vy8AmHQmeYCqC48MNE9ZxmUZMLeVHOtnAuGBUITzCPufSdBn6PAzcelW/g5Fk9PwuY8R/HcuRS/+BKlb7xJ445sej/zjIYUdTKh3qG8c8o7PLHmCT7O+Ji5KXPZUryFv0/8O74OX1eXJyIiIr9CQaeIiEgH0tPXnamjI5g6OoKmllbW5ZaRnF5IcnoR6fuqWJdXxrq8Mp7+Np2evm5MHhBCYlww42OC8HFXt6d0Mt4hMOQccwFU7TWDz5ylZvhZmg27N5jrx+fBsELYCDP4jJpoBp+OjhEkGhYLwTfeiCM6mj333Et1cjJ506cT/tJLGlLUyTisDh4Y9wBDg4by6KpHSd6ZzPSF03ku8TkG9Bjg6vJERETkFyjoFBER6aDsVgvjogMZFx3I3acOZFd5HcnphSSlFbEiq5h9lQ3MX1fA/HUF2CwGo6MCmBwbTGJcCP1DvNXtKZ2PTy8Yep65ACp2tXV8LjU7PsvzYOdacy1/Dix2CBvZNtxoAkSMBbuHS7fgd9ppOCIi2DnzehoyM8k9/3zC576gIUWd0Nn9z2ZAwABmJc0ivyqfC7+8kAePfpDT+p3m6tJERETkf1DQKSIi0kmE+Xtw4dhILhwbSUNzC2tySklKKyI5vZDs4hpWZpewMruEx79OI8zfwww9Y0M4OiYQT4f+yZdOyC8M4qeZC6A8v+1+z7bj7hUFULDKXEufBqsDwkcfmOgePhrs7ke8bI9hw4j6eAE7r5tJ/bZt5pCiBx/E/9xzjngt8ucMDhzM/NPnc8fSO1i5ZyV3LbuLzcWbuXXUrdgt6qIXERHpaPRTj4iISCfkZrMysX8wE/sH88CUQeSV1JDcdrfnyh0l7Cqv4/3V+by/Oh+H1cLYfgEkxoaQGBdC36COcdRX5Hfz7wPDLzSX0wlluWbgmds24KhqN+StMNcSwOZuhp37hxuFjQSb44iUau/Vi8j324YUffste+69l4asLEJuu1VDijoZf3d/Xjr+JealzOO1za/x/vb32VayjWePeZZgz2BXlyciIiI/oaBTRESkC4gM9GLG0V7MODqKusYWVmWXkJReyOK0QnaW1bEss5hlmcU8vHAbUYGeTI41Bxod1S8Qd7tCF+mEDAMC+pprxCVm8FmafeB+z9zlUL2v7b8vM59j84A+Y83Qs+8k6D0crIevK8/i4UHYc7MpnjuP4hdfpPStt2jMzqb3s89g9fY+bB9XDj2rxcqNI25kaNBQ7ll+DxsLNzJ14VSePeZZRvQc4eryREREpI2CThERkS7Gw2ElMc7s3nzoDCc7imrMuz3TC1mTU0puSS1v/5jL2z/m4m63cHR0ENNGR3DS4F6uLl3kjzMMCIw216jLzOCzOPPA/Z65y6G2GLKTzQVg9zIHGvWdCFGTIDQerIf222NzSNENuMVEs/vue6hessQcUvTiizgiIg7px5LDL7FPIh+d/hE3J91MVnkWf/32r9w2+jb+EvcX3YssIiLSASjoFBER6cIMwyAmxJuYEG+umNiP6oZmVmQVk5xu3u25p6KexWlm5+fDZw7mknFRri5Z5NAwDAgeYK7RV5jBZ1FaW+i5FHJXQF0p7PjBXAAOH4gc19bxORF6DQPLoel49j31VOwREey8biYNmVnkTp1G+AvP4zlq1CF5/3LkRPpG8v6p7/Pgjw/yde7XPLHmCVKLUvnbuL/hafd0dXkiIiLdmoJOERGRbsTbzcZJg3tx0uBeOJ1O0vdV8c+Veby/Op8HPt8KoLBTuibDgJCB5hp7FbS2QuE281h7zjLIWw71FZD5nbkA3Pwg8ui2js+J0HMIWCx/uASPoUOJ+uRjc0jR1q3kXXY5oQ/+Df9zzz1Em5QjxdPuyZOTnmRY8DCeWfcMX+V8RUZZBnMS5xDpG+nq8kRERLotBZ0iIiLdlGEYxPXy5dGzhuDtbuOVJdk88PlWDOBihZ3S1Vks0GuIuY66FlpbYN+Wto7PZZD3IzRUQMbX5gLw6AGR4w90fAYP/N3Bp71nTyLf+ye777mHqq+/Yc+999GQmUXI7bdpSFEnYxgGFw26iIGBA7k1+VayyrOYvnA6j018jMkRk11dnoiISLekoFNERKSbMwyDu06OAye8sjSb+z/fCobBxUepK0m6EYvVvKMzNB6Ovh5ammFv6oGOz/yVUFcGaQvNBeAZCFETzOAzaiIEx5qdo7/2oTw8CJs9m+LoGIrnzqX07bdpyMkm7NlnNaSoExrZcyQLpizgtiW3sbFwIzcsvoGrhl3FdfHXYT1EVx+IiIjIb6OgU0RERMyw85Q4nMCrS7O5/7MtGMBFCjulu7LaIGyEucbfBC1NsDvlwBT3/FVQWwLbPjcXgFdIW/A5wZzqHhjzP4NPwzAIvn6mOaTorrupWbKU3AsuIOKllzSkqBMK8QzhjRPf4Jl1z/BB2ge8mvoqW4u38sTEJ/B393d1eSIiIt2Ggk4REREBzODl7lPicDqdvLYsh/s+24JhwIVjFXaKYLVDxGhzTZwFzY2we8OBjs+C1VBTCFs/NReAd68D93tGTYCAfv8VfPqefDL2sHB2zpxJY9YOcs+fStjz/8BrzBgXbFL+DLvVzt1j72ZI0BAeXvkwK3av4IIvL2D25NkMChzk6vJERES6hT9+m7qIiIh0OYZhcM+pA7lyYl8A7v33Fj5Yne/iqkQ6IJsD+hwFk26HGf8Hd+XDpV/B5LvNYNPqBtV7YfPH8MWN8MIIeG4wfHo1bHwPyvLa35XH0CFEffwx7kOG0FJeTv7lf6Xs449duDn5M6ZET+G9U98jwieCXdW7uPiri/l35r9dXZaIiEi3oI5OEREROcj+sNPphNeX53DPvzcD8JexfVxcmUgHZnODqPHmAmiqg51r24YbLTf/e+UuSP3IXAB+fdo7Pu19JxL5z3fZc++9VH71NXvvf4DGrCxC7rhDQ4o6odiAWD487UPuXX4vS3Yu4YEfH2Bz8WbuGnMXDqvD1eWJiIh0WQo6RURE5L8YhsG9pw3ECbzRFnYaBkwfo7BT5Dexe5j3dPadZL7dWGseb89tCz53rYeKfEh531yApUcUvSdNwOGdSPGCJErfeZeG7BzCZj+L1cfHhZuRP8LPzY/nj32eV1Nf5cWUF/k442PSStOYPXk2vbx6ubo8ERGRLklH10VERORnGYbBfacN5PLx5jH2uz/dzEdrdIxd5A9xeEJ0Ihz3APz1O7gzDy76F4y/GcJGgWGFslyMlPcItrxP2NGlGDaoWbaM3LNOo3H7BlfvQP4Ai2HhmvhrmHfcPHwdvmwu3sy0hdNYvWe1q0sTERHpktTRKSIiIv+TYRjcf/pAnDh5a0Uud31qdnZOG63OTpE/xc0bYo43F0B9pTnJPXcp5CzD10jF7l3EzmUBNO4qInfadMJO9cJrwuQDA468gly6BfntJoZP5KPTP2JW8izSStO4atFV3DziZi4dfCnGfwyoEhERkT9OQaeIiIj8IsMweOB0c2Jwe9iJwdTRES6uTKQLcfeFASeaC6CuHI/8lUSN/46dLy6ifh/kf15Lr10f0iP6DfMxIYPMae77p7p7BriufvlVET4R/POUf/LIqkf4vx3/x+z1s9lcvJlHxj+Cl93L1eWJiIh0CTq6fhjMmzePQYMGMXr0aFeXIiIickjsDzsvPToKpxPu/DSVBWsLXF2WSNfl4Q+xp2Cf9hyR367D96TjwWmwd60/e9P64mwFCrfBmldhwcXwVD94aQJ8czekfQl1Za7egfwMd5s7j45/lPvG3ofNYmNR3iKmfzmd7PJsV5cmIiLSJSjoPAxmzpzJtm3bWLt2ratLEREROWQMw+BvU/4j7FynsFPkcLO4u9N7zvME3XgDAGUpDRQUnEXLaa/C6CshOA5wwr7NsOpF+Ogv8GRfeGUSfHsvpH8D9RWu3YS0MwyDaXHTePvktwnxDCGnIofpX07nu9zvXF2aiIhIp6ej6yIiIvKb7Q87nU4n76zM485/pWIA54/SMXaRw8kwDIKvuw636Bh233UXNSvXkLuvmIiXXsRx2jNQXXhgonvOMijJhD2bzLVyLhgWCE1ou99zEvQZC26a5O5K8cHxzD99PncsvYO1e9dy65Jbuaz4Mm4ccSM2i35MExER+SP0L6iIiIj8LoZh8OAZg3EC767M445/pWIYBueNDHd1aSJdnu9JJ2IPD2PndTNpzM4md+o0wv7xD7yOGgtDzjUXQOUeM/TMXWau0mzYvcFcK/5hTnkPG2He79l3IkSMBYfuiTzSgjyCePWEV/nHhn/w9ta3eWvrW2wt2cpTk54i0CPQ1eWJiIh0Ojq6LiIiIr+bYRg8dMZgLj4qEqcTbv9kE5+s3+nqskS6BY/Bg4n6eAHuw4bRUlFB/hVXUDZ/wcEP8g2FYefDGc/DjRvhlq1w9isw/CLwjwRnC+xcC8tnwz/Phici4Y2TYPGjkL0Emupcs7luyGaxceuoW3nmmGfwsHmwZu8api2cRmpRqqtLExER6XTU0SkiIiJ/iGEYPHzmYAD+uSqP2z/ZhAGcq85OkcPOHhJC5LvvsOfe+6j88kv2/u1vNGRl0fPOOzBsP/Mtvl84xF9gLoDyfPOIe+4y8z8rd0LBKnMtfRqsDggffaDjM3w02NyO7Ca7mZOiTiLGP4abk24mtzKXS7+5lLvG3MX5A87HMAxXlyciItIpKOgUERGRP2x/2OnEyXur8rntk00YBpwzQmGnyOFmcXen9zNP49Y/hqI5/6Dsn/+kMSeHsNnPYvX1/eUn+/eB4Reay+mEstwDoWfuMqjaA3krzLXkCbC5m2Fn30lm+Bk2EmyOI7LP7iTaP5oPT/uQ+1bcxw/5P/DIqkdILUrlvqPuw93m7uryREREOjwFnSIiIvKnGIbBw2cMwemE91fnc+vHZth59nCFnSKHm2EYBF1zDY5+/dh9513ULF9O7rQLiHj5JRyRkb/1nUBAX3ONuMQMPkuzIWfpgfCzpvDAfZ8ANg9zoFHURDP87D0crPbDt9FuxNvhzXOTn+PNLW/y/Mbn+XzH52SUZTB78mzCffR1VURE5Jco6BQREZE/zWIxeOTMITiBD1bnc+uCTYDCTpEjxffEE3GEh1Nw3Uwac3LImTqN8H/Mweuoo37/OzMMCIw216jLzOCzOBNyl7Z1fC6H2mLITjYXgN0LIsdB1ARzqntoPFj1o8YfZRgGfx36VwYHDeaOJXewvXQ7F3x5AU9OfJLxYeNdXZ6IiEiHpWFEIiIickhYLAaPnjmE6WP60OqEWxds4rONu1xdlki34T5oEH0/XoB7/DBaKyrIv+JKyj766M+/Y8OA4AEw+gqY+g7cngXXrYJTnoaBU8CjBzTVQNb38P2D8Pqx8FRfeH8q/PgC7N4IrS1/vo5u6KjQo5h/+nyGBA6hoqGCa7+/llc2vUKrs9XVpYmIiHRI+jWriIiIHDIWi8HfzxoCOPlwTQGzFqRgGHBmQpirSxPpFmzBwUS++y577rufyi++YO+DD9GQmUXPu+/6+SFFf4RhQMhAc429ClpboXCr2emZswzylkN9BWR+ay4Adz+IHG8edY+aAD2HgEU9F79FqHcob5/yNk+seYJPMj5hbspcthRv4e8T/46v41fuYhUREelmFHSKiIjIIWWGnUNxOuGjtQXcMj8FUNgpcqRY3Nzo/dSTuEVHUzRnDmXvv28OKXpuNlY/v8PwAS3Qa6i5jrrW7N7cu9kMPnOXQd6PZvCZ/pW5wOwCjRx/YLhRcJyCz1/gZnXjb+P+xrCgYTy66lGSdyYzfeF0nkt8jgE9Bri6PBERkQ5DQaeIiIgcchaLwWNnDwUUdoq4gjmk6Goc0f3Yfced1Pz444EhRVFRh/eDW6zQO8FcR18PLc2wd9OB+z3zV0JdGaQtNBeAZ2Db/Z5tw42CBpido3KQs/ufzYAeA7gl+Rbyq/K58MsLefDoBzmt32muLk1ERKRDUNApIiIih8X+sNPphPnrzLDTMAzOiO/t6tJEug3fE07A8UHbkKLcXHKmXUD4nOfwGjfuyBVhtUHYSHNNuBlammB3yoHhRgWrobYEtn1uLgCvEDP47DvRHG4UGK3gs83goMHMP30+dy69k5V7VnLXsrvYXLyZW0fdit1id3V5IiIiLqXzISIiInLYWCwGj58zlKmjwml1ws0fbeSLTbtdXZZIt+I+cCB9F8zHIz7+wJCiDz90XUFWO0SMhom3wiWfwZ15cPm3kHif2c1pc4eaQtj6KSy8BeaOhNkD4V9XwPp3oDTbnATfjfVw78FLx7/ElUOvBOD97e9zxbdXUFRb5OLKREREXEtBp4iIiBxWFovBE+cMOxB2zk9hYarCTpEjyRYcTJ9338H3jCnQ0sLehx5m78OP4GxudnVpYHNAn6PgmNthxhdwVz5c+hVMvhsiJ4DVAVV7YPPH8MWN8PxweG4IfHo1bHwPyvJcvQOXsFqs3DjiRv6R+A+87d5sKNzA1IVT2bBvg6tLExERcRkFnSIiInLY7Q87zx8ZTkurk5s+SuHL1D2uLkukW7G4udH7yScJnjULDIOyDz6g4KqraKmocHVpB7O5QdR4mHwXXPalGXzO+AIm3QF9xoHFDpU7IfUj+Hwm/GMYzBkKn82ElA+hYqerd3BEHdvnWD487UNi/GMorivmr9/+lfe3v4+zm3e9iohI96Q7OkVEROSIsFgMnjx3GE7gk/U7ufGjjQCcNizUtYWJdCOGYRB01ZW4Rfdj1+13UPPjSnKnXUD4Sy/i1revq8v7eXYP80h730nm24215r2eucvMOz53b4DyfEh5z1wAPfq23e/Ztny79teZKL8o3j/1ff7249/4JvcbnljzBKlFqfxt3N/wtHu6ujwREZEjRkGniIiIHDHtYacT/rXBDDsNA04d2rVDCJGOxue444j64H0KrruOxtxcM+yc8xxeRx/t6tJ+ncMTohPNBdBQDQWr2qa6L4PdG6Esx1wb3jUfExjTFnq2TXb36em6+g8TT7snT016imHBw3h23bN8lfMVmeWZzJk8hz6+fVxdnoiIyBGhoFNERESOKKvF4KnzhuHEyacbdnHDh2Znp8JOkSPLPS6OvgsWsPP6G6hLSSH/yqvoee89BPzlL64u7fdx84aY480FUF8J+SsPdHzuTYWSLHOtf8t8TFAs9J2IEXE0jqZa19V+iBmGwcWDLmZgwEBuW3IbmWWZXLDwAh6b+BiTIya7ujwREZHDTnd0ioiIyBFntRg8fV485wwPo6XVyQ0fbuTrzbqzU+RIswUF0eedt/E78wxoaWHfw4+w9+GHcTY1ubq0P87dFwacBCc+ClcvgTty4IIP4aiZ0GsoYEBxOqx9Hdunl3PKluuxvToRvroDtn8BtaWu3sGfNqrXKBZMWUBCcAJVTVXcsPgGXtj4Ai2tLa4uTURE5LBSR6eIiIi4hNVi8PT58QB8utHs7JxrwMlD1NkpciRZ3NwIfeIJHDExFM1+jrIPPqQhJ4fwOXOw+vm5urw/z8Mf4k41F5hBZt4KyFmGM3cZRuE2jKLtULQd1rwCGNBzyIE7PiOPNt9HJxPiGcKbJ73J0+ue5sO0D3k19VW2Fm/liYlP4O/u7+ryREREDgt1dIqIiIjL7A87zx4eRnOrk+s/2Mg3W/a6uiyRbscwDIKuvJLweXMxPD2pXbmK3KnTaMjOcXVph55nAAycAqc+RfOVS/l6yFyaz3kTRl8BwXGAE/ZthlUvwkfT4ckoeGUSfHsvZHxrHo3vJOxWO/eMvYfHJjyGu9WdFbtXcMGXF7CtZJurSxMRETksFHSKiIiIS1ktBs+cH89ZCb3bws4NCjtFXMTn2GOJ+vADbL1DaczLI/eCC6hescLVZR1WjXZfnAPPgNOehZmr4bZMOO9NGHkZBPYHnLBnE6ycCx9MhScj4dVEWPQAZH5vDkPq4KZET+G9U98j3DucXdW7uOTrS/gs6zNXlyUiInLIKegUERERl7NaDJ6dmnBQ2PntVoWdIq7gHhtL348/xmP4cForKym46mpK33/f1WUdOd4hMORcmDIHblgHs9LgnNdhxCUQ0A+crbB7A6z4B7x/rhl8vn4CfP8Q7FgMjR1zuFFsQCwfnf4Rk8In0dDSwP0r7ufhlQ/T2NLo6tJEREQOGQWdIiIi0iHsDzvPbAs7Z76/ge8Udoq4hC0wsG1I0ZnmkKJHHmXPQw917iFFf5RvKAw7H854AW7cCLdshbNfgYSLwL8PtDbDzjWwfDb882x4og+8eTIsfhSyl0BTnat30M7PzY8Xjn2B6xKuw8Dg44yPueyby9hbo6+1IiLSNSjoFBERkQ7DajF49vx4zohvCzs/2MCibftcXZZIt2RxOAh94nFCbr8NDIPyDz8i/8qraCkvd3VpruUXDvEXwFnz4ObNcFMqnPkixE8H33BobYL8lbD0aXj3DDP4fOs0SHoccpdDc4NLy7cYFq6Nv5Z5x83Dx+FDanEq0xZOY/We1S6tS0RE5FBQ0CkiIiIdis1qYfbUeKbE96apxcl1769X2CniIoZhEPjXvxI+by4WT09qV60iZ9o0GrKzXV1ax9EjEoZfCGe/DLdsMbs+z3gBhk4Fn1BoaYS85bDkCXj7NDP4fGcKLHka8lZCs2uOjk8Mn8j80+cTFxBHaX0pVy26ire2vIXT6XRJPSIiIoeCgk4RERHpcGxWC8/9R9j5vcJOEZfxOfZYIj/8EHtYGE15+eROu4Dq5V17SNEfYhjmPZ4jLoFzX4NZ2+H69XD6HPPeT68QaK6HnKWQ9Ci8dbJ5x+e7Z8GyZ6FgDbQcuesBInwiePeUdzkj+gxana3MXj+bW5fcSk1TzRGrQURE5FBS0CkiIiId0v6w8/RhoTS1OLlWYaeIS7nHDiBqwXw8RoygtaqKgquuovSf76kD8JcYBgTFwKjLzEnut2XAzDVw6jMw6CzwDIKmWshOgh8ehjdOgCej4L1zYfkc2LUeWpoPa4keNg8eHf8o9429D5vFxqK8RUz/cjrZFeraFRGRzkdBp4iIiHRYNquFOdMSOO0nYecP2xV2iriKLTCQPm+/hd/ZZ0NrK/v+/nf2PthNhxT9EYYBwbEw5kqY+g7cngXXroRTnoKBU8CjBzRWQ9b38P3f4LVj4am+8P5U+PEF2J0CrS2HoSyDaXHTeOuktwjxCCGnIofpC6ezKG/RIf9YIiIih5OCzsNg3rx5DBo0iNGjR7u6FBERkU7PZrXwj2kJnDa0Lex8bwOL0xR2iriKxeEg9LG/E3L77eaQovnzyb/iSprLylxdWudjGNBzEIy9Gqa9B7dnwzXL4aTHIfY0cPeDhkrI/Ba+uw9ePcYMPj+cDitfhL2bobX1kJWTEJLA/CnzGdVzFLXNtcxKnsXsdbNpbj28XaUiIiKHioLOw2DmzJls27aNtWvXuroUERGRLsFmtTDnggROHdqLxpZWrvnnBpLSCl1dlki3ZQ4pupzwF+eZQ4pWryZ32gU07Njh6tI6N4sFeg2FcdfB9A/gjhy4agmc+CgMOBncfKG+AtK/gm/vhpcnwNP94KMLYfUrsG8b/MmrBII8gnjtxNeYMWgGAG9tfYurF11NSV3JodihiIjIYaWgU0RERDoFu9XCPy4Y3h52Xv3P9Qo7RVzMJzGRyI/ahhTltw0pWrbc1WV1HRYr9E6Ao2+Av8w3g88rF8PxD0HM8WD3groySFsIX98BL42Dp2NgwSWw5jUoSv9DwafNYuO20bfx9DFP42HzYM3eNUxbOI3UotRDv0cREZFDSEGniIiIdBr7w85Thvwk7ExX2CniSu4DBhD18QI8Ro2ktbqagquvpvTddzWk6HCw2iBsJEy4GS76F9yVB3/9Ho57APolgt0Taoth2+fw1W0wbww8MwA+vgzWvQnFWb8r+Dw56mQ+PO1Donyj2Fe7j0u/uZQF6Qv0uRURkQ5LQaeIiIh0KnarheenHxx2JivsFHEpW0AAkW++id8555hDih57nL1/e1BDig43qx0iRsPEW+GSz+DOPLj8W0i8D/pOAps71BTC1k9h4S0wdyTMHgj/uhLWvwOl2b8afEb7R/PhaR9yXJ/jaGpt4pFVj/DAjw9Q31x/ZPYoIiLyOyjoFBERkU5nf9h58uBeNDa3cpXCThGXMxwOQv/+KCF33GEOKVqwgPy/XqEhRUeSzQF9joJjbocZX8Bd+XDpVzD5boicAFYHVO2BzQvgixvh+eHw3BD49zWw8X0oy/vZd+vt8Oa5yc9x84ibsRgWPsv6jEu+voRd1buO8AZFRER+mYJOERER6ZTsVgsv/GU4Jw3u2R52LskocnVZIt2aYRgEXn4Z4S+9iMXLi9o1azSkyJVsbhA1HibfBZd9aQafM76ASXdAn3FgsUPlTtj0IXx+HfxjGMwZCp/NhE0fQcXO9ndlGAZ/HfpXXj7+ZXq49WB76XamLZzGil0rXLhBERGRgynoFBERkU7LbrXwwvQRnDjIDDuvfHedwk6RDsBn8mSiPvoQe3j4T4YULXN1WWL3MI+0H3svXP6Necfnxf82j76HjwGLDcrzIeU9+PfV8Nxg+EcC/N8NkPoxVO5hXO9xzD99PoMDB1PRUMG131/LK5teodXZ6urdiYiIKOgUERGRzs1hszD3LyM44Sdh51KFnSIu59a//38MKbqG0nfe0SCbjsThBdHHmsOMrlhk3vF54b9g/M3m0CPDAmU5sOFd+PQKmB0HL4wkNOlJ3ulzFudGnYYTJ3NT5nLT4puobKx09Y5ERKSbU9ApIiIinZ7DZmHef4SdyzIVdoq4mq1HD3NI0XnnmkOKHn+CvQ88gLOx0dWlyc9x84b+x8MJD8GVi83g8y8LYNz1EJoAGFCSBevfwu3Tq3kw6SUeqrfjwELyzmSmfzGVjLIMV+9CRES6MQWdIiIi0iXsDzuPH9iThuZWrnhnHcszi11dlki3ZzgchD7yCCF33QkWC+Uff6IhRZ2Fuy8MOAlO+jtcvQTuzIULPoSjroOeQwE4Z88O3t21m9DmZvKrd3HR5+fy1acXwfYvoLbUtfWLiEi3o6BTREREugyHzcKLF47g+IEhNDS38td31irsFOkADMMg8NJLiXj5JXNI0dq15E6dRkNWlqtLk9/Dwx/iToWTH4drl8MdOTDtPQYnXMb8Bj/G1dVRZ8CdVZt4YtENND3VD16eAN/cDWlfQV25q3cgIiJdnIJOERER6VLMsHPkQWHniiyFnSIdgfekSUTN/wh7RARNBQXmkKIlS1xdlvxRngEwcAqc+hQ9rlvFSxet4MpekwB438+HK0KDKSraCqtehI+mw1N94ZVj4Nt7IeNbqNedniIicmgp6BQREZEux2GzMO/CERwXdyDs/FFhp0iH4BYTQ9SC+XiOHk1rTQ0F115Hydtva0hRF2D17smNJ81jTuIcvO3ebHB3Z2q/ODYMOxsCY8DZCntSYOVc+GAqPBkFrx0Li/4Gmd9DQ7WrtyAiIp2cgk4RERHpktxsVl68aATHxoVQ39TK5Qo7RToMW48e9HnjdfzPPw9aWyl84kn23H+/hhR1Ecf1OY4PT/uQaL9oipur+Wv1Jt4/fhbOW7bDOa/DiEugR19wtsCu9bBiDrx/LjwZCa+fAD88DDsWQ2Otq7ciIiKdjIJOERER6bLcbFZe+s+wc4fCTpGOwHA46PXww/S8+y6wWKj45F/kX/5XDSnqIqL8ovjgtA84Oepkmp3NPLHmCe7a9A9qB54GZ7wAN6XALVvh7Fcg4SLw7wOtzbBzDSx7Fv55NjzRB948GRb/HXKWQlOdq7clIiIdnIJOERER6dL2h52JscFm2Pm2wk6RjsIwDAJmzDCHFHl7U7tuHbnnT6UhM9PVpckh4Gn35KlJT3HH6DuwGla+yvmKi76+iPzKfPMBfuEQfwGcNQ9u3gw3pcKZ8yB+OviGQ2sT5K+EpU/BO1PgiUh46zRIfgJyV0Bzg2s3KCIiHY6CThEREenyzLBzJJN/Enau3FHi6rJEpM1BQ4p27iT3gulUJSe7uiw5BAzD4OJBF/P6ia8T6B5IZlkmFyy8gOSC5P9+cI9IGH4RnP0y3LIFbtwIU56HoVPBJxRaGiBvOSQ/Dm+fanZ8vjMFljwN+augWVcfiIh0dwo6RUREpFtwt1t5+aKRHDPgQNi5Klthp0hH4RYdbQ4pGjOG1poadl57HSVvvqUhRV3EqF6jmH/6fOKD46lqquKGxTfwwsYXaGlt+fknGAYE9IORM+Dc12DWdrh+PZz+HAw+B7xCoLnePNKe9Ci8eZJ5x+e7Z5lH3wvWQkvTEd2jiIi4noJOERER6Tbc7VZeudgMO+uaWrjsLYWdIh2JrUcP+rz+Gv7nnw9OJ4VPPcWee+/TkKIuoqdXT9466S0uiL0AgFdTX2XmDzOpaKj49ScbBgTFwKjL4fy34LYMmLkGTn0GBp0JnoHQVAvZSeYwozeON6e6v3cuLJ9jDj1qaT6s+xMREddT0CkiIiLdyv6wc9JPws7VCjtFOgxzSNFD9LznHnNI0aefknf55TSXlrq6NDkE7FY79x51L49NeAx3qzsrdq9g2sJpbCvZ9vvekWFAcCyMuRKmvgu3ZcG1K+GUpyDudPDoAY3VkPU9fP83eO1YeKovfDANfnwBdqfA/+omFRGRTktBp4iIiHQ77nYrr/407Hx7LWtyFKKIdBSGYRBwycVEvPIyFm9v6tatJ/f8qdRnZLi6NDlEpkRP4b1T3yPcO5xd1bu45OtL+Czrsz/+Di0W6DkIxl4NF7wPt2fDNcvhpMch9lRw84OGSsj4Br67D149xgw+P/wLrHwR9m6G1tZDtj8REXENBZ0iIiLSLe0POyf2D6K2sYVL31qjsFOkg/GeONEcUtSnD027dpF3wXSqkpJcXZYcIrEBsXx0+kdMDJtIQ0sD96+4n0dWPkJjyyG4qsBigV5DYdx1MP1DuDMHrloCJz4K/U8Chw/UV0D6l/Dt3fDyBHg6GuZfBKtfgX3bQPfDioh0Ogo6RUREpNtyt1t57ZJRB4Wda3MVdop0JG7R0UTN/wjPsWNpra1l53UzKXnjTQ0p6iL83PyYe9xcrou/DgODBRkLuOyby9hbs/fQfiCLFXonwNE3wIUL4M5cuGIxHP8QxBwPdi+oK4XtX8DXd8BL4+DpGFgwA9a+DkXpCj5FRDoBBZ0iIiLSrf1X2Pmmwk6RjqZ9SNG0aeaQoqefZs+999GqIUVdgsWwcG3Ctcw9bi4+Dh9Si1OZtnAaa/asOXwf1GqD8JEw4Wa46F9wVx78dREc9wD0SwSbB9QWw7bP4MtbYd4YeDYWPrkc1r0FxVkKPkVEOiAFnSIiItLt7Q87J8QEUdMWdq5T2CnSoRh2O70e/Bs97723fUhR/mUaUtSVTAqfxPzT5xPbI5bS+lKuXHQlb21568h071rtEDEGJt4Kl3wGd+XDZd9A4n3QdxLY3KF6H2z5Fyy8GeaOhNmD4F9XwoZ3oTRHwaeISAegoFNERESEA2Hn+JhAahpbmKGwU6TDMQyDgIsvIuLVV7H4+FC3fj25551PfbqGFHUVET4R/PPUfzKl3xRana3MXj+bW5fcSk1TzZEtxOaAyHFwzO0w4wu4Mw8u/RKOuQsiJ4DVAVW7YfMC+L8b4PkEmDMU/n0NbHwfyvOPbL0iIgIo6BQRERFp5+Gw8volozk6+kDYuT5PYadIR+M9Ybw5pCiyD027d5M3fTpVizWkqKvwsHnw9wl/596x92Kz2FiUt4jpX04nuyLbdUXZ3SFqAiTeDZd9aXZ8XvJ/MOl26DMOLHaoKIBNH8Ln15mh55xh8NlM2PQRVOxyXe0iIt2Igk4RERGRn/BwWHljxk/DzrWszytzdVki8h/c+vWj7/z5eB51lDmkaOZMyhYscHVZcogYhsEFcRfw1klvEeIRQk5FDtMXTmdR3iJXl2aye0C/Y+DY++Dyb8w7Pi/+N0yYBeFjwGKD8jxIeQ/+fTU8NwieH252f6Z+DFWHeNiSiIgACjpFRERE/sv+sHNcv0CqG5rbOjsVdop0NFZ/f/q89mr7kKKiOf/QNPYuJiEkgflT5jOq5yhqm2uZlTyL2etm09za7OrSDubwguhj4fi/wRWLzKPuF/4Lxt8EvUeAYYHSbPM+z0+vMAcbvTAKvrjZvPezutDVOxAR6RIUdIqIiIj8DA+HlTcuHcVR/QLaw84N+Qo7RToaw26n5913gcVCS2kpLSUlri5JDrEgjyBePfFVLhl0CQBvbX2LaxZdQ0ldB/5cu3lD/+PhhIfhqiS4Mxf+sgDGXQ+h8YABJZmw/i1zkvsz/WHeWPjyNtj6GdR04L2JiHRgCjpFRERE/gdPh403Lx19IOx8Yw0bFXaKdDgWd3ccffoA0JChwURdkd1i5/bRt/P0pKfxsHmweu9qpi2cxuaiza4u7bdx94MBJ8FJf4erl8KdOXDBh3DUddBzqPmYojRY+xp8PAOe7gcvHg1f3wnbv4Ba3RctIvJbKOgUERER+QU/DTurGpq5RGGnSIfk1r8/AA2ZmS6uRA6nk/uezAenfkCUbxT7avcx45sZfJzxcee7ssCjB8SdCic/DtcuhztyYNp7MOZqCBlkPqZwK6x+GeZfBE/1g5cnwDf3QNpXUFfu0vJFRDoqBZ0iIiIiv2J/2Dm274GwM6Wg3NVlichPuA0YAEC9Ojq7vJgeMXxw2gccG3EsTa1NPLzyYV7Y+IKry/pzPANg4BQ49Sm4biXcvgPOfwdGXwFBsYAT9m6GVfPgo+nwVF945Rj47j7I+A4aqly9AxGRDkFBp4iIiMhv4Omw8dZloxnTFnZe/PpqhZ0iHciBjs4sF1ciR4KPw4c5iXO4PuF6AN7b/h4trS0uruoQ8gqCwWfBac/C9Wvg1gw49w0YeRkExoCzFfakwI8vwAfnY3s2hknpD2JZ/DBkfQ8N1a7egYiISyjoFBEREfmNPB023rp0NGOi2sLON1azSWGnSIewv6OzISsLZ2uri6uRI8EwDK4YegUeNg/qmuvIq8xzdUmHj09PGHoeTJkDN6yHWdvhnNdgxCXQoy+Gs4UetdlYVz4P750LT0bC6yfADw/DjiRorHX1DkREjggFnSIiIiK/g5dbW2dnVABV9c1cpLBTpENw9InAcDhw1tbStGuXq8uRI8RqsTKghxlyby/d7uJqjiDf3jBsKpzxAtyUQtMNm9jQ5ypah/0F/PtAazPsXAPLnoV/ngVP9IE3T4bFf4ecpdBU7+odiIgcFgo6RURERH6n/WHn6Kge7WFn6s5yV5cl0q0ZNhuOmGhAk9e7m4EBAwHYXtKNgs7/5BtGQeAEWqY8DzdvhptS4cx5MOwC8A2D1ibIXwlLn4J3ppjB51unQfITkLsCmhtcvQMRkUNCQedhMG/ePAYNGsTo0aNdXYqIiIgcJmbYOYZRkW1h5+ur2byzwtVliXRr7pq83i0NCjSnlHerjs5f0yMShl8E57wCt2yFGzfClOdh6Png3QtaGiBvOSQ/Dm+fCrMHwdbPXF21iMifpqDzMJg5cybbtm1j7dq1ri5FREREDiNvNxtvX26GnZX1zVz4+iqFnSIu1H5Ppzo6u5WBgQc6Op1Op4ur6YAMAwL6wcgZcO7rcGsaXL8eTn8OBp8DXsFQWwwfz4B/XQl1Za6uWETkD+sUQWdeXh7btm2jVZeKi4iISAezP+wc2RZ2XvTGarbsUtgp4gr7J6/XK+jsVqL9orFb7FQ1VbGzeqery+n4DAOCYmDU5XD+W3DLNph4KxgW2LwAXjwasn5wdZUiIn9Ihwo633zzTWbPnn3Qn1111VX069ePoUOHMmTIEAoKClxUnYiIiMjP83az8U5b2FlR18SFryvsFHGF/R2djbl5tDY2urgaOVLsVjsx/jEApJWmubiaTsjmgOMegMu/g4BoqNoN750DC2dBY42rqxMR+V06VND56quv0qNHj/a3v/nmG9566y3effdd1q5di7+/Pw899JALKxQRERH5ed5uNt6+bDQj+vgr7BRxEVvPnlh8faG5mcacHFeXI0dQ+z2d3Xkg0Z8VMRquWQZjrjLfXvcGvDQe8le5ti4Rkd+hQwWdmZmZjBo1qv3tzz//nDPPPJMLL7yQESNG8Nhjj/HDD2qhFxERkY7Jx93OO5ePaQ87dYxd5MgyDKP9+HpDhgYSdSf7J69vK93m4ko6OYcXnPo0XPyZOa29LAfeOgUW/U2T2UWkU+hQQWddXR2+vr7tb//4449MmjSp/e1+/fqxd+9eV5QmIiIi8pvsDzuH9/GnvNYMO7fuVtgpcqS4DdgfdOqezu5EA4kOsehEuPZHiJ8OzlZYMQdeTYQ9qa6uTETkF3WooDMyMpL169cDUFxczNatWxk/fnz73+/duxc/Pz9XlSciIiLym+wPOxMizLDzwtcVdoocKe0dnZnq6OxOBvQYgNWwUlpfSmFtoavL6Ro8/OHsl2Hae+AZBIVb4bVjYekz0NLs6upERH5Whwo6Z8yYwcyZM3nkkUc4//zziYuLY+TIke1//+OPPzJkyBAXVigiIiLy2/i623n3rweHndt2V7q6LJEuz71tIJE6OrsXd5s7ff36AvD/7N13fJXl+cfxzznZARJG2CNsMEy1gIgoKoq7aK2K1i1W0Vq1tVatu8Ofo9qqaN3WOlqtq05cKIIoQ/aQjbLDTsgiOb8/DjkaQQVJ8uQkn/frxYvknPOccz14l8KX676vuRs8p7NS7XM8jJoI3Y+DshJ4/1Z4/CjIXRh0ZZK0kxoVdP7ud79j5MiRvPjii6SmpvL8889XeH78+PGMGDEioOokSZL2THnY2ScWdk407JSqWHlHZ8nKlZTm5QVcjapT+TmdBp1VoH7TaGfn8AchJQO+mgQPHgSfPgRlZUFXJ0kxNSroDIfD3HLLLXz++ee8+eab7LPPPhWef/755zn//PMDqk6SJGnPZaQm8c/z+tOnTSYbd4Sdc1cZdkpVJSEzk8TmzQG3r9c13zynU1UgFIK+I6Jnd3Y4BLYXwJtXwVPDYfNXQVcnSUANCzoB/v3vf3PGGWfw85//nAcffDDociRJkvZaZloS/zx/wDfCzk+Zt9qwU6oqTl6vm+zorCYN20ansh99BySmwZIPYfSBMO1ZcBCUpIDVqKDzgQceYMSIEUyePJkFCxZwySWXcNVVVwVdliRJ0l4rDzt7t8lkQ34xpz9s2ClVlZTyczrt6KxTujfuDsDq/NVsKNwQcDW1XDgMAy6Eiz6G1j+Bos3w8kXw719A3rqgq5NUh9WooPO+++7jxhtvZP78+UybNo0nn3yS0aNHB12WJElSpchMS+Kpb4Wd81dvDbosqdb5uqPTgUR1Sf3k+mRnZAMwb/28gKupI7I6w3lvw2HXQzgJ5r0Gow+Aua8FXZmkOqpGBZ2LFy/m7LPPjn1/+umns337dlatWhVgVZIkSZUnMy2Jp84bQK/W5WHnRMNOqZKldP066Iy4lbZOcft6ABIS4eDfwsj3oVkObMuFf58BL10MhZuDrk5SHVOjgs6ioiLq1asX+z4cDpOcnExBQUGAVUmSJFWuzPQk/nV+NOxcb9gpVbqUTp0gHKZ00yZKc3ODLkfVqHz7ukFnAFr2hgvHwqBfAyGY/kz07M7FYwMuTFJdkhh0Ad92/fXXk56eHvu+uLiYP/3pT2RmZsYe++tf/xpEaZIkSZWmPOw849GJzFqxhdMfnsizFx5A1+YNgi5Ninvh1FSS27WjeOlSihYsILFp06BLUjVx8nrAElPgiFug2zHw0kWwcQn886fQ/0IYejMkp//we0jSXqhRHZ0HH3ww8+fP5/PPP4/9OPDAA1m8eHHs+2nTpgVdpiRJUqUoDzt7tMqIdXYuWGNnp1QZygcSFXpOZ51SvnV9+dblbC3299PAtDsgOqjoJ+dHv//sIfjHYPhqcrB1Sar1alRH59ixYyt8n5ubS3JyMhkZGcEUJEmSVMUapifz9AUDOOORT5m9cgsjHp7IsyMPoIudndJeSenSha1jxjh5vY5plNqIlvVasip/FfM2zKNfi35Bl1R3pdSH4/4K3Y+BVy6F9Qvh0SPgoCvhkKshMTnoCiXVQjWqoxNg06ZNXHLJJWRlZdG8eXMaNWpEixYtuOaaa9i2bVvQ5UmSJFW68rAzp2UGuXnFjHj4UxautRNJ2hvlHZ1FXxh01jWxgURuX68ZOg+FUZ9Ar59DpAzG3QmPHAZrZgddmaRaqEYFnRs2bGDAgAE8+eST/OxnP+Ouu+7irrvu4oQTTuDee+/l4IMPprCwkM8++4y///3vQZcrSZJUaSqGnUWc9pBhp7Q3UrrsmLy+cCGRsrKAq1F16t4kOpBo3oZ5AVeimLRG8LNH4OdPQlpjWD0THhoCH98DZaVBVyepFqlRQectt9xCcnIyixYt4h//+AeXX345l19+OQ899BALFy6kuLiYM888kyOOOKLCcCJJkqTaoFG9aNi5T4WwMy/osqS4lJzdjlByMpGCAkq++iroclSNchrnAE5er5F6DIdRE6HrUVBaDO/eCI8fAxsWB12ZpFqiRgWdL7/8MnfeeSfNmzff6bkWLVpw++2389///pcrr7ySs88+O4AKJUmSqta3w84RD0807JR+hFBCAsmdOwFQ5ECiOqV88vrizYsp2F4QcDXaSYPmMOI5OOE+SK4PX06EBw6CSY9CJBJ0dZLiXI0KOletWkWPHj2+8/mePXsSDoe58cYbq7EqSZKk6tV4R9jZvUUD1m2Nhp2L1hl2SnsqtcuOczodSFSnNE1rSpPUJpRFyvhioyF3jRQKwX5nwsUTIPsgKMmH16+Ef/0MtqwMujpJcaxGBZ1ZWVksXbr0O59fsmQJzZo1q76CJEmSAtK4XjLPjDzg67DzIcNOaU+ldI2e01loR2edEgqFYl2dDiSq4Rplw9n/g2F/gcRUWPQejD4AZjxvd6ekH6VGBZ3Dhg3juuuuo7i4eKfnioqKuP766znqqKMCqEySJKn6fTPsXGvYKe2x2OR1OzrrnPLJ6w4kigPhMAwcBb/8CFrtC4Wb4cUL4PmzIX990NVJijM1Kui85ZZbmD9/Pl26dOH222/n1Vdf5ZVXXuG2226jS5cuzJ07l5tuuinoMiVJkqrNN7exl4ediw07pd1SHnQWL1lK2S6aKVR7lXd0zlk/J+BKtNuadoPz34Eh10A4Eea8Eu3unP9W0JVJiiM1Kuhs06YNn3zyCTk5OVxzzTUMHz6cE088keuuu46cnBzGjx9Pu3btgi5TkiSpWjWpn8LTFwygW/MdYefDE1mSmx90WVKNl9isGeGMDCgtpXjJkqDLUTUq7+hcsGkBJaUlAVej3ZaQBEN+Dxe8C027Q/5aePZUeOVSKNwSdHWS4kCNCjoBOnTowJtvvklubi4TJ05k4sSJrFu3jrfeeovOnTsHXZ4kSVIgmtRP4emRA+javD5rthRx2kOfGHZKPyAUCsXO6XTyet3Sun5rGiQ3YHvZdhZuWhh0OdpTrfaFCz+EgZcCIfj8KXhgECwZF3Rlkmq4Ghd0lmvUqBH9+/enf//+NG7cOOhyJEmSApdVP4VnRh4QCztHPDSRpYad0vdK6VIedHpOZ10SCoXIaZwDwNwNDiSKS0mpMOxPcM7r0LAdbF4OTx4Hb10DJQVBVyephqqxQackSZJ2Vh52dmlWn9VbCjnNsFP6XqnlA4ns6KxzPKezlmg/CC6eAPudFf1+4mj4x8GwYmqwdUmqkQw6JUmS4sy3w84RD09k2XrDTmlXYh2dTl6vc5y8XoukNIAT7oXT/wP1m0PuF/DIUPjgL+AZrJK+waBTkiQpDjVt8HXYuWpztLPTsFPaWXnQWbJyJaV5eQFXo+rUvUl3AOZvmE9pWWnA1ahSdB0GoyZCjxMhUgof3hYNPNcaZkuKMuiUJEmKU+VhZ+cdYecIw05pJwmZmSQ2bw54Tmddk90gm7TENApLC1m6ZWnQ5aiypDeGnz8BP3sUUhvCqmnRrewT7gUDbanOM+iUJEmKY9GwcwCdmtZj5Y6wc/n6bUGXJdUoKeXndLp9vU5JCCfQvXG0q9NzOmuhXidHuzs7D4XSIhjzB3jyeNi4NOjKJAXIoFOSJCnONWuQyrMXHhALO0976BPDTukbvp687kCiuqb8nE4nr9dSGS3hjBfguHsgqR4sGw8PDIIpT0IkEnR1kgJg0ClJklQLNGuQyrMjD6BjeWfnwxP5coNhpwSQ0tWBRHVV+eR1BxLVYqEQ/ORcuHg8tBsIxXnwv8vgmVNh6+qgq5NUzQw6JUmSaolmGak8tyPsXLGpgNMeMuyUAFLLt65/8QURu7zqlFhH5/q5lEXKAq5GVapxBzjndTjiVkhIhgVvw+gDYNaLQVcmqRoZdEqSJNUisbAzy7BTKpfcsSOEw5Ru2kRpbm7Q5agadWzYkeRwMnkleazYuiLoclTVwgkw6DK48ENo0RsKNsIL58IL58G2DUFXJ6kaGHRKkiTVMs0yomd2GnZKGUvo5QABAABJREFUUeHUVJKzswEo9JzOOiUpnESXRtGjC+ZscCBRndE8By54Dw7+HYQSYNZ/YfRAWPBO0JVJqmIGnZIkSbVQ8x1hZ4cdYeeIhyfy1UbDTtVdsYFEntNZ55Sf0zl3vQOJ6pTEZDjsOjj/HWjSBfJWw9Mnw/9+DUV5QVcnqYoYdEqSJNVSzTOiA4o6ZNXjq43Rzk7DTtVVKbFzOg066xonr9dxbfaHX34EAy6Ofj/lCXjgQFg2IdCyJFUNg05JkqRarEVmxbDTzk7VVXZ01l05TXKA6OR1h1HVUcnpcPRtcNarkNkWNi2Dx4+BMX+AksKgq5NUiQw6JUmSarnysLN9k3S+3BANO1dsKgi6LKlapXTdEXQuXEikzOnbdUmXRl1ICCWwoXADa7atCbocBanjIXDxeOj7CyACE+6Fh4bAqulBVyapkhh0SpIk1QEtMqNndmbvCDtPe+gTw07VKcnt2hFKSSFSUEDJl18GXY6qUUpCCh0bdgQ8p1NAaiYMvx9OexbqNYV1c+Hhw+DD26F0e9DVSdpLBp2SJEl1RMvMNJ77Rtg54qGJrDTsVB0RSkggpVMnwO3rdZHndGon3Y+BURNhn+OhbDt88Cd47EjI9fcHKZ4ZdEqSJNUhLTPTeHbkAbRrnM7yDds4zbBTdUj5OZ2FX3wRcCWqbuXndNrRqQrqZcEpT8GJD0FKJqyYAg8eBBMfBI+4kOKSQackSVId06phtLOzPOwc8fBEVm027FTtF5u8bkdnnWNHp75TKAR9ToVRn0DHQ2F7Ibx1NfzzBNi0POjqJO0hg05JkqQ6qFXDNJ698ADaNk5j2fpoZ6dhp2q72ECiLww665pujbsRIsSabWtYX7A+6HJUE2W2hjNfgmPuhKR0WDoORh8In/8LIpGgq5O0mww6q8D9999PTk4O/fr1C7oUSZKk79S6YRrPXTgwFnaOeGgiqzcXBl2WVGXKOzqLly6lrLg44GpUneol1SM7IxuAeRvmBVyNaqxQCPqPhIs+hjb9oXgrvHIJPHc65K0NujpJu8GgswpccsklzJkzh0mTJgVdiiRJ0vdq3TB6ZmebRmksXb+N0x76xLBTtVZis2aEMzOhtJTixYuDLkfVzO3r2m1NOsF5b8HhN0I4Cea/AaMPgDmvBF2ZpB9g0ClJklTHtWmUznMXfh12jnjYzk7VTqFQiJQunQHP6ayL9mkSDTrnrJ8TcCWKC+EEGHwlXPgBNO8J29bDf86CFy+Egk1BVyfpOxh0SpIkqULYuSQ3nxEPT2TNFsNO1T6p5QOJnLxe55QHnU5e1x5p0QtGvg8HXQmhMMz4N4weCIveD7oySbtg0ClJkiQgGnY+O/IAWjeMhp2nPWTYqdonpYsDieqq8q3rX+V9xZbiLQFXo7iSmAJDb4Tz3obGHWHrSnjqRHj9N1CcH3R1kr7BoFOSJEkxbRtHOzvLw84Rhp2qZcoHEhUusKOzrslMyaR1/dYAzN8wP+BqFJfa9o8OKuo3Mvr9pEfgwYNg+afB1iUpxqBTkiRJFXwz7Fy8I+xca9ipWiKlc/SMzu0rV1G6dWvA1ai6lXd1ek6nfrTkenDsnXDmS9CgFWxYDI8fBe/eBNuLgq5OqvMMOiVJkrSTb4edpz1s2KnaISEzk8QWLQAoXrQo4GpU3bo37g44eV2VoNNhMOoT6H0aRMrg47vh4cNg9aygK5PqNINOSZIk7VLbxtEzO1tlprJ4XXRA0bqtdqso/pWf01nsOZ11jgOJVKnSGsJJ/4BTnoL0JrBmFjw0BMbdBaXbg65OqpMMOiVJkvSd2jVJ57kLB9IqM5VF6/L5xWOT2VIcdFXS3knpuiPoXLgw4EpU3XKa5ACwdMtStpVsC7ga1Ro5J8CoidDtWCgrgfdugcePhvV2jUvVzaBTkiRJ36tdk3SevfAAWmamsjg3n/vmJJCbZ2en4lds8voCOzrrmqy0LJqmNaUsUsYXGx1IpUpUvxmc9jT8dDSkZMBXn0UHFX32MJSVBV2dVGcYdEqSJOkHZTepx3MXHkCLjBTWFIT4xWOT3cauuJW6Y/J68cKFEIkEXI2qW2z7uud0qrKFQrDvGXDxBOhwMJRsgzd+C/86CTavCLo6qU4w6JQkSdJuyW5Sj3+d34+GyREWrcvndM/sVJxK7tQJwmHKNm0iwcnrdU5sIJHndKqqNGwLZ74CR98Oiamw+AMYPRCmP+c/rkhVzKBTkiRJuy27cTqX5pTSPCOFBWvzDDsVl8IpKSRnZwOQsnpNwNWouuU0jp7TaUenqlQ4DAN+Cb8cB633h6LN8NIv4T9nQn5u0NVJtZZBpyRJkvZI0zR4+rx+tMhIjYWdntmpeJOyY/t68urVAVei6la+dX3hxoUUlzpdTVWsaVc4bwwc+gcIJ8Lc/8HoA2De60FXJtVKBp2SJEnaY9k7BhSVd3aO/OfkoEuS9kj5QKKUNQaddU3Lei3JTMlke2Q7CzctDLoc1QUJiXDIVTDyfWiWA/nr4LnT4eVRULg56OqkWsWgU5IkST9Kh6x6PDvyAAA+X76Jjfl2Ril+pHTdEXS6db3OCYVC7NN4x0Aiz+lUdWrZB0Z+AAdeBoRg2tPwwCBY/GHQlUm1hkGnJEmSfrSOTevTKjMVgMW5eQFXI+2+8o7O5DVriJSWBlyNqpuT1xWYpFQ48lY49w1o1B42fwn/PAHevBqKtwVdnRT3DDolSZK0Vzo2rQ/AorX5AVci7b7kdu0IpaQQLimhZMWKoMtRNbOjU4HLPhAuGg/7nxv9/tMH4R+D4aspwdYlxTmDTkmSJO2Vjk3rAbDIjk7FkVBCAskdOwJQvGBBwNWoupUHnfM3zmd72faAq1GdlVIfjr8HzvgvNGgJ6xfCo0fA+3+E7R4HI/0YBp2SJEnaK512dHQuXmdHp+JL8o7t68ULHEhT17TLaEd6YjpFpUUs3bw06HJU13UZChdPgJ4nQ6QUProDHjkc1swJujIp7hh0SpIkaa+Ud3QuXmdHp+JLcpfOgB2ddVE4FKZ74+6A53SqhkhvDCc/Cic/DmmNYPUMeOgQGP83KPMcYWl3GXRKkiRpr5Sf0bl8wza2l5YFXI20+8o7OosW2tFZF+U0yQFgznq75lSD9DwJRk2ELsOgtBjeuQGeOBY2LAm6MikuGHRKkiRpr7TMSCU1KUxJaYQvNxYEXY6028onr5csW0ZZsefh1TVOXleN1aAFnP5vOOFeSK4Pyz+BBwbB5McgEgm6OqlGM+iUJEnSXgmHQ3TIKp+87vZ1xY+Epk0pTUuD0lKKFy8OuhxVs/Kt6/M2zKMsYje6aphQCPY7Cy4eD9mDoCQfXrsCnj4ZtqwMujqpxjLolCRJ0l6LndPp5HXFkVAoRFGLFgAUffFFwNWounXM7EhKQgr5Jfl8ufXLoMuRdq1Rezj7NTjyT5CQAgvfhdEDYeYLQVcm1UgGnZIkSdprTl5XvCpu0RyAIgcS1TmJ4US6NuoKuH1dNVw4DAdeCr/8CFr2hcJN8N/z4flzYNuGgIuTahaDTkmSJO21TrHJ6wadii/lHZ2FdnTWSfs03nFO53qDTsWBZt3hgndhyDUQSoDZL8HoA+CLt4OuTKoxDDolSZK01zruOKPTreuKN0XNy7eu29FZF8UGEhl0Kl4kJMGQ30cDz6xukLcGnjkFXv0VFG0NujopcAadkiRJ2msddnR05uYVs3lbScDVSLuvfOv69lWrKN1qSFDXfHPyesRp1oonrfeDX34IB1wChGDqP+GBA2Hpx0FXJgXKoFOSJEl7rX5KIs0zUgBYZFen4khZWhqJzT2ns67q0rALiaFENhVtYnX+6qDLkfZMUhoc9Wc45zXIbAeblsMTx8Hb10FJYdDVSYEw6JQkSVKliG1f95xOxZnkLl0At6/XRckJyXRq2AmAORvmBFyN9CO1PwguHg/7nglE4JP74B8Hw4qpQVcmVTuDTkmSJFWKjrGBRHZ0Kr4kd+kMQJEDieqk8u3r8zbMC7gSaS+kZsBP74MR/4Z6zSB3PjwyFMbeBqUeKaO6w6BTkiRJlaJTUzs6FZ9iHZ1uXa+TnLyuWqXbUTBqIuQMh0gpjP0LPHoErJsfdGVStTDolCRJUqWIdXR6RqfiTEps6/oXDqSpg3Ka5AAGnapF6jWBnz8BP3sUUjNh5efw4GD45H4oKwu6OqlKGXRKkiSpUpR3dC7N3UZpmWGR4kdShw6QkEDp5s1sX7cu6HJUzbo26kqIEGsL1pJbkBt0OVLlCIWg18nR7s5Oh0NpEbx9LTx5PGxcFnR1UpUx6JQkSVKlaNUwjeTEMMWlZXy1cVvQ5Ui7LZySQnJ2NuBAorooPSmd9pntAbs6VQtltIJf/BeOuxuS6sGyj+GBA2HqP8EOdtVCBp2SJEmqFAnhEB2alA8k8pxOxZdvbl9X3VN+TqcDiVQrhULwk/Pg4o+h7QFQnAev/gqePQ22rgm6OqlSGXRKkiSp0pSf07nIyeuKMyldHUhUl8XO6dxgR6dqscYd4dw34IhbICEZvngLRh8As18KujKp0hh0SpIkqdLEJq/n2tGp+GJHZ91W3tE5Z/2cgCuRqlg4AQb9Gi4cCy16QcEGeP4c+O8FULAx6OqkvWbQKUmSpEoTm7xuR6fiTGrXrgAULVpEpLQ04GpU3bo36Q7AirwVbC7aHHA1UjVo3gMueB8OvgpCYZj5PIweCAvfDboyaa8YdEqSJKnSdNzR0bnIMzoVZ5LatiWUmkqksJCSL78MuhxVs4zkDNrUbwN4TqfqkMRkOOwPcP470KQzbF0F//oZvHYFFPkPlopPBp2SJEmqNOUdneu2FrG1sCTgaqTdF0pIIKVTJwAKPaezTtqnSXT7upPXVee0+Qn8chz0/2X0+8mPwYODYNknwdYl/QgGnZIkSao0GalJZNVPAZy8rvjjOZ11W/k5nQ4kUp2UnA7H3A5nvQIZbWDjUnj8aHjnBtheFHR10m4z6JQkSVKlip3Tmeu2N8WXlPJzOhcsDLgSBSHW0WnQqbqs4xAYNQH6ngFEYPzf4KEhsGpGwIVJu8egU5IkSZUqNnndjk7FmVjQaUdnndS9cXQg0dLNS9lWsi3gaqQApWbC8NFw2jOQngVr58DDh8JHd0Dp9qCrk76XQackSZIqVafY5HWDTsWX8q3rxcuWUVbkVs26Jisti2bpzYgQYf7G+UGXIwWv+7EwaiJ0Pw7KtsP7f4THhkGu5xir5jLolCRJUqUq37q+aJ1b1xVfEps1JSEzE0pLKV68OOhyFICcxjkAzFk/J+BKpBqiflM49V9w4j8gJRNWTIYHB8On/4CysqCrk3Zi0ClJkqRK1TErunV9SW4+ZWWRgKuRdl8oFHIgUR3XvUl0+/q8DfMCrkSqQUIh6HNa9OzOjkNgewG8+Tt46qew6cugq5MqMOiUJElSpWrTKI2khBBF28tYsakg6HKkPZLWtw+pvXsTSkkJuhQFIDZ5fb0DiaSdZLaBX7wEx9wJiWmw5CN44ECY9gxE/IdN1QwGnZIkSapUiQlhspuUT173nE7Fl2a//S0d/vNvMo46KuhSFICcJtGt64s2LaKo1HNapZ2Ew9B/JFz0MbTpB0Vb4OWL4bkzIG9d0NVJBp2SJEmqfF8PJPKcTknxo3l6cxqlNGJ7ZDsLNy4Muhyp5srqDOe+BYffAOEkmP86jD4A5v4v6MpUxxl0SpIkqdJ1bBo9p9PJ65LiSSgUYp8m0e3rczY4kEj6XgmJMPg3cOEH0KwHbMuFf/8CXvwlFGwKujrVUQadkiRJqnQds5y8Lik+dW8cHUjkOZ3SbmrRKxp2HnQFhMIw47no2Z2LPgi6MtVBBp2SJEmqdHZ0SopX5R2dTl6X9kBiCgy9KbqdvVEH2LICnhoOb1wFxduCrk51iEGnJEmSKl35GZ2rtxSSX7Q94GokafflNM6hYUpDmqQ2IeIkaWnPtBsAF4+HfhdEv//sIXjwIPhyUrB1qc4w6JQkSVKla5ieTON6yQAscfK6pDjStkFbPjr1I+49/F5CoVDQ5UjxJ7keHHsX/OJFaNAKNiyCx46Ed2+G7cVBV6dazqBTkiRJVaK8q9NzOiXFk1AoZMApVYbOh8OoCdD7VIiUwcd/hYcPg9Wzgq5MtZhBpyRJkqpExyzP6ZQkqU5LawQnPQSn/BPSGsOamfDQEPj4bigrDbo61UIGnZIkSaoSHe3olCRJADk/hUs+ha5HQ1kJvHsTPH40rF8UdGWqZQw6JUmSVCWcvC5JkmLqN4MRz8JP74fkBvDlp9FBRZMeAQd/qZIYdEqSJKlKlHd0LsnNp6zMv8BIklTnhUKw7y+iZ3e2Hwwl2+D138C/ToLNK4KuTrWAQackSZKqRLvG6SSGQxSUlLJ6S2HQ5UiSpJqiYTs461U46jZITIVF78MDA2HGf+zu1F4x6JQkSVKVSEoI065xOuD2dUmS9C3hMBxwMfxyHLTaDwo3w4sjSXjxPJJLtgRdneKUQackSZKqTOyczlwHEkmSpF1o2hXOfwcOvQ7CiYTn/Y9D511L6Is3g65MccigU5IkSVWmU/nk9bUGnZIk6TskJMIhv4ML3iOS1Y3U7VtIfP5MePkSKLS7U7vPoFOSJElVpnwg0eJct65LkqQf0Kov289/jwXNjiZCCKb9Cx4YBEs+CroyxQmDTkmSJFWZ2NZ1z+iUJEm7IzGVOa1HUHrmq9AwGzYvhyePhzd/DyUFQVenGs6gswrcf//95OTk0K9fv6BLkSRJClTHrGhH54pNBRQUlwZcjSRJiheRdgPh4vGw/znRBz59AP5xMKyYEmhdqtkMOqvAJZdcwpw5c5g0aVLQpUiSJAWqcb1kMtOSAFji9nVJkrQnUhrA8X+D05+H+i0g9wt45Ah4/09QWhJ0daqBDDolSZJUZUKhUGwgkZPXJUnSj9L1SBj1CfT8GURK4aPb4ZHDYe3coCtTDWPQKUmSpCpVfk7norV2dEqSpB8pvTGc/Fj0R1ojWDUd/nEITLgXyjweR1EGnZIkSapSHe3olCRJlaXnz2DUROhyJJQWwZg/wBPHwYYlQVemGsCgU5IkSVWqY5aT1yVJUiVq0AJO/0/0/M7k+rB8AjwwCCY/DpFI0NUpQAadkiRJqlKxMzrX5RHxLx+SJKkyhELRiewXj4d2B0JJPrx2OTz9c9iyKujqFBCDTkmSJFWpdk3SCYcgv7iUtVuLgi5HkiTVJo3awzmvwZF/hIQUWPgOjD4AZv036MoUAINOSZIkVamUxATaNU4HYNE6z+mUJEmVLJwAB/4KfvkhtOwDhZvghfPg+XNh24agq1M1MuiUJElSlYtNXvecTkmSVFWa7QMXvAeHXA2hBJj9YrS784sxQVemamLQKUmSpCrXMevrczolSZKqTEISHHotXPAOZHWFvDXwzM/h1cugaGvQ1amKGXRKkiSpypV3dDp5XZIkVYvW+8MvP4IDRkW/n/pkdDL7sgnB1qUqZdApSZKkKtexfPJ6rh2dkiSpmiSlwVF/gbNfg8x2sGkZPH4MvH0dlBQGXZ2qgEGnJEmSqlx50PnVxgIKS0oDrkaSJNUpHQbDxeNh318AEfjkPnjoEFj5edCVqZIZdEqSJKnKNa2fQoPURCIRWLZ+W9DlSJKkuiY1A356P4x4Duo1g3Xz4JGhMPb/oLQk6OpUSQw6JUmSVOVCodA3Jq+7fV2SJAWk29EwaiLk/BTKtsPYP8OjR8K6L4KuTJXAoFOSJEnVopOT1yVJUk1Qrwn8/Ek46RFIzYSVU+Efg+GT0VBWFnR12gsGnZIkSaoWsYFETl6XJElBC4Wg98+j3Z2dDoPthfD2NfDPE2DT8qCr049k0ClJkqRqEdu6nmvQKUmSaoiMVvCLF+HYuyApHZaOg9EHwuf/gkgk6Oq0hww6JUmSVC2+7ujMI+JfHCRJUk0RCkG/C+Cij6HtACjeCq9cAs+OgK1rgq5Oe8CgU5IkSdWifZN6hEKwtXA7uXnFQZcjSZJUUZNOcO6bMPRmSEiGL96E0QfA7JeDrky7yaBTkiRJ1SI1KYE2jdIAJ69LkqQaKpwAB10OF46F5r2gYAM8fzb8dyQUbAy6Ov0Ag05JkiRVm45Z0XM6HUgkSZJqtOY9YOT7MPg3EArDzP9Ez+5c+F7Qlel7GHRKkiSp2nzznE5JkqQaLTEZDr8BzhsDjTvB1pXwr5PgtSuh2H+0rYkMOiVJklRtyievL3byuiRJihdt+0UHFfW/MPr95EfhgUGwfGKwdWknBp2SJEmqNp2y7OiUJElxKDkdjrkDznwZMlrDxiXw+NHwzo2wvSjo6rSDQackSZKqTXlH55cbCyjaXhpwNZIkSXuo06Fw8QToMwIiZTD+HnjoUFg9M+jKhEGnJEmSqlHzjBTqJSdQWhZh+fptQZcjSZK059IawokPwqlPQ3oWrJ0dDTs/uhNKtwddXZ1m0ClJkqRqEwqFYl2di5y8LkmS4tk+x8GoidD9OCgrgfdvhcePgvWLgq6szjLolCRJUrWKTV7P9ZxOSZIU5+o3hVP/BcMfhJQM+GpSdFDR1KeCrqxOMuiUJElSteqYtWPyuh2dkiSpNgiFoO+I6NmdHQ6B7QXwv8tgy8qgK6tzDDolSZJUrWIdnU5elyRJtUnDttGp7C16RwcVLf046IrqHINOSZIkVavyoHPRunwikUjA1UiSJFWicBg6HBz92qCz2hl0SpIkqVqVb13fXFDChvzigKuRJEmqZO0Piv68bHywddRBBp2SJEmqVmnJCbRumAbA4lzP6ZQkSbVMu4FACNYvhK2rg66mTjHolCRJUrXznE5JklRrpTWEFr2iX9vVWa0MOiVJklTtOmaVB512dEqSpFqofPv6UoPO6mTQKUmSpGrXsWn0nM5FBp2SJKk2yh4U/dmBRNXKoFOSJEnVLrZ1Pdet65IkqRbKPjD6c+58yFsXbC11iEGnJEmSql2nHR2dy9dvo6S0LOBqJEmSKll6Y2jWI/q153RWG4NOSZIkVbsWGamkJSWwvSzC8g3bgi5HkiSp8rXfsX3doLPaGHRKkiSp2oXDITo4kEiSJNVmsXM6DTqri0GnJEmSAhE7p3Od53RKkqRaqDzoXDsbtm0ItpY6wqBTkiRJgSifvG5HpyRJqpXqN4WsbtGvl00ItpY6wqBTkiRJgejk5HVJklTbtT8o+rPndFYLg05JkiQFonzy+iI7OiVJUm1VPpBo6bhg66gjDDolSZIUiPJhRBvyi9m0rTjgaiRJkqpA9o6OztWzoGBjsLXUAQadkiRJCkS9lERaZKQCdnVKkqRaqkFzaNIZiMDyiUFXU+sZdEqSJCkwTl6XJEm1Xvn09aUfB1tHHWDQKUmSpMDEgs5cOzolSVIt5UCiamPQKUmSpMB0zIoOJLKjU5Ik1VrlHZ2rpkPhlmBrqeUMOiVJkhSY8o5Oz+iUJEm1VmZraNQeImXw5adBV1OrGXRKkiQpMJ2aRjs6l63PZ3tpWcDVSJIkVZHy7etLxwVbRy1n0ClJkqTAtG6YRkpimJLSCF9tLAi6HEmSpKqRXR50ek5nVTLolCRJUmDC4RAdssoHEnlOpyRJqqXa7zinc+XnUOSfeaqKQackSZICFZu87jmdkiSptmrYDjLbQaTUczqrkEGnJEmSAlU+ed2BRJIkqVYr7+pc5vb1qmLQKUmSpEB9PXndbVySJKkWy94RdHpOZ5Ux6JQkSVKgyievu3VdkiTVauUdnSumQPG2YGuppQw6JUmSFKjyjs7cvCK2FJYEXI0kSVIVadQBGrSCshL4alLQ1dRKBp2SJEkKVIPUJJo2SAHs6pQkSbVYKATtD4p+vfTjYGuppQw6JUmSFLiOWeWT1z2nU5Ik1WIOJKpSiUEXIEmSJJ0+oB3DerSgb9uGQZciSZJUdbJ3dHR+NRlKCiEpNdh6ahmDTkmSJAXup31bB12CJElS1WvSCeo3h7w1sGLy11vZVSncui5JkiRJkiRVh1AIsndsX1/q9vXKZtApSZIkSZIkVZfYOZ0OJKpsBp2SJEmSJElSdSk/p/PLSbC9ONhaahmDTkmSJEmSJKm6NO0G6VmwvQBWTg26mlrFoFOSJEmSJEmqLqHQ19vXl44LtpZaxqBTkiRJkiRJqk7l29cdSFSpDDolSZIkSZKk6lTe0fnlZ1BaEmwttYhBpyRJkiRJklSdmu4DaY2gJB9WTgu6mlrDoFOSJEmSJEmqTuEwZO/o6lz2cbC11CIGnZIkSZIkSVJ1Kw86Paez0hh0SpIkSZIkSdWt/JzO5ROhdHuwtdQSBp2SJEmSJElSdWveE1IyoXgrrJ4RdDW1gkGnJEmSJEmSVN3CCZB9YPTrpZ7TWRkMOiVJkiRJkqQglG9fX+Y5nZXBoFOSJEmSJEkKQmzy+idQVhpsLbWAQackSZIkSZIUhBa9IbkBFG2GNbOCribuGXRKkiRJkiRJQUhIhHYHRL9e6vb1vWXQKUmSJEmSJAXFczorjUGnJEmSJEmSFJTsg6I/LxsPZWXB1hLnDDolSZIkSZKkoLTqC0n1oGAjrJsbdDVxzaBTkiRJkiRJCkpCErTtH/166cfB1hLnDDolSZIkSZKkILXfsX3doHOvGHRKkiRJkiRJQSoPOpdNgEgk2FrimEGnJEmSJEmSFKRW+0FiGmzLhXXzg64mbhl0SpIkSZIkSUFKTIa2/aJfL3P7+o9l0ClJkiRJkiQFLbv8nM7xwdYRxww6JUmSJEmSpKC1HxT9edl4z+n8kQw6JUmSJEmSpKC1/gkkpEDeGli/KOhq4pJBpyRJkiRJkhS0pFRo85Po10vHBVtLnDLolCRJkiRJkmqC9jvO6VzmOZ0/hkGnJEmSJEmSVBNk7zinc6nndP4YBp2SJEmSJElSTdCmH4STYOtK2Lgk6GrijkGnJEmSJEmSVBMkp0Pr/aNfL3X7+p4y6JQkSZIkSZJqivY7tq97TuceM+iUJEmSJEmSaopvntOpPWLQKUmSJEmSJNUUbQdAKAE2L4eNy4KuJq4YdEqSJEmSJEk1RUp9aLVv9Gu3r+8Rg05JkiRJkiSpJml/UPRnt6/vEYNOSZIkSZIkqSYpDzqXfRxsHXHGoFOSJEmSJEmqSdoOgFAYNi6FzSuCriZuGHRKkiRJkiRJNUlqBrTsE/3aczp3m0GnJEmSJEmSVNNkD4r+vNTt67vLoFOSJEmSJEmqaWLndNrRubsMOiVJkiRJkqSapt1AIATrF8LW1UFXExcMOiVJkiRJkqSaJq0htOgZ/drt67vFoFOSJEmSJEmqidoPjv7s9vXdYtApSZIkSZIk1USxgUQGnbvDoFOSJEmSJEmqibIPjP6cOx/y1gVbSxww6PwB7du3p3fv3vTt25dDDz006HIkSZIkSZJUV6Q3hmY9ol+7ff0HJQZdQDyYMGEC9evXD7oMSZIkSZIk1TXtB8Ha2dGgs8fwoKup0ezolCRJkiRJkmoqz+ncbbU66Pzoo484/vjjadWqFaFQiJdffnmn19x///20b9+e1NRUBgwYwGeffVbh+VAoxCGHHEK/fv14+umnq6lySZIkSZIkia+DzrWzYduGYGup4Wp10Jmfn0+fPn24//77d/n8v//9b6688kpuvPFGpk6dSp8+fRg2bBhr166Nvebjjz9mypQpvPrqq/z5z39mxowZ1VW+JEmSJEmS6rr6TSGrW/Rrz+n8XrX6jM6jjz6ao48++juf/+tf/8rIkSM599xzAXjwwQd5/fXXeeyxx/j9738PQOvWrQFo2bIlxxxzDFOnTqV37967fL+ioiKKiopi32/ZsgWAkpISSkpKKuWefkj551TX50lVxbWs2sT1rNrE9azaxPWs2sK1rNrE9bxr4XYHkpA7n9LF4yjrfFTQ5VSrPVkLtTro/D7FxcVMmTKFa665JvZYOBxm6NChfPLJJ0C0I7SsrIwGDRqQl5fH+++/zymnnPKd7/mXv/yFm2++eafHx4wZQ3p6euXfxPd45513qvXzpKriWlZt4npWbeJ6Vm3ielZt4VpWbeJ6rqjVxjT6AVtnvcmH2wcFXU612rZt226/ts4Gnbm5uZSWltK8efMKjzdv3px58+YBsGbNGk488UQASktLGTlyJP369fvO97zmmmu48sorY99v2bKFtm3bcuSRR5KRkVEFd7GzkpIS3nnnHY444giSkpKq5TOlquBaVm3ielZt4npWbeJ6Vm3hWlZt4nr+Dnn7w99Gk1nwJccceiCkNQy6ompTvmN6d9TZoHN3dOzYkenTp+/261NSUkhJSdnp8aSkpGr/H2cQnylVBdeyahPXs2oT17NqE9ezagvXsmoT1/O3NGoDTToTWr+QpFWTodt3H9VY2+zJOqjVw4i+T1ZWFgkJCaxZs6bC42vWrKFFixYBVSVJkiRJkiTtQvn09aUfB1tHDVZng87k5GT2339/3nvvvdhjZWVlvPfeewwcODDAyiRJkiRJkqRvaX9Q9Gcnr3+nWr11PS8vj4ULF8a+X7JkCdOmTaNx48a0a9eOK6+8krPPPpuf/OQn9O/fn3vuuYf8/PzYFHZJkiRJkiSpRijv6Fw1HQq3QGr1zIOJJ7U66Jw8eTKHHnpo7PvyQUFnn302TzzxBKeeeirr1q3jhhtuYPXq1fTt25e33nprpwFFkiRJkiRJUqAyW0Oj9rBxKSyfCF2PDLqiGqdWB51DhgwhEol872suvfRSLr300mqqSJIkSZIkSfqR2h8UDTqXfWzQuQt19oxOSZIkSZIkKa5k7zinc6nndO6KQackSZIkSZIUD9rvOKdz5edQlBdsLTWQQackSZIkSZIUDxq2g8x2ECmFLz8Nupoax6BTkiRJkiRJihflXZ3L3L7+bQadkiRJkiRJUrzI3hF0ek7nTgw6JUmSJEmSpHhR3tG5YgoUbwu2lhrGoFOSJEmSJEmKF406QINWUFYCX30WdDU1ikGnJEmSJEmSFC9Coa+7Ot2+XoFBpyRJkiRJkhRP2h8U/dmBRBUYdFaB+++/n5ycHPr16xd0KZIkSZIkSaptsncEnV9NhpLCYGupQQw6q8All1zCnDlzmDRpUtClSJIkSZIkqbZp0gnqN4fSIlgxOehqagyDTkmSJEmSJCmehEKQ7Tmd32bQKUmSJEmSJMWb8oFEyz4Oto4axKBTkiRJkiRJijfl53R+OQm2FwVbSw1h0ClJkiRJkiTFm6bdID0LthfAiqlBV1MjGHRKkiRJkiRJ8SYUguwDo1+7fR0w6JQkSZIkSZLiU/vB0Z8dSAQYdEqSJEmSJEnxqXwg0ZefQWlJsLXUAIlBFyBJkiRJkiTpR2i6D+xzArTqGx1IlJAUdEWBMuiUJEmSJEmS4lE4DKc+FXQVNYZb1yVJkiRJkiTFPYNOSZIkSZIkSXHPoFOSJEmSJElS3DPolCRJkiRJkhT3DDolSZIkSZIkxT2DTkmSJEmSJElxz6BTkiRJkiRJUtwz6JQkSZIkSZIU9ww6JUmSJEmSJMU9g84qcP/995OTk0O/fv2CLkWSJEmSJEmqEww6q8All1zCnDlzmDRpUtClSJIkSZIkSXWCQackSZIkSZKkuGfQKUmSJEmSJCnuGXRKkiRJkiRJinsGnZIkSZIkSZLinkGnJEmSJEmSpLhn0ClJkiRJkiQp7hl0SpIkSZIkSYp7Bp2SJEmSJEmS4p5BpyRJkiRJkqS4Z9ApSZIkSZIkKe4lBl1AbRaJRADYsmVLtX1mSUkJ27ZtY8uWLSQlJVXb50qVzbWs2sT1rNrE9azaxPWs2sK1rNrE9axvK8/VynO272PQWYW2bt0KQNu2bQOuRJIkSZIkSYpfW7duJTMz83tfE4rsThyqH6WsrIyVK1fSoEEDQqFQtXzmli1baNu2LV9++SUZGRnV8plSVXAtqzZxPas2cT2rNnE9q7ZwLas2cT3r2yKRCFu3bqVVq1aEw99/CqcdnVUoHA7Tpk2bQD47IyPD3xBUK7iWVZu4nlWbuJ5Vm7ieVVu4llWbuJ71TT/UyVnOYUSSJEmSJEmS4p5BpyRJkiRJkqS4Z9BZy6SkpHDjjTeSkpISdCnSXnEtqzZxPas2cT2rNnE9q7ZwLas2cT1rbziMSJIkSZIkSVLcs6NTkiRJkiRJUtwz6JQkSZIkSZIU9ww6JUmSJEmSJMU9g05JkiRJkiRJcc+gs4a7//77ad++PampqQwYMIDPPvvsO187ZMgQQqHQTj+OPfbY2GsikQg33HADLVu2JC0tjaFDh7JgwYLquBWpUtdzSUkJV199Nb169aJevXq0atWKs846i5UrV1bX7aiOq+zfn7/poosuIhQKcc8991RR9dLXqmItz507lxNOOIHMzEzq1atHv379WL58eVXfilTp6zkvL49LL72UNm3akJaWRk5ODg8++GB13Iq0R+sZ4J577qFbt26kpaXRtm1brrjiCgoLC/fqPaXKUtnr+S9/+Qv9+vWjQYMGNGvWjOHDhzN//vyqvg3Fg4hqrOeeey6SnJwceeyxxyKzZ8+OjBw5MtKwYcPImjVrdvn69evXR1atWhX7MWvWrEhCQkLk8ccfj73mtttui2RmZkZefvnlyPTp0yMnnHBCpEOHDpGCgoJquivVVZW9njdt2hQZOnRo5N///ndk3rx5kU8++STSv3//yP7771+Nd6W6qip+fy734osvRvr06RNp1apV5O67767aG1GdVxVreeHChZHGjRtHrrrqqsjUqVMjCxcujLzyyivf+Z5SZamK9Txy5MhIp06dIh988EFkyZIlkX/84x+RhISEyCuvvFJNd6W6ak/X89NPPx1JSUmJPP3005ElS5ZE3n777UjLli0jV1xxxY9+T6myVMV6HjZsWOTxxx+PzJo1KzJt2rTIMcccE2nXrl0kLy+vum5LNZRBZw3Wv3//yCWXXBL7vrS0NNKqVavIX/7yl926/u677440aNAg9j/0srKySIsWLSJ33HFH7DWbNm2KpKSkRJ599tnKLV76lspez7vy2WefRYDIsmXL9rpe6ftU1Xr+6quvIq1bt47MmjUrkp2dbdCpKlcVa/nUU0+N/OIXv6j0WqUfUhXruUePHpFbbrmlwuv222+/yHXXXVc5RUvfYU/X8yWXXBI57LDDKjx25ZVXRgYNGvSj31OqLFWxnr9t7dq1ESDy4YcfVk7RiltuXa+hiouLmTJlCkOHDo09Fg6HGTp0KJ988sluvcejjz7KaaedRr169QBYsmQJq1evrvCemZmZDBgwYLffU/oxqmI978rmzZsJhUI0bNhwb0uWvlNVreeysjLOPPNMrrrqKnr06FHpdUvfVhVruaysjNdff52uXbsybNgwmjVrxoABA3j55Zer4hakmKr6vfnAAw/k1VdfZcWKFUQiET744AO++OILjjzyyEq/B6ncj1nPBx54IFOmTIltB168eDFvvPEGxxxzzI9+T6kyVMV63pXNmzcD0Lhx40qsXvHIoLOGys3NpbS0lObNm1d4vHnz5qxevfoHr//ss8+YNWsWF1xwQeyx8ut+7HtKP1ZVrOdvKyws5Oqrr2bEiBFkZGTsdc3Sd6mq9fx///d/JCYmctlll1VqvdJ3qYq1vHbtWvLy8rjttts46qijGDNmDCeeeCInnXQSH374YaXfg1Suqn5vvvfee8nJyaFNmzYkJydz1FFHcf/993PwwQdXav3SN/2Y9Xz66adzyy23cNBBB5GUlESnTp0YMmQI11577Y9+T6kyVMV6/raysjIuv/xyBg0aRM+ePSv9HhRfDDprqUcffZRevXrRv3//oEuR9toPreeSkhJOOeUUIpEIDzzwQDVXJ+2ZXa3nKVOm8Le//Y0nnniCUCgUYHXS7tvVWi4rKwPgpz/9KVdccQV9+/bl97//Pccdd5wDXFSjfdefNe69914mTpzIq6++ypQpU7jrrru45JJLePfddwOqVNq1sWPH8uc//5nRo0czdepUXnzxRV5//XVuvfXWoEuT9tierudLLrmEWbNm8dxzz1VzpaqJEoMuQLuWlZVFQkICa9asqfD4mjVraNGixfdem5+fz3PPPcctt9xS4fHy69asWUPLli0rvGffvn0rp3BpF6piPZcrDzmXLVvG+++/bzenqlxVrOdx48axdu1a2rVrF3ustLSU3/zmN9xzzz0sXbq00uqXylXFWs7KyiIxMZGcnJwKj++zzz58/PHHlVO4tAtVsZ4LCgq49tpreemll2KT2Hv37s20adO48847K2zDlCrTj1nP119/PWeeeWasK7lXr17k5+dz4YUXct111+3V/0akvVEV6zkc/rpn79JLL+W1117jo48+ok2bNlV3I4obdnTWUMnJyey///689957scfKysp47733GDhw4Pde+/zzz1NUVMQvfvGLCo936NCBFi1aVHjPLVu28Omnn/7ge0p7oyrWM3wdci5YsIB3332XJk2aVHrt0rdVxXo+88wzmTFjBtOmTYv9aNWqFVdddRVvv/12ldyHVBVrOTk5mX79+jF//vwKj3/xxRdkZ2dXXvHSt1TFei4pKaGkpKTCX6gBEhISYt3LUlX4Met527Ztu1yrAJFIZK/+NyLtjapYz+U/X3rppbz00ku8//77dOjQoYruQHEn0FFI+l7PPfdcJCUlJfLEE09E5syZE7nwwgsjDRs2jKxevToSiUQiZ555ZuT3v//9TtcddNBBkVNPPXWX73nbbbdFGjZsGHnllVciM2bMiPz0pz+NdOjQIVJQUFCl9yJV9nouLi6OnHDCCZE2bdpEpk2bFlm1alXsR1FRUZXfj+q2qvj9+ducuq7qUBVr+cUXX4wkJSVFHnroociCBQsi9957byQhISEybty4Kr0XqSrW8yGHHBLp0aNH5IMPPogsXrw48vjjj0dSU1Mjo0ePrtJ7kfZ0Pd94442RBg0aRJ599tnI4sWLI2PGjIl06tQpcsopp+z2e0pVpSrW88UXXxzJzMyMjB07tsLfBbdt21bt96eaxaCzhrv33nsj7dq1iyQnJ0f69+8fmThxYuy5Qw45JHL22WdXeP28efMiQGTMmDG7fL+ysrLI9ddfH2nevHkkJSUlcvjhh0fmz59flbcgxVTmel6yZEkE2OWPDz74oIrvRKr835+/zaBT1aUq1vKjjz4a6dy5cyQ1NTXSp0+fyMsvv1xV5UsVVPZ6XrVqVeScc86JtGrVKpKamhrp1q1b5K677oqUlZVV5W1IkUhkz9ZzSUlJ5Kabbop06tQpkpqaGmnbtm1k1KhRkY0bN+72e0pVqbLX83f9XfDxxx+vvptSjRSKRHb0/UqSJEmSJElSnPKMTkmSJEmSJElxz6BTkiRJkiRJUtwz6JQkSZIkSZIU9ww6JUmSJEmSJMU9g05JkiRJkiRJcc+gU5IkSZIkSVLcM+iUJEmSJEmSFPcMOiVJkiRJkiTFPYNOSZIkaQ/cdNNN9O3bN/b9Oeecw/DhwwOrR5IkSVEGnZIkSZIkSZLinkGnJEmSao3i4uKgS5AkSVJADDolSZIUt4YMGcKll17K5ZdfTlZWFsOGDWPWrFkcffTR1K9fn+bNm3PmmWeSm5sbu6asrIzbb7+dzp07k5KSQrt27fjTn/4Ue/7qq6+ma9eupKen07FjR66//npKSkqCuD1JkiTtAYNOSZIkxbUnn3yS5ORkxo8fz2233cZhhx3Gvvvuy+TJk3nrrbdYs2YNp5xySuz111xzDbfddhvXX389c+bM4ZlnnqF58+ax5xs0aMATTzzBnDlz+Nvf/sbDDz/M3XffHcStSZIkaQ+EIpFIJOgiJEmSpB9jyJAhbNmyhalTpwLwxz/+kXHjxvH222/HXvPVV1/Rtm1b5s+fT8uWLWnatCn33XcfF1xwwW59xp133slzzz3H5MmTgegwopdffplp06YB0WFEmzZt4uWXX67Ue5MkSdKeSQy6AEmSJGlv7L///rGvp0+fzgcffED9+vV3et2iRYvYtGkTRUVFHH744d/5fv/+97/5+9//zqJFi8jLy2P79u1kZGRUSe2SJEmqPAadkiRJimv16tWLfZ2Xl8fxxx/P//3f/+30upYtW7J48eLvfa9PPvmEM844g5tvvplhw4aRmZnJc889x1133VXpdUuSJKlyGXRKkiSp1thvv/3473//S/v27UlM3PmPul26dCEtLY333ntvl1vXJ0yYQHZ2Ntddd13ssWXLllVpzZIkSaocDiOSJElSrXHJJZewYcMGRowYwaRJk1i0aBFvv/025557LqWlpaSmpnL11Vfzu9/9jn/+858sWrSIiRMn8uijjwLRIHT58uU899xzLFq0iL///e+89NJLAd+VJEmSdodBpyRJkmqNVq1aMX78eEpLSznyyCPp1asXl19+OQ0bNiQcjv7R9/rrr+c3v/kNN9xwA/vssw+nnnoqa9euBeCEE07giiuu4NJLL6Vv375MmDCB66+/PshbkiRJ0m5y6rokSZIkSZKkuGdHpyRJkiRJkqS4Z9ApSZIkSZIkKe4ZdEqSJEmSJEmKewadkiRJkiRJkuKeQackSZIkSZKkuGfQKUmSJEmSJCnuGXRKkiRJkiRJinsGnZIkSZIkSZLinkGnJEmSJEmSpLhn0ClJkiRJkiQp7hl0SpIkSZIkSYp7Bp2SJEmSJEmS4p5BpyRJkiRJkqS4Z9ApSZIkSZIkKe4ZdEqSJEmSJEmKewadkiRJkiRJkuKeQackSZIkSZKkuGfQKUmSJEmSJCnuGXRKkiRJkiRJinsGnZIkSZIkSZLinkGnJEmSJEmSpLhn0ClJkiRJkiQp7hl0SpIkSZIkSYp7Bp2SJEmSJEmS4p5BpyRJkiRJkqS4Z9ApSZIkSZIkKe4ZdEqSJEmSJEmKewadkiRJkiRJkuKeQackSZIkSZKkuGfQKUmSJEmSJCnuGXRKkiRJkiRJinsGnZIkSZIkSZLinkGnJEmSJEmSpLhn0ClJkiRJkiQp7hl0SpIkSZIkSYp7Bp2SJEmSJEmS4p5BpyRJkiRJkqS4Z9ApSZIkSZIkKe4ZdEqSJEmSJEmKewadkiRJkiRJkuKeQackSZIkSZKkuGfQKUmSJEmSJCnuGXRKkiRJkiRJinsGnZIkSZIkSZLinkGnJEmSJEmSpLhn0ClJkqQ6YciQIfTs2TPoMiRJklRFDDolSZKkH6mgoIDzzz+fnj17kpmZSf369enTpw9/+9vfKCkpqfDa9957j/POO4+uXbuSnp5Ox44dueCCC1i1atV3vv+9995LZmZm7L1WrVrFhRdeSIcOHUhLS6NTp05ceeWVrF+/vkrvU5IkKR4kBl2AJEmSFK8KCgqYPXs2xxxzDO3btyccDjNhwgSuuOIKPv30U5555pnYa6+++mo2bNjAz3/+c7p06cLixYu57777eO2115g2bRotWrTY6f1ff/11jjzySJKSksjLy2PgwIHk5+czatQo2rZty/Tp07nvvvv44IMPmDJlCuGwfQySJKnuMuiUJElS3MrPz6devXqBfX7jxo2ZOHFihccuuugiMjMzue+++/jrX/8aCzD/+te/ctBBB1UII4866igOOeQQ7rvvPv74xz9WeJ9t27bx4Ycf8sADDwDw6quvsmzZMl577TWOPfbYCjXccsstTJ8+nX333beqblWSJKnG8598JUmStMe2bt3K5ZdfTvv27UlJSaFZs2YcccQRTJ06tcLrPv30U4466igyMzNJT0/nkEMOYfz48RVes2zZMkaNGkW3bt1IS0ujSZMm/PznP2fp0qUVXvfEE08QCoX48MMPGTVqFM2aNaNNmzax5998800OOeQQGjRoQEZGBv369avQUVluzpw5HHrooaSnp9O6dWtuv/32nV6zfPly5s2b96N/fdq3bw/Apk2bYo8dfPDBO3VcHnzwwTRu3Ji5c+fu9B7vvfceRUVFHH300QBs2bIFgObNm1d4XcuWLQFIS0v70fVKkiTVBnZ0SpIkaY9ddNFFvPDCC1x66aXk5OSwfv16Pv74Y+bOnct+++0HwPvvv8/RRx/N/vvvz4033kg4HObxxx/nsMMOY9y4cfTv3x+ASZMmMWHCBE477TTatGnD0qVLeeCBBxgyZAhz5swhPT29wmePGjWKpk2bcsMNN5Cfnw9EQ9DzzjuPHj16cM0119CwYUM+//xz3nrrLU4//fTYtRs3buSoo47ipJNO4pRTTuGFF17g6quvplevXrFAEeCss87iww8/JBKJ7NavR3FxMVu2bKGgoIDJkydz5513kp2dTefOnb/3ury8PPLy8sjKytrpuTfeeIP9998/FmyWB6W//vWvueuuu2jTpg0zZszgT3/6E8OHD6d79+67VaskSVJtFYrs7p/eJEmSpB0aNmzIL37xC+67775dPh+JROjWrRsdO3bkzTffJBQKAdEzLXv06EHnzp0ZM2ZM7LFvdyNOnDiRgQMH8s9//pMzzzwTiIaZ5557LgcddBBjx44lISEBgM2bN9O2bVtycnIYO3YsqampFeoo/+whQ4bw4YcfVnjP4uJisrOzGTRoEC+88ELsuvLX7u4flZ977jlGjBgR+/4nP/kJjz32GL169fre6/74xz9y/fXX895773HYYYdVeC47O5tzzz2Xm266KfbYo48+ym9/+9sKnaJnn302jzzyCImJ9jBIkqS6zT8NSZIkaY81bNiQTz/9lJUrV9KqVaudnp82bRoLFizgD3/4w04TwQ8//HCeeuopysrKCIfDFULOkpIStmzZQufOnWnYsCFTp06NhZLlRo4cGQs5Ad555x22bt3K73//+wohJxALOcvVr1+fX/ziF7Hvk5OT6d+/P4sXL67wurFjx+7eL8QOhx56KO+88w6bNm3ivffeY/r06bFu0+/y0UcfcfPNN3PKKafsFHLOmjWL5cuXVziLE6B169b079+fY445huzsbMaNG8ff//53srKyuPPOO/eoZkmSpNrGoFOSJEl77Pbbb+fss8+mbdu27L///hxzzDGcddZZdOzYEYAFCxYA0W7D77J582YaNWpEQUEBf/nLX3j88cdZsWJFhS7KzZs373Rdhw4dKny/aNEiAHr27PmDdbdp02an8LNRo0bMmDHjB6/9Ps2bN49tMT/55JP585//zBFHHMGCBQt2OU193rx5nHjiifTs2ZNHHnlkp+dff/11mjdvzk9+8pPYY+PHj+e4445j4sSJsceHDx9ORkYGN998M+eddx45OTl7dR+SJEnxzGFEkiRJ2mOnnHIKixcv5t5776VVq1bccccd9OjRgzfffBOAsrIyAO644w7eeeedXf6oX78+AL/61a/405/+xCmnnMJ//vMfxowZwzvvvEOTJk1i7/NNezN055udoN9U2ac5nXzyyeTl5fHKK6/s9NyXX37JkUceSWZmJm+88QYNGjTY6TVvvPEGRx11VIVQ9h//+MdO4SfACSecQCQSYcKECZV6D5IkSfHGjk5JkiT9KC1btmTUqFGMGjWKtWvXst9++/GnP/2Jo48+mk6dOgGQkZHB0KFDv/d9XnjhBc4++2zuuuuu2GOFhYUVzqH8PuWfNWvWrB8c/lNdCgoKgJ07UtevX8+RRx5JUVER7733Xmxi+jdt2rSJCRMmcOmll1Z4fM2aNZSWlu70+pKSEgC2b99eWeVLkiTFJTs6JUmStEdKS0t3CvCaNWtGq1atKCoqAmD//fenU6dO3HnnneTl5e30HuvWrYt9nZCQsFNH5b333rvLUG9XjjzySBo0aMBf/vIXCgsLKzz3Yzs1ly9fzrx5837wdbm5ubv8jPLt6N/svszPz+eYY45hxYoVvPHGG3Tp0mWX71k+pOnII4+s8HjXrl1Zs2bNTueHPvvsswDsu+++P1ivJElSbWZHpyRJkvbI1q1badOmDSeffDJ9+vShfv36vPvuu0yaNCnWlRkOh3nkkUc4+uij6dGjB+eeey6tW7dmxYoVfPDBB2RkZPC///0PgOOOO46nnnqKzMxMcnJy+OSTT3j33Xdp0qTJbtWTkZHB3XffzQUXXEC/fv04/fTTadSoEdOnT2fbtm08+eSTe3yPZ5111m5NXf/Xv/7Fgw8+yPDhw+nYsSNbt27l7bff5p133uH444+vMGTojDPO4LPPPuO8885j7ty5zJ07N/Zc/fr1GT58OBA9n/Oggw4iMzOzwmddeumlPP744xx//PH86le/Ijs7mw8//JBnn32WI444ggEDBuzxfUqSJNUmBp2SJEnaI+np6YwaNYoxY8bw4osvUlZWRufOnRk9ejQXX3xx7HVDhgzhk08+4dZbb+W+++4jLy+PFi1aMGDAAH75y1/GXve3v/2NhIQEnn76aQoLCxk0aBDvvvsuw4YN2+2azj//fJo1a8Ztt93GrbfeSlJSEt27d+eKK66o1Hv/toMOOogJEybw7LPPsmbNGhITE+nWrRt//etf+dWvflXhtdOmTQPgscce47HHHqvwXHZ2NsOHDycSifDWW2/x29/+dqfP6tatG1OmTOEPf/gD//rXv1i9ejWtWrXit7/9LTfffHOV3aMkSVK8CEUq++R1SZIkST/KZ599xoABA5g9e7YT1CVJkvaQZ3RKkiRJNcif//xnQ05JkqQfwY5OSZIkSZIkSXHPjk5JkiRJkiRJcc+gU5IkSZIkSVLcM+iUJEmSJEmSFPcMOiVJkiRJkiTFvcSgC6jNysrKWLlyJQ0aNCAUCgVdjiRJkiRJkhRXIpEIW7dupVWrVoTD39+zadBZhVauXEnbtm2DLkOSJEmSJEmKa19++SVt2rT53tcYdFahBg0aANH/EBkZGQFXE6ySkhLGjBnDkUceSVJSUtDlKM65nlSZXE+qTK4nVSbXkyqT60mVyfWkyuaa0vfZsmULbdu2jeVs38egswqVb1fPyMgw6CwpIT09nYyMDH/T0l5zPakyuZ5UmVxPqkyuJ1Um15Mqk+tJlc01pd2xO8dCOoxIkiRJkiRJUtwz6JQkSZIkSZIU9ww6JUmSJEmSJMU9z+iUJEmSJEkSAGVlZRQXF1frZ5aUlJCYmEhhYSGlpaXV+tmqGZKTkwmH974f06BTkiRJkiRJFBcXs2TJEsrKyqr1cyORCC1atODLL7/crYEzqn3C4TAdOnQgOTl5r97HoFOSJEmSJKmOi0QirFq1ioSEBNq2bVsp3XW7q6ysjLy8POrXr1+tn6uaoaysjJUrV7Jq1SratWu3V2G3QackSZIkSVIdt337drZt20arVq1IT0+v1s8u3y6fmppq0FlHNW3alJUrV7J9+3aSkpJ+9Pu4eiRJkiRJkuq48rMx93brsPRjlK+7vT2j1aBTkiRJkiRJAJ6RqUBU1roz6JQkSZIkSZIU9ww6JUmSJEmSpBrgnHPOYfjw4UGXEbcMOiVJkiRJkhS3VqxYwS9+8QuaNGlCWloavXr1YvLkybt87UUXXUQoFOKee+75wfedNGkShx9+OA0bNqRRo0YMGzaM6dOnV3L1qkwGnZIkSZIkSYpLGzduZNCgQSQlJfHmm28yZ84c7rrrLho1arTTa1966SUmTpxIq1atfvB98/LyOOqoo2jXrh2ffvopH3/8MQ0aNGDYsGGUlJRUxa2oEhh0SpIkSZIkKS793//9H23btuXxxx+nf//+dOjQgSOPPJJOnTpVeN2KFSv41a9+xdNPP01SUtIPvu+8efPYsGEDt9xyC926daNHjx7ceOONrFmzhmXLln3nddOnT+fQQw+lQYMGZGRksP/++8e6S2+66Sb69u1b4fX33HMP7du33+l9br75Zpo2bUpGRgYXXXQRxcXFsedeeOEFevXqRVpaGk2aNGHo0KHk5+cDX299/77r33rrLQ466CAaNmxIkyZNOO6441i0aFGFz//qq68YMWIEjRs3pl69evzkJz/h008/jT3/yiuvsN9++5GamkrHjh25+eab2b59+w/+ula1xKALkCRJkiRJUs0SiUQoKCmtls8qKyujoLiUxOLthMNh0pISdnsK96uvvsqwYcP4+c9/zocffkjr1q0ZNWoUI0eOrPD+Z555JldddRU9evTYrfft1q0bTZo04dFHH+Xaa6+ltLSURx99lH322WeXwWS5M844g3333ZcHHniAhIQEpk2btlvB6je99957pKamMnbsWJYuXcq5555LkyZN+NOf/sSqVasYMWIEt99+OyeeeCJbt25l3LhxRCKR3boeID8/nyuvvJLevXuTl5fHDTfcwIknnsi0adMIh8Pk5eVxyCGH0Lp1a1599VVatGjB1KlTKSsrA2DcuHGcddZZ/P3vf2fw4MEsWrSICy+8EIAbb7xxj+61shl0SpIkSZIkqYKCklJybng7kM+ec8sw0pN3L7JavHgxDzzwAFdeeSXXXnstkyZN4rLLLiM5OZmzzz4biHZ9JiYmctlll+12DQ0aNGDs2LEMHz6cW2+9FYAuXbrw9ttvk5j43bUtX76cq666iu7du8eu2VPJyck89thjpKen06NHD2655Rauuuoqbr31VlatWsX27ds56aSTyM7OBqBXr167fX04HOZnP/tZhdc/9thjNG3alDlz5tCzZ0+eeeYZ1q1bx6RJk2jcuDEAnTt3jr3+5ptv5ve//33s17djx47ceuut/O53vws86HTruiRJkiRJkuJSWVkZ++23H3/+85/Zd999ufDCCxk5ciQPPvggAFOmTOFvf/sbTzzxxHd2iR599NHUr1+f+vXrxzo+CwoKOP/88xk0aBATJ05k/Pjx9OzZk2OPPZaCggKA2DX169fnoosuAuDKK6/kggsuYOjQodx22207bQnfHX369CE9PT32/cCBA8nLy+PLL7+kT58+HH744fTq1Yuf//znPPzww2zcuHG3rwdYsGABI0aMoGPHjmRkZMQ6VJcvXw7AtGnT2HfffWMh57dNnz6dW265pcL9jxw5klWrVrFt27Y9vt/KZEenJEmSJEmSKkhLSmDOLcOq5bPKysrYumUrDTIaxLau766WLVuSk5NT4bF99tmH//73v0B0m/XatWtp165d7PnS0lJ+85vfcM8997B06VIeeeSRWHhZvs38mWeeYenSpXzyySeEw+HYY40aNeKVV17htNNOY9q0abH3zMjIAKLncJ5++um8/vrrvPnmm9x4440899xznHjiiYTD4QpbzIE9HmyUkJDAO++8w4QJExgzZgz33nsv1113HZ9++ikdOnTYrfc4/vjjyc7O5uGHH6ZVq1aUlZXRs2fP2DmeaWlp33t9Xl4eN998MyeddNJOz6Wmpu7R/VQ2g05JkiRJkiRVEAqFdnv7+N4qKytje3IC6cmJsVBxdw0aNIj58+dXeOyLL76Ibes+88wzGTp0aIXnhw0bxplnnsm5554LQOvWrXd6323bthEOhyt0gZZ/X35W5Te3c39T165d6dq1K1dccQUjRozg8ccf58QTT6Rp06asXr2aSCQSe99vhqXlpk+fTkFBQSxwnDhxIvXr16dt27ZA9L/NoEGDGDRoEDfccAPZ2dm89NJLXHnllT94/fr165k/fz4PP/wwgwcPBuDjjz+u8Pm9e/fmkUceYcOGDbvs6txvv/2YP3/+d95/kNy6LkmSJEmSpLh0xRVXMHHiRP785z+zcOFCnnnmGR566CEuueQSAJo0aULPnj0r/EhKSqJFixZ069btO9/3iCOOYOPGjVxyySXMnTuX2bNnc+6555KYmMihhx66y2sKCgq49NJLGTt2LMuWLWP8+PFMmjSJffbZB4AhQ4awbt06br/9dhYtWsT999/Pm2++udP7FBcXc/755zNnzhzeeOMNbrzxRi699FLC4TCffvopf/7zn5k8eTLLly/nxRdfZN26dbHP+KHrGzVqRJMmTXjooYdYuHAh77//fiwgLTdixAhatGjB8OHDGT9+PIsXL+a///0vn3zyCQA33HAD//znP7n55puZPXs2c+fO5bnnnuMPf/jDnv3HqwIGnZICt23KFDY+/zyF878gUlo9U/0kSZIkSfGvX79+vPTSSzz77LP07NmTW2+9lXvuuYczzjhjr963e/fu/O9//2PGjBkMHDiQwYMHs3LlSt566y1atmy5y2sSEhJYv349Z511Fl27duWUU07h6KOP5uabbwaiW+pHjx7N/fffT58+ffjss8/47W9/u9P7HH744XTp0oWDDz6YU089lRNOOIGbbroJiG6R/+ijjzjmmGPo2rUrf/jDH7jrrrs4+uijd+v6cDjMc889x5QpU+jZsydXXHEFd9xxR4XPT05OZsyYMTRr1oxjjjmGXr16cdttt5GQED1SYNiwYbz22muMGTOGfv36ccABB3D33XfHumiDFIp8+3AAVZotW7aQmZnJ5s2bY2c11FUlJSW88cYbHHPMMbHzLqRyq264kU3/+Q8AofR00nJySO3Tm7RevUnr3YvEli0rbBdwPakyuZ5UmVxPqkyuJ1Um15Mqk+updiosLGTJkiV06NCh2s9ZLCsrY8uWLWRkZOzx1nVVdM4557Bp0yZefvnloEvZI9+3/vYkX/OMTkmBS+nWlfQDDqBw5kzK8vPZNnky2yZPjj2f0DQrFnqm9upFYvfuAVZbM6zdtpYxS8fQM6sn+zTZh5SElKBLkiRJkiQpUAadkgLX+IwzaHzGGURKSylesoSCGTMpmDGdwhkzKfziC0rX5ZL3/vvkvf9+7Jr2TZuy5uOPSe/bl7TevUnp1o1wcnKAd1G9Jq+ezP9N+j8AEsOJdGvUjZ5ZPendtDc9s3rSPqM94ZD/EipJkiRJqjsMOiXVGKGEBFI6dyalc2cannQiAGWFhRTOnUvhjBnRAHTmTEqWLyd53Tq2/u81tv7vtei1SUmk5OxTofMzuX37Clvea5OGqQ0Z0mYIM3JnsKFwA7PXz2b2+tn8e/6/AWiQ1IAeWT3oldUr+qNpL7LSsgKuWpIkSZJUlZ544omgSwiUQaekGi2cmkr6vvuSvu++sccK165l3ONP0DstlaLZsymcPoPSTZsonD6Dwukz2Fh+bWYmaT17ktq7F2m9e5PWuzeJTZoEcyOV7MBWB3JgqwOJRCKsyl/FjNwZzFo3i5m5M5mzfg5bS7YycdVEJq6aGLumZb2WFYLPfRrvQ3pSeoB3IUmSJElS5THolBR3Eho1Ylv3bjTecfh5JBKh5KuvKJgxI9b5WThnDmWbN5M/fjz548fHrk1q1arCoKPUnBzC6fEb9oVCIVrVb0Wr+q04qv1RAGwv287CTQuZmTuTmetmMjN3Jos2LWJV/ipW5a9izLIxACSEEujcsDO9mvaKBaAdMzuSEE4I8pYkSZIkSfpRDDolxb1QKERy27Ykt21L5rHHAhApKaFowQIKyoPPmTMoWriIkpUrKVm5kq1vvhW9OCGBlC5dSOvVa0fnZx9SOncilBC/YV9iOJHujbvTvXF3ft715wDkl+QzO3d2NPzc8WPttrXM3zif+Rvn88IXLwCQnphOj6we0fM+s6Lnfbao1yLI25EkSZIkabcYdEqqlUJJSaTm5JCak0Oj004DoDQvj8JZsymY+XXn5/Y1ayiaN4+iefPg+eej16ank5aTQ+qO7e5pvXuR2LJlXJ/3WS+pHv1b9qd/y/6xx9bkr2FW7qzotvfcWczKncW27duYtHoSk1ZPir2uWVozemb1jHV+9mjSg/rJ9YO4DUmSJEmSvpNBp6Q6I6F+feodMIB6BwyIPVayZg2FM2dSMH0GBTNnUjhzJmX5+WybPJltkyd/fW1WFmm9epHWpzepvXqR1qsXCRkZQdxGpWlerznN6zXn8OzDASgtK2XJ5iUVuj4XbFzA2oK1vP/l+7z/ZXTqfYgQnRp2ioafO7a8d27UmaRwUpC3I0mSJEmq4ww6JdVpSc2bk9S8OQ2GDgUgUlZG8eLFOya8R4cbFX7xBaW5ueR98AF5H3wQuza5Q4cdE957k9anNynduhFOTg7qVvZaQjiBzo0607lRZ07sEp16X7C9gLnr534dfq6bycr8lSzctJCFmxby8sKXAUhNSGWfJvtUGHbUql6ruO6ClSRJkiTFF4NOSfqGUDhMSufOpHTuTMOTomFfWWEhhXPnVuj8LFm+nOIlSyhesoTNr7wavTYpiZR99qnQ+ZmcnU0oHA7ylvZKWmIa+zXfj/2a7xd7LLcgl1m5s2LB56zcWWwt2crnaz/n87Wfx17XOLUxvbJ6xc777JHVg8yUzCBuQ5IkSZJqjSFDhtC3b1/uueeeoEupcQw6JekHhFNTSd93X9L33Tf22PaNG6PB5zc6P0s3baJwx+T3jU8/Hb02I+PrQUc7Jr0nZmUFdSuVIistiyFthzCk7RAAyiJlLNuyLHre57roeZ/zNs5jQ+EGPvzqQz786sPYte0z2lfY8t6tcTeSE+K3C1aSJElS8D766CPuuOMOpkyZwqpVq3jppZcYPnw4ACUlJfzhD3/gjTfeYPHixWRmZjJ06FBuu+02WrVqFXuPL774gquuuorx48dTXFxM7969ufXWWzn00EO/97PffvttbrzxRmbPnk1qaioHH3wwd911F+3bt6/CO9Z3MeiUpB8hsVEj6h98MPUPPhiASCRCyVdfUTBjBoUzZkZ/njOHsi1byB8/nvzx42PXJrVqFR10VN75mZNDOD09qFvZa+FQmA6ZHeiQ2YHjOx0PQFFpEfM3zGdm7sxY+Ll863KWblnK0i1LeW3xawAkhZPo3rh7bLt7r6xetGvQzi3vkiRJknZbfn4+ffr04bzzzuOkk06q8Ny2bduYOnUq119/PX369GHjxo38+te/5oQTTmDyN+YyHHfccXTp0oX333+ftLQ07rnnHo477jgWLVpEixYtdvm5S5Ys4ac//SlXXnklTz/9NJs3b+aKK67gpJNOYurUqVV6z9o1g05JqgShUIjktm1JbtuWzGOPBSBSUkLRggUU7JjwXjhzBkULF1GyciUlK1ey9a23oheHw6R06UJa797Rzs/evUnp1IlQYvz+Fp2SkELvpr3p3bQ3Z+xzBgCbCjcxa/0sZq77etjRpqJNsa+ZF702IzmjQvDZM6snjVMbB3g3kiRJkmqyo48+mqOPPnqXz2VmZvLOO+9UeOy+++6jf//+LF++nHbt2pGbm8uCBQt49NFH6d27NwC33XYbo0ePZtasWd8ZdE6ZMoXS0lL++Mc/Et5xZNlvf/tbfvrTn1JSUkJS0q4Hto4dO5bf/e53zJ49m6SkJHr06MEzzzxDdnY255xzDps2beLll1+Ovf7yyy9n2rRpjB07NvbY9u3bufTSS3nqqadISkri4osv5pZbbok1jYwePZq7776bL7/8kszMTAYPHswLL7wARLe+9+zZE+A7r3/qqaf429/+xvz586lXrx6HHXYY99xzD82aNYvVMHv2bK6++mo++ugjIpEIffv25YknnqBTp04APPLII9x1110sWbKE9u3bc9lllzFq1Khd/ppUlvj9W7Qk1XChpCRSc3JIzcmh0WmnAVCal0/hrFnR7e47Oj+3r1lD0fz5FM2fD88/H702PZ20nJxo5+eO8DOxZcu47nRsmNqQg1ofxEGtDwKiXbBf5X1VIficu34uW4q3MH7leMav/LoLtnX91vTO6h0977Npb7o37k5qYmpQtyJJkiTVfpEIlGyrns8qK4t+VnEChMOQlA5V+HefzZs3EwqFaNiwIQBNmjShW7du/POf/2S//fYjJSWFf/zjHzRr1oz999//O99n//33JxwO8/jjj3POOeeQl5fHU089xdChQ78z5Ny+fTvDhw9n5MiRPPvssxQXF/PZZ5/t8d/1nnzySc4//3w+++wzJk+ezIUXXki7du0YOXIkkydP5rLLLuOpp57iwAMPZMOGDYwbN263r4folv9bb72Vbt26sXbtWq688krOOecc3njjDQBWrFjBwQcfzJAhQ3j//ffJyMhg/PjxbN++HYCnn36aG264gfvuu499992Xzz//nJEjR1KvXj3OPvvsPbrXPWHQKUnVKKF+PeodMIB6BwyIPVayZk2FQUeFM2dSlp/PtsmT2faNrRQJWVkVBh2l9epFQkZGELdRKUKhEG0btKVtg7Yc0/EYAEpKS/hi0xfMWjeLGbnRLe+LNy9mRd4KVuSt4M2lbwKQGEqkS6Mu9G7aOzbsqH1me8Kh+B38JEmSJNUoJdvgz61++HWVIAw0/OYD166E5HpV8lmFhYVcffXVjBgxgowdf58KhUK8++67DB8+nAYNGhAOh2nWrBlvvfUWjRo1+s736tChA2PGjOGUU07hl7/8JaWlpQwcODAWBu7Kli1b2Lx5M8cdd1ys83GfffbZ4/to27Ytd999N6FQiG7dujFz5kzuvvtuRo4cyfLly6lXrx7HHXccDRo0IDs7m32/MXPih64HOO+882Kv7dixI3//+9/p168feXl51K9fn/vvv5/MzEyee+65WKjbtWvX2DU33ngjd911V+wogQ4dOjBnzhz+8Y9/GHRKUm2W1Lw5Sc2b02DoUAAiZWUUL1myI/iMdn4Wzp9PaW4ueR98QN4HH8SuTW7ffkfwGe38TOnenXBy/A73SUpIokeTHvRo0oNTORWArcVbmZU7KzrsKHcGM9fNZH3heuZumMvcDXP59/x/A1A/qT49snrEBh31yupF0/SmQd6OJEmSpBqkpKSEU045hUgkwgMPPBB7PBKJcMkll9CsWTPGjRtHWloajzzyCMcffzyTJk2iZcuW9OjRg2XLlgEwePBg3nzzTVavXs3IkSM5++yzGTFiBFu3buWGG27g5JNP5p133uHLL78kJycn9jnXXnst1157Leeccw7Dhg3jiCOOYOjQoZxyyim0bNlyj+7lgAMOqNAFOnDgQO666y5KS0s54ogjyM7OpmPHjhx11FEcddRRnHjiiaR/YzbE912fkJDAlClTuOmmm5g+fTobN26krKwMgOXLl5OTk8O0adMYPHjwLjtX8/PzWbRoEeeff34sOIVoN2tmZuYe3eeeMuiUpBomFA6T0qkTKZ060fCkEwEoKyykcO7crye9z5hByfLlFC9dSvHSpWx+5dXotUlJpOyzT4XOz+TsbELh+O10bJDcgIGtBjKw1UAg+oeQ1fmrY9vdZ+bOZM76OeSV5PHpqk/5dNWnsWtb1GsRCz17ZvWkR5MepCfF7+AnSZIkqdokpUc7K6tBWVkZW7ZuJWNHNyVV8Gf28pBz2bJlsa3W5d5//31ee+01Nm7cGHt89OjRvPPOOzz55JP8/ve/54033qCkpASAtLQ0gFhX4+233x57r3/961+0bduWTz/9lJ/85CdMmzYt9lzjxtHZA48//jiXXXYZb731Fv/+97/5wx/+wDvvvMMBBxxAOBwmEonsVPueaNCgAVOnTmXs2LGMGTOGG264gZtuuolJkybFtut/n/z8fIYNG8awYcN4+umnadq0KcuXL2fYsGEUFxdX+DXYlby8PAAefvhhBgwYUOG5hISEPbqXPWXQKUlxIJyaSvq++5L+je0G2zdujJ73OWNGbNp76caNFM6YQeGMGWx8+unotRkZpPXsSWqf3qTt6PxMzMoK6lb2WigUomX9lrSs35Ij2x8JwPay7SzatIiZuTNjnZ+LNi1idf5qVuev5p1l0cPHw6EwnRp2ip332SurF+3qtQvydiRJkqSaKRSqsu3jOykrg6TS6OdVQZNGeci5YMECPvjgA5o0aVLh+W3bomeRhr/12eFwONbJmJ2dvdP7btu2badryoO8srIyEhMT6dy58y5r2nfffdl333255pprGDhwIM888wwHHHAATZs2ZdasWRVeO23atJ06Jz/99NMK30+cOJEuXbrEPj8xMZGhQ4cydOhQbrzxRho2bMj7778f20r+fdfPmzeP9evXc9ttt9G2bVuAChPqAXr37s2TTz65y6FLzZs3p1WrVixevJgzzjhjl/dfVQw6JSlOJTZqRP3Bg6k/eDAQ7XQsWbGCgunTo4OOZs6kcPZsyrZsIX/CBPInTIhdm9SqVXTQUa9epPXuRWqPHoTT47fTMTGcSLfG3ejWuBsndz0ZgG0l25i9fvbX4ee6GazZtoYFGxewYOMC/rvgvwCkJabRPNKcLz7/gj7N+tC7aW+apzeP68FPkiRJUl2Sl5fHwoULY98vWbKEadOm0bhxY1q2bMnJJ5/M1KlTee211ygtLWX16tVAtMMyOTmZgQMH0qhRI84++2xuuOEG0tLSePjhh1myZAnHHnvsd37usccey913380tt9wS27p+7bXX7vJMzG/W9tBDD3HCCSfQqlUr5s+fz4IFCzjrrLMAOOyww7jjjjv45z//ycCBA/nXv/7FrFmzdnq/5cuXc+WVV/LLX/6SqVOncu+993LXXXcB8Nprr7F48WIOPvhgGjVqxBtvvEFZWRndunXbrevbtWtHcnIy9957LxdddBGzZs3i1ltvrfD5l156Kffeey+nnXYa11xzDZmZmUycOJH+/fvTrVs3br75Zi677DIyMzM56qijKCoqYvLkyWzcuJErr7xyd//T7jGDTkmqJUKhEMlt2pDcpg2ZO/7POFJSQtGCBdHt7jOjnZ5FCxdRsnIlJStXsvWtt6IXh8OkdOkSDT179yatd29SOnUilBi//zeRnpROvxb96NeiX+yxtdvWxoLPmetmMmv9LPJL8lnKUpbOXQpzo6/LSsv6+qzPpr3o0aQHDZIbBHMjkiRJkr7X5MmTOfTQQ2PflwdpZ599NjfddBOvvho96qtv374Vrvvggw8YMmQIWVlZvPXWW1x33XUcdthhlJSU0KNHD1555RX69OnznZ972GGH8cwzz3D77bdz++23k56ezsCBA3nrrbe+c2t3eno68+bN48knn2T9+vW0bNmSSy65hF/+8pcADBs2jOuvv57f/e53FBYWct5553HWWWcxc+bMCu9z1llnUVBQQP/+/UlISODXv/41F154IQANGzbkxRdf5KabbqKwsJAuXbrw7LPP0qNHj926vmnTpjzxxBNce+21/P3vf2e//fbjzjvv5IQTTohd36RJE95//32uuuoqDjnkEBISEujbty+DBg0C4IILLiA9PZ077riDq666inr16tGrVy8uv/zy7/z1rAyhyLc3/qvSbNmyhczMTDZv3lzh7Ie6qKSkhDfeeINjjjlmlwfVSnvC9bR3SvPyKZw9m4IZX3d+bt/xL5rfFEpLI61Hjx3BZ3TKe2KrVrWq07EsUsaC9Qt45v1nCLcOM3vDbBZsXMD2yPYKrwsRokNmh9iE955Ne9K1UVeSwq4/VeTvT6pMridVJteTKpPrqXYqLCxkyZIldOjQgdTU1Gr97LKyMrZs2UJGRsZOW8FV+YYMGULfvn255557gi4l5vvW357ka/HbqiNJ+lES6tej3oD+1BvQP/ZYyZq1FM6c8XXn58xZlOXlsW3yZLZ94yyWhKysr7e79+pNWq+eJFTx1LyqFA6F6ZjZkf1S9uOY/tE/qBduL2TuhrnMXPf1sKMVeStYvHkxizcv5tVF0X8NTklIoXvj7hU6P9vUb1OrgmBJkiRJiicGnZIkkpo3I6n5UBoMHQpApKyM4iVLdkx4j3Z+Fs6fT2luLnkffEDeBx/Erk1u357U3r1I692HtN69SOnenXByclC3stdSE1PZt9m+7Nvs6zNw1hesZ/b62cxYNyO67T13JluKtzB93XSmr5see12jlEbRIUdNe8UC0MyU+A2CJUmSJCmeGHRKknYSCodJ6dSJlE6daHjicADKiooomjt3x5T3aOdnybLlFC9dSvHSpWx59X/Ri5OSSO3enbQdW95Te/UmuX02oTjegtIkrQkHtzmYg9scDEQHPy3furxC8Dlvwzw2Fm1k3IpxjFsxLnZtuwbtKgSf3Rt3JzkhfoNgSZIkSfFt7NixQZdQZQw69aNtyC9m0tINDOzUhIxUz2WRartwSgppffuS9o0DvLdv3EjhrFk7ws8ZFM6YSenGjRTOnEnhzJlsfHrHtRkZpPXsWaHzMzErK5gbqQShUIjsjGyyM7I5vtPxABSXFjN/w/zYdveZuTNZtmUZy7cuZ/nW5by++HUgOiG+e6Pu0fM+m/amV1Yv2mW0IxyK3yBYkiRJkmoCg079aGPnr+XK/0wnIRxiv3YNGdylKYO7ZNG7TUMSwp5RJ9UFiY0aUX/wYOoPHgxEOx1LVqygcMYMCqbPoGDmTApnz6ZsyxbyJ0wgf8KEr69t1ZK0Xr2/7vzMySFcr15Qt7LXkhOSo52bTXvFHttctDnW8TkzdyYz181kY9FGZq2fxaz1s3hu/nMANEhuQK+sXl8PO8rqSZO0JkHdiiRJkiTFJYNO7ZWOWfVYnJvPpKUbmbR0I3995wsy05I4qHMWg7tkMbhrU1o3TAu6TEnVJBQKkdymDclt2pBxzDEAREpKKFqw4OtBRzNmULRwEdtXrmLrylVsffvt6MXhMClduuzY7t6LtD59SOnUiVBi/P5fVWZKJoNaD2JQ60FANAhekbeCWbmzmJEb3fY+Z/0cthZvZcLKCUxY+XUQ3Lp+6+h5nzu2vO/TZB/SEv39VJIkSZK+S/z+7VGBO2m/Npy0Xxu+3LCNcQtyGbdgHeMX5rK5oITXZ67i9ZmrAOjYtB6DOjUhbVOIQ4q20zDJbe5SXRJKSiI1J4fUnBwanXYqAKV5+RTOnh2d9L6j83P76tUUzZ9P0fz58PwL0WvT0kjtkRPt/OzTm7RevUhs1SpuJ5uHQiHaNGhDmwZtOKrDUQCUlJWwcOPCCl2fizcvZkXeClbkreDtpdEgOCGUQJdGXb6e8p7Viw6ZHUgIJwR5S5IkSZJUYxh0aq+1bZzO6QPacfqAdmwvLWPGis2M+yKXjxasY9qXm1i8Lp/F6/KBBB7/ywfsn92IwV2acnCXpvRolUHYbe5SnZNQvx71BvSn3oD+scdK1qyNBp/lnZ8zZ1GWl0fB5CkUTJ7y9bVZWaT16hUbdJTWqycJmfE72TwpnMQ+TfZhnyb7cEq3UwDIK85j9vrZseBzZu5M1hWsY96GeczbMI/nv3gegHpJ9ejRpEeFLe/N6zUP8nYkSZIkKTAGnapUiQlh9mvXiP3aNeLXQ7uwuaCETxat58P5axgz40vWF8HExRuYuHgDd7w9n8b1kr/e5t6lKS0yU4O+BUkBSWrejKTmQ2kwdCgAkbIyipcsoWDGzFjnZ+H8+ZTm5pL3wQfkffBB7Nrk9u2jg452dH6mdO9OODl+J5vXT67PgJYDGNByABDd8r5m25oKXZ+z188mvySfz1Z/xmerP4td2yy9WSz07N20NzlNcqiXFL9nn0qSJEnS7jLoVJXKTEviqJ4tOLxbEwYkLKXnAUP4ZMlGPlqQyyeL1rMhv5hXp6/k1ekrAejavD4Hd2nK4K5N6d++MWnJbsmU6qpQOExKp06kdOoEJw4HoKyoiKK5c3dMeY92fpYsW07x0qUUL13Kllf/F704KYnU7t2/HnTUqzfJ7bMJheNzsnkoFKJFvRa0qNeCI7KPAKC0rJRFmxdFz/tcFz3vc8GmBazdtpZ3l7/Lu8vfBSAcCtMxs2N0u3vT6Jb3zg07kxj2jwCSJElSPBoyZAh9+/blnnvuCbqUGse/5fyAJUuWcN5557FmzRoSEhKYOHEi9eJ4KnCQQiHIbpJO5xaZnDmwPSWlZXy+fBPjFqzjowW5zPhqE1+syeOLNXk88vESkhPD9G/fmIO7Rrs9u7doELfn8kmqHOGUFNL69iWtb9/YY9s3bqRw1iwKZsygcMZMCmbMoHTjRgpnzqRw5kw2Pr3j2owM0nr2jHZ+9o5Oe0/MygrmRipBQjiBro260rVRV07qchIA20q2MWf9nArDjlblr2LhpoUs3LSQlxa+BEBqQio5TXKik96bRre9t6zX0t9jJUmSFJc++ugj7rjjDqZMmcKqVat46aWXGD58OAAlJSX84Q9/4I033mDx4sVkZmYydOhQbrvtNlq1ahV7jy+++IKrrrqK8ePHU1xcTO/evbn11ls59NBDv/ezI5EId911Fw899BDLli0jKyuLUaNGcd1111XlLes7GHT+gHPOOYc//vGPDB48mA0bNpCSkhJ0SbVGUkKY/h0a079DY35zZDc25hczYdF6PvpiHeMWrGPl5kI+XpjLxwtzgXk0bZDC4M5ZDO6axUGdm9K0gf8tJEFio0bUHzyY+oMHA9E/aJSsWEFhedfnjBkUzp5N2ZYt5E+YQP6EryebJ7ZqSUqPnjRKSqKgWTMSevcmHMf/mJWelM5PWvyEn7T4Seyx3ILc2DmfM3NnMit3FnkleUxdO5Wpa6fGXtcktUms67NnVk96ZvUkIzkjiNuQJEmS9kh+fj59+vThvPPO46STTqrw3LZt25g6dSrXX389ffr0YePGjfz617/mhBNOYPLkybHXHXfccXTp0oX333+ftLQ07rnnHo477jgWLVpEixYtvvOzf/3rXzNmzBjuvPNOevXqxYYNG9iwYUOV3au+n0Hn95g9ezZJSUkM3vGX58aNGwdcUe3WqF4yx/ZuybG9WxKJRFi0Lp9xC9Yxbsc293Vbi3jx8xW8+PkKAHJaZjC4axYHd2nK/tmNSE1ym7uk6Dbv5DZtSG7ThoxjjgEgUlJC0cKFOya8Rzs/ixYuZPvKVWxfuYqmwIo33oBwmJTOnUnr05vUXtHOz5TOnQklxu//XWalZXFou0M5tF30X6LLImUs3bK0Qvj5xYYvWF+4nrFfjWXsV2Nj17bPaE/vpr1jw466NupKUkJSQHciSZKk6hSJRCjYXlAtn1VWVkbB9gISSxIJh8OkJabt0W6jo48+mqOPPnqXz2VmZvLOO+9UeOy+++6jf//+LF++nHbt2pGbm8uCBQt49NFH6d27NwC33XYbo0ePZtasWd8ZdM6dO5cHHniAWbNm0a1bNwA6dOjwg/WOHTuW3/3ud7HcqUePHjzzzDNkZ2dzzjnnsGnTJl5++eXY6y+//HKmTZvG2LFjY49t376dSy+9lKeeeoqkpCQuvvhibrnlltiv2+jRo7n77rv58ssvyczMZPDgwbzwwgtAdOt7z549Ab7z+qeeeoq//e1vzJ8/n3r16nHYYYdxzz330KxZs1gNs2fP5uqrr+ajjz4iEonQt29fnnjiCTp16gTAI488wl133cWSJUto3749l112GaNGjfrBX5+9Eb9/c9sN39e6XO7+++/njjvuYPXq1fTp04d7772X/v2jU4AXLFhA/fr1Of7441mxYgUnn3wy1157bQB3UveEQiE6N6tP52b1OXdQB4q2lzJl2UbGLchl3IJ1zFqxhTmroj/+8eFiUpPCHNCxyY5p7ln/z959x0dVZg0c/90pmZn0MumF9EIqJQGlCGIBxNVdAXUt2Bs2WHt3RUUFxIK9NwR1d1VE14IQQHpLIZBCekJ678nM+8cNg6zltQCThPPdz/PZ5Xpv5jzZMSQn55yHSB9nacEUQtgoej3GuDiMcXF4XHA+AH2tbXRmZ9O2exdF33yLR00NvVVVdOXm0pWbCx+p3wQoJhPG+OHqQUf9be+6gIBB+zXm0MzOcLdwzok8B4Cuvi5y6nJsLe+ZNZmUtZZR1FxEUXMRnxV8BoCDxoFYr1i18rN/BbsED9rPhRBCCCGE+GUdvR2M+WCMXV57y9+34Kh3PGYfv6mpCUVRcHd3B8DLy4uYmBjeeecdRo4cicFg4OWXX8bHx4dRo0b94sf5/PPPCQ8PZ9WqVUydOhWr1cppp53Gk08++YvFcr29vZx77rlcffXVLF++nO7ubrZu3fq7v6d+++23ufLKK9m6dSvbt2/nmmuuISQkhKuvvprt27dz88038+6773LyySdTX1/P+vXrf/PzoLb8P/LII8TExFBdXc38+fO57LLLWL16NQDl5eVMnDiRSZMmsWbNGlxdXdm4cSO9vb0AvP/++zzwwAM8//zzjBgxgl27dnH11Vfj5OTEnDlzftdef48hnej8tdJlgBUrVjB//nxeeuklxowZw9KlSznzzDPZv38/Pj4+9Pb2sn79enbv3o2Pjw9Tp04lNTWV008/3Q67ObEZdFpOjjBzcoSZO6fGUtvaxcb8WtJz1cRndUsXa/fXsHZ/DQD+bkbbSe7jIs14Og3e05eFEMeG1tkJpzFpOIwcwSY/P0ZMnw71DXRmZfa3vO+hMzMLS2srHdt30LF9x+FnvbwwJSb2z/tMxpSYgNbNzY67+XMMWgMpPimk+KTYrjV0Ntha3Q/N+2zqaiKjJoOMmgzbfW4GN1vFZ6JZbXv3MHrYYRdCCCGEEEL8/zo7O7nzzju58MILcXVVRzUpisK3337Lueeei4uLCxqNBh8fH7766is8PH75e9sDBw5QXFzMRx99xDvvvENfXx/z5s1j5syZrFmz5mefaW5upqmpiRkzZtgqH+Pi4n73PoKDg3n66adRFIWYmBgyMzN5+umnufrqqykpKcHJyYkZM2bg4uLCsGHDGDFixG9+HuCKK66w3RseHs6zzz5Lamoqra2tODs7s2zZMtzc3Pjwww/R69Wur+joaNszDz74IIsXL7bl48LCwti7dy8vv/yyJDr/qF8rXQZYsmQJV199NZdffjkAL730El988QVvvPEGd911F4GBgYwePZrg4GAApk+fzu7du38x0dnV1UVXV5ftz83NzYCaBe/p6Tla2xqUDu3/aH0e3Awapsf7MD3eB6vVSl51K+vz69iQX8e2ogYqmzpZub2MldvLUBRICHBlfIQX46O8SAlyx0E3OE9eFqqj/X4SJ7Yfv5/0nh4YJ07EOHEiHoDVYqGnqJjOzAy6MrPozMqia/9++urqaF27ltYftY7ohw3DmJiIISEBY2IChthYFIfB+0sWZ60zJ/mexEm+JwFq61JpaynZddlk1WaRVZfF/ob9NHU1sbF8IxvLN9qeDXIOIsErgQSvBOK94on1jMWgPTHmKsvXJ3E0yftJHE3yfhJHk7yfhqaenh6sVisWiwWLxYJBY2DTBZuO2+u3tLTg4uICgEFjwGKx/OGPdWgP/6unp4dZs2ZhtVpZtmyZ7R6r1coNN9yAt7c369atw2Qy8frrr3P22WezZcsW/P39SUxMpLi4GIDx48ezevVq+vr66Orq4q233rIl+V599VVSU1PJycnBZDLZWsQB7r77bu6++27mzJnDmWeeyWmnncZpp53GrFmz8Pf3t8Vy6P+HQ6xWq21fh4wZM8Z276E/L168mJ6eHqZMmcKwYcMIDw/nzDPP5Mwzz+Svf/0rjo6Ov+l5rVbLjh07ePjhh8nIyKChocH22kVFRQwfPpxdu3Yxfvx4tFrtTz7XbW1tFBQUcOWVV9oSp6BWs7q5uf3s/zcWi0U9b6H/9f/3/7ffakgnOn9Nd3c3O3bs4O6777Zd02g0nHbaaWzapP6LnJqaSnV1NQ0NDbi5uZGens611177ix/z8ccf5+GHH/7J9a+//vqIN9OJ7H/nYhxN/sAsbzjHEw60KOxrVNjXpFDZrpBZ3kxmeTMvphdi0FiJcrMS42Yl1t2Kt1E9EV4MPsfy/SROPL/6ftLrYeQIGDkCpacHQ0UlxrJSjKWlGEtKcairo6e4mJ7iYlpWrQLAqtXS5e9PZ3AwHcFBdIaE0OPlBZrB/4uW+P7/9Lr0UtVXRWlfKWW9ZZT1lVFrqaWstYyy1jK+Kv4KAA0a/LR+BGuDCdQFEqwNxkvjhUYZ/J+LXyJfn8TRJO8ncTTJ+0kcTfJ+Glp0Oh1+fn60trbS3d193F/fpDPR26G2PbfQ8qc+VkdHh6347JCenh4uv/xyioqK+OwzdTTToXvWrVvHF198QWFhoa3K8/HHH+frr7/mlVdeYd68eSxfvtzWlm00GmlubsbT09P2eTv0sQIDAwHYt28fEyZMID093RaDh4cHzc3NLF26lCuuuIJvv/2WDz74gPvvv59//etfpKam0tfXR09PzxHxt7W10dvba7vW29v7k3s6Ojpse9JqtaxZs4YNGzawZs0aHnjgAR566CHWrFmDm5vb//t8Z2cnU6dO5dRTT+Wll17CbDZTVlbGeeedR0NDA83Nzej1+p98jEOqq6sBWLp0KaNHjz7in2m12p99pru7m46ODtLT022f50Pa29t/cv8vOWETnbW1tfT19eHr63vEdV9fX/bt2weo/5I/9thjTJw4EavVyhlnnMGMGTN+8WPefffdzJ8/3/bn5uZmgoODOeOMM2z/opyoenp6+Oabbzj99NNtJc3HS1VzJz8U1LMhv44NBbXUt/WQ1aCQ1aD+8yB3I+MizYyP9OKkcE/cTHLQxkBnz/eTGHqOxvupr7GRzuxsujIy6czKojMzE0tDA8ayMoxlZbj3/yJc4+KCIT4eY2KiWvWZmIjObD6Ku7G/lu4WteqzLsu26jvrqeiroKKvAvq/Z3bWOxPvFU+8VzyJXonEe8VjNg3+z4V8fRJHk7yfxNEk7ydxNMn7aWjq7OyktLQUZ2dnjEbjcX1tq9Vqq+g8GvPfTSbTEXmYnp4eLrvsMoqKivjuu+/w9vb+2efc3d1xdna2/Vmn0+Hg4ICrq+sRlZmHTJ48mSeffJKamhpbG3phYSGgtqN7enr+4qzO8ePHM378eB566CHGjRvHZ599xpQpUwgICCA3N/eI+HNyctDr9bZrOp2OXbt2HXFPRkYGUVFRR7Ta/+Uvf+Evf/kLjz76KJ6enmzbto2//e1v/+/zO3bsoL6+nkWLFtm6nA/lypycnHB1dWXkyJG88847mEymn3wdcHV1JSAggIMHD5KSkvKz+/9fnZ2dmEwmJk6c+JP3388lRn/JCZvo/K3+v/b3HzMYDBgMP23N0+v18sW/nz0+F0FeemZ7uTA7bRgWi5W9lc22Q422FzVQ1tjJiu1lrNhehkaBlGB39VCjaDPJQe7otEO34miwk3+3xNH0Z95Pem9vjJMmwaRJgPqNWk95OZ0ZGeq8z8xMOrOzsbS00LF5Mx2bN9ue1QX42w46MiYmYoqPR+PkdBR2ZB+eek8mOE1gQsgEQP1cVLZVqnM+a7LIrM1kb91eWnta2XJwC1sObrE96+/kf3jep3cicZ5xx3QI/bEkX5/E0STvJ3E0yftJHE3yfhpa+vr6UBQFjUaD5jh3IR1qZT70+r9Xa2sr+fn5tj8XFxeTkZGBp6cn/v7+zJ49m507d7Jq1SqsVqut4tDT0xMHBwfGjRuHh4cHl19+OQ888AAmk4lXX32VwsJCZsyY8YsxnXHGGYwcOZKrrrqKpUuXYrFYmDt3LqeffjqxsbE/+0xhYSGvvPIKf/nLXwgICGD//v3k5eVx6aWXotFomDJlCosWLeK9997jpJNO4r333iMrK4sRI0YcEUdJSQm33XYb1157LTt37uT5559n8eLFaDQaVq1axYEDB5g4cSIeHh6sXr0ai8VCXFyc7WP82vOhoaE4ODiwbNkyrrvuOrKysnj00UcBbO+Pm266ieeff56///3v3H333bi5ubF582bS0tKIiYnh4Ycf5uabb8bd3Z2pU6fS1dXF9u3baWhoOKJI8BCNRoOiKD/7deX3fJ05YROdZrMZrVZLVVXVEderqqrw8/OzU1TiWNNoFBIC3UgIdOP6SRG0d/ey5UA96Xk1rM+rJb+6lZ0ljewsaeSZ7/JwMeo4OcKLidHeTIzyJthzcP7ALYQ4vhRFwSEoCIegIFynTwfA2tNDV37+4YOOMjLpys+nt6KSlopKWv77X/VhjQZDZGT/QUdJmJKSMERGougG51/ZiqIQ4BxAgHMAU0OnAtBr6SW/MZ/M2kwyazLJrM2koLGAyrZKKtsq+aZYbYPTKloi3SPV5Kd3EgnmBCLcItBqtL/2kkIIIYQQ4gSzfft2Jk+ebPvzoUTanDlzeOihh2yt6v9bXfj9998zadIkzGYzX331Fffeey+nnnoqPT09xMfH8+mnn5KcnPyLr6vRaPj888+56aabmDhxIk5OTkybNo3Fixf/4jOOjo7s27ePt99+m7q6Ovz9/Zk7d65tVOKZZ57J/fffzx133EFnZydXXHEFl156KZmZmUd8nEsvvZSOjg7S0tLQarXccsstXHPNNYBamfqvf/2Lhx56iM7OTqKioli+fDnx8fG/6Xlvb2/eeust7rnnHp599llGjhzJokWL+Mtf/mJ73svLizVr1nD77bdzyimnoNVqSUlJYdy4cQBcddVVODo68tRTT3H77bfj5OREYmIit9566y9+bo4GxXpo6ugQpygK//73vzn33HNt18aMGUNaWhrPPfccoP4GISQkhBtvvJG77rrrT79mc3Mzbm5uNDU1Set6Tw+rV69m+vTpA/o3fhWNHWzIq2VdXg0b82tpbD9y4G2olyMToryZEGXmpAgvXIwDdy9D2WB5P4nBwZ7vp77WNjr3Zh9R+dlbWfmT+xSjEWN8fH/iMxFjYhL6wICj0tYzULT1tJFdm60mP/tXdXv1T+4z6Uxqu7t3Iolmdfk5DZxfUMrXJ3E0yftJHE3yfhJHk7yfhqbOzk4KCwsJCws77q3rFouF5uZmXF1dj3s16Ylo0qRJpKSksHTpUnuHYvNr77/fk18bnOUhv9H/li4XFhaye/duPD09CQkJYf78+cyZM4fRo0eTlpbG0qVLaWtrs53CLk48Ae4mZqcGMzs1mD6LlazyJtbn1ZCeW8vOkgaK6topqivm3c3F6DQKI0M8mBBlZkK0N4mBbmg1QyfpIIQ49rTOTjilpeGUlma71lNdTWdm5uHKz8wsLK2tdOzYQceOHYef9fLClJjYX/mZjCkxAa2bmz22cVQ46Z1I808jzf/w56KqrYqs2iy17b02i6zaLNp729letZ3tVdtt9/mYfEgwJ9iSn/Fe8Tg7OP/cywghhBBCCCGGsCGd6Py10uW33nqL888/n5qaGh544AHbgNSvvvrqJwcUiROTVqOQHOxOcrA7N54aRUtnD5sP1LO+v829sLaNrUX1bC2qZ/E3ubg76hkXaWZilJkJUd4EuJvsvQUhxCCk9/FBP2UKLlOmAGC1WOguKqIjI8NW+dm5fz99dXW0rl1L69q1tmcdhg3D2N/ubkpKxBAXh8bBwU47+fN8nXzxdfJlyjD1c9Fn6aOwqfCIqs+8hjyqO6pZU7qGNaVrAFBQCHcLP6LqM9IjEr1GKk6EEEIIIYQYyoZ0onPSpEn8f535N954IzfeeONxikgMZi5GPacP9+X04WoivLS+XZ3tmVvLxgK1zf2LjEq+yFDbTiO8nWyzPceEe+LoMKT/dRNCHCOKRoMhPBxDeDj0j1+xdHXRlZNja3fvyNhDT3EJ3cXFdBcX0/z55+rDej3G2NgjKj8dQoehDNJ2IK1GS6RHJJEekfw16q8AdPR2kFOXczj5WZNJRVsFBU0FFDQV8J/8/wBg1BqJ84qzHXaUYE4g0DlwSLX/CyGEEEII8Vus/VGxxFAjmRch/qBgT0cuGjOMi8YMo7fPwp6yRtJz1dPcd5c2UlDTRkFNG29uLMJBq2HUMA8mRqvzPYf7u6KRNnchxB+kMRgwpaRg+tEw9b7GRjoys+jIzKBzTwYdmZn01dfTmZlJZ2YmfND/rIsLpsQEjIlJmJKTMCUmovP2ts9GjgKTzsRI35GM9B1pu1bbUUtWbZYt8ZlVm0VLTwu7qnexq3qX7T5Po6fa8t5f9ZlgTsDNMHjb/4UQQgghhDjRSaJTiKNAp9Uwapgno4Z5Mu/0aJo6ethUUMu63FrSc2sob+xg04E6Nh2o44mvwMvJgfH9Le4To8z4uB7fQc9CiKFH6+6O84TxOE8YD4DVaqWnvILOjD22ys/O7GwsLS20/bCJth822Z7V+fv/6KCjREzx8WicnOy1lT/NbDIzKXgSk4InAWCxWihuLlbnfdao8z73NeyjvrOe9LJ00svSbc8Ocx1mS3wmmhOJ8YzBQTt42/+FEEIIIYQ4kUiiU4hjwM2kZ2qCP1MT/LFarRTVtdsONdpUUEtdWzef7q7g090VAMT6uaiHGkV5kxbmiVGvtfMOhBCDnaIoOAQF4hAUiOv06QBYe3vpysvrT3xm0JmRSVd+Pr2VlbRUVtLy3/+qD2s0GCIj1Xb3xP55n1FRKLrB+W2DRtEQ5hZGmFsYZ0ecDUBXXxf76/eTWZtpS36WtJRQ3FxMcXMxqw6sAkCv0RPrGWur+EzyTiLEJURa3oUQQgghhBiABudPLEIMIoqiEGZ2IszsxKUnhdLda2FXSQPr89Q294zyJvYdbGHfwRZeXV+IQachLcyTiVHeTIg2E+PrIj9QCyGOCkWnwxgXhzEuDo/zZwPQ19pG595s9aT3/pb33spKunJz6crNpenjT9RnjUaM8fGYEhMxJSdhTExCHxgwaL8+GbQGkryTSPJO4qK4iwBo7Gwkqy6LzJrDhx01djXa/vchrg6uRyQ+E8wJeBo97bUVIYQQQgghRD9JdApxnDnoNIwJ92JMuBe3nRlDQ1s3G/Jrbae5VzZ19idBa2E1+LgYGB9l5pRob8ZFmjE7G+y9BSHEEKJ1dsIpLQ2ntDTbtZ7qajXxmZFJZ6Z60rultZWOHTvo2LHj8LNeXocPOkpMwpSYgNbd3Q67ODrcje6MDxzP+MDD7f9lrWVHJD5z6nJo7m5mY8VGNlZstD0b6BxIojmR4Z7Dae1tpbO3E71eTnkXQgghhBDieJJEpxB25uHkwNnJAZydHIDVaqWgppV1/YcabT5QR3VLF//aWc6/dpYDEB/gqs72jDYzapgHBp20uQshji69jw/6KVNwmTIFAKvFQndRER0Zart7R0YGnfv301dXR+vatbT+6NRGh2HDMCYl2So/DbGxaAyD8xc0iqIQ7BJMsEsw08PV9v+evh5yG3PJqskio1ZteT/QdIDy1nLKW8v5qugrAN746A2iPKLUWZ/e6rzPMLcwNMrgPPFeCCGEEEKIwUASnUIMIIqiEOnjQqSPC1eOD6Ort48dRQ2k56mHGu2tbCa7Ql0vrSvApNcyNtzTlviM8HYetG2kQoiBS9FoMISHYwgPh3PPBcDS1UXXvn22dvfOjAy6i4ttq/nzz9WH9XqMMTGYkpLUys+kJBxCQ1E0gzPhp9fqifeKJ94rnvM5H4CW7hay67LJrMlkT/UedlTsoNXaSk59Djn1OazMXQmAk96JBK8EEr37297NSXg7Dt4T74UQQgghxLGhKAr//ve/Obf/e2/x20miU4gBzKDTcnKkmZMjzdw1LZaali425teS3t/mXtPSxff7a/h+fw0AAW5GJvTP9hwXYcbDSU4KFkIcGxqDAVNyMqbkZNu1vsZGOjKzbAcddWRk0FdfT2dWFp1ZWfBB/7MuLpgSEzD2H3RkSkpC5z14E34uDi6M9R/LWP+x9PT08MUXXzBy0kj2Ne6ztbzvrdtLW08bWw5uYcvBLbZnfR19bXM+E82JxHvF46h3tONuhBBCCCEGn/T0dJ566il27NhBZWXlEUnCnp4e7rvvPlavXs2BAwdwc3PjtNNOY+HChQQEBNg+Rm5uLrfffjsbN26ku7ubpKQkHnnkESZPnvyrr221Wlm8eDGvvPIKxcXFmM1mbrjhBu69995jueXfbO3atUyePJmGhgbcB/GYqd9KEp1CDCLeLgbOHRHIuSMCsVqt7DvYYpvtuaWwnoqmTlZsL2XF9lIUBZKC3JnYf5r7iBB39NrBWUElhBgctO7uOE8Yj/OEwzMue8orbHM+OzIy6MzOxtLSQtsPm2j7YZPtWZ2/v9runpSotr7Hx6NxcrLXVv4URVHwd/InxD2EM0LPAKDX0ktBYwGZtZlk1apt7wWNBVS1V/FN8Td8U/wNoJ4QH+EeQZL5cPIzwj0CnUa+ZRNCCCGE+CVtbW0kJydzxRVX8Le//e2If9be3s7OnTu5//77SU5OpqGhgVtuuYW//OUvbN++3XbfjBkziIqKYs2aNZhMJpYuXcqMGTMoKCjAz8/vF1/7lltu4euvv2bRokUkJiZSX19PfX39MdurvVitVvr6+tDpBvb3pQM7OiHEL1IUhTh/V+L8XblmYgSdPX1sKaxnfa6a+Nxf1cKe0kb2lDby3Jp8nA06xoZ7cUq0mvgc5uUobe5CiGNKURQcggJxCArEddo0AKy9vXTl5/e3vKuVn135+fRWVtJSWUnL11+rD2s0GCIiMCYnqQcdJSViiIpCGeDfWP0SnUZHjGcMMZ4xzIyeCUB7T7va8n4o+VmTQVV7FXkNeeQ15PFJnnrivUlnYrjXcHXeZ//yc/KTr+FCCCGEOKasVivWjo7j8loWiwVLRwcWnQ40GhST6Xd9rzNt2jSm9X+/+b/c3Nz45ptvjrj2/PPPk5aWRklJCSEhIdTW1pKXl8frr79OUlISAAsXLuSFF14gKyvrFxOdOTk5vPjii2RlZRETEwNAWFjYb4r5jTfeYPHixeTn5+Pp6cl5553H888//5P7fq4ic/fu3YwYMYLCwkJCQ0MpLi7mxhtvZMOGDXR3dxMaGspTTz3F8OHDbRWpHh4eAMyZM4e33noLi8XCE088wSuvvMLBgweJjo7m/vvvZ+bMmUe87urVq7nvvvvIzMzk66+/ZtKkSb9pf/YyOH9aEEL8hFGv5ZRob06JVts/q5rV09vTc2vYkF9LfVs33+ZU8W1OFQDBniZ1tmeUmZMizLiZ5HRgIcSxp+h0GGNjMcbG4nH+bAAsbW10ZGfbTnrvyMigt7KSrrw8uvLyaPpYTfgpRiPG+PgfVX4mow8MGLQJP0e9I6l+qaT6pdquVbdX2xKfmTWZZNVl0dbTxo6qHeyoOnzivdlkts35TDAnkGBOwMXBxR7bEEIIIcQQZe3oYP/IUcf1Nav6/ztm5w4Ux2M3zqepqQlFUWyJQy8vL2JiYnjnnXcYOXIkBoOBl19+GR8fH0aN+uXPweeff054eDirVq1i6tSpWK1WTjvtNJ588kk8PT1/8bkXX3yR+fPns3DhQqZNm0ZTUxMbN278w/uZO3cu3d3dpKen4+TkxN69e3F2diY4OJhPPvmE8847j/379+Pq6orJZALg8ccf57333uOll14iKiqK9PR0Lr74Yry9vTnllFNsH/uuu+5i0aJFhIeH25KlA5kkOoUYonxdjcwcFcTMUUFYLFb2Vjarsz1za9leXE9pfQcfbCnhgy0laDUKKcHuTOhvc08OckMnbe5CiONE4+SEU1oaTmlptms91dV0ZmXRsSdDbX3PzMLS0kLHjh107Dic8NN6emJKTDxc+ZmYgHYQzx7ycfRhSsgUpoSoJ95brBYKmwrVWZ816rzPvIY8ajtqWVu6lrWla23PhrmFHa769E4k2j0avVZ+iSWEEEII8WOdnZ3ceeedXHjhhbi6ugJqJ9K3337Lueeei4uLCxqNBh8fH7766qtfTe4dOHCA4uJiPvroI9555x36+vqYN28eM2fOZM2aNb/43IIFC/jHP/7BLbfcYruWmpr6i/f/f0pKSjjvvPNITEwEIDw83PbPDiVcfXx8bIndrq4uHnvsMb799ltOOukk2zMbNmzg5ZdfPiLR+c9//pPTTz/9D8d2vEmi8xhYtmwZy5Yto6+vz96hCAGARqOQEOhGQqAbN0yKpK2rly2FdaTn1rI+r4aCmjZ2FDewo7iBpd/m4WrUMS5STXpOiDIT7CkHYwghji+9jw/6U0/F5dRTAbBaLHQXFalzPjMy1ZPe9+2jr76e1nXraF23zvasw7Bh6pzP/spPQ1wcGoPBXlv5Uw7N7Ixwj+DcyHMB6OztJKc+x5b4zKzNpLy1nMKmQgqbCvms4DMAHDQOxHnFHZH8DHIOGrQVsEIIIYQ4vhSTiZidO/7/G48Ci8VCc0sLrv1JRqW/6vBo6+npYfbs2VitVl588UXbdavVyty5c/Hx8WH9+vWYTCZee+01zj77bLZt24a/vz/x8fEUFxcDMGHCBL788kssFgtdXV288847REdHA/D6668zatQo9u/fj8lkYvjw4bbXueeee7jqqquoqKhgypQpR21fN998M9dffz1ff/01p512Guedd56tBf/n5Ofn097e/pMEZnd3NyNGjDji2ujRo49anMeDJDqPgblz5zJ37lyam5txc3OzdzhC/ISTQcepsb6cGusLQFlDOxvyalmfV8uG/FqaOnr4MusgX2YdBCDM7GQ71GhshBfOBvnSIYQ4vhSNBkN4OIbwcOg/QdPS1UXXvn2HDzrKyKC7uNi2mj//XH1Yr8cYE3P4oKOkJBxCQ1E0g7Ny3agzMsJnBCN8Dn8TWtdRR3ZdNhk1GWrbe20mzd3N7KnZw56aPbb7PAwetkOOEr0TSfBKwN3oboddCCGEEGKgUxTlmLaPH8FiQdPbi8bREc0x+h7tUJKzuLiYNWvW2Ko5AdasWcOqVatoaGiwXX/hhRf45ptvePvtt7nrrrtYvXo1PT09ALb2b39/f3Q6nS3JCRAXFweoVZaTJ09m9+7dtn/m6emJXv/7Om4OfT6sVusRe/mxq666ijPPPJMvvviCr7/+mscff5zFixdz0003/ezHbG1tBeCLL74gMDDwiH9m+J8CAadBdkCoZCuEEAR5OHJBWggXpIXQZ7GSWd5Eem4N6/Nq2FnSSGFtG4W1bby9qRidRmHkMA8mRpmZGO1NfIAbWo1UBwkhjj+NwYApORlTcrLtWl9jIx1Z2XRk7FErPzMy6KuvpzMri86sLPhgufqsszPGxARMSclqAjQxEb2Pj7228qd5mbyYGDSRiUETAfUb4ZKWkiMSn/vq99HQ1cD68vWsL19vezbEJUSd9+mdRKI5kRjPGAzawVkBK4QQQgjxcw4lOfPy8vj+++/x8vI64p+3t7cD/CTJqtFosFgsAAwbNuwnH3fcuHH09vZSUFBAREQEALm5ubb7dTodkZGRP3kuNDSU7777znZQ0K/x9lbP4aisrLS10f84eXpIcHAw1113Hddddx133303r776KjfddBMODg4AR3QdDx8+HIPBQElJyRFt6kOBJDqFEEc4NK8zJdidm6dE0dLZw6aCOvVgo7waiuva2VpYz9bCehZ9nYuHo55xkWYmRnkzIdqMv9uxaTEQQojfQuvujvP4cTiPHweoCb+e8gp1zuehys/sbCytrbRv2kz7ps22Z3X+/ocPOkpMwpQQj2aQ/Qb7EEVRGOY6jGGuwzg74mwAuvu62V+/39bunlWbRVFzESUtJZS0lLC6cDWgnhAf6xFrS34mmBMY5joMjTI4K2CFEEIIMfS1traSn59v+3NhYSG7d+/G09MTf39/Zs6cyc6dO1m1ahV9fX0cPKh2L3p6euLg4MBJJ52Eh4cHc+bM4YEHHsBkMvHqq69SWFjIWWed9Yuve9pppzFy5EiuuOIKli5disViYe7cuZx++ulHVHn+r4ceeojrrrsOHx8fpk2bRktLCxs3bvzZCszIyEiCg4N56KGHePTRR8nNzWXx4sVH3HPrrbcybdo0oqOjaWho4Pvvv7dVlg4bNgxFUVi1ahXTp0/HZDLh4uLCbbfdxrx587BYLIwfP952IJKrqytz5sz5XZ//gUQSnUKIX+Vi1HNGvB9nxPsBUFzXxvo8dbbnD/l1NLT3sCqjklUZlQBE+Tirsz2jzYwJ88TRQb7MCCHsR1EUHIICcQgKxHXaNACsvb105efTkZFhm/nZlZ9Pb2UlLZWVtHz9tfqwRoMhIgJjUqKt8tMQFYWiG5xf1xy0DiR6qy3rhzR1NdkqPg8deNTQ1UBWXRZZdVl8uP9DAFwcXEjwSlCf75/56WXy+qWXEkIIIYQ4rrZv335EdeT8+fMBmDNnDg899BCffabOME9JSTniue+//55JkyZhNpv56quvuPfeezn11FPp6ekhPj6eTz/9lOQfdQ/9L41Gw+eff85NN93ExIkTcXJyYtq0aT9JRP6vOXPm0NnZydNPP81tt92G2Wxm5syZP3uvXq9n+fLlXH/99SQlJZGamsqCBQuYNWuW7Z6+vj7mzp1LWVkZrq6uTJ06laeffhqAwMBAHn74Ye666y4uv/xyLr30Ut566y0eeeQRvL29efzxxzlw4ADu7u6MHDmSe+6551djH+gU64+b/MVRdWhGZ1NT0xGzH05EPT09rF69munTp//ueRRi4Orps7CntJH0/sTnntJGLD/6iuKg1ZAa5mE71CjOzxXNUWhzl/eTOJrk/SQALG1tdO7d25/8zKQjM4Peisqf3KcYjRiHD8eUlGSb+akPDLQd8DPY309Wq5Xy1nKyarPIqFXb3vfW7aWrr+sn9wY4BRyR+IzzisOkk6r+o2mwv5/EwCLvJ3E0yftpaOrs7KSwsJCwsDCMRuNxfW2LxUJzczOurq7HbEanGNh+7f33e/Jrg7MkQQgxIOi1GkaHejI61JP5p0fT1N7DxgI16ZmeW0t5Ywcb8+vYmF/Hwi/B7OzA+Eh1tuf4KDM+Lsf3L08hhPglGicnHFNTcUxNtV3rqa6mMyvLdtBRR2YWlpYWOnbupGPnTtt9Wk9PTImJGJMS0cfHo+mf8TQYKYpCkEsQQS5BTA2bCkCPpYf8hvwjqj4PNB2goq2CirYK/lv0XwC0ipYojyi15d2szvsMcwtDq9Hac0tCCCGEEOIEIolOIcRR4+aoZ3qiP9MT/bFarRTWqm3u6bk1bDpQR21rN//ZXcF/dlcAEOvnwsRobyZGeTM61AOjXn4YFkIMHHofH/SnnorLqacCYLVY6C4qVud97smgIzOTzn376Kuvp3XdOlrXrQMgEih+863+qk+18tMQF4fGMDgP+NFr9MR5xRHnFcfsmNkAtHa3kl2XbUt8ZtZmUtNRw776feyr38fHuR8D4KR3It4r3pb8TDAn4Ovka8/tCCGEEEKIIUwSnUKIY0JRFMK9nQn3dmbOyaF091rYWdLA+rwa1ufVklnexL6DLew72MIr6Qcw6DSMCfdiYpSZCVHeRPs621pBhRBiIFA0GgzhYRjCw3A75xwALN3ddOXk2NrdOzIy6CkqpqekhJ6SEppXrVIf1usxxsQcPugoKRGHsDCUQdqa5ezgzBj/MYzxHwOoLe9V7VVHVH1m12XT1tPG1oNb2Xpwq+1ZH0cfW7t7ojmReHM8TvrBeeiTEEIIIYQYWCTRKYQ4Lhx0GsaGezE23Ivbz4S61i42FtSxPreG9Lwaqpq7SM+tIT23BsjB19Vgm+05PtKMl/PgrIQSQgxtGgcHTMnJmPqH1Pf09PDVxx8zMSCAnr171crPjAz66uvpzMqiMysLWK4+6+yMMTEBU2ISpuQkjImJ6H187LibP05RFPyc/PBz8uP0YacD0Gfpo6CpQJ33WaPO+8xrzKO6vZrvSr7ju5Lv1GdRiHCPINGcaDvpPdI9Ep1Gvk0VQgghhBC/j3wHKYSwCy9nA39JDuAvyQFYrVbyqltJz1WrPbcU1lHV3MXHO8r4eEcZAAmBrkyM8mZClDdJAc52jl4IIX6ZxdERx5NPRn/KKYBa7dhbUXHEQUed2XuxtLbSvmkz7Zs2257V+fkdPugoMQljfDxa58FZ7ajVaIn2iCbaI5q/Rf0NgPaedvbW7T3isKPKtkryG/PJb8zn3/n/BsCoNTLca7ia/PRW2979nfyl0l8IIYQ4DuTMamEPR+t9J4lOIYTdKYpCtK8L0b4uXDUhnM6ePnYUN6gVnnm15FQ2k1WurhfWFuDooCXUUUONRzGT4/wINzvJD79CiAFLURT0gYHoAwNxnTYNAGtvL135+epBR5mZdOzJoCs/n96DB2k5eJCWr79WH9ZoMEREYExKtFV+GqKiUHSD81s4R70jo/1GM9pvtO1abUetbc5nZm0mWbVZtPa0srN6JzurDx/65Gn0tM35TPRWqz9dHX791E0hhBBC/HZarXpmQnd3NyaTyc7RiBNNd3c3cPh9+EcNzu+ShRBDmlGvZVykmXGRZu4Gqls62ZhfS3queqJ7bWs3e7s17F29nwWr9xPobmJC/2zPcZFeuDs62HsLQgjxqxSdDmNsLMbYWJitHvBjaWujc+/eIyo/eysq6crLoysvj6ZP/qU+azRiHD78cOVnUhL6wMBB+wsfs8nM5JDJTA6ZDIDFaqGoueiI5GdufS71nfWsLVvL2rK1tmdDXUPVWZ/e6rzPGI8Y9Fq9nXYihBBCDG46nQ5HR0dqamrQ6/VojuMscYvFQnd3N52dncf1dcXAYLFYqKmpwdHREd2f/IW+JDqFEAOej4uRv44I4q8jgrBYrGSVNfDaqg3U6r3ZXtxIeWMHH24r5cNtpWgUSApyVw81ivYmJdgdvVb+ohRCDHwaJyccU1NxTE21XeutqaEjM1Ot/MzIpCMzE0tLCx07d9Kx83C1o9bTE1Niolr5mZSEKTERrbu7HXbx52kUDeFu4YS7hXNOpHroU1dfFzl1ObaW98yaTMpayyhqLqKouYjPD3wOgIPGgViv2CMOOwp2CR60SWAhhBDieFIUBX9/fwoLCykuLj6ur221Wuno6MBkMsnf2ycojUZDSEjIn/7/XxKdQohBRaNRiPN3YUqglenTR9Nr1bClsI71eWq1Z25VK7tLG9ld2siza/JxMeg4KcKLCdHeTIwyM8xrcM66E0KcmHTe3riceioup54KgNViobuomM7M/qrPjAw69+2jr76e1nXraF23zvasfliI2u7en/w0xMWhMQzOg90MWgMpPimk+KTYrjV0Ntha3Q/N+2zqaiKjJoOMmgzbfW4GN/WQo0Nt7+ZEPIwedtiFEEIIMfA5ODgQFRVlayM+Xnp6ekhPT2fixIno9dKdcSJycHA4KtW8kugUQgxqJgctk2J8mBSjnlR8sKmT9Dz1UKMNeTU0tPfw9d4qvt5bBUCIpyMTo9U295MivHA1yl+iQojBQ9FoMISHYQgPw+0ctdrR0t1N17596gnvmWrlZ3dRET3FJfQUl9C8apX6sE6HMSam/4R3NQHqEBaGMkjbwzyMHkwMmsjEoImAWglS2lJqa3fPrM1kX90+mrqa2Fi+kY3lG23PBjkH2drdE82JxHrGYtQZ7bUVIYQQYkDRaDQYjcf370WtVktvby9Go1ESneJPkUSnEGJI8XMzMnt0MLNHB2OxWMmuaCY9r4b03Bp2FDdQUt/Oe5tLeG9zCVqNwohgdyZEeTMx2kxSkDtajbRJCCEGF42DQ/+8ziTbtb7GRjqyso+o/Oyrq6MzO5vO7GxgufqsszPGxARb5acxKQm9j4+ddvLnKIpCiGsIIa4hnBV+FgA9fT3kNuTaKj4zajIoai6irLWMstYyviz8EgCdoiPaM/qIlvdQt1A0yuBMAgshhBBCnKgk0SmEGLI0GoXEIDcSg9yYOzmS1q5ethxQ29zTc2s4UNvG9uIGthc38PS3ubgadYzvP9RoQpSZIA9He29BCCH+EK27O87jx+E8fhygVjv2VlSo8z4PVX5m78XS2kr7ps20b9pse1bn5/ejeZ/JGOPj0ToPzrEfeq2eeHM88eZ427Xm7mayarPIrDnc9l7fWc/eur3srdvLiv0rAHDWOxNvjifJnGQ78MhsMttrK0IIIYQQ4jeQRKcQ4oThbNAxJc6XKXG+AJTWt7MhX53tuSGvlubOXlZnHmR15kEAwr2dmNif9Bwb7oWTQb5kCiEGJ0VR0AcGog8MxHXqVACsvb105eercz4zM+nIyKQrL4/egwdpOXiQlm++OfQwhsgIjElJtspPQ1QUyiBtK3N1cOXkgJM5OeBkQE0CV7ZVqlWfNVlk1mayt24vrT2tbKncwpbKLbZn/Z38j5j3OdxrOI56+aWYEEIIIcRAIT+1CyFOWMGejlyYFsKFaSH0WazsKWtkfa6a+NxV2siBmjYO1LTx1g9F6LUKI0M8mBjtzcQob+IDXNFIm7sQYhBTdDqMsbEYY2Nh9mwALG1tdO7dq7a7Z2bSkbGH3opKuvLy6crLp+mTf6nPGo0Yhw8/XPmZnIw+MHBQnpKqKAoBzgEEOAcwNVRNAvdaeslvzFdnfdao8z4LGguobKuksq2Sb4rVJLBG0RDpHnm45d07kQi3CLQarT23JIQQQghxwpJEpxBCAFqNmsgcGeLBLadF0dzZww/5dazPqyE9r4bS+g62FNazpbCep/67H08nB8ZFmpnY3+ru5yaHWAghBj+NkxOOqak4pqbarvXW1NCRmUVHxh46+xOglpYWOnbupGPnTtt9Wg+P/nZ3dV6oMSEBncfgPN1cp9ER6xlLrGcss6JnAdDW00Z2bfYRhx1Vt1eT25BLbkMun+R9AoBJZyLeK/6Iw458HX0HZRJYCCGEEGKwkUSnEEL8DFejnqkJfkxN8AOguK6N9P7ZnpsK6qhv6+bzPRV8vqcCgGhfZ9tszzFhXpgcpJpHCDE06Ly9cTl1Mi6nTgbAarHQXVxMZ0aGrfKzKyeHvoYG2tal07Yu3fasPiSkP/GZiDExEePw4WgMBntt5U9x0juR5p9Gmn+a7VpVW5VtzmdWbRZZtVm097azvWo726u22+7zNnnbKj4TzYnEe8Xj7OBsj20IIYQQQgxpkug8BpYtW8ayZcvo6+uzdyhCiKNkmJcTl3g5ccnYYfT0Wdhd2sj63BrS82rZU9ZIblUruVWtvL6hEAedhrRQTyb0V3vG+btIJY8QYshQNBoMYWEYwsJwO+ccACzd3XTt29d/wrta+dldVERPSQk9JSU0r1qlPqzTYYyJsR10ZEpKxCEsDEUzOE8393XyxdfJlynDpgDQZ+mjsKnwiKrPvIY8ajpqWFO6hjWlawBQUAh3C1fnfXqr8z6jPKLQawbn3FMhhBBCiIFCEp3HwNy5c5k7dy7Nzc24ubnZOxwhxFGm12pIDfUkNdST+WfE0NjezcZDbe65NVQ0dbIhv5YN+bU8/uU+zM4GtcU92sz4SG+8XQZnNZMQQvwSjYODrWUdLgKgr6mJjqysw5WfGRn01dXRmZ1NZ3Y2jcs/VJ91dsaYkPCjys8k9L4+dtzNH6fVaIn0iCTSI5K/Rv0VgI7eDnLqcg4nP2syqWiroKCpgIKmAj4t+BQAg9ZAnGfcES3vPobB+XkQQgghhLAXSXQKIcSf5O7owFlJ/pyV5I/VauVAbRvpuTWsz6tlU0Edta1d/GtXOf/aVQ5AnL8rE6PNTIzyZtQwD4x6aXMXQgw9Wjc3nMeNw3ncOEA93by3oqL/kKP+ys/svVhaW2nfvJn2zZttz+r8/A4fdJSUjDE+Hq2zk7228qeYdCZG+o5kpO9I27XajlqyarNsic+s2ixaelrYXbOb3TW7bfd5GDzw6fOhPLOcZN9kEswJuBnkl+hCCCGEEL9EEp1CCHEUKYpChLczEd7OXD4ujK7ePnYWN5KeV8P6vBqyypvJqVTXy+sOYNRrGBPmxYQoM6dEexPp4yxt7kKIIUlRFPSBgegDA3Gdqp5ubu3tpauggI6MDFvlZ1deHr0HD9Jy8CAt33xz6GEMkREYE5NslZ+GqCgU/eBs9TabzEwKnsSk4EkAWKwWipuL1XmfNeq8z30N+2joaqCBBvZn7odM9dlhrsNINCeqbe/mJGI8Y3DQOthvM0IIIYQQA4gkOoUQ4hgy6LScFOHFSRFe3Dk1lrrWLjbk17I+r5b1eTVUNXexLreGdbk1LPgiBz9XozrbM9qb8ZFmPJ3kh1chxNClHJrZGRMDs9TTzS3t7XRmZ9sOOurMyKCnooKuvHy68vJp+te/1GeNRozDh/+o8jMJfVDQoPxlkUbREOYWRphbGGdHnA1AV18X2dXZrFy/Eouvhey6bEpaSihuLqa4uZhVB9S5p3qNnljPWBLMCbaW92Guwwbl50EIIYQQ4s+SRKcQQhxHXs4GzkkJ5JyUQKxWK7lVrepsz7xathyo42BzJx/tKOOjHWUoCiQGutkONRoZ4oGDbnAe2CGEEL+VxtERx9RUHFNTbdd6a2royMyiIzODzj0ZdGRmYmlpoWPnTjp27rTdp/XwUJOeiUmYkpMwJiSg8/Cwxzb+NIPWQKI5kVJDKdNPno5er6exs5Gsuiwyaw4fdtTY1Wj738tZDoCrg+sRic9E70Q8jZ523pEQQgghxLEniU4hhLATRVGI8XMhxs+FqyaE09nTx7aietbn1ZKeW8O+gy1klDWRUdbEsu8LcHJQq0MnRHkzIcpMmNlJKnaEECcEnbc3LqdOxuXUyQBYLRa6i4sPH3SUmUlXTg59DQ20rUunbV267VmX00/D/7HH0Lq42Cv8o8bd6M74wPGMDxwPqHNPy1rLjkh85tTl0NzdzA8VP/BDxQ+2ZwOdA49IfMZ5xmHUGe21FSGEEEKIY0ISnUIIMUAY9dr+JKY390yPo7q509biviG/ltrWbr7NqebbnGoAAt1NtkONTo4w4+Y4OGfVCSHE76VoNBjCwjCEheF2zjkAWLq76dq3j46MTDozM+jYk0F3UREt33xLV14+QS8swxAebufIjy5FUQh2CSbYJZjp4dMB6OnrIbcxl6yaLDJq1XmfB5oOUN5aTnlrOV8VfQWAVtES7RF9eN6ndxJhbmFoFOkcEEIIIcTgJYlOIYQYoHxcjZw3KojzRgVhsVjJOdhsq/bcXtRAeWMHy7eWsnxrKRoFkoPdmRDlzcQoMynB7ui08sOqEOLEoXFw6D+oKAm4CICOrGzKbryR7qIiimafT8BTT+IyebJ9Az3G9Fo98V7xxHvFcz7nA9DS3UJ2XTaZNZlk1GaQWZNJXWcdOfU55NTnsDJ3JQBOeicSvBLUtnfvRMb6j8VJPzhPuxdCCCHEiUkSnUIIMQhoNArxAW7EB7hx3SkRtHf3sqWwnvW5asVnXnUru0oa2VXSyLPf5eFi0HFypFd/4tObEC9He29BCCGOO1NCPGEff0TZLbfSsWMHZTfMxfuWW/C69poTavSHi4MLY/3HMtZ/LKC2vB9sO2hrd8+szWRv3V7aetrYcnALWw5uAcDX0ZfFkxaT7J1sz/CFEEIIIX4zSXQKIcQg5OigY3KMD5NjfACoaOxgQ14t6f1t7o3tPfw3u4r/ZlcBEOrlaJvteVKEFy5GaXMXQpwYdGYzw958g4OPP07j8g+pWbqUzn37CHjsUTSOJ+YvgRRFwd/ZH39nf84IPQOAXksvBY0FZNZmklWbxcaKjRxsO8hlX13GHal3cEHMBSdUclgIIYQQg5MkOoUQYggIcDcxOzWY2anB9FmsZJU32U5z31ncQFFdO0V1xby7uRitRmFkiDsTo7yZEO1NYqAbWo388CqEGLoUBwf8H3wQY2wcBxcsoOWrrygqLCRo2fM4BAXZO7wBQafREeMZQ4xnDDOjZ9LW08b9G+/nm+JveGzLY+yu3s2DJz2Io/7ETA4LIYQQYnCQRKcQQgwxWo1CcrA7ycHu3HhqFK1dvWwuqCM9r4b1ebUU1raxraiBbUUNLP4mFzeTnvGRZiZGm5kQ5U2Au8neWxBCiGPC4/zZGCIjKLvlVrr276do5iwClz6N09ix9g5twHHSO7H4lMW8u/ddluxYwurC1eQ25LJk0hLC3MLsHZ4QQgghxM+SRKcQQgxxzgYdpw335bThvgCU1rcfcZp7U0cPX2RW8kVmJQAR3k7qbM9oM2PDvXB0kL8qhBBDh+OoUerczhtvojMri5Irr8L3zjvxuORiac3+H4qicGn8pcSb47l93e3kN+Zz4RcX8si4Rzh92On2Dk8IIYQQ4ifkp1chhDjBBHs68vcxIfx9TAi9fRb2lKlt7uvzatlV0kBBTRsFNW289UMReq3C6GGeTIg2MzHKm+H+rmikzV0IMcjp/fwY9t67HHzwQZo+/Yyqxx6jMycHv4ceRGMw2Du8AWeU7yhWnr2S29fdzvaq7cxfO585w+dwy6hb0Gtk5rMQQgghBg5JdAohxAlMp9UwapgHo4Z5cOtp0TR19LCpoJb0vFrSc2soa+hg04E6Nh2o48mv9uPl5MD4KLPtYCNfV6O9tyCEEH+IxmjEf+FCDHFxVD/5FE3//jddBQUEPfccel8fe4c34JhNZl4941We3fUsb2a9ydt73yazNpNFpyzC29Hb3uEJIYQQQgCS6BRCCPEjbiY9UxP8mZrgj9VqpbiunfS8GtJza9lUUEtdWzef7q7g090VAMT4ujAhyszEaG/Swjwx6rV23oEQQvx2iqLgddllGKKiKJ//DzozMiiceR5Bzz6L44gR9g5vwNFpdMwfNZ9kczL3bryXndU7mfX5LBadsojRfqPtHZ4QQgghhCQ6hRBC/DxFUQg1OxFqduLSk0Lp6bOwq6SR9Nwa1ufVkFHexP6qFvZXtfDahkIcdBrGhHnaEp8xvi4y704IMSg4jxtH2EcrKZt7I115eZRcOge/Bx/AfeZMe4c2IE0ZNoUI9wjmrZ1HfmM+V319FbeOvJU58XPk674QQggh7EoSnUIIIX4TvVZDWpgnaWGe3HZmDA1t3WwsqGV9bi3peTVUNnX2H3JUy2Or9+HtYlCTnlHejI8yY3aWuXdCiIHLISSE0A+XU3HX3bR88w2V991PZ84+fO+6E0Uvcyj/V6hbKO9Pf59HNj/CqgOrWLxjMXtq9vDPcf/ExcHF3uEJIYQQ4gQliU4hhBB/iIeTAzOSApiRFIDVaqWgppX0XPU0980H6qlp6eJfO8v5185yAOIDXNXT3KPMjAr1wKCTNnchxMCicXIi8Jml1L70ErXPPkfD++/TlZdH4NKn0Xl62ju8AcdR78hj4x8jxTuFhdsW8m3Jt+Q15vH0pKeJ8oiyd3hCCCGEOAFJolMIIcSfpigKkT4uRPq4cMX4MLp6+9hR1EB6npr4zK5otq2X1hVg0msZG+6pJj6jzUR4O0u7oxBiQFA0GrxvuAFjTAwVt99B+9atFM2cRdCy5zHGxdk7vAFHURTOjz2f4V7Dmb9uPsXNxVy0+iIeOOkBZoTPsHd4QgghhDjBSKJTCCHEUWfQaTk50szJkWbumhZLbWsXG/NrWZdbw/q8Wmpauvh+fw3f768BwN/NaJvtOS7CjIeTg513IIQ40blMmULoyhWUzp1LT3EJRRf+nYDHHsV1+nR7hzYgJXonsnLGSu5afxc/VPzA3evvZnf1bu5IvQMHrXxNF0IIIcTxIYlOIYQQx5zZ2cA5KYGckxKI1Wplf1WLbbbnlsJ6Kps6Wbm9jJXby1AUSAp0Y0KUNxOizIwc5oFeq7H3FoQQJyBDZCRhK1dS/o/baNuwQT2ZPScH71tvRdHK+I3/5WH04IUpL/BSxku8tOclVuxfwd66vSw+ZTH+zv72Dk8IIYQQJwBJdAohhDiuFEUh1s+VWD9Xrp4YTmdPH1sL61mfp1Z77jvYwp6yJvaUNfH89/k4OWg5KcLMxGgzE6K8CfVylDZ3IcRxo3VzI/jll6h5+mnqXnuduldfo3P/fgIXLULr6mrv8AYcrUbL3JS5JJoTuXv93WTWZjJ71WyemPAEJweebO/whBBCCDHESaJTCCGEXRn1WiZGezMx2huAquZDp7fXsCGvlrq2br7NqeLbnCoAgjxM6v1RZk6KMONmktOQ7aZkM+R/B0GpEDQaHOWwFjE0KVotPrfdhiE2jsp776UtfT1Fs2YT9MIyDBER9g5vQJoYNJGVZ69k/tr57K3by3XfXsfclLlcnXQ1GkWq9IUQQghxbEii8xhYtmwZy5Yto6+vz96hCCHEoOPramTmqCBmjgrCYrGyt7KZ9Lwa1ufWsr24nrKGDj7YUsIHW0rQKJAS7M7EaG8mRHmTHOSGTtrcj5/9q2HjM4f/7BV5OOkZlAY+w0Er32qIocNtxlk4hIVSduNNdBcXUzT7fAKeegqXUyfbO7QBKdA5kHemvcPCrQv5OPdjnt/9PHtq9vD4hMdxM7jZOzwhhBBCDEHy08cxMHfuXObOnUtzczNubvJNnBBC/FEajUJCoBsJgW7cMCmStq5ethbW9x9qVENBTRs7SxrZWdLI0m/zcDHqGBdhZkK0mYlR3gR7Otp7C0Nb8FhIroGyrVCXf3jtWa7+c70jBIxUE5/BaRA4Glx87RuzEH+SKT6esI8/ovyWW2nfvp2yuXPxvvkmvK67TsZq/AyD1sCDJz1IsncyCzYvYH35es5fdT6LJy0m3ive3uEJIYQQYoiRRKcQQohBw8mgY3KsD5NjfQAob+xgQ14N6Xm1bMirpamjh6+yD/JV9kEAwsxOTIhSZ3ueFOGFs0H+2juqYqerC6C9Hsp3QNk2KN2q/u+uZijeoK5D3EP6qz7T1P/2SwSdnMgsBhedlxchb75B1eOP0/DBcmqeeZbOnH0EPP4YGicne4c3IJ0beS6xnrHM+34eZa1lXLr6Uu4Zcw/nRZ9n79CEEEIIMYTIT3xCCCEGrUB3E+enhnB+agh9FiuZ5U2sz1UPNdpZ0kBhbRuFtW28s6kYnUZh5DAPJvYnPhMC3dBqpPrqqHH0hKjT1QVgsUBtrpr4LNsKZduhOgcaS9SV9Yl6n9YA/slq0jM4Vf1v10CQyjgxwCl6PX4PPIAhNpaDjyyg5euvKSoqImjZ8zgEB9s7vAEp1jOWFWev4N7197K2bC0PbXqI3TW7uXfMvRh1RnuHJ4QQQoghQBKdQgghhgStRiEl2J2UYHdumhJFS2cPmwrqbAcbFdW1s7Wwnq2F9Sz6OhcPRz3jIs2cHO5Jd5e9ox+CNBrwiVXXyEvUa53NULETSrf1J0C3QUd9fyJ0K2zuf9bF//Ccz6BUCEgBvcleOxHiV3nMno0hMpKym2+hKzeXopmzCFz6NE4nnWTv0AYkVwdXnjn1Gd7IeoPndj3Hf/L/w776fSw5ZQnBrpIgFkIIIcSfI4lOIYQQQ5KLUc8Z8X6cEe8HQEldu3qoUV4NP+TX0dDew6qMSlZlVAI63i3daDv9fUyYJ44O8lfkUWd0hfBJ6gKwWqH+gFrtWbZVTXwezIKWSsj5XF0AGh34JqhzPg8dduQRJlWfYsBwHDmSsI8/ouymm+nMzKTkqqvxveN2PC69VOZ2/gyNouGqxKtIMCdwZ/qd7Kvfx/mrzuexCY8xKXiSvcMTQgghxCAmP8UJIYQ4IYR4OXKx1zAuHjuM3j4Le8oaSc+tZV1uNXtKGymoaaOgpo03NxbhoNUwOtSDCVHeTIgyM9zfFY20uR99igJeEepKPl+91t0OlbvVOZ+Hqj5bq9Rrlbth6yvqfY5e/UnP/hU4EgwudtqIEKD382PYe+9y8IEHafr0U6oeX0hnzj78Hn4IjcFg7/AGpLH+Y1kxYwW3rbuNPTV7uGnNTVydeDVzU+ai1WjtHZ4QQgghBiFJdAohhDjh6LQaRg3zZNQwT26cFMbHn63GOWIUPxxoID23hvLGDn4oqOOHgjqe+ArMzg6MjzTbEp8+rjJL7phxcIRhJ6sL1KrPprLDcz7LtkHlHmivg9yv1AWgaMA77vCcz6BU8IpSW+iFOE40BgP+Cx/HODyOqiefouk//6GroICg559D7+tr7/AGJD8nP948800W71jM+znv82rmq2TUZvDEhCfwMnnZOzwhhBBCDDKS6BRCCHHCc9TB1Hhfzk4Jwmq1UljbZpvt+UNBHbWt3fxndwX/2V0BQKyfCxOj1aRnaqgnRr1UHh0zigLuwepK6D+dubcLDmb+qOpzOzSVQHW2una8pd5ndIPA0T+q/BwFJg+7bUWcGBRFwXPOHAzR0ZTfOo/OzEwKZ84k6JlncRw5wt7hDUh6rZ670u4i2TuZB394kC2VW5i9ajaLT1lMik+KvcMTQgghxCAiiU4hhBDiRxRFIdzbmXBvZ+acHEp3r4WdJQ2sz1NPc88sb2LfwRb2HWzhlfQDGHQaxoR72U5zj/Z1lpl8x5rO0H9Y0ejD11oOHm51L9sO5TuhswkKvlPXIV5R/bM++xOg3nGglW+HxNHndNJJhH78EWU3zKUrL4/iOXPwe+B+PGbNsndoA9a0sGlEe0Qzb+08CpsKufyry7kt9Tb+Hvt3+boqhBBCiN9EvrMXQgghfoWDTsPYcC/Ghntx+5lQ39bNxvxa0nPVxOfB5k7Sc2tIz60BcvBxMTAhypuJ0WbGR5rxcpbZfMeFix/Ena0ugL4eqMo+nPgs2wb1BVCXp67d76v36Z3U+Z4/nvfp7G2/fYghxSE4mNAPl1Nx9z20fP01B+9/gK6cHHzvvhtFr7d3eANShHsEy89azgMbH+Dr4q9ZuHUhe2r28NBJD+God7R3eEIIIYQY4CTRKYQQQvwOnk4OnJ0cwNnJAVitVvKrW0nvb3PffKCO6pYuPtlZxic7ywBICHS1zfYcPcwTB53MjDwutHoISFFX2tXqtbY6KN/+o8rPHdDdAkXr1XWIR+iPEp+jwTcRdA522IQYCjROTgQ+s5S6l16i5plnafhgOV25eQQ+sxSdl8yg/DlOeicWnbKI93LeY8n2JXxZ+CW59bksmbyEcLdwe4cnhBBCiAFMEp1CCCHEH6QoClG+LkT5unDl+DA6e/rYUdxAel4N63Nr2VvZTFa5ul5cW4Cjg5ax4V5M6G9zj/B2knbM48nJC6LPVBeApQ9q9h/Z8l6zDxqK1JX5kXqfzgj+KYfb3YPTwDXATpsQg5GiKJivvx5DTCwVt99O+/btFM6aRdBzz2GKj7d3eAOSoihcMvwS4r3iuW3dbRQ0FXDhqgt5ZNwjnBF6hr3DE0IIIcQAJYlOIYQQ4igx6rWMizQzLtLM3dOguqWTjfm1rM+tJT2vltrWLtbsq2bNvmoAAt1NtqTnuEgv3B2lavC40mjBd7i6Rs1Rr3U2QfmOw+3uZdugowFKN6vrENfAw4nPoFQ1Eao32mUbYvBwOXUyoStXUHbDXLqLiym+6GL8FyzAbcZZ9g5twBrpO5KVZ6/kjvQ72HZwG/9Y9w8urbmUW0fdil4j7f9CCCGEOJIkOoUQQohjxMfFyF9HBPHXEepp7vsOtthme24tqqe8sYMPt5Xy4bZSFAWSgtw5JcrMhGhvUoLd0Wulzf24M7pBxKnqArBaoa7gR1Wf29TZn83lsLcc9n6q3qfRg18imoBRBNZroTEezBHqqfFC/IghIoLQj1ZS/o/baFu/norbbqNrXw7e8+ahaLX2Dm9AMpvMvHL6Kzy36zneyHqDd/a+Q1ZtFk+d8hQ+jj72Dk8IIYQQA4gkOoUQQojjQFEU4vxdifN35dpTIujo7mNrUT3rc2tIz6sht6qVPaWN7Clt5Nk1+TgbdJwUoZ7mPjHam2FeTvbewolJUcAcqa6UC9Vr3W1Qsetwu3vpVmirhoqdaCt2Mhpg2Uvg5H14zmdQGgSMAIOzPXcjBgitqyvBL71IzdKl1L36GnWvvU7n/lwCFz2F1s3N3uENSDqNjnmj5pHkncR9G+5jZ/VOZn8+m6dOeYpUv1R7hyeEEEKIAUISnUIIIYQdmBy0nBLtzSnR6gnfB5s6WZ+nVntuyK+lvq2bb/ZW8c3eKgBCPB1tbe4nR3rhapSWTbtxcILQ8eoCteqzsQTKttFXspWm7G/x6CxBaauB/avVBaBowCf+yFmfnhGgkcrdE5Gi1eLzj39giI2l8t77aFu/nsLZswletgxDZKS9wxuwpoRMIXJGJPPWziOvIY+rv76aW0bewmXxl8nMYyGEEEJIolMIIYQYCPzcjMwaHcys0cFYLFayK5rVQ43yathR3EBJfTvvbynh/S0laDUKI4Ld1dPco80kBbqhkzZ3+1EU8BgGHsOwxJ7D+t5xTD/jVPS1OYfb3Uu3QXMZVGWqa8eb6rNG9yNnfQaOApO7PXcjjjO3s87CEBZG6Y030lNcQtH5FxDw1JO4nHqqvUMbsIa5DuP96e/zyKZH+PzA5yzZsYQ9NXt4ZNwjuDi42Ds8IYQQQtiRJDqFEEKIAUajUUgMciMxyI25kyNp6+pl84E61ufVkp5Xw4GaNrYXN7C9uIGnv83F1ahjXKTa4j4hykyQh6O9tyB0RrViMzjt8LXmiiMPOarYBZ2NkP+tug4xx/RXfPYnP71j1YOTxJBlHD6csI8/pvyWW2nfto2yG+Zivvkm3K680t6hDVgmnYlHxz9Kik8KC7cu5LuS78hryGPJpCXEeMbYOzwhhBBC2IkkOoUQQogBzsmgY0qcL1PifAEoa2hnQ3/Sc0NeLc2dvXyZdZAvsw4CEG52YkL/bM+x4V44GeSv+wHBNQCG/0VdAH09UJV1eM5n2TZoKITa/era/Z56n4MzBI5U53wemvnpZLbfPsQxofP0JOSN16la+AQN779P7bPP0bF3L8rEifYObcBSFIXZMbMZ7jWc+WvnU9JSwsWrL+aBkx7g7Iiz7R2eEEIIIexAfvIRQgghBpkgD0cuSAvhgrQQ+ixWMsoa1WrP3Bp2lTZyoLaNA7VtvL2pGL1WYWSIh63aMyHADY1G5tgNCFq9ekBRwAhIu1q91lbbX/XZn/gs3wndrVCYrq5DPMIOz/kMGg2+CerHE4Oaotfjd/99GONiqXz4n7R9+x0hWVn0jE5FHx5m7/AGrARzAitmrOCu9XfxQ8UP3LPhHnZX7+bOtDtx0DrYOzwhhBBCHEeS6BRCCCEGMa1GYUSIByNCPLh5ShTNnT1sKqhjfV4N6bm1lNS3s6Wwni2F9Tz13/14OOoZH+Xdf7CRGX83k723IH7MyQwxU9UFYOmDmn2H53yWbVOrPRsK1ZW5Ur1PZ1QTpodmfQalgqu//fYh/hT3mTNxiIig7KabMRysovSCCwha+jROJ59s79AGLA+jBy9MeYGXM17mpT0vsTJ3JXvr9rJk0hL8neXfBSGEEOJEIYlOIYQQYghxNeo5M96PM+P9ACiuayM9r5b1uTX8UFBHQ3sPn++p4PM9FQBE+zozIUo9/X18pFmqPQcajRZ849U16jL1WkcjlO84POuzbBt0NkHJJnUd4hp0eM5nUCr4JYHeaI9diD/AccQIgj9cTs7lV2AqLaXkqqvxueN2POfMkdPFf4FWo+WGlBtINCdy94a7yarLYvaq2SycsJBxgePsHZ4QQgghjgNJdAohhBBD2DAvJy7xcuKSscPo6bOwp7SR9Nwa0vNqyShrJLeqldyqVl7fUMhZif48c0GKnOA+0JncIXKKugAsFqgvODzns2w7VGerp7xnl0H2v9X7NHrwT+qf9dl/0rt7iHpqvBiQdL6+lF17DSO3bqPls8+oXvgEXTk5+D38MBqjJK1/yYSgCayYsYL5a+ezt24v1397PdenXM+1SdeiUeTrmxBCCDGUSaJTCCGEOEHotRpGh3oyOtST+WfE0NjezQ8FdaTn1vCvneV8kVmJRqPw9OxkSXYOJhoNmKPUNeIi9VpXq3qqe9nWw4cdtdeqlaDlO2BL/7POvocPOApKVdvfHZzsthXxU1a9Hp8Fj+CYEE/VE0/S9OlndBUcIOj559D7+dk7vAEr0DmQd6a9w8KtC/k492Ne2P0CGTUZLJywEDeDm73DE0IIIcQxIolOIYQQ4gTl7ujA9ER/pif6c1qcL9e/v4PP91SgVWDx7BS00sY+eBmcIWyCugCsVmgsPjzns2wbHMyA1irYt0pdAEp/q/yPZ316RUjVp50pioLnpZdiiI6m/JZb6czKonDmLIKefQbHkSPtHd6AZdAaePCkB0n2TmbB5gVsKN/A7M9ns2TyEuK94u0dnhBCCCGOAUl0CiGEEILThvvy3IUjufGDnfxndwVajYanZibJzM6hQlHAI1RdSbPUaz0dULnnR7M+t0NzuZoAPZgB219X7zN5/CjxORoCR4FRKuLswWnsWEI/+ZiyuTfStX8/xXMuw+/++/CYPdveoQ1o50aeS5xnHPPWzqO0pZRLVl/CPWPu4byo82TeqRBCCDHESKJTCCGEEABMTfDj2QtHcNPyXXyyswydRuHxvyVKsnOo0psgZKy6DmkqPzLxWbELOhog72t1AaCAd+zhdvegVPXPGhl3cDw4BAURuvwDKu65l5avvuLgAw/SmZOD3913ozg42Du8ASvGM4YPZ3zIvRvuZW3pWh7e9DC7q3dz39j7MOpk3qkQQggxVEiiUwghhBA20xP96bNYueXDXazYXopGo/DouQmS7DxRuAWqK/5c9c+93VCVqSY9DyVAG4qgJkddu95V7zO4QuDII1veHT3ttYshT+PoSODTS6iLjaXmmWdoXP4hXXl5BD3zDDovL3uHN2C5OrjyzORneDPrTZ7d9SyfFnzKvvp9PD3paYJdg+0dnhBCCCGOAkl0HgPLli1j2bJl9PX12TsUIYQQ4nc7OzkAi9XKvBW7Wb61BJ1G4Z/nxEuL54lI56C2qgeOgjHXqtdaq49MfJbvhK5mOLBWXYd4Rhxudw9OA5940Mq3nkeLoiiYr7sWQ0w0FbfdTsf2Herczueew5Qg8yd/iUbRcGXilSSaE7k9/Xb2N+zn/FXn8+j4R5kcMtne4QkhhBDiT5LvNo+BuXPnMnfuXJqbm3FzkxlWQgghBp9zUgLp7bNy28d7eHdzMVqNwoNnD5dkpwBnH4idri6Avl61uvNQu3vZNqjNhfoCdWV8qN6nd1RPdbe1vKeBi6/99jFEuEyeTOhHKym7YS7dRUUUX3QR/gsW4Hb2DHuHNqCl+aexcsZK/rHuH+yp2cPN39/MVYlXMTdlLjqN/IgkhBBCDFbyt7gQQgghftZ5o4Los1i545MM3vqhCK1G4b6z4iTZKY6k1YFforpGX6Fea69XKz1/PO+zqwmKN6rrELeQw4nP4DT1Y+gM9tnHIGYIDyd05QrKb7+dtnXpVNx+O505Ofj8Yz6KVmvv8AYsXydf3jzzTZbsWMJ7Oe/xWuZrZNZk8sTEJ/AyyQgAIYQQYjCSRKcQQgghftHs1GB6LVbu+Xcmr28oRKdRuGtarCQ7xa9z9ISo09QFYLFAXd6Ric/qvdBUoq7sf6n3aR3AP/nIWZ9uQeqp8eJXaV1dCX7hBWqeeZa6V16h/o036Nq/n8Ali9FKh9Ev0mv13Jl2J8neyTzwwwNsObiF2atms/iUxaT4pNg7PCGEEEL8TpLoFEIIIcSv+vuYEPqsVu7/TxYvpx9Ap1W47YwYSXaK306jAe8YdY24WL3W1fKjqs/tULYV2usOJ0MPcfY7surTPwUcHO2yjYFO0WrxmT8PY1wsFffcS9vGjRTOmk3wsucxREXZO7wBbWrYVKI8opi3dh6FTYVc/tXl3JZ6G3+P/bt8rRNCCCEGEUl0CiGEEOL/dcnYYfT1WXjo870s+74AnUbDvNOj7R2WGMwMLhB+iroArFZoKDw857N0K1RlQetB2LdKXQCKFvwSDs/5DBoNnuFS9fkjrtOm4RAaStncG+kpKaHo/AsIeOpJXKZMsXdoA1qEewTLz1rOgz88yH+L/svCrQvZU72Hh05+CEe9JNeFEEKIwUASnUIIIYT4TS4bF0avxcqCL3J45rs8tBqFm6dIlZg4ShRFTVh6hkPSbPVadztU7umv8twKpdvUxGflHnVte029z+TZX/HZ3+4eMBKMrvbbywBgjIsj9OOPKL91Hu1bt1I290bMN96I+YbrUTQae4c3YDnpnXhq4lOkeKewePtiviz6kv0N+3l68tOEu4XbOzwhhBBC/D8k0SmEEEKI3+yqCeFYrFYeW72PJd/kotUozJ0cae+wxFDl4AjDTlIXqFWfzeX9FZ/9Le6Vu6GjHvL+qy4AFPCJO3LWpzlabaE/geg8PQl5/TWqnniShvfeo/b55+ncl0PAwifQOjvZO7wBS1EULh5+MfHmeG5bexsHmg5w4aoLeXjcw0wNnWrv8IQQQgjxKyTRKYQQQojf5ZqJEfRarDz51X6e+u9+dBqFa0+JsHdY4kSgKOrhRG5BEP9X9VpvFxzMOlz1WbYNGkvUw46q98LOt9X7DG4QNOpw4jNwlHpo0hCn6PX43Xcvxrg4Dj70EK3ffkfxhRcQtGwZDiEh9g5vQBvhM4IVZ6/gjvQ72HZwG7evu5091XuYP3o+eo3e3uEJIYQQ4mdIolMIIYQQv9sNkyLp67Oy+JtcHv9yH1qNwlUTpK1T2IHO0J/AHAVcp15rqYLy7eqcz7LtULETupqgYI26DvGKPDznMygVfIaDdmh+e+x+3t8wRIRTdtPNdOXlUzhrNoFLFuM8bpy9QxvQzCYzr5z+Cs/teo43st7gvZz3yK7LZtEpi/Bx9LF3eEIIIYT4H0PzOzkhhBBCHHM3TYmix2Ll2e/yWPBFDjqNwmXjwuwdlhDg4guxZ6kLoK9Xre4s23r4sKO6/MNrzwfqfXonCBx5OPEZlArOQyeZZUpJIfTjjym7+SY692RQevU1+Nx2G56XXyYni/8KnUbHvFHzSPJO4r4N97GrehezPp/FolMWkeqXau/whBBCCPEjkugUQgghxB8277Qo+iwWln1fwEOf70Wr1XDJ2GH2DkuII2l14J+krtSr1Gvt9VC+o7/qc5v6v7uaoWi9ug5xD+mv+uxPfPolgs7BPvs4CvS+Pgx75x0OPvxPmv71L6qffJLOfTn4//OfaIxGe4c3oE0JmULUjCjmrZ1HbkMuV319FbeMvIXL4y+XRLEQQggxQEiiUwghhBB/mKIo3HZGDL0WKy+vO8D9/8lCp1G4ME1m/4kBztETok5XF4DFArW5h+d8lm2H6hx13mdjCWR9rN6nNUBASn/is7/y0y3Ibtv4IzQGA/6PLsAYF0fVwoU0f/Y53QUHCHr+OfT+/vYOb0ALcQ3hvenvsWDzAj4r+IyndzzNnuo9LBi/ABcHF3uHJ4QQQpzwJNEphBBCiD9FURTumhpLX5+V1zYUcve/MtEqCrNTg+0dmhC/nUYDPrHqGnmpeq2zWa30PNTuXrZNPeG9dIu6DnEJOLLdPSAF9Ca7bOO3UhQFz0suxhAVRfmtt9KZnU3hzFkEPfsMjqNG2Tu8Ac2kM7Fg3AJSfFJ4fMvjrCldwwWrLmDJpCXEeMbYOzwhhBDihCaJTiGEEEL8aYqicO9ZcfRarLz1QxF3/isDrUbhvFGDq9JNiCMYXSFisroArFaoP3A46Vm2TT3xvaUCcj5TF4BGp7a4H0p8BqWCR6h6avwA4zR2jDq3c+5cuvbvp/iyy/G79148Ljjf3qENaIqiMCt6FsM9hzN/7XxKWkq4ePXFPHDSA5wdcba9wxNCCCFOWJLoFEIIIcRRoSgKD549nD6LlXc3F3Pbx3vQahTOHRFo79CEODoUBbwi1JV8gXqtuw0qdh+Z/Gytgopd6tr6inqfo/nIdvfAkWAYGK3ODkGBhC7/gIp776Xly684+NBDdObk4HfvPSgOg3ce6fEQb45nxYwV3LX+LjZWbOSeDfewu3o3d6bdiYNWPndCCCHE8SaJTiGEEEIcNYqi8PBf4umzWvlgSwnzV+5Gq1E4OznA3qEJcWw4OEHoOHWBWvXZVHp4zmfZNqjcA+21kPulugAUDfgM/1HLexp4Raot9HagcXQkcMkS6mLjqFm6lMYVK+jKzyfomaXozGa7xDRYuBvdWTZlGa9kvMKLe15kZe5KsuuyWTJpCQHO8rVPCCGEOJ4k0SmEEEKIo0qjUVhwTgJ9fVZWbC/l1hVqsnN6ohxyIk4AiqKe1O4eAgnnqdd6u6Ay40dVn9uhqQSqstS14y31PqMbBPYnPoNTwSf5OIeuYL72Ggwx0VTcdjsdO3aoczufew5TYsJxjWWw0Wq0XJ9yPYneidy1/i6y67KZvWo2T0x4gnGB4+wdnhBCCHHCkESnEEIIIY46jUbh8b8l0mux8snOMm5evguNojA1wc/eoQlx/OkMauIyOPXwteZKKN9+OPFZvhM6m6DgO3UBeuBUgz/avi8hZIyaAPWJA432mIbrMmkSoStXUjZ3Lt2FhRRffDH+Cx7B7WyZPfn/GR84npUzVjJ/7Xyy67K5/tvruT75eq5NvhaNYp9qXSGEEOJEIolOIYQQQhwTGo3CkzOT6LNY+M/uCm5avpMXLxrFacN97R2aEPbn6g+uZ0Ncf/Kwrweqso9sea8vwKWrEjKWqwvAwRkCRvRXfaapFaDO3kc9PEN4GKErV1Bx2+20rltHxe130Lk3B59/zEfRyY8QvybAOYB3pr3Dwq0L+Sj3I17Y8wJ7avewcPxC3I3u9g5PCCGEGNLkuxQhhBBCHDNajcKiWcn0WeHzPRXc8P5OXr5kFJNjfewdmhADi1YPASnqSrsagJ6mg+z49GVS/RW0Ff1Vn90tULReXYd4hB6e8xk0GnwTQPfnD8LRurgQ9MIyap59jrqXX6b+zTfp2r+fwCWL0bq7/+mPP5Q5aB144KQHSPZO5pHNj7CxfCPnrzqfJZOWEG+Ot3d4QgghxJAliU4hhBBCHFM6rYanZydjsVj5IrOSa9/bwauXjuaU6KNfhSbEkOLoRZVbCpZJ09Hq9WDpg5r9R57wXrMPGorUlfmR+pzOCP4patIzOE1Ngrr+sUNxFK0Wn3m3YoyLpeLue2j74QcKZ59P0PPPYYyOPlo7HbLOiTyHWM9Y5q2dR2lLKZd8eQl3j7mbmVEzURTF3uEJIYQQQ44kOoUQQghxzOm0GpZekEKvxcJ/s6u45p3tvD4nlfFRcpqzEL+ZRgu+w9U1ao56raMRKnaq7e6lW9XkZ2cjlG5W16b+Z10D+0947098+ieD3vibX9p16lQcQkMpm3sjPSUlFF1wIQFPLMT19NOP9i6HnBjPGD6c8SH3bbiP70u/55+b/snu6t3cN/Y+TDqTvcMTQgghhhSZiC2EEEKI40Kv1fDchSM5Lc6Xrl4LV72zjR8Kau0dlhCDm8kdIk6FU+6Aiz+GO4vgxh1w7ksw+grwSwRFA83lsPdT+PpeeOMMeDwIXpkMX94JmR+rFaFW66++lDE2ltCPP8JxzBis7e2U33QzNc89j9ViOR47HdRcHVxZOnkpt468FY2i4bOCz7h49cWUNJfYOzQhhBBiSJFEpxBCCCGOGwedhmUXjeDUWB86eyxc+dZ2thyos3dYQgwdigLmSEi5EGY8DddtgLtK4bIv4LSHIOYscPIGS49aCbrlJfjkSngmGRZFw/K/w/olULgeulp/8uF1Hh6EvPYqHpdeAkDtsmWU3Xwzfa1tx3mjg49G0XBl4pW8evqreBo9yW3I5YJVF7CmZI29QxNCCCGGDEl0CiGEEOK4Mui0vHDRSCZGe9PR08flb21je1G9vcMSYugyOEPoeBg/Dy78AG7Lg1sy4LzXYcx1EDgKNHpoq4b9X8B3D8PbM2BhMLw0HlbNg90fQG0eWK0oej1+99yD/2OPoej1tH77HUUXnE93cbG9dzoopPmnsXLGSlK8U2jpaeGW729h6Y6l9Fp67R2aEEIIMehJolMIIYQQx51Rr+WVS0YxPtJMe3cfc97Yyo7iBnuHJcSJQVHAYxgkzoRpT8DVa+DuMrjyGzjjURh+LrgGgdUCBzNh+xvwn+vh+dHwRCi8NxPWPoF7kivDXn8RnY8P3fkFFM6aTev6Dfbe3aDg6+TLG1Pf4OK4iwF4Pet1rv3mWmo7ZJyHEEII8WdIolMIIYQQdmHUa3n10tGcFO5FW3cfl72xld2ljfYOS4gTk96ontB+8o0w+22Ynw3zc2D2u3DyTRByknqae2cj5H8Dax+D9/6GafVZhM5oxxTiiqW5mdJrr6Xutdew/j/zPgXoNXruTLuTp055CpPOxNaDWzn/8/PZXb3b3qEJIYQQg5YkOoUQQghhNyYHLa9fNpq0ME9aunq59PUtZJY12TssIQSAawAM/wucsQCu+Eqt+rxmLUx7ChJng0cYYEXfnkvImH24hbeBxUL1osVUnDcGy5cPwv6voE2qFH/N1NCpfHjWh4S7hVPdUc3lX13O+znvS7JYCCGE+AMk0SmEEEIIu3J00PHmZamMHuZBc2cvF7++hewKSXYKMeBo9RAwAsZcA+e9CrfshtsL4MIP0Uz6B/6zk/BN6wDFSvPeFor/+R49r/0dnoqAZ0fAv66Bra9CxS7o67H3bgaUcPdwlp+1nKmhU+m19rJw60LuSL+D9p52e4cmhBBCDCo6ewcghBBCCOFk0PHWFWlc+voWdpY0cvFrW/jg6rHE+bvaOzQhxK9xMkPMNIiZhgJ4XtqH4b8fU/7AE3Q2QOG3fgSdVIMjB6D+AGSsUJ/TmdSkadBoCEpV2+Zd/Oy6FXtz1Dvy5MQnSfFJYdG2RXxV9BW5Dbk8Pelpwt3D7R2eEEIIMShIRacQQgghBgTn/mRncrA7De09XPTaFvYfbLF3WEKI30OjxWna+YT953MMcXH0dVgpTvelwXMeTLobIk8Doxv0dkDJD/DDs7DyElgcA08nwEeXwaZlULoNervsvZvjTlEULoq7iDenvomPyYcDTQe44IsL+KroK3uHJoQQQgwKkugUQgghxIDhatTzzhVpJAa6Ud/WzUWvbSa/WpKdQgw2+sBAQj94H9fp06C3j4MvrKByTQfW2cvhjiKYuw3OeQFGXQ6+iaBooKkUsv8N/70HXj8NHg+CV6fAl3dB5sfQWAInyNzKFJ8UVp69kjS/NDp6O7h93e08sfUJeqTlXwghhPhV0rp+DCxbtoxly5bR19dn71CEEEKIQcfNpOfdK9P4+6tb2FvZzIWvbuHDa8YS4e1s79CEEL+DxmQiYPFiDHFx1Cx5msaVK+nKyyPo2WfQeUeDdzSMuEi9uatFnd1Ztg3KtkPpVmivhfLt6trS/0GdfdVW96DREJQGASng4GSvLR5TXiYvXj79ZZ7f9TyvZ73OeznvkVWbxaJTFuHr5Gvv8IQQQogBSSo6j4G5c+eyd+9etm3bZu9QhBBCiEHJ3dGB968aQ6yfCzUtXVz4ymYKa9vsHZYQ4ndSFAXz1VcT/NKLaFxc6Ni1i8KZs+jIzDryRoMLhE2ECf+AC5fD7flw827422uQdi0EjASNDlqrYN8q+PYheGs6PB4ML02AVfNhz4dQVzCkqj51Gh23jrqVZyY/g4vehd01u5m9ajZbK7faOzQhhBBiQJJEpxBCCCEGJA8nNdkZ7etMdUsXf391MyV1cgKxEIOR8ymnELpyBQ7h4fRWVVF80UU0ffrpLz+gKOAZBkmzYPqTcM33cHcZXPFfOGMBDD8HXALA2gcHM2D76/Dva+G5kfBkOLw/C9Y9BQXfQ2fT8dvoMXJqyKl8OONDoj2iqe+s5+pvrub1zNexDqGkrhBCCHE0SOu6EEIIIQYsL2cD7181lgtf3Ux+dSsXvrqZD68ZS7Cno71DE0L8ToawMEJXrqDi9jto/f57Ku68i869OfjcfhuK7jf8WKI3QchYdR3SVN7f7t6/KnZDRz3kfa0uABTwjlXb3YPT1NZ3cwxoBlfNR4hrCO9Nf48FmxfwWcFnLN25lD01e1gwfgGuDq72Dk8IIYQYECTRKYQQQogBzdvFwAdXj+GCVzZzoKaNC1/dzIprTyLQ3WTv0IQQv5PW2ZmgZc9T89xz1L34EvVvv01XXi6BS5agdXf//R/QLVBd8eeqf+7thqpMdc5n2TZ11mdjMdTkqGvXu+p9BlcIHKnO+Tw089PR82ht85gx6UwsGLeAFJ8UHt/yON+Xfs8Fqy7g6UlPE+MZY+/whBBCCLsbXL/GFEIIIcQJycfFyPKrxxJmdqKsoYMLX9lMZVOHvcMSQvwBikaDzy23ELh0KYqjI20/bKJw1mw69+f++Q+uc4DAUTDmWjjvNbg1A27LgwuWw/j5EDoB9E7Q1QwH1kL6k/DBLHgyDJ4dCf++Dra9BpV7oK/3z8dzDCiKwqzoWbw77V0CnAIobSnlotUX8Wn+r4wCEEIIIU4QUtEphBBCiEHB19XIB1eP4fyXN1NS386Fr6iVnb6uRnuHJoT4A1ynnolDWBhlc+fSU1pK0YUXErDwcVzPOOPovpCzD8ROVxeoCcyanP6Kz/6W97o8qC9Q157l6n16R/UQpKDR/VWfqeAycE47jzfHs2LGCu7acBcbyzdy38b72FOzhzvT7sSgNdg7PCGEEMIupKJTCCGEEIOGv5uJ5deMJcjDRFGdmuysbu60d1hCiD/IGBNN6EcrcRw7Fmt7O+U330LNs89itViO3YtqdeCXCKOvgL++CDdthzsK4aJP4JS7IOJUMLhBTzsUb4CNS2HFRbA4Gp5OhI+vgM0vqu3xvV3HLs7fwN3ozgtTXuCGlBtQUPgo9yPmfDmH8tZyu8YlhBBC2ItUdAohhBBiUAl0N7H86rHqzM7aNv7+2haWXz0WbxepYBJiMNJ5eBDy2qtUP/UU9W+/Q+0LL9K5bz8BTz6B1tn5+ATh6AlRp6kLwGJRqzwPzfks2w7Ve6GpRF1Zn6j3aQ3gn3x4zmdQKrgFqafGHycaRcP1ydeTZE7izvV3kl2XzfmrzmfhhIWMDxx/3OIQQgghBgKp6BRCCCHEoBPs6cgHV4/B381IfnUrF722mbpW+1ZWCSH+OEWnw/fuu/Ff+DiKgwOta9ZQdP4FdBcV2ScgjQa8Y2DExfCXZ+GGH+CuErj0Mzj1PoieCo5e0NcFZVth8zL4+HJYmgBL4mDFxbDxGSj+Abrbj0vI4wLHsXLGShK8EmjqauKGb2/gxd0vYrEew+pYIYQQYoCRik4hhBBCDErDvJz44OqxXPDKJnKrWrmov7LTw8nB3qEJIf4g93PPxRAeTtmNN9FdUEDh7PMJXLwI5wkT7B0aGF0h/BR1AVit0FCoVnuWblWrP6uyoKUScj5XF4BGB74Jh+d8Bo0Gz/BjUvUZ4BzA29Pe5sltT7Ji/wpe2PMCe2r3sHD8Qpy0Tkf99YQQQoiBRio6hRBCCDFohZnVZKe3i4F9B1u46LUtNLZ32zssIcSfYEpKIvTjjzClpGBpbqb02uuoe+01rFarvUM7kqKoCcuk2XDWIrh2HdxVCpd/Baf/E+LOBmc/sPRC5W7Y9ir8+xp4biQ8FQEfnA/pT6mnv3e1HLWwHLQO3Df2Ph4b/xhGrZGN5RuZvWo22XXZR+01hBBCiIFKKjqFEEIIMahFeDuz/OoxXPDKZvZWNnPJ61t576oxuJn09g5NCPEH6X18CHnnbaoeeYTGjz6metFiOnP24b/gETQmk73D+2UOjjDsJHWBWvXZXH54zmfZNjXp2V4HuV+pCwAFfIYfnvMZnAZeUWoL/R90dsTZRHtEM3/tfEpaSrjimyuYZpjGNOu0P71NIYQQYqCSRKcQQgghBr1IH5f+NvbNZJY3cekbW3n3yjRcjZLsFGKw0jg44PfPf2KIi6Pqscdp/uILugoPEPz88+gDAuwd3m+jKOrhRG5BkPA39VpvFxzMUmd7lm1TV2MJVGera+fb6n0GNwga1d/ungaBI9VDk36HGM8YPpzxIfdtuI81pWv4rOMzLJstPHDyA5h0AzhhLIQQQvxBkugUQgghxJAQ7evC+1eN4cJXN7OntJE5b2zlnSvScJFkpxCDlqIoeP797xgiIym/dR5de3MonDmLoGeW4piaau/w/hidoT+BOQq4Xr3WUnU46Vm2HSp2QlcTFKxR1yFeUYfnfAangXccaH/9RzoXBxeWTl7Kaxmv8dzu51hVuIrcxlyWTFrCMNdhx26fQgghhB3IjE4hhBBCDBlx/q68d6Xatr6rpJHL39xGW1evvcMSQvxJTmlphH38EYbhcfTV11N8+RXUf/DBwJvb+Ue5+ELcDDj9Ybj8C3XW57XpcNZiSL4QvCLV++ryYM8H8MV8eGk8LAyBt2bAtw/BvtXQWv2zH15RFC4bfhmXO12Op9GT3IZcLlh1Ad+VfHf89iiEEEIcB5LoFEIIIcSQkhDoxntXjsHFqGN7cQOXv7WN9m5Jdgox2OkDAgh9/31czzoLenup+ucjHHzgASzdQ/AAMq0O/JMh9Sr460tw0w64oxD+/hFMvAPCJ4PBFXraoGg9bHgaPrwQFkXB0iT4+ErY/BKU74Dew5+fcH04y6cuZ4TPCFp7Wrn1+1tZsmMJvRb5GimEEGJokNZ1IYQQQgw5iUFuvHvlGC55bQtbC+u58q3tvHFZKiYHrb1DE0L8CRqTiYBFT2EcHkf1osU0fvQxXfkFBD6zFL2Pj73DO7YcPSH6DHUBWCxQu//IlvfqHGgsVlfWx+p9WgMEpKAJGIl/gxbv3hReP/N1nt7xNO/ufZc3s94kqzaLJyc+idlktt/+hBBCiKNAKjqFEEIIMSSlBLvz9pVpOBt0bDpQx9XvbKezp8/eYQkh/iRFUfC68kqCX3kZjYsLHbt2UTRzFh0ZGfYO7fjSaMAnDkZeCn95Dm7YBHcVwyX/gcn3QdSZYPKEvi4o3YJ2y4ukFT2P/rkk9E8ncceBTBb5nYaj1sC2g9uY/flsdlXvsveuhBBCiD9FEp1CCCGEGLJGhnjw1uWpODpo2ZBfy7Xv7pBkpxBDhPOECYR9tBKHiAh6q6spvvgSGv/zH3uHZV9GN4iYDKfcDhethDsOwE074a8v0zfqChpNw7AqWmipgJzPOHPTGywvLiS8u4eajhqu+HIO7377D6x1B2CozD8VQghxQpFEpxBCCCGGtNGhnrx5WSomvZZ1uTXc8P5Ounol2SnEUOAQGkroig9xPvVUrN3dVN51N1WPP461V2ZOAqAo4BUByRdgmfok62Ifofe2A3DZajjtYYidQbjBi+UVB5nW2kYvVp4s/5rbPzyNtkWR8MEFsH4xFKZDV6u9dyOEEEL8v2RGpxBCCCGGvDHhXrxxWSqXv7WVNfuqmfv+Ll64aCQOOvmdrxCDndbZmaDnn6P2+WXUvvAC9W+/Q2duLoFLlqDz8LB3eAOPgxOEjlMXgNWKY1MpT5RuJTnvYxY1Z/NfZydyHXp4uvAbInK/VO9TNOAzHIJSDy+vSLWFXgghhBgg5G8lIYQQQpwQTorw4rVLUzHoNHybU8VNy3fS02exd1hCiKNA0WjwvvkmAp99BsXRkfZNmymaNZvO/bn2Dm3gUxRwD0FJnMlFf/uQN6e/i4/Jh0IHPRcGh/BlzCngFgxWC1RlwY434dMbYFkqPBkG750HaxdC/rfQ0WDv3QghhDjBSaJTCCGEECeM8VFmXrl0NA5aDf/NruKWD3fRK8lOIYYM1zPOIHT5cvTBwfSUlVF04YU0//dre4c1qKT4pLDy7JWM8RtDh7WXO7oLWTjuYnpuzYTz34Nxt0DIyaAzQWejmuBc+7ia8HwiFJ5Pg//Mhe1vwsEssMioECGEEMePtK4LIYQQ4oRySrQ3L18yimve3c7qzINoNXt4enYyOq38/leIocAYE03YRyspnz+fth82UX7LLXRefx3eN92EIm3Wv4mXyYuXT3+ZZbuX8Wrmq7yf8z7ZtdksOmURvnFnqzf19UBVNpRtO7zqD0DtfnXtfk+9z8EZAkce2fLuZLbf5oQQQgxpkugUQgghxAlncqwPL140iuvf38HneyrQKrB4dgpajWLv0IQQR4HW3Z3gV16hetFi6t96i7oXX6Jr334CnnoSrbOzvcMbFLQaLTePvJlEcyL3briX3TW7mb1qNk9OfJIx/mNAq4eAFHWlXa0+1FYH5duhdKua+CzfCd0t6mFGhemHP7hH2I8Sn6PBL1H9eEIIIcSfJIlOIYQQQpyQThvuy3MXjuTGD3byn90VaDUanpqZhEaSnUIMCYpOh+9dd2KMi6Xy/gdo/f57imafT9Cy5zGEhdk7vEFjcshkVsxYwby189jfsJ9rvrmGm0bcxBUJV6BR/qdC1skLos9UF6ht6zX7+ys+t0LZdqjZBw2F6spcqd6nM0LACDXpeSgB6hpwfDcqhBBiSJBEpxBCCCFOWFMT/Hj2whHctHwXn+wsQ6dRePxviZLsFGIIcTvnHBzCwym78Sa6DxygaPb5BC5ehPPEifYObdAIdg3mvenvsWDzAj4t+JRndj7Dnpo9PDr+UVwdXH/5QY0WfIera9Qc9VpHI1TshNIftbx3NkLJJnUd4hp0ZOLTPxn0xmO5TSGEEEOAJDqFEEIIcUKbnuhPn8XKLR/uYsX2UjQahUfPTZBkpxBDiCkxkbCPP6Lsllvp2LmT0muvw3vePLyuvgpFkX/Xfwujzsgj4x4hxSeFx7Y8xtrStVyw6gKWTFpCrGfsb/9AJneIOFVdAFYr1BX0V3z2Jz6rsqG5DPaWwd7/qPdp9OCfdOSsT/cQ9dR4IYQQop8kOoUQQghxwjs7OYA+i5V5K3ezfGsJOo3CP8+JlwSIEEOIztubYW+9ycEFj9K4ciU1S5bQtS8H/0cfRWMy2Tu8QUFRFGZGzyTOM475a+dT2lLKxasv5r6x93Fu5Ll/9IOCOVJdKX9Xr3W1QuXu/lmf29UkaFsNlO9Q15aX1PucfA7P+QxOU9vfHZyOxlaFEEIMUpLoFEIIIYQAzh0RSK/Fyu0f7+HdzcVoNQoPnj1ckp1CDCGKgwP+/3wY4/A4Di54lObVX9JVWETw88+hDwy0d3iDRrw5npVnr+Su9XexoXwD92+8n93Vu7l7zN0YtIY//wIGZwgdry5Qqz4bS4484b0yA9qqYf8X6gJQ+lvlg1IhKE39b68IqfoUQogTiCQ6hRBCCCH6zRwVhMVi5Y5PMnjrhyK0GoX7zoqTZKcQQ4zHBRdgiIig7JZb6crJoXDmLAKXLsVpTJq9Qxs03AxuLJuyjFcyXuGF3S/wSd4n5NTnsGTSEgKdj3LSWFHAY5i6Emeq13o6oXLPj5Kf29V294OZ6tr+hnqfyQMC+2d9BqdC4Cgwuh3d+IQQQgwYkugUQgghhPiR2anB9Fqs3PPvTF7fUIhOo3DXtFhJdgoxxDimpqpzO2+8ic69eym54gp8774bj4v+Lv++/0YaRcN1ydeRZE7izvV3srduL7M/n83CCQuZEDTh2L643gghY9R1SHPFkYnPil3Q0QD536gLAAW8Y/oPOuqv+vSOUQ9OEkIIMehJolMIIYQQ4n/8fUwIfVYr9/8ni5fTD6DTKtx2RowkP4QYYvQBAQx7/z0q73+A5lWrqFqwgM59Ofg98AAaBwd7hzdonBx4MitmrOAfa/9BVl0Wc7+by7XJ13Jd0nVoj2cC0TUAhp+jLoC+HrW6s2z74QRoQyHU7FPXrvfU+xxcIHCkOuczKFWtAHXyOn5xCyGEOGok0SmEEEII8TMuGTuMvj4LD32+l2XfF6DTaJh3erS9wxJCHGUak4mAp57EGBdH9eLFNH38Cd35BQQ++wx6Hx97hzdoBDgH8Pa0t3ly25Os2L+Cl/a8REZNBgsnLMTD6GGfoLR6NYEZOBLGXKNea62B8h8lPst3QncLFK5T1yGe4Uee8O4br348IYQQA5okOoUQQgghfsFl48LotVhZ8EUOz3yXh1ajcPOUKHuHJYQ4yhRFwevKKzBER1P+j3/QsXs3RTNnEfTcs5iSk+0d3qDhoHXgvrH3keydzD83/ZMfKn7g/FXns2TSEhLMCfYOT+XsDTHT1AVg6YPqnMPt7mVboTYX6g+oK2OFep/OpJ7qHvyj5KeLn/32IYQQ4mdJolMIIYQQ4ldcNSEci9XKY6v3seSbXLQahbmTI+0dlhDiGHCeMJ6wj1ZSOncu3fkFFF98CX4P/x979x0fdX34cfx1lx1Iwt4bke0AwYF7UVxF6164cAC16s/VqnVVa23dA/fede9FVRQQGSIIgspQZMqG7OTu98eRSAQV8JLvXfJ6/h55JDfzvvPT/MI7n3E1DY44POhoSeXQzofStVFXzv/gfL5f+z0nv3Uyl/a/lKO2PSrxtgAJp0CLXrGPnU6NXVe4EhZMqrrkvWg1fD829lEhr23VvT5bbgepcTh1XpK01Sw6JUmSfsOZe3amtDzKv9+Zxb/fmUVqOMRZe3UOOpakapDevj0dnnmWhZdewrr3R7Hob3+j6KuvaH7xRYTSXLq8ubZtuC3PHPIMV4y5glHfj+LaT69lytIpXLHrFWSlZgUd79dlNYRt9o99AEQisPzbDQ46mgBLZ8Dq+bGP6S/F7peSDi23Xz/jc/1J73ltY6fGS5JqhEWnJEnSZhi+zzaUR6Lc/N7X/POtmaSEQ5yxR6egY0mqBin169Hm9ttZdtfdLLvrLlY+/jjFX39N61tvIbVhQPtNJqGc9Bxu2fsWHp3+KLdOvpXX5rzGzJUzuWXvW2if2z7oeJsvHIam28Y+djwhdl3x2tip7j9MgPnry8+CZT8VoRXqt/ip9GzTD1rtAOn1AnkZklQXWHRKkiRtpnP360JZJMrto77hH298RWo4xCkDOgYdS1I1CIXDNP3zCDK6dWXRJZdSMH58bN/Ou+8is2vXoOMljVAoxCm9TqFnk55c9NFFfLPyG459/Vj+sfs/2K/dfkHH23oZOdBxz9gHQDQKK+dtsNz9s9iJ7+sWw8zXYx8AofVL5Tc86KhRJ2d9SlKcWHRKkiRtgfP370J5JMJdH8zmqtdmkBIOcdKuHYKOJama5B5wABkdOjB/+AhKv/+eecceR6t/Xk/uH/4QdLSk0q9FP5479Dku+ugiJi+dzHkfnMepvU7l3B3PJTVcC/5ZGgpBo46xj+2Oil1XWgiLvlg/6/Oz2Oe1i2LXLfoCJjwQu19Wow2Kz52gdV/IzA3utUhSEqsF/x9FkiSp5oRCIS48sCtlkSj3fjSHK16ZTko4zPE7tws6mqRqktGlCx2fe5YFF/wf+WPHsuC88yk6ayZN/3IuoXA46HhJo1l2Mx4Y+AC3TrqVx2Y8xsNfPsy0H6fx773+TZOsJkHHi7+0LGi3S+yjwuoFVff6XDgFClfAN+/EPgAIQbPuVZe8N+kaW0IvSfpVFp3V4K677uKuu+6ivLw86CiSJKkahEIhLv1DN8rLozzwyVz+9tI0UsMhDt+hRdDRJFWTlAYNaHvfvSy96WZWPPwwy++9l+JZs2j17xtJyckJOl7SSAuncVG/i9i+6fZcMeYKJi6ZyNGvHc1/9voPfZr3CTpe9ctrHfvoOTh2uawElkyLLXmvmPW56rvYYUdLZ8Dkx2L3y8iNzfRs0y9WnHbax+JTkjbBorMaDB8+nOHDh7NmzRry8vKCjiNJkqpBKBTisoO7UxaJ8sjYeVzy4lSIRsgIOpikahNKTaX5JReT2aM7iy6/gnUffsi8o4+hzV13kdHJ/Xq3xIEdDqRLwy6c/8H5zF49m9PeOY0L+l7AST1OIlSX9qtMTY8VmK37ws5nxa5bt3T9Xp+fxT4vmAzFa2DOB7EPgPa7w+EjoYGrCSRpQ/4JSJIkaSuFQiGuPLQHJ+3SnmgULnnpSyb+WIf+gS7VUXmHHkr7J58ktUULSubOZd7RR7Puo4+CjpV0OuZ15KmDn2JQx0GUR8v598R/838f/R/5pflBRwtW/WbQ7SDY/yo45XW49Hs4+xM45BbY/nhIqwfffQIjB8CUp2MHIUmSAItOSZKk3yUUCnH1YT05rn87olF44tswb0xbHHQsSdUsq1dPOj7/X7L69iWybh3zzz6HZffeR9TSaYtkp2Xzrz3+xV/7/5XUcCrvffcex75+LLNXzQ46WuJISYUWvWGn02KzOM/5BNruHJvl+fLZ8NzJkL886JSSlBAsOiVJkn6ncDjEdYN7cWSf1kQJ8X/PT+PNaYuCjiWpmqU2aUL7hx+iwbHHQDTKj7fcwoILLiBSUBB0tKQSCoU4vvvxPDzwYZplN2Pemnkc98ZxvDnnzaCjJaZGneDUt2C/v0M4Fb56FUbuCt+8F3QySQqcRackSVIchMMhrvtjD/o3jVAeiXLu05/z9pfO7JRqu1B6Oi2vuooWV10Fqamsfett5h1/AiU/LAg6WtLZodkOPHfIc+zcYmcKywq55ONL+Of4f1JaXhp0tMQTToE9/g/OGBU7kX3dEnjySHj9Aiip40v/JdVpFp2SJElxEg6HOK5zhMO2a0lZJMqIpybz3owlQceSVAMaHnsM7R99hJTGjSmeOZN5Rx5J/vjPgo6VdBpnNebeA+5laO+hADw18ylOfedUFuf7h6NNarUDnPUR7HxO7PLEB+GePWKHGElSHWTRKUmSFEfhEPzriJ4cun0ryiJRhj05iQ9mLg06lqQakN23Lx2f/y+ZPXtSvmoV3592Gisef8J9O7dQSjiFc/ucyx373kFOWg5f/PgFx7x+DJ8u+jToaIkpLQsG3QAnvQy5rWHFbHjwQPjgenA2rKQ6xqJTkiQpzlJTwtxy9PYc3LslpeVRznpiEh99/WPQsSTVgLSWLWn/5BPkHnYolJez5LrrWHTZ5URKSoKOlnT2brs3zx76LN0adWNF0QrOeu8sHpj2AJFoJOhoianzPnDOGOh9FETL4aN/xQrPZd8EnUySaoxFpyRJUjVITQlz67E7MLBnc0rKIpz52EQ++WZZ0LEk1YBwZiat/vUvml18MYTDrH7xRb476SRKlzi7e0u1zWnL44MeZ/A2g4lEI9w2+Tb+8r+/sLp4ddDRElNWQ/jTA/CnByEzDxZOji1l/+x+cGaxpDrAolOSJKmapKWEueO4PuzfvRnFZRHOeGwCY2dbdkp1QSgUovFpp9L2/vsI5+VR9MVU5h15JIVTpgQdLelkpmZy7YBruXq3q0kPp/PhDx9y7OvHMnPFzKCjJa7eR8I546DTPlBWCG9eCE/8CdYsCjqZJFUri05JkqRqlJ4a5q4T+rBP16YUlUY4/ZGJjJ+zPOhYkmpI/QED6Pjf58josg1lP/7IdyedzKoXXgw6VlI6ossRPHbQY7Su35of1v3AiW+eyMvfvhx0rMSV1xpOfBEG3QipmTB7FIzcFaa/FHQySao2Fp2SJEnVLCM1hZEn9mXPbZtSWFrOqY9MYOK8FUHHklRD0tu1o/3Tz5BzwP5ES0tZdNllLP7HdURLPShmS/Vs3JNnD3mWPVrvQXF5MVeMuYKrxl5FcXlx0NESUzgMO58FZ42GlttD4Ur47ynw4plQuCrodJIUdxadkiRJNSAzLYX7TurL7ts0oaCknCEPfcak71YGHUtSDUmpX4/Wt91Gkz+PAGDlE0/w/RlDKVvpz4EtlZeRx5373cmIHUYQIsQL37zASW+exA9rfwg6WuJq2hVOfx/2vAhCYZj6LIwcAHNHB51MkuLKolOSJKmGZKalcP/JO7Frp8bkl5RzykOfMWX+qqBjSaohoXCYpsOH0+bOOwhnZ1Mwfjzz/nQkRV99FXS0pBMOhTlr+7O454B7aJDRgK9WfMUxrx/D6B8s7n5Rajrsezmc9g407AhrfoBHD4V3LoPSoqDTSVJcWHRKkiTVoKz0FB48ZSf6d2zE2uIyTn5wPNN+8PRgqS7J2X9/Ojz7DGnt21G6cCHzjjueNW++GXSspLRbq9147pDn6N2kN2tK1jB81HDu/PxOyiPlQUdLXG37w9mfQN9TYpfH3Qn37Q2LpgaZSpLiwqJTkiSphmWnp/LwKf3YqX1D1hSVceKD45m+0LJTqksyunSh43PPUW/AAKJFRSy44P9YevMtRMst6LZUy/oteeQPj3BM12MAuHfqvZzz/jmsLHJbgF+UUR8OvQ2OexbqNYUfv4L794VPbgFLYklJzKJTkiQpAPUyUnnktP7s2K4BqwtLOfGB8Xy1aE3QsSTVoJS8PNredy+NTj8NgOX33cf8YcMoX7s24GTJJz0lnct3uZzrd7+ezJRMxi0ax9GvH820H6cFHS2xdf0DDPsUuh0CkVJ4/yp45GBYOS/oZJK0VSw6JUmSAlI/I5VHT+vP9m3yWFlQygkPjGfWYgsOqS4JpaTQ/KKLaPXvfxPKyCD/o9HMO+poiufMCTpaUjq086E8efCTtM9tz+L8xZz89sk8O/NZotFo0NESV70mcMwT8Me7IT0Hvh8XO6jo8yfA901SkrHolCRJClBuZhqPnb4zvVvnsSK/hBMe+JRvl1p2SnVN3qGH0P7JJ0lt2ZKSefOYd/QxrP3gg6BjJaVtG27L0wc/zX7t9qMsUsY/xv+Dv33yNwpKC4KOlrhCIdjxBDjnE2i3K5Ssg1eGw7MnQv6yoNNJ0maz6JQkSQpYXlYaj5/enx4tc1m2roTj7h/P7B/XBR1LUg3L6tWTjs//l6yd+hJZt44fhg1n2T33OhtxK+Sk53DL3rfwf33/j5RQCq/PeZ0T3jyBeavnBR0tsTXsAKe8AftfDeE0mPk63L0LzHo76GSStFksOiVJkhJAg+x0njxjZ7q1yOHHtcUcd9+nzF2WH3QsSTUstXFj2j/0EA2OOxaiUX689VYWnHc+kXx/HmypUCjEKb1O4f4D76dxZmO+XfUtx75xLO9/937Q0RJbOAV2Pw/O/ACa9YD8H+HpY+DVc6HYP8JJSmwWnZIkSQmiYb1Y2blt8/osXVvM8fd/yvfLXWop1TWh9HRaXnklLa65GtLSWPvOO8w77nhKlywNOlpS6teiH/899L/0adaH/NJ8zv/wfG6eeDNlkbKgoyW2Fr1h6Aew6wggBJMfhXt2h/mfBZ1Mkn6RRackSVICaVw/gyfP2IVtmtVn0eoijrv/U+avsOyU6qKGRx9N+0cfIaVJE4q//ppld98ddKSk1TS7KQ8MfIAhPYYA8PD0hznj3TNYVuj+k78qLRMGXgdDXoXcNrByLjw0kPCH/yQUtSiWlHgsOiVJkhJM05wMnhq6M52a1mPBqkKOu/9TFqwqDDqWpABk9+lDiyv/DkDh558HnCa5pYXTuLDfhdy0103US6vHpCWTOOq1o5i0ZFLQ0RJfxz3hnDGw3bEQjZAy5ib2nHUNLPs66GSSVIVFpyRJUgJqlpPJ00N3oUPjbH5YWchx933KotWWnVJdlLXd9gAUf/ute3XGwYEdDuTpg59mmwbbsKxwGcNHDWdNyZqgYyW+rAZwxL1w1CNEsxrSoHAeqQ/uC5/eA5FI0OkkCbDolCRJSljNczN5+sxdaNcom+9XFHDcfZ+yZE1R0LEk1bC05s1Ibd4cIhGKZswIOk6t0DGvI08e9CSt6rUivzSfaT9OCzpS8uh5OGVDR7MkpzehsiJ4+xJ44nBYvSDoZJKUHEXnd999x4wZM4j4VyJJklTHtMzL4ukzd6FNwyzmLY+VnUstO6U6J2u73gAUTrWQi5fstGx2aLYDANOW+b5ukZyWfNr5QsoH3gipWTDnQxi5K0x7Puhkkuq4hCo6H3roIW6++eYq15155pl06tSJ3r1706tXL+bPnx9QOkmSpGC0bpDF00N3oXWDLOYsy+f4B8bz49rioGNJqkGZvbcDoHCahVw89W4SK5CnL5secJIkFAoR2ek0OPtjaNUHilbDC6fD86dD4cqg00mqoxKq6Lzvvvto2LBh5eW3336bhx9+mMcee4wJEybQoEEDrr766gATSpIkBaNto2yeGrozLfMy+XbpOk544FOWr7PslOqKihmdRRadcdWrSS8gNqMzGo0GnCZJNekCp78Le10KoRT48nm4ezeY/UHQySTVQQlVdH7zzTfstNNOlZdfeeUV/vjHP3LCCSfQp08frr/+ekaNGhVgQkmSpOC0b1yPp4buQvPcDL5eso4THhjPyvySoGNJqgGZPXsCULpgAWXLlwecpvbo1qgbKaEUlhctZ3H+4qDjJK+UNNjnr7HCs1FnWLsQHh8Mb10KpR6kJ6nmJFTRWVhYSG5ubuXlsWPHsueee1Ze7tSpE4sX+/98JElS3dWxSazsbJqTwczFaznhgfGsKrDslGq7lJwc0jt1Aly+Hk+ZqZls23BbAL5c/mXAaWqBNjvFlrLvdHrs8viRcO9esHBKoLEk1R0JVXS2b9+eSZMmAbBs2TKmT5/OgAEDKm9fvHgxeXl5QcWTJElKCJ2b1ufpoTvTpH46Mxat4aQHP2N1YWnQsSRVs6ze65eveyBRXPVsEpst64FEcZJeDw65GU54Huo3h2Wz4IH9YPR/oLws6HSSarmEKjqHDBnC8OHDufbaaznqqKPo1q0bffv2rbx97Nix9OrVK8CEkiRJiWGbZjk8ecYuNKqXzrQFqzn5oc9YU2TZKdVmmRUnrzujM64qDiT6cpkzOuOqywFwzjjofhhEyuB/18IjB8GKOUEnk1SLJVTRefHFFzN06FBefPFFMjMz+e9//1vl9jFjxnDccccFlE6SJCmxdG2Rw5Nn7EyD7DS+mL+KIQ99xlrLTqnWytoudvJ60dSpHpwTRxUHEs1YPoPySHnAaWqZeo3h6Mfg8HshIxfmj4eRu8OkR8AxLKkaJFTRGQ6Hueaaa/j8889566236N69e5Xb//vf/3L66acHlE6SJCnxdG+ZyxOn70xeVhqff7+KUx+eQH6xSwOl2iija1dCaWmUr15N6fz5QcepNTrldSIrNYv80nzmrZkXdJzaJxSC7Y+Fc8ZA+92hNB9e+ws8fSysWxp0Okm1TEIVnQDPPvssJ5xwAkcddRT33HNP0HEkSZISXq/WeTxx+s7kZKYy8buVnPrIBApKLDul2iacnk7G+skghe7TGTep4VS6N4q9r+7TWY0atIMhr8GB/4CUdPj6bbh7V5j5RtDJJNUiCVV0jhw5kuOOO46JEyfyzTffMHz4cC666KKgY0mSJCW83m3yePz0ncnJSOWzuSs4/ZGJFJa4BFOqbSoPJJo2NeAktYv7dNaQcBh2+zOc+SE07wUFy+CZ4+GVEVC8Nuh0kmqBhCo677zzTq688kpmzZrFlClTePTRR7n77ruDjiVJkpQUdmjbgEdP70/9jFTGzVnO0McmUlRq2SnVJlmVBxJZyMVTxT6dFp01pHlPGPo/GPAXIASfPw4jB8B344JOJinJJVTROWfOHIYMGVJ5+fjjj6esrIxFixYFmEqSJCl59GnXkEdO7Ud2egqffLuMsx6fZNkp1SKZvdcfSDRjBtFSDx+Ll4qic9bKWZSUlwScpo5IzYADroFT3oC8drDqO3h4ELx/FZT530DS1kmoorO4uJh69epVXg6Hw6Snp1NYWBhgKkmSpOSyU4dGPHxKP7LSUvjo6x8Z9uRkisssO6XaIL1De8L16xMtKqL422+DjlNrtK7fmoYZDSmLlDFrxayg49QtHQbEDira4QQgCp/cAg/sC0u/CjqZpCSUGnSAn7viiivIzs6uvFxSUsJ1111HXl5e5XU333xzENEkSZKSxs6dGvPgKTtx2iMT+N/MpQx/8nPuPqEP6akJ9XduSVsoFA6T2bsXBeM+pXDqNDLXH06k3ycUCtGzSU8+WfAJ05ZNo3fT3kFHqlsyc2Hw3dB1UOxE9sXT4N69YP8rYedzYnt7StJmSKifFnvuuSezZs3i888/r/zYbbfdmDNnTuXlKVOmBB1TkiQpKezWuQkPnNyPjNQw73+1hD8/PZnS8kjQsST9Tlnrl68XeiBRXFUcSDR9+fSAk9Rh3Q+Fc8ZBl4FQXgzv/A0eOwxWzQ86maQkkVAzOj/88MMql5ctW0Z6ejq5ubnBBJIkSUpyu3dpwn0n78TQRyfyzvQl/OWZz7n92B1JTUmov3dL2gIVBxIVTZ0WcJLapWKfzmnLfF8DldMcjn8WJj0M71wG8z6OHVR08H+g91EQCgWdUFICS7jfcFetWsXw4cNp0qQJzZs3p2HDhrRo0YK//vWvFBQUBB1PkiQp6ey1bVPuPakvaSkh3py2mPOf+4IyZ3ZKSaviQKLib78lkp8fcJrao6LonLt6LmtL1gacpo4LhWCn0+DsT6D1TlC8Gl4cCs+fCgUrgk4nKYElVNG5YsUKdt55Zx599FH+9Kc/cdNNN3HTTTdx2GGHcccdd7DnnntSVFTEZ599xu233+lPqsgAAQAASURBVB50XEmSpKSxT7dmjDwhVna+9sVCLvzvF5RHokHHkrQV0po3I7V5c4hEKJoxI+g4tUajzEa0rt8agBnLfV8TQuPOcNo7sM9lEE6F6S/ByN3g2/eDTiYpQSVU0XnNNdeQnp7O7NmzuffeeznvvPM477zzuO+++/j2228pKSnhpJNO4oADDqhyOJEkSZJ+2/49mnPHcX1IDYd4ecpCLn5+KhHLTikpVSxfL3T5ely5fD0BpaTCXhfD6e9B4y6wdhE88Sd48yIocdWnpKoSquh8+eWX+c9//kPz5s03uq1FixbceOONvPDCC1xwwQUMGTIkgISSJEnJ7Q+9WnD7cTuSEg7xwuQf+OuL0yw7pSRUsXy98EsLuXjq1ThWdH657MuAk2gjrfvAWaOh/1mxy5/dB/fuCQsmB5tLUkJJqKJz0aJF9OzZ8xdv79WrF+FwmCuvvLIGU0mSJNUuB/VuyS3H7EA4BM9OnM9lL39p2SklGQ8kqh4VMzotOhNUejYcdCOc+CLktITl38CDB8BHN0J5WdDpJCWAhCo6mzRpwrx5837x9rlz59KsWbOaCyRJklRLHbZ9K24+egdCIXj6s++58tXpRKOWnVKyyFw/QaR0wQLKli8POE3t0aNxD8KhMEsKlrC0YGnQcfRLttkPzhkLPQ+HSBl8cB08NBCWzw46maSAJVTROXDgQC677DJKSko2uq24uJgrrriCP/zhDwEkkyRJqn0G79iafx+5PaEQPP7pd1z92gzLTilJpOTkkN6pEwCF05zVGS/Zadl0you9r87qTHDZjeDIh+GIByAjDxZMhHt2h4kPgf+/TKqzEqrovOaaa5g1axZdunThxhtv5NVXX+WVV17hhhtuoEuXLnz11VdcddVVQceUJEmqNY7s24Z/HRHb6++RsfP4xxtfWXZKSSKrt8vXq0PvJrH31aIzCYRCsN1RMGwsdNwTSgvg9fPhqaNh7ZKg00kKQEIVnW3atGHcuHH06NGDv/71rwwePJjDDz+cyy67jB49ejBmzBjatWsXdExJkqRa5eh+bbn+8Ng/7B/8ZC43vDXTslNKApkVJ687ozOu3KczCeW1gZNegYH/hJQM+OZduHsXmPFq0Mkk1bDUoAP8XMeOHXnrrbdYuXIl33zzDQDbbLMNjRo1CjiZJElS7XX8zu0oj0a54uUvuXf0HFJTQlx4YFdCoVDQ0ST9gqztYrOxi6ZOJRqN+r/XOKksOpd/6fuaTMJh2HUYdN4HXhwKi6fBcyfB9sfDoBsgMy/ohJJqQELN6NxQw4YN6d+/P/3790+6kvOuu+6iR48e9OvXL+gokiRJm+2kXdpz1aE9ALjrg9nc+v43ASeS9GsyunYllJZG+erVlM6fH3ScWqNLwy6kh9NZW7KW79d+H3Qcbalm3eGM/8HuF0AoDF88BSN3h3ljgk4mqQYkbNGZzIYPH86MGTOYMGFC0FEkSZK2yCkDOnL5wd0BuG3UN9w+yrJTSlTh9HQyusf+9+ry9fhJC6fRrXE3AKYt831NSqnpsP+VcOpb0LADrP4eHjkY3r0CyoqDTiepGll0SpIkqYoz9ujEXwfF/pF/83tfc9cH3wacSNIv8UCi6uGBRLVEu13g7E+gz8lAFMbeDvfvC4v97yrVVhadkiRJ2shZe3XmooFdAfj3O7O496PZASeStClZHkhULTyQqBbJyIHD7oBjn4bsJrDkS7h/HxhzO0TKg04nKc4sOiVJkrRJw/fZhgsO2BaAf741kwc+nhNwIkk/l1kxo3PGDKKlpQGnqT16NY4VnTNXzKQ04vtaK3Q7CIaNg20HQXkJvHcFPHoYrHIfVqk2seiUJEnSLzp3vy6cu18XAP7xxlc8MmZuwIkkbSi9QwfC9esTLSqi+Fu3mYiXdrntyEnPobi8mG9X+r7WGvWbwXFPw6G3Q1o9+O4TGDkApjwN0WjQ6STFgUWnJEmSftX5+3dh+D6dAbjqtRk8Pm5esIEkVQqFw2T2js0+LHSfzrgJh8KVszo9kKiWCYWg7xA45xNouzMUr4GXz4bnTob85UGnk/Q7WXRKkiTpV4VCIS48sCtn7dUJgCtemc5T413qJyWKrN7bAVA4bWrASWoX9+ms5Rp1ip3Kvt/fIZwKX70KI3eFb94LOpmk38GiU5IkSb8pFApx6R+6ccbuHQH420vTeG7C/IBTSYKfDiTy5PX4qiw6l1t01lrhFNjj/+CMUdC0G6xbAk8eCa9fACX5QaeTtBUsOiVJkrRZQqEQlx3cnVN26wDAJS9O5YVJPwQbShKZ62d0Fn/7LZGCgoDT1B69m8QK5NmrZlNQ6vtaq7XaAc78EHYZFrs88UG4Zw/4YWKQqSRtBYtOSZIkbbZQKMSVh/bgxF3aEY3Chc9/wcufLwg6llSnpTVvRmrz5hCJUDRjRtBxao2m2U1plt2MSDTCjOW+r7VeWhb84Z9w8iuQ2xpWzIYHD4QProfy0qDTSdpMFp2SJEnaIqFQiGsO68Vx/WNl5wXPTeG1LxYGHUuq0yqWr3sgUXxVzOqcvnx6wElUYzrtDeeMgd5HQbQcPvpXrPBc9k3QySRtBotOSZIkbbFwOMR1g3tx9E5tiEThvGen8Oa0RUHHkuqsTA8kqhYV+3R68nodk9UQ/vQA/OlByMyDhZNjS9k/ux+i0aDTSfoVFp2SJEnaKuFwiBuO2I4/9WlDeSTKuU9/zttfLg46llQneSBR9fDk9Tqu95Ew7FPotA+UFcKbF8ITf4I1/mFPSlQWnZIkSdpq4XCIG4/cjsE7tKIsEmXEU5N5b8aSoGNJdU5mz54AlC5YQNny5QGnqT16No69rwvWLWBF0YqA0ygQua3gxBdh0I2QmgmzR8HIXWH6S0Enk7QJFp2SJEn6XVLCIf5z1PYcun2s7Bz25CQ+mLk06FhSnZKSk0N6p04AFE5zVme85KTn0DGvI+CszjotHIadz4KzRkPLHaBwJfz3FHjxTChcFXA4SRuy6JQkSdLvlpoS5pajt+eg3i0oLY9y1hOT+OjrH4OOJdUpWb1dvl4dejV2+brWa9oVzngf9rwYQmGY+iyMHABzRwedTNJ6Fp2SJEmKi9SUMLcduyMDezanpCzCmY9N5JNvlgUdS6ozMitOXndGZ1y5T6eqSEmDfS+D096Bhh1hzQ/w6KHwzmVQWhR0OqnOs+iUJElS3KSlhLnjuD7s370ZxWURznhsAmNnW3ZKNSFru9jJ60VTpxL1ZOi46d0kViB/uexL31f9pG1/OPsT6HtK7PK4O+G+vWHR1CBTSXWeRackSZLiKj01zF0n9GGfrk0pKo1w+iMTGT/Hw1Gk6pbRtSuhtDTKV6+m9Icfgo5Ta3Rt1JXUcCori1eyYN2CoOMokWTUh0Nvg+OehXrN4Mev4P594ZNbIFIedDqpTrLolCRJUtxlpKYw8sS+7LltUwpLyzn1kQlMnOeJxVJ1Cqenk9G9OwCFU51VFi/pKel0bdgVgC+Xu3xdm9D1DzBsHHQ7BCKl8P5V8MjBsHJe0MmkOseiU5IkSdUiMy2F+07qy+7bNKGgpJwhD33GpO9WBh1LqtU8kKh6VO7T+aNFp35BvSZwzBPwx7shPQe+Hxc7qOjzJ8AtD6QaY9EpSZKkapOZlsL9J+/Erp0ak19SzikPfcaU+auCjiXVWlkeSFQtKorOact8X/UrQiHY8QQ45xNotxuUrINXhsOzJ0K++1VLNcGiU5IkSdUqKz2FB0/Zif4dG7G2uIyTHhzPtB9WBx1LqpUyK2Z0zphBtLQ04DS1R8WBRF+t+IqySFnAaZTwGnaAU16H/a+GcBrMfB3u3gVmvR10MqnWs+iUJElStctOT+XhU/qxU/uGrC0qY8jDn7Gu2LJAirf0Dh0I169PtKiI4m+/DTpOrdEhtwPZqdkUlhUyZ/WcoOMoGYRTYPfz4MwPoFkPyP8Rnj4GXj0XitcFnU6qtSw6JUmSVCPqZaTy8Kn9aJ6bwYr8Er5wCbsUd6FwmMzesWXWhe7TGTcp4RR6NukJwPRl0wNOo6TSojcM/QB2HQGEYPKjcM/uMP+zoJNJtZJFpyRJkmpMTmYaO7ZtCMCMhWsCTiPVTlm9twOgcJonr8eT+3Rqq6VlwsDrYMirkNsGVs6FhwbC//4B5W4xIcWTRackSZJqVM9WuQBMX+g+nVJ1qDiQyJPX46tX4/Unry/z5HVtpY57wrCxsN2xEI3A6H/DA/vBj7OCTibVGhadkiRJqlE9W1cUnc7olKpD5voZncXffkukoCDgNLVHxYFE36z8hqKyooDTKGll5sER98JRj0BWQ1j0Bdy7J3x6D0QiQaeTkp5FpyRJkmpUz1Z5AMz+cR2FJeUBp5Fqn7TmzUht3hwiEYpmzAg6Tq3Rol4LGmc2pixaxswVM4OOo2TX83A4Zxx03g/KiuDtS+CJw2H1gqCTSUnNolOSJEk1qllOBk3qpxOJwszFzuqUqkPF8nUPJIqfUChUuU+ny9cVF7kt4cQX4OCbIDUL5nwII3eFac8HnUxKWhadkiRJqlGhUIge62d1unxdqh6ZHkhULSqLzuUWnYqTUAj6nQFnfwyt+kDRanjhdHj+dChcGXQ6KelYdEqSJKnG/XQgkUWnVB2yescKOQ8kiq+KfTqd0am4a9IFTn8X9roUQinw5fNw924w+4Ogk0lJxaJTkiRJNa5Hy1jROcOT16VqkdkrVnSWLlhA2fLlAaepPXo27gnAd2u+Y3WxP78UZylpsM9f4fT3oFFnWLsQHh8Mb10KpYVBp5OSgkWnJEmSalzFjM6Zi9dSVu4ps1K8peTkkN6pEwCF05zVGS8NMhvQNqctANOXTw84jWqtNn1jS9n7nRG7PH4k3LsXLJwSaCwpGVh0SpIkqcZ1aFyPeukpFJdFmLMsP+g4Uq2U1Tu2zNrl6/HlgUSqEen1YocUnfA81G8Oy2bBA/vB6P9AeVnQ6aSEZdEpSZKkGhcOh+jesmKfTpd/StUhs+LkdWd0xlWvxrGic9oy31fVgC4HwDnjoPthECmD/10LjxwEK+YEnUxKSBadkiRJCkTlgUQLPJBIqg5Z28VOXi+aNo1oNBpwmtqjd9OfDiTyfVWNqNcYjn4MDr8XMnJh/ngYuTtMegQcg1IVFp2SJEkKRM9WeYAnr0vVJaNrV0JpaZSvWkXZDwuCjlNrdGvUjZRQCssKl7GkYEnQcVRXhEKw/bFwzhhovzuU5sNrf4Gnj4V1S4NOJyUMi05JkiQFokern5auOytKir9wejoZ3bsDUPSly6zjJSs1i20abAO4T6cC0KAdDHkNDvwHpKTD12/D3bvCzDeCTiYlBItOSZIkBWLb5jmkpYRYU1TGDysLg44j1UqVBxJNs5CLJw8kUqDCYdjtz3Dmh9C8FxQsg2eOh1dGQPHaoNNJgbLolCRJUiDSU8N0aZYDuHxdqi5Z6w8kKv7SQi6eejf5aZ9OKTDNe8LQ/8GAvwAh+PxxGDkAvhsXdDIpMBadkiRJCkzF8vUZnrwuVYvM9TM6i7/6CsrLA05Te1TM6Jy+fDqRaCTgNKrTUjPggGvglDcgrx2s+g4eHgTvXwVlJUGnk2qcRackSZICU3nyujM6pWqR3qED4fr1iRYVkbHEg3PipXODzmSmZLKudB3z1swLOo4EHQbEDira4QQgCp/cAg/sC0u/CjqZVKMsOiVJkhSYipPXZyyy6JSqQygcJrN3bPZh5vz5AaepPVLDqfRo3ANw+boSSGYuDL4bjnkCshvD4mlw714w7i6IOPNYdYNFpyRJkgLTvWVsj85Fq4tYke8SO6k6ZPXeDrDojLeeTXoCFp1KQN0PhXPGQZeBUF4M7/wNHv8jrPJngGo/i05JkiQFJiczjQ6NswGY7j6dUrWoOJAo84cfAk5Su3ggkRJaTnM4/lk45FZIy4a5o2MHFU19DqLRoNNJ1caiU5IkSYGqWL7uPp1S9chcP6MzffESIgUFAaepPXo1jm0JMHPFTErLSwNOI21CKAQ7nQpnfwJt+kHxanhxKDx/KhSsCDqdVC0sOiVJkhSoHh5IJFWrtObNSGnWjFA0Gjt9XXHRJqcNeRl5lEZK+Xrl10HHkX5Z485w6tuwz+UQToXpL8HI3eDb94NOJsWdRackSZIC9dPJ6y5dl6pLZu/YMuuiL11mHS+hUIheTWKzOqctmxZwGuk3pKTCXhfB6e9B4y6wdhE88Sd48yIocaa3ag+LTkmSJAWqYun63GX55BeXBZxGqp0ye8UKueJpFp3xVLF83aJTSaN1HzhrNPQ/K3b5s/vg3j1hweRgc0lxYtEpSZKkQDXNyaBpTgbRKMxc7PJ1qTpk9IqdEF70pYVcPFUcSDR92fSAk0hbID0bDroRTnwRclrC8m/gwQPgoxuh3D84KrlZdEqSJClwPd2nU6pWGT1jRWfZgoWULV8ecJrao2eT2Ps6Z/Uc1pWsCziNtIW22Q/OGQs9D4dIGXxwHTw0EJbPDjqZtNUsOiVJkhS4iqJzhkWnVC1ScnIobtoUgMJpzuqMlyZZTWhZryVRosxYPiPoONKWy24ERz4MRzwAGXmwYCLcsztMfAii0aDTSVvMolOSJEmBq9in0xmdUvUpats29nmqRWc8VRxI9OVy9z9VkgqFYLujYNhY6LgnlBbA6+fDU0fD2iVBp5O2iEWnJEmSAlcxo3PW4rWUlkcCTiPVThVFZ6H7dMZVxT6dXy6z6FSSy2sDJ70CA/8JKRnwzbtw9y4w49Wgk0mbzaJTkiRJgWvbMJucjFRKyiN8u9R97qTqUNS2Tezz1GlEXZIaNxUzOj15XbVCOAy7DoOzPoIWvaFwBTx3Erx0DhStDjqd9JssOiVJkhS4cDhEdw8kkqpVScuWkJZG+apVlP7wQ9Bxao0ejXsQIsTi/MUsK1wWdBwpPpp1hzP+B7tfAKEwfPEUjNwd5o0JOpn0qyw6JUmSlBB+OnndGSNSdYimppLRrSsAhVOnBpym9qiXVo/ODToDLl9XLZOaDvtfCae+BQ07wOrv4ZGD4d0roKw46HTSJll0SpIkKSF4IJFU/TJ7xfaT9ECi+OrZuCfg8nXVUu12gbM/gT4nA1EYezvcvy8stthX4rHolCRJUkLo0TI2o/OrhWuIRNw/UKoOGb1ihVzhNAu5eKo4kGj6sukBJ5GqSUYOHHYHHPs0ZDeBJV/C/fvAmNshUh50OqmSRackSZISQpfm9UlPCbO2uIwfVhYGHUeqlSpndM6YQbS0NOA0tUevpj8dSORBT6rVuh0Ewz6FrgdBeQm8dwU8ehis+j7oZBJg0SlJkqQEkZYSZtsW9QH36ZSqS1qH9oTr1ydaVETxt98GHafW2LbBtqSF01hTsob5a+cHHUeqXvWbwrFPxWZ4ptWD7z6BkQNgytNg0a+AWXRKkiQpYfRs6T6dUnUKhcNk9o7NPix0n864SUtJo3uj7oAHEqmOCIVie3ae8wm03RmK18DLZ8NzJ0P+8qDTqQ6z6JQkSVLC6Nnak9el6pbVezsAir606IynXk1+Wr4u1RmNOsVOZd/v7xBOha9ehZG7wjfvBZ1MdZRFpyRJkhJGz1YVRaczOqXqkrVdbJ9OZ3TGV0XROX25BxKpjgmnwB7/B0P/B027wbol8OSR8PoFUJIfdDrVMRadkiRJShjdWuQSCsHStcX8uLY46DhSrZS5fkZn8TffECkoCDhN7VFRdH61/CtKIx70pDqo5fZw5oewy7DY5YkPwj17wA8TA42lusWiU5IkSQmjXkYqHZvUA1y+LlWXtObNSG3eHCIRimbMCDpOrdE+tz05aTkUlRcxe9XsoONIwUjLgj/8E05+BXJbw4rZ8OCB8MH1UO4fAFT9LDolSZKUUHq28kAiqbq5fD3+wqEwPZr0ADyQSKLT3nDOWOh9NETL4aN/xQrPZd8EnUy1nEWnJEmSEkqPlrF9OmdYdErVpmL5euG0qQEnqV16N4kVyBadEpDVAP50Pxz5EGTmwcLJsaXsn90P0WjQ6VRLWXRKkiQpoVQcSDRjkUWnVF2yesf2kyxyRmdc9WrsyevSRnr9CYZ9Cp32gbJCePNCeOJPsGZR0MlUC1l0SpIkKaFUFJ1zl+Wzrrgs4DRS7ZTZK1bIlS5YQNny5QGnqT0qDiSavWo2BaUe9CRVym0FJ74Ig26E1EyYPQpG7grTXwo6mWoZi05JkiQllMb1M2iRmwnAV87qlKpFSk4O6Z06AVA4zdmH8dK8XnOaZTWjPFrOzBUzg44jJZZwGHY+C876GFruAIUr4b+nwItnQpEHECo+LDolSZKUcCpmdU5f4D98pOqS1Tu2n2TRNPeTjKeeTXoCLl+XflHTbeGM92HPiyEUhqnPknr/njRZOyPoZKoFLDolSZKUcCqLTg8kkqpNZsXJ6x5IFFcVBxJNXzY94CRSAktJg30vg9PegUadCK1ZwIBvbyD8/hVQWhR0OiUxi85qcNddd9GjRw/69esXdBRJkqSk1KNVHmDRKVWnrO1iJ68XTZ1G1BOQ46Zin05ndEqboW1/OOtjynccAkDK+JFw396wyD/AaOtYdFaD4cOHM2PGDCZMmBB0FEmSpKRUMaPzm6VrKSmLBJxGqp0yunYllJZG+apVlP7wQ9Bxao2Kpes/rPuBlUUrA04jJYGM+kQOuolPO11AtF4z+PEruH9f+OQWiJQHnU5JxqJTkiRJCadNwyxyM1MpLY/y9ZK1QceRaqVwejoZ3bsDUDjV2VPxkpueS4fcDgBMX+7ydWlzLcnbgbKho6HbIRAphfevgkcOhpXzgo6mJGLRKUmSpIQTCoXosX5W5wyXr0vVpvJAoqkus44nl69LW6leEzjmCfjj3ZCeA9+Pg5ED4PMnwC02tBksOiVJkpSQeq7fp3PGIotOqbpk9o4VcoXTLOTiqaLo/HKZJ9pLWywUgh1PgHM+gXa7Qck6eGU4PHsi5C8LOp0SnEWnJEmSEtJPJ6+vDjiJVHtVHkg0YwbR0tKA09QeGxadHvQkbaWGHeCU12H/qyGcBjNfh7t3gVlvB51MCcyiU5IkSQmpckbnwjVEIhYFUnVI79CBcP36RIuKKP7226Dj1BrdGnUjNZTKiqIVLMpfFHQcKXmFU2D38+DMD6BZD8j/EZ4+Bl49F4rXBZ1OCciiU5IkSQmpc9N6ZKSGyS8p57sVBUHHkWqlUDjs8vVqkJGSQZeGXQD36ZTiokVvGPoB7PZnIASTH4V7dof5nwWdTAnGolOSJEkJKTUlTLcWOYDL16XqlNV7/fJ1i8646t0kdtDT9GWevC7FRVomHPgPGPIa5LWFlXPhoYHwv39AuVtvKMaiU5IkSQmrx/rl69M9eV2qNlnbxQq5Qk9ejytPXpeqScc94JwxsN2xEI3A6H/DA/vBj7OCTqYEYNEpSZKkhPXTgUQWnVJ1yey9HfX32Yfcgw7y4Jw4qig6ZyyfQXmkPOA0Ui2TmQdH3AtHPQpZDWHRF3DvnvDpPRCJBJ1OAbLolCRJUsKqKDpnLFxtASNVk7TmzWg78m6anHUmoVAo6Di1Rqe8TmSlZlFQVsDc1XODjiPVTj0Hw7BPYZv9oawI3r4EnjgcVi8IOpkCYtEpSZKkhNWtRS7hECxbV8LStcVBx5GkzZYSTqFn456Ay9elapXTAk54Hg6+CVKzYM6HMHJXmPZ80MkUAItOSZIkJays9BQ6Na0PwAyXr0tKMqf2OpWb976ZPdvsGXQUqXYLhaDfGXD2x9CqDxSthhdOh+dPh8KVQadTDbLolCRJUkL7aZ9OT16XlFz2bLMnB7Q/gMZZjYOOItUNTbrA6e/C3n+FUAp8+TzcvRvM/iDoZKohFp2SJElKaB5IJEmSNltKGux9KZz+HjTqDGsXwuOD4a1LobQw6HSqZhadkiRJSmg9W+UBFp2SJGkLtOkbW8re74zY5fEj4d69YOGUQGOpell0SpIkKaFVzOj8fkUBa4pKA04jSZKSRnq92CFFJzwP9ZvDslnwwH4w+j9QXhZ0OlUDi05JkiQltAbZ6bRukAV4IJEkSdoKXQ6AYZ9C98MgUgb/uxYeOQhWzAk6meLMolOSJEkJr4f7dEqSpN8juxEc/Rgcfi9k5ML88TByd5j0CESjQadTnFh0SpIkKeH1aOnJ65Ik6XcKhWD7Y+GcMdBhDyjNh9f+Ak8fC+uWBp1OcWDRKUmSpIRXsU+nS9clSdLv1qAdnPwqHHgdpKTD12/D3bvCzDeCTqbfyaJTkiRJCa9n69jJ698uXUdxWXnAaSRJUtILh2G3EXDmh9C8FxQsg2eOh1dGQPHaoNNpK1l0SpIkKeG1ysukQXYaZZEoXy9eF3QcSZJUWzTvCUP/BwP+AoTg88dh5AD4blzQybQVLDolSZKU8EKhUOXydffplCRJcZWaAQdcA6e8EVvWvuo7eHgQvH8VlJUEnU5bwKJTkiRJSaFnq9jydU9elyRJ1aLDADh7DOxwIhCFT26BB/aFpV8FnUybyaJTkiRJScEZnZIkqdpl5sLgu+CYJyC7MSyeBvfuBePugkgk6HT6DRadkiRJSgoVRedXi9ZSHokGnEaSJNVq3Q+Fc8ZBl4FQXgzv/A0e/yOsmh90Mv0Ki05JkiQlhY5N6pOZFqawtJy5y/KDjiNJkmq7nOZw/LNwyK2Qlg1zR8cOKpr6HET9o2sisuiUJElSUkgJh+jWwuXrkiSpBoVCsNOpcPYn0KYfFK+GF4fC86dCwYqg0+lnLDolSZKUNCqWr89Y5IFEkiSpBjXuDKe+DftcDuFUmP4SjNwNvn0/6GTagEWnJEmSkkbFyeszPHldkiTVtJRU2OsiOP09aLItrF0ET/wJ3rwISgqCTicsOiVJkpREfjp5fQ1R98aSJElBaN0HzvwI+p8Vu/zZffDKsGAzCbDolCRJUhLp2iKHlHCIFfklLF5TFHQcSZJUV6Vnw0E3wrFPxy7PfBNKC4PNJItOSZIkJY/MtBS2aVofgOkLXL4uSZIC1nUQ5LSC8mKYPz7oNHWeRackSZKSyobL1yVJkgIVCkHHPWNfzx0dbBZZdEqSJCm59KgsOlcHnESSJAnotFfs85yPgs0hi05JkiQll4qT153RKUmSEkLFjM6Fk6HIP8QGyaJTkiRJSaVHy9iMzgWrCllVUBJwGkmSVOfltYFGnSEage/GBZ2mTrPolCRJUlLJy06jTcMsAGYsclanJElKAJX7dLp8PUgWnZIkSUo6FQcSzXD5uiRJSgQeSJQQLDolSZKUdNynU5IkJZSKonPJl7Dux2Cz1GEWnZIkSUo6PT15XZIkJZJ6TaB5r9jX8z4ONksdZtEpSZKkpFMxo3P2j/kUlZYHnEaSJAnouFfss8vXA2PRKUmSpKTTPDeDxvXSKY9Embl4bdBxJEmSPJAoAVh0SpIkKemEQiF6uHxdkiQlkva7QSgFVsyBVfODTlMnWXRKkiQpKXkgkSRJSiiZudC6T+xrl68HwqJTkiRJSemnGZ0WnZIkKUFULl+36AyCRackSZKSUsXJ6zMXraGsPBJwGkmSJDY4kOgjiEaDzVIHWXRKkiQpKXVsXI/s9BSKyyLMXZYfdBxJkiRo2x9SMmDtIlj+bdBp6hyLTkmSJCWlcDhE95YuX5ckSQkkLQva7Rz72tPXa5xFpyRJkpJWT09elyRJiaZin845Fp01zaJTkiRJSaunBxJJkqREU7FP57yPIeI+4jXJolOSJElJq2erPCBWdEbd8F+SJCWCVn0gPQcKV8KSaUGnqVMsOiVJkpS0ujSvT2o4xOrCUhasKgw6jiRJEqSkQvvdYl/PHR1sljrGolOSJElJKyM1hS7NcwCXr0uSpATSaf3ydYvOGmXRKUmSpKTWY/3J6zMsOiVJUqKoOJDou7FQXhpsljrEolOSJElJzQOJJElSwmnWE7IbQ8k6WDA56DR1hkWnJEmSklpF0Tlj4eqAk0iSJK0XDkOHPWJfz/0o2Cx1iEWnJEmSklqP9UXnwtVFrMwvCTiNJEnSehXL192ns8ZYdEqSJCmp5WSm0b5xNuDydUmSlEA67R37PH88lBQEGqWusOiUJElS0vtpn06Xr0uSpATRqBPktobykljZqWpn0SlJkqSk17NVHuCMTkmSlEBCIZev1zCLTkmSJCW9Hs7olCRJiajjXrHPHkhUIyw6JUmSlPQqlq7PWZZPQUlZwGkkSZLWq5jRufBzKPIPstXNolOSJElJr1lOJk3qZxCNwszFa4OOI0mSFJPXGhpvA9EIzBsTdJpaz6JTkiRJtcJPBxK5T6ckSUog7tNZYyw6JUmSVCtUFJ0z3KdTkiQlksp9Oi06q5tFpyRJkmoFT16XJEkJqcMesc9Lp8O6H4PNUstZdEqSJKlWqJjROXPxWkrLIwGnkSRJWq9eY2jeO/b1PGd1VieLTkmSJNUK7RplUz8jlZKyCLN/XBd0HEmSpJ90Wr98fc5Hweao5Sw6JUmSVCuEwyF6tFx/INECl69LkqQE4oFENcKiU5IkSbVGD09elyRJiaj9bhBKgZVzYdX3QaeptSw6JUmSVGv0rCw6PXldkiQlkIwcaN039rWzOquNRackSZJqjYoZnXOX5RONRgNOI0mStAGXr1c7i05JkiTVGts2z+Hd8/dk7KX7EgqFgo4jSZL0kw0PJPIPstXColOSJEm1RlpKmG2b55Ca4q+5kiQpwbTpD6mZsG4xLPsm6DS1kr8BSpIkSZIkSdUtLRPa7hz7eu5HwWappSw6JUmSJEmSpJpQuU+nRWd1sOiUJEmSJEmSakKnvWOf534MkUigUWoji05JkiRJkiSpJrTcAdJzoGgVLJ4adJpax6JTkiRJkiRJqgkpqdBhQOzruaODzVILWXRKkiRJkiRJNaXjXrHP7tMZdxadkiRJkiRJUk2pOJDou3FQVhJsllrGolOSJEmSJEmqKc16QHYTKM2HBZOCTlOrWHRKkiRJkiRJNSUcho57xL52n864suiUJEmSJEmSalLF8nWLzriy6JQkSZIkSZJqUsWBRD98BiUFwWapRSw6JUmSJEmSpJrUqBPktoHyEpj/adBpag2LTkmSJEmSJKkmhULQaf2szjkfBZulFrHolCRJkiRJkmqa+3TGnUWnJEmSJEmSVNMqis5FU6BwVZBJag2LTkmSJEmSJKmm5baCxl0gGoHvxgSdplaw6JQkSZIkSZKC4PL1uLLolCRJkiRJkoLggURxZdEpSZIkSZIkBaHDHkAIfvwK1i0NOk3Ss+iUJEmSJEmSgpDdCFr0jn3t8vXfzaJTkiRJkiRJCkrlPp0uX/+9LDolSZIkSZKkoHRcv0+nMzp/N4tOSZIkSZIkKSjtd4VwKqycByu/CzpNUrPolCRJkiRJkoKSkQOt+8a+dlbn72LRKUmSJEmSJAWpcvm6+3T+HhadkiRJkiRJUpAqDyQaDdFosFmSmEWnJEmSJEmSFKQ2/SA1E9YtgWVfB50maVl0SpIkSZIkSUFKy4R2u8S+nuPy9a1l0SlJkiRJkiQFrXL5ukXn1rLolCRJkiRJkoLWce/Y53kfQ6Q8yCRJy6JTkiRJkiRJClrL7SEjF4pWw+KpQadJShadkiRJkiRJUtBSUqH9gNjX7tO5VSw6JUmSJEmSpETQaa/Y57mjg82RpCw6JUmSJEmSpERQcSDR9+OgrCTYLEnIolOSJEmSJElKBM16QHYTKC2ABRODTpN0LDolSZIkSZKkRBAK/TSr0+XrW8yiU5IkSZIkSUoUFft0eiDRFrPolCRJkiRJkhJFxYzOHyZASX6wWZKMRackSZIkSZKUKBp2hLy2ECmF7z8NOk1SseiUJEmSJEmSEkUoBB3XL1+f6/L1LWHRKUmSJEmSJCUSDyTaKhadkiRJkiRJUiKpKDoXToHClYFGSSYWnZIkSZIkSVIiyW0JTbYFojBvTNBpkoZFpyRJkiRJkpRoKpevu0/n5rLolCRJkiRJkhJN5YFE7tO5uSw6JUmSJEmSpETTYXcgBD/OhLVLgk6TFCw6f0OHDh3Ybrvt2GGHHdhnn32CjiNJkiRJkqS6ILsRtNwu9rWzOjdLatABksHYsWOpX79+0DEkSZIkSZJUl3TcExZ9Edunc7ujgk6T8JzRKUmSJEmSJCWijnvHPnsg0Wap1UXn6NGjOfTQQ2nVqhWhUIiXX355o/vcdddddOjQgczMTHbeeWc+++yzKreHQiH22msv+vXrx5NPPllDySVJkiRJklTntdsFwqmw6ntYOS/oNAmvVhed+fn5bL/99tx1112bvP3ZZ5/lggsu4Morr2Ty5Mlsv/32DBw4kKVLl1be55NPPmHSpEm8+uqrXH/99UydOrWm4kuSJEmSJKkuy6gPrXeKfe0+nb+pVu/ROWjQIAYNGvSLt998880MHTqUU089FYB77rmHN954g4ceeohLL70UgNatWwPQsmVLDjroICZPnsx22223yecrLi6muLi48vKaNWsAKC0tpbS0NC6vKVlVvP66/j4oPhxPiifHk+LJ8aR4cjwpnhxPiifHk+LNMfXrwu13J2X+p0Rmf0B57+OCjlPjtmRchKLRaLQasySMUCjESy+9xODBgwEoKSkhOzub559/vvI6gCFDhrBq1SpeeeUV8vPziUQi5OTksG7dOvbaay/uuece+vXrt8nvcdVVV3H11VdvdP1TTz1FdnZ2dbwsSZIkSZIk1WKN185k92+vpyg1j3d63Q6hUNCRalRBQQHHH388q1evJjc391fvW6tndP6aZcuWUV5eTvPmzatc37x5c2bOnAnAkiVLOPzwwwEoLy9n6NChv1hyAvz1r3/lggsuqLy8Zs0a2rZty4EHHvib/yFqu9LSUt577z0OOOAA0tLSgo6jJOd4Ujw5nhRPjifFk+NJ8eR4Ujw5nhRvjqnfULYf0ZtuIbNsNQf17wxNuwWdqEZVrJjeHHW26NwcnTp14osvvtjs+2dkZJCRkbHR9Wlpaf4PdT3fC8WT40nx5HhSPDmeFE+OJ8WT40nx5HhSvDmmfkFaWuxQojkfkDZ/LLTqHXSiGrUlY6JWH0b0a5o0aUJKSgpLliypcv2SJUto0aJFQKkkSZIkSZKkn+m4Z+yzBxL9qjpbdKanp9O3b19GjRpVeV0kEmHUqFHsuuuuASaTJEmSJEmSNtBpr9jneR9DpDzYLAmsVi9dX7duHd9++23l5blz5zJlyhQaNWpEu3btuOCCCxgyZAg77bQT/fv359ZbbyU/P7/yFHZJkiRJkiQpcC13gIw8KFoNi76A1n2CTpSQanXROXHiRPbZZ5/KyxUHBQ0ZMoRHHnmEY445hh9//JG///3vLF68mB122IG33357owOKJEmSJEmSpMCEU6DD7jDrDZj7kUXnL6jVRefee+9NNBr91fuMGDGCESNG1FAiSZIkSZIkaSt03HN90Tkadj8/6DQJqc7u0SlJkiRJkiQljYoDib4bB2XFwWZJUBadkiRJkiRJUqJr1h3qNYWyQvhhYtBpEpJFpyRJkiRJkpToQqGfZnXOHR1slgRl0SlJkiRJkiQlg457xT7P/SjYHAnKolOSJEmSJElKBhUzOn+YACX5wWZJQBadkiRJkiRJUjJo1BEatINIWexQIlVh0SlJkiRJkiQli8p9Ol2+/nMWnZIkSZIkSVKyqNyn0wOJfs6iU5IkSZIkSUoWFTM6F30BBSuCzZJgLDolSZIkSZKkZJHTApp0BaLw3Zig0yQUi05JkiRJkiQpmXRav3x9jvt0bsiiU5IkSZIkSUomlQcSuU/nhiw6q8Fdd91Fjx496NevX9BRJEmSJEmSVNu0HwCEYNksWLMo6DQJw6KzGgwfPpwZM2YwYcKEoKNIkiRJkiSptsluBC23j3097+NgsyQQi05JkiRJkiQp2VQuX3efzgoWnZIkSZIkSVKyqTyQaDREo8FmSRAWnZIkSZIkSVKyabcrhFNh9fewcl7QaRKCRackSZIkSZKUbNLrQZv1B2G7fB2w6JQkSZIkSZKSU8f1y9fnjg42R4Kw6JQkSZIkSZKSUeWBRO7TCRadkiRJkiRJUnJq0w/qNYNWfaBoddBpApcadABJkiRJkiRJWyE1Hf5vFoSdywjO6JQkSZIkSZKSlyVnJd8JSZIkSZIkSUnPolOSJEmSJElS0rPolCRJkiRJkpT0LDolSZIkSZIkJT2LTkmSJEmSJElJz6JTkiRJkiRJUtKz6JQkSZIkSZKU9Cw6JUmSJEmSJCU9i05JkiRJkiRJSc+iU5IkSZIkSVLSs+iUJEmSJEmSlPQsOqvBXXfdRY8ePejXr1/QUSRJkiRJkqQ6waKzGgwfPpwZM2YwYcKEoKNIkiRJkiRJdYJFpyRJkiRJkqSkZ9EpSZIkSZIkKelZdEqSJEmSJElKehadkiRJkiRJkpKeRackSZIkSZKkpGfRKUmSJEmSJCnpWXRKkiRJkiRJSnqpQQeozaLRKABr1qwJOEnwSktLKSgoYM2aNaSlpQUdR0nO8aR4cjwpnhxPiifHk+LJ8aR4cjwp3hxT+jUVvVpFz/ZrLDqr0dq1awFo27ZtwEkkSZIkSZKk5LV27Vry8vJ+9T6h6ObUodoqkUiEhQsXkpOTQygUCjpOoNasWUPbtm2ZP38+ubm5QcdRknM8KZ4cT4onx5PiyfGkeHI8KZ4cT4o3x5R+TTQaZe3atbRq1Ypw+Nd34XRGZzUKh8O0adMm6BgJJTc31x9aihvHk+LJ8aR4cjwpnhxPiifHk+LJ8aR4c0zpl/zWTM4KHkYkSZIkSZIkKelZdEqSJEmSJElKehadqhEZGRlceeWVZGRkBB1FtYDjSfHkeFI8OZ4UT44nxZPjSfHkeFK8OaYULx5GJEmSJEmSJCnpOaNTkiRJkiRJUtKz6JQkSZIkSZKU9Cw6JUmSJEmSJCU9i05JkiRJkiRJSc+iU1vlrrvuokOHDmRmZrLzzjvz2Wef/er9b731Vrp27UpWVhZt27bl/PPPp6io6Hc9p2qPeI+nf/7zn/Tr14+cnByaNWvG4MGDmTVrVnW/DCWI6vj5VOGGG24gFApx3nnnVUNyJarqGFMLFizgxBNPpHHjxmRlZdG7d28mTpxYnS9DCSLe46m8vJwrrriCjh07kpWVRefOnbn22mvxvNG6YUvGU2lpKddccw2dO3cmMzOT7bffnrfffvt3Padql3iPJ38nr9uq4+dTBX8n16+KSlvomWeeiaanp0cfeuih6PTp06NDhw6NNmjQILpkyZJN3v/JJ5+MZmRkRJ988sno3Llzo++88060ZcuW0fPPP3+rn1O1R3WMp4EDB0Yffvjh6JdffhmdMmVK9KCDDoq2a9cuum7dupp6WQpIdYynCp999lm0Q4cO0e222y76l7/8pZpfiRJFdYypFStWRNu3bx895ZRTouPHj4/OmTMn+s4770S//fbbmnpZCkh1jKfrrrsu2rhx4+jrr78enTt3bvS///1vtH79+tHbbrutpl6WArKl4+niiy+OtmrVKvrGG29EZ8+eHb377rujmZmZ0cmTJ2/1c6r2qI7x5O/kdVd1jKcK/k6u32LRqS3Wv3//6PDhwysvl5eXR1u1ahX95z//ucn7Dx8+PLrvvvtWue6CCy6IDhgwYKufU7VHdYynn1u6dGkUiH700UfxCa2EVV3jae3atdEuXbpE33vvvehee+3lL1V1SHWMqUsuuSS6++67V09gJbTqGE8HH3xw9LTTTqtynyOOOCJ6wgknxDG5EtGWjqeWLVtG77zzzirX/Xys+Dt53VUd4+nn/J287qiu8eTv5NocLl3XFikpKWHSpEnsv//+ldeFw2H2339/xo0bt8nH7LbbbkyaNKlyqvqcOXN48803Oeigg7b6OVU7VMd42pTVq1cD0KhRozimV6KpzvE0fPhwDj744CrPrdqvusbUq6++yk477cRRRx1Fs2bN2HHHHbn//vur98UocNU1nnbbbTdGjRrF119/DcAXX3zBJ598wqBBg6rx1ShoWzOeiouLyczMrHJdVlYWn3zyyVY/p2qH6hhPm+Lv5HVDdY4nfyfX5kgNOoCSy7JlyygvL6d58+ZVrm/evDkzZ87c5GOOP/54li1bxu677040GqWsrIyzzz6bv/3tb1v9nKodqmM8/VwkEuG8885jwIAB9OrVK+6vQYmjusbTM888w+TJk5kwYUK15lfiqa4xNWfOHEaOHMkFF1zA3/72NyZMmMC5555Leno6Q4YMqdbXpOBU13i69NJLWbNmDd26dSMlJYXy8nKuu+46TjjhhGp9PQrW1oyngQMHcvPNN7PnnnvSuXNnRo0axYsvvkh5eflWP6dqh+oYTz/n7+R1R3WNJ38n1+ZyRqeq3Ycffsj111/P3XffzeTJk3nxxRd54403uPbaa4OOpiS0peNp+PDhfPnllzzzzDM1nFTJ4LfG0/z58/nLX/7Ck08+udFfmaVN2ZyfUZFIhD59+nD99dez4447cuaZZzJ06FDuueeeAJMrEW3OeHruued48skneeqpp5g8eTKPPvoo//nPf3j00UcDTK5EdNttt9GlSxe6detGeno6I0aM4NRTTyUc9p+E2nJbOp78nVy/5rfGk7+Ta0s4o1NbpEmTJqSkpLBkyZIq1y9ZsoQWLVps8jFXXHEFJ510EmeccQYAvXv3Jj8/nzPPPJPLLrtsq55TtUN1jKcNf7kaMWIEr7/+OqNHj6ZNmzbV90KUEKpjPE2aNImlS5fSp0+fyseUl5czevRo7rzzToqLi0lJSam+F6VAVdfPqJYtW9KjR48qj+vevTsvvPBC9bwQJYTqGk8XXXQRl156Kccee2zlfb777jv++c9/OkO4Ftua8dS0aVNefvllioqKWL58Oa1ateLSSy+lU6dOW/2cqh2qYzxtyN/J65bqGE/+Tq4t4Z/vtEXS09Pp27cvo0aNqrwuEokwatQodt11100+pqCgYKO/7FX8EIpGo1v1nKodqmM8VXweMWIEL730Ev/73//o2LFjNb0CJZLqGE/77bcf06ZNY8qUKZUfO+20EyeccAJTpkzxF6parrp+Rg0YMIBZs2ZVuc/XX39N+/bt4xlfCaa6xtMv3ScSicQzvhLM7/n9OTMzk9atW1NWVsYLL7zAH//4x9/9nEpu1TGewN/J66rqGE/+Tq4tEswZSEpmzzzzTDQjIyP6yCOPRGfMmBE988wzow0aNIguXrw4Go1GoyeddFL00ksvrbz/lVdeGc3JyYk+/fTT0Tlz5kTffffdaOfOnaNHH330Zj+naq/qGE/nnHNONC8vL/rhhx9GFy1aVPlRUFBQ469PNas6xtPPecJj3VIdY+qzzz6LpqamRq+77rroN998E33yySej2dnZ0SeeeKLGX59qVnWMpyFDhkRbt24dff3116Nz586Nvvjii9EmTZpEL7744hp/fapZWzqePv300+gLL7wQnT17dnT06NHRfffdN9qxY8foypUrN/s5VXtVx3jyd/K6qzrG08/5O7l+iUWntsodd9wRbdeuXTQ9PT3av3//6Kefflp521577RUdMmRI5eXS0tLoVVddFe3cuXM0MzMz2rZt2+iwYcM2+qH1a8+p2i3e4wnY5MfDDz9ccy9KgamOn08b8pequqc6xtRrr70W7dWrVzQjIyParVu36H333VdDr0ZBi/d4WrNmTfQvf/lLtF27dtHMzMxop06dopdddlm0uLi4Bl+VgrIl4+nDDz+Mdu/ePZqRkRFt3Lhx9KSTToouWLBgi55TtVu8x5O/k9dt1fHzaUP+Tq5fEopG1697kSRJkiRJkqQk5R6dkiRJkiRJkpKeRackSZIkSZKkpGfRKUmSJEmSJCnpWXRKkiRJkiRJSnoWnZIkSZIkSZKSnkWnJEmSJEmSpKRn0SlJkiRJkiQp6Vl0SpIkSZIkSUp6Fp2SJEnSFrjqqqvYYYcdKi+fcsopDB48OLA8kiRJirHolCRJkiRJkpT0LDolSZJUa5SUlAQdQZIkSQGx6JQkSVLS2nvvvRkxYgTnnXceTZo0YeDAgXz55ZcMGjSI+vXr07x5c0466SSWLVtW+ZhIJMKNN97INttsQ0ZGBu3ateO6666rvP2SSy5h2223JTs7m06dOnHFFVdQWloaxMuTJEnSFrDolCRJUlJ79NFHSU9PZ8yYMdxwww3su+++7LjjjkycOJG3336bJUuWcPTRR1fe/69//Ss33HADV1xxBTNmzOCpp56iefPmlbfn5OTwyCOPMGPGDG677Tbuv/9+brnlliBemiRJkrZAKBqNRoMOIUmSJG2NvffemzVr1jB58mQA/vGPf/Dxxx/zzjvvVN7nhx9+oG3btsyaNYuWLVvStGlT7rzzTs4444zN+h7/+c9/eOaZZ5g4cSIQO4zo5ZdfZsqUKUDsMKJVq1bx8ssvx/W1SZIkacukBh1AkiRJ+j369u1b+fUXX3zBBx98QP369Te63+zZs1m1ahXFxcXst99+v/h8zz77LLfffjuzZ89m3bp1lJWVkZubWy3ZJUmSFD8WnZIkSUpq9erVq/x63bp1HHroofzrX//a6H4tW7Zkzpw5v/pc48aN44QTTuDqq69m4MCB5OXl8cwzz3DTTTfFPbckSZLiy6JTkiRJtUafPn144YUX6NChA6mpG/+q26VLF7Kyshg1atQml66PHTuW9u3bc9lll1Ve991331VrZkmSJMWHhxFJkiSp1hg+fDgrVqzguOOOY8KECcyePZt33nmHU089lfLycjIzM7nkkku4+OKLeeyxx5g9ezaffvopDz74IBArQr///nueeeYZZs+eze23385LL70U8KuSJEnS5rDolCRJUq3RqlUrxowZQ3l5OQceeCC9e/fmvPPOo0GDBoTDsV99r7jiCv7v//6Pv//973Tv3p1jjjmGpUuXAnDYYYdx/vnnM2LECHbYYQfGjh3LFVdcEeRLkiRJ0mby1HVJkiRJkiRJSc8ZnZIkSZIkSZKSnkWnJEmSJEmSpKRn0SlJkiRJkiQp6Vl0SpIkSZIkSUp6Fp2SJEmSJEmSkp5FpyRJkiRJkqSkZ9EpSZIkSZIkKelZdEqSJEmSJElKehadkiRJkiRJkpKeRackSZIkSZKkpGfRKUmSJEmSJCnpWXRKkiRJkiRJSnoWnZIkSZIkSZKSnkWnJEmSJEmSpKRn0SlJkiRJkiQp6Vl0SpIkSZIkSUp6Fp2SJEmSJEmSkp5FpyRJkiRJkqSkZ9EpSZIkSZIkKelZdEqSJEmSJElKehadkiRJkiRJkpKeRackSZIkSZKkpGfRKUmSJEmSJCnpWXRKkiRJkiRJSnoWnZIkSZIkSZKSnkWnJEmSJEmSpKRn0SlJkiRJkiQp6Vl0SpIkSZIkSUp6Fp2SJEmSJEmSkp5FpyRJkiRJkqSkZ9EpSZIkSZIkKelZdEqSJEmSJElKehadkiRJkiRJkpKeRackSZIkSZKkpGfRKUmSJEmSJCnpWXRKkiRJkiRJSnoWnZIkSZIkSZKSnkWnJEmSJEmSpKRn0SlJkiRJkiQp6Vl0SpIkSZIkSUp6Fp2SJEmSJEmSkp5FpyRJkiRJkqSkZ9EpSZIkSZIkKelZdEqSJEmSJElKehadkiRJkiRJkpKeRackSZIkSZKkpGfRKUmSpKS1995706tXr6BjSJIkKQFYdEqSJEkbKCws5PTTT6dXr17k5eVRv359tt9+e2677TZKS0ur3HfUqFGcdtppbLvttmRnZ9OpUyfOOOMMFi1a9IvPf8cdd5CXl1f5XIsWLeLMM8+kY8eOZGVl0blzZy644AKWL1++yce/9tprhMNhFi9e/KuvY+bMmVx88cXssMMO5OTk0LJlSw4++GAmTpy4he+IJElSckgNOoAkSZKUSAoLC5k+fToHHXQQHTp0IBwOM3bsWM4//3zGjx/PU089VXnfSy65hBUrVnDUUUfRpUsX5syZw5133snrr7/OlClTaNGixUbP/8Ybb3DggQeSlpbGunXr2HXXXcnPz2fYsGG0bduWL774gjvvvJMPPviASZMmEQ6HN3p83759N/ncG3rggQd48MEH+dOf/sSwYcNYvXo19957L7vssgtvv/02+++/f3zeMEmSpAQRikaj0aBDSJIkSRXy8/OpV6/eZt137733ZtmyZXz55ZfVnAr+/Oc/c+edd7Jo0aLKknH06NHsvvvuVcrI0aNHs9dee3HZZZfxj3/8o8pzFBQU0LhxY0aOHMkpp5zCU089xQknnMDrr7/OwQcfXHm/K6+8kmuuuYbJkyez4447VnmOdu3acdppp3HVVVdtMmdRURHp6el8/vnndO3alfr161fetnz5crp37862227LJ5988nvfEkmSpITi0nVJkiQBsHbtWs477zw6dOhARkYGzZo144ADDmDy5MlV7jd+/Hj+8Ic/kJeXR3Z2NnvttRdjxoypcp/vvvuOYcOG0bVrV7KysmjcuDFHHXUU8+bNq3K/Rx55hFAoxEcffcSwYcNo1qwZbdq0qbz9rbfeYq+99iInJ4fc3Fz69etXZUZlhRkzZrDPPvuQnZ1N69atufHGGze6z/fff8/MmTO3+v3p0KEDAKtWraq8bs8999xoxuWee+5Jo0aN+OqrrzZ6jlGjRlFcXMygQYMAWLNmDQDNmzevcr+WLVsCkJWVVeX6adOmMX/+/MpS9MMPPyQUCvHMM89w+eWX07p1a7Kzs1mzZg19+/atUnICNG7cmD322GOT2SRJkpKdS9clSZIEwNlnn83zzz/PiBEj6NGjB8uXL+eTTz7hq6++ok+fPgD873//Y9CgQfTt25crr7yScDjMww8/zL777svHH39M//79AZgwYQJjx47l2GOPpU2bNsybN4+RI0ey9957M2PGDLKzs6t872HDhtG0aVP+/ve/k5+fD8RK0NNOO42ePXvy17/+lQYNGvD555/z9ttvc/zxx1c+duXKlfzhD3/giCOO4Oijj+b555/nkksuoXfv3pWFIsDJJ5/MRx99xOYuaCopKWHNmjUUFhYyceJE/vOf/9C+fXu22WabX33cunXrWLduHU2aNNnotjfffJO+fftWFpsVRelf/vIXbrrpJtq0acPUqVO57rrrGDx4MN26ddvo8c2aNWOnnXaqcv21115Leno6F154IcXFxaSnp/9ivsWLF28ymyRJUrKz6JQkSRIQ2/tx6NCh3HTTTZXXXXzxxZVfR6NRzj77bPbZZx/eeustQqEQAGeddRY9e/bk8ssv59133wXg4IMP5sgjj6zy/Iceeii77rorL7zwAieddFKV2xo1asSoUaNISUkBYPXq1Zx77rn079+fDz/8kMzMzCo5NrRw4UIee+yxyuc8/fTTad++PQ8++GCVonNLvfjiixx33HGVl3faaSceeughUlN//VfoW2+9lZKSEo455piNbnvzzTc59dRTKy/36NGD++67jwsvvJBdd9218vohQ4bwwAMPbPT4N954g0GDBlW+9xWKioqYOHHiRjNAf+7jjz9m3LhxXH755b96P0mSpGRk0SlJkiQAGjRowPjx41m4cCGtWrXa6PYpU6bwzTffcPnll290Ivh+++3H448/TiQSIRwOVyncSktLWbNmDdtssw0NGjRg8uTJGxWdQ4cOrSw5Ad577z3Wrl3LpZdeWqXkBDYq+erXr8+JJ55YeTk9PZ3+/fszZ86cKvf78MMPN++NWG+fffbhvffeY9WqVYwaNYovvviicrbpLxk9ejRXX301Rx99NPvuu2+V27788ku+//77KntxArRu3Zr+/ftz0EEH0b59ez7++GNuv/12mjRpwn/+85/K+61atYpx48bx5z//eaPvO2TIkN8sOZcuXcrxxx9Px44dqxTYkiRJtYVFpyRJkgC48cYbGTJkCG3btqVv374cdNBBnHzyyXTq1AmAb775BoiVar9k9erVNGzYkMLCQv75z3/y8MMPs2DBgiqzMFevXr3R4zp27Fjl8uzZswHo1avXb+Zu06bNRuVnw4YNmTp16m8+9tc0b968con5kUceyfXXX88BBxzAN998s8kTz2fOnMnhhx9Or169fnE2ZvPmzassOx8zZgyHHHIIn376aeX1gwcPJjc3l6uvvprTTjuNHj16APDOO+8AcOCBB2703D9//34uPz+fQw45hLVr1/LJJ59stHenJElSbeBhRJIkSQLg6KOPZs6cOdxxxx20atWKf//73/Ts2ZO33noLgEgkAsC///1v3nvvvU1+VBRof/7zn7nuuus4+uijee6553j33Xd57733aNy4ceXzbOi3ZiP+mg1ngm5oc/fi3FxHHnkk69at45VXXtnotvnz53PggQeSl5fHm2++SU5Ozkb3efPNN/nDH/5QpZS99957Nyo/AQ477DCi0Shjx46t8vgBAwaQl5e30XP/2vtXUlLCEUccwdSpU3nllVc2qzyWJElKRs7olCRJUqWWLVsybNgwhg0bxtKlS+nTpw/XXXcdgwYNonPnzgDk5uay//77/+rzPP/88wwZMqTKfp9FRUVVTiz/NRXf68svv/zNw39qSmFhIbDxjNTly5dz4IEHUlxczKhRoypPTN/QqlWrGDt2LCNGjKhy/ZIlSygvL9/o/qWlpQCUlZUBsdL27bff5sILL9yizJFIhJNPPplRo0bx3HPPsddee23R4yVJkpKJMzolSZJEeXn5RgVes2bNaNWqFcXFxQD07duXzp0785///Id169Zt9Bw//vhj5dcpKSkbzai84447NlnqbcqBBx5ITk4O//znPykqKqpy29bO1Pz++++ZOXPmb95v2bJlm/weFcvRN5x9mZ+fz0EHHcSCBQt488036dKlyyafs+KQpp8vO992221ZsmTJRvuHPv300wDsuOOOQOwU+6VLl260v+dv+fOf/8yzzz7L3XffzRFHHLFFj5UkSUo2zuiUJEkSa9eupU2bNhx55JFsv/321K9fn/fff58JEyZUzsoMh8M88MADDBo0iJ49e3LqqafSunVrFixYwAcffEBubi6vvfYaAIcccgiPP/44eXl59OjRg3HjxvH+++/TuHHjzcqTm5vLLbfcwhlnnEG/fv04/vjjadiwIV988QUFBQU8+uijW/waTz75ZD766KPfLEqfeOIJ7rnnHgYPHkynTp1Yu3Yt77zzDu+99x6HHnpolUOGTjjhBD777DNOO+00vvrqK7766qvK2+rXr8/gwYOB2P6cu++++0bLzkeMGMHDDz/MoYceyp///Gfat2/PRx99xNNPP80BBxzAzjvvXPn4Dh06VO7XuTluvfVW7r77bnbddVeys7N54oknqtx++OGHU69evc1+PkmSpERn0SlJkiSys7MZNmwY7777Li+++CKRSIRtttmGu+++m3POOafyfnvvvTfjxo3j2muv5c4772TdunW0aNGCnXfembPOOqvyfrfddhspKSk8+eSTFBUVMWDAAN5//30GDhy42ZlOP/10mjVrxg033MC1115LWloa3bp14/zzz4/ra/+53XffnbFjx/L000+zZMkSUlNT6dq1KzfffPNGJ55PmTIFgIceeoiHHnqoym3t27dn8ODBv7rsvGvXrkyaNInLL7+cJ554gsWLF9OqVSsuvPBCrr766sr7vfnmmxx00EFb9Doqso0bN45x48ZtdPvcuXMtOiVJUq0SisZ7l3ZJkiRJlT777DN23nlnpk+fvkUzMissWbKEli1b8vrrr29x2SlJklSXuEenJEmSVM2uv/76rSo5IXb40d///nf22WefOKeSJEmqXZzRKUmSJEmSJCnpOaNTkiRJkiRJUtKz6JQkSZIkSZKU9Cw6JUmSJEmSJCW91KAD1GaRSISFCxeSk5NDKBQKOo4kSZIkSZKUVKLRKGvXrqVVq1aEw78+Z9OisxotXLiQtm3bBh1DkiRJkiRJSmrz58+nTZs2v3ofi85qlJOTA8T+Q+Tm5gacRjWhtLSUd999lwMPPJC0tLSg40hbzbGs2sBxrNrCsazawrGs2sKxrNoiWcbymjVraNu2bWXP9mssOqtRxXL13Nxci846orS0lOzsbHJzcxP6h4T0WxzLqg0cx6otHMuqLRzLqi0cy6otkm0sb862kB5GJEmSJEmSJCnpWXRKkiRJkiRJSnoWnZIkSZIkSZKSnnt0SpIkSZIkCYBIJEJJSUnQMVQDSktLSU1NpaioiPLy8kCzpKenEw7//vmYFp2SJEmSJEmipKSEuXPnEolEgo6iGhCNRmnRogXz58/frIN+qlM4HKZjx46kp6f/ruex6JQkSZIkSarjotEoixYtIiUlhbZt28Zldp0SWyQSYd26ddSvXz/Q/96RSISFCxeyaNEi2rVr97tKV4tOSZIkSZKkOq6srIyCggJatWpFdnZ20HFUAyq2KcjMzAy82G7atCkLFy6krKyMtLS0rX4e63lJkiRJkqQ6rmKPxt+7dFjaGhXj7vfuFWrRKUmSJEmSJIDA92pU3RSvcWfRKUmSJEmSJCnpWXRKkiRJkiRJCeCUU05h8ODBQcdIWhadkiRJkiRJSloLFizgxBNPpHHjxmRlZdG7d28mTpy4yfueffbZhEIhbr311t983gkTJrDffvvRoEEDGjZsyMCBA/niiy/inF7xZNEpSZIkSZKkpLRy5UoGDBhAWloab731FjNmzOCmm26iYcOGG933pZde4tNPP6VVq1a/+bzr1q3jD3/4A+3atWP8+PF88skn5OTkMHDgQEpLS6vjpSgOLDolSZIkSZKUlP71r3/Rtm1bHn74Yfr370/Hjh058MAD6dy5c5X7LViwgD//+c88+eSTpKWl/ebzzpw5kxUrVnDNNdfQtWtXevbsyZVXXsmSJUv47rvvfvFxX3zxBfvssw85OTnk5ubSt2/fytmlV111FTvssEOV+99666106NBho+e5+uqradq0Kbm5uZx99tmUlJRU3vb888/Tu3dvsrKyaNy4Mfvvvz/5+fnAT0vff+3xb7/9NrvvvjuNGjWiU6dOHHroocyePbvK9//hhx847rjjaNSoEfXq1WOnnXZi/Pjxlbe/8sor9OnTh8zMTDp16sTVV19NWVnZb76v1S016ACSJEmSJElKLNFolMLS8kC+d1Zaymafwv3qq68ycOBAjjrqKD766CNat27NsGHDGDp0aOV9IpEIJ510EhdddBE9e/bcrOft2rUrjRs35sEHH+Rvf/sb5eXlPPjgg3Tv3n2TxWSFE044gR133JGRI0eSkpLClClTNqtY3dCoUaPIzMzkww8/ZN68eZx66qk0btyY6667jkWLFnHcccdx4403cvjhh7N27Vo+/vhjotHoZj0eID8/nwsuuIBevXqxZMmSyueaMmUK4XCYdevWsddee9G6dWteffVVWrRoweTJk4lEIgB8/PHHnHzyydx+++3ssccezJ49mzPPPBOAK6+8cotea7xZdEqSJEmSJKmKwtJyevz9nUC+94xrBpKdvnmV1Zw5cxg5ciQXXHABf/vb35gwYQLnnnsu6enpDBkyBIjN+kxNTeXcc8/d7Aw5OTl8+OGHDB48mGuvvRaALl268M4775Ca+svZvv/+ey666CK6detW+ZgtlZ6ezkMPPUR2djY9e/bkmmuu4aKLLuLaa69l0aJFlJWVccQRR9C+fXsAevfuvdmPD4fD/OlPfwJiBXCzZs148MEHad68OTNmzKBXr1489dRT/Pjjj0yYMIFGjRoBsM0221Q+/9VXX82ll15a+f526tSJa6+9losvvjjwotOl65IkSZIkSUpKkUiEPn36cP3117Pjjjty5plnMnToUO655x4AJk2axG233cYjjzzyi7NEBw0aRP369alfv37ljM/CwkJOP/10BgwYwKeffsqYMWPo1asXBx98MIWFhQCVj6lfvz5nn302ABdccAFnnHEG+++/PzfccMNGS8I3x/bbb092dnbl5V133ZV169Yxf/58tt9+e/bbbz969+7NUUcdxf3338/KlSs3+/EA33zzDccddxzbbLMN7dq1o1OnTkCspAWYMmUKO+64Y2XJ+XNffPEF11xzTZXXP3ToUBYtWkRBQcEWv954ckanJEmSJEmSqshKS2HGNQMD+96bq2XLlvTo0aPKdd27d+eFF14AYsusly5dSrt27SpvLy8v5//+7/+49dZbmTdvHg888EBleVmxzPypp55i3rx5jBs3jnA4XHldw4YNeeWVVzj22GOZMmVK5XPm5uYCsX04jz/+eN544w3eeustrrzySp555hkOP/xwwuFwlSXmwBYfbJSSksJ7773H2LFjeffdd7njjju47LLLGD9+PB07dtys5zj00ENp37499957L7m5uWRnZ7PddttV7uOZlZX1q49ft24dV199NUccccRGt2VmZm7R64k3i05JkiRJkiRVEQqFNnv5eJAGDBjArFmzqlz39ddfVy7rPumkk9h///2r3D5w4EBOOukkTj31VABat2690fMWFBQQDoerzAKtuFyxV+WGy7k3tO2227Ltttty/vnnc9xxx/Hwww9z+OGH07RpUxYvXkw0Gq183g3L0gpffPEFhYWFlYXjp59+Sv369Wnbti0Q+28zYMAABgwYwN///nfat2/PSy+9xAUXXPCbj1++fDmzZs3i/vvvZ8CAAaxZs4apU6dW+f7bbbcdDzzwACtWrNjkrM4+ffowa9asX3z9QXLpuiRJkiRJkpLS+eefz6effsr111/Pt99+y1NPPcV9993H8OHDAWjcuDG9evWq8pGWlkaLFi3o2rXrLz7vAQccwMqVKxk+fDhfffUV06dP59RTTyU1NZV99tlnk48pLCxkxIgRfPjhh3z33XeMGTOGCRMm0L17dwD23ntvfvzxR2688UZmz57NXXfdxVtvvbXR85SUlHD66aczY8YM3nzzTa688kpGjBhBOBxm/PjxXH/99UycOJHvv/+eF198kR9//LHye/zW4xs2bEjjxo257777+Pbbbxk9ejQXXnhhle9/3HHH0aJFCwYPHsyYMWOYM2cOL7zwAuPGjQPg73//O4899hhXX30106dP56uvvuKZZ57h8ssv37L/eNXAolO12rzV8/hh7Q+sLFpJcXnxRlPEJUmSJElS8urXrx8vvfQSTz/9NL169eLaa6/l1ltv5YQTTvhdz9utWzdee+01pk6dyq677soee+zBwoULefvtt2nZsuUmH5OSksLy5cs5+eST2XbbbTn66KMZNGgQV199NRBbUn/33Xdz1113sf322/PZZ59tVDIC7LfffnTp0oU999yTY445hsMOO4yrrroKiC2RHz16NAcddBDbbrstl19+OTfddBODBg3arMeHw2GeeeYZJk2axHbbbcff/vY3/vWvf1X5/unp6bz77rs0a9aMgw46iN69e3PDDTeQkhLbUmDgwIG8/vrrvPvuu/Tr149ddtmFW265pXIWbZBCUZufarNmzRry8vJYvXp15V4Nqln7/Xc/lhYsrbycGkolOy2b7LRs6qXWo15aPbLSsqiXWi92XVrsc3bq+q9TN75uw8tZqVlVprGXlpby5ptvctBBB1Xu6yElI8eyagPHsWoLx7JqC8eyaovaOpaLioqYO3cuHTt2DHyfRW29U045hVWrVvHyyy//5n0jkQhr1qwhNze3ch/SoPza+NuSfi3xN1uQfofMlEyyUrMoLIttKlwWLWNNyRrWlKyJy/OHCFWWptlpseKzcG0h73z4DvUz6lctR1OzNypZKy5vWKymhDd/02VJkiRJkiTFWHSqVnvjiDcAKI+UU1hWSH5pPvll+RSWrv+6NJ+CsgLyS/N/un3D60oLyS9bf11pAQWlBeSXxb6Orv+/isdQ+NP3nbdw3lZnzkzJ/Gl26foCtGLW6YazSSvus2GJWmUW6vrPaSm15y+MkiRJkiRJv8SiU3VCSjiF+un1qZ9ePy7PF41GKSwrpKBsffm5vhxdU7iGMRPG0LV3V4oiRVWK0Q3vt+HXFSVqWbQMgKLyIorKi1hRtCIuWdPCaVVmnW749S+WpBvOMv3ZYzNTMqss15ckSZIkSYnhkUceCTpCoCw6pa0QCoUqiz+yfrq+tLSUNV+s4aDOW75XS0l5yUbl54ZF6a+VpBVfbzgztbi8OJYpUsrq4tWsLl4dl9ceDoWpl7p+lukvlaMbXvcrS/XrpdUjKzWLcMhz0SRJkiRJ0u9j0SkliPSUdNJT0mlIw7g8X1mkrLIY/Xk5umEhuqlidcMCdcPHAkSiEdaWrmVt6dq45ATISs3aqPysnE36s1mnG84u/aU9UNPCLteXJEmSJKmusehUwvrxjjtZ8cgjhLOzCWVnEc6uRzgri3B29vrPWYSyswlnZf90Xb3Y51DW+vtn/3T/yuuyMgml1P4Df1LDqeSm55Kb/usnkm2uSDRCUVnRLxamFeVolT1QN1i2v+FjKgrU8mg5AIVlhRSWFbK8aHlcsqaH0zeaUVpRlFaUqFXK0Q0L058v20+rR3o43eX6kiRJkiQlOItOJazIunVE8vOJ5OfH/blDmZk/labZWYQ2LEvXXxfOzo6VoxW3/cp1FY+jFpdh4VD4p+X6cRCNRikuL97kEvzNXapf5fbSAkoiJQCUREooKS5hZfHKuGRNDaVuvFR/c/Y2/YX7ZaVmWZxKkiRJkhRnFp1KWE2GnUPD444lUlBApLCQSEHh+q8LiBQUEP2l6/LX37/wp9ui668jGgUgWlREeVER5SvjU4RVSk2lc2oqc2+6mZTs7A1mo66febphubrhdfV+uq3KbNSK6zIzCYVr1z6WoVCIzNRMMlMzaZTZKC7PWRop3eRS/Z+Xo5tbrBaWFQJQFi1jbcla1pbEZ7l+iFCVmaVVlupveGjUL5SoP1+qn52aTWrYH+eSJEmSpLrNfxkrYaXk5ZGSlxe354tGo0SLijYoTfPXl6UFm76uYIPbKorUDa9b/zlaUEC0tDT2TcrKSCkri5WocUseE6qYObrh8v162VVno1Zet0G5WjHzNGuDcrXeT/cPbeGhSYksLZxGXkYeeRnxGTflkXIKywp/uRzdYGn+5izVLygrIBKNECUau66sgB8Lf4xL1syUzMrSs7Ik/aUDon6hMN1wD9QQzjiVJEmSJCUXi07VGaFQaP2y8yyIzwTCStHSUiKFhZSsXs0H77zDHv36kVJS8lMhusHM0yqzTivK0sKCja6rmKFa+T0KCigvKIh/gZqWFitAN1y6n5VVdSbqBvuibjTr9Od7oFbcJyMj6Zdnp4RTqJ9en/rp9ePyfNFolKLy2D6nhaWFVQ5+qrK36QYzT6vsgbqJpfxlkTIAisqLKCovYgUr4pI1NZxKWjSNO1++k/rp9Tdagr/h/qcb7oe6qRK1Xlo9MlMyk348SJIkSVIi2Hvvvdlhhx249dZbg46ScCw6pTgIpaWRkpZGalYWpU2bktmjB2lxmCkZjURis1A3nEVasMEy/Sqlaf7PlvP/bFl/QWGV56A8VplGS0uJrl5NZPXq3523inD4p8J0E3ugbnLW6a/sgRraYCZqsh4mFQrFlqxnpWZBVnyes7S8tMps0s3a23QTs1ErHlNUXgRAWaSMMsooLCiEgt+fMxwKx4rPn804rZdar3L/0w0Pjfqt2ahZqVmkhJNzHEiSJEmKr9GjR/Pvf/+bSZMmsWjRIl566SUGDx4MQGlpKZdffjlvvvkmc+bMIS8vj/33358bbriBVq1aVT7H119/zUUXXcSYMWMoKSlhu+2249prr2Wfffb51e/9zjvvcOWVVzJ9+nQyMzPZc889uemmm+jQoUM1vmL9EotObbXS8gghIDWldu0dmUhC4XDlbMt4ikajsYKzojj92X6n0V/bF/Vne6D+fDZqtLg49k0ikdhBUvn58Z+FmpHx0/6lmyhSf2lf1F/cAzUri3C9erHZrUk26zAtJY0GKQ1oQIO4PF9ZpIzCskJWF67mrVFv0W+3fhRHizcuRzcsVn/pgKj1X0eJEolGWFe6jnWl66Dwt3NsjqzUrE3ONP3VA6I23AP1Z8VqWrj2bOMgSZIk1SX5+flsv/32nHbaaRxxxBFVbisoKGDy5MlcccUVbL/99qxcuZK//OUvHHbYYUycOLHyfocccghdunThf//7H1lZWdx6660ccsghzJ49mxYtWmzy+86dO5c//vGPXHDBBTz55JOsXr2a888/nyOOOILJkydX62vWpll0aqu9/PkCLnp+KjkZqeRmpdEgO428DT7nZqXRICudvKyq1+dlpZGXnUZORmrSlUq1RSgUIpSeDunppDRoENfnjpaXEyks2uI9UKMbXbf+/htcRyQS+x7FxZQXF1O+alVcs5OSUnX5fr2fLd3f5B6oGxaoG++BWnFbshwmlRpOJSc9h8xQJs1SmtGzcc/fNTs5Eo1QVFb0mwc//bwwrbK36fpiteI+5dFYdV5YVkhhWSHLi5bH5bWnhdM2PiDqlwrTnxWrVZb0r79fRkryb98gSZIkJYNBgwYxaNCgTd6Wl5fHe++9V+W6O++8k/79+/P999/Trl07li1bxjfffMODDz7IdtttB8ANN9zA3XffzZdffvmLReekSZMoLy/nH//4B+H1/+a78MIL+eMf/0hpaekv/lvqww8/5OKLL2b69OmkpaXRs2dPnnrqKdq3b88pp5zCqlWrePnllyvvf9555zFlyhQ+/PDDyuvKysoYMWIEjz/+OGlpaZxzzjlcc801lf8Gufvuu7nllluYP38+eXl57LHHHjz//PNAbOl7r169iEajPP7446Snp2/0+Mcff5zbbruNWbNmUa9ePfbdd19uvfVWmjVrVplh+vTpXHLJJYwePZpoNMoOO+zAI488QufOnQF44IEHuOmmm5g7dy4dOnTg3HPPZdiwYZt8T+LFolNbbXVh7ACetcVlrC0uY8GqLZumFQ7xU/GZlUZedqwUbZBVtRD9eVHaICudzLSwBUKCCqWkkFK/Hin168X1eaPRKNHi4soDoKou5//ZHqibsS9qbDn/+j1SS0pi36S8nMi6dUTWrYtrdqByf9jf2gM19PPrKpbrb2Jf1HBWVqywTmDhULhyBmWTrCa/+/mi0SglkZKfDoDaxD6mG123vkDd1KFR+aX5lERi//1LI6WsKl7FquJVvzsnQEooZZP7mf58qf6vHhC1wR6oWalZhP+fvfsOj6O8+j7+ne1FvUvuvcuWreICxjXuhBZCyYMJnZgAIRVCCAl5E55QQw0t9PaQhAC2cWxs07GKmyz3XtW7drV93j9GWmtVbK0tWy7nk2suy6udnXuFImt/e+5zlLMjMBdCCCGEEOcAVQVvF/SxOhFGG5zC1/y1tbUoikJMU+FPfHw8Q4YM4Y033mDs2LGYzWZeeOEFkpKSGDduXIePM27cOHQ6Ha+++irXX389DQ0NvPnmm8yYMaPDkNPn83HJJZdw88038+677+LxeMjLyws743j99de58cYbycvLo6CggFtuuYXevXtz8803U1BQwJ133smbb77JxIkTqaqq4quvvmpz/g033MDKlSvZtm0bt912W/B80Lb8P/TQQwwZMoSysjLuuecerr/+epYuXQrA4cOHmTx5MlOmTGHVqlVERUXxzTff4PNpcyTefvttHnjgAZ555hkyMjJYv349N998M3a7nYULF4b1XMMhQac4YddP7MulGT2obfRS2+ilptFLXaOXGqf36G3Bjz0ht7l9AQIqVDu9VDu9YV/bpNeFVJE2B6RtbmtZRdpUXWoySFBwNlIUBcViQWexQGxslz626vOdVA/UYDVqO0Fq8BqNjfgbG/FXdc2woCCjMSRADQ1S7e1v3T9OD1SdzYZ6hvZBVRQFs96MWW8m1tI13wfegBen19luYNruVv12tue3DlkB/Kqfem899d76LlkncNyt+s2VqG0+385WfZvBhkEnvwYIIYQQQogOeJ3w57Tj3+9UuO8ImLq2eKaZy+Xi17/+NVdffTVRUVGA9jrjs88+45JLLiEyMhKdTkdSUhLLli0j9hivP/v168fy5cu58sorufXWW/H7/UyYMCEYBranrq6O2tpa5s+fH6x8HDZsWNjPo1evXjzxxBMoisKQIUPYtGkTTzzxBDfffDMHDhzAbrczf/58IiMj6dOnDxkZGW3Of/zxx6mvr2fcuHFs3rw5eD7ADTfcELxv//79eeqpp8jKyqKhoYGIiAieffZZoqOjee+994Kh7uDBg4Pn/P73v+exxx4LthLo168fW7Zs4YUXXpCgU5yZDHod8RFm4iPMYZ/r8vq1ULRNIOql1ukJCU+1245+3hdQ8fgDVDS4qWhwh31tm0kfWknaOhS1mULC0+aPo6xG9DqpIj0XKQYD+shI9JGRXfq4qqqGDpNyNPU07XQP1NCt+yHDpJreJcPrJeD1Eqir69K1oygMNBrZ+9dHmkJR2zH7ooZUo7aoRG2vL6piOLP+6THqjESbo4k2R3fJ4wXUQDA07WjwU3u9TVsHqw6vg0ZvIw6fg4CqtW1w+rRzKhorumStZr25c71NO9kD1aQzSbW9EEIIIYQ4Y3m9Xq688kpUVeX5558P3q6qKosWLSIpKYmvvvoKq9XKyy+/zIIFC8jPzyc1NZURI0awf/9+AC688EI+/fRTSkpKuPnmm1m4cCFXX3019fX1PPDAA1xxxRWsWLGCgwcPMnz48OB17rvvPu677z6uv/56Zs2axcyZM5kxYwZXXnklqampYT2X8ePHh/zuPWHCBB577DH8fj8zZ86kT58+9O/fn9mzZzN79mwuvfRSbC3mfxzrfL1ez9q1a3nwwQfZuHEj1dXVBJpayR04cIDhw4ezYcMGLrzwwnYrVx0OB7t37+bGG28MBqegVbNGR3fN666OnFmvNsV5w2LUYzHqSYqyhHWeqqo4PP5g+FnT6GlbRdr0Z8vba5we6t0+VBWcHj9Oj5/iWlfY6460GFpVkZpCqkgjTDp2VyrE7qkkLsJ69HbpR3peUhQluGW9q6keT6sK0nB7oDaFq60qVFVX0/8vVBWdx4O/shJ/Zdf0wGymmEyhPVBbVqTaWwSjTdv52/RAba5GDQav2m2K6cwI2XSKLlhV2RVUVcXtdx87HG3Z2/QYW/Wb7+cNaJX0br8bt99Ntbu6S9ZqUAztb9Vvp5q0o8C05d+tBusZ8d9UCCGEEOK8ZLRplZXdde0u1hxy7t+/P7jVutmqVatYvHgx1dXVwdufe+45VqxYweuvv85vfvMbli5diter/R5tbXqN11zV+Ne//jX4WG+99Ra9evUiNzeXzMxMNmzYEPxcXFwcAK+++ip33nkny5Yt4/333+f+++9nxYoVjB8/Hp1Oh6qqbdYejsjISNatW8fnn3/O8uXLeeCBB3jwwQfJz88Pbtc/FofDwaxZs5g1axZvv/02iYmJHDhwgFmzZuFpav1mPcbr3IamdnAvvfQSOTk5IZ/Tn+LdgxJ0irOKoihEmA1EmA30iAkvPPIHVBpcPmpabaMPVpK2CE+1v/uC1aUOjzb8pN7lo97l4+Axx0breXXH2tBbdMqxq0hDbgsd4GQxnplbiEX3Ukwm9CYT+i5+N0wNBFAbG3HX1bH600+ZnJ2NzuM9qR6owWFSfu3/R6rHg9/jgdraLl07Ol1o1WnLrfsd9UC1tVN1amvVF9VqQenGrfyKomAxWLAYLMRb47vkMb1+b5vBTy0rSFsOjmq9pb+9rfrN2/V9qo86Tx11nq6pMFZQQrbdt64gPV5vU5NiotxfTqmzlGhrNDaDDb1OfqYKIYQQQnSKopyy7eOnW3PIuXPnTlavXk18fOjv1c6mtmO6VkNkdTpdsJKxT58+bR7X6XS2Oac5yAsEAhgMBgYOHNjumjIyMsjIyODee+9lwoQJvPPOO4wfP57ExESKiopC7rthw4Y2lZO5ubkhf1+zZg2DBg0KXt9gMDBjxgxmzJjB73//e2JiYli1alVwK/mxzt+2bRuVlZU8/PDD9OrVCyBkQj1Aeno6r7/+ertDl5KTk0lLS2PPnj1ce+217T7/U0WCTnHe0OsUbbiRLfwJ0h5fgDrX0XC0rvFopWht49HwtMbhYe+RMvSWCOpcPmoavXh8AfwBlSqHhyqHJ+xrmwy6kC30MTZtG31zRWm01RAMR1v3KDXqpR+pCI+i06HY7RhMJrzx8ZiHDDmpqevNVFXVqlCdLcLSYEDaYut+p3qgtqhGbWxEdTe1sAgECDgcBByOk15va4rFErpNv72t++1VnR6jL2pzFWp3MOqNROu7bru+P+A/GoL6mgLTYw2Iam/7fqt+qGrT/5rPOeb7S8fxt//8LfixRW9p29u0qeq0ZYDafJ8Ot+03/WnUn/z/P4QQQgghxMlpaGhg165dwb/v3buXDRs2EBcXR2pqKldccQXr1q1j8eLF+P1+SkpKAK3C0mQyMWHCBGJjY1m4cCEPPPAAVquVl156ib179zJv3rwOrztv3jyeeOIJ/vjHPwa3rt93333t9sRsubYXX3yRiy++mLS0NLZv387OnTu57rrrAJg2bRqPPPIIb7zxBhMmTOCtt96iqKiozeMdOHCAe+65h1tvvZV169bx9NNP89hjjwGwePFi9uzZw+TJk4mNjWXp0qUEAgGGDBkScv7Pf/5zrrnmGnbs2BFyfu/evTGZTDz99NPcdtttFBUV8dBDD4Vc/4477uDpp5/mqquu4t577yU6Opo1a9aQnZ3NkCFD+MMf/sCdd95JdHQ0s2fPxu12U1BQQHV1Nffcc09n/9OGTYJOITrBZNCREGEm4Tj9SL1eL0uXLmXu3EnBcMjl9bca0OQJrSJtr7K06fAHVDy+AGX1bsrqw+9Ham/uR2rTAtFgOGrruLI0xmoi0mJAJ/1IRRdSFAXFbEZnNp+aYVIuV5g9UDvqi9oUrjbdRtOWEdXlwu9y4a/umi3eQQZDm2FSJ9YDtVUfVYsFRXf63ujQ6/REmiKJNHVNn1tVVWn0NbZbQdo6HG2vB2pw277XQW1jLV68+FStr63L78Lld1Hl6prBYEadsU3VachW/c70QG1xrkVvke36QgghhBBhKigoYOrUqcG/NwdpCxcu5MEHH+Tjjz8GYMyYMSHnrV69milTppCQkMCyZcv47W9/y7Rp0/B6vYwYMYKPPvqI0aNHd3jdadOm8c477/DXv/6Vv/71r9hsNiZMmMCyZcs63Npts9nYtm0br7/+OpWVlaSmprJo0SJuvfVWAGbNmsXvfvc7fvWrX+Fyubjhhhu47rrr2LRpU8jjXHfddTQ2NpKdnY1er+euu+7illtuASAmJoZ///vfPPjgg7hcLgYNGsS7777LiBEj2pw/ffp0DAZDyPmJiYm89tpr3HfffTz11FOMHTuWRx99lIsvvjh4fnx8PKtWreKXv/wlF110EXq9njFjxjBp0iQAbrrpJmw2G4888gi//OUvsdvtjBo1irvvvrvDr2dXUNTWG/9Fl6mrqyM6Opra2tqQ3g/i3HU06Jx70lVwqqrS4Pa1rSJtFY5qQ51Ct+PXu3wndW1FgUjz0UrR0CrSttWlLcNTu0kvL9LPAV35vXy2Cg6Taq8Haif6orbXAzXQ2IjqdKKG2WPnRCjNwWebHqgtQtGQvqjN1agttu8332Y/en/lLPp+aP4+njNnDujpcPBTRyFp899bbvNvrkx1+8N/86kzdIoOu6GpyrSjcLTlbS226rfe3m832rEarOgUqe4/28nPZHGukO9lca44V7+XXS4Xe/fupV+/flgs4c3TEGeXKVOmMGbMGB5//HHq6uqIiopqswX/dDvW9184+ZpUdApxhlIUhUiLkUiLkZ5hFsD5A2owGA2ZXt9iqn1HVaROjx9VhTqXj7oTCEwNLfuR2tqGo9GtepA2fz7KKv1IxZklZJhUXNc+tur1Hg0/nS0GRnW6B6ojuHU/pC9q49H93arTid/pxN+1S0cxGoMDoFoGqYqt1db9zvZAbb6P2XzK3iRRFAWj3ohJbyKWrqko9gV8R4PQVoOfOupt2hystrtt36f1hQqoAeq99dR767tknQBWg7VN+BmsJm1VddpeP9TWPVCNunPnBZ0QQgghhDi3SNApxDlIr1OItZuItYff/8/jCzSFnu0PbWquLm0Znjbf5vEH8AVUKh0eKk+gH6nZoGs1oMnUdmt9B9WlBulHKs4iitGI3mhE38XV/mogoFWhhoSmLbbph4SmrapRO+iB2nx+cJiU14taW0vgVAyTslhQ7K3D0hPrgapYbQRMRmhqHt/VDDoDUaYookxd898woAZw+VzH7m3aeqt+O9v2WwarflX7b9Y8MKrSVdklazXpTG0qSpuD0uYQNSQcbRmYtt62b7Rj0plkJ4AQQgghhOgSEnQKIUKYDDoSI80kRh67H2lrqqri8gaODmZqWTHqbKe6tFWv0oAKbl+A0jo3pXXhbwmNMBs6nmrfqgdpy4rTSLP0IxXnDkWnC1ZbdiVVVbWA09l+D1S1nds66oGqNjpDKlRDhkk5ndDFVaiDgd0P/uGYPVA76osaEqS27otqt2vVrV0U0OkUXTA4TCTxpB9PVVXcfnfHvU1bbdVv734tg1an14knoL2B5Ql48Lg9VLu7pmetQTG03arfmd6mHdzParBKcCqEEEIIcQyff/45QHCi/LlEgk4hRJdQFAWrSY/VZCU1uv2myx0JBFQaPL7QQDQkHPW0mHQfWkVa79a21ze4fTS4fRyuCW80s04hWCHaUVAaYzUdrSK1Hf3TapR+pOL8oCiKNiHeZEIfE9Olj636/QQaXWH3QFVb3xYMXY/e1lzNqbrd+N1u/DU1Xbp29PqQHqjBatQ2fVHb9kDtqC9q8+dOdpiUoihYDBYsBgtxlq7pveD1ezsVjrbX27T57y3PbfRpP699qo96Tz31nq7Zrq+ghFSWhmzVbzk0qoMQtfVWfZvBhkEnvzILIYQQQpwN5Lc2IUS30+kUoixGoixGeoV5rs8foM7VPLSp1UR7Z+sq0tDw1OUNEFChxqkFp+Ey6pV2wlFTx5WlLapLzQbpRyoEgKLXo4+wo4+wd+njqqqKp6GB5Z98wvRJk9B7PB33QO1UX9Sjlauqp6k1h99PoKGBQENDl64dCPaHPV4PVCWMvqg6q1ULrE+QUW8kWh9NtDm6S56jP+A/WjHaXjjaQQ/UjoZIOX1OAmoAFVW7zeekvLG8S9Zq0VuCoWcwJO0oHO0gMG3ZA9WkP/H/DkIIIYQQomMSdAohzmoGvY44u4k4uwkILyhx+/yhgWirLfZHq0g9bW7z+lW8fpWKBg8VDeH3I7Ua9W3Cz5Z9R0N6kbYIT6MsBulHKkQnKIqCzmIhYLdjTEvr0omoqs8XRg/U9qpOW/dFPRqkBq/R2Ii/sbHLh0lhNIZWnDZXo9qagtFO9EVt7oEasp3fYgm7wl2v0xNhiiDCFNElT01VVVx+rc9po7cxZPCTw9d0W6seqCFb+tupVvUFtF0DLr8Ll99FFVVdslaDznDMrfot+5/ajXbMOjPbPduJOhxFlDWqzTkWffhffyGEEEKIc5EEnUKI85bZoCcpUk9SpCWs81RVxenxt7PNvv0BTiFDm1xeVBUavX4avX5K6lxhrzvSbAgNR5s+jmrRgzR0qNPRfqTyQliIk6cYDOgjI9FHRnbp4waHSbUITdXG9vuitt8D1RGydb/ln/i0wA6vl4DXS6CurkvXjqI0Baa2NkFqRz1Qg9WoTVv8Q3qg2o7eXzF07tdVRdG2rFsNVgivg0qHvH5vSDVpe+Fouz1Q2xkU5fQ6cfm1n/m+gI9ady217vCGer3/xfvt3q5TdFrw2ari1G6wB/ufthwaFdLvtJ3t+1aDFb1Odh4IIYQQ4uwjQacQQoRJURTsZgN2s4G0mPD7kda7fW220bfsO9pRUNrQ1I+03u2j3u3jUHX4/UiPBp+mNlWkLatLI0wKRxxQXOsiMUqHxaiTkFSIU+xUDZMCUFtu3e+iHqgBpxPV1fRmjaqekmFSAIrJ1IkeqPZ2tu530AO16WusmI4/7d2oNxKjjyGGmC55Lr6AL2TAU4fhaItqVKfXSYOngYOlB7FEWYJVq83hqYpKQA3Q4G2gwdsA4f3T0CGrwdpupekxB0S17IHaVI3a/HejruuqqoUQQgghOiJBpxBCnEY63dG+nuHy+gNHg9CmP+taDWg6GoyGVpe6fVo/0mqnl2qnFyqdx78gBv638EsATHqdVjFqCw1Ho1pVjx79+GiQajLIVnshuptiMqE3mdBHd01/zWZqINBqiNTJ90ANDpPya5Gp6vHg93igNrzqx+PS6Vr1QLWH9EUNtwfq0cDVgqJvvxrSoDMQaYok0hReNbDX62Xp0qXMnTM3pA1DQA3g8rmOOfjpeD1QW1em+lXt697oa6TR10ilq/LEv8YtGHXGtgOiOhoG1aofans9UM16s7wBJ4QQQog2JOgUQoizhFGvIz7CTHyEOexzXV5/m230zf1HW4entY1eahweymsdNAZ0+AMqHn+AigY3FQ3usK9tM+lDt9G3GdDUtro0xmYk0mJEr5MXsUKcyRSdDsVuR2fv+mFSqscTGoAGA9IWW/c71QO16f5Nt6nupp9jgQABh4OAw9GlawdQLJajoandpvU0bb11v4MeqNogqtDb/EYjis+Hqqoh19EpumDFZII14aTXraoqnoDnaJWp1xGsQD3WVv1jbdv3BLQ+1t6Alxp3DTXumpNeJ4Be0bfbz7T1Vv1jDohqUXVqNVjRKfLGnBBCiLPDlClTGDNmDE8++WR3L+WMI0GnEEKcByxGPRajnuSozvUjba4emjPne3hUXchQppAq0nam3Dfft97tQ1XB6fHj9Pgprj2BfqQWQ2jFqNXUtoq0nerSCOlHKsRZTVEUFLMZndkMsbFd+tiqz0fA5QqzB2pHfVGbwtWm22gKIlWXC7/Lhb+6usvWPQjY/fsHO98DteWW/mP0QA0Ok9JpLUrMejNmvZk4S1yXrNsb8AaDz3Z7m7YIR1tXmrYMWpvPafRpe/P9qp96bz313vouWSdw3K36zZWobT7fYtt+y4pUg05eagkhxOny5Zdf8sgjj7B27VqKi4v58MMPueSSSwDttc3999/P0qVL2bNnD9HR0cyYMYOHH36YtLS04GPs2LGDX/7yl3zzzTd4PB7S09N56KGHmDp16jGvraoqjz32GC+++CL79+8nISGBn/zkJ/z2t789lU9ZdED+9RVCCNEhRVGIMBmIMBvoEWY/Un9Apd7Vtoo0tLI0dIt9c3Wpw6Ntnax3+ah3+TgYZtM5fYsWAR1Wkraaat/8OYtRBnAIcS5TDAb0ERHoI7pm2nszVVWPDpNq3QO1RTVqR31Rg1Wnrbbwq04nqterXcTnI1BfT6C+68K9ZsFBUm16oHbUF7VtD1SlnduMRiPR5miizV3TNiGgBkL6nLauIG359/a26rfsgdrobcThcxBQAwDafXxOKhorumStZr05GIimRaSRmZxJVkoW6YnpmPXh784QQgjRMYfDwejRo7nhhhu47LLLQj7ndDpZt24dv/vd7xg9ejTV1dXcddddXHzxxRQUFATvN3/+fAYNGsSqVauwWq08+eSTzJ8/n927d5OSktLhte+66y6WL1/Oo48+yqhRo6iqqqKqquqUPVdxbBJ0CiGEOCX0OoUYm4kYmynscz2+AHWuo+FoXfPQJqeX2kZfcIBT6FAn7U+PL4A/oFLl8FDl8IR9bZNBFzKgKcYWOtE+2moIhqOtq0uNetn2KMT5SlGUpm3nVuiaYsggj9PJfz/+mBmTJqHz+pqqS4/RF7VND1RHcOt+SF/UxqNvIqlOJ/5TMUzKaAwOgGoZpCq2Vlv3O+qBam17m9Vmw2ZNRLGdfOW+qqq4/K5jh6PH6oHqa1uZ6g1owbTb78btd1PtruZww2HyS/J5fuPzGHVG0hPTyUrJIjM5k9GJo7EYOrfjQgghRPvmzJnDnDlz2v1cdHQ0K1asCLntmWeeITs7mwMHDtC7d28qKirYuXMnr7zyCunp6QA8/PDDPPfccxQVFXUYdG7dupXnn3+eoqIihgwZAkC/fv2Ou97PP/+cX/3qV2zevBmj0ciIESN455136NOnD9dffz01NTX85z//Cd7/7rvvZsOGDXz++efB23w+H3fccQdvvvkmRqOR22+/nT/+8Y/BnW3PPfccTzzxBAcPHiQ6OpoLL7yQf/7zn4C29X3kyJGoqsqbb76JyWRqc/6bb77J3/72N7Zv347dbmfatGk8+eSTJCUlBdewefNmfv3rX/Pll1+iqipjxozhtddeY8CAAQC8/PLLPPbYY+zdu5e+ffty55138pOf/OS4X5+TIUGnEEKIM47JoCMhwkzCCfYjbdmDtHUVaW2rrfd1LW73B1Q8vgBl9W7K6sPvR2pv7kdqMxFtNQS320fbOq4sjbGaiLQY0Ek/UiFEBxSjkYDViiElJWQY0clSAwGtCjVkmFSLbfohoakjtBq1gx6ozecHh0l5vai1tQS6epiUomhVpPZWYWm7VafH7ouqt1qJstqIsceii07rcJhUZ3n93pBw1OF1sKtmF/kl+RSUFFDWWMba0rWsLV0LaIOaRiWM0oLPFC34tBrC20UhhBCngqqqwZYhp5vVYD2lrahqa2tRFIWYmBgA4uPjGTJkCG+88QZjx47FbDbzwgsvkJSUxLhx4zp8nE8++YT+/fuzePFiZs+ejaqqzJgxg7/+9a/ExbX/zqfP5+OSSy7h5ptv5t1338Xj8ZCXlxf283399de58cYbycvLo6CggFtuuYXevXtz8803U1BQwJ133smbb77JxIkTqaqq4quvvmpz/g033MDKlSvZtm0bt912W/B80Lb8P/TQQwwZMoSysjLuuecerr/+epYuXQrA4cOHmTx5MlOmTGHVqlVERUXxzTff4PP5AHj77bd54IEHeOaZZ8jIyGD9+vXcfPPN2O12Fi5cGNZzDYcEnUIIIc4pFqOelGg9KdHhVceoqkqD29eqirS9rfeeNrfVu7R/zB0ePw6PnyNh9iNVFIiytK0ibT2gKVhF2iI8tZv00o9UCHFCFJ0uWG3ZlVRV1QJOZ/s9UNWQ7fzH7oGqNjpDKlSDw6RUVQtUT0UVqtnctBW/bdWpITERW1YmtpwcjMnJ7Z5v1BuJ1odu1x+TNIYrBl+BqqocrD9Ifkk++aX55JfkU+YsY13ZOtaVreOFwhcw6AykJ6QzLnkcWSlZjEkaI8GnEKJbNPoayXknp1uunXtNLjZj1/771MzlcvHrX/+aq6++mqioKEDbmfHZZ59xySWXEBkZiU6nIykpiWXLlhF7jH7he/bsYf/+/XzwwQe88cYb+P1+fvazn3HFFVewatWqds+pq6ujtraW+fPnBysfhw0bFvbz6NWrF0888QSKojBkyBA2bdrEE088wc0338yBAwew2+3Mnz+fyMhI+vTpQ0ZGRpvzH3/8cerr6xk3bhybN28Ong9www03BO/bv39/nnrqKbKysmhoaCAiIoJnn32W6Oho3nvvveAbsYMHDw6e8/vf/57HHnss2EqgX79+bNmyhRdeeEGCTiGEEOJUUxSFSIs27b1XmOf6A2q70+trnW1D0ZZVpDVOL41eP6pK8H7hMjT3I21nQFPLqfYxLe4T0xSYSj9SIcSpoCgKiskEJhP6pkqZrqL6/QQaXWH3QFVb3xYMXY/eRkDr1am63fjdbqipaXcNNR98AICpb19sOTnYx+dgy87GEB9/3PUrikLvqN70jurN5YMvR1VVDtUfCoae+SX5lDpLg8HnS5tewqAzMCphFJnJmWSmZDImccwpe/EvhBDnOq/Xy5VXXomqqjz//PPB21VVZdGiRSQlJfHVV19htVp5+eWXWbBgAfn5+aSmpjJixAj2798PwIUXXsinn35KIBDA7XbzxhtvBEO+V155hXHjxrF9+3asVivDhw8PXue+++7jvvvu4/rrr2fWrFnMnDmTGTNmcOWVV5KamhrWcxk/fnxIwcOECRN47LHH8Pv9zJw5kz59+tC/f39mz57N7NmzufTSS7G1eHPzWOfr9XrWrl3Lgw8+yMaNG6muribQ9O/kgQMHGD58OBs2bODCCy9sd7eJw+Fg9+7d3HjjjcHgFLRq1ujorunb3REJOoUQQoiTpNcpxNpNxNrD70fq9vmpa/QFK0VrnG2rSFtWl2rb8X3UNXrx+AP4AiqVDg+VJ9CP1GzQBStFmyfat9laH+xRGrr93iD9SIUQ3UDR69FH2NFH2Lv0cVVVRXW7gwOgQrfzHw1GPfv24czNw7VlC559+/Ds20fN++8DYB40EFvOeGw52dizsjoV8iqKQq+oXvSK6sVlgy7Tgs+GQxSUFASrPkscJawvW8/6svVa8KkYGJkwksyUTLKStYpPCT6FEKeC1WAl95rcbrt2V2sOOffv3x/cat1s1apVLF68mOrq6uDtzz33HCtWrOD111/nN7/5DUuXLsXbNCDQatXWl5qaisFgCKlkbK7OPHDgAFOnTmXDhg3BzzVvZ3/11Ve58847WbZsGe+//z73338/K1asYPz48eh0OlRVbbP2cERGRrJu3To+//xzli9fzgMPPMCDDz5Ifn5+cLv+sTgcDmbNmsWsWbN4++23SUxM5MCBA8yaNQuPxxPyNWhPQ0MDAC+99BI5OaFVwfqTbBFzPBJ0CiGEEN3IbNCTGKknMTK8fqSqqtLo9bfdWh8yoEkLRVtWlzZXlAZUcPsClNa5Ka0Lvx9phNnQ8UT7Vj1IWwamkWbpRyqEOPMoioJisaCzWOAYWxSb+evqcBYU4FizBmduHu7t23Hv3IV75y6q33oLFAXzsKHYs3Owjc/BlpmJPiKiU+voFdmLXpG9uHTQpaiqGhxkVFCqhZ/FjmI2lG9gQ/kGXt70MgbFwPCE4WQlZ5GVkkVGUoYEn0KILqEoyjnz86Q55Ny5cyerV68mvlUVvtPpBECnC30zX6fTBSsZ+/Tp0+ZxJ02ahM/nY/fu3cFt6Dt27Aje32AwMHDgwHbXlJGRQUZGBvfeey8TJkzgnXfeYfz48SQmJlJUVBRy3w0bNrSpnMzNDQ2h16xZw6BBg4JBosFgYMaMGcyYMYPf//73xMTEsGrVquBW8mOdv23bNiorK3n44Yfp1Uvb79ZyQj1Aeno6r7/+Ol6vt83akpOTSUtLY8+ePVx77bXtPv9TRYJO0X12r4LiQrAnQkTS0T9tCWAIvypKCCHOJ4qiYDMZsJkMpEaH9453IKDS4PGFhqLOluFoUxVpq8/XNXqpd2v9SBvcPhrcPg7XhNegXqcQrBw91oCm1hPtY2xGrEbpRyqEODPoo6KInDaNyGnTAPBVVeHMy8eZl4sjNw/P7t24t2zFvWUrVa+9Bno9lhEjsOfkYMvJwTY2o1O9URVFoWdkT3pG9uTSQZcCBIPP/JJ81pau5XDDYQrLCyksL+SVolfQK3pGxI/QKj6bgk+7sWsrYIUQ4kzT0NDArl27gn/fu3cvGzZsIC4ujtTUVK644grWrVvH4sWL8fv9lJSUAFqFpclkYsKECcTGxrJw4UIeeOABrFYrL730Env37mXevHkdXnfGjBmMHTuWG264gSeffJJAIMCiRYuYOXNmSJVnS3v37uXFF1/k4osvJi0tje3bt7Nz506uu+46AKZNm8YjjzzCG2+8wYQJE3jrrbcoKipq02PzwIED3HPPPdx6662sW7eOp59+msceewyAxYsXs2fPHiZPnkxsbCxLly4lEAgEJ8M3n//zn/+ca665hh07doSc37t3b0wmE08//TS33XYbRUVFPPTQQyHXv+OOO3j66ae56qqruPfee4mOjmbNmjVkZ2czZMgQ/vCHP3DnnXcSHR3N7NmzcbvdFBQUUF1dzT333NPZ/7Rhk6BTdJ9tSyD/5fY/Z4lpCj+TwJ7Q/scRiVo4apJf3IQQIhw6nUKUxUjUCfQj9fkD1Ll87U+1d7buURoanrq8AQIq1Di14DRcRr3SKhw1tQlMW4amdqNCnUerXO3CQdVCCNGGIS6OqNmziJo9CwBvWZkWfOauwZGbh/fAAVyFhbgKC6l86SUwGrGmp2PPycaWMx7rmNHozJ2r7O8R0YMeA3twycBLAC34LCgpCFZ8Hm44TGFFIYUVhfyj6B/oFT3D44cHt7pnJGUQYTp+dakQQpxNCgoKmDp1avDvzUHawoULefDBB/n4448BGDNmTMh5q1evZsqUKSQkJLBs2TJ++9vfMm3aNLxeLyNGjOCjjz5i9OjRHV5Xp9PxySef8NOf/pTJkydjt9uZM2dOMDBsj81mY9u2bbz++utUVlaSmprKokWLuPXWWwGYNWsWv/vd7/jVr36Fy+Xihhtu4LrrrmPTpk0hj3PdddfR2NhIdnY2er2eu+66i1tuuQWAmJgY/v3vf/Pggw/icrkYNGgQ7777LiNGjGhz/vTp0zEYDCHnJyYm8tprr3Hffffx1FNPMXbsWB599FEuvvji4Pnx8fGsWrWKX/7yl1x00UXo9XrGjBnDpEmTALjpppuw2Ww88sgj/PKXv8RutzNq1CjuvvvuDr82XUFRW2/8F12mrq6O6OhoamtrQ3o/iCYb34M9n0NDGTjKoKEcHOWghjk702hvCj2bq0KbPo5oCkaDHyeCJVobb3yKeL1eli5dyty5c9ttyCvE2UK+l8Wp4PL6g4OYWg9oOlpF2mKAU4vKUl/g5H5dsRr1bbbWx1jb9iJtGaA2D23Sy1Z70c3kZ/LZz3vkCI7cPJy5uThyc/EVF4d8XjGZsGZkaIONcnKwjhypDXQ6AUcajlBQWhDs83mo4VDI53WKjuFxw8lKySIzJZOMpAwiTZEn/NzCId/L4lxxrn4vu1wu9u7dS79+/bBYLN29HHEKTZkyhTFjxvD4449TV1dHVFRUm237p9uxvv/CydekolN0n9FXaUdLgQC4ao6Gn47ypgC0rOm2ihahaBn4XOB1QLUDqvcd/5p6kxZ4ttwuH/y4VcWoLQ50MpFYCCG6isWox2LUkxQV3i/Oqqri9PjbDGtqb4BTbUiQ6qGu0YuKQqPXT6PXT0mdK+x1R5oNoX1Hgx+b2t163xymRpoNstVeCAGAMS2NmEsvIebSS1BVFe/Bgzhyc3GuycWRl4u/vAJnbi7Opn5pitWKbdw4bbDR+PFYhg1DMXTupVtaRBoXR1zMxQO0qpsSR0lIj8+D9QcpqiyiqLKIVze/ik7RMSxumBZ8JmcyNnnsaQs+hRBCiK4mQac4s+h0WsBoiwOGHvu+qgqehqYAtPzon8GPm4LR5tvddeD3QN1h7TgeRQe2+Bbb5I9RMWpPlL6iQghxiiiKgt1swG42kBbT+X6kXq+XxUuWcuG0mTi9hGyjb9l3NKQXaYvKUodH22FQ7/ZR7/ZxqDq8fqR6nUKUpWloU4tt9jHtVZGGVJqasBh1EpIKcY5SFAVT796Yevcm9gc/QFVVPHv3BgcbOfPy8FdX4/j6axxff005oIuIwJaZiW18DvacHMxDhqB0svImxZ7CggELWDBgAaAFny0rPg/UH2Bz5WY2V27mtc2voVN0DI0bSlayVvE5NnksUSbZnSaEEOLsIEFnmJxOJ8OGDeMHP/gBjz76aHcv5/ymKGCO1I74Ace/v9fVFIS2qAjtqGLUWQVq4GhwWtaJ9VhiMNgTmOTWo//3vyAyueOKUekrKoQQp4VOgWirkYSo8LeVef0BLfRsr+9oO5WlLW93+wL4AyrVTi/VTi9UOsO6tkmvazW9Xvuz5ZCm0CrSo0GqydC9246EEOFRFAVz//6Y+/cn7pprUAMB3Dt3atvc1+TizM8nUF9Pw+ef0/D55wDoo6OxZWdjy8nBPj4H04ABnX5zJMWewvz+85nffz4ApY7SYLVnQWkB++v2s6VyC1sqt/D6ltdRUBgaNzTY43Ns8liizdGn6sshhBDiNPi86d+T5ony5xIJOsP0//7f/2P8+PHdvQxxIowWiOmlHcfj94GzIrRStE3FaPnRI+ADVw2Kq4YEgK3bj7MWe/uDldqrHrXEnNK+okIIIdpn1OuIjzATH9G5ASEtubz+NuFny+30NY3eNpWlzRWl/oCKxx+gvN5Neb077GvbTPrQbfStQ9EWPUhbfj7SIv1IhTgTKDodliFDsAwZQtx116H6/bi2bmsabJRLY8Fa/LW11K9YQf2KFQDoExKwZ2dhyxmPPScbY58+nQ4+k+3JzOs/j3n9tanCZc4yrdqzNJ+CkgL21e1ja9VWtlZt5c0tbwaDz3HJ48hKyWJc8jgJPoUQQpwxJOgMw86dO9m2bRsLFiygqKiou5cjTiW9ASJTtON4mvuKOsrx1R5h/dfLGTu4J/rGqvarR32NWl/RGgfU7O/EWpr7irYarNSyz2jzn7Z46SsqhBBngOZ+pMkn0I/U4fGHTrRvtbW+7W0eap1e6t0+VBWcHj9Oj5/i2hPoR2oxHK0YbaoSjWoVlAYD0ha3RUg/UiFOGUWvxzpyBNaRI4i/8UZUr5fGoiKcuXk4ctfQuG49/ooK6pZ+St3STwEwJCdrg42ytYpPY48enb5eki2Juf3nMrf/XADKneUhFZ97a/cGg8+3tr6FgsLg2MHB4UaZyZkSfAohhOg2Z0TQefjwYX7961/z6aef4nQ6GThwIK+++iqZmZld8vhffvkljzzyCGvXrqW4uJgPP/yQSy65pM39nn32WR555BFKSkoYPXo0Tz/9NNnZ2cHP/+IXv+CRRx7h22+/7ZJ1iXNEi76iakx/jmyuY0zWXPTtTd8L6Sta0WLLfHk71aMV4K498b6ix60YTQBD+FVKQgghTh1FUYgwG4gwG+gZG965/oBKvaudKtLGFhWjztABTs3Vpc7mfqQuH/UuHwcJvx/pMatIW020b/l5i1HeoBMiHIrRiC0jA1tGBgm33UrA48G1caO2zT03l8aNG/GVllL70cfUfvQxAMaePYODjWzZORiTkzp9vURbInP6zWFOvzkAVDRWBPt7FpQWsKd2D9urt7O9ensw+BwUO4islCyykrWKzxhLzKn4UgghhBBtdHvQWV1dzaRJk5g6dSqffvopiYmJ7Ny5k9jY9n+7/+abb8jOzsbYKkTasmUL8fHxJCcntznH4XAwevRobrjhBi677LJ2H/f999/nnnvu4e9//zs5OTk8+eSTzJo1i+3bt5OUlMRHH33E4MGDGTx4sASd4sSdTF/R4GCl5irRVv1GW/cV7QxLdNvBSu1VjNoTwRxxcs9dCCHEKaXXKcTYTMTYTPSJD+9cjy9AnatlQOoJVo6226O0xd89fq0faZXDQ5XDE/a6TQZdyICmYBVpsO+o4WhA2io8NeqlH6kQOpMJW1YWtqws+OkdBBobaVy/HkduHs41a2gsKsJ76BC1hw5R+69/A2Dq2zc42MiWnY0hvvM/NBKsCczuN5vZ/WYDTcFn03CjgpICdtfuZkf1DnZU7+DtrW8DaMFnchYZiRk4Ao6u/yIIIYQQTbo96Pzf//1fevXqxauvvhq8rV+/fu3eNxAIsGjRIgYNGsR7772HXq9VAGzfvp1p06Zxzz338Ktf/arNeXPmzGHOnDnHXMfjjz/OzTffzI9//GMA/v73v7NkyRL+8Y9/8Jvf/IY1a9bw3nvv8cEHH9DQ0IDX6yUqKooHHnjgRJ+6EMcXdl/RyraDlTrqLRrwgatWOyp3dmIttrbhZ+shS80Vo9JXVAghziomg46ECDMJYfYjVVUVlzcQ0oO0bRVpaDha1+J+AVULWcvq3ZSdQD9Se3M/UptJC0St7QeirbfjR1oM6KQfqThH6axW7BMnYp84EQB/g4PGdWuDFZ+uLVvw7NuHZ98+at57HwDzoEHBwUa2rCz00Z3fep5gTWB239nM7ns0+FxbulYLPksL2FWzi53VO9lZvZN3tr0DwAdLPiArNYvM5EwyUzKJs8R18VdBCCHE+arbg86PP/6YWbNm8YMf/IAvvviCHj168JOf/ISbb765zX11Oh1Lly5l8uTJXHfddbz55pvs3buXadOmcckll7QbcnaGx+Nh7dq13HvvvSHXmjFjBt999x0Af/nLX/jLX/4CwGuvvUZRUVGHIeezzz7Ls88+i9/vP6H1CHFC9AZt0ntk26rmNlr0FW0bhLZTPeprBK9T6ynamb6iOuPRYUotByu1Vz0qfUWFEOKspSgKVpMeq0lPSnT4/Ugb3L5WE+w7nmrf8rZ6lw8Ah8ePw+PnSJj9SBUFoiyhQWhUOwOaQqpLbdrnbSa99CMVZxV9hJ2IyZOJmDwZAH9tLc6CAhy5uTjX5OLesQP3zp24d+6k+q23QFEwDxuKPWc8tpxsbJmZ6CM6v7MnwZrArL6zmNV3FgBVrirWlq4lvySf/OJ8dtXuCh7vbnsXgIExA4OhZ2ZyJvHWMMvShRBCiCbdHnTu2bOH559/nnvuuYf77ruP/Px87rzzTkwmEwsXLmxz/7S0NFatWsWFF17INddcw3fffceMGTN4/vnnT3gNFRUV+P3+Ntvek5OT2bZtW9iPt2jRIhYtWkRdXR3RYbwbKsRp06KvKIlDjn1fVQWPo/3BSm2qR8u1vqIBL9Qf0Y7jUrSws/VgpXYrRhOlr6gQQpwjFEUh0qJNe+/EvoUQPn+AepcvpFK0eaJ9Rz1Km29r9PpRVYKfD5ehuR9puwOaTKG32Y5+HCX9SMUZQh8dTeT06UROnw6Ar6oKZ14+jtw1OHPz8OzZg3vLVtxbtlL16qug12MZOQJ7dg628TnYxo5FZ7V2+npxljhm9pnJzD4z8Xq9fLD4A+JGx7G+fD35pfnsrN7Jrppd7KrZxXvb3wNgQPQALfRsCj4TrAmn5GshhBBnKkVROpwvI46t24POQCBAZmYmf/7znwHIyMigqKiIv//97+0GnQC9e/fmzTff5KKLLqJ///688sorp/Wd9euvv/60XUuIbqcoWn9OcwTE9T/+/b0ucFa0GqzUQcWosxJQtfs7Kzq3Hkt0O4OVOqgYlb6iQghxTjLodcTaTcTaTWGf6/b5g1voQ7bWt1NZenQ7vo/aRg9ev4ovoFLp8FB5Av1ILUZdiyDURFTrrfUhVaRHg1Rbt//GLs5lhrg4ombPImq2VoHpLSvDmZuHMy8Xx5pcvAcP4tpYiGtjIZUvvQRGI9b0dK2/Z04O1jGj0Zk7/0a0XWdneq/pzO6vbXWvdlWzrnQd+aX55Jfks6N6B7trd7O7djfvb9e21veP7k9mcmZwsrsEn0KI1o41hNrr9XL//fezdOlS9uzZQ3R0NDNmzODhhx8mLS0t+Bg7duzgl7/8Jd988w0ej4f09HQeeughpk6desxrq6rKY489xosvvsj+/ftJSEjgJz/5Cb/97W9P5VPutM8//5ypU6dSXV1NTExMdy/nlOv2X5tSU1MZPnx4yG3Dhg3jX//6V4fnlJaWcsstt7BgwQLy8/P52c9+xtNPP33Ca0hISECv11NaWtrmOikpKSf8uEKcl4wWiO6pHccT7Cta3omK0XKtUjTYV3RXJ9Zia3+wUnvVo9ZY6SsqhBDnAbNBT1KknqTI8LfaN3r9bUPRYA/SpgFOjb7Q6tKmUDWggssbwOV1U1oXfj9Ss17PX7d+SXRTn9GQqfatepC23I4faZZ+pCI8xqQkohfMJ3rBfAC8R44EBxs58vLwFRfTuHYtjWvXwnPPoZjNWDMysOdka8HnqFEorQbHHkusJZbpfaYzvY9WYVrjqmFt2drgZPcd1TvYU7uHPbV7+L8d/wdAv+h+R4PP5EwSbYld/4UQQpxVjjWE2ul0sm7dOn73u98xevRoqqurueuuu7j44ospKCgI3m/+/PkMGjSIVatWYbVaefLJJ5k/fz67d+8+ZjZ01113sXz5ch599FFGjRpFVVUVVVVVp+y5dhdVVfH7/RgM3R4lHlO3r27SpEls37495LYdO3bQp0+fdu9fUVHB9OnTGTZsGB988AE7duxgypQpmM1mHn300RNag8lkYty4caxcuTKY+AcCAVauXMkdd9xxQo8phOiEcPqKqio0Vh97yFLL27zOE+sr2nqwUnvVo/YE6SsqhBDnGUVRsJkM2EwGUqM7v20XIBBQqXf7Otha7wkJTFt+vq7RS71b60fq9iscrnFxuCa8fqQ6haOVo03Voq2D0ubq0pDbbEasRulHKsCYlkbMpZcQc+klqKqK9+BBHGu0be6O3Fz8FRU416zBuWYNAIrNhm3sWG2wUU4OluHDUfSd/70pxhLD9N7Tmd5bCz5r3bXBHp9rS9eyrWobe2v3srd2Lx/s+ACAvlF9yUzJJCtZq/hMsiV1/RdCCHFGO9YQ6ujoaFasWBFy2zPPPEN2djYHDhygd+/eVFRUsHPnTl555RXS09MBePjhh3nuuecoKirqMOjcunUrzz//PEVFRQwZorWF62jAdmv/+Mc/eOyxx9i1axdxcXFcfvnlPPPMM23u115F5oYNG8jIyGDv3r307duX/fv3c8cdd/D111/j8Xjo27cvjzzyCMOHDw9WpMbGxgKwcOFCXnvtNQKBAA8//DAvvPACZWVlDB48mN/97ndcccUVIdddunQp999/P5s2bWL58uVMmTKlU8+vu3R70Pmzn/2MiRMn8uc//5krr7ySvLw8XnzxRV588cU29w0EAsyZM4c+ffrw/vvvYzAYGD58OCtWrGDatGn06NGDn/3sZ23Oa2hoYNeuo9Vfe/fuZcOGDcTFxdG7d28A7rnnHhYuXEhmZibZ2dk8+eSTOByO4BR2IUQ3U5QWfUUHH//+7ob2Byu1Vz3qOpm+oi0GK3VUMSp9RYUQ4ryma+7raT2xfqSV9Y18vOwzxmRPxOFV2x3aVONssR2/KTx1eQMEVKhxap/vxNt+IYz6o+vWwk9TyN/bBKa2o0Gq2SBvCJ6LFEXB1Ls3pt69ib3ySlRVxbNnT3CwkTMvD39NDY6vv8bx9dcA6CIisGVlYcvJxjxunDYUMwzR5mim9Z7GtN7TAC34bN7qXlBSwLaqbeyr28e+un38c8c/AegT1Sek4jPZ3ok31YUQbaiqitrY2C3XVqzWU/pmW21tLYqiBIPD+Ph4hgwZwhtvvMHYsWMxm8288MILJCUlMW7cuA4f55NPPqF///4sXryY2bNno6oqM2bM4K9//StxcXEdntc8q+bhhx9mzpw51NbW8s0335zw81m0aBEej4cvv/wSu93Oli1biIiIoFevXvzrX//i8ssvZ/v27URFRWFt6rP8l7/8hbfeeovHH3+c0aNH8/XXX/OjH/2IxMRELrroouBj/+Y3v+HRRx+lf//+wbD0TNbtQWdWVhYffvgh9957L3/84x/p168fTz75JNdee22b++p0Ov785z9z4YUXYjId7ck0evRoPvvsMxIT29+yUFBQENJT4Z577gGOptgAP/zhDykvL+eBBx6gpKSEMWPGsGzZsjYDioQQZ4lw+or63C0qQo9TMXoifUXN0S0qQo9VMZoIpgjZQi+EECLIoNcRZzeRZIUxvWIwhrEl2OX1h2yhP7rNvnnbvadFZenRKtIapxdfQMXrV6lo8FDREH4/UqtR3yb8bD3VPqpVeNpccaqXrfZnDUVRMA8YgHnAAOKuuQY1EMC9c6e2zT03D2d+PoH6ehpWr6Zh9WoABthsFK9cScSECdhzcjANGBBWmBFtjmZq76lM7a29vqvz1LGudJ221b00n21V29hft5/9dfv5106tHVrvyN7B/p6ZyZmk2KU9mRCdoTY2sn1sxyHfqTRk3VoUm+2UPLbL5eLXv/41V199NVFRUYD28+yzzz7jkksuITIyEp1OR1JSEsuWLTtmuLdnzx7279/PBx98wBtvvIHf7+dnP/sZV1xxBatWrerwvD/96U/8/Oc/56677grelpWVdcLP6cCBA1x++eWMGjUKgP79j74Obg5ck5KSgsGu2+3mz3/+M8uXL2fEiBFERUUxcOBAvv76a1544YWQoPOPf/wjM2fOPOG1nW7dHnSC1gdh/vz5nbpvR1/cjIyMDs+ZMmUKqqoe97HvuOMO2aouxPnIYO58X9GAXws7Ww9WarditKmvqLtWOzrTV9RgPf6QpeZKUekrKoQQ4hgsRj0Wo56kqPD7kTo9/pBwtLbR06aStLn/aMh2e5dX6zbj9dPo9VNSF95We4BIs+Fo39GWvUhb9SANqS61af1IZat991J0OixDhmAZMoS4hQtR/X5cW7Zqg41yc3HmF6B3OnF8thLHZysB0CckYM/W+nvac7Ix9ukT1n/HKFMUU3pNYUqvKYAWfK4vXU9Bqdbjc2vVVg7UH+BA/YFg8Nkrslew2jMrJUuCTyHOI16vlyubKtKff/754O2qqrJo0SKSkpL46quvsFqtvPzyy8HZMKmpqYwYMYL9+7X9ERdeeCGffvopgUAAt9vNG2+8weDB2s7DV155hXHjxrF9+3asVmvIXJr77ruPm266iSNHjjB9+vQue1533nknt99+O8uXL2fGjBlcfvnlwS347dm1axdOp5NZs2aF3O7xeNrka5mZmV22ztPhjAg6hRDirKHTa0FjRCd6P6kquGraGazUQcWo1wm+Rqg5oB3HXYvhGMOWWlWP2uK1nqhCCCHEcSiKgt1swG420CPmBPqRunytBjSF9h2tCRng5Gu6zYPD4weg3u2j3u3jUHV42yX1OoUoi6Ep+DQRYzWS3jOaeempDEmOlBC0Gyh6PdZRI7GOGkn8jTficTpZ/fLLjNHrcRUU0LhuPf6KCuqWLqVu6VIADCkpTYONxmvBZ48eYV0zyhTFRb0u4qJeWjVSvaee9WXrg8ONtlRt4WD9QQ7WH+TfO/8NQM+InsGKz6zkLFIjUrv2CyHEWUqxWhmybm23XburNYec+/fvZ9WqVcFqToBVq1axePFiqqurg7c/99xzrFixgtdff53f/OY3LF26FK/XCxDc/p2amorBYAiGnKAN2AatynLq1Kls2LAh+Lm4uLiwdmeAtrsZCCnga15Hs5tuuolZs2axZMkSli9fzl/+8hcee+wxfvrTn7b7mA0NDYC29T46OpqIiIjgdczm0NZrdrs9rPV2N3nVK4QQp4qiaFWX1tgw+oqWtx2s1F71qKsWAj6oL9aO4y9G62/aerBSRxWj6E722QshhDgP6XSKVo1pM9Kb8LYcev0BLfRsNdG+zaT7pgC1ZWWpxxfAH1Cpdnqpdnqh0gnAFzvKeXrVLgYmRTBvVCoLRqcyMCnyVDx10QmK0YirTx/i5s7FuGgRAY+Hxg0bcObm4czNxblxI76SEmo/+pjajz4GwNizJ7bxOdhzcrBl52BMDm/QUKQpksk9JzO552QAGjwNrC9bH+zxuaVyC4caDnFo1yE+3PUhAD0ieoRUfKZFpHXtF0KIs4SiKKds+/jp1hxy7ty5k9WrVxMfHx/yeadT+3ejOexrptPpCDT1Fm5vaPakSZPw+Xzs3r2bAQMGANqA7eb7GwwGBg4c2Oa8vn37snLlypA2ix1pbtNYXFwc3EbfMjxt1qtXL2677TZuu+027r33Xl566SV++tOfBls/+v3+4H2HDx+O2WzmwIEDfP/73ycqKqrNcz9bSdAphBBnimBf0U5M6fO5W1SHthis1F71qLMS1ID2p7MSyrce9+EN5iimY0Vf8dzRCtaOKkalr6gQQoguYNTriI8wEx8R/hA/l9ffJhAtq3exels5X+4oZ1dZA39buZO/rdzJkORI5qWnMj89lf6JEafgmYjO0plM2LOzsWdnw0/vINDYSOP69TjW5OLMzaWxqAjvoUPU/vMQtf/Utp2b+vXDlpONffx4bNnZGI4x7KM9EaYILux5IRf2vBAAh9ehBZ8lWvC5uXIzhxsOc3jXYf6z6z+AFnyOSx5HVkoWWSlZ9IgIr8pUCHHqHWsIdWpqKldccQXr1q1j8eLF+P1+SkpKAK3C0mQyMWHCBGJjY1m4cCEPPPAAVquVl156ib179zJv3rwOrztjxgzGjh3LDTfcwJNPPkkgEGDRokXMnDkzpMqztQcffJDbbruNpKQk5syZQ319Pd988027FZgDBw6kV69ePPjgg/y///f/2LFjB4899ljIfe6++27mzJnD4MGDqa6uZvXq1cHK0j5NLUEWL17M3LlzsVqtREZG8otf/IKf//znOJ1OZsyYEVxDVFQUCxcuDOvrfyaRoFMIIc5GBjNE99CO42nuK9p6sFJHFaMBL4q7jgjq4GBpJ9ZibX+wUpvq0SSwxMA58k6hEEKIM0dzP9LkVv1Ir83pQ53Ly4rNpSzZVMxXO8vZXlrP9hX1PL5iB8NSo5ifnsq8Uan0TTi7tuadi3RWK/aJE7FPnAiAv8FB49oCbbBRbi6uLVvw7N2LZ+9eat57HwDzoEHYxmvb3G1ZWeijo8O6pt1o54IeF3BBjwsALfjcULaB/JJ88kvz2VKxRQs+Gw7z8W6tyjTNnhYcbNQcfEprBCG617GGUD/44IN8/LH2/98xY8aEnLd69WqmTJlCQkICy5Yt47e//S3Tpk3D6/UyYsQIPvroI0aPHt3hdXU6HZ988gk//elPmTx5Mna7nTlz5rQJIltbuHAhLpeLJ554gl/84hckJCRwxRVXtHtfo9HIu+++y+233056ejpZWVn86U9/4gc/+EHwPn6/n0WLFnHo0CGioqKYPXs2TzzxBAA9evTgD3/4A7/5zW/48Y9/zHXXXcdrr73GQw89REJCAk888QR33XUXMTExjB07lvvuu++Yaz/TKWpnpvSIE1JXV0d0dDS1tbUhvR/Eucvr9bJ06VLmzp0bdt8NIc4ITX1FvTXF5K76hPGjBmBorGpbMdr8sdcR3uMH+4q2GqzUss9o85+2BOkrKk6K/EwW5wr5Xu46tU4v/91SwpLCYr7ZVYEvcPSl0MgeUcwblcb89FR6xZ0bWzXPNCf7veyvrcVZUBCs+HQ3bQ8NUhQsw4Zpg43G52Adl4k+4uQCbKfXqQWfTVvdiyqK8Km+kPuk2lODoWdmSiY9I3pK8HmOO1d/LrtcLvbu3Uu/fv2wWMIbZCfOToFAgLq6ujNi6/qxvv/CydfkFaQQQoijmvuKGiKojBiKOmwuHOuXN4/j+EOWmv901Zx4X9GWg5U6qhg1yi9jQgghji3aZuTKzF5cmdmLaoeH/24uYcmmYr7dXUnR4TqKDtfxv8u2MbppiNG89LSwBzKJU0cfHU3k9OlENk0q9lVV4czL0ya65+bh2bMH15YtuLZsoerVV0GvxzJyBPac8dhysrGNHYsuzAEnNqONiT0mMrGHVmXq9DrZUL6BgpICCkoL2FSxiWJHMZ/s+YRP9nwCQLItObjNPSs5i56REnwKIcTpIkGnEEKIE2eyaz1FO9VX1NMUgrYarNRQfvT25orRNn1FO7EWc1SLitDjVIxKX1EhhDjvxdpNXJXdm6uye1PZ4GbZZq3Sc82eSjYeqmXjoVr+vHQbGb1jmDcqlXnpqaRGS+h5JjHExRE1ezZRs2cD4C0tawo+1+DMzcN78CCujYW4NhZS+eKLYDRiHZ2OPTsH2/gcrGPGoGsa0tFZNqONiWkTmZh2NPjcWL6R/JJ81paupbCikFJnKYv3LGbxnsUAJNmSgqFnVkoWvSJ7SfAphBCniASdQgghTg+DKcy+olVtByt1VDEa8IK7TjuqdndiLZZW1aHtDFlqDkqlr6gQQpzz4iPMXJvTh2tz+lBW7+K/RSUsLiwmb18V6w/UsP5ADX9aspXMPrHMS09l7qjUNv1ARfczJicRvWA+0QvmA+A9fDjY39ORm4uvpITGgrU0FqyF555DMZuxZmRgH69NdLeOGokS5jZkm9HGhLQJTEibAECjr5GN5RspKCkgvySfwopCypxlLNmzhCV7lgCQZE0iMyUzONm9T1QfCT6FEKKLSNAphBDizKPTayFkRCIkjzj2fVUVXLXtD1Zqr2LU6wCfC2oPaMdx12LQ+oWGDFZKbFEx2qJ6VPqKCiHEWS8p0sL/TOjL/0zoS2mdi083FbNkUzH5+6op2K8df1y8hay+ccxPT2XOyFQSI8OfFC9OPWOPHsRcdikxl12Kqqp4DxwIbnN35Obir6jAuWYNzjVrAFBsNmzjxmmDjXLGYxk+DEWvD+uaVoOV8anjGZ86HtCCz8LyQgpKm4LP8kLKGstYuncpS/cuBSDRmhgSfPaN6ivBpxBCnCB5NSaEEOLspihgjdGOhEHHv7/H0f5gpfaqR5v7ijaUaEdnWOPaDlYKqRhtEZRKX1EhhDijJUdZuH5SP66f1I/i2kaWbiphSeER1h2oIW9vFXl7q3jw483k9ItnXnoqc0amEB8hoeeZSFEUTH36YOrTh9grr0RVVTx79uBYo21zd+bl4a+pwfHVVzi++goAXWQktsxMreIzJwfz4MEoYe7ysBqs5KTmkJOaA4DL52oTfJY3lvPp3k/5dO+nACRYE8hK1gYbZaZk0i+qnwSf4rSSmdWiO3TV950EnUIIIc4vJrt2xPY9/n19HnBWtB2s1F71qLNC6yvaWKUd5duO//jmqBYVoR1VjDYFpeZI6SsqhBDdKDXayo0X9OPGC/pxuKaRpYXFLN5UzMaDNXy3p5Lv9lTy+483M6G/FnrOHpFCrD28/o/i9FEUBfOAAZgHDCDu2mtRAwHcO3Y0bXPPw5mfT6C+nobVq2lYvRoAfUwMtuxsbDnZ2MePx9S/f9gBpMVgITs1m+zUbADcfrcWfJYUkF+az8ayjVQ0VvDpvk/5dJ8WfMZb4oPVnlkpWfSLluBTnBr6pgpmj8eDNczBXUKcLI/HAxz9PjxREnQKIYQQHTGYICpNO44n2Fe01WCljipG/Z4WfUX3dGItllY9RNurGG0KSK2x0ldUCCFOoR4xVm6e3J+bJ/fnYJWTJZuKWVJYzKbDtXy9q4Kvd1Vw/3+KmDQwgfnpqcwankK0Lbzej+L0UnQ6LEOHYhk6lLiFC1H9flxbtuLMXaMFn2vX4q+poX75cuqXLwdAn5iAPSsb2/gc7Dk5GHv3DjuANOvNwQntt3M7br+bTeWbyC/Np6CkgI3lG6l0VbJs3zKW7VsGQJwlLhh6ZqVk0T86/MBViPYYDAZsNhvl5eUYjUZ08vvkOS8QCODxeHC5XN363zsQCFBeXo7NZsNgOLmoUoJOIYQQoiu07CvK8GPft2Vf0daDldqrGPU0nFhf0ZDq0MSjQWjL6lF7AujlxbcQQpyoXnE2brtoALddNID9lQ4WF2qh55biOr7cUc6XO8r5rX4TFwxMYH56GjNHJBNlkZ+7ZzpFr8c6aiTWUSOJv+kmVK+Xxk1FOPO0wUaN69bjL6+gbulS6pZqvTYNKSnYc7Rt7vacbIw9OjGAsRWz3hzcss5o8Pg9bKrYRH6JFnxuKN9AlauK5fuXs3y/FrjGWeIYlzwuONl9QMwACT7FCVEUhdTUVPbu3cv+/fu7ezniNFBVlcbGRqxWa7f/3NDpdPQ+gTeMWpOgUwghhDjdwu4r6jz+kKXm6tHG6tC+oqWdWE/LvqId9RZtDkqNso1JCCE60ifezqKpA1k0dSB7yhtYUqgNMtpWUs/q7eWs3l6O6d86Jg9OYF56KjOGJRMpoedZQTEasY3NwDY2g4TbbiPgdtO4cSPONbk48nJp3FiIr6SE2o8+ovajjwAw9uqlbXPPGY8tJxtjUlLY1zXpTYxLHse45HHB4LOookgLPksL2FCmBZ8r9q9gxf4VAMSaY7WwtKnqc0DMAHSKVOaJzjGZTAwaNCi4jVic27xeL19++SWTJ0/GaOzef49MJlOXVJVK0CmEEEKc6Uw2MPU9gb6iLQYrtVc9eiJ9RU2RLSpCE9oGoS17i0pfUSHEeax/YgQ/nT6In04fxK6yehYXFrO4sJhdZQ18trWMz7aWYTLomDI4MRh62s3y8uxsoTObsWdnY8/OJpGfEmhsxLlunTbYKDeXxqIivAcPUnvwILX//BcApn79gtvcbdnZGOLiwr6uSW9ibPJYxiaP5VZuxev3UlRZFFLxWe2uDgk+Y8wxZCZnBie7D4wZKMGnOCadTofFIkMzzwd6vR6fz4fFYun2oLOryL+kQgghxLkkrL6iTSFn68FKHVWM+j3gqYeq+jD6irasDE1oO2SpOSiVvqJCiHPYwKRI7p4Ryd0zBrO9pJ4lhUdYXFjMngoHy7eUsnxLKWaDjmlDk5iXnsq0oUnYTPJS7Wyis1qJmDSJiEmTAPA3NNC4di2ONbk4c3Nxbd2KZ+9ePHv3UvPuewCYBw/WtrmPz8GWmYk+Ojrs6xr1RjKSMshIyuCW9Fvw+r1srtwcrPhcX7aeGncNnx34jM8OfAZAtDk6WO2ZmZzJoNhBEnwKIc4Z8q+nEEIIcb7S6ZrCxwQ61VfUXddqsFJ5B71Fy1v0FT2oHcej6FtNoG8VhLbsNyp9RYUQZ7EhKZEMSRnCz2YOZmtxPUs2aaHn/konnxaV8GlRCVajnmnDkpg/KpWpQ5OwGE9uAq04/fQREURcdBERF10EgL+2Fmd+vjbYKDcX944dwaP6zTdBUbAMG4Zt/HjsOdlYx2Wij7CHfV2j3siYpDGMSRrDzdyMN+Blc8VmCkoLKCgpYF3ZOmrdtaw8sJKVB1YCWvA5Lknr8ZmZksng2MESfAohzloSdAohhBDi+BQFLNHakTDw+Pf3ONsZrNRyAn2LitHGalD90FCqHZ3qKxrbTnVo663zsegC0l9KCHFmUhSF4WlRDE+L4hffG8LmI3XaIKNNRzhY1aj19ywsxmbSM2NYMvPSU7locKKEnmcpfXQ0kTNmEDljBgC+qiqceXk41qzBmZuHZ+9eXFu24Nqyhap//AP0eqwjRwYrPq0ZGeis4ffJNuqOBp83jboJb8DLlsotFJQUkF+az/rS9dS6a1l1cBWrDq4CIMoUxbjkccGqz8Gxg9Hr5PtOCHF2kKBTCCGEEF3PZANTH4jtc/z7NvcVbRmEtplGX3F0e73q18LRxmqo2N7hwxqBBYC69e7QIUvtbZ1vDkrNUdJXVAhx2imKwsge0YzsEc2vZw+h8FAtSzZpQefhmkY+3niEjzceIcJsYObwZOaNSuXCwQmYDRI+na0McXFEzZ5N1OzZAHhLy4IT3Z25eXgPHqRx40YaN26k8sUXwWjEOjo9ONjIOmYMOpMp7OsadUZGJ45mdOJobhx1I76Aj62VW8kvzSe/JJ91peuo89Sx+uBqVh9cDUCkKTIk+BwSO0SCTyHEGUuCTiGEEEJ0rxPpK9omCG09gb4CtaEMxe9G8TRoW+mr9x7/8fXmVv1EE1sEoa0CUmuc9BUVQnQ5RVEY3SuG0b1iuHfOUNYfrAlWd5bUufhw/WE+XH+YSIuB7w1PYX56KpMGJmAyyM+js5kxOYnoBQuIXrAAAO/hw03b3NfgyM3DV1JCY8FaGgvWwrPPopjNWMdmaIONcnKwjhyJcgKDRAw6A6MSRzEqcRQ3jLwBX8DHtqpt5Jc0BZ9l66j31PP5wc/5/ODnAEQaIxmbPDa41X1o7FAJPoUQZwwJOoUQQghx9mjZVzRp2DHv6vN4WL74X3xv4hiM7ppjT6B3VGiDlvzuE+grmthxpWjLfqPSV1QIESZFURjbO5axvWP57dxhrDtQzeLCYpZuKqas3s2/1h3iX+sOEW01MmtEMvPT05gwIB6jXkLPs52xRw9iLruUmMsuRVVVvAcOBAcbOfLy8FdU4PxuDc7v1gCg2GzYxo3TBhtl52AZPgxFH374aNAZGJkwkpEJI/nxyB/jC/jYXrVdCz5LtYrPem89Xxz6gi8OfQFAhDFCCz6Ts7SKz7ghGHQSNQghuof89BFCCCHEuUlR8OltED8QOlPl0rKvaMveog3t3Na6r2hnNPcVDekn2hyEtuo3arKd3HMXQpxzdDqFzL5xZPaN44H5w8nfV8WSTcUs3VRCRYOb/ys4xP8VHCLWZmT2yBTmp6eR0y8Og4SeZz1FUTD16YOpTx9if3glqqri2b1b2+a+JhdnXh7+2locX32F46uvANBFRmLLysKek40tJwfz4MEoJ7ALwaAzMCJhBCMSRnD9yOvxB/xsq96m9fgsORp8fnnoS7489CWgBZ8ZSRlkpWjB59C4oRJ8CiFOG/lpI4QQQggB4fUV9Xtb9A0tbzWNvqLNNvrO9hU9upaIDqpE26kYlb6iQpx3dDqFnP7x5PSP5/cLRpC3t4rFhUdYVlRCpcPDu3kHeTfvIPF2UzD0zO4Xh14nPyvOBYqiYB44EPPAgcRdey1qIIB7x47gYCNnfj6B+noaVq2iYZU2YEgfE4MtOxvb+BzsOTmY+vdHOYF/O/Q6PSPiRzAifgQLRyzEH/CzvVqr+CwoLWBt6VrqPfV8dfgrvjqsha52o/1o8JmcxbD4YRJ8CiFOGfnpIoQQQggRLr0RolK143gCAS3gbHfrfFMQ2nIbvd+t9RQNp69oyyrRNhPoE45+LH1FhTjn6HUKEwbEM2FAPH+4eAS5rULPt3MP8HbuARIizMwdpYWemX1i0Unoec5QdDosQ4diGTqU+OuvR/X5cG3dqm1zz83DuXYt/poa6pcvp375cgD0iQnYs3Ow5WRjz8nB2Lv3CQefw+OHMzx+eDD43FG9Ixh8FpQWUO+p5+vDX/P14a8BsBlsZCRnkJWs9fgcHj8co07auwghuoYEnUIIIYQQp5JOB/Z47ThOX1FUFdz1rYLQY1SMNvcVrTukHcej6MCW0H6laHu3SV9RIc4qBr2OSQMTmDQwgT9+fyTf7q5kSeER/ru5lIoGN298t583vttPcpSZOSNTWTA6lYxeEnqeaxSDAeuoUVhHjSL+pptQvV4aNxUFBxs1rl+Pv7yCuiVLqFuyBABDair27Gxs48djz8nGmNaJAYHt0Ov0DIsfxrD4YVw34jr8AT87a3YGt7oXlBZQ56njm8Pf8M3hbwCwGqyMTRpLZkommcmZjEgYIcGnEOKESdAphBBCCHGmUBSwRGlH/IDj39/b2E4Q2mrYUvPHjVWgBprC07LOrccS085gpdbT6Js+lr6iQpxRjHodFw1O5KLBifzpkgDf7KpgcWExy7eUUFrn5rVv9/Hat/tIjbYwd1Qq89NTGdMr5oSq+sSZTTEasY3NwDY2g4TbbyfgdtO4YWPTYKNcGjcW4isupvajj6j96CMAjL16BQcb2XKyMSYlndC19To9Q+OGMjRuKD8a/iMCaoCd1TspKD0afNa6a/nmyDd8c+Ro8JmRlEFmciZZKVmMiB+BUd54E0J0kgSdQgghhBBnK6MVYnprx/EE+4qWt+ohWt7Ox+VaX1FXjXZU7Dj+45siQrfJH6ti1BItfUWFOI1MBh1ThyYxdWgSbt9IvtpRwZJNxazYUkpxrYtXvt7LK1/vpUeMlXnpWug5qke0hJ7nKJ3ZjD0nG3tONon8lIDTiXP9epxrtODTVbQZ78GD1Bw8SM0H/wTA1L9/0zb38dhysjHExp7YtRUdQ+KGMCRuCNcOu5aAGmBXzS4t9CzRtrrXuGv49si3fHvkW0ALPsckjiEzRQs+R8aPlOBTCNEhCTqFEEIIIc4HJ9RX9BiVoi230ftcLfqK7uvEWkxNFaEJx6gYbfrYFgc6/Uk/fSGExmzQM2N4MjOGJ+Py+vliRzlLCov5bGsph2saefHLPbz45R56xVmZNyqN+empjEiLktDzHKaz2YiYNImISZMA8Dc04Cwo0AYb5ebi2roVz549ePbsoebd9wAwDx4cHGxky8pCHxV1YtdWdAyOHczg2MHB4HN3ze6jPT5LCqh2V/Nd8Xd8V/wdABa9hTFJY4IVnyMTRmLSm7rmiyGEOOtJ0CmEEEIIIUK17CvK0GPft2Vf0Za9RZsrQ1tPoHfXgd8Tfl/RYw1Zaq4UtSeCQV7sCtFZFqOeWSNSmDUiBZfXz+ptZSzeVMyqrWUcrGrk71/s5u9f7KZvvK2p0jONoSmREnqe4/QREUROmULklCkA+GtqcBYU4FiTizM3F/fOnbh37MC9YwfVb7wJioJl+HBsOTnYx+dgHTsOfYT9hK6tU3QMih3EoNhBXDPsGgJqgD01e8gvzSe/JJ+1pWupclWxpngNa4rXAFrwOTpxdLDic1TCKAk+hTiPSdAphBBCCCFO3En1FT1OxWjrvqKdaS0a7Cvacut8B9WjphN7IS7Euchi1DNnVCpzRqXi9PhYta2MJYXFrNpWxr5KJ8+u3s2zq3fTP9HO/FGpzB+dxuDkyO5etjgN9DExRM6YQeSMGQD4Kitx5uXhyM3FmZuHZ+9eXJs349q8map//AP0eqwjRwYHG1kzMtBZrSd0bZ2iY2DsQAbGDuTqoVejqip7aveQX5IfrPqsclWRW5JLbkkuAGa9+WjwmZzFqMRRmPXmLvt6CCHObBJ0CiGEEEKI0yesvqI+cFa0mEBfcYyK0RPoK2q0tz9Yqb2KUekrKs4jNpOB+elpzE9Pw+H28dnWUpYUFvP5jnL2lDt4atUunlq1i0FJEcxPT2NeeioDkyK6e9niNDHExxM1Zw5Rc+YA4C0t1YLPNWtw5ubhPXSIxo0bady4kcoXXkAxGrGOHh2s+LSMHo3OdGIVl4qiMCBmAANiBnDV0KtQVZW9tXuDoWd+ST6VrkrySvLIK8kDwKQzMTppNFnJWWSmZJKemC7BpxDnMAk6hRBCCCHEmUlvgMgU7TieQEALOIOhaOtp9BWh2+h9LvA6oNoRRl/RxLaDldqrGJW+ouIcYjcb+P6YHnx/TA/qXd5g6PnFjnJ2ljXwxGc7eOKzHQxNiWTeqFTmpafSP1FCz/OJMTmZ6AULiF6wAADPocM4c3Nx5uXiWJOLr7RU6/lZUEDFs8+iWCxYM8YEBxtZR45EMZ7YcCFFUegf05/+Mf354dAfasFn3V5tsFFJAfml+VQ0VgQrQNmoBZ/pielkpWSRmawFnxaDpSu/JEKIbiRBpxBCCCGEOPvpdFrAaIujU31FPQ2tBiu16jEarB4tb9FX9LB2HI+iA1t8i23yrSpGW38sfUXFWSLSYuTSjJ5cmtGT2kYvK7aUsqTwCF/trGBbST3bSup5bMUOhqdGBae394mXFhHnG1PPHph6XkbM5Zehqire/ftx5ObhzF2DIzcPf2Ulzu/W4PxO67Gps9mwZo7TBhvljMcybCiK/sTeLFIUhf7R/ekf3Z8rh1yJqqrsq9sXrPYsKCmgvLFcG3RUWgCAUWcMCT5HJ46W4FOIs5gEnUIIIYQQ4vyiKGCO1I5O9RV1tTNYqYOKUWdzX9Gm4LRTfUWj2w5Wav44IgnFHIvNXQYeBxhjTvbZC9Eloq1GrhjXkyvG9aTG6WH55lIWbyrmm10VbCmuY0txHY/8dzujekQzLz2VeaNSSYk8sao9cfZSFAVT376Y+vYl9oda8OjZvTs42MiZl4e/thbHl1/h+PIrAHRRUdgyM7GPz8GWk4N50CAUne6Er98vuh/9ovvxg8E/QFVV9tftDwk+yxrLWFu6lrWlawEt+ByVMCo43Gh04mishhPrMSqEOP0k6BRCCCGEEOJYjBaI6aUdx9PcV7T1YKX2KkYd5RDwgatWOyp3tvuQBmAmwJZfaH1F2xus1F71qCVG+oqK0yLGZuLKrF5cmdWLKoeH/24uYUlhMd/urmDT4Vo2Ha7l4U+3kd4zin56hTE1jfRJlNDzfKQoCuaBAzEPHEjcj65FDQRwb98eHGzkzM8nUFdHw6pVNKxaBYA+NhZbdja2nGzs48dj6tcP5QR/timKQt/ovvSN7ssVg69AVVUO1h/UtrY3TXYvc5axrmwd68rW8WLhixh0BtIT0hmXPI6slCzGJI3BIFGKEGcsRVVVtbsXca6qq6sjOjqa2tpaoqKiuns54jTwer0sXbqUuXPnYjzBPjNCnAnke1mcC+T7WJzxQvqKthqs1KJ6VG0ox19XgkH1hPf4wb6iCcesGMWeqG21l76iootVNLhZVqSFnrl7Kwm0eOU5tncM89LTtErPaNkmLDSqz4dr69bgYCPn2rWojY0h99EnJmDPzsE2Pgd7Tg7GXr1OOPhsc31V5VD9oWDomV+ST6mzNOQ+Bp2BkfEjia6L5ocTf8i41HHYjLYuub4Qp9vZ8vtyOPmaBJ2nkASd55+z5YeEEMcj38viXCDfx+Jc4fV6WbpkCXNnTMborm4xWKl1pWj50W307trwLtKyr+gxK0abDoNMLBbhKat3sWTjYd76Yit7GhRavgrN6hvLvFGpzB2VSlKUhJ7iKNXjobGoCGduLo7cPBrXrUP1hL7pY0hNbervmYM9JxtjWlrXXV9VOdRwSBts1FT1WeIoCb2+YmBEwgiyUrLIStYqPiX4FGeLs+X35XDyNam3FkIIIYQQ4kzX3Fc0Ii78vqLBwUrtV4y26SvaGc19RVsOVopIar961CwTuAUkRVr4UU5v4iqLGHfBNFZsq2BJYTEF+6vJ36cdf1i8hey+ccxPT2X2yFQSIyVQP98pJhO2sWOxjR1Lwu23E3C7adywMTjYqLGwEF9xMbX/+Q+1//kPAMbevbHnZGPLGY89JxtDYuKJX19R6BXZi16Rvbh00KWoqsrhhsOsObyGj9Z9RImxhBJnCRvLN7KxfCMvb3oZg2JgeMJwspKzyErJIiMpQ4JPIU4jCTqFEEIIIYQ414TdV7Sy7WCl9ipGO9lXNHQtttBt8sGP26kelb6i54XkKAs/ntSPH0/qR3FtI0sKi1myqZj1B2rI3VtF7t4qfv/xZsb3j2deeiqzR6QQHyGhpwCd2Yw9Jxt7TjaJQMDpxLluvVbxmZeLa1MR3gMHqDlwgJoP/gmAqX9/bbBRdg62nGwMsbEnfH1FUegZ2ZPvD/g+xu1G5s6dS5m7LLjNfW3pWg43HKawvJDC8kJeKXoFvaJnRPyI4HCjjKQM7EZ7F31FhBCtSdAphBBCCCHE+UxvgMhk7Tie5r6irQcrdVQx6msErxNq9mvH8eiMoVWix6oYlb6i54TUaCs3Xdifmy7sz6FqJ0s3FbOksJiNh2r5dncl3+6u5IGPNjNxQDzz01P53vAUYu2m7l62OEPobDYiLphExAWTAPA3NOAsKMC5Rgs+3Vu34dmzB8+ePVS/8y4A5iFDgoONbJmZ6E+yzVyPiB70GNiDSwZeAsDhhsMUlBQEJ7sfbjhMYUUhhRWF/KPoH+gVPcPjh5OZkklmciZjk8YSYZLKdyG6igSdQgghhBBCiM7R6cAWpx2JQ459X1UFjyN0m7yj/OjHIdWj5Vpf0YAX6o9ox3EpWth5rCFLLT+WvqJnvJ6xNm6ZPIBbJg/gYJWTxYXFLNl0hKLDdXy1s4Kvdlbw2w+LmDQwIRh6RtvO3J5y4vTTR0QQOWUKkVOmAOCvqcGRn68NNsrNxb1zJ+7t23Fv3071G2+CTodl2LDgYCPr2HHoI06u2rI5+Pz+wO8DcKThCAWlBcE+n4caDrGpYhObKjbxatGr6BQdw+OGk5WSRWZKJhlJGUSaIk/2SyHEeUuCTiGEEEIIIUTXUxStP6c5AuL6H//+Xhc4K1oNVipvv3rUWQmo2v2dFZ1bjzm6RXVo6yFLrapHpa9ot+sVZ+P2KQO4fcoA9lU4WLKpmMWFxWwtruOLHeV8saOc+/SbuHBQIvPTU5kxPJkoi4SeIpQ+JoaomTOJmjkTAF9lJc68PBxrcnHm5uLZtw/X5s24Nm+m6pV/gMGAdeRIbbDR+BysGRnoLCc3ICstIo2LIy7m4gEXA1DiKCG/JD9Y8Xmw/iBFlUUUVRbx6mYt+BwWN0wLPpMzGZs8VoJPIcIgQacQQgghhBCi+xktEN1TO44n2Fe0vBMVo+Vapai7Vjsqd3ViLba2g5Vi+8KYazu3xV90qb4JdhZNHciiqQPZXd6g9fQsLGZ7aT2rtpWxalsZJr2OyYMTWTA6lenDkokwy0td0ZYhPp6oOXOImjMHAG9padNE91ycuXl4Dx2iccMGGjdsoPKFF1CMRqyjR2Mbrw02sowejc50cq0TUuwpLBiwgAUDFgBa8Nmy4vNA/QE2V25mc+VmXtv8GjpFx9C4oWQmaz0+xyaPJcp0ctvthTiXyU9/IYQQQgghxNklnL6iqgqN1W0HK3U0bMnrbOorekA7Wvr8YRhzNUy8E+IHnJrnJo5pQGIEd04fxJ3TB7GztJ7FhcUsLjzC7nIHn20t5bOtpZgMOqYOSWR+ehrThiZhl9BTdMCYnEz0xRcTfbFWbek5dLgp+FyDMzcPX2mp1vOzoICKZ0CxWLCNzcCcmYnF50f1esF4cpXEKfYU5vefz/z+8wEodZQGqz0LSgvYX7efLZVb2FK5hTe2vIGCogWfKZlkJWvBZ7Q5+qS/FkKcK+QnvhBCCCGEEOLcpSid7ysK4G5of8jS7pVwMBfWvgZrX4fhF8Oku6HH2FP9DEQHBiVH8rOZkdw9YxDbS+tZUqhtb99b4eC/m0v57+ZSLEYd04YmMT89jalDkrCaZICV6JipZw9MPS8j5vLLUFUV7/792jb3vFwcuXn4KytxfPsdjm+/ozew57XXsGWOw54zHltODpZhQ1H0J/c9lmxPZl7/eczrPw+AMmeZVu1Zmk9BSQH76vaxtWorW6u28uaWN1FQGBI3JFjxOS55nASf4rwmQacQQgghhBBCNOuor+iUX8P+7+DrJ2Dnf2HLR9rRb7IWeA6YpoWq4rRTFIWhKVEMTYninpmD2Vpcz+LCIyzZVMz+SidLN5WwdFMJVqOe6cO00HPKkEQsRgk9RccURcHUty+mvn2JveqHqKqKZ9cuHLl5NHz3HXXffove6cTx5Vc4vvwKAF1UFLasLOw52dhyxmMeNBBFpzupdSTZkpjbfy5z+88FoNxZHqz4zC/JZ1/dPrZVbWNb1Tbe2voWCgqDYwcHhxtlJmdK8CnOKxJ0CiGEEEIIIURn9JmgHaVb4Ju/QdE/Ye+X2pGSDpPuguGXaFvrRbdQFIXhaVEMT4vil7OGsPlIHZ8UHmFJYTGHqhubtroXYzfpmTE8mfnpaUwenIDZIKGnODZFUTAPGoR50CAif3gl6xcvZvrAgbgL1uLMzcVZUECgro6GlStpWLkSAH1sLLbsbOzjc7Dl5GDq1w/lJN8QSbQlMqffHOb00/qMVjRWBPt7FpQWsKd2D9urt7O9ejtvbX0LIBh8ZiVrFZ8xlpiTWoMQZzL5F1gIIYQQQgghwpE8HC57AabdD989C+teh5JC+NeNsOohmHAHZPwIjNbuXul5TVEURvaIZmSPaH4zeyiFh2q1Ss/CYo7UuvhowxE+2nCESLOBmcOTmT86lQsGJmIynFwFnjhP6HSYhw4lYtQo4n98ParPh2vLFm2w0ZpcnOvW4a+upv6//6X+v/8FwJCYiC0nB1tONvacHIy9ep108JlgTWB2v9nM7jcbaAo+m4YbFZQUsLt2Nzuqd7Cjegdvb30bgEGxg8hK1io+xyWPI84Sd3JfCyHOIBJ0CiGEEEIIIcSJiOkFcx6Gi34F+S9D7t+heh8s/YU2uCjnNsi6UesPKrqVoiiM7hXD6F4x3DtnGBsO1bB4YzFLNxVTUufi3+sP8+/1h4myGPjeiBTmp6cyaWACRr2EnqJzFIMBa3o61vR0uPlmVI+HxqIiHGu0wUaN69fjKy+nbvFi6hYvBsCQloo9Owfb+Bwt+ExNPel1JFgTmN13NrP7Hg0+15au1YLP0gJ21exiZ/VOdlbv5J1t7wAwMGagttU9OZPMlEwJPsVZTYJOIYQQQgghhDgZtjgt7JxwB2x4G759SpvYvvpPWk/PcdfDhJ9AdM/uXqkAdDqFsb1jGds7lvvnDWPdgWoWFxazZFMx5fVu/rn2EP9ce4gYm5FZw1OYPzqVCf3jMUjoKcKgmEzYxo7FNnYs/OQnBNxuGtdv0AYbrcmlsbAQ35Fiav/zH2r/8x8AjL17Y8/Rtrnbc7IxJCae9DoSrAnM6juLWX1nAVDZWKkFn019PnfV7Aoe7257F9CCz+bQMzM5k3hr/EmvQ4jTRYJOIYQQQgghhOgKJhtk3wzjfgybP9T6eJZugjXPQt4LMOpKrY9n0tDuXqlootMpZPaNI7NvHL+bP5yCfVUsLizm06JiKho8vF9wkPcLDhJnNzFrRAoL0lPJ7hcnoacIm85sxj4+B/v4HBLvhIDTiXPdepy5a3Dk5uEqKsJ74AA1Bw5Q88EHAJgGDAgONrJlZ2GIjT3pdcRb4/le3+/xvb7fA6DKVRWs+MwvzWdn9c5g8Pne9vcAGBA9QAs9m4LPBGvCSa9DiFNFgk4hhBBCCCGE6Ep6A6T/AEZdAbtWwjdPwr6vYOM72jF4DlxwN/Qe390rFS3odQo5/ePJ6R/PgxePIHdvJYsLi1lWVEKVw8O7eQd4N+8ACREmZo9MYX56Gll949DrTq7Hojg/6Ww2Ii6YRMQFkwDw19fjLCjAmZuHIy8X99ZteHbvxrN7N9XvaJWW5iFDgoONbJmZ6KOiTnodcZY4ZvaZycw+MwGodlWzrnQd+aXaVPcd1TvYXbub3bW7eX/7+wD0j+5PZnJmcLK7BJ/iTCJBpxBCCCGEEEKcCooCg2Zox6G1WuC59RPY8al29BqvBZ6DZoFOKgTPJHqdwsQBCUwckMAfLx7Bmj1VLC48wrLNJVQ0eHhrzQHeWnOAxEgzc0emMH90GuN6x6KT0FOcIH1kJJFTpxI5dSoA/poaHPn52mCjvFzcO3fh3r4d9/btVL3+Buh0WIYP1wYbjR+PbexYdHb7Sa8j1hLL9D7Tmd5nOgA1rhrWlq0NTnbfUb2DPbV72FO7h//b8X8A9I3qq011b+rzmWg7+S33QpwoCTqFEEIIIYQQ4lTrOQ5++CZU7NJ6eG58Fw6ugXevgsSh2pb2kVeAwdTdKxWtGPQ6LhiUwAWDEnjokpF8s6uCJYXF/HdzCeX1bl7/bj+vf7ef5Cgzc0elMj89jYxeMRJ6ipOij4khauZMomZqlZa+igqceXk4cvNw5ubi2bcPV1ERrqIiql75BxgMWEeODA42smZkoLNYTnodMZYYpveezvTeWvBZ665lbela8kvyKSgtYHvVdvbV7WNf3T4+2KFtue8b1ZfMlMzgZPckW9JJr0OIzpKgUwghhBBCCCFOl4SBcPFTMPU+WPM8FPwDyrfBf26HVX+CCYtg7EIwR3T3SkU7jHodU4YkMWVIEv/v0lF8vaucxYXFrNhcSmmdm1e/2cer3+wjLdqihZ6j0xjdMxpFkdBTnBxDQgJRc+cSNXcuAN7SUpy52mAjZ24u3sOHadywgcYNG6j8+wsoRiPWMWO0wUbjc7Cmp6OYTv6NlGhzNNN6T2Na72mAFnw2b3UvKClgW9W2YPD5zx3/BKBPVJ+jW92TM0m2J5/0OoToiASdQgghhBBCCHG6RabAzD/AhfdAwauw5jmoOwz/vQ+++Ctk3QQ5t0GEbAE9U5kMOqYNTWba0GTcPj9f7qhgSeERVmwp5Uiti5e/3svLX++lR4yV+elapefIHlESeoouYUxOJvrii4m++GIAPIcOacFnbi7ONbn4yspw5ufjzM+n4plnUCwWbGMzsOWMx56TjWXkSBTDyUdC0eZopvaeytTe2pb7Ok+dFnw2VXxuq9rG/rr97K/bz792/guA3pG9g/09M5MzSbGnnPQ6hGgmQacQQgghhBBCdBdLtNanc/ztsPE9bVt75S746lH47hnI+BFMuAPi+nX3SsUxmA16Zg5PZubwZFxeP59vL2fJpmJWbi3lcE0jL3y5hxe+3EPvOBvz0lOZn57K8FQJPUXXMfXsialnT2IuvxxVVfHs26cNNspdgzM3D39VFY5vv8Px7XeUow1DsmZlYs/OwTY+B8vQoSh6/UmvI8oUxZReU5jSawqgBZ/rS9dTUKr1+NxatZUD9Qc4UH8gGHz2iuwVrPbMSsmS4FOcFAk6hRBCCCGEEKK7GcwwbqEWbG5bog0uOrwW8l/WtrePuFTr45k6urtXKo7DYtQze2QKs0em0Ojx8/n2MhYXFrNyWykHqpw8//lunv98N/0S7Mwblcr80akMSY6U0FN0GUVRMPfrh7lfP2Kv+qEWfO7apW1zz8vFkZdPoLYWxxdf4vjiSwB0UVHYsrKw52hT3c2DBqJ0wZC0KFMUF/W6iIt6XQRAvaee9WXrg8ONtlRt4WD9QQ7WH+TfO/8NQM+InsGKz6zkLFIjUk96HeL8IUGnEEIIIYQQQpwpdHoYfjEMWwD7vtYCz12fQdG/tGPANJh0N/SbrE11F2c0q0nPnFGpzBmVitPjY9W2MhZvLGb19jL2Vjh4ZvUunlm9iwGJdualp7EgPZVByZHdvWxxjlEUBfOgQZgHDSLuf36EGgjg3rZNG2y0Zg3OggICdXU0rFxJw8qVAOhjY7X+njnZ2HLGY+rXt0vC+EhTJJN7TmZyz8kANHgaWFe2joLSAgpKCthSuYVDDYc4tOsQH+76EIAeET2C1Z5ZKVmkRaSd9DrEuUuCTiGEEEIIIYQ40ygK9LtQO0o2wTd/g6J/w+5V2pGWoVV4DrtYC0fFGc9mMjA/PY356Wk0uH2s3FrK4sJivthezu5yB0+t3MlTK3cyODmC+elpzEtPZUCiDKUSXU/R6bAMH45l+HDif3w9qs+Ha8uW4GAj57p1+KurqV+2jPplywAwJCYGBxvZcnIw9uzZJcFnhCkiJPh0eB2sL1uv9fgsKWBz5WYONxzmcMNhPtr9EaAFn+OSxwWDzx4RPU56HeLcIUGnEEIIIYQQQpzJUkbB5S/DtPvhu2dh3ZtwZD18cD3E9YeJP4XR14DR0t0rFZ0UYTbw/TE9+P6YHtS5vFroubGYL3eWs6O0gcdX7ODxFTsYmhLJgtFpzBuVSt8Ee3cvW5yjFIMBa3o61vR0uOVmVI+Hxk2btMFGuXk0rl+Pr7ycusWLqVu8GABDWir2nPHYcrKx5+RgTO2a7eV2o50LelzABT0uALTgc0PZBvJL8skvzWdLxZZg8Pnx7o8BSLOnBQcbNQef0gri/CVBpxBCCCGEEEKcDWL7wtxH4KJfQ96L2lG1Bxb/DFb/BcbfBpk3gjWmu1cqwhBlMXJpRk8uzehJbaOXFVtKWVx4hK93VrCtpJ5tJdt55L/bGZEWpVV6jkqld7ytu5ctzmGKyYRt3Dhs48bBT35CwO2mcf2G4GCjxsJCfEeKqf3wQ2o/1LaXG/v0Dg42smdnY0hM7JK12I12JvWYxKQekwBwep1a8FmaT35JPpsrNnPEcYSPd38cDD5T7ClkJWcFKz57RvbskrWIs4MEnUIIIYQQQghxNrEnwNT7YOKdsP5N+PYZqDsEK/8IXz0BmT+G8T+BKBngcbaJthq5YlxPrhjXkxqnh+WbS/mk8Ajf7q5k85E6Nh+p43+XbSO9ZzTz01OZOyqVnrESeopTS2c2Yx+vbVsHCDgcONet1wYb5ebhKirCu/8ANfsPUPPBBwCYBgwIDjayZWdhiI3tkrXYjDYm9pjIxB4Tgabgs3wDBSUFFJQWsKliEyWOEj7Z8wmf7PkEBYWHJj3E9wd+v0uuL858EnQKIYQQQgghxNnIHAHjb4esm7RBRd/8Dcq2wLdPwZrnYfQPYeJdkDi4u1cqTkCMzcSVWb24MqsXVQ4Py4pKWLLpCN/trqTwUC2Fh2r589JtjOkVEww902Ks3b1scR7Q2e1EXHgBERdq28v99fU4CwpwrsnFkZeHe9s2PLt349m9m+p33gHAPHRocLCRLSsTfWTXDN2yGW1MTJvIxLSjwefG8o3kl+Tz3ZHvKKos4pkNzzCv/zwMOonAzgfyX1kIIYQQQgghzmZ6I4y+CtJ/CDuXw9dPwoFvYf1bsP5tGDpPm9TeK6u7VypOUJzdxDU5vbkmpzcVDW4+LSphSeERcvdWseFgDRsO1vCnJVsZ1yc2GHomR0nPVnF66CMjiZw6lcipUwHwVVfjzM/HmZuHMy8X985duLdtw71tG1WvvwFNw5CaBxvZxo5FZ++aHrQ2o40JaROYkDaBW0ffyswPZlLiKOGLg18wvc/0LrmGOLNJ0CmEEEIIIYQQ5wJFgcGztONgnhZ4bl8C2xZrR59JWuA5aKZ2X3FWSogw8z/j+/A/4/tQVu/i000lLCksJn9/FWv3V7N2fzV/XLyFrD5xzB+dyuyRKSRFSugpTh9DbCxR3/seUd/7HgC+igqceXnBqe6e/ftxFRXhKiqi8uVXwGDAOmqUNtho/HisY8ags5z896xZb+bywZfz8qaXeXfbuxJ0nick6BRCCCGEEEKIc02vbLj6HSjfDt88BYXvw/5vtCNpBEy6C0ZeplWDirNWUqSFhRP7snBiX0pqXSzdVMySTcWs3V9N3r4q8vZV8fuPN5PTL4556WnMGZlCQoS5u5ctzjOGhASi5s4lau5cALwlJThztf6ezjVr8B45QuP69TSuX0/l319AMRqxjhmjDTbKycGano5iMp3Qta8cfCX/KPoHuSW57KrexcDYgV351MQZSIJOIYQQQgghhDhXJQ6BS57VhheteQ7WvgZlm+HDW2DVn2DCIhj7P2Dqmm2jovukRFu44YJ+3HBBP47UNLJ0UzGLC4vZcLCGNXuqWLOnit9/VMSEAfHMG5XG7JEpxNlPLDwS4mQYU1KI/v73if6+NiDIc+iQFnw2VXz6ysq0re/5+VQ8/QyK1YotIwNbTg6RM6ZjHjCg09dKjUhlWq9pfHbgM97b/h73j7//VD0tcYaQoFMIIYQQQgghznXRPWDW/4PJv4D8VyD371B7AJb9Gr74X8i+RTvs8d29UtEF0mKs3HRhf266sD8Hq5zBSs/CQ7V8s6uSb3ZV8ruPipg4IJ4F6Wl8b0QyMTYJPUX3MPXsialnT2IuvxxVVfHs29dU8ZmLMzcPf1UVjm+/xfHtt1Q8+yx933sXy/DhnX78q4dezWcHPuPj3R9z19i7iDR1zSAkcWaSoFMIIYQQQgghzhfWWC3snLAINrwD3z4N1Xvhi4e1ae0Z/wMT74CY3t29UtFFesXZuPWiAdx60QAOVDpZvOkISwqL2Xykjq92VvDVzgru+1DhgkEJzE9PY+bwZKKt0tJAdA9FUTD364e5Xz9ir7oKVVVx79yJMzePmg//jXvLVipf+Qc9Hnu004+ZlZLFgOgB7K7dzce7P+baYdeewmcgupuuuxcghBBCCCGEEOI0M1oh60b46Vq44lVIHQ1eJ+S9AH8bA/+6GUqKunuVoov1jrfxkykDWXLnhaz+xRR+OWsIQ1Mi8QVUPt9ezi8+2Ejmn1Zw42v5fLj+EPUub3cvWZznFEXBMngwcf/zI9L+9CcA6v77X7wlJWE9xtVDrwbg3W3vElADp2St4swgQacQQgghhBBCnK90em0o0S1fwP/8B/pPAdUPm/4P/j4J3roC9n0NqtrdKxVdrF+CnUVTB7Ls7sl8ds9F3DNzMIOTI/D6VVZuK+Nn729k3J8+4+Y3Cvhow2Ea3L7uXrI4z1mGD8eWnQ0+H9Vvvx3WuQsGLCDCGMH+uv18d+S7U7RCcSaQoFMIIYQQQgghzneKAgOmwnUfwS2fw4hLQdHBrhXw2jx4eQZs/QQCUgl1LhqYFMGd0wex/GcXsfxnk7lz+iD6J9rx+AKs2FLKXe9tYNxDK7jtzbUsLjyC0yOhp+gecdcvBKD6/f8j4HB0+jyb0cYlAy8BtKpOce6SoFMIIYQQQgghxFFpGfCD17Rt7Zk3gN4Mhwvg/R/Bs9mw7g3wubt7leIUGZwcyT0zB7PynotYdveF3DF1IH3jbbh9AZZtLuGOd9Yz9qEVLHp7HZ9uKqbR4+/uJYvzSMSUKRj79CZQV0fNf/4T1rk/HPJDAL489CUH6w+egtWJM4EEnUIIIYQQQggh2orrD/OfgJ8VwYU/B0s0VO6Ej38KfxsN3/wNXHXdvUpxiiiKwtCUKH4xawirfzGFJXdewO1TBtA7zobLG2DJpmJuf3sd4/60gp++u57/bi7B5ZXQU5xaik5H3HXXAVD1xhuoYVSZ943uy6S0SaiovL/t/VO1RNHNJOgUQgghhBBCCNGxiCSY/gD8bDN8708QmQb1xbDiAXhiJHz2INSXdvcqxSmkKAoj0qL59eyhfPHLKXxyxwXcOrk/PWKsOD1+Ptl4hFvfXEvmnz7j7vfW89mWUtw+CT3FqRFzySXooqLw7j9Aw+efh3XuNcOuAeDfu/5No6/xFKxOdDcJOoUQQgghhBBCHJ85Eib+FO7aCN9/FhIGg7sWvn4CnhwFn9wFlbu7e5XiFFMUhVE9o7l37jC+/vVU/rNoEjdd0I/UaAsNbh//2XCEm94oIPOhz7jn/zawelsZHp/0dhVdR2e3E3vlDwCoeu31sM6dlDaJnhE9qffUs3TP0lOxPNHNJOgUQgghhBBCCNF5BhNk/Ah+kgtXvQu9csDvhrWvwdPj4P+ug8PrunuV4jRQFIUxvWK4f/5wvvn1NP51+0R+PKkvyVFm6t0+/r3uMD9+LZ/MP63glx9s5Isd5Xj9EnqKkxf7ox+BwYAzLw/Xli2dPk+v03PV0KsAeGfbO6iqeqqWKLqJBJ1CCCGEEEIIIcKn08HQuXDjcvjxMhg0C1Bhy0fw0lR4fQHsWgkSJJwXdDqFcX1i+f2CEXz3m+l8cNsEFk7oQ2KkmTqXjw/WHmLhP/LI/n+fce+/C/l6ZwU+CT3FCTKmpBA1axYAVa+HV9V5ycBLsOgt7KjewboyeVPmXCNBpxBCCCGEEEKIk9NnAlz7f3D7d5B+FegMsPdLeOsyeGEybPonBHzdvUpxmuh0Cll94/jD90ey5t7pvHfLeH40vjfxdhPVTi/v5h3kR6/kkvPnlfz2w018u7sCf0ACcRGeuOuvB6B26ad4y8o6fV60OZp5/ecB8O62d0/F0kQ3kqBTCCGEEEIIIUTXSB4Ol70Ad26AnNvBaIOSQvjXjRiez6Fv+WfglQEg5xO9TmF8/3j+dMkocu+bzjs35XB1dm9ibUYqHR7ezj3ANS9poecDHxWRu6dSQk/RKdZRI7GOGwdeL9XvvBPWuVcPvRqAlftXUuqQYWrnEgk6hRBCCCGEEEJ0rZheMOdhbVL71N+CLR6lZj+jD72B4ZkM+OIRcFZ19yrFaWbQ65g4MIG/XDaK/N/O4M0bs7kqqxcxNiMVDW7e+G4/P3xxDRP+spIHP95Mwb4qAhJ6imOIW3gdADXvvkegsfNvogyJG8LYpLH4VB8f7PjgVC1PdAMJOoUQQgghhBBCnBq2OLjoV3B3Ef5Z/4vDlIDirIDVf4InRsKy+6D2UHevUnQDg17HhYMSefjydPJ/O4PXfpzFFeN6EmkxUFbv5rVv93HF379j0v+u4qHFW1h3oFoGx4g2IqdPx9izJ/7aWmo/+jisc68Zdg0AH+z4AI/fcyqWJ7qBBJ1CCCGEEEIIIU4tk41A5o2sHP4IvktegORR4HXAmmfhb6Phw9uhbFt3r1J0E6Nex5QhSTz6g9GsvX8m/7g+k8syehBpNlBc6+KVr/dy2XPfcsH/rubPS7ey8WCNhJ4CAEWvJ+66/wGg6o03UAOdH3A1rfc0kqxJVLmqWLF/xalaojjNJOgUQgghhBBCCHFaqIoedcTlcNtXcO2/oO+F2pCije/AcznwzlWw/7vuXqboRiaDjmlDk3n8h2PIv38GL12XyffHpGE36Tlc08iLX+7h+89+w4V/Xc1fPt1K0eFaCT3Pc9GXXY4uIgLPnj04vvqq0+cZdUZ+MOQHALyzLbwen+LMJUGnEEIIIYQQQojTS1Fg0Ay4fjHctAqGXQwosONTeHU2vDILtn8KYVRniXOPxahn5vBk/nZVBmt/N5O//2gc89NTsRr1HKpu5IUv9jD/6a+Z8ujn/HXZNrYcqZPQ8zykj7AT8wMtsKx6/fWwzr1i8BUYdAYKywvZXLH5VCxPnGYSdAohhBBCCCGE6D49x8EP34Q7CmDsQtCb4OAaePcqeH4CbHgHfNI/73xnMeqZPTKFZ64Zy7rfzeS5a8cyd1QKFqOO/ZVOnvt8N3Of+orpj33BY8u3s72kXkLP80jcj64FnQ7Ht9/h2r690+clWBOY1XcWAO9ue/dULU+cRhJ0CiGEEEIIIYTofgkD4eKn4O5NMOluMEdB+Tb4z+3w1Bj47llwN3T3KsUZwGrSM3dUKs9dO46198/k6aszmDUiGZNBx54KB0+v2sWsJ79k5hNf8uRnO9hVVt/dSxanmLFHDyK/9z0Aql5/I6xzrx56NQCf7v2Uald1l69NnF4SdAohhBBCCCGEOHNEpsDMP8DPimDGHyAiGeoOw3/vgydGwMqHoKG8u1cpzhB2s4EFo9N44X8yWfe7mfztqjHMGJaMSa9jV1kDT362kxmPf8msJ77k6ZU72VMuYfm5Kv76hQDUffIJvoqKTp+XnpDO8PjheAIe/rXzX6dqeeI0kaBTCCGEEEIIIcSZxxINF9ytVXgueAriB4KrBr56FJ4cCYvvgaq93b1KcQaJMBv4/pgevLwwk4LfzeDxK0czbWgSRr3C9tJ6Hluxg2mPffH/27vvKKnKw//j75mt9N57kSZFiiiioogiKipGVEBEY/QXg4nol2gSoyYmlm++xo4lRgULggULqCiioCBIR+lIE5G+9LJ1fn8MbCSi7sIOd2f2/Tpnz87euc/sZzz3bJhP7vM89HrkM4Z98jWrt+wJOrKKUKkTTqBUu3ZEsrPZNrLg09BDoRD9W/QH4NWlr5KTlxOriDoGLDolSZIkScVXchp0HASDZ8BlL0KdjpCzH2Y9C491gNeugfXzg06pYqZ8egqXdKjLc1efyKzbz+b/Lm1Lt2bVSA6HWLx+J//3wVLOeGASFzz2GU9NXsHajL1BR1YRqHzgrs5to0aRl5lZ4HHnNjqXimkVWb9nPZO/nRyreDoGLDolSZIkScVfOAlaXQi/mgiDxkHTHhDJg4Vj4OnT4cU+sHIyuAGN/kuF0in07VSPEb/szMzbe3D/JW047biqJIVDLFi3k/vfX8Jp//iEix6fwjOfrmTd9n1BR9YRKnf22STXrkVuRgY7x44t8Li0pDR+cdwvADclincWnZIkSZKk+BEKQaPT4Mo34NdToE1fCCXBio/hhQvhmTNh4ZuQlxt0UhVDlcqkckXn+rx47UnM+NNZ3NunDac0qUI4BPO/3cE97y2m6/0f0+eJqTw7ZRXrd1h6xpNQcjKVrxwIQMaIEUQK8X98XNb8MsKhMF+s/4IV21fEKqJizKJTkiRJkhSfaraBX/wbfjcHOl8PyaXgu7nw2tXweCeY9Rxk7w86pYqpKmXT6H9SfUZedzJf/KkHf7u4NSc1qkwoBHO/2c7fxi2iy30fc+mTnzN86io27vRaigcV+15KuHRpMpd/zZ6pnxd4XO2ytTmz3pmAd3XGM4tOSZIkSVJ8q9QQzvu/6E7t3W6DUpUgYyWMuxkebgOf/RP2bQ86pYqxauXSGHhyA0b/vy588cez+OuFx3Niw0oAzFqzjb+MXcTJ903ksqen8fIX37AzK+DA+lFJ5cpR4dLoNPSMESMKNbZfi34AvLPiHXZl7SrybIo9i05JkiRJUmIoUxXO/BMMWQDn3g/l68KeTTDxbnioNXz4Z9i5PuiUKuaql09n0CkNee3XpzD9j2dx5wWt6FC/IpEIzFiVwV/GLeHO2UkMfG4mL3+xhq27C77pjY6NygMHQijEns8+I/Prrws8rnPNzjSp0IR9Oft4Z8U7MUyoWLHolCRJkiQllrSycPINcNM86PM0VG8FWbvg88eid3i+PRg2Lws6peJAzQrp/PLURoz5TVem/qE7fz6/JW3rlidCiOmrtnH7mwvofO9Ervz3F4ya8Q3b9nirZ3GQWq8e5XqcBUDGiBcKPC4UCuXf1TlqySjyInkxyafYseiUJEmSJCWmpBRodwXc8Dn0fxXqnwJ52TD3JRjWGUYNgLUzg06pOFGnYil+dVpj3vh/J3Nn+xxu7XkcbepUIDcvwpSvt/CHMV9x4j0fMei5Gbw6ay079mYHHblEq3z11QDseOcdcjIyCjyud5PelE0py+qdq5n+3fQYpVOsWHRKkiRJkhJbKATNesIv34drJ0Dz84EILBkHz/aA58+DZR9CIXZoVslWJR2uO7URY397KpN/fwa3ntucVrXKk5MXYfKyzdz6+pd0umcCvxw+kzdmf8vO/Zaex1qpDh1Ib92aSGYm20aNKvC40imluajpRYCbEsUji05JkiRJUslRrzP0GwmDZ8AJV0I4BdZMhZF94cmuMH805FpKqeAaVCnDb85oyns3ncbH/9ONoec0o0XNcmTnRvh4ySb+57X5dPrbR/xqxCzemruOXZaex0QoFKLyoEEAbBv5CnlZBV9W4IrmVwAw+dvJrN21Nib5FBsWnZIkSZKkkqdac7h4GNw0H7rcCKllYdNCePN6eLQ9TH8KsvYEnVJxpnG1stzY/TjGDzmdj245nZt7NOO46mXJys3jo8UbGTJ6Hh3//hH/78VZvDP/O/Zk5gQdOaGVP7cnyTVqkLtlCzvffa/A4xpWaEjX2l2JEOHVpa/GMKGKmkWnJEmSJKnkqlAHet4DNy+A7ndAmWqwYy2Mvy26U/sn98GerUGnVBxqWr0cN/U4jgm3dOPDm0/nd92b0rhqGbJy8vhg4UZ+98pcOvxtAje8NJt3v1zP3ixLz6IWSkmh0pUDAMgYPpxIIZanOLgp0ZjlY9iXsy8m+VT0LDolSZIkSSpVCU4fCkO+gvMfhEqNYF8GTL4fHm4N790K278JOqXiVLMa5bjlnOZM/J9uvH/TaQw+swkNq5QmMyeP9xdsYPDIOXT820cMHjmH8QvWsz87N+jICaPSZZcRKlWKzKVL2fvFFwUed2qdU6lTtg47s3by/qr3Y5hQRcmiU5IkSZKkg1JKwYnXwm9nw6XPQ612kL0XZjwNj5wAb1wHGxYEnVJxKhQK0bJWeX7fswWfDD2Dcb89lV93a0K9yqXYl53Lu1+u59cvzaHj3ybwu1fm8uHCDZaeRympQgUq9ukDQMbwEQUfF07Kv6tz5OKRhbobVMGx6JQkSZIk6b+Fk6D1JXD9ZBj4FjQ+AyK58NWr8FRXeOlSWD3Fndp1xEKhEK3rVOAPvVrw6e/P5J0bu/L/Tm9MnYql2JOVyzvzv+P6F2fT6e8fcfPoeUxcvJHMHEvPI1H5qoEQCrF70iQyV64q8LiLm15MelI6S7ctZe6muTFMqKJi0SlJkiRJ0o8JhaDJmXDV23D9JDi+D4TC8PUEGH4+/LsHLB4LeXlBJ1UcC4VCtK1bkT+e15Ipt53Jm785hV+d2ohaFdLZnZnDm3PXce2IWXT6+0f8z6vz+WTpJrJyvOYKKrVhQ8qecQYAGS++UOBxFdIqcH7j8wF4ZckrsYimImbRKUmSJElSQdRuD32HR6e1d/olJKXBulkw+koY1hnmvAA5mUGnVJwLhUK0r1+JP1/Qiqm3deeNG7pwTdeG1Cifxq79Obwx51uueX4mJ97zEbe9/iWfLttMdq6l58+pfPXVAOx48y1yt28v8LiD09c/WvMRm/ZuikEyFSWLTkmSJEmSCqNyY7jgoehO7af9D6RXgK3L4Z3fwiPtYOojsH9n0CmVAMLhEB0bVOau3scz7Q9n8er/68KgLg2oWjaNHfuyGT1rLVc9N4PO93zEH8d8xdSvt5Bj6XlYpTufSFrLlkT272fb6FcLPK555eZ0qN6BnEgOry17LYYJVRQsOiVJkiRJOhJlq8NZd8LNC+Gcv0O52rBrPUy4Ex5qDR/9BXZtDDqlEkQ4HKJzo8r89aLWfPGns3jlupO58uT6VCmTyra92bwy4xsG/PsLTr5vIn9+6yumrdhKbp5ryB4UCoWoPOgqALa9/DKRrKwCj+3XMnpX52tLXyM7Nzsm+VQ0LDolSZIkSToaaeXglN/CTfPhomFQtRlk7oApD8HDbWDsTbB1RdAplUCSwiG6NKnC3y9uwxd/OouXf3US/TrXp1LpFLbszuKl6d/Q75npnHzfRO56ewEzVmWQZ+lJhfPOI7laNXI2bWLnBx8UeNxZ9c+iWqlqbN2/lQlrJsQwoY6WRackSZIkSUUhORXaXwm/+QKueAXqnQS5mTB7ODzWEV69CtbNCTqlEkxyUpiuTaty3yVtmHF7D174ZWcu61SXCqVS2LwrkxHT1nDZ09Pocv9E/jp2IbPXlNzSM5SaSqUB/QHIeH44kUjB/jukhFPo27wvACOXjIxZPh09i05JkiRJkopSOAwtzoNrP4RrxkOzc4EILHobnjkTRvSGrydCAUsWqaBSksKc3qwa/7i0HTNv78Hz15zILzrUpVx6Mht3ZvL81NX84slpnPq/H/P3cYuY+822Apd9iaLi5ZcTSktj/6JF7Js1q8Dj+jbrS3I4mfmb57Nw68IYJtTRsOiUJEmSJClWGnSB/qPhhmnQrh+Ek2HVp/DSJfD06fDV65CbE3RKJaDU5DBnNq/OPy9rx6w/9+DZQZ3o074OZdOS+W7Hfv49ZRV9nvicU//3E+57bzFffru9RJSeyZUqUeHiiwHYOmJEgcdVLVWVcxqcA8CoJaNiEU1FwKJTkiRJkqRYq9EK+jwFv5sHJ90AKaVhw5fwxrXwWAeY8Qxk7ws6pRJUWnISZ7WswUOXn8CsP/fgXwM7cmG72pROTWLd9n08/elKLnx8Kt3+bxL/O34JC9btSOjS8+CmRLsnfkzWmjUFHtevRXRTovdWvse2/dtikk1Hx6JTkiRJkqRjpWI96HV/dKf2M2+H0lVg+xp4b2h0p/bJ/4C9GUGnVAJLT0ninONr8mi/9sy542yeurID57etRamUJL7J2MuTk1ZwwWNT6P7PyTzwwVIWr9+ZcKVnWuPGlDn9NIhEyHjxpQKPa1etHa2qtCIrL4sxy8fEMKGOlEWnJEmSJEnHWunK0O1WGLIAznsAKtaHvVvgk3uihef4P8GOb4NOqQSXnpLEua1rMax/B2bf0YNh/TvQq3VN0pLDrNqyh8c/+Zpej3xGjwcn8+CEZSzbuCvoyEWmytVXA7B9zBhyd+4s0JhQKJR/V+fopaPJzcuNVTwdIYtOSZIkSZKCkloaOl8Hv50Lv3gWarSB7D0wfRg80g7evAE2LQk6pUqA0qnJnN+2Fk9e2ZE5d5zNo/3a0/P4GqQmh1mxeQ+PTlzOOQ99yjkPTeaRj5bz9abdQUc+KqW7dCGtWTMie/ey/bXXCjzu3IbnUjGtIuv3rGfyt5NjmFBHwqJTkiRJkqSgJSVDm0vh15/BgDeg4WmQlwPzR8ITJ8HIK2DNtKBTqoQok5bMhe1q8/TATsz+cw8evvwEerSsQWpSmGUbd/PQR8vo8eBkzn34Ux7/eDmrtuwJOnKhhUKh/LU6M156mUh2doHGpSenc8lxlwAwcsnImOXTkbHolCRJkiSpuAiF4LgecPU4+NXH0PJCIATL3ofnz4Vnz4El70FeXtBJVUKUS0/h4vZ1+PegTsz8cw/+2bcd3VtUJyUpxJINu3jgw2Wc+cAkLn96Gjv2FawsLC7KX3ABSVWqkLN+PTs//LDA4y5vfjnhUJgv1n/Byu0rY5hQhWXRKUmSJElScVS3I1z+Itw4CzoMgqRUWPsFjOoHT3aBuS9DTlbQKVWCVCiVwi861uW5q09k1u1n849L29KtWTWSwyG+WJXBqzPXBh2xUMJpaVTqF11zM2PECwXedKl22dqcUfcMAF5Z8kqs4ukIWHRKkiRJklScVW0KFz4KQ76CrkMgrTxsXgJv/wYePQE+fxwyE2eTGMWHCqVTuKxTPUb8sjN39W4FwNgvvws4VeFV6ncFodRU9n/5JfvmzivwuH4towXpOyveYXdWfK9XmkgsOiVJkiRJigflasLZf4WbF0CPv0LZGrBzHXx4e3Sn9ol/g92bg06pEqhXm1okhUN8+e0OVsfZep3JVapQ/sLeAGQMH17gcSfVPInGFRqzN2cvb694O0bpVFgWnZIkSZIkxZP0CnDqkOgdnr0fhSpNYf92+OwBeLg1jLsFMlYFnVIlSNWyaZzSpAoA4+Lwrs7KV0U3Jdr10UdkffttgcaEQiH6tYje1TlqySjyIq6bWxxYdEqSJEmSFI+S06DjIBg8Ay57Eep0hJz9MOtZeKwDvHYNrJ8fdEqVEL3b1gZg3JfrA05SeOnNmlHmlFMgL49tL75U4HG9m/SmTEoZVu9czfT102OYUAVl0SlJkiRJUjwLJ0GrC+FXE2HQOGjaAyJ5sHAMPH06vHAxrJwEBdxoRToSPY+vmb8T+7KN8bdmbOVrrgZg++uvk7u7YGtulkkpw0VNLgLglcVuSlQcWHRKkiRJkpQIQiFodBpc+Qb8egq06QuhJFj5CbxwEfzrDFj4JuTlBp1UCahC6RS6NasGwLj58Td9vcypp5LapAl5e/aw/fXXCzzuihZXADD528l8u6tg094VOxadkiRJkiQlmppt4Bf/ht/Nhc7XQ3IpWD8PXrsaHu8Es56D7P1Bp1SC6d0uOn197JfricTZHcShUCh/rc5tL75EJCenQOMaVWjEKbVPIUKEV5e+GsuIKgCLTkmSJEmSElWlBnDe/0V3au92G5SqBBkrYdzN8HAb+OyfsG970CmVIHq0rEF6SphVW/aw8LudQccptAoXXUhSxYpkr1vHro8mFnjcwU2J3lj+Bvty9sUqngrAolOSJEmSpERXpiqc+ScYsgDOvR/K14U9m2Di3fBQa/jwz7Az/qYbq3gpk5bMWS1qADA2Dqevh9PTqdgvOhU9Y8SIAo87rc5p1Clbh51ZO3l/1fuxiqcCsOiUJEmSJKmkSCsLJ98AN82DPk9D9VaQtQs+fwwebgtvD4bNy4JOqTjWu10tILr7el5efE1fB6jUrx+kpLBv7lz2zZ9foDFJ4SSuaB4tSF9Z8krcTdtPJBadkiRJkiSVNEkp0O4KuOFz6P8q1D8F8rJh7kswrDOMGgBrZwadUnHojObVKZuWzLrt+5i7dlvQcQotpXp1Kpx/PlC4uzr7HNeHtKQ0lmQsYd7meTFKp59j0SlJkiRJUkkVCkGznvDL9+HaCdD8fCACS8bBsz3g+fNg2YfgHWoqoPSUJM5pdXD6+vqA0xyZylcPAmDnBx+S/V3BpuBXSKvA+Y2jBenIxSNjlk0/zaJTkiRJkiRBvc7QbyQMngEnXAnhFFgzFUb2hSe7wvzRkJsddErFgQu+N309Nw6nr6e3aEHpk06C3FwyXn65wOMObkr00ZqP2LR3U6zi6SdYdEqSJEmSpP+o1hwuHgY3zYcuN0JqWdi0EN68Hh5tD9Ofgqw9QadUMXZq02pUKJXClt2ZfLFya9BxjsjBuzq3v/oaeXsKdr23qNyCDtU7kBPJ4fVlr8cynn6ERackSZIkSfqhCnWg5z1w8wLofgeUqQY71sL426I7tX9yH+yJzxJLsZWaHKZX65oAjP0yPqevl+3WjdSGDcnbtYvtY94s8LiDd3W+tuw1sr0D+piz6JQkSZIkST+uVCU4fSgM+QrOfxAqNYJ9GTD5fnjoeHjvVtj+TdApVcz0blcbgPcXrCc7Ny/gNIUXCoepdNVAADJefJFIbm6Bxp3V4CyqlarGln1bmLBmQiwj6jAsOiVJkiRJ0s9LKQUnXgu/nQ2XPg+12kHOPpjxNDxyArxxHWxYEHRKFRMnN65C1bJpbN+bzZSvtwQd54hUvPhiwhUqkP3NN+z+5JMCjUkJp9C3eV8AXlnySizj6TAsOiVJkiRJUsGFk6D1JXD9ZBj4FjQ+AyK58NWr8FRXeOlSWD3FndpLuKRwiPPbHJi+Pr9gO5cXN+HSpal02WUAZAwfUeBxfZv1JTmczLzN81i0dVGs4ukwLDolSZIkSVLhhULQ5Ey46m24fhIc3wdCYfh6Agw/H/7dAxaPhbz4m7asonFw+vqHCzeyP7tgU7+Lm0pXDoDkZPbOmsW+BQsLNKZqqaqc3eBsAEYtGRXLePovFp2SJEmSJOno1G4PfYdHp7V3+iUkpcG6WTD6ShjWGea8ADmZQafUMdahfiVqV0hnd2YOk5ZuDjrOEUmpUYPyvXoBkDGi4Hd19m/RH4D3Vr3H9v3bYxFNh2HRKUmSJEmSikblxnDBQ9Gd2k/7H0ivAFuXwzu/hYfbwtRHYP/OoFPqGAmHQ1xw4K7OsV/G5/R1gMqDBgGw8/33yd64sUBj2lVrR8vKLcnMzWTM12NiGU/fY9EpSZIkSZKKVtnqcNadcPNCOOfvUK427N4AE+6Eh1rDR3+BXQUrjBTfereNFp0TF29kT2ZOwGmOTKnWx1OqU0fIyWHbyyMLNCYUCtGvRT8ARi8ZTW5efE7djzcWnZIkSZIkKTbSysEpv4Wb5sNFw6BqM8jcAVMegofbwNibYOuKoFMqhlrXKU/DKqXZn53HR4vjt9yucvXVAGwbPZq8vXsLNKZXo15UTKvId3u+Y/K3k2OYTgdZdEqSJEmSpNhKToX2V8JvvoArXoF6J0FuJsweDo91hFevgnWzg06pGAiFQlxw4K7OsfPXB5zmyJU980xS6tUjb8cOdrz9doHGpCenc8lxlwDwypJXYhlPB1h0SpIkSZKkYyMchhbnwbUfwjXjodm5QAQWvQ3PdIcRveHriRCJBJ1URejg7uufLtvMjn3ZAac5MqGkJCoPHAhAxogXiOTlFWjcZc0vIxwKM339dFZuXxnLiMKiU5IkSZIkBaFBF+g/Gm6YBu36QTgZVn0KL10CT58OX70OufG5pqMO1bxmOZrVKEtWbh4fLtwQdJwjVuGSSwiXK0fW6tXsnlywqeh1ytahW91uAIxaOiqW8YRFpyRJkiRJClKNVtDnKfjdPDjpBkgpDRu+hDeuhcc6wIxnIHtf0Cl1lA5uSjT2y/idvp5UtgwV+/YFond1FtTBTYne/vptdmftjkk2RVl0SpIkSZKk4FWsB73uj+7UfubtULoKbF8D7w2N7tQ++R+wNyPolDpCFxyYvj716y1s3Z0ZcJojV/nKAZCUxN7p09m/eHGBxpxc62QaVWjE3py9vLPinRgnLNksOiVJkiRJUvFRujJ0uxWGLIDzHoCK9WHvFvjknmjhOf6PsOPboFOqkBpVLUObOhXIzYvw/oL4nb6eUrs25XueAxT8rs5QKJR/V+crS14h4hq0MWPRKUmSJEmSip/U0tD5OvjtXPjFs1CjDWTvgelPwCPt4M1fw6aC3VGn4qF3u1oAjJ3/XcBJjk7lQYMA2PHuu2Rv2lSgMRc2uZAyKWVYvXM109ZPi2W8Es2iU5IkSZIkFV9JydDmUvj1Z3DlG9DwNMjLgfmvwBMnw8grYI3FUTw4/8A6nTNWZ7Bhx/6A0xy5Uu3aUap9e8jOZtsrrxRoTJmUMlzU5CIgelenYsOiU5IkSZIkFX+hEDTtAVePg199DC0vBEKw7H14/lx49hxY8h7k5QWdVD+iTsVSdGpQiUgE3v0qfjclgv/c1bl91Gjy9hestL28xeUATF47mXW718UsW0lm0SlJkiRJkuJL3Y5w+Ytw4yzoMAiSUmHtFzCqHzzZBea+DDlZQafUYfQ+sClRvE9fL9fjLFJq1yZ32zZ2vFOwDYYaV2hMl1pdiBBh9NLRMU5YMll0SpIkSZKk+FS1KVz4KAz5CroOgbTysHkJvP0bePQE+PwR3pTyAABAwklEQVRxyNwVdEp9T682NQmHYN7a7azN2Bt0nCMWSk6m0lUDgeimRAXdYOjgpkRjlo9hf078Tt8vriw6C2nv3r00aNCAoUOHBh1FkiRJkiQBlKsJZ/8Vbl4APf4KZWvCznXw4e3w0PEw8W+we3PQKQVUL5fOyY2rADD2y/i+q7PipZcSLlOGrBUr2DNlSoHGnF73dOqUrcOOzB28v+r9GCcseSw6C+mee+7h5JNPDjqGJEmSJEn6b+kV4NQhMORL6P0oVGkK+3fAZw/Aw61h3C2QsSrolCXewenr4+bH9zqdSWXLUvHSXwCQMXxEwcaEk7i8eXStzpFLRhb4TlAVjEVnISxfvpwlS5bQq1evoKNIkiRJkqQfk5wGHQfB4Blw2YtQpyPk7IdZz8JjHeC1a2D9/KBTlljnHl+T5HCIRet38vWm3UHHOSqVBg6EcJg9U6eyf9myAo3p07QPaUlpLMlYwvzNXodFKfCi8y9/+QuhUOiQrxYtWhTp7/j000/p3bs3tWvXJhQK8dZbbx32vGHDhtGwYUPS09M56aSTmDFjxiHPDx06lPvuu69Is0mSJEmSpBgJJ0GrC+FXE2HQuOiu7ZE8WDgGnj4dXrgYVk4C76o7piqVSeW046oCMC7Op6+n1q1LuR49AMh44YUCjamYXpHzGp0HwMjFI2OWrSQKvOgEOP7441m/fn3+15SfWNdg6tSpZGdn/+D4okWL2Lhx42HH7Nmzh3bt2jFs2LAffd3Ro0dzyy23cNdddzFnzhzatWtHz5492bRpEwBvv/02zZo1o1mzZoV8d5IkSZIkKVChEDQ6Da58A349Bdr0hVASrPwEXrgI/nUGLHwT8nKDTlpifH/39Xifvl356kEA7HxnLDlbtxZozMFNiSasmcDmva4fW1SKRdGZnJxMzZo187+qVq162PPy8vIYPHgw/fv3Jzf3P398li5dSvfu3Rkx4vDrIfTq1Yu///3v9OnT50czPPjgg1x33XVcc801tGrViqeeeorSpUvz3HPPATB9+nRGjRpFw4YNGTp0KM888wx33333UbxrSZIkSZJ0zNVsA7/4N/xuLnS+HpJLwfp58NrV8HgnmPUcZLsbdqyd3aoGqclhVmzew+L1u4KOc1RKtW9Petu2RLKy2PbKqAKNaVmlJe2rtycnksPry16PccKSo1gUncuXL6d27do0btyYAQMG8M033xz2vHA4zHvvvcfcuXO56qqryMvLY8WKFXTv3p2LL76YW2+99Yh+f1ZWFrNnz6bHgVuND/6uHj16MG3aNADuu+8+1q5dy+rVq3nggQe47rrruPPOOw/7esOGDaNVq1aceOKJR5RHkiRJkiTFWKUGcN7/RXdq73YblKoEGSth3M3wcBv47J+wb3vQKRNWufQUujevDsT/7uuhUIjKg64CYNsrr5CXmVmgcQfv6nx12atk5/5w9rIKL/Ci86STTmL48OGMHz+eJ598klWrVnHaaaexa9fh2/zatWvz8ccfM2XKFPr370/37t3p0aMHTz755BFn2LJlC7m5udSoUeOQ4zVq1GDDhg2Ffr3BgwezaNEiZs6cecSZJEmSJEnSMVCmKpz5JxiyAM69H8rXhT2bYOLd8FBr+PDPsDO+i7jiKpGmr5c/5xySa9Ykd+tWdo57t0BjetTvQbVS1diybwsfffNRjBOWDIEXnb169aJv3760bduWnj178t5777F9+3ZeffXVHx1Tv359XnzxRUaPHk1ycjLPPvssoVDomGW++uqreeCBB47Z75MkSZIkSTGWVhZOvgFumgd9nobqrSBrF3z+GDzcFt4eDJsLtqu2CqZ7i+qUTk3i2237mLd2e9BxjkooJYXKA68EIGPEiAIVtylJKfRt1heAV5a8EtN8JUXgRed/q1ixIs2aNePrr7/+0XM2btzI9ddfT+/evdm7dy8333zzUf3OqlWrkpSU9IPNjDZu3EjNmjWP6rUlSZIkSVIcSUqBdlfADZ9D/1eh/imQlw1zX4JhnUl67Soq7fnxzkIFVyo1iR4to7Nrx85fH3Cao1exb19CpUuTuWwZew8shfhzLm12KcmhZOZumsvirYtjnDDxFbuic/fu3axYsYJatWod9vktW7Zw1lln0bJlS8aMGcPEiRMZPXo0Q4cOPeLfmZqaSseOHZk4cWL+sby8PCZOnEiXLl2O+HUlSZIkSVKcCoWgWU/45ftw7QRofj4QIbzsPU5fdjdJL/aGZR9CnE+5DtrB6evvfvUdeXnx/d8yqXx5Kh7YCHvrj2yY/d+qla7G2Q3OBryrsygEXnQOHTqUyZMns3r1aj7//HP69OlDUlIS/fr1+8G5eXl59OrViwYNGuRPW2/VqhUTJkzg+eef56GHHjrs79i9ezfz5s1j3rx5AKxatYp58+YdsunRLbfcwjPPPMOIESNYvHgxN9xwA3v27OGaa66JyfuWJEmSJElxol5n6DcSBs8gr21/8kJJhL+ZBiP7wpNdYf5ocDOZI3J6s6qUS09m485MZq7OCDrOUat81UAIhdgz+VMyV6wo0Jj+LfsD8N6q99i+f3sM0yW+wIvOb7/9ln79+tG8eXMuu+wyqlSpwvTp06lWrdoPzg2Hw9x777288cYbpKam5h9v164dH330EX379j3s75g1axbt27enffv2QLTUbN++/SG7pl9++eU88MAD3HnnnZxwwgnMmzeP8ePH/2CDIkmSJEmSVEJVa05u70eZ0Oqf5J70G0gtC5sWwpvXw6PtYfpTkLUn6JRxJS05iXOPjy4bGO+7rwOkNmhA2e7dAch44cUCjWlXrR0tK7ckMzeTN79+M5bxEl7gReeoUaP47rvvyMzM5Ntvv2XUqFE0adLkR88/++yzSU9P/8Hx9u3bU7du3cOOOeOMM4hEIj/4Gj58+CHn3XjjjaxZs4bMzEy++OILTjrppKN6b5IkSZIkKfHsT61MXo+74eYF0P0OKFMNdqyF8bdFd2r/5D7YszXomHHj4PT1977aQE5uXsBpjl7lQVcBsOPtt8nZtu1nzw+FQvRrEZ3ZPHrpaHLzcmOaL5EFXnRKkiRJkiTFpVKV4PShMOQrOP9BqNQI9mXA5PvhoePhvVth25qgUxZ7pzSpQuUyqWTsyeLzFfFfEJc+8UTSW7Uisn8/20ePLtCYXo16USGtAut2r+PTbz+NccLEZdEpSZIkSZJ0NFJKwYnXwm9nw6XPQ612kLMPZjwdndL+xnWwYUHQKYut5KQw57U5MH19fvxPXw+FQlS+ehAA214eSSQr62fHpCenc8lxlwBuSnQ0LDolSZIkSZKKQjgJWl8C10+GgW9B4zMgkgtfvQpPdYWXLoXVU9yp/TB6t41OXx+/cAOZOfE/dbv8ueeSXK0aOZs3s/P99ws05vLmlxMixLT101i5Y2WMEyYmi05JkiRJkqSiFApBkzPhqrfh+klwfB8IheHrCTD8fPh3D1g8FvLifz3KonJiw8rUKJ/Grv05fLpsS9BxjlooNZVKV14JwNbhI4gUoNyuU7YO3ep1A2DUklExzZeoLDolSZIkSZJipXZ76Ds8Oq290y8hKQ3WzYLRV8KwzjDnBcjJDDpl4MLhEBccuKszEaavA1S6/DJC6elkLl7M3hkzCzSmf4v+ALz99dvsztody3gJyaJTkiRJkiQp1io3hgseiu7UftpQSK8AW5fDO7+Fh9vClIdh/86gUwbqgra1AJiwaCN7s3ICTnP0kipWpMLFFwGQMWJEgcacXOtkGpZvyN6cvYxdOTaW8RKSRackSZIkSdKxUrY6nHUH3LwQzrkHytWG3Rvgo7uiO7V/9BfYtTHolIE4oV5F6lYqxb7sXD5esinoOEWi8lXRTYl2f/IJWatX/+z5oVCIfi36AdFNiQoy5V3/YdEpSZIkSZJ0rKWVg1NuhJvmw0XDoGozyNwJUx6Ch9vA2Jtg64qgUx5ToVCI3u0Sa/p6WuNGlO3WDSIRMl54sUBjLmxyIaWTS7Nqxyqmr58e44SJxaJTkiRJkiQpKMmp0P5K+M0XcMUrUO8kyM2E2cPhsY7w6lWwbnbQKY+Zg7uvf7J0M7v2ZwecpmhUvjp6V+f2N98kd/v2nz2/bGpZLmoanfL+ypJXYhkt4RRJ0blmzRoWLVpEnruFSZIkSZIkFV44DC3Og2s/hGvGQ7NzgQgsehue6Q7DL4CvP4IEn8rcslY5mlQrQ1ZOHhMWJcYU/tInn0xa8+ZE9u1j22uvFWjMFS2uAGDyt5NZt3tdLOMllEIVnc899xwPPvjgIceuv/56GjduTJs2bWjdujVr164t0oCSJEmSJEklSoMu0H803DAN2vWDcDKs/gxe+gU8fRp89Trkxv9mPYeTiNPXQ6EQlQdF7+rc9tLLRLJ//k7VxhUac3Ktk8mL5DF66ehYR0wYhSo6//Wvf1GpUqX8n8ePH8/zzz/PCy+8wMyZM6lYsSJ//etfizykJEmSJElSiVOjFfR5Cn43D07+DaSUgQ1fwRvXwmMdYMYzkLU36JRF7oID09c/W76FbXuyAk5TNMpfcD5JVauSs3EjO8d/UKAx/Vv0B2DM8jHsz9kfy3gJo1BF5/Lly+nUqVP+z2+//TYXXXQRAwYMoEOHDtx7771MnDixyENKkiRJkiSVWBXrwbn3wc0L4MzboXQV2L4G3hsa3bho8j9gb0bQKYtM0+plaVWrPDl5EcYv3BB0nCIRTk2lUv/obuoZI0YUaDf10+ueTu0ytdmRuYP3V70f64gJoVBF5759+yhfvnz+z59//jmnn356/s+NGzdmw4bEuAAlSZIkSZKKldKVodutMGQBnPcAVKwPe7fAJ/fAQ61h/B9hx7dBpywSiTZ9HaDSFVcQSk1l/4IF7Jsz52fPTwoncXmLy4HopkQFKUdLukIVnQ0aNGD27OhOX1u2bGHhwoV07do1//kNGzZQoUKFok0oSZIkSZKk/0gtDZ2vg9/OhV88CzXaQPYemP4EPNIO3vw1bFocdMqjckHbWgBMW7mVTTsTY9p2cuXKVLjoQgAyhg8v0JhLml5CWlIaizMWM3/z/BimSwyFKjoHDRrE4MGD+dvf/kbfvn1p0aIFHTt2zH/+888/p3Xr1kUeUpIkSZIkSf8lKRnaXAq//gyufAMangZ5OTD/FXjiZBh5OayZFnTKI1Kvcmna169IJALvfbU+6DhFpvJVVwGw66OJZBVgQ++K6RU5r9F5AIxcMjKm2RJBoYrOW2+9leuuu44xY8aQnp7Oa6+9dsjzU6dOpV+/fkUaUJIkSZIkST8hFIKmPeDqcfCrj6HlhUAIlo2H58+FZ8+BJe9BXl7QSQvl4KZEY79MnKIz7bjjKHPqqRCJkPHiiwUac0WLKwCYsHoCm/dujmW8uFeoojMcDnP33Xczd+5c3n//fVq2bHnI86+99hrXXnttkQaUJEmSJElSAdXtCJe/CDfOgg6DICkV1n4Bo/rBk11g7suQEx87mZ/fphahEMxes4112/cFHafIVB40CIAdr79B7q5dP3t+qyqtOKHaCeREcnh92euxjhfXClV0AowePZoBAwbQt29fnnrqqVhkkiRJkiRJ0tGo2hQufBSGfAVdh0Baedi8BN7+DTx6Anz+OGT+fMkWpJoV0uncsDIA736ZOJsSlTm1K6lNm5C3dy/bXytYcdm/ZX/qlq1LzTI1Y5wuvhWq6HzyySfp168fs2bNYvny5QwePJjf//73scomSZIkSZKko1GuJpz9V7h5AfT4K5StCTvXwYe3w0PHw8S/we7iOx36P7uvJ8709VAolH9XZ8ZLLxLJyfnZMec0OIdxfcbR57g+sY4X1wpVdD7++OPcddddLF26lHnz5jFixAieeOKJWGWTJEmSJElSUUivAKcOgSFfQu9HoUpT2L8DPnsAHm4N426BjFVBp/yBXq1rkhQO8dW6HazasifoOEWmQu/eJFWqRM5369k1YcLPnp8UTiIpnHQMksW3QhWdK1euZNCBxhmgf//+5OTksH594rTqkiRJkiRJCSs5DToOgsEz4LIXoU5HyNkPs56FxzrAa9fA+vlBp8xXpWwaXZtWBWDc/MSZvh5OT6fSgQ29M4aPCDhN4ihU0ZmZmUmZMmX+MzgcJjU1lX37EmdBWEmSJEmSpIQXToJWF8KvJsKgcdFd2yN5sHAMPH06vHAxrJwEkUjQSendthYAYxNonU6ASv37EUpJYd/8+exbsDDoOAkhubAD7rjjDkqXLp3/c1ZWFvfccw8VKlTIP/bggw8WTTpJkiRJkiTFTigEjU6Lfm34CqY+AgvGwMpPol+1TohOeW95YbQcDcA5x9fk9jcXsGzjbpZu2EXzmuUCyVHUkqtWpfrvf09a0yakH98q6DgJoVBF5+mnn87SpUsPOXbKKaewcuXK/J9DoVDRJJMkSZIkSdKxU7MN/OLf0P0OmPY4zHkR1s+D166Gyo3hlN9Cu/6Qkn5MY1UolUK35tWYsGgjY+d/R/OazY/p74+lylcNDDpCQilU0Tlp0qRDft6yZQupqamUL1++KDNJkiRJkiQpKJUawHn/B91ugxn/in5lrIRxN8Mn98HJv4ZO10KpiscsUu92taNF55ff8T/nNPNGOx1WodboBNi+fTuDBw+matWq1KhRg0qVKlGzZk3++Mc/snfv3lhklCRJkiRJ0rFWpiqc+ScYsgDOvR/K14U9m2Di3fBQa/jwz7Dz2KybeVaL6qSnhFmzdS9frdtxTH6n4k+h7ujMyMigS5curFu3jgEDBtCyZUsAFi1axGOPPcaECROYMmUKX375JdOnT+d3v/tdTEJLkiRJkiTpGEkrCyffACf+Cha8EV3Hc9Mi+PwxmP4UtLscTrkJqjWLWYQyacmc1bIG7365nrHzv6Nt3Yox+12KX4W6o/Puu+8mNTWVFStW8PTTTzNkyBCGDBnCv/71L77++muysrIYOHAgZ5999iGbE0mSJEmSJCnOJaVAuyvghs+h/6tQ/xTIy4a5L8GwzjBqAKydGbNf37ttbQDGfbmevLzgd4NX8VOoovOtt97igQceoEaNGj94rmbNmvzjH//gjTfe4JZbbmHQoEFFFlKSJEmSJEnFRCgEzXrCL9+HaydA8/OBCCwZB8/2gOfPg2UfQqRoy8gzmlejbFoy63fsZ84324r0tZUYClV0rl+/nuOPP/5Hn2/dujXhcJi77rrrqINJkiRJkiSpmKvXGfqNhMEz4IQrIZwCa6bCyL7wZFeYPxpys4vkV6WnJHHO8dGb78bOPzZrgyq+FKrorFq1KqtXr/7R51etWkX16tWPNpMkSZIkSZLiSbXmcPEwuGk+dLkRUsvCpoXw5vXwaPvoWp5Ze4761/RuF52+/u5X68nJzTvq11NiKVTR2bNnT26//XaysrJ+8FxmZiZ33HEH5557bpGFkyRJkiRJUhypUAd63gM3L4Dud0CZarBjLYy/LbpT+yf3wZ6tR/zypzatSsXSKWzZncUXqzKKMLgSQaF2Xb/77rvp1KkTxx13HIMHD6ZFixZEIhEWL17ME088QWZmJi+88EKsskqSJEmSJCkelKoEpw+FLoNh3sjoDu3bVsHk+6O7tne4KvpcpQaFetmUpDC9WtfilRnfMHb+d3RtWjVGb0DxqFB3dNatW5dp06bRqlUr/vjHP3LxxRfTp08fbr/9dlq1asXUqVOpX79+rLJKkiRJkiQpnqSUghOvhd/Ohkufh1rtIGcfzHg6OqX9jV/BhgWFesne7WoB8P6CDWTlOH1d/1GoOzoBGjVqxPvvv8+2bdtYvnw5AE2bNqVy5cpFHk6SJEmSJEkJIJwErS+B4/vAykkw9eHo969ei3417QFdh0DDU6O7uv+EkxpVoVq5NDbvymTK15vp3qLGMXgDigeFuqPz+ypVqkTnzp3p3LmzJackSZIkSZJ+XigETc6Eq96G6ydFi89QGL7+CEZcAP8+CxaPhbwfv1MzKRzi/DbRuzrHzl9/jIIrHhxx0SlJkiRJkiQdsdrtoe/w6LT2Tr+EpDRYNxtGXwnDOsOcFyAn87BDD05f/3DhBvZn5x7D0CrOLDolSZIkSZIUnMqN4YKHoju1nzYU0ivA1uXwzm/h4bYw5WHYv/OQIe3rVaJOxVLsycrlkyWbgsmtYseiU5IkSZIkScErWx3OugNuXgjn3APlasPuDfDRXfDQ8TDhLti1AYBwOMQFbaN3dY770unrirLolCRJkiRJUvGRVg5OuRFumg8XDYOqzSBzZ3QDo4fbwDu/g60r6N2uNgATl2xkd2ZOsJlVLFh0SpIkSZIkqfhJToX2V8JvvoArXoF6J0FuFswZAY915PgpN3Jupe/Yn53HxMUbg06rYsCiU5IkSZIkScVXOAwtzoNrP4RrxkOzc4EIocXv8NS+oYxM+Tsrp70NkUjQSRUwi05JkiRJkiTFhwZdoP9ouGEatOtHJJzMKUmLuHnjH8l98jT46nXIdRp7SWXRKUmSJEmSpPhSoxX0eYrQ7+YxJu1C9kTSSNr0FbxxLTzWAWY8A1l7g06pY8yiU5IkSZIkSfGpYj3Wn3wXXTMf5fXyV0HpKrB9Dbw3FB5pC5sWB51Qx5BFpyRJkiRJkuLWBW1rsZ1y3Lr5XLZcNxvOewAq1IM9m2H6E0HH0zFk0SlJkiRJkqS41aBKGdrWrUBeBN5fsgM6XwfnPxh9cuXkYMPpmLLolCRJkiRJUlzr3bY2AGPnr48eaHAKhJOj09gzVgWYTMeSRackSZIkSZLi2vltawEwY3UG63fsg7SyUPfE6JOrvKuzpLDolCRJkiRJUlyrXbEUJzasBMC7Xx64q7NRt+j3lZOCCaVjzqJTkiRJkiRJca93uwPT1w8WnY3PiH5f9Snk5QUTSseURackSZIkSZLiXq/WtQiHYP7a7XyzdS/U6QgpZWDvVti4IOh4OgYsOiVJkiRJkhT3qpVL45QmVQEY++V3kJwKDbtGn3SdzhLBolOSJEmSJEkJoXe76KZEY+d/Fz3gOp0likWnJEmSJEmSEkLP42uSkhRiyYZdLN+46z/rdK75HHKyAs2m2LPolCRJkiRJUkKoWDqV04+rBhzYlKh6KyhdFbL3wrczA06nWLPolCRJkiRJUsK44MD09XHzvyMSCkHjA9PXXacz4Vl0SpIkSZIkKWH0aFmDtOQwK7fsYeF3O7+3TqdFZ6Kz6JQkSZIkSVLCKJeeQvcW1YEDu68fXKdz3SzI3BVcMMWcRackSZIkSZISSu92tQEYN389kYr1oVJDyMuJbkqkhGXRKUmSJEmSpIRyZvPqlElNYt32fcxdu/0/d3WunBRgKsWaRackSZIkSZISSqnUJM5uVQOAsfO/c53OEsKiU5IkSZIkSQnn4PT1d79cT27D06MHNy2E3ZsCTKVYsuiUJEmSJElSwjntuGqUT09m065MZmwMQc020SdWfRpsMMWMRackSZIkSZISTmpymF6tawEHdl/Pn77+SYCpFEsWnZIkSZIkSUpIB6evv//VenIafm+dzkgkwFSKFYtOSZIkSZIkJaSTG1emSplUtu3N5vOcZhBOgR1rIWNl0NEUAxadkiRJkiRJSkjJSWHOaxOdvv72wh1Qr3P0iVXuvp6ILDolSZIkSZKUsA5OX/9w4QayG5wWPbhyUnCBFDMWnZIkSZIkSUpYnRpUomb5dHZl5jAnqV304KpPIS8v2GAqchadkiRJkiRJSljhcIgL2kanr49cVw1Sy8K+bbDhy4CTqahZdEqSJEmSJCmh5U9fX7yV3PpdowddpzPhWHRKkiRJkiQpobWtW4H6lUuzLzuXxaXaRw+6TmfCseiUJEmSJElSQguFQvRuF52+/vq2ptGDa6ZBTmaAqVTULDolSZIkSZKU8A5OXx+5qgx5ZapDzj74dmbAqVSULDolSZIkSZKU8JrXKMdx1cuSlRvh24onRg86fT2hWHRKkiRJkiQp4YVCIS5oe2BTon0togdXuiFRIrHolCRJkiRJUolwwYF1OodvaBg9sG427N8ZXCAVKYtOSZIkSZIklQhNqpXl+Nrl+TavCjtL14dILqyZGnQsFRGLTkmSJEmSJJUYBzcl+oI20QOu05kwLDolSZIkSZJUYpzfJjp9/c0dTaMHXKczYVh0SpIkSZIkqcSoV7k0HepX5PPcVkQIwebFsGtD0LFUBCw6JUmSJEmSVKL0bleb7ZRjZXKT6IFVnwYbSEXColOSJEmSJEklyvltahEKwYT9LaIHXKczIVh0SpIkSZIkqUSpXj6dkxtVYWpe6+iBlZMhEgk2lI6aRackSZIkSZJKnN7tajMzrznZJMPOb2HriqAj6ShZdEqSJEmSJKnEObd1TbLD6czKbRY9sGpSoHl09Cw6JUmSJEmSVOJULpPKqU2rMjXv+OgB1+mMexadkiRJkiRJKpF6t6udv05nZNVnkJcbcCIdDYtOSZIkSZIklUjnHF+DJeGm7IyUIrR/O6yfH3QkHQWLTkmSJEmSJJVI5dNTOK15Tb7IaxU9sGpysIF0VCw6JUmSJEmSVGJFp69H1+mMrLTojGcWnZIkSZIkSSqxzmpZnVnhdgBE1nwO2fsDTqQjZdEpSZIkSZKkEqt0ajKNWnZgY6Qi4dxM+HZG0JF0hCw6JUmSJEmSVKIdsvv6iknBhtERs+iUJEmSJElSidateTXmJLUFYM+SiQGn0ZGy6JQkSZIkSVKJlpacRFqzMwEoveVL2Lc92EA6IhadkiRJkiRJKvFO63gCK/JqESaP3FVTgo6jI2DRKUmSJEmSpBKva9OqzA63AWDjvPEBp9GRsOiUJEmSJElSiZeSFCa7QTcAktZ8GnAaHQmLTkmSJEmSJAlodlIv8iIhamSuISvj26DjqJAsOiVJkiRJkiSgQ4vGLAk3BmD5F+8GnEaFZdEpSZIkSZIkAUnhEBnVuwCwZ/HEgNOosCw6JUmSJEmSpANqnHAuAPV3zGRfZk7AaVQYFp2SJEmSJEnSAU07nkUmKdQMZTBj1hdBx1EhWHRKkiRJkiRJB4RSS7O+fDsA1s8dH3AaFYZFpyRJkiRJkvQ9pZp3B6DKpmns2p8dcBoVlEWnJEmSJEmS9D3V2/UEoHNoIR8t+i7gNCooi05JkiRJkiTpe0K127M/qSwVQnv5auanQcdRAVl0SpIkSZIkSd8XTiKnflcASq/9jO17swIOpIKw6JQkSZIkSZL+S9kWPQA4ObSA8Qs2BJxGBWHRKUmSJEmSJP23xmcAcGLSMnKz9gabRQVi0SlJkiRJkiT9t6rHESlbizSyGVDbOzrjgUWnJEmSJEmS9N9CIUJNzog+XjkpyCQqIItOSZIkSZIk6XAadYt+Xzk52BwqEItOSZIkSZIk6XAaHyg6v5sL+7YFm0U/y6JTkiRJkiRJOpzytaFqMyACq6cEnUY/w6JTkiRJkiRJ+jH509cnBRpDP8+iU5IkSZIkSfoxjc+IfnedzmLPolOSJEmSJEn6MQ1PhVAYti6HHeuCTqOfYNEpSZIkSZIk/ZhSFaF2++jjVd7VWZxZdEqSJEmSJEk/xXU644JFpyRJkiRJkvRTvr9OZyQSaBT9OItOSZIkSZIk6afUOwmS02H3Bti8NOg0+hEWnZIkSZIkSdJPSUmH+idHH7tOZ7Fl0SlJkiRJkiT9nPx1Oi06iyuLTkmSJEmSJOnnHFync/VnkJsTaBQdnkWnJEmSJEmS9HNqtYP0CpC5E9bPCzqNDsOiU5IkSZIkSfo54SRodHr08cpPgs2iw7LolCRJkiRJkgrCdTqLNYtOSZIkSZIkqSAanxn9vvYLyNobbBb9gEWnJEmSJEmSVBBVmkD5OpCbBWunB51G/8WiU5IkSZIkSSqIUOh709cnBRpFP2TRKUmSJEmSJBVU4zOi312ns9ix6JQkSZIkSZIK6uDO6+vnw96MYLPoEBadkiRJkiRJUkGVrwXVWgARWP1Z0Gn0PRadkiRJkiRJUmG4TmexZNEpSZIkSZIkFYbrdBZLFp2SJEmSJElSYTTsCqEwZKyA7WuDTqMDLDolSZIkSZKkwkivAHU6Rh+v8q7O4sKiU5IkSZIkSSos1+ksdiw6JUmSJEmSpML6/jqdkUigURRl0SlJkiRJkiQVVr3OkFwK9myCTYuDTiMsOiVJkiRJkqTCS06DBl2ij12ns1iw6JQkSZIkSZKORP46nRadxYFFpyRJkiRJknQkDq7TuXoK5OYEGkUWnZIkSZIkSdKRqdkWSlWCrF3w3Zyg05R4Fp2SJEmSJEnSkQiHodHp0ccrJwUaRRadkiRJkiRJ0pFznc5iw6JTkiRJkiRJOlIH1+lc+wVk7Qk0Skln0SlJkiRJkiQdqcqNoUI9yMuGb6YFnaZEs+iUJEmSJEmSjlQo9L3p65MCjVLSWXRKkiRJkiRJR+Pg9HXX6QyURackSZIkSZJ0NA7uvL7hS9izNdgsJZhFpyRJkiRJknQ0ytWA6q2ij1d/GmyWEsyiU5IkSZIkSTpartMZOItOSZIkSZIk6Wi5TmfgLDolSZIkSZKko9XgFAglwbZVsG1N0GlKJItOSZIkSZIk6Will4e6naKPV3lXZxAsOiVJkiRJkqSi4DqdgbLolCRJkiRJkorCwXU6V30KkUigUUoii05JkiRJkiSpKNQ9EVJKw57NsGlR0GlKHItOSZIkSZIkqSgkp0Y3JQKnrwfAolOSJEmSJEkqKvnrdLoh0bFm0SlJkiRJkiQVlYPrdK6ZCrnZgUYpaSw6JUmSJEmSpKJSozWUrgJZu2Hd7KDTlCgWnZIkSZIkSVJRCYeh4WnRx67TeUxZdEqSJEmSJElF6eD0ddfpPKYsOiVJkiRJkqSi1PjAhkTfzoDM3cFmKUEsOiVJkiRJkqSiVKkRVKwPeTnwzbSg05QYFp2SJEmSJElSUQqFoNGBuzpdp/OYseiUJEmSJEmSiprrdB5zFp2SJEmSJElSUTt4R+fGr2D35mCzlBAWnZIkSZIkSVJRK1sNarSOPl79abBZSgiLTkmSJEmSJCkWXKfzmLLolCRJkiRJkmLBdTqPKYtOSZIkSZIkKRYanALhZNi+BjJWBZ0m4Vl0SpIkSZIkSbGQVhbqnhh9vMq7OmPNolOSJEmSJEmKlfx1Oi06Y82iU5IkSZIkSYqVg+t0rpoMeXmBRkl0Fp2SJEmSJElSrNTpCCllYO9W2LQw6DQJzaJTkiRJkiRJipXkVGjYNfp45aRAoyQ6i05JkiRJkiQpllyn85iw6JQkSZIkSZJiqfGBonPNVMjJCjZLArPolCRJkiRJkmKp+vFQuipk74V1s4JOk7AsOiVJkiRJkqRYCoeh0enRx67TGTMWnZIkSZIkSVKsNT4j+t11OmPGolOSJEmSJEmKtYPrdK6bBZm7gs2SoCw6JUmSJEmSpFir1DD6lZcDaz4POk1CsuiUJEmSJEmSjoVGB+7qdJ3OmLDoLKS9e/fSoEEDhg4dGnQUSZIkSZIkxRPX6Ywpi85Cuueeezj55JODjiFJkiRJkqR4c3Dn9U0LYfemYLMkIIvOQli+fDlLliyhV69eQUeRJEmSJElSvClTFWq2iT5e9WmwWRJQsSo677//fkKhEEOGDCnS1/3000/p3bs3tWvXJhQK8dZbbx32vGHDhtGwYUPS09M56aSTmDFjxiHPDx06lPvuu69Is0mSJEmSJKkEyV+n85NgcySgYlN0zpw5k6effpq2bdv+5HlTp04lOzv7B8cXLVrExo0bDztmz549tGvXjmHDhv3o644ePZpbbrmFu+66izlz5tCuXTt69uzJpk3R24jffvttmjVrRrNmzQrxriRJkiRJkqTvaXxm9PvKTyESCTZLgikWRefu3bsZMGAAzzzzDJUqVfrR8/Ly8hg8eDD9+/cnNzc3//jSpUvp3r07I0aMOOy4Xr168fe//50+ffr86Gs/+OCDXHfddVxzzTW0atWKp556itKlS/Pcc88BMH36dEaNGkXDhg0ZOnQozzzzDHffffcRvmNJkiRJkiSVSA26QDgFdnwD21YFnSahFIuic/DgwZx//vn06NHjJ88Lh8O89957zJ07l6uuuoq8vDxWrFhB9+7dufjii7n11luP6PdnZWUxe/bsQ35/OBymR48eTJs2DYD77ruPtWvXsnr1ah544AGuu+467rzzzsO+3rBhw2jVqhUnnnjiEeWRJEmSJElSgkotA/U6Rx+vnBRolEQTeNE5atQo5syZU+C1L2vXrs3HH3/MlClT6N+/P927d6dHjx48+eSTR5xhy5Yt5ObmUqNGjUOO16hRgw0bNhT69QYPHsyiRYuYOXPmEWeSJEmSJElSgspfp3NysDkSTHKQv3zt2rXcdNNNTJgwgfT09AKPq1+/Pi+++CLdunWjcePGPPvss4RCoRgmPdTVV199zH6XJEmSJEmSEkzjM2DSvdGd1/PyIBz4vYgJIdD/irNnz2bTpk106NCB5ORkkpOTmTx5Mo8++ijJycmHrMP5fRs3buT666+nd+/e7N27l5tvvvmoclStWpWkpKQfbGa0ceNGataseVSvLUmSJEmSJB2iTgdILQv7MmDjV0GnSRiBFp1nnXUWX331FfPmzcv/6tSpEwMGDGDevHkkJSX9YMyWLVs466yzaNmyJWPGjGHixImMHj2aoUOHHnGO1NRUOnbsyMSJE/OP5eXlMXHiRLp06XLErytJkiRJkiT9QFIKNOgafew6nUUm0Knr5cqVo3Xr1occK1OmDFWqVPnBcYiWj7169aJBgwaMHj2a5ORkWrVqxYQJE+jevTt16tQ57N2du3fv5uuvv87/edWqVcybN4/KlStTv359AG655RYGDRpEp06d6Ny5Mw8//DB79uzhmmuuKeJ3LUmSJEmSpBKv8Rmw/IPoOp1dbwo6TUIItOgsrHA4zL333stpp51Gampq/vF27drx0UcfUa1atcOOmzVrFmeeeWb+z7fccgsAgwYNYvjw4QBcfvnlbN68mTvvvJMNGzZwwgknMH78+B9sUCRJkiRJkiQdtcYHNiRa8znkZEJyWrB5EkCxKzonTZr0k8+fffbZhz3evn37Hx1zxhlnEIlEfvZ333jjjdx4440/e54kSZIkSZJ0VKq3gjLVYM9m+HYmNDw16ERxzy2dJEmSJEmSpGMtFIJGB+7qdJ3OImHRKUmSJEmSJAWh8RnR7ysnBxojUVh0SpIkSZIkSUE4uE7nutmwf2ewWRKARackSZIkSZIUhIr1oXJjiOTCmqlBp4l7Fp2SJEmSJElSUFyns8hYdEqSJEmSJElBcZ3OImPRKUmSJEmSJAWl0elACDYvhl0bgk4T1yw6JUmSJEmSpKCUrgy12kYfr/o02CxxzqJTkiRJkiRJClL+Op1OXz8aFp2SJEmSJElSkPLX6ZwEkUiQSeKaRackSZIkSZIUpPpdICkVdn4LGSuDThO3LDolSZIkSZKkIKWWhnonRR+v/CTYLHHMolOSJEmSJEkKmut0HjWLTkmSJEmSJClojQ8Unas+hbzcYLPEKYtOSZIkSZIkKWi1O0BqOdi/HTZ8GXSauGTRKUmSJEmSJAUtKRkanhp9vHJSoFHilUWnJEmSJEmSVBw0PiP63XU6j4hFpyRJkiRJklQcHFyn85tpkL0/2CxxyKJTkiRJkiRJKg6qtYCyNSBnP3w7I+g0cceiU5IkSZIkSSoOQiFodOCuTtfpLDSLTkmSJEmSJKm4cJ3OI2bRKUmSJEmSJBUXB9fp/G4O7NseaJR4Y9EpSZIkSZIkFRcV6kKVphDJgzVTg04TVyw6JUmSJEmSpOLEdTqPiEWnJEmSJEmSVJy4TucRseiUJEmSJEmSipOGpwIh2LIUdq4POk3csOiUJEmSJEmSipPSlaH2CdHHq7yrs6AsOiVJkiRJkqTiJn+dTovOgrLolCRJkiRJkoqb/HU6J0EkEmSSuGHRKUmSJEmSJBU39U+GpDTY9R1s/TroNHHBolOSJEmSJEkqblJKQb3O0ccrJwUaJV5YdEqSJEmSJEnF0fenr+tnWXRKkiRJkiRJxdHBonP1Z5CXG2iUeGDRKUmSJEmSJBVHtU6AtAqwfwesnxd0mmLPolOSJEmSJEkqjpKSoeGp0cdOX/9ZFp2SJEmSJElScZW/TufkQGPEA4tOSZIkSZIkqbhq3C36/ZvpkL0v2CzFnEWnJEmSJEmSVFxVbQblakFuJqz9Iug0xZpFpyRJkiRJklRchULQ6MBdna7T+ZMsOiVJkiRJkqTizHU6C8SiU5IkSZIkSSrODq7T+d1c2Lct2CzFmEWnJEmSJEmSVJyVrx1dq5MIrJ4SdJpiy6JTkiRJkiRJKu7y1+l0+vqPseiUJEmSJEmSirv8dTonBZmiWLPolCRJkiRJkoq7hqdCKAxbl8OOdUGnKZYsOiVJkiRJkqTirlRFqN0++niV09cPx6JTkiRJkiRJigeu0/mTLDolSZIkSZKkeND4YNE5CSKRQKMURxadkiRJkiRJUjyodzIkp8PuDbBlWdBpih2LTkmSJEmSJCkepKRDvZOij919/QcsOiVJkiRJkqR40fiM6HfX6fwBi05JkiRJkiQpXhxcp3P1Z5CbE2yWYsaiU5IkSZIkSYoXtU6A9AqQuRPWzws6TbFi0SlJkiRJkiTFi3ASNDwt+njlJ8FmKWYsOiVJkiRJkqR44jqdh2XRKUmSJEmSJMWTg0Xn2i8ga2+gUYoTi05JkiRJkiQpnlRpCuXrQG4WrJ0edJpiw6JTkiRJkiRJiiehEDQ6sPv6ykmBRilOLDolSZIkSZKkeOM6nT9g0SlJkiRJkiTFm0anR7+vnw97M4LNUkxYdEqSJEmSJEnxpnwtqNYCiMDqz4JOUyxYdEqSJEmSJEnxKH+dTqevg0WnJEmSJEmSFJ/y1+mcFGSKYsOiU5IkSZIkSYpHDbtCKAx7t7pOJ5AcdABJkiRJkiRJRyC9AvxmOlRpCuGkoNMEzqJTkiRJkiRJilfVmgedoNhw6rokSZIkSZKkuGfRKUmSJEmSJCnuWXRKkiRJkiRJinsWnZIkSZIkSZLinkWnJEmSJEmSpLhn0SlJkiRJkiQp7ll0SpIkSZIkSYp7Fp2SJEmSJEmS4p5FpyRJkiRJkqS4Z9EpSZIkSZIkKe5ZdEqSJEmSJEmKexadkiRJkiRJkuKeRackSZIkSZKkuGfRKUmSJEmSJCnuWXRKkiRJkiRJinsWnZIkSZIkSZLinkWnJEmSJEmSpLhn0SlJkiRJkiQp7ll0SpIkSZIkSYp7Fp2SJEmSJEmS4p5FpyRJkiRJkqS4lxx0gEQWiUQA2LlzZ8BJdKxkZ2ezd+9edu7cSUpKStBxpCPmtaxE4HWsROG1rEThtaxE4bWsRBEv1/LBXu1gz/ZTLDpjaNeuXQDUq1cv4CSSJEmSJElS/Nq1axcVKlT4yXNCkYLUoToieXl5fPfdd5QrV45QKBR0HB0DO3fupF69eqxdu5by5csHHUc6Yl7LSgRex0oUXstKFF7LShRey0oU8XItRyIRdu3aRe3atQmHf3oVTu/ojKFwOEzdunWDjqEAlC9fvlj/kZAKymtZicDrWInCa1mJwmtZicJrWYkiHq7ln7uT8yA3I5IkSZIkSZIU9yw6JUmSJEmSJMU9i06pCKWlpXHXXXeRlpYWdBTpqHgtKxF4HStReC0rUXgtK1F4LStRJOK17GZEkiRJkiRJkuKed3RKkiRJkiRJinsWnZIkSZIkSZLinkWnJEmSJEmSpLhn0SlJkiRJkiQp7ll0Sj9h2LBhNGzYkPT0dE466SRmzJjxk+c//PDDNG/enFKlSlGvXj1uvvlm9u/fn/98bm4ud9xxB40aNaJUqVI0adKEv/3tb7gnmGKtMNdydnY2d999N02aNCE9PZ127doxfvz4o3pNqagU9bV83333ceKJJ1KuXDmqV6/OxRdfzNKlS2P9NqSY/F0+6P777ycUCjFkyJAYJJcOFYtred26dVx55ZVUqVKFUqVK0aZNG2bNmhXLt6ESrqivYz/3KQiffvopvXv3pnbt2oRCId56662fHTNp0iQ6dOhAWloaTZs2Zfjw4T84J+4+90UkHdaoUaMiqampkeeeey6ycOHCyHXXXRepWLFiZOPGjYc9/+WXX46kpaVFXn755ciqVasiH3zwQaRWrVqRm2++Of+ce+65J1KlSpXIuHHjIqtWrYq89tprkbJly0YeeeSRY/W2VAIV9lq+9dZbI7Vr1468++67kRUrVkSeeOKJSHp6emTOnDlH/JpSUYjFtdyzZ8/I888/H1mwYEFk3rx5kfPOOy9Sv379yO7du4/V21IJFItr+aAZM2ZEGjZsGGnbtm3kpptuivE7UUkXi2s5IyMj0qBBg8jVV18d+eKLLyIrV66MfPDBB5Gvv/76WL0tlTCxuI793KcgvPfee5Hbb789MmbMmAgQefPNN3/y/JUrV0ZKly4dueWWWyKLFi2KPPbYY5GkpKTI+PHj88+Jx899Fp3Sj+jcuXNk8ODB+T/n5uZGateuHbnvvvsOe/7gwYMj3bt3P+TYLbfcEunatWv+z+eff37kl7/85SHnXHLJJZEBAwYUYXLpUIW9lmvVqhV5/PHHDzn239dpYV9TKgqxuJb/26ZNmyJAZPLkyUUTWjqMWF3Lu3btihx33HGRCRMmRLp162bRqZiLxbV82223RU499dTYBJYOIxbXsZ/7FLSCFJ233npr5Pjjjz/k2OWXXx7p2bNn/s/x+LnPqevSYWRlZTF79mx69OiRfywcDtOjRw+mTZt22DGnnHIKs2fPzr+Ne+XKlbz33nucd955h5wzceJEli1bBsD8+fOZMmUKvXr1iuG7UUl2JNdyZmYm6enphxwrVaoUU6ZMOeLXlI5WLK7lw9mxYwcAlStXLoLU0g/F8loePHgw559//iGvLcVKrK7ld955h06dOtG3b1+qV69O+/bteeaZZ2LzJlTixeo69nOf4sG0adN+8G+Gnj175l/78fq5LznoAFJxtGXLFnJzc6lRo8Yhx2vUqMGSJUsOO6Z///5s2bKFU089lUgkQk5ODr/+9a/505/+lH/OH/7wB3bu3EmLFi1ISkoiNzeXe+65hwEDBsT0/ajkOpJruWfPnjz44IOcfvrpNGnShIkTJzJmzBhyc3OP+DWloxWLa/m/5eXlMWTIELp27Urr1q2L/D1IELtredSoUcyZM4eZM2fGNL90UKyu5ZUrV/Lkk09yyy238Kc//YmZM2fyu9/9jtTUVAYNGhTT96SSJ1bXsZ/7FA82bNhw2Gt/586d7Nu3j23btsXl5z7v6JSKyKRJk7j33nt54oknmDNnDmPGjOHdd9/lb3/7W/45r776Ki+//DIjR45kzpw5jBgxggceeIARI0YEmFw61COPPMJxxx1HixYtSE1N5cYbb+Saa64hHPZ/MhRfCnstDx48mAULFjBq1KhjnFT6aT93La9du5abbrqJl19++Qd3GUnFSUH+Lufl5dGhQwfuvfde2rdvz/XXX891113HU089FWBy6T8Kch37uU8Kjp9apcOoWrUqSUlJbNy48ZDjGzdupGbNmocdc8cddzBw4EB+9atf0aZNG/r06cO9997LfffdR15eHgC///3v+cMf/sAVV1xBmzZtGDhwIDfffDP33XdfzN+TSqYjuZarVavGW2+9xZ49e1izZg1LliyhbNmyNG7c+IhfUzpasbiWv+/GG29k3LhxfPLJJ9StWzcm70GC2FzLs2fPZtOmTXTo0IHk5GSSk5OZPHkyjz76KMnJyT96F7N0NGL1d7lWrVq0atXqkHEtW7bkm2++Kfo3oRIvVtexn/sUD2rWrHnYa798+fKUKlUqbj/3WXRKh5GamkrHjh2ZOHFi/rG8vDwmTpxIly5dDjtm7969P7hLKCkpCYBIJPKT5xwsQqWidiTX8kHp6enUqVOHnJwc3njjDS666KKjfk3pSMXiWobo3+cbb7yRN998k48//phGjRrF7D1IEJtr+ayzzuKrr75i3rx5+V+dOnViwIABzJs3L//fI1JRitXf5a5du7J06dJDzl+2bBkNGjQo2jcgEbvr2M99igddunQ55NoHmDBhQv61H7ef+wLeDEkqtkaNGhVJS0uLDB8+PLJo0aLI9ddfH6lYsWJkw4YNkUgkEhk4cGDkD3/4Q/75d911V6RcuXKRV155JbJy5crIhx9+GGnSpEnksssuyz9n0KBBkTp16kTGjRsXWbVqVWTMmDGRqlWrRm699dZj/v5UchT2Wp4+fXrkjTfeiKxYsSLy6aefRrp37x5p1KhRZNu2bQV+TSkWYnEt33DDDZEKFSpEJk2aFFm/fn3+1969e4/121MJEotr+b+567qOhVhcyzNmzIgkJydH7rnnnsjy5csjL7/8cqR06dKRl1566Vi/PZUQsbiO/dynIOzatSsyd+7cyNy5cyNA5MEHH4zMnTs3smbNmkgkEon84Q9/iAwcODD//JUrV0ZKly4d+f3vfx9ZvHhxZNiwYZGkpKTI+PHj88+Jx899Fp3ST3jsscci9evXj6SmpkY6d+4cmT59ev5z3bp1iwwaNCj/5+zs7Mhf/vKXSJMmTSLp6emRevXqRX7zm98c8j94O3fujNx0002R+vXrR9LT0yONGzeO3H777ZHMzMxj+K5UEhXmWp40aVKkZcuWkbS0tEiVKlUiAwcOjKxbt65QrynFSlFfy8Bhv55//vlj9I5UUsXi7/L3WXTqWInFtTx27NhI69atI2lpaZEWLVpE/vWvfx2Lt6ISrKivYz/3KQiffPLJYf9de/D6HTRoUKRbt24/GHPCCSdEUlNTI40bNz7sv4Hj7XNfKBI5MKdWkiRJkiRJkuKUa3RKkiRJkiRJinsWnZIkSZIkSZLinkWnJEmSJEmSpLhn0SlJkiRJkiQp7ll0SpIkSZIkSYp7Fp2SJEmSJEmS4p5FpyRJkiRJkqS4Z9EpSZIkFcJf/vIXTjjhhPyfr776ai6++OLA8kiSJCnKolOSJEmSJElS3LPolCRJUsLIysoKOoIkSZICYtEpSZKkuHXGGWdw4403MmTIEKpWrUrPnj1ZsGABvXr1omzZstSoUYOBAweyZcuW/DF5eXn84x//oGnTpqSlpVG/fn3uueee/Odvu+02mjVrRunSpWncuDF33HEH2dnZQbw9SZIkFYJFpyRJkuLaiBEjSE1NZerUqdx///10796d9u3bM2vWLMaPH8/GjRu57LLL8s//4x//yP33388dd9zBokWLGDlyJDVq1Mh/vly5cgwfPpxFixbxyCOP8Mwzz/DQQw8F8dYkSZJUCKFIJBIJOoQkSZJ0JM444wx27tzJnDlzAPj73//OZ599xgcffJB/zrfffku9evVYunQptWrVolq1ajz++OP86le/KtDveOCBBxg1ahSzZs0CopsRvfXWW8ybNw+Ibka0fft23nrrrSJ9b5IkSSqc5KADSJIkSUejY8eO+Y/nz5/PJ598QtmyZX9w3ooVK9i+fTuZmZmcddZZP/p6o0eP5tFHH2XFihXs3r2bnJwcypcvH5PskiRJKjoWnZIkSYprZcqUyX+8e/duevfuzf/+7//+4LxatWqxcuXKn3ytadOmMWDAAP7617/Ss2dPKlSowKhRo/jnP/9Z5LklSZJUtCw6JUmSlDA6dOjAG2+8QcOGDUlO/uE/dY877jhKlSrFxIkTDzt1/fPPP6dBgwbcfvvt+cfWrFkT08ySJEkqGm5GJEmSpIQxePBgMjIy6NevHzNnzmTFihV88MEHXHPNNeTm5pKens5tt93GrbfeygsvvMCKFSuYPn06zz77LBAtQr/55htGjRrFihUrePTRR3nzzTcDfleSJEkqCItOSZIkJYzatWszdepUcnNzOeecc2jTpg1DhgyhYsWKhMPRf/recccd/M///A933nknLVu25PLLL2fTpk0AXHjhhdx8883ceOONnHDCCXz++efccccdQb4lSZIkFZC7rkuSJEmSJEmKe97RKUmSJEmSJCnuWXRKkiRJkiRJinsWnZIkSZIkSZLinkWnJEmSJEmSpLhn0SlJkiRJkiQp7ll0SpIkSZIkSYp7Fp2SJEmSJEmS4p5FpyRJkiRJkqS4Z9EpSZIkSZIkKe5ZdEqSJEmSJEmKexadkiRJkiRJkuKeRackSZIkSZKkuPf/Ac/+u+pc7UL0AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1600x2400 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(len(search_config_names), 1, figsize=(16, len(search_config_names)*8))\n",
     "fig.suptitle(\n",
@@ -1352,7 +932,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.13"
   },
   "vscode": {
    "interpreter": {

From 35f8b84b73010d8aed788cede62644fde9cce88f Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 30 May 2024 08:36:30 -0700
Subject: [PATCH 071/623] Expose serialization to the python / c-api (#164)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/164
---
 cpp/include/cuvs/neighbors/cagra.h            | 45 +++++++++++
 cpp/src/neighbors/cagra_c.cpp                 | 76 ++++++++++++++++--
 docs/source/working_with_ann_indexes_c.rst    |  4 -
 docs/source/working_with_ann_indexes_cpp.rst  |  6 --
 .../working_with_ann_indexes_python.rst       |  5 --
 docs/source/working_with_ann_indexes_rust.rst |  8 --
 python/cuvs/cuvs/neighbors/cagra/__init__.py  |  4 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    | 10 +++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    | 80 ++++++++++++++++++-
 python/cuvs/cuvs/test/test_cagra.py           | 50 ++++++++++++
 10 files changed, 258 insertions(+), 30 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 727c39c6ef..c82195710e 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -18,6 +18,7 @@
 
 #include <cuvs/core/c_api.h>
 #include <dlpack/dlpack.h>
+#include <stdbool.h>
 #include <stdint.h>
 
 #ifdef __cplusplus
@@ -382,6 +383,50 @@ cuvsError_t cuvsCagraSearch(cuvsResources_t res,
  * @}
  */
 
+/**
+ * @defgroup cagra_c_serialize CAGRA C-API serialize functions
+ * @{
+ */
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <cuvs/neighbors/cagra.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create an index with `cuvsCagraBuild`
+ * cuvsCagraSerialize(res, "/path/to/index", index, true);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ *
+ */
+cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
+                               const char* filename,
+                               cuvsCagraIndex_t index,
+                               bool include_dataset);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index CAGRA index loaded disk
+ */
+cuvsError_t cuvsCagraDeserialize(cuvsResources_t res, const char* filename, cuvsCagraIndex_t index);
+/**
+ * @}
+ */
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index cadda19d5d..630a5058a0 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -21,6 +21,7 @@
 #include <raft/core/error.hpp>
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/core/exceptions.hpp>
@@ -104,6 +105,27 @@ void _search(cuvsResources_t res,
     *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
 }
 
+template <typename T>
+void _serialize(cuvsResources_t res,
+                const char* filename,
+                cuvsCagraIndex_t index,
+                bool include_dataset)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index->addr);
+  cuvs::neighbors::cagra::serialize_file(
+    *res_ptr, std::string(filename), *index_ptr, include_dataset);
+}
+
+template <typename T>
+void* _deserialize(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
+  cuvs::neighbors::cagra::deserialize_file(*res_ptr, std::string(filename), index);
+  return index;
+}
+
 }  // namespace
 
 extern "C" cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index)
@@ -140,15 +162,13 @@ extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
 {
   return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
+    index->dtype = dataset.dtype;
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
-      index->addr       = reinterpret_cast<uintptr_t>(_build<float>(res, *params, dataset_tensor));
-      index->dtype.code = kDLFloat;
+      index->addr = reinterpret_cast<uintptr_t>(_build<float>(res, *params, dataset_tensor));
     } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
-      index->addr       = reinterpret_cast<uintptr_t>(_build<int8_t>(res, *params, dataset_tensor));
-      index->dtype.code = kDLInt;
+      index->addr = reinterpret_cast<uintptr_t>(_build<int8_t>(res, *params, dataset_tensor));
     } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
       index->addr = reinterpret_cast<uintptr_t>(_build<uint8_t>(res, *params, dataset_tensor));
-      index->dtype.code = kDLUInt;
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
@@ -247,3 +267,49 @@ extern "C" cuvsError_t cuvsCagraSearchParamsDestroy(cuvsCagraSearchParams_t para
 {
   return cuvs::core::translate_exceptions([=] { delete params; });
 }
+
+extern "C" cuvsError_t cuvsCagraDeserialize(cuvsResources_t res,
+                                            const char* filename,
+                                            cuvsCagraIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // read the numpy dtype from the beginning of the file
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename));
+      index->dtype.code = kDLFloat;
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<int8_t>(res, filename));
+      index->dtype.code = kDLInt;
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<uint8_t>(res, filename));
+      index->dtype.code = kDLUInt;
+    } else {
+      RAFT_FAIL("Unsupported dtype in file %s", filename);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
+                                          const char* filename,
+                                          cuvsCagraIndex_t index,
+                                          bool include_dataset)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _serialize<float>(res, filename, index, include_dataset);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _serialize<int8_t>(res, filename, index, include_dataset);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _serialize<uint8_t>(res, filename, index, include_dataset);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
diff --git a/docs/source/working_with_ann_indexes_c.rst b/docs/source/working_with_ann_indexes_c.rst
index 89f368aaac..9d0c17a1e3 100644
--- a/docs/source/working_with_ann_indexes_c.rst
+++ b/docs/source/working_with_ann_indexes_c.rst
@@ -62,7 +62,3 @@ Searching an index
     cuvsCagraIndexDestroy(index);
     cuvsCagraIndexParamsDestroy(index_params);
     cuvsResourcesDestroy(res);
-
-
-Serializing an index
---------------------
\ No newline at end of file
diff --git a/docs/source/working_with_ann_indexes_cpp.rst b/docs/source/working_with_ann_indexes_cpp.rst
index 1d3225cb85..ead64107e9 100644
--- a/docs/source/working_with_ann_indexes_cpp.rst
+++ b/docs/source/working_with_ann_indexes_cpp.rst
@@ -43,9 +43,3 @@ Searching an index
     cagra::search_params search_params;
 
     cagra::search(res, search_params, index, queries, neighbors, distances);
-
-
-Serializing an index
---------------------
-
-
diff --git a/docs/source/working_with_ann_indexes_python.rst b/docs/source/working_with_ann_indexes_python.rst
index e9aba18019..c075deaea4 100644
--- a/docs/source/working_with_ann_indexes_python.rst
+++ b/docs/source/working_with_ann_indexes_python.rst
@@ -33,8 +33,3 @@ Searching an index
     index = // ... build index ...
 
     neighbors, distances = cagra.search(search_params, index, queries, k)
-
-
-
-Serializing an index
---------------------
\ No newline at end of file
diff --git a/docs/source/working_with_ann_indexes_rust.rst b/docs/source/working_with_ann_indexes_rust.rst
index e7da4960a8..ba284d1688 100644
--- a/docs/source/working_with_ann_indexes_rust.rst
+++ b/docs/source/working_with_ann_indexes_rust.rst
@@ -33,11 +33,3 @@ Building an index
 
         Ok(())
     }
-
-
-Searching an index
-------------------
-
-
-Serializing an index
---------------------
\ No newline at end of file
diff --git a/python/cuvs/cuvs/neighbors/cagra/__init__.py b/python/cuvs/cuvs/neighbors/cagra/__init__.py
index 0b487d7ab6..dd87a644aa 100644
--- a/python/cuvs/cuvs/neighbors/cagra/__init__.py
+++ b/python/cuvs/cuvs/neighbors/cagra/__init__.py
@@ -19,6 +19,8 @@
     IndexParams,
     SearchParams,
     build_index,
+    load,
+    save,
     search,
 )
 
@@ -28,5 +30,7 @@
     "IndexParams",
     "SearchParams",
     "build_index",
+    "load",
+    "save",
     "search",
 ]
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index d229a3b09d..b23c2a4b3e 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -23,6 +23,7 @@ from libc.stdint cimport (
     uint64_t,
     uintptr_t,
 )
+from libcpp cimport bool
 
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
@@ -110,3 +111,12 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
                                 DLManagedTensor* queries,
                                 DLManagedTensor* neighbors,
                                 DLManagedTensor* distances) except +
+
+    cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
+                                   const char * filename,
+                                   cuvsCagraIndex_t index,
+                                   bool include_dataset) except +
+
+    cuvsError_t cuvsCagraDeserialize(cuvsResources_t res,
+                                     const char * filename,
+                                     cuvsCagraIndex_t index) except +
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 56439dd924..3cdff37276 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -23,6 +23,7 @@ from cuvs.common.resources import auto_sync_resources
 
 from cython.operator cimport dereference as deref
 from libcpp cimport bool, cast
+from libcpp.string cimport string
 
 from cuvs.common cimport cydlpack
 
@@ -282,7 +283,6 @@ def build_index(IndexParams index_params, dataset, resources=None):
                                     np.dtype('ubyte')])
 
     cdef Index idx = Index()
-    cdef cuvsError_t build_status
     cdef cydlpack.DLManagedTensor* dataset_dlpack = \
         cydlpack.dlpack_c(dataset_ai)
     cdef cuvsCagraIndexParams* params = index_params.params
@@ -544,7 +544,6 @@ def search(SearchParams search_params,
                        exp_rows=n_queries, exp_cols=k)
 
     cdef cuvsCagraSearchParams* params = &search_params.params
-    cdef cuvsError_t search_status
     cdef cydlpack.DLManagedTensor* queries_dlpack = \
         cydlpack.dlpack_c(queries_cai)
     cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
@@ -564,3 +563,80 @@ def search(SearchParams search_params,
         ))
 
     return (distances, neighbors)
+
+
+@auto_sync_resources
+def save(filename, Index index, bool include_dataset=True, resources=None):
+    """
+    Saves the index to a file.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    index : Index
+        Trained CAGRA index.
+    include_dataset : bool
+        Whether or not to write out the dataset along with the index. Including
+        the dataset in the serialized index will use extra disk space, and
+        might not be desired if you already have a copy of the dataset on
+        disk. If this option is set to false, you will have to call
+        `index.update_dataset(dataset)` after loading the index.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = cagra.build_index(cagra.IndexParams(), dataset)
+    >>> # Serialize and deserialize the cagra index built
+    >>> cagra.save("my_index.bin", index)
+    >>> index_loaded = cagra.load("my_index.bin")
+    """
+    cdef string c_filename = filename.encode('utf-8')
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    check_cuvs(cuvsCagraSerialize(res,
+                                  c_filename.c_str(),
+                                  index.index,
+                                  include_dataset))
+
+
+@auto_sync_resources
+def load(filename, resources=None):
+    """
+    Loads index from file.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change, therefore loading an index saved with a previous
+    version of cuvs is not guaranteed to work.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+
+    """
+    cdef Index idx = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    cdef string c_filename = filename.encode('utf-8')
+
+    check_cuvs(cuvsCagraDeserialize(
+        res,
+        c_filename.c_str(),
+        idx.index
+    ))
+    idx.trained = True
+    return idx
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
index a2b0184860..15711c854d 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -183,3 +183,53 @@ def test_cagra_vpq_compression():
     run_cagra_build_search_test(
         n_cols=dim, compression=cagra.CompressionParams(pq_dim=dim / pq_len)
     )
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.ubyte])
+# TODO: expose update_dataset
+# @pytest.mark.parametrize("include_dataset", [True, False])
+@pytest.mark.parametrize("include_dataset", [True])
+def test_save_load(dtype, include_dataset):
+    n_rows = 10000
+    n_cols = 50
+    n_queries = 1000
+
+    dataset = generate_data((n_rows, n_cols), dtype)
+    dataset_device = device_ndarray(dataset)
+
+    build_params = cagra.IndexParams()
+    index = cagra.build_index(build_params, dataset_device)
+
+    assert index.trained
+    filename = "my_index.bin"
+    cagra.save(filename, index, include_dataset=include_dataset)
+    loaded_index = cagra.load(filename)
+
+    # if we didn't save the dataset with the index, we need to update the
+    # index with an already loaded copy
+    if not include_dataset:
+        loaded_index.update_dataset(dataset)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+
+    queries_device = device_ndarray(queries)
+    search_params = cagra.SearchParams()
+    k = 10
+
+    distance_dev, neighbors_dev = cagra.search(
+        search_params, index, queries_device, k
+    )
+
+    neighbors = neighbors_dev.copy_to_host()
+    dist = distance_dev.copy_to_host()
+    del index
+
+    distance_dev, neighbors_dev = cagra.search(
+        search_params, loaded_index, queries_device, k
+    )
+
+    neighbors2 = neighbors_dev.copy_to_host()
+    dist2 = distance_dev.copy_to_host()
+
+    assert np.all(neighbors == neighbors2)
+    assert np.allclose(dist, dist2, rtol=1e-6)

From 13e0732d49709f652f045f121b5b775167617810 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20F=C3=B6rster?=
 <97973773+mfoerste4@users.noreply.github.com>
Date: Thu, 30 May 2024 19:47:33 +0200
Subject: [PATCH 072/623] CAGRA API update and allow async host refinement
 (#131)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR updates the CAGRA public API, changes defaults, and improves refinement during IVF-PQ build step.

Updated defaults:
- By default CAGRA would select NN descent. We fall back to IVF-PQ build algorithm if there is not enough memory for NN descent.
- For the IVF-PQ build algo, the search params were updated to use `n_probe = 0.01*nlist`, and both LUT and internal distance type is set to `fp16` (as opposed to `fp8/fp32` previously)

- By default build would create the index that contains both the `graph` and the `dataset` on GPU. If the `dataset` does not fit gpu, then the returned index will only contain the graph (on device). In such case the user is expected to call `index.update_dataset()`  (for example with dataset in managed memory) before we can `search` the index.

API changes:
- We can specify IVF-PQ build algo parameters the following way:
```c++
cagra::index_params params;
params.graph_degree = 32;
params.intermediate_graph_degree = 48;
auto pq_params = cagra::graph_build_algo::ivf_pq_params(dataset.extents());
// This sets reasonable defaults, but can be updated by the user, e.g.:
pq_params.pq_dim = 32;
// Select IVF-PQ algorithm by passing ivf_pq_params as graph_build_algo
params.graph_build_algo = pq_params;
```

- We can specify NN descent algo parameters a similar way
```c++
cagra::index_params params;
params.graph_degree = 32;
params.intermediate_graph_degree = 48;
// Select NN descent algo by passing nn_descent_params
params.graph_build_algo = cagra::graph_build_params::nn_descent_params(intermediate_degree) ;
```

Additionally, this PR optimizes the IVF-PQ refinement step within the CAGRA graph creation in case the dataset is in host-memory.

* depending on hardware, a certain amount of refinement comes for free
* the change also has a positive effect without refinement as the graph creation is done on host either way

Authors:
  - Malte Förster (https://github.com/mfoerste4)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/131
---
 cpp/CMakeLists.txt                            |   2 +
 cpp/include/cuvs/neighbors/cagra.h            |   2 +
 cpp/include/cuvs/neighbors/cagra.hpp          | 356 +++++++++++++++---
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  20 +-
 cpp/include/cuvs/neighbors/nn_descent.hpp     |  22 +-
 cpp/src/neighbors/cagra.cuh                   |  16 +-
 cpp/src/neighbors/cagra_build_float.cu        |  46 +--
 cpp/src/neighbors/cagra_build_int8.cu         |  46 +--
 cpp/src/neighbors/cagra_build_uint8.cu        |  46 +--
 cpp/src/neighbors/cagra_c.cpp                 |  32 +-
 .../neighbors/detail/cagra/cagra_build.cpp    |  35 ++
 .../neighbors/detail/cagra/cagra_build.cuh    | 290 +++++++++-----
 .../detail/cagra/cagra_serialize.cuh          |   3 -
 cpp/src/neighbors/detail/nn_descent.cuh       |   2 -
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |   3 +
 cpp/src/neighbors/ivf_pq_index.cpp            |  15 +
 cpp/src/neighbors/nn_descent.cu               |  59 +++
 cpp/src/neighbors/refine/refine_host.hpp      |  50 +--
 cpp/test/neighbors/ann_cagra.cuh              |  88 ++++-
 19 files changed, 813 insertions(+), 320 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/cagra/cagra_build.cpp
 create mode 100644 cpp/src/neighbors/nn_descent.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d4ab1e62bb..5f016ed884 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -243,6 +243,7 @@ add_library(
   src/neighbors/cagra_serialize_float.cu
   src/neighbors/cagra_serialize_int8.cu
   src/neighbors/cagra_serialize_uint8.cu
+  src/neighbors/detail/cagra/cagra_build.cpp
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
@@ -425,6 +426,7 @@ add_library(
   src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
+  src/neighbors/nn_descent.cu
   src/neighbors/nn_descent_float.cu
   src/neighbors/nn_descent_int8.cu
   src/neighbors/nn_descent_uint8.cu
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index c82195710e..87541f7f08 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -35,6 +35,8 @@ extern "C" {
  *
  */
 enum cuvsCagraGraphBuildAlgo {
+  /* Select build algorithm automatically */
+  AUTO_SELECT,
   /* Use IVF-PQ to build all-neighbors knn graph */
   IVF_PQ,
   /* Experimental, use NN-Descent to build all-neighbors knn graph */
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 9d976d28e0..c5f2ab87d9 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -19,6 +19,8 @@
 #include "common.hpp"
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_device_accessor.hpp>
 #include <raft/core/host_mdspan.hpp>
@@ -29,6 +31,7 @@
 #include <rmm/cuda_stream_view.hpp>
 
 #include <optional>
+#include <variant>
 
 namespace cuvs::neighbors::cagra {
 /**
@@ -37,31 +40,70 @@ namespace cuvs::neighbors::cagra {
  */
 
 /**
- * @brief ANN algorithm used by CAGRA to build knn graph
+ * @brief ANN parameters used by CAGRA to build knn graph
  *
  */
-enum class graph_build_algo {
-  /* Use IVF-PQ to build all-neighbors knn graph */
-  IVF_PQ,
-  /* Experimental, use NN-Descent to build all-neighbors knn graph */
-  NN_DESCENT
+namespace graph_build_params {
+
+/** Specialized parameters utilizing IVF-PQ to build knn graph */
+struct ivf_pq_params {
+  cuvs::neighbors::ivf_pq::index_params build_params;
+  cuvs::neighbors::ivf_pq::search_params search_params;
+  float refinement_rate;
+
+  ivf_pq_params() = default;
+  /**
+   * Set default parameters based on shape of the input dataset.
+   * Usage example:
+   * @code{.cpp}
+   *   using namespace cuvs::neighbors;
+   *   raft::resources res;
+   *   // create index_params for a [N. D] dataset
+   *   auto dataset = raft::make_device_matrix<float, int64_t>(res, N, D);
+   *   auto pq_params =
+   *     cagra::graph_build_params::ivf_pq_params(dataset.extents());
+   *   // modify/update index_params as needed
+   *   index_params.add_data_on_build = true;
+   * @endcode
+   */
+  ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
+                cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded);
 };
 
+using nn_descent_params = cuvs::neighbors::nn_descent::index_params;
+}  // namespace graph_build_params
+
 struct index_params : cuvs::neighbors::index_params {
   /** Degree of input graph for pruning. */
   size_t intermediate_graph_degree = 128;
   /** Degree of output graph. */
   size_t graph_degree = 64;
-  /** ANN algorithm to build knn graph. */
-  graph_build_algo build_algo = graph_build_algo::IVF_PQ;
-  /** Number of Iterations to run if building with NN_DESCENT */
-  size_t nn_descent_niter = 20;
   /**
    * Specify compression parameters if compression is desired.
-   *
-   * NOTE: this is experimental new API, consider it unsafe.
    */
   std::optional<cuvs::neighbors::vpq_params> compression = std::nullopt;
+
+  /** Parameters for graph building.
+   *
+   * Set ivf_pq_params or nn_descent_params to select the graph build algorithm and control their
+   * parameters. The default (std::monostate) is to use a heuristic to decide the algorithm and its
+   * parameters.
+   *
+   * @code{.cpp}
+   * cagra::index_params params;
+   * // 1. Choose IVF-PQ algorithm
+   * params.graph_build_params = cagra::graph_build_params::ivf_pq_params(dataset.extent,
+   * params.metric);
+   *
+   * // 2. Choose NN Descent algorithm for kNN graph construction
+   * params.graph_build_params =
+   * cagra::graph_build_params::nn_descent_params(params.intermediate_graph_degree);
+   * @endcode
+   */
+  std::variant<std::monostate,
+               graph_build_params::ivf_pq_params,
+               graph_build_params::nn_descent_params>
+    graph_build_params;
 };
 
 /**
@@ -283,9 +325,7 @@ struct index : cuvs::neighbors::index {
                  "Dataset and knn_graph must have equal number of rows");
     update_graph(res, knn_graph);
 
-    printf("Called update_graph\n");
     raft::resource::sync_stream(res);
-    printf("Done syncing\n");
   }
 
   /**
@@ -356,7 +396,6 @@ struct index : cuvs::neighbors::index {
   {
     RAFT_LOG_DEBUG("Copying CAGRA knn graph from host to device");
 
-    printf("Copying graph...\n");
     if ((graph_.extent(0) != knn_graph.extent(0)) || (graph_.extent(1) != knn_graph.extent(1))) {
       // clear existing memory before allocating to prevent OOM errors on large graphs
       if (graph_.size()) { graph_ = raft::make_device_matrix<IdxT, int64_t>(res, 0, 0); }
@@ -368,7 +407,6 @@ struct index : cuvs::neighbors::index {
                knn_graph.size(),
                raft::resource::get_cuda_stream(res));
     graph_view_ = graph_.view();
-    printf("Done...\n");
   }
 
  private:
@@ -385,32 +423,219 @@ struct index : cuvs::neighbors::index {
  * @defgroup cagra_cpp_index_build CAGRA index build functions
  * @{
  */
-auto build(raft::resources const& handle,
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+auto build(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::cagra::index<float, uint32_t>;
 
-auto build(raft::resources const& handle,
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+auto build(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::cagra::index<float, uint32_t>;
 
-auto build(raft::resources const& handle,
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+auto build(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::cagra::index<int8_t, uint32_t>;
 
-auto build(raft::resources const& handle,
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+auto build(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::cagra::index<int8_t, uint32_t>;
 
-auto build(raft::resources const& handle,
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+auto build(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::cagra::index<uint8_t, uint32_t>;
 
-auto build(raft::resources const& handle,
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+auto build(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::cagra::index<uint8_t, uint32_t>;
@@ -418,56 +643,77 @@ auto build(raft::resources const& handle,
  * @}
  */
 
-void build_device(raft::resources const& handle,
-                  const cuvs::neighbors::cagra::index_params& params,
-                  raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
-                  cuvs::neighbors::cagra::index<float, uint32_t>& idx);
-
-void build_host(raft::resources const& handle,
-                const cuvs::neighbors::cagra::index_params& params,
-                raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
-                cuvs::neighbors::cagra::index<float, uint32_t>& idx);
-
-void build_device(raft::resources const& handle,
-                  const cuvs::neighbors::cagra::index_params& params,
-                  raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
-                  cuvs::neighbors::cagra::index<int8_t, uint32_t>& idx);
-
-void build_host(raft::resources const& handle,
-                const cuvs::neighbors::cagra::index_params& params,
-                raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
-                cuvs::neighbors::cagra::index<int8_t, uint32_t>& idx);
-
-void build_device(raft::resources const& handle,
-                  const cuvs::neighbors::cagra::index_params& params,
-                  raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
-                  cuvs::neighbors::cagra::index<uint8_t, uint32_t>& idx);
-
-void build_host(raft::resources const& handle,
-                const cuvs::neighbors::cagra::index_params& params,
-                raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
-                cuvs::neighbors::cagra::index<uint8_t, uint32_t>& idx);
-
 /**
  * @defgroup cagra_cpp_index_search CAGRA search functions
  * @{
  */
 
-void search(raft::resources const& handle,
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& res,
             cuvs::neighbors::cagra::search_params const& params,
             const cuvs::neighbors::cagra::index<float, uint32_t>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances);
 
-void search(raft::resources const& handle,
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& res,
             cuvs::neighbors::cagra::search_params const& params,
             const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
             raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances);
 
-void search(raft::resources const& handle,
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& res,
             cuvs::neighbors::cagra::search_params const& params,
             const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 8493882b43..d7696a37b9 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -102,31 +102,19 @@ struct index_params : cuvs::neighbors::index_params {
    * Creates index_params based on shape of the input dataset.
    * Usage example:
    * @code{.cpp}
-   *   using namespace raft::neighbors;
+   *   using namespace cuvs::neighbors;
    *   raft::resources res;
    *   // create index_params for a [N. D] dataset and have InnerProduct as the distance metric
    *   auto dataset = raft::make_device_matrix<float, int64_t>(res, N, D);
    *   ivf_pq::index_params index_params =
-   *     ivf_pq::index_params::from_dataset(dataset.view(), raft::distance::InnerProduct);
+   *     ivf_pq::index_params::from_dataset(dataset.extents(), raft::distance::InnerProduct);
    *   // modify/update index_params as needed
    *   index_params.add_data_on_build = true;
    * @endcode
    */
-  template <typename DataT, typename Accessor>
   static index_params from_dataset(
-    raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
-    cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
-  {
-    index_params params;
-    params.n_lists =
-      dataset.extent(0) < 4 * 2500 ? 4 : static_cast<uint32_t>(std::sqrt(dataset.extent(0)));
-    params.pq_dim =
-      raft::round_up_safe(static_cast<uint32_t>(dataset.extent(1) / 4), static_cast<uint32_t>(8));
-    params.pq_bits                  = 8;
-    params.kmeans_trainset_fraction = dataset.extent(0) < 10000 ? 1 : 0.1;
-    params.metric                   = metric;
-    return params;
-  }
+    raft::matrix_extent<int64_t> dataset,
+    cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded);
 };
 /**
  * @}
diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index 1f216d68f4..9f43001778 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -53,6 +53,15 @@ struct index_params : cuvs::neighbors::index_params {
   size_t intermediate_graph_degree = 128;     // Degree of input graph for pruning.
   size_t max_iterations            = 20;      // Number of nn-descent iterations.
   float termination_threshold      = 0.0001;  // Termination threshold of nn-descent.
+
+  /** @brief Construct NN descent parameters for a specific kNN graph degree
+   *
+   * @param graph_degree output graph degree
+   */
+  index_params(size_t graph_degree = 64)
+    : graph_degree(graph_degree), intermediate_graph_degree(1.5 * graph_degree)
+  {
+  }
 };
 
 /**
@@ -320,6 +329,8 @@ auto build(raft::resources const& res,
            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
+/** @} */
+
 /**
  * @brief Build nn-descent Index with dataset in host memory
  *
@@ -353,7 +364,16 @@ auto build(raft::resources const& res,
   -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
- * @}
+ * @brief Test if we have enough GPU memory to run NN descent algorithm.
+ *
+ * @param res
+ * @param dataset shape of the dataset
+ * @param idx_size the size of index type in bytes
+ * @return true if enough GPU memory can be allocated
+ * @return false otherwise
  */
+bool has_enough_device_memory(raft::resources const& res,
+                              raft::matrix_extent<int64_t> dataset,
+                              size_t idx_size = 4);
 
 }  // namespace cuvs::neighbors::nn_descent
\ No newline at end of file
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 997f369a95..0e714370a4 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -75,18 +75,14 @@ namespace cuvs::neighbors::cagra {
  * @param[in] res raft resources
  * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
  * @param[out] knn_graph a host matrix view to store the output knn graph [n_rows, graph_degree]
- * @param[in] refine_rate (optional) refinement rate for ivf-pq search
- * @param[in] build_params (optional) ivf_pq index building parameters for knn graph
- * @param[in] search_params (optional) ivf_pq search parameters
+ * @param[in] ivf_pq_params ivf-pq parameters for graph build
  */
 template <typename DataT, typename IdxT, typename accessor>
 void build_knn_graph(
   raft::resources const& res,
   raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
   raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
-  std::optional<float> refine_rate                   = std::nullopt,
-  std::optional<ivf_pq::index_params> build_params   = std::nullopt,
-  std::optional<ivf_pq::search_params> search_params = std::nullopt)
+  cagra::graph_build_params::ivf_pq_params ivf_pq_params)
 {
   using internal_IdxT = typename std::make_unsigned<IdxT>::type;
 
@@ -98,8 +94,12 @@ void build_knn_graph(
     raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor>(
       dataset.data_handle(), dataset.extent(0), dataset.extent(1));
 
-  cagra::detail::build_knn_graph(
-    res, dataset_internal, knn_graph_internal, refine_rate, build_params, search_params);
+  cagra::detail::build_knn_graph(res,
+                                 dataset_internal,
+                                 knn_graph_internal,
+                                 ivf_pq_params.refinement_rate,
+                                 ivf_pq_params.build_params,
+                                 ivf_pq_params.search_params);
 }
 
 /**
diff --git a/cpp/src/neighbors/cagra_build_float.cu b/cpp/src/neighbors/cagra_build_float.cu
index c5e331925b..b990d1b329 100644
--- a/cpp/src/neighbors/cagra_build_float.cu
+++ b/cpp/src/neighbors/cagra_build_float.cu
@@ -19,37 +19,21 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                                   \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)        \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
-  {                                                                                      \
-    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
-  }                                                                                      \
-                                                                                         \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)          \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
-  {                                                                                      \
-    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
-  }                                                                                      \
-                                                                                         \
-  void build_device(raft::resources const& handle,                                       \
-                    const cuvs::neighbors::cagra::index_params& params,                  \
-                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset, \
-                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                         \
-  {                                                                                      \
-    idx = build(handle, params, dataset);                                                \
-  }                                                                                      \
-                                                                                         \
-  void build_host(raft::resources const& handle,                                         \
-                  const cuvs::neighbors::cagra::index_params& params,                    \
-                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,     \
-                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                           \
-  {                                                                                      \
-    idx = build(handle, params, dataset);                                                \
+#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                            \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::cagra::index_params& params,                  \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+  {                                                                               \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
+  }                                                                               \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::cagra::index_params& params,                  \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+  {                                                                               \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }
 
 RAFT_INST_CAGRA_BUILD(float, uint32_t);
diff --git a/cpp/src/neighbors/cagra_build_int8.cu b/cpp/src/neighbors/cagra_build_int8.cu
index e48286b0f5..624ebd060a 100644
--- a/cpp/src/neighbors/cagra_build_int8.cu
+++ b/cpp/src/neighbors/cagra_build_int8.cu
@@ -19,37 +19,21 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                                   \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)        \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
-  {                                                                                      \
-    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
-  }                                                                                      \
-                                                                                         \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)          \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
-  {                                                                                      \
-    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
-  }                                                                                      \
-                                                                                         \
-  void build_device(raft::resources const& handle,                                       \
-                    const cuvs::neighbors::cagra::index_params& params,                  \
-                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset, \
-                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                         \
-  {                                                                                      \
-    idx = build(handle, params, dataset);                                                \
-  }                                                                                      \
-                                                                                         \
-  void build_host(raft::resources const& handle,                                         \
-                  const cuvs::neighbors::cagra::index_params& params,                    \
-                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,     \
-                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                           \
-  {                                                                                      \
-    idx = build(handle, params, dataset);                                                \
+#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                            \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::cagra::index_params& params,                  \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+  {                                                                               \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
+  }                                                                               \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::cagra::index_params& params,                  \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+  {                                                                               \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }
 
 RAFT_INST_CAGRA_BUILD(int8_t, uint32_t);
diff --git a/cpp/src/neighbors/cagra_build_uint8.cu b/cpp/src/neighbors/cagra_build_uint8.cu
index 796fdda745..8fd806c64e 100644
--- a/cpp/src/neighbors/cagra_build_uint8.cu
+++ b/cpp/src/neighbors/cagra_build_uint8.cu
@@ -19,37 +19,21 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                                   \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)        \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
-  {                                                                                      \
-    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
-  }                                                                                      \
-                                                                                         \
-  auto build(raft::resources const& handle,                                              \
-             const cuvs::neighbors::cagra::index_params& params,                         \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)          \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                             \
-  {                                                                                      \
-    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);              \
-  }                                                                                      \
-                                                                                         \
-  void build_device(raft::resources const& handle,                                       \
-                    const cuvs::neighbors::cagra::index_params& params,                  \
-                    raft::device_matrix_view<const T, int64_t, raft::row_major> dataset, \
-                    cuvs::neighbors::cagra::index<T, IdxT>& idx)                         \
-  {                                                                                      \
-    idx = build(handle, params, dataset);                                                \
-  }                                                                                      \
-                                                                                         \
-  void build_host(raft::resources const& handle,                                         \
-                  const cuvs::neighbors::cagra::index_params& params,                    \
-                  raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,     \
-                  cuvs::neighbors::cagra::index<T, IdxT>& idx)                           \
-  {                                                                                      \
-    idx = build(handle, params, dataset);                                                \
+#define RAFT_INST_CAGRA_BUILD(T, IdxT)                                            \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::cagra::index_params& params,                  \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+  {                                                                               \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
+  }                                                                               \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::cagra::index_params& params,                  \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+  {                                                                               \
+    return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }
 
 RAFT_INST_CAGRA_BUILD(uint8_t, uint32_t);
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 630a5058a0..df0c4d9f25 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -39,12 +39,26 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
   auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
 
-  auto build_params                      = cuvs::neighbors::cagra::index_params();
-  build_params.intermediate_graph_degree = params.intermediate_graph_degree;
-  build_params.graph_degree              = params.graph_degree;
-  build_params.build_algo =
-    static_cast<cuvs::neighbors::cagra::graph_build_algo>(params.build_algo);
-  build_params.nn_descent_niter = params.nn_descent_niter;
+  auto index_params                      = cuvs::neighbors::cagra::index_params();
+  index_params.intermediate_graph_degree = params.intermediate_graph_degree;
+  index_params.graph_degree              = params.graph_degree;
+
+  switch (params.build_algo) {
+    case cuvsCagraGraphBuildAlgo::AUTO_SELECT: break;
+    case cuvsCagraGraphBuildAlgo::IVF_PQ: {
+      auto dataset_extent = raft::matrix_extent<int64_t>(dataset.shape[0], dataset.shape[1]);
+      index_params.graph_build_params =
+        cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(dataset_extent);
+      break;
+    }
+    case cuvsCagraGraphBuildAlgo::NN_DESCENT:
+      cuvs::neighbors::cagra::graph_build_params::nn_descent_params nn_descent_params{};
+      nn_descent_params =
+        cuvs::neighbors::nn_descent::index_params(index_params.intermediate_graph_degree);
+      nn_descent_params.max_iterations = params.nn_descent_niter;
+      index_params.graph_build_params  = nn_descent_params;
+      break;
+  };
 
   if (auto* cparams = params.compression; cparams != nullptr) {
     auto compression_params                        = cuvs::neighbors::vpq_params();
@@ -54,17 +68,17 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
     compression_params.kmeans_n_iters              = cparams->kmeans_n_iters;
     compression_params.vq_kmeans_trainset_fraction = cparams->vq_kmeans_trainset_fraction;
     compression_params.pq_kmeans_trainset_fraction = cparams->pq_kmeans_trainset_fraction;
-    build_params.compression.emplace(compression_params);
+    index_params.compression.emplace(compression_params);
   }
 
   if (cuvs::core::is_dlpack_device_compatible(dataset)) {
     using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
     auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
-    cuvs::neighbors::cagra::build_device(*res_ptr, build_params, mds, *index);
+    *index            = cuvs::neighbors::cagra::build(*res_ptr, index_params, mds);
   } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
     using mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
     auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
-    cuvs::neighbors::cagra::build_host(*res_ptr, build_params, mds, *index);
+    *index            = cuvs::neighbors::cagra::build(*res_ptr, index_params, mds);
   }
   return index;
 }
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cpp b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
new file mode 100644
index 0000000000..7ea45d0638
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/mdspan.hpp>
+
+namespace cuvs::neighbors::cagra::graph_build_params {
+ivf_pq_params::ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
+                             cuvs::distance::DistanceType metric)
+{
+  build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset_extents, metric);
+
+  search_params                         = cuvs::neighbors::ivf_pq::search_params{};
+  search_params.n_probes                = std::max<uint32_t>(10, build_params.n_lists * 0.01);
+  search_params.lut_dtype               = CUDA_R_16F;
+  search_params.internal_distance_dtype = CUDA_R_16F;
+
+  refinement_rate = 2;
+}
+}  // namespace cuvs::neighbors::cagra::graph_build_params
\ No newline at end of file
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 0ca97b9ca9..a50ac2e651 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -49,17 +49,85 @@ namespace cuvs::neighbors::cagra::detail {
 
 static const std::string RAFT_NAME = "raft";
 
+template <typename IdxT>
+void write_to_graph(raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
+                    raft::host_matrix_view<int64_t, int64_t, raft::row_major> neighbors_host_view,
+                    size_t& num_self_included,
+                    size_t batch_size,
+                    size_t batch_offset)
+{
+  uint32_t node_degree = knn_graph.extent(1);
+  size_t top_k         = neighbors_host_view.extent(1);
+  // omit itself & write out
+  for (std::size_t i = 0; i < batch_size; i++) {
+    size_t vec_idx = i + batch_offset;
+    for (std::size_t j = 0, num_added = 0; j < top_k && num_added < node_degree; j++) {
+      const auto v = neighbors_host_view(i, j);
+      if (static_cast<size_t>(v) == vec_idx) {
+        num_self_included++;
+        continue;
+      }
+      knn_graph(vec_idx, num_added) = v;
+      num_added++;
+    }
+  }
+}
+
+template <typename DataT, typename IdxT, typename accessor>
+void refine_host_and_write_graph(
+  raft::resources const& res,
+  raft::host_matrix<DataT, int64_t>& queries_host,
+  raft::host_matrix<int64_t, int64_t>& neighbors_host,
+  raft::host_matrix<int64_t, int64_t>& refined_neighbors_host,
+  raft::host_matrix<float, int64_t>& refined_distances_host,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
+  cuvs::distance::DistanceType metric,
+  size_t& num_self_included,
+  size_t batch_size,
+  size_t batch_offset,
+  int top_k,
+  int gpu_top_k)
+{
+  bool do_refine = top_k != gpu_top_k;
+
+  auto refined_neighbors_host_view = raft::make_host_matrix_view<int64_t, int64_t>(
+    do_refine ? refined_neighbors_host.data_handle() : neighbors_host.data_handle(),
+    batch_size,
+    top_k);
+
+  if (do_refine) {
+    // needed for compilation as this routine will also be run for device data with !do_refine
+    if constexpr (raft::is_host_mdspan_v<decltype(dataset)>) {
+      auto queries_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+        queries_host.data_handle(), batch_size, dataset.extent(1));
+      auto neighbors_host_view = raft::make_host_matrix_view<const int64_t, int64_t>(
+        neighbors_host.data_handle(), batch_size, neighbors_host.extent(1));
+      auto refined_distances_host_view = raft::make_host_matrix_view<float, int64_t>(
+        refined_distances_host.data_handle(), batch_size, top_k);
+      cuvs::neighbors::refine(res,
+                              dataset,
+                              queries_host_view,
+                              neighbors_host_view,
+                              refined_neighbors_host_view,
+                              refined_distances_host_view,
+                              metric);
+    }
+  }
+
+  write_to_graph(
+    knn_graph, refined_neighbors_host_view, num_self_included, batch_size, batch_offset);
+}
+
 template <typename DataT, typename IdxT, typename accessor>
 void build_knn_graph(
   raft::resources const& res,
   raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, accessor> dataset,
   raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
-  std::optional<float> refine_rate                                    = std::nullopt,
-  std::optional<cuvs::neighbors::ivf_pq::index_params> build_params   = std::nullopt,
-  std::optional<cuvs::neighbors::ivf_pq::search_params> search_params = std::nullopt)
+  cuvs::neighbors::cagra::graph_build_params::ivf_pq_params pq)
 {
-  RAFT_EXPECTS(!build_params || build_params->metric == cuvs::distance::DistanceType::L2Expanded ||
-                 build_params->metric == cuvs::distance::DistanceType::InnerProduct,
+  RAFT_EXPECTS(pq.build_params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                 pq.build_params.metric == cuvs::distance::DistanceType::InnerProduct,
                "Currently only L2Expanded or InnerProduct metric are supported");
 
   uint32_t node_degree = knn_graph.extent(1);
@@ -69,10 +137,6 @@ void build_knn_graph(
     size_t(dataset.extent(1)),
     node_degree);
 
-  if (!build_params) {
-    build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset);
-  }
-
   // Make model name
   const std::string model_name = [&]() {
     char model_name[1024];
@@ -81,29 +145,25 @@ void build_knn_graph(
             "IVF-PQ",
             static_cast<size_t>(dataset.extent(0)),
             static_cast<size_t>(dataset.extent(1)),
-            build_params->n_lists,
-            build_params->pq_dim,
-            build_params->pq_bits,
-            build_params->kmeans_n_iters,
-            build_params->metric,
-            static_cast<uint32_t>(build_params->codebook_kind));
+            pq.build_params.n_lists,
+            pq.build_params.pq_dim,
+            pq.build_params.pq_bits,
+            pq.build_params.kmeans_n_iters,
+            pq.build_params.metric,
+            static_cast<uint32_t>(pq.build_params.codebook_kind));
     return std::string(model_name);
   }();
 
   RAFT_LOG_DEBUG("# Building IVF-PQ index %s", model_name.c_str());
-  auto index = cuvs::neighbors::ivf_pq::detail::build<DataT, int64_t>(res, *build_params, dataset);
+  auto index =
+    cuvs::neighbors::ivf_pq::detail::build<DataT, int64_t>(res, pq.build_params, dataset);
 
   //
   // search top (k + 1) neighbors
   //
-  if (!search_params) {
-    search_params            = cuvs::neighbors::ivf_pq::search_params{};
-    search_params->n_probes  = std::min<IdxT>(dataset.extent(1) * 2, build_params->n_lists);
-    search_params->lut_dtype = CUDA_R_8U;
-    search_params->internal_distance_dtype = CUDA_R_32F;
-  }
+
   const auto top_k          = node_degree + 1;
-  uint32_t gpu_top_k        = node_degree * refine_rate.value_or(2.0f);
+  uint32_t gpu_top_k        = node_degree * pq.refinement_rate;
   gpu_top_k                 = std::min<IdxT>(std::max(gpu_top_k, top_k), dataset.extent(0));
   const auto num_queries    = dataset.extent(0);
   const auto max_batch_size = 1024;
@@ -113,7 +173,7 @@ void build_knn_graph(
     top_k,
     gpu_top_k,
     max_batch_size,
-    search_params->n_probes);
+    pq.search_params.n_probes);
 
   auto distances = raft::make_device_matrix<float, int64_t>(res, max_batch_size, gpu_top_k);
   auto neighbors = raft::make_device_matrix<int64_t, int64_t>(res, max_batch_size, gpu_top_k);
@@ -142,6 +202,10 @@ void build_knn_graph(
   size_t next_report_offset = 0;
   size_t d_report_offset    = dataset.extent(0) / 100;  // Report progress in 1% steps.
 
+  bool async_host_processing   = raft::is_host_mdspan_v<decltype(dataset)> || top_k == gpu_top_k;
+  size_t previous_batch_size   = 0;
+  size_t previous_batch_offset = 0;
+
   for (const auto& batch : vec_batches) {
     // Map int64_t to uint32_t because ivf_pq requires the latter.
     // TODO(tfeher): remove this mapping once ivf_pq accepts mdspan with int64_t index type
@@ -153,33 +217,62 @@ void build_knn_graph(
       distances.data_handle(), batch.size(), distances.extent(1));
 
     cuvs::neighbors::ivf_pq::search(
-      res, *search_params, index, queries_view, neighbors_view, distances_view);
-    if constexpr (raft::is_host_mdspan_v<decltype(dataset)>) {
+      res, pq.search_params, index, queries_view, neighbors_view, distances_view);
+
+    if (async_host_processing) {
+      // process previous batch async on host
+      // NOTE: the async path also covers disabled refinement (top_k == gpu_top_k)
+      if (previous_batch_size > 0) {
+        refine_host_and_write_graph(res,
+                                    queries_host,
+                                    neighbors_host,
+                                    refined_neighbors_host,
+                                    refined_distances_host,
+                                    dataset,
+                                    knn_graph,
+                                    pq.build_params.metric,
+                                    num_self_included,
+                                    previous_batch_size,
+                                    previous_batch_offset,
+                                    top_k,
+                                    gpu_top_k);
+      }
+
+      // copy next batch to host
       raft::copy(neighbors_host.data_handle(),
                  neighbors.data_handle(),
                  neighbors_view.size(),
                  raft::resource::get_cuda_stream(res));
-      raft::copy(queries_host.data_handle(),
-                 batch.data(),
-                 queries_view.size(),
-                 raft::resource::get_cuda_stream(res));
-      auto queries_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
-        queries_host.data_handle(), batch.size(), batch.row_width());
-      auto neighbors_host_view = raft::make_host_matrix_view<const int64_t, int64_t>(
-        neighbors_host.data_handle(), batch.size(), neighbors.extent(1));
-      auto refined_neighbors_host_view = raft::make_host_matrix_view<int64_t, int64_t>(
-        refined_neighbors_host.data_handle(), batch.size(), top_k);
-      auto refined_distances_host_view = raft::make_host_matrix_view<float, int64_t>(
-        refined_distances_host.data_handle(), batch.size(), top_k);
+      if (top_k != gpu_top_k) {
+        // can be skipped for disabled refinement
+        raft::copy(queries_host.data_handle(),
+                   batch.data(),
+                   queries_view.size(),
+                   raft::resource::get_cuda_stream(res));
+      }
+
+      previous_batch_size   = batch.size();
+      previous_batch_offset = batch.offset();
+
+      // we need to ensure the copy operations are done prior using the host data
       raft::resource::sync_stream(res);
 
-      cuvs::neighbors::refine(res,
-                              dataset,
-                              queries_host_view,
-                              neighbors_host_view,
-                              refined_neighbors_host_view,
-                              refined_distances_host_view,
-                              build_params->metric);
+      // process last batch
+      if (previous_batch_offset + previous_batch_size == (size_t)num_queries) {
+        refine_host_and_write_graph(res,
+                                    queries_host,
+                                    neighbors_host,
+                                    refined_neighbors_host,
+                                    refined_distances_host,
+                                    dataset,
+                                    knn_graph,
+                                    pq.build_params.metric,
+                                    num_self_included,
+                                    previous_batch_size,
+                                    previous_batch_offset,
+                                    top_k,
+                                    gpu_top_k);
+      }
     } else {
       auto neighbor_candidates_view = raft::make_device_matrix_view<const int64_t, uint64_t>(
         neighbors.data_handle(), batch.size(), gpu_top_k);
@@ -196,26 +289,17 @@ void build_knn_graph(
                               neighbor_candidates_view,
                               refined_neighbors_view,
                               refined_distances_view,
-                              build_params->metric);
+                              pq.build_params.metric);
       raft::copy(refined_neighbors_host.data_handle(),
                  refined_neighbors_view.data_handle(),
                  refined_neighbors_view.size(),
                  raft::resource::get_cuda_stream(res));
       raft::resource::sync_stream(res);
-    }
-    // omit itself & write out
-    // TODO(tfeher): do this in parallel with GPU processing of next batch
-    for (std::size_t i = 0; i < batch.size(); i++) {
-      size_t vec_idx = i + batch.offset();
-      for (std::size_t j = 0, num_added = 0; j < top_k && num_added < node_degree; j++) {
-        const auto v = refined_neighbors_host(i, j);
-        if (static_cast<size_t>(v) == vec_idx) {
-          num_self_included++;
-          continue;
-        }
-        knn_graph(vec_idx, num_added) = v;
-        num_added++;
-      }
+
+      auto refined_neighbors_host_view = raft::make_host_matrix_view<int64_t, int64_t>(
+        refined_neighbors_host.data_handle(), batch.size(), top_k);
+      write_to_graph(
+        knn_graph, refined_neighbors_host_view, num_self_included, batch.size(), batch.offset());
     }
 
     size_t num_queries_done = batch.offset() + batch.size();
@@ -303,12 +387,7 @@ template <typename T,
 index<T, IdxT> build(
   raft::resources const& res,
   const index_params& params,
-  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
-  std::optional<cuvs::neighbors::nn_descent::index_params> nn_descent_params = std::nullopt,
-  std::optional<float> refine_rate                                           = std::nullopt,
-  std::optional<cuvs::neighbors::ivf_pq::index_params> pq_build_params       = std::nullopt,
-  std::optional<cuvs::neighbors::ivf_pq::search_params> search_params        = std::nullopt,
-  bool construct_index_with_dataset                                          = true)
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
 {
   size_t intermediate_degree = params.intermediate_graph_degree;
   size_t graph_degree        = params.graph_degree;
@@ -330,20 +409,45 @@ index<T, IdxT> build(
   std::optional<raft::host_matrix<IdxT, int64_t>> knn_graph(
     raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), intermediate_degree));
 
-  if (params.build_algo == graph_build_algo::IVF_PQ) {
-    build_knn_graph(res, dataset, knn_graph->view(), refine_rate, pq_build_params, search_params);
+  // Set default value in case knn_build_params is not defined.
+  auto knn_build_params = params.graph_build_params;
+  if (std::holds_alternative<std::monostate>(params.graph_build_params)) {
+    // Heuristic to decide default build algo and its params.
+    if (params.metric == cuvs::distance::DistanceType::L2Expanded &&
+        cuvs::neighbors::nn_descent::has_enough_device_memory(
+          res, dataset.extents(), sizeof(IdxT))) {
+      RAFT_LOG_DEBUG("NN descent solver");
+      knn_build_params = cagra::graph_build_params::nn_descent_params(intermediate_degree);
+    } else {
+      RAFT_LOG_DEBUG("Selecting IVF-PQ solver");
+      knn_build_params = cagra::graph_build_params::ivf_pq_params(dataset.extents(), params.metric);
+    }
+  }
+
+  // Dispatch based on graph_build_params
+  if (std::holds_alternative<cagra::graph_build_params::ivf_pq_params>(knn_build_params)) {
+    auto ivf_pq_params =
+      std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(knn_build_params);
+    build_knn_graph(res, dataset, knn_graph->view(), ivf_pq_params);
   } else {
     RAFT_EXPECTS(
       params.metric == cuvs::distance::DistanceType::L2Expanded,
       "L2Expanded is the only distance metrics supported for CAGRA build with nn_descent");
-    // Use nn-descent to build CAGRA knn graph
-    if (!nn_descent_params) {
-      nn_descent_params                            = cuvs::neighbors::nn_descent::index_params();
-      nn_descent_params->graph_degree              = intermediate_degree;
-      nn_descent_params->intermediate_graph_degree = 1.5 * intermediate_degree;
-      nn_descent_params->max_iterations            = params.nn_descent_niter;
+    auto nn_descent_params =
+      std::get<cagra::graph_build_params::nn_descent_params>(knn_build_params);
+
+    if (nn_descent_params.graph_degree != intermediate_degree) {
+      RAFT_LOG_WARN(
+        "Graph degree (%lu) for nn-descent needs to match cagra intermediate graph degree (%lu), "
+        "aligning "
+        "nn-descent graph_degree.",
+        nn_descent_params.graph_degree,
+        intermediate_degree);
+      nn_descent_params = cagra::graph_build_params::nn_descent_params(intermediate_degree);
     }
-    build_knn_graph<T, IdxT>(res, dataset, knn_graph->view(), *nn_descent_params);
+
+    // Use nn-descent to build CAGRA knn graph
+    build_knn_graph<T, IdxT>(res, dataset, knn_graph->view(), nn_descent_params);
   }
 
   auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
@@ -355,25 +459,27 @@ index<T, IdxT> build(
   knn_graph.reset();
 
   RAFT_LOG_INFO("Graph optimized, creating index");
+
   // Construct an index from dataset and optimized knn graph.
-  if (construct_index_with_dataset) {
-    if (params.compression.has_value()) {
-      RAFT_EXPECTS(params.metric == cuvs::distance::DistanceType::L2Expanded,
-                   "VPQ compression is only supported with L2Expanded distance mertric");
-      index<T, IdxT> idx(res, params.metric);
-      idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
-      idx.update_dataset(
-        res,
-        // TODO: hardcoding codebook math to `half`, we can do runtime dispatching later
-        cuvs::neighbors::vpq_build<decltype(dataset), half, int64_t>(
-          res, *params.compression, dataset));
-
-      return idx;
-    }
+  if (params.compression.has_value()) {
+    RAFT_EXPECTS(params.metric == cuvs::distance::DistanceType::L2Expanded,
+                 "VPQ compression is only supported with L2Expanded distance mertric");
+    index<T, IdxT> idx(res, params.metric);
+    idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
+    idx.update_dataset(
+      res,
+      // TODO: hardcoding codebook math to `half`, we can do runtime dispatching later
+      cuvs::neighbors::vpq_build<decltype(dataset), half, int64_t>(
+        res, *params.compression, dataset));
+
+    return idx;
+  }
+  try {
     return index<T, IdxT>(res, params.metric, dataset, raft::make_const_mdspan(cagra_graph.view()));
-  } else {
-    // We just add the graph. User is expected to update dataset separately. This branch is used
-    // if user needs special control of memory allocations for the dataset.
+  } catch (std::bad_alloc& e) {
+    RAFT_LOG_DEBUG("Insufficient GPU memory to construct CAGRA index with dataset on GPU");
+    // We just add the graph. User is expected to update dataset separately (e.g allocating in
+    // managed memory).
     index<T, IdxT> idx(res, params.metric);
     idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
     return idx;
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index df703a04e4..4947d3148b 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -62,19 +62,16 @@ void serialize(raft::resources const& res,
   dtype_string.resize(4);
   os << dtype_string;
 
-  printf("Inside serialize...\n");
   raft::serialize_scalar(res, os, serialization_version);
   raft::serialize_scalar(res, os, index_.size());
   raft::serialize_scalar(res, os, index_.dim());
   raft::serialize_scalar(res, os, index_.graph_degree());
   raft::serialize_scalar(res, os, index_.metric());
 
-  printf("Serializing mdspan\n");
   raft::serialize_mdspan(res, os, index_.graph());
 
   include_dataset &= (index_.data().n_rows() > 0);
 
-  printf("Serializing include dataset\n");
   raft::serialize_scalar(res, os, include_dataset);
   if (include_dataset) {
     RAFT_LOG_INFO("Saving CAGRA index with dataset");
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 31a26aba4c..8c5767c50c 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -32,8 +32,6 @@
 #include <raft/util/cudart_utils.hpp>
 #include <raft/util/pow2_utils.cuh>
 
-#include <rmm/device_uvector.hpp>
-
 #include <cub/cub.cuh>
 #include <cuda_runtime.h>
 #include <thrust/execution_policy.h>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 3b0e475f4b..804f25d028 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1673,6 +1673,9 @@ auto build(raft::resources const& handle,
                   std::is_same_v<T, int8_t>,
                 "Unsupported data type");
 
+  std::cout << "using ivf_pq::index_params nrows " << (int)dataset.extent(0) << ", dim "
+            << (int)dataset.extent(1) << ", n_lits " << (int)params.n_lists << ", pq_dim "
+            << (int)params.pq_dim << std::endl;
   RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
   RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
 
diff --git a/cpp/src/neighbors/ivf_pq_index.cpp b/cpp/src/neighbors/ivf_pq_index.cpp
index 286838387c..ee6c5dfb43 100644
--- a/cpp/src/neighbors/ivf_pq_index.cpp
+++ b/cpp/src/neighbors/ivf_pq_index.cpp
@@ -17,6 +17,21 @@
 #include <cuvs/neighbors/ivf_pq.hpp>
 
 namespace cuvs::neighbors::ivf_pq {
+index_params index_params::from_dataset(raft::matrix_extent<int64_t> dataset,
+                                        cuvs::distance::DistanceType metric)
+{
+  index_params params;
+  params.n_lists =
+    dataset.extent(0) < 4 * 2500 ? 4 : static_cast<uint32_t>(std::sqrt(dataset.extent(0)));
+  params.n_lists = std::min<uint32_t>(params.n_lists, dataset.extent(0));
+  params.pq_dim =
+    raft::round_up_safe(static_cast<uint32_t>(dataset.extent(1) / 4), static_cast<uint32_t>(8));
+  if (params.pq_dim == 0) params.pq_dim = 8;
+  params.pq_bits                  = 8;
+  params.kmeans_trainset_fraction = dataset.extent(0) < 10000 ? 1 : 0.1;
+  params.metric                   = metric;
+  return params;
+}
 
 template <typename IdxT>
 index<IdxT>::index(raft::resources const& handle, const index_params& params, uint32_t dim)
diff --git a/cpp/src/neighbors/nn_descent.cu b/cpp/src/neighbors/nn_descent.cu
new file mode 100644
index 0000000000..3fa87d7ab9
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent.cu
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/nn_descent.cuh"
+#include <cuvs/neighbors/nn_descent.hpp>
+#include <raft/core/logger.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+
+/**
+ * @brief Test if we have enough GPU memory to run NN descent algorithm.
+ * *
+ * @param res
+ * @param dataset shape of the dataset
+ * @param idx_size the size of index type in bytes
+ * @return true if enough GPU memory could be allocated
+ * @return false otherwise
+ */
+bool has_enough_device_memory(raft::resources const& res,
+                              raft::matrix_extent<int64_t> dataset,
+                              size_t idx_size)
+{
+  using DistData_t = float;
+  try {
+    auto d_data_ = raft::make_device_matrix<__half, size_t, raft::row_major>(
+      res, dataset.extent(0), dataset.extent(1));
+    auto l2_norms_     = raft::make_device_vector<DistData_t, size_t>(res, dataset.extent(0));
+    auto graph_buffer_ = raft::make_device_vector<uint32_t, size_t>(
+      res, dataset.extent(0) * idx_size * detail::DEGREE_ON_DEVICE);
+
+    auto dists_buffer_ = raft::make_device_matrix<DistData_t, size_t, raft::row_major>(
+      res, dataset.extent(0), detail::DEGREE_ON_DEVICE);
+
+    auto d_locks_ = raft::make_device_vector<int, size_t>(res, dataset.extent(0));
+
+    auto d_list_sizes_new_ = raft::make_device_vector<int2, size_t>(res, dataset.extent(0));
+    auto d_list_sizes_old_ = raft::make_device_vector<int2, size_t>(res, dataset.extent(0));
+    RAFT_LOG_DEBUG("Sufficient memory for NN descent");
+    return true;
+  } catch (std::bad_alloc& e) {
+    RAFT_LOG_DEBUG("Insufficient memory for NN descent");
+    return false;
+  }
+}
+
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/src/neighbors/refine/refine_host.hpp b/cpp/src/neighbors/refine/refine_host.hpp
index 4f293e5b81..dcaccd5a87 100644
--- a/cpp/src/neighbors/refine/refine_host.hpp
+++ b/cpp/src/neighbors/refine/refine_host.hpp
@@ -101,32 +101,36 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
 
   if (size_t(suggested_n_threads) > n_queries) { suggested_n_threads = n_queries; }
 
-#pragma omp parallel num_threads(suggested_n_threads)
   {
-    std::vector<std::tuple<DistanceT, IdxT>> refined_pairs(orig_k);
-    for (size_t i = omp_get_thread_num(); i < n_queries; i += omp_get_num_threads()) {
-      // Compute the refined distance using original dataset vectors
-      const DataT* query = queries.data_handle() + dim * i;
-      for (size_t j = 0; j < orig_k; j++) {
-        IdxT id            = neighbor_candidates(i, j);
-        DistanceT distance = 0.0;
-        if (static_cast<size_t>(id) >= n_rows) {
-          distance = std::numeric_limits<DistanceT>::max();
-        } else {
-          const DataT* row = dataset.data_handle() + dim * id;
-          for (size_t k = 0; k < dim; k++) {
-            distance += DC::template eval<DistanceT>(query[k], row[k]);
+    std::vector<std::vector<std::tuple<DistanceT, IdxT>>> refined_pairs(
+      suggested_n_threads, std::vector<std::tuple<DistanceT, IdxT>>(orig_k));
+#pragma omp parallel num_threads(suggested_n_threads)
+    {
+      auto tid = omp_get_thread_num();
+      for (size_t i = tid; i < n_queries; i += omp_get_num_threads()) {
+        // Compute the refined distance using original dataset vectors
+        const DataT* query = queries.data_handle() + dim * i;
+        for (size_t j = 0; j < orig_k; j++) {
+          IdxT id            = neighbor_candidates(i, j);
+          DistanceT distance = 0.0;
+          if (static_cast<size_t>(id) >= n_rows) {
+            distance = std::numeric_limits<DistanceT>::max();
+          } else {
+            const DataT* row = dataset.data_handle() + dim * id;
+            for (size_t k = 0; k < dim; k++) {
+              distance += DC::template eval<DistanceT>(query[k], row[k]);
+            }
           }
+          refined_pairs[tid][j] = std::make_tuple(distance, id);
         }
-        refined_pairs[j] = std::make_tuple(distance, id);
-      }
-      // Sort the query neighbors by their refined distances
-      std::sort(refined_pairs.begin(), refined_pairs.end());
-      // Store first refined_k neighbors
-      for (size_t j = 0; j < refined_k; j++) {
-        indices(i, j) = std::get<1>(refined_pairs[j]);
-        if (distances.data_handle() != nullptr) {
-          distances(i, j) = DC::template postprocess(std::get<0>(refined_pairs[j]));
+        // Sort the query neighbors by their refined distances
+        std::sort(refined_pairs[tid].begin(), refined_pairs[tid].end());
+        // Store first refined_k neighbors
+        for (size_t j = 0; j < refined_k; j++) {
+          indices(i, j) = std::get<1>(refined_pairs[tid][j]);
+          if (distances.data_handle() != nullptr) {
+            distances(i, j) = DC::template postprocess(std::get<0>(refined_pairs[tid][j]));
+          }
         }
       }
     }
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 017defe390..ad7f327859 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -140,6 +140,16 @@ void GenerateRoundingErrorFreeDataset(const raft::resources& handle,
   GenerateRoundingErrorFreeDataset_kernel<<<grid_size, block_size, 0, cuda_stream>>>(
     ptr, size, resolution);
 }
+
+enum class graph_build_algo {
+  /* Use IVF-PQ to build all-neighbors knn graph */
+  IVF_PQ,
+  /* Experimental, use NN-Descent to build all-neighbors knn graph */
+  NN_DESCENT,
+  /* Choose default automatically */
+  AUTO
+};
+
 }  // namespace
 
 struct AnnCagraInputs {
@@ -158,18 +168,22 @@ struct AnnCagraInputs {
   bool include_serialized_dataset;
   // std::optional<double>
   double min_recall;  // = std::nullopt;
-  std::optional<vpq_params> compression = std::nullopt;
+  std::optional<float> ivf_pq_search_refine_ratio = std::nullopt;
+  std::optional<vpq_params> compression           = std::nullopt;
 };
 
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
 {
   std::vector<std::string> algo       = {"single-cta", "multi_cta", "multi_kernel", "auto"};
-  std::vector<std::string> build_algo = {"IVF_PQ", "NN_DESCENT"};
+  std::vector<std::string> build_algo = {"IVF_PQ", "NN_DESCENT", "AUTO"};
   os << "{n_queries=" << p.n_queries << ", dataset shape=" << p.n_rows << "x" << p.dim
      << ", k=" << p.k << ", " << algo.at((int)p.algo) << ", max_queries=" << p.max_queries
      << ", itopk_size=" << p.itopk_size << ", search_width=" << p.search_width
      << ", metric=" << static_cast<int>(p.metric) << (p.host_dataset ? ", host" : ", device")
      << ", build_algo=" << build_algo.at((int)p.build_algo);
+  if ((int)p.build_algo == 0 && p.ivf_pq_search_refine_ratio) {
+    os << "(refine_rate=" << *p.ivf_pq_search_refine_ratio << ')';
+  }
   if (p.compression.has_value()) {
     auto vpq = p.compression.value();
     os << ", pq_bits=" << vpq.pq_bits << ", pq_dim=" << vpq.pq_dim
@@ -226,7 +240,26 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
         cagra::index_params index_params;
         index_params.metric = ps.metric;  // Note: currently ony the cagra::index_params metric is
                                           // not used for knn_graph building.
-        index_params.build_algo  = ps.build_algo;
+        switch (ps.build_algo) {
+          case graph_build_algo::IVF_PQ:
+            index_params.graph_build_params =
+              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            if (ps.ivf_pq_search_refine_ratio) {
+              std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
+                index_params.graph_build_params)
+                .refinement_rate = *ps.ivf_pq_search_refine_ratio;
+            }
+            break;
+          case graph_build_algo::NN_DESCENT: {
+            index_params.graph_build_params =
+              graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
+            break;
+          }
+          case graph_build_algo::AUTO:
+            // do nothing
+            break;
+        };
+
         index_params.compression = ps.compression;
         cagra::search_params search_params;
         search_params.algo        = ps.algo;
@@ -407,21 +440,21 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
-  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
-    {100},
-    {10000, 20000},
-    {32},
-    {10},
-    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
-    {search_algo::AUTO},
-    {10},
-    {0},  // team_size
-    {64},
-    {1},
-    {cuvs::distance::DistanceType::L2Expanded},
-    {false, true},
-    {false},
-    {0.995});
+  inputs2 =
+    raft::util::itertools::product<AnnCagraInputs>({100},
+                                                   {10000, 20000},
+                                                   {32},
+                                                   {10},
+                                                   {graph_build_algo::AUTO},
+                                                   {search_algo::AUTO},
+                                                   {10},
+                                                   {0},  // team_size
+                                                   {64},
+                                                   {1},
+                                                   {cuvs::distance::DistanceType::L2Expanded},
+                                                   {false, true},
+                                                   {false},
+                                                   {0.985});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // a few PQ configurations
@@ -452,6 +485,25 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     }
   }
 
+  // refinement options
+  inputs2 =
+    raft::util::itertools::product<AnnCagraInputs>({100},
+                                                   {5000},
+                                                   {32, 64},
+                                                   {16},
+                                                   {graph_build_algo::IVF_PQ},
+                                                   {search_algo::AUTO},
+                                                   {10},
+                                                   {0},  // team_size
+                                                   {64},
+                                                   {1},
+                                                   {cuvs::distance::DistanceType::L2Expanded},
+                                                   {false, true},
+                                                   {false},
+                                                   {0.99},
+                                                   {1.0f, 2.0f, 3.0f});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
   return inputs;
 }
 

From 66959b1e1531f5a080ac19a670f1576a47d25355 Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Thu, 30 May 2024 23:16:41 +0200
Subject: [PATCH 073/623] Fix CAGRA OOM handling (#167)

Fixes handling OOM error during CAGRA index creation, that was introduced in #131.

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/167
---
 cpp/src/neighbors/detail/cagra/cagra_build.cuh | 9 ++++++---
 cpp/src/neighbors/nn_descent.cu                | 4 ++++
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index a50ac2e651..43e720077f 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -480,9 +480,12 @@ index<T, IdxT> build(
     RAFT_LOG_DEBUG("Insufficient GPU memory to construct CAGRA index with dataset on GPU");
     // We just add the graph. User is expected to update dataset separately (e.g allocating in
     // managed memory).
-    index<T, IdxT> idx(res, params.metric);
-    idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
-    return idx;
+  } catch (raft::logic_error& e) {
+    // The memory error can also manifest as logic_error.
+    RAFT_LOG_DEBUG("Insufficient GPU memory to construct CAGRA index with dataset on GPU");
   }
+  index<T, IdxT> idx(res, params.metric);
+  idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
+  return idx;
 }
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/nn_descent.cu b/cpp/src/neighbors/nn_descent.cu
index 3fa87d7ab9..548ed00f3b 100644
--- a/cpp/src/neighbors/nn_descent.cu
+++ b/cpp/src/neighbors/nn_descent.cu
@@ -18,6 +18,7 @@
 #include <cuvs/neighbors/nn_descent.hpp>
 #include <raft/core/logger.hpp>
 
+using namespace raft;
 namespace cuvs::neighbors::nn_descent {
 
 /**
@@ -53,6 +54,9 @@ bool has_enough_device_memory(raft::resources const& res,
   } catch (std::bad_alloc& e) {
     RAFT_LOG_DEBUG("Insufficient memory for NN descent");
     return false;
+  } catch (raft::logic_error& e) {
+    RAFT_LOG_DEBUG("Insufficient memory for NN descent (logic error)");
+    return false;
   }
 }
 

From 09d674b80b34fd22ead8e4a36b013df84eaeb2a1 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Fri, 31 May 2024 10:46:11 -0400
Subject: [PATCH 074/623] Use rapids-build-backend (#145)

Contributes to rapidsai/build-planning#31.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Jake Awe (https://github.com/AyodeAwe)
  - Dante Gama Dessavre (https://github.com/dantegd)
  - James Lamb (https://github.com/jameslamb)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/145
---
 .pre-commit-config.yaml                       |  2 +-
 build.sh                                      |  2 +-
 ci/build_python.sh                            |  3 --
 ci/build_wheel.sh                             | 33 +-----------
 ci/release/update-version.sh                  |  8 +--
 .../all_cuda-118_arch-aarch64.yaml            |  5 +-
 .../all_cuda-118_arch-x86_64.yaml             |  5 +-
 .../all_cuda-122_arch-aarch64.yaml            |  5 +-
 .../all_cuda-122_arch-x86_64.yaml             |  5 +-
 conda/recipes/cuvs/meta.yaml                  |  1 +
 dependencies.yaml                             | 52 ++++++++++++++-----
 python/cuvs/cuvs/_version.py                  | 20 +++++--
 python/cuvs/cuvs/test/test_version.py         | 12 +++++
 python/cuvs/pyproject.toml                    | 25 +++++----
 14 files changed, 102 insertions(+), 76 deletions(-)
 create mode 100644 python/cuvs/cuvs/test/test_version.py

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index b1de648b02..72e7dffb32 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -93,7 +93,7 @@ repos:
                 args: ["--toml", "pyproject.toml"]
                 exclude: (?x)^(^CHANGELOG.md$)
       - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.8.0
+        rev: v1.13.11
         hooks:
             - id: rapids-dependency-file-generator
               args: ["--clean"]
diff --git a/build.sh b/build.sh
index 822b845904..678df7b352 100755
--- a/build.sh
+++ b/build.sh
@@ -391,7 +391,7 @@ fi
 if (( ${NUMARGS} == 0 )) || hasArg python; then
     SKBUILD_CMAKE_ARGS="${SKBUILD_EXTRA_CMAKE_ARGS}" \
         SKBUILD_BUILD_OPTIONS="-j${PARALLEL_LEVEL}" \
-        python -m pip install --no-build-isolation --no-deps -vvv ${REPODIR}/python/cuvs
+        python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs
 fi
 
 # Build the cuvs Rust bindings
diff --git a/ci/build_python.sh b/ci/build_python.sh
index c50a413c6e..82ecba43ac 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -21,12 +21,9 @@ package_dir="python"
 CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
 
 version=$(rapids-generate-version)
-git_commit=$(git rev-parse HEAD)
 export RAPIDS_PACKAGE_VERSION=${version}
 echo "${version}" > VERSION
 
-sed -i "/^__git_commit__/ s/= .*/= \"${git_commit}\"/g" "${package_dir}/${package_name}/${package_name}/_version.py"
-
 # TODO: Remove `--no-test` flags once importing on a CPU
 # node works correctly
 rapids-conda-retry mambabuild \
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 4f9f96d19c..29d9ce4840 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -10,40 +10,9 @@ underscore_package_name=$(echo "${package_name}" | tr "-" "_")
 source rapids-configure-sccache
 source rapids-date-string
 
-version=$(rapids-generate-version)
-git_commit=$(git rev-parse HEAD)
-
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
 
-# This is the version of the suffix with a preceding hyphen. It's used
-# everywhere except in the final wheel name.
-PACKAGE_CUDA_SUFFIX="-${RAPIDS_PY_CUDA_SUFFIX}"
-
-# Patch project metadata files to include the CUDA version suffix and version override.
-pyproject_file="${package_dir}/pyproject.toml"
-version_file="${package_dir}/${underscore_package_name}/_version.py"
-
-sed -i "s/name = \"${package_name}\"/name = \"${package_name}${PACKAGE_CUDA_SUFFIX}\"/g" ${pyproject_file}
-echo "${version}" > VERSION
-sed -i "/^__git_commit__ / s/= .*/= \"${git_commit}\"/g" ${version_file}
-
-# For nightlies we want to ensure that we're pulling in alphas as well. The
-# easiest way to do so is to augment the spec with a constraint containing a
-# min alpha version that doesn't affect the version bounds but does allow usage
-# of alpha versions for that dependency without --pre
-alpha_spec=''
-if ! rapids-is-release-build; then
-    alpha_spec=',>=0.0.0a0'
-fi
-
-sed -r -i "s/rmm(.*)\"/rmm${PACKAGE_CUDA_SUFFIX}\1${alpha_spec}\"/g" ${pyproject_file}
-sed -r -i "s/pylibraft(.*)\"/pylibraft${PACKAGE_CUDA_SUFFIX}\1${alpha_spec}\"/g" ${pyproject_file}
-
-
-if [[ $PACKAGE_CUDA_SUFFIX == "-cu12" ]]; then
-    sed -i "s/cuda-python[<=>\.,0-9a]*/cuda-python>=12.0,<13.0a0/g" ${pyproject_file}
-    sed -i "s/cupy-cuda11x/cupy-cuda12x/g" ${pyproject_file}
-fi
+rapids-generate-version > VERSION
 
 cd "${package_dir}"
 
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 0082461c59..69fbb746c8 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -58,15 +58,15 @@ DEPENDENCIES=(
 )
 for FILE in dependencies.yaml conda/environments/*.yaml; do
   for DEP in "${DEPENDENCIES[@]}"; do
-    sed_runner "/-.* ${DEP}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}\.*/g" ${FILE};
+    sed_runner "/-.* ${DEP}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}\.*,>=0.0.0a0/g" ${FILE};
   done
-  sed_runner "/-.* ucx-py==/ s/==.*/==${NEXT_UCX_PY_SHORT_TAG_PEP440}\.*/g" ${FILE};
+  sed_runner "/-.* ucx-py==/ s/==.*/==${NEXT_UCX_PY_SHORT_TAG_PEP440}\.*,>=0.0.0a0/g" ${FILE};
 done
 for FILE in python/*/pyproject.toml; do
   for DEP in "${DEPENDENCIES[@]}"; do
-    sed_runner "/\"${DEP}==/ s/==.*\"/==${NEXT_SHORT_TAG_PEP440}.*\"/g" ${FILE}
+    sed_runner "/\"${DEP}==/ s/==.*\"/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" ${FILE}
   done
-  sed_runner "/\"ucx-py==/ s/==.*\"/==${NEXT_UCX_PY_SHORT_TAG_PEP440}.*\"/g" ${FILE}
+  sed_runner "/\"ucx-py==/ s/==.*\"/==${NEXT_UCX_PY_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" ${FILE}
 done
 
 for FILE in .github/workflows/*.yaml; do
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 3540039dcd..9a50bb8056 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -43,11 +43,12 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*
+- pylibraft==24.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*
+- rmm==24.8.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 51170b40d5..a0581ff458 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -43,11 +43,12 @@ dependencies:
 - nvcc_linux-64=11.8
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*
+- pylibraft==24.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*
+- rmm==24.8.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index b430fa837b..9b151e4b7e 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -39,11 +39,12 @@ dependencies:
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*
+- pylibraft==24.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*
+- rmm==24.8.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 10913378e4..d4b04daf07 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -39,11 +39,12 @@ dependencies:
 - numpydoc
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*
+- pylibraft==24.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*
+- rmm==24.8.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index 349923bf0e..b0f0c3f396 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -55,6 +55,7 @@ requirements:
     - libcuvs {{ version }}
     - python x.x
     - rmm ={{ minor_version }}
+    - rapids-build-backend>=0.3.0,<0.4.0.dev0
     - scikit-build-core >=0.7.0
     - setuptools
   run:
diff --git a/dependencies.yaml b/dependencies.yaml
index 97dae890e0..22325d5dfb 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -7,6 +7,7 @@ files:
       arch: [x86_64, aarch64]
     includes:
       - build
+      - rapids_build
       - build_py_cuvs
       - cuda
       - cuda_version
@@ -46,13 +47,13 @@ files:
       - docs
       - py_version
       - rust
-      - build
+      - rapids_build
       - cuda
   rust:
     output: none
     includes:
       - cuda_version
-      - build
+      - rapids_build
       - cuda
       - rust
   py_build_py_cuvs:
@@ -62,6 +63,14 @@ files:
       table: build-system
     includes:
       - build
+  py_rapids_build_py_cuvs:
+    output: pyproject
+    pyproject_dir: python/cuvs
+    extras:
+      table: tool.rapids-build-backend
+      key: requires
+    includes:
+      - rapids_build
       - build_py_cuvs
   py_run_py_cuvs:
     output: pyproject
@@ -88,6 +97,17 @@ channels:
   - nvidia
 dependencies:
   build:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - rapids-build-backend>=0.3.0,<0.4.0.dev0
+      - output_types: [conda]
+        packages:
+          - scikit-build-core>=0.7.0
+      - output_types: [requirements, pyproject]
+        packages:
+          - scikit-build-core[pyproject]>=0.7.0
+  rapids_build:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
@@ -99,10 +119,6 @@ dependencies:
           - c-compiler
           - cxx-compiler
           - nccl>=2.9.9
-          - scikit-build-core>=0.7.0
-      - output_types: [requirements, pyproject]
-        packages:
-          - scikit-build-core[pyproject]>=0.7.0
     specific:
       - output_types: conda
         matrices:
@@ -141,8 +157,8 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
-          - &rmm_conda rmm==24.8.*
-          - &pylibraft_conda pylibraft==24.8.*
+          - &rmm_conda rmm==24.8.*,>=0.0.0a0
+          - &pylibraft_conda pylibraft==24.8.*,>=0.0.0a0
           - dlpack>=0.8,<1.0
       - output_types: requirements
         packages:
@@ -157,19 +173,23 @@ dependencies:
               cuda: "12.*"
             packages:
               - &cuda_python12 cuda-python>=12.0,<13.0a0
-          - matrix: # All CUDA 11 versions
+          - matrix:
+              cuda: "11.*"
             packages:
               - &cuda_python11 cuda-python>=11.7.1,<12.0a0
+          - matrix:
+            packages:
+              - &cuda_python cuda-python
       - output_types: [requirements, pyproject]
         matrices:
           - matrix: {cuda: "12.*"}
             packages:
-              - &rmm_cu12 rmm-cu12==24.8.*
-              - &pylibraft_cu12 pylibraft-cu12==24.8.*
+              - &rmm_cu12 rmm-cu12==24.8.*,>=0.0.0a0
+              - &pylibraft_cu12 pylibraft-cu12==24.8.*,>=0.0.0a0
           - matrix: {cuda: "11.*"}
             packages:
-              - &rmm_cu11 rmm-cu11==24.8.*
-              - &pylibraft_cu11 pylibraft-cu11==24.8.*
+              - &rmm_cu11 rmm-cu11==24.8.*,>=0.0.0a0
+              - &pylibraft_cu11 pylibraft-cu11==24.8.*,>=0.0.0a0
           - {matrix: null, packages: [*rmm_conda, *pylibraft_conda] }
   checks:
     common:
@@ -373,9 +393,13 @@ dependencies:
               cuda: "12.*"
             packages:
               - *cuda_python12
-          - matrix: # All CUDA 11 versions
+          - matrix:
+              cuda: "11.*"
             packages:
               - *cuda_python11
+          - matrix:
+            packages:
+              - *cuda_python
       - output_types: [requirements, pyproject]
         matrices:
           - matrix: {cuda: "12.*"}
diff --git a/python/cuvs/cuvs/_version.py b/python/cuvs/cuvs/_version.py
index 72ab982ffb..6436ee0127 100644
--- a/python/cuvs/cuvs/_version.py
+++ b/python/cuvs/cuvs/_version.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,10 +13,22 @@
 # limitations under the License.
 #
 
-
 import importlib.resources
 
 __version__ = (
-    importlib.resources.files("cuvs").joinpath("VERSION").read_text().strip()
+    importlib.resources.files(__package__)
+    .joinpath("VERSION")
+    .read_text()
+    .strip()
 )
-__git_commit__ = ""
+try:
+    __git_commit__ = (
+        importlib.resources.files(__package__)
+        .joinpath("GIT_COMMIT")
+        .read_text()
+        .strip()
+    )
+except FileNotFoundError:
+    __git_commit__ = ""
+
+__all__ = ["__git_commit__", "__version__"]
diff --git a/python/cuvs/cuvs/test/test_version.py b/python/cuvs/cuvs/test/test_version.py
new file mode 100644
index 0000000000..9f2c0ddfc6
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_version.py
@@ -0,0 +1,12 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+import cuvs
+
+
+def test_version_constants_are_populated():
+    # __git_commit__ will only be non-empty in a built distribution
+    assert isinstance(cuvs.__git_commit__, str)
+
+    # __version__ should always be non-empty
+    assert isinstance(cuvs.__version__, str)
+    assert len(cuvs.__version__) > 0
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index a9660a8ca0..4fda501554 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -15,15 +15,10 @@
 [build-system]
 
 requires = [
-    "cmake>=3.26.4",
-    "cuda-python>=11.7.1,<12.0a0",
-    "cython>=3.0.0",
-    "ninja",
-    "pylibraft==24.8.*",
-    "rmm==24.8.*",
+    "rapids-build-backend>=0.3.0,<0.4.0.dev0",
     "scikit-build-core[pyproject]>=0.7.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-build-backend = "scikit_build_core.build"
+build-backend = "rapids_build_backend.build"
 
 [project]
 name = "cuvs"
@@ -36,9 +31,9 @@ authors = [
 license = { text = "Apache 2.0" }
 requires-python = ">=3.9"
 dependencies = [
-    "cuda-python>=11.7.1,<12.0a0",
+    "cuda-python",
     "numpy>=1.23,<2.0a0",
-    "pylibraft==24.8.*",
+    "pylibraft==24.8.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -121,6 +116,18 @@ provider = "scikit_build_core.metadata.regex"
 input = "cuvs/VERSION"
 regex = "(?P<value>.*)"
 
+[tool.rapids-build-backend]
+requires = [
+    "cmake>=3.26.4",
+    "cuda-python",
+    "cython>=3.0.0",
+    "ninja",
+    "pylibraft==24.8.*,>=0.0.0a0",
+    "rmm==24.8.*,>=0.0.0a0",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+build-backend = "scikit_build_core.build"
+dependencies-file = "../../dependencies.yaml"
+
 [tool.pytest.ini_options]
 filterwarnings = [
     "error",

From 8055da1739fdc19b0997b7c09c2720356ed1a780 Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Wed, 5 Jun 2024 10:11:59 -0400
Subject: [PATCH 075/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e69de29bb2..ddeafff816 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -0,0 +1,60 @@
+# cuvs 24.06.00 (5 Jun 2024)
+
+## 🐛 Bug Fixes
+
+- Fix CAGRA OOM handling ([#167](https://github.com/rapidsai/cuvs/pull/167)) [@tfeher](https://github.com/tfeher)
+- Pass through raft static CMake var ([#111](https://github.com/rapidsai/cuvs/pull/111)) [@vyasr](https://github.com/vyasr)
+- Fix rust docs build ([#84](https://github.com/rapidsai/cuvs/pull/84)) [@benfred](https://github.com/benfred)
+
+## 📖 Documentation
+
+- chore: update Doxyfile ([#162](https://github.com/rapidsai/cuvs/pull/162)) [@eltociear](https://github.com/eltociear)
+- cuVS docs updates for release ([#161](https://github.com/rapidsai/cuvs/pull/161)) [@cjnolet](https://github.com/cjnolet)
+- update: fix RAFT URL in README ([#91](https://github.com/rapidsai/cuvs/pull/91)) [@hurutoriya](https://github.com/hurutoriya)
+- Update the developer&#39;s guide with new copyright hook ([#81](https://github.com/rapidsai/cuvs/pull/81)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Add `lucene-cuvs` to integrations section of docs ([#73](https://github.com/rapidsai/cuvs/pull/73)) [@cjnolet](https://github.com/cjnolet)
+
+## 🚀 New Features
+
+- Add `refine` to public API ([#154](https://github.com/rapidsai/cuvs/pull/154)) [@lowener](https://github.com/lowener)
+- [FEA] support of prefiltered brute force ([#146](https://github.com/rapidsai/cuvs/pull/146)) [@rhdong](https://github.com/rhdong)
+- Migrate IVF-Flat from RAFT ([#94](https://github.com/rapidsai/cuvs/pull/94)) [@divyegala](https://github.com/divyegala)
+- Migrate IVF-PQ from RAFT to cuVS ([#86](https://github.com/rapidsai/cuvs/pull/86)) [@lowener](https://github.com/lowener)
+
+## 🛠️ Improvements
+
+- Expose serialization to the python / c-api ([#164](https://github.com/rapidsai/cuvs/pull/164)) [@benfred](https://github.com/benfred)
+- Select k instantiations ([#159](https://github.com/rapidsai/cuvs/pull/159)) [@benfred](https://github.com/benfred)
+- fix devcontainer name for codespaces ([#153](https://github.com/rapidsai/cuvs/pull/153)) [@trxcllnt](https://github.com/trxcllnt)
+- Accept host_mdspan for IVF-PQ build and extend ([#148](https://github.com/rapidsai/cuvs/pull/148)) [@tfeher](https://github.com/tfeher)
+- Add pairwise_distance api&#39;s for C, Python and Rust ([#142](https://github.com/rapidsai/cuvs/pull/142)) [@benfred](https://github.com/benfred)
+- Changing RAFT_EXPLICT_* to CUVS_EXPLITI_* ([#141](https://github.com/rapidsai/cuvs/pull/141)) [@cjnolet](https://github.com/cjnolet)
+- Speed-up rust build ([#138](https://github.com/rapidsai/cuvs/pull/138)) [@benfred](https://github.com/benfred)
+- Removing `libraft.so` from libcuvs dependencies ([#132](https://github.com/rapidsai/cuvs/pull/132)) [@cjnolet](https://github.com/cjnolet)
+- CAGRA API update and allow async host refinement ([#131](https://github.com/rapidsai/cuvs/pull/131)) [@mfoerste4](https://github.com/mfoerste4)
+- Fix rust api docs ([#119](https://github.com/rapidsai/cuvs/pull/119)) [@benfred](https://github.com/benfred)
+- Migrate BFKNN from raft ([#118](https://github.com/rapidsai/cuvs/pull/118)) [@benfred](https://github.com/benfred)
+- Fix IVF-PQ helper functions ([#116](https://github.com/rapidsai/cuvs/pull/116)) [@lowener](https://github.com/lowener)
+- Migrate `raft::cluster` to `cuvs::cluster` ([#115](https://github.com/rapidsai/cuvs/pull/115)) [@cjnolet](https://github.com/cjnolet)
+- hide RAFT #pragma deprecation warnings ([#114](https://github.com/rapidsai/cuvs/pull/114)) [@trxcllnt](https://github.com/trxcllnt)
+- Enable Warnings as errors in Python tests ([#102](https://github.com/rapidsai/cuvs/pull/102)) [@mroeschke](https://github.com/mroeschke)
+- Remove libnvjitlink dependency. ([#97](https://github.com/rapidsai/cuvs/pull/97)) [@bdice](https://github.com/bdice)
+- Migrate to `{{ stdlib(&quot;c&quot;) }}` ([#93](https://github.com/rapidsai/cuvs/pull/93)) [@hcho3](https://github.com/hcho3)
+- update: replace to cuvs from RAFT in PULL_REQUEST_TEMPLATE ([#92](https://github.com/rapidsai/cuvs/pull/92)) [@hurutoriya](https://github.com/hurutoriya)
+- Add python and rust bindings for Ivf-Pq ([#90](https://github.com/rapidsai/cuvs/pull/90)) [@benfred](https://github.com/benfred)
+- add --rm and --name to devcontainer run args ([#89](https://github.com/rapidsai/cuvs/pull/89)) [@trxcllnt](https://github.com/trxcllnt)
+- Update pip devcontainers to UCX v1.15.0 ([#88](https://github.com/rapidsai/cuvs/pull/88)) [@trxcllnt](https://github.com/trxcllnt)
+- Remove gtest from dependencies.yaml ([#87](https://github.com/rapidsai/cuvs/pull/87)) [@robertmaynard](https://github.com/robertmaynard)
+- Moving and renaming distance namespaces from raft -&gt; cuvs ([#85](https://github.com/rapidsai/cuvs/pull/85)) [@cjnolet](https://github.com/cjnolet)
+- Use static gtest ([#83](https://github.com/rapidsai/cuvs/pull/83)) [@robertmaynard](https://github.com/robertmaynard)
+- Add python and rust bindings for Ivf-Flat ([#82](https://github.com/rapidsai/cuvs/pull/82)) [@benfred](https://github.com/benfred)
+- Forward merge branch-24.04 to branch-24.06 ([#80](https://github.com/rapidsai/cuvs/pull/80)) [@benfred](https://github.com/benfred)
+- Update devcontainers to use cuda12.2 ([#72](https://github.com/rapidsai/cuvs/pull/72)) [@benfred](https://github.com/benfred)
+- Forward merge branch-24.04 to branch-24.06 ([#71](https://github.com/rapidsai/cuvs/pull/71)) [@benfred](https://github.com/benfred)
+- Enable forward-merger ops-bot plugin ([#70](https://github.com/rapidsai/cuvs/pull/70)) [@benfred](https://github.com/benfred)
+- Adds missing files to `update-version.sh` ([#69](https://github.com/rapidsai/cuvs/pull/69)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Add Cagra-Q compression to the python and rust api&#39;s ([#68](https://github.com/rapidsai/cuvs/pull/68)) [@benfred](https://github.com/benfred)
+- ConfigureCUDA.cmake now sets CUVS_ prefixed variables ([#66](https://github.com/rapidsai/cuvs/pull/66)) [@robertmaynard](https://github.com/robertmaynard)
+- Enable all tests for `arm` jobs ([#63](https://github.com/rapidsai/cuvs/pull/63)) [@galipremsagar](https://github.com/galipremsagar)
+
+

From 38b3147af76adfc7a1fa9de0bbd61d6b9ec6e128 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 5 Jun 2024 12:11:27 -0700
Subject: [PATCH 076/623] Allow serialization on streams (#173)

This change allows serializing to a std::ostream and deserializaing from a std::istream. This also fixes some minor docstring issues in the C++ serialization api's.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/173
---
 cpp/include/cuvs/neighbors/cagra.hpp          | 322 ++++++++++++++++--
 cpp/include/cuvs/neighbors/ivf_flat.hpp       | 122 +++----
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  38 +--
 cpp/src/neighbors/cagra_c.cpp                 |   5 +-
 cpp/src/neighbors/cagra_serialize.cuh         | 172 ++--------
 cpp/src/neighbors/cagra_serialize_float.cu    |  61 +---
 cpp/src/neighbors/cagra_serialize_int8.cu     |  61 +---
 cpp/src/neighbors/cagra_serialize_uint8.cu    |  61 +---
 .../detail/cagra/cagra_serialize.cuh          |  15 +-
 .../neighbors/ivf_flat/generate_ivf_flat.py   |  35 +-
 .../neighbors/ivf_flat/ivf_flat_serialize.cuh |  28 ++
 .../ivf_flat_serialize_float_int64_t.cu       |  31 --
 .../ivf_flat_serialize_int8_t_int64_t.cu      |  31 --
 .../ivf_flat_serialize_uint8_t_int64_t.cu     |  31 --
 .../neighbors/ivf_pq/ivf_pq_deserialize.cu    |  11 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu  |  12 +-
 cpp/test/neighbors/ann_cagra.cuh              |   4 +-
 cpp/test/neighbors/ann_ivf_flat.cuh           |   4 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |   4 +-
 19 files changed, 461 insertions(+), 587 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index c5f2ab87d9..b0668eeb0c 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -727,55 +727,321 @@ void search(raft::resources const& res,
  * @defgroup cagra_cpp_serialize CAGRA serialize functions
  * @{
  */
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::cagra::index<float, uint32_t>& index,
-                    bool include_dataset = true);
-
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::cagra::index<float, uint32_t>* index);
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ *
+ */
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::cagra::index<float, uint32_t>& index,
+               bool include_dataset = true);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+
+ * cuvs::neighbors::cagra::index<float, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index the cagra index
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::cagra::index<float, uint32_t>* index);
+
+/**
+ * Write the index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::cagra::index<float, uint32_t>& index,
                bool include_dataset = true);
 
+/**
+ * Load index from input stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * cuvs::neighbors::cagra::index<float, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, is, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ * @param[out] index the cagra index
+ */
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& is,
                  cuvs::neighbors::cagra::index<float, uint32_t>* index);
 
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
-                    bool include_dataset = true);
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
+               bool include_dataset = true);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+
+ * cuvs::neighbors::cagra::index<int8_t, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index the cagra index
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::cagra::index<int8_t, uint32_t>* index);
 
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::cagra::index<int8_t, uint32_t>* index);
+/**
+ * Write the index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
                bool include_dataset = true);
 
+/**
+ * Load index from input stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * cuvs::neighbors::cagra::index<int8_t, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, is, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ * @param[out] index the cagra index
+ */
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& is,
                  cuvs::neighbors::cagra::index<int8_t, uint32_t>* index);
 
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
-                    bool include_dataset = true);
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
+               bool include_dataset = true);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
 
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::cagra::index<uint8_t, uint32_t>* index);
+ * cuvs::neighbors::cagra::index<uint8_t, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index the cagra index
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::cagra::index<uint8_t, uint32_t>* index);
+
+/**
+ * Write the index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
                bool include_dataset = true);
 
+/**
+ * Load index from input stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * cuvs::neighbors::cagra::index<uint8_t, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, is, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ * @param[out] index the cagra index
+ */
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& is,
                  cuvs::neighbors::cagra::index<uint8_t, uint32_t>* index);
 /**
  * @}
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index 38c7be612d..b5f3b54fac 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -1197,7 +1197,7 @@ void search_with_filtering(
  * // create a string with a filepath
  * std::string filename("/path/to/index");
  * // create an index with `auto index = ivf_flat::build(...);`
- * cuvs::serialize_file(handle, filename, index);
+ * cuvs::neighbors::ivf_flat::serialize(handle, filename, index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1205,9 +1205,9 @@ void search_with_filtering(
  * @param[in] index IVF-Flat index
  *
  */
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::ivf_flat::index<float, int64_t>& index);
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::ivf_flat::index<float, int64_t>& index);
 
 /**
  * Load index from file.
@@ -1225,7 +1225,7 @@ void serialize_file(raft::resources const& handle,
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
  * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
- * cuvs::deserialize_file(handle, filename, &index);
+ * cuvs::neighbors::ivf_flat::deserialize(handle, filename, &index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1233,12 +1233,12 @@ void serialize_file(raft::resources const& handle,
  * @param[in] index IVF-Flat index
  *
  */
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::ivf_flat::index<float, int64_t>* index);
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_flat::index<float, int64_t>* index);
 
 /**
- * Write the index to an output string
+ * Write the index to an output stream
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1248,23 +1248,23 @@ void deserialize_file(raft::resources const& handle,
  *
  * raft::resources handle;
  *
- * // create an output string
- * std::string str;
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
  * // create an index with `auto index = ivf_flat::build(...);`
- * cuvs::serialize(handle, str, index);
+ * cuvs::neighbors::ivf_flat::serialize(handle, os, index);
  * @endcode
  *
  * @param[in] handle the raft handle
- * @param[out] str output string
+ * @param[in] os output stream
  * @param[in] index IVF-Flat index
  *
  */
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::ivf_flat::index<float, int64_t>& index);
 
 /**
- * Load index from input string
+ * Load index from input stream
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1274,21 +1274,21 @@ void serialize(raft::resources const& handle,
  *
  * raft::resources handle;
  *
- * // create an input string
- * std::string str;
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
  * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
- * auto index = cuvs::deserialize(handle, str, &index);
+ * cuvs::neighbors::ivf_flat::deserialize(handle, is, &index);
  * @endcode
  *
  * @param[in] handle the raft handle
- * @param[in] str output string
+ * @param[in] is input stream
  * @param[in] index IVF-Flat index
  *
  */
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& is,
                  cuvs::neighbors::ivf_flat::index<float, int64_t>* index);
 
 /**
@@ -1305,7 +1305,7 @@ void deserialize(raft::resources const& handle,
  * // create a string with a filepath
  * std::string filename("/path/to/index");
  * // create an index with `auto index = ivf_flat::build(...);`
- * cuvs::serialize_file(handle, filename, index);
+ * cuvs::neighbors::ivf_flat::serialize(handle, filename, index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1313,9 +1313,9 @@ void deserialize(raft::resources const& handle,
  * @param[in] index IVF-Flat index
  *
  */
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index);
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index);
 
 /**
  * Load index from file.
@@ -1333,7 +1333,7 @@ void serialize_file(raft::resources const& handle,
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
  * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
- * cuvs::deserialize_file(handle, filename, &index);
+ * cuvs::neighbors::ivf_flat::deserialize(handle, filename, &index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1341,12 +1341,12 @@ void serialize_file(raft::resources const& handle,
  * @param[in] index IVF-Flat index
  *
  */
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::ivf_flat::index<int8_t, int64_t>* index);
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_flat::index<int8_t, int64_t>* index);
 
 /**
- * Write the index to an output string
+ * Write the index to an output stream
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1356,23 +1356,23 @@ void deserialize_file(raft::resources const& handle,
  *
  * raft::resources handle;
  *
- * // create an output string
- * std::string str;
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
  * // create an index with `auto index = ivf_flat::build(...);`
- * cuvs::serialize(handle, str, index);
+ * cuvs::neighbors::ivf_flat::serialize(handle, os, index);
  * @endcode
  *
  * @param[in] handle the raft handle
- * @param[out] str output string
+ * @param[in] os output stream
  * @param[in] index IVF-Flat index
  *
  */
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index);
 
 /**
- * Load index from input string
+ * Load index from input stream
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1382,21 +1382,21 @@ void serialize(raft::resources const& handle,
  *
  * raft::resources handle;
  *
- * // create an input string
- * std::string str;
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
  * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
- * auto index = cuvs::deserialize(handle, str, &index);
+ * cuvs::neighbors::ivf_flat::deserialize(handle, is, &index);
  * @endcode
  *
  * @param[in] handle the raft handle
- * @param[in] str output string
+ * @param[in] is input stream
  * @param[in] index IVF-Flat index
  *
  */
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& is,
                  cuvs::neighbors::ivf_flat::index<int8_t, int64_t>* index);
 
 /**
@@ -1413,7 +1413,7 @@ void deserialize(raft::resources const& handle,
  * // create a string with a filepath
  * std::string filename("/path/to/index");
  * // create an index with `auto index = ivf_flat::build(...);`
- * cuvs::serialize_file(handle, filename, index);
+ * cuvs::neighbors::ivf_flat::serialize(handle, filename, index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1421,9 +1421,9 @@ void deserialize(raft::resources const& handle,
  * @param[in] index IVF-Flat index
  *
  */
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index);
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index);
 
 /**
  * Load index from file.
@@ -1441,7 +1441,7 @@ void serialize_file(raft::resources const& handle,
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
  * // create an empty index with ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
- * cuvs::deserialize_file(handle, filename, &index);
+ * cuvs::neighbors::ivf_flat::deserialize(handle, filename, &index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1449,12 +1449,12 @@ void serialize_file(raft::resources const& handle,
  * @param[in] index IVF-Flat index
  *
  */
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* index);
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* index);
 
 /**
- * Write the index to an output string
+ * Write the index to an output stream
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1464,23 +1464,23 @@ void deserialize_file(raft::resources const& handle,
  *
  * raft::resources handle;
  *
- * // create an output string
- * std::string str;
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
  * // create an index with `auto index = ivf_flat::build(...);`
- * cuvs::serialize(handle, str, index);
+ * cuvs::neighbors::ivf_flat::serialize(handle, os, index);
  * @endcode
  *
  * @param[in] handle the raft handle
- * @param[out] str output string
+ * @param[in] os output stream
  * @param[in] index IVF-Flat index
  *
  */
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index);
 
 /**
- * Load index from input string
+ * Load index from input stream
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1490,21 +1490,21 @@ void serialize(raft::resources const& handle,
  *
  * raft::resources handle;
  *
- * // create an input string
- * std::string str;
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
  * using T    = float; // data element type
  * using IdxT = int64_t; // type of the index
  * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
- * auto index = cuvs::deserialize(handle, str, &index);
+ * cuvs::neighbors::ivf_flat::deserialize(handle, is, &index);
  * @endcode
  *
  * @param[in] handle the raft handle
- * @param[in] str output string
+ * @param[in] is input stream
  * @param[in] index IVF-Flat index
  *
  */
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& is,
                  cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>* index);
 
 /**
@@ -1866,4 +1866,4 @@ void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index);
  */
 
 }  // namespace helpers
-}  // namespace cuvs::neighbors::ivf_flat
\ No newline at end of file
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index d7696a37b9..ea9e8b0aee 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -1308,26 +1308,26 @@ void search_with_filtering(
  * @{
  */
 /**
- * Serialize the index to an output string.
+ * Write the index to an output stream
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
  *
  * raft::resources handle;
  *
- * // create an input string
- * std::string str
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
  * // create an index with `auto index = ivf_pq::build(...);`
- * cuvs::serialize(handle, str, index);
+ * cuvs::neighbors::ivf_pq::serialize(handle, os, index);
  * @endcode
  *
  * @param[in] handle the raft handle
- * @param[out] str output string
+ * @param[in] os output stream
  * @param[in] index IVF-PQ index
  *
  */
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::ivf_pq::index<int64_t>& index);
 
 /**
@@ -1341,7 +1341,7 @@ void serialize(raft::resources const& handle,
  * // create a string with a filepath
  * std::string filename("/path/to/index");
  * // create an index with `auto index = ivf_pq::build(...);`
- * cuvs::serialize(handle, filename, index);
+ * cuvs::neighbors::ivf_pq::serialize(handle, filename, index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1349,25 +1349,26 @@ void serialize(raft::resources const& handle,
  * @param[in] index IVF-PQ index
  *
  */
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::ivf_pq::index<int64_t>& index);
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::ivf_pq::index<int64_t>& index);
 
 /**
- * Load index from input string.
+ * Load index from input stream
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
  *
  * raft::resources handle;
  *
- * std::string str = ...
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
  *
  * using IdxT = int64_t; // type of the index
  * // create an empty index
  * cuvs::neighbors::ivf_pq::index<IdxT> index(handl, index_params, dim);
  *
- * cuvs::deserialize(handle, filename, &index);
+ * cuvs::neighbors::ivf_pq::deserialize(handle, is, index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1375,9 +1376,8 @@ void serialize_file(raft::resources const& handle,
  * @param[out] index IVF-PQ index
  *
  */
-
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& str,
                  cuvs::neighbors::ivf_pq::index<int64_t>* index);
 /**
  * Load index from file.
@@ -1393,7 +1393,7 @@ void deserialize(raft::resources const& handle,
  * // create an empty index with
  * ivf_pq::index<IdxT> index(handle, index_params, dim);
  *
- * cuvs::deserialize(handle, filename, &index);
+ * cuvs::neighbors::ivf_pq::deserialize(handle, filename, &index);
  * @endcode
  *
  * @param[in] handle the raft handle
@@ -1401,9 +1401,9 @@ void deserialize(raft::resources const& handle,
  * @param[out] index IVF-PQ index
  *
  */
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::ivf_pq::index<int64_t>* index);
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_pq::index<int64_t>* index);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index df0c4d9f25..868b3dec02 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -127,8 +127,7 @@ void _serialize(cuvsResources_t res,
 {
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index->addr);
-  cuvs::neighbors::cagra::serialize_file(
-    *res_ptr, std::string(filename), *index_ptr, include_dataset);
+  cuvs::neighbors::cagra::serialize(*res_ptr, std::string(filename), *index_ptr, include_dataset);
 }
 
 template <typename T>
@@ -136,7 +135,7 @@ void* _deserialize(cuvsResources_t res, const char* filename)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
   auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
-  cuvs::neighbors::cagra::deserialize_file(*res_ptr, std::string(filename), index);
+  cuvs::neighbors::cagra::deserialize(*res_ptr, std::string(filename), index);
   return index;
 }
 
diff --git a/cpp/src/neighbors/cagra_serialize.cuh b/cpp/src/neighbors/cagra_serialize.cuh
index 100453b203..03f128cb99 100644
--- a/cpp/src/neighbors/cagra_serialize.cuh
+++ b/cpp/src/neighbors/cagra_serialize.cuh
@@ -19,82 +19,6 @@
 #include "detail/cagra/cagra_serialize.cuh"
 
 namespace cuvs::neighbors::cagra {
-
-/**
- * \defgroup cagra_serialize CAGRA Serialize
- * @{
- */
-
-/**
- * Write the index to an output stream
- *
- * Experimental, both the API and the serialization format are subject to change.
- *
- * @code{.cpp}
- * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
- *
- * raft::resources handle;
- *
- * // create an output stream
- * std::ostream os(std::cout.rdbuf());
- * // create an index with `auto index = raft::cagra::build(...);`
- * raft::cagra::serialize(handle, os, index);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
- * @param[in] handle the raft handle
- * @param[in] os output stream
- * @param[in] index CAGRA index
- * @param[in] include_dataset Whether or not to write out the dataset to the file.
- *
- */
-template <typename T, typename IdxT>
-void serialize(raft::resources const& handle,
-               std::ostream& os,
-               const index<T, IdxT>& index,
-               bool include_dataset = true)
-{
-  detail::serialize(handle, os, index, include_dataset);
-}
-
-/**
- * Save the index to file.
- *
- * Experimental, both the API and the serialization format are subject to change.
- *
- * @code{.cpp}
- * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
- *
- * raft::resources handle;
- *
- * // create a string with a filepath
- * std::string filename("/path/to/index");
- * // create an index with `auto index = raft::cagra::build(...);`
- * raft::cagra::serialize(handle, filename, index);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
- * @param[in] handle the raft handle
- * @param[in] filename the file name for saving the index
- * @param[in] index CAGRA index
- * @param[in] include_dataset Whether or not to write out the dataset to the file.
- *
- */
-template <typename T, typename IdxT>
-void serialize(raft::resources const& handle,
-               const std::string& filename,
-               const index<T, IdxT>& index,
-               bool include_dataset = true)
-{
-  detail::serialize(handle, filename, index, include_dataset);
-}
-
 /**
  * Write the CAGRA built index as a base layer HNSW index to an output stream
  *
@@ -161,70 +85,36 @@ void serialize_to_hnswlib(raft::resources const& handle,
   detail::serialize_to_hnswlib<T, IdxT>(handle, filename, index);
 }
 
-/**
- * Load index from input stream
- *
- * Experimental, both the API and the serialization format are subject to change.
- *
- * @code{.cpp}
- * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
- *
- * raft::resources handle;
- *
- * // create an input stream
- * std::istream is(std::cin.rdbuf());
- * using T    = float; // data element type
- * using IdxT = int; // type of the index
- * auto index = raft::cagra::deserialize<T, IdxT>(handle, is);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
- * @param[in] handle the raft handle
- * @param[in] is input stream
- *
- * @return cuvs::neighbors::experimental::cagra::index<T, IdxT>
- */
-template <typename T, typename IdxT>
-index<T, IdxT> deserialize(raft::resources const& handle, std::istream& is)
-{
-  return detail::deserialize<T, IdxT>(handle, is);
-}
-
-/**
- * Load index from file.
- *
- * Experimental, both the API and the serialization format are subject to change.
- *
- * @code{.cpp}
- * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
- *
- * raft::resources handle;
- *
- * // create a string with a filepath
- * std::string filename("/path/to/index");
- * using T    = float; // data element type
- * using IdxT = int; // type of the index
- * auto index = raft::cagra::deserialize<T, IdxT>(handle, filename);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
- * @param[in] handle the raft handle
- * @param[in] filename the name of the file that stores the index
- *
- * @return cuvs::neighbors::experimental::cagra::index<T, IdxT>
- */
-template <typename T, typename IdxT>
-index<T, IdxT> deserialize(raft::resources const& handle, const std::string& filename)
-{
-  return detail::deserialize<T, IdxT>(handle, filename);
-}
-
-/**@}*/
+#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                   \
+  void serialize(raft::resources const& handle,                                            \
+                 const std::string& filename,                                              \
+                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,              \
+                 bool include_dataset)                                                     \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                            \
+      handle, filename, index, include_dataset);                                           \
+  };                                                                                       \
+                                                                                           \
+  void deserialize(raft::resources const& handle,                                          \
+                   const std::string& filename,                                            \
+                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                  \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, filename, index); \
+  };                                                                                       \
+  void serialize(raft::resources const& handle,                                            \
+                 std::ostream& os,                                                         \
+                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,              \
+                 bool include_dataset)                                                     \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                            \
+      handle, os, index, include_dataset);                                                 \
+  }                                                                                        \
+                                                                                           \
+  void deserialize(raft::resources const& handle,                                          \
+                   std::istream& is,                                                       \
+                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                  \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, is, index);       \
+  }
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_serialize_float.cu b/cpp/src/neighbors/cagra_serialize_float.cu
index edaae0a0a9..b04fb51f36 100644
--- a/cpp/src/neighbors/cagra_serialize_float.cu
+++ b/cpp/src/neighbors/cagra_serialize_float.cu
@@ -15,68 +15,9 @@
  */
 
 #include "cagra_serialize.cuh"
-#include <raft/core/device_resources.hpp>
-
-#include <cuvs/neighbors/cagra.hpp>
-
-#include <cuda_fp16.h>
-
-#include <sstream>
-#include <string>
 
 namespace cuvs::neighbors::cagra {
 
-#define RAFT_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
-  void serialize_file(raft::resources const& handle,                                              \
-                      const std::string& filename,                                                \
-                      const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                \
-                      bool include_dataset)                                                       \
-  {                                                                                               \
-    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, filename, index, include_dataset); \
-  };                                                                                              \
-                                                                                                  \
-  void deserialize_file(raft::resources const& handle,                                            \
-                        const std::string& filename,                                              \
-                        cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                    \
-  {                                                                                               \
-    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
-    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, filename);              \
-  };                                                                                              \
-  void serialize(raft::resources const& handle,                                                   \
-                 std::string& str,                                                                \
-                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                     \
-                 bool include_dataset)                                                            \
-  {                                                                                               \
-    std::stringstream os;                                                                         \
-    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, os, index, include_dataset);       \
-    str = os.str();                                                                               \
-  }                                                                                               \
-                                                                                                  \
-  void serialize_to_hnswlib_file(raft::resources const& handle,                                   \
-                                 const std::string& filename,                                     \
-                                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)     \
-  {                                                                                               \
-    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, filename, index);       \
-  };                                                                                              \
-  void serialize_to_hnswlib(raft::resources const& handle,                                        \
-                            std::string& str,                                                     \
-                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)          \
-  {                                                                                               \
-    std::stringstream os;                                                                         \
-    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index);             \
-    str = os.str();                                                                               \
-  }                                                                                               \
-                                                                                                  \
-  void deserialize(raft::resources const& handle,                                                 \
-                   const std::string& str,                                                        \
-                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                         \
-  {                                                                                               \
-    std::istringstream is(str);                                                                   \
-    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
-    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, is);                    \
-  }
-
-RAFT_INST_CAGRA_SERIALIZE(float);
+CUVS_INST_CAGRA_SERIALIZE(float);
 
-#undef RAFT_INST_CAGRA_SERIALIZE
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_serialize_int8.cu b/cpp/src/neighbors/cagra_serialize_int8.cu
index bb23e0ae55..5c290bb675 100644
--- a/cpp/src/neighbors/cagra_serialize_int8.cu
+++ b/cpp/src/neighbors/cagra_serialize_int8.cu
@@ -15,68 +15,9 @@
  */
 
 #include "cagra_serialize.cuh"
-#include <raft/core/device_resources.hpp>
-
-#include <cuvs/neighbors/cagra.hpp>
-
-#include <cuda_fp16.h>
-
-#include <sstream>
-#include <string>
 
 namespace cuvs::neighbors::cagra {
 
-#define RAFT_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
-  void serialize_file(raft::resources const& handle,                                              \
-                      const std::string& filename,                                                \
-                      const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                \
-                      bool include_dataset)                                                       \
-  {                                                                                               \
-    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, filename, index, include_dataset); \
-  };                                                                                              \
-                                                                                                  \
-  void deserialize_file(raft::resources const& handle,                                            \
-                        const std::string& filename,                                              \
-                        cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                    \
-  {                                                                                               \
-    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
-    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, filename);              \
-  };                                                                                              \
-  void serialize(raft::resources const& handle,                                                   \
-                 std::string& str,                                                                \
-                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                     \
-                 bool include_dataset)                                                            \
-  {                                                                                               \
-    std::stringstream os;                                                                         \
-    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, os, index, include_dataset);       \
-    str = os.str();                                                                               \
-  }                                                                                               \
-                                                                                                  \
-  void serialize_to_hnswlib_file(raft::resources const& handle,                                   \
-                                 const std::string& filename,                                     \
-                                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)     \
-  {                                                                                               \
-    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, filename, index);       \
-  };                                                                                              \
-  void serialize_to_hnswlib(raft::resources const& handle,                                        \
-                            std::string& str,                                                     \
-                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)          \
-  {                                                                                               \
-    std::stringstream os;                                                                         \
-    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index);             \
-    str = os.str();                                                                               \
-  }                                                                                               \
-                                                                                                  \
-  void deserialize(raft::resources const& handle,                                                 \
-                   const std::string& str,                                                        \
-                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                         \
-  {                                                                                               \
-    std::istringstream is(str);                                                                   \
-    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
-    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, is);                    \
-  }
-
-RAFT_INST_CAGRA_SERIALIZE(int8_t);
+CUVS_INST_CAGRA_SERIALIZE(int8_t);
 
-#undef RAFT_INST_CAGRA_SERIALIZE
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_serialize_uint8.cu b/cpp/src/neighbors/cagra_serialize_uint8.cu
index b59b2cbe88..4494c9fbba 100644
--- a/cpp/src/neighbors/cagra_serialize_uint8.cu
+++ b/cpp/src/neighbors/cagra_serialize_uint8.cu
@@ -15,68 +15,9 @@
  */
 
 #include "cagra_serialize.cuh"
-#include <raft/core/device_resources.hpp>
-
-#include <cuvs/neighbors/cagra.hpp>
-
-#include <cuda_fp16.h>
-
-#include <sstream>
-#include <string>
 
 namespace cuvs::neighbors::cagra {
 
-#define RAFT_INST_CAGRA_SERIALIZE(DTYPE)                                                          \
-  void serialize_file(raft::resources const& handle,                                              \
-                      const std::string& filename,                                                \
-                      const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                \
-                      bool include_dataset)                                                       \
-  {                                                                                               \
-    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, filename, index, include_dataset); \
-  };                                                                                              \
-                                                                                                  \
-  void deserialize_file(raft::resources const& handle,                                            \
-                        const std::string& filename,                                              \
-                        cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                    \
-  {                                                                                               \
-    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
-    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, filename);              \
-  };                                                                                              \
-  void serialize(raft::resources const& handle,                                                   \
-                 std::string& str,                                                                \
-                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                     \
-                 bool include_dataset)                                                            \
-  {                                                                                               \
-    std::stringstream os;                                                                         \
-    cuvs::neighbors::cagra::serialize<DTYPE, uint32_t>(handle, os, index, include_dataset);       \
-    str = os.str();                                                                               \
-  }                                                                                               \
-                                                                                                  \
-  void serialize_to_hnswlib_file(raft::resources const& handle,                                   \
-                                 const std::string& filename,                                     \
-                                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)     \
-  {                                                                                               \
-    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, filename, index);       \
-  };                                                                                              \
-  void serialize_to_hnswlib(raft::resources const& handle,                                        \
-                            std::string& str,                                                     \
-                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)          \
-  {                                                                                               \
-    std::stringstream os;                                                                         \
-    cuvs::neighbors::cagra::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index);             \
-    str = os.str();                                                                               \
-  }                                                                                               \
-                                                                                                  \
-  void deserialize(raft::resources const& handle,                                                 \
-                   const std::string& str,                                                        \
-                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                         \
-  {                                                                                               \
-    std::istringstream is(str);                                                                   \
-    if (!index) { RAFT_FAIL("Invalid index pointer"); }                                           \
-    *index = cuvs::neighbors::cagra::deserialize<DTYPE, uint32_t>(handle, is);                    \
-  }
-
-RAFT_INST_CAGRA_SERIALIZE(uint8_t);
+CUVS_INST_CAGRA_SERIALIZE(uint8_t);
 
-#undef RAFT_INST_CAGRA_SERIALIZE
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index 4947d3148b..41329975b3 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -230,7 +230,7 @@ void serialize_to_hnswlib(raft::resources const& res,
  *
  */
 template <typename T, typename IdxT>
-auto deserialize(raft::resources const& res, std::istream& is) -> index<T, IdxT>
+void deserialize(raft::resources const& res, std::istream& is, index<T, IdxT>* index_)
 {
   raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("cagra::deserialize");
 
@@ -249,26 +249,23 @@ auto deserialize(raft::resources const& res, std::istream& is) -> index<T, IdxT>
   auto graph = raft::make_host_matrix<IdxT, int64_t>(n_rows, graph_degree);
   deserialize_mdspan(res, is, graph.view());
 
-  index<T, IdxT> idx(res, metric);
-  idx.update_graph(res, raft::make_const_mdspan(graph.view()));
+  *index_ = index<T, IdxT>(res, metric);
+  index_->update_graph(res, raft::make_const_mdspan(graph.view()));
   bool has_dataset = raft::deserialize_scalar<bool>(res, is);
   if (has_dataset) {
-    idx.update_dataset(res, cuvs::neighbors::detail::deserialize_dataset<int64_t>(res, is));
+    index_->update_dataset(res, cuvs::neighbors::detail::deserialize_dataset<int64_t>(res, is));
   }
-  return idx;
 }
 
 template <typename T, typename IdxT>
-auto deserialize(raft::resources const& res, const std::string& filename) -> index<T, IdxT>
+void deserialize(raft::resources const& res, const std::string& filename, index<T, IdxT>* index_)
 {
   std::ifstream is(filename, std::ios::in | std::ios::binary);
 
   if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
 
-  auto index = detail::deserialize<T, IdxT>(res, is);
+  detail::deserialize<T, IdxT>(res, is, index_);
 
   is.close();
-
-  return index;
 }
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
index 1733ca8b25..e739bddd47 100644
--- a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
+++ b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
@@ -165,40 +165,7 @@
   }
 """
 
-serialize_macro = """
-#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                                      \\
-  void serialize_file(raft::resources const& handle,                                               \\
-                      const std::string& filename,                                                 \\
-                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                      \\
-  {                                                                                                \\
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);                         \\
-  }                                                                                                \\
-                                                                                                   \\
-  void serialize(raft::resources const& handle,                                                    \\
-                 std::string& str,                                                                 \\
-                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                           \\
-  {                                                                                                \\
-    std::ostringstream os;                                                                         \\
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                               \\
-    str = os.str();                                                                                \\
-  }                                                                                                \\
-                                                                                                   \\
-  void deserialize_file(raft::resources const& handle,                                             \\
-                        const std::string& filename,                                               \\
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                          \\
-  {                                                                                                \\
-    * index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(                             \\
-      handle, filename);                                                                           \\
-  }                                                                                                \\
-    void deserialize(raft::resources const& handle,                                                \\
-                     const std::string& str,                                                       \\
-                     cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                             \\
-  {                                                                                                \\
-    std::istringstream is(str);                                                                    \\
-    * index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(                             \\
-      handle, is);                                                                                 \\
-  }                                                                                                 
-"""
+serialize_macro = ""
 
 macros = dict(
     build_extend=dict(
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
index d88e173230..fdf06b286d 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize.cuh
@@ -174,3 +174,31 @@ auto deserialize(raft::resources const& handle, const std::string& filename) ->
   return index;
 }
 }  // namespace cuvs::neighbors::ivf_flat::detail
+
+#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                           \
+  void serialize(raft::resources const& handle,                                         \
+                 const std::string& filename,                                           \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
+  {                                                                                     \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);              \
+  }                                                                                     \
+                                                                                        \
+  void serialize(raft::resources const& handle,                                         \
+                 std::ostream& os,                                                      \
+                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
+  {                                                                                     \
+    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                    \
+  }                                                                                     \
+                                                                                        \
+  void deserialize(raft::resources const& handle,                                       \
+                   const std::string& filename,                                         \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
+  {                                                                                     \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, filename); \
+  }                                                                                     \
+  void deserialize(raft::resources const& handle,                                       \
+                   std::istream& is,                                                    \
+                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
+  {                                                                                     \
+    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, is);       \
+  }
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
index 9ab00623ad..95288d1198 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
@@ -28,37 +28,6 @@
 #include "ivf_flat_serialize.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                           \
-  void serialize_file(raft::resources const& handle,                                    \
-                      const std::string& filename,                                      \
-                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)           \
-  {                                                                                     \
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);              \
-  }                                                                                     \
-                                                                                        \
-  void serialize(raft::resources const& handle,                                         \
-                 std::string& str,                                                      \
-                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
-  {                                                                                     \
-    std::ostringstream os;                                                              \
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                    \
-    str = os.str();                                                                     \
-  }                                                                                     \
-                                                                                        \
-  void deserialize_file(raft::resources const& handle,                                  \
-                        const std::string& filename,                                    \
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)               \
-  {                                                                                     \
-    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, filename); \
-  }                                                                                     \
-  void deserialize(raft::resources const& handle,                                       \
-                   const std::string& str,                                              \
-                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
-  {                                                                                     \
-    std::istringstream is(str);                                                         \
-    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, is);       \
-  }
 CUVS_INST_IVF_FLAT_SERIALIZE(float, int64_t);
 
 #undef CUVS_INST_IVF_FLAT_SERIALIZE
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
index 18d6a02873..74e0c2653b 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
@@ -28,37 +28,6 @@
 #include "ivf_flat_serialize.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                           \
-  void serialize_file(raft::resources const& handle,                                    \
-                      const std::string& filename,                                      \
-                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)           \
-  {                                                                                     \
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);              \
-  }                                                                                     \
-                                                                                        \
-  void serialize(raft::resources const& handle,                                         \
-                 std::string& str,                                                      \
-                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
-  {                                                                                     \
-    std::ostringstream os;                                                              \
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                    \
-    str = os.str();                                                                     \
-  }                                                                                     \
-                                                                                        \
-  void deserialize_file(raft::resources const& handle,                                  \
-                        const std::string& filename,                                    \
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)               \
-  {                                                                                     \
-    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, filename); \
-  }                                                                                     \
-  void deserialize(raft::resources const& handle,                                       \
-                   const std::string& str,                                              \
-                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
-  {                                                                                     \
-    std::istringstream is(str);                                                         \
-    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, is);       \
-  }
 CUVS_INST_IVF_FLAT_SERIALIZE(int8_t, int64_t);
 
 #undef CUVS_INST_IVF_FLAT_SERIALIZE
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
index c5ab7d5c12..d33be5968d 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
@@ -28,37 +28,6 @@
 #include "ivf_flat_serialize.cuh"
 
 namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_SERIALIZE(T, IdxT)                                           \
-  void serialize_file(raft::resources const& handle,                                    \
-                      const std::string& filename,                                      \
-                      const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)           \
-  {                                                                                     \
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, filename, index);              \
-  }                                                                                     \
-                                                                                        \
-  void serialize(raft::resources const& handle,                                         \
-                 std::string& str,                                                      \
-                 const cuvs::neighbors::ivf_flat::index<T, IdxT>& index)                \
-  {                                                                                     \
-    std::ostringstream os;                                                              \
-    cuvs::neighbors::ivf_flat::detail::serialize(handle, os, index);                    \
-    str = os.str();                                                                     \
-  }                                                                                     \
-                                                                                        \
-  void deserialize_file(raft::resources const& handle,                                  \
-                        const std::string& filename,                                    \
-                        cuvs::neighbors::ivf_flat::index<T, IdxT>* index)               \
-  {                                                                                     \
-    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, filename); \
-  }                                                                                     \
-  void deserialize(raft::resources const& handle,                                       \
-                   const std::string& str,                                              \
-                   cuvs::neighbors::ivf_flat::index<T, IdxT>* index)                    \
-  {                                                                                     \
-    std::istringstream is(str);                                                         \
-    *index = cuvs::neighbors::ivf_flat::detail::deserialize<T, IdxT>(handle, is);       \
-  }
 CUVS_INST_IVF_FLAT_SERIALIZE(uint8_t, int64_t);
 
 #undef CUVS_INST_IVF_FLAT_SERIALIZE
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
index 7827e7892a..3cde5d11c6 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_deserialize.cu
@@ -20,20 +20,19 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-void deserialize_file(raft::resources const& handle,
-                      const std::string& filename,
-                      cuvs::neighbors::ivf_pq::index<int64_t>* index)
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_pq::index<int64_t>* index)
 {
   if (!index) { RAFT_FAIL("Invalid index pointer"); }
   *index = cuvs::neighbors::ivf_pq::detail::deserialize<int64_t>(handle, filename);
 }
 
 void deserialize(raft::resources const& handle,
-                 const std::string& str,
+                 std::istream& is,
                  cuvs::neighbors::ivf_pq::index<int64_t>* index)
 {
   if (!index) { RAFT_FAIL("Invalid index pointer"); }
-  std::istringstream is(str);
   *index = cuvs::neighbors::ivf_pq::detail::deserialize<int64_t>(handle, is);
 }
-}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
index f0214f4bb3..7b647fd24e 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cu
@@ -20,19 +20,17 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-void serialize_file(raft::resources const& handle,
-                    const std::string& filename,
-                    const cuvs::neighbors::ivf_pq::index<int64_t>& index)
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::ivf_pq::index<int64_t>& index)
 {
   cuvs::neighbors::ivf_pq::detail::serialize(handle, filename, index);
 }
 
 void serialize(raft::resources const& handle,
-               std::string& str,
+               std::ostream& os,
                const cuvs::neighbors::ivf_pq::index<int64_t>& index)
 {
-  std::ostringstream os;
   cuvs::neighbors::ivf_pq::detail::serialize(handle, os, index);
-  str = os.str();
 }
-}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index ad7f327859..63ac06911d 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -282,11 +282,11 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
             index = cagra::build(handle_, index_params, database_view);
           };
 
-          cagra::serialize_file(handle_, "cagra_index", index, ps.include_serialized_dataset);
+          cagra::serialize(handle_, "cagra_index", index, ps.include_serialized_dataset);
         }
 
         cagra::index<DataT, IdxT> index(handle_);
-        cagra::deserialize_file(handle_, "cagra_index", &index);
+        cagra::deserialize(handle_, "cagra_index", &index);
 
         if (!ps.include_serialized_dataset) { index.update_dataset(handle_, database_view); }
 
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index 9ce23cc7df..49cb3ec2a1 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -184,9 +184,9 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
         auto dists_out_view = raft::make_device_matrix_view<T, IdxT>(
           distances_ivfflat_dev.data(), ps.num_queries, ps.k);
         const std::string filename = "ivf_flat_index";
-        cuvs::neighbors::ivf_flat::serialize_file(handle_, filename, index_2);
+        cuvs::neighbors::ivf_flat::serialize(handle_, filename, index_2);
         cuvs::neighbors::ivf_flat::index<DataT, IdxT> index_loaded(handle_, index_params, ps.dim);
-        cuvs::neighbors::ivf_flat::deserialize_file(handle_, filename, &index_loaded);
+        cuvs::neighbors::ivf_flat::deserialize(handle_, filename, &index_loaded);
         ASSERT_EQ(index_2.size(), index_loaded.size());
 
         cuvs::neighbors::ivf_flat::search(handle_,
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index f716a8efed..662b701af9 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -241,9 +241,9 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
   auto build_serialize()
   {
     std::string filename = "ivf_pq_index";
-    cuvs::neighbors::ivf_pq::serialize_file(handle_, filename, build_only());
+    cuvs::neighbors::ivf_pq::serialize(handle_, filename, build_only());
     cuvs::neighbors::ivf_pq::index<IdxT> index(handle_, ps.index_params, ps.dim);
-    cuvs::neighbors::ivf_pq::deserialize_file(handle_, filename, &index);
+    cuvs::neighbors::ivf_pq::deserialize(handle_, filename, &index);
     return index;
   }
 

From b771085ef2b09d4a6ec982915a2f8ddef6a259cc Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 6 Jun 2024 18:34:01 -0700
Subject: [PATCH 077/623] Add refine to the Python and C api's (#175)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/175
---
 cpp/CMakeLists.txt                        |   9 +-
 cpp/include/cuvs/core/detail/interop.hpp  |  14 +-
 cpp/include/cuvs/neighbors/refine.h       |  61 ++++++
 cpp/src/neighbors/refine/refine_c.cpp     | 146 +++++++++++++
 python/cuvs/cuvs/common/cydlpack.pyx      |   5 +
 python/cuvs/cuvs/neighbors/CMakeLists.txt |  11 +
 python/cuvs/cuvs/neighbors/__init__.py    |   4 +-
 python/cuvs/cuvs/neighbors/refine.pxd     |  30 +++
 python/cuvs/cuvs/neighbors/refine.pyx     | 169 +++++++++++++++
 python/cuvs/cuvs/test/test_refine.py      | 239 ++++++++++++++++++++++
 10 files changed, 682 insertions(+), 6 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/refine.h
 create mode 100644 cpp/src/neighbors/refine/refine_c.cpp
 create mode 100644 python/cuvs/cuvs/neighbors/refine.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/refine.pyx
 create mode 100644 python/cuvs/cuvs/test/test_refine.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 5f016ed884..abe6fa7b3d 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -555,8 +555,13 @@ target_link_options(cuvs PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
 if(BUILD_C_LIBRARY)
   add_library(
     cuvs_c SHARED
-    src/core/c_api.cpp src/neighbors/brute_force_c.cpp src/neighbors/ivf_flat_c.cpp
-    src/neighbors/ivf_pq_c.cpp src/neighbors/cagra_c.cpp src/distance/pairwise_distance_c.cpp
+    src/core/c_api.cpp
+    src/neighbors/brute_force_c.cpp
+    src/neighbors/ivf_flat_c.cpp
+    src/neighbors/ivf_pq_c.cpp
+    src/neighbors/cagra_c.cpp
+    src/neighbors/refine/refine_c.cpp
+    src/distance/pairwise_distance_c.cpp
   )
 
   add_library(cuvs::c_api ALIAS cuvs_c)
diff --git a/cpp/include/cuvs/core/detail/interop.hpp b/cpp/include/cuvs/core/detail/interop.hpp
index 208daaae7c..2ed0b330de 100644
--- a/cpp/include/cuvs/core/detail/interop.hpp
+++ b/cpp/include/cuvs/core/detail/interop.hpp
@@ -16,6 +16,8 @@
 
 #pragma once
 
+#include <cuda_fp16.h>
+
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/error.hpp>
 #include <raft/core/host_mdspan.hpp>
@@ -44,7 +46,9 @@ DLDataType data_type_to_DLDataType()
 {
   uint8_t const bits{sizeof(T) * 8};
   uint16_t const lanes{1};
-  if constexpr (std::is_floating_point_v<T>) {
+  // std::is_floating_point returns false for the half type - handle
+  // that here
+  if constexpr (std::is_floating_point_v<T> || std::is_same_v<T, half>) {
     return DLDataType{kDLFloat, bits, lanes};
   } else if constexpr (std::is_signed_v<T>) {
     return DLDataType{kDLInt, bits, lanes};
@@ -72,9 +76,13 @@ inline MdspanType from_dlpack(DLManagedTensor* managed_tensor)
 
   auto to_data_type = data_type_to_DLDataType<typename MdspanType::value_type>();
   RAFT_EXPECTS(to_data_type.code == tensor.dtype.code,
-               "code mismatch between return mdspan and DLTensor");
+               "code mismatch between return mdspan (%i) and DLTensor (%i)",
+               to_data_type.code,
+               tensor.dtype.code);
   RAFT_EXPECTS(to_data_type.bits == tensor.dtype.bits,
-               "bits mismatch between return mdspan and DLTensor");
+               "bits mismatch between return mdspan (%i) and DLTensor (%i)",
+               to_data_type.bits,
+               tensor.dtype.bits);
   RAFT_EXPECTS(to_data_type.lanes == tensor.dtype.lanes,
                "lanes mismatch between return mdspan and DLTensor");
   RAFT_EXPECTS(tensor.dtype.lanes == 1, "More than 1 DLTensor lanes not supported");
diff --git a/cpp/include/cuvs/neighbors/refine.h b/cpp/include/cuvs/neighbors/refine.h
new file mode 100644
index 0000000000..4e7a572b4f
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/refine.h
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+/**
+ * @defgroup ann_refine_c Approximate Nearest Neighbors Refinement C-API
+ * @{
+ */
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ */
+cuvsError_t cuvsRefine(cuvsResources_t res,
+                       DLManagedTensor* dataset,
+                       DLManagedTensor* queries,
+                       DLManagedTensor* candidates,
+                       cuvsDistanceType metric,
+                       DLManagedTensor* indices,
+                       DLManagedTensor* distances);
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/neighbors/refine/refine_c.cpp b/cpp/src/neighbors/refine/refine_c.cpp
new file mode 100644
index 0000000000..955eaded41
--- /dev/null
+++ b/cpp/src/neighbors/refine/refine_c.cpp
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/refine.h>
+#include <cuvs/neighbors/refine.hpp>
+
+namespace {
+
+template <typename T>
+void _refine(bool on_device,
+             cuvsResources_t res,
+             DLManagedTensor* dataset_tensor,
+             DLManagedTensor* queries_tensor,
+             DLManagedTensor* candidates_tensor,
+             cuvsDistanceType metric,
+             DLManagedTensor* indices_tensor,
+             DLManagedTensor* distances_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  if (on_device) {
+    using queries_type    = raft::device_matrix_view<const T, int64_t, raft::row_major>;
+    using candidates_type = raft::device_matrix_view<const int64_t, int64_t, raft::row_major>;
+    using indices_type    = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
+    using distances_type  = raft::device_matrix_view<float, int64_t, raft::row_major>;
+    auto dataset          = cuvs::core::from_dlpack<queries_type>(dataset_tensor);
+    auto queries          = cuvs::core::from_dlpack<queries_type>(queries_tensor);
+    auto candidates       = cuvs::core::from_dlpack<candidates_type>(candidates_tensor);
+    auto indices          = cuvs::core::from_dlpack<indices_type>(indices_tensor);
+    auto distances        = cuvs::core::from_dlpack<distances_type>(distances_tensor);
+    cuvs::neighbors::refine(*res_ptr, dataset, queries, candidates, indices, distances, metric);
+  } else {
+    using queries_type    = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+    using candidates_type = raft::host_matrix_view<const int64_t, int64_t, raft::row_major>;
+    using indices_type    = raft::host_matrix_view<int64_t, int64_t, raft::row_major>;
+    using distances_type  = raft::host_matrix_view<float, int64_t, raft::row_major>;
+    auto dataset          = cuvs::core::from_dlpack<queries_type>(dataset_tensor);
+    auto queries          = cuvs::core::from_dlpack<queries_type>(queries_tensor);
+    auto candidates       = cuvs::core::from_dlpack<candidates_type>(candidates_tensor);
+    auto indices          = cuvs::core::from_dlpack<indices_type>(indices_tensor);
+    auto distances        = cuvs::core::from_dlpack<distances_type>(distances_tensor);
+    cuvs::neighbors::refine(*res_ptr, dataset, queries, candidates, indices, distances, metric);
+  }
+}
+}  // namespace
+
+extern "C" cuvsError_t cuvsRefine(cuvsResources_t res,
+                                  DLManagedTensor* dataset_tensor,
+                                  DLManagedTensor* queries_tensor,
+                                  DLManagedTensor* candidates_tensor,
+                                  cuvsDistanceType metric,
+                                  DLManagedTensor* indices_tensor,
+                                  DLManagedTensor* distances_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset    = dataset_tensor->dl_tensor;
+    auto queries    = queries_tensor->dl_tensor;
+    auto candidates = candidates_tensor->dl_tensor;
+    auto indices    = indices_tensor->dl_tensor;
+    auto distances  = distances_tensor->dl_tensor;
+
+    // all matrices must either be on host or on device, can't mix and match
+    bool on_device = cuvs::core::is_dlpack_device_compatible(dataset);
+    if (on_device != cuvs::core::is_dlpack_device_compatible(queries) ||
+        on_device != cuvs::core::is_dlpack_device_compatible(candidates) ||
+        on_device != cuvs::core::is_dlpack_device_compatible(indices) ||
+        on_device != cuvs::core::is_dlpack_device_compatible(distances)) {
+      RAFT_FAIL("Tensors must either all be on device memory, or all on host memory");
+    }
+
+    RAFT_EXPECTS(candidates.dtype.code == kDLInt && candidates.dtype.bits == 64,
+                 "candidates should be of type int64_t");
+    RAFT_EXPECTS(indices.dtype.code == kDLInt && indices.dtype.bits == 64,
+                 "indices should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    RAFT_EXPECTS(queries.dtype.code == dataset.dtype.code,
+                 "type mismatch between dataset and queries");
+
+    if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
+      _refine<float>(on_device,
+                     res,
+                     dataset_tensor,
+                     queries_tensor,
+                     candidates_tensor,
+                     metric,
+                     indices_tensor,
+                     distances_tensor);
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      _refine<half>(on_device,
+                    res,
+                    dataset_tensor,
+                    queries_tensor,
+                    candidates_tensor,
+                    metric,
+                    indices_tensor,
+                    distances_tensor);
+    } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
+      _refine<int8_t>(on_device,
+                      res,
+                      dataset_tensor,
+                      queries_tensor,
+                      candidates_tensor,
+                      metric,
+                      indices_tensor,
+                      distances_tensor);
+    } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
+      _refine<uint8_t>(on_device,
+                       res,
+                       dataset_tensor,
+                       queries_tensor,
+                       candidates_tensor,
+                       metric,
+                       indices_tensor,
+                       distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
+                queries.dtype.code,
+                queries.dtype.bits);
+    }
+  });
+}
diff --git a/python/cuvs/cuvs/common/cydlpack.pyx b/python/cuvs/cuvs/common/cydlpack.pyx
index 526f6c78e9..79f88cddc9 100644
--- a/python/cuvs/cuvs/common/cydlpack.pyx
+++ b/python/cuvs/cuvs/common/cydlpack.pyx
@@ -53,6 +53,9 @@ cdef DLManagedTensor* dlpack_c(ary):
     elif ary.dtype == np.float64:
         dtype.code = DLDataTypeCode.kDLFloat
         dtype.bits = 64
+    elif ary.dtype == np.float16:
+        dtype.code = DLDataTypeCode.kDLFloat
+        dtype.bits = 16
     elif ary.dtype == np.int8:
         dtype.code = DLDataTypeCode.kDLInt
         dtype.bits = 8
@@ -74,6 +77,8 @@ cdef DLManagedTensor* dlpack_c(ary):
     elif ary.dtype == np.bool_:
         dtype.code = DLDataTypeCode.kDLFloat
         dtype.bits = 8
+    else:
+        raise ValueError(f"Unsupported dtype {ary.dtype}")
 
     dtype.lanes = 1
 
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index e0041243ab..3579215fd9 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -16,3 +16,14 @@ add_subdirectory(brute_force)
 add_subdirectory(cagra)
 add_subdirectory(ivf_flat)
 add_subdirectory(ivf_pq)
+
+# Set the list of Cython files to build
+set(cython_sources refine.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_refine_
+)
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index c431ffde7b..0ecd57d750 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -15,4 +15,6 @@
 
 from cuvs.neighbors import brute_force, cagra, ivf_flat, ivf_pq
 
-__all__ = ["brute_force", "cagra", "ivf_flat", "ivf_pq"]
+from .refine import refine
+
+__all__ = ["brute_force", "cagra", "ivf_flat", "ivf_pq", "refine"]
diff --git a/python/cuvs/cuvs/neighbors/refine.pxd b/python/cuvs/cuvs/neighbors/refine.pxd
new file mode 100644
index 0000000000..f02404734d
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/refine.pxd
@@ -0,0 +1,30 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "cuvs/neighbors/refine.h" nogil:
+    cuvsError_t cuvsRefine(cuvsResources_t res,
+                           DLManagedTensor* dataset,
+                           DLManagedTensor* queries,
+                           DLManagedTensor* candidates,
+                           cuvsDistanceType metric,
+                           DLManagedTensor* indices,
+                           DLManagedTensor* distances) except +
diff --git a/python/cuvs/cuvs/neighbors/refine.pyx b/python/cuvs/cuvs/neighbors/refine.pyx
new file mode 100644
index 0000000000..0eccc4108e
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/refine.pyx
@@ -0,0 +1,169 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libc.stdint cimport uint32_t
+from libcpp cimport bool
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+from pylibraft.neighbors.common import _check_input_array
+
+from cuvs.distance import DISTANCE_TYPES
+
+from cuvs.common.c_api cimport cuvsResources_t
+
+from cuvs.common.exceptions import check_cuvs
+
+
+@auto_sync_resources
+@auto_convert_output
+def refine(dataset,
+           queries,
+           candidates,
+           k=None,
+           metric="sqeuclidean",
+           indices=None,
+           distances=None,
+           resources=None):
+    """
+    Refine nearest neighbor search.
+
+    Refinement is an operation that follows an approximate NN search. The
+    approximate search has already selected n_candidates neighbor candidates
+    for each query. We narrow it down to k neighbors. For each query, we
+    calculate the exact distance between the query and its n_candidates
+    neighbor candidate, and select the k nearest ones.
+
+    Input arrays can be either CUDA array interface compliant matrices or
+    array interface compliant matrices in host memory. All array must be in
+    the same memory space.
+
+    Parameters
+    ----------
+    dataset : array interface compliant matrix, shape (n_samples, dim)
+        Supported dtype [float32, int8, uint8, float16]
+    queries : array interface compliant matrix, shape (n_queries, dim)
+        Supported dtype [float32, int8, uint8, float16]
+    candidates : array interface compliant matrix, shape (n_queries, k0)
+        Supported dtype int64
+    k : int
+        Number of neighbors to search (k <= k0). Optional if indices or
+        distances arrays are given (in which case their second dimension
+        is k).
+    metric : str
+        Name of distance metric to use, default ="sqeuclidean"
+    indices :  Optional array interface compliant matrix shape \
+               (n_queries, k).
+        If supplied, neighbor indices will be written here in-place.
+        (default None). Supported dtype int64.
+    distances :  Optional array interface compliant matrix shape \
+                 (n_queries, k).
+        If supplied, neighbor indices will be written here in-place.
+        (default None) Supported dtype float.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.common import Resources
+    >>> from cuvs.neighbors import ivf_pq, refine
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> resources = Resources()
+    >>> index_params = ivf_pq.IndexParams(n_lists=1024,
+    ...                                   metric="sqeuclidean",
+    ...                                   pq_dim=10)
+    >>> index = ivf_pq.build(index_params, dataset, resources=resources)
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> k = 40
+    >>> _, candidates = ivf_pq.search(ivf_pq.SearchParams(), index,
+    ...                               queries, k, resources=resources)
+    >>> k = 10
+    >>> distances, neighbors = refine(dataset, queries, candidates, k)
+    """
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    if k is None:
+        if indices is not None:
+            k = wrap_array(indices).shape[1]
+        elif distances is not None:
+            k = wrap_array(distances).shape[1]
+        else:
+            raise ValueError("Argument k must be specified if both indices "
+                             "and distances arg is None")
+
+    queries_cai = wrap_array(queries)
+    dataset_cai = wrap_array(dataset)
+    candidates_cai = wrap_array(candidates)
+    n_queries = wrap_array(queries).shape[0]
+
+    on_device = hasattr(dataset, "__cuda_array_interface__")
+    ndarray = device_ndarray if on_device else np
+    if indices is None:
+        indices = ndarray.empty((n_queries, k), dtype='int64')
+
+    if distances is None:
+        distances = ndarray.empty((n_queries, k), dtype='float32')
+
+    indices_cai = wrap_array(indices)
+    distances_cai = wrap_array(distances)
+
+    _check_input_array(indices_cai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+    _check_input_array(distances_cai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_cai)
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_cai)
+    cdef cydlpack.DLManagedTensor* candidates_dlpack = \
+        cydlpack.dlpack_c(candidates_cai)
+    cdef cydlpack.DLManagedTensor* indices_dlpack = \
+        cydlpack.dlpack_c(indices_cai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_cai)
+
+    cdef cuvsDistanceType c_metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+
+    with cuda_interruptible():
+        check_cuvs(cuvsRefine(
+            res,
+            dataset_dlpack,
+            queries_dlpack,
+            candidates_dlpack,
+            c_metric,
+            indices_dlpack,
+            distances_dlpack))
+
+    return (distances, indices)
diff --git a/python/cuvs/cuvs/test/test_refine.py b/python/cuvs/cuvs/test/test_refine.py
new file mode 100644
index 0000000000..c7f9f678e2
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_refine.py
@@ -0,0 +1,239 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     h ttp://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+from test_ivf_pq import calc_recall, generate_data
+
+from cuvs.neighbors import refine
+
+
+def run_refine(
+    n_rows=500,
+    n_cols=50,
+    n_queries=100,
+    metric="sqeuclidean",
+    k0=40,
+    k=10,
+    inplace=False,
+    dtype=np.float32,
+    memory_type="device",
+):
+
+    dataset = generate_data((n_rows, n_cols), dtype)
+    queries = generate_data((n_queries, n_cols), dtype)
+
+    if metric == "inner_product":
+        if dtype != np.float32:
+            pytest.skip("Normalized input cannot be represented in int8")
+            return
+        dataset = normalize(dataset, norm="l2", axis=1)
+        queries = normalize(queries, norm="l2", axis=1)
+
+    dataset_device = device_ndarray(dataset)
+    queries_device = device_ndarray(queries)
+
+    # Calculate reference values with sklearn
+    skl_metric = {"sqeuclidean": "euclidean", "inner_product": "cosine"}[
+        metric
+    ]
+    nn_skl = NearestNeighbors(
+        n_neighbors=k0, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_dist, candidates = nn_skl.kneighbors(queries)
+    candidates = candidates.astype(np.int64)
+    candidates_device = device_ndarray(candidates)
+
+    out_idx = np.zeros((n_queries, k), dtype=np.int64)
+    out_dist = np.zeros((n_queries, k), dtype=np.float32)
+    out_idx_device = device_ndarray(out_idx) if inplace else None
+    out_dist_device = device_ndarray(out_dist) if inplace else None
+
+    if memory_type == "device":
+        if inplace:
+            refine(
+                dataset_device,
+                queries_device,
+                candidates_device,
+                indices=out_idx_device,
+                distances=out_dist_device,
+                metric=metric,
+            )
+        else:
+            out_dist_device, out_idx_device = refine(
+                dataset_device,
+                queries_device,
+                candidates_device,
+                k=k,
+                metric=metric,
+            )
+        out_idx = out_idx_device.copy_to_host()
+        out_dist = out_dist_device.copy_to_host()
+    elif memory_type == "host":
+        if inplace:
+            refine(
+                dataset,
+                queries,
+                candidates,
+                indices=out_idx,
+                distances=out_dist,
+                metric=metric,
+            )
+        else:
+            out_dist, out_idx = refine(
+                dataset, queries, candidates, k=k, metric=metric
+            )
+
+    skl_idx = candidates[:, :k]
+
+    recall = calc_recall(out_idx, skl_idx)
+
+    if memory_type == "device" and dtype == np.float16:
+        # fp16 differences between host and device make the
+        # reference results from sklearn likely to be substantially different
+        # from those calculated on device.
+        assert recall >= 0.9
+
+    elif recall <= 0.999:
+        # We did not find the same neighbor indices.
+        # We could have found other neighbor with same distance.
+        if metric == "sqeuclidean":
+            skl_dist = np.power(skl_dist[:, :k], 2)
+        elif metric == "inner_product":
+            skl_dist = 1 - skl_dist[:, :k]
+        else:
+            raise ValueError("Invalid metric")
+
+        mask = out_idx != skl_idx
+        assert np.all(out_dist[mask] <= skl_dist[mask] + 1.0e-6)
+
+
+@pytest.mark.parametrize("n_queries", [100, 1024, 37])
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8, np.float16])
+@pytest.mark.parametrize("memory_type", ["device", "host"])
+def test_refine_dtypes(n_queries, dtype, inplace, metric, memory_type):
+    run_refine(
+        n_rows=2000,
+        n_queries=n_queries,
+        n_cols=50,
+        k0=40,
+        k=10,
+        dtype=dtype,
+        inplace=inplace,
+        metric=metric,
+        memory_type=memory_type,
+    )
+
+
+@pytest.mark.parametrize(
+    "params",
+    [
+        pytest.param(
+            {
+                "n_rows": 0,
+                "n_cols": 10,
+                "n_queries": 10,
+                "k0": 10,
+                "k": 1,
+            },
+            marks=pytest.mark.xfail(reason="empty dataset"),
+        ),
+        {"n_rows": 1, "n_cols": 10, "n_queries": 10, "k": 1, "k0": 1},
+        {"n_rows": 10, "n_cols": 1, "n_queries": 10, "k": 10, "k0": 10},
+        {"n_rows": 999, "n_cols": 42, "n_queries": 453, "k0": 137, "k": 53},
+    ],
+)
+@pytest.mark.parametrize("memory_type", ["device", "host"])
+def test_refine_row_col(params, memory_type):
+    run_refine(
+        n_rows=params["n_rows"],
+        n_queries=params["n_queries"],
+        n_cols=params["n_cols"],
+        k0=params["k0"],
+        k=params["k"],
+        memory_type=memory_type,
+    )
+
+
+@pytest.mark.parametrize("memory_type", ["device", "host"])
+def test_input_dtype(memory_type):
+    with pytest.raises(Exception):
+        run_refine(dtype=np.float64, memory_type=memory_type)
+
+
+@pytest.mark.parametrize(
+    "params",
+    [
+        {"idx_shape": None, "dist_shape": None, "k": None},
+        {"idx_shape": [100, 9], "dist_shape": None, "k": 10},
+        {"idx_shape": [101, 10], "dist_shape": None, "k": None},
+        {"idx_shape": None, "dist_shape": [100, 11], "k": 10},
+        {"idx_shape": None, "dist_shape": [99, 10], "k": None},
+    ],
+)
+@pytest.mark.parametrize("memory_type", ["device", "host"])
+def test_input_assertions(params, memory_type):
+    n_cols = 5
+    n_queries = 100
+    k0 = 40
+    dtype = np.float32
+    dataset = generate_data((500, n_cols), dtype)
+    dataset_device = device_ndarray(dataset)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    queries_device = device_ndarray(queries)
+
+    candidates = np.random.randint(
+        0, 500, size=(n_queries, k0), dtype=np.int64
+    )
+    candidates_device = device_ndarray(candidates)
+
+    if params["idx_shape"] is not None:
+        out_idx = np.zeros(params["idx_shape"], dtype=np.int64)
+        out_idx_device = device_ndarray(out_idx)
+    else:
+        out_idx_device = None
+    if params["dist_shape"] is not None:
+        out_dist = np.zeros(params["dist_shape"], dtype=np.float32)
+        out_dist_device = device_ndarray(out_dist)
+    else:
+        out_dist_device = None
+
+    if memory_type == "device":
+        with pytest.raises(Exception):
+            distances, indices = refine(
+                dataset_device,
+                queries_device,
+                candidates_device,
+                k=params["k"],
+                indices=out_idx_device,
+                distances=out_dist_device,
+            )
+    else:
+        with pytest.raises(Exception):
+            distances, indices = refine(
+                dataset,
+                queries,
+                candidates,
+                k=params["k"],
+                indices=out_idx,
+                distances=out_dist,
+            )

From 863b8bdd4ef25537d453157eae2dd2bcf8e69786 Mon Sep 17 00:00:00 2001
From: Ray Douglass <3107146+raydouglass@users.noreply.github.com>
Date: Fri, 7 Jun 2024 17:17:13 -0400
Subject: [PATCH 078/623] Adopt CI/packaging codeowners (#183)

This PR overhauls how `ops-codeowners` reviews are handled.

`ops-codeowners` is replaced by `ci-codeowners` &
`packaging-codeowners`. The coverage of files is expanded as well.

Additionally, the process will change: reviews will be assigned to a
member of the teams instead of a manual request to `ops-codeowners`.

---------

Co-authored-by: Bradley Dice <bdice@bradleydice.com>
---
 .github/CODEOWNERS | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 01dbcfc835..67d14835a9 100755
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -17,11 +17,14 @@ python/setup.py    @rapidsai/cuvs-build-codeowners
 build.sh           @rapidsai/cuvs-build-codeowners
 **/build.sh        @rapidsai/cuvs-build-codeowners
 
-#build/ops code owners
-.github/           @rapidsai/ops-codeowners
-ci/                @rapidsai/ops-codeowners
-conda/             @rapidsai/ops-codeowners
-**/Dockerfile      @rapidsai/ops-codeowners
-**/.dockerignore   @rapidsai/ops-codeowners
-docker/            @rapidsai/ops-codeowners
-dependencies.yaml  @rapidsai/ops-codeowners
+#CI code owners
+/.github/                @rapidsai/ci-codeowners
+/ci/                     @rapidsai/ci-codeowners
+/.pre-commit-config.yaml @rapidsai/ci-codeowners
+
+#packaging code owners
+/.devcontainer/    @rapidsai/packaging-codeowners
+/conda/            @rapidsai/packaging-codeowners
+/dependencies.yaml @rapidsai/packaging-codeowners
+/build.sh          @rapidsai/packaging-codeowners
+pyproject.toml     @rapidsai/packaging-codeowners

From 4847b5b384340052f2bad88a6b6dc75b0d9e6933 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Tue, 11 Jun 2024 09:18:09 -0700
Subject: [PATCH 079/623] Remove text builds of documentation (#180)

This PR removes text builds of the documentation, which we do not currently use for anything. Contributes to https://github.com/rapidsai/build-planning/issues/71.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/180
---
 ci/build_docs.sh | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index fbf24b346d..d3e916b18e 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -49,11 +49,9 @@ popd
 rapids-logger "Build Python docs"
 pushd docs
 sphinx-build -b dirhtml source _html
-sphinx-build -b text source _text
 mv ../rust/target/doc ./_html/_static/rust
-mkdir -p "${RAPIDS_DOCS_DIR}/cuvs/"{html,txt}
+mkdir -p "${RAPIDS_DOCS_DIR}/cuvs/"html
 mv _html/* "${RAPIDS_DOCS_DIR}/cuvs/html"
-mv _text/* "${RAPIDS_DOCS_DIR}/cuvs/txt"
 popd
 
 rapids-upload-docs

From 5668ef01fdd0b49bafba2c68df2e98d6d9c4adb5 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 12 Jun 2024 18:17:18 +0200
Subject: [PATCH 080/623] Scaling workspace resources (#181)

Use raft's large workspace resource for large temporary allocations during ANN index build.
This is the port of https://github.com/rapidsai/raft/pull/2194, which didn't make into raft before the algorithms were ported to cuVS.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/181
---
 .../neighbors/detail/cagra/cagra_build.cuh    | 59 +++++++----
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 30 +++---
 cpp/src/neighbors/detail/cagra/utils.hpp      |  7 +-
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh | 13 ++-
 .../neighbors/ivf_flat/ivf_flat_search.cuh    | 23 ++---
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     | 97 ++++++++++++-------
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |  5 +-
 7 files changed, 149 insertions(+), 85 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 43e720077f..a3e591b74f 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -34,6 +34,7 @@
 
 // TODO: Fixme- this needs to be migrated
 #include "../../ivf_pq/ivf_pq_build.cuh"
+#include "../../ivf_pq/ivf_pq_search.cuh"
 #include "../../nn_descent.cuh"
 
 // TODO: This shouldn't be calling spatial/knn APIs
@@ -162,42 +163,64 @@ void build_knn_graph(
   // search top (k + 1) neighbors
   //
 
-  const auto top_k          = node_degree + 1;
-  uint32_t gpu_top_k        = node_degree * pq.refinement_rate;
-  gpu_top_k                 = std::min<IdxT>(std::max(gpu_top_k, top_k), dataset.extent(0));
-  const auto num_queries    = dataset.extent(0);
-  const auto max_batch_size = 1024;
+  const auto top_k       = node_degree + 1;
+  uint32_t gpu_top_k     = node_degree * pq.refinement_rate;
+  gpu_top_k              = std::min<IdxT>(std::max(gpu_top_k, top_k), dataset.extent(0));
+  const auto num_queries = dataset.extent(0);
+
+  // Use the same maximum batch size as the ivf_pq::search to avoid allocating more than needed.
+  using cuvs::neighbors::ivf_pq::detail::kMaxQueries;
+  // Heuristic: the build_knn_graph code should use only a fraction of the workspace memory; the
+  // rest should be used by the ivf_pq::search. Here we say that the workspace size should be a good
+  // multiple of what is required for the I/O batching below.
+  constexpr size_t kMinWorkspaceRatio = 5;
+  auto desired_workspace_size         = kMaxQueries * kMinWorkspaceRatio *
+                                (sizeof(DataT) * dataset.extent(1)  // queries (dataset batch)
+                                 + sizeof(float) * gpu_top_k        // distances
+                                 + sizeof(int64_t) * gpu_top_k      // neighbors
+                                 + sizeof(float) * top_k            // refined_distances
+                                 + sizeof(int64_t) * top_k          // refined_neighbors
+                                );
+
+  // If the workspace is smaller than desired, put the I/O buffers into the large workspace.
+  rmm::device_async_resource_ref workspace_mr =
+    desired_workspace_size <= raft::resource::get_workspace_free_bytes(res)
+      ? raft::resource::get_workspace_resource(res)
+      : raft::resource::get_large_workspace_resource(res);
+
   RAFT_LOG_DEBUG(
     "IVF-PQ search node_degree: %d, top_k: %d,  gpu_top_k: %d,  max_batch_size:: %d, n_probes: %u",
     node_degree,
     top_k,
     gpu_top_k,
-    max_batch_size,
+    kMaxQueries,
     pq.search_params.n_probes);
 
-  auto distances = raft::make_device_matrix<float, int64_t>(res, max_batch_size, gpu_top_k);
-  auto neighbors = raft::make_device_matrix<int64_t, int64_t>(res, max_batch_size, gpu_top_k);
-  auto refined_distances = raft::make_device_matrix<float, int64_t>(res, max_batch_size, top_k);
-  auto refined_neighbors = raft::make_device_matrix<int64_t, int64_t>(res, max_batch_size, top_k);
-  auto neighbors_host    = raft::make_host_matrix<int64_t, int64_t>(max_batch_size, gpu_top_k);
-  auto queries_host = raft::make_host_matrix<DataT, int64_t>(max_batch_size, dataset.extent(1));
-  auto refined_neighbors_host = raft::make_host_matrix<int64_t, int64_t>(max_batch_size, top_k);
-  auto refined_distances_host = raft::make_host_matrix<float, int64_t>(max_batch_size, top_k);
+  auto distances = raft::make_device_mdarray<float>(
+    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, gpu_top_k));
+  auto neighbors = raft::make_device_mdarray<int64_t>(
+    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, gpu_top_k));
+  auto refined_distances = raft::make_device_mdarray<float>(
+    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, top_k));
+  auto refined_neighbors = raft::make_device_mdarray<int64_t>(
+    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, top_k));
+  auto neighbors_host = raft::make_host_matrix<int64_t, int64_t>(kMaxQueries, gpu_top_k);
+  auto queries_host   = raft::make_host_matrix<DataT, int64_t>(kMaxQueries, dataset.extent(1));
+  auto refined_neighbors_host = raft::make_host_matrix<int64_t, int64_t>(kMaxQueries, top_k);
+  auto refined_distances_host = raft::make_host_matrix<float, int64_t>(kMaxQueries, top_k);
 
   // TODO(tfeher): batched search with multiple GPUs
   std::size_t num_self_included = 0;
   bool first                    = true;
   const auto start_clock        = std::chrono::system_clock::now();
 
-  rmm::device_async_resource_ref device_memory = raft::resource::get_workspace_resource(res);
-
   cuvs::spatial::knn::detail::utils::batch_load_iterator<DataT> vec_batches(
     dataset.data_handle(),
     dataset.extent(0),
     dataset.extent(1),
-    (int64_t)max_batch_size,
+    static_cast<int64_t>(kMaxQueries),
     raft::resource::get_cuda_stream(res),
-    device_memory);
+    workspace_mr);
 
   size_t next_report_offset = 0;
   size_t d_report_offset    = dataset.extent(0) / 100;  // Report progress in 1% steps.
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 2e90eed64a..e10b85dd4c 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -41,8 +41,7 @@
 #include <memory>
 #include <random>
 
-namespace cuvs::neighbors::cagra::detail {
-namespace graph {
+namespace cuvs::neighbors::cagra::detail::graph {
 
 // unnamed namespace to avoid multiple definition error
 namespace {
@@ -251,7 +250,10 @@ void sort_knn_graph(
   const uint32_t input_graph_degree = knn_graph.extent(1);
   IdxT* const input_graph_ptr       = knn_graph.data_handle();
 
-  auto d_input_graph = raft::make_device_matrix<IdxT, int64_t>(res, graph_size, input_graph_degree);
+  auto large_tmp_mr = raft::resource::get_large_workspace_resource(res);
+
+  auto d_input_graph = raft::make_device_mdarray<IdxT>(
+    res, large_tmp_mr, raft::make_extents<int64_t>(graph_size, input_graph_degree));
 
   //
   // Sorting kNN graph
@@ -259,7 +261,8 @@ void sort_knn_graph(
   const double time_sort_start = cur_time();
   RAFT_LOG_DEBUG("# Sorting kNN Graph on GPUs ");
 
-  auto d_dataset = raft::make_device_matrix<DataT, int64_t>(res, dataset_size, dataset_dim);
+  auto d_dataset = raft::make_device_mdarray<DataT>(
+    res, large_tmp_mr, raft::make_extents<int64_t>(dataset_size, dataset_dim));
   raft::copy(d_dataset.data_handle(),
              dataset_ptr,
              dataset_size * dataset_dim,
@@ -332,6 +335,7 @@ void optimize(
 {
   RAFT_LOG_DEBUG(
     "# Pruning kNN graph (size=%lu, degree=%lu)\n", knn_graph.extent(0), knn_graph.extent(1));
+  auto large_tmp_mr = raft::resource::get_large_workspace_resource(res);
 
   RAFT_EXPECTS(knn_graph.extent(0) == new_graph.extent(0),
                "Each input array is expected to have the same number of rows");
@@ -347,15 +351,16 @@ void optimize(
     //
     // Prune kNN graph
     //
-    auto d_detour_count =
-      raft::make_device_matrix<uint8_t, int64_t>(res, graph_size, input_graph_degree);
+    auto d_detour_count = raft::make_device_mdarray<uint8_t>(
+      res, large_tmp_mr, raft::make_extents<int64_t>(graph_size, input_graph_degree));
 
     RAFT_CUDA_TRY(cudaMemsetAsync(d_detour_count.data_handle(),
                                   0xff,
                                   graph_size * input_graph_degree * sizeof(uint8_t),
                                   raft::resource::get_cuda_stream(res)));
 
-    auto d_num_no_detour_edges = raft::make_device_vector<uint32_t, int64_t>(res, graph_size);
+    auto d_num_no_detour_edges = raft::make_device_mdarray<uint32_t>(
+      res, large_tmp_mr, raft::make_extents<int64_t>(graph_size));
     RAFT_CUDA_TRY(cudaMemsetAsync(d_num_no_detour_edges.data_handle(),
                                   0x00,
                                   graph_size * sizeof(uint32_t),
@@ -475,14 +480,16 @@ void optimize(
                                   graph_size * output_graph_degree * sizeof(IdxT),
                                   raft::resource::get_cuda_stream(res)));
 
-    auto d_rev_graph_count = raft::make_device_vector<uint32_t, int64_t>(res, graph_size);
+    auto d_rev_graph_count = raft::make_device_mdarray<uint32_t>(
+      res, large_tmp_mr, raft::make_extents<int64_t>(graph_size));
     RAFT_CUDA_TRY(cudaMemsetAsync(d_rev_graph_count.data_handle(),
                                   0x00,
                                   graph_size * sizeof(uint32_t),
                                   raft::resource::get_cuda_stream(res)));
 
-    auto dest_nodes   = raft::make_host_vector<IdxT, int64_t>(graph_size);
-    auto d_dest_nodes = raft::make_device_vector<IdxT, int64_t>(res, graph_size);
+    auto dest_nodes = raft::make_host_vector<IdxT, int64_t>(graph_size);
+    auto d_dest_nodes =
+      raft::make_device_mdarray<IdxT>(res, large_tmp_mr, raft::make_extents<int64_t>(graph_size));
 
     for (uint64_t k = 0; k < output_graph_degree; k++) {
 #pragma omp parallel for
@@ -578,5 +585,4 @@ void optimize(
   }
 }
 
-}  // namespace graph
-}  // namespace cuvs::neighbors::cagra::detail
+}  // namespace cuvs::neighbors::cagra::detail::graph
diff --git a/cpp/src/neighbors/detail/cagra/utils.hpp b/cpp/src/neighbors/detail/cagra/utils.hpp
index 395ec15229..8ce20ec5cd 100644
--- a/cpp/src/neighbors/detail/cagra/utils.hpp
+++ b/cpp/src/neighbors/detail/cagra/utils.hpp
@@ -186,8 +186,11 @@ class device_matrix_view_from_host {
     device_ptr = reinterpret_cast<T*>(attr.devicePointer);
     if (device_ptr == NULL) {
       // allocate memory and copy over
-      device_mem_.emplace(
-        raft::make_device_matrix<T, IdxT>(res, host_view.extent(0), host_view.extent(1)));
+      // NB: We use the temporary "large" workspace resource here; this structure is supposed to
+      // live on stack and not returned to a user.
+      // The user may opt to set this resource to managed memory to allow large allocations.
+      device_mem_.emplace(raft::make_device_mdarray<T, IdxT>(
+        res, raft::resource::get_large_workspace_resource(res), host_view.extents()));
       raft::copy(device_mem_->data_handle(),
                  host_view.data_handle(),
                  host_view.extent(0) * host_view.extent(1),
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index 811f9c231d..ff1f3032d9 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -31,6 +31,7 @@
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/linalg/map.cuh>
@@ -184,7 +185,8 @@ void extend(raft::resources const& handle,
   RAFT_EXPECTS(new_indices != nullptr || index->size() == 0,
                "You must pass data indices when the index is non-empty.");
 
-  auto new_labels = raft::make_device_vector<LabelT, IdxT>(handle, n_rows);
+  auto new_labels = raft::make_device_mdarray<LabelT>(
+    handle, raft::resource::get_large_workspace_resource(handle), raft::make_extents<IdxT>(n_rows));
   cuvs::cluster::kmeans::balanced_params kmeans_params;
   kmeans_params.metric = index->metric();
   auto orig_centroids_view =
@@ -215,7 +217,8 @@ void extend(raft::resources const& handle,
   }
 
   auto* list_sizes_ptr    = index->list_sizes().data_handle();
-  auto old_list_sizes_dev = raft::make_device_vector<uint32_t, IdxT>(handle, n_lists);
+  auto old_list_sizes_dev = raft::make_device_mdarray<uint32_t>(
+    handle, raft::resource::get_workspace_resource(handle), raft::make_extents<IdxT>(n_lists));
   raft::copy(old_list_sizes_dev.data_handle(), list_sizes_ptr, n_lists, stream);
 
   // Calculate the centers and sizes on the new data, starting from the original values
@@ -371,7 +374,8 @@ inline auto build(raft::resources const& handle,
     auto trainset_ratio = std::max<size_t>(
       1, n_rows / std::max<size_t>(params.kmeans_trainset_fraction * n_rows, index.n_lists()));
     auto n_rows_train = n_rows / trainset_ratio;
-    rmm::device_uvector<T> trainset(n_rows_train * index.dim(), stream);
+    rmm::device_uvector<T> trainset(
+      n_rows_train * index.dim(), stream, raft::resource::get_large_workspace_resource(handle));
     // TODO: a proper sampling
     RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset.data(),
                                     sizeof(T) * index.dim(),
@@ -431,7 +435,8 @@ inline void fill_refinement_index(raft::resources const& handle,
   common::nvtx::range<common::nvtx::domain::cuvs> fun_scope(
     "ivf_flat::fill_refinement_index(%zu, %u)", size_t(n_queries));
 
-  rmm::device_uvector<LabelT> new_labels(n_queries * n_candidates, stream);
+  rmm::device_uvector<LabelT> new_labels(
+    n_queries * n_candidates, stream, raft::resource::get_workspace_resource(handle));
   auto new_labels_view =
     raft::make_device_vector_view<LabelT, IdxT>(new_labels.data(), n_queries * n_candidates);
   raft::linalg::map_offset(
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index d5efdeb2dc..43111a7dea 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -280,17 +280,15 @@ void search_impl(raft::resources const& handle,
 template <typename T,
           typename IdxT,
           typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
-inline void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  const index<T, IdxT>& index,
-  const T* queries,
-  uint32_t n_queries,
-  uint32_t k,
-  IdxT* neighbors,
-  float* distances,
-  rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource(),
-  IvfSampleFilterT sample_filter    = IvfSampleFilterT())
+inline void search_with_filtering(raft::resources const& handle,
+                                  const search_params& params,
+                                  const index<T, IdxT>& index,
+                                  const T* queries,
+                                  uint32_t n_queries,
+                                  uint32_t k,
+                                  IdxT* neighbors,
+                                  float* distances,
+                                  IvfSampleFilterT sample_filter = IvfSampleFilterT())
 {
   common::nvtx::range<common::nvtx::domain::cuvs> fun_scope(
     "ivf_flat::search(k = %u, n_queries = %u, dim = %zu)", k, n_queries, index.dim());
@@ -335,7 +333,7 @@ inline void search_with_filtering(
                                                   cuvs::distance::is_min_close(index.metric()),
                                                   neighbors + offset_q * k,
                                                   distances + offset_q * k,
-                                                  mr,
+                                                  raft::resource::get_workspace_resource(handle),
                                                   sample_filter);
   }
 }
@@ -367,7 +365,6 @@ void search_with_filtering(raft::resources const& handle,
                         static_cast<std::uint32_t>(neighbors.extent(1)),
                         neighbors.data_handle(),
                         distances.data_handle(),
-                        raft::resource::get_workspace_resource(handle),
                         sample_filter);
 }
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 804f25d028..8823bbbfb7 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -64,6 +64,7 @@
 #include <variant>
 
 namespace cuvs::neighbors::ivf_pq::detail {
+using raft::RAFT_NAME;                       // TODO: this is required for RAFT_LOG_XXX messages.
 using namespace cuvs::spatial::knn::detail;  // NOLINT
 
 using internal_extents_t = int64_t;  // The default mdspan extent type used internally.
@@ -184,7 +185,7 @@ void flat_compute_residuals(
   raft::device_matrix_view<const float, uint32_t, raft::row_major> centers,  // [n_lists, dim_ext]
   const T* dataset,                                                          // [n_rows, dim]
   std::variant<uint32_t, const uint32_t*> labels,                            // [n_rows]
-  rmm::mr::device_memory_resource* device_memory)
+  rmm::device_async_resource_ref device_memory)
 {
   auto stream  = raft::resource::get_cuda_stream(handle);
   auto dim     = rotation_matrix.extent(1);
@@ -357,8 +358,7 @@ void train_per_subset(raft::resources const& handle,
                       size_t n_rows,
                       const float* trainset,   // [n_rows, dim]
                       const uint32_t* labels,  // [n_rows]
-                      uint32_t kmeans_n_iters,
-                      rmm::mr::device_memory_resource* managed_memory)
+                      uint32_t kmeans_n_iters)
 {
   auto stream        = raft::resource::get_cuda_stream(handle);
   auto device_memory = raft::resource::get_workspace_resource(handle);
@@ -435,11 +435,13 @@ void train_per_cluster(raft::resources const& handle,
                        size_t n_rows,
                        const float* trainset,   // [n_rows, dim]
                        const uint32_t* labels,  // [n_rows]
-                       uint32_t kmeans_n_iters,
-                       rmm::mr::device_memory_resource* managed_memory)
+                       uint32_t kmeans_n_iters)
 {
   auto stream        = raft::resource::get_cuda_stream(handle);
   auto device_memory = raft::resource::get_workspace_resource(handle);
+  // NB: Managed memory is used for small arrays accessed from both device and host. There's no
+  // performance reasoning behind this, just avoiding the boilerplate of explicit copies.
+  rmm::mr::managed_memory_resource managed_memory;
 
   rmm::device_uvector<float> pq_centers_tmp(index.pq_centers().size(), stream, device_memory);
   rmm::device_uvector<uint32_t> cluster_sizes(index.n_lists(), stream, managed_memory);
@@ -1303,7 +1305,7 @@ void process_and_fill_codes(raft::resources const& handle,
                             std::variant<IdxT, const IdxT*> src_offset_or_indices,
                             const uint32_t* new_labels,
                             IdxT n_rows,
-                            rmm::mr::device_memory_resource* mr)
+                            rmm::device_async_resource_ref mr)
 {
   auto new_vectors_residual =
     raft::make_device_mdarray<float>(handle, mr, raft::make_extents<IdxT>(n_rows, index.rot_dim()));
@@ -1500,7 +1502,9 @@ void extend(raft::resources const& handle,
                   std::is_same_v<T, int8_t>,
                 "Unsupported data type");
 
-  rmm::mr::device_memory_resource* device_memory = raft::resource::get_workspace_resource(handle);
+  rmm::device_async_resource_ref device_memory = raft::resource::get_workspace_resource(handle);
+  rmm::device_async_resource_ref large_memory =
+    raft::resource::get_large_workspace_resource(handle);
 
   // The spec defines how the clusters look like
   auto spec = list_spec<uint32_t, IdxT>{
@@ -1515,12 +1519,22 @@ void extend(raft::resources const& handle,
     n_rows + (kIndexGroupSize - 1) * std::min<IdxT>(n_clusters, n_rows));
 
   // Available device memory
-  size_t free_mem, total_mem;
-  RAFT_CUDA_TRY(cudaMemGetInfo(&free_mem, &total_mem));
-
+  size_t free_mem = raft::resource::get_workspace_free_bytes(handle);
+
+  // We try to use the workspace memory by default here.
+  // If the workspace limit is too small, we change the resource for batch data to the
+  // `large_workspace_resource`, which does not have the explicit allocation limit. The user may opt
+  // to populate the `large_workspace_resource` memory resource with managed memory for easier
+  // scaling.
+  rmm::device_async_resource_ref labels_mr  = device_memory;
+  rmm::device_async_resource_ref batches_mr = device_memory;
+  if (n_rows * (index->dim() * sizeof(T) + index->pq_dim() + sizeof(IdxT) + sizeof(uint32_t)) >
+      free_mem) {
+    labels_mr = large_memory;
+  }
   // Allocate a buffer for the new labels (classifying the new data)
-  rmm::device_uvector<uint32_t> new_data_labels(n_rows, stream, device_memory);
-  free_mem -= sizeof(uint32_t) * n_rows;
+  rmm::device_uvector<uint32_t> new_data_labels(n_rows, stream, labels_mr);
+  free_mem = raft::resource::get_workspace_free_bytes(handle);
 
   // Calculate the batch size for the input data if it's not accessible directly from the device
   constexpr size_t kReasonableMaxBatchSize = 65536;
@@ -1549,13 +1563,19 @@ void extend(raft::resources const& handle,
     while (size_factor * max_batch_size > free_mem && max_batch_size > 128) {
       max_batch_size >>= 1;
     }
-    // If we're keeping the batches in device memory, update the available mem tracker.
-    free_mem -= size_factor * max_batch_size;
+    if (size_factor * max_batch_size > free_mem) {
+      // if that still doesn't fit, resort to the UVM
+      batches_mr     = large_memory;
+      max_batch_size = kReasonableMaxBatchSize;
+    } else {
+      // If we're keeping the batches in device memory, update the available mem tracker.
+      free_mem -= size_factor * max_batch_size;
+    }
   }
 
   // Predict the cluster labels for the new data, in batches if necessary
   utils::batch_load_iterator<T> vec_batches(
-    new_vectors, n_rows, index->dim(), max_batch_size, stream, device_memory);
+    new_vectors, n_rows, index->dim(), max_batch_size, stream, batches_mr);
   // Release the placeholder memory, because we don't intend to allocate any more long-living
   // temporary buffers before we allocate the index data.
   // This memory could potentially speed up UVM accesses, if any.
@@ -1628,7 +1648,7 @@ void extend(raft::resources const& handle,
   // By this point, the index state is updated and valid except it doesn't contain the new data
   // Fill the extended index with the new data (possibly, in batches)
   utils::batch_load_iterator<IdxT> idx_batches(
-    new_indices, n_rows, 1, max_batch_size, stream, device_memory);
+    new_indices, n_rows, 1, max_batch_size, stream, batches_mr);
   for (const auto& vec_batch : vec_batches) {
     const auto& idx_batch = *idx_batches++;
     process_and_fill_codes(handle,
@@ -1639,7 +1659,7 @@ void extend(raft::resources const& handle,
                              : std::variant<IdxT, const IdxT*>(IdxT(idx_batch.offset())),
                            new_data_labels.data() + vec_batch.offset(),
                            IdxT(vec_batch.size()),
-                           device_memory);
+                           batches_mr);
   }
 }
 
@@ -1694,12 +1714,29 @@ auto build(raft::resources const& handle,
       size_t(n_rows) / std::max<size_t>(params.kmeans_trainset_fraction * n_rows, index.n_lists()));
     size_t n_rows_train = n_rows / trainset_ratio;
 
-    auto* device_memory = raft::resource::get_workspace_resource(handle);
-    rmm::mr::managed_memory_resource managed_memory_upstream;
+    rmm::device_async_resource_ref device_memory = raft::resource::get_workspace_resource(handle);
+
+    // If the trainset is small enough to comfortably fit into device memory, put it there.
+    // Otherwise, use the managed memory.
+    constexpr size_t kTolerableRatio = 4;
+    rmm::device_async_resource_ref big_memory_resource =
+      raft::resource::get_large_workspace_resource(handle);
+    if (sizeof(float) * n_rows_train * index.dim() * kTolerableRatio <
+        raft::resource::get_workspace_free_bytes(handle)) {
+      big_memory_resource = device_memory;
+    }
 
     // Besides just sampling, we transform the input dataset into floats to make it easier
     // to use gemm operations from cublas.
-    rmm::device_uvector<float> trainset(n_rows_train * index.dim(), stream, device_memory);
+    rmm::device_uvector<float> trainset(0, stream, big_memory_resource);
+    try {
+      trainset.resize(n_rows_train * index.dim(), stream);
+    } catch (raft::logic_error& e) {
+      RAFT_LOG_ERROR(
+        "Insufficient memory for kmeans training set allocation. Please decrease "
+        "kmeans_trainset_fraction, or set large_workspace_resource appropriately.");
+      throw;
+    }
     // TODO: a proper sampling
     if constexpr (std::is_same_v<T, float>) {
       RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset.data(),
@@ -1770,7 +1807,7 @@ auto build(raft::resources const& handle,
       handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
 
     // Trainset labels are needed for training PQ codebooks
-    rmm::device_uvector<uint32_t> labels(n_rows_train, stream, device_memory);
+    rmm::device_uvector<uint32_t> labels(n_rows_train, stream, big_memory_resource);
     auto centers_const_view = raft::make_device_matrix_view<const float, internal_extents_t>(
       cluster_centers, index.n_lists(), index.dim());
     auto labels_view =
@@ -1790,22 +1827,12 @@ auto build(raft::resources const& handle,
     // Train PQ codebooks
     switch (index.codebook_kind()) {
       case codebook_gen::PER_SUBSPACE:
-        train_per_subset(handle,
-                         index,
-                         n_rows_train,
-                         trainset.data(),
-                         labels.data(),
-                         params.kmeans_n_iters,
-                         &managed_memory_upstream);
+        train_per_subset(
+          handle, index, n_rows_train, trainset.data(), labels.data(), params.kmeans_n_iters);
         break;
       case codebook_gen::PER_CLUSTER:
-        train_per_cluster(handle,
-                          index,
-                          n_rows_train,
-                          trainset.data(),
-                          labels.data(),
-                          params.kmeans_n_iters,
-                          &managed_memory_upstream);
+        train_per_cluster(
+          handle, index, n_rows_train, trainset.data(), labels.data(), params.kmeans_n_iters);
         break;
       default: RAFT_FAIL("Unreachable code");
     }
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index e131b8fb30..5f812dc4fb 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -586,6 +586,9 @@ inline auto get_max_batch_size(raft::resources const& res,
   return max_batch_size;
 }
 
+/** Maximum number of queries ivf_pq::search can process in one batch. */
+constexpr uint32_t kMaxQueries = 4096;
+
 /** See raft::spatial::knn::ivf_pq::search docs */
 template <typename T,
           typename IdxT,
@@ -648,7 +651,7 @@ inline void search(raft::resources const& handle,
   auto mr = raft::resource::get_workspace_resource(handle);
 
   // Maximum number of query vectors to search at the same time.
-  const auto max_queries = std::min<uint32_t>(std::max<uint32_t>(n_queries, 1), 4096);
+  const auto max_queries = std::min<uint32_t>(std::max<uint32_t>(n_queries, 1), kMaxQueries);
   auto max_batch_size    = get_max_batch_size(handle, k, n_probes, max_queries, max_samples);
 
   rmm::device_uvector<float> float_queries(max_queries * dim_ext, stream, mr);

From 771cc91187e28b7be5dfed4faab4efd6ede7344c Mon Sep 17 00:00:00 2001
From: James Lamb <jaylamb20@gmail.com>
Date: Thu, 13 Jun 2024 16:57:29 -0500
Subject: [PATCH 081/623] resolve dependency-file-generator warning,
 rapids-build-backend followup (#185)

Contributes to https://github.com/rapidsai/build-planning/issues/31
Contributes to https://github.com/rapidsai/dependency-file-generator/issues/89

Since #145 was merged, we've made some small adjustments to the approach for `rapids-build-backend`. This catches `cuvs` up with those changes:

* consolidates version-handling in `ci/` scripts
* uses `--file-key` instead of `--file_key` in `rapids-dependency-file-generator` calls

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/185
---
 ci/build_cpp.sh   | 4 +---
 ci/build_docs.sh  | 2 +-
 ci/build_rust.sh  | 2 +-
 ci/build_wheel.sh | 2 +-
 ci/check_style.sh | 2 +-
 ci/test_cpp.sh    | 2 +-
 ci/test_python.sh | 2 +-
 7 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index 75156eec08..7bc0be5a75 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -13,10 +13,8 @@ export CMAKE_GENERATOR=Ninja
 
 rapids-print-env
 
-version=$(rapids-generate-version)
-
 rapids-logger "Begin cpp build"
 
-RAPIDS_PACKAGE_VERSION=${version} rapids-conda-retry mambabuild conda/recipes/libcuvs
+RAPIDS_PACKAGE_VERSION=$(rapids-generate-version) rapids-conda-retry mambabuild conda/recipes/libcuvs
 
 rapids-upload-conda-to-s3 cpp
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index d3e916b18e..460cc38996 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -8,7 +8,7 @@ rapids-logger "Create test conda environment"
 
 rapids-dependency-file-generator \
   --output conda \
-  --file_key docs \
+  --file-key docs \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n docs
diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index dc4d4cf720..31d0de053f 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -8,7 +8,7 @@ rapids-logger "Create test conda environment"
 
 rapids-dependency-file-generator \
   --output conda \
-  --file_key rust \
+  --file-key rust \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n rust
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 29d9ce4840..5d48bab220 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -12,7 +12,7 @@ source rapids-date-string
 
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
 
-rapids-generate-version > VERSION
+rapids-generate-version > ./VERSION
 
 cd "${package_dir}"
 
diff --git a/ci/check_style.sh b/ci/check_style.sh
index 58cc1b944a..c22f3f9f0a 100755
--- a/ci/check_style.sh
+++ b/ci/check_style.sh
@@ -8,7 +8,7 @@ rapids-logger "Create checks conda environment"
 
 rapids-dependency-file-generator \
   --output conda \
-  --file_key checks \
+  --file-key checks \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n checks
diff --git a/ci/test_cpp.sh b/ci/test_cpp.sh
index 75ec7c5b65..6dfc2cf714 100755
--- a/ci/test_cpp.sh
+++ b/ci/test_cpp.sh
@@ -8,7 +8,7 @@ set -euo pipefail
 rapids-logger "Generate C++ testing dependencies"
 rapids-dependency-file-generator \
   --output conda \
-  --file_key test_cpp \
+  --file-key test_cpp \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch)" | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n test
diff --git a/ci/test_python.sh b/ci/test_python.sh
index 1c23c92273..93bc597cf4 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -8,7 +8,7 @@ set -euo pipefail
 rapids-logger "Generate Python testing dependencies"
 rapids-dependency-file-generator \
   --output conda \
-  --file_key test_python \
+  --file-key test_python \
   --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n test

From 615749e31d6963653ae628e968c7d8348ab76345 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 13 Jun 2024 19:38:27 -0700
Subject: [PATCH 082/623] Change cagra.build_index to cagra.build (#187)

This changes the build_index method to build in the python API for cagra. All of the other python api's use a `build` method for building the index, as do both the C++ and Rust api's as well.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/187
---
 README.md                                       |  2 +-
 docs/source/python_api/neighbors_cagra.rst      |  2 +-
 docs/source/working_with_ann_indexes_python.rst |  2 +-
 python/cuvs/cuvs/neighbors/cagra/__init__.py    |  4 ++--
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx      | 16 ++++++++++++----
 python/cuvs/cuvs/test/test_cagra.py             |  6 +++---
 6 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 08a734cb89..a932b7dd41 100755
--- a/README.md
+++ b/README.md
@@ -65,7 +65,7 @@ from cuvs.neighbors import cagra
 dataset = load_data()
 index_params = cagra.IndexParams()
 
-index = cagra.build_index(build_params, dataset)
+index = cagra.build(build_params, dataset)
 ```
 
 ### C++ API
diff --git a/docs/source/python_api/neighbors_cagra.rst b/docs/source/python_api/neighbors_cagra.rst
index 6f64f77635..09b2e2694c 100644
--- a/docs/source/python_api/neighbors_cagra.rst
+++ b/docs/source/python_api/neighbors_cagra.rst
@@ -28,7 +28,7 @@ Index
 Index build
 ###########
 
-.. autofunction:: cuvs.neighbors.cagra.build_index
+.. autofunction:: cuvs.neighbors.cagra.build
 
 Index search
 ############
diff --git a/docs/source/working_with_ann_indexes_python.rst b/docs/source/working_with_ann_indexes_python.rst
index c075deaea4..b2ab47a854 100644
--- a/docs/source/working_with_ann_indexes_python.rst
+++ b/docs/source/working_with_ann_indexes_python.rst
@@ -16,7 +16,7 @@ Building an index
     dataset = load_data()
     index_params = cagra.IndexParams()
 
-    index = cagra.build_index(build_params, dataset)
+    index = cagra.build(build_params, dataset)
 
 
 Searching an index
diff --git a/python/cuvs/cuvs/neighbors/cagra/__init__.py b/python/cuvs/cuvs/neighbors/cagra/__init__.py
index dd87a644aa..889425f950 100644
--- a/python/cuvs/cuvs/neighbors/cagra/__init__.py
+++ b/python/cuvs/cuvs/neighbors/cagra/__init__.py
@@ -18,7 +18,7 @@
     Index,
     IndexParams,
     SearchParams,
-    build_index,
+    build,
     load,
     save,
     search,
@@ -29,7 +29,7 @@
     "Index",
     "IndexParams",
     "SearchParams",
-    "build_index",
+    "build",
     "load",
     "save",
     "search",
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 3cdff37276..f940ab8bfd 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -15,6 +15,8 @@
 #
 # cython: language_level=3
 
+import warnings
+
 import numpy as np
 
 cimport cuvs.common.cydlpack
@@ -231,7 +233,7 @@ cdef class Index:
 
 
 @auto_sync_resources
-def build_index(IndexParams index_params, dataset, resources=None):
+def build(IndexParams index_params, dataset, resources=None):
     """
     Build the CAGRA index from the dataset for efficient search.
 
@@ -268,7 +270,7 @@ def build_index(IndexParams index_params, dataset, resources=None):
     >>> dataset = cp.random.random_sample((n_samples, n_features),
     ...                                   dtype=cp.float32)
     >>> build_params = cagra.IndexParams(metric="sqeuclidean")
-    >>> index = cagra.build_index(build_params, dataset)
+    >>> index = cagra.build(build_params, dataset)
     >>> distances, neighbors = cagra.search(cagra.SearchParams(),
     ...                                      index, dataset,
     ...                                      k)
@@ -301,6 +303,12 @@ def build_index(IndexParams index_params, dataset, resources=None):
     return idx
 
 
+def build_index(IndexParams index_params, dataset, resources=None):
+    warnings.warn("cagra.build_index is deprecated, use cagra.build instead",
+                  FutureWarning)
+    return build(index_params, dataset, resources=resources)
+
+
 cdef class SearchParams:
     """
     CAGRA search parameters
@@ -501,7 +509,7 @@ def search(SearchParams search_params,
     >>> dataset = cp.random.random_sample((n_samples, n_features),
     ...                                   dtype=cp.float32)
     >>> # Build index
-    >>> index = cagra.build_index(cagra.IndexParams(), dataset)
+    >>> index = cagra.build(cagra.IndexParams(), dataset)
     >>> # Search using the built index
     >>> queries = cp.random.random_sample((n_queries, n_features),
     ...                                   dtype=cp.float32)
@@ -596,7 +604,7 @@ def save(filename, Index index, bool include_dataset=True, resources=None):
     >>> dataset = cp.random.random_sample((n_samples, n_features),
     ...                                   dtype=cp.float32)
     >>> # Build index
-    >>> index = cagra.build_index(cagra.IndexParams(), dataset)
+    >>> index = cagra.build(cagra.IndexParams(), dataset)
     >>> # Serialize and deserialize the cagra index built
     >>> cagra.save("my_index.bin", index)
     >>> index_loaded = cagra.load("my_index.bin")
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
index 15711c854d..38d0d18657 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -54,9 +54,9 @@ def run_cagra_build_search_test(
     )
 
     if array_type == "device":
-        index = cagra.build_index(build_params, dataset_device)
+        index = cagra.build(build_params, dataset_device)
     else:
-        index = cagra.build_index(build_params, dataset)
+        index = cagra.build(build_params, dataset)
 
     if not add_data_on_build:
         dataset_1 = dataset[: n_rows // 2, :]
@@ -198,7 +198,7 @@ def test_save_load(dtype, include_dataset):
     dataset_device = device_ndarray(dataset)
 
     build_params = cagra.IndexParams()
-    index = cagra.build_index(build_params, dataset_device)
+    index = cagra.build(build_params, dataset_device)
 
     assert index.trained
     filename = "my_index.bin"

From 9dc3a4dc5085cb5eb92d18ca1eafd5eef3dc405e Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 14 Jun 2024 13:13:01 -0700
Subject: [PATCH 083/623] Add python serialization API's for ivf-pq and
 ivf_flat (#186)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: https://github.com/rapidsai/cuvs/pull/186
---
 cpp/include/cuvs/neighbors/ivf_flat.h         | 44 +++++++++
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |  9 ++
 cpp/include/cuvs/neighbors/ivf_pq.h           | 40 ++++++++
 cpp/include/cuvs/neighbors/ivf_pq.hpp         | 14 ++-
 .../neighbors/detail/cagra/cagra_build.cpp    | 68 +++++++-------
 cpp/src/neighbors/ivf_flat_c.cpp              | 67 +++++++++++++-
 cpp/src/neighbors/ivf_flat_index.cpp          |  6 ++
 cpp/src/neighbors/ivf_pq_c.cpp                | 92 ++++++++++---------
 cpp/src/neighbors/ivf_pq_index.cpp            | 18 ++++
 cpp/test/neighbors/ann_ivf_flat.cuh           |  2 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |  2 +-
 python/cuvs/cuvs/common/exceptions.pyx        |  2 +-
 .../cuvs/cuvs/neighbors/ivf_flat/__init__.py  | 20 +++-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |  8 ++
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx | 71 ++++++++++++++
 python/cuvs/cuvs/neighbors/ivf_pq/__init__.py | 12 ++-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd  |  8 ++
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  | 71 ++++++++++++++
 python/cuvs/cuvs/test/test_cagra.py           | 50 ----------
 python/cuvs/cuvs/test/test_serialization.py   | 76 +++++++++++++++
 20 files changed, 538 insertions(+), 142 deletions(-)
 create mode 100644 python/cuvs/cuvs/test/test_serialization.py

diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 2e8c551e48..5e91a4f619 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -278,6 +278,50 @@ cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
  * @}
  */
 
+/**
+ * @defgroup ivf_flat_c_serialize IVF-Flat C-API serialize functions
+ * @{
+ */
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <cuvs/neighbors/ivf_flat.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create an index with `cuvsIvfFlatBuild`
+ * cuvsIvfFlatSerialize(res, "/path/to/index", index, true);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-Flat index
+ */
+cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res,
+                                 const char* filename,
+                                 cuvsIvfFlatIndex_t index);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index IVF-Flat index loaded disk
+ */
+cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res,
+                                   const char* filename,
+                                   cuvsIvfFlatIndex_t index);
+/**
+ * @}
+ */
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index b5f3b54fac..ee6102f526 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -138,6 +138,15 @@ struct index : cuvs::neighbors::index {
   index& operator=(const index&) = delete;
   index& operator=(index&&)      = default;
   ~index()                       = default;
+
+  /**
+   * @brief Construct an empty index.
+   *
+   * Constructs an empty index. This index will either need to be trained with `build`
+   * or loaded from a saved copy with `deserialize`
+   */
+  index(raft::resources const& res);
+
   /** Construct an empty index. It needs to be trained and then populated. */
   index(raft::resources const& res, const index_params& params, uint32_t dim);
   /** Construct an empty index. It needs to be trained and then populated. */
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index 9424729ff6..f94448a938 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -351,6 +351,46 @@ cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
  * @}
  */
 
+/**
+ * @defgroup ivf_pq_c_serialize IVF-PQ C-API serialize functions
+ * @{
+ */
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <cuvs/neighbors/ivf_pq.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create an index with `cuvsIvfPqBuild`
+ * cuvsIvfPqSerialize(res, "/path/to/index", index, true);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-PQ index
+ */
+cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char* filename, cuvsIvfPqIndex_t index);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index IVF-PQ index loaded disk
+ */
+cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char* filename, cuvsIvfPqIndex_t index);
+/**
+ * @}
+ */
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index ea9e8b0aee..f38b6cbc48 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -279,6 +279,14 @@ struct index : cuvs::neighbors::index {
   auto operator=(index&&) -> index&      = default;
   ~index()                               = default;
 
+  /**
+   * @brief Construct an empty index.
+   *
+   * Constructs an empty index. This index will either need to be trained with `build`
+   * or loaded from a saved copy with `deserialize`
+   */
+  index(raft::resources const& handle);
+
   /** Construct an empty index. It needs to be trained and then populated. */
   index(raft::resources const& handle,
         cuvs::distance::DistanceType metric,
@@ -1366,7 +1374,7 @@ void serialize(raft::resources const& handle,
  *
  * using IdxT = int64_t; // type of the index
  * // create an empty index
- * cuvs::neighbors::ivf_pq::index<IdxT> index(handl, index_params, dim);
+ * cuvs::neighbors::ivf_pq::index<IdxT> index(handle);
  *
  * cuvs::neighbors::ivf_pq::deserialize(handle, is, index);
  * @endcode
@@ -1390,8 +1398,8 @@ void deserialize(raft::resources const& handle,
  * // create a string with a filepath
  * std::string filename("/path/to/index");
  * using IdxT = int64_t; // type of the index
- * // create an empty index with
- * ivf_pq::index<IdxT> index(handle, index_params, dim);
+ * // create an empty index
+ * ivf_pq::index<IdxT> index(handle);
  *
  * cuvs::neighbors::ivf_pq::deserialize(handle, filename, &index);
  * @endcode
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cpp b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
index 7ea45d0638..574a020977 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cpp
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
@@ -1,35 +1,35 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cuvs/distance/distance.hpp>
-#include <cuvs/neighbors/cagra.hpp>
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft/core/mdspan.hpp>
-
-namespace cuvs::neighbors::cagra::graph_build_params {
-ivf_pq_params::ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
-                             cuvs::distance::DistanceType metric)
-{
-  build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset_extents, metric);
-
-  search_params                         = cuvs::neighbors::ivf_pq::search_params{};
-  search_params.n_probes                = std::max<uint32_t>(10, build_params.n_lists * 0.01);
-  search_params.lut_dtype               = CUDA_R_16F;
-  search_params.internal_distance_dtype = CUDA_R_16F;
-
-  refinement_rate = 2;
-}
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/mdspan.hpp>
+
+namespace cuvs::neighbors::cagra::graph_build_params {
+ivf_pq_params::ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
+                             cuvs::distance::DistanceType metric)
+{
+  build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset_extents, metric);
+
+  search_params                         = cuvs::neighbors::ivf_pq::search_params{};
+  search_params.n_probes                = std::max<uint32_t>(10, build_params.n_lists * 0.01);
+  search_params.lut_dtype               = CUDA_R_16F;
+  search_params.internal_distance_dtype = CUDA_R_16F;
+
+  refinement_rate = 2;
+}
 }  // namespace cuvs::neighbors::cagra::graph_build_params
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index c79ca8df96..ae06a55b65 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -21,6 +21,7 @@
 #include <raft/core/error.hpp>
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/core/exceptions.hpp>
@@ -83,6 +84,22 @@ void _search(cuvsResources_t res,
     *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
 }
 
+template <typename T, typename IdxT>
+void _serialize(cuvsResources_t res, const char* filename, cuvsIvfFlatIndex index)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
+  cuvs::neighbors::ivf_flat::serialize(*res_ptr, std::string(filename), *index_ptr);
+}
+
+template <typename T, typename IdxT>
+void* _deserialize(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto index   = new cuvs::neighbors::ivf_flat::index<T, IdxT>(*res_ptr);
+  cuvs::neighbors::ivf_flat::deserialize(*res_ptr, std::string(filename), index);
+  return index;
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
@@ -120,18 +137,16 @@ extern "C" cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
 
+    index->dtype = dataset.dtype;
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<float, int64_t>(res, *params, dataset_tensor));
-      index->dtype.code = kDLFloat;
     } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<int8_t, int64_t>(res, *params, dataset_tensor));
-      index->dtype.code = kDLInt;
     } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<uint8_t, int64_t>(res, *params, dataset_tensor));
-      index->dtype.code = kDLUInt;
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
@@ -213,3 +228,49 @@ extern "C" cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t
 {
   return cuvs::core::translate_exceptions([=] { delete params; });
 }
+
+extern "C" cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res,
+                                              const char* filename,
+                                              cuvsIvfFlatIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // read the numpy dtype from the beginning of the file
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float, int64_t>(res, filename));
+      index->dtype.code = kDLFloat;
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<int8_t, int64_t>(res, filename));
+      index->dtype.code = kDLInt;
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->addr = reinterpret_cast<uintptr_t>(_deserialize<uint8_t, int64_t>(res, filename));
+      index->dtype.code = kDLUInt;
+    } else {
+      RAFT_FAIL(
+        "Unsupported dtype in file %s itemsize %i kind %i", filename, dtype.itemsize, dtype.kind);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res,
+                                            const char* filename,
+                                            cuvsIvfFlatIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _serialize<float, int64_t>(res, filename, *index);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _serialize<int8_t, int64_t>(res, filename, *index);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _serialize<uint8_t, int64_t>(res, filename, *index);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
index 61d720665f..b249a9c29b 100644
--- a/cpp/src/neighbors/ivf_flat_index.cpp
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -18,6 +18,12 @@
 
 namespace cuvs::neighbors::ivf_flat {
 
+template <typename T, typename IdxT>
+index<T, IdxT>::index(raft::resources const& res)
+  : index(res, cuvs::distance::DistanceType::L2Expanded, 0, false, false, 0)
+{
+}
+
 template <typename T, typename IdxT>
 index<T, IdxT>::index(raft::resources const& res, const index_params& params, uint32_t dim)
   : index(res,
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 14a8791233..3e835523a8 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -20,8 +20,10 @@
 #include <raft/core/error.hpp>
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
 #include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/ivf_pq.hpp>
@@ -88,30 +90,38 @@ void _search(cuvsResources_t res,
     *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
 }
 
+template <typename IdxT>
+void _serialize(cuvsResources_t res, const char* filename, cuvsIvfPqIndex index)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
+  cuvs::neighbors::ivf_pq::serialize(*res_ptr, std::string(filename), *index_ptr);
+}
+
+template <typename IdxT>
+void* _deserialize(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto index   = new cuvs::neighbors::ivf_pq::index<IdxT>(*res_ptr);
+  cuvs::neighbors::ivf_pq::deserialize(*res_ptr, std::string(filename), index);
+  return index;
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index)
 {
-  try {
-    *index = new cuvsIvfPqIndex{};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsIvfPqIndex{}; });
 }
 
 extern "C" cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index_c_ptr)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
     auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index.addr);
     delete index_ptr;
     delete index_c_ptr;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
@@ -119,7 +129,7 @@ extern "C" cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
                                       DLManagedTensor* dataset_tensor,
                                       cuvsIvfPqIndex_t index)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
 
     if ((dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) ||
@@ -127,15 +137,13 @@ extern "C" cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
         (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8)) {
       index->addr = reinterpret_cast<uintptr_t>(_build<int64_t>(res, *params, dataset_tensor));
       index->dtype.code = dataset.dtype.code;
+      index->dtype.bits = dataset.dtype.bits;
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
                 dataset.dtype.bits);
     }
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
@@ -145,7 +153,7 @@ extern "C" cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
                                        DLManagedTensor* neighbors_tensor,
                                        DLManagedTensor* distances_tensor)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
     auto neighbors = neighbors_tensor->dl_tensor;
     auto distances = distances_tensor->dl_tensor;
@@ -163,7 +171,6 @@ extern "C" cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
                  "distances should be of type float32");
 
     auto index = *index_c_ptr;
-    RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
 
     if ((queries.dtype.code == kDLFloat && queries.dtype.bits == 32) ||
         (queries.dtype.code == kDLInt && queries.dtype.bits == 8) ||
@@ -174,16 +181,12 @@ extern "C" cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
                 queries.dtype.code,
                 queries.dtype.bits);
     }
-
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t* params)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     *params = new cuvsIvfPqIndexParams{.metric                         = L2Expanded,
                                        .metric_arg                     = 2.0f,
                                        .add_data_on_build              = true,
@@ -195,41 +198,40 @@ extern "C" cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t* params
                                        .codebook_kind                  = codebook_gen::PER_SUBSPACE,
                                        .force_random_rotation          = false,
                                        .conservative_memory_allocation = false};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t params)
 {
-  try {
-    delete params;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { delete params; });
 }
 
 extern "C" cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t* params)
 {
-  try {
+  return cuvs::core::translate_exceptions([=] {
     *params = new cuvsIvfPqSearchParams{.n_probes                 = 20,
                                         .lut_dtype                = CUDA_R_32F,
                                         .internal_distance_dtype  = CUDA_R_32F,
                                         .preferred_shmem_carveout = 1.0};
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
 {
-  try {
-    delete params;
-    return CUVS_SUCCESS;
-  } catch (...) {
-    return CUVS_ERROR;
-  }
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res,
+                                            const char* filename,
+                                            cuvsIvfPqIndex_t index)
+{
+  return cuvs::core::translate_exceptions(
+    [=] { index->addr = reinterpret_cast<uintptr_t>(_deserialize<int64_t>(res, filename)); });
+}
+
+extern "C" cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res,
+                                          const char* filename,
+                                          cuvsIvfPqIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] { _serialize<int64_t>(res, filename, *index); });
 }
diff --git a/cpp/src/neighbors/ivf_pq_index.cpp b/cpp/src/neighbors/ivf_pq_index.cpp
index ee6c5dfb43..8f4e5b3316 100644
--- a/cpp/src/neighbors/ivf_pq_index.cpp
+++ b/cpp/src/neighbors/ivf_pq_index.cpp
@@ -33,6 +33,23 @@ index_params index_params::from_dataset(raft::matrix_extent<int64_t> dataset,
   return params;
 }
 
+template <typename IdxT>
+index<IdxT>::index(raft::resources const& handle)
+  // this constructor is just for a temporary index, for use in the deserialization
+  // api. all the parameters here will get replaced with loaded values - that aren't
+  // necessarily known ahead of time before deserialization.
+  // TODO: do we even need a handle here - could just construct one?
+  : index(handle,
+          cuvs::distance::DistanceType::L2Expanded,
+          codebook_gen::PER_SUBSPACE,
+          0,
+          0,
+          8,
+          0,
+          true)
+{
+}
+
 template <typename IdxT>
 index<IdxT>::index(raft::resources const& handle, const index_params& params, uint32_t dim)
   : index(handle,
@@ -76,6 +93,7 @@ index<IdxT>::index(raft::resources const& handle,
   check_consistency();
   accum_sorted_sizes_(n_lists) = 0;
 }
+
 template <typename IdxT>
 IdxT index<IdxT>::size() const noexcept
 {
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index 49cb3ec2a1..5dd6ebb2b1 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -185,7 +185,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
           distances_ivfflat_dev.data(), ps.num_queries, ps.k);
         const std::string filename = "ivf_flat_index";
         cuvs::neighbors::ivf_flat::serialize(handle_, filename, index_2);
-        cuvs::neighbors::ivf_flat::index<DataT, IdxT> index_loaded(handle_, index_params, ps.dim);
+        cuvs::neighbors::ivf_flat::index<DataT, IdxT> index_loaded(handle_);
         cuvs::neighbors::ivf_flat::deserialize(handle_, filename, &index_loaded);
         ASSERT_EQ(index_2.size(), index_loaded.size());
 
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index 662b701af9..fa26d7ef91 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -242,7 +242,7 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
   {
     std::string filename = "ivf_pq_index";
     cuvs::neighbors::ivf_pq::serialize(handle_, filename, build_only());
-    cuvs::neighbors::ivf_pq::index<IdxT> index(handle_, ps.index_params, ps.dim);
+    cuvs::neighbors::ivf_pq::index<IdxT> index(handle_);
     cuvs::neighbors::ivf_pq::deserialize(handle_, filename, &index);
     return index;
   }
diff --git a/python/cuvs/cuvs/common/exceptions.pyx b/python/cuvs/cuvs/common/exceptions.pyx
index 603e0711bc..30134594e8 100644
--- a/python/cuvs/cuvs/common/exceptions.pyx
+++ b/python/cuvs/cuvs/common/exceptions.pyx
@@ -28,7 +28,7 @@ def get_last_error_text():
     if c_err is NULL:
         return
     cdef bytes err = c_err
-    return err.decode("utf8")
+    return err.decode("utf8", "ignore")
 
 
 def check_cuvs(status: cuvsError_t):
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py b/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
index a9c3e9b2db..d6a4afddee 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
@@ -13,6 +13,22 @@
 # limitations under the License.
 
 
-from .ivf_flat import Index, IndexParams, SearchParams, build, search
+from .ivf_flat import (
+    Index,
+    IndexParams,
+    SearchParams,
+    build,
+    load,
+    save,
+    search,
+)
 
-__all__ = ["Index", "IndexParams", "SearchParams", "build", "search"]
+__all__ = [
+    "Index",
+    "IndexParams",
+    "SearchParams",
+    "build",
+    "load",
+    "save",
+    "search",
+]
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
index 8859d5d969..65fcad5cfc 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -72,3 +72,11 @@ cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
                                   DLManagedTensor* queries,
                                   DLManagedTensor* neighbors,
                                   DLManagedTensor* distances) except +
+
+    cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res,
+                                     const char * filename,
+                                     cuvsIvfFlatIndex_t index) except +
+
+    cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res,
+                                       const char * filename,
+                                       cuvsIvfFlatIndex_t index) except +
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index daa723099f..03d2549953 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -23,6 +23,7 @@ from cuvs.common.resources import auto_sync_resources
 
 from cython.operator cimport dereference as deref
 from libcpp cimport bool, cast
+from libcpp.string cimport string
 
 from cuvs.common cimport cydlpack
 from cuvs.distance_type cimport cuvsDistanceType
@@ -358,3 +359,73 @@ def search(SearchParams search_params,
         ))
 
     return (distances, neighbors)
+
+
+@auto_sync_resources
+def save(filename, Index index, bool include_dataset=True, resources=None):
+    """
+    Saves the index to a file.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    index : Index
+        Trained IVF-Flat index.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = ivf_flat.build(ivf_flat.IndexParams(), dataset)
+    >>> # Serialize and deserialize the ivf_flat index built
+    >>> ivf_flat.save("my_index.bin", index)
+    >>> index_loaded = ivf_flat.load("my_index.bin")
+    """
+    cdef string c_filename = filename.encode('utf-8')
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    check_cuvs(cuvsIvfFlatSerialize(res,
+                                    c_filename.c_str(),
+                                    index.index))
+
+
+@auto_sync_resources
+def load(filename, resources=None):
+    """
+    Loads index from file.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change, therefore loading an index saved with a previous
+    version of cuvs is not guaranteed to work.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+
+    """
+    cdef Index idx = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    cdef string c_filename = filename.encode('utf-8')
+
+    check_cuvs(cuvsIvfFlatDeserialize(
+        res,
+        c_filename.c_str(),
+        idx.index
+    ))
+    idx.trained = True
+    return idx
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py b/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
index 3c939aa270..bcbcff7e81 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
@@ -13,6 +13,14 @@
 # limitations under the License.
 
 
-from .ivf_pq import Index, IndexParams, SearchParams, build, search
+from .ivf_pq import Index, IndexParams, SearchParams, build, load, save, search
 
-__all__ = ["Index", "IndexParams", "SearchParams", "build", "search"]
+__all__ = [
+    "Index",
+    "IndexParams",
+    "SearchParams",
+    "build",
+    "load",
+    "save",
+    "search",
+]
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index f036c2c611..17d2e40305 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -91,3 +91,11 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
                                 DLManagedTensor* queries,
                                 DLManagedTensor* neighbors,
                                 DLManagedTensor* distances) except +
+
+    cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res,
+                                   const char * filename,
+                                   cuvsIvfPqIndex_t index) except +
+
+    cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res,
+                                     const char * filename,
+                                     cuvsIvfPqIndex_t index) except +
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 81baed1f0e..0dc10e6637 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -23,6 +23,7 @@ from cuvs.common.resources import auto_sync_resources
 
 from cython.operator cimport dereference as deref
 from libcpp cimport bool, cast
+from libcpp.string cimport string
 
 from cuvs.common cimport cydlpack
 from cuvs.distance_type cimport cuvsDistanceType
@@ -432,3 +433,73 @@ def search(SearchParams search_params,
         ))
 
     return (distances, neighbors)
+
+
+@auto_sync_resources
+def save(filename, Index index, bool include_dataset=True, resources=None):
+    """
+    Saves the index to a file.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    index : Index
+        Trained IVF-PQ index.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = ivf_pq.build(ivf_pq.IndexParams(), dataset)
+    >>> # Serialize and deserialize the ivf_pq index built
+    >>> ivf_pq.save("my_index.bin", index)
+    >>> index_loaded = ivf_pq.load("my_index.bin")
+    """
+    cdef string c_filename = filename.encode('utf-8')
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    check_cuvs(cuvsIvfPqSerialize(res,
+                                  c_filename.c_str(),
+                                  index.index))
+
+
+@auto_sync_resources
+def load(filename, resources=None):
+    """
+    Loads index from file.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change, therefore loading an index saved with a previous
+    version of cuvs is not guaranteed to work.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+
+    """
+    cdef Index idx = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    cdef string c_filename = filename.encode('utf-8')
+
+    check_cuvs(cuvsIvfPqDeserialize(
+        res,
+        c_filename.c_str(),
+        idx.index
+    ))
+    idx.trained = True
+    return idx
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
index 38d0d18657..92b88f0139 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -183,53 +183,3 @@ def test_cagra_vpq_compression():
     run_cagra_build_search_test(
         n_cols=dim, compression=cagra.CompressionParams(pq_dim=dim / pq_len)
     )
-
-
-@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.ubyte])
-# TODO: expose update_dataset
-# @pytest.mark.parametrize("include_dataset", [True, False])
-@pytest.mark.parametrize("include_dataset", [True])
-def test_save_load(dtype, include_dataset):
-    n_rows = 10000
-    n_cols = 50
-    n_queries = 1000
-
-    dataset = generate_data((n_rows, n_cols), dtype)
-    dataset_device = device_ndarray(dataset)
-
-    build_params = cagra.IndexParams()
-    index = cagra.build(build_params, dataset_device)
-
-    assert index.trained
-    filename = "my_index.bin"
-    cagra.save(filename, index, include_dataset=include_dataset)
-    loaded_index = cagra.load(filename)
-
-    # if we didn't save the dataset with the index, we need to update the
-    # index with an already loaded copy
-    if not include_dataset:
-        loaded_index.update_dataset(dataset)
-
-    queries = generate_data((n_queries, n_cols), dtype)
-
-    queries_device = device_ndarray(queries)
-    search_params = cagra.SearchParams()
-    k = 10
-
-    distance_dev, neighbors_dev = cagra.search(
-        search_params, index, queries_device, k
-    )
-
-    neighbors = neighbors_dev.copy_to_host()
-    dist = distance_dev.copy_to_host()
-    del index
-
-    distance_dev, neighbors_dev = cagra.search(
-        search_params, loaded_index, queries_device, k
-    )
-
-    neighbors2 = neighbors_dev.copy_to_host()
-    dist2 = distance_dev.copy_to_host()
-
-    assert np.all(neighbors == neighbors2)
-    assert np.allclose(dist, dist2, rtol=1e-6)
diff --git a/python/cuvs/cuvs/test/test_serialization.py b/python/cuvs/cuvs/test/test_serialization.py
new file mode 100644
index 0000000000..4ffccf1214
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_serialization.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+
+from cuvs.neighbors import cagra, ivf_flat, ivf_pq
+from cuvs.test.ann_utils import generate_data
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.ubyte])
+def test_save_load_ivf_flat(dtype):
+    run_save_load(ivf_flat, dtype)
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.ubyte])
+def test_save_load_cagra(dtype):
+    run_save_load(cagra, dtype)
+
+
+def test_save_load_ivf_pq():
+    run_save_load(ivf_pq, np.float32)
+
+
+def run_save_load(ann_module, dtype):
+    n_rows = 10000
+    n_cols = 50
+    n_queries = 1000
+
+    dataset = generate_data((n_rows, n_cols), dtype)
+    dataset_device = device_ndarray(dataset)
+
+    build_params = ann_module.IndexParams()
+    index = ann_module.build(build_params, dataset_device)
+
+    assert index.trained
+    filename = "my_index.bin"
+    ann_module.save(filename, index)
+    loaded_index = ann_module.load(filename)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+
+    queries_device = device_ndarray(queries)
+    search_params = ann_module.SearchParams()
+    k = 10
+
+    distance_dev, neighbors_dev = ann_module.search(
+        search_params, index, queries_device, k
+    )
+
+    neighbors = neighbors_dev.copy_to_host()
+    dist = distance_dev.copy_to_host()
+    del index
+
+    distance_dev, neighbors_dev = ann_module.search(
+        search_params, loaded_index, queries_device, k
+    )
+
+    neighbors2 = neighbors_dev.copy_to_host()
+    dist2 = distance_dev.copy_to_host()
+
+    assert np.all(neighbors == neighbors2)
+    assert np.allclose(dist, dist2, rtol=1e-6)

From 581c9cc9ca23c1d828d28f1ad84e1c53fe5077c6 Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Wed, 19 Jun 2024 06:56:44 +0900
Subject: [PATCH 084/623] Fix a CAGRA graph opt bug (#192)

There is a bug in the current CAGRA graph rank-based neighbor reordering process. A low recall or illegal memory access can occur if there are many detourable nodes from a node to its neighbors, e.g. there is a small subgraph in the initial kNN graph. This PR fixes this.

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/192
---
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 31 ++++++++++++++-----
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index e10b85dd4c..9a63002b34 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -435,24 +435,39 @@ void optimize(
     const auto num_full = host_stats.data_handle()[1];
 
     // Create pruned kNN graph
-    uint32_t max_detour = 0;
-#pragma omp parallel for reduction(max : max_detour)
+#pragma omp parallel for
     for (uint64_t i = 0; i < graph_size; i++) {
-      uint64_t pk = 0;
-      for (uint32_t num_detour = 0; num_detour < output_graph_degree; num_detour++) {
-        if (max_detour < num_detour) { max_detour = num_detour; /* stats */ }
+      // Find the `output_graph_degree` smallest detourable count nodes by checking the detourable
+      // count of the neighbors while increasing the target detourable count from zero.
+      uint64_t pk         = 0;
+      uint32_t num_detour = 0;
+      while (pk < output_graph_degree) {
+        uint32_t next_num_detour = std::numeric_limits<uint32_t>::max();
         for (uint64_t k = 0; k < input_graph_degree; k++) {
-          if (detour_count.data_handle()[k + (input_graph_degree * i)] != num_detour) { continue; }
+          const auto num_detour_k = detour_count.data_handle()[k + (input_graph_degree * i)];
+          // Find the detourable count to check in the next iteration
+          if (num_detour_k > num_detour) {
+            next_num_detour = std::min(static_cast<uint32_t>(num_detour_k), next_num_detour);
+          }
+
+          // Store the neighbor index if its detourable count is equal to `num_detour`.
+          if (num_detour_k != num_detour) { continue; }
           output_graph_ptr[pk + (output_graph_degree * i)] =
             input_graph_ptr[k + (input_graph_degree * i)];
           pk += 1;
           if (pk >= output_graph_degree) break;
         }
         if (pk >= output_graph_degree) break;
+
+        assert(next_num_detour != std::numeric_limits<uint32_t>::max());
+        num_detour = next_num_detour;
       }
-      assert(pk == output_graph_degree);
+      RAFT_EXPECTS(pk == output_graph_degree,
+                   "Couldn't find the output_graph_degree (%u) smallest detourable count nodes for "
+                   "node %lu in the rank-based node reranking process",
+                   output_graph_degree,
+                   static_cast<uint64_t>(i));
     }
-    // RAFT_LOG_DEBUG("# max_detour: %u\n", max_detour);
 
     const double time_prune_end = cur_time();
     RAFT_LOG_DEBUG(

From ebfd06871a4d5fde6b8860408360df5933e3521e Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Thu, 27 Jun 2024 06:21:58 -0700
Subject: [PATCH 085/623] fix library name in docs ('cuvs' not 'pycuvs') (#193)

The Python library is distributed as conda package `cuvs`, but the installation docs say `pycuvs`. This fixes that.

Looked for other uses like this:

```shell
git grep -i pycuvs
```

Didn't find any.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: https://github.com/rapidsai/cuvs/pull/193
---
 README.md             | 14 ++++++++------
 docs/source/build.rst |  2 +-
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index a932b7dd41..b84b8592d5 100755
--- a/README.md
+++ b/README.md
@@ -34,21 +34,23 @@ cuVS contains state-of-the-art implementations of several algorithms for running
 
 cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python). Different packages are available for the different languages supported by cuVS:
 
-| Python | C++ | C |
-|--------|-----|---|
-| `pycuvs`| `libcuvs` | `libcuvs_c` |
+| Python | C/C++                       |
+|--------|-----------------------------|
+| `cuvs` | `libcuvs`, `libcuvs-static` |
 
 ### Stable release
 
-It is recommended to use [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) to install the desired packages. The following command will install the Python package. You can substitute `pycuvs` for any of the packages in the table above:
+It is recommended to use [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) to install the desired packages. The following command will install the Python package. You can substitute `cuvs` for any of the packages in the table above:
+
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai pycuvs
+mamba install -c conda-forge -c nvidia -c rapidsai cuvs
 ```
 
 ### Nightlies
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
+
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai-nightly pycuvs=24.08
+mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.08
 ```
 
 Please see the [Build and Install Guide](docs/source/build.md) for more information on installing cuVS and building from source.
diff --git a/docs/source/build.rst b/docs/source/build.rst
index dbd882660f..f959fc2883 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -50,7 +50,7 @@ C Package
 
 .. code-block:: bash
 
-    mamba install -c rapidsai -c conda-forge -c nvidia libcuvs_c cuda-version=12.0
+    mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.0
 
 Python Package
 ~~~~~~~~~~~~~~

From 064a23b3f60fdea969feff1b6e89e2dbc228ea09 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 27 Jun 2024 18:15:39 +0200
Subject: [PATCH 086/623] ANN_BENCH (#130)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Porting the ANN benchmarks from RAFT.
- [x] Make it build

Sanity check that benchmarks work (runs and gives reasonable recall for Deep-1M dataset)
- [x] cuVS brute force kNN
- [x] cuVS IVF-Flat
- [x] cuVS IVF-PQ (+ refinement)
- [x] cuVS CAGRA
- [x] cuVS CAGRA-Q (+refinement)
- [x] Faiss GPU/CPU IVF-Flat & IVF-PQ
- [x] HNSW
- [x] CAGRA + HNSW
- [x] GGNN

NB: the indices built using the old ANN_BENCH in raft tend to crash in cuvs search benchmarks during index deserialization - don't forget to build the indexes anew when testing.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Malte Förster (https://github.com/mfoerste4)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/130
---
 build.sh                                      |  29 +-
 .../all_cuda-118_arch-aarch64.yaml            |   1 +
 .../all_cuda-118_arch-x86_64.yaml             |   1 +
 .../all_cuda-122_arch-aarch64.yaml            |   1 +
 .../all_cuda-122_arch-x86_64.yaml             |   1 +
 conda/recipes/libcuvs/build_libcuvs_tests.sh  |   2 +-
 conda/recipes/libcuvs/meta.yaml               |   1 +
 cpp/CMakeLists.txt                            |  14 +
 cpp/bench/ann/CMakeLists.txt                  | 367 +++++++++
 cpp/bench/ann/src/common/ann_types.hpp        | 168 ++++
 cpp/bench/ann/src/common/benchmark.cpp        | 124 +++
 cpp/bench/ann/src/common/benchmark.hpp        | 725 ++++++++++++++++++
 cpp/bench/ann/src/common/conf.hpp             | 147 ++++
 .../src/common/cuda_huge_page_resource.hpp    | 105 +++
 .../ann/src/common/cuda_pinned_resource.hpp   |  99 +++
 cpp/bench/ann/src/common/cuda_stub.hpp        | 244 ++++++
 cpp/bench/ann/src/common/dataset.hpp          | 498 ++++++++++++
 cpp/bench/ann/src/common/thread_pool.hpp      | 134 ++++
 cpp/bench/ann/src/common/util.hpp             | 561 ++++++++++++++
 .../src/cuvs/cuvs_ann_bench_param_parser.h    | 273 +++++++
 cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h | 245 ++++++
 cpp/bench/ann/src/cuvs/cuvs_benchmark.cu      | 131 ++++
 cpp/bench/ann/src/cuvs/cuvs_cagra_float.cu    |  20 +
 cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu     |  20 +
 cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu  |  88 +++
 .../ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h | 108 +++
 cpp/bench/ann/src/cuvs/cuvs_cagra_int8_t.cu   |  20 +
 cpp/bench/ann/src/cuvs/cuvs_cagra_uint8_t.cu  |  20 +
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   | 345 +++++++++
 cpp/bench/ann/src/cuvs/cuvs_ivf_flat.cu       |  22 +
 .../ann/src/cuvs/cuvs_ivf_flat_wrapper.h      | 170 ++++
 cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu         |  23 +
 cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h  | 202 +++++
 cpp/bench/ann/src/cuvs/cuvs_wrapper.h         | 167 ++++
 .../ann/src/faiss/faiss_cpu_benchmark.cpp     | 143 ++++
 cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h   | 327 ++++++++
 .../ann/src/faiss/faiss_gpu_benchmark.cu      | 142 ++++
 cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h   | 444 +++++++++++
 cpp/bench/ann/src/ggnn/ggnn_benchmark.cu      | 109 +++
 cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh       | 323 ++++++++
 .../ann/src/hnswlib/hnswlib_benchmark.cpp     |  99 +++
 cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h   | 241 ++++++
 cpp/cmake/patches/ggnn.diff                   | 230 ++++++
 cpp/cmake/patches/hnswlib.diff                | 188 +++++
 cpp/cmake/thirdparty/get_faiss.cmake          | 109 +++
 cpp/cmake/thirdparty/get_ggnn.cmake           |  58 ++
 cpp/cmake/thirdparty/get_glog.cmake           |  42 +
 cpp/cmake/thirdparty/get_hnswlib.cmake        |  92 +++
 cpp/cmake/thirdparty/get_nlohmann_json.cmake  |  42 +
 cpp/include/cuvs/neighbors/cagra.hpp          | 160 ++++
 cpp/src/neighbors/cagra_serialize.cuh         | 140 ++--
 dependencies.yaml                             |   1 +
 52 files changed, 7868 insertions(+), 98 deletions(-)
 create mode 100644 cpp/bench/ann/CMakeLists.txt
 create mode 100644 cpp/bench/ann/src/common/ann_types.hpp
 create mode 100644 cpp/bench/ann/src/common/benchmark.cpp
 create mode 100644 cpp/bench/ann/src/common/benchmark.hpp
 create mode 100644 cpp/bench/ann/src/common/conf.hpp
 create mode 100644 cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
 create mode 100644 cpp/bench/ann/src/common/cuda_pinned_resource.hpp
 create mode 100644 cpp/bench/ann/src/common/cuda_stub.hpp
 create mode 100644 cpp/bench/ann/src/common/dataset.hpp
 create mode 100644 cpp/bench/ann/src/common/thread_pool.hpp
 create mode 100644 cpp/bench/ann/src/common/util.hpp
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_float.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_int8_t.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_uint8_t.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_ivf_flat.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_wrapper.h
 create mode 100644 cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
 create mode 100644 cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
 create mode 100644 cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
 create mode 100644 cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
 create mode 100644 cpp/bench/ann/src/ggnn/ggnn_benchmark.cu
 create mode 100644 cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh
 create mode 100644 cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
 create mode 100644 cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
 create mode 100644 cpp/cmake/patches/ggnn.diff
 create mode 100644 cpp/cmake/patches/hnswlib.diff
 create mode 100644 cpp/cmake/thirdparty/get_faiss.cmake
 create mode 100644 cpp/cmake/thirdparty/get_ggnn.cmake
 create mode 100644 cpp/cmake/thirdparty/get_glog.cmake
 create mode 100644 cpp/cmake/thirdparty/get_hnswlib.cmake
 create mode 100644 cpp/cmake/thirdparty/get_nlohmann_json.cmake

diff --git a/build.sh b/build.sh
index 678df7b352..a283bcd070 100755
--- a/build.sh
+++ b/build.sh
@@ -18,8 +18,8 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python rust docs tests examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
-HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--build-metrics=<filename>]
+VALIDARGS="clean libcuvs python rust docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
    libcuvs          - build the cuvs C++ code only. Also builds the C-wrapper library
@@ -28,6 +28,7 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    rust             - build the cuvs Rust bindings
    docs             - build the documentation
    tests            - build the tests
+   bench-ann        - build end-to-end ann benchmarks
    examples         - build the examples
 
  and <flag> is:
@@ -37,6 +38,7 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    --uninstall                 - uninstall files for specified targets which were built and installed prior
    --compile-static-lib        - compile static library for all components
    --limit-tests               - semicolon-separated list of test executables to compile (e.g. NEIGHBORS_TEST;CLUSTER_TEST)
+   --limit-bench-ann           - semicolon-separated list of ann benchmark executables to compute (e.g. HNSWLIB_ANN_BENCH;RAFT_IVF_PQ_ANN_BENCH)
    --allgpuarch                - build for all supported GPU architectures
    --no-nvtx                   - disable nvtx (profiling markers), but allow enabling it in downstream projects
    --show_depr_warn            - show cmake deprecation warnings
@@ -70,6 +72,7 @@ BUILD_REPORT_METRICS=""
 BUILD_REPORT_INCL_CACHE_STATS=OFF
 
 TEST_TARGETS="NEIGHBORS_ANN_CAGRA_TEST"
+ANN_BENCH_TARGETS="CUVS_ANN_BENCH_ALL"
 
 CACHE_ARGS=""
 NVTX=ON
@@ -150,6 +153,21 @@ function limitTests {
     fi
 }
 
+function limitAnnBench {
+    # Check for option to limit the set of test binaries to build
+    if [[ -n $(echo $ARGS | { grep -E "\-\-limit\-bench-ann" || true; } ) ]]; then
+        # There are possible weird edge cases that may cause this regex filter to output nothing and fail silently
+        # the true pipe will catch any weird edge cases that may happen and will cause the program to fall back
+        # on the invalid option error
+        LIMIT_ANN_BENCH_TARGETS=$(echo $ARGS | sed -e 's/.*--limit-bench-ann=//' -e 's/ .*//')
+        if [[ -n ${LIMIT_ANN_BENCH_TARGETS} ]]; then
+            # Remove the full LIMIT_TEST_TARGETS argument from list of args so that it passes validArgs function
+            ARGS=${ARGS//--limit-bench-ann=$LIMIT_ANN_BENCH_TARGETS/}
+            ANN_BENCH_TARGETS=${LIMIT_ANN_BENCH_TARGETS}
+        fi
+    fi
+}
+
 function buildMetrics {
     # Check for multiple build-metrics options
     if [[ $(echo $ARGS | { grep -Eo "\-\-build\-metrics" || true; } | wc -l ) -gt 1 ]]; then
@@ -179,6 +197,7 @@ if (( ${NUMARGS} != 0 )); then
     cmakeArgs
     cacheTool
     limitTests
+    limitAnnBench
     buildMetrics
     for a in ${ARGS}; do
         if ! (echo " ${VALIDARGS} " | grep -q " ${a} "); then
@@ -255,6 +274,11 @@ if hasArg tests || (( ${NUMARGS} == 0 )); then
     fi
 fi
 
+if hasArg bench-ann || (( ${NUMARGS} == 0 )); then
+    BUILD_ANN_BENCH=ON
+    CMAKE_TARGET="${CMAKE_TARGET};${ANN_BENCH_TARGETS}"
+fi
+
 if hasArg --no-nvtx; then
     NVTX=OFF
 fi
@@ -327,6 +351,7 @@ if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || has
           -DDISABLE_DEPRECATION_WARNINGS=${DISABLE_DEPRECATION_WARNINGS} \
           -DBUILD_TESTS=${BUILD_TESTS} \
           -DBUILD_C_TESTS=${BUILD_TESTS} \
+          -DBUILD_ANN_BENCH=${BUILD_ANN_BENCH} \
           -DBUILD_CPU_ONLY=${BUILD_CPU_ONLY} \
           -DCMAKE_MESSAGE_LOG_LEVEL=${CMAKE_LOG_LEVEL} \
           ${CACHE_ARGS} \
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 9a50bb8056..b4bfdebcf8 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -41,6 +41,7 @@ dependencies:
 - numpy>=1.23,<2.0a0
 - numpydoc
 - nvcc_linux-aarch64=11.8
+- openblas
 - pre-commit
 - pydata-sphinx-theme
 - pylibraft==24.8.*,>=0.0.0a0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index a0581ff458..e3181d69cd 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -41,6 +41,7 @@ dependencies:
 - numpy>=1.23,<2.0a0
 - numpydoc
 - nvcc_linux-64=11.8
+- openblas
 - pre-commit
 - pydata-sphinx-theme
 - pylibraft==24.8.*,>=0.0.0a0
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index 9b151e4b7e..e22d4388ec 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -37,6 +37,7 @@ dependencies:
 - ninja
 - numpy>=1.23,<2.0a0
 - numpydoc
+- openblas
 - pre-commit
 - pydata-sphinx-theme
 - pylibraft==24.8.*,>=0.0.0a0
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index d4b04daf07..08d2f23bbb 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -37,6 +37,7 @@ dependencies:
 - ninja
 - numpy>=1.23,<2.0a0
 - numpydoc
+- openblas
 - pre-commit
 - pydata-sphinx-theme
 - pylibraft==24.8.*,>=0.0.0a0
diff --git a/conda/recipes/libcuvs/build_libcuvs_tests.sh b/conda/recipes/libcuvs/build_libcuvs_tests.sh
index fb116a6920..5d77ae2d1b 100644
--- a/conda/recipes/libcuvs/build_libcuvs_tests.sh
+++ b/conda/recipes/libcuvs/build_libcuvs_tests.sh
@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
 # Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
-./build.sh tests --allgpuarch --no-nvtx --build-metrics=tests_bench --incl-cache-stats
+./build.sh tests bench-ann --allgpuarch --no-nvtx --build-metrics=tests_bench --incl-cache-stats
 cmake --install cpp/build --component testing
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index d1e8506c87..4ffdc91e40 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -198,6 +198,7 @@ outputs:
         - libraft ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
         - cuda-version ={{ cuda_version }}
+        - openblas # required by some CPU algos in benchmarks
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
         - libcublas {{ cuda11_libcublas_host_version }}
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index abe6fa7b3d..c7b61b92f3 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -55,6 +55,7 @@ option(BUILD_SHARED_LIBS "Build cuvs shared libraries" ON)
 option(BUILD_TESTS "Build cuvs unit-tests" ON)
 option(BUILD_C_LIBRARY "Build raft C API library" OFF)
 option(BUILD_C_TESTS "Build raft C API tests" OFF)
+option(BUILD_ANN_BENCH "Build cuVS ann benchmarks" OFF)
 option(CUDA_ENABLE_KERNELINFO "Enable kernel resource usage info" OFF)
 option(CUDA_ENABLE_LINEINFO
        "Enable the -lineinfo option for nvcc (useful for cuda-memcheck / profiler)" OFF
@@ -92,6 +93,7 @@ include(CMakeDependentOption)
 
 message(VERBOSE "cuVS: Build cuVS unit-tests: ${BUILD_TESTS}")
 message(VERBOSE "cuVS: Build CPU only components: ${BUILD_CPU_ONLY}")
+message(VERBOSE "cuVS: Build ANN benchmarks: ${BUILD_ANN_BENCH}")
 message(VERBOSE "cuVS: Enable detection of conda environment for dependencies: ${DETECT_CONDA_ENV}")
 message(VERBOSE "cuVS: Disable depreaction warnings " ${DISABLE_DEPRECATION_WARNINGS})
 message(VERBOSE "cuVS: Disable OpenMP: ${DISABLE_OPENMP}")
@@ -184,6 +186,11 @@ endif()
 
 include(cmake/thirdparty/get_cutlass.cmake)
 
+if(BUILD_ANN_BENCH)
+  include(${rapids-cmake-dir}/cpm/gbench.cmake)
+  rapids_cpm_gbench(BUILD_STATIC)
+endif()
+
 # ##################################################################################################
 # * cuvs ---------------------------------------------------------------------
 
@@ -663,3 +670,10 @@ if(BUILD_TESTS OR BUILD_C_TESTS)
   include(internal/CMakeLists.txt)
   include(test/CMakeLists.txt)
 endif()
+
+# ##################################################################################################
+# * build ann benchmark executable -----------------------------------------------
+
+if(BUILD_ANN_BENCH)
+  include(bench/ann/CMakeLists.txt)
+endif()
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
new file mode 100644
index 0000000000..6e9e66fadb
--- /dev/null
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -0,0 +1,367 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# ##################################################################################################
+# * benchmark options ------------------------------------------------------------------------------
+
+option(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT "Include faiss' brute-force knn algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT "Include faiss' ivf flat algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ "Include faiss' ivf pq algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT "Include faiss' cpu brute-force algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT "Include faiss' cpu ivf flat algorithm in benchmark"
+       ON
+)
+option(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ "Include faiss' cpu ivf pq algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT "Include cuVS ivf flat algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ "Include cuVS ivf pq algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_CUVS_CAGRA "Include cuVS CAGRA in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE "Include cuVS brute force knn in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB "Include cuVS CAGRA with HNSW search in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_HNSWLIB "Include hnsw algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_GGNN "Include ggnn algorithm in benchmark" OFF)
+option(CUVS_ANN_BENCH_SINGLE_EXE
+       "Make a single executable with benchmark as shared library modules" OFF
+)
+
+# ##################################################################################################
+# * Process options ----------------------------------------------------------
+
+find_package(Threads REQUIRED)
+
+if(BUILD_CPU_ONLY)
+  set(CUVS_FAISS_ENABLE_GPU OFF)
+  set(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT OFF)
+  set(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT OFF)
+  set(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_CAGRA OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB OFF)
+  set(CUVS_ANN_BENCH_USE_GGNN OFF)
+else()
+  set(CUVS_FAISS_ENABLE_GPU ON)
+endif()
+
+set(CUVS_ANN_BENCH_USE_FAISS OFF)
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT
+   OR CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ
+   OR CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT
+   OR CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT
+   OR CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ
+   OR CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT
+)
+  set(CUVS_ANN_BENCH_USE_FAISS ON)
+  set(CUVS_USE_FAISS_STATIC ON)
+endif()
+
+set(CUVS_ANN_BENCH_USE_CUVS OFF)
+if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
+   OR CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE
+   OR CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT
+   OR CUVS_ANN_BENCH_USE_CUVS_CAGRA
+   OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB
+)
+  set(CUVS_ANN_BENCH_USE_CUVS ON)
+endif()
+
+# ##################################################################################################
+# * Fetch requirements -------------------------------------------------------------
+
+if(CUVS_ANN_BENCH_USE_HNSWLIB OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+  include(cmake/thirdparty/get_hnswlib.cmake)
+endif()
+
+include(cmake/thirdparty/get_nlohmann_json.cmake)
+
+if(CUVS_ANN_BENCH_USE_GGNN)
+  include(cmake/thirdparty/get_ggnn.cmake)
+endif()
+
+if(CUVS_ANN_BENCH_USE_FAISS)
+  # We need to ensure that faiss has all the conda information. So we currently use the very ugly
+  # hammer of `link_libraries` to ensure that all targets in this directory and the faiss directory
+  # will have the conda includes/link dirs
+  link_libraries($<TARGET_NAME_IF_EXISTS:conda_env>)
+  include(cmake/thirdparty/get_faiss.cmake)
+endif()
+
+# ##################################################################################################
+# * Enable NVTX if available
+
+# Note: ANN_BENCH wrappers have extra NVTX code not related to raft::nvtx.They track gbench
+# benchmark cases and iterations. This is to make limited NVTX available to all algos, not just
+# raft/cuVS.
+if(TARGET CUDA::nvtx3)
+  set(_CMAKE_REQUIRED_INCLUDES_ORIG ${CMAKE_REQUIRED_INCLUDES})
+  get_target_property(CMAKE_REQUIRED_INCLUDES CUDA::nvtx3 INTERFACE_INCLUDE_DIRECTORIES)
+  unset(NVTX3_HEADERS_FOUND CACHE)
+  # Check the headers explicitly to make sure the cpu-only build succeeds
+  CHECK_INCLUDE_FILE_CXX(nvtx3/nvToolsExt.h NVTX3_HEADERS_FOUND)
+  set(CMAKE_REQUIRED_INCLUDES ${_CMAKE_REQUIRED_INCLUDES_ORIG})
+endif()
+
+# ##################################################################################################
+# * Target function -------------------------------------------------------------
+
+function(ConfigureAnnBench)
+
+  set(oneValueArgs NAME)
+  set(multiValueArgs PATH LINKS CXXFLAGS)
+
+  if(NOT BUILD_CPU_ONLY)
+    set(GPU_BUILD ON)
+  endif()
+
+  cmake_parse_arguments(
+    ConfigureAnnBench "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}
+  )
+
+  set(BENCH_NAME ${ConfigureAnnBench_NAME}_ANN_BENCH)
+
+  if(CUVS_ANN_BENCH_SINGLE_EXE)
+    add_library(${BENCH_NAME} SHARED ${ConfigureAnnBench_PATH})
+    string(TOLOWER ${BENCH_NAME} BENCH_LIB_NAME)
+    set_target_properties(${BENCH_NAME} PROPERTIES OUTPUT_NAME ${BENCH_LIB_NAME})
+    add_dependencies(${BENCH_NAME} ANN_BENCH)
+  else()
+    add_executable(${BENCH_NAME} ${ConfigureAnnBench_PATH})
+    target_compile_definitions(
+      ${BENCH_NAME} PRIVATE ANN_BENCH_BUILD_MAIN
+                            $<$<BOOL:${NVTX3_HEADERS_FOUND}>:ANN_BENCH_NVTX3_HEADERS_FOUND>
+    )
+    target_link_libraries(
+      ${BENCH_NAME} PRIVATE benchmark::benchmark $<$<BOOL:${NVTX3_HEADERS_FOUND}>:CUDA::nvtx3>
+    )
+  endif()
+
+  target_link_libraries(
+    ${BENCH_NAME}
+    PRIVATE ${ConfigureAnnBench_LINKS}
+            nlohmann_json::nlohmann_json
+            Threads::Threads
+            $<$<BOOL:${GPU_BUILD}>:CUDA::cudart_static>
+            $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+            $<TARGET_NAME_IF_EXISTS:conda_env>
+            -static-libgcc
+            -static-libstdc++
+  )
+
+  set_target_properties(
+    ${BENCH_NAME}
+    PROPERTIES # set target compile options
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+               BUILD_RPATH "\$ORIGIN"
+               INSTALL_RPATH "\$ORIGIN"
+  )
+
+  set(${ConfigureAnnBench_CXXFLAGS} ${CUVS_CXX_FLAGS} ${ConfigureAnnBench_CXXFLAGS})
+
+  target_compile_options(
+    ${BENCH_NAME} PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${ConfigureAnnBench_CXXFLAGS}>"
+                          "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
+  )
+
+  if(CUVS_ANN_BENCH_USE_${ConfigureAnnBench_NAME})
+    target_compile_definitions(
+      ${BENCH_NAME}
+      PUBLIC
+        CUVS_ANN_BENCH_USE_${ConfigureAnnBench_NAME}=CUVS_ANN_BENCH_USE_${ConfigureAnnBench_NAME}
+    )
+  endif()
+
+  target_include_directories(
+    ${BENCH_NAME}
+    PUBLIC "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/include>"
+    PRIVATE ${ConfigureAnnBench_INCLUDES}
+  )
+
+  install(
+    TARGETS ${BENCH_NAME}
+    COMPONENT ann_bench
+    DESTINATION bin/ann
+  )
+
+  add_dependencies(CUVS_ANN_BENCH_ALL ${BENCH_NAME})
+endfunction()
+
+# ##################################################################################################
+# * Configure benchmark targets -------------------------------------------------------------
+
+if(NOT TARGET CUVS_ANN_BENCH_ALL)
+  add_custom_target(CUVS_ANN_BENCH_ALL)
+endif()
+
+if(CUVS_ANN_BENCH_USE_HNSWLIB)
+  ConfigureAnnBench(
+    NAME HNSWLIB PATH bench/ann/src/hnswlib/hnswlib_benchmark.cpp LINKS hnswlib::hnswlib
+  )
+
+endif()
+
+if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ)
+  ConfigureAnnBench(
+    NAME CUVS_IVF_PQ PATH bench/ann/src/cuvs/cuvs_benchmark.cu
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_IVF_PQ}>:bench/ann/src/cuvs/cuvs_ivf_pq.cu> LINKS cuvs
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT)
+  ConfigureAnnBench(
+    NAME CUVS_IVF_FLAT PATH bench/ann/src/cuvs/cuvs_benchmark.cu
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT}>:bench/ann/src/cuvs/cuvs_ivf_flat.cu> LINKS cuvs
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE)
+  ConfigureAnnBench(NAME CUVS_BRUTE_FORCE PATH bench/ann/src/cuvs/cuvs_benchmark.cu LINKS cuvs)
+endif()
+
+if(CUVS_ANN_BENCH_USE_CUVS_CAGRA)
+  ConfigureAnnBench(
+    NAME
+    CUVS_CAGRA
+    PATH
+    bench/ann/src/cuvs/cuvs_benchmark.cu
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_float.cu>
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_half.cu>
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_int8_t.cu>
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_uint8_t.cu>
+    LINKS
+    cuvs
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+  ConfigureAnnBench(
+    NAME CUVS_CAGRA_HNSWLIB PATH bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu LINKS cuvs
+    hnswlib::hnswlib
+  )
+endif()
+
+set(CUVS_FAISS_TARGETS faiss::faiss)
+if(TARGET faiss::faiss_avx2)
+  set(CUVS_FAISS_TARGETS faiss::faiss_avx2)
+endif()
+
+message("CUVS_FAISS_TARGETS: ${CUVS_FAISS_TARGETS}")
+message("CUDAToolkit_LIBRARY_DIR: ${CUDAToolkit_LIBRARY_DIR}")
+if(CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT)
+  ConfigureAnnBench(
+    NAME FAISS_CPU_FLAT PATH bench/ann/src/faiss/faiss_cpu_benchmark.cpp LINKS
+    ${CUVS_FAISS_TARGETS}
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT)
+  ConfigureAnnBench(
+    NAME FAISS_CPU_IVF_FLAT PATH bench/ann/src/faiss/faiss_cpu_benchmark.cpp LINKS
+    ${CUVS_FAISS_TARGETS}
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ)
+  ConfigureAnnBench(
+    NAME FAISS_CPU_IVF_PQ PATH bench/ann/src/faiss/faiss_cpu_benchmark.cpp LINKS
+    ${CUVS_FAISS_TARGETS}
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT)
+  ConfigureAnnBench(
+    NAME FAISS_GPU_IVF_FLAT PATH bench/ann/src/faiss/faiss_gpu_benchmark.cu LINKS
+    ${CUVS_FAISS_TARGETS}
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ)
+  ConfigureAnnBench(
+    NAME FAISS_GPU_IVF_PQ PATH bench/ann/src/faiss/faiss_gpu_benchmark.cu LINKS
+    ${CUVS_FAISS_TARGETS}
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT)
+  ConfigureAnnBench(
+    NAME FAISS_GPU_FLAT PATH bench/ann/src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_GGNN)
+  include(cmake/thirdparty/get_glog.cmake)
+  ConfigureAnnBench(
+    NAME GGNN PATH bench/ann/src/ggnn/ggnn_benchmark.cu LINKS glog::glog ggnn::ggnn CUDA::curand
+  )
+endif()
+
+# ##################################################################################################
+# * Dynamically-loading ANN_BENCH executable -------------------------------------------------------
+if(CUVS_ANN_BENCH_SINGLE_EXE)
+  add_executable(ANN_BENCH bench/ann/src/common/benchmark.cpp)
+
+  # Build and link static version of the GBench to keep ANN_BENCH self-contained.
+  get_target_property(TMP_PROP benchmark::benchmark SOURCES)
+  add_library(benchmark_static STATIC ${TMP_PROP})
+  get_target_property(TMP_PROP benchmark::benchmark INCLUDE_DIRECTORIES)
+  target_include_directories(benchmark_static PUBLIC ${TMP_PROP})
+  get_target_property(TMP_PROP benchmark::benchmark LINK_LIBRARIES)
+  target_link_libraries(benchmark_static PUBLIC ${TMP_PROP})
+
+  target_include_directories(ANN_BENCH PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+
+  target_link_libraries(
+    ANN_BENCH
+    PRIVATE raft::raft
+            nlohmann_json::nlohmann_json
+            benchmark_static
+            dl
+            -static-libgcc
+            fmt::fmt-header-only
+            spdlog::spdlog_header_only
+            -static-libstdc++
+            $<$<BOOL:${NVTX3_HEADERS_FOUND}>:CUDA::nvtx3>
+  )
+  set_target_properties(
+    ANN_BENCH
+    PROPERTIES # set target compile options
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+               BUILD_RPATH "\$ORIGIN"
+               INSTALL_RPATH "\$ORIGIN"
+  )
+  target_compile_definitions(
+    ANN_BENCH
+    PRIVATE
+      $<$<BOOL:${CUDAToolkit_FOUND}>:ANN_BENCH_LINK_CUDART="libcudart.so.${CUDAToolkit_VERSION_MAJOR}.${CUDAToolkit_VERSION_MINOR}.${CUDAToolkit_VERSION_PATCH}">
+      $<$<BOOL:${NVTX3_HEADERS_FOUND}>:ANN_BENCH_NVTX3_HEADERS_FOUND>
+  )
+
+  target_link_options(ANN_BENCH PRIVATE -export-dynamic)
+
+  install(
+    TARGETS ANN_BENCH
+    COMPONENT ann_bench
+    DESTINATION bin/ann
+    EXCLUDE_FROM_ALL
+  )
+endif()
diff --git a/cpp/bench/ann/src/common/ann_types.hpp b/cpp/bench/ann/src/common/ann_types.hpp
new file mode 100644
index 0000000000..4b17885c07
--- /dev/null
+++ b/cpp/bench/ann/src/common/ann_types.hpp
@@ -0,0 +1,168 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "cuda_stub.hpp"  // cudaStream_t
+
+#include <memory>
+#include <nlohmann/json.hpp>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+namespace cuvs::bench {
+
+/** Benchmark mode: measuring latency vs throughput. */
+enum class Mode {
+  kThroughput,  // See how many vectors we can push through
+  kLatency      // See how fast we can push a vector through
+};
+
+enum class MemoryType {
+  kHost,
+  kHostMmap,
+  kDevice,
+};
+
+enum class Metric {
+  kInnerProduct,
+  kEuclidean,
+};
+
+inline auto parse_metric(const std::string& metric_str) -> Metric
+{
+  if (metric_str == "inner_product") {
+    return cuvs::bench::Metric::kInnerProduct;
+  } else if (metric_str == "euclidean") {
+    return cuvs::bench::Metric::kEuclidean;
+  } else {
+    throw std::runtime_error("invalid metric: '" + metric_str + "'");
+  }
+}
+
+inline auto parse_memory_type(const std::string& memory_type) -> MemoryType
+{
+  if (memory_type == "host") {
+    return MemoryType::kHost;
+  } else if (memory_type == "mmap") {
+    return MemoryType::kHostMmap;
+  } else if (memory_type == "device") {
+    return MemoryType::kDevice;
+  } else {
+    throw std::runtime_error("invalid memory type: '" + memory_type + "'");
+  }
+}
+
+struct algo_property {
+  MemoryType dataset_memory_type;
+  // neighbors/distances should have same memory type as queries
+  MemoryType query_memory_type;
+};
+
+class algo_base {
+ public:
+  using index_type = int64_t;
+
+  inline algo_base(Metric metric, int dim) : metric_(metric), dim_(dim) {}
+  virtual ~algo_base() noexcept = default;
+
+ protected:
+  Metric metric_;
+  int dim_;
+};
+
+/**
+ * The GPU-based algorithms, which do not perform CPU synchronization at the end of their build or
+ * search methods, must implement this interface.
+ *
+ * The `cuda_timer` / `cuda_lap`  from `util.hpp` uses this stream to record GPU times with events
+ * and, if necessary, also synchronize (via events) between iterations.
+ *
+ * If the algo does not implement this interface, GPU timings are disabled.
+ */
+class algo_gpu {
+ public:
+  /**
+   * Return the main cuda stream for this algorithm.
+   * If any work is done in multiple streams, they should synchornize with the main stream at the
+   * end.
+   */
+  [[nodiscard]] virtual auto get_sync_stream() const noexcept -> cudaStream_t = 0;
+  /**
+   * By default a GPU algorithm uses a fixed stream to order GPU operations.
+   * However, an algorithm may need to synchronize with the host at the end of its execution.
+   * In that case, also synchronizing with a benchmark event would put it at disadvantage.
+   *
+   * We can disable event sync by passing `false` here
+   *   - ONLY IF THE ALGORITHM HAS PRODUCED ITS OUTPUT BY THE TIME IT SYNCHRONIZES WITH CPU.
+   */
+  [[nodiscard]] virtual auto uses_stream() const noexcept -> bool { return true; }
+  virtual ~algo_gpu() noexcept = default;
+};
+
+template <typename T>
+class algo : public algo_base {
+ public:
+  struct search_param {
+    virtual ~search_param() = default;
+    [[nodiscard]] virtual auto needs_dataset() const -> bool { return false; };
+  };
+
+  inline algo(Metric metric, int dim) : algo_base(metric, dim) {}
+  ~algo() noexcept override = default;
+
+  virtual void build(const T* dataset, size_t nrow) = 0;
+
+  virtual void set_search_param(const search_param& param) = 0;
+  // TODO(snanditale): this assumes that an algorithm can always return k results.
+  // This is not always possible.
+  virtual void search(const T* queries,
+                      int batch_size,
+                      int k,
+                      algo_base::index_type* neighbors,
+                      float* distances) const = 0;
+
+  virtual void save(const std::string& file) const = 0;
+  virtual void load(const std::string& file)       = 0;
+
+  [[nodiscard]] virtual auto get_preference() const -> algo_property = 0;
+
+  // Some algorithms don't save the building dataset in their indices.
+  // So they should be given the access to that dataset during searching.
+  // The advantage of this way is that index has smaller size
+  // and many indices can share one dataset.
+  //
+  // search_param::needs_dataset() of such algorithm should be true,
+  // and set_search_dataset() should save the passed-in pointer somewhere.
+  // The client code should call set_search_dataset() before searching,
+  // and should not release dataset before searching is finished.
+  virtual void set_search_dataset(const T* /*dataset*/, size_t /*nrow*/){};
+
+  /**
+   * Make a shallow copy of the algo wrapper that shares the resources and ensures thread-safe
+   * access to them. */
+  virtual auto copy() -> std::unique_ptr<algo<T>> = 0;
+};
+
+}  // namespace cuvs::bench
+
+#define REGISTER_ALGO_INSTANCE(DataT)                                                              \
+  template auto cuvs::bench::create_algo<DataT>(                                                   \
+    const std::string&, const std::string&, int, const nlohmann::json&)                            \
+    ->std::unique_ptr<cuvs::bench::algo<DataT>>;                                                   \
+  template auto cuvs::bench::create_search_param<DataT>(const std::string&, const nlohmann::json&) \
+    ->std::unique_ptr<typename cuvs::bench::algo<DataT>::search_param>;
diff --git a/cpp/bench/ann/src/common/benchmark.cpp b/cpp/bench/ann/src/common/benchmark.cpp
new file mode 100644
index 0000000000..7ec8558a4a
--- /dev/null
+++ b/cpp/bench/ann/src/common/benchmark.cpp
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// clang-format off
+#include "cuda_stub.hpp"  // must go first
+// clang-format on
+
+#include "ann_types.hpp"
+
+#include <dlfcn.h>
+
+#include <filesystem>
+#include <memory>
+#include <unordered_map>
+
+namespace cuvs::bench {
+
+struct lib_handle {
+  void* handle{nullptr};
+  explicit lib_handle(const std::string& name)
+  {
+    handle = dlopen(name.c_str(), RTLD_LAZY | RTLD_LOCAL);
+    if (handle == nullptr) {
+      auto error_msg = "Failed to load " + name;
+      auto err       = dlerror();
+      if (err != nullptr && err[0] != '\0') { error_msg += ": " + std::string(err); }
+      throw std::runtime_error(error_msg);
+    }
+  }
+  ~lib_handle() noexcept
+  {
+    if (handle != nullptr) { dlclose(handle); }
+  }
+};
+
+auto load_lib(const std::string& algo) -> void*
+{
+  static std::unordered_map<std::string, lib_handle> libs{};
+  auto found = libs.find(algo);
+
+  if (found != libs.end()) { return found->second.handle; }
+  auto lib_name = "lib" + algo + "_ann_bench.so";
+  return libs.emplace(algo, lib_name).first->second.handle;
+}
+
+/*
+  TODO(achirkin): remove this compatibility layer.
+  When reading old raft algo configs, we may encounter raft_xxx algorithms;
+  they all are renamed to cuvs_xxx algorithm.
+  This compatibility layer helps using old configs with the new benchmark executable.
+ */
+auto load_lib_raft_compat(const std::string& algo) -> void*
+{
+  try {
+    return load_lib(algo);
+  } catch (std::runtime_error& e) {
+    if (algo.rfind("raft", 0) == 0) { return load_lib("cuvs" + algo.substr(4)); }
+    throw e;
+  }
+}
+
+auto get_fun_name(void* addr) -> std::string
+{
+  Dl_info dl_info;
+  if (dladdr(addr, &dl_info) != 0) {
+    if (dl_info.dli_sname != nullptr && dl_info.dli_sname[0] != '\0') {
+      return std::string{dl_info.dli_sname};
+    }
+  }
+  throw std::logic_error("Failed to find out name of the looked up function");
+}
+
+template <typename T>
+auto create_algo(const std::string& algo,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  static auto fname = get_fun_name(reinterpret_cast<void*>(&create_algo<T>));
+  auto handle       = load_lib_raft_compat(algo);
+  auto fun_addr     = dlsym(handle, fname.c_str());
+  if (fun_addr == nullptr) {
+    throw std::runtime_error("Couldn't load the create_algo function (" + algo + ")");
+  }
+  auto fun = reinterpret_cast<decltype(&create_algo<T>)>(fun_addr);
+  return fun(algo, distance, dim, conf);
+}
+
+template <typename T>
+std::unique_ptr<typename cuvs::bench::algo<T>::search_param> create_search_param(
+  const std::string& algo, const nlohmann::json& conf)
+{
+  static auto fname = get_fun_name(reinterpret_cast<void*>(&create_search_param<T>));
+  auto handle       = load_lib_raft_compat(algo);
+  auto fun_addr     = dlsym(handle, fname.c_str());
+  if (fun_addr == nullptr) {
+    throw std::runtime_error("Couldn't load the create_search_param function (" + algo + ")");
+  }
+  auto fun = reinterpret_cast<decltype(&create_search_param<T>)>(fun_addr);
+  return fun(algo, conf);
+}
+
+};  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#include "benchmark.hpp"
+
+auto main(int argc, char** argv) -> int { return cuvs::bench::run_main(argc, argv); }
diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
new file mode 100644
index 0000000000..e6462c1571
--- /dev/null
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -0,0 +1,725 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "ann_types.hpp"
+#include "conf.hpp"
+#include "dataset.hpp"
+#include "util.hpp"
+
+#include <benchmark/benchmark.h>
+#include <unistd.h>
+
+#include <algorithm>
+#include <chrono>
+#include <cmath>
+#include <cstdint>
+#include <fstream>
+#include <limits>
+#include <memory>
+#include <mutex>
+#include <numeric>
+#include <sstream>
+#include <string>
+#include <thread>
+#include <vector>
+
+namespace cuvs::bench {
+
+static inline std::unique_ptr<algo_base> current_algo{nullptr};
+static inline std::unique_ptr<algo_property> current_algo_props{nullptr};
+
+using kv_series = std::vector<std::tuple<std::string, std::vector<nlohmann::json>>>;
+
+inline auto apply_overrides(const std::vector<nlohmann::json>& configs,
+                            const kv_series& overrides,
+                            std::size_t override_idx = 0) -> std::vector<nlohmann::json>
+{
+  std::vector<nlohmann::json> results{};
+  if (override_idx >= overrides.size()) {
+    auto n = configs.size();
+    for (size_t i = 0; i < n; i++) {
+      auto c               = configs[i];
+      c["override_suffix"] = n > 1 ? "/" + std::to_string(i) : "";
+      results.push_back(c);
+    }
+    return results;
+  }
+  auto rec_configs = apply_overrides(configs, overrides, override_idx + 1);
+  auto [key, vals] = overrides[override_idx];
+  auto n           = vals.size();
+  for (size_t i = 0; i < n; i++) {
+    const auto& val = vals[i];
+    for (auto rc : rec_configs) {
+      if (n > 1) {
+        rc["override_suffix"] =
+          static_cast<std::string>(rc["override_suffix"]) + "/" + std::to_string(i);
+      }
+      rc[key] = val;
+      results.push_back(rc);
+    }
+  }
+  return results;
+}
+
+inline auto apply_overrides(const nlohmann::json& config,
+                            const kv_series& overrides,
+                            std::size_t override_idx = 0)
+{
+  return apply_overrides(std::vector{config}, overrides, 0);
+}
+
+inline void dump_parameters(::benchmark::State& state, nlohmann::json params)
+{
+  std::string label = "";
+  bool label_empty  = true;
+  for (auto& [key, val] : params.items()) {
+    if (val.is_number()) {
+      state.counters.insert({{key, val}});
+    } else if (val.is_boolean()) {
+      state.counters.insert({{key, val ? 1.0 : 0.0}});
+    } else {
+      auto kv = key + "=" + val.dump();
+      if (label_empty) {
+        label = kv;
+      } else {
+        label += "#" + kv;
+      }
+      label_empty = false;
+    }
+  }
+  if (!label_empty) { state.SetLabel(label); }
+}
+
+inline auto parse_algo_property(algo_property prop, const nlohmann::json& conf) -> algo_property
+{
+  if (conf.contains("dataset_memory_type")) {
+    prop.dataset_memory_type = parse_memory_type(conf.at("dataset_memory_type"));
+  }
+  if (conf.contains("query_memory_type")) {
+    prop.query_memory_type = parse_memory_type(conf.at("query_memory_type"));
+  }
+  return prop;
+};
+
+template <typename T>
+void bench_build(::benchmark::State& state,
+                 std::shared_ptr<const dataset<T>> dataset,
+                 configuration::index index,
+                 bool force_overwrite)
+{
+  // NB: these two thread-local vars can be used within algo wrappers
+  cuvs::bench::benchmark_thread_id = state.thread_index();
+  cuvs::bench::benchmark_n_threads = state.threads();
+  dump_parameters(state, index.build_param);
+  if (file_exists(index.file)) {
+    if (force_overwrite) {
+      log_info("Overwriting file: %s", index.file.c_str());
+    } else {
+      return state.SkipWithMessage(
+        "Index file already exists (use --force to overwrite the index).");
+    }
+  }
+
+  std::unique_ptr<algo<T>> algo;
+  try {
+    algo = create_algo<T>(index.algo, dataset->distance(), dataset->dim(), index.build_param);
+  } catch (const std::exception& e) {
+    return state.SkipWithError("Failed to create an algo: " + std::string(e.what()));
+  }
+
+  const auto algo_property = parse_algo_property(algo->get_preference(), index.build_param);
+
+  const T* base_set      = dataset->base_set(algo_property.dataset_memory_type);
+  std::size_t index_size = dataset->base_set_size();
+
+  cuda_timer gpu_timer{algo};
+  {
+    nvtx_case nvtx{state.name()};
+    for (auto _ : state) {
+      [[maybe_unused]] auto ntx_lap = nvtx.lap();
+      [[maybe_unused]] auto gpu_lap = gpu_timer.lap();
+      try {
+        algo->build(base_set, index_size);
+      } catch (const std::exception& e) {
+        state.SkipWithError(std::string(e.what()));
+      }
+    }
+  }
+  if (gpu_timer.active()) {
+    state.counters.insert({"GPU", {gpu_timer.total_time(), benchmark::Counter::kAvgIterations}});
+  }
+  state.counters.insert({{"index_size", index_size}});
+
+  if (state.skipped()) { return; }
+  make_sure_parent_dir_exists(index.file);
+  algo->save(index.file);
+}
+
+template <typename T>
+void bench_search(::benchmark::State& state,
+                  configuration::index index,
+                  std::size_t search_param_ix,
+                  std::shared_ptr<const dataset<T>> dataset)
+{
+  // NB: these two thread-local vars can be used within algo wrappers
+  cuvs::bench::benchmark_thread_id = state.thread_index();
+  cuvs::bench::benchmark_n_threads = state.threads();
+  std::size_t queries_processed    = 0;
+
+  const auto& sp_json = index.search_params[search_param_ix];
+
+  if (state.thread_index() == 0) { dump_parameters(state, sp_json); }
+
+  // NB: `k` and `n_queries` are guaranteed to be populated in conf.cpp
+  const std::uint32_t k = sp_json["k"];
+  // Amount of data processes in one go
+  const std::size_t n_queries = sp_json["n_queries"];
+  // Round down the query data to a multiple of the batch size to loop over full batches of data
+  const std::size_t query_set_size = (dataset->query_set_size() / n_queries) * n_queries;
+
+  if (dataset->query_set_size() < n_queries) {
+    std::stringstream msg;
+    msg << "Not enough queries in benchmark set. Expected " << n_queries << ", actual "
+        << dataset->query_set_size();
+    state.SkipWithError(msg.str());
+    return;
+  }
+
+  // Each thread start from a different offset, so that the queries that they process do not
+  // overlap.
+  std::ptrdiff_t batch_offset   = (state.thread_index() * n_queries) % query_set_size;
+  std::ptrdiff_t queries_stride = state.threads() * n_queries;
+  // Output is saved into a contiguous buffer (separate buffers for each thread).
+  std::ptrdiff_t out_offset = 0;
+
+  const T* query_set = nullptr;
+
+  if (!file_exists(index.file)) {
+    state.SkipWithError("Index file is missing. Run the benchmark in the build mode first.");
+    return;
+  }
+
+  /**
+   * Make sure the first thread loads the algo and dataset
+   */
+  progress_barrier load_barrier{};
+  if (load_barrier.arrive(1) == 0) {
+    // algo is static to cache it between close search runs to save time on index loading
+    static std::string index_file = "";
+    if (index.file != index_file) {
+      current_algo.reset();
+      index_file = index.file;
+    }
+
+    std::unique_ptr<typename algo<T>::search_param> search_param;
+    algo<T>* a;
+    try {
+      if (!current_algo || (a = dynamic_cast<algo<T>*>(current_algo.get())) == nullptr) {
+        auto ualgo =
+          create_algo<T>(index.algo, dataset->distance(), dataset->dim(), index.build_param);
+        a = ualgo.get();
+        a->load(index_file);
+        current_algo = std::move(ualgo);
+      }
+      search_param = create_search_param<T>(index.algo, sp_json);
+    } catch (const std::exception& e) {
+      state.SkipWithError("Failed to create an algo: " + std::string(e.what()));
+      return;
+    }
+
+    current_algo_props =
+      std::make_unique<algo_property>(std::move(parse_algo_property(a->get_preference(), sp_json)));
+
+    if (search_param->needs_dataset()) {
+      try {
+        a->set_search_dataset(dataset->base_set(current_algo_props->dataset_memory_type),
+                              dataset->base_set_size());
+      } catch (const std::exception& ex) {
+        state.SkipWithError("The algorithm '" + index.name +
+                            "' requires the base set, but it's not available. " +
+                            "Exception: " + std::string(ex.what()));
+        return;
+      }
+    }
+    try {
+      a->set_search_param(*search_param);
+    } catch (const std::exception& ex) {
+      state.SkipWithError("An error occurred setting search parameters: " + std::string(ex.what()));
+      return;
+    }
+
+    query_set = dataset->query_set(current_algo_props->query_memory_type);
+    load_barrier.arrive(state.threads());
+  } else {
+    // All other threads will wait for the first thread to initialize the algo.
+    load_barrier.wait(state.threads() * 2);
+    // gbench ensures that all threads are synchronized at the start of the benchmark loop.
+    // We are accessing shared variables (like current_algo, current_algo_probs) before the
+    // benchmark loop, therefore the synchronization here is necessary.
+  }
+  query_set = dataset->query_set(current_algo_props->query_memory_type);
+
+  /**
+   * Each thread will manage its own outputs
+   */
+  using index_type                 = algo_base::index_type;
+  constexpr size_t kAlignResultBuf = 64;
+  size_t result_elem_count         = k * query_set_size;
+  result_elem_count =
+    ((result_elem_count + kAlignResultBuf - 1) / kAlignResultBuf) * kAlignResultBuf;
+  auto& result_buf =
+    get_result_buffer_from_global_pool(result_elem_count * (sizeof(float) + sizeof(index_type)));
+  auto* neighbors_ptr =
+    reinterpret_cast<index_type*>(result_buf.data(current_algo_props->query_memory_type));
+  auto* distances_ptr = reinterpret_cast<float*>(neighbors_ptr + result_elem_count);
+
+  {
+    nvtx_case nvtx{state.name()};
+
+    std::unique_ptr<algo<T>> a{nullptr};
+    try {
+      dynamic_cast<algo<T>*>(current_algo.get())->copy().swap(a);
+    } catch (const std::exception& e) {
+      state.SkipWithError("Algo::copy: " + std::string(e.what()));
+      return;
+    }
+    // Initialize with algo, so that the timer.lap() object can sync with algo::get_sync_stream()
+    cuda_timer gpu_timer{a};
+    auto start = std::chrono::high_resolution_clock::now();
+    for (auto _ : state) {
+      [[maybe_unused]] auto ntx_lap = nvtx.lap();
+      [[maybe_unused]] auto gpu_lap = gpu_timer.lap();
+      try {
+        a->search(query_set + batch_offset * dataset->dim(),
+                  n_queries,
+                  k,
+                  neighbors_ptr + out_offset * k,
+                  distances_ptr + out_offset * k);
+      } catch (const std::exception& e) {
+        state.SkipWithError("Benchmark loop: " + std::string(e.what()));
+        break;
+      }
+
+      // advance to the next batch
+      batch_offset = (batch_offset + queries_stride) % query_set_size;
+      out_offset   = (out_offset + n_queries) % query_set_size;
+
+      queries_processed += n_queries;
+    }
+    auto end      = std::chrono::high_resolution_clock::now();
+    auto duration = std::chrono::duration_cast<std::chrono::duration<double>>(end - start).count();
+    if (state.thread_index() == 0) { state.counters.insert({{"end_to_end", duration}}); }
+    state.counters.insert({"Latency", {duration, benchmark::Counter::kAvgIterations}});
+
+    if (gpu_timer.active()) {
+      state.counters.insert({"GPU", {gpu_timer.total_time(), benchmark::Counter::kAvgIterations}});
+    }
+  }
+
+  state.SetItemsProcessed(queries_processed);
+
+  // This will be the total number of queries across all threads
+  state.counters.insert({{"total_queries", queries_processed}});
+
+  if (state.skipped()) { return; }
+
+  // Each thread calculates recall on their partition of queries.
+  // evaluate recall
+  if (dataset->max_k() >= k) {
+    const std::int32_t* gt    = dataset->gt_set();
+    const std::uint32_t max_k = dataset->max_k();
+    result_buf.transfer_data(MemoryType::kHost, current_algo_props->query_memory_type);
+    auto* neighbors_host    = reinterpret_cast<index_type*>(result_buf.data(MemoryType::kHost));
+    std::size_t rows        = std::min(queries_processed, query_set_size);
+    std::size_t match_count = 0;
+    std::size_t total_count = rows * static_cast<size_t>(k);
+
+    // We go through the groundtruth with same stride as the benchmark loop.
+    size_t out_offset   = 0;
+    size_t batch_offset = (state.thread_index() * n_queries) % query_set_size;
+    while (out_offset < rows) {
+      for (std::size_t i = 0; i < n_queries; i++) {
+        size_t i_orig_idx = batch_offset + i;
+        size_t i_out_idx  = out_offset + i;
+        if (i_out_idx < rows) {
+          for (std::uint32_t j = 0; j < k; j++) {
+            auto act_idx = static_cast<std::int32_t>(neighbors_host[i_out_idx * k + j]);
+            for (std::uint32_t l = 0; l < k; l++) {
+              auto exp_idx = gt[i_orig_idx * max_k + l];
+              if (act_idx == exp_idx) {
+                match_count++;
+                break;
+              }
+            }
+          }
+        }
+      }
+      out_offset += n_queries;
+      batch_offset = (batch_offset + queries_stride) % query_set_size;
+    }
+    double actual_recall = static_cast<double>(match_count) / static_cast<double>(total_count);
+    state.counters.insert({"Recall", {actual_recall, benchmark::Counter::kAvgThreads}});
+  }
+}
+
+inline void printf_usage()
+{
+  ::benchmark::PrintDefaultHelp();
+  fprintf(stdout,
+          "          [--build|--search] \n"
+          "          [--force]\n"
+          "          [--data_prefix=<prefix>]\n"
+          "          [--index_prefix=<prefix>]\n"
+          "          [--override_kv=<key:value1:value2:...:valueN>]\n"
+          "          [--mode=<latency|throughput>\n"
+          "          [--threads=min[:max]]\n"
+          "          <conf>.json\n"
+          "\n"
+          "Note the non-standard benchmark parameters:\n"
+          "  --build: build mode, will build index\n"
+          "  --search: search mode, will search using the built index\n"
+          "            one and only one of --build and --search should be specified\n"
+          "  --force: force overwriting existing index files\n"
+          "  --data_prefix=<prefix>:"
+          " prepend <prefix> to dataset file paths specified in the <conf>.json (default = "
+          "'data/').\n"
+          "  --index_prefix=<prefix>:"
+          " prepend <prefix> to index file paths specified in the <conf>.json (default = "
+          "'index/').\n"
+          "  --override_kv=<key:value1:value2:...:valueN>:"
+          " override a build/search key one or more times multiplying the number of configurations;"
+          " you can use this parameter multiple times to get the Cartesian product of benchmark"
+          " configs.\n"
+          "  --mode=<latency|throughput>"
+          " run the benchmarks in latency (accumulate times spent in each batch) or "
+          " throughput (pipeline batches and measure end-to-end) mode\n"
+          "  --threads=min[:max] specify the number threads to use for throughput benchmark."
+          " Power of 2 values between 'min' and 'max' will be used. If only 'min' is specified,"
+          " then a single test is run with 'min' threads. By default min=1, max=<num hyper"
+          " threads>.\n");
+}
+
+template <typename T>
+void register_build(std::shared_ptr<const dataset<T>> dataset,
+                    std::vector<configuration::index> indices,
+                    bool force_overwrite)
+{
+  for (auto index : indices) {
+    auto suf      = static_cast<std::string>(index.build_param["override_suffix"]);
+    auto file_suf = suf;
+    index.build_param.erase("override_suffix");
+    std::replace(file_suf.begin(), file_suf.end(), '/', '-');
+    index.file += file_suf;
+    auto* b = ::benchmark::RegisterBenchmark(
+      index.name + suf, bench_build<T>, dataset, index, force_overwrite);
+    b->Unit(benchmark::kSecond);
+    b->MeasureProcessCPUTime();
+    b->UseRealTime();
+  }
+}
+
+template <typename T>
+void register_search(std::shared_ptr<const dataset<T>> dataset,
+                     std::vector<configuration::index> indices,
+                     Mode metric_objective,
+                     const std::vector<int>& threads)
+{
+  for (auto index : indices) {
+    for (std::size_t i = 0; i < index.search_params.size(); i++) {
+      auto suf = static_cast<std::string>(index.search_params[i]["override_suffix"]);
+      index.search_params[i].erase("override_suffix");
+
+      auto* b = ::benchmark::RegisterBenchmark(index.name + suf, bench_search<T>, index, i, dataset)
+                  ->Unit(benchmark::kMillisecond)
+                  /**
+                   * The following are important for getting accuracy QPS measurements on both CPU
+                   * and GPU These make sure that
+                   *   - `end_to_end` ~ (`Time` * `Iterations`)
+                   *   - `items_per_second` ~ (`total_queries` / `end_to_end`)
+                   *   - Throughput = `items_per_second`
+                   */
+                  ->MeasureProcessCPUTime()
+                  ->UseRealTime();
+
+      if (metric_objective == Mode::kThroughput) { b->ThreadRange(threads[0], threads[1]); }
+    }
+  }
+}
+
+template <typename T>
+void dispatch_benchmark(const configuration& conf,
+                        bool force_overwrite,
+                        bool build_mode,
+                        bool search_mode,
+                        std::string data_prefix,
+                        std::string index_prefix,
+                        kv_series override_kv,
+                        Mode metric_objective,
+                        const std::vector<int>& threads)
+{
+  if (cudart.found()) {
+    for (auto [key, value] : cuda_info()) {
+      ::benchmark::AddCustomContext(key, value);
+    }
+  }
+  const auto dataset_conf = conf.get_dataset_conf();
+  auto base_file          = combine_path(data_prefix, dataset_conf.base_file);
+  auto query_file         = combine_path(data_prefix, dataset_conf.query_file);
+  auto gt_file            = dataset_conf.groundtruth_neighbors_file;
+  if (gt_file.has_value()) { gt_file.emplace(combine_path(data_prefix, gt_file.value())); }
+  auto dataset = std::make_shared<bin_dataset<T>>(dataset_conf.name,
+                                                  base_file,
+                                                  dataset_conf.subset_first_row,
+                                                  dataset_conf.subset_size,
+                                                  query_file,
+                                                  dataset_conf.distance,
+                                                  gt_file);
+  ::benchmark::AddCustomContext("dataset", dataset_conf.name);
+  ::benchmark::AddCustomContext("distance", dataset_conf.distance);
+  std::vector<configuration::index> indices = conf.get_indices();
+  if (build_mode) {
+    if (file_exists(base_file)) {
+      log_info("Using the dataset file '%s'", base_file.c_str());
+      ::benchmark::AddCustomContext("n_records", std::to_string(dataset->base_set_size()));
+      ::benchmark::AddCustomContext("dim", std::to_string(dataset->dim()));
+    } else {
+      log_warn("dataset file '%s' does not exist; benchmarking index building is impossible.",
+               base_file.c_str());
+    }
+    std::vector<configuration::index> more_indices{};
+    for (auto& index : indices) {
+      for (auto param : apply_overrides(index.build_param, override_kv)) {
+        auto modified_index        = index;
+        modified_index.build_param = param;
+        modified_index.file        = combine_path(index_prefix, modified_index.file);
+        more_indices.push_back(modified_index);
+      }
+    }
+    register_build<T>(dataset, more_indices, force_overwrite);
+  } else if (search_mode) {
+    if (file_exists(query_file)) {
+      log_info("Using the query file '%s'", query_file.c_str());
+      ::benchmark::AddCustomContext("max_n_queries", std::to_string(dataset->query_set_size()));
+      ::benchmark::AddCustomContext("dim", std::to_string(dataset->dim()));
+      if (gt_file.has_value()) {
+        if (file_exists(*gt_file)) {
+          log_info("Using the ground truth file '%s'", gt_file->c_str());
+          ::benchmark::AddCustomContext("max_k", std::to_string(dataset->max_k()));
+        } else {
+          log_warn("Ground truth file '%s' does not exist; the recall won't be reported.",
+                   gt_file->c_str());
+        }
+      } else {
+        log_warn(
+          "Ground truth file is not provided; the recall won't be reported. NB: use "
+          "the 'groundtruth_neighbors_file' alongside the 'query_file' key to specify the "
+          "path to "
+          "the ground truth in your conf.json.");
+      }
+    } else {
+      log_warn("Query file '%s' does not exist; benchmarking search is impossible.",
+               query_file.c_str());
+    }
+    for (auto& index : indices) {
+      index.search_params = apply_overrides(index.search_params, override_kv);
+      index.file          = combine_path(index_prefix, index.file);
+    }
+    register_search<T>(dataset, indices, metric_objective, threads);
+  }
+}
+
+inline auto parse_bool_flag(const char* arg, const char* pat, bool& result) -> bool
+{
+  if (strcmp(arg, pat) == 0) {
+    result = true;
+    return true;
+  }
+  return false;
+}
+
+inline auto parse_string_flag(const char* arg, const char* pat, std::string& result) -> bool
+{
+  auto n = strlen(pat);
+  if (strncmp(pat, arg, strlen(pat)) == 0) {
+    result = arg + n + 1;
+    return true;
+  }
+  return false;
+}
+
+inline auto run_main(int argc, char** argv) -> int
+{
+  bool force_overwrite                = false;
+  bool build_mode                     = false;
+  bool search_mode                    = false;
+  std::string data_prefix             = "data";
+  std::string index_prefix            = "index";
+  std::string new_override_kv         = "";
+  std::string mode                    = "latency";
+  std::string threads_arg_txt         = "";
+  std::vector<int> threads            = {1, -1};  // min_thread, max_thread
+  std::string log_level_str           = "";
+  [[maybe_unused]] int raft_log_level = 0;  // raft::logger::get(RAFT_NAME).get_level();
+  kv_series override_kv{};
+
+  char arg0_default[] = "benchmark";  // NOLINT
+  char* args_default  = arg0_default;
+  if (!argv) {
+    argc = 1;
+    argv = &args_default;
+  }
+  if (argc == 1) {
+    printf_usage();
+    return -1;
+  }
+
+  char* conf_path = argv[--argc];
+  std::ifstream conf_stream(conf_path);
+
+  for (int i = 1; i < argc; i++) {
+    if (parse_bool_flag(argv[i], "--force", force_overwrite) ||
+        parse_bool_flag(argv[i], "--build", build_mode) ||
+        parse_bool_flag(argv[i], "--search", search_mode) ||
+        parse_string_flag(argv[i], "--data_prefix", data_prefix) ||
+        parse_string_flag(argv[i], "--index_prefix", index_prefix) ||
+        parse_string_flag(argv[i], "--mode", mode) ||
+        parse_string_flag(argv[i], "--override_kv", new_override_kv) ||
+        parse_string_flag(argv[i], "--threads", threads_arg_txt) ||
+        parse_string_flag(argv[i], "--raft_log_level", log_level_str)) {
+      if (!log_level_str.empty()) {
+        raft_log_level = std::stoi(log_level_str);
+        log_level_str  = "";
+      }
+      if (!threads_arg_txt.empty()) {
+        auto threads_arg = split(threads_arg_txt, ':');
+        threads[0]       = std::stoi(threads_arg[0]);
+        if (threads_arg.size() > 1) {
+          threads[1] = std::stoi(threads_arg[1]);
+        } else {
+          threads[1] = threads[0];
+        }
+        threads_arg_txt = "";
+      }
+      if (!new_override_kv.empty()) {
+        auto kvv = split(new_override_kv, ':');
+        auto key = kvv[0];
+        std::vector<nlohmann::json> vals{};
+        for (std::size_t j = 1; j < kvv.size(); j++) {
+          vals.push_back(nlohmann::json::parse(kvv[j]));
+        }
+        override_kv.emplace_back(key, vals);
+        new_override_kv = "";
+      }
+      for (int j = i; j < argc - 1; j++) {
+        argv[j] = argv[j + 1];
+      }
+      argc--;
+      i--;
+    }
+  }
+
+  // raft::logger::get(RAFT_NAME).set_level(raft_log_level);
+
+  Mode metric_objective = Mode::kLatency;
+  if (mode == "throughput") { metric_objective = Mode::kThroughput; }
+
+  int max_threads =
+    (metric_objective == Mode::kThroughput) ? std::thread::hardware_concurrency() : 1;
+  if (threads[1] == -1) threads[1] = max_threads;
+
+  if (metric_objective == Mode::kLatency) {
+    if (threads[0] != 1 || threads[1] != 1) {
+      log_warn("Latency mode enabled. Overriding threads arg, running with single thread.");
+      threads = {1, 1};
+    }
+  }
+
+  if (build_mode == search_mode) {
+    log_error("One and only one of --build and --search should be specified");
+    printf_usage();
+    return -1;
+  }
+
+  if (!conf_stream) {
+    log_error("Can't open configuration file: %s", conf_path);
+    return -1;
+  }
+
+  if (cudart.needed() && !cudart.found()) {
+    log_warn("cudart library is not found, GPU-based indices won't work.");
+  }
+
+  configuration conf(conf_stream);
+  std::string dtype = conf.get_dataset_conf().dtype;
+
+  if (dtype == "float") {
+    dispatch_benchmark<float>(conf,
+                              force_overwrite,
+                              build_mode,
+                              search_mode,
+                              data_prefix,
+                              index_prefix,
+                              override_kv,
+                              metric_objective,
+                              threads);
+    // } else if (dtype == "half") {
+    //   dispatch_benchmark<half>(conf,
+    //                            force_overwrite,
+    //                            build_mode,
+    //                            search_mode,
+    //                            data_prefix,
+    //                            index_prefix,
+    //                            override_kv,
+    //                            metric_objective,
+    //                            threads);
+  } else if (dtype == "uint8") {
+    dispatch_benchmark<std::uint8_t>(conf,
+                                     force_overwrite,
+                                     build_mode,
+                                     search_mode,
+                                     data_prefix,
+                                     index_prefix,
+                                     override_kv,
+                                     metric_objective,
+                                     threads);
+  } else if (dtype == "int8") {
+    dispatch_benchmark<std::int8_t>(conf,
+                                    force_overwrite,
+                                    build_mode,
+                                    search_mode,
+                                    data_prefix,
+                                    index_prefix,
+                                    override_kv,
+                                    metric_objective,
+                                    threads);
+  } else {
+    log_error("datatype '%s' is not supported", dtype.c_str());
+    return -1;
+  }
+
+  ::benchmark::Initialize(&argc, argv, printf_usage);
+  if (::benchmark::ReportUnrecognizedArguments(argc, argv)) return -1;
+  ::benchmark::RunSpecifiedBenchmarks();
+  ::benchmark::Shutdown();
+  // Release a possibly cached algo object, so that it cannot be alive longer than the handle
+  // to a shared library it depends on (dynamic benchmark executable).
+  current_algo.reset();
+  current_algo_props.reset();
+  reset_global_device_resources();
+  return 0;
+}
+};  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/common/conf.hpp b/cpp/bench/ann/src/common/conf.hpp
new file mode 100644
index 0000000000..1fc7327cb5
--- /dev/null
+++ b/cpp/bench/ann/src/common/conf.hpp
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "util.hpp"
+
+#include <iostream>
+#include <optional>
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+namespace cuvs::bench {
+
+class configuration {
+ public:
+  struct index {
+    std::string name;
+    std::string algo;
+    nlohmann::json build_param;
+    std::string file;
+
+    int batch_size;
+    int k;
+    std::vector<nlohmann::json> search_params;
+  };
+
+  struct dataset_conf {
+    std::string name;
+    std::string base_file;
+    // use only a subset of base_file,
+    // the range of rows is [subset_first_row, subset_first_row + subset_size)
+    // however, subset_size = 0 means using all rows after subset_first_row
+    // that is, the subset is [subset_first_row, #rows in base_file)
+    size_t subset_first_row{0};
+    size_t subset_size{0};
+    std::string query_file;
+    std::string distance;
+    std::optional<std::string> groundtruth_neighbors_file{std::nullopt};
+
+    // data type of input dataset, possible values ["float", "int8", "uint8"]
+    std::string dtype;
+  };
+
+  explicit inline configuration(std::istream& conf_stream)
+  {
+    // to enable comments in json
+    auto conf = nlohmann::json::parse(conf_stream, nullptr, true, true);
+
+    parse_dataset(conf.at("dataset"));
+    parse_index(conf.at("index"), conf.at("search_basic_param"));
+  }
+
+  [[nodiscard]] inline auto get_dataset_conf() const -> dataset_conf { return dataset_conf_; }
+  [[nodiscard]] inline auto get_indices() const -> std::vector<index> { return indices_; };
+
+ private:
+  inline void parse_dataset(const nlohmann::json& conf)
+  {
+    dataset_conf_.name       = conf.at("name");
+    dataset_conf_.base_file  = conf.at("base_file");
+    dataset_conf_.query_file = conf.at("query_file");
+    dataset_conf_.distance   = conf.at("distance");
+
+    if (conf.contains("groundtruth_neighbors_file")) {
+      dataset_conf_.groundtruth_neighbors_file = conf.at("groundtruth_neighbors_file");
+    }
+    if (conf.contains("subset_first_row")) {
+      dataset_conf_.subset_first_row = conf.at("subset_first_row");
+    }
+    if (conf.contains("subset_size")) { dataset_conf_.subset_size = conf.at("subset_size"); }
+
+    if (conf.contains("dtype")) {
+      dataset_conf_.dtype = conf.at("dtype");
+    } else {
+      auto filename = dataset_conf_.base_file;
+      if (filename.size() > 6 && filename.compare(filename.size() - 6, 6, "f16bin") == 0) {
+        dataset_conf_.dtype = "half";
+      } else if (filename.size() > 9 &&
+                 filename.compare(filename.size() - 9, 9, "fp16.fbin") == 0) {
+        dataset_conf_.dtype = "half";
+      } else if (filename.size() > 4 && filename.compare(filename.size() - 4, 4, "fbin") == 0) {
+        dataset_conf_.dtype = "float";
+      } else if (filename.size() > 5 && filename.compare(filename.size() - 5, 5, "u8bin") == 0) {
+        dataset_conf_.dtype = "uint8";
+      } else if (filename.size() > 5 && filename.compare(filename.size() - 5, 5, "i8bin") == 0) {
+        dataset_conf_.dtype = "int8";
+      } else {
+        log_error("Could not determine data type of the dataset %s", filename.c_str());
+      }
+    }
+  }
+  inline void parse_index(const nlohmann::json& index_conf, const nlohmann::json& search_basic_conf)
+  {
+    const int batch_size = search_basic_conf.at("batch_size");
+    const int k          = search_basic_conf.at("k");
+
+    for (const auto& conf : index_conf) {
+      index index;
+      index.name        = conf.at("name");
+      index.algo        = conf.at("algo");
+      index.build_param = conf.at("build_param");
+      index.file        = conf.at("file");
+      index.batch_size  = batch_size;
+      index.k           = k;
+
+      for (auto param : conf.at("search_params")) {
+        /*  ### Special parameters for backward compatibility ###
+
+          - Local values of `k` and `n_queries` take priority.
+          - The legacy "batch_size" renamed to `n_queries`.
+          - Basic search params are used otherwise.
+        */
+        if (!param.contains("k")) { param["k"] = k; }
+        if (!param.contains("n_queries")) {
+          if (param.contains("batch_size")) {
+            param["n_queries"] = param["batch_size"];
+            param.erase("batch_size");
+          } else {
+            param["n_queries"] = batch_size;
+          }
+        }
+        index.search_params.push_back(param);
+      }
+
+      indices_.push_back(index);
+    }
+  }
+
+  dataset_conf dataset_conf_;
+  std::vector<index> indices_;
+};
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp b/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
new file mode 100644
index 0000000000..a4d967dd17
--- /dev/null
+++ b/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <raft/core/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <sys/mman.h>
+
+#include <cstddef>
+#include <cstring>
+
+namespace raft::mr {
+/**
+ * @brief `device_memory_resource` derived class that uses mmap to allocate memory.
+ * This class enables memory allocation using huge pages.
+ * It is assumed that the allocated memory is directly accessible on device. This currently only
+ * works on GH systems.
+ *
+ * TODO(tfeher): consider improving or removing this helper once we made progress with
+ * https://github.com/rapidsai/raft/issues/1819
+ */
+class cuda_huge_page_resource final : public rmm::mr::device_memory_resource {
+ public:
+  cuda_huge_page_resource()                                                  = default;
+  ~cuda_huge_page_resource() override                                        = default;
+  cuda_huge_page_resource(cuda_huge_page_resource const&)                    = default;
+  cuda_huge_page_resource(cuda_huge_page_resource&&)                         = default;
+  auto operator=(cuda_huge_page_resource const&) -> cuda_huge_page_resource& = default;
+  auto operator=(cuda_huge_page_resource&&) -> cuda_huge_page_resource&      = default;
+
+ private:
+  /**
+   * @brief Allocates memory of size at least `bytes` using cudaMalloc.
+   *
+   * The returned pointer has at least 256B alignment.
+   *
+   * @note Stream argument is ignored
+   *
+   * @throws `rmm::bad_alloc` if the requested allocation could not be fulfilled
+   *
+   * @param bytes The size, in bytes, of the allocation
+   * @return void* Pointer to the newly allocated memory
+   */
+  auto do_allocate(std::size_t bytes, rmm::cuda_stream_view) -> void* override
+  {
+    void* addr{nullptr};
+    addr = mmap(nullptr, bytes, PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANONYMOUS, -1, 0);
+    if (addr == MAP_FAILED) { RAFT_FAIL("huge_page_resource::MAP FAILED"); }
+    if (madvise(addr, bytes, MADV_HUGEPAGE) == -1) {
+      munmap(addr, bytes);
+      RAFT_FAIL("huge_page_resource::madvise MADV_HUGEPAGE");
+    }
+    memset(addr, 0, bytes);
+    return addr;
+  }
+
+  /**
+   * @brief Deallocate memory pointed to by \p p.
+   *
+   * @note Stream argument is ignored.
+   *
+   * @throws Nothing.
+   *
+   * @param p Pointer to be deallocated
+   */
+  void do_deallocate(void* ptr, std::size_t size, rmm::cuda_stream_view) override
+  {
+    if (munmap(ptr, size) == -1) { RAFT_FAIL("huge_page_resource::munmap"); }
+  }
+
+  /**
+   * @brief Compare this resource to another.
+   *
+   * Two cuda_huge_page_resources always compare equal, because they can each
+   * deallocate memory allocated by the other.
+   *
+   * @throws Nothing.
+   *
+   * @param other The other resource to compare to
+   * @return true If the two resources are equivalent
+   * @return false If the two resources are not equal
+   */
+  [[nodiscard]] auto do_is_equal(device_memory_resource const& other) const noexcept
+    -> bool override
+  {
+    return dynamic_cast<cuda_huge_page_resource const*>(&other) != nullptr;
+  }
+};
+}  // namespace raft::mr
diff --git a/cpp/bench/ann/src/common/cuda_pinned_resource.hpp b/cpp/bench/ann/src/common/cuda_pinned_resource.hpp
new file mode 100644
index 0000000000..fb977cd34b
--- /dev/null
+++ b/cpp/bench/ann/src/common/cuda_pinned_resource.hpp
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/detail/error.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <cstddef>
+
+namespace raft::mr {
+/**
+ * @brief `device_memory_resource` derived class that uses cudaMallocHost/Free for
+ * allocation/deallocation.
+ *
+ * This is almost the same as rmm::mr::host::pinned_memory_resource, but it has
+ * device_memory_resource as base class. Pinned memory can be accessed from device,
+ * and using this allocator we can create device_mdarray backed by pinned allocator.
+ *
+ * TODO(tfeher): it would be preferred to just rely on the existing allocator from rmm
+ * (pinned_memory_resource), but that is incompatible with the container_policy class
+ * for device matrix, because the latter expects a device_memory_resource. We shall
+ * revise this once we progress with Issue https://github.com/rapidsai/raft/issues/1819
+ */
+class cuda_pinned_resource final : public rmm::mr::device_memory_resource {
+ public:
+  cuda_pinned_resource()                                               = default;
+  ~cuda_pinned_resource() override                                     = default;
+  cuda_pinned_resource(cuda_pinned_resource const&)                    = default;
+  cuda_pinned_resource(cuda_pinned_resource&&)                         = default;
+  auto operator=(cuda_pinned_resource const&) -> cuda_pinned_resource& = default;
+  auto operator=(cuda_pinned_resource&&) -> cuda_pinned_resource&      = default;
+
+ private:
+  /**
+   * @brief Allocates memory of size at least `bytes` using cudaMalloc.
+   *
+   * The returned pointer has at least 256B alignment.
+   *
+   * @note Stream argument is ignored
+   *
+   * @throws `rmm::bad_alloc` if the requested allocation could not be fulfilled
+   *
+   * @param bytes The size, in bytes, of the allocation
+   * @return void* Pointer to the newly allocated memory
+   */
+  auto do_allocate(std::size_t bytes, rmm::cuda_stream_view) -> void* override
+  {
+    void* ptr{nullptr};
+    RMM_CUDA_TRY_ALLOC(cudaMallocHost(&ptr, bytes));
+    return ptr;
+  }
+
+  /**
+   * @brief Deallocate memory pointed to by \p p.
+   *
+   * @note Stream argument is ignored.
+   *
+   * @throws Nothing.
+   *
+   * @param p Pointer to be deallocated
+   */
+  void do_deallocate(void* ptr, std::size_t, rmm::cuda_stream_view) override
+  {
+    RMM_ASSERT_CUDA_SUCCESS(cudaFreeHost(ptr));
+  }
+
+  /**
+   * @brief Compare this resource to another.
+   *
+   * Two cuda_pinned_resources always compare equal, because they can each
+   * deallocate memory allocated by the other.
+   *
+   * @throws Nothing.
+   *
+   * @param other The other resource to compare to
+   * @return true If the two resources are equivalent
+   * @return false If the two resources are not equal
+   */
+  [[nodiscard]] auto do_is_equal(device_memory_resource const& other) const noexcept
+    -> bool override
+  {
+    return dynamic_cast<cuda_pinned_resource const*>(&other) != nullptr;
+  }
+};
+}  // namespace raft::mr
diff --git a/cpp/bench/ann/src/common/cuda_stub.hpp b/cpp/bench/ann/src/common/cuda_stub.hpp
new file mode 100644
index 0000000000..0ee087a11d
--- /dev/null
+++ b/cpp/bench/ann/src/common/cuda_stub.hpp
@@ -0,0 +1,244 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+The content of this header is governed by two preprocessor definitions:
+
+  - BUILD_CPU_ONLY - whether none of the CUDA functions are used.
+  - ANN_BENCH_LINK_CUDART - dynamically link against this string if defined.
+
+___________________________________________________________________________________
+|BUILD_CPU_ONLY | ANN_BENCH_LINK_CUDART |         cudart      | cuda_runtime_api.h |
+|               |                       |  found    |  needed |      included      |
+|---------------|-----------------------|-----------|---------|--------------------|
+|   ON          |    <not defined>      |  false    |  false  |       NO           |
+|   ON          |   "cudart.so.xx.xx"   |  false    |  false  |       NO           |
+|  OFF          |     <not defined>     |   true    |   true  |      YES           |
+|  OFF          |   "cudart.so.xx.xx"   | <runtime> |   true  |      YES           |
+------------------------------------------------------------------------------------
+*/
+
+#pragma once
+
+#ifndef BUILD_CPU_ONLY
+#include <cuda_fp16.h>
+#include <cuda_runtime_api.h>
+#ifdef ANN_BENCH_LINK_CUDART
+#include <dlfcn.h>
+
+#include <cstring>
+#endif
+#else
+#include <cstdint>
+
+typedef void* cudaStream_t;
+typedef void* cudaEvent_t;
+typedef uint16_t half;
+#endif
+
+namespace cuvs::bench {
+
+struct cuda_lib_handle {
+  void* handle{nullptr};
+  explicit cuda_lib_handle()
+  {
+#ifdef ANN_BENCH_LINK_CUDART
+    constexpr int kFlags = RTLD_NOW | RTLD_GLOBAL | RTLD_DEEPBIND | RTLD_NODELETE;
+    // The full name of the linked cudart library 'cudart.so.MAJOR.MINOR.PATCH'
+    char libname[] = ANN_BENCH_LINK_CUDART;  // NOLINT
+    handle         = dlopen(ANN_BENCH_LINK_CUDART, kFlags);
+    if (handle != nullptr) { return; }
+    // try strip the PATCH
+    auto p = strrchr(libname, '.');
+    p[0]   = 0;
+    handle = dlopen(libname, kFlags);
+    if (handle != nullptr) { return; }
+    // try set the MINOR version to 0
+    p      = strrchr(libname, '.');
+    p[1]   = '0';
+    p[2]   = 0;
+    handle = dlopen(libname, kFlags);
+    if (handle != nullptr) { return; }
+    // try strip the MINOR
+    p[0]   = 0;
+    handle = dlopen(libname, kFlags);
+    if (handle != nullptr) { return; }
+    // try strip the MAJOR
+    p      = strrchr(libname, '.');
+    p[0]   = 0;
+    handle = dlopen(libname, kFlags);
+#endif
+  }
+  ~cuda_lib_handle() noexcept
+  {
+#ifdef ANN_BENCH_LINK_CUDART
+    if (handle != nullptr) { dlclose(handle); }
+#endif
+  }
+
+  template <typename Symbol>
+  auto sym(const char* name) -> Symbol
+  {
+#ifdef ANN_BENCH_LINK_CUDART
+    return reinterpret_cast<Symbol>(dlsym(handle, name));
+#else
+    return nullptr;
+#endif
+  }
+
+  /** Whether this is NOT a cpu-only package. */
+  [[nodiscard]] constexpr inline auto needed() const -> bool
+  {
+#if defined(BUILD_CPU_ONLY)
+    return false;
+#else
+    return true;
+#endif
+  }
+
+  /** CUDA found, either at compile time or at runtime. */
+  [[nodiscard]] inline auto found() const -> bool
+  {
+#if defined(BUILD_CPU_ONLY)
+    return false;
+#elif defined(ANN_BENCH_LINK_CUDART)
+    return handle != nullptr;
+#else
+    return true;
+#endif
+  }
+};
+
+static inline cuda_lib_handle cudart{};
+
+#ifdef ANN_BENCH_LINK_CUDART
+namespace stub {
+
+[[gnu::weak, gnu::noinline]] auto cuda_memcpy(void* dst,
+                                              const void* src,
+                                              size_t count,
+                                              enum cudaMemcpyKind kind) -> cudaError_t
+{
+  return cudaSuccess;
+}
+
+[[gnu::weak, gnu::noinline]] auto cuda_malloc(void** ptr, size_t size) -> cudaError_t
+{
+  *ptr = nullptr;
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_memset(void* devPtr, int value, size_t count) -> cudaError_t
+{
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_free(void* devPtr) -> cudaError_t { return cudaSuccess; }
+[[gnu::weak, gnu::noinline]] auto cuda_stream_create(cudaStream_t* pStream) -> cudaError_t
+{
+  *pStream = nullptr;
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_stream_create_with_flags(cudaStream_t* pStream,
+                                                                unsigned int flags) -> cudaError_t
+{
+  *pStream = nullptr;
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_stream_destroy(cudaStream_t pStream) -> cudaError_t
+{
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_device_synchronize() -> cudaError_t { return cudaSuccess; }
+
+[[gnu::weak, gnu::noinline]] auto cuda_stream_synchronize(cudaStream_t pStream) -> cudaError_t
+{
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_event_create(cudaEvent_t* event) -> cudaError_t
+{
+  *event = nullptr;
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_event_record(cudaEvent_t event, cudaStream_t stream)
+  -> cudaError_t
+{
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_event_synchronize(cudaEvent_t event) -> cudaError_t
+{
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_event_elapsed_time(float* ms,
+                                                          cudaEvent_t start,
+                                                          cudaEvent_t end) -> cudaError_t
+{
+  *ms = 0;
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_event_destroy(cudaEvent_t event) -> cudaError_t
+{
+  return cudaSuccess;
+}
+[[gnu::weak, gnu::noinline]] auto cuda_get_device(int* device) -> cudaError_t
+{
+  *device = 0;
+  return cudaSuccess;
+};
+[[gnu::weak, gnu::noinline]] auto cuda_driver_get_version(int* driver) -> cudaError_t
+{
+  *driver = 0;
+  return cudaSuccess;
+};
+[[gnu::weak, gnu::noinline]] auto cuda_runtime_get_version(int* runtime) -> cudaError_t
+{
+  *runtime = 0;
+  return cudaSuccess;
+};
+[[gnu::weak, gnu::noinline]] cudaError_t cudaGetDeviceProperties(struct cudaDeviceProp* prop,
+                                                                 int device)
+{
+  *prop = cudaDeviceProp{};
+  return cudaSuccess;
+}
+
+}  // namespace stub
+
+#define RAFT_DECLARE_CUDART(fun)           \
+  static inline decltype(&stub::fun) fun = \
+    cudart.found() ? cudart.sym<decltype(&stub::fun)>(#fun) : &stub::fun
+
+RAFT_DECLARE_CUDART(cuda_memcpy);
+RAFT_DECLARE_CUDART(cuda_malloc);
+RAFT_DECLARE_CUDART(cuda_memset);
+RAFT_DECLARE_CUDART(cuda_free);
+RAFT_DECLARE_CUDART(cuda_stream_create);
+RAFT_DECLARE_CUDART(cuda_stream_create_with_flags);
+RAFT_DECLARE_CUDART(cuda_stream_destroy);
+RAFT_DECLARE_CUDART(cuda_device_synchronize);
+RAFT_DECLARE_CUDART(cuda_stream_synchronize);
+RAFT_DECLARE_CUDART(cuda_event_create);
+RAFT_DECLARE_CUDART(cuda_event_record);
+RAFT_DECLARE_CUDART(cuda_event_synchronize);
+RAFT_DECLARE_CUDART(cuda_event_elapsed_time);
+RAFT_DECLARE_CUDART(cuda_event_destroy);
+RAFT_DECLARE_CUDART(cuda_get_device);
+RAFT_DECLARE_CUDART(cuda_driver_get_version);
+RAFT_DECLARE_CUDART(cuda_runtime_get_version);
+RAFT_DECLARE_CUDART(cudaGetDeviceProperties);
+
+#undef RAFT_DECLARE_CUDART
+#endif
+
+};  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/common/dataset.hpp b/cpp/bench/ann/src/common/dataset.hpp
new file mode 100644
index 0000000000..95f1a82a28
--- /dev/null
+++ b/cpp/bench/ann/src/common/dataset.hpp
@@ -0,0 +1,498 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "ann_types.hpp"
+#include "util.hpp"
+
+#include <cerrno>
+#include <sys/mman.h>
+#include <sys/stat.h>
+
+#include <cassert>
+#include <cstdint>
+#include <cstdio>
+#include <optional>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+namespace cuvs::bench {
+
+// http://big-algo-benchmarks.com/index.html:
+// binary format that starts with 8 bytes of data consisting of num_points(uint32_t)
+// num_dimensions(uint32) followed by num_pts x num_dimensions x sizeof(type) bytes of
+// data stored one vector after another.
+// Data files will have suffixes .fbin, .u8bin, and .i8bin to represent float32, uint8
+// and int8 type data.
+// As extensions for this benchmark, half and int data files will have suffixes .f16bin
+// and .ibin, respectively.
+template <typename T>
+class bin_file {
+ public:
+  bin_file(std::string file,
+           const std::string& mode,
+           uint32_t subset_first_row = 0,
+           uint32_t subset_size      = 0);
+  ~bin_file()
+  {
+    if (mapped_ptr_ != nullptr) { unmap(); }
+    if (fp_ != nullptr) { fclose(fp_); }
+  }
+  bin_file(const bin_file&)                    = delete;
+  auto operator=(const bin_file&) -> bin_file& = delete;
+
+  void get_shape(size_t* nrows, int* ndims) const
+  {
+    assert(read_mode_);
+    if (!fp_) { open_file(); }
+    *nrows = nrows_;
+    *ndims = ndims_;
+  }
+
+  void read(T* data) const
+  {
+    assert(read_mode_);
+    if (!fp_) { open_file(); }
+    size_t total = static_cast<size_t>(nrows_) * ndims_;
+    if (fread(data, sizeof(T), total, fp_) != total) {
+      throw std::runtime_error{"fread() bin_file " + file_ + " failed"};
+    }
+  }
+
+  void write(const T* data, uint32_t nrows, uint32_t ndims)
+  {
+    assert(!read_mode_);
+    if (!fp_) { open_file(); }
+    if (fwrite(&nrows, sizeof(uint32_t), 1, fp_) != 1) {
+      throw std::runtime_error{"fwrite() bin_file " + file_ + " failed"};
+    }
+    if (fwrite(&ndims, sizeof(uint32_t), 1, fp_) != 1) {
+      throw std::runtime_error{"fwrite() bin_file " + file_ + " failed"};
+    }
+
+    size_t total = static_cast<size_t>(nrows) * ndims;
+    if (fwrite(data, sizeof(T), total, fp_) != total) {
+      throw std::runtime_error{"fwrite() bin_file " + file_ + " failed"};
+    }
+  }
+
+  auto map() const -> T*
+  {
+    assert(read_mode_);
+    if (!fp_) { open_file(); }
+    int fid     = fileno(fp_);
+    mapped_ptr_ = mmap(nullptr, file_size_, PROT_READ, MAP_PRIVATE, fid, 0);
+    if (mapped_ptr_ == MAP_FAILED) {
+      mapped_ptr_ = nullptr;
+      throw std::runtime_error{"mmap error: Value of errno " + std::to_string(errno) + ", " +
+                               std::string(strerror(errno))};
+    }
+    return reinterpret_cast<T*>(reinterpret_cast<uint8_t*>(mapped_ptr_) + 2 * sizeof(uint32_t) +
+                                subset_first_row_ * ndims_ * sizeof(T));
+  }
+
+  void unmap() const
+  {
+    if (munmap(mapped_ptr_, file_size_) == -1) {
+      throw std::runtime_error{"munmap error: " + std::string(strerror(errno))};
+    }
+  }
+
+ private:
+  void check_suffix();
+  void open_file() const;
+
+  std::string file_;
+  bool read_mode_;
+  uint32_t subset_first_row_;
+  uint32_t subset_size_;
+
+  mutable FILE* fp_{nullptr};
+  mutable uint32_t nrows_;
+  mutable uint32_t ndims_;
+  mutable size_t file_size_;
+  mutable void* mapped_ptr_{nullptr};
+};
+
+template <typename T>
+bin_file<T>::bin_file(std::string file,
+                      const std::string& mode,
+                      uint32_t subset_first_row,
+                      uint32_t subset_size)
+  : file_(std::move(file)),
+    read_mode_(mode == "r"),
+    subset_first_row_(subset_first_row),
+    subset_size_(subset_size)
+
+{
+  check_suffix();
+
+  if (!read_mode_) {
+    if (mode == "w") {
+      if (subset_first_row != 0) {
+        throw std::runtime_error{"subset_first_row should be zero for write mode"};
+      }
+      if (subset_size != 0) {
+        throw std::runtime_error{"subset_size should be zero for write mode"};
+      }
+    } else {
+      throw std::runtime_error{"bin_file's mode must be either 'r' or 'w': " + file_};
+    }
+  }
+}
+
+template <typename T>
+void bin_file<T>::open_file() const
+{
+  fp_ = fopen(file_.c_str(), read_mode_ ? "r" : "w");
+  if (!fp_) { throw std::runtime_error{"open bin_file failed: " + file_}; }
+
+  if (read_mode_) {
+    struct stat statbuf;
+    if (stat(file_.c_str(), &statbuf) != 0) { throw std::runtime_error{"stat() failed: " + file_}; }
+    file_size_ = statbuf.st_size;
+
+    uint32_t header[2];
+    if (fread(header, sizeof(uint32_t), 2, fp_) != 2) {
+      throw std::runtime_error{"read header of bin_file failed: " + file_};
+    }
+    nrows_ = header[0];
+    ndims_ = header[1];
+
+    size_t expected_file_size =
+      2 * sizeof(uint32_t) + static_cast<size_t>(nrows_) * ndims_ * sizeof(T);
+    if (file_size_ != expected_file_size) {
+      throw std::runtime_error{"expected file size of " + file_ + " is " +
+                               std::to_string(expected_file_size) + ", however, actual size is " +
+                               std::to_string(file_size_)};
+    }
+
+    if (subset_first_row_ >= nrows_) {
+      throw std::runtime_error{file_ + ": subset_first_row (" + std::to_string(subset_first_row_) +
+                               ") >= nrows (" + std::to_string(nrows_) + ")"};
+    }
+    if (subset_first_row_ + subset_size_ > nrows_) {
+      throw std::runtime_error{file_ + ": subset_first_row (" + std::to_string(subset_first_row_) +
+                               ") + subset_size (" + std::to_string(subset_size_) + ") > nrows (" +
+                               std::to_string(nrows_) + ")"};
+    }
+
+    if (subset_first_row_) {
+      static_assert(sizeof(long) == 8, "fseek() don't support 64-bit offset");
+      if (fseek(fp_, sizeof(T) * subset_first_row_ * ndims_, SEEK_CUR) == -1) {
+        throw std::runtime_error{file_ + ": fseek failed"};
+      }
+      nrows_ -= subset_first_row_;
+    }
+    if (subset_size_) { nrows_ = subset_size_; }
+  }
+}
+
+template <typename T>
+void bin_file<T>::check_suffix()
+{
+  auto pos = file_.rfind('.');
+  if (pos == std::string::npos) {
+    throw std::runtime_error{"name of bin_file doesn't have a suffix: " + file_};
+  }
+  std::string suffix = file_.substr(pos + 1);
+
+  if constexpr (std::is_same_v<T, float>) {
+    if (suffix != "fbin") {
+      throw std::runtime_error{"bin_file<float> should has .fbin suffix: " + file_};
+    }
+  } else if constexpr (std::is_same_v<T, half>) {
+    if (suffix != "f16bin" && suffix != "fbin") {
+      throw std::runtime_error{"bin_file<half> should has .f16bin suffix: " + file_};
+    }
+  } else if constexpr (std::is_same_v<T, int>) {
+    if (suffix != "ibin") {
+      throw std::runtime_error{"bin_file<int> should has .ibin suffix: " + file_};
+    }
+  } else if constexpr (std::is_same_v<T, uint8_t>) {
+    if (suffix != "u8bin") {
+      throw std::runtime_error{"bin_file<uint8_t> should has .u8bin suffix: " + file_};
+    }
+  } else if constexpr (std::is_same_v<T, int8_t>) {
+    if (suffix != "i8bin") {
+      throw std::runtime_error{"bin_file<int8_t> should has .i8bin suffix: " + file_};
+    }
+  } else {
+    throw std::runtime_error(
+      "T of bin_file<T> should be one of float, half, int, uint8_t, or int8_t");
+  }
+}
+
+template <typename T>
+class dataset {
+ public:
+  explicit dataset(std::string name) : name_(std::move(name)) {}
+  dataset(std::string name, std::string distance)
+    : name_(std::move(name)), distance_(std::move(distance))
+  {
+  }
+  dataset(const dataset&)                    = delete;
+  auto operator=(const dataset&) -> dataset& = delete;
+  virtual ~dataset();
+
+  auto name() const -> std::string { return name_; }
+  auto distance() const -> std::string { return distance_; }
+  virtual auto dim() const -> int               = 0;
+  virtual auto max_k() const -> uint32_t        = 0;
+  virtual auto base_set_size() const -> size_t  = 0;
+  virtual auto query_set_size() const -> size_t = 0;
+
+  // load data lazily, so don't pay the overhead of reading unneeded set
+  // e.g. don't load base set when searching
+  auto base_set() const -> const T*
+  {
+    if (!base_set_) { load_base_set(); }
+    return base_set_;
+  }
+
+  auto query_set() const -> const T*
+  {
+    if (!query_set_) { load_query_set(); }
+    return query_set_;
+  }
+
+  auto gt_set() const -> const int32_t*
+  {
+    if (!gt_set_) { load_gt_set(); }
+    return gt_set_;
+  }
+
+  auto base_set_on_gpu() const -> const T*;
+  auto query_set_on_gpu() const -> const T*;
+  auto mapped_base_set() const -> const T*;
+
+  auto query_set(MemoryType memory_type) const -> const T*
+  {
+    switch (memory_type) {
+      case MemoryType::kDevice: return query_set_on_gpu();
+      default: return query_set();
+    }
+  }
+
+  auto base_set(MemoryType memory_type) const -> const T*
+  {
+    switch (memory_type) {
+      case MemoryType::kDevice: return base_set_on_gpu();
+      case MemoryType::kHost: return base_set();
+      case MemoryType::kHostMmap: return mapped_base_set();
+      default: return nullptr;
+    }
+  }
+
+ protected:
+  virtual void load_base_set() const  = 0;
+  virtual void load_gt_set() const    = 0;
+  virtual void load_query_set() const = 0;
+  virtual void map_base_set() const   = 0;
+
+  std::string name_;
+  std::string distance_;
+
+  mutable T* base_set_        = nullptr;
+  mutable T* query_set_       = nullptr;
+  mutable T* d_base_set_      = nullptr;
+  mutable T* d_query_set_     = nullptr;
+  mutable T* mapped_base_set_ = nullptr;
+  mutable int32_t* gt_set_    = nullptr;
+};
+
+template <typename T>
+dataset<T>::~dataset()
+{
+  delete[] base_set_;
+  delete[] query_set_;
+  delete[] gt_set_;
+#ifndef BUILD_CPU_ONLY
+  if (d_base_set_) { cudaFree(d_base_set_); }
+  if (d_query_set_) { cudaFree(d_query_set_); }
+#endif
+}
+
+template <typename T>
+auto dataset<T>::base_set_on_gpu() const -> const T*
+{
+#ifndef BUILD_CPU_ONLY
+  if (!d_base_set_) {
+    base_set();
+    cudaMalloc(reinterpret_cast<void**>(&d_base_set_), base_set_size() * dim() * sizeof(T));
+    cudaMemcpy(d_base_set_, base_set_, base_set_size() * dim() * sizeof(T), cudaMemcpyHostToDevice);
+  }
+#endif
+  return d_base_set_;
+}
+
+template <typename T>
+auto dataset<T>::query_set_on_gpu() const -> const T*
+{
+#ifndef BUILD_CPU_ONLY
+  if (!d_query_set_) {
+    query_set();
+    cudaMalloc(reinterpret_cast<void**>(&d_query_set_), query_set_size() * dim() * sizeof(T));
+    cudaMemcpy(
+      d_query_set_, query_set_, query_set_size() * dim() * sizeof(T), cudaMemcpyHostToDevice);
+  }
+#endif
+  return d_query_set_;
+}
+
+template <typename T>
+auto dataset<T>::mapped_base_set() const -> const T*
+{
+  if (!mapped_base_set_) { map_base_set(); }
+  return mapped_base_set_;
+}
+
+template <typename T>
+class bin_dataset : public dataset<T> {
+ public:
+  bin_dataset(const std::string& name,
+              const std::string& base_file,
+              size_t subset_first_row,
+              size_t subset_size,
+              const std::string& query_file,
+              const std::string& distance,
+              const std::optional<std::string>& groundtruth_neighbors_file);
+
+  auto dim() const -> int override;
+  auto max_k() const -> uint32_t override;
+  auto base_set_size() const -> size_t override;
+  auto query_set_size() const -> size_t override;
+
+ private:
+  void load_base_set() const;
+  void load_query_set() const;
+  void load_gt_set() const;
+  void map_base_set() const;
+
+  mutable int dim_               = 0;
+  mutable uint32_t max_k_        = 0;
+  mutable size_t base_set_size_  = 0;
+  mutable size_t query_set_size_ = 0;
+
+  bin_file<T> base_file_;
+  bin_file<T> query_file_;
+  std::optional<bin_file<std::int32_t>> gt_file_{std::nullopt};
+};
+
+template <typename T>
+bin_dataset<T>::bin_dataset(const std::string& name,
+                            const std::string& base_file,
+                            size_t subset_first_row,
+                            size_t subset_size,
+                            const std::string& query_file,
+                            const std::string& distance,
+                            const std::optional<std::string>& groundtruth_neighbors_file)
+  : dataset<T>(name, distance),
+    base_file_(base_file, "r", subset_first_row, subset_size),
+    query_file_(query_file, "r")
+{
+  if (groundtruth_neighbors_file.has_value()) {
+    gt_file_.emplace(groundtruth_neighbors_file.value(), "r");
+  }
+}
+
+template <typename T>
+auto bin_dataset<T>::dim() const -> int
+{
+  if (dim_ > 0) { return dim_; }
+  if (base_set_size() > 0) { return dim_; }
+  if (query_set_size() > 0) { return dim_; }
+  return dim_;
+}
+
+template <typename T>
+auto bin_dataset<T>::max_k() const -> uint32_t
+{
+  if (!this->gt_set_) { load_gt_set(); }
+  return max_k_;
+}
+
+template <typename T>
+auto bin_dataset<T>::query_set_size() const -> size_t
+{
+  if (query_set_size_ > 0) { return query_set_size_; }
+  int dim;
+  query_file_.get_shape(&query_set_size_, &dim);
+  if (query_set_size_ == 0) { throw std::runtime_error{"Zero query set size"}; }
+  if (dim == 0) { throw std::runtime_error{"Zero query set dim"}; }
+  if (dim_ == 0) {
+    dim_ = dim;
+  } else if (dim_ != dim) {
+    throw std::runtime_error{"base set dim (" + std::to_string(dim_) + ") != query set dim (" +
+                             std::to_string(dim)};
+  }
+  return query_set_size_;
+}
+
+template <typename T>
+auto bin_dataset<T>::base_set_size() const -> size_t
+{
+  if (base_set_size_ > 0) { return base_set_size_; }
+  int dim;
+  base_file_.get_shape(&base_set_size_, &dim);
+  if (base_set_size_ == 0) { throw std::runtime_error{"Zero base set size"}; }
+  if (dim == 0) { throw std::runtime_error{"Zero base set dim"}; }
+  if (dim_ == 0) {
+    dim_ = dim;
+  } else if (dim_ != dim) {
+    throw std::runtime_error{"base set dim (" + std::to_string(dim) + ") != query set dim (" +
+                             std::to_string(dim_)};
+  }
+  return base_set_size_;
+}
+
+template <typename T>
+void bin_dataset<T>::load_base_set() const
+{
+  this->base_set_ = new T[base_set_size() * dim()];
+  base_file_.read(this->base_set_);
+}
+
+template <typename T>
+void bin_dataset<T>::load_query_set() const
+{
+  this->query_set_ = new T[query_set_size() * dim()];
+  query_file_.read(this->query_set_);
+}
+
+template <typename T>
+void bin_dataset<T>::load_gt_set() const
+{
+  if (gt_file_.has_value()) {
+    size_t queries;
+    int k;
+    gt_file_->get_shape(&queries, &k);
+    this->gt_set_ = new std::int32_t[queries * k];
+    gt_file_->read(this->gt_set_);
+    max_k_ = k;
+  }
+}
+
+template <typename T>
+void bin_dataset<T>::map_base_set() const
+{
+  this->mapped_base_set_ = base_file_.map();
+}
+
+}  // namespace  cuvs::bench
diff --git a/cpp/bench/ann/src/common/thread_pool.hpp b/cpp/bench/ann/src/common/thread_pool.hpp
new file mode 100644
index 0000000000..9d0218606e
--- /dev/null
+++ b/cpp/bench/ann/src/common/thread_pool.hpp
@@ -0,0 +1,134 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <omp.h>
+
+#include <atomic>
+#include <future>
+#include <memory>
+#include <mutex>
+#include <stdexcept>
+#include <thread>
+#include <utility>
+
+class fixed_thread_pool {
+ public:
+  explicit fixed_thread_pool(int num_threads)
+  {
+    if (num_threads < 1) {
+      throw std::runtime_error("num_threads must >= 1");
+    } else if (num_threads == 1) {
+      return;
+    }
+
+    tasks_ = new task[num_threads];
+
+    threads_.reserve(num_threads);
+    for (int i = 0; i < num_threads; ++i) {
+      threads_.emplace_back([&, i] {
+        auto& task = tasks_[i];
+        while (true) {
+          std::unique_lock<std::mutex> lock(task.mtx);
+          task.cv.wait(lock,
+                       [&] { return task.has_task || finished_.load(std::memory_order_relaxed); });
+          if (finished_.load(std::memory_order_relaxed)) { break; }
+
+          task.task();
+          task.has_task = false;
+        }
+      });
+    }
+  }
+
+  ~fixed_thread_pool()
+  {
+    if (threads_.empty()) { return; }
+
+    finished_.store(true, std::memory_order_relaxed);
+    for (unsigned i = 0; i < threads_.size(); ++i) {
+      auto& task = tasks_[i];
+      std::lock_guard<std::mutex>(task.mtx);
+
+      task.cv.notify_one();
+      threads_[i].join();
+    }
+
+    delete[] tasks_;
+  }
+
+  template <typename Func, typename IdxT>
+  void submit(Func f, IdxT len)
+  {
+    // Run functions in main thread if thread pool has no threads
+    if (threads_.empty()) {
+      for (IdxT i = 0; i < len; ++i) {
+        f(i);
+      }
+      return;
+    }
+
+    const int num_threads = threads_.size();
+    // one extra part for competition among threads
+    const IdxT items_per_thread = len / (num_threads + 1);
+    std::atomic<IdxT> cnt(items_per_thread * num_threads);
+
+    // Wrap function
+    auto wrapped_f = [&](IdxT start, IdxT end) {
+      for (IdxT i = start; i < end; ++i) {
+        f(i);
+      }
+
+      while (true) {
+        IdxT i = cnt.fetch_add(1, std::memory_order_relaxed);
+        if (i >= len) { break; }
+        f(i);
+      }
+    };
+
+    std::vector<std::future<void>> futures;
+    futures.reserve(num_threads);
+    for (int i = 0; i < num_threads; ++i) {
+      IdxT start = i * items_per_thread;
+      auto& task = tasks_[i];
+      {
+        std::lock_guard lock(task.mtx);
+        (void)lock;  // stop nvcc warning
+        task.task = std::packaged_task<void()>([=] { wrapped_f(start, start + items_per_thread); });
+        futures.push_back(task.task.get_future());
+        task.has_task = true;
+      }
+      task.cv.notify_one();
+    }
+
+    for (auto& fut : futures) {
+      fut.wait();
+    }
+    return;
+  }
+
+ private:
+  struct alignas(64) task {
+    std::mutex mtx;
+    std::condition_variable cv;
+    bool has_task = false;
+    std::packaged_task<void()> task;
+  };
+
+  task* tasks_;
+  std::vector<std::thread> threads_;
+  std::atomic<bool> finished_{false};
+};
diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
new file mode 100644
index 0000000000..490b0326ea
--- /dev/null
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -0,0 +1,561 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "ann_types.hpp"
+#include "cuda_stub.hpp"  // cuda-related utils
+
+#ifdef ANN_BENCH_NVTX3_HEADERS_FOUND
+#include <nvtx3/nvToolsExt.h>
+#endif
+
+#include <sys/stat.h>
+#include <sys/types.h>
+
+#include <atomic>
+#include <chrono>
+#include <condition_variable>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <filesystem>
+#include <functional>
+#include <mutex>
+#include <optional>
+#include <sstream>
+#include <string>
+#include <vector>
+
+namespace cuvs::bench {
+
+/**
+ * Current thread id as given by the benchmark State.
+ * It's populated on every call of a benchmark case.
+ * It's relevant in the 'throughput' mode of the search benchmarks,
+ * where some algorithms might want to coordinate allocation of the resources.
+ */
+inline thread_local int benchmark_thread_id = 0;
+/**
+ * Total concurrent thread count as given by the benchmark State.
+ * It's populated on every call of a benchmark case.
+ * It's relevant in the 'throughput' mode of the search benchmarks,
+ * where some algorithms might want to coordinate allocation of the resources.
+ */
+inline thread_local int benchmark_n_threads = 1;
+
+struct cuda_timer {
+ private:
+  std::optional<cudaStream_t> stream_;
+  cudaEvent_t start_{nullptr};
+  cudaEvent_t stop_{nullptr};
+  double total_time_{0};
+
+  template <typename AnnT>
+  static inline auto extract_stream(AnnT* algo) -> std::optional<cudaStream_t>
+  {
+    auto gpu_ann = dynamic_cast<algo_gpu*>(algo);
+    if (gpu_ann != nullptr && gpu_ann->uses_stream()) {
+      return std::make_optional(gpu_ann->get_sync_stream());
+    }
+    return std::nullopt;
+  }
+
+ public:
+  struct cuda_lap {
+   private:
+    cudaStream_t stream_;
+    cudaEvent_t start_;
+    cudaEvent_t stop_;
+    double& total_time_;
+
+   public:
+    cuda_lap(cudaStream_t stream, cudaEvent_t start, cudaEvent_t stop, double& total_time)
+      : start_(start), stop_(stop), stream_(stream), total_time_(total_time)
+    {
+#ifndef BUILD_CPU_ONLY
+      cudaEventRecord(start_, stream_);
+#endif
+    }
+    cuda_lap() = delete;
+
+    ~cuda_lap() noexcept
+    {
+#ifndef BUILD_CPU_ONLY
+      cudaEventRecord(stop_, stream_);
+      cudaEventSynchronize(stop_);
+      float milliseconds = 0.0f;
+      cudaEventElapsedTime(&milliseconds, start_, stop_);
+      total_time_ += milliseconds / 1000.0;
+#endif
+    }
+  };
+
+  explicit cuda_timer(std::optional<cudaStream_t> stream) : stream_{stream}
+  {
+#ifndef BUILD_CPU_ONLY
+    if (stream_.has_value()) {
+      cudaEventCreate(&stop_);
+      cudaEventCreate(&start_);
+    }
+#endif
+  }
+
+  template <typename AnnT>
+  explicit cuda_timer(const std::unique_ptr<AnnT>& algo) : cuda_timer{extract_stream(algo.get())}
+  {
+  }
+
+  ~cuda_timer() noexcept
+  {
+#ifndef BUILD_CPU_ONLY
+    if (stream_.has_value()) {
+      cudaStreamSynchronize(stream_.value());
+      cudaEventDestroy(start_);
+      cudaEventDestroy(stop_);
+    }
+#endif
+  }
+
+  cuda_timer()                                     = delete;
+  cuda_timer(cuda_timer const&)                    = delete;
+  cuda_timer(cuda_timer&&)                         = delete;
+  auto operator=(cuda_timer const&) -> cuda_timer& = delete;
+  auto operator=(cuda_timer&&) -> cuda_timer&      = delete;
+
+  [[nodiscard]] auto stream() const -> std::optional<cudaStream_t> { return stream_; }
+
+  [[nodiscard]] auto active() const -> bool { return stream_.has_value(); }
+
+  [[nodiscard]] auto total_time() const -> double { return total_time_; }
+
+  [[nodiscard]] auto lap(bool enabled = true) -> std::optional<cuda_timer::cuda_lap>
+  {
+    return enabled && stream_.has_value()
+             ? std::make_optional<cuda_timer::cuda_lap>(stream_.value(), start_, stop_, total_time_)
+             : std::nullopt;
+  }
+};
+
+#ifndef BUILD_CPU_ONLY
+// ATM, rmm::stream does not support passing in flags; hence this helper type.
+struct non_blocking_stream {
+  non_blocking_stream() { cudaStreamCreateWithFlags(&stream_, cudaStreamNonBlocking); }
+  ~non_blocking_stream() noexcept
+  {
+    if (stream_ != nullptr) { cudaStreamDestroy(stream_); }
+  }
+  non_blocking_stream(non_blocking_stream const&) = delete;
+  non_blocking_stream(non_blocking_stream&& other) noexcept { std::swap(stream_, other.stream_); }
+  auto operator=(non_blocking_stream const&) -> non_blocking_stream& = delete;
+  auto operator=(non_blocking_stream&&) -> non_blocking_stream&      = delete;
+  [[nodiscard]] auto view() const noexcept -> cudaStream_t { return stream_; }
+
+ private:
+  cudaStream_t stream_{nullptr};
+};
+
+namespace detail {
+inline std::vector<non_blocking_stream> global_stream_pool(0);
+inline std::mutex gsp_mutex;
+}  // namespace detail
+#endif
+
+/**
+ * Get a stream associated with the current benchmark thread.
+ *
+ * Note, the streams are reused between the benchmark cases.
+ * This makes it easier to profile and analyse multiple benchmark cases in one timeline using tools
+ * like nsys.
+ */
+inline auto get_stream_from_global_pool() -> cudaStream_t
+{
+#ifndef BUILD_CPU_ONLY
+  std::lock_guard guard(detail::gsp_mutex);
+  if (static_cast<int>(detail::global_stream_pool.size()) < benchmark_n_threads) {
+    detail::global_stream_pool.resize(benchmark_n_threads);
+  }
+  return detail::global_stream_pool[benchmark_thread_id].view();
+#else
+  return nullptr;
+#endif
+}
+
+struct result_buffer {
+  explicit result_buffer(size_t size, cudaStream_t stream) : size_{size}, stream_{stream}
+  {
+    if (size_ == 0) { return; }
+    data_host_ = malloc(size_);
+#ifndef BUILD_CPU_ONLY
+    cudaMallocAsync(&data_device_, size_, stream_);
+    cudaStreamSynchronize(stream_);
+#endif
+  }
+  result_buffer()                                        = delete;
+  result_buffer(result_buffer&&)                         = delete;
+  auto operator=(result_buffer&&) -> result_buffer&      = delete;
+  result_buffer(const result_buffer&)                    = delete;
+  auto operator=(const result_buffer&) -> result_buffer& = delete;
+  ~result_buffer() noexcept
+  {
+    if (size_ == 0) { return; }
+#ifndef BUILD_CPU_ONLY
+    cudaFreeAsync(data_device_, stream_);
+    cudaStreamSynchronize(stream_);
+#endif
+    free(data_host_);
+  }
+
+  [[nodiscard]] auto size() const noexcept { return size_; }
+  [[nodiscard]] auto data(MemoryType loc) const noexcept
+  {
+    switch (loc) {
+      case MemoryType::kDevice: return data_device_;
+      default: return data_host_;
+    }
+  }
+
+  void transfer_data(MemoryType dst, MemoryType src)
+  {
+    auto dst_ptr = data(dst);
+    auto src_ptr = data(src);
+    if (dst_ptr == src_ptr) { return; }
+#ifndef BUILD_CPU_ONLY
+    cudaMemcpyAsync(dst_ptr, src_ptr, size_, cudaMemcpyDefault, stream_);
+    cudaStreamSynchronize(stream_);
+#endif
+  }
+
+ private:
+  size_t size_{0};
+  cudaStream_t stream_ = nullptr;
+  void* data_host_     = nullptr;
+  void* data_device_   = nullptr;
+};
+
+namespace detail {
+inline std::vector<std::unique_ptr<result_buffer>> global_result_buffer_pool(0);
+inline std::mutex grp_mutex;
+}  // namespace detail
+
+/**
+ * Get a result buffer associated with the current benchmark thread.
+ *
+ * Note, the allocations are reused between the benchmark cases.
+ * This reduces the setup overhead and number of times the context is being blocked
+ * (this is relevant if there is a persistent kernel running across multiples benchmark cases).
+ */
+inline auto get_result_buffer_from_global_pool(size_t size) -> result_buffer&
+{
+  auto stream = get_stream_from_global_pool();
+  auto& rb    = [stream, size]() -> result_buffer& {
+    std::lock_guard guard(detail::grp_mutex);
+    if (static_cast<int>(detail::global_result_buffer_pool.size()) < benchmark_n_threads) {
+      detail::global_result_buffer_pool.resize(benchmark_n_threads);
+    }
+    auto& rb = detail::global_result_buffer_pool[benchmark_thread_id];
+    if (!rb || rb->size() < size) { rb = std::make_unique<result_buffer>(size, stream); }
+    return *rb;
+  }();
+
+  memset(rb.data(MemoryType::kHost), 0, size);
+#ifndef BUILD_CPU_ONLY
+  cudaMemsetAsync(rb.data(MemoryType::kDevice), 0, size, stream);
+  cudaStreamSynchronize(stream);
+#endif
+  return rb;
+}
+
+/**
+ * Delete all streams and memory allocations in the global pool.
+ * It's called at the end of the `main` function - before global/static variables and cuda context
+ * is destroyed - to make sure they are destroyed gracefully and correctly seen by analysis tools
+ * such as nsys.
+ */
+inline void reset_global_device_resources()
+{
+#ifndef BUILD_CPU_ONLY
+  std::lock_guard guard(detail::gsp_mutex);
+  detail::global_result_buffer_pool.resize(0);
+  detail::global_stream_pool.resize(0);
+#endif
+}
+
+inline auto cuda_info()
+{
+  std::vector<std::tuple<std::string, std::string>> props;
+#ifndef BUILD_CPU_ONLY
+  int dev, driver = 0, runtime = 0;
+  cudaDriverGetVersion(&driver);
+  cudaRuntimeGetVersion(&runtime);
+
+  cudaDeviceProp device_prop;
+  cudaGetDevice(&dev);
+  cudaGetDeviceProperties(&device_prop, dev);
+  props.emplace_back("gpu_name", std::string(device_prop.name));
+  props.emplace_back("gpu_sm_count", std::to_string(device_prop.multiProcessorCount));
+  props.emplace_back("gpu_sm_freq", std::to_string(device_prop.clockRate * 1e3));
+  props.emplace_back("gpu_mem_freq", std::to_string(device_prop.memoryClockRate * 1e3));
+  props.emplace_back("gpu_mem_bus_width", std::to_string(device_prop.memoryBusWidth));
+  props.emplace_back("gpu_mem_global_size", std::to_string(device_prop.totalGlobalMem));
+  props.emplace_back("gpu_mem_shared_size", std::to_string(device_prop.sharedMemPerMultiprocessor));
+  props.emplace_back("gpu_driver_version",
+                     std::to_string(driver / 1000) + "." + std::to_string((driver % 100) / 10));
+  props.emplace_back("gpu_runtime_version",
+                     std::to_string(runtime / 1000) + "." + std::to_string((runtime % 100) / 10));
+#endif
+  return props;
+}
+
+struct nvtx_case {
+#ifdef ANN_BENCH_NVTX3_HEADERS_FOUND
+ private:
+  std::string case_name_;
+  std::array<char, 32> iter_name_{0};
+  nvtxDomainHandle_t domain_;
+  int64_t iteration_ = 0;
+  nvtxEventAttributes_t case_attrib_{0};
+  nvtxEventAttributes_t iter_attrib_{0};
+#endif
+
+ public:
+  struct nvtx_lap {
+#ifdef ANN_BENCH_NVTX3_HEADERS_FOUND
+   private:
+    nvtxDomainHandle_t domain_;
+
+   public:
+    nvtx_lap(nvtxDomainHandle_t domain, nvtxEventAttributes_t* attr) : domain_(domain)
+    {
+      nvtxDomainRangePushEx(domain_, attr);
+    }
+    nvtx_lap() = delete;
+    ~nvtx_lap() noexcept { nvtxDomainRangePop(domain_); }
+#endif
+  };
+
+#ifdef ANN_BENCH_NVTX3_HEADERS_FOUND
+  explicit nvtx_case(std::string case_name)
+    : case_name_(std::move(case_name)), domain_(nvtxDomainCreateA("algo benchmark"))
+  {
+    case_attrib_.version       = NVTX_VERSION;
+    iter_attrib_.version       = NVTX_VERSION;
+    case_attrib_.size          = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+    iter_attrib_.size          = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+    case_attrib_.colorType     = NVTX_COLOR_ARGB;
+    iter_attrib_.colorType     = NVTX_COLOR_ARGB;
+    case_attrib_.messageType   = NVTX_MESSAGE_TYPE_ASCII;
+    iter_attrib_.messageType   = NVTX_MESSAGE_TYPE_ASCII;
+    case_attrib_.message.ascii = case_name_.c_str();
+    auto c                     = std::hash<std::string>{}(case_name_);
+    case_attrib_.color         = c | 0xA0A0A0;
+    nvtxDomainRangePushEx(domain_, &case_attrib_);
+  }
+
+  ~nvtx_case()
+  {
+    nvtxDomainRangePop(domain_);
+    nvtxDomainDestroy(domain_);
+  }
+#else
+  explicit nvtx_case(std::string) {}
+#endif
+
+  [[nodiscard]] auto lap() -> nvtx_case::nvtx_lap
+  {
+#ifdef ANN_BENCH_NVTX3_HEADERS_FOUND
+    auto i     = iteration_++;
+    uint32_t c = (i % 5);
+    uint32_t r = 150 + c * 20;
+    uint32_t g = 200 + c * 10;
+    uint32_t b = 220 + c * 5;
+    std::snprintf(iter_name_.data(), iter_name_.size(), "Lap %zd", i);
+    iter_attrib_.message.ascii = iter_name_.data();
+    iter_attrib_.color         = (r << 16) + (g << 8) + b;
+    return nvtx_lap{domain_, &iter_attrib_};
+#else
+    return nvtx_lap{};
+#endif
+  }
+};
+
+/**
+ * A progress tracker that allows syncing threads multiple times and resets the global
+ * progress once the threads are done.
+ */
+struct progress_barrier {
+  progress_barrier() = default;
+  ~progress_barrier() noexcept
+  {
+    {
+      // Lock makes sure the notified threads see the updates to `done_`.
+      std::unique_lock lk(mutex_);
+      done_.store(true, std::memory_order_relaxed);
+      cv_.notify_all();
+    }
+    // This is the only place where the order of the updates to thread_progress_ and done_ is
+    // important. They are not guarded by the mutex, and `done_` must not be reset to `true` by
+    // other threads after the `total_progress_` is zero.
+    // Hence the default memory order (std::memory_order_seq_cst).
+    auto rem = total_progress_.fetch_sub(thread_progress_);
+    if (rem == thread_progress_) {
+      // the last thread to exit clears the progress state.
+      done_.store(false);
+    }
+  }
+
+  /**
+   * Advance the progress counter by `n` and return the previous `progress` value.
+   *
+   * This can be used to track which thread arrives on the call site first.
+   *
+   * @return the previous progress counter value (before incrementing it by `n`).
+   */
+  auto arrive(int n)
+  {
+    thread_progress_ += n;
+    // Lock makes sure the notified threads see the updates to `total_progress_`.
+    std::unique_lock lk(mutex_);
+    auto prev = total_progress_.fetch_add(n, std::memory_order_relaxed);
+    cv_.notify_all();
+    return prev;
+  }
+
+  /**
+   * Wait till the progress counter reaches `n` or finishes abnormally.
+   *
+   * @return the latest observed value of the progress counter.
+   */
+  auto wait(int limit)
+  {
+    int cur = total_progress_.load(std::memory_order_relaxed);
+    if (cur >= limit) { return cur; }
+    auto done = done_.load(std::memory_order_relaxed);
+    if (done) { return cur; }
+    std::unique_lock lk(mutex_);
+    while (cur < limit && !done) {
+      using namespace std::chrono_literals;
+      cv_.wait_for(lk, 10ms);
+      cur  = total_progress_.load(std::memory_order_relaxed);
+      done = done_.load(std::memory_order_relaxed);
+    }
+    return cur;
+  }
+
+ private:
+  static inline std::atomic<int> total_progress_;
+  static inline std::atomic<bool> done_;
+  static inline std::mutex mutex_;
+  static inline std::condition_variable cv_;
+  int thread_progress_{0};
+};
+
+inline auto split(const std::string& s, char delimiter) -> std::vector<std::string>
+{
+  std::vector<std::string> tokens;
+  std::string token;
+  std::istringstream iss(s);
+  while (getline(iss, token, delimiter)) {
+    if (!token.empty()) { tokens.push_back(token); }
+  }
+  return tokens;
+}
+
+inline auto file_exists(const std::string& filename) -> bool
+{
+  struct stat statbuf;
+  if (stat(filename.c_str(), &statbuf) != 0) { return false; }
+  return S_ISREG(statbuf.st_mode);
+}
+
+inline auto dir_exists(const std::string& dir) -> bool
+{
+  struct stat statbuf;
+  if (stat(dir.c_str(), &statbuf) != 0) { return false; }
+  return S_ISDIR(statbuf.st_mode);
+}
+
+inline auto create_dir(const std::string& dir) -> bool
+{
+  const auto path = split(dir, '/');
+
+  std::string cwd;
+  if (!dir.empty() && dir[0] == '/') { cwd += '/'; }
+
+  for (const auto& p : path) {
+    cwd += p + "/";
+    if (!dir_exists(cwd)) {
+      int ret = mkdir(cwd.c_str(), S_IRWXU | S_IRGRP | S_IXGRP | S_IROTH | S_IXOTH);
+      if (ret != 0) { return false; }
+    }
+  }
+  return true;
+}
+
+inline void make_sure_parent_dir_exists(const std::string& file_path)
+{
+  const auto pos = file_path.rfind('/');
+  if (pos != std::string::npos) {
+    auto dir = file_path.substr(0, pos);
+    if (!dir_exists(dir)) { create_dir(dir); }
+  }
+}
+
+inline auto combine_path(const std::string& dir, const std::string& path)
+{
+  std::filesystem::path p_dir(dir);
+  std::filesystem::path p_suf(path);
+  return (p_dir / p_suf).string();
+}
+
+template <typename... Ts>
+void log_with_level(const char* level, const Ts&... vs)
+{
+  char buf[20];
+  auto now    = std::chrono::system_clock::now();
+  auto now_tt = std::chrono::system_clock::to_time_t(now);
+  size_t millis =
+    std::chrono::duration_cast<std::chrono::microseconds>(now.time_since_epoch()).count() %
+    1000000ULL;
+  std::strftime(buf, sizeof(buf), "%H:%M:%S", std::localtime(&now_tt));
+  printf("[%s] [%s.%06zu] ", level, buf, millis);
+  if constexpr (sizeof...(Ts) == 1) {
+    printf("%s", vs...);
+  } else {
+    printf(vs...);
+  }
+  printf("\n");
+  fflush(stdout);
+}
+
+template <typename... Ts>
+void log_info(Ts&&... vs)
+{
+  log_with_level("I", std::forward<Ts>(vs)...);
+}
+
+template <typename... Ts>
+void log_warn(Ts&&... vs)
+{
+  log_with_level("W", std::forward<Ts>(vs)...);
+}
+
+template <typename... Ts>
+void log_error(Ts&&... vs)
+{
+  log_with_level("E", std::forward<Ts>(vs)...);
+}
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
new file mode 100644
index 0000000000..67f8ed39d0
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -0,0 +1,273 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#undef WARP_SIZE
+#ifdef CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE
+#include "cuvs_wrapper.h"
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT
+#include "cuvs_ivf_flat_wrapper.h"
+extern template class cuvs::bench::cuvs_ivf_flat<float, int64_t>;
+extern template class cuvs::bench::cuvs_ivf_flat<uint8_t, int64_t>;
+extern template class cuvs::bench::cuvs_ivf_flat<int8_t, int64_t>;
+#endif
+#if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || \
+  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+#include "cuvs_ivf_pq_wrapper.h"
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
+extern template class cuvs::bench::cuvs_ivf_pq<float, int64_t>;
+extern template class cuvs::bench::cuvs_ivf_pq<uint8_t, int64_t>;
+extern template class cuvs::bench::cuvs_ivf_pq<int8_t, int64_t>;
+#endif
+#if defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+#include "cuvs_cagra_wrapper.h"
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_CAGRA
+extern template class cuvs::bench::cuvs_cagra<float, uint32_t>;
+extern template class cuvs::bench::cuvs_cagra<half, uint32_t>;
+extern template class cuvs::bench::cuvs_cagra<uint8_t, uint32_t>;
+extern template class cuvs::bench::cuvs_cagra<int8_t, uint32_t>;
+#endif
+
+#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT
+template <typename T, typename IdxT>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::cuvs_ivf_flat<T, IdxT>::build_param& param)
+{
+  param.n_lists = conf.at("nlist");
+  if (conf.contains("niter")) { param.kmeans_n_iters = conf.at("niter"); }
+  if (conf.contains("ratio")) { param.kmeans_trainset_fraction = 1.0 / (double)conf.at("ratio"); }
+}
+
+template <typename T, typename IdxT>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::cuvs_ivf_flat<T, IdxT>::search_param& param)
+{
+  param.ivf_flat_params.n_probes = conf.at("nprobe");
+}
+#endif
+
+#if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || \
+  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+template <typename T, typename IdxT>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::cuvs_ivf_pq<T, IdxT>::build_param& param)
+{
+  if (conf.contains("nlist")) { param.n_lists = conf.at("nlist"); }
+  if (conf.contains("niter")) { param.kmeans_n_iters = conf.at("niter"); }
+  if (conf.contains("ratio")) { param.kmeans_trainset_fraction = 1.0 / (double)conf.at("ratio"); }
+  if (conf.contains("pq_bits")) { param.pq_bits = conf.at("pq_bits"); }
+  if (conf.contains("pq_dim")) { param.pq_dim = conf.at("pq_dim"); }
+  if (conf.contains("codebook_kind")) {
+    std::string kind = conf.at("codebook_kind");
+    if (kind == "cluster") {
+      param.codebook_kind = cuvs::neighbors::ivf_pq::codebook_gen::PER_CLUSTER;
+    } else if (kind == "subspace") {
+      param.codebook_kind = cuvs::neighbors::ivf_pq::codebook_gen::PER_SUBSPACE;
+    } else {
+      throw std::runtime_error("codebook_kind: '" + kind +
+                               "', should be either 'cluster' or 'subspace'");
+    }
+  }
+}
+
+template <typename T, typename IdxT>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::cuvs_ivf_pq<T, IdxT>::search_param& param)
+{
+  if (conf.contains("nprobe")) { param.pq_param.n_probes = conf.at("nprobe"); }
+  if (conf.contains("internalDistanceDtype")) {
+    std::string type = conf.at("internalDistanceDtype");
+    if (type == "float") {
+      param.pq_param.internal_distance_dtype = CUDA_R_32F;
+    } else if (type == "half") {
+      param.pq_param.internal_distance_dtype = CUDA_R_16F;
+    } else {
+      throw std::runtime_error("internalDistanceDtype: '" + type +
+                               "', should be either 'float' or 'half'");
+    }
+  } else {
+    // set half as default type
+    param.pq_param.internal_distance_dtype = CUDA_R_16F;
+  }
+
+  if (conf.contains("smemLutDtype")) {
+    std::string type = conf.at("smemLutDtype");
+    if (type == "float") {
+      param.pq_param.lut_dtype = CUDA_R_32F;
+    } else if (type == "half") {
+      param.pq_param.lut_dtype = CUDA_R_16F;
+    } else if (type == "fp8") {
+      param.pq_param.lut_dtype = CUDA_R_8U;
+    } else {
+      throw std::runtime_error("smemLutDtype: '" + type +
+                               "', should be either 'float', 'half' or 'fp8'");
+    }
+  } else {
+    // set half as default
+    param.pq_param.lut_dtype = CUDA_R_16F;
+  }
+  if (conf.contains("refine_ratio")) {
+    param.refine_ratio = conf.at("refine_ratio");
+    if (param.refine_ratio < 1.0f) { throw std::runtime_error("refine_ratio should be >= 1.0"); }
+  }
+}
+#endif
+
+#if defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+template <typename T, typename IdxT>
+void parse_build_param(const nlohmann::json& conf, cuvs::neighbors::nn_descent::index_params& param)
+{
+  if (conf.contains("graph_degree")) { param.graph_degree = conf.at("graph_degree"); }
+  if (conf.contains("intermediate_graph_degree")) {
+    param.intermediate_graph_degree = conf.at("intermediate_graph_degree");
+  }
+  // we allow niter shorthand for max_iterations
+  if (conf.contains("niter")) { param.max_iterations = conf.at("niter"); }
+  if (conf.contains("max_iterations")) { param.max_iterations = conf.at("max_iterations"); }
+  if (conf.contains("termination_threshold")) {
+    param.termination_threshold = conf.at("termination_threshold");
+  }
+}
+
+inline void parse_build_param(const nlohmann::json& conf, cuvs::neighbors::vpq_params& param)
+{
+  if (conf.contains("pq_bits")) { param.pq_bits = conf.at("pq_bits"); }
+  if (conf.contains("pq_dim")) { param.pq_dim = conf.at("pq_dim"); }
+  if (conf.contains("vq_n_centers")) { param.vq_n_centers = conf.at("vq_n_centers"); }
+  if (conf.contains("kmeans_n_iters")) { param.kmeans_n_iters = conf.at("kmeans_n_iters"); }
+  if (conf.contains("vq_kmeans_trainset_fraction")) {
+    param.vq_kmeans_trainset_fraction = conf.at("vq_kmeans_trainset_fraction");
+  }
+  if (conf.contains("pq_kmeans_trainset_fraction")) {
+    param.pq_kmeans_trainset_fraction = conf.at("pq_kmeans_trainset_fraction");
+  }
+}
+
+nlohmann::json collect_conf_with_prefix(const nlohmann::json& conf,
+                                        const std::string& prefix,
+                                        bool remove_prefix = true)
+{
+  nlohmann::json out;
+  for (auto& i : conf.items()) {
+    if (i.key().compare(0, prefix.size(), prefix) == 0) {
+      auto new_key = remove_prefix ? i.key().substr(prefix.size()) : i.key();
+      out[new_key] = i.value();
+    }
+  }
+  return out;
+}
+
+template <typename T, typename IdxT>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::cuvs_cagra<T, IdxT>::build_param& param)
+{
+  if (conf.contains("graph_degree")) {
+    param.cagra_params.graph_degree              = conf.at("graph_degree");
+    param.cagra_params.intermediate_graph_degree = param.cagra_params.graph_degree * 2;
+  }
+  if (conf.contains("intermediate_graph_degree")) {
+    param.cagra_params.intermediate_graph_degree = conf.at("intermediate_graph_degree");
+  }
+  if (conf.contains("graph_build_algo")) {
+    if (conf.at("graph_build_algo") == "IVF_PQ") {
+      param.algo = cuvs::bench::CagraBuildAlgo::kIvfPq;
+    } else if (conf.at("graph_build_algo") == "NN_DESCENT") {
+      param.algo = cuvs::bench::CagraBuildAlgo::kNnDescent;
+    } else {
+      param.algo = cuvs::bench::CagraBuildAlgo::kAuto;
+    }
+  }
+  nlohmann::json ivf_pq_build_conf = collect_conf_with_prefix(conf, "ivf_pq_build_");
+  if (!ivf_pq_build_conf.empty()) {
+    cuvs::neighbors::ivf_pq::index_params bparam;
+    parse_build_param<T, IdxT>(ivf_pq_build_conf, bparam);
+    param.ivf_pq_build_params = bparam;
+  }
+  nlohmann::json ivf_pq_search_conf = collect_conf_with_prefix(conf, "ivf_pq_search_");
+  if (!ivf_pq_search_conf.empty()) {
+    typename cuvs::bench::cuvs_ivf_pq<T, IdxT>::search_param sparam;
+    parse_search_param<T, IdxT>(ivf_pq_search_conf, sparam);
+    param.ivf_pq_search_params = sparam.pq_param;
+    param.ivf_pq_refine_rate   = sparam.refine_ratio;
+  }
+  nlohmann::json nn_descent_conf = collect_conf_with_prefix(conf, "nn_descent_");
+  if (!nn_descent_conf.empty()) {
+    cuvs::neighbors::nn_descent::index_params nn_param;
+    nn_param.intermediate_graph_degree = 1.5 * param.cagra_params.intermediate_graph_degree;
+    parse_build_param<T, IdxT>(nn_descent_conf, nn_param);
+    if (nn_param.graph_degree != param.cagra_params.intermediate_graph_degree) {
+      nn_param.graph_degree = param.cagra_params.intermediate_graph_degree;
+    }
+    param.nn_descent_params = nn_param;
+  }
+  nlohmann::json comp_search_conf = collect_conf_with_prefix(conf, "compression_");
+  if (!comp_search_conf.empty()) {
+    cuvs::neighbors::vpq_params vpq_pams;
+    parse_build_param(comp_search_conf, vpq_pams);
+    param.cagra_params.compression.emplace(vpq_pams);
+  }
+}
+
+cuvs::bench::AllocatorType parse_allocator(std::string mem_type)
+{
+  if (mem_type == "device") {
+    return cuvs::bench::AllocatorType::kDevice;
+  } else if (mem_type == "host_pinned") {
+    return cuvs::bench::AllocatorType::kHostPinned;
+  } else if (mem_type == "host_huge_page") {
+    return cuvs::bench::AllocatorType::kHostHugePage;
+  }
+  THROW(
+    "Invalid value for memory type %s, must be one of [\"device\", \"host_pinned\", "
+    "\"host_huge_page\"",
+    mem_type.c_str());
+}
+
+template <typename T, typename IdxT>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::cuvs_cagra<T, IdxT>::search_param& param)
+{
+  if (conf.contains("itopk")) { param.p.itopk_size = conf.at("itopk"); }
+  if (conf.contains("search_width")) { param.p.search_width = conf.at("search_width"); }
+  if (conf.contains("max_iterations")) { param.p.max_iterations = conf.at("max_iterations"); }
+  if (conf.contains("algo")) {
+    if (conf.at("algo") == "single_cta") {
+      param.p.algo = cuvs::neighbors::cagra::search_algo::SINGLE_CTA;
+    } else if (conf.at("algo") == "multi_cta") {
+      param.p.algo = cuvs::neighbors::cagra::search_algo::MULTI_CTA;
+    } else if (conf.at("algo") == "multi_kernel") {
+      param.p.algo = cuvs::neighbors::cagra::search_algo::MULTI_KERNEL;
+    } else if (conf.at("algo") == "auto") {
+      param.p.algo = cuvs::neighbors::cagra::search_algo::AUTO;
+    } else {
+      std::string tmp = conf.at("algo");
+      THROW("Invalid value for algo: %s", tmp.c_str());
+    }
+  }
+  if (conf.contains("graph_memory_type")) {
+    param.graph_mem = parse_allocator(conf.at("graph_memory_type"));
+  }
+  if (conf.contains("internal_dataset_memory_type")) {
+    param.dataset_mem = parse_allocator(conf.at("internal_dataset_memory_type"));
+  }
+  // Same ratio as in IVF-PQ
+  param.refine_ratio = conf.value("refine_ratio", 1.0f);
+}
+#endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
new file mode 100644
index 0000000000..b92785943a
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
@@ -0,0 +1,245 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "../common/util.hpp"
+#include <cuvs/neighbors/refine.hpp>
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/failure_callback_resource_adaptor.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <memory>
+#include <type_traits>
+
+namespace cuvs::bench {
+
+inline auto parse_metric_type(cuvs::bench::Metric metric) -> cuvs::distance::DistanceType
+{
+  if (metric == cuvs::bench::Metric::kInnerProduct) {
+    return cuvs::distance::DistanceType::InnerProduct;
+  } else if (metric == cuvs::bench::Metric::kEuclidean) {
+    // Even for L2 expanded RAFT IVF Flat uses unexpanded formula
+    return cuvs::distance::DistanceType::L2Expanded;
+  } else {
+    throw std::runtime_error("raft supports only metric type of inner product and L2");
+  }
+}
+
+/** Report a more verbose error with a backtrace when OOM occurs on RMM side. */
+inline auto rmm_oom_callback(std::size_t bytes, void*) -> bool
+{
+  auto cuda_status = cudaGetLastError();
+  size_t free      = 0;
+  size_t total     = 0;
+  RAFT_CUDA_TRY_NO_THROW(cudaMemGetInfo(&free, &total));
+  RAFT_FAIL(
+    "Failed to allocate %zu bytes using RMM memory resource. "
+    "NB: latest cuda status = %s, free memory = %zu, total memory = %zu.",
+    bytes,
+    cudaGetErrorName(cuda_status),
+    free,
+    total);
+}
+
+/**
+ * This container keeps the part of raft state that should be shared among multiple copies of raft
+ * handles (in different CPU threads).
+ * An example of this is an RMM memory resource: if we had an RMM memory pool per thread, we'd
+ * quickly run out of memory.
+ */
+class shared_raft_resources {
+ public:
+  using pool_mr_type = rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource>;
+  using mr_type      = rmm::mr::failure_callback_resource_adaptor<pool_mr_type>;
+
+  shared_raft_resources()
+  try : orig_resource_{rmm::mr::get_current_device_resource()},
+    pool_resource_(orig_resource_, 1024 * 1024 * 1024ull),
+    resource_(&pool_resource_, rmm_oom_callback, nullptr) {
+    rmm::mr::set_current_device_resource(&resource_);
+  } catch (const std::exception& e) {
+    auto cuda_status = cudaGetLastError();
+    size_t free      = 0;
+    size_t total     = 0;
+    RAFT_CUDA_TRY_NO_THROW(cudaMemGetInfo(&free, &total));
+    RAFT_FAIL(
+      "Failed to initialize shared raft resources (NB: latest cuda status = %s, free memory = %zu, "
+      "total memory = %zu): %s",
+      cudaGetErrorName(cuda_status),
+      free,
+      total,
+      e.what());
+  }
+
+  shared_raft_resources(shared_raft_resources&&)                               = delete;
+  auto operator=(shared_raft_resources&&) -> shared_raft_resources&            = delete;
+  shared_raft_resources(const shared_raft_resources& res)                      = delete;
+  auto operator=(const shared_raft_resources& other) -> shared_raft_resources& = delete;
+
+  ~shared_raft_resources() noexcept { rmm::mr::set_current_device_resource(orig_resource_); }
+
+ private:
+  rmm::mr::device_memory_resource* orig_resource_;
+  pool_mr_type pool_resource_;
+  mr_type resource_;
+};
+
+/**
+ * This struct is used by multiple raft benchmark wrappers. It serves as a thread-safe keeper of
+ * shared and private GPU resources (see below).
+ *
+ * - Accessing the same `configured_raft_resources` from concurrent threads is not safe.
+ * - Accessing the copies of `configured_raft_resources` from concurrent threads is safe.
+ * - There must be at most one "original" `configured_raft_resources` at any time, but as many
+ *   copies of it as needed (modifies the program static state).
+ */
+class configured_raft_resources {
+ public:
+  /**
+   * This constructor has the shared state passed unmodified but creates the local state anew.
+   * It's used by the copy constructor.
+   */
+  explicit configured_raft_resources(const std::shared_ptr<shared_raft_resources>& shared_res)
+    : shared_res_{shared_res},
+      res_{std::make_unique<raft::device_resources>(
+        rmm::cuda_stream_view(get_stream_from_global_pool()))}
+  {
+  }
+
+  /** Default constructor creates all resources anew. */
+  configured_raft_resources() : configured_raft_resources{std::make_shared<shared_raft_resources>()}
+  {
+  }
+
+  configured_raft_resources(configured_raft_resources&&);
+  auto operator=(configured_raft_resources&&) -> configured_raft_resources&;
+  ~configured_raft_resources() = default;
+  configured_raft_resources(const configured_raft_resources& res)
+    : configured_raft_resources{res.shared_res_}
+  {
+  }
+  auto operator=(const configured_raft_resources& other) -> configured_raft_resources&
+  {
+    this->shared_res_ = other.shared_res_;
+    return *this;
+  }
+
+  operator raft::resources&() noexcept { return *res_; }              // NOLINT
+  operator const raft::resources&() const noexcept { return *res_; }  // NOLINT
+
+  /** Get the main stream */
+  [[nodiscard]] auto get_sync_stream() const noexcept
+  {
+    return raft::resource::get_cuda_stream(*res_);
+  }
+
+ private:
+  /** The resources shared among multiple raft handles / threads. */
+  std::shared_ptr<shared_raft_resources> shared_res_;
+  /**
+   * Until we make the use of copies of raft::resources thread-safe, each benchmark wrapper must
+   * have its own copy of it.
+   */
+  std::unique_ptr<raft::device_resources> res_ = std::make_unique<raft::device_resources>();
+};
+
+inline configured_raft_resources::configured_raft_resources(configured_raft_resources&&) = default;
+inline auto configured_raft_resources::operator=(configured_raft_resources&&)
+  -> configured_raft_resources& = default;
+
+/** A helper to refine the neighbors when the data is on device or on host. */
+template <typename DatasetT, typename QueriesT, typename CandidatesT>
+void refine_helper(const raft::resources& res,
+                   DatasetT dataset,
+                   QueriesT queries,
+                   CandidatesT candidates,
+                   int k,
+                   algo_base::index_type* neighbors,
+                   float* distances,
+                   cuvs::distance::DistanceType metric)
+{
+  using data_type    = typename DatasetT::value_type;
+  using index_type   = algo_base::index_type;
+  using extents_type = int64_t;  // device-side refine requires this
+
+  static_assert(std::is_same_v<data_type, typename QueriesT::value_type>);
+  static_assert(std::is_same_v<data_type, typename DatasetT::value_type>);
+  static_assert(std::is_same_v<index_type, typename CandidatesT::value_type>);
+
+  extents_type batch_size = queries.extent(0);
+  extents_type dim        = queries.extent(1);
+  extents_type k0         = candidates.extent(1);
+
+  if (raft::get_device_for_address(dataset.data_handle()) >= 0) {
+    auto dataset_device = raft::make_device_matrix_view<const data_type, extents_type>(
+      dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+    auto queries_device = raft::make_device_matrix_view<const data_type, extents_type>(
+      queries.data_handle(), batch_size, dim);
+    auto candidates_device = raft::make_device_matrix_view<const index_type, extents_type>(
+      candidates.data_handle(), batch_size, k0);
+    auto neighbors_device =
+      raft::make_device_matrix_view<index_type, extents_type>(neighbors, batch_size, k);
+    auto distances_device =
+      raft::make_device_matrix_view<float, extents_type>(distances, batch_size, k);
+
+    cuvs::neighbors::refine(res,
+                            dataset_device,
+                            queries_device,
+                            candidates_device,
+                            neighbors_device,
+                            distances_device,
+                            metric);
+  } else {
+    auto dataset_host = raft::make_host_matrix_view<const data_type, extents_type>(
+      dataset.data_handle(), dataset.extent(0), dataset.extent(1));
+    auto queries_host    = raft::make_host_matrix<data_type, extents_type>(batch_size, dim);
+    auto candidates_host = raft::make_host_matrix<index_type, extents_type>(batch_size, k0);
+    auto neighbors_host  = raft::make_host_matrix<index_type, extents_type>(batch_size, k);
+    auto distances_host  = raft::make_host_matrix<float, extents_type>(batch_size, k);
+
+    auto stream = raft::resource::get_cuda_stream(res);
+    raft::copy(queries_host.data_handle(), queries.data_handle(), queries_host.size(), stream);
+    raft::copy(
+      candidates_host.data_handle(), candidates.data_handle(), candidates_host.size(), stream);
+
+    raft::resource::sync_stream(res);  // wait for the queries and candidates
+    cuvs::neighbors::refine(res,
+                            dataset_host,
+                            queries_host.view(),
+                            candidates_host.view(),
+                            neighbors_host.view(),
+                            distances_host.view(),
+                            metric);
+
+    raft::copy(neighbors, neighbors_host.data_handle(), neighbors_host.size(), stream);
+    raft::copy(distances, distances_host.data_handle(), distances_host.size(), stream);
+  }
+}
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
new file mode 100644
index 0000000000..a7495c23a9
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "cuvs_ann_bench_param_parser.h"
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace cuvs::bench {
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  [[maybe_unused]] cuvs::bench::Metric metric = parse_metric(distance);
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float>) {
+#ifdef CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE
+    if (algo_name == "raft_brute_force" || algo_name == "cuvs_brute_force") {
+      a = std::make_unique<cuvs::bench::cuvs_gpu<T>>(metric, dim);
+    }
+#endif
+  }
+
+  if constexpr (std::is_same_v<T, uint8_t>) {}
+
+#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, uint8_t> ||
+                std::is_same_v<T, int8_t>) {
+    if (algo_name == "raft_ivf_flat" || algo_name == "cuvs_ivf_flat") {
+      typename cuvs::bench::cuvs_ivf_flat<T, int64_t>::build_param param;
+      parse_build_param<T, int64_t>(conf, param);
+      a = std::make_unique<cuvs::bench::cuvs_ivf_flat<T, int64_t>>(metric, dim, param);
+    }
+  }
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
+  if (algo_name == "raft_ivf_pq" || algo_name == "cuvs_ivf_pq") {
+    typename cuvs::bench::cuvs_ivf_pq<T, int64_t>::build_param param;
+    parse_build_param<T, int64_t>(conf, param);
+    a = std::make_unique<cuvs::bench::cuvs_ivf_pq<T, int64_t>>(metric, dim, param);
+  }
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_CAGRA
+  if (algo_name == "raft_cagra" || algo_name == "cuvs_cagra") {
+    typename cuvs::bench::cuvs_cagra<T, uint32_t>::build_param param;
+    parse_build_param<T, uint32_t>(conf, param);
+    a = std::make_unique<cuvs::bench::cuvs_cagra<T, uint32_t>>(metric, dim, param);
+  }
+#endif
+
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+#ifdef CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE
+  if (algo_name == "raft_brute_force" || algo_name == "cuvs_brute_force") {
+    auto param = std::make_unique<typename cuvs::bench::cuvs_gpu<T>::search_param>();
+    return param;
+  }
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, uint8_t> ||
+                std::is_same_v<T, int8_t>) {
+    if (algo_name == "raft_ivf_flat" || algo_name == "cuvs_ivf_flat") {
+      auto param =
+        std::make_unique<typename cuvs::bench::cuvs_ivf_flat<T, int64_t>::search_param>();
+      parse_search_param<T, int64_t>(conf, *param);
+      return param;
+    }
+  }
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
+  if (algo_name == "raft_ivf_pq" || algo_name == "cuvs_ivf_pq") {
+    auto param = std::make_unique<typename cuvs::bench::cuvs_ivf_pq<T, int64_t>::search_param>();
+    parse_search_param<T, int64_t>(conf, *param);
+    return param;
+  }
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_CAGRA
+  if (algo_name == "raft_cagra" || algo_name == "cuvs_cagra") {
+    auto param = std::make_unique<typename cuvs::bench::cuvs_cagra<T, uint32_t>::search_param>();
+    parse_search_param<T, uint32_t>(conf, *param);
+    return param;
+  }
+#endif
+
+  // else
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+};  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+// REGISTER_ALGO_INSTANCE(half);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_float.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_float.cu
new file mode 100644
index 0000000000..576f1d5bd2
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_float.cu
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_cagra_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_cagra<float, uint32_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu
new file mode 100644
index 0000000000..6768034a2b
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_cagra_wrapper.h"
+
+namespace cuvs::bench {
+// template class cuvs_cagra<half, uint32_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
new file mode 100644
index 0000000000..558ba01e0a
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "cuvs_ann_bench_param_parser.h"
+#include "cuvs_cagra_hnswlib_wrapper.h"
+
+#include <rmm/cuda_device.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+#include <rmm/resource_ref.hpp>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::cuvs_cagra_hnswlib<T, IdxT>::search_param& param)
+{
+  param.ef = conf.at("ef");
+  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  [[maybe_unused]] cuvs::bench::Metric metric = parse_metric(distance);
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float> or std::is_same_v<T, std::uint8_t>) {
+    if (algo_name == "raft_cagra_hnswlib" || algo_name == "cuvs_cagra_hnswlib") {
+      typename cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>::build_param param;
+      parse_build_param<T, uint32_t>(conf, param);
+      a = std::make_unique<cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>>(metric, dim, param);
+    }
+  }
+
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+  if (algo_name == "raft_cagra_hnswlib" || algo_name == "cuvs_cagra_hnswlib") {
+    auto param =
+      std::make_unique<typename cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>::search_param>();
+    parse_search_param<T, uint32_t>(conf, *param);
+    return param;
+  }
+
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+}  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+/*
+[NOTE] Dear developer,
+
+Please don't modify the content of the `main` function; this will make the behavior of the benchmark
+executable differ depending on the cmake flags and will complicate the debugging. In particular,
+don't try to setup an RMM memory resource here; it will anyway be modified by the memory resource
+set on per-algorithm basis. For example, see `cuvs/cuvs_ann_bench_utils.h`.
+*/
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
new file mode 100644
index 0000000000..875fe0bba5
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../hnswlib/hnswlib_wrapper.h"
+#include "cuvs_cagra_wrapper.h"
+
+#include <memory>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+  using build_param       = typename cuvs_cagra<T, IdxT>::build_param;
+  using search_param      = typename hnsw_lib<T>::search_param;
+
+  cuvs_cagra_hnswlib(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
+    : algo<T>(metric, dim),
+      cagra_build_{metric, dim, param, concurrent_searches},
+      // hnsw_lib param values don't matter since we don't build with hnsw_lib
+      hnswlib_search_{metric, dim, typename hnsw_lib<T>::build_param{50, 100}}
+  {
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return cagra_build_.get_sync_stream();
+  }
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHostMmap;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override
+  {
+    return std::make_unique<cuvs_cagra_hnswlib<T, IdxT>>(*this);
+  }
+
+ private:
+  cuvs_cagra<T, IdxT> cagra_build_;
+  hnsw_lib<T> hnswlib_search_;
+};
+
+template <typename T, typename IdxT>
+void cuvs_cagra_hnswlib<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  cagra_build_.build(dataset, nrow);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_hnswlib<T, IdxT>::set_search_param(const search_param_base& param_)
+{
+  hnswlib_search_.set_search_param(param_);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_hnswlib<T, IdxT>::save(const std::string& file) const
+{
+  cagra_build_.save_to_hnswlib(file);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_hnswlib<T, IdxT>::load(const std::string& file)
+{
+  hnswlib_search_.load(file);
+  hnswlib_search_.set_base_layer_only();
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_hnswlib<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  hnswlib_search_.search(queries, batch_size, k, neighbors, distances);
+}
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_int8_t.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_int8_t.cu
new file mode 100644
index 0000000000..e2c0735f5b
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_int8_t.cu
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_cagra_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_cagra<int8_t, uint32_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_uint8_t.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_uint8_t.cu
new file mode 100644
index 0000000000..0f38498850
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_uint8_t.cu
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_cagra_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_cagra<uint8_t, uint32_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
new file mode 100644
index 0000000000..9ac25f53f2
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -0,0 +1,345 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../../../../src/neighbors/detail/cagra/utils.hpp"
+#include "../common/ann_types.hpp"
+#include "../common/cuda_huge_page_resource.hpp"
+#include "../common/cuda_pinned_resource.hpp"
+#include "cuvs_ann_bench_utils.h"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/linalg/unary_op.cuh>
+// #include <raft/neighbors/dataset.hpp>
+// #include <raft/neighbors/detail/cagra/cagra_build.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/resource_ref.hpp>
+
+#include <cassert>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <optional>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+namespace cuvs::bench {
+
+enum class AllocatorType { kHostPinned, kHostHugePage, kDevice };
+enum class CagraBuildAlgo { kAuto, kIvfPq, kNnDescent };
+
+template <typename T, typename IdxT>
+class cuvs_cagra : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+
+  struct search_param : public search_param_base {
+    cuvs::neighbors::cagra::search_params p;
+    float refine_ratio;
+    AllocatorType graph_mem   = AllocatorType::kDevice;
+    AllocatorType dataset_mem = AllocatorType::kDevice;
+    [[nodiscard]] auto needs_dataset() const -> bool override { return true; }
+  };
+
+  struct build_param {
+    cuvs::neighbors::cagra::index_params cagra_params;
+    CagraBuildAlgo algo;
+    std::optional<cuvs::neighbors::nn_descent::index_params> nn_descent_params = std::nullopt;
+    std::optional<float> ivf_pq_refine_rate                                    = std::nullopt;
+    std::optional<cuvs::neighbors::ivf_pq::index_params> ivf_pq_build_params   = std::nullopt;
+    std::optional<cuvs::neighbors::ivf_pq::search_params> ivf_pq_search_params = std::nullopt;
+  };
+
+  cuvs_cagra(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
+    : algo<T>(metric, dim),
+      index_params_(param),
+      dimension_(dim),
+
+      dataset_(std::make_shared<raft::device_matrix<T, int64_t, raft::row_major>>(
+        std::move(raft::make_device_matrix<T, int64_t>(handle_, 0, 0)))),
+      graph_(std::make_shared<raft::device_matrix<IdxT, int64_t, raft::row_major>>(
+        std::move(raft::make_device_matrix<IdxT, int64_t>(handle_, 0, 0)))),
+      input_dataset_v_(
+        std::make_shared<raft::device_matrix_view<const T, int64_t, raft::row_major>>(
+          nullptr, 0, 0))
+
+  {
+    index_params_.cagra_params.metric         = parse_metric_type(metric);
+    index_params_.ivf_pq_build_params->metric = parse_metric_type(metric);
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param) override;
+
+  void set_search_dataset(const T* dataset, size_t nrow) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+  void search_base(const T* queries,
+                   int batch_size,
+                   int k,
+                   algo_base::index_type* neighbors,
+                   float* distances) const;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return handle_.get_sync_stream();
+  }
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHostMmap;
+    property.query_memory_type   = MemoryType::kDevice;
+    return property;
+  }
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  void save_to_hnswlib(const std::string& file) const;
+  std::unique_ptr<algo<T>> copy() override;
+
+ private:
+  // handle_ must go first to make sure it dies last and all memory allocated in pool
+  configured_raft_resources handle_{};
+  raft::mr::cuda_pinned_resource mr_pinned_;
+  raft::mr::cuda_huge_page_resource mr_huge_page_;
+  AllocatorType graph_mem_{AllocatorType::kDevice};
+  AllocatorType dataset_mem_{AllocatorType::kDevice};
+  float refine_ratio_;
+  build_param index_params_;
+  bool need_dataset_update_{true};
+  cuvs::neighbors::cagra::search_params search_params_;
+  std::shared_ptr<cuvs::neighbors::cagra::index<T, IdxT>> index_;
+  int dimension_;
+  std::shared_ptr<raft::device_matrix<IdxT, int64_t, raft::row_major>> graph_;
+  std::shared_ptr<raft::device_matrix<T, int64_t, raft::row_major>> dataset_;
+  std::shared_ptr<raft::device_matrix_view<const T, int64_t, raft::row_major>> input_dataset_v_;
+
+  inline rmm::device_async_resource_ref get_mr(AllocatorType mem_type)
+  {
+    switch (mem_type) {
+      case (AllocatorType::kHostPinned): return &mr_pinned_;
+      case (AllocatorType::kHostHugePage): return &mr_huge_page_;
+      default: return rmm::mr::get_current_device_resource();
+    }
+  }
+};
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  auto dataset_view =
+    raft::make_host_matrix_view<const T, int64_t>(dataset, IdxT(nrow), dimension_);
+
+  auto& params = index_params_.cagra_params;
+
+  if (index_params_.algo == CagraBuildAlgo::kIvfPq) {
+    auto pq_params = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(
+      dataset_view.extents(), params.metric);
+    if (index_params_.ivf_pq_build_params) {
+      pq_params.build_params = *index_params_.ivf_pq_build_params;
+    }
+    if (index_params_.ivf_pq_search_params) {
+      pq_params.search_params = *index_params_.ivf_pq_search_params;
+    }
+    if (index_params_.ivf_pq_refine_rate) {
+      pq_params.refinement_rate = *index_params_.ivf_pq_refine_rate;
+    }
+    params.graph_build_params = pq_params;
+  } else if (index_params_.algo == CagraBuildAlgo::kNnDescent) {
+    auto nn_params = cuvs::neighbors::cagra::graph_build_params::nn_descent_params(
+      params.intermediate_graph_degree);
+    if (index_params_.nn_descent_params) { nn_params = *index_params_.nn_descent_params; }
+    params.graph_build_params = nn_params;
+  }
+  index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(
+    std::move(cuvs::neighbors::cagra::build(handle_, params, dataset_view)));
+}
+
+inline auto allocator_to_string(AllocatorType mem_type) -> std::string
+{
+  if (mem_type == AllocatorType::kDevice) {
+    return "device";
+  } else if (mem_type == AllocatorType::kHostPinned) {
+    return "host_pinned";
+  } else if (mem_type == AllocatorType::kHostHugePage) {
+    return "host_huge_page";
+  }
+  return "<invalid allocator type>";
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param)
+{
+  auto sp        = dynamic_cast<const search_param&>(param);
+  search_params_ = sp.p;
+  refine_ratio_  = sp.refine_ratio;
+  if (sp.graph_mem != graph_mem_) {
+    // Move graph to correct memory space
+    graph_mem_ = sp.graph_mem;
+    RAFT_LOG_DEBUG("moving graph to new memory space: %s", allocator_to_string(graph_mem_).c_str());
+    // We create a new graph and copy to it from existing graph
+    auto mr        = get_mr(graph_mem_);
+    auto new_graph = raft::make_device_mdarray<IdxT, int64_t>(
+      handle_, mr, raft::make_extents<int64_t>(index_->graph().extent(0), index_->graph_degree()));
+
+    raft::copy(new_graph.data_handle(),
+               index_->graph().data_handle(),
+               index_->graph().size(),
+               raft::resource::get_cuda_stream(handle_));
+
+    index_->update_graph(handle_, make_const_mdspan(new_graph.view()));
+    // update_graph() only stores a view in the index. We need to keep the graph object alive.
+    *graph_ = std::move(new_graph);
+  }
+
+  if (sp.dataset_mem != dataset_mem_ || need_dataset_update_) {
+    dataset_mem_ = sp.dataset_mem;
+
+    // First free up existing memory
+    *dataset_ = raft::make_device_matrix<T, int64_t>(handle_, 0, 0);
+    index_->update_dataset(handle_, make_const_mdspan(dataset_->view()));
+
+    // Allocate space using the correct memory resource.
+    RAFT_LOG_DEBUG("moving dataset to new memory space: %s",
+                   allocator_to_string(dataset_mem_).c_str());
+
+    auto mr = get_mr(dataset_mem_);
+    cuvs::neighbors::cagra::detail::copy_with_padding(handle_, *dataset_, *input_dataset_v_, mr);
+
+    auto dataset_view = raft::make_device_strided_matrix_view<const T, int64_t>(
+      dataset_->data_handle(), dataset_->extent(0), this->dim_, dataset_->extent(1));
+    index_->update_dataset(handle_, dataset_view);
+
+    need_dataset_update_ = false;
+  }
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::set_search_dataset(const T* dataset, size_t nrow)
+{
+  using ds_idx_type = decltype(index_->data().n_rows());
+  bool is_vpq =
+    dynamic_cast<const cuvs::neighbors::vpq_dataset<half, ds_idx_type>*>(&index_->data()) ||
+    dynamic_cast<const cuvs::neighbors::vpq_dataset<float, ds_idx_type>*>(&index_->data());
+  // It can happen that we are re-using a previous algo object which already has
+  // the dataset set. Check if we need update.
+  if (static_cast<size_t>(input_dataset_v_->extent(0)) != nrow ||
+      input_dataset_v_->data_handle() != dataset) {
+    *input_dataset_v_ = raft::make_device_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
+    need_dataset_update_ = !is_vpq;  // ignore update if this is a VPQ dataset.
+  }
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::save(const std::string& file) const
+{
+  cuvs::neighbors::cagra::serialize(handle_, file, *index_);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::save_to_hnswlib(const std::string& file) const
+{
+  cuvs::neighbors::cagra::serialize_to_hnswlib(handle_, file, *index_);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::load(const std::string& file)
+{
+  index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(handle_);
+  cuvs::neighbors::cagra::deserialize(handle_, file, index_.get());
+}
+
+template <typename T, typename IdxT>
+std::unique_ptr<algo<T>> cuvs_cagra<T, IdxT>::copy()
+{
+  return std::make_unique<cuvs_cagra<T, IdxT>>(*this);  // use copy constructor
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::search_base(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  static_assert(std::is_integral_v<algo_base::index_type>);
+  static_assert(std::is_integral_v<IdxT>);
+
+  IdxT* neighbors_idx_t;
+  std::optional<rmm::device_uvector<IdxT>> neighbors_storage{std::nullopt};
+  if constexpr (sizeof(IdxT) == sizeof(algo_base::index_type)) {
+    neighbors_idx_t = reinterpret_cast<IdxT*>(neighbors);
+  } else {
+    neighbors_storage.emplace(batch_size * k, raft::resource::get_cuda_stream(handle_));
+    neighbors_idx_t = neighbors_storage->data();
+  }
+
+  auto queries_view =
+    raft::make_device_matrix_view<const T, int64_t>(queries, batch_size, dimension_);
+  auto neighbors_view =
+    raft::make_device_matrix_view<IdxT, int64_t>(neighbors_idx_t, batch_size, k);
+  auto distances_view = raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k);
+
+  cuvs::neighbors::cagra::search(
+    handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+
+  if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
+    raft::linalg::unaryOp(neighbors,
+                          neighbors_idx_t,
+                          batch_size * k,
+                          raft::cast_op<algo_base::index_type>(),
+                          raft::resource::get_cuda_stream(handle_));
+  }
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  auto k0                       = static_cast<size_t>(refine_ratio_ * k);
+  const bool disable_refinement = k0 <= static_cast<size_t>(k);
+  const raft::resources& res    = handle_;
+
+  if (disable_refinement) {
+    search_base(queries, batch_size, k, neighbors, distances);
+  } else {
+    auto queries_v = raft::make_device_matrix_view<const T, algo_base::index_type>(
+      queries, batch_size, dimension_);
+    auto candidate_ixs =
+      raft::make_device_matrix<algo_base::index_type, algo_base::index_type>(res, batch_size, k0);
+    auto candidate_dists =
+      raft::make_device_matrix<float, algo_base::index_type>(res, batch_size, k0);
+    search_base(
+      queries, batch_size, k0, candidate_ixs.data_handle(), candidate_dists.data_handle());
+    refine_helper(
+      res, *input_dataset_v_, queries_v, candidate_ixs, k, neighbors, distances, index_->metric());
+  }
+}
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_flat.cu b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat.cu
new file mode 100644
index 0000000000..f38e16c9fc
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat.cu
@@ -0,0 +1,22 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_ivf_flat_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_ivf_flat<float, int64_t>;
+template class cuvs_ivf_flat<uint8_t, int64_t>;
+template class cuvs_ivf_flat<int8_t, int64_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
new file mode 100644
index 0000000000..2aaa5a2942
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
@@ -0,0 +1,170 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "cuvs_ann_bench_utils.h"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <cassert>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+class cuvs_ivf_flat : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+
+  struct search_param : public search_param_base {
+    cuvs::neighbors::ivf_flat::search_params ivf_flat_params;
+  };
+
+  using build_param = cuvs::neighbors::ivf_flat::index_params;
+
+  cuvs_ivf_flat(Metric metric, int dim, const build_param& param)
+    : algo<T>(metric, dim), index_params_(param), dimension_(dim)
+  {
+    index_params_.metric                         = parse_metric_type(metric);
+    index_params_.conservative_memory_allocation = true;
+    RAFT_CUDA_TRY(cudaGetDevice(&device_));
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return handle_.get_sync_stream();
+  }
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHostMmap;
+    property.query_memory_type   = MemoryType::kDevice;
+    return property;
+  }
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override;
+
+ private:
+  // handle_ must go first to make sure it dies last and all memory allocated in pool
+  configured_raft_resources handle_{};
+  build_param index_params_;
+  cuvs::neighbors::ivf_flat::search_params search_params_;
+  std::shared_ptr<cuvs::neighbors::ivf_flat::index<T, IdxT>> index_;
+  int device_;
+  int dimension_;
+};
+
+template <typename T, typename IdxT>
+void cuvs_ivf_flat<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  index_ = std::make_shared<cuvs::neighbors::ivf_flat::index<T, IdxT>>(
+    std::move(cuvs::neighbors::ivf_flat::build(
+      handle_,
+      index_params_,
+      raft::make_host_matrix_view<const T, int64_t>(dataset, nrow, dimension_))));
+  // Note: internally the IVF-Flat build works with simple pointers, and accepts both host and
+  // device pointer. Therefore, although we provide here a host_mdspan, this works with device
+  // pointer too.
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_flat<T, IdxT>::set_search_param(const search_param_base& param)
+{
+  auto sp        = dynamic_cast<const search_param&>(param);
+  search_params_ = sp.ivf_flat_params;
+  assert(search_params_.n_probes <= index_params_.n_lists);
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_flat<T, IdxT>::save(const std::string& file) const
+{
+  cuvs::neighbors::ivf_flat::serialize(handle_, file, *index_);
+  return;
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_flat<T, IdxT>::load(const std::string& file)
+{
+  index_ =
+    std::make_shared<cuvs::neighbors::ivf_flat::index<T, IdxT>>(handle_, index_params_, this->dim_);
+
+  cuvs::neighbors::ivf_flat::deserialize(handle_, file, index_.get());
+  return;
+}
+
+template <typename T, typename IdxT>
+std::unique_ptr<algo<T>> cuvs_ivf_flat<T, IdxT>::copy()
+{
+  return std::make_unique<cuvs_ivf_flat<T, IdxT>>(*this);  // use copy constructor
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_flat<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  static_assert(std::is_integral_v<algo_base::index_type>);
+  static_assert(std::is_integral_v<IdxT>);
+
+  IdxT* neighbors_idx_t;
+  std::optional<rmm::device_uvector<IdxT>> neighbors_storage{std::nullopt};
+  if constexpr (sizeof(IdxT) == sizeof(algo_base::index_type)) {
+    neighbors_idx_t = reinterpret_cast<IdxT*>(neighbors);
+  } else {
+    neighbors_storage.emplace(batch_size * k, raft::resource::get_cuda_stream(handle_));
+    neighbors_idx_t = neighbors_storage->data();
+  }
+  cuvs::neighbors::ivf_flat::search(
+    handle_,
+    search_params_,
+    *index_,
+    raft::make_device_matrix_view<const T, int64_t>(queries, batch_size, index_->dim()),
+    raft::make_device_matrix_view<IdxT, int64_t>(neighbors_idx_t, batch_size, k),
+    raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k));
+  if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
+    raft::linalg::unaryOp(neighbors,
+                          neighbors_idx_t,
+                          batch_size * k,
+                          raft::cast_op<algo_base::index_type>(),
+                          raft::resource::get_cuda_stream(handle_));
+  }
+}
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu
new file mode 100644
index 0000000000..3ffdd4a253
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_ivf_pq_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_ivf_pq<float, int64_t>;
+// template class cuvs_ivf_pq<half, int64_t>;
+template class cuvs_ivf_pq<uint8_t, int64_t>;
+template class cuvs_ivf_pq<int8_t, int64_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
new file mode 100644
index 0000000000..a305f14ea3
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
@@ -0,0 +1,202 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "cuvs_ann_bench_utils.h"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/neighbors/refine.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <type_traits>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+class cuvs_ivf_pq : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+  using algo<T>::dim_;
+
+  struct search_param : public search_param_base {
+    cuvs::neighbors::ivf_pq::search_params pq_param;
+    float refine_ratio = 1.0f;
+    [[nodiscard]] auto needs_dataset() const -> bool override { return refine_ratio > 1.0f; }
+  };
+
+  using build_param = cuvs::neighbors::ivf_pq::index_params;
+
+  cuvs_ivf_pq(Metric metric, int dim, const build_param& param)
+    : algo<T>(metric, dim), index_params_(param), dimension_(dim)
+  {
+    index_params_.metric = parse_metric_type(metric);
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param) override;
+  void set_search_dataset(const T* dataset, size_t nrow) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+  void search_base(const T* queries,
+                   int batch_size,
+                   int k,
+                   algo_base::index_type* neighbors,
+                   float* distances) const;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return handle_.get_sync_stream();
+  }
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kDevice;
+    return property;
+  }
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override;
+
+ private:
+  // handle_ must go first to make sure it dies last and all memory allocated in pool
+  configured_raft_resources handle_{};
+  build_param index_params_;
+  cuvs::neighbors::ivf_pq::search_params search_params_;
+  std::shared_ptr<cuvs::neighbors::ivf_pq::index<IdxT>> index_;
+  int dimension_;
+  float refine_ratio_ = 1.0;
+  raft::device_matrix_view<const T, IdxT> dataset_;
+};
+
+template <typename T, typename IdxT>
+void cuvs_ivf_pq<T, IdxT>::save(const std::string& file) const
+{
+  cuvs::neighbors::ivf_pq::serialize(handle_, file, *index_);
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_pq<T, IdxT>::load(const std::string& file)
+{
+  index_ = std::make_shared<cuvs::neighbors::ivf_pq::index<IdxT>>(handle_, index_params_, dim_);
+  cuvs::neighbors::ivf_pq::deserialize(handle_, file, index_.get());
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_pq<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  auto dataset_v = raft::make_device_matrix_view<const T, IdxT>(dataset, IdxT(nrow), dim_);
+  std::make_shared<cuvs::neighbors::ivf_pq::index<IdxT>>(
+    std::move(cuvs::neighbors::ivf_pq::build(handle_, index_params_, dataset_v)))
+    .swap(index_);
+}
+
+template <typename T, typename IdxT>
+std::unique_ptr<algo<T>> cuvs_ivf_pq<T, IdxT>::copy()
+{
+  return std::make_unique<cuvs_ivf_pq<T, IdxT>>(*this);  // use copy constructor
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param)
+{
+  auto sp        = dynamic_cast<const search_param&>(param);
+  search_params_ = sp.pq_param;
+  refine_ratio_  = sp.refine_ratio;
+  assert(search_params_.n_probes <= index_params_.n_lists);
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_pq<T, IdxT>::set_search_dataset(const T* dataset, size_t nrow)
+{
+  dataset_ = raft::make_device_matrix_view<const T, IdxT>(dataset, nrow, index_->dim());
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_pq<T, IdxT>::search_base(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  static_assert(std::is_integral_v<algo_base::index_type>);
+  static_assert(std::is_integral_v<IdxT>);
+
+  IdxT* neighbors_idx_t;
+  std::optional<rmm::device_uvector<IdxT>> neighbors_storage{std::nullopt};
+  if constexpr (sizeof(IdxT) == sizeof(algo_base::index_type)) {
+    neighbors_idx_t = reinterpret_cast<IdxT*>(neighbors);
+  } else {
+    neighbors_storage.emplace(batch_size * k, raft::resource::get_cuda_stream(handle_));
+    neighbors_idx_t = neighbors_storage->data();
+  }
+
+  auto queries_view =
+    raft::make_device_matrix_view<const T, uint32_t>(queries, batch_size, dimension_);
+  auto neighbors_view =
+    raft::make_device_matrix_view<IdxT, uint32_t>(neighbors_idx_t, batch_size, k);
+  auto distances_view = raft::make_device_matrix_view<float, uint32_t>(distances, batch_size, k);
+
+  cuvs::neighbors::ivf_pq::search(
+    handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+
+  if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
+    raft::linalg::unaryOp(neighbors,
+                          neighbors_idx_t,
+                          batch_size * k,
+                          raft::cast_op<algo_base::index_type>(),
+                          raft::resource::get_cuda_stream(handle_));
+  }
+}
+
+template <typename T, typename IdxT>
+void cuvs_ivf_pq<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  auto k0                       = static_cast<size_t>(refine_ratio_ * k);
+  const bool disable_refinement = k0 <= static_cast<size_t>(k);
+  const raft::resources& res    = handle_;
+
+  if (disable_refinement) {
+    search_base(queries, batch_size, k, neighbors, distances);
+  } else {
+    auto queries_v = raft::make_device_matrix_view<const T, algo_base::index_type>(
+      queries, batch_size, dimension_);
+    auto candidate_ixs =
+      raft::make_device_matrix<algo_base::index_type, algo_base::index_type>(res, batch_size, k0);
+    auto candidate_dists =
+      raft::make_device_matrix<float, algo_base::index_type>(res, batch_size, k0);
+    search_base(
+      queries, batch_size, k0, candidate_ixs.data_handle(), candidate_dists.data_handle());
+    refine_helper(
+      res, dataset_, queries_v, candidate_ixs, k, neighbors, distances, index_->metric());
+  }
+}
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
new file mode 100644
index 0000000000..0954e60516
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
@@ -0,0 +1,167 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "cuvs_ann_bench_utils.h"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/core/device_resources.hpp>
+
+#include <cassert>
+#include <fstream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+namespace raft_temp {
+
+inline auto parse_metric_type(cuvs::bench::Metric metric) -> cuvs::distance::DistanceType
+{
+  switch (metric) {
+    case cuvs::bench::Metric::kInnerProduct: return cuvs::distance::DistanceType::InnerProduct;
+    case cuvs::bench::Metric::kEuclidean: return cuvs::distance::DistanceType::L2Expanded;
+    default: throw std::runtime_error("raft supports only metric type of inner product and L2");
+  }
+}
+}  // namespace raft_temp
+
+namespace cuvs::bench {
+
+// brute force KNN - RAFT
+template <typename T>
+class cuvs_gpu : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+
+  struct search_param : public search_param_base {
+    [[nodiscard]] auto needs_dataset() const -> bool override { return true; }
+  };
+
+  cuvs_gpu(Metric metric, int dim);
+
+  void build(const T*, size_t) final;
+
+  void set_search_param(const search_param_base& param) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const final;
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kDevice;
+    property.query_memory_type   = MemoryType::kDevice;
+    return property;
+  }
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return handle_.get_sync_stream();
+  }
+  void set_search_dataset(const T* dataset, size_t nrow) override;
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override;
+
+ protected:
+  // handle_ must go first to make sure it dies last and all memory allocated in pool
+  configured_raft_resources handle_{};
+  std::shared_ptr<cuvs::neighbors::brute_force::index<T>> index_;
+  cuvs::distance::DistanceType metric_type_;
+  int device_;
+  const T* dataset_;
+  size_t nrow_;
+};
+
+template <typename T>
+cuvs_gpu<T>::cuvs_gpu(Metric metric, int dim)
+  : algo<T>(metric, dim), metric_type_(raft_temp::parse_metric_type(metric))
+{
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, double>,
+                "raft bfknn only supports float/double");
+  RAFT_CUDA_TRY(cudaGetDevice(&device_));
+}
+
+template <typename T>
+void cuvs_gpu<T>::build(const T* dataset, size_t nrow)
+{
+  auto dataset_view = raft::make_device_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
+  index_            = std::make_shared<cuvs::neighbors::brute_force::index<T>>(
+    std::move(cuvs::neighbors::brute_force::build(handle_, dataset_view, metric_type_)));
+}
+
+template <typename T>
+void cuvs_gpu<T>::set_search_param(const search_param_base&)
+{
+  // Nothing to set here as it is brute force implementation
+}
+
+template <typename T>
+void cuvs_gpu<T>::set_search_dataset(const T* dataset, size_t nrow)
+{
+  dataset_ = dataset;
+  nrow_    = nrow;
+  // Wrap the dataset with an index.
+  auto dataset_view = raft::make_device_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
+  index_            = std::make_shared<cuvs::neighbors::brute_force::index<T>>(
+    std::move(cuvs::neighbors::brute_force::build(handle_, dataset_view, metric_type_)));
+}
+
+template <typename T>
+void cuvs_gpu<T>::save(const std::string& file) const
+{
+  // The index is just the dataset with metadata (shape). The dataset already exist on disk,
+  // therefore we do not need to save it here.
+  // We create an empty file because the benchmark logic requires an index file to be created.
+  std::ofstream of(file);
+  of.close();
+}
+
+template <typename T>
+void cuvs_gpu<T>::load(const std::string& file)
+{
+  // We do not have serialization of brute force index. We can simply wrap the
+  // dataset into a brute force index, like it is done in set_search_dataset.
+}
+
+template <typename T>
+void cuvs_gpu<T>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  auto queries_view =
+    raft::make_device_matrix_view<const T, int64_t>(queries, batch_size, this->dim_);
+
+  auto neighbors_view =
+    raft::make_device_matrix_view<algo_base::index_type, int64_t>(neighbors, batch_size, k);
+  auto distances_view = raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k);
+
+  cuvs::neighbors::brute_force::search(
+    handle_, *index_, queries_view, neighbors_view, distances_view, std::nullopt);
+}
+
+template <typename T>
+std::unique_ptr<algo<T>> cuvs_gpu<T>::copy()
+{
+  return std::make_unique<cuvs_gpu<T>>(*this);  // use copy constructor
+}
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
new file mode 100644
index 0000000000..354a9b2910
--- /dev/null
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "faiss_cpu_wrapper.h"
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace cuvs::bench {
+
+template <typename T>
+void parse_base_build_param(const nlohmann::json& conf,
+                            typename cuvs::bench::faiss_cpu<T>::build_param& param)
+{
+  param.nlist = conf.at("nlist");
+  if (conf.contains("ratio")) { param.ratio = conf.at("ratio"); }
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_cpu_ivf_flat<T>::build_param& param)
+{
+  parse_base_build_param<T>(conf, param);
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_cpu_ivfpq<T>::build_param& param)
+{
+  parse_base_build_param<T>(conf, param);
+  param.m = conf.at("M");
+  if (conf.contains("usePrecomputed")) {
+    param.use_precomputed = conf.at("usePrecomputed");
+  } else {
+    param.use_precomputed = false;
+  }
+  if (conf.contains("bitsPerCode")) {
+    param.bits_per_code = conf.at("bitsPerCode");
+  } else {
+    param.bits_per_code = 8;
+  }
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_cpu_ivfsq<T>::build_param& param)
+{
+  parse_base_build_param<T>(conf, param);
+  param.quantizer_type = conf.at("quantizer_type");
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::faiss_cpu<T>::search_param& param)
+{
+  param.nprobe = conf.at("nprobe");
+  if (conf.contains("refine_ratio")) { param.refine_ratio = conf.at("refine_ratio"); }
+  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+}
+
+template <typename T, template <typename> class Algo>
+auto make_algo(cuvs::bench::Metric metric, int dim, const nlohmann::json& conf)
+  -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  typename Algo<T>::build_param param;
+  parse_build_param<T>(conf, param);
+  return std::make_unique<Algo<T>>(metric, dim, param);
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float>) {
+    cuvs::bench::Metric metric = parse_metric(distance);
+    if (algo_name == "faiss_cpu_ivf_flat") {
+      a = make_algo<T, cuvs::bench::faiss_cpu_ivf_flat>(metric, dim, conf);
+    } else if (algo_name == "faiss_cpu_ivf_pq") {
+      a = make_algo<T, cuvs::bench::faiss_cpu_ivfpq>(metric, dim, conf);
+    } else if (algo_name == "faiss_cpu_ivf_sq") {
+      a = make_algo<T, cuvs::bench::faiss_cpu_ivfsq>(metric, dim, conf);
+    } else if (algo_name == "faiss_cpu_flat") {
+      a = std::make_unique<cuvs::bench::faiss_cpu_flat<T>>(metric, dim);
+    }
+  }
+
+  if constexpr (std::is_same_v<T, uint8_t>) {}
+
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+  if (algo_name == "faiss_cpu_ivf_flat" || algo_name == "faiss_cpu_ivf_pq" ||
+      algo_name == "faiss_cpu_ivf_sq") {
+    auto param = std::make_unique<typename cuvs::bench::faiss_cpu<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
+  } else if (algo_name == "faiss_cpu_flat") {
+    auto param = std::make_unique<typename cuvs::bench::faiss_cpu<T>::search_param>();
+    return param;
+  }
+  // else
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+}  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
new file mode 100644
index 0000000000..0cc40de37f
--- /dev/null
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
@@ -0,0 +1,327 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "../common/thread_pool.hpp"
+#include "../common/util.hpp"
+
+#include <faiss/IndexFlat.h>
+#include <faiss/IndexIVFFlat.h>
+#include <faiss/IndexIVFPQ.h>
+#include <faiss/IndexRefine.h>
+#include <faiss/IndexScalarQuantizer.h>
+#include <faiss/index_io.h>
+
+#include <cassert>
+#include <iostream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+namespace {
+
+auto parse_metric_type(cuvs::bench::Metric metric) -> faiss::MetricType
+{
+  if (metric == cuvs::bench::Metric::kInnerProduct) {
+    return faiss::METRIC_INNER_PRODUCT;
+  } else if (metric == cuvs::bench::Metric::kEuclidean) {
+    return faiss::METRIC_L2;
+  } else {
+    throw std::runtime_error("faiss supports only metric type of inner product and L2");
+  }
+}
+}  // namespace
+
+namespace cuvs::bench {
+
+template <typename T>
+class faiss_cpu : public algo<T> {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+  struct search_param : public search_param_base {
+    int nprobe;
+    float refine_ratio = 1.0;
+    int num_threads    = omp_get_num_procs();
+  };
+
+  struct build_param {
+    int nlist = 1;
+    int ratio = 2;
+  };
+
+  faiss_cpu(Metric metric, int dim, const build_param& param)
+    : algo<T>(metric, dim),
+      metric_type_(parse_metric_type(metric)),
+      nlist_{param.nlist},
+      training_sample_fraction_{1.0 / double(param.ratio)}
+  {
+    static_assert(std::is_same_v<T, float>, "faiss support only float type");
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param) override;
+
+  void init_quantizer(int dim)
+  {
+    if (this->metric_type_ == faiss::MetricType::METRIC_L2) {
+      this->quantizer_ = std::make_shared<faiss::IndexFlatL2>(dim);
+    } else if (this->metric_type_ == faiss::MetricType::METRIC_INNER_PRODUCT) {
+      this->quantizer_ = std::make_shared<faiss::IndexFlatIP>(dim);
+    }
+  }
+
+  // TODO(snanditale): if the number of results is less than k, the remaining elements of
+  // 'neighbors' will be filled with (size_t)-1
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const final;
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    // to enable building big dataset which is larger than  memory
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+ protected:
+  template <typename Index>
+  void save_(const std::string& file) const;  // NOLINT
+
+  template <typename Index>
+  void load_(const std::string& file);  // NOLINT
+
+  std::shared_ptr<faiss::Index> index_;
+  std::shared_ptr<faiss::Index> quantizer_;
+  std::shared_ptr<faiss::IndexRefineFlat> index_refine_;
+  faiss::MetricType metric_type_;
+  int nlist_;
+  double training_sample_fraction_;
+
+  int num_threads_;
+  std::shared_ptr<fixed_thread_pool> thread_pool_;
+};
+
+template <typename T>
+void faiss_cpu<T>::build(const T* dataset, size_t nrow)
+{
+  auto index_ivf = dynamic_cast<faiss::IndexIVF*>(index_.get());
+  if (index_ivf != nullptr) {
+    // set the min/max training size for clustering to use the whole provided training set.
+    double trainset_size       = training_sample_fraction_ * static_cast<double>(nrow);
+    double points_per_centroid = trainset_size / static_cast<double>(nlist_);
+    int max_ppc                = std::ceil(points_per_centroid);
+    int min_ppc                = std::floor(points_per_centroid);
+    if (min_ppc < index_ivf->cp.min_points_per_centroid) {
+      log_warn(
+        "The suggested training set size %zu (data size %zu, training sample ratio %f) yields %d "
+        "points per cluster (n_lists = %d). This is smaller than the FAISS default "
+        "min_points_per_centroid = %d.",
+        static_cast<size_t>(trainset_size),
+        nrow,
+        training_sample_fraction_,
+        min_ppc,
+        nlist_,
+        index_ivf->cp.min_points_per_centroid);
+    }
+    index_ivf->cp.max_points_per_centroid = max_ppc;
+    index_ivf->cp.min_points_per_centroid = min_ppc;
+  }
+  index_->train(nrow, dataset);  // faiss::IndexFlat::train() will do nothing
+  assert(index_->is_trained);
+  index_->add(nrow, dataset);
+  index_refine_ = std::make_shared<faiss::IndexRefineFlat>(this->index_.get(), dataset);
+}
+
+template <typename T>
+void faiss_cpu<T>::set_search_param(const search_param_base& param)
+{
+  auto sp    = dynamic_cast<const search_param&>(param);
+  int nprobe = sp.nprobe;
+  assert(nprobe <= nlist_);
+  dynamic_cast<faiss::IndexIVF*>(index_.get())->nprobe = nprobe;
+
+  if (sp.refine_ratio > 1.0) { this->index_refine_.get()->k_factor = sp.refine_ratio; }
+
+  if (!thread_pool_ || num_threads_ != sp.num_threads) {
+    num_threads_ = sp.num_threads;
+    thread_pool_ = std::make_shared<fixed_thread_pool>(num_threads_);
+  }
+}
+
+template <typename T>
+void faiss_cpu<T>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  static_assert(sizeof(size_t) == sizeof(faiss::idx_t),
+                "sizes of size_t and faiss::idx_t are different");
+
+  thread_pool_->submit(
+    [&](int i) {
+      // Use thread pool for batch size = 1. FAISS multi-threads internally for batch size > 1.
+      index_->search(batch_size, queries, k, distances, reinterpret_cast<faiss::idx_t*>(neighbors));
+    },
+    1);
+}
+
+template <typename T>
+template <typename Index>
+void faiss_cpu<T>::save_(const std::string& file) const
+{
+  faiss::write_index(index_.get(), file.c_str());
+}
+
+template <typename T>
+template <typename Index>
+void faiss_cpu<T>::load_(const std::string& file)
+{
+  index_ = std::shared_ptr<Index>(dynamic_cast<Index*>(faiss::read_index(file.c_str())));
+}
+
+template <typename T>
+class faiss_cpu_ivf_flat : public faiss_cpu<T> {
+ public:
+  using typename faiss_cpu<T>::build_param;
+
+  faiss_cpu_ivf_flat(Metric metric, int dim, const build_param& param)
+    : faiss_cpu<T>(metric, dim, param)
+  {
+    this->init_quantizer(dim);
+    this->index_ = std::make_shared<faiss::IndexIVFFlat>(
+      this->quantizer_.get(), dim, param.nlist, this->metric_type_);
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::IndexIVFFlat>(file);
+  }
+  void load(const std::string& file) override { this->template load_<faiss::IndexIVFFlat>(file); }
+
+  std::unique_ptr<algo<T>> copy()
+  {
+    return std::make_unique<faiss_cpu_ivf_flat<T>>(*this);  // use copy constructor
+  }
+};
+
+template <typename T>
+class faiss_cpu_ivfpq : public faiss_cpu<T> {
+ public:
+  struct build_param : public faiss_cpu<T>::build_param {
+    int m;
+    int bits_per_code;
+    bool use_precomputed;
+  };
+
+  faiss_cpu_ivfpq(Metric metric, int dim, const build_param& param)
+    : faiss_cpu<T>(metric, dim, param)
+  {
+    this->init_quantizer(dim);
+    this->index_ = std::make_shared<faiss::IndexIVFPQ>(
+      this->quantizer_.get(), dim, param.nlist, param.m, param.bits_per_code, this->metric_type_);
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::IndexIVFPQ>(file);
+  }
+  void load(const std::string& file) override { this->template load_<faiss::IndexIVFPQ>(file); }
+
+  std::unique_ptr<algo<T>> copy()
+  {
+    return std::make_unique<faiss_cpu_ivfpq<T>>(*this);  // use copy constructor
+  }
+};
+
+// TODO(snanditale): Enable this in cmake
+//  ref: https://github.com/rapidsai/raft/issues/1876
+template <typename T>
+class faiss_cpu_ivfsq : public faiss_cpu<T> {
+ public:
+  struct build_param : public faiss_cpu<T>::build_param {
+    std::string quantizer_type;
+  };
+
+  faiss_cpu_ivfsq(Metric metric, int dim, const build_param& param)
+    : faiss_cpu<T>(metric, dim, param)
+  {
+    faiss::ScalarQuantizer::QuantizerType qtype;
+    if (param.quantizer_type == "fp16") {
+      qtype = faiss::ScalarQuantizer::QT_fp16;
+    } else if (param.quantizer_type == "int8") {
+      qtype = faiss::ScalarQuantizer::QT_8bit;
+    } else {
+      throw std::runtime_error("faiss_cpu_ivfsq supports only fp16 and int8 but got " +
+                               param.quantizer_type);
+    }
+
+    this->init_quantizer(dim);
+    this->index_ = std::make_shared<faiss::IndexIVFScalarQuantizer>(
+      this->quantizer_.get(), dim, param.nlist, qtype, this->metric_type_, true);
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::IndexIVFScalarQuantizer>(file);
+  }
+  void load(const std::string& file) override
+  {
+    this->template load_<faiss::IndexIVFScalarQuantizer>(file);
+  }
+
+  std::unique_ptr<algo<T>> copy()
+  {
+    return std::make_unique<faiss_cpu_ivfsq<T>>(*this);  // use copy constructor
+  }
+};
+
+template <typename T>
+class faiss_cpu_flat : public faiss_cpu<T> {
+ public:
+  faiss_cpu_flat(Metric metric, int dim)
+    : faiss_cpu<T>(metric, dim, typename faiss_cpu<T>::build_param{})
+  {
+    this->index_ = std::make_shared<faiss::IndexFlat>(dim, this->metric_type_);
+  }
+
+  // class faiss_cpu is more like a IVF class, so need special treating here
+  void set_search_param(const typename algo<T>::search_param& param) override
+  {
+    auto search_param = dynamic_cast<const typename faiss_cpu<T>::search_param&>(param);
+    if (!this->thread_pool_ || this->num_threads_ != search_param.num_threads) {
+      this->num_threads_ = search_param.num_threads;
+      this->thread_pool_ = std::make_shared<fixed_thread_pool>(this->num_threads_);
+    }
+  };
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::IndexFlat>(file);
+  }
+  void load(const std::string& file) override { this->template load_<faiss::IndexFlat>(file); }
+
+  std::unique_ptr<algo<T>> copy()
+  {
+    return std::make_unique<faiss_cpu_flat<T>>(*this);  // use copy constructor
+  }
+};
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
new file mode 100644
index 0000000000..2d92716395
--- /dev/null
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+
+#undef WARP_SIZE
+#include "faiss_gpu_wrapper.h"
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace cuvs::bench {
+
+template <typename T>
+void parse_base_build_param(const nlohmann::json& conf,
+                            typename cuvs::bench::faiss_gpu<T>::build_param& param)
+{
+  param.nlist = conf.at("nlist");
+  if (conf.contains("ratio")) { param.ratio = conf.at("ratio"); }
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_gpu_ivf_flat<T>::build_param& param)
+{
+  parse_base_build_param<T>(conf, param);
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_gpu_ivfpq<T>::build_param& param)
+{
+  parse_base_build_param<T>(conf, param);
+  param.m = conf.at("M");
+  if (conf.contains("usePrecomputed")) {
+    param.use_precomputed = conf.at("usePrecomputed");
+  } else {
+    param.use_precomputed = false;
+  }
+  if (conf.contains("useFloat16")) {
+    param.use_float16 = conf.at("useFloat16");
+  } else {
+    param.use_float16 = false;
+  }
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_gpu_ivfsq<T>::build_param& param)
+{
+  parse_base_build_param<T>(conf, param);
+  param.quantizer_type = conf.at("quantizer_type");
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::faiss_gpu<T>::search_param& param)
+{
+  param.nprobe = conf.at("nprobe");
+  if (conf.contains("refine_ratio")) { param.refine_ratio = conf.at("refine_ratio"); }
+}
+
+template <typename T, template <typename> class Algo>
+auto make_algo(cuvs::bench::Metric metric, int dim, const nlohmann::json& conf)
+  -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  typename Algo<T>::build_param param;
+  parse_build_param<T>(conf, param);
+  return std::make_unique<Algo<T>>(metric, dim, param);
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float>) {
+    cuvs::bench::Metric metric = parse_metric(distance);
+    if (algo_name == "faiss_gpu_ivf_flat") {
+      a = make_algo<T, cuvs::bench::faiss_gpu_ivf_flat>(metric, dim, conf);
+    } else if (algo_name == "faiss_gpu_ivf_pq") {
+      a = make_algo<T, cuvs::bench::faiss_gpu_ivfpq>(metric, dim, conf);
+    } else if (algo_name == "faiss_gpu_ivf_sq") {
+      a = make_algo<T, cuvs::bench::faiss_gpu_ivfsq>(metric, dim, conf);
+    } else if (algo_name == "faiss_gpu_flat") {
+      a = std::make_unique<cuvs::bench::faiss_gpu_flat<T>>(metric, dim);
+    }
+  }
+
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+  if (algo_name == "faiss_gpu_ivf_flat" || algo_name == "faiss_gpu_ivf_pq" ||
+      algo_name == "faiss_gpu_ivf_sq") {
+    auto param = std::make_unique<typename cuvs::bench::faiss_gpu<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
+  } else if (algo_name == "faiss_gpu_flat") {
+    auto param = std::make_unique<typename cuvs::bench::faiss_gpu<T>::search_param>();
+    return param;
+  }
+  // else
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+}  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
new file mode 100644
index 0000000000..f935e365fe
--- /dev/null
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
@@ -0,0 +1,444 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "../common/util.hpp"
+
+#include <faiss/IndexFlat.h>
+#include <faiss/IndexIVFFlat.h>
+#include <faiss/IndexIVFPQ.h>
+#include <faiss/IndexRefine.h>
+#include <faiss/IndexScalarQuantizer.h>
+#include <faiss/gpu/GpuIndexFlat.h>
+#include <faiss/gpu/GpuIndexIVFFlat.h>
+#include <faiss/gpu/GpuIndexIVFPQ.h>
+#include <faiss/gpu/GpuIndexIVFScalarQuantizer.h>
+#include <faiss/gpu/StandardGpuResources.h>
+#include <faiss/impl/ScalarQuantizer.h>
+#include <faiss/index_io.h>
+#include <omp.h>
+
+#include <cassert>
+#include <iostream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+namespace {
+
+auto parse_metric_type(cuvs::bench::Metric metric) -> faiss::MetricType
+{
+  if (metric == cuvs::bench::Metric::kInnerProduct) {
+    return faiss::METRIC_INNER_PRODUCT;
+  } else if (metric == cuvs::bench::Metric::kEuclidean) {
+    return faiss::METRIC_L2;
+  } else {
+    throw std::runtime_error("faiss supports only metric type of inner product and L2");
+  }
+}
+
+// note BLAS library can still use multi-threading, and
+// setting environment variable like OPENBLAS_NUM_THREADS can control it
+class omp_single_thread_scope {
+ public:
+  omp_single_thread_scope()
+  {
+    max_threads_ = omp_get_max_threads();
+    omp_set_num_threads(1);
+  }
+  ~omp_single_thread_scope()
+  {
+    // the best we can do
+    omp_set_num_threads(max_threads_);
+  }
+
+ private:
+  int max_threads_;
+};
+
+}  // namespace
+
+namespace cuvs::bench {
+
+template <typename T>
+class faiss_gpu : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+  struct search_param : public search_param_base {
+    int nprobe;
+    float refine_ratio = 1.0;
+    [[nodiscard]] auto needs_dataset() const -> bool override { return refine_ratio > 1.0f; }
+  };
+
+  struct build_param {
+    int nlist = 1;
+    int ratio = 2;
+  };
+
+  faiss_gpu(Metric metric, int dim, const build_param& param)
+    : algo<T>(metric, dim),
+      gpu_resource_{std::make_shared<faiss::gpu::StandardGpuResources>()},
+      metric_type_(parse_metric_type(metric)),
+      nlist_{param.nlist},
+      training_sample_fraction_{1.0 / double(param.ratio)}
+  {
+    static_assert(std::is_same_v<T, float>, "faiss support only float type");
+    cudaGetDevice(&device_);
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+
+  virtual void set_search_param(const search_param_base& param) {}
+
+  void set_search_dataset(const T* dataset, size_t nrow) override { dataset_ = dataset; }
+
+  // TODO(snanditale): if the number of results is less than k, the remaining elements of
+  // 'neighbors' will be filled with (size_t)-1
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const final;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return gpu_resource_->getDefaultStream(device_);
+  }
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    // to enable building big dataset which is larger than GPU memory
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+ protected:
+  template <typename GpuIndex, typename CpuIndex>
+  void save_(const std::string& file) const;  // NOLINT
+
+  template <typename GpuIndex, typename CpuIndex>
+  void load_(const std::string& file);  // NOLINT
+
+  /** [NOTE Multithreading]
+   *
+   * `gpu_resource_` is a shared resource:
+   *   1. It uses a shared_ptr under the hood, so the copies of it refer to the same
+   *      resource implementation instance
+   *   2. GpuIndex is probably keeping a reference to it, as it's passed to the constructor
+   *
+   * To avoid copying the index (database) in each thread, we make both the index and
+   * the gpu_resource shared.
+   * This means faiss GPU streams are possibly shared among the CPU threads;
+   * the throughput search mode may be inaccurate.
+   *
+   * WARNING: we haven't investigated whether faiss::gpu::GpuIndex or
+   * faiss::gpu::StandardGpuResources are thread-safe.
+   *
+   */
+  mutable std::shared_ptr<faiss::gpu::StandardGpuResources> gpu_resource_;
+  std::shared_ptr<faiss::gpu::GpuIndex> index_;
+  std::shared_ptr<faiss::IndexRefineFlat> index_refine_{nullptr};
+  faiss::MetricType metric_type_;
+  int nlist_;
+  int device_;
+  double training_sample_fraction_;
+  std::shared_ptr<faiss::SearchParameters> search_params_;
+  const T* dataset_;
+  float refine_ratio_ = 1.0;
+};
+
+template <typename T>
+void faiss_gpu<T>::build(const T* dataset, size_t nrow)
+{
+  omp_single_thread_scope omp_single_thread;
+  auto index_ivf = dynamic_cast<faiss::gpu::GpuIndexIVF*>(index_.get());
+  if (index_ivf != nullptr) {
+    // set the min/max training size for clustering to use the whole provided training set.
+    double trainset_size       = training_sample_fraction_ * static_cast<double>(nrow);
+    double points_per_centroid = trainset_size / static_cast<double>(nlist_);
+    int max_ppc                = std::ceil(points_per_centroid);
+    int min_ppc                = std::floor(points_per_centroid);
+    if (min_ppc < index_ivf->cp.min_points_per_centroid) {
+      log_warn(
+        "The suggested training set size %zu (data size %zu, training sample ratio %f) yields %d "
+        "points per cluster (n_lists = %d). This is smaller than the FAISS default "
+        "min_points_per_centroid = %d.",
+        static_cast<size_t>(trainset_size),
+        nrow,
+        training_sample_fraction_,
+        min_ppc,
+        nlist_,
+        index_ivf->cp.min_points_per_centroid);
+    }
+    index_ivf->cp.max_points_per_centroid = max_ppc;
+    index_ivf->cp.min_points_per_centroid = min_ppc;
+  }
+  index_->train(nrow, dataset);  // faiss::gpu::GpuIndexFlat::train() will do nothing
+  assert(index_->is_trained);
+  index_->add(nrow, dataset);
+}
+
+template <typename T>
+void faiss_gpu<T>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  static_assert(sizeof(size_t) == sizeof(faiss::idx_t),
+                "sizes of size_t and faiss::idx_t are different");
+
+  if (this->refine_ratio_ > 1.0) {
+    // TODO(snanditale): FAISS changed their search APIs to accept the search parameters as a struct
+    // object but their refine API doesn't allow the struct to be passed in. Once this is fixed, we
+    // need to re-enable refinement below
+    // index_refine_->search(batch_size, queries, k, distances,
+    // reinterpret_cast<faiss::idx_t*>(neighbors), this->search_params_.get()); Related FAISS issue:
+    // https://github.com/facebookresearch/faiss/issues/3118
+    throw std::runtime_error(
+      "FAISS doesn't support refinement in their new APIs so this feature is disabled in the "
+      "benchmarks for the time being.");
+  } else {
+    index_->search(batch_size,
+                   queries,
+                   k,
+                   distances,
+                   reinterpret_cast<faiss::idx_t*>(neighbors),
+                   this->search_params_.get());
+  }
+}
+
+template <typename T>
+template <typename GpuIndex, typename CpuIndex>
+void faiss_gpu<T>::save_(const std::string& file) const
+{
+  omp_single_thread_scope omp_single_thread;
+
+  auto cpu_index = std::make_unique<CpuIndex>();
+  dynamic_cast<GpuIndex*>(index_.get())->copyTo(cpu_index.get());
+  faiss::write_index(cpu_index.get(), file.c_str());
+}
+
+template <typename T>
+template <typename GpuIndex, typename CpuIndex>
+void faiss_gpu<T>::load_(const std::string& file)
+{
+  omp_single_thread_scope omp_single_thread;
+
+  std::unique_ptr<CpuIndex> cpu_index(dynamic_cast<CpuIndex*>(faiss::read_index(file.c_str())));
+  assert(cpu_index);
+
+  try {
+    dynamic_cast<GpuIndex*>(index_.get())->copyFrom(cpu_index.get());
+
+  } catch (const std::exception& e) {
+    std::cout << "Error loading index file: " << std::string(e.what()) << std::endl;
+  }
+}
+
+template <typename T>
+class faiss_gpu_ivf_flat : public faiss_gpu<T> {
+ public:
+  using typename faiss_gpu<T>::build_param;
+  using typename faiss_gpu<T>::search_param_base;
+
+  faiss_gpu_ivf_flat(Metric metric, int dim, const build_param& param)
+    : faiss_gpu<T>(metric, dim, param)
+  {
+    faiss::gpu::GpuIndexIVFFlatConfig config;
+    config.device = this->device_;
+    this->index_  = std::make_shared<faiss::gpu::GpuIndexIVFFlat>(
+      this->gpu_resource_.get(), dim, param.nlist, this->metric_type_, config);
+  }
+
+  void set_search_param(const search_param_base& param) override
+  {
+    auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
+    int nprobe = sp.nprobe;
+    assert(nprobe <= this->nlist_);
+
+    faiss::IVFSearchParameters faiss_search_params;
+    faiss_search_params.nprobe = nprobe;
+    this->search_params_       = std::make_shared<faiss::IVFSearchParameters>(faiss_search_params);
+    this->refine_ratio_        = sp.refine_ratio;
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::gpu::GpuIndexIVFFlat, faiss::IndexIVFFlat>(file);
+  }
+  void load(const std::string& file) override
+  {
+    this->template load_<faiss::gpu::GpuIndexIVFFlat, faiss::IndexIVFFlat>(file);
+  }
+  std::unique_ptr<algo<T>> copy() override
+  {
+    return std::make_unique<faiss_gpu_ivf_flat<T>>(*this);
+  };
+};
+
+template <typename T>
+class faiss_gpu_ivfpq : public faiss_gpu<T> {
+ public:
+  struct build_param : public faiss_gpu<T>::build_param {
+    int m;
+    bool use_float16;
+    bool use_precomputed;
+  };
+  using typename faiss_gpu<T>::search_param_base;
+
+  faiss_gpu_ivfpq(Metric metric, int dim, const build_param& param)
+    : faiss_gpu<T>(metric, dim, param)
+  {
+    faiss::gpu::GpuIndexIVFPQConfig config;
+    config.useFloat16LookupTables = param.use_float16;
+    config.usePrecomputedTables   = param.use_precomputed;
+    config.device                 = this->device_;
+
+    this->index_ =
+      std::make_shared<faiss::gpu::GpuIndexIVFPQ>(this->gpu_resource_.get(),
+                                                  dim,
+                                                  param.nlist,
+                                                  param.m,
+                                                  8,  // FAISS only supports bitsPerCode=8
+                                                  this->metric_type_,
+                                                  config);
+  }
+
+  void set_search_param(const search_param_base& param) override
+  {
+    auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
+    int nprobe = sp.nprobe;
+    assert(nprobe <= this->nlist_);
+    this->refine_ratio_ = sp.refine_ratio;
+    faiss::IVFPQSearchParameters faiss_search_params;
+    faiss_search_params.nprobe = nprobe;
+
+    this->search_params_ = std::make_shared<faiss::IVFPQSearchParameters>(faiss_search_params);
+
+    if (sp.refine_ratio > 1.0) {
+      this->index_refine_ =
+        std::make_shared<faiss::IndexRefineFlat>(this->index_.get(), this->dataset_);
+      this->index_refine_.get()->k_factor = sp.refine_ratio;
+    }
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::gpu::GpuIndexIVFPQ, faiss::IndexIVFPQ>(file);
+  }
+  void load(const std::string& file) override
+  {
+    this->template load_<faiss::gpu::GpuIndexIVFPQ, faiss::IndexIVFPQ>(file);
+  }
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<faiss_gpu_ivfpq<T>>(*this); };
+};
+
+// TODO(snanditale): Enable this in cmake
+//  ref: https://github.com/rapidsai/raft/issues/1876
+template <typename T>
+class faiss_gpu_ivfsq : public faiss_gpu<T> {
+ public:
+  struct build_param : public faiss_gpu<T>::build_param {
+    std::string quantizer_type;
+  };
+  using typename faiss_gpu<T>::search_param_base;
+
+  faiss_gpu_ivfsq(Metric metric, int dim, const build_param& param)
+    : faiss_gpu<T>(metric, dim, param)
+  {
+    faiss::ScalarQuantizer::QuantizerType qtype;
+    if (param.quantizer_type == "fp16") {
+      qtype = faiss::ScalarQuantizer::QT_fp16;
+    } else if (param.quantizer_type == "int8") {
+      qtype = faiss::ScalarQuantizer::QT_8bit;
+    } else {
+      throw std::runtime_error("faiss_gpu_ivfsq supports only fp16 and int8 but got " +
+                               param.quantizer_type);
+    }
+
+    faiss::gpu::GpuIndexIVFScalarQuantizerConfig config;
+    config.device = this->device_;
+    this->index_  = std::make_shared<faiss::gpu::GpuIndexIVFScalarQuantizer>(
+      this->gpu_resource_.get(), dim, param.nlist, qtype, this->metric_type_, true, config);
+  }
+
+  void set_search_param(const search_param_base& param) override
+  {
+    auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
+    int nprobe = sp.nprobe;
+    assert(nprobe <= this->nlist_);
+
+    faiss::IVFSearchParameters faiss_search_params;
+    faiss_search_params.nprobe = nprobe;
+
+    this->search_params_ = std::make_shared<faiss::IVFSearchParameters>(faiss_search_params);
+    this->refine_ratio_  = sp.refine_ratio;
+    if (sp.refine_ratio > 1.0) {
+      this->index_refine_ =
+        std::make_shared<faiss::IndexRefineFlat>(this->index_.get(), this->dataset_);
+      this->index_refine_.get()->k_factor = sp.refine_ratio;
+    }
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::gpu::GpuIndexIVFScalarQuantizer, faiss::IndexIVFScalarQuantizer>(
+      file);
+  }
+  void load(const std::string& file) override
+  {
+    this->template load_<faiss::gpu::GpuIndexIVFScalarQuantizer, faiss::IndexIVFScalarQuantizer>(
+      file);
+  }
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<faiss_gpu_ivfsq<T>>(*this); };
+};
+
+template <typename T>
+class faiss_gpu_flat : public faiss_gpu<T> {
+ public:
+  using typename faiss_gpu<T>::search_param_base;
+
+  faiss_gpu_flat(Metric metric, int dim)
+    : faiss_gpu<T>(metric, dim, typename faiss_gpu<T>::build_param{})
+  {
+    faiss::gpu::GpuIndexFlatConfig config;
+    config.device = this->device_;
+    this->index_  = std::make_shared<faiss::gpu::GpuIndexFlat>(
+      this->gpu_resource_.get(), dim, this->metric_type_, config);
+  }
+  void set_search_param(const search_param_base& param) override
+  {
+    auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
+    int nprobe = sp.nprobe;
+    assert(nprobe <= this->nlist_);
+
+    this->search_params_ = std::make_shared<faiss::SearchParameters>();
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::gpu::GpuIndexFlat, faiss::IndexFlat>(file);
+  }
+  void load(const std::string& file) override
+  {
+    this->template load_<faiss::gpu::GpuIndexFlat, faiss::IndexFlat>(file);
+  }
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<faiss_gpu_flat<T>>(*this); };
+};
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/ggnn/ggnn_benchmark.cu b/cpp/bench/ann/src/ggnn/ggnn_benchmark.cu
new file mode 100644
index 0000000000..6b1a1eaf74
--- /dev/null
+++ b/cpp/bench/ann/src/ggnn/ggnn_benchmark.cu
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "ggnn_wrapper.cuh"
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace cuvs::bench {
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::ggnn<T>::build_param& param)
+{
+  param.k = conf.at("k");
+
+  if (conf.contains("k_build")) { param.k_build = conf.at("k_build"); }
+  if (conf.contains("segment_size")) { param.segment_size = conf.at("segment_size"); }
+  if (conf.contains("num_layers")) { param.num_layers = conf.at("num_layers"); }
+  if (conf.contains("tau")) { param.tau = conf.at("tau"); }
+  if (conf.contains("refine_iterations")) {
+    param.refine_iterations = conf.at("refine_iterations");
+  }
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::ggnn<T>::search_param& param)
+{
+  param.tau = conf.at("tau");
+
+  if (conf.contains("block_dim")) { param.block_dim = conf.at("block_dim"); }
+  if (conf.contains("max_iterations")) { param.max_iterations = conf.at("max_iterations"); }
+  if (conf.contains("cache_size")) { param.cache_size = conf.at("cache_size"); }
+  if (conf.contains("sorted_size")) { param.sorted_size = conf.at("sorted_size"); }
+}
+
+template <typename T, template <typename> class Algo>
+auto make_algo(cuvs::bench::Metric metric, int dim, const nlohmann::json& conf)
+  -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  typename Algo<T>::build_param param;
+  parse_build_param<T>(conf, param);
+  return std::make_unique<Algo<T>>(metric, dim, param);
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  cuvs::bench::Metric metric = parse_metric(distance);
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, uint8_t> ||
+                std::is_same_v<T, int8_t>) {
+    if (algo_name == "ggnn") { a = make_algo<T, cuvs::bench::ggnn>(metric, dim, conf); }
+  }
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, uint8_t> ||
+                std::is_same_v<T, int8_t>) {
+    if (algo_name == "ggnn") {
+      auto param = std::make_unique<typename cuvs::bench::ggnn<T>::search_param>();
+      parse_search_param<T>(conf, *param);
+      return param;
+    }
+  }
+  // else
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+}  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh b/cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh
new file mode 100644
index 0000000000..e2ca18e223
--- /dev/null
+++ b/cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh
@@ -0,0 +1,323 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "../common/util.hpp"
+
+// #include <raft/util/cudart_utils.hpp>
+
+#include <ggnn/cuda_knn_ggnn_gpu_instance.cuh>
+
+#include <memory>
+#include <stdexcept>
+
+namespace cuvs::bench {
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+class ggnn_impl;
+
+template <typename T>
+class ggnn : public algo<T>, public algo_gpu {
+ public:
+  struct build_param {
+    int k_build{24};       // KBuild
+    int segment_size{32};  // S
+    int num_layers{4};     // L
+    float tau{0.5};
+    int refine_iterations{2};
+    int k;  // GGNN requires to know k during building
+  };
+
+  using search_param_base = typename algo<T>::search_param;
+  struct search_param : public search_param_base {
+    float tau;
+    int block_dim{32};
+    int max_iterations{400};
+    int cache_size{512};
+    int sorted_size{256};
+    [[nodiscard]] auto needs_dataset() const -> bool override { return true; }
+  };
+
+  ggnn(Metric metric, int dim, const build_param& param);
+
+  void build(const T* dataset, size_t nrow) override { impl_->build(dataset, nrow); }
+
+  void set_search_param(const search_param_base& param) override { impl_->set_search_param(param); }
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override
+  {
+    impl_->search(queries, batch_size, k, neighbors, distances);
+  }
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return dynamic_cast<algo_gpu*>(impl_.get())->get_sync_stream();
+  }
+
+  void save(const std::string& file) const override { impl_->save(file); }
+  void load(const std::string& file) override { impl_->load(file); }
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<ggnn<T>>(*this); };
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    return impl_->get_preference();
+  }
+
+  void set_search_dataset(const T* dataset, size_t nrow) override
+  {
+    impl_->set_search_dataset(dataset, nrow);
+  };
+
+ private:
+  std::shared_ptr<algo<T>> impl_;
+};
+
+template <typename T>
+ggnn<T>::ggnn(Metric metric, int dim, const build_param& param) : algo<T>(metric, dim)
+{
+  // ggnn/src/sift1m.cu
+  if (metric == Metric::kEuclidean && dim == 128 && param.k_build == 24 && param.k == 10 &&
+      param.segment_size == 32) {
+    impl_ = std::make_shared<ggnn_impl<T, Euclidean, 128, 24, 10, 32>>(metric, dim, param);
+  }
+  // ggnn/src/deep1b_multi_gpu.cu, and adapt it deep1B
+  else if (metric == Metric::kEuclidean && dim == 96 && param.k_build == 24 && param.k == 10 &&
+           param.segment_size == 32) {
+    impl_ = std::make_shared<ggnn_impl<T, Euclidean, 96, 24, 10, 32>>(metric, dim, param);
+  } else if (metric == Metric::kInnerProduct && dim == 96 && param.k_build == 24 && param.k == 10 &&
+             param.segment_size == 32) {
+    impl_ = std::make_shared<ggnn_impl<T, Cosine, 96, 24, 10, 32>>(metric, dim, param);
+  } else if (metric == Metric::kInnerProduct && dim == 96 && param.k_build == 96 && param.k == 10 &&
+             param.segment_size == 64) {
+    impl_ = std::make_shared<ggnn_impl<T, Cosine, 96, 96, 10, 64>>(metric, dim, param);
+  }
+  // ggnn/src/glove200.cu, adapt it to glove100
+  else if (metric == Metric::kInnerProduct && dim == 100 && param.k_build == 96 && param.k == 10 &&
+           param.segment_size == 64) {
+    impl_ = std::make_shared<ggnn_impl<T, Cosine, 100, 96, 10, 64>>(metric, dim, param);
+  } else {
+    throw std::runtime_error(
+      "ggnn: not supported combination of metric, dim and build param; "
+      "see Ggnn's constructor in ggnn_wrapper.cuh for available combinations");
+  }
+}
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+class ggnn_impl : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+
+  ggnn_impl(Metric metric, int dim, const typename ggnn<T>::build_param& param);
+
+  void build(const T* dataset, size_t nrow) override;
+
+  void set_search_param(const search_param_base& param) override;
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override { return stream_; }
+
+  void save(const std::string& file) const override;
+  void load(const std::string& file) override;
+  std::unique_ptr<algo<T>> copy() override
+  {
+    auto r = std::make_unique<ggnn_impl<T, measure, D, KBuild, KQuery, S>>(*this);
+    // set the thread-local stream to the copied handle.
+    r->stream_ = cuvs::bench::get_stream_from_global_pool();
+    return r;
+  };
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kDevice;
+    property.query_memory_type   = MemoryType::kDevice;
+    return property;
+  }
+
+  void set_search_dataset(const T* dataset, size_t nrow) override;
+
+ private:
+  using algo<T>::metric_;
+  using algo<T>::dim_;
+
+  using ggnngpu_instance = GGNNGPUInstance<measure,
+                                           int64_t /* KeyT */,
+                                           float /* ValueT */,
+                                           size_t /* GAddrT */,
+                                           T /* BaseT */,
+                                           size_t /* BAddrT */,
+                                           D,
+                                           KBuild,
+                                           KBuild / 2 /* KF */,
+                                           KQuery,
+                                           S>;
+  std::shared_ptr<ggnngpu_instance> ggnn_;
+  typename ggnn<T>::build_param build_param_;
+  typename ggnn<T>::search_param search_param_;
+  cudaStream_t stream_;
+  const T* base_dataset_                 = nullptr;
+  size_t base_n_rows_                    = 0;
+  std::optional<std::string> graph_file_ = std::nullopt;
+
+  void load_impl()
+  {
+    if (base_dataset_ == nullptr) { return; }
+    if (base_n_rows_ == 0) { return; }
+    int device;
+    cudaGetDevice(&device);
+    ggnn_ = std::make_shared<ggnngpu_instance>(
+      device, base_n_rows_, build_param_.num_layers, true, build_param_.tau);
+    ggnn_->set_base_data(base_dataset_);
+    ggnn_->set_stream(get_sync_stream());
+    if (graph_file_.has_value()) {
+      auto& ggnn_host   = ggnn_->ggnn_cpu_buffers.at(0);
+      auto& ggnn_device = ggnn_->ggnn_shards.at(0);
+      ggnn_->set_stream(get_sync_stream());
+
+      ggnn_host.load(graph_file_.value());
+      ggnn_host.uploadAsync(ggnn_device);
+      cudaStreamSynchronize(ggnn_device.stream);
+    }
+  }
+};
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+ggnn_impl<T, measure, D, KBuild, KQuery, S>::ggnn_impl(Metric metric,
+                                                       int dim,
+                                                       const typename ggnn<T>::build_param& param)
+  : algo<T>(metric, dim), build_param_(param), stream_(cuvs::bench::get_stream_from_global_pool())
+{
+  if (metric_ == Metric::kInnerProduct) {
+    if (measure != Cosine) { throw std::runtime_error("mis-matched metric"); }
+  } else if (metric_ == Metric::kEuclidean) {
+    if (measure != Euclidean) { throw std::runtime_error("mis-matched metric"); }
+  } else {
+    throw std::runtime_error(
+      "ggnn supports only metric type of InnerProduct, Cosine and Euclidean");
+  }
+
+  if (dim != D) { throw std::runtime_error("mis-matched dim"); }
+}
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+void ggnn_impl<T, measure, D, KBuild, KQuery, S>::build(const T* dataset, size_t nrow)
+{
+  base_dataset_ = dataset;
+  base_n_rows_  = nrow;
+  graph_file_   = std::nullopt;
+  load_impl();
+  ggnn_->build(0);
+  for (int i = 0; i < build_param_.refine_iterations; ++i) {
+    ggnn_->refine();
+  }
+}
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+void ggnn_impl<T, measure, D, KBuild, KQuery, S>::set_search_dataset(const T* dataset, size_t nrow)
+{
+  if (base_dataset_ != dataset || base_n_rows_ != nrow) {
+    base_dataset_ = dataset;
+    base_n_rows_  = nrow;
+    load_impl();
+  }
+}
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+void ggnn_impl<T, measure, D, KBuild, KQuery, S>::set_search_param(const search_param_base& param)
+{
+  search_param_ = dynamic_cast<const typename ggnn<T>::search_param&>(param);
+}
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+void ggnn_impl<T, measure, D, KBuild, KQuery, S>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  static_assert(sizeof(size_t) == sizeof(int64_t), "sizes of size_t and GGNN's KeyT are different");
+  if (k != KQuery) {
+    throw std::runtime_error(
+      "k = " + std::to_string(k) +
+      ", but this GGNN instance only supports k = " + std::to_string(KQuery));
+  }
+
+  ggnn_->set_stream(get_sync_stream());
+  cudaMemcpyToSymbol(c_tau_query, &search_param_.tau, sizeof(float));
+
+  const int block_dim      = search_param_.block_dim;
+  const int max_iterations = search_param_.max_iterations;
+  const int cache_size     = search_param_.cache_size;
+  const int sorted_size    = search_param_.sorted_size;
+  // default value
+  if (block_dim == 32 && max_iterations == 400 && cache_size == 512 && sorted_size == 256) {
+    ggnn_->template queryLayer<32, 400, 512, 256, false>(
+      queries, batch_size, reinterpret_cast<int64_t*>(neighbors), distances);
+  }
+  // ggnn/src/sift1m.cu
+  else if (block_dim == 32 && max_iterations == 200 && cache_size == 256 && sorted_size == 64) {
+    ggnn_->template queryLayer<32, 200, 256, 64, false>(
+      queries, batch_size, reinterpret_cast<int64_t*>(neighbors), distances);
+  }
+  // ggnn/src/sift1m.cu
+  else if (block_dim == 32 && max_iterations == 400 && cache_size == 448 && sorted_size == 64) {
+    ggnn_->template queryLayer<32, 400, 448, 64, false>(
+      queries, batch_size, reinterpret_cast<int64_t*>(neighbors), distances);
+  }
+  // ggnn/src/glove200.cu
+  else if (block_dim == 128 && max_iterations == 2000 && cache_size == 2048 && sorted_size == 32) {
+    ggnn_->template queryLayer<128, 2000, 2048, 32, false>(
+      queries, batch_size, reinterpret_cast<int64_t*>(neighbors), distances);
+  }
+  // for glove100
+  else if (block_dim == 64 && max_iterations == 400 && cache_size == 512 && sorted_size == 32) {
+    ggnn_->template queryLayer<64, 400, 512, 32, false>(
+      queries, batch_size, reinterpret_cast<int64_t*>(neighbors), distances);
+  } else if (block_dim == 128 && max_iterations == 2000 && cache_size == 1024 &&
+             sorted_size == 32) {
+    ggnn_->template queryLayer<128, 2000, 1024, 32, false>(
+      queries, batch_size, reinterpret_cast<int64_t*>(neighbors), distances);
+  } else {
+    throw std::runtime_error("ggnn: not supported search param");
+  }
+}
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+void ggnn_impl<T, measure, D, KBuild, KQuery, S>::save(const std::string& file) const
+{
+  auto& ggnn_host   = ggnn_->ggnn_cpu_buffers.at(0);
+  auto& ggnn_device = ggnn_->ggnn_shards.at(0);
+  ggnn_->set_stream(get_sync_stream());
+
+  ggnn_host.downloadAsync(ggnn_device);
+  cudaStreamSynchronize(ggnn_device.stream);
+  ggnn_host.store(file);
+}
+
+template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
+void ggnn_impl<T, measure, D, KBuild, KQuery, S>::load(const std::string& file)
+{
+  if (!graph_file_.has_value() || graph_file_.value() != file) {
+    graph_file_ = file;
+    load_impl();
+  }
+}
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp b/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
new file mode 100644
index 0000000000..755c7c8d69
--- /dev/null
+++ b/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "hnswlib_wrapper.h"
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace cuvs::bench {
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::hnsw_lib<T>::build_param& param)
+{
+  param.ef_construction = conf.at("efConstruction");
+  param.m               = conf.at("M");
+  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::hnsw_lib<T>::search_param& param)
+{
+  param.ef = conf.at("ef");
+  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+}
+
+template <typename T, template <typename> class Algo>
+auto make_algo(cuvs::bench::Metric metric, int dim, const nlohmann::json& conf)
+  -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  typename Algo<T>::build_param param;
+  parse_build_param<T>(conf, param);
+  return std::make_unique<Algo<T>>(metric, dim, param);
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  cuvs::bench::Metric metric = parse_metric(distance);
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float>) {
+    if (algo_name == "hnswlib") { a = make_algo<T, cuvs::bench::hnsw_lib>(metric, dim, conf); }
+  }
+
+  if constexpr (std::is_same_v<T, uint8_t>) {
+    if (algo_name == "hnswlib") { a = make_algo<T, cuvs::bench::hnsw_lib>(metric, dim, conf); }
+  }
+
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+  if (algo_name == "hnswlib") {
+    auto param = std::make_unique<typename cuvs::bench::hnsw_lib<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
+  }
+  // else
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+};  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h b/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
new file mode 100644
index 0000000000..9d643f12a1
--- /dev/null
+++ b/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
@@ -0,0 +1,241 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "../common/thread_pool.hpp"
+#include "../common/util.hpp"
+
+#include <hnswlib/hnswlib.h>
+
+#include <algorithm>
+#include <atomic>
+#include <cassert>
+#include <cmath>
+#include <condition_variable>
+#include <cstdio>
+#include <ctime>
+#include <future>
+#include <memory>
+#include <mutex>
+#include <numeric>
+#include <stdexcept>
+#include <thread>
+#include <utility>
+#include <vector>
+
+namespace cuvs::bench {
+
+template <typename T>
+struct hnsw_dist_t {
+  using type = void;
+};
+
+template <>
+struct hnsw_dist_t<float> {
+  using type = float;
+};
+
+template <>
+struct hnsw_dist_t<uint8_t> {
+  using type = int;
+};
+
+template <>
+struct hnsw_dist_t<int8_t> {
+  using type = int;
+};
+
+template <typename T>
+class hnsw_lib : public algo<T> {
+ public:
+  // https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md
+  struct build_param {
+    int m;
+    int ef_construction;
+    int num_threads = omp_get_num_procs();
+  };
+
+  using search_param_base = typename algo<T>::search_param;
+  struct search_param : public search_param_base {
+    int ef;
+    int num_threads = 1;
+  };
+
+  hnsw_lib(Metric metric, int dim, const build_param& param);
+
+  void build(const T* dataset, size_t nrow) override;
+
+  void set_search_param(const search_param_base& param) override;
+  void search(const T* query,
+              int batch_size,
+              int k,
+              algo_base::index_type* indices,
+              float* distances) const override;
+
+  void save(const std::string& path_to_index) const override;
+  void load(const std::string& path_to_index) override;
+  auto copy() -> std::unique_ptr<algo<T>> override { return std::make_unique<hnsw_lib<T>>(*this); };
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+  void set_base_layer_only() { appr_alg_->base_layer_only = true; }
+
+ private:
+  void get_search_knn_results(const T* query,
+                              int k,
+                              algo_base::index_type* indices,
+                              float* distances) const;
+
+  std::shared_ptr<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>> appr_alg_;
+  std::shared_ptr<hnswlib::SpaceInterface<typename hnsw_dist_t<T>::type>> space_;
+
+  using algo<T>::metric_;
+  using algo<T>::dim_;
+  int ef_construction_;
+  int m_;
+  int num_threads_;
+  std::shared_ptr<fixed_thread_pool> thread_pool_;
+  Mode bench_mode_;
+};
+
+template <typename T>
+hnsw_lib<T>::hnsw_lib(Metric metric, int dim, const build_param& param) : algo<T>(metric, dim)
+{
+  assert(dim_ > 0);
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, uint8_t>);
+  if constexpr (std::is_same_v<T, uint8_t>) {
+    if (metric_ != Metric::kEuclidean) {
+      throw std::runtime_error("hnswlib<uint8_t> only supports Euclidean distance");
+    }
+  }
+
+  ef_construction_ = param.ef_construction;
+  m_               = param.m;
+  num_threads_     = param.num_threads;
+}
+
+template <typename T>
+void hnsw_lib<T>::build(const T* dataset, size_t nrow)
+{
+  if constexpr (std::is_same_v<T, float>) {
+    if (metric_ == Metric::kInnerProduct) {
+      space_ = std::make_shared<hnswlib::InnerProductSpace>(dim_);
+    } else {
+      space_ = std::make_shared<hnswlib::L2Space>(dim_);
+    }
+  } else if constexpr (std::is_same_v<T, uint8_t>) {
+    space_ = std::make_shared<hnswlib::L2SpaceI<T>>(dim_);
+  }
+
+  appr_alg_ = std::make_shared<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+    space_.get(), nrow, m_, ef_construction_);
+
+  thread_pool_                  = std::make_shared<fixed_thread_pool>(num_threads_);
+  const size_t items_per_thread = nrow / (num_threads_ + 1);
+
+  thread_pool_->submit(
+    [&](size_t i) {
+      if (i < items_per_thread && i % 10000 == 0) {
+        char buf[20];
+        std::time_t now = std::time(nullptr);
+        std::strftime(buf, sizeof(buf), "%Y-%m-%d %H:%M:%S", std::localtime(&now));
+        printf("%s building %zu / %zu\n", buf, i, items_per_thread);
+        fflush(stdout);
+      }
+
+      appr_alg_->addPoint(dataset + i * dim_, i);
+    },
+    nrow);
+}
+
+template <typename T>
+void hnsw_lib<T>::set_search_param(const search_param_base& param_)
+{
+  auto param     = dynamic_cast<const search_param&>(param_);
+  appr_alg_->ef_ = param.ef;
+  num_threads_   = param.num_threads;
+  // bench_mode_ = param.metric_objective;
+  bench_mode_ = Mode::kLatency;  // TODO(achirkin): pass the benchmark mode in the algo parameters
+
+  // Create a pool if multiple query threads have been set and the pool hasn't been created already
+  bool create_pool = (bench_mode_ == Mode::kLatency && num_threads_ > 1 && !thread_pool_);
+  if (create_pool) { thread_pool_ = std::make_shared<fixed_thread_pool>(num_threads_); }
+}
+
+template <typename T>
+void hnsw_lib<T>::search(
+  const T* query, int batch_size, int k, algo_base::index_type* indices, float* distances) const
+{
+  auto f = [&](int i) {
+    // hnsw can only handle a single vector at a time.
+    get_search_knn_results(query + i * dim_, k, indices + i * k, distances + i * k);
+  };
+  if (bench_mode_ == Mode::kLatency && num_threads_ > 1) {
+    thread_pool_->submit(f, batch_size);
+  } else {
+    for (int i = 0; i < batch_size; i++) {
+      f(i);
+    }
+  }
+}
+
+template <typename T>
+void hnsw_lib<T>::save(const std::string& path_to_index) const
+{
+  appr_alg_->saveIndex(std::string(path_to_index));
+}
+
+template <typename T>
+void hnsw_lib<T>::load(const std::string& path_to_index)
+{
+  if constexpr (std::is_same_v<T, float>) {
+    if (metric_ == Metric::kInnerProduct) {
+      space_ = std::make_shared<hnswlib::InnerProductSpace>(dim_);
+    } else {
+      space_ = std::make_shared<hnswlib::L2Space>(dim_);
+    }
+  } else if constexpr (std::is_same_v<T, uint8_t>) {
+    space_ = std::make_shared<hnswlib::L2SpaceI<T>>(dim_);
+  }
+
+  appr_alg_ = std::make_shared<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+    space_.get(), path_to_index);
+}
+
+template <typename T>
+void hnsw_lib<T>::get_search_knn_results(const T* query,
+                                         int k,
+                                         algo_base::index_type* indices,
+                                         float* distances) const
+{
+  auto result = appr_alg_->searchKnn(query, k);
+  assert(result.size() >= static_cast<size_t>(k));
+
+  for (int i = k - 1; i >= 0; --i) {
+    indices[i]   = result.top().second;
+    distances[i] = result.top().first;
+    result.pop();
+  }
+}
+
+};  // namespace cuvs::bench
diff --git a/cpp/cmake/patches/ggnn.diff b/cpp/cmake/patches/ggnn.diff
new file mode 100644
index 0000000000..fc45298803
--- /dev/null
+++ b/cpp/cmake/patches/ggnn.diff
@@ -0,0 +1,230 @@
+--- a/include/ggnn/cache/cuda_simple_knn_sym_cache.cuh
++++ b/include/ggnn/cache/cuda_simple_knn_sym_cache.cuh
+@@ -62,7 +62,7 @@ struct SimpleKNNSymCache {
+                                                 const ValueT dist_half)
+         : dist_query(dist_query), dist_half(dist_half) {}
+ 
+-    __device__ __forceinline__ DistQueryAndHalf() {}
++    DistQueryAndHalf() = default;
+   };
+ 
+   struct DistanceAndNorm {
+@@ -98,8 +98,7 @@ struct SimpleKNNSymCache {
+     KeyT cache;
+     DistQueryAndHalf dist;
+     bool flag;
+-
+-    __device__ __forceinline__ SyncTempStorage() {}
++    SyncTempStorage() = default;
+   };
+ 
+  public:
+diff --git a/include/ggnn/cuda_knn_ggnn_gpu_instance.cuh b/include/ggnn/cuda_knn_ggnn_gpu_instance.cuh
+index 8cbaf0d..6eb72ac 100644
+--- a/include/ggnn/cuda_knn_ggnn_gpu_instance.cuh
++++ b/include/ggnn/cuda_knn_ggnn_gpu_instance.cuh
+@@ -41,7 +41,6 @@ limitations under the License.
+ #include "ggnn/sym/cuda_knn_sym_query_layer.cuh"
+ #include "ggnn/utils/cuda_knn_utils.cuh"
+ #include "ggnn/utils/cuda_knn_constants.cuh"
+-#include "ggnn/utils/cuda_knn_dataset.cuh"
+ 
+ template <typename ValueT>
+ __global__ void divide(ValueT* res, ValueT* input, ValueT N) {
+@@ -98,9 +97,7 @@ struct GGNNGPUInstance {
+   typedef GGNNGraphDevice<KeyT, BaseT, ValueT> GGNNGraphDevice;
+   typedef GGNNGraphHost<KeyT, BaseT, ValueT> GGNNGraphHost;
+ 
+-  const Dataset<KeyT, BaseT, BAddrT>* dataset;
+   GGNNGraphBuffer<KeyT, ValueT>* ggnn_buffer {nullptr};
+-  GGNNQuery<KeyT, ValueT, BaseT> ggnn_query;
+ 
+   // Graph Shards resident on the GPU
+   std::vector<GGNNGraphDevice> ggnn_shards;
+@@ -117,13 +114,12 @@ struct GGNNGPUInstance {
+   // number of shards that need to be processed by this instance
+   const int num_parts;
+ 
+-  GGNNGPUInstance(const int gpu_id, const Dataset<KeyT, BaseT, BAddrT>* dataset,
++  GGNNGPUInstance(const int gpu_id,
+             const int N_shard, const int L,
+             const bool enable_construction, const float tau_build,
+             const int num_parts=1, const int num_cpu_buffers=1) :
+     N_shard{N_shard}, L{L}, tau_build{tau_build},
+-    dataset{dataset}, gpu_id{gpu_id},
+-    ggnn_query{dataset->N_query, D, KQuery, num_parts},
++    gpu_id{gpu_id},
+     num_parts{num_parts}
+   {
+     CHECK_LE(L, MAX_LAYER);
+@@ -135,7 +131,6 @@ struct GGNNGPUInstance {
+       CHECK_EQ(current_gpu_id, gpu_id) << "cudaSetDevice() needs to be called in advance!";
+     }
+ 
+-    ggnn_query.loadQueriesAsync(dataset->h_query, 0);
+ 
+     computeGraphParameters();
+ 
+@@ -186,7 +181,7 @@ struct GGNNGPUInstance {
+   }
+ 
+   GGNNGPUInstance(const GGNNGPUInstance& other)
+-   : dataset{nullptr}, ggnn_query{0, D, KQuery},
++   :
+      gpu_id{0}, N_shard{0}, num_parts{0} {
+     // this exists to allow using vector::emplace_back
+     // when it triggers a reallocation, this code will be called.
+@@ -305,6 +300,7 @@ struct GGNNGPUInstance {
+ 
+   // io
+ 
++  /*
+   void waitForDiskIO(const int shard_id) {
+     auto& cpu_buffer = ggnn_cpu_buffers[shard_id%ggnn_cpu_buffers.size()];
+     if (cpu_buffer.disk_io_thread.joinable())
+@@ -468,11 +464,12 @@ struct GGNNGPUInstance {
+     CHECK_CUDA(cudaDeviceSynchronize());
+     CHECK_CUDA(cudaPeekAtLastError());
+   }
++  */
+ 
+   // graph operations
+ 
+   template <int BLOCK_DIM_X = 32, int MAX_ITERATIONS = 400, int CACHE_SIZE = 512, int SORTED_SIZE = 256, bool DIST_STATS = false>
+-  void queryLayer(const int shard_id = 0) const {
++  void queryLayer(const BaseT* d_query, int batch_size, KeyT* d_query_result_ids, ValueT* d_query_result_dists, const int shard_id = 0) const {
+     CHECK_CUDA(cudaSetDevice(gpu_id));
+     const auto& shard = ggnn_shards.at(shard_id%ggnn_shards.size());
+ 
+@@ -482,21 +479,21 @@ struct GGNNGPUInstance {
+ 
+     int* m_dist_statistics = nullptr;
+     if (DIST_STATS)
+-      cudaMallocManaged(&m_dist_statistics, dataset->N_query * sizeof(int));
++      cudaMallocManaged(&m_dist_statistics, batch_size * sizeof(int));
+ 
+     QueryKernel query_kernel;
+     query_kernel.d_base = shard.d_base;
+-    query_kernel.d_query = ggnn_query.d_query;
++    query_kernel.d_query = d_query;
+ 
+     query_kernel.d_graph = shard.d_graph;
+-    query_kernel.d_query_results = ggnn_query.d_query_result_ids;
+-    query_kernel.d_query_results_dists = ggnn_query.d_query_result_dists;
++    query_kernel.d_query_results = d_query_result_ids;
++    query_kernel.d_query_results_dists = d_query_result_dists;
+ 
+     query_kernel.d_translation = shard.d_translation;
+ 
+     query_kernel.d_nn1_stats = shard.d_nn1_stats;
+ 
+-    query_kernel.N = dataset->N_query;
++    query_kernel.N = batch_size;
+     query_kernel.N_offset = 0;
+ 
+     query_kernel.d_dist_stats = m_dist_statistics;
+@@ -771,6 +768,16 @@ struct GGNNGPUInstance {
+       sym(layer, shard_id);
+     }
+   }
++
++  void set_stream(cudaStream_t stream) {
++    assert(ggnn_shards.size() == 1);
++    ggnn_shards.at(0).stream = stream;
++  }
++
++  void set_base_data(const BaseT* dataset) {
++    assert(ggnn_shards.size() == 1);
++    ggnn_shards.at(0).d_base = dataset;
++  }
+ };
+ 
+ #endif  // INCLUDE_GGNN_CUDA_KNN_GGNN_GPU_INSTANCE_CUH_
+diff --git a/include/ggnn/graph/cuda_knn_ggnn_graph_device.cuh b/include/ggnn/graph/cuda_knn_ggnn_graph_device.cuh
+index c94a8f1..781226d 100644
+--- a/include/ggnn/graph/cuda_knn_ggnn_graph_device.cuh
++++ b/include/ggnn/graph/cuda_knn_ggnn_graph_device.cuh
+@@ -50,7 +50,7 @@ struct GGNNGraphDevice {
+   ValueT* d_nn1_stats;
+ 
+   /// base data pointer for the shard.
+-  BaseT* d_base;
++  const BaseT* d_base;
+ 
+   /// combined memory pool
+   char* d_memory;
+@@ -69,7 +69,9 @@ struct GGNNGraphDevice {
+     const size_t selection_translation_size = align8(ST_all * sizeof(KeyT));
+     const size_t nn1_stats_size = align8(2 * sizeof(ValueT));
+     total_graph_size = graph_size + 2 * selection_translation_size + nn1_stats_size;
+-    base_size = align8(static_cast<size_t>(N) * D * sizeof(BaseT));
++    // base_size = align8(static_cast<size_t>(N) * D * sizeof(BaseT));
++    (void) N;
++    (void) D;
+ 
+     const size_t total_size = base_size+total_graph_size;
+ 
+@@ -86,8 +88,7 @@ struct GGNNGraphDevice {
+     CHECK_CUDA(cudaMalloc(&d_memory, total_size));
+ 
+     size_t pos = 0;
+-    d_base = reinterpret_cast<BaseT*>(d_memory+pos);
+-    pos += base_size;
++    d_base = nullptr;
+     d_graph = reinterpret_cast<KeyT*>(d_memory+pos);
+     pos += graph_size;
+     d_translation = reinterpret_cast<KeyT*>(d_memory+pos);
+@@ -99,14 +100,14 @@ struct GGNNGraphDevice {
+ 
+     CHECK_EQ(pos, total_size);
+ 
+-    CHECK_CUDA(cudaStreamCreate(&stream));
++    // CHECK_CUDA(cudaStreamCreate(&stream));
+ 
+     CHECK_CUDA(cudaPeekAtLastError());
+     CHECK_CUDA(cudaDeviceSynchronize());
+     CHECK_CUDA(cudaPeekAtLastError());
+   }
+ 
+-  GGNNGraphDevice(const GGNNGraphDevice& other) {
++  GGNNGraphDevice(const GGNNGraphDevice&) {
+     // this exists to allow using vector::emplace_back
+     // when it triggers a reallocation, this code will be called.
+     // always make sure that enough memory is reserved ahead of time.
+@@ -116,7 +117,7 @@ struct GGNNGraphDevice {
+   ~GGNNGraphDevice() {
+     cudaFree(d_memory);
+ 
+-    CHECK_CUDA(cudaStreamDestroy(stream));
++    // CHECK_CUDA(cudaStreamDestroy(stream));
+   }
+ };
+ 
+diff --git a/include/ggnn/graph/cuda_knn_ggnn_graph_host.cuh b/include/ggnn/graph/cuda_knn_ggnn_graph_host.cuh
+index 2055f9e..ef5843a 100644
+--- a/include/ggnn/graph/cuda_knn_ggnn_graph_host.cuh
++++ b/include/ggnn/graph/cuda_knn_ggnn_graph_host.cuh
+@@ -92,7 +92,7 @@ struct GGNNGraphHost {
+     CHECK_CUDA(cudaPeekAtLastError());
+   }
+ 
+-  GGNNGraphHost(const GGNNGraphHost& other) {
++  GGNNGraphHost(const GGNNGraphHost&) {
+     // this exists to allow using vector::emplace_back
+     // when it triggers a reallocation, this code will be called.
+     // always make sure that enough memory is reserved ahead of time.
+diff --git a/include/ggnn/select/cuda_knn_wrs_select_layer.cuh b/include/ggnn/select/cuda_knn_wrs_select_layer.cuh
+index 49d76a1..eef69e6 100644
+--- a/include/ggnn/select/cuda_knn_wrs_select_layer.cuh
++++ b/include/ggnn/select/cuda_knn_wrs_select_layer.cuh
+@@ -22,7 +22,6 @@ limitations under the License.
+ #include <cuda.h>
+ #include <cuda_runtime.h>
+ 
+-#include <gflags/gflags.h>
+ #include <cub/cub.cuh>
+ 
+ #include "ggnn/utils/cuda_knn_constants.cuh"
+-- 
+2.43.0
+
diff --git a/cpp/cmake/patches/hnswlib.diff b/cpp/cmake/patches/hnswlib.diff
new file mode 100644
index 0000000000..e7f89a8cc9
--- /dev/null
+++ b/cpp/cmake/patches/hnswlib.diff
@@ -0,0 +1,188 @@
+--- a/hnswlib/hnswalg.h
++++ b/hnswlib/hnswalg.h
+@@ -3,6 +3,7 @@
+ #include "visited_list_pool.h"
+ #include "hnswlib.h"
+ #include <atomic>
++#include <limits>
+ #include <random>
+ #include <stdlib.h>
+ #include <assert.h>
+@@ -16,6 +17,8 @@ namespace hnswlib {
+     template<typename dist_t>
+     class HierarchicalNSW : public AlgorithmInterface<dist_t> {
+     public:
++        bool base_layer_only{false};
++        int num_seeds=32;
+         static const tableint max_update_element_locks = 65536;
+         HierarchicalNSW(SpaceInterface<dist_t> *s) {
+         }
+@@ -56,7 +59,7 @@ namespace hnswlib {
+             visited_list_pool_ = new VisitedListPool(1, max_elements);
+ 
+             //initializations for special treatment of the first node
+-            enterpoint_node_ = -1;
++            enterpoint_node_ = std::numeric_limits<tableint>::max();
+             maxlevel_ = -1;
+ 
+             linkLists_ = (char **) malloc(sizeof(void *) * max_elements_);
+@@ -527,7 +530,7 @@ namespace hnswlib {
+                     tableint *datal = (tableint *) (data + 1);
+                     for (int i = 0; i < size; i++) {
+                         tableint cand = datal[i];
+-                        if (cand < 0 || cand > max_elements_)
++                        if (cand > max_elements_)
+                             throw std::runtime_error("cand error");
+                         dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
+ 
+@@ -1067,7 +1070,7 @@ namespace hnswlib {
+                             tableint *datal = (tableint *) (data + 1);
+                             for (int i = 0; i < size; i++) {
+                                 tableint cand = datal[i];
+-                                if (cand < 0 || cand > max_elements_)
++                                if (cand > max_elements_)
+                                     throw std::runtime_error("cand error");
+                                 dist_t d = fstdistfunc_(data_point, getDataByInternalId(cand), dist_func_param_);
+                                 if (d < curdist) {
+@@ -1119,28 +1122,41 @@ namespace hnswlib {
+             tableint currObj = enterpoint_node_;
+             dist_t curdist = fstdistfunc_(query_data, getDataByInternalId(enterpoint_node_), dist_func_param_);
+ 
+-            for (int level = maxlevel_; level > 0; level--) {
+-                bool changed = true;
+-                while (changed) {
+-                    changed = false;
+-                    unsigned int *data;
++            if (base_layer_only) {
++                // You can increase the number of seeds when testing large-scale dataset, num_seeds = 48 for 100M-scale
++                for (int i = 0; i < num_seeds; i++) {
++                    tableint obj = i * (max_elements_ / num_seeds);
++                    dist_t dist = fstdistfunc_(query_data, getDataByInternalId(obj), dist_func_param_);
++                    if (dist < curdist) {
++                        curdist = dist;
++                        currObj = obj;
++                    }
++                }
++            }
++            else{
++                for (int level = maxlevel_; level > 0; level--) {
++                    bool changed = true;
++                    while (changed) {
++                        changed = false;
++                        unsigned int *data;
+ 
+-                    data = (unsigned int *) get_linklist(currObj, level);
+-                    int size = getListCount(data);
+-                    metric_hops++;
+-                    metric_distance_computations+=size;
++                        data = (unsigned int *) get_linklist(currObj, level);
++                        int size = getListCount(data);
++                        metric_hops++;
++                        metric_distance_computations+=size;
+ 
+-                    tableint *datal = (tableint *) (data + 1);
+-                    for (int i = 0; i < size; i++) {
+-                        tableint cand = datal[i];
+-                        if (cand < 0 || cand > max_elements_)
+-                            throw std::runtime_error("cand error");
+-                        dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
++                        tableint *datal = (tableint *) (data + 1);
++                        for (int i = 0; i < size; i++) {
++                            tableint cand = datal[i];
++                            if (cand > max_elements_)
++                                throw std::runtime_error("cand error");
++                            dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
+ 
+-                        if (d < curdist) {
+-                            curdist = d;
+-                            currObj = cand;
+-                            changed = true;
++                            if (d < curdist) {
++                                curdist = d;
++                                currObj = cand;
++                                changed = true;
++                            }
+                         }
+                     }
+                 }
+diff --git a/hnswlib/space_l2.h b/hnswlib/space_l2.h
+index 4413537..c3240f3 100644
+--- a/hnswlib/space_l2.h
++++ b/hnswlib/space_l2.h
+@@ -252,13 +252,14 @@ namespace hnswlib {
+         ~L2Space() {}
+     };
+ 
++    template <typename T>
+     static int
+     L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const void *__restrict qty_ptr) {
+ 
+         size_t qty = *((size_t *) qty_ptr);
+         int res = 0;
+-        unsigned char *a = (unsigned char *) pVect1;
+-        unsigned char *b = (unsigned char *) pVect2;
++        T *a = (T *) pVect1;
++        T *b = (T *) pVect2;
+ 
+         qty = qty >> 2;
+         for (size_t i = 0; i < qty; i++) {
+@@ -279,11 +280,12 @@ namespace hnswlib {
+         return (res);
+     }
+ 
++    template <typename T>
+     static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2, const void* __restrict qty_ptr) {
+         size_t qty = *((size_t*)qty_ptr);
+         int res = 0;
+-        unsigned char* a = (unsigned char*)pVect1;
+-        unsigned char* b = (unsigned char*)pVect2;
++        T* a = (T*)pVect1;
++        T* b = (T*)pVect2;
+ 
+         for(size_t i = 0; i < qty; i++)
+         {
+@@ -294,6 +296,7 @@ namespace hnswlib {
+         return (res);
+     }
+ 
++    template <typename T>
+     class L2SpaceI : public SpaceInterface<int> {
+ 
+         DISTFUNC<int> fstdistfunc_;
+@@ -302,10 +305,10 @@ namespace hnswlib {
+     public:
+         L2SpaceI(size_t dim) {
+             if(dim % 4 == 0) {
+-                fstdistfunc_ = L2SqrI4x;
++                fstdistfunc_ = L2SqrI4x<T>;
+             }
+             else {
+-                fstdistfunc_ = L2SqrI;
++                fstdistfunc_ = L2SqrI<T>;
+             }
+             dim_ = dim;
+             data_size_ = dim * sizeof(unsigned char);
+diff --git a/hnswlib/visited_list_pool.h b/hnswlib/visited_list_pool.h
+index 5e1a4a5..4195ebd 100644
+--- a/hnswlib/visited_list_pool.h
++++ b/hnswlib/visited_list_pool.h
+@@ -3,6 +3,7 @@
+ #include <mutex>
+ #include <string.h>
+ #include <deque>
++#include <limits>
+ 
+ namespace hnswlib {
+     typedef unsigned short int vl_type;
+@@ -14,7 +15,7 @@ namespace hnswlib {
+         unsigned int numelements;
+ 
+         VisitedList(int numelements1) {
+-            curV = -1;
++            curV = std::numeric_limits<vl_type>::max();
+             numelements = numelements1;
+             mass = new vl_type[numelements];
+         }
+-- 
+2.43.0
+
diff --git a/cpp/cmake/thirdparty/get_faiss.cmake b/cpp/cmake/thirdparty/get_faiss.cmake
new file mode 100644
index 0000000000..89446332da
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_faiss.cmake
@@ -0,0 +1,109 @@
+#=============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#=============================================================================
+
+function(find_and_configure_faiss)
+    set(oneValueArgs VERSION REPOSITORY PINNED_TAG BUILD_STATIC_LIBS EXCLUDE_FROM_ALL ENABLE_GPU)
+    cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+            "${multiValueArgs}" ${ARGN} )
+
+        rapids_find_generate_module(faiss
+                HEADER_NAMES  faiss/IndexFlat.h
+                LIBRARY_NAMES faiss
+                )
+
+        set(BUILD_SHARED_LIBS ON)
+        if (PKG_BUILD_STATIC_LIBS)
+            set(BUILD_SHARED_LIBS OFF)
+            set(CPM_DOWNLOAD_faiss ON)
+        endif()
+
+        include(cmake/modules/FindAVX.cmake)
+
+        # Link against AVX CPU lib if it exists
+        set(CUVS_FAISS_GLOBAL_TARGETS faiss::faiss)
+        set(CUVS_FAISS_EXPORT_GLOBAL_TARGETS faiss)
+        set(CUVS_FAISS_OPT_LEVEL "generic")
+        if(CXX_AVX_FOUND)
+            set(CUVS_FAISS_OPT_LEVEL "avx2")
+            list(APPEND CUVS_FAISS_GLOBAL_TARGETS faiss::faiss_avx2)
+            list(APPEND CUVS_FAISS_EXPORT_GLOBAL_TARGETS faiss_avx2)
+        endif()
+
+        rapids_cpm_find(faiss ${PKG_VERSION}
+                GLOBAL_TARGETS ${CUVS_FAISS_GLOBAL_TARGETS}
+                CPM_ARGS
+                GIT_REPOSITORY   ${PKG_REPOSITORY}
+                GIT_TAG          ${PKG_PINNED_TAG}
+                EXCLUDE_FROM_ALL ${PKG_EXCLUDE_FROM_ALL}
+                OPTIONS
+                "FAISS_ENABLE_GPU ${PKG_ENABLE_GPU}"
+                "FAISS_ENABLE_PYTHON OFF"
+                "FAISS_OPT_LEVEL ${CUVS_FAISS_OPT_LEVEL}"
+                "FAISS_USE_CUDA_TOOLKIT_STATIC ${CUDA_STATIC_RUNTIME}"
+                "BUILD_TESTING OFF"
+                "CMAKE_MESSAGE_LOG_LEVEL VERBOSE"
+                )
+
+        if(TARGET faiss AND NOT TARGET faiss::faiss)
+            add_library(faiss::faiss ALIAS faiss)
+        endif()
+
+    if(CXX_AVX_FOUND)
+
+        if(TARGET faiss_avx2 AND NOT TARGET faiss::faiss_avx2)
+            add_library(faiss::faiss_avx2 ALIAS faiss_avx2)
+        endif()
+    endif()
+
+
+    if(faiss_ADDED)
+            rapids_export(BUILD faiss
+                    EXPORT_SET faiss-targets
+                    GLOBAL_TARGETS ${CUVS_FAISS_EXPORT_GLOBAL_TARGETS}
+                    NAMESPACE faiss::)
+        endif()
+
+    # We generate the faiss-config files when we built faiss locally, so always do `find_dependency`
+    rapids_export_package(BUILD OpenMP cuvs-ann-bench-exports) # faiss uses openMP but doesn't export a need for it
+    rapids_export_package(BUILD faiss cuvs-ann-bench-exports GLOBAL_TARGETS ${CUVS_FAISS_GLOBAL_TARGETS} ${CUVS_FAISS_EXPORT_GLOBAL_TARGETS})
+    rapids_export_package(INSTALL faiss cuvs-ann-bench-exports GLOBAL_TARGETS ${CUVS_FAISS_GLOBAL_TARGETS} ${CUVS_FAISS_EXPORT_GLOBAL_TARGETS})
+
+    # Tell cmake where it can find the generated faiss-config.cmake we wrote.
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+    rapids_export_find_package_root(BUILD faiss [=[${CMAKE_CURRENT_LIST_DIR}]=]
+                                    EXPORT_SET cuvs-ann-bench-exports)
+endfunction()
+
+if(NOT CUVS_FAISS_GIT_TAG)
+    # TODO: Remove this once faiss supports FAISS_USE_CUDA_TOOLKIT_STATIC
+    # (https://github.com/facebookresearch/faiss/pull/2446)
+    set(CUVS_FAISS_GIT_TAG fea/statically-link-ctk)
+    # set(CUVS_FAISS_GIT_TAG bde7c0027191f29c9dadafe4f6e68ca0ee31fb30)
+endif()
+
+if(NOT CUVS_FAISS_GIT_REPOSITORY)
+    # TODO: Remove this once faiss supports FAISS_USE_CUDA_TOOLKIT_STATIC
+    # (https://github.com/facebookresearch/faiss/pull/2446)
+    set(CUVS_FAISS_GIT_REPOSITORY https://github.com/cjnolet/faiss.git)
+    # set(CUVS_FAISS_GIT_REPOSITORY https://github.com/facebookresearch/faiss.git)
+endif()
+
+find_and_configure_faiss(VERSION    1.7.4
+        REPOSITORY  ${CUVS_FAISS_GIT_REPOSITORY}
+        PINNED_TAG  ${CUVS_FAISS_GIT_TAG}
+        BUILD_STATIC_LIBS ${CUVS_USE_FAISS_STATIC}
+        EXCLUDE_FROM_ALL ${CUVS_EXCLUDE_FAISS_FROM_ALL}
+        ENABLE_GPU ${CUVS_FAISS_ENABLE_GPU})
diff --git a/cpp/cmake/thirdparty/get_ggnn.cmake b/cpp/cmake/thirdparty/get_ggnn.cmake
new file mode 100644
index 0000000000..8137ef84eb
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_ggnn.cmake
@@ -0,0 +1,58 @@
+#=============================================================================
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#=============================================================================
+
+function(find_and_configure_ggnn)
+  set(oneValueArgs VERSION REPOSITORY PINNED_TAG)
+  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+          "${multiValueArgs}" ${ARGN} )
+
+
+  set(patch_files_to_run "${CMAKE_CURRENT_SOURCE_DIR}/cmake/patches/ggnn.diff")
+  set(patch_issues_to_ref "fix compile issues")
+  set(patch_script "${CMAKE_BINARY_DIR}/rapids-cmake/patches/ggnn/patch.cmake")
+  set(log_file "${CMAKE_BINARY_DIR}/rapids-cmake/patches/ggnn/log")
+  string(TIMESTAMP current_year "%Y" UTC)
+  configure_file(${rapids-cmake-dir}/cpm/patches/command_template.cmake.in "${patch_script}"
+                @ONLY)
+
+  rapids_cpm_find(
+    ggnn ${PKG_VERSION}
+    GLOBAL_TARGETS ggnn::ggnn
+    CPM_ARGS
+    GIT_REPOSITORY ${PKG_REPOSITORY}
+    GIT_TAG ${PKG_PINNED_TAG}
+    GIT_SHALLOW TRUE
+    DOWNLOAD_ONLY ON
+    PATCH_COMMAND ${CMAKE_COMMAND} -P ${patch_script}
+  )
+  if(NOT TARGET ggnn::ggnn)
+    add_library(ggnn INTERFACE)
+    target_include_directories(ggnn INTERFACE "$<BUILD_INTERFACE:${ggnn_SOURCE_DIR}/include>")
+    add_library(ggnn::ggnn ALIAS ggnn)
+  endif()
+
+endfunction()
+if(NOT RAFT_GGNN_GIT_TAG)
+  set(RAFT_GGNN_GIT_TAG release_0.5)
+endif()
+
+if(NOT RAFT_GGNN_GIT_REPOSITORY)
+  set(RAFT_GGNN_GIT_REPOSITORY https://github.com/cgtuebingen/ggnn.git)
+endif()
+find_and_configure_ggnn(VERSION 0.5
+        REPOSITORY       ${RAFT_GGNN_GIT_REPOSITORY}
+        PINNED_TAG       ${RAFT_GGNN_GIT_TAG}
+        )
diff --git a/cpp/cmake/thirdparty/get_glog.cmake b/cpp/cmake/thirdparty/get_glog.cmake
new file mode 100644
index 0000000000..5839ee3d5a
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_glog.cmake
@@ -0,0 +1,42 @@
+#=============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#=============================================================================
+
+function(find_and_configure_glog)
+    set(oneValueArgs VERSION FORK PINNED_TAG EXCLUDE_FROM_ALL)
+    cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+            "${multiValueArgs}" ${ARGN} )
+
+    rapids_cpm_find(glog ${PKG_VERSION}
+            GLOBAL_TARGETS      glog::glog
+            CPM_ARGS
+            GIT_REPOSITORY         https://github.com/${PKG_FORK}/glog.git
+            GIT_TAG                ${PKG_PINNED_TAG}
+            EXCLUDE_FROM_ALL       ${PKG_EXCLUDE_FROM_ALL}
+            )
+
+    if(glog_ADDED)
+        message(VERBOSE "cuVS: Using glog located in ${glog_SOURCE_DIR}")
+    else()
+        message(VERBOSE "cuVS: Using glog located in ${glog_DIR}")
+    endif()
+
+endfunction()
+
+find_and_configure_glog(VERSION 0.6.0
+        FORK             google
+        PINNED_TAG       v0.6.0
+        EXCLUDE_FROM_ALL ON
+        )
diff --git a/cpp/cmake/thirdparty/get_hnswlib.cmake b/cpp/cmake/thirdparty/get_hnswlib.cmake
new file mode 100644
index 0000000000..054a12f1e0
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_hnswlib.cmake
@@ -0,0 +1,92 @@
+#=============================================================================
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#=============================================================================
+
+function(find_and_configure_hnswlib)
+  set(oneValueArgs VERSION REPOSITORY PINNED_TAG EXCLUDE_FROM_ALL)
+  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+          "${multiValueArgs}" ${ARGN} )
+
+  set(patch_files_to_run "${CMAKE_CURRENT_SOURCE_DIR}/cmake/patches/hnswlib.diff")
+  set(patch_issues_to_ref "fix compile issues")
+  set(patch_script "${CMAKE_BINARY_DIR}/rapids-cmake/patches/hnswlib/patch.cmake")
+  set(log_file "${CMAKE_BINARY_DIR}/rapids-cmake/patches/hnswlib/log")
+  string(TIMESTAMP current_year "%Y" UTC)
+  configure_file(${rapids-cmake-dir}/cpm/patches/command_template.cmake.in "${patch_script}"
+                @ONLY)
+
+  rapids_cpm_find(
+    hnswlib ${PKG_VERSION}
+    GLOBAL_TARGETS hnswlib::hnswlib
+    BUILD_EXPORT_SET raft-exports
+    INSTALL_EXPORT_SET raft-exports
+    CPM_ARGS
+    GIT_REPOSITORY ${PKG_REPOSITORY}
+    GIT_TAG ${PKG_PINNED_TAG}
+    GIT_SHALLOW TRUE
+    DOWNLOAD_ONLY ON
+    PATCH_COMMAND ${CMAKE_COMMAND} -P ${patch_script}
+  )
+  if(NOT TARGET hnswlib::hnswlib)
+    add_library(hnswlib INTERFACE )
+    add_library(hnswlib::hnswlib ALIAS hnswlib)
+    target_include_directories(hnswlib INTERFACE
+     "$<BUILD_INTERFACE:${hnswlib_SOURCE_DIR}>"
+     "$<INSTALL_INTERFACE:include>")
+
+    if(NOT PKG_EXCLUDE_FROM_ALL)
+      install(TARGETS hnswlib EXPORT hnswlib-exports)
+      install(DIRECTORY "${hnswlib_SOURCE_DIR}/hnswlib/" DESTINATION include/hnswlib)
+
+      # write install export rules
+      rapids_export(
+        INSTALL hnswlib
+        VERSION ${PKG_VERSION}
+        EXPORT_SET hnswlib-exports
+        GLOBAL_TARGETS hnswlib
+        NAMESPACE hnswlib::)
+    endif()
+
+    # write build export rules
+    rapids_export(
+      BUILD hnswlib
+      VERSION ${PKG_VERSION}
+      EXPORT_SET hnswlib-exports
+      GLOBAL_TARGETS hnswlib
+      NAMESPACE hnswlib::)
+
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+
+    # When using RAFT from the build dir, ensure hnswlib is also found in RAFT's build dir. This
+    # line adds `set(hnswlib_ROOT "${CMAKE_CURRENT_LIST_DIR}")` to build/raft-dependencies.cmake
+    rapids_export_find_package_root(
+      BUILD hnswlib [=[${CMAKE_CURRENT_LIST_DIR}]=] EXPORT_SET raft-exports
+    )
+  endif()
+endfunction()
+
+
+if(NOT CUVS_HNSWLIB_GIT_TAG)
+  set(CUVS_HNSWLIB_GIT_TAG v0.6.2)
+endif()
+
+if(NOT CUVS_HNSWLIB_GIT_REPOSITORY)
+  set(CUVS_HNSWLIB_GIT_REPOSITORY https://github.com/nmslib/hnswlib.git)
+endif()
+find_and_configure_hnswlib(VERSION 0.6.2
+        REPOSITORY       ${CUVS_HNSWLIB_GIT_REPOSITORY}
+        PINNED_TAG       ${CUVS_HNSWLIB_GIT_TAG}
+        EXCLUDE_FROM_ALL OFF
+        )
diff --git a/cpp/cmake/thirdparty/get_nlohmann_json.cmake b/cpp/cmake/thirdparty/get_nlohmann_json.cmake
new file mode 100644
index 0000000000..8d0b671e47
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_nlohmann_json.cmake
@@ -0,0 +1,42 @@
+#=============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#=============================================================================
+
+function(find_and_configure_nlohmann_json)
+    set(oneValueArgs VERSION FORK PINNED_TAG EXCLUDE_FROM_ALL)
+    cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+            "${multiValueArgs}" ${ARGN} )
+
+    rapids_cpm_find(nlohmann_json ${PKG_VERSION}
+            GLOBAL_TARGETS      nlohmann_json::nlohmann_json
+            CPM_ARGS
+            GIT_REPOSITORY         https://github.com/${PKG_FORK}/json.git
+            GIT_TAG                ${PKG_PINNED_TAG}
+            EXCLUDE_FROM_ALL       ${PKG_EXCLUDE_FROM_ALL}
+            )
+
+    if(glog_ADDED)
+        message(VERBOSE "cuVS: Using glog located in ${glog_SOURCE_DIR}")
+    else()
+        message(VERBOSE "cuVS: Using glog located in ${glog_DIR}")
+    endif()
+
+endfunction()
+
+find_and_configure_nlohmann_json(VERSION  3.11.2
+        FORK             nlohmann
+        PINNED_TAG       v3.11.2
+        EXCLUDE_FROM_ALL ON
+        )
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index b0668eeb0c..141fba4ee8 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1043,6 +1043,166 @@ void serialize(raft::resources const& handle,
 void deserialize(raft::resources const& handle,
                  std::istream& is,
                  cuvs::neighbors::cagra::index<uint8_t, uint32_t>* index);
+
+/**
+ * Write the CAGRA built index as a base layer HNSW index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ *
+ */
+void serialize_to_hnswlib(raft::resources const& handle,
+                          std::ostream& os,
+                          const cuvs::neighbors::cagra::index<float, uint32_t>& index);
+
+/**
+ * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, filename, index);
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ *
+ */
+void serialize_to_hnswlib(raft::resources const& handle,
+                          const std::string& filename,
+                          const cuvs::neighbors::cagra::index<float, uint32_t>& index);
+
+/**
+ * Write the CAGRA built index as a base layer HNSW index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ *
+ */
+void serialize_to_hnswlib(raft::resources const& handle,
+                          std::ostream& os,
+                          const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index);
+
+/**
+ * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, filename, index);
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ *
+ */
+void serialize_to_hnswlib(raft::resources const& handle,
+                          const std::string& filename,
+                          const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index);
+
+/**
+ * Write the CAGRA built index as a base layer HNSW index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ *
+ */
+void serialize_to_hnswlib(raft::resources const& handle,
+                          std::ostream& os,
+                          const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index);
+
+/**
+ * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra_serialize.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = raft::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, filename, index);
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ *
+ */
+void serialize_to_hnswlib(raft::resources const& handle,
+                          const std::string& filename,
+                          const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index);
+
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/cagra_serialize.cuh b/cpp/src/neighbors/cagra_serialize.cuh
index 03f128cb99..e193c06304 100644
--- a/cpp/src/neighbors/cagra_serialize.cuh
+++ b/cpp/src/neighbors/cagra_serialize.cuh
@@ -19,102 +19,52 @@
 #include "detail/cagra/cagra_serialize.cuh"
 
 namespace cuvs::neighbors::cagra {
-/**
- * Write the CAGRA built index as a base layer HNSW index to an output stream
- *
- * Experimental, both the API and the serialization format are subject to change.
- *
- * @code{.cpp}
- * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
- *
- * raft::resources handle;
- *
- * // create an output stream
- * std::ostream os(std::cout.rdbuf());
- * // create an index with `auto index = raft::cagra::build(...);`
- * raft::cagra::serialize_to_hnswlib(handle, os, index);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
- * @param[in] handle the raft handle
- * @param[in] os output stream
- * @param[in] index CAGRA index
- *
- */
-template <typename T, typename IdxT>
-void serialize_to_hnswlib(raft::resources const& handle,
-                          std::ostream& os,
-                          const cuvs::neighbors::cagra::index<T, IdxT>& index)
-{
-  detail::serialize_to_hnswlib<T, IdxT>(handle, os, index);
-}
-
-/**
- * Save a CAGRA build index in hnswlib base-layer-only serialized format
- *
- * Experimental, both the API and the serialization format are subject to change.
- *
- * @code{.cpp}
- * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
- *
- * raft::resources handle;
- *
- * // create a string with a filepath
- * std::string filename("/path/to/index");
- * // create an index with `auto index = raft::cagra::build(...);`
- * raft::cagra::serialize_to_hnswlib(handle, filename, index);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
- * @param[in] handle the raft handle
- * @param[in] filename the file name for saving the index
- * @param[in] index CAGRA index
- *
- */
-template <typename T, typename IdxT>
-void serialize_to_hnswlib(raft::resources const& handle,
-                          const std::string& filename,
-                          const cuvs::neighbors::cagra::index<T, IdxT>& index)
-{
-  detail::serialize_to_hnswlib<T, IdxT>(handle, filename, index);
-}
 
-#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                   \
-  void serialize(raft::resources const& handle,                                            \
-                 const std::string& filename,                                              \
-                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,              \
-                 bool include_dataset)                                                     \
-  {                                                                                        \
-    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                            \
-      handle, filename, index, include_dataset);                                           \
-  };                                                                                       \
-                                                                                           \
-  void deserialize(raft::resources const& handle,                                          \
-                   const std::string& filename,                                            \
-                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                  \
-  {                                                                                        \
-    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, filename, index); \
-  };                                                                                       \
-  void serialize(raft::resources const& handle,                                            \
-                 std::ostream& os,                                                         \
-                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,              \
-                 bool include_dataset)                                                     \
-  {                                                                                        \
-    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                            \
-      handle, os, index, include_dataset);                                                 \
-  }                                                                                        \
-                                                                                           \
-  void deserialize(raft::resources const& handle,                                          \
-                   std::istream& is,                                                       \
-                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                  \
-  {                                                                                        \
-    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, is, index);       \
+#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                      \
+  void serialize(raft::resources const& handle,                                               \
+                 const std::string& filename,                                                 \
+                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                 \
+                 bool include_dataset)                                                        \
+  {                                                                                           \
+    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                               \
+      handle, filename, index, include_dataset);                                              \
+  };                                                                                          \
+                                                                                              \
+  void deserialize(raft::resources const& handle,                                             \
+                   const std::string& filename,                                               \
+                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                     \
+  {                                                                                           \
+    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, filename, index);    \
+  };                                                                                          \
+  void serialize(raft::resources const& handle,                                               \
+                 std::ostream& os,                                                            \
+                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                 \
+                 bool include_dataset)                                                        \
+  {                                                                                           \
+    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                               \
+      handle, os, index, include_dataset);                                                    \
+  }                                                                                           \
+                                                                                              \
+  void deserialize(raft::resources const& handle,                                             \
+                   std::istream& is,                                                          \
+                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                     \
+  {                                                                                           \
+    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, is, index);          \
+  }                                                                                           \
+                                                                                              \
+  void serialize_to_hnswlib(raft::resources const& handle,                                    \
+                            std::ostream& os,                                                 \
+                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)      \
+  {                                                                                           \
+    cuvs::neighbors::cagra::detail::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index); \
+  }                                                                                           \
+                                                                                              \
+  void serialize_to_hnswlib(raft::resources const& handle,                                    \
+                            const std::string& filename,                                      \
+                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)      \
+  {                                                                                           \
+    cuvs::neighbors::cagra::detail::serialize_to_hnswlib<DTYPE, uint32_t>(                    \
+      handle, filename, index);                                                               \
   }
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/dependencies.yaml b/dependencies.yaml
index 22325d5dfb..689c984177 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -324,6 +324,7 @@ dependencies:
       - output_types: [conda]
         packages:
           - *cmake_ver
+          - openblas
   docs:
     common:
       - output_types: [conda]

From 25630a05581b003b7c0c393748af56fdaeb82f10 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 27 Jun 2024 16:22:35 -0400
Subject: [PATCH 087/623] Adding IVF examples (#203)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/203
---
 examples/build.sh                    |   2 +-
 examples/cpp/CMakeLists.txt          |   4 +
 examples/cpp/src/ivf_flat_example.cu | 155 +++++++++++++++++++++++++++
 examples/cpp/src/ivf_pq_example.cu   | 110 +++++++++++++++++++
 4 files changed, 270 insertions(+), 1 deletion(-)
 create mode 100644 examples/cpp/src/ivf_flat_example.cu
 create mode 100644 examples/cpp/src/ivf_pq_example.cu

diff --git a/examples/build.sh b/examples/build.sh
index cd021ca21d..59f589af2a 100755
--- a/examples/build.sh
+++ b/examples/build.sh
@@ -33,7 +33,7 @@ if [ "$1" == "clean" ]; then
 fi
 
 ################################################################################
-# Add individual libcudf examples build scripts down below
+# Add individual libcuvs examples build scripts down below
 
 build_example() {
   example_dir=${1}
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 15b12b1182..84536597e8 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -36,7 +36,11 @@ include(../cmake/thirdparty/get_cuvs.cmake)
 
 # -------------- compile tasks ----------------- #
 add_executable(CAGRA_EXAMPLE src/cagra_example.cu)
+add_executable(IVF_FLAT_EXAMPLE src/ivf_flat_example.cu)
+add_executable(IVF_PQ_EXAMPLE src/ivf_pq_example.cu)
 
 # `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets are
 # installed in a conda environment, if one exists
 target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
\ No newline at end of file
diff --git a/examples/cpp/src/ivf_flat_example.cu b/examples/cpp/src/ivf_flat_example.cu
new file mode 100644
index 0000000000..0480fa1b9a
--- /dev/null
+++ b/examples/cpp/src/ivf_flat_example.cu
@@ -0,0 +1,155 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.cuh"
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/device_ptr.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <cstdint>
+#include <optional>
+
+void ivf_flat_build_search_simple(raft::device_resources const& dev_resources,
+                                  raft::device_matrix_view<const float, int64_t> dataset,
+                                  raft::device_matrix_view<const float, int64_t> queries)
+{
+  using namespace cuvs::neighbors;
+
+  ivf_flat::index_params index_params;
+  index_params.n_lists                  = 1024;
+  index_params.kmeans_trainset_fraction = 0.1;
+  index_params.metric                   = cuvs::distance::DistanceType::L2Expanded;
+
+  std::cout << "Building IVF-Flat index" << std::endl;
+  auto index = ivf_flat::build(dev_resources, index_params, dataset);
+
+  std::cout << "Number of clusters " << index.n_lists() << ", number of vectors added to index "
+            << index.size() << std::endl;
+
+  // Create output arrays.
+  int64_t topk      = 10;
+  int64_t n_queries = queries.extent(0);
+  auto neighbors    = raft::make_device_matrix<int64_t>(dev_resources, n_queries, topk);
+  auto distances    = raft::make_device_matrix<float>(dev_resources, n_queries, topk);
+
+  // Set search parameters.
+  ivf_flat::search_params search_params;
+  search_params.n_probes = 50;
+
+  // Search K nearest neighbors for each of the queries.
+  ivf_flat::search(
+    dev_resources, search_params, index, queries, neighbors.view(), distances.view());
+
+  // The call to ivf_flat::search is asynchronous. Before accessing the data, sync by calling
+   raft::resource::sync_stream(dev_resources);
+
+  print_results(dev_resources, neighbors.view(), distances.view());
+}
+
+void ivf_flat_build_extend_search(raft::device_resources const& dev_resources,
+                                  raft::device_matrix_view<const float, int64_t> dataset,
+                                  raft::device_matrix_view<const float, int64_t> queries)
+{
+  using namespace cuvs::neighbors;
+
+  // Define dataset indices.
+  auto data_indices = raft::make_device_vector<int64_t, int64_t>(dev_resources, dataset.extent(0));
+  thrust::counting_iterator<int64_t> first(0);
+  thrust::device_ptr<int64_t> ptr(data_indices.data_handle());
+  thrust::copy(
+    raft::resource::get_thrust_policy(dev_resources), first, first + dataset.extent(0), ptr);
+
+  // Sub-sample the dataset to create a training set.
+  auto trainset =
+    subsample(dev_resources, dataset, raft::make_const_mdspan(data_indices.view()), 0.1);
+
+  ivf_flat::index_params index_params;
+  index_params.n_lists           = 100;
+  index_params.metric            = cuvs::distance::DistanceType::L2Expanded;
+  index_params.add_data_on_build = false;
+
+  std::cout << "\nRun k-means clustering using the training set" << std::endl;
+  auto index =
+    ivf_flat::build(dev_resources, index_params, raft::make_const_mdspan(trainset.view()));
+
+  std::cout << "Number of clusters " << index.n_lists() << ", number of vectors added to index "
+            << index.size() << std::endl;
+
+  std::cout << "Filling index with the dataset vectors" << std::endl;
+  index = ivf_flat::extend(dev_resources,
+                           dataset,
+                           std::make_optional(raft::make_const_mdspan(data_indices.view())),
+                           index);
+
+  std::cout << "Index size after addin dataset vectors " << index.size() << std::endl;
+
+  // Set search parameters.
+  ivf_flat::search_params search_params;
+  search_params.n_probes = 10;
+
+  // Create output arrays.
+  int64_t topk      = 10;
+  int64_t n_queries = queries.extent(0);
+  auto neighbors    = raft::make_device_matrix<int64_t, int64_t>(dev_resources, n_queries, topk);
+  auto distances    = raft::make_device_matrix<float, int64_t>(dev_resources, n_queries, topk);
+
+  // Search K nearest neighbors for each queries.
+  ivf_flat::search(
+    dev_resources, search_params, index, queries, neighbors.view(), distances.view());
+
+  // The call to ivf_flat::search is asynchronous. Before accessing the data, sync using:
+   raft::resource::sync_stream(dev_resources);
+
+  print_results(dev_resources, neighbors.view(), distances.view());
+}
+
+int main()
+{
+  raft::device_resources dev_resources;
+
+  // Set pool memory resource with 1 GiB initial pool size. All allocations use the same pool.
+  rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
+    rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+  rmm::mr::set_current_device_resource(&pool_mr);
+
+  // Create input arrays.
+  int64_t n_samples = 10000;
+  int64_t n_dim     = 3;
+  int64_t n_queries = 10;
+  auto dataset      = raft::make_device_matrix<float, int64_t>(dev_resources, n_samples, n_dim);
+  auto queries      = raft::make_device_matrix<float, int64_t>(dev_resources, n_queries, n_dim);
+  generate_dataset(dev_resources, dataset.view(), queries.view());
+
+  // Simple build and search example.
+  ivf_flat_build_search_simple(dev_resources,
+                               raft::make_const_mdspan(dataset.view()),
+                               raft::make_const_mdspan(queries.view()));
+
+  // Build and extend example.
+  ivf_flat_build_extend_search(dev_resources,
+                               raft::make_const_mdspan(dataset.view()),
+                               raft::make_const_mdspan(queries.view()));
+}
diff --git a/examples/cpp/src/ivf_pq_example.cu b/examples/cpp/src/ivf_pq_example.cu
new file mode 100644
index 0000000000..bef9787141
--- /dev/null
+++ b/examples/cpp/src/ivf_pq_example.cu
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.cuh"
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/refine.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <cstdint>
+
+void ivf_pq_build_search(raft::device_resources const& dev_resources,
+                         raft::device_matrix_view<const float, int64_t> dataset,
+                         raft::device_matrix_view<const float, int64_t> queries)
+{
+  using namespace cuvs::neighbors;  // NOLINT
+
+  ivf_pq::index_params index_params;
+  index_params.n_lists                  = 1024;
+  index_params.kmeans_trainset_fraction = 0.1;
+  index_params.metric                   = cuvs::distance::DistanceType::L2Expanded;
+  index_params.pq_bits                  = 8;
+  index_params.pq_dim                   = 2;
+
+  std::cout << "Building IVF-PQ index" << std::endl;
+  auto index = ivf_pq::build(dev_resources, index_params, dataset);
+
+  std::cout << "Number of clusters " << index.n_lists() << ", number of vectors added to index "
+            << index.size() << std::endl;
+
+  // Set search parameters.
+  ivf_pq::search_params search_params;
+  search_params.n_probes = 50;
+  // Set the internal search precision to 16-bit floats;
+  // usually, this improves the performance at a slight cost to the recall.
+  search_params.internal_distance_dtype = CUDA_R_16F;
+  search_params.lut_dtype               = CUDA_R_16F;
+
+  // Create output arrays.
+  int64_t topk      = 10;
+  int64_t n_queries = queries.extent(0);
+  auto neighbors    = raft::make_device_matrix<int64_t>(dev_resources, n_queries, topk);
+  auto distances    = raft::make_device_matrix<float>(dev_resources, n_queries, topk);
+
+  // Search K nearest neighbors for each of the queries.
+  ivf_pq::search(
+    dev_resources, search_params, index, queries, neighbors.view(), distances.view());
+
+  // Re-ranking operation: refine the initial search results by computing exact distances
+  int64_t topk_refined = 7;
+  auto neighbors_refined =
+    raft::make_device_matrix<int64_t>(dev_resources, n_queries, topk_refined);
+  auto distances_refined = raft::make_device_matrix<float>(dev_resources, n_queries, topk_refined);
+
+  // Note, refinement requires the original dataset and the queries.
+  // Don't forget to specify the same distance metric as used by the index.
+  cuvs::neighbors::refine(dev_resources,
+                          dataset,
+                          queries,
+                          raft::make_const_mdspan(neighbors.view()),
+                          neighbors_refined.view(),
+                          distances_refined.view(),
+                          index.metric());
+
+  // Show both the original and the refined results
+  std::cout << std::endl << "Original results:" << std::endl;
+  print_results(dev_resources, neighbors.view(), distances.view());
+  std::cout << std::endl << "Refined results:" << std::endl;
+  print_results(dev_resources, neighbors_refined.view(), distances_refined.view());
+}
+
+int main()
+{
+  raft::device_resources dev_resources;
+
+  // Set pool memory resource with 1 GiB initial pool size. All allocations use the same pool.
+  rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
+    rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+  rmm::mr::set_current_device_resource(&pool_mr);
+
+  // Create input arrays.
+  int64_t n_samples = 10000;
+  int64_t n_dim     = 3;
+  int64_t n_queries = 10;
+  auto dataset      = raft::make_device_matrix<float, int64_t>(dev_resources, n_samples, n_dim);
+  auto queries      = raft::make_device_matrix<float, int64_t>(dev_resources, n_queries, n_dim);
+  generate_dataset(dev_resources, dataset.view(), queries.view());
+
+  // Simple build and search example.
+  ivf_pq_build_search(dev_resources,
+                      raft::make_const_mdspan(dataset.view()),
+                      raft::make_const_mdspan(queries.view()));
+}

From 7bac20fe52d9be2d15e799bf209e3104760f717b Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Mon, 1 Jul 2024 12:44:57 -0700
Subject: [PATCH 088/623] Add rust example (#206)

Add an example project using the cuvs bindings uploaded to crates.io, as well as some basic instructions on how to compile

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/206
---
 examples/rust/Cargo.toml  |  9 +++++
 examples/rust/README.md   |  7 ++++
 examples/rust/src/main.rs | 78 +++++++++++++++++++++++++++++++++++++++
 3 files changed, 94 insertions(+)
 create mode 100644 examples/rust/Cargo.toml
 create mode 100644 examples/rust/README.md
 create mode 100644 examples/rust/src/main.rs

diff --git a/examples/rust/Cargo.toml b/examples/rust/Cargo.toml
new file mode 100644
index 0000000000..7955e08e69
--- /dev/null
+++ b/examples/rust/Cargo.toml
@@ -0,0 +1,9 @@
+[package]
+name = "cuvs-rust-example"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+cuvs = ">=24.6.0"
+ndarray = "0.15"
+ndarray-rand = "0.14"
diff --git a/examples/rust/README.md b/examples/rust/README.md
new file mode 100644
index 0000000000..255ffcfb64
--- /dev/null
+++ b/examples/rust/README.md
@@ -0,0 +1,7 @@
+# cuVS Rust Example
+
+This template project provides a drop-in sample to for using cuVS in a rust project.
+
+First, please refer to our [installation docs](https://docs.rapids.ai/api/cuvs/stable/build.html#cuda-gpu-requirements) for the minimum requirements to use cuVS. Note that you will have to have the libcuvs.so and libcuvs_c.so binaries installed to compile this example project.
+
+Once the minimum requirements are satisfied, this example template application can be built using 'cargo build', and this directory can be copied directly in order to build a new application with cuVS.
diff --git a/examples/rust/src/main.rs b/examples/rust/src/main.rs
new file mode 100644
index 0000000000..ccc1466dd9
--- /dev/null
+++ b/examples/rust/src/main.rs
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use cuvs::cagra::{Index, IndexParams, SearchParams};
+use cuvs::{ManagedTensor, Resources, Result};
+
+use ndarray::s;
+use ndarray_rand::rand_distr::Uniform;
+use ndarray_rand::RandomExt;
+
+/// Example showing how to index and search data with CAGRA
+fn cagra_example() -> Result<()> {
+    let res = Resources::new()?;
+
+    // Create a new random dataset to index
+    let n_datapoints = 65536;
+    let n_features = 512;
+    let dataset =
+        ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+
+    // build the cagra index
+    let build_params = IndexParams::new()?;
+    let index = Index::build(&res, &build_params, &dataset)?;
+    println!(
+        "Indexed {}x{} datapoints into cagra index",
+        n_datapoints, n_features
+    );
+
+    // use the first 4 points from the dataset as queries : will test that we get them back
+    // as their own nearest neighbor
+    let n_queries = 4;
+    let queries = dataset.slice(s![0..n_queries, ..]);
+
+    let k = 10;
+
+    // CAGRA search API requires queries and outputs to be on device memory
+    // copy query data over, and allocate new device memory for the distances/ neighbors
+    // outputs
+    let queries = ManagedTensor::from(&queries).to_device(&res)?;
+    let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+    let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;
+
+    let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+    let distances = ManagedTensor::from(&distances_host).to_device(&res)?;
+
+    let search_params = SearchParams::new()?;
+
+    index.search(&res, &search_params, &queries, &neighbors, &distances)?;
+
+    // Copy back to host memory
+    distances.to_host(&res, &mut distances_host)?;
+    neighbors.to_host(&res, &mut neighbors_host)?;
+
+    // nearest neighbors should be themselves, since queries are from the
+    // dataset
+    println!("Neighbors {:?}", neighbors_host);
+    println!("Distances {:?}", distances_host);
+    Ok(())
+}
+
+fn main() {
+    if let Err(e) = cagra_example() {
+        println!("Failed to run CAGRA: {:?}", e);
+    }
+}

From 34644fba54f056edfab42eb90c7f3417848bc2c9 Mon Sep 17 00:00:00 2001
From: Ray Bell <rayjohnbell0@gmail.com>
Date: Mon, 1 Jul 2024 15:53:46 -0400
Subject: [PATCH 089/623] DOC: update notebook link (#191)

This updates the notebook link from https://github.com/rapidsai/cuvs/tree/HEAD/exmples (doesn't exist) to https://github.com/rapidsai/cuvs/tree/branch-24.08/notebooks

Authors:
  - Ray Bell (https://github.com/raybellwaves)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/191
---
 docs/source/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index b8334e8589..88f3612439 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -6,7 +6,7 @@ Useful Resources
 
 .. _cuvs_reference: https://docs.rapids.ai/api/cuvs/stable/
 
-- `Example Notebooks <https://github.com/rapidsai/cuvs/tree/HEAD/exmples>`_: Self-contained Code examples
+- `Example Notebooks <https://github.com/rapidsai/cuvs/tree/HEAD/notebooks>`_: Self-contained Code examples
 - `RAPIDS Community <https://rapids.ai/community.html>`_: Get help, contribute, and collaborate.
 - `GitHub repository <https://github.com/rapidsai/cuvs>`_: Download the cuVS source code.
 - `Issue tracker <https://github.com/rapidsai/cuvs/issues>`_: Report issues or request features.

From 307ea6766ca1afa5c2f1d5e672f564edc31d20d7 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Mon, 1 Jul 2024 13:15:43 -0700
Subject: [PATCH 090/623] Fixes for publishing rust package to crates.io (#207)

Some minor fixes that are required to publish our rust bindings to crates.io:

 * using relative paths in the cuvs-sys cmake files didn't work, get around this by symlinking required files instead
 * Need to specify an actual version for cuvs-sys and ndarray-rand packages in the rust/cuvs/Cargo.toml file

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/207
---
 ci/release/update-version.sh      | 1 +
 rust/cuvs-sys/CMakeLists.txt      | 4 ++--
 rust/cuvs-sys/VERSION             | 1 +
 rust/cuvs-sys/get_dlpack.cmake    | 1 +
 rust/cuvs-sys/rapids_config.cmake | 1 +
 rust/cuvs/Cargo.toml              | 4 ++--
 6 files changed, 8 insertions(+), 4 deletions(-)
 create mode 120000 rust/cuvs-sys/VERSION
 create mode 120000 rust/cuvs-sys/get_dlpack.cmake
 create mode 120000 rust/cuvs-sys/rapids_config.cmake

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 69fbb746c8..08bc319562 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -80,6 +80,7 @@ sed_runner "s|=[0-9][0-9].[0-9][0-9]|=${NEXT_SHORT_TAG}|g" README.md
 # rust can't handle leading 0's in the major/minor/patch version - remove
 NEXT_FULL_RUST_TAG=$(printf "%d.%d.%d" $((10#$NEXT_MAJOR)) $((10#$NEXT_MINOR)) $((10#$NEXT_PATCH)))
 sed_runner "s/version = \".*\"/version = \"${NEXT_FULL_RUST_TAG}\"/g" rust/Cargo.toml
+sed_runner "s/version = \".*\"/version = \"${NEXT_FULL_RUST_TAG}\"/g" rust/cuvs/Cargo.toml
 
 # .devcontainer files
 find .devcontainer/ -type f -name devcontainer.json -print0 | while IFS= read -r -d '' filename; do
diff --git a/rust/cuvs-sys/CMakeLists.txt b/rust/cuvs-sys/CMakeLists.txt
index e2ca3a444f..759152d907 100644
--- a/rust/cuvs-sys/CMakeLists.txt
+++ b/rust/cuvs-sys/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
 
-include(../../rapids_config.cmake)
+include(rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-export)
@@ -57,7 +57,7 @@ if(NOT cuvs_FOUND)
   add_subdirectory(../../cpp cuvs-cpp EXCLUDE_FROM_ALL)
 endif()
 
-include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
+include(get_dlpack.cmake)
 
 # add a dummy target here,
 add_library(cuvs-rust INTERFACE)
diff --git a/rust/cuvs-sys/VERSION b/rust/cuvs-sys/VERSION
new file mode 120000
index 0000000000..558194c5a5
--- /dev/null
+++ b/rust/cuvs-sys/VERSION
@@ -0,0 +1 @@
+../../VERSION
\ No newline at end of file
diff --git a/rust/cuvs-sys/get_dlpack.cmake b/rust/cuvs-sys/get_dlpack.cmake
new file mode 120000
index 0000000000..93a54ca4d7
--- /dev/null
+++ b/rust/cuvs-sys/get_dlpack.cmake
@@ -0,0 +1 @@
+../../cpp/cmake/thirdparty/get_dlpack.cmake
\ No newline at end of file
diff --git a/rust/cuvs-sys/rapids_config.cmake b/rust/cuvs-sys/rapids_config.cmake
new file mode 120000
index 0000000000..398eea52da
--- /dev/null
+++ b/rust/cuvs-sys/rapids_config.cmake
@@ -0,0 +1 @@
+../../rapids_config.cmake
\ No newline at end of file
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index cc52db026e..d69e86f9bc 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,8 +9,8 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "24.8.0" }
 ndarray = "0.15"
 
 [dev-dependencies]
-ndarray-rand = "*"
+ndarray-rand = "0.14"

From 8dfef2b56cc0acad3fb65d8e78bdaded80fbc114 Mon Sep 17 00:00:00 2001
From: Yinzuo Jiang <jiangyinzuo@foxmail.com>
Date: Wed, 3 Jul 2024 01:13:46 +0800
Subject: [PATCH 091/623] Fix compilation error when _CLK_BREAKDOWN is defined
 in cagra. (#202)

Port from https://github.com/rapidsai/raft/pull/2350

Authors:
  - Yinzuo Jiang (https://github.com/jiangyinzuo)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/202
---
 .../cagra/search_multi_cta_kernel-inl.cuh      | 12 ++++++++----
 .../cagra/search_single_cta_kernel-inl.cuh     | 18 +++++++-----------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 3c5c07a84f..90e699f48c 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -41,6 +41,7 @@
 
 #include <algorithm>
 #include <cassert>
+#include <cstdio>
 #include <iostream>
 #include <memory>
 #include <numeric>
@@ -214,7 +215,7 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
 
 #if 0
     /* debug */
-    for (unsigned i = threadIdx.x; i < result_buffer_size_32; i += BLOCK_SIZE) {
+    for (unsigned i = threadIdx.x; i < result_buffer_size_32; i += blockDim.x) {
         result_indices_buffer[i] = utils::get_max_value<INDEX_T>();
         result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
     }
@@ -356,16 +357,19 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
   }
 
 #ifdef _CLK_BREAKDOWN
-  if ((threadIdx.x == 0 || threadIdx.x == BLOCK_SIZE - 1) && (blockIdx.x == 0) &&
+  if ((threadIdx.x == 0 || threadIdx.x == blockDim.x - 1) && (blockIdx.x == 0) &&
       ((query_id * 3) % gridDim.y < 3)) {
-    RAFT_LOG_DEBUG(
+    printf(
+      "%s:%d "
       "query, %d, thread, %d"
-      ", init, %d"
+      ", init, %lu"
       ", 1st_distance, %lu"
       ", topk, %lu"
       ", pickup_parents, %lu"
       ", distance, %lu"
       "\n",
+      __FILE__,
+      __LINE__,
       query_id,
       threadIdx.x,
       clk_init,
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index 99543b9d35..e58167432b 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -44,6 +44,7 @@
 #include <algorithm>
 #include <cassert>
 #include <cstdint>
+#include <cstdio>
 #include <iostream>
 #include <memory>
 #include <numeric>
@@ -452,14 +453,6 @@ __device__ inline void hashmap_restore(INDEX_T* const hashmap_ptr,
   }
 }
 
-template <class T, unsigned BLOCK_SIZE>
-__device__ inline void set_value_device(T* const ptr, const T fill, const std::uint32_t count)
-{
-  for (std::uint32_t i = threadIdx.x; i < count; i += BLOCK_SIZE) {
-    ptr[i] = fill;
-  }
-}
-
 // One query one thread block
 template <uint32_t TEAM_SIZE,
           uint32_t DATASET_BLOCK_DIM,
@@ -795,10 +788,11 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
     num_executed_iterations[query_id] = iter + 1;
   }
 #ifdef _CLK_BREAKDOWN
-  if ((threadIdx.x == 0 || threadIdx.x == BLOCK_SIZE - 1) && ((query_id * 3) % gridDim.y < 3)) {
-    RAFT_LOG_DEBUG(
+  if ((threadIdx.x == 0 || threadIdx.x == blockDim.x - 1) && ((query_id * 3) % gridDim.y < 3)) {
+    printf(
+      "%s:%d "
       "query, %d, thread, %d"
-      ", init, %d"
+      ", init, %lu"
       ", 1st_distance, %lu"
       ", topk, %lu"
       ", reset_hash, %lu"
@@ -806,6 +800,8 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
       ", restore_hash, %lu"
       ", distance, %lu"
       "\n",
+      __FILE__,
+      __LINE__,
       query_id,
       threadIdx.x,
       clk_init,

From 1f2d1b54eeafdfd3cfcb6dd293a89687cf3a0338 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 2 Jul 2024 14:15:08 -0400
Subject: [PATCH 092/623] Use verify-alpha-spec hook (#209)

With the deployment of rapids-build-backend, we need to make sure our dependencies have alpha specs.

Contributes to https://github.com/rapidsai/build-planning/issues/31

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/209
---
 .pre-commit-config.yaml | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 72e7dffb32..c6960af6df 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -92,17 +92,12 @@ repos:
                 additional_dependencies: [tomli]
                 args: ["--toml", "pyproject.toml"]
                 exclude: (?x)^(^CHANGELOG.md$)
-      - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.13.11
-        hooks:
-            - id: rapids-dependency-file-generator
-              args: ["--clean"]
       - repo: https://github.com/pre-commit/pre-commit-hooks
         rev: v4.5.0
         hooks:
               - id: check-json
       - repo: https://github.com/rapidsai/pre-commit-hooks
-        rev: v0.0.3
+        rev: v0.2.0
         hooks:
           - id: verify-copyright
             files: |
@@ -116,6 +111,12 @@ repos:
               (?x)
                   docs/source/sphinxext/github_link\.py|
                   cpp/cmake/modules/FindAVX\.cmake|
+          - id: verify-alpha-spec
+      - repo: https://github.com/rapidsai/dependency-file-generator
+        rev: v1.13.11
+        hooks:
+            - id: rapids-dependency-file-generator
+              args: ["--clean"]
 
 default_language_version:
       python: python3

From d814ed80fae6a3f0fcc650bc873a43e95ee51925 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 5 Jul 2024 17:01:22 -0500
Subject: [PATCH 093/623] skip CMake 3.30.0 (#214)

Contributes to https://github.com/rapidsai/build-planning/issues/80

Adds constraints to avoid pulling in CMake 3.30.0, for the reasons described in that issue.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/214
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/all_cuda-122_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-122_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs/conda_build_config.yaml        | 2 +-
 conda/recipes/libcuvs/conda_build_config.yaml     | 2 +-
 dependencies.yaml                                 | 2 +-
 python/cuvs/pyproject.toml                        | 2 +-
 8 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index b4bfdebcf8..47c69217fe 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang
 - clang-tools=16.0.6
 - clang==16.0.6
-- cmake>=3.26.4
+- cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
 - cuda-python>=11.7.1,<12.0a0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index e3181d69cd..ced6ae83d3 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang
 - clang-tools=16.0.6
 - clang==16.0.6
-- cmake>=3.26.4
+- cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
 - cuda-python>=11.7.1,<12.0a0
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-122_arch-aarch64.yaml
index e22d4388ec..e12a2f153f 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-122_arch-aarch64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang
 - clang-tools=16.0.6
 - clang==16.0.6
-- cmake>=3.26.4
+- cmake>=3.26.4,!=3.30.0
 - cuda-cudart-dev
 - cuda-nvcc
 - cuda-nvtx-dev
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-122_arch-x86_64.yaml
index 08d2f23bbb..100d2ec4dc 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-122_arch-x86_64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang
 - clang-tools=16.0.6
 - clang==16.0.6
-- cmake>=3.26.4
+- cmake>=3.26.4,!=3.30.0
 - cuda-cudart-dev
 - cuda-nvcc
 - cuda-nvtx-dev
diff --git a/conda/recipes/cuvs/conda_build_config.yaml b/conda/recipes/cuvs/conda_build_config.yaml
index e3ca633eb9..001878ff25 100644
--- a/conda/recipes/cuvs/conda_build_config.yaml
+++ b/conda/recipes/cuvs/conda_build_config.yaml
@@ -17,4 +17,4 @@ c_stdlib_version:
   - "2.17"
 
 cmake_version:
-  - ">=3.26.4"
+  - ">=3.26.4,!=3.30.0"
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index 8141af1543..0e14163424 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -17,7 +17,7 @@ c_stdlib_version:
   - "2.17"
 
 cmake_version:
-  - ">=3.26.4"
+  - ">=3.26.4,!=3.30.0"
 
 nccl_version:
   - ">=2.9.9"
diff --git a/dependencies.yaml b/dependencies.yaml
index 689c984177..e6d151d82c 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -111,7 +111,7 @@ dependencies:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - &cmake_ver cmake>=3.26.4
+          - &cmake_ver cmake>=3.26.4,!=3.30.0
           - cython>=3.0.0
           - ninja
       - output_types: [conda]
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 4fda501554..65fb51196f 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -118,7 +118,7 @@ regex = "(?P<value>.*)"
 
 [tool.rapids-build-backend]
 requires = [
-    "cmake>=3.26.4",
+    "cmake>=3.26.4,!=3.30.0",
     "cuda-python",
     "cython>=3.0.0",
     "ninja",

From c5e3ec82e804baf2e31de2d7db7114284eec6250 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 9 Jul 2024 14:37:37 -0400
Subject: [PATCH 094/623] Add CUDA_STATIC_MATH_LIBRARIES (#216)

Usage of the CUDA math libraries is independent of the CUDA runtime. Make their static/shared status separately controllable.

Contributes to https://github.com/rapidsai/build-planning/issues/35

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Robert Maynard (https://github.com/robertmaynard)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/216
---
 cpp/CMakeLists.txt         | 10 +++++++---
 docs/source/build.rst      |  5 +++++
 python/cuvs/CMakeLists.txt |  1 +
 3 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index c7b61b92f3..5ba47d799b 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -60,7 +60,8 @@ option(CUDA_ENABLE_KERNELINFO "Enable kernel resource usage info" OFF)
 option(CUDA_ENABLE_LINEINFO
        "Enable the -lineinfo option for nvcc (useful for cuda-memcheck / profiler)" OFF
 )
-option(CUDA_STATIC_RUNTIME "Statically link the CUDA toolkit runtime and libraries" OFF)
+option(CUDA_STATIC_RUNTIME "Statically link the CUDA runtime" OFF)
+option(CUDA_STATIC_MATH_LIBRARIES "Statically link the CUDA math libraries" OFF)
 option(CUVS_USE_RAFT_STATIC "Build and statically link the RAFT libraries" OFF)
 option(CUDA_LOG_COMPILE_TIME "Write a log of compilation times to nvcc_compile_log.csv" OFF)
 option(DETECT_CONDA_ENV "Enable detection of conda environment for dependencies" ON)
@@ -101,7 +102,10 @@ message(VERBOSE "cuVS: Enable kernel resource usage info: ${CUDA_ENABLE_KERNELIN
 message(VERBOSE "cuVS: Enable lineinfo in nvcc: ${CUDA_ENABLE_LINEINFO}")
 message(VERBOSE "cuVS: Enable nvtx markers: ${CUVS_NVTX}")
 message(VERBOSE
-        "cuVS: Statically link the CUDA toolkit runtime and libraries: ${CUDA_STATIC_RUNTIME}"
+        "cuVS: Statically link the CUDA runtime: ${CUDA_STATIC_RUNTIME}"
+)
+message(VERBOSE
+        "cuVS: Statically link the CUDA math libraries: ${CUDA_STATIC_MATH_LIBRARIES}"
 )
 message(VERBOSE "cuVS: Build and statically link RAFT libraries: ${CUVS_USE_RAFT_STATIC}")
 
@@ -132,7 +136,7 @@ endif()
 # * compiler options ----------------------------------------------------------
 
 set(_ctk_static_suffix "")
-if(CUDA_STATIC_RUNTIME)
+if(CUDA_STATIC_MATH_LIBRARIES)
   set(_ctk_static_suffix "_static")
 endif()
 
diff --git a/docs/source/build.rst b/docs/source/build.rst
index f959fc2883..86f4590fc5 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -218,6 +218,11 @@ cuVS has the following configurable cmake flags available:
    - OFF
    - Statically link the CUDA runtime
 
+ * - CUDA_STATIC_MATH_LIBRARIES
+   - ON, OFF
+   - OFF
+   - Statically link the CUDA math libraries
+
  * - DETECT_CONDA_ENV
    - ON, OFF
    - ON
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index d48cc7405a..4d24682cfb 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -67,6 +67,7 @@ if(NOT cuvs_FOUND)
 
   # Statically link dependencies if building wheels
   set(CUDA_STATIC_RUNTIME ON)
+  set(CUDA_STATIC_MATH_LIBRARIES ON)
   set(CUVS_USE_RAFT_STATIC ON)
 
   add_subdirectory(../../cpp cuvs-cpp EXCLUDE_FROM_ALL)

From bf539409f974f43c62c60ab14671499225802fad Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Wed, 10 Jul 2024 04:32:12 +0900
Subject: [PATCH 095/623] CAGRA new vector addition (#151)

This PR introduces the new vector addition feature to CAGRA.

Rel: https://github.com/rapidsai/raft/issues/1775
Original PR: https://github.com/rapidsai/raft/pull/2157

CAGRA-Q is not supported

## Usage
```cpp
auto additional_dataset = raft::make_host_matrix<float, int64_t>(res,updated_dataset_size, dim);
cuvs::neighbors::cagra::extend(handle, raft::make_const_mdspan(additiona_dataset.view()), cagra_index);
```

## Algorithm

Graph degree: d

The algorithm consists of two stages: rank-based reordering and reverse edge addition.
1. Rank-based reordering
1-1. Obtain d' (=2d) nearest neighbor vectors (V) of a given new vector using the CAGRA search
1-2. Count the number of detourable edges using the result of step 1 and the neighbor list of the input index. Then we prune (3*d/2) edges in the same way as the CAGRA graph optimization. Through this operation, we decide d/2 neighbors.
2. Reverse edge addition
2-1. Count the number of incoming edges for all nodes.
2-2. Add d/2 reverse edges from the nodes added to the neighbor list in Step 1 by replacing a node with a new node. To prevent the connection to the replaced node from being lost, we add the node to the neighbor list of the new node. This allow us to make a detour connection. The replaced nodes are the largest number of incoming edge nodes in the 2/d nodes from the back of the neighbor list without duplication with the nodes already in the neighbor list.

## Performance
In this experiment, we first split the dataset into two parts: the initial and the additional part. Then, we extend the CAGRA index built by the initial part to include the additional part.
![search-eval](https://github.com/rapidsai/raft/assets/12711693/0fbae9e5-defc-4263-9d34-176667fb3359)


We can see a larger recall drop compared to the baseline by increasing the number of added vectors.
Therefore, rebuilding the CAGRA index is recommended when one wants to add a lot of vectors.

Authors:
  - tsuki (https://github.com/enp1s0)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/151
---
 cpp/CMakeLists.txt                            |   3 +
 cpp/include/cuvs/neighbors/cagra.hpp          | 258 ++++++++++-
 cpp/src/neighbors/cagra.cuh                   |  70 +--
 cpp/src/neighbors/cagra_extend_float.cu       |  47 ++
 cpp/src/neighbors/cagra_extend_int8.cu        |  47 ++
 cpp/src/neighbors/cagra_extend_uint8.cu       |  47 ++
 cpp/src/neighbors/detail/cagra/add_nodes.cuh  | 438 ++++++++++++++++++
 cpp/test/neighbors/ann_cagra.cuh              | 356 +++++++++++++-
 .../ann_cagra/test_float_uint32_t.cu          |   6 +
 .../ann_cagra/test_int8_t_uint32_t.cu         |   5 +
 .../ann_cagra/test_uint8_t_uint32_t.cu        |   5 +
 docs/source/cpp_api/neighbors_cagra.rst       |  24 +
 12 files changed, 1225 insertions(+), 81 deletions(-)
 create mode 100644 cpp/src/neighbors/cagra_extend_float.cu
 create mode 100644 cpp/src/neighbors/cagra_extend_int8.cu
 create mode 100644 cpp/src/neighbors/cagra_extend_uint8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/add_nodes.cuh

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 5ba47d799b..0fe44f5117 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -247,6 +247,9 @@ add_library(
   src/neighbors/cagra_build_float.cu
   src/neighbors/cagra_build_int8.cu
   src/neighbors/cagra_build_uint8.cu
+  src/neighbors/cagra_extend_float.cu
+  src/neighbors/cagra_extend_int8.cu
+  src/neighbors/cagra_extend_uint8.cu
   src/neighbors/cagra_optimize.cu
   src/neighbors/cagra_search_float.cu
   src/neighbors/cagra_search_int8.cu
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 141fba4ee8..fffea4ae56 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -25,6 +25,7 @@
 #include <raft/core/host_device_accessor.hpp>
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/mdspan.hpp>
+#include <raft/core/mdspan_types.hpp>
 #include <raft/core/resource/stream_view.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/util/integer_utils.hpp>
@@ -173,6 +174,25 @@ struct search_params : cuvs::neighbors::search_params {
 /**
  * @}
  */
+
+/**
+ * @defgroup cagra_cpp_extend_params CAGRA index extend parameters
+ * @{
+ */
+
+struct extend_params {
+  /** The additional dataset is divided into chunks and added to the graph. This is the knob to
+   * adjust the tradeoff between the recall and operation throughput. Large chunk sizes can result
+   * in high throughput, but use more working memory (O(max_chunk_size*degree^2)). This can also
+   * degrade recall because no edges are added between the nodes in the same chunk. Auto select when
+   * 0. */
+  uint32_t max_chunk_size = 0;
+};
+
+/**
+ * @}
+ */
+
 static_assert(std::is_aggregate_v<index_params>);
 static_assert(std::is_aggregate_v<search_params>);
 
@@ -644,11 +664,244 @@ auto build(raft::resources const& res,
  */
 
 /**
- * @defgroup cagra_cpp_index_search CAGRA search functions
+ * @defgroup cagra_cpp_index_extend CAGRA extend functions
  * @{
  */
 
+/** @brief Add new vectors to a CAGRA index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto additional_dataset = raft::make_device_matrix<float, int64_t>(handle,add_size,dim);
+ *   // set_additional_dataset(additional_dataset.view());
+ *
+ *   cagra::extend_params params;
+ *   cagra::extend(res, params, raft::make_const_mdspan(additional_dataset.view()), index);
+ * @endcode
+ *
+ * @param[in] handle raft resources
+ * @param[in] params extend params
+ * @param[in] additional_dataset additional dataset on device memory
+ * @param[in,out] idx CAGRA index
+ * @param[out] new_dataset_buffer_view memory buffer view for the dataset including the additional
+ * part. The data will be copied from the current index in this function. The num rows must be the
+ * sum of the original and additional datasets, cols must be the dimension of the dataset, and the
+ * stride must be the same as the original index dataset. This view will be stored in the output
+ * index. It is the caller's responsibility to ensure that dataset stays alive as long as the index.
+ * This option is useful when users want to manage the memory space for the dataset themselves.
+ * @param[out] new_graph_buffer_view memory buffer view for the graph including the additional part.
+ * The data will be copied from the current index in this function. The num rows must be the sum of
+ * the original and additional datasets and cols must be the graph degree. This view will be stored
+ * in the output index. It is the caller's responsibility to ensure that dataset stays alive as long
+ * as the index. This option is useful when users want to manage the memory space for the graph
+ * themselves.
+ */
+void extend(
+  raft::resources const& handle,
+  const cagra::extend_params& params,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> additional_dataset,
+  cuvs::neighbors::cagra::index<float, uint32_t>& idx,
+  std::optional<raft::device_matrix_view<float, int64_t, raft::layout_stride>>
+    new_dataset_buffer_view                                                        = std::nullopt,
+  std::optional<raft::device_matrix_view<uint32_t, int64_t>> new_graph_buffer_view = std::nullopt);
+
+/** @brief Add new vectors to a CAGRA index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto additional_dataset = raft::make_host_matrix<float, int64_t>(handle,add_size,dim);
+ *   // set_additional_dataset(additional_dataset.view());
+ *
+ *   cagra::extend_params params;
+ *   cagra::extend(res, params, raft::make_const_mdspan(additional_dataset.view()), index);
+ * @endcode
+ *
+ * @param[in] handle raft resources
+ * @param[in] params extend params
+ * @param[in] additional_dataset additional dataset on host memory
+ * @param[in,out] idx CAGRA index
+ * @param[out] new_dataset_buffer_view memory buffer view for the dataset including the additional
+ * part. The data will be copied from the current index in this function. The num rows must be the
+ * sum of the original and additional datasets, cols must be the dimension of the dataset, and the
+ * stride must be the same as the original index dataset. This view will be stored in the output
+ * index. It is the caller's responsibility to ensure that dataset stays alive as long as the index.
+ * This option is useful when users want to manage the memory space for the dataset themselves.
+ * @param[out] new_graph_buffer_view memory buffer view for the graph including the additional part.
+ * The data will be copied from the current index in this function. The num rows must be the sum of
+ * the original and additional datasets and cols must be the graph degree. This view will be stored
+ * in the output index. It is the caller's responsibility to ensure that dataset stays alive as long
+ * as the index. This option is useful when users want to manage the memory space for the graph
+ * themselves.
+ */
+void extend(
+  raft::resources const& handle,
+  const cagra::extend_params& params,
+  raft::host_matrix_view<const float, int64_t, raft::row_major> additional_dataset,
+  cuvs::neighbors::cagra::index<float, uint32_t>& idx,
+  std::optional<raft::device_matrix_view<float, int64_t, raft::layout_stride>>
+    new_dataset_buffer_view                                                        = std::nullopt,
+  std::optional<raft::device_matrix_view<uint32_t, int64_t>> new_graph_buffer_view = std::nullopt);
+
+/** @brief Add new vectors to a CAGRA index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto additional_dataset = raft::make_device_matrix<int8_t, int64_t>(handle,add_size,dim);
+ *   // set_additional_dataset(additional_dataset.view());
+ *
+ *   cagra::extend_params params;
+ *   cagra::extend(res, params, raft::make_const_mdspan(additional_dataset.view()), index);
+ * @endcode
+ *
+ * @param[in] handle raft resources
+ * @param[in] params extend params
+ * @param[in] additional_dataset additional dataset on device memory
+ * @param[in,out] idx CAGRA index
+ * @param[out] new_dataset_buffer_view memory buffer view for the dataset including the additional
+ * part. The data will be copied from the current index in this function. The num rows must be the
+ * sum of the original and additional datasets, cols must be the dimension of the dataset, and the
+ * stride must be the same as the original index dataset. This view will be stored in the output
+ * index. It is the caller's responsibility to ensure that dataset stays alive as long as the index.
+ * This option is useful when users want to manage the memory space for the dataset themselves.
+ * @param[out] new_graph_buffer_view memory buffer view for the graph including the additional part.
+ * The data will be copied from the current index in this function. The num rows must be the sum of
+ * the original and additional datasets and cols must be the graph degree. This view will be stored
+ * in the output index. It is the caller's responsibility to ensure that dataset stays alive as long
+ * as the index. This option is useful when users want to manage the memory space for the graph
+ * themselves.
+ */
+void extend(
+  raft::resources const& handle,
+  const cagra::extend_params& params,
+  raft::device_matrix_view<const int8_t, int64_t, raft::row_major> additional_dataset,
+  cuvs::neighbors::cagra::index<int8_t, uint32_t>& idx,
+  std::optional<raft::device_matrix_view<int8_t, int64_t, raft::layout_stride>>
+    new_dataset_buffer_view                                                        = std::nullopt,
+  std::optional<raft::device_matrix_view<uint32_t, int64_t>> new_graph_buffer_view = std::nullopt);
+
+/** @brief Add new vectors to a CAGRA index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto additional_dataset = raft::make_host_matrix<int8_t, int64_t>(handle,add_size,dim);
+ *   // set_additional_dataset(additional_dataset.view());
+ *
+ *   cagra::extend_params params;
+ *   cagra::extend(res, params, raft::make_const_mdspan(additional_dataset.view()), index);
+ * @endcode
+ *
+ * @param[in] handle raft resources
+ * @param[in] params extend params
+ * @param[in] additional_dataset additional dataset on host memory
+ * @param[in,out] idx CAGRA index
+ * @param[out] new_dataset_buffer_view memory buffer view for the dataset including the additional
+ * part. The data will be copied from the current index in this function. The num rows must be the
+ * sum of the original and additional datasets, cols must be the dimension of the dataset, and the
+ * stride must be the same as the original index dataset. This view will be stored in the output
+ * index. It is the caller's responsibility to ensure that dataset stays alive as long as the index.
+ * This option is useful when users want to manage the memory space for the dataset themselves.
+ * @param[out] new_graph_buffer_view memory buffer view for the graph including the additional part.
+ * The data will be copied from the current index in this function. The num rows must be the sum of
+ * the original and additional datasets and cols must be the graph degree. This view will be stored
+ * in the output index. It is the caller's responsibility to ensure that dataset stays alive as long
+ * as the index. This option is useful when users want to manage the memory space for the graph
+ * themselves.
+ */
+void extend(
+  raft::resources const& handle,
+  const cagra::extend_params& params,
+  raft::host_matrix_view<const int8_t, int64_t, raft::row_major> additional_dataset,
+  cuvs::neighbors::cagra::index<int8_t, uint32_t>& idx,
+  std::optional<raft::device_matrix_view<int8_t, int64_t, raft::layout_stride>>
+    new_dataset_buffer_view                                                        = std::nullopt,
+  std::optional<raft::device_matrix_view<uint32_t, int64_t>> new_graph_buffer_view = std::nullopt);
+
+/** @brief Add new vectors to a CAGRA index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto additional_dataset = raft::make_host_matrix<uint8_t, int64_t>(handle,add_size,dim);
+ *   // set_additional_dataset(additional_dataset.view());
+ *
+ *   cagra::extend_params params;
+ *   cagra::extend(res, params, raft::make_const_mdspan(additional_dataset.view()), index);
+ * @endcode
+ *
+ * @param[in] handle raft resources
+ * @param[in] params extend params
+ * @param[in] additional_dataset additional dataset on host memory
+ * @param[in,out] idx CAGRA index
+ * @param[out] new_dataset_buffer_view memory buffer view for the dataset including the additional
+ * part. The data will be copied from the current index in this function. The num rows must be the
+ * sum of the original and additional datasets, cols must be the dimension of the dataset, and the
+ * stride must be the same as the original index dataset. This view will be stored in the output
+ * index. It is the caller's responsibility to ensure that dataset stays alive as long as the index.
+ * This option is useful when users want to manage the memory space for the dataset themselves.
+ * @param[out] new_graph_buffer_view memory buffer view for the graph including the additional part.
+ * The data will be copied from the current index in this function. The num rows must be the sum of
+ * the original and additional datasets and cols must be the graph degree. This view will be stored
+ * in the output index. It is the caller's responsibility to ensure that dataset stays alive as long
+ * as the index. This option is useful when users want to manage the memory space for the graph
+ * themselves.
+ */
+void extend(
+  raft::resources const& handle,
+  const cagra::extend_params& params,
+  raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> additional_dataset,
+  cuvs::neighbors::cagra::index<uint8_t, uint32_t>& idx,
+  std::optional<raft::device_matrix_view<uint8_t, int64_t, raft::layout_stride>>
+    new_dataset_buffer_view                                                        = std::nullopt,
+  std::optional<raft::device_matrix_view<uint32_t, int64_t>> new_graph_buffer_view = std::nullopt);
+
+/** @brief Add new vectors to a CAGRA index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto additional_dataset = raft::make_host_matrix<uint8_t, int64_t>(handle,add_size,dim);
+ *   // set_additional_dataset(additional_dataset.view());
+ *
+ *   cagra::extend_params params;
+ *   cagra::extend(res, params, raft::make_const_mdspan(additional_dataset.view()), index);
+ * @endcode
+ *
+ * @param[in] handle raft resources
+ * @param[in] params extend params
+ * @param[in] additional_dataset additional dataset on host memory
+ * @param[in,out] idx CAGRA index
+ * @param[out] new_dataset_buffer_view memory buffer view for the dataset including the additional
+ * part. The data will be copied from the current index in this function. The num rows must be the
+ * sum of the original and additional datasets, cols must be the dimension of the dataset, and the
+ * stride must be the same as the original index dataset. This view will be stored in the output
+ * index. It is the caller's responsibility to ensure that dataset stays alive as long as the index.
+ * This option is useful when users want to manage the memory space for the dataset themselves.
+ * @param[out] new_graph_buffer_view memory buffer view for the graph including the additional part.
+ * The data will be copied from the current index in this function. The num rows must be the sum of
+ * the original and additional datasets and cols must be the graph degree. This view will be stored
+ * in the output index. It is the caller's responsibility to ensure that dataset stays alive as long
+ * as the index. This option is useful when users want to manage the memory space for the graph
+ * themselves.
+ */
+void extend(
+  raft::resources const& handle,
+  const cagra::extend_params& params,
+  raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> additional_dataset,
+  cuvs::neighbors::cagra::index<uint8_t, uint32_t>& idx,
+  std::optional<raft::device_matrix_view<uint8_t, int64_t, raft::layout_stride>>
+    new_dataset_buffer_view                                                        = std::nullopt,
+  std::optional<raft::device_matrix_view<uint32_t, int64_t>> new_graph_buffer_view = std::nullopt);
 /**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_cpp_index_search CAGRA search functions
+ * @{
  * @brief Search ANN using the constructed index.
  *
  * See the [cagra::build](#cagra::build) documentation for a usage example.
@@ -658,13 +911,14 @@ auto build(raft::resources const& res,
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
- * @param[in] index cagra index
+ * @param[in] idx cagra index
  * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
  */
+
 void search(raft::resources const& res,
             cuvs::neighbors::cagra::search_params const& params,
             const cuvs::neighbors::cagra::index<float, uint32_t>& index,
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 0e714370a4..89e0db1c12 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include "detail/cagra/add_nodes.cuh"
 #include "detail/cagra/cagra_build.cuh"
 #include "detail/cagra/cagra_search.cuh"
 #include "detail/cagra/graph_core.cuh"
@@ -242,46 +243,6 @@ void optimize(
   detail::optimize(res, knn_graph, new_graph);
 }
 
-/**
- * @brief Build the index from the dataset for efficient search.
- *
- * The build consist of two steps: build an intermediate knn-graph, and optimize it to
- * create the final graph. The index_params struct controls the node degree of these
- * graphs.
- *
- * It is required that dataset and the optimized graph fit the GPU memory.
- *
- * To customize the parameters for knn-graph building and pruning, and to reuse the
- * intermediate results, you could build the index in two steps using
- * [cagra::build_knn_graph](#cagra::build_knn_graph) and [cagra::optimize](#cagra::optimize).
- *
- * The following distance metrics are supported:
- * - L2
- *
- * Usage example:
- * @code{.cpp}
- *   using namespace cuvs::neighbors;
- *   // use default index parameters
- *   cagra::index_params index_params;
- *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build(res, index_params, dataset);
- *   // use default search parameters
- *   cagra::search_params search_params;
- *   // search K nearest neighbours
- *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
- *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
- * @endcode
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices in the source dataset
- *
- * @param[in] res
- * @param[in] params parameters for building the index
- * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
- *
- * @return the constructed cagra index
- */
 template <typename T,
           typename IdxT     = uint32_t,
           typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
@@ -364,23 +325,6 @@ void search_with_filtering(raft::resources const& res,
     res, params, idx, queries_internal, neighbors_internal, distances_internal, sample_filter);
 }
 
-/**
- * @brief Search ANN using the constructed index.
- *
- * See the [cagra::build](#cagra::build) documentation for a usage example.
- *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
- * @param[in] res raft resources
- * @param[in] params configure the search
- * @param[in] idx cagra index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- */
 template <typename T, typename IdxT>
 void search(raft::resources const& res,
             const search_params& params,
@@ -394,6 +338,18 @@ void search(raft::resources const& res,
     res, params, idx, queries, neighbors, distances, none_filter_type{});
 }
 
+template <class T, class IdxT, class Accessor>
+void extend(
+  raft::resources const& handle,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> additional_dataset,
+  cuvs::neighbors::cagra::index<T, IdxT>& index,
+  const cagra::extend_params& params,
+  std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> ndv,
+  std::optional<raft::device_matrix_view<IdxT, int64_t>> ngv)
+{
+  cagra::extend_core<T, IdxT, Accessor>(handle, additional_dataset, index, params, ndv, ngv);
+}
+
 /** @} */  // end group cagra
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_extend_float.cu b/cpp/src/neighbors/cagra_extend_float.cu
new file mode 100644
index 0000000000..7e928a2df6
--- /dev/null
+++ b/cpp/src/neighbors/cagra_extend_float.cu
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define RAFT_INST_CAGRA_EXTEND(T, IdxT)                                                         \
+  void extend(raft::resources const& handle,                                                    \
+              const cagra::extend_params& params,                                               \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> additional_dataset,   \
+              cuvs::neighbors::cagra::index<T, IdxT>& idx,                                      \
+              std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> ndv,     \
+              std::optional<raft::device_matrix_view<IdxT, int64_t>> ngv)                       \
+  {                                                                                             \
+    cuvs::neighbors::cagra::extend<T, IdxT>(handle, additional_dataset, idx, params, ndv, ngv); \
+  }                                                                                             \
+                                                                                                \
+  void extend(raft::resources const& handle,                                                    \
+              const cagra::extend_params& params,                                               \
+              raft::host_matrix_view<const T, int64_t, raft::row_major> additional_dataset,     \
+              cuvs::neighbors::cagra::index<T, IdxT>& idx,                                      \
+              std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> ndv,     \
+              std::optional<raft::device_matrix_view<IdxT, int64_t>> ngv)                       \
+  {                                                                                             \
+    cuvs::neighbors::cagra::extend<T, IdxT>(handle, additional_dataset, idx, params, ndv, ngv); \
+  }
+
+RAFT_INST_CAGRA_EXTEND(float, uint32_t);
+
+#undef RAFT_INST_CAGRA_EXTEND
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_extend_int8.cu b/cpp/src/neighbors/cagra_extend_int8.cu
new file mode 100644
index 0000000000..8a220399d0
--- /dev/null
+++ b/cpp/src/neighbors/cagra_extend_int8.cu
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define RAFT_INST_CAGRA_EXTEND(T, IdxT)                                                         \
+  void extend(raft::resources const& handle,                                                    \
+              const cagra::extend_params& params,                                               \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> additional_dataset,   \
+              cuvs::neighbors::cagra::index<T, IdxT>& idx,                                      \
+              std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> ndv,     \
+              std::optional<raft::device_matrix_view<IdxT, int64_t>> ngv)                       \
+  {                                                                                             \
+    cuvs::neighbors::cagra::extend<T, IdxT>(handle, additional_dataset, idx, params, ndv, ngv); \
+  }                                                                                             \
+                                                                                                \
+  void extend(raft::resources const& handle,                                                    \
+              const cagra::extend_params& params,                                               \
+              raft::host_matrix_view<const T, int64_t, raft::row_major> additional_dataset,     \
+              cuvs::neighbors::cagra::index<T, IdxT>& idx,                                      \
+              std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> ndv,     \
+              std::optional<raft::device_matrix_view<IdxT, int64_t>> ngv)                       \
+  {                                                                                             \
+    cuvs::neighbors::cagra::extend<T, IdxT>(handle, additional_dataset, idx, params, ndv, ngv); \
+  }
+
+RAFT_INST_CAGRA_EXTEND(int8_t, uint32_t);
+
+#undef RAFT_INST_CAGRA_EXTEND
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_extend_uint8.cu b/cpp/src/neighbors/cagra_extend_uint8.cu
new file mode 100644
index 0000000000..ef2f80b3a7
--- /dev/null
+++ b/cpp/src/neighbors/cagra_extend_uint8.cu
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define RAFT_INST_CAGRA_EXTEND(T, IdxT)                                                         \
+  void extend(raft::resources const& handle,                                                    \
+              const cagra::extend_params& params,                                               \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> additional_dataset,   \
+              cuvs::neighbors::cagra::index<T, IdxT>& idx,                                      \
+              std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> ndv,     \
+              std::optional<raft::device_matrix_view<IdxT, int64_t>> ngv)                       \
+  {                                                                                             \
+    cuvs::neighbors::cagra::extend<T, IdxT>(handle, additional_dataset, idx, params, ndv, ngv); \
+  }                                                                                             \
+                                                                                                \
+  void extend(raft::resources const& handle,                                                    \
+              const cagra::extend_params& params,                                               \
+              raft::host_matrix_view<const T, int64_t, raft::row_major> additional_dataset,     \
+              cuvs::neighbors::cagra::index<T, IdxT>& idx,                                      \
+              std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> ndv,     \
+              std::optional<raft::device_matrix_view<IdxT, int64_t>> ngv)                       \
+  {                                                                                             \
+    cuvs::neighbors::cagra::extend<T, IdxT>(handle, additional_dataset, idx, params, ndv, ngv); \
+  }
+
+RAFT_INST_CAGRA_EXTEND(uint8_t, uint32_t);
+
+#undef RAFT_INST_CAGRA_EXTEND
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
new file mode 100644
index 0000000000..9694a3e7a2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -0,0 +1,438 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "../ann_utils.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/neighbors/cagra_types.hpp>
+#include <raft/spatial/knn/detail/ann_utils.cuh>
+#include <raft/stats/histogram.cuh>
+
+#include <rmm/device_buffer.hpp>
+
+#include <omp.h>
+
+#include <cstdint>
+
+static const std::string RAFT_NAME = "raft";
+
+namespace cuvs::neighbors::cagra {
+template <class T, class IdxT, class Accessor>
+void add_node_core(
+  raft::resources const& handle,
+  const cuvs::neighbors::cagra::index<T, IdxT>& idx,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::layout_stride, Accessor>
+    additional_dataset_view,
+  raft::host_matrix_view<IdxT, std::int64_t> updated_graph)
+{
+  using DistanceT                 = float;
+  const std::size_t degree        = idx.graph_degree();
+  const std::size_t dim           = idx.dim();
+  const std::size_t old_size      = idx.dataset().extent(0);
+  const std::size_t num_add       = additional_dataset_view.extent(0);
+  const std::size_t new_size      = old_size + num_add;
+  const std::uint32_t base_degree = degree * 2;
+
+  // Step 0: Calculate the number of incoming edges for each node
+  auto dev_num_incoming_edges = raft::make_device_vector<int, std::uint64_t>(handle, new_size);
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(dev_num_incoming_edges.data_handle(),
+                                0,
+                                sizeof(int) * new_size,
+                                raft::resource::get_cuda_stream(handle)));
+  raft::stats::histogram<IdxT, std::int64_t>(raft::stats::HistTypeAuto,
+                                             dev_num_incoming_edges.data_handle(),
+                                             old_size,
+                                             idx.graph().data_handle(),
+                                             old_size * degree,
+                                             1,
+                                             raft::resource::get_cuda_stream(handle));
+
+  auto host_num_incoming_edges = raft::make_host_vector<int, std::uint64_t>(new_size);
+  raft::copy(host_num_incoming_edges.data_handle(),
+             dev_num_incoming_edges.data_handle(),
+             new_size,
+             raft::resource::get_cuda_stream(handle));
+
+  const std::size_t max_chunk_size = 1024;
+
+  cuvs::neighbors::cagra::search_params params;
+  params.itopk_size = std::max(base_degree * 2lu, 256lu);
+
+  // Memory space for rank-based neighbor list
+  auto mr = raft::resource::get_workspace_resource(handle);
+
+  auto neighbor_indices = raft::make_device_mdarray<IdxT, std::int64_t>(
+    handle, mr, raft::make_extents<std::int64_t>(max_chunk_size, base_degree));
+
+  auto neighbor_distances = raft::make_device_mdarray<DistanceT, std::int64_t>(
+    handle, mr, raft::make_extents<std::int64_t>(max_chunk_size, base_degree));
+
+  auto queries = raft::make_device_mdarray<T, std::int64_t>(
+    handle, mr, raft::make_extents<std::int64_t>(max_chunk_size, dim));
+
+  auto host_neighbor_indices =
+    raft::make_host_matrix<IdxT, std::int64_t>(max_chunk_size, base_degree);
+
+  cuvs::spatial::knn::detail::utils::batch_load_iterator<T> additional_dataset_batch(
+    additional_dataset_view.data_handle(),
+    num_add,
+    additional_dataset_view.stride(0),
+    max_chunk_size,
+    raft::resource::get_cuda_stream(handle),
+    raft::resource::get_workspace_resource(handle));
+  for (const auto& batch : additional_dataset_batch) {
+    // Step 1: Obtain K (=base_degree) nearest neighbors of the new vectors by CAGRA search
+    // Create queries
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(queries.data_handle(),
+                                    sizeof(T) * dim,
+                                    batch.data(),
+                                    sizeof(T) * additional_dataset_view.stride(0),
+                                    sizeof(T) * dim,
+                                    batch.size(),
+                                    cudaMemcpyDefault,
+                                    raft::resource::get_cuda_stream(handle)));
+
+    const auto queries_view = raft::make_device_matrix_view<const T, std::int64_t>(
+      queries.data_handle(), batch.size(), dim);
+
+    auto neighbor_indices_view = raft::make_device_matrix_view<IdxT, std::int64_t>(
+      neighbor_indices.data_handle(), batch.size(), base_degree);
+    auto neighbor_distances_view = raft::make_device_matrix_view<float, std::int64_t>(
+      neighbor_distances.data_handle(), batch.size(), base_degree);
+
+    neighbors::cagra::search(
+      handle, params, idx, queries_view, neighbor_indices_view, neighbor_distances_view);
+
+    raft::copy(host_neighbor_indices.data_handle(),
+               neighbor_indices.data_handle(),
+               batch.size() * base_degree,
+               raft::resource::get_cuda_stream(handle));
+    raft::resource::sync_stream(handle);
+
+    // Step 2: rank-based reordering
+#pragma omp parallel
+    {
+      std::vector<std::pair<IdxT, std::size_t>> detourable_node_count_list(base_degree);
+      for (std::size_t vec_i = omp_get_thread_num(); vec_i < batch.size();
+           vec_i += omp_get_num_threads()) {
+        // Count detourable edges
+        for (std::uint32_t i = 0; i < base_degree; i++) {
+          std::uint32_t detourable_node_count = 0;
+          const auto a_id                     = host_neighbor_indices(vec_i, i);
+          for (std::uint32_t j = 0; j < i; j++) {
+            const auto b0_id = host_neighbor_indices(vec_i, j);
+            assert(b0_id < idx.size());
+            for (std::uint32_t k = 0; k < degree; k++) {
+              const auto b1_id = updated_graph(b0_id, k);
+              if (a_id == b1_id) {
+                detourable_node_count++;
+                break;
+              }
+            }
+          }
+          detourable_node_count_list[i] = std::make_pair(a_id, detourable_node_count);
+        }
+        std::sort(detourable_node_count_list.begin(),
+                  detourable_node_count_list.end(),
+                  [&](const std::pair<IdxT, std::size_t> a, const std::pair<IdxT, std::size_t> b) {
+                    return a.second < b.second;
+                  });
+
+        for (std::size_t i = 0; i < degree; i++) {
+          updated_graph(old_size + batch.offset() + vec_i, i) = detourable_node_count_list[i].first;
+        }
+      }
+    }
+
+    // Step 3: Add reverse edges
+    const std::uint32_t rev_edge_search_range = degree / 2;
+    const std::uint32_t num_rev_edges         = degree / 2;
+    std::vector<IdxT> rev_edges(num_rev_edges), temp(degree);
+    for (std::size_t vec_i = 0; vec_i < batch.size(); vec_i++) {
+      // Create a reverse edge list
+      const auto target_new_node_id = old_size + batch.offset() + vec_i;
+      for (std::size_t i = 0; i < num_rev_edges; i++) {
+        const auto target_node_id = updated_graph(old_size + batch.offset() + vec_i, i);
+
+        IdxT replace_id                        = new_size;
+        IdxT replace_id_j                      = 0;
+        std::size_t replace_num_incoming_edges = 0;
+        for (std::int32_t j = degree - 1; j >= static_cast<std::int32_t>(rev_edge_search_range);
+             j--) {
+          const auto neighbor_id               = updated_graph(target_node_id, j);
+          const std::size_t num_incoming_edges = host_num_incoming_edges(neighbor_id);
+          if (num_incoming_edges > replace_num_incoming_edges) {
+            // Check duplication
+            bool dup = false;
+            for (std::uint32_t k = 0; k < i; k++) {
+              if (rev_edges[k] == neighbor_id) {
+                dup = true;
+                break;
+              }
+            }
+            if (dup) { continue; }
+
+            // Update rev edge candidate
+            replace_num_incoming_edges = num_incoming_edges;
+            replace_id                 = neighbor_id;
+            replace_id_j               = j;
+          }
+        }
+        if (replace_id >= new_size) {
+          std::fprintf(stderr, "Invalid rev edge index (%u)\n", replace_id);
+          return;
+        }
+        updated_graph(target_node_id, replace_id_j) = target_new_node_id;
+        rev_edges[i]                                = replace_id;
+      }
+      host_num_incoming_edges(target_new_node_id) = num_rev_edges;
+
+      // Create a neighbor list of a new node by interleaving the kNN neighbor list and reverse edge
+      // list
+      std::uint32_t interleave_switch = 0, rank_base_i = 0, rev_edges_return_i = 0, num_add = 0;
+      const auto rank_based_list_ptr =
+        updated_graph.data_handle() + (old_size + batch.offset() + vec_i) * degree;
+      const auto rev_edges_return_list_ptr = rev_edges.data();
+      while (num_add < degree) {
+        const auto node_list_ptr =
+          interleave_switch == 0 ? rank_based_list_ptr : rev_edges_return_list_ptr;
+        auto& node_list_index          = interleave_switch == 0 ? rank_base_i : rev_edges_return_i;
+        const auto max_node_list_index = interleave_switch == 0 ? degree : num_rev_edges;
+        for (; node_list_index < max_node_list_index; node_list_index++) {
+          const auto candidate = node_list_ptr[node_list_index];
+          // Check duplication
+          bool dup = false;
+          for (std::uint32_t j = 0; j < num_add; j++) {
+            if (temp[j] == candidate) {
+              dup = true;
+              break;
+            }
+          }
+          if (!dup) {
+            temp[num_add] = candidate;
+            num_add++;
+            break;
+          }
+        }
+        interleave_switch = 1 - interleave_switch;
+      }
+      for (std::uint32_t i = 0; i < degree; i++) {
+        updated_graph(target_new_node_id, i) = temp[i];
+      }
+    }
+  }
+}
+
+template <class T, class IdxT>
+void add_graph_nodes(
+  raft::resources const& handle,
+  raft::device_matrix_view<const T, int64_t, raft::layout_stride> input_updated_dataset_view,
+  const neighbors::cagra::index<T, IdxT>& index,
+  raft::host_matrix_view<IdxT, std::int64_t> updated_graph_view,
+  const cagra::extend_params& params)
+{
+  assert(input_updated_dataset_view.extent(0) >= index.size());
+
+  const std::size_t initial_dataset_size = index.size();
+  const std::size_t new_dataset_size     = input_updated_dataset_view.extent(0);
+  const std::size_t num_new_nodes        = new_dataset_size - initial_dataset_size;
+  const std::size_t degree               = index.graph_degree();
+  const std::size_t dim                  = index.dim();
+  const std::size_t stride               = input_updated_dataset_view.stride(0);
+  const std::size_t max_chunk_size_      = params.max_chunk_size == 0 ? 1 : params.max_chunk_size;
+
+  raft::copy(updated_graph_view.data_handle(),
+             index.graph().data_handle(),
+             index.graph().size(),
+             raft::resource::get_cuda_stream(handle));
+
+  neighbors::cagra::index<T, IdxT> internal_index(
+    handle,
+    index.metric(),
+    raft::make_device_matrix_view<const T, int64_t>(nullptr, 0, dim),
+    raft::make_device_matrix_view<const IdxT, int64_t>(nullptr, 0, degree));
+
+  for (std::size_t additional_dataset_offset = 0; additional_dataset_offset < num_new_nodes;
+       additional_dataset_offset += max_chunk_size_) {
+    const auto actual_chunk_size =
+      std::min(num_new_nodes - additional_dataset_offset, max_chunk_size_);
+
+    auto dataset_view = raft::make_device_strided_matrix_view<const T, std::int64_t>(
+      input_updated_dataset_view.data_handle(),
+      initial_dataset_size + additional_dataset_offset,
+      dim,
+      stride);
+    auto graph_view = raft::make_host_matrix_view<const IdxT, std::int64_t>(
+      updated_graph_view.data_handle(), initial_dataset_size + additional_dataset_offset, degree);
+
+    internal_index.update_dataset(handle, dataset_view);
+
+    // Note: The graph is copied to the device memory.
+    internal_index.update_graph(handle, graph_view);
+    raft::resource::sync_stream(handle);
+
+    auto updated_graph = raft::make_host_matrix_view<IdxT, std::int64_t>(
+      updated_graph_view.data_handle(),
+      initial_dataset_size + additional_dataset_offset + actual_chunk_size,
+      degree);
+    auto additional_dataset_view = raft::make_device_strided_matrix_view<const T, std::int64_t>(
+      input_updated_dataset_view.data_handle() +
+        (initial_dataset_size + additional_dataset_offset) * stride,
+      actual_chunk_size,
+      dim,
+      stride);
+
+    neighbors::cagra::add_node_core<T, IdxT>(
+      handle, internal_index, additional_dataset_view, updated_graph);
+    raft::resource::sync_stream(handle);
+  }
+}
+
+template <class T, class IdxT, class Accessor>
+void extend_core(
+  raft::resources const& handle,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> additional_dataset,
+  cuvs::neighbors::cagra::index<T, IdxT>& index,
+  const cagra::extend_params& params,
+  std::optional<raft::device_matrix_view<T, int64_t, raft::layout_stride>> new_dataset_buffer_view,
+  std::optional<raft::device_matrix_view<IdxT, int64_t>> new_graph_buffer_view)
+{
+  if (dynamic_cast<const non_owning_dataset<T, IdxT>*>(&index.data()) != nullptr &&
+      !new_dataset_buffer_view.has_value()) {
+    RAFT_LOG_WARN(
+      "New memory space for extended dataset will be allocated while the memory space for the old "
+      "dataset is allocated by user.");
+  }
+  const std::size_t num_new_nodes        = additional_dataset.extent(0);
+  const std::size_t initial_dataset_size = index.size();
+  const std::size_t new_dataset_size     = initial_dataset_size + num_new_nodes;
+  const std::size_t degree               = index.graph_degree();
+  const std::size_t dim                  = index.dim();
+
+  if (new_dataset_buffer_view.has_value() &&
+      static_cast<std::size_t>(new_dataset_buffer_view.value().extent(0)) != new_dataset_size) {
+    RAFT_LOG_ERROR(
+      "The extended dataset size (%lu) must be the initial dataset size (%lu) + additional dataset "
+      "size (%lu). "
+      "Please fix the memory buffer size for the extended dataset.",
+      new_dataset_buffer_view.value().extent(0),
+      initial_dataset_size,
+      num_new_nodes);
+  }
+
+  if (new_graph_buffer_view.has_value() &&
+      static_cast<std::size_t>(new_graph_buffer_view.value().extent(0)) != new_dataset_size) {
+    RAFT_LOG_ERROR(
+      "The extended graph size (%lu) must be the initial dataset size (%lu) + additional dataset "
+      "size (%lu). "
+      "Please fix the memory buffer size for the extended graph.",
+      new_graph_buffer_view.value().extent(0),
+      initial_dataset_size,
+      num_new_nodes);
+  }
+
+  using ds_idx_type = decltype(index.data().n_rows());
+  if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index.data());
+      strided_dset != nullptr) {
+    // Allocate memory space for updated graph on host
+    auto updated_graph = raft::make_host_matrix<IdxT, std::int64_t>(new_dataset_size, degree);
+
+    const auto stride    = strided_dset->stride();
+    auto updated_dataset = raft::make_device_matrix<T, std::int64_t>(handle, 0, stride);
+    auto updated_dataset_view =
+      raft::make_device_strided_matrix_view<T, std::int64_t>(nullptr, 0, dim, stride);
+
+    // Update dataset
+    auto host_updated_dataset = raft::make_host_matrix<T, std::int64_t>(new_dataset_size, stride);
+
+    // The padding area must be filled with zeros.!!!!!!!!!!!!!!!!!!!
+    memset(host_updated_dataset.data_handle(), 0, sizeof(T) * host_updated_dataset.size());
+
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_updated_dataset.data_handle(),
+                                    sizeof(T) * stride,
+                                    strided_dset->view().data_handle(),
+                                    sizeof(T) * stride,
+                                    sizeof(T) * dim,
+                                    initial_dataset_size,
+                                    cudaMemcpyDefault,
+                                    raft::resource::get_cuda_stream(handle)));
+    RAFT_CUDA_TRY(
+      cudaMemcpy2DAsync(host_updated_dataset.data_handle() + initial_dataset_size * stride,
+                        sizeof(T) * stride,
+                        additional_dataset.data_handle(),
+                        sizeof(T) * additional_dataset.stride(0),
+                        sizeof(T) * dim,
+                        num_new_nodes,
+                        cudaMemcpyDefault,
+                        raft::resource::get_cuda_stream(handle)));
+
+    if (new_dataset_buffer_view.has_value()) {
+      updated_dataset_view = new_dataset_buffer_view.value();
+    } else {
+      // Deallocate the current dataset memory space if the dataset is `owning'.
+      index.update_dataset(
+        handle, raft::make_device_strided_matrix_view<const T, int64_t>(nullptr, 0, dim, stride));
+
+      // Allocate the new dataset
+      updated_dataset = raft::make_device_matrix<T, std::int64_t>(handle, new_dataset_size, stride);
+      updated_dataset_view = raft::make_device_strided_matrix_view<T, std::int64_t>(
+        updated_dataset.data_handle(), new_dataset_size, dim, stride);
+    }
+
+    // Copy updated dataset on host memory to device memory
+    raft::copy(updated_dataset_view.data_handle(),
+               host_updated_dataset.data_handle(),
+               new_dataset_size * stride,
+               raft::resource::get_cuda_stream(handle));
+
+    // Add graph nodes
+    cuvs::neighbors::cagra::add_graph_nodes<T, IdxT>(
+      handle, raft::make_const_mdspan(updated_dataset_view), index, updated_graph.view(), params);
+
+    // Update index dataset
+    if (new_dataset_buffer_view.has_value()) {
+      index.update_dataset(handle, raft::make_const_mdspan(updated_dataset_view));
+    } else {
+      using out_mdarray_type          = decltype(updated_dataset);
+      using out_layout_type           = typename out_mdarray_type::layout_type;
+      using out_container_policy_type = typename out_mdarray_type::container_policy_type;
+      using out_owning_type =
+        owning_dataset<T, int64_t, out_layout_type, out_container_policy_type>;
+      auto out_layout = raft::make_strided_layout(updated_dataset_view.extents(),
+                                                  std::array<int64_t, 2>{stride, 1});
+
+      index.update_dataset(handle, out_owning_type{std::move(updated_dataset), out_layout});
+    }
+
+    // Update index graph
+    if (new_graph_buffer_view.has_value()) {
+      auto device_graph_view = new_graph_buffer_view.value();
+      raft::copy(device_graph_view.data_handle(),
+                 updated_graph.data_handle(),
+                 updated_graph.size(),
+                 raft::resource::get_cuda_stream(handle));
+      index.update_graph(handle, raft::make_const_mdspan(device_graph_view));
+    } else {
+      index.update_graph(handle, raft::make_const_mdspan(updated_graph.view()));
+    }
+  } else {
+    RAFT_FAIL("Only uncompressed dataset is supported");
+  }
+}
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 63ac06911d..9d2f9c175b 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -29,6 +29,9 @@
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/linalg/add.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/normalize.cuh>
+#include <raft/linalg/reduce.cuh>
 #include <raft/random/rng.cuh>
 #include <raft/util/itertools.hpp>
 
@@ -108,37 +111,130 @@ testing::AssertionResult CheckOrder(raft::host_matrix_view<IdxT, int64_t> index_
   return testing::AssertionSuccess();
 }
 
+template <typename T>
+struct fpi_mapper {};
+
+template <>
+struct fpi_mapper<double> {
+  using type                         = int64_t;
+  static constexpr int kBitshiftBase = 53;
+};
+
+template <>
+struct fpi_mapper<float> {
+  using type                         = int32_t;
+  static constexpr int kBitshiftBase = 24;
+};
+
+template <>
+struct fpi_mapper<half> {
+  using type                         = int16_t;
+  static constexpr int kBitshiftBase = 11;
+};
+
 // Generate dataset to ensure no rounding error occurs in the norm computation of any two vectors.
 // When testing the CAGRA index sorting function, rounding errors can affect the norm and alter the
 // order of the index. To ensure the accuracy of the test, we utilize the dataset. The generation
 // method is based on the error-free transformation (EFT) method.
-RAFT_KERNEL GenerateRoundingErrorFreeDataset_kernel(float* const ptr,
+template <typename T>
+RAFT_KERNEL GenerateRoundingErrorFreeDataset_kernel(T* const ptr,
                                                     const uint32_t size,
-                                                    const uint32_t resolution)
+                                                    const typename fpi_mapper<T>::type resolution)
 {
   const auto tid = threadIdx.x + blockIdx.x * blockDim.x;
   if (tid >= size) { return; }
 
-  const float u32 = *reinterpret_cast<const uint32_t*>(ptr + tid);
+  const float u32 = *reinterpret_cast<const typename fpi_mapper<T>::type*>(ptr + tid);
   ptr[tid]        = u32 / resolution;
 }
 
-void GenerateRoundingErrorFreeDataset(const raft::resources& handle,
-                                      float* const ptr,
-                                      const uint32_t n_row,
-                                      const uint32_t dim,
-                                      raft::random::RngState& rng)
+template <typename T>
+void GenerateRoundingErrorFreeDataset(
+  const raft::resources& handle,
+  T* const ptr,
+  const uint32_t n_row,
+  const uint32_t dim,
+  raft::random::RngState& rng,
+  const bool diff_flag  // true if compute the norm between two vectors
+)
 {
+  using mapper_type         = fpi_mapper<T>;
+  using int_type            = typename mapper_type::type;
   auto cuda_stream          = raft::resource::get_cuda_stream(handle);
   const uint32_t size       = n_row * dim;
   const uint32_t block_size = 256;
   const uint32_t grid_size  = (size + block_size - 1) / block_size;
 
-  const uint32_t resolution = 1u << static_cast<unsigned>(std::floor((24 - std::log2(dim)) / 2));
-  raft::random::uniformInt(handle, rng, reinterpret_cast<uint32_t*>(ptr), size, 0u, resolution - 1);
+  const auto bitshift = (mapper_type::kBitshiftBase - std::log2(dim) - (diff_flag ? 1 : 0)) / 2;
+  // Skip the test when `dim` is too big for type `T` to allow rounding error-free test.
+  if (bitshift <= 1) { GTEST_SKIP(); }
+  const int_type resolution = int_type{1} << static_cast<unsigned>(std::floor(bitshift));
+  raft::random::uniformInt<int_type>(
+    handle, rng, reinterpret_cast<int_type*>(ptr), size, -resolution, resolution - 1);
 
-  GenerateRoundingErrorFreeDataset_kernel<<<grid_size, block_size, 0, cuda_stream>>>(
-    ptr, size, resolution);
+  GenerateRoundingErrorFreeDataset_kernel<T>
+    <<<grid_size, block_size, 0, cuda_stream>>>(ptr, size, resolution);
+}
+
+template <class DataT>
+void InitDataset(const raft::resources& handle,
+                 DataT* const datatset_ptr,
+                 std::uint32_t size,
+                 std::uint32_t dim,
+                 distance::DistanceType metric,
+                 raft::random::RngState& r)
+{
+  if constexpr (std::is_same_v<DataT, float> || std::is_same_v<DataT, half>) {
+    GenerateRoundingErrorFreeDataset(handle, datatset_ptr, size, dim, r, true);
+
+    if (metric == InnerProduct) {
+      auto dataset_view = raft::make_device_matrix_view(datatset_ptr, size, dim);
+      raft::linalg::row_normalize(
+        handle, raft::make_const_mdspan(dataset_view), dataset_view, raft::linalg::L2Norm);
+    }
+  } else if constexpr (std::is_same_v<DataT, std::uint8_t> || std::is_same_v<DataT, std::int8_t>) {
+    if constexpr (std::is_same_v<DataT, std::int8_t>) {
+      raft::random::uniformInt(handle, r, datatset_ptr, size * dim, DataT(-10), DataT(10));
+    } else {
+      raft::random::uniformInt(handle, r, datatset_ptr, size * dim, DataT(1), DataT(20));
+    }
+
+    if (metric == InnerProduct) {
+      // TODO (enp1s0): Change this once row_normalize supports (u)int8 matrices.
+      // https://github.com/rapidsai/raft/issues/2291
+
+      using ComputeT    = float;
+      auto dataset_view = raft::make_device_matrix_view(datatset_ptr, size, dim);
+      auto dev_row_norm = raft::make_device_vector<ComputeT>(handle, size);
+      const auto normalized_norm =
+        (std::is_same_v<DataT, std::uint8_t> ? 40 : 20) * std::sqrt(static_cast<ComputeT>(dim));
+
+      raft::linalg::reduce(dev_row_norm.data_handle(),
+                           datatset_ptr,
+                           dim,
+                           size,
+                           0.f,
+                           true,
+                           true,
+                           raft::resource::get_cuda_stream(handle),
+                           false,
+                           raft::sq_op(),
+                           raft::add_op(),
+                           raft::sqrt_op());
+      raft::linalg::matrix_vector_op(
+        handle,
+        raft::make_const_mdspan(dataset_view),
+        raft::make_const_mdspan(dev_row_norm.view()),
+        dataset_view,
+        raft::linalg::Apply::ALONG_COLUMNS,
+        [normalized_norm] __device__(DataT elm, ComputeT norm) {
+          const ComputeT v           = elm / norm * normalized_norm;
+          const ComputeT max_v_range = std::numeric_limits<DataT>::max();
+          const ComputeT min_v_range = std::numeric_limits<DataT>::min();
+          return static_cast<DataT>(std::min(max_v_range, std::max(min_v_range, v)));
+        });
+    }
+  }
 }
 
 enum class graph_build_algo {
@@ -170,16 +266,27 @@ struct AnnCagraInputs {
   double min_recall;  // = std::nullopt;
   std::optional<float> ivf_pq_search_refine_ratio = std::nullopt;
   std::optional<vpq_params> compression           = std::nullopt;
+
+  std::optional<bool> non_owning_memory_buffer_flag = std::nullopt;
 };
 
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
 {
+  const auto metric_str = [](const cuvs::distance::DistanceType dist) -> std::string {
+    switch (dist) {
+      case InnerProduct: return "InnerProduct";
+      case L2Expanded: return "L2";
+      default: break;
+    }
+    return "Unknown";
+  };
+
   std::vector<std::string> algo       = {"single-cta", "multi_cta", "multi_kernel", "auto"};
   std::vector<std::string> build_algo = {"IVF_PQ", "NN_DESCENT", "AUTO"};
   os << "{n_queries=" << p.n_queries << ", dataset shape=" << p.n_rows << "x" << p.dim
      << ", k=" << p.k << ", " << algo.at((int)p.algo) << ", max_queries=" << p.max_queries
      << ", itopk_size=" << p.itopk_size << ", search_width=" << p.search_width
-     << ", metric=" << static_cast<int>(p.metric) << (p.host_dataset ? ", host" : ", device")
+     << ", metric=" << metric_str(p.metric) << ", " << (p.host_dataset ? "host" : "device")
      << ", build_algo=" << build_algo.at((int)p.build_algo);
   if ((int)p.build_algo == 0 && p.ivf_pq_search_refine_ratio) {
     os << "(refine_rate=" << *p.ivf_pq_search_refine_ratio << ')';
@@ -343,16 +450,201 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
     database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
     search_queries.resize(ps.n_queries * ps.dim, stream_);
     raft::random::RngState r(1234ULL);
-    if constexpr (std::is_same<DataT, float>{}) {
-      raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
-      raft::random::normal(
-        handle_, r, search_queries.data(), ps.n_queries * ps.dim, DataT(0.1), DataT(2.0));
-    } else {
-      raft::random::uniformInt(
-        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(20));
-      raft::random::uniformInt(
-        handle_, r, search_queries.data(), ps.n_queries * ps.dim, DataT(1), DataT(20));
+    InitDataset(handle_, database.data(), ps.n_rows, ps.dim, ps.metric, r);
+    InitDataset(handle_, search_queries.data(), ps.n_queries, ps.dim, ps.metric, r);
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnCagraInputs ps;
+  rmm::device_uvector<DataT> database;
+  rmm::device_uvector<DataT> search_queries;
+};
+
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
+ public:
+  AnnCagraAddNodesTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnCagraInputs>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+ protected:
+  void testCagra()
+  {
+    // TODO (tarang-jain): remove when NN Descent index building support InnerProduct. Reference
+    // issue: https://github.com/rapidsai/raft/issues/2276
+    if (ps.metric == InnerProduct && ps.build_algo == graph_build_algo::NN_DESCENT) GTEST_SKIP();
+    if (ps.compression != std::nullopt) GTEST_SKIP();
+
+    size_t queries_size = ps.n_queries * ps.k;
+    std::vector<IdxT> indices_Cagra(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<DistanceT> distances_Cagra(queries_size);
+    std::vector<DistanceT> distances_naive(queries_size);
+
+    {
+      rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+
+      cuvs::neighbors::naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                                         distances_naive_dev.data(),
+                                                         indices_naive_dev.data(),
+                                                         search_queries.data(),
+                                                         database.data(),
+                                                         ps.n_queries,
+                                                         ps.n_rows,
+                                                         ps.dim,
+                                                         ps.k,
+                                                         ps.metric);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
     }
+
+    {
+      rmm::device_uvector<DistanceT> distances_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_dev(queries_size, stream_);
+
+      {
+        cagra::index_params index_params;
+        index_params.metric = ps.metric;  // Note: currently ony the cagra::index_params metric is
+                                          // not used for knn_graph building.
+
+        switch (ps.build_algo) {
+          case graph_build_algo::IVF_PQ:
+            index_params.graph_build_params =
+              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            if (ps.ivf_pq_search_refine_ratio) {
+              std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
+                index_params.graph_build_params)
+                .refinement_rate = *ps.ivf_pq_search_refine_ratio;
+            }
+            break;
+          case graph_build_algo::NN_DESCENT: {
+            index_params.graph_build_params =
+              graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
+            break;
+          }
+          case graph_build_algo::AUTO:
+            // do nothing
+            break;
+        };
+
+        cagra::search_params search_params;
+        search_params.algo        = ps.algo;
+        search_params.max_queries = ps.max_queries;
+        search_params.team_size   = ps.team_size;
+        search_params.itopk_size  = ps.itopk_size;
+
+        const double initial_dataset_ratio      = 0.90;
+        const std::size_t initial_database_size = ps.n_rows * initial_dataset_ratio;
+
+        auto initial_database_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          (const DataT*)database.data(), initial_database_size, ps.dim);
+
+        cagra::index<DataT, IdxT> index(handle_);
+        if (ps.host_dataset) {
+          auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+          raft::copy(
+            database_host.data_handle(), database.data(), initial_database_view.size(), stream_);
+          auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+            (const DataT*)database_host.data_handle(), initial_database_size, ps.dim);
+          index = cagra::build(handle_, index_params, database_host_view);
+        } else {
+          index = cagra::build(handle_, index_params, initial_database_view);
+        };
+
+        auto additional_dataset =
+          raft::make_host_matrix<DataT, int64_t>(ps.n_rows - initial_database_size, index.dim());
+        raft::copy(additional_dataset.data_handle(),
+                   static_cast<const DataT*>(database.data()) + initial_database_view.size(),
+                   additional_dataset.size(),
+                   stream_);
+
+        auto new_dataset_buffer = raft::make_device_matrix<DataT, int64_t>(handle_, 0, 0);
+        auto new_graph_buffer   = raft::make_device_matrix<IdxT, int64_t>(handle_, 0, 0);
+        std::optional<raft::device_matrix_view<DataT, int64_t, raft::layout_stride>>
+          new_dataset_buffer_view                                                    = std::nullopt;
+        std::optional<raft::device_matrix_view<IdxT, int64_t>> new_graph_buffer_view = std::nullopt;
+        if (ps.non_owning_memory_buffer_flag.has_value() &&
+            ps.non_owning_memory_buffer_flag.value()) {
+          const auto stride =
+            dynamic_cast<const cuvs::neighbors::strided_dataset<DataT, int64_t>*>(&index.data())
+              ->stride();
+          new_dataset_buffer = raft::make_device_matrix<DataT, int64_t>(handle_, ps.n_rows, stride);
+          new_graph_buffer =
+            raft::make_device_matrix<IdxT, int64_t>(handle_, ps.n_rows, index.graph_degree());
+
+          new_dataset_buffer_view = raft::make_device_strided_matrix_view<DataT, int64_t>(
+            new_dataset_buffer.data_handle(), ps.n_rows, ps.dim, stride);
+          new_graph_buffer_view = new_graph_buffer.view();
+        }
+
+        cagra::extend_params extend_params;
+        cagra::extend(handle_,
+                      extend_params,
+                      raft::make_const_mdspan(additional_dataset.view()),
+                      index,
+                      new_dataset_buffer_view,
+                      new_graph_buffer_view);
+
+        auto search_queries_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          search_queries.data(), ps.n_queries, ps.dim);
+        auto indices_out_view =
+          raft::make_device_matrix_view<IdxT, int64_t>(indices_dev.data(), ps.n_queries, ps.k);
+        auto dists_out_view = raft::make_device_matrix_view<DistanceT, int64_t>(
+          distances_dev.data(), ps.n_queries, ps.k);
+
+        cagra::search(
+          handle_, search_params, index, search_queries_view, indices_out_view, dists_out_view);
+        raft::update_host(distances_Cagra.data(), distances_dev.data(), queries_size, stream_);
+        raft::update_host(indices_Cagra.data(), indices_dev.data(), queries_size, stream_);
+        raft::resource::sync_stream(handle_);
+      }
+
+      double min_recall = ps.min_recall;
+      EXPECT_TRUE(eval_neighbours(indices_naive,
+                                  indices_Cagra,
+                                  distances_naive,
+                                  distances_Cagra,
+                                  ps.n_queries,
+                                  ps.k,
+                                  0.006,
+                                  min_recall));
+      EXPECT_TRUE(eval_distances(handle_,
+                                 database.data(),
+                                 search_queries.data(),
+                                 indices_dev.data(),
+                                 distances_dev.data(),
+                                 ps.n_rows,
+                                 ps.dim,
+                                 ps.n_queries,
+                                 ps.k,
+                                 ps.metric,
+                                 1.0e-4));
+    }
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    search_queries.resize(ps.n_queries * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    InitDataset(handle_, database.data(), ps.n_rows, ps.dim, ps.metric, r);
+    InitDataset(handle_, search_queries.data(), ps.n_queries, ps.dim, ps.metric, r);
     raft::resource::sync_stream(handle_);
   }
 
@@ -504,6 +796,26 @@ inline std::vector<AnnCagraInputs> generate_inputs()
                                                    {1.0f, 2.0f, 3.0f});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {1000},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // dim
+    {10},
+    {graph_build_algo::IVF_PQ},
+    {search_algo::AUTO},
+    {10},
+    {0},  // team_size
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {false},
+    {0.995});
+  for (auto input : inputs2) {
+    input.non_owning_memory_buffer_flag = true;
+    inputs.push_back(input);
+  }
+
   return inputs;
 }
 
diff --git a/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
index c510dfd932..d4e6347193 100644
--- a/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
@@ -23,6 +23,12 @@ namespace cuvs::neighbors::cagra {
 typedef AnnCagraTest<float, float, std::uint32_t> AnnCagraTestF_U32;
 TEST_P(AnnCagraTestF_U32, AnnCagra) { this->testCagra(); }
 
+typedef AnnCagraAddNodesTest<float, float, std::uint32_t> AnnCagraAddNodesTestF_U32;
+TEST_P(AnnCagraAddNodesTestF_U32, AnnCagraAddNodes) { this->testCagra(); }
+
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF_U32, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
+                        AnnCagraAddNodesTestF_U32,
+                        ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
index df5db45aad..72bdee4282 100644
--- a/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
@@ -22,7 +22,12 @@ namespace cuvs::neighbors::cagra {
 
 typedef AnnCagraTest<float, std::int8_t, std::uint32_t> AnnCagraTestI8_U32;
 TEST_P(AnnCagraTestI8_U32, AnnCagra) { this->testCagra(); }
+typedef AnnCagraAddNodesTest<float, std::int8_t, std::uint32_t> AnnCagraAddNodesTestI8_U32;
+TEST_P(AnnCagraAddNodesTestI8_U32, AnnCagra) { this->testCagra(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestI8_U32, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
+                        AnnCagraAddNodesTestI8_U32,
+                        ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
index 27ffcd9158..b68bfa5745 100644
--- a/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
@@ -22,7 +22,12 @@ namespace cuvs::neighbors::cagra {
 
 typedef AnnCagraTest<float, std::uint8_t, std::uint32_t> AnnCagraTestU8_U32;
 TEST_P(AnnCagraTestU8_U32, AnnCagra) { this->testCagra(); }
+typedef AnnCagraAddNodesTest<float, std::uint8_t, std::uint32_t> AnnCagraAddNodesTestU8_U32;
+TEST_P(AnnCagraAddNodesTestU8_U32, AnnCagra) { this->testCagra(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestU8_U32, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
+                        AnnCagraAddNodesTestU8_U32,
+                        ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/docs/source/cpp_api/neighbors_cagra.rst b/docs/source/cpp_api/neighbors_cagra.rst
index aed843dad7..0e07406d1a 100644
--- a/docs/source/cpp_api/neighbors_cagra.rst
+++ b/docs/source/cpp_api/neighbors_cagra.rst
@@ -27,6 +27,22 @@ Index search parameters
     :members:
     :content-only:
 
+Index extend parameters
+----------------------
+
+.. doxygengroup:: cagra_cpp_extend_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index extend memory buffers
+----------------------
+
+.. doxygengroup:: cagra_cpp_extend_memory_buffers
+    :project: cuvs
+    :members:
+    :content-only:
+
 Index
 -----
 
@@ -51,3 +67,11 @@ Index search
     :members:
     :content-only:
 
+Index extend
+-----------
+
+.. doxygengroup:: cagra_cpp_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+

From 37fe9f47c58268129296a58503b1d38aad51822d Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Mon, 15 Jul 2024 07:58:55 -0700
Subject: [PATCH 096/623] Add python bindings for ivf-* extend functions (#220)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/220
---
 cpp/include/cuvs/neighbors/ivf_flat.h         | 20 +++++
 cpp/include/cuvs/neighbors/ivf_pq.h           | 20 +++++
 cpp/src/neighbors/ivf_flat_c.cpp              | 35 +++++++++
 cpp/src/neighbors/ivf_pq_c.cpp                | 34 +++++++++
 .../cuvs/cuvs/neighbors/ivf_flat/__init__.py  |  2 +
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |  5 ++
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx | 73 ++++++++++++++++++-
 python/cuvs/cuvs/neighbors/ivf_pq/__init__.py | 12 ++-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd  |  5 ++
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  | 72 ++++++++++++++++++
 python/cuvs/cuvs/test/test_doctests.py        | 10 ++-
 python/cuvs/cuvs/test/test_ivf_flat.py        | 28 +++++++
 python/cuvs/cuvs/test/test_ivf_pq.py          | 28 ++++++-
 13 files changed, 338 insertions(+), 6 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 5e91a4f619..5c61620418 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -322,6 +322,26 @@ cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res,
  * @}
  */
 
+/**
+ * @defgroup ivf_flat_c_index_extend IVF-Flat index extend
+ * @{
+ */
+/**
+ * @brief Extend the index with the new data.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] new_vectors DLManagedTensor* the new vectors to add to the index
+ * @param[in] new_indices DLManagedTensor* vector of new indices for the new vectors
+ * @param[inout] index IVF-Flat index to be extended
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res,
+                              DLManagedTensor* new_vectors,
+                              DLManagedTensor* new_indices,
+                              cuvsIvfFlatIndex_t index);
+/**
+ * @}
+ */
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index f94448a938..5a975a72c6 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -391,6 +391,26 @@ cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char* filename, cuvs
  * @}
  */
 
+/**
+ * @defgroup ivf_pq_c_index_extend IVF-PQ index extend
+ * @{
+ */
+/**
+ * @brief Extend the index with the new data.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] new_vectors DLManagedTensor* the new vectors to add to the index
+ * @param[in] new_indices DLManagedTensor* vector of new indices for the new vectors
+ * @param[inout] index IVF-PQ index to be extended
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
+                            DLManagedTensor* new_vectors,
+                            DLManagedTensor* new_indices,
+                            cuvsIvfPqIndex_t index);
+/**
+ * @}
+ */
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index ae06a55b65..decc88ab3e 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -100,6 +100,23 @@ void* _deserialize(cuvsResources_t res, const char* filename)
   cuvs::neighbors::ivf_flat::deserialize(*res_ptr, std::string(filename), index);
   return index;
 }
+
+template <typename T, typename IdxT>
+void _extend(cuvsResources_t res,
+             DLManagedTensor* new_vectors,
+             DLManagedTensor* new_indices,
+             cuvsIvfFlatIndex index)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
+  using vectors_mdspan_type = raft::device_matrix_view<T const, IdxT, raft::row_major>;
+  using indices_mdspan_type = raft::device_vector_view<IdxT, IdxT>;
+
+  auto vectors_mds = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors);
+  auto indices_mds = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices);
+
+  cuvs::neighbors::ivf_flat::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
@@ -274,3 +291,21 @@ extern "C" cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res,
     }
   });
 }
+
+extern "C" cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res,
+                                         DLManagedTensor* new_vectors,
+                                         DLManagedTensor* new_indices,
+                                         cuvsIvfFlatIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _extend<float, int64_t>(res, new_vectors, new_indices, *index);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _extend<int8_t, int64_t>(res, new_vectors, new_indices, *index);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _extend<uint8_t, int64_t>(res, new_vectors, new_indices, *index);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 3e835523a8..e2578345b1 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -106,6 +106,23 @@ void* _deserialize(cuvsResources_t res, const char* filename)
   cuvs::neighbors::ivf_pq::deserialize(*res_ptr, std::string(filename), index);
   return index;
 }
+
+template <typename IdxT>
+void _extend(cuvsResources_t res,
+             DLManagedTensor* new_vectors,
+             DLManagedTensor* new_indices,
+             cuvsIvfPqIndex index)
+{
+  auto res_ptr              = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr            = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
+  using vectors_mdspan_type = raft::device_matrix_view<float const, IdxT, raft::row_major>;
+  using indices_mdspan_type = raft::device_vector_view<IdxT, IdxT>;
+
+  auto vectors_mds = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors);
+  auto indices_mds = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices);
+
+  cuvs::neighbors::ivf_pq::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index)
@@ -235,3 +252,20 @@ extern "C" cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res,
 {
   return cuvs::core::translate_exceptions([=] { _serialize<int64_t>(res, filename, *index); });
 }
+
+extern "C" cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
+                                       DLManagedTensor* new_vectors,
+                                       DLManagedTensor* new_indices,
+                                       cuvsIvfPqIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto vectors = new_vectors->dl_tensor;
+    if ((vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) ||
+        (vectors.dtype.code == kDLInt && vectors.dtype.bits == 8) ||
+        (vectors.dtype.code == kDLUInt && vectors.dtype.bits == 8)) {
+      _extend<int64_t>(res, new_vectors, new_indices, *index);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", vectors.dtype.code, vectors.dtype.bits);
+    }
+  });
+}
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py b/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
index d6a4afddee..d014b10b4d 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/__init__.py
@@ -18,6 +18,7 @@
     IndexParams,
     SearchParams,
     build,
+    extend,
     load,
     save,
     search,
@@ -28,6 +29,7 @@
     "IndexParams",
     "SearchParams",
     "build",
+    "extend",
     "load",
     "save",
     "search",
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
index 65fcad5cfc..96bc557e6a 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -80,3 +80,8 @@ cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
     cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res,
                                        const char * filename,
                                        cuvsIvfFlatIndex_t index) except +
+
+    cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res,
+                                  DLManagedTensor* new_vectors,
+                                  DLManagedTensor* new_indices,
+                                  cuvsIvfFlatIndex_t index)
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 03d2549953..a3799144c7 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -220,7 +220,6 @@ def build(IndexParams index_params, dataset, resources=None):
                                     np.dtype('ubyte')])
 
     cdef Index idx = Index()
-    cdef cuvsError_t build_status
     cdef cydlpack.DLManagedTensor* dataset_dlpack = \
         cydlpack.dlpack_c(dataset_ai)
     cdef cuvsIvfFlatIndexParams* params = index_params.params
@@ -429,3 +428,75 @@ def load(filename, resources=None):
     ))
     idx.trained = True
     return idx
+
+
+@auto_sync_resources
+def extend(Index index, new_vectors, new_indices, resources=None):
+    """
+    Extend an existing index with new vectors.
+
+    The input array can be either CUDA array interface compliant matrix or
+    array interface compliant matrix in host memory.
+
+
+    Parameters
+    ----------
+    index : ivf_flat.Index
+        Trained ivf_flat object.
+    new_vectors : array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    new_indices : array interface compliant vector shape (n_samples)
+        Supported dtype [int64]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.ivf_flat.Index`
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> index = ivf_flat.build(ivf_flat.IndexParams(), dataset)
+    >>> n_rows = 100
+    >>> more_data = cp.random.random_sample((n_rows, n_features),
+    ...                                     dtype=cp.float32)
+    >>> indices = n_samples + cp.arange(n_rows, dtype=cp.int64)
+    >>> index = ivf_flat.extend(index, more_data, indices)
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> distances, neighbors = ivf_flat.search(ivf_flat.SearchParams(),
+    ...                                      index, queries,
+    ...                                      k=10)
+    """
+
+    new_vectors_ai = wrap_array(new_vectors)
+    _check_input_array(new_vectors_ai, [np.dtype('float32'), np.dtype('byte'),
+                                        np.dtype('ubyte')])
+
+    new_indices_ai = wrap_array(new_indices)
+    _check_input_array(new_indices_ai, [np.dtype('int64')])
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* new_vectors_dlpack = \
+        cydlpack.dlpack_c(new_vectors_ai)
+
+    cdef cydlpack.DLManagedTensor* new_indices_dlpack = \
+        cydlpack.dlpack_c(new_indices_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsIvfFlatExtend(
+            res,
+            new_vectors_dlpack,
+            new_indices_dlpack,
+            index.index
+        ))
+
+    return index
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py b/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
index bcbcff7e81..299266df90 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/__init__.py
@@ -13,13 +13,23 @@
 # limitations under the License.
 
 
-from .ivf_pq import Index, IndexParams, SearchParams, build, load, save, search
+from .ivf_pq import (
+    Index,
+    IndexParams,
+    SearchParams,
+    build,
+    extend,
+    load,
+    save,
+    search,
+)
 
 __all__ = [
     "Index",
     "IndexParams",
     "SearchParams",
     "build",
+    "extend",
     "load",
     "save",
     "search",
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index 17d2e40305..c6eb85e35d 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -99,3 +99,8 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
     cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res,
                                      const char * filename,
                                      cuvsIvfPqIndex_t index) except +
+
+    cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
+                                DLManagedTensor* new_vectors,
+                                DLManagedTensor* new_indices,
+                                cuvsIvfPqIndex_t index)
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 0dc10e6637..de52cb2da8 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -503,3 +503,75 @@ def load(filename, resources=None):
     ))
     idx.trained = True
     return idx
+
+
+@auto_sync_resources
+def extend(Index index, new_vectors, new_indices, resources=None):
+    """
+    Extend an existing index with new vectors.
+
+    The input array can be either CUDA array interface compliant matrix or
+    array interface compliant matrix in host memory.
+
+
+    Parameters
+    ----------
+    index : ivf_pq.Index
+        Trained ivf_pq object.
+    new_vectors : array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    new_indices : array interface compliant vector shape (n_samples)
+        Supported dtype [int64]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.ivf_pq.Index`
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> index = ivf_pq.build(ivf_pq.IndexParams(), dataset)
+    >>> n_rows = 100
+    >>> more_data = cp.random.random_sample((n_rows, n_features),
+    ...                                     dtype=cp.float32)
+    >>> indices = n_samples + cp.arange(n_rows, dtype=cp.int64)
+    >>> index = ivf_pq.extend(index, more_data, indices)
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> distances, neighbors = ivf_pq.search(ivf_pq.SearchParams(),
+    ...                                      index, queries,
+    ...                                      k=10)
+    """
+
+    new_vectors_ai = wrap_array(new_vectors)
+    _check_input_array(new_vectors_ai, [np.dtype('float32'), np.dtype('byte'),
+                                        np.dtype('ubyte')])
+
+    new_indices_ai = wrap_array(new_indices)
+    _check_input_array(new_indices_ai, [np.dtype('int64')])
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* new_vectors_dlpack = \
+        cydlpack.dlpack_c(new_vectors_ai)
+
+    cdef cydlpack.DLManagedTensor* new_indices_dlpack = \
+        cydlpack.dlpack_c(new_indices_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsIvfPqExtend(
+            res,
+            new_vectors_dlpack,
+            new_indices_dlpack,
+            index.index
+        ))
+
+    return index
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
index 64b0c5da6d..4407bda526 100644
--- a/python/cuvs/cuvs/test/test_doctests.py
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -17,6 +17,7 @@
 import doctest
 import inspect
 import io
+from pathlib import Path
 
 import pytest
 
@@ -97,10 +98,13 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.distance))
 
 
+def _test_name_from_docstring(docstring):
+    filename = Path(docstring.filename).name.split(".")[0]
+    return f"{filename}:{docstring.name}"
+
+
 @pytest.mark.parametrize(
-    "docstring",
-    DOC_STRINGS,
-    ids=lambda docstring: docstring.name,
+    "docstring", DOC_STRINGS, ids=_test_name_from_docstring
 )
 def test_docstring(docstring):
     # We ignore differences in whitespace in the doctest output, and enable
diff --git a/python/cuvs/cuvs/test/test_ivf_flat.py b/python/cuvs/cuvs/test/test_ivf_flat.py
index 81917b0e3c..bb50d35734 100644
--- a/python/cuvs/cuvs/test/test_ivf_flat.py
+++ b/python/cuvs/cuvs/test/test_ivf_flat.py
@@ -29,6 +29,7 @@ def run_ivf_flat_build_search_test(
     n_queries=100,
     k=10,
     dtype=np.float32,
+    add_data_on_build=True,
     metric="euclidean",
     compare=True,
     inplace=True,
@@ -41,10 +42,24 @@ def run_ivf_flat_build_search_test(
 
     build_params = ivf_flat.IndexParams(
         metric=metric,
+        add_data_on_build=add_data_on_build,
     )
 
     index = ivf_flat.build(build_params, dataset_device)
 
+    if not add_data_on_build:
+        dataset_1 = dataset[: n_rows // 2, :]
+        dataset_2 = dataset[n_rows // 2 :, :]
+        indices_1 = np.arange(n_rows // 2, dtype=np.int64)
+        indices_2 = np.arange(n_rows // 2, n_rows, dtype=np.int64)
+
+        dataset_1_device = device_ndarray(dataset_1)
+        dataset_2_device = device_ndarray(dataset_2)
+        indices_1_device = device_ndarray(indices_1)
+        indices_2_device = device_ndarray(indices_2)
+        index = ivf_flat.extend(index, dataset_1_device, indices_1_device)
+        index = ivf_flat.extend(index, dataset_2_device, indices_2_device)
+
     queries = generate_data((n_queries, n_cols), dtype)
     out_idx = np.zeros((n_queries, k), dtype=np.int64)
     out_dist = np.zeros((n_queries, k), dtype=np.float32)
@@ -100,3 +115,16 @@ def test_ivf_flat(inplace, dtype, metric):
         inplace=inplace,
         metric=metric,
     )
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+def test_extend(dtype):
+    run_ivf_flat_build_search_test(
+        n_rows=10000,
+        n_cols=10,
+        n_queries=100,
+        k=10,
+        metric="sqeuclidean",
+        dtype=dtype,
+        add_data_on_build=False,
+    )
diff --git a/python/cuvs/cuvs/test/test_ivf_pq.py b/python/cuvs/cuvs/test/test_ivf_pq.py
index 86fdf1a23f..8af389a93f 100644
--- a/python/cuvs/cuvs/test/test_ivf_pq.py
+++ b/python/cuvs/cuvs/test/test_ivf_pq.py
@@ -34,7 +34,7 @@ def run_ivf_pq_build_search_test(
     pq_bits=8,
     pq_dim=0,
     codebook_kind="subspace",
-    add_data_on_build="True",
+    add_data_on_build=True,
     n_probes=100,
     lut_dtype=np.float32,
     internal_distance_dtype=np.float32,
@@ -62,6 +62,18 @@ def run_ivf_pq_build_search_test(
     )
 
     index = ivf_pq.build(build_params, dataset_device)
+    if not add_data_on_build:
+        dataset_1 = dataset[: n_rows // 2, :]
+        dataset_2 = dataset[n_rows // 2 :, :]
+        indices_1 = np.arange(n_rows // 2, dtype=np.int64)
+        indices_2 = np.arange(n_rows // 2, n_rows, dtype=np.int64)
+
+        dataset_1_device = device_ndarray(dataset_1)
+        dataset_2_device = device_ndarray(dataset_2)
+        indices_1_device = device_ndarray(indices_1)
+        indices_2_device = device_ndarray(indices_2)
+        index = ivf_pq.extend(index, dataset_1_device, indices_1_device)
+        index = ivf_pq.extend(index, dataset_2_device, indices_2_device)
 
     queries = generate_data((n_queries, n_cols), dtype)
     out_idx = np.zeros((n_queries, k), dtype=np.int64)
@@ -166,3 +178,17 @@ def test_ivf_pq_search_params(params):
         lut_dtype=params["lut"],
         internal_distance_dtype=params["idd"],
     )
+
+
+@pytest.mark.parametrize("dtype", [np.float32])
+def test_extend(dtype):
+    run_ivf_pq_build_search_test(
+        n_rows=10000,
+        n_cols=10,
+        n_queries=100,
+        k=10,
+        n_lists=100,
+        metric="sqeuclidean",
+        dtype=dtype,
+        add_data_on_build=False,
+    )

From 27f816c942774de32812fd1a9def1b7ff18061cd Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 16 Jul 2024 17:24:52 -0400
Subject: [PATCH 097/623] Build and test with CUDA 12.5.1 (#219)

This PR updates the latest CUDA build/test version 12.2.2 to 12.5.1.

Contributes to https://github.com/rapidsai/build-planning/issues/73

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - https://github.com/jakirkham
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/219
---
 .../cuda12.2-conda/devcontainer.json          |  8 +++---
 .devcontainer/cuda12.2-pip/devcontainer.json  | 12 ++++-----
 .github/workflows/build.yaml                  | 14 +++++-----
 .github/workflows/pr.yaml                     | 26 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++---
 ...64.yaml => all_cuda-125_arch-aarch64.yaml} |  4 +--
 ..._64.yaml => all_cuda-125_arch-x86_64.yaml} |  4 +--
 dependencies.yaml                             |  6 ++++-
 docs/source/build.rst                         |  8 +++---
 9 files changed, 47 insertions(+), 43 deletions(-)
 rename conda/environments/{all_cuda-122_arch-aarch64.yaml => all_cuda-125_arch-aarch64.yaml} (95%)
 rename conda/environments/{all_cuda-122_arch-x86_64.yaml => all_cuda-125_arch-x86_64.yaml} (95%)

diff --git a/.devcontainer/cuda12.2-conda/devcontainer.json b/.devcontainer/cuda12.2-conda/devcontainer.json
index 1c5c4a637d..db825a269d 100644
--- a/.devcontainer/cuda12.2-conda/devcontainer.json
+++ b/.devcontainer/cuda12.2-conda/devcontainer.json
@@ -3,7 +3,7 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.2",
+      "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "conda",
       "BASE": "rapidsai/devcontainers:24.08-cpp-mambaforge-ubuntu22.04"
     }
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.2-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.5-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
@@ -20,7 +20,7 @@
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.2-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.5-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -29,7 +29,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.2-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.5-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.devcontainer/cuda12.2-pip/devcontainer.json b/.devcontainer/cuda12.2-pip/devcontainer.json
index 9730c7b0ad..fe6ec3dd26 100644
--- a/.devcontainer/cuda12.2-pip/devcontainer.json
+++ b/.devcontainer/cuda12.2-pip/devcontainer.json
@@ -3,15 +3,15 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.2",
+      "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda12.2-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda12.5-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.2-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.5-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
@@ -19,7 +19,7 @@
       "version": "1.15.0"
     },
     "ghcr.io/rapidsai/devcontainers/features/cuda:24.8": {
-      "version": "12.2",
+      "version": "12.5",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
@@ -32,7 +32,7 @@
     "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.2-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.5-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -40,7 +40,7 @@
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.2-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.5-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index b71b4ff045..1ef4e0c35d 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.5.1
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.5.1
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda-12.5.1
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.5.1
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.5.1
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 647cf6df85..a82b72c84c 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda-12.5.1
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda-12.5.1
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.5.1
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.5.1
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.5.1
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.5.1
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.5.1
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,23 +86,23 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.5.1
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.5.1
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-12.5.1
     with:
       arch: '["amd64"]'
-      cuda: '["12.2"]'
+      cuda: '["12.5"]'
       build_command: |
         sccache -z;
         build-all --verbose;
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 55262b9433..5f3067ea7b 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.5.1
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.5.1
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.5.1
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.5.1
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/conda/environments/all_cuda-122_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
similarity index 95%
rename from conda/environments/all_cuda-122_arch-aarch64.yaml
rename to conda/environments/all_cuda-125_arch-aarch64.yaml
index e12a2f153f..cfb68dcf1d 100644
--- a/conda/environments/all_cuda-122_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.0,<13.0a0
-- cuda-version=12.2
+- cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -52,4 +52,4 @@ dependencies:
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sysroot_linux-aarch64==2.17
-name: all_cuda-122_arch-aarch64
+name: all_cuda-125_arch-aarch64
diff --git a/conda/environments/all_cuda-122_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
similarity index 95%
rename from conda/environments/all_cuda-122_arch-x86_64.yaml
rename to conda/environments/all_cuda-125_arch-x86_64.yaml
index 100d2ec4dc..3be64448d6 100644
--- a/conda/environments/all_cuda-122_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.0,<13.0a0
-- cuda-version=12.2
+- cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -52,4 +52,4 @@ dependencies:
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sysroot_linux-64==2.17
-name: all_cuda-122_arch-x86_64
+name: all_cuda-125_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index e6d151d82c..60ce82aa74 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,7 +3,7 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["11.8", "12.2"]
+      cuda: ["11.8", "12.5"]
       arch: [x86_64, aarch64]
     includes:
       - build
@@ -230,6 +230,10 @@ dependencies:
               cuda: "12.2"
             packages:
               - cuda-version=12.2
+          - matrix:
+              cuda: "12.5"
+            packages:
+              - cuda-version=12.5
   cuda:
     specific:
       - output_types: conda
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 86f4590fc5..a7d020c461 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -43,21 +43,21 @@ C++ Package
 
 .. code-block:: bash
 
-  mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.0
+  mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
 
 C Package
 ~~~~~~~~~
 
 .. code-block:: bash
 
-    mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.0
+    mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-    mamba install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.0
+    mamba install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.5
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
@@ -102,7 +102,7 @@ Create a build environment
 
 .. code-block:: bash
 
-    mamba env create --name cuvs -f conda/environments/all_cuda-120_arch-x86_64.yaml
+    mamba env create --name cuvs -f conda/environments/all_cuda-125_arch-x86_64.yaml
     mamba activate cuvs
 
 The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment

From 90be4848f38a4425771248f3eb67c4c62e7479bb Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 17 Jul 2024 06:43:19 -0700
Subject: [PATCH 098/623] Add col-major support for brute force knn (#217)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/217
---
 cpp/include/cuvs/neighbors/brute_force.hpp   |  72 +++++-
 cpp/src/neighbors/brute_force.cu             |  52 ++++
 cpp/src/neighbors/detail/knn_brute_force.cuh |  27 +--
 cpp/test/neighbors/brute_force.cu            | 240 ++++++++++++++++++-
 cpp/test/neighbors/knn_utils.cuh             |  15 +-
 5 files changed, 364 insertions(+), 42 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 13a5ea0cbf..1ec7e81f73 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -49,7 +49,8 @@ struct index : cuvs::neighbors::index {
    *
    * Constructs a brute force index from a dataset. This lets us precompute norms for
    * the dataset, providing a speed benefit over doing this at query time.
-   * This index will store a non-owning reference to the dataset.
+   * This index will copy the host dataset onto the device, and take ownership of any
+   * precaculated norms.
    */
   index(raft::resources const& res,
         raft::host_matrix_view<const T, int64_t, raft::row_major> dataset_view,
@@ -61,7 +62,8 @@ struct index : cuvs::neighbors::index {
    *
    * Constructs a brute force index from a dataset. This lets us precompute norms for
    * the dataset, providing a speed benefit over doing this at query time.
-   * The dataset will be copied to the device and the index will own the device memory.
+   * This index will store a non-owning reference to the dataset, but will move
+   * any norms supplied.
    */
   index(raft::resources const& res,
         raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
@@ -71,7 +73,7 @@ struct index : cuvs::neighbors::index {
 
   /** Construct a brute force index from dataset
    *
-   * This class stores a non-owning reference to the dataset and norms here.
+   * This class stores a non-owning reference to the dataset and norms.
    * Having precomputed norms gives us a performance advantage at query time.
    */
   index(raft::resources const& res,
@@ -80,6 +82,17 @@ struct index : cuvs::neighbors::index {
         cuvs::distance::DistanceType metric,
         T metric_arg = 0.0);
 
+  /** Construct a brute force index from dataset
+   *
+   * This class stores a non-owning reference to the dataset and norms, with
+   * the dataset being supplied on device in a col_major format
+   */
+  index(raft::resources const& res,
+        raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
+        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        cuvs::distance::DistanceType metric,
+        T metric_arg = 0.0);
+
   /**
    * Replace the dataset with a new dataset.
    */
@@ -152,12 +165,34 @@ struct index : cuvs::neighbors::index {
  * @param[in] metric cuvs::distance::DistanceType
  * @param[in] metric_arg metric argument
  *
- * @return the constructed ivf-flat index
+ * @return the constructed bruteforce index
  */
 auto build(raft::resources const& handle,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
            float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<float>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = brute_force::build(handle, dataset, metric);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] dataset a device pointer to a col-major matrix [n_rows, dim]
+ * @param[in] metric cuvs::distance::DistanceType
+ * @param[in] metric_arg metric argument
+ *
+ * @return the constructed bruteforce index
+ */
+auto build(raft::resources const& handle,
+           raft::device_matrix_view<const float, int64_t, raft::col_major> dataset,
+           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+           float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<float>;
 /**
  * @}
  */
@@ -169,7 +204,7 @@ auto build(raft::resources const& handle,
 /**
  * @brief Search ANN using the constructed index.
  *
- * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ * See the [brute_force::build](#brute_force::build) documentation for a usage example.
  *
  * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
  * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
@@ -186,13 +221,13 @@ auto build(raft::resources const& handle,
  * @endcode
  *
  * @param[in] handle
- * @param[in] index ivf-flat constructed index
+ * @param[in] index bruteforce constructed index
  * @param[in] queries a device pointer to a row-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
- * @param[in] sample_filter a optional device bitmap filter function that greenlights samples for a
- * given
+ * @param[in] sample_filter an optional device bitmap filter function that greenlights samples for a
+ * given query
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::brute_force::index<float>& index,
@@ -200,6 +235,27 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [brute_force::build](#brute_force::build) documentation for a usage example.
+ *
+ * @param[in] handle
+ * @param[in] index bruteforce constructed index
+ * @param[in] queries a device pointer to a col-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
+ * @param[in] sample_filter an optional device bitmap filter function that greenlights samples for a
+ * given query
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::index<float>& index,
+            raft::device_matrix_view<const float, int64_t, raft::col_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index 13554c0b5d..c0414079c5 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -69,6 +69,36 @@ index<T>::index(raft::resources const& res,
 {
 }
 
+template <typename T>
+index<T>::index(raft::resources const& res,
+                raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
+                std::optional<raft::device_vector<T, int64_t>>&& norms,
+                cuvs::distance::DistanceType metric,
+                T metric_arg)
+  : cuvs::neighbors::index(),
+    metric_(metric),
+    dataset_(
+      raft::make_device_matrix<T, int64_t>(res, dataset_view.extent(0), dataset_view.extent(1))),
+    norms_(std::move(norms)),
+    metric_arg_(metric_arg)
+{
+  // currently we don't support col_major inside tiled_brute_force_knn, because
+  // of limitations of the pairwise_distance API:
+  // 1) paiwise_distance takes a single 'isRowMajor' parameter - and we have
+  // multiple options here (both dataset and queries)
+  // 2) because of tiling, we need to be able to set a custom stride in the PW
+  // api, which isn't supported
+  // Instead, transpose the input matrices if they are passed as col-major.
+  // (note: we're doing the transpose here to avoid doing per query)
+  raft::linalg::transpose(res,
+                          const_cast<T*>(dataset_view.data_handle()),
+                          dataset_.data_handle(),
+                          dataset_view.extent(0),
+                          dataset_view.extent(1),
+                          raft::resource::get_cuda_stream(res));
+  dataset_view_ = raft::make_const_mdspan(dataset_.view());
+}
+
 template <typename T>
 void index<T>::update_dataset(raft::resources const& res,
                               raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
@@ -93,6 +123,14 @@ void index<T>::update_dataset(raft::resources const& res,
     ->cuvs::neighbors::brute_force::index<T>                                                      \
   {                                                                                               \
     return detail::build<T>(res, dataset, metric, metric_arg);                                    \
+  }                                                                                               \
+  auto build(raft::resources const& res,                                                          \
+             raft::device_matrix_view<const T, int64_t, raft::col_major> dataset,                 \
+             cuvs::distance::DistanceType metric,                                                 \
+             T metric_arg)                                                                        \
+    ->cuvs::neighbors::brute_force::index<T>                                                      \
+  {                                                                                               \
+    return detail::build<T>(res, dataset, metric, metric_arg);                                    \
   }                                                                                               \
                                                                                                   \
   void search(                                                                                    \
@@ -109,6 +147,20 @@ void index<T>::update_dataset(raft::resources const& res,
       detail::brute_force_search_filtered<T, int64_t>(                                            \
         res, idx, queries, *sample_filter, neighbors, distances);                                 \
     }                                                                                             \
+  }                                                                                               \
+  void search(                                                                                    \
+    raft::resources const& res,                                                                   \
+    const cuvs::neighbors::brute_force::index<T>& idx,                                            \
+    raft::device_matrix_view<const T, int64_t, raft::col_major> queries,                          \
+    raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,                        \
+    raft::device_matrix_view<T, int64_t, raft::row_major> distances,                              \
+    std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter = std::nullopt) \
+  {                                                                                               \
+    if (!sample_filter.has_value()) {                                                             \
+      detail::brute_force_search<T, int64_t>(res, idx, queries, neighbors, distances);            \
+    } else {                                                                                      \
+      RAFT_FAIL("filtered search isn't available with col_major queries yet");                    \
+    }                                                                                             \
   }                                                                                               \
                                                                                                   \
   template struct cuvs::neighbors::brute_force::index<T>;
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 97f7fba75d..fe425fe8f6 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -513,11 +513,11 @@ void brute_force_knn_impl(
   if (translations == nullptr) delete id_ranges;
 };
 
-template <typename T, typename IdxT>
+template <typename T, typename IdxT, typename QueryLayoutT = raft::row_major>
 void brute_force_search(
   raft::resources const& res,
   const cuvs::neighbors::brute_force::index<T>& idx,
-  raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<const T, int64_t, QueryLayoutT> queries,
   raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
   raft::device_matrix_view<T, int64_t, raft::row_major> distances,
   std::optional<raft::device_vector_view<const T, int64_t>> query_norms = std::nullopt)
@@ -544,7 +544,7 @@ void brute_force_search(
                                          distances.data_handle(),
                                          k,
                                          true,
-                                         true,
+                                         std::is_same_v<QueryLayoutT, raft::row_major>,
                                          nullptr,
                                          idx.metric(),
                                          idx.metric_arg(),
@@ -719,28 +719,17 @@ void brute_force_search_filtered(
   return;
 }
 
-template <typename T>
+template <typename T, typename LayoutT = raft::row_major>
 cuvs::neighbors::brute_force::index<T> build(
   raft::resources const& res,
-  raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+  raft::device_matrix_view<const T, int64_t, LayoutT> dataset,
   cuvs::distance::DistanceType metric,
   T metric_arg)
 {
   // certain distance metrics can benefit by pre-calculating the norms for the index dataset
   // which lets us avoid calculating these at query time
   std::optional<raft::device_vector<T, int64_t>> norms;
-  auto dataset_storage = std::optional<raft::device_matrix<T, int64_t>>{};
-  auto dataset_view    = [&res, &dataset_storage, dataset]() {
-    if constexpr (std::is_same_v<decltype(dataset),
-                                 raft::device_matrix_view<const T, int64_t, raft::row_major>>) {
-      return dataset;
-    } else {
-      dataset_storage =
-        raft::make_device_matrix<T, int64_t>(res, dataset.extent(0), dataset.extent(1));
-      raft::copy(res, dataset_storage->view(), dataset);
-      return raft::make_const_mdspan(dataset_storage->view());
-    }
-  }();
+
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
       metric == cuvs::distance::DistanceType::CosineExpanded) {
@@ -748,14 +737,14 @@ cuvs::neighbors::brute_force::index<T> build(
     // cosine needs the l2norm, where as l2 distances needs the squared norm
     if (metric == cuvs::distance::DistanceType::CosineExpanded) {
       raft::linalg::norm(res,
-                         dataset_view,
+                         dataset,
                          norms->view(),
                          raft::linalg::NormType::L2Norm,
                          raft::linalg::Apply::ALONG_ROWS,
                          raft::sqrt_op{});
     } else {
       raft::linalg::norm(res,
-                         dataset_view,
+                         dataset,
                          norms->view(),
                          raft::linalg::NormType::L2Norm,
                          raft::linalg::Apply::ALONG_ROWS);
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index 081a2966ee..c97bb5531c 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -15,8 +15,15 @@
  */
 
 #include "../test_utils.cuh"
+#include "./knn_utils.cuh"
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/selection/select_k.hpp>
+
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/transpose.cuh>
+#include <raft/matrix/init.cuh>
 
 namespace cuvs::neighbors::brute_force {
 struct KNNInputs {
@@ -179,4 +186,235 @@ typedef KNNTest<float, int64_t> KNNTestFint64_t;
 TEST_P(KNNTestFint64_t, BruteForce) { this->testBruteForce(); }
 
 INSTANTIATE_TEST_CASE_P(KNNTest, KNNTestFint64_t, ::testing::ValuesIn(inputs));
-}  // namespace cuvs::neighbors::brute_force
\ No newline at end of file
+
+// Also test with larger random inputs, including col-major inputs
+struct RandomKNNInputs {
+  int num_queries;
+  int num_db_vecs;
+  int dim;
+  int k;
+  cuvs::distance::DistanceType metric;
+  bool row_major;
+};
+
+std::ostream& operator<<(std::ostream& os, const RandomKNNInputs& input)
+{
+  return os << "num_queries:" << input.num_queries << " num_vecs:" << input.num_db_vecs
+            << " dim:" << input.dim << " k:" << input.k
+            << " metric:" << cuvs::neighbors::print_metric{input.metric}
+            << " row_major:" << input.row_major;
+}
+
+template <typename T>
+class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs> {
+ public:
+  RandomBruteForceKNNTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      params_(::testing::TestWithParam<RandomKNNInputs>::GetParam()),
+      database(params_.num_db_vecs * params_.dim, stream_),
+      search_queries(params_.num_queries * params_.dim, stream_),
+      cuvs_indices_(params_.num_queries * params_.k, stream_),
+      cuvs_distances_(params_.num_queries * params_.k, stream_),
+      ref_indices_(params_.num_queries * params_.k, stream_),
+      ref_distances_(params_.num_queries * params_.k, stream_)
+  {
+    raft::matrix::fill(
+      handle_,
+      raft::make_device_matrix_view(database.data(), params_.num_db_vecs, params_.dim),
+      T{0.0});
+    raft::matrix::fill(
+      handle_,
+      raft::make_device_matrix_view(search_queries.data(), params_.num_queries, params_.dim),
+      T{0.0});
+    raft::matrix::fill(
+      handle_,
+      raft::make_device_matrix_view(cuvs_distances_.data(), params_.num_queries, params_.k),
+      T{0.0});
+    raft::matrix::fill(
+      handle_,
+      raft::make_device_matrix_view(ref_distances_.data(), params_.num_queries, params_.k),
+      T{0.0});
+  }
+
+ protected:
+  void testBruteForce()
+  {
+    float metric_arg = 3.0;
+
+    // calculate the naive knn, by calculating the full pairwise distances and doing a k-select
+    rmm::device_uvector<T> temp_distances(num_db_vecs * num_queries, stream_);
+    rmm::device_uvector<char> workspace(0, stream_);
+
+    auto temp_dist = temp_distances.data();
+    rmm::device_uvector<T> temp_row_major_dist(num_db_vecs * num_queries, stream_);
+
+    if (params_.row_major) {
+      distance::pairwise_distance(
+        handle_,
+        raft::make_device_matrix_view<const T, int64_t>(
+          search_queries.data(), params_.num_queries, params_.dim),
+        raft::make_device_matrix_view<const T, int64_t>(
+          database.data(), params_.num_db_vecs, params_.dim),
+        raft::make_device_matrix_view<T, int64_t>(temp_distances.data(), num_queries, num_db_vecs),
+        metric,
+        metric_arg);
+
+    } else {
+      distance::pairwise_distance(handle_,
+                                  raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+                                    search_queries.data(), params_.num_queries, params_.dim),
+                                  raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+                                    database.data(), params_.num_db_vecs, params_.dim),
+                                  raft::make_device_matrix_view<T, int64_t, raft::col_major>(
+                                    temp_distances.data(), num_queries, num_db_vecs),
+                                  metric,
+                                  metric_arg);
+
+      // the pairwisse_distance call assumes that the inputs and outputs are all either row-major
+      // or col-major - meaning we have to transpose the output back for col-major queries
+      // for comparison
+      raft::linalg::transpose(
+        handle_, temp_dist, temp_row_major_dist.data(), num_queries, num_db_vecs, stream_);
+      temp_dist = temp_row_major_dist.data();
+    }
+
+    cuvs::selection::select_k(
+      handle_,
+      raft::make_device_matrix_view<const T, int64_t>(temp_dist, num_queries, num_db_vecs),
+      std::nullopt,
+      raft::make_device_matrix_view(ref_distances_.data(), params_.num_queries, params_.k),
+      raft::make_device_matrix_view(ref_indices_.data(), params_.num_queries, params_.k),
+      cuvs::distance::is_min_close(metric),
+      true);
+
+    auto indices = raft::make_device_matrix_view<int64_t, int64_t>(
+      cuvs_indices_.data(), params_.num_queries, params_.k);
+    auto distances = raft::make_device_matrix_view<T, int64_t>(
+      cuvs_distances_.data(), params_.num_queries, params_.k);
+
+    if (params_.row_major) {
+      auto idx =
+        cuvs::neighbors::brute_force::build(handle_,
+                                            raft::make_device_matrix_view<const T, int64_t>(
+                                              database.data(), params_.num_db_vecs, params_.dim),
+                                            metric,
+                                            metric_arg);
+
+      cuvs::neighbors::brute_force::search(
+        handle_,
+        idx,
+        raft::make_device_matrix_view<const T, int64_t>(
+          search_queries.data(), params_.num_queries, params_.dim),
+        indices,
+        distances,
+        std::nullopt);
+    } else {
+      auto idx = cuvs::neighbors::brute_force::build(
+        handle_,
+        raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+          database.data(), params_.num_db_vecs, params_.dim),
+        metric,
+        metric_arg);
+
+      cuvs::neighbors::brute_force::search(
+        handle_,
+        idx,
+        raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+          search_queries.data(), params_.num_queries, params_.dim),
+        indices,
+        distances,
+        std::nullopt);
+    }
+
+    ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(ref_indices_.data(),
+                                                    cuvs_indices_.data(),
+                                                    ref_distances_.data(),
+                                                    cuvs_distances_.data(),
+                                                    num_queries,
+                                                    k_,
+                                                    float(0.001),
+                                                    stream_,
+                                                    true));
+  }
+
+  void SetUp() override
+  {
+    num_queries = params_.num_queries;
+    num_db_vecs = params_.num_db_vecs;
+    dim         = params_.dim;
+    k_          = params_.k;
+    metric      = params_.metric;
+
+    unsigned long long int seed = 1234ULL;
+    raft::random::RngState r(seed);
+
+    // JensenShannon distance requires positive values
+    T min_val = metric == cuvs::distance::DistanceType::JensenShannon ? T(0.0) : T(-1.0);
+    uniform(handle_, r, database.data(), num_db_vecs * dim, min_val, T(1.0));
+    uniform(handle_, r, search_queries.data(), num_queries * dim, min_val, T(1.0));
+  }
+
+ private:
+  raft::resources handle_;
+  cudaStream_t stream_ = 0;
+  RandomKNNInputs params_;
+  int num_queries;
+  int num_db_vecs;
+  int dim;
+  rmm::device_uvector<T> database;
+  rmm::device_uvector<T> search_queries;
+  rmm::device_uvector<int64_t> cuvs_indices_;
+  rmm::device_uvector<T> cuvs_distances_;
+  rmm::device_uvector<int64_t> ref_indices_;
+  rmm::device_uvector<T> ref_distances_;
+  int k_;
+  cuvs::distance::DistanceType metric;
+};
+
+const std::vector<RandomKNNInputs> random_inputs = {
+  // test each distance metric on a small-ish input, with row-major inputs
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Expanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L1, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Linf, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CorrelationExpanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CosineExpanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::LpUnexpanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::JensenShannon, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Canberra, true},
+  // test each distance metric with col-major inputs
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Expanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L1, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Linf, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CorrelationExpanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CosineExpanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::LpUnexpanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::JensenShannon, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Canberra, false},
+  // larger tests on different sized data / k values
+  {10000, 40000, 32, 30, cuvs::distance::DistanceType::L2Expanded, false},
+  {345, 1023, 16, 128, cuvs::distance::DistanceType::CosineExpanded, true},
+  {789, 20516, 64, 256, cuvs::distance::DistanceType::L2SqrtExpanded, false},
+  {1000, 500000, 128, 128, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 500000, 128, 128, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 5000, 128, 128, cuvs::distance::DistanceType::LpUnexpanded, true},
+  {1000, 5000, 128, 128, cuvs::distance::DistanceType::L2SqrtExpanded, false},
+  {1000, 5000, 128, 128, cuvs::distance::DistanceType::InnerProduct, false}};
+
+typedef RandomBruteForceKNNTest<float> RandomBruteForceKNNTestF;
+TEST_P(RandomBruteForceKNNTestF, BruteForce) { this->testBruteForce(); }
+
+INSTANTIATE_TEST_CASE_P(RandomBruteForceKNNTest,
+                        RandomBruteForceKNNTestF,
+                        ::testing::ValuesIn(random_inputs));
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/neighbors/knn_utils.cuh b/cpp/test/neighbors/knn_utils.cuh
index d95174ef65..75cc909169 100644
--- a/cpp/test/neighbors/knn_utils.cuh
+++ b/cpp/test/neighbors/knn_utils.cuh
@@ -17,6 +17,7 @@
 #pragma once
 
 #include "../test_utils.cuh"
+#include "./ann_utils.cuh"
 
 #include <raft/util/cudart_utils.hpp>
 
@@ -25,20 +26,6 @@
 #include <memory>
 
 namespace cuvs::neighbors {
-template <typename IdxT, typename DistT, typename compareDist>
-struct idx_dist_pair {
-  IdxT idx;
-  DistT dist;
-  compareDist eq_compare;
-  bool operator==(const idx_dist_pair<IdxT, DistT, compareDist>& a) const
-  {
-    if (idx == a.idx) return true;
-    if (eq_compare(dist, a.dist)) return true;
-    return false;
-  }
-  idx_dist_pair(IdxT x, DistT y, compareDist op) : idx(x), dist(y), eq_compare(op) {}
-};
-
 template <typename T, typename DistT>
 testing::AssertionResult devArrMatchKnnPair(const T* expected_idx,
                                             const T* actual_idx,

From 5cd039d0cce9e967f2bd1152dc8eef56971edc92 Mon Sep 17 00:00:00 2001
From: jakirkham <jakirkham@gmail.com>
Date: Wed, 17 Jul 2024 10:23:33 -0700
Subject: [PATCH 099/623] Rename `.devcontainer`s for CUDA 12.5 (#224)

Follow up to PR: https://github.com/rapidsai/cuvs/pull/219
Partially addresses issue: https://github.com/rapidsai/build-planning/issues/73

Renames the `.devcontainer`s for CUDA 12.5

cc @KyleFromNVIDIA @jameslamb @trxcllnt (for awareness)

Authors:
  - https://github.com/jakirkham

Approvers:
  - Paul Taylor (https://github.com/trxcllnt)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/224
---
 .../{cuda12.2-conda => cuda12.5-conda}/devcontainer.json          | 0
 .devcontainer/{cuda12.2-pip => cuda12.5-pip}/devcontainer.json    | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename .devcontainer/{cuda12.2-conda => cuda12.5-conda}/devcontainer.json (100%)
 rename .devcontainer/{cuda12.2-pip => cuda12.5-pip}/devcontainer.json (100%)

diff --git a/.devcontainer/cuda12.2-conda/devcontainer.json b/.devcontainer/cuda12.5-conda/devcontainer.json
similarity index 100%
rename from .devcontainer/cuda12.2-conda/devcontainer.json
rename to .devcontainer/cuda12.5-conda/devcontainer.json
diff --git a/.devcontainer/cuda12.2-pip/devcontainer.json b/.devcontainer/cuda12.5-pip/devcontainer.json
similarity index 100%
rename from .devcontainer/cuda12.2-pip/devcontainer.json
rename to .devcontainer/cuda12.5-pip/devcontainer.json

From 63b3ded2bb6056fbc54b46a8f9deb2edcf06073c Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 17 Jul 2024 23:19:51 +0200
Subject: [PATCH 100/623] CAGRA bench: use device-side refinement when the data
 is on device (#228)

Fix CAGRA build benchmark always assuming the dataset is on host.
Before the fix, this would result in the refinement step being performed on host even if the data is on device; this would lead to a segfault.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/228
---
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index 9ac25f53f2..53db717a61 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -157,14 +157,13 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
 {
-  auto dataset_view =
-    raft::make_host_matrix_view<const T, int64_t>(dataset, IdxT(nrow), dimension_);
+  auto dataset_extents = raft::make_extents<IdxT>(nrow, dimension_);
 
   auto& params = index_params_.cagra_params;
 
   if (index_params_.algo == CagraBuildAlgo::kIvfPq) {
-    auto pq_params = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(
-      dataset_view.extents(), params.metric);
+    auto pq_params =
+      cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(dataset_extents, params.metric);
     if (index_params_.ivf_pq_build_params) {
       pq_params.build_params = *index_params_.ivf_pq_build_params;
     }
@@ -181,8 +180,15 @@ void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
     if (index_params_.nn_descent_params) { nn_params = *index_params_.nn_descent_params; }
     params.graph_build_params = nn_params;
   }
-  index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(
-    std::move(cuvs::neighbors::cagra::build(handle_, params, dataset_view)));
+  auto dataset_view_host =
+    raft::make_mdspan<const T, IdxT, raft::row_major, true, false>(dataset, dataset_extents);
+  auto dataset_view_device =
+    raft::make_mdspan<const T, IdxT, raft::row_major, false, true>(dataset, dataset_extents);
+  bool dataset_is_on_host = raft::get_device_for_address(dataset) == -1;
+
+  index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(std::move(
+    dataset_is_on_host ? cuvs::neighbors::cagra::build(handle_, params, dataset_view_host)
+                       : cuvs::neighbors::cagra::build(handle_, params, dataset_view_device)));
 }
 
 inline auto allocator_to_string(AllocatorType mem_type) -> std::string

From 43ed8ccd9bf2a7f6297d396db36f1a0795a66ba7 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 17 Jul 2024 14:25:16 -0700
Subject: [PATCH 101/623] Mark the rust brute force unittest as flaky (#226)

The rust `brute_force::tests::test_l2` test can sometimes randomly fail (https://github.com/rapidsai/cuvs/issues/127).

We should figure out whats causing the test failure, but in the meantime - this PR marks the test as flaky using the `mark_flaky_tests` crate, which will retry the test 3 times and pass it if any of those runs succeed.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/226
---
 rust/cuvs/Cargo.toml          | 1 +
 rust/cuvs/src/brute_force.rs  | 3 ++-
 rust/cuvs/src/distance/mod.rs | 3 ---
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index d69e86f9bc..8ae4374c1c 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -14,3 +14,4 @@ ndarray = "0.15"
 
 [dev-dependencies]
 ndarray-rand = "0.14"
+mark-flaky-tests = "1"
diff --git a/rust/cuvs/src/brute_force.rs b/rust/cuvs/src/brute_force.rs
index 975a4ddc38..c153359b8a 100644
--- a/rust/cuvs/src/brute_force.rs
+++ b/rust/cuvs/src/brute_force.rs
@@ -106,6 +106,7 @@ mod tests {
     use ndarray::s;
     use ndarray_rand::rand_distr::Uniform;
     use ndarray_rand::RandomExt;
+    use mark_flaky_tests::flaky;
 
     fn test_bfknn(metric: DistanceType) {
         let res = Resources::new().unwrap();
@@ -174,7 +175,7 @@ mod tests {
     }
 */
 
-    #[test]
+    #[flaky]
     fn test_l2() {
         test_bfknn(DistanceType::L2Expanded);
     }
diff --git a/rust/cuvs/src/distance/mod.rs b/rust/cuvs/src/distance/mod.rs
index 0377f3e323..566bf0129e 100644
--- a/rust/cuvs/src/distance/mod.rs
+++ b/rust/cuvs/src/distance/mod.rs
@@ -15,8 +15,6 @@
  */
 
 
-use std::io::{stderr, Write};
-
 use crate::distance_type::DistanceType;
 use crate::dlpack::ManagedTensor;
 use crate::error::{check_cuvs, Result};
@@ -55,7 +53,6 @@ pub fn pairwise_distance(
 #[cfg(test)]
 mod tests {
     use super::*;
-    use ndarray::s;
     use ndarray_rand::rand_distr::Uniform;
     use ndarray_rand::RandomExt;
 

From f4d7de007120cf100554809b1388d0131ef38eff Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Fri, 19 Jul 2024 13:23:49 -0400
Subject: [PATCH 102/623] Use workflow branch 24.08 again (#234)

After updating everything to CUDA 12.5.1, use `shared-workflows@branch-24.08` again.

Contributes to https://github.com/rapidsai/build-planning/issues/73

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/234
---
 .github/workflows/build.yaml | 14 +++++++-------
 .github/workflows/pr.yaml    | 24 ++++++++++++------------
 .github/workflows/test.yaml  |  8 ++++----
 3 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 1ef4e0c35d..b71b4ff045 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index a82b72c84c..a54738368f 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.08
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.08
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,20 +86,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.08
     with:
       arch: '["amd64"]'
       cuda: '["12.5"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 5f3067ea7b..55262b9433 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.5.1
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}

From 1e0bdc218e89a4282fa8d5106cd075d89847b76a Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Fri, 19 Jul 2024 15:07:00 -0400
Subject: [PATCH 103/623] DOC v24.10 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda11.8-pip/devcontainer.json  | 10 ++++----
 .../cuda12.5-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda12.5-pip/devcontainer.json  | 10 ++++----
 .github/workflows/build.yaml                  | 14 +++++------
 .github/workflows/pr.yaml                     | 24 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++----
 README.md                                     |  2 +-
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-118_arch-x86_64.yaml             |  4 ++--
 .../all_cuda-125_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-125_arch-x86_64.yaml             |  4 ++--
 dependencies.yaml                             | 12 +++++-----
 docs/source/developer_guide.md                |  6 ++---
 examples/cmake/thirdparty/fetch_rapids.cmake  |  2 +-
 python/cuvs/pyproject.toml                    |  6 ++---
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 19 files changed, 64 insertions(+), 64 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index 9cb608e522..13103e8f71 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda11.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 733f23f789..146753ae07 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,27 +5,27 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda11.8-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda11.8-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda11.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.8": {
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.10": {
       "version": "1.15.0"
     },
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.8": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.10": {
       "version": "11.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.5-conda/devcontainer.json b/.devcontainer/cuda12.5-conda/devcontainer.json
index db825a269d..d6902d3f91 100644
--- a/.devcontainer/cuda12.5-conda/devcontainer.json
+++ b/.devcontainer/cuda12.5-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.08-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.10-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.5-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda12.5-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.5-pip/devcontainer.json b/.devcontainer/cuda12.5-pip/devcontainer.json
index fe6ec3dd26..49e4943ebb 100644
--- a/.devcontainer/cuda12.5-pip/devcontainer.json
+++ b/.devcontainer/cuda12.5-pip/devcontainer.json
@@ -5,27 +5,27 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.08-cpp-cuda12.5-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda12.5-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.08-cuda12.5-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda12.5-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.8": {
+    "ghcr.io/rapidsai/devcontainers/features/ucx:24.10": {
       "version": "1.15.0"
     },
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.8": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.10": {
       "version": "12.5",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.8": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index b71b4ff045..db20bdbc12 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index a54738368f..8ea2fa503c 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.10
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.10
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,20 +86,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.10
     with:
       arch: '["amd64"]'
       cuda: '["12.5"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 55262b9433..c0d07297b8 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/README.md b/README.md
index b84b8592d5..23856060de 100755
--- a/README.md
+++ b/README.md
@@ -50,7 +50,7 @@ mamba install -c conda-forge -c nvidia -c rapidsai cuvs
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.08
+mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.10
 ```
 
 Please see the [Build and Install Guide](docs/source/build.md) for more information on installing cuVS and building from source.
diff --git a/VERSION b/VERSION
index ec8489fda9..7c7ba04436 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.08.00
+24.10.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 47c69217fe..86b0b80ef3 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -44,12 +44,12 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*,>=0.0.0a0
+- pylibraft==24.10.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*,>=0.0.0a0
+- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index ced6ae83d3..2aeff85562 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -44,12 +44,12 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*,>=0.0.0a0
+- pylibraft==24.10.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*,>=0.0.0a0
+- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index cfb68dcf1d..461de0d30c 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -40,12 +40,12 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*,>=0.0.0a0
+- pylibraft==24.10.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*,>=0.0.0a0
+- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 3be64448d6..e1cadb7fef 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -40,12 +40,12 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.8.*,>=0.0.0a0
+- pylibraft==24.10.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.8.*,>=0.0.0a0
+- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.7.0
 - scikit-learn
diff --git a/dependencies.yaml b/dependencies.yaml
index 60ce82aa74..92ceacbc2e 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -157,8 +157,8 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
-          - &rmm_conda rmm==24.8.*,>=0.0.0a0
-          - &pylibraft_conda pylibraft==24.8.*,>=0.0.0a0
+          - &rmm_conda rmm==24.10.*,>=0.0.0a0
+          - &pylibraft_conda pylibraft==24.10.*,>=0.0.0a0
           - dlpack>=0.8,<1.0
       - output_types: requirements
         packages:
@@ -184,12 +184,12 @@ dependencies:
         matrices:
           - matrix: {cuda: "12.*"}
             packages:
-              - &rmm_cu12 rmm-cu12==24.8.*,>=0.0.0a0
-              - &pylibraft_cu12 pylibraft-cu12==24.8.*,>=0.0.0a0
+              - &rmm_cu12 rmm-cu12==24.10.*,>=0.0.0a0
+              - &pylibraft_cu12 pylibraft-cu12==24.10.*,>=0.0.0a0
           - matrix: {cuda: "11.*"}
             packages:
-              - &rmm_cu11 rmm-cu11==24.8.*,>=0.0.0a0
-              - &pylibraft_cu11 pylibraft-cu11==24.8.*,>=0.0.0a0
+              - &rmm_cu11 rmm-cu11==24.10.*,>=0.0.0a0
+              - &pylibraft_cu11 pylibraft-cu11==24.10.*,>=0.0.0a0
           - {matrix: null, packages: [*rmm_conda, *pylibraft_conda] }
   checks:
     common:
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 36588f3450..516819b1c1 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.08/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.10/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.08/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.10/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
@@ -230,7 +230,7 @@ Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY`
 ## Logging
 
 ### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.08/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
+Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.10/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
 
 ### Usage
 ```cpp
diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
index 0f1d5ff020..f64a924cf5 100644
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and RAFT versions
-set(RAPIDS_VERSION "24.08")
+set(RAPIDS_VERSION "24.10")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 65fb51196f..88158ea81e 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -33,7 +33,7 @@ requires-python = ">=3.9"
 dependencies = [
     "cuda-python",
     "numpy>=1.23,<2.0a0",
-    "pylibraft==24.8.*,>=0.0.0a0",
+    "pylibraft==24.10.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -122,8 +122,8 @@ requires = [
     "cuda-python",
     "cython>=3.0.0",
     "ninja",
-    "pylibraft==24.8.*,>=0.0.0a0",
-    "rmm==24.8.*,>=0.0.0a0",
+    "pylibraft==24.10.*,>=0.0.0a0",
+    "rmm==24.10.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
 dependencies-file = "../../dependencies.yaml"
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index d35ab66ad0..52125bef3b 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "24.8.0"
+version = "24.10.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index 8ae4374c1c..7e5b181434 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "24.8.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "24.10.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From 0786591f23ab2af26be65c3cd5399071edb2eb50 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 19 Jul 2024 14:16:54 -0700
Subject: [PATCH 104/623] Fix pinning to a different RAFT tag (#235)

Attempting to pin the version of a raft to a custom fork wasn't working, and it was still using the version installed by conda. Fix by mirroing the `CUML_RAFT_CLONE_ON_PIN` logic found in the cuml cmake files.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/235
---
 cpp/CMakeLists.txt                  |  9 +++------
 cpp/cmake/thirdparty/get_raft.cmake | 10 ++++++++--
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 0fe44f5117..ab883ef77e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -68,6 +68,7 @@ option(DETECT_CONDA_ENV "Enable detection of conda environment for dependencies"
 option(DISABLE_DEPRECATION_WARNINGS "Disable deprecaction warnings " ON)
 option(DISABLE_OPENMP "Disable OpenMP" OFF)
 option(CUVS_NVTX "Enable nvtx markers" OFF)
+option(CUVS_RAFT_CLONE_ON_PIN "Explicitly clone RAFT branch when pinned to non-feature branch" ON)
 
 if((BUILD_TESTS OR BUILD_C_LIBRARY) AND NOT BUILD_CPU_ONLY)
 
@@ -101,12 +102,8 @@ message(VERBOSE "cuVS: Disable OpenMP: ${DISABLE_OPENMP}")
 message(VERBOSE "cuVS: Enable kernel resource usage info: ${CUDA_ENABLE_KERNELINFO}")
 message(VERBOSE "cuVS: Enable lineinfo in nvcc: ${CUDA_ENABLE_LINEINFO}")
 message(VERBOSE "cuVS: Enable nvtx markers: ${CUVS_NVTX}")
-message(VERBOSE
-        "cuVS: Statically link the CUDA runtime: ${CUDA_STATIC_RUNTIME}"
-)
-message(VERBOSE
-        "cuVS: Statically link the CUDA math libraries: ${CUDA_STATIC_MATH_LIBRARIES}"
-)
+message(VERBOSE "cuVS: Statically link the CUDA runtime: ${CUDA_STATIC_RUNTIME}")
+message(VERBOSE "cuVS: Statically link the CUDA math libraries: ${CUDA_STATIC_MATH_LIBRARIES}")
 message(VERBOSE "cuVS: Build and statically link RAFT libraries: ${CUVS_USE_RAFT_STATIC}")
 
 # Set RMM logging level
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 44825f91cb..7640fbfa6e 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -17,11 +17,11 @@ set(RAFT_FORK "rapidsai")
 set(RAFT_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
 
 function(find_and_configure_raft)
-    set(oneValueArgs VERSION FORK PINNED_TAG USE_RAFT_STATIC ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES)
+    set(oneValueArgs VERSION FORK PINNED_TAG USE_RAFT_STATIC ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES CLONE_ON_PIN)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
-    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "branch-${CUML_BRANCH_VERSION_raft}")
+    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "branch-${RAFT_VERSION}")
         message(STATUS "cuVS: RAFT pinned tag found: ${PKG_PINNED_TAG}. Cloning raft locally.")
         set(CPM_DOWNLOAD_raft ON)
     elseif(PKG_USE_RAFT_STATIC AND (NOT CPM_raft_SOURCE))
@@ -56,6 +56,7 @@ function(find_and_configure_raft)
             )
 endfunction()
 
+
 # Change pinned tag here to test a commit in CI
 # To use a different RAFT locally, set the CMake variable
 # CPM_raft_SOURCE=/path/to/local/raft
@@ -65,4 +66,9 @@ find_and_configure_raft(VERSION  ${RAFT_VERSION}.00
         ENABLE_MNMG_DEPENDENCIES OFF
         ENABLE_NVTX              OFF
         USE_RAFT_STATIC ${CUVS_USE_RAFT_STATIC}
+        # When PINNED_TAG above doesn't match the default rapids branch,
+        # force local raft clone in build directory
+        # even if it's already installed.
+        CLONE_ON_PIN     ${CUVS_RAFT_CLONE_ON_PIN}
+
 )

From 63285f77634607080274b4613a312d48d79ea747 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Mon, 22 Jul 2024 14:47:33 -0400
Subject: [PATCH 105/623] Moving over C++ API of CAGRA+hnswlib from RAFT (#229)

Authors:
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/229
---
 cpp/CMakeLists.txt                            |  11 +
 cpp/include/cuvs/neighbors/hnsw.hpp           | 386 ++++++++++++++++++
 .../detail/cagra/cagra_serialize.cuh          |  26 +-
 cpp/src/neighbors/detail/hnsw.hpp             | 183 +++++++++
 cpp/src/neighbors/hnsw.cpp                    |  70 ++++
 cpp/test/CMakeLists.txt                       |  16 +-
 cpp/test/neighbors/hnsw.cu                    | 187 +++++++++
 7 files changed, 856 insertions(+), 23 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/hnsw.hpp
 create mode 100644 cpp/src/neighbors/detail/hnsw.hpp
 create mode 100644 cpp/src/neighbors/hnsw.cpp
 create mode 100644 cpp/test/neighbors/hnsw.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index ab883ef77e..5060f4591c 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -56,6 +56,7 @@ option(BUILD_TESTS "Build cuvs unit-tests" ON)
 option(BUILD_C_LIBRARY "Build raft C API library" OFF)
 option(BUILD_C_TESTS "Build raft C API tests" OFF)
 option(BUILD_ANN_BENCH "Build cuVS ann benchmarks" OFF)
+option(BUILD_CAGRA_HNSWLIB "Build CAGRA+hnswlib interface" ON)
 option(CUDA_ENABLE_KERNELINFO "Enable kernel resource usage info" OFF)
 option(CUDA_ENABLE_LINEINFO
        "Enable the -lineinfo option for nvcc (useful for cuda-memcheck / profiler)" OFF
@@ -192,6 +193,10 @@ if(BUILD_ANN_BENCH)
   rapids_cpm_gbench(BUILD_STATIC)
 endif()
 
+if(BUILD_CAGRA_HNSWLIB)
+  include(cmake/thirdparty/get_hnswlib.cmake)
+endif()
+
 # ##################################################################################################
 # * cuvs ---------------------------------------------------------------------
 
@@ -399,6 +404,7 @@ add_library(
   src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
   src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
   src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
+  $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
   src/neighbors/ivf_flat_index.cpp
   src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
   src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
@@ -492,6 +498,11 @@ if(NOT BUILD_CPU_ONLY)
   )
 endif()
 
+if(BUILD_CAGRA_HNSWLIB)
+  target_link_libraries(cuvs PRIVATE hnswlib::hnswlib)
+  target_compile_definitions(cuvs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
+endif()
+
 # Endian detection
 include(TestBigEndian)
 test_big_endian(BIG_ENDIAN)
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
new file mode 100644
index 0000000000..86f3215649
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -0,0 +1,386 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifdef CUVS_BUILD_CAGRA_HNSWLIB
+
+#pragma once
+
+#include "common.hpp"
+
+#include <cuvs/distance/distance.hpp>
+
+#include "cagra.hpp"
+#include <raft/core/host_mdspan.hpp>
+
+#include <sys/types.h>
+
+#include <cstdint>
+#include <memory>
+#include <type_traits>
+
+namespace cuvs::neighbors::hnsw {
+
+/**
+ * @defgroup hnsw Build CAGRA index and search with hnswlib
+ * @{
+ */
+
+struct search_params : cuvs::neighbors::search_params {
+  int ef;               // size of the candidate list
+  int num_threads = 0;  // number of host threads to use for concurrent searches. Value of 0
+                        // automatically maximizes parallelism
+};
+
+template <typename T>
+struct index : cuvs::neighbors::index {
+ public:
+  /**
+   * @brief load a base-layer-only hnswlib index originally saved from a built CAGRA index.
+   *  This is a virtual class and it cannot be used directly. To create an index, use the factory
+   *  function `cuvs::neighbors::hnsw::from_cagra` from the header
+   *  `cuvs/neighbors/hnsw.hpp`
+   *
+   * @param[in] dim dimensions of the training dataset
+   * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+   */
+  index(int dim, cuvs::distance::DistanceType metric) : dim_{dim}, metric_{metric} {}
+
+  /**
+  @brief Get underlying index
+  */
+  virtual auto get_index() const -> void const* = 0;
+
+  auto dim() const -> int const { return dim_; }
+
+  auto metric() const -> cuvs::distance::DistanceType { return metric_; }
+
+  /**
+  @brief Set ef for search
+  */
+  virtual void set_ef(int ef) const;
+
+ private:
+  int dim_;
+  cuvs::distance::DistanceType metric_;
+};
+
+/**
+ * @brief Construct an hnswlib base-layer-only index from a CAGRA index
+ * NOTE: 1. This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
+ * before reading it as an hnswlib index, then deleting the temporary file.
+ *       2. This function is only offered as a compiled symbol in `libraft.so`
+ *
+ * @param[in] res raft resources
+ * @param[in] cagra_index cagra index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace raft::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<float, uint32_t>(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as base-layer-only hnswlib index
+ *   auto hnsw_index = hnsw::from_cagra(res, index);
+ * @endcode
+ */
+std::unique_ptr<index<float>> from_cagra(
+  raft::resources const& res, const cuvs::neighbors::cagra::index<float, uint32_t>& cagra_index);
+
+/**
+ * @brief Construct an hnswlib base-layer-only index from a CAGRA index
+ * NOTE: 1. This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
+ * before reading it as an hnswlib index, then deleting the temporary file.
+ *       2. This function is only offered as a compiled symbol in `libraft.so`
+ *
+ * @param[in] res raft resources
+ * @param[in] cagra_index cagra index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace raft::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<uint8_t, uint32_t>(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as base-layer-only hnswlib index
+ *   auto hnsw_index = hnsw::from_cagra(res, index);
+ * @endcode
+ */
+std::unique_ptr<index<uint8_t>> from_cagra(
+  raft::resources const& res, const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& cagra_index);
+
+/**
+ * @brief Construct an hnswlib base-layer-only index from a CAGRA index
+ * NOTE: 1. This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
+ * before reading it as an hnswlib index, then deleting the temporary file.
+ *       2. This function is only offered as a compiled symbol in `libraft.so`
+ *
+ * @param[in] res raft resources
+ * @param[in] cagra_index cagra index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace raft::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<int8_t, uint32_t>(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as base-layer-only hnswlib index
+ *   auto hnsw_index = hnsw::from_cagra(res, index);
+ * @endcode
+ */
+std::unique_ptr<index<int8_t>> from_cagra(
+  raft::resources const& res, const cuvs::neighbors::cagra::index<int8_t, uint32_t>& cagra_index);
+
+/**
+ * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] idx cagra index
+ * @param[in] queries a host matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a host matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a host matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<float, uint32_t>(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as a base-layer HNSW index using the filesystem
+ *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *
+ *   // Search K nearest neighbors as an hnswlib index
+ *   // using host threads for concurrency
+ *   hnsw::search_params search_params;
+ *   search_params.ef = 50 // ef >= K;
+ *   search_params.num_threads = 10;
+ *   auto neighbors = raft::make_host_matrix<uint64_t>(res, n_queries, k);
+ *   auto distances = raft::make_host_matrix<float>(res, n_queries, k);
+ *   hnsw::search(res, search_params, *index.get(), queries, neighbors, distances);
+ * @endcode
+ */
+void search(raft::resources const& res,
+            const search_params& params,
+            const index<float>& idx,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] idx cagra index
+ * @param[in] queries a host matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a host matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a host matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<uint8_t, uint32_t>(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as a base-layer HNSW index using the filesystem
+ *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *
+ *   // Search K nearest neighbors as an hnswlib index
+ *   // using host threads for concurrency
+ *   hnsw::search_params search_params;
+ *   search_params.ef = 50 // ef >= K;
+ *   search_params.num_threads = 10;
+ *   auto neighbors = raft::make_host_matrix<uint64_t>(res, n_queries, k);
+ *   auto distances = raft::make_host_matrix<float>(res, n_queries, k);
+ *   hnsw::search(res, search_params, *index.get(), queries, neighbors, distances);
+ * @endcode
+ */
+void search(raft::resources const& res,
+            const search_params& params,
+            const index<uint8_t>& idx,
+            raft::host_matrix_view<const int, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] idx cagra index
+ * @param[in] queries a host matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a host matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a host matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<int8_t, uint32_t>(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as a base-layer HNSW index using the filesystem
+ *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *
+ *   // Search K nearest neighbors as an hnswlib index
+ *   // using host threads for concurrency
+ *   hnsw::search_params search_params;
+ *   search_params.ef = 50 // ef >= K;
+ *   search_params.num_threads = 10;
+ *   auto neighbors = raft::make_host_matrix<uint64_t>(res, n_queries, k);
+ *   auto distances = raft::make_host_matrix<float>(res, n_queries, k);
+ *   hnsw::search(res, search_params, *index.get(), queries, neighbors, distances);
+ * @endcode
+ */
+void search(raft::resources const& res,
+            const search_params& params,
+            const index<int8_t>& idx,
+            raft::host_matrix_view<const int, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief De-serialize a CAGRA index saved to a file as an hnsw index
+ *
+ * @param[in] res raft resources
+ * @param[in] filename path to the file containing the serialized CAGRA index
+ * @param[in] dim dimensions of the training dataset
+ * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+ * @param[out] index hnsw index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<float, uint32_t>(res, index_params, dataset);
+ *
+ *   // save a CAGRA index to a file
+ *   cagra::serialize(res, index, "index.bin");
+ *   // De-serialize a CAGRA index as a base-layer HNSW index using the filesystem
+ *   index<float>* hnsw_index = nullptr;
+ *   hnsw::deserialize(res, "index.bin", index->dim(), index->metric(), &hnsw_index);
+ *
+ *   // Delete index after use
+ *   delete hnsw_index;
+ * @endcode
+ */
+void deserialize(raft::resources const& res,
+                 const std::string& filename,
+                 int dim,
+                 cuvs::distance::DistanceType metric,
+                 index<float>** index);
+
+/**
+ * @brief De-serialize a CAGRA index saved to a file as an hnsw index
+ *
+ * @param[in] res raft resources
+ * @param[in] filename path to the file containing the serialized CAGRA index
+ * @param[in] dim dimensions of the training dataset
+ * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+ * @param[out] index hnsw index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<uint8_t, uint32_t>(res, index_params, dataset);
+ *
+ *   // save a CAGRA index to a file
+ *   cagra::serialize(res, index, "index.bin");
+ *   // De-serialize a CAGRA index as a base-layer HNSW index using the filesystem
+ *   index<uint8_t>* hnsw_index = nullptr;
+ *   hnsw::deserialize(res, "index.bin", index->dim(), index->metric(), &hnsw_index);
+ *
+ *   // Delete index after use
+ *   delete hnsw_index;
+ * @endcode
+ */
+void deserialize(raft::resources const& res,
+                 const std::string& filename,
+                 int dim,
+                 cuvs::distance::DistanceType metric,
+                 index<uint8_t>** index);
+
+/**
+ * @brief De-serialize a CAGRA index saved to a file as an hnsw index
+ *
+ * @param[in] res raft resources
+ * @param[in] filename path to the file containing the serialized CAGRA index
+ * @param[in] dim dimensions of the training dataset
+ * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+ * @param[out] index hnsw index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build<int8_t, uint32_t>(res, index_params, dataset);
+ *
+ *   // save a CAGRA index to a file
+ *   cagra::serialize(res, index, "index.bin");
+ *   // De-serialize a CAGRA index as a base-layer HNSW index using the filesystem
+ *   index<int8_t>* hnsw_index = nullptr;
+ *   hnsw::deserialize(res, "index.bin", index->dim(), index->metric(), &hnsw_index);
+ *
+ *   // Delete index after use
+ *   delete hnsw_index;
+ * @endcode
+ */
+void deserialize(raft::resources const& res,
+                 const std::string& filename,
+                 int dim,
+                 cuvs::distance::DistanceType metric,
+                 index<int8_t>** index);
+
+/**@}*/
+
+}  // namespace cuvs::neighbors::hnsw
+
+#else
+#error "This header is only available if cuVS CMake option `BUILD_CAGRA_HNSWLIB=ON"
+#endif
\ No newline at end of file
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index 41329975b3..24cc2a22f4 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -119,9 +119,9 @@ void serialize_to_hnswlib(raft::resources const& res,
   os.write(reinterpret_cast<char*>(&curr_element_count), sizeof(std::size_t));
   // Example:M: 16, dim = 128, data_t = float, index_t = uint32_t, list_size_type = uint32_t,
   // labeltype: size_t size_data_per_element_ = M * 2 * sizeof(index_t) + sizeof(list_size_type) +
-  // dim * sizeof(data_t) + sizeof(labeltype)
-  auto size_data_per_element = static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4 +
-                                                        index_.dim() * sizeof(T) + 8);
+  // dim * 4 + sizeof(labeltype)
+  auto size_data_per_element =
+    static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4 + index_.dim() * 4 + 8);
   os.write(reinterpret_cast<char*>(&size_data_per_element), sizeof(std::size_t));
   // label_offset
   std::size_t label_offset = size_data_per_element - 8;
@@ -184,17 +184,17 @@ void serialize_to_hnswlib(raft::resources const& res,
     }
 
     auto data_row = host_dataset.data_handle() + (index_.dim() * i);
-    // if constexpr (std::is_same_v<T, float>) {
-    for (std::size_t j = 0; j < index_.dim(); ++j) {
-      auto data_elem = host_dataset(i, j);
-      os.write(reinterpret_cast<char*>(&data_elem), sizeof(T));
+    if constexpr (std::is_same_v<T, float>) {
+      for (std::size_t j = 0; j < index_.dim(); ++j) {
+        auto data_elem = static_cast<float>(host_dataset(i, j));
+        os.write(reinterpret_cast<char*>(&data_elem), sizeof(float));
+      }
+    } else if constexpr (std::is_same_v<T, std::int8_t> or std::is_same_v<T, std::uint8_t>) {
+      for (std::size_t j = 0; j < index_.dim(); ++j) {
+        auto data_elem = static_cast<int>(host_dataset(i, j));
+        os.write(reinterpret_cast<char*>(&data_elem), sizeof(int));
+      }
     }
-    // } else if constexpr (std::is_same_v<T, std::int8_t> or std::is_same_v<T, std::uint8_t>) {
-    //   for (std::size_t j = 0; j < index_.dim(); ++j) {
-    //     auto data_elem = static_cast<int>(host_dataset(i, j));
-    //     os.write(reinterpret_cast<char*>(&data_elem), sizeof(int));
-    //   }
-    // }
 
     os.write(reinterpret_cast<char*>(&i), sizeof(std::size_t));
   }
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
new file mode 100644
index 0000000000..0d1ae4ec96
--- /dev/null
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/hnsw.hpp>
+#include <filesystem>
+#include <hnswlib/hnswalg.h>
+#include <hnswlib/hnswlib.h>
+#include <memory>
+#include <random>
+
+namespace cuvs::neighbors::hnsw::detail {
+
+template <typename T>
+struct hnsw_dist_t {
+  using type = void;
+};
+
+template <>
+struct hnsw_dist_t<float> {
+  using type = float;
+};
+
+template <>
+struct hnsw_dist_t<uint8_t> {
+  using type = int;
+};
+
+template <>
+struct hnsw_dist_t<int8_t> {
+  using type = int;
+};
+
+template <typename T>
+struct index_impl : index<T> {
+ public:
+  /**
+   * @brief load a base-layer-only hnswlib index originally saved from a built CAGRA index
+   *
+   * @param[in] filepath path to the index
+   * @param[in] dim dimensions of the training dataset
+   * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+   */
+  index_impl(const std::string& filepath, int dim, cuvs::distance::DistanceType metric)
+    : index<T>{dim, metric}
+  {
+    if constexpr (std::is_same_v<T, float>) {
+      if (metric == cuvs::distance::DistanceType::L2Expanded) {
+        space_ = std::make_unique<hnswlib::L2Space>(dim);
+      } else if (metric == cuvs::distance::DistanceType::InnerProduct) {
+        space_ = std::make_unique<hnswlib::InnerProductSpace>(dim);
+      }
+    } else if constexpr (std::is_same_v<T, std::int8_t> or std::is_same_v<T, std::uint8_t>) {
+      if (metric == cuvs::distance::DistanceType::L2Expanded) {
+        space_ = std::make_unique<hnswlib::L2SpaceI<T>>(dim);
+      }
+    }
+
+    RAFT_EXPECTS(space_ != nullptr, "Unsupported metric type was used");
+
+    appr_alg_ = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+      space_.get(), filepath);
+
+    appr_alg_->base_layer_only = true;
+  }
+
+  /**
+  @brief Get hnswlib index
+  */
+  auto get_index() const -> void const* override { return appr_alg_.get(); }
+
+  /**
+  @brief Set ef for search
+  */
+  void set_ef(int ef) const override { appr_alg_->ef_ = ef; }
+
+ private:
+  std::unique_ptr<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>> appr_alg_;
+  std::unique_ptr<hnswlib::SpaceInterface<typename hnsw_dist_t<T>::type>> space_;
+};
+
+template <typename T>
+std::unique_ptr<index<T>> from_cagra(raft::resources const& res,
+                                     const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index)
+{
+  std::random_device dev;
+  std::mt19937 rng(dev());
+  std::uniform_int_distribution<std::mt19937::result_type> dist(0);
+  auto uuid            = std::to_string(dist(rng));
+  std::string filepath = "/tmp/" + uuid + ".bin";
+  cuvs::neighbors::cagra::serialize_to_hnswlib(res, filepath, cagra_index);
+  index<T>* hnsw_index = nullptr;
+  cuvs::neighbors::hnsw::deserialize(
+    res, filepath, cagra_index.dim(), cagra_index.metric(), &hnsw_index);
+  std::filesystem::remove(filepath);
+  return std::unique_ptr<index<T>>(hnsw_index);
+}
+
+template <typename QueriesT>
+void get_search_knn_results(hnswlib::HierarchicalNSW<QueriesT> const* idx,
+                            const QueriesT* query,
+                            int k,
+                            uint64_t* indices,
+                            float* distances)
+{
+  auto result = idx->searchKnn(query, k);
+  assert(result.size() >= static_cast<size_t>(k));
+
+  for (int i = k - 1; i >= 0; --i) {
+    indices[i]   = result.top().second;
+    distances[i] = result.top().first;
+    result.pop();
+  }
+}
+
+template <typename T, typename QueriesT>
+void search(raft::resources const& res,
+            const search_params& params,
+            const index<T>& idx,
+            raft::host_matrix_view<const QueriesT, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances)
+{
+  RAFT_EXPECTS(
+    queries.extent(0) == neighbors.extent(0) && queries.extent(0) == distances.extent(0),
+    "Number of rows in output neighbors and distances matrices must equal the number of queries.");
+
+  RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1),
+               "Number of columns in output neighbors and distances matrices must equal k");
+  RAFT_EXPECTS(queries.extent(1) == idx.dim(),
+               "Number of query dimensions should equal number of dimensions in the index.");
+
+  idx.set_ef(params.ef);
+  auto const* hnswlib_index =
+    reinterpret_cast<hnswlib::HierarchicalNSW<QueriesT> const*>(idx.get_index());
+
+  // when num_threads == 0, automatically maximize parallelism
+  if (params.num_threads) {
+#pragma omp parallel for num_threads(params.num_threads)
+    for (int64_t i = 0; i < queries.extent(0); ++i) {
+      get_search_knn_results(hnswlib_index,
+                             queries.data_handle() + i * queries.extent(1),
+                             neighbors.extent(1),
+                             neighbors.data_handle() + i * neighbors.extent(1),
+                             distances.data_handle() + i * distances.extent(1));
+    }
+  } else {
+#pragma omp parallel for
+    for (int64_t i = 0; i < queries.extent(0); ++i) {
+      get_search_knn_results(hnswlib_index,
+                             queries.data_handle() + i * queries.extent(1),
+                             neighbors.extent(1),
+                             neighbors.data_handle() + i * neighbors.extent(1),
+                             distances.data_handle() + i * distances.extent(1));
+    }
+  }
+}
+
+template <typename T>
+void deserialize(raft::resources const& res,
+                 const std::string& filename,
+                 int dim,
+                 cuvs::distance::DistanceType metric,
+                 index<T>** idx)
+{
+  *idx = new detail::index_impl<T>(filename, dim, metric);
+}
+
+}  // namespace cuvs::neighbors::hnsw::detail
diff --git a/cpp/src/neighbors/hnsw.cpp b/cpp/src/neighbors/hnsw.cpp
new file mode 100644
index 0000000000..36cbb16c9d
--- /dev/null
+++ b/cpp/src/neighbors/hnsw.cpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/hnsw.hpp"
+#include <cstdint>
+#include <cuvs/neighbors/hnsw.hpp>
+#include <sys/types.h>
+
+namespace cuvs::neighbors::hnsw {
+
+#define CUVS_INST_HNSW_FROM_CAGRA(T)                                                           \
+  std::unique_ptr<index<T>> from_cagra(                                                        \
+    raft::resources const& res, const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index) \
+  {                                                                                            \
+    return detail::from_cagra<T>(res, cagra_index);                                            \
+  }
+
+CUVS_INST_HNSW_FROM_CAGRA(float);
+CUVS_INST_HNSW_FROM_CAGRA(uint8_t);
+CUVS_INST_HNSW_FROM_CAGRA(int8_t);
+
+#undef CUVS_INST_HNSW_FROM_CAGRA
+
+#define CUVS_INST_HNSW_SEARCH(T, QueriesT)                                              \
+  void search(raft::resources const& res,                                               \
+              const search_params& params,                                              \
+              const index<T>& idx,                                                      \
+              raft::host_matrix_view<const QueriesT, int64_t, raft::row_major> queries, \
+              raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,     \
+              raft::host_matrix_view<float, int64_t, raft::row_major> distances)        \
+  {                                                                                     \
+    detail::search<T, QueriesT>(res, params, idx, queries, neighbors, distances);       \
+  }
+
+CUVS_INST_HNSW_SEARCH(float, float);
+CUVS_INST_HNSW_SEARCH(uint8_t, int);
+CUVS_INST_HNSW_SEARCH(int8_t, int);
+
+#undef CUVS_INST_HNSW_SEARCH
+
+#define CUVS_INST_HNSW_DESERIALIZE(T)                        \
+  void deserialize(raft::resources const& res,               \
+                   const std::string& filename,              \
+                   int dim,                                  \
+                   cuvs::distance::DistanceType metric,      \
+                   index<T>** idx)                           \
+  {                                                          \
+    detail::deserialize<T>(res, filename, dim, metric, idx); \
+  }
+
+CUVS_INST_HNSW_DESERIALIZE(float);
+CUVS_INST_HNSW_DESERIALIZE(uint8_t);
+CUVS_INST_HNSW_DESERIALIZE(int8_t);
+
+#undef CUVS_INST_HNSW_DESERIALIZE
+
+}  // namespace cuvs::neighbors::hnsw
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 1fae2f70ba..7921fffd30 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -91,16 +91,8 @@ endfunction()
 
 if(BUILD_TESTS)
   ConfigureTest(
-    NAME
-    NEIGHBORS_TEST
-    PATH
-    test/neighbors/brute_force.cu
-    test/neighbors/brute_force_prefiltered.cu
-    test/neighbors/refine.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_TEST PATH test/neighbors/brute_force.cu
+    test/neighbors/brute_force_prefiltered.cu test/neighbors/refine.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
@@ -160,6 +152,10 @@ if(BUILD_TESTS)
     100
   )
 
+  if(BUILD_CAGRA_HNSWLIB)
+    ConfigureTest(NAME NEIGHBORS_HNSW_TEST PATH test/neighbors/hnsw.cu GPUS 1 PERCENT 100)
+  endif()
+
   ConfigureTest(
     NAME
     DISTANCE_TEST
diff --git a/cpp/test/neighbors/hnsw.cu b/cpp/test/neighbors/hnsw.cu
new file mode 100644
index 0000000000..9fb88be05b
--- /dev/null
+++ b/cpp/test/neighbors/hnsw.cu
@@ -0,0 +1,187 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+
+#include <cstdint>
+#include <cuvs/neighbors/hnsw.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/itertools.hpp>
+
+#include "naive_knn.cuh"
+
+#include <gtest/gtest.h>
+
+#include <cstddef>
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace cuvs::neighbors::hnsw {
+
+struct AnnHNSWInputs {
+  int n_rows;
+  int dim;
+  int graph_degree;
+  cuvs::distance::DistanceType metric;
+  int k;
+  int n_queries;
+  int ef;
+  double min_recall;
+};
+
+inline ::std::ostream& operator<<(::std::ostream& os, const AnnHNSWInputs& p)
+{
+  os << "dataset shape=" << p.n_rows << "x" << p.dim << ", graph_degree=" << p.graph_degree
+     << ", metric=" << static_cast<int>(p.metric) << ", ef=" << (p.ef) << std::endl;
+  return os;
+}
+
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnHNSWTest : public ::testing::TestWithParam<AnnHNSWInputs> {
+ public:
+  AnnHNSWTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnHNSWInputs>::GetParam()),
+      database(0, stream_),
+      queries(0, stream_)
+  {
+  }
+
+ protected:
+  void testHNSW()
+  {
+    std::vector<IdxT> indices_HNSW(ps.n_queries * ps.k);
+    std::vector<DataT> distances_HNSW(ps.n_queries * ps.k);
+    std::vector<IdxT> indices_naive(ps.n_queries * ps.k);
+    std::vector<DataT> distances_naive(ps.n_queries * ps.k);
+
+    std::vector<DataT> queries_h(ps.n_queries * ps.dim);
+    raft::update_host(queries_h.data(), queries.data(), ps.n_queries * ps.dim, stream_);
+
+    {
+      rmm::device_uvector<DistanceT> distances_naive_dev(ps.n_queries * ps.k, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(ps.n_queries * ps.k, stream_);
+      naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                        distances_naive_dev.data(),
+                                        indices_naive_dev.data(),
+                                        queries.data(),
+                                        database.data(),
+                                        ps.n_queries,
+                                        ps.n_rows,
+                                        ps.dim,
+                                        ps.k,
+                                        ps.metric);
+      raft::update_host(
+        indices_naive.data(), indices_naive_dev.data(), ps.n_queries * ps.k, stream_);
+      raft::update_host(
+        distances_naive.data(), distances_naive_dev.data(), ps.n_queries * ps.k, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    {
+      cuvs::neighbors::cagra::index_params index_params;
+      index_params.metric                    = ps.metric;
+      index_params.graph_degree              = ps.graph_degree;
+      index_params.intermediate_graph_degree = 2 * ps.graph_degree;
+
+      auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
+        (const DataT*)database.data(), ps.n_rows, ps.dim);
+
+      auto index = cuvs::neighbors::cagra::build(handle_, index_params, database_view);
+      raft::resource::sync_stream(handle_);
+
+      cuvs::neighbors::hnsw::search_params search_params;
+      search_params.ef = ps.ef;
+      auto hnsw_index  = cuvs::neighbors::hnsw::from_cagra(handle_, index);
+      auto queries_HNSW_view =
+        raft::make_host_matrix_view<DataT, int64_t>(queries_h.data(), ps.n_queries, ps.dim);
+      auto indices_HNSW_view =
+        raft::make_host_matrix_view<uint64_t, int64_t>(indices_HNSW.data(), ps.n_queries, ps.k);
+      auto distances_HNSW_view =
+        raft::make_host_matrix_view<float, int64_t>(distances_HNSW.data(), ps.n_queries, ps.k);
+      cuvs::neighbors::hnsw::search(handle_,
+                                    search_params,
+                                    *hnsw_index.get(),
+                                    queries_HNSW_view,
+                                    indices_HNSW_view,
+                                    distances_HNSW_view);
+    }
+
+    double min_recall = ps.min_recall;
+    EXPECT_TRUE(eval_neighbours(indices_naive,
+                                indices_HNSW,
+                                distances_naive,
+                                distances_HNSW,
+                                ps.n_queries,
+                                ps.k,
+                                0.006,
+                                min_recall));
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    queries.resize(((size_t)ps.n_queries) * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
+      raft::random::normal(
+        handle_, r, queries.data(), ps.n_queries * ps.dim, DataT(0.1), DataT(2.0));
+
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(20));
+      raft::random::uniformInt(
+        handle_, r, queries.data(), ps.n_queries * ps.dim, DataT(1), DataT(20));
+    }
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnHNSWInputs ps;
+  rmm::device_uvector<DataT> database;
+  rmm::device_uvector<DataT> queries;
+};
+
+const std::vector<AnnHNSWInputs> inputs = raft::util::itertools::product<AnnHNSWInputs>(
+  {1000, 2000},                                // n_rows
+  {5, 10, 25, 50, 100, 250, 500, 1000},        // dim
+  {32, 64},                                    // graph_degree
+  {cuvs::distance::DistanceType::L2Expanded},  // metric
+  {50},                                        // k
+  {500},                                       // n_queries
+  {200},                                       // ef
+  {0.98}                                       // min_recall
+);
+
+typedef AnnHNSWTest<float, float, uint64_t> AnnHNSW_F;
+TEST_P(AnnHNSW_F, AnnHNSW) { this->testHNSW(); }
+
+INSTANTIATE_TEST_CASE_P(AnnHNSWTest, AnnHNSW_F, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::hnsw

From 9e6d311138e1cada8627687b22040d595ba69963 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 24 Jul 2024 00:22:01 +0200
Subject: [PATCH 106/623] CAGRA: reduce argument count in select_and_run()
 kernel wrappers (#227)

A small change that reduces the number of arguments in one of the wrapper layers in the detail namespace of CAGRA. The goal is twofold:
  1) Simplify the overly long signature of `selet_and_run` (which has many instances)
  2) Give access to all search parameters for future upgrades of the search kernel

This is to simplify the integration (and review) of the persistent kernel (https://github.com/rapidsai/cuvs/pull/215).
No performance or functional changes expected.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/227
---
 .../detail/cagra/search_multi_cta.cuh         |  7 +---
 .../detail/cagra/search_multi_cta_inst.cuh    |  7 +---
 .../cagra/search_multi_cta_kernel-ext.cuh     | 40 ++++++-------------
 .../cagra/search_multi_cta_kernel-inl.cuh     | 19 ++++-----
 .../detail/cagra/search_single_cta.cuh        |  7 +---
 .../detail/cagra/search_single_cta_inst.cuh   |  7 +---
 .../cagra/search_single_cta_kernel-ext.cuh    | 27 +++----------
 .../cagra/search_single_cta_kernel-inl.cuh    | 21 ++++------
 8 files changed, 38 insertions(+), 97 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index a6a20ca49d..efbf9b56d8 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -230,6 +230,7 @@ struct search : public search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
       num_queries,
       dev_seed_ptr,
       num_executed_iterations,
+      *this,
       topk,
       thread_block_size,
       result_buffer_size,
@@ -237,13 +238,7 @@ struct search : public search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
       hash_bitlen,
       hashmap.data(),
       num_cta_per_query,
-      num_random_samplings,
-      rand_xor_mask,
       num_seeds,
-      itopk_size,
-      search_width,
-      min_iterations,
-      max_iterations,
       sample_filter,
       this->metric,
       stream);
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
index e28389f388..b1cfaf8702 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
@@ -32,6 +32,7 @@ namespace cuvs::neighbors::cagra::detail::multi_cta_search {
     const uint32_t num_queries,                                                                   \
     const typename DATASET_DESC_T::INDEX_T* dev_seed_ptr,                                         \
     uint32_t* const num_executed_iterations,                                                      \
+    const search_params& ps,                                                                      \
     uint32_t topk,                                                                                \
     uint32_t block_size,                                                                          \
     uint32_t result_buffer_size,                                                                  \
@@ -39,13 +40,7 @@ namespace cuvs::neighbors::cagra::detail::multi_cta_search {
     int64_t hash_bitlen,                                                                          \
     typename DATASET_DESC_T::INDEX_T* hashmap_ptr,                                                \
     uint32_t num_cta_per_query,                                                                   \
-    uint32_t num_random_samplings,                                                                \
-    uint64_t rand_xor_mask,                                                                       \
     uint32_t num_seeds,                                                                           \
-    size_t itopk_size,                                                                            \
-    size_t search_width,                                                                          \
-    size_t min_iterations,                                                                        \
-    size_t max_iterations,                                                                        \
     SAMPLE_FILTER_T sample_filter,                                                                \
     cuvs::distance::DistanceType metric,                                                          \
     cudaStream_t stream);
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
index 495ec6a4d8..b00d6617cd 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
@@ -27,33 +27,29 @@ namespace multi_cta_search {
 #ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 template <unsigned TEAM_SIZE,
-          unsigned MAX_DATASET_DIM,
-          class DATASET_DESCRIPTOR_T,
-          class SAMPLE_FILTER_T>
+          unsigned DATASET_BLOCK_DIM,
+          typename DATASET_DESCRIPTOR_T,
+          typename SAMPLE_FILTER_T>
 void select_and_run(
   DATASET_DESCRIPTOR_T dataset_desc,
   raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
     graph,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,
-  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
   const uint32_t num_queries,
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,
-  uint32_t* const num_executed_iterations,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
+  uint32_t* const num_executed_iterations,                     // [num_queries,]
+  const search_params& ps,
   uint32_t topk,
-  uint32_t block_size,
+  // multi_cta_search (params struct)
+  uint32_t block_size,  //
   uint32_t result_buffer_size,
   uint32_t smem_size,
   int64_t hash_bitlen,
   typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
   uint32_t num_cta_per_query,
-  uint32_t num_random_samplings,
-  uint64_t rand_xor_mask,
   uint32_t num_seeds,
-  size_t itopk_size,
-  size_t search_width,
-  size_t min_iterations,
-  size_t max_iterations,
   SAMPLE_FILTER_T sample_filter,
   cuvs::distance::DistanceType metric,
   cudaStream_t stream) RAFT_EXPLICIT;
@@ -75,6 +71,7 @@ void select_and_run(
     const uint32_t num_queries,                                                                 \
     const INDEX_T* dev_seed_ptr,                                                                \
     uint32_t* const num_executed_iterations,                                                    \
+    const search_params& ps,                                                                    \
     uint32_t topk,                                                                              \
     uint32_t block_size,                                                                        \
     uint32_t result_buffer_size,                                                                \
@@ -82,13 +79,7 @@ void select_and_run(
     int64_t hash_bitlen,                                                                        \
     INDEX_T* hashmap_ptr,                                                                       \
     uint32_t num_cta_per_query,                                                                 \
-    uint32_t num_random_samplings,                                                              \
-    uint64_t rand_xor_mask,                                                                     \
     uint32_t num_seeds,                                                                         \
-    size_t itopk_size,                                                                          \
-    size_t search_width,                                                                        \
-    size_t min_iterations,                                                                      \
-    size_t max_iterations,                                                                      \
     SAMPLE_FILTER_T sample_filter,                                                              \
     cuvs::distance::DistanceType metric,                                                        \
     cudaStream_t stream);
@@ -160,6 +151,7 @@ instantiate_kernel_selection(
     const uint32_t num_queries,                                                                 \
     const INDEX_T* dev_seed_ptr,                                                                \
     uint32_t* const num_executed_iterations,                                                    \
+    const search_params& ps,                                                                    \
     uint32_t topk,                                                                              \
     uint32_t block_size,                                                                        \
     uint32_t result_buffer_size,                                                                \
@@ -167,13 +159,7 @@ instantiate_kernel_selection(
     int64_t hash_bitlen,                                                                        \
     INDEX_T* hashmap_ptr,                                                                       \
     uint32_t num_cta_per_query,                                                                 \
-    uint32_t num_random_samplings,                                                              \
-    uint64_t rand_xor_mask,                                                                     \
     uint32_t num_seeds,                                                                         \
-    size_t itopk_size,                                                                          \
-    size_t search_width,                                                                        \
-    size_t min_iterations,                                                                      \
-    size_t max_iterations,                                                                      \
     SAMPLE_FILTER_T sample_filter,                                                              \
     cuvs::distance::DistanceType metric,                                                        \
     cudaStream_t stream);
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 90e699f48c..4d2030c6c9 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -458,6 +458,7 @@ void select_and_run(
   const uint32_t num_queries,
   const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
   uint32_t* const num_executed_iterations,                     // [num_queries,]
+  const search_params& ps,
   uint32_t topk,
   // multi_cta_search (params struct)
   uint32_t block_size,  //
@@ -466,13 +467,7 @@ void select_and_run(
   int64_t hash_bitlen,
   typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
   uint32_t num_cta_per_query,
-  uint32_t num_random_samplings,
-  uint64_t rand_xor_mask,
   uint32_t num_seeds,
-  size_t itopk_size,
-  size_t search_width,
-  size_t min_iterations,
-  size_t max_iterations,
   SAMPLE_FILTER_T sample_filter,
   cuvs::distance::DistanceType metric,
   cudaStream_t stream)
@@ -507,16 +502,16 @@ void select_and_run(
                                                        queries_ptr,
                                                        graph.data_handle(),
                                                        graph.extent(1),
-                                                       num_random_samplings,
-                                                       rand_xor_mask,
+                                                       ps.num_random_samplings,
+                                                       ps.rand_xor_mask,
                                                        dev_seed_ptr,
                                                        num_seeds,
                                                        hashmap_ptr,
                                                        hash_bitlen,
-                                                       itopk_size,
-                                                       search_width,
-                                                       min_iterations,
-                                                       max_iterations,
+                                                       ps.itopk_size,
+                                                       ps.search_width,
+                                                       ps.min_iterations,
+                                                       ps.max_iterations,
                                                        num_executed_iterations,
                                                        sample_filter,
                                                        metric);
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index b8e1726e78..0a101cbfef 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -233,6 +233,7 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
       num_queries,
       dev_seed_ptr,
       num_executed_iterations,
+      *this,
       topk,
       num_itopk_candidates,
       static_cast<uint32_t>(thread_block_size),
@@ -241,13 +242,7 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
       hashmap.data(),
       small_hash_bitlen,
       small_hash_reset_interval,
-      num_random_samplings,
-      rand_xor_mask,
       num_seeds,
-      itopk_size,
-      search_width,
-      min_iterations,
-      max_iterations,
       sample_filter,
       this->metric,
       stream);
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
index b3d75e9234..a4581d15ef 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
@@ -32,6 +32,7 @@ namespace cuvs::neighbors::cagra::detail::single_cta_search {
     const uint32_t num_queries,                                                                   \
     const typename DATASET_DESC_T::INDEX_T* dev_seed_ptr,                                         \
     uint32_t* const num_executed_iterations,                                                      \
+    const search_params& ps,                                                                      \
     uint32_t topk,                                                                                \
     uint32_t num_itopk_candidates,                                                                \
     uint32_t block_size,                                                                          \
@@ -40,13 +41,7 @@ namespace cuvs::neighbors::cagra::detail::single_cta_search {
     typename DATASET_DESC_T::INDEX_T* hashmap_ptr,                                                \
     size_t small_hash_bitlen,                                                                     \
     size_t small_hash_reset_interval,                                                             \
-    uint32_t num_random_samplings,                                                                \
-    uint64_t rand_xor_mask,                                                                       \
     uint32_t num_seeds,                                                                           \
-    size_t itopk_size,                                                                            \
-    size_t search_width,                                                                          \
-    size_t min_iterations,                                                                        \
-    size_t max_iterations,                                                                        \
     SAMPLE_FILTER_T sample_filter,                                                                \
     cuvs::distance::DistanceType metric,                                                          \
     cudaStream_t stream);
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
index dfcdec28fe..79f6e153cb 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
@@ -26,10 +26,10 @@ namespace single_cta_search {
 #ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 template <unsigned TEAM_SIZE,
-          unsigned MAX_DATASET_DIM,
+          unsigned DATASET_BLOCK_DIM,
           typename DATASET_DESCRIPTOR_T,
           typename SAMPLE_FILTER_T>
-void select_and_run(  // raft::resources const& res,
+void select_and_run(
   DATASET_DESCRIPTOR_T dataset_desc,
   raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
     graph,
@@ -39,21 +39,16 @@ void select_and_run(  // raft::resources const& res,
   const uint32_t num_queries,
   const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
   uint32_t* const num_executed_iterations,                     // [num_queries,]
+  const search_params& ps,
   uint32_t topk,
   uint32_t num_itopk_candidates,
-  uint32_t block_size,
+  uint32_t block_size,  //
   uint32_t smem_size,
   int64_t hash_bitlen,
   typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
   size_t small_hash_bitlen,
   size_t small_hash_reset_interval,
-  uint32_t num_random_samplings,
-  uint64_t rand_xor_mask,
   uint32_t num_seeds,
-  size_t itopk_size,
-  size_t search_width,
-  size_t min_iterations,
-  size_t max_iterations,
   SAMPLE_FILTER_T sample_filter,
   cuvs::distance::DistanceType metric,
   cudaStream_t stream) RAFT_EXPLICIT;
@@ -76,6 +71,7 @@ void select_and_run(  // raft::resources const& res,
     const uint32_t num_queries,                                                                 \
     const INDEX_T* dev_seed_ptr,                                                                \
     uint32_t* const num_executed_iterations,                                                    \
+    const search_params& ps,                                                                    \
     uint32_t topk,                                                                              \
     uint32_t num_itopk_candidates,                                                              \
     uint32_t block_size,                                                                        \
@@ -84,13 +80,7 @@ void select_and_run(  // raft::resources const& res,
     INDEX_T* hashmap_ptr,                                                                       \
     size_t small_hash_bitlen,                                                                   \
     size_t small_hash_reset_interval,                                                           \
-    uint32_t num_random_samplings,                                                              \
-    uint64_t rand_xor_mask,                                                                     \
     uint32_t num_seeds,                                                                         \
-    size_t itopk_size,                                                                          \
-    size_t search_width,                                                                        \
-    size_t min_iterations,                                                                      \
-    size_t max_iterations,                                                                      \
     SAMPLE_FILTER_T sample_filter,                                                              \
     cuvs::distance::DistanceType metric,                                                        \
     cudaStream_t stream);
@@ -162,6 +152,7 @@ instantiate_single_cta_select_and_run(
     const uint32_t num_queries,                                                                 \
     const INDEX_T* dev_seed_ptr,                                                                \
     uint32_t* const num_executed_iterations,                                                    \
+    const search_params& ps,                                                                    \
     uint32_t topk,                                                                              \
     uint32_t num_itopk_candidates,                                                              \
     uint32_t block_size,                                                                        \
@@ -170,13 +161,7 @@ instantiate_single_cta_select_and_run(
     INDEX_T* hashmap_ptr,                                                                       \
     size_t small_hash_bitlen,                                                                   \
     size_t small_hash_reset_interval,                                                           \
-    uint32_t num_random_samplings,                                                              \
-    uint64_t rand_xor_mask,                                                                     \
     uint32_t num_seeds,                                                                         \
-    size_t itopk_size,                                                                          \
-    size_t search_width,                                                                        \
-    size_t min_iterations,                                                                      \
-    size_t max_iterations,                                                                      \
     SAMPLE_FILTER_T sample_filter,                                                              \
     cuvs::distance::DistanceType metric,                                                        \
     cudaStream_t stream);
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index e58167432b..a101cdc1f6 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -919,6 +919,7 @@ void select_and_run(
   const uint32_t num_queries,
   const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
   uint32_t* const num_executed_iterations,                     // [num_queries,]
+  const search_params& ps,
   uint32_t topk,
   uint32_t num_itopk_candidates,
   uint32_t block_size,  //
@@ -927,20 +928,14 @@ void select_and_run(
   typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
   size_t small_hash_bitlen,
   size_t small_hash_reset_interval,
-  uint32_t num_random_samplings,
-  uint64_t rand_xor_mask,
   uint32_t num_seeds,
-  size_t itopk_size,
-  size_t search_width,
-  size_t min_iterations,
-  size_t max_iterations,
   SAMPLE_FILTER_T sample_filter,
   cuvs::distance::DistanceType metric,
   cudaStream_t stream)
 {
   auto kernel =
     search_kernel_config<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::
-      choose_itopk_and_mx_candidates(itopk_size, num_itopk_candidates, block_size);
+      choose_itopk_and_mx_candidates(ps.itopk_size, num_itopk_candidates, block_size);
   RAFT_CUDA_TRY(cudaFuncSetAttribute(kernel,
                                      cudaFuncAttributeMaxDynamicSharedMemorySize,
                                      smem_size + DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte));
@@ -955,15 +950,15 @@ void select_and_run(
                                                          queries_ptr,
                                                          graph.data_handle(),
                                                          graph.extent(1),
-                                                         num_random_samplings,
-                                                         rand_xor_mask,
+                                                         ps.num_random_samplings,
+                                                         ps.rand_xor_mask,
                                                          dev_seed_ptr,
                                                          num_seeds,
                                                          hashmap_ptr,
-                                                         itopk_size,
-                                                         search_width,
-                                                         min_iterations,
-                                                         max_iterations,
+                                                         ps.itopk_size,
+                                                         ps.search_width,
+                                                         ps.min_iterations,
+                                                         ps.max_iterations,
                                                          num_executed_iterations,
                                                          hash_bitlen,
                                                          small_hash_bitlen,

From b4427561591e6e752f023ab2d805ef8d97bcbf3a Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Thu, 25 Jul 2024 22:17:58 -0400
Subject: [PATCH 107/623] Use `raft::util::popc(...)` public API (#249)

https://github.com/rapidsai/raft/pull/2346 introduced a breaking change in the API. This PR fixes the API usage.

Authors:
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/249
---
 cpp/src/neighbors/detail/knn_brute_force.cuh  | 9 +++++----
 cpp/test/neighbors/brute_force_prefiltered.cu | 9 +++++----
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index fe425fe8f6..f05bebf3fb 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -28,8 +28,8 @@
 #include "./knn_utils.cuh"
 
 #include <raft/core/bitmap.cuh>
-#include <raft/core/detail/popc.cuh>
 #include <raft/core/device_csr_matrix.hpp>
+#include <raft/core/host_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
@@ -46,6 +46,7 @@
 #include <raft/sparse/matrix/select_k.cuh>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
+#include <raft/util/popc.cuh>
 
 #include <rmm/cuda_device.hpp>
 #include <rmm/device_uvector.hpp>
@@ -591,10 +592,10 @@ void brute_force_search_filtered(
   auto nnz_view = raft::make_device_scalar_view<IdxT>(nnz.data());
   auto filter_view =
     raft::make_device_vector_view<const BitmapT, IdxT>(filter.data(), filter.n_elements());
+  IdxT size_h    = n_queries * n_dataset;
+  auto size_view = raft::make_host_scalar_view<IdxT>(&size_h);
 
-  // TODO(rhdong): Need to switch to the public API,
-  // with the issue: https://github.com/rapidsai/cuvs/issues/158
-  raft::detail::popc(res, filter_view, n_queries * n_dataset, nnz_view);
+  raft::popc(res, filter_view, size_view, nnz_view);
   raft::copy(&nnz_h, nnz.data(), 1, stream);
 
   raft::resource::sync_stream(res, stream);
diff --git a/cpp/test/neighbors/brute_force_prefiltered.cu b/cpp/test/neighbors/brute_force_prefiltered.cu
index 17166fd7a5..2b8ae9d9a6 100644
--- a/cpp/test/neighbors/brute_force_prefiltered.cu
+++ b/cpp/test/neighbors/brute_force_prefiltered.cu
@@ -20,12 +20,13 @@
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
 
-#include <raft/core/detail/popc.cuh>
+#include <raft/core/host_mdspan.hpp>
 #include <raft/matrix/copy.cuh>
 #include <raft/random/make_blobs.cuh>
 #include <raft/random/rmat_rectangular_generator.cuh>
 #include <raft/random/rng.cuh>
 #include <raft/random/rng_state.hpp>
+#include <raft/util/popc.cuh>
 
 #include <gtest/gtest.h>
 
@@ -192,12 +193,12 @@ class PrefilteredBruteForceTest
       auto nnz_view = raft::make_device_scalar_view<index_t>(nnz.data());
       auto filter_view =
         raft::make_device_vector_view<const uint32_t, index_t>(filter_d.data(), filter_d.size());
+      index_t size_h = m * n;
+      auto size_view = raft::make_host_scalar_view<index_t>(&size_h);
 
       set_bitmap(src, dst, bitmap, n_edges, n, stream);
 
-      // TODO(rhdong): Need to switch to the public API,
-      // with the issue: https://github.com/rapidsai/cuvs/issues/158
-      raft::detail::popc(handle, filter_view, m * n, nnz_view);
+      raft::popc(handle, filter_view, size_view, nnz_view);
       raft::copy(&nnz_h, nnz.data(), 1, stream);
 
       raft::resource::sync_stream(handle, stream);

From 337c696bb312bf6da0c0d301c0aff2970231a648 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 26 Jul 2024 15:23:50 -0500
Subject: [PATCH 108/623] split up CUDA-suffixed dependencies in
 dependencies.yaml (#247)

Contributes to https://github.com/rapidsai/build-planning/issues/31

In short, RAPIDS DLFW builds want to produce wheels with unsuffixed dependencies, e.g. `cudf` depending on `rmm`, not `rmm-cu12`.

This PR is part of a series across all of RAPIDS to try to support that type of build by setting up CUDA-suffixed and CUDA-unsuffixed dependency lists in `dependencies.yaml`.

For more details, see:
* https://github.com/rapidsai/build-planning/issues/31#issuecomment-2245815818
* https://github.com/rapidsai/cudf/pull/16183

## Notes for Reviewers

### Why target 24.08?

This is targeting 24.08 because:

1. it should be very low-risk
2. getting these changes into 24.08 prevents the need to carry around patches for every library in DLFW builds using RAPIDS 24.08

Authors:
  - James Lamb (https://github.com/jameslamb)
  - Paul Taylor (https://github.com/trxcllnt)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/247
---
 dependencies.yaml          | 20 ++++++++++++--------
 python/cuvs/pyproject.toml |  1 +
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/dependencies.yaml b/dependencies.yaml
index 60ce82aa74..79bb0d26a6 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -157,8 +157,8 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
-          - &rmm_conda rmm==24.8.*,>=0.0.0a0
-          - &pylibraft_conda pylibraft==24.8.*,>=0.0.0a0
+          - &rmm_unsuffixed rmm==24.8.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==24.8.*,>=0.0.0a0
           - dlpack>=0.8,<1.0
       - output_types: requirements
         packages:
@@ -182,15 +182,19 @@ dependencies:
               - &cuda_python cuda-python
       - output_types: [requirements, pyproject]
         matrices:
-          - matrix: {cuda: "12.*"}
+          - matrix:
+              cuda: "12.*"
+              cuda_suffixed: "true"
             packages:
               - &rmm_cu12 rmm-cu12==24.8.*,>=0.0.0a0
               - &pylibraft_cu12 pylibraft-cu12==24.8.*,>=0.0.0a0
-          - matrix: {cuda: "11.*"}
+          - matrix:
+              cuda: "11.*"
+              cuda_suffixed: "true"
             packages:
               - &rmm_cu11 rmm-cu11==24.8.*,>=0.0.0a0
               - &pylibraft_cu11 pylibraft-cu11==24.8.*,>=0.0.0a0
-          - {matrix: null, packages: [*rmm_conda, *pylibraft_conda] }
+          - {matrix: null, packages: [*rmm_unsuffixed, *pylibraft_unsuffixed] }
   checks:
     common:
       - output_types: [conda, requirements]
@@ -383,8 +387,8 @@ dependencies:
           - &numpy numpy>=1.23,<2.0a0
       - output_types: [conda]
         packages:
-          - *rmm_conda
-          - *pylibraft_conda
+          - *rmm_unsuffixed
+          - *pylibraft_unsuffixed
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -413,7 +417,7 @@ dependencies:
           - matrix: {cuda: "11.*"}
             packages:
               - *pylibraft_cu11
-          - {matrix: null, packages: [*pylibraft_conda]}
+          - {matrix: null, packages: [*pylibraft_unsuffixed]}
   test_python_common:
     common:
       - output_types: [conda, requirements, pyproject]
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 65fb51196f..b0598b9005 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -127,6 +127,7 @@ requires = [
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
 dependencies-file = "../../dependencies.yaml"
+matrix-entry = "cuda_suffixed=true"
 
 [tool.pytest.ini_options]
 filterwarnings = [

From b882590a6525e7aa2fcc521bbb42316aaa5a3b5e Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Sat, 27 Jul 2024 15:26:36 -0400
Subject: [PATCH 109/623] Enable building FAISS main statically (#241)

Port https://github.com/rapidsai/raft/pull/2323 PR from RAFT
[Cleans up a collection of anti-patterns in the cuvs CMake code while also enabling building faiss from latest main]

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Robert Maynard (https://github.com/robertmaynard)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Paul Taylor (https://github.com/trxcllnt)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/241
---
 .devcontainer/Dockerfile                |   7 +
 cpp/CMakeLists.txt                      |   6 +-
 cpp/bench/ann/CMakeLists.txt            | 109 ++++++--------
 cpp/cmake/patches/faiss_override.json   |   9 ++
 cpp/cmake/patches/ggnn_override.json    |  16 ++
 cpp/cmake/patches/hnswlib_override.json |  16 ++
 cpp/cmake/thirdparty/get_faiss.cmake    | 187 +++++++++++++-----------
 cpp/cmake/thirdparty/get_ggnn.cmake     |  43 +++---
 cpp/cmake/thirdparty/get_hnswlib.cmake  |  76 +++++-----
 cpp/test/CMakeLists.txt                 |  84 +++++------
 10 files changed, 286 insertions(+), 267 deletions(-)
 create mode 100644 cpp/cmake/patches/faiss_override.json
 create mode 100644 cpp/cmake/patches/ggnn_override.json
 create mode 100644 cpp/cmake/patches/hnswlib_override.json

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 9d35e3f97f..594ba8c3c4 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -5,6 +5,13 @@ ARG PYTHON_PACKAGE_MANAGER=conda
 
 FROM ${BASE} as pip-base
 
+RUN apt update -y \
+ && DEBIAN_FRONTEND=noninteractive apt install -y --no-install-recommends \
+    # faiss dependencies
+    libblas-dev \
+    liblapack-dev \
+ && rm -rf /tmp/* /var/tmp/* /var/cache/apt/* /var/lib/apt/lists/*;
+
 ENV DEFAULT_VIRTUAL_ENV=rapids
 
 FROM ${BASE} as conda-base
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 5060f4591c..d204a9a231 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -682,13 +682,13 @@ rapids_export(
 # * build test executable ----------------------------------------------------
 
 if(BUILD_TESTS OR BUILD_C_TESTS)
-  include(internal/CMakeLists.txt)
-  include(test/CMakeLists.txt)
+  add_subdirectory(internal)
+  add_subdirectory(test)
 endif()
 
 # ##################################################################################################
 # * build ann benchmark executable -----------------------------------------------
 
 if(BUILD_ANN_BENCH)
-  include(bench/ann/CMakeLists.txt)
+  add_subdirectory(bench/ann/)
 endif()
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 6e9e66fadb..80c1f35306 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -12,6 +12,8 @@
 # the License.
 # =============================================================================
 
+list(APPEND CMAKE_MODULE_PATH "${CUVS_SOURCE_DIR}")
+
 # ##################################################################################################
 # * benchmark options ------------------------------------------------------------------------------
 
@@ -39,31 +41,18 @@ option(CUVS_ANN_BENCH_SINGLE_EXE
 
 find_package(Threads REQUIRED)
 
+set(CUVS_ANN_BENCH_USE_FAISS ON)
+set(CUVS_FAISS_ENABLE_GPU ON)
+set(CUVS_USE_FAISS_STATIC ON)
+
 if(BUILD_CPU_ONLY)
   set(CUVS_FAISS_ENABLE_GPU OFF)
-  set(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT OFF)
-  set(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT OFF)
-  set(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_CAGRA OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB OFF)
   set(CUVS_ANN_BENCH_USE_GGNN OFF)
-else()
-  set(CUVS_FAISS_ENABLE_GPU ON)
-endif()
-
-set(CUVS_ANN_BENCH_USE_FAISS OFF)
-if(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT
-   OR CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ
-   OR CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT
-   OR CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT
-   OR CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ
-   OR CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT
-)
-  set(CUVS_ANN_BENCH_USE_FAISS ON)
-  set(CUVS_USE_FAISS_STATIC ON)
 endif()
 
 set(CUVS_ANN_BENCH_USE_CUVS OFF)
@@ -80,21 +69,17 @@ endif()
 # * Fetch requirements -------------------------------------------------------------
 
 if(CUVS_ANN_BENCH_USE_HNSWLIB OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
-  include(cmake/thirdparty/get_hnswlib.cmake)
+  include(cmake/thirdparty/get_hnswlib)
 endif()
 
-include(cmake/thirdparty/get_nlohmann_json.cmake)
+include(cmake/thirdparty/get_nlohmann_json)
 
 if(CUVS_ANN_BENCH_USE_GGNN)
-  include(cmake/thirdparty/get_ggnn.cmake)
+  include(cmake/thirdparty/get_ggnn)
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS)
-  # We need to ensure that faiss has all the conda information. So we currently use the very ugly
-  # hammer of `link_libraries` to ensure that all targets in this directory and the faiss directory
-  # will have the conda includes/link dirs
-  link_libraries($<TARGET_NAME_IF_EXISTS:conda_env>)
-  include(cmake/thirdparty/get_faiss.cmake)
+  include(cmake/thirdparty/get_faiss)
 endif()
 
 # ##################################################################################################
@@ -154,8 +139,6 @@ function(ConfigureAnnBench)
             $<$<BOOL:${GPU_BUILD}>:CUDA::cudart_static>
             $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
             $<TARGET_NAME_IF_EXISTS:conda_env>
-            -static-libgcc
-            -static-libstdc++
   )
 
   set_target_properties(
@@ -210,27 +193,34 @@ endif()
 
 if(CUVS_ANN_BENCH_USE_HNSWLIB)
   ConfigureAnnBench(
-    NAME HNSWLIB PATH bench/ann/src/hnswlib/hnswlib_benchmark.cpp LINKS hnswlib::hnswlib
+    NAME HNSWLIB PATH src/hnswlib/hnswlib_benchmark.cpp LINKS hnswlib::hnswlib
   )
 
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ)
   ConfigureAnnBench(
-    NAME CUVS_IVF_PQ PATH bench/ann/src/cuvs/cuvs_benchmark.cu
-    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_IVF_PQ}>:bench/ann/src/cuvs/cuvs_ivf_pq.cu> LINKS cuvs
+    NAME CUVS_IVF_PQ
+    PATH
+    src/cuvs/cuvs_benchmark.cu
+    src/cuvs/cuvs_ivf_pq.cu
+    LINKS cuvs
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT)
   ConfigureAnnBench(
-    NAME CUVS_IVF_FLAT PATH bench/ann/src/cuvs/cuvs_benchmark.cu
-    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT}>:bench/ann/src/cuvs/cuvs_ivf_flat.cu> LINKS cuvs
+    NAME CUVS_IVF_FLAT
+    PATH
+    src/cuvs/cuvs_benchmark.cu
+    src/cuvs/cuvs_ivf_flat.cu
+    LINKS
+    cuvs
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE)
-  ConfigureAnnBench(NAME CUVS_BRUTE_FORCE PATH bench/ann/src/cuvs/cuvs_benchmark.cu LINKS cuvs)
+  ConfigureAnnBench(NAME CUVS_BRUTE_FORCE PATH src/cuvs/cuvs_benchmark.cu LINKS cuvs)
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_CAGRA)
@@ -238,11 +228,11 @@ if(CUVS_ANN_BENCH_USE_CUVS_CAGRA)
     NAME
     CUVS_CAGRA
     PATH
-    bench/ann/src/cuvs/cuvs_benchmark.cu
-    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_float.cu>
-    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_half.cu>
-    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_int8_t.cu>
-    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_CAGRA}>:bench/ann/src/cuvs/cuvs_cagra_uint8_t.cu>
+    src/cuvs/cuvs_benchmark.cu
+    src/cuvs/cuvs_cagra_float.cu
+    src/cuvs/cuvs_cagra_half.cu
+    src/cuvs/cuvs_cagra_int8_t.cu
+    src/cuvs/cuvs_cagra_uint8_t.cu
     LINKS
     cuvs
   )
@@ -250,78 +240,65 @@ endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
   ConfigureAnnBench(
-    NAME CUVS_CAGRA_HNSWLIB PATH bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu LINKS cuvs
+    NAME CUVS_CAGRA_HNSWLIB PATH src/cuvs/cuvs_cagra_hnswlib.cu LINKS cuvs
     hnswlib::hnswlib
   )
 endif()
 
-set(CUVS_FAISS_TARGETS faiss::faiss)
-if(TARGET faiss::faiss_avx2)
-  set(CUVS_FAISS_TARGETS faiss::faiss_avx2)
-endif()
-
 message("CUVS_FAISS_TARGETS: ${CUVS_FAISS_TARGETS}")
 message("CUDAToolkit_LIBRARY_DIR: ${CUDAToolkit_LIBRARY_DIR}")
 if(CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT)
   ConfigureAnnBench(
-    NAME FAISS_CPU_FLAT PATH bench/ann/src/faiss/faiss_cpu_benchmark.cpp LINKS
+    NAME FAISS_CPU_FLAT PATH src/faiss/faiss_cpu_benchmark.cpp LINKS
     ${CUVS_FAISS_TARGETS}
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT)
   ConfigureAnnBench(
-    NAME FAISS_CPU_IVF_FLAT PATH bench/ann/src/faiss/faiss_cpu_benchmark.cpp LINKS
+    NAME FAISS_CPU_IVF_FLAT PATH src/faiss/faiss_cpu_benchmark.cpp LINKS
     ${CUVS_FAISS_TARGETS}
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ)
   ConfigureAnnBench(
-    NAME FAISS_CPU_IVF_PQ PATH bench/ann/src/faiss/faiss_cpu_benchmark.cpp LINKS
+    NAME FAISS_CPU_IVF_PQ PATH src/faiss/faiss_cpu_benchmark.cpp LINKS
     ${CUVS_FAISS_TARGETS}
   )
 endif()
 
-if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT)
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
-    NAME FAISS_GPU_IVF_FLAT PATH bench/ann/src/faiss/faiss_gpu_benchmark.cu LINKS
+    NAME FAISS_GPU_IVF_FLAT PATH src/faiss/faiss_gpu_benchmark.cu LINKS
     ${CUVS_FAISS_TARGETS}
   )
 endif()
 
-if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ)
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
-    NAME FAISS_GPU_IVF_PQ PATH bench/ann/src/faiss/faiss_gpu_benchmark.cu LINKS
+    NAME FAISS_GPU_IVF_PQ PATH src/faiss/faiss_gpu_benchmark.cu LINKS
     ${CUVS_FAISS_TARGETS}
   )
 endif()
 
-if(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT)
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
-    NAME FAISS_GPU_FLAT PATH bench/ann/src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
+    NAME FAISS_GPU_FLAT PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_GGNN)
-  include(cmake/thirdparty/get_glog.cmake)
+  include(cmake/thirdparty/get_glog)
   ConfigureAnnBench(
-    NAME GGNN PATH bench/ann/src/ggnn/ggnn_benchmark.cu LINKS glog::glog ggnn::ggnn CUDA::curand
+    NAME GGNN PATH src/ggnn/ggnn_benchmark.cu LINKS glog::glog ggnn::ggnn CUDA::curand
   )
 endif()
 
 # ##################################################################################################
 # * Dynamically-loading ANN_BENCH executable -------------------------------------------------------
 if(CUVS_ANN_BENCH_SINGLE_EXE)
-  add_executable(ANN_BENCH bench/ann/src/common/benchmark.cpp)
-
-  # Build and link static version of the GBench to keep ANN_BENCH self-contained.
-  get_target_property(TMP_PROP benchmark::benchmark SOURCES)
-  add_library(benchmark_static STATIC ${TMP_PROP})
-  get_target_property(TMP_PROP benchmark::benchmark INCLUDE_DIRECTORIES)
-  target_include_directories(benchmark_static PUBLIC ${TMP_PROP})
-  get_target_property(TMP_PROP benchmark::benchmark LINK_LIBRARIES)
-  target_link_libraries(benchmark_static PUBLIC ${TMP_PROP})
+  add_executable(ANN_BENCH src/common/benchmark.cpp)
 
   target_include_directories(ANN_BENCH PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
 
@@ -329,12 +306,10 @@ if(CUVS_ANN_BENCH_SINGLE_EXE)
     ANN_BENCH
     PRIVATE raft::raft
             nlohmann_json::nlohmann_json
-            benchmark_static
+            benchmark::benchmark
             dl
-            -static-libgcc
             fmt::fmt-header-only
             spdlog::spdlog_header_only
-            -static-libstdc++
             $<$<BOOL:${NVTX3_HEADERS_FOUND}>:CUDA::nvtx3>
   )
   set_target_properties(
diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
new file mode 100644
index 0000000000..c39abdc2b7
--- /dev/null
+++ b/cpp/cmake/patches/faiss_override.json
@@ -0,0 +1,9 @@
+{
+    "packages" : {
+      "faiss" : {
+        "version": "1.7.4",
+        "git_url": "https://github.com/facebookresearch/faiss.git",
+        "git_tag": "main"
+      }
+    }
+  }
\ No newline at end of file
diff --git a/cpp/cmake/patches/ggnn_override.json b/cpp/cmake/patches/ggnn_override.json
new file mode 100644
index 0000000000..c9a1b69782
--- /dev/null
+++ b/cpp/cmake/patches/ggnn_override.json
@@ -0,0 +1,16 @@
+{
+    "packages" : {
+      "ggnn" : {
+        "version": "0.5",
+        "git_url": "https://github.com/cgtuebingen/ggnn.git",
+        "git_tag": "release_${version}",
+        "patches" : [
+          {
+            "file" : "${current_json_dir}/ggnn.diff",
+            "issue" : "Correct compilation issues",
+            "fixed_in" : ""
+          }
+        ]
+      }
+    }
+  }
\ No newline at end of file
diff --git a/cpp/cmake/patches/hnswlib_override.json b/cpp/cmake/patches/hnswlib_override.json
new file mode 100644
index 0000000000..aef2da772c
--- /dev/null
+++ b/cpp/cmake/patches/hnswlib_override.json
@@ -0,0 +1,16 @@
+{
+    "packages" : {
+      "hnswlib" : {
+        "version": "0.6.2",
+        "git_url": "https://github.com/nmslib/hnswlib.git",
+        "git_tag": "v${version}",
+        "patches" : [
+          {
+            "file" : "${current_json_dir}/hnswlib.diff",
+            "issue" : "Correct compilation issues",
+            "fixed_in" : ""
+          }
+        ]
+      }
+    }
+  }
\ No newline at end of file
diff --git a/cpp/cmake/thirdparty/get_faiss.cmake b/cpp/cmake/thirdparty/get_faiss.cmake
index 89446332da..d6261d2484 100644
--- a/cpp/cmake/thirdparty/get_faiss.cmake
+++ b/cpp/cmake/thirdparty/get_faiss.cmake
@@ -15,95 +15,104 @@
 #=============================================================================
 
 function(find_and_configure_faiss)
-    set(oneValueArgs VERSION REPOSITORY PINNED_TAG BUILD_STATIC_LIBS EXCLUDE_FROM_ALL ENABLE_GPU)
-    cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
-            "${multiValueArgs}" ${ARGN} )
+  set(oneValueArgs VERSION REPOSITORY PINNED_TAG BUILD_STATIC_LIBS EXCLUDE_FROM_ALL ENABLE_GPU)
+  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+                        "${multiValueArgs}" ${ARGN} )
+
+  rapids_find_generate_module(faiss
+    HEADER_NAMES  faiss/IndexFlat.h
+    LIBRARY_NAMES faiss
+    )
+  
+  set(patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/../patches")
+  rapids_cpm_package_override("${patch_dir}/faiss_override.json")
+
+  include("${rapids-cmake-dir}/cpm/detail/package_details.cmake")
+  rapids_cpm_package_details(faiss version repository tag shallow exclude)
+
+  include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
+  rapids_cpm_generate_patch_command(faiss ${version} patch_command)
+
+  set(BUILD_SHARED_LIBS ON)
+  if (PKG_BUILD_STATIC_LIBS)
+    set(BUILD_SHARED_LIBS OFF)
+    set(CPM_DOWNLOAD_faiss ON)
+  endif()
+
+  include(cmake/modules/FindAVX)
+  # Link against AVX CPU lib if it exists
+  set(CUVS_FAISS_OPT_LEVEL "generic")
+  if(CXX_AVX2_FOUND)
+    set(CUVS_FAISS_OPT_LEVEL "avx2")
+  endif()
+
+  rapids_cpm_find(faiss ${version}
+    GLOBAL_TARGETS faiss faiss_avx2 faiss_gpu faiss::faiss faiss::faiss_avx2
+    CPM_ARGS
+    GIT_REPOSITORY ${repository}
+    GIT_TAG ${tag}
+    GIT_SHALLOW ${shallow} ${patch_command}
+    EXCLUDE_FROM_ALL ${exclude}
+    OPTIONS
+    "FAISS_ENABLE_GPU ${PKG_ENABLE_GPU}"
+    "FAISS_ENABLE_PYTHON OFF"
+    "FAISS_OPT_LEVEL ${CUVS_FAISS_OPT_LEVEL}"
+    "FAISS_USE_CUDA_TOOLKIT_STATIC ${CUDA_STATIC_RUNTIME}"
+    "BUILD_TESTING OFF"
+    "CMAKE_MESSAGE_LOG_LEVEL VERBOSE"
+    )
+
+  
+  include("${rapids-cmake-dir}/cpm/detail/display_patch_status.cmake")
+  rapids_cpm_display_patch_status(faiss)
+
+  if(TARGET faiss AND NOT TARGET faiss::faiss)
+    add_library(faiss::faiss ALIAS faiss)
+    # We need to ensure that faiss has all the conda information. So we use this approach so that
+    # faiss will have the conda includes/link dirs
+    target_link_libraries(faiss PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>)
+  endif()
+  if(TARGET faiss_avx2 AND NOT TARGET faiss::faiss_avx2)
+    add_library(faiss::faiss_avx2 ALIAS faiss_avx2)
+    # We need to ensure that faiss has all the conda information. So we use this approach so that
+    # faiss will have the conda includes/link dirs
+    target_link_libraries(faiss_avx2 PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>)
+  endif()
+  if(TARGET faiss_gpu AND NOT TARGET faiss::faiss_gpu)
+    add_library(faiss::faiss_gpu ALIAS faiss_gpu)
+    # We need to ensure that faiss has all the conda information. So we use this approach so that
+    # faiss will have the conda includes/link dirs
+    target_link_libraries(faiss_gpu PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>)
+  endif()
+
+  if(faiss_ADDED)
+    rapids_export(BUILD faiss
+                  EXPORT_SET faiss-targets
+                  GLOBAL_TARGETS ${CUVS_FAISS_EXPORT_GLOBAL_TARGETS}
+                  NAMESPACE faiss::)
+  endif()
+
+  # Need to tell CMake to rescan the link group of faiss::faiss_gpu and faiss
+  # so that we get proper link order when they are static
+  #
+  # We don't look at the existence of `faiss_avx2` as it will always exist
+  # even when CXX_AVX2_FOUND is false. In addition for arm builds the
+  # faiss_avx2 is marked as `EXCLUDE_FROM_ALL` so we don't want to add
+  # a dependency to it. Adding a dependency will cause it to compile,
+  # and fail due to invalid compiler flags.
+  if(PKG_ENABLE_GPU AND PKG_BUILD_STATIC_LIBS AND CXX_AVX2_FOUND)
+    set(CUVS_FAISS_TARGETS "$<LINK_GROUP:RESCAN,$<LINK_LIBRARY:WHOLE_ARCHIVE,faiss_gpu>,faiss::faiss_avx2>" PARENT_SCOPE)
+  elseif(PKG_ENABLE_GPU AND  PKG_BUILD_STATIC_LIBS)
+    set(CUVS_FAISS_TARGETS "$<LINK_GROUP:RESCAN,$<LINK_LIBRARY:WHOLE_ARCHIVE,faiss_gpu>,faiss::faiss>" PARENT_SCOPE)
+  elseif(CXX_AVX2_FOUND)
+    set(CUVS_FAISS_TARGETS faiss::faiss_avx2 PARENT_SCOPE)
+  else()
+    set(CUVS_FAISS_TARGETS faiss::faiss PARENT_SCOPE)
+  endif()
 
-        rapids_find_generate_module(faiss
-                HEADER_NAMES  faiss/IndexFlat.h
-                LIBRARY_NAMES faiss
-                )
-
-        set(BUILD_SHARED_LIBS ON)
-        if (PKG_BUILD_STATIC_LIBS)
-            set(BUILD_SHARED_LIBS OFF)
-            set(CPM_DOWNLOAD_faiss ON)
-        endif()
-
-        include(cmake/modules/FindAVX.cmake)
-
-        # Link against AVX CPU lib if it exists
-        set(CUVS_FAISS_GLOBAL_TARGETS faiss::faiss)
-        set(CUVS_FAISS_EXPORT_GLOBAL_TARGETS faiss)
-        set(CUVS_FAISS_OPT_LEVEL "generic")
-        if(CXX_AVX_FOUND)
-            set(CUVS_FAISS_OPT_LEVEL "avx2")
-            list(APPEND CUVS_FAISS_GLOBAL_TARGETS faiss::faiss_avx2)
-            list(APPEND CUVS_FAISS_EXPORT_GLOBAL_TARGETS faiss_avx2)
-        endif()
-
-        rapids_cpm_find(faiss ${PKG_VERSION}
-                GLOBAL_TARGETS ${CUVS_FAISS_GLOBAL_TARGETS}
-                CPM_ARGS
-                GIT_REPOSITORY   ${PKG_REPOSITORY}
-                GIT_TAG          ${PKG_PINNED_TAG}
-                EXCLUDE_FROM_ALL ${PKG_EXCLUDE_FROM_ALL}
-                OPTIONS
-                "FAISS_ENABLE_GPU ${PKG_ENABLE_GPU}"
-                "FAISS_ENABLE_PYTHON OFF"
-                "FAISS_OPT_LEVEL ${CUVS_FAISS_OPT_LEVEL}"
-                "FAISS_USE_CUDA_TOOLKIT_STATIC ${CUDA_STATIC_RUNTIME}"
-                "BUILD_TESTING OFF"
-                "CMAKE_MESSAGE_LOG_LEVEL VERBOSE"
-                )
-
-        if(TARGET faiss AND NOT TARGET faiss::faiss)
-            add_library(faiss::faiss ALIAS faiss)
-        endif()
-
-    if(CXX_AVX_FOUND)
-
-        if(TARGET faiss_avx2 AND NOT TARGET faiss::faiss_avx2)
-            add_library(faiss::faiss_avx2 ALIAS faiss_avx2)
-        endif()
-    endif()
-
-
-    if(faiss_ADDED)
-            rapids_export(BUILD faiss
-                    EXPORT_SET faiss-targets
-                    GLOBAL_TARGETS ${CUVS_FAISS_EXPORT_GLOBAL_TARGETS}
-                    NAMESPACE faiss::)
-        endif()
-
-    # We generate the faiss-config files when we built faiss locally, so always do `find_dependency`
-    rapids_export_package(BUILD OpenMP cuvs-ann-bench-exports) # faiss uses openMP but doesn't export a need for it
-    rapids_export_package(BUILD faiss cuvs-ann-bench-exports GLOBAL_TARGETS ${CUVS_FAISS_GLOBAL_TARGETS} ${CUVS_FAISS_EXPORT_GLOBAL_TARGETS})
-    rapids_export_package(INSTALL faiss cuvs-ann-bench-exports GLOBAL_TARGETS ${CUVS_FAISS_GLOBAL_TARGETS} ${CUVS_FAISS_EXPORT_GLOBAL_TARGETS})
-
-    # Tell cmake where it can find the generated faiss-config.cmake we wrote.
-    include("${rapids-cmake-dir}/export/find_package_root.cmake")
-    rapids_export_find_package_root(BUILD faiss [=[${CMAKE_CURRENT_LIST_DIR}]=]
-                                    EXPORT_SET cuvs-ann-bench-exports)
 endfunction()
 
-if(NOT CUVS_FAISS_GIT_TAG)
-    # TODO: Remove this once faiss supports FAISS_USE_CUDA_TOOLKIT_STATIC
-    # (https://github.com/facebookresearch/faiss/pull/2446)
-    set(CUVS_FAISS_GIT_TAG fea/statically-link-ctk)
-    # set(CUVS_FAISS_GIT_TAG bde7c0027191f29c9dadafe4f6e68ca0ee31fb30)
-endif()
-
-if(NOT CUVS_FAISS_GIT_REPOSITORY)
-    # TODO: Remove this once faiss supports FAISS_USE_CUDA_TOOLKIT_STATIC
-    # (https://github.com/facebookresearch/faiss/pull/2446)
-    set(CUVS_FAISS_GIT_REPOSITORY https://github.com/cjnolet/faiss.git)
-    # set(CUVS_FAISS_GIT_REPOSITORY https://github.com/facebookresearch/faiss.git)
-endif()
-
-find_and_configure_faiss(VERSION    1.7.4
-        REPOSITORY  ${CUVS_FAISS_GIT_REPOSITORY}
-        PINNED_TAG  ${CUVS_FAISS_GIT_TAG}
-        BUILD_STATIC_LIBS ${CUVS_USE_FAISS_STATIC}
-        EXCLUDE_FROM_ALL ${CUVS_EXCLUDE_FAISS_FROM_ALL}
-        ENABLE_GPU ${CUVS_FAISS_ENABLE_GPU})
+find_and_configure_faiss(
+  BUILD_STATIC_LIBS ${CUVS_USE_FAISS_STATIC}
+  ENABLE_GPU ${CUVS_FAISS_ENABLE_GPU}
+)
diff --git a/cpp/cmake/thirdparty/get_ggnn.cmake b/cpp/cmake/thirdparty/get_ggnn.cmake
index 8137ef84eb..2ccfbc64df 100644
--- a/cpp/cmake/thirdparty/get_ggnn.cmake
+++ b/cpp/cmake/thirdparty/get_ggnn.cmake
@@ -15,29 +15,30 @@
 #=============================================================================
 
 function(find_and_configure_ggnn)
-  set(oneValueArgs VERSION REPOSITORY PINNED_TAG)
-  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
-          "${multiValueArgs}" ${ARGN} )
 
+  include(${rapids-cmake-dir}/cpm/package_override.cmake)
+  set(patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/../patches")
+  rapids_cpm_package_override("${patch_dir}/ggnn_override.json")
 
-  set(patch_files_to_run "${CMAKE_CURRENT_SOURCE_DIR}/cmake/patches/ggnn.diff")
-  set(patch_issues_to_ref "fix compile issues")
-  set(patch_script "${CMAKE_BINARY_DIR}/rapids-cmake/patches/ggnn/patch.cmake")
-  set(log_file "${CMAKE_BINARY_DIR}/rapids-cmake/patches/ggnn/log")
-  string(TIMESTAMP current_year "%Y" UTC)
-  configure_file(${rapids-cmake-dir}/cpm/patches/command_template.cmake.in "${patch_script}"
-                @ONLY)
+  include("${rapids-cmake-dir}/cpm/detail/package_details.cmake")
+  rapids_cpm_package_details(ggnn version repository tag shallow exclude)
+
+  include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
+  rapids_cpm_generate_patch_command(ggnn ${version} patch_command)
 
   rapids_cpm_find(
-    ggnn ${PKG_VERSION}
+    ggnn ${version}
     GLOBAL_TARGETS ggnn::ggnn
     CPM_ARGS
-    GIT_REPOSITORY ${PKG_REPOSITORY}
-    GIT_TAG ${PKG_PINNED_TAG}
-    GIT_SHALLOW TRUE
+    GIT_REPOSITORY ${repository}
+    GIT_TAG ${tag}
+    GIT_SHALLOW ${shallow} ${patch_command}
     DOWNLOAD_ONLY ON
-    PATCH_COMMAND ${CMAKE_COMMAND} -P ${patch_script}
   )
+
+  include("${rapids-cmake-dir}/cpm/detail/display_patch_status.cmake")
+  rapids_cpm_display_patch_status(ggnn)
+  
   if(NOT TARGET ggnn::ggnn)
     add_library(ggnn INTERFACE)
     target_include_directories(ggnn INTERFACE "$<BUILD_INTERFACE:${ggnn_SOURCE_DIR}/include>")
@@ -45,14 +46,4 @@ function(find_and_configure_ggnn)
   endif()
 
 endfunction()
-if(NOT RAFT_GGNN_GIT_TAG)
-  set(RAFT_GGNN_GIT_TAG release_0.5)
-endif()
-
-if(NOT RAFT_GGNN_GIT_REPOSITORY)
-  set(RAFT_GGNN_GIT_REPOSITORY https://github.com/cgtuebingen/ggnn.git)
-endif()
-find_and_configure_ggnn(VERSION 0.5
-        REPOSITORY       ${RAFT_GGNN_GIT_REPOSITORY}
-        PINNED_TAG       ${RAFT_GGNN_GIT_TAG}
-        )
+find_and_configure_ggnn()
diff --git a/cpp/cmake/thirdparty/get_hnswlib.cmake b/cpp/cmake/thirdparty/get_hnswlib.cmake
index 054a12f1e0..2e6c895e5f 100644
--- a/cpp/cmake/thirdparty/get_hnswlib.cmake
+++ b/cpp/cmake/thirdparty/get_hnswlib.cmake
@@ -15,78 +15,74 @@
 #=============================================================================
 
 function(find_and_configure_hnswlib)
-  set(oneValueArgs VERSION REPOSITORY PINNED_TAG EXCLUDE_FROM_ALL)
-  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
-          "${multiValueArgs}" ${ARGN} )
+  set(oneValueArgs)
 
-  set(patch_files_to_run "${CMAKE_CURRENT_SOURCE_DIR}/cmake/patches/hnswlib.diff")
-  set(patch_issues_to_ref "fix compile issues")
-  set(patch_script "${CMAKE_BINARY_DIR}/rapids-cmake/patches/hnswlib/patch.cmake")
-  set(log_file "${CMAKE_BINARY_DIR}/rapids-cmake/patches/hnswlib/log")
-  string(TIMESTAMP current_year "%Y" UTC)
-  configure_file(${rapids-cmake-dir}/cpm/patches/command_template.cmake.in "${patch_script}"
-                @ONLY)
+  include(${rapids-cmake-dir}/cpm/package_override.cmake)
+  set(patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/../patches")
+  rapids_cpm_package_override("${patch_dir}/hnswlib_override.json")
+
+  include("${rapids-cmake-dir}/cpm/detail/package_details.cmake")
+  rapids_cpm_package_details(hnswlib version repository tag shallow exclude)
+
+  include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
+  rapids_cpm_generate_patch_command(hnswlib ${version} patch_command)
 
   rapids_cpm_find(
-    hnswlib ${PKG_VERSION}
-    GLOBAL_TARGETS hnswlib::hnswlib
-    BUILD_EXPORT_SET raft-exports
-    INSTALL_EXPORT_SET raft-exports
+    hnswlib ${version}
+    GLOBAL_TARGETS hnswlib hnswlib::hnswlib
     CPM_ARGS
-    GIT_REPOSITORY ${PKG_REPOSITORY}
-    GIT_TAG ${PKG_PINNED_TAG}
-    GIT_SHALLOW TRUE
+    GIT_REPOSITORY ${repository}
+    GIT_TAG ${tag}
+    GIT_SHALLOW ${shallow} ${patch_command}
+    EXCLUDE_FROM_ALL ${exclude}
     DOWNLOAD_ONLY ON
-    PATCH_COMMAND ${CMAKE_COMMAND} -P ${patch_script}
   )
+
+  include("${rapids-cmake-dir}/cpm/detail/display_patch_status.cmake")
+  rapids_cpm_display_patch_status(hnswlib)
+
   if(NOT TARGET hnswlib::hnswlib)
     add_library(hnswlib INTERFACE )
     add_library(hnswlib::hnswlib ALIAS hnswlib)
     target_include_directories(hnswlib INTERFACE
      "$<BUILD_INTERFACE:${hnswlib_SOURCE_DIR}>"
      "$<INSTALL_INTERFACE:include>")
+  endif()
 
-    if(NOT PKG_EXCLUDE_FROM_ALL)
-      install(TARGETS hnswlib EXPORT hnswlib-exports)
+  if(hnswlib_ADDED)
+    # write build export rules
+    install(TARGETS hnswlib EXPORT hnswlib-exports)
+    if(NOT exclude)
       install(DIRECTORY "${hnswlib_SOURCE_DIR}/hnswlib/" DESTINATION include/hnswlib)
 
       # write install export rules
       rapids_export(
         INSTALL hnswlib
-        VERSION ${PKG_VERSION}
+        VERSION ${version}
         EXPORT_SET hnswlib-exports
         GLOBAL_TARGETS hnswlib
         NAMESPACE hnswlib::)
     endif()
 
-    # write build export rules
     rapids_export(
       BUILD hnswlib
-      VERSION ${PKG_VERSION}
+      VERSION ${version}
       EXPORT_SET hnswlib-exports
       GLOBAL_TARGETS hnswlib
       NAMESPACE hnswlib::)
 
-    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+    include("${rapids-cmake-dir}/export/package.cmake")
+    rapids_export_package(INSTALL hnswlib cuvs-exports VERSION ${version} GLOBAL_TARGETS hnswlib hnswlib::hnswlib)
+    rapids_export_package(BUILD hnswlib cuvs-exports VERSION ${version} GLOBAL_TARGETS hnswlib hnswlib::hnswlib)
+
 
-    # When using RAFT from the build dir, ensure hnswlib is also found in RAFT's build dir. This
-    # line adds `set(hnswlib_ROOT "${CMAKE_CURRENT_LIST_DIR}")` to build/raft-dependencies.cmake
+    # When using cuVS from the build dir, ensure hnswlib is also found in cuVS' build dir. This
+    # line adds `set(hnswlib_ROOT "${CMAKE_CURRENT_LIST_DIR}")` to build/cuvs-dependencies.cmake
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
     rapids_export_find_package_root(
-      BUILD hnswlib [=[${CMAKE_CURRENT_LIST_DIR}]=] EXPORT_SET raft-exports
+      BUILD hnswlib [=[${CMAKE_CURRENT_LIST_DIR}]=] EXPORT_SET cuvs-exports
     )
   endif()
 endfunction()
 
-
-if(NOT CUVS_HNSWLIB_GIT_TAG)
-  set(CUVS_HNSWLIB_GIT_TAG v0.6.2)
-endif()
-
-if(NOT CUVS_HNSWLIB_GIT_REPOSITORY)
-  set(CUVS_HNSWLIB_GIT_REPOSITORY https://github.com/nmslib/hnswlib.git)
-endif()
-find_and_configure_hnswlib(VERSION 0.6.2
-        REPOSITORY       ${CUVS_HNSWLIB_GIT_REPOSITORY}
-        PINNED_TAG       ${CUVS_HNSWLIB_GIT_TAG}
-        EXCLUDE_FROM_ALL OFF
-        )
+find_and_configure_hnswlib()
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 7921fffd30..3495b23442 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -91,22 +91,22 @@ endfunction()
 
 if(BUILD_TESTS)
   ConfigureTest(
-    NAME NEIGHBORS_TEST PATH test/neighbors/brute_force.cu
-    test/neighbors/brute_force_prefiltered.cu test/neighbors/refine.cu GPUS 1 PERCENT 100
+    NAME NEIGHBORS_TEST PATH neighbors/brute_force.cu
+    neighbors/brute_force_prefiltered.cu neighbors/refine.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
-    NAME CLUSTER_TEST PATH test/cluster/kmeans.cu test/cluster/kmeans_balanced.cu
-    test/cluster/kmeans_find_k.cu test/cluster/linkage.cu GPUS 1 PERCENT 100
+    NAME CLUSTER_TEST PATH cluster/kmeans.cu cluster/kmeans_balanced.cu
+    cluster/kmeans_find_k.cu cluster/linkage.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
     NAME
     NEIGHBORS_ANN_IVF_FLAT_TEST
     PATH
-    test/neighbors/ann_ivf_flat/test_float_int64_t.cu
-    test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
-    test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
+    neighbors/ann_ivf_flat/test_float_int64_t.cu
+    neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
+    neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
     GPUS
     1
     PERCENT
@@ -117,9 +117,9 @@ if(BUILD_TESTS)
     NAME
     NEIGHBORS_ANN_IVF_PQ_TEST
     PATH
-    test/neighbors/ann_ivf_pq/test_float_int64_t.cu
-    test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
-    test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+    neighbors/ann_ivf_pq/test_float_int64_t.cu
+    neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+    neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
     GPUS
     1
     PERCENT
@@ -130,9 +130,9 @@ if(BUILD_TESTS)
     NAME
     NEIGHBORS_ANN_CAGRA_TEST
     PATH
-    test/neighbors/ann_cagra/test_float_uint32_t.cu
-    test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
-    test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+    neighbors/ann_cagra/test_float_uint32_t.cu
+    neighbors/ann_cagra/test_int8_t_uint32_t.cu
+    neighbors/ann_cagra/test_uint8_t_uint32_t.cu
     GPUS
     1
     PERCENT
@@ -143,9 +143,9 @@ if(BUILD_TESTS)
     NAME
     NEIGHBORS_ANN_NN_DESCENT_TEST
     PATH
-    test/neighbors/ann_nn_descent/test_float_uint32_t.cu
-    test/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
-    test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
+    neighbors/ann_nn_descent/test_float_uint32_t.cu
+    neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
+    neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
     GPUS
     1
     PERCENT
@@ -153,29 +153,29 @@ if(BUILD_TESTS)
   )
 
   if(BUILD_CAGRA_HNSWLIB)
-    ConfigureTest(NAME NEIGHBORS_HNSW_TEST PATH test/neighbors/hnsw.cu GPUS 1 PERCENT 100)
+    ConfigureTest(NAME NEIGHBORS_HNSW_TEST PATH neighbors/hnsw.cu GPUS 1 PERCENT 100)
   endif()
 
   ConfigureTest(
     NAME
     DISTANCE_TEST
     PATH
-    test/distance/dist_canberra.cu
-    test/distance/dist_correlation.cu
-    test/distance/dist_cos.cu
-    test/distance/dist_hamming.cu
-    test/distance/dist_hellinger.cu
-    test/distance/dist_inner_product.cu
-    test/distance/dist_jensen_shannon.cu
-    test/distance/dist_kl_divergence.cu
-    test/distance/dist_l1.cu
-    test/distance/dist_l2_exp.cu
-    test/distance/dist_l2_sqrt_exp.cu
-    test/distance/dist_l_inf.cu
-    test/distance/dist_lp_unexp.cu
-    test/distance/dist_russell_rao.cu
-    test/distance/masked_nn.cu
-    test/sparse/neighbors/cross_component_nn.cu
+    distance/dist_canberra.cu
+    distance/dist_correlation.cu
+    distance/dist_cos.cu
+    distance/dist_hamming.cu
+    distance/dist_hellinger.cu
+    distance/dist_inner_product.cu
+    distance/dist_jensen_shannon.cu
+    distance/dist_kl_divergence.cu
+    distance/dist_l1.cu
+    distance/dist_l2_exp.cu
+    distance/dist_l2_sqrt_exp.cu
+    distance/dist_l_inf.cu
+    distance/dist_lp_unexp.cu
+    distance/dist_russell_rao.cu
+    distance/masked_nn.cu
+    sparse/neighbors/cross_component_nn.cu
     GPUS
     1
     PERCENT
@@ -184,27 +184,27 @@ if(BUILD_TESTS)
 endif()
 
 if(BUILD_C_TESTS)
-  ConfigureTest(NAME INTEROP_TEST PATH test/core/interop.cu C_LIB)
+  ConfigureTest(NAME INTEROP_TEST PATH core/interop.cu C_LIB)
   ConfigureTest(
-    NAME DISTANCE_C_TEST PATH test/distance/run_pairwise_distance_c.c
-    test/distance/pairwise_distance_c.cu C_LIB
+    NAME DISTANCE_C_TEST PATH distance/run_pairwise_distance_c.c
+    distance/pairwise_distance_c.cu C_LIB
   )
 
   ConfigureTest(
-    NAME BRUTEFORCE_C_TEST PATH test/neighbors/run_brute_force_c.c test/neighbors/brute_force_c.cu
+    NAME BRUTEFORCE_C_TEST PATH neighbors/run_brute_force_c.c neighbors/brute_force_c.cu
     C_LIB
   )
 
   ConfigureTest(
-    NAME IVF_FLAT_C_TEST PATH test/neighbors/run_ivf_flat_c.c test/neighbors/ann_ivf_flat_c.cu
+    NAME IVF_FLAT_C_TEST PATH neighbors/run_ivf_flat_c.c neighbors/ann_ivf_flat_c.cu
     C_LIB
   )
 
   ConfigureTest(
-    NAME IVF_PQ_C_TEST PATH test/neighbors/run_ivf_pq_c.c test/neighbors/ann_ivf_pq_c.cu C_LIB
+    NAME IVF_PQ_C_TEST PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu C_LIB
   )
 
-  ConfigureTest(NAME CAGRA_C_TEST PATH test/neighbors/ann_cagra_c.cu C_LIB)
+  ConfigureTest(NAME CAGRA_C_TEST PATH neighbors/ann_cagra_c.cu C_LIB)
 endif()
 
 # ##################################################################################################
@@ -215,9 +215,9 @@ rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gt
 if(BUILD_C_TESTS)
   enable_language(C)
 
-  add_executable(cuvs_c_test test/core/c_api.c)
+  add_executable(cuvs_c_test core/c_api.c)
   target_link_libraries(cuvs_c_test PUBLIC cuvs::c_api)
 
-  add_executable(cuvs_c_neighbors_test test/neighbors/c_api.c)
+  add_executable(cuvs_c_neighbors_test neighbors/c_api.c)
   target_link_libraries(cuvs_c_neighbors_test PUBLIC cuvs::c_api)
 endif()

From 98c07f90a30c04b8bd6a90e7a6535a19c9ab9731 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Mon, 29 Jul 2024 12:58:39 +0200
Subject: [PATCH 110/623] Add more info to ANN_BENCH context (#248)

Add extra information to benchmark context for better reproducibility and performance analysis:

  1. Full command line used to call the executable (so you can copy-paste and run again).
  2. More CUDA device information: whether HMM, AST, or host atomics are available (how GPU can efficiently communicate with CPU).
  3. Host information: min/max frequences, used virtual processors and cores, available physical memory and swap (does the benchmark segfault due to not enough host memory? is SMT enabled? etc).

Addresses parts of https://github.com/rapidsai/cuvs/issues/160

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/248
---
 cpp/bench/ann/src/common/benchmark.hpp | 24 +++++++--
 cpp/bench/ann/src/common/util.hpp      | 72 ++++++++++++++++++++++++++
 2 files changed, 91 insertions(+), 5 deletions(-)

diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index e6462c1571..5d7b8934fe 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -461,7 +461,8 @@ void register_search(std::shared_ptr<const dataset<T>> dataset,
 }
 
 template <typename T>
-void dispatch_benchmark(const configuration& conf,
+void dispatch_benchmark(std::string cmdline,
+                        const configuration& conf,
                         bool force_overwrite,
                         bool build_mode,
                         bool search_mode,
@@ -471,6 +472,10 @@ void dispatch_benchmark(const configuration& conf,
                         Mode metric_objective,
                         const std::vector<int>& threads)
 {
+  ::benchmark::AddCustomContext("command_line", cmdline);
+  for (auto [key, value] : host_info()) {
+    ::benchmark::AddCustomContext(key, value);
+  }
   if (cudart.found()) {
     for (auto [key, value] : cuda_info()) {
       ::benchmark::AddCustomContext(key, value);
@@ -586,6 +591,11 @@ inline auto run_main(int argc, char** argv) -> int
     printf_usage();
     return -1;
   }
+  // Save command line for reproducibility.
+  std::string cmdline(argv[0]);
+  for (int i = 1; i < argc; i++) {
+    cmdline += " " + std::string(argv[i]);
+  }
 
   char* conf_path = argv[--argc];
   std::ifstream conf_stream(conf_path);
@@ -667,7 +677,8 @@ inline auto run_main(int argc, char** argv) -> int
   std::string dtype = conf.get_dataset_conf().dtype;
 
   if (dtype == "float") {
-    dispatch_benchmark<float>(conf,
+    dispatch_benchmark<float>(cmdline,
+                              conf,
                               force_overwrite,
                               build_mode,
                               search_mode,
@@ -677,7 +688,8 @@ inline auto run_main(int argc, char** argv) -> int
                               metric_objective,
                               threads);
     // } else if (dtype == "half") {
-    //   dispatch_benchmark<half>(conf,
+    //   dispatch_benchmark<half>(cmdline
+    //                            conf,
     //                            force_overwrite,
     //                            build_mode,
     //                            search_mode,
@@ -687,7 +699,8 @@ inline auto run_main(int argc, char** argv) -> int
     //                            metric_objective,
     //                            threads);
   } else if (dtype == "uint8") {
-    dispatch_benchmark<std::uint8_t>(conf,
+    dispatch_benchmark<std::uint8_t>(cmdline,
+                                     conf,
                                      force_overwrite,
                                      build_mode,
                                      search_mode,
@@ -697,7 +710,8 @@ inline auto run_main(int argc, char** argv) -> int
                                      metric_objective,
                                      threads);
   } else if (dtype == "int8") {
-    dispatch_benchmark<std::int8_t>(conf,
+    dispatch_benchmark<std::int8_t>(cmdline,
+                                    conf,
                                     force_overwrite,
                                     build_mode,
                                     search_mode,
diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
index 490b0326ea..e01e3847be 100644
--- a/cpp/bench/ann/src/common/util.hpp
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -22,8 +22,11 @@
 #include <nvtx3/nvToolsExt.h>
 #endif
 
+#include <sched.h>  // sched_getaffinity
 #include <sys/stat.h>
+#include <sys/sysinfo.h>
 #include <sys/types.h>
+#include <unistd.h>
 
 #include <atomic>
 #include <chrono>
@@ -32,9 +35,11 @@
 #include <cstring>
 #include <ctime>
 #include <filesystem>
+#include <fstream>
 #include <functional>
 #include <mutex>
 #include <optional>
+#include <set>
 #include <sstream>
 #include <string>
 #include <vector>
@@ -293,6 +298,64 @@ inline void reset_global_device_resources()
 #endif
 }
 
+inline auto host_info()
+{
+  std::vector<std::tuple<std::string, std::string>> props;
+
+  // Memory info
+  auto page_size = sysconf(_SC_PAGE_SIZE);
+  props.emplace_back("host_pagesize", std::to_string(page_size));
+
+  struct sysinfo sys_info;
+  if (sysinfo(&sys_info) != -1) {
+    props.emplace_back("host_total_ram_size",
+                       std::to_string(size_t(sys_info.totalram) * size_t(sys_info.mem_unit)));
+    props.emplace_back("host_total_swap_size",
+                       std::to_string(size_t(sys_info.totalswap) * size_t(sys_info.mem_unit)));
+  }
+
+  // CPU info
+  int host_processors_configured = sysconf(_SC_NPROCESSORS_CONF);
+  props.emplace_back("host_processors_sysconf", std::to_string(host_processors_configured));
+  std::vector<uint8_t> affinity_mask_buf(CPU_ALLOC_SIZE(host_processors_configured));
+  cpu_set_t* affinity_mask = reinterpret_cast<cpu_set_t*>(affinity_mask_buf.data());
+  sched_getaffinity(0, affinity_mask_buf.size(), affinity_mask);
+  uint64_t cpu_freq_min    = 0;
+  uint64_t cpu_freq_max    = 0;
+  int host_processors_used = 0;
+  int host_cores_used      = 0;
+  std::set<int> host_cores_selected{};
+  for (int cpu_id = 0; cpu_id < host_processors_configured; cpu_id++) {
+    if (CPU_ISSET_S(cpu_id, affinity_mask_buf.size(), affinity_mask) == 0) { continue; }
+    host_processors_used++;
+    std::string cpu_fpath = "/sys/devices/system/cpu/cpu" + std::to_string(cpu_id);
+    if (!std::filesystem::exists(cpu_fpath)) { continue; }
+
+    int this_cpu_core          = 0;
+    uint64_t this_cpu_freq_min = 0;
+    uint64_t this_cpu_freq_max = 0;
+    std::ifstream(cpu_fpath + "/topology/core_id") >> this_cpu_core;
+    std::ifstream(cpu_fpath + "/cpufreq/scaling_min_freq") >> this_cpu_freq_min;
+    std::ifstream(cpu_fpath + "/cpufreq/scaling_max_freq") >> this_cpu_freq_max;
+    host_cores_selected.insert(this_cpu_core);
+    cpu_freq_min = cpu_freq_min == 0
+                     ? (this_cpu_freq_min * 1000ull)
+                     : std::min<uint64_t>(this_cpu_freq_min * 1000ull, cpu_freq_min);
+    cpu_freq_max = std::max<uint64_t>(this_cpu_freq_max * 1000ull, cpu_freq_max);
+  }
+  host_cores_used = host_cores_selected.size();
+  if (host_processors_used != 0) {
+    props.emplace_back("host_processors_used", std::to_string(host_processors_used));
+  }
+  if (host_cores_used != 0) {
+    props.emplace_back("host_cores_used", std::to_string(host_cores_used));
+  }
+  if (cpu_freq_min != 0) { props.emplace_back("host_cpu_freq_min", std::to_string(cpu_freq_min)); }
+  if (cpu_freq_max != 0) { props.emplace_back("host_cpu_freq_max", std::to_string(cpu_freq_max)); }
+
+  return props;
+}
+
 inline auto cuda_info()
 {
   std::vector<std::tuple<std::string, std::string>> props;
@@ -315,6 +378,15 @@ inline auto cuda_info()
                      std::to_string(driver / 1000) + "." + std::to_string((driver % 100) / 10));
   props.emplace_back("gpu_runtime_version",
                      std::to_string(runtime / 1000) + "." + std::to_string((runtime % 100) / 10));
+  props.emplace_back("gpu_hostNativeAtomicSupported",
+                     std::to_string(device_prop.hostNativeAtomicSupported));
+  props.emplace_back("gpu_pageableMemoryAccess", std::to_string(device_prop.pageableMemoryAccess));
+  props.emplace_back("gpu_pageableMemoryAccessUsesHostPageTables",
+                     std::to_string(device_prop.pageableMemoryAccessUsesHostPageTables));
+#if defined(CUDART_VERSION) && CUDART_VERSION >= 12000
+  props.emplace_back("gpu_gpuDirectRDMASupported",
+                     std::to_string(device_prop.gpuDirectRDMASupported));
+#endif
 #endif
   return props;
 }

From 25178260d0f1cc6408ba9ee131c904cd1ce94613 Mon Sep 17 00:00:00 2001
From: rhdong <hrong@nvidia.com>
Date: Mon, 29 Jul 2024 13:37:24 -0700
Subject: [PATCH 111/623] [FEA] expose python & C API for prefiltered brute
 force (#174)

Authors:
  - rhdong (https://github.com/rhdong)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/174
---
 cpp/include/cuvs/neighbors/brute_force.h      |  12 +-
 cpp/include/cuvs/neighbors/brute_force.hpp    |   9 +-
 cpp/include/cuvs/neighbors/common.h           |  61 +++
 cpp/src/neighbors/brute_force_c.cpp           |  34 +-
 cpp/test/neighbors/ann_cagra_c.cu             |  29 +-
 cpp/test/neighbors/ann_ivf_flat_c.cu          |  39 +-
 cpp/test/neighbors/ann_ivf_pq_c.cu            |  39 +-
 cpp/test/neighbors/brute_force_c.cu           | 373 +++++++++++++++++-
 cpp/test/neighbors/run_brute_force_c.c        |  26 +-
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 python/cuvs/cuvs/neighbors/__init__.py        |  11 +-
 .../neighbors/brute_force/brute_force.pxd     |   4 +-
 .../neighbors/brute_force/brute_force.pyx     |  48 ++-
 .../cuvs/neighbors/filters/CMakeLists.txt     |  25 ++
 .../cuvs/cuvs/neighbors/filters/__init__.pxd  |   0
 .../cuvs/cuvs/neighbors/filters/__init__.py   |  18 +
 .../cuvs/cuvs/neighbors/filters/filters.pxd   |  30 ++
 .../cuvs/cuvs/neighbors/filters/filters.pyx   |  97 +++++
 python/cuvs/cuvs/test/test_brute_force.py     |  98 ++++-
 rust/cuvs/src/brute_force.rs                  |  22 +-
 20 files changed, 865 insertions(+), 111 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/common.h
 create mode 100644 python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/filters/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/filters/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/filters/filters.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/filters/filters.pyx

diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
index e285eae37e..c9e172f629 100644
--- a/cpp/include/cuvs/neighbors/brute_force.h
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -18,6 +18,7 @@
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.h>
+#include <cuvs/neighbors/common.h>
 #include <dlpack/dlpack.h>
 #include <stdint.h>
 
@@ -135,9 +136,13 @@ cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
  * DLManagedTensor dataset;
  * DLManagedTensor queries;
  * DLManagedTensor neighbors;
+ * DLManagedTensor bitmap;
+ *
+ * cuvsFilter prefilter{(uintptr_t)&bitmap, BITMAP};
  *
  * // Search the `index` built using `cuvsBruteForceBuild`
- * cuvsError_t search_status = cuvsBruteForceSearch(res, index, &queries, &neighbors, &distances);
+ * cuvsError_t search_status = cuvsBruteForceSearch(res, index, &queries, &neighbors, &distances,
+ * prefilter);
  *
  * // de-allocate `res`
  * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
@@ -148,12 +153,15 @@ cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
  * @param[in] queries DLManagedTensor* queries dataset to search
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
+ * @param[in] prefilter cuvsFilter input prefilter that can be used
+              to filter queries and neighbors based on the given bitmap.
  */
 cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
                                  cuvsBruteForceIndex_t index,
                                  DLManagedTensor* queries,
                                  DLManagedTensor* neighbors,
-                                 DLManagedTensor* distances);
+                                 DLManagedTensor* distances,
+                                 cuvsFilter prefilter);
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 1ec7e81f73..0a5071b6da 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -165,7 +165,7 @@ struct index : cuvs::neighbors::index {
  * @param[in] metric cuvs::distance::DistanceType
  * @param[in] metric_arg metric argument
  *
- * @return the constructed bruteforce index
+ * @return the constructed brute-force index
  */
 auto build(raft::resources const& handle,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
@@ -221,13 +221,14 @@ auto build(raft::resources const& handle,
  * @endcode
  *
  * @param[in] handle
- * @param[in] index bruteforce constructed index
+ * @param[in] index brute-force constructed index
  * @param[in] queries a device pointer to a row-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
- * @param[in] sample_filter an optional device bitmap filter function that greenlights samples for a
- * given query
+ * @param[in] sample_filter An optional device bitmap filter function with a `row-major` layout and
+ * the shape of [n_queries, index->size()], which means the filter will use the first
+ * `index->size()` bits to indicate whether queries[0] should compute the distance with dataset.
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::brute_force::index<float>& index,
diff --git a/cpp/include/cuvs/neighbors/common.h b/cpp/include/cuvs/neighbors/common.h
new file mode 100644
index 0000000000..02cbeea960
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/common.h
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup filters Filters APIs
+ * @brief APIs related to filter functionality.
+ * @{
+ */
+
+/**
+ * @brief Enum to denote filter type.
+ */
+enum cuvsFilterType {
+  /* No filter */
+  NO_FILTER,
+  /* Filter an index with a bitset */
+  BITSET,
+  /* Filter an index with a bitmap */
+  BITMAP
+};
+
+/**
+ * @brief Struct to hold address of cuvs::neighbor::prefilter and its type
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  enum cuvsFilterType type;
+} cuvsFilter;
+
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index 5f04ffa340..f3ca2e7309 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -27,6 +27,7 @@
 #include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/neighbors/common.h>
 
 namespace {
 
@@ -53,7 +54,8 @@ void _search(cuvsResources_t res,
              cuvsBruteForceIndex index,
              DLManagedTensor* queries_tensor,
              DLManagedTensor* neighbors_tensor,
-             DLManagedTensor* distances_tensor)
+             DLManagedTensor* distances_tensor,
+             cuvsFilter prefilter)
 {
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T>*>(index.addr);
@@ -61,12 +63,29 @@ void _search(cuvsResources_t res,
   using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, raft::row_major>;
   using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
   using distances_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
-  auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
-  auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
-  auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+  using prefilter_mds_type    = raft::device_vector_view<const uint32_t, int64_t>;
+  using prefilter_opt_type    = cuvs::core::bitmap_view<const uint32_t, int64_t>;
+
+  auto queries_mds   = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> filter_opt;
+
+  if (prefilter.type == NO_FILTER) {
+    filter_opt = std::nullopt;
+  } else {
+    auto prefilter_ptr  = reinterpret_cast<DLManagedTensor*>(prefilter.addr);
+    auto prefilter_mds  = cuvs::core::from_dlpack<prefilter_mds_type>(prefilter_ptr);
+    auto prefilter_view = prefilter_opt_type((const uint32_t*)prefilter_mds.data_handle(),
+                                             queries_mds.extent(0),
+                                             index_ptr->dataset().extent(0));
+
+    filter_opt = std::make_optional<prefilter_opt_type>(prefilter_view);
+  }
 
   cuvs::neighbors::brute_force::search(
-    *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds, std::nullopt);
+    *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds, filter_opt);
 }
 
 }  // namespace
@@ -120,7 +139,8 @@ extern "C" cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
                                             cuvsBruteForceIndex_t index_c_ptr,
                                             DLManagedTensor* queries_tensor,
                                             DLManagedTensor* neighbors_tensor,
-                                            DLManagedTensor* distances_tensor)
+                                            DLManagedTensor* distances_tensor,
+                                            cuvsFilter prefilter)
 {
   return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
@@ -143,7 +163,7 @@ extern "C" cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
     RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
 
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
-      _search<float>(res, index, queries_tensor, neighbors_tensor, distances_tensor);
+      _search<float>(res, index, queries_tensor, neighbors_tensor, distances_tensor, prefilter);
     } else {
       RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
                 queries.dtype.code,
diff --git a/cpp/test/neighbors/ann_cagra_c.cu b/cpp/test/neighbors/ann_cagra_c.cu
index 6e3a3cbd1f..599d2d8428 100644
--- a/cpp/test/neighbors/ann_cagra_c.cu
+++ b/cpp/test/neighbors/ann_cagra_c.cu
@@ -42,6 +42,8 @@ TEST(CagraC, BuildSearch)
   // create cuvsResources_t
   cuvsResources_t res;
   cuvsResourcesCreate(&res);
+  cudaStream_t stream;
+  cuvsStreamGet(res, &stream);
 
   // create dataset DLTensor
   DLManagedTensor dataset_tensor;
@@ -65,12 +67,11 @@ TEST(CagraC, BuildSearch)
   cuvsCagraBuild(res, build_params, &dataset_tensor, index);
 
   // create queries DLTensor
-  float* queries_d;
-  cudaMalloc(&queries_d, sizeof(float) * 4 * 2);
-  cudaMemcpy(queries_d, queries, sizeof(float) * 4 * 2, cudaMemcpyDefault);
+  rmm::device_uvector<float> queries_d(4 * 2, stream);
+  raft::copy(queries_d.data(), (float*)queries, 4 * 2, stream);
 
   DLManagedTensor queries_tensor;
-  queries_tensor.dl_tensor.data               = queries_d;
+  queries_tensor.dl_tensor.data               = queries_d.data();
   queries_tensor.dl_tensor.device.device_type = kDLCUDA;
   queries_tensor.dl_tensor.ndim               = 2;
   queries_tensor.dl_tensor.dtype.code         = kDLFloat;
@@ -81,11 +82,10 @@ TEST(CagraC, BuildSearch)
   queries_tensor.dl_tensor.strides            = nullptr;
 
   // create neighbors DLTensor
-  uint32_t* neighbors_d;
-  cudaMalloc(&neighbors_d, sizeof(uint32_t) * 4);
+  rmm::device_uvector<uint32_t> neighbors_d(4, stream);
 
   DLManagedTensor neighbors_tensor;
-  neighbors_tensor.dl_tensor.data               = neighbors_d;
+  neighbors_tensor.dl_tensor.data               = neighbors_d.data();
   neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
   neighbors_tensor.dl_tensor.ndim               = 2;
   neighbors_tensor.dl_tensor.dtype.code         = kDLUInt;
@@ -96,11 +96,10 @@ TEST(CagraC, BuildSearch)
   neighbors_tensor.dl_tensor.strides            = nullptr;
 
   // create distances DLTensor
-  float* distances_d;
-  cudaMalloc(&distances_d, sizeof(float) * 4);
+  rmm::device_uvector<float> distances_d(4, stream);
 
   DLManagedTensor distances_tensor;
-  distances_tensor.dl_tensor.data               = distances_d;
+  distances_tensor.dl_tensor.data               = distances_d.data();
   distances_tensor.dl_tensor.device.device_type = kDLCUDA;
   distances_tensor.dl_tensor.ndim               = 2;
   distances_tensor.dl_tensor.dtype.code         = kDLFloat;
@@ -116,14 +115,10 @@ TEST(CagraC, BuildSearch)
   cuvsCagraSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
 
   // verify output
-  ASSERT_TRUE(cuvs::devArrMatchHost(neighbors_exp, neighbors_d, 4, cuvs::Compare<uint32_t>()));
   ASSERT_TRUE(
-    cuvs::devArrMatchHost(distances_exp, distances_d, 4, cuvs::CompareApprox<float>(0.001f)));
-
-  // delete device memory
-  cudaFree(queries_d);
-  cudaFree(neighbors_d);
-  cudaFree(distances_d);
+    cuvs::devArrMatchHost(neighbors_exp, neighbors_d.data(), 4, cuvs::Compare<uint32_t>()));
+  ASSERT_TRUE(cuvs::devArrMatchHost(
+    distances_exp, distances_d.data(), 4, cuvs::CompareApprox<float>(0.001f)));
 
   // de-allocate index and res
   cuvsCagraSearchParamsDestroy(search_params);
diff --git a/cpp/test/neighbors/ann_ivf_flat_c.cu b/cpp/test/neighbors/ann_ivf_flat_c.cu
index 7844188603..8c23e6cff8 100644
--- a/cpp/test/neighbors/ann_ivf_flat_c.cu
+++ b/cpp/test/neighbors/ann_ivf_flat_c.cu
@@ -101,36 +101,37 @@ TEST(IvfFlatC, BuildSearch)
   int64_t n_dim        = 32;
   uint32_t n_neighbors = 8;
 
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+
   cuvsDistanceType metric = L2Expanded;
   size_t n_probes         = 20;
   size_t n_lists          = 1024;
 
-  float *index_data, *query_data, *distances_data;
-  int64_t* neighbors_data;
-  cudaMalloc(&index_data, sizeof(float) * n_rows * n_dim);
-  cudaMalloc(&query_data, sizeof(float) * n_queries * n_dim);
-  cudaMalloc(&neighbors_data, sizeof(int64_t) * n_queries * n_neighbors);
-  cudaMalloc(&distances_data, sizeof(float) * n_queries * n_neighbors);
+  rmm::device_uvector<float> index_data(n_rows * n_dim, stream);
+  rmm::device_uvector<float> query_data(n_queries * n_dim, stream);
+  rmm::device_uvector<int64_t> neighbors_data(n_queries * n_neighbors, stream);
+  rmm::device_uvector<float> distances_data(n_queries * n_neighbors, stream);
 
-  generate_random_data(index_data, n_rows * n_dim);
-  generate_random_data(query_data, n_queries * n_dim);
+  generate_random_data(index_data.data(), n_rows * n_dim);
+  generate_random_data(query_data.data(), n_queries * n_dim);
 
   run_ivf_flat(n_rows,
                n_queries,
                n_dim,
                n_neighbors,
-               index_data,
-               query_data,
-               distances_data,
-               neighbors_data,
+               index_data.data(),
+               query_data.data(),
+               distances_data.data(),
+               neighbors_data.data(),
                metric,
                n_probes,
                n_lists);
 
-  recall_eval(query_data,
-              index_data,
-              neighbors_data,
-              distances_data,
+  recall_eval(query_data.data(),
+              index_data.data(),
+              neighbors_data.data(),
+              distances_data.data(),
               n_queries,
               n_rows,
               n_dim,
@@ -138,10 +139,4 @@ TEST(IvfFlatC, BuildSearch)
               metric,
               n_probes,
               n_lists);
-
-  // delete device memory
-  cudaFree(index_data);
-  cudaFree(query_data);
-  cudaFree(neighbors_data);
-  cudaFree(distances_data);
 }
diff --git a/cpp/test/neighbors/ann_ivf_pq_c.cu b/cpp/test/neighbors/ann_ivf_pq_c.cu
index 88cd1bd93a..21e4805b90 100644
--- a/cpp/test/neighbors/ann_ivf_pq_c.cu
+++ b/cpp/test/neighbors/ann_ivf_pq_c.cu
@@ -101,36 +101,37 @@ TEST(IvfPqC, BuildSearch)
   int64_t n_dim        = 32;
   uint32_t n_neighbors = 8;
 
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+
   cuvsDistanceType metric = L2Expanded;
   size_t n_probes         = 20;
   size_t n_lists          = 1024;
 
-  float *index_data, *query_data, *distances_data;
-  int64_t* neighbors_data;
-  cudaMalloc(&index_data, sizeof(float) * n_rows * n_dim);
-  cudaMalloc(&query_data, sizeof(float) * n_queries * n_dim);
-  cudaMalloc(&neighbors_data, sizeof(int64_t) * n_queries * n_neighbors);
-  cudaMalloc(&distances_data, sizeof(float) * n_queries * n_neighbors);
+  rmm::device_uvector<float> index_data(n_rows * n_dim, stream);
+  rmm::device_uvector<float> query_data(n_queries * n_dim, stream);
+  rmm::device_uvector<int64_t> neighbors_data(n_queries * n_neighbors, stream);
+  rmm::device_uvector<float> distances_data(n_queries * n_neighbors, stream);
 
-  generate_random_data(index_data, n_rows * n_dim);
-  generate_random_data(query_data, n_queries * n_dim);
+  generate_random_data(index_data.data(), n_rows * n_dim);
+  generate_random_data(query_data.data(), n_queries * n_dim);
 
   run_ivf_pq(n_rows,
              n_queries,
              n_dim,
              n_neighbors,
-             index_data,
-             query_data,
-             distances_data,
-             neighbors_data,
+             index_data.data(),
+             query_data.data(),
+             distances_data.data(),
+             neighbors_data.data(),
              metric,
              n_probes,
              n_lists);
 
-  recall_eval(query_data,
-              index_data,
-              neighbors_data,
-              distances_data,
+  recall_eval(query_data.data(),
+              index_data.data(),
+              neighbors_data.data(),
+              distances_data.data(),
               n_queries,
               n_rows,
               n_dim,
@@ -138,10 +139,4 @@ TEST(IvfPqC, BuildSearch)
               metric,
               n_probes,
               n_lists);
-
-  // delete device memory
-  cudaFree(index_data);
-  cudaFree(query_data);
-  cudaFree(neighbors_data);
-  cudaFree(distances_data);
 }
diff --git a/cpp/test/neighbors/brute_force_c.cu b/cpp/test/neighbors/brute_force_c.cu
index 8caf1c9d1c..8abc340715 100644
--- a/cpp/test/neighbors/brute_force_c.cu
+++ b/cpp/test/neighbors/brute_force_c.cu
@@ -18,16 +18,27 @@
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/handle.hpp>
 #include <raft/random/rng.cuh>
+#include <rmm/device_uvector.hpp>
 
 #include "ann_utils.cuh"
 #include <cuvs/neighbors/brute_force.h>
 
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <optional>
+#include <queue>
+#include <random>
+#include <unordered_set>
+#include <vector>
+
 extern "C" void run_brute_force(int64_t n_rows,
                                 int64_t n_queries,
                                 int64_t n_dim,
                                 uint32_t n_neighbors,
                                 float* index_data,
                                 float* query_data,
+                                uint32_t* prefilter_data,
                                 float* distances_data,
                                 int64_t* neighbors_data,
                                 cuvsDistanceType metric);
@@ -40,9 +51,210 @@ void generate_random_data(T* devPtr, size_t size)
   raft::random::uniform(handle, r, devPtr, size, T(0.1), T(2.0));
 };
 
+template <typename index_t, typename bitmap_t = uint32_t>
+index_t create_sparse_matrix(index_t m, index_t n, float sparsity, std::vector<bitmap_t>& bitmap)
+{
+  index_t total    = static_cast<index_t>(m * n);
+  index_t num_ones = static_cast<index_t>((total * 1.0f) * sparsity);
+  index_t nnz      = num_ones;
+
+  for (auto& item : bitmap) {
+    item = static_cast<bitmap_t>(0);
+  }
+
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::uniform_int_distribution<index_t> dis(0, total - 1);
+
+  while (num_ones > 0) {
+    index_t index = dis(gen);
+
+    bitmap_t& element    = bitmap[index / (8 * sizeof(bitmap_t))];
+    index_t bit_position = index % (8 * sizeof(bitmap_t));
+
+    if (((element >> bit_position) & 1) == 0) {
+      element |= (static_cast<bitmap_t>(1) << bit_position);
+      num_ones--;
+    }
+  }
+  return nnz;
+}
+
+template <typename index_t, typename bitmap_t = uint32_t>
+void cpu_convert_to_csr(std::vector<bitmap_t>& bitmap,
+                        index_t rows,
+                        index_t cols,
+                        std::vector<index_t>& indices,
+                        std::vector<index_t>& indptr)
+{
+  index_t offset_indptr   = 0;
+  index_t offset_values   = 0;
+  indptr[offset_indptr++] = 0;
+
+  index_t index        = 0;
+  bitmap_t element     = 0;
+  index_t bit_position = 0;
+
+  for (index_t i = 0; i < rows; ++i) {
+    for (index_t j = 0; j < cols; ++j) {
+      index        = i * cols + j;
+      element      = bitmap[index / (8 * sizeof(bitmap_t))];
+      bit_position = index % (8 * sizeof(bitmap_t));
+
+      if (((element >> bit_position) & 1)) {
+        indices[offset_values] = static_cast<index_t>(j);
+        offset_values++;
+      }
+    }
+    indptr[offset_indptr++] = static_cast<index_t>(offset_values);
+  }
+}
+
+template <typename value_t, typename index_t>
+void cpu_sddmm(value_t* A,
+               value_t* B,
+               std::vector<value_t>& vals,
+               const std::vector<index_t>& cols,
+               const std::vector<index_t>& row_ptrs,
+               bool is_row_major_A,
+               bool is_row_major_B,
+               index_t n_queries,
+               index_t n_dataset,
+               index_t dim,
+               cuvsDistanceType metric,
+               value_t alpha = 1.0,
+               value_t beta  = 0.0)
+{
+  bool trans_a = is_row_major_A;
+  bool trans_b = is_row_major_B;
+
+  for (index_t i = 0; i < n_queries; ++i) {
+    for (index_t j = row_ptrs[i]; j < row_ptrs[i + 1]; ++j) {
+      value_t sum     = 0;
+      value_t norms_A = 0;
+      value_t norms_B = 0;
+      for (index_t l = 0; l < dim; ++l) {
+        index_t a_index = trans_a ? i * dim + l : l * n_queries + i;
+        index_t b_index = trans_b ? l * n_dataset + cols[j] : cols[j] * dim + l;
+        sum += A[a_index] * B[b_index];
+
+        norms_A += A[a_index] * A[a_index];
+        norms_B += B[b_index] * B[b_index];
+      }
+      vals[j] = alpha * sum + beta * vals[j];
+      if (metric == cuvs::distance::DistanceType::L2Expanded) {
+        vals[j] = value_t(-2.0) * vals[j] + norms_A + norms_B;
+      } else if (metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+        vals[j] = std::sqrt(value_t(-2.0) * vals[j] + norms_A + norms_B);
+      } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+        vals[j] = value_t(1.0) - vals[j] / std::sqrt(norms_A * norms_B);
+      }
+    }
+  }
+}
+
+template <typename value_t, typename index_t>
+void cpu_select_k(const std::vector<index_t>& indptr_h,
+                  const std::vector<index_t>& indices_h,
+                  const std::vector<value_t>& values_h,
+                  std::optional<std::vector<index_t>>& in_idx_h,
+                  index_t n_queries,
+                  index_t n_dataset,
+                  index_t n_neighbors,
+                  std::vector<value_t>& out_values_h,
+                  std::vector<index_t>& out_indices_h,
+                  bool select_min = true)
+{
+  auto comp = [select_min](const std::pair<value_t, index_t>& a,
+                           const std::pair<value_t, index_t>& b) {
+    return select_min ? a.first < b.first : a.first >= b.first;
+  };
+
+  for (index_t row = 0; row < n_queries; ++row) {
+    std::priority_queue<std::pair<value_t, index_t>,
+                        std::vector<std::pair<value_t, index_t>>,
+                        decltype(comp)>
+      pq(comp);
+
+    for (index_t idx = indptr_h[row]; idx < indptr_h[row + 1]; ++idx) {
+      pq.push({values_h[idx], (in_idx_h.has_value()) ? (*in_idx_h)[idx] : indices_h[idx]});
+      if (pq.size() > size_t(n_neighbors)) { pq.pop(); }
+    }
+
+    std::vector<std::pair<value_t, index_t>> row_pairs;
+    while (!pq.empty()) {
+      row_pairs.push_back(pq.top());
+      pq.pop();
+    }
+
+    if (select_min) {
+      std::sort(row_pairs.begin(), row_pairs.end(), [](const auto& a, const auto& b) {
+        return a.first <= b.first;
+      });
+    } else {
+      std::sort(row_pairs.begin(), row_pairs.end(), [](const auto& a, const auto& b) {
+        return a.first >= b.first;
+      });
+    }
+    for (index_t col = 0; col < n_neighbors; col++) {
+      if (col < index_t(row_pairs.size())) {
+        out_values_h[row * n_neighbors + col]  = row_pairs[col].first;
+        out_indices_h[row * n_neighbors + col] = row_pairs[col].second;
+      }
+    }
+  }
+}
+
+template <typename value_t, typename index_t, typename bitmap_t = uint32_t>
+void cpu_brute_force_with_filter(value_t* query_data,
+                                 value_t* index_data,
+                                 std::vector<bitmap_t>& filter,
+                                 std::vector<index_t>& out_indices_h,
+                                 std::vector<value_t>& out_values_h,
+                                 size_t n_queries,
+                                 size_t n_dataset,
+                                 size_t n_dim,
+                                 size_t n_neighbors,
+                                 size_t nnz,
+                                 bool select_min,
+                                 cuvsDistanceType metric)
+{
+  std::vector<value_t> values_h(nnz);
+  std::vector<index_t> indices_h(nnz);
+  std::vector<index_t> indptr_h(n_queries + 1);
+
+  cpu_convert_to_csr(filter, (index_t)n_queries, (index_t)n_dataset, indices_h, indptr_h);
+
+  cpu_sddmm(query_data,
+            index_data,
+            values_h,
+            indices_h,
+            indptr_h,
+            true,
+            false,
+            (index_t)n_queries,
+            (index_t)n_dataset,
+            (index_t)n_dim,
+            metric);
+
+  std::optional<std::vector<index_t>> optional_indices_h = std::nullopt;
+
+  cpu_select_k(indptr_h,
+               indices_h,
+               values_h,
+               optional_indices_h,
+               (index_t)n_queries,
+               (index_t)n_dataset,
+               (index_t)n_neighbors,
+               out_values_h,
+               out_indices_h,
+               select_min);
+}
+
 template <typename T, typename IdxT>
 void recall_eval(T* query_data,
                  T* index_data,
+                 uint32_t* filter,
                  IdxT* neighbors,
                  T* distances,
                  size_t n_queries,
@@ -90,6 +302,63 @@ void recall_eval(T* query_data,
                                                min_recall));
 };
 
+template <typename T, typename IdxT, typename bitmap_t = uint32_t>
+void recall_eval_with_filter(T* query_data,
+                             T* index_data,
+                             std::vector<bitmap_t>& filter_h,
+                             IdxT* neighbors_d,
+                             T* distances_d,
+                             std::vector<T>& distances_ref_h,
+                             std::vector<IdxT>& neighbors_ref_h,
+                             size_t n_queries,
+                             size_t n_rows,
+                             size_t n_dim,
+                             uint32_t n_neighbors,
+                             size_t nnz,
+                             cuvsDistanceType metric)
+{
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  std::vector<T> queries_h(n_queries * n_dim);
+  std::vector<T> indices_h(n_rows * n_dim);
+
+  size_t size = n_queries * n_neighbors;
+  std::vector<IdxT> neighbors_h(size);
+  std::vector<T> distances_h(size);
+
+  raft::copy(neighbors_h.data(), neighbors_d, size, stream);
+  raft::copy(distances_h.data(), distances_d, size, stream);
+  raft::copy(queries_h.data(), query_data, n_queries * n_dim, stream);
+  raft::copy(indices_h.data(), index_data, n_rows * n_dim, stream);
+
+  bool select_min = cuvs::distance::is_min_close(metric);
+
+  cpu_brute_force_with_filter(queries_h.data(),
+                              indices_h.data(),
+                              filter_h,
+                              neighbors_ref_h,
+                              distances_ref_h,
+                              n_queries,
+                              n_rows,
+                              n_dim,
+                              n_neighbors,
+                              nnz,
+                              select_min,
+                              static_cast<cuvs::distance::DistanceType>((uint16_t)metric));
+
+  // verify output
+  double min_recall = 0.95;
+  ASSERT_TRUE(cuvs::neighbors::eval_neighbours(neighbors_ref_h,
+                                               neighbors_h,
+                                               distances_ref_h,
+                                               distances_h,
+                                               n_queries,
+                                               n_neighbors,
+                                               0.001,
+                                               min_recall));
+};
+
 TEST(BruteForceC, BuildSearch)
 {
   int64_t n_rows       = 8096;
@@ -97,41 +366,103 @@ TEST(BruteForceC, BuildSearch)
   int64_t n_dim        = 32;
   uint32_t n_neighbors = 8;
 
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+
   cuvsDistanceType metric = L2Expanded;
 
-  float *index_data, *query_data, *distances_data;
-  int64_t* neighbors_data;
-  cudaMalloc(&index_data, sizeof(float) * n_rows * n_dim);
-  cudaMalloc(&query_data, sizeof(float) * n_queries * n_dim);
-  cudaMalloc(&neighbors_data, sizeof(int64_t) * n_queries * n_neighbors);
-  cudaMalloc(&distances_data, sizeof(float) * n_queries * n_neighbors);
+  uint32_t* filter_data = NULL;
+
+  rmm::device_uvector<float> index_data(n_rows * n_dim, stream);
+  rmm::device_uvector<float> query_data(n_queries * n_dim, stream);
+  rmm::device_uvector<int64_t> neighbors_data(n_queries * n_neighbors, stream);
+  rmm::device_uvector<float> distances_data(n_queries * n_neighbors, stream);
 
-  generate_random_data(index_data, n_rows * n_dim);
-  generate_random_data(query_data, n_queries * n_dim);
+  generate_random_data(index_data.data(), n_rows * n_dim);
+  generate_random_data(query_data.data(), n_queries * n_dim);
 
   run_brute_force(n_rows,
                   n_queries,
                   n_dim,
                   n_neighbors,
-                  index_data,
-                  query_data,
-                  distances_data,
-                  neighbors_data,
+                  index_data.data(),
+                  query_data.data(),
+                  filter_data,
+                  distances_data.data(),
+                  neighbors_data.data(),
                   metric);
 
-  recall_eval(query_data,
-              index_data,
-              neighbors_data,
-              distances_data,
+  recall_eval(query_data.data(),
+              index_data.data(),
+              filter_data,
+              neighbors_data.data(),
+              distances_data.data(),
               n_queries,
               n_rows,
               n_dim,
               n_neighbors,
               metric);
+}
+
+TEST(BruteForceC, BuildSearchWithFilter)
+{
+  int64_t n_rows       = 8096;
+  int64_t n_queries    = 128;
+  int64_t n_dim        = 32;
+  uint32_t n_neighbors = 8;
+
+  raft::resources handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  float sparsity   = 0.2;
+  int64_t n_filter = (n_queries * n_rows + 31) / 32;
+  std::vector<uint32_t> filter_h(n_filter);
+  int64_t nnz = create_sparse_matrix(n_queries, n_rows, sparsity, filter_h);
+
+  cuvsDistanceType metric = L2Expanded;
+  bool select_min         = cuvs::distance::is_min_close(metric);
+
+  std::vector<float> distances_ref_h(
+    n_queries * n_neighbors,
+    select_min ? std::numeric_limits<float>::infinity() : std::numeric_limits<float>::lowest());
+  std::vector<int64_t> neighbors_ref_h(n_queries * n_neighbors, static_cast<int64_t>(0));
+
+  rmm::device_uvector<float> index_data(n_rows * n_dim, stream);
+  rmm::device_uvector<float> query_data(n_queries * n_dim, stream);
+  rmm::device_uvector<int64_t> neighbors_data(n_queries * n_neighbors, stream);
+  rmm::device_uvector<float> distances_data(n_queries * n_neighbors, stream);
+  rmm::device_uvector<uint32_t> filter_data(n_filter, stream);
+
+  raft::copy(neighbors_data.data(), neighbors_ref_h.data(), n_queries * n_neighbors, stream);
+  raft::copy(distances_data.data(), distances_ref_h.data(), n_queries * n_neighbors, stream);
+
+  generate_random_data(index_data.data(), n_rows * n_dim);
+  generate_random_data(query_data.data(), n_queries * n_dim);
+
+  raft::copy(filter_data.data(), filter_h.data(), n_filter, stream);
+
+  run_brute_force(n_rows,
+                  n_queries,
+                  n_dim,
+                  n_neighbors,
+                  index_data.data(),
+                  query_data.data(),
+                  filter_data.data(),
+                  distances_data.data(),
+                  neighbors_data.data(),
+                  metric);
 
-  // delete device memory
-  cudaFree(index_data);
-  cudaFree(query_data);
-  cudaFree(neighbors_data);
-  cudaFree(distances_data);
+  recall_eval_with_filter(query_data.data(),
+                          index_data.data(),
+                          filter_h,
+                          neighbors_data.data(),
+                          distances_data.data(),
+                          distances_ref_h,
+                          neighbors_ref_h,
+                          n_queries,
+                          n_rows,
+                          n_dim,
+                          n_neighbors,
+                          nnz,
+                          metric);
 }
diff --git a/cpp/test/neighbors/run_brute_force_c.c b/cpp/test/neighbors/run_brute_force_c.c
index ed9e999709..ceb1247314 100644
--- a/cpp/test/neighbors/run_brute_force_c.c
+++ b/cpp/test/neighbors/run_brute_force_c.c
@@ -15,6 +15,7 @@
  */
 
 #include <cuvs/neighbors/brute_force.h>
+#include <stdint.h>
 
 void run_brute_force(int64_t n_rows,
                      int64_t n_queries,
@@ -22,6 +23,7 @@ void run_brute_force(int64_t n_rows,
                      uint32_t n_neighbors,
                      float* index_data,
                      float* query_data,
+                     uint32_t* prefilter_data,
                      float* distances_data,
                      int64_t* neighbors_data,
                      cuvsDistanceType metric)
@@ -85,8 +87,30 @@ void run_brute_force(int64_t n_rows,
   distances_tensor.dl_tensor.shape              = distances_shape;
   distances_tensor.dl_tensor.strides            = NULL;
 
+  cuvsFilter prefilter;
+
+  DLManagedTensor prefilter_tensor;
+  if (prefilter_data == NULL) {
+    prefilter.type = NO_FILTER;
+    prefilter.addr = (uintptr_t)NULL;
+  } else {
+    prefilter_tensor.dl_tensor.data               = (void*)prefilter_data;
+    prefilter_tensor.dl_tensor.device.device_type = kDLCUDA;
+    prefilter_tensor.dl_tensor.ndim               = 1;
+    prefilter_tensor.dl_tensor.dtype.code         = kDLUInt;
+    prefilter_tensor.dl_tensor.dtype.bits         = 32;
+    prefilter_tensor.dl_tensor.dtype.lanes        = 1;
+    int64_t prefilter_shape[1]                    = {(n_queries * n_rows + 31) / 32};
+    prefilter_tensor.dl_tensor.shape              = prefilter_shape;
+    prefilter_tensor.dl_tensor.strides            = NULL;
+
+    prefilter.type = BITMAP;
+    prefilter.addr = (uintptr_t)&prefilter_tensor;
+  }
+
   // search index
-  cuvsBruteForceSearch(res, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  cuvsBruteForceSearch(
+    res, index, &queries_tensor, &neighbors_tensor, &distances_tensor, prefilter);
 
   // de-allocate index and res
   cuvsBruteForceIndexDestroy(index);
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 3579215fd9..21c3db5da3 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -16,6 +16,7 @@ add_subdirectory(brute_force)
 add_subdirectory(cagra)
 add_subdirectory(ivf_flat)
 add_subdirectory(ivf_pq)
+add_subdirectory(filters)
 
 # Set the list of Cython files to build
 set(cython_sources refine.pyx)
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index 0ecd57d750..52bb1eef84 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -13,8 +13,15 @@
 # limitations under the License.
 
 
-from cuvs.neighbors import brute_force, cagra, ivf_flat, ivf_pq
+from cuvs.neighbors import brute_force, cagra, filters, ivf_flat, ivf_pq
 
 from .refine import refine
 
-__all__ = ["brute_force", "cagra", "ivf_flat", "ivf_pq", "refine"]
+__all__ = [
+    "brute_force",
+    "cagra",
+    "filters",
+    "ivf_flat",
+    "ivf_pq",
+    "refine",
+]
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
index c57fa9e8da..1838279169 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
@@ -20,6 +20,7 @@ from libc.stdint cimport uintptr_t
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
 from cuvs.distance_type cimport cuvsDistanceType
+from cuvs.neighbors.filters.filters cimport cuvsFilter, cuvsFilterType
 
 
 cdef extern from "cuvs/neighbors/brute_force.h" nogil:
@@ -44,4 +45,5 @@ cdef extern from "cuvs/neighbors/brute_force.h" nogil:
                                      cuvsBruteForceIndex_t index,
                                      DLManagedTensor* queries,
                                      DLManagedTensor* neighbors,
-                                     DLManagedTensor* distances) except +
+                                     DLManagedTensor* distances,
+                                     cuvsFilter filter) except +
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 490f1d3ac2..559302ccc2 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -38,6 +38,7 @@ from cuvs.distance import DISTANCE_TYPES
 from cuvs.common.c_api cimport cuvsResources_t
 
 from cuvs.common.exceptions import check_cuvs
+from cuvs.neighbors.filters import no_filter
 
 
 cdef class Index:
@@ -129,7 +130,8 @@ def search(Index index,
            k,
            neighbors=None,
            distances=None,
-           resources=None):
+           resources=None,
+           prefilter=None):
     """
     Find the k nearest neighbors for each query.
 
@@ -147,10 +149,18 @@ def search(Index index,
     distances : Optional CUDA array interface compliant matrix shape
                 (n_queries, k) If supplied, the distances to the
                 neighbors will be written here in-place. (default None)
+    prefilter : Optional cuvs.neighbors.cuvsFilter can be used to filter
+                queries and neighbors based on a given bitmap. The filter
+                function should have a row-major layout and logical shape
+                [n_queries, n_samples], using the first n_samples bits to
+                indicate whether queries[0] should compute the distance with
+                index.
+        (default None)
     {resources_docstring}
 
     Examples
     --------
+    >>> # Example without pre-filter
     >>> import cupy as cp
     >>> from cuvs.neighbors import brute_force
     >>> n_samples = 50000
@@ -170,6 +180,36 @@ def search(Index index,
     >>> distances, neighbors = brute_force.search(index, queries, k)
     >>> neighbors = cp.asarray(neighbors)
     >>> distances = cp.asarray(distances)
+
+    Examples
+    --------
+    >>> # Example with pre-filter
+    >>> import numpy as np
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import brute_force, filters
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = brute_force.build(dataset, metric="sqeuclidean")
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build filters
+    >>> n_bitmap = np.ceil(n_samples * n_queries / 32).astype(int)
+    >>> # Create your own bitmap as the filter by replacing the random one.
+    >>> bitmap = cp.random.randint(1, 1000, size=(n_bitmap,), dtype=cp.uint32)
+    >>> prefilter = filters.from_bitmap(bitmap)
+    >>> k = 10
+    >>> # Using a pooling allocator reduces overhead of temporary array
+    >>> # creation during search. This is useful if multiple searches
+    >>> # are performed with same query size.
+    >>> distances, neighbors = brute_force.search(index, queries, k,
+    ...                                           prefilter=prefilter)
+    >>> neighbors = cp.asarray(neighbors)
+    >>> distances = cp.asarray(distances)
     """
     if not index.trained:
         raise ValueError("Index needs to be built before calling search.")
@@ -202,13 +242,17 @@ def search(Index index,
     cdef cydlpack.DLManagedTensor* distances_dlpack = \
         cydlpack.dlpack_c(distances_cai)
 
+    if prefilter is None:
+        prefilter = no_filter()
+
     with cuda_interruptible():
         check_cuvs(cuvsBruteForceSearch(
             res,
             index.index,
             queries_dlpack,
             neighbors_dlpack,
-            distances_dlpack
+            distances_dlpack,
+            prefilter.prefilter
         ))
 
     return (distances, neighbors)
diff --git a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
new file mode 100644
index 0000000000..8f281d1c84
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
@@ -0,0 +1,25 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources filters.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
+                   neighbors_prefilter_
+)
diff --git a/python/cuvs/cuvs/neighbors/filters/__init__.pxd b/python/cuvs/cuvs/neighbors/filters/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/filters/__init__.py b/python/cuvs/cuvs/neighbors/filters/__init__.py
new file mode 100644
index 0000000000..2ad118965a
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/filters/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .filters import Prefilter, from_bitmap, no_filter
+
+__all__ = ["no_filter", "from_bitmap", "Prefilter"]
diff --git a/python/cuvs/cuvs/neighbors/filters/filters.pxd b/python/cuvs/cuvs/neighbors/filters/filters.pxd
new file mode 100644
index 0000000000..6ab4e91f9c
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/filters/filters.pxd
@@ -0,0 +1,30 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uintptr_t
+
+
+cdef extern from "cuvs/neighbors/common.h" nogil:
+
+    ctypedef enum cuvsFilterType:
+        NO_FILTER
+        BITSET
+        BITMAP
+
+    ctypedef struct cuvsFilter:
+        uintptr_t addr
+        cuvsFilterType type
diff --git a/python/cuvs/cuvs/neighbors/filters/filters.pyx b/python/cuvs/cuvs/neighbors/filters/filters.pyx
new file mode 100644
index 0000000000..3a81cb7867
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/filters/filters.pyx
@@ -0,0 +1,97 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from libc.stdint cimport uintptr_t
+
+from cuvs.common cimport cydlpack
+
+from .filters cimport BITMAP, NO_FILTER, cuvsFilter
+
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.neighbors.common import _check_input_array
+
+
+cdef class Prefilter:
+    cdef public cuvsFilter prefilter
+    cdef public object parent
+
+    def __init__(self, cuvsFilter prefilter, parent=None):
+        if parent is not None:
+            self.parent = parent
+        self.prefilter = prefilter
+
+
+def no_filter():
+    """
+    Create a default pre-filter which filters nothing.
+    """
+    cdef cuvsFilter filter
+    filter.type = NO_FILTER
+    filter.addr = <uintptr_t> NULL
+    return Prefilter(filter)
+
+
+def from_bitmap(bitmap):
+    """
+    Create a pre-filter from an array with type of uint32.
+
+    Parameters
+    ----------
+    bitmap : numpy.ndarray
+        An array with type of `uint32` where each bit in the array corresponds
+        to if a sample and query pair is greenlit (not filtered) or filtered.
+        The array is row-major, meaning the bits are ordered by rows first.
+        Each bit in a `uint32` element represents a different sample-query
+        pair.
+
+        - Bit value of 1: The sample-query pair is greenlit (allowed).
+        - Bit value of 0: The sample-query pair is filtered.
+
+    Returns
+    -------
+    filter : cuvs.neighbors.filters.Prefilter
+        An instance of `Prefilter` that can be used to filter neighbors
+        based on the given bitmap.
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> import numpy as np
+    >>> from cuvs.neighbors import filters
+    >>>
+    >>> n_samples = 50000
+    >>> n_queries = 1000
+    >>>
+    >>> n_bitmap = np.ceil(n_samples * n_queries / 32).astype(int)
+    >>> bitmap = cp.random.randint(1, 100, size=(n_bitmap,), dtype=cp.uint32)
+    >>> prefilter = filters.from_bitmap(bitmap)
+    """
+    bitmap_cai = wrap_array(bitmap)
+    _check_input_array(bitmap_cai, [np.dtype('uint32')])
+
+    cdef cydlpack.DLManagedTensor* bitmap_dlpack = \
+        cydlpack.dlpack_c(bitmap_cai)
+
+    cdef cuvsFilter filter
+    filter.type = BITMAP
+    filter.addr = <uintptr_t> bitmap_dlpack
+
+    return Prefilter(filter, parent=bitmap)
diff --git a/python/cuvs/cuvs/test/test_brute_force.py b/python/cuvs/cuvs/test/test_brute_force.py
index 146ab59f01..acf347ec35 100644
--- a/python/cuvs/cuvs/test/test_brute_force.py
+++ b/python/cuvs/cuvs/test/test_brute_force.py
@@ -18,7 +18,7 @@
 from pylibraft.common import device_ndarray
 from scipy.spatial.distance import cdist
 
-from cuvs.neighbors import brute_force
+from cuvs.neighbors import brute_force, filters
 
 
 @pytest.mark.parametrize("n_index_rows", [32, 100])
@@ -62,6 +62,8 @@ def test_brute_force_knn(
     indices_device = device_ndarray(indices)
     distances_device = device_ndarray(distances)
 
+    prefilter = filters.no_filter()
+
     brute_force_index = brute_force.build(index_device, metric)
     ret_distances, ret_indices = brute_force.search(
         brute_force_index,
@@ -69,6 +71,7 @@ def test_brute_force_knn(
         k,
         neighbors=indices_device,
         distances=distances_device,
+        prefilter=prefilter,
     )
 
     pw_dists = cdist(queries, index, metric=metric)
@@ -88,3 +91,96 @@ def test_brute_force_knn(
         np.testing.assert_allclose(
             cpu_ordered[:k], gpu_dists, atol=1e-3, rtol=1e-3
         )
+
+
+def create_sparse_array(shape, sparsity):
+    bits_per_uint32 = 32
+
+    num_bits = np.prod(shape) * bits_per_uint32
+    num_ones = int(num_bits * sparsity)
+
+    array = np.zeros(shape, dtype=np.uint32)
+    indices = np.random.choice(num_bits, num_ones, replace=False)
+
+    for index in indices:
+        i = index // bits_per_uint32
+        bit_position = index % bits_per_uint32
+        array.flat[i] |= 1 << bit_position
+
+    return array
+
+
+@pytest.mark.parametrize("n_index_rows", [32, 100])
+@pytest.mark.parametrize("n_query_rows", [32, 100])
+@pytest.mark.parametrize("n_cols", [40, 100])
+@pytest.mark.parametrize("k", [1, 5, 32])
+@pytest.mark.parametrize("sparsity", [0.01, 0.2, 0.4])
+@pytest.mark.parametrize(
+    "metric",
+    [
+        "euclidean",
+        "cosine",
+        "sqeuclidean",
+    ],
+)
+@pytest.mark.parametrize("inplace", [True])
+@pytest.mark.parametrize("dtype", [np.float32])
+def test_prefiltered_brute_force_knn(
+    n_index_rows, n_query_rows, n_cols, k, sparsity, inplace, metric, dtype
+):
+    index = np.random.random_sample((n_index_rows, n_cols)).astype(dtype)
+    queries = np.random.random_sample((n_query_rows, n_cols)).astype(dtype)
+    bitmap = create_sparse_array(
+        (np.ceil(n_query_rows * n_index_rows / 32).astype(int)), sparsity
+    )
+
+    is_min = metric != "inner_product"
+
+    indices = np.zeros((n_query_rows, k), dtype="int64")
+    distances = np.zeros((n_query_rows, k), dtype=dtype)
+
+    index_device = device_ndarray(index)
+    queries_device = device_ndarray(queries)
+    indices_device = device_ndarray(indices)
+    distances_device = device_ndarray(distances)
+    bitmap_device = device_ndarray(bitmap)
+    prefilter = filters.from_bitmap(bitmap_device)
+    bitmap_device = None
+    brute_force_index = brute_force.build(index_device, metric)
+    ret_distances, ret_indices = brute_force.search(
+        brute_force_index,
+        queries_device,
+        k,
+        neighbors=indices_device,
+        distances=distances_device,
+        prefilter=prefilter,
+    )
+
+    pw_dists = cdist(queries, index, metric=metric)
+
+    # convert bitmap to bool array.
+    bitmap_as_uint8 = bitmap.view(np.uint8)
+    bool_filter = np.unpackbits(bitmap_as_uint8)
+    bool_filter = bool_filter.reshape(-1, 4, 8)
+    bool_filter = np.flip(bool_filter, axis=2)
+    bool_filter = bool_filter.reshape(-1)[: (n_query_rows * n_index_rows)]
+    bool_filter = bool_filter.reshape(-1, n_index_rows)
+    bool_filter = np.logical_not(bool_filter)
+
+    pw_dists[bool_filter] = np.inf if is_min else -np.inf
+
+    distances_device = ret_distances if not inplace else distances_device
+
+    actual_distances = distances_device.copy_to_host()
+
+    actual_distances[actual_distances <= 1e-5] = 0.0
+    argsort = np.argsort(pw_dists, axis=1)
+
+    for i in range(pw_dists.shape[0]):
+        expected_indices = argsort[i]
+        gpu_dists = actual_distances[i]
+
+        cpu_ordered = pw_dists[i, expected_indices]
+        np.testing.assert_allclose(
+            cpu_ordered[:k], gpu_dists, atol=1e-3, rtol=1e-3
+        )
diff --git a/rust/cuvs/src/brute_force.rs b/rust/cuvs/src/brute_force.rs
index c153359b8a..63752ee9c1 100644
--- a/rust/cuvs/src/brute_force.rs
+++ b/rust/cuvs/src/brute_force.rs
@@ -80,12 +80,18 @@ impl Index {
         distances: &ManagedTensor,
     ) -> Result<()> {
         unsafe {
+            let prefilter = ffi::cuvsFilter {
+                addr: 0,
+                type_: ffi::cuvsFilterType::NO_FILTER,
+            };
+
             check_cuvs(ffi::cuvsBruteForceSearch(
                 res.0,
                 self.0,
                 queries.as_ptr(),
                 neighbors.as_ptr(),
                 distances.as_ptr(),
+                prefilter,
             ))
         }
     }
@@ -117,9 +123,7 @@ mod tests {
         let dataset_host =
             ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
 
-        let dataset = ManagedTensor::from(&dataset_host)
-            .to_device(&res)
-            .unwrap();
+        let dataset = ManagedTensor::from(&dataset_host).to_device(&res).unwrap();
 
         println!("dataset {:#?}", dataset_host);
 
@@ -168,12 +172,12 @@ mod tests {
         assert_eq!(neighbors_host[[3, 0]], 3);
     }
 
-/*
-    #[test]
-    fn test_cosine() {
-        test_bfknn(DistanceType::CosineExpanded);
-    }
-*/
+    /*
+        #[test]
+        fn test_cosine() {
+            test_bfknn(DistanceType::CosineExpanded);
+        }
+    */
 
     #[flaky]
     fn test_l2() {

From 33698a56cfe02693e90fb4774dc949331fbc2924 Mon Sep 17 00:00:00 2001
From: rhdong <hrong@nvidia.com>
Date: Mon, 29 Jul 2024 13:39:07 -0700
Subject: [PATCH 112/623] [Opt] introduce the `masked_matmul` to prefiltered
 brute force. (#251)

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/251
---
 cpp/src/neighbors/detail/knn_brute_force.cuh | 39 ++++----------------
 1 file changed, 8 insertions(+), 31 deletions(-)

diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index f05bebf3fb..559d33cc28 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -42,7 +42,7 @@
 #include <raft/sparse/convert/coo.cuh>
 #include <raft/sparse/convert/csr.cuh>
 #include <raft/sparse/distance/detail/utils.cuh>
-#include <raft/sparse/linalg/sddmm.hpp>
+#include <raft/sparse/linalg/masked_matmul.hpp>
 #include <raft/sparse/matrix/select_k.cuh>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
@@ -636,36 +636,13 @@ void brute_force_search_filtered(
                                       rows.data(),
                                       compressed_csr_view.get_nnz(),
                                       stream);
-    if (n_queries > 10) {
-      auto csr_view = raft::make_device_csr_matrix_view<T, IdxT, IdxT, IdxT>(
-        csr.get_elements().data(), compressed_csr_view);
-
-      // create dataset view
-      auto dataset_view = raft::make_device_matrix_view<const T, IdxT, raft::col_major>(
-        idx.dataset().data_handle(), dim, n_dataset);
-
-      // calc dot
-      T alpha = static_cast<T>(1.0f);
-      T beta  = static_cast<T>(0.0f);
-      raft::sparse::linalg::sddmm(res,
-                                  queries,
-                                  dataset_view,
-                                  csr_view,
-                                  raft::linalg::Operation::NON_TRANSPOSE,
-                                  raft::linalg::Operation::NON_TRANSPOSE,
-                                  raft::make_host_scalar_view<T>(&alpha),
-                                  raft::make_host_scalar_view<T>(&beta));
-    } else {
-      raft::sparse::distance::detail::faster_dot_on_csr(res,
-                                                        csr.get_elements().data(),
-                                                        compressed_csr_view.get_nnz(),
-                                                        compressed_csr_view.get_indptr().data(),
-                                                        compressed_csr_view.get_indices().data(),
-                                                        queries.data_handle(),
-                                                        idx.dataset().data_handle(),
-                                                        compressed_csr_view.get_n_rows(),
-                                                        dim);
-    }
+    auto dataset_view = raft::make_device_matrix_view<const T, IdxT, raft::row_major>(
+      idx.dataset().data_handle(), n_dataset, dim);
+
+    auto csr_view = raft::make_device_csr_matrix_view<T, IdxT, IdxT, IdxT>(
+      csr.get_elements().data(), compressed_csr_view);
+
+    raft::sparse::linalg::masked_matmul(res, queries, dataset_view, filter, csr_view);
 
     // post process
     std::optional<raft::device_vector<T, IdxT>> query_norms_;

From 6658c3178baff7753a114a18a31ea719a63bc461 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Tue, 30 Jul 2024 11:38:34 -0700
Subject: [PATCH 113/623] Complete Migration of IVF Helpers / Features from
 RAFT (#213)

Utils / Helpers to enable FAISS migration to cuVS from RAFT.

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/213
---
 cpp/cmake/modules/ConfigureCUDA.cmake         |  4 +-
 cpp/include/cuvs/neighbors/brute_force.hpp    | 15 ++-
 cpp/include/cuvs/neighbors/cagra.hpp          | 52 +++++++++--
 cpp/include/cuvs/neighbors/common.hpp         | 10 --
 cpp/include/cuvs/neighbors/ivf_flat.hpp       | 92 +++++++++++++++++++
 cpp/include/cuvs/neighbors/ivf_pq.hpp         | 89 +++++++++++++-----
 cpp/src/neighbors/brute_force.cu              | 30 ++++++
 .../neighbors/detail/cagra/cagra_build.cuh    | 25 +++--
 cpp/src/neighbors/detail/div_utils.cuh        | 52 -----------
 .../neighbors/ivf_flat/ivf_flat_helpers.cu    | 17 ++++
 .../neighbors/ivf_flat/ivf_flat_helpers.cuh   | 11 +--
 cpp/src/neighbors/ivf_list.cuh                |  2 +
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |  1 -
 .../neighbors/ivf_pq/ivf_pq_build_common.cu   | 29 +++++-
 .../neighbors/ivf_pq/ivf_pq_codepacking.cuh   |  1 -
 cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh      | 55 -----------
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh |  1 -
 examples/cpp/CMakeLists.txt                   |  2 +-
 18 files changed, 320 insertions(+), 168 deletions(-)
 delete mode 100644 cpp/src/neighbors/detail/div_utils.cuh
 delete mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh

diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index e2dd829da2..74da256604 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -14,7 +14,9 @@
 
 if(DISABLE_DEPRECATION_WARNINGS)
   list(APPEND CUVS_CXX_FLAGS -Wno-deprecated-declarations -DRAFT_HIDE_DEPRECATION_WARNINGS)
-  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wno-deprecated-declarations -DRAFT_HIDE_DEPRECATION_WARNINGS)
+  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wno-deprecated-declarations
+       -DRAFT_HIDE_DEPRECATION_WARNINGS
+  )
 endif()
 
 # Be very strict when compiling with GCC as host compiler (and thus more lenient when compiling with
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 0a5071b6da..db70a7fa65 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -82,6 +82,19 @@ struct index : cuvs::neighbors::index {
         cuvs::distance::DistanceType metric,
         T metric_arg = 0.0);
 
+  /** Construct a brute force index from dataset
+   *
+   * Constructs a brute force index from a dataset. This lets us precompute norms for
+   * the dataset, providing a speed benefit over doing this at query time.
+   * This index will store a non-owning reference to the dataset, but will move
+   * any norms supplied.
+   */
+  index(raft::resources const& res,
+        raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
+        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        cuvs::distance::DistanceType metric,
+        T metric_arg = 0.0);
+
   /** Construct a brute force index from dataset
    *
    * This class stores a non-owning reference to the dataset and norms, with
@@ -89,7 +102,7 @@ struct index : cuvs::neighbors::index {
    */
   index(raft::resources const& res,
         raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
-        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
         cuvs::distance::DistanceType metric,
         T metric_arg = 0.0);
 
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index fffea4ae56..eb2151c466 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -64,7 +64,7 @@ struct ivf_pq_params {
    *   auto pq_params =
    *     cagra::graph_build_params::ivf_pq_params(dataset.extents());
    *   // modify/update index_params as needed
-   *   index_params.add_data_on_build = true;
+   *   pq_params.kmeans_trainset_fraction = 0.1;
    * @endcode
    */
   ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
@@ -80,7 +80,8 @@ struct index_params : cuvs::neighbors::index_params {
   /** Degree of output graph. */
   size_t graph_degree = 64;
   /**
-   * Specify compression parameters if compression is desired.
+   * Specify compression parameters if compression is desired. If set, overrides the
+   * attach_dataset_on_build (and the compressed dataset is always added to the index).
    */
   std::optional<cuvs::neighbors::vpq_params> compression = std::nullopt;
 
@@ -105,6 +106,36 @@ struct index_params : cuvs::neighbors::index_params {
                graph_build_params::ivf_pq_params,
                graph_build_params::nn_descent_params>
     graph_build_params;
+  /**
+   * Whether to add the dataset content to the index, i.e.:
+   *
+   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
+   * `build` provided there is enough memory available.
+   *  - `false` means `build` only builds the graph and the user is expected to
+   * update the dataset using cuvs::neighbors::cagra::update_dataset.
+   *
+   * Regardless of the value of `attach_dataset_on_build`, the search graph is created using all
+   * the vectors in the dataset.  Setting `attach_dataset_on_build = false` can be useful if
+   * the user needs to build only the search graph but does not intend to search it using CAGRA
+   * (e.g. search using another graph search algorithm), or if specific memory placement options
+   * need to be applied on the dataset before it is attached to the index using `update_dataset`.
+   * API.
+   * @code{.cpp}
+   *   auto dataset = raft::make_device_matrix<float, int64_t>(res, n_rows, n_cols);
+   *   // use default index_parameters
+   *   cagra::index_params index_params;
+   *   // update index_params to only build the CAGRA graph
+   *   index_params.attach_dataset_on_build = false;
+   *   auto index = cagra::build(res, index_params, dataset.view());
+   *   // assert that the dataset is not attached to the index
+   *   ASSERT(index.dataset().extent(0) == 0);
+   *   // update dataset
+   *   index.update_dataset(res, dataset.view());
+   *   // The index is now ready for search
+   *   cagra::search(res, search_params, index, queries, neighbors, distances);
+   * @endcode
+   */
+  bool attach_dataset_on_build = true;
 };
 
 /**
@@ -328,7 +359,6 @@ struct index : cuvs::neighbors::index {
    *   // the index only stores a reference to these.
    *   cagra::search(res, search_params, index, queries, neighbors, distances);
    * @endcode
-   *
    */
   template <typename data_accessor, typename graph_accessor>
   index(raft::resources const& res,
@@ -353,7 +383,7 @@ struct index : cuvs::neighbors::index {
    *
    * If the new dataset rows are aligned on 16 bytes, then only a reference is stored to the
    * dataset. It is the caller's responsibility to ensure that dataset stays alive as long as the
-   * index.
+   * index. It is expected that the same set of vectors are used for update_dataset and index build.
    */
   void update_dataset(raft::resources const& res,
                       raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
@@ -371,7 +401,8 @@ struct index : cuvs::neighbors::index {
   /**
    * Replace the dataset with a new dataset.
    *
-   * We create a copy of the dataset on the device. The index manages the lifetime of this copy.
+   * We create a copy of the dataset on the device. The index manages the lifetime of this copy. It
+   * is expected that the same set of vectors are used for update_dataset and index build.
    */
   void update_dataset(raft::resources const& res,
                       raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)
@@ -379,7 +410,10 @@ struct index : cuvs::neighbors::index {
     dataset_ = make_aligned_dataset(res, dataset, 16);
   }
 
-  /** Replace the dataset with a new dataset. */
+  /**
+   * Replace the dataset with a new dataset. It is expected that the same set of vectors are used
+   * for update_dataset and index build.
+   */
   template <typename DatasetT>
   auto update_dataset(raft::resources const& res, DatasetT&& dataset)
     -> std::enable_if_t<std::is_base_of_v<cuvs::neighbors::dataset<int64_t>, DatasetT>>
@@ -453,6 +487,7 @@ struct index : cuvs::neighbors::index {
  *
  * The following distance metrics are supported:
  * - L2
+ * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
  *
  * Usage example:
  * @code{.cpp}
@@ -489,6 +524,7 @@ auto build(raft::resources const& res,
  *
  * The following distance metrics are supported:
  * - L2
+ * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
  *
  * Usage example:
  * @code{.cpp}
@@ -525,6 +561,7 @@ auto build(raft::resources const& res,
  *
  * The following distance metrics are supported:
  * - L2
+ * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
  *
  * Usage example:
  * @code{.cpp}
@@ -561,6 +598,7 @@ auto build(raft::resources const& res,
  *
  * The following distance metrics are supported:
  * - L2
+ * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
  *
  * Usage example:
  * @code{.cpp}
@@ -597,6 +635,7 @@ auto build(raft::resources const& res,
  *
  * The following distance metrics are supported:
  * - L2
+ * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
  *
  * Usage example:
  * @code{.cpp}
@@ -633,6 +672,7 @@ auto build(raft::resources const& res,
  *
  * The following distance metrics are supported:
  * - L2
+ * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
  *
  * Usage example:
  * @code{.cpp}
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 72d35961f0..4144380673 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -90,15 +90,6 @@ struct index_params {
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
   /** The argument used by some distance metrics. */
   float metric_arg = 2.0f;
-  /**
-   * Whether to add the dataset content to the index, i.e.:
-   *
-   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
-   * `build`.
-   *  - `false` means `build` only trains the underlying model (e.g. quantizer or clustering), but
-   * the index is left empty; you'd need to call `extend` on the index afterwards to populate it.
-   */
-  bool add_data_on_build = true;
 };
 
 struct search_params {};
@@ -596,7 +587,6 @@ enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
                                                std::shared_ptr<ListT>& ld,
                                                const typename ListT::spec_type& store_spec,
                                                const typename ListT::spec_type& device_spec);
-
 }  // namespace ivf
 
 };  // namespace cuvs::neighbors
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index ee6102f526..ad0ca49dbf 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -61,6 +61,15 @@ struct index_params : cuvs::neighbors::index_params {
    * flag to `true` if you prefer to use as little GPU memory for the database as possible.
    */
   bool conservative_memory_allocation = false;
+  /**
+   * Whether to add the dataset content to the index, i.e.:
+   *
+   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
+   * `build`.
+   *  - `false` means `build` only trains the underlying model (e.g. quantizer or clustering), but
+   * the index is left empty; you'd need to call `extend` on the index afterwards to populate it.
+   */
+  bool add_data_on_build = true;
 };
 /**
  * @}
@@ -1870,6 +1879,89 @@ void reset_index(const raft::resources& res, index<int8_t, int64_t>* index);
  */
 void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index);
 
+/**
+ * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
+ * modified externally.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   raft::resources res;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_pq::index<int64_t> index(res, index_params, D);
+ *   ivf_pq::helpers::reset_index(res, &index);
+ *   // resize the first IVF list to hold 5 records
+ *   auto spec = list_spec<uint32_t, int64_t>{
+ *     index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+ *   uint32_t new_size = 5;
+ *   ivf::resize_list(res, list, spec, new_size, 0);
+ *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
+ *   // recompute the internal state of the index
+ *   ivf_pq::helpers::recompute_internal_state(res, index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ */
+void recompute_internal_state(const raft::resources& res, index<float, int64_t>* index);
+
+/**
+ * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
+ * modified externally.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   raft::resources res;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_pq::index<int64_t> index(res, index_params, D);
+ *   ivf_pq::helpers::reset_index(res, &index);
+ *   // resize the first IVF list to hold 5 records
+ *   auto spec = list_spec<uint32_t, int64_t>{
+ *     index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+ *   uint32_t new_size = 5;
+ *   ivf::resize_list(res, list, spec, new_size, 0);
+ *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
+ *   // recompute the internal state of the index
+ *   ivf_pq::helpers::recompute_internal_state(res, index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ */
+void recompute_internal_state(const raft::resources& res, index<int8_t, int64_t>* index);
+
+/**
+ * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
+ * modified externally.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   raft::resources res;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_pq::index<int64_t> index(res, index_params, D);
+ *   ivf_pq::helpers::reset_index(res, &index);
+ *   // resize the first IVF list to hold 5 records
+ *   auto spec = list_spec<uint32_t, int64_t>{
+ *     index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+ *   uint32_t new_size = 5;
+ *   ivf::resize_list(res, list, spec, new_size, 0);
+ *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
+ *   // recompute the internal state of the index
+ *   ivf_pq::helpers::recompute_internal_state(res, index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-Flat index
+ */
+void recompute_internal_state(const raft::resources& res, index<uint8_t, int64_t>* index);
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index f38b6cbc48..2702dba3bf 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -97,6 +97,15 @@ struct index_params : cuvs::neighbors::index_params {
    * flag to `true` if you prefer to use as little GPU memory for the database as possible.
    */
   bool conservative_memory_allocation = false;
+  /**
+   * Whether to add the dataset content to the index, i.e.:
+   *
+   *  - `true` means the index is filled with the dataset vectors and ready to search after calling
+   * `build`.
+   *  - `false` means `build` only trains the underlying model (e.g. quantizer or clustering), but
+   * the index is left empty; you'd need to call `extend` on the index afterwards to populate it.
+   */
+  bool add_data_on_build = true;
 
   /**
    * Creates index_params based on shape of the input dataset.
@@ -212,6 +221,39 @@ struct list_spec {
   constexpr list_extents make_list_extents(SizeT n_rows) const;
 };
 
+template <typename SizeT, typename IdxT>
+constexpr list_spec<SizeT, IdxT>::list_spec(uint32_t pq_bits,
+                                            uint32_t pq_dim,
+                                            bool conservative_memory_allocation)
+  : pq_bits(pq_bits),
+    pq_dim(pq_dim),
+    align_min(kIndexGroupSize),
+    align_max(conservative_memory_allocation ? kIndexGroupSize : 1024)
+{
+}
+
+template <typename SizeT, typename IdxT>
+template <typename OtherSizeT>
+constexpr list_spec<SizeT, IdxT>::list_spec(const list_spec<OtherSizeT, IdxT>& other_spec)
+  : pq_bits{other_spec.pq_bits},
+    pq_dim{other_spec.pq_dim},
+    align_min{other_spec.align_min},
+    align_max{other_spec.align_max}
+{
+}
+
+template <typename SizeT, typename IdxT>
+constexpr typename list_spec<SizeT, IdxT>::list_extents list_spec<SizeT, IdxT>::make_list_extents(
+  SizeT n_rows) const
+{
+  // how many elems of pq_dim fit into one kIndexGroupVecLen-byte chunk
+  auto pq_chunk = (kIndexGroupVecLen * 8u) / pq_bits;
+  return raft::make_extents<SizeT>(raft::div_rounding_up_safe<SizeT>(n_rows, kIndexGroupSize),
+                                   raft::div_rounding_up_safe<SizeT>(pq_dim, pq_chunk),
+                                   kIndexGroupSize,
+                                   kIndexGroupVecLen);
+}
+
 template <typename IdxT, typename SizeT = uint32_t>
 using list_data = ivf::list<list_spec, SizeT, IdxT>;
 
@@ -2034,9 +2076,31 @@ void make_rotation_matrix(raft::resources const& res,
 void set_centers(raft::resources const& res,
                  index<int64_t>* index,
                  raft::device_matrix_view<const float, uint32_t> cluster_centers);
+
+/**
+ * @brief Public helper API for fetching a trained index's IVF centroids
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   // allocate the buffer for the output centers
+ *   auto cluster_centers = raft::make_device_matrix<float, uint32_t>(
+ *     res, index.n_lists(), index.dim());
+ *   // Extract the IVF centroids into the buffer
+ *   cuvs::neighbors::ivf_pq::helpers::extract_centers(res, index, cluster_centers.data_handle());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] index IVF-PQ index (passed by reference)
+ * @param[out] cluster_centers IVF cluster centers [index.n_lists(), index.dim]
+ */
+void extract_centers(raft::resources const& res,
+                     const index<int64_t>& index,
+                     raft::device_matrix_view<float, uint32_t, raft::row_major> cluster_centers);
+
 /**
  * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
- * modified.
+ * modified externally.
  *
  * Usage example:
  * @code{.cpp}
@@ -2054,7 +2118,7 @@ void set_centers(raft::resources const& res,
  *   ivf::resize_list(res, list, spec, new_size, 0);
  *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
  *   // recompute the internal state of the index
- *   ivf_pq::helpers::recompute_internal_state(res, &index);
+ *   ivf_pq::helpers::recompute_internal_state(res, index);
  * @endcode
  *
  * @param[in] res raft resource
@@ -2062,27 +2126,6 @@ void set_centers(raft::resources const& res,
  */
 void recompute_internal_state(const raft::resources& res, index<int64_t>* index);
 
-/**
- * @brief Public helper API for fetching a trained index's IVF centroids into a buffer that may be
- * allocated on either host or device.
- *
- * Usage example:
- * @code{.cpp}
- *   raft::resources res;
- *   // allocate the buffer for the output centers
- *   auto cluster_centers = raft::make_device_matrix<float, uint32_t>(
- *     res, index.n_lists(), index.dim());
- *   // Extract the IVF centroids into the buffer
- *   cuvs::neighbors::ivf_pq::helpers::extract_centers(res, index, cluster_centers.data_handle());
- * @endcode
- *
- * @param[in] res raft resource
- * @param[in] index IVF-PQ index (passed by reference)
- * @param[out] cluster_centers IVF cluster centers [index.n_lists(), index.dim]
- */
-void extract_centers(raft::resources const& res,
-                     const index<int64_t>& index,
-                     raft::device_matrix_view<float> cluster_centers);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index c0414079c5..ce21e2d395 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -99,6 +99,36 @@ index<T>::index(raft::resources const& res,
   dataset_view_ = raft::make_const_mdspan(dataset_.view());
 }
 
+template <typename T>
+index<T>::index(raft::resources const& res,
+                raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
+                std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
+                cuvs::distance::DistanceType metric,
+                T metric_arg)
+  : cuvs::neighbors::index(),
+    metric_(metric),
+    dataset_(
+      raft::make_device_matrix<T, int64_t>(res, dataset_view.extent(0), dataset_view.extent(1))),
+    norms_view_(norms_view),
+    metric_arg_(metric_arg)
+{
+  // currently we don't support col_major inside tiled_brute_force_knn, because
+  // of limitations of the pairwise_distance API:
+  // 1) paiwise_distance takes a single 'isRowMajor' parameter - and we have
+  // multiple options here (both dataset and queries)
+  // 2) because of tiling, we need to be able to set a custom stride in the PW
+  // api, which isn't supported
+  // Instead, transpose the input matrices if they are passed as col-major.
+  // (note: we're doing the transpose here to avoid doing per query)
+  raft::linalg::transpose(res,
+                          const_cast<T*>(dataset_view.data_handle()),
+                          dataset_.data_handle(),
+                          dataset_view.extent(0),
+                          dataset_view.extent(1),
+                          raft::resource::get_cuda_stream(res));
+  dataset_view_ = raft::make_const_mdspan(dataset_.view());
+}
+
 template <typename T>
 void index<T>::update_dataset(raft::resources const& res,
                               raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index a3e591b74f..73fe86098d 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -497,15 +497,22 @@ index<T, IdxT> build(
 
     return idx;
   }
-  try {
-    return index<T, IdxT>(res, params.metric, dataset, raft::make_const_mdspan(cagra_graph.view()));
-  } catch (std::bad_alloc& e) {
-    RAFT_LOG_DEBUG("Insufficient GPU memory to construct CAGRA index with dataset on GPU");
-    // We just add the graph. User is expected to update dataset separately (e.g allocating in
-    // managed memory).
-  } catch (raft::logic_error& e) {
-    // The memory error can also manifest as logic_error.
-    RAFT_LOG_DEBUG("Insufficient GPU memory to construct CAGRA index with dataset on GPU");
+  if (params.attach_dataset_on_build) {
+    try {
+      return index<T, IdxT>(
+        res, params.metric, dataset, raft::make_const_mdspan(cagra_graph.view()));
+    } catch (std::bad_alloc& e) {
+      RAFT_LOG_WARN(
+        "Insufficient GPU memory to construct CAGRA index with dataset on GPU. Only the graph will "
+        "be added to the index");
+      // We just add the graph. User is expected to update dataset separately (e.g allocating in
+      // managed memory).
+    } catch (raft::logic_error& e) {
+      // The memory error can also manifest as logic_error.
+      RAFT_LOG_WARN(
+        "Insufficient GPU memory to construct CAGRA index with dataset on GPU. Only the graph will "
+        "be added to the index");
+    }
   }
   index<T, IdxT> idx(res, params.metric);
   idx.update_graph(res, raft::make_const_mdspan(cagra_graph.view()));
diff --git a/cpp/src/neighbors/detail/div_utils.cuh b/cpp/src/neighbors/detail/div_utils.cuh
deleted file mode 100644
index eb10b728a4..0000000000
--- a/cpp/src/neighbors/detail/div_utils.cuh
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (c) 2023, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <raft/util/pow2_utils.cuh>
-
-/**
- * @brief A simple wrapper for raft::Pow2 which uses Pow2 utils only when available and regular
- * integer division otherwise. This is done to allow a common interface for division arithmetic for
- * non CUDA headers.
- *
- * @tparam Value_ a compile-time value representable as a power-of-two.
- */
-namespace cuvs::neighbors::detail {
-template <auto Value_>
-struct div_utils {
-  typedef decltype(Value_) Type;
-  static constexpr Type Value = Value_;
-
-  template <typename T>
-  static constexpr _RAFT_HOST_DEVICE inline auto roundDown(T x)
-  {
-    return raft::Pow2<Value_>::roundDown(x);
-  }
-
-  template <typename T>
-  static constexpr _RAFT_HOST_DEVICE inline auto mod(T x)
-  {
-    return raft::Pow2<Value_>::mod(x);
-  }
-
-  template <typename T>
-  static constexpr _RAFT_HOST_DEVICE inline auto div(T x)
-  {
-    return raft::Pow2<Value_>::div(x);
-  }
-};
-}  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
index 03243221d1..7a34e92627 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
@@ -16,10 +16,12 @@
 
 #include <cstdint>
 
+#include "../ivf_common.cuh"
 #include "ivf_flat_helpers.cuh"
 #include <cuvs/neighbors/ivf_flat.hpp>
 
 namespace cuvs::neighbors::ivf_flat::helpers {
+
 namespace codepacker {
 
 void pack(raft::resources const& res,
@@ -197,4 +199,19 @@ void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index)
   detail::reset_index<uint8_t, int64_t>(res, index);
 }
 
+void recompute_internal_state(const raft::resources& res, index<float, int64_t>* index)
+{
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
+void recompute_internal_state(const raft::resources& res, index<int8_t, int64_t>* index)
+{
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
+void recompute_internal_state(const raft::resources& res, index<uint8_t, int64_t>* index)
+{
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
 }  // namespace cuvs::neighbors::ivf_flat::helpers
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
index 936c78fc5e..12042f5554 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cuh
@@ -18,21 +18,20 @@
 
 #include "../detail/ann_utils.cuh"
 
-#include "../detail/div_utils.cuh"
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/util/pow2_utils.cuh>
 #include <variant>
 
 namespace cuvs::neighbors::ivf_flat::helpers::codepacker {
 
 namespace {
-
 template <typename T>
 __device__ void pack_1(const T* flat_code, T* block, uint32_t dim, uint32_t veclen, uint32_t offset)
 {
   // The data is written in interleaved groups of `index::kGroupSize` vectors
-  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+  using interleaved_group = raft::Pow2<kIndexGroupSize>;
 
   // Interleave dimensions of the source vector while recording it.
   // NB: such `veclen` is selected, that `dim % veclen == 0`
@@ -51,7 +50,7 @@ __device__ void unpack_1(
   const T* block, T* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
 {
   // The data is written in interleaved groups of `index::kGroupSize` vectors
-  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+  using interleaved_group = raft::Pow2<kIndexGroupSize>;
 
   // NB: such `veclen` is selected, that `dim % veclen == 0`
   auto group_offset = interleaved_group::roundDown(offset);
@@ -168,7 +167,7 @@ template <typename T>
 void pack_1(const T* flat_code, T* block, uint32_t dim, uint32_t veclen, uint32_t offset)
 {
   // The data is written in interleaved groups of `index::kGroupSize` vectors
-  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+  using interleaved_group = raft::Pow2<kIndexGroupSize>;
 
   // Interleave dimensions of the source vector while recording it.
   // NB: such `veclen` is selected, that `dim % veclen == 0`
@@ -186,7 +185,7 @@ template <typename T>
 void unpack_1(const T* block, T* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
 {
   // The data is written in interleaved groups of `index::kGroupSize` vectors
-  using interleaved_group = cuvs::neighbors::detail::div_utils<kIndexGroupSize>;
+  using interleaved_group = raft::Pow2<kIndexGroupSize>;
 
   // NB: such `veclen` is selected, that `dim % veclen == 0`
   auto group_offset = interleaved_group::roundDown(offset);
diff --git a/cpp/src/neighbors/ivf_list.cuh b/cpp/src/neighbors/ivf_list.cuh
index 604fafe6bc..c6335b1142 100644
--- a/cpp/src/neighbors/ivf_list.cuh
+++ b/cpp/src/neighbors/ivf_list.cuh
@@ -29,6 +29,8 @@
 #include <raft/core/serialize.hpp>
 #include <raft/util/integer_utils.hpp>
 
+#include "ivf_common.cuh"
+
 #include <thrust/fill.h>
 
 #include <atomic>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 8823bbbfb7..02b6fbeb91 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -20,7 +20,6 @@
 #include "../ivf_common.cuh"
 #include "../ivf_list.cuh"
 #include "ivf_pq_codepacking.cuh"
-#include "ivf_pq_list.cuh"
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
index 6a8d07a0f0..b34611c1d6 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
@@ -19,6 +19,7 @@
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/mdspan_types.hpp>
 
 namespace cuvs::neighbors::ivf_pq::helpers {
 
@@ -279,7 +280,7 @@ void make_rotation_matrix(raft::resources const& res,
 
 void set_centers(raft::resources const& handle,
                  index<int64_t>* index,
-                 raft::device_matrix_view<const float, uint32_t> cluster_centers)
+                 raft::device_matrix_view<const float, uint32_t, raft::row_major> cluster_centers)
 {
   RAFT_EXPECTS(cluster_centers.extent(0) == index->n_lists(),
                "Number of rows in the new centers must be equal to the number of IVF lists");
@@ -289,4 +290,30 @@ void set_centers(raft::resources const& handle,
   detail::set_centers(handle, index, cluster_centers.data_handle());
 }
 
+void extract_centers(raft::resources const& res,
+                     const cuvs::neighbors::ivf_pq::index<int64_t>& index,
+                     raft::device_matrix_view<float, uint32_t, raft::row_major> cluster_centers)
+{
+  RAFT_EXPECTS(cluster_centers.extent(0) == index.n_lists(),
+               "Number of rows in the output buffer for cluster centers must be equal to the "
+               "number of IVF lists");
+  RAFT_EXPECTS(
+    cluster_centers.extent(1) == index.dim(),
+    "Number of columns in the output buffer for cluster centers and index dim are different");
+  auto stream = raft::resource::get_cuda_stream(res);
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(cluster_centers.data_handle(),
+                                  sizeof(float) * index.dim(),
+                                  index.centers().data_handle(),
+                                  sizeof(float) * index.dim_ext(),
+                                  sizeof(float) * index.dim(),
+                                  index.n_lists(),
+                                  cudaMemcpyDefault,
+                                  stream));
+}
+
+void recompute_internal_state(const raft::resources& res, index<int64_t>* index)
+{
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
 }  // namespace cuvs::neighbors::ivf_pq::helpers
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
index f15ec293a0..40b9f74677 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_codepacking.cuh
@@ -16,7 +16,6 @@
 
 #pragma once
 
-#include "ivf_pq_list.cuh"
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/device_mdspan.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
deleted file mode 100644
index 57f074328e..0000000000
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_list.cuh
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#include <cuvs/neighbors/common.hpp>
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft/util/integer_utils.hpp>
-
-namespace cuvs::neighbors::ivf_pq {
-template <typename SizeT, typename IdxT>
-constexpr list_spec<SizeT, IdxT>::list_spec(uint32_t pq_bits,
-                                            uint32_t pq_dim,
-                                            bool conservative_memory_allocation)
-  : pq_bits(pq_bits),
-    pq_dim(pq_dim),
-    align_min(kIndexGroupSize),
-    align_max(conservative_memory_allocation ? kIndexGroupSize : 1024)
-{
-}
-
-template <typename SizeT, typename IdxT>
-template <typename OtherSizeT>
-constexpr list_spec<SizeT, IdxT>::list_spec(const list_spec<OtherSizeT, IdxT>& other_spec)
-  : pq_bits{other_spec.pq_bits},
-    pq_dim{other_spec.pq_dim},
-    align_min{other_spec.align_min},
-    align_max{other_spec.align_max}
-{
-}
-
-template <typename SizeT, typename IdxT>
-constexpr typename list_spec<SizeT, IdxT>::list_extents list_spec<SizeT, IdxT>::make_list_extents(
-  SizeT n_rows) const
-{
-  // how many elems of pq_dim fit into one kIndexGroupVecLen-byte chunk
-  auto pq_chunk = (kIndexGroupVecLen * 8u) / pq_bits;
-  return raft::make_extents<SizeT>(raft::div_rounding_up_safe<SizeT>(n_rows, kIndexGroupSize),
-                                   raft::div_rounding_up_safe<SizeT>(pq_dim, pq_chunk),
-                                   kIndexGroupSize,
-                                   kIndexGroupVecLen);
-}
-}  // namespace cuvs::neighbors::ivf_pq
\ No newline at end of file
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
index 9f0c085d15..5eaebe69d7 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
@@ -18,7 +18,6 @@
 
 #include "../ivf_common.cuh"
 #include "../ivf_list.cuh"
-#include "ivf_pq_list.cuh"
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/host_mdarray.hpp>
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 84536597e8..d744a8178a 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -43,4 +43,4 @@ add_executable(IVF_PQ_EXAMPLE src/ivf_pq_example.cu)
 # installed in a conda environment, if one exists
 target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
-target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
\ No newline at end of file
+target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)

From 6b3c06f7ea902b94720519ee18f4d3e5e060ddb6 Mon Sep 17 00:00:00 2001
From: Mike McCarty <mmccarty@nvidia.com>
Date: Tue, 30 Jul 2024 18:45:01 -0400
Subject: [PATCH 114/623] Fixed link to build docs and corrected
 ivf_flat_example (#255)

Fixed link to build docs and corrected ivf_flat_example notebook for handles to resources kwarg change.

Authors:
  - Mike McCarty (https://github.com/mmccarty)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/255
---
 README.md                        |  2 +-
 notebooks/ivf_flat_example.ipynb | 12 ++++++------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 23856060de..543ce941cf 100755
--- a/README.md
+++ b/README.md
@@ -53,7 +53,7 @@ If installing a version that has not yet been released, the `rapidsai` channel c
 mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.10
 ```
 
-Please see the [Build and Install Guide](docs/source/build.md) for more information on installing cuVS and building from source.
+Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/stable/build/) for more information on installing cuVS and building from source.
 
 ## Getting Started
 
diff --git a/notebooks/ivf_flat_example.ipynb b/notebooks/ivf_flat_example.ipynb
index 38bacb8a74..2d9c5fb58e 100644
--- a/notebooks/ivf_flat_example.ipynb
+++ b/notebooks/ivf_flat_example.ipynb
@@ -216,7 +216,7 @@
     "search_params = ivf_flat.SearchParams(n_probes=30)\n",
     "\n",
     "# Search 10 nearest neighbors.\n",
-    "distances, indices = ivf_flat.search(search_params, index, cp.asarray(queries[:n_queries,:]), k=10, handle=handle)\n",
+    "distances, indices = ivf_flat.search(search_params, index, cp.asarray(queries[:n_queries,:]), k=10, resources=handle)\n",
     "    \n",
     "# cuVS calls are asynchronous (when handle arg is provided), we need to sync before accessing the results.\n",
     "handle.sync()\n",
@@ -302,7 +302,7 @@
     "            index,\n",
     "            cp.asarray(queries),\n",
     "            k=10,\n",
-    "            handle=handle,\n",
+    "            resources=handle,\n",
     "        )\n",
     "        handle.sync()\n",
     "    \n",
@@ -382,7 +382,7 @@
     "        add_data_on_build=True\n",
     "    )\n",
     "\n",
-    "index = ivf_flat.build(build_params, dataset, handle=handle)"
+    "index = ivf_flat.build(build_params, dataset, resources=handle)"
    ]
   },
   {
@@ -406,7 +406,7 @@
     "search_params = ivf_flat.SearchParams(n_probes=10)\n",
     "\n",
     "# Search 10 nearest neighbors.\n",
-    "distances, indices = ivf_flat.search(search_params, index, cp.asarray(queries[:n_queries,:]), k=10, handle=handle)\n",
+    "distances, indices = ivf_flat.search(search_params, index, cp.asarray(queries[:n_queries,:]), k=10, resources=handle)\n",
     "    \n",
     "handle.sync()\n",
     "distances, neighbors = cp.asnumpy(distances), cp.asnumpy(indices)"
@@ -445,7 +445,7 @@
     "        kmeans_trainset_fraction=0.1, \n",
     "        kmeans_n_iters=20 \n",
     "    ) \n",
-    "index = ivf_flat.build(build_params, dataset, handle=handle)"
+    "index = ivf_flat.build(build_params, dataset, resources=handle)"
    ]
   },
   {
@@ -465,7 +465,7 @@
    "source": [
     "search_params = ivf_flat.SearchParams(n_probes=10)\n",
     "\n",
-    "distances, indices = ivf_flat.search(search_params, index, cp.asarray(queries[:n_queries,:]), k=10, handle=handle)\n",
+    "distances, indices = ivf_flat.search(search_params, index, cp.asarray(queries[:n_queries,:]), k=10, resources=handle)\n",
     "    \n",
     "handle.sync()\n",
     "distances, neighbors = cp.asnumpy(distances), cp.asnumpy(indices)\n",

From 02275272d56c55bc2096b710e77b25ddea262654 Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Wed, 31 Jul 2024 08:42:19 +0900
Subject: [PATCH 115/623] chore: update search_plan.cuh (#232)

iteraton -> iteration

Authors:
  - Ikko Eltociear Ashimine (https://github.com/eltociear)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/232
---
 cpp/src/neighbors/detail/cagra/search_plan.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index d46814e6cb..0543224b3d 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -187,7 +187,7 @@ struct search_plan_impl : public search_plan_impl_base {
     while (hashmap_mode == hash_mode::AUTO || hashmap_mode == hash_mode::SMALL) {
       //
       // The small-hash reduces hash table size by initializing the hash table
-      // for each iteraton and re-registering only the nodes that should not be
+      // for each iteration and re-registering only the nodes that should not be
       // re-visited in that iteration. Therefore, the size of small-hash should
       // be determined based on the internal topk size and the number of nodes
       // visited per iteration.

From 812fffdebdece7aca8bb1c7f8ec7f945835540c2 Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Tue, 30 Jul 2024 21:08:50 -0500
Subject: [PATCH 116/623] Add cuvs_bench python folder, config files and
 constraints (#244)

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/244
---
 .../config/algos/cuvs_brute_force.yaml        |   5 +
 .../cuvs_bench/config/algos/cuvs_cagra.yaml   |  13 +
 .../config/algos/cuvs_cagra_hnswlib.yaml      |  11 +
 .../config/algos/cuvs_ivf_flat.yaml           |   9 +
 .../cuvs_bench/config/algos/cuvs_ivf_pq.yaml  |  41 +
 .../config/algos/faiss_cpu_flat.yaml          |   5 +
 .../config/algos/faiss_cpu_ivf_flat.yaml      |  10 +
 .../config/algos/faiss_cpu_ivf_pq.yaml        |  18 +
 .../config/algos/faiss_gpu_flat.yaml          |   5 +
 .../config/algos/faiss_gpu_ivf_flat.yaml      |  21 +
 .../config/algos/faiss_gpu_ivf_pq.yaml        |  77 ++
 .../cuvs_bench/config/algos/hnswlib.yaml      |  10 +
 .../cuvs_bench/config/bigann-100M.yaml        | 295 ++++++
 .../cuvs_bench/config/constraints.py          | 101 ++
 .../cuvs_bench/config/datasets.yaml           | 127 +++
 .../cuvs_bench/config/deep-100M.yaml          | 896 ++++++++++++++++++
 .../cuvs_bench/cuvs_bench/config/deep-1B.yaml |  26 +
 .../config/deep-image-96-inner.yaml           | 712 ++++++++++++++
 .../config/fashion-mnist-784-euclidean.yaml   | 778 +++++++++++++++
 .../cuvs_bench/config/gist-960-euclidean.yaml | 777 +++++++++++++++
 .../cuvs_bench/config/glove-100-angular.yaml  | 777 +++++++++++++++
 .../cuvs_bench/config/glove-100-inner.yaml    | 777 +++++++++++++++
 .../cuvs_bench/config/glove-50-angular.yaml   | 777 +++++++++++++++
 .../cuvs_bench/config/glove-50-inner.yaml     | 777 +++++++++++++++
 .../cuvs_bench/config/lastfm-65-angular.yaml  | 777 +++++++++++++++
 .../config/mnist-784-euclidean.yaml           | 778 +++++++++++++++
 .../config/nytimes-256-angular.yaml           | 778 +++++++++++++++
 .../cuvs_bench/config/nytimes-256-inner.yaml  | 778 +++++++++++++++
 .../cuvs_bench/config/sift-128-euclidean.yaml | 562 +++++++++++
 .../cuvs_bench/config/wiki_all_10M.yaml       | 357 +++++++
 .../cuvs_bench/config/wiki_all_1M.yaml        | 371 ++++++++
 .../cuvs_bench/config/wiki_all_88M.yaml       | 357 +++++++
 32 files changed, 11803 insertions(+)
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_brute_force.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_flat.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_flat.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/bigann-100M.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/constraints.py
 create mode 100644 python/cuvs_bench/cuvs_bench/config/datasets.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/deep-100M.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/deep-1B.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/deep-image-96-inner.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/fashion-mnist-784-euclidean.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/gist-960-euclidean.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/glove-100-angular.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/glove-100-inner.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/glove-50-angular.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/glove-50-inner.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/lastfm-65-angular.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/mnist-784-euclidean.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/nytimes-256-angular.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/nytimes-256-inner.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/sift-128-euclidean.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/wiki_all_10M.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/wiki_all_1M.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/wiki_all_88M.yaml

diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_brute_force.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_brute_force.yaml
new file mode 100644
index 0000000000..df6855fb42
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_brute_force.yaml
@@ -0,0 +1,5 @@
+name: cuvs_brute_force
+groups:
+  base:
+    build:
+    search:
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
new file mode 100644
index 0000000000..e7b049d0c0
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
@@ -0,0 +1,13 @@
+name: cuvs_cagra
+constraints:
+  build: cuvs_bench.constraints.raft_cagra_build_constraints
+  search: cuvs_bench.constraints.raft_cagra_search_constraints
+groups:
+  base:
+    build:
+      graph_degree: [32, 64, 128, 256]
+      intermediate_graph_degree: [32, 64, 96, 128]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      itopk: [32, 64, 128, 256, 512]
+      search_width: [1, 2, 4, 8, 16, 32, 64]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
new file mode 100644
index 0000000000..70e344dfd6
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
@@ -0,0 +1,11 @@
+name: cuvs_cagra_hnswlib
+constraints:
+  search: cuvs_bench.constraints.hnswlib_search
+groups:
+  base:
+    build:
+      graph_degree: [32, 64, 128, 256]
+      intermediate_graph_degree: [32, 64, 96, 128]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      ef: [10, 20, 40, 60, 80, 120, 200, 400, 600, 800]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml
new file mode 100644
index 0000000000..22afc79ab1
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml
@@ -0,0 +1,9 @@
+name: cuvs_ivf_flat
+groups:
+  base:
+    build:
+      nlist: [1024, 2048, 4096, 8192, 16384, 32000, 64000]
+      ratio: [1, 2, 4]
+      niter: [20, 25]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200, 500, 1000, 2000]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
new file mode 100644
index 0000000000..aa95d6716f
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
@@ -0,0 +1,41 @@
+name: cuvs_ivf_pq
+constraints:
+  build: cuvs_bench.constraints.cuvs_ivf_pq_build
+  search: cuvs_bench.constraints.cuvs_ivf_pq_search
+groups:
+  base:
+    build:
+      nlist: [1024, 2048, 4096, 8192]
+      pq_dim: [64, 32, 16]
+      pq_bits: [8, 6, 5, 4]
+      ratio: [10]
+      niter: [25]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1, 2, 4]
+  large:
+    build:
+      nlist: [8192, 16384, 32768, 65536]
+      pq_dim: [48, 32, 16]
+      pq_bits: [8, 6, 5, 4]
+      ratio: [4]
+      niter: [20]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1, 2, 4]
+  100M:
+    build:
+      nlist: [50000]
+      pq_dim: [48]
+      pq_bits: [8, 6, 5, 4]
+      ratio: [10]
+      niter: [10]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_flat.yaml
new file mode 100644
index 0000000000..25eaf03d40
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_flat.yaml
@@ -0,0 +1,5 @@
+name: faiss_cpu_flat
+groups:
+  base:
+    build:
+    search:
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
new file mode 100644
index 0000000000..29c145f86d
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
@@ -0,0 +1,10 @@
+name: faiss_cpu_ivf_flat
+groups:
+  base:
+    build:
+      nlist: [2048]
+      ratio: [10]
+      useFloat16: [False]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+      refine_ratio: [1]
\ No newline at end of file
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml
new file mode 100644
index 0000000000..a531ec8294
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml
@@ -0,0 +1,18 @@
+name: faiss_cpu_ivf_pq
+groups:
+  base:
+    build:
+      nlist: [1024, 2048, 4096, 8192]
+      M: [48, 32, 16]
+      ratio: [10]
+      bitsPerCode: [8, 6, 5, 4]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+  large:
+    build:
+      nlist: [8192, 16384, 32768, 65536]
+      M: [48, 32, 16]
+      ratio: [10]
+      bitsPerCode: [8, 6, 5, 4]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_flat.yaml
new file mode 100644
index 0000000000..a722e1b91c
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_flat.yaml
@@ -0,0 +1,5 @@
+name: faiss_gpu_flat
+groups:
+  base:
+    build:
+    search:
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
new file mode 100644
index 0000000000..e4abc35f5c
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
@@ -0,0 +1,21 @@
+name: faiss_gpu_ivf_flat
+groups:
+  base:
+    build:
+      nlist: [2048]
+      ratio: [10]
+      useFloat16: [False, True]
+      use_raft: [False]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+      refine_ratio: [1]
+groups:
+  baseraft:
+    build:
+      nlist: [2048]
+      ratio: [10]
+      useFloat16: [False, True]
+      use_raft: [True]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+      refine_ratio: [1]
\ No newline at end of file
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
new file mode 100644
index 0000000000..1bd78b736f
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
@@ -0,0 +1,77 @@
+name: faiss_gpu_ivf_pq
+constraints:
+  build: cuvs_bench.constraints.faiss_gpu_ivf_pq_build
+  search: cuvs_bench.constraints.faiss_gpu_ivf_pq_search
+groups:
+  base:
+    build:
+      nlist: [1024, 2048, 4096, 8192]
+      M: [64, 32, 16]
+      ratio: [10]
+      usePrecomputed: [False, True]
+      useFloat16: [False, True]
+      use_raft: [False]
+      bitsPerCode: [8]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+      refine_ratio: [1, 2, 4]
+  baseraft:
+    build:
+      nlist: [1024, 2048, 4096, 8192]
+      M: [64, 32, 16]
+      ratio: [10]
+      usePrecomputed: [False]
+      useFloat16: [False, True]
+      use_raft: [True]
+      bitsPerCode: [8, 6, 5, 4]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+      refine_ratio: [1, 2, 4]
+  large:
+    build:
+      nlist: [8192, 16384, 32768, 65536]
+      M: [48, 32, 16]
+      ratio: [4]
+      usePrecomputed: [False, True]
+      useFloat16: [False, True]
+      use_raft: [False]
+      bitsPerCode: [8]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      refine_ratio: [1, 2, 4]
+  largeraft:
+    build:
+      nlist: [8192, 16384, 32768, 65536]
+      M: [48, 32, 16]
+      ratio: [4]
+      usePrecomputed: [False]
+      useFloat16: [False, True]
+      use_raft: [True]
+      bitsPerCode: [8, 6, 5, 4]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      refine_ratio: [1, 2, 4]
+  100M:
+    build:
+      nlist: [50000]
+      M: [48]
+      ratio: [10]
+      usePrecomputed: [False, True]
+      useFloat16: [False, True]
+      use_raft: [False]
+      bitsPerCode: [8]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      refine_ratio: [1]
+  100Mraft:
+    build:
+      nlist: [50000]
+      M: [48]
+      ratio: [10]
+      usePrecomputed: [False, True]
+      useFloat16: [False, True]
+      use_raft: [True]
+      bitsPerCode: [8, 6, 5, 4]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      refine_ratio: [1]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
new file mode 100644
index 0000000000..dbd73155d5
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
@@ -0,0 +1,10 @@
+name: hnswlib
+constraints:
+  search: cuvs_bench.constraints.hnswlib_search
+groups:
+  base:
+    build:
+      M: [12, 16, 24, 36]
+      efConstruction: [64, 128, 256, 512]
+    search:
+      ef: [10, 20, 40, 60, 80, 120, 200, 400, 600, 800]
diff --git a/python/cuvs_bench/cuvs_bench/config/bigann-100M.yaml b/python/cuvs_bench/cuvs_bench/config/bigann-100M.yaml
new file mode 100644
index 0000000000..69f75d1f71
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/bigann-100M.yaml
@@ -0,0 +1,295 @@
+dataset:
+  base_file: bigann-1B/base.1B.u8bin
+  distance: euclidean
+  groundtruth_neighbors_file: bigann-100M/groundtruth.neighbors.ibin
+  name: bigann-100M
+  query_file: bigann-1B/query.public.10K.u8bin
+  subset_size: 100000000
+index:
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 5000
+    pq_dim: 64
+    ratio: 10
+  file: bigann-100M/raft_ivf_pq/dimpq64-cluster5K
+  name: raft_ivf_pq.dimpq64-cluster5K
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 20
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 1000
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 10000
+    pq_dim: 64
+    ratio: 10
+  file: bigann-100M/raft_ivf_pq/dimpq64-cluster5K
+  name: raft_ivf_pq.dimpq64-cluster10K
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 20
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 1000
+    smemLutDtype: half
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: bigann-100M/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: bigann-100M/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: bigann-100M/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: bigann-100M/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 100000
+    ratio: 5
+  file: bigann-100M/raft_ivf_flat/nlist100K
+  name: raft_ivf_flat.nlist100K
+  search_params:
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 20
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 30
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 40
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 50
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 100
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 200
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 500
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 1000
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: bigann-100M/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: bigann-100M/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+search_basic_param:
+  batch_size: 10000
+  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/constraints.py b/python/cuvs_bench/cuvs_bench/config/constraints.py
new file mode 100644
index 0000000000..ff451c0565
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/constraints.py
@@ -0,0 +1,101 @@
+#
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+###############################################################################
+#                                 Utilities                                   #
+###############################################################################
+
+dtype_sizes = {
+    "float": 4,
+    "fp8": 1,
+    "half": 2,
+}
+
+
+###############################################################################
+#                              cuVS constraints                               #
+###############################################################################
+
+
+def cuvs_cagra_build(params, dims):
+    if "graph_degree" in params and "intermediate_graph_degree" in params:
+        return params["graph_degree"] <= params["intermediate_graph_degree"]
+    return True
+
+
+def cuvs_ivf_pq_build(params, dims):
+    if "pq_dim" in params:
+        return params["pq_dim"] <= dims
+    return True
+
+
+def cuvs_ivf_pq_search(params, build_params, k, batch_size):
+    ret = True
+    if "internalDistanceDtype" in params and "smemLutDtype" in params:
+        ret = (
+            dtype_sizes[params["smemLutDtype"]]
+            <= dtype_sizes[params["internalDistanceDtype"]]
+        )
+
+    if "nlist" in build_params and "nprobe" in params:
+        ret = ret and build_params["nlist"] >= params["nprobe"]
+    return ret
+
+
+def cuvs_cagra_search(params, build_params, k, batch_size):
+    if "itopk" in params:
+        return params["itopk"] >= k
+    return True
+
+
+###############################################################################
+#                              FAISS constraints                              #
+###############################################################################
+
+
+def faiss_gpu_ivf_pq_build(params, dims):
+    ret = True
+    # M must be defined
+    ret = params["M"] <= dims and dims % params["M"] == 0
+    if "use_cuvs" in params and params["use_cuvs"]:
+        return ret
+    pq_bits = 8
+    if "bitsPerCode" in params:
+        pq_bits = params["bitsPerCode"]
+    lookup_table_size = 4
+    if "useFloat16" in params and params["useFloat16"]:
+        lookup_table_size = 2
+    # FAISS constraint to check if lookup table fits in shared memory
+    # for now hard code maximum shared memory per block to 49 kB
+    # (the value for A100 and V100)
+    return ret and lookup_table_size * params["M"] * (2**pq_bits) <= 49152
+
+
+def faiss_gpu_ivf_pq_search(params, build_params, k, batch_size):
+    ret = True
+    if "nlist" in build_params and "nprobe" in params:
+        ret = ret and build_params["nlist"] >= params["nprobe"]
+    return ret
+
+
+###############################################################################
+#                              hnswlib constraints                            #
+###############################################################################
+
+
+def hnswlib_search(params, build_params, k, batch_size):
+    if "ef" in params:
+        return params["ef"] >= k
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets.yaml b/python/cuvs_bench/cuvs_bench/config/datasets.yaml
new file mode 100644
index 0000000000..188d24d20f
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/datasets.yaml
@@ -0,0 +1,127 @@
+- name: bigann-1B
+  base_file: bigann-1B/base.1B.u8bin
+  subset_size: 100000000
+  dims: 128
+  query_file: bigann-1B/query.public.10K.u8bin
+  groundtruth_neighbors_file: bigann-1B/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: deep-1B
+  base_file: deep-1B/base.1B.fbin
+  query_file: deep-1B/query.public.10K.fbin
+  dims: 96
+  groundtruth_neighbors_file: deep-1B/groundtruth.neighbors.ibin
+  distance: inner_product
+
+- name: bigann-100M
+  base_file: bigann-100M/base.1B.u8bin
+  subset_size: 100000000
+  dims: 128
+  query_file: bigann-100M/query.public.10K.u8bin
+  groundtruth_neighbors_file: bigann-100M/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: deep-image-96-inner
+  base_file: deep-image-96-inner/base.fbin
+  query_file: deep-image-96-inner/query.fbin
+  dims: 96
+  groundtruth_neighbors_file: deep-image-96-inner/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: fashion-mnist-784-euclidean
+  dims: 784
+  base_file: fashion-mnist-784-euclidean/base.fbin
+  query_file: fashion-mnist-784-euclidean/query.fbin
+  groundtruth_neighbors_file: fashion-mnist-784-euclidean/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: gist-960-euclidean
+  dims: 960
+  base_file: gist-960-euclidean/base.fbin
+  query_file: gist-960-euclidean/query.fbin
+  groundtruth_neighbors_file: gist-960-euclidean/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: glove-50-angular
+  dims: 50
+  base_file: glove-50-angular/base.fbin
+  query_file: glove-50-angular/query.fbin
+  groundtruth_neighbors_file: glove-50-angular/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: glove-50-inner
+  dims: 50
+  base_file: glove-50-inner/base.fbin
+  query_file: glove-50-inner/query.fbin
+  groundtruth_neighbors_file: glove-50-inner/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: glove-100-angular
+  dims: 100
+  base_file: glove-100-angular/base.fbin
+  query_file: glove-100-angular/query.fbin
+  groundtruth_neighbors_file: glove-100-angular/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: glove-100-inner
+  dims: 100
+  base_file: glove-100-inner/base.fbin
+  query_file: glove-100-inner/query.fbin
+  groundtruth_neighbors_file: glove-100-inner/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: lastfm-65-angular
+  dims: 65
+  base_file: lastfm-65-angular/base.fbin
+  query_file: lastfm-65-angular/query.fbin
+  groundtruth_neighbors_file: lastfm-65-angular/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: mnist-784-euclidean
+  dims: 784
+  base_file: mnist-784-euclidean/base.fbin
+  query_file: mnist-784-euclidean/query.fbin
+  groundtruth_neighbors_file: mnist-784-euclidean/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: nytimes-256-angular
+  dims: 256
+  base_file: nytimes-256-angular/base.fbin
+  query_file: nytimes-256-angular/query.fbin
+  groundtruth_neighbors_file: nytimes-256-angular/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: nytimes-256-inner
+  dims: 256
+  base_file: nytimes-256-inner/base.fbin
+  query_file: nytimes-256-inner/query.fbin
+  groundtruth_neighbors_file: nytimes-256-inner/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: sift-128-euclidean
+  dims: 128
+  base_file: sift-128-euclidean/base.fbin
+  query_file: sift-128-euclidean/query.fbin
+  groundtruth_neighbors_file: sift-128-euclidean/groundtruth.neighbors.ibin
+  distance: euclidean
+
+- name: wiki_all_1M
+  dims: 768
+  base_file: wiki_all_1M/base.1M.fbin
+  query_file: wiki_all_1M/queries.fbin
+  groundtruth_neighbors_file: wiki_all_1M/groundtruth.1M.neighbors.ibin
+  distance: euclidean
+
+- name: wiki_all_10M
+  dims: 768
+  base_file: wiki_all_10M/base.10M.fbin
+  query_file: wiki_all_10M/queries.fbin
+  groundtruth_neighbors_file: wiki_all_10M/groundtruth.10M.neighbors.ibin
+  distance: euclidean
+
+- name: wiki_all_88M
+  dims: 768
+  base_file: wiki_all_88M/base.88M.fbin
+  query_file: wiki_all_88M/queries.fbin
+  groundtruth_neighbors_file: wiki_all_88M/groundtruth.88M.neighbors.ibin
+  distance: euclidean
diff --git a/python/cuvs_bench/cuvs_bench/config/deep-100M.yaml b/python/cuvs_bench/cuvs_bench/config/deep-100M.yaml
new file mode 100644
index 0000000000..90b1f9721b
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/deep-100M.yaml
@@ -0,0 +1,896 @@
+dataset:
+  base_file: deep-100M/base.1B.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: deep-100M/groundtruth.neighbors.ibin
+  name: deep-100M
+  query_file: deep-100M/query.public.10K.fbin
+  subset_size: 100000000
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: deep-100M/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: deep-100M/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: deep-100M/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: deep-100M/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 50000
+  file: deep-100M/faiss_gpu_ivf_flat/nlist50K
+  name: faiss_gpu_ivf_flat.nlist50K
+  search_params:
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 100000
+  file: deep-100M/faiss_gpu_ivf_flat/nlist100K
+  name: faiss_gpu_ivf_flat.nlist100K
+  search_params:
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 200000
+  file: deep-100M/faiss_gpu_ivf_flat/nlist200K
+  name: faiss_gpu_ivf_flat.nlist200K
+  search_params:
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 48
+    nlist: 16384
+  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist16K
+  name: faiss_gpu_ivf_pq.M48-nlist16K
+  search_params:
+  - nprobe: 10
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 48
+    nlist: 50000
+  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist50K
+  name: faiss_gpu_ivf_pq.M48-nlist50K
+  search_params:
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 48
+    nlist: 100000
+  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist100K
+  name: faiss_gpu_ivf_pq.M48-nlist100K
+  search_params:
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 48
+    nlist: 200000
+  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist200K
+  name: faiss_gpu_ivf_pq.M48-nlist200K
+  search_params:
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 50000
+    ratio: 5
+  file: deep-100M/raft_ivf_flat/nlist50K
+  name: raft_ivf_flat.nlist50K
+  search_params:
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 20
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 30
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 40
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 50
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 100
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 200
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 500
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 1000
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 100000
+    ratio: 5
+  file: deep-100M/raft_ivf_flat/nlist100K
+  name: raft_ivf_flat.nlist100K
+  search_params:
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 20
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 30
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 40
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 50
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 100
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 200
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 500
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 1000
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 200000
+    ratio: 5
+  file: deep-100M/raft_ivf_flat/nlist200K
+  name: raft_ivf_flat.nlist200K
+  search_params:
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 20
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 30
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 40
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 50
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 100
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 200
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 500
+  - max_batch: 10000
+    max_k: 10
+    nprobe: 1000
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 50000
+    pq_bits: 5
+    pq_dim: 96
+    ratio: 10
+  file: deep-100M/raft_ivf_pq/d96b5n50K
+  name: raft_ivf_pq.d96b5n50K
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 20
+    refine_ratio: 2
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 30
+    refine_ratio: 2
+    smemLutDtype: float
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+    refine_ratio: 2
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+    refine_ratio: 2
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+    nprobe: 100
+    refine_ratio: 2
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+    refine_ratio: 2
+    smemLutDtype: float
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+    refine_ratio: 2
+    smemLutDtype: float
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+    refine_ratio: 2
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+    refine_ratio: 2
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+    refine_ratio: 2
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+    refine_ratio: 2
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+    refine_ratio: 2
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+    refine_ratio: 2
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+    nprobe: 5000
+    refine_ratio: 2
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+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 2
+    smemLutDtype: fp8
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+    nprobe: 30
+    refine_ratio: 2
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+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 2
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 2
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 2
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 2
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+  - internalDistanceDtype: half
+    nprobe: 1000
+    refine_ratio: 2
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+    nprobe: 2000
+    refine_ratio: 2
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 5000
+    refine_ratio: 2
+    smemLutDtype: fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 50000
+    pq_bits: 5
+    pq_dim: 64
+    ratio: 10
+  file: deep-100M/raft_ivf_pq/d64b5n50K
+  name: raft_ivf_pq.d64b5n50K
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 20
+    refine_ratio: 4
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 30
+    refine_ratio: 4
+    smemLutDtype: float
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+    nprobe: 40
+    refine_ratio: 4
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+    nprobe: 50
+    refine_ratio: 4
+    smemLutDtype: float
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+    nprobe: 100
+    refine_ratio: 4
+    smemLutDtype: float
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+    nprobe: 200
+    refine_ratio: 4
+    smemLutDtype: float
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+    nprobe: 1000
+    refine_ratio: 4
+    smemLutDtype: float
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+    nprobe: 2000
+    refine_ratio: 4
+    smemLutDtype: float
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+    nprobe: 5000
+    refine_ratio: 4
+    smemLutDtype: float
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+    nprobe: 20
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+  - internalDistanceDtype: half
+    nprobe: 5000
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+    smemLutDtype: fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/deep-image-96-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: float
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+    nprobe: 100
+    smemLutDtype: float
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+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/deep-image-96-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+    intermediate_graph_degree: 48
+  file: deep-100M/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - algo: single_cta
+    itopk: 32
+    max_iterations: 0
+    search_width: 1
+  - algo: single_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 1
+  - algo: single_cta
+    itopk: 64
+    max_iterations: 16
+    search_width: 4
+  - algo: single_cta
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+    max_iterations: 64
+    search_width: 1
+  - algo: single_cta
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+    max_iterations: 48
+    search_width: 2
+  - algo: single_cta
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+    max_iterations: 16
+    search_width: 8
+  - algo: single_cta
+    itopk: 128
+    max_iterations: 64
+    search_width: 2
+  - algo: single_cta
+    itopk: 192
+    max_iterations: 24
+    search_width: 8
+  - algo: single_cta
+    itopk: 192
+    max_iterations: 96
+    search_width: 2
+  - algo: single_cta
+    itopk: 256
+    max_iterations: 32
+    search_width: 8
+  - algo: single_cta
+    itopk: 384
+    max_iterations: 48
+    search_width: 8
+  - algo: single_cta
+    itopk: 512
+    max_iterations: 64
+    search_width: 8
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+    intermediate_graph_degree: 48
+  file: deep-100M/raft_cagra/dim32
+  name: raft_cagra.dim32.multi_cta
+  search_params:
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 0
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 1
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 16
+    search_width: 4
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 64
+    search_width: 1
+  - algo: multi_cta
+    itopk: 96
+    max_iterations: 48
+    search_width: 2
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 16
+    search_width: 8
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 64
+    search_width: 2
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 24
+    search_width: 8
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 96
+    search_width: 2
+  - algo: multi_cta
+    itopk: 256
+    max_iterations: 32
+    search_width: 8
+  - algo: multi_cta
+    itopk: 384
+    max_iterations: 48
+    search_width: 8
+  - algo: multi_cta
+    itopk: 512
+    max_iterations: 64
+    search_width: 8
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+    intermediate_graph_degree: 48
+  file: deep-100M/raft_cagra/dim32
+  name: raft_cagra.dim32.multi_kernel
+  search_params:
+  - algo: multi_kernel
+    itopk: 32
+    max_iterations: 0
+    search_width: 1
+  - algo: multi_kernel
+    itopk: 32
+    max_iterations: 32
+    search_width: 1
+  - algo: multi_kernel
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+    max_iterations: 16
+    search_width: 4
+  - algo: multi_kernel
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+    max_iterations: 64
+    search_width: 1
+  - algo: multi_kernel
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+    max_iterations: 48
+    search_width: 2
+  - algo: multi_kernel
+    itopk: 128
+    max_iterations: 16
+    search_width: 8
+  - algo: multi_kernel
+    itopk: 128
+    max_iterations: 64
+    search_width: 2
+  - algo: multi_kernel
+    itopk: 192
+    max_iterations: 24
+    search_width: 8
+  - algo: multi_kernel
+    itopk: 192
+    max_iterations: 96
+    search_width: 2
+  - algo: multi_kernel
+    itopk: 256
+    max_iterations: 32
+    search_width: 8
+  - algo: multi_kernel
+    itopk: 384
+    max_iterations: 48
+    search_width: 8
+  - algo: multi_kernel
+    itopk: 512
+    max_iterations: 64
+    search_width: 8
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: deep-100M/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+    max_iterations: 0
+    search_width: 1
+  - itopk: 32
+    max_iterations: 32
+    search_width: 1
+  - itopk: 64
+    max_iterations: 16
+    search_width: 4
+  - itopk: 64
+    max_iterations: 64
+    search_width: 1
+  - itopk: 96
+    max_iterations: 48
+    search_width: 2
+  - itopk: 128
+    max_iterations: 16
+    search_width: 8
+  - itopk: 128
+    max_iterations: 64
+    search_width: 2
+  - itopk: 192
+    max_iterations: 24
+    search_width: 8
+  - itopk: 192
+    max_iterations: 96
+    search_width: 2
+  - itopk: 256
+    max_iterations: 32
+    search_width: 8
+  - itopk: 384
+    max_iterations: 48
+    search_width: 8
+  - itopk: 512
+    max_iterations: 64
+    search_width: 8
+search_basic_param:
+  batch_size: 10000
+  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/deep-1B.yaml b/python/cuvs_bench/cuvs_bench/config/deep-1B.yaml
new file mode 100644
index 0000000000..061677e621
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/deep-1B.yaml
@@ -0,0 +1,26 @@
+dataset:
+  base_file: deep-1B/base.1B.fbin
+  distance: inner_product
+  groundtruth_neighbors_file: deep-1B/groundtruth.neighbors.ibin
+  name: deep-1B
+  query_file: deep-1B/query.public.10K.fbin
+index:
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 48
+    nlist: 50000
+  file: deep-1B/faiss_gpu_ivf_pq/M48-nlist50K
+  name: faiss_gpu_ivf_pq.M48-nlist50K
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+search_basic_param:
+  batch_size: 10000
+  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/deep-image-96-inner.yaml b/python/cuvs_bench/cuvs_bench/config/deep-image-96-inner.yaml
new file mode 100644
index 0000000000..2ec584eb56
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/deep-image-96-inner.yaml
@@ -0,0 +1,712 @@
+dataset:
+  base_file: deep-image-96-inner/base.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: deep-image-96-inner/groundtruth.neighbors.ibin
+  name: deep-image-96-inner
+  query_file: deep-image-96-inner/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/deep-image-96-inner/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/deep-image-96-inner/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/deep-image-96-inner/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/deep-image-96-inner/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/deep-image-96-inner/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/deep-image-96-inner/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/deep-image-96-inner/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/deep-image-96-inner/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/deep-image-96-inner/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/deep-image-96-inner/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/deep-image-96-inner/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/deep-image-96-inner/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/deep-image-96-inner/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/deep-image-96-inner/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/deep-image-96-inner/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/deep-image-96-inner/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/deep-image-96-inner/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/deep-image-96-inner/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/deep-image-96-inner/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/deep-image-96-inner/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/fashion-mnist-784-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/fashion-mnist-784-euclidean.yaml
new file mode 100644
index 0000000000..e92d3badcb
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/fashion-mnist-784-euclidean.yaml
@@ -0,0 +1,778 @@
+dataset:
+  base_file: fashion-mnist-784-euclidean/base.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: fashion-mnist-784-euclidean/groundtruth.neighbors.ibin
+  name: fashion-mnist-784-euclidean
+  query_file: fashion-mnist-784-euclidean/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/fashion-mnist-784-euclidean/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/fashion-mnist-784-euclidean/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/fashion-mnist-784-euclidean/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/fashion-mnist-784-euclidean/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/fashion-mnist-784-euclidean/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/fashion-mnist-784-euclidean/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/fashion-mnist-784-euclidean/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/fashion-mnist-784-euclidean/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/fashion-mnist-784-euclidean/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/fashion-mnist-784-euclidean/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/fashion-mnist-784-euclidean/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/fashion-mnist-784-euclidean/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/fashion-mnist-784-euclidean/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/gist-960-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/gist-960-euclidean.yaml
new file mode 100644
index 0000000000..d72dff74d2
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/gist-960-euclidean.yaml
@@ -0,0 +1,777 @@
+dataset:
+  base_file: gist-960-euclidean/base.fbin
+  distance: euclidean
+  name: gist-960-euclidean
+  query_file: gist-960-euclidean/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/gist-960-euclidean/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/gist-960-euclidean/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/gist-960-euclidean/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/gist-960-euclidean/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/gist-960-euclidean/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/gist-960-euclidean/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/gist-960-euclidean/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/gist-960-euclidean/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/gist-960-euclidean/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/gist-960-euclidean/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/gist-960-euclidean/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/gist-960-euclidean/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/gist-960-euclidean/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/gist-960-euclidean/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/gist-960-euclidean/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/gist-960-euclidean/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/gist-960-euclidean/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/gist-960-euclidean/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/gist-960-euclidean/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/gist-960-euclidean/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-100-angular.yaml b/python/cuvs_bench/cuvs_bench/config/glove-100-angular.yaml
new file mode 100644
index 0000000000..dab60ed4cf
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/glove-100-angular.yaml
@@ -0,0 +1,777 @@
+dataset:
+  base_file: glove-100-angular/base.fbin
+  distance: euclidean
+  name: glove-100-angular
+  query_file: glove-100-angular/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-angular/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-angular/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-angular/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-angular/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-angular/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-angular/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-angular/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-angular/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/glove-100-angular/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/glove-100-angular/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/glove-100-angular/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/glove-100-angular/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/glove-100-angular/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-angular/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/glove-100-angular/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-angular/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/glove-100-angular/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-100-angular/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/glove-100-angular/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-100-angular/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-100-inner.yaml b/python/cuvs_bench/cuvs_bench/config/glove-100-inner.yaml
new file mode 100644
index 0000000000..972703735f
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/glove-100-inner.yaml
@@ -0,0 +1,777 @@
+dataset:
+  base_file: glove-100-inner/base.fbin
+  distance: euclidean
+  name: glove-100-inner
+  query_file: glove-100-inner/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-inner/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-inner/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-inner/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-inner/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-inner/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-inner/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-100-inner/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-100-inner/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/glove-100-inner/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/glove-100-inner/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: glove-100-inner/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: glove-100-inner/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: glove-100-inner/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: glove-100-inner/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/glove-100-inner/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-inner/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/glove-100-inner/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/glove-100-inner/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/glove-100-inner/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: glove-100-inner/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: glove-100-inner/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/glove-100-inner/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-100-inner/raft_gpu_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/glove-100-inner/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-100-inner/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/glove-100-inner/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-100-inner/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/glove-100-inner/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-100-inner/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/glove-100-inner/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-100-inner/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-50-angular.yaml b/python/cuvs_bench/cuvs_bench/config/glove-50-angular.yaml
new file mode 100644
index 0000000000..d68d1700c8
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/glove-50-angular.yaml
@@ -0,0 +1,777 @@
+dataset:
+  base_file: glove-50-angular/base.fbin
+  distance: euclidean
+  name: glove-50-angular
+  query_file: glove-50-angular/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-angular/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-angular/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-angular/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-angular/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-angular/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-angular/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-angular/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-angular/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/glove-50-angular/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/glove-50-angular/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/glove-50-angular/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/glove-50-angular/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/glove-50-angular/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-angular/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/glove-50-angular/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-angular/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/glove-50-angular/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-50-angular/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/glove-50-angular/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-50-angular/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-50-inner.yaml b/python/cuvs_bench/cuvs_bench/config/glove-50-inner.yaml
new file mode 100644
index 0000000000..379c797964
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/glove-50-inner.yaml
@@ -0,0 +1,777 @@
+dataset:
+  base_file: glove-50-inner/base.fbin
+  distance: euclidean
+  name: glove-50-inner
+  query_file: glove-50-inner/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-inner/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-inner/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-inner/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-inner/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-inner/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-inner/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/glove-50-inner/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/glove-50-inner/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/glove-50-inner/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/glove-50-inner/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/glove-50-inner/faiss_ivf_flat/nlist1024
+  name: faiss_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/glove-50-inner/faiss_ivf_flat/nlist2048
+  name: faiss_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/glove-50-inner/faiss_ivf_flat/nlist4096
+  name: faiss_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/glove-50-inner/faiss_ivf_flat/nlist8192
+  name: faiss_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/glove-50-inner/faiss_ivf_flat/nlist16384
+  name: faiss_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/glove-50-inner/faiss_ivf_pq/M64-nlist1024
+  name: faiss_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/glove-50-inner/faiss_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/glove-50-inner/faiss_ivf_sq/nlist1024-fp16
+  name: faiss_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/glove-50-inner/faiss_ivf_sq/nlist2048-fp16
+  name: faiss_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/glove-50-inner/faiss_ivf_sq/nlist4096-fp16
+  name: faiss_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/glove-50-inner/faiss_ivf_sq/nlist8192-fp16
+  name: faiss_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/glove-50-inner/faiss_ivf_sq/nlist16384-fp16
+  name: faiss_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/glove-50-inner/faiss_ivf_sq/nlist1024-int8
+  name: faiss_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/glove-50-inner/faiss_ivf_sq/nlist2048-int8
+  name: faiss_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/glove-50-inner/faiss_ivf_sq/nlist4096-int8
+  name: faiss_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/glove-50-inner/faiss_ivf_sq/nlist8192-int8
+  name: faiss_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/glove-50-inner/faiss_ivf_sq/nlist16384-int8
+  name: faiss_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/glove-50-inner/faiss_flat/flat
+  name: faiss_flat
+  search_params:
+  - {}
+  search_result_file: result/glove-50-inner/faiss_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: half
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+    k: 10
+    nprobe: 100
+    smemLutDtype: half
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+    k: 10
+    nprobe: 200
+    smemLutDtype: half
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+    k: 10
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1
+    smemLutDtype: float
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+    nprobe: 1
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+    nprobe: 5
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+    smemLutDtype: float
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+    smemLutDtype: float
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+    nprobe: 200
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+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
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+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
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+    nprobe: 50
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+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
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+    nprobe: 50
+    smemLutDtype: fp8
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+    nprobe: 100
+    smemLutDtype: fp8
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+    nprobe: 200
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+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: half
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+    nprobe: 50
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+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: half
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
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+    nprobe: 100
+    smemLutDtype: fp8
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+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: fp8
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+    smemLutDtype: fp8
+  - internalDistanceDtype: float
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+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: fp8
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
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+    k: 10
+    nprobe: 100
+    smemLutDtype: float
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+    nprobe: 200
+    smemLutDtype: float
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+    k: 10
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 50
+    smemLutDtype: float
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+    smemLutDtype: float
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+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
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+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    nprobe: 1024
+    smemLutDtype: float
+  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/glove-50-inner/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/glove-50-inner/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/glove-50-inner/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/glove-50-inner/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/glove-50-inner/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-50-inner/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/glove-50-inner/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/glove-50-inner/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/lastfm-65-angular.yaml b/python/cuvs_bench/cuvs_bench/config/lastfm-65-angular.yaml
new file mode 100644
index 0000000000..f28dc5850d
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/lastfm-65-angular.yaml
@@ -0,0 +1,777 @@
+dataset:
+  base_file: lastfm-65-angular/base.fbin
+  distance: euclidean
+  name: lastfm-65-angular
+  query_file: lastfm-65-angular/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/lastfm-65-angular/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/lastfm-65-angular/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/lastfm-65-angular/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/lastfm-65-angular/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/lastfm-65-angular/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/lastfm-65-angular/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/lastfm-65-angular/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/lastfm-65-angular/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/lastfm-65-angular/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/lastfm-65-angular/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/lastfm-65-angular/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/lastfm-65-angular/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/lastfm-65-angular/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/lastfm-65-angular/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/lastfm-65-angular/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/lastfm-65-angular/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/lastfm-65-angular/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/lastfm-65-angular/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/lastfm-65-angular/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/lastfm-65-angular/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/mnist-784-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/mnist-784-euclidean.yaml
new file mode 100644
index 0000000000..1ffdd21244
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/mnist-784-euclidean.yaml
@@ -0,0 +1,778 @@
+dataset:
+  base_file: mnist-784-euclidean/base.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: mnist-784-euclidean/groundtruth.neighbors.ibin
+  name: mnist-784-euclidean
+  query_file: mnist-784-euclidean/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/mnist-784-euclidean/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/mnist-784-euclidean/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/mnist-784-euclidean/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/mnist-784-euclidean/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/mnist-784-euclidean/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/mnist-784-euclidean/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/mnist-784-euclidean/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/mnist-784-euclidean/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/mnist-784-euclidean/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/mnist-784-euclidean/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/mnist-784-euclidean/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/mnist-784-euclidean/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/mnist-784-euclidean/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/mnist-784-euclidean/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/mnist-784-euclidean/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/mnist-784-euclidean/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/mnist-784-euclidean/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/mnist-784-euclidean/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/mnist-784-euclidean/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/mnist-784-euclidean/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/nytimes-256-angular.yaml b/python/cuvs_bench/cuvs_bench/config/nytimes-256-angular.yaml
new file mode 100644
index 0000000000..e9dcd83178
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/nytimes-256-angular.yaml
@@ -0,0 +1,778 @@
+dataset:
+  base_file: nytimes-256-angular/base.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: nytimes-256-angular/groundtruth.neighbors.ibin
+  name: nytimes-256-angular
+  query_file: nytimes-256-angular/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-angular/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-angular/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-angular/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-angular/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-angular/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-angular/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-angular/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-angular/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/nytimes-256-angular/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/nytimes-256-angular/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/nytimes-256-angular/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+  search_result_file: result/nytimes-256-angular/faiss_gpu_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/nytimes-256-angular/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-angular/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/nytimes-256-angular/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-angular/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/nytimes-256-angular/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/nytimes-256-angular/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/nytimes-256-angular/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/nytimes-256-angular/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/nytimes-256-inner.yaml b/python/cuvs_bench/cuvs_bench/config/nytimes-256-inner.yaml
new file mode 100644
index 0000000000..fa0094e11f
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/nytimes-256-inner.yaml
@@ -0,0 +1,778 @@
+dataset:
+  base_file: nytimes-256-inner/base.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: nytimes-256-inner/groundtruth.neighbors.ibin
+  name: nytimes-256-inner
+  query_file: nytimes-256-inner/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-inner/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-inner/hnswlib/M12
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-inner/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-inner/hnswlib/M16
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-inner/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-inner/hnswlib/M24
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: index/nytimes-256-inner/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+  search_result_file: result/nytimes-256-inner/hnswlib/M36
+- algo: raft_bfknn
+  build_param: {}
+  file: index/nytimes-256-inner/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+  search_result_file: result/nytimes-256-inner/raft_bfknn/bfknn
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: index/nytimes-256-inner/faiss_ivf_flat/nlist1024
+  name: faiss_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist1024
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: index/nytimes-256-inner/faiss_ivf_flat/nlist2048
+  name: faiss_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist2048
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: index/nytimes-256-inner/faiss_ivf_flat/nlist4096
+  name: faiss_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist4096
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: index/nytimes-256-inner/faiss_ivf_flat/nlist8192
+  name: faiss_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist8192
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: index/nytimes-256-inner/faiss_ivf_flat/nlist16384
+  name: faiss_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist16384
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: index/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024
+  name: faiss_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: index/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist1024-fp16
+  name: faiss_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist1024-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist2048-fp16
+  name: faiss_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist2048-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist4096-fp16
+  name: faiss_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist4096-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist8192-fp16
+  name: faiss_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist8192-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist16384-fp16
+  name: faiss_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist16384-fp16
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist1024-int8
+  name: faiss_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist1024-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist2048-int8
+  name: faiss_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist2048-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist4096-int8
+  name: faiss_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist4096-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist8192-int8
+  name: faiss_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist8192-int8
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: index/nytimes-256-inner/faiss_ivf_sq/nlist16384-int8
+  name: faiss_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist16384-int8
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: index/nytimes-256-inner/faiss_flat/flat
+  name: faiss_flat
+  search_params:
+  - {}
+  search_result_file: result/nytimes-256-inner/faiss_flat/flat
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024
+  name: raft_ivf_pq.dimpq128-cluster1024
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+  name: raft_ivf_pq.dimpq128-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 5
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+  name: raft_ivf_pq.dimpq128-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 64
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+  name: raft_ivf_pq.dimpq64-cluster1024-float-half
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: half
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: half
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 32
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 16
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: fp8
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 128
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+  name: raft_ivf_pq.dimpq128-cluster1024-half-float
+  search_params:
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: half
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1024
+    pq_dim: 512
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+  name: raft_ivf_pq.dimpq512-cluster1024-float-float
+  search_params:
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 10
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    k: 10
+    numProbes: 1024
+    smemLutDtype: float
+  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: index/nytimes-256-inner/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  search_result_file: result/nytimes-256-inner/raft_ivf_flat/nlist1024
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: index/nytimes-256-inner/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+  search_result_file: result/nytimes-256-inner/raft_ivf_flat/nlist16384
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: index/nytimes-256-inner/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/nytimes-256-inner/raft_cagra/dim32
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: index/nytimes-256-inner/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+  search_result_file: result/nytimes-256-inner/raft_cagra/dim64
+search_basic_param:
+  batch_size: 5000
+  k: 10
+  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/sift-128-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/sift-128-euclidean.yaml
new file mode 100644
index 0000000000..2588d8a366
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/sift-128-euclidean.yaml
@@ -0,0 +1,562 @@
+dataset:
+  base_file: sift-128-euclidean/base.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: sift-128-euclidean/groundtruth.neighbors.ibin
+  name: sift-128-euclidean
+  query_file: sift-128-euclidean/query.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 12
+    efConstruction: 500
+    numThreads: 32
+  file: sift-128-euclidean/hnswlib/M12
+  name: hnswlib.M12
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 500
+    numThreads: 32
+  file: sift-128-euclidean/hnswlib/M16
+  name: hnswlib.M16
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 24
+    efConstruction: 500
+    numThreads: 32
+  file: sift-128-euclidean/hnswlib/M24
+  name: hnswlib.M24
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: hnswlib
+  build_param:
+    M: 36
+    efConstruction: 500
+    numThreads: 32
+  file: sift-128-euclidean/hnswlib/M36
+  name: hnswlib.M36
+  search_params:
+  - ef: 10
+  - ef: 20
+  - ef: 40
+  - ef: 60
+  - ef: 80
+  - ef: 120
+  - ef: 200
+  - ef: 400
+  - ef: 600
+  - ef: 800
+- algo: raft_bfknn
+  build_param: {}
+  file: sift-128-euclidean/raft_bfknn/bfknn
+  name: raft_bfknn
+  search_params:
+  - probe: 1
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 1024
+  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist1024
+  name: faiss_gpu_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 2048
+  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist2048
+  name: faiss_gpu_ivf_flat.nlist2048
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 4096
+  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist4096
+  name: faiss_gpu_ivf_flat.nlist4096
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 8192
+  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist8192
+  name: faiss_gpu_ivf_flat.nlist8192
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_flat
+  build_param:
+    nlist: 16384
+  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist16384
+  name: faiss_gpu_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: true
+  file: sift-128-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
+  name: faiss_gpu_ivf_pq.M64-nlist1024
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 1024
+    useFloat16: true
+    usePrecomputed: false
+  file: sift-128-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
+  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
+  search_params:
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: fp16
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
+  name: faiss_gpu_ivf_sq.nlist1024-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: fp16
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
+  name: faiss_gpu_ivf_sq.nlist2048-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: fp16
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
+  name: faiss_gpu_ivf_sq.nlist4096-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: fp16
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
+  name: faiss_gpu_ivf_sq.nlist8192-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: fp16
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
+  name: faiss_gpu_ivf_sq.nlist16384-fp16
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 1024
+    quantizer_type: int8
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
+  name: faiss_gpu_ivf_sq.nlist1024-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 2048
+    quantizer_type: int8
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
+  name: faiss_gpu_ivf_sq.nlist2048-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 4096
+    quantizer_type: int8
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
+  name: faiss_gpu_ivf_sq.nlist4096-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 8192
+    quantizer_type: int8
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
+  name: faiss_gpu_ivf_sq.nlist8192-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: faiss_gpu_ivf_sq
+  build_param:
+    nlist: 16384
+    quantizer_type: int8
+  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
+  name: faiss_gpu_ivf_sq.nlist16384-int8
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+- algo: faiss_gpu_flat
+  build_param: {}
+  file: sift-128-euclidean/faiss_gpu_flat/flat
+  name: faiss_gpu_flat
+  search_params:
+  - {}
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1000
+    pq_bits: 8
+    pq_dim: 64
+    ratio: 1
+  file: sift-128-euclidean/raft_ivf_pq/dimpq64-bitpq8-cluster1K
+  name: raft_ivf_pq.dimpq64-bitpq8-cluster1K
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 20
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 1000
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 25
+    nlist: 1000
+    pq_bits: 6
+    pq_dim: 128
+    ratio: 1
+  file: sift-128-euclidean/raft_ivf_pq/dimpq128-bitpq6-cluster1K
+  name: raft_ivf_pq.dimpq128-bitpq6-cluster1K
+  search_params:
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: float
+  - internalDistanceDtype: float
+    nprobe: 20
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 30
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 40
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 50
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 100
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 200
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 500
+    smemLutDtype: fp8
+  - internalDistanceDtype: float
+    nprobe: 1000
+    smemLutDtype: fp8
+  - internalDistanceDtype: half
+    nprobe: 20
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 1000
+    smemLutDtype: half
+- algo: raft_ivf_flat
+  build_param:
+    niter: 25
+    nlist: 1024
+    ratio: 1
+  file: sift-128-euclidean/raft_ivf_flat/nlist1024
+  name: raft_ivf_flat.nlist1024
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+- algo: raft_ivf_flat
+  build_param:
+    niter: 20
+    nlist: 16384
+    ratio: 2
+  file: sift-128-euclidean/raft_ivf_flat/nlist16384
+  name: raft_ivf_flat.nlist16384
+  search_params:
+  - nprobe: 1
+  - nprobe: 5
+  - nprobe: 10
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+  - nprobe: 1000
+  - nprobe: 2000
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+  file: sift-128-euclidean/raft_cagra/dim32
+  name: raft_cagra.dim32
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+- algo: raft_cagra
+  build_param:
+    graph_degree: 64
+  file: sift-128-euclidean/raft_cagra/dim64
+  name: raft_cagra.dim64
+  search_params:
+  - itopk: 32
+  - itopk: 64
+  - itopk: 128
+search_basic_param:
+  batch_size: 5000
+  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/wiki_all_10M.yaml b/python/cuvs_bench/cuvs_bench/config/wiki_all_10M.yaml
new file mode 100644
index 0000000000..090247be9d
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/wiki_all_10M.yaml
@@ -0,0 +1,357 @@
+dataset:
+  base_file: wiki_all_10M/base.88M.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: wiki_all_10M/groundtruth.88M.neighbors.ibin
+  name: wiki_all_10M
+  query_file: wiki_all_10M/queries.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 50
+    numThreads: 56
+  file: wiki_all_10M/hnswlib/M16.ef50
+  name: hnswlib.M16.ef50
+  search_params:
+  - ef: 10
+    numThreads: 56
+  - ef: 20
+    numThreads: 56
+  - ef: 40
+    numThreads: 56
+  - ef: 60
+    numThreads: 56
+  - ef: 80
+    numThreads: 56
+  - ef: 120
+    numThreads: 56
+  - ef: 200
+    numThreads: 56
+  - ef: 400
+    numThreads: 56
+  - ef: 600
+    numThreads: 56
+  - ef: 800
+    numThreads: 56
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 32
+    nlist: 16384
+    ratio: 2
+  file: wiki_all_10M/faiss_ivf_pq/M32-nlist16K_ratio2
+  name: faiss_ivf_pq.M32-nlist16K
+  search_params:
+  - nprobe: 10
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 16384
+    ratio: 2
+  file: wiki_all_10M/faiss_ivf_pq/M64-nlist16K_ratio2
+  name: faiss_ivf_pq.M64-nlist16K
+  search_params:
+  - nprobe: 10
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 128
+    ratio: 10
+  file: wiki_all_10M/raft_ivf_pq/d128-nlist16K
+  name: raft_ivf_pq.d128-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 1
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 64
+    ratio: 10
+  file: wiki_all_10M/raft_ivf_pq/d64-nlist16K
+  name: raft_ivf_pq.d64-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 4
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 32
+    ratio: 10
+  file: wiki_all_10M/raft_ivf_pq/d32-nlist16K
+  name: raft_ivf_pq.d32-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 32
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 32
+    ratio: 10
+  file: wiki_all_10M/raft_ivf_pq/d32-nlist16K
+  name: raft_ivf_pq.d32X-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 4
+    smemLutDtype: half
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+    intermediate_graph_degree: 48
+  file: wiki_all_10M/raft_cagra/dim32.ibin
+  name: raft_cagra.dim32.multi_cta
+  search_params:
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 0
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 36
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 40
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 44
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 48
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 16
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 24
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 26
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 2
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 16
+    search_width: 4
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 64
+    search_width: 1
+  - algo: multi_cta
+    itopk: 96
+    max_iterations: 48
+    search_width: 2
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 16
+    search_width: 8
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 64
+    search_width: 2
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 24
+    search_width: 8
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 96
+    search_width: 2
+  - algo: multi_cta
+    itopk: 256
+    max_iterations: 32
+    search_width: 8
+  - algo: multi_cta
+    itopk: 384
+    max_iterations: 48
+    search_width: 8
+  - algo: multi_cta
+    itopk: 512
+    max_iterations: 64
+    search_width: 8
+search_basic_param:
+  batch_size: 10000
+  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/wiki_all_1M.yaml b/python/cuvs_bench/cuvs_bench/config/wiki_all_1M.yaml
new file mode 100644
index 0000000000..bc5abaca2a
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/wiki_all_1M.yaml
@@ -0,0 +1,371 @@
+dataset:
+  base_file: wiki_all_1M/base.1M.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: wiki_all_1M/groundtruth.1M.neighbors.ibin
+  name: wiki_all_1M
+  query_file: wiki_all_1M/queries.fbin
+  subset_size: 1000000
+index:
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 50
+    numThreads: 56
+  file: wiki_all_1M/hnswlib/M16.ef50
+  name: hnswlib.M16.ef50
+  search_params:
+  - ef: 10
+    numThreads: 56
+  - ef: 20
+    numThreads: 56
+  - ef: 40
+    numThreads: 56
+  - ef: 60
+    numThreads: 56
+  - ef: 80
+    numThreads: 56
+  - ef: 120
+    numThreads: 56
+  - ef: 200
+    numThreads: 56
+  - ef: 400
+    numThreads: 56
+  - ef: 600
+    numThreads: 56
+  - ef: 800
+    numThreads: 56
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 32
+    nlist: 16384
+    ratio: 2
+  file: wiki_all_1M/faiss_ivf_pq/M32-nlist16K_ratio2
+  name: faiss_ivf_pq.M32-nlist16K
+  search_params:
+  - nprobe: 10
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 16384
+    ratio: 2
+  file: wiki_all_1M/faiss_ivf_pq/M64-nlist16K_ratio2
+  name: faiss_ivf_pq.M64-nlist16K
+  search_params:
+  - nprobe: 10
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 128
+    ratio: 10
+  file: wiki_all_1M/raft_ivf_pq/d128-nlist16K
+  name: raft_ivf_pq.d128-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 1
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 64
+    ratio: 10
+  file: wiki_all_1M/raft_ivf_pq/d64-nlist16K
+  name: raft_ivf_pq.d64-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 4
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 32
+    ratio: 10
+  file: wiki_all_1M/raft_ivf_pq/d32-nlist16K
+  name: raft_ivf_pq.d32-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 32
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 32
+    ratio: 10
+  file: wiki_all_1M/raft_ivf_pq/d32-nlist16K
+  name: raft_ivf_pq.d32X-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 4
+    smemLutDtype: half
+- algo: raft_cagra
+  build_param:
+    graph_build_algo: NN_DESCENT
+    graph_degree: 32
+    intermediate_graph_degree: 48
+    ivf_pq_build_niter: 10
+    ivf_pq_build_nlist: 16384
+    ivf_pq_build_pq_bits: 8
+    ivf_pq_build_pq_dim: 32
+    ivf_pq_build_ratio: 10
+    ivf_pq_search_internalDistanceDtype: half
+    ivf_pq_search_nprobe: 30
+    ivf_pq_search_refine_ratio: 8
+    ivf_pq_search_smemLutDtype: half
+    nn_descent_intermediate_graph_degree: 72
+    nn_descent_max_iterations: 10
+    nn_descent_termination_threshold: 0.001
+  file: wiki_all_1M/raft_cagra/dim32.ibin
+  name: raft_cagra.dim32.multi_cta
+  search_params:
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 0
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 36
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 40
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 44
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 48
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 16
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 24
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 26
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 2
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 16
+    search_width: 4
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 64
+    search_width: 1
+  - algo: multi_cta
+    itopk: 96
+    max_iterations: 48
+    search_width: 2
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 16
+    search_width: 8
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 64
+    search_width: 2
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 24
+    search_width: 8
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 96
+    search_width: 2
+  - algo: multi_cta
+    itopk: 256
+    max_iterations: 32
+    search_width: 8
+  - algo: multi_cta
+    itopk: 384
+    max_iterations: 48
+    search_width: 8
+  - algo: multi_cta
+    itopk: 512
+    max_iterations: 64
+    search_width: 8
+search_basic_param:
+  batch_size: 10000
+  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/wiki_all_88M.yaml b/python/cuvs_bench/cuvs_bench/config/wiki_all_88M.yaml
new file mode 100644
index 0000000000..a06d6de54e
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/wiki_all_88M.yaml
@@ -0,0 +1,357 @@
+dataset:
+  base_file: wiki_all_88M/base.88M.fbin
+  distance: euclidean
+  groundtruth_neighbors_file: wiki_all_88M/groundtruth.88M.neighbors.ibin
+  name: wiki_all_88M
+  query_file: wiki_all_88M/queries.fbin
+index:
+- algo: hnswlib
+  build_param:
+    M: 16
+    efConstruction: 50
+    numThreads: 56
+  file: wiki_all_88M/hnswlib/M16.ef50
+  name: hnswlib.M16.ef50
+  search_params:
+  - ef: 10
+    numThreads: 56
+  - ef: 20
+    numThreads: 56
+  - ef: 40
+    numThreads: 56
+  - ef: 60
+    numThreads: 56
+  - ef: 80
+    numThreads: 56
+  - ef: 120
+    numThreads: 56
+  - ef: 200
+    numThreads: 56
+  - ef: 400
+    numThreads: 56
+  - ef: 600
+    numThreads: 56
+  - ef: 800
+    numThreads: 56
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 32
+    nlist: 16384
+    ratio: 2
+  file: wiki_all_88M/faiss_ivf_pq/M32-nlist16K_ratio2
+  name: faiss_ivf_pq.M32-nlist16K
+  search_params:
+  - nprobe: 10
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+- algo: faiss_gpu_ivf_pq
+  build_param:
+    M: 64
+    nlist: 16384
+    ratio: 2
+  file: wiki_all_88M/faiss_ivf_pq/M64-nlist16K_ratio2
+  name: faiss_ivf_pq.M64-nlist16K
+  search_params:
+  - nprobe: 10
+  - nprobe: 20
+  - nprobe: 30
+  - nprobe: 40
+  - nprobe: 50
+  - nprobe: 100
+  - nprobe: 200
+  - nprobe: 500
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 128
+    ratio: 10
+  file: wiki_all_88M/raft_ivf_pq/d128-nlist16K
+  name: raft_ivf_pq.d128-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 1
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 1
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 64
+    ratio: 10
+  file: wiki_all_88M/raft_ivf_pq/d64-nlist16K
+  name: raft_ivf_pq.d64-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 4
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 32
+    ratio: 10
+  file: wiki_all_88M/raft_ivf_pq/d32-nlist16K
+  name: raft_ivf_pq.d32-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 32
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 32
+    smemLutDtype: half
+- algo: raft_ivf_pq
+  build_param:
+    niter: 10
+    nlist: 16384
+    pq_bits: 8
+    pq_dim: 32
+    ratio: 10
+  file: wiki_all_88M/raft_ivf_pq/d32-nlist16K
+  name: raft_ivf_pq.d32X-nlist16K
+  search_params:
+  - internalDistanceDtype: half
+    nprobe: 20
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 16
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 8
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 30
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 40
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 50
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 100
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 200
+    refine_ratio: 4
+    smemLutDtype: half
+  - internalDistanceDtype: half
+    nprobe: 500
+    refine_ratio: 4
+    smemLutDtype: half
+- algo: raft_cagra
+  build_param:
+    graph_degree: 32
+    intermediate_graph_degree: 48
+  file: wiki_all_88M/raft_cagra/dim32.ibin
+  name: raft_cagra.dim32.multi_cta
+  search_params:
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 0
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 36
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 40
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 44
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 48
+    search_width: 1
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 16
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 24
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 26
+    search_width: 2
+  - algo: multi_cta
+    itopk: 32
+    max_iterations: 32
+    search_width: 2
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 16
+    search_width: 4
+  - algo: multi_cta
+    itopk: 64
+    max_iterations: 64
+    search_width: 1
+  - algo: multi_cta
+    itopk: 96
+    max_iterations: 48
+    search_width: 2
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 16
+    search_width: 8
+  - algo: multi_cta
+    itopk: 128
+    max_iterations: 64
+    search_width: 2
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 24
+    search_width: 8
+  - algo: multi_cta
+    itopk: 192
+    max_iterations: 96
+    search_width: 2
+  - algo: multi_cta
+    itopk: 256
+    max_iterations: 32
+    search_width: 8
+  - algo: multi_cta
+    itopk: 384
+    max_iterations: 48
+    search_width: 8
+  - algo: multi_cta
+    itopk: 512
+    max_iterations: 64
+    search_width: 8
+search_basic_param:
+  batch_size: 10000
+  k: 10

From e67caa5a82e84480a2f58fc1af4cdd4204892402 Mon Sep 17 00:00:00 2001
From: Akira Naruse <naruse.12251624@gmail.com>
Date: Thu, 1 Aug 2024 00:09:59 +0900
Subject: [PATCH 117/623] Add MST optimization to guarantee the connectivity of
 CAGRA graphs (#237)

This PR allows us to guarantee the connectivity of the CAGRA search graph using approximate MST.

It has been empirically shown that the graph indexes generated by CAGRA for search provide comparable search accuracy to other libraries, but reachability from any node to all nodes is not guaranteed. In fact, it has been confirmed that the number of strongly connected components (SCC) of graph indexes created by CAGRA is not 1 in some 100M scale datasets.

This problem can be alleviated by increasing the number of degrees in the search graph, but this would increase the size of the graph index. It is desirable to address this problem without increasing the number of degrees of the search graph.

Prior study has shown that this can be solved by using a Minimum Spanning Tree (MST)-like approach, but in general, MST calculation takes a long time. However, what is needed here is not an exact MST, but, for example, an approximate MST in which the total number of edges is not necessarily minimum. Such an approximate MST could be computed quickly on GPUs.

This PR contains implementation to create a approximate MST on the GPU at high speed based on the above policy and use it to guarantee the connectivity of the search graph.

This functionality is not always required, so it is considered an opt-in feature. A member variable named `guarantee_connectivity` is added to `index_params`, so set this variable to `true` if you wish to use this featgure.

> cuvs::neighbors::cagra::index_params index_params;
> index_params.guarantee_connectivity = true;
> auto index = cuvs::neighbors::cagra::build(res, index_params, dataset_view);

Authors:
  - Akira Naruse (https://github.com/anaruse)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/237
---
 cpp/include/cuvs/neighbors/cagra.hpp          |   5 +
 cpp/src/neighbors/cagra.cuh                   |   5 +-
 .../neighbors/detail/cagra/cagra_build.cuh    |   8 +-
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 871 +++++++++++++++++-
 4 files changed, 858 insertions(+), 31 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index eb2151c466..f74eac7111 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -106,6 +106,11 @@ struct index_params : cuvs::neighbors::index_params {
                graph_build_params::ivf_pq_params,
                graph_build_params::nn_descent_params>
     graph_build_params;
+
+  /**
+   * Whether to use MST optimization to guarantee graph connectivity.
+   */
+  bool guarantee_connectivity = false;
   /**
    * Whether to add the dataset content to the index, i.e.:
    *
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 89e0db1c12..033f080e23 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -238,9 +238,10 @@ template <
 void optimize(
   raft::resources const& res,
   raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph,
-  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph)
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph,
+  const bool guarantee_connectivity = false)
 {
-  detail::optimize(res, knn_graph, new_graph);
+  detail::optimize(res, knn_graph, new_graph, guarantee_connectivity);
 }
 
 template <typename T,
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 73fe86098d..4a927add53 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -382,7 +382,8 @@ template <
 void optimize(
   raft::resources const& res,
   raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph,
-  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph)
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph,
+  const bool guarantee_connectivity = false)
 {
   using internal_IdxT = typename std::make_unsigned<IdxT>::type;
 
@@ -400,7 +401,8 @@ void optimize(
       knn_graph.extent(0),
       knn_graph.extent(1));
 
-  cagra::detail::graph::optimize(res, knn_graph_internal, new_graph_internal);
+  cagra::detail::graph::optimize(
+    res, knn_graph_internal, new_graph_internal, guarantee_connectivity);
 }
 
 template <typename T,
@@ -476,7 +478,7 @@ index<T, IdxT> build(
   auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
 
   RAFT_LOG_INFO("optimizing graph");
-  optimize<IdxT>(res, knn_graph->view(), cagra_graph.view());
+  optimize<IdxT>(res, knn_graph->view(), cagra_graph.view(), params.guarantee_connectivity);
 
   // free intermediate graph before trying to create the index
   knn_graph.reset();
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 9a63002b34..515be75df0 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -210,6 +210,239 @@ RAFT_KERNEL kern_make_rev_graph(const IdxT* const dest_nodes,     // [graph_size
   }
 }
 
+template <class IdxT, class LabelT>
+__device__ __host__ LabelT get_root_label(IdxT i, const LabelT* label)
+{
+  LabelT l = label[i];
+  while (l != label[l]) {
+    l = label[l];
+  }
+  return l;
+}
+
+template <class IdxT>
+RAFT_KERNEL kern_mst_opt_update_graph(IdxT* mst_graph,                 // [graph_size, graph_degree]
+                                      const IdxT* candidate_edges,     // [graph_size]
+                                      IdxT* outgoing_num_edges,        // [graph_size]
+                                      IdxT* incoming_num_edges,        // [graph_size]
+                                      const IdxT* outgoing_max_edges,  // [graph_size]
+                                      const IdxT* incoming_max_edges,  // [graph_size]
+                                      const IdxT* label,               // [graph_size]
+                                      const uint32_t graph_size,
+                                      const uint32_t graph_degree,
+                                      uint64_t* stats)
+{
+  const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
+  if (i >= graph_size) return;
+
+  int ret = 0;  // 0: No edge, 1: Direct edge, 2: Alternate edge, 3: Failure
+
+  if (outgoing_num_edges[i] >= outgoing_max_edges[i]) return;
+  uint64_t j = candidate_edges[i];
+  if (j >= graph_size) return;
+  const uint32_t ri = get_root_label(i, label);
+  const uint32_t rj = get_root_label(j, label);
+  if (ri == rj) return;
+
+  // Try to add a direct edge to destination node with different label.
+  if (incoming_num_edges[j] < incoming_max_edges[j]) {
+    ret = 1;
+    // Check to avoid duplication
+    for (uint64_t kj = 0; kj < graph_degree; kj++) {
+      uint64_t l = mst_graph[(graph_degree * j) + kj];
+      if (l >= graph_size) continue;
+      const uint32_t rl = get_root_label(l, label);
+      if (ri == rl) {
+        ret = 0;
+        break;
+      }
+    }
+    if (ret == 0) return;
+
+    ret     = 0;
+    auto kj = atomicAdd(incoming_num_edges + j, (IdxT)1);
+    if (kj < incoming_max_edges[j]) {
+      auto ki                                      = outgoing_num_edges[i]++;
+      mst_graph[(graph_degree * (i)) + ki]         = j;  // outgoing
+      mst_graph[(graph_degree * (j + 1)) - 1 - kj] = i;  // incoming
+      ret                                          = 1;
+    }
+  }
+  if (ret > 0) {
+    atomicAdd((unsigned long long int*)stats + ret, 1);
+    return;
+  }
+
+  // Try to add an edge to an alternate node instead
+  ret = 3;
+  for (uint64_t kj = 0; kj < graph_degree; kj++) {
+    uint64_t l = mst_graph[(graph_degree * (j + 1)) - 1 - kj];
+    if (l >= graph_size) continue;
+    uint32_t rl = get_root_label(l, label);
+    if (ri == rl) {
+      ret = 0;
+      break;
+    }
+    if (incoming_num_edges[l] >= incoming_max_edges[l]) continue;
+
+    // Check to avoid duplication
+    for (uint64_t kl = 0; kl < graph_degree; kl++) {
+      uint64_t m = mst_graph[(graph_degree * l) + kl];
+      if (m > graph_size) continue;
+      uint32_t rm = get_root_label(m, label);
+      if (ri == rm) {
+        ret = 0;
+        break;
+      }
+    }
+    if (ret == 0) { break; }
+
+    auto kl = atomicAdd(incoming_num_edges + l, (IdxT)1);
+    if (kl < incoming_max_edges[l]) {
+      auto ki                                      = outgoing_num_edges[i]++;
+      mst_graph[(graph_degree * (i)) + ki]         = l;  // outgoing
+      mst_graph[(graph_degree * (l + 1)) - 1 - kl] = i;  // incoming
+      ret                                          = 2;
+      break;
+    }
+  }
+  if (ret > 0) { atomicAdd((unsigned long long int*)stats + ret, 1); }
+}
+
+template <class IdxT>
+RAFT_KERNEL kern_mst_opt_labeling(IdxT* label,            // [graph_size]
+                                  const IdxT* mst_graph,  // [graph_size, graph_degree]
+                                  const uint32_t graph_size,
+                                  const uint32_t graph_degree,
+                                  uint64_t* stats)
+{
+  const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
+  if (i >= graph_size) return;
+
+  __shared__ uint32_t smem_updated[1];
+  if (threadIdx.x == 0) { smem_updated[0] = 0; }
+  __syncthreads();
+
+  for (uint64_t ki = 0; ki < graph_degree; ki++) {
+    uint64_t j = mst_graph[(graph_degree * i) + ki];
+    if (j >= graph_size) continue;
+
+    IdxT li = label[i];
+    IdxT ri = get_root_label(i, label);
+    if (ri < li) { atomicMin(label + i, ri); }
+    IdxT lj = label[j];
+    IdxT rj = get_root_label(j, label);
+    if (rj < lj) { atomicMin(label + j, rj); }
+    if (ri == rj) continue;
+
+    if (ri > rj) {
+      atomicCAS(label + i, ri, rj);
+    } else if (rj > ri) {
+      atomicCAS(label + j, rj, ri);
+    }
+    smem_updated[0] = 1;
+  }
+
+  __syncthreads();
+  if ((threadIdx.x == 0) && (smem_updated[0] > 0)) { stats[0] = 1; }
+}
+
+template <class IdxT>
+RAFT_KERNEL kern_mst_opt_cluster_size(IdxT* cluster_size,  // [graph_size]
+                                      const IdxT* label,   // [graph_size]
+                                      const uint32_t graph_size,
+                                      uint64_t* stats)
+{
+  const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
+  if (i >= graph_size) return;
+
+  __shared__ uint64_t smem_num_clusters[1];
+  if (threadIdx.x == 0) { smem_num_clusters[0] = 0; }
+  __syncthreads();
+
+  IdxT ri = get_root_label(i, label);
+  if (ri == i) {
+    atomicAdd((unsigned long long int*)smem_num_clusters, 1);
+  } else {
+    atomicAdd(cluster_size + ri, cluster_size[i]);
+    cluster_size[i] = 0;
+  }
+
+  __syncthreads();
+  if ((threadIdx.x == 0) && (smem_num_clusters[0] > 0)) {
+    atomicAdd((unsigned long long int*)stats, (unsigned long long int)(smem_num_clusters[0]));
+  }
+}
+
+template <class IdxT>
+RAFT_KERNEL kern_mst_opt_postprocessing(IdxT* outgoing_num_edges,  // [graph_size]
+                                        IdxT* incoming_num_edges,  // [graph_size]
+                                        IdxT* outgoing_max_edges,  // [graph_size]
+                                        IdxT* incoming_max_edges,  // [graph_size]
+                                        const IdxT* cluster_size,  // [graph_size]
+                                        const uint32_t graph_size,
+                                        const uint32_t graph_degree,
+                                        uint64_t* stats)
+{
+  const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
+  if (i >= graph_size) return;
+
+  __shared__ uint64_t smem_cluster_size_min[1];
+  __shared__ uint64_t smem_cluster_size_max[1];
+  __shared__ uint64_t smem_total_outgoing_edges[1];
+  __shared__ uint64_t smem_total_incoming_edges[1];
+  if (threadIdx.x == 0) {
+    smem_cluster_size_min[0]     = stats[0];
+    smem_cluster_size_max[0]     = stats[1];
+    smem_total_outgoing_edges[0] = 0;
+    smem_total_incoming_edges[0] = 0;
+  }
+  __syncthreads();
+
+  // Adjust incoming_num_edges
+  if (incoming_num_edges[i] > incoming_max_edges[i]) {
+    incoming_num_edges[i] = incoming_max_edges[i];
+  }
+
+  // Calculate min/max of cluster_size
+  if (cluster_size[i] > 0) {
+    if (smem_cluster_size_min[0] > cluster_size[i]) {
+      atomicMin((unsigned long long int*)smem_cluster_size_min,
+                (unsigned long long int)(cluster_size[i]));
+    }
+    if (smem_cluster_size_max[0] < cluster_size[i]) {
+      atomicMax((unsigned long long int*)smem_cluster_size_max,
+                (unsigned long long int)(cluster_size[i]));
+    }
+  }
+
+  // Calculate total number of outgoing/incoming edges
+  atomicAdd((unsigned long long int*)smem_total_outgoing_edges,
+            (unsigned long long int)(outgoing_num_edges[i]));
+  atomicAdd((unsigned long long int*)smem_total_incoming_edges,
+            (unsigned long long int)(incoming_num_edges[i]));
+
+  // Adjust incoming/outgoing_max_edges
+  if (outgoing_num_edges[i] == outgoing_max_edges[i]) {
+    if (outgoing_num_edges[i] + incoming_num_edges[i] < graph_degree) {
+      outgoing_max_edges[i] += 1;
+      incoming_max_edges[i] -= 1;
+    }
+  }
+
+  __syncthreads();
+  if (threadIdx.x == 0) {
+    atomicMin((unsigned long long int*)stats + 0,
+              (unsigned long long int)(smem_cluster_size_min[0]));
+    atomicMax((unsigned long long int*)stats + 1,
+              (unsigned long long int)(smem_cluster_size_max[0]));
+    atomicAdd((unsigned long long int*)stats + 2,
+              (unsigned long long int)(smem_total_outgoing_edges[0]));
+    atomicAdd((unsigned long long int*)stats + 3,
+              (unsigned long long int)(smem_total_incoming_edges[0]));
+  }
+}
+
 template <class T>
 uint64_t pos_in_array(T val, const T* array, uint64_t num)
 {
@@ -324,6 +557,507 @@ void sort_knn_graph(
   RAFT_LOG_DEBUG("# Sorting kNN graph time: %.1lf sec\n", time_sort_end - time_sort_start);
 }
 
+template <typename IdxT = uint32_t>
+void mst_opt_update_graph(IdxT* mst_graph_ptr,
+                          IdxT* candidate_edges_ptr,
+                          IdxT* outgoing_num_edges_ptr,
+                          IdxT* incoming_num_edges_ptr,
+                          IdxT* outgoing_max_edges_ptr,
+                          IdxT* incoming_max_edges_ptr,
+                          IdxT* label_ptr,
+                          IdxT graph_size,
+                          uint32_t mst_graph_degree,
+                          uint64_t k,
+                          int& num_direct,
+                          int& num_alternate,
+                          int& num_failure)
+{
+#pragma omp parallel for reduction(+ : num_direct, num_alternate, num_failure)
+  for (uint64_t ii = 0; ii < graph_size; ii++) {
+    uint64_t i = ii;
+    if (k % 2 == 0) { i = graph_size - (ii + 1); }
+    int ret = 0;  // 0: No edge, 1: Direct edge, 2: Alternate edge, 3: Failure
+
+    if (outgoing_num_edges_ptr[i] >= outgoing_max_edges_ptr[i]) continue;
+    uint64_t j = candidate_edges_ptr[i];
+    if (j >= graph_size) continue;
+    if (label_ptr[i] == label_ptr[j]) continue;
+
+    // Try to add a direct edge to destination node with different label.
+    if (incoming_num_edges_ptr[j] < incoming_max_edges_ptr[j]) {
+      ret = 1;
+      // Check to avoid duplication
+      for (uint64_t kj = 0; kj < mst_graph_degree; kj++) {
+        uint64_t l = mst_graph_ptr[(mst_graph_degree * j) + kj];
+        if (l >= graph_size) continue;
+        if (label_ptr[i] == label_ptr[l]) {
+          ret = 0;
+          break;
+        }
+      }
+      if (ret == 0) continue;
+
+      // Use atomic to avoid conflicts, since 'incoming_num_edges_ptr[j]'
+      // can be updated by other threads.
+      ret = 0;
+      uint32_t kj;
+#pragma omp atomic capture
+      kj = incoming_num_edges_ptr[j]++;
+      if (kj < incoming_max_edges_ptr[j]) {
+        auto ki                                              = outgoing_num_edges_ptr[i]++;
+        mst_graph_ptr[(mst_graph_degree * (i)) + ki]         = j;  // OUT
+        mst_graph_ptr[(mst_graph_degree * (j + 1)) - 1 - kj] = i;  // IN
+        ret                                                  = 1;
+      }
+    }
+    if (ret == 1) {
+      num_direct += 1;
+      continue;
+    }
+
+    // Try to add an edge to an alternate node instead
+    ret = 3;
+    for (uint64_t kj = 0; kj < mst_graph_degree; kj++) {
+      uint64_t l = mst_graph_ptr[(mst_graph_degree * (j + 1)) - 1 - kj];
+      if (l >= graph_size) continue;
+      if (label_ptr[i] == label_ptr[l]) {
+        ret = 0;
+        break;
+      }
+      if (incoming_num_edges_ptr[l] >= incoming_max_edges_ptr[l]) continue;
+
+      // Check to avoid duplication
+      for (uint64_t kl = 0; kl < mst_graph_degree; kl++) {
+        uint64_t m = mst_graph_ptr[(mst_graph_degree * l) + kl];
+        if (m > graph_size) continue;
+        if (label_ptr[i] == label_ptr[m]) {
+          ret = 0;
+          break;
+        }
+      }
+      if (ret == 0) { break; }
+
+      // Use atomic to avoid conflicts, since 'incoming_num_edges_ptr[l]'
+      // can be updated by other threads.
+      uint32_t kl;
+#pragma omp atomic capture
+      kl = incoming_num_edges_ptr[l]++;
+      if (kl < incoming_max_edges_ptr[l]) {
+        auto ki                                              = outgoing_num_edges_ptr[i]++;
+        mst_graph_ptr[(mst_graph_degree * (i)) + ki]         = l;  // OUT
+        mst_graph_ptr[(mst_graph_degree * (l + 1)) - 1 - kl] = i;  // IN
+        ret                                                  = 2;
+        break;
+      }
+    }
+    if (ret == 2) {
+      num_alternate += 1;
+    } else if (ret == 3) {
+      num_failure += 1;
+    }
+  }
+}
+
+//
+// Create approximate MSTs with kNN graphs as input to guarantee connectivity of search graphs
+//
+// * Since there is an upper limit to the degree of a graph for search, what is created is a
+//   degree-constraied MST.
+// * The number of edges is not a minimum because strict MST is not required. Therefore, it is
+//   an approximate MST.
+// * If the input kNN graph is disconnected, random connection is added to the largest cluster.
+//
+template <typename IdxT = uint32_t>
+void mst_optimization(raft::resources const& res,
+                      raft::host_matrix_view<IdxT, int64_t, raft::row_major> input_graph,
+                      raft::host_matrix_view<IdxT, int64_t, raft::row_major> output_graph,
+                      raft::host_vector_view<uint32_t, int64_t> mst_graph_num_edges,
+                      bool use_gpu = true)
+{
+  if (use_gpu) {
+    RAFT_LOG_DEBUG("# MST optimization on GPU");
+  } else {
+    RAFT_LOG_DEBUG("# MST optimization on CPU");
+  }
+  const double time_mst_opt_start = cur_time();
+
+  const IdxT graph_size              = input_graph.extent(0);
+  const uint32_t input_graph_degree  = input_graph.extent(1);
+  const uint32_t output_graph_degree = output_graph.extent(1);
+  auto input_graph_ptr               = input_graph.data_handle();
+  auto output_graph_ptr              = output_graph.data_handle();
+  auto mst_graph_num_edges_ptr       = mst_graph_num_edges.data_handle();
+
+  // Allocate temporal arrays
+  const uint32_t mst_graph_degree = output_graph_degree;
+  auto mst_graph              = raft::make_host_matrix<IdxT, int64_t>(graph_size, mst_graph_degree);
+  auto outgoing_max_edges     = raft::make_host_vector<IdxT, int64_t>(graph_size);
+  auto incoming_max_edges     = raft::make_host_vector<IdxT, int64_t>(graph_size);
+  auto outgoing_num_edges     = raft::make_host_vector<IdxT, int64_t>(graph_size);
+  auto incoming_num_edges     = raft::make_host_vector<IdxT, int64_t>(graph_size);
+  auto label                  = raft::make_host_vector<IdxT, int64_t>(graph_size);
+  auto cluster_size           = raft::make_host_vector<IdxT, int64_t>(graph_size);
+  auto candidate_edges        = raft::make_host_vector<IdxT, int64_t>(graph_size);
+  auto mst_graph_ptr          = mst_graph.data_handle();
+  auto outgoing_max_edges_ptr = outgoing_max_edges.data_handle();
+  auto incoming_max_edges_ptr = incoming_max_edges.data_handle();
+  auto outgoing_num_edges_ptr = outgoing_num_edges.data_handle();
+  auto incoming_num_edges_ptr = incoming_num_edges.data_handle();
+  auto label_ptr              = label.data_handle();
+  auto cluster_size_ptr       = cluster_size.data_handle();
+  auto candidate_edges_ptr    = candidate_edges.data_handle();
+
+  // Initialize arrays
+#pragma omp parallel for
+  for (uint64_t i = 0; i < graph_size; i++) {
+    for (uint64_t k = 0; k < mst_graph_degree; k++) {
+      // mst_graph_ptr[(mst_graph_degree * i) + k] = graph_size;
+      mst_graph(i, k) = graph_size;
+    }
+    outgoing_max_edges_ptr[i] = 2;
+    incoming_max_edges_ptr[i] = mst_graph_degree - outgoing_max_edges_ptr[i];
+    outgoing_num_edges_ptr[i] = 0;
+    incoming_num_edges_ptr[i] = 0;
+    label_ptr[i]              = i;
+    cluster_size_ptr[i]       = 1;
+  }
+
+  // Allocate arrays on GPU
+  uint32_t d_graph_size = graph_size;
+  if (!use_gpu) {
+    // (*) If GPU is not used, arrays of size 0 are created.
+    d_graph_size = 0;
+  }
+  auto d_mst_graph_num_edges = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_mst_graph = raft::make_device_matrix<IdxT, int64_t>(res, d_graph_size, mst_graph_degree);
+  auto d_outgoing_max_edges      = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_incoming_max_edges      = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_outgoing_num_edges      = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_incoming_num_edges      = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_label                   = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_cluster_size            = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_candidate_edges         = raft::make_device_vector<IdxT, int64_t>(res, d_graph_size);
+  auto d_mst_graph_num_edges_ptr = d_mst_graph_num_edges.data_handle();
+  auto d_mst_graph_ptr           = d_mst_graph.data_handle();
+  auto d_outgoing_max_edges_ptr  = d_outgoing_max_edges.data_handle();
+  auto d_incoming_max_edges_ptr  = d_incoming_max_edges.data_handle();
+  auto d_outgoing_num_edges_ptr  = d_outgoing_num_edges.data_handle();
+  auto d_incoming_num_edges_ptr  = d_incoming_num_edges.data_handle();
+  auto d_label_ptr               = d_label.data_handle();
+  auto d_cluster_size_ptr        = d_cluster_size.data_handle();
+  auto d_candidate_edges_ptr     = d_candidate_edges.data_handle();
+
+  constexpr int stats_size = 4;
+  auto stats               = raft::make_host_vector<uint64_t, int64_t>(stats_size);
+  auto d_stats             = raft::make_device_vector<uint64_t, int64_t>(res, stats_size);
+  auto stats_ptr           = stats.data_handle();
+  auto d_stats_ptr         = d_stats.data_handle();
+
+  if (use_gpu) {
+    raft::copy(d_mst_graph_ptr,
+               mst_graph_ptr,
+               (size_t)graph_size * mst_graph_degree,
+               raft::resource::get_cuda_stream(res));
+    raft::copy(d_outgoing_num_edges_ptr,
+               outgoing_num_edges_ptr,
+               (size_t)graph_size,
+               raft::resource::get_cuda_stream(res));
+    raft::copy(d_incoming_num_edges_ptr,
+               incoming_num_edges_ptr,
+               (size_t)graph_size,
+               raft::resource::get_cuda_stream(res));
+    raft::copy(d_outgoing_max_edges_ptr,
+               outgoing_max_edges_ptr,
+               (size_t)graph_size,
+               raft::resource::get_cuda_stream(res));
+    raft::copy(d_incoming_max_edges_ptr,
+               incoming_max_edges_ptr,
+               (size_t)graph_size,
+               raft::resource::get_cuda_stream(res));
+    raft::copy(d_label_ptr, label_ptr, (size_t)graph_size, raft::resource::get_cuda_stream(res));
+    raft::copy(d_cluster_size_ptr,
+               cluster_size_ptr,
+               (size_t)graph_size,
+               raft::resource::get_cuda_stream(res));
+  }
+
+  IdxT num_clusters     = 0;
+  IdxT num_clusters_pre = graph_size;
+  IdxT cluster_size_min = graph_size;
+  IdxT cluster_size_max = 0;
+  for (uint64_t k = 0; k <= input_graph_degree; k++) {
+    int num_direct    = 0;
+    int num_alternate = 0;
+    int num_failure   = 0;
+
+    // 1. Prepare candidate edges
+    if (k == input_graph_degree) {
+      // If the number of clusters does not converge to 1, then edges are
+      // made from all nodes not belonging to the main cluster to any node
+      // in the main cluster.
+      raft::copy(cluster_size_ptr,
+                 d_cluster_size_ptr,
+                 (size_t)graph_size,
+                 raft::resource::get_cuda_stream(res));
+      raft::copy(label_ptr, d_label_ptr, (size_t)graph_size, raft::resource::get_cuda_stream(res));
+      raft::resource::sync_stream(res);
+      uint32_t main_cluster_label = graph_size;
+#pragma omp parallel for reduction(min : main_cluster_label)
+      for (uint64_t i = 0; i < graph_size; i++) {
+        if ((cluster_size_ptr[i] == cluster_size_max) && (main_cluster_label > i)) {
+          main_cluster_label = i;
+        }
+      }
+#pragma omp parallel for
+      for (uint64_t i = 0; i < graph_size; i++) {
+        candidate_edges_ptr[i] = graph_size;
+        if (label_ptr[i] == main_cluster_label) continue;
+        uint64_t j = i;
+        while (label_ptr[j] != main_cluster_label) {
+          constexpr uint32_t ofst = 97;
+          j                       = (j + ofst) % graph_size;
+        }
+        candidate_edges_ptr[i] = j;
+      }
+    } else {
+      // Copy rank-k edges from the input knn graph to 'candidate_edges'
+#pragma omp parallel for
+      for (uint64_t i = 0; i < graph_size; i++) {
+        candidate_edges_ptr[i] = input_graph_ptr[k + (input_graph_degree * i)];
+      }
+    }
+
+    // 2. Update MST graph
+    //  * Try to add candidate edges to MST graph
+    if (use_gpu) {
+      raft::copy(d_candidate_edges_ptr,
+                 candidate_edges_ptr,
+                 graph_size,
+                 raft::resource::get_cuda_stream(res));
+      stats_ptr[0] = 0;
+      stats_ptr[1] = num_direct;
+      stats_ptr[2] = num_alternate;
+      stats_ptr[3] = num_failure;
+      raft::copy(d_stats_ptr, stats_ptr, 4, raft::resource::get_cuda_stream(res));
+
+      constexpr uint64_t n_threads = 256;
+      const dim3 threads(n_threads, 1, 1);
+      const dim3 blocks(raft::ceildiv<uint64_t>(graph_size, n_threads), 1, 1);
+      kern_mst_opt_update_graph<<<blocks, threads, 0, raft::resource::get_cuda_stream(res)>>>(
+        d_mst_graph_ptr,
+        d_candidate_edges_ptr,
+        d_outgoing_num_edges_ptr,
+        d_incoming_num_edges_ptr,
+        d_outgoing_max_edges_ptr,
+        d_incoming_max_edges_ptr,
+        d_label_ptr,
+        graph_size,
+        mst_graph_degree,
+        d_stats_ptr);
+
+      raft::copy(stats_ptr, d_stats_ptr, 4, raft::resource::get_cuda_stream(res));
+      raft::resource::sync_stream(res);
+      num_direct    = stats_ptr[1];
+      num_alternate = stats_ptr[2];
+      num_failure   = stats_ptr[3];
+    } else {
+      mst_opt_update_graph(mst_graph_ptr,
+                           candidate_edges_ptr,
+                           outgoing_num_edges_ptr,
+                           incoming_num_edges_ptr,
+                           outgoing_max_edges_ptr,
+                           incoming_max_edges_ptr,
+                           label_ptr,
+                           graph_size,
+                           mst_graph_degree,
+                           k,
+                           num_direct,
+                           num_alternate,
+                           num_failure);
+    }
+
+    // 3. Labeling
+    uint32_t flag_update = 1;
+    while (flag_update) {
+      flag_update = 0;
+      if (use_gpu) {
+        stats_ptr[0] = flag_update;
+        raft::copy(d_stats_ptr, stats_ptr, 1, raft::resource::get_cuda_stream(res));
+
+        constexpr uint64_t n_threads = 256;
+        const dim3 threads(n_threads, 1, 1);
+        const dim3 blocks((graph_size + n_threads - 1) / n_threads, 1, 1);
+        kern_mst_opt_labeling<<<blocks, threads, 0, raft::resource::get_cuda_stream(res)>>>(
+          d_label_ptr, d_mst_graph_ptr, graph_size, mst_graph_degree, d_stats_ptr);
+
+        raft::copy(stats_ptr, d_stats_ptr, 1, raft::resource::get_cuda_stream(res));
+        raft::resource::sync_stream(res);
+        flag_update = stats_ptr[0];
+      } else {
+#pragma omp parallel for reduction(+ : flag_update)
+        for (uint64_t i = 0; i < graph_size; i++) {
+          for (uint64_t ki = 0; ki < mst_graph_degree; ki++) {
+            uint64_t j = mst_graph_ptr[(mst_graph_degree * i) + ki];
+            if (j >= graph_size) continue;
+            if (label_ptr[i] > label_ptr[j]) {
+              flag_update += 1;
+              label_ptr[i] = label_ptr[j];
+            }
+          }
+        }
+      }
+    }
+
+    // 4. Calculate the number of clusters and the size of each cluster
+    num_clusters = 0;
+    if (use_gpu) {
+      stats_ptr[0] = num_clusters;
+      raft::copy(d_stats_ptr, stats_ptr, 1, raft::resource::get_cuda_stream(res));
+
+      constexpr uint64_t n_threads = 256;
+      const dim3 threads(n_threads, 1, 1);
+      const dim3 blocks(raft::ceildiv<uint64_t>(graph_size, n_threads), 1, 1);
+      kern_mst_opt_cluster_size<<<blocks, threads, 0, raft::resource::get_cuda_stream(res)>>>(
+        d_cluster_size_ptr, d_label_ptr, graph_size, d_stats_ptr);
+
+      raft::copy(stats_ptr, d_stats_ptr, 1, raft::resource::get_cuda_stream(res));
+      raft::resource::sync_stream(res);
+      num_clusters = stats_ptr[0];
+    } else {
+#pragma omp parallel for reduction(+ : num_clusters)
+      for (uint64_t i = 0; i < graph_size; i++) {
+        uint64_t ri = get_root_label(i, label_ptr);
+        if (ri == i) {
+          num_clusters += 1;
+        } else {
+#pragma omp atomic update
+          cluster_size_ptr[ri] += cluster_size_ptr[i];
+          cluster_size_ptr[i] = 0;
+        }
+      }
+    }
+
+    // 5. Postprocessings
+    //  * Adjust incoming_num_edges
+    //  * Calculate the min/max size of clusters.
+    //  * Calculate the total number of outgoing/incoming edges
+    //  * Increase the limit of outgoing edges as needed
+    cluster_size_min              = graph_size;
+    cluster_size_max              = 0;
+    uint64_t total_outgoing_edges = 0;
+    uint64_t total_incoming_edges = 0;
+    if (use_gpu) {
+      stats_ptr[0] = cluster_size_min;
+      stats_ptr[1] = cluster_size_max;
+      stats_ptr[2] = total_outgoing_edges;
+      stats_ptr[3] = total_incoming_edges;
+      raft::copy(d_stats_ptr, stats_ptr, 4, raft::resource::get_cuda_stream(res));
+
+      constexpr uint64_t n_threads = 256;
+      const dim3 threads(n_threads, 1, 1);
+      const dim3 blocks((graph_size + n_threads - 1) / n_threads, 1, 1);
+      kern_mst_opt_postprocessing<<<blocks, threads, 0, raft::resource::get_cuda_stream(res)>>>(
+        d_outgoing_num_edges_ptr,
+        d_incoming_num_edges_ptr,
+        d_outgoing_max_edges_ptr,
+        d_incoming_max_edges_ptr,
+        d_cluster_size_ptr,
+        graph_size,
+        mst_graph_degree,
+        d_stats_ptr);
+
+      raft::copy(stats_ptr, d_stats_ptr, 4, raft::resource::get_cuda_stream(res));
+      raft::resource::sync_stream(res);
+      cluster_size_min     = stats_ptr[0];
+      cluster_size_max     = stats_ptr[1];
+      total_outgoing_edges = stats_ptr[2];
+      total_incoming_edges = stats_ptr[3];
+    } else {
+#pragma omp parallel for
+      for (uint64_t i = 0; i < graph_size; i++) {
+        if (incoming_num_edges_ptr[i] > incoming_max_edges_ptr[i]) {
+          incoming_num_edges_ptr[i] = incoming_max_edges_ptr[i];
+        }
+      }
+
+#pragma omp parallel for reduction(max : cluster_size_max) reduction(min : cluster_size_min)
+      for (uint64_t i = 0; i < graph_size; i++) {
+        if (cluster_size_ptr[i] == 0) continue;
+        cluster_size_min = min(cluster_size_min, cluster_size_ptr[i]);
+        cluster_size_max = max(cluster_size_max, cluster_size_ptr[i]);
+      }
+
+#pragma omp parallel for reduction(+ : total_outgoing_edges, total_incoming_edges)
+      for (uint64_t i = 0; i < graph_size; i++) {
+        total_outgoing_edges += outgoing_num_edges_ptr[i];
+        total_incoming_edges += incoming_num_edges_ptr[i];
+      }
+
+#pragma omp parallel for
+      for (uint64_t i = 0; i < graph_size; i++) {
+        if (outgoing_num_edges_ptr[i] < outgoing_max_edges_ptr[i]) continue;
+        if (outgoing_num_edges_ptr[i] + incoming_num_edges_ptr[i] == mst_graph_degree) continue;
+        assert(outgoing_num_edges_ptr[i] + incoming_num_edges_ptr[i] < mst_graph_degree);
+        outgoing_max_edges_ptr[i] += 1;
+        incoming_max_edges_ptr[i] = mst_graph_degree - outgoing_max_edges_ptr[i];
+      }
+    }
+
+    // 6. Show stats
+    if (num_clusters != num_clusters_pre) {
+      std::string msg = "# k: " + std::to_string(k);
+      msg += ", num_clusters: " + std::to_string(num_clusters);
+      msg += ", cluster_size: " + std::to_string(cluster_size_min) + " to " +
+             std::to_string(cluster_size_max);
+      msg += ", total_num_edges: " + std::to_string(total_outgoing_edges) + ", " +
+             std::to_string(total_incoming_edges);
+      if (num_alternate + num_failure > 0) {
+        msg += ", altenate: " + std::to_string(num_alternate);
+        if (num_failure > 0) { msg += ", failure: " + std::to_string(num_failure); }
+      }
+      RAFT_LOG_DEBUG("%s", msg.c_str());
+    }
+    assert(num_clusters > 0);
+    assert(total_outgoing_edges == total_incoming_edges);
+    if (num_clusters == 1) { break; }
+    num_clusters_pre = num_clusters;
+  }
+
+  // The edges that make up the MST are stored as edges in the output graph.
+  if (use_gpu) {
+    raft::copy(mst_graph_ptr,
+               d_mst_graph_ptr,
+               (size_t)graph_size * mst_graph_degree,
+               raft::resource::get_cuda_stream(res));
+    raft::resource::sync_stream(res);
+  }
+#pragma omp parallel for
+  for (uint64_t i = 0; i < graph_size; i++) {
+    uint64_t k = 0;
+    for (uint64_t kj = 0; kj < mst_graph_degree; kj++) {
+      uint64_t j = mst_graph_ptr[(mst_graph_degree * i) + kj];
+      if (j >= graph_size) continue;
+
+      // Check to avoid duplication
+      auto flag_match = false;
+      for (uint64_t ki = 0; ki < k; ki++) {
+        if (j == output_graph_ptr[(output_graph_degree * i) + ki]) {
+          flag_match = true;
+          break;
+        }
+      }
+      if (flag_match) continue;
+
+      output_graph_ptr[(output_graph_degree * i) + k] = j;
+      k += 1;
+    }
+    mst_graph_num_edges_ptr[i] = k;
+  }
+
+  const double time_mst_opt_end = cur_time();
+  RAFT_LOG_DEBUG("# MST optimization time: %.1lf sec", time_mst_opt_end - time_mst_opt_start);
+}
+
 template <
   typename IdxT = uint32_t,
   typename g_accessor =
@@ -331,7 +1065,8 @@ template <
 void optimize(
   raft::resources const& res,
   raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph,
-  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph)
+  raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph,
+  const bool guarantee_connectivity = true)
 {
   RAFT_LOG_DEBUG(
     "# Pruning kNN graph (size=%lu, degree=%lu)\n", knn_graph.extent(0), knn_graph.extent(1));
@@ -347,6 +1082,26 @@ void optimize(
   auto output_graph_ptr              = new_graph.data_handle();
   const IdxT graph_size              = new_graph.extent(0);
 
+  // MST optimization
+  auto mst_graph_num_edges     = raft::make_host_vector<uint32_t, int64_t>(graph_size);
+  auto mst_graph_num_edges_ptr = mst_graph_num_edges.data_handle();
+#pragma omp parallel for
+  for (uint64_t i = 0; i < graph_size; i++) {
+    mst_graph_num_edges_ptr[i] = 0;
+  }
+  if (guarantee_connectivity) {
+    RAFT_LOG_INFO("MST optimization is used to guarantee graph connectivity.");
+    constexpr bool use_gpu = true;
+    mst_optimization(res, knn_graph, new_graph, mst_graph_num_edges.view(), use_gpu);
+
+    for (uint64_t i = 0; i < graph_size; i++) {
+      if (i < 8 || i >= graph_size - 8) {
+        RAFT_LOG_DEBUG("# mst_graph_num_edges_ptr[%lu]: %u\n", i, mst_graph_num_edges_ptr[i]);
+      }
+    }
+  }
+
+  auto pruned_graph = raft::make_host_matrix<uint32_t, int64_t>(graph_size, output_graph_degree);
   {
     //
     // Prune kNN graph
@@ -509,7 +1264,8 @@ void optimize(
     for (uint64_t k = 0; k < output_graph_degree; k++) {
 #pragma omp parallel for
       for (uint64_t i = 0; i < graph_size; i++) {
-        dest_nodes.data_handle()[i] = output_graph_ptr[k + (output_graph_degree * i)];
+        // dest_nodes.data_handle()[i] = output_graph_ptr[k + (output_graph_degree * i)];
+        dest_nodes(i) = output_graph_ptr[k + (output_graph_degree * i)];
       }
       raft::resource::sync_stream(res);
 
@@ -549,37 +1305,58 @@ void optimize(
 
   {
     //
-    // Replace some edges with reverse edges
+    // Create search graphs from MST and pruned and reverse graphs
     //
     const double time_replace_start = cur_time();
 
-    const uint64_t num_protected_edges = output_graph_degree / 2;
-    RAFT_LOG_DEBUG("# num_protected_edges: %lu", num_protected_edges);
-
-    constexpr int _omp_chunk = 1024;
-#pragma omp parallel for schedule(dynamic, _omp_chunk)
-    for (uint64_t j = 0; j < graph_size; j++) {
-      uint64_t k = std::min(rev_graph_count.data_handle()[j], output_graph_degree);
-      while (k) {
-        k--;
-        uint64_t i = rev_graph.data_handle()[k + (output_graph_degree * j)];
-
-        uint64_t pos =
-          pos_in_array<IdxT>(i, output_graph_ptr + (output_graph_degree * j), output_graph_degree);
-        if (pos < num_protected_edges) { continue; }
-        uint64_t num_shift = pos - num_protected_edges;
-        if (pos == output_graph_degree) {
-          num_shift = output_graph_degree - num_protected_edges - 1;
+#pragma omp parallel for
+    for (uint64_t i = 0; i < graph_size; i++) {
+      auto my_fwd_graph = pruned_graph.data_handle() + (output_graph_degree * i);
+      auto my_rev_graph = rev_graph.data_handle() + (output_graph_degree * i);
+      auto my_out_graph = output_graph_ptr + (output_graph_degree * i);
+      uint32_t kf       = 0;
+      uint32_t k        = mst_graph_num_edges_ptr[i];
+
+      const uint64_t num_protected_edges = max(k, output_graph_degree / 2);
+      assert(num_protected_edges <= output_graph_degree);
+      if (num_protected_edges == output_graph_degree) continue;
+
+      // Append edges from the pruned graph to output graph
+      while (k < output_graph_degree && kf < output_graph_degree) {
+        if (my_fwd_graph[kf] < graph_size) {
+          auto flag_match = false;
+          for (uint32_t kk = 0; kk < k; kk++) {
+            if (my_out_graph[kk] == my_fwd_graph[kf]) {
+              flag_match = true;
+              break;
+            }
+          }
+          if (!flag_match) {
+            my_out_graph[k] = my_fwd_graph[kf];
+            k += 1;
+          }
         }
-        shift_array<IdxT>(output_graph_ptr + num_protected_edges + (output_graph_degree * j),
-                          num_shift);
-        output_graph_ptr[num_protected_edges + (output_graph_degree * j)] = i;
+        kf += 1;
       }
-      if ((omp_get_thread_num() == 0) && ((j % _omp_chunk) == 0)) {
-        RAFT_LOG_DEBUG("# Replacing reverse edges: %lu / %lu    ", j, graph_size);
+      assert(k == output_graph_degree);
+      assert(kf <= output_graph_degree);
+
+      // Replace some edges of the output graph with edges of the reverse graph.
+      uint32_t kr = std::min(rev_graph_count.data_handle()[i], output_graph_degree);
+      while (kr) {
+        kr -= 1;
+        if (my_rev_graph[kr] < graph_size) {
+          uint64_t pos = pos_in_array<IdxT>(my_rev_graph[kr], my_out_graph, output_graph_degree);
+          if (pos < num_protected_edges) { continue; }
+          uint64_t num_shift = pos - num_protected_edges;
+          if (pos >= output_graph_degree) {
+            num_shift = output_graph_degree - num_protected_edges - 1;
+          }
+          shift_array<IdxT>(my_out_graph + num_protected_edges, num_shift);
+          my_out_graph[num_protected_edges] = my_rev_graph[kr];
+        }
       }
     }
-    RAFT_LOG_DEBUG("\n");
 
     const double time_replace_end = cur_time();
     RAFT_LOG_DEBUG("# Replacing edges time: %.1lf sec", time_replace_end - time_replace_start);
@@ -598,6 +1375,48 @@ void optimize(
     RAFT_LOG_DEBUG("# Average number of replaced edges per node: %.2f",
                    (double)num_replaced_edges / graph_size);
   }
+
+  // Check number of incoming edges
+  {
+    auto in_edge_count     = raft::make_host_vector<uint32_t, int64_t>(graph_size);
+    auto in_edge_count_ptr = in_edge_count.data_handle();
+#pragma omp parallel for
+    for (uint64_t i = 0; i < graph_size; i++) {
+      in_edge_count_ptr[i] = 0;
+    }
+#pragma omp parallel for
+    for (uint64_t i = 0; i < graph_size; i++) {
+      for (uint64_t k = 0; k < output_graph_degree; k++) {
+        const uint64_t j = output_graph_ptr[k + (output_graph_degree * i)];
+        if (j >= graph_size) continue;
+#pragma omp atomic
+        in_edge_count_ptr[j] += 1;
+      }
+    }
+    auto hist     = raft::make_host_vector<uint32_t, int64_t>(output_graph_degree);
+    auto hist_ptr = hist.data_handle();
+    for (uint64_t k = 0; k < output_graph_degree; k++) {
+      hist_ptr[k] = 0;
+    }
+#pragma omp parallel for
+    for (uint64_t i = 0; i < graph_size; i++) {
+      uint32_t count = in_edge_count_ptr[i];
+      if (count >= output_graph_degree) continue;
+#pragma omp atomic
+      hist_ptr[count] += 1;
+    }
+    RAFT_LOG_DEBUG("# Histogram for number of incoming edges\n");
+    uint32_t sum_hist = 0;
+    for (uint64_t k = 0; k < output_graph_degree; k++) {
+      sum_hist += hist_ptr[k];
+      RAFT_LOG_DEBUG("# %3lu, %8u, %lf, (%8u, %lf)\n",
+                     k,
+                     hist_ptr[k],
+                     (double)hist_ptr[k] / graph_size,
+                     sum_hist,
+                     (double)sum_hist / graph_size);
+    }
+  }
 }
 
 }  // namespace cuvs::neighbors::cagra::detail::graph

From 631a432a0215d4f8a3a3b1a3397d06297f2735d7 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Wed, 31 Jul 2024 13:15:58 -0500
Subject: [PATCH 118/623] Update pip devcontainers to UCX v1.17.0 (#262)

Contributes to https://github.com/rapidsai/build-planning/issues/77.

Follow-up to https://github.com/rapidsai/devcontainers/pull/338

Proposes updating the pip devcontainers to use v1.17.0, as part of an effort to use that version across RAPIDS.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Peter Andreas Entschev (https://github.com/pentschev)
  - Mike Sarahan (https://github.com/msarahan)

URL: https://github.com/rapidsai/cuvs/pull/262
---
 .devcontainer/cuda11.8-pip/devcontainer.json | 5 +----
 .devcontainer/cuda12.5-pip/devcontainer.json | 5 +----
 2 files changed, 2 insertions(+), 8 deletions(-)

diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 146753ae07..74d62afcce 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda11.8-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
@@ -15,9 +15,6 @@
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.10": {
-      "version": "1.15.0"
-    },
     "ghcr.io/rapidsai/devcontainers/features/cuda:24.10": {
       "version": "11.8",
       "installcuBLAS": true,
diff --git a/.devcontainer/cuda12.5-pip/devcontainer.json b/.devcontainer/cuda12.5-pip/devcontainer.json
index 49e4943ebb..3dcf52e835 100644
--- a/.devcontainer/cuda12.5-pip/devcontainer.json
+++ b/.devcontainer/cuda12.5-pip/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda12.5-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
@@ -15,9 +15,6 @@
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/ucx:24.10": {
-      "version": "1.15.0"
-    },
     "ghcr.io/rapidsai/devcontainers/features/cuda:24.10": {
       "version": "12.5",
       "installcuBLAS": true,

From eb4d38ef0559fbabff7f73d32f3ca9abdf862f6f Mon Sep 17 00:00:00 2001
From: Rui Lan <abc99lr@gmail.com>
Date: Wed, 31 Jul 2024 16:11:10 -0700
Subject: [PATCH 119/623] Enable kernel & memcpy overlapping in IVF index
 building (#230)

Currently, in IVF index building (both IVF-Flat and IVF-PQ), large dataset is usually in pageable host memory or mmap-ed file. In both case, after the cluster centers are trained, the entire dataset needs to be copied twice to the GPU -- one for assigning vectors to clusters, the other for copying vectors to the corresponding clusters. Both copies are done using `batch_load_iterator` in a chunk-by-chunk fashion. Since the source buffer is in pageable memory, the current `batch_load_iterator` implementation doesn't support kernel and memcopy overlapping. This PR adds support on prefetching with `cudaMemcpyAsync` on pageable memory. We achieve kernel copy overlapping by launching kernel first following by the prefetching of the next chunk.

We benchmarked the change on L40S. The results show 3%-21% speedup on index building, without impacting the search recall (about 1-2%, similar to run-to-run variance).
algo | dataset | model | with prefetching (s) | without prefetching (s) | speedup
-- | -- | -- | -- | -- | --
IVF-PQ | deep-100M | d64b5n50K | 97.3547 | 100.36 | 1.03
IVF-PQ | wiki-all-10M | d64-nlist16K | 14.9763 | 18.1602 | 1.21
IVF-Flat | deep-100M | nlist50K | 78.8188 | 81.4461 | 1.03

This PR is related to the issue submitted to RAFT: https://github.com/rapidsai/raft/issues/2106

Authors:
  - Rui Lan (https://github.com/abc99lr)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/230
---
 .../ann/src/cuvs/cuvs_ivf_flat_wrapper.h      |   4 +
 cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h  |   4 +
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |  79 ++++++++++++++
 cpp/include/cuvs/neighbors/ivf_pq.hpp         | 103 ++++++++++++++++--
 cpp/src/neighbors/detail/ann_utils.cuh        | 101 ++++++++++++++---
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |  27 ++++-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |  24 +++-
 cpp/test/neighbors/ann_ivf_flat.cuh           |  24 +++-
 cpp/test/neighbors/ann_ivf_pq.cuh             |  41 +++++++
 .../ann_ivf_pq/test_float_int64_t.cu          |   2 +
 .../ann_ivf_pq/test_int8_t_int64_t.cu         |   2 +
 .../ann_ivf_pq/test_uint8_t_int64_t.cu        |   2 +
 12 files changed, 382 insertions(+), 31 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
index 2aaa5a2942..b7e335622d 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
@@ -26,6 +26,7 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/util/cudart_utils.hpp>
+#include <rmm/cuda_stream_pool.hpp>
 
 #include <cassert>
 #include <fstream>
@@ -96,6 +97,9 @@ class cuvs_ivf_flat : public algo<T>, public algo_gpu {
 template <typename T, typename IdxT>
 void cuvs_ivf_flat<T, IdxT>::build(const T* dataset, size_t nrow)
 {
+  // Create a CUDA stream pool with 1 stream (besides main stream) for kernel/copy overlapping.
+  size_t n_streams = 1;
+  raft::resource::set_cuda_stream_pool(handle_, std::make_shared<rmm::cuda_stream_pool>(n_streams));
   index_ = std::make_shared<cuvs::neighbors::ivf_flat::index<T, IdxT>>(
     std::move(cuvs::neighbors::ivf_flat::build(
       handle_,
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
index a305f14ea3..4c8a91f23d 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
@@ -30,6 +30,7 @@
 #include <raft/linalg/unary_op.cuh>
 #include <raft/neighbors/refine.cuh>
 #include <raft/util/cudart_utils.hpp>
+#include <rmm/cuda_stream_pool.hpp>
 
 #include <type_traits>
 
@@ -115,6 +116,9 @@ void cuvs_ivf_pq<T, IdxT>::load(const std::string& file)
 template <typename T, typename IdxT>
 void cuvs_ivf_pq<T, IdxT>::build(const T* dataset, size_t nrow)
 {
+  // Create a CUDA stream pool with 1 stream (besides main stream) for kernel/copy overlapping.
+  size_t n_streams = 1;
+  raft::resource::set_cuda_stream_pool(handle_, std::make_shared<rmm::cuda_stream_pool>(n_streams));
   auto dataset_v = raft::make_device_matrix_view<const T, IdxT>(dataset, IdxT(nrow), dim_);
   std::make_shared<cuvs::neighbors::ivf_pq::index<IdxT>>(
     std::move(cuvs::neighbors::ivf_pq::build(handle_, index_params_, dataset_v)))
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index ad0ca49dbf..918fef5aff 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -445,11 +445,18 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   auto index = ivf_flat::build(handle, dataset, index_params);
  * @endcode
@@ -468,11 +475,18 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
  *   ivf_flat::build(handle, dataset, index_params, index);
@@ -492,11 +506,18 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   auto index = ivf_flat::build(handle, dataset, index_params);
  * @endcode
@@ -515,11 +536,18 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
  *   ivf_flat::build(handle, dataset, index_params, index);
@@ -539,11 +567,18 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   auto index = ivf_flat::build(handle, dataset, index_params);
  * @endcode
@@ -562,11 +597,18 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
  *   ivf_flat::build(handle, dataset, index_params, index);
@@ -710,6 +752,7 @@ auto extend(raft::resources const& handle,
  * @param[in] handle
  * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
  * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *
  *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
  *    here to imply a continuous range `[0...n_rows)`.
  * @param[inout] idx pointer to index, to be overwritten in-place
@@ -786,6 +829,9 @@ void extend(raft::resources const& handle,
 /**
  * @brief Build a new index containing the data of the original plus new extra vectors.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Implementation note:
  *    The new data is clustered according to existing kmeans clusters, then the cluster
  *    centers are adjusted to match the newly labeled data.
@@ -798,6 +844,9 @@ void extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
@@ -821,6 +870,9 @@ auto extend(raft::resources const& handle,
 /**
  * @brief Extend the index in-place with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -829,6 +881,9 @@ auto extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
@@ -850,6 +905,9 @@ void extend(raft::resources const& handle,
 /**
  * @brief Build a new index containing the data of the original plus new extra vectors.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Implementation note:
  *    The new data is clustered according to existing kmeans clusters, then the cluster
  *    centers are adjusted to match the newly labeled data.
@@ -862,6 +920,9 @@ void extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_flat::build(handle, dataset, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
@@ -885,6 +946,9 @@ auto extend(raft::resources const& handle,
 /**
  * @brief Extend the index in-place with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -893,6 +957,9 @@ auto extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
@@ -914,6 +981,9 @@ void extend(raft::resources const& handle,
 /**
  * @brief Build a new index containing the data of the original plus new extra vectors.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Implementation note:
  *    The new data is clustered according to existing kmeans clusters, then the cluster
  *    centers are adjusted to match the newly labeled data.
@@ -926,6 +996,9 @@ void extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_flat::build(handle, dataset, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
@@ -949,6 +1022,9 @@ auto extend(raft::resources const& handle,
 /**
  * @brief Extend the index in-place with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -957,6 +1033,9 @@ auto extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 2702dba3bf..70f56bf583 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -634,11 +634,18 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_pq::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   auto index = ivf_pq::build(handle, index_params, dataset);
  * @endcode
@@ -657,11 +664,18 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_pq::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
  *   ivf_pq::build(handle, index_params, dataset, index);
@@ -681,11 +695,18 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_pq::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   auto index = ivf_pq::build(handle, index_params, dataset);
  * @endcode
@@ -704,11 +725,18 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_pq::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
  *   ivf_pq::build(handle, index_params, dataset, index);
@@ -728,11 +756,18 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_pq::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   auto index = ivf_pq::build(handle, index_params, dataset);
  * @endcode
@@ -751,11 +786,18 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   ivf_pq::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // create and fill the index from a [N, D] dataset
  *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
  *   ivf_pq::build(handle, index_params, dataset, index);
@@ -953,6 +995,9 @@ void extend(raft::resources const& handle,
 /**
  * @brief Extend the index with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -961,14 +1006,17 @@ void extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
  * @endcode
  *
  * @param[in] handle
- * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
- * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
  *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
  *    here to imply a continuous range `[0...n_rows)`.
  * @param[inout] idx
@@ -982,6 +1030,9 @@ auto extend(raft::resources const& handle,
 /**
  * @brief Extend the index with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -990,14 +1041,17 @@ auto extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
  * @endcode
  *
  * @param[in] handle
- * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
- * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
  *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
  *    here to imply a continuous range `[0...n_rows)`.
  * @param[inout] idx
@@ -1010,6 +1064,9 @@ void extend(raft::resources const& handle,
 /**
  * @brief Extend the index with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -1018,14 +1075,17 @@ void extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
  * @endcode
  *
  * @param[in] handle
- * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
- * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
  *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
  *    here to imply a continuous range `[0...n_rows)`.
  * @param[inout] idx
@@ -1039,6 +1099,9 @@ auto extend(raft::resources const& handle,
 /**
  * @brief Extend the index with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -1047,14 +1110,17 @@ auto extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
  * @endcode
  *
  * @param[in] handle
- * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
- * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
  *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
  *    here to imply a continuous range `[0...n_rows)`.
  * @param[inout] idx
@@ -1067,6 +1133,9 @@ void extend(raft::resources const& handle,
 /**
  * @brief Extend the index with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -1075,14 +1144,17 @@ void extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
  * @endcode
  *
  * @param[in] handle
- * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
- * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
  *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
  *    here to imply a continuous range `[0...n_rows)`.
  * @param[inout] idx
@@ -1096,6 +1168,9 @@ auto extend(raft::resources const& handle,
 /**
  * @brief Extend the index with the new data.
  *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -1104,14 +1179,18 @@ auto extend(raft::resources const& handle,
  *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
  *   // train the index from a [N, D] dataset
  *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
  *   // fill the index with the data
  *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
  *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ *
  * @endcode
  *
  * @param[in] handle
- * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
- * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
  *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
  *    here to imply a continuous range `[0...n_rows)`.
  * @param[inout] idx
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index f73524b7aa..1db2dca64f 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -17,6 +17,7 @@
 #pragma once
 
 #include <cuvs/distance/distance.hpp>
+#include <raft/common/nvtx.hpp>
 #include <raft/core/logger-ext.hpp>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
@@ -25,6 +26,7 @@
 #include <rmm/cuda_stream_view.hpp>
 #include <rmm/device_scalar.hpp>
 #include <rmm/device_uvector.hpp>
+#include <rmm/mr/host/pinned_memory_resource.hpp>
 #include <rmm/resource_ref.hpp>
 
 #include <cuda_fp16.hpp>
@@ -393,6 +395,12 @@ void copy_selected(IdxT n_rows,
  *
  * The iterator can be reused. If the number of iterations is one, at most one raft::copy will ever
  * be invoked (i.e. small datasets are not reloaded multiple times).
+ *
+ * In the case of pageable host buffer input, the iterator is by default (almost) synchronous due to
+ * the behavior of raft::copy. In order to achieve kernel and copy overlapping, a
+ * prefetch_next_batch (synchronous) API is provided. Note that since prefetch API is synchronous,
+ * user may want to schedule kernel, which is asynchronous, first. User is responsible to properly
+ * manage the order of prefetch and kernel to ensure overlapping.
  */
 template <typename T>
 struct batch_load_iterator {
@@ -417,41 +425,52 @@ struct batch_load_iterator {
           size_type row_width,
           size_type batch_size,
           rmm::cuda_stream_view stream,
-          rmm::device_async_resource_ref mr)
+          rmm::device_async_resource_ref mr,
+          bool prefetch = false)
       : stream_(stream),
-        buf_(0, stream, mr),
+        buf_0_(0, stream, mr),
+        buf_1_(0, stream, mr),
         source_(source),
         dev_ptr_(nullptr),
         n_rows_(n_rows),
         row_width_(row_width),
         batch_size_(std::min(batch_size, n_rows)),
         pos_(std::nullopt),
+        prefetch_pos_(std::nullopt),
         n_iters_(raft::div_rounding_up_safe(n_rows, batch_size)),
-        needs_copy_(false)
+        needs_copy_(false),
+        prefetch_(prefetch)
     {
       if (source_ == nullptr) { return; }
       cudaPointerAttributes attr;
       RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, source_));
       dev_ptr_ = reinterpret_cast<T*>(attr.devicePointer);
       if (dev_ptr_ == nullptr) {
-        buf_.resize(row_width_ * batch_size_, stream);
-        dev_ptr_    = buf_.data();
+        buf_0_.resize(row_width_ * batch_size_, stream);
+        dev_ptr_    = buf_0_.data();
         needs_copy_ = true;
+        if (prefetch_) {
+          buf_1_.resize(row_width_ * batch_size_, stream);
+          prefetch_dev_ptr_ = buf_1_.data();
+        }
       }
     }
     rmm::cuda_stream_view stream_;
-    rmm::device_uvector<T> buf_;
+    rmm::device_uvector<T> buf_0_;
+    rmm::device_uvector<T> buf_1_;
     const T* source_;
     size_type n_rows_;
     size_type row_width_;
     size_type batch_size_;
     size_type n_iters_;
     bool needs_copy_;
+    bool prefetch_;
 
     std::optional<size_type> pos_;
+    std::optional<size_type> prefetch_pos_;
     size_type batch_len_;
     T* dev_ptr_;
-
+    T* prefetch_dev_ptr_;
     friend class batch_load_iterator<T>;
 
     /**
@@ -471,12 +490,38 @@ struct batch_load_iterator {
                          size_t(offset()),
                          size_t(size()),
                          size_t(row_width()));
-          raft::copy(dev_ptr_, source_ + offset() * row_width(), size() * row_width(), stream_);
+          if (prefetch_ && prefetch_pos_ == pos_) {
+            std::swap(dev_ptr_, prefetch_dev_ptr_);
+          } else {
+            raft::copy(dev_ptr_, source_ + offset() * row_width(), size() * row_width(), stream_);
+          }
         }
       } else {
         dev_ptr_ = const_cast<T*>(source_) + offset() * row_width();
       }
     }
+
+    /**
+     * Helper function for prefetch. NOP if prefetch option is not enabled. This API is synchronous.
+     */
+    void prefetch(const size_type& pos)
+    {
+      if (pos >= n_iters_ || !prefetch_ || !needs_copy_ || source_ == nullptr) { return; }
+      size_type prefetch_offset = batch_size_ * pos;
+      size_type prefetch_size = std::min(batch_size_, n_rows_ - std::min(prefetch_offset, n_rows_));
+      raft::common::nvtx::push_range(
+        "batch_load_iterator::prefetch(offset = %zu, size = %zu, row_width = %zu)",
+        size_t(prefetch_offset),
+        size_t(prefetch_size),
+        size_t(row_width()));
+      raft::copy(prefetch_dev_ptr_,
+                 source_ + prefetch_offset * row_width(),
+                 prefetch_size * row_width(),
+                 stream_);
+      raft::common::nvtx::pop_range();
+      stream_.synchronize();
+      prefetch_pos_.emplace(pos);
+    }
   };
 
   using value_type = batch;
@@ -491,20 +536,36 @@ struct batch_load_iterator {
    * row-major matrix of size [n_rows, row_width], and the batches are the sub-matrices of size
    * [x<=batch_size, n_rows].
    *
+   * If prefetch option is enabled, the batch_load_iterator could help to achieve overlapping with
+   * prefetch_next_batch() with other workloads. This is useful if source buffer is in host memory.
+   * To achieve overlapping, the other workloads have to be async and scheduled before
+   * prefetch_next_batch(). Users also need to use a different stream for the workloads. E.g.,
+   * utils::batch_load_iterator<T> batches(..., stream_1, ..., true);
+   * batches.prefetch_next_batch();
+   * for (const auto& batch : batches) {
+   *     // The following kernel and prefetch_next_batch() could be overlapped.
+   *     kernel<<<..., stream_2>>>(...);
+   *     batches.prefetch_next_batch();
+   * }
+   *
    * @param source the input data -- host, device, or nullptr.
    * @param n_rows the size of the input in logical rows.
    * @param row_width the size of the logical row in the elements of type `T`.
    * @param batch_size the desired size of the batch.
    * @param stream the ordering for the host->device copies, if applicable.
    * @param mr a custom memory resource for the intermediate buffer, if applicable.
+   * @param prefetch enable prefetch feature in order to achieve kernel/copy overlapping.
    */
   batch_load_iterator(const T* source,
                       size_type n_rows,
                       size_type row_width,
                       size_type batch_size,
                       rmm::cuda_stream_view stream,
-                      rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())
-    : cur_batch_(new batch(source, n_rows, row_width, batch_size, stream, mr)), cur_pos_(0)
+                      rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource(),
+                      bool prefetch                     = false)
+    : cur_batch_(new batch(source, n_rows, row_width, batch_size, stream, mr, prefetch)),
+      cur_pos_(0),
+      cur_prefetch_pos_(0)
   {
   }
   /**
@@ -512,10 +573,18 @@ struct batch_load_iterator {
    * (i.e. the source is inaccessible from the device).
    */
   [[nodiscard]] auto does_copy() const -> bool { return cur_batch_->does_copy(); }
-  /** Reset the iterator position to `begin()` */
-  void reset() { cur_pos_ = 0; }
-  /** Reset the iterator position to `end()` */
-  void reset_to_end() { cur_pos_ = cur_batch_->n_iters_; }
+  /** Reset the iterator position and prefetch position to `begin()` */
+  void reset()
+  {
+    cur_pos_          = 0;
+    cur_prefetch_pos_ = 0;
+  }
+  /** Reset the iterator position and prefetch position to `end()` */
+  void reset_to_end()
+  {
+    cur_pos_          = cur_batch_->n_iters_;
+    cur_prefetch_pos_ = cur_batch_->n_iters_;
+  }
   [[nodiscard]] auto begin() const -> const batch_load_iterator<T>
   {
     batch_load_iterator<T> x(*this);
@@ -538,6 +607,9 @@ struct batch_load_iterator {
     cur_batch_->load(cur_pos_);
     return cur_batch_.get();
   }
+  /* Prefetch next batch. Users are responsible for calling this method to enable kernel/copy
+   * overlapping. Note that this API is synchronous. */
+  void prefetch_next_batch() { cur_batch_->prefetch(cur_prefetch_pos_++); }
   friend auto operator==(const batch_load_iterator<T>& x, const batch_load_iterator<T>& y) -> bool
   {
     return x.cur_batch_ == y.cur_batch_ && x.cur_pos_ == y.cur_pos_;
@@ -572,6 +644,7 @@ struct batch_load_iterator {
  private:
   std::shared_ptr<value_type> cur_batch_;
   size_type cur_pos_;
+  size_type cur_prefetch_pos_;
 };
 
 }  // namespace cuvs::spatial::knn::detail::utils
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index ff1f3032d9..e8df3e3d6a 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -31,6 +31,7 @@
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/linalg/add.cuh>
@@ -195,13 +196,25 @@ void extend(raft::resources const& handle,
   constexpr size_t kReasonableMaxBatchSize = 65536;
   size_t max_batch_size                    = std::min<size_t>(n_rows, kReasonableMaxBatchSize);
 
+  // Determine if a stream pool exist and make sure there is at least one stream in it so we
+  // could use the stream for kernel/copy overlapping by enabling prefetch.
+  auto copy_stream = raft::resource::get_cuda_stream(handle);  // Using the main stream by default
+  bool enable_prefetch = false;
+  if (handle.has_resource_factory(raft::resource::resource_type::CUDA_STREAM_POOL)) {
+    if (raft::resource::get_stream_pool_size(handle) >= 1) {
+      enable_prefetch = true;
+      copy_stream     = raft::resource::get_stream_from_stream_pool(handle);
+    }
+  }
   // Predict the cluster labels for the new data, in batches if necessary
   utils::batch_load_iterator<T> vec_batches(new_vectors,
                                             n_rows,
                                             index->dim(),
                                             max_batch_size,
-                                            stream,
-                                            raft::resource::get_workspace_resource(handle));
+                                            copy_stream,
+                                            raft::resource::get_workspace_resource(handle),
+                                            enable_prefetch);
+  vec_batches.prefetch_next_batch();
 
   for (const auto& batch : vec_batches) {
     auto batch_data_view =
@@ -214,6 +227,10 @@ void extend(raft::resources const& handle,
                                             orig_centroids_view,
                                             batch_labels_view,
                                             utils::mapping<float>{});
+    vec_batches.prefetch_next_batch();
+    // User needs to make sure kernel finishes its work before we overwrite batch in the next
+    // iteration if different streams are used for kernel and copy.
+    raft::resource::sync_stream(handle);
   }
 
   auto* list_sizes_ptr    = index->list_sizes().data_handle();
@@ -277,6 +294,8 @@ void extend(raft::resources const& handle,
 
   utils::batch_load_iterator<IdxT> vec_indices(
     new_indices, n_rows, 1, max_batch_size, stream, raft::resource::get_workspace_resource(handle));
+  vec_batches.reset();
+  vec_batches.prefetch_next_batch();
   utils::batch_load_iterator<IdxT> idx_batch = vec_indices.begin();
   size_t next_report_offset                  = 0;
   size_t d_report_offset                     = n_rows * 5 / 100;
@@ -297,6 +316,10 @@ void extend(raft::resources const& handle,
                                            dim,
                                            index->veclen(),
                                            batch.offset());
+    vec_batches.prefetch_next_batch();
+    // User needs to make sure kernel finishes its work before we overwrite batch in the next
+    // iteration if different streams are used for kernel and copy.
+    raft::resource::sync_stream(handle);
     RAFT_CUDA_TRY(cudaPeekAtLastError());
 
     if (batch.offset() > next_report_offset) {
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 02b6fbeb91..d35394c99b 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -33,6 +33,7 @@
 #include <raft/core/logger-ext.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/linalg/add.cuh>
@@ -1572,9 +1573,19 @@ void extend(raft::resources const& handle,
     }
   }
 
+  // Determine if a stream pool exist and make sure there is at least one stream in it so we
+  // could use the stream for kernel/copy overlapping by enabling prefetch.
+  auto copy_stream = raft::resource::get_cuda_stream(handle);  // Using the main stream by default
+  bool enable_prefetch = false;
+  if (handle.has_resource_factory(raft::resource::resource_type::CUDA_STREAM_POOL)) {
+    if (raft::resource::get_stream_pool_size(handle) >= 1) {
+      enable_prefetch = true;
+      copy_stream     = raft::resource::get_stream_from_stream_pool(handle);
+    }
+  }
   // Predict the cluster labels for the new data, in batches if necessary
   utils::batch_load_iterator<T> vec_batches(
-    new_vectors, n_rows, index->dim(), max_batch_size, stream, batches_mr);
+    new_vectors, n_rows, index->dim(), max_batch_size, copy_stream, device_memory, enable_prefetch);
   // Release the placeholder memory, because we don't intend to allocate any more long-living
   // temporary buffers before we allocate the index data.
   // This memory could potentially speed up UVM accesses, if any.
@@ -1592,6 +1603,7 @@ void extend(raft::resources const& handle,
                                     n_clusters,
                                     cudaMemcpyDefault,
                                     stream));
+    vec_batches.prefetch_next_batch();
     for (const auto& batch : vec_batches) {
       auto batch_data_view = raft::make_device_matrix_view<const T, internal_extents_t>(
         batch.data(), batch.size(), index->dim());
@@ -1607,6 +1619,10 @@ void extend(raft::resources const& handle,
                                               centers_view,
                                               batch_labels_view,
                                               utils::mapping<float>{});
+      vec_batches.prefetch_next_batch();
+      // User needs to make sure kernel finishes its work before we overwrite batch in the next
+      // iteration if different streams are used for kernel and copy.
+      raft::resource::sync_stream(handle);
     }
   }
 
@@ -1648,6 +1664,8 @@ void extend(raft::resources const& handle,
   // Fill the extended index with the new data (possibly, in batches)
   utils::batch_load_iterator<IdxT> idx_batches(
     new_indices, n_rows, 1, max_batch_size, stream, batches_mr);
+  vec_batches.reset();
+  vec_batches.prefetch_next_batch();
   for (const auto& vec_batch : vec_batches) {
     const auto& idx_batch = *idx_batches++;
     process_and_fill_codes(handle,
@@ -1659,6 +1677,10 @@ void extend(raft::resources const& handle,
                            new_data_labels.data() + vec_batch.offset(),
                            IdxT(vec_batch.size()),
                            batches_mr);
+    vec_batches.prefetch_next_batch();
+    // User needs to make sure kernel finishes its work before we overwrite batch in the next
+    // iteration if different streams are used for kernel and copy.
+    raft::resource::sync_stream(handle);
   }
 }
 
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index 5dd6ebb2b1..f907baa1fb 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -24,9 +24,11 @@
 #include <raft/stats/mean.cuh>
 #include <thrust/sequence.h>
 
+#include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/matrix/gather.cuh>
 #include <raft/util/fast_int_div.cuh>
+#include <rmm/cuda_stream_pool.hpp>
 
 namespace cuvs::neighbors::ivf_flat {
 
@@ -44,7 +46,9 @@ struct AnnIvfFlatInputs {
   IdxT nlist;
   cuvs::distance::DistanceType metric;
   bool adaptive_centers;
-  bool host_dataset;
+  bool host_dataset = false;
+  // The kernel_copy_overlapping option is only applicable when host dataset is enabled.
+  bool kernel_copy_overlapping = false;
 };
 
 template <typename IdxT>
@@ -52,7 +56,8 @@ template <typename IdxT>
 {
   os << "{ " << p.num_queries << ", " << p.num_db_vecs << ", " << p.dim << ", " << p.k << ", "
      << p.nprobe << ", " << p.nlist << ", " << static_cast<int>(p.metric) << ", "
-     << p.adaptive_centers << '}' << std::endl;
+     << p.adaptive_centers << "," << p.host_dataset << "," << p.kernel_copy_overlapping << '}'
+     << std::endl;
   return os;
 }
 
@@ -75,6 +80,12 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
     std::vector<T> distances_ivfflat(queries_size);
     std::vector<T> distances_naive(queries_size);
 
+    if (ps.kernel_copy_overlapping) {
+      size_t n_streams = 1;
+      raft::resource::set_cuda_stream_pool(handle_,
+                                           std::make_shared<rmm::cuda_stream_pool>(n_streams));
+    }
+
     {
       rmm::device_uvector<T> distances_naive_dev(queries_size, stream_);
       rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
@@ -556,6 +567,15 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
   {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
 
+  // // host input data with prefetching for kernel copy overlapping
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+
   {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
   {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::InnerProduct, true},
   {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::InnerProduct, false},
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index fa26d7ef91..e6d8efc93f 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -22,8 +22,10 @@
 #include <cuvs/neighbors/ivf_pq.hpp>
 
 #include <raft/core/bitset.cuh>
+#include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/matrix/gather.cuh>
+#include <rmm/cuda_stream_pool.hpp>
 #include <rmm/mr/device/managed_memory_resource.hpp>
 #include <thrust/sequence.h>
 
@@ -208,6 +210,33 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     return cuvs::neighbors::ivf_pq::build(handle_, ipams, index_view);
   }
 
+  auto build_only_host_input()
+  {
+    auto ipams              = ps.index_params;
+    ipams.add_data_on_build = true;
+
+    auto host_database = raft::make_host_matrix<DataT, int64_t>(ps.num_db_vecs, ps.dim);
+    raft::copy(host_database.data_handle(), database.data(), ps.num_db_vecs * ps.dim, stream_);
+    auto index_view = raft::make_host_matrix_view<const DataT, int64_t>(
+      host_database.data_handle(), ps.num_db_vecs, ps.dim);
+    return cuvs::neighbors::ivf_pq::build(handle_, ipams, index_view);
+  }
+
+  auto build_only_host_input_overlap()
+  {
+    auto ipams              = ps.index_params;
+    ipams.add_data_on_build = true;
+
+    size_t n_streams = 1;
+    raft::resource::set_cuda_stream_pool(handle_,
+                                         std::make_shared<rmm::cuda_stream_pool>(n_streams));
+    auto host_database = raft::make_host_matrix<DataT, int64_t>(ps.num_db_vecs, ps.dim);
+    raft::copy(host_database.data_handle(), database.data(), ps.num_db_vecs * ps.dim, stream_);
+    auto index_view = raft::make_host_matrix_view<const DataT, int64_t>(
+      host_database.data_handle(), ps.num_db_vecs, ps.dim);
+    return cuvs::neighbors::ivf_pq::build(handle_, ipams, index_view);
+  }
+
   auto build_2_extends()
   {
     auto db_indices = raft::make_device_vector<IdxT>(handle_, ps.num_db_vecs);
@@ -968,6 +997,18 @@ inline auto special_cases() -> test_cases_t
     this->run([this]() { return this->build_only(); }); \
   }
 
+#define TEST_BUILD_HOST_INPUT_SEARCH(type)                         \
+  TEST_P(type, build_host_input_search) /* NOLINT */               \
+  {                                                                \
+    this->run([this]() { return this->build_only_host_input(); }); \
+  }
+
+#define TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(type)                         \
+  TEST_P(type, build_host_input_overlap_search) /* NOLINT */               \
+  {                                                                        \
+    this->run([this]() { return this->build_only_host_input_overlap(); }); \
+  }
+
 #define TEST_BUILD_EXTEND_SEARCH(type)                       \
   TEST_P(type, build_extend_search) /* NOLINT */             \
   {                                                          \
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
index 4c55c7b396..cdc6c1b7e4 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
@@ -21,6 +21,8 @@ namespace cuvs::neighbors::ivf_pq {
 using f32_f32_i64        = ivf_pq_test<float, float, int64_t>;
 using f32_f32_i64_filter = ivf_pq_filter_test<float, float, int64_t>;
 
+TEST_BUILD_HOST_INPUT_SEARCH(f32_f32_i64)
+TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_f32_i64)
 TEST_BUILD_EXTEND_SEARCH(f32_f32_i64)
 TEST_BUILD_SERIALIZE_SEARCH(f32_f32_i64)
 INSTANTIATE(f32_f32_i64, defaults() + small_dims() + big_dims_moderate_lut());
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
index 1e2c0ebd21..80b0e2ccb9 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
@@ -22,6 +22,8 @@ using f32_i08_i64        = ivf_pq_test<float, int8_t, int64_t>;
 using f32_i08_i64_filter = ivf_pq_filter_test<float, int8_t, int64_t>;
 
 TEST_BUILD_SEARCH(f32_i08_i64)
+TEST_BUILD_HOST_INPUT_SEARCH(f32_i08_i64)
+TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_i08_i64)
 TEST_BUILD_SERIALIZE_SEARCH(f32_i08_i64)
 INSTANTIATE(f32_i08_i64, defaults() + big_dims() + var_k());
 
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
index 6b78c73b88..0216a1e80f 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
@@ -22,6 +22,8 @@ using f32_u08_i64        = ivf_pq_test<float, uint8_t, int64_t>;
 using f32_u08_i64_filter = ivf_pq_filter_test<float, uint8_t, int64_t>;
 
 TEST_BUILD_SEARCH(f32_u08_i64)
+TEST_BUILD_HOST_INPUT_SEARCH(f32_u08_i64)
+TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_u08_i64)
 TEST_BUILD_EXTEND_SEARCH(f32_u08_i64)
 INSTANTIATE(f32_u08_i64, small_dims_per_cluster() + enum_variety());
 

From 2ebbeca41290430aeb84ebd52a6acc5893ec2491 Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Thu, 1 Aug 2024 03:37:33 +0200
Subject: [PATCH 120/623] Remove fp16 kernels that have no public entry point
 (#268)

`libcuvs.so` contains fp16 kernels that are not accessible (missing headers and missing public entry points). This PR removes the unused kernel.

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/268
---
 cpp/CMakeLists.txt | 48 ----------------------------------------------
 1 file changed, 48 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d204a9a231..3b483538a1 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -268,14 +268,6 @@ add_library(
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -300,14 +292,6 @@ add_library(
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
@@ -316,14 +300,6 @@ add_library(
   src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -348,22 +324,10 @@ add_library(
   src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
   src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
   src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
   src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
   src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
   src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
   src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
   src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
   src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
@@ -376,18 +340,10 @@ add_library(
   src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
   src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
   src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
   src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
   src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
   src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
   src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
   src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
   src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
   src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
@@ -400,10 +356,6 @@ add_library(
   src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
   src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
   src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
   $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
   src/neighbors/ivf_flat_index.cpp
   src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu

From e599ab248c2903e0714cf6e6b25d3b625b2274ee Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Thu, 1 Aug 2024 08:31:22 +0200
Subject: [PATCH 121/623] Enable random subsampling (#122)

Random sampling of training set for IVF methods was reverted in https://github.com/rapidsai/raft/pull/2144 due to the large memory usage of the subsample method.

Since then, PR https://github.com/rapidsai/raft/pull/2155 has implemented a new random sampling method with improved memory utilization.  Using that we can now enable random sampling of IVF methods (https://github.com/rapidsai/raft/pull/2052 and https://github.com/rapidsai/raft/pull/2077).

Random subsampling has measurable overhead for IVF-Flat, therefore it is only enabled for IVF-PQ.

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/122
---
 cpp/include/cuvs/neighbors/ivf_pq.h          |   9 +
 cpp/include/cuvs/neighbors/ivf_pq.hpp        |   9 +
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh    | 163 ++++++-------------
 cpp/src/neighbors/ivf_pq_c.cpp               |   4 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd |   1 +
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx |  24 ++-
 rust/cuvs/src/ivf_pq/index_params.rs         |  12 ++
 7 files changed, 109 insertions(+), 113 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index 5a975a72c6..d588fec415 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -116,6 +116,15 @@ struct cuvsIvfPqIndexParams {
    * flag to `true` if you prefer to use as little GPU memory for the database as possible.
    */
   bool conservative_memory_allocation;
+
+  /**
+   * The max number of data points to use per PQ code during PQ codebook training. Using more data
+   * points per PQ code may increase the quality of PQ codebook but may also increase the build
+   * time. The parameter is applied to both PQ codebook generation methods, i.e., PER_SUBSPACE and
+   * PER_CLUSTER. In both cases, we will use `pq_book_size * max_train_points_per_pq_code` training
+   * points to train each codebook.
+   */
+  uint32_t max_train_points_per_pq_code;
 };
 
 typedef struct cuvsIvfPqIndexParams* cuvsIvfPqIndexParams_t;
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 70f56bf583..b2db966864 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -107,6 +107,15 @@ struct index_params : cuvs::neighbors::index_params {
    */
   bool add_data_on_build = true;
 
+  /**
+   * The max number of data points to use per PQ code during PQ codebook training. Using more data
+   * points per PQ code may increase the quality of PQ codebook but may also increase the build
+   * time. The parameter is applied to both PQ codebook generation methods, i.e., PER_SUBSPACE and
+   * PER_CLUSTER. In both cases, we will use `pq_book_size * max_train_points_per_pq_code` training
+   * points to train each codebook.
+   */
+  uint32_t max_train_points_per_pq_code = 256;
+
   /**
    * Creates index_params based on shape of the input dataset.
    * Usage example:
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index d35394c99b..c65ea81082 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -44,6 +44,7 @@
 #include <raft/linalg/unary_op.cuh>
 #include <raft/matrix/gather.cuh>
 #include <raft/matrix/linewise_op.cuh>
+#include <raft/matrix/sample_rows.cuh>
 #include <raft/random/rng.cuh>
 #include <raft/stats/histogram.cuh>
 #include <raft/util/cuda_utils.cuh>
@@ -69,51 +70,6 @@ using namespace cuvs::spatial::knn::detail;  // NOLINT
 
 using internal_extents_t = int64_t;  // The default mdspan extent type used internally.
 
-template <uint32_t BlockDim, typename T, typename S>
-__launch_bounds__(BlockDim) static __global__ void copy_warped_kernel(
-  T* out, uint32_t ld_out, const S* in, uint32_t ld_in, uint32_t n_cols, size_t n_rows)
-{
-  using warp    = raft::Pow2<raft::WarpSize>;
-  size_t row_ix = warp::div(size_t(threadIdx.x) + size_t(BlockDim) * size_t(blockIdx.x));
-  uint32_t i    = warp::mod(threadIdx.x);
-  if (row_ix >= n_rows) return;
-  out += row_ix * ld_out;
-  in += row_ix * ld_in;
-  auto f = utils::mapping<T>{};
-  for (uint32_t col_ix = i; col_ix < n_cols; col_ix += warp::Value) {
-    auto x = f(in[col_ix]);
-    __syncwarp();
-    out[col_ix] = x;
-  }
-}
-
-/**
- * raft::copy the data one warp-per-row:
- *
- *  1. load the data per-warp
- *  2. apply the `utils::mapping<T>{}`
- *  3. sync within warp
- *  4. store the data.
- *
- * Assuming sizeof(T) >= sizeof(S) and the data is properly aligned (see the usage in `build`), this
- * allows to re-structure the data within rows in-place.
- */
-template <typename T, typename S>
-void copy_warped(T* out,
-                 uint32_t ld_out,
-                 const S* in,
-                 uint32_t ld_in,
-                 uint32_t n_cols,
-                 size_t n_rows,
-                 rmm::cuda_stream_view stream)
-{
-  constexpr uint32_t kBlockDim = 128;
-  dim3 threads(kBlockDim, 1, 1);
-  dim3 blocks(raft::div_rounding_up_safe<size_t>(n_rows, kBlockDim / raft::WarpSize), 1, 1);
-  copy_warped_kernel<kBlockDim, T, S>
-    <<<blocks, threads, 0, stream>>>(out, ld_out, in, ld_in, n_cols, n_rows);
-}
-
 /**
  * @brief Compute residual vectors from the source dataset given by selected indices.
  *
@@ -358,14 +314,19 @@ void train_per_subset(raft::resources const& handle,
                       size_t n_rows,
                       const float* trainset,   // [n_rows, dim]
                       const uint32_t* labels,  // [n_rows]
-                      uint32_t kmeans_n_iters)
+                      uint32_t kmeans_n_iters,
+                      uint32_t max_train_points_per_pq_code)
 {
   auto stream        = raft::resource::get_cuda_stream(handle);
   auto device_memory = raft::resource::get_workspace_resource(handle);
 
   rmm::device_uvector<float> pq_centers_tmp(index.pq_centers().size(), stream, device_memory);
-  rmm::device_uvector<float> sub_trainset(n_rows * size_t(index.pq_len()), stream, device_memory);
-  rmm::device_uvector<uint32_t> sub_labels(n_rows, stream, device_memory);
+  // Subsampling the train set for codebook generation based on max_train_points_per_pq_code.
+  size_t big_enough = max_train_points_per_pq_code * size_t(index.pq_book_size());
+  auto pq_n_rows    = uint32_t(std::min(big_enough, n_rows));
+  rmm::device_uvector<float> sub_trainset(
+    pq_n_rows * size_t(index.pq_len()), stream, device_memory);
+  rmm::device_uvector<uint32_t> sub_labels(pq_n_rows, stream, device_memory);
 
   rmm::device_uvector<uint32_t> pq_cluster_sizes(index.pq_book_size(), stream, device_memory);
 
@@ -376,7 +337,7 @@ void train_per_subset(raft::resources const& handle,
     // Get the rotated cluster centers for each training vector.
     // This will be subtracted from the input vectors afterwards.
     utils::copy_selected<float, float, size_t, uint32_t>(
-      n_rows,
+      pq_n_rows,
       index.pq_len(),
       index.centers_rot().data_handle() + index.pq_len() * j,
       labels,
@@ -392,7 +353,7 @@ void train_per_subset(raft::resources const& handle,
                        true,
                        false,
                        index.pq_len(),
-                       n_rows,
+                       pq_n_rows,
                        index.dim(),
                        &alpha,
                        index.rotation_matrix().data_handle() + index.dim() * index.pq_len() * j,
@@ -406,13 +367,13 @@ void train_per_subset(raft::resources const& handle,
 
     // train PQ codebook for this subspace
     auto sub_trainset_view = raft::make_device_matrix_view<const float, internal_extents_t>(
-      sub_trainset.data(), n_rows, index.pq_len());
+      sub_trainset.data(), pq_n_rows, index.pq_len());
     auto centers_tmp_view = raft::make_device_matrix_view<float, internal_extents_t>(
       pq_centers_tmp.data() + index.pq_book_size() * index.pq_len() * j,
       index.pq_book_size(),
       index.pq_len());
     auto sub_labels_view =
-      raft::make_device_vector_view<uint32_t, internal_extents_t>(sub_labels.data(), n_rows);
+      raft::make_device_vector_view<uint32_t, internal_extents_t>(sub_labels.data(), pq_n_rows);
     auto cluster_sizes_view = raft::make_device_vector_view<uint32_t, internal_extents_t>(
       pq_cluster_sizes.data(), index.pq_book_size());
     cuvs::cluster::kmeans::balanced_params kmeans_params;
@@ -435,7 +396,8 @@ void train_per_cluster(raft::resources const& handle,
                        size_t n_rows,
                        const float* trainset,   // [n_rows, dim]
                        const uint32_t* labels,  // [n_rows]
-                       uint32_t kmeans_n_iters)
+                       uint32_t kmeans_n_iters,
+                       uint32_t max_train_points_per_pq_code)
 {
   auto stream        = raft::resource::get_cuda_stream(handle);
   auto device_memory = raft::resource::get_workspace_resource(handle);
@@ -485,9 +447,11 @@ void train_per_cluster(raft::resources const& handle,
                      indices + cluster_offsets[l],
                      device_memory);
 
-    // limit the cluster size to bound the training time.
+    // limit the cluster size to bound the training time based on max_train_points_per_pq_code
+    // If pq_book_size is less than pq_dim, use max_train_points_per_pq_code per pq_dim instead
     // [sic] we interpret the data as pq_len-dimensional
-    size_t big_enough     = 256ul * std::max<size_t>(index.pq_book_size(), index.pq_dim());
+    size_t big_enough =
+      max_train_points_per_pq_code * std::max<size_t>(index.pq_book_size(), index.pq_dim());
     size_t available_rows = size_t(cluster_size) * size_t(index.pq_dim());
     auto pq_n_rows        = uint32_t(std::min(big_enough, available_rows));
     // train PQ codebook for this cluster
@@ -1730,6 +1694,7 @@ auto build(raft::resources const& handle,
   utils::memzero(index.inds_ptrs().data_handle(), index.inds_ptrs().size(), stream);
 
   {
+    raft::random::RngState random_state{137};
     auto trainset_ratio = std::max<size_t>(
       1,
       size_t(n_rows) / std::max<size_t>(params.kmeans_trainset_fraction * n_rows, index.n_lists()));
@@ -1749,9 +1714,11 @@ auto build(raft::resources const& handle,
 
     // Besides just sampling, we transform the input dataset into floats to make it easier
     // to use gemm operations from cublas.
-    rmm::device_uvector<float> trainset(0, stream, big_memory_resource);
+    auto trainset = raft::make_device_mdarray<float>(
+      handle, big_memory_resource, raft::make_extents<int64_t>(0, 0));
     try {
-      trainset.resize(n_rows_train * index.dim(), stream);
+      trainset = raft::make_device_mdarray<float>(
+        handle, big_memory_resource, raft::make_extents<int64_t>(n_rows_train, dim));
     } catch (raft::logic_error& e) {
       RAFT_LOG_ERROR(
         "Insufficient memory for kmeans training set allocation. Please decrease "
@@ -1760,54 +1727,19 @@ auto build(raft::resources const& handle,
     }
     // TODO: a proper sampling
     if constexpr (std::is_same_v<T, float>) {
-      RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset.data(),
-                                      sizeof(T) * index.dim(),
-                                      dataset.data_handle(),
-                                      sizeof(T) * index.dim() * trainset_ratio,
-                                      sizeof(T) * index.dim(),
-                                      n_rows_train,
-                                      cudaMemcpyDefault,
-                                      stream));
+      raft::matrix::sample_rows<T, int64_t>(handle, random_state, dataset, trainset.view());
     } else {
-      size_t dim = index.dim();
-      cudaPointerAttributes dataset_attr;
-      RAFT_CUDA_TRY(cudaPointerGetAttributes(&dataset_attr, dataset.data_handle()));
-      if (dataset_attr.devicePointer != nullptr) {
-        // data is available on device: just run the kernel to raft::copy and map the data
-        auto p = reinterpret_cast<T*>(dataset_attr.devicePointer);
-        auto trainset_view =
-          raft::make_device_vector_view<float, IdxT>(trainset.data(), dim * n_rows_train);
-        raft::linalg::map_offset(
-          handle, trainset_view, [p, trainset_ratio, dim] __device__(size_t i) {
-            auto col = i % dim;
-            return utils::mapping<float>{}(p[(i - col) * size_t(trainset_ratio) + col]);
-          });
-      } else {
-        // data is not available: first raft::copy, then map inplace
-        auto trainset_tmp = reinterpret_cast<T*>(reinterpret_cast<uint8_t*>(trainset.data()) +
-                                                 (sizeof(float) - sizeof(T)) * index.dim());
-        // We raft::copy the data in strides, one row at a time, and place the smaller rows of type
-        // T at the end of float rows.
-        RAFT_CUDA_TRY(cudaMemcpy2DAsync(trainset_tmp,
-                                        sizeof(float) * index.dim(),
-                                        dataset.data_handle(),
-                                        sizeof(T) * index.dim() * trainset_ratio,
-                                        sizeof(T) * index.dim(),
-                                        n_rows_train,
-                                        cudaMemcpyDefault,
-                                        stream));
-        // Transform the input `{T -> float}`, one row per warp.
-        // The threads in each warp raft::copy the data synchronously; this and the layout of the
-        // data (content is aligned to the end of the rows) together allow doing the transform
-        // in-place.
-        copy_warped(trainset.data(),
-                    index.dim(),
-                    trainset_tmp,
-                    index.dim() * sizeof(float) / sizeof(T),
-                    index.dim(),
-                    n_rows_train,
-                    stream);
-      }
+      // TODO(tfeher): Enable codebook generation with any type T, and then remove trainset tmp.
+      auto trainset_tmp = raft::make_device_mdarray<T>(
+        handle, big_memory_resource, raft::make_extents<int64_t>(n_rows_train, dim));
+
+      raft::matrix::sample_rows<T, int64_t>(handle, random_state, dataset, trainset_tmp.view());
+
+      raft::linalg::unaryOp(trainset.data_handle(),
+                            trainset_tmp.data_handle(),
+                            trainset.size(),
+                            utils::mapping<float>{},
+                            raft::resource::get_cuda_stream(handle));
     }
 
     // NB: here cluster_centers is used as if it is [n_clusters, data_dim] not [n_clusters,
@@ -1817,9 +1749,8 @@ auto build(raft::resources const& handle,
     auto cluster_centers = cluster_centers_buf.data();
 
     // Train balanced hierarchical kmeans clustering
-    auto trainset_const_view = raft::make_device_matrix_view<const float, internal_extents_t>(
-      trainset.data(), n_rows_train, index.dim());
-    auto centers_view = raft::make_device_matrix_view<float, internal_extents_t>(
+    auto trainset_const_view = raft::make_const_mdspan(trainset.view());
+    auto centers_view        = raft::make_device_matrix_view<float, internal_extents_t>(
       cluster_centers, index.n_lists(), index.dim());
     cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = params.kmeans_n_iters;
@@ -1848,12 +1779,22 @@ auto build(raft::resources const& handle,
     // Train PQ codebooks
     switch (index.codebook_kind()) {
       case codebook_gen::PER_SUBSPACE:
-        train_per_subset(
-          handle, index, n_rows_train, trainset.data(), labels.data(), params.kmeans_n_iters);
+        train_per_subset(handle,
+                         index,
+                         n_rows_train,
+                         trainset.data_handle(),
+                         labels.data(),
+                         params.kmeans_n_iters,
+                         params.max_train_points_per_pq_code);
         break;
       case codebook_gen::PER_CLUSTER:
-        train_per_cluster(
-          handle, index, n_rows_train, trainset.data(), labels.data(), params.kmeans_n_iters);
+        train_per_cluster(handle,
+                          index,
+                          n_rows_train,
+                          trainset.data_handle(),
+                          labels.data(),
+                          params.kmeans_n_iters,
+                          params.max_train_points_per_pq_code);
         break;
       default: RAFT_FAIL("Unreachable code");
     }
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index e2578345b1..256d760e67 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -48,6 +48,7 @@ void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor*
     static_cast<cuvs::neighbors::ivf_pq::codebook_gen>((int)params.codebook_kind);
   build_params.force_random_rotation          = params.force_random_rotation;
   build_params.conservative_memory_allocation = params.conservative_memory_allocation;
+  build_params.max_train_points_per_pq_code   = params.max_train_points_per_pq_code;
 
   auto dataset = dataset_tensor->dl_tensor;
   auto dim     = dataset.shape[0];
@@ -214,7 +215,8 @@ extern "C" cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t* params
                                        .pq_dim                         = 0,
                                        .codebook_kind                  = codebook_gen::PER_SUBSPACE,
                                        .force_random_rotation          = false,
-                                       .conservative_memory_allocation = false};
+                                       .conservative_memory_allocation = false,
+                                       .max_train_points_per_pq_code   = 256};
   });
 }
 
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index c6eb85e35d..e732cd7d25 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -50,6 +50,7 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
         codebook_gen codebook_kind
         bool force_random_rotation
         bool conservative_memory_allocation
+        uint32_t max_train_points_per_pq_code
 
     ctypedef cuvsIvfPqIndexParams* cuvsIvfPqIndexParams_t
 
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index de52cb2da8..3add1df75f 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -97,11 +97,27 @@ cdef class IndexParams:
         default, if `dim == rot_dim`, the rotation transform is
         initialized with the identity matrix. When
         `force_random_rotation == True`, a random orthogonal transform
+        matrix is generated regardless of the values of `dim` and `pq_dim`.
     add_data_on_build : bool, default = True
         After training the coarse and fine quantizers, we will populate
         the index with the dataset if add_data_on_build == True, otherwise
         the index is left empty, and the extend method can be used
         to add new vectors to the index.
+    conservative_memory_allocation : bool, default = True
+        By default, the algorithm allocates more space than necessary for
+        individual clusters (`list_data`). This allows to amortize the cost
+        of memory allocation and reduce the number of data copies during
+        repeated calls to `extend` (extending the database).
+        To disable this behavior and use as little GPU memory for the
+        database as possible, set this flat to `True`.
+    max_train_points_per_pq_code : int, default = 256
+        The max number of data points to use per PQ code during PQ codebook
+        training. Using more data points per PQ code may increase the
+        quality of PQ codebook but may also increase the build time. The
+        parameter is applied to both PQ codebook generation methods, i.e.,
+        PER_SUBSPACE and PER_CLUSTER. In both cases, we will use
+        pq_book_size * max_train_points_per_pq_code training points to
+        train each codebook.
     """
 
     cdef cuvsIvfPqIndexParams* params
@@ -124,7 +140,8 @@ cdef class IndexParams:
                  codebook_kind="subspace",
                  force_random_rotation=False,
                  add_data_on_build=True,
-                 conservative_memory_allocation=False):
+                 conservative_memory_allocation=False,
+                 max_train_points_per_pq_code=256):
         self.params.n_lists = n_lists
         self._metric = metric
         self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
@@ -143,6 +160,8 @@ cdef class IndexParams:
         self.params.add_data_on_build = add_data_on_build
         self.params.conservative_memory_allocation = \
             conservative_memory_allocation
+        self.params.max_train_points_per_pq_code = \
+            max_train_points_per_pq_code
 
     @property
     def metric(self):
@@ -192,6 +211,9 @@ cdef class IndexParams:
     def conservative_memory_allocation(self):
         return self.params.conservative_memory_allocation
 
+    @property
+    def max_train_points_per_pq_code(self):
+        return self.params.max_train_points_per_pq_code
 
 cdef class Index:
     """
diff --git a/rust/cuvs/src/ivf_pq/index_params.rs b/rust/cuvs/src/ivf_pq/index_params.rs
index e38c0945fa..321821bc32 100644
--- a/rust/cuvs/src/ivf_pq/index_params.rs
+++ b/rust/cuvs/src/ivf_pq/index_params.rs
@@ -124,6 +124,18 @@ impl IndexParams {
         self
     }
 
+    /// The max number of data points to use per PQ code during PQ codebook training. Using more data
+    /// points per PQ code may increase the quality of PQ codebook but may also increase the build
+    /// time. The parameter is applied to both PQ codebook generation methods, i.e., PER_SUBSPACE and
+    /// PER_CLUSTER. In both cases, we will use `pq_book_size * max_train_points_per_pq_code` training
+    /// points to train each codebook.
+    pub fn set_max_train_points_per_pq_code(self, max_pq_points: u32)-> IndexParams {
+        unsafe {
+            (*self.0).max_train_points_per_pq_code = max_pq_points;
+        }
+        self
+    }
+
     /// After training the coarse and fine quantizers, we will populate
     /// the index with the dataset if add_data_on_build == true, otherwise
     /// the index is left empty, and the extend method can be used

From 653bf276a1ae7fdfd3c8daa2a79eceac8344af83 Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Wed, 7 Aug 2024 10:42:13 -0400
Subject: [PATCH 122/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 65 ++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 63 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ddeafff816..1330990c6d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,66 @@
+# cuvs 24.08.00 (7 Aug 2024)
+
+## 🚨 Breaking Changes
+
+- Allow serialization on streams ([#173](https://github.com/rapidsai/cuvs/pull/173)) [@benfred](https://github.com/benfred)
+
+## 🐛 Bug Fixes
+
+- Remove fp16 kernels that have no public entry point ([#268](https://github.com/rapidsai/cuvs/pull/268)) [@tfeher](https://github.com/tfeher)
+- Use `raft::util::popc(...)` public API ([#249](https://github.com/rapidsai/cuvs/pull/249)) [@divyegala](https://github.com/divyegala)
+- Enable building FAISS main statically ([#241](https://github.com/rapidsai/cuvs/pull/241)) [@tarang-jain](https://github.com/tarang-jain)
+- CAGRA bench: use device-side refinement when the data is on device ([#228](https://github.com/rapidsai/cuvs/pull/228)) [@achirkin](https://github.com/achirkin)
+- Rename `.devcontainer`s for CUDA 12.5 ([#224](https://github.com/rapidsai/cuvs/pull/224)) [@jakirkham](https://github.com/jakirkham)
+- Fix a CAGRA graph opt bug ([#192](https://github.com/rapidsai/cuvs/pull/192)) [@enp1s0](https://github.com/enp1s0)
+
+## 📖 Documentation
+
+- fix library name in docs (&#39;cuvs&#39; not &#39;pycuvs&#39;) ([#193](https://github.com/rapidsai/cuvs/pull/193)) [@jameslamb](https://github.com/jameslamb)
+
+## 🚀 New Features
+
+- Add cuvs_bench python folder, config files and constraints ([#244](https://github.com/rapidsai/cuvs/pull/244)) [@dantegd](https://github.com/dantegd)
+- Add MST optimization to guarantee the connectivity of CAGRA graphs ([#237](https://github.com/rapidsai/cuvs/pull/237)) [@anaruse](https://github.com/anaruse)
+- Moving over C++ API of CAGRA+hnswlib from RAFT ([#229](https://github.com/rapidsai/cuvs/pull/229)) [@divyegala](https://github.com/divyegala)
+- [FEA] expose python &amp; C API for prefiltered brute force ([#174](https://github.com/rapidsai/cuvs/pull/174)) [@rhdong](https://github.com/rhdong)
+- CAGRA new vector addition ([#151](https://github.com/rapidsai/cuvs/pull/151)) [@enp1s0](https://github.com/enp1s0)
+
+## 🛠️ Improvements
+
+- [Opt] introduce the `masked_matmul` to prefiltered brute force. ([#251](https://github.com/rapidsai/cuvs/pull/251)) [@rhdong](https://github.com/rhdong)
+- Add more info to ANN_BENCH context ([#248](https://github.com/rapidsai/cuvs/pull/248)) [@achirkin](https://github.com/achirkin)
+- split up CUDA-suffixed dependencies in dependencies.yaml ([#247](https://github.com/rapidsai/cuvs/pull/247)) [@jameslamb](https://github.com/jameslamb)
+- Fix pinning to a different RAFT tag ([#235](https://github.com/rapidsai/cuvs/pull/235)) [@benfred](https://github.com/benfred)
+- Use workflow branch 24.08 again ([#234](https://github.com/rapidsai/cuvs/pull/234)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- chore: update search_plan.cuh ([#232](https://github.com/rapidsai/cuvs/pull/232)) [@eltociear](https://github.com/eltociear)
+- Enable kernel &amp; memcpy overlapping in IVF index building ([#230](https://github.com/rapidsai/cuvs/pull/230)) [@abc99lr](https://github.com/abc99lr)
+- CAGRA: reduce argument count in select_and_run() kernel wrappers ([#227](https://github.com/rapidsai/cuvs/pull/227)) [@achirkin](https://github.com/achirkin)
+- Mark the rust brute force unittest as flaky ([#226](https://github.com/rapidsai/cuvs/pull/226)) [@benfred](https://github.com/benfred)
+- Add python bindings for ivf-* extend functions ([#220](https://github.com/rapidsai/cuvs/pull/220)) [@benfred](https://github.com/benfred)
+- Build and test with CUDA 12.5.1 ([#219](https://github.com/rapidsai/cuvs/pull/219)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Add col-major support for brute force knn ([#217](https://github.com/rapidsai/cuvs/pull/217)) [@benfred](https://github.com/benfred)
+- Add CUDA_STATIC_MATH_LIBRARIES ([#216](https://github.com/rapidsai/cuvs/pull/216)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- skip CMake 3.30.0 ([#214](https://github.com/rapidsai/cuvs/pull/214)) [@jameslamb](https://github.com/jameslamb)
+- Complete Migration of IVF Helpers / Features from RAFT ([#213](https://github.com/rapidsai/cuvs/pull/213)) [@tarang-jain](https://github.com/tarang-jain)
+- Use verify-alpha-spec hook ([#209](https://github.com/rapidsai/cuvs/pull/209)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Fixes for publishing rust package to crates.io ([#207](https://github.com/rapidsai/cuvs/pull/207)) [@benfred](https://github.com/benfred)
+- Add rust example ([#206](https://github.com/rapidsai/cuvs/pull/206)) [@benfred](https://github.com/benfred)
+- Adding IVF examples ([#203](https://github.com/rapidsai/cuvs/pull/203)) [@cjnolet](https://github.com/cjnolet)
+- Fix compilation error when _CLK_BREAKDOWN is defined in cagra. ([#202](https://github.com/rapidsai/cuvs/pull/202)) [@jiangyinzuo](https://github.com/jiangyinzuo)
+- DOC: update notebook link ([#191](https://github.com/rapidsai/cuvs/pull/191)) [@raybellwaves](https://github.com/raybellwaves)
+- Change cagra.build_index to cagra.build ([#187](https://github.com/rapidsai/cuvs/pull/187)) [@benfred](https://github.com/benfred)
+- Add python serialization API&#39;s for ivf-pq and ivf_flat ([#186](https://github.com/rapidsai/cuvs/pull/186)) [@benfred](https://github.com/benfred)
+- resolve dependency-file-generator warning, rapids-build-backend followup ([#185](https://github.com/rapidsai/cuvs/pull/185)) [@jameslamb](https://github.com/jameslamb)
+- Adopt CI/packaging codeowners ([#183](https://github.com/rapidsai/cuvs/pull/183)) [@raydouglass](https://github.com/raydouglass)
+- Scaling workspace resources ([#181](https://github.com/rapidsai/cuvs/pull/181)) [@achirkin](https://github.com/achirkin)
+- Remove text builds of documentation ([#180](https://github.com/rapidsai/cuvs/pull/180)) [@vyasr](https://github.com/vyasr)
+- Add refine to the Python and C api&#39;s ([#175](https://github.com/rapidsai/cuvs/pull/175)) [@benfred](https://github.com/benfred)
+- Allow serialization on streams ([#173](https://github.com/rapidsai/cuvs/pull/173)) [@benfred](https://github.com/benfred)
+- Forward-merge branch-24.06 into branch-24.08 ([#169](https://github.com/rapidsai/cuvs/pull/169)) [@benfred](https://github.com/benfred)
+- Use rapids-build-backend ([#145](https://github.com/rapidsai/cuvs/pull/145)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- ANN_BENCH ([#130](https://github.com/rapidsai/cuvs/pull/130)) [@achirkin](https://github.com/achirkin)
+- Enable random subsampling ([#122](https://github.com/rapidsai/cuvs/pull/122)) [@tfeher](https://github.com/tfeher)
+
 # cuvs 24.06.00 (5 Jun 2024)
 
 ## 🐛 Bug Fixes
@@ -56,5 +119,3 @@
 - Add Cagra-Q compression to the python and rust api&#39;s ([#68](https://github.com/rapidsai/cuvs/pull/68)) [@benfred](https://github.com/benfred)
 - ConfigureCUDA.cmake now sets CUVS_ prefixed variables ([#66](https://github.com/rapidsai/cuvs/pull/66)) [@robertmaynard](https://github.com/robertmaynard)
 - Enable all tests for `arm` jobs ([#63](https://github.com/rapidsai/cuvs/pull/63)) [@galipremsagar](https://github.com/galipremsagar)
-
-

From 0e2270fbb6606ae51a4b826493b5f6ac0682a9ab Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Wed, 7 Aug 2024 22:04:22 -0500
Subject: [PATCH 123/623] Use tool.scikit-build.cmake.version, set
 scikit-build-core minimum-version (#280)

Contributes to https://github.com/rapidsai/build-planning/issues/58.

`scikit-build-core==0.10.0` was released today (https://github.com/scikit-build/scikit-build-core/releases/tag/v0.10.0), and wheel-building configurations across RAPIDS are incompatible with it.

This proposes upgrading to that version and fixing configuration here in a way that:

* is compatible with that new `scikit-build-core` version
* takes advantage of the forward-compatibility mechanism (`minimum-version`) that `scikit-build-core` provides, to reduce the risk of needing to do this again in the future

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cuvs/pull/280
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/all_cuda-125_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-125_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs/meta.yaml                      | 2 +-
 dependencies.yaml                                 | 4 ++--
 python/cuvs/pyproject.toml                        | 5 +++--
 7 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 86b0b80ef3..54866d0e53 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -51,7 +51,7 @@ dependencies:
 - recommonmark
 - rmm==24.10.*,>=0.0.0a0
 - rust
-- scikit-build-core>=0.7.0
+- scikit-build-core>=0.10.0
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 2aeff85562..b29fe1b50e 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -51,7 +51,7 @@ dependencies:
 - recommonmark
 - rmm==24.10.*,>=0.0.0a0
 - rust
-- scikit-build-core>=0.7.0
+- scikit-build-core>=0.10.0
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index 461de0d30c..c6deb93f8b 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -47,7 +47,7 @@ dependencies:
 - recommonmark
 - rmm==24.10.*,>=0.0.0a0
 - rust
-- scikit-build-core>=0.7.0
+- scikit-build-core>=0.10.0
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index e1cadb7fef..538fdf08be 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -47,7 +47,7 @@ dependencies:
 - recommonmark
 - rmm==24.10.*,>=0.0.0a0
 - rust
-- scikit-build-core>=0.7.0
+- scikit-build-core>=0.10.0
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index b0f0c3f396..df6cfeec68 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -56,7 +56,7 @@ requirements:
     - python x.x
     - rmm ={{ minor_version }}
     - rapids-build-backend>=0.3.0,<0.4.0.dev0
-    - scikit-build-core >=0.7.0
+    - scikit-build-core >=0.10.0
     - setuptools
   run:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
diff --git a/dependencies.yaml b/dependencies.yaml
index 9bb8682bf1..98c9043aa9 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -103,10 +103,10 @@ dependencies:
           - rapids-build-backend>=0.3.0,<0.4.0.dev0
       - output_types: [conda]
         packages:
-          - scikit-build-core>=0.7.0
+          - scikit-build-core>=0.10.0
       - output_types: [requirements, pyproject]
         packages:
-          - scikit-build-core[pyproject]>=0.7.0
+          - scikit-build-core[pyproject]>=0.10.0
   rapids_build:
     common:
       - output_types: [conda, requirements, pyproject]
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 6bee508332..cc2aa6a7fe 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -16,7 +16,7 @@
 
 requires = [
     "rapids-build-backend>=0.3.0,<0.4.0.dev0",
-    "scikit-build-core[pyproject]>=0.7.0",
+    "scikit-build-core[pyproject]>=0.10.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "rapids_build_backend.build"
 
@@ -106,7 +106,8 @@ skip = [
 [tool.scikit-build]
 build-dir = "build/{wheel_tag}"
 cmake.build-type = "Release"
-cmake.minimum-version = "3.26.4"
+cmake.version = "CMakeLists.txt"
+minimum-version = "build-system.requires"
 ninja.make-fallback = true
 sdist.reproducible = true
 wheel.packages = ["cuvs"]

From aadf81079c41428ce7bdcce04c165cc52cbfc91b Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 8 Aug 2024 07:56:21 -0700
Subject: [PATCH 124/623] Fix python ivf-pq for int8/uint8 dtypes (#271)

The ivf-pq python bindings weren't working appropriately for int8/uint8 dtypes. Fix.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/271
---
 cpp/src/neighbors/ivf_pq_c.cpp       | 58 +++++++++++++++++-----------
 python/cuvs/cuvs/test/test_ivf_pq.py |  9 +++++
 2 files changed, 44 insertions(+), 23 deletions(-)

diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 256d760e67..0afe9356d1 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -30,7 +30,7 @@
 
 namespace {
 
-template <typename IdxT>
+template <typename T, typename IdxT>
 void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor* dataset_tensor)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
@@ -55,7 +55,7 @@ void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor*
 
   auto index = new cuvs::neighbors::ivf_pq::index<IdxT>(*res_ptr, build_params, dim);
 
-  using mdspan_type = raft::device_matrix_view<float const, IdxT, raft::row_major>;
+  using mdspan_type = raft::device_matrix_view<const T, IdxT, raft::row_major>;
   auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
 
   cuvs::neighbors::ivf_pq::build(*res_ptr, build_params, mds, index);
@@ -63,7 +63,7 @@ void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor*
   return index;
 }
 
-template <typename IdxT>
+template <typename T, typename IdxT>
 void _search(cuvsResources_t res,
              cuvsIvfPqSearchParams params,
              cuvsIvfPqIndex index,
@@ -80,7 +80,7 @@ void _search(cuvsResources_t res,
   search_params.internal_distance_dtype  = params.internal_distance_dtype;
   search_params.preferred_shmem_carveout = params.preferred_shmem_carveout;
 
-  using queries_mdspan_type   = raft::device_matrix_view<float const, IdxT, raft::row_major>;
+  using queries_mdspan_type   = raft::device_matrix_view<const T, IdxT, raft::row_major>;
   using neighbors_mdspan_type = raft::device_matrix_view<IdxT, IdxT, raft::row_major>;
   using distances_mdspan_type = raft::device_matrix_view<float, IdxT, raft::row_major>;
   auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
@@ -108,7 +108,7 @@ void* _deserialize(cuvsResources_t res, const char* filename)
   return index;
 }
 
-template <typename IdxT>
+template <typename T, typename IdxT>
 void _extend(cuvsResources_t res,
              DLManagedTensor* new_vectors,
              DLManagedTensor* new_indices,
@@ -116,7 +116,7 @@ void _extend(cuvsResources_t res,
 {
   auto res_ptr              = reinterpret_cast<raft::resources*>(res);
   auto index_ptr            = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
-  using vectors_mdspan_type = raft::device_matrix_view<float const, IdxT, raft::row_major>;
+  using vectors_mdspan_type = raft::device_matrix_view<const T, IdxT, raft::row_major>;
   using indices_mdspan_type = raft::device_vector_view<IdxT, IdxT>;
 
   auto vectors_mds = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors);
@@ -148,14 +148,19 @@ extern "C" cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
                                       cuvsIvfPqIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto dataset = dataset_tensor->dl_tensor;
-
-    if ((dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) ||
-        (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) ||
-        (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8)) {
-      index->addr = reinterpret_cast<uintptr_t>(_build<int64_t>(res, *params, dataset_tensor));
-      index->dtype.code = dataset.dtype.code;
-      index->dtype.bits = dataset.dtype.bits;
+    auto dataset      = dataset_tensor->dl_tensor;
+    index->dtype.code = dataset.dtype.code;
+    index->dtype.bits = dataset.dtype.bits;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<float, int64_t>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<int8_t, int64_t>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<uint8_t, int64_t>(res, *params, dataset_tensor));
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
@@ -189,11 +194,15 @@ extern "C" cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
                  "distances should be of type float32");
 
     auto index = *index_c_ptr;
-
-    if ((queries.dtype.code == kDLFloat && queries.dtype.bits == 32) ||
-        (queries.dtype.code == kDLInt && queries.dtype.bits == 8) ||
-        (queries.dtype.code == kDLUInt && queries.dtype.bits == 8)) {
-      _search<int64_t>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
+      _search<float, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
+      _search<int8_t, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
+      _search<uint8_t, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
     } else {
       RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
                 queries.dtype.code,
@@ -262,10 +271,13 @@ extern "C" cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
 {
   return cuvs::core::translate_exceptions([=] {
     auto vectors = new_vectors->dl_tensor;
-    if ((vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) ||
-        (vectors.dtype.code == kDLInt && vectors.dtype.bits == 8) ||
-        (vectors.dtype.code == kDLUInt && vectors.dtype.bits == 8)) {
-      _extend<int64_t>(res, new_vectors, new_indices, *index);
+
+    if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
+      _extend<float, int64_t>(res, new_vectors, new_indices, *index);
+    } else if (vectors.dtype.code == kDLInt && vectors.dtype.bits == 8) {
+      _extend<int8_t, int64_t>(res, new_vectors, new_indices, *index);
+    } else if (vectors.dtype.code == kDLUInt && vectors.dtype.bits == 8) {
+      _extend<uint8_t, int64_t>(res, new_vectors, new_indices, *index);
     } else {
       RAFT_FAIL("Unsupported index dtype: %d and bits: %d", vectors.dtype.code, vectors.dtype.bits);
     }
diff --git a/python/cuvs/cuvs/test/test_ivf_pq.py b/python/cuvs/cuvs/test/test_ivf_pq.py
index 8af389a93f..dff65b18cd 100644
--- a/python/cuvs/cuvs/test/test_ivf_pq.py
+++ b/python/cuvs/cuvs/test/test_ivf_pq.py
@@ -192,3 +192,12 @@ def test_extend(dtype):
         dtype=dtype,
         add_data_on_build=False,
     )
+
+
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+def test_ivf_pq_dtype(inplace, dtype):
+    run_ivf_pq_build_search_test(
+        dtype=dtype,
+        inplace=inplace,
+    )

From e74ab05f5e40b6533abd99ea5bb81752ca2d8a64 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 8 Aug 2024 10:18:27 -0500
Subject: [PATCH 125/623] Improve update-version.sh (#282)

This PR improves `update-version.sh` by clarifying suffix handling and ucxx/ucx-py version handling.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/282
---
 ci/release/update-version.sh | 29 ++++++++++++++---------------
 1 file changed, 14 insertions(+), 15 deletions(-)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 08bc319562..6d7d022c2d 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -18,17 +18,16 @@ CURRENT_MINOR=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[2]}')
 CURRENT_PATCH=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[3]}')
 CURRENT_SHORT_TAG=${CURRENT_MAJOR}.${CURRENT_MINOR}
 
-#Get <major>.<minor> for next version
+# Get <major>.<minor> for next version
 NEXT_MAJOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[1]}')
 NEXT_MINOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[2]}')
 NEXT_PATCH=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[3]}')
 NEXT_SHORT_TAG=${NEXT_MAJOR}.${NEXT_MINOR}
-NEXT_UCX_PY_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/${NEXT_SHORT_TAG})"
-NEXT_UCX_PY_VERSION="${NEXT_UCX_PY_SHORT_TAG}.*"
+NEXT_UCXX_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/${NEXT_SHORT_TAG})"
 
 # Need to distutils-normalize the original version
 NEXT_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${NEXT_SHORT_TAG}'))")
-NEXT_UCX_PY_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${NEXT_UCX_PY_SHORT_TAG}'))")
+NEXT_UCXX_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${NEXT_UCXX_SHORT_TAG}'))")
 
 echo "Preparing release $CURRENT_TAG => $NEXT_FULL_TAG"
 
@@ -45,28 +44,28 @@ echo "${NEXT_FULL_TAG}" > VERSION
 DEPENDENCIES=(
   dask-cuda
   cuvs
-  cuvs-cu11
-  cuvs-cu12
   pylibraft
-  pylibraft-cu11
-  pylibraft-cu12
   rmm
-  rmm-cu11
-  rmm-cu12
   rapids-dask-dependency
-  # ucx-py is handled separately below
+)
+UCXX_DEPENDENCIES=(
+  ucx-py
 )
 for FILE in dependencies.yaml conda/environments/*.yaml; do
   for DEP in "${DEPENDENCIES[@]}"; do
-    sed_runner "/-.* ${DEP}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}\.*,>=0.0.0a0/g" ${FILE};
+    sed_runner "/-.* ${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0/g" "${FILE}"
+  done
+  for DEP in "${UCXX_DEPENDENCIES[@]}"; do
+    sed_runner "/-.* ${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}==/ s/==.*/==${NEXT_UCXX_SHORT_TAG_PEP440}.*,>=0.0.0a0/g" "${FILE}"
   done
-  sed_runner "/-.* ucx-py==/ s/==.*/==${NEXT_UCX_PY_SHORT_TAG_PEP440}\.*,>=0.0.0a0/g" ${FILE};
 done
 for FILE in python/*/pyproject.toml; do
   for DEP in "${DEPENDENCIES[@]}"; do
-    sed_runner "/\"${DEP}==/ s/==.*\"/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" ${FILE}
+    sed_runner "/\"${DEP}==/ s/==.*\"/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" "${FILE}"
+  done
+  for DEP in "${UCXX_DEPENDENCIES[@]}"; do
+    sed_runner "/\"${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}==/ s/==.*\"/==${NEXT_UCXX_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" "${FILE}"
   done
-  sed_runner "/\"ucx-py==/ s/==.*\"/==${NEXT_UCX_PY_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" ${FILE}
 done
 
 for FILE in .github/workflows/*.yaml; do

From bf281cac1da99d8cf8f5abffad861a52f771f449 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Thu, 8 Aug 2024 12:23:15 -0400
Subject: [PATCH 126/623] Update pre-commit hooks (#283)

This PR updates pre-commit hooks to the latest versions that are supported without causing style check errors.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/283
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c6960af6df..592693adb6 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -97,7 +97,7 @@ repos:
         hooks:
               - id: check-json
       - repo: https://github.com/rapidsai/pre-commit-hooks
-        rev: v0.2.0
+        rev: v0.3.1
         hooks:
           - id: verify-copyright
             files: |

From 829ae16c5862a985a1077b3c57a71e71ab9895ba Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Wed, 14 Aug 2024 21:59:42 +0200
Subject: [PATCH 127/623] [FEA] Support for Cosine distance in IVF-Flat (#179)

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/179
---
 cpp/include/cuvs/cluster/kmeans.hpp           |   1 +
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |  72 +++++
 cpp/src/cluster/detail/kmeans_balanced.cuh    | 115 ++++++--
 cpp/src/cluster/kmeans_balanced.cuh           |  47 ++--
 cpp/src/neighbors/ivf_common.cuh              |   1 +
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |  45 ++-
 .../ivf_flat/ivf_flat_build_float_int64_t.cu  |  69 -----
 .../ivf_flat/ivf_flat_build_int8_t_int64_t.cu |  69 -----
 .../ivf_flat_build_uint8_t_int64_t.cu         |  69 -----
 .../ivf_flat/ivf_flat_extend_float_int64_t.cu |  71 -----
 .../ivf_flat_extend_int8_t_int64_t.cu         |  71 -----
 .../ivf_flat_extend_uint8_t_int64_t.cu        |  71 -----
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    | 263 ++++++++++++++----
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |  32 ++-
 cpp/src/neighbors/ivf_flat_index.cpp          |   1 +
 cpp/test/neighbors/ann_ivf_flat.cuh           |  56 ++++
 cpp/test/neighbors/naive_knn.cuh              |  14 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |   4 +-
 python/cuvs/cuvs/test/test_ivf_flat.py        |   3 +-
 19 files changed, 544 insertions(+), 530 deletions(-)
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu

diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index c6fe4d0465..75205fa4f1 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -120,6 +120,7 @@ struct params : base_params {
  * Simple object to specify hyper-parameters to the balanced k-means algorithm.
  *
  * The following metrics are currently supported in k-means balanced:
+ *  - CosineExpanded
  *  - InnerProduct
  *  - L2Expanded
  *  - L2SqrtExpanded
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index 918fef5aff..44502f942f 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -304,6 +304,12 @@ struct index : cuvs::neighbors::index {
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -327,6 +333,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -351,6 +363,12 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -374,6 +392,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -398,6 +422,12 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -421,6 +451,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -445,6 +481,12 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
@@ -475,6 +517,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
@@ -506,6 +554,12 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
@@ -536,6 +590,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
@@ -567,6 +627,12 @@ void build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
@@ -597,6 +663,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index a09b17532b..34bb22e85b 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -33,6 +33,7 @@
 #include <raft/linalg/add.cuh>
 #include <raft/linalg/gemm.cuh>
 #include <raft/linalg/map.cuh>
+#include <raft/linalg/matrix_vector.cuh>
 #include <raft/linalg/matrix_vector_op.cuh>
 #include <raft/linalg/norm.cuh>
 #include <raft/linalg/normalize.cuh>
@@ -141,6 +142,53 @@ inline std::enable_if_t<std::is_floating_point_v<MathT>> predict_core(
                         raft::compose_op<raft::cast_op<LabelT>, raft::key_op>());
       break;
     }
+    case cuvs::distance::DistanceType::CosineExpanded: {
+      auto workspace = raft::make_device_mdarray<char, IdxT>(
+        handle, mr, raft::make_extents<IdxT>((sizeof(int)) * n_rows));
+
+      auto minClusterAndDistance = raft::make_device_mdarray<raft::KeyValuePair<IdxT, MathT>, IdxT>(
+        handle, mr, raft::make_extents<IdxT>(n_rows));
+      raft::KeyValuePair<IdxT, MathT> initial_value(0, std::numeric_limits<MathT>::max());
+      thrust::fill(raft::resource::get_thrust_policy(handle),
+                   minClusterAndDistance.data_handle(),
+                   minClusterAndDistance.data_handle() + minClusterAndDistance.size(),
+                   initial_value);
+
+      auto centroidsNorm =
+        raft::make_device_mdarray<MathT, IdxT>(handle, mr, raft::make_extents<IdxT>(n_clusters));
+      raft::linalg::rowNorm<MathT, IdxT>(centroidsNorm.data_handle(),
+                                         centers,
+                                         dim,
+                                         n_clusters,
+                                         raft::linalg::L2Norm,
+                                         true,
+                                         stream,
+                                         raft::sqrt_op{});
+
+      cuvs::distance::fusedDistanceNNMinReduce<MathT, raft::KeyValuePair<IdxT, MathT>, IdxT>(
+        minClusterAndDistance.data_handle(),
+        dataset,
+        centers,
+        dataset_norm,
+        centroidsNorm.data_handle(),
+        n_rows,
+        n_clusters,
+        dim,
+        (void*)workspace.data_handle(),
+        false,
+        false,
+        true,
+        params.metric,
+        0.0f,
+        stream);
+      // Copy keys to output labels
+      thrust::transform(raft::resource::get_thrust_policy(handle),
+                        minClusterAndDistance.data_handle(),
+                        minClusterAndDistance.data_handle() + n_rows,
+                        labels,
+                        raft::compose_op<raft::cast_op<LabelT>, raft::key_op>());
+      break;
+    }
     case cuvs::distance::DistanceType::InnerProduct: {
       // TODO: pass buffer
       rmm::device_uvector<MathT> distances(n_rows * n_clusters, stream, mr);
@@ -320,13 +368,14 @@ void calc_centers_and_sizes(const raft::resources& handle,
 }
 
 /** Computes the L2 norm of the dataset, converting to MathT if necessary */
-template <typename T, typename MathT, typename IdxT, typename MappingOpT>
+template <typename T, typename MathT, typename IdxT, typename MappingOpT, typename FinOpT>
 void compute_norm(const raft::resources& handle,
                   MathT* dataset_norm,
                   const T* dataset,
                   IdxT dim,
                   IdxT n_rows,
                   MappingOpT mapping_op,
+                  FinOpT norm_fin_op,
                   std::optional<rmm::device_async_resource_ref> mr = std::nullopt)
 {
   raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("compute_norm");
@@ -347,7 +396,7 @@ void compute_norm(const raft::resources& handle,
   }
 
   raft::linalg::rowNorm<MathT, IdxT>(
-    dataset_norm, dataset_ptr, dim, n_rows, raft::linalg::L2Norm, true, stream);
+    dataset_norm, dataset_ptr, dim, n_rows, raft::linalg::L2Norm, true, stream, norm_fin_op);
 }
 
 /**
@@ -394,7 +443,8 @@ void predict(const raft::resources& handle,
     std::is_same_v<T, MathT> ? 0 : max_minibatch_size * dim, stream, mem_res);
   bool need_compute_norm =
     dataset_norm == nullptr && (params.metric == cuvs::distance::DistanceType::L2Expanded ||
-                                params.metric == cuvs::distance::DistanceType::L2SqrtExpanded);
+                                params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                                params.metric == cuvs::distance::DistanceType::CosineExpanded);
   rmm::device_uvector<MathT> cur_dataset_norm(
     need_compute_norm ? max_minibatch_size : 0, stream, mem_res);
   const MathT* dataset_norm_ptr = nullptr;
@@ -411,8 +461,24 @@ void predict(const raft::resources& handle,
 
     // Compute the norm now if it hasn't been pre-computed.
     if (need_compute_norm) {
-      compute_norm(
-        handle, cur_dataset_norm.data(), cur_dataset_ptr, dim, minibatch_size, mapping_op, mem_res);
+      if (params.metric == cuvs::distance::DistanceType::CosineExpanded)
+        compute_norm(handle,
+                     cur_dataset_norm.data(),
+                     cur_dataset_ptr,
+                     dim,
+                     minibatch_size,
+                     mapping_op,
+                     raft::sqrt_op{},
+                     mr);
+      else
+        compute_norm(handle,
+                     cur_dataset_norm.data(),
+                     cur_dataset_ptr,
+                     dim,
+                     minibatch_size,
+                     mapping_op,
+                     raft::identity_op{},
+                     mr);
       dataset_norm_ptr = cur_dataset_norm.data();
     } else if (dataset_norm != nullptr) {
       dataset_norm_ptr = dataset_norm + offset;
@@ -904,7 +970,8 @@ auto build_fine_clusters(const raft::resources& handle,
     cub::TransformInputIterator<MathT, MappingOpT, const T*> mapping_itr(dataset_mptr, mapping_op);
     raft::matrix::gather(mapping_itr, dim, n_rows, mc_trainset_ids, k, mc_trainset, stream);
     if (params.metric == cuvs::distance::DistanceType::L2Expanded ||
-        params.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+        params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+        params.metric == cuvs::distance::DistanceType::CosineExpanded) {
       thrust::gather(raft::resource::get_thrust_policy(handle),
                      mc_trainset_ids,
                      mc_trainset_ids + k,
@@ -963,7 +1030,8 @@ void build_hierarchical(const raft::resources& handle,
                         IdxT n_rows,
                         MathT* cluster_centers,
                         IdxT n_clusters,
-                        MappingOpT mapping_op)
+                        MappingOpT mapping_op,
+                        const MathT* dataset_norm = nullptr)
 {
   auto stream  = raft::resource::get_cuda_stream(handle);
   using LabelT = uint32_t;
@@ -980,21 +1048,32 @@ void build_hierarchical(const raft::resources& handle,
   auto [max_minibatch_size, mem_per_row] =
     calc_minibatch_size<MathT>(n_clusters, n_rows, dim, params.metric, std::is_same_v<T, MathT>);
 
-  // Precompute the L2 norm of the dataset if relevant.
-  const MathT* dataset_norm = nullptr;
+  // Precompute the L2 norm of the dataset if relevant and not yet computed.
   rmm::device_uvector<MathT> dataset_norm_buf(0, stream, device_memory);
-  if (params.metric == cuvs::distance::DistanceType::L2Expanded ||
-      params.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+  if (dataset_norm == nullptr && (params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                                  params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                                  params.metric == cuvs::distance::DistanceType::CosineExpanded)) {
     dataset_norm_buf.resize(n_rows, stream);
     for (IdxT offset = 0; offset < n_rows; offset += max_minibatch_size) {
       IdxT minibatch_size = std::min<IdxT>(max_minibatch_size, n_rows - offset);
-      compute_norm(handle,
-                   dataset_norm_buf.data() + offset,
-                   dataset + dim * offset,
-                   dim,
-                   minibatch_size,
-                   mapping_op,
-                   device_memory);
+      if (params.metric == cuvs::distance::DistanceType::CosineExpanded)
+        compute_norm(handle,
+                     dataset_norm_buf.data() + offset,
+                     dataset + dim * offset,
+                     dim,
+                     minibatch_size,
+                     mapping_op,
+                     raft::sqrt_op{},
+                     device_memory);
+      else
+        compute_norm(handle,
+                     dataset_norm_buf.data() + offset,
+                     dataset + dim * offset,
+                     dim,
+                     minibatch_size,
+                     mapping_op,
+                     raft::identity_op{},
+                     device_memory);
     }
     dataset_norm = (const MathT*)dataset_norm_buf.data();
   }
diff --git a/cpp/src/cluster/kmeans_balanced.cuh b/cpp/src/cluster/kmeans_balanced.cuh
index 040d17b369..306989891e 100644
--- a/cpp/src/cluster/kmeans_balanced.cuh
+++ b/cpp/src/cluster/kmeans_balanced.cuh
@@ -71,13 +71,15 @@ namespace cuvs::cluster::kmeans_balanced {
  * @param[out] centroids  The generated centroids [dim = n_clusters x n_features]
  * @param[in]  mapping_op (optional) Functor to convert from the input datatype to the arithmetic
  *                        datatype. If DataT == MathT, this must be the identity.
+ * @param[in]  X_norm        (optional) Dataset's row norms [dim = n_samples]
  */
 template <typename DataT, typename MathT, typename IndexT, typename MappingOpT = raft::identity_op>
 void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
          raft::device_matrix_view<const DataT, IndexT> X,
          raft::device_matrix_view<MathT, IndexT> centroids,
-         MappingOpT mapping_op = raft::identity_op())
+         MappingOpT mapping_op                                               = raft::identity_op(),
+         std::optional<raft::device_vector_view<const MathT, IndexT>> X_norm = std::nullopt)
 {
   RAFT_EXPECTS(X.extent(1) == centroids.extent(1),
                "Number of features in dataset and centroids are different");
@@ -88,14 +90,16 @@ void fit(const raft::resources& handle,
                "The number of centroids must be strictly positive and cannot exceed the number of "
                "points in the training dataset.");
 
-  cuvs::cluster::kmeans::detail::build_hierarchical(handle,
-                                                    params,
-                                                    X.extent(1),
-                                                    X.data_handle(),
-                                                    X.extent(0),
-                                                    centroids.data_handle(),
-                                                    centroids.extent(0),
-                                                    mapping_op);
+  cuvs::cluster::kmeans::detail::build_hierarchical(
+    handle,
+    params,
+    X.extent(1),
+    X.data_handle(),
+    X.extent(0),
+    centroids.data_handle(),
+    centroids.extent(0),
+    mapping_op,
+    X_norm.has_value() ? X_norm.value().data_handle() : nullptr);
 }
 
 /**
@@ -125,6 +129,7 @@ void fit(const raft::resources& handle,
  * @param[out] labels     The output labels [dim = n_samples]
  * @param[in]  mapping_op (optional) Functor to convert from the input datatype to the arithmetic
  *                        datatype. If DataT == MathT, this must be the identity.
+ * @param[in]  X_norm     (optional) Dataset's row norms [dim = n_samples]
  */
 template <typename DataT,
           typename MathT,
@@ -136,7 +141,8 @@ void predict(const raft::resources& handle,
              raft::device_matrix_view<const DataT, IndexT> X,
              raft::device_matrix_view<const MathT, IndexT> centroids,
              raft::device_vector_view<LabelT, IndexT> labels,
-             MappingOpT mapping_op = raft::identity_op())
+             MappingOpT mapping_op = raft::identity_op(),
+             std::optional<raft::device_vector_view<const MathT, IndexT>> X_norm = std::nullopt)
 {
   RAFT_EXPECTS(X.extent(0) == labels.extent(0),
                "Number of rows in dataset and labels are different");
@@ -149,15 +155,18 @@ void predict(const raft::resources& handle,
                  static_cast<uint64_t>(std::numeric_limits<LabelT>::max()),
                "The chosen label type cannot represent all cluster labels");
 
-  cuvs::cluster::kmeans::detail::predict(handle,
-                                         params,
-                                         centroids.data_handle(),
-                                         centroids.extent(0),
-                                         X.extent(1),
-                                         X.data_handle(),
-                                         X.extent(0),
-                                         labels.data_handle(),
-                                         mapping_op);
+  cuvs::cluster::kmeans::detail::predict(
+    handle,
+    params,
+    centroids.data_handle(),
+    centroids.extent(0),
+    X.extent(1),
+    X.data_handle(),
+    X.extent(0),
+    labels.data_handle(),
+    mapping_op,
+    raft::resource::get_workspace_resource(handle),
+    X_norm.has_value() ? X_norm.value().data_handle() : nullptr);
 }
 
 /**
diff --git a/cpp/src/neighbors/ivf_common.cuh b/cpp/src/neighbors/ivf_common.cuh
index 60d43bed6c..fb73fb8a90 100644
--- a/cpp/src/neighbors/ivf_common.cuh
+++ b/cpp/src/neighbors/ivf_common.cuh
@@ -254,6 +254,7 @@ void postprocess_distances(ScoreOutT* out,      // [n_queries, topk]
         raft::linalg::unaryOp(out, in, len, raft::sqrt_op{}, stream);
       }
     } break;
+    case distance::DistanceType::CosineExpanded:
     case distance::DistanceType::InnerProduct: {
       float factor = (account_for_max_close ? -1.0 : 1.0) * scaling_factor * scaling_factor;
       if (factor != 1.0) {
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index e8df3e3d6a..fb110d8104 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -335,6 +335,37 @@ void extend(raft::resources const& handle,
   if (!index->center_norms().has_value()) {
     index->allocate_center_norms(handle);
     if (index->center_norms().has_value()) {
+      if (index->metric() == cuvs::distance::DistanceType::CosineExpanded) {
+        raft::linalg::rowNorm(index->center_norms()->data_handle(),
+                              index->centers().data_handle(),
+                              dim,
+                              n_lists,
+                              raft::linalg::L2Norm,
+                              true,
+                              stream,
+                              raft::sqrt_op{});
+      } else {
+        raft::linalg::rowNorm(index->center_norms()->data_handle(),
+                              index->centers().data_handle(),
+                              dim,
+                              n_lists,
+                              raft::linalg::L2Norm,
+                              true,
+                              stream);
+      }
+      RAFT_LOG_TRACE_VEC(index->center_norms()->data_handle(), std::min<uint32_t>(dim, 20));
+    }
+  } else if (index->center_norms().has_value() && index->adaptive_centers()) {
+    if (index->metric() == cuvs::distance::DistanceType::CosineExpanded) {
+      raft::linalg::rowNorm(index->center_norms()->data_handle(),
+                            index->centers().data_handle(),
+                            dim,
+                            n_lists,
+                            raft::linalg::L2Norm,
+                            true,
+                            stream,
+                            raft::sqrt_op{});
+    } else {
       raft::linalg::rowNorm(index->center_norms()->data_handle(),
                             index->centers().data_handle(),
                             dim,
@@ -342,16 +373,7 @@ void extend(raft::resources const& handle,
                             raft::linalg::L2Norm,
                             true,
                             stream);
-      RAFT_LOG_TRACE_VEC(index->center_norms()->data_handle(), std::min<uint32_t>(dim, 20));
     }
-  } else if (index->center_norms().has_value() && index->adaptive_centers()) {
-    raft::linalg::rowNorm(index->center_norms()->data_handle(),
-                          index->centers().data_handle(),
-                          dim,
-                          n_lists,
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
     RAFT_LOG_TRACE_VEC(index->center_norms()->data_handle(), std::min<uint32_t>(dim, 20));
   }
 }
@@ -384,7 +406,8 @@ inline auto build(raft::resources const& handle,
                 "unsupported data type");
   RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
   RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
-
+  RAFT_EXPECTS(params.metric != cuvs::distance::DistanceType::CosineExpanded || dim > 1,
+               "Cosine metric requires more than one dim");
   index<T, IdxT> index(handle, params, dim);
   utils::memzero(
     index.accum_sorted_sizes().data_handle(), index.accum_sorted_sizes().size(), stream);
@@ -414,7 +437,7 @@ inline auto build(raft::resources const& handle,
       index.centers().data_handle(), index.n_lists(), index.dim());
     cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = params.kmeans_n_iters;
-    kmeans_params.metric  = static_cast<cuvs::distance::DistanceType>(index.metric());
+    kmeans_params.metric  = index.metric();
     cuvs::cluster::kmeans_balanced::fit(
       handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
   }
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu
deleted file mode 100644
index 56bb71094f..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_float_int64_t.cu
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-
-#include "ivf_flat_build.cuh"
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                            \
-  auto build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
-  }                                                                                  \
-                                                                                     \
-  void build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,       \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
-  {                                                                                  \
-    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
-  }                                                                                  \
-  auto build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)         \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
-  }                                                                                  \
-                                                                                     \
-  void build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,         \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
-  {                                                                                  \
-    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
-  }
-CUVS_INST_IVF_FLAT_BUILD(float, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_BUILD
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu
deleted file mode 100644
index 4803868c0c..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_int8_t_int64_t.cu
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-
-#include "ivf_flat_build.cuh"
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                            \
-  auto build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
-  }                                                                                  \
-                                                                                     \
-  void build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,       \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
-  {                                                                                  \
-    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
-  }                                                                                  \
-  auto build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)         \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
-  }                                                                                  \
-                                                                                     \
-  void build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,         \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
-  {                                                                                  \
-    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
-  }
-CUVS_INST_IVF_FLAT_BUILD(int8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_BUILD
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu
deleted file mode 100644
index e087f94c4b..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_uint8_t_int64_t.cu
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-
-#include "ivf_flat_build.cuh"
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_BUILD(T, IdxT)                                            \
-  auto build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
-  }                                                                                  \
-                                                                                     \
-  void build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,       \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
-  {                                                                                  \
-    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
-  }                                                                                  \
-  auto build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)         \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                      \
-  {                                                                                  \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                \
-      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset))); \
-  }                                                                                  \
-                                                                                     \
-  void build(raft::resources const& handle,                                          \
-             const cuvs::neighbors::ivf_flat::index_params& params,                  \
-             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,         \
-             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                         \
-  {                                                                                  \
-    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);          \
-  }
-CUVS_INST_IVF_FLAT_BUILD(uint8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_BUILD
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu
deleted file mode 100644
index 2636067bf8..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_float_int64_t.cu
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-
-#include "ivf_flat_build.cuh"
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                                       \
-  auto extend(raft::resources const& handle,                                                     \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
-  {                                                                                              \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
-      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
-  }                                                                                              \
-                                                                                                 \
-  void extend(raft::resources const& handle,                                                     \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
-  {                                                                                              \
-    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
-  }                                                                                              \
-  auto extend(raft::resources const& handle,                                                     \
-              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
-              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
-  {                                                                                              \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
-      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
-  }                                                                                              \
-                                                                                                 \
-  void extend(raft::resources const& handle,                                                     \
-              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
-              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
-  {                                                                                              \
-    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
-  }
-CUVS_INST_IVF_FLAT_EXTEND(float, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu
deleted file mode 100644
index 191cb9f391..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_int8_t_int64_t.cu
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-
-#include "ivf_flat_build.cuh"
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                                       \
-  auto extend(raft::resources const& handle,                                                     \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
-  {                                                                                              \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
-      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
-  }                                                                                              \
-                                                                                                 \
-  void extend(raft::resources const& handle,                                                     \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
-  {                                                                                              \
-    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
-  }                                                                                              \
-  auto extend(raft::resources const& handle,                                                     \
-              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
-              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
-  {                                                                                              \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
-      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
-  }                                                                                              \
-                                                                                                 \
-  void extend(raft::resources const& handle,                                                     \
-              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
-              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
-  {                                                                                              \
-    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
-  }
-CUVS_INST_IVF_FLAT_EXTEND(int8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu
deleted file mode 100644
index 29b7e7b69d..0000000000
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_extend_uint8_t_int64_t.cu
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_flat.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_flat.py
- *
- */
-
-#include <cuvs/neighbors/ivf_flat.hpp>
-
-#include "ivf_flat_build.cuh"
-
-namespace cuvs::neighbors::ivf_flat {
-
-#define CUVS_INST_IVF_FLAT_EXTEND(T, IdxT)                                                       \
-  auto extend(raft::resources const& handle,                                                     \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
-  {                                                                                              \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
-      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
-  }                                                                                              \
-                                                                                                 \
-  void extend(raft::resources const& handle,                                                     \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
-              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
-  {                                                                                              \
-    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
-  }                                                                                              \
-  auto extend(raft::resources const& handle,                                                     \
-              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
-              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
-              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
-  {                                                                                              \
-    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
-      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
-  }                                                                                              \
-                                                                                                 \
-  void extend(raft::resources const& handle,                                                     \
-              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
-              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
-  {                                                                                              \
-    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
-  }
-CUVS_INST_IVF_FLAT_EXTEND(uint8_t, int64_t);
-
-#undef CUVS_INST_IVF_FLAT_EXTEND
-
-}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index ce29a7e7c9..86ef559289 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -104,13 +104,16 @@ __device__ inline void copy_vectorized(T* out, const T* in, uint32_t n)
  * @tparam AccT type of the accumulated value (an optimization for 8bit values to be loaded as 32bit
  * values)
  */
-template <int kUnroll, typename Lambda, int Veclen, typename T, typename AccT>
+template <int kUnroll, typename Lambda, int Veclen, typename T, typename AccT, bool ComputeNorm>
 struct loadAndComputeDist {
   Lambda compute_dist;
   AccT& dist;
+  AccT& norm_query;
+  AccT& norm_data;
 
-  __device__ __forceinline__ loadAndComputeDist(AccT& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  __device__ __forceinline__
+  loadAndComputeDist(AccT& dist, Lambda op, AccT& norm_query, AccT& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
 
@@ -134,6 +137,10 @@ struct loadAndComputeDist {
 #pragma unroll
       for (int k = 0; k < Veclen; ++k) {
         compute_dist(dist, queryRegs[k], encV[k]);
+        if constexpr (ComputeNorm) {
+          norm_query += queryRegs[k] * queryRegs[k];
+          norm_data += encV[k] * encV[k];
+        }
       }
     }
   }
@@ -163,7 +170,12 @@ struct loadAndComputeDist {
         const int d = (i * kUnroll + j) * Veclen;
 #pragma unroll
         for (int k = 0; k < Veclen; ++k) {
-          compute_dist(dist, raft::shfl(queryReg, d + k, raft::WarpSize), encV[k]);
+          T q = raft::shfl(queryReg, d + k, raft::WarpSize);
+          compute_dist(dist, q, encV[k]);
+          if constexpr (ComputeNorm) {
+            norm_query += q * q;
+            norm_data += encV[k] * encV[k];
+          }
         }
       }
     }
@@ -184,20 +196,28 @@ struct loadAndComputeDist {
       raft::ldg(enc, data + loadDataIdx);
 #pragma unroll
       for (int k = 0; k < Veclen; k++) {
-        compute_dist(dist, raft::shfl(queryReg, d + k, raft::WarpSize), enc[k]);
+        T q = raft::shfl(queryReg, d + k, raft::WarpSize);
+        compute_dist(dist, q, enc[k]);
+        if constexpr (ComputeNorm) {
+          norm_query += q * q;
+          norm_data += enc[k] * enc[k];
+        }
       }
     }
   }
 };
 
 // This handles uint8_t 8, 16 Veclens
-template <int kUnroll, typename Lambda, int uint8_veclen>
-struct loadAndComputeDist<kUnroll, Lambda, uint8_veclen, uint8_t, uint32_t> {
+template <int kUnroll, typename Lambda, int uint8_veclen, bool ComputeNorm>
+struct loadAndComputeDist<kUnroll, Lambda, uint8_veclen, uint8_t, uint32_t, ComputeNorm> {
   Lambda compute_dist;
   uint32_t& dist;
+  uint32_t& norm_query;
+  uint32_t& norm_data;
 
-  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  __device__ __forceinline__
+  loadAndComputeDist(uint32_t& dist, Lambda op, uint32_t& norm_query, uint32_t& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
 
@@ -220,6 +240,10 @@ struct loadAndComputeDist<kUnroll, Lambda, uint8_veclen, uint8_t, uint32_t> {
 #pragma unroll
       for (int k = 0; k < veclen_int; k++) {
         compute_dist(dist, queryRegs[k], encV[k]);
+        if constexpr (ComputeNorm) {
+          norm_query = raft::dp4a(queryRegs[k], queryRegs[k], norm_query);
+          norm_data  = raft::dp4a(encV[k], encV[k], norm_data);
+        }
       }
     }
   }
@@ -244,7 +268,12 @@ struct loadAndComputeDist<kUnroll, Lambda, uint8_veclen, uint8_t, uint32_t> {
         const int d = (i * kUnroll + j) * veclen_int;
 #pragma unroll
         for (int k = 0; k < veclen_int; ++k) {
-          compute_dist(dist, raft::shfl(queryReg, d + k, raft::WarpSize), encV[k]);
+          uint32_t q = raft::shfl(queryReg, d + k, raft::WarpSize);
+          compute_dist(dist, q, encV[k]);
+          if constexpr (ComputeNorm) {
+            norm_query = raft::dp4a(q, q, norm_query);
+            norm_data  = raft::dp4a(encV[k], encV[k], norm_data);
+          }
         }
       }
     }
@@ -267,6 +296,10 @@ struct loadAndComputeDist<kUnroll, Lambda, uint8_veclen, uint8_t, uint32_t> {
       for (int k = 0; k < veclen_int; k++) {
         uint32_t q = raft::shfl(queryReg, (d / 4) + k, raft::WarpSize);
         compute_dist(dist, q, enc[k]);
+        if constexpr (ComputeNorm) {
+          norm_query = raft::dp4a(q, q, norm_query);
+          norm_data  = raft::dp4a(enc[k], enc[k], norm_data);
+        }
       }
     }
   }
@@ -274,13 +307,16 @@ struct loadAndComputeDist<kUnroll, Lambda, uint8_veclen, uint8_t, uint32_t> {
 
 // Keep this specialized uint8 Veclen = 4, because compiler is generating suboptimal code while
 // using above common template of int2/int4
-template <int kUnroll, typename Lambda>
-struct loadAndComputeDist<kUnroll, Lambda, 4, uint8_t, uint32_t> {
+template <int kUnroll, typename Lambda, bool ComputeNorm>
+struct loadAndComputeDist<kUnroll, Lambda, 4, uint8_t, uint32_t, ComputeNorm> {
   Lambda compute_dist;
   uint32_t& dist;
+  uint32_t& norm_query;
+  uint32_t& norm_data;
 
-  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  __device__ __forceinline__
+  loadAndComputeDist(uint32_t& dist, Lambda op, uint32_t& norm_query, uint32_t& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
 
@@ -294,6 +330,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 4, uint8_t, uint32_t> {
       uint32_t encV      = reinterpret_cast<unsigned const*>(data)[loadIndex + j * kIndexGroupSize];
       uint32_t queryRegs = reinterpret_cast<unsigned const*>(query_shared + shmemIndex)[j];
       compute_dist(dist, queryRegs, encV);
+      if constexpr (ComputeNorm) {
+        norm_query = raft::dp4a(queryRegs, queryRegs, norm_query);
+        norm_data  = raft::dp4a(encV, encV, norm_data);
+      }
     }
   }
   __device__ __forceinline__ void runLoadShflAndCompute(const uint8_t*& data,
@@ -313,6 +353,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 4, uint8_t, uint32_t> {
         uint32_t encV = reinterpret_cast<unsigned const*>(data)[lane_id + j * kIndexGroupSize];
         uint32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
         compute_dist(dist, q, encV);
+        if constexpr (ComputeNorm) {
+          norm_query = raft::dp4a(q, q, norm_query);
+          norm_data  = raft::dp4a(encV, encV, norm_data);
+        }
       }
     }
   }
@@ -330,17 +374,24 @@ struct loadAndComputeDist<kUnroll, Lambda, 4, uint8_t, uint32_t> {
       uint32_t enc = reinterpret_cast<unsigned const*>(data)[lane_id];
       uint32_t q   = raft::shfl(queryReg, d / veclen, raft::WarpSize);
       compute_dist(dist, q, enc);
+      if constexpr (ComputeNorm) {
+        norm_query = raft::dp4a(q, q, norm_query);
+        norm_data  = raft::dp4a(enc, enc, norm_data);
+      }
     }
   }
 };
 
-template <int kUnroll, typename Lambda>
-struct loadAndComputeDist<kUnroll, Lambda, 2, uint8_t, uint32_t> {
+template <int kUnroll, typename Lambda, bool ComputeNorm>
+struct loadAndComputeDist<kUnroll, Lambda, 2, uint8_t, uint32_t, ComputeNorm> {
   Lambda compute_dist;
   uint32_t& dist;
+  uint32_t& norm_query;
+  uint32_t& norm_data;
 
-  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  __device__ __forceinline__
+  loadAndComputeDist(uint32_t& dist, Lambda op, uint32_t& norm_query, uint32_t& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
 
@@ -354,6 +405,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 2, uint8_t, uint32_t> {
       uint32_t encV      = reinterpret_cast<uint16_t const*>(data)[loadIndex + j * kIndexGroupSize];
       uint32_t queryRegs = reinterpret_cast<uint16_t const*>(query_shared + shmemIndex)[j];
       compute_dist(dist, queryRegs, encV);
+      if constexpr (ComputeNorm) {
+        norm_query = raft::dp4a(queryRegs, queryRegs, norm_query);
+        norm_data  = raft::dp4a(encV, encV, norm_data);
+      }
     }
   }
 
@@ -374,6 +429,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 2, uint8_t, uint32_t> {
         uint32_t encV = reinterpret_cast<uint16_t const*>(data)[lane_id + j * kIndexGroupSize];
         uint32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
         compute_dist(dist, q, encV);
+        if constexpr (ComputeNorm) {
+          norm_query = raft::dp4a(q, q, norm_query);
+          norm_data  = raft::dp4a(encV, encV, norm_data);
+        }
       }
     }
   }
@@ -391,17 +450,24 @@ struct loadAndComputeDist<kUnroll, Lambda, 2, uint8_t, uint32_t> {
       uint32_t enc = reinterpret_cast<uint16_t const*>(data)[lane_id];
       uint32_t q   = raft::shfl(queryReg, d / veclen, raft::WarpSize);
       compute_dist(dist, q, enc);
+      if constexpr (ComputeNorm) {
+        norm_query = raft::dp4a(q, q, norm_query);
+        norm_data  = raft::dp4a(enc, enc, norm_data);
+      }
     }
   }
 };
 
-template <int kUnroll, typename Lambda>
-struct loadAndComputeDist<kUnroll, Lambda, 1, uint8_t, uint32_t> {
+template <int kUnroll, typename Lambda, bool ComputeNorm>
+struct loadAndComputeDist<kUnroll, Lambda, 1, uint8_t, uint32_t, ComputeNorm> {
   Lambda compute_dist;
   uint32_t& dist;
+  uint32_t& norm_query;
+  uint32_t& norm_data;
 
-  __device__ __forceinline__ loadAndComputeDist(uint32_t& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  __device__ __forceinline__
+  loadAndComputeDist(uint32_t& dist, Lambda op, uint32_t& norm_query, uint32_t& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
 
@@ -415,6 +481,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 1, uint8_t, uint32_t> {
       uint32_t encV      = data[loadIndex + j * kIndexGroupSize];
       uint32_t queryRegs = query_shared[shmemIndex + j];
       compute_dist(dist, queryRegs, encV);
+      if constexpr (ComputeNorm) {
+        norm_query += queryRegs * queryRegs;
+        norm_data += encV * encV;
+      }
     }
   }
 
@@ -434,6 +504,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 1, uint8_t, uint32_t> {
         uint32_t encV = data[lane_id + j * kIndexGroupSize];
         uint32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
         compute_dist(dist, q, encV);
+        if constexpr (ComputeNorm) {
+          norm_query += q * q;
+          norm_data += encV * encV;
+        }
       }
     }
   }
@@ -451,18 +525,25 @@ struct loadAndComputeDist<kUnroll, Lambda, 1, uint8_t, uint32_t> {
       uint32_t enc = data[lane_id];
       uint32_t q   = raft::shfl(queryReg, d, raft::WarpSize);
       compute_dist(dist, q, enc);
+      if constexpr (ComputeNorm) {
+        norm_query += q * q;
+        norm_data += enc * enc;
+      }
     }
   }
 };
 
 // This device function is for int8 veclens 4, 8 and 16
-template <int kUnroll, typename Lambda, int int8_veclen>
-struct loadAndComputeDist<kUnroll, Lambda, int8_veclen, int8_t, int32_t> {
+template <int kUnroll, typename Lambda, int int8_veclen, bool ComputeNorm>
+struct loadAndComputeDist<kUnroll, Lambda, int8_veclen, int8_t, int32_t, ComputeNorm> {
   Lambda compute_dist;
   int32_t& dist;
+  int32_t& norm_query;
+  int32_t& norm_data;
 
-  __device__ __forceinline__ loadAndComputeDist(int32_t& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  __device__ __forceinline__
+  loadAndComputeDist(int32_t& dist, Lambda op, int32_t& norm_query, int32_t& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
 
@@ -485,6 +566,10 @@ struct loadAndComputeDist<kUnroll, Lambda, int8_veclen, int8_t, int32_t> {
 #pragma unroll
       for (int k = 0; k < veclen_int; k++) {
         compute_dist(dist, queryRegs[k], encV[k]);
+        if constexpr (ComputeNorm) {
+          norm_query = raft::dp4a(queryRegs[k], queryRegs[k], norm_query);
+          norm_data  = raft::dp4a(encV[k], encV[k], norm_data);
+        }
       }
     }
   }
@@ -513,6 +598,10 @@ struct loadAndComputeDist<kUnroll, Lambda, int8_veclen, int8_t, int32_t> {
         for (int k = 0; k < veclen_int; ++k) {
           int32_t q = raft::shfl(queryReg, d + k, raft::WarpSize);
           compute_dist(dist, q, encV[k]);
+          if constexpr (ComputeNorm) {
+            norm_query = raft::dp4a(q, q, norm_query);
+            norm_data  = raft::dp4a(encV[k], encV[k], norm_data);
+          }
         }
       }
     }
@@ -531,17 +620,24 @@ struct loadAndComputeDist<kUnroll, Lambda, int8_veclen, int8_t, int32_t> {
       for (int k = 0; k < veclen_int; k++) {
         int32_t q = raft::shfl(queryReg, (d / 4) + k, raft::WarpSize);  // Here 4 is for 1 - int;
         compute_dist(dist, q, enc[k]);
+        if constexpr (ComputeNorm) {
+          norm_query = raft::dp4a(q, q, norm_query);
+          norm_data  = raft::dp4a(enc[k], enc[k], norm_data);
+        }
       }
     }
   }
 };
 
-template <int kUnroll, typename Lambda>
-struct loadAndComputeDist<kUnroll, Lambda, 2, int8_t, int32_t> {
+template <int kUnroll, typename Lambda, bool ComputeNorm>
+struct loadAndComputeDist<kUnroll, Lambda, 2, int8_t, int32_t, ComputeNorm> {
   Lambda compute_dist;
   int32_t& dist;
-  __device__ __forceinline__ loadAndComputeDist(int32_t& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  int32_t& norm_query;
+  int32_t& norm_data;
+  __device__ __forceinline__
+  loadAndComputeDist(int32_t& dist, Lambda op, int32_t& norm_query, int32_t& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
   __device__ __forceinline__ void runLoadShmemCompute(const int8_t* const& data,
@@ -554,6 +650,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 2, int8_t, int32_t> {
       int32_t encV      = reinterpret_cast<uint16_t const*>(data)[loadIndex + j * kIndexGroupSize];
       int32_t queryRegs = reinterpret_cast<uint16_t const*>(query_shared + shmemIndex)[j];
       compute_dist(dist, queryRegs, encV);
+      if constexpr (ComputeNorm) {
+        norm_query = raft::dp4a(queryRegs, queryRegs, norm_query);
+        norm_data  = raft::dp4a(encV, encV, norm_data);
+      }
     }
   }
 
@@ -574,6 +674,10 @@ struct loadAndComputeDist<kUnroll, Lambda, 2, int8_t, int32_t> {
         int32_t encV = reinterpret_cast<uint16_t const*>(data)[lane_id + j * kIndexGroupSize];
         int32_t q    = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
         compute_dist(dist, q, encV);
+        if constexpr (ComputeNorm) {
+          norm_query = raft::dp4a(queryReg, queryReg, norm_query);
+          norm_data  = raft::dp4a(encV, encV, norm_data);
+        }
       }
     }
   }
@@ -588,16 +692,23 @@ struct loadAndComputeDist<kUnroll, Lambda, 2, int8_t, int32_t> {
       int32_t enc = reinterpret_cast<uint16_t const*>(data + lane_id * veclen)[0];
       int32_t q   = raft::shfl(queryReg, d / veclen, raft::WarpSize);
       compute_dist(dist, q, enc);
+      if constexpr (ComputeNorm) {
+        norm_query = raft::dp4a(q, q, norm_query);
+        norm_data  = raft::dp4a(enc, enc, norm_data);
+      }
     }
   }
 };
 
-template <int kUnroll, typename Lambda>
-struct loadAndComputeDist<kUnroll, Lambda, 1, int8_t, int32_t> {
+template <int kUnroll, typename Lambda, bool ComputeNorm>
+struct loadAndComputeDist<kUnroll, Lambda, 1, int8_t, int32_t, ComputeNorm> {
   Lambda compute_dist;
   int32_t& dist;
-  __device__ __forceinline__ loadAndComputeDist(int32_t& dist, Lambda op)
-    : dist(dist), compute_dist(op)
+  int32_t& norm_query;
+  int32_t& norm_data;
+  __device__ __forceinline__
+  loadAndComputeDist(int32_t& dist, Lambda op, int32_t& norm_query, int32_t& norm_data)
+    : dist(dist), compute_dist(op), norm_query(norm_query), norm_data(norm_data)
   {
   }
 
@@ -609,6 +720,11 @@ struct loadAndComputeDist<kUnroll, Lambda, 1, int8_t, int32_t> {
 #pragma unroll
     for (int j = 0; j < kUnroll; ++j) {
       compute_dist(dist, query_shared[shmemIndex + j], data[loadIndex + j * kIndexGroupSize]);
+      if constexpr (ComputeNorm) {
+        norm_query += int32_t{query_shared[shmemIndex + j]} * int32_t{query_shared[shmemIndex + j]};
+        norm_data += int32_t{data[loadIndex + j * kIndexGroupSize]} *
+                     int32_t{data[loadIndex + j * kIndexGroupSize]};
+      }
     }
   }
 
@@ -625,9 +741,12 @@ struct loadAndComputeDist<kUnroll, Lambda, 1, int8_t, int32_t> {
     for (int i = 0; i < raft::WarpSize / stride; ++i, data += stride * kIndexGroupSize) {
 #pragma unroll
       for (int j = 0; j < kUnroll; ++j) {
-        compute_dist(dist,
-                     raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize),
-                     data[lane_id + j * kIndexGroupSize]);
+        int32_t q = raft::shfl(queryReg, i * kUnroll + j, raft::WarpSize);
+        compute_dist(dist, q, data[lane_id + j * kIndexGroupSize]);
+        if constexpr (ComputeNorm) {
+          norm_query += q * q;
+          norm_data += data[lane_id + j * kIndexGroupSize] * data[lane_id + j * kIndexGroupSize];
+        }
       }
     }
   }
@@ -638,7 +757,12 @@ struct loadAndComputeDist<kUnroll, Lambda, 1, int8_t, int32_t> {
     const int loadDim    = dimBlocks + lane_id;
     int32_t queryReg     = loadDim < dim ? query[loadDim] : 0;
     for (int d = 0; d < dim - dimBlocks; d += veclen, data += kIndexGroupSize * veclen) {
-      compute_dist(dist, raft::shfl(queryReg, d, raft::WarpSize), data[lane_id]);
+      int32_t q = raft::shfl(queryReg, d, raft::WarpSize);
+      compute_dist(dist, q, data[lane_id]);
+      if constexpr (ComputeNorm) {
+        norm_query += q * q;
+        norm_data += int32_t{data[lane_id]} * int32_t{data[lane_id]};
+      }
     }
   }
 };
@@ -691,6 +815,7 @@ using block_sort_t = typename flat_block_sort<Capacity, Ascending, T, IdxT>::typ
 template <int Capacity,
           int Veclen,
           bool Ascending,
+          bool ComputeNorm,
           typename T,
           typename AccT,
           typename IdxT,
@@ -775,7 +900,9 @@ RAFT_KERNEL __launch_bounds__(kThreadsPerBlock)
       // and each thread adds one (id, dist) pair to the filtering queue.
       for (uint32_t group_id = align_warp::div(threadIdx.x); group_id < num_groups;
            group_id += kNumWarps) {
-        AccT dist = 0;
+        AccT dist         = 0;
+        AccT norm_query   = 0;
+        AccT norm_dataset = 0;
         // This is where this warp begins reading data (start position of an interleaved group)
         const T* data = list_data_ptrs[list_id] + (group_id * kIndexGroupSize) * dim;
 
@@ -784,28 +911,27 @@ RAFT_KERNEL __launch_bounds__(kThreadsPerBlock)
         const bool valid =
           vec_id < list_length && sample_filter(queries_offset + blockIdx.y, list_id, vec_id);
 
-        // Process first shm_assisted_dim dimensions (always using shared memory)
         if (valid) {
-          loadAndComputeDist<kUnroll, decltype(compute_dist), Veclen, T, AccT> lc(dist,
-                                                                                  compute_dist);
+          // Process first shm_assisted_dim dimensions (always using shared memory)
+          loadAndComputeDist<kUnroll, decltype(compute_dist), Veclen, T, AccT, ComputeNorm> lc(
+            dist, compute_dist, norm_query, norm_dataset);
           for (int pos = 0; pos < shm_assisted_dim;
                pos += raft::WarpSize, data += kIndexGroupSize * raft::WarpSize) {
             lc.runLoadShmemCompute(data, query_shared, lane_id, pos);
           }
-        }
 
-        if (dim > query_smem_elems) {
-          // The default path - using shfl ops - for dimensions beyond query_smem_elems
-          loadAndComputeDist<kUnroll, decltype(compute_dist), Veclen, T, AccT> lc(dist,
-                                                                                  compute_dist);
-          for (int pos = shm_assisted_dim; pos < full_warps_along_dim; pos += raft::WarpSize) {
-            lc.runLoadShflAndCompute(data, query, pos, lane_id);
-          }
-          lc.runLoadShflAndComputeRemainder(data, query, lane_id, dim, full_warps_along_dim);
-        } else {
-          // when  shm_assisted_dim == full_warps_along_dim < dim
-          if (valid) {
-            loadAndComputeDist<1, decltype(compute_dist), Veclen, T, AccT> lc(dist, compute_dist);
+          if (dim > query_smem_elems) {
+            // The default path - using shfl ops - for dimensions beyond query_smem_elems
+            loadAndComputeDist<kUnroll, decltype(compute_dist), Veclen, T, AccT, ComputeNorm> lc(
+              dist, compute_dist, norm_query, norm_dataset);
+            for (int pos = shm_assisted_dim; pos < full_warps_along_dim; pos += raft::WarpSize) {
+              lc.runLoadShflAndCompute(data, query, pos, lane_id);
+            }
+            lc.runLoadShflAndComputeRemainder(data, query, lane_id, dim, full_warps_along_dim);
+          } else {
+            // when  shm_assisted_dim == full_warps_along_dim < dim
+            loadAndComputeDist<1, decltype(compute_dist), Veclen, T, AccT, ComputeNorm> lc(
+              dist, compute_dist, norm_query, norm_dataset);
             for (int pos = full_warps_along_dim; pos < dim;
                  pos += Veclen, data += kIndexGroupSize * Veclen) {
               lc.runLoadShmemCompute(data, query_shared, lane_id, pos);
@@ -814,7 +940,13 @@ RAFT_KERNEL __launch_bounds__(kThreadsPerBlock)
         }
 
         // Enqueue one element per thread
-        const float val = valid ? static_cast<float>(dist) : local_topk_t::queue_t::kDummy;
+        float val = valid ? static_cast<float>(dist) : local_topk_t::queue_t::kDummy;
+
+        if constexpr (ComputeNorm) {
+          if (valid)
+            val = val / (raft::sqrt(static_cast<float>(norm_query)) *
+                         raft::sqrt(static_cast<float>(norm_dataset)));
+        }
         if constexpr (kManageLocalTopK) {
           queue.add(val, sample_offset + vec_id);
         } else {
@@ -864,6 +996,7 @@ uint32_t configure_launch_x(uint32_t numQueries, uint32_t n_probes, int32_t sMem
 template <int Capacity,
           int Veclen,
           bool Ascending,
+          bool ComputeNorm,
           typename T,
           typename AccT,
           typename IdxT,
@@ -892,6 +1025,7 @@ void launch_kernel(Lambda lambda,
   constexpr auto kKernel   = interleaved_scan_kernel<Capacity,
                                                    Veclen,
                                                    Ascending,
+                                                   ComputeNorm,
                                                    T,
                                                    AccT,
                                                    IdxT,
@@ -1029,6 +1163,7 @@ void launch_with_fixed_consts(cuvs::distance::DistanceType metric, Args&&... arg
       return launch_kernel<Capacity,
                            Veclen,
                            Ascending,
+                           false,
                            T,
                            AccT,
                            IdxT,
@@ -1040,6 +1175,7 @@ void launch_with_fixed_consts(cuvs::distance::DistanceType metric, Args&&... arg
       return launch_kernel<Capacity,
                            Veclen,
                            Ascending,
+                           false,
                            T,
                            AccT,
                            IdxT,
@@ -1050,12 +1186,27 @@ void launch_with_fixed_consts(cuvs::distance::DistanceType metric, Args&&... arg
       return launch_kernel<Capacity,
                            Veclen,
                            Ascending,
+                           false,
                            T,
                            AccT,
                            IdxT,
                            IvfSampleFilterT,
                            inner_prod_dist<Veclen, T, AccT>,
                            raft::identity_op>({}, {}, std::forward<Args>(args)...);
+    case cuvs::distance::DistanceType::CosineExpanded:
+      // NB: "Ascending" is reversed because the post-processing step is done after that sort
+      return launch_kernel<Capacity,
+                           Veclen,
+                           !Ascending,
+                           true,
+                           T,
+                           AccT,
+                           IdxT,
+                           IvfSampleFilterT,
+                           inner_prod_dist<Veclen, T, AccT>>(
+        {},
+        raft::compose_op(raft::mul_const_op<float>{-1.0f}, raft::add_const_op<float>{1.0f}),
+        std::forward<Args>(args)...);
     // NB: update the description of `knn::ivf_flat::build` when adding here a new metric.
     default: RAFT_FAIL("The chosen distance metric is not supported (%d)", int(metric));
   }
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index 43111a7dea..b7dac3ef88 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -79,6 +79,8 @@ void search_impl(raft::resources const& handle,
   // also we might need additional storage for select_k
   rmm::device_uvector<uint32_t> indices_tmp_dev(0, stream, search_mr);
   rmm::device_uvector<uint32_t> neighbors_uint32_buf(0, stream, search_mr);
+  auto distance_buffer_dev_view = raft::make_device_matrix_view<AccT, int64_t>(
+    distance_buffer_dev.data(), n_queries, index.n_lists());
 
   size_t float_query_size;
   if constexpr (std::is_integral_v<T>) {
@@ -122,6 +124,19 @@ void search_impl(raft::resources const& handle,
       RAFT_LOG_TRACE_VEC(distance_buffer_dev.data(), std::min<uint32_t>(20, index.n_lists()));
       break;
     }
+    case cuvs::distance::DistanceType::CosineExpanded: {
+      raft::linalg::rowNorm(query_norm_dev.data(),
+                            converted_queries_ptr,
+                            static_cast<IdxT>(index.dim()),
+                            static_cast<IdxT>(n_queries),
+                            raft::linalg::L2Norm,
+                            true,
+                            stream,
+                            raft::sqrt_op{});
+      alpha = -1.0f;
+      beta  = 0.0f;
+      break;
+    }
     default: {
       alpha = 1.0f;
       beta  = 0.0f;
@@ -144,12 +159,25 @@ void search_impl(raft::resources const& handle,
                      index.n_lists(),
                      stream);
 
+  if (index.metric() == cuvs::distance::DistanceType::CosineExpanded) {
+    auto n_lists                      = index.n_lists();
+    const auto* q_norm_ptr            = query_norm_dev.data();
+    const auto* index_center_norm_ptr = index.center_norms()->data_handle();
+    raft::linalg::map_offset(
+      handle,
+      distance_buffer_dev_view,
+      [=] __device__(const uint32_t idx, const float dist) {
+        const auto query   = idx / n_lists;
+        const auto cluster = idx % n_lists;
+        return dist / (q_norm_ptr[query] * index_center_norm_ptr[cluster]);
+      },
+      raft::make_const_mdspan(distance_buffer_dev_view));
+  }
   RAFT_LOG_TRACE_VEC(distance_buffer_dev.data(), std::min<uint32_t>(20, index.n_lists()));
 
   cuvs::selection::select_k(
     handle,
-    raft::make_device_matrix_view<const AccT, int64_t>(
-      distance_buffer_dev.data(), n_queries, index.n_lists()),
+    raft::make_const_mdspan(distance_buffer_dev_view),
     std::nullopt,
     raft::make_device_matrix_view<AccT, int64_t>(coarse_distances_dev.data(), n_queries, n_probes),
     raft::make_device_matrix_view<uint32_t, int64_t>(
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
index b249a9c29b..6f7d11e500 100644
--- a/cpp/src/neighbors/ivf_flat_index.cpp
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -193,6 +193,7 @@ void index<T, IdxT>::allocate_center_norms(raft::resources const& res)
     case cuvs::distance::DistanceType::L2SqrtExpanded:
     case cuvs::distance::DistanceType::L2Unexpanded:
     case cuvs::distance::DistanceType::L2SqrtUnexpanded:
+    case cuvs::distance::DistanceType::CosineExpanded:
       center_norms_ = raft::make_device_vector<float, uint32_t>(res, n_lists());
       break;
     default: center_norms_ = std::nullopt;
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index f907baa1fb..17ec84097c 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -20,7 +20,9 @@
 #include "naive_knn.cuh"
 
 #include <cuvs/core/bitset.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft/linalg/normalize.cuh>
 #include <raft/stats/mean.cuh>
 #include <thrust/sequence.h>
 
@@ -533,63 +535,109 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   // test various dims (aligned and not aligned to vector sizes)
   {1000, 10000, 1, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
   {1000, 10000, 2, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 2, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 3, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 10000, 3, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 4, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 4, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 5, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 5, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 8, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 8, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 5, 16, 40, 1024, cuvs::distance::DistanceType::L2SqrtExpanded, false},
+  {1000, 10000, 5, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 8, 16, 40, 1024, cuvs::distance::DistanceType::L2SqrtExpanded, true},
+  {1000, 10000, 8, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
 
   // test dims that do not fit into kernel shared memory limits
   {1000, 10000, 2048, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 2048, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 2049, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 2049, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 2050, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 2050, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 2052, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 2052, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 2053, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 10000, 2053, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 2056, 16, 40, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 10000, 2056, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
 
   // various random combinations
   {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::L2Expanded, false},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::CosineExpanded, false},
   {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false},
+  {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
 
   // host input data
   {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true},
   {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true},
   {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true},
   {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::CosineExpanded, false, true},
   {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true},
   {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true},
   {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true},
+  {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true},
 
   // // host input data with prefetching for kernel copy overlapping
   {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true, true},
   {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true, true},
   {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true, true},
   {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::CosineExpanded, false, true, true},
   {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true, true},
   {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true, true},
   {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::L2Expanded, false, true, true},
+  {10000, 131072, 8, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false, true, true},
 
   {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 16, 10, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {1000, 10000, 16, 10, 50, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 16, 10, 70, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::InnerProduct, true},
+  {100, 10000, 16, 10, 20, 512, cuvs::distance::DistanceType::CosineExpanded, true},
   {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {20, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 100000, 16, 10, 20, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {10000, 131072, 8, 10, 50, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  {10000, 131072, 8, 10, 50, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
 
   {1000, 10000, 4096, 20, 50, 1024, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000, 10000, 4096, 20, 50, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
 
   // test splitting the big query batches  (> max gridDim.y) into smaller batches
   {100000, 1024, 32, 10, 64, 64, cuvs::distance::DistanceType::InnerProduct, false},
+  {100000, 1024, 32, 10, 64, 64, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000000, 1024, 32, 10, 256, 256, cuvs::distance::DistanceType::InnerProduct, false},
+  {1000000, 1024, 32, 10, 256, 256, cuvs::distance::DistanceType::CosineExpanded, false},
   {98306, 1024, 32, 10, 64, 64, cuvs::distance::DistanceType::InnerProduct, true},
+  {98306, 1024, 32, 10, 64, 64, cuvs::distance::DistanceType::CosineExpanded, true},
 
   // test radix_sort for getting the cluster selection
   {1000,
@@ -608,6 +656,14 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
    raft::matrix::detail::select::warpsort::kMaxCapacity * 4,
    cuvs::distance::DistanceType::InnerProduct,
    false},
+  {1000,
+   10000,
+   16,
+   10,
+   raft::matrix::detail::select::warpsort::kMaxCapacity * 4,
+   raft::matrix::detail::select::warpsort::kMaxCapacity * 4,
+   cuvs::distance::DistanceType::CosineExpanded,
+   false},
 
   // The following two test cases should show very similar recall.
   // num_queries, num_db_vecs, dim, k, nprobe, nlist, metric, adaptive_centers
diff --git a/cpp/test/neighbors/naive_knn.cuh b/cpp/test/neighbors/naive_knn.cuh
index 90547150f2..553e667aa1 100644
--- a/cpp/test/neighbors/naive_knn.cuh
+++ b/cpp/test/neighbors/naive_knn.cuh
@@ -41,7 +41,9 @@ RAFT_KERNEL naive_distance_kernel(EvalT* dist,
   if (midx >= m) return;
   IdxT grid_size = IdxT(blockDim.y) * IdxT(gridDim.y);
   for (IdxT nidx = threadIdx.y + blockIdx.y * blockDim.y; nidx < n; nidx += grid_size) {
-    EvalT acc = EvalT(0);
+    EvalT acc   = EvalT(0);
+    EvalT normX = EvalT(0);
+    EvalT normY = EvalT(0);
     for (IdxT i = 0; i < k; ++i) {
       IdxT xidx = i + midx * k;
       IdxT yidx = i + nidx * k;
@@ -51,6 +53,11 @@ RAFT_KERNEL naive_distance_kernel(EvalT* dist,
         case cuvs::distance::DistanceType::InnerProduct: {
           acc += xv * yv;
         } break;
+        case cuvs::distance::DistanceType::CosineExpanded: {
+          acc += xv * yv;
+          normX += xv * xv;
+          normY += yv * yv;
+        } break;
         case cuvs::distance::DistanceType::L2SqrtExpanded:
         case cuvs::distance::DistanceType::L2SqrtUnexpanded:
         case cuvs::distance::DistanceType::L2Expanded:
@@ -66,6 +73,9 @@ RAFT_KERNEL naive_distance_kernel(EvalT* dist,
       case cuvs::distance::DistanceType::L2SqrtUnexpanded: {
         acc = raft::sqrt(acc);
       } break;
+      case cuvs::distance::DistanceType::CosineExpanded: {
+        acc = 1 - acc / (raft::sqrt(normX) * raft::sqrt(normY));
+      }
       default: break;
     }
     dist[midx * n + nidx] = acc;
@@ -118,7 +128,7 @@ void naive_knn(raft::resources const& handle,
                                                 static_cast<int>(k),
                                                 dist_topk + offset * k,
                                                 indices_topk + offset * k,
-                                                type != cuvs::distance::DistanceType::InnerProduct,
+                                                cuvs::distance::is_min_close(type),
                                                 mr);
   }
   RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index a3799144c7..25b9b2aeeb 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -58,12 +58,14 @@ cdef class IndexParams:
     metric : str, default = "sqeuclidean"
         String denoting the metric type.
         Valid values for metric: ["sqeuclidean", "inner_product",
-        "euclidean"], where
+        "euclidean", "cosine"], where
             - sqeuclidean is the euclidean distance without the square root
               operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2,
             - euclidean is the euclidean distance
             - inner product distance is defined as
               distance(a, b) = \\sum_i a_i * b_i.
+            - cosine distance is defined as
+              distance(a, b) = 1 - \\sum_i a_i * b_i / ( ||a||_2 * ||b||_2).
     kmeans_n_iters : int, default = 20
         The number of iterations searching for kmeans centers during index
         building.
diff --git a/python/cuvs/cuvs/test/test_ivf_flat.py b/python/cuvs/cuvs/test/test_ivf_flat.py
index bb50d35734..9dd4097dcb 100644
--- a/python/cuvs/cuvs/test/test_ivf_flat.py
+++ b/python/cuvs/cuvs/test/test_ivf_flat.py
@@ -92,6 +92,7 @@ def run_ivf_flat_build_search_test(
     skl_metric = {
         "sqeuclidean": "sqeuclidean",
         "inner_product": "cosine",
+        "cosine": "cosine",
         "euclidean": "euclidean",
     }[metric]
     nn_skl = NearestNeighbors(
@@ -107,7 +108,7 @@ def run_ivf_flat_build_search_test(
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("dtype", [np.float32])
 @pytest.mark.parametrize(
-    "metric", ["sqeuclidean", "inner_product", "euclidean"]
+    "metric", ["sqeuclidean", "inner_product", "euclidean", "cosine"]
 )
 def test_ivf_flat(inplace, dtype, metric):
     run_ivf_flat_build_search_test(

From 9cf4800d79936670d9507c4f4bb287bfdad5d9aa Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Wed, 14 Aug 2024 22:20:06 +0200
Subject: [PATCH 128/623] Fix dataset dimension in IVF-PQ C wrappers (#292)

fixes #288

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/292
---
 cpp/src/neighbors/ivf_flat_c.cpp | 2 +-
 cpp/src/neighbors/ivf_pq_c.cpp   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)
 mode change 100644 => 100755 cpp/src/neighbors/ivf_flat_c.cpp
 mode change 100644 => 100755 cpp/src/neighbors/ivf_pq_c.cpp

diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
old mode 100644
new mode 100755
index decc88ab3e..c14c1edc0c
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -47,7 +47,7 @@ void* _build(cuvsResources_t res, cuvsIvfFlatIndexParams params, DLManagedTensor
   build_params.conservative_memory_allocation = params.conservative_memory_allocation;
 
   auto dataset = dataset_tensor->dl_tensor;
-  auto dim     = dataset.shape[0];
+  auto dim     = dataset.shape[1];
 
   auto index = new cuvs::neighbors::ivf_flat::index<T, IdxT>(*res_ptr, build_params, dim);
 
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
old mode 100644
new mode 100755
index 0afe9356d1..2ecad6ada8
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -51,7 +51,7 @@ void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor*
   build_params.max_train_points_per_pq_code   = params.max_train_points_per_pq_code;
 
   auto dataset = dataset_tensor->dl_tensor;
-  auto dim     = dataset.shape[0];
+  auto dim     = dataset.shape[1];
 
   auto index = new cuvs::neighbors::ivf_pq::index<IdxT>(*res_ptr, build_params, dim);
 

From 934645cc7c6a6c19b535d770163ac1f776b95eea Mon Sep 17 00:00:00 2001
From: rhdong <hrong@nvidia.com>
Date: Tue, 20 Aug 2024 10:21:11 -0700
Subject: [PATCH 129/623] [FEA] Support for half-float mixed precise in
 brute-force (#225)

- distance supports half-float mixed precision
- prefiltered_brute_force supports half
- migrate the ann brute force test cases and support half

Authors:
  - rhdong (https://github.com/rhdong)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/225
---
 cpp/CMakeLists.txt                            |   15 +
 cpp/bench/ann/CMakeLists.txt                  |   18 +
 .../ann/src/cuvs/cuvs_brute_force_knn.cu      |  333 +++++
 cpp/cmake/thirdparty/get_raft.cmake           |    4 +-
 cpp/include/cuvs/distance/distance.hpp        |   87 ++
 cpp/include/cuvs/neighbors/brute_force.hpp    |  139 +-
 cpp/src/distance/detail/distance.cuh          |   84 +-
 .../distance/detail/distance_ops/canberra.cuh |   26 +-
 .../detail/distance_ops/correlation.cuh       |   32 +-
 .../distance/detail/distance_ops/cosine.cuh   |   27 +-
 .../distance/detail/distance_ops/hamming.cuh  |    6 +-
 .../detail/distance_ops/hellinger.cuh         |    6 +-
 .../detail/distance_ops/jensen_shannon.cuh    |   14 +-
 .../detail/distance_ops/kl_divergence.cuh     |   26 +-
 cpp/src/distance/detail/distance_ops/l1.cuh   |    9 +-
 .../distance/detail/distance_ops/l2_exp.cuh   |   28 +-
 .../distance/detail/distance_ops/l2_unexp.cuh |   15 +-
 .../distance/detail/distance_ops/l_inf.cuh    |    4 +-
 .../distance/detail/distance_ops/lp_unexp.cuh |   10 +-
 .../detail/distance_ops/russel_rao.cuh        |    9 +-
 .../distance/detail/distance_ops/template.cuh |    4 +-
 .../distance/detail/masked_distance_base.cuh  |    2 +-
 .../detail/pairwise_distance_base.cuh         |   26 +-
 .../detail/pairwise_distance_cutlass_base.cuh |   30 +-
 .../distance/detail/pairwise_distance_gemm.h  |  103 +-
 .../detail/pairwise_matrix/dispatch-ext.cuh   |  174 +--
 .../detail/pairwise_matrix/dispatch-inl.cuh   |   12 +-
 .../detail/pairwise_matrix/dispatch.cuh       |    2 -
 .../pairwise_matrix/dispatch_00_generate.py   |   10 +-
 ...patch_canberra_double_double_double_int.cu |    4 +-
 ...dispatch_canberra_float_float_float_int.cu |    4 +-
 .../dispatch_canberra_half_float_float_int.cu |   50 +
 ...ch_correlation_double_double_double_int.cu |    4 +-
 ...patch_correlation_float_float_float_int.cu |    4 +-
 ...spatch_correlation_half_float_float_int.cu |   50 +
 ...ispatch_cosine_double_double_double_int.cu |    4 +-
 .../dispatch_cosine_float_float_float_int.cu  |    4 +-
 .../dispatch_cosine_half_float_float_int.cu   |   51 +
 ...ing_unexpanded_double_double_double_int.cu |    4 +-
 ...amming_unexpanded_float_float_float_int.cu |    4 +-
 ...hamming_unexpanded_half_float_float_int.cu |   50 +
 ...inger_expanded_double_double_double_int.cu |    4 +-
 ...ellinger_expanded_float_float_float_int.cu |    4 +-
 ...hellinger_expanded_half_float_float_int.cu |   50 +
 ...jensen_shannon_double_double_double_int.cu |    4 +-
 ...ch_jensen_shannon_float_float_float_int.cu |    4 +-
 ...tch_jensen_shannon_half_float_float_int.cu |   55 +
 ..._kl_divergence_double_double_double_int.cu |    4 +-
 ...tch_kl_divergence_float_float_float_int.cu |    4 +-
 ...atch_kl_divergence_half_float_float_int.cu |   50 +
 .../dispatch_l1_double_double_double_int.cu   |    4 +-
 .../dispatch_l1_float_float_float_int.cu      |    4 +-
 .../dispatch_l1_half_float_float_int.cu       |   50 +
 ...ch_l2_expanded_double_double_double_int.cu |    4 +-
 ...patch_l2_expanded_float_float_float_int.cu |    4 +-
 ...spatch_l2_expanded_half_float_float_int.cu |   51 +
 ..._l2_unexpanded_double_double_double_int.cu |    4 +-
 ...tch_l2_unexpanded_float_float_float_int.cu |    4 +-
 ...atch_l2_unexpanded_half_float_float_int.cu |   50 +
 ...dispatch_l_inf_double_double_double_int.cu |    4 +-
 .../dispatch_l_inf_float_float_float_int.cu   |    4 +-
 .../dispatch_l_inf_half_float_float_int.cu    |   50 +
 ..._lp_unexpanded_double_double_double_int.cu |    4 +-
 ...tch_lp_unexpanded_float_float_float_int.cu |    4 +-
 ...atch_lp_unexpanded_half_float_float_int.cu |   50 +
 .../detail/pairwise_matrix/dispatch_rbf.cu    |   12 +-
 ...tch_russel_rao_double_double_double_int.cu |    4 +-
 ...spatch_russel_rao_float_float_float_int.cu |    4 +-
 ...ispatch_russel_rao_half_float_float_int.cu |   50 +
 .../detail/pairwise_matrix/params.cuh         |    4 +-
 cpp/src/distance/distance-ext.cuh             | 1122 ++++-------------
 cpp/src/distance/distance-inl.cuh             |   48 +-
 cpp/src/distance/distance.cu                  | 1073 +++-------------
 cpp/src/distance/distance.cuh                 |    2 -
 cpp/src/distance/pairwise_distance.cu         |   36 +
 cpp/src/neighbors/brute_force.cu              |  105 +-
 .../cagra/search_multi_cta_kernel-ext.cuh     |    4 +-
 .../detail/cagra/search_multi_cta_kernel.cuh  |    2 +-
 .../cagra/search_single_cta_kernel-ext.cuh    |    4 +-
 .../detail/cagra/search_single_cta_kernel.cuh |    2 +-
 cpp/src/neighbors/detail/fused_l2_knn.cuh     |  179 +--
 .../neighbors/detail/haversine_distance.cuh   |   52 +-
 cpp/src/neighbors/detail/knn_brute_force.cuh  |  214 ++--
 cpp/src/neighbors/detail/knn_utils.cuh        |   37 +-
 cpp/test/CMakeLists.txt                       |   15 +
 cpp/test/distance/dist_canberra.cu            |   25 +-
 cpp/test/distance/dist_correlation.cu         |   49 +-
 cpp/test/distance/dist_cos.cu                 |   69 +-
 cpp/test/distance/dist_hamming.cu             |   24 +-
 cpp/test/distance/dist_hellinger.cu           |   24 +-
 cpp/test/distance/dist_inner_product.cu       |   25 +-
 cpp/test/distance/dist_jensen_shannon.cu      |   24 +-
 cpp/test/distance/dist_kl_divergence.cu       |   24 +-
 cpp/test/distance/dist_l1.cu                  |   25 +-
 cpp/test/distance/dist_l2_exp.cu              |   72 +-
 cpp/test/distance/dist_l2_sqrt_exp.cu         |   27 +-
 cpp/test/distance/dist_l2_unexp.cu            |   24 +-
 cpp/test/distance/dist_l_inf.cu               |   25 +-
 cpp/test/distance/dist_lp_unexp.cu            |   26 +-
 cpp/test/distance/dist_russell_rao.cu         |   24 +-
 cpp/test/distance/distance_base.cuh           |  347 ++---
 cpp/test/neighbors/ann_brute_force.cuh        |  200 +++
 .../neighbors/ann_brute_force/test_float.cu   |   28 +
 .../neighbors/ann_brute_force/test_half.cu    |   30 +
 cpp/test/neighbors/brute_force.cu             |  231 +++-
 cpp/test/neighbors/brute_force_prefiltered.cu |  202 ++-
 106 files changed, 3692 insertions(+), 2760 deletions(-)
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l1_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_half_float_float_int.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu
 create mode 100644 cpp/test/neighbors/ann_brute_force.cuh
 create mode 100644 cpp/test/neighbors/ann_brute_force/test_float.cu
 create mode 100644 cpp/test/neighbors/ann_brute_force/test_half.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 3b483538a1..76fd09b582 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -216,30 +216,43 @@ add_library(
   src/cluster/kmeans_transform_float.cu
   src/cluster/single_linkage_float.cu
   src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_correlation_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_cosine_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l1_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_l_inf_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
+  src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_rbf.cu
   src/distance/detail/fused_distance_nn.cu
@@ -413,6 +426,8 @@ add_library(
   src/selection/select_k_half_uint32_t.cu
 )
 
+target_compile_definitions(cuvs PRIVATE "CUVS_EXPLICIT_INSTANTIATE_ONLY")
+
 target_compile_options(
   cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
                  --expt-relaxed-constexpr>
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 80c1f35306..6fe23483e0 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -35,6 +35,7 @@ option(CUVS_ANN_BENCH_USE_GGNN "Include ggnn algorithm in benchmark" OFF)
 option(CUVS_ANN_BENCH_SINGLE_EXE
        "Make a single executable with benchmark as shared library modules" OFF
 )
+option(CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE "Include cuVS brute force knn in benchmark" ON)
 
 # ##################################################################################################
 # * Process options ----------------------------------------------------------
@@ -53,6 +54,9 @@ if(BUILD_CPU_ONLY)
   set(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB OFF)
   set(CUVS_ANN_BENCH_USE_GGNN OFF)
+  set(CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE OFF)
+else()
+  set(CUVS_FAISS_ENABLE_GPU ON)
 endif()
 
 set(CUVS_ANN_BENCH_USE_CUVS OFF)
@@ -61,6 +65,7 @@ if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
    OR CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT
    OR CUVS_ANN_BENCH_USE_CUVS_CAGRA
    OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB
+   OR CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE
 )
   set(CUVS_ANN_BENCH_USE_CUVS ON)
 endif()
@@ -169,6 +174,8 @@ function(ConfigureAnnBench)
     )
   endif()
 
+  target_compile_definitions(${BENCH_NAME} PRIVATE "CUVS_EXPLICIT_INSTANTIATE_ONLY")
+
   target_include_directories(
     ${BENCH_NAME}
     PUBLIC "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/include>"
@@ -223,6 +230,17 @@ if(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE)
   ConfigureAnnBench(NAME CUVS_BRUTE_FORCE PATH src/cuvs/cuvs_benchmark.cu LINKS cuvs)
 endif()
 
+if(CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE)
+  ConfigureAnnBench(
+    NAME
+    CUVS_KNN_BRUTE_FORCE
+    PATH
+    $<$<BOOL:${CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE}>:src/cuvs/cuvs_brute_force_knn.cu>
+    LINKS
+    cuvs
+  )
+endif()
+
 if(CUVS_ANN_BENCH_USE_CUVS_CAGRA)
   ConfigureAnnBench(
     NAME
diff --git a/cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu b/cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu
new file mode 100644
index 0000000000..4c38b3420d
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu
@@ -0,0 +1,333 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/transpose.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/random/rng_state.hpp>
+#include <raft/util/itertools.hpp>
+
+#include <chrono>
+#include <cuda_fp16.h>
+#include <iomanip>
+#include <iostream>
+
+namespace cuvs::neighbors::brute_force {
+
+struct print_metric {
+  cuvs::distance::DistanceType value;
+};
+
+struct RandomKNNInputs {
+  int num_queries;
+  int num_db_vecs;
+  int dim;
+  int k;
+  cuvs::distance::DistanceType metric;
+  bool row_major;
+};
+
+inline auto operator<<(std::ostream& os, const print_metric& p) -> std::ostream&
+{
+  switch (p.value) {
+    case cuvs::distance::DistanceType::L2Expanded: os << "L2Expanded"; break;
+    case cuvs::distance::DistanceType::L2SqrtExpanded: os << "L2SqrtExpanded"; break;
+    case cuvs::distance::DistanceType::CosineExpanded: os << "CosineExpanded"; break;
+    case cuvs::distance::DistanceType::L1: os << "L1"; break;
+    case cuvs::distance::DistanceType::L2Unexpanded: os << "L2Unexpanded"; break;
+    case cuvs::distance::DistanceType::L2SqrtUnexpanded: os << "L2SqrtUnexpanded"; break;
+    case cuvs::distance::DistanceType::InnerProduct: os << "InnerProduct"; break;
+    case cuvs::distance::DistanceType::Linf: os << "Linf"; break;
+    case cuvs::distance::DistanceType::Canberra: os << "Canberra"; break;
+    case cuvs::distance::DistanceType::LpUnexpanded: os << "LpUnexpanded"; break;
+    case cuvs::distance::DistanceType::CorrelationExpanded: os << "CorrelationExpanded"; break;
+    case cuvs::distance::DistanceType::JaccardExpanded: os << "JaccardExpanded"; break;
+    case cuvs::distance::DistanceType::HellingerExpanded: os << "HellingerExpanded"; break;
+    case cuvs::distance::DistanceType::Haversine: os << "Haversine"; break;
+    case cuvs::distance::DistanceType::BrayCurtis: os << "BrayCurtis"; break;
+    case cuvs::distance::DistanceType::JensenShannon: os << "JensenShannon"; break;
+    case cuvs::distance::DistanceType::HammingUnexpanded: os << "HammingUnexpanded"; break;
+    case cuvs::distance::DistanceType::KLDivergence: os << "KLDivergence"; break;
+    case cuvs::distance::DistanceType::RusselRaoExpanded: os << "RusselRaoExpanded"; break;
+    case cuvs::distance::DistanceType::DiceExpanded: os << "DiceExpanded"; break;
+    case cuvs::distance::DistanceType::Precomputed: os << "Precomputed"; break;
+    default: RAFT_FAIL("unreachable code");
+  }
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const RandomKNNInputs& input)
+{
+  return os << "num_queries:" << input.num_queries << " num_vecs:" << input.num_db_vecs
+            << " dim:" << input.dim << " k:" << input.k << " metric:" << print_metric{input.metric}
+            << " row_major:" << input.row_major;
+}
+
+template <typename T, typename DistT = T>
+class BruteForceKNNBenchmark {
+ public:
+  BruteForceKNNBenchmark(const RandomKNNInputs& params, const std::string& type_str)
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      params_(params),
+      type_str_(type_str),
+      database(params_.num_db_vecs * params_.dim, stream_),
+      search_queries(params_.num_queries * params_.dim, stream_),
+      cuvs_indices_(params_.num_queries * params_.k, stream_),
+      cuvs_distances_(params_.num_queries * params_.k, stream_)
+  {
+    raft::matrix::fill(
+      handle_,
+      raft::make_device_matrix_view(database.data(), params_.num_db_vecs, params_.dim),
+      T{0.0});
+    raft::matrix::fill(
+      handle_,
+      raft::make_device_matrix_view(search_queries.data(), params_.num_queries, params_.dim),
+      T{0.0});
+    raft::matrix::fill(
+      handle_,
+      raft::make_device_matrix_view(cuvs_distances_.data(), params_.num_queries, params_.k),
+      DistT{0.0});
+  }
+
+  void runBenchmark()
+  {
+    DistT metric_arg = 3.0;
+    rmm::device_uvector<char> workspace(0, stream_);
+
+    std::chrono::duration<double, std::milli> build_dur;
+    std::chrono::duration<double, std::milli> search_dur;
+
+    auto indices = raft::make_device_matrix_view<int64_t, int64_t, raft::row_major>(
+      cuvs_indices_.data(), params_.num_queries, params_.k);
+    auto distances = raft::make_device_matrix_view<DistT, int64_t, raft::row_major>(
+      cuvs_distances_.data(), params_.num_queries, params_.k);
+    raft::resource::sync_stream(handle_, stream_);
+
+    if (params_.row_major) {
+      {
+        auto idx_warm =
+          cuvs::neighbors::brute_force::build(handle_,
+                                              raft::make_device_matrix_view<const T, int64_t>(
+                                                database.data(), params_.num_db_vecs, params_.dim),
+                                              params_.metric,
+                                              metric_arg);
+        cuvs::neighbors::brute_force::search(
+          handle_,
+          idx_warm,
+          raft::make_device_matrix_view<const T, int64_t>(
+            search_queries.data(), params_.num_queries, params_.dim),
+          indices,
+          distances,
+          std::nullopt);
+        flush_l2_cache();
+        raft::resource::sync_stream(handle_, stream_);
+      }
+
+      auto start = std::chrono::high_resolution_clock::now();
+      auto idx =
+        cuvs::neighbors::brute_force::build(handle_,
+                                            raft::make_device_matrix_view<const T, int64_t>(
+                                              database.data(), params_.num_db_vecs, params_.dim),
+                                            params_.metric,
+                                            metric_arg);
+      raft::resource::sync_stream(handle_, stream_);
+      auto end  = std::chrono::high_resolution_clock::now();
+      build_dur = end - start;
+
+      start = std::chrono::high_resolution_clock::now();
+      cuvs::neighbors::brute_force::search(
+        handle_,
+        idx,
+        raft::make_device_matrix_view<const T, int64_t>(
+          search_queries.data(), params_.num_queries, params_.dim),
+        indices,
+        distances,
+        std::nullopt);
+      raft::resource::sync_stream(handle_, stream_);
+      end        = std::chrono::high_resolution_clock::now();
+      search_dur = end - start;
+
+    } else {
+      {
+        auto idx_warm =
+          cuvs::neighbors::brute_force::build(handle_,
+                                              raft::make_device_matrix_view<const T, int64_t>(
+                                                database.data(), params_.num_db_vecs, params_.dim),
+                                              params_.metric,
+                                              metric_arg);
+        cuvs::neighbors::brute_force::search(
+          handle_,
+          idx_warm,
+          raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+            search_queries.data(), params_.num_queries, params_.dim),
+          indices,
+          distances,
+          std::nullopt);
+        flush_l2_cache();
+        raft::resource::sync_stream(handle_, stream_);
+      }
+
+      auto start = std::chrono::high_resolution_clock::now();
+      auto idx   = cuvs::neighbors::brute_force::build(
+        handle_,
+        raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+          database.data(), params_.num_db_vecs, params_.dim),
+        params_.metric,
+        metric_arg);
+      raft::resource::sync_stream(handle_, stream_);
+      auto end  = std::chrono::high_resolution_clock::now();
+      build_dur = end - start;
+
+      start = std::chrono::high_resolution_clock::now();
+      cuvs::neighbors::brute_force::search(
+        handle_,
+        idx,
+        raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+          search_queries.data(), params_.num_queries, params_.dim),
+        indices,
+        distances,
+        std::nullopt);
+      raft::resource::sync_stream(handle_, stream_);
+      end        = std::chrono::high_resolution_clock::now();
+      search_dur = end - start;
+    }
+
+    double total_dur  = build_dur.count() + search_dur.count();
+    double throughput = static_cast<double>(params_.num_queries) / (total_dur / 1000.0);
+    ;
+    printResult(params_, build_dur.count(), search_dur.count(), total_dur, throughput);
+  }
+
+  void setUp()
+  {
+    unsigned long long int seed = 1234ULL;
+    raft::random::RngState r(seed);
+
+    // JensenShannon distance requires positive values
+    T min_val = params_.metric == cuvs::distance::DistanceType::JensenShannon ? T(0.0) : T(-1.0);
+    uniform(handle_, r, database.data(), params_.num_db_vecs * params_.dim, min_val, T(1.0));
+    uniform(handle_, r, search_queries.data(), params_.num_queries * params_.dim, min_val, T(1.0));
+  }
+
+ private:
+  void flush_l2_cache()
+  {
+    int l2_cache_size = 0;
+    int device_id     = 0;
+    RAFT_CUDA_TRY(cudaGetDevice(&device_id));
+    RAFT_CUDA_TRY(cudaDeviceGetAttribute(&l2_cache_size, cudaDevAttrL2CacheSize, device_id));
+    scratch_buf_ = rmm::device_buffer(l2_cache_size * 3, stream_);
+    RAFT_CUDA_TRY(cudaMemsetAsync(scratch_buf_.data(), 0, scratch_buf_.size(), stream_));
+  };
+
+  void printResult(const RandomKNNInputs& params,
+                   double build_time,
+                   double search_time,
+                   double total_time,
+                   double throughput)
+  {
+    std::cout << std::left << std::setw(15) << type_str_ << std::setw(10) << params.num_queries
+              << std::setw(10) << params.num_db_vecs << std::setw(10) << params.dim << std::setw(10)
+              << params.k << std::setw(20) << print_metric{params.metric} << std::setw(15)
+              << (params.row_major ? "row" : "col") << std::right << std::setw(20) << std::fixed
+              << std::setprecision(3) << build_time << std::right << std::setw(20) << std::fixed
+              << std::setprecision(3) << search_time << std::right << std::setw(20) << std::fixed
+              << std::setprecision(3) << total_time << std::right << std::setw(20) << std::fixed
+              << std::setprecision(3) << throughput << "\n";
+  }
+  raft::resources handle_;
+  cudaStream_t stream_ = 0;
+  RandomKNNInputs params_;
+  rmm::device_uvector<T> database;
+  rmm::device_uvector<T> search_queries;
+  rmm::device_uvector<int64_t> cuvs_indices_;
+  rmm::device_uvector<DistT> cuvs_distances_;
+  rmm::device_buffer scratch_buf_;
+  std::string type_str_;
+};
+
+static std::vector<RandomKNNInputs> getInputs()
+{
+  std::vector<RandomKNNInputs> param_vec;
+  struct TestParams {
+    int num_queries;
+    int num_db_vecs;
+    int dim;
+    int k;
+    cuvs::distance::DistanceType metric;
+    bool row_major;
+  };
+
+  const std::vector<TestParams> params_group = raft::util::itertools::product<TestParams>(
+    {int(10), int(100), int(1024)},
+    {int(1000000)},
+    {int(32), int(256), int(1024)},
+    {int(128), int(1024)},
+    {cuvs::distance::DistanceType::InnerProduct, cuvs::distance::DistanceType::L2SqrtExpanded},
+    {true, false});
+
+  param_vec.reserve(params_group.size());
+  for (TestParams params : params_group) {
+    param_vec.push_back(RandomKNNInputs({params.num_queries,
+                                         params.num_db_vecs,
+                                         params.dim,
+                                         params.k,
+                                         params.metric,
+                                         params.row_major}));
+  }
+  return param_vec;
+}
+
+void printHeader()
+{
+  std::cout << std::left << std::setw(15) << "Type" << std::setw(10) << "Queries" << std::setw(10)
+            << "Vectors" << std::setw(10) << "Dim" << std::setw(10) << "K" << std::setw(20)
+            << "Metric" << std::setw(15) << "Layout" << std::right << std::setw(20)
+            << "Build Time (ms)" << std::right << std::setw(20) << "Search Time (ms)" << std::right
+            << std::setw(20) << "Total Time (ms)" << std::right << std::setw(20)
+            << "Throughput (q/s)"
+            << "\n";
+  std::cout << std::string(165, '-') << "\n";
+}
+
+void runBenchmarkForType()
+{
+  auto selected_inputs = getInputs();
+  for (const auto& input : selected_inputs) {
+    {
+      BruteForceKNNBenchmark<float, float> benchmark(input, "float");
+      benchmark.setUp();
+      benchmark.runBenchmark();
+    }
+    {
+      BruteForceKNNBenchmark<half, float> benchmark(input, "half");
+      benchmark.setUp();
+      benchmark.runBenchmark();
+    }
+  }
+}
+
+}  // namespace cuvs::neighbors::brute_force
+
+int main()
+{
+  cuvs::neighbors::brute_force::printHeader();
+  cuvs::neighbors::brute_force::runBenchmarkForType();
+  return 0;
+}
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 7640fbfa6e..fb25623ce3 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -61,8 +61,8 @@ endfunction()
 # To use a different RAFT locally, set the CMake variable
 # CPM_raft_SOURCE=/path/to/local/raft
 find_and_configure_raft(VERSION  ${RAFT_VERSION}.00
-        FORK                     ${RAFT_FORK}
-        PINNED_TAG               ${RAFT_PINNED_TAG}
+        FORK                     rhdong
+        PINNED_TAG               half_knn
         ENABLE_MNMG_DEPENDENCIES OFF
         ENABLE_NVTX              OFF
         USE_RAFT_STATIC ${CUVS_USE_RAFT_STATIC}
diff --git a/cpp/include/cuvs/distance/distance.hpp b/cpp/include/cuvs/distance/distance.hpp
index 5786b0a32b..def72641ec 100644
--- a/cpp/include/cuvs/distance/distance.hpp
+++ b/cpp/include/cuvs/distance/distance.hpp
@@ -19,6 +19,7 @@
 #include "distance.h"
 
 #include <cstdint>
+#include <cuda_fp16.h>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
 
@@ -156,6 +157,49 @@ void pairwise_distance(
   raft::device_matrix_view<double, std::int64_t, raft::layout_c_contiguous> dist,
   cuvs::distance::DistanceType metric,
   double metric_arg = 2.0f);
+/**
+ * @brief Compute pairwise distances for two matrices
+ *
+ * Note: Only contiguous row- or column-major layouts supported currently.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/core/device_mdarray.hpp>
+ * #include <cuvs/distance/distance.hpp>
+ *
+ * raft::resources handle;
+ * int n_samples = 5000;
+ * int n_features = 50;
+ *
+ * auto input = raft::make_device_matrix<float>(handle, n_samples, n_features);
+ *
+ * // ... fill input with data ...
+ *
+ * auto output = raft::make_device_matrix<float>(handle, n_samples, n_samples);
+ *
+ * auto metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+ * cuvs::distance::pairwise_distance(handle,
+ *                                   raft::make_const(input.view()),
+ *                                   raft::make_const(input.view()),
+ *                                   output.view(),
+ *                                   metric);
+ * @endcode
+ *
+ * @param[in] handle raft handle for managing expensive resources
+ * @param[in] x first set of points (size n*k)
+ * @param[in] y second set of points (size m*k)
+ * @param[out] dist output distance matrix (size n*m)
+ * @param[in] metric distance to evaluate
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_c_contiguous> const x,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_c_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_c_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg = 2.0f);
 
 /**
  * @brief Compute pairwise distances for two matrices
@@ -243,6 +287,49 @@ void pairwise_distance(
   raft::device_matrix_view<double, std::int64_t, raft::layout_f_contiguous> dist,
   cuvs::distance::DistanceType metric,
   double metric_arg = 2.0f);
+/**
+ * @brief Compute pairwise distances for two matrices
+ *
+ * Note: Only contiguous row- or column-major layouts supported currently.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <raft/core/device_mdarray.hpp>
+ * #include <cuvs/distance/distance.hpp>
+ *
+ * raft::resources handle;
+ * int n_samples = 5000;
+ * int n_features = 50;
+ *
+ * auto input = raft::make_device_matrix<float>(handle, n_samples, n_features);
+ *
+ * // ... fill input with data ...
+ *
+ * auto output = raft::make_device_matrix<float>(handle, n_samples, n_samples);
+ *
+ * auto metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+ * cuvs::distance::pairwise_distance(handle,
+ *                                   raft::make_const(input.view()),
+ *                                   raft::make_const(input.view()),
+ *                                   output.view(),
+ *                                   metric);
+ * @endcode
+ *
+ * @param[in] handle raft handle for managing expensive resources
+ * @param[in] x first set of points (size n*k)
+ * @param[in] y second set of points (size m*k)
+ * @param[out] dist output distance matrix (size n*m)
+ * @param[in] metric distance to evaluate
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_f_contiguous> const x,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_f_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_f_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg = 2.0f);
 
 /** @} */  // end group pairwise_distance_runtime
 
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index db70a7fa65..5408eb1a00 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -23,6 +23,8 @@
 #include <raft/core/handle.hpp>
 #include <raft/core/host_mdspan.hpp>
 
+#include <cuda_fp16.h>
+
 namespace cuvs::neighbors::brute_force {
 
 /**
@@ -36,7 +38,7 @@ namespace cuvs::neighbors::brute_force {
  *
  * @tparam T data element type
  */
-template <typename T>
+template <typename T, typename DistT = T>
 struct index : cuvs::neighbors::index {
  public:
   index(const index&)            = delete;
@@ -54,9 +56,9 @@ struct index : cuvs::neighbors::index {
    */
   index(raft::resources const& res,
         raft::host_matrix_view<const T, int64_t, raft::row_major> dataset_view,
-        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        std::optional<raft::device_vector<DistT, int64_t>>&& norms,
         cuvs::distance::DistanceType metric,
-        T metric_arg = 0.0);
+        DistT metric_arg = 0.0);
 
   /** Construct a brute force index from dataset
    *
@@ -67,9 +69,9 @@ struct index : cuvs::neighbors::index {
    */
   index(raft::resources const& res,
         raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
-        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        std::optional<raft::device_vector<DistT, int64_t>>&& norms,
         cuvs::distance::DistanceType metric,
-        T metric_arg = 0.0);
+        DistT metric_arg = 0.0);
 
   /** Construct a brute force index from dataset
    *
@@ -78,9 +80,9 @@ struct index : cuvs::neighbors::index {
    */
   index(raft::resources const& res,
         raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
-        std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
+        std::optional<raft::device_vector_view<const DistT, int64_t>> norms_view,
         cuvs::distance::DistanceType metric,
-        T metric_arg = 0.0);
+        DistT metric_arg = 0.0);
 
   /** Construct a brute force index from dataset
    *
@@ -91,9 +93,9 @@ struct index : cuvs::neighbors::index {
    */
   index(raft::resources const& res,
         raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
-        std::optional<raft::device_vector<T, int64_t>>&& norms,
+        std::optional<raft::device_vector<DistT, int64_t>>&& norms,
         cuvs::distance::DistanceType metric,
-        T metric_arg = 0.0);
+        DistT metric_arg = 0.0);
 
   /** Construct a brute force index from dataset
    *
@@ -102,9 +104,9 @@ struct index : cuvs::neighbors::index {
    */
   index(raft::resources const& res,
         raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
-        std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
+        std::optional<raft::device_vector_view<const DistT, int64_t>> norms_view,
         cuvs::distance::DistanceType metric,
-        T metric_arg = 0.0);
+        DistT metric_arg = 0.0);
 
   /**
    * Replace the dataset with a new dataset.
@@ -124,7 +126,7 @@ struct index : cuvs::neighbors::index {
   cuvs::distance::DistanceType metric() const noexcept { return metric_; }
 
   /** Metric argument */
-  T metric_arg() const noexcept { return metric_arg_; }
+  DistT metric_arg() const noexcept { return metric_arg_; }
 
   /** Total length of the index (number of vectors). */
   size_t size() const noexcept { return dataset_view_.extent(0); }
@@ -139,7 +141,7 @@ struct index : cuvs::neighbors::index {
   }
 
   /** Dataset norms */
-  raft::device_vector_view<const T, int64_t, raft::row_major> norms() const
+  raft::device_vector_view<const DistT, int64_t, raft::row_major> norms() const
   {
     return norms_view_.value();
   }
@@ -150,10 +152,10 @@ struct index : cuvs::neighbors::index {
  private:
   cuvs::distance::DistanceType metric_;
   raft::device_matrix<T, int64_t, raft::row_major> dataset_;
-  std::optional<raft::device_vector<T, int64_t>> norms_;
-  std::optional<raft::device_vector_view<const T, int64_t>> norms_view_;
+  std::optional<raft::device_vector<DistT, int64_t>> norms_;
+  std::optional<raft::device_vector_view<const DistT, int64_t>> norms_view_;
   raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view_;
-  T metric_arg_;
+  DistT metric_arg_;
 };
 /**
  * @}
@@ -183,8 +185,28 @@ struct index : cuvs::neighbors::index {
 auto build(raft::resources const& handle,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
-           float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<float>;
-
+           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<float, float>;
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = brute_force::build(handle, dataset, metric);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ * @param[in] metric cuvs::distance::DistanceType
+ * @param[in] metric_arg metric argument
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
+           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<half, float>;
 /**
  * @brief Build the index from the dataset for efficient search.
  *
@@ -205,7 +227,28 @@ auto build(raft::resources const& handle,
 auto build(raft::resources const& handle,
            raft::device_matrix_view<const float, int64_t, raft::col_major> dataset,
            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
-           float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<float>;
+           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<float, float>;
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = brute_force::build(handle, dataset, metric);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] dataset a device pointer to a col-major matrix [n_rows, dim]
+ * @param[in] metric cuvs::distance::DistanceType
+ * @param[in] metric_arg metric argument
+ *
+ * @return the constructed bruteforce index
+ */
+auto build(raft::resources const& handle,
+           raft::device_matrix_view<const half, int64_t, raft::col_major> dataset,
+           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<half, float>;
 /**
  * @}
  */
@@ -244,12 +287,46 @@ auto build(raft::resources const& handle,
  * `index->size()` bits to indicate whether queries[0] should compute the distance with dataset.
  */
 void search(raft::resources const& handle,
-            const cuvs::neighbors::brute_force::index<float>& index,
+            const cuvs::neighbors::brute_force::index<float, float>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
 
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [brute_force::build](#brute_force::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`:
+ * @code{.cpp}
+ *   ...
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   brute_force::search(handle, index, queries1, out_inds1, out_dists1);
+ *   brute_force::search(handle, index, queries2, out_inds2, out_dists2);
+ *   brute_force::search(handle, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index ivf-flat constructed index
+ * @param[in] queries a device pointer to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
+ * @param[in] sample_filter a optional device bitmap filter function that greenlights samples for a
+ * given
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::index<half, float>& index,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
 /**
  * @brief Search ANN using the constructed index.
  *
@@ -265,11 +342,31 @@ void search(raft::resources const& handle,
  * given query
  */
 void search(raft::resources const& handle,
-            const cuvs::neighbors::brute_force::index<float>& index,
+            const cuvs::neighbors::brute_force::index<float, float>& index,
             raft::device_matrix_view<const float, int64_t, raft::col_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [brute_force::build](#brute_force::build) documentation for a usage example.
+ *
+ * @param[in] handle
+ * @param[in] index bruteforce constructed index
+ * @param[in] queries a device pointer to a col-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
+ * @param[in] sample_filter an optional device bitmap filter function that greenlights samples for a
+ * given query
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::index<half, float>& index,
+            raft::device_matrix_view<const half, int64_t, raft::col_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
 /**
  * @}
  */
diff --git a/cpp/src/distance/detail/distance.cuh b/cpp/src/distance/detail/distance.cuh
index c8dde4ea4f..d6fd046469 100644
--- a/cpp/src/distance/detail/distance.cuh
+++ b/cpp/src/distance/detail/distance.cuh
@@ -27,6 +27,7 @@
 #include <raft/linalg/norm.cuh>
 #include <raft/linalg/reduce.cuh>
 #include <raft/linalg/unary_op.cuh>
+#include <raft/util/cuda_dev_essentials.cuh>  // to_float
 
 #include <type_traits>
 
@@ -104,8 +105,8 @@ void distance_impl(raft::resources const& handle,
 {
   ops::canberra_distance_op<DataT, AccT, IdxT> distance_op{};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
@@ -217,8 +218,8 @@ void distance_impl(raft::resources const& handle,
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
-  DataT* x_norm = workspace;
-  DataT* y_norm = workspace;
+  OutT* x_norm = reinterpret_cast<OutT*>(workspace);
+  OutT* y_norm = reinterpret_cast<OutT*>(workspace);
   // TODO: Column major case looks to have lower accuracy for X == Y,
   // perhaps the use of stridedSummationKernel could be causing this,
   // need to investigate and fix.
@@ -255,8 +256,8 @@ void distance_impl(raft::resources const& handle,
 {
   ops::hamming_distance_op<DataT, AccT, IdxT> distance_op{k};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
@@ -319,8 +320,8 @@ void distance_impl(raft::resources const& handle,
   // Then calculate Hellinger distance
   ops::hellinger_distance_op<DataT, AccT, IdxT> distance_op{};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
     distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
@@ -349,8 +350,8 @@ void distance_impl(raft::resources const& handle,
 {
   ops::jensen_shannon_distance_op<DataT, AccT, IdxT> distance_op{};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
@@ -376,14 +377,24 @@ void distance_impl(raft::resources const& handle,
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
   auto unaryOp_lambda = [] __device__(DataT input) {
-    const bool x_zero = (input == 0);
-    return (!x_zero) * raft::log(input + x_zero);
+    auto input_       = raft::to_float(input);
+    const bool x_zero = (input_ == 0);
+    if constexpr (std::is_same_v<DataT, half>) {
+      return __float2half((!x_zero) * raft::log(input_ + x_zero));
+    } else {
+      return (!x_zero) * raft::log(input_ + x_zero);
+    }
   };
 
   auto unaryOp_lambda_reverse = [] __device__(DataT input) {
     // reverse previous log (x) back to x using (e ^ log(x))
-    const bool x_zero = (input == 0);
-    return (!x_zero) * raft::exp(input);
+    auto input_       = raft::to_float(input);
+    const bool x_zero = (input_ == 0);
+    if constexpr (std::is_same_v<DataT, half>) {
+      return __float2half((!x_zero) * raft::exp(input_));
+    } else {
+      return (!x_zero) * raft::exp(input_);
+    }
   };
 
   if (x != y) {
@@ -391,8 +402,8 @@ void distance_impl(raft::resources const& handle,
       (DataT*)y, y, n * k, unaryOp_lambda, stream);
   }
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   // This op takes some shortcuts when x equals y. So its behavior changes based
   // on this.
@@ -425,8 +436,8 @@ void distance_impl(raft::resources const& handle,
 {
   ops::l1_distance_op<DataT, AccT, IdxT> distance_op{};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
@@ -460,8 +471,13 @@ void distance_impl_l2_expanded(  // NOTE: different name
   ASSERT(!(worksize < (m + n) * sizeof(AccT)), "workspace size error");
   ASSERT(workspace != nullptr, "workspace is null");
 
-  DataT* x_norm = workspace;
-  DataT* y_norm = workspace;
+  // TODO: May we have a better method to avoid misalignment?
+  uintptr_t offset = alignof(OutT) - (reinterpret_cast<uintptr_t>(workspace) % alignof(OutT));
+  if (offset == alignof(OutT)) { offset = 0; }
+  OutT* x_norm = reinterpret_cast<OutT*>(reinterpret_cast<char*>(workspace) + offset);
+
+  offset       = (reinterpret_cast<uintptr_t>(x_norm) % alignof(OutT));
+  OutT* y_norm = x_norm;
   // TODO: Column major case looks to have lower accuracy for X == Y,
   // perhaps the use of stridedSummationKernel could be causing this,
   // need to investigate and fix.
@@ -548,8 +564,8 @@ void distance_impl(raft::resources const& handle,
   ops::l2_unexp_distance_op<DataT, AccT, IdxT> l2_op(perform_sqrt);
 
   // The unexpanded L2 does not require the norms of a and b to be calculated.
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
@@ -576,8 +592,8 @@ void distance_impl(raft::resources const& handle,
   ops::l2_unexp_distance_op<DataT, AccT, IdxT> l2_op(perform_sqrt);
 
   // The unexpanded L2 does not require the norms of a and b to be calculated.
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
@@ -602,8 +618,8 @@ void distance_impl(raft::resources const& handle,
 {
   ops::l_inf_distance_op<DataT, AccT, IdxT> distance_op{};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
@@ -628,8 +644,8 @@ void distance_impl(raft::resources const& handle,
 {
   ops::lp_unexp_distance_op<DataT, AccT, IdxT> distance_op{metric_arg};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
@@ -654,8 +670,8 @@ void distance_impl(raft::resources const& handle,
 {
   ops::russel_rao_distance_op<DataT, AccT, IdxT> distance_op{k};
 
-  const DataT* x_norm = nullptr;
-  const DataT* y_norm = nullptr;
+  const OutT* x_norm = nullptr;
+  const OutT* y_norm = nullptr;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
@@ -705,8 +721,8 @@ void distance(raft::resources const& handle,
               void* workspace,
               size_t worksize,
               FinalLambda fin_op,
-              bool isRowMajor   = true,
-              InType metric_arg = 2.0f)
+              bool isRowMajor    = true,
+              OutType metric_arg = 2.0f)
 {
   // raft distance support inputs as float/double and output as uint8_t/float/double.
   static_assert(!((sizeof(OutType) > 1) && (sizeof(AccType) != sizeof(OutType))),
@@ -762,8 +778,8 @@ void distance(raft::resources const& handle,
               Index_ k,
               void* workspace,
               size_t worksize,
-              bool isRowMajor   = true,
-              InType metric_arg = 2.0f)
+              bool isRowMajor    = true,
+              OutType metric_arg = 2.0f)
 {
   auto fin_op = raft::identity_op();
 
diff --git a/cpp/src/distance/detail/distance_ops/canberra.cuh b/cpp/src/distance/detail/distance_ops/canberra.cuh
index 8bbdc9945b..bf01caf986 100644
--- a/cpp/src/distance/detail/distance_ops/canberra.cuh
+++ b/cpp/src/distance/detail/distance_ops/canberra.cuh
@@ -19,6 +19,8 @@
 #include <raft/core/operators.hpp>            // raft::abs
 #include <raft/util/cuda_dev_essentials.cuh>  // DI
 
+#include <cuda_fp16.h>
+
 namespace cuvs::distance::detail::ops {
 
 /**
@@ -50,17 +52,27 @@ struct canberra_distance_op {
 
   DI void core(AccT& acc, DataT& x, DataT& y) const
   {
-    const auto diff = raft::abs(x - y);
-    const auto add  = raft::abs(x) + raft::abs(y);
-    // deal with potential for 0 in denominator by
-    // forcing 0/1 instead
-    acc += ((add != 0) * diff / (add + (add == 0)));
+    if constexpr ((std::is_same_v<AccT, float> && std::is_same_v<DataT, half>)) {
+      AccT _x         = __half2float(x);
+      AccT _y         = __half2float(y);
+      const auto diff = raft::abs(_x - _y);
+      const auto add  = raft::abs(_x) + raft::abs(_y);
+      // deal with potential for 0 in denominator by
+      // forcing 0/1 instead
+      acc += ((add != 0) * diff / (add + (add == 0)));
+    } else {
+      const auto diff = raft::abs(x - y);
+      const auto add  = raft::abs(x) + raft::abs(y);
+      // deal with potential for 0 in denominator by
+      // forcing 0/1 instead
+      acc += ((add != 0) * diff / (add + (add == 0)));
+    }
   };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/correlation.cuh b/cpp/src/distance/detail/distance_ops/correlation.cuh
index f033f3dfad..810d3f90c4 100644
--- a/cpp/src/distance/detail/distance_ops/correlation.cuh
+++ b/cpp/src/distance/detail/distance_ops/correlation.cuh
@@ -18,6 +18,8 @@
 
 #include <raft/util/cuda_dev_essentials.cuh>  // DI
 
+#include <cuda_fp16.h>
+
 namespace cuvs::distance::detail::ops {
 
 /** @brief The correlation distance
@@ -34,20 +36,20 @@ struct correlation_distance_op {
   using AccT  = AccType;
   using IdxT  = IdxType;
 
-  const DataT* x2n;
-  const DataT* y2n;
+  const AccT* x2n;
+  const AccT* y2n;
   IdxT m;
   IdxT n;
   IdxT k;
 
   correlation_distance_op(
-    bool is_row_major, const DataT* x2n_, const DataT* y2n_, IdxT m_, IdxT n_, IdxT k_) noexcept
+    bool is_row_major, const AccT* x2n_, const AccT* y2n_, IdxT m_, IdxT n_, IdxT k_) noexcept
     : x2n(x2n_), y2n(y2n_), m(m_), n(n_), k(k_)
   {
     // The distance op is typically created before the row-major/col-major
     // swapping has been done. So we do it here.
     if (!is_row_major) {
-      std::swap<const DataT*>(x2n, y2n);
+      std::swap<const AccT*>(x2n, y2n);
       std::swap(m, n);
     }
   }
@@ -63,15 +65,18 @@ struct correlation_distance_op {
   template <typename Policy>
   static constexpr size_t shared_mem_size()
   {
-    return Policy::SmemSize + (2 * (Policy::Mblk + Policy::Nblk) * sizeof(DataT));
+    return Policy::SmemSize + (2 * (Policy::Mblk + Policy::Nblk) * sizeof(AccT));
   }
 
-  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    acc += raft::to_float(x) * raft::to_float(y);
+  };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
@@ -80,23 +85,22 @@ struct correlation_distance_op {
     // changes, this will be where we find the bugs.
     extern __shared__ char smem[];
 
-    DataT regx2n[Policy::AccRowsPerTh], regy2n[Policy::AccColsPerTh];
+    AccT regx2n[Policy::AccRowsPerTh], regy2n[Policy::AccColsPerTh];
 
-    DataT* sx2Norm =
-      (DataT*)(&smem[Policy::SmemSize + (Policy::Mblk + Policy::Nblk) * sizeof(DataT)]);
-    DataT* sy2Norm = (&sx2Norm[Policy::Mblk]);
+    AccT* sx2Norm = (AccT*)(&smem[Policy::SmemSize + (Policy::Mblk + Policy::Nblk) * sizeof(AccT)]);
+    AccT* sy2Norm = (&sx2Norm[Policy::Mblk]);
 
     // Load x & y norms required by this threadblock in shmem buffer
     if (gridStrideX == blockIdx.x * Policy::Nblk) {
       for (int i = threadIdx.x; i < Policy::Mblk; i += Policy::Nthreads) {
         auto idx   = gridStrideY + i;
-        sx2Norm[i] = idx < m ? x2n[idx] : 0;
+        sx2Norm[i] = idx < m ? raft::to_float(x2n[idx]) : 0;
       }
     }
 
     for (int i = threadIdx.x; i < Policy::Nblk; i += Policy::Nthreads) {
       auto idx   = gridStrideX + i;
-      sy2Norm[i] = idx < n ? y2n[idx] : 0;
+      sy2Norm[i] = idx < n ? raft::to_float(y2n[idx]) : 0;
     }
     __syncthreads();
 
diff --git a/cpp/src/distance/detail/distance_ops/cosine.cuh b/cpp/src/distance/detail/distance_ops/cosine.cuh
index d487316513..b0a8b867c1 100644
--- a/cpp/src/distance/detail/distance_ops/cosine.cuh
+++ b/cpp/src/distance/detail/distance_ops/cosine.cuh
@@ -18,17 +18,19 @@
 
 #include <raft/util/cuda_dev_essentials.cuh>  // DI
 
+#include <cuda_fp16.h>
+
 namespace cuvs::distance::detail::ops {
 
 // Epilogue operator for CUTLASS based kernel
 template <typename DataT, typename AccT>
 struct cosine_cutlass_op {
   __device__ cosine_cutlass_op() noexcept {}
-  __device__ AccT operator()(DataT& aNorm, const DataT& bNorm, DataT& accVal) const noexcept
+  __device__ AccT operator()(AccT& aNorm, const AccT& bNorm, AccT& accVal) const noexcept
   {
     return static_cast<AccT>(1.0) - static_cast<AccT>(accVal / (aNorm * bNorm));
   }
-  __device__ AccT operator()(DataT aData) const noexcept { return aData; }
+  __device__ AccT operator()(DataT aData) const noexcept { return raft::to_float(aData); }
 };
 
 /**
@@ -55,15 +57,22 @@ struct cosine_distance_op {
   template <typename Policy>
   static constexpr size_t shared_mem_size()
   {
-    return Policy::SmemSize + ((Policy::Mblk + Policy::Nblk) * sizeof(DataT));
+    return Policy::SmemSize + ((Policy::Mblk + Policy::Nblk) * sizeof(AccT));
   }
 
-  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    if constexpr ((std::is_same_v<AccT, float> && std::is_same_v<DataT, half>)) {
+      acc += __half2float(x) * __half2float(y);
+    } else {
+      acc += x * y;
+    }
+  };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
@@ -71,7 +80,11 @@ struct cosine_distance_op {
     for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
 #pragma unroll
       for (int j = 0; j < Policy::AccColsPerTh; ++j) {
-        acc[i][j] = 1.0 - (acc[i][j] / (regxn[i] * regyn[j]));
+        if constexpr ((std::is_same_v<AccT, float> && std::is_same_v<AccT, half>)) {
+          acc[i][j] = 1.0 - (acc[i][j] / (__half2float(regxn[i]) * __half2float(regyn[j])));
+        } else {
+          acc[i][j] = 1.0 - (acc[i][j] / (regxn[i] * regyn[j]));
+        }
       }
     }
   }
diff --git a/cpp/src/distance/detail/distance_ops/hamming.cuh b/cpp/src/distance/detail/distance_ops/hamming.cuh
index 7c6553f387..8548df7520 100644
--- a/cpp/src/distance/detail/distance_ops/hamming.cuh
+++ b/cpp/src/distance/detail/distance_ops/hamming.cuh
@@ -54,12 +54,12 @@ struct hamming_distance_op {
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
-    const DataT one_over_k = DataT(1.0) / k;
+    const AccT one_over_k = AccT(1.0) / k;
 #pragma unroll
     for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
 #pragma unroll
diff --git a/cpp/src/distance/detail/distance_ops/hellinger.cuh b/cpp/src/distance/detail/distance_ops/hellinger.cuh
index ad5ca31564..5d9dd22591 100644
--- a/cpp/src/distance/detail/distance_ops/hellinger.cuh
+++ b/cpp/src/distance/detail/distance_ops/hellinger.cuh
@@ -50,14 +50,14 @@ struct hellinger_distance_op {
   DI void core(AccT& acc, DataT& x, DataT& y) const
   {
     // This is sqrt(x) * sqrt(y).
-    const auto product = x * y;
+    const AccT product = raft::to_float(x) * raft::to_float(y);
     acc += product;
   };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/jensen_shannon.cuh b/cpp/src/distance/detail/distance_ops/jensen_shannon.cuh
index 2166394941..8f8324ed0e 100644
--- a/cpp/src/distance/detail/distance_ops/jensen_shannon.cuh
+++ b/cpp/src/distance/detail/distance_ops/jensen_shannon.cuh
@@ -52,19 +52,21 @@ struct jensen_shannon_distance_op {
 
   DI void core(AccT& acc, DataT& x, DataT& y) const
   {
-    const DataT m     = 0.5f * (x + y);
+    AccT x_           = raft::to_float(x);
+    AccT y_           = raft::to_float(y);
+    const AccT m      = 0.5f * (x_ + y_);
     const bool m_zero = (m == 0);
     const auto logM   = (!m_zero) * raft::log(m + m_zero);
 
-    const bool x_zero = (x == 0);
-    const bool y_zero = (y == 0);
-    acc += (-x * (logM - raft::log(x + x_zero))) + (-y * (logM - raft::log(y + y_zero)));
+    const bool x_zero = (x_ == 0);
+    const bool y_zero = (y_ == 0);
+    acc += (-x_ * (logM - raft::log(x_ + x_zero))) + (-y_ * (logM - raft::log(y_ + y_zero)));
   };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/kl_divergence.cuh b/cpp/src/distance/detail/distance_ops/kl_divergence.cuh
index 929c3a5596..7f183b159e 100644
--- a/cpp/src/distance/detail/distance_ops/kl_divergence.cuh
+++ b/cpp/src/distance/detail/distance_ops/kl_divergence.cuh
@@ -59,31 +59,33 @@ struct kl_divergence_op {
   {
     // TODO: make sure that these branches get hoisted out of main loop.. Could
     // be quite expensive otherwise.
+    AccT x_ = raft::to_float(x);
+    AccT y_ = raft::to_float(y);
     if (x_equal_y) {
       if (is_row_major) {
-        const bool x_zero = (x == 0);
-        const bool y_zero = (y == 0);
-        acc += x * (raft::log(x + x_zero) - (!y_zero) * raft::log(y + y_zero));
+        const bool x_zero = (x_ == 0);
+        const bool y_zero = (y_ == 0);
+        acc += x_ * (raft::log(x_ + x_zero) - (!y_zero) * raft::log(y_ + y_zero));
       } else {
-        const bool y_zero = (y == 0);
-        const bool x_zero = (x == 0);
-        acc += y * (raft::log(y + y_zero) - (!x_zero) * raft::log(x + x_zero));
+        const bool y_zero = (y_ == 0);
+        const bool x_zero = (x_ == 0);
+        acc += y_ * (raft::log(y_ + y_zero) - (!x_zero) * raft::log(x_ + x_zero));
       }
     } else {
       if (is_row_major) {
-        const bool x_zero = (x == 0);
-        acc += x * (raft::log(x + x_zero) - y);
+        const bool x_zero = (x_ == 0);
+        acc += x_ * (raft::log(x_ + x_zero) - y_);
       } else {
-        const bool y_zero = (y == 0);
-        acc += y * (raft::log(y + y_zero) - x);
+        const bool y_zero = (y_ == 0);
+        acc += y_ * (raft::log(y_ + y_zero) - x_);
       }
     }
   };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/l1.cuh b/cpp/src/distance/detail/distance_ops/l1.cuh
index 76eaffaf30..278702ea33 100644
--- a/cpp/src/distance/detail/distance_ops/l1.cuh
+++ b/cpp/src/distance/detail/distance_ops/l1.cuh
@@ -46,12 +46,15 @@ struct l1_distance_op {
     return Policy::SmemSize;
   }
 
-  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += raft::abs(x - y); };
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    acc += raft::abs(raft::to_float(x) - raft::to_float(y));
+  };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/l2_exp.cuh b/cpp/src/distance/detail/distance_ops/l2_exp.cuh
index f45c412069..04817aa8b1 100644
--- a/cpp/src/distance/detail/distance_ops/l2_exp.cuh
+++ b/cpp/src/distance/detail/distance_ops/l2_exp.cuh
@@ -19,6 +19,8 @@
 #include <raft/core/math.hpp>
 #include <raft/util/cuda_dev_essentials.cuh>  // DI
 
+#include <cuda_fp16.h>
+
 namespace cuvs::distance::detail::ops {
 
 /**
@@ -52,8 +54,8 @@ struct l2_exp_cutlass_op {
      * Self-neighboring points should have (aNorm == bNorm) == accVal and the dot product (accVal)
      * can sometimes have round-off errors, which will cause (aNorm == bNorm) ~ accVal instead.
      */
-    outVal = outVal * !((outVal * outVal < get_clamp_precision<DataT>()) * (aNorm == bNorm));
-    return sqrt ? raft::sqrt(outVal * (outVal > 0)) : outVal;
+    outVal = outVal * AccT(!((outVal * outVal < get_clamp_precision<AccT>()) * (aNorm == bNorm)));
+    return sqrt ? raft::sqrt(outVal * static_cast<AccT>(outVal > AccT(0))) : outVal;
   }
 
   __device__ AccT operator()(DataT aData) const noexcept { return aData; }
@@ -88,15 +90,22 @@ struct l2_exp_distance_op {
   template <typename Policy>
   static constexpr size_t shared_mem_size()
   {
-    return Policy::SmemSize + ((Policy::Mblk + Policy::Nblk) * sizeof(DataT));
+    return Policy::SmemSize + ((Policy::Mblk + Policy::Nblk) * sizeof(AccT));
   }
 
-  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    if constexpr ((std::is_same_v<AccT, float> && std::is_same_v<DataT, half>)) {
+      acc += __half2float(x) * __half2float(y);
+    } else {
+      acc += x * y;
+    }
+  };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
@@ -104,8 +113,8 @@ struct l2_exp_distance_op {
     for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
 #pragma unroll
       for (int j = 0; j < Policy::AccColsPerTh; ++j) {
-        DataT accVal = acc[i][j];
-        DataT val    = regxn[i] + regyn[j] - (DataT)2.0 * accVal;
+        AccT accVal = acc[i][j];
+        AccT val    = regxn[i] + regyn[j] - (AccT)2.0 * accVal;
 
         /**
          * Self-neighboring points should have (aNorm == bNorm) == accVal and the dot product
@@ -113,7 +122,8 @@ struct l2_exp_distance_op {
          * instead.
          */
         acc[i][j] =
-          val * (val > 0) * !((val * val < get_clamp_precision<DataT>()) * (regxn[i] == regyn[j]));
+          val * static_cast<AccT>((val > AccT(0))) *
+          static_cast<AccT>(!((val * val < get_clamp_precision<AccT>()) * (regxn[i] == regyn[j])));
       }
     }
     if (sqrt) {
diff --git a/cpp/src/distance/detail/distance_ops/l2_unexp.cuh b/cpp/src/distance/detail/distance_ops/l2_unexp.cuh
index aa6cc27f3e..f12820d8ec 100644
--- a/cpp/src/distance/detail/distance_ops/l2_unexp.cuh
+++ b/cpp/src/distance/detail/distance_ops/l2_unexp.cuh
@@ -18,6 +18,8 @@
 
 #include <raft/util/cuda_dev_essentials.cuh>  // DI
 
+#include <cuda_fp16.h>
+
 namespace cuvs::distance::detail::ops {
 
 /**
@@ -53,14 +55,19 @@ struct l2_unexp_distance_op {
 
   DI void core(AccT& acc, DataT& x, DataT& y) const
   {
-    const auto diff = x - y;
-    acc += diff * diff;
+    if constexpr ((std::is_same_v<AccT, float> && std::is_same_v<DataT, half>)) {
+      const auto diff = __half2float(x) - __half2float(y);
+      acc += diff * diff;
+    } else {
+      const auto diff = x - y;
+      acc += diff * diff;
+    }
   };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/l_inf.cuh b/cpp/src/distance/detail/distance_ops/l_inf.cuh
index d8f9384d7e..d8559a7d1b 100644
--- a/cpp/src/distance/detail/distance_ops/l_inf.cuh
+++ b/cpp/src/distance/detail/distance_ops/l_inf.cuh
@@ -55,8 +55,8 @@ struct l_inf_distance_op {
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/lp_unexp.cuh b/cpp/src/distance/detail/distance_ops/lp_unexp.cuh
index 6136f9f3eb..2adf0befa0 100644
--- a/cpp/src/distance/detail/distance_ops/lp_unexp.cuh
+++ b/cpp/src/distance/detail/distance_ops/lp_unexp.cuh
@@ -53,18 +53,18 @@ struct lp_unexp_distance_op {
 
   DI void core(AccT& acc, DataT& x, DataT& y) const
   {
-    const auto diff = raft::abs(x - y);
-    acc += raft::pow(diff, p);
+    const AccT diff = raft::abs(raft::to_float(x) - raft::to_float(y));
+    acc += raft::pow(diff, raft::to_float(p));
   };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
-    const auto one_over_p = 1.0f / p;
+    const AccT one_over_p = 1.0f / static_cast<AccT>(raft::to_float(p));
 #pragma unroll
     for (int i = 0; i < Policy::AccRowsPerTh; ++i) {
 #pragma unroll
diff --git a/cpp/src/distance/detail/distance_ops/russel_rao.cuh b/cpp/src/distance/detail/distance_ops/russel_rao.cuh
index 5dffdcdb89..4988c73536 100644
--- a/cpp/src/distance/detail/distance_ops/russel_rao.cuh
+++ b/cpp/src/distance/detail/distance_ops/russel_rao.cuh
@@ -52,12 +52,15 @@ struct russel_rao_distance_op {
     return Policy::SmemSize;
   }
 
-  DI void core(AccT& acc, DataT& x, DataT& y) const { acc += x * y; };
+  DI void core(AccT& acc, DataT& x, DataT& y) const
+  {
+    acc += raft::to_float(x) * raft::to_float(y);
+  };
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/distance_ops/template.cuh b/cpp/src/distance/detail/distance_ops/template.cuh
index bdb9332379..cb26e210db 100644
--- a/cpp/src/distance/detail/distance_ops/template.cuh
+++ b/cpp/src/distance/detail/distance_ops/template.cuh
@@ -52,8 +52,8 @@ struct template_distance_op {
 
   template <typename Policy>
   DI void epilog(AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-                 DataT* regxn,
-                 DataT* regyn,
+                 AccT* regxn,
+                 AccT* regyn,
                  IdxT gridStrideX,
                  IdxT gridStrideY) const
   {
diff --git a/cpp/src/distance/detail/masked_distance_base.cuh b/cpp/src/distance/detail/masked_distance_base.cuh
index 2c41ee3be0..ec7270baa2 100644
--- a/cpp/src/distance/detail/masked_distance_base.cuh
+++ b/cpp/src/distance/detail/masked_distance_base.cuh
@@ -266,7 +266,7 @@ struct MaskedDistances : public BaseClass {
     for (int i = 0; i < P::AccRowsPerTh; ++i) {
 #pragma unroll
       for (int j = 0; j < P::AccColsPerTh; ++j) {
-        acc[i][j] = BaseClass::Zero;
+        acc[i][j] = BaseClass::Zero();
       }
     }
   }
diff --git a/cpp/src/distance/detail/pairwise_distance_base.cuh b/cpp/src/distance/detail/pairwise_distance_base.cuh
index 990f845fd4..72d75ec12b 100644
--- a/cpp/src/distance/detail/pairwise_distance_base.cuh
+++ b/cpp/src/distance/detail/pairwise_distance_base.cuh
@@ -72,8 +72,8 @@ struct PairwiseDistances : public BaseClass {
 
  private:
   typedef Policy P;
-  const DataT* xn;
-  const DataT* yn;
+  const OutT* xn;
+  const OutT* yn;
   const DataT* const yBase;
   OutT* dOutput;
   char* smem;
@@ -99,8 +99,8 @@ struct PairwiseDistances : public BaseClass {
                        IdxT _lda,
                        IdxT _ldb,
                        IdxT _ldd,
-                       const DataT* _xn,
-                       const DataT* _yn,
+                       const OutT* _xn,
+                       const OutT* _yn,
                        OutT* _dOutput,
                        char* _smem,
                        OpT _distance_op,
@@ -154,7 +154,7 @@ struct PairwiseDistances : public BaseClass {
 
         // Epilog:
         if (distance_op.use_norms) {
-          DataT regxn[P::AccRowsPerTh], regyn[P::AccColsPerTh];
+          OutT regxn[P::AccRowsPerTh], regyn[P::AccColsPerTh];
           load_norms(tile_idx_m, tile_idx_n, regxn, regyn);
           // Overlap ldg with epilog computation
           ldgNextGridStride(tile_idx_m, tile_idx_n);
@@ -200,7 +200,7 @@ struct PairwiseDistances : public BaseClass {
     for (int i = 0; i < P::AccRowsPerTh; ++i) {
 #pragma unroll
       for (int j = 0; j < P::AccColsPerTh; ++j) {
-        acc[i][j] = BaseClass::Zero;
+        acc[i][j] = BaseClass::Zero();
       }
     }
   }
@@ -242,23 +242,23 @@ struct PairwiseDistances : public BaseClass {
 
   DI void load_norms(IdxT tile_idx_m,
                      IdxT tile_idx_n,
-                     DataT (&regxn)[P::AccRowsPerTh],
-                     DataT (&regyn)[P::AccColsPerTh])
+                     OutT (&regxn)[P::AccRowsPerTh],
+                     OutT (&regyn)[P::AccColsPerTh])
   {
-    DataT* sxNorm = (DataT*)(&smem[P::SmemSize]);
-    DataT* syNorm = (&sxNorm[P::Mblk]);
+    OutT* sxNorm = (OutT*)(&smem[P::SmemSize]);
+    OutT* syNorm = (&sxNorm[P::Mblk]);
 
     // Load x & y norms required by this threadblock in shmem buffer
     if (tile_idx_n == blockIdx.x * P::Nblk) {
       for (int i = threadIdx.x; i < P::Mblk; i += P::Nthreads) {
         auto idx  = tile_idx_m + i;
-        sxNorm[i] = idx < this->m ? xn[idx] : 0;
+        sxNorm[i] = idx < this->m ? xn[idx] : OutT(0);
       }
     }
 
     for (int i = threadIdx.x; i < P::Nblk; i += P::Nthreads) {
       auto idx  = tile_idx_n + i;
-      syNorm[i] = idx < this->n ? yn[idx] : 0;
+      syNorm[i] = idx < this->n ? yn[idx] : OutT(0);
     }
     __syncthreads();
 
@@ -285,7 +285,7 @@ struct PairwiseDistances : public BaseClass {
         auto colId = startx + j * P::AccThCols;
         if (rowId < this->m && colId < this->n) {
           // Promote to 64 bit index for final write, as output array can be > 2^31
-          dOutput[std::size_t(rowId) * this->n + colId] = fin_op(acc[i][j], 0);
+          dOutput[std::size_t(rowId) * this->n + colId] = fin_op(acc[i][j], AccT(0));
         }
       }
     }
diff --git a/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh b/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
index d41c5d30cd..d4d86d7f4d 100644
--- a/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
+++ b/cpp/src/distance/detail/pairwise_distance_cutlass_base.cuh
@@ -58,8 +58,8 @@ template <typename DataT,
           bool isRowMajor>
 std::enable_if_t<ops::has_cutlass_op<OpT>::value> cutlassDistanceKernel(const DataT* x,
                                                                         const DataT* y,
-                                                                        const DataT* xn,
-                                                                        const DataT* yn,
+                                                                        const OutT* xn,
+                                                                        const OutT* yn,
                                                                         IdxT m,
                                                                         IdxT n,
                                                                         IdxT k,
@@ -77,12 +77,12 @@ std::enable_if_t<ops::has_cutlass_op<OpT>::value> cutlassDistanceKernel(const Da
   auto dist_op     = distance_op.get_cutlass_op();
   using DistanceFn = decltype(dist_op);
   using EpilogueOutputOp =
-    epilogue::thread::PairwiseDistanceEpilogueElementwise<DataT,  // ElementC_
-                                                          AccT,   // ElementAccumulator_
-                                                          DataT,  // ElementCompute_
-                                                          AccT,   // ElementZ_
-                                                          OutT,   // ElementT_
-                                                          1,      // Elements per access 1
+    epilogue::thread::PairwiseDistanceEpilogueElementwise<OutT,  // ElementC_
+                                                          AccT,  // ElementAccumulator_
+                                                          AccT,  // ElementCompute_
+                                                          OutT,  // ElementZ_
+                                                          OutT,  // ElementT_
+                                                          1,     // Elements per access 1
                                                           DistanceFn,
                                                           FinalLambda>;
   constexpr int batch_count = 1;
@@ -143,13 +143,13 @@ std::enable_if_t<ops::has_cutlass_op<OpT>::value> cutlassDistanceKernel(const Da
       epilog_op_param,
       a,
       b,
-      xn,                    // C matrix eq vector param, which here is A norm
-      nullptr,               // tensor_Z,
-      (DataT*)yn + offsetN,  // this is broadcast vec, which is required to be non-const param
-      dOutput + offsetN,     // Output distance matrix
-      (int64_t)0,            // batch stride A
-      (int64_t)0,            // batch stride B
-      (int64_t)0,            // batch stride Norm A
+      xn,                   // C matrix eq vector param, which here is A norm
+      nullptr,              // tensor_Z,
+      (OutT*)yn + offsetN,  // this is broadcast vec, which is required to be non-const param
+      dOutput + offsetN,    // Output distance matrix
+      (int64_t)0,           // batch stride A
+      (int64_t)0,           // batch stride B
+      (int64_t)0,           // batch stride Norm A
       (int64_t)0,
       (int64_t)0,  // batch stride Norm B
       (int64_t)0,  // batch stride Output
diff --git a/cpp/src/distance/detail/pairwise_distance_gemm.h b/cpp/src/distance/detail/pairwise_distance_gemm.h
index 6ac13f27b6..a746138125 100644
--- a/cpp/src/distance/detail/pairwise_distance_gemm.h
+++ b/cpp/src/distance/detail/pairwise_distance_gemm.h
@@ -19,11 +19,15 @@
 #include "./pairwise_distance_epilogue.h"
 
 #include <cutlass/cutlass.h>
+#include <cutlass/epilogue/threadblock/default_epilogue_direct_store.h>
+#include <cutlass/gemm/device/gemm_universal.h>
 #include <cutlass/gemm/kernel/default_gemm_universal.h>
 #include <cutlass/gemm/kernel/gemm_with_fused_epilogue.h>
 #include <cutlass/layout/matrix.h>
 #include <cutlass/layout/tensor.h>
 
+#include <cuda_fp16.h>
+
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 namespace cuvs {
@@ -235,8 +239,105 @@ struct PairwiseDistanceGemm<double,
     GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
 };
 
+template <
+  /// Layout type for A matrix operand
+  int kAlignmentA,
+  /// Layout type for B matrix operand
+  int kAlignmentB,
+  /// Element type for C and D matrix operands
+  typename ElementC_,
+  /// Element type for internal accumulation
+  typename ElementAccumulator,
+  /// Epilogue output operator      - must satisfy concept of 'EpilogueWithBroadcastOp'
+  typename EpilogueOutputOp,
+  /// Number of stages used in the pipelined mainloop
+  int Stages,
+  /// data layout row/column major of inputs
+  bool isRowMajor>
+struct PairwiseDistanceGemm<half,
+                            kAlignmentA,
+                            half,
+                            kAlignmentB,
+                            ElementC_,
+                            ElementAccumulator,
+                            EpilogueOutputOp,
+                            Stages,
+                            isRowMajor> {
+  // using Transform = cutlass::ComplexTransform::kNone;
+  // Threadblock-level tile size (concept: GemmShape)
+  using ThreadblockShape =
+    cutlass::gemm::GemmShape<128, 128, 32>;  // <- threadblock tile M = 64, N = 64, K = 16
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes tile size a warp will compute
+  using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;  // <- warp tile M = 32, N = 32, K = 16
+  /// Warp-level tile size (concept: GemmShape)
+  // This code section describes the size of MMA op
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
+
+  // Operation performed by GEMM
+  using Operator = cutlass::arch::OpMultiplyAdd;
+  // This code section describes whether you want to use tensor cores or regular SIMT cores on GPU
+  // SM
+  using OperatorClass = cutlass::arch::OpClassTensorOp;
+
+  // This code section describes CUDA SM architecture number
+  using ArchTag = cutlass::arch::Sm80;
+
+  // This code section describes how threadblocks are scheduled on GPU
+  /// Threadblock-level swizzling operator
+  using ThreadblockSwizzle = cutlass::gemm::threadblock::GemmBatchedIdentityThreadblockSwizzle;
+
+  /// data layout for final output matrix.
+  // we keep this same layout even for column major inputs
+  using LayoutOutput = cutlass::layout::RowMajor;
+
+  typedef typename std::conditional<isRowMajor,
+                                    cutlass::layout::RowMajor,
+                                    cutlass::layout::ColumnMajor>::type NormXLayout;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::RowMajor, cutlass::layout::ColumnMajor>::type LayoutA_;
+
+  typedef typename std::
+    conditional<isRowMajor, cutlass::layout::ColumnMajor, cutlass::layout::RowMajor>::type LayoutB_;
+
+  using GemmBase = typename cutlass::gemm::device::GemmUniversal<cutlass::half_t,
+                                                                 LayoutA_,
+                                                                 cutlass::half_t,
+                                                                 LayoutB_,
+                                                                 ElementC_,
+                                                                 LayoutOutput,
+                                                                 ElementAccumulator,
+                                                                 OperatorClass,
+                                                                 ArchTag,
+                                                                 ThreadblockShape,
+                                                                 WarpShape,
+                                                                 InstructionShape,
+                                                                 EpilogueOutputOp,
+                                                                 ThreadblockSwizzle,
+                                                                 3,
+                                                                 2,
+                                                                 2>::GemmKernel;
+
+  // Replace epilogue
+  using Epilogue = typename cuvs::epilogue::threadblock::PairwiseDistanceEpilogue<
+    typename GemmBase::Epilogue::Shape,
+    typename GemmBase::Epilogue::WarpMmaOperator,
+    GemmBase::Epilogue::kPartitionsK,
+    ElementC_,
+    typename EpilogueOutputOp::ElementT,
+    ElementC_,
+    EpilogueOutputOp,
+    NormXLayout,
+    GemmBase::Epilogue::kElementsPerAccess>::Epilogue;
+
+  // Compose the GEMM kernel
+  using GemmKernel = cutlass::gemm::kernel::
+    GemmWithFusedEpilogue<typename GemmBase::Mma, Epilogue, ThreadblockSwizzle>;
+};
+
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 }  // namespace kernel
 }  // namespace gemm
-}  // namespace cuvs
\ No newline at end of file
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
index bc8189c709..3107f0fa44 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
@@ -38,8 +38,8 @@ void pairwise_matrix_dispatch(OpT distance_op,
                               IdxT k,
                               const DataT* x,
                               const DataT* y,
-                              const DataT* x_norm,
-                              const DataT* y_norm,
+                              const OutT* x_norm,
+                              const OutT* y_norm,
                               OutT* out,
                               FinOpT fin_op,
                               cudaStream_t stream,
@@ -47,9 +47,9 @@ void pairwise_matrix_dispatch(OpT distance_op,
 
 };  // namespace cuvs::distance::detail
 
-#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
 
-#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+#define instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                     \
   OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
   extern template void cuvs::distance::detail::                                        \
     pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
@@ -59,136 +59,70 @@ void pairwise_matrix_dispatch(OpT distance_op,
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
       bool is_row_major)
 
+#define instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(OpT, IdxT) \
+  instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                                 \
+    OpT, float, float, float, raft::identity_op, IdxT);                                      \
+  instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                                 \
+    OpT, double, double, double, raft::identity_op, IdxT);                                   \
+  instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                                 \
+    OpT, half, float, float, raft::identity_op, IdxT);
+
+#define instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo(OpT, IdxT, FinOpT) \
+  instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                                 \
+    OpT, float, float, float, FinOpT<float>, IdxT);                                          \
+  instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                                 \
+    OpT, double, double, double, FinOpT<double>, IdxT);                                      \
+  instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                                 \
+    OpT, half, float, float, FinOpT<float>, IdxT);
+
 /*
  * Hierarchy of instantiations:
  *
  * This file defines extern template instantiations of the distance kernels. The
- * instantiation of the public API is handled in raft/distance/distance-ext.cuh.
+ * instantiation of the public API is handled in cuvs/distance/distance-ext.cuh.
  *
  * After adding an instance here, make sure to also add the instance there.
  */
 
 // The following two instances are used in the RBF kernel object. Note the use of int64_t for the
 // index type.
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l2_unexp_distance_op,
-  float,
-  float,
-  float,
-  cuvs::distance::kernels::detail::rbf_fin_op<float>,
-  int64_t);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l2_unexp_distance_op,
-  double,
-  double,
-  double,
-  cuvs::distance::kernels::detail::rbf_fin_op<double>,
-  int64_t);
-
-// Rest of instances
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::canberra_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::canberra_distance_op,
-  double,
-  double,
-  double,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::correlation_distance_op,
-  float,
-  float,
-  float,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::correlation_distance_op,
-  double,
-  double,
-  double,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::cosine_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::cosine_distance_op, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::hamming_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::hamming_distance_op, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::hellinger_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::hellinger_distance_op,
-  double,
-  double,
-  double,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::jensen_shannon_distance_op,
-  float,
-  float,
-  float,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::jensen_shannon_distance_op,
-  double,
-  double,
-  double,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::kl_divergence_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::kl_divergence_op, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l1_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l1_distance_op, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l2_exp_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l2_exp_distance_op, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l2_unexp_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::canberra_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::correlation_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::cosine_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::hamming_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::hellinger_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::jensen_shannon_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::kl_divergence_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::l1_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::l2_exp_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::l2_unexp_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::l_inf_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::lp_unexp_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::russel_rao_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo(
   cuvs::distance::detail::ops::l2_unexp_distance_op,
-  double,
-  double,
-  double,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l_inf_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::l_inf_distance_op, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::lp_unexp_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::lp_unexp_distance_op,
-  double,
-  double,
-  double,
-  raft::identity_op,
-  int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::russel_rao_distance_op, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_detail_pairwise_matrix_dispatch(
-  cuvs::distance::detail::ops::russel_rao_distance_op,
-  double,
-  double,
-  double,
-  raft::identity_op,
-  int);
+  int64_t,
+  cuvs::distance::kernels::detail::rbf_fin_op);
 
-#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
+#undef instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo
+#undef instantiate_cuvs_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
index fc3849febe..96d7c265e7 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-inl.cuh
@@ -72,8 +72,8 @@ void pairwise_matrix_dispatch(OpT distance_op,
                               IdxT k,
                               const DataT* x,
                               const DataT* y,
-                              const DataT* x_norm,
-                              const DataT* y_norm,
+                              const OutT* x_norm,
+                              const OutT* y_norm,
                               OutT* out,
                               FinOpT fin_op,
                               cudaStream_t stream,
@@ -113,7 +113,13 @@ void pairwise_matrix_dispatch(OpT distance_op,
     void* kernel_ptr  = reinterpret_cast<void*>(sm60_wrapper.kernel_ptr);
     auto runtime_arch = arch::kernel_virtual_arch(kernel_ptr);
 
-    if (cutlass_range.contains(runtime_arch)) {
+    // TODO: the cutlass doesn't support the odd `k` on half DataT.
+    bool if_unsupported_on_half = (sizeof(DataT) == 2) && ((k % 2) != 0);
+
+    if (if_unsupported_on_half) {
+      auto any_range = arch::SM_range(arch::SM_min(), arch::SM_future());
+      pairwise_matrix_sm60_dispatch(distance_op, params, any_range, stream);
+    } else if (cutlass_range.contains(runtime_arch) && !if_unsupported_on_half) {
       // If device is SM_80 or later, use CUTLASS-based kernel.
       pairwise_matrix_sm80_dispatch(distance_op, params, cutlass_range, stream);
     } else {
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
index 06b039c3a1..0521a5713a 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
@@ -19,6 +19,4 @@
 #include "dispatch-inl.cuh"
 #endif
 
-#ifdef RAFT_COMPILED
 #include "dispatch-ext.cuh"
-#endif
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
index e013db1e18..1bd51aef91 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
@@ -56,8 +56,8 @@
       IdxT k,                                                                          \\
       const DataT* x,                                                                  \\
       const DataT* y,                                                                  \\
-      const DataT* x_norm,                                                             \\
-      const DataT* y_norm,                                                             \\
+      const OutT* x_norm,                                                              \\
+      const OutT* y_norm,                                                              \\
       OutT* out,                                                                       \\
       FinOpT fin_op,                                                                   \\
       cudaStream_t stream,                                                             \\
@@ -77,6 +77,12 @@
         OutT="double",
         IdxT="int",
     ),
+    dict(
+        DataT="half",
+        AccT="float",
+        OutT="float",
+        IdxT="int",
+    ),
 ]
 
 op_instances = [
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
index f82df6cc03..c2c44dc53c 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
index a20ca5f472..00099dcae4 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
new file mode 100644
index 0000000000..0b70f23413
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::canberra_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
index 7bb7e4a96e..d9b6feb9c4 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
index 34fcc4be42..dfb6f62a8b 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_half_float_float_int.cu
new file mode 100644
index 0000000000..b2c959b556
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_correlation_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::correlation_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
index cb23743c1f..d7046d4e25 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
@@ -38,8 +38,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
index ad71ff2957..215805ddea 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
@@ -38,8 +38,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_half_float_float_int.cu
new file mode 100644
index 0000000000..5f9928958b
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_cosine_half_float_float_int.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::cosine_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
index e81d544112..d558604d8d 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
index ddbdab6021..632523194a 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_half_float_float_int.cu
new file mode 100644
index 0000000000..76360ebd87
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hamming_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
index d2acecaf0e..707d9c08df 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
index 034d76679e..7dceab56c8 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_half_float_float_int.cu
new file mode 100644
index 0000000000..35e7adf062
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::hellinger_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
index 030faeecd2..e3e0744797 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
index f7551a5665..6eff405503 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_half_float_float_int.cu
new file mode 100644
index 0000000000..24302c6e6c
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_half_float_float_int.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::jensen_shannon_distance_op,
+  half,
+  float,
+  float,
+  raft::identity_op,
+  int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
index 6640d39492..4f45adf274 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
index 60cafa4742..b2cac754fe 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_half_float_float_int.cu
new file mode 100644
index 0000000000..9347a026c1
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_kl_divergence_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::kl_divergence_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
index 8f6e3a35d2..82d9d1abe0 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
index 73868a486b..ad5f06048a 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_half_float_float_int.cu
new file mode 100644
index 0000000000..99043958f0
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l1_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l1_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
index 8ac80b77d2..b2911f16b4 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
@@ -38,8 +38,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
index abebb91219..93a4166439 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
@@ -38,8 +38,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_half_float_float_int.cu
new file mode 100644
index 0000000000..e30499ae7c
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_half_float_float_int.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_exp_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
index ffa6bf02ba..eecab9ec40 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
index acef42a4e0..9f58f5f859 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_half_float_float_int.cu
new file mode 100644
index 0000000000..73e9352eed
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
index c2bbbf06b5..812dda4500 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
index 163b9f37ba..f95dd7a870 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_half_float_float_int.cu
new file mode 100644
index 0000000000..ba9c976c46
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l_inf_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l_inf_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
index d13532ac60..a88875f3a2 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
index 65e0163d78..b8b3775d1c 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_half_float_float_int.cu
new file mode 100644
index 0000000000..ef323f57d0
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::lp_unexp_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
index 23f2b34e8c..1cb0ed8aeb 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
@@ -38,8 +38,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
@@ -61,4 +61,12 @@ instantiate_raft_distance_detail_pairwise_matrix_dispatch(
   cuvs::distance::kernels::detail::rbf_fin_op<double>,
   int64_t);
 
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_unexp_distance_op,
+  half,
+  float,
+  float,
+  cuvs::distance::kernels::detail::rbf_fin_op<half>,
+  int64_t);
+
 #undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
index 1a5e5cf981..1afbd690e4 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
index a9b1f6bb43..217f61b84f 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
@@ -37,8 +37,8 @@
       IdxT k,                                                                          \
       const DataT* x,                                                                  \
       const DataT* y,                                                                  \
-      const DataT* x_norm,                                                             \
-      const DataT* y_norm,                                                             \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
       OutT* out,                                                                       \
       FinOpT fin_op,                                                                   \
       cudaStream_t stream,                                                             \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu
new file mode 100644
index 0000000000..c65fb24f62
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::russel_rao_distance_op, half, float, float, raft::identity_op, int);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/params.cuh b/cpp/src/distance/detail/pairwise_matrix/params.cuh
index aa419aca0d..739c4a9f62 100644
--- a/cpp/src/distance/detail/pairwise_matrix/params.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/params.cuh
@@ -27,8 +27,8 @@ struct pairwise_matrix_params {
   IdxT ld_out;
   const DataT* x;
   const DataT* y;
-  const DataT* x_norm;
-  const DataT* y_norm;
+  const OutT* x_norm;
+  const OutT* y_norm;
   OutT* out;
   FinOpT fin_op;
   bool is_row_major;
diff --git a/cpp/src/distance/distance-ext.cuh b/cpp/src/distance/distance-ext.cuh
index 148951afad..e7fa30f03b 100644
--- a/cpp/src/distance/distance-ext.cuh
+++ b/cpp/src/distance/distance-ext.cuh
@@ -24,6 +24,8 @@
 
 #include <rmm/device_uvector.hpp>  // rmm::device_uvector
 
+#include <cuda_fp16.h>
+
 #ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 namespace cuvs {
@@ -45,8 +47,8 @@ void distance(raft::resources const& handle,
               void* workspace,
               size_t worksize,
               FinalLambda fin_op,
-              bool isRowMajor  = true,
-              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+              bool isRowMajor = true,
+              OutT metric_arg = 2.0f) RAFT_EXPLICIT;
 
 template <cuvs::distance::DistanceType DistT,
           typename DataT,
@@ -62,8 +64,8 @@ void distance(raft::resources const& handle,
               IdxT k,
               void* workspace,
               size_t worksize,
-              bool isRowMajor  = true,
-              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+              bool isRowMajor = true,
+              OutT metric_arg = 2.0f) RAFT_EXPLICIT;
 
 template <cuvs::distance::DistanceType DistT,
           typename DataT,
@@ -93,33 +95,33 @@ void distance(raft::resources const& handle,
               IdxT m,
               IdxT n,
               IdxT k,
-              bool isRowMajor  = true,
-              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+              bool isRowMajor = true,
+              OutT metric_arg = 2.0f) RAFT_EXPLICIT;
 
-template <typename Type, typename IdxT = int>
+template <typename Type, typename IdxT = int, typename DistT = Type>
 void pairwise_distance(raft::resources const& handle,
                        const Type* x,
                        const Type* y,
-                       Type* dist,
+                       DistT* dist,
                        IdxT m,
                        IdxT n,
                        IdxT k,
                        rmm::device_uvector<char>& workspace,
                        cuvs::distance::DistanceType metric,
-                       bool isRowMajor = true,
-                       Type metric_arg = 2.0f) RAFT_EXPLICIT;
+                       bool isRowMajor  = true,
+                       DistT metric_arg = DistT(2.0f)) RAFT_EXPLICIT;
 
-template <typename Type, typename IdxT = int>
+template <typename Type, typename IdxT = int, typename DistT = Type>
 void pairwise_distance(raft::resources const& handle,
                        const Type* x,
                        const Type* y,
-                       Type* dist,
+                       DistT* dist,
                        IdxT m,
                        IdxT n,
                        IdxT k,
                        cuvs::distance::DistanceType metric,
-                       bool isRowMajor = true,
-                       Type metric_arg = 2.0f) RAFT_EXPLICIT;
+                       bool isRowMajor  = true,
+                       DistT metric_arg = DistT(2.0f)) RAFT_EXPLICIT;
 
 template <cuvs::distance::DistanceType DistT,
           typename DataT,
@@ -128,23 +130,26 @@ template <cuvs::distance::DistanceType DistT,
           typename layout = raft::layout_c_contiguous,
           typename IdxT   = int>
 void distance(raft::resources const& handle,
-              raft::device_matrix_view<DataT, IdxT, layout> const x,
-              raft::device_matrix_view<DataT, IdxT, layout> const y,
+              raft::device_matrix_view<const DataT, IdxT, layout> const x,
+              raft::device_matrix_view<const DataT, IdxT, layout> const y,
               raft::device_matrix_view<OutT, IdxT, layout> dist,
-              DataT metric_arg = 2.0f) RAFT_EXPLICIT;
+              OutT metric_arg = 2.0f) RAFT_EXPLICIT;
 
-template <typename Type, typename layout = layout_c_contiguous, typename IdxT = int>
+template <typename Type,
+          typename layout = raft::layout_c_contiguous,
+          typename IdxT   = int,
+          typename DistT  = Type>
 void pairwise_distance(raft::resources const& handle,
-                       device_matrix_view<Type, IdxT, layout> const x,
-                       device_matrix_view<Type, IdxT, layout> const y,
-                       device_matrix_view<Type, IdxT, layout> dist,
+                       raft::device_matrix_view<const Type, IdxT, layout> const x,
+                       raft::device_matrix_view<const Type, IdxT, layout> const y,
+                       raft::device_matrix_view<DistT, IdxT, layout> dist,
                        cuvs::distance::DistanceType metric,
-                       Type metric_arg = 2.0f) RAFT_EXPLICIT;
+                       DistT metric_arg = DistT(2.0f)) RAFT_EXPLICIT;
 
 };  // namespace distance
 };  // namespace cuvs
 
-#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 /*
  * Hierarchy of instantiations:
@@ -158,909 +163,220 @@ void pairwise_distance(raft::resources const& handle,
  * dispatch-ext.cuh and the corresponding .cu files.
  */
 
-#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, FinalLambda, IdxT)       \
-  extern template void cuvs::distance::distance<DT, DataT, AccT, OutT, FinalLambda, IdxT>( \
-    raft::resources const& handle,                                                         \
-    const DataT* x,                                                                        \
-    const DataT* y,                                                                        \
-    OutT* dist,                                                                            \
-    IdxT m,                                                                                \
-    IdxT n,                                                                                \
-    IdxT k,                                                                                \
-    void* workspace,                                                                       \
-    size_t worksize,                                                                       \
-    FinalLambda fin_op,                                                                    \
-    bool isRowMajor,                                                                       \
-    DataT metric_arg)
+#define instantiate_cuvs_distance_distance(DistT, DataT, AccT, OutT, IdxT)             \
+  extern template void                                                                 \
+  cuvs::distance::distance<DistT, DataT, AccT, OutT, raft::identity_op, IdxT>(         \
+    raft::resources const& handle,                                                     \
+    const DataT* x,                                                                    \
+    const DataT* y,                                                                    \
+    OutT* dist,                                                                        \
+    IdxT m,                                                                            \
+    IdxT n,                                                                            \
+    IdxT k,                                                                            \
+    void* workspace,                                                                   \
+    size_t worksize,                                                                   \
+    raft::identity_op fin_op,                                                          \
+    bool isRowMajor,                                                                   \
+    OutT metric_arg);                                                                  \
+                                                                                       \
+  extern template void cuvs::distance::distance<DistT, DataT, AccT, OutT, IdxT>(       \
+    raft::resources const& handle,                                                     \
+    const DataT* x,                                                                    \
+    const DataT* y,                                                                    \
+    OutT* dist,                                                                        \
+    IdxT m,                                                                            \
+    IdxT n,                                                                            \
+    IdxT k,                                                                            \
+    void* workspace,                                                                   \
+    size_t worksize,                                                                   \
+    bool isRowMajor,                                                                   \
+    OutT metric_arg);                                                                  \
+                                                                                       \
+  extern template void cuvs::distance::distance<DistT, DataT, AccT, OutT, IdxT>(       \
+    raft::resources const& handle,                                                     \
+    const DataT* x,                                                                    \
+    const DataT* y,                                                                    \
+    OutT* dist,                                                                        \
+    IdxT m,                                                                            \
+    IdxT n,                                                                            \
+    IdxT k,                                                                            \
+    bool isRowMajor,                                                                   \
+    OutT metric_arg);                                                                  \
+                                                                                       \
+  extern template void                                                                 \
+  cuvs::distance::distance<DistT, DataT, AccT, OutT, raft::layout_f_contiguous, IdxT>( \
+    raft::resources const& handle,                                                     \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_f_contiguous> const x,    \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_f_contiguous> const y,    \
+    raft::device_matrix_view<OutT, IdxT, raft::layout_f_contiguous> dist,              \
+    OutT metric_arg);                                                                  \
+                                                                                       \
+  extern template void                                                                 \
+  cuvs::distance::distance<DistT, DataT, AccT, OutT, raft::layout_c_contiguous, IdxT>( \
+    raft::resources const& handle,                                                     \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> const x,    \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> const y,    \
+    raft::device_matrix_view<OutT, IdxT, raft::layout_c_contiguous> dist,              \
+    OutT metric_arg)
+
+#define instantiate_cuvs_distance_distance_by_algo(DistT)                 \
+  instantiate_cuvs_distance_distance(DistT, float, float, float, int);    \
+  instantiate_cuvs_distance_distance(DistT, double, double, double, int); \
+  instantiate_cuvs_distance_distance(DistT, half, float, float, int)
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::Canberra);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::CorrelationExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::CosineExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::HammingUnexpanded);
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::HellingerExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::InnerProduct);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::JensenShannon);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::KLDivergence);
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L1);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2Expanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2SqrtExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2SqrtUnexpanded);
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2Unexpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::Linf);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::LpUnexpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::RusselRaoExpanded);
+
+#undef instantiate_cuvs_distance_distance_by_algo
+#undef instantiate_cuvs_distance_distance
 
 // The following two instances are used in test/distance/gram.cu. Note the use
 // of int64_t for the index type.
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   cuvs::distance::kernels::detail::rbf_fin_op<float>,
-                                   int64_t);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   cuvs::distance::kernels::detail::rbf_fin_op<double>,
-                                   int64_t);
-
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::identity_op,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, raft::identity_op, int);
-
-#undef instantiate_raft_distance_distance
-
-// Same, but without raft::identity_op
-#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT)       \
-  extern template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>( \
-    raft::resources const& handle,                                            \
-    const DataT* x,                                                           \
-    const DataT* y,                                                           \
-    OutT* dist,                                                               \
-    IdxT m,                                                                   \
-    IdxT n,                                                                   \
-    IdxT k,                                                                   \
-    void* workspace,                                                          \
-    size_t worksize,                                                          \
-    bool isRowMajor,                                                          \
+#define instantiate_cuvs_distance_distance_extra(DistT, DataT, AccT, OutT, FinalLambda, IdxT) \
+  extern template void cuvs::distance::distance<DistT, DataT, AccT, OutT, FinalLambda, IdxT>( \
+    raft::resources const& handle,                                                            \
+    const DataT* x,                                                                           \
+    const DataT* y,                                                                           \
+    OutT* dist,                                                                               \
+    IdxT m,                                                                                   \
+    IdxT n,                                                                                   \
+    IdxT k,                                                                                   \
+    void* workspace,                                                                          \
+    size_t worksize,                                                                          \
+    FinalLambda fin_op,                                                                       \
+    bool isRowMajor,                                                                          \
     DataT metric_arg)
 
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
-
-#undef instantiate_raft_distance_distance
-
-// Same, but without workspace
-#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT)       \
-  extern template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>( \
-    raft::resources const& handle,                                            \
-    const DataT* x,                                                           \
-    const DataT* y,                                                           \
-    OutT* dist,                                                               \
-    IdxT m,                                                                   \
-    IdxT n,                                                                   \
-    IdxT k,                                                                   \
-    bool isRowMajor,                                                          \
-    DataT metric_arg)
-
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
-
-#undef instantiate_raft_distance_distance
-
-#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT)         \
-  extern template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>( \
-    const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k)
-
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Linf, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Linf, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
-
-#undef instantiate_raft_distance_getWorkspaceSize
-
-#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT, layout)         \
-  extern template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, layout>( \
-    raft::device_matrix_view<DataT, IdxT, layout> const& x,                                        \
-    raft::device_matrix_view<DataT, IdxT, layout> const& y)
-
-// We could consider not taking template parameters for this function. The
-// number of instantiations seems a bit excessive..
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2Unexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_f_contiguous);
-
-#undef instantiate_raft_distance_getWorkspaceSize
-
-#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                               \
+instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpanded,
+                                         float,
+                                         float,
+                                         float,
+                                         cuvs::distance::kernels::detail::rbf_fin_op<float>,
+                                         int64_t);
+instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpanded,
+                                         double,
+                                         double,
+                                         double,
+                                         cuvs::distance::kernels::detail::rbf_fin_op<double>,
+                                         int64_t);
+
+#undef instantiate_cuvs_distance_distance_extra
+
+#define instantiate_cuvs_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT)             \
+  extern template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>(     \
+    const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k);                                   \
+                                                                                               \
+  extern template size_t                                                                       \
+  cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, raft::layout_f_contiguous>( \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_f_contiguous> const& x,                 \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_f_contiguous> const& y);                \
+                                                                                               \
+  extern template size_t                                                                       \
+  cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, raft::layout_c_contiguous>( \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_c_contiguous> const& x,                 \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_c_contiguous> const& y)
+
+#define instantiate_cuvs_distance_getWorkspaceSize_by_algo(DistT)                     \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, float, float, float, int);        \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, double, double, double, int);     \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, half, float, float, int);         \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, float, float, float, int64_t);    \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, double, double, double, int64_t); \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, half, float, float, int64_t)
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::Canberra);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(
+  cuvs::distance::DistanceType::CorrelationExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::CosineExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::HammingUnexpanded);
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::HellingerExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::InnerProduct);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::JensenShannon);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::KLDivergence);
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L1);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2Expanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2SqrtExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2SqrtUnexpanded);
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2Unexpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::Linf);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::LpUnexpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::RusselRaoExpanded);
+
+#undef instantiate_cuvs_distance_getWorkspaceSize_by_algo
+#undef instantiate_cuvs_distance_getWorkspaceSize
+
+#define instantiate_cuvs_distance_pairwise_distance(DataT, IdxT, DistT)                        \
   extern template void cuvs::distance::pairwise_distance(raft::resources const& handle,        \
                                                          const DataT* x,                       \
                                                          const DataT* y,                       \
-                                                         DataT* dist,                          \
+                                                         DistT* dist,                          \
                                                          IdxT m,                               \
                                                          IdxT n,                               \
                                                          IdxT k,                               \
                                                          rmm::device_uvector<char>& workspace, \
                                                          cuvs::distance::DistanceType metric,  \
                                                          bool isRowMajor,                      \
-                                                         DataT metric_arg)
+                                                         DistT metric_arg)
 
-instantiate_raft_distance_pairwise_distance(float, int);
-instantiate_raft_distance_pairwise_distance(double, int);
+instantiate_cuvs_distance_pairwise_distance(float, int, float);
+instantiate_cuvs_distance_pairwise_distance(double, int, double);
+instantiate_cuvs_distance_pairwise_distance(half, int, float);
 
-#undef instantiate_raft_distance_pairwise_distance
+#undef instantiate_cuvs_distance_pairwise_distance
 
 // Same, but without workspace
-#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                              \
+#define instantiate_cuvs_distance_pairwise_distance(DataT, IdxT, DistT)                       \
   extern template void cuvs::distance::pairwise_distance(raft::resources const& handle,       \
                                                          const DataT* x,                      \
                                                          const DataT* y,                      \
-                                                         DataT* dist,                         \
+                                                         DistT* dist,                         \
                                                          IdxT m,                              \
                                                          IdxT n,                              \
                                                          IdxT k,                              \
                                                          cuvs::distance::DistanceType metric, \
                                                          bool isRowMajor,                     \
-                                                         DataT metric_arg)
-
-instantiate_raft_distance_pairwise_distance(float, int);
-instantiate_raft_distance_pairwise_distance(double, int);
-
-#undef instantiate_raft_distance_pairwise_distance
-
-// Version with mdspan
-#define instantiate_raft_distance_distance(DistT, DataT, AccT, OutT, layout, IdxT)       \
-  extern template void cuvs::distance::distance<DistT, DataT, AccT, OutT, layout, IdxT>( \
-    raft::resources const& handle,                                                       \
-    raft::device_matrix_view<const DataT, IdxT, layout> const x,                         \
-    raft::device_matrix_view<const DataT, IdxT, layout> const y,                         \
-    raft::device_matrix_view<OutT, IdxT, layout> dist,                                   \
-    DataT metric_arg)
-
-// Again, we might want to consider reigning in the number of instantiations...
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-
-#undef instantiate_raft_distance_distance
-
-#define instantiate_raft_distance_pairwise_distance(DataT, layout, IdxT) \
-  extern template void cuvs::distance::pairwise_distance(                \
-    raft::resources const& handle,                                       \
-    raft::device_matrix_view<const DataT, IdxT, layout> const x,         \
-    raft::device_matrix_view<const DataT, IdxT, layout> const y,         \
-    raft::device_matrix_view<DataT, IdxT, layout> dist,                  \
-    cuvs::distance::DistanceType metric,                                 \
-    DataT metric_arg)
-
-instantiate_raft_distance_pairwise_distance(float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_pairwise_distance(float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_pairwise_distance(double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_pairwise_distance(double, raft::layout_f_contiguous, int);
-
-#undef instantiate_raft_distance_pairwise_distance
+                                                         DistT metric_arg)
+
+instantiate_cuvs_distance_pairwise_distance(float, int, float);
+instantiate_cuvs_distance_pairwise_distance(double, int, double);
+instantiate_cuvs_distance_pairwise_distance(half, int, float);
+
+#undef instantiate_cuvs_distance_pairwise_distance
+
+#define instantiate_cuvs_distance_pairwise_distance(DataT, layout, IdxT, DistT) \
+  extern template void cuvs::distance::pairwise_distance(                       \
+    raft::resources const& handle,                                              \
+    raft::device_matrix_view<const DataT, IdxT, layout> const x,                \
+    raft::device_matrix_view<const DataT, IdxT, layout> const y,                \
+    raft::device_matrix_view<DistT, IdxT, layout> dist,                         \
+    cuvs::distance::DistanceType metric,                                        \
+    DistT metric_arg)
+
+instantiate_cuvs_distance_pairwise_distance(float, raft::layout_c_contiguous, int, float);
+instantiate_cuvs_distance_pairwise_distance(float, raft::layout_f_contiguous, int, float);
+instantiate_cuvs_distance_pairwise_distance(double, raft::layout_c_contiguous, int, double);
+instantiate_cuvs_distance_pairwise_distance(double, raft::layout_f_contiguous, int, double);
+instantiate_cuvs_distance_pairwise_distance(half, raft::layout_c_contiguous, int, float);
+instantiate_cuvs_distance_pairwise_distance(half, raft::layout_f_contiguous, int, float);
+
+#undef instantiate_cuvs_distance_pairwise_distance
diff --git a/cpp/src/distance/distance-inl.cuh b/cpp/src/distance/distance-inl.cuh
index 6236901c3d..e047d31448 100644
--- a/cpp/src/distance/distance-inl.cuh
+++ b/cpp/src/distance/distance-inl.cuh
@@ -75,8 +75,8 @@ void distance(raft::resources const& handle,
               void* workspace,
               size_t worksize,
               FinalLambda fin_op,
-              bool isRowMajor  = true,
-              DataT metric_arg = 2.0f)
+              bool isRowMajor = true,
+              OutT metric_arg = 2.0f)
 {
   detail::distance<DistT, DataT, AccT, OutT, FinalLambda, IdxT>(
     handle, x, y, dist, m, n, k, workspace, worksize, fin_op, isRowMajor, metric_arg);
@@ -115,8 +115,8 @@ void distance(raft::resources const& handle,
               IdxT k,
               void* workspace,
               size_t worksize,
-              bool isRowMajor  = true,
-              DataT metric_arg = 2.0f)
+              bool isRowMajor = true,
+              OutT metric_arg = 2.0f)
 {
   detail::distance<DistT, DataT, AccT, OutT, IdxT>(
     handle, x, y, dist, m, n, k, workspace, worksize, isRowMajor, metric_arg);
@@ -206,8 +206,8 @@ void distance(raft::resources const& handle,
               IdxT m,
               IdxT n,
               IdxT k,
-              bool isRowMajor  = true,
-              DataT metric_arg = 2.0f)
+              bool isRowMajor = true,
+              OutT metric_arg = 2.0f)
 {
   auto stream = raft::resource::get_cuda_stream(handle);
   rmm::device_uvector<char> workspace(0, stream);
@@ -222,6 +222,7 @@ void distance(raft::resources const& handle,
  * into compile time for the purpose of dispatch
  * @tparam Type input/accumulation/output data-type
  * @tparam IdxT indexing type
+ * @tparam DistT output type, equal to Type by default
  * @param handle raft handle for managing expensive resources
  * @param x first set of points
  * @param y second set of points
@@ -235,25 +236,25 @@ void distance(raft::resources const& handle,
  * @param isRowMajor whether the matrices are row-major or col-major
  * @param metric_arg metric argument (used for Minkowski distance)
  */
-template <typename Type, typename IdxT = int>
+template <typename Type, typename IdxT = int, typename DistT = Type>
 void pairwise_distance(raft::resources const& handle,
                        const Type* x,
                        const Type* y,
-                       Type* dist,
+                       DistT* dist,
                        IdxT m,
                        IdxT n,
                        IdxT k,
                        rmm::device_uvector<char>& workspace,
                        cuvs::distance::DistanceType metric,
-                       bool isRowMajor = true,
-                       Type metric_arg = 2.0f)
+                       bool isRowMajor  = true,
+                       DistT metric_arg = 2.0f)
 {
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
   auto dispatch = [&](auto distance_type) {
-    auto worksize = getWorkspaceSize<distance_type(), Type, Type, Type, IdxT>(x, y, m, n, k);
+    auto worksize = getWorkspaceSize<distance_type(), Type, DistT, DistT, IdxT>(x, y, m, n, k);
     workspace.resize(worksize, stream);
-    detail::distance<distance_type(), Type, Type, Type, IdxT>(
+    detail::distance<distance_type(), Type, DistT, DistT, IdxT>(
       handle, x, y, dist, m, n, k, workspace.data(), worksize, isRowMajor, metric_arg);
   };
 
@@ -315,6 +316,7 @@ void pairwise_distance(raft::resources const& handle,
  * into compile time for the purpose of dispatch
  * @tparam Type input/accumulation/output data-type
  * @tparam IdxT indexing type
+ * @tparam DistT output type, equal to Type by default
  * @param handle raft handle for managing expensive resources
  * @param x first set of points
  * @param y second set of points
@@ -326,21 +328,21 @@ void pairwise_distance(raft::resources const& handle,
  * @param isRowMajor whether the matrices are row-major or col-major
  * @param metric_arg metric argument (used for Minkowski distance)
  */
-template <typename Type, typename IdxT = int>
+template <typename Type, typename IdxT = int, typename DistT = Type>
 void pairwise_distance(raft::resources const& handle,
                        const Type* x,
                        const Type* y,
-                       Type* dist,
+                       DistT* dist,
                        IdxT m,
                        IdxT n,
                        IdxT k,
                        cuvs::distance::DistanceType metric,
-                       bool isRowMajor = true,
-                       Type metric_arg = 2.0f)
+                       bool isRowMajor  = true,
+                       DistT metric_arg = 2.0f)
 {
   auto stream = raft::resource::get_cuda_stream(handle);
   rmm::device_uvector<char> workspace(0, stream);
-  pairwise_distance<Type, IdxT>(
+  pairwise_distance<Type, IdxT, DistT>(
     handle, x, y, dist, m, n, k, workspace, metric, isRowMajor, metric_arg);
 }
 
@@ -397,7 +399,7 @@ void distance(raft::resources const& handle,
               raft::device_matrix_view<const DataT, IdxT, layout> const x,
               raft::device_matrix_view<const DataT, IdxT, layout> const y,
               raft::device_matrix_view<OutT, IdxT, layout> dist,
-              DataT metric_arg = 2.0f)
+              OutT metric_arg = 2.0f)
 {
   RAFT_EXPECTS(x.extent(1) == y.extent(1), "Number of columns must be equal.");
   RAFT_EXPECTS(dist.extent(0) == x.extent(0),
@@ -428,6 +430,7 @@ void distance(raft::resources const& handle,
  * into compile time for the purpose of dispatch
  * @tparam Type input/accumulation/output data-type
  * @tparam IdxT indexing type
+ * @tparam DistT output type, equal to Type by default
  * @param handle raft handle for managing expensive resources
  * @param x first matrix of points (size mxk)
  * @param y second matrix of points (size nxk)
@@ -435,13 +438,16 @@ void distance(raft::resources const& handle,
  * @param metric distance metric
  * @param metric_arg metric argument (used for Minkowski distance)
  */
-template <typename Type, typename layout = raft::layout_c_contiguous, typename IdxT = int>
+template <typename Type,
+          typename layout = raft::layout_c_contiguous,
+          typename IdxT   = int,
+          typename DistT  = Type>
 void pairwise_distance(raft::resources const& handle,
                        raft::device_matrix_view<const Type, IdxT, layout> const x,
                        raft::device_matrix_view<const Type, IdxT, layout> const y,
-                       raft::device_matrix_view<Type, IdxT, layout> dist,
+                       raft::device_matrix_view<DistT, IdxT, layout> dist,
                        cuvs::distance::DistanceType metric,
-                       Type metric_arg = 2.0f)
+                       DistT metric_arg = DistT(2.0f))
 {
   RAFT_EXPECTS(x.extent(1) == y.extent(1), "Number of columns must be equal.");
   RAFT_EXPECTS(dist.extent(0) == x.extent(0),
diff --git a/cpp/src/distance/distance.cu b/cpp/src/distance/distance.cu
index 02c071d13c..72be93f10a 100644
--- a/cpp/src/distance/distance.cu
+++ b/cpp/src/distance/distance.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,910 +25,219 @@
  * kernels is handled in distance/detail/pairwise_matrix/dispatch_*.cu.
  *
  */
-
-#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, FinalLambda, IdxT) \
-  template void cuvs::distance::distance<DT, DataT, AccT, OutT, FinalLambda, IdxT>(  \
-    raft::resources const& handle,                                                   \
-    const DataT* x,                                                                  \
-    const DataT* y,                                                                  \
-    OutT* dist,                                                                      \
-    IdxT m,                                                                          \
-    IdxT n,                                                                          \
-    IdxT k,                                                                          \
-    void* workspace,                                                                 \
-    size_t worksize,                                                                 \
-    FinalLambda fin_op,                                                              \
-    bool isRowMajor,                                                                 \
-    DataT metric_arg)
+#define instantiate_cuvs_distance_distance(DistT, DataT, AccT, OutT, IdxT)                   \
+  template void cuvs::distance::distance<DistT, DataT, AccT, OutT, raft::identity_op, IdxT>( \
+    raft::resources const& handle,                                                           \
+    const DataT* x,                                                                          \
+    const DataT* y,                                                                          \
+    OutT* dist,                                                                              \
+    IdxT m,                                                                                  \
+    IdxT n,                                                                                  \
+    IdxT k,                                                                                  \
+    void* workspace,                                                                         \
+    size_t worksize,                                                                         \
+    raft::identity_op fin_op,                                                                \
+    bool isRowMajor,                                                                         \
+    OutT metric_arg);                                                                        \
+                                                                                             \
+  template void cuvs::distance::distance<DistT, DataT, AccT, OutT, IdxT>(                    \
+    raft::resources const& handle,                                                           \
+    const DataT* x,                                                                          \
+    const DataT* y,                                                                          \
+    OutT* dist,                                                                              \
+    IdxT m,                                                                                  \
+    IdxT n,                                                                                  \
+    IdxT k,                                                                                  \
+    void* workspace,                                                                         \
+    size_t worksize,                                                                         \
+    bool isRowMajor,                                                                         \
+    OutT metric_arg);                                                                        \
+                                                                                             \
+  template void cuvs::distance::distance<DistT, DataT, AccT, OutT, IdxT>(                    \
+    raft::resources const& handle,                                                           \
+    const DataT* x,                                                                          \
+    const DataT* y,                                                                          \
+    OutT* dist,                                                                              \
+    IdxT m,                                                                                  \
+    IdxT n,                                                                                  \
+    IdxT k,                                                                                  \
+    bool isRowMajor,                                                                         \
+    OutT metric_arg);                                                                        \
+                                                                                             \
+  template void                                                                              \
+  cuvs::distance::distance<DistT, DataT, AccT, OutT, raft::layout_f_contiguous, IdxT>(       \
+    raft::resources const& handle,                                                           \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_f_contiguous> const x,          \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_f_contiguous> const y,          \
+    raft::device_matrix_view<OutT, IdxT, raft::layout_f_contiguous> dist,                    \
+    OutT metric_arg);                                                                        \
+                                                                                             \
+  template void                                                                              \
+  cuvs::distance::distance<DistT, DataT, AccT, OutT, raft::layout_c_contiguous, IdxT>(       \
+    raft::resources const& handle,                                                           \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> const x,          \
+    raft::device_matrix_view<const DataT, IdxT, raft::layout_c_contiguous> const y,          \
+    raft::device_matrix_view<OutT, IdxT, raft::layout_c_contiguous> dist,                    \
+    OutT metric_arg)
+
+#define instantiate_cuvs_distance_distance_by_algo(DistT)                 \
+  instantiate_cuvs_distance_distance(DistT, float, float, float, int);    \
+  instantiate_cuvs_distance_distance(DistT, double, double, double, int); \
+  instantiate_cuvs_distance_distance(DistT, half, float, float, int)
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::Canberra);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::CorrelationExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::CosineExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::HammingUnexpanded);
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::HellingerExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::InnerProduct);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::JensenShannon);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::KLDivergence);
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L1);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2Expanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2SqrtExpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2SqrtUnexpanded);
+
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::L2Unexpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::Linf);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::LpUnexpanded);
+instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::RusselRaoExpanded);
+
+#undef instantiate_cuvs_distance_distance_by_algo
+#undef instantiate_cuvs_distance_distance
 
 // The following two instances are used in test/distance/gram.cu. Note the use
 // of int64_t for the index type.
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   cuvs::distance::kernels::detail::rbf_fin_op<float>,
-                                   int64_t);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   cuvs::distance::kernels::detail::rbf_fin_op<double>,
-                                   int64_t);
-
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::identity_op,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, raft::identity_op, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, raft::identity_op, int);
-
-#undef instantiate_raft_distance_distance
-
-// Same, but without raft::identity_op
-#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT) \
-  template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>(  \
-    raft::resources const& handle,                                      \
-    const DataT* x,                                                     \
-    const DataT* y,                                                     \
-    OutT* dist,                                                         \
-    IdxT m,                                                             \
-    IdxT n,                                                             \
-    IdxT k,                                                             \
-    void* workspace,                                                    \
-    size_t worksize,                                                    \
-    bool isRowMajor,                                                    \
-    DataT metric_arg)
-
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
-
-#undef instantiate_raft_distance_distance
-
-// Same, but without workspace
-#define instantiate_raft_distance_distance(DT, DataT, AccT, OutT, IdxT) \
-  template void cuvs::distance::distance<DT, DataT, AccT, OutT, IdxT>(  \
-    raft::resources const& handle,                                      \
-    const DataT* x,                                                     \
-    const DataT* y,                                                     \
-    OutT* dist,                                                         \
-    IdxT m,                                                             \
-    IdxT n,                                                             \
-    IdxT k,                                                             \
-    bool isRowMajor,                                                    \
+#define instantiate_cuvs_distance_distance_extra(DistT, DataT, AccT, OutT, FinalLambda, IdxT) \
+  template void cuvs::distance::distance<DistT, DataT, AccT, OutT, FinalLambda, IdxT>(        \
+    raft::resources const& handle,                                                            \
+    const DataT* x,                                                                           \
+    const DataT* y,                                                                           \
+    OutT* dist,                                                                               \
+    IdxT m,                                                                                   \
+    IdxT n,                                                                                   \
+    IdxT k,                                                                                   \
+    void* workspace,                                                                          \
+    size_t worksize,                                                                          \
+    FinalLambda fin_op,                                                                       \
+    bool isRowMajor,                                                                          \
     DataT metric_arg)
 
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L1, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, float, float, float, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::Linf, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
-
-#undef instantiate_raft_distance_distance
-
-#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT)  \
-  template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>( \
-    const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k)
-
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CorrelationExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CorrelationExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CosineExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::CosineExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HammingUnexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HammingUnexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HellingerExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::HellingerExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2SqrtUnexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Linf, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Linf, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::LpUnexpanded, double, double, double, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::RusselRaoExpanded, float, float, float, int);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::RusselRaoExpanded, double, double, double, int);
-
-#undef instantiate_raft_distance_getWorkspaceSize
-
-#define instantiate_raft_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT, layout)  \
-  template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, layout>( \
-    raft::device_matrix_view<DataT, IdxT, layout> const& x,                                 \
-    raft::device_matrix_view<DataT, IdxT, layout> const& y)
-
-// We could consider not taking template parameters for this function. The
-// number of instantiations seems a bit excessive..
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::Canberra, double, double, double, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CorrelationExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::CosineExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HammingUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::HellingerExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::InnerProduct,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::JensenShannon,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::KLDivergence,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L1, double, double, double, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, int, raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           float,
-                                           float,
-                                           float,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_f_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(cuvs::distance::DistanceType::L2Unexpanded,
-                                           double,
-                                           double,
-                                           double,
-                                           int,
-                                           raft::layout_c_contiguous);
-instantiate_raft_distance_getWorkspaceSize(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, int, raft::layout_f_contiguous);
-
-#undef instantiate_raft_distance_getWorkspaceSize
-
-#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                        \
+instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpanded,
+                                         float,
+                                         float,
+                                         float,
+                                         cuvs::distance::kernels::detail::rbf_fin_op<float>,
+                                         int64_t);
+instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpanded,
+                                         double,
+                                         double,
+                                         double,
+                                         cuvs::distance::kernels::detail::rbf_fin_op<double>,
+                                         int64_t);
+
+#undef instantiate_cuvs_distance_distance_extra
+
+#define instantiate_cuvs_distance_getWorkspaceSize(DistT, DataT, AccT, OutT, IdxT)             \
+  template size_t cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT>(            \
+    const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k);                                   \
+                                                                                               \
+  template size_t                                                                              \
+  cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, raft::layout_f_contiguous>( \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_f_contiguous> const& x,                 \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_f_contiguous> const& y);                \
+                                                                                               \
+  template size_t                                                                              \
+  cuvs::distance::getWorkspaceSize<DistT, DataT, AccT, OutT, IdxT, raft::layout_c_contiguous>( \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_c_contiguous> const& x,                 \
+    raft::device_matrix_view<DataT, IdxT, raft::layout_c_contiguous> const& y)
+
+#define instantiate_cuvs_distance_getWorkspaceSize_by_algo(DistT)                     \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, float, float, float, int);        \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, double, double, double, int);     \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, half, float, float, int);         \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, float, float, float, int64_t);    \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, double, double, double, int64_t); \
+  instantiate_cuvs_distance_getWorkspaceSize(DistT, half, float, float, int64_t)
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::Canberra);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(
+  cuvs::distance::DistanceType::CorrelationExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::CosineExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::HammingUnexpanded);
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::HellingerExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::InnerProduct);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::JensenShannon);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::KLDivergence);
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L1);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2Expanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2SqrtExpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2SqrtUnexpanded);
+
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::L2Unexpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::Linf);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::LpUnexpanded);
+instantiate_cuvs_distance_getWorkspaceSize_by_algo(cuvs::distance::DistanceType::RusselRaoExpanded);
+
+#undef instantiate_cuvs_distance_getWorkspaceSize_by_algo
+#undef instantiate_cuvs_distance_getWorkspaceSize
+
+#define instantiate_cuvs_distance_pairwise_distance(DataT, IdxT, DistT)                 \
   template void cuvs::distance::pairwise_distance(raft::resources const& handle,        \
                                                   const DataT* x,                       \
                                                   const DataT* y,                       \
-                                                  DataT* dist,                          \
+                                                  DistT* dist,                          \
                                                   IdxT m,                               \
                                                   IdxT n,                               \
                                                   IdxT k,                               \
                                                   rmm::device_uvector<char>& workspace, \
                                                   cuvs::distance::DistanceType metric,  \
                                                   bool isRowMajor,                      \
-                                                  DataT metric_arg)
+                                                  DistT metric_arg)
 
-instantiate_raft_distance_pairwise_distance(float, int);
-instantiate_raft_distance_pairwise_distance(double, int);
+instantiate_cuvs_distance_pairwise_distance(float, int, float);
+instantiate_cuvs_distance_pairwise_distance(double, int, double);
+instantiate_cuvs_distance_pairwise_distance(half, int, float);
 
-#undef instantiate_raft_distance_pairwise_distance
+#undef instantiate_cuvs_distance_pairwise_distance
 
 // Same, but without workspace
-#define instantiate_raft_distance_pairwise_distance(DataT, IdxT)                       \
+#define instantiate_cuvs_distance_pairwise_distance(DataT, IdxT, DistT)                \
   template void cuvs::distance::pairwise_distance(raft::resources const& handle,       \
                                                   const DataT* x,                      \
                                                   const DataT* y,                      \
-                                                  DataT* dist,                         \
+                                                  DistT* dist,                         \
                                                   IdxT m,                              \
                                                   IdxT n,                              \
                                                   IdxT k,                              \
                                                   cuvs::distance::DistanceType metric, \
                                                   bool isRowMajor,                     \
-                                                  DataT metric_arg)
-
-instantiate_raft_distance_pairwise_distance(float, int);
-instantiate_raft_distance_pairwise_distance(double, int);
-
-#undef instantiate_raft_distance_pairwise_distance
-
-// Version with mdspan
-#define instantiate_raft_distance_distance(DistT, DataT, AccT, OutT, layout, IdxT) \
-  template void cuvs::distance::distance<DistT, DataT, AccT, OutT, layout, IdxT>(  \
-    raft::resources const& handle,                                                 \
-    raft::device_matrix_view<const DataT, IdxT, layout> const x,                   \
-    raft::device_matrix_view<const DataT, IdxT, layout> const y,                   \
-    raft::device_matrix_view<OutT, IdxT, layout> dist,                             \
-    DataT metric_arg)
-
-// Again, we might want to consider reigning in the number of instantiations...
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Canberra, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CorrelationExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::CosineExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HammingUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::HellingerExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::InnerProduct, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::InnerProduct,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::JensenShannon, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::JensenShannon,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::KLDivergence, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::KLDivergence,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L1, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Expanded, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2SqrtUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::L2Unexpanded, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::L2Unexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::Linf, double, double, double, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(
-  cuvs::distance::DistanceType::LpUnexpanded, float, float, float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::LpUnexpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_c_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   float,
-                                   float,
-                                   float,
-                                   raft::layout_f_contiguous,
-                                   int);
-instantiate_raft_distance_distance(cuvs::distance::DistanceType::RusselRaoExpanded,
-                                   double,
-                                   double,
-                                   double,
-                                   raft::layout_f_contiguous,
-                                   int);
-
-#undef instantiate_raft_distance_distance
-
-#define instantiate_raft_distance_pairwise_distance(DataT, layout, IdxT) \
-  template void cuvs::distance::pairwise_distance(                       \
-    raft::resources const& handle,                                       \
-    raft::device_matrix_view<const DataT, IdxT, layout> const x,         \
-    raft::device_matrix_view<const DataT, IdxT, layout> const y,         \
-    raft::device_matrix_view<DataT, IdxT, layout> dist,                  \
-    cuvs::distance::DistanceType metric,                                 \
-    DataT metric_arg)
-
-instantiate_raft_distance_pairwise_distance(float, raft::layout_c_contiguous, int);
-instantiate_raft_distance_pairwise_distance(float, raft::layout_f_contiguous, int);
-instantiate_raft_distance_pairwise_distance(double, raft::layout_c_contiguous, int);
-instantiate_raft_distance_pairwise_distance(double, raft::layout_f_contiguous, int);
-
-#undef instantiate_raft_distance_pairwise_distance
+                                                  DistT metric_arg)
+
+instantiate_cuvs_distance_pairwise_distance(float, int, float);
+instantiate_cuvs_distance_pairwise_distance(double, int, double);
+instantiate_cuvs_distance_pairwise_distance(half, int, float);
+
+#undef instantiate_cuvs_distance_pairwise_distance
+
+#define instantiate_cuvs_distance_pairwise_distance(DataT, layout, IdxT, DistT) \
+  template void cuvs::distance::pairwise_distance(                              \
+    raft::resources const& handle,                                              \
+    raft::device_matrix_view<const DataT, IdxT, layout> const x,                \
+    raft::device_matrix_view<const DataT, IdxT, layout> const y,                \
+    raft::device_matrix_view<DistT, IdxT, layout> dist,                         \
+    cuvs::distance::DistanceType metric,                                        \
+    DistT metric_arg)
+
+instantiate_cuvs_distance_pairwise_distance(float, raft::layout_c_contiguous, int, float);
+instantiate_cuvs_distance_pairwise_distance(float, raft::layout_f_contiguous, int, float);
+instantiate_cuvs_distance_pairwise_distance(double, raft::layout_c_contiguous, int, double);
+instantiate_cuvs_distance_pairwise_distance(double, raft::layout_f_contiguous, int, double);
+instantiate_cuvs_distance_pairwise_distance(half, raft::layout_c_contiguous, int, float);
+instantiate_cuvs_distance_pairwise_distance(half, raft::layout_f_contiguous, int, float);
+
+#undef instantiate_cuvs_distance_pairwise_distance
diff --git a/cpp/src/distance/distance.cuh b/cpp/src/distance/distance.cuh
index b5bfc07cb2..d1bfc8212e 100644
--- a/cpp/src/distance/distance.cuh
+++ b/cpp/src/distance/distance.cuh
@@ -19,6 +19,4 @@
 #include "distance-inl.cuh"
 #endif
 
-#ifdef RAFT_COMPILED
 #include "distance-ext.cuh"
-#endif
diff --git a/cpp/src/distance/pairwise_distance.cu b/cpp/src/distance/pairwise_distance.cu
index 10f096b966..a802ce91c6 100644
--- a/cpp/src/distance/pairwise_distance.cu
+++ b/cpp/src/distance/pairwise_distance.cu
@@ -61,6 +61,24 @@ void pairwise_distance(
     handle, x_v, y_v, d_v, metric, metric_arg);
 }
 
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_c_contiguous> const x,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_c_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_c_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg)
+{
+  auto x_v = raft::make_device_matrix_view<const half, int, raft::layout_c_contiguous>(
+    x.data_handle(), x.extent(0), x.extent(1));
+  auto y_v = raft::make_device_matrix_view<const half, int, raft::layout_c_contiguous>(
+    y.data_handle(), y.extent(0), y.extent(1));
+  auto d_v = raft::make_device_matrix_view<float, int, raft::layout_c_contiguous>(
+    dist.data_handle(), dist.extent(0), dist.extent(1));
+  pairwise_distance<half, raft::layout_c_contiguous, int, float>(
+    handle, x_v, y_v, d_v, metric, metric_arg);
+}
+
 void pairwise_distance(
   raft::resources const& handle,
   raft::device_matrix_view<const float, std::int64_t, raft::layout_f_contiguous> const x,
@@ -97,6 +115,24 @@ void pairwise_distance(
     handle, x_v, y_v, d_v, metric, metric_arg);
 }
 
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_f_contiguous> const x,
+  raft::device_matrix_view<const half, std::int64_t, raft::layout_f_contiguous> const y,
+  raft::device_matrix_view<float, std::int64_t, raft::layout_f_contiguous> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg)
+{
+  auto x_v = raft::make_device_matrix_view<const half, int, raft::layout_f_contiguous>(
+    x.data_handle(), x.extent(0), x.extent(1));
+  auto y_v = raft::make_device_matrix_view<const half, int, raft::layout_f_contiguous>(
+    y.data_handle(), y.extent(0), y.extent(1));
+  auto d_v = raft::make_device_matrix_view<float, int, raft::layout_f_contiguous>(
+    dist.data_handle(), dist.extent(0), dist.extent(1));
+  pairwise_distance<half, raft::layout_f_contiguous, int, float>(
+    handle, x_v, y_v, d_v, metric, metric_arg);
+}
+
 /** @} */  // end group pairwise_distance_runtime
 
 }  // namespace cuvs::distance
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index ce21e2d395..c76feb0159 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -21,12 +21,12 @@
 #include <raft/core/copy.hpp>
 
 namespace cuvs::neighbors::brute_force {
-template <typename T>
-index<T>::index(raft::resources const& res,
-                raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
-                std::optional<raft::device_vector<T, int64_t>>&& norms,
-                cuvs::distance::DistanceType metric,
-                T metric_arg)
+template <typename T, typename DistT>
+index<T, DistT>::index(raft::resources const& res,
+                       raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
+                       std::optional<raft::device_vector<DistT, int64_t>>&& norms,
+                       cuvs::distance::DistanceType metric,
+                       DistT metric_arg)
   : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
@@ -38,12 +38,12 @@ index<T>::index(raft::resources const& res,
   raft::resource::sync_stream(res);
 }
 
-template <typename T>
-index<T>::index(raft::resources const& res,
-                raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
-                std::optional<raft::device_vector<T, int64_t>>&& norms,
-                cuvs::distance::DistanceType metric,
-                T metric_arg)
+template <typename T, typename DistT>
+index<T, DistT>::index(raft::resources const& res,
+                       raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
+                       std::optional<raft::device_vector<DistT, int64_t>>&& norms,
+                       cuvs::distance::DistanceType metric,
+                       DistT metric_arg)
   : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
@@ -54,12 +54,12 @@ index<T>::index(raft::resources const& res,
   update_dataset(res, dataset);
 }
 
-template <typename T>
-index<T>::index(raft::resources const& res,
-                raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
-                std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
-                cuvs::distance::DistanceType metric,
-                T metric_arg)
+template <typename T, typename DistT>
+index<T, DistT>::index(raft::resources const& res,
+                       raft::device_matrix_view<const T, int64_t, raft::row_major> dataset_view,
+                       std::optional<raft::device_vector_view<const DistT, int64_t>> norms_view,
+                       cuvs::distance::DistanceType metric,
+                       DistT metric_arg)
   : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
@@ -69,12 +69,12 @@ index<T>::index(raft::resources const& res,
 {
 }
 
-template <typename T>
-index<T>::index(raft::resources const& res,
-                raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
-                std::optional<raft::device_vector<T, int64_t>>&& norms,
-                cuvs::distance::DistanceType metric,
-                T metric_arg)
+template <typename T, typename DistT>
+index<T, DistT>::index(raft::resources const& res,
+                       raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
+                       std::optional<raft::device_vector<DistT, int64_t>>&& norms,
+                       cuvs::distance::DistanceType metric,
+                       DistT metric_arg)
   : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(
@@ -99,12 +99,12 @@ index<T>::index(raft::resources const& res,
   dataset_view_ = raft::make_const_mdspan(dataset_.view());
 }
 
-template <typename T>
-index<T>::index(raft::resources const& res,
-                raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
-                std::optional<raft::device_vector_view<const T, int64_t>> norms_view,
-                cuvs::distance::DistanceType metric,
-                T metric_arg)
+template <typename T, typename DistT>
+index<T, DistT>::index(raft::resources const& res,
+                       raft::device_matrix_view<const T, int64_t, raft::col_major> dataset_view,
+                       std::optional<raft::device_vector_view<const DistT, int64_t>> norms_view,
+                       cuvs::distance::DistanceType metric,
+                       DistT metric_arg)
   : cuvs::neighbors::index(),
     metric_(metric),
     dataset_(
@@ -129,73 +129,74 @@ index<T>::index(raft::resources const& res,
   dataset_view_ = raft::make_const_mdspan(dataset_.view());
 }
 
-template <typename T>
-void index<T>::update_dataset(raft::resources const& res,
-                              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
+template <typename T, typename DistT>
+void index<T, DistT>::update_dataset(
+  raft::resources const& res, raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
 {
   dataset_view_ = dataset;
 }
 
-template <typename T>
-void index<T>::update_dataset(raft::resources const& res,
-                              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)
+template <typename T, typename DistT>
+void index<T, DistT>::update_dataset(
+  raft::resources const& res, raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)
 {
   dataset_ = raft::make_device_matrix<T, int64_t>(res, dataset.extent(0), dataset.extent(1));
   raft::copy(res, dataset_.view(), dataset);
   dataset_view_ = raft::make_const_mdspan(dataset_.view());
 }
 
-#define CUVS_INST_BFKNN(T)                                                                        \
+#define CUVS_INST_BFKNN(T, DistT)                                                                 \
   auto build(raft::resources const& res,                                                          \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,                 \
              cuvs::distance::DistanceType metric,                                                 \
-             T metric_arg)                                                                        \
-    ->cuvs::neighbors::brute_force::index<T>                                                      \
+             DistT metric_arg)                                                                    \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                               \
   {                                                                                               \
-    return detail::build<T>(res, dataset, metric, metric_arg);                                    \
+    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                             \
   }                                                                                               \
   auto build(raft::resources const& res,                                                          \
              raft::device_matrix_view<const T, int64_t, raft::col_major> dataset,                 \
              cuvs::distance::DistanceType metric,                                                 \
-             T metric_arg)                                                                        \
-    ->cuvs::neighbors::brute_force::index<T>                                                      \
+             DistT metric_arg)                                                                    \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                               \
   {                                                                                               \
-    return detail::build<T>(res, dataset, metric, metric_arg);                                    \
+    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                             \
   }                                                                                               \
                                                                                                   \
   void search(                                                                                    \
     raft::resources const& res,                                                                   \
-    const cuvs::neighbors::brute_force::index<T>& idx,                                            \
+    const cuvs::neighbors::brute_force::index<T, DistT>& idx,                                     \
     raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                          \
     raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,                        \
-    raft::device_matrix_view<T, int64_t, raft::row_major> distances,                              \
+    raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,                          \
     std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter = std::nullopt) \
   {                                                                                               \
     if (!sample_filter.has_value()) {                                                             \
-      detail::brute_force_search<T, int64_t>(res, idx, queries, neighbors, distances);            \
+      detail::brute_force_search<T, int64_t, DistT>(res, idx, queries, neighbors, distances);     \
     } else {                                                                                      \
-      detail::brute_force_search_filtered<T, int64_t>(                                            \
+      detail::brute_force_search_filtered<T, int64_t, uint32_t, DistT>(                           \
         res, idx, queries, *sample_filter, neighbors, distances);                                 \
     }                                                                                             \
   }                                                                                               \
   void search(                                                                                    \
     raft::resources const& res,                                                                   \
-    const cuvs::neighbors::brute_force::index<T>& idx,                                            \
+    const cuvs::neighbors::brute_force::index<T, DistT>& idx,                                     \
     raft::device_matrix_view<const T, int64_t, raft::col_major> queries,                          \
     raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,                        \
-    raft::device_matrix_view<T, int64_t, raft::row_major> distances,                              \
+    raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,                          \
     std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter = std::nullopt) \
   {                                                                                               \
     if (!sample_filter.has_value()) {                                                             \
-      detail::brute_force_search<T, int64_t>(res, idx, queries, neighbors, distances);            \
+      detail::brute_force_search<T, int64_t, DistT>(res, idx, queries, neighbors, distances);     \
     } else {                                                                                      \
       RAFT_FAIL("filtered search isn't available with col_major queries yet");                    \
     }                                                                                             \
   }                                                                                               \
                                                                                                   \
-  template struct cuvs::neighbors::brute_force::index<T>;
+  template struct cuvs::neighbors::brute_force::index<T, DistT>;
 
-CUVS_INST_BFKNN(float);
+CUVS_INST_BFKNN(float, float);
+CUVS_INST_BFKNN(half, float);
 
 #undef CUVS_INST_BFKNN
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
index b00d6617cd..e907568f5e 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
@@ -24,7 +24,7 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace multi_cta_search {
 
-#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
+#ifdef _CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 template <unsigned TEAM_SIZE,
           unsigned DATASET_BLOCK_DIM,
@@ -53,7 +53,7 @@ void select_and_run(
   SAMPLE_FILTER_T sample_filter,
   cuvs::distance::DistanceType metric,
   cudaStream_t stream) RAFT_EXPLICIT;
-#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 #define instantiate_kernel_selection(                                                           \
   TEAM_SIZE, MAX_DATASET_DIM, DATA_T, INDEX_T, DISTANCE_T, SAMPLE_FILTER_T)                     \
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
index 2cbb758f91..673fc5473f 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
+#ifndef _CUVS_EXPLICIT_INSTANTIATE_ONLY
 #include "search_multi_cta_kernel-inl.cuh"
 #endif
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
index 79f6e153cb..10dda03898 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
@@ -23,7 +23,7 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace single_cta_search {
 
-#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
+#ifdef _CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 template <unsigned TEAM_SIZE,
           unsigned DATASET_BLOCK_DIM,
@@ -53,7 +53,7 @@ void select_and_run(
   cuvs::distance::DistanceType metric,
   cudaStream_t stream) RAFT_EXPLICIT;
 
-#endif  // RAFT_EXPLICIT_INSTANTIATE_ONLY
+#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
 
 #define instantiate_single_cta_select_and_run(                                                  \
   TEAM_SIZE, MAX_DATASET_DIM, DATA_T, INDEX_T, DISTANCE_T, SAMPLE_FILTER_T)                     \
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
index 40de22f2e3..1ccec9219e 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
@@ -15,7 +15,7 @@
  */
 #pragma once
 
-#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
+#ifndef _CUVS_EXPLICIT_INSTANTIATE_ONLY
 #include "search_single_cta_kernel-inl.cuh"
 #endif
 
diff --git a/cpp/src/neighbors/detail/fused_l2_knn.cuh b/cpp/src/neighbors/detail/fused_l2_knn.cuh
index 13ea4d4189..be904673ea 100644
--- a/cpp/src/neighbors/detail/fused_l2_knn.cuh
+++ b/cpp/src/neighbors/detail/fused_l2_knn.cuh
@@ -196,8 +196,8 @@ template <typename DataT,
           bool isRowMajor   = true>
 __launch_bounds__(Policy::Nthreads, 2) RAFT_KERNEL fusedL2kNN(const DataT* x,
                                                               const DataT* y,
-                                                              const DataT* _xn,
-                                                              const DataT* _yn,
+                                                              const OutT* _xn,
+                                                              const OutT* _yn,
                                                               const IdxT m,
                                                               const IdxT n,
                                                               const IdxT k,
@@ -342,8 +342,8 @@ __launch_bounds__(Policy::Nthreads, 2) RAFT_KERNEL fusedL2kNN(const DataT* x,
   auto epilog_lambda =
     [&distance_op, numOfNN, m, n, ldd, out_dists, out_inds, keyMax, identity] __device__(
       AccT acc[Policy::AccRowsPerTh][Policy::AccColsPerTh],
-      DataT * regxn,
-      DataT * regyn,
+      OutT * regxn,
+      OutT * regyn,
       IdxT gridStrideX,
       IdxT gridStrideY) {
       // Use ::template to disambiguate (See:
@@ -536,8 +536,8 @@ void fusedL2UnexpKnnImpl(const DataT* x,
                          void* workspace,
                          size_t& worksize)
 {
-  typedef typename raft::linalg::Policy2x8<DataT, 1>::Policy RowPolicy;
-  typedef typename raft::linalg::Policy4x4<DataT, VecLen>::ColPolicy ColPolicy;
+  typedef typename raft::linalg::Policy2x8<AccT, 1>::Policy RowPolicy;
+  typedef typename raft::linalg::Policy4x4<AccT, VecLen>::ColPolicy ColPolicy;
 
   typedef typename std::conditional<true, RowPolicy, ColPolicy>::type KPolicy;
 
@@ -705,8 +705,8 @@ template <typename DataT,
           bool isRowMajor>
 void fusedL2ExpKnnImpl(const DataT* x,
                        const DataT* y,
-                       const DataT* xn,
-                       const DataT* yn,
+                       const AccT* xn,
+                       const AccT* yn,
                        IdxT m,
                        IdxT n,
                        IdxT k,
@@ -721,8 +721,8 @@ void fusedL2ExpKnnImpl(const DataT* x,
                        void* workspace,
                        size_t& worksize)
 {
-  typedef typename raft::linalg::Policy2x8<DataT, 1>::Policy RowPolicy;
-  typedef typename raft::linalg::Policy4x4<DataT, VecLen>::ColPolicy ColPolicy;
+  typedef typename raft::linalg::Policy2x8<AccT, 1>::Policy RowPolicy;
+  typedef typename raft::linalg::Policy4x4<AccT, VecLen>::ColPolicy ColPolicy;
 
   typedef typename std::conditional<true, RowPolicy, ColPolicy>::type KPolicy;
 
@@ -777,7 +777,7 @@ void fusedL2ExpKnnImpl(const DataT* x,
     int32_t* mutexes = nullptr;
     if (grid.x > 1) {
       const auto numMutexes   = raft::ceildiv<int>(m, KPolicy::Mblk);
-      const auto normsSize    = (x != y) ? (m + n) * sizeof(DataT) : n * sizeof(DataT);
+      const auto normsSize    = (x != y) ? (m + n) * sizeof(AccT) : n * sizeof(AccT);
       const auto requiredSize = sizeof(int32_t) * numMutexes + normsSize;
       if (worksize < requiredSize) {
         worksize = requiredSize;
@@ -790,8 +790,8 @@ void fusedL2ExpKnnImpl(const DataT* x,
 
     // calculate norms if they haven't been passed in
     if (!xn) {
-      DataT* xn_ = (DataT*)workspace;
-      workspace  = xn_ + m;
+      AccT* xn_ = (AccT*)workspace;
+      workspace = xn_ + m;
       raft::linalg::rowNorm(
         xn_, x, k, m, raft::linalg::L2Norm, isRowMajor, stream, raft::identity_op{});
       xn = xn_;
@@ -800,7 +800,7 @@ void fusedL2ExpKnnImpl(const DataT* x,
       if (x == y) {
         yn = xn;
       } else {
-        DataT* yn_ = (DataT*)(workspace);
+        AccT* yn_ = (AccT*)(workspace);
         raft::linalg::rowNorm(
           yn_, y, k, n, raft::linalg::L2Norm, isRowMajor, stream, raft::identity_op{});
         yn = yn_;
@@ -843,8 +843,8 @@ void fusedL2ExpKnn(IdxT m,
                    IdxT ldd,
                    const DataT* x,
                    const DataT* y,
-                   const DataT* xn,
-                   const DataT* yn,
+                   const AccT* xn,
+                   const AccT* yn,
                    bool sqrt,
                    OutT* out_dists,
                    IdxT* out_inds,
@@ -930,10 +930,13 @@ void fusedL2ExpKnn(IdxT m,
  * @param[in] rowMajorQuery are the query array in row-major layout?
  * @param[in] stream stream to order kernel launch
  */
-template <typename value_idx, typename value_t, bool usePrevTopKs = false>
+template <typename value_idx,
+          typename value_t,
+          bool usePrevTopKs   = false,
+          typename distance_t = float>
 void fusedL2Knn(size_t D,
                 value_idx* out_inds,
-                value_t* out_dists,
+                distance_t* out_dists,
                 const value_t* index,
                 const value_t* query,
                 size_t n_index_rows,
@@ -943,8 +946,8 @@ void fusedL2Knn(size_t D,
                 bool rowMajorQuery,
                 cudaStream_t stream,
                 cuvs::distance::DistanceType metric,
-                const value_t* index_norms = NULL,
-                const value_t* query_norms = NULL)
+                const distance_t* index_norms = NULL,
+                const distance_t* query_norms = NULL)
 {
   // Validate the input data
   ASSERT(k > 0, "l2Knn: k must be > 0");
@@ -975,83 +978,87 @@ void fusedL2Knn(size_t D,
       tempWorksize =
         cuvs::distance::getWorkspaceSize<cuvs::distance::DistanceType::L2Expanded,
                                          value_t,
-                                         value_t,
-                                         value_t,
+                                         distance_t,
+                                         distance_t,
                                          value_idx>(query, index, n_query_rows, n_index_rows, D);
       worksize = tempWorksize;
       workspace.resize(worksize, stream);
-      fusedL2ExpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
-                                                                              n_index_rows,
-                                                                              D,
-                                                                              lda,
-                                                                              ldb,
-                                                                              ldd,
-                                                                              query,
-                                                                              index,
-                                                                              query_norms,
-                                                                              index_norms,
-                                                                              sqrt,
-                                                                              out_dists,
-                                                                              out_inds,
-                                                                              k,
-                                                                              stream,
-                                                                              workspace.data(),
-                                                                              worksize);
+      fusedL2ExpKnn<value_t, distance_t, distance_t, value_idx, usePrevTopKs, true>(
+        n_query_rows,
+        n_index_rows,
+        D,
+        lda,
+        ldb,
+        ldd,
+        query,
+        index,
+        query_norms,
+        index_norms,
+        sqrt,
+        out_dists,
+        out_inds,
+        k,
+        stream,
+        workspace.data(),
+        worksize);
       if (worksize > tempWorksize) {
         workspace.resize(worksize, stream);
-        fusedL2ExpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
-                                                                                n_index_rows,
-                                                                                D,
-                                                                                lda,
-                                                                                ldb,
-                                                                                ldd,
-                                                                                query,
-                                                                                index,
-                                                                                query_norms,
-                                                                                index_norms,
-                                                                                sqrt,
-                                                                                out_dists,
-                                                                                out_inds,
-                                                                                k,
-                                                                                stream,
-                                                                                workspace.data(),
-                                                                                worksize);
+        fusedL2ExpKnn<value_t, distance_t, distance_t, value_idx, usePrevTopKs, true>(
+          n_query_rows,
+          n_index_rows,
+          D,
+          lda,
+          ldb,
+          ldd,
+          query,
+          index,
+          query_norms,
+          index_norms,
+          sqrt,
+          out_dists,
+          out_inds,
+          k,
+          stream,
+          workspace.data(),
+          worksize);
       }
       break;
     case cuvs::distance::DistanceType::L2Unexpanded:
     case cuvs::distance::DistanceType::L2SqrtUnexpanded:
-      fusedL2UnexpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
-                                                                                n_index_rows,
-                                                                                D,
-                                                                                lda,
-                                                                                ldb,
-                                                                                ldd,
-                                                                                query,
-                                                                                index,
-                                                                                sqrt,
-                                                                                out_dists,
-                                                                                out_inds,
-                                                                                k,
-                                                                                stream,
-                                                                                workspace.data(),
-                                                                                worksize);
+      fusedL2UnexpKnn<value_t, distance_t, distance_t, value_idx, usePrevTopKs, true>(
+        n_query_rows,
+        n_index_rows,
+        D,
+        lda,
+        ldb,
+        ldd,
+        query,
+        index,
+        sqrt,
+        out_dists,
+        out_inds,
+        k,
+        stream,
+        workspace.data(),
+        worksize);
       if (worksize) {
         workspace.resize(worksize, stream);
-        fusedL2UnexpKnn<value_t, value_t, value_t, value_idx, usePrevTopKs, true>(n_query_rows,
-                                                                                  n_index_rows,
-                                                                                  D,
-                                                                                  lda,
-                                                                                  ldb,
-                                                                                  ldd,
-                                                                                  query,
-                                                                                  index,
-                                                                                  sqrt,
-                                                                                  out_dists,
-                                                                                  out_inds,
-                                                                                  k,
-                                                                                  stream,
-                                                                                  workspace.data(),
-                                                                                  worksize);
+        fusedL2UnexpKnn<value_t, distance_t, distance_t, value_idx, usePrevTopKs, true>(
+          n_query_rows,
+          n_index_rows,
+          D,
+          lda,
+          ldb,
+          ldd,
+          query,
+          index,
+          sqrt,
+          out_dists,
+          out_inds,
+          k,
+          stream,
+          workspace.data(),
+          worksize);
       }
       break;
     default: printf("only L2 distance metric is supported\n"); break;
diff --git a/cpp/src/neighbors/detail/haversine_distance.cuh b/cpp/src/neighbors/detail/haversine_distance.cuh
index fc6aa477d1..ee972cf350 100644
--- a/cpp/src/neighbors/detail/haversine_distance.cuh
+++ b/cpp/src/neighbors/detail/haversine_distance.cuh
@@ -22,15 +22,30 @@
 #include <raft/util/cudart_utils.hpp>
 #include <raft/util/pow2_utils.cuh>
 
+#include <cuda_fp16.h>
+
 namespace cuvs::neighbors::detail {
-template <typename value_t>
-DI value_t compute_haversine(value_t x1, value_t y1, value_t x2, value_t y2)
+template <typename value_t, typename distance_t>
+DI distance_t compute_haversine(value_t x1, value_t y1, value_t x2, value_t y2)
 {
-  value_t sin_0 = raft::sin(0.5 * (x1 - y1));
-  value_t sin_1 = raft::sin(0.5 * (x2 - y2));
-  value_t rdist = sin_0 * sin_0 + raft::cos(x1) * raft::cos(y1) * sin_1 * sin_1;
-
-  return 2 * raft::asin(raft::sqrt(rdist));
+  if constexpr ((std::is_same_v<distance_t, float> && std::is_same_v<value_t, half>)) {
+    distance_t _x1 = __half2float(x1);
+    distance_t _y1 = __half2float(y1);
+    distance_t _x2 = __half2float(x2);
+    distance_t _y2 = __half2float(y2);
+
+    distance_t sin_0 = raft::sin(distance_t(0.5) * (_x1 - _y1));
+    distance_t sin_1 = raft::sin(distance_t(0.5) * (_x2 - _y2));
+    distance_t rdist = sin_0 * sin_0 + raft::cos(_x1) * raft::cos(_y1) * sin_1 * sin_1;
+
+    return static_cast<distance_t>(2) * raft::asin(raft::sqrt(rdist));
+  } else {
+    distance_t sin_0 = raft::sin(distance_t(0.5) * (x1 - y1));
+    distance_t sin_1 = raft::sin(distance_t(0.5) * (x2 - y2));
+    distance_t rdist = sin_0 * sin_0 + raft::cos(x1) * raft::cos(y1) * sin_1 * sin_1;
+
+    return static_cast<distance_t>(2) * raft::asin(raft::sqrt(rdist));
+  }
 }
 
 /**
@@ -46,9 +61,14 @@ DI value_t compute_haversine(value_t x1, value_t y1, value_t x2, value_t y2)
  * @param[in] n_index_rows number of rows in index array
  * @param[in] k number of closest neighbors to return
  */
-template <typename value_idx, typename value_t, int warp_q = 1024, int thread_q = 8, int tpb = 128>
+template <typename value_idx,
+          typename value_t,
+          int warp_q          = 1024,
+          int thread_q        = 8,
+          int tpb             = 128,
+          typename distance_t = float>
 RAFT_KERNEL haversine_knn_kernel(value_idx* out_inds,
-                                 value_t* out_dists,
+                                 distance_t* out_dists,
                                  const value_t* index,
                                  const value_t* query,
                                  size_t n_index_rows,
@@ -56,12 +76,12 @@ RAFT_KERNEL haversine_knn_kernel(value_idx* out_inds,
 {
   constexpr int kNumWarps = tpb / raft::WarpSize;
 
-  __shared__ value_t smemK[kNumWarps * warp_q];
+  __shared__ distance_t smemK[kNumWarps * warp_q];
   __shared__ value_idx smemV[kNumWarps * warp_q];
 
   using namespace raft::neighbors::detail::faiss_select;
-  BlockSelect<value_t, value_idx, false, Comparator<value_t>, warp_q, thread_q, tpb> heap(
-    std::numeric_limits<value_t>::max(), std::numeric_limits<value_idx>::max(), smemK, smemV, k);
+  BlockSelect<distance_t, value_idx, false, Comparator<distance_t>, warp_q, thread_q, tpb> heap(
+    std::numeric_limits<distance_t>::max(), std::numeric_limits<value_idx>::max(), smemK, smemV, k);
 
   // Grid is exactly sized to rows available
   int limit = raft::Pow2<raft::WarpSize>::roundDown(n_index_rows);
@@ -77,7 +97,7 @@ RAFT_KERNEL haversine_knn_kernel(value_idx* out_inds,
     value_t y1             = idx_ptr[0];
     value_t y2             = idx_ptr[1];
 
-    value_t dist = compute_haversine(x1, y1, x2, y2);
+    distance_t dist = compute_haversine<value_t, distance_t>(x1, y1, x2, y2);
 
     heap.add(dist, i);
   }
@@ -88,7 +108,7 @@ RAFT_KERNEL haversine_knn_kernel(value_idx* out_inds,
     value_t y1             = idx_ptr[0];
     value_t y2             = idx_ptr[1];
 
-    value_t dist = compute_haversine(x1, y1, x2, y2);
+    distance_t dist = compute_haversine<value_t, distance_t>(x1, y1, x2, y2);
 
     heap.addThreadQ(dist, i);
   }
@@ -117,9 +137,9 @@ RAFT_KERNEL haversine_knn_kernel(value_idx* out_inds,
  * @param[in] k number of closest neighbors to return
  * @param[in] stream stream to order kernel launch
  */
-template <typename value_idx, typename value_t>
+template <typename value_idx, typename value_t, typename distance_t = float>
 void haversine_knn(value_idx* out_inds,
-                   value_t* out_dists,
+                   distance_t* out_dists,
                    const value_t* index,
                    const value_t* query,
                    size_t n_index_rows,
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 559d33cc28..e3f7acc96a 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -48,9 +48,9 @@
 #include <raft/util/cudart_utils.hpp>
 #include <raft/util/popc.cuh>
 
+#include <cuda_fp16.h>
 #include <rmm/cuda_device.hpp>
 #include <rmm/device_uvector.hpp>
-
 #include <thrust/iterator/transform_iterator.h>
 
 #include <cstdint>
@@ -62,7 +62,7 @@ namespace cuvs::neighbors::detail {
  * Calculates brute force knn, using a fixed memory budget
  * by tiling over both the rows and columns of pairwise_distances
  */
-template <typename ElementType = float, typename IndexType = int64_t>
+template <typename ElementType = float, typename IndexType = int64_t, typename DistanceT = float>
 void tiled_brute_force_knn(const raft::resources& handle,
                            const ElementType* search,  // size (m ,d)
                            const ElementType* index,   // size (n ,d)
@@ -70,15 +70,15 @@ void tiled_brute_force_knn(const raft::resources& handle,
                            size_t n,
                            size_t d,
                            size_t k,
-                           ElementType* distances,  // size (m, k)
-                           IndexType* indices,      // size (m, k)
+                           DistanceT* distances,  // size (m, k)
+                           IndexType* indices,    // size (m, k)
                            cuvs::distance::DistanceType metric,
-                           float metric_arg                            = 2.0,
-                           size_t max_row_tile_size                    = 0,
-                           size_t max_col_tile_size                    = 0,
-                           const ElementType* precomputed_index_norms  = nullptr,
-                           const ElementType* precomputed_search_norms = nullptr,
-                           const uint32_t* filter_bitmap               = nullptr)
+                           DistanceT metric_arg                      = 2.0,
+                           size_t max_row_tile_size                  = 0,
+                           size_t max_col_tile_size                  = 0,
+                           const DistanceT* precomputed_index_norms  = nullptr,
+                           const DistanceT* precomputed_search_norms = nullptr,
+                           const uint32_t* filter_bitmap             = nullptr)
 {
   // Figure out the number of rows/cols to tile for
   size_t tile_rows   = 0;
@@ -88,7 +88,7 @@ void tiled_brute_force_knn(const raft::resources& handle,
   auto total_mem     = rmm::available_device_memory().second;
 
   cuvs::neighbors::detail::faiss_select::chooseTileSize(
-    m, n, d, sizeof(ElementType), total_mem, tile_rows, tile_cols);
+    m, n, d, sizeof(DistanceT), total_mem, tile_rows, tile_cols);
 
   // for unittesting, its convenient to be able to put a max size on the tiles
   // so we can test the tiling logic without having to use huge inputs.
@@ -99,13 +99,13 @@ void tiled_brute_force_knn(const raft::resources& handle,
   tile_cols = std::max(tile_cols, k);
 
   // stores pairwise distances for the current tile
-  rmm::device_uvector<ElementType> temp_distances(tile_rows * tile_cols, stream);
+  rmm::device_uvector<DistanceT> temp_distances(tile_rows * tile_cols, stream);
 
   // calculate norms for L2 expanded distances - this lets us avoid calculating
   // norms repeatedly per-tile, and just do once for the entire input
   auto pairwise_metric = metric;
-  rmm::device_uvector<ElementType> search_norms(0, stream);
-  rmm::device_uvector<ElementType> index_norms(0, stream);
+  rmm::device_uvector<DistanceT> search_norms(0, stream);
+  rmm::device_uvector<DistanceT> index_norms(0, stream);
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
       metric == cuvs::distance::DistanceType::CosineExpanded) {
@@ -162,14 +162,14 @@ void tiled_brute_force_knn(const raft::resources& handle,
   if (n < k) {
     raft::matrix::fill(handle,
                        raft::make_device_matrix_view(distances, m, k),
-                       std::numeric_limits<ElementType>::lowest());
+                       std::numeric_limits<DistanceT>::lowest());
 
     if constexpr (std::is_signed_v<IndexType>) {
       raft::matrix::fill(handle, raft::make_device_matrix_view(indices, m, k), IndexType{-1});
     }
   }
 
-  rmm::device_uvector<ElementType> temp_out_distances(tile_rows * temp_out_cols, stream);
+  rmm::device_uvector<DistanceT> temp_out_distances(tile_rows * temp_out_cols, stream);
   rmm::device_uvector<IndexType> temp_out_indices(tile_rows * temp_out_cols, stream);
 
   bool select_min = cuvs::distance::is_min_close(metric);
@@ -189,7 +189,7 @@ void tiled_brute_force_knn(const raft::resources& handle,
           search + i * d, current_query_size, d),
         raft::make_device_matrix_view<const ElementType, int64_t>(
           index + j * d, current_centroid_size, d),
-        raft::make_device_matrix_view<ElementType, int64_t>(
+        raft::make_device_matrix_view<DistanceT, int64_t>(
           temp_distances.data(), current_query_size, current_centroid_size),
         pairwise_metric,
         metric_arg);
@@ -208,7 +208,7 @@ void tiled_brute_force_knn(const raft::resources& handle,
             IndexType row = i + (idx / current_centroid_size);
             IndexType col = j + (idx % current_centroid_size);
 
-            cuvs::distance::detail::ops::l2_exp_cutlass_op<ElementType, ElementType> l2_op(sqrt);
+            cuvs::distance::detail::ops::l2_exp_cutlass_op<DistanceT, DistanceT> l2_op(sqrt);
             return l2_op(row_norms[row], col_norms[col], dist[idx]);
           });
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
@@ -222,16 +222,16 @@ void tiled_brute_force_knn(const raft::resources& handle,
           [=] __device__(IndexType idx) {
             IndexType row = i + (idx / current_centroid_size);
             IndexType col = j + (idx % current_centroid_size);
-            auto val      = 1.0 - dist[idx] / (row_norms[row] * col_norms[col]);
+            auto val      = DistanceT(1.0) - dist[idx] / DistanceT(row_norms[row] * col_norms[col]);
             return val;
           });
       }
 
       if (filter_bitmap != nullptr) {
-        auto distances_ptr          = temp_distances.data();
-        auto count                  = thrust::make_counting_iterator<IndexType>(0);
-        ElementType masked_distance = select_min ? std::numeric_limits<ElementType>::infinity()
-                                                 : std::numeric_limits<ElementType>::lowest();
+        auto distances_ptr        = temp_distances.data();
+        auto count                = thrust::make_counting_iterator<IndexType>(0);
+        DistanceT masked_distance = select_min ? std::numeric_limits<DistanceT>::infinity()
+                                               : std::numeric_limits<DistanceT>::lowest();
         thrust::for_each(raft::resource::get_thrust_policy(handle),
                          count,
                          count + current_query_size * current_centroid_size,
@@ -250,10 +250,10 @@ void tiled_brute_force_knn(const raft::resources& handle,
 
       cuvs::selection::select_k(
         handle,
-        raft::make_device_matrix_view<const ElementType, int64_t, raft::row_major>(
+        raft::make_device_matrix_view<const DistanceT, int64_t, raft::row_major>(
           temp_distances.data(), current_query_size, current_centroid_size),
         std::nullopt,
-        raft::make_device_matrix_view<ElementType, int64_t, raft::row_major>(
+        raft::make_device_matrix_view<DistanceT, int64_t, raft::row_major>(
           distances + i * k, current_query_size, current_k),
         raft::make_device_matrix_view<IndexType, int64_t, raft::row_major>(
           indices + i * k, current_query_size, current_k),
@@ -269,10 +269,10 @@ void tiled_brute_force_knn(const raft::resources& handle,
       // concatenation.
       // Fix both of these problems in a single pass here
       if (tile_cols != n) {
-        const ElementType* in_distances = distances + i * k;
-        const IndexType* in_indices     = indices + i * k;
-        ElementType* out_distances      = temp_out_distances.data();
-        IndexType* out_indices          = temp_out_indices.data();
+        const DistanceT* in_distances = distances + i * k;
+        const IndexType* in_indices   = indices + i * k;
+        DistanceT* out_distances      = temp_out_distances.data();
+        IndexType* out_indices        = temp_out_indices.data();
 
         auto count = thrust::make_counting_iterator<IndexType>(0);
         thrust::for_each(raft::resource::get_thrust_policy(handle),
@@ -292,11 +292,11 @@ void tiled_brute_force_knn(const raft::resources& handle,
       // select the actual top-k items here from the temporary output
       cuvs::selection::select_k(
         handle,
-        raft::make_device_matrix_view<const ElementType, int64_t, raft::row_major>(
+        raft::make_device_matrix_view<const DistanceT, int64_t, raft::row_major>(
           temp_out_distances.data(), current_query_size, temp_out_cols),
         raft::make_device_matrix_view<const IndexType, int64_t, raft::row_major>(
           temp_out_indices.data(), current_query_size, temp_out_cols),
-        raft::make_device_matrix_view<ElementType, int64_t, raft::row_major>(
+        raft::make_device_matrix_view<DistanceT, int64_t, raft::row_major>(
           distances + i * k, current_query_size, k),
         raft::make_device_matrix_view<IndexType, int64_t, raft::row_major>(
           indices + i * k, current_query_size, k),
@@ -330,7 +330,10 @@ void tiled_brute_force_knn(const raft::resources& handle,
  * @param[in] metric corresponds to the cuvs::distance::DistanceType enum (default is L2Expanded)
  * @param[in] metricArg metric argument to use. Corresponds to the p arg for lp norm
  */
-template <typename IntType = int, typename IdxType = std::int64_t, typename value_t = float>
+template <typename IntType  = int,
+          typename IdxType  = std::int64_t,
+          typename value_t  = float,
+          typename DistType = float>
 void brute_force_knn_impl(
   raft::resources const& handle,
   std::vector<value_t*>& input,
@@ -339,15 +342,15 @@ void brute_force_knn_impl(
   value_t* search_items,
   IntType n,
   IdxType* res_I,
-  value_t* res_D,
+  DistType* res_D,
   IntType k,
   bool rowMajorIndex                  = true,
   bool rowMajorQuery                  = true,
   std::vector<IdxType>* translations  = nullptr,
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded,
-  float metricArg                     = 0,
-  std::vector<value_t*>* input_norms  = nullptr,
-  const value_t* search_norms         = nullptr)
+  DistType metricArg                  = 0,
+  std::vector<DistType*>* input_norms = nullptr,
+  const DistType* search_norms        = nullptr)
 {
   auto userStream = raft::resource::get_cuda_stream(handle);
 
@@ -375,11 +378,11 @@ void brute_force_knn_impl(
   rmm::device_uvector<IdxType> trans(id_ranges->size(), userStream);
   raft::update_device(trans.data(), id_ranges->data(), id_ranges->size(), userStream);
 
-  rmm::device_uvector<value_t> all_D(0, userStream);
+  rmm::device_uvector<DistType> all_D(0, userStream);
   rmm::device_uvector<IdxType> all_I(0, userStream);
 
-  value_t* out_D = res_D;
-  IdxType* out_I = res_I;
+  DistType* out_D = res_D;
+  IdxType* out_I  = res_I;
 
   if (input.size() > 1) {
     all_D.resize(input.size() * k * n, userStream);
@@ -419,8 +422,8 @@ void brute_force_knn_impl(
 
   size_t total_rows_processed = 0;
   for (size_t i = 0; i < input.size(); i++) {
-    value_t* out_d_ptr = out_D + (i * k * n);
-    IdxType* out_i_ptr = out_I + (i * k * n);
+    DistType* out_d_ptr = out_D + (i * k * n);
+    IdxType* out_i_ptr  = out_I + (i * k * n);
 
     auto stream = raft::resource::get_next_usable_stream(handle, i);
 
@@ -448,13 +451,13 @@ void brute_force_knn_impl(
       if (metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
           metric == cuvs::distance::DistanceType::L2SqrtUnexpanded ||
           metric == cuvs::distance::DistanceType::LpUnexpanded) {
-        value_t p = 0.5;  // standard l2
+        DistType p = 0.5;  // standard l2
         if (metric == cuvs::distance::DistanceType::LpUnexpanded) p = 1.0 / metricArg;
-        raft::linalg::unaryOp<value_t>(
+        raft::linalg::unaryOp<DistType>(
           res_D,
           res_D,
           n * k,
-          [p] __device__(value_t input) { return powf(fabsf(input), p); },
+          [p] __device__(DistType input) { return powf(fabsf(input), p); },
           stream);
       }
     } else {
@@ -514,14 +517,17 @@ void brute_force_knn_impl(
   if (translations == nullptr) delete id_ranges;
 };
 
-template <typename T, typename IdxT, typename QueryLayoutT = raft::row_major>
+template <typename T,
+          typename IdxT,
+          typename DistanceT    = float,
+          typename QueryLayoutT = raft::row_major>
 void brute_force_search(
   raft::resources const& res,
-  const cuvs::neighbors::brute_force::index<T>& idx,
+  const cuvs::neighbors::brute_force::index<T, DistanceT>& idx,
   raft::device_matrix_view<const T, int64_t, QueryLayoutT> queries,
   raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<T, int64_t, raft::row_major> distances,
-  std::optional<raft::device_vector_view<const T, int64_t>> query_norms = std::nullopt)
+  raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
+  std::optional<raft::device_vector_view<const DistanceT, int64_t>> query_norms = std::nullopt)
 {
   RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1), "Value of k must match for outputs");
   RAFT_EXPECTS(idx.dataset().extent(1) == queries.extent(1),
@@ -532,36 +538,37 @@ void brute_force_search(
 
   std::vector<T*> dataset    = {const_cast<T*>(idx.dataset().data_handle())};
   std::vector<int64_t> sizes = {idx.dataset().extent(0)};
-  std::vector<T*> norms;
-  if (idx.has_norms()) { norms.push_back(const_cast<T*>(idx.norms().data_handle())); }
-
-  brute_force_knn_impl<int64_t, IdxT, T>(res,
-                                         dataset,
-                                         sizes,
-                                         d,
-                                         const_cast<T*>(queries.data_handle()),
-                                         queries.extent(0),
-                                         neighbors.data_handle(),
-                                         distances.data_handle(),
-                                         k,
-                                         true,
-                                         std::is_same_v<QueryLayoutT, raft::row_major>,
-                                         nullptr,
-                                         idx.metric(),
-                                         idx.metric_arg(),
-                                         norms.size() ? &norms : nullptr,
-                                         query_norms ? query_norms->data_handle() : nullptr);
+  std::vector<DistanceT*> norms;
+  if (idx.has_norms()) { norms.push_back(const_cast<DistanceT*>(idx.norms().data_handle())); }
+
+  brute_force_knn_impl<int64_t, IdxT, T, DistanceT>(
+    res,
+    dataset,
+    sizes,
+    d,
+    const_cast<T*>(queries.data_handle()),
+    queries.extent(0),
+    neighbors.data_handle(),
+    distances.data_handle(),
+    k,
+    true,
+    std::is_same_v<QueryLayoutT, raft::row_major>,
+    nullptr,
+    idx.metric(),
+    idx.metric_arg(),
+    norms.size() ? &norms : nullptr,
+    query_norms ? query_norms->data_handle() : nullptr);
 }
 
-template <typename T, typename IdxT, typename BitmapT>
+template <typename T, typename IdxT, typename BitmapT, typename DistanceT = float>
 void brute_force_search_filtered(
   raft::resources const& res,
-  const cuvs::neighbors::brute_force::index<T>& idx,
+  const cuvs::neighbors::brute_force::index<T, DistanceT>& idx,
   raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
   cuvs::core::bitmap_view<const BitmapT, IdxT> filter,
   raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
-  raft::device_matrix_view<T, IdxT, raft::row_major> distances,
-  std::optional<raft::device_vector_view<const T, IdxT>> query_norms = std::nullopt)
+  raft::device_matrix_view<DistanceT, IdxT, raft::row_major> distances,
+  std::optional<raft::device_vector_view<const DistanceT, IdxT>> query_norms = std::nullopt)
 {
   auto metric = idx.metric();
 
@@ -604,26 +611,26 @@ void brute_force_search_filtered(
   if (sparsity > 0.01f) {
     raft::resources stream_pool_handle(res);
     raft::resource::set_cuda_stream(stream_pool_handle, stream);
-    auto idx_norm = idx.has_norms() ? const_cast<T*>(idx.norms().data_handle()) : nullptr;
-
-    tiled_brute_force_knn<T, IdxT>(stream_pool_handle,
-                                   queries.data_handle(),
-                                   idx.dataset().data_handle(),
-                                   n_queries,
-                                   n_dataset,
-                                   dim,
-                                   k,
-                                   distances.data_handle(),
-                                   neighbors.data_handle(),
-                                   metric,
-                                   2.0,
-                                   0,
-                                   0,
-                                   idx_norm,
-                                   nullptr,
-                                   filter.data());
+    auto idx_norm = idx.has_norms() ? const_cast<DistanceT*>(idx.norms().data_handle()) : nullptr;
+
+    tiled_brute_force_knn<T, IdxT, DistanceT>(stream_pool_handle,
+                                              queries.data_handle(),
+                                              idx.dataset().data_handle(),
+                                              n_queries,
+                                              n_dataset,
+                                              dim,
+                                              k,
+                                              distances.data_handle(),
+                                              neighbors.data_handle(),
+                                              metric,
+                                              DistanceT{2.0},
+                                              0,
+                                              0,
+                                              idx_norm,
+                                              nullptr,
+                                              filter.data());
   } else {
-    auto csr = raft::make_device_csr_matrix<T, IdxT>(res, n_queries, n_dataset, nnz_h);
+    auto csr = raft::make_device_csr_matrix<DistanceT, IdxT>(res, n_queries, n_dataset, nnz_h);
 
     // fill csr
     raft::sparse::convert::bitmap_to_csr(res, filter, csr);
@@ -639,20 +646,20 @@ void brute_force_search_filtered(
     auto dataset_view = raft::make_device_matrix_view<const T, IdxT, raft::row_major>(
       idx.dataset().data_handle(), n_dataset, dim);
 
-    auto csr_view = raft::make_device_csr_matrix_view<T, IdxT, IdxT, IdxT>(
+    auto csr_view = raft::make_device_csr_matrix_view<DistanceT, IdxT, IdxT, IdxT>(
       csr.get_elements().data(), compressed_csr_view);
 
     raft::sparse::linalg::masked_matmul(res, queries, dataset_view, filter, csr_view);
 
     // post process
-    std::optional<raft::device_vector<T, IdxT>> query_norms_;
+    std::optional<raft::device_vector<DistanceT, IdxT>> query_norms_;
     if (metric == cuvs::distance::DistanceType::L2Expanded ||
         metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
         metric == cuvs::distance::DistanceType::CosineExpanded) {
       if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         if (!query_norms) {
-          query_norms_ = raft::make_device_vector<T, IdxT>(res, n_queries);
-          raft::linalg::rowNorm((T*)(query_norms_->data_handle()),
+          query_norms_ = raft::make_device_vector<DistanceT, IdxT>(res, n_queries);
+          raft::linalg::rowNorm((DistanceT*)(query_norms_->data_handle()),
                                 queries.data_handle(),
                                 dim,
                                 n_queries,
@@ -663,8 +670,8 @@ void brute_force_search_filtered(
         }
       } else {
         if (!query_norms) {
-          query_norms_ = raft::make_device_vector<T, IdxT>(res, n_queries);
-          raft::linalg::rowNorm((T*)(query_norms_->data_handle()),
+          query_norms_ = raft::make_device_vector<DistanceT, IdxT>(res, n_queries);
+          raft::linalg::rowNorm((DistanceT*)(query_norms_->data_handle()),
                                 queries.data_handle(),
                                 dim,
                                 n_queries,
@@ -686,7 +693,7 @@ void brute_force_search_filtered(
     }
 
     // select k
-    auto const_csr_view = raft::make_device_csr_matrix_view<const T, IdxT, IdxT, IdxT>(
+    auto const_csr_view = raft::make_device_csr_matrix_view<const DistanceT, IdxT, IdxT, IdxT>(
       csr.get_elements().data(), compressed_csr_view);
     std::optional<raft::device_vector_view<const IdxT, IdxT>> no_opt = std::nullopt;
     bool select_min = cuvs::distance::is_min_close(metric);
@@ -697,21 +704,21 @@ void brute_force_search_filtered(
   return;
 }
 
-template <typename T, typename LayoutT = raft::row_major>
-cuvs::neighbors::brute_force::index<T> build(
+template <typename T, typename DistT, typename LayoutT = raft::row_major>
+cuvs::neighbors::brute_force::index<T, DistT> build(
   raft::resources const& res,
   raft::device_matrix_view<const T, int64_t, LayoutT> dataset,
   cuvs::distance::DistanceType metric,
-  T metric_arg)
+  DistT metric_arg)
 {
   // certain distance metrics can benefit by pre-calculating the norms for the index dataset
   // which lets us avoid calculating these at query time
-  std::optional<raft::device_vector<T, int64_t>> norms;
+  std::optional<raft::device_vector<DistT, int64_t>> norms;
 
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
       metric == cuvs::distance::DistanceType::CosineExpanded) {
-    norms = raft::make_device_vector<T, int64_t>(res, dataset.extent(0));
+    norms = raft::make_device_vector<DistT, int64_t>(res, dataset.extent(0));
     // cosine needs the l2norm, where as l2 distances needs the squared norm
     if (metric == cuvs::distance::DistanceType::CosineExpanded) {
       raft::linalg::norm(res,
@@ -729,6 +736,7 @@ cuvs::neighbors::brute_force::index<T> build(
     }
   }
 
-  return cuvs::neighbors::brute_force::index<T>(res, dataset, std::move(norms), metric, metric_arg);
+  return cuvs::neighbors::brute_force::index<T, DistT>(
+    res, dataset, std::move(norms), metric, metric_arg);
 }
 }  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/knn_utils.cuh b/cpp/src/neighbors/detail/knn_utils.cuh
index 1cc709fa40..60d5f6e30e 100644
--- a/cpp/src/neighbors/detail/knn_utils.cuh
+++ b/cpp/src/neighbors/detail/knn_utils.cuh
@@ -21,15 +21,16 @@
 #include <cuvs/distance/distance.hpp>
 
 #include <cub/cub.cuh>
+#include <cuda_fp16.h>
 #include <cuda_pipeline.h>
 
 namespace cuvs::neighbors::detail {
 
-template <typename value_idx, typename value_t, typename expansion_f>
-RAFT_KERNEL epilogue_on_csr_kernel(value_t* __restrict__ compressed_C,
+template <typename value_idx, typename value_t, typename output_t, typename expansion_f>
+RAFT_KERNEL epilogue_on_csr_kernel(output_t* __restrict__ compressed_C,
                                    const value_idx* __restrict__ rows,
                                    const value_idx* __restrict__ cols,
-                                   const value_t* __restrict__ Q_sq_norms,
+                                   const output_t* __restrict__ Q_sq_norms,
                                    const value_t* __restrict__ R_sq_norms,
                                    value_idx nnz,
                                    expansion_f expansion_func)
@@ -43,13 +44,13 @@ RAFT_KERNEL epilogue_on_csr_kernel(value_t* __restrict__ compressed_C,
   compressed_C[tid] = expansion_func(compressed_C[tid], Q_sq_norms[i], R_sq_norms[j]);
 }
 
-template <typename value_idx, typename value_t, int tpb = 256>
+template <typename value_idx, typename value_t, typename output_t, int tpb = 256>
 void epilogue_on_csr(raft::resources const& handle,
-                     value_t* compressed_C,
+                     output_t* compressed_C,
                      const value_idx nnz,
                      const value_idx* rows,
                      const value_idx* cols,
-                     const value_t* Q_sq_norms,
+                     const output_t* Q_sq_norms,
                      const value_t* R_sq_norms,
                      cuvs::distance::DistanceType metric)
 {
@@ -65,8 +66,12 @@ void epilogue_on_csr(raft::resources const& handle,
       Q_sq_norms,
       R_sq_norms,
       nnz,
-      [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) -> value_t {
-        return value_t(-2.0) * dot + q_norm + r_norm;
+      [] __device__ __host__(output_t dot, output_t q_norm, value_t r_norm) -> output_t {
+        if constexpr (std::is_same_v<value_t, half>) {
+          return output_t(-2.0) * dot + q_norm + __half2float(r_norm);
+        } else {
+          return output_t(-2.0) * dot + q_norm + r_norm;
+        }
       });
   } else if (metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
     epilogue_on_csr_kernel<<<blocks, tpb, 0, stream>>>(
@@ -76,8 +81,12 @@ void epilogue_on_csr(raft::resources const& handle,
       Q_sq_norms,
       R_sq_norms,
       nnz,
-      [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) -> value_t {
-        return raft::sqrt(value_t(-2.0) * dot + q_norm + r_norm);
+      [] __device__ __host__(output_t dot, output_t q_norm, value_t r_norm) -> output_t {
+        if constexpr (std::is_same_v<value_t, half>) {
+          return raft::sqrt(output_t(-2.0) * dot + q_norm + __half2float(r_norm));
+        } else {
+          return raft::sqrt(output_t(-2.0) * dot + q_norm + r_norm);
+        }
       });
   } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
     epilogue_on_csr_kernel<<<blocks, tpb, 0, stream>>>(
@@ -87,8 +96,12 @@ void epilogue_on_csr(raft::resources const& handle,
       Q_sq_norms,
       R_sq_norms,
       nnz,
-      [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) -> value_t {
-        return value_t(1.0) - dot / (q_norm * r_norm);
+      [] __device__ __host__(output_t dot, output_t q_norm, value_t r_norm) -> output_t {
+        if constexpr (std::is_same_v<value_t, half>) {
+          return output_t(1.0) - dot / (q_norm * __half2float(r_norm));
+        } else {
+          return output_t(1.0) - dot / (q_norm * r_norm);
+        }
       });
   }
   RAFT_CUDA_TRY(cudaPeekAtLastError());
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 3495b23442..a30e2dec72 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -70,6 +70,9 @@ function(ConfigureTest)
     ${TEST_NAME} PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
                          "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
+
+  target_compile_definitions(${TEST_NAME} PRIVATE "CUVS_EXPLICIT_INSTANTIATE_ONLY")
+
   if(_CUVS_TEST_NOCUDA)
     target_compile_definitions(${TEST_NAME} PRIVATE "CUVS_DISABLE_CUDA")
   endif()
@@ -126,6 +129,18 @@ if(BUILD_TESTS)
     100
   )
 
+  ConfigureTest(
+    NAME
+    NEIGHBORS_ANN_BRUTE_FORCE_TEST
+    PATH
+    neighbors/ann_brute_force/test_float.cu
+    neighbors/ann_brute_force/test_half.cu
+    GPUS
+    1
+    PERCENT
+    100
+  )
+
   ConfigureTest(
     NAME
     NEIGHBORS_ANN_CAGRA_TEST
diff --git a/cpp/test/distance/dist_canberra.cu b/cpp/test/distance/dist_canberra.cu
index 2bf5906013..e7ffa9d0f5 100644
--- a/cpp/test/distance/dist_canberra.cu
+++ b/cpp/test/distance/dist_canberra.cu
@@ -20,8 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
-class DistanceCanberra : public DistanceTest<cuvs::distance::DistanceType::Canberra, DataType> {};
+template <typename DataType, typename OutputType = DataType>
+class DistanceCanberra
+  : public DistanceTest<cuvs::distance::DistanceType::Canberra, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -63,6 +64,26 @@ TEST_P(DistanceCanberraD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCanberraD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceCanberra<half, float> DistanceCanberraH;
+TEST_P(DistanceCanberraH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCanberraH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixCanberra : public BigMatrixDistanceTest<cuvs::distance::DistanceType::Canberra> {};
 TEST_F(BigMatrixCanberra, Result) {}
 
diff --git a/cpp/test/distance/dist_correlation.cu b/cpp/test/distance/dist_correlation.cu
index 9e061bebc3..70f3a9adb5 100644
--- a/cpp/test/distance/dist_correlation.cu
+++ b/cpp/test/distance/dist_correlation.cu
@@ -20,13 +20,15 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceCorrelation
-  : public DistanceTest<cuvs::distance::DistanceType::CorrelationExpanded, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::CorrelationExpanded, DataType, OutputType> {};
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceCorrelationXequalY
-  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::CorrelationExpanded, DataType> {};
+  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::CorrelationExpanded,
+                                  DataType,
+                                  OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -48,6 +50,26 @@ TEST_P(DistanceCorrelationF, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCorrelationF, ::testing::ValuesIn(inputsf));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceCorrelation<half, float> DistanceCorrelationH;
+TEST_P(DistanceCorrelationH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCorrelationH, ::testing::ValuesIn(inputsh));
+
 typedef DistanceCorrelationXequalY<float> DistanceCorrelationXequalYF;
 TEST_P(DistanceCorrelationXequalYF, Result)
 {
@@ -87,6 +109,25 @@ TEST_P(DistanceCorrelationD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCorrelationD, ::testing::ValuesIn(inputsd));
 
+typedef DistanceCorrelationXequalY<half, float> DistanceCorrelationXequalYH;
+TEST_P(DistanceCorrelationXequalYH, Result)
+{
+  int m = params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[0].data(),
+                                dist[0].data(),
+                                m,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[1].data(),
+                                dist[1].data(),
+                                m / 2,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceCorrelationXequalYH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixCorrelation
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::CorrelationExpanded> {};
 TEST_F(BigMatrixCorrelation, Result) {}
diff --git a/cpp/test/distance/dist_cos.cu b/cpp/test/distance/dist_cos.cu
index e134f045fc..78e2c745fe 100644
--- a/cpp/test/distance/dist_cos.cu
+++ b/cpp/test/distance/dist_cos.cu
@@ -20,13 +20,15 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
-class DistanceExpCos : public DistanceTest<cuvs::distance::DistanceType::CosineExpanded, DataType> {
-};
+template <typename DataType, typename OutputType = DataType>
+class DistanceExpCos
+  : public DistanceTest<cuvs::distance::DistanceType::CosineExpanded, DataType, OutputType> {};
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceExpCosXequalY
-  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::CosineExpanded, DataType> {};
+  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::CosineExpanded,
+                                  DataType,
+                                  OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 128, (65536 + 128) * 128, 8, true, 1234ULL},
@@ -52,6 +54,30 @@ const std::vector<DistanceInputs<float>> inputsXeqYf = {
   {0.03f, 1024, 1024, 1024, false, 1234ULL},
 };
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 128, (65536 + 128) * 128, 8, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, (65536 + 128) * 128, 128, 8, false, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+
+const std::vector<DistanceInputs<half, float>> inputsXeqYh = {
+  {0.01f, 1024, 1024, 32, true, 1234ULL},
+  {0.01f, 1024, 32, 1024, true, 1234ULL},
+  {0.01f, 32, 1024, 1024, true, 1234ULL},
+  {0.03f, 1024, 1024, 1024, true, 1234ULL},
+  {0.01f, 1024, 1024, 32, false, 1234ULL},
+  {0.01f, 1024, 32, 1024, false, 1234ULL},
+  {0.01f, 32, 1024, 1024, false, 1234ULL},
+  {0.03f, 1024, 1024, 1024, false, 1234ULL},
+};
+
 typedef DistanceExpCos<float> DistanceExpCosF;
 TEST_P(DistanceExpCosF, Result)
 {
@@ -62,6 +88,16 @@ TEST_P(DistanceExpCosF, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceExpCosF, ::testing::ValuesIn(inputsf));
 
+typedef DistanceExpCos<half, float> DistanceExpCosH;
+TEST_P(DistanceExpCosH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceExpCosH, ::testing::ValuesIn(inputsh));
+
 typedef DistanceExpCosXequalY<float> DistanceExpCosXequalYF;
 TEST_P(DistanceExpCosXequalYF, Result)
 {
@@ -85,6 +121,29 @@ TEST_P(DistanceExpCosXequalYF, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceExpCosXequalYF, ::testing::ValuesIn(inputsXeqYf));
 
+typedef DistanceExpCosXequalY<half, float> DistanceExpCosXequalYH;
+TEST_P(DistanceExpCosXequalYH, Result)
+{
+  int m = params.m;
+  int n = params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[0].data(),
+                                dist[0].data(),
+                                m,
+                                n,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+  n = params.isRowMajor ? m : m / 2;
+  m = params.isRowMajor ? m / 2 : m;
+
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[1].data(),
+                                dist[1].data(),
+                                m,
+                                n,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceExpCosXequalYH, ::testing::ValuesIn(inputsXeqYh));
+
 const std::vector<DistanceInputs<double>> inputsd = {
   {0.001, 1024, 1024, 32, true, 1234ULL},
   {0.001, 1024, 32, 1024, true, 1234ULL},
diff --git a/cpp/test/distance/dist_hamming.cu b/cpp/test/distance/dist_hamming.cu
index 0cf753ecae..3073ed9399 100644
--- a/cpp/test/distance/dist_hamming.cu
+++ b/cpp/test/distance/dist_hamming.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceHamming
-  : public DistanceTest<cuvs::distance::DistanceType::HammingUnexpanded, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::HammingUnexpanded, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -64,6 +64,26 @@ TEST_P(DistanceHammingD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHammingD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceHamming<half, float> DistanceHammingH;
+TEST_P(DistanceHammingH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHammingH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixHamming
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::HammingUnexpanded> {};
 TEST_F(BigMatrixHamming, Result) {}
diff --git a/cpp/test/distance/dist_hellinger.cu b/cpp/test/distance/dist_hellinger.cu
index 3998a60ab1..692bfeeffe 100644
--- a/cpp/test/distance/dist_hellinger.cu
+++ b/cpp/test/distance/dist_hellinger.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceHellingerExp
-  : public DistanceTest<cuvs::distance::DistanceType::HellingerExpanded, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::HellingerExpanded, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -64,6 +64,26 @@ TEST_P(DistanceHellingerExpD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHellingerExpD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceHellingerExp<half, float> DistanceHellingerExpH;
+TEST_P(DistanceHellingerExpH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceHellingerExpH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixHellingerExp
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::HellingerExpanded> {};
 TEST_F(BigMatrixHellingerExp, Result) {}
diff --git a/cpp/test/distance/dist_inner_product.cu b/cpp/test/distance/dist_inner_product.cu
index 1d6709d52b..aaedb5bf11 100644
--- a/cpp/test/distance/dist_inner_product.cu
+++ b/cpp/test/distance/dist_inner_product.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceInnerProduct
-  : public DistanceTest<cuvs::distance::DistanceType::InnerProduct, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::InnerProduct, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 10, 5, 32, true, 1234ULL},
@@ -66,6 +66,27 @@ TEST_P(DistanceInnerProductD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceInnerProductD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 10, 5, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceInnerProduct<half, float> DistanceInnerProductH;
+TEST_P(DistanceInnerProductH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceInnerProductH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixInnerProduct
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::InnerProduct> {};
 TEST_F(BigMatrixInnerProduct, Result) {}
diff --git a/cpp/test/distance/dist_jensen_shannon.cu b/cpp/test/distance/dist_jensen_shannon.cu
index 43b7b361d1..f50830d4ee 100644
--- a/cpp/test/distance/dist_jensen_shannon.cu
+++ b/cpp/test/distance/dist_jensen_shannon.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceJensenShannon
-  : public DistanceTest<cuvs::distance::DistanceType::JensenShannon, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::JensenShannon, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -64,6 +64,26 @@ TEST_P(DistanceJensenShannonD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceJensenShannonD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceJensenShannon<half, float> DistanceJensenShannonH;
+TEST_P(DistanceJensenShannonH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceJensenShannonH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixJensenShannon
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::JensenShannon> {};
 TEST_F(BigMatrixJensenShannon, Result) {}
diff --git a/cpp/test/distance/dist_kl_divergence.cu b/cpp/test/distance/dist_kl_divergence.cu
index 5e56928411..3d2373bf73 100644
--- a/cpp/test/distance/dist_kl_divergence.cu
+++ b/cpp/test/distance/dist_kl_divergence.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceKLDivergence
-  : public DistanceTest<cuvs::distance::DistanceType::KLDivergence, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::KLDivergence, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -64,6 +64,26 @@ TEST_P(DistanceKLDivergenceD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceKLDivergenceD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceKLDivergence<half, float> DistanceKLDivergenceH;
+TEST_P(DistanceKLDivergenceH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceKLDivergenceH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixKLDivergence
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::KLDivergence> {};
 TEST_F(BigMatrixKLDivergence, Result) {}
diff --git a/cpp/test/distance/dist_l1.cu b/cpp/test/distance/dist_l1.cu
index a3ecd21fe0..cd9a9219b5 100644
--- a/cpp/test/distance/dist_l1.cu
+++ b/cpp/test/distance/dist_l1.cu
@@ -20,8 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
-class DistanceUnexpL1 : public DistanceTest<cuvs::distance::DistanceType::L1, DataType> {};
+template <typename DataType, typename OutputType = DataType>
+class DistanceUnexpL1
+  : public DistanceTest<cuvs::distance::DistanceType::L1, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -63,6 +64,26 @@ TEST_P(DistanceUnexpL1D, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceUnexpL1D, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceUnexpL1<half, float> DistanceUnexpL1H;
+TEST_P(DistanceUnexpL1H, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceUnexpL1H, ::testing::ValuesIn(inputsh));
+
 class BigMatrixUnexpL1 : public BigMatrixDistanceTest<cuvs::distance::DistanceType::L1> {};
 TEST_F(BigMatrixUnexpL1, Result) {}
 
diff --git a/cpp/test/distance/dist_l2_exp.cu b/cpp/test/distance/dist_l2_exp.cu
index f3d038cbc9..5804bb6c57 100644
--- a/cpp/test/distance/dist_l2_exp.cu
+++ b/cpp/test/distance/dist_l2_exp.cu
@@ -20,13 +20,14 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
-class DistanceEucExpTest : public DistanceTest<cuvs::distance::DistanceType::L2Expanded, DataType> {
-};
+template <typename DataType, typename OutputType = DataType>
+class DistanceEucExpTest
+  : public DistanceTest<cuvs::distance::DistanceType::L2Expanded, DataType, OutputType> {};
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceEucExpTestXequalY
-  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::L2Expanded, DataType> {};
+  : public DistanceTestSameBuffer<cuvs::distance::DistanceType::L2Expanded, DataType, OutputType> {
+};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 128, (65536 + 128) * 128, 8, true, 1234ULL},
@@ -58,6 +59,36 @@ const std::vector<DistanceInputs<float>> inputsXeqYf = {
   {0.03f, 1021, 1021, 1021, false, 1234ULL},
 };
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 128, (65536 + 128) * 128, 8, true, 1234ULL},
+  {0.001f, 2048, 4096, 128, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.003f, 1021, 1021, 1021, true, 1234ULL},
+  {0.001f, (65536 + 128) * 128, 128, 8, false, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+  {0.003f, 1021, 1021, 1021, false, 1234ULL},
+};
+
+const std::vector<DistanceInputs<half, float>> inputsXeqYh = {
+  {0.01f, 2048, 4096, 128, true, 1234ULL},
+  {0.01f, 1024, 1024, 32, true, 1234ULL},
+  {0.01f, 1024, 32, 1024, true, 1234ULL},
+  {0.01f, 32, 1024, 1024, true, 1234ULL},
+  {0.03f, 1024, 1024, 1024, true, 1234ULL},
+  {0.03f, 1021, 1021, 1021, true, 1234ULL},
+  {0.01f, 1024, 1024, 32, false, 1234ULL},
+  {0.01f, 1024, 32, 1024, false, 1234ULL},
+  {0.01f, 32, 1024, 1024, false, 1234ULL},
+  {0.03f, 1024, 1024, 1024, false, 1234ULL},
+  {0.03f, 1021, 1021, 1021, false, 1234ULL},
+};
+
 typedef DistanceEucExpTest<float> DistanceEucExpTestF;
 TEST_P(DistanceEucExpTestF, Result)
 {
@@ -68,6 +99,16 @@ TEST_P(DistanceEucExpTestF, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucExpTestF, ::testing::ValuesIn(inputsf));
 
+typedef DistanceEucExpTest<half, float> DistanceEucExpTestH;
+TEST_P(DistanceEucExpTestH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucExpTestH, ::testing::ValuesIn(inputsh));
+
 typedef DistanceEucExpTestXequalY<float> DistanceEucExpTestXequalYF;
 TEST_P(DistanceEucExpTestXequalYF, Result)
 {
@@ -89,6 +130,27 @@ INSTANTIATE_TEST_CASE_P(DistanceTests,
                         DistanceEucExpTestXequalYF,
                         ::testing::ValuesIn(inputsXeqYf));
 
+typedef DistanceEucExpTestXequalY<half, float> DistanceEucExpTestXequalYH;
+TEST_P(DistanceEucExpTestXequalYH, Result)
+{
+  int m = params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[0].data(),
+                                dist[0].data(),
+                                m,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+  ASSERT_TRUE(cuvs::devArrMatch(dist_ref[1].data(),
+                                dist[1].data(),
+                                m / 2,
+                                m,
+                                cuvs::CompareApprox<float>(params.tolerance),
+                                stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests,
+                        DistanceEucExpTestXequalYH,
+                        ::testing::ValuesIn(inputsXeqYh));
+
 const std::vector<DistanceInputs<double>> inputsd = {
   {0.001, 1024, 1024, 32, true, 1234ULL},
   {0.001, 1024, 32, 1024, true, 1234ULL},
diff --git a/cpp/test/distance/dist_l2_sqrt_exp.cu b/cpp/test/distance/dist_l2_sqrt_exp.cu
index b24384be8a..a2e09fdb08 100644
--- a/cpp/test/distance/dist_l2_sqrt_exp.cu
+++ b/cpp/test/distance/dist_l2_sqrt_exp.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceEucSqrtExpTest
-  : public DistanceTest<cuvs::distance::DistanceType::L2SqrtExpanded, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::L2SqrtExpanded, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 2048, 4096, 128, true, 1234ULL},
@@ -67,6 +67,29 @@ TEST_P(DistanceEucSqrtExpTestD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucSqrtExpTestD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 2048, 4096, 128, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.003f, 1021, 1021, 1021, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+  {0.003f, 1021, 1021, 1021, false, 1234ULL},
+};
+typedef DistanceEucSqrtExpTest<half, float> DistanceEucSqrtExpTestH;
+TEST_P(DistanceEucSqrtExpTestH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucSqrtExpTestH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixEucSqrtExp
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::L2SqrtExpanded> {};
 TEST_F(BigMatrixEucSqrtExp, Result) {}
diff --git a/cpp/test/distance/dist_l2_unexp.cu b/cpp/test/distance/dist_l2_unexp.cu
index c057434fae..3f9e6458f3 100644
--- a/cpp/test/distance/dist_l2_unexp.cu
+++ b/cpp/test/distance/dist_l2_unexp.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceEucUnexpTest
-  : public DistanceTest<cuvs::distance::DistanceType::L2Unexpanded, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::L2Unexpanded, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -64,6 +64,26 @@ TEST_P(DistanceEucUnexpTestD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucUnexpTestD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceEucUnexpTest<half, float> DistanceEucUnexpTestH;
+TEST_P(DistanceEucUnexpTestH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceEucUnexpTestH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixEucUnexp : public BigMatrixDistanceTest<cuvs::distance::DistanceType::L2Unexpanded> {
 };
 TEST_F(BigMatrixEucUnexp, Result) {}
diff --git a/cpp/test/distance/dist_l_inf.cu b/cpp/test/distance/dist_l_inf.cu
index b9ced68f34..21e9a6c871 100644
--- a/cpp/test/distance/dist_l_inf.cu
+++ b/cpp/test/distance/dist_l_inf.cu
@@ -20,8 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
-class DistanceLinf : public DistanceTest<cuvs::distance::DistanceType::Linf, DataType> {};
+template <typename DataType, typename OutputType = DataType>
+class DistanceLinf : public DistanceTest<cuvs::distance::DistanceType::Linf, DataType, OutputType> {
+};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -63,6 +64,26 @@ TEST_P(DistanceLinfD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLinfD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceLinf<half, float> DistanceLinfH;
+TEST_P(DistanceLinfH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLinfH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixLinf : public BigMatrixDistanceTest<cuvs::distance::DistanceType::Linf> {};
 TEST_F(BigMatrixLinf, Result) {}
 
diff --git a/cpp/test/distance/dist_lp_unexp.cu b/cpp/test/distance/dist_lp_unexp.cu
index 26620b44b3..95e521fb3a 100644
--- a/cpp/test/distance/dist_lp_unexp.cu
+++ b/cpp/test/distance/dist_lp_unexp.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
-class DistanceLpUnexp : public DistanceTest<cuvs::distance::DistanceType::LpUnexpanded, DataType> {
-};
+template <typename DataType, typename OutputType = DataType>
+class DistanceLpUnexp
+  : public DistanceTest<cuvs::distance::DistanceType::LpUnexpanded, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL, 4.0f},
@@ -64,6 +64,26 @@ TEST_P(DistanceLpUnexpD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLpUnexpD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL, 4.0f},
+  {0.001f, 1024, 32, 1024, true, 1234ULL, 3.0f},
+  {0.001f, 32, 1024, 1024, true, 1234ULL, 4.0f},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL, 3.0f},
+  {0.001f, 1024, 1024, 32, false, 1234ULL, 4.0f},
+  {0.001f, 1024, 32, 1024, false, 1234ULL, 3.0f},
+  {0.001f, 32, 1024, 1024, false, 1234ULL, 4.0f},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL, 3.0f},
+};
+typedef DistanceLpUnexp<half, float> DistanceLpUnexpH;
+TEST_P(DistanceLpUnexpH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceLpUnexpH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixLpUnexp : public BigMatrixDistanceTest<cuvs::distance::DistanceType::LpUnexpanded> {
 };
 TEST_F(BigMatrixLpUnexp, Result) {}
diff --git a/cpp/test/distance/dist_russell_rao.cu b/cpp/test/distance/dist_russell_rao.cu
index 46da7f9cd0..814a0503f9 100644
--- a/cpp/test/distance/dist_russell_rao.cu
+++ b/cpp/test/distance/dist_russell_rao.cu
@@ -20,9 +20,9 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 class DistanceRussellRao
-  : public DistanceTest<cuvs::distance::DistanceType::RusselRaoExpanded, DataType> {};
+  : public DistanceTest<cuvs::distance::DistanceType::RusselRaoExpanded, DataType, OutputType> {};
 
 const std::vector<DistanceInputs<float>> inputsf = {
   {0.001f, 1024, 1024, 32, true, 1234ULL},
@@ -64,6 +64,26 @@ TEST_P(DistanceRussellRaoD, Result)
 }
 INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceRussellRaoD, ::testing::ValuesIn(inputsd));
 
+const std::vector<DistanceInputs<half, float>> inputsh = {
+  {0.001f, 1024, 1024, 32, true, 1234ULL},
+  {0.001f, 1024, 32, 1024, true, 1234ULL},
+  {0.001f, 32, 1024, 1024, true, 1234ULL},
+  {0.003f, 1024, 1024, 1024, true, 1234ULL},
+  {0.001f, 1024, 1024, 32, false, 1234ULL},
+  {0.001f, 1024, 32, 1024, false, 1234ULL},
+  {0.001f, 32, 1024, 1024, false, 1234ULL},
+  {0.003f, 1024, 1024, 1024, false, 1234ULL},
+};
+typedef DistanceRussellRao<half, float> DistanceRussellRaoH;
+TEST_P(DistanceRussellRaoH, Result)
+{
+  int m = params.isRowMajor ? params.m : params.n;
+  int n = params.isRowMajor ? params.n : params.m;
+  ASSERT_TRUE(cuvs::devArrMatch(
+    dist_ref.data(), dist.data(), m, n, cuvs::CompareApprox<float>(params.tolerance), stream));
+}
+INSTANTIATE_TEST_CASE_P(DistanceTests, DistanceRussellRaoH, ::testing::ValuesIn(inputsh));
+
 class BigMatrixRussellRao
   : public BigMatrixDistanceTest<cuvs::distance::DistanceType::RusselRaoExpanded> {};
 TEST_F(BigMatrixRussellRao, Result) {}
diff --git a/cpp/test/distance/distance_base.cuh b/cpp/test/distance/distance_base.cuh
index 2213db87eb..8a431f49a1 100644
--- a/cpp/test/distance/distance_base.cuh
+++ b/cpp/test/distance/distance_base.cuh
@@ -18,7 +18,6 @@
 
 #include "../test_utils.cuh"
 
-#include <cuvs/distance/distance.hpp>
 #include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
 #include <raft/common/nvtx.hpp>         // raft::common::nvtx::range
 #include <raft/core/device_mdspan.hpp>  //raft::make_device_matrix_view
@@ -34,8 +33,18 @@
 namespace cuvs {
 namespace distance {
 
-template <typename DataType>
-RAFT_KERNEL naiveDistanceKernel(DataType* dist,
+template <typename T>
+_RAFT_DEVICE inline auto half2float(T& a)
+{
+  if constexpr (std::is_same_v<typename std::remove_const<T>::type, half>) {
+    return __half2float(a);
+  } else {
+    return a;
+  }
+}
+
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveDistanceKernel(OutputType* dist,
                                 const DataType* x,
                                 const DataType* y,
                                 std::int64_t m,
@@ -47,11 +56,11 @@ RAFT_KERNEL naiveDistanceKernel(DataType* dist,
   std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) return;
-  DataType acc = DataType(0);
+  OutputType acc = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto diff         = x[xidx] - y[yidx];
+    auto diff         = half2float(x[xidx]) - half2float(y[yidx]);
     acc += diff * diff;
   }
   if (type == cuvs::distance::DistanceType::L2SqrtExpanded ||
@@ -61,8 +70,8 @@ RAFT_KERNEL naiveDistanceKernel(DataType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType>
-RAFT_KERNEL naiveL1_Linf_CanberraDistanceKernel(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveL1_Linf_CanberraDistanceKernel(OutputType* dist,
                                                 const DataType* x,
                                                 const DataType* y,
                                                 std::int64_t m,
@@ -75,12 +84,12 @@ RAFT_KERNEL naiveL1_Linf_CanberraDistanceKernel(DataType* dist,
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) { return; }
 
-  DataType acc = DataType(0);
+  OutputType acc = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     auto diff         = (a > b) ? (a - b) : (b - a);
     if (type == cuvs::distance::DistanceType::Linf) {
       acc = raft::max(acc, diff);
@@ -98,8 +107,8 @@ RAFT_KERNEL naiveL1_Linf_CanberraDistanceKernel(DataType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType>
-RAFT_KERNEL naiveCosineDistanceKernel(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveCosineDistanceKernel(OutputType* dist,
                                       const DataType* x,
                                       const DataType* y,
                                       std::int64_t m,
@@ -111,15 +120,15 @@ RAFT_KERNEL naiveCosineDistanceKernel(DataType* dist,
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) { return; }
 
-  DataType acc_a  = DataType(0);
-  DataType acc_b  = DataType(0);
-  DataType acc_ab = DataType(0);
+  OutputType acc_a  = OutputType(0);
+  OutputType acc_b  = OutputType(0);
+  OutputType acc_ab = OutputType(0);
 
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     acc_a += a * a;
     acc_b += b * b;
     acc_ab += a * b;
@@ -128,11 +137,11 @@ RAFT_KERNEL naiveCosineDistanceKernel(DataType* dist,
   std::int64_t outidx = isRowMajor ? midx * n + nidx : midx + m * nidx;
 
   // Use 1.0 - (cosine similarity) to calc the distance
-  dist[outidx] = (DataType)1.0 - acc_ab / (raft::sqrt(acc_a) * raft::sqrt(acc_b));
+  dist[outidx] = (OutputType)1.0 - acc_ab / (raft::sqrt(acc_a) * raft::sqrt(acc_b));
 }
 
-template <typename DataType>
-RAFT_KERNEL naiveInnerProductKernel(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveInnerProductKernel(OutputType* dist,
                                     const DataType* x,
                                     const DataType* y,
                                     std::int64_t m,
@@ -144,13 +153,13 @@ RAFT_KERNEL naiveInnerProductKernel(DataType* dist,
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) { return; }
 
-  DataType acc_ab = DataType(0);
+  OutputType acc_ab = OutputType(0);
 
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     acc_ab += a * b;
   }
 
@@ -158,8 +167,8 @@ RAFT_KERNEL naiveInnerProductKernel(DataType* dist,
   dist[outidx]        = acc_ab;
 }
 
-template <typename DataType>
-RAFT_KERNEL naiveHellingerDistanceKernel(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveHellingerDistanceKernel(OutputType* dist,
                                          const DataType* x,
                                          const DataType* y,
                                          std::int64_t m,
@@ -171,13 +180,13 @@ RAFT_KERNEL naiveHellingerDistanceKernel(DataType* dist,
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) { return; }
 
-  DataType acc_ab = DataType(0);
+  OutputType acc_ab = OutputType(0);
 
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     acc_ab += raft::sqrt(a) * raft::sqrt(b);
   }
 
@@ -189,25 +198,25 @@ RAFT_KERNEL naiveHellingerDistanceKernel(DataType* dist,
   dist[outidx]   = raft::sqrt(rectifier * acc_ab);
 }
 
-template <typename DataType>
-RAFT_KERNEL naiveLpUnexpDistanceKernel(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveLpUnexpDistanceKernel(OutputType* dist,
                                        const DataType* x,
                                        const DataType* y,
                                        std::int64_t m,
                                        std::int64_t n,
                                        std::int64_t k,
                                        bool isRowMajor,
-                                       DataType p)
+                                       OutputType p)
 {
   std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) return;
-  DataType acc = DataType(0);
+  OutputType acc = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     auto diff         = raft::abs(a - b);
     acc += raft::pow(diff, p);
   }
@@ -217,8 +226,8 @@ RAFT_KERNEL naiveLpUnexpDistanceKernel(DataType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType>
-RAFT_KERNEL naiveHammingDistanceKernel(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveHammingDistanceKernel(OutputType* dist,
                                        const DataType* x,
                                        const DataType* y,
                                        std::int64_t m,
@@ -229,12 +238,12 @@ RAFT_KERNEL naiveHammingDistanceKernel(DataType* dist,
   std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) return;
-  DataType acc = DataType(0);
+  OutputType acc = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     acc += (a != b);
   }
   acc                 = acc / k;
@@ -242,8 +251,8 @@ RAFT_KERNEL naiveHammingDistanceKernel(DataType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType>
-RAFT_KERNEL naiveJensenShannonDistanceKernel(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveJensenShannonDistanceKernel(OutputType* dist,
                                              const DataType* x,
                                              const DataType* y,
                                              std::int64_t m,
@@ -254,19 +263,19 @@ RAFT_KERNEL naiveJensenShannonDistanceKernel(DataType* dist,
   std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) return;
-  DataType acc = DataType(0);
+  OutputType acc = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
 
-    DataType m  = 0.5f * (a + b);
-    bool a_zero = a == 0;
-    bool b_zero = b == 0;
+    OutputType m = 0.5f * (a + b);
+    bool a_zero  = a == 0;
+    bool b_zero  = b == 0;
 
-    DataType p = (!a_zero * m) / (a_zero + a);
-    DataType q = (!b_zero * m) / (b_zero + b);
+    OutputType p = (!a_zero * m) / (a_zero + a);
+    OutputType q = (!b_zero * m) / (b_zero + b);
 
     bool p_zero = p == 0;
     bool q_zero = q == 0;
@@ -278,8 +287,8 @@ RAFT_KERNEL naiveJensenShannonDistanceKernel(DataType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType, typename OutType>
-RAFT_KERNEL naiveRussellRaoDistanceKernel(OutType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveRussellRaoDistanceKernel(OutputType* dist,
                                           const DataType* x,
                                           const DataType* y,
                                           std::int64_t m,
@@ -290,12 +299,12 @@ RAFT_KERNEL naiveRussellRaoDistanceKernel(OutType* dist,
   std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) return;
-  OutType acc = OutType(0);
+  OutputType acc = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     acc += (a * b);
   }
   acc                 = (k - acc) / k;
@@ -303,8 +312,8 @@ RAFT_KERNEL naiveRussellRaoDistanceKernel(OutType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType, typename OutType>
-RAFT_KERNEL naiveKLDivergenceDistanceKernel(OutType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveKLDivergenceDistanceKernel(OutputType* dist,
                                             const DataType* x,
                                             const DataType* y,
                                             std::int64_t m,
@@ -315,12 +324,12 @@ RAFT_KERNEL naiveKLDivergenceDistanceKernel(OutType* dist,
   std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) return;
-  OutType acc = OutType(0);
+  OutputType acc = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     bool b_zero       = (b == 0);
     bool a_zero       = (a == 0);
     acc += a * (log(a + a_zero) - log(b + b_zero));
@@ -330,8 +339,8 @@ RAFT_KERNEL naiveKLDivergenceDistanceKernel(OutType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType, typename OutType>
-RAFT_KERNEL naiveCorrelationDistanceKernel(OutType* dist,
+template <typename DataType, typename OutputType = DataType>
+RAFT_KERNEL naiveCorrelationDistanceKernel(OutputType* dist,
                                            const DataType* x,
                                            const DataType* y,
                                            std::int64_t m,
@@ -342,16 +351,16 @@ RAFT_KERNEL naiveCorrelationDistanceKernel(OutType* dist,
   std::int64_t midx = threadIdx.x + blockIdx.x * blockDim.x;
   std::int64_t nidx = threadIdx.y + blockIdx.y * blockDim.y;
   if (midx >= m || nidx >= n) return;
-  OutType acc    = OutType(0);
-  auto a_norm    = DataType(0);
-  auto b_norm    = DataType(0);
-  auto a_sq_norm = DataType(0);
-  auto b_sq_norm = DataType(0);
+  OutputType acc = OutputType(0);
+  auto a_norm    = OutputType(0);
+  auto b_norm    = OutputType(0);
+  auto a_sq_norm = OutputType(0);
+  auto b_sq_norm = OutputType(0);
   for (std::int64_t i = 0; i < k; ++i) {
     std::int64_t xidx = isRowMajor ? i + midx * k : i * m + midx;
     std::int64_t yidx = isRowMajor ? i + nidx * k : i * n + nidx;
-    auto a            = x[xidx];
-    auto b            = y[yidx];
+    auto a            = half2float(x[xidx]);
+    auto b            = half2float(y[yidx]);
     a_norm += a;
     b_norm += b;
     a_sq_norm += (a * a);
@@ -369,8 +378,8 @@ RAFT_KERNEL naiveCorrelationDistanceKernel(OutType* dist,
   dist[outidx]        = acc;
 }
 
-template <typename DataType>
-void naiveDistance(DataType* dist,
+template <typename DataType, typename OutputType = DataType>
+void naiveDistance(OutputType* dist,
                    const DataType* x,
                    const DataType* y,
                    std::int64_t m,
@@ -378,8 +387,8 @@ void naiveDistance(DataType* dist,
                    std::int64_t k,
                    cuvs::distance::DistanceType type,
                    bool isRowMajor,
-                   DataType metric_arg = 2.0f,
-                   cudaStream_t stream = 0)
+                   OutputType metric_arg = 2.0f,
+                   cudaStream_t stream   = 0)
 {
   static const dim3 TPB(4, 256, 1);
   dim3 nblks(raft::ceildiv(m, (std::int64_t)TPB.x), raft::ceildiv(n, (std::int64_t)TPB.y), 1);
@@ -388,49 +397,50 @@ void naiveDistance(DataType* dist,
     case cuvs::distance::DistanceType::Canberra:
     case cuvs::distance::DistanceType::Linf:
     case cuvs::distance::DistanceType::L1:
-      naiveL1_Linf_CanberraDistanceKernel<DataType>
+      naiveL1_Linf_CanberraDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, type, isRowMajor);
       break;
     case cuvs::distance::DistanceType::L2SqrtUnexpanded:
     case cuvs::distance::DistanceType::L2Unexpanded:
     case cuvs::distance::DistanceType::L2SqrtExpanded:
     case cuvs::distance::DistanceType::L2Expanded:
-      naiveDistanceKernel<DataType>
+      naiveDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, type, isRowMajor);
       break;
     case cuvs::distance::DistanceType::CosineExpanded:
-      naiveCosineDistanceKernel<DataType>
+      naiveCosineDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     case cuvs::distance::DistanceType::HellingerExpanded:
-      naiveHellingerDistanceKernel<DataType>
+      naiveHellingerDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     case cuvs::distance::DistanceType::LpUnexpanded:
-      naiveLpUnexpDistanceKernel<DataType>
+      naiveLpUnexpDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor, metric_arg);
       break;
     case cuvs::distance::DistanceType::HammingUnexpanded:
-      naiveHammingDistanceKernel<DataType>
+      naiveHammingDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     case cuvs::distance::DistanceType::InnerProduct:
-      naiveInnerProductKernel<DataType><<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
+      naiveInnerProductKernel<DataType, OutputType>
+        <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     case cuvs::distance::DistanceType::JensenShannon:
-      naiveJensenShannonDistanceKernel<DataType>
+      naiveJensenShannonDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     case cuvs::distance::DistanceType::RusselRaoExpanded:
-      naiveRussellRaoDistanceKernel<DataType>
+      naiveRussellRaoDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     case cuvs::distance::DistanceType::KLDivergence:
-      naiveKLDivergenceDistanceKernel<DataType>
+      naiveKLDivergenceDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     case cuvs::distance::DistanceType::CorrelationExpanded:
-      naiveCorrelationDistanceKernel<DataType>
+      naiveCorrelationDistanceKernel<DataType, OutputType>
         <<<nblks, TPB, 0, stream>>>(dist, x, y, m, n, k, isRowMajor);
       break;
     default: FAIL() << "should be here\n";
@@ -438,13 +448,13 @@ void naiveDistance(DataType* dist,
   RAFT_CUDA_TRY(cudaPeekAtLastError());
 }
 
-template <typename DataType>
+template <typename DataType, typename OutputType = DataType>
 struct DistanceInputs {
-  DataType tolerance;
+  OutputType tolerance;
   std::int64_t m, n, k;
   bool isRowMajor;
   unsigned long long int seed;
-  DataType metric_arg = 2.0f;
+  OutputType metric_arg = 2.0f;
 };
 
 template <typename DataType>
@@ -472,31 +482,38 @@ constexpr bool layout_to_row_major<raft::layout_f_contiguous>()
   return false;
 }
 
-template <cuvs::distance::DistanceType distanceType, typename DataType, typename layout>
+template <cuvs::distance::DistanceType distanceType,
+          typename DataType,
+          typename Layout,
+          typename OutputType = float>
 void distanceLauncher(raft::resources const& handle,
                       DataType* x,
                       DataType* y,
-                      DataType* dist,
-                      DataType* dist2,
+                      OutputType* dist,
+                      OutputType* dist2,
                       std::int64_t m,
                       std::int64_t n,
                       std::int64_t k,
-                      DistanceInputs<DataType>& params,
-                      DataType threshold,
-                      DataType metric_arg = 2.0f)
+                      DistanceInputs<OutputType>& params,
+                      OutputType threshold,
+                      OutputType metric_arg = 2.0f)
 {
-  auto x_v    = raft::make_device_matrix_view<DataType, std::int64_t, layout>(x, m, k);
-  auto y_v    = raft::make_device_matrix_view<DataType, std::int64_t, layout>(y, n, k);
-  auto dist_v = raft::make_device_matrix_view<DataType, std::int64_t, layout>(dist, m, n);
+  // Create device matrix views for the input and output data
+  auto x_v    = raft::make_device_matrix_view<DataType, std::int64_t, Layout>(x, m, k);
+  auto y_v    = raft::make_device_matrix_view<DataType, std::int64_t, Layout>(y, n, k);
+  auto dist_v = raft::make_device_matrix_view<OutputType, std::int64_t, Layout>(dist, m, n);
 
+  // Explicitly instantiate the template function
   cuvs::distance::pairwise_distance(handle, x_v, y_v, dist_v, distanceType, metric_arg);
 }
 
-template <cuvs::distance::DistanceType distanceType, typename DataType>
-class DistanceTest : public ::testing::TestWithParam<DistanceInputs<DataType>> {
+template <cuvs::distance::DistanceType distanceType,
+          typename DataType,
+          typename OutputType = DataType>
+class DistanceTest : public ::testing::TestWithParam<DistanceInputs<DataType, OutputType>> {
  public:
   DistanceTest()
-    : params(::testing::TestWithParam<DistanceInputs<DataType>>::GetParam()),
+    : params(::testing::TestWithParam<DistanceInputs<OutputType>>::GetParam()),
       stream(raft::resource::get_cuda_stream(handle)),
       x(params.m * params.k, stream),
       y(params.n * params.k, stream),
@@ -513,11 +530,11 @@ class DistanceTest : public ::testing::TestWithParam<DistanceInputs<DataType>> {
       "test::%s/%s", testInfo->test_suite_name(), testInfo->name());
 
     raft::random::RngState r(params.seed);
-    std::int64_t m      = params.m;
-    std::int64_t n      = params.n;
-    std::int64_t k      = params.k;
-    DataType metric_arg = params.metric_arg;
-    bool isRowMajor     = params.isRowMajor;
+    std::int64_t m        = params.m;
+    std::int64_t n        = params.n;
+    std::int64_t k        = params.k;
+    OutputType metric_arg = params.metric_arg;
+    bool isRowMajor       = params.isRowMajor;
     if (distanceType == cuvs::distance::DistanceType::HellingerExpanded ||
         distanceType == cuvs::distance::DistanceType::JensenShannon ||
         distanceType == cuvs::distance::DistanceType::KLDivergence) {
@@ -537,33 +554,33 @@ class DistanceTest : public ::testing::TestWithParam<DistanceInputs<DataType>> {
     naiveDistance(
       dist_ref.data(), x.data(), y.data(), m, n, k, distanceType, isRowMajor, metric_arg, stream);
 
-    DataType threshold = -10000.f;
+    OutputType threshold = -10000.f;
 
     if (isRowMajor) {
-      distanceLauncher<distanceType, DataType, raft::layout_c_contiguous>(handle,
-                                                                          x.data(),
-                                                                          y.data(),
-                                                                          dist.data(),
-                                                                          dist2.data(),
-                                                                          m,
-                                                                          n,
-                                                                          k,
-                                                                          params,
-                                                                          threshold,
-                                                                          metric_arg);
+      distanceLauncher<distanceType, DataType, raft::layout_c_contiguous, OutputType>(handle,
+                                                                                      x.data(),
+                                                                                      y.data(),
+                                                                                      dist.data(),
+                                                                                      dist2.data(),
+                                                                                      m,
+                                                                                      n,
+                                                                                      k,
+                                                                                      params,
+                                                                                      threshold,
+                                                                                      metric_arg);
 
     } else {
-      distanceLauncher<distanceType, DataType, raft::layout_f_contiguous>(handle,
-                                                                          x.data(),
-                                                                          y.data(),
-                                                                          dist.data(),
-                                                                          dist2.data(),
-                                                                          m,
-                                                                          n,
-                                                                          k,
-                                                                          params,
-                                                                          threshold,
-                                                                          metric_arg);
+      distanceLauncher<distanceType, DataType, raft::layout_f_contiguous, OutputType>(handle,
+                                                                                      x.data(),
+                                                                                      y.data(),
+                                                                                      dist.data(),
+                                                                                      dist2.data(),
+                                                                                      m,
+                                                                                      n,
+                                                                                      k,
+                                                                                      params,
+                                                                                      threshold,
+                                                                                      metric_arg);
     }
     raft::resource::sync_stream(handle, stream);
   }
@@ -572,8 +589,9 @@ class DistanceTest : public ::testing::TestWithParam<DistanceInputs<DataType>> {
   raft::resources handle;
   cudaStream_t stream;
 
-  DistanceInputs<DataType> params;
-  rmm::device_uvector<DataType> x, y, dist_ref, dist, dist2;
+  DistanceInputs<OutputType> params;
+  rmm::device_uvector<DataType> x, y;
+  rmm::device_uvector<OutputType> dist_ref, dist, dist2;
 };
 
 /*
@@ -583,12 +601,15 @@ class DistanceTest : public ::testing::TestWithParam<DistanceInputs<DataType>> {
  * It may happen that though both X and Y are same buffer but user passes
  * different dimensions for them like in case of tiled_brute_force_knn.
  */
-template <cuvs::distance::DistanceType distanceType, typename DataType>
-class DistanceTestSameBuffer : public ::testing::TestWithParam<DistanceInputs<DataType>> {
+template <cuvs::distance::DistanceType distanceType,
+          typename DataType,
+          typename OutputType = DataType>
+class DistanceTestSameBuffer
+  : public ::testing::TestWithParam<DistanceInputs<DataType, OutputType>> {
  public:
-  using dev_vector = rmm::device_uvector<DataType>;
+  using dev_vector = rmm::device_uvector<OutputType>;
   DistanceTestSameBuffer()
-    : params(::testing::TestWithParam<DistanceInputs<DataType>>::GetParam()),
+    : params(::testing::TestWithParam<DistanceInputs<OutputType>>::GetParam()),
       stream(raft::resource::get_cuda_stream(handle)),
       x(params.m * params.k, stream),
       dist_ref({dev_vector(params.m * params.m, stream), dev_vector(params.m * params.m, stream)}),
@@ -604,11 +625,11 @@ class DistanceTestSameBuffer : public ::testing::TestWithParam<DistanceInputs<Da
       "test::%s/%s", testInfo->test_suite_name(), testInfo->name());
 
     raft::random::RngState r(params.seed);
-    std::int64_t m      = params.m;
-    std::int64_t n      = params.m;
-    std::int64_t k      = params.k;
-    DataType metric_arg = params.metric_arg;
-    bool isRowMajor     = params.isRowMajor;
+    std::int64_t m        = params.m;
+    std::int64_t n        = params.m;
+    std::int64_t k        = params.k;
+    OutputType metric_arg = params.metric_arg;
+    bool isRowMajor       = params.isRowMajor;
     if (distanceType == cuvs::distance::DistanceType::HellingerExpanded ||
         distanceType == cuvs::distance::DistanceType::JensenShannon ||
         distanceType == cuvs::distance::DistanceType::KLDivergence) {
@@ -637,33 +658,35 @@ class DistanceTestSameBuffer : public ::testing::TestWithParam<DistanceInputs<Da
                     metric_arg,
                     stream);
 
-      DataType threshold = -10000.f;
+      OutputType threshold = -10000.f;
 
       if (isRowMajor) {
-        distanceLauncher<distanceType, DataType, raft::layout_c_contiguous>(handle,
-                                                                            x.data(),
-                                                                            x.data(),
-                                                                            dist[i].data(),
-                                                                            dist2[i].data(),
-                                                                            m,
-                                                                            n,
-                                                                            k,
-                                                                            params,
-                                                                            threshold,
-                                                                            metric_arg);
+        distanceLauncher<distanceType, DataType, raft::layout_c_contiguous, OutputType>(
+          handle,
+          x.data(),
+          x.data(),
+          dist[i].data(),
+          dist2[i].data(),
+          m,
+          n,
+          k,
+          params,
+          threshold,
+          metric_arg);
 
       } else {
-        distanceLauncher<distanceType, DataType, raft::layout_f_contiguous>(handle,
-                                                                            x.data(),
-                                                                            x.data(),
-                                                                            dist[i].data(),
-                                                                            dist2[i].data(),
-                                                                            m,
-                                                                            n,
-                                                                            k,
-                                                                            params,
-                                                                            threshold,
-                                                                            metric_arg);
+        distanceLauncher<distanceType, DataType, raft::layout_f_contiguous, OutputType>(
+          handle,
+          x.data(),
+          x.data(),
+          dist[i].data(),
+          dist2[i].data(),
+          m,
+          n,
+          k,
+          params,
+          threshold,
+          metric_arg);
       }
     }
     raft::resource::sync_stream(handle, stream);
@@ -673,8 +696,8 @@ class DistanceTestSameBuffer : public ::testing::TestWithParam<DistanceInputs<Da
   raft::resources handle;
   cudaStream_t stream;
 
-  DistanceInputs<DataType> params;
-  dev_vector x;
+  DistanceInputs<OutputType> params;
+  rmm::device_uvector<DataType> x;
   static const std::int64_t N = 2;
   std::array<dev_vector, N> dist_ref, dist, dist2;
 };
diff --git a/cpp/test/neighbors/ann_brute_force.cuh b/cpp/test/neighbors/ann_brute_force.cuh
new file mode 100644
index 0000000000..461a202f22
--- /dev/null
+++ b/cpp/test/neighbors/ann_brute_force.cuh
@@ -0,0 +1,200 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../test_utils.cuh"
+
+#include "knn_utils.cuh"
+#include "naive_knn.cuh"
+
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+
+#include <gtest/gtest.h>
+
+namespace cuvs::neighbors::brute_force {
+
+template <typename IdxT>
+struct AnnBruteForceInputs {
+  IdxT num_queries;
+  IdxT num_db_vecs;
+  IdxT dim;
+  IdxT k;
+  cuvs::distance::DistanceType metric;
+  float metric_arg = 0.0f;
+};
+
+template <typename T, typename IdxT>
+::std::ostream& operator<<(::std::ostream& os, const AnnBruteForceInputs<IdxT>& p)
+{
+  os << "{ " << p.num_queries << ", " << p.num_db_vecs << ", " << p.dim << ", " << p.k << ", "
+     << static_cast<int>(p.metric) << static_cast<T>(p.metric_arg) << '}' << std::endl;
+  return os;
+}
+
+template <typename T, typename DataT, typename IdxT>
+class AnnBruteForceTest : public ::testing::TestWithParam<AnnBruteForceInputs<IdxT>> {
+ public:
+  AnnBruteForceTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnBruteForceInputs<IdxT>>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+  void testBruteForce()
+  {
+    size_t queries_size = ps.num_queries * ps.k;
+
+    rmm::device_uvector<T> distances_naive_dev(queries_size, stream_);
+    rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+
+    cuvs::neighbors::naive_knn<T, DataT, IdxT>(handle_,
+                                               distances_naive_dev.data(),
+                                               indices_naive_dev.data(),
+                                               search_queries.data(),
+                                               database.data(),
+                                               ps.num_queries,
+                                               ps.num_db_vecs,
+                                               ps.dim,
+                                               ps.k,
+                                               ps.metric);
+    raft::resource::sync_stream(handle_);
+
+    {
+      // Require exact result for brute force
+      rmm::device_uvector<T> distances_bruteforce_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_bruteforce_dev(queries_size, stream_);
+
+      auto idx = [this]() {
+        auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
+          (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
+
+        return brute_force::build(handle_, database_view, ps.metric, ps.metric_arg);
+      }();
+
+      auto search_queries_view = raft::make_device_matrix_view<const DataT, IdxT>(
+        search_queries.data(), ps.num_queries, ps.dim);
+      auto indices_out_view = raft::make_device_matrix_view<IdxT, IdxT>(
+        indices_bruteforce_dev.data(), ps.num_queries, ps.k);
+      auto dists_out_view = raft::make_device_matrix_view<T, IdxT>(
+        distances_bruteforce_dev.data(), ps.num_queries, ps.k);
+
+      brute_force::search(
+        handle_, idx, search_queries_view, indices_out_view, dists_out_view, std::nullopt);
+
+      raft::resource::sync_stream(handle_);
+
+      ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(indices_naive_dev.data(),
+                                                      indices_bruteforce_dev.data(),
+                                                      distances_naive_dev.data(),
+                                                      distances_bruteforce_dev.data(),
+                                                      ps.num_queries,
+                                                      ps.k,
+                                                      0.001f,
+                                                      stream_,
+                                                      true));
+      brute_force::search(
+        handle_, idx, search_queries_view, indices_out_view, dists_out_view, std::nullopt);
+    }
+  }
+
+  void SetUp() override
+  {
+    database.resize(ps.num_db_vecs * ps.dim, stream_);
+    search_queries.resize(ps.num_queries * ps.dim, stream_);
+
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{} || std::is_same<DataT, half>{}) {
+      raft::random::uniform(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(0.1), DataT(2.0));
+      raft::random::uniform(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(1), DataT(20));
+      raft::random::uniformInt(
+        handle_, r, search_queries.data(), ps.num_queries * ps.dim, DataT(1), DataT(20));
+    }
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnBruteForceInputs<IdxT> ps;
+  rmm::device_uvector<DataT> database;
+  rmm::device_uvector<DataT> search_queries;
+};
+
+const std::vector<AnnBruteForceInputs<int64_t>> inputs = {
+  // test various dims (aligned and not aligned to vector sizes)
+  {1000, 10000, 1, 16, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 2, 16, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 3, 16, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 4, 16, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 5, 16, cuvs::distance::DistanceType::InnerProduct},
+  {1000, 10000, 8, 16, cuvs::distance::DistanceType::InnerProduct},
+  {1000, 10000, 5, 16, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {1000, 10000, 8, 16, cuvs::distance::DistanceType::L2SqrtExpanded},
+
+  // test dims that do not fit into kernel shared memory limits
+  {1000, 10000, 2048, 16, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 2049, 16, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 2050, 16, cuvs::distance::DistanceType::InnerProduct},
+  {1000, 10000, 2051, 16, cuvs::distance::DistanceType::InnerProduct},
+  {1000, 10000, 2052, 16, cuvs::distance::DistanceType::InnerProduct},
+  {1000, 10000, 2053, 16, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 2056, 16, cuvs::distance::DistanceType::L2Expanded},
+
+  // test fused_l2_knn
+  {100, 1000, 16, 10, cuvs::distance::DistanceType::L2Expanded},
+  {256, 256, 30, 10, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 16, 10, cuvs::distance::DistanceType::L2Expanded},
+  {100, 1000, 16, 50, cuvs::distance::DistanceType::L2Expanded},
+  {20, 10000, 16, 10, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 16, 50, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 10000, 32, 50, cuvs::distance::DistanceType::L2Expanded},
+  {10000, 40000, 32, 30, cuvs::distance::DistanceType::L2Expanded},
+  {100, 1000, 16, 10, cuvs::distance::DistanceType::L2Unexpanded},
+  {1000, 10000, 16, 10, cuvs::distance::DistanceType::L2Unexpanded},
+  {100, 1000, 16, 50, cuvs::distance::DistanceType::L2Unexpanded},
+  {20, 10000, 16, 50, cuvs::distance::DistanceType::L2Unexpanded},
+  {1000, 10000, 16, 50, cuvs::distance::DistanceType::L2Unexpanded},
+  {1000, 10000, 32, 50, cuvs::distance::DistanceType::L2Unexpanded},
+  {10000, 40000, 32, 30, cuvs::distance::DistanceType::L2Unexpanded},
+
+  // test tile
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Expanded},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {10000, 40000, 32, 30, cuvs::distance::DistanceType::L2Expanded},
+  {789, 20516, 64, 256, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {4, 12, 32, 6, cuvs::distance::DistanceType::L2Expanded},
+  {1, 40, 32, 30, cuvs::distance::DistanceType::L2Expanded},
+  {1000, 500000, 128, 128, cuvs::distance::DistanceType::L2Expanded}};
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/neighbors/ann_brute_force/test_float.cu b/cpp/test/neighbors/ann_brute_force/test_float.cu
new file mode 100644
index 0000000000..ded371c427
--- /dev/null
+++ b/cpp/test/neighbors/ann_brute_force/test_float.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_brute_force.cuh"
+
+#include <gtest/gtest.h>
+
+namespace cuvs::neighbors::brute_force {
+
+using AnnBruteForceTest_float = AnnBruteForceTest<float, float, std::int64_t>;
+TEST_P(AnnBruteForceTest_float, AnnBruteForce) { this->testBruteForce(); }
+
+INSTANTIATE_TEST_CASE_P(AnnBruteForceTest, AnnBruteForceTest_float, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/neighbors/ann_brute_force/test_half.cu b/cpp/test/neighbors/ann_brute_force/test_half.cu
new file mode 100644
index 0000000000..39b7b79822
--- /dev/null
+++ b/cpp/test/neighbors/ann_brute_force/test_half.cu
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_brute_force.cuh"
+
+#include <gtest/gtest.h>
+
+namespace cuvs::neighbors::brute_force {
+
+using AnnBruteForceTest_half_float = AnnBruteForceTest<float, half, std::int64_t>;
+TEST_P(AnnBruteForceTest_half_float, AnnBruteForce) { this->testBruteForce(); }
+
+INSTANTIATE_TEST_CASE_P(AnnBruteForceTest,
+                        AnnBruteForceTest_half_float,
+                        ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index c97bb5531c..f1a05e045d 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -25,9 +25,13 @@
 #include <raft/linalg/transpose.cuh>
 #include <raft/matrix/init.cuh>
 
+#include <cuda_fp16.h>
+
 namespace cuvs::neighbors::brute_force {
+
+template <typename T>
 struct KNNInputs {
-  std::vector<std::vector<float>> input;
+  std::vector<std::vector<T>> input;
   int k;
   std::vector<int> labels;
 };
@@ -53,11 +57,11 @@ RAFT_KERNEL build_expected_output(int* output, int n_rows, int k, const int* lab
   }
 }
 
-template <typename T, typename IdxT>
-class KNNTest : public ::testing::TestWithParam<KNNInputs> {
+template <typename T, typename DistT, typename IdxT>
+class KNNTest : public ::testing::TestWithParam<KNNInputs<T>> {
  public:
   KNNTest()
-    : params_(::testing::TestWithParam<KNNInputs>::GetParam()),
+    : params_(::testing::TestWithParam<KNNInputs<T>>::GetParam()),
       stream(raft::resource::get_cuda_stream(handle)),
       actual_labels_(0, stream),
       expected_labels_(0, stream),
@@ -85,7 +89,7 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs> {
     auto indices =
       raft::make_device_matrix_view<IdxT, IdxT, raft::row_major>(indices_.data(), rows_, k_);
     auto distances =
-      raft::make_device_matrix_view<T, IdxT, raft::row_major>(distances_.data(), rows_, k_);
+      raft::make_device_matrix_view<DistT, IdxT, raft::row_major>(distances_.data(), rows_, k_);
 
     auto metric = cuvs::distance::DistanceType::L2Unexpanded;
     auto idx    = cuvs::neighbors::brute_force::build(handle, index, metric);
@@ -119,23 +123,22 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs> {
       cudaMemsetAsync(actual_labels_.data(), 0, actual_labels_.size() * sizeof(int), stream));
     RAFT_CUDA_TRY(
       cudaMemsetAsync(expected_labels_.data(), 0, expected_labels_.size() * sizeof(int), stream));
-    RAFT_CUDA_TRY(cudaMemsetAsync(input_.data(), 0, input_.size() * sizeof(float), stream));
-    RAFT_CUDA_TRY(
-      cudaMemsetAsync(search_data_.data(), 0, search_data_.size() * sizeof(float), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(input_.data(), 0, input_.size() * sizeof(T), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(search_data_.data(), 0, search_data_.size() * sizeof(T), stream));
     RAFT_CUDA_TRY(cudaMemsetAsync(indices_.data(), 0, indices_.size() * sizeof(IdxT), stream));
-    RAFT_CUDA_TRY(cudaMemsetAsync(distances_.data(), 0, distances_.size() * sizeof(float), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(distances_.data(), 0, distances_.size() * sizeof(DistT), stream));
     RAFT_CUDA_TRY(
       cudaMemsetAsync(search_labels_.data(), 0, search_labels_.size() * sizeof(int), stream));
 
-    std::vector<float> row_major_input;
+    std::vector<T> row_major_input;
     for (std::size_t i = 0; i < params_.input.size(); ++i) {
       for (std::size_t j = 0; j < params_.input[i].size(); ++j) {
         row_major_input.push_back(params_.input[i][j]);
       }
     }
     rmm::device_buffer input_d =
-      rmm::device_buffer(row_major_input.data(), row_major_input.size() * sizeof(float), stream);
-    float* input_ptr = static_cast<float*>(input_d.data());
+      rmm::device_buffer(row_major_input.data(), row_major_input.size() * sizeof(T), stream);
+    T* input_ptr = static_cast<T*>(input_d.data());
 
     rmm::device_buffer labels_d =
       rmm::device_buffer(params_.labels.data(), params_.labels.size() * sizeof(int), stream);
@@ -151,13 +154,13 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs> {
   raft::resources handle;
   cudaStream_t stream;
 
-  KNNInputs params_;
+  KNNInputs<T> params_;
   int rows_;
   int cols_;
-  rmm::device_uvector<float> input_;
-  rmm::device_uvector<float> search_data_;
+  rmm::device_uvector<T> input_;
+  rmm::device_uvector<T> search_data_;
   rmm::device_uvector<IdxT> indices_;
-  rmm::device_uvector<float> distances_;
+  rmm::device_uvector<DistT> distances_;
   int k_;
 
   rmm::device_uvector<int> search_labels_;
@@ -165,7 +168,8 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs> {
   rmm::device_uvector<int> expected_labels_;
 };
 
-const std::vector<KNNInputs> inputs = {
+template <typename T>
+const std::vector<KNNInputs<T>> inputs = {
   // 2D
   {{
      {2.7810836, 2.550537003},
@@ -182,10 +186,14 @@ const std::vector<KNNInputs> inputs = {
    2,
    {0, 0, 0, 0, 0, 1, 1, 1, 1, 1}}};
 
-typedef KNNTest<float, int64_t> KNNTestFint64_t;
-TEST_P(KNNTestFint64_t, BruteForce) { this->testBruteForce(); }
+typedef KNNTest<float, float, int64_t> KNNTest_float_int64_t;
+TEST_P(KNNTest_float_int64_t, BruteForce) { this->testBruteForce(); }
+
+typedef KNNTest<half, float, int64_t> KNNTest_half_int64_t;
+TEST_P(KNNTest_half_int64_t, BruteForce) { this->testBruteForce(); }
 
-INSTANTIATE_TEST_CASE_P(KNNTest, KNNTestFint64_t, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(KNNTest, KNNTest_float_int64_t, ::testing::ValuesIn(inputs<float>));
+INSTANTIATE_TEST_CASE_P(KNNTest, KNNTest_half_int64_t, ::testing::ValuesIn(inputs<half>));
 
 // Also test with larger random inputs, including col-major inputs
 struct RandomKNNInputs {
@@ -205,7 +213,7 @@ std::ostream& operator<<(std::ostream& os, const RandomKNNInputs& input)
             << " row_major:" << input.row_major;
 }
 
-template <typename T>
+template <typename T, typename DistT = T>
 class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs> {
  public:
   RandomBruteForceKNNTest()
@@ -229,67 +237,153 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
     raft::matrix::fill(
       handle_,
       raft::make_device_matrix_view(cuvs_distances_.data(), params_.num_queries, params_.k),
-      T{0.0});
+      DistT{0.0});
     raft::matrix::fill(
       handle_,
       raft::make_device_matrix_view(ref_distances_.data(), params_.num_queries, params_.k),
-      T{0.0});
+      DistT{0.0});
   }
 
  protected:
+  void cpu_distance(const T* d_A,
+                    const T* d_B,
+                    DistT* d_vals,
+                    bool is_row_major_A,
+                    bool is_row_major_B,
+                    bool is_row_major_C,
+                    cudaStream_t stream,
+                    DistT alpha = 1.0,
+                    DistT beta  = 0.0)
+  {
+    size_t size_A    = params_.num_queries * params_.dim * sizeof(T);
+    size_t size_B    = params_.num_db_vecs * params_.dim * sizeof(T);
+    size_t size_vals = params_.num_queries * params_.num_db_vecs * sizeof(DistT);
+
+    T* h_A        = static_cast<T*>(malloc(size_A));
+    T* h_B        = static_cast<T*>(malloc(size_B));
+    DistT* h_vals = static_cast<DistT*>(malloc(size_vals));
+
+    cudaMemcpyAsync(h_A, d_A, size_A, cudaMemcpyDeviceToHost, stream);
+    cudaMemcpyAsync(h_B, d_B, size_B, cudaMemcpyDeviceToHost, stream);
+    cudaMemcpyAsync(h_vals, d_vals, size_vals, cudaMemcpyDeviceToHost, stream);
+    cudaStreamSynchronize(stream);
+
+    bool trans_a = is_row_major_A;
+    bool trans_b = is_row_major_B;
+    bool trans_c = is_row_major_C;
+
+    for (int64_t i = 0; i < params_.num_queries; ++i) {
+      for (int64_t j = 0; j < params_.num_db_vecs; ++j) {
+        DistT sum     = 0;
+        DistT norms_A = 0;
+        DistT norms_B = 0;
+
+        for (int64_t l = 0; l < params_.dim; ++l) {
+          int64_t a_index = trans_a ? i * params_.dim + l : l * params_.num_queries + i;
+          int64_t b_index = trans_b ? j * params_.dim + l : l * params_.num_db_vecs + j;
+          DistT A_v;
+          DistT B_v;
+          if constexpr (sizeof(T) == 2) {
+            A_v = __half2float(h_A[a_index]);
+            B_v = __half2float(h_B[b_index]);
+          } else {
+            A_v = h_A[a_index];
+            B_v = h_B[b_index];
+          }
+
+          sum += A_v * B_v;
+
+          norms_A += A_v * A_v;
+          norms_B += B_v * B_v;
+        }
+
+        int64_t c_index = trans_c ? i * params_.num_db_vecs + j : j * params_.num_queries + i;
+
+        h_vals[c_index] = alpha * sum + beta * h_vals[c_index];
+        if (params_.metric == cuvs::distance::DistanceType::L2Expanded) {
+          h_vals[c_index] = DistT(-2.0) * h_vals[c_index] + norms_A + norms_B;
+        } else if (params_.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+          h_vals[c_index] = std::sqrt(DistT(-2.0) * h_vals[c_index] + norms_A + norms_B);
+        } else if (params_.metric == cuvs::distance::DistanceType::CosineExpanded) {
+          h_vals[c_index] = DistT(1.0) - h_vals[c_index] / std::sqrt(norms_A * norms_B);
+        }
+      }
+    }
+    cudaMemcpyAsync(d_vals, h_vals, size_vals, cudaMemcpyHostToDevice, stream);
+    cudaStreamSynchronize(stream);
+
+    free(h_A);
+    free(h_B);
+    free(h_vals);
+  }
+
   void testBruteForce()
   {
-    float metric_arg = 3.0;
+    DistT metric_arg = 3.0;
 
     // calculate the naive knn, by calculating the full pairwise distances and doing a k-select
-    rmm::device_uvector<T> temp_distances(num_db_vecs * num_queries, stream_);
+    rmm::device_uvector<DistT> temp_distances(num_db_vecs * num_queries, stream_);
     rmm::device_uvector<char> workspace(0, stream_);
 
     auto temp_dist = temp_distances.data();
-    rmm::device_uvector<T> temp_row_major_dist(num_db_vecs * num_queries, stream_);
-
-    if (params_.row_major) {
-      distance::pairwise_distance(
-        handle_,
-        raft::make_device_matrix_view<const T, int64_t>(
-          search_queries.data(), params_.num_queries, params_.dim),
-        raft::make_device_matrix_view<const T, int64_t>(
-          database.data(), params_.num_db_vecs, params_.dim),
-        raft::make_device_matrix_view<T, int64_t>(temp_distances.data(), num_queries, num_db_vecs),
-        metric,
-        metric_arg);
-
+    rmm::device_uvector<DistT> temp_row_major_dist(num_db_vecs * num_queries, stream_);
+
+    // For the complex post processes in these algorithms, we use CPU logic to make the baseline.
+    if (metric == cuvs::distance::DistanceType::L2Expanded ||
+        metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+        metric == cuvs::distance::DistanceType::CosineExpanded) {
+      cpu_distance(search_queries.data(),
+                   database.data(),
+                   temp_distances.data(),
+                   params_.row_major,
+                   params_.row_major,
+                   true,
+                   stream_);
     } else {
-      distance::pairwise_distance(handle_,
-                                  raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
-                                    search_queries.data(), params_.num_queries, params_.dim),
-                                  raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
-                                    database.data(), params_.num_db_vecs, params_.dim),
-                                  raft::make_device_matrix_view<T, int64_t, raft::col_major>(
-                                    temp_distances.data(), num_queries, num_db_vecs),
-                                  metric,
-                                  metric_arg);
-
-      // the pairwisse_distance call assumes that the inputs and outputs are all either row-major
-      // or col-major - meaning we have to transpose the output back for col-major queries
-      // for comparison
-      raft::linalg::transpose(
-        handle_, temp_dist, temp_row_major_dist.data(), num_queries, num_db_vecs, stream_);
-      temp_dist = temp_row_major_dist.data();
+      if (params_.row_major) {
+        distance::pairwise_distance(handle_,
+                                    raft::make_device_matrix_view<const T, int64_t>(
+                                      search_queries.data(), params_.num_queries, params_.dim),
+                                    raft::make_device_matrix_view<const T, int64_t>(
+                                      database.data(), params_.num_db_vecs, params_.dim),
+                                    raft::make_device_matrix_view<DistT, int64_t>(
+                                      temp_distances.data(), num_queries, num_db_vecs),
+                                    metric,
+                                    metric_arg);
+
+      } else {
+        distance::pairwise_distance(
+          handle_,
+          raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+            search_queries.data(), params_.num_queries, params_.dim),
+          raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+            database.data(), params_.num_db_vecs, params_.dim),
+          raft::make_device_matrix_view<DistT, int64_t, raft::col_major>(
+            temp_distances.data(), num_queries, num_db_vecs),
+          metric,
+          metric_arg);
+
+        // the pairwise_distance call assumes that the inputs and outputs are all either row-major
+        // or col-major - meaning we have to transpose the output back for col-major queries
+        // for comparison
+        raft::linalg::transpose(
+          handle_, temp_dist, temp_row_major_dist.data(), num_queries, num_db_vecs, stream_);
+        temp_dist = temp_row_major_dist.data();
+      }
     }
 
     cuvs::selection::select_k(
       handle_,
-      raft::make_device_matrix_view<const T, int64_t>(temp_dist, num_queries, num_db_vecs),
+      raft::make_device_matrix_view<const DistT, int64_t>(temp_dist, num_queries, num_db_vecs),
       std::nullopt,
       raft::make_device_matrix_view(ref_distances_.data(), params_.num_queries, params_.k),
       raft::make_device_matrix_view(ref_indices_.data(), params_.num_queries, params_.k),
       cuvs::distance::is_min_close(metric),
       true);
 
-    auto indices = raft::make_device_matrix_view<int64_t, int64_t>(
+    auto indices = raft::make_device_matrix_view<int64_t, int64_t, raft::row_major>(
       cuvs_indices_.data(), params_.num_queries, params_.k);
-    auto distances = raft::make_device_matrix_view<T, int64_t>(
+    auto distances = raft::make_device_matrix_view<DistT, int64_t, raft::row_major>(
       cuvs_distances_.data(), params_.num_queries, params_.k);
 
     if (params_.row_major) {
@@ -332,7 +426,7 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
                                                     cuvs_distances_.data(),
                                                     num_queries,
                                                     k_,
-                                                    float(0.001),
+                                                    DistT(0.001),
                                                     stream_,
                                                     true));
   }
@@ -364,16 +458,16 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
   rmm::device_uvector<T> database;
   rmm::device_uvector<T> search_queries;
   rmm::device_uvector<int64_t> cuvs_indices_;
-  rmm::device_uvector<T> cuvs_distances_;
+  rmm::device_uvector<DistT> cuvs_distances_;
   rmm::device_uvector<int64_t> ref_indices_;
-  rmm::device_uvector<T> ref_distances_;
+  rmm::device_uvector<DistT> ref_distances_;
   int k_;
   cuvs::distance::DistanceType metric;
 };
 
 const std::vector<RandomKNNInputs> random_inputs = {
   // test each distance metric on a small-ish input, with row-major inputs
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Expanded, true},
+  {100, 256, 2, 65, cuvs::distance::DistanceType::L2Expanded, true},
   {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, true},
   {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true},
   {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, true},
@@ -387,7 +481,7 @@ const std::vector<RandomKNNInputs> random_inputs = {
   {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true},
   {256, 512, 16, 8, cuvs::distance::DistanceType::Canberra, true},
   // test each distance metric with col-major inputs
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Expanded, false},
+  {256, 512, 16, 7, cuvs::distance::DistanceType::L2Expanded, false},
   {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, false},
   {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, false},
   {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, false},
@@ -404,17 +498,24 @@ const std::vector<RandomKNNInputs> random_inputs = {
   {10000, 40000, 32, 30, cuvs::distance::DistanceType::L2Expanded, false},
   {345, 1023, 16, 128, cuvs::distance::DistanceType::CosineExpanded, true},
   {789, 20516, 64, 256, cuvs::distance::DistanceType::L2SqrtExpanded, false},
-  {1000, 500000, 128, 128, cuvs::distance::DistanceType::L2Expanded, true},
-  {1000, 500000, 128, 128, cuvs::distance::DistanceType::L2Expanded, false},
+  {1000, 200000, 128, 128, cuvs::distance::DistanceType::L2Expanded, true},
+  {1000, 200000, 128, 128, cuvs::distance::DistanceType::L2Expanded, false},
   {1000, 5000, 128, 128, cuvs::distance::DistanceType::LpUnexpanded, true},
   {1000, 5000, 128, 128, cuvs::distance::DistanceType::L2SqrtExpanded, false},
   {1000, 5000, 128, 128, cuvs::distance::DistanceType::InnerProduct, false}};
 
-typedef RandomBruteForceKNNTest<float> RandomBruteForceKNNTestF;
+typedef RandomBruteForceKNNTest<float, float> RandomBruteForceKNNTestF;
 TEST_P(RandomBruteForceKNNTestF, BruteForce) { this->testBruteForce(); }
 
+typedef RandomBruteForceKNNTest<half, float> RandomBruteForceKNNTestH;
+TEST_P(RandomBruteForceKNNTestH, BruteForce) { this->testBruteForce(); }
+
 INSTANTIATE_TEST_CASE_P(RandomBruteForceKNNTest,
                         RandomBruteForceKNNTestF,
                         ::testing::ValuesIn(random_inputs));
 
+INSTANTIATE_TEST_CASE_P(RandomBruteForceKNNTest,
+                        RandomBruteForceKNNTestH,
+                        ::testing::ValuesIn(random_inputs));
+
 }  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/neighbors/brute_force_prefiltered.cu b/cpp/test/neighbors/brute_force_prefiltered.cu
index 2b8ae9d9a6..9304ee0451 100644
--- a/cpp/test/neighbors/brute_force_prefiltered.cu
+++ b/cpp/test/neighbors/brute_force_prefiltered.cu
@@ -30,7 +30,11 @@
 
 #include <gtest/gtest.h>
 
+#include <cuda_fp16.h>
+#include <thrust/device_vector.h>
 #include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
 
 #include <algorithm>
 #include <cmath>
@@ -94,6 +98,10 @@ RAFT_KERNEL normalize_kernel(
   }
 }
 
+struct float_to_half {
+  __host__ __device__ __half operator()(const float x) const { return __float2half(x); }
+};
+
 template <typename OutT, typename InT>
 void normalize(OutT* theta,
                const InT* in_vals,
@@ -137,7 +145,8 @@ void set_bitmap(const index_t* src,
     <<<blocks, block_size, 0, stream>>>(src, dst, bitmap, n_edges, n_cols);
   RAFT_CUDA_TRY(cudaGetLastError());
 }
-template <typename value_t, typename index_t, typename bitmap_t = uint32_t>
+
+template <typename value_t, typename dist_t, typename index_t, typename bitmap_t = uint32_t>
 class PrefilteredBruteForceTest
   : public ::testing::TestWithParam<PrefilteredBruteForceInputs<index_t>> {
  public:
@@ -157,22 +166,22 @@ class PrefilteredBruteForceTest
  protected:
   index_t create_sparse_matrix_with_rmat(index_t m,
                                          index_t n,
-                                         value_t sparsity,
+                                         float sparsity,
                                          rmm::device_uvector<bitmap_t>& filter_d)
   {
     index_t r_scale   = (index_t)std::log2(m);
     index_t c_scale   = (index_t)std::log2(n);
-    index_t n_edges   = (index_t)(m * n * 1.0 * sparsity);
+    index_t n_edges   = (index_t)(m * n * 1.0f * sparsity);
     index_t max_scale = std::max(r_scale, c_scale);
 
     rmm::device_uvector<index_t> out_src{(unsigned long)n_edges, stream};
     rmm::device_uvector<index_t> out_dst{(unsigned long)n_edges, stream};
-    rmm::device_uvector<value_t> theta{(unsigned long)(4 * max_scale), stream};
+    rmm::device_uvector<float> theta{(unsigned long)(4 * max_scale), stream};
 
     raft::random::RngState state{2024ULL, raft::random::GeneratorType::GenPC};
 
-    raft::random::uniform<value_t>(handle, state, theta.data(), theta.size(), 0.0f, 1.0f);
-    normalize<value_t, value_t>(
+    raft::random::uniform<float>(handle, state, theta.data(), theta.size(), 0.0f, 1.0f);
+    normalize<float, float>(
       theta.data(), theta.data(), max_scale, r_scale, c_scale, r_scale != c_scale, true, stream);
     raft::random::rmat_rectangular_gen((index_t*)nullptr,
                                        out_src.data(),
@@ -236,15 +245,15 @@ class PrefilteredBruteForceTest
     }
   }
 
-  void cpu_sddmm(const std::vector<value_t>& A,
-                 const std::vector<value_t>& B,
-                 std::vector<value_t>& vals,
+  void cpu_sddmm(const std::vector<dist_t>& A,
+                 const std::vector<dist_t>& B,
+                 std::vector<dist_t>& vals,
                  const std::vector<index_t>& cols,
                  const std::vector<index_t>& row_ptrs,
                  bool is_row_major_A,
                  bool is_row_major_B,
-                 value_t alpha = 1.0,
-                 value_t beta  = 0.0)
+                 dist_t alpha = 1.0,
+                 dist_t beta  = 0.0)
   {
     if (params.n_queries * params.dim != static_cast<index_t>(A.size()) ||
         params.dim * params.n_dataset != static_cast<index_t>(B.size())) {
@@ -257,24 +266,35 @@ class PrefilteredBruteForceTest
 
     for (index_t i = 0; i < params.n_queries; ++i) {
       for (index_t j = row_ptrs[i]; j < row_ptrs[i + 1]; ++j) {
-        value_t sum     = 0;
-        value_t norms_A = 0;
-        value_t norms_B = 0;
+        dist_t sum     = 0;
+        dist_t norms_A = 0;
+        dist_t norms_B = 0;
+
         for (index_t l = 0; l < params.dim; ++l) {
           index_t a_index = trans_a ? i * params.dim + l : l * params.n_queries + i;
           index_t b_index = trans_b ? l * params.n_dataset + cols[j] : cols[j] * params.dim + l;
-          sum += A[a_index] * B[b_index];
-
-          norms_A += A[a_index] * A[a_index];
-          norms_B += B[b_index] * B[b_index];
+          dist_t A_v;
+          dist_t B_v;
+          if constexpr (sizeof(value_t) == 2) {
+            A_v = __half2float(__float2half(A[a_index]));
+            B_v = __half2float(__float2half(B[b_index]));
+          } else {
+            A_v = A[a_index];
+            B_v = B[b_index];
+          }
+
+          sum += A_v * B_v;
+
+          norms_A += A_v * A_v;
+          norms_B += B_v * B_v;
         }
         vals[j] = alpha * sum + beta * vals[j];
         if (params.metric == cuvs::distance::DistanceType::L2Expanded) {
-          vals[j] = value_t(-2.0) * vals[j] + norms_A + norms_B;
+          vals[j] = dist_t(-2.0) * vals[j] + norms_A + norms_B;
         } else if (params.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
-          vals[j] = std::sqrt(value_t(-2.0) * vals[j] + norms_A + norms_B);
+          vals[j] = std::sqrt(dist_t(-2.0) * vals[j] + norms_A + norms_B);
         } else if (params.metric == cuvs::distance::DistanceType::CosineExpanded) {
-          vals[j] = value_t(1.0) - vals[j] / std::sqrt(norms_A * norms_B);
+          vals[j] = dist_t(1.0) - vals[j] / std::sqrt(norms_A * norms_B);
         }
       }
     }
@@ -282,32 +302,31 @@ class PrefilteredBruteForceTest
 
   void cpu_select_k(const std::vector<index_t>& indptr_h,
                     const std::vector<index_t>& indices_h,
-                    const std::vector<value_t>& values_h,
+                    const std::vector<dist_t>& values_h,
                     std::optional<std::vector<index_t>>& in_idx_h,
                     index_t n_queries,
                     index_t n_dataset,
                     index_t top_k,
-                    std::vector<value_t>& out_values_h,
+                    std::vector<dist_t>& out_values_h,
                     std::vector<index_t>& out_indices_h,
                     bool select_min = true)
   {
-    auto comp = [select_min](const std::pair<value_t, index_t>& a,
-                             const std::pair<value_t, index_t>& b) {
+    auto comp = [select_min](const std::pair<dist_t, index_t>& a,
+                             const std::pair<dist_t, index_t>& b) {
       return select_min ? a.first < b.first : a.first >= b.first;
     };
 
     for (index_t row = 0; row < n_queries; ++row) {
-      std::priority_queue<std::pair<value_t, index_t>,
-                          std::vector<std::pair<value_t, index_t>>,
+      std::priority_queue<std::pair<dist_t, index_t>,
+                          std::vector<std::pair<dist_t, index_t>>,
                           decltype(comp)>
         pq(comp);
-
       for (index_t idx = indptr_h[row]; idx < indptr_h[row + 1]; ++idx) {
         pq.push({values_h[idx], (in_idx_h.has_value()) ? (*in_idx_h)[idx] : indices_h[idx]});
         if (pq.size() > size_t(top_k)) { pq.pop(); }
       }
 
-      std::vector<std::pair<value_t, index_t>> row_pairs;
+      std::vector<std::pair<dist_t, index_t>> row_pairs;
       while (!pq.empty()) {
         row_pairs.push_back(pq.top());
         pq.pop();
@@ -347,40 +366,80 @@ class PrefilteredBruteForceTest
     index_t dataset_size = params.n_dataset * params.dim;
     index_t queries_size = params.n_queries * params.dim;
 
-    std::vector<value_t> dataset_h(dataset_size);
-    std::vector<value_t> queries_h(queries_size);
+    std::vector<dist_t> dataset_h(dataset_size);
+    std::vector<dist_t> queries_h(queries_size);
 
     dataset_d.resize(dataset_size, stream);
     queries_d.resize(queries_size, stream);
 
     auto blobs_in_val =
-      raft::make_device_matrix<value_t, index_t>(handle, 1, dataset_size + queries_size);
+      raft::make_device_matrix<dist_t, index_t>(handle, 1, dataset_size + queries_size);
     auto labels = raft::make_device_vector<index_t, index_t>(handle, 1);
 
-    raft::random::make_blobs<value_t, index_t>(blobs_in_val.data_handle(),
-                                               labels.data_handle(),
-                                               1,
-                                               dataset_size + queries_size,
-                                               1,
-                                               stream,
-                                               false,
-                                               nullptr,
-                                               nullptr,
-                                               value_t(1.0),
-                                               false,
-                                               value_t(-1.0f),
-                                               value_t(1.0f),
-                                               uint64_t(2024));
+    if constexpr (!std::is_same_v<value_t, half>) {
+      raft::random::make_blobs<value_t, index_t>(blobs_in_val.data_handle(),
+                                                 labels.data_handle(),
+                                                 1,
+                                                 dataset_size + queries_size,
+                                                 1,
+                                                 stream,
+                                                 false,
+                                                 nullptr,
+                                                 nullptr,
+                                                 value_t(1.0),
+                                                 false,
+                                                 value_t(-1.0f),
+                                                 value_t(1.0f),
+                                                 uint64_t(2024));
+    } else {
+      raft::random::make_blobs<dist_t, index_t>(blobs_in_val.data_handle(),
+                                                labels.data_handle(),
+                                                1,
+                                                dataset_size + queries_size,
+                                                1,
+                                                stream,
+                                                false,
+                                                nullptr,
+                                                nullptr,
+                                                dist_t(1.0),
+                                                false,
+                                                dist_t(-1.0f),
+                                                dist_t(1.0f),
+                                                uint64_t(2024));
+    }
 
     raft::copy(dataset_h.data(), blobs_in_val.data_handle(), dataset_size, stream);
-    raft::copy(dataset_d.data(), blobs_in_val.data_handle(), dataset_size, stream);
+
+    if constexpr (std::is_same_v<value_t, half>) {
+      thrust::device_ptr<dist_t> d_output_ptr =
+        thrust::device_pointer_cast(blobs_in_val.data_handle());
+      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(dataset_d.data());
+      thrust::transform(thrust::cuda::par.on(stream),
+                        d_output_ptr,
+                        d_output_ptr + dataset_size,
+                        d_value_ptr,
+                        float_to_half());
+    } else {
+      raft::copy(dataset_d.data(), blobs_in_val.data_handle(), dataset_size, stream);
+    }
 
     raft::copy(queries_h.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
-    raft::copy(queries_d.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
+    if constexpr (std::is_same_v<value_t, half>) {
+      thrust::device_ptr<dist_t> d_output_ptr =
+        thrust::device_pointer_cast(blobs_in_val.data_handle() + dataset_size);
+      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(queries_d.data());
+      thrust::transform(thrust::cuda::par.on(stream),
+                        d_output_ptr,
+                        d_output_ptr + queries_size,
+                        d_value_ptr,
+                        float_to_half());
+    } else {
+      raft::copy(queries_d.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
+    }
 
     raft::resource::sync_stream(handle);
 
-    std::vector<value_t> values_h(nnz);
+    std::vector<dist_t> values_h(nnz);
     std::vector<index_t> indices_h(nnz);
     std::vector<index_t> indptr_h(params.n_queries + 1);
 
@@ -390,9 +449,9 @@ class PrefilteredBruteForceTest
 
     bool select_min = cuvs::distance::is_min_close(params.metric);
 
-    std::vector<value_t> out_val_h(params.n_queries * params.top_k,
-                                   select_min ? std::numeric_limits<value_t>::infinity()
-                                              : std::numeric_limits<value_t>::lowest());
+    std::vector<dist_t> out_val_h(
+      params.n_queries * params.top_k,
+      select_min ? std::numeric_limits<dist_t>::infinity() : std::numeric_limits<dist_t>::lowest());
     std::vector<index_t> out_idx_h(params.n_queries * params.top_k, static_cast<index_t>(0));
 
     out_val_d.resize(params.n_queries * params.top_k, stream);
@@ -404,7 +463,6 @@ class PrefilteredBruteForceTest
     raft::resource::sync_stream(handle);
 
     std::optional<std::vector<index_t>> optional_indices_h = std::nullopt;
-
     cpu_select_k(indptr_h,
                  indices_h,
                  values_h,
@@ -415,10 +473,11 @@ class PrefilteredBruteForceTest
                  out_val_h,
                  out_idx_h,
                  select_min);
-
     out_val_expected_d.resize(params.n_queries * params.top_k, stream);
     out_idx_expected_d.resize(params.n_queries * params.top_k, stream);
 
+    // dump_vector(out_val_h.data(), out_val_h.size(), "out_val_h");
+
     raft::update_device(out_val_expected_d.data(), out_val_h.data(), out_val_h.size(), stream);
     raft::update_device(out_idx_expected_d.data(), out_idx_h.data(), out_idx_h.size(), stream);
 
@@ -438,12 +497,17 @@ class PrefilteredBruteForceTest
     auto filter = cuvs::core::bitmap_view<const bitmap_t, index_t>(
       (const bitmap_t*)filter_d.data(), params.n_queries, params.n_dataset);
 
-    auto out_val = raft::make_device_matrix_view<value_t, index_t, raft::row_major>(
+    auto out_val = raft::make_device_matrix_view<dist_t, index_t, raft::row_major>(
       out_val_d.data(), params.n_queries, params.top_k);
     auto out_idx = raft::make_device_matrix_view<index_t, index_t, raft::row_major>(
       out_idx_d.data(), params.n_queries, params.top_k);
 
     brute_force::search(handle, dataset, queries, out_idx, out_val, std::make_optional(filter));
+    std::vector<dist_t> out_val_h(params.n_queries * params.top_k,
+                                  std::numeric_limits<dist_t>::infinity());
+
+    raft::update_host(out_val_h.data(), out_val_d.data(), out_val_h.size(), stream);
+    raft::resource::sync_stream(handle);
 
     ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(out_idx_expected_d.data(),
                                                     out_idx.data_handle(),
@@ -468,49 +532,61 @@ class PrefilteredBruteForceTest
   rmm::device_uvector<value_t> queries_d;
   rmm::device_uvector<bitmap_t> filter_d;
 
-  rmm::device_uvector<value_t> out_val_d;
-  rmm::device_uvector<value_t> out_val_expected_d;
+  rmm::device_uvector<dist_t> out_val_d;
+  rmm::device_uvector<dist_t> out_val_expected_d;
 
   rmm::device_uvector<index_t> out_idx_d;
   rmm::device_uvector<index_t> out_idx_expected_d;
 };
 
-using PrefilteredBruteForceTest_float_int64 = PrefilteredBruteForceTest<float, int64_t>;
+using PrefilteredBruteForceTest_float_int64 = PrefilteredBruteForceTest<float, float, int64_t>;
 TEST_P(PrefilteredBruteForceTest_float_int64, Result) { Run(); }
 
+using PrefilteredBruteForceTest_half_int64 = PrefilteredBruteForceTest<half, float, int64_t>;
+TEST_P(PrefilteredBruteForceTest_half_int64, Result) { Run(); }
+
 template <typename index_t>
 const std::vector<PrefilteredBruteForceInputs<index_t>> selectk_inputs = {
+  {8, 131072, 255, 255, 0.01, cuvs::distance::DistanceType::L2Expanded},
+  {8, 131072, 255, 255, 0.01, cuvs::distance::DistanceType::InnerProduct},
+  {8, 131072, 255, 255, 0.01, cuvs::distance::DistanceType::L2SqrtExpanded},
+  {8, 131072, 255, 255, 0.01, cuvs::distance::DistanceType::CosineExpanded},
   {2, 131072, 255, 255, 0.4, cuvs::distance::DistanceType::L2Expanded},
+
   {8, 131072, 512, 16, 0.5, cuvs::distance::DistanceType::L2Expanded},
   {16, 131072, 2052, 16, 0.2, cuvs::distance::DistanceType::L2Expanded},
+  {2, 8192, 255, 16, 0.01, cuvs::distance::DistanceType::InnerProduct},
   {2, 8192, 255, 16, 0.4, cuvs::distance::DistanceType::InnerProduct},
   {16, 8192, 512, 16, 0.5, cuvs::distance::DistanceType::InnerProduct},
+
   {128, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 1, 0, 0.1, cuvs::distance::DistanceType::L2Expanded},
   {1024, 8192, 3, 0, 0.1, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 5, 0, 0.1, cuvs::distance::DistanceType::L2SqrtExpanded},
   {1024, 8192, 8, 0, 0.1, cuvs::distance::DistanceType::CosineExpanded},
 
-  {1024, 8192, 1, 1, 0.1, cuvs::distance::DistanceType::L2Expanded},
+  {1024, 8192, 1, 1, 0.1, cuvs::distance::DistanceType::L2Expanded},  //--
   {1024, 8192, 3, 1, 0.1, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 5, 1, 0.1, cuvs::distance::DistanceType::L2SqrtExpanded},
   {1024, 8192, 8, 1, 0.1, cuvs::distance::DistanceType::CosineExpanded},
-
   {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::L2Expanded},
+
   {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::L2Expanded},
   {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::L2Expanded},
   {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::InnerProduct},
+
   {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::L2SqrtExpanded},
   {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::L2SqrtExpanded},
   {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::L2SqrtExpanded},
   {1024, 8192, 2050, 16, 0.4, cuvs::distance::DistanceType::CosineExpanded},
   {1024, 8192, 2051, 16, 0.5, cuvs::distance::DistanceType::CosineExpanded},
-  {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::CosineExpanded},
 
+  {1024, 8192, 2052, 16, 0.2, cuvs::distance::DistanceType::CosineExpanded},
   {1024, 8192, 1, 16, 0.5, cuvs::distance::DistanceType::L2Expanded},
   {1024, 8192, 2, 16, 0.2, cuvs::distance::DistanceType::L2Expanded},
+
   {1024, 8192, 3, 16, 0.4, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 4, 16, 0.5, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 5, 16, 0.2, cuvs::distance::DistanceType::L2SqrtExpanded},
@@ -522,4 +598,8 @@ INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceTest,
                         PrefilteredBruteForceTest_float_int64,
                         ::testing::ValuesIn(selectk_inputs<int64_t>));
 
+INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceTest,
+                        PrefilteredBruteForceTest_half_int64,
+                        ::testing::ValuesIn(selectk_inputs<int64_t>));
+
 }  // namespace cuvs::neighbors::brute_force

From 1d6e8da16ae98bbacb4bae17bf8c9e21d0564265 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Wed, 21 Aug 2024 20:54:43 -0400
Subject: [PATCH 130/623] Use CUDA math wheels (#298)

Use CUDA math wheels to reduce wheel size by not statically linking CUDA math libraries.

Contributes to https://github.com/rapidsai/build-planning/issues/35

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Robert Maynard (https://github.com/robertmaynard)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/298
---
 ci/build_wheel.sh          | 18 +++++++++++++++++-
 ci/build_wheel_cuvs.sh     | 11 ++++++++++-
 dependencies.yaml          | 31 +++++++++++++++++++++++++++++++
 python/cuvs/CMakeLists.txt | 21 +++++++++++++++++++++
 python/cuvs/pyproject.toml |  6 +++++-
 5 files changed, 84 insertions(+), 3 deletions(-)

diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 5d48bab220..d1030276f9 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -16,10 +16,26 @@ rapids-generate-version > ./VERSION
 
 cd "${package_dir}"
 
+case "${RAPIDS_CUDA_VERSION}" in
+  12.*)
+    EXCLUDE_ARGS=(
+      --exclude "libcublas.so.12"
+      --exclude "libcublasLt.so.12"
+      --exclude "libcurand.so.10"
+      --exclude "libcusolver.so.11"
+      --exclude "libcusparse.so.12"
+      --exclude "libnvJitLink.so.12"
+    )
+  ;;
+  11.*)
+    EXCLUDE_ARGS=()
+  ;;
+esac
+
 # Hardcode the output dir
 python -m pip wheel . -w dist -vvv --no-deps --disable-pip-version-check
 
 mkdir -p final_dist
-python -m auditwheel repair -w final_dist dist/*
+python -m auditwheel repair -w final_dist "${EXCLUDE_ARGS[@]}" dist/*
 
 RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 final_dist
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index 0fe28c2f7f..e03da9f19d 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -3,7 +3,16 @@
 
 set -euo pipefail
 
+case "${RAPIDS_CUDA_VERSION}" in
+  12.*)
+    EXTRA_CMAKE_ARGS=";-DUSE_CUDA_MATH_WHEELS=ON"
+  ;;
+  11.*)
+    EXTRA_CMAKE_ARGS=";-DUSE_CUDA_MATH_WHEELS=OFF"
+  ;;
+esac
+
 # Set up skbuild options. Enable sccache in skbuild config options
-export SKBUILD_CMAKE_ARGS="-DDETECT_CONDA_ENV=OFF -DFIND_CUVS_CPP=OFF"
+export SKBUILD_CMAKE_ARGS="-DDETECT_CONDA_ENV=OFF;-DFIND_CUVS_CPP=OFF${EXTRA_CMAKE_ARGS}"
 
 ci/build_wheel.sh cuvs python/cuvs
diff --git a/dependencies.yaml b/dependencies.yaml
index 98c9043aa9..f39e307706 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -78,6 +78,7 @@ files:
     extras:
       table: project
     includes:
+      - cuda_wheels
       - run_py_cuvs
   py_test_py_cuvs:
     output: pyproject
@@ -310,6 +311,36 @@ dependencies:
               - *libcusolver114
               - *libcusparse_dev114
               - *libcusparse114
+  cuda_wheels:
+    specific:
+      - output_types: pyproject
+        matrices:
+          - matrix:
+              cuda: "12.*"
+              use_cuda_wheels: "true"
+            packages:
+              - nvidia-cublas-cu12
+              - nvidia-curand-cu12
+              - nvidia-cusolver-cu12
+              - nvidia-cusparse-cu12
+          # CUDA 11 does not provide wheels, so use the system libraries instead
+          - matrix:
+              cuda: "11.*"
+              use_cuda_wheels: "true"
+            packages:
+          # if use_cuda_wheels=false is provided, do not add dependencies on any CUDA wheels
+          # (e.g. for DLFW and pip devcontainers)
+          - matrix:
+              use_cuda_wheels: "false"
+            packages:
+          # if no matching matrix selectors passed, list the unsuffixed packages
+          # (just as a source of documentation, as this populates pyproject.toml in source control)
+          - matrix:
+            packages:
+              - nvidia-cublas
+              - nvidia-curand
+              - nvidia-cusolver
+              - nvidia-cusparse
 
   cupy:
     common:
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 4d24682cfb..7d2f8dcf90 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -37,6 +37,7 @@ project(
 option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulting to local files"
        OFF
 )
+option(USE_CUDA_MATH_WHEELS "Use the CUDA math wheels instead of the system libraries" OFF)
 
 message(
   "CUVS_PY: Searching for existing cuVS C/C++ installations before defaulting to local files: ${FIND_CUVS_CPP}"
@@ -62,6 +63,8 @@ else()
 endif()
 
 if(NOT cuvs_FOUND)
+  find_package(CUDAToolkit REQUIRED)
+
   set(BUILD_TESTS OFF)
   set(BUILD_C_LIBRARY ON)
 
@@ -70,8 +73,26 @@ if(NOT cuvs_FOUND)
   set(CUDA_STATIC_MATH_LIBRARIES ON)
   set(CUVS_USE_RAFT_STATIC ON)
 
+  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 12.0)
+    set(CUDA_STATIC_MATH_LIBRARIES OFF)
+  elseif(USE_CUDA_MATH_WHEELS)
+    message(FATAL_ERROR "Cannot use CUDA math wheels with CUDA < 12.0")
+  endif()
+
   add_subdirectory(../../cpp cuvs-cpp EXCLUDE_FROM_ALL)
 
+  if(NOT CUDA_STATIC_MATH_LIBRARIES AND USE_CUDA_MATH_WHEELS)
+    set(rpaths
+      "$ORIGIN/../nvidia/cublas/lib"
+      "$ORIGIN/../nvidia/curand/lib"
+      "$ORIGIN/../nvidia/cusolver/lib"
+      "$ORIGIN/../nvidia/cusparse/lib"
+      "$ORIGIN/../nvidia/nvjitlink/lib"
+    )
+    set_property(TARGET cuvs PROPERTY INSTALL_RPATH ${rpaths} APPEND)
+    set_property(TARGET cuvs_c PROPERTY INSTALL_RPATH ${rpaths} APPEND)
+  endif()
+
   set(cython_lib_dir cuvs)
   install(TARGETS cuvs cuvs_c DESTINATION ${cython_lib_dir})
 endif()
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index cc2aa6a7fe..0a3c30a7cf 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -33,6 +33,10 @@ requires-python = ">=3.9"
 dependencies = [
     "cuda-python",
     "numpy>=1.23,<2.0a0",
+    "nvidia-cublas",
+    "nvidia-curand",
+    "nvidia-cusolver",
+    "nvidia-cusparse",
     "pylibraft==24.10.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
@@ -128,7 +132,7 @@ requires = [
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
 dependencies-file = "../../dependencies.yaml"
-matrix-entry = "cuda_suffixed=true"
+matrix-entry = "cuda_suffixed=true;use_cuda_wheels=true"
 
 [tool.pytest.ini_options]
 filterwarnings = [

From d113e7c9e9f3757911c3605c817bb9bd4e9eb363 Mon Sep 17 00:00:00 2001
From: rhdong <hrong@nvidia.com>
Date: Thu, 22 Aug 2024 13:35:30 -0700
Subject: [PATCH 131/623] [Fix] pin raft dependent to rapidsai (#299)

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/299
---
 cpp/cmake/thirdparty/get_raft.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index fb25623ce3..7640fbfa6e 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -61,8 +61,8 @@ endfunction()
 # To use a different RAFT locally, set the CMake variable
 # CPM_raft_SOURCE=/path/to/local/raft
 find_and_configure_raft(VERSION  ${RAFT_VERSION}.00
-        FORK                     rhdong
-        PINNED_TAG               half_knn
+        FORK                     ${RAFT_FORK}
+        PINNED_TAG               ${RAFT_PINNED_TAG}
         ENABLE_MNMG_DEPENDENCIES OFF
         ENABLE_NVTX              OFF
         USE_RAFT_STATIC ${CUVS_USE_RAFT_STATIC}

From 108ee25467c36fd098de0d0d95ba85321a4e76a5 Mon Sep 17 00:00:00 2001
From: Ajit Mistry <55892788+ajit283@users.noreply.github.com>
Date: Thu, 22 Aug 2024 23:43:58 +0200
Subject: [PATCH 132/623] Add RMM Pool memory resource to C API (#285)

Enables switching to the pool allocator for better performance

Authors:
  - Ajit Mistry (https://github.com/ajit283)
  - Ray Douglass (https://github.com/raydouglass)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/285
---
 cpp/include/cuvs/core/c_api.h | 21 +++++++++++++++++++++
 cpp/src/core/c_api.cpp        | 29 +++++++++++++++++++++++++++++
 cpp/test/core/c_api.c         | 27 +++++++++++++++++++++++++++
 3 files changed, 77 insertions(+)

diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index d931d6c13d..4db7fd12c0 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -127,6 +127,27 @@ cuvsError_t cuvsRMMAlloc(cuvsResources_t res, void** ptr, size_t bytes);
  */
 cuvsError_t cuvsRMMFree(cuvsResources_t res, void* ptr, size_t bytes);
 
+/**
+ * @brief Switches the working memory resource to use the RMM pool memory resource, which will
+ * bypass unnecessary synchronizations by allocating a chunk of device memory up front and carving
+ * that up for temporary memory allocations within algorithms. Be aware that this function will
+ * change the memory resource for the whole process and the new memory resource will be used until
+ * explicitly changed.
+ *
+ * @param[in] initial_pool_size_percent The initial pool size as a percentage of the total
+ * available memory
+ * @param[in] max_pool_size_percent The maximum pool size as a percentage of the total
+ * available memory
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsRMMPoolMemoryResourceEnable(int initial_pool_size_percent,
+                                            int max_pool_size_percent);
+/**
+ * @brief Resets the memory resource to use the default memory resource (cuda_memory_resource)
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsRMMMemoryResourceReset();
+
 /** @} */
 
 #ifdef __cplusplus
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 96504a2fee..a75e5a1ddf 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -22,6 +22,7 @@
 #include <raft/core/resources.hpp>
 #include <rmm/cuda_stream_view.hpp>
 #include <rmm/mr/device/per_device_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
 #include <thread>
 
 extern "C" cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
@@ -82,6 +83,34 @@ extern "C" cuvsError_t cuvsRMMFree(cuvsResources_t res, void* ptr, size_t bytes)
   });
 }
 
+thread_local std::unique_ptr<rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource>> pool_mr;
+
+extern "C" cuvsError_t cuvsRMMPoolMemoryResourceEnable(int initial_pool_size_percent,
+                                                       int max_pool_size_percent)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Upstream memory resource needs to be a cuda_memory_resource
+    auto cuda_mr         = rmm::mr::get_current_device_resource();
+    auto* cuda_mr_casted = dynamic_cast<rmm::mr::cuda_memory_resource*>(cuda_mr);
+    if (cuda_mr_casted == nullptr) {
+      throw std::runtime_error("Current memory resource is not a cuda_memory_resource");
+    }
+    auto initial_size = rmm::percent_of_free_device_memory(initial_pool_size_percent);
+    auto max_size     = rmm::percent_of_free_device_memory(max_pool_size_percent);
+    pool_mr = std::make_unique<rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource>>(
+      cuda_mr_casted, initial_size, max_size);
+    rmm::mr::set_current_device_resource(pool_mr.get());
+  });
+}
+
+extern "C" cuvsError_t cuvsRMMMemoryResourceReset()
+{
+  return cuvs::core::translate_exceptions([=] {
+    rmm::mr::set_current_device_resource(nullptr);
+    pool_mr.reset();
+  });
+}
+
 thread_local std::string last_error_text = "";
 
 extern "C" const char* cuvsGetLastErrorText()
diff --git a/cpp/test/core/c_api.c b/cpp/test/core/c_api.c
index a5b73d8fb1..27973c2ddf 100644
--- a/cpp/test/core/c_api.c
+++ b/cpp/test/core/c_api.c
@@ -31,6 +31,33 @@ int main()
   cuvsError_t stream_error = cuvsStreamSet(res, stream);
   if (stream_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
+  // Allocate memory
+  void* ptr;
+  size_t bytes            = 1024;
+  cuvsError_t alloc_error = cuvsRMMAlloc(res, &ptr, bytes);
+  if (alloc_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Free memory
+  cuvsError_t free_error = cuvsRMMFree(res, ptr, bytes);
+  if (free_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Enable pool memory resource
+  cuvsError_t pool_error = cuvsRMMPoolMemoryResourceEnable(10, 100);
+  if (pool_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Allocate memory again
+  void* ptr2;
+  cuvsError_t alloc_error_pool = cuvsRMMAlloc(res, &ptr2, 1024);
+  if (alloc_error_pool == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Free memory
+  cuvsError_t free_error_pool = cuvsRMMFree(res, ptr2, 1024);
+  if (free_error_pool == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Reset pool memory resource
+  cuvsError_t reset_error = cuvsRMMMemoryResourceReset();
+  if (reset_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
   // Destroy resources
   cuvsError_t destroy_error = cuvsResourcesDestroy(res);
   if (destroy_error == CUVS_ERROR) { exit(EXIT_FAILURE); }

From 3b5ef26c6b574b894da584213c49aea3b61407e7 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Thu, 22 Aug 2024 21:18:08 -0500
Subject: [PATCH 133/623] Drop Python 3.9 support (#301)

Contributes to https://github.com/rapidsai/build-planning/issues/88

Finishes the work of dropping Python 3.9 support.

This project stopped building / testing against Python 3.9 as of https://github.com/rapidsai/shared-workflows/pull/235.
This PR updates configuration and docs to reflect that.

## Notes for Reviewers

### How I tested this

Checked that there were no remaining uses like this:

```shell
git grep -E '3\.9'
git grep '39'
git grep 'py39'
```

And similar for variations on Python 3.8 (to catch things that were missed the last time this was done).

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cuvs/pull/301
---
 dependencies.yaml          | 6 +-----
 pyproject.toml             | 2 +-
 python/cuvs/pyproject.toml | 4 ++--
 3 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/dependencies.yaml b/dependencies.yaml
index f39e307706..a60065671f 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -396,10 +396,6 @@ dependencies:
     specific:
       - output_types: conda
         matrices:
-          - matrix:
-              py: "3.9"
-            packages:
-              - python=3.9
           - matrix:
               py: "3.10"
             packages:
@@ -410,7 +406,7 @@ dependencies:
               - python=3.11
           - matrix:
             packages:
-              - python>=3.9,<3.12
+              - python>=3.10,<3.12
   run_py_cuvs:
     common:
       - output_types: [conda, pyproject]
diff --git a/pyproject.toml b/pyproject.toml
index 2982db2a23..fbf4cf41fe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.black]
 line-length = 79
-target-version = ["py39"]
+target-version = ["py310"]
 include = '\.py?$'
 force-exclude = '''
 /(
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 0a3c30a7cf..568017f0f0 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -29,7 +29,7 @@ authors = [
     { name = "NVIDIA Corporation" },
 ]
 license = { text = "Apache 2.0" }
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
     "numpy>=1.23,<2.0a0",
@@ -42,8 +42,8 @@ dependencies = [
 classifiers = [
     "Intended Audience :: Developers",
     "Programming Language :: Python",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
 ]
 
 [project.optional-dependencies]

From 49e45bb65c6e822ebe60c28a593781be7f2fae33 Mon Sep 17 00:00:00 2001
From: Sebastian Berg <sebastian@sipsolutions.net>
Date: Sat, 24 Aug 2024 07:59:23 +0200
Subject: [PATCH 134/623] Remove NumPy <2 pin (#297)

This PR removes the NumPy<2 pin which is expected to work for
RAPIDS projects once CuPy 13.3.0 is released (CuPy 13.2.0 had
some issues preventing the use with NumPy 2).

Authors:
  - Sebastian Berg (https://github.com/seberg)
  - https://github.com/jakirkham

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/297
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/all_cuda-125_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-125_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs/meta.yaml                      | 2 ++
 dependencies.yaml                                 | 2 +-
 python/cuvs/pyproject.toml                        | 2 +-
 7 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 54866d0e53..4bbdc36503 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -38,7 +38,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23,<2.0a0
+- numpy>=1.23,<3.0a0
 - numpydoc
 - nvcc_linux-aarch64=11.8
 - openblas
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index b29fe1b50e..908421d084 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -38,7 +38,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23,<2.0a0
+- numpy>=1.23,<3.0a0
 - numpydoc
 - nvcc_linux-64=11.8
 - openblas
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index c6deb93f8b..3131d0b771 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23,<2.0a0
+- numpy>=1.23,<3.0a0
 - numpydoc
 - openblas
 - pre-commit
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 538fdf08be..2f107c4fb4 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - make
 - nccl>=2.9.9
 - ninja
-- numpy>=1.23,<2.0a0
+- numpy>=1.23,<3.0a0
 - numpydoc
 - openblas
 - pre-commit
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index df6cfeec68..2633b3db8f 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -69,6 +69,8 @@ requirements:
     - libcuvs {{ version }}
     - python x.x
     - rmm ={{ minor_version }}
+    - cuda-python
+    - numpy >=1.23,<3.0a0
 
 tests:
   requirements:
diff --git a/dependencies.yaml b/dependencies.yaml
index a60065671f..d8763e0767 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -411,7 +411,7 @@ dependencies:
     common:
       - output_types: [conda, pyproject]
         packages:
-          - &numpy numpy>=1.23,<2.0a0
+          - &numpy numpy>=1.23,<3.0a0
       - output_types: [conda]
         packages:
           - *rmm_unsuffixed
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 568017f0f0..fe0e9022fd 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -32,7 +32,7 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
-    "numpy>=1.23,<2.0a0",
+    "numpy>=1.23,<3.0a0",
     "nvidia-cublas",
     "nvidia-curand",
     "nvidia-cusolver",

From 4fd3ed631d2755101fa41e2322ee81c9a7499c79 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 27 Aug 2024 15:28:31 -0400
Subject: [PATCH 135/623] Update rapidsai/pre-commit-hooks (#303)

This PR updates rapidsai/pre-commit-hooks to the version 0.4.0.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/303
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 592693adb6..3e3623f24f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -97,7 +97,7 @@ repos:
         hooks:
               - id: check-json
       - repo: https://github.com/rapidsai/pre-commit-hooks
-        rev: v0.3.1
+        rev: v0.4.0
         hooks:
           - id: verify-copyright
             files: |

From 4a71d30109765056ea82bb0111436ecc7c8fb635 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 5 Sep 2024 16:25:25 -0700
Subject: [PATCH 136/623] Exclude any kernel symbol that uses cutlass (#314)

Similar to https://github.com/rapidsai/raft/pull/2425, this excludes kernels using cutlass in order to unblock CI

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/314
---
 .github/workflows/pr.yaml   | 2 +-
 .github/workflows/test.yaml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 8ea2fa503c..07b10e85a8 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -50,7 +50,7 @@ jobs:
     with:
       build_type: pull-request
       enable_check_symbols: true
-      symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
+      symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index c0d07297b8..0821233a1e 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -23,7 +23,7 @@ jobs:
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
       enable_check_symbols: true
-      symbol_exclusions: (void (thrust::|cub::)|_ZN\d+raft_cutlass)
+      symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10

From 9e8ec39235fbda1e2a06b649d3f67f31e3e74c21 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 5 Sep 2024 21:17:13 -0400
Subject: [PATCH 137/623] Updating example notebooks (#294)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/294
---
 .gitignore                                    |  5 +++
 cpp/src/neighbors/cagra_optimize.cu           | 10 ++++-
 .../VectorSearch_QuestionRetrieval.ipynb      |  2 +-
 notebooks/ivf_flat_example.ipynb              | 26 ++++++++++-
 notebooks/tutorial_ivf_pq.ipynb               | 44 ++++++++++---------
 5 files changed, 62 insertions(+), 25 deletions(-)

diff --git a/.gitignore b/.gitignore
index 4b6f46320f..fcbe0fa3a2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -28,6 +28,11 @@ bench/ann/data
 temporary_*.json
 rust/target/
 rust/Cargo.lock
+rmm_log.txt
+
+## example notebooks
+notebooks/simplewiki-2020-11-01-nq-distilbert-base-v1.pt
+notebooks/data/
 
 ## scikit-build
 _skbuild
diff --git a/cpp/src/neighbors/cagra_optimize.cu b/cpp/src/neighbors/cagra_optimize.cu
index 4c152cf121..cba66a5e9b 100644
--- a/cpp/src/neighbors/cagra_optimize.cu
+++ b/cpp/src/neighbors/cagra_optimize.cu
@@ -23,13 +23,19 @@ void optimize(raft::resources const& handle,
               raft::device_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
               raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
 {
-  cuvs::neighbors::cagra::optimize(handle, knn_graph, new_graph);
+  cuvs::neighbors::cagra::optimize<
+    uint32_t,
+    raft::host_device_accessor<std::experimental::default_accessor<uint32_t>,
+                               raft::memory_type::device>>(handle, knn_graph, new_graph);
 }
 void optimize(raft::resources const& handle,
               raft::host_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
               raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
 {
-  cuvs::neighbors::cagra::optimize(handle, knn_graph, new_graph);
+  cuvs::neighbors::cagra::optimize<
+    uint32_t,
+    raft::host_device_accessor<std::experimental::default_accessor<uint32_t>,
+                               raft::memory_type::host>>(handle, knn_graph, new_graph);
 }
 
 }  // namespace cuvs::neighbors::cagra
\ No newline at end of file
diff --git a/notebooks/VectorSearch_QuestionRetrieval.ipynb b/notebooks/VectorSearch_QuestionRetrieval.ipynb
index 4023a1821b..21d59975bb 100644
--- a/notebooks/VectorSearch_QuestionRetrieval.ipynb
+++ b/notebooks/VectorSearch_QuestionRetrieval.ipynb
@@ -344,7 +344,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/ivf_flat_example.ipynb b/notebooks/ivf_flat_example.ipynb
index 2d9c5fb58e..ce35866833 100644
--- a/notebooks/ivf_flat_example.ipynb
+++ b/notebooks/ivf_flat_example.ipynb
@@ -520,6 +520,30 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23010fbc-8f5a-4403-a112-33f190a85498",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "774848e8-fa45-4223-bd2a-e8585650531e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6309b8a7-f4eb-4976-a824-cd4499a0000d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -538,7 +562,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/tutorial_ivf_pq.ipynb b/notebooks/tutorial_ivf_pq.ipynb
index cc0fe41428..9d59daea23 100644
--- a/notebooks/tutorial_ivf_pq.ipynb
+++ b/notebooks/tutorial_ivf_pq.ipynb
@@ -124,6 +124,7 @@
    "outputs": [],
    "source": [
     "DATASET_URL = \"http://ann-benchmarks.com/sift-128-euclidean.hdf5\"\n",
+    "DATASET_NAME = \"SIFT-128\"\n",
     "f = load_dataset(DATASET_URL)"
    ]
   },
@@ -206,7 +207,7 @@
     "# This function takes a row-major either numpy or cupy (GPU) array.\n",
     "# Generally, it's a bit faster with GPU inputs, but the CPU version may come in handy\n",
     "# if the whole dataset cannot fit into GPU memory.\n",
-    "index = ivf_pq.build(index_params, dataset, handle=resources)\n",
+    "index = ivf_pq.build(index_params, dataset, resources=resources)\n",
     "# This function is asynchronous so we need to explicitly synchronize the GPU before we can measure the execution time\n",
     "resources.sync()\n",
     "index"
@@ -262,7 +263,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "distances, neighbors = ivf_pq.search(search_params, index, queries, k, handle=resources)\n",
+    "distances, neighbors = ivf_pq.search(search_params, index, queries, k, resources=resources)\n",
     "# Sync the GPU to make sure we've got the timing right\n",
     "resources.sync()"
    ]
@@ -303,8 +304,8 @@
    "source": [
     "%%time\n",
     "\n",
-    "candidates = ivf_pq.search(search_params, index, queries, k * 2, handle=resources)[1]\n",
-    "distances, neighbors = refine(dataset, queries, candidates, k, handle=resources)\n",
+    "candidates = ivf_pq.search(search_params, index, queries, k * 2, resources=resources)[1]\n",
+    "distances, neighbors = refine(dataset, queries, candidates, k, resources=resources)\n",
     "resources.sync()"
    ]
   },
@@ -349,7 +350,7 @@
     "bench_avg = np.zeros_like(bench_k, dtype=np.float32)\n",
     "bench_std = np.zeros_like(bench_k, dtype=np.float32)\n",
     "for i, k in enumerate(bench_k):\n",
-    "    r = %timeit -o ivf_pq.search(search_params, index, queries, k, handle=resources); resources.sync()\n",
+    "    r = %timeit -o ivf_pq.search(search_params, index, queries, k, resources=resources); resources.sync()\n",
     "    bench_avg[i] = (queries.shape[0] * r.loops / np.array(r.all_runs)).mean()\n",
     "    bench_std[i] = (queries.shape[0] * r.loops / np.array(r.all_runs)).std()\n",
     "\n",
@@ -387,9 +388,9 @@
     "k = 100\n",
     "for i, n_probes in enumerate(bench_probes):\n",
     "    sp = ivf_pq.SearchParams(n_probes=n_probes)\n",
-    "    r = %timeit -o ivf_pq.search(sp, index, queries, k, handle=resources); resources.sync()\n",
+    "    r = %timeit -o ivf_pq.search(sp, index, queries, k, resources=resources); resources.sync()\n",
     "    bench_qps[i] = (queries.shape[0] * r.loops / np.array(r.all_runs)).mean()\n",
-    "    bench_recall[i] = calc_recall(ivf_pq.search(sp, index, queries, k, handle=resources)[1], gt_neighbors)\n",
+    "    bench_recall[i] = calc_recall(ivf_pq.search(sp, index, queries, k, resources=resources)[1], gt_neighbors)\n",
     "    "
    ]
   },
@@ -492,9 +493,9 @@
     "bench_names = ['32/32', '32/16', '32/8', '16/16', '16/8']\n",
     "\n",
     "for i, sp in enumerate(search_ps):\n",
-    "    r = %timeit -o ivf_pq.search(sp, index, queries, k, handle=resources); resources.sync()\n",
+    "    r = %timeit -o ivf_pq.search(sp, index, queries, k, resources=resources); resources.sync()\n",
     "    bench_qps_s1[i] = (queries.shape[0] * r.loops / np.array(r.all_runs)).mean()\n",
-    "    bench_recall_s1[i] = calc_recall(ivf_pq.search(sp, index, queries, k, handle=resources)[1], gt_neighbors)"
+    "    bench_recall_s1[i] = calc_recall(ivf_pq.search(sp, index, queries, k, resources=resources)[1], gt_neighbors)"
    ]
   },
   {
@@ -505,7 +506,7 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=plt.figaspect(1/2))\n",
     "fig.suptitle(\n",
-    "    f'Effects of search parameters on QPS/recall trade-off ({DATASET_FILENAME})\\n' + \\\n",
+    "    f'Effects of search parameters on QPS/recall trade-off ({DATASET_NAME})\\n' + \\\n",
     "    f'k = {k}, n_probes = {n_probes}, pq_dim = {pq_dim}')\n",
     "ax.plot(bench_recall_s1, bench_qps_s1, 'o')\n",
     "ax.set_xlabel('recall')\n",
@@ -553,8 +554,8 @@
    "source": [
     "def search_refine(ps, ratio):\n",
     "    k_search = k * ratio\n",
-    "    candidates = ivf_pq.search(ps, index, queries, k_search, handle=resources)[1]\n",
-    "    return candidates if ratio == 1 else refine(dataset, queries, candidates, k, handle=resources)[1]\n",
+    "    candidates = ivf_pq.search(ps, index, queries, k_search, resources=resources)[1]\n",
+    "    return candidates if ratio == 1 else refine(dataset, queries, candidates, k, resources=resources)[1]\n",
     "\n",
     "ratios = [1, 2, 4]\n",
     "bench_qps_sr = np.zeros((len(ratios), len(search_ps)), dtype=np.float32)\n",
@@ -575,7 +576,7 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=plt.figaspect(1/2))\n",
     "fig.suptitle(\n",
-    "    f'Effects of search parameters on QPS/recall trade-off ({DATASET_FILENAME})\\n' + \\\n",
+    "    f'Effects of search parameters on QPS/recall trade-off ({DATASET_NAME})\\n' + \\\n",
     "    f'k = {k}, n_probes = {n_probes}, pq_dim = {pq_dim}')\n",
     "labels = []\n",
     "for j, ratio in enumerate(ratios):\n",
@@ -629,8 +630,8 @@
     "        n_probes=n_probes,\n",
     "        internal_distance_dtype=internal_distance_dtype,\n",
     "        lut_dtype=lut_dtype)\n",
-    "    candidates = ivf_pq.search(ps, index, queries, k_search, handle=resources)[1]\n",
-    "    return candidates if ratio == 1 else refine(dataset, queries, candidates, k, handle=resources)[1]\n",
+    "    candidates = ivf_pq.search(ps, index, queries, k_search, resources=resources)[1]\n",
+    "    return candidates if ratio == 1 else refine(dataset, queries, candidates, k, resources=resources)[1]\n",
     "\n",
     "search_configs = [\n",
     "    lambda n_probes: search_refine(np.float16, np.float16, 1, n_probes),\n",
@@ -703,12 +704,13 @@
     "\n",
     "for i, n_lists in enumerate(n_list_variants):\n",
     "    index_params = ivf_pq.IndexParams(n_lists=n_lists, metric=metric, pq_dim=pq_dim)\n",
-    "    index = ivf_pq.build(index_params, dataset, handle=resources)\n",
+    "    index = ivf_pq.build(index_params, dataset, resources=resources)\n",
     "    for j, pl_ratio in enumerate(pl_ratio_variants):\n",
     "        n_probes = max(1, n_lists // pl_ratio)\n",
     "        r = %timeit -o search_fun(n_probes);  resources.sync()\n",
     "        bench_qps_nl[i, j] = (queries.shape[0] * r.loops / np.array(r.all_runs)).mean()\n",
-    "        bench_recall_nl[i, j] = calc_recall(search_fun(n_probes), gt_neighbors)"
+    "        bench_recall_nl[i, j] = calc_recall(search_fun(n_probes), gt_neighbors)\n",
+    "    del index"
    ]
   },
   {
@@ -719,7 +721,7 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=plt.figaspect(1/2))\n",
     "fig.suptitle(\n",
-    "    f'Effects of n_list on QPS/recall trade-off ({DATASET_FILENAME})\\n' + \\\n",
+    "    f'Effects of n_list on QPS/recall trade-off ({DATASET_NAME})\\n' + \\\n",
     "    f'k = {k}, pq_dim = {pq_dim}, search = {search_label}')\n",
     "labels = []\n",
     "for i, n_lists in enumerate(n_list_variants):\n",
@@ -875,7 +877,7 @@
     "bench_recall_ip = np.zeros_like(bench_qps_ip, dtype=np.float32)\n",
     "\n",
     "for i, index_params in enumerate(build_configs.values()):\n",
-    "    index = ivf_pq.build(index_params, dataset, handle=resources)\n",
+    "    index = ivf_pq.build(index_params, dataset, resources=resources)\n",
     "    for l, search_fun in enumerate(search_configs):\n",
     "        for j, n_probes in enumerate(n_probes_variants):\n",
     "            r = %timeit -o search_fun(n_probes);  resources.sync()\n",
@@ -891,7 +893,7 @@
    "source": [
     "fig, ax = plt.subplots(len(search_config_names), 1, figsize=(16, len(search_config_names)*8))\n",
     "fig.suptitle(\n",
-    "    f'Effects of index parameters on QPS/recall trade-off ({DATASET_FILENAME})\\n' + \\\n",
+    "    f'Effects of index parameters on QPS/recall trade-off ({DATASET_NAME})\\n' + \\\n",
     "    f'k = {k}, n_lists = {n_lists}')\n",
     "\n",
     "for j, search_label in enumerate(search_config_names):\n",
@@ -932,7 +934,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.9"
   },
   "vscode": {
    "interpreter": {

From 7d144cf1285f7113cfbeed68dda5362efb8f2657 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 6 Sep 2024 13:27:10 -0700
Subject: [PATCH 138/623] Migrate trustworthiness and silhouette_score stats
 from RAFT (#313)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/313
---
 cpp/CMakeLists.txt                            |   2 +
 cpp/include/cuvs/stats/silhouette_score.hpp   | 121 ++++++
 .../cuvs/stats/trustworthiness_score.hpp      |  51 +++
 .../stats/detail/batched/silhouette_score.cuh | 285 ++++++++++++++
 cpp/src/stats/detail/silhouette_score.cuh     | 328 ++++++++++++++++
 .../stats/detail/trustworthiness_score.cuh    | 220 +++++++++++
 cpp/src/stats/silhouette_score.cu             | 138 +++++++
 cpp/src/stats/trustworthiness_score.cu        |  48 +++
 cpp/test/CMakeLists.txt                       |  36 +-
 cpp/test/stats/silhouette_score.cu            | 235 ++++++++++++
 cpp/test/stats/trustworthiness.cu             | 356 ++++++++++++++++++
 docs/source/cpp_api.rst                       |   1 +
 docs/source/cpp_api/neighbors_cagra.rst       |   6 +-
 docs/source/cpp_api/stats.rst                 |  35 ++
 14 files changed, 1837 insertions(+), 25 deletions(-)
 create mode 100644 cpp/include/cuvs/stats/silhouette_score.hpp
 create mode 100644 cpp/include/cuvs/stats/trustworthiness_score.hpp
 create mode 100644 cpp/src/stats/detail/batched/silhouette_score.cuh
 create mode 100644 cpp/src/stats/detail/silhouette_score.cuh
 create mode 100644 cpp/src/stats/detail/trustworthiness_score.cuh
 create mode 100644 cpp/src/stats/silhouette_score.cu
 create mode 100644 cpp/src/stats/trustworthiness_score.cu
 create mode 100644 cpp/test/stats/silhouette_score.cu
 create mode 100644 cpp/test/stats/trustworthiness.cu
 create mode 100644 docs/source/cpp_api/stats.rst

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 76fd09b582..90ed0b1601 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -424,6 +424,8 @@ add_library(
   src/selection/select_k_float_int64_t.cu
   src/selection/select_k_float_uint32_t.cu
   src/selection/select_k_half_uint32_t.cu
+  src/stats/silhouette_score.cu
+  src/stats/trustworthiness_score.cu
 )
 
 target_compile_definitions(cuvs PRIVATE "CUVS_EXPLICIT_INSTANTIATE_ONLY")
diff --git a/cpp/include/cuvs/stats/silhouette_score.hpp b/cpp/include/cuvs/stats/silhouette_score.hpp
new file mode 100644
index 0000000000..1771cc21cd
--- /dev/null
+++ b/cpp/include/cuvs/stats/silhouette_score.hpp
@@ -0,0 +1,121 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs {
+namespace stats {
+
+/**
+ * @defgroup stats_silhouette_score Silhouette Score
+ * @{
+ */
+/**
+ * @brief main function that returns the average silhouette score for a given set of data and its
+ * clusterings
+ * @param[in]  handle: raft handle for managing expensive resources
+ * @param[in]  X_in: input matrix Data in row-major format (nRows x nCols)
+ * @param[in]  labels: the pointer to the array containing labels for every data sample (length:
+ * nRows)
+ * @param[out] silhouette_score_per_sample: optional array populated with the silhouette score
+ * for every sample (length: nRows)
+ * @param[in]  n_unique_labels: number of unique labels in the labels array
+ * @param[in]  metric: Distance metric to use. Euclidean (L2) is used by default
+ * @return: The silhouette score.
+ */
+float silhouette_score(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X_in,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<float, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief function that returns the average silhouette score for a given set of data and its
+ * clusterings
+ * @param[in]  handle: raft handle for managing expensive resources
+ * @param[in]  X: input matrix Data in row-major format (nRows x nCols)
+ * @param[in]  labels: the pointer to the array containing labels for every data sample (length:
+ * nRows)
+ * @param[out] silhouette_score_per_sample: optional array populated with the silhouette score
+ * for every sample (length: nRows)
+ * @param[in]  n_unique_labels: number of unique labels in the labels array
+ * @param[in]  batch_size: number of samples per batch
+ * @param[in]  metric: the numerical value that maps to the type of distance metric to be used in
+ * the calculations
+ * @return: The silhouette score.
+ */
+float silhouette_score_batched(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<float, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  int64_t batch_size,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief main function that returns the average silhouette score for a given set of data and its
+ * clusterings
+ * @param[in]  handle: raft handle for managing expensive resources
+ * @param[in]  X_in: input matrix Data in row-major format (nRows x nCols)
+ * @param[in]  labels: the pointer to the array containing labels for every data sample (length:
+ * nRows)
+ * @param[out] silhouette_score_per_sample: optional array populated with the silhouette score
+ * for every sample (length: nRows)
+ * @param[in]  n_unique_labels: number of unique labels in the labels array
+ * @param[in]  metric: the numerical value that maps to the type of distance metric to be used in
+ * the calculations
+ * @return: The silhouette score.
+ */
+double silhouette_score(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> X_in,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<double, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/**
+ * @brief function that returns the average silhouette score for a given set of data and its
+ * clusterings
+ * @param[in]  handle: raft handle for managing expensive resources
+ * @param[in]  X: input matrix Data in row-major format (nRows x nCols)
+ * @param[in]  labels: the pointer to the array containing labels for every data sample (length:
+ * nRows)
+ * @param[out] silhouette_score_per_sample: optional array populated with the silhouette score
+ * for every sample (length: nRows)
+ * @param[in]  n_unique_labels: number of unique labels in the labels array
+ * @param[in]  batch_size: number of samples per batch
+ * @param[in]  metric: the numerical value that maps to the type of distance metric to be used in
+ * the calculations
+ * @return: The silhouette score.
+ */
+double silhouette_score_batched(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> X,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<double, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  int64_t batch_size,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+}  // namespace stats
+}  // namespace cuvs
diff --git a/cpp/include/cuvs/stats/trustworthiness_score.hpp b/cpp/include/cuvs/stats/trustworthiness_score.hpp
new file mode 100644
index 0000000000..08a26fad51
--- /dev/null
+++ b/cpp/include/cuvs/stats/trustworthiness_score.hpp
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs {
+namespace stats {
+/**
+ * @defgroup stats_trustworthiness Trustworthiness
+ * @{
+ */
+
+/**
+ * @brief Compute the trustworthiness score
+ * @param[in] handle the raft handle
+ * @param[in] X: Data in original dimension
+ * @param[in] X_embedded: Data in target dimension (embedding)
+ * @param[in] n_neighbors Number of neighbors considered by trustworthiness score
+ * @param[in] metric Distance metric to use. Euclidean (L2) is used by default
+ * @param[in] batch_size Batch size
+ * @return Trustworthiness score
+ * @note The constness of the data in X_embedded is currently casted away and the data is slightly
+ * modified.
+ */
+double trustworthiness_score(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X_embedded,
+  int n_neighbors,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtUnexpanded,
+  int batch_size                      = 512);
+
+/** @} */  // end group stats_trustworthiness
+}  // namespace stats
+}  // namespace cuvs
diff --git a/cpp/src/stats/detail/batched/silhouette_score.cuh b/cpp/src/stats/detail/batched/silhouette_score.cuh
new file mode 100644
index 0000000000..aae14ea6cc
--- /dev/null
+++ b/cpp/src/stats/detail/batched/silhouette_score.cuh
@@ -0,0 +1,285 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../silhouette_score.cuh"
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/cuda_stream_pool.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/device_atomics.cuh>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/device_vector.h>
+#include <thrust/fill.h>
+#include <thrust/reduce.h>
+
+namespace cuvs {
+namespace stats {
+namespace batched {
+namespace detail {
+
+/**
+ * This kernel initializes matrix b (n_rows * n_labels)
+ * For each label that the corresponding row is not a part of is initialized as 0
+ * If the corresponding row is the only sample in its label, again 0
+ * Only if the there are > 1 samples in the label, row is initialized to max
+ */
+template <typename value_t, typename value_idx, typename label_idx>
+RAFT_KERNEL fill_b_kernel(value_t* b,
+                          const label_idx* y,
+                          value_idx n_rows,
+                          label_idx n_labels,
+                          const value_idx* cluster_counts)
+{
+  value_idx idx = threadIdx.x + blockIdx.x * blockDim.x;
+  label_idx idy = threadIdx.y + blockIdx.y * blockDim.y;
+
+  if (idx >= n_rows || idy >= n_labels) { return; }
+
+  auto row_cluster = y[idx];
+
+  auto col_cluster_count = cluster_counts[idy];
+
+  // b for own cluster should be max value
+  // so that it does not interfere with min operator
+  // b is also max if col cluster count is 0
+  // however, b is 0 if self cluster count is 1
+  if (row_cluster == idy || col_cluster_count == 0) {
+    if (cluster_counts[row_cluster] == 1) {
+      b[idx * n_labels + idy] = 0;
+    } else {
+      b[idx * n_labels + idy] = std::numeric_limits<value_t>::max();
+    }
+  } else {
+    b[idx * n_labels + idy] = 0;
+  }
+}
+
+/**
+ * This kernel does an elementwise sweep of chunked pairwise distance matrix
+ * By knowing the offsets of the chunked pairwise distance matrix in the
+ * global pairwise distance matrix, we are able to calculate
+ * intermediate values of a and b for the rows and columns present in the
+ * current chunked pairwise distance matrix.
+ */
+template <typename value_t, typename value_idx, typename label_idx>
+RAFT_KERNEL compute_chunked_a_b_kernel(value_t* a,
+                                       value_t* b,
+                                       value_idx row_offset,
+                                       value_idx col_offset,
+                                       const label_idx* y,
+                                       label_idx n_labels,
+                                       const value_idx* cluster_counts,
+                                       const value_t* distances,
+                                       value_idx dist_rows,
+                                       value_idx dist_cols)
+{
+  value_idx row_id = threadIdx.x + blockIdx.x * blockDim.x;
+  value_idx col_id = threadIdx.y + blockIdx.y * blockDim.y;
+
+  // these are global offsets of current element
+  // in the full pairwise distance matrix
+  value_idx pw_row_id = row_id + row_offset;
+  value_idx pw_col_id = col_id + col_offset;
+
+  if (row_id >= dist_rows || col_id >= dist_cols || pw_row_id == pw_col_id) { return; }
+
+  auto row_cluster = y[pw_row_id];
+  if (cluster_counts[row_cluster] == 1) { return; }
+
+  auto col_cluster        = y[pw_col_id];
+  auto col_cluster_counts = cluster_counts[col_cluster];
+
+  if (col_cluster == row_cluster) {
+    atomicAdd(&a[pw_row_id], distances[row_id * dist_cols + col_id] / (col_cluster_counts - 1));
+  } else {
+    atomicAdd(&b[pw_row_id * n_labels + col_cluster],
+              distances[row_id * dist_cols + col_id] / col_cluster_counts);
+  }
+}
+
+template <typename value_idx, typename label_idx>
+rmm::device_uvector<value_idx> get_cluster_counts(raft::resources const& handle,
+                                                  const label_idx* y,
+                                                  value_idx& n_rows,
+                                                  label_idx& n_labels)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  rmm::device_uvector<value_idx> cluster_counts(n_labels, stream);
+
+  rmm::device_uvector<char> workspace(1, stream);
+
+  cuvs::stats::detail::countLabels(y, cluster_counts.data(), n_rows, n_labels, workspace, stream);
+
+  return cluster_counts;
+}
+
+template <typename value_t, typename value_idx>
+rmm::device_uvector<value_t> get_pairwise_distance(raft::resources const& handle,
+                                                   const value_t* left_begin,
+                                                   const value_t* right_begin,
+                                                   value_idx& n_left_rows,
+                                                   value_idx& n_right_rows,
+                                                   value_idx& n_cols,
+                                                   cuvs::distance::DistanceType metric,
+                                                   cudaStream_t stream)
+{
+  rmm::device_uvector<value_t> distances(n_left_rows * n_right_rows, stream);
+
+  cuvs::distance::pairwise_distance(
+    handle,
+    raft::make_device_matrix_view<const value_t, int64_t>(left_begin, n_left_rows, n_cols),
+    raft::make_device_matrix_view<const value_t, int64_t>(right_begin, n_right_rows, n_cols),
+    raft::make_device_matrix_view<value_t, int64_t>(distances.data(), n_left_rows, n_right_rows),
+    metric);
+
+  return distances;
+}
+
+template <typename value_t, typename value_idx, typename label_idx>
+void compute_chunked_a_b(raft::resources const& handle,
+                         value_t* a,
+                         value_t* b,
+                         value_idx& row_offset,
+                         value_idx& col_offset,
+                         const label_idx* y,
+                         label_idx& n_labels,
+                         const value_idx* cluster_counts,
+                         const value_t* distances,
+                         value_idx& dist_rows,
+                         value_idx& dist_cols,
+                         cudaStream_t stream)
+{
+  dim3 block_size(std::min(dist_rows, 32), std::min(dist_cols, 32));
+  dim3 grid_size(raft::ceildiv(dist_rows, (value_idx)block_size.x),
+                 raft::ceildiv(dist_cols, (value_idx)block_size.y));
+
+  detail::compute_chunked_a_b_kernel<<<grid_size, block_size, 0, stream>>>(
+    a, b, row_offset, col_offset, y, n_labels, cluster_counts, distances, dist_rows, dist_cols);
+}
+
+template <typename value_t, typename value_idx, typename label_idx>
+value_t silhouette_score(
+  raft::resources const& handle,
+  const value_t* X,
+  value_idx n_rows,
+  value_idx n_cols,
+  const label_idx* y,
+  label_idx n_labels,
+  value_t* scores,
+  value_idx chunk,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  ASSERT(n_labels >= 2 && n_labels <= (n_rows - 1),
+         "silhouette Score not defined for the given number of labels!");
+
+  rmm::device_uvector<value_idx> cluster_counts = get_cluster_counts(handle, y, n_rows, n_labels);
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+  auto policy = raft::resource::get_thrust_policy(handle);
+
+  auto b_size = n_rows * n_labels;
+
+  value_t *a_ptr, *b_ptr;
+  rmm::device_uvector<value_t> a(0, stream);
+  rmm::device_uvector<value_t> b(b_size, stream);
+
+  b_ptr = b.data();
+
+  // since a and silhouette score per sample are same size, reusing
+  if (scores == nullptr || scores == NULL) {
+    a.resize(n_rows, stream);
+    a_ptr = a.data();
+  } else {
+    a_ptr = scores;
+  }
+
+  thrust::fill(policy, a_ptr, a_ptr + n_rows, 0);
+
+  dim3 block_size(std::min(n_rows, 32), std::min(n_labels, 32));
+  dim3 grid_size(raft::ceildiv(n_rows, (value_idx)block_size.x),
+                 raft::ceildiv(n_labels, (label_idx)block_size.y));
+  detail::fill_b_kernel<<<grid_size, block_size, 0, stream>>>(
+    b_ptr, y, n_rows, n_labels, cluster_counts.data());
+
+  raft::resource::wait_stream_pool_on_stream(handle);
+
+  auto n_iters = 0;
+
+  for (value_idx i = 0; i < n_rows; i += chunk) {
+    for (value_idx j = 0; j < n_rows; j += chunk) {
+      ++n_iters;
+
+      auto chunk_stream = raft::resource::get_next_usable_stream(handle, i + chunk * j);
+
+      const auto* left_begin  = X + (i * n_cols);
+      const auto* right_begin = X + (j * n_cols);
+
+      auto n_left_rows  = (i + chunk) < n_rows ? chunk : (n_rows - i);
+      auto n_right_rows = (j + chunk) < n_rows ? chunk : (n_rows - j);
+
+      rmm::device_uvector<value_t> distances = get_pairwise_distance(
+        handle, left_begin, right_begin, n_left_rows, n_right_rows, n_cols, metric, chunk_stream);
+
+      compute_chunked_a_b(handle,
+                          a_ptr,
+                          b_ptr,
+                          i,
+                          j,
+                          y,
+                          n_labels,
+                          cluster_counts.data(),
+                          distances.data(),
+                          n_left_rows,
+                          n_right_rows,
+                          chunk_stream);
+    }
+  }
+
+  raft::resource::sync_stream_pool(handle);
+
+  // calculating row-wise minimum in b
+  // this prim only supports int indices for now
+  raft::linalg::reduce<value_t, value_t, value_idx, raft::identity_op, raft::min_op>(
+    b_ptr,
+    b_ptr,
+    n_labels,
+    n_rows,
+    std::numeric_limits<value_t>::max(),
+    true,
+    true,
+    stream,
+    false,
+    raft::identity_op(),
+    raft::min_op());
+
+  // calculating the silhouette score per sample
+  raft::linalg::binaryOp<value_t, cuvs::stats::detail::SilOp<value_t>, value_t, value_idx>(
+    a_ptr, a_ptr, b_ptr, n_rows, cuvs::stats::detail::SilOp<value_t>(), stream);
+
+  return thrust::reduce(policy, a_ptr, a_ptr + n_rows, value_t(0)) / n_rows;
+}
+
+}  // namespace detail
+}  // namespace batched
+}  // namespace stats
+}  // namespace cuvs
diff --git a/cpp/src/stats/detail/silhouette_score.cuh b/cpp/src/stats/detail/silhouette_score.cuh
new file mode 100644
index 0000000000..6c876ce6e6
--- /dev/null
+++ b/cpp/src/stats/detail/silhouette_score.cuh
@@ -0,0 +1,328 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/eltwise.cuh>
+#include <raft/linalg/map_then_reduce.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/reduce.cuh>
+#include <raft/linalg/reduce_cols_by_key.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <rmm/device_scalar.hpp>
+
+#include <cub/cub.cuh>
+
+#include <math.h>
+
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+
+namespace cuvs {
+namespace stats {
+namespace detail {
+
+/**
+ * @brief kernel that calculates the average intra-cluster distance for every sample data point and
+ * updates the cluster distance to max value
+ * @tparam DataT: type of the data samples
+ * @tparam LabelT: type of the labels
+ * @param sampleToClusterSumOfDistances: the pointer to the 2D array that contains the sum of
+ * distances from every sample to every cluster (nRows x nLabels)
+ * @param binCountArray: pointer to the 1D array that contains the count of samples per cluster (1 x
+ * nLabels)
+ * @param d_aArray: the pointer to the array of average intra-cluster distances for every sample in
+ * device memory (1 x nRows)
+ * @param labels: the pointer to the array containing labels for every data sample (1 x nRows)
+ * @param nRows: number of data samples
+ * @param nLabels: number of Labels
+ * @param MAX_VAL: DataT specific upper limit
+ */
+template <typename DataT, typename LabelT>
+RAFT_KERNEL populateAKernel(DataT* sampleToClusterSumOfDistances,
+                            DataT* binCountArray,
+                            DataT* d_aArray,
+                            const LabelT* labels,
+                            int nRows,
+                            int nLabels,
+                            const DataT MAX_VAL)
+{
+  // getting the current index
+  int sampleIndex = threadIdx.x + blockIdx.x * blockDim.x;
+
+  if (sampleIndex >= nRows) return;
+
+  // sampleDistanceVector is an array that stores that particular row of the distanceMatrix
+  DataT* sampleToClusterSumOfDistancesVector =
+    &sampleToClusterSumOfDistances[sampleIndex * nLabels];
+
+  LabelT sampleCluster = labels[sampleIndex];
+
+  int sampleClusterIndex = (int)sampleCluster;
+
+  if (binCountArray[sampleClusterIndex] - 1 <= 0) {
+    d_aArray[sampleIndex] = -1;
+    return;
+
+  }
+
+  else {
+    d_aArray[sampleIndex] = (sampleToClusterSumOfDistancesVector[sampleClusterIndex]) /
+                            (binCountArray[sampleClusterIndex] - 1);
+
+    // modifying the sampleDistanceVector to give sample average distance
+    sampleToClusterSumOfDistancesVector[sampleClusterIndex] = MAX_VAL;
+  }
+}
+
+/**
+ * @brief function to calculate the bincounts of number of samples in every label
+ * @tparam DataT: type of the data samples
+ * @tparam LabelT: type of the labels
+ * @param labels: the pointer to the array containing labels for every data sample (1 x nRows)
+ * @param binCountArray: pointer to the 1D array that contains the count of samples per cluster (1 x
+ * nLabels)
+ * @param nRows: number of data samples
+ * @param nUniqueLabels: number of Labels
+ * @param workspace: device buffer containing workspace memory
+ * @param stream: the cuda stream where to launch this kernel
+ */
+template <typename DataT, typename LabelT>
+void countLabels(const LabelT* labels,
+                 DataT* binCountArray,
+                 int nRows,
+                 int nUniqueLabels,
+                 rmm::device_uvector<char>& workspace,
+                 cudaStream_t stream)
+{
+  int num_levels            = nUniqueLabels + 1;
+  LabelT lower_level        = 0;
+  LabelT upper_level        = nUniqueLabels;
+  size_t temp_storage_bytes = 0;
+
+  rmm::device_uvector<int> countArray(nUniqueLabels, stream);
+
+  RAFT_CUDA_TRY(cub::DeviceHistogram::HistogramEven(nullptr,
+                                                    temp_storage_bytes,
+                                                    labels,
+                                                    binCountArray,
+                                                    num_levels,
+                                                    lower_level,
+                                                    upper_level,
+                                                    nRows,
+                                                    stream));
+
+  workspace.resize(temp_storage_bytes, stream);
+
+  RAFT_CUDA_TRY(cub::DeviceHistogram::HistogramEven(workspace.data(),
+                                                    temp_storage_bytes,
+                                                    labels,
+                                                    binCountArray,
+                                                    num_levels,
+                                                    lower_level,
+                                                    upper_level,
+                                                    nRows,
+                                                    stream));
+}
+
+/**
+ * @brief structure that defines the division Lambda for elementwise op
+ */
+template <typename DataT>
+struct DivOp {
+  HDI DataT operator()(DataT a, int b, int c)
+  {
+    if (b == 0)
+      return ULLONG_MAX;
+    else
+      return a / b;
+  }
+};
+
+/**
+ * @brief structure that defines the elementwise operation to calculate silhouette score using
+ * params 'a' and 'b'
+ */
+template <typename DataT>
+struct SilOp {
+  HDI DataT operator()(DataT a, DataT b)
+  {
+    if (a == 0 && b == 0 || a == b)
+      return 0;
+    else if (a == -1)
+      return 0;
+    else if (a > b)
+      return (b - a) / a;
+    else
+      return (b - a) / b;
+  }
+};
+
+/**
+ * @brief main function that returns the average silhouette score for a given set of data and its
+ * clusterings
+ * @tparam DataT: type of the data samples
+ * @tparam LabelT: type of the labels
+ * @param X_in: pointer to the input Data samples array (nRows x nCols)
+ * @param nRows: number of data samples
+ * @param nCols: number of features
+ * @param labels: the pointer to the array containing labels for every data sample (1 x nRows)
+ * @param nLabels: number of Labels
+ * @param silhouette_scorePerSample: pointer to the array that is optionally taken in as input and
+ * is populated with the silhouette score for every sample (1 x nRows)
+ * @param stream: the cuda stream where to launch this kernel
+ * @param metric: the numerical value that maps to the type of distance metric to be used in the
+ * calculations
+ */
+template <typename DataT, typename LabelT>
+DataT silhouette_score(
+  raft::resources const& handle,
+  const DataT* X_in,
+  int nRows,
+  int nCols,
+  const LabelT* labels,
+  int nLabels,
+  DataT* silhouette_scorePerSample,
+  cudaStream_t stream,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  ASSERT(nLabels >= 2 && nLabels <= (nRows - 1),
+         "silhouette Score not defined for the given number of labels!");
+
+  // compute the distance matrix
+  rmm::device_uvector<DataT> distanceMatrix(nRows * nRows, stream);
+  rmm::device_uvector<char> workspace(1, stream);
+
+  auto X_in_view = raft::make_device_matrix_view<const DataT, int64_t>(X_in, nRows, nCols);
+
+  cuvs::distance::pairwise_distance(
+    handle,
+    X_in_view,
+    X_in_view,
+    raft::make_device_matrix_view<DataT, int64_t>(distanceMatrix.data(), nRows, nRows),
+    metric);
+
+  // deciding on the array of silhouette scores for each dataPoint
+  rmm::device_uvector<DataT> silhouette_scoreSamples(0, stream);
+  DataT* perSampleSilScore = nullptr;
+  if (silhouette_scorePerSample == nullptr) {
+    silhouette_scoreSamples.resize(nRows, stream);
+    perSampleSilScore = silhouette_scoreSamples.data();
+  } else {
+    perSampleSilScore = silhouette_scorePerSample;
+  }
+  RAFT_CUDA_TRY(cudaMemsetAsync(perSampleSilScore, 0, nRows * sizeof(DataT), stream));
+
+  // getting the sample count per cluster
+  rmm::device_uvector<DataT> binCountArray(nLabels, stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(binCountArray.data(), 0, nLabels * sizeof(DataT), stream));
+  countLabels(labels, binCountArray.data(), nRows, nLabels, workspace, stream);
+
+  // calculating the sample-cluster-distance-sum-array
+  rmm::device_uvector<DataT> sampleToClusterSumOfDistances(nRows * nLabels, stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(
+    sampleToClusterSumOfDistances.data(), 0, nRows * nLabels * sizeof(DataT), stream));
+  raft::linalg::reduce_cols_by_key(distanceMatrix.data(),
+                                   labels,
+                                   sampleToClusterSumOfDistances.data(),
+                                   nRows,
+                                   nRows,
+                                   nLabels,
+                                   stream);
+
+  // creating the a array and b array
+  rmm::device_uvector<DataT> d_aArray(nRows, stream);
+  rmm::device_uvector<DataT> d_bArray(nRows, stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(d_aArray.data(), 0, nRows * sizeof(DataT), stream));
+  RAFT_CUDA_TRY(cudaMemsetAsync(d_bArray.data(), 0, nRows * sizeof(DataT), stream));
+
+  // kernel that populates the d_aArray
+  // kernel configuration
+  dim3 numThreadsPerBlock(32, 1, 1);
+  dim3 numBlocks(raft::ceildiv<int>(nRows, numThreadsPerBlock.x), 1, 1);
+
+  // calling the kernel
+  populateAKernel<<<numBlocks, numThreadsPerBlock, 0, stream>>>(
+    sampleToClusterSumOfDistances.data(),
+    binCountArray.data(),
+    d_aArray.data(),
+    labels,
+    nRows,
+    nLabels,
+    std::numeric_limits<DataT>::max());
+
+  // elementwise dividing by bincounts
+  rmm::device_uvector<DataT> averageDistanceBetweenSampleAndCluster(nRows * nLabels, stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(
+    averageDistanceBetweenSampleAndCluster.data(), 0, nRows * nLabels * sizeof(DataT), stream));
+
+  raft::linalg::matrixVectorOp(averageDistanceBetweenSampleAndCluster.data(),
+                               sampleToClusterSumOfDistances.data(),
+                               binCountArray.data(),
+                               binCountArray.data(),
+                               nLabels,
+                               nRows,
+                               true,
+                               true,
+                               DivOp<DataT>(),
+                               stream);
+
+  // calculating row-wise minimum
+  raft::linalg::reduce<DataT, DataT, int, raft::identity_op, raft::min_op>(
+    d_bArray.data(),
+    averageDistanceBetweenSampleAndCluster.data(),
+    nLabels,
+    nRows,
+    std::numeric_limits<DataT>::max(),
+    true,
+    true,
+    stream,
+    false,
+    raft::identity_op{},
+    raft::min_op{});
+
+  // calculating the silhouette score per sample using the d_aArray and d_bArray
+  raft::linalg::binaryOp<DataT, SilOp<DataT>>(
+    perSampleSilScore, d_aArray.data(), d_bArray.data(), nRows, SilOp<DataT>(), stream);
+
+  // calculating the sum of all the silhouette score
+  rmm::device_scalar<DataT> d_avgSilhouetteScore(stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(d_avgSilhouetteScore.data(), 0, sizeof(DataT), stream));
+
+  raft::linalg::mapThenSumReduce<DataT, raft::identity_op>(d_avgSilhouetteScore.data(),
+                                                           nRows,
+                                                           raft::identity_op(),
+                                                           stream,
+                                                           perSampleSilScore,
+                                                           perSampleSilScore);
+
+  DataT avgSilhouetteScore = d_avgSilhouetteScore.value(stream);
+
+  raft::resource::sync_stream(handle, stream);
+
+  avgSilhouetteScore /= nRows;
+
+  return avgSilhouetteScore;
+}
+
+};  // namespace detail
+};  // namespace stats
+};  // namespace cuvs
diff --git a/cpp/src/stats/detail/trustworthiness_score.cuh b/cpp/src/stats/detail/trustworthiness_score.cuh
new file mode 100644
index 0000000000..f4725a2e8f
--- /dev/null
+++ b/cpp/src/stats/detail/trustworthiness_score.cuh
@@ -0,0 +1,220 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/matrix/col_wise_sort.cuh>
+
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+
+#define N_THREADS 512
+
+namespace cuvs {
+namespace stats {
+namespace detail {
+
+/**
+ * @brief Build the lookup table
+ * @param[out] lookup_table: Lookup table giving nearest neighbor order
+ *                of pairwise distance calculations given sample index
+ * @param[in] X_ind: Sorted indexes of pairwise distance calculations of X
+ * @param n: Number of samples
+ * @param work: Number of elements to consider
+ */
+RAFT_KERNEL build_lookup_table(int* lookup_table, const int* X_ind, int n, int work)
+{
+  int i = blockIdx.x * blockDim.x + threadIdx.x;
+  if (i >= work) return;
+
+  int sample_idx = i / n;
+  int nn_idx     = i % n;
+
+  int idx                              = X_ind[i];
+  lookup_table[(sample_idx * n) + idx] = nn_idx;
+}
+
+/**
+ * @brief Compute a the rank of trustworthiness score
+ * @param[out] rank: Resulting rank
+ * @param[out] lookup_table: Lookup table giving nearest neighbor order
+ *                of pairwise distance calculations given sample index
+ * @param[in] emb_ind: Indexes of KNN on embeddings
+ * @param n: Number of samples
+ * @param n_neighbors: Number of neighbors considered by trustworthiness score
+ * @param work: Batch to consider (to do it at once use n * n_neighbors)
+ */
+template <typename knn_index_t>
+RAFT_KERNEL compute_rank(double* rank,
+                         const int* lookup_table,
+                         const knn_index_t* emb_ind,
+                         int n,
+                         int n_neighbors,
+                         int work)
+{
+  int i = blockIdx.x * blockDim.x + threadIdx.x;
+  if (i >= work) return;
+
+  int sample_idx = i / n_neighbors;
+
+  knn_index_t emb_nn_ind = emb_ind[i];
+
+  int r   = lookup_table[(sample_idx * n) + emb_nn_ind];
+  int tmp = r - n_neighbors + 1;
+  if (tmp > 0) raft::myAtomicAdd<double>(rank, tmp);
+}
+
+/**
+ * @brief Compute a kNN and returns the indices of the nearest neighbors
+ * @param h Raft handle
+ * @param[in] input Input matrix containing the dataset
+ * @param n Number of samples
+ * @param d Number of features
+ * @param n_neighbors number of neighbors
+ * @param[out] indices KNN indexes
+ * @param[out] distances KNN distances
+ */
+template <typename math_t>
+void run_knn(const raft::resources& h,
+             math_t* input,
+             cuvs::distance::DistanceType metric,
+             int n,
+             int d,
+             int n_neighbors,
+             int64_t* indices,
+             math_t* distances)
+{
+  auto input_view = raft::make_device_matrix_view<const math_t, int64_t>(input, n, d);
+  auto index      = cuvs::neighbors::brute_force::build(h, input_view, metric);
+
+  cuvs::neighbors::brute_force::search(
+    h,
+    index,
+    input_view,
+    raft::make_device_matrix_view<int64_t, int64_t>(indices, n, n_neighbors),
+    raft::make_device_matrix_view<math_t, int64_t>(distances, n, n_neighbors),
+    std::nullopt);
+}
+
+/**
+ * @brief Compute the trustworthiness score
+ * @param h Raft handle
+ * @param X[in]: Data in original dimension
+ * @param X_embedded[in]: Data in target dimension (embedding)
+ * @param n: Number of samples
+ * @param m: Number of features in high/original dimension
+ * @param d: Number of features in low/embedded dimension
+ * @param n_neighbors Number of neighbors considered by trustworthiness score
+ * @param batchSize Batch size
+ * @return Trustworthiness score
+ */
+template <typename math_t>
+double trustworthiness_score(const raft::resources& h,
+                             const math_t* X,
+                             math_t* X_embedded,
+                             cuvs::distance::DistanceType metric,
+                             int n,
+                             int m,
+                             int d,
+                             int n_neighbors,
+                             int batchSize = 512)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(h);
+
+  const int KNN_ALLOC = n * (n_neighbors + 1);
+  rmm::device_uvector<int64_t> emb_ind(KNN_ALLOC, stream);
+  rmm::device_uvector<math_t> emb_dist(KNN_ALLOC, stream);
+
+  run_knn(h, X_embedded, metric, n, d, n_neighbors + 1, emb_ind.data(), emb_dist.data());
+
+  const int PAIRWISE_ALLOC = batchSize * n;
+  rmm::device_uvector<int> X_ind(PAIRWISE_ALLOC, stream);
+  rmm::device_uvector<math_t> X_dist(PAIRWISE_ALLOC, stream);
+  rmm::device_uvector<int> lookup_table(PAIRWISE_ALLOC, stream);
+
+  double t = 0.0;
+  rmm::device_scalar<double> t_dbuf(stream);
+
+  int toDo = n;
+  while (toDo > 0) {
+    int curBatchSize = min(toDo, batchSize);
+
+    // Takes at most batchSize vectors at a time
+    cuvs::distance::pairwise_distance(
+      h,
+      raft::make_device_matrix_view<const float, std::int64_t>(&X[(n - toDo) * m], curBatchSize, m),
+      raft::make_device_matrix_view<const float, std::int64_t>(X, n, m),
+      raft::make_device_matrix_view<float, std::int64_t>(X_dist.data(), curBatchSize, n),
+      metric);
+
+    size_t colSortWorkspaceSize = 0;
+    bool bAllocWorkspace        = false;
+
+    raft::matrix::sort_cols_per_row(X_dist.data(),
+                                    X_ind.data(),
+                                    curBatchSize,
+                                    n,
+                                    bAllocWorkspace,
+                                    nullptr,
+                                    colSortWorkspaceSize,
+                                    stream);
+
+    if (bAllocWorkspace) {
+      rmm::device_uvector<char> sortColsWorkspace(colSortWorkspaceSize, stream);
+
+      raft::matrix::sort_cols_per_row(X_dist.data(),
+                                      X_ind.data(),
+                                      curBatchSize,
+                                      n,
+                                      bAllocWorkspace,
+                                      sortColsWorkspace.data(),
+                                      colSortWorkspaceSize,
+                                      stream);
+    }
+
+    int work     = curBatchSize * n;
+    int n_blocks = raft::ceildiv(work, N_THREADS);
+    build_lookup_table<<<n_blocks, N_THREADS, 0, stream>>>(
+      lookup_table.data(), X_ind.data(), n, work);
+
+    RAFT_CUDA_TRY(cudaMemsetAsync(t_dbuf.data(), 0, sizeof(double), stream));
+
+    work     = curBatchSize * (n_neighbors + 1);
+    n_blocks = raft::ceildiv(work, N_THREADS);
+    compute_rank<<<n_blocks, N_THREADS, 0, stream>>>(
+      t_dbuf.data(),
+      lookup_table.data(),
+      &emb_ind.data()[(n - toDo) * (n_neighbors + 1)],
+      n,
+      n_neighbors + 1,
+      work);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+    t += t_dbuf.value(stream);
+
+    toDo -= curBatchSize;
+  }
+
+  t = 1.0 - ((2.0 / ((n * n_neighbors) * ((2.0 * n) - (3.0 * n_neighbors) - 1.0))) * t);
+
+  return t;
+}
+
+}  // namespace detail
+}  // namespace stats
+}  // namespace cuvs
diff --git a/cpp/src/stats/silhouette_score.cu b/cpp/src/stats/silhouette_score.cu
new file mode 100644
index 0000000000..a3bd5f24da
--- /dev/null
+++ b/cpp/src/stats/silhouette_score.cu
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/stats/silhouette_score.hpp>
+
+#include "./detail/batched/silhouette_score.cuh"
+#include "./detail/silhouette_score.cuh"
+
+namespace cuvs {
+namespace stats {
+namespace {
+template <typename value_t, typename label_t, typename idx_t>
+value_t _silhouette_score(
+  raft::resources const& handle,
+  raft::device_matrix_view<const value_t, idx_t, raft::row_major> X_in,
+  raft::device_vector_view<const label_t, idx_t> labels,
+  std::optional<raft::device_vector_view<value_t, idx_t>> silhouette_score_per_sample,
+  idx_t n_unique_labels,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  RAFT_EXPECTS(labels.extent(0) == X_in.extent(0), "Size mismatch between labels and data");
+
+  value_t* silhouette_score_per_sample_ptr = nullptr;
+  if (silhouette_score_per_sample.has_value()) {
+    silhouette_score_per_sample_ptr = silhouette_score_per_sample.value().data_handle();
+    RAFT_EXPECTS(silhouette_score_per_sample.value().extent(0) == X_in.extent(0),
+                 "Size mismatch between silhouette_score_per_sample and data");
+  }
+  return detail::silhouette_score(handle,
+                                  X_in.data_handle(),
+                                  X_in.extent(0),
+                                  X_in.extent(1),
+                                  labels.data_handle(),
+                                  n_unique_labels,
+                                  silhouette_score_per_sample_ptr,
+                                  raft::resource::get_cuda_stream(handle),
+                                  metric);
+}
+
+template <typename value_t, typename label_t, typename idx_t>
+value_t _silhouette_score_batched(
+  raft::resources const& handle,
+  raft::device_matrix_view<const value_t, idx_t, raft::row_major> X,
+  raft::device_vector_view<const label_t, idx_t> labels,
+  std::optional<raft::device_vector_view<value_t, idx_t>> silhouette_score_per_sample,
+  idx_t n_unique_labels,
+  idx_t batch_size,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded)
+{
+  static_assert(std::is_integral_v<idx_t>,
+                "silhouette_score_batched: The index type "
+                "of each mdspan argument must be an integral type.");
+  static_assert(std::is_integral_v<label_t>,
+                "silhouette_score_batched: The label type must be an integral type.");
+  RAFT_EXPECTS(labels.extent(0) == X.extent(0), "Size mismatch between labels and data");
+
+  value_t* scores_ptr = nullptr;
+  if (silhouette_score_per_sample.has_value()) {
+    scores_ptr = silhouette_score_per_sample.value().data_handle();
+    RAFT_EXPECTS(silhouette_score_per_sample.value().extent(0) == X.extent(0),
+                 "Size mismatch between silhouette_score_per_sample and data");
+  }
+  return cuvs::stats::batched::detail::silhouette_score<value_t, int, label_t>(handle,
+                                                                               X.data_handle(),
+                                                                               X.extent(0),
+                                                                               X.extent(1),
+                                                                               labels.data_handle(),
+                                                                               n_unique_labels,
+                                                                               scores_ptr,
+                                                                               batch_size,
+                                                                               metric);
+}
+}  // namespace
+
+float silhouette_score(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X_in,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<float, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  cuvs::distance::DistanceType metric)
+{
+  return _silhouette_score(
+    handle, X_in, labels, silhouette_score_per_sample, n_unique_labels, metric);
+}
+
+double silhouette_score(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> X_in,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<double, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  cuvs::distance::DistanceType metric)
+{
+  return _silhouette_score(
+    handle, X_in, labels, silhouette_score_per_sample, n_unique_labels, metric);
+}
+
+float silhouette_score_batched(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<float, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  int64_t batch_size,
+  cuvs::distance::DistanceType metric)
+{
+  return _silhouette_score_batched(
+    handle, X, labels, silhouette_score_per_sample, n_unique_labels, batch_size, metric);
+}
+
+double silhouette_score_batched(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> X,
+  raft::device_vector_view<const int, int64_t> labels,
+  std::optional<raft::device_vector_view<double, int64_t>> silhouette_score_per_sample,
+  int64_t n_unique_labels,
+  int64_t batch_size,
+  cuvs::distance::DistanceType metric)
+{
+  return _silhouette_score_batched(
+    handle, X, labels, silhouette_score_per_sample, n_unique_labels, batch_size, metric);
+}
+};  // namespace stats
+};  // namespace cuvs
diff --git a/cpp/src/stats/trustworthiness_score.cu b/cpp/src/stats/trustworthiness_score.cu
new file mode 100644
index 0000000000..9ab4cb7062
--- /dev/null
+++ b/cpp/src/stats/trustworthiness_score.cu
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/stats/trustworthiness_score.hpp>
+
+#include "./detail/trustworthiness_score.cuh"
+
+namespace cuvs {
+namespace stats {
+
+double trustworthiness_score(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X_embedded,
+  int n_neighbors,
+  cuvs::distance::DistanceType metric,
+  int batch_size)
+{
+  RAFT_EXPECTS(X.extent(0) == X_embedded.extent(0), "Size mismatch between X and X_embedded");
+  RAFT_EXPECTS(X.extent(0) <= std::numeric_limits<int>::max(), "Index type not supported");
+
+  // TODO: Change the underlying implementation to remove the need to const_cast X_embedded.
+  return detail::trustworthiness_score<float>(handle,
+                                              X.data_handle(),
+                                              const_cast<float*>(X_embedded.data_handle()),
+                                              metric,
+                                              static_cast<int>(X.extent(0)),
+                                              static_cast<int>(X.extent(1)),
+                                              static_cast<int>(X_embedded.extent(1)),
+                                              n_neighbors,
+                                              batch_size);
+}
+
+}  // namespace stats
+}  // namespace cuvs
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index a30e2dec72..570220084f 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -94,13 +94,13 @@ endfunction()
 
 if(BUILD_TESTS)
   ConfigureTest(
-    NAME NEIGHBORS_TEST PATH neighbors/brute_force.cu
-    neighbors/brute_force_prefiltered.cu neighbors/refine.cu GPUS 1 PERCENT 100
+    NAME NEIGHBORS_TEST PATH neighbors/brute_force.cu neighbors/brute_force_prefiltered.cu
+    neighbors/refine.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
-    NAME CLUSTER_TEST PATH cluster/kmeans.cu cluster/kmeans_balanced.cu
-    cluster/kmeans_find_k.cu cluster/linkage.cu GPUS 1 PERCENT 100
+    NAME CLUSTER_TEST PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu
+    cluster/linkage.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
@@ -130,15 +130,8 @@ if(BUILD_TESTS)
   )
 
   ConfigureTest(
-    NAME
-    NEIGHBORS_ANN_BRUTE_FORCE_TEST
-    PATH
-    neighbors/ann_brute_force/test_float.cu
-    neighbors/ann_brute_force/test_half.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_ANN_BRUTE_FORCE_TEST PATH neighbors/ann_brute_force/test_float.cu
+    neighbors/ann_brute_force/test_half.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
@@ -196,29 +189,28 @@ if(BUILD_TESTS)
     PERCENT
     100
   )
+  ConfigureTest(
+    NAME STATS_TEST PATH stats/trustworthiness.cu stats/silhouette_score.cu GPUS 1 PERCENT 100
+  )
 endif()
 
 if(BUILD_C_TESTS)
   ConfigureTest(NAME INTEROP_TEST PATH core/interop.cu C_LIB)
   ConfigureTest(
-    NAME DISTANCE_C_TEST PATH distance/run_pairwise_distance_c.c
-    distance/pairwise_distance_c.cu C_LIB
-  )
-
-  ConfigureTest(
-    NAME BRUTEFORCE_C_TEST PATH neighbors/run_brute_force_c.c neighbors/brute_force_c.cu
+    NAME DISTANCE_C_TEST PATH distance/run_pairwise_distance_c.c distance/pairwise_distance_c.cu
     C_LIB
   )
 
   ConfigureTest(
-    NAME IVF_FLAT_C_TEST PATH neighbors/run_ivf_flat_c.c neighbors/ann_ivf_flat_c.cu
-    C_LIB
+    NAME BRUTEFORCE_C_TEST PATH neighbors/run_brute_force_c.c neighbors/brute_force_c.cu C_LIB
   )
 
   ConfigureTest(
-    NAME IVF_PQ_C_TEST PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu C_LIB
+    NAME IVF_FLAT_C_TEST PATH neighbors/run_ivf_flat_c.c neighbors/ann_ivf_flat_c.cu C_LIB
   )
 
+  ConfigureTest(NAME IVF_PQ_C_TEST PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu C_LIB)
+
   ConfigureTest(NAME CAGRA_C_TEST PATH neighbors/ann_cagra_c.cu C_LIB)
 endif()
 
diff --git a/cpp/test/stats/silhouette_score.cu b/cpp/test/stats/silhouette_score.cu
new file mode 100644
index 0000000000..7662763fd7
--- /dev/null
+++ b/cpp/test/stats/silhouette_score.cu
@@ -0,0 +1,235 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "../test_utils.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/stats/silhouette_score.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <gtest/gtest.h>
+
+#include <algorithm>
+#include <iostream>
+#include <random>
+
+namespace cuvs {
+namespace stats {
+
+// parameter structure definition
+struct silhouetteScoreParam {
+  int nRows;
+  int nCols;
+  int nLabels;
+  cuvs::distance::DistanceType metric;
+  int chunk;
+  double tolerance;
+};
+
+// test fixture class
+template <typename LabelT, typename DataT>
+class silhouetteScoreTest : public ::testing::TestWithParam<silhouetteScoreParam> {
+ protected:
+  silhouetteScoreTest()
+    : d_X(0, raft::resource::get_cuda_stream(handle)),
+      sampleSilScore(0, raft::resource::get_cuda_stream(handle)),
+      d_labels(0, raft::resource::get_cuda_stream(handle))
+  {
+  }
+
+  void host_silhouette_score()
+  {
+    // generating random value test input
+    std::vector<double> h_X(nElements, 0.0);
+    std::vector<int> h_labels(nRows, 0);
+    std::random_device rd;
+    std::default_random_engine dre(nElements * nLabels);
+    std::uniform_int_distribution<int> intGenerator(0, nLabels - 1);
+    std::uniform_real_distribution<double> realGenerator(0, 100);
+
+    std::generate(h_X.begin(), h_X.end(), [&]() { return realGenerator(dre); });
+    std::generate(h_labels.begin(), h_labels.end(), [&]() { return intGenerator(dre); });
+
+    // allocating and initializing memory to the GPU
+    auto stream = raft::resource::get_cuda_stream(handle);
+    d_X.resize(nElements, stream);
+    d_labels.resize(nElements, stream);
+    RAFT_CUDA_TRY(cudaMemsetAsync(d_X.data(), 0, d_X.size() * sizeof(DataT), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(d_labels.data(), 0, d_labels.size() * sizeof(LabelT), stream));
+    sampleSilScore.resize(nElements, stream);
+
+    raft::update_device(d_X.data(), &h_X[0], (int)nElements, stream);
+    raft::update_device(d_labels.data(), &h_labels[0], (int)nElements, stream);
+
+    // finding the distance matrix
+
+    rmm::device_uvector<double> d_distanceMatrix(nRows * nRows, stream);
+    double* h_distanceMatrix = (double*)malloc(nRows * nRows * sizeof(double*));
+
+    auto d_X_view = raft::make_device_matrix_view<const DataT, int64_t>(d_X.data(), nRows, nCols);
+    cuvs::distance::pairwise_distance(
+      handle,
+      d_X_view,
+      d_X_view,
+      raft::make_device_matrix_view<DataT, int64_t>(d_distanceMatrix.data(), nRows, nRows),
+      params.metric);
+
+    raft::resource::sync_stream(handle, stream);
+
+    raft::update_host(h_distanceMatrix, d_distanceMatrix.data(), nRows * nRows, stream);
+
+    // finding the bincount array
+
+    double* binCountArray = (double*)malloc(nLabels * sizeof(double*));
+    memset(binCountArray, 0, nLabels * sizeof(double));
+
+    for (int i = 0; i < nRows; ++i) {
+      binCountArray[h_labels[i]] += 1;
+    }
+
+    // finding the average intra cluster distance for every element
+
+    double* a = (double*)malloc(nRows * sizeof(double*));
+
+    for (int i = 0; i < nRows; ++i) {
+      int myLabel               = h_labels[i];
+      double sumOfIntraClusterD = 0;
+
+      for (int j = 0; j < nRows; ++j) {
+        if (h_labels[j] == myLabel) { sumOfIntraClusterD += h_distanceMatrix[i * nRows + j]; }
+      }
+
+      if (binCountArray[myLabel] <= 1)
+        a[i] = -1;
+      else
+        a[i] = sumOfIntraClusterD / (binCountArray[myLabel] - 1);
+    }
+
+    // finding the average inter cluster distance for every element
+
+    double* b = (double*)malloc(nRows * sizeof(double*));
+
+    for (int i = 0; i < nRows; ++i) {
+      int myLabel          = h_labels[i];
+      double minAvgInterCD = ULLONG_MAX;
+
+      for (int j = 0; j < nLabels; ++j) {
+        int curClLabel = j;
+        if (curClLabel == myLabel) continue;
+        double avgInterCD = 0;
+
+        for (int k = 0; k < nRows; ++k) {
+          if (h_labels[k] == curClLabel) { avgInterCD += h_distanceMatrix[i * nRows + k]; }
+        }
+
+        if (binCountArray[curClLabel])
+          avgInterCD /= binCountArray[curClLabel];
+        else
+          avgInterCD = ULLONG_MAX;
+        minAvgInterCD = min(minAvgInterCD, avgInterCD);
+      }
+
+      b[i] = minAvgInterCD;
+    }
+
+    // finding the silhouette score for every element
+
+    double* truthSampleSilScore = (double*)malloc(nRows * sizeof(double*));
+    for (int i = 0; i < nRows; ++i) {
+      if (a[i] == -1)
+        truthSampleSilScore[i] = 0;
+      else if (a[i] == 0 && b[i] == 0)
+        truthSampleSilScore[i] = 0;
+      else
+        truthSampleSilScore[i] = (b[i] - a[i]) / max(a[i], b[i]);
+      truthSilhouetteScore += truthSampleSilScore[i];
+    }
+
+    truthSilhouetteScore /= nRows;
+  }
+
+  // the constructor
+  void SetUp() override
+  {
+    // getting the parameters
+    params = ::testing::TestWithParam<silhouetteScoreParam>::GetParam();
+
+    nRows     = params.nRows;
+    nCols     = params.nCols;
+    nLabels   = params.nLabels;
+    chunk     = params.chunk;
+    nElements = nRows * nCols;
+
+    host_silhouette_score();
+
+    // calling the silhouette_score CUDA implementation
+    computedSilhouetteScore = cuvs::stats::silhouette_score(
+      handle,
+      raft::make_device_matrix_view<const DataT>(d_X.data(), nRows, nCols),
+      raft::make_device_vector_view<const LabelT>(d_labels.data(), nRows),
+      std::make_optional(raft::make_device_vector_view(sampleSilScore.data(), nRows)),
+      nLabels,
+      params.metric);
+
+    batchedSilhouetteScore = cuvs::stats::silhouette_score_batched(
+      handle,
+      raft::make_device_matrix_view<const DataT>(d_X.data(), nRows, nCols),
+      raft::make_device_vector_view<const LabelT>(d_labels.data(), nRows),
+      std::make_optional(raft::make_device_vector_view(sampleSilScore.data(), nRows)),
+      nLabels,
+      chunk,
+      params.metric);
+  }
+
+  // declaring the data values
+  raft::resources handle;
+  silhouetteScoreParam params;
+  int nLabels;
+  rmm::device_uvector<DataT> d_X;
+  rmm::device_uvector<DataT> sampleSilScore;
+  rmm::device_uvector<LabelT> d_labels;
+  int nRows;
+  int nCols;
+  int nElements;
+  double truthSilhouetteScore    = 0;
+  double computedSilhouetteScore = 0;
+  double batchedSilhouetteScore  = 0;
+  int chunk;
+};
+
+// setting test parameter values
+const std::vector<silhouetteScoreParam> inputs = {
+  {4, 2, 3, cuvs::distance::DistanceType::L2Expanded, 4, 0.00001},
+  {4, 2, 2, cuvs::distance::DistanceType::L2SqrtUnexpanded, 2, 0.00001},
+  {8, 8, 3, cuvs::distance::DistanceType::L2Unexpanded, 4, 0.00001},
+  {11, 2, 5, cuvs::distance::DistanceType::L2Expanded, 3, 0.00001},
+  {40, 2, 8, cuvs::distance::DistanceType::L2Expanded, 10, 0.00001},
+  {12, 7, 3, cuvs::distance::DistanceType::CosineExpanded, 8, 0.00001},
+  {7, 5, 5, cuvs::distance::DistanceType::L1, 2, 0.00001}};
+
+// writing the test suite
+typedef silhouetteScoreTest<int, double> silhouetteScoreTestClass;
+TEST_P(silhouetteScoreTestClass, Result)
+{
+  ASSERT_NEAR(computedSilhouetteScore, truthSilhouetteScore, params.tolerance);
+  ASSERT_NEAR(batchedSilhouetteScore, truthSilhouetteScore, params.tolerance);
+}
+INSTANTIATE_TEST_CASE_P(silhouetteScore, silhouetteScoreTestClass, ::testing::ValuesIn(inputs));
+
+}  // end namespace stats
+}  // end namespace cuvs
diff --git a/cpp/test/stats/trustworthiness.cu b/cpp/test/stats/trustworthiness.cu
new file mode 100644
index 0000000000..e2ed04a03a
--- /dev/null
+++ b/cpp/test/stats/trustworthiness.cu
@@ -0,0 +1,356 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/stats/trustworthiness_score.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <gtest/gtest.h>
+
+#include <iostream>
+#include <vector>
+
+namespace cuvs {
+namespace stats {
+
+class TrustworthinessScoreTest : public ::testing::Test {
+ public:
+  TrustworthinessScoreTest()
+    : d_X(0, raft::resource::get_cuda_stream(handle)),
+      d_X_embedded(0, raft::resource::get_cuda_stream(handle))
+  {
+  }
+
+ protected:
+  void basicTest()
+  {
+    std::vector<float> X = {
+      5.6142087,   8.59787,     -4.382763,   -3.6452143,  -5.8816037,  -0.6330313,  4.6920023,
+      -0.79210913, 0.6106314,   2.1210914,   5.919943,    -8.43784,    -6.4819884,  0.41001374,
+      -6.1052523,  -4.0825715,  -5.314755,   -2.834671,   5.751696,    -6.5012555,  -0.4719201,
+      -7.53353,    7.6789393,   -1.4959852,  -5.5977287,  -9.564147,   1.2902534,   3.559834,
+      -6.7659483,  8.265964,    4.595404,    9.133477,    -6.1553917,  -6.319754,   -2.9039452,
+      4.4150834,   -3.094395,   -4.426273,   9.584571,    -5.64133,    6.6209483,   7.4044604,
+      3.9620576,   5.639907,    10.33007,    -0.8792053,  5.143776,    -7.464049,   1.2448754,
+      -5.6300974,  5.4518576,   4.119535,    6.749645,    7.627064,    -7.2298336,  1.9681473,
+      -6.9083176,  6.404673,    0.07186685,  9.0994835,   8.51037,     -8.986389,   0.40534487,
+      2.115397,    4.086756,    1.2284287,   -2.6272132,  0.06527536,  -9.587425,   -7.206078,
+      7.864875,    7.4397306,   -6.9233336,  -2.6643622,  3.3466153,   7.0408177,   -3.6069896,
+      -9.971769,   4.4075623,   7.9063697,   2.559074,    4.323717,    1.6867131,   -1.1576937,
+      -9.893141,   -3.251416,   -7.4889135,  -4.0588717,  -2.73338,    -7.4852257,  3.4460473,
+      9.759119,    -5.4680476,  -4.722435,   -8.032619,   -1.4598992,  4.227361,    3.135568,
+      1.1950601,   1.1982028,   6.998856,    -6.131138,   -6.6921015,  0.5361224,   -7.1213965,
+      -5.6104236,  -7.2212887,  -2.2710054,  8.544764,    -6.0254574,  1.4582269,   -5.5587835,
+      8.031556,    -0.26328218, -5.2591386,  -9.262641,   2.8691363,   5.299787,    -9.209455,
+      8.523085,    5.180329,    10.655528,   -5.7171874,  -6.7739563,  -3.6306462,  4.067106,
+      -1.5912259,  -3.2345476,  8.042973,    -3.6364832,  4.1242137,   9.886953,    5.4743724,
+      6.3058076,   9.369645,    -0.5175337,  4.9859877,   -7.879498,   1.358422,    -4.147944,
+      3.8984218,   5.894656,    6.4903927,   8.702036,    -8.023722,   2.802145,    -7.748032,
+      5.8461113,   -0.34215945, 11.298865,   1.4107164,   -9.949621,   -1.6257563,  -10.655836,
+      2.4528909,   1.1570255,   5.170669,    2.8398793,   7.1838694,   9.088459,    2.631155,
+      3.964414,    2.8769252,   0.04198391,  -0.16993195, 3.6747139,   -2.8377378,  6.1782537,
+      10.759618,   -4.5642614,  -8.522967,   0.8614642,   6.623416,    -1.029324,   5.5488334,
+      -7.804511,   2.128833,    7.9042315,   7.789576,    -2.7944536,  0.72271067,  -10.511495,
+      -0.78634536, -10.661714,  2.9376361,   1.9148129,   6.22859,     0.26264945,  8.028384,
+      6.8743043,   0.9351067,   7.0690722,   4.2846055,   1.4134506,   -0.18144785, 5.2778087,
+      -1.7140163,  9.217541,    8.602799,    -2.6537218,  -7.8377395,  1.1244944,   5.4540544,
+      -0.38506773, 3.9885726,   -10.76455,   1.4440702,   9.136163,    6.664117,    -5.7046547,
+      8.038592,    -9.229767,   -0.2799413,  3.6064725,   4.187257,    1.0516582,   -2.0707326,
+      -0.7615968,  -8.561018,   -3.7831352,  10.300297,   5.332594,    -6.5880876,  -4.2508664,
+      1.7985519,   5.7226253,   -4.1223383,  -9.6697855,  1.4885283,   7.524974,    1.7206005,
+      4.890457,    3.7264557,   0.4428284,   -9.922455,   -4.250455,   -6.4410596,  -2.107994,
+      -1.4109765,  -6.1325397,  0.32883006,  6.0489736,   7.7257385,   -8.281174,   1.0129383,
+      -10.792166,  8.378851,    10.802716,   9.848448,    -9.188757,   1.3151443,   1.9971865,
+      -2.521849,   4.3268294,   -7.775683,   -2.2902298,  3.0824065,   -7.17559,    9.6100855,
+      7.3965735,   -10.476525,  5.895973,    -3.6974669,  -7.6688933,  1.7354839,   -7.4045196,
+      -1.7992063,  -4.0394845,  5.2471714,   -2.250571,   2.528036,    -8.343515,   -2.2374575,
+      -10.019771,  0.73371273,  3.1853926,   2.7994921,   2.6637669,   7.620401,    7.515571,
+      0.68636256,  5.834537,    4.650282,    -1.0362619,  0.4461701,   3.7870514,   -4.1340904,
+      7.202998,    9.736904,    -3.005512,   -8.920467,   1.1228397,   6.2598724,   1.2812365,
+      4.5442104,   -8.791537,   0.92113096,  8.464749,    8.359035,    -4.3923397,  1.2252625,
+      -10.1986475, -1.4409319,  -10.013967,  3.9071581,   1.683064,    4.877419,    1.6570637,
+      9.559105,    7.3546534,   0.36635467,  5.220211,    4.6303267,   0.6601065,   0.16149978,
+      3.8818731,   -3.4438233,  8.42085,     8.659159,    -3.0935583,  -8.039611,   2.3060374,
+      5.134666,    1.0458113,   6.0190983,   -9.143728,   0.99048865,  9.210842,    6.670241,
+      -5.9614363,  0.8747396,   7.078824,    8.067469,    -10.314754,  0.45977542,  -9.28306,
+      9.1838665,   9.318644,    7.189082,    -11.092555,  1.0320464,   3.882163,    0.10953151,
+      7.9029684,   -6.9068265,  -1.3526366,  5.3996363,   -8.430931,   11.452577,   6.39663,
+      -11.090514,  4.6662245,   -3.1268113,  -8.357452,   2.2276728,   -10.357126,  -0.9291848,
+      -3.4193344,  3.1289792,   -2.5030103,  6.772719,    11.457757,   -4.2125936,  -6.684548,
+      -4.7611327,  3.6960156,   -2.3030636,  -3.0591488,  10.452471,   -4.1267314,  5.66614,
+      7.501461,    5.072407,    6.636537,    8.990381,    -0.2559256,  4.737867,    -6.2149944,
+      2.535682,    -5.5484023,  5.7113924,   3.4742818,   7.9915137,   7.0052586,   -7.156467,
+      1.4354781,   -8.286235,   5.7523417,   -2.4175215,  9.678009,    0.05066403,  -9.645226,
+      -2.2658763,  -9.518178,   4.493372,    2.3232365,   2.1659086,   0.42507997,  8.360246,
+      8.23535,     2.6878164,   5.236947,    3.4924245,   -0.6089895,  0.8884741,   4.359464,
+      -4.6073823,  7.83441,     8.958755,    -3.4690795,  -9.182282,   1.2478025,   5.6311107,
+      -1.2408862,  3.6316886,   -8.684654,   2.1078515,   7.2813864,   7.9265943,   -3.6135032,
+      0.4571511,   8.493568,    10.496853,   -7.432897,   0.8625995,   -9.607528,   7.2899456,
+      8.83158,     8.908199,    -10.300263,  1.1451302,   3.7871468,   -0.97040755, 5.7664757,
+      -8.9688,     -2.146672,   5.9641485,   -6.2908535,  10.126465,   6.1553903,   -12.066902,
+      6.301596,    -5.0419583,  -8.228695,   2.4879954,   -8.918582,   -3.7434099,  -4.1593685,
+      3.7431836,   -1.1704745,  0.5524103,   9.109399,    9.571567,    -11.209955,  1.2462777,
+      -9.554555,   9.091726,    11.477966,   7.630937,    -10.450911,  1.9205878,   5.358983,
+      -0.44546837, 6.7611346,   -9.74753,    -0.5939732,  3.8892255,   -6.437991,   10.294727,
+      5.6723895,   -10.7883,    6.192348,    -5.293862,   -10.811491,  1.0194173,   -7.074576,
+      -3.192368,   -2.5231771,  4.2791643,   -0.53309685, 0.501366,    9.636625,    7.710316,
+      -6.4219728,  1.0975566,   -8.218886,   6.9011984,   9.873679,    8.903804,    -9.316832,
+      1.2404599,   4.9039655,   1.2272617,   4.541515,    -5.2753224,  -3.2196746,  3.1303136,
+      -7.285681,   9.041425,    5.6417427,   -9.93667,    5.7548947,   -5.113397,   -8.544622,
+      4.182665,    -7.7709813,  -3.2810235,  -3.312072,   3.8900535,   -2.0604856,  6.709082,
+      -8.461194,   1.2666026,   4.8770437,   2.6955879,   3.0340345,   -1.1614609,  -3.536341,
+      -7.090382,   -5.36146,    9.072544,    6.4554095,   -4.4728956,  -1.88395,    3.1095037,
+      8.782348,    -3.316743,   -8.65248,    1.6802986,   8.186188,    2.1783829,   4.931278,
+      4.158475,    1.4033595,   -11.320101,  -3.7084908,  -6.740436,   -2.5555193,  -1.0451177,
+      -6.5569925,  0.82810307,  8.505919,    8.332857,    -9.488569,   -0.21588463, -8.056692,
+      8.493993,    7.6401625,   8.812983,    -9.377281,   2.4369764,   3.1766508,   0.6300803,
+      5.6666765,   -7.913654,   -0.42301777, 4.506412,    -7.8954244,  10.904591,   5.042256,
+      -9.626183,   8.347351,    -3.605006,   -7.923387,   1.1024277,   -8.705793,   -2.5151258,
+      -2.5066147,  4.0515003,   -2.060757,   6.2635093,   8.286584,    -6.0509276,  -6.76452,
+      -3.1158175,  1.6578803,   -1.4608748,  -1.24211,    8.151246,    -4.2970877,  6.093071,
+      7.4911637,   4.51018,     4.8425875,   9.211085,    -2.4386222,  4.5830803,   -5.6079445,
+      2.3713675,   -4.0707507,  3.1787417,   5.462342,    6.915912,    6.3928423,   -7.2970796,
+      5.0112796,   -9.140893,   4.9990606,   0.38391754,  7.7088532,   1.9340848,   8.18833,
+      8.16617,     -9.42086,    -0.3388326,  -9.659727,   8.243045,    8.099073,    8.439428,
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+      5.393288,    -5.6455317,  0.7829499,   -4.0338907,  2.479272,    6.5080743,   8.582535,
+      7.0097537,   -6.9823785,  3.984318,    -7.225381,   5.3135114,   -1.0391048,  8.951443,
+      -0.70119005, -8.510742,   -0.42949116, -10.9224825, 2.8176029,   1.6800792,   5.778404,
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+      1.2911975,   5.950357,    0.19028673,  4.657226,    -8.199647,   2.246055,    8.989509,
+      5.3101015,   -4.2400866};
+
+    std::vector<float> X_embedded = {
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+      -0.21353109, -0.54769957, -0.55086273, 0.37093949,  -0.12714292, -0.06639574, -0.36098689,
+      -0.13060696, -0.07362658, -1.01205945, -0.39285606, 0.2864089,   -0.32031146, -0.19595343,
+      0.08900568,  -0.04813879, -0.06563424, -0.42655188, -0.69014251, 0.51459783,  -0.1942696,
+      -0.07767916, -0.6119386,  0.04813685,  -0.22557008, -0.56890118, -0.60293794, 0.43429622,
+      -0.09240723, -0.00624062, -0.25800395, -0.1886092,  0.01655941,  -0.01961523, -0.14147359,
+      0.41414487,  -0.8512944,  -0.61199242, -0.18586016, 0.14024924,  -0.41635606, -0.02890144,
+      0.1065347,   0.39700791,  -1.14060664, -0.95313865, 0.14416681,  0.17306046,  -0.53189689,
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+      -0.0980606,  -0.00971657, 0.29432917,  0.36575687,  -1.1645509,  -0.89094597, 0.03718805,
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+      0.02149482,  0.21720445,  -0.49869928, -0.95655465, -0.51680422, 0.45761383,  -0.08351214,
+      -0.12151554, 0.00819737,  -0.20813803, -0.01055793, 0.25319234,  0.36154974,  0.1822421,
+      -1.15837133, -0.92209691, -0.0501582,  0.08535917,  -0.54003763, -1.08675635, -1.04009593,
+      0.09408128,  0.07009826,  -0.01762833, -0.19180447, -0.18029785, -0.20342001, 0.04034991,
+      0.1814747,   0.36906669,  -1.13532007, -0.8852452,  0.0782818,   0.16825101,  -0.50301319,
+      -0.29128098, -0.65341312, 0.51484352,  -0.38758236, -0.22531103, -0.55021971, 0.10804344,
+      -0.3521522,  -0.38849035, -0.74110794, 0.53761131,  -0.25142813, -0.1118066,  -0.47453368,
+      0.06347904,  -0.23796193, -1.02682328, -0.47594091, 0.39515916,  -0.2782529,  -0.16566519,
+      0.08063579,  0.00810116,  -0.06213913, -1.059654,   -0.62496334, 0.53698546,  -0.11806234,
+      0.00356161,  0.11513405,  -0.14213292, 0.04102662,  -0.36622161, -0.73686272, 0.48323864,
+      -0.27338892, -0.14203401, -0.41736352, 0.03332564,  -0.21907479, -0.06396769, 0.01831361,
+      0.46263444,  -1.01878166, -0.86486858, 0.17622118,  -0.01249686, -0.74530888, -0.9354887,
+      -0.5027945,  0.38170099,  -0.15547098, 0.00677824,  -0.04677663, -0.13541745, 0.07253501,
+      -0.97933143, -0.58001202, 0.48235369,  -0.18836913, -0.02430783, 0.07572441,  -0.08101331,
+      0.00630076,  -0.16881248, -0.67989182, 0.46083611,  -0.43910736, -0.29321918, -0.38735861,
+      0.07669903,  -0.29749861, -0.40047669, -0.56722462, 0.33168188,  -0.13118173, -0.06672747,
+      -0.56856316, -0.26269144, -0.14236671, 0.10651901,  0.4962585,   0.38848072,  -1.06653547,
+      -0.64079332, -0.47378591, 0.43195483,  -0.04856951, -0.9840439,  -0.70610428, 0.34028092,
+      -0.2089237,  -0.05382041, 0.01625874,  -0.02080803, -0.12535211, -0.04146428, -1.24533033,
+      0.48944879,  0.0578458,   0.26708388,  -0.90321028, 0.35377088,  -0.36791429, -0.35382384,
+      -0.52748734, 0.42854419,  -0.31744713, -0.19174226, -0.39073724, -0.03258846, -0.19978228,
+      -0.36185205, -0.57412046, 0.43681973,  -0.25414538, -0.12904905, -0.46334973, -0.03123853,
+      -0.11303604, -0.87073672, -0.45441297, 0.41825858,  -0.25303507, -0.21845073, 0.10248682,
+      -0.11045569, -0.10002795, -0.00572806, 0.16519061,  0.42651513,  -1.11417019, -0.83789682,
+      0.02995787,  0.16843079,  -0.53874511, 0.03056994,  0.17877036,  0.49632853,  -1.03276777,
+      -0.74778616, -0.03971953, 0.10907949,  -0.67385727, -0.9523471,  -0.56550741, 0.40409449,
+      -0.2703723,  -0.10175014, 0.13605487,  -0.06306008, -0.01768126, -0.4749442,  -0.56964815,
+      0.39389887,  -0.19248079, -0.04161081, -0.38728487, -0.20341556, -0.12656988, -0.35949609,
+      -0.46137866, 0.28798422,  -0.06603147, -0.04363992, -0.60343552, -0.23565227, -0.10242701,
+      -0.06792886, 0.09689897,  0.33259571,  -0.98854214, -0.84444433, 0.00673901,  0.13457057,
+      -0.43145794, -0.51500046, -0.50821936, 0.38000089,  0.0132636,   0.0580942,   -0.40157595,
+      -0.11967677, 0.02549113,  -0.10350953, 0.22918226,  0.40411913,  -1.05619383, -0.71218503,
+      -0.02197581, 0.26422262,  -0.34765676, 0.06601537,  0.21712676,  0.34723559,  -1.20982027,
+      -0.95646334, 0.00793948,  0.27620381,  -0.43475035, -0.67326003, -0.6137197,  0.43724492,
+      -0.17666136, -0.06591748, -0.18937394, -0.07400128, -0.06881691, -0.5201112,  -0.61088628,
+      0.4225319,   -0.18969463, -0.06921366, -0.33993208, -0.06990873, -0.10288513, -0.70659858,
+      -0.56003648, 0.46628812,  -0.16090363, -0.0185108,  -0.1431348,  -0.1128775,  -0.0078648,
+      -0.02323332, 0.04292452,  0.39291084,  -0.94897962, -0.63863206, -0.16546988, 0.23698957,
+      -0.30633628};
+
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    d_X.resize(X.size(), stream);
+    d_X_embedded.resize(X_embedded.size(), stream);
+    raft::update_device(d_X.data(), X.data(), X.size(), stream);
+    raft::update_device(d_X_embedded.data(), X_embedded.data(), X_embedded.size(), stream);
+    auto n_sample            = 50;
+    auto n_features_origin   = 30;
+    auto n_features_embedded = 8;
+
+    // euclidean test
+    score = cuvs::stats::trustworthiness_score(
+      handle,
+      raft::make_device_matrix_view<const float>(d_X.data(), n_sample, n_features_origin),
+      raft::make_device_matrix_view<const float>(
+        d_X_embedded.data(), n_sample, n_features_embedded),
+      5,
+      cuvs::distance::DistanceType::L2SqrtUnexpanded);
+  }
+
+  void SetUp() override { basicTest(); }
+
+  void TearDown() override {}
+
+ protected:
+  raft::resources handle;
+
+  rmm::device_uvector<float> d_X;
+  rmm::device_uvector<float> d_X_embedded;
+
+  double score;
+};
+
+typedef TrustworthinessScoreTest TrustworthinessScoreTestF;
+TEST_F(TrustworthinessScoreTestF, Result) { ASSERT_TRUE(0.9375 < score && score < 0.9379); }
+};  // namespace stats
+};  // namespace cuvs
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index 5b8b4841af..49732dc92f 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -11,3 +11,4 @@ C++ API Documentation
    cpp_api/distance.rst
    cpp_api/neighbors.rst
    cpp_api/selection.rst
+   cpp_api/stats.rst
diff --git a/docs/source/cpp_api/neighbors_cagra.rst b/docs/source/cpp_api/neighbors_cagra.rst
index 0e07406d1a..3a1ed861af 100644
--- a/docs/source/cpp_api/neighbors_cagra.rst
+++ b/docs/source/cpp_api/neighbors_cagra.rst
@@ -28,7 +28,7 @@ Index search parameters
     :content-only:
 
 Index extend parameters
-----------------------
+-----------------------
 
 .. doxygengroup:: cagra_cpp_extend_params
     :project: cuvs
@@ -36,7 +36,7 @@ Index extend parameters
     :content-only:
 
 Index extend memory buffers
-----------------------
+---------------------------
 
 .. doxygengroup:: cagra_cpp_extend_memory_buffers
     :project: cuvs
@@ -68,7 +68,7 @@ Index search
     :content-only:
 
 Index extend
------------
+------------
 
 .. doxygengroup:: cagra_cpp_index_extend
     :project: cuvs
diff --git a/docs/source/cpp_api/stats.rst b/docs/source/cpp_api/stats.rst
new file mode 100644
index 0000000000..80d6c65fc9
--- /dev/null
+++ b/docs/source/cpp_api/stats.rst
@@ -0,0 +1,35 @@
+Stats
+=====
+
+
+This page provides C++ class references for the publicly-exposed elements of the `cuvs/stats`
+package.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+Silhouette Score
+----------------
+
+``#include <cuvs/stats/silhouette_score.hpp>``
+
+namespace *cuvs::stats*
+
+.. doxygengroup:: stats_silhouette_score
+    :project: cuvs
+    :members:
+    :content-only:
+
+Trustworthiness Score
+---------------------
+
+``#include <cuvs/stats/trustworthiness_score.hpp>``
+
+namespace *cuvs::stats*
+
+.. doxygengroup:: stats_trustworthiness
+    :project: cuvs
+    :members:
+    :content-only:
+

From bdea78ebcc9e540ccd62dbe24f46f954b853d816 Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Tue, 10 Sep 2024 01:40:01 +0100
Subject: [PATCH 139/623] Add cuvs-bench to dependencies and conda environments
 (#275)

Adding conda environments and a few extra files needed for the upcoming cuvs-bench.

cc @tarang-jain @cjnolet could use a look at the correct version of packages like hnsw

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/275
---
 .../bench_ann_cuda-118_arch-aarch64.yaml      | 47 +++++++++++++
 .../bench_ann_cuda-118_arch-x86_64.yaml       | 47 +++++++++++++
 .../bench_ann_cuda-125_arch-aarch64.yaml      | 43 ++++++++++++
 .../bench_ann_cuda-125_arch-x86_64.yaml       | 43 ++++++++++++
 dependencies.yaml                             | 55 +++++++++++++++
 python/cuvs_bench/LICENSE                     |  1 +
 python/cuvs_bench/cuvs_bench/VERSION          |  1 +
 python/cuvs_bench/pyproject.toml              | 70 +++++++++++++++++++
 8 files changed, 307 insertions(+)
 create mode 100644 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
 create mode 100644 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
 create mode 100644 conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
 create mode 100644 conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
 create mode 120000 python/cuvs_bench/LICENSE
 create mode 120000 python/cuvs_bench/cuvs_bench/VERSION
 create mode 100644 python/cuvs_bench/pyproject.toml

diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
new file mode 100644
index 0000000000..9b23faa676
--- /dev/null
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -0,0 +1,47 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- benchmark>=1.8.2
+- c-compiler
+- clang-tools=16.0.6
+- clang==16.0.6
+- click
+- cmake>=3.26.4,!=3.30.0
+- cuda-nvtx=11.8
+- cuda-profiler-api=11.8.86
+- cuda-python>=11.7.1,<12.0a0
+- cuda-version=11.8
+- cudatoolkit
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- gcc_linux-aarch64=11.*
+- glog>=0.6.0
+- h5py>=3.8.0
+- hnswlib=0.6.2
+- libcublas-dev=11.11.3.6
+- libcublas=11.11.3.6
+- libcurand-dev=10.3.0.86
+- libcurand=10.3.0.86
+- libcusolver-dev=11.4.1.48
+- libcusolver=11.4.1.48
+- libcusparse-dev=11.7.5.86
+- libcusparse=11.7.5.86
+- matplotlib
+- nccl>=2.9.9
+- ninja
+- nlohmann_json>=3.11.2
+- nvcc_linux-aarch64=11.8
+- openblas
+- pandas
+- pylibraft==24.10.*,>=0.0.0a0
+- pyyaml
+- rmm==24.10.*,>=0.0.0a0
+- sysroot_linux-aarch64==2.17
+name: bench_ann_cuda-118_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
new file mode 100644
index 0000000000..e73efd60c7
--- /dev/null
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -0,0 +1,47 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- benchmark>=1.8.2
+- c-compiler
+- clang-tools=16.0.6
+- clang==16.0.6
+- click
+- cmake>=3.26.4,!=3.30.0
+- cuda-nvtx=11.8
+- cuda-profiler-api=11.8.86
+- cuda-python>=11.7.1,<12.0a0
+- cuda-version=11.8
+- cudatoolkit
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- gcc_linux-64=11.*
+- glog>=0.6.0
+- h5py>=3.8.0
+- hnswlib=0.6.2
+- libcublas-dev=11.11.3.6
+- libcublas=11.11.3.6
+- libcurand-dev=10.3.0.86
+- libcurand=10.3.0.86
+- libcusolver-dev=11.4.1.48
+- libcusolver=11.4.1.48
+- libcusparse-dev=11.7.5.86
+- libcusparse=11.7.5.86
+- matplotlib
+- nccl>=2.9.9
+- ninja
+- nlohmann_json>=3.11.2
+- nvcc_linux-64=11.8
+- openblas
+- pandas
+- pylibraft==24.10.*,>=0.0.0a0
+- pyyaml
+- rmm==24.10.*,>=0.0.0a0
+- sysroot_linux-64==2.17
+name: bench_ann_cuda-118_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
new file mode 100644
index 0000000000..dec41007ac
--- /dev/null
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -0,0 +1,43 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- benchmark>=1.8.2
+- c-compiler
+- clang-tools=16.0.6
+- clang==16.0.6
+- click
+- cmake>=3.26.4,!=3.30.0
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-python>=12.0,<13.0a0
+- cuda-version=12.5
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- gcc_linux-aarch64=11.*
+- glog>=0.6.0
+- h5py>=3.8.0
+- hnswlib=0.6.2
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- matplotlib
+- nccl>=2.9.9
+- ninja
+- nlohmann_json>=3.11.2
+- openblas
+- pandas
+- pylibraft==24.10.*,>=0.0.0a0
+- pyyaml
+- rmm==24.10.*,>=0.0.0a0
+- sysroot_linux-aarch64==2.17
+name: bench_ann_cuda-125_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
new file mode 100644
index 0000000000..f106644cde
--- /dev/null
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -0,0 +1,43 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- benchmark>=1.8.2
+- c-compiler
+- clang-tools=16.0.6
+- clang==16.0.6
+- click
+- cmake>=3.26.4,!=3.30.0
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-python>=12.0,<13.0a0
+- cuda-version=12.5
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- gcc_linux-64=11.*
+- glog>=0.6.0
+- h5py>=3.8.0
+- hnswlib=0.6.2
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- matplotlib
+- nccl>=2.9.9
+- ninja
+- nlohmann_json>=3.11.2
+- openblas
+- pandas
+- pylibraft==24.10.*,>=0.0.0a0
+- pyyaml
+- rmm==24.10.*,>=0.0.0a0
+- sysroot_linux-64==2.17
+name: bench_ann_cuda-125_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index d8763e0767..bd6a2f224a 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -21,6 +21,19 @@ files:
       - test_py_cuvs
       - cupy
       - rust
+  bench_ann:
+    output: conda
+    matrix:
+      cuda: ["11.8", "12.5"]
+      arch: [x86_64, aarch64]
+    includes:
+      - rapids_build
+      - build_py_cuvs
+      - cuda
+      - cuda_version
+      - develop
+      - bench
+      - bench_python
   test_cpp:
     output: none
     includes:
@@ -90,6 +103,20 @@ files:
       - test_python_common
       - test_py_cuvs
       - cupy
+  py_build_cuvs_bench:
+    output: pyproject
+    pyproject_dir: python/cuvs_bench
+    extras:
+      table: build-system
+    includes:
+      - rapids_build_setuptools
+  py_run_cuvs_bench:
+    output: pyproject
+    pyproject_dir: python/cuvs_bench
+    extras:
+      table: project
+    includes:
+      - bench_python
 channels:
   - rapidsai
   - rapidsai-nightly
@@ -154,6 +181,14 @@ dependencies:
           - matrix: {cuda: "11.2", arch: aarch64}
             packages: [nvcc_linux-aarch64=11.2]
 
+  rapids_build_setuptools:
+    common:
+      - output_types: [requirements, pyproject]
+        packages:
+          - &rapids_build_backend rapids-build-backend>=0.3.0,<0.4.0.dev0
+          - setuptools
+          - wheel
+
   build_py_cuvs:
     common:
       - output_types: [conda]
@@ -456,3 +491,23 @@ dependencies:
       - output_types: [conda, requirements, pyproject]
         packages:
           - scikit-learn
+  bench:
+    common:
+      - output_types: [conda, pyproject, requirements]
+        packages:
+          - hnswlib=0.6.2
+          - nlohmann_json>=3.11.2
+          - glog>=0.6.0
+          - h5py>=3.8.0
+          - benchmark>=1.8.2
+          - openblas
+          - *rmm_unsuffixed
+  bench_python:
+    common:
+      - output_types: [conda]
+        packages:
+          - matplotlib
+          - pandas
+          - pyyaml
+          - pandas
+          - click
diff --git a/python/cuvs_bench/LICENSE b/python/cuvs_bench/LICENSE
new file mode 120000
index 0000000000..30cff7403d
--- /dev/null
+++ b/python/cuvs_bench/LICENSE
@@ -0,0 +1 @@
+../../LICENSE
\ No newline at end of file
diff --git a/python/cuvs_bench/cuvs_bench/VERSION b/python/cuvs_bench/cuvs_bench/VERSION
new file mode 120000
index 0000000000..d62dc733ef
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/VERSION
@@ -0,0 +1 @@
+../../../VERSION
\ No newline at end of file
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
new file mode 100644
index 0000000000..fe49d3d224
--- /dev/null
+++ b/python/cuvs_bench/pyproject.toml
@@ -0,0 +1,70 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+[build-system]
+build-backend = "rapids_build_backend.build"
+requires = [
+    "rapids-build-backend>=0.3.0,<0.4.0.dev0",
+    "setuptools",
+    "wheel",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project]
+name = "cuvs-bench"
+dynamic = ["version"]
+description = "cuVS benchmarks"
+authors = [
+    { name = "NVIDIA Corporation" },
+]
+license = { text = "Apache 2.0" }
+requires-python = ">=3.9"
+dependencies = [
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+classifiers = [
+    "Intended Audience :: Developers",
+    "Topic :: Database",
+    "Topic :: Scientific/Engineering",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+]
+
+[project.urls]
+Homepage = "https://github.com/rapidsai/raft"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.setuptools.package-data]
+"*" = ["*.*", "VERSION"]
+
+[tool.isort]
+line_length = 79
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+combine_as_imports = true
+order_by_type = true
+skip = [
+    "thirdparty",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".tox",
+    ".venv",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+]
+
+[tool.setuptools.dynamic]
+version = { file = "cuvs_bench/VERSION" }
+
+[tool.rapids-build-backend]
+build-backend = "scikit_build_core.build"
+requires = []
+dependencies-file = "../../dependencies.yaml"
+matrix-entry = "cuda_suffixed=true"

From 6f0070d9f4dbccaed40a49da7d39ef494c13c30d Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 10 Sep 2024 07:11:02 -0700
Subject: [PATCH 140/623] Add support for Python 3.12 (#312)

Contributes to https://github.com/rapidsai/build-planning/issues/40

This PR adds support for Python 3.12.

## Notes for Reviewers

This is part of ongoing work to add Python 3.12 support across RAPIDS.
It temporarily introduces a build/test matrix including Python 3.12, from https://github.com/rapidsai/shared-workflows/pull/213.

A follow-up PR will revert back to pointing at the `branch-24.10` branch of `shared-workflows` once all
RAPIDS repos have added Python 3.12 support.

### This will fail until all dependencies have been updates to Python 3.12

CI here is expected to fail until all of this project's upstream dependencies support Python 3.12.

This can be merged whenever all CI jobs are passing.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/312
---
 .github/workflows/build.yaml | 14 +++++++-------
 .github/workflows/pr.yaml    | 24 ++++++++++++------------
 .github/workflows/test.yaml  |  8 ++++----
 dependencies.yaml            |  6 +++++-
 python/cuvs/pyproject.toml   |  1 +
 5 files changed, 29 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index db20bdbc12..b73a8eea98 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@python-3.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@python-3.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 07b10e85a8..d34f74062f 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@python-3.12
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@python-3.12
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.12
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.12
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.12
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.12
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.12
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,20 +86,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.12
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.12
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@python-3.12
     with:
       arch: '["amd64"]'
       cuda: '["12.5"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 0821233a1e..f2daecbec6 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/dependencies.yaml b/dependencies.yaml
index bd6a2f224a..a53aef0f03 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -440,8 +440,12 @@ dependencies:
             packages:
               - python=3.11
           - matrix:
+              py: "3.12"
             packages:
-              - python>=3.10,<3.12
+              - python=3.12
+          - matrix:
+            packages:
+              - python>=3.10,<3.13
   run_py_cuvs:
     common:
       - output_types: [conda, pyproject]
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index fe0e9022fd..0eb98d6018 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -44,6 +44,7 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
 ]
 
 [project.optional-dependencies]

From 1e9793e9c49843109e20031f2c3317a777bdd34c Mon Sep 17 00:00:00 2001
From: abner-ma <969023674@qq.com>
Date: Tue, 10 Sep 2024 23:23:11 +0800
Subject: [PATCH 141/623] Euclidean distance example (#315)

Add euclidean distance calculation example in c.

Authors:
  - https://github.com/abner-ma

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/315
---
 examples/c/CMakeLists.txt     |   4 ++
 examples/c/src/L2_c_example.c | 123 ++++++++++++++++++++++++++++++++++
 2 files changed, 127 insertions(+)
 create mode 100644 examples/c/src/L2_c_example.c

diff --git a/examples/c/CMakeLists.txt b/examples/c/CMakeLists.txt
index fb508728f0..d47cd4f1cb 100644
--- a/examples/c/CMakeLists.txt
+++ b/examples/c/CMakeLists.txt
@@ -35,3 +35,7 @@ include(../cmake/thirdparty/get_cuvs.cmake)
 add_executable(CAGRA_C_EXAMPLE src/cagra_c_example.c)
 target_include_directories(CAGRA_C_EXAMPLE PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
 target_link_libraries(CAGRA_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env>)
+
+add_executable(L2_C_EXAMPLE src/L2_c_example.c)
+target_include_directories(L2_C_EXAMPLE PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
+target_link_libraries(L2_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/c/src/L2_c_example.c b/examples/c/src/L2_c_example.c
new file mode 100644
index 0000000000..73ddf61039
--- /dev/null
+++ b/examples/c/src/L2_c_example.c
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/pairwise_distance.h>
+#include <cuvs/neighbors/cagra.h>
+
+#include <dlpack/dlpack.h>
+
+#include <cuda_runtime.h>
+#include <stdio.h>
+#include <stdlib.h>
+
+#define DIM 4
+#define N_ROWS 1
+
+float PointA[N_ROWS][DIM] = {1.0,2.0,3.0,4.0};
+float PointB[N_ROWS][DIM] = {2.0,3.0,4.0,5.0};
+
+cuvsResources_t res;
+
+void outputVector(float * Vec) {
+  printf("Vector is ");
+  for (int i = 0; i < DIM; ++i){
+    printf(" %f",Vec[i]);
+  }
+  printf("\n");
+}
+
+/**
+ * @brief Initialize Tensor.
+ *
+ * @param[in] x_d Pointer to a vector
+ * @param[in] x_shape[] Two-dimensional array, which stores the number of rows and columns of vectors.
+ * @param[out] x_tensor Stores the initialized DLManagedTensor.
+ */
+void tensor_initialize(float* x_d, int64_t x_shape[2], DLManagedTensor* x_tensor) {
+  x_tensor->dl_tensor.data = x_d;
+  x_tensor->dl_tensor.device.device_type = kDLCUDA;
+  x_tensor->dl_tensor.ndim = 2;
+  x_tensor->dl_tensor.dtype.code = kDLFloat;
+  x_tensor->dl_tensor.dtype.bits = 32;
+  x_tensor->dl_tensor.dtype.lanes = 1;
+  x_tensor->dl_tensor.shape = x_shape;
+  x_tensor->dl_tensor.strides = NULL;
+}
+
+/**
+ * @brief Calculate the euclidean distance between two arrays.
+ *
+ * @param[in] n_cols array length，also the dimension of the vector
+ * @param[in] x[] Pointer to a vector
+ * @param[in] y[] Pointer to another vector
+ * @param[out] ret will store the result about the euclidean distance
+ */
+void l2_distance_calc(int64_t n_cols,float x[], float y[], float *ret) {
+  float *x_d, *y_d;
+  float *distance_d;
+  cuvsRMMAlloc(res, (void**) &x_d, sizeof(float) * N_ROWS * n_cols);
+  cuvsRMMAlloc(res, (void**) &y_d, sizeof(float) * N_ROWS * n_cols);
+  cuvsRMMAlloc(res, (void**) &distance_d, sizeof(float) * N_ROWS * N_ROWS);
+
+  // Use DLPack to represent x[] and y[] as tensors
+  cudaMemcpy(x_d, x, sizeof(float) * N_ROWS * n_cols, cudaMemcpyDefault);
+  cudaMemcpy(y_d, y, sizeof(float) * N_ROWS * n_cols, cudaMemcpyDefault);
+
+  DLManagedTensor x_tensor;
+  int64_t x_shape[2] = {N_ROWS, n_cols};
+  tensor_initialize(x_d, x_shape, &x_tensor);
+
+  DLManagedTensor y_tensor;
+  int64_t y_shape[2] = {N_ROWS, n_cols};
+  tensor_initialize(y_d, y_shape, &y_tensor);
+  
+  DLManagedTensor dist_tensor;
+  int64_t distances_shape[2] = {N_ROWS, N_ROWS};
+  tensor_initialize(distance_d, distances_shape, &dist_tensor);
+
+  // metric_arg default value is 2.0,used for Minkowski distance
+  cuvsPairwiseDistance(res, &x_tensor, &y_tensor, &dist_tensor, L2SqrtUnexpanded, 2.0);
+
+  cudaMemcpy(ret, distance_d, sizeof(float) * N_ROWS * N_ROWS, cudaMemcpyDefault);
+  
+  cuvsRMMFree(res, distance_d, sizeof(float) * N_ROWS * N_ROWS);
+  cuvsRMMFree(res, x_d, sizeof(float) * N_ROWS * n_cols);
+  cuvsRMMFree(res, y_d, sizeof(float) * N_ROWS * n_cols);
+
+}
+
+int euclidean_distance_calculation_example() {
+  // Create a cuvsResources_t object
+  cuvsResourcesCreate(&res);
+
+  outputVector((float *)PointA);
+  outputVector((float *)PointB);
+  
+  float ret;
+  
+  l2_distance_calc(DIM, (float *)PointA, (float *)PointB, &ret);
+  printf("L2 distance is %f.\n", ret);
+  
+  cuvsResourcesDestroy(res);
+
+  return 0;
+}
+
+int main() {
+    euclidean_distance_calculation_example();
+    return 0;
+}

From 0728fc6eaad220c7b0d8e056b07c988432327e48 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 10 Sep 2024 12:25:13 -0500
Subject: [PATCH 142/623] Update Python versions in cuvs_bench pyproject.toml.
 (#318)

This is a follow-up for #275 and #312. I updated `cuvs_bench`'s `pyproject.toml` to drop Python 3.9 and add Python 3.12.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/318
---
 python/cuvs_bench/pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index fe49d3d224..7bb9e2f8d7 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -16,7 +16,7 @@ authors = [
     { name = "NVIDIA Corporation" },
 ]
 license = { text = "Apache 2.0" }
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
@@ -25,9 +25,9 @@ classifiers = [
     "Topic :: Scientific/Engineering",
     "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
 ]
 
 [project.urls]

From c40abaeb0e9bfa3f7e13db35cc30105574412952 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Tue, 10 Sep 2024 13:27:49 -0400
Subject: [PATCH 143/623] C API for CAGRA+HNSW (#240)

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Artem M. Chirkin (https://github.com/achirkin)
  - James Lamb (https://github.com/jameslamb)
  - Tarang Jain (https://github.com/tarang-jain)
  - rhdong (https://github.com/rhdong)
  - Ikko Eltociear Ashimine (https://github.com/eltociear)
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Akira Naruse (https://github.com/anaruse)
  - Mike McCarty (https://github.com/mmccarty)
  - Rui Lan (https://github.com/abc99lr)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Ray Douglass (https://github.com/raydouglass)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/240
---
 cpp/CMakeLists.txt                      |   1 +
 cpp/include/cuvs/neighbors/cagra.h      |  35 +++-
 cpp/include/cuvs/neighbors/cagra.hpp    |  12 ++
 cpp/include/cuvs/neighbors/hnsw.h       | 207 ++++++++++++++++++++++++
 cpp/include/cuvs/neighbors/hnsw.hpp     |  77 +++++++--
 cpp/src/neighbors/cagra_c.cpp           |  25 +++
 cpp/src/neighbors/hnsw_c.cpp            | 166 +++++++++++++++++++
 cpp/test/CMakeLists.txt                 |   4 +
 cpp/test/neighbors/ann_hnsw_c.cu        | 131 +++++++++++++++
 docs/source/c_api/neighbors_cagra_c.rst |   6 +
 docs/source/c_api/neighbors_hnsw_c.rst  |  43 +++++
 docs/source/cpp_api/neighbors_cagra.rst |   7 +
 docs/source/cpp_api/neighbors_hnsw.rst  |  52 ++++++
 13 files changed, 748 insertions(+), 18 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/hnsw.h
 create mode 100644 cpp/src/neighbors/hnsw_c.cpp
 create mode 100644 cpp/test/neighbors/ann_hnsw_c.cu
 create mode 100644 docs/source/c_api/neighbors_hnsw_c.rst
 create mode 100644 docs/source/cpp_api/neighbors_hnsw.rst

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 90ed0b1601..ba46e60b41 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -551,6 +551,7 @@ if(BUILD_C_LIBRARY)
     src/neighbors/ivf_flat_c.cpp
     src/neighbors/ivf_pq_c.cpp
     src/neighbors/cagra_c.cpp
+    src/neighbors/hnsw_c.cpp
     src/neighbors/refine/refine_c.cpp
     src/distance/pairwise_distance_c.cpp
   )
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 87541f7f08..241f5d8b01 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -337,7 +337,10 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  *        It is also important to note that the CAGRA Index must have been built
  *        with the same type of `queries`, such that `index.dtype.code ==
  * queries.dl_tensor.dtype.code` Types for input are:
- *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        1. `queries`:
+ *`         a. kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *          b. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *          c. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *
@@ -394,7 +397,7 @@ cuvsError_t cuvsCagraSearch(cuvsResources_t res,
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
- * @code{.cpp}
+ * @code{.c}
  * #include <cuvs/neighbors/cagra.h>
  *
  * // Create cuvsResources_t
@@ -416,6 +419,34 @@ cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
                                cuvsCagraIndex_t index,
                                bool include_dataset);
 
+/**
+ * Save the CAGRA index to file in hnswlib format.
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/cagra.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create an index with `cuvsCagraBuild`
+ * cuvsCagraSerializeHnswlib(res, "/path/to/index", index);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ *
+ */
+cuvsError_t cuvsCagraSerializeToHnswlib(cuvsResources_t res,
+                                        const char* filename,
+                                        cuvsCagraIndex_t index);
+
 /**
  * Load index from file.
  *
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index f74eac7111..5f77eb8a3c 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1345,6 +1345,8 @@ void deserialize(raft::resources const& handle,
 
 /**
  * Write the CAGRA built index as a base layer HNSW index to an output stream
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1371,6 +1373,8 @@ void serialize_to_hnswlib(raft::resources const& handle,
 
 /**
  * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1398,6 +1402,8 @@ void serialize_to_hnswlib(raft::resources const& handle,
 
 /**
  * Write the CAGRA built index as a base layer HNSW index to an output stream
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1424,6 +1430,8 @@ void serialize_to_hnswlib(raft::resources const& handle,
 
 /**
  * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1451,6 +1459,8 @@ void serialize_to_hnswlib(raft::resources const& handle,
 
 /**
  * Write the CAGRA built index as a base layer HNSW index to an output stream
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -1477,6 +1487,8 @@ void serialize_to_hnswlib(raft::resources const& handle,
 
 /**
  * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
  *
  * Experimental, both the API and the serialization format are subject to change.
  *
diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
new file mode 100644
index 0000000000..5e94de60a1
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <dlpack/dlpack.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup hnsw_c_search_params C API for hnswlib wrapper search params
+ * @{
+ */
+
+struct cuvsHnswSearchParams {
+  int32_t ef;
+  int32_t numThreads;
+};
+
+typedef struct cuvsHnswSearchParams* cuvsHnswSearchParams_t;
+
+/**
+ * @brief Allocate HNSW search params, and populate with default values
+ *
+ * @param[in] params cuvsHnswSearchParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsHnswSearchParamsCreate(cuvsHnswSearchParams_t* params);
+
+/**
+ * @brief De-allocate HNSW search params
+ *
+ * @param[in] params cuvsHnswSearchParams_t to de-allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsHnswSearchParamsDestroy(cuvsHnswSearchParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup hnsw_c_index C API for hnswlib wrapper index
+ * @{
+ */
+
+/**
+ * @brief Struct to hold address of cuvs::neighbors::Hnsw::index and its active trained dtype
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+
+} cuvsHnswIndex;
+
+typedef cuvsHnswIndex* cuvsHnswIndex_t;
+
+/**
+ * @brief Allocate HNSW index
+ *
+ * @param[in] index cuvsHnswIndex_t to allocate
+ * @return HnswError_t
+ */
+cuvsError_t cuvsHnswIndexCreate(cuvsHnswIndex_t* index);
+
+/**
+ * @brief De-allocate HNSW index
+ *
+ * @param[in] index cuvsHnswIndex_t to de-allocate
+ */
+cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup hnsw_c_index_search C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+/**
+ * @brief Search a HNSW index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCPU`, `kDLCUDAHost`, or `kDLCUDAManaged`.
+ *        It is also important to note that the HNSW Index must have been built
+ *        with the same type of `queries`, such that `index.dtype.code ==
+ *        queries.dl_tensor.dtype.code`
+ *        Supported types for input are:
+ *        1. `queries`: `kDLDataType.code == kDLFloat` or `kDLDataType.code == kDLInt` and
+ * `kDLDataType.bits = 32`
+ *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 64`
+ *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
+ *       as the format is not compatible with the original hnswlib.
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/hnsw.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ * DLManagedTensor queries;
+ * DLManagedTensor neighbors;
+ *
+ * // Create default search params
+ * cuvsHnswSearchParams_t params;
+ * cuvsError_t params_create_status = cuvsHnswSearchParamsCreate(&params);
+ *
+ * // Search the `index` built using `cuvsHnswBuild`
+ * cuvsError_t search_status = cuvsHnswSearch(res, params, index, &queries, &neighbors,
+ * &distances);
+ *
+ * // de-allocate `params` and `res`
+ * cuvsError_t params_destroy_status = cuvsHnswSearchParamsDestroy(params);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsHnswSearchParams_t used to search Hnsw index
+ * @param[in] index cuvsHnswIndex which has been returned by `cuvsHnswBuild`
+ * @param[in] queries DLManagedTensor* queries dataset to search
+ * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
+ * @param[out] distances DLManagedTensor* output `k` distances for queries
+ */
+cuvsError_t cuvsHnswSearch(cuvsResources_t res,
+                           cuvsHnswSearchParams_t params,
+                           cuvsHnswIndex_t index,
+                           DLManagedTensor* queries,
+                           DLManagedTensor* neighbors,
+                           DLManagedTensor* distances);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup hnsw_c_serialize HNSW C-API serialize functions
+ * @{
+ */
+
+/**
+ * Load hnswlib index from file which was serialized from a HNSW index.
+ * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/cagra.h>
+ * #include <cuvs/neighbors/hnsw.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create an index with `cuvsCagraBuild`
+ * cuvsCagraSerializeHnswlib(res, "/path/to/index", index);
+ *
+ * // Load the serialized CAGRA index from file as an hnswlib index
+ * // The index should have the same dtype as the one used to build CAGRA the index
+ * cuvsHnswIndex_t hnsw_index;
+ * cuvsHnswIndexCreate(&hnsw_index);
+ * hnsw_index->dtype = index->dtype;
+ * cuvsCagraDeserialize(res, "/path/to/index", hnsw_index);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[in] dim the dimension of the vectors in the index
+ * @param[in] metric the distance metric used to build the index
+ * @param[out] index HNSW index loaded disk
+ */
+cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
+                                const char* filename,
+                                int dim,
+                                cuvsDistanceType metric,
+                                cuvsHnswIndex_t index);
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 86f3215649..007adef0d4 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -34,7 +34,7 @@
 namespace cuvs::neighbors::hnsw {
 
 /**
- * @defgroup hnsw Build CAGRA index and search with hnswlib
+ * @defgroup hnsw_cpp_search_params Build CAGRA index and search with hnswlib
  * @{
  */
 
@@ -44,6 +44,13 @@ struct search_params : cuvs::neighbors::search_params {
                         // automatically maximizes parallelism
 };
 
+/**@}*/
+
+/**
+ * @defgroup hnsw_cpp_index hnswlib index wrapper
+ * @{
+ */
+
 template <typename T>
 struct index : cuvs::neighbors::index {
  public:
@@ -58,6 +65,8 @@ struct index : cuvs::neighbors::index {
    */
   index(int dim, cuvs::distance::DistanceType metric) : dim_{dim}, metric_{metric} {}
 
+  virtual ~index() {}
+
   /**
   @brief Get underlying index
   */
@@ -77,11 +86,19 @@ struct index : cuvs::neighbors::index {
   cuvs::distance::DistanceType metric_;
 };
 
+/**@}*/
+
+/**
+ * @defgroup hnsw_cpp_index_load Load CAGRA index as hnswlib index
+ * @{
+ */
+
 /**
- * @brief Construct an hnswlib base-layer-only index from a CAGRA index
- * NOTE: 1. This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
- * before reading it as an hnswlib index, then deleting the temporary file.
- *       2. This function is only offered as a compiled symbol in `libraft.so`
+ * @brief Construct an immutable hnswlib base-layer-only index from a CAGRA index
+ * NOTE: This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
+ * before reading it as an hnswlib index, then deleting the temporary file. The returned index
+ * is immutable and can only be searched by the hnswlib wrapper in cuVS, as the format is not
+ * compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] cagra_index cagra index
@@ -103,10 +120,11 @@ std::unique_ptr<index<float>> from_cagra(
   raft::resources const& res, const cuvs::neighbors::cagra::index<float, uint32_t>& cagra_index);
 
 /**
- * @brief Construct an hnswlib base-layer-only index from a CAGRA index
- * NOTE: 1. This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
- * before reading it as an hnswlib index, then deleting the temporary file.
- *       2. This function is only offered as a compiled symbol in `libraft.so`
+ * @brief Construct an immutable hnswlib base-layer-only index from a CAGRA index
+ * NOTE: This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
+ * before reading it as an hnswlib index, then deleting the temporary file.  The returned index
+ * is immutable and can only be searched by the hnswlib wrapper in cuVS, as the format is not
+ * compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] cagra_index cagra index
@@ -128,10 +146,11 @@ std::unique_ptr<index<uint8_t>> from_cagra(
   raft::resources const& res, const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& cagra_index);
 
 /**
- * @brief Construct an hnswlib base-layer-only index from a CAGRA index
- * NOTE: 1. This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
- * before reading it as an hnswlib index, then deleting the temporary file.
- *       2. This function is only offered as a compiled symbol in `libraft.so`
+ * @brief Construct an immutable hnswlib base-layer-only index from a CAGRA index
+ * NOTE: This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
+ * before reading it as an hnswlib index, then deleting the temporary file.  The returned index
+ * is immutable and can only be searched by the hnswlib wrapper in cuVS, as the format is not
+ * compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] cagra_index cagra index
@@ -152,8 +171,17 @@ std::unique_ptr<index<uint8_t>> from_cagra(
 std::unique_ptr<index<int8_t>> from_cagra(
   raft::resources const& res, const cuvs::neighbors::cagra::index<int8_t, uint32_t>& cagra_index);
 
+/**@}*/
+
+/**
+ * @defgroup hnsw_cpp_index_search Search hnswlib index
+ * @{
+ */
+
 /**
  * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
+ *       as the format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -195,6 +223,8 @@ void search(raft::resources const& res,
 
 /**
  * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
+ *       as the format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -236,6 +266,8 @@ void search(raft::resources const& res,
 
 /**
  * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
+ *       as the format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -275,8 +307,17 @@ void search(raft::resources const& res,
             raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances);
 
+/**@}*/
+
+/**
+ * @defgroup hnsw_cpp_index_deserialize Deserialize CAGRA index as hnswlib index
+ * @{
+ */
+
 /**
- * @brief De-serialize a CAGRA index saved to a file as an hnsw index
+ * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
+ * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] filename path to the file containing the serialized CAGRA index
@@ -310,7 +351,9 @@ void deserialize(raft::resources const& res,
                  index<float>** index);
 
 /**
- * @brief De-serialize a CAGRA index saved to a file as an hnsw index
+ * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
+ * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] filename path to the file containing the serialized CAGRA index
@@ -344,7 +387,9 @@ void deserialize(raft::resources const& res,
                  index<uint8_t>** index);
 
 /**
- * @brief De-serialize a CAGRA index saved to a file as an hnsw index
+ * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
+ * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] filename path to the file containing the serialized CAGRA index
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 868b3dec02..164448f2c9 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -130,6 +130,14 @@ void _serialize(cuvsResources_t res,
   cuvs::neighbors::cagra::serialize(*res_ptr, std::string(filename), *index_ptr, include_dataset);
 }
 
+template <typename T>
+void _serialize_to_hnswlib(cuvsResources_t res, const char* filename, cuvsCagraIndex_t index)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index->addr);
+  cuvs::neighbors::cagra::serialize_to_hnswlib(*res_ptr, std::string(filename), *index_ptr);
+}
+
 template <typename T>
 void* _deserialize(cuvsResources_t res, const char* filename)
 {
@@ -326,3 +334,20 @@ extern "C" cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
     }
   });
 }
+
+extern "C" cuvsError_t cuvsCagraSerializeToHnswlib(cuvsResources_t res,
+                                                   const char* filename,
+                                                   cuvsCagraIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _serialize_to_hnswlib<float>(res, filename, index);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _serialize_to_hnswlib<int8_t>(res, filename, index);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _serialize_to_hnswlib<uint8_t>(res, filename, index);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
diff --git a/cpp/src/neighbors/hnsw_c.cpp b/cpp/src/neighbors/hnsw_c.cpp
new file mode 100644
index 0000000000..ab5268a6d3
--- /dev/null
+++ b/cpp/src/neighbors/hnsw_c.cpp
@@ -0,0 +1,166 @@
+
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cuvs/distance/distance.h"
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/hnsw.h>
+#include <cuvs/neighbors/hnsw.hpp>
+
+namespace {
+template <typename T, typename QueriesT>
+void _search(cuvsResources_t res,
+             cuvsHnswSearchParams params,
+             cuvsHnswIndex index,
+             DLManagedTensor* queries_tensor,
+             DLManagedTensor* neighbors_tensor,
+             DLManagedTensor* distances_tensor)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<T>*>(index.addr);
+
+  auto search_params        = cuvs::neighbors::hnsw::search_params();
+  search_params.ef          = params.ef;
+  search_params.num_threads = params.numThreads;
+
+  using queries_mdspan_type   = raft::host_matrix_view<QueriesT const, int64_t, raft::row_major>;
+  using neighbors_mdspan_type = raft::host_matrix_view<uint64_t, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
+  auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+  cuvs::neighbors::hnsw::search(
+    *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+}
+
+template <typename T>
+void* _deserialize(cuvsResources_t res, const char* filename, int dim, cuvsDistanceType metric)
+{
+  auto res_ptr                           = reinterpret_cast<raft::resources*>(res);
+  cuvs::neighbors::hnsw::index<T>* index = nullptr;
+  cuvs::neighbors::hnsw::deserialize(*res_ptr, std::string(filename), dim, metric, &index);
+  return index;
+}
+}  // namespace
+
+extern "C" cuvsError_t cuvsHnswSearchParamsCreate(cuvsHnswSearchParams_t* params)
+{
+  return cuvs::core::translate_exceptions(
+    [=] { *params = new cuvsHnswSearchParams{.ef = 200, .numThreads = 0}; });
+}
+
+extern "C" cuvsError_t cuvsHnswSearchParamsDestroy(cuvsHnswSearchParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsHnswIndexCreate(cuvsHnswIndex_t* index)
+{
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsHnswIndex{}; });
+}
+
+extern "C" cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index_c_ptr)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index = *index_c_ptr;
+
+    if (index.dtype.code == kDLFloat) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<float>*>(index.addr);
+      delete index_ptr;
+    } else if (index.dtype.code == kDLInt) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<int8_t>*>(index.addr);
+      delete index_ptr;
+    } else if (index.dtype.code == kDLUInt) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<uint8_t>*>(index.addr);
+      delete index_ptr;
+    }
+    delete index_c_ptr;
+  });
+}
+
+extern "C" cuvsError_t cuvsHnswSearch(cuvsResources_t res,
+                                      cuvsHnswSearchParams_t params,
+                                      cuvsHnswIndex_t index_c_ptr,
+                                      DLManagedTensor* queries_tensor,
+                                      DLManagedTensor* neighbors_tensor,
+                                      DLManagedTensor* distances_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(queries),
+                 "queries should have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(neighbors),
+                 "neighbors should have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(distances),
+                 "distances should have host compatible memory");
+
+    RAFT_EXPECTS(neighbors.dtype.code == kDLUInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type uint64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    auto index = *index_c_ptr;
+    RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
+    RAFT_EXPECTS(queries.dtype.bits == 32, "number of bits in queries dtype should be 32");
+
+    if (index.dtype.code == kDLFloat) {
+      _search<float, float>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (index.dtype.code == kDLUInt) {
+      _search<uint8_t, int>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (index.dtype.code == kDLInt) {
+      _search<int8_t, int>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", queries.dtype.code, queries.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
+                                           const char* filename,
+                                           int dim,
+                                           cuvsDistanceType metric,
+                                           cuvsHnswIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      index->addr = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename, dim, metric));
+      index->dtype.code = kDLFloat;
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_deserialize<uint8_t>(res, filename, dim, metric));
+      index->dtype.code = kDLInt;
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_deserialize<int8_t>(res, filename, dim, metric));
+      index->dtype.code = kDLUInt;
+    } else {
+      RAFT_FAIL("Unsupported dtype in file %s", filename);
+    }
+  });
+}
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 570220084f..e04c39318d 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -212,6 +212,10 @@ if(BUILD_C_TESTS)
   ConfigureTest(NAME IVF_PQ_C_TEST PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu C_LIB)
 
   ConfigureTest(NAME CAGRA_C_TEST PATH neighbors/ann_cagra_c.cu C_LIB)
+
+  if(BUILD_CAGRA_HNSWLIB)
+    ConfigureTest(NAME HNSW_C_TEST PATH neighbors/ann_hnsw_c.cu C_LIB)
+  endif()
 endif()
 
 # ##################################################################################################
diff --git a/cpp/test/neighbors/ann_hnsw_c.cu b/cpp/test/neighbors/ann_hnsw_c.cu
new file mode 100644
index 0000000000..fc740b924c
--- /dev/null
+++ b/cpp/test/neighbors/ann_hnsw_c.cu
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <dlpack/dlpack.h>
+
+#include <cstdint>
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/hnsw.h>
+
+#include <cuda_runtime.h>
+#include <gtest/gtest.h>
+#include <sys/types.h>
+#include <vector>
+
+float dataset[4][2] = {{0.74021935, 0.9209938},
+                       {0.03902049, 0.9689629},
+                       {0.92514056, 0.4463501},
+                       {0.6673192, 0.10993068}};
+float queries[4][2] = {{0.48216683, 0.0428398},
+                       {0.5084142, 0.6545497},
+                       {0.51260436, 0.2643005},
+                       {0.05198065, 0.5789965}};
+
+std::vector<uint64_t> neighbors_exp = {3, 0, 3, 1};
+std::vector<float> distances_exp    = {0.03878258, 0.12472608, 0.04776672, 0.15224178};
+
+TEST(CagraHnswC, BuildSearch)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // create dataset DLTensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = dataset;
+  dataset_tensor.dl_tensor.device.device_type = kDLCPU;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {4, 2};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = nullptr;
+
+  // create index
+  cuvsCagraIndex_t index;
+  cuvsCagraIndexCreate(&index);
+
+  // build index
+  cuvsCagraIndexParams_t build_params;
+  cuvsCagraIndexParamsCreate(&build_params);
+  cuvsCagraBuild(res, build_params, &dataset_tensor, index);
+  cuvsCagraSerializeToHnswlib(res, "/tmp/cagra_hnswlib.index", index);
+
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = queries;
+  queries_tensor.dl_tensor.device.device_type = kDLCPU;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {4, 2};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = nullptr;
+
+  // create neighbors DLTensor
+  std::vector<uint64_t> neighbors(4);
+
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = neighbors.data();
+  neighbors_tensor.dl_tensor.device.device_type = kDLCPU;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLUInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {4, 1};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = nullptr;
+
+  // create distances DLTensor
+  std::vector<float> distances(4);
+
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = distances.data();
+  distances_tensor.dl_tensor.device.device_type = kDLCPU;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {4, 1};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = nullptr;
+
+  // create hnsw index
+  cuvsHnswIndex_t hnsw_index;
+  cuvsHnswIndexCreate(&hnsw_index);
+  hnsw_index->dtype = index->dtype;
+  cuvsHnswDeserialize(res, "/tmp/cagra_hnswlib.index", 2, L2Expanded, hnsw_index);
+
+  // search index
+  cuvsHnswSearchParams_t search_params;
+  cuvsHnswSearchParamsCreate(&search_params);
+  cuvsHnswSearch(
+    res, search_params, hnsw_index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+
+  // verify output
+  ASSERT_TRUE(cuvs::hostVecMatch(neighbors_exp, neighbors, cuvs::Compare<uint64_t>()));
+  ASSERT_TRUE(cuvs::hostVecMatch(distances_exp, distances, cuvs::CompareApprox<float>(0.001f)));
+
+  cuvsCagraIndexParamsDestroy(build_params);
+  cuvsCagraIndexDestroy(index);
+  cuvsHnswSearchParamsDestroy(search_params);
+  cuvsHnswIndexDestroy(hnsw_index);
+  cuvsResourcesDestroy(res);
+}
diff --git a/docs/source/c_api/neighbors_cagra_c.rst b/docs/source/c_api/neighbors_cagra_c.rst
index eb40d55782..a5ffc45b9c 100644
--- a/docs/source/c_api/neighbors_cagra_c.rst
+++ b/docs/source/c_api/neighbors_cagra_c.rst
@@ -50,4 +50,10 @@ Index search
     :members:
     :content-only:
 
+Index serialize
+------------
 
+.. doxygengroup:: cagra_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/c_api/neighbors_hnsw_c.rst b/docs/source/c_api/neighbors_hnsw_c.rst
new file mode 100644
index 0000000000..4d83cd3e38
--- /dev/null
+++ b/docs/source/c_api/neighbors_hnsw_c.rst
@@ -0,0 +1,43 @@
+HNSW
+====
+
+This is a wrapper for hnswlib, to load a CAGRA index as an immutable HNSW index. The loaded HNSW index is only compatible in cuVS, and can be searched using wrapper functions.
+
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+``#include <raft/neighbors/hnsw.h>``
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: hnsw_c_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: hnsw_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: cagra_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index serialize
+------------
+
+.. doxygengroup:: hnsw_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cpp_api/neighbors_cagra.rst b/docs/source/cpp_api/neighbors_cagra.rst
index 3a1ed861af..d9f5038718 100644
--- a/docs/source/cpp_api/neighbors_cagra.rst
+++ b/docs/source/cpp_api/neighbors_cagra.rst
@@ -75,3 +75,10 @@ Index extend
     :members:
     :content-only:
 
+Index serialize
+---------------
+
+.. doxygengroup:: cagra_cpp_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cpp_api/neighbors_hnsw.rst b/docs/source/cpp_api/neighbors_hnsw.rst
new file mode 100644
index 0000000000..b0af88af00
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_hnsw.rst
@@ -0,0 +1,52 @@
+HNSW
+====
+
+This is a wrapper for hnswlib, to load a CAGRA index as an immutable HNSW index. The loaded HNSW index is only compatible in cuVS, and can be searched using wrapper functions.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/hnsw.hpp>``
+
+namespace *cuvs::neighbors::hnsw*
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: hnsw_cpp_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: hnsw_cpp_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index load
+------------
+
+.. doxygengroup:: hnsw_cpp_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: hnsw_cpp_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index deserialize
+---------------
+
+.. doxygengroup:: hnsw_cpp_index_deserialize
+    :project: cuvs
+    :members:
+    :content-only:

From 21247890038f004435691c93acc3e2b3c770913e Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 11 Sep 2024 00:11:13 -0500
Subject: [PATCH 144/623] Refactor dependencies.yaml to use depends-on pattern.
 (#321)

This refactors cuvs's `dependencies.yaml` to follow a convention we've established in other repos. For RAPIDS packages we declare a `depends_on_X` dependency list, and use that rather than specifying packages multiple times. This enables more reuse and easier automation for version migrations.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/321
---
 ci/release/update-version.sh                  |  1 +
 .../all_cuda-118_arch-aarch64.yaml            |  2 +-
 .../all_cuda-118_arch-x86_64.yaml             |  2 +-
 .../all_cuda-125_arch-aarch64.yaml            |  2 +-
 .../all_cuda-125_arch-x86_64.yaml             |  2 +-
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  2 +-
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  2 +-
 .../bench_ann_cuda-125_arch-aarch64.yaml      |  2 +-
 .../bench_ann_cuda-125_arch-x86_64.yaml       |  2 +-
 dependencies.yaml                             | 98 ++++++++++---------
 python/cuvs/pyproject.toml                    |  2 -
 11 files changed, 63 insertions(+), 54 deletions(-)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 6d7d022c2d..feb0a400ce 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -45,6 +45,7 @@ DEPENDENCIES=(
   dask-cuda
   cuvs
   pylibraft
+  librmm
   rmm
   rapids-dask-dependency
 )
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 4bbdc36503..cfcb562258 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -35,6 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- librmm==24.10.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -49,7 +50,6 @@ dependencies:
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.10.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 908421d084..dc519d1b58 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -35,6 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- librmm==24.10.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -49,7 +50,6 @@ dependencies:
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.10.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index 3131d0b771..b32650e449 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -32,6 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- librmm==24.10.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -45,7 +46,6 @@ dependencies:
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.10.0
 - scikit-learn
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 2f107c4fb4..d40fc3b99b 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -32,6 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- librmm==24.10.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -45,7 +46,6 @@ dependencies:
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
-- rmm==24.10.*,>=0.0.0a0
 - rust
 - scikit-build-core>=0.10.0
 - scikit-learn
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 9b23faa676..c6e8b05a2c 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -33,6 +33,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- librmm==24.10.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
@@ -42,6 +43,5 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
-- rmm==24.10.*,>=0.0.0a0
 - sysroot_linux-aarch64==2.17
 name: bench_ann_cuda-118_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index e73efd60c7..d6c023ae97 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -33,6 +33,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- librmm==24.10.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
@@ -42,6 +43,5 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
-- rmm==24.10.*,>=0.0.0a0
 - sysroot_linux-64==2.17
 name: bench_ann_cuda-118_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index dec41007ac..4d0ca94966 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -30,6 +30,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- librmm==24.10.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
@@ -38,6 +39,5 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
-- rmm==24.10.*,>=0.0.0a0
 - sysroot_linux-aarch64==2.17
 name: bench_ann_cuda-125_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index f106644cde..7dd67ab5e0 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -30,6 +30,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- librmm==24.10.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
@@ -38,6 +39,5 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
-- rmm==24.10.*,>=0.0.0a0
 - sysroot_linux-64==2.17
 name: bench_ann_cuda-125_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index a53aef0f03..9fcbeaae2f 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -11,6 +11,8 @@ files:
       - build_py_cuvs
       - cuda
       - cuda_version
+      - depends_on_pylibraft
+      - depends_on_librmm
       - develop
       - checks
       - build_wheels
@@ -31,6 +33,8 @@ files:
       - build_py_cuvs
       - cuda
       - cuda_version
+      - depends_on_pylibraft
+      - depends_on_librmm
       - develop
       - bench
       - bench_python
@@ -93,6 +97,7 @@ files:
     includes:
       - cuda_wheels
       - run_py_cuvs
+      - depends_on_pylibraft
   py_test_py_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
@@ -193,15 +198,7 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
-          - &rmm_unsuffixed rmm==24.10.*,>=0.0.0a0
-          - &pylibraft_unsuffixed pylibraft==24.10.*,>=0.0.0a0
           - dlpack>=0.8,<1.0
-      - output_types: requirements
-        packages:
-          # pip recognizes the index as a global option for the requirements.txt file
-          # This index is needed for rmm-cu{11,12}.
-          - --extra-index-url=https://pypi.nvidia.com
-          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
     specific:
       - output_types: [conda, requirements, pyproject]
         matrices:
@@ -216,21 +213,6 @@ dependencies:
           - matrix:
             packages:
               - &cuda_python cuda-python
-      - output_types: [requirements, pyproject]
-        matrices:
-          - matrix:
-              cuda: "12.*"
-              cuda_suffixed: "true"
-            packages:
-              - &rmm_cu12 rmm-cu12==24.10.*,>=0.0.0a0
-              - &pylibraft_cu12 pylibraft-cu12==24.10.*,>=0.0.0a0
-          - matrix:
-              cuda: "11.*"
-              cuda_suffixed: "true"
-            packages:
-              - &rmm_cu11 rmm-cu11==24.10.*,>=0.0.0a0
-              - &pylibraft_cu11 pylibraft-cu11==24.10.*,>=0.0.0a0
-          - {matrix: null, packages: [*rmm_unsuffixed, *pylibraft_unsuffixed] }
   checks:
     common:
       - output_types: [conda, requirements]
@@ -448,19 +430,9 @@ dependencies:
               - python>=3.10,<3.13
   run_py_cuvs:
     common:
-      - output_types: [conda, pyproject]
+      - output_types: [conda, requirements, pyproject]
         packages:
           - &numpy numpy>=1.23,<3.0a0
-      - output_types: [conda]
-        packages:
-          - *rmm_unsuffixed
-          - *pylibraft_unsuffixed
-      - output_types: requirements
-        packages:
-          # pip recognizes the index as a global option for the requirements.txt file
-          # This index is needed for cudf and rmm.
-          - --extra-index-url=https://pypi.nvidia.com
-          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
     specific:
       - output_types: [conda, requirements, pyproject]
         matrices:
@@ -475,15 +447,6 @@ dependencies:
           - matrix:
             packages:
               - *cuda_python
-      - output_types: [requirements, pyproject]
-        matrices:
-          - matrix: {cuda: "12.*"}
-            packages:
-              - *pylibraft_cu12
-          - matrix: {cuda: "11.*"}
-            packages:
-              - *pylibraft_cu11
-          - {matrix: null, packages: [*pylibraft_unsuffixed]}
   test_python_common:
     common:
       - output_types: [conda, requirements, pyproject]
@@ -505,7 +468,6 @@ dependencies:
           - h5py>=3.8.0
           - benchmark>=1.8.2
           - openblas
-          - *rmm_unsuffixed
   bench_python:
     common:
       - output_types: [conda]
@@ -515,3 +477,51 @@ dependencies:
           - pyyaml
           - pandas
           - click
+  depends_on_librmm:
+    common:
+      - output_types: conda
+        packages:
+          - &librmm_unsuffixed librmm==24.10.*,>=0.0.0a0
+      - output_types: requirements
+        packages:
+          # pip recognizes the index as a global option for the requirements.txt file
+          - --extra-index-url=https://pypi.nvidia.com
+          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
+    specific:
+      - output_types: [requirements, pyproject]
+        matrices:
+          - matrix:
+              cuda: "12.*"
+              cuda_suffixed: "true"
+            packages:
+              - librmm-cu12==24.10.*,>=0.0.0a0
+          - matrix:
+              cuda: "11.*"
+              cuda_suffixed: "true"
+            packages:
+              - librmm-cu11==24.10.*,>=0.0.0a0
+          - {matrix: null, packages: [*librmm_unsuffixed]}
+  depends_on_pylibraft:
+    common:
+      - output_types: conda
+        packages:
+          - &pylibraft_unsuffixed pylibraft==24.10.*,>=0.0.0a0
+      - output_types: requirements
+        packages:
+          # pip recognizes the index as a global option for the requirements.txt file
+          - --extra-index-url=https://pypi.nvidia.com
+          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
+    specific:
+      - output_types: [requirements, pyproject]
+        matrices:
+          - matrix:
+              cuda: "12.*"
+              cuda_suffixed: "true"
+            packages:
+              - pylibraft-cu12==24.10.*,>=0.0.0a0
+          - matrix:
+              cuda: "11.*"
+              cuda_suffixed: "true"
+            packages:
+              - pylibraft-cu11==24.10.*,>=0.0.0a0
+          - {matrix: null, packages: [*pylibraft_unsuffixed]}
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 0eb98d6018..68bd9a8688 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -128,8 +128,6 @@ requires = [
     "cuda-python",
     "cython>=3.0.0",
     "ninja",
-    "pylibraft==24.10.*,>=0.0.0a0",
-    "rmm==24.10.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
 dependencies-file = "../../dependencies.yaml"

From 68480c92cec6da2d7289482146a85725e6f1235a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20F=C3=B6rster?=
 <97973773+mfoerste4@users.noreply.github.com>
Date: Wed, 11 Sep 2024 16:16:55 +0200
Subject: [PATCH 145/623] Brute force knn tile size heuristic (#316)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR modifies the tile size heuristic for brute force knn as mentioned in (#277).

It also removes some unneeded cuda calls to save a couple of microseconds which might be relevant when running smaller batches.

CC @tfeher

Authors:
  - Malte Förster (https://github.com/mfoerste4)
  - Ben Frederickson (https://github.com/benfred)
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/316
---
 .../neighbors/detail/faiss_distance_utils.h   | 42 +++++++++----------
 cpp/src/neighbors/detail/knn_brute_force.cuh  | 35 +++++++---------
 2 files changed, 36 insertions(+), 41 deletions(-)

diff --git a/cpp/src/neighbors/detail/faiss_distance_utils.h b/cpp/src/neighbors/detail/faiss_distance_utils.h
index e8a41c1aa6..63f0c88c27 100644
--- a/cpp/src/neighbors/detail/faiss_distance_utils.h
+++ b/cpp/src/neighbors/detail/faiss_distance_utils.h
@@ -14,10 +14,18 @@ inline void chooseTileSize(size_t numQueries,
                            size_t numCentroids,
                            size_t dim,
                            size_t elementSize,
-                           size_t totalMem,
                            size_t& tileRows,
                            size_t& tileCols)
 {
+  // 512 seems to be a batch size sweetspot for float32.
+  // If we are on float16, increase to 512.
+  // If the k size (vec dim) of the matrix multiplication is small (<= 32),
+  // increase to 1024.
+  size_t preferredTileRows = 512;
+  if (dim <= 32) { preferredTileRows = 1024; }
+
+  tileRows = std::min(preferredTileRows, numQueries);
+
   // The matrix multiplication should be large enough to be efficient, but if
   // it is too large, we seem to lose efficiency as opposed to
   // double-streaming. Each tile size here defines 1/2 of the memory use due
@@ -25,28 +33,20 @@ inline void chooseTileSize(size_t numQueries,
   // adjusted independently by the user and can thus meet these requirements
   // (or not). For <= 4 GB GPUs, prefer 512 MB of usage. For <= 8 GB GPUs,
   // prefer 768 MB of usage. Otherwise, prefer 1 GB of usage.
-  size_t targetUsage = 0;
-
-  if (totalMem <= ((size_t)4) * 1024 * 1024 * 1024) {
-    targetUsage = 512 * 1024 * 1024;
-  } else if (totalMem <= ((size_t)8) * 1024 * 1024 * 1024) {
-    targetUsage = 768 * 1024 * 1024;
+  size_t targetUsage = 512 * 1024 * 1024;
+  if (tileRows * numCentroids * elementSize * 2 <= targetUsage) {
+    tileCols = numCentroids;
   } else {
-    targetUsage = 1024 * 1024 * 1024;
-  }
+    // only query total memory in case it potentially impacts tilesize
+    size_t totalMem = rmm::available_device_memory().second;
 
-  targetUsage /= 2 * elementSize;
+    if (totalMem > ((size_t)8) * 1024 * 1024 * 1024) {
+      targetUsage = 1024 * 1024 * 1024;
+    } else if (totalMem > ((size_t)4) * 1024 * 1024 * 1024) {
+      targetUsage = 768 * 1024 * 1024;
+    }
 
-  // 512 seems to be a batch size sweetspot for float32.
-  // If we are on float16, increase to 512.
-  // If the k size (vec dim) of the matrix multiplication is small (<= 32),
-  // increase to 1024.
-  size_t preferredTileRows = 512;
-  if (dim <= 32) { preferredTileRows = 1024; }
-
-  tileRows = std::min(preferredTileRows, numQueries);
-
-  // tileCols is the remainder size
-  tileCols = std::min(targetUsage / preferredTileRows, numCentroids);
+    tileCols = std::min(targetUsage / (2 * elementSize * tileRows), numCentroids);
+  }
 }
 }  // namespace cuvs::neighbors::detail::faiss_select
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index e3f7acc96a..88986af7d9 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -81,14 +81,12 @@ void tiled_brute_force_knn(const raft::resources& handle,
                            const uint32_t* filter_bitmap             = nullptr)
 {
   // Figure out the number of rows/cols to tile for
-  size_t tile_rows   = 0;
-  size_t tile_cols   = 0;
-  auto stream        = raft::resource::get_cuda_stream(handle);
-  auto device_memory = raft::resource::get_workspace_resource(handle);
-  auto total_mem     = rmm::available_device_memory().second;
+  size_t tile_rows = 0;
+  size_t tile_cols = 0;
+  auto stream      = raft::resource::get_cuda_stream(handle);
 
   cuvs::neighbors::detail::faiss_select::chooseTileSize(
-    m, n, d, sizeof(DistanceT), total_mem, tile_rows, tile_cols);
+    m, n, d, sizeof(DistanceT), tile_rows, tile_cols);
 
   // for unittesting, its convenient to be able to put a max size on the tiles
   // so we can test the tiling logic without having to use huge inputs.
@@ -356,27 +354,26 @@ void brute_force_knn_impl(
 
   ASSERT(input.size() == sizes.size(), "input and sizes vectors should be the same size");
 
-  std::vector<IdxType>* id_ranges;
-  if (translations == nullptr) {
+  std::vector<IdxType> id_ranges;
+  if (translations != nullptr) {
+    // use the given translations
+    id_ranges.insert(id_ranges.end(), translations->begin(), translations->end());
+  } else if (input.size() > 1) {
     // If we don't have explicit translations
     // for offsets of the indices, build them
     // from the local partitions
-    id_ranges       = new std::vector<IdxType>();
     IdxType total_n = 0;
     for (size_t i = 0; i < input.size(); i++) {
-      id_ranges->push_back(total_n);
+      id_ranges.push_back(total_n);
       total_n += sizes[i];
     }
-  } else {
-    // otherwise, use the given translations
-    id_ranges = translations;
   }
 
-  int device;
-  RAFT_CUDA_TRY(cudaGetDevice(&device));
-
-  rmm::device_uvector<IdxType> trans(id_ranges->size(), userStream);
-  raft::update_device(trans.data(), id_ranges->data(), id_ranges->size(), userStream);
+  rmm::device_uvector<IdxType> trans(0, userStream);
+  if (id_ranges.size() > 0) {
+    trans.resize(id_ranges.size(), userStream);
+    raft::update_device(trans.data(), id_ranges.data(), id_ranges.size(), userStream);
+  }
 
   rmm::device_uvector<DistType> all_D(0, userStream);
   rmm::device_uvector<IdxType> all_I(0, userStream);
@@ -513,8 +510,6 @@ void brute_force_knn_impl(
     // no translations or partitions to combine, it can be skipped.
     knn_merge_parts(out_D, out_I, res_D, res_I, n, input.size(), k, userStream, trans.data());
   }
-
-  if (translations == nullptr) delete id_ranges;
 };
 
 template <typename T,

From a6b71d72ed349a118887ff730475eaeda43ef2aa Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 11 Sep 2024 12:57:52 -0500
Subject: [PATCH 146/623] Simplify libcuvs conda recipe. (#322)

This PR refactors the `libcuvs` conda recipe to use `libraft-headers-only` instead of `libraft`.

I removed dependencies on `rmm` from the `cuvs` build -- it is not imported.

I included a couple small fixes in CMake and the `cuvs` recipe.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Mike Sarahan (https://github.com/msarahan)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/322
---
 conda/recipes/cuvs/meta.yaml    |  6 +-----
 conda/recipes/libcuvs/meta.yaml | 33 ++++++++++++++++++++++++---------
 cpp/CMakeLists.txt              |  4 ++--
 3 files changed, 27 insertions(+), 16 deletions(-)

diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index 2633b3db8f..e7e2daf0c6 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -1,7 +1,7 @@
 # Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 # Usage:
-#   conda build . -c conda-forge -c numba -c rapidsai -c pytorch
+#   conda build . -c rapidsai -c conda-forge -c nvidia
 {% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
 {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
 {% set py_version = environ['CONDA_PY'] %}
@@ -54,10 +54,8 @@ requirements:
     - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
     - python x.x
-    - rmm ={{ minor_version }}
     - rapids-build-backend>=0.3.0,<0.4.0.dev0
     - scikit-build-core >=0.10.0
-    - setuptools
   run:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major == "11" %}
@@ -68,7 +66,6 @@ requirements:
     - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
     - python x.x
-    - rmm ={{ minor_version }}
     - cuda-python
     - numpy >=1.23,<3.0a0
 
@@ -81,5 +78,4 @@ tests:
 about:
   home: https://rapids.ai/
   license: Apache-2.0
-  # license_file: LICENSE
   summary: cuvs python library
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index 4ffdc91e40..e154ccf410 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -1,7 +1,7 @@
 # Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
 # Usage:
-#   conda build . -c conda-forge -c nvidia -c rapidsai
+#   conda build . -c rapidsai -c conda-forge -c nvidia
 {% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
 {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
 {% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
@@ -63,7 +63,8 @@ outputs:
         - ninja
         - {{ stdlib("c") }}
       host:
-        - libraft ={{ minor_version }}
+        - librmm ={{ minor_version }}
+        - libraft-headers ={{ minor_version }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_host_version }}
@@ -84,7 +85,6 @@ outputs:
         - libcusparse-dev
         {% endif %}
       run:
-        - libraft ={{ minor_version }}
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
         {% if cuda_major != "11" %}
         - cuda-cudart
@@ -129,7 +129,8 @@ outputs:
         - ninja
         - {{ stdlib("c") }}
       host:
-        - libraft ={{ minor_version }}
+        - librmm ={{ minor_version }}
+        - libraft-headers ={{ minor_version }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_host_version }}
@@ -150,7 +151,6 @@ outputs:
         - libcusparse-dev
         {% endif %}
       run:
-        - libraft ={{ minor_version }}
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
         {% if cuda_major != "11" %}
         - cuda-cudart
@@ -195,7 +195,8 @@ outputs:
         - ninja
         - {{ stdlib("c") }}
       host:
-        - libraft ={{ minor_version }}
+        - librmm ={{ minor_version }}
+        - libraft-headers ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
         - cuda-version ={{ cuda_version }}
         - openblas # required by some CPU algos in benchmarks
@@ -228,7 +229,6 @@ outputs:
         - libcusolver
         - libcusparse
         {% endif %}
-        - libraft ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
     about:
       home: https://rapids.ai/
@@ -248,6 +248,9 @@ outputs:
         - {{ compiler('cuda') }}
         - cuda-cudart-dev
         - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
         {% endif %}
     requirements:
       build:
@@ -263,17 +266,27 @@ outputs:
         - ninja
         - {{ stdlib("c") }}
       host:
-        - libraft ={{ minor_version }}
+        - librmm ={{ minor_version }}
+        - libraft-headers ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
         - libcublas {{ cuda11_libcublas_host_version }}
         - libcublas-dev {{ cuda11_libcublas_host_version }}
+        - libcurand {{ cuda11_libcurand_host_version }}
+        - libcurand-dev {{ cuda11_libcurand_host_version }}
+        - libcusolver {{ cuda11_libcusolver_host_version }}
+        - libcusolver-dev {{ cuda11_libcusolver_host_version }}
+        - libcusparse {{ cuda11_libcusparse_host_version }}
+        - libcusparse-dev {{ cuda11_libcusparse_host_version }}
         {% else %}
         - cuda-cudart-dev
         - cuda-profiler-api
         - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
         {% endif %}
       run:
         - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
@@ -282,8 +295,10 @@ outputs:
         {% else %}
         - cuda-cudart
         - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
         {% endif %}
-        - libraft ={{ minor_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
     about:
       home: https://rapids.ai/
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index ba46e60b41..fec1248bbe 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -53,8 +53,8 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 option(BUILD_SHARED_LIBS "Build cuvs shared libraries" ON)
 option(BUILD_TESTS "Build cuvs unit-tests" ON)
-option(BUILD_C_LIBRARY "Build raft C API library" OFF)
-option(BUILD_C_TESTS "Build raft C API tests" OFF)
+option(BUILD_C_LIBRARY "Build cuVS C API library" OFF)
+option(BUILD_C_TESTS "Build cuVS C API tests" OFF)
 option(BUILD_ANN_BENCH "Build cuVS ann benchmarks" OFF)
 option(BUILD_CAGRA_HNSWLIB "Build CAGRA+hnswlib interface" ON)
 option(CUDA_ENABLE_KERNELINFO "Enable kernel resource usage info" OFF)

From e58b1d04ec7b014065d39f6d9d7f6cec77c670b6 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 17 Sep 2024 11:33:37 -0500
Subject: [PATCH 147/623] fix style checks on Python 3.12 (#328)

After merging https://github.com/rapidsai/ci-imgs/pull/188, style checks
here started breaking like this:

```text
python/cuvs/cuvs/test/test_doctests.py:103:24: E231 missing whitespace after ':'
python/cuvs/cuvs/test/test_doctests.py:124:27: E231 missing whitespace after ':'
```

([build
link](https://github.com/rapidsai/cuvs/actions/runs/10905609739/job/30264867507?pr=325))

Looks like both of those are false positives (we really do intend to not
have a space after the `:` on those lines). This proposes ignoring them
with `#noqa` comments.
---
 python/cuvs/cuvs/test/test_doctests.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
index 4407bda526..bd8ed1a6d5 100644
--- a/python/cuvs/cuvs/test/test_doctests.py
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -100,7 +100,7 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 
 def _test_name_from_docstring(docstring):
     filename = Path(docstring.filename).name.split(".")[0]
-    return f"{filename}:{docstring.name}"
+    return f"{filename}:{docstring.name}"  # noqa: E231
 
 
 @pytest.mark.parametrize(
@@ -121,5 +121,5 @@ def test_docstring(docstring):
         results = runner.summarize()
     assert not results.failed, (
         f"{results.failed} of {results.attempted} doctests failed for "
-        f"{docstring.name}:\n{doctest_stdout.getvalue()}"
+        f"{docstring.name}:\n{doctest_stdout.getvalue()}"  # noqa: E231
     )

From 09cd67c5f77530b3a37eb9d4b023f287b3ebb2ef Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 17 Sep 2024 15:44:26 -0500
Subject: [PATCH 148/623] Update flake8 to 7.1.1. (#327)

We need to update flake8 to fix a false-positive that appears with older flake8 versions on Python 3.12.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/327
---
 .pre-commit-config.yaml                | 2 +-
 python/cuvs/cuvs/test/test_doctests.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3e3623f24f..439b429592 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -18,7 +18,7 @@ repos:
                 # Explicitly specify the pyproject.toml at the repo root, not per-project.
                 args: ["--config", "pyproject.toml"]
       - repo: https://github.com/PyCQA/flake8
-        rev: 5.0.4
+        rev: 7.1.1
         hooks:
               - id: flake8
                 args: ["--config=.flake8"]
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
index bd8ed1a6d5..4407bda526 100644
--- a/python/cuvs/cuvs/test/test_doctests.py
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -100,7 +100,7 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 
 def _test_name_from_docstring(docstring):
     filename = Path(docstring.filename).name.split(".")[0]
-    return f"{filename}:{docstring.name}"  # noqa: E231
+    return f"{filename}:{docstring.name}"
 
 
 @pytest.mark.parametrize(
@@ -121,5 +121,5 @@ def test_docstring(docstring):
         results = runner.summarize()
     assert not results.failed, (
         f"{results.failed} of {results.attempted} doctests failed for "
-        f"{docstring.name}:\n{doctest_stdout.getvalue()}"  # noqa: E231
+        f"{docstring.name}:\n{doctest_stdout.getvalue()}"
     )

From c945e5f30cd360b4a00ea53ffd181ccb89d838b5 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Wed, 18 Sep 2024 14:43:00 -0500
Subject: [PATCH 149/623] Use CI workflow branch 'branch-24.10' again (#331)

All RAPIDS libraries have been updated with Python 3.12 support, so
Python 3.12 changes
have been merged into `branch-24.10` of `shared-workflows`:
https://github.com/rapidsai/shared-workflows/pull/213

This updates GitHub Actions configs here to that branch.
---
 .github/workflows/build.yaml | 14 +++++++-------
 .github/workflows/pr.yaml    | 24 ++++++++++++------------
 .github/workflows/test.yaml  |  8 ++++----
 3 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index b73a8eea98..db20bdbc12 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index d34f74062f..07b10e85a8 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.10
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.10
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,20 +86,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.10
     with:
       arch: '["amd64"]'
       cuda: '["12.5"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index f2daecbec6..0821233a1e 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}

From 8d42b81dc131ebe95c1cb0f7f9c00621c5552fc4 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Thu, 19 Sep 2024 01:53:35 +0200
Subject: [PATCH 150/623] Fix order of operations for cosine IVF Flat (#329)

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/329
---
 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index 86ef559289..a4f7697413 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -1205,7 +1205,7 @@ void launch_with_fixed_consts(cuvs::distance::DistanceType metric, Args&&... arg
                            IvfSampleFilterT,
                            inner_prod_dist<Veclen, T, AccT>>(
         {},
-        raft::compose_op(raft::mul_const_op<float>{-1.0f}, raft::add_const_op<float>{1.0f}),
+        raft::compose_op(raft::add_const_op<float>{1.0f}, raft::mul_const_op<float>{-1.0f}),
         std::forward<Args>(args)...);
     // NB: update the description of `knn::ivf_flat::build` when adding here a new metric.
     default: RAFT_FAIL("The chosen distance metric is not supported (%d)", int(metric));

From bfc611aba4ca530ae82b2f58a55f050b706f3905 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 18 Sep 2024 18:53:48 -0500
Subject: [PATCH 151/623] Fix broken examples link in README. (#326)

Fixes a broken link to the examples in the README.

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/326
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 543ce941cf..e697c61ed8 100755
--- a/README.md
+++ b/README.md
@@ -85,7 +85,7 @@ cagra::index_params index_params;
 auto index = cagra::build(res, index_params, dataset);
 ```
 
-For more example of the C++ APIs, refer to [cpp/examples](https://github.com/rapidsai/cuvs/tree/HEAD/cpp/examples) directory in the codebase.
+For more examples of the C++ APIs, refer to the [examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples) directory in the codebase.
 
 ### C API
 

From 6f72fe7c4161b030c212e68c5858dcd59199a813 Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Thu, 19 Sep 2024 12:06:59 -0400
Subject: [PATCH 152/623] DOC v24.12 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda11.8-pip/devcontainer.json  |  8 +++----
 .../cuda12.5-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda12.5-pip/devcontainer.json  |  8 +++----
 .github/workflows/build.yaml                  | 14 +++++------
 .github/workflows/pr.yaml                     | 24 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++----
 README.md                                     |  2 +-
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-118_arch-x86_64.yaml             |  4 ++--
 .../all_cuda-125_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-125_arch-x86_64.yaml             |  4 ++--
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  4 ++--
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  4 ++--
 .../bench_ann_cuda-125_arch-aarch64.yaml      |  4 ++--
 .../bench_ann_cuda-125_arch-x86_64.yaml       |  4 ++--
 dependencies.yaml                             | 12 +++++-----
 docs/source/developer_guide.md                |  6 ++---
 examples/cmake/thirdparty/fetch_rapids.cmake  |  2 +-
 python/cuvs/pyproject.toml                    |  2 +-
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 23 files changed, 68 insertions(+), 68 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index 13103e8f71..05f11c005e 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.12-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda11.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 74d62afcce..b4c507f867 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.12-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda11.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.10": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.12": {
       "version": "11.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.5-conda/devcontainer.json b/.devcontainer/cuda12.5-conda/devcontainer.json
index d6902d3f91..4f8d628c2b 100644
--- a/.devcontainer/cuda12.5-conda/devcontainer.json
+++ b/.devcontainer/cuda12.5-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.10-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.12-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda12.5-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda12.5-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.5-pip/devcontainer.json b/.devcontainer/cuda12.5-pip/devcontainer.json
index 3dcf52e835..8e6ba4de8c 100644
--- a/.devcontainer/cuda12.5-pip/devcontainer.json
+++ b/.devcontainer/cuda12.5-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.10-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:24.12-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.10-cuda12.5-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda12.5-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.10": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:24.12": {
       "version": "12.5",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index db20bdbc12..7ac02e3655 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 07b10e85a8..4e3fb600a7 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -24,29 +24,29 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.12
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.12
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.12
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.12
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.12
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -54,19 +54,19 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.12
     with:
       build_type: pull-request
   conda-python-tests:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.12
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -76,7 +76,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -86,20 +86,20 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.12
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.12
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.12
     with:
       arch: '["amd64"]'
       cuda: '["12.5"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 0821233a1e..5f60c0a348 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.12
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/README.md b/README.md
index e697c61ed8..e23b946164 100755
--- a/README.md
+++ b/README.md
@@ -50,7 +50,7 @@ mamba install -c conda-forge -c nvidia -c rapidsai cuvs
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.10
+mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.12
 ```
 
 Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/stable/build/) for more information on installing cuVS and building from source.
diff --git a/VERSION b/VERSION
index 7c7ba04436..af28c42b52 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.10.00
+24.12.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index cfcb562258..cb5c804d91 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -45,7 +45,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index dc519d1b58..3b126c1dcd 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -45,7 +45,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index b32650e449..0eafb709cf 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index d40fc3b99b..fc15743c0d 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - make
 - nccl>=2.9.9
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index c6e8b05a2c..47d012c034 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -33,7 +33,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pandas
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pyyaml
 - sysroot_linux-aarch64==2.17
 name: bench_ann_cuda-118_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index d6c023ae97..ae7a64e441 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -33,7 +33,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pandas
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pyyaml
 - sysroot_linux-64==2.17
 name: bench_ann_cuda-118_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 4d0ca94966..3807661eb6 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -30,14 +30,14 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pyyaml
 - sysroot_linux-aarch64==2.17
 name: bench_ann_cuda-125_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index 7dd67ab5e0..14182f8656 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -30,14 +30,14 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.10.*,>=0.0.0a0
+- librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.9.9
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==24.10.*,>=0.0.0a0
+- pylibraft==24.12.*,>=0.0.0a0
 - pyyaml
 - sysroot_linux-64==2.17
 name: bench_ann_cuda-125_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index 9fcbeaae2f..956f33196b 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -481,7 +481,7 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - &librmm_unsuffixed librmm==24.10.*,>=0.0.0a0
+          - &librmm_unsuffixed librmm==24.12.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -494,18 +494,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu12==24.10.*,>=0.0.0a0
+              - librmm-cu12==24.12.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu11==24.10.*,>=0.0.0a0
+              - librmm-cu11==24.12.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
       - output_types: conda
         packages:
-          - &pylibraft_unsuffixed pylibraft==24.10.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==24.12.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -518,10 +518,10 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu12==24.10.*,>=0.0.0a0
+              - pylibraft-cu12==24.12.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu11==24.10.*,>=0.0.0a0
+              - pylibraft-cu11==24.12.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 516819b1c1..c4a099fabb 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.10/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.12/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.10/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.12/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
@@ -230,7 +230,7 @@ Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY`
 ## Logging
 
 ### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.10/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
+Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.12/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
 
 ### Usage
 ```cpp
diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
index f64a924cf5..6f4c627ed4 100644
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and RAFT versions
-set(RAPIDS_VERSION "24.10")
+set(RAPIDS_VERSION "24.12")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 68bd9a8688..bf62f5adf4 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -37,7 +37,7 @@ dependencies = [
     "nvidia-curand",
     "nvidia-cusolver",
     "nvidia-cusparse",
-    "pylibraft==24.10.*,>=0.0.0a0",
+    "pylibraft==24.12.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index 52125bef3b..79aa5756a1 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "24.10.0"
+version = "24.12.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index 7e5b181434..13cc658e36 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "24.10.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "24.12.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From b3c0a10d9a108113edf8d610eb2ce82041825919 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Fri, 20 Sep 2024 12:49:46 -0500
Subject: [PATCH 153/623] Recommend `miniforge` for conda install (#325)

Recommending `miniforge` for conda install in installation docs.

I also cleaned up a few other small pieces in this document.

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/325
---
 docs/source/build.rst | 31 +++++++++++++------------------
 1 file changed, 13 insertions(+), 18 deletions(-)

diff --git a/docs/source/build.rst b/docs/source/build.rst
index a7d020c461..9c7c98989a 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -1,7 +1,7 @@
 Installation
 ============
 
-The cuVS software development kit provides APIs for C, C++, Python, and Rust languages. This guide outlines how to install the pre-compiled packages, build it from source, and use it in downstream applications. 
+The cuVS software development kit provides APIs for C, C++, Python, and Rust languages. This guide outlines how to install the pre-compiled packages, build it from source, and use it in downstream applications.
 
 - `Installing pre-compiled packages`_
 
@@ -25,7 +25,7 @@ The cuVS software development kit provides APIs for C, C++, Python, and Rust lan
 
   * `Rust Library`_
 
-  * `Using Cmake Directly`_
+  * `Using CMake Directly`_
 
 - `Build Documentation`_
 
@@ -36,17 +36,12 @@ Installing Pre-compiled Packages
 C, C++, and Python through Conda
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The easiest way to install the pre-compiled C, C++, and Python packages is through `conda <https://docs.anaconda.com/free/miniconda/index.html>`_. Use the following commands, depending on your CUDA version, to install cuVS packages (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
+The easiest way to install the pre-compiled C, C++, and Python packages is through conda. You can get a minimal conda installation with `miniforge <https://github.com/conda-forge/miniforge>`__.
 
-C++ Package
-~~~~~~~~~~~
+Use the following commands, depending on your CUDA version, to install cuVS packages (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
 
-.. code-block:: bash
-
-  mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
-
-C Package
-~~~~~~~~~
+C/C++ Package
+~~~~~~~~~~~~~
 
 .. code-block:: bash
 
@@ -62,7 +57,7 @@ Python Package
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
 
-The cuVS Python package can also be `installed through pip <https://rapids.ai/pip.html#install>`_.
+The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.
 
 For CUDA 11 packages:
 
@@ -76,7 +71,7 @@ And CUDA 12 packages:
 
     pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 
-Note: these packages statically link the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code. 
+Note: these packages statically link the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code.
 
 Rust through crates.io
 ^^^^^^^^^^^^^^^^^^^^^^
@@ -84,13 +79,13 @@ Rust through crates.io
 Build from source
 -----------------
 
-The core cuVS source code is written in C++ and wrapped through a C API. The C API is wrapped around the C++ APIs and the other supported languages are built around the C API. 
+The core cuVS source code is written in C++ and wrapped through a C API. The C API is wrapped around the C++ APIs and the other supported languages are built around the C API.
 
 
 Prerequisites
 ^^^^^^^^^^^^^
 
-- Cmake 3.26.4+
+- CMake 3.26.4+
 - GCC 9.3+ (11.4+ recommended)
 - CUDA Toolkit 11.4+
 - Volta architecture or better (compute capability >= 7.0)
@@ -98,7 +93,7 @@ Prerequisites
 Create a build environment
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-`Conda <https://docs.anaconda.com/free/miniconda/index.html>`_ environment scripts are provided for installing the necessary dependencies to build cuVS from source. It is preferred to use `mamba`, as it provides significant speedup over `conda`:
+Conda environment scripts are provided for installing the necessary dependencies to build cuVS from source. It is preferred to use `mamba`, as it provides significant speedup over `conda`:
 
 .. code-block:: bash
 
@@ -177,7 +172,7 @@ The Rust bindings can be built with
 Using CMake directly
 ^^^^^^^^^^^^^^^^^^^^
 
-When building cuVS from source, the `build.sh` script offers a nice wrapper around the `cmake` commands to ease the burdens of manually configuring the various available cmake options. When more fine-grained control over the CMake configuration is desired, the `cmake` command can be invoked directly as the below example demonstrates. 
+When building cuVS from source, the `build.sh` script offers a nice wrapper around the `cmake` commands to ease the burdens of manually configuring the various available cmake options. When more fine-grained control over the CMake configuration is desired, the `cmake` command can be invoked directly as the below example demonstrates.
 
 The `CMAKE_INSTALL_PREFIX` installs cuVS into a specific location. The example below installs cuVS into the current Conda environment:
 
@@ -191,7 +186,7 @@ The `CMAKE_INSTALL_PREFIX` installs cuVS into a specific location. The example b
 
 cuVS has the following configurable cmake flags available:
 
-.. list-table:: Cmake Flags
+.. list-table:: CMake Flags
 
  * - Flag
    - Possible Values

From 3da9f040345c93e96424945100ff98da48e2cd81 Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Tue, 24 Sep 2024 14:12:39 -0500
Subject: [PATCH 154/623] Update update-version.sh to use packaging lib (#344)

This PR updates the update-version.sh script to use the packaging
library, given that setuptools is no longer included by default in
Python 3.12.
---
 ci/release/update-version.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index feb0a400ce..4cf1f06175 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -26,8 +26,8 @@ NEXT_SHORT_TAG=${NEXT_MAJOR}.${NEXT_MINOR}
 NEXT_UCXX_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/${NEXT_SHORT_TAG})"
 
 # Need to distutils-normalize the original version
-NEXT_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${NEXT_SHORT_TAG}'))")
-NEXT_UCXX_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${NEXT_UCXX_SHORT_TAG}'))")
+NEXT_SHORT_TAG_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_SHORT_TAG}'))")
+NEXT_UCXX_SHORT_TAG_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_UCXX_SHORT_TAG}'))")
 
 echo "Preparing release $CURRENT_TAG => $NEXT_FULL_TAG"
 

From 0a4298adfd5175a0dcbd792147d300f6dc57e30c Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 25 Sep 2024 18:06:11 +0200
Subject: [PATCH 155/623] CAGRA - separable compilation for distance
 computation (#296)

Factor the `compute_distance` function and related template parameters out of the CAGRA search kernels.
This reduces the total number of kernel instances, thus reducing the binary size and the compile time.

The change, however, has a few drawbacks:
 - CUDA separable compilation needs to be enabled to allow `compute_distance` functions being compiled in separate object files. I introduced a static library component for the affected sources to minimize the impact of the change.
 - The separable compilation and dynamic dispatch of `compute_distance` function means the compiler cannot optimize across the kernel-`compute_distance` boundary, which results in higher register usage and occasional register spilling. Most of the cases are optimized in this PR, but some compromises seem unavoidable.
 - Dynamic dispatch (constructing a dataset descriptor) requires an extra kernel call (`xxx_init_kernel`) to get the function pointer, which adds extra latency. This is mitigated to some extent by caching the constructed descriptor using raft custom resource.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/296
---
 cpp/CMakeLists.txt                            | 194 +++---
 cpp/include/cuvs/neighbors/common.hpp         |  41 +-
 cpp/src/neighbors/detail/ann_utils.cuh        |  16 +-
 cpp/src/neighbors/detail/cagra/bitonic.hpp    |  37 +-
 .../neighbors/detail/cagra/cagra_search.cuh   | 152 +----
 .../detail/cagra/compute_distance-ext.cuh     | 511 +++++++++++++++
 .../detail/cagra/compute_distance.cu          |  82 +++
 .../detail/cagra/compute_distance.hpp         | 576 +++++++++--------
 .../cagra/compute_distance_00_generate.py     | 162 +++++
 .../cagra/compute_distance_standard-impl.cuh  | 279 +++++++++
 .../cagra/compute_distance_standard.hpp       |  80 +++
 ...rd_InnerProduct_float_uint32_dim128_t8.cu} |  24 +-
 ...d_InnerProduct_float_uint32_dim256_t16.cu} |  24 +-
 ...d_InnerProduct_float_uint32_dim512_t32.cu} |  24 +-
 ...dard_InnerProduct_half_uint32_dim128_t8.cu |  33 +
 ...rd_InnerProduct_half_uint32_dim256_t16.cu} |  24 +-
 ...ard_InnerProduct_half_uint32_dim512_t32.cu |  38 ++
 ...dard_InnerProduct_int8_uint32_dim128_t8.cu |  38 ++
 ...ard_InnerProduct_int8_uint32_dim256_t16.cu |  38 ++
 ...ard_InnerProduct_int8_uint32_dim512_t32.cu |  38 ++
 ...ard_InnerProduct_uint8_uint32_dim128_t8.cu |  38 ++
 ...rd_InnerProduct_uint8_uint32_dim256_t16.cu |  38 ++
 ...rd_InnerProduct_uint8_uint32_dim512_t32.cu |  38 ++
 ...ndard_L2Expanded_float_uint32_dim128_t8.cu |  33 +
 ...dard_L2Expanded_float_uint32_dim256_t16.cu |  33 +
 ...dard_L2Expanded_float_uint32_dim512_t32.cu |  33 +
 ...andard_L2Expanded_half_uint32_dim128_t8.cu |  33 +
 ...ndard_L2Expanded_half_uint32_dim256_t16.cu |  33 +
 ...ndard_L2Expanded_half_uint32_dim512_t32.cu |  33 +
 ...andard_L2Expanded_int8_uint32_dim128_t8.cu |  33 +
 ...ndard_L2Expanded_int8_uint32_dim256_t16.cu |  38 ++
 ...ndard_L2Expanded_int8_uint32_dim512_t32.cu |  38 ++
 ...ndard_L2Expanded_uint8_uint32_dim128_t8.cu |  38 ++
 ...dard_L2Expanded_uint8_uint32_dim256_t16.cu |  38 ++
 ...dard_L2Expanded_uint8_uint32_dim512_t32.cu |  38 ++
 .../cagra/compute_distance_vpq-impl.cuh       | 466 ++++++++++++++
 .../detail/cagra/compute_distance_vpq.cuh     | 231 -------
 .../detail/cagra/compute_distance_vpq.hpp     | 100 +++
 ..._float_uint32_dim128_t8_8pq_2subd_half.cu} |  27 +-
 ..._float_uint32_dim128_t8_8pq_4subd_half.cu} |  27 +-
 ..._float_uint32_dim256_t16_8pq_2subd_half.cu |  41 ++
 ..._float_uint32_dim256_t16_8pq_4subd_half.cu |  41 ++
 ..._float_uint32_dim512_t32_8pq_2subd_half.cu |  41 ++
 ..._float_uint32_dim512_t32_8pq_4subd_half.cu |  41 ++
 ...d_half_uint32_dim128_t8_8pq_2subd_half.cu} |  27 +-
 ...d_half_uint32_dim128_t8_8pq_4subd_half.cu} |  27 +-
 ...d_half_uint32_dim256_t16_8pq_2subd_half.cu |  41 ++
 ...d_half_uint32_dim256_t16_8pq_4subd_half.cu |  41 ++
 ...d_half_uint32_dim512_t32_8pq_2subd_half.cu |  41 ++
 ...d_half_uint32_dim512_t32_8pq_4subd_half.cu |  41 ++
 ...ed_int8_uint32_dim128_t8_8pq_2subd_half.cu |  41 ++
 ...ed_int8_uint32_dim128_t8_8pq_4subd_half.cu |  41 ++
 ...d_int8_uint32_dim256_t16_8pq_2subd_half.cu |  41 ++
 ...d_int8_uint32_dim256_t16_8pq_4subd_half.cu |  41 ++
 ...d_int8_uint32_dim512_t32_8pq_2subd_half.cu |  41 ++
 ...d_int8_uint32_dim512_t32_8pq_4subd_half.cu |  41 ++
 ...d_uint8_uint32_dim128_t8_8pq_2subd_half.cu |  41 ++
 ...d_uint8_uint32_dim128_t8_8pq_4subd_half.cu |  41 ++
 ..._uint8_uint32_dim256_t16_8pq_2subd_half.cu |  41 ++
 ..._uint8_uint32_dim256_t16_8pq_4subd_half.cu |  41 ++
 ..._uint8_uint32_dim512_t32_8pq_2subd_half.cu |  41 ++
 ..._uint8_uint32_dim512_t32_8pq_4subd_half.cu |  41 ++
 .../neighbors/detail/cagra/device_common.hpp  | 306 ++++++++-
 cpp/src/neighbors/detail/cagra/factory.cuh    | 175 ++++--
 cpp/src/neighbors/detail/cagra/graph_core.cuh |  94 +--
 cpp/src/neighbors/detail/cagra/hashmap.hpp    |  26 +-
 .../cagra/q_search_multi_cta_00_generate.py   |  83 ---
 ..._float_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ..._float_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ..._float_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ..._float_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 ...float_uint64_dim1024_t32_8pq_2subd_half.cu |  36 --
 ...float_uint64_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...a_float_uint64_dim128_t8_8pq_2subd_half.cu |  36 --
 ...a_float_uint64_dim128_t8_8pq_4subd_half.cu |  36 --
 ..._float_uint64_dim256_t16_8pq_2subd_half.cu |  36 --
 ..._float_uint64_dim256_t16_8pq_4subd_half.cu |  36 --
 ..._float_uint64_dim512_t32_8pq_2subd_half.cu |  36 --
 ..._float_uint64_dim512_t32_8pq_4subd_half.cu |  36 --
 ..._half_uint32_dim1024_t32_8pq_2subd_half.cu |  36 --
 ..._half_uint32_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...a_half_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ...a_half_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ...a_half_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ...a_half_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 ..._half_uint64_dim1024_t32_8pq_2subd_half.cu |  36 --
 ..._half_uint64_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...a_half_uint64_dim256_t16_8pq_2subd_half.cu |  36 --
 ...a_half_uint64_dim256_t16_8pq_4subd_half.cu |  36 --
 ...a_half_uint64_dim512_t32_8pq_2subd_half.cu |  36 --
 ...a_half_uint64_dim512_t32_8pq_4subd_half.cu |  36 --
 ..._int8_uint32_dim1024_t32_8pq_2subd_half.cu |  36 --
 ..._int8_uint32_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...ta_int8_uint32_dim128_t8_8pq_2subd_half.cu |  36 --
 ...ta_int8_uint32_dim128_t8_8pq_4subd_half.cu |  36 --
 ...a_int8_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ...a_int8_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ...a_int8_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ...a_int8_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 ...uint8_uint32_dim1024_t32_8pq_2subd_half.cu |  36 --
 ...uint8_uint32_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...a_uint8_uint32_dim128_t8_8pq_2subd_half.cu |  36 --
 ...a_uint8_uint32_dim128_t8_8pq_4subd_half.cu |  36 --
 ..._uint8_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ..._uint8_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ..._uint8_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ..._uint8_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 .../cagra/q_search_single_cta_00_generate.py  |  88 ---
 ...float_uint32_dim1024_t32_8pq_2subd_half.cu |  36 --
 ...float_uint32_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...a_float_uint32_dim128_t8_8pq_2subd_half.cu |  36 --
 ...a_float_uint32_dim128_t8_8pq_4subd_half.cu |  36 --
 ..._float_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ..._float_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ..._float_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ..._float_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 ...float_uint64_dim1024_t32_8pq_2subd_half.cu |  36 --
 ...float_uint64_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...a_float_uint64_dim128_t8_8pq_2subd_half.cu |  36 --
 ...a_float_uint64_dim128_t8_8pq_4subd_half.cu |  36 --
 ..._float_uint64_dim256_t16_8pq_2subd_half.cu |  36 --
 ..._float_uint64_dim256_t16_8pq_4subd_half.cu |  36 --
 ..._float_uint64_dim512_t32_8pq_2subd_half.cu |  36 --
 ..._float_uint64_dim512_t32_8pq_4subd_half.cu |  36 --
 ..._half_uint32_dim1024_t32_8pq_2subd_half.cu |  36 --
 ..._half_uint32_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...ta_half_uint32_dim128_t8_8pq_2subd_half.cu |  36 --
 ...ta_half_uint32_dim128_t8_8pq_4subd_half.cu |  36 --
 ...a_half_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ...a_half_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ...a_half_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ...a_half_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 ..._half_uint64_dim1024_t32_8pq_2subd_half.cu |  36 --
 ..._half_uint64_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...ta_half_uint64_dim128_t8_8pq_2subd_half.cu |  36 --
 ...ta_half_uint64_dim128_t8_8pq_4subd_half.cu |  36 --
 ...a_half_uint64_dim256_t16_8pq_2subd_half.cu |  36 --
 ...a_half_uint64_dim256_t16_8pq_4subd_half.cu |  36 --
 ...a_half_uint64_dim512_t32_8pq_2subd_half.cu |  36 --
 ...a_half_uint64_dim512_t32_8pq_4subd_half.cu |  36 --
 ..._int8_uint32_dim1024_t32_8pq_2subd_half.cu |  36 --
 ..._int8_uint32_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...ta_int8_uint32_dim128_t8_8pq_2subd_half.cu |  36 --
 ...ta_int8_uint32_dim128_t8_8pq_4subd_half.cu |  36 --
 ...a_int8_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ...a_int8_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ...a_int8_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ...a_int8_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 ...uint8_uint32_dim1024_t32_8pq_2subd_half.cu |  36 --
 ...uint8_uint32_dim1024_t32_8pq_4subd_half.cu |  36 --
 ...a_uint8_uint32_dim128_t8_8pq_2subd_half.cu |  36 --
 ...a_uint8_uint32_dim128_t8_8pq_4subd_half.cu |  36 --
 ..._uint8_uint32_dim256_t16_8pq_2subd_half.cu |  36 --
 ..._uint8_uint32_dim256_t16_8pq_4subd_half.cu |  36 --
 ..._uint8_uint32_dim512_t32_8pq_2subd_half.cu |  36 --
 ..._uint8_uint32_dim512_t32_8pq_4subd_half.cu |  36 --
 .../detail/cagra/search_multi_cta.cuh         | 166 +++--
 .../cagra/search_multi_cta_00_generate.py     |  22 +-
 ...t8.cu => search_multi_cta_float_uint32.cu} |  11 +-
 ...arch_multi_cta_float_uint32_dim1024_t32.cu |  37 --
 ...earch_multi_cta_float_uint32_dim256_t16.cu |  37 --
 ...earch_multi_cta_float_uint32_dim512_t32.cu |  37 --
 ...t8.cu => search_multi_cta_float_uint64.cu} |  11 +-
 ...arch_multi_cta_float_uint64_dim1024_t32.cu |  37 --
 ...search_multi_cta_float_uint64_dim128_t8.cu |  37 --
 ...earch_multi_cta_float_uint64_dim256_t16.cu |  37 --
 ...earch_multi_cta_float_uint64_dim512_t32.cu |  37 --
 ..._t8.cu => search_multi_cta_half_uint32.cu} |  11 +-
 ...earch_multi_cta_half_uint32_dim1024_t32.cu |  37 --
 ...search_multi_cta_half_uint32_dim512_t32.cu |  37 --
 ...t16.cu => search_multi_cta_half_uint64.cu} |  11 +-
 ...earch_multi_cta_half_uint64_dim1024_t32.cu |  37 --
 ...search_multi_cta_half_uint64_dim256_t16.cu |  37 --
 ...search_multi_cta_half_uint64_dim512_t32.cu |  37 --
 .../detail/cagra/search_multi_cta_inst.cuh    |  44 +-
 .../cagra/search_multi_cta_int8_uint32.cu     |  34 +
 ...earch_multi_cta_int8_uint32_dim1024_t32.cu |  37 --
 .../search_multi_cta_int8_uint32_dim128_t8.cu |  37 --
 ...search_multi_cta_int8_uint32_dim256_t16.cu |  37 --
 ...search_multi_cta_int8_uint32_dim512_t32.cu |  37 --
 .../cagra/search_multi_cta_kernel-ext.cuh     | 405 ------------
 .../cagra/search_multi_cta_kernel-inl.cuh     | 216 +++----
 .../detail/cagra/search_multi_cta_kernel.cuh  |  36 +-
 .../cagra/search_multi_cta_uint8_uint32.cu    |  34 +
 ...arch_multi_cta_uint8_uint32_dim1024_t32.cu |  37 --
 ...search_multi_cta_uint8_uint32_dim128_t8.cu |  37 --
 ...earch_multi_cta_uint8_uint32_dim256_t16.cu |  37 --
 ...earch_multi_cta_uint8_uint32_dim512_t32.cu |  37 --
 .../detail/cagra/search_multi_kernel.cuh      | 458 +++++---------
 .../neighbors/detail/cagra/search_plan.cuh    |  71 +--
 .../detail/cagra/search_single_cta.cuh        | 142 ++---
 .../cagra/search_single_cta_00_generate.py    |  22 +-
 ...8.cu => search_single_cta_float_uint32.cu} |  11 +-
 ...rch_single_cta_float_uint32_dim1024_t32.cu |  37 --
 ...arch_single_cta_float_uint32_dim256_t16.cu |  37 --
 ...arch_single_cta_float_uint32_dim512_t32.cu |  37 --
 ...8.cu => search_single_cta_float_uint64.cu} |  11 +-
 ...rch_single_cta_float_uint64_dim1024_t32.cu |  37 --
 ...earch_single_cta_float_uint64_dim128_t8.cu |  37 --
 ...arch_single_cta_float_uint64_dim256_t16.cu |  37 --
 ...arch_single_cta_float_uint64_dim512_t32.cu |  37 --
 ...t8.cu => search_single_cta_half_uint32.cu} |  11 +-
 ...arch_single_cta_half_uint32_dim1024_t32.cu |  37 --
 ...earch_single_cta_half_uint32_dim512_t32.cu |  37 --
 ...16.cu => search_single_cta_half_uint64.cu} |  11 +-
 ...arch_single_cta_half_uint64_dim1024_t32.cu |  37 --
 ...earch_single_cta_half_uint64_dim256_t16.cu |  37 --
 ...earch_single_cta_half_uint64_dim512_t32.cu |  37 --
 .../detail/cagra/search_single_cta_inst.cuh   |  46 +-
 .../cagra/search_single_cta_int8_uint32.cu    |  34 +
 ...arch_single_cta_int8_uint32_dim1024_t32.cu |  37 --
 ...search_single_cta_int8_uint32_dim128_t8.cu |  37 --
 ...earch_single_cta_int8_uint32_dim256_t16.cu |  37 --
 ...earch_single_cta_int8_uint32_dim512_t32.cu |  37 --
 .../cagra/search_single_cta_kernel-ext.cuh    | 588 ------------------
 .../cagra/search_single_cta_kernel-inl.cuh    | 299 ++++-----
 .../detail/cagra/search_single_cta_kernel.cuh |  36 +-
 .../cagra/search_single_cta_uint8_uint32.cu   |  34 +
 ...rch_single_cta_uint8_uint32_dim1024_t32.cu |  37 --
 ...earch_single_cta_uint8_uint32_dim128_t8.cu |  37 --
 ...arch_single_cta_uint8_uint32_dim256_t16.cu |  37 --
 ...arch_single_cta_uint8_uint32_dim512_t32.cu |  37 --
 .../neighbors/detail/cagra/topk_by_radix.cuh  |  44 +-
 .../detail/cagra/topk_for_cagra/topk.cu       | 171 +++++
 .../detail/cagra/topk_for_cagra/topk_core.cuh | 258 ++------
 cpp/src/neighbors/detail/cagra/utils.hpp      |  10 +-
 cpp/test/neighbors/ann_cagra.cuh              |   2 +-
 227 files changed, 5416 insertions(+), 7935 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu => compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu} (51%)
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu => compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu} (51%)
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu => compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu} (51%)
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu => compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu} (51%)
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh
 delete mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu => compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_2subd_half.cu} (50%)
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu => compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_4subd_half.cu} (50%)
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_4subd_half.cu
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu => compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_2subd_half.cu} (50%)
 rename cpp/src/neighbors/detail/cagra/{q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu => compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_4subd_half.cu} (50%)
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_4subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_2subd_half.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
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 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
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 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py
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 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
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 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
 rename cpp/src/neighbors/detail/cagra/{search_multi_cta_half_uint64_dim128_t8.cu => search_multi_cta_float_uint32.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
 rename cpp/src/neighbors/detail/cagra/{search_multi_cta_half_uint32_dim128_t8.cu => search_multi_cta_float_uint64.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
 rename cpp/src/neighbors/detail/cagra/{search_multi_cta_float_uint32_dim128_t8.cu => search_multi_cta_half_uint32.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
 rename cpp/src/neighbors/detail/cagra/{search_multi_cta_half_uint32_dim256_t16.cu => search_multi_cta_half_uint64.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
 rename cpp/src/neighbors/detail/cagra/{search_single_cta_half_uint64_dim128_t8.cu => search_single_cta_float_uint32.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
 rename cpp/src/neighbors/detail/cagra/{search_single_cta_half_uint32_dim128_t8.cu => search_single_cta_float_uint64.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
 rename cpp/src/neighbors/detail/cagra/{search_single_cta_float_uint32_dim128_t8.cu => search_single_cta_half_uint32.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
 rename cpp/src/neighbors/detail/cagra/{search_single_cta_half_uint32_dim256_t16.cu => search_single_cta_half_uint64.cu} (80%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
 create mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index fec1248bbe..d8d554648b 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -199,6 +199,96 @@ endif()
 
 # ##################################################################################################
 # * cuvs ---------------------------------------------------------------------
+add_library(
+  cuvs-cagra-search STATIC
+  src/neighbors/cagra_search_float.cu
+  src/neighbors/cagra_search_int8.cu
+  src/neighbors/cagra_search_uint8.cu
+  src/neighbors/detail/cagra/compute_distance.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim128_t8.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim256_t16.cu
+  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim512_t32.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_2subd_half.cu
+  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_4subd_half.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
+  src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
+  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
+  src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu
+  src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
+  src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
+  src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
+  src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
+  src/neighbors/detail/cagra/search_single_cta_half_uint64.cu
+)
+
+file(GLOB_RECURSE compute_distance_sources "src/neighbors/detail/cagra/compute_distance_*.cu")
+set_source_files_properties(${compute_distance_sources} PROPERTIES COMPILE_FLAGS -maxrregcount=64)
+
+set_target_properties(
+  cuvs-cagra-search
+  PROPERTIES BUILD_RPATH "\$ORIGIN"
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             CUDA_STANDARD 17
+             CUDA_STANDARD_REQUIRED ON
+             CUDA_SEPARABLE_COMPILATION ON
+             INTERFACE_POSITION_INDEPENDENT_CODE ON
+             POSITION_INDEPENDENT_CODE ON
+)
+target_link_libraries(cuvs-cagra-search PRIVATE raft::raft)
+target_include_directories(
+  cuvs-cagra-search PRIVATE "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+)
+target_compile_options(
+  cuvs-cagra-search PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
+                            "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
+)
 
 add_library(
   cuvs SHARED
@@ -266,109 +356,11 @@ add_library(
   src/neighbors/cagra_extend_int8.cu
   src/neighbors/cagra_extend_uint8.cu
   src/neighbors/cagra_optimize.cu
-  src/neighbors/cagra_search_float.cu
-  src/neighbors/cagra_search_int8.cu
-  src/neighbors/cagra_search_uint8.cu
   src/neighbors/cagra_serialize_float.cu
   src/neighbors/cagra_serialize_int8.cu
   src/neighbors/cagra_serialize_uint8.cu
   src/neighbors/detail/cagra/cagra_build.cpp
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
+  src/neighbors/detail/cagra/topk_for_cagra/topk.cu
   $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
   src/neighbors/ivf_flat_index.cpp
   src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
@@ -463,7 +455,7 @@ if(NOT BUILD_CPU_ONLY)
   target_link_libraries(
     cuvs
     PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
-    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX> cuvs-cagra-search
   )
 endif()
 
@@ -539,7 +531,7 @@ target_compile_options(
                "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
 )
 # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
-target_link_options(cuvs PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
+target_link_options(cuvs PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
 
 # ##################################################################################################
 # * cuvs_c -------------------------------------------------------------------------------
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 4144380673..8218b5f52b 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -172,6 +172,22 @@ struct owning_dataset : public strided_dataset<DataT, IdxT> {
   };
 };
 
+template <typename DatasetT>
+struct is_strided_dataset : std::false_type {};
+
+template <typename DataT, typename IdxT>
+struct is_strided_dataset<strided_dataset<DataT, IdxT>> : std::true_type {};
+
+template <typename DataT, typename IdxT>
+struct is_strided_dataset<non_owning_dataset<DataT, IdxT>> : std::true_type {};
+
+template <typename DataT, typename IdxT, typename LayoutPolicy, typename ContainerPolicy>
+struct is_strided_dataset<owning_dataset<DataT, IdxT, LayoutPolicy, ContainerPolicy>>
+  : std::true_type {};
+
+template <typename DatasetT>
+inline constexpr bool is_strided_dataset_v = is_strided_dataset<DatasetT>::value;
+
 /**
  * @brief Contstruct a strided matrix from any mdarray or mdspan.
  *
@@ -284,23 +300,25 @@ auto make_aligned_dataset(const raft::resources& res, const SrcT& src, uint32_t
  */
 template <typename MathT, typename IdxT>
 struct vpq_dataset : public dataset<IdxT> {
+  using index_type = IdxT;
+  using math_type  = MathT;
   /** Vector Quantization codebook - "coarse cluster centers". */
-  raft::device_matrix<MathT, uint32_t, raft::row_major> vq_code_book;
+  raft::device_matrix<math_type, uint32_t, raft::row_major> vq_code_book;
   /** Product Quantization codebook - "fine cluster centers".  */
-  raft::device_matrix<MathT, uint32_t, raft::row_major> pq_code_book;
+  raft::device_matrix<math_type, uint32_t, raft::row_major> pq_code_book;
   /** Compressed dataset.  */
-  raft::device_matrix<uint8_t, IdxT, raft::row_major> data;
+  raft::device_matrix<uint8_t, index_type, raft::row_major> data;
 
-  vpq_dataset(raft::device_matrix<MathT, uint32_t, raft::row_major>&& vq_code_book,
-              raft::device_matrix<MathT, uint32_t, raft::row_major>&& pq_code_book,
-              raft::device_matrix<uint8_t, IdxT, raft::row_major>&& data)
+  vpq_dataset(raft::device_matrix<math_type, uint32_t, raft::row_major>&& vq_code_book,
+              raft::device_matrix<math_type, uint32_t, raft::row_major>&& pq_code_book,
+              raft::device_matrix<uint8_t, index_type, raft::row_major>&& data)
     : vq_code_book{std::move(vq_code_book)},
       pq_code_book{std::move(pq_code_book)},
       data{std::move(data)}
   {
   }
 
-  [[nodiscard]] auto n_rows() const noexcept -> IdxT final { return data.extent(0); }
+  [[nodiscard]] auto n_rows() const noexcept -> index_type final { return data.extent(0); }
   [[nodiscard]] auto dim() const noexcept -> uint32_t final { return vq_code_book.extent(1); }
   [[nodiscard]] auto is_owning() const noexcept -> bool final { return true; }
 
@@ -354,6 +372,15 @@ struct vpq_dataset : public dataset<IdxT> {
   }
 };
 
+template <typename DatasetT>
+struct is_vpq_dataset : std::false_type {};
+
+template <typename MathT, typename IdxT>
+struct is_vpq_dataset<vpq_dataset<MathT, IdxT>> : std::true_type {};
+
+template <typename DatasetT>
+inline constexpr bool is_vpq_dataset_v = is_vpq_dataset<DatasetT>::value;
+
 namespace filtering {
 
 /* A filter that filters nothing. This is the default behavior. */
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index 1db2dca64f..29f790ec51 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -224,7 +224,7 @@ inline void memzero(T* ptr, IdxT n_elems, rmm::cuda_stream_view stream)
 }
 
 template <typename T, typename IdxT>
-RAFT_KERNEL outer_add_kernel(const T* a, IdxT len_a, const T* b, IdxT len_b, T* c)
+static __global__ void outer_add_kernel(const T* a, IdxT len_a, const T* b, IdxT len_b, T* c)
 {
   IdxT gid = threadIdx.x + blockDim.x * static_cast<IdxT>(blockIdx.x);
   IdxT i   = gid / len_b;
@@ -234,12 +234,12 @@ RAFT_KERNEL outer_add_kernel(const T* a, IdxT len_a, const T* b, IdxT len_b, T*
 }
 
 template <typename T, typename IdxT>
-RAFT_KERNEL block_copy_kernel(const IdxT* in_offsets,
-                              const IdxT* out_offsets,
-                              IdxT n_blocks,
-                              const T* in_data,
-                              T* out_data,
-                              IdxT n_mult)
+static __global__ void block_copy_kernel(const IdxT* in_offsets,
+                                         const IdxT* out_offsets,
+                                         IdxT n_blocks,
+                                         const T* in_data,
+                                         T* out_data,
+                                         IdxT n_mult)
 {
   IdxT i = static_cast<IdxT>(blockDim.x) * static_cast<IdxT>(blockIdx.x) + threadIdx.x;
   // find the source offset using the binary search.
@@ -317,7 +317,7 @@ void outer_add(const T* a, IdxT len_a, const T* b, IdxT len_b, T* c, rmm::cuda_s
 }
 
 template <typename T, typename S, typename IdxT, typename LabelT>
-RAFT_KERNEL copy_selected_kernel(
+static __global__ void copy_selected_kernel(
   IdxT n_rows, IdxT n_cols, const S* src, const LabelT* row_ids, IdxT ld_src, T* dst, IdxT ld_dst)
 {
   IdxT gid   = threadIdx.x + blockDim.x * static_cast<IdxT>(blockIdx.x);
diff --git a/cpp/src/neighbors/detail/cagra/bitonic.hpp b/cpp/src/neighbors/detail/cagra/bitonic.hpp
index 26195bd9c9..ed609d6fdf 100644
--- a/cpp/src/neighbors/detail/cagra/bitonic.hpp
+++ b/cpp/src/neighbors/detail/cagra/bitonic.hpp
@@ -26,7 +26,7 @@ namespace bitonic {
 namespace detail {
 
 template <class K, class V>
-_RAFT_DEVICE inline void swap_if_needed(K& k0, V& v0, K& k1, V& v1, const bool asc)
+RAFT_DEVICE_INLINE_FUNCTION void swap_if_needed(K& k0, V& v0, K& k1, V& v1, const bool asc)
 {
   if ((k0 != k1) && ((k0 < k1) != asc)) {
     const auto tmp_k = k0;
@@ -39,7 +39,10 @@ _RAFT_DEVICE inline void swap_if_needed(K& k0, V& v0, K& k1, V& v1, const bool a
 }
 
 template <class K, class V>
-_RAFT_DEVICE inline void swap_if_needed(K& k0, V& v0, const unsigned lane_offset, const bool asc)
+RAFT_DEVICE_INLINE_FUNCTION void swap_if_needed(K& k0,
+                                                V& v0,
+                                                const unsigned lane_offset,
+                                                const bool asc)
 {
   auto k1 = __shfl_xor_sync(~0u, k0, lane_offset);
   auto v1 = __shfl_xor_sync(~0u, v0, lane_offset);
@@ -51,7 +54,10 @@ _RAFT_DEVICE inline void swap_if_needed(K& k0, V& v0, const unsigned lane_offset
 
 template <class K, class V, unsigned N, unsigned warp_size = 32>
 struct warp_merge_core {
-  _RAFT_DEVICE inline void operator()(K k[N], V v[N], const std::uint32_t range, const bool asc)
+  RAFT_DEVICE_INLINE_FUNCTION void operator()(K k[N],
+                                              V v[N],
+                                              const std::uint32_t range,
+                                              const bool asc)
   {
     const auto lane_id = threadIdx.x % warp_size;
 
@@ -93,7 +99,10 @@ struct warp_merge_core {
 
 template <class K, class V, unsigned warp_size>
 struct warp_merge_core<K, V, 6, warp_size> {
-  _RAFT_DEVICE inline void operator()(K k[6], V v[6], const std::uint32_t range, const bool asc)
+  RAFT_DEVICE_INLINE_FUNCTION void operator()(K k[6],
+                                              V v[6],
+                                              const std::uint32_t range,
+                                              const bool asc)
   {
     constexpr unsigned N = 6;
     const auto lane_id   = threadIdx.x % warp_size;
@@ -141,7 +150,10 @@ struct warp_merge_core<K, V, 6, warp_size> {
 
 template <class K, class V, unsigned warp_size>
 struct warp_merge_core<K, V, 3, warp_size> {
-  _RAFT_DEVICE inline void operator()(K k[3], V v[3], const std::uint32_t range, const bool asc)
+  RAFT_DEVICE_INLINE_FUNCTION void operator()(K k[3],
+                                              V v[3],
+                                              const std::uint32_t range,
+                                              const bool asc)
   {
     constexpr unsigned N = 3;
     const auto lane_id   = threadIdx.x % warp_size;
@@ -171,7 +183,10 @@ struct warp_merge_core<K, V, 3, warp_size> {
 
 template <class K, class V, unsigned warp_size>
 struct warp_merge_core<K, V, 2, warp_size> {
-  _RAFT_DEVICE inline void operator()(K k[2], V v[2], const std::uint32_t range, const bool asc)
+  RAFT_DEVICE_INLINE_FUNCTION void operator()(K k[2],
+                                              V v[2],
+                                              const std::uint32_t range,
+                                              const bool asc)
   {
     constexpr unsigned N = 2;
     const auto lane_id   = threadIdx.x % warp_size;
@@ -197,7 +212,10 @@ struct warp_merge_core<K, V, 2, warp_size> {
 
 template <class K, class V, unsigned warp_size>
 struct warp_merge_core<K, V, 1, warp_size> {
-  _RAFT_DEVICE inline void operator()(K k[1], V v[1], const std::uint32_t range, const bool asc)
+  RAFT_DEVICE_INLINE_FUNCTION void operator()(K k[1],
+                                              V v[1],
+                                              const std::uint32_t range,
+                                              const bool asc)
   {
     const auto lane_id    = threadIdx.x % warp_size;
     const std::uint32_t b = range;
@@ -211,14 +229,15 @@ struct warp_merge_core<K, V, 1, warp_size> {
 }  // namespace detail
 
 template <class K, class V, unsigned N, unsigned warp_size = 32>
-__device__ void warp_merge(K k[N], V v[N], unsigned range, const bool asc = true)
+RAFT_DEVICE_INLINE_FUNCTION void warp_merge(K k[N], V v[N], unsigned range, const bool asc = true)
 {
   detail::warp_merge_core<K, V, N, warp_size>{}(k, v, range, asc);
 }
 
 template <class K, class V, unsigned N, unsigned warp_size = 32>
-__device__ void warp_sort(K k[N], V v[N], const bool asc = true)
+RAFT_DEVICE_INLINE_FUNCTION void warp_sort(K k[N], V v[N], const bool asc = true)
 {
+#pragma unroll
   for (std::uint32_t range = 1; range <= warp_size; range <<= 1) {
     warp_merge<K, V, N, warp_size>(k, v, range, asc);
   }
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index cfb5f7919c..6dc601f320 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -16,7 +16,6 @@
 
 #pragma once
 
-#include "compute_distance_vpq.cuh"
 #include "factory.cuh"
 #include "search_plan.cuh"
 #include "search_single_cta_inst.cuh"
@@ -85,29 +84,22 @@ inline
   return filter;
 }
 
-template <typename DatasetDescriptorT, typename CagraSampleFilterT>
-void search_main_core(
-  raft::resources const& res,
-  search_params params,
-  DatasetDescriptorT dataset_desc,
-  raft::device_matrix_view<const typename DatasetDescriptorT::INDEX_T, int64_t, raft::row_major>
-    graph,
-  raft::device_matrix_view<const typename DatasetDescriptorT::DATA_T, int64_t, raft::row_major>
-    queries,
-  raft::device_matrix_view<typename DatasetDescriptorT::INDEX_T, int64_t, raft::row_major>
-    neighbors,
-  raft::device_matrix_view<typename DatasetDescriptorT::DISTANCE_T, int64_t, raft::row_major>
-    distances,
-  CagraSampleFilterT sample_filter    = CagraSampleFilterT(),
-  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
+template <typename DataT, typename IndexT, typename DistanceT, typename CagraSampleFilterT>
+void search_main_core(raft::resources const& res,
+                      search_params params,
+                      const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
+                      raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
+                      raft::device_matrix_view<const DataT, int64_t, raft::row_major> queries,
+                      raft::device_matrix_view<IndexT, int64_t, raft::row_major> neighbors,
+                      raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
+                      CagraSampleFilterT sample_filter = CagraSampleFilterT())
 {
   RAFT_LOG_DEBUG("# dataset size = %lu, dim = %lu\n",
-                 static_cast<size_t>(dataset_desc.size),
-                 static_cast<size_t>(dataset_desc.dim));
+                 static_cast<size_t>(graph.extent(0)),
+                 static_cast<size_t>(queries.extent(1)));
   RAFT_LOG_DEBUG("# query size = %lu, dim = %lu\n",
                  static_cast<size_t>(queries.extent(0)),
                  static_cast<size_t>(queries.extent(1)));
-  RAFT_EXPECTS(queries.extent(1) == dataset_desc.dim, "Queries and index dim must match");
   const uint32_t topk = neighbors.extent(1);
 
   cudaDeviceProp deviceProp = raft::resource::get_device_properties(res);
@@ -119,12 +111,12 @@ void search_main_core(
     "cagra::search(max_queries = %u, k = %u, dim = %zu)",
     params.max_queries,
     topk,
-    dataset_desc.dim);
+    queries.extent(1));
 
   using CagraSampleFilterT_s = typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type;
-  std::unique_ptr<search_plan_impl<DatasetDescriptorT, CagraSampleFilterT_s>> plan =
-    factory<DatasetDescriptorT, CagraSampleFilterT_s>::create(
-      res, params, dataset_desc.dim, graph.extent(1), topk, metric);
+  std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT_s>> plan =
+    factory<DataT, IndexT, DistanceT, CagraSampleFilterT_s>::create(
+      res, params, dataset_desc, queries.extent(1), graph.extent(1), topk);
 
   plan->check(topk);
 
@@ -134,21 +126,17 @@ void search_main_core(
 
   for (unsigned qid = 0; qid < queries.extent(0); qid += max_queries) {
     const uint32_t n_queries = std::min<std::size_t>(max_queries, queries.extent(0) - qid);
-    auto _topk_indices_ptr =
-      reinterpret_cast<typename DatasetDescriptorT::INDEX_T*>(neighbors.data_handle()) +
-      (topk * qid);
+    auto _topk_indices_ptr   = reinterpret_cast<IndexT*>(neighbors.data_handle()) + (topk * qid);
     auto _topk_distances_ptr = distances.data_handle() + (topk * qid);
     // todo(tfeher): one could keep distances optional and pass nullptr
     const auto* _query_ptr = queries.data_handle() + (query_dim * qid);
     const auto* _seed_ptr =
       plan->num_seeds > 0
-        ? reinterpret_cast<const typename DatasetDescriptorT::INDEX_T*>(plan->dev_seed.data()) +
-            (plan->num_seeds * qid)
+        ? reinterpret_cast<const IndexT*>(plan->dev_seed.data()) + (plan->num_seeds * qid)
         : nullptr;
     uint32_t* _num_executed_iterations = nullptr;
 
     (*plan)(res,
-            dataset_desc,
             graph,
             _topk_indices_ptr,
             _topk_distances_ptr,
@@ -161,77 +149,6 @@ void search_main_core(
   }
 }
 
-template <class T,
-          class DatasetT,
-          class DatasetIdxT,
-          class InternalIdxT,
-          class DistanceT,
-          class CagraSampleFilterT>
-void launch_vpq_search_main_core(
-  raft::resources const& res,
-  const vpq_dataset<DatasetT, DatasetIdxT>* vpq_dset,
-  search_params params,
-  raft::device_matrix_view<const InternalIdxT, int64_t, raft::row_major> graph,
-  raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<InternalIdxT, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
-  CagraSampleFilterT sample_filter,
-  const cuvs::distance::DistanceType metric)
-{
-  RAFT_EXPECTS(vpq_dset->pq_bits() == 8, "Only pq_bits = 8 is supported for now");
-  RAFT_EXPECTS(vpq_dset->pq_len() == 2 || vpq_dset->pq_len() == 4,
-               "Only pq_len 2 or 4 is supported for now");
-  RAFT_EXPECTS(vpq_dset->dim() % vpq_dset->pq_dim() == 0,
-               "dim must be a multiple of pq_dim at the moment");
-
-  const float vq_scale = 1.0f;
-  const float pq_scale = 1.0f;
-
-  if (vpq_dset->pq_bits() == 8) {
-    if (vpq_dset->pq_len() == 2) {
-      using dataset_desc_t = cagra_q_dataset_descriptor_t<T,
-                                                          DatasetT,
-                                                          8 /*PQ bit*/,
-                                                          2 /* Subspace dimension*/,
-                                                          DistanceT,
-                                                          InternalIdxT>;
-      dataset_desc_t dataset_desc(vpq_dset->data.data_handle(),
-                                  vpq_dset->encoded_row_length(),
-                                  vpq_dset->pq_dim(),
-                                  vpq_dset->vq_code_book.data_handle(),
-                                  vq_scale,
-                                  vpq_dset->pq_code_book.data_handle(),
-                                  pq_scale,
-                                  size_t(vpq_dset->n_rows()),
-                                  vpq_dset->dim());
-      search_main_core(
-        res, params, dataset_desc, graph, queries, neighbors, distances, sample_filter, metric);
-    } else if (vpq_dset->pq_len() == 4) {
-      using dataset_desc_t = cagra_q_dataset_descriptor_t<T,
-                                                          DatasetT,
-                                                          8 /*PQ bit*/,
-                                                          4 /* Subspace dimension*/,
-                                                          DistanceT,
-                                                          InternalIdxT>;
-      dataset_desc_t dataset_desc(vpq_dset->data.data_handle(),
-                                  vpq_dset->encoded_row_length(),
-                                  vpq_dset->pq_dim(),
-                                  vpq_dset->vq_code_book.data_handle(),
-                                  vq_scale,
-                                  vpq_dset->pq_code_book.data_handle(),
-                                  pq_scale,
-                                  size_t(vpq_dset->n_rows()),
-                                  vpq_dset->dim());
-      search_main_core(
-        res, params, dataset_desc, graph, queries, neighbors, distances, sample_filter, metric);
-    } else {
-      RAFT_FAIL("Subspace dimension must be 2 or 4");
-    }
-  } else {
-    RAFT_FAIL("Only 8-bit PQ is supported now");
-  }
-}
-
 /**
  * @brief Search ANN using the constructed index.
  *
@@ -264,6 +181,7 @@ void search_main(raft::resources const& res,
                  raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
                  CagraSampleFilterT sample_filter = CagraSampleFilterT())
 {
+  auto stream         = raft::resource::get_cuda_stream(res);
   const auto& graph   = index.graph();
   auto graph_internal = raft::make_device_matrix_view<const InternalIdxT, int64_t, raft::row_major>(
     reinterpret_cast<const InternalIdxT*>(graph.data_handle()), graph.extent(0), graph.extent(1));
@@ -273,39 +191,21 @@ void search_main(raft::resources const& res,
   // Dispatch search parameters based on the dataset kind.
   if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index.data());
       strided_dset != nullptr) {
-    // Set TEAM_SIZE and DATASET_BLOCK_SIZE to zero tentatively since these parameters cannot be
-    // determined here. They are set just before kernel launch.
-    using dataset_desc_t = standard_dataset_descriptor_t<T, InternalIdxT, DistanceT>;
     // Search using a plain (strided) row-major dataset
-    const dataset_desc_t dataset_desc(strided_dset->view().data_handle(),
-                                      strided_dset->n_rows(),
-                                      strided_dset->dim(),
-                                      strided_dset->stride());
-    search_main_core<dataset_desc_t, CagraSampleFilterT>(res,
-                                                         params,
-                                                         dataset_desc,
-                                                         graph_internal,
-                                                         queries,
-                                                         neighbors,
-                                                         distances,
-                                                         sample_filter,
-                                                         index.metric());
+    auto& desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
+      res, params, *strided_dset, index.metric());
+    search_main_core<T, InternalIdxT, DistanceT, CagraSampleFilterT>(
+      res, params, desc, graph_internal, queries, neighbors, distances, sample_filter);
   } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<float, ds_idx_type>*>(&index.data());
              vpq_dset != nullptr) {
     // Search using a compressed dataset
     RAFT_FAIL("FP32 VPQ dataset support is coming soon");
   } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<half, ds_idx_type>*>(&index.data());
              vpq_dset != nullptr) {
-    launch_vpq_search_main_core<T, half, ds_idx_type, InternalIdxT, DistanceT, CagraSampleFilterT>(
-      res,
-      vpq_dset,
-      params,
-      graph_internal,
-      queries,
-      neighbors,
-      distances,
-      sample_filter,
-      index.metric());
+    auto& desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
+      res, params, *vpq_dset, index.metric());
+    search_main_core<T, InternalIdxT, DistanceT, CagraSampleFilterT>(
+      res, params, desc, graph_internal, queries, neighbors, distances, sample_filter);
   } else if (auto* empty_dset = dynamic_cast<const empty_dataset<ds_idx_type>*>(&index.data());
              empty_dset != nullptr) {
     // Forgot to add a dataset.
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
new file mode 100644
index 0000000000..8407ef055a
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
@@ -0,0 +1,511 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#pragma once
+
+#include "compute_distance_standard.hpp"
+#include "compute_distance_vpq.hpp"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                8,
+                                                128,
+                                                float,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                8,
+                                                128,
+                                                float,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           2,
+                                           half,
+                                           float,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           4,
+                                           half,
+                                           float,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                16,
+                                                256,
+                                                float,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                16,
+                                                256,
+                                                float,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           2,
+                                           half,
+                                           float,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           4,
+                                           half,
+                                           float,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                32,
+                                                512,
+                                                float,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                32,
+                                                512,
+                                                float,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           2,
+                                           half,
+                                           float,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           4,
+                                           half,
+                                           float,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                8,
+                                                128,
+                                                half,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                8,
+                                                128,
+                                                half,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           2,
+                                           half,
+                                           half,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           4,
+                                           half,
+                                           half,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                16,
+                                                256,
+                                                half,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                16,
+                                                256,
+                                                half,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           2,
+                                           half,
+                                           half,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           4,
+                                           half,
+                                           half,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                32,
+                                                512,
+                                                half,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                32,
+                                                512,
+                                                half,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           2,
+                                           half,
+                                           half,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           4,
+                                           half,
+                                           half,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                8,
+                                                128,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                8,
+                                                128,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           2,
+                                           half,
+                                           int8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           4,
+                                           half,
+                                           int8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                16,
+                                                256,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                16,
+                                                256,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           2,
+                                           half,
+                                           int8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           4,
+                                           half,
+                                           int8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                32,
+                                                512,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                32,
+                                                512,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           2,
+                                           half,
+                                           int8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           4,
+                                           half,
+                                           int8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                8,
+                                                128,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                8,
+                                                128,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           2,
+                                           half,
+                                           uint8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           8,
+                                           128,
+                                           8,
+                                           4,
+                                           half,
+                                           uint8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                16,
+                                                256,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                16,
+                                                256,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           2,
+                                           half,
+                                           uint8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           16,
+                                           256,
+                                           8,
+                                           4,
+                                           half,
+                                           uint8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                                32,
+                                                512,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                                32,
+                                                512,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           2,
+                                           half,
+                                           uint8_t,
+                                           uint32_t,
+                                           float>;
+extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                           32,
+                                           512,
+                                           8,
+                                           4,
+                                           half,
+                                           uint8_t,
+                                           uint32_t,
+                                           float>;
+
+extern template struct instance_selector<
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>>;
+
+using descriptor_instances = instance_selector<
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>>;
+
+template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
+auto dataset_descriptor_init(const cagra::search_params& params,
+                             const DatasetT& dataset,
+                             cuvs::distance::DistanceType metric,
+                             rmm::cuda_stream_view stream)
+  -> dataset_descriptor_host<DataT, IndexT, DistanceT>
+{
+  auto [init, priority] =
+    descriptor_instances::select<DataT, IndexT, DistanceT>(params, dataset, metric);
+  if (init == nullptr || priority < 0) {
+    RAFT_FAIL("No dataset descriptor instance compiled for this parameter combination.");
+  }
+  return init(params, dataset, metric, stream);
+}
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.cu b/cpp/src/neighbors/detail/cagra/compute_distance.cu
new file mode 100644
index 0000000000..45316e59bd
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.cu
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance-ext.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+
+template struct instance_selector<
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, float, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, half, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, int8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.hpp b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
index 2b0c750ffa..4bed275abd 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
@@ -20,303 +20,363 @@
 #include "utils.hpp"
 
 #include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <raft/core/logger-macros.hpp>
 #include <raft/core/operators.hpp>
 
 // TODO: This shouldn't be invoking spatial/knn
 #include "../ann_utils.cuh"
 
+#include <raft/util/device_loads_stores.cuh>
 #include <raft/util/vectorized.cuh>
 
+#include <functional>
+#include <memory>
 #include <type_traits>
 
 namespace cuvs::neighbors::cagra::detail {
-namespace device {
 
-// using LOAD_256BIT_T = ulonglong4;
-using LOAD_128BIT_T = uint4;
-using LOAD_64BIT_T  = uint64_t;
-
-template <class LOAD_T, class DATA_T>
-_RAFT_DEVICE constexpr unsigned get_vlen()
-{
-  return utils::size_of<LOAD_T>() / utils::size_of<DATA_T>();
-}
-
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          class DATASET_DESCRIPTOR_T,
-          class DISTANCE_T,
-          class INDEX_T>
-_RAFT_DEVICE void compute_distance_to_random_nodes(
-  INDEX_T* const result_indices_ptr,       // [num_pickup]
-  DISTANCE_T* const result_distances_ptr,  // [num_pickup]
-  const typename DATASET_DESCRIPTOR_T::QUERY_T* const query_buffer,
-  const DATASET_DESCRIPTOR_T& dataset_desc,
-  const std::size_t num_pickup,
-  const unsigned num_distilation,
-  const uint64_t rand_xor_mask,
-  const INDEX_T* const seed_ptr,  // [num_seeds]
-  const uint32_t num_seeds,
-  INDEX_T* const visited_hash_ptr,
-  const uint32_t hash_bitlen,
-  const cuvs::distance::DistanceType metric,
-  const uint32_t block_id   = 0,
-  const uint32_t num_blocks = 1)
-{
-  uint32_t max_i = num_pickup;
-  if (max_i % (32 / TEAM_SIZE)) { max_i += (32 / TEAM_SIZE) - (max_i % (32 / TEAM_SIZE)); }
-
-  for (uint32_t i = threadIdx.x / TEAM_SIZE; i < max_i; i += blockDim.x / TEAM_SIZE) {
-    const bool valid_i = (i < num_pickup);
-
-    INDEX_T best_index_team_local;
-    DISTANCE_T best_norm2_team_local = utils::get_max_value<DISTANCE_T>();
-    for (uint32_t j = 0; j < num_distilation; j++) {
-      // Select a node randomly and compute the distance to it
-      INDEX_T seed_index;
-      if (valid_i) {
-        // uint32_t gid = i + (num_pickup * (j + (num_distilation * block_id)));
-        uint32_t gid = block_id + (num_blocks * (i + (num_pickup * j)));
-        if (seed_ptr && (gid < num_seeds)) {
-          seed_index = seed_ptr[gid];
-        } else {
-          seed_index = device::xorshift64(gid ^ rand_xor_mask) % dataset_desc.size;
-        }
-      }
-
-      DISTANCE_T norm2;
-      switch (metric) {
-        case cuvs::distance::DistanceType::L2Expanded:
-          norm2 =
-            dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                     TEAM_SIZE,
-                                                     cuvs::distance::DistanceType::L2Expanded>(
-              query_buffer, seed_index, valid_i);
-          break;
-        case cuvs::distance::DistanceType::InnerProduct:
-          norm2 =
-            dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                     TEAM_SIZE,
-                                                     cuvs::distance::DistanceType::InnerProduct>(
-              query_buffer, seed_index, valid_i);
-          break;
-        default: break;
-      }
-
-      if (valid_i && (norm2 < best_norm2_team_local)) {
-        best_norm2_team_local = norm2;
-        best_index_team_local = seed_index;
+/**
+ * @brief Dataset and distance description.
+ *
+ * This is the base type for the dataset/distance descriptors.
+ * The actual implementations are hidden in `compute_distance_***-impl.cuh` files, which should be
+ * included only in `compute_distance_***.cu` files to enforce separable compilation.
+ *
+ * [Note: manual dispatch]
+ * The descriptor type hierarchy declared here resembles the usual C++ inheritance: the search
+ * kernels take a pointer to the base type as an argument, but the actual implementation types are
+ * passed by the host. The kernels only ever need two functions `setup_workspace` and
+ * `compute_distance`; the choice of the implementation happens at the runtime.
+ *
+ * However, for performance reasons, we don't use the C++ virtual dispatch mechanics here.
+ * The extra pointer-chasing and register usage overheads associated with virtual tables turn out to
+ * cause a significant slowdown in the performance-critical `compute_distance`.
+ * Instead, we manually dispatch the two polymorphic functions and store them as fields in the
+ * descriptor structure.
+ *
+ * [Note: initialization/dispatch]
+ * The host doesn't know the addresses of the device symbols. That means we either need to resolve
+ * the device functions and store them in the descriptor directly on the device, or use
+ * `cudaMemcpyFromSymbolAsync` to fetch them (note, there is same problem with classes: if an object
+ * is created on the host, its pointer to the vtable would be invalid on device).
+ * We take the first approach: there's an `***_init_kernel` for each descriptor instance that is
+ * called before the search kernel; all it does is call a (placement) new with an appropriate type
+ * and arguments in a single GPU thread.
+ *
+ */
+template <typename DataT, typename IndexT, typename DistanceT>
+struct alignas(device::LOAD_128BIT_T) dataset_descriptor_base_t {
+  using base_type  = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+  using LOAD_T     = device::LOAD_128BIT_T;
+  using DATA_T     = DataT;
+  using INDEX_T    = IndexT;
+  using DISTANCE_T = DistanceT;
+
+  /**
+   * @brief "polymorphic" `compute_distance` arguments.
+   *
+   * This is a tightly-packed POD arguments of `compute_distance`.
+   * **Important** this structure is passed by value to `compute_distance`; it's important it
+   * remains small.
+   *
+   * [Note: arguments layout]
+   * The descriptor implementations require different sets of arguments (with couple arguments
+   * overlapping). At the same time the `compute_distance` is defined such that it accepts the
+   * `args_t` by value. That means the layout of the struct must be identical for all descriptor
+   * implementations. We workaround this requirement by defining generic fields in this struct and
+   * assignging the meaning to them on the implementation side.
+   */
+  struct alignas(LOAD_T) args_t {
+    void* extra_ptr1;
+    void* extra_ptr2;
+    /** Pointer to the workspace in the shared memory (filled in every copy by a thread block). */
+    uint32_t smem_ws_ptr;
+    /** Dimensionality of the data/queries. */
+    uint32_t dim;
+    uint32_t extra_word1;
+    uint32_t extra_word2;
+
+    /**
+     * Load this struct from shared memory.
+     *
+     * NB: until `compute_distance` is called, the arguments struct is stored in the shared memory
+     * as a member of the descriptor struct. This helper functions saves a few instructions by
+     * forcing the compiler to assume it is indeed in the shared memory address space.
+     */
+    RAFT_DEVICE_INLINE_FUNCTION auto load() const -> args_t
+    {
+      constexpr int kCount = sizeof(*this) / sizeof(LOAD_T);
+      using blob_type      = LOAD_T[kCount];
+      args_t r;
+      auto& src = reinterpret_cast<const blob_type&>(*this);
+      auto& dst = reinterpret_cast<blob_type&>(r);
+#pragma unroll
+      for (int i = 0; i < kCount; i++) {
+        device::lds(dst[i], src + i);
       }
+      return r;
     }
-
-    const unsigned lane_id = threadIdx.x % TEAM_SIZE;
-    if (valid_i && lane_id == 0) {
-      if (hashmap::insert(visited_hash_ptr, hash_bitlen, best_index_team_local)) {
-        result_distances_ptr[i] = best_norm2_team_local;
-        result_indices_ptr[i]   = best_index_team_local;
-      } else {
-        result_distances_ptr[i] = utils::get_max_value<DISTANCE_T>();
-        result_indices_ptr[i]   = utils::get_max_value<INDEX_T>();
-      }
+  };
+
+  /** Shared memory usage and team_size packed into a single uint32_t to save on memory requests. */
+  struct smem_and_team_size_t {
+    uint32_t value;
+    RAFT_INLINE_FUNCTION constexpr smem_and_team_size_t(uint32_t smem_size_bytes,
+                                                        uint32_t team_size_bitshift)
+      : value{(team_size_bitshift << 24) | smem_size_bytes}
+    {
     }
-  }
-}
-
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          unsigned MAX_N_FRAGS,
-          class DATASET_DESCRIPTOR_T,
-          class DISTANCE_T,
-          class INDEX_T>
-_RAFT_DEVICE void compute_distance_to_child_nodes(
-  INDEX_T* const result_child_indices_ptr,
-  DISTANCE_T* const result_child_distances_ptr,
-  // query
-  const typename DATASET_DESCRIPTOR_T::QUERY_T* const query_buffer,
-  // [dataset_dim, dataset_size]
-  const DATASET_DESCRIPTOR_T& dataset_desc,
-  // [knn_k, dataset_size]
-  const INDEX_T* const knn_graph,
-  const std::uint32_t knn_k,
-  // hashmap
-  INDEX_T* const visited_hashmap_ptr,
-  const std::uint32_t hash_bitlen,
-  const INDEX_T* const parent_indices,
-  const INDEX_T* const internal_topk_list,
-  const std::uint32_t search_width,
-  const cuvs::distance::DistanceType metric)
-{
-  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
-  const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
-
-  // Read child indices of parents from knn graph and check if the distance
-  // computaiton is necessary.
-  for (uint32_t i = threadIdx.x; i < knn_k * search_width; i += blockDim.x) {
-    const INDEX_T smem_parent_id = parent_indices[i / knn_k];
-    INDEX_T child_id             = invalid_index;
-    if (smem_parent_id != invalid_index) {
-      const auto parent_id = internal_topk_list[smem_parent_id] & ~index_msb_1_mask;
-      child_id             = knn_graph[(i % knn_k) + (static_cast<int64_t>(knn_k) * parent_id)];
+    /** Total dynamic shared memory required by the descriptor.  */
+    RAFT_INLINE_FUNCTION constexpr auto smem_ws_size_in_bytes() const noexcept -> uint32_t
+    {
+      return value & 0xffffffu;
     }
-    if (child_id != invalid_index) {
-      if (hashmap::insert(visited_hashmap_ptr, hash_bitlen, child_id) == 0) {
-        child_id = invalid_index;
-      }
+    RAFT_INLINE_FUNCTION constexpr auto team_size_bitshift() const noexcept -> uint32_t
+    {
+      return (value >> 24) & 0xffu;
+    }
+    /** How many threads are involved in computing a single distance. */
+    RAFT_INLINE_FUNCTION constexpr auto team_size() const noexcept -> uint32_t
+    {
+      return 1u << team_size_bitshift();
     }
-    result_child_indices_ptr[i] = child_id;
+  };
+  static_assert(sizeof(smem_and_team_size_t) == sizeof(uint32_t));
+
+  using setup_workspace_type  = const base_type*(const base_type*, void*, const DATA_T*, uint32_t);
+  using compute_distance_type = DISTANCE_T(const args_t, const INDEX_T);
+
+  args_t args;
+
+  /** Copy the descriptor and the query into shared memory and do any other work, such as
+   * initializing the codebook. */
+  setup_workspace_type* setup_workspace_impl;
+  /** Compute the distance from the query vector (stored in the smem_workspace) and a dataset vector
+   * given by the dataset_index. */
+  compute_distance_type* compute_distance_impl;
+  /** A placeholder for an implementation-specific pointer. */
+  void* extra_ptr3;
+  smem_and_team_size_t smem_and_team_size;
+
+  /** Number of records in the database. */
+  INDEX_T size;
+
+  RAFT_INLINE_FUNCTION dataset_descriptor_base_t(setup_workspace_type* setup_workspace_impl,
+                                                 compute_distance_type* compute_distance_impl,
+                                                 INDEX_T size,
+                                                 uint32_t dim,
+                                                 uint32_t team_size_bitshift,
+                                                 uint32_t smem_ws_size_in_bytes)
+    : setup_workspace_impl(setup_workspace_impl),
+      compute_distance_impl(compute_distance_impl),
+      size(size),
+      smem_and_team_size(smem_ws_size_in_bytes, team_size_bitshift),
+      args{nullptr, nullptr, 0, dim, 0, 0}
+  {
   }
-  __syncthreads();
 
-  // Compute the distance to child nodes
-  std::uint32_t max_i = knn_k * search_width;
-  if (max_i % (32 / TEAM_SIZE)) { max_i += (32 / TEAM_SIZE) - (max_i % (32 / TEAM_SIZE)); }
-  for (std::uint32_t tid = threadIdx.x; tid < max_i * TEAM_SIZE; tid += blockDim.x) {
-    const auto i       = tid / TEAM_SIZE;
-    const bool valid_i = (i < (knn_k * search_width));
-    INDEX_T child_id   = invalid_index;
-    if (valid_i) { child_id = result_child_indices_ptr[i]; }
+  /** Total dynamic shared memory required by the descriptor.  */
+  RAFT_INLINE_FUNCTION constexpr auto smem_ws_size_in_bytes() const noexcept -> uint32_t
+  {
+    return smem_and_team_size.smem_ws_size_in_bytes();
+  }
+  RAFT_INLINE_FUNCTION constexpr auto team_size_bitshift() const noexcept -> uint32_t
+  {
+    return smem_and_team_size.team_size_bitshift();
+  }
+  RAFT_DEVICE_INLINE_FUNCTION constexpr auto team_size_bitshift_from_smem() const noexcept
+    -> uint32_t
+  {
+    uint32_t sts;
+    raft::lds(sts, reinterpret_cast<const uint32_t*>(&smem_and_team_size));
+    return reinterpret_cast<smem_and_team_size_t&>(sts).team_size_bitshift();
+  }
 
-    DISTANCE_T norm2;
-    switch (metric) {
-      case cuvs::distance::DistanceType::L2Expanded:
-        norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                         TEAM_SIZE,
-                                                         cuvs::distance::DistanceType::L2Expanded>(
-          query_buffer, child_id, child_id != invalid_index);
-        break;
-      case cuvs::distance::DistanceType::InnerProduct:
-        norm2 =
-          dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                   TEAM_SIZE,
-                                                   cuvs::distance::DistanceType::InnerProduct>(
-            query_buffer, child_id, child_id != invalid_index);
-        break;
-      default: break;
-    }
+  /** How many threads are involved in computing a single distance. */
+  RAFT_INLINE_FUNCTION constexpr auto team_size() const noexcept -> uint32_t
+  {
+    return smem_and_team_size.team_size();
+  }
 
-    // Store the distance
-    const unsigned lane_id = threadIdx.x % TEAM_SIZE;
-    if (valid_i && lane_id == 0) {
-      if (child_id != invalid_index) {
-        result_child_distances_ptr[i] = norm2;
-      } else {
-        result_child_distances_ptr[i] = utils::get_max_value<DISTANCE_T>();
-      }
-    }
+  RAFT_DEVICE_INLINE_FUNCTION auto setup_workspace(void* smem_ptr,
+                                                   const DATA_T* queries_ptr,
+                                                   uint32_t query_id) const -> const base_type*
+  {
+    return setup_workspace_impl(this, smem_ptr, queries_ptr, query_id);
   }
-}
 
-}  // namespace device
+  RAFT_DEVICE_INLINE_FUNCTION auto compute_distance(INDEX_T dataset_index, bool valid) const
+    -> DISTANCE_T
+  {
+    auto per_thread_distances = valid ? compute_distance_impl(args.load(), dataset_index) : 0;
+    return device::team_sum(per_thread_distances, team_size_bitshift_from_smem());
+  }
+};
 
-template <class QUERY_T_, class DISTANCE_T_, class INDEX_T_>
-struct dataset_descriptor_base_t {
-  using INDEX_T    = INDEX_T_;
-  using QUERY_T    = QUERY_T_;
-  using DISTANCE_T = DISTANCE_T_;
+/**
+ * @brief Hosting a device descriptor.
+ *
+ * The dataset descriptor is initialized on the device side and stays there.
+ * The host struct manages the lifetime of the associated device pointer and a couple parameters
+ * affecting the search kernel launch config.
+ *
+ */
+template <typename DataT, typename IndexT, typename DistanceT>
+struct dataset_descriptor_host {
+  using dev_descriptor_t         = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+  uint32_t smem_ws_size_in_bytes = 0;
+  uint32_t team_size             = 0;
+
+  template <typename DescriptorImpl>
+  dataset_descriptor_host(const DescriptorImpl& dd_host, rmm::cuda_stream_view stream)
+    : dev_ptr_{[stream]() {
+                 dev_descriptor_t* p;
+                 RAFT_CUDA_TRY(cudaMallocAsync(&p, sizeof(DescriptorImpl), stream));
+                 return p;
+               }(),
+               [stream](dev_descriptor_t* p) { RAFT_CUDA_TRY_NO_THROW(cudaFreeAsync(p, stream)); }},
+      smem_ws_size_in_bytes{dd_host.smem_ws_size_in_bytes()},
+      team_size{dd_host.team_size()}
+  {
+  }
 
-  const INDEX_T size;
-  const std::uint32_t dim;
+  [[nodiscard]] auto dev_ptr() const -> const dev_descriptor_t* { return dev_ptr_.get(); }
+  [[nodiscard]] auto dev_ptr() -> dev_descriptor_t* { return dev_ptr_.get(); }
 
-  dataset_descriptor_base_t(const INDEX_T size, const std::uint32_t dim) : size(size), dim(dim) {}
+ private:
+  std::unique_ptr<dev_descriptor_t, std::function<void(dev_descriptor_t*)>> dev_ptr_;
 };
 
-template <class DATA_T_, class INDEX_T, class DISTANCE_T = float>
-struct standard_dataset_descriptor_t
-  : public dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T> {
-  using LOAD_T  = device::LOAD_128BIT_T;
-  using DATA_T  = DATA_T_;
-  using QUERY_T = typename dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>::QUERY_T;
-
-  const DATA_T* const ptr;
-  const std::size_t ld;
-  using dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>::size;
-  using dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>::dim;
-
-  standard_dataset_descriptor_t(const DATA_T* const ptr,
-                                const std::size_t size,
-                                const std::uint32_t dim,
-                                const std::size_t ld)
-    : dataset_descriptor_base_t<float, DISTANCE_T, INDEX_T>(size, dim), ptr(ptr), ld(ld)
+/**
+ * @brief The signature for descriptor initialization.
+ *
+ * There is an init function associated with every descriptor implementation. It's responsible for
+ * initializing the device-side descriptor instance (calling the init kernel).
+ *
+ */
+template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
+using init_desc_type =
+  dataset_descriptor_host<DataT, IndexT, DistanceT> (*)(const cagra::search_params&,
+                                                        const DatasetT&,
+                                                        cuvs::distance::DistanceType,
+                                                        rmm::cuda_stream_view);
+
+/**
+ * @brief Descriptor instance specification.
+ *
+ * This type provides a decentralized way for selecting a descriptor instance best suitable for the
+ * given dataset and distance metric.
+ * There is a spec for every descriptor (described in the interface files
+ * `compute_distance_***.hpp`).
+ *
+ * The `instance_spec` implementation must have the following static member template functions:
+ *   * constexpr bool accepts_dataset()
+ *     - tells whether the spec is compatible with the dataset type, executed at compile time.
+ *   * double priority(..)
+ *     - tells how to select a single spec out of possibly several compatible specs
+ *   * init_desc_type init
+ *     - (see `init_desc_type` above) the function to initialize the descriptor.
+ */
+template <typename DataT, typename IndexT, typename DistanceT>
+struct instance_spec {
+  using data_type     = DataT;
+  using index_type    = IndexT;
+  using distance_type = DistanceT;
+  using host_type     = dataset_descriptor_host<DataT, IndexT, DistanceT>;
+  /** Use this to constrain the input dataset type. */
+  template <typename DatasetT>
+  constexpr static inline bool accepts_dataset()
   {
+    return false;
   }
+};
 
-  static const std::uint32_t smem_buffer_size_in_byte = 0;
-  __device__ void set_smem_ptr(void* const){};
-
-  template <uint32_t DATASET_BLOCK_DIM>
-  __device__ void copy_query(const DATA_T* const dmem_query_ptr,
-                             QUERY_T* const smem_query_ptr,
-                             const std::uint32_t query_smem_buffer_length)
-  {
-    for (unsigned i = threadIdx.x; i < query_smem_buffer_length; i += blockDim.x) {
-      unsigned j = device::swizzling(i);
-      if (i < dim) {
-        smem_query_ptr[j] =
-          cuvs::spatial::knn::detail::utils::mapping<QUERY_T>{}(dmem_query_ptr[i]);
-      } else {
-        smem_query_ptr[j] = 0.0;
-      }
-    }
+/** Whether the descriptor is compatible with the dataset and arguments at the type level
+ * (compile-time check).
+ */
+template <typename InstanceSpec,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          typename DatasetT>
+constexpr bool spec_sound = std::is_same_v<DataT, typename InstanceSpec::data_type> &&
+                            std::is_same_v<IndexT, typename InstanceSpec::index_type> &&
+                            std::is_same_v<DistanceT, typename InstanceSpec::distance_type> &&
+                            InstanceSpec::template accepts_dataset<DatasetT>();
+
+/**
+ * @brief Get the init function and the priority of the descriptor given by the InstanceSpec.
+ *
+ * @return (init function, priority)
+ */
+template <typename InstanceSpec,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          typename DatasetT>
+constexpr auto spec_match(const cagra::search_params& params,
+                          const DatasetT& dataset,
+                          cuvs::distance::DistanceType metric)
+  -> std::tuple<init_desc_type<DataT, IndexT, DistanceT, DatasetT>, double>
+{
+  if constexpr (spec_sound<InstanceSpec, DataT, IndexT, DistanceT, DatasetT>) {
+    return std::make_tuple(InstanceSpec::template init<DatasetT>,
+                           InstanceSpec::template priority(params, dataset, metric));
   }
+  return std::make_tuple(nullptr, -1.0);
+}
 
-  template <typename T, cuvs::distance::DistanceType METRIC>
-  std::enable_if_t<METRIC == cuvs::distance::DistanceType::L2Expanded, T> __device__
-  dist_op(T a, T b) const
+/**
+ * @brief Select the best matching descriptor instance from the given type-level list.
+ *
+ * This is a helper struct that goes through the given list of specs (given as template arguments),
+ * filters is (partially at compile time and partially at runtime), and selects the descriptor with
+ * the highest priority.
+ *
+ * There is a single point in the codebase, where all specs are brought together; it's in the
+ * `neighbors/detail/cagra/compute_distance-ext.cuh`, which is generated by
+ * `neighbors/detail/cagra/compute_distance_00_generate.py`.
+ * Hence, `compute_distance_00_generate.py` is the only place you need to manually change to modify
+ * or extend the list supported dataset descriptors.
+ * The logic of selecting the descriptor is fully defined in this file, whereas the priorities of
+ * specific implementations are defined next to the implementations.
+ */
+template <typename... Specs>
+struct instance_selector {
+  template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
+  static auto select(const cagra::search_params&, const DatasetT&, cuvs::distance::DistanceType)
+    -> std::tuple<init_desc_type<DataT, IndexT, DistanceT, DatasetT>, double>
   {
-    T diff = a - b;
-    return diff * diff;
+    return std::make_tuple(nullptr, -1.0);
   }
+};
 
-  template <typename T, cuvs::distance::DistanceType METRIC>
-  std::enable_if_t<METRIC == cuvs::distance::DistanceType::InnerProduct, T> __device__
-  dist_op(T a, T b) const
+template <typename Spec, typename... Specs>
+struct instance_selector<Spec, Specs...> {
+  template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
+  static auto select(const cagra::search_params& params,
+                     const DatasetT& dataset,
+                     cuvs::distance::DistanceType metric)
+    -> std::enable_if_t<spec_sound<Spec, DataT, IndexT, DistanceT, DatasetT>,
+                        std::tuple<init_desc_type<DataT, IndexT, DistanceT, DatasetT>, double>>
   {
-    return -a * b;
+    auto s0 = spec_match<Spec, DataT, IndexT, DistanceT, DatasetT>(params, dataset, metric);
+    auto ss = instance_selector<Specs...>::template select<DataT, IndexT, DistanceT, DatasetT>(
+      params, dataset, metric);
+    return std::get<1>(s0) >= std::get<1>(ss) ? s0 : ss;
   }
 
-  template <uint32_t DATASET_BLOCK_DIM, uint32_t TEAM_SIZE, cuvs::distance::DistanceType METRIC>
-  __device__ DISTANCE_T compute_similarity(const QUERY_T* const query_ptr,
-                                           const INDEX_T dataset_i,
-                                           const bool valid) const
+  template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
+  static auto select(const cagra::search_params& params,
+                     const DatasetT& dataset,
+                     cuvs::distance::DistanceType metric)
+    -> std::enable_if_t<!spec_sound<Spec, DataT, IndexT, DistanceT, DatasetT>,
+                        std::tuple<init_desc_type<DataT, IndexT, DistanceT, DatasetT>, double>>
   {
-    const auto dataset_ptr  = ptr + dataset_i * ld;
-    const unsigned lane_id  = threadIdx.x % TEAM_SIZE;
-    constexpr unsigned vlen = device::get_vlen<LOAD_T, DATA_T>();
-    // #include <raft/util/cuda_dev_essentials.cuh
-    constexpr unsigned reg_nelem = raft::ceildiv<unsigned>(DATASET_BLOCK_DIM, TEAM_SIZE * vlen);
-    raft::TxN_t<DATA_T, vlen> dl_buff[reg_nelem];
-
-    DISTANCE_T norm2 = 0;
-    if (valid) {
-      for (uint32_t elem_offset = 0; elem_offset < dim; elem_offset += DATASET_BLOCK_DIM) {
-#pragma unroll
-        for (uint32_t e = 0; e < reg_nelem; e++) {
-          const uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset;
-          if (k >= dim) break;
-          dl_buff[e].load(dataset_ptr, k);
-        }
-#pragma unroll
-        for (uint32_t e = 0; e < reg_nelem; e++) {
-          const uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset;
-          if (k >= dim) break;
-#pragma unroll
-          for (uint32_t v = 0; v < vlen; v++) {
-            const uint32_t kv = k + v;
-            // Note this loop can go above the dataset_dim for padded arrays. This is not a problem
-            // because:
-            // - Above the last element (dataset_dim-1), the query array is filled with zeros.
-            // - The data buffer has to be also padded with zeros.
-            DISTANCE_T d = query_ptr[device::swizzling(kv)];
-            norm2 += dist_op<DISTANCE_T, METRIC>(
-              d, cuvs::spatial::knn::detail::utils::mapping<float>{}(dl_buff[e].val.data[v]));
-          }
-        }
-      }
-    }
-    for (uint32_t offset = TEAM_SIZE / 2; offset > 0; offset >>= 1) {
-      norm2 += __shfl_xor_sync(0xffffffff, norm2, offset);
-    }
-    return norm2;
+    return instance_selector<Specs...>::template select<DataT, IndexT, DistanceT, DatasetT>(
+      params, dataset, metric);
   }
 };
 
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
new file mode 100644
index 0000000000..52a15e2a16
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
@@ -0,0 +1,162 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import glob
+
+template = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+{includes}
+
+namespace cuvs::neighbors::cagra::detail {{
+
+using namespace cuvs::distance;
+{content}
+
+}}  // namespace cuvs::neighbors::cagra::detail
+"""
+
+mxdim_team = [(128, 8), (256, 16), (512, 32)]
+#mxdim_team = [(64, 8), (128, 16), (256, 32)]
+#mxdim_team = [(32, 8), (64, 16), (128, 32)]
+
+pq_bits = [8]
+pq_lens = [2, 4]
+
+# rblock = [(256, 4), (512, 2), (1024, 1)]
+# rcandidates = [32]
+# rsize = [256, 512]
+code_book_types = ["half"]
+
+search_types = dict(
+    float_uint32=("float", "uint32_t", "float"),  # data_t, idx_t, distance_t
+    half_uint32=("half", "uint32_t", "float"),
+    int8_uint32=("int8_t", "uint32_t", "float"),
+    uint8_uint32=("uint8_t", "uint32_t", "float"),
+    # float_uint64=("float", "uint64_t", "float"),
+    # half_uint64=("half", "uint64_t", "float"),
+)
+
+metric_prefix = 'DistanceType::'
+
+specs = []
+descs = []
+cmake_list = []
+
+
+
+
+# Cleanup first
+for f in glob.glob("compute_distance_standard_*.cu"):
+  os.remove(f)
+for f in glob.glob("compute_distance_vpq_*.cu"):
+  os.remove(f)
+
+# Generate new files
+for type_path, (data_t, idx_t, distance_t) in search_types.items():
+    for (mxdim, team) in mxdim_team:
+        # CAGRA
+        for metric in ['L2Expanded', 'InnerProduct']:
+            path = f"compute_distance_standard_{metric}_{type_path}_dim{mxdim}_t{team}.cu"
+            includes = '#include "compute_distance_standard-impl.cuh"'
+            params = f"{metric_prefix}{metric}, {team}, {mxdim}, {data_t}, {idx_t}, {distance_t}"
+            spec = f"standard_descriptor_spec<{params}>"
+            content = f"""template struct {spec};"""
+            specs.append(spec)
+            with open(path, "w") as f:
+                f.write(template.format(includes=includes, content=content))
+                cmake_list.append(f"  src/neighbors/detail/cagra/{path}")
+
+        # CAGRA-Q
+        for code_book_t in code_book_types:
+            for pq_len in pq_lens:
+                for pq_bit in pq_bits:
+                    for metric in ['L2Expanded']:
+                        path = f"compute_distance_vpq_{metric}_{type_path}_dim{mxdim}_t{team}_{pq_bit}pq_{pq_len}subd_{code_book_t}.cu"
+                        includes = '#include "compute_distance_vpq-impl.cuh"'
+                        params = f"{metric_prefix}{metric}, {team}, {mxdim}, {pq_bit}, {pq_len}, {code_book_t}, {data_t}, {idx_t}, {distance_t}"
+                        spec = f"vpq_descriptor_spec<{params}>"
+                        content = f"""template struct {spec};"""
+                        specs.append(spec)
+                        with open(path, "w") as f:
+                            f.write(template.format(includes=includes, content=content))
+                            cmake_list.append(f"  src/neighbors/detail/cagra/{path}")
+
+with open("compute_distance-ext.cuh", "w") as f:
+    includes = '''
+#pragma once
+
+#include "compute_distance_standard.hpp"
+#include "compute_distance_vpq.hpp"
+'''
+    newline = "\n"
+    contents = f'''
+{newline.join(map(lambda s: "extern template struct " + s + ";", specs))}
+
+extern template struct
+  instance_selector<{("," + newline + "                    ").join(specs)}>;
+
+using descriptor_instances =
+  instance_selector<{("," + newline + "                    ").join(specs)}>;
+
+template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
+auto dataset_descriptor_init(const cagra::search_params& params,
+                             const DatasetT& dataset,
+                             cuvs::distance::DistanceType metric,
+                             rmm::cuda_stream_view stream)
+  -> dataset_descriptor_host<DataT, IndexT, DistanceT>
+{{
+  auto [init, priority] = descriptor_instances::select<DataT, IndexT, DistanceT>(params, dataset, metric);
+  if (init == nullptr || priority < 0) {{
+    RAFT_FAIL("No dataset descriptor instance compiled for this parameter combination.");
+  }}
+  return init(params, dataset, metric, stream);
+}}
+'''
+    f.write(template.format(includes=includes, content=contents))
+
+
+with open("compute_distance.cu", "w") as f:
+    includes = '#include "compute_distance-ext.cuh"'
+    newline = "\n"
+    contents = f'''
+template struct instance_selector<{("," + newline + "                    ").join(specs)}>;
+'''
+    f.write(template.format(includes=includes, content=contents))
+
+cmake_list.sort()
+for path in cmake_list:
+    print(path)
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
new file mode 100644
index 0000000000..b0205508ac
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
@@ -0,0 +1,279 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "compute_distance_standard.hpp"
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra::detail {
+namespace {
+template <typename T, cuvs::distance::DistanceType Metric>
+RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(T a, T b)
+  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::L2Expanded, T>
+{
+  T diff = a - b;
+  return diff * diff;
+}
+
+template <typename T, cuvs::distance::DistanceType Metric>
+RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(T a, T b)
+  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::InnerProduct, T>
+{
+  return -a * b;
+}
+}  // namespace
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+struct standard_dataset_descriptor_t : public dataset_descriptor_base_t<DataT, IndexT, DistanceT> {
+  using base_type = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+  using QUERY_T   = float;
+  using base_type::args;
+  using base_type::smem_ws_size_in_bytes;
+  using typename base_type::args_t;
+  using typename base_type::compute_distance_type;
+  using typename base_type::DATA_T;
+  using typename base_type::DISTANCE_T;
+  using typename base_type::INDEX_T;
+  using typename base_type::LOAD_T;
+  using typename base_type::setup_workspace_type;
+  constexpr static inline auto kMetric          = Metric;
+  constexpr static inline auto kTeamSize        = TeamSize;
+  constexpr static inline auto kDatasetBlockDim = DatasetBlockDim;
+
+  static constexpr RAFT_INLINE_FUNCTION auto ptr(const args_t& args) noexcept
+    -> const DATA_T* const&
+  {
+    return (const DATA_T* const&)(args.extra_ptr1);
+  }
+  static constexpr RAFT_INLINE_FUNCTION auto ptr(args_t& args) noexcept -> const DATA_T*&
+  {
+    return (const DATA_T*&)(args.extra_ptr1);
+  }
+
+  static constexpr RAFT_INLINE_FUNCTION auto ld(const args_t& args) noexcept -> const uint32_t&
+  {
+    return args.extra_word1;
+  }
+  static constexpr RAFT_INLINE_FUNCTION auto ld(args_t& args) noexcept -> uint32_t&
+  {
+    return args.extra_word1;
+  }
+
+  _RAFT_HOST_DEVICE standard_dataset_descriptor_t(setup_workspace_type* setup_workspace_impl,
+                                                  compute_distance_type* compute_distance_impl,
+                                                  const DATA_T* ptr,
+                                                  INDEX_T size,
+                                                  uint32_t dim,
+                                                  uint32_t ld)
+    : base_type(setup_workspace_impl,
+                compute_distance_impl,
+                size,
+                dim,
+                raft::Pow2<TeamSize>::Log2,
+                get_smem_ws_size_in_bytes(dim))
+  {
+    standard_dataset_descriptor_t::ptr(args) = ptr;
+    standard_dataset_descriptor_t::ld(args)  = ld;
+    static_assert(sizeof(*this) == sizeof(base_type));
+    static_assert(alignof(standard_dataset_descriptor_t) == alignof(base_type));
+  }
+
+ private:
+  RAFT_INLINE_FUNCTION constexpr static auto get_smem_ws_size_in_bytes(uint32_t dim) -> uint32_t
+  {
+    return sizeof(standard_dataset_descriptor_t) +
+           raft::round_up_safe<uint32_t>(dim, DatasetBlockDim) * sizeof(QUERY_T);
+  }
+};
+
+template <typename DescriptorT>
+_RAFT_DEVICE __noinline__ auto setup_workspace_standard(
+  const DescriptorT* that,
+  void* smem_ptr,
+  const typename DescriptorT::DATA_T* queries_ptr,
+  uint32_t query_id) -> const DescriptorT*
+{
+  using DATA_T                    = typename DescriptorT::DATA_T;
+  using LOAD_T                    = typename DescriptorT::LOAD_T;
+  using base_type                 = typename DescriptorT::base_type;
+  using QUERY_T                   = typename DescriptorT::QUERY_T;
+  using word_type                 = uint32_t;
+  constexpr auto kTeamSize        = DescriptorT::kTeamSize;
+  constexpr auto kDatasetBlockDim = DescriptorT::kDatasetBlockDim;
+  auto* r                         = reinterpret_cast<DescriptorT*>(smem_ptr);
+  auto* buf                       = reinterpret_cast<QUERY_T*>(r + 1);
+  if (r != that) {
+    constexpr uint32_t kCount = sizeof(DescriptorT) / sizeof(word_type);
+    using blob_type           = word_type[kCount];
+    auto& src                 = reinterpret_cast<const blob_type&>(*that);
+    auto& dst                 = reinterpret_cast<blob_type&>(*r);
+    for (uint32_t i = threadIdx.x; i < kCount; i += blockDim.x) {
+      dst[i] = src[i];
+    }
+    const auto smem_ptr_offset =
+      reinterpret_cast<uint8_t*>(&(r->args.smem_ws_ptr)) - reinterpret_cast<uint8_t*>(r);
+    if (threadIdx.x == uint32_t(smem_ptr_offset / sizeof(word_type))) {
+      r->args.smem_ws_ptr = uint32_t(__cvta_generic_to_shared(buf));
+    }
+    __syncthreads();
+  }
+
+  uint32_t dim        = r->args.dim;
+  auto buf_len        = raft::round_up_safe<uint32_t>(dim, kDatasetBlockDim);
+  constexpr auto vlen = device::get_vlen<LOAD_T, DATA_T>();
+  queries_ptr += dim * query_id;
+  for (unsigned i = threadIdx.x; i < buf_len; i += blockDim.x) {
+    unsigned j = device::swizzling<kDatasetBlockDim, vlen * kTeamSize>(i);
+    if (i < dim) {
+      buf[j] = cuvs::spatial::knn::detail::utils::mapping<QUERY_T>{}(queries_ptr[i]);
+    } else {
+      buf[j] = 0.0;
+    }
+  }
+
+  return const_cast<const DescriptorT*>(r);
+}
+
+template <typename DescriptorT>
+RAFT_DEVICE_INLINE_FUNCTION auto compute_distance_standard_worker(
+  const typename DescriptorT::DATA_T* __restrict__ dataset_ptr,
+  uint32_t dim,
+  uint32_t query_smem_ptr) -> typename DescriptorT::DISTANCE_T
+{
+  using DATA_T                    = typename DescriptorT::DATA_T;
+  using DISTANCE_T                = typename DescriptorT::DISTANCE_T;
+  using LOAD_T                    = typename DescriptorT::LOAD_T;
+  using QUERY_T                   = typename DescriptorT::QUERY_T;
+  constexpr auto kTeamSize        = DescriptorT::kTeamSize;
+  constexpr auto kDatasetBlockDim = DescriptorT::kDatasetBlockDim;
+  constexpr auto vlen             = device::get_vlen<LOAD_T, DATA_T>();
+  constexpr auto reg_nelem =
+    raft::div_rounding_up_unsafe<uint32_t>(kDatasetBlockDim, kTeamSize * vlen);
+
+  DISTANCE_T r = 0;
+  for (uint32_t elem_offset = (threadIdx.x % kTeamSize) * vlen; elem_offset < dim;
+       elem_offset += kDatasetBlockDim) {
+    DATA_T data[reg_nelem][vlen];
+#pragma unroll
+    for (uint32_t e = 0; e < reg_nelem; e++) {
+      const uint32_t k = e * (kTeamSize * vlen) + elem_offset;
+      if (k >= dim) break;
+      device::ldg_cg(reinterpret_cast<LOAD_T&>(data[e]),
+                     reinterpret_cast<const LOAD_T*>(dataset_ptr + k));
+    }
+#pragma unroll
+    for (uint32_t e = 0; e < reg_nelem; e++) {
+      const uint32_t k = e * (kTeamSize * vlen) + elem_offset;
+      if (k >= dim) break;
+#pragma unroll
+      for (uint32_t v = 0; v < vlen; v++) {
+        // Note this loop can go above the dataset_dim for padded arrays. This is not a problem
+        // because:
+        // - Above the last element (dataset_dim-1), the query array is filled with zeros.
+        // - The data buffer has to be also padded with zeros.
+        DISTANCE_T d;
+        device::lds(
+          d,
+          query_smem_ptr +
+            sizeof(QUERY_T) * device::swizzling<kDatasetBlockDim, vlen * kTeamSize>(k + v));
+        r += dist_op<DISTANCE_T, DescriptorT::kMetric>(
+          d, cuvs::spatial::knn::detail::utils::mapping<DISTANCE_T>{}(data[e][v]));
+      }
+    }
+  }
+  return r;
+}
+
+template <typename DescriptorT>
+_RAFT_DEVICE __noinline__ auto compute_distance_standard(
+  const typename DescriptorT::args_t args, const typename DescriptorT::INDEX_T dataset_index) ->
+  typename DescriptorT::DISTANCE_T
+{
+  return compute_distance_standard_worker<DescriptorT>(
+    DescriptorT::ptr(args) + (static_cast<std::uint64_t>(DescriptorT::ld(args)) * dataset_index),
+    args.dim,
+    args.smem_ws_ptr);
+}
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+RAFT_KERNEL __launch_bounds__(1, 1)
+  standard_dataset_descriptor_init_kernel(dataset_descriptor_base_t<DataT, IndexT, DistanceT>* out,
+                                          const DataT* ptr,
+                                          IndexT size,
+                                          uint32_t dim,
+                                          uint32_t ld)
+{
+  using desc_type =
+    standard_dataset_descriptor_t<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>;
+  using base_type = typename desc_type::base_type;
+  new (out) desc_type(reinterpret_cast<typename base_type::setup_workspace_type*>(
+                        &setup_workspace_standard<desc_type>),
+                      reinterpret_cast<typename base_type::compute_distance_type*>(
+                        &compute_distance_standard<desc_type>),
+                      ptr,
+                      size,
+                      dim,
+                      ld);
+}
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+dataset_descriptor_host<DataT, IndexT, DistanceT>
+standard_descriptor_spec<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>::init_(
+  const cagra::search_params& params,
+  const DataT* ptr,
+  IndexT size,
+  uint32_t dim,
+  uint32_t ld,
+  rmm::cuda_stream_view stream)
+{
+  using desc_type =
+    standard_dataset_descriptor_t<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>;
+  using base_type = typename desc_type::base_type;
+  desc_type dd_host{nullptr, nullptr, ptr, size, dim, ld};
+  host_type result{dd_host, stream};
+
+  standard_dataset_descriptor_init_kernel<Metric,
+                                          TeamSize,
+                                          DatasetBlockDim,
+                                          DataT,
+                                          IndexT,
+                                          DistanceT>
+    <<<1, 1, 0, stream>>>(result.dev_ptr(), ptr, size, dim, desc_type::ld(dd_host.args));
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+  return result;
+}
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp b/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
new file mode 100644
index 0000000000..df1b77e86c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "compute_distance.hpp"
+
+#include <cuvs/distance/distance.hpp>
+
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra::detail {
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+struct standard_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT> {
+  using base_type = instance_spec<DataT, IndexT, DistanceT>;
+  using typename base_type::data_type;
+  using typename base_type::distance_type;
+  using typename base_type::host_type;
+  using typename base_type::index_type;
+
+  template <typename DatasetT>
+  constexpr static inline bool accepts_dataset()
+  {
+    return is_strided_dataset_v<DatasetT>;
+  }
+
+  template <typename DatasetT>
+  static auto init(const cagra::search_params& params,
+                   const DatasetT& dataset,
+                   cuvs::distance::DistanceType metric,
+                   rmm::cuda_stream_view stream) -> host_type
+  {
+    return init_(params,
+                 dataset.view().data_handle(),
+                 IndexT(dataset.n_rows()),
+                 dataset.dim(),
+                 dataset.stride(),
+                 stream);
+  }
+
+  template <typename DatasetT>
+  static auto priority(const cagra::search_params& params,
+                       const DatasetT& dataset,
+                       cuvs::distance::DistanceType metric) -> double
+  {
+    // If explicit team_size is specified and doesn't match the instance, discard it
+    if (params.team_size != 0 && TeamSize != params.team_size) { return -1.0; }
+    if (Metric != metric) { return -1.0; }
+    // Otherwise, favor the closest dataset dimensionality.
+    return 1.0 / (0.1 + std::abs(double(dataset.dim()) - double(DatasetBlockDim)));
+  }
+
+ private:
+  static dataset_descriptor_host<DataT, IndexT, DistanceT> init_(const cagra::search_params& params,
+                                                                 const DataT* ptr,
+                                                                 IndexT size,
+                                                                 uint32_t dim,
+                                                                 uint32_t ld,
+                                                                 rmm::cuda_stream_view stream);
+};
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
similarity index 51%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
index 1116eaaa49..af5e89a769 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
@@ -15,22 +15,24 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_standard-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         8,
+                                         128,
+                                         float,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
similarity index 51%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
index 7e3ec363d9..332eb6bf95 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
@@ -15,22 +15,24 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_standard-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         16,
+                                         256,
+                                         float,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
similarity index 51%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
index af60c776a2..3e5c11240e 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
@@ -15,22 +15,24 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_standard-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         32,
+                                         512,
+                                         float,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..92ca114f7d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, half, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu
similarity index 51%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu
index 5dd79a79b4..cfad79f3ac 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu
@@ -15,22 +15,24 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_standard-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         16,
+                                         256,
+                                         half,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..8c208044b5
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         32,
+                                         512,
+                                         half,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..929df5bbef
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         8,
+                                         128,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..3cc4a2c952
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         16,
+                                         256,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..a87e866eb2
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         32,
+                                         512,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..650d9ecac7
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         8,
+                                         128,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..6f7f4b97fe
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         16,
+                                         256,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..e7b96ab497
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::InnerProduct,
+                                         32,
+                                         512,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..b45cf3669a
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim128_t8.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..7d1206c37c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim256_t16.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, float, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..251316b2cc
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim512_t32.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, float, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..e3870df404
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, half, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..1253d7cd4c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, half, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..792532c2cd
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, half, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..c9c960cf94
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, int8_t, uint32_t, float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..d7a12804bc
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                         16,
+                                         256,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..a4f06c2833
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                         32,
+                                         512,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..199f05e497
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                         8,
+                                         128,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..0962ecd822
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                         16,
+                                         256,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..9c7e4ab032
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::L2Expanded,
+                                         32,
+                                         512,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh
new file mode 100644
index 0000000000..86c5925026
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh
@@ -0,0 +1,466 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "compute_distance_vpq.hpp"
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/util/integer_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
+
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra::detail {
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          uint32_t PQ_BITS,
+          uint32_t PQ_LEN,
+          typename CodebookT,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+struct cagra_q_dataset_descriptor_t : public dataset_descriptor_base_t<DataT, IndexT, DistanceT> {
+  using base_type   = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+  using CODE_BOOK_T = CodebookT;
+  using QUERY_T     = half;
+  using base_type::args;
+  using base_type::extra_ptr3;
+  using typename base_type::args_t;
+  using typename base_type::compute_distance_type;
+  using typename base_type::DATA_T;
+  using typename base_type::DISTANCE_T;
+  using typename base_type::INDEX_T;
+  using typename base_type::LOAD_T;
+  using typename base_type::setup_workspace_type;
+  constexpr static inline auto kMetric          = Metric;
+  constexpr static inline auto kTeamSize        = TeamSize;
+  constexpr static inline auto kDatasetBlockDim = DatasetBlockDim;
+  constexpr static inline auto kPqBits          = PQ_BITS;
+  constexpr static inline auto kPqLen           = PQ_LEN;
+
+  static_assert(std::is_same_v<CODE_BOOK_T, half>, "Only CODE_BOOK_T = `half` is supported now");
+
+  RAFT_INLINE_FUNCTION static constexpr auto encoded_dataset_ptr(args_t& args) noexcept
+    -> const uint8_t*&
+  {
+    return (const uint8_t*&)args.extra_ptr1;
+  }
+  RAFT_INLINE_FUNCTION static constexpr auto vq_code_book_ptr(args_t& args) noexcept
+    -> const CODE_BOOK_T*&
+  {
+    return (const CODE_BOOK_T*&)args.extra_ptr2;
+  }
+  RAFT_INLINE_FUNCTION constexpr auto pq_code_book_ptr() noexcept -> const CODE_BOOK_T*&
+  {
+    return (const CODE_BOOK_T*&)extra_ptr3;
+  }
+  RAFT_INLINE_FUNCTION static constexpr auto encoded_dataset_dim(args_t& args) noexcept -> uint32_t&
+  {
+    return args.extra_word1;
+  }
+
+  RAFT_INLINE_FUNCTION static constexpr auto encoded_dataset_ptr(const args_t& args) noexcept
+    -> const uint8_t* const&
+  {
+    return (const uint8_t*&)args.extra_ptr1;
+  }
+  RAFT_INLINE_FUNCTION static constexpr auto vq_code_book_ptr(const args_t& args) noexcept
+    -> const CODE_BOOK_T* const&
+  {
+    return (const CODE_BOOK_T*&)args.extra_ptr2;
+  }
+  RAFT_INLINE_FUNCTION constexpr auto pq_code_book_ptr() const noexcept -> const CODE_BOOK_T* const&
+  {
+    return (const CODE_BOOK_T*&)extra_ptr3;
+  }
+  RAFT_INLINE_FUNCTION static constexpr auto encoded_dataset_dim(const args_t& args) noexcept
+    -> const uint32_t&
+  {
+    return args.extra_word1;
+  }
+
+  static constexpr std::uint32_t kSMemCodeBookSizeInBytes =
+    (1 << PQ_BITS) * PQ_LEN * utils::size_of<CODE_BOOK_T>();
+
+  _RAFT_HOST_DEVICE cagra_q_dataset_descriptor_t(setup_workspace_type* setup_workspace_impl,
+                                                 compute_distance_type* compute_distance_impl,
+                                                 const std::uint8_t* encoded_dataset_ptr,
+                                                 std::uint32_t encoded_dataset_dim,
+                                                 const CODE_BOOK_T* vq_code_book_ptr,
+                                                 const CODE_BOOK_T* pq_code_book_ptr,
+                                                 IndexT size,
+                                                 std::uint32_t dim)
+    : base_type(setup_workspace_impl,
+                compute_distance_impl,
+                size,
+                dim,
+                raft::Pow2<TeamSize>::Log2,
+                get_smem_ws_size_in_bytes(dim))
+  {
+    cagra_q_dataset_descriptor_t::encoded_dataset_ptr(args) = encoded_dataset_ptr;
+    cagra_q_dataset_descriptor_t::vq_code_book_ptr(args)    = vq_code_book_ptr;
+    this->pq_code_book_ptr()                                = pq_code_book_ptr;
+    cagra_q_dataset_descriptor_t::encoded_dataset_dim(args) = encoded_dataset_dim;
+    static_assert(sizeof(*this) == sizeof(base_type));
+    static_assert(alignof(cagra_q_dataset_descriptor_t) == alignof(base_type));
+  }
+
+ private:
+  RAFT_INLINE_FUNCTION constexpr static auto get_smem_ws_size_in_bytes(uint32_t dim) -> uint32_t
+  {
+    /* SMEM workspace layout:
+      1. The descriptor itself
+      2. Codebook (kSMemCodeBookSizeInBytes bytes)
+      3. Queries (smem_query_buffer_length elems)
+    */
+    return sizeof(cagra_q_dataset_descriptor_t) + kSMemCodeBookSizeInBytes +
+           raft::round_up_safe<uint32_t>(dim, DatasetBlockDim) * sizeof(QUERY_T);
+  }
+};
+
+template <auto Block, auto Stride, typename T>
+RAFT_DEVICE_INLINE_FUNCTION constexpr auto transpose(T x) -> T
+{
+  auto i = x % Block;
+  auto j = x / Block;
+  auto k = i % Stride;
+  auto l = i / Stride;
+  return j * Block + k * (Block / Stride) + l;
+}
+
+template <typename DescriptorT>
+_RAFT_DEVICE __noinline__ auto setup_workspace_vpq(const DescriptorT* that,
+                                                   void* smem_ptr,
+                                                   const typename DescriptorT::DATA_T* queries_ptr,
+                                                   uint32_t query_id) -> const DescriptorT*
+{
+  using QUERY_T                   = typename DescriptorT::QUERY_T;
+  using CODE_BOOK_T               = typename DescriptorT::CODE_BOOK_T;
+  using word_type                 = uint32_t;
+  constexpr auto kDatasetBlockDim = DescriptorT::kDatasetBlockDim;
+  constexpr auto PQ_BITS          = DescriptorT::kPqBits;
+  constexpr auto PQ_LEN           = DescriptorT::kPqLen;
+
+  auto* r = reinterpret_cast<DescriptorT*>(smem_ptr);
+
+  if (r != that) {
+    constexpr uint32_t kCount = sizeof(DescriptorT) / sizeof(word_type);
+    using blob_type           = word_type[kCount];
+    auto& src                 = reinterpret_cast<const blob_type&>(*that);
+    auto& dst                 = reinterpret_cast<blob_type&>(*r);
+    for (uint32_t i = threadIdx.x; i < kCount; i += blockDim.x) {
+      dst[i] = src[i];
+    }
+
+    auto codebook_buf = uint32_t(__cvta_generic_to_shared(r + 1));
+    const auto smem_ptr_offset =
+      reinterpret_cast<uint8_t*>(&(r->args.smem_ws_ptr)) - reinterpret_cast<uint8_t*>(r);
+    if (threadIdx.x == uint32_t(smem_ptr_offset / sizeof(word_type))) {
+      r->args.smem_ws_ptr = codebook_buf;
+    }
+    __syncthreads();
+
+    // Copy PQ table
+    for (unsigned i = threadIdx.x * 2; i < (1 << PQ_BITS) * PQ_LEN; i += blockDim.x * 2) {
+      half2 buf2;
+      buf2.x = r->pq_code_book_ptr()[i];
+      buf2.y = r->pq_code_book_ptr()[i + 1];
+
+      // Change the order of PQ code book array to reduce the
+      // frequency of bank conflicts.
+      constexpr auto num_elements_per_bank  = 4 / utils::size_of<CODE_BOOK_T>();
+      constexpr auto num_banks_per_subspace = PQ_LEN / num_elements_per_bank;
+      const auto j                          = i / num_elements_per_bank;
+      const auto smem_index =
+        (j / num_banks_per_subspace) + (j % num_banks_per_subspace) * (1 << PQ_BITS);
+
+      device::sts(codebook_buf + smem_index * sizeof(half2), buf2);
+    }
+  }
+
+  uint32_t dim = r->args.dim;
+  queries_ptr += dim * query_id;
+
+  constexpr cuvs::spatial::knn::detail::utils::mapping<QUERY_T> mapping{};
+  auto smem_query_ptr =
+    reinterpret_cast<QUERY_T*>(reinterpret_cast<uint8_t*>(smem_ptr) + sizeof(DescriptorT) +
+                               DescriptorT::kSMemCodeBookSizeInBytes);
+  for (unsigned i = threadIdx.x * 2; i < dim; i += blockDim.x * 2) {
+    half2 buf2{0, 0};
+    if (i < dim) { buf2.x = mapping(queries_ptr[i]); }
+    if (i + 1 < dim) { buf2.y = mapping(queries_ptr[i + 1]); }
+    if constexpr ((PQ_BITS == 8) && (PQ_LEN % 2 == 0)) {
+      // Transpose the queries buffer to avoid bank conflicts in compute_distance.
+      constexpr uint32_t vlen = 4;  // **** DO NOT CHANGE ****
+      constexpr auto kStride  = vlen * PQ_LEN / 2;
+      reinterpret_cast<half2*>(smem_query_ptr)[transpose<kDatasetBlockDim / 2, kStride>(i / 2)] =
+        buf2;
+    } else {
+      (reinterpret_cast<half2*>(smem_query_ptr + i))[0] = buf2;
+    }
+  }
+
+  return const_cast<const DescriptorT*>(r);
+}
+
+template <typename DescriptorT>
+_RAFT_DEVICE RAFT_DEVICE_INLINE_FUNCTION auto compute_distance_vpq_worker(
+  const uint8_t* __restrict__ dataset_ptr,
+  const typename DescriptorT::CODE_BOOK_T* __restrict__ vq_code_book_ptr,
+  uint32_t dim,
+  uint32_t pq_codebook_ptr) -> typename DescriptorT::DISTANCE_T
+{
+  using DISTANCE_T               = typename DescriptorT::DISTANCE_T;
+  using LOAD_T                   = typename DescriptorT::LOAD_T;
+  using QUERY_T                  = typename DescriptorT::QUERY_T;
+  using CODE_BOOK_T              = typename DescriptorT::CODE_BOOK_T;
+  constexpr auto TeamSize        = DescriptorT::kTeamSize;
+  constexpr auto DatasetBlockDim = DescriptorT::kDatasetBlockDim;
+  constexpr auto PQ_BITS         = DescriptorT::kPqBits;
+  constexpr auto PQ_LEN          = DescriptorT::kPqLen;
+
+  const uint32_t query_ptr = pq_codebook_ptr + DescriptorT::kSMemCodeBookSizeInBytes;
+  static_assert(PQ_BITS == 8, "Only pq_bits == 8 is supported at the moment.");
+  constexpr uint32_t vlen = 4;  // **** DO NOT CHANGE ****
+  constexpr uint32_t nelem =
+    raft::div_rounding_up_unsafe<uint32_t>(DatasetBlockDim / PQ_LEN, TeamSize * vlen);
+
+  constexpr auto kTeamMask = DescriptorT::kTeamSize - 1;
+  constexpr auto kTeamVLen = TeamSize * vlen;
+
+  const auto n_subspace = raft::div_rounding_up_unsafe(dim, PQ_LEN);
+  const auto laneId     = threadIdx.x & kTeamMask;
+  DISTANCE_T norm       = 0;
+  for (uint32_t elem_offset = 0; elem_offset * PQ_LEN < dim;
+       elem_offset += DatasetBlockDim / PQ_LEN) {
+    // Loading PQ codes
+    uint32_t pq_codes[nelem];
+#pragma unroll
+    for (std::uint32_t e = 0; e < nelem; e++) {
+      const std::uint32_t k = e * kTeamVLen + elem_offset + laneId * vlen;
+      if (k >= n_subspace) break;
+      // Loading 4 x 8-bit PQ-codes using 32-bit load ops (from device memory)
+      device::ldg_cg(pq_codes[e], reinterpret_cast<const std::uint32_t*>(dataset_ptr + 4 + k));
+    }
+    //
+    if constexpr (PQ_LEN % 2 == 0) {
+      // **** Use half2 for distance computation ****
+#pragma unroll
+      for (std::uint32_t e = 0; e < nelem; e++) {
+        const std::uint32_t k = e * kTeamVLen + elem_offset + laneId * vlen;
+        if (k >= n_subspace) break;
+        // Loading VQ code-book
+        half2 vq_vals[PQ_LEN][vlen / 2];
+#pragma unroll
+        for (std::uint32_t m = 0; m < PQ_LEN; m++) {
+          const uint32_t d = (vlen * m) + (PQ_LEN * k);
+          if (d >= dim) break;
+          device::ldg_ca(vq_vals[m], vq_code_book_ptr + d);
+        }
+        // Compute distance
+        std::uint32_t pq_code = pq_codes[e];
+#pragma unroll
+        for (std::uint32_t v = 0; v < vlen; v++) {
+          if (PQ_LEN * (v + k) >= dim) break;
+#pragma unroll
+          for (std::uint32_t m = 0; m < PQ_LEN / 2; m++) {
+            constexpr auto kQueryBlock = DatasetBlockDim / (vlen * PQ_LEN);
+            const std::uint32_t d1     = m + (PQ_LEN / 2) * v;
+            const std::uint32_t d =
+              d1 * kQueryBlock + elem_offset * (PQ_LEN / 2) + e * TeamSize + laneId;
+            half2 q2, c2;
+            // Loading query vector from smem
+            device::lds(q2, query_ptr + sizeof(half2) * d);
+            // Loading PQ code book from smem
+            device::lds(c2,
+                        pq_codebook_ptr +
+                          sizeof(CODE_BOOK_T) * ((1 << PQ_BITS) * 2 * m + (2 * (pq_code & 0xff))));
+            // L2 distance
+            auto dist = q2 - c2 - reinterpret_cast<half2(&)[PQ_LEN * vlen / 2]>(vq_vals)[d1];
+            dist      = dist * dist;
+            norm += static_cast<DISTANCE_T>(dist.x + dist.y);
+          }
+          pq_code >>= 8;
+        }
+      }
+    } else {
+      // **** Use float for distance computation ****
+#pragma unroll
+      for (std::uint32_t e = 0; e < nelem; e++) {
+        const std::uint32_t k = e * kTeamVLen + elem_offset + laneId * vlen;
+        if (k >= n_subspace) break;
+        // Loading VQ code-book
+        CODE_BOOK_T vq_vals[PQ_LEN][vlen];
+#pragma unroll
+        for (std::uint32_t m = 0; m < PQ_LEN; m++) {
+          const std::uint32_t d = (vlen * m) + (PQ_LEN * k);
+          if (d >= dim) break;
+          // Loading 4 x 8/16-bit VQ-values using 32/64-bit load ops (from L2$ or device memory)
+          device::ldg_ca(vq_vals[m], vq_code_book_ptr + d);
+        }
+        // Compute distance
+        std::uint32_t pq_code = pq_codes[e];
+#pragma unroll
+        for (std::uint32_t v = 0; v < vlen; v++) {
+          if (PQ_LEN * (v + k) >= dim) break;
+          CODE_BOOK_T pq_vals[PQ_LEN];
+          device::lds(pq_vals, pq_codebook_ptr + sizeof(CODE_BOOK_T) * PQ_LEN * (pq_code & 0xff));
+#pragma unroll
+          for (std::uint32_t m = 0; m < PQ_LEN; m++) {
+            const std::uint32_t d1 = m + (PQ_LEN * v);
+            const std::uint32_t d  = d1 + (PQ_LEN * k);
+            // if (d >= dataset_dim) break;
+            DISTANCE_T diff;
+            device::lds(diff, query_ptr + sizeof(QUERY_T) * d);
+            diff -= static_cast<DISTANCE_T>(pq_vals[m]);
+            diff -=
+              static_cast<DISTANCE_T>(reinterpret_cast<CODE_BOOK_T(&)[PQ_LEN * vlen]>(vq_vals)[d1]);
+            norm += diff * diff;
+          }
+          pq_code >>= 8;
+        }
+      }
+    }
+  }
+  return norm;
+}
+
+template <typename DescriptorT>
+_RAFT_DEVICE __noinline__ auto compute_distance_vpq(
+  const typename DescriptorT::args_t args, const typename DescriptorT::INDEX_T dataset_index) ->
+  typename DescriptorT::DISTANCE_T
+{
+  const auto* dataset_ptr =
+    DescriptorT::encoded_dataset_ptr(args) +
+    (static_cast<std::uint64_t>(DescriptorT::encoded_dataset_dim(args)) * dataset_index);
+  uint32_t vq_code;
+  device::ldg_cg(vq_code, reinterpret_cast<const std::uint32_t*>(dataset_ptr));
+  return compute_distance_vpq_worker<DescriptorT>(
+    dataset_ptr /* advance dataset pointer by the size of vq_code */,
+    DescriptorT::vq_code_book_ptr(args) + args.dim * vq_code,
+    args.dim,
+    args.smem_ws_ptr);
+}
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          uint32_t PqBits,
+          uint32_t PqLen,
+          typename CodebookT,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+RAFT_KERNEL __launch_bounds__(1, 1)
+  vpq_dataset_descriptor_init_kernel(dataset_descriptor_base_t<DataT, IndexT, DistanceT>* out,
+                                     const std::uint8_t* encoded_dataset_ptr,
+                                     uint32_t encoded_dataset_dim,
+                                     const CodebookT* vq_code_book_ptr,
+                                     const CodebookT* pq_code_book_ptr,
+                                     IndexT size,
+                                     uint32_t dim)
+{
+  using desc_type = cagra_q_dataset_descriptor_t<Metric,
+                                                 TeamSize,
+                                                 DatasetBlockDim,
+                                                 PqBits,
+                                                 PqLen,
+                                                 CodebookT,
+                                                 DataT,
+                                                 IndexT,
+                                                 DistanceT>;
+  using base_type = typename desc_type::base_type;
+  new (out) desc_type(
+    reinterpret_cast<typename base_type::setup_workspace_type*>(&setup_workspace_vpq<desc_type>),
+    reinterpret_cast<typename base_type::compute_distance_type*>(&compute_distance_vpq<desc_type>),
+    encoded_dataset_ptr,
+    encoded_dataset_dim,
+    vq_code_book_ptr,
+    pq_code_book_ptr,
+    size,
+    dim);
+}
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          uint32_t PqBits,
+          uint32_t PqLen,
+          typename CodebookT,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+dataset_descriptor_host<DataT, IndexT, DistanceT>
+vpq_descriptor_spec<Metric,
+                    TeamSize,
+                    DatasetBlockDim,
+                    PqBits,
+                    PqLen,
+                    CodebookT,
+                    DataT,
+                    IndexT,
+                    DistanceT>::init_(const cagra::search_params& params,
+                                      const std::uint8_t* encoded_dataset_ptr,
+                                      uint32_t encoded_dataset_dim,
+                                      const CodebookT* vq_code_book_ptr,
+                                      const CodebookT* pq_code_book_ptr,
+                                      IndexT size,
+                                      uint32_t dim,
+                                      rmm::cuda_stream_view stream)
+{
+  using desc_type = cagra_q_dataset_descriptor_t<Metric,
+                                                 TeamSize,
+                                                 DatasetBlockDim,
+                                                 PqBits,
+                                                 PqLen,
+                                                 CodebookT,
+                                                 DataT,
+                                                 IndexT,
+                                                 DistanceT>;
+  using base_type = typename desc_type::base_type;
+
+  desc_type dd_host{nullptr,
+                    nullptr,
+                    encoded_dataset_ptr,
+                    encoded_dataset_dim,
+                    vq_code_book_ptr,
+                    pq_code_book_ptr,
+                    size,
+                    dim};
+  host_type result{dd_host, stream};
+  vpq_dataset_descriptor_init_kernel<Metric,
+                                     TeamSize,
+                                     DatasetBlockDim,
+                                     PqBits,
+                                     PqLen,
+                                     CodebookT,
+                                     DataT,
+                                     IndexT,
+                                     DistanceT><<<1, 1, 0, stream>>>(result.dev_ptr(),
+                                                                     encoded_dataset_ptr,
+                                                                     encoded_dataset_dim,
+                                                                     vq_code_book_ptr,
+                                                                     pq_code_book_ptr,
+                                                                     size,
+                                                                     dim);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+  return result;
+}
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
deleted file mode 100644
index 68973662fd..0000000000
--- a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.cuh
+++ /dev/null
@@ -1,231 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "compute_distance.hpp"
-
-#include <cuvs/distance/distance.hpp>
-#include <raft/util/integer_utils.hpp>
-
-namespace cuvs::neighbors::cagra::detail {
-template <class DATA_T_,
-          class CODE_BOOK_T_,
-          unsigned PQ_BITS,
-          unsigned PQ_LEN,
-          class DISTANCE_T,
-          class INDEX_T>
-struct cagra_q_dataset_descriptor_t : public dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T> {
-  using LOAD_T      = device::LOAD_128BIT_T;
-  using DATA_T      = DATA_T_;
-  using CODE_BOOK_T = CODE_BOOK_T_;
-  using QUERY_T     = typename dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>::QUERY_T;
-
-  static_assert(std::is_same_v<CODE_BOOK_T, half>, "Only CODE_BOOK_T = `half` is supported now");
-
-  const std::uint8_t* encoded_dataset_ptr;
-  const std::uint32_t encoded_dataset_dim;
-  const std::uint32_t n_subspace;
-  const CODE_BOOK_T* vq_code_book_ptr;
-  const float vq_scale;
-  const CODE_BOOK_T* pq_code_book_ptr;
-  const float pq_scale;
-  using dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>::size;
-  using dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>::dim;
-
-  // Set on device
-  CODE_BOOK_T* smem_pq_code_book_ptr;
-  static const std::uint32_t smem_buffer_size_in_byte =
-    (1 << PQ_BITS) * PQ_LEN * utils::size_of<CODE_BOOK_T>();
-
-  __device__ void set_smem_ptr(void* const smem_ptr)
-  {
-    smem_pq_code_book_ptr = reinterpret_cast<CODE_BOOK_T*>(smem_ptr);
-
-    // Copy PQ table
-    for (unsigned i = threadIdx.x * 2; i < (1 << PQ_BITS) * PQ_LEN; i += blockDim.x * 2) {
-      half2 buf2;
-      buf2.x = pq_code_book_ptr[i];
-      buf2.y = pq_code_book_ptr[i + 1];
-
-      // Change the order of PQ code book array to reduce the
-      // frequency of bank conflicts.
-      constexpr auto num_elements_per_bank  = 4 / utils::size_of<CODE_BOOK_T>();
-      constexpr auto num_banks_per_subspace = PQ_LEN / num_elements_per_bank;
-      const auto j                          = i / num_elements_per_bank;
-      const auto smem_index =
-        (j / num_banks_per_subspace) + (j % num_banks_per_subspace) * (1 << PQ_BITS);
-      reinterpret_cast<half2*>(smem_pq_code_book_ptr)[smem_index] = buf2;
-    }
-  }
-
-  cagra_q_dataset_descriptor_t(const std::uint8_t* encoded_dataset_ptr,
-                               const std::uint32_t encoded_dataset_dim,
-                               const std::uint32_t n_subspace,
-                               const CODE_BOOK_T* const vq_code_book_ptr,
-                               const float vq_scale,
-                               const CODE_BOOK_T* const pq_code_book_ptr,
-                               const float pq_scale,
-                               const std::size_t size,
-                               const std::uint32_t dim)
-    : dataset_descriptor_base_t<half, DISTANCE_T, INDEX_T>(size, dim),
-      encoded_dataset_ptr(encoded_dataset_ptr),
-      encoded_dataset_dim(encoded_dataset_dim),
-      n_subspace(n_subspace),
-      vq_code_book_ptr(vq_code_book_ptr),
-      vq_scale(vq_scale),
-      pq_code_book_ptr(pq_code_book_ptr),
-      pq_scale(pq_scale)
-  {
-  }
-
-  template <uint32_t DATASET_BLOCK_DIM>
-  __device__ void copy_query(const DATA_T* const dmem_query_ptr,
-                             QUERY_T* const smem_query_ptr,
-                             const std::uint32_t query_smem_buffer_length)
-  {
-    constexpr cuvs::spatial::knn::detail::utils::mapping<half> mapping{};
-    for (unsigned i = threadIdx.x * 2; i < dim; i += blockDim.x * 2) {
-      half2 buf2{0, 0};
-      if (i < dim) { buf2.x = mapping(dmem_query_ptr[i]); }
-      if (i + 1 < dim) { buf2.y = mapping(dmem_query_ptr[i + 1]); }
-      if ((PQ_BITS == 8) && (PQ_LEN % 2 == 0)) {
-        // Use swizzling in the condition to reduce bank conflicts in shared
-        // memory, which are likely to occur when pq_code_book_dim is large.
-        ((half2*)smem_query_ptr)[device::swizzling<std::uint32_t, DATASET_BLOCK_DIM / 2>(i / 2)] =
-          buf2;
-      } else {
-        (reinterpret_cast<half2*>(smem_query_ptr + i))[0] = buf2;
-      }
-    }
-  }
-
-  template <uint32_t DATASET_BLOCK_DIM, uint32_t TEAM_SIZE, cuvs::distance::DistanceType METRIC>
-  __device__ DISTANCE_T compute_similarity(const QUERY_T* const query_ptr,
-                                           const INDEX_T node_id,
-                                           const bool valid) const
-  {
-    float norm = 0;
-    if (valid) {
-      const unsigned lane_id = threadIdx.x % TEAM_SIZE;
-      const uint32_t vq_code = *(reinterpret_cast<const std::uint32_t*>(
-        encoded_dataset_ptr + (static_cast<std::uint64_t>(encoded_dataset_dim) * node_id)));
-      if (PQ_BITS == 8) {
-        for (uint32_t elem_offset = 0; elem_offset < dim; elem_offset += DATASET_BLOCK_DIM) {
-          constexpr unsigned vlen = 4;  // **** DO NOT CHANGE ****
-          constexpr unsigned nelem =
-            raft::div_rounding_up_unsafe<unsigned>(DATASET_BLOCK_DIM / PQ_LEN, TEAM_SIZE * vlen);
-          // Loading PQ codes
-          uint32_t pq_codes[nelem];
-#pragma unroll
-          for (std::uint32_t e = 0; e < nelem; e++) {
-            const std::uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset / PQ_LEN;
-            if (k >= n_subspace) break;
-            // Loading 4 x 8-bit PQ-codes using 32-bit load ops (from device memory)
-            pq_codes[e] = *(reinterpret_cast<const std::uint32_t*>(
-              encoded_dataset_ptr + (static_cast<std::uint64_t>(encoded_dataset_dim) * node_id) +
-              4 + k));
-          }
-          //
-          if constexpr (PQ_LEN % 2 == 0) {
-            // **** Use half2 for distance computation ****
-            half2 norm2{0, 0};
-#pragma unroll
-            for (std::uint32_t e = 0; e < nelem; e++) {
-              const std::uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset / PQ_LEN;
-              if (k >= n_subspace) break;
-              // Loading VQ code-book
-              raft::TxN_t<half2, vlen / 2> vq_vals[PQ_LEN];
-#pragma unroll
-              for (std::uint32_t m = 0; m < PQ_LEN; m += 1) {
-                const uint32_t d = (vlen * m) + (PQ_LEN * k);
-                if (d >= dim) break;
-                vq_vals[m].load(
-                  reinterpret_cast<const half2*>(vq_code_book_ptr + d + (dim * vq_code)), 0);
-              }
-              // Compute distance
-              std::uint32_t pq_code = pq_codes[e];
-#pragma unroll
-              for (std::uint32_t v = 0; v < vlen; v++) {
-                if (PQ_LEN * (v + k) >= dim) break;
-#pragma unroll
-                for (std::uint32_t m = 0; m < PQ_LEN; m += 2) {
-                  const std::uint32_t d1 = m + (PQ_LEN * v);
-                  const std::uint32_t d  = d1 + (PQ_LEN * k);
-                  // Loading query vector in smem
-                  half2 diff2 = (reinterpret_cast<const half2*>(
-                    query_ptr))[device::swizzling<std::uint32_t, DATASET_BLOCK_DIM / 2>(d / 2)];
-                  // Loading PQ code book in smem
-                  diff2 -= *(reinterpret_cast<half2*>(
-                    smem_pq_code_book_ptr + (1 << PQ_BITS) * 2 * (m / 2) + (2 * (pq_code & 0xff))));
-                  diff2 -= vq_vals[d1 / vlen].val.data[(d1 % vlen) / 2];
-                  norm2 += diff2 * diff2;
-                }
-                pq_code >>= 8;
-              }
-            }
-            norm += static_cast<float>(norm2.x + norm2.y);
-          } else {
-            // **** Use float for distance computation ****
-#pragma unroll
-            for (std::uint32_t e = 0; e < nelem; e++) {
-              const std::uint32_t k = (lane_id + (TEAM_SIZE * e)) * vlen + elem_offset / PQ_LEN;
-              if (k >= n_subspace) break;
-              // Loading VQ code-book
-              raft::TxN_t<CODE_BOOK_T, vlen> vq_vals[PQ_LEN];
-#pragma unroll
-              for (std::uint32_t m = 0; m < PQ_LEN; m++) {
-                const std::uint32_t d = (vlen * m) + (PQ_LEN * k);
-                if (d >= dim) break;
-                // Loading 4 x 8/16-bit VQ-values using 32/64-bit load ops (from L2$ or device
-                // memory)
-                vq_vals[m].load(
-                  reinterpret_cast<const half2*>(vq_code_book_ptr + d + (dim * vq_code)), 0);
-              }
-              // Compute distance
-              std::uint32_t pq_code = pq_codes[e];
-#pragma unroll
-              for (std::uint32_t v = 0; v < vlen; v++) {
-                if (PQ_LEN * (v + k) >= dim) break;
-                raft::TxN_t<CODE_BOOK_T, PQ_LEN> pq_vals;
-                pq_vals.load(
-                  reinterpret_cast<const half2*>(smem_pq_code_book_ptr + PQ_LEN * (pq_code & 0xff)),
-                  0);  // (from L1$ or smem)
-#pragma unroll
-                for (std::uint32_t m = 0; m < PQ_LEN; m++) {
-                  const std::uint32_t d1 = m + (PQ_LEN * v);
-                  const std::uint32_t d  = d1 + (PQ_LEN * k);
-                  // if (d >= dataset_dim) break;
-                  DISTANCE_T diff = query_ptr[d];  // (from smem)
-                  diff -= pq_scale * static_cast<float>(pq_vals.data[m]);
-                  diff -= vq_scale * static_cast<float>(vq_vals[d1 / vlen].val.data[d1 % vlen]);
-                  norm += diff * diff;
-                }
-                pq_code >>= 8;
-              }
-            }
-          }
-        }
-      }
-    }
-    for (uint32_t offset = TEAM_SIZE / 2; offset > 0; offset >>= 1) {
-      norm += __shfl_xor_sync(0xffffffff, norm, offset);
-    }
-    return norm;
-  }
-};
-
-}  // namespace cuvs::neighbors::cagra::detail
\ No newline at end of file
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
new file mode 100644
index 0000000000..378d2943ee
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "compute_distance.hpp"
+
+#include <cuvs/distance/distance.hpp>
+
+#include <type_traits>
+
+namespace cuvs::neighbors::cagra::detail {
+
+template <cuvs::distance::DistanceType Metric,
+          uint32_t TeamSize,
+          uint32_t DatasetBlockDim,
+          uint32_t PqBits,
+          uint32_t PqLen,
+          typename CodebookT,
+          typename DataT,
+          typename IndexT,
+          typename DistanceT>
+struct vpq_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT> {
+  using base_type = instance_spec<DataT, IndexT, DistanceT>;
+  using typename base_type::data_type;
+  using typename base_type::distance_type;
+  using typename base_type::host_type;
+  using typename base_type::index_type;
+
+  template <typename DatasetT>
+  constexpr static inline auto accepts_dataset()
+    -> std::enable_if_t<is_vpq_dataset_v<DatasetT>, bool>
+  {
+    return std::is_same_v<typename DatasetT::math_type, CodebookT>;
+  }
+
+  template <typename DatasetT>
+  constexpr static inline auto accepts_dataset()
+    -> std::enable_if_t<!is_vpq_dataset_v<DatasetT>, bool>
+  {
+    return false;
+  }
+
+  template <typename DatasetT>
+  static auto init(const cagra::search_params& params,
+                   const DatasetT& dataset,
+                   cuvs::distance::DistanceType metric,
+                   rmm::cuda_stream_view stream) -> host_type
+  {
+    return init_(params,
+                 dataset.data.data_handle(),
+                 dataset.encoded_row_length(),
+                 dataset.vq_code_book.data_handle(),
+                 dataset.pq_code_book.data_handle(),
+                 IndexT(dataset.n_rows()),
+                 dataset.dim(),
+                 stream);
+  }
+
+  template <typename DatasetT>
+  static auto priority(const cagra::search_params& params,
+                       const DatasetT& dataset,
+                       cuvs::distance::DistanceType metric) -> double
+  {
+    // If explicit team_size is specified and doesn't match the instance, discard it
+    if (params.team_size != 0 && TeamSize != params.team_size) { return -1.0; }
+    if (cuvs::distance::DistanceType::L2Expanded != metric) { return -1.0; }
+    // Match codebook params
+    if (dataset.pq_bits() != PqBits) { return -1.0; }
+    if (dataset.pq_len() != PqLen) { return -1.0; }
+    // Otherwise, favor the closest dataset dimensionality.
+    return 1.0 / (0.1 + std::abs(double(dataset.dim()) - double(DatasetBlockDim)));
+  }
+
+ private:
+  static dataset_descriptor_host<DataT, IndexT, DistanceT> init_(
+    const cagra::search_params& params,
+    const std::uint8_t* encoded_dataset_ptr,
+    uint32_t encoded_dataset_dim,
+    const CodebookT* vq_code_book_ptr,
+    const CodebookT* pq_code_book_ptr,
+    IndexT size,
+    uint32_t dim,
+    rmm::cuda_stream_view stream);
+};
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_2subd_half.cu
similarity index 50%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_2subd_half.cu
index 9ec7ce3dd0..a56a5a9dfa 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_2subd_half.cu
@@ -15,22 +15,27 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_vpq-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    2,
+                                    half,
+                                    float,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_4subd_half.cu
similarity index 50%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_4subd_half.cu
index 292a1429ad..f58a8c7dfd 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_4subd_half.cu
@@ -15,22 +15,27 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_vpq-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    4,
+                                    half,
+                                    float,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..bdc072e613
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    2,
+                                    half,
+                                    float,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..301c8c55bf
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    4,
+                                    half,
+                                    float,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..05ebeae2b5
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    2,
+                                    half,
+                                    float,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..e343d938c9
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    4,
+                                    half,
+                                    float,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_2subd_half.cu
similarity index 50%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_2subd_half.cu
index 1a5ad50e31..5d950351fa 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_2subd_half.cu
@@ -15,22 +15,27 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_vpq-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    2,
+                                    half,
+                                    half,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_4subd_half.cu
similarity index 50%
rename from cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
rename to cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_4subd_half.cu
index 0ab23d7eb2..453e15df36 100644
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_4subd_half.cu
@@ -15,22 +15,27 @@
  */
 
 /*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
+ * NOTE: this file is generated by compute_distance_00_generate.py
  *
  * Make changes there and run in this directory:
  *
- * > python q_search_multi_cta_00_generate.py
+ * > python compute_distance_00_generate.py
  *
  */
 
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
+#include "compute_distance_vpq-impl.cuh"
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+namespace cuvs::neighbors::cagra::detail {
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    4,
+                                    half,
+                                    half,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..c79cb74b63
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    2,
+                                    half,
+                                    half,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..dee326d543
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    4,
+                                    half,
+                                    half,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..a1ef9ba924
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    2,
+                                    half,
+                                    half,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..f2f01c8d43
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    4,
+                                    half,
+                                    half,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..1afccb8fde
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    2,
+                                    half,
+                                    int8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..28ea523eed
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    4,
+                                    half,
+                                    int8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..eca36cc36f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    2,
+                                    half,
+                                    int8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..89aed8afc8
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    4,
+                                    half,
+                                    int8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..ff646b22c6
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    2,
+                                    half,
+                                    int8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..633a805c77
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    4,
+                                    half,
+                                    int8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_2subd_half.cu
new file mode 100644
index 0000000000..3a09161ead
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    2,
+                                    half,
+                                    uint8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_4subd_half.cu
new file mode 100644
index 0000000000..85331d2435
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    8,
+                                    128,
+                                    8,
+                                    4,
+                                    half,
+                                    uint8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_2subd_half.cu
new file mode 100644
index 0000000000..a7719074ae
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    2,
+                                    half,
+                                    uint8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_4subd_half.cu
new file mode 100644
index 0000000000..7dd028b823
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    16,
+                                    256,
+                                    8,
+                                    4,
+                                    half,
+                                    uint8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_2subd_half.cu
new file mode 100644
index 0000000000..78f37b135f
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_2subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    2,
+                                    half,
+                                    uint8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_4subd_half.cu
new file mode 100644
index 0000000000..d3eb20a057
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_4subd_half.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_vpq-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct vpq_descriptor_spec<DistanceType::L2Expanded,
+                                    32,
+                                    512,
+                                    8,
+                                    4,
+                                    half,
+                                    uint8_t,
+                                    uint32_t,
+                                    float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/device_common.hpp b/cpp/src/neighbors/detail/cagra/device_common.hpp
index 192d81aa85..b7cb9c42df 100644
--- a/cpp/src/neighbors/detail/cagra/device_common.hpp
+++ b/cpp/src/neighbors/detail/cagra/device_common.hpp
@@ -15,10 +15,15 @@
  */
 #pragma once
 
+#include "hashmap.hpp"
 #include "utils.hpp"
 
+#include <cuvs/distance/distance.hpp>
+
 // TODO: This shouldn't be invoking anything in detail APIs outside of cuvs/neighbors
 #include <raft/core/detail/macros.hpp>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/warp_primitives.cuh>
 
 #include <cuda_fp16.h>
 
@@ -31,6 +36,16 @@ namespace device {
 // warpSize for compile time calculation
 constexpr unsigned warp_size = 32;
 
+// using LOAD_256BIT_T = ulonglong4;
+using LOAD_128BIT_T = uint4;
+using LOAD_64BIT_T  = uint64_t;
+
+template <class LOAD_T, class DATA_T>
+RAFT_DEVICE_INLINE_FUNCTION constexpr unsigned get_vlen()
+{
+  return utils::size_of<LOAD_T>() / utils::size_of<DATA_T>();
+}
+
 /** Xorshift rondem number generator.
  *
  * See https://en.wikipedia.org/wiki/Xorshift#xorshift for reference.
@@ -43,18 +58,299 @@ _RAFT_HOST_DEVICE inline uint64_t xorshift64(uint64_t u)
   return u * 0x2545F4914F6CDD1DULL;
 }
 
-template <class T, unsigned X_MAX = 1024>
-_RAFT_DEVICE inline T swizzling(T x)
+template <uint32_t Dim = 1024, uint32_t Stride = 128, typename T>
+RAFT_DEVICE_INLINE_FUNCTION constexpr auto swizzling(T x) -> T
 {
   // Address swizzling reduces bank conflicts in shared memory, but increases
   // the amount of operation instead.
   // return x;
-  if constexpr (X_MAX <= 1024) {
-    return (x) ^ ((x) >> 5);
+  if constexpr (Stride <= 32) {
+    return x;
+  } else if constexpr (Dim <= 1024) {
+    return x ^ (x >> 5);
   } else {
-    return (x) ^ (((x) >> 5) & 0x1f);
+    return x ^ ((x >> 5) & 0x1f);
+  }
+}
+
+template <uint32_t TeamSize, typename T>
+RAFT_DEVICE_INLINE_FUNCTION auto team_sum(T x) -> T
+{
+#pragma unroll
+  for (uint32_t stride = TeamSize >> 1; stride > 0; stride >>= 1) {
+    x += raft::shfl_xor(x, stride, TeamSize);
+  }
+  return x;
+}
+
+template <typename T>
+RAFT_DEVICE_INLINE_FUNCTION auto team_sum(T x, uint32_t team_size_bitshift) -> T
+{
+  switch (team_size_bitshift) {
+    case 5: x += raft::shfl_xor(x, 16);
+    case 4: x += raft::shfl_xor(x, 8);
+    case 3: x += raft::shfl_xor(x, 4);
+    case 2: x += raft::shfl_xor(x, 2);
+    case 1: x += raft::shfl_xor(x, 1);
+    default: return x;
+  }
+}
+
+template <typename IndexT,
+          typename DistanceT,
+          typename DATASET_DESCRIPTOR_T>
+RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_random_nodes(
+  IndexT* __restrict__ result_indices_ptr,       // [num_pickup]
+  DistanceT* __restrict__ result_distances_ptr,  // [num_pickup]
+  const DATASET_DESCRIPTOR_T& dataset_desc,
+  const uint32_t num_pickup,
+  const uint32_t num_distilation,
+  const uint64_t rand_xor_mask,
+  const IndexT* __restrict__ seed_ptr,  // [num_seeds]
+  const uint32_t num_seeds,
+  IndexT* __restrict__ visited_hash_ptr,
+  const uint32_t hash_bitlen,
+  const uint32_t block_id   = 0,
+  const uint32_t num_blocks = 1)
+{
+  const auto team_size_bits = dataset_desc.team_size_bitshift_from_smem();
+  const auto max_i = raft::round_up_safe<uint32_t>(num_pickup, warp_size >> team_size_bits);
+  const auto compute_distance = dataset_desc.compute_distance_impl;
+
+  for (uint32_t i = threadIdx.x >> team_size_bits; i < max_i; i += (blockDim.x >> team_size_bits)) {
+    const bool valid_i = (i < num_pickup);
+
+    IndexT best_index_team_local;
+    DistanceT best_norm2_team_local = raft::upper_bound<DistanceT>();
+    for (uint32_t j = 0; j < num_distilation; j++) {
+      // Select a node randomly and compute the distance to it
+      IndexT seed_index;
+      if (valid_i) {
+        // uint32_t gid = i + (num_pickup * (j + (num_distilation * block_id)));
+        uint32_t gid = block_id + (num_blocks * (i + (num_pickup * j)));
+        if (seed_ptr && (gid < num_seeds)) {
+          seed_index = seed_ptr[gid];
+        } else {
+          seed_index = device::xorshift64(gid ^ rand_xor_mask) % dataset_desc.size;
+        }
+      }
+
+      const auto norm2 = dataset_desc.compute_distance(seed_index, valid_i);
+
+      if (valid_i && (norm2 < best_norm2_team_local)) {
+        best_norm2_team_local = norm2;
+        best_index_team_local = seed_index;
+      }
+    }
+
+    const unsigned lane_id = threadIdx.x & ((1u << team_size_bits) - 1u);
+    if (valid_i && lane_id == 0) {
+      if (hashmap::insert(visited_hash_ptr, hash_bitlen, best_index_team_local)) {
+        result_distances_ptr[i] = best_norm2_team_local;
+        result_indices_ptr[i]   = best_index_team_local;
+      } else {
+        result_distances_ptr[i] = raft::upper_bound<DistanceT>();
+        result_indices_ptr[i]   = raft::upper_bound<IndexT>();
+      }
+    }
   }
 }
 
+template <typename IndexT, typename DistanceT, typename DATASET_DESCRIPTOR_T>
+RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_child_nodes(
+  IndexT* __restrict__ result_child_indices_ptr,
+  DistanceT* __restrict__ result_child_distances_ptr,
+  // [dataset_dim, dataset_size]
+  const DATASET_DESCRIPTOR_T& dataset_desc,
+  // [knn_k, dataset_size]
+  const IndexT* __restrict__ knn_graph,
+  const uint32_t knn_k,
+  // hashmap
+  IndexT* __restrict__ visited_hashmap_ptr,
+  const uint32_t hash_bitlen,
+  const IndexT* __restrict__ parent_indices,
+  const IndexT* __restrict__ internal_topk_list,
+  const uint32_t search_width)
+{
+  constexpr IndexT index_msb_1_mask = utils::gen_index_msb_1_mask<IndexT>::value;
+  constexpr IndexT invalid_index    = raft::upper_bound<IndexT>();
+
+  // Read child indices of parents from knn graph and check if the distance
+  // computaiton is necessary.
+  for (uint32_t i = threadIdx.x; i < knn_k * search_width; i += blockDim.x) {
+    const IndexT smem_parent_id = parent_indices[i / knn_k];
+    IndexT child_id             = invalid_index;
+    if (smem_parent_id != invalid_index) {
+      const auto parent_id = internal_topk_list[smem_parent_id] & ~index_msb_1_mask;
+      child_id             = knn_graph[(i % knn_k) + (static_cast<int64_t>(knn_k) * parent_id)];
+    }
+    if (child_id != invalid_index) {
+      if (hashmap::insert(visited_hashmap_ptr, hash_bitlen, child_id) == 0) {
+        child_id = invalid_index;
+      }
+    }
+    result_child_indices_ptr[i] = child_id;
+  }
+  __syncthreads();
+
+  // Compute the distance to child nodes
+  const auto team_size_bits   = dataset_desc.team_size_bitshift_from_smem();
+  const auto num_k            = knn_k * search_width;
+  const auto max_i            = raft::round_up_safe(num_k, warp_size >> team_size_bits);
+  const auto compute_distance = dataset_desc.compute_distance_impl;
+  const auto args             = dataset_desc.args.load();
+  const bool lead_lane        = (threadIdx.x & ((1u << team_size_bits) - 1u)) == 0;
+  for (uint32_t i = threadIdx.x >> team_size_bits; i < max_i; i += blockDim.x >> team_size_bits) {
+    const bool valid_i  = i < num_k;
+    const auto child_id = valid_i ? result_child_indices_ptr[i] : invalid_index;
+
+    // We should be calling `dataset_desc.compute_distance(..)` here as follows:
+    // > const auto child_dist = dataset_desc.compute_distance(child_id, child_id != invalid_index);
+    // Instead, we manually inline this function for performance reasons.
+    // This allows us to move the fetching of the arguments from shared memory out of the loop.
+    const DistanceT child_dist = device::team_sum(
+      (child_id != invalid_index) ? compute_distance(args, child_id)
+                                  : (lead_lane ? raft::upper_bound<DistanceT>() : 0),
+      team_size_bits);
+
+    // Store the distance
+    if (valid_i && lead_lane) { result_child_distances_ptr[i] = child_dist; }
+  }
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void lds(float& x, uint32_t addr)
+{
+  asm volatile("ld.shared.f32 {%0}, [%1];" : "=f"(x) : "r"(addr));
+}
+RAFT_DEVICE_INLINE_FUNCTION void lds(half& x, uint32_t addr)
+{
+  asm volatile("ld.shared.u16 {%0}, [%1];" : "=h"(reinterpret_cast<uint16_t&>(x)) : "r"(addr));
+}
+RAFT_DEVICE_INLINE_FUNCTION void lds(half2& x, uint32_t addr)
+{
+  asm volatile("ld.shared.u32 {%0}, [%1];" : "=r"(reinterpret_cast<uint32_t&>(x)) : "r"(addr));
+}
+RAFT_DEVICE_INLINE_FUNCTION void lds(half (&x)[1], uint32_t addr)
+{
+  asm volatile("ld.shared.u16 {%0}, [%1];" : "=h"(*reinterpret_cast<uint16_t*>(x)) : "r"(addr));
+}
+RAFT_DEVICE_INLINE_FUNCTION void lds(half (&x)[2], uint32_t addr)
+{
+  asm volatile("ld.shared.v2.u16 {%0, %1}, [%2];"
+               : "=h"(*reinterpret_cast<uint16_t*>(x)), "=h"(*reinterpret_cast<uint16_t*>(x + 1))
+               : "r"(addr));
+}
+RAFT_DEVICE_INLINE_FUNCTION void lds(half (&x)[4], uint32_t addr)
+{
+  asm volatile("ld.shared.v4.u16 {%0, %1, %2, %3}, [%4];"
+               : "=h"(*reinterpret_cast<uint16_t*>(x)),
+                 "=h"(*reinterpret_cast<uint16_t*>(x + 1)),
+                 "=h"(*reinterpret_cast<uint16_t*>(x + 2)),
+                 "=h"(*reinterpret_cast<uint16_t*>(x + 3))
+               : "r"(addr));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void lds(uint32_t& x, uint32_t addr)
+{
+  asm volatile("ld.shared.u32 {%0}, [%1];" : "=r"(x) : "r"(addr));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void lds(uint32_t& x, const uint32_t* addr)
+{
+  lds(x, uint32_t(__cvta_generic_to_shared(addr)));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void lds(uint4& x, uint32_t addr)
+{
+  asm volatile("ld.shared.v4.u32 {%0, %1, %2, %3}, [%4];"
+               : "=r"(x.x), "=r"(x.y), "=r"(x.z), "=r"(x.w)
+               : "r"(addr));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void lds(uint4& x, const uint4* addr)
+{
+  lds(x, uint32_t(__cvta_generic_to_shared(addr)));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void sts(uint32_t addr, const half2& x)
+{
+  asm volatile("st.shared.v2.u16 [%0], {%1, %2};"
+               :
+               : "r"(addr),
+                 "h"(reinterpret_cast<const uint16_t&>(x.x)),
+                 "h"(reinterpret_cast<const uint16_t&>(x.y)));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void ldg_cg(uint4& x, const uint4* addr)
+{
+  asm volatile("ld.global.cg.v4.u32 {%0, %1, %2, %3}, [%4];"
+               : "=r"(x.x), "=r"(x.y), "=r"(x.z), "=r"(x.w)
+               : "l"(addr));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(uint4& x, const uint4* addr)
+{
+  asm volatile("ld.global.ca.v4.u32 {%0, %1, %2, %3}, [%4];"
+               : "=r"(x.x), "=r"(x.y), "=r"(x.z), "=r"(x.w)
+               : "l"(addr));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(uint32_t& x, const uint32_t* addr)
+{
+  asm volatile("ld.global.ca.u32 %0, [%1];" : "=r"(x) : "l"(addr));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void ldg_cg(uint32_t& x, const uint32_t* addr)
+{
+  asm volatile("ld.global.cg.u32 %0, [%1];" : "=r"(x) : "l"(addr));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(half& x, const half* addr)
+{
+  asm volatile("ld.global.ca.u16 {%0}, [%1];"
+               : "=h"(reinterpret_cast<uint16_t&>(x))
+               : "l"(reinterpret_cast<const uint16_t*>(addr)));
+}
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(half (&x)[1], const half* addr)
+{
+  asm volatile("ld.global.ca.u16 {%0}, [%1];"
+               : "=h"(*reinterpret_cast<uint16_t*>(x))
+               : "l"(reinterpret_cast<const uint16_t*>(addr)));
+}
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(half (&x)[2], const half* addr)
+{
+  asm volatile("ld.global.ca.v2.u16 {%0, %1}, [%2];"
+               : "=h"(*reinterpret_cast<uint16_t*>(x)), "=h"(*reinterpret_cast<uint16_t*>(x + 1))
+               : "l"(reinterpret_cast<const uint16_t*>(addr)));
+}
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(half (&x)[4], const half* addr)
+{
+  asm volatile("ld.global.ca.v4.u16 {%0, %1, %2, %3}, [%4];"
+               : "=h"(*reinterpret_cast<uint16_t*>(x)),
+                 "=h"(*reinterpret_cast<uint16_t*>(x + 1)),
+                 "=h"(*reinterpret_cast<uint16_t*>(x + 2)),
+                 "=h"(*reinterpret_cast<uint16_t*>(x + 3))
+               : "l"(reinterpret_cast<const uint16_t*>(addr)));
+}
+
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(half2& x, const half* addr)
+{
+  asm volatile("ld.global.ca.u32 %0, [%1];"
+               : "=r"(reinterpret_cast<uint32_t&>(x))
+               : "l"(reinterpret_cast<const uint32_t*>(addr)));
+}
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(half2 (&x)[1], const half* addr)
+{
+  asm volatile("ld.global.ca.u32 %0, [%1];"
+               : "=r"(*reinterpret_cast<uint32_t*>(x))
+               : "l"(reinterpret_cast<const uint32_t*>(addr)));
+}
+RAFT_DEVICE_INLINE_FUNCTION void ldg_ca(half2 (&x)[2], const half* addr)
+{
+  asm volatile("ld.global.ca.v2.u32 {%0, %1}, [%2];"
+               : "=r"(*reinterpret_cast<uint32_t*>(x)), "=r"(*reinterpret_cast<uint32_t*>(x + 1))
+               : "l"(reinterpret_cast<const uint32_t*>(addr)));
+}
+
 }  // namespace device
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index 183d6051fc..1c99f72f77 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include "compute_distance-ext.cuh"
 #include "search_multi_cta.cuh"
 #include "search_multi_kernel.cuh"
 #include "search_plan.cuh"
@@ -25,71 +26,153 @@
 
 namespace cuvs::neighbors::cagra::detail {
 
-template <typename DATASET_DESCRIPTOR_T,
+template <typename DataT,
+          typename IndexT,
+          typename DistanceT,
           typename CagraSampleFilterT = cuvs::neighbors::filtering::none_cagra_sample_filter>
 class factory {
-  using T         = typename DATASET_DESCRIPTOR_T::DATA_T;
-  using IdxT      = typename DATASET_DESCRIPTOR_T::INDEX_T;
-  using DistanceT = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
-
  public:
   /**
    * Create a search structure for dataset with dim features.
    */
-  static std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>> create(
+  static std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT>> create(
     raft::resources const& res,
     search_params const& params,
+    const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
     int64_t dim,
     int64_t graph_degree,
-    uint32_t topk,
-    const cuvs::distance::DistanceType metric)
+    uint32_t topk)
   {
-    search_plan_impl_base plan(params, dim, graph_degree, topk, metric);
-    switch (plan.dataset_block_dim) {
-      case 128:
-        switch (plan.team_size) {
-          case 8: return dispatch_kernel<128, 8>(res, plan); break;
-          default: THROW("Incorrect team size %lu", plan.team_size);
-        }
-        break;
-      case 256:
-        switch (plan.team_size) {
-          case 16: return dispatch_kernel<256, 16>(res, plan); break;
-          default: THROW("Incorrect team size %lu", plan.team_size);
-        }
-        break;
-      case 512:
-        switch (plan.team_size) {
-          case 32: return dispatch_kernel<512, 32>(res, plan); break;
-          default: THROW("Incorrect team size %lu", plan.team_size);
-        }
-        break;
-      default: THROW("Incorrect dataset_block_dim (%lu)\n", plan.dataset_block_dim);
-    }
-    return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>();
+    search_plan_impl_base plan(params, dim, graph_degree, topk);
+    return dispatch_kernel(res, plan, dataset_desc);
   }
 
  private:
-  template <unsigned DATASET_BLOCK_DIM, unsigned TEAM_SIZE>
-  static std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>
-  dispatch_kernel(raft::resources const& res, search_plan_impl_base& plan)
+  static std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT>>
+  dispatch_kernel(raft::resources const& res,
+                  search_plan_impl_base& plan,
+                  const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc)
   {
     if (plan.algo == search_algo::SINGLE_CTA) {
-      return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>(
-        new single_cta_search::
-          search<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, CagraSampleFilterT>(
-            res, plan, plan.dim, plan.graph_degree, plan.topk, plan.metric));
+      return std::make_unique<
+        single_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
+        res, plan, dataset_desc, plan.dim, plan.graph_degree, plan.topk);
     } else if (plan.algo == search_algo::MULTI_CTA) {
-      return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>(
-        new multi_cta_search::
-          search<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, CagraSampleFilterT>(
-            res, plan, plan.dim, plan.graph_degree, plan.topk, plan.metric));
+      return std::make_unique<
+        multi_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
+        res, plan, dataset_desc, plan.dim, plan.graph_degree, plan.topk);
     } else {
-      return std::unique_ptr<search_plan_impl<DATASET_DESCRIPTOR_T, CagraSampleFilterT>>(
-        new multi_kernel_search::
-          search<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, CagraSampleFilterT>(
-            res, plan, plan.dim, plan.graph_degree, plan.topk, plan.metric));
+      return std::make_unique<
+        multi_kernel_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
+        res, plan, dataset_desc, plan.dim, plan.graph_degree, plan.topk);
     }
   }
 };
+
+/*
+Caching of dataset/distance descriptor initialization
+  (see `dataset_descriptor_init_with_cache` below).
+ */
+namespace descriptor_cache {
+
+/**
+ * The key for caching consists of a minimal set of fields that uniquely define the descriptor.
+ * The key field names are the same as of the descriptor and the contents are not relevant for
+ * caching.
+ */
+struct key {
+  uint64_t data_ptr;
+  uint64_t n_rows;
+  uint32_t dim;
+  uint32_t extra_val;  // this one has different meanings for different descriptor types
+  uint32_t team_size;
+  uint32_t metric;
+};
+
+template <typename DatasetT>
+auto make_key(const cagra::search_params& params,
+              const DatasetT& dataset,
+              cuvs::distance::DistanceType metric)
+  -> std::enable_if_t<is_strided_dataset_v<DatasetT>, key>
+{
+  return key{reinterpret_cast<uint64_t>(dataset.view().data_handle()),
+             uint64_t(dataset.n_rows()),
+             dataset.dim(),
+             dataset.stride(),
+             uint32_t(params.team_size),
+             uint32_t(metric)};
+}
+
+template <typename DatasetT>
+auto make_key(const cagra::search_params& params,
+              const DatasetT& dataset,
+              cuvs::distance::DistanceType metric)
+  -> std::enable_if_t<is_vpq_dataset_v<DatasetT>, key>
+{
+  return key{reinterpret_cast<uint64_t>(dataset.data.data_handle()),
+             uint64_t(dataset.n_rows()),
+             dataset.dim(),
+             uint32_t(reinterpret_cast<uint64_t>(dataset.pq_code_book.data_handle()) >> 6),
+             uint32_t(params.team_size),
+             uint32_t(metric)};
+}
+
+inline auto operator==(const key& a, const key& b) -> bool
+{
+  return a.data_ptr == b.data_ptr && a.n_rows == b.n_rows && a.dim == b.dim &&
+         a.extra_val == b.extra_val && a.team_size == b.team_size && a.metric == b.metric;
+}
+
+struct key_hash {
+  inline auto operator()(const key& x) const noexcept -> std::size_t
+  {
+    return size_t{x.data_ptr} + size_t{x.n_rows} * size_t{x.dim} * size_t{x.extra_val} +
+           (size_t{x.team_size} ^ size_t{x.metric});
+  }
+};
+
+template <typename DataT, typename IndexT, typename DistanceT>
+struct store {
+  /** Number of descriptors to cache. */
+  static constexpr size_t kDefaultSize = 100;
+  raft::cache::lru<key,
+                   key_hash,
+                   std::equal_to<>,
+                   std::shared_ptr<dataset_descriptor_host<DataT, IndexT, DistanceT>>>
+    value{kDefaultSize};
+};
+
+}  // namespace descriptor_cache
+
+/**
+ * Call `dataset_descriptor_init` with memoization.
+ * (NB: `dataset_descriptor_init` is a function in a generated header file
+ * `neighbors/detail/cagra/compute_distance-ext.cuh`).
+ *
+ * `dataset_descriptor_init`  involves calling a CUDA kernel to resolve device symbols before the
+ * main search kernel runs. This adds an extra unwanted latency.
+ * Caching the the descriptor helps to hide this latency for repeated searches.
+ *
+ */
+template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
+auto dataset_descriptor_init_with_cache(const raft::resources& res,
+                                        const cagra::search_params& params,
+                                        const DatasetT& dataset,
+                                        cuvs::distance::DistanceType metric)
+  -> const dataset_descriptor_host<DataT, IndexT, DistanceT>&
+{
+  using desc_t = dataset_descriptor_host<DataT, IndexT, DistanceT>;
+  auto key     = descriptor_cache::make_key(params, dataset, metric);
+  auto& cache =
+    raft::resource::get_custom_resource<descriptor_cache::store<DataT, IndexT, DistanceT>>(res)
+      ->value;
+  std::shared_ptr<desc_t> desc{nullptr};
+  if (!cache.get(key, &desc)) {
+    desc = std::make_shared<desc_t>(std::move(dataset_descriptor_init<DataT, IndexT, DistanceT>(
+      params, dataset, metric, raft::resource::get_cuda_stream(res))));
+    cache.set(key, desc);
+  }
+  return *desc;
+}
+
 };  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 515be75df0..9edbbf5c1d 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -73,12 +73,12 @@ __device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2, bool a
 }
 
 template <class DATA_T, class IdxT, int numElementsPerThread>
-RAFT_KERNEL kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size, dataset_dim]
-                      const IdxT dataset_size,
-                      const uint32_t dataset_dim,
-                      IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
-                      const uint32_t graph_size,
-                      const uint32_t graph_degree)
+__global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size, dataset_dim]
+                          const IdxT dataset_size,
+                          const uint32_t dataset_dim,
+                          IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
+                          const uint32_t graph_size,
+                          const uint32_t graph_degree)
 {
   const IdxT srcNode = (blockDim.x * blockIdx.x + threadIdx.x) / raft::WarpSize;
   if (srcNode >= graph_size) { return; }
@@ -129,15 +129,15 @@ RAFT_KERNEL kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size, data
 }
 
 template <int MAX_DEGREE, class IdxT>
-RAFT_KERNEL kern_prune(const IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
-                       const uint32_t graph_size,
-                       const uint32_t graph_degree,
-                       const uint32_t degree,
-                       const uint32_t batch_size,
-                       const uint32_t batch_id,
-                       uint8_t* const detour_count,          // [graph_chunk_size, graph_degree]
-                       uint32_t* const num_no_detour_edges,  // [graph_size]
-                       uint64_t* const stats)
+__global__ void kern_prune(const IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
+                           const uint32_t graph_size,
+                           const uint32_t graph_degree,
+                           const uint32_t degree,
+                           const uint32_t batch_size,
+                           const uint32_t batch_id,
+                           uint8_t* const detour_count,          // [graph_chunk_size, graph_degree]
+                           uint32_t* const num_no_detour_edges,  // [graph_size]
+                           uint64_t* const stats)
 {
   __shared__ uint32_t smem_num_detour[MAX_DEGREE];
   uint64_t* const num_retain = stats;
@@ -192,11 +192,11 @@ RAFT_KERNEL kern_prune(const IdxT* const knn_graph,  // [graph_chunk_size, graph
 }
 
 template <class IdxT>
-RAFT_KERNEL kern_make_rev_graph(const IdxT* const dest_nodes,     // [graph_size]
-                                IdxT* const rev_graph,            // [size, degree]
-                                uint32_t* const rev_graph_count,  // [graph_size]
-                                const uint32_t graph_size,
-                                const uint32_t degree)
+__global__ void kern_make_rev_graph(const IdxT* const dest_nodes,     // [graph_size]
+                                    IdxT* const rev_graph,            // [size, degree]
+                                    uint32_t* const rev_graph_count,  // [graph_size]
+                                    const uint32_t graph_size,
+                                    const uint32_t degree)
 {
   const uint32_t tid  = threadIdx.x + (blockDim.x * blockIdx.x);
   const uint32_t tnum = blockDim.x * gridDim.x;
@@ -221,16 +221,16 @@ __device__ __host__ LabelT get_root_label(IdxT i, const LabelT* label)
 }
 
 template <class IdxT>
-RAFT_KERNEL kern_mst_opt_update_graph(IdxT* mst_graph,                 // [graph_size, graph_degree]
-                                      const IdxT* candidate_edges,     // [graph_size]
-                                      IdxT* outgoing_num_edges,        // [graph_size]
-                                      IdxT* incoming_num_edges,        // [graph_size]
-                                      const IdxT* outgoing_max_edges,  // [graph_size]
-                                      const IdxT* incoming_max_edges,  // [graph_size]
-                                      const IdxT* label,               // [graph_size]
-                                      const uint32_t graph_size,
-                                      const uint32_t graph_degree,
-                                      uint64_t* stats)
+__global__ void kern_mst_opt_update_graph(IdxT* mst_graph,  // [graph_size, graph_degree]
+                                          const IdxT* candidate_edges,     // [graph_size]
+                                          IdxT* outgoing_num_edges,        // [graph_size]
+                                          IdxT* incoming_num_edges,        // [graph_size]
+                                          const IdxT* outgoing_max_edges,  // [graph_size]
+                                          const IdxT* incoming_max_edges,  // [graph_size]
+                                          const IdxT* label,               // [graph_size]
+                                          const uint32_t graph_size,
+                                          const uint32_t graph_degree,
+                                          uint64_t* stats)
 {
   const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
   if (i >= graph_size) return;
@@ -310,11 +310,11 @@ RAFT_KERNEL kern_mst_opt_update_graph(IdxT* mst_graph,                 // [graph
 }
 
 template <class IdxT>
-RAFT_KERNEL kern_mst_opt_labeling(IdxT* label,            // [graph_size]
-                                  const IdxT* mst_graph,  // [graph_size, graph_degree]
-                                  const uint32_t graph_size,
-                                  const uint32_t graph_degree,
-                                  uint64_t* stats)
+__global__ void kern_mst_opt_labeling(IdxT* label,            // [graph_size]
+                                      const IdxT* mst_graph,  // [graph_size, graph_degree]
+                                      const uint32_t graph_size,
+                                      const uint32_t graph_degree,
+                                      uint64_t* stats)
 {
   const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
   if (i >= graph_size) return;
@@ -348,10 +348,10 @@ RAFT_KERNEL kern_mst_opt_labeling(IdxT* label,            // [graph_size]
 }
 
 template <class IdxT>
-RAFT_KERNEL kern_mst_opt_cluster_size(IdxT* cluster_size,  // [graph_size]
-                                      const IdxT* label,   // [graph_size]
-                                      const uint32_t graph_size,
-                                      uint64_t* stats)
+__global__ void kern_mst_opt_cluster_size(IdxT* cluster_size,  // [graph_size]
+                                          const IdxT* label,   // [graph_size]
+                                          const uint32_t graph_size,
+                                          uint64_t* stats)
 {
   const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
   if (i >= graph_size) return;
@@ -375,14 +375,14 @@ RAFT_KERNEL kern_mst_opt_cluster_size(IdxT* cluster_size,  // [graph_size]
 }
 
 template <class IdxT>
-RAFT_KERNEL kern_mst_opt_postprocessing(IdxT* outgoing_num_edges,  // [graph_size]
-                                        IdxT* incoming_num_edges,  // [graph_size]
-                                        IdxT* outgoing_max_edges,  // [graph_size]
-                                        IdxT* incoming_max_edges,  // [graph_size]
-                                        const IdxT* cluster_size,  // [graph_size]
-                                        const uint32_t graph_size,
-                                        const uint32_t graph_degree,
-                                        uint64_t* stats)
+__global__ void kern_mst_opt_postprocessing(IdxT* outgoing_num_edges,  // [graph_size]
+                                            IdxT* incoming_num_edges,  // [graph_size]
+                                            IdxT* outgoing_max_edges,  // [graph_size]
+                                            IdxT* incoming_max_edges,  // [graph_size]
+                                            const IdxT* cluster_size,  // [graph_size]
+                                            const uint32_t graph_size,
+                                            const uint32_t graph_degree,
+                                            uint64_t* stats)
 {
   const uint64_t i = threadIdx.x + (blockDim.x * blockIdx.x);
   if (i >= graph_size) return;
diff --git a/cpp/src/neighbors/detail/cagra/hashmap.hpp b/cpp/src/neighbors/detail/cagra/hashmap.hpp
index dd6c6c844e..2c62dda90e 100644
--- a/cpp/src/neighbors/detail/cagra/hashmap.hpp
+++ b/cpp/src/neighbors/detail/cagra/hashmap.hpp
@@ -29,10 +29,12 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace hashmap {
 
-_RAFT_HOST_DEVICE inline uint32_t get_size(const uint32_t bitlen) { return 1U << bitlen; }
+RAFT_INLINE_FUNCTION uint32_t get_size(const uint32_t bitlen) { return 1U << bitlen; }
 
 template <class IdxT>
-_RAFT_DEVICE inline void init(IdxT* const table, const unsigned bitlen, unsigned FIRST_TID = 0)
+RAFT_DEVICE_INLINE_FUNCTION void init(IdxT* const table,
+                                      const unsigned bitlen,
+                                      unsigned FIRST_TID = 0)
 {
   if (threadIdx.x < FIRST_TID) return;
   for (unsigned i = threadIdx.x - FIRST_TID; i < get_size(bitlen); i += blockDim.x - FIRST_TID) {
@@ -41,7 +43,9 @@ _RAFT_DEVICE inline void init(IdxT* const table, const unsigned bitlen, unsigned
 }
 
 template <class IdxT>
-_RAFT_DEVICE inline uint32_t insert(IdxT* const table, const uint32_t bitlen, const IdxT key)
+RAFT_DEVICE_INLINE_FUNCTION uint32_t insert(IdxT* const table,
+                                            const uint32_t bitlen,
+                                            const IdxT key)
 {
   // Open addressing is used for collision resolution
   const uint32_t size     = get_size(bitlen);
@@ -68,7 +72,9 @@ _RAFT_DEVICE inline uint32_t insert(IdxT* const table, const uint32_t bitlen, co
 }
 
 template <unsigned TEAM_SIZE, class IdxT>
-_RAFT_DEVICE inline uint32_t insert(IdxT* const table, const uint32_t bitlen, const IdxT key)
+RAFT_DEVICE_INLINE_FUNCTION uint32_t insert(IdxT* const table,
+                                            const uint32_t bitlen,
+                                            const IdxT key)
 {
   IdxT ret = 0;
   if (threadIdx.x % TEAM_SIZE == 0) { ret = insert(table, bitlen, key); }
@@ -78,5 +84,17 @@ _RAFT_DEVICE inline uint32_t insert(IdxT* const table, const uint32_t bitlen, co
   return ret;
 }
 
+template <class IdxT>
+RAFT_DEVICE_INLINE_FUNCTION uint32_t
+insert(unsigned team_size, IdxT* const table, const uint32_t bitlen, const IdxT key)
+{
+  IdxT ret = 0;
+  if (threadIdx.x % team_size == 0) { ret = insert(table, bitlen, key); }
+  for (unsigned offset = 1; offset < team_size; offset *= 2) {
+    ret |= __shfl_xor_sync(0xffffffff, ret, offset);
+  }
+  return ret;
+}
+
 }  // namespace hashmap
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py
deleted file mode 100644
index 63171373f7..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_00_generate.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-header = """/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-#include "compute_distance_vpq.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-"""
-
-trailer = """
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
-"""
-
-mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
-pq_bits = [8]
-subspace_dims = [2, 4]
-# block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
-# mxelem = [64, 128, 256]
-load_types = ["uint4"]
-code_book_types = ["half"]
-search_types = dict(
-    float_uint32=(
-        "float",
-        "uint32_t",
-        "float",
-    ),  # data_t, vec_idx_t, distance_t
-    half_uint32=("half", "uint32_t", "float"),
-    int8_uint32=("int8_t", "uint32_t", "float"),
-    uint8_uint32=("uint8_t", "uint32_t", "float"),
-    float_uint64=("float", "uint64_t", "float"),
-    half_uint64=("half", "uint64_t", "float"),
-)
-# knn
-for type_path, (data_t, idx_t, distance_t) in search_types.items():
-    for (mxdim, team) in mxdim_team:
-        for code_book_t in code_book_types:
-            for subspace_dim in subspace_dims:
-                for pq_bit in pq_bits:
-                    path = f"q_search_multi_cta_{type_path}_dim{mxdim}_t{team}_{pq_bit}pq_{subspace_dim}subd_{code_book_t}.cu"
-                    with open(path, "w") as f:
-                        f.write(header)
-                        f.write(
-                                f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<{data_t} COMMA {code_book_t} COMMA {pq_bit} COMMA {subspace_dim} COMMA {distance_t} COMMA {idx_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
-                        )
-                        f.write(trailer)
-                        # For pasting into CMakeLists.txt
-                    print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 5d94a501aa..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index 56534dc05e..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 7ff962058b..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 3387a32a3f..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 2d3f2cb1da..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index 73dd8cd4b6..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index b5e33602d6..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index 32fe0d6289..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index e2726ea26c..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index b4ebd49c44..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 72f198c92b..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index dfb667a7fd..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index c583569f6f..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index fedfb51464..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 2b6e8e3da8..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index 4a97fb7525..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 675cd3c937..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index b42b3289c0..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 0db4296f1e..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index 4a2610dc7a..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index b1c15662ef..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index 201f68fb53..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 26744ed766..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 1bce71bef6..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 694304f3c4..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index e6a563731e..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index 5c554af3f0..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index 965b43c076..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 97a4f80921..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index bdd1719b36..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index e39bc1e2d2..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 599cf327aa..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 621c5a249d..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index cbed3ef8a0..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index 7428bfd9eb..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index 70efefdb0d..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 4039b85822..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index 022eb0e051..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index e48b2ed714..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 64f08530f4..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_multi_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_multi_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_multi_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py
deleted file mode 100644
index bc5f506ac2..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_00_generate.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-header = """/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-#include "compute_distance_vpq.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-"""
-
-trailer = """
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
-"""
-
-mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
-# block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
-# itopk_candidates = [64, 128, 256]
-# itopk_size = [64, 128, 256, 512]
-# mxelem = [64, 128, 256]
-
-pq_bits = [8]
-subspace_dims = [2, 4]
-
-# rblock = [(256, 4), (512, 2), (1024, 1)]
-# rcandidates = [32]
-# rsize = [256, 512]
-code_book_types = ["half"]
-
-search_types = dict(
-    float_uint32=("float", "uint32_t", "float"),  # data_t, idx_t, distance_t
-    half_uint32=("half", "uint32_t", "float"),
-    int8_uint32=("int8_t", "uint32_t", "float"),
-    uint8_uint32=("uint8_t", "uint32_t", "float"),
-    float_uint64=("float", "uint64_t", "float"),
-    half_uint64=("half", "uint64_t", "float"),
-)
-
-# knn
-for type_path, (data_t, idx_t, distance_t) in search_types.items():
-    for (mxdim, team) in mxdim_team:
-        for code_book_t in code_book_types:
-            for subspace_dim in subspace_dims:
-                for pq_bit in pq_bits:
-                    path = f"q_search_single_cta_{type_path}_dim{mxdim}_t{team}_{pq_bit}pq_{subspace_dim}subd_{code_book_t}.cu"
-                    with open(path, "w") as f:
-                        f.write(header)
-                        f.write(
-                                f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<{data_t} COMMA {code_book_t} COMMA {pq_bit} COMMA {subspace_dim} COMMA {distance_t} COMMA {idx_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
-                        )
-
-                        f.write(trailer)
-                        # For pasting into CMakeLists.txt
-                        print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index b403227418..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index 36273d0d42..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index ef483437ad..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index d9ebb1b851..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index e86524ee0d..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index 9f2b7fbc77..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 1ce4f5520e..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 2d6f93ef0b..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 5f3267410e..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index 631ac7938b..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index ea8faee1c9..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index 061b1a04ef..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 15610d853d..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index f984b46f0a..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 45299f2727..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index fcb91be8c8..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_float_uint64_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               float COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index b594fedab5..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index a82be6b554..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index d80fef52c8..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index e2c3ef4f76..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 98889811db..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index f5e9d12c9c..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 4f14910b4e..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 67d52f8d5c..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 1420918a1b..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index eb0a72da3e..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index 7a98b59a9f..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index 7e07033c73..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 857f327122..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index 3c00c52234..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index e5c4c7b69f..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 22359d71b1..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_half_uint64_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               half COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint64_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 37c783f192..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index 0a4049d793..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index 773f567c46..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index dfc176abd9..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 680c326557..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index e57881e828..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 525004f2e2..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 7af2ef124c..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_int8_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               int8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
deleted file mode 100644
index 0fd36c31b5..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
deleted file mode 100644
index d4cc5f4490..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim1024_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             1024,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
deleted file mode 100644
index aa58ac2b7b..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
deleted file mode 100644
index 189c3ed9c7..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim128_t8_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(8,
-                             128,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
deleted file mode 100644
index 9dc9aaae33..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
deleted file mode 100644
index 1001103138..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim256_t16_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(16,
-                             256,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
deleted file mode 100644
index 8d4e0aeeed..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_2subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 2 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu b/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
deleted file mode 100644
index 4c73187351..0000000000
--- a/cpp/src/neighbors/detail/cagra/q_search_single_cta_uint8_uint32_dim512_t32_8pq_4subd_half.cu
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by q_search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python q_search_single_cta_00_generate.py
- *
- */
-
-#include "compute_distance_vpq.cuh"
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(32,
-                             512,
-                             cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<
-                               uint8_t COMMA half COMMA 8 COMMA 4 COMMA float COMMA uint32_t>,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index efbf9b56d8..9bcccd9f9e 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -16,12 +16,12 @@
 #pragma once
 
 #include "bitonic.hpp"
-#include "compute_distance.hpp"
+#include "compute_distance-ext.cuh"
 #include "device_common.hpp"
 #include "hashmap.hpp"
 #include "search_multi_cta_kernel.cuh"
 #include "search_plan.cuh"
-#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk if possible
+#include "topk_for_cagra/topk.h"  // TODO replace with raft topk if possible
 #include "utils.hpp"
 
 #include <raft/core/detail/macros.hpp>
@@ -51,48 +51,46 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace multi_cta_search {
 
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
-
-struct search : public search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
-  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
-  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
-  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_queries;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::itopk_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::algo;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::team_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::search_width;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::min_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::thread_block_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_mode;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_min_bitlen;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_max_fill_rate;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_random_samplings;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::rand_xor_mask;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dim;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::graph_degree;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::topk;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hash_bitlen;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_bitlen;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_reset_interval;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dataset_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::result_buffer_size;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::smem_size;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_executed_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dev_seed;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_seeds;
+template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
+struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
+  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T>;
+  using DATA_T     = typename base_type::DATA_T;
+  using INDEX_T    = typename base_type ::INDEX_T;
+  using DISTANCE_T = typename base_type::DISTANCE_T;
+
+  using base_type::algo;
+  using base_type::hashmap_max_fill_rate;
+  using base_type::hashmap_min_bitlen;
+  using base_type::hashmap_mode;
+  using base_type::itopk_size;
+  using base_type::max_iterations;
+  using base_type::max_queries;
+  using base_type::min_iterations;
+  using base_type::num_random_samplings;
+  using base_type::rand_xor_mask;
+  using base_type::search_width;
+  using base_type::team_size;
+  using base_type::thread_block_size;
+
+  using base_type::dim;
+  using base_type::graph_degree;
+  using base_type::topk;
+
+  using base_type::hash_bitlen;
+
+  using base_type::dataset_size;
+  using base_type::hashmap_size;
+  using base_type::result_buffer_size;
+  using base_type::small_hash_bitlen;
+  using base_type::small_hash_reset_interval;
+
+  using base_type::smem_size;
+
+  using base_type::dataset_desc;
+  using base_type::dev_seed;
+  using base_type::hashmap;
+  using base_type::num_executed_iterations;
+  using base_type::num_seeds;
 
   uint32_t num_cta_per_query;
   rmm::device_uvector<INDEX_T> intermediate_indices;
@@ -102,12 +100,11 @@ struct search : public search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
 
   search(raft::resources const& res,
          search_params params,
+         const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
          int64_t dim,
          int64_t graph_degree,
-         uint32_t topk,
-         cuvs::distance::DistanceType metric)
-    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
-        res, params, dim, graph_degree, topk, metric),
+         uint32_t topk)
+    : base_type(res, params, dataset_desc, dim, graph_degree, topk),
       intermediate_indices(0, raft::resource::get_cuda_stream(res)),
       intermediate_distances(0, raft::resource::get_cuda_stream(res)),
       topk_workspace(0, raft::resource::get_cuda_stream(res))
@@ -129,13 +126,9 @@ struct search : public search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
     // constexpr unsigned max_result_buffer_size = 256;
     RAFT_EXPECTS(result_buffer_size_32 <= 256, "Result buffer size cannot exceed 256");
 
-    const auto query_smem_buffer_length =
-      raft::ceildiv<uint32_t>(dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
-
-    smem_size = sizeof(float) * query_smem_buffer_length +
+    smem_size = dataset_desc.smem_ws_size_in_bytes +
                 (sizeof(INDEX_T) + sizeof(DISTANCE_T)) * result_buffer_size_32 +
-                sizeof(uint32_t) * search_width + sizeof(uint32_t) +
-                DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte;
+                sizeof(uint32_t) * search_width + sizeof(uint32_t);
     RAFT_LOG_DEBUG("# smem_size: %u", smem_size);
 
     //
@@ -204,44 +197,37 @@ struct search : public search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
 
   ~search() {}
 
-  void operator()(
-    raft::resources const& res,
-    // raft::device_matrix_view<const DATA_T, int64_t, layout_stride> dataset,
-    DATASET_DESCRIPTOR_T dataset_desc,
-    raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
-      graph,
-    typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
-    typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
-    const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
-    const uint32_t num_queries,
-    const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
-    uint32_t* const num_executed_iterations,                     // [num_queries,]
-    uint32_t topk,
-    SAMPLE_FILTER_T sample_filter)
+  void operator()(raft::resources const& res,
+                  raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
+                  INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+                  DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
+                  const DATA_T* const queries_ptr,       // [num_queries, dataset_dim]
+                  const uint32_t num_queries,
+                  const INDEX_T* dev_seed_ptr,              // [num_queries, num_seeds]
+                  uint32_t* const num_executed_iterations,  // [num_queries,]
+                  uint32_t topk,
+                  SAMPLE_FILTER_T sample_filter)
   {
     cudaStream_t stream = raft::resource::get_cuda_stream(res);
-
-    select_and_run<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
-      dataset_desc,
-      graph,
-      intermediate_indices.data(),
-      intermediate_distances.data(),
-      queries_ptr,
-      num_queries,
-      dev_seed_ptr,
-      num_executed_iterations,
-      *this,
-      topk,
-      thread_block_size,
-      result_buffer_size,
-      smem_size,
-      hash_bitlen,
-      hashmap.data(),
-      num_cta_per_query,
-      num_seeds,
-      sample_filter,
-      this->metric,
-      stream);
+    select_and_run(dataset_desc.dev_ptr(),
+                   graph,
+                   intermediate_indices.data(),
+                   intermediate_distances.data(),
+                   queries_ptr,
+                   num_queries,
+                   dev_seed_ptr,
+                   num_executed_iterations,
+                   *this,
+                   topk,
+                   thread_block_size,
+                   result_buffer_size,
+                   smem_size,
+                   hash_bitlen,
+                   hashmap.data(),
+                   num_cta_per_query,
+                   num_seeds,
+                   sample_filter,
+                   stream);
     RAFT_CUDA_TRY(cudaPeekAtLastError());
 
     // Select the top-k results from the intermediate results
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
index cb63c0e03b..3153a3a9f4 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
@@ -39,8 +39,6 @@
 
 #include "search_multi_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 """
 
@@ -48,7 +46,6 @@
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
 """
 
-mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
 # block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
 # mxelem = [64, 128, 256]
 load_types = ["uint4"]
@@ -66,13 +63,12 @@
 )
 # knn
 for type_path, (data_t, idx_t, distance_t) in search_types.items():
-    for (mxdim, team) in mxdim_team:
-        path = f"search_multi_cta_{type_path}_dim{mxdim}_t{team}.cu"
-        with open(path, "w") as f:
-            f.write(header)
-            f.write(
-                    f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<{data_t} COMMA {idx_t} COMMA {distance_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
-            )
-            f.write(trailer)
-            # For pasting into CMakeLists.txt
-        print(f"src/neighbors/detail/cagra/{path}")
+    path = f"search_multi_cta_{type_path}.cu"
+    with open(path, "w") as f:
+        f.write(header)
+        f.write(
+                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+        )
+        f.write(trailer)
+        # For pasting into CMakeLists.txt
+    print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu
rename to cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
index 2a14699f45..fae5a93877 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim128_t8.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
@@ -25,13 +25,10 @@
 
 #include "search_multi_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(float,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
deleted file mode 100644
index 0bf4a192ff..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
deleted file mode 100644
index a77859b7d2..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
deleted file mode 100644
index ab49fa9f22..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu
rename to cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu
index 157942dc52..88167b8432 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim128_t8.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu
@@ -25,13 +25,10 @@
 
 #include "search_multi_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(float,
+                             uint64_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
deleted file mode 100644
index c38eeb009c..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
deleted file mode 100644
index 3094ddaeb4..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim128_t8.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
deleted file mode 100644
index 91725d1851..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
deleted file mode 100644
index 0f452a6faf..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
rename to cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
index ea38b60c0b..9606d510fe 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32_dim128_t8.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
@@ -25,13 +25,10 @@
 
 #include "search_multi_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(half,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
deleted file mode 100644
index cfe7a7aef1..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
deleted file mode 100644
index 292859382b..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu
rename to cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu
index ee24000379..dafb89cc3d 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32_dim256_t16.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu
@@ -25,13 +25,10 @@
 
 #include "search_multi_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(half,
+                             uint64_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
deleted file mode 100644
index 13044f12d8..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
deleted file mode 100644
index 2ce6f292db..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
deleted file mode 100644
index 2d607eb8db..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
index b1cfaf8702..036a4e414c 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
@@ -21,30 +21,26 @@
 
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 
-#define instantiate_kernel_selection(TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T) \
-  template void select_and_run<TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T>(      \
-    DATASET_DESC_T dataset_desc,                                                                  \
-    raft::device_matrix_view<const typename DATASET_DESC_T::INDEX_T, int64_t, raft::row_major>    \
-      graph,                                                                                      \
-    typename DATASET_DESC_T::INDEX_T* const topk_indices_ptr,                                     \
-    typename DATASET_DESC_T::DISTANCE_T* const topk_distances_ptr,                                \
-    const typename DATASET_DESC_T::DATA_T* const queries_ptr,                                     \
-    const uint32_t num_queries,                                                                   \
-    const typename DATASET_DESC_T::INDEX_T* dev_seed_ptr,                                         \
-    uint32_t* const num_executed_iterations,                                                      \
-    const search_params& ps,                                                                      \
-    uint32_t topk,                                                                                \
-    uint32_t block_size,                                                                          \
-    uint32_t result_buffer_size,                                                                  \
-    uint32_t smem_size,                                                                           \
-    int64_t hash_bitlen,                                                                          \
-    typename DATASET_DESC_T::INDEX_T* hashmap_ptr,                                                \
-    uint32_t num_cta_per_query,                                                                   \
-    uint32_t num_seeds,                                                                           \
-    SAMPLE_FILTER_T sample_filter,                                                                \
-    cuvs::distance::DistanceType metric,                                                          \
+#define instantiate_kernel_selection(DataT, IndexT, DistanceT, SampleFilterT) \
+  template void select_and_run<DataT, IndexT, DistanceT, SampleFilterT>(      \
+    const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,  \
+    raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,   \
+    IndexT* topk_indices_ptr,                                                 \
+    DistanceT* topk_distances_ptr,                                            \
+    const DataT* queries_ptr,                                                 \
+    uint32_t num_queries,                                                     \
+    const IndexT* dev_seed_ptr,                                               \
+    uint32_t* num_executed_iterations,                                        \
+    const search_params& ps,                                                  \
+    uint32_t topk,                                                            \
+    uint32_t block_size,                                                      \
+    uint32_t result_buffer_size,                                              \
+    uint32_t smem_size,                                                       \
+    int64_t hash_bitlen,                                                      \
+    IndexT* hashmap_ptr,                                                      \
+    uint32_t num_cta_per_query,                                               \
+    uint32_t num_seeds,                                                       \
+    SampleFilterT sample_filter,                                              \
     cudaStream_t stream);
 
-#define COMMA ,
-
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
new file mode 100644
index 0000000000..a3322c4359
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(int8_t,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
deleted file mode 100644
index c28adbf808..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
deleted file mode 100644
index af5f133974..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim128_t8.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
deleted file mode 100644
index bcc7b9b8c1..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
deleted file mode 100644
index 916196c356..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
deleted file mode 100644
index e907568f5e..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-ext.cuh
+++ /dev/null
@@ -1,405 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#include "compute_distance_vpq.cuh"
-#include <cuvs/neighbors/common.hpp>    // none_cagra_sample_filter
-#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
-
-#include <cuda_fp16.h>
-
-namespace cuvs::neighbors::cagra::detail {
-namespace multi_cta_search {
-
-#ifdef _CUVS_EXPLICIT_INSTANTIATE_ONLY
-
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
-void select_and_run(
-  DATASET_DESCRIPTOR_T dataset_desc,
-  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
-    graph,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
-  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
-  const uint32_t num_queries,
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
-  uint32_t* const num_executed_iterations,                     // [num_queries,]
-  const search_params& ps,
-  uint32_t topk,
-  // multi_cta_search (params struct)
-  uint32_t block_size,  //
-  uint32_t result_buffer_size,
-  uint32_t smem_size,
-  int64_t hash_bitlen,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
-  uint32_t num_cta_per_query,
-  uint32_t num_seeds,
-  SAMPLE_FILTER_T sample_filter,
-  cuvs::distance::DistanceType metric,
-  cudaStream_t stream) RAFT_EXPLICIT;
-#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
-
-#define instantiate_kernel_selection(                                                           \
-  TEAM_SIZE, MAX_DATASET_DIM, DATA_T, INDEX_T, DISTANCE_T, SAMPLE_FILTER_T)                     \
-  extern template void select_and_run<                                                          \
-    TEAM_SIZE,                                                                                  \
-    MAX_DATASET_DIM,                                                                            \
-    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>, \
-    SAMPLE_FILTER_T>(                                                                           \
-    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>  \
-      dataset_desc,                                                                             \
-    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
-    INDEX_T* const topk_indices_ptr,                                                            \
-    DISTANCE_T* const topk_distances_ptr,                                                       \
-    const DATA_T* const queries_ptr,                                                            \
-    const uint32_t num_queries,                                                                 \
-    const INDEX_T* dev_seed_ptr,                                                                \
-    uint32_t* const num_executed_iterations,                                                    \
-    const search_params& ps,                                                                    \
-    uint32_t topk,                                                                              \
-    uint32_t block_size,                                                                        \
-    uint32_t result_buffer_size,                                                                \
-    uint32_t smem_size,                                                                         \
-    int64_t hash_bitlen,                                                                        \
-    INDEX_T* hashmap_ptr,                                                                       \
-    uint32_t num_cta_per_query,                                                                 \
-    uint32_t num_seeds,                                                                         \
-    SAMPLE_FILTER_T sample_filter,                                                              \
-    cuvs::distance::DistanceType metric,                                                        \
-    cudaStream_t stream);
-
-instantiate_kernel_selection(
-  32, 1024, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  8, 128, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  16, 256, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  32, 512, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  32, 1024, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  8, 128, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  16, 256, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  32, 512, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  32, 1024, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  8, 128, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  16, 256, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  32, 512, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  32, 1024, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  8, 128, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  16, 256, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_kernel_selection(
-  32, 512, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-#undef instantiate_kernel_selection
-
-#define instantiate_q_kernel_selection(TEAM_SIZE,                                               \
-                                       MAX_DATASET_DIM,                                         \
-                                       CODE_BOOK_T,                                             \
-                                       PQ_BITS,                                                 \
-                                       PQ_CODE_BOOK_DIM,                                        \
-                                       DATA_T,                                                  \
-                                       INDEX_T,                                                 \
-                                       DISTANCE_T,                                              \
-                                       SAMPLE_FILTER_T)                                         \
-  extern template void                                                                          \
-  select_and_run<TEAM_SIZE,                                                                     \
-                 MAX_DATASET_DIM,                                                               \
-                 cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,           \
-                                                                              CODE_BOOK_T,      \
-                                                                              PQ_BITS,          \
-                                                                              PQ_CODE_BOOK_DIM, \
-                                                                              DISTANCE_T,       \
-                                                                              INDEX_T>,         \
-                 SAMPLE_FILTER_T>(                                                              \
-    cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,                        \
-                                                                 CODE_BOOK_T,                   \
-                                                                 PQ_BITS,                       \
-                                                                 PQ_CODE_BOOK_DIM,              \
-                                                                 DISTANCE_T,                    \
-                                                                 INDEX_T> dataset_desc,         \
-    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
-    INDEX_T* const topk_indices_ptr,                                                            \
-    DISTANCE_T* const topk_distances_ptr,                                                       \
-    const DATA_T* const queries_ptr,                                                            \
-    const uint32_t num_queries,                                                                 \
-    const INDEX_T* dev_seed_ptr,                                                                \
-    uint32_t* const num_executed_iterations,                                                    \
-    const search_params& ps,                                                                    \
-    uint32_t topk,                                                                              \
-    uint32_t block_size,                                                                        \
-    uint32_t result_buffer_size,                                                                \
-    uint32_t smem_size,                                                                         \
-    int64_t hash_bitlen,                                                                        \
-    INDEX_T* hashmap_ptr,                                                                       \
-    uint32_t num_cta_per_query,                                                                 \
-    uint32_t num_seeds,                                                                         \
-    SAMPLE_FILTER_T sample_filter,                                                              \
-    cuvs::distance::DistanceType metric,                                                        \
-    cudaStream_t stream);
-
-instantiate_q_kernel_selection(
-  8, 128, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(
-  16, 256, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(
-  32, 512, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               2,
-                               half,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(
-  8, 128, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(
-  16, 256, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(
-  32, 512, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               4,
-                               half,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_kernel_selection(
-  8, 128, half, 8, 2, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(16,
-                               256,
-                               half,
-                               8,
-                               2,
-                               float,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               512,
-                               half,
-                               8,
-                               2,
-                               float,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               2,
-                               float,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(
-  8, 128, half, 8, 4, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(16,
-                               256,
-                               half,
-                               8,
-                               4,
-                               float,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               512,
-                               half,
-                               8,
-                               4,
-                               float,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               4,
-                               float,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_kernel_selection(8,
-                               128,
-                               half,
-                               8,
-                               2,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(16,
-                               256,
-                               half,
-                               8,
-                               2,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               512,
-                               half,
-                               8,
-                               2,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               2,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(8,
-                               128,
-                               half,
-                               8,
-                               4,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(16,
-                               256,
-                               half,
-                               8,
-                               4,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               512,
-                               half,
-                               8,
-                               4,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               4,
-                               uint8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_kernel_selection(8,
-                               128,
-                               half,
-                               8,
-                               2,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(16,
-                               256,
-                               half,
-                               8,
-                               2,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               512,
-                               half,
-                               8,
-                               2,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               2,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(8,
-                               128,
-                               half,
-                               8,
-                               4,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(16,
-                               256,
-                               half,
-                               8,
-                               4,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               512,
-                               half,
-                               8,
-                               4,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_kernel_selection(32,
-                               1024,
-                               half,
-                               8,
-                               4,
-                               int8_t,
-                               uint32_t,
-                               float,
-                               cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-#undef instantiate_q_kernel_selection
-}  // namespace multi_cta_search
-}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 4d2030c6c9..dd74ba44bc 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -15,12 +15,14 @@
  */
 #pragma once
 
+#include "search_multi_cta_kernel.cuh"
+
 #include "bitonic.hpp"
-#include "compute_distance.hpp"
+#include "compute_distance-ext.cuh"
 #include "device_common.hpp"
 #include "hashmap.hpp"
 #include "search_plan.cuh"
-#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk if possible
+#include "topk_for_cagra/topk.h"  // TODO replace with raft topk if possible
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
@@ -53,11 +55,12 @@ namespace multi_cta_search {
 // #define _CLK_BREAKDOWN
 
 template <class INDEX_T>
-__device__ void pickup_next_parents(INDEX_T* const next_parent_indices,  // [search_width]
-                                    const uint32_t search_width,
-                                    INDEX_T* const itopk_indices,  // [num_itopk]
-                                    const size_t num_itopk,
-                                    uint32_t* const terminate_flag)
+RAFT_DEVICE_INLINE_FUNCTION void pickup_next_parents(
+  INDEX_T* const next_parent_indices,  // [search_width]
+  const uint32_t search_width,
+  INDEX_T* const itopk_indices,  // [num_itopk]
+  const size_t num_itopk,
+  uint32_t* const terminate_flag)
 {
   constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
   const unsigned warp_id             = threadIdx.x / 32;
@@ -93,10 +96,11 @@ __device__ void pickup_next_parents(INDEX_T* const next_parent_indices,  // [sea
 }
 
 template <unsigned MAX_ELEMENTS, class INDEX_T>
-__device__ inline void topk_by_bitonic_sort(float* distances,  // [num_elements]
-                                            INDEX_T* indices,  // [num_elements]
-                                            const uint32_t num_elements,
-                                            const uint32_t num_itopk  // num_itopk <= num_elements
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort(
+  float* distances,  // [num_elements]
+  INDEX_T* indices,  // [num_elements]
+  const uint32_t num_elements,
+  const uint32_t num_itopk  // num_itopk <= num_elements
 )
 {
   const unsigned warp_id = threadIdx.x / 32;
@@ -130,17 +134,13 @@ __device__ inline void topk_by_bitonic_sort(float* distances,  // [num_elements]
 //
 // multiple CTAs per single query
 //
-template <int32_t TEAM_SIZE,
-          uint32_t DATASET_BLOCK_DIM,
-          std::uint32_t MAX_ELEMENTS,
-          class DATASET_DESCRIPTOR_T,
-          class SAMPLE_FILTER_T>
-__launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
+template <std::uint32_t MAX_ELEMENTS, class DATASET_DESCRIPTOR_T, class SAMPLE_FILTER_T>
+RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   typename DATASET_DESCRIPTOR_T::INDEX_T* const
     result_indices_ptr,  // [num_queries, num_cta_per_query, itopk_size]
   typename DATASET_DESCRIPTOR_T::DISTANCE_T* const
     result_distances_ptr,  // [num_queries, num_cta_per_query, itopk_size]
-  DATASET_DESCRIPTOR_T dataset_desc,
+  const DATASET_DESCRIPTOR_T* dataset_desc,
   const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
   const typename DATASET_DESCRIPTOR_T::INDEX_T* const knn_graph,   // [dataset_size, graph_degree]
   const uint32_t graph_degree,
@@ -156,13 +156,11 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
   const uint32_t min_iteration,
   const uint32_t max_iteration,
   uint32_t* const num_executed_iterations, /* stats */
-  SAMPLE_FILTER_T sample_filter,
-  const cuvs::distance::DistanceType metric)
+  SAMPLE_FILTER_T sample_filter)
 {
   using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
   using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
   using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
-  using QUERY_T    = typename DATASET_DESCRIPTOR_T::QUERY_T;
 
   const auto num_queries       = gridDim.y;
   const auto query_id          = blockIdx.y;
@@ -184,7 +182,7 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
 #endif
   _CLK_START();
 
-  extern __shared__ uint32_t smem[];
+  extern __shared__ uint8_t smem[];
 
   // Layout of result_buffer
   // +----------------+------------------------------+---------+
@@ -192,26 +190,21 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
   // | <itopk_size>   | <search_width * graph_degree> | upto 32 |
   // +----------------+------------------------------+---------+
   // |<---          result_buffer_size           --->|
-  uint32_t result_buffer_size    = itopk_size + (search_width * graph_degree);
-  uint32_t result_buffer_size_32 = result_buffer_size;
-  if (result_buffer_size % 32) { result_buffer_size_32 += 32 - (result_buffer_size % 32); }
+  const auto result_buffer_size    = itopk_size + (search_width * graph_degree);
+  const auto result_buffer_size_32 = raft::round_up_safe<uint32_t>(result_buffer_size, 32);
   assert(result_buffer_size_32 <= MAX_ELEMENTS);
 
-  const auto query_smem_buffer_length =
-    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
-  auto query_buffer          = reinterpret_cast<QUERY_T*>(smem);
-  auto result_indices_buffer = reinterpret_cast<INDEX_T*>(query_buffer + query_smem_buffer_length);
-  auto result_distances_buffer =
+  // Set smem working buffer for the distance calculation
+  dataset_desc = dataset_desc->setup_workspace(smem, queries_ptr, query_id);
+
+  auto* __restrict__ result_indices_buffer =
+    reinterpret_cast<INDEX_T*>(smem + dataset_desc->smem_ws_size_in_bytes());
+  auto* __restrict__ result_distances_buffer =
     reinterpret_cast<DISTANCE_T*>(result_indices_buffer + result_buffer_size_32);
-  auto parent_indices_buffer =
+  auto* __restrict__ parent_indices_buffer =
     reinterpret_cast<INDEX_T*>(result_distances_buffer + result_buffer_size_32);
-  auto distance_work_buffer_ptr =
-    reinterpret_cast<std::uint8_t*>(parent_indices_buffer + search_width);
-  auto terminate_flag = reinterpret_cast<uint32_t*>(distance_work_buffer_ptr +
-                                                    DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte);
-
-  // Set smem working buffer for the distance calculation
-  dataset_desc.set_smem_ptr(distance_work_buffer_ptr);
+  auto* __restrict__ terminate_flag =
+    reinterpret_cast<uint32_t*>(parent_indices_buffer + search_width);
 
 #if 0
     /* debug */
@@ -220,9 +213,6 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
         result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
     }
 #endif
-  const DATA_T* const query_ptr = queries_ptr + (dataset_desc.dim * query_id);
-  dataset_desc.template copy_query<DATASET_BLOCK_DIM>(
-    query_ptr, query_buffer, query_smem_buffer_length);
 
   if (threadIdx.x == 0) { terminate_flag[0] = 0; }
   INDEX_T* const local_visited_hashmap_ptr =
@@ -236,20 +226,18 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
   uint32_t block_id                   = cta_id + (num_cta_per_query * query_id);
   uint32_t num_blocks                 = num_cta_per_query * num_queries;
 
-  device::compute_distance_to_random_nodes<TEAM_SIZE, DATASET_BLOCK_DIM>(result_indices_buffer,
-                                                                         result_distances_buffer,
-                                                                         query_buffer,
-                                                                         dataset_desc,
-                                                                         result_buffer_size,
-                                                                         num_distilation,
-                                                                         rand_xor_mask,
-                                                                         local_seed_ptr,
-                                                                         num_seeds,
-                                                                         local_visited_hashmap_ptr,
-                                                                         hash_bitlen,
-                                                                         metric,
-                                                                         block_id,
-                                                                         num_blocks);
+  device::compute_distance_to_random_nodes(result_indices_buffer,
+                                           result_distances_buffer,
+                                           *dataset_desc,
+                                           result_buffer_size,
+                                           num_distilation,
+                                           rand_xor_mask,
+                                           local_seed_ptr,
+                                           num_seeds,
+                                           local_visited_hashmap_ptr,
+                                           hash_bitlen,
+                                           block_id,
+                                           num_blocks);
   __syncthreads();
   _CLK_REC(clk_compute_1st_distance);
 
@@ -279,21 +267,16 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
 
     // compute the norms between child nodes and query node
     _CLK_START();
-    // constexpr unsigned max_n_frags = 16;
-    constexpr unsigned max_n_frags = 0;
-    device::compute_distance_to_child_nodes<TEAM_SIZE, DATASET_BLOCK_DIM, max_n_frags>(
-      result_indices_buffer + itopk_size,
-      result_distances_buffer + itopk_size,
-      query_buffer,
-      dataset_desc,
-      knn_graph,
-      graph_degree,
-      local_visited_hashmap_ptr,
-      hash_bitlen,
-      parent_indices_buffer,
-      result_indices_buffer,
-      search_width,
-      metric);
+    device::compute_distance_to_child_nodes(result_indices_buffer + itopk_size,
+                                            result_distances_buffer + itopk_size,
+                                            *dataset_desc,
+                                            knn_graph,
+                                            graph_degree,
+                                            local_visited_hashmap_ptr,
+                                            hash_bitlen,
+                                            parent_indices_buffer,
+                                            result_indices_buffer,
+                                            search_width);
     _CLK_REC(clk_compute_distance);
     __syncthreads();
 
@@ -409,84 +392,58 @@ void set_value_batch(T* const dev_ptr,
     <<<grid_size, block_size, 0, cuda_stream>>>(dev_ptr, ld, val, count, batch_size);
 }
 
-template <uint32_t TEAM_SIZE,
-          uint32_t DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
+template <typename DATASET_DESCRIPTOR_T, typename SAMPLE_FILTER_T>
 struct search_kernel_config {
   // Search kernel function type. Note that the actual values for the template value
   // parameters do not matter, because they are not part of the function signature. The
   // second to fourth value parameters will be selected by the choose_* functions below.
-  using kernel_t = decltype(&search_kernel<TEAM_SIZE,
-                                           DATASET_BLOCK_DIM,
-                                           128,
-                                           DATASET_DESCRIPTOR_T,
-                                           SAMPLE_FILTER_T>);
+  using kernel_t = decltype(&search_kernel<128, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>);
 
   static auto choose_buffer_size(unsigned result_buffer_size, unsigned block_size) -> kernel_t
   {
     if (result_buffer_size <= 64) {
-      return search_kernel<TEAM_SIZE, DATASET_BLOCK_DIM, 64, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
+      return search_kernel<64, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
     } else if (result_buffer_size <= 128) {
-      return search_kernel<TEAM_SIZE,
-                           DATASET_BLOCK_DIM,
-                           128,
-                           DATASET_DESCRIPTOR_T,
-                           SAMPLE_FILTER_T>;
+      return search_kernel<128, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
     } else if (result_buffer_size <= 256) {
-      return search_kernel<TEAM_SIZE,
-                           DATASET_BLOCK_DIM,
-                           256,
-                           DATASET_DESCRIPTOR_T,
-                           SAMPLE_FILTER_T>;
+      return search_kernel<256, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
     }
     THROW("Result buffer size %u larger than max buffer size %u", result_buffer_size, 256);
   }
 };
 
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
-void select_and_run(
-  DATASET_DESCRIPTOR_T dataset_desc,
-  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
-    graph,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
-  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
-  const uint32_t num_queries,
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
-  uint32_t* const num_executed_iterations,                     // [num_queries,]
-  const search_params& ps,
-  uint32_t topk,
-  // multi_cta_search (params struct)
-  uint32_t block_size,  //
-  uint32_t result_buffer_size,
-  uint32_t smem_size,
-  int64_t hash_bitlen,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
-  uint32_t num_cta_per_query,
-  uint32_t num_seeds,
-  SAMPLE_FILTER_T sample_filter,
-  cuvs::distance::DistanceType metric,
-  cudaStream_t stream)
+template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
+void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+                    raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
+                    IndexT* topk_indices_ptr,       // [num_queries, topk]
+                    DistanceT* topk_distances_ptr,  // [num_queries, topk]
+                    const DataT* queries_ptr,       // [num_queries, dataset_dim]
+                    uint32_t num_queries,
+                    const IndexT* dev_seed_ptr,         // [num_queries, num_seeds]
+                    uint32_t* num_executed_iterations,  // [num_queries,]
+                    const search_params& ps,
+                    uint32_t topk,
+                    // multi_cta_search (params struct)
+                    uint32_t block_size,  //
+                    uint32_t result_buffer_size,
+                    uint32_t smem_size,
+                    int64_t hash_bitlen,
+                    IndexT* hashmap_ptr,
+                    uint32_t num_cta_per_query,
+                    uint32_t num_seeds,
+                    SampleFilterT sample_filter,
+                    cudaStream_t stream)
 {
   auto kernel =
-    search_kernel_config<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::
-      choose_buffer_size(result_buffer_size, block_size);
+    search_kernel_config<dataset_descriptor_base_t<DataT, IndexT, DistanceT>,
+                         SampleFilterT>::choose_buffer_size(result_buffer_size, block_size);
 
-  RAFT_CUDA_TRY(cudaFuncSetAttribute(kernel,
-                                     cudaFuncAttributeMaxDynamicSharedMemorySize,
-                                     smem_size + DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte));
+  RAFT_CUDA_TRY(
+    cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
   // Initialize hash table
   const uint32_t hash_size = hashmap::get_size(hash_bitlen);
-  set_value_batch(hashmap_ptr,
-                  hash_size,
-                  utils::get_max_value<typename DATASET_DESCRIPTOR_T::INDEX_T>(),
-                  hash_size,
-                  num_queries,
-                  stream);
+  set_value_batch(
+    hashmap_ptr, hash_size, utils::get_max_value<IndexT>(), hash_size, num_queries, stream);
 
   dim3 block_dims(block_size, 1, 1);
   dim3 grid_dims(num_cta_per_query, num_queries, 1);
@@ -513,8 +470,7 @@ void select_and_run(
                                                        ps.min_iterations,
                                                        ps.max_iterations,
                                                        num_executed_iterations,
-                                                       sample_filter,
-                                                       metric);
+                                                       sample_filter);
 }
 
 }  // namespace multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
index 673fc5473f..1ef35f9470 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,10 +15,32 @@
  */
 #pragma once
 
-#ifndef _CUVS_EXPLICIT_INSTANTIATE_ONLY
-#include "search_multi_cta_kernel-inl.cuh"
-#endif
+#include "compute_distance-ext.cuh"
 
-#ifdef RAFT_COMPILED
-#include "search_multi_cta_kernel-ext.cuh"
-#endif
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+
+template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
+void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+                    raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
+                    IndexT* topk_indices_ptr,       // [num_queries, topk]
+                    DistanceT* topk_distances_ptr,  // [num_queries, topk]
+                    const DataT* queries_ptr,       // [num_queries, dataset_dim]
+                    uint32_t num_queries,
+                    const IndexT* dev_seed_ptr,         // [num_queries, num_seeds]
+                    uint32_t* num_executed_iterations,  // [num_queries,]
+                    const search_params& ps,
+                    uint32_t topk,
+                    // multi_cta_search (params struct)
+                    uint32_t block_size,  //
+                    uint32_t result_buffer_size,
+                    uint32_t smem_size,
+                    int64_t hash_bitlen,
+                    IndexT* hashmap_ptr,
+                    uint32_t num_cta_per_query,
+                    uint32_t num_seeds,
+                    SampleFilterT sample_filter,
+                    cudaStream_t stream);
+
+}
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
new file mode 100644
index 0000000000..51fc6526f7
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_multi_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_multi_cta_00_generate.py
+ *
+ */
+
+#include "search_multi_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(uint8_t,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
deleted file mode 100644
index 3fa12d9338..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
deleted file mode 100644
index e2f25a1c24..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim128_t8.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
deleted file mode 100644
index 4cd206d8ce..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
deleted file mode 100644
index 56989a1d57..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
-
-#include "search_multi_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index bc1266fb4b..7b3ecabf37 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -15,12 +15,11 @@
  */
 #pragma once
 
-#include "compute_distance.hpp"
-#include "compute_distance_vpq.cuh"
+#include "compute_distance-ext.cuh"
 #include "device_common.hpp"
 #include "hashmap.hpp"
 #include "search_plan.cuh"
-#include "topk_for_cagra/topk_core.cuh"  //todo replace with raft kernel
+#include "topk_for_cagra/topk.h"  //todo replace with raft kernel
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
@@ -93,9 +92,9 @@ void get_value(T* const host_ptr, const T* const dev_ptr, cudaStream_t cuda_stre
 }
 
 // MAX_DATASET_DIM : must equal to or greater than dataset_dim
-template <unsigned TEAM_SIZE, unsigned DATASET_BLOCK_DIM, class DATASET_DESCRIPTOR_T>
+template <class DATASET_DESCRIPTOR_T>
 RAFT_KERNEL random_pickup_kernel(
-  const DATASET_DESCRIPTOR_T dataset_desc,
+  const DATASET_DESCRIPTOR_T* dataset_desc,
   const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
   const std::size_t num_pickup,
   const unsigned num_distilation,
@@ -106,30 +105,19 @@ RAFT_KERNEL random_pickup_kernel(
   typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldr]
   const std::uint32_t ldr,                                                // (*) ldr >= num_pickup
   typename DATASET_DESCRIPTOR_T::INDEX_T* const visited_hashmap_ptr,  // [num_queries, 1 << bitlen]
-  const std::uint32_t hash_bitlen,
-  const cuvs::distance::DistanceType metric)
+  const std::uint32_t hash_bitlen)
 {
   using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
   using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
   using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
 
+  const auto team_size_bits    = dataset_desc->team_size_bitshift();
   const auto ldb               = hashmap::get_size(hash_bitlen);
-  const auto global_team_index = (blockIdx.x * blockDim.x + threadIdx.x) / TEAM_SIZE;
+  const auto global_team_index = (blockIdx.x * blockDim.x + threadIdx.x) >> team_size_bits;
   const uint32_t query_id      = blockIdx.y;
   if (global_team_index >= num_pickup) { return; }
-  // Load a query
-  extern __shared__ float query_buffer[];
-  const auto query_smem_buffer_length =
-    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
-  for (uint32_t i = threadIdx.x; i < query_smem_buffer_length; i += blockDim.x) {
-    unsigned j = device::swizzling(i);
-    if (i < dataset_desc.dim) {
-      query_buffer[j] = cuvs::spatial::knn::detail::utils::mapping<float>{}(
-        (queries_ptr + query_id * dataset_desc.dim)[i]);
-    } else {
-      query_buffer[j] = 0.0;
-    }
-  }
+  extern __shared__ uint8_t smem[];
+  dataset_desc = dataset_desc->setup_workspace(smem, queries_ptr, query_id);
   __syncthreads();
 
   INDEX_T best_index_team_local;
@@ -141,27 +129,10 @@ RAFT_KERNEL random_pickup_kernel(
     } else {
       // Chose a seed node randomly
       seed_index =
-        device::xorshift64((global_team_index ^ rand_xor_mask) * (i + 1)) % dataset_desc.size;
-    }
-
-    DISTANCE_T norm2;
-    switch (metric) {
-      case cuvs::distance::DistanceType::L2Expanded:
-        norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                         TEAM_SIZE,
-                                                         cuvs::distance::DistanceType::L2Expanded>(
-          query_buffer, seed_index, true);
-        break;
-      case cuvs::distance::DistanceType::InnerProduct:
-        norm2 =
-          dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                   TEAM_SIZE,
-                                                   cuvs::distance::DistanceType::InnerProduct>(
-            query_buffer, seed_index, true);
-        break;
-      default: break;
+        device::xorshift64((global_team_index ^ rand_xor_mask) * (i + 1)) % dataset_desc->size;
     }
 
+    DISTANCE_T norm2 = dataset_desc->compute_distance(seed_index, true);
     if (norm2 < best_norm2_team_local) {
       best_norm2_team_local = norm2;
       best_index_team_local = seed_index;
@@ -169,7 +140,7 @@ RAFT_KERNEL random_pickup_kernel(
   }
 
   const auto store_gmem_index = global_team_index + (ldr * query_id);
-  if (threadIdx.x % TEAM_SIZE == 0) {
+  if ((threadIdx.x & ((1u << team_size_bits) - 1u)) == 0) {
     if (hashmap::insert(
           visited_hashmap_ptr + (ldb * query_id), hash_bitlen, best_index_team_local)) {
       result_distances_ptr[store_gmem_index] = best_norm2_team_local;
@@ -182,47 +153,40 @@ RAFT_KERNEL random_pickup_kernel(
 }
 
 // MAX_DATASET_DIM : must be equal to or greater than dataset_dim
-template <unsigned TEAM_SIZE, unsigned DATASET_BLOCK_DIM, class DATASET_DESCRIPTOR_T>
-void random_pickup(
-  const DATASET_DESCRIPTOR_T dataset_desc,
-  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
-  const std::size_t num_queries,
-  const std::size_t num_pickup,
-  const unsigned num_distilation,
-  const uint64_t rand_xor_mask,
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
-  const uint32_t num_seeds,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, ldr]
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldr]
-  const std::size_t ldr,                                                  // (*) ldr >= num_pickup
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const visited_hashmap_ptr,  // [num_queries, 1 << bitlen]
-  const std::uint32_t hash_bitlen,
-  const cuvs::distance::DistanceType metric,
-  cudaStream_t const cuda_stream = 0)
+template <typename DataT, typename IndexT, typename DistanceT>
+void random_pickup(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
+                   const DataT* queries_ptr,  // [num_queries, dataset_dim]
+                   std::size_t num_queries,
+                   std::size_t num_pickup,
+                   unsigned num_distilation,
+                   uint64_t rand_xor_mask,
+                   const IndexT* seed_ptr,  // [num_queries, num_seeds]
+                   uint32_t num_seeds,
+                   IndexT* result_indices_ptr,       // [num_queries, ldr]
+                   DistanceT* result_distances_ptr,  // [num_queries, ldr]
+                   std::size_t ldr,                  // (*) ldr >= num_pickup
+                   IndexT* visited_hashmap_ptr,      // [num_queries, 1 << bitlen]
+                   std::uint32_t hash_bitlen,
+                   cudaStream_t cuda_stream)
 {
   const auto block_size                = 256u;
-  const auto num_teams_per_threadblock = block_size / TEAM_SIZE;
+  const auto num_teams_per_threadblock = block_size / dataset_desc.team_size;
   const dim3 grid_size((num_pickup + num_teams_per_threadblock - 1) / num_teams_per_threadblock,
                        num_queries);
 
-  const auto query_smem_buffer_length =
-    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
-  const auto smem_size = query_smem_buffer_length * sizeof(float);
-
-  random_pickup_kernel<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T>
-    <<<grid_size, block_size, smem_size, cuda_stream>>>(dataset_desc,
-                                                        queries_ptr,
-                                                        num_pickup,
-                                                        num_distilation,
-                                                        rand_xor_mask,
-                                                        seed_ptr,
-                                                        num_seeds,
-                                                        result_indices_ptr,
-                                                        result_distances_ptr,
-                                                        ldr,
-                                                        visited_hashmap_ptr,
-                                                        hash_bitlen,
-                                                        metric);
+  random_pickup_kernel<<<grid_size, block_size, dataset_desc.smem_ws_size_in_bytes, cuda_stream>>>(
+    dataset_desc.dev_ptr(),
+    queries_ptr,
+    num_pickup,
+    num_distilation,
+    rand_xor_mask,
+    seed_ptr,
+    num_seeds,
+    result_indices_ptr,
+    result_distances_ptr,
+    ldr,
+    visited_hashmap_ptr,
+    hash_bitlen);
 }
 
 template <class INDEX_T>
@@ -325,9 +289,7 @@ void pickup_next_parents(INDEX_T* const parent_candidates_ptr,  // [num_queries,
                                                   terminate_flag);
 }
 
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          class DATASET_DESCRIPTOR_T,
+template <class DATASET_DESCRIPTOR_T,
           class SAMPLE_FILTER_T>
 RAFT_KERNEL compute_distance_to_child_nodes_kernel(
   const typename DATASET_DESCRIPTOR_T::INDEX_T* const
@@ -338,7 +300,7 @@ RAFT_KERNEL compute_distance_to_child_nodes_kernel(
     parent_distance_ptr,  // [num_queries, search_width]
   const std::size_t lds,
   const std::uint32_t search_width,
-  const DATASET_DESCRIPTOR_T dataset_desc,
+  const DATASET_DESCRIPTOR_T* dataset_desc,
   const typename DATASET_DESCRIPTOR_T::INDEX_T* const
     neighbor_graph_ptr,  // [dataset_size, graph_degree]
   const std::uint32_t graph_degree,
@@ -349,29 +311,22 @@ RAFT_KERNEL compute_distance_to_child_nodes_kernel(
   typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, ldd]
   typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldd]
   const std::uint32_t ldd,  // (*) ldd >= search_width * graph_degree
-  SAMPLE_FILTER_T sample_filter,
-  const cuvs::distance::DistanceType metric)
+  SAMPLE_FILTER_T sample_filter)
 {
   using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
   using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
 
+  const auto team_size_bits = dataset_desc->team_size_bitshift();
+  const auto team_size      = 1u << team_size_bits;
   const uint32_t ldb        = hashmap::get_size(hash_bitlen);
   const auto tid            = threadIdx.x + blockDim.x * blockIdx.x;
-  const auto global_team_id = tid / TEAM_SIZE;
+  const auto global_team_id = tid >> team_size_bits;
   const auto query_id       = blockIdx.y;
 
-  extern __shared__ float query_buffer[];
-  const auto query_smem_buffer_length =
-    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
-  for (uint32_t i = threadIdx.x; i < query_smem_buffer_length; i += blockDim.x) {
-    unsigned j = device::swizzling(i);
-    if (i < dataset_desc.dim) {
-      query_buffer[j] = cuvs::spatial::knn::detail::utils::mapping<float>{}(
-        (query_ptr + query_id * dataset_desc.dim)[i]);
-    } else {
-      query_buffer[j] = 0.0;
-    }
-  }
+  extern __shared__ uint8_t smem[];
+  // Load a query
+  dataset_desc = dataset_desc->setup_workspace(smem, query_ptr, query_id);
+
   __syncthreads();
   if (global_team_id >= search_width * graph_degree) { return; }
 
@@ -393,33 +348,18 @@ RAFT_KERNEL compute_distance_to_child_nodes_kernel(
 
   const std::size_t child_id = neighbor_list_head_ptr[global_team_id % graph_degree];
 
-  const auto compute_distance_flag = hashmap::insert<TEAM_SIZE, INDEX_T>(
-    visited_hashmap_ptr + (ldb * blockIdx.y), hash_bitlen, child_id);
-
-  DISTANCE_T norm2;
-  switch (metric) {
-    case cuvs::distance::DistanceType::L2Expanded:
-      norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                       TEAM_SIZE,
-                                                       cuvs::distance::DistanceType::L2Expanded>(
-        query_buffer, child_id, compute_distance_flag);
-      break;
-    case cuvs::distance::DistanceType::InnerProduct:
-      norm2 = dataset_desc.template compute_similarity<DATASET_BLOCK_DIM,
-                                                       TEAM_SIZE,
-                                                       cuvs::distance::DistanceType::InnerProduct>(
-        query_buffer, child_id, compute_distance_flag);
-      break;
-    default: break;
-  }
+  const auto compute_distance_flag = hashmap::insert<INDEX_T>(
+    team_size, visited_hashmap_ptr + (ldb * blockIdx.y), hash_bitlen, child_id);
+
+  DISTANCE_T norm2 = dataset_desc->compute_distance(child_id, compute_distance_flag);
 
   if (compute_distance_flag) {
-    if (threadIdx.x % TEAM_SIZE == 0) {
+    if ((threadIdx.x & (team_size - 1)) == 0) {
       result_indices_ptr[ldd * blockIdx.y + global_team_id]   = child_id;
       result_distances_ptr[ldd * blockIdx.y + global_team_id] = norm2;
     }
   } else {
-    if (threadIdx.x % TEAM_SIZE == 0) {
+    if ((threadIdx.x & (team_size - 1)) == 0) {
       result_distances_ptr[ldd * blockIdx.y + global_team_id] = utils::get_max_value<DISTANCE_T>();
     }
   }
@@ -434,66 +374,52 @@ RAFT_KERNEL compute_distance_to_child_nodes_kernel(
   }
 }
 
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          class SAMPLE_FILTER_T,
-          class DATASET_DESCRIPTOR_T>
+template <typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          class SAMPLE_FILTER_T>
 void compute_distance_to_child_nodes(
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* const
-    parent_node_list,  // [num_queries, search_width]
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const
-    parent_candidates_ptr,  // [num_queries, search_width]
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const
-    parent_distance_ptr,  // [num_queries, search_width]
-  const std::size_t lds,
-  const uint32_t search_width,
-  const DATASET_DESCRIPTOR_T dataset_desc,
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* const
-    neighbor_graph_ptr,  // [dataset_size, graph_degree]
-  const std::uint32_t graph_degree,
-  const typename DATASET_DESCRIPTOR_T::DATA_T* query_ptr,  // [num_queries, data_dim]
-  const std::uint32_t num_queries,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const
-    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
-  const std::uint32_t hash_bitlen,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, ldd]
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, ldd]
-  const std::uint32_t ldd,  // (*) ldd >= search_width * graph_degree
+  const IndexT* parent_node_list,        // [num_queries, search_width]
+  IndexT* const parent_candidates_ptr,   // [num_queries, search_width]
+  DistanceT* const parent_distance_ptr,  // [num_queries, search_width]
+  std::size_t lds,
+  uint32_t search_width,
+  const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
+  const IndexT* neighbor_graph_ptr,  // [dataset_size, graph_degree]
+  std::uint32_t graph_degree,
+  const DataT* query_ptr,  // [num_queries, data_dim]
+  std::uint32_t num_queries,
+  IndexT* visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+  std::uint32_t hash_bitlen,
+  IndexT* result_indices_ptr,       // [num_queries, ldd]
+  DistanceT* result_distances_ptr,  // [num_queries, ldd]
+  std::uint32_t ldd,                // (*) ldd >= search_width * graph_degree
   SAMPLE_FILTER_T sample_filter,
-  const cuvs::distance::DistanceType metric,
-  cudaStream_t cuda_stream = 0)
+  cudaStream_t cuda_stream)
 {
-  const auto block_size = 128;
-  const dim3 grid_size(
-    (search_width * graph_degree + (block_size / TEAM_SIZE) - 1) / (block_size / TEAM_SIZE),
-    num_queries);
-
-  const auto query_smem_buffer_length =
-    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
-
-  const auto smem_size =
-    query_smem_buffer_length * sizeof(float) + DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte;
-
-  compute_distance_to_child_nodes_kernel<TEAM_SIZE,
-                                         DATASET_BLOCK_DIM,
-                                         DATASET_DESCRIPTOR_T,
-                                         SAMPLE_FILTER_T>
-    <<<grid_size, block_size, smem_size, cuda_stream>>>(parent_node_list,
-                                                        parent_candidates_ptr,
-                                                        parent_distance_ptr,
-                                                        lds,
-                                                        search_width,
-                                                        dataset_desc,
-                                                        neighbor_graph_ptr,
-                                                        graph_degree,
-                                                        query_ptr,
-                                                        visited_hashmap_ptr,
-                                                        hash_bitlen,
-                                                        result_indices_ptr,
-                                                        result_distances_ptr,
-                                                        ldd,
-                                                        sample_filter,
-                                                        metric);
+  const auto block_size      = 128;
+  const auto teams_per_block = block_size / dataset_desc.team_size;
+  const dim3 grid_size((search_width * graph_degree + teams_per_block - 1) / teams_per_block,
+                       num_queries);
+
+  compute_distance_to_child_nodes_kernel<<<grid_size,
+                                           block_size,
+                                           dataset_desc.smem_ws_size_in_bytes,
+                                           cuda_stream>>>(parent_node_list,
+                                                          parent_candidates_ptr,
+                                                          parent_distance_ptr,
+                                                          lds,
+                                                          search_width,
+                                                          dataset_desc.dev_ptr(),
+                                                          neighbor_graph_ptr,
+                                                          graph_degree,
+                                                          query_ptr,
+                                                          visited_hashmap_ptr,
+                                                          hash_bitlen,
+                                                          result_indices_ptr,
+                                                          result_distances_ptr,
+                                                          ldd,
+                                                          sample_filter);
 }
 
 template <class INDEX_T>
@@ -639,49 +565,48 @@ void set_value_batch(T* const dev_ptr,
 // |<---                 result_buffer_allocation_size                 --->|
 // |<---                       result_buffer_size  --->|                     // Double buffer (A)
 //                      |<---  result_buffer_size                      --->| // Double buffer (B)
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
-struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
-  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
-  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
-  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
+struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
+  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T>;
+  using DATA_T     = typename base_type::DATA_T;
+  using INDEX_T    = typename base_type::INDEX_T;
+  using DISTANCE_T = typename base_type::DISTANCE_T;
 
   static_assert(std::is_same_v<DISTANCE_T, float>, "Only float is supported as resulting distance");
 
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_queries;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::itopk_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::algo;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::team_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::search_width;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::min_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::thread_block_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_mode;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_min_bitlen;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_max_fill_rate;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_random_samplings;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::rand_xor_mask;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dim;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::graph_degree;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::topk;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hash_bitlen;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_bitlen;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_reset_interval;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dataset_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::result_buffer_size;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::smem_size;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_executed_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dev_seed;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_seeds;
+  using base_type::algo;
+  using base_type::hashmap_max_fill_rate;
+  using base_type::hashmap_min_bitlen;
+  using base_type::hashmap_mode;
+  using base_type::itopk_size;
+  using base_type::max_iterations;
+  using base_type::max_queries;
+  using base_type::min_iterations;
+  using base_type::num_random_samplings;
+  using base_type::rand_xor_mask;
+  using base_type::search_width;
+  using base_type::team_size;
+  using base_type::thread_block_size;
+
+  using base_type::dim;
+  using base_type::graph_degree;
+  using base_type::topk;
+
+  using base_type::hash_bitlen;
+
+  using base_type::dataset_size;
+  using base_type::hashmap_size;
+  using base_type::result_buffer_size;
+  using base_type::small_hash_bitlen;
+  using base_type::small_hash_reset_interval;
+
+  using base_type::smem_size;
+
+  using base_type::dataset_desc;
+  using base_type::dev_seed;
+  using base_type::hashmap;
+  using base_type::num_executed_iterations;
+  using base_type::num_seeds;
 
   size_t result_buffer_allocation_size;
   rmm::device_uvector<INDEX_T> result_indices;       // results_indices_buffer
@@ -699,12 +624,11 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
 
   search(raft::resources const& res,
          search_params params,
+         const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
          int64_t dim,
          int64_t graph_degree,
-         uint32_t topk,
-         cuvs::distance::DistanceType metric)
-    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
-        res, params, dim, graph_degree, topk, metric),
+         uint32_t topk)
+    : base_type(res, params, dataset_desc, dim, graph_degree, topk),
       result_indices(0, raft::resource::get_cuda_stream(res)),
       result_distances(0, raft::resource::get_cuda_stream(res)),
       parent_node_list(0, raft::resource::get_cuda_stream(res)),
@@ -837,7 +761,6 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
   }
 
   void operator()(raft::resources const& res,
-                  DATASET_DESCRIPTOR_T dataset_desc,
                   raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
                   INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
                   DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
@@ -865,21 +788,20 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
     }
 
     // Choose initial entry point candidates at random
-    random_pickup<TEAM_SIZE, DATASET_BLOCK_DIM>(dataset_desc,
-                                                queries_ptr,
-                                                num_queries,
-                                                result_buffer_size,
-                                                num_random_samplings,
-                                                rand_xor_mask,
-                                                dev_seed_ptr,
-                                                num_seeds,
-                                                result_indices.data(),
-                                                result_distances.data(),
-                                                result_buffer_allocation_size,
-                                                hashmap.data(),
-                                                hash_bitlen,
-                                                this->metric,
-                                                stream);
+    random_pickup<DataT, IndexT, DistanceT>(dataset_desc,
+                                            queries_ptr,
+                                            num_queries,
+                                            result_buffer_size,
+                                            num_random_samplings,
+                                            rand_xor_mask,
+                                            dev_seed_ptr,
+                                            num_seeds,
+                                            result_indices.data(),
+                                            result_distances.data(),
+                                            result_buffer_allocation_size,
+                                            hashmap.data(),
+                                            hash_bitlen,
+                                            stream);
 
     unsigned iter = 0;
     while (1) {
@@ -931,7 +853,7 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
       }
 
       // Compute distance to child nodes that are adjacent to the parent node
-      compute_distance_to_child_nodes<TEAM_SIZE, DATASET_BLOCK_DIM, SAMPLE_FILTER_T>(
+      compute_distance_to_child_nodes(
         parent_node_list.data(),
         result_indices.data() + (1 - (iter & 0x1)) * result_buffer_size,
         result_distances.data() + (1 - (iter & 0x1)) * result_buffer_size,
@@ -948,7 +870,6 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
         result_distances.data() + itopk_size,
         result_buffer_allocation_size,
         sample_filter,
-        this->metric,
         stream);
 
       iter++;
@@ -1025,70 +946,5 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
   }
 };
 
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          class CODE_BOOK_T,
-          unsigned PQ_BITS,
-          unsigned PQ_CODE_BOOK_DIM,
-          class DATA_T,
-          class DISTANCE_T_,
-          class INDEX_T_,
-          typename SAMPLE_FILTER_T>
-struct search<TEAM_SIZE,
-              DATASET_BLOCK_DIM,
-              cagra_q_dataset_descriptor_t<DATA_T,
-                                           CODE_BOOK_T,
-                                           PQ_BITS,
-                                           PQ_CODE_BOOK_DIM,
-                                           DISTANCE_T_,
-                                           INDEX_T_>,
-              SAMPLE_FILTER_T>
-  : public search_plan_impl<cagra_q_dataset_descriptor_t<DATA_T,
-                                                         CODE_BOOK_T,
-                                                         PQ_BITS,
-                                                         PQ_CODE_BOOK_DIM,
-
-                                                         DISTANCE_T_,
-                                                         INDEX_T_>,
-                            SAMPLE_FILTER_T> {
-  using DATASET_DESCRIPTOR_T = cagra_q_dataset_descriptor_t<DATA_T,
-                                                            CODE_BOOK_T,
-                                                            PQ_BITS,
-                                                            PQ_CODE_BOOK_DIM,
-
-                                                            DISTANCE_T_,
-                                                            INDEX_T_>;
-  using INDEX_T              = typename DATASET_DESCRIPTOR_T::INDEX_T;
-  using DISTANCE_T           = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
-
-  search(raft::resources const& res,
-         search_params params,
-         int64_t dim,
-         int64_t graph_degree,
-         uint32_t topk,
-         cuvs::distance::DistanceType metric)
-    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
-        res, params, dim, graph_degree, topk, metric)
-  {
-    THROW("The multi-kernel mode does not support VPQ");
-  }
-
-  void set_params(raft::resources const& res) {}
-
-  void operator()(raft::resources const& res,
-                  DATASET_DESCRIPTOR_T dataset_desc,
-                  raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
-                  INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
-                  DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
-                  const DATA_T* const queries_ptr,       // [num_queries, dataset_dim]
-                  const uint32_t num_queries,
-                  const INDEX_T* dev_seed_ptr,              // [num_queries, num_seeds]
-                  uint32_t* const num_executed_iterations,  // [num_queries,]
-                  uint32_t topk,
-                  SAMPLE_FILTER_T sample_filter)
-  {
-  }
-};
-
 }  // namespace multi_kernel_search
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index 0543224b3d..16864ed193 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -18,10 +18,11 @@
 
 #include "hashmap.hpp"
 
+#include "compute_distance-ext.cuh"
 #include <cuvs/neighbors/common.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 // #include "search_single_cta_inst.cuh"
-// #include "topk_for_cagra/topk_core.cuh"
+// #include "topk_for_cagra/topk.h"
 
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
@@ -34,19 +35,12 @@
 namespace cuvs::neighbors::cagra::detail {
 
 struct search_plan_impl_base : public search_params {
-  int64_t dataset_block_dim;
   int64_t dim;
   int64_t graph_degree;
   uint32_t topk;
-  cuvs::distance::DistanceType metric;
-  search_plan_impl_base(search_params params,
-                        int64_t dim,
-                        int64_t graph_degree,
-                        uint32_t topk,
-                        cuvs::distance::DistanceType metric)
-    : search_params(params), dim(dim), graph_degree(graph_degree), topk(topk), metric(metric)
+  search_plan_impl_base(search_params params, int64_t dim, int64_t graph_degree, uint32_t topk)
+    : search_params(params), dim(dim), graph_degree(graph_degree), topk(topk)
   {
-    set_dataset_block_and_team_size(dim);
     if (algo == search_algo::AUTO) {
       const size_t num_sm = raft::getMultiProcessorCount();
       if (itopk_size <= 512 && search_params::max_queries >= num_sm * 2lu) {
@@ -61,29 +55,13 @@ struct search_plan_impl_base : public search_params {
       }
     }
   }
-
-  void set_dataset_block_and_team_size(int64_t dim)
-  {
-    constexpr int64_t max_dataset_block_dim = 512;
-    dataset_block_dim                       = 128;
-    while (dataset_block_dim < dim && dataset_block_dim < max_dataset_block_dim) {
-      dataset_block_dim *= 2;
-    }
-    // To keep binary size in check we limit only one team size specialization for each max_dim.
-    // TODO(tfeher): revise this decision.
-    switch (dataset_block_dim) {
-      case 128: team_size = 8; break;
-      case 256: team_size = 16; break;
-      default: team_size = 32; break;
-    }
-  }
 };
 
-template <class DATASET_DESCRIPTOR_T, class SAMPLE_FILTER_T>
+template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
 struct search_plan_impl : public search_plan_impl_base {
-  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
-  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
-  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  using DATA_T     = DataT;
+  using INDEX_T    = IndexT;
+  using DISTANCE_T = DistanceT;
 
   int64_t hash_bitlen;
 
@@ -100,23 +78,24 @@ struct search_plan_impl : public search_plan_impl_base {
   rmm::device_uvector<INDEX_T> hashmap;
   rmm::device_uvector<uint32_t> num_executed_iterations;  // device or managed?
   rmm::device_uvector<INDEX_T> dev_seed;
+  const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc;
 
   search_plan_impl(raft::resources const& res,
                    search_params params,
+                   const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                    int64_t dim,
                    int64_t graph_degree,
-                   uint32_t topk,
-                   cuvs::distance::DistanceType metric)
-    : search_plan_impl_base(params, dim, graph_degree, topk, metric),
+                   uint32_t topk)
+    : search_plan_impl_base(params, dim, graph_degree, topk),
       hashmap(0, raft::resource::get_cuda_stream(res)),
       num_executed_iterations(0, raft::resource::get_cuda_stream(res)),
       dev_seed(0, raft::resource::get_cuda_stream(res)),
-      num_seeds(0)
+      num_seeds(0),
+      dataset_desc(dataset_desc)
   {
     adjust_search_params();
     check_params();
     calc_hashmap_params(res);
-    set_dataset_block_and_team_size(dim);
     num_executed_iterations.resize(max_queries, raft::resource::get_cuda_stream(res));
     RAFT_LOG_DEBUG("# algo = %d", static_cast<int>(algo));
   }
@@ -124,7 +103,6 @@ struct search_plan_impl : public search_plan_impl_base {
   virtual ~search_plan_impl() {}
 
   virtual void operator()(raft::resources const& res,
-                          DATASET_DESCRIPTOR_T dataset_desc,
                           raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
                           INDEX_T* const result_indices_ptr,       // [num_queries, topk]
                           DISTANCE_T* const result_distances_ptr,  // [num_queries, topk]
@@ -160,6 +138,7 @@ struct search_plan_impl : public search_plan_impl_base {
                      itopk32);
       itopk_size = itopk32;
     }
+    team_size = dataset_desc.team_size;
   }
 
   // defines hash_bitlen, small_hash_bitlen, small_hash_reset interval, hash_size
@@ -292,10 +271,6 @@ struct search_plan_impl : public search_plan_impl_base {
         algo != search_algo::MULTI_KERNEL) {
       error_message += "An invalid kernel mode has been given: " + std::to_string((int)algo) + "";
     }
-    if (team_size != 0 && team_size != 4 && team_size != 8 && team_size != 16 && team_size != 32) {
-      error_message +=
-        "`team_size` must be 0, 4, 8, 16 or 32. " + std::to_string(team_size) + " has been given.";
-    }
     if (thread_block_size != 0 && thread_block_size != 64 && thread_block_size != 128 &&
         thread_block_size != 256 && thread_block_size != 512 && thread_block_size != 1024) {
       error_message += "`thread_block_size` must be 0, 64, 128, 256 or 512. " +
@@ -330,20 +305,4 @@ struct search_plan_impl : public search_plan_impl_base {
   }
 };
 
-// template <class DATA_T, class DISTANCE_T, class INDEX_T>
-// struct search_plan {
-//   search_plan(raft::resources const& res,
-//               search_params param,
-//               int64_t dim,
-//               int64_t graph_degree)
-//     : plan(res, param, dim, graph_degree)
-//   {
-//   }
-//   void check(uint32_t topk) { plan.check(topk); }
-
-//   // private:
-//   detail::search_plan_impl<DATA_T, DISTANCE_T, INDEX_T> plan;
-// };
-/** @} */  // end group cagra
-
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index 0a101cbfef..4abed6760e 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -16,13 +16,13 @@
 #pragma once
 
 #include "bitonic.hpp"
-#include "compute_distance.hpp"
+#include "compute_distance-ext.cuh"
 #include "device_common.hpp"
 #include "hashmap.hpp"
 #include "search_plan.cuh"
 #include "search_single_cta_kernel.cuh"
 #include "topk_by_radix.cuh"
-#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk
+#include "topk_for_cagra/topk.h"  // TODO replace with raft topk
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
@@ -49,58 +49,56 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace single_cta_search {
 
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
-struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
-  using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
-  using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
-  using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_queries;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::itopk_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::algo;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::team_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::search_width;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::min_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::max_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::thread_block_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_mode;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_min_bitlen;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_max_fill_rate;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_random_samplings;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::rand_xor_mask;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dim;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::graph_degree;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::topk;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hash_bitlen;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_bitlen;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::small_hash_reset_interval;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dataset_size;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::result_buffer_size;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::smem_size;
-
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::hashmap;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_executed_iterations;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::dev_seed;
-  using search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::num_seeds;
+template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
+struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
+  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T>;
+  using DATA_T     = typename base_type::DATA_T;
+  using INDEX_T    = typename base_type::INDEX_T;
+  using DISTANCE_T = typename base_type::DISTANCE_T;
+
+  using base_type::algo;
+  using base_type::hashmap_max_fill_rate;
+  using base_type::hashmap_min_bitlen;
+  using base_type::hashmap_mode;
+  using base_type::itopk_size;
+  using base_type::max_iterations;
+  using base_type::max_queries;
+  using base_type::min_iterations;
+  using base_type::num_random_samplings;
+  using base_type::rand_xor_mask;
+  using base_type::search_width;
+  using base_type::team_size;
+  using base_type::thread_block_size;
+
+  using base_type::dim;
+  using base_type::graph_degree;
+  using base_type::topk;
+
+  using base_type::hash_bitlen;
+
+  using base_type::dataset_size;
+  using base_type::hashmap_size;
+  using base_type::result_buffer_size;
+  using base_type::small_hash_bitlen;
+  using base_type::small_hash_reset_interval;
+
+  using base_type::smem_size;
+
+  using base_type::dataset_desc;
+  using base_type::dev_seed;
+  using base_type::hashmap;
+  using base_type::num_executed_iterations;
+  using base_type::num_seeds;
 
   uint32_t num_itopk_candidates;
 
   search(raft::resources const& res,
          search_params params,
+         const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
          int64_t dim,
          int64_t graph_degree,
-         uint32_t topk,
-         cuvs::distance::DistanceType metric)
-    : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>(
-        res, params, dim, graph_degree, topk, metric)
+         uint32_t topk)
+    : base_type(res, params, dataset_desc, dim, graph_degree, topk)
   {
     set_params(res);
   }
@@ -128,14 +126,11 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
     constexpr unsigned max_block_size       = 1024;
     //
     const std::uint32_t topk_ws_size = 3;
-    const auto query_smem_buffer_length =
-      raft::ceildiv<uint32_t>(dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
     const std::uint32_t base_smem_size =
-      sizeof(float) * query_smem_buffer_length +
+      dataset_desc.smem_ws_size_in_bytes +
       (sizeof(INDEX_T) + sizeof(DISTANCE_T)) * result_buffer_size_32 +
       sizeof(INDEX_T) * hashmap::get_size(small_hash_bitlen) + sizeof(INDEX_T) * search_width +
-      sizeof(std::uint32_t) * topk_ws_size + sizeof(std::uint32_t) +
-      DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte;
+      sizeof(std::uint32_t) * topk_ws_size + sizeof(std::uint32_t);
     smem_size = base_smem_size;
     if (num_itopk_candidates > 256) {
       // Tentatively calculate the required share memory size when radix
@@ -212,7 +207,6 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
   }
 
   void operator()(raft::resources const& res,
-                  DATASET_DESCRIPTOR_T dataset_desc,
                   raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
                   INDEX_T* const result_indices_ptr,       // [num_queries, topk]
                   DISTANCE_T* const result_distances_ptr,  // [num_queries, topk]
@@ -224,28 +218,26 @@ struct search : search_plan_impl<DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T> {
                   SAMPLE_FILTER_T sample_filter)
   {
     cudaStream_t stream = raft::resource::get_cuda_stream(res);
-    select_and_run<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T>(
-      dataset_desc,
-      graph,
-      result_indices_ptr,
-      result_distances_ptr,
-      queries_ptr,
-      num_queries,
-      dev_seed_ptr,
-      num_executed_iterations,
-      *this,
-      topk,
-      num_itopk_candidates,
-      static_cast<uint32_t>(thread_block_size),
-      smem_size,
-      hash_bitlen,
-      hashmap.data(),
-      small_hash_bitlen,
-      small_hash_reset_interval,
-      num_seeds,
-      sample_filter,
-      this->metric,
-      stream);
+    select_and_run(dataset_desc.dev_ptr(),
+                   graph,
+                   result_indices_ptr,
+                   result_distances_ptr,
+                   queries_ptr,
+                   num_queries,
+                   dev_seed_ptr,
+                   num_executed_iterations,
+                   *this,
+                   topk,
+                   num_itopk_candidates,
+                   static_cast<uint32_t>(thread_block_size),
+                   smem_size,
+                   hash_bitlen,
+                   hashmap.data(),
+                   small_hash_bitlen,
+                   small_hash_reset_interval,
+                   num_seeds,
+                   sample_filter,
+                   stream);
   }
 };
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
index a361269a6f..e37ceb1faa 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
@@ -39,8 +39,6 @@
 
 #include "search_single_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 """
 
@@ -48,7 +46,6 @@
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
 """
 
-mxdim_team = [(128, 8), (256, 16), (512, 32), (1024, 32)]
 # block = [(64, 16), (128, 8), (256, 4), (512, 2), (1024, 1)]
 # itopk_candidates = [64, 128, 256]
 # itopk_size = [64, 128, 256, 512]
@@ -69,14 +66,13 @@
 
 # knn
 for type_path, (data_t, idx_t, distance_t) in search_types.items():
-    for (mxdim, team) in mxdim_team:
-        path = f"search_single_cta_{type_path}_dim{mxdim}_t{team}.cu"
-        with open(path, "w") as f:
-            f.write(header)
-            f.write(
-                    f"instantiate_kernel_selection(\n  {team}, {mxdim}, cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<{data_t} COMMA {idx_t} COMMA  {distance_t}>, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
-            )
+    path = f"search_single_cta_{type_path}.cu"
+    with open(path, "w") as f:
+        f.write(header)
+        f.write(
+                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+        )
 
-            f.write(trailer)
-            # For pasting into CMakeLists.txt
-            print(f"src/neighbors/detail/cagra/{path}")
+        f.write(trailer)
+        # For pasting into CMakeLists.txt
+        print(f"src/neighbors/detail/cagra/{path}")
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu
rename to cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
index c2cfb13c43..f8495bc01b 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim128_t8.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
@@ -25,13 +25,10 @@
 
 #include "search_single_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(float,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
deleted file mode 100644
index 4cf4a26f74..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
deleted file mode 100644
index 6927104762..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
deleted file mode 100644
index ed3a900ff9..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu
rename to cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
index 1e2b834924..0ef5c366f3 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim128_t8.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
@@ -25,13 +25,10 @@
 
 #include "search_single_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(float,
+                             uint64_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
deleted file mode 100644
index 2c4da00db3..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
deleted file mode 100644
index 8b26a595f0..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim128_t8.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
deleted file mode 100644
index a93f893d4d..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
deleted file mode 100644
index 4a7502e3ed..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
rename to cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
index 7d3e86f383..c21e6d1f42 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32_dim128_t8.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
@@ -25,13 +25,10 @@
 
 #include "search_single_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<float COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(half,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
deleted file mode 100644
index 6c13df91a7..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
deleted file mode 100644
index 12aa72a248..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64.cu
similarity index 80%
rename from cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu
rename to cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64.cu
index cfae9e367d..b96ed0b22b 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32_dim256_t16.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64.cu
@@ -25,13 +25,10 @@
 
 #include "search_single_cta_inst.cuh"
 
-#include "compute_distance.hpp"
-
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
+instantiate_kernel_selection(half,
+                             uint64_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
deleted file mode 100644
index 84a173d6d0..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
deleted file mode 100644
index d9c5198ebe..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
deleted file mode 100644
index 3ba8f4e4d1..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<half COMMA uint64_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
index a4581d15ef..26ca7b672d 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
@@ -21,31 +21,27 @@
 
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 
-#define instantiate_kernel_selection(TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T) \
-  template void select_and_run<TEAM_SIZE, MAX_DATASET_DIM, DATASET_DESC_T, SAMPLE_FILTER_T>(      \
-    DATASET_DESC_T dataset_desc,                                                                  \
-    raft::device_matrix_view<const typename DATASET_DESC_T::INDEX_T, int64_t, raft::row_major>    \
-      graph,                                                                                      \
-    typename DATASET_DESC_T::INDEX_T* const topk_indices_ptr,                                     \
-    typename DATASET_DESC_T::DISTANCE_T* const topk_distances_ptr,                                \
-    const typename DATASET_DESC_T::DATA_T* const queries_ptr,                                     \
-    const uint32_t num_queries,                                                                   \
-    const typename DATASET_DESC_T::INDEX_T* dev_seed_ptr,                                         \
-    uint32_t* const num_executed_iterations,                                                      \
-    const search_params& ps,                                                                      \
-    uint32_t topk,                                                                                \
-    uint32_t num_itopk_candidates,                                                                \
-    uint32_t block_size,                                                                          \
-    uint32_t smem_size,                                                                           \
-    int64_t hash_bitlen,                                                                          \
-    typename DATASET_DESC_T::INDEX_T* hashmap_ptr,                                                \
-    size_t small_hash_bitlen,                                                                     \
-    size_t small_hash_reset_interval,                                                             \
-    uint32_t num_seeds,                                                                           \
-    SAMPLE_FILTER_T sample_filter,                                                                \
-    cuvs::distance::DistanceType metric,                                                          \
+#define instantiate_kernel_selection(DataT, IndexT, DistanceT, SampleFilterT) \
+  template void select_and_run<DataT, IndexT, DistanceT, SampleFilterT>(      \
+    const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,  \
+    raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,   \
+    IndexT* topk_indices_ptr,                                                 \
+    DistanceT* topk_distances_ptr,                                            \
+    const DataT* queries_ptr,                                                 \
+    uint32_t num_queries,                                                     \
+    const IndexT* dev_seed_ptr,                                               \
+    uint32_t* num_executed_iterations,                                        \
+    const search_params& ps,                                                  \
+    uint32_t topk,                                                            \
+    uint32_t num_itopk_candidates,                                            \
+    uint32_t block_size,                                                      \
+    uint32_t smem_size,                                                       \
+    int64_t hash_bitlen,                                                      \
+    IndexT* hashmap_ptr,                                                      \
+    size_t small_hash_bitlen,                                                 \
+    size_t small_hash_reset_interval,                                         \
+    uint32_t num_seeds,                                                       \
+    SampleFilterT sample_filter,                                              \
     cudaStream_t stream);
 
-#define COMMA ,
-
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
new file mode 100644
index 0000000000..56a0d8ba99
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(int8_t,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
deleted file mode 100644
index ad2ca16fc0..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
deleted file mode 100644
index 6130a84bcb..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim128_t8.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
deleted file mode 100644
index 1e7bee57c7..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
deleted file mode 100644
index 7f789e3d0c..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<int8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
deleted file mode 100644
index 10dda03898..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-ext.cuh
+++ /dev/null
@@ -1,588 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#include <cuvs/neighbors/common.hpp>
-#include <raft/util/raft_explicit.hpp>  // RAFT_EXPLICIT
-
-#include <cuda_fp16.h>
-
-namespace cuvs::neighbors::cagra::detail {
-namespace single_cta_search {
-
-#ifdef _CUVS_EXPLICIT_INSTANTIATE_ONLY
-
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
-void select_and_run(
-  DATASET_DESCRIPTOR_T dataset_desc,
-  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
-    graph,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
-  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
-  const uint32_t num_queries,
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
-  uint32_t* const num_executed_iterations,                     // [num_queries,]
-  const search_params& ps,
-  uint32_t topk,
-  uint32_t num_itopk_candidates,
-  uint32_t block_size,  //
-  uint32_t smem_size,
-  int64_t hash_bitlen,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
-  size_t small_hash_bitlen,
-  size_t small_hash_reset_interval,
-  uint32_t num_seeds,
-  SAMPLE_FILTER_T sample_filter,
-  cuvs::distance::DistanceType metric,
-  cudaStream_t stream) RAFT_EXPLICIT;
-
-#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
-
-#define instantiate_single_cta_select_and_run(                                                  \
-  TEAM_SIZE, MAX_DATASET_DIM, DATA_T, INDEX_T, DISTANCE_T, SAMPLE_FILTER_T)                     \
-  extern template void select_and_run<                                                          \
-    TEAM_SIZE,                                                                                  \
-    MAX_DATASET_DIM,                                                                            \
-    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>, \
-    SAMPLE_FILTER_T>(                                                                           \
-    cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<DATA_T, INDEX_T, DISTANCE_T>  \
-      dataset,                                                                                  \
-    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
-    INDEX_T* const topk_indices_ptr,                                                            \
-    DISTANCE_T* const topk_distances_ptr,                                                       \
-    const DATA_T* const queries_ptr,                                                            \
-    const uint32_t num_queries,                                                                 \
-    const INDEX_T* dev_seed_ptr,                                                                \
-    uint32_t* const num_executed_iterations,                                                    \
-    const search_params& ps,                                                                    \
-    uint32_t topk,                                                                              \
-    uint32_t num_itopk_candidates,                                                              \
-    uint32_t block_size,                                                                        \
-    uint32_t smem_size,                                                                         \
-    int64_t hash_bitlen,                                                                        \
-    INDEX_T* hashmap_ptr,                                                                       \
-    size_t small_hash_bitlen,                                                                   \
-    size_t small_hash_reset_interval,                                                           \
-    uint32_t num_seeds,                                                                         \
-    SAMPLE_FILTER_T sample_filter,                                                              \
-    cuvs::distance::DistanceType metric,                                                        \
-    cudaStream_t stream);
-
-instantiate_single_cta_select_and_run(
-  32, 1024, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  8, 128, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  16, 256, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  32, 512, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  32, 1024, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  8, 128, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  16, 256, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  32, 512, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  32, 1024, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  8, 128, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  16, 256, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  32, 512, int8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  32, 1024, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  8, 128, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  16, 256, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_single_cta_select_and_run(
-  32, 512, uint8_t, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-#undef instantiate_single_cta_select_and_run
-
-#define instantiate_q_single_cta_select_and_run(TEAM_SIZE,                                      \
-                                                MAX_DATASET_DIM,                                \
-                                                CODE_BOOK_T,                                    \
-                                                PQ_BITS,                                        \
-                                                PQ_CODE_BOOK_DIM,                               \
-                                                DATA_T,                                         \
-                                                INDEX_T,                                        \
-                                                DISTANCE_T,                                     \
-                                                SAMPLE_FILTER_T)                                \
-  extern template void                                                                          \
-  select_and_run<TEAM_SIZE,                                                                     \
-                 MAX_DATASET_DIM,                                                               \
-                 cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,           \
-                                                                              CODE_BOOK_T,      \
-                                                                              PQ_BITS,          \
-                                                                              PQ_CODE_BOOK_DIM, \
-                                                                              DISTANCE_T,       \
-                                                                              INDEX_T>,         \
-                 SAMPLE_FILTER_T>(                                                              \
-    cuvs::neighbors::cagra::detail::cagra_q_dataset_descriptor_t<DATA_T,                        \
-                                                                 CODE_BOOK_T,                   \
-                                                                 PQ_BITS,                       \
-                                                                 PQ_CODE_BOOK_DIM,              \
-                                                                 DISTANCE_T,                    \
-                                                                 INDEX_T> dataset,              \
-    raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,                    \
-    INDEX_T* const topk_indices_ptr,                                                            \
-    DISTANCE_T* const topk_distances_ptr,                                                       \
-    const DATA_T* const queries_ptr,                                                            \
-    const uint32_t num_queries,                                                                 \
-    const INDEX_T* dev_seed_ptr,                                                                \
-    uint32_t* const num_executed_iterations,                                                    \
-    const search_params& ps,                                                                    \
-    uint32_t topk,                                                                              \
-    uint32_t num_itopk_candidates,                                                              \
-    uint32_t block_size,                                                                        \
-    uint32_t smem_size,                                                                         \
-    int64_t hash_bitlen,                                                                        \
-    INDEX_T* hashmap_ptr,                                                                       \
-    size_t small_hash_bitlen,                                                                   \
-    size_t small_hash_reset_interval,                                                           \
-    uint32_t num_seeds,                                                                         \
-    SAMPLE_FILTER_T sample_filter,                                                              \
-    cuvs::distance::DistanceType metric,                                                        \
-    cudaStream_t stream);
-
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  16, 256, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 512, half, 8, 2, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        2,
-                                        half,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  16, 256, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 512, half, 8, 4, half, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        4,
-                                        half,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 2, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        2,
-                                        float,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        2,
-                                        float,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        2,
-                                        float,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 4, float, uint32_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        4,
-                                        float,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        4,
-                                        float,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        4,
-                                        float,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  16, 256, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 512, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 1024, half, 8, 2, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  16, 256, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 512, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 1024, half, 8, 4, half, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 2, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  16, 256, half, 8, 2, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 512, half, 8, 2, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        2,
-                                        float,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 4, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  16, 256, half, 8, 4, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  32, 512, half, 8, 4, float, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        4,
-                                        float,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_single_cta_select_and_run(8,
-                                        128,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(8,
-                                        128,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_single_cta_select_and_run(8,
-                                        128,
-                                        half,
-                                        8,
-                                        2,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        2,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        2,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        2,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(8,
-                                        128,
-                                        half,
-                                        8,
-                                        4,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        4,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        4,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        4,
-                                        int8_t,
-                                        uint32_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_single_cta_select_and_run(8,
-                                        128,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        2,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(8,
-                                        128,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        4,
-                                        uint8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 2, int8_t, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        2,
-                                        int8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        2,
-                                        int8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        2,
-                                        int8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(
-  8, 128, half, 8, 4, int8_t, int64_t, float, cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(16,
-                                        256,
-                                        half,
-                                        8,
-                                        4,
-                                        int8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        512,
-                                        half,
-                                        8,
-                                        4,
-                                        int8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-instantiate_q_single_cta_select_and_run(32,
-                                        1024,
-                                        half,
-                                        8,
-                                        4,
-                                        int8_t,
-                                        int64_t,
-                                        float,
-                                        cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-#undef instantiate_q_single_cta_select_and_run
-
-}  // namespace single_cta_search
-}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index a101cdc1f6..d10313c5b2 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -15,13 +15,15 @@
  */
 #pragma once
 
+#include "search_single_cta_kernel.cuh"
+
 #include "bitonic.hpp"
-#include "compute_distance.hpp"
+#include "compute_distance-ext.cuh"
 #include "device_common.hpp"
 #include "hashmap.hpp"
 #include "search_plan.cuh"
 #include "topk_by_radix.cuh"
-#include "topk_for_cagra/topk_core.cuh"  // TODO replace with raft topk
+#include "topk_for_cagra/topk.h"  // TODO replace with raft topk
 #include "utils.hpp"
 
 #include <cuvs/distance/distance.hpp>
@@ -56,12 +58,11 @@ namespace single_cta_search {
 // #define _CLK_BREAKDOWN
 
 template <unsigned TOPK_BY_BITONIC_SORT, class INDEX_T>
-__device__ void pickup_next_parents(std::uint32_t* const terminate_flag,
-                                    INDEX_T* const next_parent_indices,
-                                    INDEX_T* const internal_topk_indices,
-                                    const std::size_t internal_topk_size,
-                                    const std::size_t dataset_size,
-                                    const std::uint32_t search_width)
+RAFT_DEVICE_INLINE_FUNCTION void pickup_next_parents(std::uint32_t* const terminate_flag,
+                                                     INDEX_T* const next_parent_indices,
+                                                     INDEX_T* const internal_topk_indices,
+                                                     const std::size_t internal_topk_size,
+                                                     const std::uint32_t search_width)
 {
   constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
   // if (threadIdx.x >= 32) return;
@@ -99,11 +100,12 @@ __device__ void pickup_next_parents(std::uint32_t* const terminate_flag,
 }
 
 template <unsigned MAX_CANDIDATES, class IdxT = void>
-__device__ inline void topk_by_bitonic_sort_1st(float* candidate_distances,  // [num_candidates]
-                                                IdxT* candidate_indices,     // [num_candidates]
-                                                const std::uint32_t num_candidates,
-                                                const std::uint32_t num_itopk,
-                                                unsigned MULTI_WARPS = 0)
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_1st(
+  float* candidate_distances,  // [num_candidates]
+  IdxT* candidate_indices,     // [num_candidates]
+  const std::uint32_t num_candidates,
+  const std::uint32_t num_itopk,
+  unsigned MULTI_WARPS = 0)
 {
   const unsigned lane_id = threadIdx.x % 32;
   const unsigned warp_id = threadIdx.x / 32;
@@ -202,15 +204,16 @@ __device__ inline void topk_by_bitonic_sort_1st(float* candidate_distances,  //
 }
 
 template <unsigned MAX_ITOPK, class IdxT = void>
-__device__ inline void topk_by_bitonic_sort_2nd(float* itopk_distances,  // [num_itopk]
-                                                IdxT* itopk_indices,     // [num_itopk]
-                                                const std::uint32_t num_itopk,
-                                                float* candidate_distances,  // [num_candidates]
-                                                IdxT* candidate_indices,     // [num_candidates]
-                                                const std::uint32_t num_candidates,
-                                                std::uint32_t* work_buf,
-                                                const bool first,
-                                                unsigned MULTI_WARPS = 0)
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_2nd(
+  float* itopk_distances,  // [num_itopk]
+  IdxT* itopk_indices,     // [num_itopk]
+  const std::uint32_t num_itopk,
+  float* candidate_distances,  // [num_candidates]
+  IdxT* candidate_indices,     // [num_candidates]
+  const std::uint32_t num_candidates,
+  std::uint32_t* work_buf,
+  const bool first,
+  unsigned MULTI_WARPS = 0)
 {
   const unsigned lane_id = threadIdx.x % 32;
   const unsigned warp_id = threadIdx.x / 32;
@@ -410,16 +413,17 @@ __device__ inline void topk_by_bitonic_sort_2nd(float* itopk_distances,  // [num
 template <unsigned MAX_ITOPK,
           unsigned MAX_CANDIDATES,
           class IdxT>
-__device__ void topk_by_bitonic_sort(float* itopk_distances,  // [num_itopk]
-                                     IdxT* itopk_indices,     // [num_itopk]
-                                     const std::uint32_t num_itopk,
-                                     float* candidate_distances,  // [num_candidates]
-                                     IdxT* candidate_indices,     // [num_candidates]
-                                     const std::uint32_t num_candidates,
-                                     std::uint32_t* work_buf,
-                                     const bool first,
-                                     const unsigned MULTI_WARPS_1,
-                                     const unsigned MULTI_WARPS_2)
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort(
+  float* itopk_distances,  // [num_itopk]
+  IdxT* itopk_indices,     // [num_itopk]
+  const std::uint32_t num_itopk,
+  float* candidate_distances,  // [num_candidates]
+  IdxT* candidate_indices,     // [num_candidates]
+  const std::uint32_t num_candidates,
+  std::uint32_t* work_buf,
+  const bool first,
+  const unsigned MULTI_WARPS_1,
+  const unsigned MULTI_WARPS_2)
 {
   // The results in candidate_distances/indices are sorted by bitonic sort.
   topk_by_bitonic_sort_1st<MAX_CANDIDATES, IdxT>(
@@ -439,11 +443,11 @@ __device__ void topk_by_bitonic_sort(float* itopk_distances,  // [num_itopk]
 }
 
 template <class INDEX_T>
-__device__ inline void hashmap_restore(INDEX_T* const hashmap_ptr,
-                                       const size_t hashmap_bitlen,
-                                       const INDEX_T* itopk_indices,
-                                       const uint32_t itopk_size,
-                                       const uint32_t first_tid = 0)
+RAFT_DEVICE_INLINE_FUNCTION void hashmap_restore(INDEX_T* const hashmap_ptr,
+                                                 const size_t hashmap_bitlen,
+                                                 const INDEX_T* itopk_indices,
+                                                 const uint32_t itopk_size,
+                                                 const uint32_t first_tid = 0)
 {
   constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
   if (threadIdx.x < first_tid) return;
@@ -454,18 +458,16 @@ __device__ inline void hashmap_restore(INDEX_T* const hashmap_ptr,
 }
 
 // One query one thread block
-template <uint32_t TEAM_SIZE,
-          uint32_t DATASET_BLOCK_DIM,
-          unsigned MAX_ITOPK,
+template <unsigned MAX_ITOPK,
           unsigned MAX_CANDIDATES,
           unsigned TOPK_BY_BITONIC_SORT,
           class DATASET_DESCRIPTOR_T,
           class SAMPLE_FILTER_T>
-__launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
+RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, top_k]
   typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, top_k]
   const std::uint32_t top_k,
-  DATASET_DESCRIPTOR_T dataset_desc,
+  const DATASET_DESCRIPTOR_T* dataset_desc,
   const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
   const typename DATASET_DESCRIPTOR_T::INDEX_T* const knn_graph,   // [dataset_size, graph_degree]
   const std::uint32_t graph_degree,
@@ -483,15 +485,13 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
   const std::uint32_t hash_bitlen,
   const std::uint32_t small_hash_bitlen,
   const std::uint32_t small_hash_reset_interval,
-  SAMPLE_FILTER_T sample_filter,
-  cuvs::distance::DistanceType metric)
+  SAMPLE_FILTER_T sample_filter)
 {
   using LOAD_T = device::LOAD_128BIT_T;
 
   using DATA_T     = typename DATASET_DESCRIPTOR_T::DATA_T;
   using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
   using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
-  using QUERY_T    = typename DATASET_DESCRIPTOR_T::QUERY_T;
 
   const auto query_id = blockIdx.y;
 
@@ -512,7 +512,7 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
 #endif
   _CLK_START();
 
-  extern __shared__ std::uint32_t smem[];
+  extern __shared__ uint8_t smem[];
 
   // Layout of result_buffer
   // +----------------------+------------------------------+---------+
@@ -520,37 +520,28 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
   // | <internal_topk_size> | <search_width * graph_degree> | upto 32 |
   // +----------------------+------------------------------+---------+
   // |<---             result_buffer_size              --->|
-  std::uint32_t result_buffer_size    = internal_topk + (search_width * graph_degree);
-  std::uint32_t result_buffer_size_32 = result_buffer_size;
-  if (result_buffer_size % 32) { result_buffer_size_32 += 32 - (result_buffer_size % 32); }
-  const auto small_hash_size = hashmap::get_size(small_hash_bitlen);
-
-  const auto query_smem_buffer_length =
-    raft::ceildiv<uint32_t>(dataset_desc.dim, DATASET_BLOCK_DIM) * DATASET_BLOCK_DIM;
-  auto query_buffer          = reinterpret_cast<QUERY_T*>(smem);
-  auto result_indices_buffer = reinterpret_cast<INDEX_T*>(query_buffer + query_smem_buffer_length);
-  auto result_distances_buffer =
-    reinterpret_cast<DISTANCE_T*>(result_indices_buffer + result_buffer_size_32);
-  auto visited_hash_buffer =
-    reinterpret_cast<INDEX_T*>(result_distances_buffer + result_buffer_size_32);
-  auto parent_list_buffer = reinterpret_cast<INDEX_T*>(visited_hash_buffer + small_hash_size);
-  auto distance_work_buffer_ptr =
-    reinterpret_cast<std::uint8_t*>(parent_list_buffer + search_width);
-  auto topk_ws        = reinterpret_cast<std::uint32_t*>(distance_work_buffer_ptr +
-                                                  DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte);
-  auto terminate_flag = reinterpret_cast<std::uint32_t*>(topk_ws + 3);
-  auto smem_work_ptr  = reinterpret_cast<std::uint32_t*>(terminate_flag + 1);
+  const auto result_buffer_size    = internal_topk + (search_width * graph_degree);
+  const auto result_buffer_size_32 = raft::round_up_safe<uint32_t>(result_buffer_size, 32);
+  const auto small_hash_size       = hashmap::get_size(small_hash_bitlen);
 
   // Set smem working buffer for the distance calculation
-  dataset_desc.set_smem_ptr(distance_work_buffer_ptr);
+  dataset_desc = dataset_desc->setup_workspace(smem, queries_ptr, query_id);
+
+  auto* __restrict__ result_indices_buffer =
+    reinterpret_cast<INDEX_T*>(smem + dataset_desc->smem_ws_size_in_bytes());
+  auto* __restrict__ result_distances_buffer =
+    reinterpret_cast<DISTANCE_T*>(result_indices_buffer + result_buffer_size_32);
+  auto* __restrict__ visited_hash_buffer =
+    reinterpret_cast<INDEX_T*>(result_distances_buffer + result_buffer_size_32);
+  auto* __restrict__ parent_list_buffer =
+    reinterpret_cast<INDEX_T*>(visited_hash_buffer + small_hash_size);
+  auto* __restrict__ topk_ws = reinterpret_cast<std::uint32_t*>(parent_list_buffer + search_width);
+  auto* terminate_flag       = reinterpret_cast<std::uint32_t*>(topk_ws + 3);
+  auto* __restrict__ smem_work_ptr = reinterpret_cast<std::uint32_t*>(terminate_flag + 1);
 
   // A flag for filtering.
   auto filter_flag = terminate_flag;
 
-  const DATA_T* const query_ptr = queries_ptr + query_id * dataset_desc.dim;
-  dataset_desc.template copy_query<DATASET_BLOCK_DIM>(
-    query_ptr, query_buffer, query_smem_buffer_length);
-
   if (threadIdx.x == 0) {
     terminate_flag[0] = 0;
     topk_ws[0]        = ~0u;
@@ -570,18 +561,16 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
   // compute distance to randomly selecting nodes
   _CLK_START();
   const INDEX_T* const local_seed_ptr = seed_ptr ? seed_ptr + (num_seeds * query_id) : nullptr;
-  device::compute_distance_to_random_nodes<TEAM_SIZE, DATASET_BLOCK_DIM>(result_indices_buffer,
-                                                                         result_distances_buffer,
-                                                                         query_buffer,
-                                                                         dataset_desc,
-                                                                         result_buffer_size,
-                                                                         num_distilation,
-                                                                         rand_xor_mask,
-                                                                         local_seed_ptr,
-                                                                         num_seeds,
-                                                                         local_visited_hashmap_ptr,
-                                                                         hash_bitlen,
-                                                                         metric);
+  device::compute_distance_to_random_nodes(result_indices_buffer,
+                                           result_distances_buffer,
+                                           *dataset_desc,
+                                           result_buffer_size,
+                                           num_distilation,
+                                           rand_xor_mask,
+                                           local_seed_ptr,
+                                           num_seeds,
+                                           local_visited_hashmap_ptr,
+                                           hash_bitlen);
   __syncthreads();
   _CLK_REC(clk_compute_1st_distance);
 
@@ -666,7 +655,7 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
         nullptr,
         topk_ws,
         true,
-        reinterpret_cast<std::uint32_t*>(smem_work_ptr));
+        smem_work_ptr);
       _CLK_REC(clk_topk);
 
       // reset small-hash table
@@ -683,12 +672,8 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
     // pick up next parents
     if (threadIdx.x < 32) {
       _CLK_START();
-      pickup_next_parents<TOPK_BY_BITONIC_SORT, INDEX_T>(terminate_flag,
-                                                         parent_list_buffer,
-                                                         result_indices_buffer,
-                                                         internal_topk,
-                                                         dataset_desc.size,
-                                                         search_width);
+      pickup_next_parents<TOPK_BY_BITONIC_SORT, INDEX_T>(
+        terminate_flag, parent_list_buffer, result_indices_buffer, internal_topk, search_width);
       _CLK_REC(clk_pickup_parents);
     }
 
@@ -706,20 +691,16 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
 
     // compute the norms between child nodes and query node
     _CLK_START();
-    constexpr unsigned max_n_frags = 8;
-    device::compute_distance_to_child_nodes<TEAM_SIZE, DATASET_BLOCK_DIM, max_n_frags>(
-      result_indices_buffer + internal_topk,
-      result_distances_buffer + internal_topk,
-      query_buffer,
-      dataset_desc,
-      knn_graph,
-      graph_degree,
-      local_visited_hashmap_ptr,
-      hash_bitlen,
-      parent_list_buffer,
-      result_indices_buffer,
-      search_width,
-      metric);
+    device::compute_distance_to_child_nodes(result_indices_buffer + internal_topk,
+                                            result_distances_buffer + internal_topk,
+                                            *dataset_desc,
+                                            knn_graph,
+                                            graph_degree,
+                                            local_visited_hashmap_ptr,
+                                            hash_bitlen,
+                                            parent_list_buffer,
+                                            result_indices_buffer,
+                                            search_width);
     __syncthreads();
     _CLK_REC(clk_compute_distance);
 
@@ -815,50 +796,33 @@ __launch_bounds__(1024, 1) RAFT_KERNEL search_kernel(
 #endif
 }
 
-template <uint32_t TEAM_SIZE,
-          uint32_t DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
+template <typename DATASET_DESCRIPTOR_T, typename SAMPLE_FILTER_T>
 struct search_kernel_config {
-  using kernel_t = decltype(&search_kernel<TEAM_SIZE,
-                                           DATASET_BLOCK_DIM,
-                                           64,
-                                           64,
-                                           0,
-                                           DATASET_DESCRIPTOR_T,
-                                           SAMPLE_FILTER_T>);
+  using kernel_t = decltype(&search_kernel<64, 64, 0, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>);
 
   template <unsigned MAX_CANDIDATES, unsigned USE_BITONIC_SORT>
   static auto choose_search_kernel(unsigned itopk_size) -> kernel_t
   {
     if (itopk_size <= 64) {
-      return search_kernel<TEAM_SIZE,
-                           DATASET_BLOCK_DIM,
-                           64,
+      return search_kernel<64,
                            MAX_CANDIDATES,
                            USE_BITONIC_SORT,
                            DATASET_DESCRIPTOR_T,
                            SAMPLE_FILTER_T>;
     } else if (itopk_size <= 128) {
-      return search_kernel<TEAM_SIZE,
-                           DATASET_BLOCK_DIM,
-                           128,
+      return search_kernel<128,
                            MAX_CANDIDATES,
                            USE_BITONIC_SORT,
                            DATASET_DESCRIPTOR_T,
                            SAMPLE_FILTER_T>;
     } else if (itopk_size <= 256) {
-      return search_kernel<TEAM_SIZE,
-                           DATASET_BLOCK_DIM,
-                           256,
+      return search_kernel<256,
                            MAX_CANDIDATES,
                            USE_BITONIC_SORT,
                            DATASET_DESCRIPTOR_T,
                            SAMPLE_FILTER_T>;
     } else if (itopk_size <= 512) {
-      return search_kernel<TEAM_SIZE,
-                           DATASET_BLOCK_DIM,
-                           512,
+      return search_kernel<512,
                            MAX_CANDIDATES,
                            USE_BITONIC_SORT,
                            DATASET_DESCRIPTOR_T,
@@ -882,21 +846,9 @@ struct search_kernel_config {
       // Radix-based topk is used
       constexpr unsigned max_candidates = 32;  // to avoid build failure
       if (itopk_size <= 256) {
-        return search_kernel<TEAM_SIZE,
-                             DATASET_BLOCK_DIM,
-                             256,
-                             max_candidates,
-                             0,
-                             DATASET_DESCRIPTOR_T,
-                             SAMPLE_FILTER_T>;
+        return search_kernel<256, max_candidates, 0, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
       } else if (itopk_size <= 512) {
-        return search_kernel<TEAM_SIZE,
-                             DATASET_BLOCK_DIM,
-                             512,
-                             max_candidates,
-                             0,
-                             DATASET_DESCRIPTOR_T,
-                             SAMPLE_FILTER_T>;
+        return search_kernel<512, max_candidates, 0, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
       }
     }
     THROW("No kernel for parametels itopk_size %u, num_itopk_candidates %u",
@@ -905,40 +857,35 @@ struct search_kernel_config {
   }
 };
 
-template <unsigned TEAM_SIZE,
-          unsigned DATASET_BLOCK_DIM,
-          typename DATASET_DESCRIPTOR_T,
-          typename SAMPLE_FILTER_T>
-void select_and_run(
-  DATASET_DESCRIPTOR_T dataset_desc,
-  raft::device_matrix_view<const typename DATASET_DESCRIPTOR_T::INDEX_T, int64_t, raft::row_major>
-    graph,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
-  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
-  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
-  const uint32_t num_queries,
-  const typename DATASET_DESCRIPTOR_T::INDEX_T* dev_seed_ptr,  // [num_queries, num_seeds]
-  uint32_t* const num_executed_iterations,                     // [num_queries,]
-  const search_params& ps,
-  uint32_t topk,
-  uint32_t num_itopk_candidates,
-  uint32_t block_size,  //
-  uint32_t smem_size,
-  int64_t hash_bitlen,
-  typename DATASET_DESCRIPTOR_T::INDEX_T* hashmap_ptr,
-  size_t small_hash_bitlen,
-  size_t small_hash_reset_interval,
-  uint32_t num_seeds,
-  SAMPLE_FILTER_T sample_filter,
-  cuvs::distance::DistanceType metric,
-  cudaStream_t stream)
+template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
+void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+                    raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
+                    IndexT* topk_indices_ptr,       // [num_queries, topk]
+                    DistanceT* topk_distances_ptr,  // [num_queries, topk]
+                    const DataT* queries_ptr,       // [num_queries, dataset_dim]
+                    uint32_t num_queries,
+                    const IndexT* dev_seed_ptr,         // [num_queries, num_seeds]
+                    uint32_t* num_executed_iterations,  // [num_queries,]
+                    const search_params& ps,
+                    uint32_t topk,
+                    uint32_t num_itopk_candidates,
+                    uint32_t block_size,  //
+                    uint32_t smem_size,
+                    int64_t hash_bitlen,
+                    IndexT* hashmap_ptr,
+                    size_t small_hash_bitlen,
+                    size_t small_hash_reset_interval,
+                    uint32_t num_seeds,
+                    SampleFilterT sample_filter,
+                    cudaStream_t stream)
 {
   auto kernel =
-    search_kernel_config<TEAM_SIZE, DATASET_BLOCK_DIM, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>::
-      choose_itopk_and_mx_candidates(ps.itopk_size, num_itopk_candidates, block_size);
-  RAFT_CUDA_TRY(cudaFuncSetAttribute(kernel,
-                                     cudaFuncAttributeMaxDynamicSharedMemorySize,
-                                     smem_size + DATASET_DESCRIPTOR_T::smem_buffer_size_in_byte));
+    search_kernel_config<dataset_descriptor_base_t<DataT, IndexT, DistanceT>,
+                         SampleFilterT>::choose_itopk_and_mx_candidates(ps.itopk_size,
+                                                                        num_itopk_candidates,
+                                                                        block_size);
+  RAFT_CUDA_TRY(
+    cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
   dim3 thread_dims(block_size, 1, 1);
   dim3 block_dims(1, num_queries, 1);
   RAFT_LOG_DEBUG(
@@ -963,9 +910,9 @@ void select_and_run(
                                                          hash_bitlen,
                                                          small_hash_bitlen,
                                                          small_hash_reset_interval,
-                                                         sample_filter,
-                                                         metric);
-  RAFT_CUDA_TRY(cudaPeekAtLastError());
+                                                         sample_filter);
+  // RAFT_CUDA_TRY(cudaPeekAtLastError());
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
 }
 }  // namespace single_cta_search
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
index 1ccec9219e..7b7f44db71 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,10 +15,32 @@
  */
 #pragma once
 
-#ifndef _CUVS_EXPLICIT_INSTANTIATE_ONLY
-#include "search_single_cta_kernel-inl.cuh"
-#endif
+#include "compute_distance-ext.cuh"
 
-#ifdef RAFT_COMPILED
-#include "search_single_cta_kernel-ext.cuh"
-#endif
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+
+template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
+void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+                    raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
+                    IndexT* topk_indices_ptr,       // [num_queries, topk]
+                    DistanceT* topk_distances_ptr,  // [num_queries, topk]
+                    const DataT* queries_ptr,       // [num_queries, dataset_dim]
+                    uint32_t num_queries,
+                    const IndexT* dev_seed_ptr,         // [num_queries, num_seeds]
+                    uint32_t* num_executed_iterations,  // [num_queries,]
+                    const search_params& ps,
+                    uint32_t topk,
+                    uint32_t num_itopk_candidates,
+                    uint32_t block_size,  //
+                    uint32_t smem_size,
+                    int64_t hash_bitlen,
+                    IndexT* hashmap_ptr,
+                    size_t small_hash_bitlen,
+                    size_t small_hash_reset_interval,
+                    uint32_t num_seeds,
+                    SampleFilterT sample_filter,
+                    cudaStream_t stream);
+
+}
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
new file mode 100644
index 0000000000..ee64271700
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by search_single_cta_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python search_single_cta_00_generate.py
+ *
+ */
+
+#include "search_single_cta_inst.cuh"
+
+namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(uint8_t,
+                             uint32_t,
+                             float,
+                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+
+}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
deleted file mode 100644
index 35e04ea6a0..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim1024_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  1024,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
deleted file mode 100644
index 614e6ca014..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim128_t8.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  8,
-  128,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
deleted file mode 100644
index 005afb5669..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim256_t16.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  16,
-  256,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
deleted file mode 100644
index af30b2e244..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32_dim512_t32.cu
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-#include "compute_distance.hpp"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(
-  32,
-  512,
-  cuvs::neighbors::cagra::detail::standard_dataset_descriptor_t<uint8_t COMMA uint32_t COMMA float>,
-  cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/topk_by_radix.cuh b/cpp/src/neighbors/detail/cagra/topk_by_radix.cuh
index 67173026b3..b6f97cb263 100644
--- a/cpp/src/neighbors/detail/cagra/topk_by_radix.cuh
+++ b/cpp/src/neighbors/detail/cagra/topk_by_radix.cuh
@@ -32,17 +32,17 @@ struct topk_by_radix_sort : topk_by_radix_sort_base<MAX_INTERNAL_TOPK> {};
 template <unsigned MAX_INTERNAL_TOPK, class IdxT>
 struct topk_by_radix_sort<MAX_INTERNAL_TOPK, IdxT, std::enable_if_t<((MAX_INTERNAL_TOPK <= 64))>>
   : topk_by_radix_sort_base<MAX_INTERNAL_TOPK> {
-  __device__ void operator()(uint32_t topk,
-                             uint32_t batch_size,
-                             uint32_t len_x,
-                             const uint32_t* _x,
-                             const IdxT* _in_vals,
-                             uint32_t* _y,
-                             IdxT* _out_vals,
-                             uint32_t* work,
-                             uint32_t* _hints,
-                             bool sort,
-                             uint32_t* _smem)
+  RAFT_DEVICE_INLINE_FUNCTION void operator()(uint32_t topk,
+                                              uint32_t batch_size,
+                                              uint32_t len_x,
+                                              const uint32_t* _x,
+                                              const IdxT* _in_vals,
+                                              uint32_t* _y,
+                                              IdxT* _out_vals,
+                                              uint32_t* work,
+                                              uint32_t* _hints,
+                                              bool sort,
+                                              uint32_t* _smem)
   {
     std::uint8_t* const state = reinterpret_cast<std::uint8_t*>(work);
     topk_cta_11_core<topk_by_radix_sort_base<MAX_INTERNAL_TOPK>::state_bit_lenght,
@@ -60,17 +60,17 @@ struct topk_by_radix_sort<MAX_INTERNAL_TOPK, IdxT, std::enable_if_t<((MAX_INTERN
     IdxT,                                                                            \
     std::enable_if_t<((MAX_INTERNAL_TOPK <= V) && (2 * MAX_INTERNAL_TOPK > V))>>     \
     : topk_by_radix_sort_base<MAX_INTERNAL_TOPK> {                                   \
-    __device__ void operator()(uint32_t topk,                                        \
-                               uint32_t batch_size,                                  \
-                               uint32_t len_x,                                       \
-                               const uint32_t* _x,                                   \
-                               const IdxT* _in_vals,                                 \
-                               uint32_t* _y,                                         \
-                               IdxT* _out_vals,                                      \
-                               uint32_t* work,                                       \
-                               uint32_t* _hints,                                     \
-                               bool sort,                                            \
-                               uint32_t* _smem)                                      \
+    RAFT_DEVICE_INLINE_FUNCTION void operator()(uint32_t topk,                       \
+                                                uint32_t batch_size,                 \
+                                                uint32_t len_x,                      \
+                                                const uint32_t* _x,                  \
+                                                const IdxT* _in_vals,                \
+                                                uint32_t* _y,                        \
+                                                IdxT* _out_vals,                     \
+                                                uint32_t* work,                      \
+                                                uint32_t* _hints,                    \
+                                                bool sort,                           \
+                                                uint32_t* _smem)                     \
     {                                                                                \
       assert(blockDim.x >= V / 4);                                                   \
       std::uint8_t* state = (std::uint8_t*)work;                                     \
diff --git a/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.cu b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.cu
new file mode 100644
index 0000000000..72ff2cb853
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk.cu
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "topk_core.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+//
+size_t _cuann_find_topk_bufferSize(uint32_t topK,
+                                   uint32_t sizeBatch,
+                                   uint32_t numElements,
+                                   cudaDataType_t sampleDtype)
+{
+  constexpr int numThreads  = NUM_THREADS;
+  constexpr int stateBitLen = STATE_BIT_LENGTH;
+  assert(stateBitLen == 0 || stateBitLen == 8);
+
+  size_t workspaceSize = 1;
+  // state
+  if (stateBitLen == 8) {
+    workspaceSize = _cuann_aligned(
+      sizeof(uint8_t) * get_state_size<stateBitLen, numThreads>(numElements) * sizeBatch);
+  }
+
+  return workspaceSize;
+}
+
+template <class ValT>
+void _cuann_find_topk(uint32_t topK,
+                      uint32_t sizeBatch,
+                      uint32_t numElements,
+                      const float* inputKeys,  // [sizeBatch, ldIK,]
+                      uint32_t ldIK,           // (*) ldIK >= numElements
+                      const ValT* inputVals,   // [sizeBatch, ldIV,]
+                      uint32_t ldIV,           // (*) ldIV >= numElements
+                      float* outputKeys,       // [sizeBatch, ldOK,]
+                      uint32_t ldOK,           // (*) ldOK >= topK
+                      ValT* outputVals,        // [sizeBatch, ldOV,]
+                      uint32_t ldOV,           // (*) ldOV >= topK
+                      void* workspace,
+                      bool sort,
+                      uint32_t* hints,
+                      cudaStream_t stream)
+{
+  assert(ldIK >= numElements);
+  assert(ldIV >= numElements);
+  assert(ldOK >= topK);
+  assert(ldOV >= topK);
+
+  constexpr int numThreads  = NUM_THREADS;
+  constexpr int stateBitLen = STATE_BIT_LENGTH;
+  assert(stateBitLen == 0 || stateBitLen == 8);
+
+  uint8_t* state = NULL;
+  if (stateBitLen == 8) { state = (uint8_t*)workspace; }
+
+  dim3 threads(numThreads, 1, 1);
+  dim3 blocks(sizeBatch, 1, 1);
+
+  void (*cta_kernel)(uint32_t,
+                     uint32_t,
+                     uint32_t,
+                     const uint32_t*,
+                     uint32_t,
+                     const ValT*,
+                     uint32_t,
+                     uint32_t*,
+                     uint32_t,
+                     ValT*,
+                     uint32_t,
+                     uint8_t*,
+                     uint32_t*,
+                     bool) = nullptr;
+
+  // V:vecLen, K:maxTopk, T:numSortThreads
+#define SET_KERNEL_VKT(V, K, T, ValT)                          \
+  do {                                                         \
+    assert(numThreads >= T);                                   \
+    assert((K % T) == 0);                                      \
+    assert((K / T) <= 4);                                      \
+    cta_kernel = kern_topk_cta_11<stateBitLen, V, K, T, ValT>; \
+  } while (0)
+
+  // V: vecLen
+#define SET_KERNEL_V(V, ValT)                                \
+  do {                                                       \
+    if (topK <= 32) {                                        \
+      SET_KERNEL_VKT(V, 32, 32, ValT);                       \
+    } else if (topK <= 64) {                                 \
+      SET_KERNEL_VKT(V, 64, 32, ValT);                       \
+    } else if (topK <= 96) {                                 \
+      SET_KERNEL_VKT(V, 96, 32, ValT);                       \
+    } else if (topK <= 128) {                                \
+      SET_KERNEL_VKT(V, 128, 32, ValT);                      \
+    } else if (topK <= 192) {                                \
+      SET_KERNEL_VKT(V, 192, 64, ValT);                      \
+    } else if (topK <= 256) {                                \
+      SET_KERNEL_VKT(V, 256, 64, ValT);                      \
+    } else if (topK <= 384) {                                \
+      SET_KERNEL_VKT(V, 384, 128, ValT);                     \
+    } else if (topK <= 512) {                                \
+      SET_KERNEL_VKT(V, 512, 128, ValT);                     \
+    } else if (topK <= 768) {                                \
+      SET_KERNEL_VKT(V, 768, 256, ValT);                     \
+    } else if (topK <= 1024) {                               \
+      SET_KERNEL_VKT(V, 1024, 256, ValT);                    \
+    } \
+        /* else if (topK <= 1536) { SET_KERNEL_VKT(V, 1536, 512); } */ \
+        /* else if (topK <= 2048) { SET_KERNEL_VKT(V, 2048, 512); } */ \
+        /* else if (topK <= 3072) { SET_KERNEL_VKT(V, 3072, 1024); } */ \
+        /* else if (topK <= 4096) { SET_KERNEL_VKT(V, 4096, 1024); } */ \
+        else {                                                      \
+      RAFT_FAIL("topk must be lower than or equal to 1024"); \
+    }                                                        \
+  } while (0)
+
+  int _vecLen = _get_vecLen(ldIK, 2);
+  if (_vecLen == 2) {
+    SET_KERNEL_V(2, ValT);
+  } else if (_vecLen == 1) {
+    SET_KERNEL_V(1, ValT);
+  }
+
+  cta_kernel<<<blocks, threads, 0, stream>>>(topK,
+                                             sizeBatch,
+                                             numElements,
+                                             (const uint32_t*)inputKeys,
+                                             ldIK,
+                                             inputVals,
+                                             ldIV,
+                                             (uint32_t*)outputKeys,
+                                             ldOK,
+                                             outputVals,
+                                             ldOV,
+                                             state,
+                                             hints,
+                                             sort);
+
+  return;
+}
+
+template void _cuann_find_topk<uint32_t>(uint32_t topK,
+                                         uint32_t sizeBatch,
+                                         uint32_t numElements,
+                                         const float* inputKeys,     // [sizeBatch, ldIK,]
+                                         uint32_t ldIK,              // (*) ldIK >= numElements
+                                         const uint32_t* inputVals,  // [sizeBatch, ldIV,]
+                                         uint32_t ldIV,              // (*) ldIV >= numElements
+                                         float* outputKeys,          // [sizeBatch, ldOK,]
+                                         uint32_t ldOK,              // (*) ldOK >= topK
+                                         uint32_t* outputVals,       // [sizeBatch, ldOV,]
+                                         uint32_t ldOV,              // (*) ldOV >= topK
+                                         void* workspace,
+                                         bool sort,
+                                         uint32_t* hint,
+                                         cudaStream_t stream);
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh
index cbf99a5565..65f9cfade4 100644
--- a/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/topk_for_cagra/topk_core.cuh
@@ -14,10 +14,15 @@
  * limitations under the License.
  */
 #pragma once
+
+#include "../utils.hpp"
 #include "topk.h"
 
 #include <cub/cub.cuh>
 
+#include <raft/core/detail/macros.hpp>
+#include <raft/core/error.hpp>
+
 #include <assert.h>
 #include <float.h>
 #include <stdint.h>
@@ -25,7 +30,7 @@
 
 namespace cuvs::neighbors::cagra::detail {
 //
-__device__ inline uint32_t convert(uint32_t x)
+RAFT_DEVICE_INLINE_FUNCTION constexpr uint32_t convert(uint32_t x)
 {
   if (x & 0x80000000) {
     return x ^ 0xffffffff;
@@ -35,7 +40,7 @@ __device__ inline uint32_t convert(uint32_t x)
 }
 
 //
-__device__ inline uint16_t convert(uint16_t x)
+RAFT_DEVICE_INLINE_FUNCTION constexpr uint16_t convert(uint16_t x)
 {
   if (x & 0x8000) {
     return x ^ 0xffff;
@@ -62,7 +67,7 @@ struct u16_vector {
 
 //
 template <int vecLen>
-__device__ inline void load_u32_vector(struct u32_vector& vec, const uint32_t* x, int i)
+RAFT_DEVICE_INLINE_FUNCTION void load_u32_vector(struct u32_vector& vec, const uint32_t* x, int i)
 {
   if (vecLen == 1) {
     vec.x1 = ((uint1*)(x + i))[0];
@@ -77,7 +82,7 @@ __device__ inline void load_u32_vector(struct u32_vector& vec, const uint32_t* x
 
 //
 template <int vecLen>
-__device__ inline void load_u16_vector(struct u16_vector& vec, const uint16_t* x, int i)
+RAFT_DEVICE_INLINE_FUNCTION void load_u16_vector(struct u16_vector& vec, const uint16_t* x, int i)
 {
   if (vecLen == 1) {
     vec.x1 = ((ushort1*)(x + i))[0];
@@ -92,7 +97,7 @@ __device__ inline void load_u16_vector(struct u16_vector& vec, const uint16_t* x
 
 //
 template <int vecLen>
-__device__ inline uint32_t get_element_from_u32_vector(struct u32_vector& vec, int i)
+RAFT_DEVICE_INLINE_FUNCTION uint32_t get_element_from_u32_vector(struct u32_vector& vec, int i)
 {
   uint32_t xi;
   if (vecLen == 1) {
@@ -134,7 +139,7 @@ __device__ inline uint32_t get_element_from_u32_vector(struct u32_vector& vec, i
 
 //
 template <int vecLen>
-__device__ inline uint16_t get_element_from_u16_vector(struct u16_vector& vec, int i)
+RAFT_DEVICE_INLINE_FUNCTION uint16_t get_element_from_u16_vector(struct u16_vector& vec, int i)
 {
   uint16_t xi;
   if (vecLen == 1) {
@@ -175,7 +180,7 @@ __device__ inline uint16_t get_element_from_u16_vector(struct u16_vector& vec, i
 }
 
 template <typename T>
-__device__ inline void block_scan(const T input, T& output)
+RAFT_DEVICE_INLINE_FUNCTION void block_scan(const T input, T& output)
 {
   switch (blockDim.x) {
     case 32: {
@@ -214,19 +219,19 @@ __device__ inline void block_scan(const T input, T& output)
 
 //
 template <typename T, int stateBitLen, int vecLen>
-__device__ inline void update_histogram(int itr,
-                                        uint32_t thread_id,
-                                        uint32_t num_threads,
-                                        uint32_t hint,
-                                        uint32_t threshold,
-                                        uint32_t& num_bins,
-                                        uint32_t& shift,
-                                        const T* x,  // [nx,]
-                                        uint32_t nx,
-                                        uint32_t* hist,  // [num_bins]
-                                        uint8_t* state,
-                                        uint32_t* output,  // [topk]
-                                        uint32_t* output_count)
+RAFT_DEVICE_INLINE_FUNCTION void update_histogram(int itr,
+                                                  uint32_t thread_id,
+                                                  uint32_t num_threads,
+                                                  uint32_t hint,
+                                                  uint32_t threshold,
+                                                  uint32_t& num_bins,
+                                                  uint32_t& shift,
+                                                  const T* x,  // [nx,]
+                                                  uint32_t nx,
+                                                  uint32_t* hist,  // [num_bins]
+                                                  uint8_t* state,
+                                                  uint32_t* output,  // [topk]
+                                                  uint32_t* output_count)
 {
   if (sizeof(T) == 4) {
     // 32-bit (uint32_t)
@@ -324,15 +329,16 @@ __device__ inline void update_histogram(int itr,
 }
 
 template <unsigned blockDim_x>
-__device__ inline void select_best_index_for_next_threshold_core(uint32_t& my_index,
-                                                                 uint32_t& my_csum,
-                                                                 const unsigned num_bins,
-                                                                 const uint32_t* const hist,
-                                                                 const uint32_t nx_below_threshold,
-                                                                 const uint32_t max_threshold,
-                                                                 const uint32_t threshold,
-                                                                 const uint32_t shift,
-                                                                 const uint32_t topk)
+RAFT_DEVICE_INLINE_FUNCTION void select_best_index_for_next_threshold_core(
+  uint32_t& my_index,
+  uint32_t& my_csum,
+  const unsigned num_bins,
+  const uint32_t* const hist,
+  const uint32_t nx_below_threshold,
+  const uint32_t max_threshold,
+  const uint32_t threshold,
+  const uint32_t shift,
+  const uint32_t topk)
 {
   typedef cub::BlockScan<uint32_t, blockDim_x> BlockScanT;
   __shared__ typename BlockScanT::TempStorage temp_storage;
@@ -370,7 +376,7 @@ __device__ inline void select_best_index_for_next_threshold_core(uint32_t& my_in
 }
 
 //
-__device__ inline void select_best_index_for_next_threshold(
+RAFT_DEVICE_INLINE_FUNCTION void select_best_index_for_next_threshold(
   const uint32_t topk,
   const uint32_t threshold,
   const uint32_t max_threshold,
@@ -469,17 +475,17 @@ __device__ inline void select_best_index_for_next_threshold(
 
 //
 template <typename T, int stateBitLen, int vecLen>
-__device__ inline void output_index_below_threshold(const uint32_t topk,
-                                                    const uint32_t thread_id,
-                                                    const uint32_t num_threads,
-                                                    const uint32_t threshold,
-                                                    const uint32_t nx_below_threshold,
-                                                    const T* const x,  // [nx,]
-                                                    const uint32_t nx,
-                                                    const uint8_t* state,
-                                                    uint32_t* const output,  // [topk]
-                                                    uint32_t* const output_count,
-                                                    uint32_t* const output_count_eq)
+RAFT_DEVICE_INLINE_FUNCTION void output_index_below_threshold(const uint32_t topk,
+                                                              const uint32_t thread_id,
+                                                              const uint32_t num_threads,
+                                                              const uint32_t threshold,
+                                                              const uint32_t nx_below_threshold,
+                                                              const T* const x,  // [nx,]
+                                                              const uint32_t nx,
+                                                              const uint8_t* state,
+                                                              uint32_t* const output,  // [topk]
+                                                              uint32_t* const output_count,
+                                                              uint32_t* const output_count_eq)
 {
   int ii = 0;
   for (int i = thread_id * vecLen; i < nx; i += num_threads * max(vecLen, stateBitLen), ii++) {
@@ -530,7 +536,7 @@ __device__ inline void output_index_below_threshold(const uint32_t topk,
 
 //
 template <typename T>
-__device__ inline void swap(T& val1, T& val2)
+RAFT_DEVICE_INLINE_FUNCTION constexpr void swap(T& val1, T& val2)
 {
   const T val0 = val1;
   val1         = val2;
@@ -539,7 +545,7 @@ __device__ inline void swap(T& val1, T& val2)
 
 //
 template <typename K>
-__device__ inline bool swap_if_needed(K& key1, K& key2)
+RAFT_DEVICE_INLINE_FUNCTION constexpr bool swap_if_needed(K& key1, K& key2)
 {
   if (key1 > key2) {
     swap<K>(key1, key2);
@@ -550,7 +556,7 @@ __device__ inline bool swap_if_needed(K& key1, K& key2)
 
 //
 template <typename K, typename V>
-__device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2)
+RAFT_DEVICE_INLINE_FUNCTION constexpr bool swap_if_needed(K& key1, K& key2, V& val1, V& val2)
 {
   if (key1 > key2) {
     swap<K>(key1, key2);
@@ -562,7 +568,8 @@ __device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2)
 
 //
 template <typename K, typename V>
-__device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2, bool ascending)
+RAFT_DEVICE_INLINE_FUNCTION constexpr bool swap_if_needed(
+  K& key1, K& key2, V& val1, V& val2, bool ascending)
 {
   if (key1 == key2) { return false; }
   if ((key1 > key2) == ascending) {
@@ -575,20 +582,20 @@ __device__ inline bool swap_if_needed(K& key1, K& key2, V& val1, V& val2, bool a
 
 //
 template <typename T>
-__device__ inline T max_value_of();
+RAFT_DEVICE_INLINE_FUNCTION T max_value_of();
 template <>
-__device__ inline float max_value_of<float>()
+RAFT_DEVICE_INLINE_FUNCTION float max_value_of<float>()
 {
   return FLT_MAX;
 }
 template <>
-__device__ inline uint32_t max_value_of<uint32_t>()
+RAFT_DEVICE_INLINE_FUNCTION uint32_t max_value_of<uint32_t>()
 {
   return ~0u;
 }
 
 template <int stateBitLen, unsigned BLOCK_SIZE = 0>
-__device__ __host__ inline uint32_t get_state_size(uint32_t len_x)
+RAFT_INLINE_FUNCTION constexpr uint32_t get_state_size(uint32_t len_x)
 {
 #ifdef __CUDA_ARCH__
   const uint32_t num_threads = blockDim.x;
@@ -605,16 +612,16 @@ __device__ __host__ inline uint32_t get_state_size(uint32_t len_x)
 
 //
 template <int stateBitLen, int vecLen, int maxTopk, int numSortThreads, class ValT>
-__device__ inline void topk_cta_11_core(uint32_t topk,
-                                        uint32_t len_x,
-                                        const uint32_t* _x,    // [size_batch, ld_x,]
-                                        const ValT* _in_vals,  // [size_batch, ld_iv,]
-                                        uint32_t* _y,          // [size_batch, ld_y,]
-                                        ValT* _out_vals,       // [size_batch, ld_ov,]
-                                        uint8_t* _state,       // [size_batch, ...,]
-                                        uint32_t* _hint,
-                                        bool sort,
-                                        uint32_t* _smem)
+RAFT_DEVICE_INLINE_FUNCTION void topk_cta_11_core(uint32_t topk,
+                                                  uint32_t len_x,
+                                                  const uint32_t* _x,    // [size_batch, ld_x,]
+                                                  const ValT* _in_vals,  // [size_batch, ld_iv,]
+                                                  uint32_t* _y,          // [size_batch, ld_y,]
+                                                  ValT* _out_vals,       // [size_batch, ld_ov,]
+                                                  uint8_t* _state,       // [size_batch, ...,]
+                                                  uint32_t* _hint,
+                                                  bool sort,
+                                                  uint32_t* _smem)
 {
   uint32_t* const smem_out_vals = _smem;
   uint32_t* const hist          = &(_smem[2 * maxTopk]);
@@ -904,137 +911,4 @@ __launch_bounds__(1024, 1) RAFT_KERNEL
     _smem);
 }
 
-//
-size_t inline _cuann_find_topk_bufferSize(uint32_t topK,
-                                          uint32_t sizeBatch,
-                                          uint32_t numElements,
-                                          cudaDataType_t sampleDtype)
-{
-  constexpr int numThreads  = NUM_THREADS;
-  constexpr int stateBitLen = STATE_BIT_LENGTH;
-  assert(stateBitLen == 0 || stateBitLen == 8);
-
-  size_t workspaceSize = 1;
-  // state
-  if (stateBitLen == 8) {
-    workspaceSize = _cuann_aligned(
-      sizeof(uint8_t) * get_state_size<stateBitLen, numThreads>(numElements) * sizeBatch);
-  }
-
-  return workspaceSize;
-}
-
-template <class ValT>
-inline void _cuann_find_topk(uint32_t topK,
-                             uint32_t sizeBatch,
-                             uint32_t numElements,
-                             const float* inputKeys,  // [sizeBatch, ldIK,]
-                             uint32_t ldIK,           // (*) ldIK >= numElements
-                             const ValT* inputVals,   // [sizeBatch, ldIV,]
-                             uint32_t ldIV,           // (*) ldIV >= numElements
-                             float* outputKeys,       // [sizeBatch, ldOK,]
-                             uint32_t ldOK,           // (*) ldOK >= topK
-                             ValT* outputVals,        // [sizeBatch, ldOV,]
-                             uint32_t ldOV,           // (*) ldOV >= topK
-                             void* workspace,
-                             bool sort,
-                             uint32_t* hints,
-                             cudaStream_t stream)
-{
-  assert(ldIK >= numElements);
-  assert(ldIV >= numElements);
-  assert(ldOK >= topK);
-  assert(ldOV >= topK);
-
-  constexpr int numThreads  = NUM_THREADS;
-  constexpr int stateBitLen = STATE_BIT_LENGTH;
-  assert(stateBitLen == 0 || stateBitLen == 8);
-
-  uint8_t* state = NULL;
-  if (stateBitLen == 8) { state = (uint8_t*)workspace; }
-
-  dim3 threads(numThreads, 1, 1);
-  dim3 blocks(sizeBatch, 1, 1);
-
-  void (*cta_kernel)(uint32_t,
-                     uint32_t,
-                     uint32_t,
-                     const uint32_t*,
-                     uint32_t,
-                     const ValT*,
-                     uint32_t,
-                     uint32_t*,
-                     uint32_t,
-                     ValT*,
-                     uint32_t,
-                     uint8_t*,
-                     uint32_t*,
-                     bool) = nullptr;
-
-  // V:vecLen, K:maxTopk, T:numSortThreads
-#define SET_KERNEL_VKT(V, K, T, ValT)                          \
-  do {                                                         \
-    assert(numThreads >= T);                                   \
-    assert((K % T) == 0);                                      \
-    assert((K / T) <= 4);                                      \
-    cta_kernel = kern_topk_cta_11<stateBitLen, V, K, T, ValT>; \
-  } while (0)
-
-  // V: vecLen
-#define SET_KERNEL_V(V, ValT)                                \
-  do {                                                       \
-    if (topK <= 32) {                                        \
-      SET_KERNEL_VKT(V, 32, 32, ValT);                       \
-    } else if (topK <= 64) {                                 \
-      SET_KERNEL_VKT(V, 64, 32, ValT);                       \
-    } else if (topK <= 96) {                                 \
-      SET_KERNEL_VKT(V, 96, 32, ValT);                       \
-    } else if (topK <= 128) {                                \
-      SET_KERNEL_VKT(V, 128, 32, ValT);                      \
-    } else if (topK <= 192) {                                \
-      SET_KERNEL_VKT(V, 192, 64, ValT);                      \
-    } else if (topK <= 256) {                                \
-      SET_KERNEL_VKT(V, 256, 64, ValT);                      \
-    } else if (topK <= 384) {                                \
-      SET_KERNEL_VKT(V, 384, 128, ValT);                     \
-    } else if (topK <= 512) {                                \
-      SET_KERNEL_VKT(V, 512, 128, ValT);                     \
-    } else if (topK <= 768) {                                \
-      SET_KERNEL_VKT(V, 768, 256, ValT);                     \
-    } else if (topK <= 1024) {                               \
-      SET_KERNEL_VKT(V, 1024, 256, ValT);                    \
-    } \
-        /* else if (topK <= 1536) { SET_KERNEL_VKT(V, 1536, 512); } */ \
-        /* else if (topK <= 2048) { SET_KERNEL_VKT(V, 2048, 512); } */ \
-        /* else if (topK <= 3072) { SET_KERNEL_VKT(V, 3072, 1024); } */ \
-        /* else if (topK <= 4096) { SET_KERNEL_VKT(V, 4096, 1024); } */ \
-        else {                                                      \
-      RAFT_FAIL("topk must be lower than or equal to 1024"); \
-    }                                                        \
-  } while (0)
-
-  int _vecLen = _get_vecLen(ldIK, 2);
-  if (_vecLen == 2) {
-    SET_KERNEL_V(2, ValT);
-  } else if (_vecLen == 1) {
-    SET_KERNEL_V(1, ValT);
-  }
-
-  cta_kernel<<<blocks, threads, 0, stream>>>(topK,
-                                             sizeBatch,
-                                             numElements,
-                                             (const uint32_t*)inputKeys,
-                                             ldIK,
-                                             inputVals,
-                                             ldIV,
-                                             (uint32_t*)outputKeys,
-                                             ldOK,
-                                             outputVals,
-                                             ldOV,
-                                             state,
-                                             hints,
-                                             sort);
-
-  return;
-}
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/utils.hpp b/cpp/src/neighbors/detail/cagra/utils.hpp
index 8ce20ec5cd..0f83093282 100644
--- a/cpp/src/neighbors/detail/cagra/utils.hpp
+++ b/cpp/src/neighbors/detail/cagra/utils.hpp
@@ -125,24 +125,24 @@ union fp_conv {
   FP_T fp;
 };
 template <class T>
-_RAFT_HOST_DEVICE inline T get_max_value();
+_RAFT_HOST_DEVICE constexpr inline T get_max_value();
 template <>
-_RAFT_HOST_DEVICE inline float get_max_value<float>()
+_RAFT_HOST_DEVICE constexpr inline float get_max_value<float>()
 {
   return FLT_MAX;
 };
 template <>
-_RAFT_HOST_DEVICE inline half get_max_value<half>()
+_RAFT_HOST_DEVICE constexpr inline half get_max_value<half>()
 {
   return fp_conv<std::uint16_t, half>{.bs = 0x7aff}.fp;
 };
 template <>
-_RAFT_HOST_DEVICE inline std::uint32_t get_max_value<std::uint32_t>()
+_RAFT_HOST_DEVICE constexpr inline std::uint32_t get_max_value<std::uint32_t>()
 {
   return 0xffffffffu;
 };
 template <>
-_RAFT_HOST_DEVICE inline std::uint64_t get_max_value<std::uint64_t>()
+_RAFT_HOST_DEVICE constexpr inline std::uint64_t get_max_value<std::uint64_t>()
 {
   return 0xfffffffffffffffflu;
 };
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 9d2f9c175b..4ce0849fd1 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -706,7 +706,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
     {search_algo::AUTO},
     {10},
-    {0, 4, 8, 16, 32},  // team_size
+    {0, 8, 16, 32},  // team_size
     {64},
     {1},
     {cuvs::distance::DistanceType::L2Expanded},

From 8e0215021454c51f9c03b1310f8c8ea39e56b041 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Wed, 25 Sep 2024 13:27:15 -0500
Subject: [PATCH 156/623] remove NCCL pins in build and test environments
 (#341)

Contributes to https://github.com/rapidsai/build-planning/issues/102

Some RAPIDS libraries are using `ncclCommSplit()`, which was introduced in `nccl==2.18.1.1`. This is part of a series of PRs across RAPIDS updating libraries' pins to `nccl>=2.18.1.1` to ensure they get a new-enough version that supports that.

`cuvs` doesn't have any *direct* uses of NCCL... it only uses it via raft. This PR proposes removing `cuvs`'s dependency pinnings on NCCL, in favor of just using whatever it gets transitively via raft.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/341
---
 conda/environments/all_cuda-118_arch-aarch64.yaml       | 1 -
 conda/environments/all_cuda-118_arch-x86_64.yaml        | 1 -
 conda/environments/all_cuda-125_arch-aarch64.yaml       | 1 -
 conda/environments/all_cuda-125_arch-x86_64.yaml        | 1 -
 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml | 1 -
 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml  | 1 -
 conda/environments/bench_ann_cuda-125_arch-aarch64.yaml | 1 -
 conda/environments/bench_ann_cuda-125_arch-x86_64.yaml  | 1 -
 conda/recipes/libcuvs/conda_build_config.yaml           | 3 ---
 dependencies.yaml                                       | 1 -
 10 files changed, 12 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index cfcb562258..5c599fcc2f 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -37,7 +37,6 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - make
-- nccl>=2.9.9
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index dc519d1b58..ce9a7f058d 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -37,7 +37,6 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - make
-- nccl>=2.9.9
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index b32650e449..116e80ac2e 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -34,7 +34,6 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - make
-- nccl>=2.9.9
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index d40fc3b99b..7f7ad045d2 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -34,7 +34,6 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - make
-- nccl>=2.9.9
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index c6e8b05a2c..7e1014f250 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -35,7 +35,6 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
-- nccl>=2.9.9
 - ninja
 - nlohmann_json>=3.11.2
 - nvcc_linux-aarch64=11.8
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index d6c023ae97..120b7afca0 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -35,7 +35,6 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
-- nccl>=2.9.9
 - ninja
 - nlohmann_json>=3.11.2
 - nvcc_linux-64=11.8
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 4d0ca94966..ac0ea97e6a 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -32,7 +32,6 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
-- nccl>=2.9.9
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index 7dd67ab5e0..e593c240d1 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -32,7 +32,6 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
-- nccl>=2.9.9
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index 0e14163424..e165f7ed9a 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -19,9 +19,6 @@ c_stdlib_version:
 cmake_version:
   - ">=3.26.4,!=3.30.0"
 
-nccl_version:
-  - ">=2.9.9"
-
 h5py_version:
   - ">=3.8.0"
 
diff --git a/dependencies.yaml b/dependencies.yaml
index 9fcbeaae2f..c63cecbbec 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -151,7 +151,6 @@ dependencies:
         packages:
           - c-compiler
           - cxx-compiler
-          - nccl>=2.9.9
     specific:
       - output_types: conda
         matrices:

From edba189053c1b830ca2b02c63d7a9a1c2f10010f Mon Sep 17 00:00:00 2001
From: rhdong <hrong@nvidia.com>
Date: Thu, 26 Sep 2024 15:50:21 -0700
Subject: [PATCH 157/623] [Feat] Relative change with `bitset` API feature
 #2439 in raft (#350)

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/350
---
 cpp/src/neighbors/detail/knn_brute_force.cuh  | 2 +-
 cpp/test/neighbors/brute_force_prefiltered.cu | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 88986af7d9..cf27bcde70 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -595,7 +595,7 @@ void brute_force_search_filtered(
   auto filter_view =
     raft::make_device_vector_view<const BitmapT, IdxT>(filter.data(), filter.n_elements());
   IdxT size_h    = n_queries * n_dataset;
-  auto size_view = raft::make_host_scalar_view<IdxT>(&size_h);
+  auto size_view = raft::make_host_scalar_view<const IdxT, IdxT>(&size_h);
 
   raft::popc(res, filter_view, size_view, nnz_view);
   raft::copy(&nnz_h, nnz.data(), 1, stream);
diff --git a/cpp/test/neighbors/brute_force_prefiltered.cu b/cpp/test/neighbors/brute_force_prefiltered.cu
index 9304ee0451..ae9111ea15 100644
--- a/cpp/test/neighbors/brute_force_prefiltered.cu
+++ b/cpp/test/neighbors/brute_force_prefiltered.cu
@@ -203,7 +203,7 @@ class PrefilteredBruteForceTest
       auto filter_view =
         raft::make_device_vector_view<const uint32_t, index_t>(filter_d.data(), filter_d.size());
       index_t size_h = m * n;
-      auto size_view = raft::make_host_scalar_view<index_t>(&size_h);
+      auto size_view = raft::make_host_scalar_view<const index_t, index_t>(&size_h);
 
       set_bitmap(src, dst, bitmap, n_edges, n, stream);
 

From b93b8f639b2e4caa8c542b643d615035a6dee754 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 27 Sep 2024 03:04:34 +0200
Subject: [PATCH 158/623] Persistent CAGRA kernel (#215)

An experimental version of the single-cta CAGRA kernel that run persistently while allowing many CPU threads submit the queries in small batches very efficiently.

<p align="center">
  <img alt="CAGRA throughput @ Recall = 0.94, n_queries = 1" src="https://github.com/user-attachments/assets/68884aef-2d5f-49f6-b75c-2cb86d17c0fb" width="49%">
  <img alt="CAGRA throughput @ Recall = 0.94, n_queries = 10" src="https://github.com/user-attachments/assets/e6fbfb73-7c29-4887-8e97-31a30a346d13" width="49%">
</p>

## API

In the current implementation, the public API does not change. An extra parameter `persistent` is added to the `ann::cagra::search_params` (only valid when `algo == SINGLE_CTA`).
The persistent kernel is managed by a global runner object in a `shared_ptr`; the first CPU thread to call the kernel spawns the runner, subsequent calls/threads only update a global "heartbeat" atomic variable (`runner_base_t::last_touch`). When there's no heartbeat in the last few seconds (`kLiveInterval`), the runner shuts down the kernel and cleans up the associated resources.

An alternative solution would be to control the kernel explicitly, in a client-server style. This would be more controllable, but would require significant re-thinking of the RAFT/cuVS API.

### Synchronization behavior and CUDA streams

The kernel is managed in a dedicated thread & a non-blocking stream; it's independent of any other (i.e. calling) threads.

Although we pass a CUDA stream to the search function to preserve the api, this **CUDA stream is never used**; in fact, there are no CUDA API calls happening in the calling thread.
All communication between the host calling thread and GPU workers happens via atomic variables.

**The search function blocks the CPU thread**, i.e. it waits till the results are back before returning.

### Exceptions and safety

The kernel runner object is stored in a shared pointer. Hence, it provides all the same safety guarantees as smart pointers. For example, if a C++ exception is raised in the runner thread, the kernel is stopped during the destruction of the runner/last shared pointer.

It's hard to detect if something happens to the kernel or CUDA context. If the kernel does not return the results to the calling thread within the configured kernel lifetime (`persistent_lifetime` ), the calling thread abandons the request and throws an exception.
The designed behavior here is that all components can gracefully shutdown within the configured kernel lifetime independently.

## Integration notes

### lightweight_uvector

RMM memory resources and device buffers are not zero-cost, even when the allocation size is zero (a common pattern for conditionally-used buffers). They do at least couple `cudaGetDevice` calls during initialization. Normally, the overhead of this is negligible. However, when the number of concurrent threads is large (hundreds of threads), any CUDA call can become a bottleneck due to a single mutex guarding a critical section somewhere in the driver.

To workaround this, I introduce a `lightweight_uvector` in `/detail/cagra/search_plan.cuh` for several buffers used in CAGRA kernels. This is a stripped "multi-device-unsafe" version of `rmm::uvector`: it does not check during resize/destruction whether the current device has changed since construction.
We may consider putting this in a common folder to use across other RAFT/cuVS algorithms.

### Shared resource queues / ring buffers

`resource_queue_t` is an atomic counter-based ring buffer used to distribute the worker resources (CTAs) and pre-allocated job descriptors across CPU I/O threads.
We may consider putting this in a common public namespace in raft if we envision more uses for it.

### Persistent runner structs

`launcher_t` and `persistent_runner_base_t` look like they could be abstracted from the cagra kernel and re-used in other algos. The code in its current state, however, is not ready for this.

### Adjusted benchmarks

 1. I introduced a global temporary buffer for keeping the intermediate results (e.g. neighbor candidates before refinement). This is needed to avoid unnecessary allocations alongside the persistent kernel (but also positively affects performance of the original non-persistent implementation)
 2. I adjusted cuvs common benchmark utils to avoid extra d2h copies and syncs during refinement.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/215
---
 cpp/bench/ann/src/common/ann_types.hpp        |    3 +
 cpp/bench/ann/src/common/dataset.hpp          |   56 +-
 cpp/bench/ann/src/common/util.hpp             |  133 +-
 .../src/cuvs/cuvs_ann_bench_param_parser.h    |   10 +
 cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h |   55 +-
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |   88 +-
 cpp/bench/ann/src/cuvs/cuvs_wrapper.h         |    1 +
 cpp/include/cuvs/neighbors/cagra.hpp          |   24 +
 .../detail/cagra/compute_distance-ext.cuh     |    5 +-
 .../detail/cagra/compute_distance.hpp         |   62 +-
 .../cagra/compute_distance_00_generate.py     |    5 +-
 .../cagra/compute_distance_standard-impl.cuh  |   30 +-
 .../cagra/compute_distance_standard.hpp       |   14 +-
 .../cagra/compute_distance_vpq-impl.cuh       |   42 +-
 .../detail/cagra/compute_distance_vpq.hpp     |    9 +-
 cpp/src/neighbors/detail/cagra/factory.cuh    |    4 +-
 .../detail/cagra/search_multi_cta.cuh         |    2 +-
 .../detail/cagra/search_multi_cta_inst.cuh    |    2 +-
 .../cagra/search_multi_cta_kernel-inl.cuh     |    4 +-
 .../detail/cagra/search_multi_cta_kernel.cuh  |    2 +-
 .../detail/cagra/search_multi_kernel.cuh      |    4 +-
 .../neighbors/detail/cagra/search_plan.cuh    |   89 +-
 .../detail/cagra/search_single_cta.cuh        |    8 +-
 .../detail/cagra/search_single_cta_inst.cuh   |    2 +-
 .../cagra/search_single_cta_kernel-inl.cuh    | 1220 ++++++++++++++++-
 .../detail/cagra/search_single_cta_kernel.cuh |    2 +-
 examples/cpp/CMakeLists.txt                   |    5 +
 examples/cpp/src/cagra_persistent_example.cu  |  258 ++++
 28 files changed, 1896 insertions(+), 243 deletions(-)
 create mode 100644 examples/cpp/src/cagra_persistent_example.cu

diff --git a/cpp/bench/ann/src/common/ann_types.hpp b/cpp/bench/ann/src/common/ann_types.hpp
index 4b17885c07..c2f85e5396 100644
--- a/cpp/bench/ann/src/common/ann_types.hpp
+++ b/cpp/bench/ann/src/common/ann_types.hpp
@@ -35,6 +35,7 @@ enum class Mode {
 enum class MemoryType {
   kHost,
   kHostMmap,
+  kHostPinned,
   kDevice,
 };
 
@@ -60,6 +61,8 @@ inline auto parse_memory_type(const std::string& memory_type) -> MemoryType
     return MemoryType::kHost;
   } else if (memory_type == "mmap") {
     return MemoryType::kHostMmap;
+  } else if (memory_type == "pinned") {
+    return MemoryType::kHostPinned;
   } else if (memory_type == "device") {
     return MemoryType::kDevice;
   } else {
diff --git a/cpp/bench/ann/src/common/dataset.hpp b/cpp/bench/ann/src/common/dataset.hpp
index 95f1a82a28..49020fe36e 100644
--- a/cpp/bench/ann/src/common/dataset.hpp
+++ b/cpp/bench/ann/src/common/dataset.hpp
@@ -286,7 +286,28 @@ class dataset {
   {
     switch (memory_type) {
       case MemoryType::kDevice: return query_set_on_gpu();
-      default: return query_set();
+      case MemoryType::kHost: {
+        auto r = query_set();
+#ifndef BUILD_CPU_ONLY
+        if (query_set_pinned_) {
+          cudaHostUnregister(const_cast<T*>(r));
+          query_set_pinned_ = false;
+        }
+#endif
+        return r;
+      }
+      case MemoryType::kHostPinned: {
+        auto r = query_set();
+#ifndef BUILD_CPU_ONLY
+        if (!query_set_pinned_) {
+          cudaHostRegister(
+            const_cast<T*>(r), query_set_size() * dim() * sizeof(T), cudaHostRegisterDefault);
+          query_set_pinned_ = true;
+        }
+#endif
+        return r;
+      }
+      default: return nullptr;
     }
   }
 
@@ -294,7 +315,27 @@ class dataset {
   {
     switch (memory_type) {
       case MemoryType::kDevice: return base_set_on_gpu();
-      case MemoryType::kHost: return base_set();
+      case MemoryType::kHost: {
+        auto r = base_set();
+#ifndef BUILD_CPU_ONLY
+        if (base_set_pinned_) {
+          cudaHostUnregister(const_cast<T*>(r));
+          base_set_pinned_ = false;
+        }
+#endif
+        return r;
+      }
+      case MemoryType::kHostPinned: {
+        auto r = base_set();
+#ifndef BUILD_CPU_ONLY
+        if (!base_set_pinned_) {
+          cudaHostRegister(
+            const_cast<T*>(r), base_set_size() * dim() * sizeof(T), cudaHostRegisterDefault);
+          base_set_pinned_ = true;
+        }
+#endif
+        return r;
+      }
       case MemoryType::kHostMmap: return mapped_base_set();
       default: return nullptr;
     }
@@ -315,18 +356,23 @@ class dataset {
   mutable T* d_query_set_     = nullptr;
   mutable T* mapped_base_set_ = nullptr;
   mutable int32_t* gt_set_    = nullptr;
+
+  mutable bool base_set_pinned_  = false;
+  mutable bool query_set_pinned_ = false;
 };
 
 template <typename T>
 dataset<T>::~dataset()
 {
-  delete[] base_set_;
-  delete[] query_set_;
-  delete[] gt_set_;
 #ifndef BUILD_CPU_ONLY
   if (d_base_set_) { cudaFree(d_base_set_); }
   if (d_query_set_) { cudaFree(d_query_set_); }
+  if (base_set_pinned_) { cudaHostUnregister(base_set_); }
+  if (query_set_pinned_) { cudaHostUnregister(query_set_); }
 #endif
+  delete[] base_set_;
+  delete[] query_set_;
+  delete[] gt_set_;
 }
 
 template <typename T>
diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
index e01e3847be..c3db2bb4bf 100644
--- a/cpp/bench/ann/src/common/util.hpp
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -198,42 +198,71 @@ inline auto get_stream_from_global_pool() -> cudaStream_t
 #endif
 }
 
-struct result_buffer {
-  explicit result_buffer(size_t size, cudaStream_t stream) : size_{size}, stream_{stream}
+/** The workspace buffer for use thread-locally. */
+struct ws_buffer {
+  explicit ws_buffer(size_t size, cudaStream_t stream) : size_{size}, stream_{stream} {}
+  ws_buffer()                                    = delete;
+  ws_buffer(ws_buffer&&)                         = delete;
+  auto operator=(ws_buffer&&) -> ws_buffer&      = delete;
+  ws_buffer(const ws_buffer&)                    = delete;
+  auto operator=(const ws_buffer&) -> ws_buffer& = delete;
+  ~ws_buffer() noexcept
   {
-    if (size_ == 0) { return; }
-    data_host_ = malloc(size_);
 #ifndef BUILD_CPU_ONLY
-    cudaMallocAsync(&data_device_, size_, stream_);
-    cudaStreamSynchronize(stream_);
-#endif
-  }
-  result_buffer()                                        = delete;
-  result_buffer(result_buffer&&)                         = delete;
-  auto operator=(result_buffer&&) -> result_buffer&      = delete;
-  result_buffer(const result_buffer&)                    = delete;
-  auto operator=(const result_buffer&) -> result_buffer& = delete;
-  ~result_buffer() noexcept
-  {
-    if (size_ == 0) { return; }
-#ifndef BUILD_CPU_ONLY
-    cudaFreeAsync(data_device_, stream_);
-    cudaStreamSynchronize(stream_);
+    if (data_device_ != nullptr) {
+      cudaFreeAsync(data_device_, stream_);
+      cudaStreamSynchronize(stream_);
+    }
+    if (data_host_ != nullptr) { cudaFreeHost(data_host_); }
+#else
+    if (data_host_ != nullptr) { free(data_host_); }
 #endif
-    free(data_host_);
   }
 
   [[nodiscard]] auto size() const noexcept { return size_; }
-  [[nodiscard]] auto data(MemoryType loc) const noexcept
+  [[nodiscard]] auto data(MemoryType loc) const noexcept -> void*
   {
+    if (size_ == 0) { return nullptr; }
     switch (loc) {
-      case MemoryType::kDevice: return data_device_;
-      default: return data_host_;
+#ifndef BUILD_CPU_ONLY
+      case MemoryType::kDevice: {
+        if (data_device_ == nullptr) {
+          cudaMallocAsync(&data_device_, size_, stream_);
+          cudaStreamSynchronize(stream_);
+          needs_cleanup_device_ = false;
+        } else if (needs_cleanup_device_) {
+          cudaMemsetAsync(data_device_, 0, size_, stream_);
+          cudaStreamSynchronize(stream_);
+          needs_cleanup_device_ = false;
+        }
+        return data_device_;
+      }
+#endif
+      default: {
+        if (data_host_ == nullptr) {
+#ifndef BUILD_CPU_ONLY
+          cudaMallocHost(&data_host_, size_);
+#else
+          data_host_ = malloc(size_);
+#endif
+          needs_cleanup_host_ = false;
+        } else if (needs_cleanup_host_) {
+          memset(data_host_, 0, size_);
+          needs_cleanup_host_ = false;
+        }
+        return data_host_;
+      }
     }
   }
 
   void transfer_data(MemoryType dst, MemoryType src)
   {
+    // The destination is overwritten and thus does not need cleanup
+    if (dst == MemoryType::kDevice) {
+      needs_cleanup_device_ = false;
+    } else {
+      needs_cleanup_host_ = false;
+    }
     auto dst_ptr = data(dst);
     auto src_ptr = data(src);
     if (dst_ptr == src_ptr) { return; }
@@ -243,15 +272,25 @@ struct result_buffer {
 #endif
   }
 
+  /** Mark the buffer for reuse - it needs to be cleared to make sure the previous results are not
+   * leaked to the new iteration. */
+  void reuse()
+  {
+    needs_cleanup_host_   = true;
+    needs_cleanup_device_ = true;
+  }
+
  private:
   size_t size_{0};
-  cudaStream_t stream_ = nullptr;
-  void* data_host_     = nullptr;
-  void* data_device_   = nullptr;
+  cudaStream_t stream_               = nullptr;
+  mutable void* data_host_           = nullptr;
+  mutable void* data_device_         = nullptr;
+  mutable bool needs_cleanup_host_   = false;
+  mutable bool needs_cleanup_device_ = false;
 };
 
 namespace detail {
-inline std::vector<std::unique_ptr<result_buffer>> global_result_buffer_pool(0);
+inline std::vector<std::unique_ptr<ws_buffer>> global_result_buffer_pool(0);
 inline std::mutex grp_mutex;
 }  // namespace detail
 
@@ -262,24 +301,47 @@ inline std::mutex grp_mutex;
  * This reduces the setup overhead and number of times the context is being blocked
  * (this is relevant if there is a persistent kernel running across multiples benchmark cases).
  */
-inline auto get_result_buffer_from_global_pool(size_t size) -> result_buffer&
+inline auto get_result_buffer_from_global_pool(size_t size) -> ws_buffer&
 {
   auto stream = get_stream_from_global_pool();
-  auto& rb    = [stream, size]() -> result_buffer& {
+  auto& rb    = [stream, size]() -> ws_buffer& {
     std::lock_guard guard(detail::grp_mutex);
     if (static_cast<int>(detail::global_result_buffer_pool.size()) < benchmark_n_threads) {
       detail::global_result_buffer_pool.resize(benchmark_n_threads);
     }
     auto& rb = detail::global_result_buffer_pool[benchmark_thread_id];
-    if (!rb || rb->size() < size) { rb = std::make_unique<result_buffer>(size, stream); }
+    if (!rb || rb->size() < size) {
+      rb = std::make_unique<ws_buffer>(size, stream);
+    } else {
+      rb->reuse();
+    }
     return *rb;
   }();
+  return rb;
+}
 
-  memset(rb.data(MemoryType::kHost), 0, size);
-#ifndef BUILD_CPU_ONLY
-  cudaMemsetAsync(rb.data(MemoryType::kDevice), 0, size, stream);
-  cudaStreamSynchronize(stream);
-#endif
+namespace detail {
+inline std::vector<std::unique_ptr<ws_buffer>> global_tmp_buffer_pool(0);
+inline std::mutex gtp_mutex;
+}  // namespace detail
+
+/**
+ * Global temporary buffer pool for use by algorithms.
+ * In contrast to `get_result_buffer_from_global_pool`, the content of these buffers is never
+ * initialized.
+ */
+inline auto get_tmp_buffer_from_global_pool(size_t size) -> ws_buffer&
+{
+  auto stream = get_stream_from_global_pool();
+  auto& rb    = [stream, size]() -> ws_buffer& {
+    std::lock_guard guard(detail::gtp_mutex);
+    if (static_cast<int>(detail::global_tmp_buffer_pool.size()) < benchmark_n_threads) {
+      detail::global_tmp_buffer_pool.resize(benchmark_n_threads);
+    }
+    auto& rb = detail::global_tmp_buffer_pool[benchmark_thread_id];
+    if (!rb || rb->size() < size) { rb = std::make_unique<ws_buffer>(size, stream); }
+    return *rb;
+  }();
   return rb;
 }
 
@@ -293,6 +355,7 @@ inline void reset_global_device_resources()
 {
 #ifndef BUILD_CPU_ONLY
   std::lock_guard guard(detail::gsp_mutex);
+  detail::global_tmp_buffer_pool.resize(0);
   detail::global_result_buffer_pool.resize(0);
   detail::global_stream_pool.resize(0);
 #endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 67f8ed39d0..22f0cab6fc 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -247,6 +247,16 @@ void parse_search_param(const nlohmann::json& conf,
   if (conf.contains("itopk")) { param.p.itopk_size = conf.at("itopk"); }
   if (conf.contains("search_width")) { param.p.search_width = conf.at("search_width"); }
   if (conf.contains("max_iterations")) { param.p.max_iterations = conf.at("max_iterations"); }
+  if (conf.contains("persistent")) { param.p.persistent = conf.at("persistent"); }
+  if (conf.contains("persistent_lifetime")) {
+    param.p.persistent_lifetime = conf.at("persistent_lifetime");
+  }
+  if (conf.contains("persistent_device_usage")) {
+    param.p.persistent_device_usage = conf.at("persistent_device_usage");
+  }
+  if (conf.contains("thread_block_size")) {
+    param.p.thread_block_size = conf.at("thread_block_size");
+  }
   if (conf.contains("algo")) {
     if (conf.at("algo") == "single_cta") {
       param.p.algo = cuvs::neighbors::cagra::search_algo::SINGLE_CTA;
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
index b92785943a..92274e2636 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
@@ -218,27 +218,46 @@ void refine_helper(const raft::resources& res,
   } else {
     auto dataset_host = raft::make_host_matrix_view<const data_type, extents_type>(
       dataset.data_handle(), dataset.extent(0), dataset.extent(1));
-    auto queries_host    = raft::make_host_matrix<data_type, extents_type>(batch_size, dim);
-    auto candidates_host = raft::make_host_matrix<index_type, extents_type>(batch_size, k0);
-    auto neighbors_host  = raft::make_host_matrix<index_type, extents_type>(batch_size, k);
-    auto distances_host  = raft::make_host_matrix<float, extents_type>(batch_size, k);
+    if (raft::get_device_for_address(queries.data_handle()) >= 0) {
+      // Queries & results are on the device
 
-    auto stream = raft::resource::get_cuda_stream(res);
-    raft::copy(queries_host.data_handle(), queries.data_handle(), queries_host.size(), stream);
-    raft::copy(
-      candidates_host.data_handle(), candidates.data_handle(), candidates_host.size(), stream);
+      auto queries_host    = raft::make_host_matrix<data_type, extents_type>(batch_size, dim);
+      auto candidates_host = raft::make_host_matrix<index_type, extents_type>(batch_size, k0);
+      auto neighbors_host  = raft::make_host_matrix<index_type, extents_type>(batch_size, k);
+      auto distances_host  = raft::make_host_matrix<float, extents_type>(batch_size, k);
 
-    raft::resource::sync_stream(res);  // wait for the queries and candidates
-    cuvs::neighbors::refine(res,
-                            dataset_host,
-                            queries_host.view(),
-                            candidates_host.view(),
-                            neighbors_host.view(),
-                            distances_host.view(),
-                            metric);
+      auto stream = raft::resource::get_cuda_stream(res);
+      raft::copy(queries_host.data_handle(), queries.data_handle(), queries_host.size(), stream);
+      raft::copy(
+        candidates_host.data_handle(), candidates.data_handle(), candidates_host.size(), stream);
+
+      raft::resource::sync_stream(res);  // wait for the queries and candidates
+      cuvs::neighbors::refine(res,
+                              dataset_host,
+                              queries_host.view(),
+                              candidates_host.view(),
+                              neighbors_host.view(),
+                              distances_host.view(),
+                              metric);
+
+      raft::copy(neighbors, neighbors_host.data_handle(), neighbors_host.size(), stream);
+      raft::copy(distances, distances_host.data_handle(), distances_host.size(), stream);
+
+    } else {
+      // Queries & results are on the host - no device sync / copy needed
+
+      auto queries_host = raft::make_host_matrix_view<const data_type, extents_type>(
+        queries.data_handle(), batch_size, dim);
+      auto candidates_host = raft::make_host_matrix_view<const index_type, extents_type>(
+        candidates.data_handle(), batch_size, k0);
+      auto neighbors_host =
+        raft::make_host_matrix_view<index_type, extents_type>(neighbors, batch_size, k);
+      auto distances_host =
+        raft::make_host_matrix_view<float, extents_type>(distances, batch_size, k);
 
-    raft::copy(neighbors, neighbors_host.data_handle(), neighbors_host.size(), stream);
-    raft::copy(distances, distances_host.data_handle(), distances_host.size(), stream);
+      cuvs::neighbors::refine(
+        res, dataset_host, queries_host, candidates_host, neighbors_host, distances_host, metric);
+    }
   }
 }
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index 53db717a61..9ca41cab04 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -107,13 +107,23 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
                    int batch_size,
                    int k,
                    algo_base::index_type* neighbors,
-                   float* distances) const;
+                   float* distances,
+                   IdxT* neighbors_idx_t) const;
 
   [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
   {
     return handle_.get_sync_stream();
   }
 
+  [[nodiscard]] auto uses_stream() const noexcept -> bool override
+  {
+    // If the algorithm uses persistent kernel, the CPU has to synchronize by the end of computing
+    // the result. Hence it guarantees the benchmark CUDA stream is empty by the end of the
+    // execution. Hence we inform the benchmark to not waste the time on recording & synchronizing
+    // the event.
+    return !search_params_.persistent;
+  }
+
   // to enable dataset access from GPU memory
   [[nodiscard]] auto get_preference() const -> algo_property override
   {
@@ -269,7 +279,11 @@ void cuvs_cagra<T, IdxT>::set_search_dataset(const T* dataset, size_t nrow)
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::save(const std::string& file) const
 {
-  cuvs::neighbors::cagra::serialize(handle_, file, *index_);
+  using ds_idx_type = decltype(index_->data().n_rows());
+  bool is_vpq =
+    dynamic_cast<const cuvs::neighbors::vpq_dataset<half, ds_idx_type>*>(&index_->data()) ||
+    dynamic_cast<const cuvs::neighbors::vpq_dataset<float, ds_idx_type>*>(&index_->data());
+  cuvs::neighbors::cagra::serialize(handle_, file, *index_, is_vpq);
 }
 
 template <typename T, typename IdxT>
@@ -292,19 +306,18 @@ std::unique_ptr<algo<T>> cuvs_cagra<T, IdxT>::copy()
 }
 
 template <typename T, typename IdxT>
-void cuvs_cagra<T, IdxT>::search_base(
-  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+void cuvs_cagra<T, IdxT>::search_base(const T* queries,
+                                      int batch_size,
+                                      int k,
+                                      algo_base::index_type* neighbors,
+                                      float* distances,
+                                      IdxT* neighbors_idx_t) const
 {
   static_assert(std::is_integral_v<algo_base::index_type>);
   static_assert(std::is_integral_v<IdxT>);
 
-  IdxT* neighbors_idx_t;
-  std::optional<rmm::device_uvector<IdxT>> neighbors_storage{std::nullopt};
   if constexpr (sizeof(IdxT) == sizeof(algo_base::index_type)) {
     neighbors_idx_t = reinterpret_cast<IdxT*>(neighbors);
-  } else {
-    neighbors_storage.emplace(batch_size * k, raft::resource::get_cuda_stream(handle_));
-    neighbors_idx_t = neighbors_storage->data();
   }
 
   auto queries_view =
@@ -317,11 +330,23 @@ void cuvs_cagra<T, IdxT>::search_base(
     handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
 
   if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
-    raft::linalg::unaryOp(neighbors,
-                          neighbors_idx_t,
-                          batch_size * k,
-                          raft::cast_op<algo_base::index_type>(),
-                          raft::resource::get_cuda_stream(handle_));
+    if (raft::get_device_for_address(neighbors) < 0 &&
+        raft::get_device_for_address(neighbors_idx_t) < 0) {
+      // Both pointers on the host, let's use host-side mapping
+      if (uses_stream()) {
+        // Need to wait for GPU to finish filling source
+        raft::resource::sync_stream(handle_);
+      }
+      for (int i = 0; i < batch_size * k; i++) {
+        neighbors[i] = algo_base::index_type(neighbors_idx_t[i]);
+      }
+    } else {
+      raft::linalg::unaryOp(neighbors,
+                            neighbors_idx_t,
+                            batch_size * k,
+                            raft::cast_op<algo_base::index_type>(),
+                            raft::resource::get_cuda_stream(handle_));
+    }
   }
 }
 
@@ -329,21 +354,42 @@ template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::search(
   const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
 {
+  static_assert(std::is_integral_v<algo_base::index_type>);
+  static_assert(std::is_integral_v<IdxT>);
+  constexpr bool kNeedsIoMapping = sizeof(IdxT) != sizeof(algo_base::index_type);
+
   auto k0                       = static_cast<size_t>(refine_ratio_ * k);
   const bool disable_refinement = k0 <= static_cast<size_t>(k);
   const raft::resources& res    = handle_;
+  auto mem_type =
+    raft::get_device_for_address(neighbors) >= 0 ? MemoryType::kDevice : MemoryType::kHostPinned;
+  auto& tmp_buf = get_tmp_buffer_from_global_pool(
+    ((disable_refinement ? 0 : (sizeof(float) + sizeof(algo_base::index_type))) +
+     (kNeedsIoMapping ? sizeof(IdxT) : 0)) *
+    batch_size * k0);
+  auto* candidates_ptr = reinterpret_cast<algo_base::index_type*>(tmp_buf.data(mem_type));
+  auto* candidate_dists_ptr =
+    reinterpret_cast<float*>(candidates_ptr + (disable_refinement ? 0 : batch_size * k0));
+  auto* neighbors_idx_t =
+    reinterpret_cast<IdxT*>(candidate_dists_ptr + (disable_refinement ? 0 : batch_size * k0));
 
   if (disable_refinement) {
-    search_base(queries, batch_size, k, neighbors, distances);
+    search_base(queries, batch_size, k, neighbors, distances, neighbors_idx_t);
   } else {
+    search_base(queries, batch_size, k0, candidates_ptr, candidate_dists_ptr, neighbors_idx_t);
+
+    if (mem_type == MemoryType::kHostPinned && uses_stream()) {
+      // If the algorithm uses a stream to synchronize (non-persistent kernel), but the data is in
+      // the pinned host memory, we need to synchronize before the refinement operation to wait for
+      // the data being available for the host.
+      raft::resource::sync_stream(res);
+    }
+
+    auto candidate_ixs =
+      raft::make_device_matrix_view<const algo_base::index_type, algo_base::index_type>(
+        candidates_ptr, batch_size, k0);
     auto queries_v = raft::make_device_matrix_view<const T, algo_base::index_type>(
       queries, batch_size, dimension_);
-    auto candidate_ixs =
-      raft::make_device_matrix<algo_base::index_type, algo_base::index_type>(res, batch_size, k0);
-    auto candidate_dists =
-      raft::make_device_matrix<float, algo_base::index_type>(res, batch_size, k0);
-    search_base(
-      queries, batch_size, k0, candidate_ixs.data_handle(), candidate_dists.data_handle());
     refine_helper(
       res, *input_dataset_v_, queries_v, candidate_ixs, k, neighbors, distances, index_->metric());
   }
diff --git a/cpp/bench/ann/src/cuvs/cuvs_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
index 0954e60516..ea052533d6 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
@@ -26,6 +26,7 @@
 #include <fstream>
 #include <memory>
 #include <stdexcept>
+#include <stdint.h>
 #include <string>
 #include <type_traits>
 
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 5f77eb8a3c..fec95b5637 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -205,6 +205,30 @@ struct search_params : cuvs::neighbors::search_params {
   uint32_t num_random_samplings = 1;
   /** Bit mask used for initial random seed node selection. */
   uint64_t rand_xor_mask = 0x128394;
+
+  /** Whether to use the persistent version of the kernel (only SINGLE_CTA is supported a.t.m.) */
+  bool persistent = false;
+  /** Persistent kernel: time in seconds before the kernel stops if no requests received. */
+  float persistent_lifetime = 2;
+  /**
+   * Set the fraction of maximum grid size used by persistent kernel.
+   * Value 1.0 means the kernel grid size is maximum possible for the selected device.
+   * The value must be greater than 0.0 and not greater than 1.0.
+   *
+   * One may need to run other kernels alongside this persistent kernel. This parameter can
+   * be used to reduce the grid size of the persistent kernel to leave a few SMs idle.
+   * Note: running any other work on GPU alongside with the persistent kernel makes the setup
+   * fragile.
+   *   - Running another kernel in another thread usually works, but no progress guaranteed
+   *   - Any CUDA allocations block the context (this issue may be obscured by using pools)
+   *   - Memory copies to not-pinned host memory may block the context
+   *
+   * Even when we know there are no other kernels working at the same time, setting
+   * kDeviceUsage to 1.0 surprisingly sometimes hurts performance. Proceed with care.
+   * If you suspect this is an issue, you can reduce this number to ~0.9 without a significant
+   * impact on the throughput.
+   */
+  float persistent_device_usage = 1.0;
 };
 
 /**
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
index 8407ef055a..df447d1961 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
@@ -496,8 +496,7 @@ using descriptor_instances = instance_selector<
 template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
 auto dataset_descriptor_init(const cagra::search_params& params,
                              const DatasetT& dataset,
-                             cuvs::distance::DistanceType metric,
-                             rmm::cuda_stream_view stream)
+                             cuvs::distance::DistanceType metric)
   -> dataset_descriptor_host<DataT, IndexT, DistanceT>
 {
   auto [init, priority] =
@@ -505,7 +504,7 @@ auto dataset_descriptor_init(const cagra::search_params& params,
   if (init == nullptr || priority < 0) {
     RAFT_FAIL("No dataset descriptor instance compiled for this parameter combination.");
   }
-  return init(params, dataset, metric, stream);
+  return init(params, dataset, metric);
 }
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.hpp b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
index 4bed275abd..297eb1f559 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
@@ -34,6 +34,7 @@
 #include <functional>
 #include <memory>
 #include <type_traits>
+#include <variant>
 
 namespace cuvs::neighbors::cagra::detail {
 
@@ -222,31 +223,61 @@ struct alignas(device::LOAD_128BIT_T) dataset_descriptor_base_t {
  * The host struct manages the lifetime of the associated device pointer and a couple parameters
  * affecting the search kernel launch config.
  *
+ * [Note: lazy initialization]
+ * Initialization of the descriptor involves allocating device memory and calling a kernel.
+ * This can interfere with other workloads (such as the persistent kernel) and generally adds
+ * overhead. To mitigate this, we don't call any CUDA api at the construction of the descriptor
+ * host. Instead, we postpone the initialization till the device pointer is requested.
+ *
  */
 template <typename DataT, typename IndexT, typename DistanceT>
 struct dataset_descriptor_host {
-  using dev_descriptor_t         = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+  using dev_descriptor_t = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+  using dd_ptr_t         = std::shared_ptr<dev_descriptor_t>;
+  using init_f =
+    std::tuple<std::function<void(dev_descriptor_t*, rmm::cuda_stream_view stream)>, size_t>;
   uint32_t smem_ws_size_in_bytes = 0;
   uint32_t team_size             = 0;
 
-  template <typename DescriptorImpl>
-  dataset_descriptor_host(const DescriptorImpl& dd_host, rmm::cuda_stream_view stream)
-    : dev_ptr_{[stream]() {
-                 dev_descriptor_t* p;
-                 RAFT_CUDA_TRY(cudaMallocAsync(&p, sizeof(DescriptorImpl), stream));
-                 return p;
-               }(),
-               [stream](dev_descriptor_t* p) { RAFT_CUDA_TRY_NO_THROW(cudaFreeAsync(p, stream)); }},
+  template <typename DescriptorImpl, typename InitF>
+  dataset_descriptor_host(const DescriptorImpl& dd_host, InitF init)
+    : value_{std::make_tuple(init, sizeof(DescriptorImpl))},
       smem_ws_size_in_bytes{dd_host.smem_ws_size_in_bytes()},
       team_size{dd_host.team_size()}
   {
   }
 
-  [[nodiscard]] auto dev_ptr() const -> const dev_descriptor_t* { return dev_ptr_.get(); }
-  [[nodiscard]] auto dev_ptr() -> dev_descriptor_t* { return dev_ptr_.get(); }
+  /**
+   * Return the device pointer, possibly evaluating it in the given thread.
+   */
+  [[nodiscard]] auto dev_ptr(rmm::cuda_stream_view stream) const -> const dev_descriptor_t*
+  {
+    if (std::holds_alternative<init_f>(value_)) { value_ = eval(std::get<init_f>(value_), stream); }
+    return std::get<dd_ptr_t>(value_).get();
+  }
+  [[nodiscard]] auto dev_ptr(rmm::cuda_stream_view stream) -> dev_descriptor_t*
+  {
+    if (std::holds_alternative<init_f>(value_)) { value_ = eval(std::get<init_f>(value_), stream); }
+    return std::get<dd_ptr_t>(value_).get();
+  }
 
  private:
-  std::unique_ptr<dev_descriptor_t, std::function<void(dev_descriptor_t*)>> dev_ptr_;
+  mutable std::variant<dd_ptr_t, init_f> value_;
+
+  static auto eval(init_f init, rmm::cuda_stream_view stream) -> dd_ptr_t
+  {
+    using raft::RAFT_NAME;
+    auto& [fun, size] = init;
+    dd_ptr_t dev_ptr{
+      [stream, s = size]() {
+        dev_descriptor_t* p;
+        RAFT_CUDA_TRY(cudaMallocAsync(&p, s, stream));
+        return p;
+      }(),
+      [stream](dev_descriptor_t* p) { RAFT_CUDA_TRY_NO_THROW(cudaFreeAsync(p, stream)); }};
+    fun(dev_ptr.get(), stream);
+    return dev_ptr;
+  }
 };
 
 /**
@@ -257,11 +288,8 @@ struct dataset_descriptor_host {
  *
  */
 template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
-using init_desc_type =
-  dataset_descriptor_host<DataT, IndexT, DistanceT> (*)(const cagra::search_params&,
-                                                        const DatasetT&,
-                                                        cuvs::distance::DistanceType,
-                                                        rmm::cuda_stream_view);
+using init_desc_type = dataset_descriptor_host<DataT, IndexT, DistanceT> (*)(
+  const cagra::search_params&, const DatasetT&, cuvs::distance::DistanceType);
 
 /**
  * @brief Descriptor instance specification.
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
index 52a15e2a16..f8584c62ec 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
@@ -135,15 +135,14 @@
 template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
 auto dataset_descriptor_init(const cagra::search_params& params,
                              const DatasetT& dataset,
-                             cuvs::distance::DistanceType metric,
-                             rmm::cuda_stream_view stream)
+                             cuvs::distance::DistanceType metric)
   -> dataset_descriptor_host<DataT, IndexT, DistanceT>
 {{
   auto [init, priority] = descriptor_instances::select<DataT, IndexT, DistanceT>(params, dataset, metric);
   if (init == nullptr || priority < 0) {{
     RAFT_FAIL("No dataset descriptor instance compiled for this parameter combination.");
   }}
-  return init(params, dataset, metric, stream);
+  return init(params, dataset, metric);
 }}
 '''
     f.write(template.format(includes=includes, content=contents))
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
index b0205508ac..877d83fff7 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
@@ -252,28 +252,24 @@ template <cuvs::distance::DistanceType Metric,
           typename DistanceT>
 dataset_descriptor_host<DataT, IndexT, DistanceT>
 standard_descriptor_spec<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>::init_(
-  const cagra::search_params& params,
-  const DataT* ptr,
-  IndexT size,
-  uint32_t dim,
-  uint32_t ld,
-  rmm::cuda_stream_view stream)
+  const cagra::search_params& params, const DataT* ptr, IndexT size, uint32_t dim, uint32_t ld)
 {
   using desc_type =
     standard_dataset_descriptor_t<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>;
   using base_type = typename desc_type::base_type;
   desc_type dd_host{nullptr, nullptr, ptr, size, dim, ld};
-  host_type result{dd_host, stream};
-
-  standard_dataset_descriptor_init_kernel<Metric,
-                                          TeamSize,
-                                          DatasetBlockDim,
-                                          DataT,
-                                          IndexT,
-                                          DistanceT>
-    <<<1, 1, 0, stream>>>(result.dev_ptr(), ptr, size, dim, desc_type::ld(dd_host.args));
-  RAFT_CUDA_TRY(cudaPeekAtLastError());
-  return result;
+  return host_type{dd_host,
+                   [=](dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dev_ptr,
+                       rmm::cuda_stream_view stream) {
+                     standard_dataset_descriptor_init_kernel<Metric,
+                                                             TeamSize,
+                                                             DatasetBlockDim,
+                                                             DataT,
+                                                             IndexT,
+                                                             DistanceT>
+                       <<<1, 1, 0, stream>>>(dev_ptr, ptr, size, dim, ld);
+                     RAFT_CUDA_TRY(cudaPeekAtLastError());
+                   }};
 }
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp b/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
index df1b77e86c..fec14d7136 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
@@ -45,15 +45,13 @@ struct standard_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT>
   template <typename DatasetT>
   static auto init(const cagra::search_params& params,
                    const DatasetT& dataset,
-                   cuvs::distance::DistanceType metric,
-                   rmm::cuda_stream_view stream) -> host_type
+                   cuvs::distance::DistanceType metric) -> host_type
   {
     return init_(params,
                  dataset.view().data_handle(),
                  IndexT(dataset.n_rows()),
                  dataset.dim(),
-                 dataset.stride(),
-                 stream);
+                 dataset.stride());
   }
 
   template <typename DatasetT>
@@ -69,12 +67,8 @@ struct standard_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT>
   }
 
  private:
-  static dataset_descriptor_host<DataT, IndexT, DistanceT> init_(const cagra::search_params& params,
-                                                                 const DataT* ptr,
-                                                                 IndexT size,
-                                                                 uint32_t dim,
-                                                                 uint32_t ld,
-                                                                 rmm::cuda_stream_view stream);
+  static dataset_descriptor_host<DataT, IndexT, DistanceT> init_(
+    const cagra::search_params& params, const DataT* ptr, IndexT size, uint32_t dim, uint32_t ld);
 };
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh
index 86c5925026..6caa173f2c 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq-impl.cuh
@@ -421,8 +421,7 @@ vpq_descriptor_spec<Metric,
                                       const CodebookT* vq_code_book_ptr,
                                       const CodebookT* pq_code_book_ptr,
                                       IndexT size,
-                                      uint32_t dim,
-                                      rmm::cuda_stream_view stream)
+                                      uint32_t dim)
 {
   using desc_type = cagra_q_dataset_descriptor_t<Metric,
                                                  TeamSize,
@@ -443,24 +442,27 @@ vpq_descriptor_spec<Metric,
                     pq_code_book_ptr,
                     size,
                     dim};
-  host_type result{dd_host, stream};
-  vpq_dataset_descriptor_init_kernel<Metric,
-                                     TeamSize,
-                                     DatasetBlockDim,
-                                     PqBits,
-                                     PqLen,
-                                     CodebookT,
-                                     DataT,
-                                     IndexT,
-                                     DistanceT><<<1, 1, 0, stream>>>(result.dev_ptr(),
-                                                                     encoded_dataset_ptr,
-                                                                     encoded_dataset_dim,
-                                                                     vq_code_book_ptr,
-                                                                     pq_code_book_ptr,
-                                                                     size,
-                                                                     dim);
-  RAFT_CUDA_TRY(cudaPeekAtLastError());
-  return result;
+  return host_type{dd_host,
+                   [=](dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dev_ptr,
+                       rmm::cuda_stream_view stream) {
+                     vpq_dataset_descriptor_init_kernel<Metric,
+                                                        TeamSize,
+                                                        DatasetBlockDim,
+                                                        PqBits,
+                                                        PqLen,
+                                                        CodebookT,
+                                                        DataT,
+                                                        IndexT,
+                                                        DistanceT>
+                       <<<1, 1, 0, stream>>>(dev_ptr,
+                                             encoded_dataset_ptr,
+                                             encoded_dataset_dim,
+                                             vq_code_book_ptr,
+                                             pq_code_book_ptr,
+                                             size,
+                                             dim);
+                     RAFT_CUDA_TRY(cudaPeekAtLastError());
+                   }};
 }
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
index 378d2943ee..4f7d24f176 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
@@ -57,8 +57,7 @@ struct vpq_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT> {
   template <typename DatasetT>
   static auto init(const cagra::search_params& params,
                    const DatasetT& dataset,
-                   cuvs::distance::DistanceType metric,
-                   rmm::cuda_stream_view stream) -> host_type
+                   cuvs::distance::DistanceType metric) -> host_type
   {
     return init_(params,
                  dataset.data.data_handle(),
@@ -66,8 +65,7 @@ struct vpq_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT> {
                  dataset.vq_code_book.data_handle(),
                  dataset.pq_code_book.data_handle(),
                  IndexT(dataset.n_rows()),
-                 dataset.dim(),
-                 stream);
+                 dataset.dim());
   }
 
   template <typename DatasetT>
@@ -93,8 +91,7 @@ struct vpq_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT> {
     const CodebookT* vq_code_book_ptr,
     const CodebookT* pq_code_book_ptr,
     IndexT size,
-    uint32_t dim,
-    rmm::cuda_stream_view stream);
+    uint32_t dim);
 };
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index 1c99f72f77..2f201de3b1 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -168,8 +168,8 @@ auto dataset_descriptor_init_with_cache(const raft::resources& res,
       ->value;
   std::shared_ptr<desc_t> desc{nullptr};
   if (!cache.get(key, &desc)) {
-    desc = std::make_shared<desc_t>(std::move(dataset_descriptor_init<DataT, IndexT, DistanceT>(
-      params, dataset, metric, raft::resource::get_cuda_stream(res))));
+    desc = std::make_shared<desc_t>(
+      std::move(dataset_descriptor_init<DataT, IndexT, DistanceT>(params, dataset, metric)));
     cache.set(key, desc);
   }
   return *desc;
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index 9bcccd9f9e..0003f24952 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -209,7 +209,7 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
                   SAMPLE_FILTER_T sample_filter)
   {
     cudaStream_t stream = raft::resource::get_cuda_stream(res);
-    select_and_run(dataset_desc.dev_ptr(),
+    select_and_run(dataset_desc,
                    graph,
                    intermediate_indices.data(),
                    intermediate_distances.data(),
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
index 036a4e414c..8d34ab0d68 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 
 #define instantiate_kernel_selection(DataT, IndexT, DistanceT, SampleFilterT) \
   template void select_and_run<DataT, IndexT, DistanceT, SampleFilterT>(      \
-    const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,  \
+    const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,    \
     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,   \
     IndexT* topk_indices_ptr,                                                 \
     DistanceT* topk_distances_ptr,                                            \
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index dd74ba44bc..4dfc462563 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -413,7 +413,7 @@ struct search_kernel_config {
 };
 
 template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
-void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
                     IndexT* topk_indices_ptr,       // [num_queries, topk]
                     DistanceT* topk_distances_ptr,  // [num_queries, topk]
@@ -455,7 +455,7 @@ void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* d
 
   kernel<<<grid_dims, block_dims, smem_size, stream>>>(topk_indices_ptr,
                                                        topk_distances_ptr,
-                                                       dataset_desc,
+                                                       dataset_desc.dev_ptr(stream),
                                                        queries_ptr,
                                                        graph.data_handle(),
                                                        graph.extent(1),
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
index 1ef35f9470..1a1dcd579a 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
@@ -22,7 +22,7 @@
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 
 template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
-void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
                     IndexT* topk_indices_ptr,       // [num_queries, topk]
                     DistanceT* topk_distances_ptr,  // [num_queries, topk]
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index 7b3ecabf37..0daae17b38 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -175,7 +175,7 @@ void random_pickup(const dataset_descriptor_host<DataT, IndexT, DistanceT>& data
                        num_queries);
 
   random_pickup_kernel<<<grid_size, block_size, dataset_desc.smem_ws_size_in_bytes, cuda_stream>>>(
-    dataset_desc.dev_ptr(),
+    dataset_desc.dev_ptr(cuda_stream),
     queries_ptr,
     num_pickup,
     num_distilation,
@@ -410,7 +410,7 @@ void compute_distance_to_child_nodes(
                                                           parent_distance_ptr,
                                                           lds,
                                                           search_width,
-                                                          dataset_desc.dev_ptr(),
+                                                          dataset_desc.dev_ptr(cuda_stream),
                                                           neighbor_graph_ptr,
                                                           graph_degree,
                                                           query_ptr,
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index 16864ed193..6ecbbc2e89 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -32,8 +32,81 @@
 #include <cuvs/neighbors/cagra.hpp>
 #include <raft/util/pow2_utils.cuh>
 
+#include <optional>
+#include <tuple>
+#include <variant>
+
 namespace cuvs::neighbors::cagra::detail {
 
+/**
+ * A lightweight version of rmm::device_uvector.
+ * This version avoids calling cudaSetDevice / cudaGetDevice, and therefore it is required that
+ * the current cuda device does not change during the lifetime of this object. This is expected
+ * to be useful in multi-threaded scenarios where we want to minimize overhead due to
+ * thread sincronization during cuda API calls.
+ * If the size stays at zero, this struct never calls any CUDA driver / RAFT resource functions.
+ */
+template <typename T>
+struct lightweight_uvector {
+ private:
+  using raft_res_type = const raft::resources*;
+  using rmm_res_type  = std::tuple<rmm::device_async_resource_ref, rmm::cuda_stream_view>;
+  static constexpr size_t kAlign = 256;
+
+  std::variant<raft_res_type, rmm_res_type> res_;
+  T* ptr_;
+  size_t size_;
+
+ public:
+  explicit lightweight_uvector(const raft::resources& res) : res_(&res), ptr_{nullptr}, size_{0} {}
+
+  [[nodiscard]] auto data() noexcept -> T* { return ptr_; }
+  [[nodiscard]] auto data() const noexcept -> const T* { return ptr_; }
+  [[nodiscard]] auto size() const noexcept -> size_t { return size_; }
+
+  void resize(size_t new_size)
+  {
+    if (new_size == size_) { return; }
+    if (std::holds_alternative<raft_res_type>(res_)) {
+      auto& h = std::get<raft_res_type>(res_);
+      res_    = rmm_res_type{raft::resource::get_workspace_resource(*h),
+                          raft::resource::get_cuda_stream(*h)};
+    }
+    auto& [r, s] = std::get<rmm_res_type>(res_);
+    T* new_ptr   = nullptr;
+    if (new_size > 0) {
+      new_ptr = reinterpret_cast<T*>(r.allocate_async(new_size * sizeof(T), kAlign, s));
+    }
+    auto copy_size = std::min(size_, new_size);
+    if (copy_size > 0) {
+      cudaMemcpyAsync(new_ptr, ptr_, copy_size * sizeof(T), cudaMemcpyDefault, s);
+    }
+    if (size_ > 0) { r.deallocate_async(ptr_, size_ * sizeof(T), kAlign, s); }
+    ptr_  = new_ptr;
+    size_ = new_size;
+  }
+
+  void resize(size_t new_size, rmm::cuda_stream_view stream)
+  {
+    if (new_size == size_) { return; }
+    if (std::holds_alternative<raft_res_type>(res_)) {
+      auto& h = std::get<raft_res_type>(res_);
+      res_    = rmm_res_type{raft::resource::get_workspace_resource(*h), stream};
+    } else {
+      std::get<rmm::cuda_stream_view>(std::get<rmm_res_type>(res_)) = stream;
+    }
+    resize(new_size);
+  }
+
+  ~lightweight_uvector() noexcept
+  {
+    if (size_ > 0) {
+      auto& [r, s] = std::get<rmm_res_type>(res_);
+      r.deallocate_async(ptr_, size_ * sizeof(T), kAlign, s);
+    }
+  }
+};
+
 struct search_plan_impl_base : public search_params {
   int64_t dim;
   int64_t graph_degree;
@@ -75,9 +148,9 @@ struct search_plan_impl : public search_plan_impl_base {
   uint32_t topk;
   uint32_t num_seeds;
 
-  rmm::device_uvector<INDEX_T> hashmap;
-  rmm::device_uvector<uint32_t> num_executed_iterations;  // device or managed?
-  rmm::device_uvector<INDEX_T> dev_seed;
+  lightweight_uvector<INDEX_T> hashmap;
+  lightweight_uvector<uint32_t> num_executed_iterations;  // device or managed?
+  lightweight_uvector<INDEX_T> dev_seed;
   const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc;
 
   search_plan_impl(raft::resources const& res,
@@ -87,16 +160,18 @@ struct search_plan_impl : public search_plan_impl_base {
                    int64_t graph_degree,
                    uint32_t topk)
     : search_plan_impl_base(params, dim, graph_degree, topk),
-      hashmap(0, raft::resource::get_cuda_stream(res)),
-      num_executed_iterations(0, raft::resource::get_cuda_stream(res)),
-      dev_seed(0, raft::resource::get_cuda_stream(res)),
+      hashmap(res),
+      num_executed_iterations(res),
+      dev_seed(res),
       num_seeds(0),
       dataset_desc(dataset_desc)
   {
     adjust_search_params();
     check_params();
     calc_hashmap_params(res);
-    num_executed_iterations.resize(max_queries, raft::resource::get_cuda_stream(res));
+    if (!persistent) {  // Persistent kernel does not provide this functionality
+      num_executed_iterations.resize(max_queries, raft::resource::get_cuda_stream(res));
+    }
     RAFT_LOG_DEBUG("# algo = %d", static_cast<int>(algo));
   }
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index 4abed6760e..2bed190090 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -37,8 +37,6 @@
 #include <raft/util/cuda_rt_essentials.hpp>
 #include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
 
-#include <rmm/device_uvector.hpp>
-
 #include <algorithm>
 #include <cassert>
 #include <iostream>
@@ -199,8 +197,8 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
     }
     RAFT_LOG_DEBUG("# smem_size: %u", smem_size);
     hashmap_size = 0;
-    if (small_hash_bitlen == 0) {
-      hashmap_size = sizeof(INDEX_T) * max_queries * hashmap::get_size(hash_bitlen);
+    if (small_hash_bitlen == 0 && !this->persistent) {
+      hashmap_size = max_queries * hashmap::get_size(hash_bitlen);
       hashmap.resize(hashmap_size, raft::resource::get_cuda_stream(res));
     }
     RAFT_LOG_DEBUG("# hashmap_size: %lu", hashmap_size);
@@ -218,7 +216,7 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
                   SAMPLE_FILTER_T sample_filter)
   {
     cudaStream_t stream = raft::resource::get_cuda_stream(res);
-    select_and_run(dataset_desc.dev_ptr(),
+    select_and_run(dataset_desc,
                    graph,
                    result_indices_ptr,
                    result_distances_ptr,
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
index 26ca7b672d..f734b05827 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra::detail::single_cta_search {
 
 #define instantiate_kernel_selection(DataT, IndexT, DistanceT, SampleFilterT) \
   template void select_and_run<DataT, IndexT, DistanceT, SampleFilterT>(      \
-    const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,  \
+    const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,    \
     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,   \
     IndexT* topk_indices_ptr,                                                 \
     DistanceT* topk_distances_ptr,                                            \
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index d10313c5b2..21a0f6bb27 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -39,21 +39,32 @@
 #include "../ann_utils.cuh"
 
 #include <raft/util/cuda_rt_essentials.hpp>
-#include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
+#include <raft/util/integer_utils.hpp>
 
+#include <rmm/cuda_stream.hpp>
 #include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/cuda_memory_resource.hpp>
+#include <rmm/mr/pinned_host_memory_resource.hpp>
+
+#include <cuda/atomic>
+#include <cuda/std/atomic>
 
 #include <algorithm>
+#include <array>
 #include <cassert>
-#include <cstdint>
+#include <chrono>
 #include <cstdio>
 #include <iostream>
+#include <limits>
 #include <memory>
 #include <numeric>
+#include <stdint.h>
+#include <thread>
 #include <vector>
 
 namespace cuvs::neighbors::cagra::detail {
 namespace single_cta_search {
+using raft::RAFT_NAME;  // TODO: this is required for RAFT_LOG_XXX messages.
 
 // #define _CLK_BREAKDOWN
 
@@ -463,7 +474,7 @@ template <unsigned MAX_ITOPK,
           unsigned TOPK_BY_BITONIC_SORT,
           class DATASET_DESCRIPTOR_T,
           class SAMPLE_FILTER_T>
-RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
+__device__ void search_core(
   typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, top_k]
   typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, top_k]
   const std::uint32_t top_k,
@@ -485,6 +496,7 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   const std::uint32_t hash_bitlen,
   const std::uint32_t small_hash_bitlen,
   const std::uint32_t small_hash_reset_interval,
+  const std::uint32_t query_id,
   SAMPLE_FILTER_T sample_filter)
 {
   using LOAD_T = device::LOAD_128BIT_T;
@@ -493,8 +505,6 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   using INDEX_T    = typename DATASET_DESCRIPTOR_T::INDEX_T;
   using DISTANCE_T = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
 
-  const auto query_id = blockIdx.y;
-
 #ifdef _CLK_BREAKDOWN
   std::uint64_t clk_init                 = 0;
   std::uint64_t clk_compute_1st_distance = 0;
@@ -552,7 +562,7 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   if (small_hash_bitlen) {
     local_visited_hashmap_ptr = visited_hash_buffer;
   } else {
-    local_visited_hashmap_ptr = visited_hashmap_ptr + (hashmap::get_size(hash_bitlen) * query_id);
+    local_visited_hashmap_ptr = visited_hashmap_ptr + (hashmap::get_size(hash_bitlen) * blockIdx.y);
   }
   hashmap::init(local_visited_hashmap_ptr, hash_bitlen, 0);
   __syncthreads();
@@ -796,37 +806,292 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
 #endif
 }
 
-template <typename DATASET_DESCRIPTOR_T, typename SAMPLE_FILTER_T>
+template <unsigned MAX_ITOPK,
+          unsigned MAX_CANDIDATES,
+          unsigned TOPK_BY_BITONIC_SORT,
+          class DATASET_DESCRIPTOR_T,
+          class SAMPLE_FILTER_T>
+RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, top_k]
+  typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, top_k]
+  const std::uint32_t top_k,
+  const DATASET_DESCRIPTOR_T* dataset_desc,
+  const typename DATASET_DESCRIPTOR_T::DATA_T* const queries_ptr,  // [num_queries, dataset_dim]
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const knn_graph,   // [dataset_size, graph_degree]
+  const std::uint32_t graph_degree,
+  const unsigned num_distilation,
+  const uint64_t rand_xor_mask,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
+  const uint32_t num_seeds,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+  const std::uint32_t internal_topk,
+  const std::uint32_t search_width,
+  const std::uint32_t min_iteration,
+  const std::uint32_t max_iteration,
+  std::uint32_t* const num_executed_iterations,  // [num_queries]
+  const std::uint32_t hash_bitlen,
+  const std::uint32_t small_hash_bitlen,
+  const std::uint32_t small_hash_reset_interval,
+  SAMPLE_FILTER_T sample_filter)
+{
+  const auto query_id = blockIdx.y;
+  search_core<MAX_ITOPK,
+              MAX_CANDIDATES,
+              TOPK_BY_BITONIC_SORT,
+              DATASET_DESCRIPTOR_T,
+              SAMPLE_FILTER_T>(result_indices_ptr,
+                               result_distances_ptr,
+                               top_k,
+                               dataset_desc,
+                               queries_ptr,
+                               knn_graph,
+                               graph_degree,
+                               num_distilation,
+                               rand_xor_mask,
+                               seed_ptr,
+                               num_seeds,
+                               visited_hashmap_ptr,
+                               internal_topk,
+                               search_width,
+                               min_iteration,
+                               max_iteration,
+                               num_executed_iterations,
+                               hash_bitlen,
+                               small_hash_bitlen,
+                               small_hash_reset_interval,
+                               query_id,
+                               sample_filter);
+}
+
+// To make sure we avoid false sharing on both CPU and GPU, we enforce cache line size to the
+// maximum of the two.
+// This makes sync atomic significantly faster.
+constexpr size_t kCacheLineBytes = 64;
+
+constexpr uint32_t kMaxJobsNum              = 8192;
+constexpr uint32_t kMaxWorkersNum           = 4096;
+constexpr uint32_t kMaxWorkersPerThread     = 256;
+constexpr uint32_t kSoftMaxWorkersPerThread = 16;
+
+template <typename DATASET_DESCRIPTOR_T>
+struct alignas(kCacheLineBytes) job_desc_t {
+  using index_type    = typename DATASET_DESCRIPTOR_T::INDEX_T;
+  using distance_type = typename DATASET_DESCRIPTOR_T::DISTANCE_T;
+  using data_type     = typename DATASET_DESCRIPTOR_T::DATA_T;
+  // The algorithm input parameters
+  struct value_t {
+    index_type* result_indices_ptr;       // [num_queries, top_k]
+    distance_type* result_distances_ptr;  // [num_queries, top_k]
+    const data_type* queries_ptr;         // [num_queries, dataset_dim]
+    uint32_t top_k;
+    uint32_t n_queries;
+  };
+  using blob_elem_type = uint4;
+  constexpr static inline size_t kBlobSize =
+    raft::div_rounding_up_safe(sizeof(value_t), sizeof(blob_elem_type));
+  // Union facilitates loading the input by a warp in a single request
+  union input_t {
+    blob_elem_type blob[kBlobSize];  // NOLINT
+    value_t value;
+  } input;
+  // Last thread triggers this flag.
+  cuda::atomic<bool, cuda::thread_scope_system> completion_flag;
+};
+
+struct alignas(kCacheLineBytes) worker_handle_t {
+  using handle_t = uint64_t;
+  struct value_t {
+    uint32_t desc_id;
+    uint32_t query_id;
+  };
+  union data_t {
+    handle_t handle;
+    value_t value;
+  };
+  cuda::atomic<data_t, cuda::thread_scope_system> data;
+};
+static_assert(sizeof(worker_handle_t::value_t) == sizeof(worker_handle_t::handle_t));
+static_assert(
+  cuda::atomic<worker_handle_t::data_t, cuda::thread_scope_system>::is_always_lock_free);
+
+constexpr worker_handle_t::handle_t kWaitForWork = std::numeric_limits<uint64_t>::max();
+constexpr worker_handle_t::handle_t kNoMoreWork  = kWaitForWork - 1;
+
+constexpr auto is_worker_busy(worker_handle_t::handle_t h) -> bool
+{
+  return (h != kWaitForWork) && (h != kNoMoreWork);
+}
+
+template <unsigned MAX_ITOPK,
+          unsigned MAX_CANDIDATES,
+          unsigned TOPK_BY_BITONIC_SORT,
+          class DATASET_DESCRIPTOR_T,
+          class SAMPLE_FILTER_T>
+RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel_p(
+  const DATASET_DESCRIPTOR_T* dataset_desc,
+  worker_handle_t* worker_handles,
+  job_desc_t<DATASET_DESCRIPTOR_T>* job_descriptors,
+  uint32_t* completion_counters,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* const knn_graph,  // [dataset_size, graph_degree]
+  const std::uint32_t graph_degree,
+  const unsigned num_distilation,
+  const uint64_t rand_xor_mask,
+  const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
+  const uint32_t num_seeds,
+  typename DATASET_DESCRIPTOR_T::INDEX_T* const
+    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
+  const std::uint32_t internal_topk,
+  const std::uint32_t search_width,
+  const std::uint32_t min_iteration,
+  const std::uint32_t max_iteration,
+  std::uint32_t* const num_executed_iterations,  // [num_queries]
+  const std::uint32_t hash_bitlen,
+  const std::uint32_t small_hash_bitlen,
+  const std::uint32_t small_hash_reset_interval,
+  SAMPLE_FILTER_T sample_filter)
+{
+  using job_desc_type = job_desc_t<DATASET_DESCRIPTOR_T>;
+  __shared__ typename job_desc_type::input_t job_descriptor;
+  __shared__ worker_handle_t::data_t worker_data;
+
+  auto& worker_handle = worker_handles[blockIdx.y].data;
+  uint32_t job_ix;
+
+  while (true) {
+    // wait the writing phase
+    if (threadIdx.x == 0) {
+      worker_handle_t::data_t worker_data_local;
+      do {
+        worker_data_local = worker_handle.load(cuda::memory_order_relaxed);
+      } while (worker_data_local.handle == kWaitForWork);
+      if (worker_data_local.handle != kNoMoreWork) {
+        worker_handle.store({kWaitForWork}, cuda::memory_order_relaxed);
+      }
+      job_ix = worker_data_local.value.desc_id;
+      cuda::atomic_thread_fence(cuda::memory_order_acquire, cuda::thread_scope_system);
+      worker_data = worker_data_local;
+    }
+    if (threadIdx.x < raft::WarpSize) {
+      // Sync one warp and copy descriptor data
+      static_assert(job_desc_type::kBlobSize <= raft::WarpSize);
+      job_ix = raft::shfl(job_ix, 0);
+      if (threadIdx.x < job_desc_type::kBlobSize && job_ix < kMaxJobsNum) {
+        job_descriptor.blob[threadIdx.x] = job_descriptors[job_ix].input.blob[threadIdx.x];
+      }
+    }
+    __syncthreads();
+    if (worker_data.handle == kNoMoreWork) { break; }
+
+    // reading phase
+    auto* result_indices_ptr   = job_descriptor.value.result_indices_ptr;
+    auto* result_distances_ptr = job_descriptor.value.result_distances_ptr;
+    auto* queries_ptr          = job_descriptor.value.queries_ptr;
+    auto top_k                 = job_descriptor.value.top_k;
+    auto n_queries             = job_descriptor.value.n_queries;
+    auto query_id              = worker_data.value.query_id;
+
+    // work phase
+    search_core<MAX_ITOPK,
+                MAX_CANDIDATES,
+                TOPK_BY_BITONIC_SORT,
+                DATASET_DESCRIPTOR_T,
+                SAMPLE_FILTER_T>(result_indices_ptr,
+                                 result_distances_ptr,
+                                 top_k,
+                                 dataset_desc,
+                                 queries_ptr,
+                                 knn_graph,
+                                 graph_degree,
+                                 num_distilation,
+                                 rand_xor_mask,
+                                 seed_ptr,
+                                 num_seeds,
+                                 visited_hashmap_ptr,
+                                 internal_topk,
+                                 search_width,
+                                 min_iteration,
+                                 max_iteration,
+                                 num_executed_iterations,
+                                 hash_bitlen,
+                                 small_hash_bitlen,
+                                 small_hash_reset_interval,
+                                 query_id,
+                                 sample_filter);
+
+    // make sure all writes are visible even for the host
+    //     (e.g. when result buffers are in pinned memory)
+    cuda::atomic_thread_fence(cuda::memory_order_release, cuda::thread_scope_system);
+
+    // arrive to mark the end of the work phase
+    __syncthreads();
+    if (threadIdx.x == 0) {
+      auto completed_count = atomicInc(completion_counters + job_ix, n_queries - 1) + 1;
+      if (completed_count >= n_queries) {
+        job_descriptors[job_ix].completion_flag.store(true, cuda::memory_order_relaxed);
+      }
+    }
+  }
+}
+
+template <bool Persistent,
+          unsigned MAX_ITOPK,
+          unsigned MAX_CANDIDATES,
+          unsigned TOPK_BY_BITONIC_SORT,
+          class DATASET_DESCRIPTOR_T,
+          class SAMPLE_FILTER_T>
+auto dispatch_kernel = []() {
+  if constexpr (Persistent) {
+    return search_kernel_p<MAX_ITOPK,
+                           MAX_CANDIDATES,
+                           TOPK_BY_BITONIC_SORT,
+                           DATASET_DESCRIPTOR_T,
+                           SAMPLE_FILTER_T>;
+  } else {
+    return search_kernel<MAX_ITOPK,
+                         MAX_CANDIDATES,
+                         TOPK_BY_BITONIC_SORT,
+                         DATASET_DESCRIPTOR_T,
+                         SAMPLE_FILTER_T>;
+  }
+}();
+
+template <bool Persistent, typename DATASET_DESCRIPTOR_T, typename SAMPLE_FILTER_T>
 struct search_kernel_config {
-  using kernel_t = decltype(&search_kernel<64, 64, 0, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>);
+  using kernel_t =
+    decltype(dispatch_kernel<Persistent, 64, 64, 0, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>);
 
   template <unsigned MAX_CANDIDATES, unsigned USE_BITONIC_SORT>
   static auto choose_search_kernel(unsigned itopk_size) -> kernel_t
   {
     if (itopk_size <= 64) {
-      return search_kernel<64,
-                           MAX_CANDIDATES,
-                           USE_BITONIC_SORT,
-                           DATASET_DESCRIPTOR_T,
-                           SAMPLE_FILTER_T>;
+      return dispatch_kernel<Persistent,
+                             64,
+                             MAX_CANDIDATES,
+                             USE_BITONIC_SORT,
+                             DATASET_DESCRIPTOR_T,
+                             SAMPLE_FILTER_T>;
     } else if (itopk_size <= 128) {
-      return search_kernel<128,
-                           MAX_CANDIDATES,
-                           USE_BITONIC_SORT,
-                           DATASET_DESCRIPTOR_T,
-                           SAMPLE_FILTER_T>;
+      return dispatch_kernel<Persistent,
+                             128,
+                             MAX_CANDIDATES,
+                             USE_BITONIC_SORT,
+                             DATASET_DESCRIPTOR_T,
+                             SAMPLE_FILTER_T>;
     } else if (itopk_size <= 256) {
-      return search_kernel<256,
-                           MAX_CANDIDATES,
-                           USE_BITONIC_SORT,
-                           DATASET_DESCRIPTOR_T,
-                           SAMPLE_FILTER_T>;
+      return dispatch_kernel<Persistent,
+                             256,
+                             MAX_CANDIDATES,
+                             USE_BITONIC_SORT,
+                             DATASET_DESCRIPTOR_T,
+                             SAMPLE_FILTER_T>;
     } else if (itopk_size <= 512) {
-      return search_kernel<512,
-                           MAX_CANDIDATES,
-                           USE_BITONIC_SORT,
-                           DATASET_DESCRIPTOR_T,
-                           SAMPLE_FILTER_T>;
+      return dispatch_kernel<Persistent,
+                             512,
+                             MAX_CANDIDATES,
+                             USE_BITONIC_SORT,
+                             DATASET_DESCRIPTOR_T,
+                             SAMPLE_FILTER_T>;
     }
     THROW("No kernel for parametels itopk_size %u, max_candidates %u", itopk_size, MAX_CANDIDATES);
   }
@@ -846,9 +1111,19 @@ struct search_kernel_config {
       // Radix-based topk is used
       constexpr unsigned max_candidates = 32;  // to avoid build failure
       if (itopk_size <= 256) {
-        return search_kernel<256, max_candidates, 0, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
+        return dispatch_kernel<Persistent,
+                               256,
+                               max_candidates,
+                               0,
+                               DATASET_DESCRIPTOR_T,
+                               SAMPLE_FILTER_T>;
       } else if (itopk_size <= 512) {
-        return search_kernel<512, max_candidates, 0, DATASET_DESCRIPTOR_T, SAMPLE_FILTER_T>;
+        return dispatch_kernel<Persistent,
+                               512,
+                               max_candidates,
+                               0,
+                               DATASET_DESCRIPTOR_T,
+                               SAMPLE_FILTER_T>;
       }
     }
     THROW("No kernel for parametels itopk_size %u, num_itopk_candidates %u",
@@ -857,8 +1132,797 @@ struct search_kernel_config {
   }
 };
 
+/**
+ * @brief Resource queue
+ *
+ * @tparam T the element type
+ * @tparam Size the maximum capacity of the queue (power-of-two)
+ * @tparam Empty a special element value designating an empty queue slot. NB: storing `Empty` is UB.
+ *
+ * A shared atomic ring buffer based queue optimized for throughput when bottlenecked on `pop`
+ * operation.
+ *
+ * @code{.cpp}
+ *   // allocate the queue
+ *   resource_queue_t<int32_t, 256> resource_ids;
+ *
+ *   // store couple values
+ *   resource_ids.push(42);
+ *   resource_ids.push(7);
+ *
+ *   // wait to get the value from the queue
+ *   auto id_x = resource_ids.pop().wait();
+ *
+ *   // stand in line to get the value from the queue, but don't wait
+ *   auto ticket_y = resource_ids.pop();
+ *   // do other stuff and check if the value is available
+ *   int32_t id_y;
+ *   while (!ticket_y.test(id_y)) {
+ *     do_some_important_business(...);
+ *     std::this_thread::sleep_for(std::chrono::microseconds(10);
+ *   }
+ *   // `id_y` is set by now and `ticket_y.wait()` won't block anymore
+ *   assert(ticket_y.wait() == id_y);
+ * @endcode
+ */
+template <typename T, uint32_t Size, T Empty = std::numeric_limits<T>::max()>
+struct alignas(kCacheLineBytes) resource_queue_t {
+  using value_type                   = T;
+  static constexpr uint32_t kSize    = Size;
+  static constexpr value_type kEmpty = Empty;
+  static_assert(cuda::std::atomic<value_type>::is_always_lock_free,
+                "The value type must be lock-free.");
+  static_assert(raft::is_a_power_of_two(kSize), "The size must be a power-of-two for efficiency.");
+  static constexpr uint32_t kElemsPerCacheLine =
+    raft::div_rounding_up_safe<uint32_t>(kCacheLineBytes, sizeof(value_type));
+  /* [Note: cache-friendly indexing]
+     To avoid false sharing, the queue pushes and pops values not sequentially, but with an
+     increment that is larger than the cache line size.
+     Hence we introduce the `kCounterIncrement > kCacheLineBytes`.
+     However, to make sure all indices are used, we choose the increment to be coprime with the
+     buffer size. We also require that the buffer size is a power-of-two for two reasons:
+       1) Fast modulus operation - reduces to binary `and` (with `kCounterLocMask`).
+       2) Easy to ensure GCD(kCounterIncrement, kSize) == 1 by construction
+          (see the definition below).
+   */
+  static constexpr uint32_t kCounterIncrement = raft::bound_by_power_of_two(kElemsPerCacheLine) + 1;
+  static constexpr uint32_t kCounterLocMask   = kSize - 1;
+  // These props hold by design, but we add them here as a documentation and a sanity check.
+  static_assert(
+    kCounterIncrement * sizeof(value_type) >= kCacheLineBytes,
+    "The counter increment should be larger than the cache line size to avoid false sharing.");
+  static_assert(
+    std::gcd(kCounterIncrement, kSize) == 1,
+    "The counter increment and the size must be coprime to allow using all of the queue slots.");
+
+  static constexpr auto kMemOrder = cuda::std::memory_order_relaxed;
+
+  explicit resource_queue_t(uint32_t capacity = Size) noexcept : capacity_{capacity}
+  {
+    head_.store(0, kMemOrder);
+    tail_.store(0, kMemOrder);
+    for (uint32_t i = 0; i < kSize; i++) {
+      buf_[i].store(kEmpty, kMemOrder);
+    }
+  }
+
+  /** Nominal capacity of the queue. */
+  [[nodiscard]] auto capacity() const { return capacity_; }
+
+  /** This does not affect the queue behavior, but merely declares a nominal capacity. */
+  void set_capacity(uint32_t capacity) { capacity_ = capacity; }
+
+  /**
+   * A slot in the queue to take the value from.
+   * Once it's obtained, the corresponding value in the queue is lost for other users.
+   */
+  struct promise_t {
+    explicit promise_t(cuda::std::atomic<value_type>& loc) : loc_{loc}, val_{Empty} {}
+    ~promise_t() noexcept { wait(); }
+
+    auto test() noexcept -> bool
+    {
+      if (val_ != Empty) { return true; }
+      val_ = loc_.exchange(kEmpty, kMemOrder);
+      return val_ != Empty;
+    }
+
+    auto test(value_type& e) noexcept -> bool
+    {
+      if (test()) {
+        e = val_;
+        return true;
+      }
+      return false;
+    }
+
+    auto wait() noexcept -> value_type
+    {
+      if (val_ == Empty) {
+        // [HOT SPOT]
+        // Optimize for the case of contention: expect the loc is empty.
+        do {
+          loc_.wait(kEmpty, kMemOrder);
+          val_ = loc_.exchange(kEmpty, kMemOrder);
+        } while (val_ == kEmpty);
+      }
+      return val_;
+    }
+
+   private:
+    cuda::std::atomic<value_type>& loc_;
+    value_type val_;
+  };
+
+  void push(value_type x) noexcept
+  {
+    auto& loc = buf_[head_.fetch_add(kCounterIncrement, kMemOrder) & kCounterLocMask];
+    /* [NOT A HOT SPOT]
+     We expect there's always enough place in the queue to push the item,
+     but also we expect a few pop waiters - notify them the data is available.
+     */
+    value_type e = kEmpty;
+    while (!loc.compare_exchange_weak(e, x, kMemOrder, kMemOrder)) {
+      e = kEmpty;
+    }
+    loc.notify_one();
+  }
+
+  auto pop() noexcept -> promise_t
+  {
+    auto& loc = buf_[tail_.fetch_add(kCounterIncrement, kMemOrder) & kCounterLocMask];
+    return promise_t{loc};
+  }
+
+ private:
+  alignas(kCacheLineBytes) cuda::std::atomic<uint32_t> head_{};
+  alignas(kCacheLineBytes) cuda::std::atomic<uint32_t> tail_{};
+  alignas(kCacheLineBytes) std::array<cuda::std::atomic<value_type>, kSize> buf_{};
+  alignas(kCacheLineBytes) uint32_t capacity_;
+};
+
+/** Primitive fixed-size deque for single-threaded use. */
+template <typename T>
+struct local_deque_t {
+  explicit local_deque_t(uint32_t size) : store_(size) {}
+
+  [[nodiscard]] auto capacity() const -> uint32_t { return store_.size(); }
+  [[nodiscard]] auto size() const -> uint32_t { return end_ - start_; }
+
+  void push_back(T x) { store_[end_++ % capacity()] = x; }
+
+  void push_front(T x)
+  {
+    if (start_ == 0) {
+      start_ += capacity();
+      end_ += capacity();
+    }
+    store_[--start_ % capacity()] = x;
+  }
+
+  // NB: unsafe functions - do not check if the queue is full/empty.
+  auto pop_back() -> T { return store_[--end_ % capacity()]; }
+  auto pop_front() -> T { return store_[start_++ % capacity()]; }
+
+  auto try_push_back(T x) -> bool
+  {
+    if (size() >= capacity()) { return false; }
+    push_back(x);
+    return true;
+  }
+
+  auto try_push_front(T x) -> bool
+  {
+    if (size() >= capacity()) { return false; }
+    push_front(x);
+    return true;
+  }
+
+  auto try_pop_back(T& x) -> bool
+  {
+    if (start_ >= end_) { return false; }
+    x = pop_back();
+    return true;
+  }
+
+  auto try_pop_front(T& x) -> bool
+  {
+    if (start_ >= end_) { return false; }
+    x = pop_front();
+    return true;
+  }
+
+ private:
+  std::vector<T> store_;
+  uint32_t start_{0};
+  uint32_t end_{0};
+};
+
+struct persistent_runner_base_t {
+  using job_queue_type    = resource_queue_t<uint32_t, kMaxJobsNum>;
+  using worker_queue_type = resource_queue_t<uint32_t, kMaxWorkersNum>;
+  rmm::mr::pinned_host_memory_resource worker_handles_mr;
+  rmm::mr::pinned_host_memory_resource job_descriptor_mr;
+  rmm::mr::cuda_memory_resource device_mr;
+  cudaStream_t stream{};
+  job_queue_type job_queue{};
+  worker_queue_type worker_queue{};
+  // This should be large enough to make the runner live through restarts of the benchmark cases.
+  // Otherwise, the benchmarks slowdown significantly.
+  std::chrono::milliseconds lifetime;
+
+  persistent_runner_base_t(float persistent_lifetime)
+    : lifetime(size_t(persistent_lifetime * 1000)), job_queue(), worker_queue()
+  {
+    cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking);
+  }
+  virtual ~persistent_runner_base_t() noexcept { cudaStreamDestroy(stream); };
+};
+
+struct alignas(kCacheLineBytes) launcher_t {
+  using job_queue_type           = persistent_runner_base_t::job_queue_type;
+  using worker_queue_type        = persistent_runner_base_t::worker_queue_type;
+  using pending_reads_queue_type = local_deque_t<uint32_t>;
+  using completion_flag_type     = cuda::atomic<bool, cuda::thread_scope_system>;
+
+  pending_reads_queue_type pending_reads;
+  job_queue_type& job_ids;
+  worker_queue_type& idle_worker_ids;
+  worker_handle_t* worker_handles;
+  uint32_t job_id;
+  completion_flag_type* completion_flag;
+  bool all_done = false;
+
+  /* [Note: sleeping]
+  When the number of threads is greater than the number of cores, the threads start to fight for
+  the CPU time, which reduces the throughput.
+  To ease the competition, we track the expected GPU latency and let a thread sleep for some
+  time, and only start to spin when it's about a time to get the result.
+  */
+  static inline constexpr auto kDefaultLatency = std::chrono::nanoseconds(50000);
+  /* This is the base for computing maximum time a thread is allowed to sleep. */
+  static inline constexpr auto kMaxExpectedLatency =
+    kDefaultLatency * std::max<std::uint32_t>(10, kMaxJobsNum / 128);
+  static inline thread_local auto expected_latency = kDefaultLatency;
+  const std::chrono::time_point<std::chrono::system_clock> start;
+  std::chrono::time_point<std::chrono::system_clock> now;
+  const int64_t pause_factor;
+  int pause_count = 0;
+  /**
+   * Beyond this threshold, the launcher (calling thread) does not wait for the results anymore and
+   * throws an exception.
+   */
+  std::chrono::time_point<std::chrono::system_clock> deadline;
+
+  template <typename RecordWork>
+  launcher_t(job_queue_type& job_ids,
+             worker_queue_type& idle_worker_ids,
+             worker_handle_t* worker_handles,
+             uint32_t n_queries,
+             std::chrono::milliseconds max_wait_time,
+             RecordWork record_work)
+    : pending_reads{std::min(n_queries, kMaxWorkersPerThread)},
+      job_ids{job_ids},
+      idle_worker_ids{idle_worker_ids},
+      worker_handles{worker_handles},
+      job_id{job_ids.pop().wait()},
+      completion_flag{record_work(job_id)},
+      start{std::chrono::system_clock::now()},
+      pause_factor{calc_pause_factor(n_queries)},
+      now{start},
+      deadline{start + max_wait_time + expected_latency}
+  {
+    // Wait for the first worker and submit the query immediately.
+    submit_query(idle_worker_ids.pop().wait(), 0);
+    // Submit the rest of the queries in the batch
+    for (uint32_t i = 1; i < n_queries; i++) {
+      auto promised_worker = idle_worker_ids.pop();
+      uint32_t worker_id;
+      while (!promised_worker.test(worker_id)) {
+        if (pending_reads.try_pop_front(worker_id)) {
+          bool returned_some = false;
+          for (bool keep_returning = true; keep_returning;) {
+            if (try_return_worker(worker_id)) {
+              keep_returning = pending_reads.try_pop_front(worker_id);
+              returned_some  = true;
+            } else {
+              pending_reads.push_front(worker_id);
+              keep_returning = false;
+            }
+          }
+          if (!returned_some) { pause(); }
+        } else {
+          // Calmly wait for the promised worker instead of spinning.
+          worker_id = promised_worker.wait();
+          break;
+        }
+      }
+      pause_count = 0;  // reset the pause behavior
+      submit_query(worker_id, i);
+      // Try to not hold too many workers in one thread
+      if (i >= kSoftMaxWorkersPerThread && pending_reads.try_pop_front(worker_id)) {
+        if (!try_return_worker(worker_id)) { pending_reads.push_front(worker_id); }
+      }
+    }
+  }
+
+  inline ~launcher_t() noexcept  // NOLINT
+  {
+    // bookkeeping: update the expected latency to wait more efficiently later
+    constexpr size_t kWindow = 100;  // moving average memory
+    expected_latency         = std::min<std::chrono::nanoseconds>(
+      ((kWindow - 1) * expected_latency + now - start) / kWindow, kMaxExpectedLatency);
+
+    // Try to gracefully cleanup the queue resources if the launcher is being destructed after an
+    // exception.
+    if (job_id != job_queue_type::kEmpty) { job_ids.push(job_id); }
+    uint32_t worker_id;
+    while (pending_reads.try_pop_front(worker_id)) {
+      idle_worker_ids.push(worker_id);
+    }
+  }
+
+  inline void submit_query(uint32_t worker_id, uint32_t query_id)
+  {
+    worker_handles[worker_id].data.store(worker_handle_t::data_t{.value = {job_id, query_id}},
+                                         cuda::memory_order_relaxed);
+
+    while (!pending_reads.try_push_back(worker_id)) {
+      // The only reason pending_reads cannot push is that the queue is full.
+      // It's local, so we must pop and wait for the returned worker to finish its work.
+      auto pending_worker_id = pending_reads.pop_front();
+      while (!try_return_worker(pending_worker_id)) {
+        pause();
+      }
+    }
+    pause_count = 0;  // reset the pause behavior
+  }
+
+  /** Check if the worker has finished the work; if so, return it to the shared pool. */
+  inline auto try_return_worker(uint32_t worker_id) -> bool
+  {
+    // Use the cached `all_done` - makes sense when called from the `wait()` routine.
+    if (all_done ||
+        !is_worker_busy(worker_handles[worker_id].data.load(cuda::memory_order_relaxed).handle)) {
+      idle_worker_ids.push(worker_id);
+      return true;
+    } else {
+      return false;
+    }
+  }
+
+  /** Check if all workers finished their work. */
+  inline auto is_all_done()
+  {
+    // Cache the result of the check to avoid doing unnecessary atomic loads.
+    if (all_done) { return true; }
+    all_done = completion_flag->load(cuda::memory_order_relaxed);
+    return all_done;
+  }
+
+  /** The launcher shouldn't attempt to wait past the returned time. */
+  [[nodiscard]] inline auto sleep_limit() const
+  {
+    constexpr auto kMinWakeTime  = std::chrono::nanoseconds(10000);
+    constexpr double kSleepLimit = 0.6;
+    return start + expected_latency * kSleepLimit - kMinWakeTime;
+  }
+
+  /**
+   * When the latency is much larger than expected, it's a sign that there is a thread contention.
+   * Then we switch to sleeping instead of waiting to give the cpu cycles to other threads.
+   */
+  [[nodiscard]] inline auto overtime_threshold() const
+  {
+    constexpr auto kOvertimeFactor = 3;
+    return start + expected_latency * kOvertimeFactor;
+  }
+
+  /**
+   * Calculate the fraction of time can be spent sleeping in a single call to `pause()`.
+   * Naturally it depends on the number of queries in a batch and the number of parallel workers.
+   */
+  [[nodiscard]] inline auto calc_pause_factor(uint32_t n_queries) const -> uint32_t
+  {
+    constexpr uint32_t kMultiplier = 10;
+    return kMultiplier * raft::div_rounding_up_safe(n_queries, idle_worker_ids.capacity());
+  }
+
+  /** Wait a little bit (called in a loop). */
+  inline void pause()
+  {
+    // Don't sleep this many times hoping for smoother run
+    constexpr auto kSpinLimit = 3;
+    // It doesn't make much sense to slee less than this
+    constexpr auto kPauseTimeMin = std::chrono::nanoseconds(1000);
+    // Bound sleeping time
+    constexpr auto kPauseTimeMax = std::chrono::nanoseconds(50000);
+    if (pause_count++ < kSpinLimit) {
+      std::this_thread::yield();
+      return;
+    }
+    now                  = std::chrono::system_clock::now();
+    auto pause_time_base = std::max(now - start, expected_latency);
+    auto pause_time      = std::clamp(pause_time_base / pause_factor, kPauseTimeMin, kPauseTimeMax);
+    if (now + pause_time < sleep_limit()) {
+      // It's too early: sleep for a bit
+      std::this_thread::sleep_for(pause_time);
+    } else if (now <= overtime_threshold()) {
+      // It's about time to check the results, don't sleep
+      std::this_thread::yield();
+    } else if (now <= deadline) {
+      // Too late; perhaps the system is too busy - sleep again
+      std::this_thread::sleep_for(pause_time);
+    } else {
+      // Missed the deadline: throw an exception
+      throw raft::exception(
+        "The calling thread didn't receive the results from the persistent CAGRA kernel within the "
+        "expected kernel lifetime. Here are possible reasons of this failure:\n"
+        "  (1) `persistent_lifetime` search parameter is too small - increase it;\n"
+        "  (2) there is other work being executed on the same device and the kernel failed to "
+        "progress - decreasing `persistent_device_usage` may help (but not guaranteed);\n"
+        "  (3) there is a bug in the implementation - please report it to cuVS team.");
+    }
+  }
+
+  /** Wait for all work to finish and don't forget to return the workers to the shared pool. */
+  inline void wait()
+  {
+    uint32_t worker_id;
+    while (pending_reads.try_pop_front(worker_id)) {
+      while (!try_return_worker(worker_id)) {
+        if (!is_all_done()) { pause(); }
+      }
+    }
+    pause_count = 0;  // reset the pause behavior
+    // terminal state, should be engaged only after the `pending_reads` is empty
+    // and `queries_submitted == n_queries`
+    now = std::chrono::system_clock::now();
+    while (!is_all_done()) {
+      auto till_time = sleep_limit();
+      if (now < till_time) {
+        std::this_thread::sleep_until(till_time);
+        now = std::chrono::system_clock::now();
+      } else {
+        pause();
+      }
+    }
+
+    // Return the job descriptor
+    job_ids.push(job_id);
+    job_id = job_queue_type::kEmpty;
+  }
+};
+
+template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
+struct alignas(kCacheLineBytes) persistent_runner_t : public persistent_runner_base_t {
+  using descriptor_base_type = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+  using index_type           = IndexT;
+  using distance_type        = DistanceT;
+  using data_type            = DataT;
+  using kernel_config_type   = search_kernel_config<true, descriptor_base_type, SampleFilterT>;
+  using kernel_type          = typename kernel_config_type::kernel_t;
+  using job_desc_type        = job_desc_t<descriptor_base_type>;
+  kernel_type kernel;
+  uint32_t block_size;
+  dataset_descriptor_host<DataT, IndexT, DistanceT> dd_host;
+  rmm::device_uvector<worker_handle_t> worker_handles;
+  rmm::device_uvector<job_desc_type> job_descriptors;
+  rmm::device_uvector<uint32_t> completion_counters;
+  rmm::device_uvector<index_type> hashmap;
+  std::atomic<std::chrono::time_point<std::chrono::system_clock>> last_touch;
+  uint64_t param_hash;
+
+  /**
+   * Calculate the hash of the parameters to detect if they've changed across the calls.
+   * NB: this must have the same argument types as the constructor.
+   */
+  static inline auto calculate_parameter_hash(
+    std::reference_wrapper<const dataset_descriptor_host<DataT, IndexT, DistanceT>> dataset_desc,
+    raft::device_matrix_view<const index_type, int64_t, raft::row_major> graph,
+    uint32_t num_itopk_candidates,
+    uint32_t block_size,  //
+    uint32_t smem_size,
+    int64_t hash_bitlen,
+    size_t small_hash_bitlen,
+    size_t small_hash_reset_interval,
+    uint32_t num_random_samplings,
+    uint64_t rand_xor_mask,
+    uint32_t num_seeds,
+    size_t itopk_size,
+    size_t search_width,
+    size_t min_iterations,
+    size_t max_iterations,
+    SampleFilterT sample_filter,
+    float persistent_lifetime,
+    float persistent_device_usage) -> uint64_t
+  {
+    return uint64_t(graph.data_handle()) ^ dataset_desc.get().team_size ^ num_itopk_candidates ^
+           block_size ^ smem_size ^ hash_bitlen ^ small_hash_reset_interval ^ num_random_samplings ^
+           rand_xor_mask ^ num_seeds ^ itopk_size ^ search_width ^ min_iterations ^ max_iterations ^
+           uint64_t(persistent_lifetime * 1000) ^ uint64_t(persistent_device_usage * 1000);
+  }
+
+  persistent_runner_t(
+    std::reference_wrapper<const dataset_descriptor_host<DataT, IndexT, DistanceT>> dataset_desc,
+    raft::device_matrix_view<const index_type, int64_t, raft::row_major> graph,
+    uint32_t num_itopk_candidates,
+    uint32_t block_size,  //
+    uint32_t smem_size,
+    int64_t hash_bitlen,
+    size_t small_hash_bitlen,
+    size_t small_hash_reset_interval,
+    uint32_t num_random_samplings,
+    uint64_t rand_xor_mask,
+    uint32_t num_seeds,
+    size_t itopk_size,
+    size_t search_width,
+    size_t min_iterations,
+    size_t max_iterations,
+    SampleFilterT sample_filter,
+    float persistent_lifetime,
+    float persistent_device_usage)
+    : persistent_runner_base_t{persistent_lifetime},
+      kernel{kernel_config_type::choose_itopk_and_mx_candidates(
+        itopk_size, num_itopk_candidates, block_size)},
+      block_size{block_size},
+      worker_handles(0, stream, worker_handles_mr),
+      job_descriptors(kMaxJobsNum, stream, job_descriptor_mr),
+      completion_counters(kMaxJobsNum, stream, device_mr),
+      hashmap(0, stream, device_mr),
+      dd_host{dataset_desc.get()},
+      param_hash(calculate_parameter_hash(dd_host,
+                                          graph,
+                                          num_itopk_candidates,
+                                          block_size,
+                                          smem_size,
+                                          hash_bitlen,
+                                          small_hash_bitlen,
+                                          small_hash_reset_interval,
+                                          num_random_samplings,
+                                          rand_xor_mask,
+                                          num_seeds,
+                                          itopk_size,
+                                          search_width,
+                                          min_iterations,
+                                          max_iterations,
+                                          sample_filter,
+                                          persistent_lifetime,
+                                          persistent_device_usage))
+  {
+    // initialize the dataset/distance descriptor
+    auto* dd_dev_ptr = dd_host.dev_ptr(stream);
+
+    // set kernel attributes same as in normal kernel
+    RAFT_CUDA_TRY(
+      cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
+
+    // set kernel launch parameters
+    dim3 gs = calc_coop_grid_size(block_size, smem_size, persistent_device_usage);
+    dim3 bs(block_size, 1, 1);
+    RAFT_LOG_DEBUG(
+      "Launching persistent kernel with %u threads, %u block %u smem", bs.x, gs.y, smem_size);
+
+    // initialize the job queue
+    auto* completion_counters_ptr = completion_counters.data();
+    auto* job_descriptors_ptr     = job_descriptors.data();
+    for (uint32_t i = 0; i < kMaxJobsNum; i++) {
+      auto& jd                = job_descriptors_ptr[i].input.value;
+      jd.result_indices_ptr   = nullptr;
+      jd.result_distances_ptr = nullptr;
+      jd.queries_ptr          = nullptr;
+      jd.top_k                = 0;
+      jd.n_queries            = 0;
+      job_descriptors_ptr[i].completion_flag.store(false);
+      job_queue.push(i);
+    }
+
+    // initialize the worker queue
+    worker_queue.set_capacity(gs.y);
+    worker_handles.resize(gs.y, stream);
+    auto* worker_handles_ptr = worker_handles.data();
+    RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+    for (uint32_t i = 0; i < gs.y; i++) {
+      worker_handles_ptr[i].data.store({kWaitForWork});
+      worker_queue.push(i);
+    }
+
+    index_type* hashmap_ptr = nullptr;
+    if (small_hash_bitlen == 0) {
+      hashmap.resize(gs.y * hashmap::get_size(hash_bitlen), stream);
+      hashmap_ptr = hashmap.data();
+    }
+
+    // launch the kernel
+    auto* graph_ptr                   = graph.data_handle();
+    uint32_t graph_degree             = graph.extent(1);
+    uint32_t* num_executed_iterations = nullptr;  // optional arg [num_queries]
+    const index_type* dev_seed_ptr    = nullptr;  // optional arg [num_queries, num_seeds]
+
+    void* args[] =  // NOLINT
+      {&dd_dev_ptr,
+       &worker_handles_ptr,
+       &job_descriptors_ptr,
+       &completion_counters_ptr,
+       &graph_ptr,  // [dataset_size, graph_degree]
+       &graph_degree,
+       &num_random_samplings,
+       &rand_xor_mask,
+       &dev_seed_ptr,
+       &num_seeds,
+       &hashmap_ptr,  // visited_hashmap_ptr: [num_queries, 1 << hash_bitlen]
+       &itopk_size,
+       &search_width,
+       &min_iterations,
+       &max_iterations,
+       &num_executed_iterations,
+       &hash_bitlen,
+       &small_hash_bitlen,
+       &small_hash_reset_interval,
+       &sample_filter};
+    cuda::atomic_thread_fence(cuda::memory_order_seq_cst, cuda::thread_scope_system);
+    RAFT_CUDA_TRY(cudaLaunchCooperativeKernel<std::remove_pointer_t<kernel_type>>(
+      kernel, gs, bs, args, smem_size, stream));
+    RAFT_LOG_INFO(
+      "Initialized the kernel %p in stream %zd; job_queue size = %u; worker_queue size = %u",
+      reinterpret_cast<void*>(kernel),
+      int64_t((cudaStream_t)stream),
+      job_queue.capacity(),
+      worker_queue.capacity());
+    last_touch.store(std::chrono::system_clock::now(), std::memory_order_relaxed);
+  }
+
+  ~persistent_runner_t() noexcept override
+  {
+    auto whs = worker_handles.data();
+    for (auto i = worker_handles.size(); i > 0; i--) {
+      whs[worker_queue.pop().wait()].data.store({kNoMoreWork}, cuda::memory_order_relaxed);
+    }
+    RAFT_CUDA_TRY_NO_THROW(cudaStreamSynchronize(stream));
+    RAFT_LOG_INFO("Destroyed the persistent runner.");
+  }
+
+  void launch(index_type* result_indices_ptr,       // [num_queries, top_k]
+              distance_type* result_distances_ptr,  // [num_queries, top_k]
+              const data_type* queries_ptr,         // [num_queries, dataset_dim]
+              uint32_t num_queries,
+              uint32_t top_k)
+  {
+    // submit all queries
+    launcher_t launcher{job_queue,
+                        worker_queue,
+                        worker_handles.data(),
+                        num_queries,
+                        this->lifetime,
+                        [=](uint32_t job_ix) {
+                          auto& jd                = job_descriptors.data()[job_ix].input.value;
+                          auto* cflag             = &job_descriptors.data()[job_ix].completion_flag;
+                          jd.result_indices_ptr   = result_indices_ptr;
+                          jd.result_distances_ptr = result_distances_ptr;
+                          jd.queries_ptr          = queries_ptr;
+                          jd.top_k                = top_k;
+                          jd.n_queries            = num_queries;
+                          cflag->store(false, cuda::memory_order_relaxed);
+                          cuda::atomic_thread_fence(cuda::memory_order_release,
+                                                    cuda::thread_scope_system);
+                          return cflag;
+                        }};
+
+    // Update the state of the keep-alive atomic in the meanwhile
+    auto prev_touch = last_touch.load(std::memory_order_relaxed);
+    if (prev_touch + lifetime / 10 < launcher.now) {
+      // to avoid congestion at this atomic, we only update it if a significant fraction of the live
+      // interval has passed.
+      last_touch.store(launcher.now, std::memory_order_relaxed);
+    }
+    // wait for the results to arrive
+    launcher.wait();
+  }
+
+  auto calc_coop_grid_size(uint32_t block_size, uint32_t smem_size, float persistent_device_usage)
+    -> dim3
+  {
+    // determine the grid size
+    int ctas_per_sm = 1;
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor<kernel_type>(
+      &ctas_per_sm, kernel, block_size, smem_size);
+    int num_sm    = raft::getMultiProcessorCount();
+    auto n_blocks = static_cast<uint32_t>(persistent_device_usage * (ctas_per_sm * num_sm));
+    if (n_blocks > kMaxWorkersNum) {
+      RAFT_LOG_WARN("Limiting the grid size limit due to the size of the queue: %u -> %u",
+                    n_blocks,
+                    kMaxWorkersNum);
+      n_blocks = kMaxWorkersNum;
+    }
+
+    return {1, n_blocks, 1};
+  }
+};
+
+struct alignas(kCacheLineBytes) persistent_state {
+  std::shared_ptr<persistent_runner_base_t> runner{nullptr};
+  std::mutex lock;
+};
+
+inline persistent_state persistent{};
+
+template <typename RunnerT, typename... Args>
+auto create_runner(Args... args) -> std::shared_ptr<RunnerT>  // it's ok.. pass everything by values
+{
+  std::lock_guard<std::mutex> guard(persistent.lock);
+  // Check if the runner has already been created
+  std::shared_ptr<RunnerT> runner_outer = std::dynamic_pointer_cast<RunnerT>(persistent.runner);
+  if (runner_outer) {
+    if (runner_outer->param_hash == RunnerT::calculate_parameter_hash(args...)) {
+      return runner_outer;
+    } else {
+      runner_outer.reset();
+    }
+  }
+  // Runner has not yet been created (or it's incompatible):
+  //   create it in another thread and only then release the lock.
+  // Free the resources (if any) in advance
+  persistent.runner.reset();
+
+  cuda::std::atomic_flag ready{};
+  ready.clear(cuda::std::memory_order_relaxed);
+  std::thread(
+    [&runner_outer, &ready](Args... thread_args) {  // pass everything by values
+      // create the runner (the lock is acquired in the parent thread).
+      runner_outer      = std::make_shared<RunnerT>(thread_args...);
+      auto lifetime     = runner_outer->lifetime;
+      persistent.runner = std::static_pointer_cast<persistent_runner_base_t>(runner_outer);
+      std::weak_ptr<RunnerT> runner_weak = runner_outer;
+      ready.test_and_set(cuda::std::memory_order_release);
+      ready.notify_one();
+      // NB: runner_outer is passed by reference and may be dead by this time.
+
+      while (true) {
+        std::this_thread::sleep_for(lifetime);
+        auto runner = runner_weak.lock();  // runner_weak is local - thread-safe
+        if (!runner) {
+          return;  // dead already
+        }
+        if (runner->last_touch.load(std::memory_order_relaxed) + lifetime <
+            std::chrono::system_clock::now()) {
+          std::lock_guard<std::mutex> guard(persistent.lock);
+          if (runner == persistent.runner) { persistent.runner.reset(); }
+          return;
+        }
+      }
+    },
+    args...)
+    .detach();
+  ready.wait(false, cuda::std::memory_order_acquire);
+  return runner_outer;
+}
+
+template <typename RunnerT, typename... Args>
+auto get_runner(Args... args) -> std::shared_ptr<RunnerT>
+{
+  // Using a thread-local weak pointer allows us to avoid using locks/atomics,
+  // since the control block of weak/shared pointers is thread-safe.
+  static thread_local std::weak_ptr<RunnerT> weak;
+  auto runner = weak.lock();
+  if (runner) {
+    if (runner->param_hash == RunnerT::calculate_parameter_hash(args...)) {
+      return runner;
+    } else {
+      weak.reset();
+      runner.reset();
+    }
+  }
+  // Thread-local variable expected_latency makes sense only for a current RunnerT configuration.
+  // If `weak` is not alive, it's a hint the configuration has changed and we should reset our
+  // estimate of the expected launch latency.
+  launcher_t::expected_latency = launcher_t::kDefaultLatency;
+  runner                       = create_runner<RunnerT>(args...);
+  weak                         = runner;
+  return runner;
+}
+
 template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
-void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
                     IndexT* topk_indices_ptr,       // [num_queries, topk]
                     DistanceT* topk_distances_ptr,  // [num_queries, topk]
@@ -879,40 +1943,66 @@ void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* d
                     SampleFilterT sample_filter,
                     cudaStream_t stream)
 {
-  auto kernel =
-    search_kernel_config<dataset_descriptor_base_t<DataT, IndexT, DistanceT>,
-                         SampleFilterT>::choose_itopk_and_mx_candidates(ps.itopk_size,
-                                                                        num_itopk_candidates,
-                                                                        block_size);
-  RAFT_CUDA_TRY(
-    cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
-  dim3 thread_dims(block_size, 1, 1);
-  dim3 block_dims(1, num_queries, 1);
-  RAFT_LOG_DEBUG(
-    "Launching kernel with %u threads, %u block %u smem", block_size, num_queries, smem_size);
-  kernel<<<block_dims, thread_dims, smem_size, stream>>>(topk_indices_ptr,
-                                                         topk_distances_ptr,
-                                                         topk,
-                                                         dataset_desc,
-                                                         queries_ptr,
-                                                         graph.data_handle(),
-                                                         graph.extent(1),
-                                                         ps.num_random_samplings,
-                                                         ps.rand_xor_mask,
-                                                         dev_seed_ptr,
-                                                         num_seeds,
-                                                         hashmap_ptr,
-                                                         ps.itopk_size,
-                                                         ps.search_width,
-                                                         ps.min_iterations,
-                                                         ps.max_iterations,
-                                                         num_executed_iterations,
-                                                         hash_bitlen,
-                                                         small_hash_bitlen,
-                                                         small_hash_reset_interval,
-                                                         sample_filter);
-  // RAFT_CUDA_TRY(cudaPeekAtLastError());
-  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+  if (ps.persistent) {
+    using runner_type = persistent_runner_t<DataT, IndexT, DistanceT, SampleFilterT>;
+
+    get_runner<runner_type>(/*
+Note, we're passing the descriptor by reference here, and this reference is going to be passed to a
+new spawned thread, which is dangerous. However, the descriptor is copied in that thread before the
+control is returned in this thread (in persistent_runner_t constructor), so we're safe.
+*/
+                            std::cref(dataset_desc),
+                            graph,
+                            num_itopk_candidates,
+                            block_size,
+                            smem_size,
+                            hash_bitlen,
+                            small_hash_bitlen,
+                            small_hash_reset_interval,
+                            ps.num_random_samplings,
+                            ps.rand_xor_mask,
+                            num_seeds,
+                            ps.itopk_size,
+                            ps.search_width,
+                            ps.min_iterations,
+                            ps.max_iterations,
+                            sample_filter,
+                            ps.persistent_lifetime,
+                            ps.persistent_device_usage)
+      ->launch(topk_indices_ptr, topk_distances_ptr, queries_ptr, num_queries, topk);
+  } else {
+    using descriptor_base_type = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
+    auto kernel                = search_kernel_config<false, descriptor_base_type, SampleFilterT>::
+      choose_itopk_and_mx_candidates(ps.itopk_size, num_itopk_candidates, block_size);
+    RAFT_CUDA_TRY(
+      cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
+    dim3 thread_dims(block_size, 1, 1);
+    dim3 block_dims(1, num_queries, 1);
+    RAFT_LOG_DEBUG(
+      "Launching kernel with %u threads, %u block %u smem", block_size, num_queries, smem_size);
+    kernel<<<block_dims, thread_dims, smem_size, stream>>>(topk_indices_ptr,
+                                                           topk_distances_ptr,
+                                                           topk,
+                                                           dataset_desc.dev_ptr(stream),
+                                                           queries_ptr,
+                                                           graph.data_handle(),
+                                                           graph.extent(1),
+                                                           ps.num_random_samplings,
+                                                           ps.rand_xor_mask,
+                                                           dev_seed_ptr,
+                                                           num_seeds,
+                                                           hashmap_ptr,
+                                                           ps.itopk_size,
+                                                           ps.search_width,
+                                                           ps.min_iterations,
+                                                           ps.max_iterations,
+                                                           num_executed_iterations,
+                                                           hash_bitlen,
+                                                           small_hash_bitlen,
+                                                           small_hash_reset_interval,
+                                                           sample_filter);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+  }
 }
 }  // namespace single_cta_search
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
index 7b7f44db71..4d8b72b417 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
@@ -22,7 +22,7 @@
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 
 template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
-void select_and_run(const dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dataset_desc,
+void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
                     IndexT* topk_indices_ptr,       // [num_queries, topk]
                     DistanceT* topk_distances_ptr,  // [num_queries, topk]
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index d744a8178a..15f494d3dc 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -27,6 +27,7 @@ include(rapids-find)
 rapids_cuda_init_architectures(test_cuvs)
 
 project(test_cuvs LANGUAGES CXX CUDA)
+find_package(Threads)
 
 # ------------- configure cuvs -----------------#
 
@@ -36,11 +37,15 @@ include(../cmake/thirdparty/get_cuvs.cmake)
 
 # -------------- compile tasks ----------------- #
 add_executable(CAGRA_EXAMPLE src/cagra_example.cu)
+add_executable(CAGRA_PERSISTENT_EXAMPLE src/cagra_persistent_example.cu)
 add_executable(IVF_FLAT_EXAMPLE src/ivf_flat_example.cu)
 add_executable(IVF_PQ_EXAMPLE src/ivf_pq_example.cu)
 
 # `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets are
 # installed in a conda environment, if one exists
 target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(
+  CAGRA_PERSISTENT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
+)
 target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/cpp/src/cagra_persistent_example.cu b/examples/cpp/src/cagra_persistent_example.cu
new file mode 100644
index 0000000000..9258a7311c
--- /dev/null
+++ b/examples/cpp/src/cagra_persistent_example.cu
@@ -0,0 +1,258 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.cuh"
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <array>
+#include <chrono>
+#include <cstdint>
+#include <future>
+
+// A helper to split the dataset into chunks
+template <typename DeviceMatrixOrView>
+auto slice_matrix(DeviceMatrixOrView source,
+                  typename DeviceMatrixOrView::index_type offset_rows,
+                  typename DeviceMatrixOrView::index_type count_rows) {
+  auto n_cols = source.extent(1);
+  return raft::make_device_matrix_view<
+      typename DeviceMatrixOrView::element_type,
+      typename DeviceMatrixOrView::index_type>(
+      source.data_handle() + offset_rows * n_cols, count_rows, n_cols);
+}
+
+// A helper to measure the execution time of a function
+template <typename F, typename... Args>
+void time_it(std::string label, F f, Args &&...xs) {
+  auto start = std::chrono::system_clock::now();
+  f(std::forward<Args>(xs)...);
+  auto end = std::chrono::system_clock::now();
+  auto t = std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+  auto t_ms = double(t.count()) / 1000.0;
+  std::cout << "[" << label << "] execution time: " << t_ms << " ms"
+            << std::endl;
+}
+
+void cagra_build_search_variants(
+    raft::device_resources const &res,
+    raft::device_matrix_view<const float, int64_t> dataset,
+    raft::device_matrix_view<const float, int64_t> queries) {
+  using namespace cuvs::neighbors;
+
+  // Number of neighbors to search
+  int64_t topk = 100;
+  // We split the queries set into three subsets for our experiment, one for a
+  // sanity check and two for measuring the performance.
+  int64_t n_queries_a = queries.extent(0) / 2;
+  int64_t n_queries_b = queries.extent(0) - n_queries_a;
+
+  auto queries_a = slice_matrix(queries, 0, n_queries_a);
+  auto queries_b = slice_matrix(queries, n_queries_a, n_queries_b);
+
+  // create output arrays
+  auto neighbors =
+      raft::make_device_matrix<uint32_t>(res, queries.extent(0), topk);
+  auto distances =
+      raft::make_device_matrix<float>(res, queries.extent(0), topk);
+  // slice them same as queries
+  auto neighbors_a = slice_matrix(neighbors, 0, n_queries_a);
+  auto distances_a = slice_matrix(distances, 0, n_queries_a);
+  auto neighbors_b = slice_matrix(neighbors, n_queries_a, n_queries_b);
+  auto distances_b = slice_matrix(distances, n_queries_a, n_queries_b);
+
+  // use default index parameters
+  cagra::index_params index_params;
+
+  std::cout << "Building CAGRA index (search graph)" << std::endl;
+  auto index = cagra::build(res, index_params, dataset);
+
+  std::cout << "CAGRA index has " << index.size() << " vectors" << std::endl;
+  std::cout << "CAGRA graph has degree " << index.graph_degree()
+            << ", graph size [" << index.graph().extent(0) << ", "
+            << index.graph().extent(1) << "]" << std::endl;
+
+  // use default search parameters
+  cagra::search_params search_params;
+  // get a decent recall by increasing the internal topk list
+  search_params.itopk_size = 512;
+
+  // Another copy of search parameters to enable persistent kernel
+  cagra::search_params search_params_persistent = search_params;
+  search_params_persistent.persistent = true;
+  // Persistent kernel only support single-cta search algorithm for now.
+  search_params_persistent.algo = cagra::search_algo::SINGLE_CTA;
+  // Slightly reduce the kernel grid size to make this example program work
+  // smooth on workstations, which use the same GPU for other tasks (e.g.
+  // rendering GUI).
+  search_params_persistent.persistent_device_usage = 0.95;
+
+  /*
+  Define the big-batch setting as a baseline for measuring the throughput.
+
+  Note, this lambda can be used by the standard and the persistent
+  implementation interchangeably: the index stays the same, only search
+  parameters need some adjustment.
+  */
+  auto search_batch =
+      [&res, &index](bool needs_sync, const cagra::search_params &ps,
+                     raft::device_matrix_view<const float, int64_t> queries,
+                     raft::device_matrix_view<uint32_t, int64_t> neighbors,
+                     raft::device_matrix_view<float, int64_t> distances) {
+        cagra::search(res, ps, index, queries, neighbors, distances);
+        /*
+        To make a fair comparison, standard implementation needs to synchronize
+        with the device to make sure the kernel has finished the work.
+        Persistent kernel does not make any use of CUDA streams and blocks till
+        the results are available. Hence, synchronizing with the stream is a
+        waste of time in this case.
+         */
+        if (needs_sync) {
+          raft::resource::sync_stream(res);
+        }
+      };
+
+  /*
+  Define the asynchronous small-batch search setting.
+  The same lambda is used for both the standard and the persistent
+  implementations.
+
+  There are a few things to remember about this example though:
+    1. The standard kernel is launched in the given stream (behind the `res`);
+       The persistent kernel is launched implicitly; the public api call does
+       not touch the stream and blocks till the results are returned. (Hence the
+       optional sync at the end of the lambda.)
+    2. When launched asynchronously, the standard kernel should actually have a
+       separate raft::resource per-thread to achieve best performance. However,
+       this requires extra management of the resource/stream pools, we don't
+       include that for simplicity.
+       The persistent implementation does not require any special care; you can
+       safely pass a single raft::resources to all threads.
+    3. This example relies on the compiler implementation to launch the async
+       jobs in separate threads. This is not guaranteed, however.
+       In the real world, we'd advise to use a custom thread pool for managing
+       the requests.
+    4. Although the API defines the arguments as device-side mdspans, we advise
+       to use the host-side buffers accessible from the device, such as
+       allocated by cudaHostAlloc/cudaHostRegister (or any host memory if
+       HMM/ATS is enabled).
+       This way, you can save some GPU resources by not manually copying the
+       data in cuda streams.
+  */
+  auto search_async =
+      [&res, &index](bool needs_sync, const cagra::search_params &ps,
+                     raft::device_matrix_view<const float, int64_t> queries,
+                     raft::device_matrix_view<uint32_t, int64_t> neighbors,
+                     raft::device_matrix_view<float, int64_t> distances) {
+        auto work_size = queries.extent(0);
+        using index_type = typeof(work_size);
+        // Limit the maximum number of concurrent jobs
+        constexpr index_type kMaxJobs = 1000;
+        std::array<std::future<void>, kMaxJobs> futures;
+        for (index_type i = 0; i < work_size + kMaxJobs; i++) {
+          // wait for previous job in the same slot to finish
+          if (i >= kMaxJobs) {
+            futures[i % kMaxJobs].wait();
+          }
+          // submit a new job
+          if (i < work_size) {
+            futures[i % kMaxJobs] = std::async(std::launch::async, [&]() {
+              cagra::search(res, ps, index, slice_matrix(queries, i, 1),
+                            slice_matrix(neighbors, i, 1),
+                            slice_matrix(distances, i, 1));
+            });
+          }
+        }
+        /* See the remark for search_batch */
+        if (needs_sync) {
+          raft::resource::sync_stream(res);
+        }
+      };
+
+  // Launch the baseline search: check the big-batch performance
+  time_it("standard/batch A", search_batch, true, search_params, queries_a,
+          neighbors_a, distances_a);
+  time_it("standard/batch B", search_batch, true, search_params, queries_b,
+          neighbors_b, distances_b);
+
+  // Try to handle the same amount of work in the async setting using the
+  // standard implementation.
+  // (Warning: suboptimal - it uses a single stream for all async jobs)
+  time_it("standard/async A", search_async, true, search_params, queries_a,
+          neighbors_a, distances_a);
+  time_it("standard/async B", search_async, true, search_params, queries_b,
+          neighbors_b, distances_b);
+
+  // Do the same using persistent kernel.
+  time_it("persistent/async A", search_async, false, search_params_persistent,
+          queries_a, neighbors_a, distances_a);
+  time_it("persistent/async B", search_async, false, search_params_persistent,
+          queries_b, neighbors_b, distances_b);
+  /*
+Here's an example output, which shows the wall time of processing the same
+amount of data in a single batch vs in async mode (1 query per job):
+```
+CAGRA index has 1000000 vectors
+CAGRA graph has degree 64, graph size [1000000, 64]
+[standard/batch A] execution time: 854.645 ms
+[standard/batch B] execution time: 698.58 ms
+[standard/async A] execution time: 19190.6 ms
+[standard/async B] execution time: 18292 ms
+[I] [15:56:49.756754] Initialized the kernel 0x7ea4e55a5350 in stream
+              139227270582864; job_queue size = 8192; worker_queue size = 155
+[persistent/async A] execution time: 1285.65 ms
+[persistent/async B] execution time: 1316.97 ms
+[I] [15:56:55.756952] Destroyed the persistent runner.
+```
+Note, while the persistent kernel provides minimal latency for each search
+request, the wall time to process all the queries in async mode (1 query per
+job) is up to 2x slower than the standard kernel with the huge batch
+size (100K queries). One reason for this is the non-optimal CTA size: CAGRA
+kernels are automatically tuned for latency and so use large CTA sizes when the
+batch size is small. Try explicitly setting the search parameter
+`thread_block_size` to a small value, such as `64` or `128` if this is an issue
+for you. This increases the latency of individual jobs though.
+  */
+}
+
+int main() {
+  raft::device_resources res;
+
+  // Set pool memory resource with 1 GiB initial pool size. All allocations use
+  // the same pool.
+  rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
+      rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+  rmm::mr::set_current_device_resource(&pool_mr);
+
+  // Create input arrays.
+  int64_t n_samples = 1000000;
+  int64_t n_dim = 128;
+  int64_t n_queries = 100000;
+  auto dataset =
+      raft::make_device_matrix<float, int64_t>(res, n_samples, n_dim);
+  auto queries =
+      raft::make_device_matrix<float, int64_t>(res, n_queries, n_dim);
+  generate_dataset(res, dataset.view(), queries.view());
+
+  // run the interesting part of the program
+  cagra_build_search_variants(res, raft::make_const_mdspan(dataset.view()),
+                              raft::make_const_mdspan(queries.view()));
+}

From d9eec694b31ac78b0c13e2a02dee4e6b7d01c954 Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Fri, 27 Sep 2024 11:19:27 -0500
Subject: [PATCH 159/623] Add cuvs_bench.run python code and build (#279)

Builds on top of #275

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)
  - rhdong (https://github.com/rhdong)
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/279
---
 .gitignore                                    |   3 +
 build.sh                                      |   9 +-
 .../bench_ann_cuda-118_arch-aarch64.yaml      |   3 +
 .../bench_ann_cuda-118_arch-x86_64.yaml       |   3 +
 .../bench_ann_cuda-125_arch-aarch64.yaml      |   3 +
 .../bench_ann_cuda-125_arch-x86_64.yaml       |   3 +
 conda/recipes/libcuvs/build_libcuvs_tests.sh  |   2 +-
 cpp/CMakeLists.txt                            |   8 +-
 cpp/bench/ann/CMakeLists.txt                  |  52 +-
 dependencies.yaml                             |  15 +-
 python/cuvs/CMakeLists.txt                    |  22 +-
 .../cuvs/neighbors/filters/CMakeLists.txt     |   3 +-
 .../cuvs_bench/cuvs_bench/config/__init__.py  |  17 +
 .../cuvs_bench/config/algorithms.yaml         |  42 ++
 .../cuvs_bench/config/algos/__init__.py       |   0
 .../constraints/__init__.py}                  |   2 +-
 .../cuvs_bench/config/algos/cuvs_cagra.yaml   |   6 +-
 .../config/algos/cuvs_cagra_hnswlib.yaml      |   2 +-
 .../cuvs_bench/config/algos/cuvs_ivf_pq.yaml  |   4 +-
 .../config/algos/faiss_gpu_ivf_pq.yaml        |   4 +-
 .../cuvs_bench/config/algos/hnswlib.yaml      |   2 +-
 .../config/{ => datasets}/bigann-100M.yaml    |   0
 .../config/{ => datasets}/datasets.yaml       |   0
 .../config/{ => datasets}/deep-100M.yaml      |   0
 .../config/{ => datasets}/deep-1B.yaml        |   0
 .../{ => datasets}/deep-image-96-inner.yaml   |   0
 .../fashion-mnist-784-euclidean.yaml          |   0
 .../{ => datasets}/gist-960-euclidean.yaml    |   0
 .../{ => datasets}/glove-100-angular.yaml     |   0
 .../{ => datasets}/glove-100-inner.yaml       |   0
 .../{ => datasets}/glove-50-angular.yaml      |   0
 .../config/{ => datasets}/glove-50-inner.yaml |   0
 .../{ => datasets}/lastfm-65-angular.yaml     |   0
 .../{ => datasets}/mnist-784-euclidean.yaml   |   0
 .../{ => datasets}/nytimes-256-angular.yaml   |   0
 .../{ => datasets}/nytimes-256-inner.yaml     |   0
 .../{ => datasets}/sift-128-euclidean.yaml    |   0
 .../config/{ => datasets}/wiki_all_10M.yaml   |   0
 .../config/{ => datasets}/wiki_all_1M.yaml    |   0
 .../config/{ => datasets}/wiki_all_88M.yaml   |   0
 python/cuvs_bench/cuvs_bench/run/__init__.py  |  17 +
 python/cuvs_bench/cuvs_bench/run/__main__.py  | 216 ++++++
 python/cuvs_bench/cuvs_bench/run/run.py       | 685 ++++++++++++++++++
 python/cuvs_bench/cuvs_bench/run/runners.py   | 273 +++++++
 .../cuvs_bench/cuvs_bench/tests/test_run.py   | 227 ++++++
 python/cuvs_bench/pyproject.toml              |  14 +-
 46 files changed, 1562 insertions(+), 75 deletions(-)
 create mode 100644 python/cuvs_bench/cuvs_bench/config/__init__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algorithms.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/__init__.py
 rename python/cuvs_bench/cuvs_bench/config/{constraints.py => algos/constraints/__init__.py} (98%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/bigann-100M.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/datasets.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/deep-100M.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/deep-1B.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/deep-image-96-inner.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/fashion-mnist-784-euclidean.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/gist-960-euclidean.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/glove-100-angular.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/glove-100-inner.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/glove-50-angular.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/glove-50-inner.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/lastfm-65-angular.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/mnist-784-euclidean.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/nytimes-256-angular.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/nytimes-256-inner.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/sift-128-euclidean.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/wiki_all_10M.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/wiki_all_1M.yaml (100%)
 rename python/cuvs_bench/cuvs_bench/config/{ => datasets}/wiki_all_88M.yaml (100%)
 create mode 100644 python/cuvs_bench/cuvs_bench/run/__init__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/run/__main__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/run/run.py
 create mode 100644 python/cuvs_bench/cuvs_bench/run/runners.py
 create mode 100644 python/cuvs_bench/cuvs_bench/tests/test_run.py

diff --git a/.gitignore b/.gitignore
index fcbe0fa3a2..17258e3dee 100644
--- a/.gitignore
+++ b/.gitignore
@@ -79,3 +79,6 @@ cagra_index
 ivf_flat_index
 ivf_pq_index
 
+# cuvs_bench
+datasets/
+/*.json
\ No newline at end of file
diff --git a/build.sh b/build.sh
index a283bcd070..b463f0f0d9 100755
--- a/build.sh
+++ b/build.sh
@@ -275,7 +275,7 @@ if hasArg tests || (( ${NUMARGS} == 0 )); then
 fi
 
 if hasArg bench-ann || (( ${NUMARGS} == 0 )); then
-    BUILD_ANN_BENCH=ON
+    BUILD_CUVS_BENCH=ON
     CMAKE_TARGET="${CMAKE_TARGET};${ANN_BENCH_TARGETS}"
 fi
 
@@ -351,7 +351,7 @@ if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || has
           -DDISABLE_DEPRECATION_WARNINGS=${DISABLE_DEPRECATION_WARNINGS} \
           -DBUILD_TESTS=${BUILD_TESTS} \
           -DBUILD_C_TESTS=${BUILD_TESTS} \
-          -DBUILD_ANN_BENCH=${BUILD_ANN_BENCH} \
+          -DBUILD_CUVS_BENCH=${BUILD_CUVS_BENCH} \
           -DBUILD_CPU_ONLY=${BUILD_CPU_ONLY} \
           -DCMAKE_MESSAGE_LOG_LEVEL=${CMAKE_LOG_LEVEL} \
           ${CACHE_ARGS} \
@@ -419,6 +419,11 @@ if (( ${NUMARGS} == 0 )) || hasArg python; then
         python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs
 fi
 
+# Build and (optionally) install the cuvs_bench Python package
+if (( ${NUMARGS} == 0 )) || hasArg bench-ann; then
+    python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs_bench
+fi
+
 # Build the cuvs Rust bindings
 if (( ${NUMARGS} == 0 )) || hasArg rust; then
     cd ${REPODIR}/rust
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 7e1014f250..73c42ca71c 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -42,5 +42,8 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- setuptools
 - sysroot_linux-aarch64==2.17
+- wheel
 name: bench_ann_cuda-118_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 120b7afca0..473e50bc6c 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -42,5 +42,8 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- setuptools
 - sysroot_linux-64==2.17
+- wheel
 name: bench_ann_cuda-118_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index ac0ea97e6a..8a877c4c0e 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -38,5 +38,8 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- setuptools
 - sysroot_linux-aarch64==2.17
+- wheel
 name: bench_ann_cuda-125_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index e593c240d1..54859a77f1 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -38,5 +38,8 @@ dependencies:
 - pandas
 - pylibraft==24.10.*,>=0.0.0a0
 - pyyaml
+- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- setuptools
 - sysroot_linux-64==2.17
+- wheel
 name: bench_ann_cuda-125_arch-x86_64
diff --git a/conda/recipes/libcuvs/build_libcuvs_tests.sh b/conda/recipes/libcuvs/build_libcuvs_tests.sh
index 5d77ae2d1b..b077dbe609 100644
--- a/conda/recipes/libcuvs/build_libcuvs_tests.sh
+++ b/conda/recipes/libcuvs/build_libcuvs_tests.sh
@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
 # Copyright (c) 2022-2024, NVIDIA CORPORATION.
 
-./build.sh tests bench-ann --allgpuarch --no-nvtx --build-metrics=tests_bench --incl-cache-stats
+./build.sh tests --allgpuarch --no-nvtx --build-metrics=tests --incl-cache-stats
 cmake --install cpp/build --component testing
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d8d554648b..b72d7f1658 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -55,7 +55,7 @@ option(BUILD_SHARED_LIBS "Build cuvs shared libraries" ON)
 option(BUILD_TESTS "Build cuvs unit-tests" ON)
 option(BUILD_C_LIBRARY "Build cuVS C API library" OFF)
 option(BUILD_C_TESTS "Build cuVS C API tests" OFF)
-option(BUILD_ANN_BENCH "Build cuVS ann benchmarks" OFF)
+option(BUILD_CUVS_BENCH "Build cuVS ann benchmarks" OFF)
 option(BUILD_CAGRA_HNSWLIB "Build CAGRA+hnswlib interface" ON)
 option(CUDA_ENABLE_KERNELINFO "Enable kernel resource usage info" OFF)
 option(CUDA_ENABLE_LINEINFO
@@ -96,7 +96,7 @@ include(CMakeDependentOption)
 
 message(VERBOSE "cuVS: Build cuVS unit-tests: ${BUILD_TESTS}")
 message(VERBOSE "cuVS: Build CPU only components: ${BUILD_CPU_ONLY}")
-message(VERBOSE "cuVS: Build ANN benchmarks: ${BUILD_ANN_BENCH}")
+message(VERBOSE "cuVS: Build ANN benchmarks: ${BUILD_CUVS_BENCH}")
 message(VERBOSE "cuVS: Enable detection of conda environment for dependencies: ${DETECT_CONDA_ENV}")
 message(VERBOSE "cuVS: Disable depreaction warnings " ${DISABLE_DEPRECATION_WARNINGS})
 message(VERBOSE "cuVS: Disable OpenMP: ${DISABLE_OPENMP}")
@@ -188,7 +188,7 @@ endif()
 
 include(cmake/thirdparty/get_cutlass.cmake)
 
-if(BUILD_ANN_BENCH)
+if(BUILD_CUVS_BENCH)
   include(${rapids-cmake-dir}/cpm/gbench.cmake)
   rapids_cpm_gbench(BUILD_STATIC)
 endif()
@@ -651,6 +651,6 @@ endif()
 # ##################################################################################################
 # * build ann benchmark executable -----------------------------------------------
 
-if(BUILD_ANN_BENCH)
+if(BUILD_CUVS_BENCH)
   add_subdirectory(bench/ann/)
 endif()
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 6fe23483e0..3224587e44 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -199,30 +199,19 @@ if(NOT TARGET CUVS_ANN_BENCH_ALL)
 endif()
 
 if(CUVS_ANN_BENCH_USE_HNSWLIB)
-  ConfigureAnnBench(
-    NAME HNSWLIB PATH src/hnswlib/hnswlib_benchmark.cpp LINKS hnswlib::hnswlib
-  )
+  ConfigureAnnBench(NAME HNSWLIB PATH src/hnswlib/hnswlib_benchmark.cpp LINKS hnswlib::hnswlib)
 
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ)
   ConfigureAnnBench(
-    NAME CUVS_IVF_PQ
-    PATH
-    src/cuvs/cuvs_benchmark.cu
-    src/cuvs/cuvs_ivf_pq.cu
-    LINKS cuvs
+    NAME CUVS_IVF_PQ PATH src/cuvs/cuvs_benchmark.cu src/cuvs/cuvs_ivf_pq.cu LINKS cuvs
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT)
   ConfigureAnnBench(
-    NAME CUVS_IVF_FLAT
-    PATH
-    src/cuvs/cuvs_benchmark.cu
-    src/cuvs/cuvs_ivf_flat.cu
-    LINKS
-    cuvs
+    NAME CUVS_IVF_FLAT PATH src/cuvs/cuvs_benchmark.cu src/cuvs/cuvs_ivf_flat.cu LINKS cuvs
   )
 endif()
 
@@ -232,12 +221,8 @@ endif()
 
 if(CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE)
   ConfigureAnnBench(
-    NAME
-    CUVS_KNN_BRUTE_FORCE
-    PATH
-    $<$<BOOL:${CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE}>:src/cuvs/cuvs_brute_force_knn.cu>
-    LINKS
-    cuvs
+    NAME CUVS_KNN_BRUTE_FORCE PATH
+    $<$<BOOL:${CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE}>:src/cuvs/cuvs_brute_force_knn.cu> LINKS cuvs
   )
 endif()
 
@@ -258,8 +243,7 @@ endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
   ConfigureAnnBench(
-    NAME CUVS_CAGRA_HNSWLIB PATH src/cuvs/cuvs_cagra_hnswlib.cu LINKS cuvs
-    hnswlib::hnswlib
+    NAME CUVS_CAGRA_HNSWLIB PATH src/cuvs/cuvs_cagra_hnswlib.cu LINKS cuvs hnswlib::hnswlib
   )
 endif()
 
@@ -267,36 +251,31 @@ message("CUVS_FAISS_TARGETS: ${CUVS_FAISS_TARGETS}")
 message("CUDAToolkit_LIBRARY_DIR: ${CUDAToolkit_LIBRARY_DIR}")
 if(CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT)
   ConfigureAnnBench(
-    NAME FAISS_CPU_FLAT PATH src/faiss/faiss_cpu_benchmark.cpp LINKS
-    ${CUVS_FAISS_TARGETS}
+    NAME FAISS_CPU_FLAT PATH src/faiss/faiss_cpu_benchmark.cpp LINKS ${CUVS_FAISS_TARGETS}
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT)
   ConfigureAnnBench(
-    NAME FAISS_CPU_IVF_FLAT PATH src/faiss/faiss_cpu_benchmark.cpp LINKS
-    ${CUVS_FAISS_TARGETS}
+    NAME FAISS_CPU_IVF_FLAT PATH src/faiss/faiss_cpu_benchmark.cpp LINKS ${CUVS_FAISS_TARGETS}
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ)
   ConfigureAnnBench(
-    NAME FAISS_CPU_IVF_PQ PATH src/faiss/faiss_cpu_benchmark.cpp LINKS
-    ${CUVS_FAISS_TARGETS}
+    NAME FAISS_CPU_IVF_PQ PATH src/faiss/faiss_cpu_benchmark.cpp LINKS ${CUVS_FAISS_TARGETS}
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
-    NAME FAISS_GPU_IVF_FLAT PATH src/faiss/faiss_gpu_benchmark.cu LINKS
-    ${CUVS_FAISS_TARGETS}
+    NAME FAISS_GPU_IVF_FLAT PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
-    NAME FAISS_GPU_IVF_PQ PATH src/faiss/faiss_gpu_benchmark.cu LINKS
-    ${CUVS_FAISS_TARGETS}
+    NAME FAISS_GPU_IVF_PQ PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
   )
 endif()
 
@@ -322,13 +301,8 @@ if(CUVS_ANN_BENCH_SINGLE_EXE)
 
   target_link_libraries(
     ANN_BENCH
-    PRIVATE raft::raft
-            nlohmann_json::nlohmann_json
-            benchmark::benchmark
-            dl
-            fmt::fmt-header-only
-            spdlog::spdlog_header_only
-            $<$<BOOL:${NVTX3_HEADERS_FOUND}>:CUDA::nvtx3>
+    PRIVATE raft::raft nlohmann_json::nlohmann_json benchmark::benchmark dl fmt::fmt-header-only
+            spdlog::spdlog_header_only $<$<BOOL:${NVTX3_HEADERS_FOUND}>:CUDA::nvtx3>
   )
   set_target_properties(
     ANN_BENCH
diff --git a/dependencies.yaml b/dependencies.yaml
index c63cecbbec..c18f53305d 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -38,6 +38,7 @@ files:
       - develop
       - bench
       - bench_python
+      - rapids_build_setuptools
   test_cpp:
     output: none
     includes:
@@ -115,6 +116,13 @@ files:
       table: build-system
     includes:
       - rapids_build_setuptools
+  py_rapids_build_py_cuvs_bench:
+    output: pyproject
+    pyproject_dir: python/cuvs_bench
+    extras:
+      table: tool.rapids-build-backend
+      key: requires
+    includes: []
   py_run_cuvs_bench:
     output: pyproject
     pyproject_dir: python/cuvs_bench
@@ -187,7 +195,7 @@ dependencies:
 
   rapids_build_setuptools:
     common:
-      - output_types: [requirements, pyproject]
+      - output_types: [conda, requirements, pyproject]
         packages:
           - &rapids_build_backend rapids-build-backend>=0.3.0,<0.4.0.dev0
           - setuptools
@@ -469,13 +477,12 @@ dependencies:
           - openblas
   bench_python:
     common:
-      - output_types: [conda]
+      - output_types: [conda, pyproject, requirements]
         packages:
+          - click
           - matplotlib
           - pandas
           - pyyaml
-          - pandas
-          - click
   depends_on_librmm:
     common:
       - output_types: conda
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 7d2f8dcf90..feb3bd58c4 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -83,14 +83,22 @@ if(NOT cuvs_FOUND)
 
   if(NOT CUDA_STATIC_MATH_LIBRARIES AND USE_CUDA_MATH_WHEELS)
     set(rpaths
-      "$ORIGIN/../nvidia/cublas/lib"
-      "$ORIGIN/../nvidia/curand/lib"
-      "$ORIGIN/../nvidia/cusolver/lib"
-      "$ORIGIN/../nvidia/cusparse/lib"
-      "$ORIGIN/../nvidia/nvjitlink/lib"
+        "$ORIGIN/../nvidia/cublas/lib"
+        "$ORIGIN/../nvidia/curand/lib"
+        "$ORIGIN/../nvidia/cusolver/lib"
+        "$ORIGIN/../nvidia/cusparse/lib"
+        "$ORIGIN/../nvidia/nvjitlink/lib"
+    )
+    set_property(
+      TARGET cuvs
+      PROPERTY INSTALL_RPATH ${rpaths}
+      APPEND
+    )
+    set_property(
+      TARGET cuvs_c
+      PROPERTY INSTALL_RPATH ${rpaths}
+      APPEND
     )
-    set_property(TARGET cuvs PROPERTY INSTALL_RPATH ${rpaths} APPEND)
-    set_property(TARGET cuvs_c PROPERTY INSTALL_RPATH ${rpaths} APPEND)
   endif()
 
   set(cython_lib_dir cuvs)
diff --git a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
index 8f281d1c84..c90615feb4 100644
--- a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
@@ -20,6 +20,5 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
-                   neighbors_prefilter_
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_prefilter_
 )
diff --git a/python/cuvs_bench/cuvs_bench/config/__init__.py b/python/cuvs_bench/cuvs_bench/config/__init__.py
new file mode 100644
index 0000000000..7c04e3fd8c
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/__init__.py
@@ -0,0 +1,17 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .algos.constraints import *
diff --git a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
new file mode 100644
index 0000000000..dc1127fbc5
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
@@ -0,0 +1,42 @@
+faiss_gpu_flat:
+  executable: FAISS_GPU_FLAT_ANN_BENCH
+  requires_gpu: true
+faiss_gpu_ivf_flat:
+  executable: FAISS_GPU_IVF_FLAT_ANN_BENCH
+  requires_gpu: true
+faiss_gpu_ivf_pq:
+  executable: FAISS_GPU_IVF_PQ_ANN_BENCH
+  requires_gpu: true
+faiss_gpu_ivf_sq:
+  executable: FAISS_GPU_IVF_PQ_ANN_BENCH
+  requires_gpu: true
+faiss_cpu_flat:
+  executable: FAISS_CPU_FLAT_ANN_BENCH
+  requires_gpu: false
+faiss_cpu_ivf_flat:
+  executable: FAISS_CPU_IVF_FLAT_ANN_BENCH
+  requires_gpu: false
+faiss_cpu_ivf_pq:
+  executable: FAISS_CPU_IVF_PQ_ANN_BENCH
+  requires_gpu: false
+cuvs_ivf_flat:
+  executable: CUVS_IVF_FLAT_ANN_BENCH
+  requires_gpu: true
+cuvs_ivf_pq:
+  executable: CUVS_IVF_PQ_ANN_BENCH
+  requires_gpu: true
+cuvs_cagra:
+  executable: CUVS_CAGRA_ANN_BENCH
+  requires_gpu: true
+cuvs_brute_force:
+  executable: CUVS_BRUTE_FORCE_ANN_BENCH
+  requires_gpu: true
+ggnn:
+  executable: GGNN_ANN_BENCH
+  requires_gpu: true
+hnswlib:
+  executable: HNSWLIB_ANN_BENCH
+  requires_gpu: false
+cuvs_cagra_hnswlib:
+  executable: CUVS_CAGRA_HNSWLIB_ANN_BENCH
+  requires_gpu: true
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/__init__.py b/python/cuvs_bench/cuvs_bench/config/algos/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs_bench/cuvs_bench/config/constraints.py b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
similarity index 98%
rename from python/cuvs_bench/cuvs_bench/config/constraints.py
rename to python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
index ff451c0565..de05bd752b 100644
--- a/python/cuvs_bench/cuvs_bench/config/constraints.py
+++ b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
index e7b049d0c0..edacb25b5f 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
@@ -1,11 +1,11 @@
 name: cuvs_cagra
 constraints:
-  build: cuvs_bench.constraints.raft_cagra_build_constraints
-  search: cuvs_bench.constraints.raft_cagra_search_constraints
+  build: cuvs_bench.config.algos.constraints.cuvs_cagra_build
+  search: cuvs_bench.config.algos.constraints.cuvs_cagra_search
 groups:
   base:
     build:
-      graph_degree: [32, 64, 128, 256]
+      graph_degree: [32, 64, 96, 128]
       intermediate_graph_degree: [32, 64, 96, 128]
       graph_build_algo: ["NN_DESCENT"]
     search:
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
index 70e344dfd6..f1a7f272cb 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
@@ -1,6 +1,6 @@
 name: cuvs_cagra_hnswlib
 constraints:
-  search: cuvs_bench.constraints.hnswlib_search
+  search: cuvs_bench.config.algos.constraints.hnswlib_search
 groups:
   base:
     build:
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
index aa95d6716f..d68e7973ab 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
@@ -1,7 +1,7 @@
 name: cuvs_ivf_pq
 constraints:
-  build: cuvs_bench.constraints.cuvs_ivf_pq_build
-  search: cuvs_bench.constraints.cuvs_ivf_pq_search
+  build: cuvs_bench.config.algos.constraints.cuvs_ivf_pq_build
+  search: cuvs_bench.config.algos.constraints.cuvs_ivf_pq_search
 groups:
   base:
     build:
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
index 1bd78b736f..782f3aed12 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
@@ -1,7 +1,7 @@
 name: faiss_gpu_ivf_pq
 constraints:
-  build: cuvs_bench.constraints.faiss_gpu_ivf_pq_build
-  search: cuvs_bench.constraints.faiss_gpu_ivf_pq_search
+  build: cuvs_bench.config.algos.constraints.faiss_gpu_ivf_pq_build
+  search: cuvs_bench.config.algos.constraints.faiss_gpu_ivf_pq_search
 groups:
   base:
     build:
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
index dbd73155d5..93d8cff2d1 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
@@ -1,6 +1,6 @@
 name: hnswlib
 constraints:
-  search: cuvs_bench.constraints.hnswlib_search
+  search: cuvs_bench.config.algos.constraints.hnswlib_search
 groups:
   base:
     build:
diff --git a/python/cuvs_bench/cuvs_bench/config/bigann-100M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/bigann-100M.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/bigann-100M.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/bigann-100M.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/datasets.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/deep-100M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/deep-100M.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/deep-100M.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/deep-100M.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/deep-1B.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/deep-1B.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/deep-1B.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/deep-1B.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/deep-image-96-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/deep-image-96-inner.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/deep-image-96-inner.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/deep-image-96-inner.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/fashion-mnist-784-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/fashion-mnist-784-euclidean.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/fashion-mnist-784-euclidean.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/fashion-mnist-784-euclidean.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/gist-960-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/gist-960-euclidean.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/gist-960-euclidean.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/gist-960-euclidean.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-100-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-angular.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/glove-100-angular.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/glove-100-angular.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-100-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-inner.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/glove-100-inner.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/glove-100-inner.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-50-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-angular.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/glove-50-angular.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/glove-50-angular.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/glove-50-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-inner.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/glove-50-inner.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/glove-50-inner.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/lastfm-65-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/lastfm-65-angular.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/lastfm-65-angular.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/lastfm-65-angular.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/mnist-784-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/mnist-784-euclidean.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/mnist-784-euclidean.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/mnist-784-euclidean.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/nytimes-256-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-angular.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/nytimes-256-angular.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-angular.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/nytimes-256-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-inner.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/nytimes-256-inner.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-inner.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/sift-128-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/sift-128-euclidean.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/sift-128-euclidean.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/sift-128-euclidean.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/wiki_all_10M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_10M.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/wiki_all_10M.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_10M.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/wiki_all_1M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_1M.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/wiki_all_1M.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_1M.yaml
diff --git a/python/cuvs_bench/cuvs_bench/config/wiki_all_88M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_88M.yaml
similarity index 100%
rename from python/cuvs_bench/cuvs_bench/config/wiki_all_88M.yaml
rename to python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_88M.yaml
diff --git a/python/cuvs_bench/cuvs_bench/run/__init__.py b/python/cuvs_bench/cuvs_bench/run/__init__.py
new file mode 100644
index 0000000000..7cb04e6f8e
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/run/__init__.py
@@ -0,0 +1,17 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .run import run_benchmark
diff --git a/python/cuvs_bench/cuvs_bench/run/__main__.py b/python/cuvs_bench/cuvs_bench/run/__main__.py
new file mode 100644
index 0000000000..b5d99a4bfe
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/run/__main__.py
@@ -0,0 +1,216 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+from pathlib import Path
+from typing import Optional
+
+import click
+from run import run_benchmark
+
+
+@click.command()
+@click.option(
+    "--subset-size",
+    type=click.IntRange(min=1),
+    help="The number of subset rows of the dataset to build the index",
+)
+@click.option(
+    "-k",
+    "--count",
+    default=10,
+    show_default=True,
+    type=click.IntRange(min=1),
+    prompt="Enter the number of  neighbors to search for",
+    help="The number of nearest neighbors to search for",
+)
+@click.option(
+    "-bs",
+    "--batch-size",
+    default=10000,
+    show_default=True,
+    type=click.IntRange(min=1),
+    prompt="Enter the batch size",
+    help="Number of query vectors to use in each query trial",
+)
+@click.option(
+    "--dataset-configuration",
+    default=None,
+    show_default=True,
+    help="Path to YAML configuration file for datasets",
+)
+@click.option(
+    "--configuration",
+    help="Path to YAML configuration file or directory for algorithms. "
+    "Any run groups found in the specified file/directory will "
+    "automatically override groups of the same name present in the "
+    "default configurations, including `base`.",
+)
+@click.option(
+    "--dataset",
+    default="glove-100-inner",
+    show_default=True,
+    prompt="Enter the name of dataset",
+    help="Name of dataset",
+)
+@click.option(
+    "--dataset-path",
+    default=lambda: os.environ.get(
+        "RAPIDS_DATASET_ROOT_DIR",
+        os.path.join(Path(__file__).parent, "datasets/"),
+    ),
+    show_default=True,
+    prompt="Enter the path to dataset folder",
+    help="Path to dataset folder, by default will look in "
+    "RAPIDS_DATASET_ROOT_DIR if defined, otherwise a datasets "
+    "subdirectory from the calling directory.",
+)
+@click.option("--build", is_flag=True, help="Build the index")
+@click.option("--search", is_flag=True, help="Perform the search")
+@click.option(
+    "--algorithms",
+    default="cuvs_cagra",
+    show_default=True,
+    prompt="Enter the comma separated list of named algorithms to run",
+    help="Run only comma separated list of named algorithms. If parameters "
+    "`groups` and `algo-groups` are both undefined, then group `base` "
+    "is run by default.",
+)
+@click.option(
+    "--groups",
+    default="base",
+    show_default=True,
+    prompt="Enter the comma separated groups of parameters",
+    help="Run only comma separated groups of parameters",
+)
+@click.option(
+    "--algo-groups",
+    help="Add comma separated <algorithm>.<group> to run. Example usage: "
+    ' "--algo-groups=cuvs_cagra.large,hnswlib.large".',
+)
+@click.option(
+    "-f",
+    "--force",
+    is_flag=True,
+    help="Re-run algorithms even if their results already exist",
+)
+@click.option(
+    "-m",
+    "--search-mode",
+    default="latency",
+    show_default=True,
+    prompt='Enter the search mode ("latency" or "throughput")',
+    help="Run search in 'latency' (measure individual batches) or "
+    "'throughput' (pipeline batches and measure end-to-end) mode.",
+)
+@click.option(
+    "-t",
+    "--search-threads",
+    default=None,
+    show_default=True,
+    help="Specify the number threads to use for throughput benchmark. "
+    "Single value or a pair of min and max separated by ':'. "
+    "Example: --search-threads=1:4. Power of 2 values between 'min' "
+    "and 'max' will be used. If only 'min' is specified, then a single "
+    "test is run with 'min' threads. By default min=1, "
+    "max=<num hyper threads>.",
+)
+@click.option(
+    "-r",
+    "--dry-run",
+    is_flag=True,
+    help="Dry-run mode will convert the yaml config for the specified "
+    "algorithms and datasets to the json format that’s consumed "
+    "by the lower-level c++ binaries and then print the command to "
+    "run execute the benchmarks but will not actually execute "
+    "the command.",
+)
+@click.option(
+    "--raft-log-level",
+    default="info",
+    show_default=True,
+    prompt="Enter the log level",
+    help="Log level, possible values are [off, error, warn, info, debug, "
+    "trace]. Default: 'info'. Note that 'debug' or more detailed "
+    "logging level requires that the library is compiled with "
+    "-DRAFT_ACTIVE_LEVEL=<L> where <L> >= <requested log level>.",
+)
+def main(
+    subset_size: Optional[int],
+    count: int,
+    batch_size: int,
+    dataset_configuration: Optional[str],
+    configuration: Optional[str],
+    dataset: str,
+    dataset_path: str,
+    build: bool,
+    search: bool,
+    algorithms: Optional[str],
+    groups: str,
+    algo_groups: Optional[str],
+    force: bool,
+    search_mode: str,
+    search_threads: Optional[str],
+    dry_run: bool,
+    raft_log_level: str,
+) -> None:
+    """
+    Main function to run the benchmark with the provided options.
+
+    Parameters
+    ----------
+    subset_size : Optional[int]
+        The number of subset rows of the dataset to build the index.
+    count : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        Number of query vectors to use in each query trial.
+    dataset_configuration : Optional[str]
+        Path to YAML configuration file for datasets.
+    configuration : Optional[str]
+        Path to YAML configuration file or directory for algorithms.
+    dataset : str
+        Name of the dataset to use.
+    dataset_path : str
+        Path to the dataset folder.
+    build : bool
+        Whether to build the indices.
+    search : bool
+        Whether to perform the search.
+    algorithms : Optional[str]
+        Comma-separated list of algorithm names to use.
+    groups : str
+        Comma-separated list of groups to consider.
+    algo_groups : Optional[str]
+        Comma-separated list of algorithm groups to consider.
+    force : bool
+        Whether to force the execution regardless of warnings.
+    search_mode : str
+        The mode of search to perform ('latency' or 'throughput').
+    search_threads : Optional[str]
+        The number of threads to use for throughput benchmark.
+    dry_run : bool
+        Whether to perform a dry run without actual execution.
+    raft_log_level : str
+        The logging level for the RAFT library.
+
+    """
+
+    run_benchmark(**locals())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/cuvs_bench/cuvs_bench/run/run.py b/python/cuvs_bench/cuvs_bench/run/run.py
new file mode 100644
index 0000000000..dbedcc183e
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/run/run.py
@@ -0,0 +1,685 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import itertools
+import os
+import warnings
+from importlib import import_module
+from typing import Any, Dict, Optional, Tuple
+
+import yaml
+from runners import cuvs_bench_cpp
+
+
+def rmm_present() -> bool:
+    """
+    Check if RMM (RAPIDS Memory Manager) is present.
+
+    Returns
+    -------
+    bool
+        True if RMM is present, False otherwise.
+    """
+    try:
+        import rmm  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
+def load_yaml_file(file_path: str) -> dict:
+    """
+    Load a YAML file and return its contents as a dictionary.
+
+    Parameters
+    ----------
+    file_path : str
+        The path to the YAML file.
+
+    Returns
+    -------
+    dict
+        The contents of the YAML file.
+    """
+    with open(file_path, "r") as f:
+        return yaml.safe_load(f)
+
+
+def get_dataset_configuration(dataset: str, dataset_conf_all: list) -> dict:
+    """
+    Retrieve the configuration for a specific dataset.
+
+    Parameters
+    ----------
+    dataset : str
+        The name of the dataset to retrieve the configuration for.
+    dataset_conf_all : list
+        A list of dataset configurations.
+
+    Returns
+    -------
+    dict
+        The configuration for the specified dataset.
+
+    Raises
+    ------
+    ValueError
+        If the dataset configuration is not found.
+    """
+    for dset in dataset_conf_all:
+        if dataset == dset["name"]:
+            return dset
+    raise ValueError("Could not find a dataset configuration")
+
+
+def prepare_conf_file(
+    dataset_conf: dict, subset_size: Optional[int], count: int, batch_size: int
+) -> dict:
+    """
+    Prepare the main configuration file for the benchmark.
+
+    Parameters
+    ----------
+    dataset_conf : dict
+        The configuration for the dataset.
+    subset_size : Optional[int]
+        The subset size of the dataset.
+    count : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        The size of each batch for processing.
+
+    Returns
+    -------
+    dict
+        The prepared configuration file.
+    """
+    conf_file = {"dataset": dataset_conf}
+    if subset_size:
+        conf_file["dataset"]["subset_size"] = subset_size
+    conf_file["search_basic_param"] = {"k": count, "batch_size": batch_size}
+    return conf_file
+
+
+def gather_algorithm_configs(
+    scripts_path: str, configuration: Optional[str]
+) -> list:
+    """
+    Gather the list of algorithm configuration files.
+
+    Parameters
+    ----------
+    scripts_path : str
+        The path to the script directory.
+    configuration : Optional[str]
+        The path to the algorithm configuration directory or file.
+
+    Returns
+    -------
+    list
+        A list of paths to the algorithm configuration files.
+    """
+    algos_conf_fs = os.listdir(
+        os.path.join(scripts_path, "../config", "algos")
+    )
+    algos_conf_fs = [
+        os.path.join(scripts_path, "../config", "algos", f)
+        for f in algos_conf_fs
+        if ".json" not in f and "constraint" not in f and ".py" not in f
+    ]
+
+    if configuration:
+        if os.path.isdir(configuration):
+            algos_conf_fs += [
+                os.path.join(configuration, f)
+                for f in os.listdir(configuration)
+                if ".json" not in f
+            ]
+        elif os.path.isfile(configuration):
+            algos_conf_fs.append(configuration)
+    return algos_conf_fs
+
+
+def load_algorithms_conf(
+    algos_conf_fs: list,
+    allowed_algos: Optional[list],
+    allowed_algo_groups: Optional[tuple],
+) -> dict:
+    """
+    Load and filter the algorithm configurations.
+
+    Parameters
+    ----------
+    algos_conf_fs : list
+        A list of paths to algorithm configuration files.
+    allowed_algos : Optional[list]
+        A list of allowed algorithm names to filter by.
+    allowed_algo_groups : Optional[tuple]
+        A tuple of allowed algorithm groups to filter by.
+
+    Returns
+    -------
+    dict
+        A dictionary containing the loaded and filtered algorithm
+        configurations.
+    """
+    algos_conf = {}
+    for algo_f in algos_conf_fs:
+        try:
+            algo = load_yaml_file(algo_f)
+        except Exception as e:
+            warnings.warn(f"Could not load YAML config {algo_f} due to {e}")
+            continue
+        if allowed_algos and algo["name"] not in allowed_algos:
+            continue
+        algos_conf[algo["name"]] = {
+            "groups": algo.get("groups", {}),
+            "constraints": algo.get("constraints", {}),
+        }
+        if allowed_algo_groups and algo["name"] in allowed_algo_groups[0]:
+            algos_conf[algo["name"]]["groups"].update(
+                {
+                    group: algo["groups"][group]
+                    for group in allowed_algo_groups[1]
+                    if group in algo["groups"]
+                }
+            )
+    return algos_conf
+
+
+def prepare_executables(
+    algos_conf: dict,
+    algos_yaml: dict,
+    gpu_present: bool,
+    conf_file: dict,
+    dataset_path: str,
+    dataset: str,
+    count: int,
+    batch_size: int,
+) -> dict:
+    """
+    Prepare the list of executables to run based on the configurations.
+
+    Parameters
+    ----------
+    algos_conf : dict
+        The loaded algorithm configurations.
+    algos_yaml : dict
+        The global algorithms configuration.
+    gpu_present : bool
+        Whether a GPU is present.
+    conf_file : dict
+        The main configuration file.
+    dataset_path : str
+        The path to the dataset directory.
+    dataset : str
+        The name of the dataset.
+    count : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        The size of each batch for processing.
+
+    Returns
+    -------
+    dict
+        A dictionary of executables to run with their associated
+        configurations.
+    """
+    executables_to_run = {}
+    for algo, algo_conf in algos_conf.items():
+        validate_algorithm(algos_yaml, algo, gpu_present)
+        for group, group_conf in algo_conf["groups"].items():
+            executable = find_executable(
+                algos_yaml, algo, group, count, batch_size
+            )
+            if executable not in executables_to_run:
+                executables_to_run[executable] = {"index": []}
+            indexes = prepare_indexes(
+                group_conf,
+                algo,
+                group,
+                conf_file,
+                algos_conf,
+                dataset_path,
+                dataset,
+                count,
+                batch_size,
+            )
+            executables_to_run[executable]["index"].extend(indexes)
+    return executables_to_run
+
+
+def validate_algorithm(algos_conf: dict, algo: str, gpu_present: bool) -> bool:
+    """
+    Validate the algorithm based on the available hardware (GPU presence).
+
+    Parameters
+    ----------
+    algos_conf : dict
+        The configuration dictionary for the algorithms.
+    algo : str
+        The name of the algorithm.
+    gpu_present : bool
+        Whether a GPU is present.
+
+    Returns
+    -------
+    bool
+        True if the algorithm is valid for the current hardware
+        configuration, False otherwise.
+    """
+    algos_conf_keys = set(algos_conf.keys())
+    if gpu_present:
+        return algo in algos_conf_keys
+    return (
+        algo in algos_conf_keys and algos_conf[algo]["requires_gpu"] is False
+    )
+
+
+def find_executable(
+    algos_conf: dict, algo: str, group: str, k: int, batch_size: int
+) -> Tuple[str, str, Tuple[str, str]]:
+    """
+    Find the executable for the given algorithm and group.
+
+    Parameters
+    ----------
+    algos_conf : dict
+        The configuration dictionary for the algorithms.
+    algo : str
+        The name of the algorithm.
+    group : str
+        The name of the group.
+    k : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        The size of each batch for processing.
+
+    Returns
+    -------
+    Tuple[str, str, Tuple[str, str]]
+        A tuple containing the executable name, the path to the executable,
+        and the file name.
+    """
+    executable = algos_conf[algo]["executable"]
+    file_name = (f"{algo},{group}", f"{algo},{group},k{k},bs{batch_size}")
+    build_path = get_build_path(executable)
+    if build_path:
+        return executable, build_path, file_name
+    raise FileNotFoundError(executable)
+
+
+def get_build_path(executable: str) -> Optional[str]:
+    """
+    Get the build path for the given executable.
+
+    Parameters
+    ----------
+    executable : str
+        The name of the executable.
+
+    Returns
+    -------
+    Optional[str]
+        The build path for the executable, if found.
+    """
+
+    devcontainer_path = "/home/coder/cuvs/cpp/build/latest/bench/ann"
+    if os.path.exists(devcontainer_path):
+        print(f"-- Detected devcontainer artifacts in {devcontainer_path}.")
+        return devcontainer_path
+
+    build_path = os.getenv("CUVS_HOME")
+    if build_path:
+        build_path = os.path.join(
+            build_path, "cpp", "build", "release", executable
+        )
+        if os.path.exists(build_path):
+            print(f"-- Using RAFT bench from repository in {build_path}.")
+            return build_path
+
+    conda_path = os.getenv("CONDA_PREFIX")
+    if conda_path:
+        conda_executable = os.path.join(conda_path, "bin", "ann", executable)
+        if os.path.exists(conda_executable):
+            print("-- Using cuVS bench found in conda environment.")
+            return conda_executable
+
+    return None
+
+
+def prepare_indexes(
+    group_conf: dict,
+    algo: str,
+    group: str,
+    conf_file: dict,
+    algos_conf: dict,
+    dataset_path: str,
+    dataset: str,
+    count: int,
+    batch_size: int,
+) -> list:
+    """
+    Prepare the index configurations for the given algorithm and group.
+
+    Parameters
+    ----------
+    group_conf : dict
+        The configuration for the algorithm group.
+    algo : str
+        The name of the algorithm.
+    group : str
+        The name of the group.
+    conf_file : dict
+        The main configuration file.
+    dataset_path : str
+        The path to the dataset directory.
+    dataset : str
+        The name of the dataset.
+    count : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        The size of each batch for processing.
+
+    Returns
+    -------
+    list
+        A list of index configurations.
+    """
+    indexes = []
+    build_params = group_conf.get("build", {})
+    search_params = group_conf.get("search", {})
+    all_build_params = itertools.product(*build_params.values())
+    search_param_names, search_param_lists = (
+        zip(*search_params.items()) if search_params else ([], [])
+    )
+    param_names = list(build_params.keys())
+    for params in all_build_params:
+        index = {
+            "algo": algo,
+            "build_param": dict(zip(build_params.keys(), params)),
+        }
+        index_name = f"{algo}_{group}" if group != "base" else f"{algo}"
+        for i in range(len(params)):
+            index["build_param"][param_names[i]] = params[i]
+            index_name += "." + f"{param_names[i]}{params[i]}"
+
+        if not validate_constraints(
+            algos_conf,
+            algo,
+            "build",
+            index["build_param"],
+            None,
+            conf_file["dataset"].get("dims"),
+            count,
+            batch_size,
+        ):
+            continue
+
+        index_filename = (
+            index_name if len(index_name) < 128 else str(hash(index_name))
+        )
+        index["name"] = index_name
+        index["file"] = os.path.join(
+            dataset_path, dataset, "index", index_filename
+        )
+        index["search_params"] = validate_search_params(
+            itertools.product(*search_param_lists),
+            search_param_names,
+            index["build_param"],
+            algo,
+            group_conf,
+            algos_conf,
+            conf_file,
+            count,
+            batch_size,
+        )
+        if index["search_params"]:
+            indexes.append(index)
+    return indexes
+
+
+def validate_search_params(
+    all_search_params,
+    search_param_names,
+    build_params,
+    algo,
+    group_conf,
+    algos_conf,
+    conf_file,
+    count,
+    batch_size,
+) -> list:
+    """
+    Validate and prepare the search parameters for the given algorithm
+    and group.
+
+    Parameters
+    ----------
+    all_search_params : itertools.product
+        The Cartesian product of search parameter values.
+    search_param_names : list
+        The names of the search parameters.
+    algo : str
+        The name of the algorithm.
+    group_conf : dict
+        The configuration for the algorithm group.
+    conf_file : dict
+        The main configuration file.
+    count : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        The size of each batch for processing.
+
+    Returns
+    -------
+    list
+        A list of validated search parameters.
+    """
+    search_params_list = []
+    for search_params in all_search_params:
+        search_dict = dict(zip(search_param_names, search_params))
+        if validate_constraints(
+            algos_conf,
+            algo,
+            "search",
+            search_dict,
+            build_params,
+            conf_file["dataset"].get("dims"),
+            count,
+            batch_size,
+        ):
+            search_params_list.append(search_dict)
+    return search_params_list
+
+
+def validate_constraints(
+    algos_conf: Dict[str, Any],
+    algo: str,
+    constraint_type: str,
+    param: Dict[str, Any],
+    build_param: dict,
+    dims: Any,
+    k: Optional[int],
+    batch_size: Optional[int],
+) -> bool:
+    """
+    Validate the constraints for the given algorithm and constraint type.
+
+    Parameters
+    ----------
+    algos_conf : Dict[str, Any]
+        The configuration dictionary for the algorithms.
+    algo : str
+        The name of the algorithm.
+    constraint_type : str
+        The type of constraint to validate ('build' or 'search').
+    param : Dict[str, Any]
+        The parameters to validate against the constraints.
+    dims : Any
+        The dimensions required for the constraints.
+    k : Optional[int]
+        The number of nearest neighbors to search for.
+    batch_size : Optional[int]
+        The size of each batch for processing.
+
+    Returns
+    -------
+    bool
+        True if the constraints are valid, False otherwise.
+
+    Raises
+    ------
+    ValueError
+        If `dims` are needed for build constraints but not specified in the
+        dataset configuration.
+    """
+    if constraint_type in algos_conf[algo]["constraints"]:
+        importable = algos_conf[algo]["constraints"][constraint_type]
+        module, func = (
+            ".".join(importable.split(".")[:-1]),
+            importable.split(".")[-1],
+        )
+        validator = import_module(module)
+        constraints_func = getattr(validator, func)
+        if constraint_type == "build":
+            return constraints_func(param, dims)
+        else:
+            return constraints_func(param, build_param, k, batch_size)
+    return True
+
+
+def run_benchmark(
+    subset_size: int,
+    count: int,
+    batch_size: int,
+    dataset_configuration: Optional[str],
+    configuration: Optional[str],
+    dataset: str,
+    dataset_path: str,
+    build: Optional[bool],
+    search: Optional[bool],
+    algorithms: Optional[str],
+    groups: str,
+    algo_groups: Optional[str],
+    force: bool,
+    search_mode: str,
+    search_threads: int,
+    dry_run: bool,
+    raft_log_level: int,
+) -> None:
+    """
+    Runs a benchmarking process based on the provided configurations.
+
+    Parameters
+    ----------
+    subset_size : int
+        The subset size of the dataset.
+    count : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        The size of each batch for processing.
+    dataset_configuration : Optional[str]
+        Path to the dataset configuration file.
+    configuration : Optional[str]
+        Path to the algorithm configuration directory or file.
+    dataset : str
+        The name of the dataset to use.
+    dataset_path : str
+        The path to the dataset directory.
+    build : Optional[bool]
+        Whether to build the indices.
+    search : Optional[bool]
+        Whether to perform the search.
+    algorithms : Optional[str]
+        Comma-separated list of algorithm names to use.
+    groups : str
+        Comma-separated list of groups to consider.
+    algo_groups : Optional[str]
+        Comma-separated list of algorithm groups to consider.
+    force : bool
+        Whether to force the execution regardless of warnings.
+    search_mode : str
+        The mode of search to perform.
+    search_threads : int
+        The number of threads to use for searching.
+    dry_run : bool
+        Whether to perform a dry run without actual execution.
+    raft_log_level : int
+        The logging level for the RAFT library.
+
+    Returns
+    -------
+    None
+    """
+    scripts_path = os.path.dirname(os.path.realpath(__file__))
+    gpu_present = rmm_present()
+
+    if not build and not search:
+        build, search = True, True
+
+    dataset_conf_all = load_yaml_file(
+        dataset_configuration
+        or os.path.join(scripts_path, "../config/datasets", "datasets.yaml")
+    )
+    dataset_conf = get_dataset_configuration(dataset, dataset_conf_all)
+    conf_file = prepare_conf_file(dataset_conf, subset_size, count, batch_size)
+    algos_conf_fs = gather_algorithm_configs(scripts_path, configuration)
+
+    allowed_algos = algorithms.split(",") if algorithms else None
+    allowed_algo_groups = (
+        [algo_group.split(".") for algo_group in algo_groups.split(",")]
+        if algo_groups
+        else None
+    )
+    algos_conf = load_algorithms_conf(
+        algos_conf_fs,
+        allowed_algos,
+        list(zip(*allowed_algo_groups)) if allowed_algo_groups else None,
+    )
+
+    executables_to_run = prepare_executables(
+        algos_conf,
+        load_yaml_file(
+            os.path.join(scripts_path, "../config", "algorithms.yaml")
+        ),
+        gpu_present,
+        conf_file,
+        dataset_path,
+        dataset,
+        count,
+        batch_size,
+    )
+
+    cuvs_bench_cpp(
+        conf_file,
+        dataset,
+        os.path.dirname(configuration)
+        if configuration and os.path.isfile(configuration)
+        else os.path.join(scripts_path, "conf", "algos"),
+        executables_to_run,
+        dataset_path,
+        force,
+        build,
+        search,
+        dry_run,
+        count,
+        batch_size,
+        search_threads,
+        search_mode,
+        raft_log_level,
+    )
diff --git a/python/cuvs_bench/cuvs_bench/run/runners.py b/python/cuvs_bench/cuvs_bench/run/runners.py
new file mode 100644
index 0000000000..5a540d2e5e
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/run/runners.py
@@ -0,0 +1,273 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import json
+import os
+import subprocess
+import uuid
+from typing import Dict, List, Optional, Tuple
+
+
+def cuvs_bench_cpp(
+    conf_file: Dict,
+    conf_filename: str,
+    conf_filedir: str,
+    executables_to_run: Dict[
+        Tuple[str, str, Tuple[str, str]], Dict[str, List[Dict]]
+    ],
+    dataset_path: str,
+    force: bool,
+    build: bool,
+    search: bool,
+    dry_run: bool,
+    k: int,
+    batch_size: int,
+    search_threads: Optional[int],
+    mode: str = "throughput",
+    raft_log_level: str = "info",
+) -> None:
+    """
+    Run the CUVS benchmarking tool with the provided configuration.
+
+    Parameters
+    ----------
+    conf_file : Dict
+        The configuration file content.
+    conf_filename : str
+        The name of the configuration file.
+    conf_filedir : str
+        The directory of the configuration file.
+    executables_to_run : Dict[Tuple[str, str, Tuple[str, str]],
+                         Dict[str, List[Dict]]]
+        Dictionary of executables to run and their configurations.
+    dataset_path : str
+        The path to the dataset.
+    force : bool
+        Whether to force the execution regardless of existing results.
+    build : bool
+        Whether to build the indices.
+    search : bool
+        Whether to perform the search.
+    dry_run : bool
+        Whether to perform a dry run without actual execution.
+    k : int
+        The number of nearest neighbors to search for.
+    batch_size : int
+        The size of each batch for processing.
+    search_threads : Optional[int]
+        The number of threads to use for searching.
+    mode : str, optional
+        The mode of search to perform ('latency' or 'throughput'),
+        by default 'throughput'.
+    raft_log_level : str, optional
+        The logging level for the RAFT library, by default 'info'.
+
+    Returns
+    -------
+    None
+    """
+    for (
+        executable,
+        ann_executable_path,
+        output_filename,
+    ) in executables_to_run.keys():
+        # Need to write temporary configuration
+        temp_conf_filename = (
+            f"{conf_filename}_{output_filename[1]}_{uuid.uuid1()}.json"
+        )
+        with open(temp_conf_filename, "w") as f:
+            temp_conf = {
+                "dataset": conf_file["dataset"],
+                "search_basic_param": conf_file["search_basic_param"],
+                "index": executables_to_run[
+                    (executable, ann_executable_path, output_filename)
+                ]["index"],
+            }
+            json_str = json.dumps(temp_conf, indent=2)
+            f.write(json_str)
+
+        legacy_result_folder = os.path.join(
+            dataset_path, conf_file["dataset"]["name"], "result"
+        )
+        os.makedirs(legacy_result_folder, exist_ok=True)
+
+        if build:
+            build_folder = os.path.join(legacy_result_folder, "build")
+            os.makedirs(build_folder, exist_ok=True)
+            build_file = f"{output_filename[0]}.json"
+            temp_build_file = f"{build_file}.lock"
+            benchmark_out = os.path.join(build_folder, temp_build_file)
+            cmd = [
+                ann_executable_path,
+                "--build",
+                f"--data_prefix={dataset_path}",
+                "--benchmark_out_format=json",
+                "--benchmark_counters_tabular=true",
+                f"--benchmark_out={os.path.join(benchmark_out)}",
+                f"--raft_log_level={parse_log_level(raft_log_level)}",
+            ]
+            if force:
+                cmd.append("--force")
+            cmd.append(temp_conf_filename)
+
+            if dry_run:
+                print(
+                    f"Benchmark command for {output_filename[0]}:\n"
+                    f"{' '.join(cmd)}\n"
+                )
+            else:
+                try:
+                    subprocess.run(cmd, check=True)
+                    merge_build_files(
+                        build_folder, build_file, temp_build_file
+                    )
+                except Exception as e:
+                    print(f"Error occurred running benchmark: {e}")
+                finally:
+                    os.remove(os.path.join(build_folder, temp_build_file))
+                    if not search:
+                        os.remove(temp_conf_filename)
+
+        if search:
+            search_folder = os.path.join(legacy_result_folder, "search")
+            os.makedirs(search_folder, exist_ok=True)
+            search_file = f"{output_filename[1]}.json"
+            cmd = [
+                ann_executable_path,
+                "--search",
+                f"--data_prefix={dataset_path}",
+                "--benchmark_counters_tabular=true",
+                f"--override_kv=k:{k}",
+                f"--override_kv=n_queries:{batch_size}",
+                "--benchmark_min_warmup_time=1",
+                "--benchmark_out_format=json",
+                f"--mode={mode}",
+                f"--benchmark_out={os.path.join(search_folder, search_file)}",
+                f"--raft_log_level={parse_log_level(raft_log_level)}",
+            ]
+            if force:
+                cmd.append("--force")
+            if search_threads:
+                cmd.append(f"--threads={search_threads}")
+            cmd.append(temp_conf_filename)
+
+            if dry_run:
+                print(
+                    f"Benchmark command for {output_filename[1]}:\n"
+                    f"{' '.join(cmd)}\n"
+                )
+            else:
+                try:
+                    subprocess.run(cmd, check=True)
+                except Exception as e:
+                    print(f"Error occurred running benchmark: {e}")
+                finally:
+                    os.remove(temp_conf_filename)
+
+
+log_levels = {
+    "off": 0,
+    "error": 1,
+    "warn": 2,
+    "info": 3,
+    "debug": 4,
+    "trace": 5,
+}
+
+
+def parse_log_level(level_str: str) -> int:
+    """
+    Parse the log level from string to integer.
+
+    Parameters
+    ----------
+    level_str : str
+        The log level as a string.
+
+    Returns
+    -------
+    int
+        The corresponding integer value of the log level.
+
+    Raises
+    ------
+    ValueError
+        If the log level string is invalid.
+    """
+    if level_str not in log_levels:
+        raise ValueError(f"Invalid log level: {level_str}")
+    return log_levels[level_str.lower()]
+
+
+def merge_build_files(
+    build_dir: str, build_file: str, temp_build_file: str
+) -> None:
+    """
+    Merge temporary build files into the main build file.
+
+    Parameters
+    ----------
+    build_dir : str
+        The directory of the build files.
+    build_file : str
+        The main build file.
+    temp_build_file : str
+        The temporary build file to merge.
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    ValueError
+        If the temporary build file is not found.
+    """
+    build_dict = {}
+
+    # If build file exists, read it
+    build_json_path = os.path.join(build_dir, build_file)
+    tmp_build_json_path = os.path.join(build_dir, temp_build_file)
+    if os.path.isfile(build_json_path):
+        try:
+            with open(build_json_path, "r") as f:
+                build_dict = json.load(f)
+        except Exception as e:
+            print(
+                f"Error loading existing build file: {build_json_path} ({e})"
+            )
+
+    temp_build_dict = {}
+    if os.path.isfile(tmp_build_json_path):
+        with open(tmp_build_json_path, "r") as f:
+            temp_build_dict = json.load(f)
+    else:
+        raise ValueError(f"Temp build file not found: {tmp_build_json_path}")
+
+    tmp_benchmarks = temp_build_dict.get("benchmarks", {})
+    benchmarks = build_dict.get("benchmarks", {})
+
+    # If the build time is absolute 0 then an error occurred
+    final_bench_dict = {b["name"]: b for b in benchmarks if b["real_time"] > 0}
+
+    for tmp_bench in tmp_benchmarks:
+        if tmp_bench["real_time"] > 0:
+            final_bench_dict[tmp_bench["name"]] = tmp_bench
+
+    temp_build_dict["benchmarks"] = list(final_bench_dict.values())
+    with open(build_json_path, "w") as f:
+        json_str = json.dumps(temp_build_dict, indent=2)
+        f.write(json_str)
diff --git a/python/cuvs_bench/cuvs_bench/tests/test_run.py b/python/cuvs_bench/cuvs_bench/tests/test_run.py
new file mode 100644
index 0000000000..7b7a481a07
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/tests/test_run.py
@@ -0,0 +1,227 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import itertools
+from unittest.mock import MagicMock, mock_open, patch
+
+import pytest
+from benchmark import (
+    find_executable,
+    gather_algorithm_configs,
+    get_dataset_configuration,
+    load_algorithms_conf,
+    load_yaml_file,
+    prepare_conf_file,
+    prepare_executables,
+    prepare_indexes,
+    rmm_present,
+    validate_algorithm,
+    validate_constraints,
+    validate_search_params,
+)
+
+
+def test_load_yaml_file():
+    yaml_content = """
+    key: value
+    """
+    with patch("builtins.open", mock_open(read_data=yaml_content)):
+        result = load_yaml_file("dummy_path.yaml")
+        assert result == {"key": "value"}
+
+
+def test_get_dataset_configuration():
+    dataset_conf_all = [{"name": "dataset1"}, {"name": "dataset2"}]
+    result = get_dataset_configuration("dataset1", dataset_conf_all)
+    assert result == {"name": "dataset1"}
+    with pytest.raises(ValueError):
+        get_dataset_configuration("non_existent_dataset", dataset_conf_all)
+
+
+def test_prepare_conf_file():
+    dataset_conf = {"name": "dataset1"}
+    result = prepare_conf_file(dataset_conf, 1000, 10, 128)
+    expected_result = {
+        "dataset": {"name": "dataset1", "subset_size": 1000},
+        "search_basic_param": {"k": 10, "batch_size": 128},
+    }
+    assert result == expected_result
+    result_no_subset = prepare_conf_file(dataset_conf, None, 10, 128)
+    assert result_no_subset["dataset"].get("subset_size") is None
+
+
+def test_gather_algorithm_configs(tmpdir):
+    scripts_path = tmpdir.mkdir("scripts")
+    algos_path = scripts_path.mkdir("algos")
+    algos_path.join("algo1.yaml").write("key: value")
+    algos_path.join("algo2.yaml").write("key: value")
+    result = gather_algorithm_configs(str(scripts_path), None)
+    assert len(result) == 2
+
+    custom_conf_dir = tmpdir.mkdir("custom_conf")
+    custom_conf_dir.join("custom_algo.yaml").write("key: value")
+    result = gather_algorithm_configs(str(scripts_path), str(custom_conf_dir))
+    assert len(result) == 3
+
+    custom_conf_file = custom_conf_dir.join("custom_algo_file.yaml")
+    custom_conf_file.write("key: value")
+    result = gather_algorithm_configs(str(scripts_path), str(custom_conf_file))
+    assert len(result) == 4
+
+
+def test_load_algorithms_conf():
+    algos_conf_fs = ["path/to/algo1.yaml", "path/to/algo2.yaml"]
+    yaml_content = """
+    name: algo1
+    groups:
+      group1: {}
+    """
+    with patch("builtins.open", mock_open(read_data=yaml_content)):
+        result = load_algorithms_conf(algos_conf_fs, None, None)
+        assert "algo1" in result
+
+    with patch("builtins.open", mock_open(read_data=yaml_content)):
+        result = load_algorithms_conf(algos_conf_fs, ["algo1"], None)
+        assert "algo1" in result
+        result = load_algorithms_conf(algos_conf_fs, ["algo2"], None)
+        assert "algo1" not in result
+
+
+@patch(
+    "benchmark.find_executable",
+    return_value=("executable", "path", "filename"),
+)
+@patch("benchmark.validate_algorithm", return_value=True)
+@patch(
+    "benchmark.prepare_indexes", return_value=[{"index_key": "index_value"}]
+)
+def test_prepare_executables(
+    mock_prepare_indexes, mock_validate_algorithm, mock_find_executable
+):
+    algos_conf = {"algo1": {"groups": {"group1": {"build": {}, "search": {}}}}}
+    algos_yaml = {"algo1": {}}
+    gpu_present = True
+    conf_file = {}
+    dataset_path = "dataset_path"
+    dataset = "dataset"
+    count = 10
+    batch_size = 128
+    result = prepare_executables(
+        algos_conf,
+        algos_yaml,
+        gpu_present,
+        conf_file,
+        dataset_path,
+        dataset,
+        count,
+        batch_size,
+    )
+    assert "executable" in result
+    assert len(result["executable"]["index"]) == 1
+
+
+def test_prepare_indexes():
+    group_conf = {"build": {"param1": [1, 2]}, "search": {"param2": [3, 4]}}
+    conf_file = {"dataset": {"dims": 128}}
+    result = prepare_indexes(
+        group_conf,
+        "algo",
+        "group",
+        conf_file,
+        "dataset_path",
+        "dataset",
+        10,
+        128,
+    )
+    assert len(result) == 2
+    assert "param1" in result[0]["build_param"]
+
+
+def test_validate_search_params():
+    all_search_params = itertools.product([1, 2], [3, 4])
+    search_param_names = ["param1", "param2"]
+    group_conf = {}
+    conf_file = {"dataset": {"dims": 128}}
+    result = validate_search_params(
+        all_search_params,
+        search_param_names,
+        "algo",
+        group_conf,
+        conf_file,
+        10,
+        128,
+    )
+    assert len(result) == 4
+
+
+def test_rmm_present():
+    with patch.dict("sys.modules", {"rmm": MagicMock()}):
+        assert rmm_present() is True
+    with patch.dict("sys.modules", {"rmm": None}):
+        assert rmm_present() is False
+
+
+@patch("benchmark.get_build_path", return_value="build_path")
+def test_find_executable(mock_get_build_path):
+    algos_conf = {"algo1": {"executable": "executable1"}}
+    result = find_executable(algos_conf, "algo1", "group1", 10, 128)
+    assert result == (
+        "executable1",
+        "build_path",
+        ("algo1,group1", "algo1,group1,k10,bs128"),
+    )
+    mock_get_build_path.return_value = None
+    with pytest.raises(FileNotFoundError):
+        find_executable(algos_conf, "algo1", "group1", 10, 128)
+
+
+def test_validate_algorithm():
+    algos_conf = {"algo1": {"requires_gpu": False}}
+    result = validate_algorithm(algos_conf, "algo1", gpu_present=True)
+    assert result is True
+    result = validate_algorithm(algos_conf, "algo1", gpu_present=False)
+    assert result is True
+    algos_conf["algo1"]["requires_gpu"] = True
+    result = validate_algorithm(algos_conf, "algo1", gpu_present=False)
+    assert result is False
+
+
+@patch("benchmark.import_module")
+def test_validate_constraints(mock_import_module):
+    mock_validator = MagicMock()
+    mock_import_module.return_value = mock_validator
+    mock_validator.constraint_func.return_value = True
+    algos_conf = {
+        "algo1": {"constraints": {"build": "module.constraint_func"}}
+    }
+    result = validate_constraints(
+        algos_conf, "algo1", "build", {"param1": "value1"}, 128, None, None
+    )
+    assert result is True
+
+    algos_conf = {"algo1": {"constraints": {}}}
+    result = validate_constraints(
+        algos_conf, "algo1", "build", {"param1": "value1"}, 128, None, None
+    )
+    assert result is True
+
+    mock_validator.constraint_func.return_value = False
+    algos_conf["algo1"]["constraints"]["build"] = "module.constraint_func"
+    result = validate_constraints(
+        algos_conf, "algo1", "build", {"param1": "value1"}, 128, None, None
+    )
+    assert result is False
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 7bb9e2f8d7..41ebad1165 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -18,6 +18,10 @@ authors = [
 license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
+    "click",
+    "matplotlib",
+    "pandas",
+    "pyyaml",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -31,10 +35,7 @@ classifiers = [
 ]
 
 [project.urls]
-Homepage = "https://github.com/rapidsai/raft"
-
-[tool.setuptools.packages.find]
-where = ["src"]
+Homepage = "https://github.com/rapidsai/cuvs"
 
 [tool.setuptools.package-data]
 "*" = ["*.*", "VERSION"]
@@ -64,7 +65,8 @@ skip = [
 version = { file = "cuvs_bench/VERSION" }
 
 [tool.rapids-build-backend]
-build-backend = "scikit_build_core.build"
-requires = []
+build-backend = "setuptools.build_meta"
+requires = [
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 dependencies-file = "../../dependencies.yaml"
 matrix-entry = "cuda_suffixed=true"

From 2ad639702b3912e4eb037a6817335e48dc90ad73 Mon Sep 17 00:00:00 2001
From: Micka <ide.mickael@gmail.com>
Date: Fri, 27 Sep 2024 20:21:10 +0200
Subject: [PATCH 160/623] Fix Question Retrieval notebook (#352)

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)
  - rhdong (https://github.com/rhdong)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/352
---
 notebooks/VectorSearch_QuestionRetrieval.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/VectorSearch_QuestionRetrieval.ipynb b/notebooks/VectorSearch_QuestionRetrieval.ipynb
index 21d59975bb..1115a5920d 100644
--- a/notebooks/VectorSearch_QuestionRetrieval.ipynb
+++ b/notebooks/VectorSearch_QuestionRetrieval.ipynb
@@ -160,7 +160,7 @@
    },
    "outputs": [],
    "source": [
-    "pq_index_mem = pq_index.pq_dim * pq_index.size * pq_index.pq_bits\n",
+    "pq_index_mem = params.pq_dim * corpus_embeddings.shape[0] * params.pq_bits\n",
     "print(\"IVF-PQ memory footprint: {:.1f} MB\".format(pq_index_mem / 2**20))\n",
     "\n",
     "original_mem = corpus_embeddings.shape[0] * corpus_embeddings.shape[1] * 4\n",

From 28ee7d2184312b1313ab894d72eabaf2dbdf58fb Mon Sep 17 00:00:00 2001
From: Ajit Mistry <55892788+ajit283@users.noreply.github.com>
Date: Fri, 27 Sep 2024 20:52:27 +0200
Subject: [PATCH 161/623] Add `managed` option for RMM Pool memory resource to
 C API (#305)

Authors:
  - Ajit Mistry (https://github.com/ajit283)
  - Ray Douglass (https://github.com/raydouglass)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/305
---
 cpp/include/cuvs/core/c_api.h |  5 +++-
 cpp/src/core/c_api.cpp        | 27 +++++++++++++++++---
 cpp/test/core/c_api.c         | 47 +++++++++++++++++++++++------------
 3 files changed, 58 insertions(+), 21 deletions(-)

diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index 4db7fd12c0..c8c8d3934a 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -17,6 +17,7 @@
 #pragma once
 
 #include <cuda_runtime.h>
+#include <stdbool.h>
 #include <stdint.h>
 
 #ifdef __cplusplus
@@ -138,10 +139,12 @@ cuvsError_t cuvsRMMFree(cuvsResources_t res, void* ptr, size_t bytes);
  * available memory
  * @param[in] max_pool_size_percent The maximum pool size as a percentage of the total
  * available memory
+ * @param[in] managed Whether to use a managed memory resource as upstream resource or not
  * @return cuvsError_t
  */
 cuvsError_t cuvsRMMPoolMemoryResourceEnable(int initial_pool_size_percent,
-                                            int max_pool_size_percent);
+                                            int max_pool_size_percent,
+                                            bool managed);
 /**
  * @brief Resets the memory resource to use the default memory resource (cuda_memory_resource)
  * @return cuvsError_t
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index a75e5a1ddf..cfbeed2d55 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -21,6 +21,9 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+#include <rmm/mr/device/owning_wrapper.hpp>
 #include <rmm/mr/device/per_device_resource.hpp>
 #include <rmm/mr/device/pool_memory_resource.hpp>
 #include <thread>
@@ -83,10 +86,14 @@ extern "C" cuvsError_t cuvsRMMFree(cuvsResources_t res, void* ptr, size_t bytes)
   });
 }
 
-thread_local std::unique_ptr<rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource>> pool_mr;
+thread_local std::shared_ptr<
+  rmm::mr::owning_wrapper<rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource>,
+                          rmm::mr::device_memory_resource>>
+  pool_mr;
 
 extern "C" cuvsError_t cuvsRMMPoolMemoryResourceEnable(int initial_pool_size_percent,
-                                                       int max_pool_size_percent)
+                                                       int max_pool_size_percent,
+                                                       bool managed)
 {
   return cuvs::core::translate_exceptions([=] {
     // Upstream memory resource needs to be a cuda_memory_resource
@@ -95,10 +102,22 @@ extern "C" cuvsError_t cuvsRMMPoolMemoryResourceEnable(int initial_pool_size_per
     if (cuda_mr_casted == nullptr) {
       throw std::runtime_error("Current memory resource is not a cuda_memory_resource");
     }
+
     auto initial_size = rmm::percent_of_free_device_memory(initial_pool_size_percent);
     auto max_size     = rmm::percent_of_free_device_memory(max_pool_size_percent);
-    pool_mr = std::make_unique<rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource>>(
-      cuda_mr_casted, initial_size, max_size);
+
+    auto mr = std::shared_ptr<rmm::mr::device_memory_resource>();
+    if (managed) {
+      mr = std::static_pointer_cast<rmm::mr::device_memory_resource>(
+        std::make_shared<rmm::mr::managed_memory_resource>());
+    } else {
+      mr = std::static_pointer_cast<rmm::mr::device_memory_resource>(
+        std::make_shared<rmm::mr::cuda_memory_resource>());
+    }
+
+    pool_mr =
+      rmm::mr::make_owning_wrapper<rmm::mr::pool_memory_resource>(mr, initial_size, max_size);
+
     rmm::mr::set_current_device_resource(pool_mr.get());
   });
 }
diff --git a/cpp/test/core/c_api.c b/cpp/test/core/c_api.c
index 27973c2ddf..a3dae60044 100644
--- a/cpp/test/core/c_api.c
+++ b/cpp/test/core/c_api.c
@@ -33,34 +33,49 @@ int main()
 
   // Allocate memory
   void* ptr;
-  size_t bytes            = 1024;
-  cuvsError_t alloc_error = cuvsRMMAlloc(res, &ptr, bytes);
-  if (alloc_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+  size_t bytes      = 1024;
+  cuvsError_t error = cuvsRMMAlloc(res, &ptr, bytes);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
   // Free memory
-  cuvsError_t free_error = cuvsRMMFree(res, ptr, bytes);
-  if (free_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+  error = cuvsRMMFree(res, ptr, bytes);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
   // Enable pool memory resource
-  cuvsError_t pool_error = cuvsRMMPoolMemoryResourceEnable(10, 100);
-  if (pool_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+  error = cuvsRMMPoolMemoryResourceEnable(10, 100, false);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
   // Allocate memory again
-  void* ptr2;
-  cuvsError_t alloc_error_pool = cuvsRMMAlloc(res, &ptr2, 1024);
-  if (alloc_error_pool == CUVS_ERROR) { exit(EXIT_FAILURE); }
+  error = cuvsRMMAlloc(res, &ptr, 1024);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
   // Free memory
-  cuvsError_t free_error_pool = cuvsRMMFree(res, ptr2, 1024);
-  if (free_error_pool == CUVS_ERROR) { exit(EXIT_FAILURE); }
+  error = cuvsRMMFree(res, ptr, 1024);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
   // Reset pool memory resource
-  cuvsError_t reset_error = cuvsRMMMemoryResourceReset();
-  if (reset_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+  error = cuvsRMMMemoryResourceReset();
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Enable pool memory resource (managed)
+  error = cuvsRMMPoolMemoryResourceEnable(10, 100, true);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Allocate memory again
+  error = cuvsRMMAlloc(res, &ptr, 1024);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Free memory
+  error = cuvsRMMFree(res, ptr, 1024);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Reset pool memory resource
+  error = cuvsRMMMemoryResourceReset();
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
   // Destroy resources
-  cuvsError_t destroy_error = cuvsResourcesDestroy(res);
-  if (destroy_error == CUVS_ERROR) { exit(EXIT_FAILURE); }
+  error = cuvsResourcesDestroy(res);
+  if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
   return 0;
 }

From c616a229ba975c83f9ac13e87db57ebf365f87b0 Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Fri, 27 Sep 2024 22:02:28 +0200
Subject: [PATCH 162/623] FP16 API for CAGRA and IVF-PQ (#264)

This PR adds public API to CAGRA and IVF-PQ ANN search using FP16 input data.

Note that the fp16 kernels are already compiled in libcuvs. This PR just adds the missing public API declarations into the C++ headers, and restores the instantiations of public API functions.

This PR partially fixes https://github.com/rapidsai/cuvs/issues/144 (the IVF-Flat API is not yet added here).

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/264
---
 cpp/CMakeLists.txt                            |  11 +-
 cpp/bench/ann/src/common/benchmark.hpp        |  22 +-
 cpp/bench/ann/src/cuvs/cuvs_benchmark.cu      |   2 +-
 cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu     |   2 +-
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |   6 +-
 cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu         |   2 +-
 cpp/include/cuvs/neighbors/cagra.hpp          | 205 ++++++++++++++-
 cpp/include/cuvs/neighbors/ivf_pq.hpp         | 236 ++++++++++++++++++
 cpp/include/cuvs/neighbors/nn_descent.hpp     |  64 +++++
 cpp/src/neighbors/cagra_build_half.cu         |  39 +++
 cpp/src/neighbors/cagra_search_half.cu        |  37 +++
 ...loat_uint64.cu => cagra_serialize_half.cu} |  21 +-
 .../neighbors/detail/cagra/cagra_build.cuh    |   8 +-
 .../detail/cagra/cagra_serialize.cuh          |   2 +
 .../cagra/compute_distance_00_generate.py     |   2 -
 .../cagra/search_multi_cta_00_generate.py     |   2 -
 .../cagra/search_single_cta_00_generate.py    |   2 -
 .../cagra/search_single_cta_float_uint64.cu   |  34 ---
 .../ivf_pq/detail/generate_ivf_pq.py          |   1 +
 .../ivf_pq_build_extend_half_int64_t.cu}      |  21 +-
 .../detail/ivf_pq_search_half_int64_t.cu      |  46 ++++
 .../ivf_pq_search_with_filter_half_int64_t.cu |  49 ++++
 cpp/src/neighbors/nn_descent_half.cu          |  43 ++++
 cpp/test/CMakeLists.txt                       |   1 +
 .../ann_cagra/test_half_uint32_t.cu}          |  24 +-
 25 files changed, 770 insertions(+), 112 deletions(-)
 create mode 100644 cpp/src/neighbors/cagra_build_half.cu
 create mode 100644 cpp/src/neighbors/cagra_search_half.cu
 rename cpp/src/neighbors/{detail/cagra/search_multi_cta_float_uint64.cu => cagra_serialize_half.cu} (53%)
 delete mode 100644 cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
 rename cpp/src/neighbors/{detail/cagra/search_multi_cta_half_uint64.cu => ivf_pq/detail/ivf_pq_build_extend_half_int64_t.cu} (55%)
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/nn_descent_half.cu
 rename cpp/{src/neighbors/detail/cagra/search_single_cta_half_uint64.cu => test/neighbors/ann_cagra/test_half_uint32_t.cu} (53%)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index b72d7f1658..d4821a273e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -202,6 +202,7 @@ endif()
 add_library(
   cuvs-cagra-search STATIC
   src/neighbors/cagra_search_float.cu
+  src/neighbors/cagra_search_half.cu
   src/neighbors/cagra_search_int8.cu
   src/neighbors/cagra_search_uint8.cu
   src/neighbors/detail/cagra/compute_distance.cu
@@ -257,14 +258,10 @@ add_library(
   src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
   src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
   src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu
   src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
   src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
   src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
   src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint64.cu
 )
 
 file(GLOB_RECURSE compute_distance_sources "src/neighbors/detail/cagra/compute_distance_*.cu")
@@ -350,6 +347,7 @@ add_library(
   src/distance/pairwise_distance.cu
   src/neighbors/brute_force.cu
   src/neighbors/cagra_build_float.cu
+  src/neighbors/cagra_build_half.cu
   src/neighbors/cagra_build_int8.cu
   src/neighbors/cagra_build_uint8.cu
   src/neighbors/cagra_extend_float.cu
@@ -357,6 +355,7 @@ add_library(
   src/neighbors/cagra_extend_uint8.cu
   src/neighbors/cagra_optimize.cu
   src/neighbors/cagra_serialize_float.cu
+  src/neighbors/cagra_serialize_half.cu
   src/neighbors/cagra_serialize_int8.cu
   src/neighbors/cagra_serialize_uint8.cu
   src/neighbors/detail/cagra/cagra_build.cpp
@@ -378,6 +377,7 @@ add_library(
   src/neighbors/ivf_pq/ivf_pq_serialize.cu
   src/neighbors/ivf_pq/ivf_pq_deserialize.cu
   src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_half_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
@@ -395,13 +395,16 @@ add_library(
   src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
   src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
+  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
   src/neighbors/nn_descent.cu
   src/neighbors/nn_descent_float.cu
+  src/neighbors/nn_descent_half.cu
   src/neighbors/nn_descent_int8.cu
   src/neighbors/nn_descent_uint8.cu
   src/neighbors/refine/detail/refine_device_float_float.cu
diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index 5d7b8934fe..db3e533e07 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -687,17 +687,17 @@ inline auto run_main(int argc, char** argv) -> int
                               override_kv,
                               metric_objective,
                               threads);
-    // } else if (dtype == "half") {
-    //   dispatch_benchmark<half>(cmdline
-    //                            conf,
-    //                            force_overwrite,
-    //                            build_mode,
-    //                            search_mode,
-    //                            data_prefix,
-    //                            index_prefix,
-    //                            override_kv,
-    //                            metric_objective,
-    //                            threads);
+  } else if (dtype == "half") {
+    dispatch_benchmark<half>(cmdline,
+                             conf,
+                             force_overwrite,
+                             build_mode,
+                             search_mode,
+                             data_prefix,
+                             index_prefix,
+                             override_kv,
+                             metric_objective,
+                             threads);
   } else if (dtype == "uint8") {
     dispatch_benchmark<std::uint8_t>(cmdline,
                                      conf,
diff --git a/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
index a7495c23a9..a956ab139c 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
@@ -121,7 +121,7 @@ auto create_search_param(const std::string& algo_name, const nlohmann::json& con
 };  // namespace cuvs::bench
 
 REGISTER_ALGO_INSTANCE(float);
-// REGISTER_ALGO_INSTANCE(half);
+REGISTER_ALGO_INSTANCE(half);
 REGISTER_ALGO_INSTANCE(std::int8_t);
 REGISTER_ALGO_INSTANCE(std::uint8_t);
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu
index 6768034a2b..b4a3235c41 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_half.cu
@@ -16,5 +16,5 @@
 #include "cuvs_cagra_wrapper.h"
 
 namespace cuvs::bench {
-// template class cuvs_cagra<half, uint32_t>;
+template class cuvs_cagra<half, uint32_t>;
 }  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index 9ca41cab04..ff854f890a 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -289,7 +289,11 @@ void cuvs_cagra<T, IdxT>::save(const std::string& file) const
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::save_to_hnswlib(const std::string& file) const
 {
-  cuvs::neighbors::cagra::serialize_to_hnswlib(handle_, file, *index_);
+  if constexpr (!std::is_same_v<T, half>) {
+    cuvs::neighbors::cagra::serialize_to_hnswlib(handle_, file, *index_);
+  } else {
+    RAFT_FAIL("Cannot save fp16 index to hnswlib format");
+  }
 }
 
 template <typename T, typename IdxT>
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu
index 3ffdd4a253..2df4609666 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq.cu
@@ -17,7 +17,7 @@
 
 namespace cuvs::bench {
 template class cuvs_ivf_pq<float, int64_t>;
-// template class cuvs_ivf_pq<half, int64_t>;
+template class cuvs_ivf_pq<half, int64_t>;
 template class cuvs_ivf_pq<uint8_t, int64_t>;
 template class cuvs_ivf_pq<int8_t, int64_t>;
 }  // namespace cuvs::bench
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index fec95b5637..20db7e8b7d 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -613,6 +613,78 @@ auto build(raft::resources const& res,
  *
  * @return the constructed cagra index
  */
+auto build(raft::resources const& res,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<half, uint32_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
+auto build(raft::resources const& res,
+           const cuvs::neighbors::cagra::index_params& params,
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::cagra::index<half, uint32_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * The build consist of two steps: build an intermediate knn-graph, and optimize it to
+ * create the final graph. The index_params struct controls the node degree of these
+ * graphs.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // use default search parameters
+ *   cagra::search_params search_params;
+ *   // search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed cagra index
+ */
 auto build(raft::resources const& res,
            const cuvs::neighbors::cagra::index_params& params,
            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
@@ -975,9 +1047,6 @@ void extend(
  *
  * See the [cagra::build](#cagra::build) documentation for a usage example.
  *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
  * @param[in] res raft resources
  * @param[in] params configure the search
  * @param[in] idx cagra index
@@ -1000,8 +1069,26 @@ void search(raft::resources const& res,
  *
  * See the [cagra::build](#cagra::build) documentation for a usage example.
  *
- * @tparam T data element type
- * @tparam IdxT type of the indices
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& res,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<half, uint32_t>& index,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -1024,9 +1111,6 @@ void search(raft::resources const& res,
  *
  * See the [cagra::build](#cagra::build) documentation for a usage example.
  *
- * @tparam T data element type
- * @tparam IdxT type of the indices
- *
  * @param[in] res raft resources
  * @param[in] params configure the search
  * @param[in] index cagra index
@@ -1156,6 +1240,111 @@ void serialize(raft::resources const& handle,
 void deserialize(raft::resources const& handle,
                  std::istream& is,
                  cuvs::neighbors::cagra::index<float, uint32_t>* index);
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ *
+ */
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::cagra::index<half, uint32_t>& index,
+               bool include_dataset = true);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+
+ * cuvs::neighbors::cagra::index<half, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index the cagra index
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::cagra::index<half, uint32_t>* index);
+
+/**
+ * Write the index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const cuvs::neighbors::cagra::index<half, uint32_t>& index,
+               bool include_dataset = true);
+
+/**
+ * Load index from input stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * cuvs::neighbors::cagra::index<half, uint32_t> index;
+ * cuvs::neighbors::cagra::deserialize(handle, is, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ * @param[out] index the cagra index
+ */
+void deserialize(raft::resources const& handle,
+                 std::istream& is,
+                 cuvs::neighbors::cagra::index<half, uint32_t>* index);
 
 /**
  * Save the index to file.
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index b2db966864..8c378b1f0b 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -16,6 +16,8 @@
 
 #pragma once
 
+#include <cuda_fp16.h>
+
 #include <cuvs/neighbors/common.hpp>
 
 #include <raft/core/device_mdarray.hpp>
@@ -547,6 +549,52 @@ void build(raft::resources const& handle,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
 
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device matrix view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
 /**
  * @brief Build the index from the dataset for efficient search.
  *
@@ -726,6 +774,53 @@ void build(raft::resources const& handle,
  *
  * @return the constructed ivf-pq index
  */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_pq::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_pq::build(handle, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_pq::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_pq::index_params& index_params,
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a host_matrix_view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-pq index
+ */
 auto build(raft::resources const& handle,
            const cuvs::neighbors::ivf_pq::index_params& index_params,
            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
@@ -887,6 +982,62 @@ void extend(raft::resources const& handle,
             std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
             cuvs::neighbors::ivf_pq::index<int64_t>* idx);
 
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a device vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
 /**
  * @brief Extend the index with the new data.
  *
@@ -1257,6 +1408,47 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances);
 
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_pq::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_pq::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_pq::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] search_params configure the search
+ * @param[in] index ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_pq::search_params& search_params,
+            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
 /**
  * @brief Search ANN using the constructed index.
  *
@@ -1372,6 +1564,39 @@ void search_with_filtering(
   raft::device_matrix_view<float, int64_t, raft::row_major> distances,
   cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
 
+/**
+ * @brief Search ANN using the constructed index with the given filter.
+ *
+ * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ * The exact size of the temporary buffer depends on multiple factors and is an implementation
+ * detail. However, you can safely specify a small initial size for the memory pool, so that only a
+ * few allocations happen to grow it during the first invocations of the `search`.
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] idx ivf-pq constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
+ * query.
+ */
+void search_with_filtering(
+  raft::resources const& handle,
+  const search_params& params,
+  index<int64_t>& idx,
+  raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
+
 /**
  * @brief Search ANN using the constructed index with the given filter.
  *
@@ -1924,6 +2149,12 @@ void reconstruct_list_data(raft::resources const& res,
                            uint32_t label,
                            uint32_t offset);
 
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_matrix_view<half, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label,
+                           uint32_t offset);
+
 void reconstruct_list_data(raft::resources const& res,
                            const index<int64_t>& index,
                            raft::device_matrix_view<int8_t, uint32_t, raft::row_major> out_vectors,
@@ -1972,6 +2203,11 @@ void reconstruct_list_data(raft::resources const& res,
                            raft::device_vector_view<const uint32_t> in_cluster_indices,
                            raft::device_matrix_view<float, uint32_t, raft::row_major> out_vectors,
                            uint32_t label);
+void reconstruct_list_data(raft::resources const& res,
+                           const index<int64_t>& index,
+                           raft::device_vector_view<const uint32_t> in_cluster_indices,
+                           raft::device_matrix_view<half, uint32_t, raft::row_major> out_vectors,
+                           uint32_t label);
 void reconstruct_list_data(raft::resources const& res,
                            const index<int64_t>& index,
                            raft::device_vector_view<const uint32_t> in_cluster_indices,
diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index 9f43001778..347ccf8894 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -27,6 +27,8 @@
 
 #include <cuvs/distance/distance.hpp>
 
+#include <cuda_fp16.h>
+
 namespace cuvs::neighbors::nn_descent {
 /**
  * @defgroup nn_descent_cpp_index_params The nn-descent algorithm parameters.
@@ -237,6 +239,68 @@ auto build(raft::resources const& res,
            raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
+/**
+ * @brief Build nn-descent Index with dataset in device memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
+ *   auto index = nn_descent::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @param[in] res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::device_matrix_view input dataset expected to be located
+ *                in device memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+/**
+ * @brief Build nn-descent Index with dataset in host memory
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors::experimental;
+ *   // use default index parameters
+ *   nn_descent::index_params index_params;
+ *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *   // index.graph() provides a raft::host_matrix_view of an
+ *   // all-neighbors knn graph of dimensions [N, k] of the input
+ *   // dataset
+ * @endcode
+ *
+ * @tparam T data-type of the input dataset
+ * @tparam IdxT data-type for the output index
+ * @param res raft::resources is an object mangaging resources
+ * @param[in] params an instance of nn_descent::index_params that are parameters
+ *               to run the nn-descent algorithm
+ * @param[in] dataset raft::host_matrix_view input dataset expected to be located
+ *                in host memory
+ * @return index<IdxT> index containing all-neighbors knn graph in host memory
+ */
+auto build(raft::resources const& res,
+           index_params const& params,
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
 /**
  * @brief Build nn-descent Index with dataset in device memory
  *
diff --git a/cpp/src/neighbors/cagra_build_half.cu b/cpp/src/neighbors/cagra_build_half.cu
new file mode 100644
index 0000000000..2aba1dada5
--- /dev/null
+++ b/cpp/src/neighbors/cagra_build_half.cu
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuda_fp16.h>
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+cuvs::neighbors::cagra::index<half, uint32_t> build(
+  raft::resources const& handle,
+  const cuvs::neighbors::cagra::index_params& params,
+  raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
+{
+  return cuvs::neighbors::cagra::build<half, uint32_t>(handle, params, dataset);
+}
+
+cuvs::neighbors::cagra::index<half, uint32_t> build(
+  raft::resources const& handle,
+  const cuvs::neighbors::cagra::index_params& params,
+  raft::host_matrix_view<const half, int64_t, raft::row_major> dataset)
+{
+  return cuvs::neighbors::cagra::build<half, uint32_t>(handle, params, dataset);
+}
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_search_half.cu b/cpp/src/neighbors/cagra_search_half.cu
new file mode 100644
index 0000000000..d80f2bc00e
--- /dev/null
+++ b/cpp/src/neighbors/cagra_search_half.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
+  void search(raft::resources const& handle,                                                       \
+              cuvs::neighbors::cagra::search_params const& params,                                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
+  {                                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
+  }
+
+CUVS_INST_CAGRA_SEARCH(half, uint32_t);
+
+#undef CUVS_INST_CAGRA_SEARCH
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu b/cpp/src/neighbors/cagra_serialize_half.cu
similarity index 53%
rename from cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu
rename to cpp/src/neighbors/cagra_serialize_half.cu
index 88167b8432..92ebd9b711 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint64.cu
+++ b/cpp/src/neighbors/cagra_serialize_half.cu
@@ -14,21 +14,12 @@
  * limitations under the License.
  */
 
-/*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_multi_cta_00_generate.py
- *
- */
+#include "cagra_serialize.cuh"
+
+#include <cuda_fp16.h>
 
-#include "search_multi_cta_inst.cuh"
+namespace cuvs::neighbors::cagra {
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(float,
-                             uint64_t,
-                             float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+CUVS_INST_CAGRA_SERIALIZE(half);
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 4a927add53..e5495dc3e8 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -33,8 +33,6 @@
 #include <cuvs/neighbors/refine.hpp>
 
 // TODO: Fixme- this needs to be migrated
-#include "../../ivf_pq/ivf_pq_build.cuh"
-#include "../../ivf_pq/ivf_pq_search.cuh"
 #include "../../nn_descent.cuh"
 
 // TODO: This shouldn't be calling spatial/knn APIs
@@ -156,8 +154,7 @@ void build_knn_graph(
   }();
 
   RAFT_LOG_DEBUG("# Building IVF-PQ index %s", model_name.c_str());
-  auto index =
-    cuvs::neighbors::ivf_pq::detail::build<DataT, int64_t>(res, pq.build_params, dataset);
+  auto index = cuvs::neighbors::ivf_pq::build(res, pq.build_params, dataset);
 
   //
   // search top (k + 1) neighbors
@@ -169,7 +166,8 @@ void build_knn_graph(
   const auto num_queries = dataset.extent(0);
 
   // Use the same maximum batch size as the ivf_pq::search to avoid allocating more than needed.
-  using cuvs::neighbors::ivf_pq::detail::kMaxQueries;
+  constexpr uint32_t kMaxQueries = 4096;
+
   // Heuristic: the build_knn_graph code should use only a fraction of the workspace memory; the
   // rest should be used by the ivf_pq::search. Here we say that the workspace size should be a good
   // multiple of what is required for the I/O batching below.
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index 24cc2a22f4..f86ed9ef62 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -194,6 +194,8 @@ void serialize_to_hnswlib(raft::resources const& res,
         auto data_elem = static_cast<int>(host_dataset(i, j));
         os.write(reinterpret_cast<char*>(&data_elem), sizeof(int));
       }
+    } else {
+      RAFT_FAIL("Unsupported dataset type while saving CAGRA dataset to HNSWlib format");
     }
 
     os.write(reinterpret_cast<char*>(&i), sizeof(std::size_t));
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
index f8584c62ec..aef31d1618 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
@@ -66,8 +66,6 @@
     half_uint32=("half", "uint32_t", "float"),
     int8_uint32=("int8_t", "uint32_t", "float"),
     uint8_uint32=("uint8_t", "uint32_t", "float"),
-    # float_uint64=("float", "uint64_t", "float"),
-    # half_uint64=("half", "uint64_t", "float"),
 )
 
 metric_prefix = 'DistanceType::'
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
index 3153a3a9f4..4e3983e3f2 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
@@ -58,8 +58,6 @@
     half_uint32=("half", "uint32_t", "float"),
     int8_uint32=("int8_t", "uint32_t", "float"),
     uint8_uint32=("uint8_t", "uint32_t", "float"),
-    float_uint64=("float", "uint64_t", "float"),
-    half_uint64=("half", "uint64_t", "float"),
 )
 # knn
 for type_path, (data_t, idx_t, distance_t) in search_types.items():
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
index e37ceb1faa..4693cd54d2 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
@@ -60,8 +60,6 @@
     half_uint32=("half", "uint32_t", "float"),
     int8_uint32=("int8_t", "uint32_t", "float"),
     uint8_uint32=("uint8_t", "uint32_t", "float"),
-    float_uint64=("float", "uint64_t", "float"),
-    half_uint64=("half", "uint64_t", "float"),
 )
 
 # knn
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
deleted file mode 100644
index 0ef5c366f3..0000000000
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint64.cu
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
-
-#include "search_single_cta_inst.cuh"
-
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(float,
-                             uint64_t,
-                             float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
-
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
index 878c7ee214..9b3083c3bc 100644
--- a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
+++ b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
@@ -57,6 +57,7 @@
 
 types = dict(
     float_int64_t=("float", "int64_t"),
+    half_int64_t=("half", "int64_t"),
     int8_t_int64_t=("int8_t", "int64_t"),
     uint8_t_int64_t=("uint8_t", "int64_t"),
 )
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_half_int64_t.cu
similarity index 55%
rename from cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu
rename to cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_half_int64_t.cu
index dafb89cc3d..2d7270957a 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_half_int64_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,20 +15,21 @@
  */
 
 /*
- * NOTE: this file is generated by search_multi_cta_00_generate.py
+ * NOTE: this file is generated by generate_ivf_pq.py
  *
  * Make changes there and run in this directory:
  *
- * > python search_multi_cta_00_generate.py
+ * > python generate_ivf_pq.py
  *
  */
 
-#include "search_multi_cta_inst.cuh"
+#include <cuvs/neighbors/ivf_pq.hpp>
 
-namespace cuvs::neighbors::cagra::detail::multi_cta_search {
-instantiate_kernel_selection(half,
-                             uint64_t,
-                             float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+#include "ivf_pq_build_extend_inst.cuh"
 
-}  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
+namespace cuvs::neighbors::ivf_pq {
+CUVS_INST_IVF_PQ_BUILD_EXTEND(half, int64_t);
+
+#undef CUVS_INST_IVF_PQ_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
new file mode 100644
index 0000000000..e5556e593e
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
+  void search(raft::resources const& handle,                                                       \
+              const cuvs::neighbors::ivf_pq::search_params& params,                                \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
+  {                                                                                                \
+    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
+  }
+CUVS_INST_IVF_PQ_SEARCH(half, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
new file mode 100644
index 0000000000..5874fba6cb
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_pq.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_pq.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include "../ivf_pq_search.cuh"
+
+namespace cuvs::neighbors::ivf_pq {
+
+#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
+  void search_with_filtering(                                                \
+    raft::resources const& handle,                                           \
+    const cuvs::neighbors::ivf_pq::search_params& params,                    \
+    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
+    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
+  {                                                                          \
+    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
+      handle, params, index, queries, neighbors, distances, sample_filter);  \
+  }
+CUVS_INST_IVF_PQ_SEARCH_FILTER(half, int64_t);
+
+#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
+
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/nn_descent_half.cu b/cpp/src/neighbors/nn_descent_half.cu
new file mode 100644
index 0000000000..5879930315
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent_half.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nn_descent.cuh"
+#include <cuvs/neighbors/nn_descent.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };                                                                              \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::nn_descent::index_params& params,             \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
+  {                                                                               \
+    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+  };
+
+CUVS_INST_NN_DESCENT_BUILD(half, uint32_t);
+
+#undef CUVS_INST_NN_DESCENT_BUILD
+
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index e04c39318d..b81ef6bfa5 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -139,6 +139,7 @@ if(BUILD_TESTS)
     NEIGHBORS_ANN_CAGRA_TEST
     PATH
     neighbors/ann_cagra/test_float_uint32_t.cu
+    neighbors/ann_cagra/test_half_uint32_t.cu
     neighbors/ann_cagra/test_int8_t_uint32_t.cu
     neighbors/ann_cagra/test_uint8_t_uint32_t.cu
     GPUS
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64.cu b/cpp/test/neighbors/ann_cagra/test_half_uint32_t.cu
similarity index 53%
rename from cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64.cu
rename to cpp/test/neighbors/ann_cagra/test_half_uint32_t.cu
index b96ed0b22b..f03de69d2c 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint64.cu
+++ b/cpp/test/neighbors/ann_cagra/test_half_uint32_t.cu
@@ -14,21 +14,15 @@
  * limitations under the License.
  */
 
-/*
- * NOTE: this file is generated by search_single_cta_00_generate.py
- *
- * Make changes there and run in this directory:
- *
- * > python search_single_cta_00_generate.py
- *
- */
+#include <gtest/gtest.h>
+
+#include "../ann_cagra.cuh"
+
+namespace cuvs::neighbors::cagra {
 
-#include "search_single_cta_inst.cuh"
+typedef AnnCagraTest<float, half, std::uint32_t> AnnCagraTestF16_U32;
+TEST_P(AnnCagraTestF16_U32, AnnCagra) { this->testCagra(); }
 
-namespace cuvs::neighbors::cagra::detail::single_cta_search {
-instantiate_kernel_selection(half,
-                             uint64_t,
-                             float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF16_U32, ::testing::ValuesIn(inputs));
 
-}  // namespace cuvs::neighbors::cagra::detail::single_cta_search
+}  // namespace cuvs::neighbors::cagra

From f39b8d512355bb46a247b1591bcb3f4725e5a85c Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Tue, 1 Oct 2024 13:35:49 -0500
Subject: [PATCH 163/623] cuvs_bench plotting functions (#347)

Builds on top of #279, will change from draft after that merge, or we can merge into one single PR if reviewers prefer so.

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)
  - rhdong (https://github.com/rhdong)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/347
---
 .gitignore                                    |   2 +-
 python/cuvs_bench/cuvs_bench/plot/__init__.py |   0
 python/cuvs_bench/cuvs_bench/plot/__main__.py | 617 ++++++++++++++++++
 python/cuvs_bench/cuvs_bench/run/__main__.py  |  18 +-
 .../cuvs_bench/cuvs_bench/run/data_export.py  | 326 +++++++++
 python/cuvs_bench/cuvs_bench/run/run.py       |   1 +
 6 files changed, 962 insertions(+), 2 deletions(-)
 create mode 100644 python/cuvs_bench/cuvs_bench/plot/__init__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/plot/__main__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/run/data_export.py

diff --git a/.gitignore b/.gitignore
index 17258e3dee..97eab287de 100644
--- a/.gitignore
+++ b/.gitignore
@@ -81,4 +81,4 @@ ivf_pq_index
 
 # cuvs_bench
 datasets/
-/*.json
\ No newline at end of file
+/*.json
diff --git a/python/cuvs_bench/cuvs_bench/plot/__init__.py b/python/cuvs_bench/cuvs_bench/plot/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs_bench/cuvs_bench/plot/__main__.py b/python/cuvs_bench/cuvs_bench/plot/__main__.py
new file mode 100644
index 0000000000..93deb69c78
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/plot/__main__.py
@@ -0,0 +1,617 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script is inspired by
+# 1: https://github.com/erikbern/ann-benchmarks/blob/main/plot.py
+# 2: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/utils.py  # noqa: E501
+# 3: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/metrics.py  # noqa: E501
+# License: https://github.com/rapidsai/cuvs/blob/branch-24.10/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
+
+import itertools
+import os
+from collections import OrderedDict
+
+import click
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+mpl.use("Agg")
+
+metrics = {
+    "k-nn": {
+        "description": "Recall",
+        "worst": float("-inf"),
+        "lim": [0.0, 1.03],
+    },
+    "throughput": {
+        "description": "Queries per second (1/s)",
+        "worst": float("-inf"),
+    },
+    "latency": {
+        "description": "Search Latency (s)",
+        "worst": float("inf"),
+    },
+}
+
+
+def positive_int(value):
+    ivalue = int(value)
+    if ivalue <= 0:
+        raise click.BadParameter(f"{value} is not a positive integer")
+    return ivalue
+
+
+def positive_float(value):
+    fvalue = float(value)
+    if fvalue <= 0:
+        raise click.BadParameter(f"{value} is not a positive float")
+    return fvalue
+
+
+def generate_n_colors(n):
+    vs = np.linspace(0.3, 0.9, 7)
+    colors = [(0.9, 0.4, 0.4, 1.0)]
+
+    def euclidean(a, b):
+        return sum((x - y) ** 2 for x, y in zip(a, b))
+
+    while len(colors) < n:
+        new_color = max(
+            itertools.product(vs, vs, vs),
+            key=lambda a: min(euclidean(a, b) for b in colors),
+        )
+        colors.append(new_color + (1.0,))
+    return colors
+
+
+def create_linestyles(unique_algorithms):
+    colors = dict(
+        zip(unique_algorithms, generate_n_colors(len(unique_algorithms)))
+    )
+    linestyles = dict(
+        (algo, ["--", "-.", "-", ":"][i % 4])
+        for i, algo in enumerate(unique_algorithms)
+    )
+    markerstyles = dict(
+        (algo, ["+", "<", "o", "*", "x"][i % 5])
+        for i, algo in enumerate(unique_algorithms)
+    )
+    faded = dict(
+        (algo, (r, g, b, 0.3)) for algo, (r, g, b, a) in colors.items()
+    )
+    return dict(
+        (
+            algo,
+            (colors[algo], faded[algo], linestyles[algo], markerstyles[algo]),
+        )
+        for algo in unique_algorithms
+    )
+
+
+def create_plot_search(
+    all_data,
+    x_scale,
+    y_scale,
+    fn_out,
+    linestyles,
+    dataset,
+    k,
+    batch_size,
+    mode,
+    time_unit,
+    x_start,
+):
+    xn = "k-nn"
+    xm, ym = (metrics[xn], metrics[mode])
+    xm["lim"][0] = x_start
+    # Now generate each plot
+    handles = []
+    labels = []
+    plt.figure(figsize=(12, 9))
+
+    # Sorting by mean y-value helps aligning plots with labels
+    def mean_y(algo):
+        points = np.array(all_data[algo], dtype=object)
+        return -np.log(np.array(points[:, 3], dtype=np.float32)).mean()
+
+    # Find range for logit x-scale
+    min_x, max_x = 1, 0
+    for algo in sorted(all_data.keys(), key=mean_y):
+        points = np.array(all_data[algo], dtype=object)
+        xs = points[:, 2]
+        ys = points[:, 3]
+        min_x = min([min_x] + [x for x in xs if x > 0])
+        max_x = max([max_x] + [x for x in xs if x < 1])
+        color, faded, linestyle, marker = linestyles[algo]
+        (handle,) = plt.plot(
+            xs,
+            ys,
+            "-",
+            label=algo,
+            color=color,
+            ms=7,
+            mew=3,
+            lw=3,
+            marker=marker,
+        )
+        handles.append(handle)
+
+        labels.append(algo)
+
+    ax = plt.gca()
+    y_description = ym["description"]
+    if mode == "latency":
+        y_description = y_description.replace("(s)", f"({time_unit})")
+    ax.set_ylabel(y_description)
+    ax.set_xlabel("Recall")
+    # Custom scales of the type --x-scale a3
+    if x_scale[0] == "a":
+        alpha = float(x_scale[1:])
+
+        def fun(x):
+            return 1 - (1 - x) ** (1 / alpha)
+
+        def inv_fun(x):
+            return 1 - (1 - x) ** alpha
+
+        ax.set_xscale("function", functions=(fun, inv_fun))
+        if alpha <= 3:
+            ticks = [inv_fun(x) for x in np.arange(0, 1.2, 0.2)]
+            plt.xticks(ticks)
+        if alpha > 3:
+            from matplotlib import ticker
+
+            ax.xaxis.set_major_formatter(ticker.LogitFormatter())
+            # plt.xticks(ticker.LogitLocator().tick_values(min_x, max_x))
+            plt.xticks([0, 1 / 2, 1 - 1e-1, 1 - 1e-2, 1 - 1e-3, 1 - 1e-4, 1])
+    # Other x-scales
+    else:
+        ax.set_xscale(x_scale)
+    ax.set_yscale(y_scale)
+    ax.set_title(f"{dataset} k={k} batch_size={batch_size}")
+    plt.gca().get_position()
+    # plt.gca().set_position([box.x0, box.y0, box.width * 0.8, box.height])
+    ax.legend(
+        handles,
+        labels,
+        loc="center left",
+        bbox_to_anchor=(1, 0.5),
+        prop={"size": 9},
+    )
+    plt.grid(visible=True, which="major", color="0.65", linestyle="-")
+    plt.setp(ax.get_xminorticklabels(), visible=True)
+
+    # Logit scale has to be a subset of (0,1)
+    if "lim" in xm and x_scale != "logit":
+        x0, x1 = xm["lim"]
+        plt.xlim(max(x0, 0), min(x1, 1))
+    elif x_scale == "logit":
+        plt.xlim(min_x, max_x)
+    if "lim" in ym:
+        plt.ylim(ym["lim"])
+
+    # Workaround for bug https://github.com/matplotlib/matplotlib/issues/6789
+    ax.spines["bottom"]._adjust_location()
+
+    print(f"writing search output to {fn_out}")
+    plt.savefig(fn_out, bbox_inches="tight")
+    plt.close()
+
+
+def create_plot_build(
+    build_results, search_results, linestyles, fn_out, dataset, k, batch_size
+):
+    bt_80 = [0] * len(linestyles)
+
+    bt_90 = [0] * len(linestyles)
+
+    bt_95 = [0] * len(linestyles)
+
+    bt_99 = [0] * len(linestyles)
+
+    data = OrderedDict()
+    colors = OrderedDict()
+
+    # Sorting by mean y-value helps aligning plots with labels
+
+    def mean_y(algo):
+        points = np.array(search_results[algo], dtype=object)
+        return -np.log(np.array(points[:, 3], dtype=np.float32)).mean()
+
+    for pos, algo in enumerate(sorted(search_results.keys(), key=mean_y)):
+        points = np.array(search_results[algo], dtype=object)
+        # x is recall, ls is algo_name, idxs is index_name
+        xs = points[:, 2]
+        ls = points[:, 0]
+        idxs = points[:, 1]
+
+        len_80, len_90, len_95, len_99 = 0, 0, 0, 0
+        for i in range(len(xs)):
+            if xs[i] >= 0.80 and xs[i] < 0.90:
+                bt_80[pos] = bt_80[pos] + build_results[(ls[i], idxs[i])][0][2]
+                len_80 = len_80 + 1
+            elif xs[i] >= 0.9 and xs[i] < 0.95:
+                bt_90[pos] = bt_90[pos] + build_results[(ls[i], idxs[i])][0][2]
+                len_90 = len_90 + 1
+            elif xs[i] >= 0.95 and xs[i] < 0.99:
+                bt_95[pos] = bt_95[pos] + build_results[(ls[i], idxs[i])][0][2]
+                len_95 = len_95 + 1
+            elif xs[i] >= 0.99:
+                bt_99[pos] = bt_99[pos] + build_results[(ls[i], idxs[i])][0][2]
+                len_99 = len_99 + 1
+        if len_80 > 0:
+            bt_80[pos] = bt_80[pos] / len_80
+        if len_90 > 0:
+            bt_90[pos] = bt_90[pos] / len_90
+        if len_95 > 0:
+            bt_95[pos] = bt_95[pos] / len_95
+        if len_99 > 0:
+            bt_99[pos] = bt_99[pos] / len_99
+        data[algo] = [
+            bt_80[pos],
+            bt_90[pos],
+            bt_95[pos],
+            bt_99[pos],
+        ]
+        colors[algo] = linestyles[algo][0]
+
+    index = [
+        "@80% Recall",
+        "@90% Recall",
+        "@95% Recall",
+        "@99% Recall",
+    ]
+
+    df = pd.DataFrame(data, index=index)
+    df.replace(0.0, np.nan, inplace=True)
+    df = df.dropna(how="all")
+    plt.figure(figsize=(12, 9))
+    ax = df.plot.bar(rot=0, color=colors)
+    fig = ax.get_figure()
+    print(f"writing build output to {fn_out}")
+    plt.title(
+        "Average Build Time within Recall Range "
+        f"for k={k} batch_size={batch_size}"
+    )
+    plt.suptitle(f"{dataset}")
+    plt.ylabel("Build Time (s)")
+    fig.savefig(fn_out)
+
+
+def load_lines(results_path, result_files, method, index_key, mode, time_unit):
+    results = dict()
+
+    for result_filename in result_files:
+        try:
+            with open(os.path.join(results_path, result_filename), "r") as f:
+                lines = f.readlines()
+                lines = lines[:-1] if lines[-1] == "\n" else lines
+
+                if method == "build":
+                    key_idx = [2]
+                elif method == "search":
+                    y_idx = 3 if mode == "throughput" else 4
+                    key_idx = [2, y_idx]
+
+                for line in lines[1:]:
+                    split_lines = line.split(",")
+
+                    algo_name = split_lines[0]
+                    index_name = split_lines[1]
+
+                    if index_key == "algo":
+                        dict_key = algo_name
+                    elif index_key == "index":
+                        dict_key = (algo_name, index_name)
+                    if dict_key not in results:
+                        results[dict_key] = []
+                    to_add = [algo_name, index_name]
+                    for key_i in key_idx:
+                        to_add.append(float(split_lines[key_i]))
+                    if (
+                        mode == "latency"
+                        and time_unit != "s"
+                        and method == "search"
+                    ):
+                        to_add[-1] = (
+                            to_add[-1] * (10**3)
+                            if time_unit == "ms"
+                            else to_add[-1] * (10**6)
+                        )
+                    results[dict_key].append(to_add)
+        except Exception:
+            print(
+                f"An error occurred processing file {result_filename}. "
+                "Skipping..."
+            )
+
+    return results
+
+
+def load_all_results(
+    dataset_path,
+    algorithms,
+    groups,
+    algo_groups,
+    k,
+    batch_size,
+    method,
+    index_key,
+    raw,
+    mode,
+    time_unit,
+):
+    results_path = os.path.join(dataset_path, "result", method)
+    result_files = os.listdir(results_path)
+    if method == "build":
+        result_files = [
+            result_file
+            for result_file in result_files
+            if ".csv" in result_file
+        ]
+    elif method == "search":
+        if raw:
+            suffix = ",raw"
+        else:
+            suffix = f",{mode}"
+        result_files = [
+            result_file
+            for result_file in result_files
+            if f"{suffix}.csv" in result_file
+        ]
+    if len(result_files) == 0:
+        raise FileNotFoundError(f"No CSV result files found in {results_path}")
+
+    if method == "search":
+        filter_k_bs = []
+        for result_filename in result_files:
+            filename_split = result_filename.split(",")
+            if (
+                int(filename_split[-3][1:]) == k
+                and int(filename_split[-2][2:]) == batch_size
+            ):
+                filter_k_bs.append(result_filename)
+        result_files = filter_k_bs
+
+    algo_group_files = [
+        result_filename.replace(".csv", "").split(",")[:2]
+        for result_filename in result_files
+    ]
+    algo_group_files = list(zip(*algo_group_files))
+
+    if len(algorithms) > 0:
+        final_results = [
+            result_files[i]
+            for i in range(len(result_files))
+            if (algo_group_files[0][i] in algorithms)
+            and (algo_group_files[1][i] in groups)
+        ]
+    else:
+        final_results = [
+            result_files[i]
+            for i in range(len(result_files))
+            if (algo_group_files[1][i] in groups)
+        ]
+
+    if len(algo_groups) > 0:
+        split_algo_groups = [
+            algo_group.split(".") for algo_group in algo_groups
+        ]
+        split_algo_groups = list(zip(*split_algo_groups))
+        final_algo_groups = [
+            result_files[i]
+            for i in range(len(result_files))
+            if (algo_group_files[0][i] in split_algo_groups[0])
+            and (algo_group_files[1][i] in split_algo_groups[1])
+        ]
+        final_results = final_results + final_algo_groups
+        final_results = set(final_results)
+
+    results = load_lines(
+        results_path, final_results, method, index_key, mode, time_unit
+    )
+
+    return results
+
+
+@click.command()
+@click.option("--dataset", default="glove-100-inner", help="Dataset to plot.")
+@click.option(
+    "--dataset-path",
+    default=lambda: os.getenv(
+        "RAPIDS_DATASET_ROOT_DIR", os.path.join(os.getcwd(), "datasets/")
+    ),
+    help="Path to dataset folder.",
+)
+@click.option(
+    "--output-filepath",
+    default=os.getcwd(),
+    help="Directory where PNG will be saved.",
+)
+@click.option(
+    "--algorithms",
+    default=None,
+    help="Comma-separated list of named algorithms to plot. If `groups` and "
+    "`algo-groups` are both undefined, then group `base` is plotted by "
+    "default.",
+)
+@click.option(
+    "--groups",
+    default="base",
+    help="Comma-separated groups of parameters to plot.",
+)
+@click.option(
+    "--algo-groups",
+    help="Comma-separated <algorithm>.<group> to plot. Example usage: "
+    '--algo-groups=raft_cagra.large,hnswlib.large".',
+)
+@click.option(
+    "-k",
+    "--count",
+    default=10,
+    type=positive_int,
+    help="The number of nearest neighbors to search for.",
+)
+@click.option(
+    "-bs",
+    "--batch-size",
+    default=10000,
+    type=positive_int,
+    help="Number of query vectors to use in each query trial.",
+)
+@click.option("--build", is_flag=True, help="Flag to indicate build mode.")
+@click.option("--search", is_flag=True, help="Flag to indicate search mode.")
+@click.option(
+    "--x-scale",
+    default="linear",
+    help="Scale to use when drawing the X-axis. Typically linear, "
+    "logit, or a2.",
+)
+@click.option(
+    "--y-scale",
+    type=click.Choice(
+        ["linear", "log", "symlog", "logit"], case_sensitive=False
+    ),
+    default="linear",
+    help="Scale to use when drawing the Y-axis.",
+)
+@click.option(
+    "--x-start",
+    default=0.8,
+    type=positive_float,
+    help="Recall values to start the x-axis from.",
+)
+@click.option(
+    "--mode",
+    type=click.Choice(["throughput", "latency"], case_sensitive=False),
+    default="throughput",
+    help="Search mode whose Pareto frontier is used on the Y-axis.",
+)
+@click.option(
+    "--time-unit",
+    type=click.Choice(["s", "ms", "us"], case_sensitive=False),
+    default="ms",
+    help="Time unit to plot when mode is latency.",
+)
+@click.option(
+    "--raw",
+    is_flag=True,
+    help="Show raw results (not just Pareto frontier) of the mode argument.",
+)
+def main(
+    dataset: str,
+    dataset_path: str,
+    output_filepath: str,
+    algorithms: str,
+    groups: str,
+    algo_groups: str,
+    count: int,
+    batch_size: int,
+    build: bool,
+    search: bool,
+    x_scale: str,
+    y_scale: str,
+    x_start: float,
+    mode: str,
+    time_unit: str,
+    raw: bool,
+) -> None:
+
+    args = locals()
+
+    if args["algorithms"]:
+        algorithms = args["algorithms"].split(",")
+    else:
+        algorithms = []
+    groups = args["groups"].split(",")
+    if args["algo_groups"]:
+        algo_groups = args["algo_groups"].split(",")
+    else:
+        algo_groups = []
+    k = args["count"]
+    batch_size = args["batch_size"]
+    if not args["build"] and not args["search"]:
+        build = True
+        search = True
+    else:
+        build = args["build"]
+        search = args["search"]
+
+    search_output_filepath = os.path.join(
+        args["output_filepath"],
+        f"search-{args['dataset']}-k{k}-batch_size{batch_size}.png",
+    )
+    build_output_filepath = os.path.join(
+        args["output_filepath"],
+        f"build-{args['dataset']}-k{k}-batch_size{batch_size}.png",
+    )
+
+    search_results = load_all_results(
+        os.path.join(args["dataset_path"], args["dataset"]),
+        algorithms,
+        groups,
+        algo_groups,
+        k,
+        batch_size,
+        "search",
+        "algo",
+        args["raw"],
+        args["mode"],
+        args["time_unit"],
+    )
+    linestyles = create_linestyles(sorted(search_results.keys()))
+    if search:
+        create_plot_search(
+            search_results,
+            args["x_scale"],
+            args["y_scale"],
+            search_output_filepath,
+            linestyles,
+            args["dataset"],
+            k,
+            batch_size,
+            args["mode"],
+            args["time_unit"],
+            args["x_start"],
+        )
+    if build:
+        build_results = load_all_results(
+            os.path.join(args["dataset_path"], args["dataset"]),
+            algorithms,
+            groups,
+            algo_groups,
+            k,
+            batch_size,
+            "build",
+            "index",
+            args["raw"],
+            args["mode"],
+            args["time_unit"],
+        )
+        create_plot_build(
+            build_results,
+            search_results,
+            linestyles,
+            build_output_filepath,
+            args["dataset"],
+            k,
+            batch_size,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/cuvs_bench/cuvs_bench/run/__main__.py b/python/cuvs_bench/cuvs_bench/run/__main__.py
index b5d99a4bfe..bf9f8586dc 100644
--- a/python/cuvs_bench/cuvs_bench/run/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/run/__main__.py
@@ -19,6 +19,7 @@
 from typing import Optional
 
 import click
+from data_export import convert_json_to_csv_build, convert_json_to_csv_search
 from run import run_benchmark
 
 
@@ -138,6 +139,16 @@
     "run execute the benchmarks but will not actually execute "
     "the command.",
 )
+@click.option(
+    "--data-export",
+    is_flag=True,
+    help="By default, the intermediate JSON outputs produced by "
+    "cuvs_bench.run to more easily readable CSV files is done "
+    "automatically, which are needed to build charts made by "
+    "cuvs_bench.plot. But if some of the benchmark runs failed or "
+    "were interrupted, use this option to convert those intermediate "
+    "files manually.",
+)
 @click.option(
     "--raft-log-level",
     default="info",
@@ -165,6 +176,7 @@ def main(
     search_mode: str,
     search_threads: Optional[str],
     dry_run: bool,
+    data_export: bool,
     raft_log_level: str,
 ) -> None:
     """
@@ -209,7 +221,11 @@ def main(
 
     """
 
-    run_benchmark(**locals())
+    if not data_export:
+        run_benchmark(**locals())
+
+    convert_json_to_csv_build(dataset, dataset_path)
+    convert_json_to_csv_search(dataset, dataset_path)
 
 
 if __name__ == "__main__":
diff --git a/python/cuvs_bench/cuvs_bench/run/data_export.py b/python/cuvs_bench/cuvs_bench/run/data_export.py
new file mode 100644
index 0000000000..997dab5001
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/run/data_export.py
@@ -0,0 +1,326 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import json
+import os
+import traceback
+import warnings
+
+import pandas as pd
+
+skip_build_cols = set(
+    [
+        "algo_name",
+        "index_name",
+        "time",
+        "name",
+        "family_index",
+        "per_family_instance_index",
+        "run_name",
+        "run_type",
+        "repetitions",
+        "repetition_index",
+        "iterations",
+        "real_time",
+        "time_unit",
+        "index_size",
+    ]
+)
+
+skip_search_cols = (
+    set(["recall", "qps", "latency", "items_per_second", "Recall", "Latency"])
+    | skip_build_cols
+)
+
+metrics = {
+    "k-nn": {
+        "description": "Recall",
+        "worst": float("-inf"),
+        "lim": [0.0, 1.03],
+    },
+    "throughput": {
+        "description": "Queries per second (1/s)",
+        "worst": float("-inf"),
+    },
+    "latency": {
+        "description": "Search Latency (s)",
+        "worst": float("inf"),
+    },
+}
+
+
+def read_json_files(dataset, dataset_path, method):
+    """
+    Yield file paths, algo names, and loaded JSON data as pandas DataFrames.
+
+    Parameters
+    ----------
+    dataset : str
+        The name of the dataset.
+    dataset_path : str
+        The base path where datasets are stored.
+    method : str
+        The method subdirectory to search within (e.g., "build" or "search").
+
+    Yields
+    ------
+    tuple
+        A tuple containing the file path, algorithm name, and the
+        DataFrame of JSON content.
+    """
+    dir_path = os.path.join(dataset_path, dataset, "result", method)
+    for file in os.listdir(dir_path):
+        if file.endswith(".json"):
+            file_path = os.path.join(dir_path, file)
+            try:
+                with open(file_path, "r", encoding="ISO-8859-1") as f:
+                    data = json.load(f)
+                    df = pd.DataFrame(data["benchmarks"])
+                    algo_name = tuple(file.split(",")[:2])
+                    yield file_path, algo_name, df
+            except Exception as e:
+                print(f"Error processing file {file}: {e}. Skipping...")
+                traceback.print_exc()
+
+
+def clean_algo_name(algo_name):
+    """
+    Clean and format the algorithm name.
+
+    Parameters
+    ----------
+    algo_name : tuple
+        Tuple containing parts of the algorithm name.
+
+    Returns
+    -------
+    str
+        Cleaned algorithm name.
+    """
+
+    return algo_name[0] if "base" in algo_name[1] else "_".join(algo_name)
+
+
+def write_csv(file, algo_name, df, extra_columns=None, skip_cols=None):
+    """
+    Write a DataFrame to CSV with specified columns skipped.
+
+    Parameters
+    ----------
+    file : str
+        The path to the file to be written.
+    algo_name : str
+        The algorithm name to be included in the CSV.
+    df : pandas.DataFrame
+        The DataFrame containing the data to write.
+    extra_columns : list, optional
+        List of extra columns to add (default is None).
+    skip_cols : set, optional
+        Set of columns to skip when writing to CSV (default is None).
+    """
+    df["name"] = df["name"].str.split("/").str[0]
+    write_data = pd.DataFrame(
+        {
+            "algo_name": [algo_name] * len(df),
+            "index_name": df["name"],
+            "time": df["real_time"],
+        }
+    )
+    # Add extra columns if provided
+    if extra_columns:
+        for col in extra_columns:
+            write_data[col] = None
+    # Include columns not in skip list
+    for name in df:
+        if name not in skip_cols:
+            write_data[name] = df[name]
+    write_data.to_csv(file.replace(".json", ".csv"), index=False)
+
+
+def convert_json_to_csv_build(dataset, dataset_path):
+    """
+    Convert build JSON files to CSV format.
+
+    Parameters
+    ----------
+    dataset : str
+        The name of the dataset.
+    dataset_path : str
+        The base path where datasets are stored.
+    """
+    for file, algo_name, df in read_json_files(dataset, dataset_path, "build"):
+        try:
+            algo_name = clean_algo_name(algo_name)
+            write_csv(file, algo_name, df, skip_cols=skip_build_cols)
+        except Exception as e:
+            print(f"Error processing build file {file}: {e}. Skipping...")
+            traceback.print_exc()
+
+
+def append_build_data(write, build_file):
+    """
+    Append build data to the search DataFrame.
+
+    Parameters
+    ----------
+    write : pandas.DataFrame
+        The DataFrame containing the search data to which build
+        data will be appended.
+    build_file : str
+        The file path to the build CSV file.
+    """
+    if os.path.exists(build_file):
+        build_df = pd.read_csv(build_file)
+        write_ncols = len(write.columns)
+        # Initialize columns for build data
+        build_columns = [
+            "build time",
+            "build threads",
+            "build cpu_time",
+            "build GPU",
+        ]
+        write = write.assign(**{col: None for col in build_columns})
+        # Append additional columns if available
+        for col_name in build_df.columns[6:]:
+            write[col_name] = None
+        # Match build rows with search rows by index_name
+        for s_index, search_row in write.iterrows():
+            for b_index, build_row in build_df.iterrows():
+                if search_row["index_name"] == build_row["index_name"]:
+                    write.iloc[s_index, write_ncols:] = build_row[2:].values
+                    break
+    else:
+        warnings.warn(
+            f"Build CSV not found for {build_file}, build params not appended."
+        )
+
+
+def convert_json_to_csv_search(dataset, dataset_path):
+    """
+    Convert search JSON files to CSV format.
+
+    Parameters
+    ----------
+    dataset : str
+        The name of the dataset.
+    dataset_path : str
+        The base path where datasets are stored.
+    """
+    for file, algo_name, df in read_json_files(
+        dataset, dataset_path, "search"
+    ):
+        try:
+            build_file = os.path.join(
+                dataset_path,
+                dataset,
+                "result",
+                "build",
+                f"{','.join(algo_name)}.csv",
+            )
+            algo_name = clean_algo_name(algo_name)
+            df["name"] = df["name"].str.split("/").str[0]
+            write_data = pd.DataFrame(
+                {
+                    "algo_name": [algo_name] * len(df),
+                    "index_name": df["name"],
+                    "recall": df["Recall"],
+                    "throughput": df["items_per_second"],
+                    "latency": df["Latency"],
+                }
+            )
+            # Append build data
+            append_build_data(write_data, build_file)
+            # Write search data and compute frontiers
+            write_data.to_csv(file.replace(".json", ",raw.csv"), index=False)
+            write_frontier(file, write_data, "throughput")
+            write_frontier(file, write_data, "latency")
+        except Exception as e:
+            print(f"Error processing search file {file}: {e}. Skipping...")
+            traceback.print_exc()
+
+
+def create_pointset(data, xn, yn):
+    """
+    Create a pointset by sorting and filtering data based on metrics.
+
+    Parameters
+    ----------
+    data : list
+        A list of data points.
+    xn : str
+        X-axis metric name.
+    yn : str
+        Y-axis metric name.
+
+    Returns
+    -------
+    list
+        Filtered list of data points sorted by x and y metrics.
+    """
+    xm, ym = metrics[xn], metrics[yn]
+    rev_x, rev_y = (-1 if xm["worst"] < 0 else 1), (
+        -1 if ym["worst"] < 0 else 1
+    )
+    # Sort data based on x and y metrics
+    data.sort(key=lambda t: (rev_y * t[4], rev_x * t[2]))
+    lines = []
+    last_x = xm["worst"]
+    comparator = (
+        (lambda xv, lx: xv > lx) if last_x < 0 else (lambda xv, lx: xv < lx)
+    )
+    for d in data:
+        if comparator(d[2], last_x):
+            last_x = d[2]
+            lines.append(d)
+    return lines
+
+
+def get_frontier(df, metric):
+    """
+    Get the frontier of the data for a given metric.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        The DataFrame containing the data.
+    metric : str
+        The metric for which to compute the frontier.
+
+    Returns
+    -------
+    pandas.DataFrame
+        DataFrame containing the frontier points for the given metric.
+    """
+    lines = create_pointset(df.values.tolist(), "k-nn", metric)
+    return pd.DataFrame(lines, columns=df.columns)
+
+
+def write_frontier(file, write_data, metric):
+    """
+    Write the frontier data to CSV for a given metric.
+
+    Parameters
+    ----------
+    file : str
+        The path to the file to write the frontier data.
+    write_data : pandas.DataFrame
+        The DataFrame containing the original data.
+    metric : str
+        The metric for which the frontier is computed
+        (e.g., "throughput", "latency").
+    """
+    frontier_data = get_frontier(write_data, metric)
+    frontier_data.to_csv(file.replace(".json", f",{metric}.csv"), index=False)
diff --git a/python/cuvs_bench/cuvs_bench/run/run.py b/python/cuvs_bench/cuvs_bench/run/run.py
index dbedcc183e..a65d4b5fea 100644
--- a/python/cuvs_bench/cuvs_bench/run/run.py
+++ b/python/cuvs_bench/cuvs_bench/run/run.py
@@ -580,6 +580,7 @@ def run_benchmark(
     search_mode: str,
     search_threads: int,
     dry_run: bool,
+    data_export: bool,
     raft_log_level: int,
 ) -> None:
     """

From ce01a0b7829065b53403c8e9018828c02b6be911 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 1 Oct 2024 21:38:43 -0700
Subject: [PATCH 164/623] Add function for calculating the
 mutual_reachability_graph (#323)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/323
---
 cpp/CMakeLists.txt                           |   2 +
 cpp/include/cuvs/neighbors/reachability.hpp  |  79 ++++++
 cpp/include/cuvs/selection/select_k.hpp      |  10 +
 cpp/src/neighbors/detail/knn_brute_force.cuh |  27 +-
 cpp/src/neighbors/detail/reachability.cuh    | 251 +++++++++++++++++++
 cpp/src/neighbors/reachability.cu            |  49 ++++
 cpp/src/selection/select_k_float_int32_t.cu  |  19 ++
 7 files changed, 433 insertions(+), 4 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/reachability.hpp
 create mode 100644 cpp/src/neighbors/detail/reachability.cuh
 create mode 100644 cpp/src/neighbors/reachability.cu
 create mode 100644 cpp/src/selection/select_k_float_int32_t.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d4821a273e..d9bea1864e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -407,6 +407,7 @@ add_library(
   src/neighbors/nn_descent_half.cu
   src/neighbors/nn_descent_int8.cu
   src/neighbors/nn_descent_uint8.cu
+  src/neighbors/reachability.cu
   src/neighbors/refine/detail/refine_device_float_float.cu
   src/neighbors/refine/detail/refine_device_half_float.cu
   src/neighbors/refine/detail/refine_device_int8_t_float.cu
@@ -417,6 +418,7 @@ add_library(
   src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
   src/neighbors/sample_filter.cu
   src/selection/select_k_float_int64_t.cu
+  src/selection/select_k_float_int32_t.cu
   src/selection/select_k_float_uint32_t.cu
   src/selection/select_k_half_uint32_t.cu
   src/stats/silhouette_score.cu
diff --git a/cpp/include/cuvs/neighbors/reachability.hpp b/cpp/include/cuvs/neighbors/reachability.hpp
new file mode 100644
index 0000000000..9746ac856a
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/reachability.hpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/sparse/coo.hpp>
+
+#include <cuvs/distance/distance.hpp>
+
+namespace cuvs::neighbors::reachability {
+
+/**
+ * @defgroup reachability_cpp Mutual Reachability
+ * @{
+ */
+/**
+ * Constructs a mutual reachability graph, which is a k-nearest neighbors
+ * graph projected into mutual reachability space using the following
+ * function for each data point, where core_distance is the distance
+ * to the kth neighbor: max(core_distance(a), core_distance(b), d(a, b))
+ *
+ * Unfortunately, points in the tails of the pdf (e.g. in sparse regions
+ * of the space) can have very large neighborhoods, which will impact
+ * nearby neighborhoods. Because of this, it's possible that the
+ * radius for points in the main mass, which might have a very small
+ * radius initially, to expand very large. As a result, the initial
+ * knn which was used to compute the core distances may no longer
+ * capture the actual neighborhoods after projection into mutual
+ * reachability space.
+ *
+ * For the experimental version, we execute the knn twice- once
+ * to compute the radii (core distances) and again to capture
+ * the final neighborhoods. Future iterations of this algorithm
+ * will work improve upon this "exact" version, by using
+ * more specialized data structures, such as space-partitioning
+ * structures. It has also been shown that approximate nearest
+ * neighbors can yield reasonable neighborhoods as the
+ * data sizes increase.
+ *
+ * @param[in] handle raft handle for resource reuse
+ * @param[in] X input data points (size m * n)
+ * @param[in] min_samples this neighborhood will be selected for core distances
+ * @param[out] indptr CSR indptr of output knn graph (size m + 1)
+ * @param[out] core_dists output core distances array (size m)
+ * @param[out] out COO object, uninitialized on entry, on exit it stores the
+ *             (symmetrized) maximum reachability distance for the k nearest
+ *             neighbors.
+ * @param[in] metric distance metric to use, default Euclidean
+ * @param[in] alpha weight applied when internal distance is chosen for
+ *            mutual reachability (value of 1.0 disables the weighting)
+ */
+void mutual_reachability_graph(
+  const raft::resources& handle,
+  raft::device_matrix_view<const float, int, raft::row_major> X,
+  int min_samples,
+  raft::device_vector_view<int> indptr,
+  raft::device_vector_view<float> core_dists,
+  raft::sparse::COO<float, int>& out,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded,
+  float alpha                         = 1.0);
+/**
+ * @}
+ */
+}  // namespace cuvs::neighbors::reachability
diff --git a/cpp/include/cuvs/selection/select_k.hpp b/cpp/include/cuvs/selection/select_k.hpp
index dc34caf419..e4dfdb12ce 100644
--- a/cpp/include/cuvs/selection/select_k.hpp
+++ b/cpp/include/cuvs/selection/select_k.hpp
@@ -87,6 +87,16 @@ void select_k(
   SelectAlgo algo                                                       = SelectAlgo::kAuto,
   std::optional<raft::device_vector_view<const int64_t, int64_t>> len_i = std::nullopt);
 
+void select_k(raft::resources const& handle,
+              raft::device_matrix_view<const float, int64_t, raft::row_major> in_val,
+              std::optional<raft::device_matrix_view<const int, int64_t, raft::row_major>> in_idx,
+              raft::device_matrix_view<float, int64_t, raft::row_major> out_val,
+              raft::device_matrix_view<int, int64_t, raft::row_major> out_idx,
+              bool select_min,
+              bool sorted                                                       = false,
+              SelectAlgo algo                                                   = SelectAlgo::kAuto,
+              std::optional<raft::device_vector_view<const int, int64_t>> len_i = std::nullopt);
+
 /**
  * Select k smallest or largest key/values from each row in the input data.
  *
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index cf27bcde70..3aa1d7529f 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -62,7 +62,10 @@ namespace cuvs::neighbors::detail {
  * Calculates brute force knn, using a fixed memory budget
  * by tiling over both the rows and columns of pairwise_distances
  */
-template <typename ElementType = float, typename IndexType = int64_t, typename DistanceT = float>
+template <typename ElementType      = float,
+          typename IndexType        = int64_t,
+          typename DistanceT        = float,
+          typename DistanceEpilogue = raft::identity_op>
 void tiled_brute_force_knn(const raft::resources& handle,
                            const ElementType* search,  // size (m ,d)
                            const ElementType* index,   // size (n ,d)
@@ -78,7 +81,8 @@ void tiled_brute_force_knn(const raft::resources& handle,
                            size_t max_col_tile_size                  = 0,
                            const DistanceT* precomputed_index_norms  = nullptr,
                            const DistanceT* precomputed_search_norms = nullptr,
-                           const uint32_t* filter_bitmap             = nullptr)
+                           const uint32_t* filter_bitmap             = nullptr,
+                           DistanceEpilogue distance_epilogue        = raft::identity_op())
 {
   // Figure out the number of rows/cols to tile for
   size_t tile_rows = 0;
@@ -207,7 +211,8 @@ void tiled_brute_force_knn(const raft::resources& handle,
             IndexType col = j + (idx % current_centroid_size);
 
             cuvs::distance::detail::ops::l2_exp_cutlass_op<DistanceT, DistanceT> l2_op(sqrt);
-            return l2_op(row_norms[row], col_norms[col], dist[idx]);
+            auto val = l2_op(row_norms[row], col_norms[col], dist[idx]);
+            return distance_epilogue(val, row, col);
           });
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         auto row_norms = precomputed_search_norms ? precomputed_search_norms : search_norms.data();
@@ -221,8 +226,22 @@ void tiled_brute_force_knn(const raft::resources& handle,
             IndexType row = i + (idx / current_centroid_size);
             IndexType col = j + (idx % current_centroid_size);
             auto val      = DistanceT(1.0) - dist[idx] / DistanceT(row_norms[row] * col_norms[col]);
-            return val;
+            return distance_epilogue(val, row, col);
           });
+      } else {
+        // if we're not l2 distance, and we have a distance epilogue - run it now
+        if constexpr (!std::is_same_v<DistanceEpilogue, raft::identity_op>) {
+          auto distances_ptr = temp_distances.data();
+          raft::linalg::map_offset(
+            handle,
+            raft::make_device_vector_view(temp_distances.data(),
+                                          current_query_size * current_centroid_size),
+            [=] __device__(size_t idx) {
+              IndexType row = i + (idx / current_centroid_size);
+              IndexType col = j + (idx % current_centroid_size);
+              return distance_epilogue(distances_ptr[idx], row, col);
+            });
+        }
       }
 
       if (filter_bitmap != nullptr) {
diff --git a/cpp/src/neighbors/detail/reachability.cuh b/cpp/src/neighbors/detail/reachability.cuh
new file mode 100644
index 0000000000..903c6f1da8
--- /dev/null
+++ b/cpp/src/neighbors/detail/reachability.cuh
@@ -0,0 +1,251 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "./knn_brute_force.cuh"
+
+#include <raft/linalg/unary_op.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/sparse/linalg/symmetrize.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cuvs::neighbors::detail::reachability {
+
+/**
+ * Extract core distances from KNN graph. This is essentially
+ * performing a knn_dists[:,min_pts]
+ * @tparam value_idx data type for integrals
+ * @tparam value_t data type for distance
+ * @tparam tpb block size for kernel
+ * @param[in] knn_dists knn distance array (size n * k)
+ * @param[in] min_samples this neighbor will be selected for core distances
+ * @param[in] n_neighbors the number of neighbors of each point in the knn graph
+ * @param[in] n number of samples
+ * @param[out] out output array (size n)
+ * @param[in] stream stream for which to order cuda operations
+ */
+template <typename value_idx, typename value_t, int tpb = 256>
+void core_distances(
+  value_t* knn_dists, int min_samples, int n_neighbors, size_t n, value_t* out, cudaStream_t stream)
+{
+  ASSERT(n_neighbors >= min_samples,
+         "the size of the neighborhood should be greater than or equal to min_samples");
+
+  auto exec_policy = rmm::exec_policy(stream);
+
+  auto indices = thrust::make_counting_iterator<value_idx>(0);
+
+  thrust::transform(exec_policy, indices, indices + n, out, [=] __device__(value_idx row) {
+    return knn_dists[row * n_neighbors + (min_samples - 1)];
+  });
+}
+
+/**
+ * Wraps the brute force knn API, to be used for both training and prediction
+ * @tparam value_idx data type for integrals
+ * @tparam value_t data type for distance
+ * @param[in] handle raft handle for resource reuse
+ * @param[in] X input data points (size m * n)
+ * @param[out] inds nearest neighbor indices (size n_search_items * k)
+ * @param[out] dists nearest neighbor distances (size n_search_items * k)
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[in] search_items array of items to search of dimensionality D (size n_search_items * n)
+ * @param[in] n_search_items number of rows in search_items
+ * @param[in] k number of nearest neighbors
+ * @param[in] metric distance metric to use
+ */
+template <typename value_idx, typename value_t>
+void compute_knn(const raft::resources& handle,
+                 const value_t* X,
+                 value_idx* inds,
+                 value_t* dists,
+                 size_t m,
+                 size_t n,
+                 const value_t* search_items,
+                 size_t n_search_items,
+                 int k,
+                 cuvs::distance::DistanceType metric)
+{
+  // perform knn
+  tiled_brute_force_knn(handle, X, search_items, m, n_search_items, n, k, dists, inds, metric);
+}
+
+/*
+  @brief Internal function for CPU->GPU interop
+         to compute core_dists
+*/
+template <typename value_idx, typename value_t>
+void _compute_core_dists(const raft::resources& handle,
+                         const value_t* X,
+                         value_t* core_dists,
+                         size_t m,
+                         size_t n,
+                         cuvs::distance::DistanceType metric,
+                         int min_samples)
+{
+  RAFT_EXPECTS(metric == cuvs::distance::DistanceType::L2SqrtExpanded,
+               "Currently only L2 expanded distance is supported");
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  rmm::device_uvector<value_idx> inds(min_samples * m, stream);
+  rmm::device_uvector<value_t> dists(min_samples * m, stream);
+
+  // perform knn
+  compute_knn(handle, X, inds.data(), dists.data(), m, n, X, m, min_samples, metric);
+
+  // Slice core distances (distances to kth nearest neighbor)
+  core_distances<value_idx>(dists.data(), min_samples, min_samples, m, core_dists, stream);
+}
+
+//  Functor to post-process distances into reachability space
+template <typename value_idx, typename value_t>
+struct ReachabilityPostProcess {
+  DI value_t operator()(value_t value, value_idx row, value_idx col) const
+  {
+    return max(core_dists[col], max(core_dists[row], alpha * value));
+  }
+
+  const value_t* core_dists;
+  value_t alpha;
+};
+
+/**
+ * Given core distances, Fuses computations of L2 distances between all
+ * points, projection into mutual reachability space, and k-selection.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param[in] handle raft handle for resource reuse
+ * @param[out] out_inds  output indices array (size m * k)
+ * @param[out] out_dists output distances array (size m * k)
+ * @param[in] X input data points (size m * n)
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[in] k neighborhood size (includes self-loop)
+ * @param[in] core_dists array of core distances (size m)
+ */
+template <typename value_idx, typename value_t>
+void mutual_reachability_knn_l2(const raft::resources& handle,
+                                value_idx* out_inds,
+                                value_t* out_dists,
+                                const value_t* X,
+                                size_t m,
+                                size_t n,
+                                int k,
+                                value_t* core_dists,
+                                value_t alpha)
+{
+  // Create a functor to postprocess distances into mutual reachability space
+  // Note that we can't use a lambda for this here, since we get errors like:
+  // `A type local to a function cannot be used in the template argument of the
+  // enclosing parent function (and any parent classes) of an extended __device__
+  // or __host__ __device__ lambda`
+  auto epilogue = ReachabilityPostProcess<value_idx, value_t>{core_dists, alpha};
+
+  cuvs::neighbors::detail::
+    tiled_brute_force_knn<value_t, value_idx, value_t, ReachabilityPostProcess<value_idx, value_t>>(
+      handle,
+      X,
+      X,
+      m,
+      m,
+      n,
+      k,
+      out_dists,
+      out_inds,
+      cuvs::distance::DistanceType::L2SqrtExpanded,
+      2.0,
+      0,
+      0,
+      nullptr,
+      nullptr,
+      nullptr,
+      epilogue);
+}
+
+template <typename value_idx, typename value_t>
+void mutual_reachability_graph(const raft::resources& handle,
+                               const value_t* X,
+                               size_t m,
+                               size_t n,
+                               cuvs::distance::DistanceType metric,
+                               int min_samples,
+                               value_t alpha,
+                               value_idx* indptr,
+                               value_t* core_dists,
+                               raft::sparse::COO<value_t, value_idx>& out)
+{
+  RAFT_EXPECTS(metric == cuvs::distance::DistanceType::L2SqrtExpanded,
+               "Currently only L2 expanded distance is supported");
+
+  auto stream      = raft::resource::get_cuda_stream(handle);
+  auto exec_policy = raft::resource::get_thrust_policy(handle);
+
+  rmm::device_uvector<value_idx> coo_rows(min_samples * m, stream);
+  rmm::device_uvector<value_idx> inds(min_samples * m, stream);
+  rmm::device_uvector<value_t> dists(min_samples * m, stream);
+
+  // perform knn
+  compute_knn(handle, X, inds.data(), dists.data(), m, n, X, m, min_samples, metric);
+
+  // Slice core distances (distances to kth nearest neighbor)
+  core_distances<value_idx>(dists.data(), min_samples, min_samples, m, core_dists, stream);
+
+  /**
+   * Compute L2 norm
+   */
+  mutual_reachability_knn_l2(
+    handle, inds.data(), dists.data(), X, m, n, min_samples, core_dists, (value_t)1.0 / alpha);
+
+  // self-loops get max distance
+  auto coo_rows_counting_itr = thrust::make_counting_iterator<value_idx>(0);
+  thrust::transform(exec_policy,
+                    coo_rows_counting_itr,
+                    coo_rows_counting_itr + (m * min_samples),
+                    coo_rows.data(),
+                    [min_samples] __device__(value_idx c) -> value_idx { return c / min_samples; });
+
+  raft::sparse::linalg::symmetrize(
+    handle, coo_rows.data(), inds.data(), dists.data(), m, m, min_samples * m, out);
+
+  raft::sparse::convert::sorted_coo_to_csr(out.rows(), out.nnz, indptr, m + 1, stream);
+
+  // self-loops get max distance
+  auto transform_in =
+    thrust::make_zip_iterator(thrust::make_tuple(out.rows(), out.cols(), out.vals()));
+
+  thrust::transform(exec_policy,
+                    transform_in,
+                    transform_in + out.nnz,
+                    out.vals(),
+                    [=] __device__(const thrust::tuple<value_idx, value_idx, value_t>& tup) {
+                      return thrust::get<0>(tup) == thrust::get<1>(tup)
+                               ? std::numeric_limits<value_t>::max()
+                               : thrust::get<2>(tup);
+                    });
+}
+
+}  // namespace cuvs::neighbors::detail::reachability
diff --git a/cpp/src/neighbors/reachability.cu b/cpp/src/neighbors/reachability.cu
new file mode 100644
index 0000000000..2e366106c3
--- /dev/null
+++ b/cpp/src/neighbors/reachability.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/reachability.hpp>
+
+#include "./detail/reachability.cuh"
+
+namespace cuvs::neighbors::reachability {
+
+void mutual_reachability_graph(const raft::resources& handle,
+                               raft::device_matrix_view<const float, int, raft::row_major> X,
+                               int min_samples,
+                               raft::device_vector_view<int> indptr,
+                               raft::device_vector_view<float> core_dists,
+                               raft::sparse::COO<float, int>& out,
+                               cuvs::distance::DistanceType metric,
+                               float alpha)
+{
+  RAFT_EXPECTS(core_dists.extent(0) == static_cast<size_t>(X.extent(0)),
+               "core_dists doesn't have expected size");
+  RAFT_EXPECTS(indptr.extent(0) == static_cast<size_t>(X.extent(0) + 1),
+               "indptr doesn't have expected size");
+
+  cuvs::neighbors::detail::reachability::mutual_reachability_graph<int, float>(
+    handle,
+    X.data_handle(),
+    X.extent(0),
+    X.extent(1),
+    metric,
+    min_samples,
+    alpha,
+    indptr.data_handle(),
+    core_dists.data_handle(),
+    out);
+}
+}  // namespace cuvs::neighbors::reachability
diff --git a/cpp/src/selection/select_k_float_int32_t.cu b/cpp/src/selection/select_k_float_int32_t.cu
new file mode 100644
index 0000000000..66672a6423
--- /dev/null
+++ b/cpp/src/selection/select_k_float_int32_t.cu
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./select_k.cuh"
+
+instantiate_cuvs_selection_select_k(float, int);

From 2fe2e8853738a3ddf5248b2ceb9bab7c92adebb5 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 2 Oct 2024 08:00:40 -0700
Subject: [PATCH 165/623] Add multigpu kmeans fit function (#348)

Changes to support using kmeans clustering inside of cuml, so we can transition cuml off of the RAFT kmeans code

* Add a multigpu kmeans fit function
* Adds instantiations for kmeans on int64_t indicies, which unfortunately also requires int64_t indices for the PW distance functions
* Add support for `double` precision kmeans

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/348
---
 cpp/CMakeLists.txt                            |   8 +
 cpp/include/cuvs/cluster/kmeans.hpp           | 506 +++++++++++-
 cpp/src/cluster/detail/connectivities.cuh     |   8 +-
 cpp/src/cluster/detail/kmeans.cuh             |   4 +-
 cpp/src/cluster/detail/kmeans_common.cuh      |  31 +-
 cpp/src/cluster/detail/kmeans_mg.cuh          | 781 ++++++++++++++++++
 cpp/src/cluster/kmeans.cuh                    |  11 +-
 cpp/src/cluster/kmeans_fit_double.cu          |  45 +
 cpp/src/cluster/kmeans_fit_float.cu           |  14 +-
 cpp/src/cluster/kmeans_fit_mg_double.cu       |  50 ++
 cpp/src/cluster/kmeans_fit_mg_float.cu        |  50 ++
 cpp/src/cluster/kmeans_fit_predict_double.cu  |  49 ++
 cpp/src/cluster/kmeans_fit_predict_float.cu   |  14 +
 cpp/src/cluster/kmeans_mg.hpp                 |  75 ++
 cpp/src/cluster/kmeans_predict_double.cu      |  49 ++
 cpp/src/cluster/kmeans_predict_float.cu       |  13 +
 cpp/src/cluster/kmeans_transform_double.cu    |  31 +
 .../detail/pairwise_matrix/dispatch-ext.cuh   |   5 +
 .../pairwise_matrix/dispatch_00_generate.py   |  30 +-
 ...2_expanded_double_double_double_int64_t.cu |  56 ++
 ...h_l2_expanded_float_float_float_int64_t.cu |  51 ++
 .../detail/pairwise_matrix/dispatch_rbf.cu    |   2 +-
 cpp/src/distance/distance-ext.cuh             |   9 +
 cpp/src/distance/distance.cu                  |  10 +
 cpp/test/cluster/kmeans_mg.cu                 | 200 +++++
 25 files changed, 2059 insertions(+), 43 deletions(-)
 create mode 100644 cpp/src/cluster/detail/kmeans_mg.cuh
 create mode 100644 cpp/src/cluster/kmeans_fit_double.cu
 create mode 100644 cpp/src/cluster/kmeans_fit_mg_double.cu
 create mode 100644 cpp/src/cluster/kmeans_fit_mg_float.cu
 create mode 100644 cpp/src/cluster/kmeans_fit_predict_double.cu
 create mode 100644 cpp/src/cluster/kmeans_mg.hpp
 create mode 100644 cpp/src/cluster/kmeans_predict_double.cu
 create mode 100644 cpp/src/cluster/kmeans_transform_double.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu
 create mode 100644 cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu
 create mode 100644 cpp/test/cluster/kmeans_mg.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d9bea1864e..78c67d9c87 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -290,9 +290,14 @@ target_compile_options(
 add_library(
   cuvs SHARED
   src/cluster/kmeans_balanced_fit_float.cu
+  src/cluster/kmeans_fit_mg_float.cu
+  src/cluster/kmeans_fit_mg_double.cu
+  src/cluster/kmeans_fit_double.cu
   src/cluster/kmeans_fit_float.cu
   src/cluster/kmeans_auto_find_k_float.cu
+  src/cluster/kmeans_fit_predict_double.cu
   src/cluster/kmeans_fit_predict_float.cu
+  src/cluster/kmeans_predict_double.cu
   src/cluster/kmeans_predict_float.cu
   src/cluster/kmeans_balanced_fit_float.cu
   src/cluster/kmeans_balanced_fit_predict_float.cu
@@ -300,6 +305,7 @@ add_library(
   src/cluster/kmeans_balanced_fit_int8.cu
   src/cluster/kmeans_balanced_fit_predict_int8.cu
   src/cluster/kmeans_balanced_predict_int8.cu
+  src/cluster/kmeans_transform_double.cu
   src/cluster/kmeans_transform_float.cu
   src/cluster/single_linkage_float.cu
   src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
@@ -342,6 +348,8 @@ add_library(
   src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
   src/distance/detail/pairwise_matrix/dispatch_rbf.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu
+  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu
   src/distance/detail/fused_distance_nn.cu
   src/distance/distance.cu
   src/distance/pairwise_distance.cu
diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index 75205fa4f1..89b3acc24f 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -153,7 +153,7 @@ struct balanced_params : base_params {
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::params params;
+ *   cuvs::cluster::kmeans::params params;
  *   int n_features = 15, inertia, n_iter;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *
@@ -203,7 +203,159 @@ void fit(raft::resources const& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::params params;
+ *   cuvs::cluster::kmeans::params params;
+ *   int64_t n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, params.n_clusters,
+ * n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids,
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int64_t> X,
+         std::optional<raft::device_vector_view<const float, int64_t>> sample_weight,
+         raft::device_matrix_view<float, int64_t> centroids,
+         raft::host_scalar_view<float, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter);
+
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<double, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids,
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int> X,
+         std::optional<raft::device_vector_view<const double, int>> sample_weight,
+         raft::device_matrix_view<double, int> centroids,
+         raft::host_scalar_view<double, int> inertia,
+         raft::host_scalar_view<int, int> n_iter);
+
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
+ *   int64_t n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<double, int64_t>(handle, params.n_clusters,
+ * n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids,
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int64_t> X,
+         std::optional<raft::device_vector_view<const double, int64_t>> sample_weight,
+         raft::device_matrix_view<double, int64_t> centroids,
+         raft::host_scalar_view<double, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter);
+
+/**
+ * @brief Find clusters with k-means algorithm.
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
  *   int n_features = 15, inertia, n_iter;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *
@@ -250,7 +402,7 @@ void fit(raft::resources const& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::balanced_params params;
+ *   cuvs::cluster::kmeans::balanced_params params;
  *   int n_features = 15;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *
@@ -284,7 +436,7 @@ void fit(const raft::resources& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::balanced_params params;
+ *   cuvs::cluster::kmeans::balanced_params params;
  *   int n_features = 15;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *
@@ -308,7 +460,6 @@ void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
          raft::device_matrix_view<const int8_t, int> X,
          raft::device_matrix_view<int8_t, int> centroids);
-
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -318,7 +469,7 @@ void fit(const raft::resources& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::params params;
+ *   cuvs::cluster::kmeans::params params;
  *   int n_features = 15, inertia, n_iter;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *
@@ -363,7 +514,7 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<uint32_t, int> labels,
+             raft::device_vector_view<int, int> labels,
              bool normalize_weight,
              raft::host_scalar_view<float> inertia);
 
@@ -376,7 +527,7 @@ void predict(raft::resources const& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::params params;
+ *   cuvs::cluster::kmeans::params params;
  *   int n_features = 15, inertia, n_iter;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *
@@ -388,7 +539,7 @@ void predict(raft::resources const& handle,
  *               raft::make_scalar_view(&inertia),
  *               raft::make_scalar_view(&n_iter));
  *   ...
- *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *   auto labels = raft::make_device_vector<int64_t, int>(handle, X.extent(0));
  *
  *   kmeans::predict(handle,
  *                   params,
@@ -404,18 +555,26 @@ void predict(raft::resources const& handle,
  * @param[in]     params           Parameters for KMeans model.
  * @param[in]     X                New data to predict.
  *                                 [dim = n_samples x n_features]
+ * @param[in]     sample_weight    Optional weights for each observation in X.
+ *                                 [len = n_samples]
  * @param[in]     centroids        Cluster centroids. The data must be in
  *                                 row-major format.
  *                                 [dim = n_clusters x n_features]
+ * @param[in]     normalize_weight True if the weights should be normalized
  * @param[out]    labels           Index of the cluster each sample in X
  *                                 belongs to.
  *                                 [len = n_samples]
+ * @param[out]    inertia          Sum of squared distances of samples to
+ *                                 their closest cluster center.
  */
-void predict(const raft::resources& handle,
-             cuvs::cluster::kmeans::balanced_params const& params,
-             raft::device_matrix_view<const int8_t, int> X,
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const float, int> X,
+             std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<uint32_t, int> labels);
+             raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<float> inertia);
 
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
@@ -426,22 +585,144 @@ void predict(const raft::resources& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15;
+ *   cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<double, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids.view(),
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ *   ...
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   std::nullopt,
+ *                   centroids.view(),
+ *                   false,
+ *                   labels.view(),
+ *                   raft::make_scalar_view(&ineratia));
+ * @endcode
+ *
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     sample_weight    Optional weights for each observation in X.
+ *                                 [len = n_samples]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[in]     normalize_weight True if the weights should be normalized
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ * @param[out]    inertia          Sum of squared distances of samples to
+ *                                 their closest cluster center.
+ */
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const double, int> X,
+             std::optional<raft::device_vector_view<const double, int>> sample_weight,
+             raft::device_matrix_view<const double, int> centroids,
+             raft::device_vector_view<int, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<double> inertia);
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<double, int>(handle, params.n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               std::nullopt,
+ *               centroids.view(),
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
+ *   ...
+ *   auto labels = raft::make_device_vector<int64_t, int>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   std::nullopt,
+ *                   centroids.view(),
+ *                   false,
+ *                   labels.view(),
+ *                   raft::make_scalar_view(&ineratia));
+ * @endcode
+ *
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     sample_weight    Optional weights for each observation in X.
+ *                                 [len = n_samples]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[in]     normalize_weight True if the weights should be normalized
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ * @param[out]    inertia          Sum of squared distances of samples to
+ *                                 their closest cluster center.
+ */
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const double, int> X,
+             std::optional<raft::device_vector_view<const double, int>> sample_weight,
+             raft::device_matrix_view<const double, int> centroids,
+             raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<double> inertia);
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
  *               X,
- *               centroids.view());
+ *               std::nullopt,
+ *               centroids.view(),
+ *               raft::make_scalar_view(&inertia),
+ *               raft::make_scalar_view(&n_iter));
  *   ...
  *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
  *
  *   kmeans::predict(handle,
  *                   params,
  *                   X,
+ *                   std::nullopt,
  *                   centroids.view(),
- *                   labels.view());
+ *                   false,
+ *                   labels.view(),
+ *                   raft::make_scalar_view(&ineratia));
  * @endcode
  *
  * @param[in]     handle           The raft handle.
@@ -457,7 +738,7 @@ void predict(const raft::resources& handle,
  */
 void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
-             raft::device_matrix_view<const float, int> X,
+             raft::device_matrix_view<const int8_t, int> X,
              raft::device_matrix_view<const float, int> centroids,
              raft::device_vector_view<uint32_t, int> labels);
 
@@ -471,7 +752,7 @@ void predict(const raft::resources& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::params params;
+ *   cuvs::cluster::kmeans::params params;
  *   int n_features = 15, inertia, n_iter;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
@@ -516,6 +797,171 @@ void fit_predict(raft::resources const& handle,
                  raft::host_scalar_view<float> inertia,
                  raft::host_scalar_view<int> n_iter);
 
+/**
+ * @brief Compute k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
+ *   int64_t n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, params.n_clusters,
+ * n_features); auto labels = raft::make_device_vector<int64_t, int64_t>(handle, X.extent(0));
+ *
+ *   kmeans::fit_predict(handle,
+ *                       params,
+ *                       X,
+ *                       std::nullopt,
+ *                       centroids.view(),
+ *                       labels.view(),
+ *                       raft::make_scalar_view(&inertia),
+ *                       raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const float, int64_t> X,
+                 std::optional<raft::device_vector_view<const float, int64_t>> sample_weight,
+                 std::optional<raft::device_matrix_view<float, int64_t>> centroids,
+                 raft::device_vector_view<int64_t, int64_t> labels,
+                 raft::host_scalar_view<float> inertia,
+                 raft::host_scalar_view<int64_t> n_iter);
+
+/**
+ * @brief Compute k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
+ *   int n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<double, int>(handle, params.n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::fit_predict(handle,
+ *                       params,
+ *                       X,
+ *                       std::nullopt,
+ *                       centroids.view(),
+ *                       labels.view(),
+ *                       raft::make_scalar_view(&inertia),
+ *                       raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const double, int> X,
+                 std::optional<raft::device_vector_view<const double, int>> sample_weight,
+                 std::optional<raft::device_matrix_view<double, int>> centroids,
+                 raft::device_vector_view<int, int> labels,
+                 raft::host_scalar_view<double> inertia,
+                 raft::host_scalar_view<int> n_iter);
+
+/**
+ * @brief Compute k-means clustering and predicts cluster index for each sample
+ * in the input.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::params params;
+ *   int64_t n_features = 15, inertia, n_iter;
+ *   auto centroids = raft::make_device_matrix<double, int64_t>(handle, params.n_clusters,
+ * n_features); auto labels = raft::make_device_vector<int64_t, int64_t>(handle, X.extent(0));
+ *
+ *   kmeans::fit_predict(handle,
+ *                       params,
+ *                       X,
+ *                       std::nullopt,
+ *                       centroids.view(),
+ *                       labels.view(),
+ *                       raft::make_scalar_view(&inertia),
+ *                       raft::make_scalar_view(&n_iter));
+ * @endcode
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must be
+ *                              in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     Optional
+ *                              [in] When init is InitMethod::Array, use
+ *                              centroids  as the initial cluster centers
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    labels        Index of the cluster each sample in X belongs
+ *                              to.
+ *                              [len = n_samples]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const double, int64_t> X,
+                 std::optional<raft::device_vector_view<const double, int64_t>> sample_weight,
+                 std::optional<raft::device_matrix_view<double, int64_t>> centroids,
+                 raft::device_vector_view<int64_t, int64_t> labels,
+                 raft::host_scalar_view<double> inertia,
+                 raft::host_scalar_view<int64_t> n_iter);
+
 /**
  * @brief Compute balanced k-means clustering and predicts cluster index for each sample
  * in the input.
@@ -526,7 +972,7 @@ void fit_predict(raft::resources const& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::balanced_params params;
+ *   cuvs::cluster::kmeans::balanced_params params;
  *   int n_features = 15;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
@@ -570,7 +1016,7 @@ void fit_predict(const raft::resources& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *    cuvs::cluster::kmeans::balanced_params params;
+ *   cuvs::cluster::kmeans::balanced_params params;
  *   int n_features = 15;
  *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
  *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
@@ -623,6 +1069,24 @@ void transform(raft::resources const& handle,
                raft::device_matrix_view<const float, int> centroids,
                raft::device_matrix_view<float, int> X_new);
 
+/**
+ * @brief Transform X to a cluster-distance space.
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format
+ *                              [dim = n_samples x n_features]
+ * @param[in]     centroids     Cluster centroids. The data must be in row-major format.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    X_new         X transformed in the new space.
+ *                              [dim = n_samples x n_features]
+ */
+void transform(raft::resources const& handle,
+               const kmeans::params& params,
+               raft::device_matrix_view<const double, int> X,
+               raft::device_matrix_view<const double, int> centroids,
+               raft::device_matrix_view<double, int> X_new);
 /**
  * @}
  */
diff --git a/cpp/src/cluster/detail/connectivities.cuh b/cpp/src/cluster/detail/connectivities.cuh
index ada4241924..e61c9166f0 100644
--- a/cpp/src/cluster/detail/connectivities.cuh
+++ b/cpp/src/cluster/detail/connectivities.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include "../../distance/distance.cuh"
+#include "./kmeans_common.cuh"
 #include <cuvs/cluster/agglomerative.hpp>
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
@@ -153,7 +153,11 @@ void pairwise_distances(const raft::resources& handle,
   // TODO: It would ultimately be nice if the MST could accept
   // dense inputs directly so we don't need to double the memory
   // usage to hand it a sparse array here.
-  distance::pairwise_distance<value_t, value_idx>(handle, X, X, data, m, m, n, metric);
+  auto X_view = raft::make_device_matrix_view<const value_t, value_idx>(X, m, n);
+
+  cuvs::cluster::kmeans::detail::pairwise_distance_kmeans<value_t, value_idx>(
+    handle, X_view, X_view, raft::make_device_matrix_view<value_t, value_idx>(data, m, m), metric);
+
   // self-loops get max distance
   auto transform_in =
     thrust::make_zip_iterator(thrust::make_tuple(thrust::make_counting_iterator(0), data));
diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
index e7d4bdf76a..9b673bca34 100644
--- a/cpp/src/cluster/detail/kmeans.cuh
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -198,7 +198,7 @@ void kmeansPlusPlus(raft::resources const& handle,
     // Output - pwd [n_trials x n_samples]
     auto pwd = distBuffer.view();
     cuvs::cluster::kmeans::detail::pairwise_distance_kmeans<DataT, IndexT>(
-      handle, centroidCandidates.view(), X, pwd, workspace, metric);
+      handle, centroidCandidates.view(), X, pwd, metric);
 
     // Update nearest cluster distance for each centroid candidate
     // Note pwd and minDistBuf points to same buffer which currently holds pairwise distance values.
@@ -1247,7 +1247,7 @@ void kmeans_transform(raft::resources const& handle,
     // calculate pairwise distance between cluster centroids and current batch
     // of input dataset
     pairwise_distance_kmeans<DataT, IndexT>(
-      handle, datasetView, centroids, pairwiseDistanceView, workspace, metric);
+      handle, datasetView, centroids, pairwiseDistanceView, metric);
   }
 }
 
diff --git a/cpp/src/cluster/detail/kmeans_common.cuh b/cpp/src/cluster/detail/kmeans_common.cuh
index 04c1a68020..eec71b5d2b 100644
--- a/cpp/src/cluster/detail/kmeans_common.cuh
+++ b/cpp/src/cluster/detail/kmeans_common.cuh
@@ -293,7 +293,6 @@ void pairwise_distance_kmeans(raft::resources const& handle,
                               raft::device_matrix_view<const DataT, IndexT> X,
                               raft::device_matrix_view<const DataT, IndexT> centroids,
                               raft::device_matrix_view<DataT, IndexT> pairwiseDistance,
-                              rmm::device_uvector<char>& workspace,
                               cuvs::distance::DistanceType metric)
 {
   auto n_samples  = X.extent(0);
@@ -303,15 +302,23 @@ void pairwise_distance_kmeans(raft::resources const& handle,
   ASSERT(X.extent(1) == centroids.extent(1),
          "# features in dataset and centroids are different (must be same)");
 
-  cuvs::distance::pairwise_distance(handle,
-                                    X.data_handle(),
-                                    centroids.data_handle(),
-                                    pairwiseDistance.data_handle(),
-                                    n_samples,
-                                    n_clusters,
-                                    n_features,
-                                    workspace,
-                                    metric);
+  if (metric == cuvs::distance::DistanceType::L2Expanded) {
+    cuvs::distance::distance<cuvs::distance::DistanceType::L2Expanded,
+                             DataT,
+                             DataT,
+                             DataT,
+                             raft::layout_c_contiguous,
+                             IndexT>(handle, X, centroids, pairwiseDistance);
+  } else if (metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    cuvs::distance::distance<cuvs::distance::DistanceType::L2SqrtExpanded,
+                             DataT,
+                             DataT,
+                             DataT,
+                             raft::layout_c_contiguous,
+                             IndexT>(handle, X, centroids, pairwiseDistance);
+  } else {
+    RAFT_FAIL("kmeans requires L2Expanded or L2SqrtExpanded distance, have %i", metric);
+  }
 }
 
 // shuffle and randomly select 'n_samples_to_gather' from input 'in' and stores
@@ -461,7 +468,7 @@ void minClusterAndDistanceCompute(
         // calculate pairwise distance between current tile of cluster centroids
         // and input dataset
         pairwise_distance_kmeans<DataT, IndexT>(
-          handle, datasetView, centroidsView, pairwiseDistanceView, workspace, metric);
+          handle, datasetView, centroidsView, pairwiseDistanceView, metric);
 
         // argmin reduction returning <index, value> pair
         // calculates the closest centroid and the distance to the closest
@@ -591,7 +598,7 @@ void minClusterDistanceCompute(raft::resources const& handle,
         // calculate pairwise distance between current tile of cluster centroids
         // and input dataset
         pairwise_distance_kmeans<DataT, IndexT>(
-          handle, datasetView, centroidsView, pairwiseDistanceView, workspace, metric);
+          handle, datasetView, centroidsView, pairwiseDistanceView, metric);
 
         raft::linalg::coalescedReduction(minClusterDistanceView.data_handle(),
                                          pairwiseDistanceView.data_handle(),
diff --git a/cpp/src/cluster/detail/kmeans_mg.cuh b/cpp/src/cluster/detail/kmeans_mg.cuh
new file mode 100644
index 0000000000..b0f4355020
--- /dev/null
+++ b/cpp/src/cluster/detail/kmeans_mg.cuh
@@ -0,0 +1,781 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../kmeans.cuh"
+
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/kvp.hpp>
+#include <raft/core/resource/comms.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/linalg/map_then_reduce.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/reduce_rows_by_key.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/integer_utils.hpp>
+
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cuda/functional>
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+#include <cstdint>
+#include <random>
+
+namespace cuvs::cluster::kmeans::mg::detail {
+
+#define CUVS_LOG_KMEANS(handle, fmt, ...)                    \
+  do {                                                       \
+    bool isRoot = true;                                      \
+    if (raft::resource::comms_initialized(handle)) {         \
+      const auto& comm  = raft::resource::get_comms(handle); \
+      const int my_rank = comm.get_rank();                   \
+      isRoot            = my_rank == 0;                      \
+    }                                                        \
+    if (isRoot) { RAFT_LOG_DEBUG(fmt, ##__VA_ARGS__); }      \
+  } while (0)
+
+template <typename IndexT, typename DataT>
+struct KeyValueIndexOp {
+  __host__ __device__ __forceinline__ IndexT
+  operator()(const raft::KeyValuePair<IndexT, DataT>& a) const
+  {
+    return a.key;
+  }
+};
+
+#define KMEANS_COMM_ROOT 0
+
+static cuvs::cluster::kmeans::params default_params;
+
+// Selects 'n_clusters' samples randomly from X
+template <typename DataT, typename IndexT>
+void initRandom(const raft::resources& handle,
+                const cuvs::cluster::kmeans::params& params,
+                raft::device_matrix_view<const DataT, IndexT> X,
+                raft::device_matrix_view<DataT, IndexT> centroids)
+{
+  const auto& comm     = raft::resource::get_comms(handle);
+  cudaStream_t stream  = raft::resource::get_cuda_stream(handle);
+  auto n_local_samples = X.extent(0);
+  auto n_features      = X.extent(1);
+  auto n_clusters      = params.n_clusters;
+
+  const int my_rank = comm.get_rank();
+  const int n_ranks = comm.get_size();
+
+  std::vector<int> nCentroidsSampledByRank(n_ranks, 0);
+  std::vector<size_t> nCentroidsElementsToReceiveFromRank(n_ranks, 0);
+
+  const int nranks_reqd = std::min(n_ranks, n_clusters);
+  ASSERT(KMEANS_COMM_ROOT < nranks_reqd, "KMEANS_COMM_ROOT must be in [0,  %d)\n", nranks_reqd);
+
+  for (int rank = 0; rank < nranks_reqd; ++rank) {
+    int nCentroidsSampledInRank = n_clusters / nranks_reqd;
+    if (rank == KMEANS_COMM_ROOT) {
+      nCentroidsSampledInRank += n_clusters - nCentroidsSampledInRank * nranks_reqd;
+    }
+    nCentroidsSampledByRank[rank]             = nCentroidsSampledInRank;
+    nCentroidsElementsToReceiveFromRank[rank] = nCentroidsSampledInRank * n_features;
+  }
+
+  auto nCentroidsSampledInRank = nCentroidsSampledByRank[my_rank];
+  ASSERT((IndexT)nCentroidsSampledInRank <= (IndexT)n_local_samples,
+         "# random samples requested from rank-%d is larger than the available "
+         "samples at the rank (requested is %lu, available is %lu)",
+         my_rank,
+         (size_t)nCentroidsSampledInRank,
+         (size_t)n_local_samples);
+
+  auto centroidsSampledInRank =
+    raft::make_device_matrix<DataT, IndexT>(handle, nCentroidsSampledInRank, n_features);
+
+  cuvs::cluster::kmeans::shuffle_and_gather(
+    handle, X, centroidsSampledInRank.view(), nCentroidsSampledInRank, params.rng_state.seed);
+
+  std::vector<size_t> displs(n_ranks);
+  thrust::exclusive_scan(thrust::host,
+                         nCentroidsElementsToReceiveFromRank.begin(),
+                         nCentroidsElementsToReceiveFromRank.end(),
+                         displs.begin());
+
+  // gather centroids from all ranks
+  comm.allgatherv<DataT>(centroidsSampledInRank.data_handle(),        // sendbuff
+                         centroids.data_handle(),                     // recvbuff
+                         nCentroidsElementsToReceiveFromRank.data(),  // recvcount
+                         displs.data(),
+                         stream);
+}
+
+/*
+ * @brief Selects 'n_clusters' samples from X using scalable kmeans++ algorithm
+ * Scalable kmeans++ pseudocode
+ * 1: C = sample a point uniformly at random from X
+ * 2: psi = phi_X (C)
+ * 3: for O( log(psi) ) times do
+ * 4:   C' = sample each point x in X independently with probability
+ *           p_x = l * ( d^2(x, C) / phi_X (C) )
+ * 5:   C = C U C'
+ * 6: end for
+ * 7: For x in C, set w_x to be the number of points in X closer to x than any
+ *    other point in C
+ * 8: Recluster the weighted points in C into k clusters
+ */
+template <typename DataT, typename IndexT>
+void initKMeansPlusPlus(const raft::resources& handle,
+                        const cuvs::cluster::kmeans::params& params,
+                        raft::device_matrix_view<const DataT, IndexT> X,
+                        raft::device_matrix_view<DataT, IndexT> centroidsRawData,
+                        rmm::device_uvector<char>& workspace)
+{
+  const auto& comm    = raft::resource::get_comms(handle);
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  const int my_rank   = comm.get_rank();
+  const int n_rank    = comm.get_size();
+
+  auto n_samples  = X.extent(0);
+  auto n_features = X.extent(1);
+  auto n_clusters = params.n_clusters;
+  auto metric     = params.metric;
+
+  raft::random::RngState rng(params.rng_state.seed, raft::random::GeneratorType::GenPhilox);
+
+  // <<<< Step-1 >>> : C <- sample a point uniformly at random from X
+  //    1.1 - Select a rank r' at random from the available n_rank ranks with a
+  //          probability of 1/n_rank [Note - with same seed all rank selects
+  //          the same r' which avoids a call to comm]
+  //    1.2 - Rank r' samples a point uniformly at random from the local dataset
+  //          X which will be used as the initial centroid for kmeans++
+  //    1.3 - Communicate the initial centroid chosen by rank-r' to all other
+  //          ranks
+  std::mt19937 gen(params.rng_state.seed);
+  std::uniform_int_distribution<> dis(0, n_rank - 1);
+  int rp = dis(gen);
+
+  // buffer to flag the sample that is chosen as initial centroids
+  std::vector<std::uint8_t> h_isSampleCentroid(n_samples);
+  std::fill(h_isSampleCentroid.begin(), h_isSampleCentroid.end(), 0);
+
+  auto initialCentroid = raft::make_device_matrix<DataT, IndexT>(handle, 1, n_features);
+  CUVS_LOG_KMEANS(
+    handle, "@Rank-%d : KMeans|| : initial centroid is sampled at rank-%d\n", my_rank, rp);
+
+  //    1.2 - Rank r' samples a point uniformly at random from the local dataset
+  //          X which will be used as the initial centroid for kmeans++
+  if (my_rank == rp) {
+    std::mt19937 gen(params.rng_state.seed);
+    std::uniform_int_distribution<> dis(0, n_samples - 1);
+
+    int cIdx           = dis(gen);
+    auto centroidsView = raft::make_device_matrix_view<const DataT, IndexT>(
+      X.data_handle() + cIdx * n_features, 1, n_features);
+
+    raft::copy(
+      initialCentroid.data_handle(), centroidsView.data_handle(), centroidsView.size(), stream);
+
+    h_isSampleCentroid[cIdx] = 1;
+  }
+
+  // 1.3 - Communicate the initial centroid chosen by rank-r' to all other ranks
+  comm.bcast<DataT>(initialCentroid.data_handle(), initialCentroid.size(), rp, stream);
+
+  // device buffer to flag the sample that is chosen as initial centroid
+  auto isSampleCentroid = raft::make_device_vector<std::uint8_t, IndexT>(handle, n_samples);
+
+  raft::copy(
+    isSampleCentroid.data_handle(), h_isSampleCentroid.data(), isSampleCentroid.size(), stream);
+
+  rmm::device_uvector<DataT> centroidsBuf(0, stream);
+
+  // reset buffer to store the chosen centroid
+  centroidsBuf.resize(initialCentroid.size(), stream);
+  raft::copy(centroidsBuf.begin(), initialCentroid.data_handle(), initialCentroid.size(), stream);
+
+  auto potentialCentroids = raft::make_device_matrix_view<DataT, IndexT>(
+    centroidsBuf.data(), initialCentroid.extent(0), initialCentroid.extent(1));
+  // <<< End of Step-1 >>>
+
+  rmm::device_uvector<DataT> L2NormBuf_OR_DistBuf(0, stream);
+
+  // L2 norm of X: ||x||^2
+  auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    raft::linalg::rowNorm(L2NormX.data_handle(),
+                          X.data_handle(),
+                          X.extent(1),
+                          X.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  }
+
+  auto minClusterDistance = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  auto uniformRands       = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+
+  // <<< Step-2 >>>: psi <- phi_X (C)
+  auto clusterCost = raft::make_device_scalar<DataT>(handle, 0);
+
+  cuvs::cluster::kmeans::min_cluster_distance(handle,
+                                              X,
+                                              potentialCentroids,
+                                              minClusterDistance.view(),
+                                              L2NormX.view(),
+                                              L2NormBuf_OR_DistBuf,
+                                              params.metric,
+                                              params.batch_samples,
+                                              params.batch_centroids,
+                                              workspace);
+
+  // compute partial cluster cost from the samples in rank
+  cuvs::cluster::kmeans::cluster_cost(
+    handle,
+    minClusterDistance.view(),
+    workspace,
+    clusterCost.view(),
+    cuda::proclaim_return_type<DataT>(
+      [] __device__(const DataT& a, const DataT& b) { return a + b; }));
+
+  // compute total cluster cost by accumulating the partial cost from all the
+  // ranks
+  comm.allreduce(
+    clusterCost.data_handle(), clusterCost.data_handle(), 1, raft::comms::op_t::SUM, stream);
+
+  DataT psi = 0;
+  raft::copy(&psi, clusterCost.data_handle(), 1, stream);
+
+  // <<< End of Step-2 >>>
+
+  ASSERT(comm.sync_stream(stream) == raft::comms::status_t::SUCCESS,
+         "An error occurred in the distributed operation. This can result from "
+         "a failed rank");
+
+  // Scalable kmeans++ paper claims 8 rounds is sufficient
+  int niter = std::min(8, (int)ceil(log(psi)));
+  CUVS_LOG_KMEANS(handle,
+                  "@Rank-%d:KMeans|| :phi - %f, max # of iterations for kmeans++ loop - "
+                  "%d\n",
+                  my_rank,
+                  psi,
+                  niter);
+
+  // <<<< Step-3 >>> : for O( log(psi) ) times do
+  for (int iter = 0; iter < niter; ++iter) {
+    CUVS_LOG_KMEANS(handle,
+                    "@Rank-%d:KMeans|| - Iteration %d: # potential centroids sampled - "
+                    "%d\n",
+                    my_rank,
+                    iter,
+                    potentialCentroids.extent(0));
+
+    cuvs::cluster::kmeans::min_cluster_distance(handle,
+                                                X,
+                                                potentialCentroids,
+                                                minClusterDistance.view(),
+                                                L2NormX.view(),
+                                                L2NormBuf_OR_DistBuf,
+                                                params.metric,
+                                                params.batch_samples,
+                                                params.batch_centroids,
+                                                workspace);
+
+    cuvs::cluster::kmeans::cluster_cost(
+      handle,
+      minClusterDistance.view(),
+      workspace,
+      clusterCost.view(),
+      cuda::proclaim_return_type<DataT>(
+        [] __device__(const DataT& a, const DataT& b) { return a + b; }));
+    comm.allreduce(
+      clusterCost.data_handle(), clusterCost.data_handle(), 1, raft::comms::op_t::SUM, stream);
+    raft::copy(&psi, clusterCost.data_handle(), 1, stream);
+    ASSERT(comm.sync_stream(stream) == raft::comms::status_t::SUCCESS,
+           "An error occurred in the distributed operation. This can result "
+           "from a failed rank");
+
+    // <<<< Step-4 >>> : Sample each point x in X independently and identify new
+    // potentialCentroids
+    raft::random::uniform(
+      handle, rng, uniformRands.data_handle(), uniformRands.extent(0), (DataT)0, (DataT)1);
+    cuvs::cluster::kmeans::SamplingOp<DataT, IndexT> select_op(psi,
+                                                               params.oversampling_factor,
+                                                               n_clusters,
+                                                               uniformRands.data_handle(),
+                                                               isSampleCentroid.data_handle());
+
+    rmm::device_uvector<DataT> inRankCp(0, stream);
+    cuvs::cluster::kmeans::sample_centroids(handle,
+                                            X,
+                                            minClusterDistance.view(),
+                                            isSampleCentroid.view(),
+                                            select_op,
+                                            inRankCp,
+                                            workspace);
+    /// <<<< End of Step-4 >>>>
+
+    int* nPtsSampledByRank;
+    RAFT_CUDA_TRY(cudaMallocHost(&nPtsSampledByRank, n_rank * sizeof(int)));
+
+    /// <<<< Step-5 >>> : C = C U C'
+    // append the data in Cp from all ranks to the buffer holding the
+    // potentialCentroids
+    // RAFT_CUDA_TRY(cudaMemsetAsync(nPtsSampledByRank, 0, n_rank * sizeof(int), stream));
+    std::fill(nPtsSampledByRank, nPtsSampledByRank + n_rank, 0);
+    nPtsSampledByRank[my_rank] = inRankCp.size() / n_features;
+    comm.allgather(&(nPtsSampledByRank[my_rank]), nPtsSampledByRank, 1, stream);
+    ASSERT(comm.sync_stream(stream) == raft::comms::status_t::SUCCESS,
+           "An error occurred in the distributed operation. This can result "
+           "from a failed rank");
+
+    auto nPtsSampled =
+      thrust::reduce(thrust::host, nPtsSampledByRank, nPtsSampledByRank + n_rank, 0);
+
+    // gather centroids from all ranks
+    std::vector<size_t> sizes(n_rank);
+    thrust::transform(
+      thrust::host, nPtsSampledByRank, nPtsSampledByRank + n_rank, sizes.begin(), [&](int val) {
+        return val * n_features;
+      });
+
+    RAFT_CUDA_TRY_NO_THROW(cudaFreeHost(nPtsSampledByRank));
+
+    std::vector<size_t> displs(n_rank);
+    thrust::exclusive_scan(thrust::host, sizes.begin(), sizes.end(), displs.begin());
+
+    centroidsBuf.resize(centroidsBuf.size() + nPtsSampled * n_features, stream);
+    comm.allgatherv<DataT>(inRankCp.data(),
+                           centroidsBuf.end() - nPtsSampled * n_features,
+                           sizes.data(),
+                           displs.data(),
+                           stream);
+
+    auto tot_centroids = potentialCentroids.extent(0) + nPtsSampled;
+    potentialCentroids =
+      raft::make_device_matrix_view<DataT, IndexT>(centroidsBuf.data(), tot_centroids, n_features);
+    /// <<<< End of Step-5 >>>
+  }  /// <<<< Step-6 >>>
+
+  CUVS_LOG_KMEANS(handle,
+                  "@Rank-%d:KMeans||: # potential centroids sampled - %d\n",
+                  my_rank,
+                  potentialCentroids.extent(0));
+
+  if ((IndexT)potentialCentroids.extent(0) > (IndexT)n_clusters) {
+    // <<< Step-7 >>>: For x in C, set w_x to be the number of pts closest to X
+    // temporary buffer to store the sample count per cluster, destructor
+    // releases the resource
+
+    auto weight = raft::make_device_vector<DataT, IndexT>(handle, potentialCentroids.extent(0));
+
+    cuvs::cluster::kmeans::count_samples_in_cluster(
+      handle, params, X, L2NormX.view(), potentialCentroids, workspace, weight.view());
+
+    // merge the local histogram from all ranks
+    comm.allreduce<DataT>(weight.data_handle(),  // sendbuff
+                          weight.data_handle(),  // recvbuff
+                          weight.size(),         // count
+                          raft::comms::op_t::SUM,
+                          stream);
+
+    // <<< end of Step-7 >>>
+
+    // Step-8: Recluster the weighted points in C into k clusters
+    // Note - reclustering step is duplicated across all ranks and with the same
+    // seed they should generate the same potentialCentroids
+    auto const_centroids = raft::make_device_matrix_view<const DataT, IndexT>(
+      potentialCentroids.data_handle(), potentialCentroids.extent(0), potentialCentroids.extent(1));
+    cuvs::cluster::kmeans::init_plus_plus(
+      handle, params, const_centroids, centroidsRawData, workspace);
+
+    auto inertia = raft::make_host_scalar<DataT>(0);
+    auto n_iter  = raft::make_host_scalar<IndexT>(0);
+    auto weight_view =
+      raft::make_device_vector_view<const DataT, IndexT>(weight.data_handle(), weight.extent(0));
+    cuvs::cluster::kmeans::params params_copy = params;
+    params_copy.rng_state                     = default_params.rng_state;
+
+    cuvs::cluster::kmeans::fit_main(handle,
+                                    params_copy,
+                                    const_centroids,
+                                    weight_view,
+                                    centroidsRawData,
+                                    inertia.view(),
+                                    n_iter.view(),
+                                    workspace);
+
+  } else if ((IndexT)potentialCentroids.extent(0) < (IndexT)n_clusters) {
+    // supplement with random
+    auto n_random_clusters = n_clusters - potentialCentroids.extent(0);
+    CUVS_LOG_KMEANS(handle,
+                    "[Warning!] KMeans||: found fewer than %d centroids during "
+                    "initialization (found %d centroids, remaining %d centroids will be "
+                    "chosen randomly from input samples)\n",
+                    n_clusters,
+                    potentialCentroids.extent(0),
+                    n_random_clusters);
+
+    // generate `n_random_clusters` centroids
+    cuvs::cluster::kmeans::params rand_params = params;
+    rand_params.rng_state                     = default_params.rng_state;
+    rand_params.init                          = cuvs::cluster::kmeans::params::InitMethod::Random;
+    rand_params.n_clusters                    = n_random_clusters;
+    initRandom(handle, rand_params, X, centroidsRawData);
+
+    // copy centroids generated during kmeans|| iteration to the buffer
+    raft::copy(centroidsRawData.data_handle() + n_random_clusters * n_features,
+               potentialCentroids.data_handle(),
+               potentialCentroids.size(),
+               stream);
+
+  } else {
+    // found the required n_clusters
+    raft::copy(centroidsRawData.data_handle(),
+               potentialCentroids.data_handle(),
+               potentialCentroids.size(),
+               stream);
+  }
+}
+
+template <typename DataT, typename IndexT>
+void checkWeights(const raft::resources& handle,
+                  rmm::device_uvector<char>& workspace,
+                  raft::device_vector_view<DataT, IndexT> weight)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_scalar<DataT> wt_aggr(stream);
+
+  const auto& comm = raft::resource::get_comms(handle);
+
+  auto n_samples            = weight.extent(0);
+  size_t temp_storage_bytes = 0;
+  RAFT_CUDA_TRY(cub::DeviceReduce::Sum(
+    nullptr, temp_storage_bytes, weight.data_handle(), wt_aggr.data(), n_samples, stream));
+
+  workspace.resize(temp_storage_bytes, stream);
+
+  RAFT_CUDA_TRY(cub::DeviceReduce::Sum(
+    workspace.data(), temp_storage_bytes, weight.data_handle(), wt_aggr.data(), n_samples, stream));
+
+  comm.allreduce<DataT>(wt_aggr.data(),  // sendbuff
+                        wt_aggr.data(),  // recvbuff
+                        1,               // count
+                        raft::comms::op_t::SUM,
+                        stream);
+  DataT wt_sum = wt_aggr.value(stream);
+  raft::resource::sync_stream(handle, stream);
+
+  if (wt_sum != n_samples) {
+    CUVS_LOG_KMEANS(handle,
+                    "[Warning!] KMeans: normalizing the user provided sample weights to "
+                    "sum up to %d samples",
+                    n_samples);
+
+    DataT scale = n_samples / wt_sum;
+    raft::linalg::unaryOp(
+      weight.data_handle(),
+      weight.data_handle(),
+      weight.size(),
+      cuda::proclaim_return_type<DataT>([=] __device__(const DataT& wt) { return wt * scale; }),
+      stream);
+  }
+}
+
+template <typename DataT, typename IndexT>
+void fit(const raft::resources& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const DataT, IndexT> X,
+         std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
+         raft::device_matrix_view<DataT, IndexT> centroids,
+         raft::host_scalar_view<DataT> inertia,
+         raft::host_scalar_view<IndexT> n_iter,
+         rmm::device_uvector<char>& workspace)
+{
+  const auto& comm    = raft::resource::get_comms(handle);
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  auto n_samples      = X.extent(0);
+  auto n_features     = X.extent(1);
+  auto n_clusters     = params.n_clusters;
+  auto metric         = params.metric;
+
+  auto weight = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  if (sample_weight) {
+    raft::copy(weight.data_handle(), sample_weight->data_handle(), n_samples, stream);
+  } else {
+    thrust::fill(raft::resource::get_thrust_policy(handle),
+                 weight.data_handle(),
+                 weight.data_handle() + weight.size(),
+                 1);
+  }
+
+  // check if weights sum up to n_samples
+  checkWeights(handle, workspace, weight.view());
+
+  if (params.init == cuvs::cluster::kmeans::params::InitMethod::Random) {
+    // initializing with random samples from input dataset
+    CUVS_LOG_KMEANS(handle,
+                    "KMeans.fit: initialize cluster centers by randomly choosing from the "
+                    "input data.\n");
+    initRandom<DataT, IndexT>(handle, params, X, centroids);
+  } else if (params.init == cuvs::cluster::kmeans::params::InitMethod::KMeansPlusPlus) {
+    // default method to initialize is kmeans++
+    CUVS_LOG_KMEANS(handle, "KMeans.fit: initialize cluster centers using k-means++ algorithm.\n");
+    initKMeansPlusPlus<DataT, IndexT>(handle, params, X, centroids, workspace);
+  } else if (params.init == cuvs::cluster::kmeans::params::InitMethod::Array) {
+    CUVS_LOG_KMEANS(handle,
+                    "KMeans.fit: initialize cluster centers from the ndarray array input "
+                    "passed to init argument.\n");
+
+  } else {
+    THROW("unknown initialization method to select initial centers");
+  }
+
+  // stores (key, value) pair corresponding to each sample where
+  //   - key is the index of nearest cluster
+  //   - value is the distance to the nearest cluster
+  auto minClusterAndDistance =
+    raft::make_device_vector<raft::KeyValuePair<IndexT, DataT>, IndexT>(handle, n_samples);
+
+  // temporary buffer to store L2 norm of centroids or distance matrix,
+  // destructor releases the resource
+  rmm::device_uvector<DataT> L2NormBuf_OR_DistBuf(0, stream);
+
+  // temporary buffer to store intermediate centroids, destructor releases the
+  // resource
+  auto newCentroids = raft::make_device_matrix<DataT, IndexT>(handle, n_clusters, n_features);
+
+  // temporary buffer to store the weights per cluster, destructor releases
+  // the resource
+  auto wtInCluster = raft::make_device_vector<DataT, IndexT>(handle, n_clusters);
+
+  // L2 norm of X: ||x||^2
+  auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    raft::linalg::rowNorm(L2NormX.data_handle(),
+                          X.data_handle(),
+                          X.extent(1),
+                          X.extent(0),
+                          raft::linalg::L2Norm,
+                          true,
+                          stream);
+  }
+
+  DataT priorClusteringCost = 0;
+  for (n_iter[0] = 1; n_iter[0] <= params.max_iter; ++n_iter[0]) {
+    CUVS_LOG_KMEANS(handle,
+                    "KMeans.fit: Iteration-%d: fitting the model using the initialize "
+                    "cluster centers\n",
+                    n_iter[0]);
+
+    auto const_centroids = raft::make_device_matrix_view<const DataT, IndexT>(
+      centroids.data_handle(), centroids.extent(0), centroids.extent(1));
+    // computes minClusterAndDistance[0:n_samples) where
+    // minClusterAndDistance[i] is a <key, value> pair where
+    //   'key' is index to an sample in 'centroids' (index of the nearest
+    //   centroid) and 'value' is the distance between the sample 'X[i]' and the
+    //   'centroid[key]'
+    cuvs::cluster::kmeans::min_cluster_and_distance(handle,
+                                                    X,
+                                                    const_centroids,
+                                                    minClusterAndDistance.view(),
+                                                    L2NormX.view(),
+                                                    L2NormBuf_OR_DistBuf,
+                                                    params.metric,
+                                                    params.batch_samples,
+                                                    params.batch_centroids,
+                                                    workspace);
+
+    // Using TransformInputIteratorT to dereference an array of
+    // cub::KeyValuePair and converting them to just return the Key to be used
+    // in reduce_rows_by_key prims
+    KeyValueIndexOp<IndexT, DataT> conversion_op;
+    cub::TransformInputIterator<IndexT,
+                                KeyValueIndexOp<IndexT, DataT>,
+                                raft::KeyValuePair<IndexT, DataT>*>
+      itr(minClusterAndDistance.data_handle(), conversion_op);
+
+    workspace.resize(n_samples, stream);
+
+    // Calculates weighted sum of all the samples assigned to cluster-i and
+    // store the result in newCentroids[i]
+    raft::linalg::reduce_rows_by_key((DataT*)X.data_handle(),
+                                     X.extent(1),
+                                     itr,
+                                     weight.data_handle(),
+                                     workspace.data(),
+                                     X.extent(0),
+                                     X.extent(1),
+                                     static_cast<IndexT>(n_clusters),
+                                     newCentroids.data_handle(),
+                                     stream);
+
+    // Reduce weights by key to compute weight in each cluster
+    raft::linalg::reduce_cols_by_key(weight.data_handle(),
+                                     itr,
+                                     wtInCluster.data_handle(),
+                                     (IndexT)1,
+                                     (IndexT)weight.extent(0),
+                                     (IndexT)n_clusters,
+                                     stream);
+
+    // merge the local histogram from all ranks
+    comm.allreduce<DataT>(wtInCluster.data_handle(),  // sendbuff
+                          wtInCluster.data_handle(),  // recvbuff
+                          wtInCluster.size(),         // count
+                          raft::comms::op_t::SUM,
+                          stream);
+
+    // reduces newCentroids from all ranks
+    comm.allreduce<DataT>(newCentroids.data_handle(),  // sendbuff
+                          newCentroids.data_handle(),  // recvbuff
+                          newCentroids.size(),         // count
+                          raft::comms::op_t::SUM,
+                          stream);
+
+    // Computes newCentroids[i] = newCentroids[i]/wtInCluster[i] where
+    //   newCentroids[n_clusters x n_features] - 2D array, newCentroids[i] has
+    //   sum of all the samples assigned to cluster-i
+    //   wtInCluster[n_clusters] - 1D array, wtInCluster[i] contains # of
+    //   samples in cluster-i.
+    // Note - when wtInCluster[i] is 0, newCentroid[i] is reset to 0
+
+    raft::linalg::matrixVectorOp(
+      newCentroids.data_handle(),
+      newCentroids.data_handle(),
+      wtInCluster.data_handle(),
+      newCentroids.extent(1),
+      newCentroids.extent(0),
+      true,
+      false,
+      cuda::proclaim_return_type<DataT>([=] __device__(DataT mat, DataT vec) {
+        if (vec == 0)
+          return DataT(0);
+        else
+          return mat / vec;
+      }),
+      stream);
+
+    // copy the centroids[i] to newCentroids[i] when wtInCluster[i] is 0
+    cub::ArgIndexInputIterator<DataT*> itr_wt(wtInCluster.data_handle());
+    raft::matrix::gather_if(
+      centroids.data_handle(),
+      centroids.extent(1),
+      centroids.extent(0),
+      itr_wt,
+      itr_wt,
+      wtInCluster.extent(0),
+      newCentroids.data_handle(),
+      cuda::proclaim_return_type<bool>(
+        [=] __device__(raft::KeyValuePair<ptrdiff_t, DataT> map) {  // predicate
+          // copy when the # of samples in the cluster is 0
+          if (map.value == 0)
+            return true;
+          else
+            return false;
+        }),
+      cuda::proclaim_return_type<ptrdiff_t>(
+        [=] __device__(raft::KeyValuePair<ptrdiff_t, DataT> map) {  // map
+          return map.key;
+        }),
+      stream);
+
+    // compute the squared norm between the newCentroids and the original
+    // centroids, destructor releases the resource
+    auto sqrdNorm = raft::make_device_scalar<DataT>(handle, 1);
+    raft::linalg::mapThenSumReduce(
+      sqrdNorm.data_handle(),
+      newCentroids.size(),
+      cuda::proclaim_return_type<DataT>([=] __device__(const DataT a, const DataT b) {
+        DataT diff = a - b;
+        return diff * diff;
+      }),
+      stream,
+      centroids.data_handle(),
+      newCentroids.data_handle());
+
+    DataT sqrdNormError = 0;
+    raft::copy(&sqrdNormError, sqrdNorm.data_handle(), sqrdNorm.size(), stream);
+
+    raft::copy(centroids.data_handle(), newCentroids.data_handle(), newCentroids.size(), stream);
+
+    bool done = false;
+    if (params.inertia_check) {
+      rmm::device_scalar<raft::KeyValuePair<IndexT, DataT>> clusterCostD(stream);
+
+      // calculate cluster cost phi_x(C)
+      cuvs::cluster::kmeans::cluster_cost(
+        handle,
+        minClusterAndDistance.view(),
+        workspace,
+        raft::make_device_scalar_view(clusterCostD.data()),
+        cuda::proclaim_return_type<raft::KeyValuePair<IndexT, DataT>>(
+          [] __device__(const raft::KeyValuePair<IndexT, DataT>& a,
+                        const raft::KeyValuePair<IndexT, DataT>& b) {
+            raft::KeyValuePair<IndexT, DataT> res;
+            res.key   = 0;
+            res.value = a.value + b.value;
+            return res;
+          }));
+
+      // Cluster cost phi_x(C) from all ranks
+      comm.allreduce(&(clusterCostD.data()->value),
+                     &(clusterCostD.data()->value),
+                     1,
+                     raft::comms::op_t::SUM,
+                     stream);
+
+      DataT curClusteringCost = 0;
+      raft::copy(&curClusteringCost, &(clusterCostD.data()->value), 1, stream);
+
+      ASSERT(comm.sync_stream(stream) == raft::comms::status_t::SUCCESS,
+             "An error occurred in the distributed operation. This can result "
+             "from a failed rank");
+      ASSERT(curClusteringCost != (DataT)0.0,
+             "Too few points and centroids being found is getting 0 cost from "
+             "centers\n");
+
+      if (n_iter[0] > 0) {
+        DataT delta = curClusteringCost / priorClusteringCost;
+        if (delta > 1 - params.tol) done = true;
+      }
+      priorClusteringCost = curClusteringCost;
+    }
+
+    raft::resource::sync_stream(handle, stream);
+    if (sqrdNormError < params.tol) done = true;
+
+    if (done) {
+      CUVS_LOG_KMEANS(
+        handle, "Threshold triggered after %d iterations. Terminating early.\n", n_iter[0]);
+      break;
+    }
+  }
+}
+
+};  // namespace cuvs::cluster::kmeans::mg::detail
diff --git a/cpp/src/cluster/kmeans.cuh b/cpp/src/cluster/kmeans.cuh
index 1d12142da6..5e6d756cca 100644
--- a/cpp/src/cluster/kmeans.cuh
+++ b/cpp/src/cluster/kmeans.cuh
@@ -17,10 +17,12 @@
 
 #include "detail/kmeans.cuh"
 #include "detail/kmeans_auto_find_k.cuh"
+#include "kmeans_mg.hpp"
 #include <cuvs/cluster/kmeans.hpp>
 #include <raft/core/kvp.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
+#include <raft/core/resource/comms.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 
 #include <optional>
@@ -94,8 +96,13 @@ void fit(raft::resources const& handle,
          raft::host_scalar_view<DataT> inertia,
          raft::host_scalar_view<IndexT> n_iter)
 {
-  cuvs::cluster::kmeans::detail::kmeans_fit<DataT, IndexT>(
-    handle, params, X, sample_weight, centroids, inertia, n_iter);
+  // use the mnmg kmeans fit if we have comms initialize, single gpu otherwise
+  if (raft::resource::comms_initialized(handle)) {
+    cuvs::cluster::kmeans::mg::fit(handle, params, X, sample_weight, centroids, inertia, n_iter);
+  } else {
+    cuvs::cluster::kmeans::detail::kmeans_fit<DataT, IndexT>(
+      handle, params, X, sample_weight, centroids, inertia, n_iter);
+  }
 }
 
 /**
diff --git a/cpp/src/cluster/kmeans_fit_double.cu b/cpp/src/cluster/kmeans_fit_double.cu
new file mode 100644
index 0000000000..4f193da096
--- /dev/null
+++ b/cpp/src/cluster/kmeans_fit_double.cu
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int> X,
+         std::optional<raft::device_vector_view<const double, int>> sample_weight,
+         raft::device_matrix_view<double, int> centroids,
+         raft::host_scalar_view<double, int> inertia,
+         raft::host_scalar_view<int, int> n_iter)
+{
+  cuvs::cluster::kmeans::fit<double, int>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter);
+}
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int64_t> X,
+         std::optional<raft::device_vector_view<const double, int64_t>> sample_weight,
+         raft::device_matrix_view<double, int64_t> centroids,
+         raft::host_scalar_view<double, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter)
+{
+  cuvs::cluster::kmeans::fit<double, int64_t>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_fit_float.cu b/cpp/src/cluster/kmeans_fit_float.cu
index 89862a46c7..3888ae4926 100644
--- a/cpp/src/cluster/kmeans_fit_float.cu
+++ b/cpp/src/cluster/kmeans_fit_float.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -30,4 +30,16 @@ void fit(raft::resources const& handle,
   cuvs::cluster::kmeans::fit<float, int>(
     handle, params, X, sample_weight, centroids, inertia, n_iter);
 }
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int64_t> X,
+         std::optional<raft::device_vector_view<const float, int64_t>> sample_weight,
+         raft::device_matrix_view<float, int64_t> centroids,
+         raft::host_scalar_view<float, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter)
+{
+  cuvs::cluster::kmeans::fit<float, int64_t>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter);
+}
 }  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_fit_mg_double.cu b/cpp/src/cluster/kmeans_fit_mg_double.cu
new file mode 100644
index 0000000000..15081dfba4
--- /dev/null
+++ b/cpp/src/cluster/kmeans_fit_mg_double.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./detail/kmeans_mg.cuh"
+#include "kmeans_mg.hpp"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans::mg {
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int> X,
+         std::optional<raft::device_vector_view<const double, int>> sample_weight,
+         raft::device_matrix_view<double, int> centroids,
+         raft::host_scalar_view<double, int> inertia,
+         raft::host_scalar_view<int, int> n_iter)
+{
+  rmm::device_uvector<char> workspace(0, raft::resource::get_cuda_stream(handle));
+
+  cuvs::cluster::kmeans::mg::detail::fit<double, int>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter, workspace);
+}
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int64_t> X,
+         std::optional<raft::device_vector_view<const double, int64_t>> sample_weight,
+         raft::device_matrix_view<double, int64_t> centroids,
+         raft::host_scalar_view<double, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter)
+{
+  rmm::device_uvector<char> workspace(0, raft::resource::get_cuda_stream(handle));
+
+  cuvs::cluster::kmeans::mg::detail::fit<double, int64_t>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter, workspace);
+}
+}  // namespace cuvs::cluster::kmeans::mg
diff --git a/cpp/src/cluster/kmeans_fit_mg_float.cu b/cpp/src/cluster/kmeans_fit_mg_float.cu
new file mode 100644
index 0000000000..54fbd6763f
--- /dev/null
+++ b/cpp/src/cluster/kmeans_fit_mg_float.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./detail/kmeans_mg.cuh"
+#include "kmeans_mg.hpp"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans::mg {
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int> X,
+         std::optional<raft::device_vector_view<const float, int>> sample_weight,
+         raft::device_matrix_view<float, int> centroids,
+         raft::host_scalar_view<float, int> inertia,
+         raft::host_scalar_view<int, int> n_iter)
+{
+  rmm::device_uvector<char> workspace(0, raft::resource::get_cuda_stream(handle));
+
+  cuvs::cluster::kmeans::mg::detail::fit<float, int>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter, workspace);
+}
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int64_t> X,
+         std::optional<raft::device_vector_view<const float, int64_t>> sample_weight,
+         raft::device_matrix_view<float, int64_t> centroids,
+         raft::host_scalar_view<float, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter)
+{
+  rmm::device_uvector<char> workspace(0, raft::resource::get_cuda_stream(handle));
+
+  cuvs::cluster::kmeans::mg::detail::fit<float, int64_t>(
+    handle, params, X, sample_weight, centroids, inertia, n_iter, workspace);
+}
+}  // namespace cuvs::cluster::kmeans::mg
diff --git a/cpp/src/cluster/kmeans_fit_predict_double.cu b/cpp/src/cluster/kmeans_fit_predict_double.cu
new file mode 100644
index 0000000000..28a1d70c0c
--- /dev/null
+++ b/cpp/src/cluster/kmeans_fit_predict_double.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const double, int> X,
+                 std::optional<raft::device_vector_view<const double, int>> sample_weight,
+                 std::optional<raft::device_matrix_view<double, int>> centroids,
+                 raft::device_vector_view<int, int> labels,
+                 raft::host_scalar_view<double> inertia,
+                 raft::host_scalar_view<int> n_iter)
+
+{
+  cuvs::cluster::kmeans::fit_predict<double, int>(
+    handle, params, X, sample_weight, centroids, labels, inertia, n_iter);
+}
+
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const double, int64_t> X,
+                 std::optional<raft::device_vector_view<const double, int64_t>> sample_weight,
+                 std::optional<raft::device_matrix_view<double, int64_t>> centroids,
+                 raft::device_vector_view<int64_t, int64_t> labels,
+                 raft::host_scalar_view<double> inertia,
+                 raft::host_scalar_view<int64_t> n_iter)
+
+{
+  cuvs::cluster::kmeans::fit_predict<double, int64_t>(
+    handle, params, X, sample_weight, centroids, labels, inertia, n_iter);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_fit_predict_float.cu b/cpp/src/cluster/kmeans_fit_predict_float.cu
index f043f76244..be3652db57 100644
--- a/cpp/src/cluster/kmeans_fit_predict_float.cu
+++ b/cpp/src/cluster/kmeans_fit_predict_float.cu
@@ -32,4 +32,18 @@ void fit_predict(raft::resources const& handle,
   cuvs::cluster::kmeans::fit_predict<float, int>(
     handle, params, X, sample_weight, centroids, labels, inertia, n_iter);
 }
+
+void fit_predict(raft::resources const& handle,
+                 const kmeans::params& params,
+                 raft::device_matrix_view<const float, int64_t> X,
+                 std::optional<raft::device_vector_view<const float, int64_t>> sample_weight,
+                 std::optional<raft::device_matrix_view<float, int64_t>> centroids,
+                 raft::device_vector_view<int64_t, int64_t> labels,
+                 raft::host_scalar_view<float> inertia,
+                 raft::host_scalar_view<int64_t> n_iter)
+
+{
+  cuvs::cluster::kmeans::fit_predict<float, int64_t>(
+    handle, params, X, sample_weight, centroids, labels, inertia, n_iter);
+}
 }  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_mg.hpp b/cpp/src/cluster/kmeans_mg.hpp
new file mode 100644
index 0000000000..34f38314a7
--- /dev/null
+++ b/cpp/src/cluster/kmeans_mg.hpp
@@ -0,0 +1,75 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans::mg {
+
+/**
+ * @brief MNMG kmeans fit
+ *
+ * @param[in]     handle        The raft handle.
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int> X,
+         std::optional<raft::device_vector_view<const float, int>> sample_weight,
+         raft::device_matrix_view<float, int> centroids,
+         raft::host_scalar_view<float, int> inertia,
+         raft::host_scalar_view<int, int> n_iter);
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const float, int64_t> X,
+         std::optional<raft::device_vector_view<const float, int64_t>> sample_weight,
+         raft::device_matrix_view<float, int64_t> centroids,
+         raft::host_scalar_view<float, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter);
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int> X,
+         std::optional<raft::device_vector_view<const double, int>> sample_weight,
+         raft::device_matrix_view<double, int> centroids,
+         raft::host_scalar_view<double, int> inertia,
+         raft::host_scalar_view<int, int> n_iter);
+
+void fit(raft::resources const& handle,
+         const cuvs::cluster::kmeans::params& params,
+         raft::device_matrix_view<const double, int64_t> X,
+         std::optional<raft::device_vector_view<const double, int64_t>> sample_weight,
+         raft::device_matrix_view<double, int64_t> centroids,
+         raft::host_scalar_view<double, int64_t> inertia,
+         raft::host_scalar_view<int64_t, int64_t> n_iter);
+}  // namespace cuvs::cluster::kmeans::mg
diff --git a/cpp/src/cluster/kmeans_predict_double.cu b/cpp/src/cluster/kmeans_predict_double.cu
new file mode 100644
index 0000000000..1fcc393acb
--- /dev/null
+++ b/cpp/src/cluster/kmeans_predict_double.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const double, int> X,
+             std::optional<raft::device_vector_view<const double, int>> sample_weight,
+             raft::device_matrix_view<const double, int> centroids,
+             raft::device_vector_view<int, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<double> inertia)
+
+{
+  cuvs::cluster::kmeans::predict<double, int>(
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+}
+
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const double, int> X,
+             std::optional<raft::device_vector_view<const double, int>> sample_weight,
+             raft::device_matrix_view<const double, int> centroids,
+             raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<double> inertia)
+
+{
+  cuvs::cluster::kmeans::predict<double, int64_t>(
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_predict_float.cu b/cpp/src/cluster/kmeans_predict_float.cu
index d092152f14..b5f9f9e510 100644
--- a/cpp/src/cluster/kmeans_predict_float.cu
+++ b/cpp/src/cluster/kmeans_predict_float.cu
@@ -32,4 +32,17 @@ void predict(raft::resources const& handle,
   cuvs::cluster::kmeans::predict<float, int>(
     handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
 }
+void predict(raft::resources const& handle,
+             const kmeans::params& params,
+             raft::device_matrix_view<const float, int> X,
+             std::optional<raft::device_vector_view<const float, int>> sample_weight,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
+             raft::host_scalar_view<float> inertia)
+
+{
+  cuvs::cluster::kmeans::predict<float, int64_t>(
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+}
 }  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_transform_double.cu b/cpp/src/cluster/kmeans_transform_double.cu
new file mode 100644
index 0000000000..4a026812eb
--- /dev/null
+++ b/cpp/src/cluster/kmeans_transform_double.cu
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+
+void transform(raft::resources const& handle,
+               const kmeans::params& params,
+               raft::device_matrix_view<const double, int> X,
+               raft::device_matrix_view<const double, int> centroids,
+               raft::device_matrix_view<double, int> X_new)
+
+{
+  cuvs::cluster::kmeans::transform<double, int>(handle, params, X, centroids, X_new);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
index 3107f0fa44..4fd194f6cf 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
@@ -111,6 +111,8 @@ instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
   cuvs::distance::detail::ops::l1_distance_op, int);
 instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
   cuvs::distance::detail::ops::l2_exp_distance_op, int);
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::l2_exp_distance_op, int64_t);
 instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
   cuvs::distance::detail::ops::l2_unexp_distance_op, int);
 instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
@@ -124,5 +126,8 @@ instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo(
   int64_t,
   cuvs::distance::kernels::detail::rbf_fin_op);
 
+instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
+  cuvs::distance::detail::ops::l2_exp_distance_op, int64_t);
+
 #undef instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo
 #undef instantiate_cuvs_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
index 1bd51aef91..d0913833f1 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_00_generate.py
@@ -15,7 +15,7 @@
 # NOTE: this template is not perfectly formatted. Use pre-commit to get
 # everything in shape again.
 header = """/*
- * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -197,4 +197,30 @@ def arch_headers(archs):
 
         f.write("\n#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch\n")
 
-print("src/distance/detail/pairwise_matrix/dispatch_rbf.cu")
+
+        print("src/distance/detail/pairwise_matrix/dispatch_rbf.cu")
+
+# L2 with int64_t indices for kmeans code
+int64_t_op_instances = [
+    dict(
+        path_prefix="l2_expanded",
+        OpT="cuvs::distance::detail::ops::l2_exp_distance_op",
+        archs = [60, 80],
+    )]
+
+for op in int64_t_op_instances:
+    for dt in data_type_instances:
+        DataT, AccT, OutT, IdxT = (dt[k] for k in ["DataT", "AccT", "OutT", "IdxT"]);
+
+        IdxT = "int64_t"
+        path = f"dispatch_{op['path_prefix']}_{DataT}_{AccT}_{OutT}_{IdxT}.cu"
+        with open(path, "w") as f:
+            f.write(header)
+            f.write(arch_headers(op["archs"]))
+            f.write(macro)
+
+            OpT = op['OpT']
+            FinOpT = "raft::identity_op"
+            f.write(f"\ninstantiate_raft_distance_detail_pairwise_matrix_dispatch({OpT}, {DataT}, {AccT}, {OutT}, {FinOpT}, {IdxT});\n")
+            f.write("\n#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch\n")
+        print(f"src/distance/detail/pairwise_matrix/{path}")
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu
new file mode 100644
index 0000000000..7567391585
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu
@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_exp_distance_op,
+  double,
+  double,
+  double,
+  raft::identity_op,
+  int64_t);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu
new file mode 100644
index 0000000000..94910875cc
--- /dev/null
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by dispatch_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python dispatch_00_generate.py
+ *
+ */
+
+#include "../distance_ops/all_ops.cuh"  // ops::*
+#include "dispatch-inl.cuh"             // dispatch
+#include "dispatch_sm60.cuh"
+#include "dispatch_sm80.cuh"
+#include <raft/core/operators.hpp>  // raft::identity_op
+#define instantiate_raft_distance_detail_pairwise_matrix_dispatch(                     \
+  OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
+  template void cuvs::distance::detail::                                               \
+    pairwise_matrix_dispatch<OpT<DataT, AccT, IdxT>, DataT, AccT, OutT, FinOpT, IdxT>( \
+      OpT<DataT, AccT, IdxT> distance_op,                                              \
+      IdxT m,                                                                          \
+      IdxT n,                                                                          \
+      IdxT k,                                                                          \
+      const DataT* x,                                                                  \
+      const DataT* y,                                                                  \
+      const OutT* x_norm,                                                              \
+      const OutT* y_norm,                                                              \
+      OutT* out,                                                                       \
+      FinOpT fin_op,                                                                   \
+      cudaStream_t stream,                                                             \
+      bool is_row_major)
+
+instantiate_raft_distance_detail_pairwise_matrix_dispatch(
+  cuvs::distance::detail::ops::l2_exp_distance_op, float, float, float, raft::identity_op, int64_t);
+
+#undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
index 1cb0ed8aeb..3c8f251097 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/cpp/src/distance/distance-ext.cuh b/cpp/src/distance/distance-ext.cuh
index e7fa30f03b..8ce7ef6905 100644
--- a/cpp/src/distance/distance-ext.cuh
+++ b/cpp/src/distance/distance-ext.cuh
@@ -244,6 +244,15 @@ instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::Linf);
 instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::LpUnexpanded);
 instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::RusselRaoExpanded);
 
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int64_t);
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int64_t);
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int64_t);
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int64_t);
+
 #undef instantiate_cuvs_distance_distance_by_algo
 #undef instantiate_cuvs_distance_distance
 
diff --git a/cpp/src/distance/distance.cu b/cpp/src/distance/distance.cu
index 72be93f10a..c1d39f360f 100644
--- a/cpp/src/distance/distance.cu
+++ b/cpp/src/distance/distance.cu
@@ -105,6 +105,16 @@ instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::Linf);
 instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::LpUnexpanded);
 instantiate_cuvs_distance_distance_by_algo(cuvs::distance::DistanceType::RusselRaoExpanded);
 
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, float, float, float, int64_t);
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2Expanded, double, double, double, int64_t);
+
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, float, float, float, int64_t);
+instantiate_cuvs_distance_distance(
+  cuvs::distance::DistanceType::L2SqrtExpanded, double, double, double, int64_t);
+
 #undef instantiate_cuvs_distance_distance_by_algo
 #undef instantiate_cuvs_distance_distance
 
diff --git a/cpp/test/cluster/kmeans_mg.cu b/cpp/test/cluster/kmeans_mg.cu
new file mode 100644
index 0000000000..b9e06b2f13
--- /dev/null
+++ b/cpp/test/cluster/kmeans_mg.cu
@@ -0,0 +1,200 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/stats/adjusted_rand_index.cuh>
+
+#include <raft/comms/std_comms.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/fill.h>
+
+#include <gtest/gtest.h>
+#include <nccl.h>
+#include <stdio.h>
+#include <test_utils.h>
+
+#include <vector>
+
+#define NCCLCHECK(cmd)                                                                        \
+  do {                                                                                        \
+    ncclResult_t res = cmd;                                                                   \
+    if (res != ncclSuccess) {                                                                 \
+      printf("Failed, NCCL error %s:%d '%s'\n", __FILE__, __LINE__, ncclGetErrorString(res)); \
+      exit(EXIT_FAILURE);                                                                     \
+    }                                                                                         \
+  } while (0)
+
+namespace cuvs {
+
+template <typename T>
+struct KmeansInputs {
+  int n_row;
+  int n_col;
+  int n_clusters;
+  T tol;
+  bool weighted;
+};
+
+template <typename T>
+class KmeansTest : public ::testing::TestWithParam<KmeansInputs<T>> {
+ protected:
+  KmeansTest()
+    : stream(handle.get_stream()),
+      d_labels(0, stream),
+      d_labels_ref(0, stream),
+      d_centroids(0, stream),
+      d_sample_weight(0, stream)
+  {
+  }
+
+  void basicTest()
+  {
+    testparams = ::testing::TestWithParam<KmeansInputs<T>>::GetParam();
+    ncclComm_t nccl_comm;
+    NCCLCHECK(ncclCommInitAll(&nccl_comm, 1, {0}));
+    raft::comms::build_comms_nccl_only(&handle, nccl_comm, 1, 0);
+
+    int n_samples              = testparams.n_row;
+    int n_features             = testparams.n_col;
+    params.n_clusters          = testparams.n_clusters;
+    params.tol                 = testparams.tol;
+    params.n_init              = 5;
+    params.rng_state.seed      = 1;
+    params.oversampling_factor = 1;
+
+    auto stream = handle.get_stream();
+    rmm::device_uvector<T> X(n_samples * n_features, stream);
+    rmm::device_uvector<int> labels(n_samples, stream);
+
+    raft::random::make_blobs<T, int>(handle,
+                                     X.data(),
+                                     labels.data(),
+                                     n_samples,
+                                     n_features,
+                                     params.n_clusters,
+                                     true,
+                                     nullptr,
+                                     nullptr,
+                                     1.0,
+                                     false,
+                                     -10.0f,
+                                     10.0f,
+                                     1234ULL);
+
+    d_labels.resize(n_samples, stream);
+    d_labels_ref.resize(n_samples, stream);
+    d_centroids.resize(params.n_clusters * n_features, stream);
+
+    std::optional<raft::device_vector_view<const T, int>> d_sw = std::nullopt;
+    if (testparams.weighted) {
+      d_sample_weight.resize(n_samples, stream);
+      thrust::fill(thrust::cuda::par.on(stream),
+                   d_sample_weight.data(),
+                   d_sample_weight.data() + n_samples,
+                   1);
+      d_sw = raft::make_device_vector_view<const T, int>(d_sample_weight.data(), n_samples);
+    }
+    raft::copy(d_labels_ref.data(), labels.data(), n_samples, stream);
+
+    handle.sync_stream(stream);
+
+    T inertia  = 0;
+    int n_iter = 0;
+
+    auto X_view = raft::make_device_matrix_view<const T, int>(X.data(), n_samples, n_features);
+    auto centroids_view =
+      raft::make_device_matrix_view<T, int>(d_centroids.data(), params.n_clusters, n_features);
+
+    cuvs::cluster::kmeans::fit(handle,
+                               params,
+                               X_view,
+                               d_sw,
+                               centroids_view,
+                               raft::make_host_scalar_view<T>(&inertia),
+                               raft::make_host_scalar_view<int>(&n_iter));
+
+    cuvs::cluster::kmeans::predict(
+      handle,
+      params,
+      X_view,
+      d_sw,
+      d_centroids.data(),
+      raft::make_device_vector_view<int, int>(d_labels.data(), n_samples),
+      true,
+      raft::make_host_scalar_view<T>(&inertia));
+    score = raft::stats::adjusted_rand_index(
+      d_labels_ref.data(), d_labels.data(), n_samples, raft::resource::get_cuda_stream(handle));
+    handle.sync_stream(stream);
+
+    if (score < 0.99) {
+      std::cout << "Expected: " << raft::arr2Str(d_labels_ref.data(), 25, "d_labels_ref", stream)
+                << std::endl;
+      std::cout << "Actual: " << raft::arr2Str(d_labels.data(), 25, "d_labels", stream)
+                << std::endl;
+      std::cout << "score = " << score << std::endl;
+    }
+    ncclCommDestroy(nccl_comm);
+  }
+
+  void SetUp() override { basicTest(); }
+
+ protected:
+  raft::handle_t handle;
+  cudaStream_t stream;
+  KmeansInputs<T> testparams;
+  rmm::device_uvector<int> d_labels;
+  rmm::device_uvector<int> d_labels_ref;
+  rmm::device_uvector<T> d_centroids;
+  rmm::device_uvector<T> d_sample_weight;
+  double score;
+  cuvs::cluster::kmeans::params params;
+};
+
+const std::vector<KmeansInputs<float>> inputsf2 = {{1000, 32, 5, 0.0001, true},
+                                                   {1000, 32, 5, 0.0001, false},
+                                                   {1000, 100, 20, 0.0001, true},
+                                                   {1000, 100, 20, 0.0001, false},
+                                                   {10000, 32, 10, 0.0001, true},
+                                                   {10000, 32, 10, 0.0001, false},
+                                                   {10000, 100, 50, 0.0001, true},
+                                                   {10000, 100, 50, 0.0001, false}};
+
+const std::vector<KmeansInputs<double>> inputsd2 = {{1000, 32, 5, 0.0001, true},
+                                                    {1000, 32, 5, 0.0001, false},
+                                                    {1000, 100, 20, 0.0001, true},
+                                                    {1000, 100, 20, 0.0001, false},
+                                                    {10000, 32, 10, 0.0001, true},
+                                                    {10000, 32, 10, 0.0001, false},
+                                                    {10000, 100, 50, 0.0001, true},
+                                                    {10000, 100, 50, 0.0001, false}};
+
+typedef KmeansTest<float> KmeansTestF;
+TEST_P(KmeansTestF, Result) { ASSERT_TRUE(score >= 0.99); }
+
+typedef KmeansTest<double> KmeansTestD;
+TEST_P(KmeansTestD, Result) { ASSERT_TRUE(score >= 0.99); }
+
+INSTANTIATE_TEST_CASE_P(KmeansTests, KmeansTestF, ::testing::ValuesIn(inputsf2));
+
+INSTANTIATE_TEST_CASE_P(KmeansTests, KmeansTestD, ::testing::ValuesIn(inputsd2));
+
+}  // end namespace cuvs

From 70bd63e2fb0b5ede74c2b68f13a74a15e28b426e Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 2 Oct 2024 17:59:22 +0200
Subject: [PATCH 166/623] Put the ann-bench large_workspace_resource in managed
 memory (#372)

Setup the `raft::resource::large_workspace_resource` to an `rmm::mr::managed_memory_resource` for the benchmarks.

This change was first introduced in https://github.com/rapidsai/raft/pull/2322 but somehow went missing in cuVS. It allows to run large (not fitting in GPU memory)  ANN problemsto completion at the cost to speed.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/372
---
 cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h | 20 ++++++++++++++++---
 1 file changed, 17 insertions(+), 3 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
index 92274e2636..11e0e4ad3f 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
@@ -32,6 +32,7 @@
 #include <rmm/device_uvector.hpp>
 #include <rmm/mr/device/device_memory_resource.hpp>
 #include <rmm/mr/device/failure_callback_resource_adaptor.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
 #include <rmm/mr/device/pool_memory_resource.hpp>
 
 #include <memory>
@@ -75,13 +76,14 @@ inline auto rmm_oom_callback(std::size_t bytes, void*) -> bool
  */
 class shared_raft_resources {
  public:
-  using pool_mr_type = rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource>;
-  using mr_type      = rmm::mr::failure_callback_resource_adaptor<pool_mr_type>;
+  using pool_mr_type  = rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource>;
+  using mr_type       = rmm::mr::failure_callback_resource_adaptor<pool_mr_type>;
+  using large_mr_type = rmm::mr::managed_memory_resource;
 
   shared_raft_resources()
   try : orig_resource_{rmm::mr::get_current_device_resource()},
     pool_resource_(orig_resource_, 1024 * 1024 * 1024ull),
-    resource_(&pool_resource_, rmm_oom_callback, nullptr) {
+    resource_(&pool_resource_, rmm_oom_callback, nullptr), large_mr_() {
     rmm::mr::set_current_device_resource(&resource_);
   } catch (const std::exception& e) {
     auto cuda_status = cudaGetLastError();
@@ -104,10 +106,16 @@ class shared_raft_resources {
 
   ~shared_raft_resources() noexcept { rmm::mr::set_current_device_resource(orig_resource_); }
 
+  auto get_large_memory_resource() noexcept
+  {
+    return static_cast<rmm::mr::device_memory_resource*>(&large_mr_);
+  }
+
  private:
   rmm::mr::device_memory_resource* orig_resource_;
   pool_mr_type pool_resource_;
   mr_type resource_;
+  large_mr_type large_mr_;
 };
 
 /**
@@ -130,6 +138,12 @@ class configured_raft_resources {
       res_{std::make_unique<raft::device_resources>(
         rmm::cuda_stream_view(get_stream_from_global_pool()))}
   {
+    // set the large workspace resource to the raft handle, but without the deleter
+    // (this resource is managed by the shared_res).
+    raft::resource::set_large_workspace_resource(
+      *res_,
+      std::shared_ptr<rmm::mr::device_memory_resource>(shared_res_->get_large_memory_resource(),
+                                                       raft::void_op{}));
   }
 
   /** Default constructor creates all resources anew. */

From 6c0fd14590f77e38068c7cb088a2dd02427201b7 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Wed, 2 Oct 2024 19:47:59 +0200
Subject: [PATCH 167/623] Expose search function with pre-filter for ANN (#302)

Related to #136

This PR:
- Expose the `search()` function with optional filters in the public API of ANNs (CAGRA, IVF-PQ, IVF-Flat)
- Add compiled bitset objects
- Restore tests for filters in IVF-Flat
- Add tests for filter in CAGRA

Authors:
  - Micka (https://github.com/lowener)
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/302
---
 cpp/CMakeLists.txt                            |   5 +-
 .../ann/src/cuvs/cuvs_brute_force_knn.cu      |   8 +-
 cpp/bench/ann/src/cuvs/cuvs_wrapper.h         |   8 +-
 cpp/include/cuvs/core/bitset.hpp              |   6 +
 cpp/include/cuvs/neighbors/brute_force.hpp    |  12 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |  25 ++-
 cpp/include/cuvs/neighbors/common.h           |   2 +-
 cpp/include/cuvs/neighbors/common.hpp         |  31 ++-
 cpp/include/cuvs/neighbors/ivf_flat.hpp       | 152 +++----------
 cpp/include/cuvs/neighbors/ivf_pq.hpp         | 155 ++-----------
 cpp/src/core/bitset.cu                        |  23 ++
 cpp/src/neighbors/brute_force.cu              |  89 ++++----
 cpp/src/neighbors/brute_force_c.cpp           |  29 +--
 cpp/src/neighbors/cagra.cuh                   |  26 ++-
 cpp/src/neighbors/cagra_search_float.cu       |  20 +-
 cpp/src/neighbors/cagra_search_half.cu        |  20 +-
 cpp/src/neighbors/cagra_search_int8.cu        |  20 +-
 cpp/src/neighbors/cagra_search_uint8.cu       |  20 +-
 .../neighbors/detail/cagra/cagra_search.cuh   |  43 +---
 cpp/src/neighbors/detail/cagra/factory.cuh    |   2 +-
 .../detail/cagra/sample_filter_utils.cuh      |  65 ++++++
 .../cagra/search_multi_cta_00_generate.py     |   8 +-
 .../cagra/search_multi_cta_float_uint32.cu    |  10 +-
 .../cagra/search_multi_cta_half_uint32.cu     |   7 +-
 .../cagra/search_multi_cta_int8_uint32.cu     |  10 +-
 .../cagra/search_multi_cta_kernel-inl.cuh     |   4 +-
 .../cagra/search_multi_cta_uint8_uint32.cu    |  10 +-
 .../detail/cagra/search_multi_kernel.cuh      |   6 +-
 .../neighbors/detail/cagra/search_plan.cuh    |   2 +-
 .../cagra/search_single_cta_00_generate.py    |   8 +-
 .../cagra/search_single_cta_float_uint32.cu   |  10 +-
 .../cagra/search_single_cta_half_uint32.cu    |   7 +-
 .../cagra/search_single_cta_int8_uint32.cu    |  10 +-
 .../cagra/search_single_cta_kernel-inl.cuh    |   7 +-
 .../cagra/search_single_cta_uint8_uint32.cu   |  10 +-
 cpp/src/neighbors/detail/knn_brute_force.cuh  |  32 +++
 .../neighbors/ivf_flat/generate_ivf_flat.py   |  34 +--
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    |   2 +-
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |  32 ++-
 .../ivf_flat/ivf_flat_search_float_int64_t.cu |  17 +-
 .../ivf_flat_search_int8_t_int64_t.cu         |  17 +-
 .../ivf_flat_search_uint8_t_int64_t.cu        |  17 +-
 .../ivf_pq/detail/generate_ivf_pq.py          |  25 +--
 .../generate_ivf_pq_compute_similarity.py     |   2 +-
 .../ivf_pq_compute_similarity_float_float.cu  |   2 +-
 ...f_pq_compute_similarity_float_fp8_false.cu |   2 +-
 ...vf_pq_compute_similarity_float_fp8_true.cu |   2 +-
 .../ivf_pq_compute_similarity_float_half.cu   |   2 +-
 ...vf_pq_compute_similarity_half_fp8_false.cu |   2 +-
 ...ivf_pq_compute_similarity_half_fp8_true.cu |   2 +-
 .../ivf_pq_compute_similarity_half_half.cu    |   2 +-
 .../detail/ivf_pq_search_float_int64_t.cu     |  20 +-
 .../detail/ivf_pq_search_half_int64_t.cu      |  20 +-
 .../detail/ivf_pq_search_int8_t_int64_t.cu    |  20 +-
 .../detail/ivf_pq_search_uint8_t_int64_t.cu   |  20 +-
 ...ivf_pq_search_with_filter_float_int64_t.cu |  49 ----
 .../ivf_pq_search_with_filter_half_int64_t.cu |  49 ----
 ...vf_pq_search_with_filter_int8_t_int64_t.cu |  49 ----
 ...f_pq_search_with_filter_uint8_t_int64_t.cu |  49 ----
 .../ivf_pq/ivf_pq_compute_similarity.cuh      |  16 +-
 .../ivf_pq/ivf_pq_compute_similarity_impl.cuh |  14 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |  29 ++-
 cpp/src/neighbors/refine/refine_device.cuh    |   2 +-
 cpp/src/neighbors/sample_filter.cu            |   5 +
 cpp/src/neighbors/sample_filter.cuh           |  21 +-
 .../stats/detail/trustworthiness_score.cuh    |   2 +-
 cpp/test/neighbors/ann_brute_force.cuh        |  16 +-
 cpp/test/neighbors/ann_cagra.cuh              | 209 ++++++++++++++++++
 .../ann_cagra/test_float_uint32_t.cu          |   4 +
 .../ann_cagra/test_int8_t_uint32_t.cu         |   3 +
 .../ann_cagra/test_uint8_t_uint32_t.cu        |   3 +
 cpp/test/neighbors/ann_ivf_flat.cuh           |  19 +-
 .../ann_ivf_flat/test_float_int64_t.cu        |   7 +-
 .../ann_ivf_flat/test_int8_t_int64_t.cu       |   7 +-
 .../ann_ivf_flat/test_uint8_t_int64_t.cu      |   7 +-
 cpp/test/neighbors/ann_ivf_pq.cuh             |  14 +-
 cpp/test/neighbors/brute_force.cu             |   7 +-
 cpp/test/neighbors/brute_force_prefiltered.cu |   7 +-
 78 files changed, 891 insertions(+), 839 deletions(-)
 create mode 100644 cpp/src/core/bitset.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/sample_filter_utils.cuh
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 78c67d9c87..063a41b1ef 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -308,6 +308,7 @@ add_library(
   src/cluster/kmeans_transform_double.cu
   src/cluster/kmeans_transform_float.cu
   src/cluster/single_linkage_float.cu
+  src/core/bitset.cu
   src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
   src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
@@ -406,10 +407,6 @@ add_library(
   src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
   src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
   src/neighbors/nn_descent.cu
   src/neighbors/nn_descent_float.cu
   src/neighbors/nn_descent_half.cu
diff --git a/cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu b/cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu
index 4c38b3420d..55d5b8c704 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_brute_force_knn.cu
@@ -134,7 +134,7 @@ class BruteForceKNNBenchmark {
             search_queries.data(), params_.num_queries, params_.dim),
           indices,
           distances,
-          std::nullopt);
+          cuvs::neighbors::filtering::none_sample_filter{});
         flush_l2_cache();
         raft::resource::sync_stream(handle_, stream_);
       }
@@ -158,7 +158,7 @@ class BruteForceKNNBenchmark {
           search_queries.data(), params_.num_queries, params_.dim),
         indices,
         distances,
-        std::nullopt);
+        cuvs::neighbors::filtering::none_sample_filter{});
       raft::resource::sync_stream(handle_, stream_);
       end        = std::chrono::high_resolution_clock::now();
       search_dur = end - start;
@@ -178,7 +178,7 @@ class BruteForceKNNBenchmark {
             search_queries.data(), params_.num_queries, params_.dim),
           indices,
           distances,
-          std::nullopt);
+          cuvs::neighbors::filtering::none_sample_filter{});
         flush_l2_cache();
         raft::resource::sync_stream(handle_, stream_);
       }
@@ -202,7 +202,7 @@ class BruteForceKNNBenchmark {
           search_queries.data(), params_.num_queries, params_.dim),
         indices,
         distances,
-        std::nullopt);
+        cuvs::neighbors::filtering::none_sample_filter{});
       raft::resource::sync_stream(handle_, stream_);
       end        = std::chrono::high_resolution_clock::now();
       search_dur = end - start;
diff --git a/cpp/bench/ann/src/cuvs/cuvs_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
index ea052533d6..bf0fa59340 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
@@ -155,8 +155,12 @@ void cuvs_gpu<T>::search(
     raft::make_device_matrix_view<algo_base::index_type, int64_t>(neighbors, batch_size, k);
   auto distances_view = raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k);
 
-  cuvs::neighbors::brute_force::search(
-    handle_, *index_, queries_view, neighbors_view, distances_view, std::nullopt);
+  cuvs::neighbors::brute_force::search(handle_,
+                                       *index_,
+                                       queries_view,
+                                       neighbors_view,
+                                       distances_view,
+                                       cuvs::neighbors::filtering::none_sample_filter{});
 }
 
 template <typename T>
diff --git a/cpp/include/cuvs/core/bitset.hpp b/cpp/include/cuvs/core/bitset.hpp
index 99942e21c4..8236bbf07d 100644
--- a/cpp/include/cuvs/core/bitset.hpp
+++ b/cpp/include/cuvs/core/bitset.hpp
@@ -18,6 +18,12 @@
 
 #include <raft/core/bitset.hpp>
 
+extern template struct raft::core::bitset<uint8_t, uint32_t>;
+extern template struct raft::core::bitset<uint16_t, uint32_t>;
+extern template struct raft::core::bitset<uint32_t, uint32_t>;
+extern template struct raft::core::bitset<uint32_t, int64_t>;
+extern template struct raft::core::bitset<uint64_t, int64_t>;
+
 namespace cuvs::core {
 /* To use bitset functions containing CUDA code, include <raft/core/bitset.cuh> */
 
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 5408eb1a00..428fa592aa 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -291,7 +291,8 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -326,7 +327,8 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 /**
  * @brief Search ANN using the constructed index.
  *
@@ -346,7 +348,8 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<const float, int64_t, raft::col_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 /**
  * @brief Search ANN using the constructed index.
  *
@@ -366,7 +369,8 @@ void search(raft::resources const& handle,
             raft::device_matrix_view<const half, int64_t, raft::col_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-            std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter);
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 20db7e8b7d..e480507564 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1055,6 +1055,8 @@ void extend(
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 
 void search(raft::resources const& res,
@@ -1062,7 +1064,9 @@ void search(raft::resources const& res,
             const cuvs::neighbors::cagra::index<float, uint32_t>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1077,13 +1081,17 @@ void search(raft::resources const& res,
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& res,
             cuvs::neighbors::cagra::search_params const& params,
             const cuvs::neighbors::cagra::index<half, uint32_t>& index,
             raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
             raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1098,13 +1106,17 @@ void search(raft::resources const& res,
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& res,
             cuvs::neighbors::cagra::search_params const& params,
             const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
             raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1119,13 +1131,18 @@ void search(raft::resources const& res,
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& res,
             cuvs::neighbors::cagra::search_params const& params,
             const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/common.h b/cpp/include/cuvs/neighbors/common.h
index 02cbeea960..d7ca878b99 100644
--- a/cpp/include/cuvs/neighbors/common.h
+++ b/cpp/include/cuvs/neighbors/common.h
@@ -44,7 +44,7 @@ enum cuvsFilterType {
 };
 
 /**
- * @brief Struct to hold address of cuvs::neighbor::prefilter and its type
+ * @brief Struct to hold address of cuvs::neighbors::prefilter and its type
  *
  */
 typedef struct {
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 8218b5f52b..73ce80b41b 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -383,8 +383,12 @@ inline constexpr bool is_vpq_dataset_v = is_vpq_dataset<DatasetT>::value;
 
 namespace filtering {
 
+struct base_filter {
+  virtual ~base_filter() = default;
+};
+
 /* A filter that filters nothing. This is the default behavior. */
-struct none_ivf_sample_filter {
+struct none_sample_filter : public base_filter {
   inline _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
@@ -392,10 +396,7 @@ struct none_ivf_sample_filter {
     const uint32_t cluster_ix,
     // the index of the current sample inside the current inverted list
     const uint32_t sample_ix) const;
-};
 
-/* A filter that filters nothing. This is the default behavior. */
-struct none_cagra_sample_filter {
   inline _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
@@ -431,13 +432,33 @@ struct ivf_to_sample_filter {
     const uint32_t sample_ix) const;
 };
 
+/**
+ * @brief Filter an index with a bitmap
+ *
+ * @tparam bitmap_t Data type of the bitmap
+ * @tparam index_t Indexing type
+ */
+template <typename bitmap_t, typename index_t>
+struct bitmap_filter : public base_filter {
+  // View of the bitset to use as a filter
+  const cuvs::core::bitmap_view<bitmap_t, index_t> bitmap_view_;
+
+  bitmap_filter(const cuvs::core::bitmap_view<bitmap_t, index_t> bitmap_for_filtering);
+  inline _RAFT_HOST_DEVICE bool operator()(
+    // query index
+    const uint32_t query_ix,
+    // the index of the current sample
+    const uint32_t sample_ix) const;
+};
+
 /**
  * @brief Filter an index with a bitset
  *
+ * @tparam bitset_t Data type of the bitset
  * @tparam index_t Indexing type
  */
 template <typename bitset_t, typename index_t>
-struct bitset_filter {
+struct bitset_filter : public base_filter {
   // View of the bitset to use as a filter
   const cuvs::core::bitset_view<bitset_t, index_t> bitset_view_;
 
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index 44502f942f..67d1b46c0f 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -1163,13 +1163,17 @@ void extend(raft::resources const& handle,
  * dataset [n_queries, k]
  * @param[out] distances raft::device_matrix_view to the distances to the selected neighbors
  * [n_queries, k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_flat::search_params& params,
             cuvs::neighbors::ivf_flat::index<float, int64_t>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1200,13 +1204,17 @@ void search(raft::resources const& handle,
  * dataset [n_queries, k]
  * @param[out] distances raft::device_matrix_view to the distances to the selected neighbors
  * [n_queries, k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_flat::search_params& params,
             cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index,
             raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1237,112 +1245,18 @@ void search(raft::resources const& handle,
  * dataset [n_queries, k]
  * @param[out] distances raft::device_matrix_view to the distances to the selected neighbors
  * [n_queries, k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_flat::search_params& params,
             cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
-/**
- * @brief Search ANN using the constructed index with the given filter.
- *
- * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
- *
- * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
- * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
- * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`.
- * The exact size of the temporary buffer depends on multiple factors and is an implementation
- * detail. However, you can safely specify a small initial size for the memory pool, so that only a
- * few allocations happen to grow it during the first invocations of the `search`.
- *
- * @param[in] handle
- * @param[in] params configure the search
- * @param[in] idx ivf-flat constructed index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
- * query.
- */
-void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  index<float, int64_t>& idx,
-  raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
-
-/**
- * @brief Search ANN using the constructed index with the given filter.
- *
- * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
- *
- * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
- * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
- * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`.
- * The exact size of the temporary buffer depends on multiple factors and is an implementation
- * detail. However, you can safely specify a small initial size for the memory pool, so that only a
- * few allocations happen to grow it during the first invocations of the `search`.
- *
- * @param[in] handle
- * @param[in] params configure the search
- * @param[in] idx ivf-flat constructed index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
- * query.
- */
-void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  index<int8_t, int64_t>& idx,
-  raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
-
-/**
- * @brief Search ANN using the constructed index with the given filter.
- *
- * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
- *
- * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
- * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
- * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`.
- * The exact size of the temporary buffer depends on multiple factors and is an implementation
- * detail. However, you can safely specify a small initial size for the memory pool, so that only a
- * few allocations happen to grow it during the first invocations of the `search`.
- *
- * @param[in] handle
- * @param[in] params configure the search
- * @param[in] idx ivf-flat constructed index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
- * query.
- */
-void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  index<uint8_t, int64_t>& idx,
-  raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
 /**
  * @}
  */
@@ -2039,18 +1953,18 @@ void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index);
  *   using namespace cuvs::neighbors;
  *   raft::resources res;
  *   // use default index parameters
- *   ivf_pq::index_params index_params;
+ *   ivf_flat::index_params index_params;
  *   // initialize an empty index
- *   ivf_pq::index<int64_t> index(res, index_params, D);
- *   ivf_pq::helpers::reset_index(res, &index);
+ *   ivf_flat::index<uint8_t, int64_t> index(res, index_params, D);
+ *   ivf_flat::helpers::reset_index(res, &index);
  *   // resize the first IVF list to hold 5 records
- *   auto spec = list_spec<uint32_t, int64_t>{
- *     index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+ *   auto spec = list_spec<uint32_t, uint8_t, int64_t>{
+ *     index->dim(), index->conservative_memory_allocation()};
  *   uint32_t new_size = 5;
  *   ivf::resize_list(res, list, spec, new_size, 0);
  *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
  *   // recompute the internal state of the index
- *   ivf_pq::helpers::recompute_internal_state(res, index);
+ *   ivf_flat::helpers::recompute_internal_state(res, index);
  * @endcode
  *
  * @param[in] res raft resource
@@ -2067,18 +1981,18 @@ void recompute_internal_state(const raft::resources& res, index<float, int64_t>*
  *   using namespace cuvs::neighbors;
  *   raft::resources res;
  *   // use default index parameters
- *   ivf_pq::index_params index_params;
+ *   ivf_flat::index_params index_params;
  *   // initialize an empty index
- *   ivf_pq::index<int64_t> index(res, index_params, D);
- *   ivf_pq::helpers::reset_index(res, &index);
+ *   ivf_flat::index<uint8_t, int64_t> index(res, index_params, D);
+ *   ivf_flat::helpers::reset_index(res, &index);
  *   // resize the first IVF list to hold 5 records
- *   auto spec = list_spec<uint32_t, int64_t>{
- *     index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+ *   auto spec = list_spec<uint32_t, uint8_t, int64_t>{
+ *     index->dim(), index->conservative_memory_allocation()};
  *   uint32_t new_size = 5;
  *   ivf::resize_list(res, list, spec, new_size, 0);
  *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
  *   // recompute the internal state of the index
- *   ivf_pq::helpers::recompute_internal_state(res, index);
+ *   ivf_flat::helpers::recompute_internal_state(res, index);
  * @endcode
  *
  * @param[in] res raft resource
@@ -2095,18 +2009,18 @@ void recompute_internal_state(const raft::resources& res, index<int8_t, int64_t>
  *   using namespace cuvs::neighbors;
  *   raft::resources res;
  *   // use default index parameters
- *   ivf_pq::index_params index_params;
+ *   ivf_flat::index_params index_params;
  *   // initialize an empty index
- *   ivf_pq::index<int64_t> index(res, index_params, D);
- *   ivf_pq::helpers::reset_index(res, &index);
+ *   ivf_flat::index<uint8_t, int64_t> index(res, index_params, D);
+ *   ivf_flat::helpers::reset_index(res, &index);
  *   // resize the first IVF list to hold 5 records
- *   auto spec = list_spec<uint32_t, int64_t>{
- *     index->pq_bits(), index->pq_dim(), index->conservative_memory_allocation()};
+ *   auto spec = list_spec<uint32_t, uint8_t, int64_t>{
+ *     index->dim(), index->conservative_memory_allocation()};
  *   uint32_t new_size = 5;
  *   ivf::resize_list(res, list, spec, new_size, 0);
  *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
  *   // recompute the internal state of the index
- *   ivf_pq::helpers::recompute_internal_state(res, index);
+ *   ivf_flat::helpers::recompute_internal_state(res, index);
  * @endcode
  *
  * @param[in] res raft resource
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 8c378b1f0b..3ce5f382f2 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -1400,13 +1400,17 @@ void extend(raft::resources const& handle,
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
             cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1441,13 +1445,17 @@ void search(raft::resources const& handle,
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
             cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1482,13 +1490,17 @@ void search(raft::resources const& handle,
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
             cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
 /**
  * @brief Search ANN using the constructed index.
@@ -1523,145 +1535,18 @@ void search(raft::resources const& handle,
  * [n_queries, k]
  * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
  * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
             cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
 
-/**
- * @brief Search ANN using the constructed index with the given filter.
- *
- * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
- *
- * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
- * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
- * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`.
- * The exact size of the temporary buffer depends on multiple factors and is an implementation
- * detail. However, you can safely specify a small initial size for the memory pool, so that only a
- * few allocations happen to grow it during the first invocations of the `search`.
- *
- * @param[in] handle
- * @param[in] params configure the search
- * @param[in] idx ivf-pq constructed index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
- * query.
- */
-void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  index<int64_t>& idx,
-  raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
-
-/**
- * @brief Search ANN using the constructed index with the given filter.
- *
- * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
- *
- * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
- * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
- * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`.
- * The exact size of the temporary buffer depends on multiple factors and is an implementation
- * detail. However, you can safely specify a small initial size for the memory pool, so that only a
- * few allocations happen to grow it during the first invocations of the `search`.
- *
- * @param[in] handle
- * @param[in] params configure the search
- * @param[in] idx ivf-pq constructed index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
- * query.
- */
-void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  index<int64_t>& idx,
-  raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
-
-/**
- * @brief Search ANN using the constructed index with the given filter.
- *
- * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
- *
- * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
- * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
- * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`.
- * The exact size of the temporary buffer depends on multiple factors and is an implementation
- * detail. However, you can safely specify a small initial size for the memory pool, so that only a
- * few allocations happen to grow it during the first invocations of the `search`.
- *
- * @param[in] handle
- * @param[in] params configure the search
- * @param[in] idx ivf-pq constructed index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
- * query.
- */
-void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  index<int64_t>& idx,
-  raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
-
-/**
- * @brief Search ANN using the constructed index with the given filter.
- *
- * See the [ivf_pq::build](#ivf_pq::build) documentation for a usage example.
- *
- * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
- * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
- * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`.
- * The exact size of the temporary buffer depends on multiple factors and is an implementation
- * detail. However, you can safely specify a small initial size for the memory pool, so that only a
- * few allocations happen to grow it during the first invocations of the `search`.
- *
- * @param[in] handle
- * @param[in] params configure the search
- * @param[in] idx ivf-pq constructed index
- * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
- * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
- * [n_queries, k]
- * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
- * k]
- * @param[in] sample_filter a device bitset filter function that greenlights samples for a given
- * query.
- */
-void search_with_filtering(
-  raft::resources const& handle,
-  const search_params& params,
-  index<int64_t>& idx,
-  raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
-  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
-  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
-  cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t> sample_filter);
 /**
  * @}
  */
diff --git a/cpp/src/core/bitset.cu b/cpp/src/core/bitset.cu
new file mode 100644
index 0000000000..c791747a9d
--- /dev/null
+++ b/cpp/src/core/bitset.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cuvs/core/bitset.hpp>
+#include <raft/core/bitset.cuh>
+
+template struct raft::core::bitset<uint8_t, uint32_t>;
+template struct raft::core::bitset<uint16_t, uint32_t>;
+template struct raft::core::bitset<uint32_t, uint32_t>;
+template struct raft::core::bitset<uint32_t, int64_t>;
+template struct raft::core::bitset<uint64_t, int64_t>;
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index c76feb0159..b0f87e9acd 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -145,54 +145,45 @@ void index<T, DistT>::update_dataset(
   dataset_view_ = raft::make_const_mdspan(dataset_.view());
 }
 
-#define CUVS_INST_BFKNN(T, DistT)                                                                 \
-  auto build(raft::resources const& res,                                                          \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,                 \
-             cuvs::distance::DistanceType metric,                                                 \
-             DistT metric_arg)                                                                    \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                               \
-  {                                                                                               \
-    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                             \
-  }                                                                                               \
-  auto build(raft::resources const& res,                                                          \
-             raft::device_matrix_view<const T, int64_t, raft::col_major> dataset,                 \
-             cuvs::distance::DistanceType metric,                                                 \
-             DistT metric_arg)                                                                    \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                               \
-  {                                                                                               \
-    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                             \
-  }                                                                                               \
-                                                                                                  \
-  void search(                                                                                    \
-    raft::resources const& res,                                                                   \
-    const cuvs::neighbors::brute_force::index<T, DistT>& idx,                                     \
-    raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                          \
-    raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,                        \
-    raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,                          \
-    std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter = std::nullopt) \
-  {                                                                                               \
-    if (!sample_filter.has_value()) {                                                             \
-      detail::brute_force_search<T, int64_t, DistT>(res, idx, queries, neighbors, distances);     \
-    } else {                                                                                      \
-      detail::brute_force_search_filtered<T, int64_t, uint32_t, DistT>(                           \
-        res, idx, queries, *sample_filter, neighbors, distances);                                 \
-    }                                                                                             \
-  }                                                                                               \
-  void search(                                                                                    \
-    raft::resources const& res,                                                                   \
-    const cuvs::neighbors::brute_force::index<T, DistT>& idx,                                     \
-    raft::device_matrix_view<const T, int64_t, raft::col_major> queries,                          \
-    raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,                        \
-    raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,                          \
-    std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> sample_filter = std::nullopt) \
-  {                                                                                               \
-    if (!sample_filter.has_value()) {                                                             \
-      detail::brute_force_search<T, int64_t, DistT>(res, idx, queries, neighbors, distances);     \
-    } else {                                                                                      \
-      RAFT_FAIL("filtered search isn't available with col_major queries yet");                    \
-    }                                                                                             \
-  }                                                                                               \
-                                                                                                  \
+#define CUVS_INST_BFKNN(T, DistT)                                                    \
+  auto build(raft::resources const& res,                                             \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,    \
+             cuvs::distance::DistanceType metric,                                    \
+             DistT metric_arg)                                                       \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                  \
+  {                                                                                  \
+    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                \
+  }                                                                                  \
+  auto build(raft::resources const& res,                                             \
+             raft::device_matrix_view<const T, int64_t, raft::col_major> dataset,    \
+             cuvs::distance::DistanceType metric,                                    \
+             DistT metric_arg)                                                       \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                  \
+  {                                                                                  \
+    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                \
+  }                                                                                  \
+                                                                                     \
+  void search(raft::resources const& res,                                            \
+              const cuvs::neighbors::brute_force::index<T, DistT>& idx,              \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,   \
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors, \
+              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,   \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)          \
+  {                                                                                  \
+    detail::search<T, int64_t, DistT, raft::row_major>(                              \
+      res, idx, queries, neighbors, distances, sample_filter);                       \
+  }                                                                                  \
+  void search(raft::resources const& res,                                            \
+              const cuvs::neighbors::brute_force::index<T, DistT>& idx,              \
+              raft::device_matrix_view<const T, int64_t, raft::col_major> queries,   \
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors, \
+              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,   \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)          \
+  {                                                                                  \
+    detail::search<T, int64_t, DistT, raft::col_major>(                              \
+      res, idx, queries, neighbors, distances, sample_filter);                       \
+  }                                                                                  \
+                                                                                     \
   template struct cuvs::neighbors::brute_force::index<T, DistT>;
 
 CUVS_INST_BFKNN(float, float);
@@ -200,4 +191,4 @@ CUVS_INST_BFKNN(half, float);
 
 #undef CUVS_INST_BFKNN
 
-}  // namespace cuvs::neighbors::brute_force
+}  // namespace cuvs::neighbors::brute_force
\ No newline at end of file
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index f3ca2e7309..eda79aa319 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -64,28 +64,31 @@ void _search(cuvsResources_t res,
   using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
   using distances_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
   using prefilter_mds_type    = raft::device_vector_view<const uint32_t, int64_t>;
-  using prefilter_opt_type    = cuvs::core::bitmap_view<const uint32_t, int64_t>;
+  using prefilter_bmp_type    = cuvs::core::bitmap_view<const uint32_t, int64_t>;
 
   auto queries_mds   = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
   auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
   auto distances_mds = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
 
-  std::optional<cuvs::core::bitmap_view<const uint32_t, int64_t>> filter_opt;
-
   if (prefilter.type == NO_FILTER) {
-    filter_opt = std::nullopt;
-  } else {
+    cuvs::neighbors::brute_force::search(*res_ptr,
+                                         *index_ptr,
+                                         queries_mds,
+                                         neighbors_mds,
+                                         distances_mds,
+                                         cuvs::neighbors::filtering::none_sample_filter{});
+  } else if (prefilter.type == BITMAP) {
     auto prefilter_ptr  = reinterpret_cast<DLManagedTensor*>(prefilter.addr);
     auto prefilter_mds  = cuvs::core::from_dlpack<prefilter_mds_type>(prefilter_ptr);
-    auto prefilter_view = prefilter_opt_type((const uint32_t*)prefilter_mds.data_handle(),
-                                             queries_mds.extent(0),
-                                             index_ptr->dataset().extent(0));
-
-    filter_opt = std::make_optional<prefilter_opt_type>(prefilter_view);
+    auto prefilter_view = cuvs::neighbors::filtering::bitmap_filter(
+      prefilter_bmp_type((const uint32_t*)prefilter_mds.data_handle(),
+                         queries_mds.extent(0),
+                         index_ptr->dataset().extent(0)));
+    cuvs::neighbors::brute_force::search(
+      *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds, prefilter_view);
+  } else {
+    RAFT_FAIL("Unsupported prefilter type: BITSET");
   }
-
-  cuvs::neighbors::brute_force::search(
-    *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds, filter_opt);
 }
 
 }  // namespace
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 033f080e23..dacfd6f630 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -332,11 +332,29 @@ void search(raft::resources const& res,
             const index<T, IdxT>& idx,
             raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
             raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
-            raft::device_matrix_view<float, int64_t, raft::row_major> distances)
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter_ref)
 {
-  using none_filter_type = cuvs::neighbors::filtering::none_cagra_sample_filter;
-  return cagra::search_with_filtering<T, IdxT, none_filter_type>(
-    res, params, idx, queries, neighbors, distances, none_filter_type{});
+  try {
+    using none_filter_type  = cuvs::neighbors::filtering::none_sample_filter;
+    auto& sample_filter     = dynamic_cast<const none_filter_type&>(sample_filter_ref);
+    auto sample_filter_copy = sample_filter;
+    return search_with_filtering<T, IdxT, none_filter_type>(
+      res, params, idx, queries, neighbors, distances, sample_filter_copy);
+    return;
+  } catch (const std::bad_cast&) {
+  }
+
+  try {
+    auto& sample_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t>&>(
+        sample_filter_ref);
+    auto sample_filter_copy = sample_filter;
+    return search_with_filtering<T, IdxT, decltype(sample_filter_copy)>(
+      res, params, idx, queries, neighbors, distances, sample_filter_copy);
+  } catch (const std::bad_cast&) {
+    RAFT_FAIL("Unsupported sample filter type");
+  }
 }
 
 template <class T, class IdxT, class Accessor>
diff --git a/cpp/src/neighbors/cagra_search_float.cu b/cpp/src/neighbors/cagra_search_float.cu
index e981d91274..3aca84f74b 100644
--- a/cpp/src/neighbors/cagra_search_float.cu
+++ b/cpp/src/neighbors/cagra_search_float.cu
@@ -19,15 +19,17 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
-  void search(raft::resources const& handle,                                                       \
-              cuvs::neighbors::cagra::search_params const& params,                                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
-  {                                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
+  void search(raft::resources const& handle,                                       \
+              cuvs::neighbors::cagra::search_params const& params,                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
+  {                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
+      handle, params, index, queries, neighbors, distances, sample_filter);        \
   }
 
 CUVS_INST_CAGRA_SEARCH(float, uint32_t);
diff --git a/cpp/src/neighbors/cagra_search_half.cu b/cpp/src/neighbors/cagra_search_half.cu
index d80f2bc00e..02be127318 100644
--- a/cpp/src/neighbors/cagra_search_half.cu
+++ b/cpp/src/neighbors/cagra_search_half.cu
@@ -19,15 +19,17 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
-  void search(raft::resources const& handle,                                                       \
-              cuvs::neighbors::cagra::search_params const& params,                                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
-  {                                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
+  void search(raft::resources const& handle,                                       \
+              cuvs::neighbors::cagra::search_params const& params,                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
+  {                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
+      handle, params, index, queries, neighbors, distances, sample_filter);        \
   }
 
 CUVS_INST_CAGRA_SEARCH(half, uint32_t);
diff --git a/cpp/src/neighbors/cagra_search_int8.cu b/cpp/src/neighbors/cagra_search_int8.cu
index b44a7507d1..3442ef55fb 100644
--- a/cpp/src/neighbors/cagra_search_int8.cu
+++ b/cpp/src/neighbors/cagra_search_int8.cu
@@ -18,15 +18,17 @@
 #include <cuvs/neighbors/cagra.hpp>
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
-  void search(raft::resources const& handle,                                                       \
-              cuvs::neighbors::cagra::search_params const& params,                                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
-  {                                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
+  void search(raft::resources const& handle,                                       \
+              cuvs::neighbors::cagra::search_params const& params,                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
+  {                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
+      handle, params, index, queries, neighbors, distances, sample_filter);        \
   }
 
 CUVS_INST_CAGRA_SEARCH(int8_t, uint32_t);
diff --git a/cpp/src/neighbors/cagra_search_uint8.cu b/cpp/src/neighbors/cagra_search_uint8.cu
index cbb7d6652c..08fe1861b1 100644
--- a/cpp/src/neighbors/cagra_search_uint8.cu
+++ b/cpp/src/neighbors/cagra_search_uint8.cu
@@ -19,15 +19,17 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                                            \
-  void search(raft::resources const& handle,                                                       \
-              cuvs::neighbors::cagra::search_params const& params,                                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,                 \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,                  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances)                 \
-  {                                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
+  void search(raft::resources const& handle,                                       \
+              cuvs::neighbors::cagra::search_params const& params,                 \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
+  {                                                                                \
+    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
+      handle, params, index, queries, neighbors, distances, sample_filter);        \
   }
 
 CUVS_INST_CAGRA_SEARCH(uint8_t, uint32_t);
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index 6dc601f320..4c15b8e14b 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -17,6 +17,7 @@
 #pragma once
 
 #include "factory.cuh"
+#include "sample_filter_utils.cuh"
 #include "search_plan.cuh"
 #include "search_single_cta_inst.cuh"
 
@@ -42,48 +43,6 @@
 
 namespace cuvs::neighbors::cagra::detail {
 
-template <class CagraSampleFilterT>
-struct CagraSampleFilterWithQueryIdOffset {
-  const uint32_t offset;
-  CagraSampleFilterT filter;
-
-  CagraSampleFilterWithQueryIdOffset(const uint32_t offset, const CagraSampleFilterT filter)
-    : offset(offset), filter(filter)
-  {
-  }
-
-  _RAFT_DEVICE auto operator()(const uint32_t query_id, const uint32_t sample_id)
-  {
-    return filter(query_id + offset, sample_id);
-  }
-};
-
-template <class CagraSampleFilterT>
-struct CagraSampleFilterT_Selector {
-  using type = CagraSampleFilterWithQueryIdOffset<CagraSampleFilterT>;
-};
-template <>
-struct CagraSampleFilterT_Selector<cuvs::neighbors::filtering::none_cagra_sample_filter> {
-  using type = cuvs::neighbors::filtering::none_cagra_sample_filter;
-};
-
-// A helper function to set a query id offset
-template <class CagraSampleFilterT>
-inline typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type set_offset(
-  CagraSampleFilterT filter, const uint32_t offset)
-{
-  typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type new_filter(offset, filter);
-  return new_filter;
-}
-template <>
-inline
-  typename CagraSampleFilterT_Selector<cuvs::neighbors::filtering::none_cagra_sample_filter>::type
-  set_offset<cuvs::neighbors::filtering::none_cagra_sample_filter>(
-    cuvs::neighbors::filtering::none_cagra_sample_filter filter, const uint32_t)
-{
-  return filter;
-}
-
 template <typename DataT, typename IndexT, typename DistanceT, typename CagraSampleFilterT>
 void search_main_core(raft::resources const& res,
                       search_params params,
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index 2f201de3b1..abc907da5e 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -29,7 +29,7 @@ namespace cuvs::neighbors::cagra::detail {
 template <typename DataT,
           typename IndexT,
           typename DistanceT,
-          typename CagraSampleFilterT = cuvs::neighbors::filtering::none_cagra_sample_filter>
+          typename CagraSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 class factory {
  public:
   /**
diff --git a/cpp/src/neighbors/detail/cagra/sample_filter_utils.cuh b/cpp/src/neighbors/detail/cagra/sample_filter_utils.cuh
new file mode 100644
index 0000000000..cd77b9b6b3
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/sample_filter_utils.cuh
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../sample_filter.cuh"
+
+#include <cuvs/neighbors/common.hpp>
+
+namespace cuvs::neighbors::cagra::detail {
+
+template <class CagraSampleFilterT>
+struct CagraSampleFilterWithQueryIdOffset {
+  const uint32_t offset;
+  CagraSampleFilterT filter;
+
+  CagraSampleFilterWithQueryIdOffset(const uint32_t offset, const CagraSampleFilterT filter)
+    : offset(offset), filter(filter)
+  {
+  }
+
+  _RAFT_DEVICE auto operator()(const uint32_t query_id, const uint32_t sample_id)
+  {
+    return filter(query_id + offset, sample_id);
+  }
+};
+
+template <class CagraSampleFilterT>
+struct CagraSampleFilterT_Selector {
+  using type = CagraSampleFilterWithQueryIdOffset<CagraSampleFilterT>;
+};
+template <>
+struct CagraSampleFilterT_Selector<cuvs::neighbors::filtering::none_sample_filter> {
+  using type = cuvs::neighbors::filtering::none_sample_filter;
+};
+
+// A helper function to set a query id offset
+template <class CagraSampleFilterT>
+inline typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type set_offset(
+  CagraSampleFilterT filter, const uint32_t offset)
+{
+  typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type new_filter(offset, filter);
+  return new_filter;
+}
+template <>
+inline typename CagraSampleFilterT_Selector<cuvs::neighbors::filtering::none_sample_filter>::type
+set_offset<cuvs::neighbors::filtering::none_sample_filter>(
+  cuvs::neighbors::filtering::none_sample_filter filter, const uint32_t)
+{
+  return filter;
+}
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
index 4e3983e3f2..b05afd2c94 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_00_generate.py
@@ -38,6 +38,9 @@
  */
 
 #include "search_multi_cta_inst.cuh"
+#include "sample_filter_utils.cuh"
+
+#define COMMA ,
 
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 """
@@ -65,7 +68,10 @@
     with open(path, "w") as f:
         f.write(header)
         f.write(
-                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, cuvs::neighbors::filtering::none_sample_filter);\n"
+        )
+        f.write(
+                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, CagraSampleFilterWithQueryIdOffset<cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);\n"
         )
         f.write(trailer)
         # For pasting into CMakeLists.txt
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
index fae5a93877..0ee0fa0821 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
@@ -23,12 +23,20 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_multi_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 instantiate_kernel_selection(float,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             cuvs::neighbors::filtering::none_sample_filter);
+instantiate_kernel_selection(float,
+                             uint32_t,
+                             float,
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
index 9606d510fe..3bd4df1721 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
@@ -23,12 +23,17 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_multi_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
+instantiate_kernel_selection(half, uint32_t, float, cuvs::neighbors::filtering::none_sample_filter);
 instantiate_kernel_selection(half,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
index a3322c4359..4e7389b4bb 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
@@ -23,12 +23,20 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_multi_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 instantiate_kernel_selection(int8_t,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             cuvs::neighbors::filtering::none_sample_filter);
+instantiate_kernel_selection(int8_t,
+                             uint32_t,
+                             float,
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 4dfc462563..9fa9d58944 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -282,7 +282,7 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
 
     // Filtering
     if constexpr (!std::is_same<SAMPLE_FILTER_T,
-                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+                                cuvs::neighbors::filtering::none_sample_filter>::value) {
       constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
       const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
 
@@ -305,7 +305,7 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
 
   // Post process for filtering
   if constexpr (!std::is_same<SAMPLE_FILTER_T,
-                              cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+                              cuvs::neighbors::filtering::none_sample_filter>::value) {
     constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
     const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
index 51fc6526f7..ed0e0387ca 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
@@ -23,12 +23,20 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_multi_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::multi_cta_search {
 instantiate_kernel_selection(uint8_t,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             cuvs::neighbors::filtering::none_sample_filter);
+instantiate_kernel_selection(uint8_t,
+                             uint32_t,
+                             float,
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::multi_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index 0daae17b38..9c22134a60 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -365,7 +365,7 @@ RAFT_KERNEL compute_distance_to_child_nodes_kernel(
   }
 
   if constexpr (!std::is_same<SAMPLE_FILTER_T,
-                              cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+                              cuvs::neighbors::filtering::none_sample_filter>::value) {
     if (!sample_filter(query_id, parent_index)) {
       parent_candidates_ptr[parent_list_index + (lds * query_id)] = utils::get_max_value<INDEX_T>();
       parent_distance_ptr[parent_list_index + (lds * query_id)] =
@@ -779,7 +779,7 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
 
     // Topk hint can not be used when applying a filter
     uint32_t* const top_hint_ptr =
-      std::is_same<SAMPLE_FILTER_T, cuvs::neighbors::filtering::none_cagra_sample_filter>::value
+      std::is_same<SAMPLE_FILTER_T, cuvs::neighbors::filtering::none_sample_filter>::value
         ? topk_hint.data()
         : nullptr;
     // Init topk_hint
@@ -878,7 +878,7 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
     auto result_distances_ptr = result_distances.data() + (iter & 0x1) * result_buffer_size;
 
     if constexpr (!std::is_same<SAMPLE_FILTER_T,
-                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+                                cuvs::neighbors::filtering::none_sample_filter>::value) {
       // Remove parent bit in search results
       remove_parent_bit(num_queries,
                         result_buffer_size,
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index 6ecbbc2e89..f23b966318 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -361,7 +361,7 @@ struct search_plan_impl : public search_plan_impl_base {
         std::to_string(hashmap_max_fill_rate) + " has been given.";
     }
     if constexpr (!std::is_same<SAMPLE_FILTER_T,
-                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+                                cuvs::neighbors::filtering::none_sample_filter>::value) {
       if (hashmap_mode == hash_mode::SMALL) {
         error_message += "`SMALL` hash is not available when filtering";
       } else {
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
index 4693cd54d2..d59201061b 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_00_generate.py
@@ -37,8 +37,11 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_single_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 """
 
@@ -68,7 +71,10 @@
     with open(path, "w") as f:
         f.write(header)
         f.write(
-                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, cuvs::neighbors::filtering::none_cagra_sample_filter);\n"
+                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, cuvs::neighbors::filtering::none_sample_filter);\n"
+        )
+        f.write(
+                f"instantiate_kernel_selection(\n  {data_t}, {idx_t}, {distance_t}, CagraSampleFilterWithQueryIdOffset<cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);\n"
         )
 
         f.write(trailer)
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
index f8495bc01b..7de479e97c 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
@@ -23,12 +23,20 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_single_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 instantiate_kernel_selection(float,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             cuvs::neighbors::filtering::none_sample_filter);
+instantiate_kernel_selection(float,
+                             uint32_t,
+                             float,
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
index c21e6d1f42..10abe1b24f 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
@@ -23,12 +23,17 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_single_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
+instantiate_kernel_selection(half, uint32_t, float, cuvs::neighbors::filtering::none_sample_filter);
 instantiate_kernel_selection(half,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
index 56a0d8ba99..ec0ea974cf 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
@@ -23,12 +23,20 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_single_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 instantiate_kernel_selection(int8_t,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             cuvs::neighbors::filtering::none_sample_filter);
+instantiate_kernel_selection(int8_t,
+                             uint32_t,
+                             float,
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index 21a0f6bb27..79cb6bc101 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -627,8 +627,7 @@ __device__ void search_core(
 
       // topk with bitonic sort
       _CLK_START();
-      if (std::is_same<SAMPLE_FILTER_T,
-                       cuvs::neighbors::filtering::none_cagra_sample_filter>::value ||
+      if (std::is_same<SAMPLE_FILTER_T, cuvs::neighbors::filtering::none_sample_filter>::value ||
           *filter_flag == 0) {
         topk_by_bitonic_sort<MAX_ITOPK, MAX_CANDIDATES>(result_distances_buffer,
                                                         result_indices_buffer,
@@ -716,7 +715,7 @@ __device__ void search_core(
 
     // Filtering
     if constexpr (!std::is_same<SAMPLE_FILTER_T,
-                                cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+                                cuvs::neighbors::filtering::none_sample_filter>::value) {
       if (threadIdx.x == 0) { *filter_flag = 0; }
       __syncthreads();
 
@@ -742,7 +741,7 @@ __device__ void search_core(
 
   // Post process for filtering
   if constexpr (!std::is_same<SAMPLE_FILTER_T,
-                              cuvs::neighbors::filtering::none_cagra_sample_filter>::value) {
+                              cuvs::neighbors::filtering::none_sample_filter>::value) {
     constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
     const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
index ee64271700..9df50513ca 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
@@ -23,12 +23,20 @@
  *
  */
 
+#include "sample_filter_utils.cuh"
 #include "search_single_cta_inst.cuh"
 
+#define COMMA ,
+
 namespace cuvs::neighbors::cagra::detail::single_cta_search {
 instantiate_kernel_selection(uint8_t,
                              uint32_t,
                              float,
-                             cuvs::neighbors::filtering::none_cagra_sample_filter);
+                             cuvs::neighbors::filtering::none_sample_filter);
+instantiate_kernel_selection(uint8_t,
+                             uint32_t,
+                             float,
+                             CagraSampleFilterWithQueryIdOffset<
+                               cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
 
 }  // namespace cuvs::neighbors::cagra::detail::single_cta_search
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 3aa1d7529f..e5eeecbc96 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -718,6 +718,38 @@ void brute_force_search_filtered(
   return;
 }
 
+template <typename T, typename IdxT, typename DistT, typename LayoutT>
+void search(raft::resources const& res,
+            const cuvs::neighbors::brute_force::index<T, DistT>& idx,
+            raft::device_matrix_view<const T, int64_t, LayoutT> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter_ref)
+{
+  try {
+    auto& sample_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::none_sample_filter&>(sample_filter_ref);
+    return brute_force_search<T, int64_t, DistT>(res, idx, queries, neighbors, distances);
+  } catch (const std::bad_cast&) {
+  }
+
+  try {
+    auto& sample_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::bitmap_filter<const uint32_t, int64_t>&>(
+        sample_filter_ref);
+    if constexpr (std::is_same_v<LayoutT, raft::col_major>) {
+      RAFT_FAIL("filtered search isn't available with col_major queries yet");
+    } else {
+      cuvs::core::bitmap_view<const uint32_t, int64_t> sample_filter_view =
+        sample_filter.bitmap_view_;
+      return brute_force_search_filtered<T, int64_t, uint32_t, DistT>(
+        res, idx, queries, sample_filter_view, neighbors, distances);
+    }
+  } catch (const std::bad_cast&) {
+    RAFT_FAIL("Unsupported sample filter type");
+  }
+}
+
 template <typename T, typename DistT, typename LayoutT = raft::row_major>
 cuvs::neighbors::brute_force::index<T, DistT> build(
   raft::resources const& res,
diff --git a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
index e739bddd47..1fabcca8c1 100644
--- a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
+++ b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
@@ -140,28 +140,18 @@
 """
 
 search_macro = """
-#define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \\
-  void search(raft::resources const& handle,                                    \\
-              const cuvs::neighbors::ivf_flat::search_params& params,           \\
-              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \\
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \\
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \\
-  {                                                                             \\
-    cuvs::neighbors::ivf_flat::detail::search(                                  \\
-      handle, params, index, queries, neighbors, distances);                    \\
-  }                                                                             \\
-  void search_with_filtering(                                                   \\
-    raft::resources const& handle,                                              \\
-    const search_params& params,                                                \\
-    index<T, IdxT>& idx,                                                        \\
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \\
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \\
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \\
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \\
-  {                                                                             \\
-    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \\
-      handle, params, idx, queries, neighbors, distances, sample_filter);       \\
+#define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                  \\
+  void search(                                                              \\
+    raft::resources const& handle,                                          \\
+    const cuvs::neighbors::ivf_flat::search_params& params,                 \\
+    cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                       \\
+    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,       \\
+    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,        \\
+    raft::device_matrix_view<float, IdxT, raft::row_major> distances,       \\
+    const cuvs::neighbors::filtering::base_filter& sample_filter)           \\
+  {                                                                         \\
+    cuvs::neighbors::ivf_flat::detail::search(                              \\
+      handle, params, index, queries, neighbors, distances, sample_filter); \\
   }
 """
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index a4f7697413..9626b2ce50 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -1304,7 +1304,7 @@ struct select_interleaved_scan_kernel {
  *               (one block processes one or more probes, hence: 1 <= grid_dim_x <= n_probes)
  * @param stream
  * @param sample_filter
- *   A filter that selects samples for a given query. Use an instance of none_ivf_sample_filter to
+ *   A filter that selects samples for a given query. Use an instance of none_sample_filter to
  *   provide a green light for every sample.
  */
 template <typename T, typename AccT, typename IdxT, typename IvfSampleFilterT>
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index b7dac3ef88..032b6a8ff0 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -20,13 +20,14 @@
 #include "../detail/ann_utils.cuh"
 #include "../ivf_common.cuh"              // cuvs::neighbors::detail::ivf
 #include "ivf_flat_interleaved_scan.cuh"  // interleaved_scan
-#include <cuvs/neighbors/common.hpp>      // none_ivf_sample_filter
+#include <cuvs/neighbors/common.hpp>      // none_sample_filter
 #include <cuvs/neighbors/ivf_flat.hpp>    // raft::neighbors::ivf_flat::index
 
 #include "../detail/ann_utils.cuh"      // utils::mapping
 #include <cuvs/distance/distance.hpp>   // is_min_close, DistanceType
 #include <cuvs/selection/select_k.hpp>  // cuvs::selection::select_k
-#include <raft/core/logger-ext.hpp>     // RAFT_LOG_TRACE
+#include <raft/core/error.hpp>
+#include <raft/core/logger-ext.hpp>  // RAFT_LOG_TRACE
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>   // raft::resources
 #include <raft/linalg/gemm.cuh>      // raft::linalg::gemm
@@ -307,7 +308,7 @@ void search_impl(raft::resources const& handle,
 /** See raft::neighbors::ivf_flat::search docs */
 template <typename T,
           typename IdxT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 inline void search_with_filtering(raft::resources const& handle,
                                   const search_params& params,
                                   const index<T, IdxT>& index,
@@ -402,15 +403,24 @@ void search(raft::resources const& handle,
             const index<T, IdxT>& idx,
             raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
             raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
-            raft::device_matrix_view<float, IdxT, raft::row_major> distances)
+            raft::device_matrix_view<float, IdxT, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter_ref)
 {
-  search_with_filtering(handle,
-                        params,
-                        idx,
-                        queries,
-                        neighbors,
-                        distances,
-                        cuvs::neighbors::filtering::none_ivf_sample_filter());
+  try {
+    auto& sample_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::none_sample_filter&>(sample_filter_ref);
+    return search_with_filtering(handle, params, idx, queries, neighbors, distances, sample_filter);
+  } catch (const std::bad_cast&) {
+  }
+
+  try {
+    auto& sample_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t>&>(
+        sample_filter_ref);
+    return search_with_filtering(handle, params, idx, queries, neighbors, distances, sample_filter);
+  } catch (const std::bad_cast&) {
+    RAFT_FAIL("Unsupported sample filter type");
+  }
 }
 
 }  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
index 93e46cbefa..3f262d6120 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
@@ -35,22 +35,11 @@ namespace cuvs::neighbors::ivf_flat {
               cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)     \
   {                                                                             \
     cuvs::neighbors::ivf_flat::detail::search(                                  \
-      handle, params, index, queries, neighbors, distances);                    \
-  }                                                                             \
-  void search_with_filtering(                                                   \
-    raft::resources const& handle,                                              \
-    const search_params& params,                                                \
-    index<T, IdxT>& idx,                                                        \
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \
-  {                                                                             \
-    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \
-      handle, params, idx, queries, neighbors, distances, sample_filter);       \
+      handle, params, index, queries, neighbors, distances, sample_filter);     \
   }
 CUVS_INST_IVF_FLAT_SEARCH(float, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
index 5f75d3d481..4357afb0ab 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
@@ -35,22 +35,11 @@ namespace cuvs::neighbors::ivf_flat {
               cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)     \
   {                                                                             \
     cuvs::neighbors::ivf_flat::detail::search(                                  \
-      handle, params, index, queries, neighbors, distances);                    \
-  }                                                                             \
-  void search_with_filtering(                                                   \
-    raft::resources const& handle,                                              \
-    const search_params& params,                                                \
-    index<T, IdxT>& idx,                                                        \
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \
-  {                                                                             \
-    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \
-      handle, params, idx, queries, neighbors, distances, sample_filter);       \
+      handle, params, index, queries, neighbors, distances, sample_filter);     \
   }
 CUVS_INST_IVF_FLAT_SEARCH(int8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
index a2696dc842..8265a3e177 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
@@ -35,22 +35,11 @@ namespace cuvs::neighbors::ivf_flat {
               cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)     \
   {                                                                             \
     cuvs::neighbors::ivf_flat::detail::search(                                  \
-      handle, params, index, queries, neighbors, distances);                    \
-  }                                                                             \
-  void search_with_filtering(                                                   \
-    raft::resources const& handle,                                              \
-    const search_params& params,                                                \
-    index<T, IdxT>& idx,                                                        \
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,           \
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,            \
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,           \
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter)    \
-  {                                                                             \
-    cuvs::neighbors::ivf_flat::detail::search_with_filtering(                   \
-      handle, params, idx, queries, neighbors, distances, sample_filter);       \
+      handle, params, index, queries, neighbors, distances, sample_filter);     \
   }
 CUVS_INST_IVF_FLAT_SEARCH(uint8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
index 9b3083c3bc..a5a8299678 100644
--- a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
+++ b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
@@ -67,29 +67,15 @@
 search_macro = """
 #define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \\
   void search(raft::resources const& handle,                                    \\
-              const cuvs::neighbors::ivf_pq::search_params& params,             \\
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \\
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \\
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances) \\
-  {                                                                             \\
-    cuvs::neighbors::ivf_pq::detail::search(                                   \\
-      handle, params, index, queries, neighbors, distances);  \\
-  }
-"""
-search_with_filter_macro = """
-#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                                 \\
-  void search_with_filtering(raft::resources const& handle,                     \\
               const cuvs::neighbors::ivf_pq::search_params& params,             \\
               cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \\
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \\
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \\
-              cuvs::neighbors::filtering::bitset_filter<                        \\
-                  uint32_t, IdxT> sample_filter)                                \\
+              const cuvs::neighbors::filtering::base_filter& sample_filter_ref) \\
   {                                                                             \\
-    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                     \\
-      handle, params, index, queries, neighbors, distances, sample_filter);     \\
+    cuvs::neighbors::ivf_pq::detail::search(                                    \\
+      handle, params, index, queries, neighbors, distances, sample_filter_ref); \\
   }
 """
 
@@ -104,11 +90,6 @@
         definition=search_macro,
         name="CUVS_INST_IVF_PQ_SEARCH",
     ),
-    search_with_filter=dict(
-        include=search_include_macro,
-        definition=search_with_filter_macro,
-        name="CUVS_INST_IVF_PQ_SEARCH_FILTER",
-    ),
 )
 
 for type_path, (T, IdxT) in types.items():
diff --git a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py
index 4c35b28361..75373e746a 100644
--- a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py
+++ b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq_compute_similarity.py
@@ -86,7 +86,7 @@
 """
 
 none_filter_int64 = "cuvs::neighbors::filtering::ivf_to_sample_filter" \
-                    "<int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>"
+                    "<int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>"
 bitset_filter64 = "cuvs::neighbors::filtering::ivf_to_sample_filter" \
                   "<int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>"
 
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
index 26312a4ae1..bc73ff5a36 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
@@ -71,4 +71,4 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   float,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
index f08f1700c0..2aa0bacf4c 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
@@ -71,4 +71,4 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
index 588c89604d..d4e3fdf5cc 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
@@ -71,4 +71,4 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
index 6c2f77412d..02e118158d 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
@@ -71,4 +71,4 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   half,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
index 7170e49db0..cde961c72c 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
@@ -71,4 +71,4 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   half,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
index c552065ab2..f1efe79f99 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
@@ -71,4 +71,4 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   half,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
index 8d9399da3a..bb56fd08d5 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
@@ -71,4 +71,4 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   half,
   half,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
index 0f54eede77..07ee110bc5 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
@@ -29,15 +29,17 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
-  void search(raft::resources const& handle,                                                       \
-              const cuvs::neighbors::ivf_pq::search_params& params,                                \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
-  {                                                                                                \
-    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_pq::search_params& params,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter_ref) \
+  {                                                                             \
+    cuvs::neighbors::ivf_pq::detail::search(                                    \
+      handle, params, index, queries, neighbors, distances, sample_filter_ref); \
   }
 CUVS_INST_IVF_PQ_SEARCH(float, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
index e5556e593e..cf387cb67d 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
@@ -29,15 +29,17 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
-  void search(raft::resources const& handle,                                                       \
-              const cuvs::neighbors::ivf_pq::search_params& params,                                \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
-  {                                                                                                \
-    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_pq::search_params& params,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter_ref) \
+  {                                                                             \
+    cuvs::neighbors::ivf_pq::detail::search(                                    \
+      handle, params, index, queries, neighbors, distances, sample_filter_ref); \
   }
 CUVS_INST_IVF_PQ_SEARCH(half, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
index 297e615d20..5ec9093dfa 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
@@ -29,15 +29,17 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
-  void search(raft::resources const& handle,                                                       \
-              const cuvs::neighbors::ivf_pq::search_params& params,                                \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
-  {                                                                                                \
-    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_pq::search_params& params,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter_ref) \
+  {                                                                             \
+    cuvs::neighbors::ivf_pq::detail::search(                                    \
+      handle, params, index, queries, neighbors, distances, sample_filter_ref); \
   }
 CUVS_INST_IVF_PQ_SEARCH(int8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
index 3cf8bfaff0..d2e2f3b005 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
@@ -29,15 +29,17 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                                           \
-  void search(raft::resources const& handle,                                                       \
-              const cuvs::neighbors::ivf_pq::search_params& params,                                \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                                         \
-              raft::device_matrix_view<const T, IdxT, raft::row_major> queries,                    \
-              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,                     \
-              raft::device_matrix_view<float, IdxT, raft::row_major> distances)                    \
-  {                                                                                                \
-    cuvs::neighbors::ivf_pq::detail::search(handle, params, index, queries, neighbors, distances); \
+#define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_pq::search_params& params,             \
+              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter_ref) \
+  {                                                                             \
+    cuvs::neighbors::ivf_pq::detail::search(                                    \
+      handle, params, index, queries, neighbors, distances, sample_filter_ref); \
   }
 CUVS_INST_IVF_PQ_SEARCH(uint8_t, int64_t);
 
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
deleted file mode 100644
index 4e75418827..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_float_int64_t.cu
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-
-#include "../ivf_pq_search.cuh"
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
-  void search_with_filtering(                                                \
-    raft::resources const& handle,                                           \
-    const cuvs::neighbors::ivf_pq::search_params& params,                    \
-    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
-  {                                                                          \
-    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
-      handle, params, index, queries, neighbors, distances, sample_filter);  \
-  }
-CUVS_INST_IVF_PQ_SEARCH_FILTER(float, int64_t);
-
-#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
deleted file mode 100644
index 5874fba6cb..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_half_int64_t.cu
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-
-#include "../ivf_pq_search.cuh"
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
-  void search_with_filtering(                                                \
-    raft::resources const& handle,                                           \
-    const cuvs::neighbors::ivf_pq::search_params& params,                    \
-    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
-  {                                                                          \
-    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
-      handle, params, index, queries, neighbors, distances, sample_filter);  \
-  }
-CUVS_INST_IVF_PQ_SEARCH_FILTER(half, int64_t);
-
-#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
deleted file mode 100644
index 52b1c68e7a..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_int8_t_int64_t.cu
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-
-#include "../ivf_pq_search.cuh"
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
-  void search_with_filtering(                                                \
-    raft::resources const& handle,                                           \
-    const cuvs::neighbors::ivf_pq::search_params& params,                    \
-    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
-  {                                                                          \
-    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
-      handle, params, index, queries, neighbors, distances, sample_filter);  \
-  }
-CUVS_INST_IVF_PQ_SEARCH_FILTER(int8_t, int64_t);
-
-#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
deleted file mode 100644
index e3d9361559..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_with_filter_uint8_t_int64_t.cu
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq.py
- *
- * Make changes there and run in this directory:
- *
- * > python generate_ivf_pq.py
- *
- */
-
-#include <cuvs/neighbors/ivf_pq.hpp>
-
-#include "../ivf_pq_search.cuh"
-
-namespace cuvs::neighbors::ivf_pq {
-
-#define CUVS_INST_IVF_PQ_SEARCH_FILTER(T, IdxT)                              \
-  void search_with_filtering(                                                \
-    raft::resources const& handle,                                           \
-    const cuvs::neighbors::ivf_pq::search_params& params,                    \
-    cuvs::neighbors::ivf_pq::index<IdxT>& index,                             \
-    raft::device_matrix_view<const T, IdxT, raft::row_major> queries,        \
-    raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,         \
-    raft::device_matrix_view<float, IdxT, raft::row_major> distances,        \
-    cuvs::neighbors::filtering::bitset_filter<uint32_t, IdxT> sample_filter) \
-  {                                                                          \
-    cuvs::neighbors::ivf_pq::detail::search_with_filtering(                  \
-      handle, params, index, queries, neighbors, distances, sample_filter);  \
-  }
-CUVS_INST_IVF_PQ_SEARCH_FILTER(uint8_t, int64_t);
-
-#undef CUVS_INST_IVF_PQ_SEARCH_FILTER
-
-}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
index 48e2bf2221..37612402c4 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include "../sample_filter.cuh"  // none_ivf_sample_filter
+#include "../sample_filter.cuh"  // none_sample_filter
 #include "ivf_pq_fp_8bit.cuh"    // cuvs::neighbors::ivf_pq::detail::fp_8bit
 
 #include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
@@ -177,37 +177,37 @@ instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   half,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   half,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   half,
   half,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   half,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   float,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   float,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
   cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_ivf_sample_filter>);
+    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
   half,
   cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
index 5fccbb3857..8404ca1f9f 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
@@ -17,7 +17,7 @@
 #pragma once
 
 #include "../ivf_common.cuh"                       // dummy_block_sort_t
-#include "../sample_filter.cuh"                    // none_ivf_sample_filter
+#include "../sample_filter.cuh"                    // none_sample_filter
 #include <cuvs/distance/distance.hpp>              // cuvs::distance::DistanceType
 #include <cuvs/neighbors/ivf_pq.hpp>               // codebook_gen
 #include <raft/matrix/detail/select_warpsort.cuh>  // matrix::detail::select::warpsort::warp_sort_distributed
@@ -247,7 +247,7 @@ __device__ auto ivfpq_compute_score(uint32_t pq_dim,
  *   query_kths keep the current state of the filtering - atomically updated distances to the
  *   k-th closest neighbors for each query [n_queries].
  * @param sample_filter
- *   A filter that selects samples for a given query. Use an instance of none_ivf_sample_filter to
+ *   A filter that selects samples for a given query. Use an instance of none_sample_filter to
  *   provide a green light for every sample.
  * @param lut_scores
  *   The device pointer for storing the lookup table globally [gridDim.x, pq_dim << PqBits].
@@ -513,7 +513,7 @@ RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
 // The signature of the kernel defined by a minimal set of template parameters
 template <typename OutT,
           typename LutT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 using compute_similarity_kernel_t =
   decltype(&compute_similarity_kernel<OutT, LutT, IvfSampleFilterT, 8, 0, true, true>);
 
@@ -522,7 +522,7 @@ template <typename OutT,
           typename LutT,
           bool PrecompBaseDiff,
           bool EnableSMemLut,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 struct compute_similarity_kernel_config {
  public:
   static auto get(uint32_t pq_bits, uint32_t k_max)
@@ -572,7 +572,7 @@ template <typename OutT,
           typename LutT,
           bool PrecompBaseDiff,
           bool EnableSMemLut,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 auto get_compute_similarity_kernel(uint32_t pq_bits, uint32_t k_max)
   -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
 {
@@ -617,7 +617,7 @@ struct selected {
 
 template <typename OutT,
           typename LutT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
                             rmm::cuda_stream_view stream,
                             uint32_t dim,
@@ -682,7 +682,7 @@ void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
  */
 template <typename OutT,
           typename LutT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 auto compute_similarity_select(const cudaDeviceProp& dev_props,
                                bool manage_local_topk,
                                int locality_hint,
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index 5f812dc4fb..e185f18dce 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -19,7 +19,7 @@
 #include "../../core/nvtx.hpp"
 #include "../detail/ann_utils.cuh"
 #include "../ivf_common.cuh"
-#include "../sample_filter.cuh"  // none_ivf_sample_filter
+#include "../sample_filter.cuh"  // none_sample_filter
 #include "ivf_pq_compute_similarity.cuh"
 #include "ivf_pq_fp_8bit.cuh"
 
@@ -592,7 +592,7 @@ constexpr uint32_t kMaxQueries = 4096;
 /** See raft::spatial::knn::ivf_pq::search docs */
 template <typename T,
           typename IdxT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_ivf_sample_filter>
+          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
 inline void search(raft::resources const& handle,
                    const search_params& params,
                    const index<IdxT>& index,
@@ -789,14 +789,23 @@ void search(raft::resources const& handle,
             const index<IdxT>& idx,
             raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
             raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
-            raft::device_matrix_view<float, IdxT, raft::row_major> distances)
+            raft::device_matrix_view<float, IdxT, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter_ref)
 {
-  search_with_filtering(handle,
-                        params,
-                        idx,
-                        queries,
-                        neighbors,
-                        distances,
-                        cuvs::neighbors::filtering::none_ivf_sample_filter{});
+  try {
+    auto& sample_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::none_sample_filter&>(sample_filter_ref);
+    return search_with_filtering(handle, params, idx, queries, neighbors, distances, sample_filter);
+  } catch (const std::bad_cast&) {
+  }
+
+  try {
+    auto& sample_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t>&>(
+        sample_filter_ref);
+    return search_with_filtering(handle, params, idx, queries, neighbors, distances, sample_filter);
+  } catch (const std::bad_cast&) {
+    RAFT_FAIL("Unsupported sample filter type");
+  }
 }
 }  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/refine/refine_device.cuh b/cpp/src/neighbors/refine/refine_device.cuh
index 5bf315ae5b..6184e540b7 100644
--- a/cpp/src/neighbors/refine/refine_device.cuh
+++ b/cpp/src/neighbors/refine/refine_device.cuh
@@ -126,7 +126,7 @@ void refine_device(
            0,
            chunk_index.data(),
            cuvs::distance::is_min_close(cuvs::distance::DistanceType(metric)),
-           cuvs::neighbors::filtering::none_ivf_sample_filter(),
+           cuvs::neighbors::filtering::none_sample_filter(),
            neighbors_uint32,
            distances.data_handle(),
            grid_dim_x,
diff --git a/cpp/src/neighbors/sample_filter.cu b/cpp/src/neighbors/sample_filter.cu
index 32a0d3bfb3..2da4bea4ef 100644
--- a/cpp/src/neighbors/sample_filter.cu
+++ b/cpp/src/neighbors/sample_filter.cu
@@ -18,6 +18,11 @@
 
 namespace cuvs::neighbors::filtering {
 
+template struct bitmap_filter<uint32_t, uint32_t>;
+template struct bitmap_filter<const uint32_t, int64_t>;
+template struct bitmap_filter<uint32_t, int64_t>;
+template struct bitmap_filter<uint64_t, int64_t>;
+
 template struct bitset_filter<uint8_t, uint32_t>;
 template struct bitset_filter<uint16_t, uint32_t>;
 template struct bitset_filter<uint32_t, uint32_t>;
diff --git a/cpp/src/neighbors/sample_filter.cuh b/cpp/src/neighbors/sample_filter.cuh
index e49d54920a..258116ed36 100644
--- a/cpp/src/neighbors/sample_filter.cuh
+++ b/cpp/src/neighbors/sample_filter.cuh
@@ -17,6 +17,7 @@
 #pragma once
 
 #include <cuvs/neighbors/common.hpp>
+#include <raft/core/bitmap.cuh>
 #include <raft/core/bitset.cuh>
 #include <raft/core/detail/macros.hpp>
 
@@ -26,7 +27,7 @@
 namespace cuvs::neighbors::filtering {
 
 /* A filter that filters nothing. This is the default behavior. */
-inline _RAFT_HOST_DEVICE bool none_ivf_sample_filter::operator()(
+inline _RAFT_HOST_DEVICE bool none_sample_filter::operator()(
   // query index
   const uint32_t query_ix,
   // the current inverted list index
@@ -38,7 +39,7 @@ inline _RAFT_HOST_DEVICE bool none_ivf_sample_filter::operator()(
 }
 
 /* A filter that filters nothing. This is the default behavior. */
-inline _RAFT_HOST_DEVICE bool none_cagra_sample_filter::operator()(
+inline _RAFT_HOST_DEVICE bool none_sample_filter::operator()(
   // query index
   const uint32_t query_ix,
   // the index of the current sample
@@ -107,4 +108,20 @@ inline _RAFT_HOST_DEVICE bool bitset_filter<bitset_t, index_t>::operator()(
   return bitset_view_.test(sample_ix);
 }
 
+template <typename bitmap_t, typename index_t>
+bitmap_filter<bitmap_t, index_t>::bitmap_filter(
+  const cuvs::core::bitmap_view<bitmap_t, index_t> bitmap_for_filtering)
+  : bitmap_view_{bitmap_for_filtering}
+{
+}
+
+template <typename bitmap_t, typename index_t>
+inline _RAFT_HOST_DEVICE bool bitmap_filter<bitmap_t, index_t>::operator()(
+  // query index
+  const uint32_t query_ix,
+  // the index of the current sample
+  const uint32_t sample_ix) const
+{
+  return bitmap_view_.test(query_ix, sample_ix);
+}
 }  // namespace cuvs::neighbors::filtering
diff --git a/cpp/src/stats/detail/trustworthiness_score.cuh b/cpp/src/stats/detail/trustworthiness_score.cuh
index f4725a2e8f..4d9c3af753 100644
--- a/cpp/src/stats/detail/trustworthiness_score.cuh
+++ b/cpp/src/stats/detail/trustworthiness_score.cuh
@@ -108,7 +108,7 @@ void run_knn(const raft::resources& h,
     input_view,
     raft::make_device_matrix_view<int64_t, int64_t>(indices, n, n_neighbors),
     raft::make_device_matrix_view<math_t, int64_t>(distances, n, n_neighbors),
-    std::nullopt);
+    cuvs::neighbors::filtering::none_sample_filter{});
 }
 
 /**
diff --git a/cpp/test/neighbors/ann_brute_force.cuh b/cpp/test/neighbors/ann_brute_force.cuh
index 461a202f22..c2afa4e8b3 100644
--- a/cpp/test/neighbors/ann_brute_force.cuh
+++ b/cpp/test/neighbors/ann_brute_force.cuh
@@ -96,8 +96,12 @@ class AnnBruteForceTest : public ::testing::TestWithParam<AnnBruteForceInputs<Id
       auto dists_out_view = raft::make_device_matrix_view<T, IdxT>(
         distances_bruteforce_dev.data(), ps.num_queries, ps.k);
 
-      brute_force::search(
-        handle_, idx, search_queries_view, indices_out_view, dists_out_view, std::nullopt);
+      brute_force::search(handle_,
+                          idx,
+                          search_queries_view,
+                          indices_out_view,
+                          dists_out_view,
+                          cuvs::neighbors::filtering::none_sample_filter{});
 
       raft::resource::sync_stream(handle_);
 
@@ -110,8 +114,12 @@ class AnnBruteForceTest : public ::testing::TestWithParam<AnnBruteForceInputs<Id
                                                       0.001f,
                                                       stream_,
                                                       true));
-      brute_force::search(
-        handle_, idx, search_queries_view, indices_out_view, dists_out_view, std::nullopt);
+      brute_force::search(handle_,
+                          idx,
+                          search_queries_view,
+                          indices_out_view,
+                          dists_out_view,
+                          cuvs::neighbors::filtering::none_sample_filter{});
     }
   }
 
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 4ce0849fd1..37d42dd1d3 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -50,6 +50,18 @@
 namespace cuvs::neighbors::cagra {
 namespace {
 
+struct test_cagra_sample_filter {
+  static constexpr unsigned offset = 300;
+  inline _RAFT_HOST_DEVICE auto operator()(
+    // query index
+    const uint32_t query_ix,
+    // the index of the current sample inside the current inverted list
+    const uint32_t sample_ix) const
+  {
+    return sample_ix >= offset;
+  }
+};
+
 /** Xorshift rondem number generator.
  *
  * See https://en.wikipedia.org/wiki/Xorshift#xorshift for reference.
@@ -663,6 +675,203 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
   rmm::device_uvector<DataT> search_queries;
 };
 
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
+ public:
+  AnnCagraFilterTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnCagraInputs>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+ protected:
+  void testCagra()
+  {
+    if (ps.metric == cuvs::distance::DistanceType::InnerProduct &&
+        ps.build_algo == graph_build_algo::NN_DESCENT)
+      GTEST_SKIP();
+
+    size_t queries_size = ps.n_queries * ps.k;
+    std::vector<IdxT> indices_Cagra(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<DistanceT> distances_Cagra(queries_size);
+    std::vector<DistanceT> distances_naive(queries_size);
+
+    {
+      rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+      auto* database_filtered_ptr = database.data() + test_cagra_sample_filter::offset * ps.dim;
+      cuvs::neighbors::naive_knn<DistanceT, DataT, IdxT>(
+        handle_,
+        distances_naive_dev.data(),
+        indices_naive_dev.data(),
+        search_queries.data(),
+        database_filtered_ptr,
+        ps.n_queries,
+        ps.n_rows - test_cagra_sample_filter::offset,
+        ps.dim,
+        ps.k,
+        ps.metric);
+      raft::linalg::addScalar(indices_naive_dev.data(),
+                              indices_naive_dev.data(),
+                              IdxT(test_cagra_sample_filter::offset),
+                              queries_size,
+                              stream_);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    {
+      rmm::device_uvector<DistanceT> distances_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_dev(queries_size, stream_);
+
+      {
+        cagra::index_params index_params;
+        index_params.metric = ps.metric;  // Note: currently ony the cagra::index_params metric is
+                                          // not used for knn_graph building.
+
+        switch (ps.build_algo) {
+          case graph_build_algo::IVF_PQ:
+            index_params.graph_build_params =
+              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            if (ps.ivf_pq_search_refine_ratio) {
+              std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
+                index_params.graph_build_params)
+                .refinement_rate = *ps.ivf_pq_search_refine_ratio;
+            }
+            break;
+          case graph_build_algo::NN_DESCENT: {
+            index_params.graph_build_params =
+              graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
+            break;
+          }
+          case graph_build_algo::AUTO:
+            // do nothing
+            break;
+        };
+
+        index_params.compression = ps.compression;
+        cagra::search_params search_params;
+        search_params.algo        = ps.algo;
+        search_params.max_queries = ps.max_queries;
+        search_params.team_size   = ps.team_size;
+
+        // TODO: setting search_params.itopk_size here breaks the filter tests, but is required for
+        // k>1024 skip these tests until fixed
+        if (ps.k >= 1024) { GTEST_SKIP(); }
+        // search_params.itopk_size   = ps.itopk_size;
+
+        auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          (const DataT*)database.data(), ps.n_rows, ps.dim);
+
+        cagra::index<DataT, IdxT> index(handle_);
+        if (ps.host_dataset) {
+          auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+          raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+          auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+            (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+          index = cagra::build(handle_, index_params, database_host_view);
+        } else {
+          index = cagra::build(handle_, index_params, database_view);
+        }
+
+        if (!ps.include_serialized_dataset) { index.update_dataset(handle_, database_view); }
+
+        auto search_queries_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          search_queries.data(), ps.n_queries, ps.dim);
+        auto indices_out_view =
+          raft::make_device_matrix_view<IdxT, int64_t>(indices_dev.data(), ps.n_queries, ps.k);
+        auto dists_out_view = raft::make_device_matrix_view<DistanceT, int64_t>(
+          distances_dev.data(), ps.n_queries, ps.k);
+        auto removed_indices =
+          raft::make_device_vector<int64_t, int64_t>(handle_, test_cagra_sample_filter::offset);
+        thrust::sequence(
+          raft::resource::get_thrust_policy(handle_),
+          thrust::device_pointer_cast(removed_indices.data_handle()),
+          thrust::device_pointer_cast(removed_indices.data_handle() + removed_indices.extent(0)));
+        raft::resource::sync_stream(handle_);
+        cuvs::core::bitset<std::uint32_t, int64_t> removed_indices_bitset(
+          handle_, removed_indices.view(), ps.n_rows);
+        auto bitset_filter_obj =
+          cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view());
+        cagra::search(handle_,
+                      search_params,
+                      index,
+                      search_queries_view,
+                      indices_out_view,
+                      dists_out_view,
+                      bitset_filter_obj);
+        raft::update_host(distances_Cagra.data(), distances_dev.data(), queries_size, stream_);
+        raft::update_host(indices_Cagra.data(), indices_dev.data(), queries_size, stream_);
+        raft::resource::sync_stream(handle_);
+      }
+
+      // Test search results for nodes marked as filtered
+      bool unacceptable_node = false;
+      for (int q = 0; q < ps.n_queries; q++) {
+        for (int i = 0; i < ps.k; i++) {
+          const auto n      = indices_Cagra[q * ps.k + i];
+          unacceptable_node = unacceptable_node | !test_cagra_sample_filter()(q, n);
+        }
+      }
+      EXPECT_FALSE(unacceptable_node);
+
+      double min_recall = ps.min_recall;
+      // TODO(mfoerster): re-enable uniquenes test
+      EXPECT_TRUE(eval_neighbours(indices_naive,
+                                  indices_Cagra,
+                                  distances_naive,
+                                  distances_Cagra,
+                                  ps.n_queries,
+                                  ps.k,
+                                  0.003,
+                                  min_recall,
+                                  false));
+      if (!ps.compression.has_value()) {
+        // Don't evaluate distances for CAGRA-Q for now as the error can be somewhat large
+        EXPECT_TRUE(eval_distances(handle_,
+                                   database.data(),
+                                   search_queries.data(),
+                                   indices_dev.data(),
+                                   distances_dev.data(),
+                                   ps.n_rows,
+                                   ps.dim,
+                                   ps.n_queries,
+                                   ps.k,
+                                   ps.metric,
+                                   1.0e-4));
+      }
+    }
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    search_queries.resize(ps.n_queries * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    InitDataset(handle_, database.data(), ps.n_rows, ps.dim, ps.metric, r);
+    InitDataset(handle_, search_queries.data(), ps.n_queries, ps.dim, ps.metric, r);
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnCagraInputs ps;
+  rmm::device_uvector<DataT> database;
+  rmm::device_uvector<DataT> search_queries;
+};
+
 inline std::vector<AnnCagraInputs> generate_inputs()
 {
   // TODO(tfeher): test MULTI_CTA kernel with search_width > 1 to allow multiple CTA per queries
diff --git a/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
index d4e6347193..ca188d1320 100644
--- a/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
@@ -26,9 +26,13 @@ TEST_P(AnnCagraTestF_U32, AnnCagra) { this->testCagra(); }
 typedef AnnCagraAddNodesTest<float, float, std::uint32_t> AnnCagraAddNodesTestF_U32;
 TEST_P(AnnCagraAddNodesTestF_U32, AnnCagraAddNodes) { this->testCagra(); }
 
+typedef AnnCagraFilterTest<float, float, std::uint32_t> AnnCagraFilterTestF_U32;
+TEST_P(AnnCagraFilterTestF_U32, AnnCagra) { this->testCagra(); }
+
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
                         AnnCagraAddNodesTestF_U32,
                         ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest, AnnCagraFilterTestF_U32, ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
index 72bdee4282..4aa03afd58 100644
--- a/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
@@ -24,10 +24,13 @@ typedef AnnCagraTest<float, std::int8_t, std::uint32_t> AnnCagraTestI8_U32;
 TEST_P(AnnCagraTestI8_U32, AnnCagra) { this->testCagra(); }
 typedef AnnCagraAddNodesTest<float, std::int8_t, std::uint32_t> AnnCagraAddNodesTestI8_U32;
 TEST_P(AnnCagraAddNodesTestI8_U32, AnnCagra) { this->testCagra(); }
+typedef AnnCagraFilterTest<float, std::int8_t, std::uint32_t> AnnCagraFilterTestI8_U32;
+TEST_P(AnnCagraFilterTestI8_U32, AnnCagra) { this->testCagra(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestI8_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
                         AnnCagraAddNodesTestI8_U32,
                         ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest, AnnCagraFilterTestI8_U32, ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
index b68bfa5745..b8e2a6b770 100644
--- a/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
@@ -24,10 +24,13 @@ typedef AnnCagraTest<float, std::uint8_t, std::uint32_t> AnnCagraTestU8_U32;
 TEST_P(AnnCagraTestU8_U32, AnnCagra) { this->testCagra(); }
 typedef AnnCagraAddNodesTest<float, std::uint8_t, std::uint32_t> AnnCagraAddNodesTestU8_U32;
 TEST_P(AnnCagraAddNodesTestU8_U32, AnnCagra) { this->testCagra(); }
+typedef AnnCagraFilterTest<float, std::uint8_t, std::uint32_t> AnnCagraFilterTestU8_U32;
+TEST_P(AnnCagraFilterTestU8_U32, AnnCagra) { this->testCagra(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestU8_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
                         AnnCagraAddNodesTestU8_U32,
                         ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest, AnnCagraFilterTestU8_U32, ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index 17ec84097c..8cc46b2f7e 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -304,7 +304,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
       ivf::resize_list(handle_, lists[label], list_device_spec, list_size, 0);
     }
 
-    idx.recompute_internal_state(handle_);
+    ivf_flat::helpers::recompute_internal_state(handle_, &idx);
 
     using interleaved_group = raft::Pow2<kIndexGroupSize>;
 
@@ -466,18 +466,19 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
 
         cuvs::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
           handle_, removed_indices.view(), ps.num_db_vecs);
+        auto bitset_filter_obj =
+          cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view());
 
         // Search with the filter
         auto search_queries_view = raft::make_device_matrix_view<const DataT, IdxT>(
           search_queries.data(), ps.num_queries, ps.dim);
-        ivf_flat::search_with_filtering(
-          handle_,
-          search_params,
-          index,
-          search_queries_view,
-          indices_ivfflat_dev.view(),
-          distances_ivfflat_dev.view(),
-          cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
+        ivf_flat::search(handle_,
+                         search_params,
+                         index,
+                         search_queries_view,
+                         indices_ivfflat_dev.view(),
+                         distances_ivfflat_dev.view(),
+                         bitset_filter_obj);
 
         raft::update_host(
           distances_ivfflat.data(), distances_ivfflat_dev.data_handle(), queries_size, stream_);
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu b/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu
index 0ce168f5ef..6a4a345169 100644
--- a/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu
@@ -21,7 +21,12 @@
 namespace cuvs::neighbors::ivf_flat {
 
 typedef AnnIVFFlatTest<float, float, int64_t> AnnIVFFlatTestF_float;
-TEST_P(AnnIVFFlatTestF_float, AnnIVFFlat) { this->testIVFFlat(); }
+TEST_P(AnnIVFFlatTestF_float, AnnIVFFlat)
+{
+  this->testIVFFlat();
+  this->testPacker();
+  this->testFilter();
+}
 
 INSTANTIATE_TEST_CASE_P(AnnIVFFlatTest, AnnIVFFlatTestF_float, ::testing::ValuesIn(inputs));
 
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
index 15935fd889..5335b1656a 100644
--- a/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
@@ -21,7 +21,12 @@
 namespace cuvs::neighbors::ivf_flat {
 
 typedef AnnIVFFlatTest<float, int8_t, int64_t> AnnIVFFlatTestF_int8;
-TEST_P(AnnIVFFlatTestF_int8, AnnIVFFlat) { this->testIVFFlat(); }
+TEST_P(AnnIVFFlatTestF_int8, AnnIVFFlat)
+{
+  this->testIVFFlat();
+  this->testPacker();
+  this->testFilter();
+}
 
 INSTANTIATE_TEST_CASE_P(AnnIVFFlatTest, AnnIVFFlatTestF_int8, ::testing::ValuesIn(inputs));
 
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
index 42a8dab2e1..e5573bcbcb 100644
--- a/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
@@ -21,7 +21,12 @@
 namespace cuvs::neighbors::ivf_flat {
 
 typedef AnnIVFFlatTest<float, uint8_t, int64_t> AnnIVFFlatTestF_uint8;
-TEST_P(AnnIVFFlatTestF_uint8, AnnIVFFlat) { this->testIVFFlat(); }
+TEST_P(AnnIVFFlatTestF_uint8, AnnIVFFlat)
+{
+  this->testIVFFlat();
+  this->testPacker();
+  this->testFilter();
+}
 
 INSTANTIATE_TEST_CASE_P(AnnIVFFlatTest, AnnIVFFlatTestF_uint8, ::testing::ValuesIn(inputs));
 
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index e6d8efc93f..f02568b744 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -18,10 +18,10 @@
 #include "../test_utils.cuh"
 #include "ann_utils.cuh"
 #include "naive_knn.cuh"
+#include <cuvs/core/bitset.hpp>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
-#include <raft/core/bitset.cuh>
 #include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/matrix/gather.cuh>
@@ -629,14 +629,10 @@ class ivf_pq_filter_test : public ::testing::TestWithParam<ivf_pq_inputs> {
 
     cuvs::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
       handle_, removed_indices.view(), ps.num_db_vecs);
-    cuvs::neighbors::ivf_pq::search_with_filtering(
-      handle_,
-      ps.search_params,
-      index,
-      query_view,
-      inds_view,
-      dists_view,
-      cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view()));
+    auto bitset_filter_obj =
+      cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view());
+    cuvs::neighbors::ivf_pq::search(
+      handle_, ps.search_params, index, query_view, inds_view, dists_view, bitset_filter_obj);
 
     raft::update_host(distances_ivf_pq.data(), distances_ivf_pq_dev.data(), queries_size, stream_);
     raft::update_host(indices_ivf_pq.data(), indices_ivf_pq_dev.data(), queries_size, stream_);
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index f1a05e045d..8c354baa96 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -93,7 +93,8 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs<T>> {
 
     auto metric = cuvs::distance::DistanceType::L2Unexpanded;
     auto idx    = cuvs::neighbors::brute_force::build(handle, index, metric);
-    cuvs::neighbors::brute_force::search(handle, idx, search, indices, distances, std::nullopt);
+    cuvs::neighbors::brute_force::search(
+      handle, idx, search, indices, distances, cuvs::neighbors::filtering::none_sample_filter{});
 
     build_actual_output<<<raft::ceildiv(rows_ * k_, 32), 32, 0, stream>>>(
       actual_labels_.data(), rows_, k_, search_labels_.data(), indices_.data());
@@ -401,7 +402,7 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
           search_queries.data(), params_.num_queries, params_.dim),
         indices,
         distances,
-        std::nullopt);
+        cuvs::neighbors::filtering::none_sample_filter{});
     } else {
       auto idx = cuvs::neighbors::brute_force::build(
         handle_,
@@ -417,7 +418,7 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
           search_queries.data(), params_.num_queries, params_.dim),
         indices,
         distances,
-        std::nullopt);
+        cuvs::neighbors::filtering::none_sample_filter{});
     }
 
     ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(ref_indices_.data(),
diff --git a/cpp/test/neighbors/brute_force_prefiltered.cu b/cpp/test/neighbors/brute_force_prefiltered.cu
index ae9111ea15..12b1c529e2 100644
--- a/cpp/test/neighbors/brute_force_prefiltered.cu
+++ b/cpp/test/neighbors/brute_force_prefiltered.cu
@@ -502,7 +502,12 @@ class PrefilteredBruteForceTest
     auto out_idx = raft::make_device_matrix_view<index_t, index_t, raft::row_major>(
       out_idx_d.data(), params.n_queries, params.top_k);
 
-    brute_force::search(handle, dataset, queries, out_idx, out_val, std::make_optional(filter));
+    brute_force::search(handle,
+                        dataset,
+                        queries,
+                        out_idx,
+                        out_val,
+                        cuvs::neighbors::filtering::bitmap_filter(filter));
     std::vector<dist_t> out_val_h(params.n_queries * params.top_k,
                                   std::numeric_limits<dist_t>::infinity());
 

From 67684ba4b5b347023306e194b2d3765280a49767 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 2 Oct 2024 22:01:34 +0200
Subject: [PATCH 168/623] Remove EXPLICIT_INSTANTIATE_ONLY macros (#358)

`CUVS_EXPLICIT_INSTANTIATE_ONLY` macro carried over from raft; it is not very useful in the fully compiled library, such as cuVS, hence is removed.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/358
---
 cpp/CMakeLists.txt                            |  2 -
 cpp/bench/ann/CMakeLists.txt                  |  2 -
 .../detail/pairwise_matrix/dispatch-ext.cuh   |  4 -
 .../detail/pairwise_matrix/dispatch.cuh       |  4 -
 cpp/src/distance/distance-ext.cuh             |  4 -
 cpp/src/distance/distance.cuh                 |  4 -
 cpp/test/CMakeLists.txt                       |  2 -
 cpp/test/cluster/linkage.cu                   |  9 --
 docs/source/developer_guide.md                | 91 -------------------
 9 files changed, 122 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 063a41b1ef..6dc32cafeb 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -430,8 +430,6 @@ add_library(
   src/stats/trustworthiness_score.cu
 )
 
-target_compile_definitions(cuvs PRIVATE "CUVS_EXPLICIT_INSTANTIATE_ONLY")
-
 target_compile_options(
   cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
                  --expt-relaxed-constexpr>
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 3224587e44..8cbf8c8b37 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -174,8 +174,6 @@ function(ConfigureAnnBench)
     )
   endif()
 
-  target_compile_definitions(${BENCH_NAME} PRIVATE "CUVS_EXPLICIT_INSTANTIATE_ONLY")
-
   target_include_directories(
     ${BENCH_NAME}
     PUBLIC "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/include>"
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
index 4fd194f6cf..edfd7cf5f2 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
@@ -22,8 +22,6 @@
 #include <raft/core/operators.hpp>        // raft::identity_op
 #include <raft/util/raft_explicit.hpp>    // RAFT_EXPLICIT
 
-#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
-
 namespace cuvs::distance::detail {
 
 template <typename OpT,
@@ -47,8 +45,6 @@ void pairwise_matrix_dispatch(OpT distance_op,
 
 };  // namespace cuvs::distance::detail
 
-#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
-
 #define instantiate_cuvs_distance_detail_pairwise_matrix_dispatch(                     \
   OpT, DataT, AccT, OutT, FinOpT, IdxT)                                                \
   extern template void cuvs::distance::detail::                                        \
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
index 0521a5713a..3f9304f7ae 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch.cuh
@@ -15,8 +15,4 @@
  */
 #pragma once
 
-#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
-#include "dispatch-inl.cuh"
-#endif
-
 #include "dispatch-ext.cuh"
diff --git a/cpp/src/distance/distance-ext.cuh b/cpp/src/distance/distance-ext.cuh
index 8ce7ef6905..e623f76bac 100644
--- a/cpp/src/distance/distance-ext.cuh
+++ b/cpp/src/distance/distance-ext.cuh
@@ -26,8 +26,6 @@
 
 #include <cuda_fp16.h>
 
-#ifdef CUVS_EXPLICIT_INSTANTIATE_ONLY
-
 namespace cuvs {
 namespace distance {
 
@@ -149,8 +147,6 @@ void pairwise_distance(raft::resources const& handle,
 };  // namespace distance
 };  // namespace cuvs
 
-#endif  // CUVS_EXPLICIT_INSTANTIATE_ONLY
-
 /*
  * Hierarchy of instantiations:
  *
diff --git a/cpp/src/distance/distance.cuh b/cpp/src/distance/distance.cuh
index d1bfc8212e..005cb212d7 100644
--- a/cpp/src/distance/distance.cuh
+++ b/cpp/src/distance/distance.cuh
@@ -15,8 +15,4 @@
  */
 #pragma once
 
-#ifndef CUVS_EXPLICIT_INSTANTIATE_ONLY
-#include "distance-inl.cuh"
-#endif
-
 #include "distance-ext.cuh"
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index b81ef6bfa5..5f7b5b02b2 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -71,8 +71,6 @@ function(ConfigureTest)
                          "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
 
-  target_compile_definitions(${TEST_NAME} PRIVATE "CUVS_EXPLICIT_INSTANTIATE_ONLY")
-
   if(_CUVS_TEST_NOCUDA)
     target_compile_definitions(${TEST_NAME} PRIVATE "CUVS_DISABLE_CUDA")
   endif()
diff --git a/cpp/test/cluster/linkage.cu b/cpp/test/cluster/linkage.cu
index 0f2461fa72..c9f9a50e7b 100644
--- a/cpp/test/cluster/linkage.cu
+++ b/cpp/test/cluster/linkage.cu
@@ -14,15 +14,6 @@
  * limitations under the License.
  */
 
-// XXX: We allow the instantiation of masked_l2_nn here:
-// raft::linkage::FixConnectivitiesRedOp<value_idx, value_t> red_op(params.n_row);
-// raft::linkage::cross_component_nn<value_idx, value_t>(
-//   handle, out_edges, data.data(), colors.data(), params.n_row, params.n_col, red_op);
-//
-// TODO: consider adding this to libraft.so or creating an instance in a
-// separate translation unit for this test.
-#undef CUVS_EXPLICIT_INSTANTIATE_ONLY
-
 #include "../test_utils.cuh"
 
 #include <cuvs/cluster/agglomerative.hpp>
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 516819b1c1..e54336852a 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -292,97 +292,6 @@ Sometimes, we need to temporarily change the log pattern (eg: for reporting deci
 
 4. Before creating a new primitive, check to see if one exists already. If one exists but the API isn't flexible enough to include your use-case, consider first refactoring the existing primitive. If that is not possible without an extreme number of changes, consider how the public API could be made more flexible. If the new primitive is different enough from all existing primitives, consider whether an existing public API could invoke the new primitive as an option or argument. If the new primitive is different enough from what exists already, add a header for the new public API function to the appropriate subdirectory and namespace.
 
-## Header organization of expensive function templates
-
-RAFT is a heavily templated library. Several core functions are expensive to compile and we want to prevent duplicate compilation of this functionality. To limit build time, RAFT provides a precompiled library (libraft.so) where expensive function templates are instantiated for the most commonly used template parameters. To prevent (1) accidental instantiation of these templates and (2) unnecessary dependency on the internals of these templates, we use a split header structure and define macros to control template instantiation. This section describes the macros and header structure.
-
-**Macros.** We define the macros `RAFT_COMPILED` and `RAFT_EXPLICIT_INSTANTIATE_ONLY`. The `RAFT_COMPILED` macro is defined by `CMake` when compiling code that (1) is part of `libraft.so` or (2) is linked with `libraft.so`. It indicates that a precompiled `libraft.so` is present at runtime.
-
-The `RAFT_EXPLICIT_INSTANTIATE_ONLY` macro is defined by `CMake` during compilation of `libraft.so` itself. When defined, it indicates that implicit instantiations of expensive function templates are forbidden (they result in a compiler error). In the RAFT project, we additionally define this macro during compilation of the tests and benchmarks. 
-
-Below, we summarize which combinations of `RAFT_COMPILED` and `RAFT_EXPLICIT_INSTANTIATE_ONLY` are used in practice and what the effect of the combination is. 
-
-| RAFT_COMPILED | RAFT_EXPLICIT_INSTANTIATE_ONLY | Which targets                                                                                        |
-|---------------|--------------------------------|------------------------------------------------------------------------------------------------------|
-| defined       | defined                        | `raft::compiled`, RAFT tests, RAFT benchmarks                                                        |
-| defined       |                                | Downstream libraries depending on `libraft` like cuML, cuGraph.                                      |
-|               |                                | Downstream libraries depending on `libraft-headers` like cugraph-ops.                                |
-
-
-| RAFT_COMPILED | RAFT_EXPLICIT_INSTANTIATE_ONLY | Effect                                                                                                |
-|---------------|--------------------------------|-------------------------------------------------------------------------------------------------------|
-| defined       | defined                        | Templates are precompiled. Compiler error on accidental instantiation of expensive function template. |
-| defined       |                                | Templates are precompiled. Implicit instantiation allowed.                                            |
-|               |                                | Nothing precompiled. Implicit instantiation allowed.                                                  |
-|               | defined                        | Avoid this: nothing precompiled. Compiler error on any instantiation of expensive function template.  |
-
-
-
-**Header organization.** Any header file that defines an expensive function template (say `expensive.cuh`) should be split in three parts: `expensive.cuh`, `expensive-inl.cuh`, and `expensive-ext.cuh`. The file `expensive-inl.cuh` ("inl" for "inline") contains the template definitions, i.e., the actual code. The file `expensive.cuh` includes one or both of the other two files, depending on the values of the `RAFT_COMPILED` and `RAFT_EXPLICIT_INSTANTIATE_ONLY` macros. The file `expensive-ext.cuh` contains `extern template` instantiations. In addition, if `RAFT_EXPLICIT_INSTANTIATE_ONLY` is set, it contains template definitions to ensure that a compiler error is raised in case of accidental instantiation.
-
-The dispatching by `expensive.cuh` is performed as follows:
-``` c++
-#ifndef RAFT_EXPLICIT_INSTANTIATE_ONLY
-// If implicit instantiation is allowed, include template definitions.
-#include "expensive-inl.cuh"
-#endif
-
-#ifdef RAFT_COMPILED
-// Include extern template instantiations when RAFT is compiled.
-#include "expensive-ext.cuh"
-#endif
-```
-
-The file `expensive-inl.cuh` is unchanged:
-``` c++
-namespace raft {
-template <typename T>
-void expensive(T arg) {
-  // .. function body
-}
-} // namespace raft
-```
-
-The file `expensive-ext.cuh` contains the following:
-``` c++
-#include <raft/util/raft_explicit.cuh> // RAFT_EXPLICIT
-
-#ifdef RAFT_EXPLICIT_INSTANTIATE_ONLY
-namespace raft {
-// (1) define templates to raise an error in case of accidental instantiation 
-template <typename T> void expensive(T arg) RAFT_EXPLICIT;
-} // namespace raft
-#endif //RAFT_EXPLICIT_INSTANTIATE_ONLY
-
-// (2) Provide extern template instantiations.
-extern template void raft::expensive<int>(int);
-extern template void raft::expensive<float>(float);
-```
-
-This header has two responsibilities: (1) define templates to raise an error in case of accidental instantiation and (2) provide `extern template` instantiations.
-First, if `RAFT_EXPLICIT_INSTANTIATE_ONLY` is set, `expensive` is defined. This is done for two reasons: (1) to give a definition, because the definition in `expensive-inl.cuh` was skipped and (2) to indicate that the template should be explicitly instantiated by taging it with the `RAFT_EXPLICIT` macro. This macro defines the function body, and it ensures that an informative error message is generated when an implicit instantiation erroneously occurs. Finally, the `extern template` instantiations are listed.
-
-To actually generate the code for the template instances, the file `src/expensive.cu` contains the following. Note that the only difference between the extern template instantiations in `expensive-ext.cuh` and these lines are the removal of the word `extern`:
-
-``` c++
-#include <raft/expensive-inl.cuh>
-
-template void raft::expensive<int>(int);
-template void raft::expensive<float>(float);
-```
-
-**Design considerations**: 
-
-1. In the `-ext.cuh` header, do not include implementation headers. Only include function parameter types and types that are used to instantiate the templates. If a primitive takes custom parameter types, define them in a separate header called `<primitive_name>_types.hpp`. (see [Common Design Considerations](https://github.com/rapidsai/raft/blob/7b065aff81a0b1976e2a9e2f3de6690361a1111b/docs/source/developer_guide.md#common-design-considerations)).
-
-2. Keep docstrings in the `-inl.cuh` header, as it is closer to the code. Remove docstrings from template definitions in the `-ext.cuh` header. Make sure to explicitly include public APIs in the RAFT API docs. That is, add `#include <raft/expensive.cuh>` to the docs in `docs/source/cpp_api/expensive.rst` (instead of `#include <raft/expensive-inl.cuh>`).
-
-3. The order of inclusion in `expensive.cuh` is extremely important. If `RAFT_EXPLICIT_INSTANTIATE_ONLY` is not defined, but `RAFT_COMPILED` is defined, then we must include the template definitions before the `extern template` instantiations.
-
-4. If a header file defines multiple expensive templates, it can be that one of them is not instantiated. In this case, **do define** the template with `RAFT_EXPLICIT` in the `-ext` header. This way, when the template is instantiated, the developer gets a helpful error message instead of a confusing "function not found".
-
-This header structure was proposed in [issue #1416](https://github.com/rapidsai/raft/issues/1416), which contains more background on the motivation of this structure and the mechanics of C++ template instantiation. 
-
 ## Testing
 
 It's important for RAFT to maintain a high test coverage of the public APIs in order to minimize the potential for downstream projects to encounter unexpected build or runtime behavior as a result of changes.

From 8a3fac3d001a1f1a1b17e3637e4959f0e0f83bad Mon Sep 17 00:00:00 2001
From: Deepika Padmanabhan <163203718+dpadmanabhan03@users.noreply.github.com>
Date: Wed, 2 Oct 2024 16:53:15 -0700
Subject: [PATCH 169/623] Adding example for tuning build and search params
 using Optuna (#257)

Authors:
  - Deepika Padmanabhan (https://github.com/dpadmanabhan03)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/257
---
 notebooks/cuvs_hpo_example.ipynb | 7181 ++++++++++++++++++++++++++++++
 1 file changed, 7181 insertions(+)
 create mode 100644 notebooks/cuvs_hpo_example.ipynb

diff --git a/notebooks/cuvs_hpo_example.ipynb b/notebooks/cuvs_hpo_example.ipynb
new file mode 100644
index 0000000000..d8b11a82c2
--- /dev/null
+++ b/notebooks/cuvs_hpo_example.ipynb
@@ -0,0 +1,7181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6c47d37e-fd75-4604-a787-ccccf392e9d3",
+   "metadata": {},
+   "source": [
+    "## Background \n",
+    "This notebook showcases how to leverage Optuna for hyperparameter tuning, specifically for the n_lists and n_probes parameters. We will demonstrate how to optimize these parameters using Optuna's Bayesian optimization capabilities. \n",
+    "\n",
+    "Note: This notebook has been tested on Sagemaker Studio with an instance type of ml.g5.12xlarge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d35868b-93ad-43b4-ae15-59e75aa89e3c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Install Required Packages\n",
+    "%mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs optuna -y\n",
+    "%pip install cupy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9ebbb352-260c-4078-8589-e0538338a275",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import cupy as cp\n",
+    "import numpy as np\n",
+    "from cuvs.neighbors import ivf_flat\n",
+    "import urllib.request\n",
+    "import numpy as np\n",
+    "import time\n",
+    "import optuna\n",
+    "from utils import calc_recall\n",
+    "from optuna.visualization import plot_optimization_history\n",
+    "import math\n",
+    "import os\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "933b9ab7-4c81-4124-9890-dbda75eb9fd9",
+   "metadata": {},
+   "source": [
+    "## Download wiki-all dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4f819371-019b-4378-8a47-389de1689c05",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import tarfile\n",
+    "home_dir = os.path.expanduser(\"~/\")\n",
+    "#wiki-all datasets are in tar format\n",
+    "def download_files(url, file):\n",
+    "    if os.path.exists(home_dir + \"/\" + file):\n",
+    "        print(\"tar file is already downloaded\")\n",
+    "    else:\n",
+    "        urllib.request.urlretrieve(url, home_dir + \"/\" + file)\n",
+    "    # Open the .tar file\n",
+    "    with tarfile.open(home_dir + \"/\" + file, 'r') as tar:\n",
+    "        filename = file.split(\".\")[0]\n",
+    "        if os.path.exists(home_dir + \"/\" + filename + \"/\"):\n",
+    "            print(\"Files already extracted\")\n",
+    "            return home_dir + \"/\" + filename + \"/\"\n",
+    "        # Extract all contents into the specified directory\n",
+    "        extract_path=home_dir + \"/\" +file.split(\".\")[0]\n",
+    "        tar.extractall(extract_path)\n",
+    "    return extract_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "26197a6e-f5c6-443f-9c1d-95105cc7038d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tar file is already downloaded\n",
+      "Files already extracted\n"
+     ]
+    }
+   ],
+   "source": [
+    "extracted_path=download_files('https://data.rapids.ai/raft/datasets/wiki_all_1M/wiki_all_1M.tar', 'wiki_all_1M.tar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b0ead9f-417b-4305-8277-92ca34352ed1",
+   "metadata": {},
+   "source": [
+    "## Dataset Preparation: Load fbin, ibin files \n",
+    "This example utilizes the Wiki-1M dataset, a collection of four binary files containing: \n",
+    "\n",
+    "Database vectors: Used for index building and searching.\n",
+    "Query vectors: Used for index building and searching.\n",
+    "Ground truth neighbors: Associated with a particular distance, used for evaluation.\n",
+    "Distances: Associated with a particular distance, used for evaluation.\n",
+    "The file suffixes denote the data type of vectors stored in the file: \n",
+    "\n",
+    ".fbin: float32\n",
+    ".ibin: int\n",
+    "For more information on the Wiki-1M dataset, please refer to the [RAPIDS documentation](https://docs.rapids.ai/api/raft/nightly/ann_benchmarks_dataset)\n",
+    "."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "2c6a0772-bb7c-43b7-aecc-8864140b0353",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def read_data(file_path, dtype):\n",
+    "    with open(file_path, \"rb\") as f:\n",
+    "        rows,cols = np.fromfile(f, count=2, dtype= np.int32)\n",
+    "        d = np.fromfile(f,count=rows*cols,dtype=dtype).reshape(rows, cols)\n",
+    "    return cp.asarray(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b46775d5-37c2-4be2-8c89-019ee5474f6b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "vectors= read_data(extracted_path + \"/base.1M.fbin\",np.float32)\n",
+    "queries = read_data(extracted_path + \"/queries.fbin\",np.float32)\n",
+    "gt_neighbors = read_data(extracted_path + \"/groundtruth.1M.neighbors.ibin\",np.int32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "fae0ab44-a9da-4443-817c-325364ff0ee7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Get the dataset size of database vectors\n",
+    "dataset_size = vectors.shape[0]\n",
+    "dim = vectors.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d35b88c4-a005-4bef-90bd-da33fee63f8b",
+   "metadata": {},
+   "source": [
+    "## Visualization\n",
+    "\n",
+    "Generates and displays Pareto front plots for a given Optuna study object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "d321bd84-b6f7-4bdb-889f-578a8512cd8c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def visualization(study_obj):\n",
+    "    \"\"\"\n",
+    "    This function creates two Pareto front plots to visualize trade-offs between different \n",
+    "    optimization objectives. The plots help in understanding the balance between competing \n",
+    "    objectives in the optimization process.\n",
+    "\n",
+    "    Args:\n",
+    "        study_obj (optuna.Study): The Optuna study object containing the optimization results.\n",
+    "\n",
+    "    The function produces the following plots:\n",
+    "    1. **Figure 1**: A Pareto front plot showing the trade-off between `build_time_in_secs` \n",
+    "       and `recall`. It visualizes how the optimization process balances the build time \n",
+    "       and recall score.\n",
+    "    2. **Figure 2**: A Pareto front plot showing the trade-off between `latency_in_ms` \n",
+    "       and `recall`. This plot illustrates the relationship between latency and recall score.\n",
+    "       \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    fig1 = optuna.visualization.plot_pareto_front(\n",
+    "    study_obj,\n",
+    "    targets=lambda t: (t.values[0], t.values[2]),\n",
+    "    target_names=[\"build_time_in_secs\", \"recall\"],\n",
+    "    )\n",
+    "    fig1.show()\n",
+    "\n",
+    "    fig2 = optuna.visualization.plot_pareto_front(\n",
+    "        study_obj,\n",
+    "        targets=lambda t: (t.values[1], t.values[2]),\n",
+    "        target_names=[\"latency_in_ms\", \"recall\"],\n",
+    "    )\n",
+    "    fig2.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "449d2a28-4286-451f-89bc-10ce535aa134",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def print_target_instance_summary(target_instance):\n",
+    "    print(f\"\\tnumber: {target_instance.number}\")\n",
+    "    print(f\"\\tparams: {target_instance.params}\")\n",
+    "    print(f\"\\tvalues: {target_instance.values}\")\n",
+    "    \n",
+    "def print_best_trial_values(optuna_study):\n",
+    "    \"\"\"\n",
+    "    Prints details about the trials on the Pareto front of an Optuna study.\n",
+    "\n",
+    "    This function analyzes the best trials from an Optuna study, which are typically \n",
+    "    those with the most favorable trade-offs among multiple objectives. It prints \n",
+    "    information on three specific metrics:\n",
+    "\n",
+    "    1. The number of trials on the Pareto front.\n",
+    "    2. The trial with the highest accuracy among the best trials.\n",
+    "    3. The trial with the lowest build time among the best trials.\n",
+    "    4. The trial with the lowest latency among the best trials.\n",
+    "\n",
+    "    Parameters:\n",
+    "    optuna_study (optuna.study.Study): An Optuna study object that contains information\n",
+    "    about the trials and their respective metrics.\n",
+    "\n",
+    "    The function assumes that each trial has three metrics recorded in the `values` list:\n",
+    "    - `values[0]`: Build time\n",
+    "    - `values[1]`: latency\n",
+    "    - `values[2]`: Accuracy\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    print(f\"Number of trials on the Pareto front: {len(optuna_study.best_trials)}\")\n",
+    "\n",
+    "    trial_with_lowest_build_time = min(optuna_study.best_trials, key=lambda t: t.values[0])\n",
+    "    print(f\"Trial with lowest build time in secs: \")\n",
+    "    print_target_instance_summary(trial_with_lowest_build_time)\n",
+    "\n",
+    "    trial_with_lowest_latency = min(optuna_study.best_trials, key=lambda t: t.values[1])\n",
+    "    print(f\"Trial with lowest latency in ms: \")\n",
+    "    print_target_instance_summary(trial_with_lowest_latency)\n",
+    "    \n",
+    "    trial_with_highest_accuracy = max(optuna_study.best_trials, key=lambda t: t.values[2])\n",
+    "    print(f\"Trial with highest accuracy: \")\n",
+    "    print_target_instance_summary(trial_with_highest_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f08a88d8-b805-4ed3-8521-f9cb0c2bd28b",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter Optimization (HPO) for CUVS Libraries\n",
+    "\n",
+    "An Optuna trial object used to suggest values for the hyperparameters of various CUVS libraries (such as ivf_flat, ivf_pq, and cagra).\n",
+    "\n",
+    "The multi-objective function returns a tuple of three float values, each rounded to four decimal places:\n",
+    "\n",
+    "build_time_in_secs: Time taken to build the index, measured in seconds.\n",
+    "latency_in_ms: Average search latency, measured in milliseconds. Calculated as the total search time divided by the number of queries.\n",
+    "recall: Recall metric, indicating the proportion of relevant neighbors retrieved.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47c43a77-f3d0-4920-a45d-9c4566d5b01f",
+   "metadata": {},
+   "source": [
+    "## ivf_flat HPO example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "bfdde156-f624-495d-aa52-bc03e9113120",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def multi_objective_ivf_flat(trial):\n",
+    "    \"\"\"\n",
+    "    Optimizes the parameters for an Inverted File Index (IVF) Flat index in a multi-objective setting.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Suggest an integer for the number of lists\n",
+    "    n_lists = trial.suggest_int(\"n_lists\", 10, dataset_size*0.1)\n",
+    "    # Suggest an integer for the number of probes\n",
+    "    n_probes = trial.suggest_int(\"n_probes\",n_lists*0.01 , n_lists*0.1)\n",
+    "    build_params = ivf_flat.IndexParams(\n",
+    "        n_lists=n_lists,\n",
+    "    )\n",
+    "    start_build_time = time.time()\n",
+    "    index = ivf_flat.build(build_params, vectors)\n",
+    "    build_time_in_secs = time.time() - start_build_time\n",
+    "\n",
+    "    # Configure search parameters\n",
+    "    search_params = ivf_flat.SearchParams(n_probes=n_probes)\n",
+    "    # Perform the search\n",
+    "    start_search_time = time.time()\n",
+    "    distances, indices = ivf_flat.search(search_params, index, queries, k=10)\n",
+    "    search_time = time.time() - start_search_time\n",
+    "    \n",
+    "    latency_in_ms = (search_time * 1000)/queries.shape[0]\n",
+    "            \n",
+    "    found_distances, found_indices = cp.asnumpy(distances), cp.asnumpy(indices)\n",
+    "    recall = calc_recall(found_indices, gt_neighbors)\n",
+    "    return round(build_time_in_secs,4), round(latency_in_ms,4), round(recall,4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "23944214-d72f-486a-bb9c-22a6c8b9516e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:24:19,149] A new study created in memory with name: no-name-ab0e88bd-1516-4eb2-a60d-01ae30ad08c4\n",
+      "[I 2024-08-19 16:24:51,762] Trial 0 finished with values: [23.1406, 0.3441, 0.9994] and parameters: {'n_lists': 48570, 'n_probes': 2511}. \n",
+      "[I 2024-08-19 16:25:28,379] Trial 1 finished with values: [24.232, 0.6336, 0.9999] and parameters: {'n_lists': 51021, 'n_probes': 4945}. \n",
+      "[I 2024-08-19 16:25:58,230] Trial 2 finished with values: [22.675, 0.1172, 0.9949] and parameters: {'n_lists': 47692, 'n_probes': 760}. \n",
+      "[I 2024-08-19 16:26:54,339] Trial 3 finished with values: [42.3469, 0.7272, 0.9997] and parameters: {'n_lists': 91882, 'n_probes': 6687}. \n",
+      "[I 2024-08-19 16:27:29,832] Trial 4 finished with values: [27.2722, 0.2141, 0.9985] and parameters: {'n_lists': 57879, 'n_probes': 1657}. \n",
+      "[I 2024-08-19 16:28:22,050] Trial 5 finished with values: [42.7912, 0.2936, 0.999] and parameters: {'n_lists': 92899, 'n_probes': 2804}. \n",
+      "[I 2024-08-19 16:28:54,048] Trial 6 finished with values: [24.9842, 0.0949, 0.9935] and parameters: {'n_lists': 52962, 'n_probes': 668}. \n",
+      "[I 2024-08-19 16:29:32,878] Trial 7 finished with values: [26.306, 0.6545, 0.9999] and parameters: {'n_lists': 55683, 'n_probes': 5439}. \n",
+      "[I 2024-08-19 16:30:02,789] Trial 8 finished with values: [20.7545, 0.3232, 0.9992] and parameters: {'n_lists': 43405, 'n_probes': 2093}. \n",
+      "[I 2024-08-19 16:30:31,208] Trial 9 finished with values: [17.2627, 0.5324, 0.9998] and parameters: {'n_lists': 35266, 'n_probes': 3028}. \n"
+     ]
+    }
+   ],
+   "source": [
+    "ivf_flat_study = optuna.create_study(directions=['minimize', 'minimize', 'maximize'])\n",
+    "ivf_flat_study.optimize(multi_objective_ivf_flat, n_trials=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "3c6c30d1-13e1-4f5f-99c2-6f2c6c1d389b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of trials on the Pareto front: 8\n",
+      "Trial with lowest build time in secs: \n",
+      "\tnumber: 9\n",
+      "\tparams: {'n_lists': 35266, 'n_probes': 3028}\n",
+      "\tvalues: [17.2627, 0.5324, 0.9998]\n",
+      "Trial with lowest latency in ms: \n",
+      "\tnumber: 6\n",
+      "\tparams: {'n_lists': 52962, 'n_probes': 668}\n",
+      "\tvalues: [24.9842, 0.0949, 0.9935]\n",
+      "Trial with highest accuracy: \n",
+      "\tnumber: 1\n",
+      "\tparams: {'n_lists': 51021, 'n_probes': 4945}\n",
+      "\tvalues: [24.232, 0.6336, 0.9999]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_best_trial_values(ivf_flat_study)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "1ed2ec62-0b0b-43e5-abbc-9bd7d06e5ebd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "%{text}<extra>Trial</extra>",
+         "marker": {
+          "color": [
+           3,
+           7
+          ],
+          "colorbar": {
+           "title": {
+            "text": "Trial"
+           }
+          },
+          "colorscale": [
+           [
+            0,
+            "rgb(247,251,255)"
+           ],
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+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Pareto-front Plot"
+        },
+        "xaxis": {
+         "autorange": true,
+         "range": [
+          15.745768536042302,
+          44.3081314639577
+         ],
+         "title": {
+          "text": "build_time_in_secs"
+         },
+         "type": "linear"
+        },
+        "yaxis": {
+         "autorange": true,
+         "range": [
+          0.9929718446601943,
+          1.0004281553398058
+         ],
+         "title": {
+          "text": "recall"
+         },
+         "type": "linear"
+        }
+       }
+      },
+      "image/png": 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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('c90cac75-c479-415f-8cee-55d62a752652');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualization(ivf_flat_study)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cca41e4-b697-4a0e-98f8-f3b682bf552f",
+   "metadata": {},
+   "source": [
+    "## ivf_pq HPO example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "dc4f0458-52e8-4663-82d7-afa33d68f242",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from cuvs.neighbors import ivf_pq,refine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "81c092df-fd11-472c-aa90-85b31f66d494",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def multi_objective_ivf_pq(trial):\n",
+    "    \"\"\"\n",
+    "    Optimizes hyperparameters for Inverted File Product Quantization (IVF-PQ) in a multi-objective setting..\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Suggest values for build parameters\n",
+    "    pq_dim = trial.suggest_int(\"pq_dim\", dim*0.25, dim, step=2)\n",
+    "    n_lists = 1000\n",
+    "\n",
+    "    # Suggest an integer for the number of probes\n",
+    "    n_probes = trial.suggest_int(\"n_probes\",n_lists*0.01 , n_lists*0.1)\n",
+    "\n",
+    "    build_params = ivf_pq.IndexParams(\n",
+    "    n_lists=n_lists,\n",
+    "    pq_dim=pq_dim,\n",
+    "    )\n",
+    "\n",
+    "    start_build_time = time.time()\n",
+    "    index = ivf_pq.build(build_params, vectors)\n",
+    "    build_time_in_secs = time.time() - start_build_time\n",
+    "\n",
+    "    # Configure search parameters\n",
+    "    search_params = ivf_pq.SearchParams(n_probes=n_probes)\n",
+    "\n",
+    "    # perform search and refine to increase recall/accuracy\n",
+    "    start_search_time = time.time()\n",
+    "    distances, indices = ivf_pq.search(search_params, index, queries, k=10)\n",
+    "    search_time = time.time() - start_search_time\n",
+    "\n",
+    "    latency_in_ms = (search_time * 1000)/queries.shape[0]\n",
+    "\n",
+    "    found_distances, found_indices = cp.asnumpy(distances), cp.asnumpy(indices)\n",
+    "    recall = calc_recall(found_indices, gt_neighbors)\n",
+    "\n",
+    "    return round(build_time_in_secs,4), round(latency_in_ms, 4), round(recall,4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "6d805aee-d93c-4423-84f7-0c0a38f036fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:41:17,476] A new study created in memory with name: no-name-f823ce34-eccc-4c5a-a90d-1963f50a3a89\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 662\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:41:39,686] Trial 0 finished with values: [9.7626, 0.6995, 0.9562] and parameters: {'pq_dim': 662, 'n_probes': 79}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 210\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:41:51,544] Trial 1 finished with values: [4.9705, 0.1443, 0.8414] and parameters: {'pq_dim': 210, 'n_probes': 57}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 678\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:42:15,891] Trial 2 finished with values: [10.0147, 0.8856, 0.9581] and parameters: {'pq_dim': 678, 'n_probes': 98}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 432\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:42:34,553] Trial 3 finished with values: [9.3991, 0.3831, 0.9497] and parameters: {'pq_dim': 432, 'n_probes': 70}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 410\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:42:52,606] Trial 4 finished with values: [9.018, 0.3598, 0.9471] and parameters: {'pq_dim': 410, 'n_probes': 69}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 242\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:43:04,224] Trial 5 finished with values: [4.2757, 0.1919, 0.8495] and parameters: {'pq_dim': 242, 'n_probes': 66}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 688\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:43:24,543] Trial 6 finished with values: [10.1002, 0.4773, 0.9518] and parameters: {'pq_dim': 688, 'n_probes': 50}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 388\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:43:42,703] Trial 7 finished with values: [8.6237, 0.411, 0.9475] and parameters: {'pq_dim': 388, 'n_probes': 85}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 286\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:43:57,057] Trial 8 finished with values: [6.0929, 0.2856, 0.8991] and parameters: {'pq_dim': 286, 'n_probes': 79}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 284\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:44:09,228] Trial 9 finished with values: [6.0429, 0.0726, 0.8775] and parameters: {'pq_dim': 284, 'n_probes': 19}. \n"
+     ]
+    }
+   ],
+   "source": [
+    "ivf_pq_study = optuna.create_study(directions=['minimize', 'minimize', 'maximize'])\n",
+    "ivf_pq_study.optimize(multi_objective_ivf_pq, n_trials=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "b7479455-9859-4e17-94df-19834360a0e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of trials on the Pareto front: 10\n",
+      "Trial with lowest build time in secs: \n",
+      "\tnumber: 5\n",
+      "\tparams: {'pq_dim': 242, 'n_probes': 66}\n",
+      "\tvalues: [4.2757, 0.1919, 0.8495]\n",
+      "Trial with lowest latency in ms: \n",
+      "\tnumber: 9\n",
+      "\tparams: {'pq_dim': 284, 'n_probes': 19}\n",
+      "\tvalues: [6.0429, 0.0726, 0.8775]\n",
+      "Trial with highest accuracy: \n",
+      "\tnumber: 2\n",
+      "\tparams: {'pq_dim': 678, 'n_probes': 98}\n",
+      "\tvalues: [10.0147, 0.8856, 0.9581]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_best_trial_values(ivf_pq_study)"
+   ]
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+   "id": "15ce945b-0c46-4f55-bcbb-3b836c80db2c",
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+             "#46039f"
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+            [
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+             "#7201a8"
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+            [
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+             "#9c179e"
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+            [
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+             "#bd3786"
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+            [
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+             "#d8576b"
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+            [
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+             "#ed7953"
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+            [
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+            [
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+            [
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+            [
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+            [
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+            [
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+             "#ed7953"
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+            [
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+             "#fdca26"
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+            [
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+             "#f0f921"
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+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Pareto-front Plot"
+        },
+        "xaxis": {
+         "autorange": true,
+         "range": [
+          3.929601822989145,
+          10.446298177010853
+         ],
+         "title": {
+          "text": "build_time_in_secs"
+         },
+         "type": "linear"
+        },
+        "yaxis": {
+         "autorange": true,
+         "range": [
+          0.8317694174757282,
+          0.9677305825242718
+         ],
+         "title": {
+          "text": "recall"
+         },
+         "type": "linear"
+        }
+       }
+      },
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('7ff8b42f-0953-4389-a0f4-b260cd662c1e');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
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+       "}}\n",
+       "\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualization(ivf_pq_study)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d5494bb-b254-4dfd-9029-f546365be894",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## cagra HPO example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "98828ce8-fec0-4096-8a5a-9be3e181a0ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cuvs.neighbors import cagra\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "c644ded3-644b-4f51-81f6-15b612123678",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multi_objective_cagra(trial):\n",
+    "    \"\"\"\n",
+    "    Optimizes the parameters for the cagra index using a multi-objective approach.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Suggest values for build parameters\n",
+    "    intermediate_graph_degree = trial.suggest_int(\"intermediate_graph_degree\", 64, 128, step=2 )\n",
+    "\n",
+    "    # Suggest an integer for the number of probes\n",
+    "    itopk_size = trial.suggest_int(\"itopk_size\", 64, 128, step=2)\n",
+    "\n",
+    "    build_params = cagra.IndexParams(\n",
+    "    intermediate_graph_degree=intermediate_graph_degree\n",
+    "    )\n",
+    "\n",
+    "    start_build_time = time.time()\n",
+    "    cagra_index = cagra.build(build_params, vectors)\n",
+    "    build_time_in_secs = time.time() - start_build_time\n",
+    "\n",
+    "    # Configure search parameters\n",
+    "    search_params = cagra.SearchParams(itopk_size=itopk_size)\n",
+    "\n",
+    "    # perform search and refine to increase recall/accuracy\n",
+    "    start_search_time = time.time()\n",
+    "    distances, indices = cagra.search(search_params, cagra_index, queries, k=10)\n",
+    "    search_time = time.time() - start_search_time\n",
+    "\n",
+    "    latency_in_ms = (search_time * 1000)/queries.shape[0]\n",
+    "\n",
+    "    found_distances, found_indices = cp.asnumpy(distances), cp.asnumpy(indices)\n",
+    "    recall = calc_recall(found_indices, gt_neighbors)\n",
+    "\n",
+    "    return round(build_time_in_secs,4), round(latency_in_ms,4), round(recall,4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "516b9b78-ba13-490c-ba47-d6786fb33606",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:53:39,324] A new study created in memory with name: no-name-b457b87f-2a54-4e19-944d-5902bf10ea8e\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 192\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:56:22,063] Trial 0 finished with values: [157.0238, 0.0412, 0.9903] and parameters: {'intermediate_graph_degree': 76, 'itopk_size': 124}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I] [16:56:15.120128] optimizing graph\n",
+      "[I] [16:56:16.300838] Graph optimized, creating index\n",
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 192\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 16:59:03,390] Trial 1 finished with values: [155.633, 0.0364, 0.9884] and parameters: {'intermediate_graph_degree': 72, 'itopk_size': 106}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I] [16:58:56.534874] optimizing graph\n",
+      "[I] [16:58:57.649931] Graph optimized, creating index\n",
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 192\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 17:01:51,648] Trial 2 finished with values: [162.741, 0.0231, 0.9801] and parameters: {'intermediate_graph_degree': 90, 'itopk_size': 66}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I] [17:01:44.581010] optimizing graph\n",
+      "[I] [17:01:46.084702] Graph optimized, creating index\n",
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 192\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 17:04:46,930] Trial 3 finished with values: [169.5533, 0.0396, 0.9915] and parameters: {'intermediate_graph_degree': 110, 'itopk_size': 114}. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I] [17:04:39.128583] optimizing graph\n",
+      "[I] [17:04:41.155907] Graph optimized, creating index\n",
+      "using ivf_pq::index_params nrows 1000000, dim 768, n_lits 1000, pq_dim 192\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-08-19 17:07:31,215] Trial 4 finished with values: [158.4761, 0.0414, 0.9905] and parameters: {'intermediate_graph_degree': 80, 'itopk_size': 126}. \n"
+     ]
+    }
+   ],
+   "source": [
+    "cagra_study = optuna.create_study(directions=['minimize', 'minimize', 'maximize'])\n",
+    "cagra_study.optimize(multi_objective_cagra, n_trials=5)"
+   ]
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+   "id": "293050f3-09ad-4333-b2e7-5887f2a25465",
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+    {
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+     "text": [
+      "Number of trials on the Pareto front: 5\n",
+      "Trial with lowest build time in secs: \n",
+      "\tnumber: 1\n",
+      "\tparams: {'intermediate_graph_degree': 72, 'itopk_size': 106}\n",
+      "\tvalues: [155.633, 0.0364, 0.9884]\n",
+      "Trial with lowest latency in ms: \n",
+      "\tnumber: 2\n",
+      "\tparams: {'intermediate_graph_degree': 90, 'itopk_size': 66}\n",
+      "\tvalues: [162.741, 0.0231, 0.9801]\n",
+      "Trial with highest accuracy: \n",
+      "\tnumber: 3\n",
+      "\tparams: {'intermediate_graph_degree': 110, 'itopk_size': 114}\n",
+      "\tvalues: [169.5533, 0.0396, 0.9915]\n"
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+    "print_best_trial_values(cagra_study)"
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+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Pareto-front Plot"
+        },
+        "xaxis": {
+         "autorange": true,
+         "range": [
+          154.8058405093237,
+          170.38045949067632
+         ],
+         "title": {
+          "text": "build_time_in_secs"
+         },
+         "type": "linear"
+        },
+        "yaxis": {
+         "autorange": true,
+         "range": [
+          0.9791592233009708,
+          0.9924407766990292
+         ],
+         "title": {
+          "text": "recall"
+         },
+         "type": "linear"
+        }
+       }
+      },
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('c6b45f92-94f8-401c-bfa0-f3e5acf30116');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualization(cagra_study)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_python3",
+   "language": "python",
+   "name": "conda_python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From d0939862b9e2db0674ca61613ebf22a2c205f1f1 Mon Sep 17 00:00:00 2001
From: Ben Karsin <bkarsin@nvidia.com>
Date: Wed, 2 Oct 2024 16:05:28 -1000
Subject: [PATCH 170/623] Vamana/DiskANN index build (#339)

Adds Vamana (DiskANN index construction support. Includes all files and changes to expose the vamana graph index in the C++ API, **build** and **serialize** operations only.  Build runs the iterative Vamana index construction, and serialize writes the graph to a file that matches the format of the Microsoft DiskANN library. This lets users use cuVS for index construction while still doing search with their current infrastructure.  Search support to be added at a later time.

Some items missing / not included in the PR:

- Documentation
- Python and C API support
- Tests that verify recall (can't do this without search)
- Additional distance metrics (only L2 currently)

Current known issues / bugs:
- Odd dimension datasets not supported (causes alignment error)
- Memory footprint is still large, need some batching / alg changes to improve
- Adding a more sophisticated medoid selection (minor recall benefit)
- Improving shared memory usage for high-degree construction should improve perf

Authors:
  - Ben Karsin (https://github.com/bkarsin)
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Dante Gama Dessavre (https://github.com/dantegd)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Tarang Jain (https://github.com/tarang-jain)

URL: https://github.com/rapidsai/cuvs/pull/339
---
 cpp/CMakeLists.txt                            |   6 +
 cpp/include/cuvs/neighbors/vamana.hpp         | 275 ++++++++++
 .../neighbors/detail/vamana/greedy_search.cuh | 286 +++++++++++
 cpp/src/neighbors/detail/vamana/macros.cuh    |  82 +++
 .../detail/vamana/priority_queue.cuh          | 329 ++++++++++++
 .../neighbors/detail/vamana/robust_prune.cuh  | 248 +++++++++
 .../neighbors/detail/vamana/vamana_build.cuh  | 410 +++++++++++++++
 .../detail/vamana/vamana_serialize.cuh        | 120 +++++
 .../detail/vamana/vamana_structs.cuh          | 478 ++++++++++++++++++
 cpp/src/neighbors/vamana.cuh                  | 102 ++++
 cpp/src/neighbors/vamana_build_float.cu       |  43 ++
 cpp/src/neighbors/vamana_build_int8.cu        |  43 ++
 cpp/src/neighbors/vamana_build_uint8.cu       |  43 ++
 cpp/src/neighbors/vamana_serialize.cuh        |  39 ++
 cpp/src/neighbors/vamana_serialize_float.cu   |  23 +
 cpp/src/neighbors/vamana_serialize_int8.cu    |  23 +
 cpp/src/neighbors/vamana_serialize_uint8.cu   |  23 +
 cpp/test/CMakeLists.txt                       |  13 +
 cpp/test/neighbors/ann_vamana.cuh             | 339 +++++++++++++
 .../ann_vamana/test_float_uint32_t.cu         |  28 +
 .../ann_vamana/test_int8_t_uint32_t.cu        |  28 +
 .../ann_vamana/test_uint8_t_uint32_t.cu       |  28 +
 examples/build.sh                             |  16 +-
 examples/c/CMakeLists.txt                     |   3 +
 examples/cpp/CMakeLists.txt                   |   2 +
 examples/cpp/src/common.cuh                   |  26 +
 examples/cpp/src/vamana_example.cu            | 108 ++++
 27 files changed, 3162 insertions(+), 2 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/vamana.hpp
 create mode 100644 cpp/src/neighbors/detail/vamana/greedy_search.cuh
 create mode 100644 cpp/src/neighbors/detail/vamana/macros.cuh
 create mode 100644 cpp/src/neighbors/detail/vamana/priority_queue.cuh
 create mode 100644 cpp/src/neighbors/detail/vamana/robust_prune.cuh
 create mode 100644 cpp/src/neighbors/detail/vamana/vamana_build.cuh
 create mode 100644 cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
 create mode 100644 cpp/src/neighbors/detail/vamana/vamana_structs.cuh
 create mode 100644 cpp/src/neighbors/vamana.cuh
 create mode 100644 cpp/src/neighbors/vamana_build_float.cu
 create mode 100644 cpp/src/neighbors/vamana_build_int8.cu
 create mode 100644 cpp/src/neighbors/vamana_build_uint8.cu
 create mode 100644 cpp/src/neighbors/vamana_serialize.cuh
 create mode 100644 cpp/src/neighbors/vamana_serialize_float.cu
 create mode 100644 cpp/src/neighbors/vamana_serialize_int8.cu
 create mode 100644 cpp/src/neighbors/vamana_serialize_uint8.cu
 create mode 100644 cpp/test/neighbors/ann_vamana.cuh
 create mode 100644 cpp/test/neighbors/ann_vamana/test_float_uint32_t.cu
 create mode 100644 cpp/test/neighbors/ann_vamana/test_int8_t_uint32_t.cu
 create mode 100644 cpp/test/neighbors/ann_vamana/test_uint8_t_uint32_t.cu
 create mode 100644 examples/cpp/src/vamana_example.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 6dc32cafeb..b05030cef3 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -422,6 +422,12 @@ add_library(
   src/neighbors/refine/detail/refine_host_int8_t_float.cpp
   src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
   src/neighbors/sample_filter.cu
+  src/neighbors/vamana_build_float.cu
+  src/neighbors/vamana_build_uint8.cu
+  src/neighbors/vamana_build_int8.cu
+  src/neighbors/vamana_serialize_float.cu
+  src/neighbors/vamana_serialize_uint8.cu
+  src/neighbors/vamana_serialize_int8.cu
   src/selection/select_k_float_int64_t.cu
   src/selection/select_k_float_int32_t.cu
   src/selection/select_k_float_uint32_t.cu
diff --git a/cpp/include/cuvs/neighbors/vamana.hpp b/cpp/include/cuvs/neighbors/vamana.hpp
new file mode 100644
index 0000000000..bec17937f9
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/vamana.hpp
@@ -0,0 +1,275 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "common.hpp"
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resource/stream_view.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/integer_utils.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+#include <variant>
+
+namespace cuvs::neighbors::experimental::vamana {
+/**
+ * @defgroup vamana_cpp_index_params Vamana index build parameters
+ * @{
+ */
+
+/**
+ * @brief ANN parameters used by VAMANA to build index
+ *
+ */
+struct index_params : cuvs::neighbors::index_params {
+  /** Maximum degree of output graph corresponds to the R parameter in the original Vamana
+   * literature. */
+  uint32_t graph_degree = 32;
+  /** Maximum number of visited nodes per search corresponds to the L parameter in the Vamana
+   * literature **/
+  uint32_t visited_size = 64;
+  /** Number of Vamana vector insertion iterations (each iteration inserts all vectors). */
+  uint32_t vamana_iters = 1;
+  /** Alpha for pruning parameter */
+  float alpha = 1.2;
+  /** Maximum fraction of dataset inserted per batch.              *
+   * Larger max batch decreases graph quality, but improves speed */
+  float max_fraction = 0.06;
+  /** Base of growth rate of batch sies **/
+  float batch_base = 2;
+  /** Size of candidate queue structure - should be (2^x)-1 */
+  uint32_t queue_size = 127;
+};
+
+/**
+ * @}
+ */
+
+static_assert(std::is_aggregate_v<index_params>);
+
+/**
+ * @defgroup vamana_cpp_index Vamana index type
+ * @{
+ */
+
+/**
+ * @brief Vamana index.
+ *
+ * The index stores the dataset and the Vamana graph in device memory.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the vector indices (represent dataset.extent(0))
+ *
+ */
+template <typename T, typename IdxT>
+struct index : cuvs::neighbors::index {
+  static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
+                "IdxT must be able to represent all values of uint32_t");
+
+ public:
+  /** Distance metric used for clustering. */
+  [[nodiscard]] constexpr inline auto metric() const noexcept -> cuvs::distance::DistanceType
+  {
+    return metric_;
+  }
+
+  /** Total length of the index (number of vectors). */
+  [[nodiscard]] constexpr inline auto size() const noexcept -> IdxT
+  {
+    auto data_rows = dataset_->n_rows();
+    return data_rows > 0 ? data_rows : graph_view_.extent(0);
+  }
+
+  /** Dimensionality of the data. */
+  [[nodiscard]] constexpr inline auto dim() const noexcept -> uint32_t { return dataset_->dim(); }
+  /** Graph degree */
+  [[nodiscard]] constexpr inline auto graph_degree() const noexcept -> uint32_t
+  {
+    return graph_view_.extent(1);
+  }
+
+  /** Dataset [size, dim] */
+  [[nodiscard]] inline auto data() const noexcept -> const cuvs::neighbors::dataset<int64_t>&
+  {
+    return *dataset_;
+  }
+
+  /** vamana graph [size, graph-degree] */
+  [[nodiscard]] inline auto graph() const noexcept
+    -> raft::device_matrix_view<const IdxT, int64_t, raft::row_major>
+  {
+    return graph_view_;
+  }
+
+  /** Return the id of the vector selected as the medoid. */
+  [[nodiscard]] inline auto medoid() const noexcept -> IdxT { return medoid_id_; }
+
+  // Don't allow copying the index for performance reasons (try avoiding copying data)
+  index(const index&)                    = delete;
+  index(index&&)                         = default;
+  auto operator=(const index&) -> index& = delete;
+  auto operator=(index&&) -> index&      = default;
+  ~index()                               = default;
+
+  /** Construct an empty index. */
+  index(raft::resources const& res,
+        cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
+    : cuvs::neighbors::index(),
+      metric_(metric),
+      graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
+      dataset_(new cuvs::neighbors::empty_dataset<int64_t>(0))
+  {
+  }
+
+  /** Construct an index from dataset and vamana graph
+   *
+   */
+  template <typename data_accessor, typename graph_accessor>
+  index(raft::resources const& res,
+        cuvs::distance::DistanceType metric,
+        raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, data_accessor> dataset,
+        raft::mdspan<const IdxT, raft::matrix_extent<int64_t>, raft::row_major, graph_accessor>
+          vamana_graph,
+        IdxT medoid_id)
+    : cuvs::neighbors::index(),
+      metric_(metric),
+      graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
+      dataset_(make_aligned_dataset(res, dataset, 16)),
+      medoid_id_(medoid_id)
+  {
+    RAFT_EXPECTS(dataset.extent(0) == vamana_graph.extent(0),
+                 "Dataset and vamana_graph must have equal number of rows");
+    update_graph(res, vamana_graph);
+
+    raft::resource::sync_stream(res);
+  }
+
+  /**
+   * Replace the graph with a new graph.
+   *
+   * Since the new graph is a device array, we store a reference to that, and it is
+   * the caller's responsibility to ensure that knn_graph stays alive as long as the index.
+   */
+  void update_graph(raft::resources const& res,
+                    raft::device_matrix_view<const IdxT, int64_t, raft::row_major> new_graph)
+  {
+    graph_view_ = new_graph;
+  }
+
+  /**
+   * Replace the graph with a new graph.
+   *
+   * We create a copy of the graph on the device. The index manages the lifetime of this copy.
+   */
+  void update_graph(raft::resources const& res,
+                    raft::host_matrix_view<const IdxT, int64_t, raft::row_major> new_graph)
+  {
+    RAFT_LOG_DEBUG("Copying Vamana graph from host to device");
+
+    if ((graph_.extent(0) != new_graph.extent(0)) || (graph_.extent(1) != new_graph.extent(1))) {
+      // clear existing memory before allocating to prevent OOM errors on large graphs
+      if (graph_.size()) { graph_ = raft::make_device_matrix<IdxT, int64_t>(res, 0, 0); }
+      graph_ =
+        raft::make_device_matrix<IdxT, int64_t>(res, new_graph.extent(0), new_graph.extent(1));
+    }
+    raft::copy(graph_.data_handle(),
+               new_graph.data_handle(),
+               new_graph.size(),
+               raft::resource::get_cuda_stream(res));
+    graph_view_ = graph_.view();
+  }
+
+ private:
+  cuvs::distance::DistanceType metric_;
+  raft::device_matrix<IdxT, int64_t, raft::row_major> graph_;
+  raft::device_matrix_view<const IdxT, int64_t, raft::row_major> graph_view_;
+  std::unique_ptr<neighbors::dataset<int64_t>> dataset_;
+  IdxT medoid_id_;
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup vamana_cpp_index_build Vamana index build functions
+ * @{
+ */
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::vamana::index_params& params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::vamana::index<float, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::vamana::index_params& params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::vamana::index<float, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::vamana::index_params& params,
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::vamana::index<int8_t, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::vamana::index_params& params,
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::vamana::index<int8_t, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::vamana::index_params& params,
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::vamana::index<uint8_t, uint32_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::vamana::index_params& params,
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::vamana::index<uint8_t, uint32_t>;
+
+/**
+ * @defgroup vamana_cpp_serialize Vamana serialize functions
+ * @{
+ */
+/**
+ * Save the index to file.
+ */
+
+void serialize(raft::resources const& handle,
+               const std::string& file_prefix,
+               const cuvs::neighbors::experimental::vamana::index<float, uint32_t>& index);
+
+void serialize(raft::resources const& handle,
+               const std::string& file_prefix,
+               const cuvs::neighbors::experimental::vamana::index<int8_t, uint32_t>& index);
+
+void serialize(raft::resources const& handle,
+               const std::string& file_prefix,
+               const cuvs::neighbors::experimental::vamana::index<uint8_t, uint32_t>& index);
+
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/detail/vamana/greedy_search.cuh b/cpp/src/neighbors/detail/vamana/greedy_search.cuh
new file mode 100644
index 0000000000..f51c6c91bf
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/greedy_search.cuh
@@ -0,0 +1,286 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "macros.cuh"
+#include "priority_queue.cuh"
+#include "vamana_structs.cuh"
+#include <cub/cub.cuh>
+#include <cuvs/neighbors/vamana.hpp>
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/util/warp_primitives.cuh>
+#include <rmm/resource_ref.hpp>
+
+#include <chrono>
+#include <cstdio>
+#include <vector>
+
+namespace cuvs::neighbors::experimental::vamana::detail {
+
+/* @defgroup greedy_search_detail greedy search
+ * @{
+ */
+
+/* Combines edge and candidate lists, removes duplicates, and sorts by distance
+ * Uses CUB primitives, so needs to be templated. Called with Macros for supported sizes above */
+template <typename accT, typename IdxT, int CANDS>
+__forceinline__ __device__ void sort_visited(
+  QueryCandidates<IdxT, accT>* query,
+  typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, (CANDS / 32)>::TempStorage* sort_mem)
+{
+  const int ELTS   = CANDS / 32;
+  using BlockSortT = cub::BlockMergeSort<DistPair<IdxT, accT>, 32, ELTS>;
+  DistPair<IdxT, accT> tmp[ELTS];
+  for (int i = 0; i < ELTS; i++) {
+    tmp[i].idx  = query->ids[ELTS * threadIdx.x + i];
+    tmp[i].dist = query->dists[ELTS * threadIdx.x + i];
+  }
+
+  __syncthreads();
+  BlockSortT(*sort_mem).Sort(tmp, CmpDist());
+  __syncthreads();
+
+  for (int i = 0; i < ELTS; i++) {
+    query->ids[ELTS * threadIdx.x + i]   = tmp[i].idx;
+    query->dists[ELTS * threadIdx.x + i] = tmp[i].dist;
+  }
+  __syncthreads();
+}
+
+namespace {
+
+/********************************************************************************************
+  GPU kernel to perform a batched GreedySearch on a graph. Since this is used for
+  Vamana construction, the entire visited list is kept and stored within the query_list.
+  Input - graph with edge lists, dataset vectors, query_list_ptr with the ids of dataset
+          vectors to be searched. All inputs, including dataset,  must be device accessible.
+
+  Output - the id and dist lists in query_list_ptr will be updated with the nodes visited
+           during the GreedySearch.
+**********************************************************************************************/
+template <typename T,
+          typename accT,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+__global__ void GreedySearchKernel(
+  raft::device_matrix_view<IdxT, int64_t> graph,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  void* query_list_ptr,
+  int num_queries,
+  int medoid_id,
+  int topk,
+  cuvs::distance::DistanceType metric,
+  int max_queue_size,
+  int sort_smem_size)
+{
+  int n      = dataset.extent(0);
+  int dim    = dataset.extent(1);
+  int degree = graph.extent(1);
+
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  static __shared__ int topk_q_size;
+  static __shared__ int cand_q_size;
+  static __shared__ accT cur_k_max;
+  static __shared__ int k_max_idx;
+
+  static __shared__ Point<T, accT> s_query;
+
+  union ShmemLayout {
+    // All blocksort sizes have same alignment (16)
+    typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, 1>::TempStorage sort_mem;
+    T coords;
+    Node<accT> topk_pq;
+    int neighborhood_arr;
+    DistPair<IdxT, accT> candidate_queue;
+  };
+
+  // Dynamic shared memory used for blocksort, temp vector storage, and neighborhood list
+  extern __shared__ __align__(alignof(ShmemLayout)) char smem[];
+
+  size_t smem_offset = sort_smem_size;  // temp sorting memory takes first chunk
+
+  T* s_coords = reinterpret_cast<T*>(&smem[smem_offset]);
+  smem_offset += dim * sizeof(T);
+
+  Node<accT>* topk_pq = reinterpret_cast<Node<accT>*>(&smem[smem_offset]);
+  smem_offset += topk * sizeof(Node<accT>);
+
+  int* neighbor_array = reinterpret_cast<int*>(&smem[smem_offset]);
+  smem_offset += degree * sizeof(int);
+
+  DistPair<IdxT, accT>* candidate_queue_smem =
+    reinterpret_cast<DistPair<IdxT, accT>*>(&smem[smem_offset]);
+
+  s_query.coords = s_coords;
+  s_query.Dim    = dim;
+
+  PriorityQueue<IdxT, accT> heap_queue;
+
+  if (threadIdx.x == 0) {
+    heap_queue.initialize(candidate_queue_smem, max_queue_size, &cand_q_size);
+  }
+
+  static __shared__ int num_neighbors;
+
+  for (int i = blockIdx.x; i < num_queries; i += gridDim.x) {
+    __syncthreads();
+
+    // resetting visited list
+    query_list[i].reset();
+
+    // storing the current query vector into shared memory
+    update_shared_point<T, accT>(&s_query, &dataset(0, 0), query_list[i].queryId, dim);
+
+    if (threadIdx.x == 0) {
+      topk_q_size = 0;
+      cand_q_size = 0;
+      s_query.id  = query_list[i].queryId;
+      cur_k_max   = 0;
+      k_max_idx   = 0;
+      heap_queue.reset();
+    }
+
+    __syncthreads();
+
+    Point<T, accT>* query_vec;
+
+    // Just start from medoid every time, rather than multiple set_ups
+    query_vec        = &s_query;
+    query_vec->Dim   = dim;
+    const T* medoid  = &dataset((size_t)medoid_id, 0);
+    accT medoid_dist = dist<T, accT>(query_vec->coords, medoid, dim, metric);
+
+    if (threadIdx.x == 0) { heap_queue.insert_back(medoid_dist, medoid_id); }
+    __syncthreads();
+
+    while (cand_q_size != 0) {
+      __syncthreads();
+
+      int cand_num;
+      accT cur_distance;
+      if (threadIdx.x == 0) {
+        Node<accT> test_cand;
+        DistPair<IdxT, accT> test_cand_out = heap_queue.pop();
+        test_cand.distance                 = test_cand_out.dist;
+        test_cand.nodeid                   = test_cand_out.idx;
+        cand_num                           = test_cand.nodeid;
+        cur_distance                       = test_cand_out.dist;
+      }
+      __syncthreads();
+
+      cand_num = raft::shfl(cand_num, 0);
+
+      __syncthreads();
+
+      if (query_list[i].check_visited(cand_num, cur_distance)) { continue; }
+
+      cur_distance = raft::shfl(cur_distance, 0);
+
+      // stop condition for the graph traversal process
+      bool done      = false;
+      bool pass_flag = false;
+
+      if (topk_q_size == topk) {
+        // Check the current node with the worst candidate in top-k queue
+        if (threadIdx.x == 0) {
+          if (cur_k_max <= cur_distance) { done = true; }
+        }
+
+        done = raft::shfl(done, 0);
+        if (done) {
+          if (query_list[i].size < topk) {
+            pass_flag = true;
+          }
+
+          else if (query_list[i].size >= topk) {
+            break;
+          }
+        }
+      }
+
+      // The current node is closer to the query vector than the worst candidate in top-K queue, so
+      // enquee the current node in top-k queue
+      Node<accT> new_cand;
+      new_cand.distance = cur_distance;
+      new_cand.nodeid   = cand_num;
+
+      if (check_duplicate(topk_pq, topk_q_size, new_cand) == false) {
+        if (!pass_flag) {
+          parallel_pq_max_enqueue<accT>(
+            topk_pq, &topk_q_size, topk, new_cand, &cur_k_max, &k_max_idx);
+
+          __syncthreads();
+        }
+      } else {
+        // already visited
+        continue;
+      }
+
+      num_neighbors = degree;
+      __syncthreads();
+
+      for (size_t j = threadIdx.x; j < degree; j += blockDim.x) {
+        // Load neighbors from the graph array and store them in neighbor array (shared memory)
+        neighbor_array[j] = graph(cand_num, j);
+        if (neighbor_array[j] == raft::upper_bound<IdxT>())
+          atomicMin(&num_neighbors, (int)j);  // warp-wide min to find the number of neighbors
+      }
+
+      // computing distances between the query vector and neighbor vectors then enqueue in priority
+      // queue.
+      enqueue_all_neighbors<T, accT, IdxT>(
+        num_neighbors, query_vec, &dataset(0, 0), neighbor_array, heap_queue, dim, metric);
+
+      __syncthreads();
+
+    }  // End cand_q_size != 0 loop
+
+    bool self_found = false;
+    // Remove self edges
+    for (int j = threadIdx.x; j < query_list[i].size; j += blockDim.x) {
+      if (query_list[i].ids[j] == query_vec->id) {
+        query_list[i].dists[j] = raft::upper_bound<accT>();
+        query_list[i].ids[j]   = raft::upper_bound<IdxT>();
+        self_found             = true;  // Flag to reduce size by 1
+      }
+    }
+
+    for (int j = query_list[i].size + threadIdx.x; j < query_list[i].maxSize; j += blockDim.x) {
+      query_list[i].ids[j]   = raft::upper_bound<IdxT>();
+      query_list[i].dists[j] = raft::upper_bound<accT>();
+    }
+
+    __syncthreads();
+    if (self_found) query_list[i].size--;
+
+    SEARCH_SELECT_SORT(topk);
+  }
+
+  return;
+}
+
+}  // namespace
+
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::experimental::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/macros.cuh b/cpp/src/neighbors/detail/vamana/macros.cuh
new file mode 100644
index 0000000000..5692650a0e
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/macros.cuh
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+namespace cuvs::neighbors::experimental::vamana::detail {
+
+/* Macros to compute the shared memory requirements for CUB primitives used by search and prune */
+#define COMPUTE_SMEM_SIZES(degree, visited_size, DEG, CANDS)                                     \
+  if (degree == DEG && visited_size <= CANDS && visited_size > CANDS / 2) {                      \
+    search_smem_sort_size = static_cast<int>(                                                    \
+      sizeof(typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, CANDS / 32>::TempStorage));  \
+                                                                                                 \
+    prune_smem_sort_size = static_cast<int>(sizeof(                                              \
+      typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, (CANDS + DEG) / 32>::TempStorage)); \
+  }
+
+// Current supported sizes for degree and visited_size. Note that visited_size must be > degree
+#define SELECT_SMEM_SIZES(degree, visited_size)       \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 64);   \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 128);  \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 256);  \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 512);  \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 64, 128);  \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 64, 256);  \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 64, 512);  \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 128, 256); \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 128, 512); \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 256, 512); \
+  COMPUTE_SMEM_SIZES(degree, visited_size, 256, 1024);
+
+/* Macros to call the CUB BlockSort primitives for supported sizes for ROBUST_PRUNE*/
+#define PRUNE_CALL_SORT(degree, visited_list, DEG, CANDS)                                  \
+  if (degree == DEG && visited_list <= CANDS && visited_list > CANDS / 2) {                \
+    using BlockSortT = cub::BlockMergeSort<DistPair<IdxT, accT>, 32, (DEG + CANDS) / 32>;  \
+    auto& sort_mem   = reinterpret_cast<typename BlockSortT::TempStorage&>(smem);          \
+    sort_edges_and_cands<accT, IdxT, DEG, CANDS>(new_nbh_list, &query_list[i], &sort_mem); \
+  }
+
+#define PRUNE_SELECT_SORT(degree, visited_list)    \
+  PRUNE_CALL_SORT(degree, visited_size, 32, 64);   \
+  PRUNE_CALL_SORT(degree, visited_size, 32, 128);  \
+  PRUNE_CALL_SORT(degree, visited_size, 32, 256);  \
+  PRUNE_CALL_SORT(degree, visited_size, 32, 512);  \
+  PRUNE_CALL_SORT(degree, visited_size, 64, 128);  \
+  PRUNE_CALL_SORT(degree, visited_size, 64, 256);  \
+  PRUNE_CALL_SORT(degree, visited_size, 64, 512);  \
+  PRUNE_CALL_SORT(degree, visited_size, 128, 256); \
+  PRUNE_CALL_SORT(degree, visited_size, 128, 512); \
+  PRUNE_CALL_SORT(degree, visited_size, 256, 512); \
+  PRUNE_CALL_SORT(degree, visited_size, 256, 1024);
+
+/* Macros to call the CUB BlockSort primitives for supported sizes for GREEDY SEARCH */
+#define SEARCH_CALL_SORT(topk, CANDS)                                             \
+  if (topk <= CANDS && topk > CANDS / 2) {                                        \
+    using BlockSortT = cub::BlockMergeSort<DistPair<IdxT, accT>, 32, CANDS / 32>; \
+    auto& sort_mem   = reinterpret_cast<typename BlockSortT::TempStorage&>(smem); \
+    sort_visited<accT, IdxT, CANDS>(&query_list[i], &sort_mem);                   \
+  }
+
+// SEARCH only relies on visited_size (not degree) for shared memory.
+#define SEARCH_SELECT_SORT(topk) \
+  SEARCH_CALL_SORT(topk, 64);    \
+  SEARCH_CALL_SORT(topk, 128);   \
+  SEARCH_CALL_SORT(topk, 256);   \
+  SEARCH_CALL_SORT(topk, 512);   \
+  SEARCH_CALL_SORT(topk, 1024);
+
+}  // namespace cuvs::neighbors::experimental::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/priority_queue.cuh b/cpp/src/neighbors/detail/vamana/priority_queue.cuh
new file mode 100644
index 0000000000..4b3bd84661
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/priority_queue.cuh
@@ -0,0 +1,329 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "vamana_structs.cuh"
+#include <raft/util/warp_primitives.cuh>
+#include <stdio.h>
+
+namespace cuvs::neighbors::experimental::vamana::detail {
+
+/***************************************************************************************
+***************************************************************************************/
+/**
+ * @defgroup vamana_priority_queue Vamana Priority queue structure
+ * @{
+ */
+
+/**
+ * @brief Priority Queue structure used by Vamana GreedySearch during
+ * graph construction.
+ *
+ * The structure keeps the nearest visited neighbors seen thus far during
+ * search and lets us efficiently find the next node to visit during the search.
+ * Stores a total of KVAL pairs, where currently KVAL must be 2i-1 for some integer
+ * i since the heap must be complete.
+ * This size is determined during vamana build with the "queue_size" parameter (default 127)
+ *
+ * The queue and all methods are device-side, with a work group size or 32 (one warp).
+ * During search, each warp creates their own queue to search a single query at a time.
+ * The device memory pointed to by `vals` is assigned during the call to `initialize`.
+ * The Vamana GreedySearch call uses shared memory, but any device-accessible memory is applicable.
+ *
+ *
+ * @tparam IdxT type of the vector indices (represent dataset.extent(0))
+ * @tparam accT type of distances between vectors (accumuator type)
+ *
+ */
+template <typename IdxT, typename accT>
+class PriorityQueue {
+ public:
+  int KVAL;
+  int insert_pointer;
+  DistPair<IdxT, accT>* vals;
+  DistPair<IdxT, accT> temp;
+
+  int* q_size;
+  // Enforce max-heap property on the entries
+  __forceinline__ __device__ void heapify()
+  {
+    int i        = 0;
+    int swapDest = 0;
+
+    while (2 * i + 2 < KVAL) {
+      swapDest = 2 * i;
+      swapDest +=
+        (vals[i].dist > vals[2 * i + 1].dist && vals[2 * i + 2].dist >= vals[2 * i + 1].dist);
+      swapDest +=
+        2 * (vals[i].dist > vals[2 * i + 2].dist && vals[2 * i + 1].dist > vals[2 * i + 2].dist);
+
+      if (swapDest == 2 * i) return;
+
+      swap(&vals[i], &vals[swapDest]);
+
+      i = swapDest;
+    }
+  }
+
+  // Starts the heapify process starting at a particular index
+  __forceinline__ __device__ void heapifyAt(int idx)
+  {
+    int i        = idx;
+    int swapDest = 0;
+
+    while (2 * i + 2 < KVAL) {
+      swapDest = 2 * i;
+      swapDest +=
+        (vals[i].dist > vals[2 * i + 1].dist && vals[2 * i + 2].dist <= vals[2 * i + 1].dist);
+      swapDest +=
+        2 * (vals[i].dist > vals[2 * i + 2].dist && vals[2 * i + 1].dist < vals[2 * i + 2].dist);
+
+      if (swapDest == 2 * i) return;
+
+      swap(&vals[i], &vals[swapDest]);
+      i = swapDest;
+    }
+  }
+
+  // Heapify from the bottom up, used with insert_back
+  __forceinline__ __device__ void heapifyReverseAt(int idx)
+  {
+    int i        = idx;
+    int swapDest = 0;
+    while (i > 0) {
+      swapDest = ((i - 1) / 2);
+      if (vals[swapDest].dist <= vals[i].dist) return;
+
+      swap(&vals[i], &vals[swapDest]);
+      i = swapDest;
+    }
+  }
+
+  __device__ void reset()
+  {
+    *q_size = 0;
+    for (int i = 0; i < KVAL; i++) {
+      vals[i].dist = raft::upper_bound<accT>();
+      vals[i].idx  = raft::upper_bound<IdxT>();
+    }
+  }
+
+  __device__ void initialize(DistPair<IdxT, accT>* v, int _kval, int* _q_size)
+  {
+    vals           = v;
+    KVAL           = _kval;
+    insert_pointer = _kval / 2;
+    q_size         = _q_size;
+    reset();
+  }
+
+  // Initialize all nodes of the heap to +infinity
+  __device__ void initialize()
+  {
+    for (int i = 0; i < KVAL; i++) {
+      vals[i].idx  = raft::upper_bound<IdxT>();
+      vals[i].dist = raft::upper_bound<accT>();
+    }
+  }
+
+  __device__ void write_to_gmem(int* gmem)
+  {
+    for (int i = 0; i < KVAL; i++) {
+      gmem[i] = vals[i].idx;
+    }
+  }
+
+  // Replace the root of the heap with new pair
+  __device__ void insert(accT newDist, IdxT newIdx)
+  {
+    vals[0].dist = newDist;
+    vals[0].idx  = newIdx;
+
+    heapify();
+  }
+
+  // Replace a specific element in the heap (and maintain heap properties)
+  __device__ void insertAt(accT newDist, IdxT newIdx, int idx)
+  {
+    vals[idx].dist = newDist;
+    vals[idx].idx  = newIdx;
+
+    heapifyAt(idx);
+  }
+
+  // Return value of the root of the heap (largest value)
+  __device__ accT top() { return vals[0].dist; }
+
+  __device__ IdxT top_node() { return vals[0].idx; }
+
+  __device__ void insert_back(accT newDist, IdxT newIdx)
+  {
+    if (newDist < vals[insert_pointer].dist) {
+      if (vals[insert_pointer].idx == raft::upper_bound<IdxT>()) *q_size += 1;
+      vals[insert_pointer].dist = newDist;
+      vals[insert_pointer].idx  = newIdx;
+      heapifyReverseAt(insert_pointer);
+    }
+    insert_pointer++;
+
+    if (insert_pointer == KVAL) insert_pointer = KVAL / 2;
+  }
+
+  // Pop root node off and heapify
+  __device__ DistPair<IdxT, accT> pop()
+  {
+    DistPair<IdxT, accT> result;
+    result.dist  = vals[0].dist;
+    result.idx   = vals[0].idx;
+    vals[0].dist = raft::upper_bound<accT>();
+    vals[0].idx  = raft::upper_bound<IdxT>();
+    heapify();
+    *q_size -= 1;
+    return result;
+  }
+};
+
+/***************************************************************************************
+ * Node structure used for simplified lists during GreedySearch.
+ * Used for other operations like checking for duplicates, etc.
+ ****************************************************************************************/
+template <typename SUMTYPE>
+class __align__(16) Node
+{
+ public:
+  SUMTYPE distance;
+  int nodeid;
+};
+
+// Less-than operator between two Nodes.
+template <typename SUMTYPE>
+__host__ __device__ bool operator<(const Node<SUMTYPE>& first, const Node<SUMTYPE>& other)
+{
+  return first.distance < other.distance;
+}
+
+// Less-than operator between two Nodes.
+template <typename SUMTYPE>
+__host__ __device__ bool operator>(const Node<SUMTYPE>& first, const Node<SUMTYPE>& other)
+{
+  return first.distance > other.distance;
+}
+
+template <typename accT>
+__device__ bool check_duplicate(const Node<accT>* pq, const int size, Node<accT> new_node)
+{
+  bool found = false;
+  for (int i = threadIdx.x; i < size; i += blockDim.x) {
+    if (pq[i].nodeid == new_node.nodeid) {
+      found = true;
+      break;
+    }
+  }
+
+  unsigned mask = raft::ballot(found);
+
+  if (mask == 0)
+    return false;
+
+  else
+    return true;
+}
+
+/*
+  Enqueuing a input value into parallel queue with tracker
+*/
+template <typename SUMTYPE>
+__inline__ __device__ void parallel_pq_max_enqueue(Node<SUMTYPE>* pq,
+                                                   int* size,
+                                                   const int pq_size,
+                                                   Node<SUMTYPE> input_data,
+                                                   SUMTYPE* cur_max_val,
+                                                   int* max_idx)
+{
+  if (*size < pq_size) {
+    __syncthreads();
+    if (threadIdx.x == 0) {
+      pq[*size].distance = input_data.distance;
+      pq[*size].nodeid   = input_data.nodeid;
+      *size              = *size + 1;
+      if (input_data.distance > (*cur_max_val)) {
+        *cur_max_val = input_data.distance;
+        *max_idx     = *size - 1;
+      }
+    }
+    __syncthreads();
+    return;
+  } else {
+    if (input_data.distance >= (*cur_max_val)) {
+      __syncthreads();
+      return;
+    }
+    if (threadIdx.x == 0) {
+      pq[*max_idx].distance = input_data.distance;
+      pq[*max_idx].nodeid   = input_data.nodeid;
+    }
+    int idx         = 0;
+    SUMTYPE max_val = pq[0].distance;
+
+    for (int i = threadIdx.x; i < pq_size; i += 32) {
+      if (pq[i].distance > max_val) {
+        max_val = pq[i].distance;
+        idx     = i;
+      }
+    }
+
+    for (int offset = 16; offset > 0; offset /= 2) {
+      SUMTYPE new_max_val = raft::shfl_up(max_val, offset);
+      int new_idx         = raft::shfl_up(idx, offset);
+      if (new_max_val > max_val) {
+        max_val = new_max_val;
+        idx     = new_idx;
+      }
+    }
+
+    if (threadIdx.x == 31) {
+      *max_idx     = idx;
+      *cur_max_val = max_val;
+    }
+  }
+  __syncthreads();
+}
+
+/*
+  Compute the distances between the source vector and all nodes in the neighbor_array and enqueue
+  them in the PQ
+*/
+template <typename T, typename accT, typename IdxT>
+__forceinline__ __device__ void enqueue_all_neighbors(int num_neighbors,
+                                                      Point<T, accT>* query_vec,
+                                                      const T* vec_ptr,
+                                                      int* neighbor_array,
+                                                      PriorityQueue<IdxT, accT>& heap_queue,
+                                                      int dim,
+                                                      cuvs::distance::DistanceType metric)
+{
+  for (int i = 0; i < num_neighbors; i++) {
+    accT dist_out = dist<T, accT>(
+      query_vec->coords, &vec_ptr[(size_t)(neighbor_array[i]) * (size_t)(dim)], dim, metric);
+
+    __syncthreads();
+    if (threadIdx.x == 0) { heap_queue.insert_back(dist_out, neighbor_array[i]); }
+    __syncthreads();
+  }
+}
+
+}  // namespace cuvs::neighbors::experimental::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/robust_prune.cuh b/cpp/src/neighbors/detail/vamana/robust_prune.cuh
new file mode 100644
index 0000000000..8446ac136d
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/robust_prune.cuh
@@ -0,0 +1,248 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cub/cub.cuh>
+#include <thrust/sort.h>
+
+#include "macros.cuh"
+#include "vamana_structs.cuh"
+
+namespace cuvs::neighbors::experimental::vamana::detail {
+
+// Load candidates (from query) and previous edges (from nbh_list) into registers (tmp) spanning
+// warp
+template <typename accT, typename IdxT = uint32_t, int DEG, int CANDS>
+__forceinline__ __device__ void load_to_registers(DistPair<IdxT, accT>* tmp,
+                                                  QueryCandidates<IdxT, accT>* query,
+                                                  DistPair<IdxT, accT>* nbh_list)
+{
+  int cands_per_thread = CANDS / 32;
+  for (int i = 0; i < cands_per_thread; i++) {
+    tmp[i].idx  = query->ids[cands_per_thread * threadIdx.x + i];
+    tmp[i].dist = query->dists[cands_per_thread * threadIdx.x + i];
+
+    if (cands_per_thread * threadIdx.x + i >= query->size) {
+      tmp[i].idx  = raft::upper_bound<IdxT>();
+      tmp[i].dist = raft::upper_bound<accT>();
+    }
+  }
+  int nbh_per_thread = DEG / 32;
+  for (int i = 0; i < nbh_per_thread; i++) {
+    tmp[cands_per_thread + i] = nbh_list[nbh_per_thread * threadIdx.x + i];
+  }
+}
+
+/* Combines edge and candidate lists, removes duplicates, and sorts by distance
+ * Uses CUB primitives, so needs to be templated. Called with Macros for supported sizes above */
+template <typename accT, typename IdxT, int DEG, int CANDS>
+__forceinline__ __device__ void sort_edges_and_cands(
+  DistPair<IdxT, accT>* new_nbh_list,
+  QueryCandidates<IdxT, accT>* query,
+  typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, (DEG + CANDS) / 32>::TempStorage* sort_mem)
+{
+  const int ELTS   = (DEG + CANDS) / 32;
+  using BlockSortT = cub::BlockMergeSort<DistPair<IdxT, accT>, 32, ELTS>;
+  DistPair<IdxT, accT> tmp[ELTS];
+
+  load_to_registers<accT, IdxT, DEG, CANDS>(tmp, query, new_nbh_list);
+
+  __syncthreads();
+  BlockSortT(*sort_mem).Sort(tmp, CmpDist());
+  __syncthreads();
+
+  // Mark duplicates and re-sort
+  // Copy last element over and shuffle to check for duplicate between threads
+  new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)] = tmp[ELTS - 1];
+  if (tmp[ELTS - 1].idx == tmp[ELTS - 2].idx) {
+    new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].idx  = raft::upper_bound<IdxT>();
+    new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].dist = raft::upper_bound<accT>();
+  }
+  __shfl_up_sync(0xffffffff, tmp[ELTS - 1].idx, 1);
+  __syncthreads();
+
+  for (int i = ELTS - 2; i > 0; i--) {
+    if (tmp[i].idx == tmp[i - 1].idx) {
+      tmp[i].idx  = raft::upper_bound<IdxT>();
+      tmp[i].dist = raft::upper_bound<accT>();
+    }
+  }
+  if (threadIdx.x == 0) {
+    if (tmp[0].idx == tmp[ELTS - 1].idx) {
+      tmp[0].idx  = raft::upper_bound<IdxT>();
+      tmp[0].dist = raft::upper_bound<accT>();
+    }
+  }
+
+  tmp[ELTS - 1].idx =
+    new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].idx;  // copy back to tmp for re-shuffling
+  tmp[ELTS - 1].dist = new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].dist;
+
+  __syncthreads();
+  BlockSortT(*sort_mem).Sort(tmp, CmpDist());
+  __syncthreads();
+
+  for (int i = 0; i < ELTS; i++) {
+    new_nbh_list[ELTS * threadIdx.x + i].idx  = tmp[i].idx;
+    new_nbh_list[ELTS * threadIdx.x + i].dist = tmp[i].dist;
+  }
+  __syncthreads();
+}
+
+namespace {
+
+/********************************************************************************************
+  GPU kernel for RobustPrune operation for Vamana graph creation
+  Input - *graph to be an edgelist of degree number of edges per vector,
+  query_list should contain the list of visited nodes during GreedySearch.
+  All inputs, including dataset, must be device accessible.
+
+  Output - candidate_ptr contains the new set of *degree* new neighbors that each node
+           should have.
+**********************************************************************************************/
+template <typename T,
+          typename accT,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+__global__ void RobustPruneKernel(
+  raft::device_matrix_view<IdxT, int64_t> graph,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  void* query_list_ptr,
+  int num_queries,
+  int visited_size,
+  cuvs::distance::DistanceType metric,
+  float alpha,
+  int sort_smem_size)
+{
+  int n      = dataset.extent(0);
+  int dim    = dataset.extent(1);
+  int degree = graph.extent(1);
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  union ShmemLayout {
+    // All blocksort sizes have same alignment (16)
+    typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, 3>::TempStorage sort_mem;
+    T coords;
+    DistPair<IdxT, accT> nbh_list;
+  };
+
+  // Dynamic shared memory used for blocksort, temp vector storage, and neighborhood list
+  extern __shared__ __align__(alignof(ShmemLayout)) char smem[];
+
+  T* s_coords = reinterpret_cast<T*>(&smem[sort_smem_size]);
+  DistPair<IdxT, accT>* new_nbh_list =
+    reinterpret_cast<DistPair<IdxT, accT>*>(&smem[dim * sizeof(T) + sort_smem_size]);
+
+  static __shared__ Point<T, accT> s_query;
+  s_query.coords = s_coords;
+  s_query.Dim    = dim;
+
+  for (int i = blockIdx.x; i < num_queries; i += gridDim.x) {
+    int queryId = query_list[i].queryId;
+
+    update_shared_point<T, accT>(&s_query, &dataset(0, 0), query_list[i].queryId, dim);
+
+    // Load new neighbors to be sorted with candidates
+    for (int j = threadIdx.x; j < degree; j += blockDim.x) {
+      new_nbh_list[j].idx = graph(queryId, j);
+    }
+    __syncthreads();
+    for (int j = 0; j < degree; j++) {
+      if (new_nbh_list[j].idx != raft::upper_bound<IdxT>()) {
+        new_nbh_list[j].dist =
+          dist<T, accT>(s_query.coords, &dataset((size_t)new_nbh_list[j].idx, 0), dim, metric);
+      } else {
+        new_nbh_list[j].dist = raft::upper_bound<accT>();
+      }
+    }
+    __syncthreads();
+
+    // combine and sort candidates and existing edges (and removes duplicates)
+    // Resulting list is stored in new_nbh_list
+    PRUNE_SELECT_SORT(degree, visited_size);
+
+    __syncthreads();
+
+    // If less than degree total neighbors, don't need to prune
+    if (new_nbh_list[degree].idx == raft::upper_bound<IdxT>()) {
+      if (threadIdx.x == 0) {
+        int writeId = 0;
+        for (; new_nbh_list[writeId].idx != raft::upper_bound<IdxT>(); writeId++) {
+          query_list[i].ids[writeId]   = new_nbh_list[writeId].idx;
+          query_list[i].dists[writeId] = new_nbh_list[writeId].dist;
+        }
+        query_list[i].size = writeId;
+        for (; writeId < degree; writeId++) {
+          query_list[i].ids[writeId]   = raft::upper_bound<IdxT>();
+          query_list[i].dists[writeId] = raft::upper_bound<accT>();
+        }
+      }
+    } else {
+      // loop through list, writing nearest to visited_list,
+      // while nulling out violating neighbors in shared memory
+      if (threadIdx.x == 0) {
+        query_list[i].ids[0]   = new_nbh_list[0].idx;
+        query_list[i].dists[0] = new_nbh_list[0].dist;
+      }
+
+      int writeId = 1;
+      for (int j = 1; j < degree + query_list[i].size && writeId < degree; j++) {
+        __syncthreads();
+        if (new_nbh_list[j].idx == queryId || new_nbh_list[j].idx == raft::upper_bound<IdxT>()) {
+          continue;
+        }
+        __syncthreads();
+        if (threadIdx.x == 0) {
+          query_list[i].ids[writeId]   = new_nbh_list[j].idx;
+          query_list[i].dists[writeId] = new_nbh_list[j].dist;
+        }
+        writeId++;
+        __syncthreads();
+
+        update_shared_point<T, accT>(&s_query, &dataset(0, 0), new_nbh_list[j].idx, dim);
+
+        int tot_size = degree + query_list[i].size;
+        for (int k = j + 1; k < tot_size; k++) {
+          T* mem_ptr = const_cast<T*>(&dataset((size_t)new_nbh_list[k].idx, 0));
+          if (new_nbh_list[k].idx != raft::upper_bound<IdxT>()) {
+            accT dist_starprime = dist<T, accT>(s_query.coords, mem_ptr, dim, metric);
+            // TODO - create cosine and selector fcn
+
+            if (threadIdx.x == 0 && alpha * dist_starprime <= new_nbh_list[k].dist) {
+              new_nbh_list[k].idx = raft::upper_bound<IdxT>();
+            }
+          }
+        }
+      }
+      __syncthreads();
+      if (threadIdx.x == 0) { query_list[i].size = writeId; }
+
+      __syncthreads();
+      for (int j = writeId + threadIdx.x; j < degree;
+           j += blockDim.x) {  // Zero out any unfilled neighbors
+        query_list[i].ids[j]   = raft::upper_bound<IdxT>();
+        query_list[i].dists[j] = raft::upper_bound<accT>();
+      }
+    }
+  }
+}
+
+}  // namespace
+
+}  // namespace cuvs::neighbors::experimental::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_build.cuh b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
new file mode 100644
index 0000000000..da24decb36
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
@@ -0,0 +1,410 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "greedy_search.cuh"
+#include "robust_prune.cuh"
+#include "vamana_structs.cuh"
+#include <cuvs/neighbors/vamana.hpp>
+
+#include <raft/cluster/kmeans.cuh>
+#include <raft/cluster/kmeans_types.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/matrix/copy.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/random/make_blobs.cuh>
+#include <raft/sparse/neighbors/cross_component_nn.cuh>
+
+#include <thrust/device_vector.h>
+#include <thrust/sequence.h>
+#include <thrust/unique.h>
+
+#include <cuvs/distance/distance.hpp>
+
+#include <chrono>
+#include <cstdio>
+#include <vector>
+
+namespace cuvs::neighbors::experimental::vamana::detail {
+
+/* @defgroup vamana_build_detail vamana build
+ * @{
+ */
+
+static const std::string RAFT_NAME = "raft";
+
+static const int blockD    = 32;
+static const int maxBlocks = 10000;
+
+// generate random permutation of inserts - TODO do this on GPU / faster
+template <typename IdxT>
+void create_insert_permutation(std::vector<IdxT>& insert_order, uint32_t N)
+{
+  insert_order.resize(N);
+  for (uint32_t i = 0; i < N; i++) {
+    insert_order[i] = (IdxT)i;
+  }
+  for (uint32_t i = 0; i < N; i++) {
+    uint32_t temp;
+    uint32_t rand_idx      = rand() % N;
+    temp                   = insert_order[i];
+    insert_order[i]        = insert_order[rand_idx];
+    insert_order[rand_idx] = temp;
+  }
+}
+
+/********************************************************************************************
+ * Main Vamana building function - insert vectors into empty graph in batches
+ * Pre - dataset contains the vector data, host matrix allocated to store the graph
+ * Post - graph matrix contains the graph edges of the final Vamana graph
+ *******************************************************************************************/
+template <typename T,
+          typename accT,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+void batched_insert_vamana(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  raft::host_matrix_view<IdxT, int64_t> graph,
+  IdxT* medoid_id,
+  cuvs::distance::DistanceType metric)
+//  int dim)
+{
+  auto stream = raft::resource::get_cuda_stream(res);
+  int N       = dataset.extent(0);
+  int dim     = dataset.extent(1);
+  int degree  = graph.extent(1);
+
+  // Algorithm params
+  int max_batchsize = (int)(params.max_fraction * (float)N);
+  if (max_batchsize > (int)dataset.extent(0)) {
+    RAFT_LOG_WARN(
+      "Max fraction is the fraction of the total dataset, so it cannot be larger 1.0, reducing it "
+      "to 1.0");
+    max_batchsize = (int)dataset.extent(0);
+  }
+  int insert_iters = (int)(params.vamana_iters);
+  double base      = (double)(params.batch_base);
+  float alpha      = (float)(params.alpha);
+  int visited_size = params.visited_size;
+  int queue_size   = params.queue_size;
+
+  if ((visited_size & (visited_size - 1)) != 0) {
+    RAFT_LOG_WARN("visited_size must be a power of 2, rounding up.");
+    int power = params.graph_degree;
+    while (power < visited_size)
+      power <<= 1;
+    visited_size = power;
+  }
+
+  // create gpu graph and set to all -1s
+  auto d_graph = raft::make_device_matrix<IdxT, int64_t>(res, graph.extent(0), graph.extent(1));
+  raft::linalg::map(res, d_graph.view(), raft::const_op<IdxT>{raft::upper_bound<IdxT>()});
+
+  // Temp storage about each batch of inserts being performed
+  auto query_ids      = raft::make_device_vector<IdxT>(res, max_batchsize);
+  auto query_list_ptr = raft::make_device_mdarray<QueryCandidates<IdxT, accT>>(
+    res,
+    raft::resource::get_large_workspace_resource(res),
+    raft::make_extents<int64_t>(max_batchsize + 1));
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr.data_handle());
+
+  // Results of each batch of inserts during build - Memory is used by query_list structure
+  auto visited_ids =
+    raft::make_device_mdarray<IdxT>(res,
+                                    raft::resource::get_large_workspace_resource(res),
+                                    raft::make_extents<int64_t>(max_batchsize, visited_size));
+  auto visited_dists =
+    raft::make_device_mdarray<accT>(res,
+                                    raft::resource::get_large_workspace_resource(res),
+                                    raft::make_extents<int64_t>(max_batchsize, visited_size));
+
+  // Assign memory to query_list structures and initiailize
+  init_query_candidate_list<IdxT, accT><<<256, blockD, 0, stream>>>(query_list,
+                                                                    visited_ids.data_handle(),
+                                                                    visited_dists.data_handle(),
+                                                                    (int)max_batchsize,
+                                                                    visited_size);
+
+  // Create random permutation for order of node inserts into graph
+  std::vector<IdxT> insert_order;
+  create_insert_permutation<IdxT>(insert_order, (uint32_t)N);
+
+  // Memory needed to sort reverse edges - potentially large memory footprint
+  auto edge_dest =
+    raft::make_device_mdarray<IdxT>(res,
+                                    raft::resource::get_large_workspace_resource(res),
+                                    raft::make_extents<int64_t>(max_batchsize, degree));
+  auto edge_src =
+    raft::make_device_mdarray<IdxT>(res,
+                                    raft::resource::get_large_workspace_resource(res),
+                                    raft::make_extents<int64_t>(max_batchsize, degree));
+
+  size_t temp_storage_bytes = max_batchsize * degree * (2 * sizeof(IdxT));
+  RAFT_LOG_DEBUG("Temp storage needed for sorting (bytes): %lu", temp_storage_bytes);
+  auto temp_sort_storage =
+    raft::make_device_mdarray<IdxT>(res,
+                                    raft::resource::get_large_workspace_resource(res),
+                                    raft::make_extents<int64_t>(2 * max_batchsize, degree));
+
+  // Calculate the shared memory sizes of each kernel
+  int search_smem_sort_size = 0;
+  int prune_smem_sort_size  = 0;
+  SELECT_SMEM_SIZES(degree, visited_size);  // Sets above 2 variables to appropriate sizes
+
+  // Total dynamic shared memory used by GreedySearch
+  int search_smem_total_size =
+    static_cast<int>(search_smem_sort_size + dim * sizeof(T) + visited_size * sizeof(Node<accT>) +
+                     degree * sizeof(int) + queue_size * sizeof(DistPair<IdxT, accT>));
+
+  // Total dynamic shared memory size needed by both RobustPrune calls
+  int prune_smem_total_size =
+    prune_smem_sort_size + dim * sizeof(T) + (degree + visited_size) * sizeof(DistPair<IdxT, accT>);
+
+  RAFT_LOG_DEBUG("Dynamic shared memory usage (bytes): GreedySearch: %d, RobustPrune: %d",
+                 search_smem_total_size,
+                 prune_smem_total_size);
+
+  if (prune_smem_sort_size == 0) {  // If sizes not supported, smem sizes will be 0
+    RAFT_FAIL("Vamana graph parameters not supported: graph_degree=%d, visited_size:%d\n",
+              degree,
+              visited_size);
+  }
+
+  // Random medoid has minor impact on recall
+  // TODO: use heuristic for better medoid selection, issue:
+  // https://github.com/rapidsai/cuvs/issues/355
+  *medoid_id = rand() % N;
+
+  // size of current batch of inserts, increases logarithmically until max_batchsize
+  int step_size = 1;
+  // Number of passes over dataset (default 1)
+  for (int iter = 0; iter < insert_iters; iter++) {
+    // Loop through batches and call the insert and prune kernels
+    for (int start = 0; start < N;) {
+      if (start + step_size > N) {
+        int new_size = N - start;
+        step_size    = new_size;
+      }
+      RAFT_LOG_DEBUG("Starting batch of inserts indices_start:%d, batch_size:%d", start, step_size);
+
+      int num_blocks = min(maxBlocks, step_size);
+
+      // Copy ids to be inserted for this batch
+      raft::copy(query_ids.data_handle(), &insert_order.data()[start], step_size, stream);
+      set_query_ids<IdxT, accT><<<num_blocks, blockD, 0, stream>>>(
+        query_list_ptr.data_handle(), query_ids.data_handle(), step_size);
+
+      // Call greedy search to get candidates for every vector being inserted
+      GreedySearchKernel<T, accT, IdxT, Accessor>
+        <<<num_blocks, blockD, search_smem_total_size, stream>>>(d_graph.view(),
+                                                                 dataset,
+                                                                 query_list_ptr.data_handle(),
+                                                                 step_size,
+                                                                 *medoid_id,
+                                                                 visited_size,
+                                                                 metric,
+                                                                 queue_size,
+                                                                 search_smem_sort_size);
+
+      // Run on candidates of vectors being inserted
+      RobustPruneKernel<T, accT, IdxT>
+        <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
+                                                                dataset,
+                                                                query_list_ptr.data_handle(),
+                                                                step_size,
+                                                                visited_size,
+                                                                metric,
+                                                                alpha,
+                                                                prune_smem_sort_size);
+
+      // Write results from first prune to graph edge list
+      write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
+        d_graph.view(), query_list_ptr.data_handle(), degree, step_size);
+
+      // compute prefix sums of query_list sizes - TODO parallelize prefix sums
+      auto d_total_edges = raft::make_device_mdarray<int>(
+        res, raft::resource::get_workspace_resource(res), raft::make_extents<int64_t>(1));
+      prefix_sums_sizes<accT, IdxT>
+        <<<1, 1, 0, stream>>>(query_list, step_size, d_total_edges.data_handle());
+
+      int total_edges;
+      raft::copy(&total_edges, d_total_edges.data_handle(), 1, stream);
+
+      // Create reverse edge list
+      create_reverse_edge_list<accT, IdxT>
+        <<<num_blocks, blockD, 0, stream>>>(query_list_ptr.data_handle(),
+                                            step_size,
+                                            degree,
+                                            edge_src.data_handle(),
+                                            edge_dest.data_handle());
+
+      // Sort to group reverse edges by destination
+      cub::DeviceMergeSort::SortPairs(temp_sort_storage.data_handle(),
+                                      temp_storage_bytes,
+                                      edge_dest.data_handle(),
+                                      edge_src.data_handle(),
+                                      total_edges,
+                                      CmpEdge<IdxT>(),
+                                      stream);
+
+      // Get number of unique node destinations
+      IdxT unique_dests =
+        raft::sparse::neighbors::get_n_components(edge_dest.data_handle(), total_edges, stream);
+
+      // Find which node IDs have reverse edges and their indices in the reverse edge list
+      thrust::device_vector<IdxT> edge_dest_vec(edge_dest.data_handle(),
+                                                edge_dest.data_handle() + total_edges);
+      auto unique_indices = raft::make_device_vector<int>(res, total_edges);
+      raft::linalg::map_offset(res, unique_indices.view(), raft::identity_op{});
+      thrust::unique_by_key(
+        edge_dest_vec.begin(), edge_dest_vec.end(), unique_indices.data_handle());
+
+      // Allocate reverse QueryCandidate list based on number of unique destinations
+      // TODO - Do this in batches to reduce memory footprint / support larger datasets
+      auto reverse_list_ptr = raft::make_device_mdarray<QueryCandidates<IdxT, accT>>(
+        res,
+        raft::resource::get_large_workspace_resource(res),
+        raft::make_extents<int64_t>(unique_dests));
+      auto rev_ids =
+        raft::make_device_mdarray<IdxT>(res,
+                                        raft::resource::get_large_workspace_resource(res),
+                                        raft::make_extents<int64_t>(unique_dests, visited_size));
+      auto rev_dists =
+        raft::make_device_mdarray<accT>(res,
+                                        raft::resource::get_large_workspace_resource(res),
+                                        raft::make_extents<int64_t>(unique_dests, visited_size));
+
+      QueryCandidates<IdxT, accT>* reverse_list =
+        static_cast<QueryCandidates<IdxT, accT>*>(reverse_list_ptr.data_handle());
+
+      init_query_candidate_list<IdxT, accT><<<256, blockD, 0, stream>>>(reverse_list,
+                                                                        rev_ids.data_handle(),
+                                                                        rev_dists.data_handle(),
+                                                                        (int)unique_dests,
+                                                                        visited_size);
+
+      // May need more blocks for reverse list
+      num_blocks = min(maxBlocks, unique_dests);
+
+      // Populate reverse list ids and candidate lists from edge_src and edge_dest
+      populate_reverse_list_struct<T, accT, IdxT>
+        <<<num_blocks, blockD, 0, stream>>>(reverse_list,
+                                            edge_src.data_handle(),
+                                            edge_dest.data_handle(),
+                                            unique_indices.data_handle(),
+                                            unique_dests,
+                                            total_edges,
+                                            dataset.extent(0));
+
+      // Recompute distances (avoided keeping it during sorting)
+      recompute_reverse_dists<T, accT, IdxT>
+        <<<num_blocks, blockD, 0, stream>>>(reverse_list, dataset, unique_dests, metric);
+
+      // Call 2nd RobustPrune on reverse query_list
+      RobustPruneKernel<T, accT, IdxT>
+        <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
+                                                                raft::make_const_mdspan(dataset),
+                                                                reverse_list_ptr.data_handle(),
+                                                                unique_dests,
+                                                                visited_size,
+                                                                metric,
+                                                                alpha,
+                                                                prune_smem_sort_size);
+
+      // Write new edge lists to graph
+      write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
+        d_graph.view(), reverse_list_ptr.data_handle(), degree, unique_dests);
+
+      start += step_size;
+      step_size *= base;
+      if (step_size > max_batchsize) step_size = max_batchsize;
+
+    }  // Batch of inserts
+
+  }  // insert iterations
+
+  raft::copy(graph.data_handle(), d_graph.data_handle(), d_graph.size(), stream);
+
+  RAFT_CHECK_CUDA(stream);
+}
+
+template <typename T,
+          typename IdxT     = uint64_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+index<T, IdxT> build(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
+{
+  uint32_t graph_degree = params.graph_degree;
+
+  RAFT_EXPECTS(params.metric == cuvs::distance::DistanceType::L2Expanded,
+               "Currently only L2Expanded metric is supported");
+
+  const int* deg_size = std::find(std::begin(DEGREE_SIZES), std::end(DEGREE_SIZES), graph_degree);
+  RAFT_EXPECTS(deg_size != std::end(DEGREE_SIZES), "Provided graph_degree not currently supported");
+
+  RAFT_EXPECTS(params.visited_size > graph_degree, "visited_size must be > graph_degree");
+
+  int dim = dataset.extent(1);
+  // TODO - Fix issue with alignment when dataset dimension is odd
+  RAFT_EXPECTS(dim % 2 == 0, "Datasets with an odd number of dimensions not currently supported");
+
+  RAFT_LOG_DEBUG("Creating empty graph structure");
+  auto vamana_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
+
+  RAFT_LOG_DEBUG("Running Vamana batched insert algorithm");
+
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
+
+  IdxT medoid_id;
+  batched_insert_vamana<T, float, IdxT, Accessor>(
+    res, params, dataset, vamana_graph.view(), &medoid_id, metric);
+
+  try {
+    return index<T, IdxT>(
+      res, params.metric, dataset, raft::make_const_mdspan(vamana_graph.view()), medoid_id);
+  } catch (std::bad_alloc& e) {
+    RAFT_LOG_DEBUG("Insufficient GPU memory to construct VAMANA index with dataset on GPU");
+    // We just add the graph. User is expected to update dataset separately (e.g allocating in
+    // managed memory).
+  } catch (raft::logic_error& e) {
+    // The memory error can also manifest as logic_error.
+    RAFT_LOG_DEBUG("Insufficient GPU memory to construct VAMANA index with dataset on GPU");
+  }
+  index<T, IdxT> idx(res, params.metric);
+  RAFT_LOG_WARN("Constructor not called, returning empty index");
+  return idx;
+}
+
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::experimental::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
new file mode 100644
index 0000000000..a554464f6c
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
@@ -0,0 +1,120 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "vamana_structs.cuh"
+
+#include <cuvs/neighbors/vamana.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/nvtx.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/serialize.hpp>
+
+#include "../dataset_serialize.hpp"
+
+#include <cstddef>
+#include <cstdint>
+#include <fstream>
+#include <type_traits>
+
+namespace cuvs::neighbors::experimental::vamana::detail {
+
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res the raft resource handle
+ * @param[in] file_name the path and name of the DiskAN index file generated
+ * @param[in] index_ VAMANA index
+ *
+ */
+
+template <typename T, typename IdxT>
+void serialize(raft::resources const& res,
+               const std::string& file_name,
+               const index<T, IdxT>& index_)
+{
+  // Write graph to first index file (format from MSFT DiskANN OSS)
+  std::ofstream index_of(file_name, std::ios::out | std::ios::binary);
+  if (!index_of) { RAFT_FAIL("Cannot open file %s", file_name.c_str()); }
+
+  size_t file_offset = 0;
+  index_of.seekp(file_offset, index_of.beg);
+  uint32_t max_degree          = 0;
+  size_t index_size            = 24;  // Starting metadata
+  uint32_t start               = static_cast<uint32_t>(index_.medoid());
+  size_t num_frozen_points     = 0;
+  uint32_t max_observed_degree = 0;
+
+  index_of.write((char*)&index_size, sizeof(uint64_t));
+  index_of.write((char*)&max_observed_degree, sizeof(uint32_t));
+  index_of.write((char*)&start, sizeof(uint32_t));
+  index_of.write((char*)&num_frozen_points, sizeof(size_t));
+
+  auto d_graph = index_.graph();
+  auto h_graph = raft::make_host_matrix<IdxT, int64_t>(d_graph.extent(0), d_graph.extent(1));
+  raft::copy(h_graph.data_handle(),
+             d_graph.data_handle(),
+             d_graph.size(),
+             raft::resource::get_cuda_stream(res));
+
+  size_t total_edges = 0;
+  size_t num_sparse  = 0;
+  size_t num_single  = 0;
+
+  for (uint32_t i = 0; i < h_graph.extent(0); i++) {
+    uint32_t node_edges = 0;
+    for (; node_edges < h_graph.extent(1); node_edges++) {
+      if (h_graph(i, node_edges) == raft::upper_bound<IdxT>()) { break; }
+    }
+
+    if (node_edges < 3) num_sparse++;
+    if (node_edges < 2) num_single++;
+    total_edges += node_edges;
+
+    index_of.write((char*)&node_edges, sizeof(uint32_t));
+    if constexpr (!std::is_same_v<IdxT, uint32_t>) {
+      RAFT_FAIL("serialization is only implemented for uint32_t graph");
+    }
+    index_of.write((char*)&h_graph(i, 0), node_edges * sizeof(uint32_t));
+
+    max_degree = node_edges > max_degree ? (uint32_t)node_edges : max_degree;
+    index_size += (size_t)(sizeof(uint32_t) * (node_edges + 1));
+  }
+  index_of.seekp(file_offset, index_of.beg);
+  index_of.write((char*)&index_size, sizeof(uint64_t));
+  index_of.write((char*)&max_degree, sizeof(uint32_t));
+
+  RAFT_LOG_DEBUG(
+    "Wrote file out, index size:%lu, max_degree:%u, num_sparse:%ld, num_single:%ld, total "
+    "edges:%ld, avg degree:%f",
+    index_size,
+    max_degree,
+    num_sparse,
+    num_single,
+    total_edges,
+    (float)total_edges / (float)h_graph.extent(0));
+
+  index_of.close();
+  if (!index_of) { RAFT_FAIL("Error writing output %s", file_name.c_str()); }
+}
+
+}  // namespace cuvs::neighbors::experimental::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
new file mode 100644
index 0000000000..86cb4e1f8f
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
@@ -0,0 +1,478 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <climits>
+#include <cstdint>
+#include <cstdio>
+#include <float.h>
+#include <unordered_set>
+#include <vector>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger-ext.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+#include <cuvs/distance/distance.hpp>
+
+namespace cuvs::neighbors::experimental::vamana::detail {
+
+/* @defgroup vamana_structures vamana structures
+ * @{
+ */
+
+#define FULL_BITMASK 0xFFFFFFFF
+
+// Currently supported values for graph_degree.
+static const int DEGREE_SIZES[4] = {32, 64, 128, 256};
+
+// Object used to store id,distance combination graph construction operations
+template <typename IdxT, typename accT>
+struct __align__(16) DistPair
+{
+  accT dist;
+  IdxT idx;
+
+  __device__ __host__ DistPair& operator=(const DistPair& other)
+  {
+    dist = other.dist;
+    idx  = other.idx;
+    return *this;
+  }
+
+  __device__ __host__ DistPair& operator=(const volatile DistPair& other)
+  {
+    dist = other.dist;
+    idx  = other.idx;
+    return *this;
+  }
+};
+
+// Swap the values of two DistPair<SUMTYPE> objects
+template <typename IdxT, typename accT>
+__device__ __host__ void swap(DistPair<IdxT, accT>* a, DistPair<IdxT, accT>* b)
+{
+  DistPair<IdxT, accT> temp;
+  temp.dist = a->dist;
+  temp.idx  = a->idx;
+  a->dist   = b->dist;
+  a->idx    = b->idx;
+  b->dist   = temp.dist;
+  b->idx    = temp.idx;
+}
+
+// Structure to sort by distance
+struct CmpDist {
+  template <typename IdxT, typename accT>
+  __device__ bool operator()(const DistPair<IdxT, accT>& lhs, const DistPair<IdxT, accT>& rhs)
+  {
+    return lhs.dist < rhs.dist;
+  }
+};
+
+// Used to sort reverse edges by destination
+template <typename IdxT>
+struct CmpEdge {
+  __device__ bool operator()(const IdxT& lhs, const IdxT& rhs) { return lhs < rhs; }
+};
+
+/*********************************************************************
+ * Object representing a Dim-dimensional point, with each coordinate
+ * represented by a element of datatype T
+ * Note, memory is allocated separately and coords set to offsets
+ *********************************************************************/
+template <typename T, typename SUMTYPE>
+class Point {
+ public:
+  int id;
+  int Dim;
+  T* coords;
+
+  __host__ __device__ Point& operator=(const Point& other)
+  {
+    for (int i = 0; i < Dim; i++) {
+      coords[i] = other.coords[i];
+    }
+    id = other.id;
+    return *this;
+  }
+};
+
+/* L2 fallback for low dimension when ILP is not possible */
+template <typename T, typename SUMTYPE>
+__device__ SUMTYPE l2_SEQ(Point<T, SUMTYPE>* src_vec, Point<T, SUMTYPE>* dst_vec)
+{
+  SUMTYPE partial_sum = 0;
+
+  for (int i = threadIdx.x; i < src_vec->Dim; i += blockDim.x) {
+    partial_sum = fmaf((src_vec[0].coords[i] - dst_vec[0].coords[i]),
+                       (src_vec[0].coords[i] - dst_vec[0].coords[i]),
+                       partial_sum);
+  }
+
+  for (int offset = 16; offset > 0; offset /= 2) {
+    partial_sum += __shfl_down_sync(FULL_BITMASK, partial_sum, offset);
+  }
+  return partial_sum;
+}
+
+/* L2 optimized with 2-way ILP for DIM >= 64 */
+template <typename T, typename SUMTYPE>
+__device__ SUMTYPE l2_ILP2(Point<T, SUMTYPE>* src_vec, Point<T, SUMTYPE>* dst_vec)
+{
+  T temp_dst[2]          = {0, 0};
+  SUMTYPE partial_sum[2] = {0, 0};
+  for (int i = threadIdx.x; i < src_vec->Dim; i += 2 * blockDim.x) {
+    temp_dst[0] = dst_vec->coords[i];
+    if (i + 32 < src_vec->Dim) temp_dst[1] = dst_vec->coords[i + 32];
+
+    partial_sum[0] = fmaf(
+      (src_vec[0].coords[i] - temp_dst[0]), (src_vec[0].coords[i] - temp_dst[0]), partial_sum[0]);
+    if (i + 32 < src_vec->Dim)
+      partial_sum[1] = fmaf((src_vec[0].coords[i + 32] - temp_dst[1]),
+                            (src_vec[0].coords[i + 32] - temp_dst[1]),
+                            partial_sum[1]);
+  }
+  partial_sum[0] += partial_sum[1];
+
+  for (int offset = 16; offset > 0; offset /= 2) {
+    partial_sum[0] += __shfl_down_sync(FULL_BITMASK, partial_sum[0], offset);
+  }
+  return partial_sum[0];
+}
+
+/* L2 optimized with 4-way ILP for optimal performance for DIM >= 128 */
+template <typename T, typename SUMTYPE>
+__device__ SUMTYPE l2_ILP4(Point<T, SUMTYPE>* src_vec, Point<T, SUMTYPE>* dst_vec)
+{
+  T temp_dst[4]          = {0, 0, 0, 0};
+  SUMTYPE partial_sum[4] = {0, 0, 0, 0};
+  for (int i = threadIdx.x; i < src_vec->Dim; i += 4 * blockDim.x) {
+    temp_dst[0] = dst_vec->coords[i];
+    if (i + 32 < src_vec->Dim) temp_dst[1] = dst_vec->coords[i + 32];
+    if (i + 64 < src_vec->Dim) temp_dst[2] = dst_vec->coords[i + 64];
+    if (i + 92 < src_vec->Dim) temp_dst[3] = dst_vec->coords[i + 96];
+
+    partial_sum[0] = fmaf(
+      (src_vec[0].coords[i] - temp_dst[0]), (src_vec[0].coords[i] - temp_dst[0]), partial_sum[0]);
+    if (i + 32 < src_vec->Dim)
+      partial_sum[1] = fmaf((src_vec[0].coords[i + 32] - temp_dst[1]),
+                            (src_vec[0].coords[i + 32] - temp_dst[1]),
+                            partial_sum[1]);
+    if (i + 64 < src_vec->Dim)
+      partial_sum[2] = fmaf((src_vec[0].coords[i + 64] - temp_dst[2]),
+                            (src_vec[0].coords[i + 64] - temp_dst[2]),
+                            partial_sum[2]);
+    if (i + 92 < src_vec->Dim)
+      partial_sum[3] = fmaf((src_vec[0].coords[i + 96] - temp_dst[3]),
+                            (src_vec[0].coords[i + 96] - temp_dst[3]),
+                            partial_sum[3]);
+  }
+  partial_sum[0] += partial_sum[1] + partial_sum[2] + partial_sum[3];
+
+  for (int offset = 16; offset > 0; offset /= 2) {
+    partial_sum[0] += __shfl_down_sync(FULL_BITMASK, partial_sum[0], offset);
+  }
+  return partial_sum[0];
+}
+
+/* Selects ILP optimization level based on dimension */
+template <typename T, typename SUMTYPE>
+__forceinline__ __device__ SUMTYPE l2(Point<T, SUMTYPE>* src_vec, Point<T, SUMTYPE>* dst_vec)
+{
+  if (src_vec->Dim >= 128) {
+    return l2_ILP4<T, SUMTYPE>(src_vec, dst_vec);
+  } else if (src_vec->Dim >= 64) {
+    return l2_ILP2<T, SUMTYPE>(src_vec, dst_vec);
+  } else {
+    return l2_SEQ<T, SUMTYPE>(src_vec, dst_vec);
+  }
+}
+
+/* Convert vectors to point structure to performance distance comparison */
+template <typename T, typename SUMTYPE>
+__host__ __device__ SUMTYPE l2(const T* src, const T* dest, int dim)
+{
+  Point<T, SUMTYPE> src_p;
+  src_p.coords = const_cast<T*>(src);
+  src_p.Dim    = dim;
+  Point<T, SUMTYPE> dest_p;
+  dest_p.coords = const_cast<T*>(dest);
+  dest_p.Dim    = dim;
+
+  return l2<T, SUMTYPE>(&src_p, &dest_p);
+}
+
+// Currently only L2Expanded is supported
+template <typename T, typename SUMTYPE>
+__host__ __device__ SUMTYPE
+dist(const T* src, const T* dest, int dim, cuvs::distance::DistanceType metric)
+{
+  return l2<T, SUMTYPE>(src, dest, dim);
+}
+
+/***************************************************************************************
+ * Structure that holds information about and results of a query. Use by both
+ * GreedySearch and RobustPrune, as well as reverse edge lists.
+ ***************************************************************************************/
+template <typename IdxT, typename accT>
+struct QueryCandidates {
+  IdxT* ids;
+  accT* dists;
+  int queryId;
+  int size;
+  int maxSize;
+
+  __device__ void reset()
+  {
+    for (int i = threadIdx.x; i < maxSize; i += blockDim.x) {
+      ids[i]   = raft::upper_bound<IdxT>();
+      dists[i] = raft::upper_bound<accT>();
+    }
+    size = 0;
+  }
+
+  // Checks current list to see if a node as previously been visited
+  __inline__ __device__ bool check_visited(IdxT target, accT dist)
+  {
+    __syncthreads();
+    __shared__ bool found;
+    found = false;
+    __syncthreads();
+
+    if (size < maxSize) {
+      __syncthreads();
+      for (int i = threadIdx.x; i < size; i += blockDim.x) {
+        if (ids[i] == target) { found = true; }
+      }
+      __syncthreads();
+      if (!found && threadIdx.x == 0) {
+        ids[size]   = target;
+        dists[size] = dist;
+        size++;
+      }
+      __syncthreads();
+    }
+    return found;
+  }
+  // For debugging
+  /*
+  __inline__ __device__ void print_visited() {
+    printf("queryId:%d, size:%d\n", queryId, size);
+    for(int i=0; i<size; i++) {
+      printf("%d (%f), ", ids[i], dists[i]);
+    }
+    printf("\n");
+  }
+  */
+};
+
+namespace {
+
+/********************************************************************************************
+ * Kernels that work on QueryCandidates objects *
+ *******************************************************************************************/
+// For debugging
+template <typename accT, typename IdxT = uint32_t>
+__global__ void print_query_results(void* query_list_ptr, int count)
+{
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  for (int i = 0; i < count; i++) {
+    query_list[i].print_visited();
+  }
+}
+
+// Initialize a list of QueryCandidates objects: assign memory to mpointers and initialize values
+template <typename IdxT, typename accT>
+__global__ void init_query_candidate_list(QueryCandidates<IdxT, accT>* query_list,
+                                          IdxT* visited_id_ptr,
+                                          accT* visited_dist_ptr,
+                                          int num_queries,
+                                          int maxSize)
+{
+  IdxT* ids_ptr  = static_cast<IdxT*>(visited_id_ptr);
+  accT* dist_ptr = static_cast<accT*>(visited_dist_ptr);
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num_queries * maxSize;
+       i += blockDim.x + gridDim.x) {
+    ids_ptr[i]  = raft::upper_bound<IdxT>();
+    dist_ptr[i] = raft::upper_bound<accT>();
+  }
+
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_queries;
+       i += blockDim.x + gridDim.x) {
+    query_list[i].maxSize = maxSize;
+    query_list[i].size    = 0;
+    query_list[i].ids     = &ids_ptr[i * (size_t)(maxSize)];
+    query_list[i].dists   = &dist_ptr[i * (size_t)(maxSize)];
+  }
+}
+
+// Copy query ID values from input array
+template <typename IdxT, typename accT>
+__global__ void set_query_ids(void* query_list_ptr, IdxT* d_query_ids, int step_size)
+{
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < step_size; i += blockDim.x * gridDim.x) {
+    query_list[i].queryId = d_query_ids[i];
+    query_list[i].size    = 0;
+  }
+}
+
+// Compute prefix sums on sizes. Currently only works with 1 thread
+// TODO replace with parallel version
+template <typename accT, typename IdxT = uint32_t>
+__global__ void prefix_sums_sizes(QueryCandidates<IdxT, accT>* query_list,
+                                  int num_queries,
+                                  int* total_edges)
+{
+  if (threadIdx.x == 0 && blockIdx.x == 0) {
+    int sum = 0;
+    for (int i = 0; i < num_queries + 1; i++) {
+      sum += query_list[i].size;
+      query_list[i].size = sum - query_list[i].size;  // exclusive prefix sum
+    }
+    *total_edges = query_list[num_queries].size;
+  }
+}
+
+// Device fcn to have a threadblock copy coordinates into shared memory
+template <typename T, typename accT>
+__device__ void update_shared_point(Point<T, accT>* shared_point,
+                                    const T* data_ptr,
+                                    int id,
+                                    int dim)
+{
+  shared_point->id  = id;
+  shared_point->Dim = dim;
+  for (size_t i = threadIdx.x; i < dim; i += blockDim.x) {
+    shared_point->coords[i] = data_ptr[(size_t)(id) * (size_t)(dim) + i];
+  }
+}
+
+// Update the graph from the results of the query list (or reverse edge list)
+template <typename accT, typename IdxT = uint32_t>
+__global__ void write_graph_edges_kernel(raft::device_matrix_view<IdxT, int64_t> graph,
+                                         void* query_list_ptr,
+                                         int degree,
+                                         int num_queries)
+{
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  for (int i = blockIdx.x; i < num_queries; i += gridDim.x) {
+    for (int j = threadIdx.x; j < query_list[i].size; j += blockDim.x) {
+      graph(query_list[i].queryId, j) = query_list[i].ids[j];
+    }
+  }
+}
+
+// Create src and dest edge lists used to sort and create reverse edges
+template <typename accT, typename IdxT = uint32_t>
+__global__ void create_reverse_edge_list(
+  void* query_list_ptr, int num_queries, int degree, IdxT* edge_src, IdxT* edge_dest)
+{
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num_queries;
+       i += blockDim.x * gridDim.x) {
+    int read_idx   = i * query_list[i].maxSize;
+    int cand_count = query_list[i + 1].size - query_list[i].size;
+
+    for (int j = 0; j < cand_count; j++) {
+      edge_src[query_list[i].size + j]  = query_list[i].queryId;
+      edge_dest[query_list[i].size + j] = query_list[i].ids[j];
+    }
+  }
+}
+
+// Populate reverse edge QueryCandidates structure based on sorted edge list and unique indices
+// values
+template <typename T, typename accT, typename IdxT = uint32_t>
+__global__ void populate_reverse_list_struct(QueryCandidates<IdxT, accT>* reverse_list,
+                                             IdxT* edge_src,
+                                             IdxT* edge_dest,
+                                             int* unique_indices,
+                                             int unique_dests,
+                                             int total_edges,
+                                             int N)
+{
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < unique_dests;
+       i += blockDim.x * gridDim.x) {
+    reverse_list[i].queryId = edge_dest[unique_indices[i]];
+    if (i == unique_dests - 1) {
+      reverse_list[i].size = total_edges - unique_indices[i];
+    } else {
+      reverse_list[i].size = unique_indices[i + 1] - unique_indices[i];
+    }
+    if (reverse_list[i].size > reverse_list[i].maxSize) {
+      reverse_list[i].size = reverse_list[i].maxSize;
+    }
+
+    for (int j = 0; j < reverse_list[i].size; j++) {
+      reverse_list[i].ids[j] = edge_src[unique_indices[i] + j];
+    }
+    for (int j = reverse_list[i].size; j < reverse_list[i].maxSize; j++) {
+      reverse_list[i].ids[j]   = raft::upper_bound<IdxT>();
+      reverse_list[i].dists[j] = raft::upper_bound<accT>();
+    }
+  }
+}
+
+// Recompute distances of reverse list. Allows us to avoid keeping distances during sort
+template <typename T,
+          typename accT,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+__global__ void recompute_reverse_dists(
+  QueryCandidates<IdxT, accT>* reverse_list,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  int unique_dests,
+  cuvs::distance::DistanceType metric)
+{
+  int dim          = dataset.extent(1);
+  const T* vec_ptr = dataset.data_handle();
+
+  for (int i = blockIdx.x; i < unique_dests; i += gridDim.x) {
+    for (int j = 0; j < reverse_list[i].size; j++) {
+      reverse_list[i].dists[j] =
+        dist<T, accT>(&vec_ptr[(size_t)(reverse_list[i].queryId) * (size_t)dim],
+                      &vec_ptr[(size_t)(reverse_list[i].ids[j]) * (size_t)dim],
+                      dim,
+                      metric);
+    }
+  }
+}
+
+}  // namespace
+
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::experimental::vamana::detail
diff --git a/cpp/src/neighbors/vamana.cuh b/cpp/src/neighbors/vamana.cuh
new file mode 100644
index 0000000000..9b9e8d271d
--- /dev/null
+++ b/cpp/src/neighbors/vamana.cuh
@@ -0,0 +1,102 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/vamana/vamana_build.cuh"
+#include "detail/vamana/vamana_serialize.cuh"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/vamana.hpp>
+
+#include <cuvs/neighbors/common.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cuvs::neighbors::experimental::vamana {
+
+/**
+ * @defgroup VAMANA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
+/**
+ * @brief Build the VAMANA / DiskANN index from the dataset.
+ *
+ * The Vamana index construction algorithm is an iterative insertion based algorithm.
+ * We start with an empty graph and insert batches of new nodes into the graph until
+ * all nodes have been inserted. Each insertion involves:
+ * - Perform GreedySearch into the current graph and collect all visited nodes.
+ * - Perform RobustPrune on the list of visited nodes to get the edge list for new node.
+ * - Compute the reverse edges for all edges from the newly inserted node.
+ * - Combine reverse edges with existing edge lists and perform RobustPrune as needed.
+ *
+ * Currently only build and serialize (write to graph) is supported in cuVS. The format
+ * of the serialized graph matches the DiskANN format, so search can be done with other
+ * CPU DiskANN libraries as needed.
+ *
+ * The following distance metrics are supported:
+ * - L2Expanded
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   vamana::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = vamana::build(res, index_params, dataset);
+ *   // write graph to file for later use.
+     vamana.serialize(res, filename, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed vamana index
+ */
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+index<T, IdxT> build(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
+{
+  return cuvs::neighbors::experimental::vamana::detail::build<T, IdxT, Accessor>(
+    res, params, dataset);
+}
+
+template <typename T, typename IdxT>
+void serialize(raft::resources const& res,
+               const std::string& file_prefix,
+               const index<T, IdxT>& index_)
+{
+  cuvs::neighbors::experimental::vamana::detail::build<T, IdxT>(res, file_prefix, index_);
+}
+
+/** @} */  // end group vamana
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/vamana_build_float.cu b/cpp/src/neighbors/vamana_build_float.cu
new file mode 100644
index 0000000000..b83af61220
--- /dev/null
+++ b/cpp/src/neighbors/vamana_build_float.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "vamana.cuh"
+#include <cuvs/neighbors/vamana.hpp>
+
+namespace cuvs::neighbors::experimental::vamana {
+
+#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                                \
+             const cuvs::neighbors::experimental::vamana::index_params& params,            \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)          \
+    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
+  {                                                                                        \
+    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+  }                                                                                        \
+                                                                                           \
+  auto build(raft::resources const& handle,                                                \
+             const cuvs::neighbors::experimental::vamana::index_params& params,            \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)            \
+    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
+  {                                                                                        \
+    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+  }
+
+RAFT_INST_VAMANA_BUILD(float, uint32_t);
+
+#undef RAFT_INST_VAMANA_BUILD
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/vamana_build_int8.cu b/cpp/src/neighbors/vamana_build_int8.cu
new file mode 100644
index 0000000000..91d2cf0280
--- /dev/null
+++ b/cpp/src/neighbors/vamana_build_int8.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "vamana.cuh"
+#include <cuvs/neighbors/vamana.hpp>
+
+namespace cuvs::neighbors::experimental::vamana {
+
+#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                                \
+             const cuvs::neighbors::experimental::vamana::index_params& params,            \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)          \
+    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
+  {                                                                                        \
+    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+  }                                                                                        \
+                                                                                           \
+  auto build(raft::resources const& handle,                                                \
+             const cuvs::neighbors::experimental::vamana::index_params& params,            \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)            \
+    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
+  {                                                                                        \
+    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+  }
+
+RAFT_INST_VAMANA_BUILD(int8_t, uint32_t);
+
+#undef RAFT_INST_VAMANA_BUILD
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/vamana_build_uint8.cu b/cpp/src/neighbors/vamana_build_uint8.cu
new file mode 100644
index 0000000000..bba93e7f45
--- /dev/null
+++ b/cpp/src/neighbors/vamana_build_uint8.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "vamana.cuh"
+#include <cuvs/neighbors/vamana.hpp>
+
+namespace cuvs::neighbors::experimental::vamana {
+
+#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                                \
+             const cuvs::neighbors::experimental::vamana::index_params& params,            \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)          \
+    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
+  {                                                                                        \
+    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+  }                                                                                        \
+                                                                                           \
+  auto build(raft::resources const& handle,                                                \
+             const cuvs::neighbors::experimental::vamana::index_params& params,            \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)            \
+    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
+  {                                                                                        \
+    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+  }
+
+RAFT_INST_VAMANA_BUILD(uint8_t, uint32_t);
+
+#undef RAFT_INST_VAMANA_BUILD
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/vamana_serialize.cuh b/cpp/src/neighbors/vamana_serialize.cuh
new file mode 100644
index 0000000000..a49d267b3a
--- /dev/null
+++ b/cpp/src/neighbors/vamana_serialize.cuh
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/vamana/vamana_serialize.cuh"
+
+namespace cuvs::neighbors::experimental::vamana {
+
+/**
+ * @defgroup VAMANA graph serialize/derserialize
+ * @{
+ */
+
+#define CUVS_INST_VAMANA_SERIALIZE(DTYPE)                                                     \
+  void serialize(raft::resources const& handle,                                               \
+                 const std::string& file_prefix,                                              \
+                 const cuvs::neighbors::experimental::vamana::index<DTYPE, uint32_t>& index_) \
+  {                                                                                           \
+    cuvs::neighbors::experimental::vamana::detail::serialize<DTYPE, uint32_t>(                \
+      handle, file_prefix, index_);                                                           \
+  };
+
+/** @} */  // end group vamana
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/vamana_serialize_float.cu b/cpp/src/neighbors/vamana_serialize_float.cu
new file mode 100644
index 0000000000..f253693687
--- /dev/null
+++ b/cpp/src/neighbors/vamana_serialize_float.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "vamana_serialize.cuh"
+
+namespace cuvs::neighbors::experimental::vamana {
+
+CUVS_INST_VAMANA_SERIALIZE(float);
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/vamana_serialize_int8.cu b/cpp/src/neighbors/vamana_serialize_int8.cu
new file mode 100644
index 0000000000..1cd54b1983
--- /dev/null
+++ b/cpp/src/neighbors/vamana_serialize_int8.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "vamana_serialize.cuh"
+
+namespace cuvs::neighbors::experimental::vamana {
+
+CUVS_INST_VAMANA_SERIALIZE(int8_t);
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/src/neighbors/vamana_serialize_uint8.cu b/cpp/src/neighbors/vamana_serialize_uint8.cu
new file mode 100644
index 0000000000..3e6d945b80
--- /dev/null
+++ b/cpp/src/neighbors/vamana_serialize_uint8.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "vamana_serialize.cuh"
+
+namespace cuvs::neighbors::experimental::vamana {
+
+CUVS_INST_VAMANA_SERIALIZE(uint8_t);
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 5f7b5b02b2..58cfc38627 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -159,6 +159,19 @@ if(BUILD_TESTS)
     100
   )
 
+ ConfigureTest(
+    NAME
+    NEIGHBORS_ANN_VAMANA_TEST
+    PATH
+    neighbors/ann_vamana/test_float_uint32_t.cu
+    neighbors/ann_vamana/test_int8_t_uint32_t.cu
+    neighbors/ann_vamana/test_uint8_t_uint32_t.cu
+    GPUS
+    1
+    PERCENT
+    100
+  )
+
   if(BUILD_CAGRA_HNSWLIB)
     ConfigureTest(NAME NEIGHBORS_HNSW_TEST PATH neighbors/hnsw.cu GPUS 1 PERCENT 100)
   endif()
diff --git a/cpp/test/neighbors/ann_vamana.cuh b/cpp/test/neighbors/ann_vamana.cuh
new file mode 100644
index 0000000000..9d9df44703
--- /dev/null
+++ b/cpp/test/neighbors/ann_vamana.cuh
@@ -0,0 +1,339 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+#include <raft/core/resource/cuda_stream.hpp>
+
+#include "naive_knn.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/vamana.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/itertools.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <gtest/gtest.h>
+
+#include <thrust/sequence.h>
+
+#include <cstddef>
+#include <iostream>
+#include <optional>
+#include <string>
+#include <vector>
+
+namespace cuvs::neighbors::experimental::vamana {
+
+struct edge_op {
+  template <typename Type, typename... UnusedArgs>
+  constexpr RAFT_INLINE_FUNCTION auto operator()(const Type& in, UnusedArgs...) const
+  {
+    return in == raft::upper_bound<Type>() ? Type(0) : in;
+  }
+};
+
+struct AnnVamanaInputs {
+  int n_rows;
+  int dim;
+  int graph_degree;
+  int visited_size;
+  double max_fraction;
+  cuvs::distance::DistanceType metric;
+  bool host_dataset;
+
+  // cagra search params
+  int n_queries;
+  int k;
+  cagra::search_algo algo;
+  int max_queries;
+  int itopk_size;
+  int search_width;
+  double min_recall;
+};
+
+template <typename DataT, typename IdxT>
+inline void CheckGraph(vamana::index<DataT, IdxT>* index_,
+                       AnnVamanaInputs inputs,
+                       cudaStream_t stream)
+{
+  EXPECT_TRUE(index_->graph().size() == (inputs.n_rows * inputs.graph_degree));
+  EXPECT_TRUE(index_->graph().extent(0) == inputs.n_rows);
+  EXPECT_TRUE(index_->graph().extent(1) == inputs.graph_degree);
+
+  // Copy graph to host
+  auto h_graph = raft::make_host_matrix<IdxT, int64_t>(inputs.n_rows, inputs.graph_degree);
+  raft::copy(h_graph.data_handle(), index_->graph().data_handle(), index_->graph().size(), stream);
+
+  size_t edge_count = 0;
+  int max_degree    = 0;
+  for (int i = 0; i < h_graph.extent(0); i++) {
+    int temp_degree = 0;
+    for (int j = 0; j < h_graph.extent(1); j++) {
+      if (h_graph(i, j) < (uint32_t)(inputs.n_rows)) temp_degree++;
+    }
+    if (temp_degree > max_degree) max_degree = temp_degree;
+    edge_count += (size_t)temp_degree;
+  }
+
+  // Tests for acceptable range of edges - low dim can also impact this
+  // Minimum expected maximum degree across the whole graph
+  EXPECT_TRUE(max_degree >= std::min(inputs.graph_degree, inputs.dim));
+
+  float max_edges = (float)(inputs.n_rows * std::min(inputs.graph_degree, inputs.dim));
+
+  RAFT_LOG_INFO("dim:%d, degree:%d, visited_size:%d, Total edges:%lu, Maximum edges:%lu",
+                inputs.dim,
+                inputs.graph_degree,
+                inputs.visited_size,
+                edge_count,
+                (size_t)max_edges);
+
+  // Graph won't always be full, but <75% is very unlikely
+  EXPECT_TRUE(((float)edge_count / max_edges) > 0.75);
+}
+
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
+ public:
+  AnnVamanaTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnVamanaInputs>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+ protected:
+  void testVamana()
+  {
+    vamana::index_params index_params;
+    index_params.metric       = ps.metric;
+    index_params.graph_degree = ps.graph_degree;
+    index_params.visited_size = ps.visited_size;
+    index_params.max_fraction = ps.max_fraction;
+
+    auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
+      (const DataT*)database.data(), ps.n_rows, ps.dim);
+
+    vamana::index<DataT, IdxT> index(handle_);
+    if (ps.host_dataset) {
+      auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+      raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+      auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+        (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+
+      index = vamana::build(handle_, index_params, database_host_view);
+    } else {
+      index = vamana::build(handle_, index_params, database_view);
+    };
+
+    CheckGraph<DataT, IdxT>(&index, ps, stream_);
+
+    vamana::serialize(handle_, "vamana_index", index);
+
+    // Test recall by searching with CAGRA search
+    if (ps.graph_degree < 256) {  // CAGRA search result buffer cannot support larger graph degree
+      size_t queries_size = ps.n_queries * ps.k;
+      std::vector<IdxT> indices_Cagra(queries_size);
+      std::vector<IdxT> indices_naive(queries_size);
+      std::vector<DistanceT> distances_Cagra(queries_size);
+      std::vector<DistanceT> distances_naive(queries_size);
+
+      {
+        rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
+        rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+
+        cuvs::neighbors::naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                                           distances_naive_dev.data(),
+                                                           indices_naive_dev.data(),
+                                                           search_queries.data(),
+                                                           database.data(),
+                                                           ps.n_queries,
+                                                           ps.n_rows,
+                                                           ps.dim,
+                                                           ps.k,
+                                                           ps.metric);
+        raft::update_host(
+          distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+        raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+        raft::resource::sync_stream(handle_);
+      }
+
+      // Replace invalid edges with an edge to node 0
+      auto graph_valid = raft::make_device_matrix<IdxT, int64_t>(
+        handle_, index.graph().extent(0), index.graph().extent(1));
+      raft::linalg::map(handle_, graph_valid.view(), edge_op{}, index.graph());
+
+      auto cagra_index = cagra::index<DataT, IdxT>(handle_,
+                                                   ps.metric,
+                                                   raft::make_const_mdspan(database_view),
+                                                   raft::make_const_mdspan(graph_valid.view()));
+
+      cagra::search_params search_params;
+      search_params.algo        = ps.algo;
+      search_params.max_queries = ps.max_queries;
+      search_params.team_size   = 0;
+
+      rmm::device_uvector<DistanceT> distances_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_dev(queries_size, stream_);
+
+      auto search_queries_view = raft::make_device_matrix_view<const DataT, int64_t>(
+        search_queries.data(), ps.n_queries, ps.dim);
+      auto indices_out_view =
+        raft::make_device_matrix_view<IdxT, int64_t>(indices_dev.data(), ps.n_queries, ps.k);
+      auto dists_out_view =
+        raft::make_device_matrix_view<DistanceT, int64_t>(distances_dev.data(), ps.n_queries, ps.k);
+
+      cagra::search(
+        handle_, search_params, cagra_index, search_queries_view, indices_out_view, dists_out_view);
+      raft::update_host(distances_Cagra.data(), distances_dev.data(), queries_size, stream_);
+      raft::update_host(indices_Cagra.data(), indices_dev.data(), queries_size, stream_);
+
+      raft::resource::sync_stream(handle_);
+
+      double min_recall = ps.min_recall;
+      EXPECT_TRUE(eval_neighbours(indices_naive,
+                                  indices_Cagra,
+                                  distances_naive,
+                                  distances_Cagra,
+                                  ps.n_queries,
+                                  ps.k,
+                                  0.003,
+                                  min_recall));
+    }
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    search_queries.resize(((size_t)ps.n_queries) * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
+      raft::random::normal(
+        handle_, r, search_queries.data(), ps.n_queries * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(20));
+      raft::random::uniformInt(
+        handle_, r, search_queries.data(), ps.n_queries * ps.dim, DataT(1), DataT(20));
+    }
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnVamanaInputs ps;
+  rmm::device_uvector<DataT> database;
+  rmm::device_uvector<DataT> search_queries;
+};
+
+inline std::vector<AnnVamanaInputs> generate_inputs()
+{
+  std::vector<AnnVamanaInputs> inputs = raft::util::itertools::product<AnnVamanaInputs>(
+    {1000},
+    //    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // TODO - fix alignment
+    //    issue for odd dims
+    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
+    {32},                                    // graph degree
+    {64, 128, 256},                          // visited_size
+    {0.06, 0.1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {100},
+    {10},
+    {cagra::search_algo::AUTO},
+    {10},
+    {64},
+    {1},
+    {0.2});
+
+  std::vector<AnnVamanaInputs> inputs2 =
+    raft::util::itertools::product<AnnVamanaInputs>({1000},
+                                                    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
+                                                    {64},             // graph degree
+                                                    {128, 256, 512},  // visited_size
+                                                    {0.06, 0.1},
+                                                    {cuvs::distance::DistanceType::L2Expanded},
+                                                    {false},
+                                                    {100},
+                                                    {10},
+                                                    {cagra::search_algo::AUTO},
+                                                    {10},
+                                                    {32},
+                                                    {1},
+                                                    {0.2});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  inputs2 =
+    raft::util::itertools::product<AnnVamanaInputs>({1000},
+                                                    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
+                                                    {128},       // graph degree
+                                                    {256, 512},  // visited_size
+                                                    {0.06, 0.1},
+                                                    {cuvs::distance::DistanceType::L2Expanded},
+                                                    {false},
+                                                    {100},
+                                                    {10},
+                                                    {cagra::search_algo::AUTO},
+                                                    {10},
+                                                    {64},
+                                                    {1},
+                                                    {0.2});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  inputs2 =
+    raft::util::itertools::product<AnnVamanaInputs>({1000},
+                                                    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
+                                                    {256},        // graph degree
+                                                    {512, 1024},  // visited_size
+                                                    {0.06, 0.1},
+                                                    {cuvs::distance::DistanceType::L2Expanded},
+                                                    {false},
+                                                    {100},
+                                                    {10},
+                                                    {cagra::search_algo::AUTO},
+                                                    {10},
+                                                    {64},
+                                                    {1},
+                                                    {0.2});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  return inputs;
+}
+
+const std::vector<AnnVamanaInputs> inputs = generate_inputs();
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/test/neighbors/ann_vamana/test_float_uint32_t.cu b/cpp/test/neighbors/ann_vamana/test_float_uint32_t.cu
new file mode 100644
index 0000000000..9aa9da1b8c
--- /dev/null
+++ b/cpp/test/neighbors/ann_vamana/test_float_uint32_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_vamana.cuh"
+
+namespace cuvs::neighbors::experimental::vamana {
+
+typedef AnnVamanaTest<float, float, std::uint32_t> AnnVamanaTestF_U32;
+TEST_P(AnnVamanaTestF_U32, AnnVamana) { this->testVamana(); }
+
+INSTANTIATE_TEST_CASE_P(AnnVamanaTest, AnnVamanaTestF_U32, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/test/neighbors/ann_vamana/test_int8_t_uint32_t.cu b/cpp/test/neighbors/ann_vamana/test_int8_t_uint32_t.cu
new file mode 100644
index 0000000000..0a6b563b2c
--- /dev/null
+++ b/cpp/test/neighbors/ann_vamana/test_int8_t_uint32_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_vamana.cuh"
+
+namespace cuvs::neighbors::experimental::vamana {
+
+typedef AnnVamanaTest<float, int8_t, std::uint32_t> AnnVamanaTestI8_U32;
+TEST_P(AnnVamanaTestI8_U32, AnnVamana) { this->testVamana(); }
+
+INSTANTIATE_TEST_CASE_P(AnnVamanaTest, AnnVamanaTestI8_U32, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/cpp/test/neighbors/ann_vamana/test_uint8_t_uint32_t.cu b/cpp/test/neighbors/ann_vamana/test_uint8_t_uint32_t.cu
new file mode 100644
index 0000000000..c0680dc188
--- /dev/null
+++ b/cpp/test/neighbors/ann_vamana/test_uint8_t_uint32_t.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_vamana.cuh"
+
+namespace cuvs::neighbors::experimental::vamana {
+
+typedef AnnVamanaTest<float, uint8_t, std::uint32_t> AnnVamanaTestU8_U32;
+TEST_P(AnnVamanaTestU8_U32, AnnVamana) { this->testVamana(); }
+
+INSTANTIATE_TEST_CASE_P(AnnVamanaTest, AnnVamanaTestU8_U32, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::experimental::vamana
diff --git a/examples/build.sh b/examples/build.sh
index 59f589af2a..6aad8fe46c 100755
--- a/examples/build.sh
+++ b/examples/build.sh
@@ -7,6 +7,7 @@
 # Abort script on first error
 set -e
 
+
 PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
 
 BUILD_TYPE=Release
@@ -14,7 +15,10 @@ BUILD_DIR=build/
 
 CUVS_REPO_REL=""
 EXTRA_CMAKE_ARGS=""
-set -e
+BUILD_ALL_GPU_ARCH=0
+if hasArg --allgpuarch; then
+    BUILD_ALL_GPU_ARCH=1
+fi
 
 # Root of examples
 EXAMPLES_DIR=$(dirname "$(realpath "$0")")
@@ -32,6 +36,14 @@ if [ "$1" == "clean" ]; then
   exit 0
 fi
 
+if (( ${BUILD_ALL_GPU_ARCH} == 0 )); then
+    CUVS_CMAKE_CUDA_ARCHITECTURES="NATIVE"
+    echo "Building for the architecture of the GPU in the system..."
+else
+    CUVS_CMAKE_CUDA_ARCHITECTURES="RAPIDS"
+    echo "Building for *ALL* supported GPU architectures..."
+fi
+
 ################################################################################
 # Add individual libcuvs examples build scripts down below
 
@@ -44,7 +56,7 @@ build_example() {
   cmake -S ${example_dir} -B ${build_dir} \
   -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
   -DCUVS_NVTX=OFF \
-  -DCMAKE_CUDA_ARCHITECTURES="native" \
+  -DCMAKE_CUDA_ARCHITECTURES=${CUVS_CMAKE_CUDA_ARCHITECTURES} \
   -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
   ${EXTRA_CMAKE_ARGS}
   # Build
diff --git a/examples/c/CMakeLists.txt b/examples/c/CMakeLists.txt
index d47cd4f1cb..ec8ca827a5 100644
--- a/examples/c/CMakeLists.txt
+++ b/examples/c/CMakeLists.txt
@@ -18,11 +18,14 @@ cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
 include(../cmake/thirdparty/fetch_rapids.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
+include(rapids-cuda)
 include(rapids-export)
 include(rapids-find)
 
 # ------------- configure project --------------#
 
+rapids_cuda_init_architectures(test_cuvs)
+
 project(test_cuvs_c LANGUAGES C CXX CUDA)
 
 # ------------- configure cuvs -----------------#
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 15f494d3dc..092b65ed98 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -40,6 +40,7 @@ add_executable(CAGRA_EXAMPLE src/cagra_example.cu)
 add_executable(CAGRA_PERSISTENT_EXAMPLE src/cagra_persistent_example.cu)
 add_executable(IVF_FLAT_EXAMPLE src/ivf_flat_example.cu)
 add_executable(IVF_PQ_EXAMPLE src/ivf_pq_example.cu)
+add_executable(VAMANA_EXAMPLE src/vamana_example.cu)
 
 # `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets are
 # installed in a conda environment, if one exists
@@ -49,3 +50,4 @@ target_link_libraries(
 )
 target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/cpp/src/common.cuh b/examples/cpp/src/common.cuh
index aade6d4afe..1c93dec0e8 100644
--- a/examples/cpp/src/common.cuh
+++ b/examples/cpp/src/common.cuh
@@ -98,3 +98,29 @@ subsample(raft::device_resources const &dev_resources,
 
   return trainset;
 }
+
+template<typename T, typename idxT>
+raft::device_matrix<T,idxT> read_bin_dataset(raft::device_resources const &dev_resources,
+		       std::string fname,
+                       int max_N = INT_MAX) {
+
+  // Read datafile in
+  std::ifstream datafile(fname, std::ifstream::binary);
+  uint32_t N;
+  uint32_t dim;
+  datafile.read((char*)&N, sizeof(uint32_t));
+  datafile.read((char*)&dim, sizeof(uint32_t));
+
+  if(N > max_N) N = max_N;
+  printf("Read in file - N:%u, dim:%u\n", N, dim);
+  std::vector<T> data;
+  data.resize((size_t)N*(size_t)dim);
+  datafile.read(reinterpret_cast<char*>(data.data()), (size_t)N*(size_t)dim*sizeof(T));
+  datafile.close();
+
+  auto dataset = raft::make_device_matrix<T, idxT>(dev_resources, N, dim);
+  raft::copy(dataset.data_handle(), data.data(), data.size(), raft::resource::get_cuda_stream(dev_resources));
+ 
+  return dataset;
+}
+
diff --git a/examples/cpp/src/vamana_example.cu b/examples/cpp/src/vamana_example.cu
new file mode 100644
index 0000000000..60bf14d56a
--- /dev/null
+++ b/examples/cpp/src/vamana_example.cu
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <cstdlib>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/sparse/neighbors/cross_component_nn.cuh>
+
+#include <cuvs/neighbors/vamana.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include "common.cuh"
+
+template <typename T>
+void vamana_build_and_write(raft::device_resources const &dev_resources,
+                            raft::device_matrix_view<const T, int64_t> dataset,
+                            std::string out_fname, int degree, int visited_size,
+                            float max_fraction, int iters) {
+  using namespace cuvs::neighbors::experimental;
+
+  // use default index parameters
+  vamana::index_params index_params;
+  index_params.max_fraction = max_fraction;
+  index_params.visited_size = visited_size;
+  index_params.graph_degree = degree;
+  index_params.vamana_iters = iters;
+
+  std::cout << "Building Vamana index (search graph)" << std::endl;
+
+  auto start = std::chrono::system_clock::now();
+  auto index = vamana::build(dev_resources, index_params, dataset);
+  auto end = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+
+  std::cout << "Vamana index has " << index.size() << " vectors" << std::endl;
+  std::cout << "Vamana graph has degree " << index.graph_degree()
+            << ", graph size [" << index.graph().extent(0) << ", "
+            << index.graph().extent(1) << "]" << std::endl;
+
+  std::cout << "Time to build index: " << elapsed_seconds.count() << "s\n";
+
+  // Output index to file
+  serialize(dev_resources, out_fname, index);
+}
+
+void usage() {
+  printf("Usage: ./vamana_example <data filename> <output filename> <graph "
+         "degree> <visited_size> <max_fraction> <iterations> \n");
+  printf("Input file expected to be binary file of fp32 vectors.\n");
+  printf("Graph degree sizes supported: 32, 64, 128, 256\n");
+  printf("Visited_size must be > degree and a power of 2.\n");
+  printf("max_fraction > 0 and <= 1. Typical values are 0.06 or 0.1.\n");
+  printf("Default iterations = 1, increase for better quality graph.\n");
+  exit(1);
+}
+
+int main(int argc, char *argv[]) {
+  raft::device_resources dev_resources;
+
+  // Set pool memory resource with 1 GiB initial pool size. All allocations use
+  // the same pool.
+  rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
+      rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+  rmm::mr::set_current_device_resource(&pool_mr);
+
+  // Alternatively, one could define a pool allocator for temporary arrays (used
+  // within RAFT algorithms). In that case only the internal arrays would use
+  // the pool, any other allocation uses the default RMM memory resource. Here
+  // is how to change the workspace memory resource to a pool with 2 GiB upper
+  // limit. raft::resource::set_workspace_to_pool_resource(dev_resources, 2 *
+  // 1024 * 1024 * 1024ull);
+
+  if (argc != 7)
+    usage();
+
+  std::string data_fname = (std::string)(argv[1]); // Input filename
+  std::string out_fname = (std::string)argv[2];    // Output index filename
+  int degree = atoi(argv[3]);
+  int max_visited = atoi(argv[4]);
+  float max_fraction = atof(argv[5]);
+  int iters = atoi(argv[6]);
+
+  // Read in binary dataset file
+  auto dataset =
+      read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
+
+  // Simple build example to create graph and write to a file
+  vamana_build_and_write<uint8_t>(
+      dev_resources, raft::make_const_mdspan(dataset.view()), out_fname, degree,
+      max_visited, max_fraction, iters);
+}

From 59bc6bd3f71357694c1113e3e634a40dac69e7f4 Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Thu, 3 Oct 2024 05:09:08 +0200
Subject: [PATCH 171/623] Use 64 bit types for dataset size calculation in
 CAGRA graph optimizer (#380)

This PR fixes #375.

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/380
---
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 9edbbf5c1d..43bf1ba2b2 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -475,12 +475,12 @@ void sort_knn_graph(
 {
   RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
                "dataset size is expected to have the same number of graph index size");
-  const uint32_t dataset_size = dataset.extent(0);
-  const uint32_t dataset_dim  = dataset.extent(1);
+  const uint64_t dataset_size = dataset.extent(0);
+  const uint64_t dataset_dim  = dataset.extent(1);
   const DataT* dataset_ptr    = dataset.data_handle();
 
   const IdxT graph_size             = dataset_size;
-  const uint32_t input_graph_degree = knn_graph.extent(1);
+  const uint64_t input_graph_degree = knn_graph.extent(1);
   IdxT* const input_graph_ptr       = knn_graph.data_handle();
 
   auto large_tmp_mr = raft::resource::get_large_workspace_resource(res);
@@ -528,7 +528,7 @@ void sort_knn_graph(
     kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
   } else {
     RAFT_FAIL(
-      "The degree of input knn graph is too large (%u). "
+      "The degree of input knn graph is too large (%lu). "
       "It must be equal to or smaller than %d.",
       input_graph_degree,
       1024);

From 562997733b4f007829b46a083b092d4780792ced Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 3 Oct 2024 06:06:28 -0700
Subject: [PATCH 172/623] Add a static library for cuvs (#382)

For the cuml integration, we need to be able to statically link to cuvs in order build the python wheels. This change adds a static target that lets us do that

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/382
---
 cpp/CMakeLists.txt      | 76 ++++++++++++++++++++++++++++++++++++++---
 cpp/test/CMakeLists.txt |  4 +++
 2 files changed, 75 insertions(+), 5 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index b05030cef3..6f5178251e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -288,7 +288,7 @@ target_compile_options(
 )
 
 add_library(
-  cuvs SHARED
+  cuvs_objs OBJECT
   src/cluster/kmeans_balanced_fit_float.cu
   src/cluster/kmeans_fit_mg_float.cu
   src/cluster/kmeans_fit_mg_double.cu
@@ -436,12 +436,67 @@ add_library(
   src/stats/trustworthiness_score.cu
 )
 
+set_target_properties(
+  cuvs_objs
+  PROPERTIES CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             CUDA_STANDARD 17
+             CUDA_STANDARD_REQUIRED ON
+             POSITION_INDEPENDENT_CODE ON
+)
+target_compile_options(
+  cuvs_objs PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
+                    "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
+)
+target_link_libraries(
+  cuvs_objs PUBLIC raft::raft rmm::rmm ${CUVS_CTK_MATH_DEPENDENCIES}
+                   $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+)
+
+add_library(cuvs SHARED $<TARGET_OBJECTS:cuvs_objs>)
+add_library(cuvs_static STATIC $<TARGET_OBJECTS:cuvs_objs>)
+
 target_compile_options(
   cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
                  --expt-relaxed-constexpr>
 )
 
 add_library(cuvs::cuvs ALIAS cuvs)
+add_library(cuvs::cuvs_static ALIAS cuvs_static)
+
+set_target_properties(
+  cuvs_static
+  PROPERTIES BUILD_RPATH "\$ORIGIN"
+             INSTALL_RPATH "\$ORIGIN"
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             POSITION_INDEPENDENT_CODE ON
+             INTERFACE_POSITION_INDEPENDENT_CODE ON
+             EXPORT_NAME cuvs_static
+)
+
+target_compile_options(cuvs_static PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
+
+target_include_directories(
+  cuvs_objs
+  PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+         "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+  INTERFACE "$<INSTALL_INTERFACE:include>"
+)
+
+target_include_directories(
+  cuvs_static
+  PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+  INTERFACE "$<INSTALL_INTERFACE:include>"
+)
+
+# ensure CUDA symbols aren't relocated to the middle of the debug build binaries
+target_link_options(cuvs_static PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
+
+target_include_directories(
+  cuvs_static PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+                     "$<INSTALL_INTERFACE:include>"
+)
 
 target_include_directories(
   cuvs PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
@@ -471,11 +526,17 @@ if(NOT BUILD_CPU_ONLY)
     PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
     PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX> cuvs-cagra-search
   )
+
+  target_link_libraries(
+    cuvs_static
+    PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
+    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX> cuvs-cagra-search
+  )
 endif()
 
 if(BUILD_CAGRA_HNSWLIB)
-  target_link_libraries(cuvs PRIVATE hnswlib::hnswlib)
-  target_compile_definitions(cuvs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
+  target_link_libraries(cuvs_objs PRIVATE hnswlib::hnswlib)
+  target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
 endif()
 
 # Endian detection
@@ -557,11 +618,16 @@ if(BUILD_C_LIBRARY)
     src/neighbors/ivf_flat_c.cpp
     src/neighbors/ivf_pq_c.cpp
     src/neighbors/cagra_c.cpp
-    src/neighbors/hnsw_c.cpp
+    $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
     src/neighbors/refine/refine_c.cpp
     src/distance/pairwise_distance_c.cpp
   )
 
+  if(BUILD_CAGRA_HNSWLIB)
+    target_link_libraries(cuvs_c PRIVATE hnswlib::hnswlib)
+    target_compile_definitions(cuvs_c PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
+  endif()
+
   add_library(cuvs::c_api ALIAS cuvs_c)
 
   set_target_properties(
@@ -600,7 +666,7 @@ include(GNUInstallDirs)
 include(CPack)
 
 install(
-  TARGETS cuvs
+  TARGETS cuvs cuvs_static cuvs-cagra-search
   DESTINATION ${lib_dir}
   COMPONENT cuvs
   EXPORT cuvs-exports
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 58cfc38627..bd07bebeef 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -174,6 +174,8 @@ if(BUILD_TESTS)
 
   if(BUILD_CAGRA_HNSWLIB)
     ConfigureTest(NAME NEIGHBORS_HNSW_TEST PATH neighbors/hnsw.cu GPUS 1 PERCENT 100)
+    target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
+    target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()
 
   ConfigureTest(
@@ -227,6 +229,8 @@ if(BUILD_C_TESTS)
 
   if(BUILD_CAGRA_HNSWLIB)
     ConfigureTest(NAME HNSW_C_TEST PATH neighbors/ann_hnsw_c.cu C_LIB)
+    target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
+    target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()
 endif()
 

From 3383f281acea0afa34d72aedc08ac3e828a8c4fd Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Thu, 3 Oct 2024 20:10:13 +0200
Subject: [PATCH 173/623] SNMG ANN (#231)

This PR implements a distributed (single-node-multiple-GPUs) implementation of ANN indexes. It allows to build, extend and search an index on multiple GPUs.

Before building the index, the user has to choose between two modes :

**Sharding mode** : The index dataset is split, each GPU trains its own index with its respective share of the dataset. This is intended to both increase the search throughput and the maximal size of the index.
**Index duplication mode** : The index is built once on a GPU and then copied over to others. Alternatively, the index dataset is sent to each GPU to be built there. This intended to increase the search throughput.

SNMG indexes can be serialized and de-serialized. Local models can also be deserialized and deployed in index duplication mode.

![bench](https://github.com/user-attachments/assets/e313d0ef-02eb-482a-9104-9e1bb400456d)

Migrated from https://github.com/rapidsai/raft/pull/1993

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/231
---
 build.sh                                      |    9 +-
 .../all_cuda-118_arch-aarch64.yaml            |    1 +
 .../all_cuda-118_arch-x86_64.yaml             |    1 +
 .../all_cuda-125_arch-aarch64.yaml            |    1 +
 .../all_cuda-125_arch-x86_64.yaml             |    1 +
 .../bench_ann_cuda-118_arch-aarch64.yaml      |    1 +
 .../bench_ann_cuda-118_arch-x86_64.yaml       |    1 +
 .../bench_ann_cuda-125_arch-aarch64.yaml      |    1 +
 .../bench_ann_cuda-125_arch-x86_64.yaml       |    1 +
 conda/recipes/libcuvs/conda_build_config.yaml |    3 +
 conda/recipes/libcuvs/meta.yaml               |    4 +
 cpp/CMakeLists.txt                            |   41 +
 cpp/bench/ann/CMakeLists.txt                  |   18 +
 .../src/cuvs/cuvs_ann_bench_param_parser.h    |   18 +-
 cpp/bench/ann/src/cuvs/cuvs_benchmark.cu      |   89 ++
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |   38 +-
 cpp/bench/ann/src/cuvs/cuvs_mg_cagra.cu       |   23 +
 .../ann/src/cuvs/cuvs_mg_cagra_wrapper.h      |  183 +++
 cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat.cu    |   23 +
 .../ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h   |  140 ++
 cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq.cu      |   23 +
 .../ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h     |  139 ++
 cpp/doxygen/Doxyfile                          |    2 +-
 cpp/include/cuvs/neighbors/common.hpp         |   54 +-
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |    6 +-
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |   77 +-
 cpp/include/cuvs/neighbors/mg.hpp             | 1367 +++++++++++++++++
 cpp/src/neighbors/detail/cagra/add_nodes.cuh  |    3 +-
 .../detail/cagra/cagra_serialize.cuh          |    3 +-
 cpp/src/neighbors/iface/generate_iface.py     |  273 ++++
 cpp/src/neighbors/iface/iface.hpp             |  198 +++
 .../iface/iface_cagra_float_uint32_t.cu       |   95 ++
 .../iface/iface_cagra_half_uint32_t.cu        |   95 ++
 .../iface/iface_cagra_int8_t_uint32_t.cu      |   95 ++
 .../iface/iface_cagra_uint8_t_uint32_t.cu     |   95 ++
 .../iface/iface_flat_float_int64_t.cu         |   96 ++
 .../iface/iface_flat_int8_t_int64_t.cu        |   96 ++
 .../iface/iface_flat_uint8_t_int64_t.cu       |   96 ++
 .../neighbors/iface/iface_pq_float_int64_t.cu |   95 ++
 .../neighbors/iface/iface_pq_half_int64_t.cu  |   95 ++
 .../iface/iface_pq_int8_t_int64_t.cu          |   95 ++
 .../iface/iface_pq_uint8_t_int64_t.cu         |   95 ++
 .../neighbors/ivf_flat/generate_ivf_flat.py   |    2 +-
 .../ivf_flat/ivf_flat_search_float_int64_t.cu |    2 +-
 .../ivf_flat_search_int8_t_int64_t.cu         |    2 +-
 .../ivf_flat_search_uint8_t_int64_t.cu        |    2 +-
 .../ivf_pq/detail/generate_ivf_pq.py          |    2 +-
 .../detail/ivf_pq_search_float_int64_t.cu     |    2 +-
 .../detail/ivf_pq_search_half_int64_t.cu      |    2 +-
 .../detail/ivf_pq_search_int8_t_int64_t.cu    |    2 +-
 .../detail/ivf_pq_search_uint8_t_int64_t.cu   |    2 +-
 cpp/src/neighbors/mg/generate_mg.py           |  286 ++++
 cpp/src/neighbors/mg/mg.cuh                   |  690 +++++++++
 .../neighbors/mg/mg_cagra_float_uint32_t.cu   |   92 ++
 .../neighbors/mg/mg_cagra_half_uint32_t.cu    |   92 ++
 .../neighbors/mg/mg_cagra_int8_t_uint32_t.cu  |   92 ++
 .../neighbors/mg/mg_cagra_uint8_t_uint32_t.cu |   92 ++
 cpp/src/neighbors/mg/mg_flat_float_int64_t.cu |  100 ++
 .../neighbors/mg/mg_flat_int8_t_int64_t.cu    |  100 ++
 .../neighbors/mg/mg_flat_uint8_t_int64_t.cu   |  100 ++
 cpp/src/neighbors/mg/mg_pq_float_int64_t.cu   |  100 ++
 cpp/src/neighbors/mg/mg_pq_half_int64_t.cu    |  100 ++
 cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu  |  100 ++
 cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu |  100 ++
 cpp/src/neighbors/mg/nccl_comm.cpp            |    8 +
 cpp/src/neighbors/mg/omp_checks.cpp           |   34 +
 cpp/test/CMakeLists.txt                       |   11 +-
 cpp/test/neighbors/mg.cuh                     |  825 ++++++++++
 cpp/test/neighbors/mg/test_float.cu           |   28 +
 dependencies.yaml                             |    1 +
 docs/source/build.rst                         |   10 +
 docs/source/cpp_api/neighbors.rst             |    1 +
 docs/source/cpp_api/neighbors_mg.rst          |   76 +
 73 files changed, 6800 insertions(+), 46 deletions(-)
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_mg_cagra.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
 create mode 100644 cpp/include/cuvs/neighbors/mg.hpp
 create mode 100644 cpp/src/neighbors/iface/generate_iface.py
 create mode 100644 cpp/src/neighbors/iface/iface.hpp
 create mode 100644 cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/generate_mg.py
 create mode 100644 cpp/src/neighbors/mg/mg.cuh
 create mode 100644 cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_flat_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_pq_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_pq_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/nccl_comm.cpp
 create mode 100644 cpp/src/neighbors/mg/omp_checks.cpp
 create mode 100644 cpp/test/neighbors/mg.cuh
 create mode 100644 cpp/test/neighbors/mg/test_float.cu
 create mode 100644 docs/source/cpp_api/neighbors_mg.rst

diff --git a/build.sh b/build.sh
index b463f0f0d9..c66a0c35ed 100755
--- a/build.sh
+++ b/build.sh
@@ -18,7 +18,7 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python rust docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python rust docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
@@ -40,6 +40,7 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    --limit-tests               - semicolon-separated list of test executables to compile (e.g. NEIGHBORS_TEST;CLUSTER_TEST)
    --limit-bench-ann           - semicolon-separated list of ann benchmark executables to compute (e.g. HNSWLIB_ANN_BENCH;RAFT_IVF_PQ_ANN_BENCH)
    --allgpuarch                - build for all supported GPU architectures
+   --no-mg                     - disable multi-GPU support
    --no-nvtx                   - disable nvtx (profiling markers), but allow enabling it in downstream projects
    --show_depr_warn            - show cmake deprecation warnings
    --build-metrics             - filename for generating build metrics report for libcuvs
@@ -65,6 +66,7 @@ CMAKE_LOG_LEVEL=""
 VERBOSE_FLAG=""
 BUILD_ALL_GPU_ARCH=0
 BUILD_TESTS=ON
+BUILD_MG_ALGOS=ON
 BUILD_TYPE=Release
 COMPILE_LIBRARY=OFF
 INSTALL_TARGET=install
@@ -261,6 +263,10 @@ if hasArg --allgpuarch; then
     BUILD_ALL_GPU_ARCH=1
 fi
 
+if hasArg --no-mg; then
+    BUILD_MG_ALGOS=OFF
+fi
+
 if hasArg tests || (( ${NUMARGS} == 0 )); then
     BUILD_TESTS=ON
     CMAKE_TARGET="${CMAKE_TARGET};${TEST_TARGETS}"
@@ -353,6 +359,7 @@ if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || has
           -DBUILD_C_TESTS=${BUILD_TESTS} \
           -DBUILD_CUVS_BENCH=${BUILD_CUVS_BENCH} \
           -DBUILD_CPU_ONLY=${BUILD_CPU_ONLY} \
+          -DBUILD_MG_ALGOS=${BUILD_MG_ALGOS} \
           -DCMAKE_MESSAGE_LOG_LEVEL=${CMAKE_LOG_LEVEL} \
           ${CACHE_ARGS} \
           ${EXTRA_CMAKE_ARGS}
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 5c599fcc2f..0658510649 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -37,6 +37,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - make
+- nccl>=2.19
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index ce9a7f058d..a253930508 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -37,6 +37,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - make
+- nccl>=2.19
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index 116e80ac2e..bb4a96d48d 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -34,6 +34,7 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - make
+- nccl>=2.19
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 7f7ad045d2..bd1b95ae89 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -34,6 +34,7 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - make
+- nccl>=2.19
 - ninja
 - numpy>=1.23,<3.0a0
 - numpydoc
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 73c42ca71c..554ad41ab0 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -35,6 +35,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
+- nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - nvcc_linux-aarch64=11.8
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 473e50bc6c..dc38f35654 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -35,6 +35,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
+- nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - nvcc_linux-64=11.8
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 8a877c4c0e..aeb23a9efe 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -32,6 +32,7 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
+- nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index 54859a77f1..3a408cd643 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -32,6 +32,7 @@ dependencies:
 - libcusparse-dev
 - librmm==24.10.*,>=0.0.0a0
 - matplotlib
+- nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index e165f7ed9a..b8c49943eb 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -22,6 +22,9 @@ cmake_version:
 h5py_version:
   - ">=3.8.0"
 
+nccl_version:
+  - ">=2.19"
+
 # The CTK libraries below are missing from the conda-forge::cudatoolkit package
 # for CUDA 11. The "*_host_*" version specifiers correspond to `11.8` packages
 # and the "*_run_*" version specifiers correspond to `11.x` packages.
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index e154ccf410..3dd7c8f2ef 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -65,6 +65,7 @@ outputs:
       host:
         - librmm ={{ minor_version }}
         - libraft-headers ={{ minor_version }}
+        - nccl {{ nccl_version }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_host_version }}
@@ -131,6 +132,7 @@ outputs:
       host:
         - librmm ={{ minor_version }}
         - libraft-headers ={{ minor_version }}
+        - nccl {{ nccl_version }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
         - cuda-profiler-api {{ cuda11_cuda_profiler_api_host_version }}
@@ -197,6 +199,7 @@ outputs:
       host:
         - librmm ={{ minor_version }}
         - libraft-headers ={{ minor_version }}
+        - nccl {{ nccl_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
         - cuda-version ={{ cuda_version }}
         - openblas # required by some CPU algos in benchmarks
@@ -268,6 +271,7 @@ outputs:
       host:
         - librmm ={{ minor_version }}
         - libraft-headers ={{ minor_version }}
+        - nccl {{ nccl_version }}
         - {{ pin_subpackage('libcuvs', exact=True) }}
         - cuda-version ={{ cuda_version }}
         {% if cuda_major == "11" %}
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 6f5178251e..52c5f66244 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -57,6 +57,7 @@ option(BUILD_C_LIBRARY "Build cuVS C API library" OFF)
 option(BUILD_C_TESTS "Build cuVS C API tests" OFF)
 option(BUILD_CUVS_BENCH "Build cuVS ann benchmarks" OFF)
 option(BUILD_CAGRA_HNSWLIB "Build CAGRA+hnswlib interface" ON)
+option(BUILD_MG_ALGOS "Build with multi-GPU support" ON)
 option(CUDA_ENABLE_KERNELINFO "Enable kernel resource usage info" OFF)
 option(CUDA_ENABLE_LINEINFO
        "Enable the -lineinfo option for nvcc (useful for cuda-memcheck / profiler)" OFF
@@ -287,6 +288,24 @@ target_compile_options(
                             "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
 )
 
+if(BUILD_MG_ALGOS)
+  set(CUVS_MG_ALGOS
+      src/neighbors/mg/mg_flat_float_int64_t.cu
+      src/neighbors/mg/mg_flat_int8_t_int64_t.cu
+      src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
+      src/neighbors/mg/mg_pq_float_int64_t.cu
+      src/neighbors/mg/mg_pq_half_int64_t.cu
+      src/neighbors/mg/mg_pq_int8_t_int64_t.cu
+      src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
+      src/neighbors/mg/mg_cagra_float_uint32_t.cu
+      src/neighbors/mg/mg_cagra_half_uint32_t.cu
+      src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
+      src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
+      src/neighbors/mg/omp_checks.cpp
+      src/neighbors/mg/nccl_comm.cpp
+  )
+endif()
+
 add_library(
   cuvs_objs OBJECT
   src/cluster/kmeans_balanced_fit_float.cu
@@ -367,6 +386,17 @@ add_library(
   src/neighbors/cagra_serialize_half.cu
   src/neighbors/cagra_serialize_int8.cu
   src/neighbors/cagra_serialize_uint8.cu
+  src/neighbors/iface/iface_cagra_float_uint32_t.cu
+  src/neighbors/iface/iface_cagra_half_uint32_t.cu
+  src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
+  src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
+  src/neighbors/iface/iface_flat_float_int64_t.cu
+  src/neighbors/iface/iface_flat_int8_t_int64_t.cu
+  src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
+  src/neighbors/iface/iface_pq_float_int64_t.cu
+  src/neighbors/iface/iface_pq_half_int64_t.cu
+  src/neighbors/iface/iface_pq_int8_t_int64_t.cu
+  src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
   src/neighbors/detail/cagra/cagra_build.cpp
   src/neighbors/detail/cagra/topk_for_cagra/topk.cu
   $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
@@ -434,6 +464,7 @@ add_library(
   src/selection/select_k_half_uint32_t.cu
   src/stats/silhouette_score.cu
   src/stats/trustworthiness_score.cu
+  ${CUVS_MG_ALGOS}
 )
 
 set_target_properties(
@@ -520,11 +551,16 @@ if(NOT BUILD_CPU_ONLY)
                                  ${CUVS_CUSPARSE_DEPENDENCY} ${CUVS_CURAND_DEPENDENCY}
   )
 
+  if(BUILD_MG_ALGOS)
+    set(CUVS_COMMS_DEPENDENCY nccl)
+  endif()
+
   # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
   target_link_libraries(
     cuvs
     PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
     PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX> cuvs-cagra-search
+            ${CUVS_COMMS_DEPENDENCY}
   )
 
   target_link_libraries(
@@ -534,6 +570,11 @@ if(NOT BUILD_CPU_ONLY)
   )
 endif()
 
+if(BUILD_MG_ALGOS)
+  target_compile_definitions(cuvs PUBLIC CUVS_BUILD_MG_ALGOS)
+  target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_MG_ALGOS)
+endif()
+
 if(BUILD_CAGRA_HNSWLIB)
   target_link_libraries(cuvs_objs PRIVATE hnswlib::hnswlib)
   target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 8cbf8c8b37..c36e70ace4 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -32,6 +32,7 @@ option(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE "Include cuVS brute force knn in benc
 option(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB "Include cuVS CAGRA with HNSW search in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_HNSWLIB "Include hnsw algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_GGNN "Include ggnn algorithm in benchmark" OFF)
+option(CUVS_ANN_BENCH_USE_CUVS_MG "Include cuVS ann mg algorithm in benchmark" ${BUILD_MG_ALGOS})
 option(CUVS_ANN_BENCH_SINGLE_EXE
        "Make a single executable with benchmark as shared library modules" OFF
 )
@@ -55,6 +56,7 @@ if(BUILD_CPU_ONLY)
   set(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB OFF)
   set(CUVS_ANN_BENCH_USE_GGNN OFF)
   set(CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_MG OFF)
 else()
   set(CUVS_FAISS_ENABLE_GPU ON)
 endif()
@@ -66,6 +68,7 @@ if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
    OR CUVS_ANN_BENCH_USE_CUVS_CAGRA
    OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB
    OR CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE
+   OR CUVS_ANN_BENCH_USE_CUVS_MG
 )
   set(CUVS_ANN_BENCH_USE_CUVS ON)
 endif()
@@ -245,6 +248,21 @@ if(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
   )
 endif()
 
+if(CUVS_ANN_BENCH_USE_CUVS_MG)
+  ConfigureAnnBench(
+    NAME
+    CUVS_MG
+    PATH
+    src/cuvs/cuvs_benchmark.cu
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_MG}>:src/cuvs/cuvs_mg_ivf_flat.cu>
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_MG}>:src/cuvs/cuvs_mg_ivf_pq.cu>
+    $<$<BOOL:${CUVS_ANN_BENCH_USE_CUVS_MG}>:src/cuvs/cuvs_mg_cagra.cu>
+    LINKS
+    cuvs
+    nccl
+  )
+endif()
+
 message("CUVS_FAISS_TARGETS: ${CUVS_FAISS_TARGETS}")
 message("CUDAToolkit_LIBRARY_DIR: ${CUDAToolkit_LIBRARY_DIR}")
 if(CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT)
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 22f0cab6fc..57d5b19106 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -45,7 +45,18 @@ extern template class cuvs::bench::cuvs_cagra<uint8_t, uint32_t>;
 extern template class cuvs::bench::cuvs_cagra<int8_t, uint32_t>;
 #endif
 
-#ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT
+#ifdef CUVS_ANN_BENCH_USE_CUVS_MG
+#include "cuvs_ivf_flat_wrapper.h"
+#include "cuvs_mg_ivf_flat_wrapper.h"
+
+#include "cuvs_ivf_pq_wrapper.h"
+#include "cuvs_mg_ivf_pq_wrapper.h"
+
+#include "cuvs_cagra_wrapper.h"
+#include "cuvs_mg_cagra_wrapper.h"
+#endif
+
+#if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT) || defined(CUVS_ANN_BENCH_USE_CUVS_MG)
 template <typename T, typename IdxT>
 void parse_build_param(const nlohmann::json& conf,
                        typename cuvs::bench::cuvs_ivf_flat<T, IdxT>::build_param& param)
@@ -64,7 +75,7 @@ void parse_search_param(const nlohmann::json& conf,
 #endif
 
 #if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || \
-  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB) || defined(CUVS_ANN_BENCH_USE_CUVS_MG)
 template <typename T, typename IdxT>
 void parse_build_param(const nlohmann::json& conf,
                        typename cuvs::bench::cuvs_ivf_pq<T, IdxT>::build_param& param)
@@ -130,7 +141,8 @@ void parse_search_param(const nlohmann::json& conf,
 }
 #endif
 
-#if defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+#if defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB) || \
+  defined(CUVS_ANN_BENCH_USE_CUVS_MG)
 template <typename T, typename IdxT>
 void parse_build_param(const nlohmann::json& conf, cuvs::neighbors::nn_descent::index_params& param)
 {
diff --git a/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
index a956ab139c..8930972365 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
@@ -29,6 +29,43 @@
 
 namespace cuvs::bench {
 
+#ifdef CUVS_ANN_BENCH_USE_CUVS_MG
+void add_distribution_mode(cuvs::neighbors::mg::distribution_mode* dist_mode,
+                           const nlohmann::json& conf)
+{
+  if (conf.contains("distribution_mode")) {
+    std::string distribution_mode = conf.at("distribution_mode");
+    if (distribution_mode == "replicated") {
+      *dist_mode = cuvs::neighbors::mg::distribution_mode::REPLICATED;
+    } else if (distribution_mode == "sharded") {
+      *dist_mode = cuvs::neighbors::mg::distribution_mode::SHARDED;
+    } else {
+      throw std::runtime_error("invalid value for distribution_mode");
+    }
+  } else {
+    // default
+    *dist_mode = cuvs::neighbors::mg::distribution_mode::SHARDED;
+  }
+};
+
+void add_merge_mode(cuvs::neighbors::mg::sharded_merge_mode* merge_mode, const nlohmann::json& conf)
+{
+  if (conf.contains("merge_mode")) {
+    std::string sharded_merge_mode = conf.at("merge_mode");
+    if (sharded_merge_mode == "tree_merge") {
+      *merge_mode = cuvs::neighbors::mg::sharded_merge_mode::TREE_MERGE;
+    } else if (sharded_merge_mode == "merge_on_root_rank") {
+      *merge_mode = cuvs::neighbors::mg::sharded_merge_mode::MERGE_ON_ROOT_RANK;
+    } else {
+      throw std::runtime_error("invalid value for merge_mode");
+    }
+  } else {
+    // default
+    *merge_mode = cuvs::neighbors::mg::sharded_merge_mode::TREE_MERGE;
+  }
+};
+#endif
+
 template <typename T>
 auto create_algo(const std::string& algo_name,
                  const std::string& distance,
@@ -71,6 +108,32 @@ auto create_algo(const std::string& algo_name,
     parse_build_param<T, uint32_t>(conf, param);
     a = std::make_unique<cuvs::bench::cuvs_cagra<T, uint32_t>>(metric, dim, param);
   }
+#endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_MG
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, uint8_t> ||
+                std::is_same_v<T, int8_t>) {
+    if (algo_name == "raft_mg_ivf_flat" || algo_name == "cuvs_mg_ivf_flat") {
+      typename cuvs::bench::cuvs_mg_ivf_flat<T, int64_t>::build_param param;
+      parse_build_param<T, int64_t>(conf, param);
+      add_distribution_mode(&param.mode, conf);
+      a = std::make_unique<cuvs::bench::cuvs_mg_ivf_flat<T, int64_t>>(metric, dim, param);
+    }
+  }
+
+  if (algo_name == "raft_mg_ivf_pq" || algo_name == "cuvs_mg_ivf_pq") {
+    typename cuvs::bench::cuvs_mg_ivf_pq<T, int64_t>::build_param param;
+    parse_build_param<T, int64_t>(conf, param);
+    add_distribution_mode(&param.mode, conf);
+    a = std::make_unique<cuvs::bench::cuvs_mg_ivf_pq<T, int64_t>>(metric, dim, param);
+  }
+
+  if (algo_name == "raft_mg_cagra" || algo_name == "cuvs_mg_cagra") {
+    typename cuvs::bench::cuvs_mg_cagra<T, uint32_t>::build_param param;
+    parse_build_param<T, uint32_t>(conf, param);
+    add_distribution_mode(&param.mode, conf);
+    a = std::make_unique<cuvs::bench::cuvs_mg_cagra<T, uint32_t>>(metric, dim, param);
+  }
+
 #endif
 
   if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
@@ -113,6 +176,32 @@ auto create_search_param(const std::string& algo_name, const nlohmann::json& con
     return param;
   }
 #endif
+#ifdef CUVS_ANN_BENCH_USE_CUVS_MG
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, uint8_t> ||
+                std::is_same_v<T, int8_t>) {
+    if (algo_name == "raft_mg_ivf_flat" || algo_name == "cuvs_mg_ivf_flat") {
+      auto param =
+        std::make_unique<typename cuvs::bench::cuvs_mg_ivf_flat<T, int64_t>::search_param>();
+      parse_search_param<T, int64_t>(conf, *param);
+      add_merge_mode(&param->merge_mode, conf);
+      return param;
+    }
+  }
+
+  if (algo_name == "raft_mg_ivf_pq" || algo_name == "cuvs_mg_ivf_pq") {
+    auto param = std::make_unique<typename cuvs::bench::cuvs_mg_ivf_pq<T, int64_t>::search_param>();
+    parse_search_param<T, int64_t>(conf, *param);
+    add_merge_mode(&param->merge_mode, conf);
+    return param;
+  }
+
+  if (algo_name == "raft_mg_cagra" || algo_name == "cuvs_mg_cagra") {
+    auto param = std::make_unique<typename cuvs::bench::cuvs_mg_cagra<T, uint32_t>::search_param>();
+    parse_search_param<T, uint32_t>(conf, *param);
+    add_merge_mode(&param->merge_mode, conf);
+    return param;
+  }
+#endif
 
   // else
   throw std::runtime_error("invalid algo: '" + algo_name + "'");
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index ff854f890a..b2ba35eeec 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -72,6 +72,23 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
     std::optional<float> ivf_pq_refine_rate                                    = std::nullopt;
     std::optional<cuvs::neighbors::ivf_pq::index_params> ivf_pq_build_params   = std::nullopt;
     std::optional<cuvs::neighbors::ivf_pq::search_params> ivf_pq_search_params = std::nullopt;
+
+    void prepare_build_params(const raft::extent_2d<IdxT>& dataset_extents)
+    {
+      if (algo == CagraBuildAlgo::kIvfPq) {
+        auto pq_params = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(
+          dataset_extents, cagra_params.metric);
+        if (ivf_pq_build_params) { pq_params.build_params = *ivf_pq_build_params; }
+        if (ivf_pq_search_params) { pq_params.search_params = *ivf_pq_search_params; }
+        if (ivf_pq_refine_rate) { pq_params.refinement_rate = *ivf_pq_refine_rate; }
+        cagra_params.graph_build_params = pq_params;
+      } else if (algo == CagraBuildAlgo::kNnDescent) {
+        auto nn_params = cuvs::neighbors::cagra::graph_build_params::nn_descent_params(
+          cagra_params.intermediate_graph_degree);
+        if (nn_descent_params) { nn_params = *nn_descent_params; }
+        cagra_params.graph_build_params = nn_params;
+      }
+    }
   };
 
   cuvs_cagra(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
@@ -168,28 +185,9 @@ template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
 {
   auto dataset_extents = raft::make_extents<IdxT>(nrow, dimension_);
+  index_params_.prepare_build_params(dataset_extents);
 
   auto& params = index_params_.cagra_params;
-
-  if (index_params_.algo == CagraBuildAlgo::kIvfPq) {
-    auto pq_params =
-      cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(dataset_extents, params.metric);
-    if (index_params_.ivf_pq_build_params) {
-      pq_params.build_params = *index_params_.ivf_pq_build_params;
-    }
-    if (index_params_.ivf_pq_search_params) {
-      pq_params.search_params = *index_params_.ivf_pq_search_params;
-    }
-    if (index_params_.ivf_pq_refine_rate) {
-      pq_params.refinement_rate = *index_params_.ivf_pq_refine_rate;
-    }
-    params.graph_build_params = pq_params;
-  } else if (index_params_.algo == CagraBuildAlgo::kNnDescent) {
-    auto nn_params = cuvs::neighbors::cagra::graph_build_params::nn_descent_params(
-      params.intermediate_graph_degree);
-    if (index_params_.nn_descent_params) { nn_params = *index_params_.nn_descent_params; }
-    params.graph_build_params = nn_params;
-  }
   auto dataset_view_host =
     raft::make_mdspan<const T, IdxT, raft::row_major, true, false>(dataset, dataset_extents);
   auto dataset_view_device =
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra.cu b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra.cu
new file mode 100644
index 0000000000..801caa85f2
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_mg_cagra_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_mg_cagra<float, uint32_t>;
+template class cuvs_mg_cagra<half, uint32_t>;
+template class cuvs_mg_cagra<uint8_t, uint32_t>;
+template class cuvs_mg_cagra<int8_t, uint32_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
new file mode 100644
index 0000000000..50c1ff4dbf
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "cuvs_ann_bench_utils.h"
+#include "cuvs_cagra_wrapper.h"
+#include <cuvs/neighbors/mg.hpp>
+#include <raft/core/resource/nccl_clique.hpp>
+
+namespace cuvs::bench {
+using namespace cuvs::neighbors;
+
+enum class AllocatorType;
+enum class CagraBuildAlgo;
+
+template <typename T, typename IdxT>
+class cuvs_mg_cagra : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+  using algo<T>::dim_;
+
+  struct build_param : public cuvs::bench::cuvs_cagra<T, IdxT>::build_param {
+    cuvs::neighbors::mg::distribution_mode mode;
+  };
+
+  struct search_param : public cuvs::bench::cuvs_cagra<T, IdxT>::search_param {
+    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+  };
+
+  cuvs_mg_cagra(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
+    : algo<T>(metric, dim), index_params_(param)
+  {
+    index_params_.cagra_params.metric         = parse_metric_type(metric);
+    index_params_.ivf_pq_build_params->metric = parse_metric_type(metric);
+
+    // init nccl clique outside as to not affect benchmark
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle_);
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param) override;
+
+  void set_search_dataset(const T* dataset, size_t nrow) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+  void search_base(const T* queries,
+                   int batch_size,
+                   int k,
+                   algo_base::index_type* neighbors,
+                   float* distances) const;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    auto stream = raft::resource::get_cuda_stream(handle_);
+    return stream;
+  }
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  void save_to_hnswlib(const std::string& file) const;
+  std::unique_ptr<algo<T>> copy() override;
+
+ private:
+  raft::device_resources handle_;
+  float refine_ratio_;
+  build_param index_params_;
+  cuvs::neighbors::mg::search_params<cagra::search_params> search_params_;
+  std::shared_ptr<cuvs::neighbors::mg::index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>
+    index_;
+};
+
+template <typename T, typename IdxT>
+void cuvs_mg_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  auto dataset_extents = raft::make_extents<IdxT>(nrow, dim_);
+  index_params_.prepare_build_params(dataset_extents);
+  cuvs::neighbors::mg::index_params<cagra::index_params> build_params = index_params_.cagra_params;
+  build_params.mode                                                   = index_params_.mode;
+
+  auto dataset_view =
+    raft::make_host_matrix_view<const T, int64_t, raft::row_major>(dataset, nrow, dim_);
+  auto idx = cuvs::neighbors::mg::build(handle_, build_params, dataset_view);
+  index_ =
+    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>(
+      std::move(idx));
+}
+
+inline auto allocator_to_string(AllocatorType mem_type) -> std::string;
+
+template <typename T, typename IdxT>
+void cuvs_mg_cagra<T, IdxT>::set_search_param(const search_param_base& param)
+{
+  auto sp = dynamic_cast<const search_param&>(param);
+  // search_params_ = static_cast<mg::search_params<cagra::search_params>>(sp.p);
+  cagra::search_params* search_params_ptr_ = static_cast<cagra::search_params*>(&search_params_);
+  *search_params_ptr_                      = sp.p;
+  search_params_.merge_mode                = sp.merge_mode;
+  refine_ratio_                            = sp.refine_ratio;
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_cagra<T, IdxT>::set_search_dataset(const T* dataset, size_t nrow)
+{
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_cagra<T, IdxT>::save(const std::string& file) const
+{
+  cuvs::neighbors::mg::serialize(handle_, *index_, file);
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_cagra<T, IdxT>::load(const std::string& file)
+{
+  index_ =
+    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>(
+      std::move(cuvs::neighbors::mg::deserialize_cagra<T, IdxT>(handle_, file)));
+}
+
+template <typename T, typename IdxT>
+std::unique_ptr<algo<T>> cuvs_mg_cagra<T, IdxT>::copy()
+{
+  return std::make_unique<cuvs_mg_cagra<T, IdxT>>(*this);  // use copy constructor
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_cagra<T, IdxT>::search_base(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  static_assert(std::is_integral_v<algo_base::index_type>);
+  static_assert(std::is_integral_v<IdxT>);
+
+  auto queries_view =
+    raft::make_host_matrix_view<const T, int64_t, raft::row_major>(queries, batch_size, dim_);
+  auto neighbors_view =
+    raft::make_host_matrix_view<IdxT, int64_t, raft::row_major>((IdxT*)neighbors, batch_size, k);
+  auto distances_view =
+    raft::make_host_matrix_view<float, int64_t, raft::row_major>(distances, batch_size, k);
+
+  cuvs::neighbors::mg::search(
+    handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_cagra<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  auto k0                       = static_cast<size_t>(refine_ratio_ * k);
+  const bool disable_refinement = k0 <= static_cast<size_t>(k);
+
+  if (disable_refinement) {
+    search_base(queries, batch_size, k, neighbors, distances);
+  } else {
+    throw std::runtime_error("refinement not supported");
+  }
+}
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat.cu b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat.cu
new file mode 100644
index 0000000000..20cdc41e36
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_mg_ivf_flat_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_mg_ivf_flat<float, int64_t>;
+// template class cuvs_mg_ivf_flat<half, int64_t>;
+template class cuvs_mg_ivf_flat<uint8_t, int64_t>;
+template class cuvs_mg_ivf_flat<int8_t, int64_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
new file mode 100644
index 0000000000..54a0d2facb
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "cuvs_ann_bench_utils.h"
+#include "cuvs_ivf_flat_wrapper.h"
+#include <cuvs/neighbors/mg.hpp>
+#include <raft/core/resource/nccl_clique.hpp>
+
+namespace cuvs::bench {
+using namespace cuvs::neighbors;
+
+template <typename T, typename IdxT>
+class cuvs_mg_ivf_flat : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+  using algo<T>::dim_;
+
+  using build_param = cuvs::neighbors::mg::index_params<ivf_flat::index_params>;
+
+  struct search_param : public cuvs::bench::cuvs_ivf_flat<T, IdxT>::search_param {
+    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+  };
+
+  cuvs_mg_ivf_flat(Metric metric, int dim, const build_param& param)
+    : algo<T>(metric, dim), index_params_(param)
+  {
+    index_params_.metric = parse_metric_type(metric);
+    // init nccl clique outside as to not affect benchmark
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle_);
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+  void set_search_param(const search_param_base& param) override;
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    auto stream = raft::resource::get_cuda_stream(handle_);
+    return stream;
+  }
+
+  [[nodiscard]] auto uses_stream() const noexcept -> bool override { return false; }
+
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override;
+
+ private:
+  raft::device_resources handle_;
+  build_param index_params_;
+  cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params_;
+  std::shared_ptr<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>
+    index_;
+};
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_flat<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  auto dataset_view =
+    raft::make_host_matrix_view<const T, int64_t, raft::row_major>(dataset, IdxT(nrow), IdxT(dim_));
+  auto idx = cuvs::neighbors::mg::build(handle_, index_params_, dataset_view);
+  index_   = std::make_shared<
+    cuvs::neighbors::mg::index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>(std::move(idx));
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_flat<T, IdxT>::set_search_param(const search_param_base& param)
+{
+  auto sp = dynamic_cast<const search_param&>(param);
+  // search_params_ = sp.ivf_flat_params;
+  ivf_flat::search_params* search_params_ptr_ =
+    static_cast<ivf_flat::search_params*>(&search_params_);
+  *search_params_ptr_       = sp.ivf_flat_params;
+  search_params_.merge_mode = sp.merge_mode;
+  assert(search_params_.n_probes <= index_params_.n_lists);
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_flat<T, IdxT>::save(const std::string& file) const
+{
+  cuvs::neighbors::mg::serialize(handle_, *index_, file);
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_flat<T, IdxT>::load(const std::string& file)
+{
+  index_ = std::make_shared<
+    cuvs::neighbors::mg::index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>(
+    std::move(cuvs::neighbors::mg::deserialize_flat<T, IdxT>(handle_, file)));
+}
+
+template <typename T, typename IdxT>
+std::unique_ptr<algo<T>> cuvs_mg_ivf_flat<T, IdxT>::copy()
+{
+  return std::make_unique<cuvs_mg_ivf_flat<T, IdxT>>(*this);  // use copy constructor
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_flat<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  auto queries_view = raft::make_host_matrix_view<const T, int64_t, raft::row_major>(
+    queries, IdxT(batch_size), IdxT(dim_));
+  auto neighbors_view = raft::make_host_matrix_view<IdxT, int64_t, raft::row_major>(
+    (IdxT*)neighbors, IdxT(batch_size), IdxT(k));
+  auto distances_view = raft::make_host_matrix_view<float, int64_t, raft::row_major>(
+    distances, IdxT(batch_size), IdxT(k));
+
+  cuvs::neighbors::mg::search(
+    handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
+}
+
+}  // namespace cuvs::bench
\ No newline at end of file
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq.cu b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq.cu
new file mode 100644
index 0000000000..a74bab6f5e
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "cuvs_mg_ivf_pq_wrapper.h"
+
+namespace cuvs::bench {
+template class cuvs_mg_ivf_pq<float, int64_t>;
+template class cuvs_mg_ivf_pq<half, int64_t>;
+template class cuvs_mg_ivf_pq<uint8_t, int64_t>;
+template class cuvs_mg_ivf_pq<int8_t, int64_t>;
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
new file mode 100644
index 0000000000..84aea7d4a3
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "cuvs_ann_bench_utils.h"
+#include "cuvs_ivf_pq_wrapper.h"
+#include <cuvs/neighbors/mg.hpp>
+#include <raft/core/resource/nccl_clique.hpp>
+
+namespace cuvs::bench {
+using namespace cuvs::neighbors;
+
+template <typename T, typename IdxT>
+class cuvs_mg_ivf_pq : public algo<T>, public algo_gpu {
+ public:
+  using search_param_base = typename algo<T>::search_param;
+  using algo<T>::dim_;
+
+  using build_param = cuvs::neighbors::mg::index_params<ivf_pq::index_params>;
+
+  struct search_param : public cuvs::bench::cuvs_ivf_pq<T, IdxT>::search_param {
+    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+  };
+
+  cuvs_mg_ivf_pq(Metric metric, int dim, const build_param& param)
+    : algo<T>(metric, dim), index_params_(param)
+  {
+    index_params_.metric = parse_metric_type(metric);
+    // init nccl clique outside as to not affect benchmark
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle_);
+  }
+
+  void build(const T* dataset, size_t nrow) final;
+  void set_search_param(const search_param_base& param) override;
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    auto stream = raft::resource::get_cuda_stream(handle_);
+    return stream;
+  }
+
+  [[nodiscard]] auto uses_stream() const noexcept -> bool override { return false; }
+
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override;
+
+ private:
+  raft::device_resources handle_;
+  build_param index_params_;
+  cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params_;
+  std::shared_ptr<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>> index_;
+};
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_pq<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  auto dataset_view =
+    raft::make_host_matrix_view<const T, int64_t, raft::row_major>(dataset, IdxT(nrow), IdxT(dim_));
+  auto idx = cuvs::neighbors::mg::build(handle_, index_params_, dataset_view);
+  index_ =
+    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>>(
+      std::move(idx));
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param)
+{
+  auto sp = dynamic_cast<const search_param&>(param);
+  // search_params_ = static_cast<mg::search_params<ivf_pq::search_params>>(sp.pq_param);
+  ivf_pq::search_params* search_params_ptr_ = static_cast<ivf_pq::search_params*>(&search_params_);
+  *search_params_ptr_                       = sp.pq_param;
+  search_params_.merge_mode                 = sp.merge_mode;
+  assert(search_params_.n_probes <= index_params_.n_lists);
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_pq<T, IdxT>::save(const std::string& file) const
+{
+  cuvs::neighbors::mg::serialize(handle_, *index_, file);
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_pq<T, IdxT>::load(const std::string& file)
+{
+  index_ =
+    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>>(
+      std::move(cuvs::neighbors::mg::deserialize_pq<T, IdxT>(handle_, file)));
+}
+
+template <typename T, typename IdxT>
+std::unique_ptr<algo<T>> cuvs_mg_ivf_pq<T, IdxT>::copy()
+{
+  return std::make_unique<cuvs_mg_ivf_pq<T, IdxT>>(*this);  // use copy constructor
+}
+
+template <typename T, typename IdxT>
+void cuvs_mg_ivf_pq<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  auto queries_view = raft::make_host_matrix_view<const T, int64_t, raft::row_major>(
+    queries, IdxT(batch_size), IdxT(dim_));
+  auto neighbors_view = raft::make_host_matrix_view<IdxT, int64_t, raft::row_major>(
+    (IdxT*)neighbors, IdxT(batch_size), IdxT(k));
+  auto distances_view = raft::make_host_matrix_view<float, int64_t, raft::row_major>(
+    distances, IdxT(batch_size), IdxT(k));
+
+  cuvs::neighbors::mg::search(
+    handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
+}
+
+}  // namespace cuvs::bench
\ No newline at end of file
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index 2459d521d5..e28572457b 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -2149,7 +2149,7 @@ INCLUDE_FILE_PATTERNS  =
 # recursively expanded use the := operator instead of the = operator.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-PREDEFINED             =
+PREDEFINED             = "CUVS_BUILD_MG_ALGOS=1"
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 73ce80b41b..60b8cc1222 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -19,7 +19,8 @@
 #include <cstdint>
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/host_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/util/cudart_utils.hpp>   // get_device_for_address
@@ -636,5 +637,56 @@ enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
                                                const typename ListT::spec_type& store_spec,
                                                const typename ListT::spec_type& device_spec);
 }  // namespace ivf
+}  // namespace cuvs::neighbors
+
+namespace cuvs::neighbors {
+using namespace raft;
+
+template <typename AnnIndexType, typename T, typename IdxT>
+struct iface {
+  iface() : mutex_(std::make_shared<std::mutex>()) {}
+
+  const IdxT size() const { return index_.value().size(); }
+
+  std::optional<AnnIndexType> index_;
+  std::shared_ptr<std::mutex> mutex_;
+};
+
+template <typename AnnIndexType, typename T, typename IdxT, typename Accessor>
+void build(const raft::device_resources& handle,
+           cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+           const cuvs::neighbors::index_params* index_params,
+           raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> index_dataset);
+
+template <typename AnnIndexType, typename T, typename IdxT, typename Accessor1, typename Accessor2>
+void extend(
+  const raft::device_resources& handle,
+  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+  raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor1> new_vectors,
+  std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, Accessor2>>
+    new_indices);
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void search(const raft::device_resources& handle,
+            const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+            const cuvs::neighbors::search_params* search_params,
+            raft::device_matrix_view<const T, int64_t, row_major> h_queries,
+            raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,
+            raft::device_matrix_view<float, int64_t, row_major> d_distances);
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void serialize(const raft::device_resources& handle,
+               const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+               std::ostream& os);
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void deserialize(const raft::device_resources& handle,
+                 cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+                 std::istream& is);
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void deserialize(const raft::device_resources& handle,
+                 cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+                 const std::string& filename);
 
 };  // namespace cuvs::neighbors
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index 67d1b46c0f..7f852d635d 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -1168,7 +1168,7 @@ void extend(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_flat::search_params& params,
-            cuvs::neighbors::ivf_flat::index<float, int64_t>& index,
+            const cuvs::neighbors::ivf_flat::index<float, int64_t>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
@@ -1209,7 +1209,7 @@ void search(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_flat::search_params& params,
-            cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index,
+            const cuvs::neighbors::ivf_flat::index<int8_t, int64_t>& index,
             raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
@@ -1250,7 +1250,7 @@ void search(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_flat::search_params& params,
-            cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index,
+            const cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>& index,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 3ce5f382f2..ae543c9e9a 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -1221,6 +1221,75 @@ void extend(raft::resources const& handle,
             std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
             cuvs::neighbors::ivf_pq::index<int64_t>* idx);
 
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_pq::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& idx)
+  -> cuvs::neighbors::ivf_pq::index<int64_t>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_pq::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_pq::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_pq::extend(handle, new_vectors, no_op, &index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors a host matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[in] new_indices a host vector view to a vector of indices [n_rows].
+ *    If the original index is empty (`idx.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_pq::index<int64_t>* idx);
+
 /**
  * @brief Extend the index with the new data.
  *
@@ -1405,7 +1474,7 @@ void extend(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
-            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
@@ -1450,7 +1519,7 @@ void search(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
-            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
@@ -1495,7 +1564,7 @@ void search(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
-            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
@@ -1540,7 +1609,7 @@ void search(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_pq::search_params& search_params,
-            cuvs::neighbors::ivf_pq::index<int64_t>& index,
+            const cuvs::neighbors::ivf_pq::index<int64_t>& index,
             raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
diff --git a/cpp/include/cuvs/neighbors/mg.hpp b/cpp/include/cuvs/neighbors/mg.hpp
new file mode 100644
index 0000000000..4657fa8fb0
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/mg.hpp
@@ -0,0 +1,1367 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#ifdef CUVS_BUILD_MG_ALGOS
+
+#include <atomic>
+#include <memory>
+
+#include <raft/core/device_resources.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#define DEFAULT_SEARCH_BATCH_SIZE 1 << 20
+
+/// \defgroup mg_cpp_index_params ANN MG index build parameters
+
+namespace cuvs::neighbors::mg {
+/** Distribution mode */
+/// \ingroup mg_cpp_index_params
+enum distribution_mode {
+  /** Index is replicated on each device, favors throughput */
+  REPLICATED,
+  /** Index is split on several devices, favors scaling */
+  SHARDED
+};
+
+/// \defgroup mg_cpp_search_params ANN MG search parameters
+
+/** Search mode when using a replicated index */
+/// \ingroup mg_cpp_search_params
+enum replicated_search_mode {
+  /** Search queries are splited to maintain equal load on GPUs */
+  LOAD_BALANCER,
+  /** Each search query is processed by a single GPU in a round-robin fashion */
+  ROUND_ROBIN
+};
+
+/** Merge mode when using a sharded index */
+/// \ingroup mg_cpp_search_params
+enum sharded_merge_mode {
+  /** Search batches are merged on the root rank */
+  MERGE_ON_ROOT_RANK,
+  /** Search batches are merged in a tree reduction fashion */
+  TREE_MERGE
+};
+
+/** Build parameters */
+/// \ingroup mg_cpp_index_params
+template <typename Upstream>
+struct index_params : public Upstream {
+  index_params() : mode(SHARDED) {}
+
+  index_params(const Upstream& sp) : Upstream(sp), mode(SHARDED) {}
+
+  /** Distribution mode */
+  cuvs::neighbors::mg::distribution_mode mode = SHARDED;
+};
+
+/** Search parameters */
+/// \ingroup mg_cpp_search_params
+template <typename Upstream>
+struct search_params : public Upstream {
+  search_params() : search_mode(LOAD_BALANCER), merge_mode(TREE_MERGE) {}
+
+  search_params(const Upstream& sp)
+    : Upstream(sp), search_mode(LOAD_BALANCER), merge_mode(TREE_MERGE)
+  {
+  }
+
+  /** Replicated search mode */
+  cuvs::neighbors::mg::replicated_search_mode search_mode = LOAD_BALANCER;
+  /** Sharded merge mode */
+  cuvs::neighbors::mg::sharded_merge_mode merge_mode = TREE_MERGE;
+};
+
+}  // namespace cuvs::neighbors::mg
+
+namespace cuvs::neighbors::mg {
+
+using namespace raft;
+
+template <typename AnnIndexType, typename T, typename IdxT>
+struct index {
+  index(distribution_mode mode, int num_ranks_);
+  index(const raft::device_resources& handle, const std::string& filename);
+
+  index(const index&)                    = delete;
+  index(index&&)                         = default;
+  auto operator=(const index&) -> index& = delete;
+  auto operator=(index&&) -> index&      = default;
+
+  distribution_mode mode_;
+  int num_ranks_;
+  std::vector<iface<AnnIndexType, T, IdxT>> ann_interfaces_;
+
+  // for load balancing mechanism
+  std::shared_ptr<std::atomic<int64_t>> round_robin_counter_;
+};
+
+/// \defgroup mg_cpp_index_build ANN MG index build
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-Flat MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<ivf_flat::index_params>& index_params,
+           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
+  -> index<ivf_flat::index<float, int64_t>, float, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-Flat MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<ivf_flat::index_params>& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
+  -> index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-Flat MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<ivf_flat::index_params>& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
+  -> index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
+  -> index<ivf_pq::index<int64_t>, float, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const half, int64_t, row_major> index_dataset)
+  -> index<ivf_pq::index<int64_t>, half, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
+  -> index<ivf_pq::index<int64_t>, int8_t, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
+  -> index<ivf_pq::index<int64_t>, uint8_t, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
+  -> index<cagra::index<float, uint32_t>, float, uint32_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const half, int64_t, row_major> index_dataset)
+  -> index<cagra::index<half, uint32_t>, half, uint32_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
+  -> index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::device_resources& handle,
+           const mg::index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
+  -> index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>;
+
+/// \defgroup mg_cpp_index_extend ANN MG index extend
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
+            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<ivf_pq::index<int64_t>, float, int64_t>& index,
+            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<ivf_pq::index<int64_t>, half, int64_t>& index,
+            raft::host_matrix_view<const half, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<cagra::index<float, uint32_t>, float, uint32_t>& index,
+            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<cagra::index<half, uint32_t>, half, uint32_t>& index,
+            raft::host_matrix_view<const half, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::device_resources& handle,
+            index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \defgroup mg_cpp_index_search ANN MG index search
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
+            const mg::search_params<ivf_flat::search_params>& search_params,
+            raft::host_matrix_view<const float, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
+            const mg::search_params<ivf_flat::search_params>& search_params,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
+            const mg::search_params<ivf_flat::search_params>& search_params,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<ivf_pq::index<int64_t>, float, int64_t>& index,
+            const mg::search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const float, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<ivf_pq::index<int64_t>, half, int64_t>& index,
+            const mg::search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const half, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
+            const mg::search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
+            const mg::search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<cagra::index<float, uint32_t>, float, uint32_t>& index,
+            const mg::search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const float, int64_t, row_major> queries,
+            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<cagra::index<half, uint32_t>, half, uint32_t>& index,
+            const mg::search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const half, int64_t, row_major> queries,
+            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
+            const mg::search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ * @param[in] n_rows_per_batch (optional) search batch size
+ *
+ */
+void search(const raft::device_resources& handle,
+            const index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
+            const mg::search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
+
+/// \defgroup mg_cpp_serialize ANN MG index serialization
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<ivf_pq::index<int64_t>, float, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<ivf_pq::index<int64_t>, half, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<cagra::index<float, uint32_t>, float, uint32_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<cagra::index<half, uint32_t>, half, uint32_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::device_resources& handle,
+               const index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
+               const std::string& filename);
+
+/// \defgroup mg_cpp_deserialize ANN MG index deserialization
+
+/// \ingroup mg_cpp_deserialize
+/**
+ * @brief Deserializes an IVF-Flat multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * auto new_index = cuvs::neighbors::mg::deserialize_flat<float, int64_t>(handle, filename);
+ *
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] filename path to the file to be deserialized
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize_flat(const raft::device_resources& handle, const std::string& filename)
+  -> index<ivf_flat::index<T, IdxT>, T, IdxT>;
+
+/// \ingroup mg_cpp_deserialize
+/**
+ * @brief Deserializes an IVF-PQ multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * auto new_index = cuvs::neighbors::mg::deserialize_pq<float, int64_t>(handle, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] filename path to the file to be deserialized
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize_pq(const raft::device_resources& handle, const std::string& filename)
+  -> index<ivf_pq::index<IdxT>, T, IdxT>;
+
+/// \ingroup mg_cpp_deserialize
+/**
+ * @brief Deserializes a CAGRA multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::mg::serialize(handle, index, filename);
+ * auto new_index = cuvs::neighbors::mg::deserialize_cagra<float, uint32_t>(handle, filename);
+ *
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] filename path to the file to be deserialized
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize_cagra(const raft::device_resources& handle, const std::string& filename)
+  -> index<cagra::index<T, IdxT>, T, IdxT>;
+
+/// \defgroup mg_cpp_distribute ANN MG local index distribution
+
+/// \ingroup mg_cpp_distribute
+/**
+ * @brief Replicates a locally built and serialized IVF-Flat index to all GPUs to form a distributed
+ * multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::ivf_flat::index_params index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(handle, index_params, index_dataset);
+ * const std::string filename = "local_index.cuvs";
+ * cuvs::neighbors::ivf_flat::serialize(handle, filename, index);
+ * auto new_index = cuvs::neighbors::mg::distribute_flat<float, int64_t>(handle, filename);
+ *
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] filename path to the file to be deserialized : a local index
+ *
+ */
+template <typename T, typename IdxT>
+auto distribute_flat(const raft::device_resources& handle, const std::string& filename)
+  -> index<ivf_flat::index<T, IdxT>, T, IdxT>;
+
+/// \ingroup mg_cpp_distribute
+/**
+ * @brief Replicates a locally built and serialized IVF-PQ index to all GPUs to form a distributed
+ * multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::ivf_pq::index_params index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(handle, index_params, index_dataset);
+ * const std::string filename = "local_index.cuvs";
+ * cuvs::neighbors::ivf_pq::serialize(handle, filename, index);
+ * auto new_index = cuvs::neighbors::mg::distribute_pq<float, int64_t>(handle, filename);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] filename path to the file to be deserialized : a local index
+ *
+ */
+template <typename T, typename IdxT>
+auto distribute_pq(const raft::device_resources& handle, const std::string& filename)
+  -> index<ivf_pq::index<IdxT>, T, IdxT>;
+
+/// \ingroup mg_cpp_distribute
+/**
+ * @brief Replicates a locally built and serialized CAGRA index to all GPUs to form a distributed
+ * multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::neighbors::cagra::index_params index_params;
+ * auto index = cuvs::neighbors::cagra::build(handle, index_params, index_dataset);
+ * const std::string filename = "local_index.cuvs";
+ * cuvs::neighbors::cagra::serialize(handle, filename, index);
+ * auto new_index = cuvs::neighbors::mg::distribute_cagra<float, uint32_t>(handle, filename);
+ *
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] filename path to the file to be deserialized : a local index
+ *
+ */
+template <typename T, typename IdxT>
+auto distribute_cagra(const raft::device_resources& handle, const std::string& filename)
+  -> index<cagra::index<T, IdxT>, T, IdxT>;
+
+}  // namespace cuvs::neighbors::mg
+
+#else
+
+static_assert(false,
+              "FORBIDEN_MG_ALGORITHM_IMPORT\n\n"
+              "Please recompile the cuVS library with MG algorithms BUILD_MG_ALGOS=ON.\n");
+
+#endif
diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
index 9694a3e7a2..b03b8214b9 100644
--- a/cpp/src/neighbors/detail/cagra/add_nodes.cuh
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -29,9 +29,10 @@
 
 #include <cstdint>
 
+namespace cuvs::neighbors::cagra {
+
 static const std::string RAFT_NAME = "raft";
 
-namespace cuvs::neighbors::cagra {
 template <class T, class IdxT, class Accessor>
 void add_node_core(
   raft::resources const& handle,
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index f86ed9ef62..b92ef0ace4 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -32,9 +32,10 @@
 #include <fstream>
 #include <type_traits>
 
-static const std::string RAFT_NAME = "raft";
 namespace cuvs::neighbors::cagra::detail {
 
+static const std::string RAFT_NAME = "raft";
+
 constexpr int serialization_version = 4;
 
 /**
diff --git a/cpp/src/neighbors/iface/generate_iface.py b/cpp/src/neighbors/iface/generate_iface.py
new file mode 100644
index 0000000000..794219bbf3
--- /dev/null
+++ b/cpp/src/neighbors/iface/generate_iface.py
@@ -0,0 +1,273 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+"""
+
+include_macro = """
+#include "iface.hpp"
+"""
+
+namespace_macro = """
+namespace cuvs::neighbors {
+"""
+
+footer = """
+}  // namespace cuvs::neighbors
+"""
+
+flat_macro = """
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                                                              \\
+  using T_ha = raft::host_device_accessor<std::experimental::default_accessor<const T>, raft::memory_type::device>;             \\
+  using T_da= raft::host_device_accessor<std::experimental::default_accessor<const T>, raft::memory_type::host>;                \\
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::device>;       \\
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::host>;         \\
+                                                                                                                                \\
+  template void build(const raft::device_resources& handle,                                                                     \\
+             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                              \\
+             const cuvs::neighbors::index_params* index_params,                                                                 \\
+             raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                                     \\
+                                                                                                                                \\
+  template void build(const raft::device_resources& handle,                                                                     \\
+             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                              \\
+             const cuvs::neighbors::index_params* index_params,                                                                 \\
+             raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                                     \\
+                                                                                                                                \\
+  template void extend(const raft::device_resources& handle,                                                                    \\
+              cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                             \\
+              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                                       \\
+              std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> new_indices);       \\
+                                                                                                                                \\
+  template void extend(const raft::device_resources& handle,                                                                    \\
+              cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                             \\
+              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                                       \\
+              std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> new_indices);       \\
+                                                                                                                                \\
+  template void search(const raft::device_resources& handle,                                                                    \\
+              const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                       \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
+              raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,                                                     \\
+              raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
+                                                                                                                                \\
+  template void search(const raft::device_resources& handle,                                                                    \\
+              const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                       \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
+              raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,                                                   \\
+              raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
+                                                                                                                                \\
+  template void serialize(const raft::device_resources& handle,                                                                 \\
+                 const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                    \\
+                 std::ostream& os);                                                                                             \\
+                                                                                                                                \\
+  template void deserialize(const raft::device_resources& handle,                                                               \\
+                   cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                        \\
+                   std::istream& is);                                                                                           \\
+                                                                                                                                \\
+  template void deserialize(const raft::device_resources& handle,                                                               \\
+                   cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                        \\
+                   const std::string& filename);
+"""
+
+pq_macro = """
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                                                \\
+  using T_ha = raft::host_device_accessor<std::experimental::default_accessor<const T>, raft::memory_type::device>;             \\
+  using T_da= raft::host_device_accessor<std::experimental::default_accessor<const T>, raft::memory_type::host>;                \\
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::device>;       \\
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::host>;         \\
+                                                                                                                                \\
+  template void build(const raft::device_resources& handle,                                                                     \\
+             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                   \\
+             const cuvs::neighbors::index_params* index_params,                                                                 \\
+             raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                                     \\
+                                                                                                                                \\
+  template void build(const raft::device_resources& handle,                                                                     \\
+             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                   \\
+             const cuvs::neighbors::index_params* index_params,                                                                 \\
+             raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                                     \\
+                                                                                                                                \\
+  template void extend(const raft::device_resources& handle,                                                                    \\
+              cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                  \\
+              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                                       \\
+              std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> new_indices);       \\
+                                                                                                                                \\
+  template void extend(const raft::device_resources& handle,                                                                    \\
+              cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                  \\
+              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                                       \\
+              std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> new_indices);       \\
+                                                                                                                                \\
+  template void search(const raft::device_resources& handle,                                                                    \\
+              const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                            \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
+              raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,                                                     \\
+              raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
+                                                                                                                                \\
+  template void search(const raft::device_resources& handle,                                                                    \\
+              const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                            \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
+              raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,                                                   \\
+              raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
+                                                                                                                                \\
+  template void serialize(const raft::device_resources& handle,                                                                 \\
+                 const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                         \\
+                 std::ostream& os);                                                                                             \\
+                                                                                                                                \\
+  template void deserialize(const raft::device_resources& handle,                                                               \\
+                   cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                             \\
+                   std::istream& is);                                                                                           \\
+                                                                                                                                \\
+  template void deserialize(const raft::device_resources& handle,                                                               \\
+                   cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                             \\
+                   const std::string& filename);
+"""
+
+cagra_macro = """
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                                             \\
+  using T_ha = raft::host_device_accessor<std::experimental::default_accessor<const T>, raft::memory_type::device>;             \\
+  using T_da= raft::host_device_accessor<std::experimental::default_accessor<const T>, raft::memory_type::host>;                \\
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::device>;       \\
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::host>;         \\
+                                                                                                                                \\
+  template void build(const raft::device_resources& handle,                                                                     \\
+             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                 \\
+             const cuvs::neighbors::index_params* index_params,                                                                 \\
+             raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                                     \\
+                                                                                                                                \\
+  template void build(const raft::device_resources& handle,                                                                     \\
+             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                 \\
+             const cuvs::neighbors::index_params* index_params,                                                                 \\
+             raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                                     \\
+                                                                                                                                \\
+  template void extend(const raft::device_resources& handle,                                                                    \\
+              cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                \\
+              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                                       \\
+              std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> new_indices);       \\
+                                                                                                                                \\
+  template void extend(const raft::device_resources& handle,                                                                    \\
+              cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                \\
+              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                                       \\
+              std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> new_indices);       \\
+                                                                                                                                \\
+  template void search(const raft::device_resources& handle,                                                                    \\
+              const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
+              raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,                                                     \\
+              raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
+                                                                                                                                \\
+    template void search(const raft::device_resources& handle,                                                                  \\
+              const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
+              raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,                                                   \\
+              raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
+                                                                                                                                \\
+  template void serialize(const raft::device_resources& handle,                                                                 \\
+                 const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                       \\
+                 std::ostream& os);                                                                                             \\
+                                                                                                                                \\
+  template void deserialize(const raft::device_resources& handle,                                                               \\
+                   cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                           \\
+                   std::istream& is);                                                                                           \\
+                                                                                                                                \\
+  template void deserialize(const raft::device_resources& handle,                                                               \\
+                   cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                           \\
+                   const std::string& filename);
+"""
+
+flat_macros = dict (
+    flat = dict(
+        include=include_macro,
+        definition=flat_macro,
+        name="CUVS_INST_MG_FLAT",
+    )
+)
+
+pq_macros = dict (
+    pq = dict(
+        include=include_macro,
+        definition=pq_macro,
+        name="CUVS_INST_MG_PQ",
+    )
+)
+
+cagra_macros = dict (
+    cagra = dict(
+        include=include_macro,
+        definition=cagra_macro,
+        name="CUVS_INST_MG_CAGRA",
+    )
+)
+
+flat_types = dict(
+    float_int64_t=("float", "int64_t"),
+    int8_t_int64_t=("int8_t", "int64_t"),
+    uint8_t_int64_t=("uint8_t", "int64_t"),
+)
+
+pq_types = dict(
+    float_int64_t=("float", "int64_t"),
+    half_int64_t=("half", "int64_t"),
+    int8_t_int64_t=("int8_t", "int64_t"),
+    uint8_t_int64_t=("uint8_t", "int64_t"),
+)
+
+cagra_types = dict(
+    float_uint32_t=("float", "uint32_t"),
+    half_uint32_t=("half", "uint32_t"),
+    int8_t_uint32_t=("int8_t", "uint32_t"),
+    uint8_t_uint32_t=("uint8_t", "uint32_t"),
+)
+
+for macros, types in [(flat_macros, flat_types), (pq_macros, pq_types), (cagra_macros, cagra_types)]:
+  for type_path, (T, IdxT) in types.items():
+      for macro_path, macro in macros.items():
+          path = f"iface_{macro_path}_{type_path}.cu"
+          with open(path, "w") as f:
+              f.write(header)
+              f.write(macro['include'])
+              f.write(namespace_macro)
+              f.write(macro["definition"])
+              f.write(f"{macro['name']}({T}, {IdxT});\n\n")
+              f.write(f"#undef {macro['name']}\n")
+              f.write(footer)
+
+          print(f"src/neighbors/iface/{path}")
diff --git a/cpp/src/neighbors/iface/iface.hpp b/cpp/src/neighbors/iface/iface.hpp
new file mode 100644
index 0000000000..a329db4298
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface.hpp
@@ -0,0 +1,198 @@
+#include <mutex>
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_resources.hpp>
+
+namespace cuvs::neighbors {
+
+using namespace raft;
+
+template <typename AnnIndexType, typename T, typename IdxT, typename Accessor>
+void build(const raft::device_resources& handle,
+           cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+           const cuvs::neighbors::index_params* index_params,
+           raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> index_dataset)
+{
+  interface.mutex_->lock();
+
+  if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
+    auto idx = cuvs::neighbors::ivf_flat::build(
+      handle, *static_cast<const ivf_flat::index_params*>(index_params), index_dataset);
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
+    auto idx = cuvs::neighbors::ivf_pq::build(
+      handle, *static_cast<const ivf_pq::index_params*>(index_params), index_dataset);
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
+    auto idx = cuvs::neighbors::cagra::build(
+      handle, *static_cast<const cagra::index_params*>(index_params), index_dataset);
+    interface.index_.emplace(std::move(idx));
+  }
+  resource::sync_stream(handle);
+
+  interface.mutex_->unlock();
+}
+
+template <typename AnnIndexType, typename T, typename IdxT, typename Accessor1, typename Accessor2>
+void extend(
+  const raft::device_resources& handle,
+  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+  raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor1> new_vectors,
+  std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, Accessor2>>
+    new_indices)
+{
+  interface.mutex_->lock();
+
+  if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
+    auto idx =
+      cuvs::neighbors::ivf_flat::extend(handle, new_vectors, new_indices, interface.index_.value());
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
+    auto idx =
+      cuvs::neighbors::ivf_pq::extend(handle, new_vectors, new_indices, interface.index_.value());
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
+    RAFT_FAIL("CAGRA does not implement the extend method");
+  }
+  resource::sync_stream(handle);
+
+  interface.mutex_->unlock();
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void search(const raft::device_resources& handle,
+            const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+            const cuvs::neighbors::search_params* search_params,
+            raft::device_matrix_view<const T, int64_t, row_major> queries,
+            raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, row_major> distances)
+{
+  // interface.mutex_->lock();
+  if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, int64_t>>::value) {
+    cuvs::neighbors::ivf_flat::search(
+      handle,
+      *reinterpret_cast<const ivf_flat::search_params*>(search_params),
+      interface.index_.value(),
+      queries,
+      neighbors,
+      distances);
+  } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<int64_t>>::value) {
+    cuvs::neighbors::ivf_pq::search(handle,
+                                    *reinterpret_cast<const ivf_pq::search_params*>(search_params),
+                                    interface.index_.value(),
+                                    queries,
+                                    neighbors,
+                                    distances);
+  } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, uint32_t>>::value) {
+    cuvs::neighbors::cagra::search(handle,
+                                   *reinterpret_cast<const cagra::search_params*>(search_params),
+                                   interface.index_.value(),
+                                   queries,
+                                   neighbors,
+                                   distances);
+  }
+  resource::sync_stream(handle);
+
+  // interface.mutex_->unlock();
+}
+
+// for MG ANN only
+template <typename AnnIndexType, typename T, typename IdxT>
+void search(const raft::device_resources& handle,
+            const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+            const cuvs::neighbors::search_params* search_params,
+            raft::host_matrix_view<const T, int64_t, row_major> h_queries,
+            raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,
+            raft::device_matrix_view<float, int64_t, row_major> d_distances)
+{
+  // interface.mutex_->lock();
+
+  int64_t n_rows = h_queries.extent(0);
+  int64_t n_dims = h_queries.extent(1);
+  auto d_queries = raft::make_device_matrix<T, int64_t, row_major>(handle, n_rows, n_dims);
+  raft::copy(d_queries.data_handle(),
+             h_queries.data_handle(),
+             n_rows * n_dims,
+             resource::get_cuda_stream(handle));
+  auto d_query_view = raft::make_const_mdspan(d_queries.view());
+
+  search(handle, interface, search_params, d_query_view, d_neighbors, d_distances);
+
+  // interface.mutex_->unlock();
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void serialize(const raft::device_resources& handle,
+               const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+               std::ostream& os)
+{
+  interface.mutex_->lock();
+
+  if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
+    ivf_flat::serialize(handle, os, interface.index_.value());
+  } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
+    ivf_pq::serialize(handle, os, interface.index_.value());
+  } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
+    cagra::serialize(handle, os, interface.index_.value(), true);
+  }
+
+  interface.mutex_->unlock();
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void deserialize(const raft::device_resources& handle,
+                 cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+                 std::istream& is)
+{
+  interface.mutex_->lock();
+
+  if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
+    ivf_flat::index<T, IdxT> idx(handle);
+    ivf_flat::deserialize(handle, is, &idx);
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
+    ivf_pq::index<IdxT> idx(handle);
+    ivf_pq::deserialize(handle, is, &idx);
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
+    cagra::index<T, IdxT> idx(handle);
+    cagra::deserialize(handle, is, &idx);
+    interface.index_.emplace(std::move(idx));
+  }
+
+  interface.mutex_->unlock();
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void deserialize(const raft::device_resources& handle,
+                 cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+                 const std::string& filename)
+{
+  interface.mutex_->lock();
+
+  std::ifstream is(filename, std::ios::in | std::ios::binary);
+  if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
+    ivf_flat::index<T, IdxT> idx(handle);
+    ivf_flat::deserialize(handle, is, &idx);
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
+    ivf_pq::index<IdxT> idx(handle);
+    ivf_pq::deserialize(handle, is, &idx);
+    interface.index_.emplace(std::move(idx));
+  } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
+    cagra::index<T, IdxT> idx(handle);
+    cagra::deserialize(handle, is, &idx);
+    interface.index_.emplace(std::move(idx));
+  }
+
+  is.close();
+
+  interface.mutex_->unlock();
+}
+
+};  // namespace cuvs::neighbors
\ No newline at end of file
diff --git a/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
new file mode 100644
index 0000000000..b5e329dd82
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(const raft::device_resources& handle,                                    \
+                          const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
+                          std::ostream& os);                                                       \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            const std::string& filename);
+CUVS_INST_MG_CAGRA(float, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
new file mode 100644
index 0000000000..23fcffc59a
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(const raft::device_resources& handle,                                    \
+                          const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
+                          std::ostream& os);                                                       \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            const std::string& filename);
+CUVS_INST_MG_CAGRA(half, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
new file mode 100644
index 0000000000..30377ab666
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(const raft::device_resources& handle,                                    \
+                          const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
+                          std::ostream& os);                                                       \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            const std::string& filename);
+CUVS_INST_MG_CAGRA(int8_t, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
new file mode 100644
index 0000000000..59a1640e89
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(const raft::device_resources& handle,                                    \
+                          const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
+                          std::ostream& os);                                                       \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
+                            const std::string& filename);
+CUVS_INST_MG_CAGRA(uint8_t, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
new file mode 100644
index 0000000000..a0a4553753
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                                 \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(                                                                         \
+    const raft::device_resources& handle,                                                          \
+    const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                    \
+    std::ostream& os);                                                                             \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            const std::string& filename);
+CUVS_INST_MG_FLAT(float, int64_t);
+
+#undef CUVS_INST_MG_FLAT
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
new file mode 100644
index 0000000000..9fdd6464ff
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                                 \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(                                                                         \
+    const raft::device_resources& handle,                                                          \
+    const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                    \
+    std::ostream& os);                                                                             \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            const std::string& filename);
+CUVS_INST_MG_FLAT(int8_t, int64_t);
+
+#undef CUVS_INST_MG_FLAT
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..daee59c4a8
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                                 \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                          raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::device_resources& handle,                                                          \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::device_resources& handle,                                       \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(                                                                         \
+    const raft::device_resources& handle,                                                          \
+    const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                    \
+    std::ostream& os);                                                                             \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::device_resources& handle,                                  \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            const std::string& filename);
+CUVS_INST_MG_FLAT(uint8_t, int64_t);
+
+#undef CUVS_INST_MG_FLAT
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
new file mode 100644
index 0000000000..7282d6bd07
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::device>;                          \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::host>;                            \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::device>;                          \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::host>;                            \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);            \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
+                                                                                                  \
+  template void serialize(const raft::device_resources& handle,                                   \
+                          const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
+                          std::ostream& os);                                                      \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            std::istream& is);                                                    \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            const std::string& filename);
+CUVS_INST_MG_PQ(float, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
new file mode 100644
index 0000000000..4d67f9aed1
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::device>;                          \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::host>;                            \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::device>;                          \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::host>;                            \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);            \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
+                                                                                                  \
+  template void serialize(const raft::device_resources& handle,                                   \
+                          const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
+                          std::ostream& os);                                                      \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            std::istream& is);                                                    \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            const std::string& filename);
+CUVS_INST_MG_PQ(half, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
new file mode 100644
index 0000000000..46537b3f9f
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::device>;                          \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::host>;                            \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::device>;                          \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::host>;                            \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);            \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
+                                                                                                  \
+  template void serialize(const raft::device_resources& handle,                                   \
+                          const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
+                          std::ostream& os);                                                      \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            std::istream& is);                                                    \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            const std::string& filename);
+CUVS_INST_MG_PQ(int8_t, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..591ac881a8
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::device>;                          \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
+                                          raft::memory_type::host>;                            \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::device>;                          \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
+                                             raft::memory_type::host>;                            \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
+                                                                                                  \
+  template void build(                                                                            \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    const cuvs::neighbors::index_params* index_params,                                            \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void extend(                                                                           \
+    const raft::device_resources& handle,                                                         \
+    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
+      new_indices);                                                                               \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);            \
+                                                                                                  \
+  template void search(const raft::device_resources& handle,                                      \
+                       const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
+                       const cuvs::neighbors::search_params* search_params,                       \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
+                                                                                                  \
+  template void serialize(const raft::device_resources& handle,                                   \
+                          const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
+                          std::ostream& os);                                                      \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            std::istream& is);                                                    \
+                                                                                                  \
+  template void deserialize(const raft::device_resources& handle,                                 \
+                            cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
+                            const std::string& filename);
+CUVS_INST_MG_PQ(uint8_t, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
index 1fabcca8c1..c435cc6d95 100644
--- a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
+++ b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
@@ -144,7 +144,7 @@
   void search(                                                              \\
     raft::resources const& handle,                                          \\
     const cuvs::neighbors::ivf_flat::search_params& params,                 \\
-    cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                       \\
+    const cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \\
     raft::device_matrix_view<const T, IdxT, raft::row_major> queries,       \\
     raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,        \\
     raft::device_matrix_view<float, IdxT, raft::row_major> distances,       \\
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
index 3f262d6120..87abc0bc05 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::ivf_flat {
 #define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \
   void search(raft::resources const& handle,                                    \
               const cuvs::neighbors::ivf_flat::search_params& params,           \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& index,           \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
index 4357afb0ab..c1e92ae5b2 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::ivf_flat {
 #define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \
   void search(raft::resources const& handle,                                    \
               const cuvs::neighbors::ivf_flat::search_params& params,           \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& index,           \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
index 8265a3e177..4ff8ed7705 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::ivf_flat {
 #define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \
   void search(raft::resources const& handle,                                    \
               const cuvs::neighbors::ivf_flat::search_params& params,           \
-              cuvs::neighbors::ivf_flat::index<T, IdxT>& index,                 \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& index,           \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
diff --git a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
index a5a8299678..a2ac048ff5 100644
--- a/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
+++ b/cpp/src/neighbors/ivf_pq/detail/generate_ivf_pq.py
@@ -68,7 +68,7 @@
 #define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \\
   void search(raft::resources const& handle,                                    \\
               const cuvs::neighbors::ivf_pq::search_params& params,             \\
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \\
+              const cuvs::neighbors::ivf_pq::index<IdxT>& index,                \\
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \\
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \\
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \\
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
index 07ee110bc5..44e9777ba6 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::ivf_pq {
 #define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
   void search(raft::resources const& handle,                                    \
               const cuvs::neighbors::ivf_pq::search_params& params,             \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& index,                \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
index cf387cb67d..d7446e8464 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::ivf_pq {
 #define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
   void search(raft::resources const& handle,                                    \
               const cuvs::neighbors::ivf_pq::search_params& params,             \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& index,                \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
index 5ec9093dfa..c1ffede978 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::ivf_pq {
 #define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
   void search(raft::resources const& handle,                                    \
               const cuvs::neighbors::ivf_pq::search_params& params,             \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& index,                \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
index d2e2f3b005..08e4f0536a 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
@@ -32,7 +32,7 @@ namespace cuvs::neighbors::ivf_pq {
 #define CUVS_INST_IVF_PQ_SEARCH(T, IdxT)                                        \
   void search(raft::resources const& handle,                                    \
               const cuvs::neighbors::ivf_pq::search_params& params,             \
-              cuvs::neighbors::ivf_pq::index<IdxT>& index,                      \
+              const cuvs::neighbors::ivf_pq::index<IdxT>& index,                \
               raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
               raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
               raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
diff --git a/cpp/src/neighbors/mg/generate_mg.py b/cpp/src/neighbors/mg/generate_mg.py
new file mode 100644
index 0000000000..af5e605456
--- /dev/null
+++ b/cpp/src/neighbors/mg/generate_mg.py
@@ -0,0 +1,286 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+"""
+
+include_macro = """
+#include "mg.cuh"
+"""
+
+namespace_macro = """
+namespace cuvs::neighbors::mg {
+"""
+
+footer = """
+}  // namespace cuvs::neighbors::mg
+"""
+
+flat_macro = """
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                                                          \\
+  index<ivf_flat::index<T, IdxT>, T, IdxT> build(const raft::device_resources& handle,                                      \\
+                                                 const mg::index_params<ivf_flat::index_params>& index_params,              \\
+                                                 raft::host_matrix_view<const T, int64_t, row_major> index_dataset)         \\
+  {                                                                                                                         \\
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                       \\
+    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);                                   \\
+    cuvs::neighbors::mg::detail::build(handle, index,                                                                       \\
+                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),                    \\
+                                       index_dataset);                                                                      \\
+    return index;                                                                                                           \\
+  }                                                                                                                         \\
+                                                                                                                            \\
+  void extend(const raft::device_resources& handle,                                                                         \\
+              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                                                              \\
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                                              \\
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)                                       \\
+  {                                                                                                                         \\
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                                           \\
+  }                                                                                                                         \\
+                                                                                                                            \\
+  void search(const raft::device_resources& handle,                                                                         \\
+              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                                                        \\
+              const mg::search_params<ivf_flat::search_params>& search_params,                                              \\
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                                                  \\
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                                   \\
+              raft::host_matrix_view<float, int64_t, row_major> distances,                                                  \\
+              int64_t n_rows_per_batch)                                                                                     \\
+  {                                                                                                                         \\
+    cuvs::neighbors::mg::detail::search(handle, index,                                                                      \\
+                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \\
+                                        queries, neighbors, distances, n_rows_per_batch);                                   \\
+  }                                                                                                                         \\
+                                                                                                                            \\
+  void serialize(const raft::device_resources& handle,                                                                      \\
+                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                                                     \\
+                 const std::string& filename)                                                                               \\
+  {                                                                                                                         \\
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                                                        \\
+  }                                                                                                                         \\
+                                                                                                                            \\
+  template<>                                                                                                                \\
+  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(const raft::device_resources& handle,                  \\
+                                                                     const std::string& filename)                           \\
+  {                                                                                                                         \\
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                                                  \\
+    return idx;                                                                                                             \\
+  }                                                                                                                         \\
+                                                                                                                            \\
+  template<>                                                                                                                \\
+  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(const raft::device_resources& handle,                   \\
+                                                                    const std::string& filename)                            \\
+  {                                                                                                                         \\
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                       \\
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                                     \\
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                                         \\
+    return idx;                                                                                                             \\
+  }
+"""
+
+pq_macro = """
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                                          \\
+  index<ivf_pq::index<IdxT>, T, IdxT> build(const raft::device_resources& handle,                                         \\
+                                            const mg::index_params<ivf_pq::index_params>& index_params,                   \\
+                                            raft::host_matrix_view<const T, int64_t, row_major> index_dataset)            \\
+  {                                                                                                                       \\
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
+    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);                                      \\
+    cuvs::neighbors::mg::detail::build(handle, index,                                                                     \\
+                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),                  \\
+                                       index_dataset);                                                                    \\
+    return index;                                                                                                         \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  void extend(const raft::device_resources& handle,                                                                       \\
+              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                                                 \\
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                                            \\
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)                                     \\
+  {                                                                                                                       \\
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                                         \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  void search(const raft::device_resources& handle,                                                                       \\
+              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                                           \\
+              const mg::search_params<ivf_pq::search_params>& search_params,                                              \\
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                                                \\
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                                 \\
+              raft::host_matrix_view<float, int64_t, row_major> distances,                                                \\
+              int64_t n_rows_per_batch)                                                                                   \\
+  {                                                                                                                       \\
+    cuvs::neighbors::mg::detail::search(handle, index,                                                                    \\
+                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),               \\
+                                        queries, neighbors, distances, n_rows_per_batch);                                 \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  void serialize(const raft::device_resources& handle,                                                                    \\
+                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                                        \\
+                 const std::string& filename)                                                                             \\
+  {                                                                                                                       \\
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                                                      \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  template<>                                                                                                              \\
+  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(const raft::device_resources& handle,                       \\
+                                                              const std::string& filename)                                \\
+  {                                                                                                                       \\
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                                                     \\
+    return idx;                                                                                                           \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  template<>                                                                                                              \\
+  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle,                        \\
+                                                             const std::string& filename)                                 \\
+  {                                                                                                                       \\
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                                        \\
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                                       \\
+    return idx;                                                                                                           \\
+  }
+"""
+
+cagra_macro = """
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                                       \\
+  index<cagra::index<T, IdxT>, T, IdxT> build(const raft::device_resources& handle,                                       \\
+                                              const mg::index_params<cagra::index_params>& index_params,                  \\
+                                              raft::host_matrix_view<const T, int64_t, row_major> index_dataset)          \\
+  {                                                                                                                       \\
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
+    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);                                    \\
+    cuvs::neighbors::mg::detail::build(handle, index,                                                                     \\
+                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),                  \\
+                                       index_dataset);                                                                    \\
+    return index;                                                                                                         \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  void search(const raft::device_resources& handle,                                                                       \\
+              const index<cagra::index<T, IdxT>, T, IdxT>& index,                                                         \\
+              const mg::search_params<cagra::search_params>& search_params,                                               \\
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                                                \\
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                                 \\
+              raft::host_matrix_view<float, int64_t, row_major> distances,                                                \\
+              int64_t n_rows_per_batch)                                                                                   \\
+  {                                                                                                                       \\
+    cuvs::neighbors::mg::detail::search(handle, index,                                                                    \\
+                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),               \\
+                                        queries, neighbors, distances, n_rows_per_batch);                                 \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  void serialize(const raft::device_resources& handle,                                                                    \\
+                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                                                      \\
+                 const std::string& filename)                                                                             \\
+  {                                                                                                                       \\
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                                                      \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  template<>                                                                                                              \\
+  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(const raft::device_resources& handle,                  \\
+                                                                   const std::string& filename)                           \\
+  {                                                                                                                       \\
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                                                   \\
+    return idx;                                                                                                           \\
+  }                                                                                                                       \\
+                                                                                                                          \\
+  template<>                                                                                                              \\
+  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(const raft::device_resources& handle,                   \\
+                                                                  const std::string& filename)                            \\
+  {                                                                                                                       \\
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                                      \\
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                                       \\
+    return idx;                                                                                                           \\
+  }
+"""
+
+flat_macros = dict (
+    flat = dict(
+        include=include_macro,
+        definition=flat_macro,
+        name="CUVS_INST_MG_FLAT",
+    )
+)
+
+pq_macros = dict (
+    pq = dict(
+        include=include_macro,
+        definition=pq_macro,
+        name="CUVS_INST_MG_PQ",
+    )
+)
+
+cagra_macros = dict (
+    cagra = dict(
+        include=include_macro,
+        definition=cagra_macro,
+        name="CUVS_INST_MG_CAGRA",
+    )
+)
+
+flat_types = dict(
+    float_int64_t=("float", "int64_t"),
+    int8_t_int64_t=("int8_t", "int64_t"),
+    uint8_t_int64_t=("uint8_t", "int64_t"),
+)
+
+pq_types = dict(
+    float_int64_t=("float", "int64_t"),
+    half_int64_t=("half", "int64_t"),
+    int8_t_int64_t=("int8_t", "int64_t"),
+    uint8_t_int64_t=("uint8_t", "int64_t"),
+)
+
+cagra_types = dict(
+    float_uint32_t=("float", "uint32_t"),
+    half_uint32_t=("half", "uint32_t"),
+    int8_t_uint32_t=("int8_t", "uint32_t"),
+    uint8_t_uint32_t=("uint8_t", "uint32_t"),
+)
+
+for macros, types in [(flat_macros, flat_types), (pq_macros, pq_types), (cagra_macros, cagra_types)]:
+  for type_path, (T, IdxT) in types.items():
+      for macro_path, macro in macros.items():
+          path = f"mg_{macro_path}_{type_path}.cu"
+          with open(path, "w") as f:
+              f.write(header)
+              f.write(macro['include'])
+              f.write(namespace_macro)
+              f.write(macro["definition"])
+              f.write(f"{macro['name']}({T}, {IdxT});\n\n")
+              f.write(f"#undef {macro['name']}\n")
+              f.write(footer)
+
+          print(f"src/neighbors/mg/{path}")
diff --git a/cpp/src/neighbors/mg/mg.cuh b/cpp/src/neighbors/mg/mg.cuh
new file mode 100644
index 0000000000..d3f635bc40
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg.cuh
@@ -0,0 +1,690 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../detail/knn_merge_parts.cuh"
+#include <raft/core/resource/nccl_clique.hpp>
+#include <raft/core/serialize.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/util/cuda_dev_essentials.cuh>
+
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/mg.hpp>
+
+namespace cuvs::neighbors {
+using namespace raft;
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void search(const raft::device_resources& handle,
+            const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
+            const cuvs::neighbors::search_params* search_params,
+            raft::host_matrix_view<const T, int64_t, row_major> h_queries,
+            raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,
+            raft::device_matrix_view<float, int64_t, row_major> d_distances);
+}  // namespace cuvs::neighbors
+
+namespace cuvs::neighbors::mg {
+void check_omp_threads(const int requirements);
+}  // namespace cuvs::neighbors::mg
+
+namespace cuvs::neighbors::mg::detail {
+using namespace cuvs::neighbors;
+using namespace raft;
+
+// local index deserialization and distribution
+template <typename AnnIndexType, typename T, typename IdxT>
+void deserialize_and_distribute(const raft::device_resources& handle,
+                                index<AnnIndexType, T, IdxT>& index,
+                                const std::string& filename)
+{
+  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
+  for (int rank = 0; rank < index.num_ranks_; rank++) {
+    int dev_id                            = clique.device_ids_[rank];
+    const raft::device_resources& dev_res = clique.device_resources_[rank];
+    RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+    auto& ann_if = index.ann_interfaces_.emplace_back();
+    cuvs::neighbors::deserialize(dev_res, ann_if, filename);
+  }
+}
+
+// MG index deserialization
+template <typename AnnIndexType, typename T, typename IdxT>
+void deserialize(const raft::device_resources& handle,
+                 index<AnnIndexType, T, IdxT>& index,
+                 const std::string& filename)
+{
+  std::ifstream is(filename, std::ios::in | std::ios::binary);
+  if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
+
+  index.mode_      = (cuvs::neighbors::mg::distribution_mode)deserialize_scalar<int>(handle, is);
+  index.num_ranks_ = deserialize_scalar<int>(handle, is);
+
+  if (index.num_ranks_ != clique.num_ranks_) {
+    RAFT_FAIL("Serialized index has %d ranks whereas NCCL clique has %d ranks",
+              index.num_ranks_,
+              clique.num_ranks_);
+  }
+
+  for (int rank = 0; rank < index.num_ranks_; rank++) {
+    int dev_id                            = clique.device_ids_[rank];
+    const raft::device_resources& dev_res = clique.device_resources_[rank];
+    RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+    auto& ann_if = index.ann_interfaces_.emplace_back();
+    cuvs::neighbors::deserialize(dev_res, ann_if, is);
+  }
+
+  is.close();
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void build(const raft::device_resources& handle,
+           index<AnnIndexType, T, IdxT>& index,
+           const cuvs::neighbors::index_params* index_params,
+           raft::host_matrix_view<const T, int64_t, row_major> index_dataset)
+{
+  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
+
+  if (index.mode_ == REPLICATED) {
+    int64_t n_rows = index_dataset.extent(0);
+    RAFT_LOG_INFO("REPLICATED BUILD: %d*%drows", index.num_ranks_, n_rows);
+
+    index.ann_interfaces_.resize(index.num_ranks_);
+#pragma omp parallel for
+    for (int rank = 0; rank < index.num_ranks_; rank++) {
+      int dev_id                            = clique.device_ids_[rank];
+      const raft::device_resources& dev_res = clique.device_resources_[rank];
+      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      auto& ann_if = index.ann_interfaces_[rank];
+      cuvs::neighbors::build(dev_res, ann_if, index_params, index_dataset);
+      resource::sync_stream(dev_res);
+    }
+  } else if (index.mode_ == SHARDED) {
+    int64_t n_rows           = index_dataset.extent(0);
+    int64_t n_cols           = index_dataset.extent(1);
+    int64_t n_rows_per_shard = raft::ceildiv(n_rows, (int64_t)index.num_ranks_);
+
+    RAFT_LOG_INFO("SHARDED BUILD: %d*%drows", index.num_ranks_, n_rows_per_shard);
+
+    index.ann_interfaces_.resize(index.num_ranks_);
+#pragma omp parallel for
+    for (int rank = 0; rank < index.num_ranks_; rank++) {
+      int dev_id                            = clique.device_ids_[rank];
+      const raft::device_resources& dev_res = clique.device_resources_[rank];
+      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      int64_t offset                  = rank * n_rows_per_shard;
+      int64_t n_rows_of_current_shard = std::min(n_rows_per_shard, n_rows - offset);
+      const T* partition_ptr          = index_dataset.data_handle() + (offset * n_cols);
+      auto partition                  = raft::make_host_matrix_view<const T, int64_t, row_major>(
+        partition_ptr, n_rows_of_current_shard, n_cols);
+      auto& ann_if = index.ann_interfaces_[rank];
+      cuvs::neighbors::build(dev_res, ann_if, index_params, partition);
+      resource::sync_stream(dev_res);
+    }
+  }
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void extend(const raft::device_resources& handle,
+            index<AnnIndexType, T, IdxT>& index,
+            raft::host_matrix_view<const T, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)
+{
+  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
+
+  int64_t n_rows = new_vectors.extent(0);
+  if (index.mode_ == REPLICATED) {
+    RAFT_LOG_INFO("REPLICATED EXTEND: %d*%drows", index.num_ranks_, n_rows);
+
+#pragma omp parallel for
+    for (int rank = 0; rank < index.num_ranks_; rank++) {
+      int dev_id                            = clique.device_ids_[rank];
+      const raft::device_resources& dev_res = clique.device_resources_[rank];
+      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      auto& ann_if = index.ann_interfaces_[rank];
+      cuvs::neighbors::extend(dev_res, ann_if, new_vectors, new_indices);
+      resource::sync_stream(dev_res);
+    }
+  } else if (index.mode_ == SHARDED) {
+    int64_t n_cols           = new_vectors.extent(1);
+    int64_t n_rows_per_shard = raft::ceildiv(n_rows, (int64_t)index.num_ranks_);
+
+    RAFT_LOG_INFO("SHARDED EXTEND: %d*%drows", index.num_ranks_, n_rows_per_shard);
+
+#pragma omp parallel for
+    for (int rank = 0; rank < index.num_ranks_; rank++) {
+      int dev_id                            = clique.device_ids_[rank];
+      const raft::device_resources& dev_res = clique.device_resources_[rank];
+      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      int64_t offset                  = rank * n_rows_per_shard;
+      int64_t n_rows_of_current_shard = std::min(n_rows_per_shard, n_rows - offset);
+      const T* new_vectors_ptr        = new_vectors.data_handle() + (offset * n_cols);
+      auto new_vectors_part           = raft::make_host_matrix_view<const T, int64_t, row_major>(
+        new_vectors_ptr, n_rows_of_current_shard, n_cols);
+
+      std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices_part = std::nullopt;
+      if (new_indices.has_value()) {
+        const IdxT* new_indices_ptr = new_indices.value().data_handle() + offset;
+        new_indices_part            = raft::make_host_vector_view<const IdxT, int64_t>(
+          new_indices_ptr, n_rows_of_current_shard);
+      }
+      auto& ann_if = index.ann_interfaces_[rank];
+      cuvs::neighbors::extend(dev_res, ann_if, new_vectors_part, new_indices_part);
+      resource::sync_stream(dev_res);
+    }
+  }
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
+                                      const index<AnnIndexType, T, IdxT>& index,
+                                      const cuvs::neighbors::search_params* search_params,
+                                      raft::host_matrix_view<const T, int64_t, row_major> queries,
+                                      raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
+                                      raft::host_matrix_view<float, int64_t, row_major> distances,
+                                      int64_t n_rows_per_batch,
+                                      int64_t n_rows,
+                                      int64_t n_cols,
+                                      int64_t n_neighbors,
+                                      int64_t n_batches)
+{
+  const auto& root_handle = clique.set_current_device_to_root_rank();
+  auto in_neighbors       = raft::make_device_matrix<IdxT, int64_t, row_major>(
+    root_handle, index.num_ranks_ * n_rows_per_batch, n_neighbors);
+  auto in_distances = raft::make_device_matrix<float, int64_t, row_major>(
+    root_handle, index.num_ranks_ * n_rows_per_batch, n_neighbors);
+  auto out_neighbors =
+    raft::make_device_matrix<IdxT, int64_t, row_major>(root_handle, n_rows_per_batch, n_neighbors);
+  auto out_distances =
+    raft::make_device_matrix<float, int64_t, row_major>(root_handle, n_rows_per_batch, n_neighbors);
+
+  for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+    int64_t offset                  = batch_idx * n_rows_per_batch;
+    int64_t query_offset            = offset * n_cols;
+    int64_t output_offset           = offset * n_neighbors;
+    int64_t n_rows_of_current_batch = std::min((int64_t)n_rows_per_batch, n_rows - offset);
+    int64_t part_size               = n_rows_of_current_batch * n_neighbors;
+    auto query_partition            = raft::make_host_matrix_view<const T, int64_t, row_major>(
+      queries.data_handle() + query_offset, n_rows_of_current_batch, n_cols);
+
+    const int& requirements = index.num_ranks_;
+    check_omp_threads(requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
+#pragma omp parallel for num_threads(index.num_ranks_)
+    for (int rank = 0; rank < index.num_ranks_; rank++) {
+      int dev_id                            = clique.device_ids_[rank];
+      const raft::device_resources& dev_res = clique.device_resources_[rank];
+      auto& ann_if                          = index.ann_interfaces_[rank];
+      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+
+      if (rank == clique.root_rank_) {  // root rank
+        uint64_t batch_offset = clique.root_rank_ * part_size;
+        auto d_neighbors      = raft::make_device_matrix_view<IdxT, int64_t, row_major>(
+          in_neighbors.data_handle() + batch_offset, n_rows_of_current_batch, n_neighbors);
+        auto d_distances = raft::make_device_matrix_view<float, int64_t, row_major>(
+          in_distances.data_handle() + batch_offset, n_rows_of_current_batch, n_neighbors);
+        cuvs::neighbors::search(
+          dev_res, ann_if, search_params, query_partition, d_neighbors, d_distances);
+
+        // wait for other ranks
+        ncclGroupStart();
+        for (int from_rank = 0; from_rank < index.num_ranks_; from_rank++) {
+          if (from_rank == clique.root_rank_) continue;
+
+          batch_offset = from_rank * part_size;
+          ncclRecv(in_neighbors.data_handle() + batch_offset,
+                   part_size * sizeof(IdxT),
+                   ncclUint8,
+                   from_rank,
+                   clique.nccl_comms_[rank],
+                   resource::get_cuda_stream(dev_res));
+          ncclRecv(in_distances.data_handle() + batch_offset,
+                   part_size * sizeof(float),
+                   ncclUint8,
+                   from_rank,
+                   clique.nccl_comms_[rank],
+                   resource::get_cuda_stream(dev_res));
+        }
+        ncclGroupEnd();
+        resource::sync_stream(dev_res);
+      } else {  // non-root ranks
+        auto d_neighbors = raft::make_device_matrix<IdxT, int64_t, row_major>(
+          dev_res, n_rows_of_current_batch, n_neighbors);
+        auto d_distances = raft::make_device_matrix<float, int64_t, row_major>(
+          dev_res, n_rows_of_current_batch, n_neighbors);
+        cuvs::neighbors::search(
+          dev_res, ann_if, search_params, query_partition, d_neighbors.view(), d_distances.view());
+
+        // send results to root rank
+        ncclGroupStart();
+        ncclSend(d_neighbors.data_handle(),
+                 part_size * sizeof(IdxT),
+                 ncclUint8,
+                 clique.root_rank_,
+                 clique.nccl_comms_[rank],
+                 resource::get_cuda_stream(dev_res));
+        ncclSend(d_distances.data_handle(),
+                 part_size * sizeof(float),
+                 ncclUint8,
+                 clique.root_rank_,
+                 clique.nccl_comms_[rank],
+                 resource::get_cuda_stream(dev_res));
+        ncclGroupEnd();
+        resource::sync_stream(dev_res);
+      }
+    }
+
+    const auto& root_handle_   = clique.set_current_device_to_root_rank();
+    auto h_trans               = std::vector<IdxT>(index.num_ranks_);
+    int64_t translation_offset = 0;
+    for (int rank = 0; rank < index.num_ranks_; rank++) {
+      h_trans[rank] = translation_offset;
+      translation_offset += index.ann_interfaces_[rank].size();
+    }
+    auto d_trans = raft::make_device_vector<IdxT, IdxT>(root_handle_, index.num_ranks_);
+    raft::copy(d_trans.data_handle(),
+               h_trans.data(),
+               index.num_ranks_,
+               resource::get_cuda_stream(root_handle_));
+
+    cuvs::neighbors::detail::knn_merge_parts(in_distances.data_handle(),
+                                             in_neighbors.data_handle(),
+                                             out_distances.data_handle(),
+                                             out_neighbors.data_handle(),
+                                             n_rows_of_current_batch,
+                                             index.num_ranks_,
+                                             n_neighbors,
+                                             resource::get_cuda_stream(root_handle_),
+                                             d_trans.data_handle());
+
+    raft::copy(neighbors.data_handle() + output_offset,
+               out_neighbors.data_handle(),
+               part_size,
+               resource::get_cuda_stream(root_handle_));
+    raft::copy(distances.data_handle() + output_offset,
+               out_distances.data_handle(),
+               part_size,
+               resource::get_cuda_stream(root_handle_));
+
+    resource::sync_stream(root_handle_);
+  }
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
+                                    const index<AnnIndexType, T, IdxT>& index,
+                                    const cuvs::neighbors::search_params* search_params,
+                                    raft::host_matrix_view<const T, int64_t, row_major> queries,
+                                    raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
+                                    raft::host_matrix_view<float, int64_t, row_major> distances,
+                                    int64_t n_rows_per_batch,
+                                    int64_t n_rows,
+                                    int64_t n_cols,
+                                    int64_t n_neighbors,
+                                    int64_t n_batches)
+{
+  for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+    int64_t offset                  = batch_idx * n_rows_per_batch;
+    int64_t query_offset            = offset * n_cols;
+    int64_t output_offset           = offset * n_neighbors;
+    int64_t n_rows_of_current_batch = std::min((int64_t)n_rows_per_batch, n_rows - offset);
+    auto query_partition            = raft::make_host_matrix_view<const T, int64_t, row_major>(
+      queries.data_handle() + query_offset, n_rows_of_current_batch, n_cols);
+
+    const int& requirements = index.num_ranks_;
+    check_omp_threads(requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
+#pragma omp parallel for num_threads(index.num_ranks_)
+    for (int rank = 0; rank < index.num_ranks_; rank++) {
+      int dev_id                            = clique.device_ids_[rank];
+      const raft::device_resources& dev_res = clique.device_resources_[rank];
+      auto& ann_if                          = index.ann_interfaces_[rank];
+      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+
+      int64_t part_size = n_rows_of_current_batch * n_neighbors;
+
+      auto tmp_neighbors = raft::make_device_matrix<IdxT, int64_t, row_major>(
+        dev_res, 2 * n_rows_of_current_batch, n_neighbors);
+      auto tmp_distances = raft::make_device_matrix<float, int64_t, row_major>(
+        dev_res, 2 * n_rows_of_current_batch, n_neighbors);
+      auto neighbors_view = raft::make_device_matrix_view<IdxT, int64_t, row_major>(
+        tmp_neighbors.data_handle(), n_rows_of_current_batch, n_neighbors);
+      auto distances_view = raft::make_device_matrix_view<float, int64_t, row_major>(
+        tmp_distances.data_handle(), n_rows_of_current_batch, n_neighbors);
+      cuvs::neighbors::search(
+        dev_res, ann_if, search_params, query_partition, neighbors_view, distances_view);
+
+      int64_t translation_offset = 0;
+      for (int r = 0; r < rank; r++) {
+        translation_offset += index.ann_interfaces_[r].size();
+      }
+      raft::linalg::addScalar(neighbors_view.data_handle(),
+                              neighbors_view.data_handle(),
+                              (IdxT)translation_offset,
+                              part_size,
+                              resource::get_cuda_stream(dev_res));
+
+      auto d_trans = raft::make_device_vector<IdxT, IdxT>(dev_res, 2);
+      cudaMemsetAsync(
+        d_trans.data_handle(), 0, 2 * sizeof(IdxT), resource::get_cuda_stream(dev_res));
+
+      int64_t remaining = index.num_ranks_;
+      int64_t radix     = 2;
+
+      while (remaining > 1) {
+        bool received_something = false;
+        int64_t offset          = radix / 2;
+        ncclGroupStart();
+        if (rank % radix == 0)  // This is one of the receivers
+        {
+          int other_id = rank + offset;
+          if (other_id < index.num_ranks_)  // Make sure someone's sending anything
+          {
+            ncclRecv(tmp_neighbors.data_handle() + part_size,
+                     part_size * sizeof(IdxT),
+                     ncclUint8,
+                     other_id,
+                     clique.nccl_comms_[rank],
+                     resource::get_cuda_stream(dev_res));
+            ncclRecv(tmp_distances.data_handle() + part_size,
+                     part_size * sizeof(float),
+                     ncclUint8,
+                     other_id,
+                     clique.nccl_comms_[rank],
+                     resource::get_cuda_stream(dev_res));
+            received_something = true;
+          }
+        } else if (rank % radix == offset)  // This is one of the senders
+        {
+          int other_id = rank - offset;
+          ncclSend(tmp_neighbors.data_handle(),
+                   part_size * sizeof(IdxT),
+                   ncclUint8,
+                   other_id,
+                   clique.nccl_comms_[rank],
+                   resource::get_cuda_stream(dev_res));
+          ncclSend(tmp_distances.data_handle(),
+                   part_size * sizeof(float),
+                   ncclUint8,
+                   other_id,
+                   clique.nccl_comms_[rank],
+                   resource::get_cuda_stream(dev_res));
+        }
+        ncclGroupEnd();
+
+        remaining = (remaining + 1) / 2;
+        radix *= 2;
+
+        if (received_something) {
+          // merge inplace
+          cuvs::neighbors::detail::knn_merge_parts(tmp_distances.data_handle(),
+                                                   tmp_neighbors.data_handle(),
+                                                   tmp_distances.data_handle(),
+                                                   tmp_neighbors.data_handle(),
+                                                   n_rows_of_current_batch,
+                                                   2,
+                                                   n_neighbors,
+                                                   resource::get_cuda_stream(dev_res),
+                                                   d_trans.data_handle());
+
+          // If done, copy the final result
+          if (remaining <= 1) {
+            raft::copy(neighbors.data_handle() + output_offset,
+                       tmp_neighbors.data_handle(),
+                       part_size,
+                       resource::get_cuda_stream(dev_res));
+            raft::copy(distances.data_handle() + output_offset,
+                       tmp_distances.data_handle(),
+                       part_size,
+                       resource::get_cuda_stream(dev_res));
+
+            resource::sync_stream(dev_res);
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void run_search_batch(const raft::comms::nccl_clique& clique,
+                      const index<AnnIndexType, T, IdxT>& index,
+                      int rank,
+                      const cuvs::neighbors::search_params* search_params,
+                      raft::host_matrix_view<const T, int64_t, row_major>& queries,
+                      raft::host_matrix_view<IdxT, int64_t, row_major>& neighbors,
+                      raft::host_matrix_view<float, int64_t, row_major>& distances,
+                      int64_t query_offset,
+                      int64_t output_offset,
+                      int64_t n_rows_of_current_batch,
+                      int64_t n_cols,
+                      int64_t n_neighbors)
+{
+  int dev_id = clique.device_ids_[rank];
+  RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+  const raft::device_resources& dev_res = clique.device_resources_[rank];
+  auto& ann_if                          = index.ann_interfaces_[rank];
+
+  auto query_partition = raft::make_host_matrix_view<const T, int64_t, row_major>(
+    queries.data_handle() + query_offset, n_rows_of_current_batch, n_cols);
+  auto d_neighbors = raft::make_device_matrix<IdxT, int64_t, row_major>(
+    dev_res, n_rows_of_current_batch, n_neighbors);
+  auto d_distances = raft::make_device_matrix<float, int64_t, row_major>(
+    dev_res, n_rows_of_current_batch, n_neighbors);
+
+  cuvs::neighbors::search(
+    dev_res, ann_if, search_params, query_partition, d_neighbors.view(), d_distances.view());
+
+  raft::copy(neighbors.data_handle() + output_offset,
+             d_neighbors.data_handle(),
+             n_rows_of_current_batch * n_neighbors,
+             resource::get_cuda_stream(dev_res));
+  raft::copy(distances.data_handle() + output_offset,
+             d_distances.data_handle(),
+             n_rows_of_current_batch * n_neighbors,
+             resource::get_cuda_stream(dev_res));
+
+  resource::sync_stream(dev_res);
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void search(const raft::device_resources& handle,
+            const index<AnnIndexType, T, IdxT>& index,
+            const cuvs::neighbors::search_params* search_params,
+            raft::host_matrix_view<const T, int64_t, row_major> queries,
+            raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances,
+            int64_t n_rows_per_batch)
+{
+  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
+
+  int64_t n_rows      = queries.extent(0);
+  int64_t n_cols      = queries.extent(1);
+  int64_t n_neighbors = neighbors.extent(1);
+
+  if (index.mode_ == REPLICATED) {
+    cuvs::neighbors::mg::replicated_search_mode search_mode;
+    if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
+      const cuvs::neighbors::mg::search_params<ivf_flat::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg::search_params<ivf_flat::search_params>*>(
+          search_params);
+      search_mode = mg_search_params->search_mode;
+    } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
+      const cuvs::neighbors::mg::search_params<ivf_pq::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg::search_params<ivf_pq::search_params>*>(
+          search_params);
+      search_mode = mg_search_params->search_mode;
+    } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
+      const cuvs::neighbors::mg::search_params<cagra::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg::search_params<cagra::search_params>*>(search_params);
+      search_mode = mg_search_params->search_mode;
+    }
+
+    if (search_mode == LOAD_BALANCER) {
+      int64_t n_rows_per_rank = raft::ceildiv(n_rows, (int64_t)index.num_ranks_);
+      n_rows_per_batch =
+        std::min(n_rows_per_batch, n_rows_per_rank);  // get at least num_ranks_ batches
+      int64_t n_batches = raft::ceildiv(n_rows, (int64_t)n_rows_per_batch);
+      if (n_batches <= 1) n_rows_per_batch = n_rows;
+
+      RAFT_LOG_INFO(
+        "REPLICATED SEARCH IN LOAD BALANCER MODE: %d*%drows", n_batches, n_rows_per_batch);
+
+#pragma omp parallel for
+      for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+        int rank                        = batch_idx % index.num_ranks_;  // alternate GPUs
+        int64_t offset                  = batch_idx * n_rows_per_batch;
+        int64_t query_offset            = offset * n_cols;
+        int64_t output_offset           = offset * n_neighbors;
+        int64_t n_rows_of_current_batch = std::min(n_rows_per_batch, n_rows - offset);
+
+        run_search_batch(clique,
+                         index,
+                         rank,
+                         search_params,
+                         queries,
+                         neighbors,
+                         distances,
+                         query_offset,
+                         output_offset,
+                         n_rows_of_current_batch,
+                         n_cols,
+                         n_neighbors);
+      }
+    } else if (search_mode == ROUND_ROBIN) {
+      RAFT_LOG_INFO("REPLICATED SEARCH IN ROUND ROBIN MODE: %d*%drows", 1, n_rows);
+
+      ASSERT(n_rows <= n_rows_per_batch,
+             "In round-robin mode, n_rows must lower or equal to n_rows_per_batch");
+
+      auto& rrc    = *index.round_robin_counter_;
+      int64_t rank = rrc++;
+      rank %= index.num_ranks_;
+
+      run_search_batch(clique,
+                       index,
+                       rank,
+                       search_params,
+                       queries,
+                       neighbors,
+                       distances,
+                       0,
+                       0,
+                       n_rows,
+                       n_cols,
+                       n_neighbors);
+    }
+  } else if (index.mode_ == SHARDED) {
+    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+    if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
+      const cuvs::neighbors::mg::search_params<ivf_flat::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg::search_params<ivf_flat::search_params>*>(
+          search_params);
+      merge_mode = mg_search_params->merge_mode;
+    } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
+      const cuvs::neighbors::mg::search_params<ivf_pq::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg::search_params<ivf_pq::search_params>*>(
+          search_params);
+      merge_mode = mg_search_params->merge_mode;
+    } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
+      const cuvs::neighbors::mg::search_params<cagra::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg::search_params<cagra::search_params>*>(search_params);
+      merge_mode = mg_search_params->merge_mode;
+    }
+
+    int64_t n_batches = raft::ceildiv(n_rows, (int64_t)n_rows_per_batch);
+    if (n_batches <= 1) n_rows_per_batch = n_rows;
+
+    if (merge_mode == MERGE_ON_ROOT_RANK) {
+      RAFT_LOG_INFO("SHARDED SEARCH WITH MERGE_ON_ROOT_RANK MERGE MODE: %d*%drows",
+                    n_batches,
+                    n_rows_per_batch);
+      sharded_search_with_direct_merge(clique,
+                                       index,
+                                       search_params,
+                                       queries,
+                                       neighbors,
+                                       distances,
+                                       n_rows_per_batch,
+                                       n_rows,
+                                       n_cols,
+                                       n_neighbors,
+                                       n_batches);
+    } else if (merge_mode == TREE_MERGE) {
+      RAFT_LOG_INFO(
+        "SHARDED SEARCH WITH TREE_MERGE MERGE MODE %d*%drows", n_batches, n_rows_per_batch);
+      sharded_search_with_tree_merge(clique,
+                                     index,
+                                     search_params,
+                                     queries,
+                                     neighbors,
+                                     distances,
+                                     n_rows_per_batch,
+                                     n_rows,
+                                     n_cols,
+                                     n_neighbors,
+                                     n_batches);
+    }
+  }
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+void serialize(const raft::device_resources& handle,
+               const index<AnnIndexType, T, IdxT>& index,
+               const std::string& filename)
+{
+  std::ofstream of(filename, std::ios::out | std::ios::binary);
+  if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
+
+  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
+
+  serialize_scalar(handle, of, (int)index.mode_);
+  serialize_scalar(handle, of, index.num_ranks_);
+
+  for (int rank = 0; rank < index.num_ranks_; rank++) {
+    int dev_id                            = clique.device_ids_[rank];
+    const raft::device_resources& dev_res = clique.device_resources_[rank];
+    RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+    auto& ann_if = index.ann_interfaces_[rank];
+    cuvs::neighbors::serialize(dev_res, ann_if, of);
+  }
+
+  of.close();
+  if (!of) { RAFT_FAIL("Error writing output %s", filename.c_str()); }
+}
+
+}  // namespace cuvs::neighbors::mg::detail
+
+namespace cuvs::neighbors::mg {
+using namespace cuvs::neighbors;
+using namespace raft;
+
+template <typename AnnIndexType, typename T, typename IdxT>
+index<AnnIndexType, T, IdxT>::index(distribution_mode mode, int num_ranks_)
+  : mode_(mode),
+    num_ranks_(num_ranks_),
+    round_robin_counter_(std::make_shared<std::atomic<int64_t>>(0))
+{
+}
+
+template <typename AnnIndexType, typename T, typename IdxT>
+index<AnnIndexType, T, IdxT>::index(const raft::device_resources& handle,
+                                    const std::string& filename)
+  : round_robin_counter_(std::make_shared<std::atomic<int64_t>>(0))
+{
+  cuvs::neighbors::mg::detail::deserialize(handle, *this, filename);
+}
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
new file mode 100644
index 0000000000..b11610fb48
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
+  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
+    const raft::device_resources& handle,                                              \
+    const mg::index_params<cagra::index_params>& index_params,                         \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
+    cuvs::neighbors::mg::detail::build(                                                \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
+      index_dataset);                                                                  \
+    return index;                                                                      \
+  }                                                                                    \
+                                                                                       \
+  void search(const raft::device_resources& handle,                                    \
+              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
+              const mg::search_params<cagra::search_params>& search_params,            \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+              raft::host_matrix_view<float, int64_t, row_major> distances,             \
+              int64_t n_rows_per_batch)                                                \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::search(                                               \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
+      queries,                                                                         \
+      neighbors,                                                                       \
+      distances,                                                                       \
+      n_rows_per_batch);                                                               \
+  }                                                                                    \
+                                                                                       \
+  void serialize(const raft::device_resources& handle,                                 \
+                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
+                 const std::string& filename)                                          \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
+    return idx;                                                                        \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
+    return idx;                                                                        \
+  }
+CUVS_INST_MG_CAGRA(float, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
new file mode 100644
index 0000000000..8f76c69a34
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
+  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
+    const raft::device_resources& handle,                                              \
+    const mg::index_params<cagra::index_params>& index_params,                         \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
+    cuvs::neighbors::mg::detail::build(                                                \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
+      index_dataset);                                                                  \
+    return index;                                                                      \
+  }                                                                                    \
+                                                                                       \
+  void search(const raft::device_resources& handle,                                    \
+              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
+              const mg::search_params<cagra::search_params>& search_params,            \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+              raft::host_matrix_view<float, int64_t, row_major> distances,             \
+              int64_t n_rows_per_batch)                                                \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::search(                                               \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
+      queries,                                                                         \
+      neighbors,                                                                       \
+      distances,                                                                       \
+      n_rows_per_batch);                                                               \
+  }                                                                                    \
+                                                                                       \
+  void serialize(const raft::device_resources& handle,                                 \
+                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
+                 const std::string& filename)                                          \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
+    return idx;                                                                        \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
+    return idx;                                                                        \
+  }
+CUVS_INST_MG_CAGRA(half, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
new file mode 100644
index 0000000000..67b88d7429
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
+  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
+    const raft::device_resources& handle,                                              \
+    const mg::index_params<cagra::index_params>& index_params,                         \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
+    cuvs::neighbors::mg::detail::build(                                                \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
+      index_dataset);                                                                  \
+    return index;                                                                      \
+  }                                                                                    \
+                                                                                       \
+  void search(const raft::device_resources& handle,                                    \
+              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
+              const mg::search_params<cagra::search_params>& search_params,            \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+              raft::host_matrix_view<float, int64_t, row_major> distances,             \
+              int64_t n_rows_per_batch)                                                \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::search(                                               \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
+      queries,                                                                         \
+      neighbors,                                                                       \
+      distances,                                                                       \
+      n_rows_per_batch);                                                               \
+  }                                                                                    \
+                                                                                       \
+  void serialize(const raft::device_resources& handle,                                 \
+                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
+                 const std::string& filename)                                          \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
+    return idx;                                                                        \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
+    return idx;                                                                        \
+  }
+CUVS_INST_MG_CAGRA(int8_t, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
new file mode 100644
index 0000000000..f721749233
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
+  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
+    const raft::device_resources& handle,                                              \
+    const mg::index_params<cagra::index_params>& index_params,                         \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
+    cuvs::neighbors::mg::detail::build(                                                \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
+      index_dataset);                                                                  \
+    return index;                                                                      \
+  }                                                                                    \
+                                                                                       \
+  void search(const raft::device_resources& handle,                                    \
+              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
+              const mg::search_params<cagra::search_params>& search_params,            \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
+              raft::host_matrix_view<float, int64_t, row_major> distances,             \
+              int64_t n_rows_per_batch)                                                \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::search(                                               \
+      handle,                                                                          \
+      index,                                                                           \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
+      queries,                                                                         \
+      neighbors,                                                                       \
+      distances,                                                                       \
+      n_rows_per_batch);                                                               \
+  }                                                                                    \
+                                                                                       \
+  void serialize(const raft::device_resources& handle,                                 \
+                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
+                 const std::string& filename)                                          \
+  {                                                                                    \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
+    return idx;                                                                        \
+  }                                                                                    \
+                                                                                       \
+  template <>                                                                          \
+  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
+    const raft::device_resources& handle, const std::string& filename)                 \
+  {                                                                                    \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
+    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
+    return idx;                                                                        \
+  }
+CUVS_INST_MG_CAGRA(uint8_t, uint32_t);
+
+#undef CUVS_INST_MG_CAGRA
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_flat_float_int64_t.cu b/cpp/src/neighbors/mg/mg_flat_float_int64_t.cu
new file mode 100644
index 0000000000..4495e2527c
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_flat_float_int64_t.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                        \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> build(                                         \
+    const raft::device_resources& handle,                                                 \
+    const mg::index_params<ivf_flat::index_params>& index_params,                         \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                    \
+  {                                                                                       \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
+    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
+    cuvs::neighbors::mg::detail::build(                                                   \
+      handle,                                                                             \
+      index,                                                                              \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                   \
+      index_dataset);                                                                     \
+    return index;                                                                         \
+  }                                                                                       \
+                                                                                          \
+  void extend(const raft::device_resources& handle,                                       \
+              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                            \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,            \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)     \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);         \
+  }                                                                                       \
+                                                                                          \
+  void search(const raft::device_resources& handle,                                       \
+              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                      \
+              const mg::search_params<ivf_flat::search_params>& search_params,            \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                 \
+              raft::host_matrix_view<float, int64_t, row_major> distances,                \
+              int64_t n_rows_per_batch)                                                   \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::search(                                                  \
+      handle,                                                                             \
+      index,                                                                              \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \
+      queries,                                                                            \
+      neighbors,                                                                          \
+      distances,                                                                          \
+      n_rows_per_batch);                                                                  \
+  }                                                                                       \
+                                                                                          \
+  void serialize(const raft::device_resources& handle,                                    \
+                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                   \
+                 const std::string& filename)                                             \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                      \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(                     \
+    const raft::device_resources& handle, const std::string& filename)                    \
+  {                                                                                       \
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                \
+    return idx;                                                                           \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(                      \
+    const raft::device_resources& handle, const std::string& filename)                    \
+  {                                                                                       \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);       \
+    return idx;                                                                           \
+  }
+CUVS_INST_MG_FLAT(float, int64_t);
+
+#undef CUVS_INST_MG_FLAT
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu
new file mode 100644
index 0000000000..5494414a6a
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                        \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> build(                                         \
+    const raft::device_resources& handle,                                                 \
+    const mg::index_params<ivf_flat::index_params>& index_params,                         \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                    \
+  {                                                                                       \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
+    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
+    cuvs::neighbors::mg::detail::build(                                                   \
+      handle,                                                                             \
+      index,                                                                              \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                   \
+      index_dataset);                                                                     \
+    return index;                                                                         \
+  }                                                                                       \
+                                                                                          \
+  void extend(const raft::device_resources& handle,                                       \
+              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                            \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,            \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)     \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);         \
+  }                                                                                       \
+                                                                                          \
+  void search(const raft::device_resources& handle,                                       \
+              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                      \
+              const mg::search_params<ivf_flat::search_params>& search_params,            \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                 \
+              raft::host_matrix_view<float, int64_t, row_major> distances,                \
+              int64_t n_rows_per_batch)                                                   \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::search(                                                  \
+      handle,                                                                             \
+      index,                                                                              \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \
+      queries,                                                                            \
+      neighbors,                                                                          \
+      distances,                                                                          \
+      n_rows_per_batch);                                                                  \
+  }                                                                                       \
+                                                                                          \
+  void serialize(const raft::device_resources& handle,                                    \
+                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                   \
+                 const std::string& filename)                                             \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                      \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(                     \
+    const raft::device_resources& handle, const std::string& filename)                    \
+  {                                                                                       \
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                \
+    return idx;                                                                           \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(                      \
+    const raft::device_resources& handle, const std::string& filename)                    \
+  {                                                                                       \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);       \
+    return idx;                                                                           \
+  }
+CUVS_INST_MG_FLAT(int8_t, int64_t);
+
+#undef CUVS_INST_MG_FLAT
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..35df2146bf
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                        \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> build(                                         \
+    const raft::device_resources& handle,                                                 \
+    const mg::index_params<ivf_flat::index_params>& index_params,                         \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                    \
+  {                                                                                       \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
+    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
+    cuvs::neighbors::mg::detail::build(                                                   \
+      handle,                                                                             \
+      index,                                                                              \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                   \
+      index_dataset);                                                                     \
+    return index;                                                                         \
+  }                                                                                       \
+                                                                                          \
+  void extend(const raft::device_resources& handle,                                       \
+              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                            \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,            \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)     \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);         \
+  }                                                                                       \
+                                                                                          \
+  void search(const raft::device_resources& handle,                                       \
+              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                      \
+              const mg::search_params<ivf_flat::search_params>& search_params,            \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                 \
+              raft::host_matrix_view<float, int64_t, row_major> distances,                \
+              int64_t n_rows_per_batch)                                                   \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::search(                                                  \
+      handle,                                                                             \
+      index,                                                                              \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \
+      queries,                                                                            \
+      neighbors,                                                                          \
+      distances,                                                                          \
+      n_rows_per_batch);                                                                  \
+  }                                                                                       \
+                                                                                          \
+  void serialize(const raft::device_resources& handle,                                    \
+                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                   \
+                 const std::string& filename)                                             \
+  {                                                                                       \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                      \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(                     \
+    const raft::device_resources& handle, const std::string& filename)                    \
+  {                                                                                       \
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                \
+    return idx;                                                                           \
+  }                                                                                       \
+                                                                                          \
+  template <>                                                                             \
+  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(                      \
+    const raft::device_resources& handle, const std::string& filename)                    \
+  {                                                                                       \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
+    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);       \
+    return idx;                                                                           \
+  }
+CUVS_INST_MG_FLAT(uint8_t, int64_t);
+
+#undef CUVS_INST_MG_FLAT
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_float_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_float_int64_t.cu
new file mode 100644
index 0000000000..c671740e61
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_pq_float_int64_t.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
+  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
+    const raft::device_resources& handle,                                                          \
+    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
+    cuvs::neighbors::mg::detail::build(                                                            \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
+      index_dataset);                                                                              \
+    return index;                                                                                  \
+  }                                                                                                \
+                                                                                                   \
+  void extend(const raft::device_resources& handle,                                                \
+              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
+  }                                                                                                \
+                                                                                                   \
+  void search(const raft::device_resources& handle,                                                \
+              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
+              const mg::search_params<ivf_pq::search_params>& search_params,                       \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
+              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
+              int64_t n_rows_per_batch)                                                            \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::search(                                                           \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
+      queries,                                                                                     \
+      neighbors,                                                                                   \
+      distances,                                                                                   \
+      n_rows_per_batch);                                                                           \
+  }                                                                                                \
+                                                                                                   \
+  void serialize(const raft::device_resources& handle,                                             \
+                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
+                 const std::string& filename)                                                      \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
+    const raft::device_resources& handle, const std::string& filename)                             \
+  {                                                                                                \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
+    return idx;                                                                                    \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
+                                                             const std::string& filename)          \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
+    return idx;                                                                                    \
+  }
+CUVS_INST_MG_PQ(float, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_half_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_half_int64_t.cu
new file mode 100644
index 0000000000..b167239c68
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_pq_half_int64_t.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
+  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
+    const raft::device_resources& handle,                                                          \
+    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
+    cuvs::neighbors::mg::detail::build(                                                            \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
+      index_dataset);                                                                              \
+    return index;                                                                                  \
+  }                                                                                                \
+                                                                                                   \
+  void extend(const raft::device_resources& handle,                                                \
+              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
+  }                                                                                                \
+                                                                                                   \
+  void search(const raft::device_resources& handle,                                                \
+              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
+              const mg::search_params<ivf_pq::search_params>& search_params,                       \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
+              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
+              int64_t n_rows_per_batch)                                                            \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::search(                                                           \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
+      queries,                                                                                     \
+      neighbors,                                                                                   \
+      distances,                                                                                   \
+      n_rows_per_batch);                                                                           \
+  }                                                                                                \
+                                                                                                   \
+  void serialize(const raft::device_resources& handle,                                             \
+                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
+                 const std::string& filename)                                                      \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
+    const raft::device_resources& handle, const std::string& filename)                             \
+  {                                                                                                \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
+    return idx;                                                                                    \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
+                                                             const std::string& filename)          \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
+    return idx;                                                                                    \
+  }
+CUVS_INST_MG_PQ(half, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu
new file mode 100644
index 0000000000..127baf8fd5
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
+  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
+    const raft::device_resources& handle,                                                          \
+    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
+    cuvs::neighbors::mg::detail::build(                                                            \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
+      index_dataset);                                                                              \
+    return index;                                                                                  \
+  }                                                                                                \
+                                                                                                   \
+  void extend(const raft::device_resources& handle,                                                \
+              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
+  }                                                                                                \
+                                                                                                   \
+  void search(const raft::device_resources& handle,                                                \
+              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
+              const mg::search_params<ivf_pq::search_params>& search_params,                       \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
+              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
+              int64_t n_rows_per_batch)                                                            \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::search(                                                           \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
+      queries,                                                                                     \
+      neighbors,                                                                                   \
+      distances,                                                                                   \
+      n_rows_per_batch);                                                                           \
+  }                                                                                                \
+                                                                                                   \
+  void serialize(const raft::device_resources& handle,                                             \
+                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
+                 const std::string& filename)                                                      \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
+    const raft::device_resources& handle, const std::string& filename)                             \
+  {                                                                                                \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
+    return idx;                                                                                    \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
+                                                             const std::string& filename)          \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
+    return idx;                                                                                    \
+  }
+CUVS_INST_MG_PQ(int8_t, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..869e009a5b
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
+  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
+    const raft::device_resources& handle,                                                          \
+    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
+    cuvs::neighbors::mg::detail::build(                                                            \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
+      index_dataset);                                                                              \
+    return index;                                                                                  \
+  }                                                                                                \
+                                                                                                   \
+  void extend(const raft::device_resources& handle,                                                \
+              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
+  }                                                                                                \
+                                                                                                   \
+  void search(const raft::device_resources& handle,                                                \
+              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
+              const mg::search_params<ivf_pq::search_params>& search_params,                       \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
+              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
+              int64_t n_rows_per_batch)                                                            \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::search(                                                           \
+      handle,                                                                                      \
+      index,                                                                                       \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
+      queries,                                                                                     \
+      neighbors,                                                                                   \
+      distances,                                                                                   \
+      n_rows_per_batch);                                                                           \
+  }                                                                                                \
+                                                                                                   \
+  void serialize(const raft::device_resources& handle,                                             \
+                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
+                 const std::string& filename)                                                      \
+  {                                                                                                \
+    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
+    const raft::device_resources& handle, const std::string& filename)                             \
+  {                                                                                                \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
+    return idx;                                                                                    \
+  }                                                                                                \
+                                                                                                   \
+  template <>                                                                                      \
+  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
+                                                             const std::string& filename)          \
+  {                                                                                                \
+    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
+    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
+    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
+    return idx;                                                                                    \
+  }
+CUVS_INST_MG_PQ(uint8_t, int64_t);
+
+#undef CUVS_INST_MG_PQ
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/nccl_comm.cpp b/cpp/src/neighbors/mg/nccl_comm.cpp
new file mode 100644
index 0000000000..c4556957ae
--- /dev/null
+++ b/cpp/src/neighbors/mg/nccl_comm.cpp
@@ -0,0 +1,8 @@
+#include <nccl.h>
+#include <raft/core/resources.hpp>
+
+namespace raft::comms {
+void build_comms_nccl_only(raft::resources* handle, ncclComm_t nccl_comm, int num_ranks, int rank)
+{
+}
+}  // namespace raft::comms
diff --git a/cpp/src/neighbors/mg/omp_checks.cpp b/cpp/src/neighbors/mg/omp_checks.cpp
new file mode 100644
index 0000000000..e09182dfe8
--- /dev/null
+++ b/cpp/src/neighbors/mg/omp_checks.cpp
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <omp.h>
+#include <raft/core/logger.hpp>
+
+namespace cuvs::neighbors::mg {
+using raft::RAFT_NAME;
+
+void check_omp_threads(const int requirements)
+{
+  const int max_threads = omp_get_max_threads();
+  if (max_threads < requirements)
+    RAFT_LOG_WARN(
+      "OpenMP is only allowed %d threads to run %d GPUs. Please increase the number of OpenMP "
+      "threads to avoid NCCL hangs by modifying the environment variable OMP_NUM_THREADS.",
+      max_threads,
+      requirements);
+}
+
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index bd07bebeef..f4d35e4383 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -22,7 +22,7 @@ rapids_test_init()
 function(ConfigureTest)
 
   set(options OPTIONAL NOCUDA C_LIB)
-  set(oneValueArgs NAME GPUS PERCENT)
+  set(oneValueArgs NAME GPUS PERCENT ADDITIONAL_DEP)
   set(multiValueArgs PATH TARGETS CONFIGURATIONS)
 
   cmake_parse_arguments(_CUVS_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
@@ -56,6 +56,7 @@ function(ConfigureTest)
             $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
             $<TARGET_NAME_IF_EXISTS:conda_env>
             $<$<BOOL:${_CUVS_TEST_C_LIB}>:cuvs::c_api>
+            ${_CUVS_TEST_ADDITIONAL_DEP}
   )
   set_target_properties(
     ${TEST_NAME}
@@ -159,7 +160,7 @@ if(BUILD_TESTS)
     100
   )
 
- ConfigureTest(
+  ConfigureTest(
     NAME
     NEIGHBORS_ANN_VAMANA_TEST
     PATH
@@ -178,6 +179,12 @@ if(BUILD_TESTS)
     target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()
 
+  if(BUILD_MG_ALGOS)
+    ConfigureTest(
+      NAME NEIGHBORS_MG_TEST PATH neighbors/mg/test_float.cu GPUS 1 PERCENT 100 ADDITIONAL_DEP nccl
+    )
+  endif()
+
   ConfigureTest(
     NAME
     DISTANCE_TEST
diff --git a/cpp/test/neighbors/mg.cuh b/cpp/test/neighbors/mg.cuh
new file mode 100644
index 0000000000..be30ca6153
--- /dev/null
+++ b/cpp/test/neighbors/mg.cuh
@@ -0,0 +1,825 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+#include "naive_knn.cuh"
+
+#include <cuvs/neighbors/mg.hpp>
+#include <raft/core/resource/nccl_clique.hpp>
+
+namespace cuvs::neighbors::mg {
+
+enum class algo_t { IVF_FLAT, IVF_PQ, CAGRA };
+enum class d_mode_t { REPLICATED, SHARDED, LOCAL_THEN_DISTRIBUTED, ROUND_ROBIN };
+enum class m_mode_t { MERGE_ON_ROOT_RANK, TREE_MERGE, UNDEFINED };
+
+struct AnnMGInputs {
+  int64_t num_queries;
+  int64_t num_db_vecs;
+  int64_t dim;
+  int64_t k;
+  d_mode_t d_mode;
+  m_mode_t m_mode;
+  algo_t algo;
+  int64_t nprobe;
+  int64_t nlist;
+  cuvs::distance::DistanceType metric;
+  bool adaptive_centers;
+};
+
+template <typename T, typename DataT>
+class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
+ public:
+  AnnMGTest()
+    : stream_(resource::get_cuda_stream(handle_)),
+      clique_(raft::resource::get_nccl_clique(handle_)),
+      ps(::testing::TestWithParam<AnnMGInputs>::GetParam()),
+      d_index_dataset(0, stream_),
+      d_queries(0, stream_),
+      h_index_dataset(0),
+      h_queries(0)
+  {
+  }
+
+  void testAnnMG()
+  {
+    size_t queries_size = ps.num_queries * ps.k;
+    std::vector<int64_t> neighbors_ref(queries_size);
+    std::vector<T> distances_ref(queries_size);
+    std::vector<int64_t> neighbors_snmg_ann(queries_size);
+    std::vector<T> distances_snmg_ann(queries_size);
+    std::vector<uint32_t> neighbors_ref_32bits(queries_size);
+    std::vector<uint32_t> neighbors_snmg_ann_32bits(queries_size);
+
+    {
+      rmm::device_uvector<T> distances_ref_dev(queries_size, stream_);
+      rmm::device_uvector<int64_t> neighbors_ref_dev(queries_size, stream_);
+      cuvs::neighbors::naive_knn<T, DataT, int64_t>(handle_,
+                                                    distances_ref_dev.data(),
+                                                    neighbors_ref_dev.data(),
+                                                    d_queries.data(),
+                                                    d_index_dataset.data(),
+                                                    ps.num_queries,
+                                                    ps.num_db_vecs,
+                                                    ps.dim,
+                                                    ps.k,
+                                                    ps.metric);
+      update_host(distances_ref.data(), distances_ref_dev.data(), queries_size, stream_);
+      update_host(neighbors_ref.data(), neighbors_ref_dev.data(), queries_size, stream_);
+      resource::sync_stream(handle_);
+    }
+
+    int64_t n_rows_per_search_batch = 3000;  // [3000, 3000, 1000] == 7000 rows
+
+    // IVF-Flat
+    if (ps.algo == algo_t::IVF_FLAT &&
+        (ps.d_mode == d_mode_t::REPLICATED || ps.d_mode == d_mode_t::SHARDED)) {
+      distribution_mode d_mode;
+      if (ps.d_mode == d_mode_t::REPLICATED)
+        d_mode = distribution_mode::REPLICATED;
+      else
+        d_mode = distribution_mode::SHARDED;
+
+      mg::index_params<ivf_flat::index_params> index_params;
+      index_params.n_lists                  = ps.nlist;
+      index_params.metric                   = ps.metric;
+      index_params.adaptive_centers         = ps.adaptive_centers;
+      index_params.add_data_on_build        = false;
+      index_params.kmeans_trainset_fraction = 1.0;
+      index_params.metric_arg               = 0;
+      index_params.mode                     = d_mode;
+
+      mg::search_params<ivf_flat::search_params> search_params;
+      search_params.n_probes    = ps.nprobe;
+      search_params.search_mode = LOAD_BALANCER;
+
+      auto index_dataset = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_index_dataset.data(), ps.num_db_vecs, ps.dim);
+      auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+      auto neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+        neighbors_snmg_ann.data(), ps.num_queries, ps.k);
+      auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+        distances_snmg_ann.data(), ps.num_queries, ps.k);
+
+      {
+        auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
+        cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
+        cuvs::neighbors::mg::serialize(handle_, index, "mg_ivf_flat_index");
+      }
+      auto new_index =
+        cuvs::neighbors::mg::deserialize_flat<DataT, int64_t>(handle_, "mg_ivf_flat_index");
+
+      if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
+        search_params.merge_mode = MERGE_ON_ROOT_RANK;
+      else
+        search_params.merge_mode = TREE_MERGE;
+      cuvs::neighbors::mg::search(
+        handle_, new_index, search_params, queries, neighbors, distances, n_rows_per_search_batch);
+      resource::sync_stream(handle_);
+
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+      ASSERT_TRUE(eval_neighbours(neighbors_ref,
+                                  neighbors_snmg_ann,
+                                  distances_ref,
+                                  distances_snmg_ann,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+      std::fill(neighbors_snmg_ann.begin(), neighbors_snmg_ann.end(), 0);
+      std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
+    }
+
+    // IVF-PQ
+    if (ps.algo == algo_t::IVF_PQ &&
+        (ps.d_mode == d_mode_t::REPLICATED || ps.d_mode == d_mode_t::SHARDED)) {
+      distribution_mode d_mode;
+      if (ps.d_mode == d_mode_t::REPLICATED)
+        d_mode = distribution_mode::REPLICATED;
+      else
+        d_mode = distribution_mode::SHARDED;
+
+      mg::index_params<ivf_pq::index_params> index_params;
+      index_params.n_lists                  = ps.nlist;
+      index_params.metric                   = ps.metric;
+      index_params.add_data_on_build        = false;
+      index_params.kmeans_trainset_fraction = 1.0;
+      index_params.metric_arg               = 0;
+      index_params.mode                     = d_mode;
+
+      mg::search_params<ivf_pq::search_params> search_params;
+      search_params.n_probes    = ps.nprobe;
+      search_params.search_mode = LOAD_BALANCER;
+
+      auto index_dataset = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_index_dataset.data(), ps.num_db_vecs, ps.dim);
+      auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+      auto neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+        neighbors_snmg_ann.data(), ps.num_queries, ps.k);
+      auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+        distances_snmg_ann.data(), ps.num_queries, ps.k);
+
+      {
+        auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
+        cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
+        cuvs::neighbors::mg::serialize(handle_, index, "mg_ivf_pq_index");
+      }
+      auto new_index =
+        cuvs::neighbors::mg::deserialize_pq<DataT, int64_t>(handle_, "mg_ivf_pq_index");
+
+      if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
+        search_params.merge_mode = MERGE_ON_ROOT_RANK;
+      else
+        search_params.merge_mode = TREE_MERGE;
+      cuvs::neighbors::mg::search(
+        handle_, new_index, search_params, queries, neighbors, distances, n_rows_per_search_batch);
+      resource::sync_stream(handle_);
+
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+      ASSERT_TRUE(eval_neighbours(neighbors_ref,
+                                  neighbors_snmg_ann,
+                                  distances_ref,
+                                  distances_snmg_ann,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+      std::fill(neighbors_snmg_ann.begin(), neighbors_snmg_ann.end(), 0);
+      std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
+    }
+
+    // CAGRA
+    if (ps.algo == algo_t::CAGRA &&
+        (ps.d_mode == d_mode_t::REPLICATED || ps.d_mode == d_mode_t::SHARDED)) {
+      distribution_mode d_mode;
+      if (ps.d_mode == d_mode_t::REPLICATED)
+        d_mode = distribution_mode::REPLICATED;
+      else
+        d_mode = distribution_mode::SHARDED;
+
+      mg::index_params<cagra::index_params> index_params;
+      index_params.graph_build_params = cagra::graph_build_params::ivf_pq_params(
+        raft::matrix_extent<int64_t>(ps.num_db_vecs, ps.dim));
+      index_params.mode = d_mode;
+
+      mg::search_params<cagra::search_params> search_params;
+
+      auto index_dataset = raft::make_host_matrix_view<const DataT, uint32_t, row_major>(
+        h_index_dataset.data(), ps.num_db_vecs, ps.dim);
+      auto queries = raft::make_host_matrix_view<const DataT, uint32_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+      auto neighbors = raft::make_host_matrix_view<uint32_t, uint32_t, row_major>(
+        neighbors_snmg_ann_32bits.data(), ps.num_queries, ps.k);
+      auto distances = raft::make_host_matrix_view<float, uint32_t, row_major>(
+        distances_snmg_ann.data(), ps.num_queries, ps.k);
+
+      {
+        auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
+        cuvs::neighbors::mg::serialize(handle_, index, "mg_cagra_index");
+      }
+      auto new_index =
+        cuvs::neighbors::mg::deserialize_cagra<DataT, uint32_t>(handle_, "mg_cagra_index");
+
+      if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
+        search_params.merge_mode = MERGE_ON_ROOT_RANK;
+      else
+        search_params.merge_mode = TREE_MERGE;
+      cuvs::neighbors::mg::search(
+        handle_, new_index, search_params, queries, neighbors, distances, n_rows_per_search_batch);
+      resource::sync_stream(handle_);
+
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+      ASSERT_TRUE(eval_neighbours(neighbors_ref_32bits,
+                                  neighbors_snmg_ann_32bits,
+                                  distances_ref,
+                                  distances_snmg_ann,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+      std::fill(neighbors_snmg_ann_32bits.begin(), neighbors_snmg_ann_32bits.end(), 0);
+      std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
+    }
+
+    if (ps.algo == algo_t::IVF_FLAT && ps.d_mode == d_mode_t::LOCAL_THEN_DISTRIBUTED) {
+      ivf_flat::index_params index_params;
+      index_params.n_lists                  = ps.nlist;
+      index_params.metric                   = ps.metric;
+      index_params.adaptive_centers         = ps.adaptive_centers;
+      index_params.add_data_on_build        = true;
+      index_params.kmeans_trainset_fraction = 1.0;
+      index_params.metric_arg               = 0;
+
+      mg::search_params<ivf_flat::search_params> search_params;
+      search_params.n_probes    = ps.nprobe;
+      search_params.search_mode = LOAD_BALANCER;
+
+      {
+        auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
+          d_index_dataset.data(), ps.num_db_vecs, ps.dim);
+        auto index = cuvs::neighbors::ivf_flat::build(handle_, index_params, index_dataset);
+        ivf_flat::serialize(handle_, "local_ivf_flat_index", index);
+      }
+
+      auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+      auto neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+        neighbors_snmg_ann.data(), ps.num_queries, ps.k);
+      auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+        distances_snmg_ann.data(), ps.num_queries, ps.k);
+
+      auto distributed_index =
+        cuvs::neighbors::mg::distribute_flat<DataT, int64_t>(handle_, "local_ivf_flat_index");
+      search_params.merge_mode = TREE_MERGE;
+      cuvs::neighbors::mg::search(handle_,
+                                  distributed_index,
+                                  search_params,
+                                  queries,
+                                  neighbors,
+                                  distances,
+                                  n_rows_per_search_batch);
+
+      resource::sync_stream(handle_);
+
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+      ASSERT_TRUE(eval_neighbours(neighbors_ref,
+                                  neighbors_snmg_ann,
+                                  distances_ref,
+                                  distances_snmg_ann,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+      std::fill(neighbors_snmg_ann.begin(), neighbors_snmg_ann.end(), 0);
+      std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
+    }
+
+    if (ps.algo == algo_t::IVF_PQ && ps.d_mode == d_mode_t::LOCAL_THEN_DISTRIBUTED) {
+      ivf_pq::index_params index_params;
+      index_params.n_lists                  = ps.nlist;
+      index_params.metric                   = ps.metric;
+      index_params.add_data_on_build        = true;
+      index_params.kmeans_trainset_fraction = 1.0;
+      index_params.metric_arg               = 0;
+
+      mg::search_params<ivf_pq::search_params> search_params;
+      search_params.n_probes    = ps.nprobe;
+      search_params.search_mode = LOAD_BALANCER;
+
+      {
+        auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
+          d_index_dataset.data(), ps.num_db_vecs, ps.dim);
+        auto index = cuvs::neighbors::ivf_pq::build(handle_, index_params, index_dataset);
+        ivf_pq::serialize(handle_, "local_ivf_pq_index", index);
+      }
+
+      auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+      auto neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+        neighbors_snmg_ann.data(), ps.num_queries, ps.k);
+      auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+        distances_snmg_ann.data(), ps.num_queries, ps.k);
+
+      auto distributed_index =
+        cuvs::neighbors::mg::distribute_pq<DataT, int64_t>(handle_, "local_ivf_pq_index");
+      search_params.merge_mode = TREE_MERGE;
+      cuvs::neighbors::mg::search(handle_,
+                                  distributed_index,
+                                  search_params,
+                                  queries,
+                                  neighbors,
+                                  distances,
+                                  n_rows_per_search_batch);
+
+      resource::sync_stream(handle_);
+
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+      ASSERT_TRUE(eval_neighbours(neighbors_ref,
+                                  neighbors_snmg_ann,
+                                  distances_ref,
+                                  distances_snmg_ann,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+      std::fill(neighbors_snmg_ann.begin(), neighbors_snmg_ann.end(), 0);
+      std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
+    }
+
+    if (ps.algo == algo_t::CAGRA && ps.d_mode == d_mode_t::LOCAL_THEN_DISTRIBUTED) {
+      cagra::index_params index_params;
+      index_params.graph_build_params = cagra::graph_build_params::ivf_pq_params(
+        raft::matrix_extent<int64_t>(ps.num_db_vecs, ps.dim));
+
+      mg::search_params<cagra::search_params> search_params;
+
+      {
+        auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
+          d_index_dataset.data(), ps.num_db_vecs, ps.dim);
+        auto index = cuvs::neighbors::cagra::build(handle_, index_params, index_dataset);
+        cuvs::neighbors::cagra::serialize(handle_, "local_cagra_index", index);
+      }
+
+      auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+      auto neighbors = raft::make_host_matrix_view<uint32_t, int64_t, row_major>(
+        neighbors_snmg_ann_32bits.data(), ps.num_queries, ps.k);
+      auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+        distances_snmg_ann.data(), ps.num_queries, ps.k);
+
+      auto distributed_index =
+        cuvs::neighbors::mg::distribute_cagra<DataT, uint32_t>(handle_, "local_cagra_index");
+
+      search_params.merge_mode = TREE_MERGE;
+      cuvs::neighbors::mg::search(handle_,
+                                  distributed_index,
+                                  search_params,
+                                  queries,
+                                  neighbors,
+                                  distances,
+                                  n_rows_per_search_batch);
+
+      resource::sync_stream(handle_);
+
+      double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
+      ASSERT_TRUE(eval_neighbours(neighbors_ref_32bits,
+                                  neighbors_snmg_ann_32bits,
+                                  distances_ref,
+                                  distances_snmg_ann,
+                                  ps.num_queries,
+                                  ps.k,
+                                  0.001,
+                                  min_recall));
+      std::fill(neighbors_snmg_ann_32bits.begin(), neighbors_snmg_ann_32bits.end(), 0);
+      std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
+    }
+
+    if (ps.algo == algo_t::IVF_FLAT && ps.d_mode == d_mode_t::ROUND_ROBIN) {
+      ASSERT_TRUE(ps.num_queries <= 4);
+
+      mg::index_params<ivf_flat::index_params> index_params;
+      index_params.n_lists                  = ps.nlist;
+      index_params.metric                   = ps.metric;
+      index_params.adaptive_centers         = ps.adaptive_centers;
+      index_params.add_data_on_build        = false;
+      index_params.kmeans_trainset_fraction = 1.0;
+      index_params.metric_arg               = 0;
+      index_params.mode                     = REPLICATED;
+
+      mg::search_params<ivf_flat::search_params> search_params;
+      search_params.n_probes    = ps.nprobe;
+      search_params.search_mode = ROUND_ROBIN;
+
+      auto index_dataset = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_index_dataset.data(), ps.num_db_vecs, ps.dim);
+      auto small_batch_query = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+
+      auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
+      cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
+
+      int n_parallel_searches = 16;
+      std::vector<char> searches_correctness(n_parallel_searches);
+      std::vector<int64_t> load_balancer_neighbors_snmg_ann(n_parallel_searches * ps.num_queries *
+                                                            ps.k);
+      std::vector<float> load_balancer_distances_snmg_ann(n_parallel_searches * ps.num_queries *
+                                                          ps.k);
+#pragma omp parallel for
+      for (uint64_t search_idx = 0; search_idx < searches_correctness.size(); search_idx++) {
+        uint64_t offset            = search_idx * ps.num_queries * ps.k;
+        auto small_batch_neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+          load_balancer_neighbors_snmg_ann.data() + offset, ps.num_queries, ps.k);
+        auto small_batch_distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+          load_balancer_distances_snmg_ann.data() + offset, ps.num_queries, ps.k);
+        cuvs::neighbors::mg::search(handle_,
+                                    index,
+                                    search_params,
+                                    small_batch_query,
+                                    small_batch_neighbors,
+                                    small_batch_distances,
+                                    n_rows_per_search_batch);
+
+        std::vector<int64_t> small_batch_neighbors_vec(
+          small_batch_neighbors.data_handle(),
+          small_batch_neighbors.data_handle() + small_batch_neighbors.size());
+        std::vector<float> small_batch_distances_vec(
+          small_batch_distances.data_handle(),
+          small_batch_distances.data_handle() + small_batch_distances.size());
+        searches_correctness[search_idx] = eval_neighbours(neighbors_ref,
+                                                           small_batch_neighbors_vec,
+                                                           distances_ref,
+                                                           small_batch_distances_vec,
+                                                           ps.num_queries,
+                                                           ps.k,
+                                                           0.001,
+                                                           0.9);
+      }
+      ASSERT_TRUE(std::all_of(searches_correctness.begin(),
+                              searches_correctness.end(),
+                              [](char val) { return val != 0; }));
+    }
+
+    if (ps.algo == algo_t::IVF_PQ && ps.d_mode == d_mode_t::ROUND_ROBIN) {
+      ASSERT_TRUE(ps.num_queries <= 4);
+
+      mg::index_params<ivf_pq::index_params> index_params;
+      index_params.n_lists                  = ps.nlist;
+      index_params.metric                   = ps.metric;
+      index_params.add_data_on_build        = false;
+      index_params.kmeans_trainset_fraction = 1.0;
+      index_params.metric_arg               = 0;
+      index_params.mode                     = REPLICATED;
+
+      mg::search_params<ivf_pq::search_params> search_params;
+      search_params.n_probes    = ps.nprobe;
+      search_params.search_mode = ROUND_ROBIN;
+
+      auto index_dataset = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_index_dataset.data(), ps.num_db_vecs, ps.dim);
+      auto small_batch_query = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+
+      auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
+      cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
+
+      int n_parallel_searches = 16;
+      std::vector<char> searches_correctness(n_parallel_searches);
+      std::vector<int64_t> load_balancer_neighbors_snmg_ann(n_parallel_searches * ps.num_queries *
+                                                            ps.k);
+      std::vector<float> load_balancer_distances_snmg_ann(n_parallel_searches * ps.num_queries *
+                                                          ps.k);
+#pragma omp parallel for
+      for (uint64_t search_idx = 0; search_idx < searches_correctness.size(); search_idx++) {
+        uint64_t offset            = search_idx * ps.num_queries * ps.k;
+        auto small_batch_neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+          load_balancer_neighbors_snmg_ann.data() + offset, ps.num_queries, ps.k);
+        auto small_batch_distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+          load_balancer_distances_snmg_ann.data() + offset, ps.num_queries, ps.k);
+        cuvs::neighbors::mg::search(handle_,
+                                    index,
+                                    search_params,
+                                    small_batch_query,
+                                    small_batch_neighbors,
+                                    small_batch_distances,
+                                    n_rows_per_search_batch);
+
+        std::vector<int64_t> small_batch_neighbors_vec(
+          small_batch_neighbors.data_handle(),
+          small_batch_neighbors.data_handle() + small_batch_neighbors.size());
+        std::vector<float> small_batch_distances_vec(
+          small_batch_distances.data_handle(),
+          small_batch_distances.data_handle() + small_batch_distances.size());
+        searches_correctness[search_idx] = eval_neighbours(neighbors_ref,
+                                                           small_batch_neighbors_vec,
+                                                           distances_ref,
+                                                           small_batch_distances_vec,
+                                                           ps.num_queries,
+                                                           ps.k,
+                                                           0.001,
+                                                           0.9);
+      }
+      ASSERT_TRUE(std::all_of(searches_correctness.begin(),
+                              searches_correctness.end(),
+                              [](char val) { return val != 0; }));
+    }
+
+    if (ps.algo == algo_t::CAGRA && ps.d_mode == d_mode_t::ROUND_ROBIN) {
+      ASSERT_TRUE(ps.num_queries <= 4);
+
+      mg::index_params<cagra::index_params> index_params;
+      index_params.graph_build_params = cagra::graph_build_params::ivf_pq_params(
+        raft::matrix_extent<int64_t>(ps.num_db_vecs, ps.dim));
+      index_params.mode = REPLICATED;
+
+      mg::search_params<cagra::search_params> search_params;
+      search_params.search_mode = ROUND_ROBIN;
+
+      auto index_dataset = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_index_dataset.data(), ps.num_db_vecs, ps.dim);
+      auto small_batch_query = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
+        h_queries.data(), ps.num_queries, ps.dim);
+
+      auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
+
+      int n_parallel_searches = 16;
+      std::vector<char> searches_correctness(n_parallel_searches);
+      std::vector<uint32_t> load_balancer_neighbors_snmg_ann(n_parallel_searches * ps.num_queries *
+                                                             ps.k);
+      std::vector<float> load_balancer_distances_snmg_ann(n_parallel_searches * ps.num_queries *
+                                                          ps.k);
+#pragma omp parallel for
+      for (uint64_t search_idx = 0; search_idx < searches_correctness.size(); search_idx++) {
+        uint64_t offset            = search_idx * ps.num_queries * ps.k;
+        auto small_batch_neighbors = raft::make_host_matrix_view<uint32_t, int64_t, row_major>(
+          load_balancer_neighbors_snmg_ann.data() + offset, ps.num_queries, ps.k);
+        auto small_batch_distances = raft::make_host_matrix_view<float, int64_t, row_major>(
+          load_balancer_distances_snmg_ann.data() + offset, ps.num_queries, ps.k);
+        cuvs::neighbors::mg::search(handle_,
+                                    index,
+                                    search_params,
+                                    small_batch_query,
+                                    small_batch_neighbors,
+                                    small_batch_distances,
+                                    n_rows_per_search_batch);
+
+        std::vector<uint32_t> small_batch_neighbors_vec(
+          small_batch_neighbors.data_handle(),
+          small_batch_neighbors.data_handle() + small_batch_neighbors.size());
+        std::vector<float> small_batch_distances_vec(
+          small_batch_distances.data_handle(),
+          small_batch_distances.data_handle() + small_batch_distances.size());
+        searches_correctness[search_idx] = eval_neighbours(neighbors_ref_32bits,
+                                                           small_batch_neighbors_vec,
+                                                           distances_ref,
+                                                           small_batch_distances_vec,
+                                                           ps.num_queries,
+                                                           ps.k,
+                                                           0.001,
+                                                           0.9);
+      }
+      ASSERT_TRUE(std::all_of(searches_correctness.begin(),
+                              searches_correctness.end(),
+                              [](char val) { return val != 0; }));
+    }
+  }
+
+  void SetUp() override
+  {
+    d_index_dataset.resize(ps.num_db_vecs * ps.dim, stream_);
+    d_queries.resize(ps.num_queries * ps.dim, stream_);
+    h_index_dataset.resize(ps.num_db_vecs * ps.dim);
+    h_queries.resize(ps.num_queries * ps.dim);
+
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::uniform(
+        handle_, r, d_index_dataset.data(), d_index_dataset.size(), DataT(0.1), DataT(2.0));
+      raft::random::uniform(handle_, r, d_queries.data(), d_queries.size(), DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, d_index_dataset.data(), d_index_dataset.size(), DataT(1), DataT(20));
+      raft::random::uniformInt(handle_, r, d_queries.data(), d_queries.size(), DataT(1), DataT(20));
+    }
+
+    raft::copy(h_index_dataset.data(),
+               d_index_dataset.data(),
+               d_index_dataset.size(),
+               resource::get_cuda_stream(handle_));
+    raft::copy(
+      h_queries.data(), d_queries.data(), d_queries.size(), resource::get_cuda_stream(handle_));
+    resource::sync_stream(handle_);
+  }
+
+  void TearDown() override {}
+
+ private:
+  raft::device_resources handle_;
+  rmm::cuda_stream_view stream_;
+  raft::comms::nccl_clique clique_;
+  AnnMGInputs ps;
+  std::vector<DataT> h_index_dataset;
+  std::vector<DataT> h_queries;
+  rmm::device_uvector<DataT> d_index_dataset;
+  rmm::device_uvector<DataT> d_queries;
+};
+
+const std::vector<AnnMGInputs> inputs = {
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::REPLICATED,
+   m_mode_t::UNDEFINED,
+   algo_t::IVF_FLAT,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::REPLICATED,
+   m_mode_t::UNDEFINED,
+   algo_t::IVF_PQ,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+
+  /*
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::REPLICATED,
+   m_mode_t::UNDEFINED,
+   algo_t::CAGRA,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  */
+
+  /*
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::SHARDED,
+   m_mode_t::MERGE_ON_ROOT_RANK,
+   algo_t::IVF_FLAT,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::SHARDED,
+   m_mode_t::MERGE_ON_ROOT_RANK,
+   algo_t::IVF_PQ,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::SHARDED,
+   m_mode_t::MERGE_ON_ROOT_RANK,
+   algo_t::CAGRA,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::SHARDED,
+   m_mode_t::TREE_MERGE,
+   algo_t::IVF_FLAT,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::SHARDED,
+   m_mode_t::TREE_MERGE,
+   algo_t::IVF_PQ,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::SHARDED,
+   m_mode_t::TREE_MERGE,
+   algo_t::CAGRA,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  */
+
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::LOCAL_THEN_DISTRIBUTED,
+   m_mode_t::UNDEFINED,
+   algo_t::IVF_FLAT,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::LOCAL_THEN_DISTRIBUTED,
+   m_mode_t::UNDEFINED,
+   algo_t::IVF_PQ,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+
+  /*
+  {7000,
+   10000,
+   8,
+   16,
+   d_mode_t::LOCAL_THEN_DISTRIBUTED,
+   m_mode_t::UNDEFINED,
+   algo_t::CAGRA,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  */
+
+  {3,
+   10000,
+   8,
+   16,
+   d_mode_t::ROUND_ROBIN,
+   m_mode_t::UNDEFINED,
+   algo_t::IVF_FLAT,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  {3,
+   10000,
+   8,
+   16,
+   d_mode_t::ROUND_ROBIN,
+   m_mode_t::UNDEFINED,
+   algo_t::IVF_PQ,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+
+  /*
+  {3,
+   10000,
+   8,
+   16,
+   d_mode_t::ROUND_ROBIN,
+   m_mode_t::UNDEFINED,
+   algo_t::CAGRA,
+   40,
+   1024,
+   cuvs::distance::DistanceType::L2Expanded,
+   true},
+  */
+};
+}  // namespace cuvs::neighbors::mg
diff --git a/cpp/test/neighbors/mg/test_float.cu b/cpp/test/neighbors/mg/test_float.cu
new file mode 100644
index 0000000000..ef9c9a043c
--- /dev/null
+++ b/cpp/test/neighbors/mg/test_float.cu
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../mg.cuh"
+
+namespace cuvs::neighbors::mg {
+
+typedef AnnMGTest<float, float> AnnMGTestF_float;
+TEST_P(AnnMGTestF_float, AnnMG) { this->testAnnMG(); }
+
+INSTANTIATE_TEST_CASE_P(AnnMGTest, AnnMGTestF_float, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::mg
diff --git a/dependencies.yaml b/dependencies.yaml
index c18f53305d..2b19b987f6 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -159,6 +159,7 @@ dependencies:
         packages:
           - c-compiler
           - cxx-compiler
+          - nccl>=2.19
     specific:
       - output_types: conda
         matrices:
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 9c7c98989a..9d0d391dc7 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -128,6 +128,16 @@ Once installed, the shared libraries, headers (and any dependencies downloaded a
     ./build.sh libcuvs --uninstall
 
 
+Multi-GPU features
+^^^^^^^^^^^^^^^^^^
+
+To disable the multi-gpu features run :
+
+.. code-block:: bash
+
+    ./build.sh libcuvs --no-mg
+
+
 Building the Googletests
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index e5a9fc9773..0c68c8415e 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -15,3 +15,4 @@ Nearest Neighbors
    neighbors_ivf_pq.rst
    neighbors_nn_descent.rst
    neighbors_refine.rst
+   neighbors_mg.rst
diff --git a/docs/source/cpp_api/neighbors_mg.rst b/docs/source/cpp_api/neighbors_mg.rst
new file mode 100644
index 0000000000..b68defec9f
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_mg.rst
@@ -0,0 +1,76 @@
+Distributed ANN
+===============
+
+The SNMG (single-node multi-GPUs) ANN API provides a set of functions to deploy ANN indexes on multiple GPUs.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/mg.hpp>``
+
+namespace *cuvs::neighbors::mg*
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: mg_cpp_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Search parameters
+----------------------
+
+.. doxygengroup:: mg_cpp_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: mg_cpp_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index extend
+------------
+
+.. doxygengroup:: mg_cpp_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search
+------------
+
+.. doxygengroup:: mg_cpp_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: mg_cpp_serialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index deserialize
+-----------------
+
+.. doxygengroup:: mg_cpp_deserialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+Distribute pre-built local index
+--------------------------------
+
+.. doxygengroup:: mg_cpp_distribute
+    :project: cuvs
+    :members:
+    :content-only:

From 496427f6668c9430957b0077970ae52b2f591c49 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 3 Oct 2024 12:28:06 -0700
Subject: [PATCH 174/623] Remove cuvs-cagra-search from cuvs_static link (#388)

Seeing build errors in the cuml wheels, and the only difference afaict
was adding the cuvs-cagra-search to link to the static library.

Removing this since cuml is the only consumer of the static library this
release and doesn't use cagra (will revisit in 24.12)
---
 cpp/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 52c5f66244..66d489c242 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -566,7 +566,7 @@ if(NOT BUILD_CPU_ONLY)
   target_link_libraries(
     cuvs_static
     PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
-    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX> cuvs-cagra-search
+    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
   )
 endif()
 
@@ -707,7 +707,7 @@ include(GNUInstallDirs)
 include(CPack)
 
 install(
-  TARGETS cuvs cuvs_static cuvs-cagra-search
+  TARGETS cuvs cuvs_static
   DESTINATION ${lib_dir}
   COMPONENT cuvs
   EXPORT cuvs-exports

From 3c7f1177536f2cb7b35265f42811d3e6b2f41631 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Thu, 3 Oct 2024 16:30:02 -0400
Subject: [PATCH 175/623] Python API for CAGRA+HNSW (#246)

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/246
---
 cpp/include/cuvs/neighbors/cagra.h            |  11 +-
 cpp/include/cuvs/neighbors/hnsw.h             |   6 +-
 cpp/include/cuvs/neighbors/hnsw.hpp           |   6 +-
 cpp/src/neighbors/cagra_c.cpp                 |   8 +
 .../detail/cagra/cagra_serialize.cuh          |  21 +-
 cpp/src/neighbors/detail/hnsw.hpp             |  13 +-
 cpp/src/neighbors/hnsw.cpp                    |  24 +-
 cpp/src/neighbors/hnsw_c.cpp                  |  16 +-
 docs/source/c_api/neighbors.rst               |   1 +
 docs/source/cpp_api/neighbors.rst             |   1 +
 docs/source/python_api/neighbors.rst          |   1 +
 docs/source/python_api/neighbors_hnsw.rst     |  30 ++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |  17 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  17 +-
 .../cuvs/cuvs/neighbors/hnsw/CMakeLists.txt   |  24 ++
 python/cuvs/cuvs/neighbors/hnsw/__init__.pxd  |   0
 python/cuvs/cuvs/neighbors/hnsw/__init__.py   |  25 ++
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd      |  53 +++
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx      | 380 ++++++++++++++++++
 python/cuvs/cuvs/test/test_hnsw.py            |  97 +++++
 21 files changed, 695 insertions(+), 57 deletions(-)
 create mode 100644 docs/source/python_api/neighbors_hnsw.rst
 create mode 100644 python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/hnsw/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/hnsw/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
 create mode 100644 python/cuvs/cuvs/test/test_hnsw.py

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 241f5d8b01..14331ebbca 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -267,6 +267,15 @@ cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index);
  */
 cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index);
 
+/**
+ * @brief Get dimension of the CAGRA index
+ *
+ * @param[in] index CAGRA index
+ * @param[out] dim return dimension of the index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim);
+
 /**
  * @}
  */
@@ -338,7 +347,7 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  *        with the same type of `queries`, such that `index.dtype.code ==
  * queries.dl_tensor.dtype.code` Types for input are:
  *        1. `queries`:
- *`         a. kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *          a. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *          b. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
  *          c. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
index 5e94de60a1..0495c574a6 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -105,8 +105,10 @@ cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index);
  *        with the same type of `queries`, such that `index.dtype.code ==
  *        queries.dl_tensor.dtype.code`
  *        Supported types for input are:
- *        1. `queries`: `kDLDataType.code == kDLFloat` or `kDLDataType.code == kDLInt` and
- * `kDLDataType.bits = 32`
+ *        1. `queries`:
+ *          a. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *          b. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *          c. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 64`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 007adef0d4..d5abd6d559 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -173,6 +173,8 @@ std::unique_ptr<index<int8_t>> from_cagra(
 
 /**@}*/
 
+// TODO: Filtered Search APIs: https://github.com/rapidsai/cuvs/issues/363
+
 /**
  * @defgroup hnsw_cpp_index_search Search hnswlib index
  * @{
@@ -260,7 +262,7 @@ void search(raft::resources const& res,
 void search(raft::resources const& res,
             const search_params& params,
             const index<uint8_t>& idx,
-            raft::host_matrix_view<const int, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
             raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances);
 
@@ -303,7 +305,7 @@ void search(raft::resources const& res,
 void search(raft::resources const& res,
             const search_params& params,
             const index<int8_t>& idx,
-            raft::host_matrix_view<const int, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> queries,
             raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances);
 
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 164448f2c9..6985ff0948 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -176,6 +176,14 @@ extern "C" cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index_c_ptr)
   });
 }
 
+extern "C" cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<float, uint32_t>*>(index->addr);
+    *dim           = index_ptr->dim();
+  });
+}
+
 extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                                       cuvsCagraIndexParams_t params,
                                       DLManagedTensor* dataset_tensor,
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index b92ef0ace4..a077c098f5 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -120,9 +120,9 @@ void serialize_to_hnswlib(raft::resources const& res,
   os.write(reinterpret_cast<char*>(&curr_element_count), sizeof(std::size_t));
   // Example:M: 16, dim = 128, data_t = float, index_t = uint32_t, list_size_type = uint32_t,
   // labeltype: size_t size_data_per_element_ = M * 2 * sizeof(index_t) + sizeof(list_size_type) +
-  // dim * 4 + sizeof(labeltype)
-  auto size_data_per_element =
-    static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4 + index_.dim() * 4 + 8);
+  // dim * sizeof(T) + sizeof(labeltype)
+  auto size_data_per_element = static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4 +
+                                                        index_.dim() * sizeof(T) + 8);
   os.write(reinterpret_cast<char*>(&size_data_per_element), sizeof(std::size_t));
   // label_offset
   std::size_t label_offset = size_data_per_element - 8;
@@ -185,18 +185,9 @@ void serialize_to_hnswlib(raft::resources const& res,
     }
 
     auto data_row = host_dataset.data_handle() + (index_.dim() * i);
-    if constexpr (std::is_same_v<T, float>) {
-      for (std::size_t j = 0; j < index_.dim(); ++j) {
-        auto data_elem = static_cast<float>(host_dataset(i, j));
-        os.write(reinterpret_cast<char*>(&data_elem), sizeof(float));
-      }
-    } else if constexpr (std::is_same_v<T, std::int8_t> or std::is_same_v<T, std::uint8_t>) {
-      for (std::size_t j = 0; j < index_.dim(); ++j) {
-        auto data_elem = static_cast<int>(host_dataset(i, j));
-        os.write(reinterpret_cast<char*>(&data_elem), sizeof(int));
-      }
-    } else {
-      RAFT_FAIL("Unsupported dataset type while saving CAGRA dataset to HNSWlib format");
+    for (std::size_t j = 0; j < index_.dim(); ++j) {
+      auto data_elem = static_cast<T>(host_dataset(i, j));
+      os.write(reinterpret_cast<char*>(&data_elem), sizeof(T));
     }
 
     os.write(reinterpret_cast<char*>(&i), sizeof(std::size_t));
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index 0d1ae4ec96..ce1e032648 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -110,9 +110,9 @@ std::unique_ptr<index<T>> from_cagra(raft::resources const& res,
   return std::unique_ptr<index<T>>(hnsw_index);
 }
 
-template <typename QueriesT>
-void get_search_knn_results(hnswlib::HierarchicalNSW<QueriesT> const* idx,
-                            const QueriesT* query,
+template <typename T>
+void get_search_knn_results(hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type> const* idx,
+                            const T* query,
                             int k,
                             uint64_t* indices,
                             float* distances)
@@ -127,11 +127,11 @@ void get_search_knn_results(hnswlib::HierarchicalNSW<QueriesT> const* idx,
   }
 }
 
-template <typename T, typename QueriesT>
+template <typename T>
 void search(raft::resources const& res,
             const search_params& params,
             const index<T>& idx,
-            raft::host_matrix_view<const QueriesT, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<const T, int64_t, raft::row_major> queries,
             raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances)
 {
@@ -146,7 +146,8 @@ void search(raft::resources const& res,
 
   idx.set_ef(params.ef);
   auto const* hnswlib_index =
-    reinterpret_cast<hnswlib::HierarchicalNSW<QueriesT> const*>(idx.get_index());
+    reinterpret_cast<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type> const*>(
+      idx.get_index());
 
   // when num_threads == 0, automatically maximize parallelism
   if (params.num_threads) {
diff --git a/cpp/src/neighbors/hnsw.cpp b/cpp/src/neighbors/hnsw.cpp
index 36cbb16c9d..e6f3fbcc7a 100644
--- a/cpp/src/neighbors/hnsw.cpp
+++ b/cpp/src/neighbors/hnsw.cpp
@@ -34,20 +34,20 @@ CUVS_INST_HNSW_FROM_CAGRA(int8_t);
 
 #undef CUVS_INST_HNSW_FROM_CAGRA
 
-#define CUVS_INST_HNSW_SEARCH(T, QueriesT)                                              \
-  void search(raft::resources const& res,                                               \
-              const search_params& params,                                              \
-              const index<T>& idx,                                                      \
-              raft::host_matrix_view<const QueriesT, int64_t, raft::row_major> queries, \
-              raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,     \
-              raft::host_matrix_view<float, int64_t, raft::row_major> distances)        \
-  {                                                                                     \
-    detail::search<T, QueriesT>(res, params, idx, queries, neighbors, distances);       \
+#define CUVS_INST_HNSW_SEARCH(T)                                                    \
+  void search(raft::resources const& res,                                           \
+              const search_params& params,                                          \
+              const index<T>& idx,                                                  \
+              raft::host_matrix_view<const T, int64_t, raft::row_major> queries,    \
+              raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors, \
+              raft::host_matrix_view<float, int64_t, raft::row_major> distances)    \
+  {                                                                                 \
+    detail::search<T>(res, params, idx, queries, neighbors, distances);             \
   }
 
-CUVS_INST_HNSW_SEARCH(float, float);
-CUVS_INST_HNSW_SEARCH(uint8_t, int);
-CUVS_INST_HNSW_SEARCH(int8_t, int);
+CUVS_INST_HNSW_SEARCH(float);
+CUVS_INST_HNSW_SEARCH(uint8_t);
+CUVS_INST_HNSW_SEARCH(int8_t);
 
 #undef CUVS_INST_HNSW_SEARCH
 
diff --git a/cpp/src/neighbors/hnsw_c.cpp b/cpp/src/neighbors/hnsw_c.cpp
index ab5268a6d3..a198756417 100644
--- a/cpp/src/neighbors/hnsw_c.cpp
+++ b/cpp/src/neighbors/hnsw_c.cpp
@@ -31,7 +31,7 @@
 #include <cuvs/neighbors/hnsw.hpp>
 
 namespace {
-template <typename T, typename QueriesT>
+template <typename T>
 void _search(cuvsResources_t res,
              cuvsHnswSearchParams params,
              cuvsHnswIndex index,
@@ -46,7 +46,7 @@ void _search(cuvsResources_t res,
   search_params.ef          = params.ef;
   search_params.num_threads = params.numThreads;
 
-  using queries_mdspan_type   = raft::host_matrix_view<QueriesT const, int64_t, raft::row_major>;
+  using queries_mdspan_type   = raft::host_matrix_view<T const, int64_t, raft::row_major>;
   using neighbors_mdspan_type = raft::host_matrix_view<uint64_t, int64_t, raft::row_major>;
   using distances_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
   auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
@@ -127,16 +127,13 @@ extern "C" cuvsError_t cuvsHnswSearch(cuvsResources_t res,
 
     auto index = *index_c_ptr;
     RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
-    RAFT_EXPECTS(queries.dtype.bits == 32, "number of bits in queries dtype should be 32");
 
     if (index.dtype.code == kDLFloat) {
-      _search<float, float>(
-        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+      _search<float>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
     } else if (index.dtype.code == kDLUInt) {
-      _search<uint8_t, int>(
-        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+      _search<uint8_t>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
     } else if (index.dtype.code == kDLInt) {
-      _search<int8_t, int>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+      _search<int8_t>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
     } else {
       RAFT_FAIL("Unsupported index dtype: %d and bits: %d", queries.dtype.code, queries.dtype.bits);
     }
@@ -152,13 +149,10 @@ extern "C" cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       index->addr = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename, dim, metric));
-      index->dtype.code = kDLFloat;
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
       index->addr = reinterpret_cast<uintptr_t>(_deserialize<uint8_t>(res, filename, dim, metric));
-      index->dtype.code = kDLInt;
     } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
       index->addr = reinterpret_cast<uintptr_t>(_deserialize<int8_t>(res, filename, dim, metric));
-      index->dtype.code = kDLUInt;
     } else {
       RAFT_FAIL("Unsupported dtype in file %s", filename);
     }
diff --git a/docs/source/c_api/neighbors.rst b/docs/source/c_api/neighbors.rst
index dc55a74dca..9c3fce672a 100644
--- a/docs/source/c_api/neighbors.rst
+++ b/docs/source/c_api/neighbors.rst
@@ -13,3 +13,4 @@ Nearest Neighbors
    neighbors_ivf_flat_c.rst
    neighbors_ivf_pq_c.rst
    neighbors_cagra_c.rst
+   neighbors_hnsw_c.rst
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index 0c68c8415e..d55d58eb0a 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -11,6 +11,7 @@ Nearest Neighbors
 
    neighbors_bruteforce.rst
    neighbors_cagra.rst
+   neighbors_hnsw.rst
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
    neighbors_nn_descent.rst
diff --git a/docs/source/python_api/neighbors.rst b/docs/source/python_api/neighbors.rst
index 022c50de34..cd4f2609cd 100644
--- a/docs/source/python_api/neighbors.rst
+++ b/docs/source/python_api/neighbors.rst
@@ -11,5 +11,6 @@ Nearest Neighbors
 
    neighbors_brute_force.rst
    neighbors_cagra.rst
+   neighbors_hnsw.rst
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
diff --git a/docs/source/python_api/neighbors_hnsw.rst b/docs/source/python_api/neighbors_hnsw.rst
new file mode 100644
index 0000000000..9922805b36
--- /dev/null
+++ b/docs/source/python_api/neighbors_hnsw.rst
@@ -0,0 +1,30 @@
+HNSW
+====
+
+This is a wrapper for hnswlib, to load a CAGRA index as an immutable HNSW index. The loaded HNSW index is only compatible in cuVS, and can be searched using wrapper functions.
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Index search parameters
+#######################
+
+.. autoclass:: cuvs.neighbors.hnsw.SearchParams
+    :members:
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.hnsw.Index
+    :members:
+
+Index Conversion
+################
+
+.. autofunction:: cuvs.neighbors.hnsw.from_cagra
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.hnsw.search
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 21c3db5da3..f68bbea533 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -14,6 +14,7 @@
 
 add_subdirectory(brute_force)
 add_subdirectory(cagra)
+add_subdirectory(hnsw)
 add_subdirectory(ivf_flat)
 add_subdirectory(ivf_pq)
 add_subdirectory(filters)
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index b23c2a4b3e..bba5a91a8e 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -17,6 +17,7 @@
 
 from libc.stdint cimport (
     int8_t,
+    int32_t,
     int64_t,
     uint8_t,
     uint32_t,
@@ -100,6 +101,8 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
 
     cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index)
 
+    cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int32_t* dim)
+
     cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                                cuvsCagraIndexParams* params,
                                DLManagedTensor* dataset,
@@ -117,6 +120,20 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
                                    cuvsCagraIndex_t index,
                                    bool include_dataset) except +
 
+    cuvsError_t cuvsCagraSerializeToHnswlib(cuvsResources_t res,
+                                            const char * filename,
+                                            cuvsCagraIndex_t index) except +
+
     cuvsError_t cuvsCagraDeserialize(cuvsResources_t res,
                                      const char * filename,
                                      cuvsCagraIndex_t index) except +
+
+cdef class Index:
+    """
+    CAGRA index object. This object stores the trained CAGRA index state
+    which can be used to perform nearest neighbors searches.
+    """
+
+    cdef cuvsCagraIndex_t index
+    cdef bool trained
+    cdef str active_index_type
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index f940ab8bfd..95209dbeb9 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -36,6 +36,7 @@ from pylibraft.neighbors.common import _check_input_array
 
 from libc.stdint cimport (
     int8_t,
+    int32_t,
     int64_t,
     uint8_t,
     uint32_t,
@@ -206,16 +207,9 @@ cdef class IndexParams:
 
 
 cdef class Index:
-    """
-    CAGRA index object. This object stores the trained CAGRA index state
-    which can be used to perform nearest neighbors searches.
-    """
-
-    cdef cuvsCagraIndex_t index
-    cdef bool trained
-
     def __cinit__(self):
         self.trained = False
+        self.active_index_type = None
         check_cuvs(cuvsCagraIndexCreate(&self.index))
 
     def __dealloc__(self):
@@ -226,6 +220,12 @@ cdef class Index:
     def trained(self):
         return self.trained
 
+    @property
+    def dim(self):
+        cdef int32_t dim
+        check_cuvs(cuvsCagraIndexGetDims(self.index, &dim))
+        return dim
+
     def __repr__(self):
         # todo(dgd): update repr as we expose data through C API
         attr_str = []
@@ -299,6 +299,7 @@ def build(IndexParams index_params, dataset, resources=None):
             idx.index
         ))
         idx.trained = True
+        idx.active_index_type = dataset_ai.dtype.name
 
     return idx
 
diff --git a/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
new file mode 100644
index 0000000000..1f9c422ca5
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources hnsw.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_hnsw_
+)
diff --git a/python/cuvs/cuvs/neighbors/hnsw/__init__.pxd b/python/cuvs/cuvs/neighbors/hnsw/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/hnsw/__init__.py b/python/cuvs/cuvs/neighbors/hnsw/__init__.py
new file mode 100644
index 0000000000..5efcdf68b2
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/hnsw/__init__.py
@@ -0,0 +1,25 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .hnsw import Index, SearchParams, from_cagra, load, save, search
+
+__all__ = [
+    "Index",
+    "SearchParams",
+    "load",
+    "save",
+    "search",
+    "from_cagra",
+]
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
new file mode 100644
index 0000000000..1cdc974064
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
@@ -0,0 +1,53 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport int32_t, uintptr_t
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "cuvs/neighbors/hnsw.h" nogil:
+    ctypedef struct cuvsHnswSearchParams:
+        int32_t ef
+        int32_t numThreads
+
+    ctypedef cuvsHnswSearchParams* cuvsHnswSearchParams_t
+
+    ctypedef struct cuvsHnswIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsHnswIndex* cuvsHnswIndex_t
+
+    cuvsError_t cuvsHnswIndexCreate(cuvsHnswIndex_t* index)
+
+    cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index)
+
+    cuvsError_t cuvsHnswSearch(cuvsResources_t res,
+                               cuvsHnswSearchParams* params,
+                               cuvsHnswIndex_t index,
+                               DLManagedTensor* queries,
+                               DLManagedTensor* neighbors,
+                               DLManagedTensor* distances) except +
+
+    cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
+                                    const char * filename,
+                                    int32_t dim,
+                                    cuvsDistanceType metric,
+                                    cuvsHnswIndex_t index) except +
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
new file mode 100644
index 0000000000..018fcfef93
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
@@ -0,0 +1,380 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uint32_t
+from libcpp cimport bool
+from libcpp.string cimport string
+
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.resources import auto_sync_resources
+
+from cuvs.common cimport cydlpack
+
+import numpy as np
+
+from cuvs.distance import DISTANCE_TYPES
+
+from cuvs.neighbors.cagra cimport cagra
+
+import os
+import uuid
+
+from pylibraft.common import auto_convert_output
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+from pylibraft.neighbors.common import _check_input_array
+
+
+cdef class SearchParams:
+    """
+    HNSW search parameters
+
+    Parameters
+    ----------
+    ef: int, default = 200
+        Maximum number of candidate list size used during search.
+    num_threads: int, default = 0
+        Number of CPU threads used to increase search parallelism.
+        When set to 0, the number of threads is automatically determined
+        using OpenMP's `omp_get_max_threads()`.
+    """
+
+    cdef cuvsHnswSearchParams params
+
+    def __init__(self, *,
+                 ef=200,
+                 num_threads=0):
+        self.params.ef = ef
+        self.params.numThreads = num_threads
+
+    def __repr__(self):
+        attr_str = [attr + "=" + str(getattr(self, attr))
+                    for attr in [
+                        "ef", "num_threads"]]
+        return "SearchParams(type=HNSW, " + (", ".join(attr_str)) + ")"
+
+    @property
+    def ef(self):
+        return self.params.ef
+
+    @property
+    def num_threads(self):
+        return self.params.numThreads
+
+
+cdef class Index:
+    """
+    HNSW index object. This object stores the trained HNSW index state
+    which can be used to perform nearest neighbors searches.
+    """
+
+    cdef cuvsHnswIndex_t index
+    cdef bool trained
+
+    def __cinit__(self):
+        self.trained = False
+        check_cuvs(cuvsHnswIndexCreate(&self.index))
+
+    def __dealloc__(self):
+        if self.index is not NULL:
+            check_cuvs(cuvsHnswIndexDestroy(self.index))
+
+    @property
+    def trained(self):
+        return self.trained
+
+    def __repr__(self):
+        # todo(dgd): update repr as we expose data through C API
+        attr_str = []
+        return "Index(type=HNSW, metric=L2" + (", ".join(attr_str)) + ")"
+
+
+@auto_sync_resources
+def save(filename, cagra.Index index, resources=None):
+    """
+    Saves the CAGRA index to a file as an hnswlib index.
+    The saved index is immutable and can only be searched by the hnswlib
+    wrapper in cuVS, as the format is not compatible with the original
+    hnswlib.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    index : Index
+        Trained CAGRA index.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = cagra.build(cagra.IndexParams(), dataset)
+    >>> # Serialize and deserialize the cagra index built
+    >>> hnsw.save("my_index.bin", index)
+    """
+    cdef string c_filename = filename.encode('utf-8')
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    check_cuvs(cagra.cuvsCagraSerializeToHnswlib(res,
+                                                 c_filename.c_str(),
+                                                 index.index))
+
+
+@auto_sync_resources
+def load(filename, dim, dtype, metric="sqeuclidean", resources=None):
+    """
+    Loads base-layer-only hnswlib index from file, which was originally
+    saved as a built CAGRA index. The loaded index is immutable and can only
+    be searched by the hnswlib wrapper in cuVS, as the format is not
+    compatible with the original hnswlib.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change, therefore loading an index saved with a previous
+    version of cuVS is not guaranteed to work.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    dim : int
+        Dimensions of the training dataest
+    dtype : np.dtype of the saved index
+        Valid values for dtype: [np.float32, np.byte, np.ubyte]
+    metric : string denoting the metric type, default="sqeuclidean"
+        Valid values for metric: ["sqeuclidean", "inner_product"], where
+            - sqeuclidean is the euclidean distance without the square root
+              operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2,
+            - inner_product distance is defined as
+              distance(a, b) = \\sum_i a_i * b_i.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : HnswIndex
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra
+    >>> from cuvs.neighbors import hnsw
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = cagra.build(cagra.IndexParams(), dataset)
+    >>> # Serialize the CAGRA index to hnswlib base layer only index format
+    >>> hnsw.save("my_index.bin", index)
+    >>> index = hnsw.load("my_index.bin", n_features, np.float32,
+    ...                   "sqeuclidean")
+    """
+    cdef Index idx = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    cdef string c_filename = filename.encode('utf-8')
+    cdef cydlpack.DLDataType dl_dtype
+    if dtype == np.float32:
+        dl_dtype.code = cydlpack.kDLFloat
+        dl_dtype.bits = 32
+        dl_dtype.lanes = 1
+    elif dtype == np.ubyte:
+        dl_dtype.code = cydlpack.kDLUInt
+        dl_dtype.bits = 8
+        dl_dtype.lanes = 1
+    elif dtype == np.byte:
+        dl_dtype.code = cydlpack.kDLInt
+        dl_dtype.bits = 8
+        dl_dtype.lanes = 1
+    else:
+        raise ValueError("Only float32 is supported for dtype")
+
+    idx.index.dtype = dl_dtype
+    cdef cuvsDistanceType distance_type = DISTANCE_TYPES[metric]
+
+    check_cuvs(cuvsHnswDeserialize(
+        res,
+        c_filename.c_str(),
+        dim,
+        distance_type,
+        idx.index
+    ))
+    idx.trained = True
+    return idx
+
+
+@auto_sync_resources
+def from_cagra(cagra.Index index, temporary_index_path=None, resources=None):
+    """
+    Returns an hnsw base-layer-only index from a CAGRA index.
+
+    NOTE: This method uses the filesystem to write the CAGRA index in
+          `/tmp/<random_number>.bin` or the parameter `temporary_index_path`
+          if not None before reading it as an hnsw index,
+          then deleting the temporary file. The returned index is immutable
+          and can only be searched by the hnsw wrapper in cuVS, as the
+          format is not compatible with the original hnswlib library.
+          By `base_layer_only`, we mean that the hnsw index is created
+          without the additional layers that are used for the hierarchical
+          search in hnswlib. Instead, the base layer is used for the search.
+
+    Saving / loading the index is experimental. The serialization format is
+    subject to change.
+
+    Parameters
+    ----------
+    index : Index
+        Trained CAGRA index.
+    temporary_index_path : string, default = None
+        Path to save the temporary index file. If None, the temporary file
+        will be saved in `/tmp/<random_number>.bin`.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra
+    >>> from cuvs.neighbors import hnsw
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = cagra.build(cagra.IndexParams(), dataset)
+    >>> # Serialize the CAGRA index to hnswlib base layer only index format
+    >>> hnsw_index = hnsw.from_cagra(index)
+    """
+    uuid_num = uuid.uuid4()
+    filename = temporary_index_path if temporary_index_path else \
+        f"/tmp/{uuid_num}.bin"
+    save(filename, index, resources=resources)
+    hnsw_index = load(filename, index.dim, np.dtype(index.active_index_type),
+                      "sqeuclidean", resources=resources)
+    os.remove(filename)
+    return hnsw_index
+
+
+@auto_sync_resources
+@auto_convert_output
+def search(SearchParams search_params,
+           Index index,
+           queries,
+           k,
+           neighbors=None,
+           distances=None,
+           resources=None):
+    """
+    Find the k nearest neighbors for each query.
+
+    Parameters
+    ----------
+    search_params : SearchParams
+    index : Index
+        Trained CAGRA index.
+    queries : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int]
+    k : int
+        The number of neighbors.
+    neighbors : Optional CUDA array interface compliant matrix shape
+                (n_queries, k), dtype uint64_t. If supplied, neighbor
+                indices will be written here in-place. (default None)
+    distances : Optional CUDA array interface compliant matrix shape
+                (n_queries, k) If supplied, the distances to the
+                neighbors will be written here in-place. (default None)
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra, hnsw
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = cagra.build(cagra.IndexParams(), dataset)
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> k = 10
+    >>> search_params = hnsw.SearchParams(
+    ...     ef=200,
+    ...     num_threads=0
+    ... )
+    >>> # Convert CAGRA index to HNSW
+    >>> hnsw_index = hnsw.from_cagra(index)
+    >>> # Using a pooling allocator reduces overhead of temporary array
+    >>> # creation during search. This is useful if multiple searches
+    >>> # are performed with same query size.
+    >>> distances, neighbors = hnsw.search(search_params, index, queries,
+    ...                                     k)
+    >>> neighbors = cp.asarray(neighbors)
+    >>> distances = cp.asarray(distances)
+    """
+    if not index.trained:
+        raise ValueError("Index needs to be built before calling search.")
+
+    # todo(dgd): we can make the check of dtype a parameter of wrap_array
+    # in RAFT to make this a single call
+    queries_ai = wrap_array(queries)
+    _check_input_array(queries_ai, [np.dtype('float32'),
+                                    np.dtype('uint8'),
+                                    np.dtype('int8')])
+
+    cdef uint32_t n_queries = queries_ai.shape[0]
+
+    if neighbors is None:
+        neighbors = np.empty((n_queries, k), dtype='uint64')
+
+    neighbors_ai = wrap_array(neighbors)
+    _check_input_array(neighbors_ai, [np.dtype('uint64')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    if distances is None:
+        distances = np.empty((n_queries, k), dtype='float32')
+
+    distances_ai = wrap_array(distances)
+    _check_input_array(distances_ai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    cdef cuvsHnswSearchParams* params = &search_params.params
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_ai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_ai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_ai)
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsHnswSearch(
+            res,
+            params,
+            index.index,
+            queries_dlpack,
+            neighbors_dlpack,
+            distances_dlpack
+        ))
+
+    return (distances, neighbors)
diff --git a/python/cuvs/cuvs/test/test_hnsw.py b/python/cuvs/cuvs/test/test_hnsw.py
new file mode 100644
index 0000000000..0ae97266b9
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_hnsw.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     h ttp://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+
+from cuvs.neighbors import cagra, hnsw
+from cuvs.test.ann_utils import calc_recall, generate_data
+
+
+def run_hnsw_build_search_test(
+    n_rows=1000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    metric="sqeuclidean",
+    build_algo="ivf_pq",
+    intermediate_graph_degree=128,
+    graph_degree=64,
+    search_params={},
+):
+    dataset = generate_data((n_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+        if dtype in [np.int8, np.uint8]:
+            pytest.skip(
+                "inner_product metric is not supported for int8/uint8 data"
+            )
+        if build_algo == "nn_descent":
+            pytest.skip("inner_product metric is not supported for nn_descent")
+
+    build_params = cagra.IndexParams(
+        metric=metric,
+        intermediate_graph_degree=intermediate_graph_degree,
+        graph_degree=graph_degree,
+        build_algo=build_algo,
+    )
+
+    index = cagra.build(build_params, dataset)
+
+    assert index.trained
+
+    hnsw_index = hnsw.from_cagra(index)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+
+    search_params = hnsw.SearchParams(**search_params)
+
+    out_dist, out_idx = hnsw.search(search_params, hnsw_index, queries, k)
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_dist, skl_idx = nn_skl.kneighbors(queries, return_distance=True)
+
+    recall = calc_recall(out_idx, skl_idx)
+    assert recall > 0.95
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("k", [10, 20])
+@pytest.mark.parametrize("ef", [30, 40])
+@pytest.mark.parametrize("num_threads", [2, 4])
+@pytest.mark.parametrize("metric", ["sqeuclidean"])
+@pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
+def test_hnsw(dtype, k, ef, num_threads, metric, build_algo):
+    # Note that inner_product tests use normalized input which we cannot
+    # represent in int8, therefore we test only sqeuclidean metric here.
+    run_hnsw_build_search_test(
+        dtype=dtype,
+        k=k,
+        metric=metric,
+        build_algo=build_algo,
+        search_params={"ef": ef, "num_threads": num_threads},
+    )

From cc86ffc99d184074feba40a6d5e95838f6c43153 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 3 Oct 2024 16:46:36 -0400
Subject: [PATCH 176/623] Improving getting started materials (#342)

Adding documentation for each index type. This decouples the major
details in the docs from the individual language API docs to present
users with a better overal experience. The major topcs in the new
index-level docs are meant to be more exhaustive than the API docs in
providing

1. Description of each index type, when it's useful and shortcomings
2. Links to any relevant research material to learn more foundational
info
3. Hyper-parameters
4. Links to API docs for each supported language
5. Links to example projects and notebooks
6. Formulas for rough estimates of memory footprint
7. How to use in cuvs-bench, along with some rough benchmarks on
different hardware

---------

Co-authored-by: Micka <mide@nvidia.com>
---
 README.md                                     | 109 +--
 docs/source/{basics.rst => api_basics.rst}    |   0
 docs/source/api_docs.rst                      |   6 +-
 ...erability.rst => api_interoperability.rst} |   0
 docs/source/build.rst                         |  14 +-
 .../choosing_and_configuring_indexes.rst      |  98 +++
 docs/source/comparing_indexes.rst             |  60 ++
 docs/source/cuvs_bench/build.rst              |  59 ++
 docs/source/cuvs_bench/index.rst              | 702 ++++++++++++++++++
 docs/source/cuvs_bench/param_tuning.rst       | 674 +++++++++++++++++
 docs/source/cuvs_bench/wiki_all_dataset.rst   |  55 ++
 docs/source/getting_started.rst               | 122 ++-
 docs/source/images/build_benchmarks.png       | Bin 0 -> 43332 bytes
 docs/source/images/index_recalls.png          | Bin 0 -> 148929 bytes
 docs/source/images/recall_buckets.png         | Bin 0 -> 70402 bytes
 docs/source/index.rst                         |  34 +-
 docs/source/indexes/bruteforce.rst            |  62 ++
 docs/source/indexes/cagra.rst                 | 149 ++++
 docs/source/indexes/indexes.rst               |  19 +
 docs/source/indexes/ivfflat.rst               | 116 +++
 docs/source/indexes/ivfpq.rst                 | 140 ++++
 docs/source/integrations.rst                  |  53 +-
 docs/source/integrations/faiss.rst            |  14 +
 docs/source/integrations/kinetica.rst         |   6 +
 docs/source/integrations/lucene.rst           |   6 +
 docs/source/integrations/milvus.rst           |   8 +
 docs/source/tuning_guide.rst                  |  56 ++
 .../vector_databases_vs_vector_search.rst     |  57 ++
 28 files changed, 2469 insertions(+), 150 deletions(-)
 rename docs/source/{basics.rst => api_basics.rst} (100%)
 rename docs/source/{interoperability.rst => api_interoperability.rst} (100%)
 create mode 100644 docs/source/choosing_and_configuring_indexes.rst
 create mode 100644 docs/source/comparing_indexes.rst
 create mode 100644 docs/source/cuvs_bench/build.rst
 create mode 100644 docs/source/cuvs_bench/index.rst
 create mode 100644 docs/source/cuvs_bench/param_tuning.rst
 create mode 100644 docs/source/cuvs_bench/wiki_all_dataset.rst
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 create mode 100644 docs/source/indexes/cagra.rst
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 create mode 100644 docs/source/indexes/ivfpq.rst
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 create mode 100644 docs/source/integrations/lucene.rst
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 create mode 100644 docs/source/tuning_guide.rst
 create mode 100644 docs/source/vector_databases_vs_vector_search.rst

diff --git a/README.md b/README.md
index e697c61ed8..38933b4c88 100755
--- a/README.md
+++ b/README.md
@@ -1,11 +1,7 @@
 # <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuVS: Vector Search and Clustering on the GPU</div>
 
 > [!note]
-> cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai/raft) library of data mining primitives. RAPIDS RAFT currently contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms in cuVS. We are in the process of migrating the algorithms from RAFT to cuVS, but if you are unsure of which to use, please consider the following:
-> 1. RAFT contains C++ and Python APIs for all of the approximate nearest neighbors and clustering algorithms.
-> 2. cuVS contains a growing support for different languages, including C, C++, Python, and Rust. We will be adding more language support to cuVS in the future but will not be improving the language support for RAFT.
-> 3. Once all of RAFT's approximate nearest neighbors and clustering algorithms are moved to cuVS, the RAFT APIs will be deprecated and eventually removed altogether. Once removed, RAFT will become a lightweight header-only library. In the meantime, there's no harm in using RAFT if support for additional languages is not needed.
-
+> cuVS is a new library mostly derived from the approximate nearest neighbors and clustering algorithms in the [RAPIDS RAFT](https://github.com/rapidsai/raft) library of machine learning and data mining primitives. As of version 24.10 (Release in October 2024), cuVS contains the most fully-featured versions of the approximate nearest neighbors and clustering algorithms from RAFT. The algorithms which have been migrated over to cuVS will be removed from RAFT in version 24.12 (released in December 2024).
 
 ## Contents
 
@@ -18,10 +14,11 @@
 
 ## Useful Resources
 
+- [Documentation](https://docs.rapids.ai/api/cuvs/): Library documentation.
+- [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build): Instructions for installing and building cuVS.
+- [Getting Started Guide](https://docs.rapids.ai/api/cuvs/nightly/getting_started): Guide to getting started with cuVS.
 - [Code Examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples): Self-contained Code Examples.
 - [API Reference Documentation](https://docs.rapids.ai/api/cuvs/nightly/api_docs): API Documentation.
-- [Getting Started Guide](https://docs.rapids.ai/api/cuvs/nightly/getting_started): Getting started with RAFT.
-- [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build): Instructions for installing and building cuVS.
 - [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
 - [GitHub repository](https://github.com/rapidsai/cuvs): Download the cuVS source code.
 - [Issue tracker](https://github.com/rapidsai/cuvs/issues): Report issues or request features.
@@ -32,32 +29,44 @@ cuVS contains state-of-the-art implementations of several algorithms for running
 
 ## Installing cuVS
 
-cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python). Different packages are available for the different languages supported by cuVS:
+cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python) and [pip](https://pip.pypa.io/en/stable/). Different packages are available for the different languages supported by cuVS:
 
-| Python | C/C++                       |
-|--------|-----------------------------|
-| `cuvs` | `libcuvs`, `libcuvs-static` |
+| Python | C/C++     |
+|--------|-----------|
+| `cuvs` | `libcuvs` |
 
 ### Stable release
 
-It is recommended to use [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) to install the desired packages. The following command will install the Python package. You can substitute `cuvs` for any of the packages in the table above:
+It is recommended to use [mamba](https://conda.github.io/conda-libmamba-solver/user-guide/) to install the desired packages. The following command will install the Python package. You can substitute `cuvs` for any of the packages in the table above:
 
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai cuvs
+conda install -c conda-forge -c nvidia -c rapidsai cuvs
 ```
 
+The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.
+
+For CUDA 11 packages:
+```bash
+pip install cuvs-cu11 --extra-index-url=https://pypi.nvidia.com
+````
+
+And CUDA 12 packages:
+```bash
+pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
+```    
+
 ### Nightlies
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.10
+conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.10
 ```
 
-Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/stable/build/) for more information on installing cuVS and building from source.
+cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
 
 ## Getting Started
 
-The following code snippets train an approximate nearest neighbors index for the CAGRA algorithm.
+The following code snippets train an approximate nearest neighbors index for the CAGRA algorithm in the various different languages supported by cuVS.
 
 ### Python API
 
@@ -85,7 +94,7 @@ cagra::index_params index_params;
 auto index = cagra::build(res, index_params, dataset);
 ```
 
-For more examples of the C++ APIs, refer to the [examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples) directory in the codebase.
+For more code examples of the C++ APIs, including drop-in Cmake project templates, please refer to the [C++ examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples) directory in the codebase.
 
 ### C API
 
@@ -110,6 +119,8 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-24.10/examples/c)
+
 ### Rust API
 
 ```rust
@@ -171,6 +182,7 @@ fn cagra_example() -> Result<()> {
 }
 ```
 
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-24.10/examples/rust).
 
 ## Contributing
 
@@ -178,60 +190,9 @@ If you are interested in contributing to the cuVS library, please read our [Cont
 
 ## References
 
-When citing cuVS generally, please consider referencing this Github repository.
-```bibtex
-@misc{rapidsai,
-  title={Rapidsai/cuVS: Vector Search and Clustering on the GPU.},
-  url={https://github.com/rapidsai/cuvs},
-  journal={GitHub},
-  publisher={Nvidia RAPIDS},
-  author={Rapidsai},
-  year={2024}
-}
-```
-
-If citing CAGRA, please consider the following bibtex:
-```bibtex
-@misc{ootomo2023cagra,
-      title={CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs},
-      author={Hiroyuki Ootomo and Akira Naruse and Corey Nolet and Ray Wang and Tamas Feher and Yong Wang},
-      year={2023},
-      eprint={2308.15136},
-      archivePrefix={arXiv},
-      primaryClass={cs.DS}
-}
-```
-
-If citing the k-selection routines, please consider the following bibtex:
-```bibtex
-@proceedings{10.1145/3581784,
-    title = {SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
-    year = {2023},
-    isbn = {9798400701092},
-    publisher = {Association for Computing Machinery},
-    address = {New York, NY, USA},
-    abstract = {Started in 1988, the SC Conference has become the annual nexus for researchers and practitioners from academia, industry and government to share information and foster collaborations to advance the state of the art in High Performance Computing (HPC), Networking, Storage, and Analysis.},
-    location = {, Denver, CO, USA, }
-}
-```
-
-If citing the nearest neighbors descent API, please consider the following bibtex:
-```bibtex
-@inproceedings{10.1145/3459637.3482344,
-    author = {Wang, Hui and Zhao, Wan-Lei and Zeng, Xiangxiang and Yang, Jianye},
-    title = {Fast K-NN Graph Construction by GPU Based NN-Descent},
-    year = {2021},
-    isbn = {9781450384469},
-    publisher = {Association for Computing Machinery},
-    address = {New York, NY, USA},
-    url = {https://doi.org/10.1145/3459637.3482344},
-    doi = {10.1145/3459637.3482344},
-    abstract = {NN-Descent is a classic k-NN graph construction approach. It is still widely employed in machine learning, computer vision, and information retrieval tasks due to its efficiency and genericness. However, the current design only works well on CPU. In this paper, NN-Descent has been redesigned to adapt to the GPU architecture. A new graph update strategy called selective update is proposed. It reduces the data exchange between GPU cores and GPU global memory significantly, which is the processing bottleneck under GPU computation architecture. This redesign leads to full exploitation of the parallelism of the GPU hardware. In the meantime, the genericness, as well as the simplicity of NN-Descent, are well-preserved. Moreover, a procedure that allows to k-NN graph to be merged efficiently on GPU is proposed. It makes the construction of high-quality k-NN graphs for out-of-GPU-memory datasets tractable. Our approach is 100-250\texttimes{} faster than the single-thread NN-Descent and is 2.5-5\texttimes{} faster than the existing GPU-based approaches as we tested on million as well as billion scale datasets.},
-    booktitle = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
-    pages = {1929–1938},
-    numpages = {10},
-    keywords = {high-dimensional, nn-descent, gpu, k-nearest neighbor graph},
-    location = {Virtual Event, Queensland, Australia},
-    series = {CIKM '21}
-}
-```
+For the interested reader, many of the accelerated implementations in cuVS are also based on research papers which can provide a lot more background. We also ask you to please cite the corresponding algorithms by referencing them in your own research. 
+- [CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search](https://arxiv.org/abs/2308.15136)
+- [Top-K Algorithms on GPU: A Comprehensive Study and New Methods](https://dl.acm.org/doi/10.1145/3581784.3607062>)
+- [Fast K-NN Graph Construction by GPU Based NN-Descent](https://dl.acm.org/doi/abs/10.1145/3459637.3482344?casa_token=O_nan1B1F5cAAAAA:QHWDEhh0wmd6UUTLY9_Gv6c3XI-5DXM9mXVaUXOYeStlpxTPmV3nKvABRfoivZAaQ3n8FWyrkWw>)
+- [cuSLINK: Single-linkage Agglomerative Clustering on the GPU](https://arxiv.org/abs/2306.16354)
+- [GPU Semiring Primitives for Sparse Neighborhood Methods](https://arxiv.org/abs/2104.06357)
diff --git a/docs/source/basics.rst b/docs/source/api_basics.rst
similarity index 100%
rename from docs/source/basics.rst
rename to docs/source/api_basics.rst
diff --git a/docs/source/api_docs.rst b/docs/source/api_docs.rst
index fe71ff3131..f4deef5064 100644
--- a/docs/source/api_docs.rst
+++ b/docs/source/api_docs.rst
@@ -2,10 +2,12 @@ API Reference
 =============
 
 .. toctree::
-   :maxdepth: 1
-   :caption: Contents:
+   :maxdepth: 3
 
    c_api.rst
    cpp_api.rst
    python_api.rst
    rust_api/index.rst
+
+* :ref:`genindex`
+* :ref:`search`
\ No newline at end of file
diff --git a/docs/source/interoperability.rst b/docs/source/api_interoperability.rst
similarity index 100%
rename from docs/source/interoperability.rst
rename to docs/source/api_interoperability.rst
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 9d0d391dc7..f4e1aab6d2 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -38,21 +38,21 @@ C, C++, and Python through Conda
 
 The easiest way to install the pre-compiled C, C++, and Python packages is through conda. You can get a minimal conda installation with `miniforge <https://github.com/conda-forge/miniforge>`__.
 
-Use the following commands, depending on your CUDA version, to install cuVS packages (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
+Use the following commands, depending on your CUDA version, to install cuVS packages (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command and can be enabled using `this guide <https://conda.github.io/conda-libmamba-solver/user-guide/>`_.
 
 C/C++ Package
 ~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-    mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
+   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-    mamba install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.5
+   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.5
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
@@ -97,15 +97,15 @@ Conda environment scripts are provided for installing the necessary dependencies
 
 .. code-block:: bash
 
-    mamba env create --name cuvs -f conda/environments/all_cuda-125_arch-x86_64.yaml
-    mamba activate cuvs
+    conda env create --name cuvs -f conda/environments/all_cuda-125_arch-x86_64.yaml
+    conda activate cuvs
 
 The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
 
 .. code-block:: bash
 
-    mamba env create --name cuvs -f conda/environments/all_cuda-118_arch-x86_64.yaml
-    mamba activate cuvs
+    conda env create --name cuvs -f conda/environments/all_cuda-118_arch-x86_64.yaml
+    conda activate cuvs
 
 The recommended way to build and install cuVS from source is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides CMake options for building and installing the headers, tests, benchmarks, and the pre-compiled shared library.
 
diff --git a/docs/source/choosing_and_configuring_indexes.rst b/docs/source/choosing_and_configuring_indexes.rst
new file mode 100644
index 0000000000..b4c140f295
--- /dev/null
+++ b/docs/source/choosing_and_configuring_indexes.rst
@@ -0,0 +1,98 @@
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Primer on vector search indexes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Vector search indexes often use approximations to trade-off accuracy of the results for speed, either through lowering latency (end-to-end single query speed) or by increasing throughput (the number of query vectors that can be satisfied in a short period of time). Vector search indexes, especially ones that use approximations, are very closely related to machine learning models but they are optimized for fast search and accuracy of results.
+
+When the number of vectors is very small, such as less than 100 thousand vectors, it could be fast enough to use a brute-force (also known as a flat index), which returns exact results but at the expense of exhaustively searching all possible neighbors
+
+Objectives
+==========
+
+This primer addresses the challenge of configuring vector search indexes, but its primary goal is to get a user up and running quickly with acceptable enough results for a good choice of index type and a small and manageable tuning knob, rather than providing a comprehensive guide to tuning each and every hyper-parameter.
+
+For this reason, we focus on 4 primary data sizes:
+
+#. Tiny datasets where GPU is likely not needed (< 100 thousand vectors)
+#. Small datasets where GPU might not be needed (< 1 million vectors)
+#. Large datasets (> 1 million vectors), goal is fast index creation at the expense of search quality
+#. Large datasets where high quality is preferred at the expense of fast index creation
+
+Like other machine learning algorithms, vector search indexes generally have a training step – which means building the index – and an inference – or search step. The hyper-parameters also tend to be broken down into build and search parameters.
+
+While not always the case, a general trend is often observed where the search speed decreases as the quality increases. This also tends to be the case with the index build performance, though different algorithms have different relationships between build time, quality, and search time. It’s important to understand that there’s no free lunch so there will always be trade-offs for each index type.
+
+Definition of quality
+=====================
+
+What do we mean when we say quality of an index? In machine learning terminology, we measure this using recall, which is sometimes used interchangeably to mean accuracy, even though the two are slightly different measures. Recall, when used in vector search, essentially means “out of all of my results, which results would have been included in the exact results?” In vector search, the objective is to find some number of vectors that are closest to a given query vector so recall tends to be more relaxed than accuracy, discriminating only on set inclusion, rather than on exact ordered list matching, which would be closer to an accuracy measure.
+
+Choosing vector search indexes
+==============================
+
+Many vector search algorithms improve scalability while reducing the number of distances by partitioning the vector space into smaller pieces, often through the use of clustering, hashing, trees, and other techniques. Another popular technique is to reduce the width or dimensionality of the space in order to decrease the cost of computing each distance.
+
+Tiny datasets (< 100 thousand vectors)
+--------------------------------------
+
+These datasets are very small and it’s questionable whether or not the GPU would provide any value at all. If the dimensionality is also relatively small (< 1024), you could just use brute-force or HNSW on the CPU and get great performance. If the dimensionality is relatively large (1536, 2048, 4096), you should consider using HNSW. If build time performance is critical, you should consider using CAGRA to build the graph and convert it to an HNSW graph for search (this capability exists today in the standalone cuVS/RAFT libraries and will soon be added to Milvus). An IVF flat index can also be a great candidate here, as it can improve the search performance over brute-force by partitioning the vector space and thus reducing the search space.
+
+Small datasets where GPU might not be needed (< 1 million vectors)
+------------------------------------------------------------------
+
+For smaller dimensionality, such as 1024 or below, you could consider using a brute-force (aka flat) index on GPU and get very good search performance with exact results. You could also use a graph-based index like HNSW on the CPU or CAGRA on the GPU. If build time is critical, you could even build a CAGRA graph on the GPU and convert it to HNSW graph on the CPU.
+
+For larger dimensionality (1536, 2048, 4096), you will start to see lower build-time performance with HNSW for higher quality search settings, and so it becomes more clear that building a CAGRA graph can be useful instead.
+
+Large datasets (> 1 million vectors), goal is fast index creation at the expense of search quality
+--------------------------------------------------------------------------------------------------
+
+For fast ingest where slightly lower search quality is acceptable (85% recall and above), the IVF (inverted file index) methods can be very useful, as they can be very fast to build and still have acceptable search performance. IVF-flat index will partition the vectors into some number of clusters (specified by the user as n_lists) and at search time, some number of closest clusters (defined by n_probes) will be searched with brute-force for each query vector.
+
+IVF-PQ is similar to IVF-flat with the major difference that the vectors are compressed using a lossy product quantized compression so the index can have a much smaller footprint on the GPU. In general, it’s advised to set n_lists = sqrt(n_vectors) and set n_probes to some percentage of n_lists (e.g. 1%, 2%, 4%, 8%, 16%). Because IVF-PQ is a lossy compression, a refinement step can be performed by initially increasing the number of neighbors (by some multiple factor) and using the raw vectors to compute the exact distances, ultimately reducing the neighborhoods down to size k. Even a refinement of 2x (which would query initially for k*2) can be quite effective in making up for recall lost by the PQ compression, but it does come at the expense of having to keep the raw vectors around (keeping in mind many databases store the raw vectors anyways).
+
+Large datasets (> 1 million vectors), goal is high quality search at the expense of fast index creation
+-------------------------------------------------------------------------------------------------------
+
+By trading off index creation performance, an extremely high quality search model can be built. Generally, all of the vector search index types have hyperparameters that have a direct correlation with the search accuracy and so they can be cranked up to yield better recall. Unfortunately, this can also significantly increase the index build time and reduce the search throughput. The trick here is to find the fastest build time that can achieve the best recall with the lowest latency or highest throughput possible.
+
+As for suggested index types, graph-based algorithms like HNSW and CAGRA tend to scale very well to larger datasets while having superior search performance with respect to quality. The challenge is that graph-based indexes require learning a graph and so, as the subtitle of this section suggests, have a tendency to be slower to build than other options. Using the CAGRA algorithm on the GPU can reduce the build time significantly over HNSW, while also having a superior throughput (and lower latency) than searching on the CPU. Currently, the downside to using CAGRA on the GPU is that it requires both the graph and the raw vectors to fit into GPU memory. A middle-ground can be reached by building a CAGRA graph on the GPU and converting it to an HNSW for high quality (and moderately fast) search on the CPU.
+
+
+Tuning and hyperparameter optimization
+======================================
+
+Unfortunately, for large datasets, doing a hyper-parameter optimization on the whole dataset is not always feasible. It is possible, however, to perform a hyper-parameter optimization on the smaller subsets and find reasonably acceptable parameters that should generalize fairly well to the entire dataset. Generally this hyper-parameter optimization will require computing a ground truth on the subset with an exact method like brute-force and then using it to evaluate several searches on randomly sampled vectors.
+
+Full hyper-parameter optimization may also not always be necessary- for example, once you have built a ground truth dataset on a subset, many times you can start by building an index with the default build parameters and then playing around with different search parameters until you get the desired quality and search performance.  For massive indexes that might be multiple terabytes, you could also take this subsampling of, say, 10M vectors, train an index and then tune the search parameters from there. While there might be a small margin of error, the chosen build/search parameters should generalize fairly well for the databases that build locally partitioned indexes.
+
+
+Summary of vector search index types
+====================================
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Trade-offs
+     - Best to use with...
+   * - Brute-force (aka flat)
+     - Exact search but requires exhaustive distance computations
+     - Tiny datasets (< 100k vectors)
+   * - IVF-Flat
+     - Partitions the vector space to reduce distance computations for brute-force search at the expense of recall
+     - Small datasets (<1M vectors) or larger datasets (>1M vectors) where fast index build time is prioritized over quality.
+   * - IVF-PQ
+     - Adds product quantization to IVF-Flat to achieve scale at the expense of recall
+     - Large datasets (>>1M vectors) where fast index build is prioritized over quality
+   * - HNSW
+     - Significantly reduces distance computations at the expense of longer build times
+     - Small datasets (<1M vectors) or large datasets (>1M vectors) where quality and speed of search are prioritized over index build times
+   * - CAGRA
+     - Significantly reduces distance computations at the expense of longer build times (though build times improve over HNSW)
+     - Large datasets (>>1M vectors) where quality and speed of search are prioritized over index build times but index build times are still important.
+   * - CAGRA build +HNSW search
+     - (coming soon to Milvus)
+     - Significantly reduces distance computations and improves build times at the expense of higher search latency / lower throughput.
+       Large datasets (>>1M vectors) where index build times and quality of search is important but GPU resources are limited and latency of search is not.
diff --git a/docs/source/comparing_indexes.rst b/docs/source/comparing_indexes.rst
new file mode 100644
index 0000000000..221aab6d75
--- /dev/null
+++ b/docs/source/comparing_indexes.rst
@@ -0,0 +1,60 @@
+.. _comparing_indexes:
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Comparing performance of vector indexes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This document provides a brief overview methodology for comparing vector search indexes and models. For guidance on how to choose and configure an index type, please refer to :doc:`this <vector_databases_vs_vector_search>` guide.
+
+Unlike traditional database indexes, which will generally return correct results even without performance tuning, vector search indexes are more closely related to ML models and they can return absolutely garbage results if they have not been tuned.
+
+For this reason, it’s important to consider the parameters that an index is built upon, both for its potential quality and throughput/latency, when comparing two trained indexes. While easier to build an index on its default parameters than having to tune them, a well tuned index can have a significantly better search quality AND perform within search perf constraints like maximal throughput and minimal latency.
+
+
+What is recall?
+===============
+
+Recall is a measure of model quality. Imagine for a particular vector, we know the exact nearest neighbors because we computed them already. The recall for a query result can be computed by taking the set intersection between the exact nearest neighbors and the actual nearest neighbors. The number of neighbors in that intersection list gets divided by k, the number of neighbors being requested. To really give a fair estimate of the recall of a model, we use several query vectors, all with ground truth computed, and we take the total neighbors across all intersected neighbor lists and divide by n_queries * k.
+
+Parameter settings dictate the quality of an index. The graph below shows eight indexes from the same data but with different tuning parameters. Generally speaking, the indexes with higher average recall took longer to build. Which index is fair to report?
+
+.. image:: images/index_recalls.png
+
+
+How do I compare models or indexing algorithms?
+===============================================
+
+In order to fairly compare the performance (e.g. latency and throughput) of an indexing algorithm or model against another, we always need to do so with respect to its potential recall. This is important and draws from the ML roots of vector search, but is often confusing to newcomers who might be more familiar with the database world.
+
+Best practice: Latency and throughput can only be compared at similar levels of recall. If you measure the performance of two indexes at different levels of recall, you are making an unfair comparison.
+
+Because recall levels can vary quite a bit across parameter settings, we tend to compare recall within a small set of potential buckets, so that parameter settings that perform within each bucket can be fairly compared.
+
+We suggest averaging performance within a range of recall. For general guidance, we tend to use the following buckets:
+
+#. 85% - 89%
+#. 90% - 94%
+#. 95% - 99%
+#. >99%
+
+.. image:: images/recall_buckets.png
+
+
+This allows us to make observations such as “at 95% recall level, model A can be built 3x faster than model B, but model B has 2x lower latency than model A”
+
+.. image:: images/build_benchmarks.png
+
+
+Another important detail is that we compare these models against their best-case search performance within each recall window. This means that we aim to find models that not only have great recall quality but also have either the highest throughput or lowest latency within the window of interest. These best-cases are most often computed by doing a parameter sweep in a grid search (or other types of search optimizers) and looking at the best cases for each level of recall.
+
+The resulting data points will construct a curve known as a Pareto optimum. Please note that this process is specifically for showing best-case across recall and throughput/latency, but when we care about finding the parameters that yield the best recall and search performance, we are essentially performing a  hyperparameter optimization, which is common in machine learning.
+
+
+How do I do this on large vector databases?
+===========================================
+
+It turns out that most vector databases, like Milvus for example, make many smaller vector search indexing models for a single “index”, and the distribution of the vectors across the smaller index models are assumed to be completely uniform. This means we can use subsampling to our benefit, and tune on smaller sub-samples of the overall dataset.
+
+Please note, however, that there are often caps on the size of each of these smaller indexes, and that needs to be taken into consideration when choosing the size of the sub sample to tune.
+
+Please see :doc:`this guide <tuning_guide>` for more information on the steps one would take to do this subsampling and tuning process.
\ No newline at end of file
diff --git a/docs/source/cuvs_bench/build.rst b/docs/source/cuvs_bench/build.rst
new file mode 100644
index 0000000000..de84326915
--- /dev/null
+++ b/docs/source/cuvs_bench/build.rst
@@ -0,0 +1,59 @@
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Build cuVS Bench From Source
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Dependencies
+============
+
+CUDA 11 and a GPU with Volta architecture or later are required to run the benchmarks.
+
+Please refer to the  :doc:`installation docs <../build>` for the base requirements to build cuVS.
+
+In addition to the base requirements for building cuVS, additional dependencies needed to build the ANN benchmarks include:
+1. FAISS GPU >= 1.7.1
+2. Google Logging (GLog)
+3. H5Py
+4. HNSWLib
+5. nlohmann_json
+6. GGNN
+
+`rapids-cmake <https://github.com/rapidsai/rapids-cmake>`_ is used to build the ANN benchmarks so the code for dependencies not already supplied in the CUDA toolkit will be downloaded and built automatically.
+
+The easiest (and most reproducible) way to install the dependencies needed to build the ANN benchmarks is to use the conda environment file located in the `conda/environments` directory of the cuVS repository. The following command will use `mamba` (which is preferred over `conda`) to build and activate a new environment for compiling the benchmarks:
+
+.. code-block:: bash
+
+    conda env create --name cuvs_benchmarks -f conda/environments/cuvs_bench_cuda-118_arch-x86_64.yaml
+    conda activate cuvs_benchmarks
+
+The above conda environment will also reduce the compile times as dependencies like FAISS will already be installed and not need to be compiled with `rapids-cmake`.
+
+Compiling the Benchmarks
+========================
+
+After the needed dependencies are satisfied, the easiest way to compile ANN benchmarks is through the `build.sh` script in the root of the RAFT source code repository. The following will build the executables for all the support algorithms:
+
+.. code-block:: bash
+
+    ./build.sh bench-ann
+
+You can limit the algorithms that are built by providing a semicolon-delimited list of executable names (each algorithm is suffixed with `_ANN_BENCH`):
+
+.. code-block:: bash
+
+    ./build.sh bench-ann -n --limit-bench-ann=HNSWLIB_ANN_BENCH;CUVS_IVF_PQ_ANN_BENCH
+
+Available targets to use with `--limit-bench-ann` are:
+- FAISS_GPU_IVF_FLAT_ANN_BENCH
+- FAISS_GPU_IVF_PQ_ANN_BENCH
+- FAISS_CPU_IVF_FLAT_ANN_BENCH
+- FAISS_CPU_IVF_PQ_ANN_BENCH
+- FAISS_GPU_FLAT_ANN_BENCH
+- FAISS_CPU_FLAT_ANN_BENCH
+- GGNN_ANN_BENCH
+- HNSWLIB_ANN_BENCH
+- CUVS_CAGRA_ANN_BENCH
+- CUVS_IVF_PQ_ANN_BENCH
+- CUVS_IVF_FLAT_ANN_BENCH
+
+By default, the `*_ANN_BENCH` executables program infer the dataset's datatype from the filename's extension. For example, an extension of `fbin` uses a `float` datatype, `f16bin` uses a `float16` datatype, extension of `i8bin` uses `int8_t` datatype, and `u8bin` uses `uint8_t` type. Currently, only `float`, `float16`, int8_t`, and `unit8_t` are supported.
\ No newline at end of file
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
new file mode 100644
index 0000000000..a1e31b98f2
--- /dev/null
+++ b/docs/source/cuvs_bench/index.rst
@@ -0,0 +1,702 @@
+~~~~~~~~~~
+cuVS Bench
+~~~~~~~~~~
+
+cuVS bench provides a benchmarking tool for various ANN search implementations. It's especially suitable for comparing GPU implementations as well as comparing GPU against CPU.
+
+- `Installing the benchmarks`_
+
+  * `Conda`_
+
+  * `Docker`_
+
+
+Installing the benchmarks
+=========================
+
+There are two main ways pre-compiled benchmarks are distributed:
+
+- `Conda`_ For users not using containers but want an easy to install and use Python package. Pip wheels are planned to be added as an alternative for users that cannot use conda and prefer to not use containers.
+- `Docker`_ Only needs docker and [NVIDIA docker](https://github.com/NVIDIA/nvidia-docker) to use. Provides a single docker run command for basic dataset benchmarking, as well as all the functionality of the conda solution inside the containers.
+
+Conda
+-----
+
+.. code-block:: bash
+
+   conda create --name cuvs_benchmarks
+   conda activate cuvs_benchmarks
+
+   # to install GPU package:
+   conda install -c rapidsai -c conda-forge -c nvidia cuvs-ann-bench=<rapids_version> cuda-version=11.8*
+
+   # to install CPU package for usage in CPU-only systems:
+   conda install -c rapidsai -c conda-forge  cuvs-bench-cpu
+
+The channel `rapidsai` can easily be substituted `rapidsai-nightly` if nightly benchmarks are desired. The CPU package currently allows to run the HNSW benchmarks.
+
+Please see the :doc:`build instructions <../build>` to build the benchmarks from source.
+
+Docker
+------
+
+We provide images for GPU enabled systems, as well as systems without a GPU. The following images are available:
+
+- `cuvs-bench`: Contains GPU and CPU benchmarks, can run all algorithms supported. Will download million-scale datasets as required. Best suited for users that prefer a smaller container size for GPU based systems. Requires the NVIDIA Container Toolkit to run GPU algorithms, can run CPU algorithms without it.
+- `cuvs-bench-datasets`: Contains the GPU and CPU benchmarks with million-scale datasets already included in the container. Best suited for users that want to run multiple million scale datasets already included in the image.
+- `cuvs-bench-cpu`: Contains only CPU benchmarks with minimal size. Best suited for users that want the smallest containers to reproduce benchmarks on systems without a GPU.
+
+Nightly images are located in `dockerhub <https://hub.docker.com/r/rapidsai/cuvs-ann-bench/tags>`_, meanwhile release (stable) versions are located in `NGC <https://hub.docker.com/r/rapidsai/cuvs_bench>`_, starting with release 24.10.
+
+The following command pulls the nightly container for python version 10, cuda version 12, and CUVS version 23.10:
+
+.. code-block:: bash
+
+   docker pull rapidsai/cuvs_bench:24.10a-cuda12.0-py3.10 #substitute cuvs_bench for the exact desired container.
+
+The CUDA and python versions can be changed for the supported values:
+- Supported CUDA versions: 11.4 and 12.x
+- Supported Python versions: 3.9 and 3.10.
+
+You can see the exact versions as well in the dockerhub site:
+- `cuVS bench images <https://hub.docker.com/r/rapidsai/cuvs_bench/tags>`_
+- `cuVS bench with datasets preloaded images <https://hub.docker.com/r/rapidsai/cuvs-bench-cpu/tags>`_
+- `cuVS bench CPU only images <https://hub.docker.com/r/rapidsai/cuvs-bench-datasets/tags>`_
+
+**Note:** GPU containers use the CUDA toolkit from inside the container, the only requirement is a driver installed on the host machine that supports that version. So, for example, CUDA 11.8 containers can run in systems with a CUDA 12.x capable driver. Please also note that the Nvidia-Docker runtime from the `Nvidia Container Toolkit <https://github.com/NVIDIA/nvidia-docker>`_ is required to use GPUs inside docker containers.
+
+How to run the benchmarks
+=========================
+
+We provide a collection of lightweight Python scripts to run the benchmarks. There are 4 general steps to running the benchmarks and visualizing the results.
+#. Prepare Dataset
+#. Build Index and Search Index
+#. Data Export
+#. Plot Results
+
+Step 1: Prepare the dataset
+---------------------------
+
+The script `cuvs_bench.get_dataset` will download and unpack the dataset in directory that the user provides. As of now, only million-scale datasets are supported by this script. For more information on :doc:`datasets and formats <ann_benchmarks_dataset>`.
+
+The usage of this script is:
+
+.. code-block:: bash
+
+    usage: get_dataset.py [-h] [--name NAME] [--dataset-path DATASET_PATH] [--normalize]
+
+    options:
+      -h, --help            show this help message and exit
+      --dataset DATASET     dataset to download (default: glove-100-angular)
+      --dataset-path DATASET_PATH
+                            path to download dataset (default: ${RAPIDS_DATASET_ROOT_DIR})
+      --normalize           normalize cosine distance to inner product (default: False)
+
+When option `normalize` is provided to the script, any dataset that has cosine distances
+will be normalized to inner product. So, for example, the dataset `glove-100-angular`
+will be written at location `datasets/glove-100-inner/`.
+
+Step 2: Build and search index
+------------------------------
+
+The script `cuvs_bench.run` will build and search indices for a given dataset and its
+specified configuration.
+
+The usage of the script `cuvs_bench.run` is:
+
+.. code-block:: bash
+
+    usage: __main__.py [-h] [--subset-size SUBSET_SIZE] [-k COUNT] [-bs BATCH_SIZE] [--dataset-configuration DATASET_CONFIGURATION] [--configuration CONFIGURATION] [--dataset DATASET]
+                       [--dataset-path DATASET_PATH] [--build] [--search] [--algorithms ALGORITHMS] [--groups GROUPS] [--algo-groups ALGO_GROUPS] [-f] [-m SEARCH_MODE]
+
+    options:
+      -h, --help            show this help message and exit
+      --subset-size SUBSET_SIZE
+                            the number of subset rows of the dataset to build the index (default: None)
+      -k COUNT, --count COUNT
+                            the number of nearest neighbors to search for (default: 10)
+      -bs BATCH_SIZE, --batch-size BATCH_SIZE
+                            number of query vectors to use in each query trial (default: 10000)
+      --dataset-configuration DATASET_CONFIGURATION
+                            path to YAML configuration file for datasets (default: None)
+      --configuration CONFIGURATION
+                            path to YAML configuration file or directory for algorithms Any run groups found in the specified file/directory will automatically override groups of the same name
+                            present in the default configurations, including `base` (default: None)
+      --dataset DATASET     name of dataset (default: glove-100-inner)
+      --dataset-path DATASET_PATH
+                            path to dataset folder, by default will look in RAPIDS_DATASET_ROOT_DIR if defined, otherwise a datasets subdirectory from the calling directory (default:
+                            os.getcwd()/datasets/)
+      --build
+      --search
+      --algorithms ALGORITHMS
+                            run only comma separated list of named algorithms. If parameters `groups` and `algo-groups` are both undefined, then group `base` is run by default (default: None)
+      --groups GROUPS       run only comma separated groups of parameters (default: base)
+      --algo-groups ALGO_GROUPS
+                            add comma separated <algorithm>.<group> to run. Example usage: "--algo-groups=cuvs_cagra.large,hnswlib.large" (default: None)
+      -f, --force           re-run algorithms even if their results already exist (default: False)
+      -m SEARCH_MODE, --search-mode SEARCH_MODE
+                            run search in 'latency' (measure individual batches) or 'throughput' (pipeline batches and measure end-to-end) mode (default: throughput)
+      -t SEARCH_THREADS, --search-threads SEARCH_THREADS
+                            specify the number threads to use for throughput benchmark. Single value or a pair of min and max separated by ':'. Example --search-threads=1:4. Power of 2 values between 'min' and 'max' will be used. If only 'min' is
+                            specified, then a single test is run with 'min' threads. By default min=1, max=<num hyper threads>. (default: None)
+      -r, --dry-run         dry-run mode will convert the yaml config for the specified algorithms and datasets to the json format that's consumed by the lower-level c++ binaries and then print the command to run execute the benchmarks but
+                            will not actually execute the command. (default: False)
+
+`dataset`: name of the dataset to be searched in `datasets.yaml`_
+
+`dataset-configuration`: optional filepath to custom dataset YAML config which has an entry for arg `dataset`
+
+`configuration`: optional filepath to YAML configuration for an algorithm or to directory that contains YAML configurations for several algorithms. Refer to `Dataset.yaml config`_ for more info.
+
+`algorithms`: runs all algorithms that it can find in YAML configs found by `configuration`. By default, only `base` group will be run.
+
+`groups`: run only specific groups of parameters configurations for an algorithm. Groups are defined in YAML configs (see `configuration`), and by default run `base` group
+
+`algo-groups`: this parameter is helpful to append any specific algorithm+group combination to run the benchmark for in addition to all the arguments from `algorithms` and `groups`. It is of the format `<algorithm>.<group>`, or for example, `cuvs_cagra.large`
+
+For every algorithm run by this script, it outputs an index build statistics JSON file in `<dataset-path/<dataset>/result/build/<{algo},{group}.json>`
+and an index search statistics JSON file in `<dataset-path/<dataset>/result/search/<{algo},{group},k{k},bs{batch_size}.json>`. NOTE: The filenames will not have ",{group}" if `group = "base"`.
+
+For every algorithm run by this script, it outputs an index build statistics JSON file in `<dataset-path/<dataset>/result/build/<{algo},{group}.json>`
+and an index search statistics JSON file in `<dataset-path/<dataset>/result/search/<{algo},{group},k{k},bs{batch_size}.json>`. NOTE: The filenames will not have ",{group}" if `group = "base"`.
+
+`dataset-path` :
+#. data is read from `<dataset-path>/<dataset>`
+#. indices are built in `<dataset-path>/<dataset>/index`
+#. build/search results are stored in `<dataset-path>/<dataset>/result`
+
+`build` and `search` : if both parameters are not supplied to the script then it is assumed both are `True`.
+
+`indices` and `algorithms` : these parameters ensure that the algorithm specified for an index is available in `algos.yaml` and not disabled, as well as having an associated executable.
+
+Step 3: Data export
+-------------------
+
+The script `cuvs_bench.data_export` will convert the intermediate JSON outputs produced by `cuvs_bench.run` to more easily readable CSV files, which are needed to build charts made by `cuvs_bench.plot`.
+
+.. code-block:: bash
+
+    usage: data_export.py [-h] [--dataset DATASET] [--dataset-path DATASET_PATH]
+
+    options:
+      -h, --help            show this help message and exit
+      --dataset DATASET     dataset to download (default: glove-100-inner)
+      --dataset-path DATASET_PATH
+                            path to dataset folder (default: ${RAPIDS_DATASET_ROOT_DIR})
+
+Build statistics CSV file is stored in `<dataset-path/<dataset>/result/build/<{algo},{group}.csv>`
+and index search statistics CSV file in `<dataset-path/<dataset>/result/search/<{algo},{group},k{k},bs{batch_size},{suffix}.csv>`, where suffix has three values:
+#. `raw`: All search results are exported
+#. `throughput`: Pareto frontier of throughput results is exported
+#. `latency`: Pareto frontier of latency results is exported
+
+Step 4: Plot results
+--------------------
+
+The script `cuvs_bench.plot` will plot results for all algorithms found in index search statistics CSV files `<dataset-path/<dataset>/result/search/*.csv`.
+
+The usage of this script is:
+
+.. code-block:: bash
+
+    usage:  [-h] [--dataset DATASET] [--dataset-path DATASET_PATH] [--output-filepath OUTPUT_FILEPATH] [--algorithms ALGORITHMS] [--groups GROUPS] [--algo-groups ALGO_GROUPS]
+            [-k COUNT] [-bs BATCH_SIZE] [--build] [--search] [--x-scale X_SCALE] [--y-scale {linear,log,symlog,logit}] [--x-start X_START] [--mode {throughput,latency}]
+            [--time-unit {s,ms,us}] [--raw]
+
+    options:
+      -h, --help            show this help message and exit
+      --dataset DATASET     dataset to plot (default: glove-100-inner)
+      --dataset-path DATASET_PATH
+                            path to dataset folder (default: /home/coder/cuvs/datasets/)
+      --output-filepath OUTPUT_FILEPATH
+                            directory for PNG to be saved (default: /home/coder/cuvs)
+      --algorithms ALGORITHMS
+                            plot only comma separated list of named algorithms. If parameters `groups` and `algo-groups are both undefined, then group `base` is plot by default
+                            (default: None)
+      --groups GROUPS       plot only comma separated groups of parameters (default: base)
+      --algo-groups ALGO_GROUPS, --algo-groups ALGO_GROUPS
+                            add comma separated <algorithm>.<group> to plot. Example usage: "--algo-groups=cuvs_cagra.large,hnswlib.large" (default: None)
+      -k COUNT, --count COUNT
+                            the number of nearest neighbors to search for (default: 10)
+      -bs BATCH_SIZE, --batch-size BATCH_SIZE
+                            number of query vectors to use in each query trial (default: 10000)
+      --build
+      --search
+      --x-scale X_SCALE     Scale to use when drawing the X-axis. Typically linear, logit or a2 (default: linear)
+      --y-scale {linear,log,symlog,logit}
+                            Scale to use when drawing the Y-axis (default: linear)
+      --x-start X_START     Recall values to start the x-axis from (default: 0.8)
+      --mode {throughput,latency}
+                            search mode whose Pareto frontier is used on the y-axis (default: throughput)
+      --time-unit {s,ms,us}
+                            time unit to plot when mode is latency (default: ms)
+      --raw                 Show raw results (not just Pareto frontier) of mode arg (default: False)
+
+`mode`: plots pareto frontier of `throughput` or `latency` results exported in the previous step
+
+`algorithms`: plots all algorithms that it can find results for the specified `dataset`. By default, only `base` group will be plotted.
+
+`groups`: plot only specific groups of parameters configurations for an algorithm. Groups are defined in YAML configs (see `configuration`), and by default run `base` group
+
+`algo-groups`: this parameter is helpful to append any specific algorithm+group combination to plot results for in addition to all the arguments from `algorithms` and `groups`. It is of the format `<algorithm>.<group>`, or for example, `cuvs_cagra.large`
+
+Running the benchmarks
+======================
+
+End-to-end: smaller-scale benchmarks (<1M to 10M)
+-------------------------------------------------
+
+The steps below demonstrate how to download, install, and run benchmarks on a subset of 10M vectors from the Yandex Deep-1B dataset By default the datasets will be stored and used from the folder indicated by the `RAPIDS_DATASET_ROOT_DIR` environment variable if defined, otherwise a datasets sub-folder from where the script is being called:
+
+.. code-block:: bash
+
+
+    # (1) prepare dataset.
+    python -m cuvs_bench.get_dataset --dataset deep-image-96-angular --normalize
+
+    # (2) build and search index
+    python -m cuvs_bench.run --dataset deep-image-96-inner --algorithms cuvs_cagra --batch-size 10 -k 10
+
+    # (3) export data
+    python -m cuvs_bench.data_export --dataset deep-image-96-inner
+
+    # (4) plot results
+    python -m cuvs_bench.plot --dataset deep-image-96-inner
+
+
+.. list-table::
+
+ * - Dataset name
+   - Train rows
+   - Columns
+   - Test rows
+   - Distance
+
+ * - `deep-image-96-angular`
+   - 10M
+   - 96
+   - 10K
+   - Angular
+
+ * - `fashion-mnist-784-euclidean`
+   - 60K
+   - 784
+   - 10K
+   - Euclidean
+
+ * - `glove-50-angular`
+   - 1.1M
+   - 50
+   - 10K
+   - Angular
+
+ * - `glove-100-angular`
+   - 1.1M
+   - 100
+   - 10K
+   - Angular
+
+ * - `mnist-784-euclidean`
+   - 60K
+   - 784
+   - 10K
+   - Euclidean
+
+ * - `nytimes-256-angular`
+   - 290K
+   - 256
+   - 10K
+   - Angular
+
+ * - `sift-128-euclidean`
+   - 1M
+   - 128
+   - 10K
+   - Euclidean
+
+All of the datasets above contain ground test datasets with 100 neighbors. Thus `k` for these datasets must be  less than or equal to 100.
+
+End-to-end: large-scale benchmarks (>10M vectors)
+-------------------------------------------------
+
+`cuvs_bench.get_dataset` cannot be used to download the `billion-scale datasets`_ due to their size. You should instead use our billion-scale datasets guide to download and prepare them.
+All other python commands mentioned below work as intended once the billion-scale dataset has been downloaded.
+
+To download billion-scale datasets, visit `big-ann-benchmarks <http://big-ann-benchmarks.com/neurips21.html>`_
+
+We also provide a new dataset called `wiki-all` containing 88 million 768-dimensional vectors. This dataset is meant for benchmarking a realistic retrieval-augmented generation (RAG)/LLM embedding size at scale. It also contains 1M and 10M vector subsets for smaller-scale experiments. See our :doc:`Wiki-all Dataset Guide <wiki_all_dataset>` for more information and to download the dataset.
+
+
+The steps below demonstrate how to download, install, and run benchmarks on a subset of 100M vectors from the Yandex Deep-1B dataset. Please note that datasets of this scale are recommended for GPUs with larger amounts of memory, such as the A100 or H100.
+
+.. code-block:: bash
+
+    mkdir -p datasets/deep-1B
+    # (1) prepare dataset
+    # download manually "Ground Truth" file of "Yandex DEEP"
+    # suppose the file name is deep_new_groundtruth.public.10K.bin
+    python -m cuvs_bench.split_groundtruth --groundtruth datasets/deep-1B/deep_new_groundtruth.public.10K.bin
+    # two files 'groundtruth.neighbors.ibin' and 'groundtruth.distances.fbin' should be produced
+
+    # (2) build and search index
+    python -m cuvs_bench.run --dataset deep-1B --algorithms cuvs_cagra --batch-size 10 -k 10
+
+    # (3) export data
+    python -m cuvs_bench.data_export --dataset deep-1B
+
+    # (4) plot results
+    python -m cuvs_bench.plot --dataset deep-1B
+
+The usage of `python -m cuvs_bench.split_groundtruth` is:
+
+.. code-block:: bash
+    usage: split_groundtruth.py [-h] --groundtruth GROUNDTRUTH
+
+    options:
+      -h, --help            show this help message and exit
+      --groundtruth GROUNDTRUTH
+                            Path to billion-scale dataset groundtruth file (default: None)
+
+Running with Docker containers
+------------------------------
+
+Two methods are provided for running the benchmarks with the Docker containers.
+
+End-to-end run on GPU
+~~~~~~~~~~~~~~~~~~~~~
+
+When no other entrypoint is provided, an end-to-end script will run through all the steps in `Running the benchmarks`_ above.
+
+For GPU-enabled systems, the `DATA_FOLDER` variable should be a local folder where you want datasets stored in `$DATA_FOLDER/datasets` and results in `$DATA_FOLDER/result` (we highly recommend `$DATA_FOLDER` to be a dedicated folder for the datasets and results of the containers):
+
+.. code-block:: bash
+
+    export DATA_FOLDER=path/to/store/datasets/and/results
+    docker run --gpus all --rm -it -u $(id -u)                      \
+        -v $DATA_FOLDER:/data/benchmarks                            \
+        rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10              \
+        "--dataset deep-image-96-angular"                           \
+        "--normalize"                                               \
+        "--algorithms cuvs_cagra,cuvs_ivf_pq --batch-size 10 -k 10" \
+        ""
+
+Usage of the above command is as follows:
+
+.. list-table::
+
+ * - Argument
+   - Description
+
+ * - `rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10`
+   - Image to use. Can be either `cuvs-bench` or `cuvs-bench-datasets`
+
+ * - `"--dataset deep-image-96-angular"`
+   - Dataset name
+
+ * - `"--normalize"`
+   - Whether to normalize the dataset
+
+ * - `"--algorithms cuvs_cagra,hnswlib --batch-size 10 -k 10"`
+   - Arguments passed to the `run` script, such as the algorithms to benchmark, the batch size, and `k`
+
+ * - `""`
+   - Additional (optional) arguments that will be passed to the `plot` script.
+
+***Note about user and file permissions:*** The flag `-u $(id -u)` allows the user inside the container to match the `uid` of the user outside the container, allowing the container to read and write to the mounted volume indicated by the `$DATA_FOLDER` variable.
+
+End-to-end run on CPU
+~~~~~~~~~~~~~~~~~~~~~
+
+The container arguments in the above section also be used for the CPU-only container, which can be used on systems that don't have a GPU installed.
+
+***Note:*** the image changes to `cuvs-bench-cpu` container and the `--gpus all` argument is no longer used:
+
+.. code-block:: bash
+
+    export DATA_FOLDER=path/to/store/datasets/and/results
+    docker run  --rm -it -u $(id -u)                  \
+        -v $DATA_FOLDER:/data/benchmarks              \
+        rapidsai/cuvs-bench-cpu:24.10a-py3.10     \
+         "--dataset deep-image-96-angular"            \
+         "--normalize"                                \
+         "--algorithms hnswlib --batch-size 10 -k 10" \
+         ""
+
+Manually run the scripts inside the container
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+All of the `cuvs-bench` images contain the Conda packages, so they can be used directly by logging directly into the container itself:
+
+.. code-block:: bash
+
+    export DATA_FOLDER=path/to/store/datasets/and/results
+    docker run --gpus all --rm -it -u $(id -u)          \
+        --entrypoint /bin/bash                          \
+        --workdir /data/benchmarks                      \
+        -v $DATA_FOLDER:/data/benchmarks                \
+        rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10
+
+This will drop you into a command line in the container, with the `cuvs-bench` python package ready to use, as described in the [Running the benchmarks](#running-the-benchmarks) section above:
+
+.. code-block:: bash
+
+    (base) root@00b068fbb862:/data/benchmarks# python -m cuvs_bench.get_dataset --dataset deep-image-96-angular --normalize
+
+Additionally, the containers can be run in detached mode without any issue.
+
+Evaluating the results
+----------------------
+
+The benchmarks capture several different measurements. The table below describes each of the measurements for index build benchmarks:
+
+.. list-table::
+
+ * - Name
+   - Description
+
+ * - Benchmark
+   - A name that uniquely identifies the benchmark instance
+
+ * - Time
+   - Wall-time spent training the index
+
+ * - CPU
+   - CPU time spent training the index
+
+ * - Iterations
+   - Number of iterations (this is usually 1)
+
+ * - GPU
+   - GU time spent building
+
+ * - index_size
+   - Number of vectors used to train index
+
+The table below describes each of the measurements for the index search benchmarks. The most important measurements `Latency`, `items_per_second`, `end_to_end`.
+
+.. list-table::
+
+ * - Name
+   - Description
+
+ * - Benchmark
+   - A name that uniquely identifies the benchmark instance
+
+ * - Time
+   - The wall-clock time of a single iteration (batch) divided by the number of threads.
+
+ * - CPU
+   - The average CPU time (user + sys time). This does not include idle time (which can also happen while waiting for GPU sync).
+
+ * - Iterations
+   - Total number of batches. This is going to be `total_queries` / `n_queries`.
+
+ * - GPU
+   - GPU latency of a single batch (seconds). In throughput mode this is averaged over multiple threads.
+
+ * - Latency
+   - Latency of a single batch (seconds), calculated from wall-clock time. In throughput mode this is averaged over multiple threads.
+
+ * - Recall
+   - Proportion of correct neighbors to ground truth neighbors. Note this column is only present if groundtruth file is specified in dataset configuration.
+
+ * - items_per_second
+   - Total throughput, a.k.a Queries per second (QPS). This is approximately `total_queries` / `end_to_end`.
+
+ * - k
+   - Number of neighbors being queried in each iteration
+
+ * - end_to_end
+   - Total time taken to run all batches for all iterations
+
+ * - n_queries
+   - Total number of query vectors in each batch
+
+ * - total_queries
+   - Total number of vectors queries across all iterations ( = `iterations` * `n_queries`)
+
+Note the following:
+- A slightly different method is used to measure `Time` and `end_to_end`. That is why `end_to_end` = `Time` * `Iterations` holds only approximately.
+- The actual table displayed on the screen may differ slightly as the hyper-parameters will also be displayed for each different combination being benchmarked.
+- Recall calculation: the number of queries processed per test depends on the number of iterations. Because of this, recall can show slight fluctuations if less neighbors are processed then it is available for the benchmark.
+
+Creating and customizing dataset configurations
+===============================================
+
+A single configuration will often define a set of algorithms, with associated index and search parameters, that can be generalize across datasets. We use YAML to define dataset specific and algorithm specific configurations.
+
+A default `datasets.yaml` is provided by CUVS in `${CUVS_HOME}/python/cuvs-ann-bench/src/cuvs_bench/run/conf` with configurations available for several datasets. Here's a simple example entry for the `sift-128-euclidean` dataset:
+
+.. code-block:: yaml
+
+    - name: sift-128-euclidean
+      base_file: sift-128-euclidean/base.fbin
+      query_file: sift-128-euclidean/query.fbin
+      groundtruth_neighbors_file: sift-128-euclidean/groundtruth.neighbors.ibin
+      dims: 128
+      distance: euclidean
+
+Configuration files for ANN algorithms supported by `cuvs-bench` are provided in `${CUVS_HOME}/python/cuvs-bench/src/cuvs_bench/run/conf`. `cuvs_cagra` algorithm configuration looks like:
+
+.. code-block:: yaml
+
+    name: cuvs_cagra
+    groups:
+      base:
+        build:
+          graph_degree: [32, 64]
+          intermediate_graph_degree: [64, 96]
+          graph_build_algo: ["NN_DESCENT"]
+        search:
+          itopk: [32, 64, 128]
+
+      large:
+        build:
+          graph_degree: [32, 64]
+        search:
+          itopk: [32, 64, 128]
+
+The default parameters for which the benchmarks are run can be overridden by creating a custom YAML file for algorithms with a `base` group.
+
+There config above has 2 fields:
+1. `name` - define the name of the algorithm for which the parameters are being specified.
+2. `groups` - define a run group which has a particular set of parameters. Each group helps create a cross-product of all hyper-parameter fields for `build` and `search`.
+
+The table below contains all algorithms supported by cuVS. Each unique algorithm will have its own set of `build` and `search` settings. The :doc:`ANN Algorithm Parameter Tuning Guide <ann_benchmarks_param_tuning>` contains detailed instructions on choosing build and search parameters for each supported algorithm.
+
+.. list-table::
+
+ * - Library
+   - Algorithms
+
+ * - FAISS_GPU
+   - `faiss_gpu_flat`, `faiss_gpu_ivf_flat`, `faiss_gpu_ivf_pq`
+
+ * - FAISS_CPU
+   - `faiss_cpu_flat`, `faiss_cpu_ivf_flat`, `faiss_cpu_ivf_pq`
+
+ * - GGNN
+   - `ggnn`
+
+ * - HNSWLIB
+   - `hnswlib`
+
+ * - cuVS
+   - `cuvs_brute_force`, `cuvs_cagra`, `cuvs_ivf_flat`, `cuvs_ivf_pq`, `cuvs_cagra_hnswlib`
+
+Adding a new ANN algorithm
+==========================
+
+Implementation and configuration
+--------------------------------
+
+Implementation of a new algorithm should be a C++ class that inherits `class ANN` (defined in `cpp/bench/ann/src/ann.h`) and implements all the pure virtual functions.
+
+In addition, it should define two `struct`s for building and searching parameters. The searching parameter class should inherit `struct ANN<T>::AnnSearchParam`. Take `class HnswLib` as an example, its definition is:
+
+.. code-block:: c++
+    template<typename T>
+    class HnswLib : public ANN<T> {
+    public:
+      struct BuildParam {
+        int M;
+        int ef_construction;
+        int num_threads;
+      };
+
+      using typename ANN<T>::AnnSearchParam;
+      struct SearchParam : public AnnSearchParam {
+        int ef;
+        int num_threads;
+      };
+
+      // ...
+    };
+
+
+The benchmark program uses JSON format natively in a configuration file to specify indexes to build, along with the build and search parameters. However the JSON config files are overly verbose and are not meant to be used directly. Instead, the Python scripts parse YAML and create these json files automatically. It's important to realize that these json objects align with the yaml objects for `build_param`, whose value is a JSON object, and `search_param`, whose value is an array of JSON objects. Take the json configuration for `HnswLib` as an example of the json after it's been parsed from yaml:
+
+.. code-block:: json
+    {
+      "name" : "hnswlib.M12.ef500.th32",
+      "algo" : "hnswlib",
+      "build_param": {"M":12, "efConstruction":500, "numThreads":32},
+      "file" : "/path/to/file",
+      "search_params" : [
+        {"ef":10, "numThreads":1},
+        {"ef":20, "numThreads":1},
+        {"ef":40, "numThreads":1},
+      ],
+      "search_result_file" : "/path/to/file"
+    },
+
+The build and search params are ultimately passed to the C++ layer as json objects for each param configuration to benchmark. The code below shows how to parse these params for `Hnswlib`:
+
+1. First, add two functions for parsing JSON object to `struct BuildParam` and `struct SearchParam`, respectively:
+
+.. code-block:: c++
+
+    template<typename T>
+    void parse_build_param(const nlohmann::json& conf,
+                           typename cuann::HnswLib<T>::BuildParam& param) {
+      param.ef_construction = conf.at("efConstruction");
+      param.M = conf.at("M");
+      if (conf.contains("numThreads")) {
+        param.num_threads = conf.at("numThreads");
+      }
+    }
+
+    template<typename T>
+    void parse_search_param(const nlohmann::json& conf,
+                            typename cuann::HnswLib<T>::SearchParam& param) {
+      param.ef = conf.at("ef");
+      if (conf.contains("numThreads")) {
+        param.num_threads = conf.at("numThreads");
+      }
+    }
+
+
+
+2. Next, add corresponding `if` case to functions `create_algo()` (in `cpp/bench/ann/) and `create_search_param()` by calling parsing functions. The string literal in `if` condition statement must be the same as the value of `algo` in configuration file. For example,
+
+.. code-block:: c++
+      // JSON configuration file contains a line like:  "algo" : "hnswlib"
+      if (algo == "hnswlib") {
+         // ...
+      }
+
+Adding a Cmake target
+---------------------
+
+In `cuvs/cpp/bench/ann/CMakeLists.txt`, we provide a `CMake` function to configure a new Benchmark target with the following signature:
+
+
+.. code-block:: cmake
+    ConfigureAnnBench(
+      NAME <algo_name>
+      PATH </path/to/algo/benchmark/source/file>
+      INCLUDES <additional_include_directories>
+      CXXFLAGS <additional_cxx_flags>
+      LINKS <additional_link_library_targets>
+    )
+
+To add a target for `HNSWLIB`, we would call the function as:
+
+.. code-block:: cmake
+
+    ConfigureAnnBench(
+      NAME HNSWLIB PATH bench/ann/src/hnswlib/hnswlib_benchmark.cpp INCLUDES
+      ${CMAKE_CURRENT_BINARY_DIR}/_deps/hnswlib-src/hnswlib CXXFLAGS "${HNSW_CXX_FLAGS}"
+    )
+
+This will create an executable called `HNSWLIB_ANN_BENCH`, which can then be used to run `HNSWLIB` benchmarks.
+
+Add a new entry to `algos.yaml` to map the name of the algorithm to its binary executable and specify whether the algorithm requires GPU support.
+
+.. code-block:: yaml
+    cuvs_ivf_pq:
+      executable: CUVS_IVF_PQ_ANN_BENCH
+      requires_gpu: true
+
+`executable` : specifies the name of the binary that will build/search the index. It is assumed to be available in `cuvs/cpp/build/`.
+`requires_gpu` : denotes whether an algorithm requires GPU to run.
\ No newline at end of file
diff --git a/docs/source/cuvs_bench/param_tuning.rst b/docs/source/cuvs_bench/param_tuning.rst
new file mode 100644
index 0000000000..faffa9daff
--- /dev/null
+++ b/docs/source/cuvs_bench/param_tuning.rst
@@ -0,0 +1,674 @@
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+cuVS Bench Parameter Tuning Guide
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This guide outlines the various parameter settings that can be specified in :doc:`cuVS Benchmarks <index>` yaml configuration files and explains the impact they have on corresponding algorithms to help inform their settings for benchmarking across desired levels of recall.
+
+cuVS Indexes
+============
+
+cuvs_brute_force
+----------------
+
+Use cuVS brute-force index for exact search. Brute-force has no further build or search parameters.
+
+cuvs_ivf_flat
+-------------
+
+IVF-flat uses an inverted-file index, which partitions the vectors into a series of clusters, or lists, storing them in an interleaved format which is optimized for fast distance computation. The searching of an IVF-flat index reduces the total vectors in the index to those within some user-specified nearest clusters called probes.
+
+IVF-flat is a simple algorithm which won't save any space, but it provides competitive search times even at higher levels of recall.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `nlist`
+   - `build`
+   - Y
+   - Positive integer >0
+   - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
+
+ * - `niter`
+   - `build`
+   - N
+   - Positive integer >0
+   - 20
+   - Number of kmeans iterations to use when training the ivf clusters
+
+ * - `ratio`
+   - `build`
+   - N
+   - Positive integer >0
+   - 2
+   - `1/ratio` is the number of training points which should be used to train the clusters.
+
+  * - `dataset_memory_type`
+    - `build`
+    - N
+    - [`device`, `host`, `mmap`]
+    - `mmap`
+    - Where should the dataset reside?
+
+  * - `query_memory_type`
+    - `search`
+    - [`device`, `host`, `mmap`]
+    - `device`
+    - Where should the queries reside?
+
+  * - `nprobe`
+    - `search`
+    - Y
+    - Positive integer >0
+    -
+    - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
+cuvs_ivf_pq
+-----------
+
+IVF-pq is an inverted-file index, which partitions the vectors into a series of clusters, or lists, in a similar way to IVF-flat above. The difference is that IVF-PQ uses product quantization to also compress the vectors, giving the index a smaller memory footprint. Unfortunately, higher levels of compression can also shrink recall, which a refinement step can improve when the original vectors are still available.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `nlist`
+   - `build`
+   - Y
+   - Positive integer >0
+   - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
+
+ * - `niter`
+   - `build`
+   - N
+   - Positive integer >0
+   - 20
+   - Number of kmeans iterations to use when training the ivf clusters
+
+ * - `ratio`
+   - `build`
+   - N
+   - Positive integer >0
+   - 2
+   - `1/ratio` is the number of training points which should be used to train the clusters.
+
+ * - `pq_dim`
+   - `build`
+   - N
+   - Positive integer. Multiple of 8.
+   - 0
+   - Dimensionality of the vector after product quantization. When 0, a heuristic is used to select this value.
+
+ * - `pq_bits`
+   - `build`
+   - N
+   - Positive integer [4-8]
+   - 8
+   - Bit length of the vector element after quantization.
+
+ * - `codebook_kind`
+   - `build`
+   - N
+   - [`cluster`, `subspace`]
+   - `subspace`
+   - Type of codebook. See :doc:`IVF-PQ index overview <../indexes/ivfpq>` for more detail
+
+ * - `dataset_memory_type`
+   - `build`
+   - N
+   - [`device`, `host`, `mmap`]
+   - `mmap`
+   - Where should the dataset reside?
+
+ * - `query_memory_type`
+   - `search`
+   - [`device`, `host`, `mmap`]
+   - `device`
+   - Where should the queries reside?
+
+ * - `nprobe`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
+ * - `internalDistanceDtype`
+   - `search`
+   - N
+   - [`float`, `half`]
+   - `half`
+   - The precision to use for the distance computations. Lower precision can increase performance at the cost of accuracy.
+
+ * - `smemLutDtype`
+   - `search`
+   - N
+   - [`float`, `half`, `fp8`]
+   - `half`
+   - The precision to use for the lookup table in shared memory. Lower precision can increase performance at the cost of accuracy.
+
+ * - `refine_ratio`
+   - `search`
+   - N
+   - Positive integer >0
+   - 1
+   - `refine_ratio * k` nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best `k` neighbors.
+
+
+cuvs_cagra
+----------
+
+CAGRA uses a graph-based index, which creates an intermediate, approximate kNN graph using IVF-PQ and then further refining and optimizing to create a final kNN graph. This kNN graph is used by CAGRA as an index for search.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `graph_degree`
+   - `build`
+   - N
+   - Positive integer >0
+   - 64
+   - Degree of the final kNN graph index.
+
+ * - `intermediate_graph_degree`
+   - `build`
+   - N
+   - Positive integer >0
+   - 128
+   - Degree of the intermediate kNN graph before the CAGRA graph is optimized
+
+ * - `graph_build_algo`
+   - `build`
+   - `N
+   - [`IVF_PQ`, NN_DESCENT`]
+   - `IVF_PQ`
+   - Algorithm to use for building the initial kNN graph, from which CAGRA will optimize into the navigable CAGRA graph
+
+ * - `dataset_memory_type`
+   - `build`
+   - N
+   - [`device`, `host`, `mmap`]
+   - `mmap`
+   - Where should the dataset reside?
+
+ * - `query_memory_type`
+   - `search`
+   - [`device`, `host`, `mmap`]
+   - `device`
+   - Where should the queries reside?
+
+ * - `itopk`
+   - `search`
+   - N
+   - Positive integer >0
+   - 64
+   - Number of intermediate search results retained during the search. Higher values improve search accuracy at the cost of speed
+
+ * - `search_width`
+   - `search`
+   - N
+   - Positive integer >0
+   - 1
+   - Number of graph nodes to select as the starting point for the search in each iteration.
+
+ * - `max_iterations`
+   - `search`
+   - N
+   - Positive integer >=0
+   - 0
+   - Upper limit of search iterations. Auto select when 0
+
+ * - `algo`
+   - `search`
+   - N
+   - [`auto`, `single_cta`, `multi_cta`, `multi_kernel`]
+   - `auto`
+   - Algorithm to use for search. It's usually best to leave this to `auto`.
+
+ * - `graph_memory_type`
+   - `search`
+   - N
+   - [`device`, `host_pinned`, `host_huge_page`]
+   - `device`
+   - Memory type to store graph
+
+ * - `internal_dataset_memory_type`
+   - `search`
+   - N
+   - [`device`, `host_pinned`, `host_huge_page`]
+   - `device`
+   - Memory type to store dataset
+
+The `graph_memory_type` or `internal_dataset_memory_type` options can be useful for large datasets that do not fit the device memory. Setting `internal_dataset_memory_type` other than `device` has negative impact on search speed. Using `host_huge_page` option is only supported on systems with Heterogeneous Memory Management or on platforms that natively support GPU access to system allocated memory, for example Grace Hopper.
+
+To fine tune CAGRA index building we can customize IVF-PQ index builder options using the following settings. These take effect only if `graph_build_algo == "IVF_PQ"`. It is recommended to experiment using a separate IVF-PQ index to find the config that gives the largest QPS for large batch. Recall does not need to be very high, since CAGRA further optimizes the kNN neighbor graph. Some of the default values are derived from the dataset size which is assumed to be [n_vecs, dim].
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `ivf_pq_build_nlist`
+   - `build`
+   - N
+   - Positive integer >0
+   - sqrt(n_vecs)
+   - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
+
+ * - `ivf_pq_build_niter`
+   - `build`
+   - N
+   - Positive integer >0
+   - 25
+   - Number of k-means iterations to use when training the clusters.
+
+ * - `ivf_pq_build_ratio`
+   - `build`
+   - N
+   - Positive integer >0
+   - 10
+   - `1/ratio` is the number of training points which should be used to train the clusters.
+
+ * - `ivf_pq_pq_dim`
+   - `build`
+   - N
+   - Positive integer. Multiple of 8
+   - dim/2 rounded up to 8
+   - Dimensionality of the vector after product quantization. When 0, a heuristic is used to select this value. `pq_dim` * `pq_bits` must be a multiple of 8.
+
+ * - `ivf_pq_build_pq_bits`
+   - `build`
+   - N
+   - Positive integer [4-8]
+   - 8
+   - Bit length of the vector element after quantization.
+
+ * - `ivf_pq_build_codebook_kind`
+   - `build`
+   - N
+   - [`cluster`, `subspace`]
+   - `subspace`
+   - Type of codebook. See :doc:`IVF-PQ index overview <../indexes/ivfpq>` for more detail
+
+ * - `ivf_pq_build_nprobe`
+   - `search`
+   - N
+   - Positive integer >0
+   - min(2*dim, nlist)
+   - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
+ * - `ivf_pq_build_internalDistanceDtype`
+   - `search`
+   - N
+   - [`float`, `half`]
+   - `half`
+   - The precision to use for the distance computations. Lower precision can increase performance at the cost of accuracy.
+
+ * - `ivf_pq_build_smemLutDtype`
+   - `search`
+   - N
+   - [`float`, `half`, `fp8`]
+   - `fp8`
+   - The precision to use for the lookup table in shared memory. Lower precision can increase performance at the cost of accuracy.
+
+ * - `ivf_pq_build_refine_ratio`
+   - `search`
+   - N
+   - Positive integer >0
+   - 2
+   - `refine_ratio * k` nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best `k` neighbors.
+
+Alternatively, if `graph_build_algo == "NN_DESCENT"`, then we can customize the following parameters
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `nn_descent_niter`
+   - `build`
+   - N
+   - Positive integer >0
+   - 20
+   - Number of nn-descent iterations
+
+ * - `nn_descent_intermediate_graph_degree
+   - `build`
+   - N
+   - Positive integer >0
+   - `cagra.intermediate_graph_degree` * 1.5
+   - Intermadiate graph degree during nn-descent iterations
+
+ * - nn_descent_termination_threshold
+   - `build`
+   - N
+   - Positive float >0
+   - 1e-4
+   - Early stopping threshold for nn-descent convergence
+
+cuvs_cagra_hnswlib
+------------------
+
+This is a benchmark that enables interoperability between `CAGRA` built `HNSW` search. It uses the `CAGRA` built graph as the base layer of an `hnswlib` index to search queries only within the base layer (this is enabled with a simple patch to `hnswlib`).
+
+`build` : Same as `build` of CAGRA
+
+`search` : Same as `search` of Hnswlib
+
+FAISS Indexes
+=============
+
+faiss_gpu_flat
+--------------
+
+Use FAISS flat index on the GPU, which performs an exact search using brute-force and doesn't have any further build or search parameters.
+
+faiss_gpu_ivf_flat
+------------------
+
+IVF-flat uses an inverted-file index, which partitions the vectors into a series of clusters, or lists, storing them in an interleaved format which is optimized for fast distance computation. The searching of an IVF-flat index reduces the total vectors in the index to those within some user-specified nearest clusters called probes.
+
+IVF-flat is a simple algorithm which won't save any space, but it provides competitive search times even at higher levels of recall.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `nlists`
+   - `build`
+   - Y
+   - Positive integer >0
+   -
+   - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained
+
+ * - `ratio`
+   - `build`
+   - N
+   - Positive integer >0
+   - 2
+   - `1/ratio` is the number of training points which should be used to train the clusters.
+
+ * - `nprobe`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
+faiss_gpu_ivf_pq
+----------------
+
+IVF-pq is an inverted-file index, which partitions the vectors into a series of clusters, or lists, in a similar way to IVF-flat above. The difference is that IVF-PQ uses product quantization to also compress the vectors, giving the index a smaller memory footprint. Unfortunately, higher levels of compression can also shrink recall, which a refinement step can improve when the original vectors are still available.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `nlist`
+   - `build`
+   - Y
+   - Positive integer >0
+   -
+   - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
+
+ * - `ratio`
+   - `build`
+   - N
+   - Positive integer >0
+   - 2
+   - `1/ratio` is the number of training points which should be used to train the clusters.
+
+ * - `M_ratio`
+   - `build`
+   - Y
+   - Positive integer. Power of 2 [8-64]
+   -
+   - Ratio of numbeer of chunks or subquantizers for each vector. Computed by `dims` / `M_ratio`
+
+ * - `usePrecomputed`
+   - `build`
+   - N
+   - Boolean
+   - `false`
+   - Use pre-computed lookup tables to speed up search at the cost of increased memory usage.
+
+ * - `useFloat16`
+   - `build`
+   - N
+   - Boolean
+   - `false`
+   - Use half-precision floats for clustering step.
+
+ * - `nprobe`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
+ * - `refine_ratio`
+   - `search`
+   - N
+   - Positive number >=1
+   - 1
+   - `refine_ratio * k` nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best `k` neighbors.
+
+
+faiss_cpu_flat
+--------------
+
+Use FAISS flat index on the CPU, which performs an exact search using brute-force and doesn't have any further build or search parameters.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `numThreads`
+   - `search`
+   - N
+   - Positive integer >0
+   - 1
+   - Number of threads to use for queries.
+
+faiss_cpu_ivf_flat
+------------------
+
+Use FAISS IVF-Flat index on CPU
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `nlists`
+   - `build`
+   - Y
+   - Positive integer >0
+   -
+   - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained
+
+ * - `ratio`
+   - `build`
+   - N
+   - Positive integer >0
+   - 2
+   - `1/ratio` is the number of training points which should be used to train the clusters.
+
+ * - `nprobe`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
+ * - `numThreads`
+   - `search`
+   - N
+   - Positive integer >0
+   - 1
+   - Number of threads to use for queries.
+
+faiss_cpu_ivf_pq
+----------------
+
+Use FAISS IVF-PQ index on CPU
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `nlist`
+   - `build`
+   - Y
+   - Positive integer >0
+   -
+   - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
+
+ * - `ratio`
+   - `build`
+   - N
+   - Positive integer >0
+   - 2
+   - `1/ratio` is the number of training points which should be used to train the clusters.
+
+ * - `M`
+   - `build`
+   - Y
+   - Positive integer. Power of 2 [8-64]
+   -
+   - Ratio of number of chunks or subquantizers for each vector. Computed by `dims` / `M_ratio`
+
+ * - `usePrecomputed`
+   - `build`
+   - N
+   - Boolean
+   - `false`
+   - Use pre-computed lookup tables to speed up search at the cost of increased memory usage.
+
+ * - `bitsPerCode`
+   - `build`
+   - N
+   - Positive integer [4-8]
+   - 8
+   - Number of bits for representing each quantized code.
+
+ * - `nprobe`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
+ * - `refine_ratio`
+   - `search`
+   - N
+   - Positive number >=1
+   - 1
+   - `refine_ratio * k` nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best `k` neighbors.
+
+ * - `numThreads`
+   - `search`
+   - N
+   - Positive integer >0
+   - 1
+   - Number of threads to use for queries.
+
+HNSW
+====
+
+hnswlib
+-------
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `efConstruction`
+   - `build`
+   - Y
+   - Positive integer >0
+   -
+   - Controls index time and accuracy. Bigger values increase the index quality. At some point, increasing this will no longer improve the quality.
+
+ * - `M`
+   - `build`
+   - Y
+   - Positive integer. Often between 2-100
+   -
+   - umber of bi-directional links create for every new element during construction. Higher values work for higher intrinsic dimensionality and/or high recall, low values can work for datasets with low intrinsic dimensionality and/or low recalls. Also affects the algorithm's memory consumption.
+
+ * - `numThreads`
+   - `build`
+   - N
+   - Positive integer >0
+   - 1
+   - Number of threads to use to build the index.
+
+ * - `ef`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - Size of the dynamic list for the nearest neighbors used for search. Higher value leads to more accurate but slower search. Cannot be lower than `k`.
+
+ * - `numThreads`
+   - `search`
+   - N
+   - Positive integer >0
+   - 1
+   - Number of threads to use for queries.
+
+Please refer to `HNSW algorithm parameters guide <https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md>`_ from `hnswlib` to learn more about these arguments.
\ No newline at end of file
diff --git a/docs/source/cuvs_bench/wiki_all_dataset.rst b/docs/source/cuvs_bench/wiki_all_dataset.rst
new file mode 100644
index 0000000000..04ac7d9a4b
--- /dev/null
+++ b/docs/source/cuvs_bench/wiki_all_dataset.rst
@@ -0,0 +1,55 @@
+~~~~~~~~~~~~~~~~
+Wiki-all Dataset
+~~~~~~~~~~~~~~~~
+
+
+The `wiki-all` dataset was created to stress vector search algorithms at scale with both a large number of vectors and dimensions. The entire dataset contains 88M vectors with 768 dimensions and is meant for testing the types of vectors one would typically encounter in retrieval augmented generation (RAG) workloads. The full dataset is ~251GB in size, which is intentionally larger than the typical memory of GPUs. The massive scale is intended to promote the use of compression and efficient out-of-core methods for both indexing and search.
+
+The dataset is composed of English wiki texts from `Kaggle <https://www.kaggle.com/datasets/jjinho/wikipedia-20230701>`_ and multi-lingual wiki texts from `Cohere Wikipedia <https://huggingface.co/datasets/Cohere/wikipedia-22-12>`_.
+
+Cohere's English Texts are older (2022) and smaller than the Kaggle English Wiki texts (2023) so the English texts have been removed from Cohere completely. The final Wiki texts include English Wiki from Kaggle and the other languages from Cohere. The English texts constitute 50% of the total text size.
+
+To form the final dataset, the Wiki texts were chunked into 85 million 128-token pieces. For reference, Cohere chunks Wiki texts into 104-token pieces. Finally, the embeddings of each chunk were computed using the `paraphrase-multilingual-mpnet-base-v2 <https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2>`_ embedding model. The resulting dataset is an embedding matrix of size 88 million by 768. Also included with the dataset is a query file containing 10k query vectors and a groundtruth file to evaluate nearest neighbors algorithms.
+
+Getting the dataset
+===================
+
+Full dataset
+------------
+
+A version of the dataset is made available in the binary format that can be used directly by the :doc:`cuvs-bench <index>` tool. The full 88M dataset is ~251GB and the download link below contains tarballs that have been split into multiple parts.
+
+The following will download all 10 the parts and untar them to a `wiki_all_88M` directory:
+
+.. code-block:: bash
+    curl -s https://data.rapids.ai/raft/datasets/wiki_all/wiki_all.tar.{00..9} | tar -xf - -C wiki_all_88M/
+
+The above has the unfortunate drawback that if the command should fail for any reason, all the parts need to be re-downloaded. The files can also be downloaded individually and then untarred to the directory. Each file is ~27GB and there are 10 of them.
+
+.. code-block:: bash
+
+    curl -s https://data.rapids.ai/raft/datasets/wiki_all/wiki_all.tar.00
+    ...
+    curl -s https://data.rapids.ai/raft/datasets/wiki_all/wiki_all.tar.09
+
+    cat wiki_all.tar.* | tar -xf - -C wiki_all_88M/
+
+1M and 10M subsets
+------------------
+
+Also available are 1M and 10M subsets of the full dataset which are 2.9GB and 29GB, respectively. These subsets also include query sets of 10k vectors and corresponding groundtruth files.
+
+.. code-block:: bash
+
+    curl -s https://data.rapids.ai/raft/datasets/wiki_all_1M/wiki_all_1M.tar
+    curl -s https://data.rapids.ai/raft/datasets/wiki_all_10M/wiki_all_10M.tar
+
+Using the dataset
+=================
+
+After the dataset is downloaded and extracted to the `wiki_all_88M` directory (or `wiki_all_1M`/`wiki_all_10M` depending on whether the subsets are used), the files can be used in the benchmarking tool. The dataset name is `wiki_all` (or `wiki_all_1M`/`wiki_all_10M`), and the benchmarking tool can be used by specifying the appropriate name `--dataset wiki_all_88M` in the scripts.
+
+License info
+============
+
+The English wiki texts available on Kaggle come with the `CC BY-NCSA 4.0 <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_ license and the Cohere wikipedia data set comes with the `Apache 2.0 <https://choosealicense.com/licenses/apache-2.0/>`_ license.
\ No newline at end of file
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
index 79b35c2d50..fa38c45ee0 100644
--- a/docs/source/getting_started.rst
+++ b/docs/source/getting_started.rst
@@ -1,12 +1,120 @@
+~~~~~~~~~~~~~~~
 Getting Started
+~~~~~~~~~~~~~~~
+
+- `New to vector search?`_
+
+  * :doc:`Primer on vector search <choosing_and_configuring_indexes>`
+
+  * :doc:`Vector search indexes vs vector databases <vector_databases_vs_vector_search>`
+
+  * :doc:`Index tuning guide <tuning_guide>`
+
+  * :doc:`Comparing vector search index performance <comparing_indexes>`
+
+- `Supported indexes`_
+
+  * :doc:`Vector search index guide <indexes/indexes>`
+
+- `Using cuVS APIs`_
+
+  * :doc:`C API Docs <c_api>`
+
+  * :doc:`C++ API Docs <cpp_api>`
+
+  * :doc:`Python API Docs <python_api>`
+
+  * :doc:`Rust API Docs <rust_api/index>`
+
+  * :doc:`API basics <api_basics>`
+
+  * :doc:`API interoperability <api_interoperability>`
+
+- `Where to next?`_
+
+  * `Social media`_
+
+  * `Blogs`_
+
+  * `Research`_
+
+  * `Get involved`_
+
+New to vector search?
+=====================
+
+If you are unfamiliar with the basics of vector search or how vector search differs from vector databases, then :doc:`this primer on vector search guide <choosing_and_configuring_index>` should provide some good insight. Another good resource for the uninitiated is our :doc:`vector databases vs vector search <vector_databases_vs_vector_search>` guide. As outlined in the primer, vector search as used in vector databases is often closer to machine learning than to traditional databases. This means that while traditional databases can often be slow without any performance tuning, they will usually still yield the correct results. Unfortunately, vector search indexes, like other machine learning models, can yield garbage results of not tuned correctly.
+
+Fortunately, this opens up the whole world of hyperparamer optimization to improve vector search performance and quality. Please see our :doc:`index tuning guide <tuning_guide>` for more information.
+
+When comparing the performance of vector search indexes, it is important that considerations are made with respect to three main dimensions:
+
+#. Build time
+#. Search quality
+#. Search performance
+
+Please see the :doc:`primer on comparing vector search index performance <comparing_indexes>` for more information on methodologies and how to make a fair apples-to-apples comparison during your evaluations.
+
+Supported indexes
+=================
+
+cuVS supports many of the standard index types with the list continuing to grow and stay current with the state-of-the-art. Please refer to our :doc:`vector search index guide <indexes/indexes>` for to learn more about each individual index type, when they can be useful on the GPU, the tuning knobs they offer to trade off performance and quality.
+
+The primary goal of cuVS is to enable speed, scale, and flexibility (in that order)- and one of the important value propositions is to enhance existing software deployments with extensible GPU capabilities to improve pain points while not interrupting parts of the system that work well today with CPU.
+
+
+Using cuVS APIs
 ===============
 
-This guide provides an initial starting point of the basic concepts and using the various APIs in the cuVS software development kit.
+cuVS is a C++ library at its core, which is wrapped with a C library and exposed further through various different languages. cuVS currently provides APIs and documentation for :doc:`C <c_api>`, :doc:`C++ <cpp_api>`, :doc:`Python <python_api>`, and :doc:`Rust <rust_api/index>` with more languages in the works. our :doc:`API basics <api_basics>` provides some background and context about the important paradigms and vocabulary types you'll encounter when working with cuVS types.
+
+Please refer to the :doc:`guide on API interoperability <api_interoperability>` for more information on how cuVS can work seamlessly with other libraries like numpy, cupy, tensorflow, and pytorch, even without having to copy device memory.
+
+
+Where to next?
+==============
+
+cuVS is free and open source software, licesed under Apache 2.0 Once you are familiar with and/or have used cuVS, you can access the developer community most easily through `Github <https://github.com/rapidsai/cuvs>`_. Please open Github issues for any bugs, questions or feature requests.
+
+Social media
+------------
+
+You can access the RAPIDS community through `Slack <https://rapids.ai/slack-invite>`_ , `Stack Overflow <https://stackoverflow.com/tags/rapids>`_ and `X <https://twitter.com/rapidsai>`_
+
+Blogs
+-----
+
+We frequently publish blogs on GPU-enabled vector search, which can provide great deep dives into various important topics and breakthroughs:
+
+#. `Accelerated Vector Search: Approximating with cuVS IVF-Flat <https://developer.nvidia.com/blog/accelerated-vector-search-approximating-with-rapids-raft-ivf-flat/>`_
+#. `Accelerating Vector Search with cuVS IVF-PQ <https://developer.nvidia.com/blog/accelerating-vector-search-rapids-cuvs-ivf-pq-deep-dive-part-1/>`_
+
+Research
+--------
+
+For the interested reader, many of the accelerated implementations in cuVS are also based on research papers which can provide a lot more background. We also ask you to please cite the corresponding algorithms by referencing them in your own research.
+
+#. `CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search <https://arxiv.org/abs/2308.15136>`_
+#. `Top-K Algorithms on GPU: A Comprehensive Study and New Methods <https://dl.acm.org/doi/10.1145/3581784.3607062>`_
+#. `Fast K-NN Graph Construction by GPU Based NN-Descent <https://dl.acm.org/doi/abs/10.1145/3459637.3482344?casa_token=O_nan1B1F5cAAAAA:QHWDEhh0wmd6UUTLY9_Gv6c3XI-5DXM9mXVaUXOYeStlpxTPmV3nKvABRfoivZAaQ3n8FWyrkWw>`_
+#. `cuSLINK: Single-linkage Agglomerative Clustering on the GPU <https://arxiv.org/abs/2306.16354>`_
+#. `GPU Semiring Primitives for Sparse Neighborhood Methods <https://arxiv.org/abs/2104.06357>`_
+
+
+Get involved
+------------
+
+We always welcome patches for new features and bug fixes. Please read our `contributing guide <contributing.md>`_ for more information on contributing patches to cuVS.
+
+
 
 .. toctree::
-   :maxdepth: 1
-   :caption: Contents:
-    
-   basics.rst
-   interoperability.rst
-   working_with_ann_indexes.rst
\ No newline at end of file
+   :hidden:
+
+   choosing_and_configuring_indexes.rst
+   vector_databases_vs_vector_search.rst
+   tuning_guide.rst
+   comparing_indexes.rst
+   indexes/indexes.rst
+   api_basics.rst
+   api_interoperability.rst
\ No newline at end of file
diff --git a/docs/source/images/build_benchmarks.png b/docs/source/images/build_benchmarks.png
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diff --git a/docs/source/index.rst b/docs/source/index.rst
index 88f3612439..f2d57866f6 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,36 +1,40 @@
 cuVS: Vector Search and Clustering on the GPU
 =============================================
 
+
+Welcome to cuVS, the premier library for GPU-accelerated vector search and clustering! cuVS provides several core building blocks for constructing new algorithms, as well as end-to-end vector search and clustering algorithms for use either standalone or through a growing list of :doc:`integrations <integrations>`.
+
+There are several benefits to using cuVS and GPUs for vector search, including
+
+#. Fast index build
+#. Latency critical and high throughput search
+#. Parameter tuning
+#. Cost savings
+#. Interoperability (build on GPU, deploy on CPU)
+#. Multiple language support
+#. Building blocks for composing new or accelerating existing algorithms
+
 Useful Resources
 ################
 
 .. _cuvs_reference: https://docs.rapids.ai/api/cuvs/stable/
 
-- `Example Notebooks <https://github.com/rapidsai/cuvs/tree/HEAD/notebooks>`_: Self-contained Code examples
+- `Example Notebooks <https://github.com/rapidsai/cuvs/tree/HEAD/notebooks>`_: Example notebooks
+- `Code Examples <https://github.com/rapidsai/cuvs/tree/HEAD/examples>`_: Self-contained code examples
 - `RAPIDS Community <https://rapids.ai/community.html>`_: Get help, contribute, and collaborate.
 - `GitHub repository <https://github.com/rapidsai/cuvs>`_: Download the cuVS source code.
 - `Issue tracker <https://github.com/rapidsai/cuvs/issues>`_: Report issues or request features.
 
 
-What is cuVS?
-#############
-
-cuVS is a library for vector search and clustering on the GPU.
+Contents
+########
 
 .. toctree::
-   :maxdepth: 1
-   :caption: Contents:
+   :maxdepth: 4
 
+   build.rst
    getting_started.rst
-   build.md
    integrations.rst
    api_docs.rst
    contributing.md
 
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/docs/source/indexes/bruteforce.rst b/docs/source/indexes/bruteforce.rst
new file mode 100644
index 0000000000..0bd17dbf15
--- /dev/null
+++ b/docs/source/indexes/bruteforce.rst
@@ -0,0 +1,62 @@
+Brute-force
+===========
+
+Brute-force, or flat index, is the most simple index type, as it ultimately boils down to an exhaustive matrix multiplication.
+
+While it scales with :math:`O(N^2*D)`, brute-force can be a great choice when
+
+1. exact nearest neighbors are required, and
+2. when the number of vectors is relatively small (a few thousand to a few million)
+
+Brute-force can also be a good choice for heavily filtered queries where other algorithms might struggle returning the expected results. For example,
+when filtering out 90%-95% of the vectors from a search, the IVF methods could struggle to return anything at all with smaller number of probes and
+graph-based algorithms with limited hash table memory could end up skipping over important unfiltered entries.
+
+[ :doc:`C API <../c_api/neighbors_bruteforce_c>` | :doc:`C++ API <../cpp_api/neighbors_bruteforce>` | :doc:`Python API <../python_api/neighbors_bruteforce>` | :doc:`Rust API <../rust_api/index>` ]
+
+Filtering considerations
+------------------------
+
+Because it is exhaustive, brute-force can quickly become the slowest, albeit most accurate form of search. However, even
+when the number of vectors in an index are very large, brute-force can still be used to search vectors efficiently with a filter.
+
+This is especially true for cases where the filter is excluding 90%-99% of the vectors in the index where the partitioning
+inherent in other approximate algorithms would simply not include expected vectors in the results. In the case of pre-filtered
+brute-force, the computation is inverted so distances are only computed between vectors that pass the filter, significantly reducing
+the amount of computation required.
+
+Configuration parameters
+------------------------
+
+Build parameters
+~~~~~~~~~~~~~~~~
+
+None
+
+Search Parameters
+~~~~~~~~~~~~~~~~~
+
+None
+
+
+Tuning Considerations
+---------------------
+
+Brute-force is exact but that doesn't always mean it's deterministic. For example, when there are many nearest neighbors with
+the same distances it's possible they might be ordered differently across different runs. This especially becomes apparent in
+cases where there are points with the same distance right near the cutoff of `k`, which can cause the final list of neighbors
+to differ from ground truth. This is not often a problem in practice and can usually be mitigated by increasing `k`.
+
+
+Memory footprint
+----------------
+
+:math:`precision` is the number of bytes in each element of each vector (e.g. 32-bit = 4-bytes)
+
+
+Index footprint
+~~~~~~~~~~~~~~~
+
+Raw vectors: :math:`n_vectors * n_dimensions * precision`
+
+Vector norms (for distances which require them): :math:`n_vectors * precision`
diff --git a/docs/source/indexes/cagra.rst b/docs/source/indexes/cagra.rst
new file mode 100644
index 0000000000..de8821e745
--- /dev/null
+++ b/docs/source/indexes/cagra.rst
@@ -0,0 +1,149 @@
+CAGRA
+=====
+
+CAGRA, or (C)UDA (A)NN (GRA)ph-based, is a graph-based index that is based loosely on the popular navigable small-world graph (NSG) algorithm, but which has been
+built from the ground-up specifically for the GPU. CAGRA constructs a flat graph representation by first building a kNN graph
+of the training points and then removing redundant paths between neighbors.
+
+The CAGRA algorithm has two basic steps-
+* 1. Construct a kNN graph
+* 2. Prune redundant routes from the kNN graph.
+
+I-force could be used to construct the initial kNN graph. This would yield the most accurate graph but would be very slow and
+we find that in practice the kNN graph does not need to be very accurate since the pruning step helps to boost the overall recall of
+the index. cuVS provides IVF-PQ and NN-Descent strategies for building the initial kNN graph and these can be selected in index params object during index construction.
+
+[ :doc:`C API <../c_api/neighbors_cagra_c>` | :doc:`C++ API <../cpp_api/neighbors_cagra>` | :doc:`Python API <../python_api/neighbors_cagra>` | :doc:`Rust API <../rust_api/index>` ]
+
+Interoperability with HNSW
+--------------------------
+
+cuVS provides the capability to convert a CAGRA graph to an HNSW graph, which enables the GPU to be used only for building the index
+while the CPU can be leveraged for search.
+
+Filtering considerations
+------------------------
+
+CAGRA supports filtered search which can work well for moderately small filters (such as filtering out only a small percentage of the vectors in the index (e.g. <<50%).
+
+When a filter is expected to remove 80%-99% of the vectors in the index, it is preferred to use brute-force with pre-filtering instead, as that will compute only those distances
+between the vectors not being filtered out. By default, CAGRA will pass the filter to the pre-filtered brute-force when the number of vevtors being filtered out is >90% of the vectors in the index.
+
+Configuration parameters
+------------------------
+
+Build parameters
+~~~~~~~~~~~~~~~~
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Default
+     - Description
+   * - compression
+     - None
+     - For large datasets, the raw vectors can be compressed using product quantization so they can be placed on device. This comes at the cost of lowering recall, though a refinement reranking step can be used to make up the lost recall after search.
+   * - graph_build_algo
+     - 'IVF_PQ'
+     - The graph build algorithm to use for building
+   * - graph_build_params
+     - None
+     - Specify explicit build parameters for the corresponding graph build algorithms
+   * - graph_degree
+     - 32
+     - The degree of the final CAGRA graph. All vertices in the graph will have this degree. During search, a larger graph degree allows for more exploration of the search space and improves recall but at the expense of searching more vertices.
+   * - intermediate_graph_degree
+     - 64
+     - The degree of the initial knn graph before it is optimized into the final CAGRA graph. A larger value increases connectivity of the initial graph so that it performs better once pruned. Larger values come at the cost of increased device memory usage and increases the time of initial knn graph construction.
+   * - guarantee_connectivity
+     - False
+     - Uses a degree-constrained minimum spanning tree to guarantee the initial knn graph is connected. This can improve recall on some datasets.
+   * - attach_data_on_build
+     - True
+     - Should the dataset be attached to the index after the index is built? Setting this to `False` can improve memory usage and performance, for example if the graph is being serialized to disk or converted to HNSW right after building it.
+
+Search parameters
+~~~~~~~~~~~~~~~~~
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Default
+     - Description
+   * - itopk_size
+     - 64
+     - Number of intermediate search results retained during search. This value needs to be >=k. This is the main knob to tweak search performance.
+   * - max_iterations
+     - 0
+     - The maximum number of iterations during search. Default is to auto-select.
+   * - max_queries
+     - 0
+     - Max number of search queries to perform concurrently (batch size). Default is to auto-select.
+   * - team_size
+     - 0
+     - Number of CUDA threads for calculating each distance. Can be 4, 8, 16, or 32. Default is to auto-select.
+   * - search_width
+     - 1
+     - Number of vertices to select as the starting point for the search in each iteration.
+   * - min_iterations
+     - 0
+     - Minimum number of search iterations to perform
+
+Tuning Considerations
+---------------------
+
+The 3 hyper-parameters that are most often tuned are `graph_degree`, `intermediate_graph_degree`, and `itopk_size`.
+
+Memory footprint
+----------------
+
+CAGRA builds a graph that ultimately ends up on the host while it needs to keep the original dataset around (can be on host or device).
+
+IVFPQ or NN-DESCENT can be used to build the graph (additions to the peak memory usage calculated as in the respective build algo above).
+
+Dataset on device (graph on host):
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Index memory footprint (device): :math:`n_index_vectors * n_dims * sizeof(T)`
+
+Index memory footprint (host): :math:`graph_degree * n_index_vectors * sizeof(T)``
+
+Dataset on host (graph on host):
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Index memory footprint (host): :math:`n_index_vectors * n_dims * sizeof(T) + graph_degree * n_index_vectors * sizeof(T)`
+
+Build peak memory usage:
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+When built using NN-descent / IVF-PQ, the build process consists of two phases: (1) building an initial/(intermediate) graph and then (2) optimizing the graph. Key input parameters are n_vectors, intermediate_graph_degree, graph_degree.
+The memory usage in the first phase (building) depends on the chosen method. The biggest allocation is the graph (n_vectors*intermediate_graph_degree), but it’s stored in the host memory.
+Usually, the second phase (optimize) uses the most device memory. The peak memory usage is achieved during the pruning step (graph_core.cuh/optimize)
+Optimize: formula for peak memory usage (device): :math:`n_vectors * (4 + (sizeof(IdxT) + 1) * intermediate_degree)``
+
+Build with out-of-core IVF-PQ peak memory usage:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Out-of-core CAGA build consists of IVF-PQ build, IVF-PQ search, CAGRA optimization. Note that these steps are performed sequentially, so they are not additive.
+
+IVF-PQ Build:
+
+.. math::
+
+   n_vectors / train_set_ratio * dim * sizeof(float)   // trainset, may be in managed mem
+   + n_vectors / train_set_ratio * sizeof(uint32_t)    // labels, may be in managed mem
+   + n_clusters * n_dim * sizeof(float)                // cluster centers
+
+IVF-PQ Search (max batch size 1024 vectors on device at a time):
+
+.. math::
+
+   [n_vectors * (pq_dim * pq_bits / 8 + sizeof(int64_t)) + O(n_clusters)]
+   + [batch_size * n_dim * sizeof(float)] + [batch_size * intermediate_degree * sizeof(uint32_t)] +
+   [batch_size * intermediate_degree * sizeof(float)]
+
+
diff --git a/docs/source/indexes/indexes.rst b/docs/source/indexes/indexes.rst
new file mode 100644
index 0000000000..a2fb1434a5
--- /dev/null
+++ b/docs/source/indexes/indexes.rst
@@ -0,0 +1,19 @@
+Nearest Neighbor Indexes
+========================
+
+.. toctree::
+   :maxdepth: 3
+   :caption: Contents:
+
+   bruteforce.rst
+   cagra.rst
+   ivfflat.rst
+   ivfpq.rst
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
\ No newline at end of file
diff --git a/docs/source/indexes/ivfflat.rst b/docs/source/indexes/ivfflat.rst
new file mode 100644
index 0000000000..14dd1798c5
--- /dev/null
+++ b/docs/source/indexes/ivfflat.rst
@@ -0,0 +1,116 @@
+IVF-Flat
+========
+
+IVF-Flat is an inverted file index (IVF) algorithm, which in the context of nearest neighbors means that data points are
+partitioned into clusters. At search time, brute-force is performed only in a (user-defined) subset of the closest clusters.
+In practice, this algorithm can search the index much faster than brute-force and often still maintain an acceptable
+recall, though this comes with the drawback that the index itself copies the original training vectors into a memory layout
+that is optimized for fast memory reads and adds some additional memory storage overheads. Once the index is trained,
+this algorithm no longer requires the original raw training vectors.
+
+IVF-Flat tends to be a great choice when
+
+1. like brute-force, there is enough device memory available to fit all of the vectors
+in the index, and
+2. exact recall is not needed. as with the other index types, the tuning parameters are used to trade-off recall for search latency / throughput.
+
+[ :doc:`C API <../c_api/neighbors_ivf_flat_c>` | :doc:`C++ API <../cpp_api/neighbors_ivf_flat>` | :doc:`Python API <../python_api/neighbors_ivf_flat>` | :doc:`Rust API <../rust_api/index>` ]
+
+Filtering considerations
+------------------------
+
+IVF methods only apply filters to the lists which are probed for each query point. As a result, the results of a filtered query will likely differ signficiantly from the results of a filtering applid to an exact method like brute-force. For example. imagine you have 3 IVF lists each containing 2 vectors and you perform a query against only the closest 2 lists but you filter out all but 1 element. If that remaining element happens to be in one of the lists which was not proved, it will not be considered at all in the search results. It's important to consider this when using any of the IVF methods in your applications.
+
+
+Configuration parameters
+------------------------
+
+Build parameters
+~~~~~~~~~~~~~~~~
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Default
+     - Description
+   * - n_lists
+     - sqrt(n)
+     - Number of coarse clusters used to partition the index. A good heuristic for this value is sqrt(n_vectors_in_index)
+   * - add_data_on_build
+     - True
+     - Should the training points be added to the index after the index is built?
+   * - kmeans_train_iters
+     - 20
+     - Max number of iterations for k-means training before convergence is assumed. Note that convergence could happen before this number of iterations.
+   * - kmeans_trainset_fraction
+     - 0.5
+     - Fraction of points that should be subsampled from the original dataset to train the k-means clusters. Default is 1/2 the training dataset. This can often be reduced for very large datasets to improve both cluster quality and the build time.
+   * - adaptive_centers
+     - false
+     - Should the existing trained centroids adapt to new points that are added to the index? This provides a trade-off between improving recall at the expense of having to compute new centroids for clusters when new points are added. When points are added in large batches, the performance cost may not be noticeable.
+   * - conservative_memory_allocation
+     - false
+     - To support dynamic indexes, where points are expected to be added later, the individual IVF lists can be imtentionally overallocated up front to reduce the amount and impact of increasing list sizes, which requires allocating more memory and copying the old list to the new, larger, list.
+
+
+Search parameters
+~~~~~~~~~~~~~~~~~
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Default
+     - Description
+   * - n_probes
+     - 20
+     - Number of closest IVF lists to scan for each query point.
+
+Tuning Considerations
+---------------------
+
+Since IVF methods use clustering to establish spatial locality and partition data points into individual lists, there's an inherent
+assumption that the number of lists, and thus the max size of the data in the index is known up front. For some use-cases, this
+might not matter. For example, most vector databases build many smaller physical approximate nearest neighbors indexes, each from
+fixed-size or maximum-sized immutable segments and so the number of lists can be tuned based on the number of vectors in the indexes.
+
+Empirically, we've found :math:`\sqrt{n\_index\_vectors}` to be a good starting point for the :math:`n\_lists` hyper-parameter. Remember, having more
+lists means less points to search within each list, but it could also mean more :math:`n\_probes` are needed at search time to reach an acceptable
+recall.
+
+
+Memory footprint
+----------------
+
+Each cluster is padded to at least 32 vectors (but potentially up to 1024). Assuming uniform random distribution of vectors/list, we would have
+:math:`cluster\_overhead = (conservative\_memory\_allocation ? 16 : 512 ) * dim * sizeof_{float})`
+
+Note that each cluster is allocated as a separate allocation. If we use a `cuda_memory_resource`, that would grab memory in 1 MiB chunks, so on average we might have 0.5 MiB overhead per cluster. If we us 10s of thousands of clusters, it becomes essential to use pool allocator to avoid this overhead.
+
+:math:`cluster\_overhead =  0.5 MiB` // if we do not use pool allocator
+
+
+Index (device memory):
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. math::
+
+   n\_vectors * n\_dimensions * sizeof(T) +
+
+   n\_vectors  * sizeof(int_type) +
+
+   n\_clusters * n\_dimensions * sizeof(T) +
+
+   n\_clusters * cluster_overhead`
+
+
+Peak device memory usage for index build:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+:math:`workspace = min(1GB, n\_queries * [(n\_lists + 1 + n\_probes * (k + 1)) * sizeof_{float}) + n\_probes * k * sizeof_{idx}])`
+
+:math:`index\_size + workspace`
+
diff --git a/docs/source/indexes/ivfpq.rst b/docs/source/indexes/ivfpq.rst
new file mode 100644
index 0000000000..e4bd81395b
--- /dev/null
+++ b/docs/source/indexes/ivfpq.rst
@@ -0,0 +1,140 @@
+IVF-PQ
+======
+
+IVF-PQ is an inverted file index (IVF) algorithm, which is an extension to the IVF-Flat algorithm (e.g. data points are first
+partitioned into clusters) where product quantization is performed within each cluster in order to shrink the memory footprint
+of the index. Product quantization is a lossy compression method and it is capable of storing larger number of vectors
+on the GPU by offloading the original vectors to main memory, however higher compression levels often lead to reduced recall.
+Often a strategy called refinement reranking is employed to make up for the lost recall by querying the IVF-PQ index for a larger
+`k` than desired and performing a reordering and reduction to `k` based on the distances from the unquantized vectors. Unfortunately,
+this does mean that the unquantized raw vectors need to be available and often this can be done efficiently using multiple CPU threads.
+
+[ :doc:`C API <../c_api/neighbors_ivf_pq_c>` | :doc:`C++ API <../cpp_api/neighbors_ivf_pq>` | :doc:`Python API <../python_api/neighbors_ivf_pq>` | :doc:`Rust API <../rust_api/index>` ]
+
+
+Configuration parameters
+------------------------
+
+Build parameters
+~~~~~~~~~~~~~~~~
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Default
+     - Description
+   * - n_lists
+     - sqrt(n)
+     - Number of coarse clusters used to partition the index. A good heuristic for this value is sqrt(n_vectors_in_index)
+   * - kmeans_n_iters
+     - 20
+     - The number of iterations when searching for k-means centers
+   * - kmeans_trainset_fraction
+     - 0.5
+     - The fraction of training data to use for iterative k-means building
+   * - pq_bits
+     - 8
+     - The bit length of each vector element after compressing with PQ. Possible values are any integer between 4 and 8.
+   * - pq_dim
+     - 0
+     - The dimensionality of each vector after compressing with PQ. When 0, the dim is set heuristically.
+   * - codebook_kind
+     - per_subspace
+     - How codebooks are created. `per_subspace` trains kmeans on some number of sub-dimensions while `per_cluster`
+   * - force_random_rotation
+     - false
+     - Apply a random rotation matrix on the input data and queries even if `dim % pq_dim == 0`
+   * - conservative_memory_allocation
+     - false
+     - To support dynamic indexes, where points are expected to be added later, the individual IVF lists can be imtentionally overallocated up front to reduce the amount and impact of increasing list sizes, which requires allocating more memory and copying the old list to the new, larger, list.
+   * - add_data_on_build
+     - True
+     - Should the training points be added to the index after the index is built?
+   * - max_train_points_per_pq_code
+     - 256
+     - The max number of data points to use per PQ code during PQ codebook training.
+
+
+Search parameters
+~~~~~~~~~~~~~~~~
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Default
+     - Description
+   * - n_probes
+     - 20
+     - Number of closest IVF lists to scan for each query point.
+   * - lut_dtype
+     - cuda_r_32f
+     - Datatype to store the pq lookup tables. Can also use cuda_r_16f for half-precision and cuda_r_8u for 8-bit precision. Smaller lookup tables can fit into shared memory and significantly improve search times.
+   * - internal_distance_dtype
+     - cuda_r_32f
+     - Storage data type for distance/similarity computed at search time. Can also use cuda_r_16f for half-precision.
+   * - preferred_smem_carveout
+     - 1.0
+     - Preferred fraction of SM's unified memory / L1 cache to be used as shared memory. Default is 100%
+
+Tuning Considerations
+---------------------
+
+IVF-PQ has similar tuning considerations to IVF-flat, though the PQ compression ratio adds an additional variable to trade-off index size for search quality.
+
+It's important to note that IVF-PQ becomes very lossy very quickly, and so refinement reranking is often needed to get a reasonable recall. This step usually consists of searching initially for more k-neighbors than needed and then reducing the resulting neighborhoods down to k by computing exact distances. This step can be performed efficiently on CPU or GPU and generally has only a marginal impact on search latency.
+
+Memory footprint
+----------------
+
+Index (device memory):
+~~~~~~~~~~~~~~~~~~~~~~
+
+Simple approximate formula: :math:`n\_vectors * (pq\_dim * \frac{pq\_bits}{8} + sizeof_{idx}) + n\_clusters`
+
+The IVF lists end up being represented by a sparse data structure that stores the pointers to each list, an indices array that contains the indexes of each vector in each list, and an array with the encoded (and interleaved) data for each list.
+
+IVF list pointers: :math:`n\_clusters * sizeof_{uint32_t}`
+
+Indices: :math:`n\_vectors * sizeof_{idx}``
+
+Encoded data (interleaved): :math:`n\_vectors * pq\_dim * \frac{pq\_bits}{8}`
+
+Per subspace method: :math:`4 * pq\_dim * pq\_len * 2^pq\_bits`
+
+Per cluster method: :math:`4 * n\_clusters * pq\_len * 2^pq\_bits`
+
+Extras: :math:`n\_clusters * (20 + 8 * dim)`
+
+Index (host memory):
+~~~~~~~~~~~~~~~~~~~~
+
+When refinement is used with the dataset on host, the original raw vectors are needed: :math:`n\_vectors * dims * sizeof_{Tloat}`
+
+Search peak memory usage (device);
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Total usage: :math:`index + queries + output\_indices + output\_distances + workspace`
+
+Workspace size is not trivial, a heuristic controls the batch size to make sure the workspace fits the `raft::resource::get_workspace_free_bytes(res)``.
+
+Build peak memory usage (device):
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. math::
+
+   \frac{n\_vectors}{trainset\_ratio * dims * sizeof_{float}}
+
+   + \frac{n\_vectors}{trainset\_ratio * sizeof_{uint32_t}}
+
+   + n\_clusters * dim * sizeof_{float}
+
+Note, if there’s not enough space left in the workspace memory resource, IVF-PQ build automatically switches to the managed memory for the training set and labels.
+
+
+
+
+
diff --git a/docs/source/integrations.rst b/docs/source/integrations.rst
index 19d72fd90e..760892a98a 100644
--- a/docs/source/integrations.rst
+++ b/docs/source/integrations.rst
@@ -1,50 +1,13 @@
+============
 Integrations
 ============
 
-Aside from using cuVS directly, it can be consumed through a number of sdk and vector database integrations.
-
-- `FAISS`_
-- `Milvus`_
-- `Lucene`_
-- `Kinetica`_
-
-
-FAISS
------
-
-FAISS v1.8 provides a special conda package that enables a RAFT backend for the Flat, IVF-Flat and IVF-PQ indexes on the GPU. Like the classical FAISS GPU indexes, the RAFT backend also enables interoperability between FAISS CPU indexes, allowing an index to be trained on GPU, searched on CPU, and vice versa.
-
-The RAFT backend can be enabled by building FAISS from source with the `FAISS_USE_RAFT` cmake flag enabled and setting the `use_raft` configuration option for the RAFT-enabled GPU indexes.
-
-A pre-compiled conda package can also be installed using the following command:
-
-.. code-block:: bash
-
-    conda install -c conda-forge -c pytorch -c rapidsai -c nvidia -c "nvidia/label/cuda-11.8.0" faiss-gpu-raft
-
-The next release of FAISS will feature cuVS support as we continue to migrate the vector search algorithms from RAFT to cuVS.
-
-Milvus
-------
-
-In version 2.3, Milvus released support for IVF-Flat and IVF-PQ indexes on the GPU through RAFT. Version 2.4 adds support for brute-force and the graph-based CAGRA index on the GPU. Please refer to the `Milvus documentation <https://milvus.io/docs/install_standalone-docker-compose-gpu.md>`_ to install Milvus with GPU support.
-
-The GPU indexes can be enabled by using the index types prefixed with `GPU_`, as outlined in the `Milvus index build guide <https://milvus.io/docs/build_index.md#Prepare-index-parameter>`_.
-
-Milvus will be migrating their GPU support from RAFT to cuVS as we continue to move the vector search algorithms out of RAFT and into cuVS.
-
-
-Lucene
-------
-
-An experimental Lucene connector for cuVS enables GPU-accelerated vector search indexes through Lucene. Initial benchmarks are showing that this connector can drastically improve the performance of both indexing and search in Lucene. This connector will continue to be improved over time and any interested developers are encouraged to contribute.
-
-Install and evaluate the `lucene-cuvs` connector on `Github <https://github.com/SearchScale/lucene-cuvs>`_.
-
-
-Kinetica
---------
+Aside from using cuVS standalone, it can be consumed through a number of sdk and vector database integrations.
 
-Starting with release 7.2, Kinetica supports the graph-based the CAGRA algorithm from RAFT. Kinetica will continue to improve its support over coming versions, while also migrating to cuVS as we work to move the vector search algorithms out of RAFT and into cuVS.
+.. toctree::
+   :maxdepth: 4
 
-Kinetica currently offers the ability to create a CAGRA index in a SQL `CREATE_TABLE` statement, as outlined in their `vector search indexing docs <https://docs.kinetica.com/7.2/concepts/indexes/#cagra-index>`_. Kinetica is not open source, but the RAFT indexes can be enabled in the developer edition, which can be installed `here <https://www.kinetica.com/try/#download_instructions>`_.
+   integrations/faiss.rst
+   integrations/milvus.rst
+   integrations/lucene.rst
+   integrations/kinetica.rst
diff --git a/docs/source/integrations/faiss.rst b/docs/source/integrations/faiss.rst
new file mode 100644
index 0000000000..bf8be8225f
--- /dev/null
+++ b/docs/source/integrations/faiss.rst
@@ -0,0 +1,14 @@
+FAISS
+-----
+
+FAISS v1.8 provides a special conda package that enables a RAFT backend for the Flat, IVF-Flat and IVF-PQ indexes on the GPU. Like the classical FAISS GPU indexes, the RAFT backend also enables interoperability between FAISS CPU indexes, allowing an index to be trained on GPU, searched on CPU, and vice versa.
+
+The RAFT backend can be enabled by building FAISS from source with the `FAISS_USE_RAFT` cmake flag enabled and setting the `use_raft` configuration option for the RAFT-enabled GPU indexes.
+
+A pre-compiled conda package can also be installed using the following command:
+
+.. code-block:: bash
+
+    conda install -c conda-forge -c pytorch -c rapidsai -c nvidia -c "nvidia/label/cuda-11.8.0" faiss-gpu-raft
+
+The next release of FAISS will feature cuVS support as we continue to migrate the vector search algorithms from RAFT to cuVS.
diff --git a/docs/source/integrations/kinetica.rst b/docs/source/integrations/kinetica.rst
new file mode 100644
index 0000000000..e74cfe82fd
--- /dev/null
+++ b/docs/source/integrations/kinetica.rst
@@ -0,0 +1,6 @@
+Kinetica
+--------
+
+Starting with release 7.2, Kinetica supports the graph-based the CAGRA algorithm from RAFT. Kinetica will continue to improve its support over coming versions, while also migrating to cuVS as we work to move the vector search algorithms out of RAFT and into cuVS.
+
+Kinetica currently offers the ability to create a CAGRA index in a SQL `CREATE_TABLE` statement, as outlined in their `vector search indexing docs <https://docs.kinetica.com/7.2/concepts/indexes/#cagra-index>`_. Kinetica is not open source, but the RAFT indexes can be enabled in the developer edition, which can be installed `here <https://www.kinetica.com/try/#download_instructions>`_.
diff --git a/docs/source/integrations/lucene.rst b/docs/source/integrations/lucene.rst
new file mode 100644
index 0000000000..d20052545b
--- /dev/null
+++ b/docs/source/integrations/lucene.rst
@@ -0,0 +1,6 @@
+Lucene
+------
+
+An experimental Lucene connector for cuVS enables GPU-accelerated vector search indexes through Lucene. Initial benchmarks are showing that this connector can drastically improve the performance of both indexing and search in Lucene. This connector will continue to be improved over time and any interested developers are encouraged to contribute.
+
+Install and evaluate the `lucene-cuvs` connector on `Github <https://github.com/SearchScale/lucene-cuvs>`_.
diff --git a/docs/source/integrations/milvus.rst b/docs/source/integrations/milvus.rst
new file mode 100644
index 0000000000..4139cca526
--- /dev/null
+++ b/docs/source/integrations/milvus.rst
@@ -0,0 +1,8 @@
+Milvus
+------
+
+In version 2.3, Milvus released support for IVF-Flat and IVF-PQ indexes on the GPU through RAFT. Version 2.4 adds support for brute-force and the graph-based CAGRA index on the GPU. Please refer to the `Milvus documentation <https://milvus.io/docs/install_standalone-docker-compose-gpu.md>`_ to install Milvus with GPU support.
+
+The GPU indexes can be enabled by using the index types prefixed with `GPU_`, as outlined in the `Milvus index build guide <https://milvus.io/docs/build_index.md#Prepare-index-parameter>`_.
+
+Milvus will be migrating their GPU support from RAFT to cuVS as we continue to move the vector search algorithms out of RAFT and into cuVS.
diff --git a/docs/source/tuning_guide.rst b/docs/source/tuning_guide.rst
new file mode 100644
index 0000000000..26cb2a7010
--- /dev/null
+++ b/docs/source/tuning_guide.rst
@@ -0,0 +1,56 @@
+~~~~~~~~~~~~~~~~~~~~~~
+Automated tuning Guide
+~~~~~~~~~~~~~~~~~~~~~~
+
+Introduction
+============
+
+A Method for tuning and evaluating Vector Search Indexes At Scale in Locally Indexed Vector Databases. For more information on the differences between locally and globally indexed vector databases, please see :doc:`this guide <vector_databases_vs_vector_search>`. The goal of this guide is to give users a scalable and effective approach for tuning a vector search index, no matter how large.  Evaluation of a vector search index “model” that measures recall in proportion to build time so that it penalizes the recall when the build time is really high (should ultimately optimize for finding a lower build time and higher recall).
+
+For more information on the various different types of vector search indexes, please see our :doc:`guide to choosing vector search indexes <choosing_and_configuring_indexes>`
+
+Why automated tuning?
+=====================
+
+As much as 75% of users have told us they will not be able to tune a vector database beyond one or two simple knobs and we suggest that an ideal “knob” would be to balance training time and search time with search quality. The more time, the higher the quality, and the more needed to find an acceptable search performance. Even the 25% of users that want to tune are still asking for simple tools for doing so. These users also ask for some simple guidelines for setting tuning parameters, like :doc:`this guide <indexes/indexes>`.
+
+Since vector search indexes are more closely related to machine learning models than traditional databases indexes, one option for easing the parameter tuning burden is to use hyper-parameter optimization tools like `Ray Tune <https://medium.com/rapids-ai/30x-faster-hyperparameter-search-with-raytune-and-rapids-403013fbefc5>`_ and `Optuna <https://docs.rapids.ai/deployment/stable/examples/rapids-optuna-hpo/notebook/>`_. to verify this.
+
+How to tune?
+============
+
+But how would this work when we have an index that's massively large- like 1TB?
+
+One benefit to locally indexed vector databases is that they often scale by breaking the larger set of vectors down into a smaller set by uniformly random subsampling and training smaller vector search index models on the sub-samples. Most often, the same set of tuning parameters are applied to all of the smaller sub-index models, rather than trying to set them individually for each one. During search, the query vectors are often sent to all of the sub-indexes and the resulting neighbors list reduced down to `k` based on the closest distances (or similarities).
+
+Because many databases use this sub-sampling trick, it's possible to perform an automated parameter tuning on the larger index just by randomly samplnig some number of vectors from it, splitting them into disjoint train/test/eval datasets, computing ground truth with brute-force, and then performing a hyper-parameter optimization on it. This procedure can also be repeated multiple times to simulate a monte-carlo cross validation.
+
+GPUs are naturally great at performing massively parallel tasks, especially when they are largely independent tasks, such as training and evaluating models with different hyper-parameter settings in parallel. Hyper-parameter optimization also lends itself well to distributed processing, such as multi-node multi-GPU operation.
+
+Steps to achieve automated tuning
+=================================
+
+More formally, an automated parameter tuning workflow with monte-carlo cross-validaton looks likes something like this:
+
+#. Ingest a large dataset into the vector database of your choice
+
+#. Choose an index size based on number of vectors. This should usually align with the average number of vectors the database will end up putting in a single ANN sub-index model.
+
+#. Uniformly random sample the number of vectors specified above from the database for a training set. This is often accomplished by generating some number of random (unique) numbers up to the dataset size.
+
+#. Uniformly sample some number of vectors for a test set and do this again for an evaluation set. 1-10% of the vectors in the training set.
+
+#. Use the test set to compute ground truth on the vectors from prior step against all vectors in the training set.
+
+#. Start the HPO tuning process for the training set, using the test vectors for the query set. It's important to make sure your HPO is multi-objective and optimizes for: a) low build time, b) high throughput or low latency sarch (depending on needs), and c) acceptable recall.
+
+#. Use the evaluation dataset to test that the optimal hyper-parameters generalize to unseen points that were not used in the optimization process.
+
+#. Optionally, the above steps multiple times on different uniform sub-samplings. Optimal parameters can then be combined over the multiple monte-carlo optimization iterations. For example, many hyper-parameters can simply be averaged but care might need to be taken for other parameters.
+
+#. Create a new index in the database using the ideal params from above that meet the target constraints (e.g. build vs search vs quality)
+
+Conclusion
+==========
+
+By the end of this process, you should have a set of parameters that meet your target constraints while demonstrating how well the optimal hyper-parameters generalize across the dataset. The major benefit to this approach is that it breaks a potentially unbounded dataset size down into manageable chunks and accelerates tuning on those chunks. We see this process as a major value add for vector search on the GPU.
diff --git a/docs/source/vector_databases_vs_vector_search.rst b/docs/source/vector_databases_vs_vector_search.rst
new file mode 100644
index 0000000000..446737c11c
--- /dev/null
+++ b/docs/source/vector_databases_vs_vector_search.rst
@@ -0,0 +1,57 @@
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Vector search indexes vs vector databases
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This guide provides information on the differences between vector search indexes and fully-fledged vector databases. For more information on selecting and configuring vector search indexes, please refer to our :doc:`guide on choosing and configuring indexes <choosing_and_configuring_indexes>`
+
+One of the primary differences between vector database indexes and traditional database indexes is that vector search often uses approximations to trade-off accuracy of the results for speed. Because of this, while many mature databases offer mechanisms to tune their indexes and achieve better performance, vector database indexes can return completely garbage results if they aren’t tuned for a reasonable level of search quality in addition to performance tuning. This is because vector database indexes are more closely related to machine learning models than they are to traditional database indexes.
+
+What are the differences between vector databases and vector search indexes?
+============================================================================
+
+Vector search in and of itself refers to the objective of finding the closest vectors in an index around a given set of query vectors. At the lowest level, vector search indexes are just machine learning models, which have a build, search, and recall performance that can be traded off, depending on the algorithm and various hyper-parameters.
+
+Vector search indexes alone are considered building blocks that enable, but are not considered by themselves to be, a fully-fledged vector database. Vector databases provide more production-level features that often use vector search algorithms in concert with other popular database design techniques to add important capabilities like durability, fault tolerance, vertical scalability, partition tolerance, and horizontal scalability.
+
+In the world of vector databases, there are special purpose-built databases that focus primarily on vector search but might also provide some small capability of more general-purpose databases, like being able to perform a hybrid search across both vectors and metadata. Many general-purpose databases, both relational and nosql / document databases for example, are beginning to add first-class vector types also.
+
+So what does all this mean to you? Sometimes a simple standalone vector search index is enough. Usually they can be trained and serialized to a file for later use, and often provide a capability to filter out specific vectors during search. Sometimes they even provide a mechanism to scale up to utilize multiple GPUs, for example, but they generally stop there- and suggest either using your own distributed system (like Spark or Dask) or a fully-fledged vector database to scale out.
+
+FAISS and cuVS are examples of standalone vector search libraries, which again are more closely related to machine learning libraries than to fully-fledged databases. Milvus is an example of a special-purpose vector database and Elastic, MongoDB, and OpenSearch are examples of general-purpose databases that have added vector search capabilities.
+
+How is vector search used by vector databases?
+==============================================
+
+Within the context of vector databases, there are two primary ways in which vector search indexes are used and it’s important to understand which you are working with because it can have an effect on the behavior of the parameters with respect to the data.
+
+Many vector search algorithms improve scalability while reducing the number of distances by partitioning the vector space into smaller pieces, often through the use of clustering, hashing, trees, and other techniques. Another popular technique is to reduce the width or dimensionality of the space in order to decrease the cost of computing each distance. In contrast, databases often partition the data, but may only do so to improve things like io performance, partition tolerance, or scale, without regards to the underlying data distributions which are ultimately going to be used for vector search.
+
+This leads us to two core architectural designs that we encounter in vector databases:
+
+Locally partitioned vector search indexes
+-----------------------------------------
+
+Most databases follow this design, and vectors are often first written to a write-ahead log for durability. After some number of vectors are written, the write-ahead logs become immutable and may be merged with other write-ahead logs before eventually being converted to a new vector search index.
+
+The search is generally done over each locally partitioned index and the results combined. When setting hyperparameters, only the local vector search indexes need to be considered, though the same hyperparameters are going to be used across all of the local partitions. So, for example, if you’ve ingested 100M vectors but each partition only contains about 10M vectors, the size of the index only needs to consider its local 10M vectors. Details like number of vectors in the index are important, for example, when setting the number of clusters in an IVF-based (inverted file index) method, as I’ll cover below.
+
+
+Globally partitioned vector search indexes
+------------------------------------------
+
+Some special-purpose vector databases follow this design, such as Yahoo’s Vespa and Google’s Spanner. A global index is trained to partition the entire database’s vectors up front as soon as there are enough vectors to do so (usually these databases are at a large enough scale that a significant number of vectors are bootstrapped initially and so it avoids the cold start problem). Ingested vectors are first run through the global index (clustering, for example, but tree- and graph-based methods have also been used) to determine which partition they belong to and the vectors are then (sent to, and) written  directly to that partition. The individual partitions can contain a graph, tree, or a simple IVF list. These types of indexes have been able to scale to hundreds of billions to trillions of vectors, and since the partitions are themselves often implicitly based on neighborhoods, rather than being based on uniformly random distributed vectors like the locally partitioned architectures, the partitions can be grouped together or intentionally separated to support localized searches or load balancing, depending upon the needs of the system.
+
+The challenge when setting hyper-parameters for globally partitioned indexes is that they need to account for the entire set of vectors, and thus the hyperparameters of the global index generally account for all of the vectors in the database, rather than any local partition.
+
+Of course, the two approaches outlined above can also be used together (e.g. training a global “coarse” index and then creating localized vector search indexes within each of the global indexes) but to my knowledge, no such architecture has implemented this pattern.
+
+A challenge with GPUs in vector databases today is that the resulting vector indexes are expected to fit into the memory of available GPUs for fast search. That is to say, there doesn’t exist today an efficient mechanism for offloading or swapping GPU indexes so they can be cached from disk or host memory, for example. We are working on mechanisms to do this, and to also utilize technologies like GPUDirect Storage and GPUDirect RDMA to improve the IO performance further.
+
+Tuning and hyperparameter optimization
+======================================
+
+Unfortunately, for large datasets, doing a hyper-parameter optimization on the whole dataset is not always feasible and this is actually where the locally partitioned vector search indexes have an advantage because you can think of each smaller segment of the larger index as a uniform random sample of the total vectors in the dataset. This means that it is possible to perform a hyperparameter optimization on the smaller subsets and find reasonably acceptable parameters that should generalize fairly well to the entire dataset. Generally this hyperparameter optimization will require computing a ground truth on the subset with an exact method like brute-force and then using it to evaluate several searches on randomly sampled vectors.
+
+Full hyper-parameter optimization may also not always be necessary- for example, once you have built a ground truth dataset on a subset, many times you can start by building an index with the default build parameters and then playing around with different search parameters until you get the desired quality and search performance.  For massive indexes that might be multiple terabytes, you could also take this subsampling of, say, 10M vectors, train an index and then tune the search parameters from there. While there might be a small margin of error, the chosen build/search parameters should generalize fairly well for the databases that build locally partitioned indexes.
+
+Refer to our :doc:`tuning guide <tuning_guide>` for more information and examples on how to efficiently and automatically tune your vector search indexes based on your needs.
\ No newline at end of file

From d5ca51e5c799f0a0ad3220539c7c7763c70bd826 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Thu, 3 Oct 2024 16:28:49 -0700
Subject: [PATCH 177/623] CosineExpanded Metric for IVF-PQ (normalize inputs)
 (#346)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/346
---
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    |  4 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     | 34 +++++++++++++++
 .../ivf_pq/ivf_pq_compute_similarity_impl.cuh |  3 ++
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    | 42 +++++++++++++++++--
 cpp/test/neighbors/ann_ivf_pq.cuh             | 24 ++++++++++-
 .../ann_ivf_pq/test_float_int64_t.cu          |  8 +++-
 .../ann_ivf_pq/test_int8_t_int64_t.cu         |  5 ++-
 .../ann_ivf_pq/test_uint8_t_int64_t.cu        |  8 +++-
 8 files changed, 115 insertions(+), 13 deletions(-)

diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index 9626b2ce50..f5a4267cd8 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -1206,8 +1206,8 @@ void launch_with_fixed_consts(cuvs::distance::DistanceType metric, Args&&... arg
                            inner_prod_dist<Veclen, T, AccT>>(
         {},
         raft::compose_op(raft::add_const_op<float>{1.0f}, raft::mul_const_op<float>{-1.0f}),
-        std::forward<Args>(args)...);
-    // NB: update the description of `knn::ivf_flat::build` when adding here a new metric.
+        std::forward<Args>(args)...);  // NB: update the description of `knn::ivf_flat::build` when
+                                       // adding here a new metric.
     default: RAFT_FAIL("The chosen distance metric is not supported (%d)", int(metric));
   }
 }
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index c65ea81082..4c9867126e 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -31,6 +31,7 @@
 
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/logger-ext.hpp>
+#include <raft/core/mdspan.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/cuda_stream_pool.hpp>
@@ -41,6 +42,8 @@
 #include <raft/linalg/gemm.cuh>
 #include <raft/linalg/map.cuh>
 #include <raft/linalg/norm.cuh>
+#include <raft/linalg/norm_types.hpp>
+#include <raft/linalg/normalize.cuh>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/matrix/gather.cuh>
 #include <raft/matrix/linewise_op.cuh>
@@ -1466,6 +1469,13 @@ void extend(raft::resources const& handle,
                   std::is_same_v<T, int8_t>,
                 "Unsupported data type");
 
+  if (index->metric() == distance::DistanceType::CosineExpanded) {
+    if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>)
+      RAFT_FAIL(
+        "CosineExpanded distance metric is currently not supported for uint8_t and int8_t data "
+        "type");
+  }
+
   rmm::device_async_resource_ref device_memory = raft::resource::get_workspace_resource(handle);
   rmm::device_async_resource_ref large_memory =
     raft::resource::get_large_workspace_resource(handle);
@@ -1632,6 +1642,14 @@ void extend(raft::resources const& handle,
   vec_batches.prefetch_next_batch();
   for (const auto& vec_batch : vec_batches) {
     const auto& idx_batch = *idx_batches++;
+    if (index->metric() == CosineExpanded) {
+      auto vec_batch_view = raft::make_device_matrix_view<T, internal_extents_t>(
+        const_cast<T*>(vec_batch.data()), vec_batch.size(), index->dim());
+      raft::linalg::row_normalize(handle,
+                                  raft::make_const_mdspan(vec_batch_view),
+                                  vec_batch_view,
+                                  raft::linalg::NormType::L2Norm);
+    }
     process_and_fill_codes(handle,
                            *index,
                            vec_batch.data(),
@@ -1683,6 +1701,13 @@ auto build(raft::resources const& handle,
             << (int)params.pq_dim << std::endl;
   RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
   RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
+  if (params.metric == distance::DistanceType::CosineExpanded) {
+    // TODO: support int8_t and uint8_t types (https://github.com/rapidsai/cuvs/issues/389)
+    if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>)
+      RAFT_FAIL(
+        "CosineExpanded distance metric is currently not supported for uint8_t and int8_t data "
+        "type");
+  }
 
   auto stream = raft::resource::get_cuda_stream(handle);
 
@@ -1755,6 +1780,11 @@ auto build(raft::resources const& handle,
     cuvs::cluster::kmeans::balanced_params kmeans_params;
     kmeans_params.n_iters = params.kmeans_n_iters;
     kmeans_params.metric  = static_cast<cuvs::distance::DistanceType>((int)index.metric());
+
+    if (index.metric() == distance::DistanceType::CosineExpanded) {
+      raft::linalg::row_normalize(
+        handle, trainset_const_view, trainset.view(), raft::linalg::NormType::L2Norm);
+    }
     cuvs::cluster::kmeans_balanced::fit(
       handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
 
@@ -1762,6 +1792,10 @@ auto build(raft::resources const& handle,
     rmm::device_uvector<uint32_t> labels(n_rows_train, stream, big_memory_resource);
     auto centers_const_view = raft::make_device_matrix_view<const float, internal_extents_t>(
       cluster_centers, index.n_lists(), index.dim());
+    if (index.metric() == distance::DistanceType::CosineExpanded) {
+      raft::linalg::row_normalize(
+        handle, centers_const_view, centers_view, raft::linalg::NormType::L2Norm);
+    }
     auto labels_view =
       raft::make_device_vector_view<uint32_t, internal_extents_t>(labels.data(), n_rows_train);
     cuvs::cluster::kmeans_balanced::predict(handle,
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
index 8404ca1f9f..fbbdd06c24 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
@@ -369,6 +369,7 @@ RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
             reinterpret_cast<float*>(lut_end)[i] = query[i] - cluster_center[i];
           }
         } break;
+        case distance::DistanceType::CosineExpanded:
         case distance::DistanceType::InnerProduct: {
           float2 pvals;
           for (uint32_t i = threadIdx.x; i < dim; i += blockDim.x) {
@@ -408,6 +409,7 @@ RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
               diff -= pq_c;
               score += diff * diff;
             } break;
+            case distance::DistanceType::CosineExpanded:
             case distance::DistanceType::InnerProduct: {
               // NB: we negate the scores as we hardcoded select-topk to always compute the minimum
               float q;
@@ -485,6 +487,7 @@ RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
           reinterpret_cast<const vec_t::io_t*>(pq_thread_data),
           lut_scores,
           early_stop_limit);
+        if (metric == distance::DistanceType::CosineExpanded) { score = OutT(1) + score; }
       }
       if constexpr (kManageLocalTopK) {
         block_topk.add(score, sample_offset + i);
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index e185f18dce..db8f9fbd3b 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -37,6 +37,9 @@
 #include <raft/core/resources.hpp>
 #include <raft/linalg/gemm.cuh>
 #include <raft/linalg/map.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/norm_types.hpp>
+#include <raft/linalg/normalize.cuh>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/matrix/detail/select_warpsort.cuh>
 #include <raft/util/cache.hpp>
@@ -104,12 +107,21 @@ void select_clusters(raft::resources const& handle,
 
       This is a negative inner-product distance. We minimize it to find the similar clusters.
 
+      NB: qc_distances is NOT used further in ivfpq_search.
+
+    Cosine distance:
+      `qc_distances[i, j] = - (queries[i], cluster_centers[j])`
+
+      This is a negative inner-product distance. The queries and cluster centers are row normalized.
+      We minimize it to find the similar clusters.
+
       NB: qc_distances is NOT used further in ivfpq_search.
  */
   float norm_factor;
   switch (metric) {
     case cuvs::distance::DistanceType::L2SqrtExpanded:
     case cuvs::distance::DistanceType::L2Expanded: norm_factor = 1.0 / -2.0; break;
+    case cuvs::distance::DistanceType::CosineExpanded:
     case cuvs::distance::DistanceType::InnerProduct: norm_factor = 0.0; break;
     default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
   }
@@ -133,6 +145,7 @@ void select_clusters(raft::resources const& handle,
       gemm_k = dim + 1;
       RAFT_EXPECTS(gemm_k <= dim_ext, "unexpected gemm_k or dim_ext");
     } break;
+    case cuvs::distance::DistanceType::CosineExpanded:
     case cuvs::distance::DistanceType::InnerProduct: {
       alpha = -1.0;
       beta  = 0.0;
@@ -363,8 +376,9 @@ void ivfpq_search_worker(raft::resources const& handle,
       // stores basediff (query[i] - center[i])
       precomp_data_count = index.rot_dim();
     } break;
+    case distance::DistanceType::CosineExpanded:
     case distance::DistanceType::InnerProduct: {
-      // stores two components (query[i] * center[i], query[i] * center[i])
+      // stores two components (query[i], query[i] * center[i])
       precomp_data_count = index.rot_dim() * 2;
     } break;
     default: {
@@ -457,8 +471,14 @@ void ivfpq_search_worker(raft::resources const& handle,
     num_samples_vector);
 
   // Postprocessing
-  ivf::detail::postprocess_distances(
-    distances, topk_dists.data(), index.metric(), n_queries, topK, scaling_factor, true, stream);
+  ivf::detail::postprocess_distances(distances,
+                                     topk_dists.data(),
+                                     index.metric(),
+                                     n_queries,
+                                     topK,
+                                     scaling_factor,
+                                     index.metric() != distance::DistanceType::CosineExpanded,
+                                     stream);
   ivf::detail::postprocess_neighbors(neighbors,
                                      neighbors_uint32,
                                      index.inds_ptrs().data_handle(),
@@ -508,6 +528,7 @@ struct ivfpq_search {
   {
     bool signed_metric = false;
     switch (metric) {
+      case cuvs::distance::DistanceType::CosineExpanded: signed_metric = true; break;
       case cuvs::distance::DistanceType::InnerProduct: signed_metric = true; break;
       default: break;
     }
@@ -606,6 +627,12 @@ inline void search(raft::resources const& handle,
   static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, uint8_t> ||
                   std::is_same_v<T, int8_t>,
                 "Unsupported element type.");
+  if (index.metric() == distance::DistanceType::CosineExpanded) {
+    if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>)
+      RAFT_FAIL(
+        "CosineExpanded distance metric is currently not supported for uint8_t and int8_t data "
+        "type");
+  }
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "ivf_pq::search(n_queries = %u, n_probes = %u, k = %u, dim = %zu)",
     n_queries,
@@ -698,7 +725,14 @@ inline void search(raft::resources const& handle,
                        rot_queries.data(),
                        index.rot_dim(),
                        stream);
-
+    if (index.metric() == distance::DistanceType::CosineExpanded) {
+      auto rot_queries_view = raft::make_device_matrix_view<float, uint32_t>(
+        rot_queries.data(), max_queries, index.rot_dim());
+      raft::linalg::row_normalize(handle,
+                                  raft::make_const_mdspan(rot_queries_view),
+                                  rot_queries_view,
+                                  raft::linalg::NormType::L2Norm);
+    }
     for (uint32_t offset_b = 0; offset_b < queries_batch; offset_b += max_batch_size) {
       uint32_t batch_size = min(max_batch_size, queries_batch - offset_b);
       /* The distance calculation is done in the rotated/transformed space;
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index f02568b744..fd4e330db2 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -282,6 +282,8 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
                             uint32_t n_take,
                             uint32_t n_skip)
   {
+    // the original data cannot be reconstructed since the dataset was normalized
+    if (index.metric() == cuvs::distance::DistanceType::CosineExpanded) { return; }
     auto& rec_list = index.lists()[label];
     auto dim       = index.dim();
     n_take         = std::min<uint32_t>(n_take, rec_list->size.load());
@@ -313,6 +315,7 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     auto old_list = index->lists()[label];
     auto n_rows   = old_list->size.load();
     if (n_rows == 0) { return; }
+    if (index->metric() == cuvs::distance::DistanceType::CosineExpanded) { return; }
 
     auto vectors_1 = raft::make_device_matrix<EvalT>(handle_, n_rows, index->dim());
     auto indices   = raft::make_device_vector<IdxT>(handle_, n_rows);
@@ -374,7 +377,7 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
                                   cuvs::Compare<uint8_t>{}));
 
     // Pack a few vectors back to the list.
-    int row_offset = 9;
+    int row_offset = 5;
     int n_vec      = 3;
     ASSERT_TRUE(row_offset + n_vec < n_rows);
     size_t offset      = row_offset * index->pq_dim();
@@ -884,6 +887,25 @@ inline auto enum_variety_l2sqrt() -> test_cases_t
   });
 }
 
+inline auto enum_variety_cosine() -> test_cases_t
+{
+  return map<ivf_pq_inputs>(enum_variety(), [](const ivf_pq_inputs& x) {
+    ivf_pq_inputs y(x);
+    if (y.min_recall.has_value()) {
+      if (y.search_params.lut_dtype == CUDA_R_8U) {
+        // TODO: Increase this recall threshold for 8 bit lut
+        // (https://github.com/rapidsai/cuvs/issues/390)
+        y.min_recall = y.min_recall.value() * 0.70;
+      } else {
+        // In other cases it seems to perform a little bit better, still worse than L2
+        y.min_recall = y.min_recall.value() * 0.94;
+      }
+    }
+    y.index_params.metric = distance::DistanceType::CosineExpanded;
+    return y;
+  });
+}
+
 /**
  * Try different number of n_probes, some of which may trigger the non-fused version of the search
  * kernel.
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
index cdc6c1b7e4..834fdb3d09 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
@@ -25,9 +25,13 @@ TEST_BUILD_HOST_INPUT_SEARCH(f32_f32_i64)
 TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_f32_i64)
 TEST_BUILD_EXTEND_SEARCH(f32_f32_i64)
 TEST_BUILD_SERIALIZE_SEARCH(f32_f32_i64)
-INSTANTIATE(f32_f32_i64, defaults() + small_dims() + big_dims_moderate_lut());
+INSTANTIATE(f32_f32_i64,
+            defaults() + small_dims() + big_dims_moderate_lut() + enum_variety_l2() +
+              enum_variety_l2sqrt() + enum_variety_ip() + enum_variety_cosine());
 
 TEST_BUILD_SEARCH(f32_f32_i64_filter)
-INSTANTIATE(f32_f32_i64_filter, defaults() + small_dims() + big_dims_moderate_lut());
+INSTANTIATE(f32_f32_i64_filter,
+            defaults() + small_dims() + big_dims_moderate_lut() + enum_variety_l2() +
+              enum_variety_l2sqrt() + enum_variety_ip() + enum_variety_cosine());
 
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
index 80b0e2ccb9..c9e5d4f010 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
@@ -25,8 +25,9 @@ TEST_BUILD_SEARCH(f32_i08_i64)
 TEST_BUILD_HOST_INPUT_SEARCH(f32_i08_i64)
 TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_i08_i64)
 TEST_BUILD_SERIALIZE_SEARCH(f32_i08_i64)
-INSTANTIATE(f32_i08_i64, defaults() + big_dims() + var_k());
+INSTANTIATE(f32_i08_i64, defaults() + big_dims() + var_k() + enum_variety_l2() + enum_variety_ip());
 
 TEST_BUILD_SEARCH(f32_i08_i64_filter)
-INSTANTIATE(f32_i08_i64_filter, defaults() + big_dims() + var_k());
+INSTANTIATE(f32_i08_i64_filter,
+            defaults() + big_dims() + var_k() + enum_variety_l2() + enum_variety_ip());
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
index 0216a1e80f..6e07322273 100644
--- a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+++ b/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
@@ -25,8 +25,12 @@ TEST_BUILD_SEARCH(f32_u08_i64)
 TEST_BUILD_HOST_INPUT_SEARCH(f32_u08_i64)
 TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_u08_i64)
 TEST_BUILD_EXTEND_SEARCH(f32_u08_i64)
-INSTANTIATE(f32_u08_i64, small_dims_per_cluster() + enum_variety());
+INSTANTIATE(f32_u08_i64,
+            small_dims_per_cluster() + enum_variety() + enum_variety_l2() + enum_variety_l2sqrt() +
+              enum_variety_ip());
 
 TEST_BUILD_SEARCH(f32_u08_i64_filter)
-INSTANTIATE(f32_u08_i64_filter, small_dims_per_cluster() + enum_variety());
+INSTANTIATE(f32_u08_i64_filter,
+            small_dims_per_cluster() + enum_variety() + enum_variety_l2() + enum_variety_l2sqrt() +
+              enum_variety_ip());
 }  // namespace cuvs::neighbors::ivf_pq

From b754166ec0988fe3d0fd597db56eac50bde828a5 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Thu, 3 Oct 2024 20:40:33 -0400
Subject: [PATCH 178/623] Port remaining scripts to `cuvs_bench` (#368)

Builds upon #367

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/368
---
 build.sh                                      |   25 +-
 ci/build_python.sh                            |   18 +
 conda/recipes/cuvs_bench/build.sh             |    5 +
 .../cuvs_bench/conda_build_config.yaml        |   70 ++
 conda/recipes/cuvs_bench/meta.yaml            |  105 ++
 conda/recipes/cuvs_bench_cpu/build.sh         |    5 +
 .../cuvs_bench_cpu/conda_build_config.yaml    |   29 +
 conda/recipes/cuvs_bench_cpu/meta.yaml        |   67 ++
 conda/recipes/libcuvs/meta.yaml               |    1 +
 cpp/CMakeLists.txt                            | 1011 +++++++++--------
 cpp/cmake/thirdparty/get_cuvs.cmake           |   64 ++
 .../generate_groundtruth/__main__.py          |  241 ++++
 .../cuvs_bench/generate_groundtruth/utils.py  |  101 ++
 .../cuvs_bench/get_dataset/__main__.py        |  115 ++
 .../cuvs_bench/get_dataset/fbin_to_f16bin.py  |   49 +
 .../cuvs_bench/get_dataset/hdf5_to_fbin.py    |   90 ++
 python/cuvs_bench/cuvs_bench/run/__main__.py  |    5 +-
 python/cuvs_bench/cuvs_bench/run/run.py       |    3 +-
 .../cuvs_bench/split_groundtruth/__main__.py  |   57 +
 .../split_groundtruth/split_groundtruth.pl    |   45 +
 20 files changed, 1599 insertions(+), 507 deletions(-)
 create mode 100644 conda/recipes/cuvs_bench/build.sh
 create mode 100644 conda/recipes/cuvs_bench/conda_build_config.yaml
 create mode 100644 conda/recipes/cuvs_bench/meta.yaml
 create mode 100644 conda/recipes/cuvs_bench_cpu/build.sh
 create mode 100644 conda/recipes/cuvs_bench_cpu/conda_build_config.yaml
 create mode 100644 conda/recipes/cuvs_bench_cpu/meta.yaml
 create mode 100644 cpp/cmake/thirdparty/get_cuvs.cmake
 create mode 100644 python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/generate_groundtruth/utils.py
 create mode 100644 python/cuvs_bench/cuvs_bench/get_dataset/__main__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/get_dataset/fbin_to_f16bin.py
 create mode 100644 python/cuvs_bench/cuvs_bench/get_dataset/hdf5_to_fbin.py
 create mode 100644 python/cuvs_bench/cuvs_bench/split_groundtruth/__main__.py
 create mode 100644 python/cuvs_bench/cuvs_bench/split_groundtruth/split_groundtruth.pl

diff --git a/build.sh b/build.sh
index c66a0c35ed..b787d3a41f 100755
--- a/build.sh
+++ b/build.sh
@@ -18,7 +18,7 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python rust docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python rust docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
@@ -37,11 +37,13 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    -n                          - no install step
    --uninstall                 - uninstall files for specified targets which were built and installed prior
    --compile-static-lib        - compile static library for all components
+   --cpu-only                  - build CPU only components without CUDA. Currently only applies to bench-ann.
    --limit-tests               - semicolon-separated list of test executables to compile (e.g. NEIGHBORS_TEST;CLUSTER_TEST)
    --limit-bench-ann           - semicolon-separated list of ann benchmark executables to compute (e.g. HNSWLIB_ANN_BENCH;RAFT_IVF_PQ_ANN_BENCH)
    --allgpuarch                - build for all supported GPU architectures
    --no-mg                     - disable multi-GPU support
    --no-nvtx                   - disable nvtx (profiling markers), but allow enabling it in downstream projects
+   --no-shared-libs            - build without shared libraries
    --show_depr_warn            - show cmake deprecation warnings
    --build-metrics             - filename for generating build metrics report for libcuvs
    --incl-cache-stats          - include cache statistics in build metrics report
@@ -72,6 +74,7 @@ COMPILE_LIBRARY=OFF
 INSTALL_TARGET=install
 BUILD_REPORT_METRICS=""
 BUILD_REPORT_INCL_CACHE_STATS=OFF
+BUILD_SHARED_LIBS=ON
 
 TEST_TARGETS="NEIGHBORS_ANN_CAGRA_TEST"
 ANN_BENCH_TARGETS="CUVS_ANN_BENCH_ALL"
@@ -282,7 +285,20 @@ fi
 
 if hasArg bench-ann || (( ${NUMARGS} == 0 )); then
     BUILD_CUVS_BENCH=ON
+    if ! hasArg tests; then
+        BUILD_TESTS=OFF
+    fi
+    COMPILE_LIBRARY=OFF
     CMAKE_TARGET="${CMAKE_TARGET};${ANN_BENCH_TARGETS}"
+    if hasArg --cpu-only; then
+        BUILD_CPU_ONLY=ON
+        BUILD_SHARED_LIBS=OFF
+        NVTX=OFF
+    fi
+fi
+
+if hasArg --no-shared-libs; then
+    BUILD_SHARED_LIBS=OFF
 fi
 
 if hasArg --no-nvtx; then
@@ -329,7 +345,11 @@ fi
 # Configure for building all C++ targets
 if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg bench-prims || hasArg bench-ann; then
     COMPILE_LIBRARY=ON
-    CMAKE_TARGET="${CMAKE_TARGET};cuvs"
+    if (( ${BUILD_SHARED_LIBS} == "OFF" )); then
+        CMAKE_TARGET="${CMAKE_TARGET};"
+    else
+        CMAKE_TARGET="${CMAKE_TARGET};cuvs"
+    fi
 
     if (( ${BUILD_ALL_GPU_ARCH} == 0 )); then
         CUVS_CMAKE_CUDA_ARCHITECTURES="NATIVE"
@@ -361,6 +381,7 @@ if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || has
           -DBUILD_CPU_ONLY=${BUILD_CPU_ONLY} \
           -DBUILD_MG_ALGOS=${BUILD_MG_ALGOS} \
           -DCMAKE_MESSAGE_LOG_LEVEL=${CMAKE_LOG_LEVEL} \
+          -DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS} \
           ${CACHE_ARGS} \
           ${EXTRA_CMAKE_ARGS}
 
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 82ecba43ac..7b0c639afd 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -31,4 +31,22 @@ rapids-conda-retry mambabuild \
   --channel "${CPP_CHANNEL}" \
   conda/recipes/cuvs
 
+# Build cuvs_bench for each cuda and python version
+rapids-conda-retry mambabuild \
+  --no-test \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
+  conda/recipes/cuvs_bench
+
+# Build cuvs_bench_cpu only in CUDA 12 jobs since it only depends on python
+# version
+RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
+if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
+  rapids-conda-retry mambabuild \
+  --no-test \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
+  conda/recipes/cuvs_bench_cpu
+fi
+
 rapids-upload-conda-to-s3 python
diff --git a/conda/recipes/cuvs_bench/build.sh b/conda/recipes/cuvs_bench/build.sh
new file mode 100644
index 0000000000..05fb7bada3
--- /dev/null
+++ b/conda/recipes/cuvs_bench/build.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+./build.sh bench-ann --allgpuarch --no-nvtx --build-metrics=bench_ann --incl-cache-stats
+cmake --install cpp/build --component ann_bench
diff --git a/conda/recipes/cuvs_bench/conda_build_config.yaml b/conda/recipes/cuvs_bench/conda_build_config.yaml
new file mode 100644
index 0000000000..47bd730daf
--- /dev/null
+++ b/conda/recipes/cuvs_bench/conda_build_config.yaml
@@ -0,0 +1,70 @@
+c_compiler_version:
+  - 11
+
+cxx_compiler_version:
+  - 11
+
+cuda_compiler:
+  - cuda-nvcc
+
+cuda11_compiler:
+  - nvcc
+
+c_stdlib:
+  - sysroot
+
+c_stdlib_version:
+  - "2.17"
+
+cmake_version:
+  - ">=3.26.4,!=3.30.0"
+
+nccl_version:
+  - ">=2.19"
+
+glog_version:
+  - ">=0.6.0"
+
+h5py_version:
+  - ">=3.8.0"
+
+nlohmann_json_version:
+  - ">=3.11.2"
+
+# The CTK libraries below are missing from the conda-forge::cudatoolkit package
+# for CUDA 11. The "*_host_*" version specifiers correspond to `11.8` packages
+# and the "*_run_*" version specifiers correspond to `11.x` packages.
+
+cuda11_libcublas_host_version:
+  - "=11.11.3.6"
+
+cuda11_libcublas_run_version:
+  - ">=11.5.2.43,<12.0.0"
+
+cuda11_libcurand_host_version:
+  - "=10.3.0.86"
+
+cuda11_libcurand_run_version:
+  - ">=10.2.5.43,<10.3.1"
+
+cuda11_libcusolver_host_version:
+  - "=11.4.1.48"
+
+cuda11_libcusolver_run_version:
+  - ">=11.2.0.43,<11.4.2"
+
+cuda11_libcusparse_host_version:
+  - "=11.7.5.86"
+
+cuda11_libcusparse_run_version:
+  - ">=11.6.0.43,<12.0.0"
+
+# `cuda-profiler-api` only has `11.8.0` and `12.0.0` packages for all
+# architectures. The "*_host_*" version specifiers correspond to `11.8` packages and the
+# "*_run_*" version specifiers correspond to `11.x` packages.
+
+cuda11_cuda_profiler_api_host_version:
+  - "=11.8.86"
+
+cuda11_cuda_profiler_api_run_version:
+  - ">=11.4.240,<12"
diff --git a/conda/recipes/cuvs_bench/meta.yaml b/conda/recipes/cuvs_bench/meta.yaml
new file mode 100644
index 0000000000..9ecbf82bb0
--- /dev/null
+++ b/conda/recipes/cuvs_bench/meta.yaml
@@ -0,0 +1,105 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Usage:
+#   conda build . -c rapidsai -c conda-forge -c nvidia
+{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set py_version = environ['CONDA_PY'] %}
+{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
+{% set cuda_major = cuda_version.split('.')[0] %}
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
+
+package:
+  name: cuvs_bench
+  version: {{ version }}
+  script: build.sh
+
+source:
+  path: ../../..
+
+build:
+  script_env:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN
+    - CMAKE_C_COMPILER_LAUNCHER
+    - CMAKE_CUDA_COMPILER_LAUNCHER
+    - CMAKE_CXX_COMPILER_LAUNCHER
+    - CMAKE_GENERATOR
+    - PARALLEL_LEVEL
+    - RAPIDS_ARTIFACTS_DIR
+    - SCCACHE_BUCKET
+    - SCCACHE_IDLE_TIMEOUT
+    - SCCACHE_REGION
+    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-aarch64 # [aarch64]
+    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-linux64 # [linux64]
+    - SCCACHE_S3_USE_SSL
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+  ignore_run_exports_from:
+    {% if cuda_major == "11" %}
+    - {{ compiler('cuda11') }}
+    {% else %}
+    - {{ compiler('cuda') }}
+    - cuda-cudart-dev
+    - libcublas-dev
+    {% endif %}
+
+requirements:
+  build:
+    - {{ compiler('c') }}
+    - {{ compiler('cxx') }}
+    {% if cuda_major == "11" %}
+    - {{ compiler('cuda11') }} ={{ cuda_version }}
+    {% else %}
+    - {{ compiler('cuda') }}
+    {% endif %}
+    - cuda-version ={{ cuda_version }}
+    - cmake {{ cmake_version }}
+    - ninja
+    - {{ stdlib("c") }}
+
+  host:
+    - benchmark
+    - cuda-version ={{ cuda_version }}
+    {% if cuda_major == "11" %}
+    - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
+    - libcublas {{ cuda11_libcublas_host_version }}
+    - libcublas-dev {{ cuda11_libcublas_host_version }}
+    {% else %}
+    - cuda-cudart-dev
+    - cuda-profiler-api
+    - libcublas-dev
+    {% endif %}
+    - glog {{ glog_version }}
+    - libcuvs {{ version }}
+    - nlohmann_json {{ nlohmann_json_version }}
+    - openblas
+    # rmm is needed to determine if package is gpu-enabled
+    - python
+    - rapids-build-backend>=0.3.0,<0.4.0.dev0
+    - rmm ={{ minor_version }}
+
+  run:
+    - benchmark
+    - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
+    {% if cuda_major == "11" %}
+    - cudatoolkit
+    {% else %}
+    - cuda-cudart
+    - libcublas
+    {% endif %}
+    - glog {{ glog_version }}
+    - libcuvs {{ version }}
+    - h5py {{ h5py_version }}
+    - matplotlib
+    - pandas
+    - pyyaml
+    # rmm is needed to determine if package is gpu-enabled
+    - pylibraft ={{ minor_version }}
+    - python
+    - rmm ={{ minor_version }}
+about:
+  home: https://rapids.ai/
+  license: Apache-2.0
+  summary: cuVS GPU and CPU benchmarks
diff --git a/conda/recipes/cuvs_bench_cpu/build.sh b/conda/recipes/cuvs_bench_cpu/build.sh
new file mode 100644
index 0000000000..163872053e
--- /dev/null
+++ b/conda/recipes/cuvs_bench_cpu/build.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+./build.sh bench-ann --cpu-only --no-nvtx --build-metrics=bench_ann_cpu --incl-cache-stats
+cmake --install cpp/build --component ann_bench
diff --git a/conda/recipes/cuvs_bench_cpu/conda_build_config.yaml b/conda/recipes/cuvs_bench_cpu/conda_build_config.yaml
new file mode 100644
index 0000000000..ed6f708e14
--- /dev/null
+++ b/conda/recipes/cuvs_bench_cpu/conda_build_config.yaml
@@ -0,0 +1,29 @@
+c_compiler_version:
+  - 11
+
+cxx_compiler_version:
+  - 11
+
+c_stdlib:
+  - sysroot
+
+c_stdlib_version:
+  - "2.17"
+
+cmake_version:
+  - ">=3.26.4,!=3.30.0"
+
+glog_version:
+  - ">=0.6.0"
+
+h5py_version:
+  - ">=3.8.0"
+
+nlohmann_json_version:
+  - ">=3.11.2"
+
+spdlog_version:
+  - ">=1.14.1,<1.15"
+
+fmt_version:
+  - ">=11.0.2,<12"
diff --git a/conda/recipes/cuvs_bench_cpu/meta.yaml b/conda/recipes/cuvs_bench_cpu/meta.yaml
new file mode 100644
index 0000000000..0ce5db7449
--- /dev/null
+++ b/conda/recipes/cuvs_bench_cpu/meta.yaml
@@ -0,0 +1,67 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+# Usage:
+#   conda build .  -c rapidsai -c conda-forge -c nvidia
+{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set py_version = environ['CONDA_PY'] %}
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
+
+package:
+  name: cuvs_bench_cpu
+  version: {{ version }}
+  script: build.sh
+
+source:
+  path: ../../..
+
+build:
+  script_env:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN
+    - CMAKE_C_COMPILER_LAUNCHER
+    - CMAKE_CUDA_COMPILER_LAUNCHER
+    - CMAKE_CXX_COMPILER_LAUNCHER
+    - CMAKE_GENERATOR
+    - PARALLEL_LEVEL
+    - RAPIDS_ARTIFACTS_DIR
+    - SCCACHE_BUCKET
+    - SCCACHE_IDLE_TIMEOUT
+    - SCCACHE_REGION
+    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-cpu-aarch64 # [aarch64]
+    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-cpu-linux64 # [linux64]
+    - SCCACHE_S3_USE_SSL
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+
+requirements:
+  build:
+    - {{ compiler('c') }}
+    - {{ compiler('cxx') }}
+    - cmake {{ cmake_version }}
+    - ninja
+    - {{ stdlib("c") }}
+
+  host:
+    - benchmark
+    - fmt {{ fmt_version }}
+    - glog {{ glog_version }}
+    - nlohmann_json {{ nlohmann_json_version }}
+    - openblas
+    - python
+    - rapids-build-backend>=0.3.0,<0.4.0.dev0
+    - spdlog {{ spdlog_version }}
+
+  run:
+    - benchmark
+    - glog {{ glog_version }}
+    - h5py {{ h5py_version }}
+    - matplotlib
+    - pandas
+    - pyyaml
+    - python
+about:
+  home: https://rapids.ai/
+  license: Apache-2.0
+  summary: cuVS CPU benchmarks
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index 3dd7c8f2ef..46552c3975 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -161,6 +161,7 @@ outputs:
         - libcusolver
         - libcusparse
         {% endif %}
+        - libraft-headers ={{ minor_version }}
     about:
       home: https://rapids.ai/
       license: Apache-2.0
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 66d489c242..3e98a247e7 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -86,6 +86,12 @@ if(NOT BUILD_C_LIBRARY)
   set(BUILD_C_TESTS OFF)
 endif()
 
+if(NOT BUILD_SHARED_LIBS)
+  set(BUILD_TESTS OFF)
+  set(BUILD_C_LIBRARY OFF)
+  set(BUILD_CAGRA_HNSWLIB OFF)
+endif()
+
 # Needed because GoogleBenchmark changes the state of FindThreads.cmake, causing subsequent runs to
 # have different values for the `Threads::Threads` target. Setting this flag ensures
 # `Threads::Threads` is the same value across all builds so that cache hits occur
@@ -176,6 +182,7 @@ rapids_cpm_init()
 
 if(NOT BUILD_CPU_ONLY)
   include(cmake/thirdparty/get_raft.cmake)
+  include(cmake/thirdparty/get_cutlass.cmake)
 endif()
 
 if(BUILD_C_LIBRARY)
@@ -187,8 +194,6 @@ if(BUILD_TESTS OR BUILD_C_TESTS)
   rapids_cpm_gtest(BUILD_STATIC)
 endif()
 
-include(cmake/thirdparty/get_cutlass.cmake)
-
 if(BUILD_CUVS_BENCH)
   include(${rapids-cmake-dir}/cpm/gbench.cmake)
   rapids_cpm_gbench(BUILD_STATIC)
@@ -200,566 +205,568 @@ endif()
 
 # ##################################################################################################
 # * cuvs ---------------------------------------------------------------------
-add_library(
-  cuvs-cagra-search STATIC
-  src/neighbors/cagra_search_float.cu
-  src/neighbors/cagra_search_half.cu
-  src/neighbors/cagra_search_int8.cu
-  src/neighbors/cagra_search_uint8.cu
-  src/neighbors/detail/cagra/compute_distance.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim128_t8.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim256_t16.cu
-  src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim512_t32.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_2subd_half.cu
-  src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_4subd_half.cu
-  src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
-  src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
-  src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
-  src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
-  src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
-  src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
-  src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
-  src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
-)
+if(BUILD_SHARED_LIBS)
+  add_library(
+    cuvs-cagra-search STATIC
+    src/neighbors/cagra_search_float.cu
+    src/neighbors/cagra_search_half.cu
+    src/neighbors/cagra_search_int8.cu
+    src/neighbors/cagra_search_uint8.cu
+    src/neighbors/detail/cagra/compute_distance.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_int8_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_uint8_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_float_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_uint8_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim128_t8_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim256_t16_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_float_uint32_dim512_t32_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim128_t8_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim256_t16_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_half_uint32_dim512_t32_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim128_t8_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim256_t16_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_int8_uint32_dim512_t32_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim128_t8_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim256_t16_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_2subd_half.cu
+    src/neighbors/detail/cagra/compute_distance_vpq_L2Expanded_uint8_uint32_dim512_t32_8pq_4subd_half.cu
+    src/neighbors/detail/cagra/search_multi_cta_float_uint32.cu
+    src/neighbors/detail/cagra/search_multi_cta_half_uint32.cu
+    src/neighbors/detail/cagra/search_multi_cta_int8_uint32.cu
+    src/neighbors/detail/cagra/search_multi_cta_uint8_uint32.cu
+    src/neighbors/detail/cagra/search_single_cta_float_uint32.cu
+    src/neighbors/detail/cagra/search_single_cta_half_uint32.cu
+    src/neighbors/detail/cagra/search_single_cta_int8_uint32.cu
+    src/neighbors/detail/cagra/search_single_cta_uint8_uint32.cu
+  )
 
-file(GLOB_RECURSE compute_distance_sources "src/neighbors/detail/cagra/compute_distance_*.cu")
-set_source_files_properties(${compute_distance_sources} PROPERTIES COMPILE_FLAGS -maxrregcount=64)
-
-set_target_properties(
-  cuvs-cagra-search
-  PROPERTIES BUILD_RPATH "\$ORIGIN"
-             CXX_STANDARD 17
-             CXX_STANDARD_REQUIRED ON
-             CUDA_STANDARD 17
-             CUDA_STANDARD_REQUIRED ON
-             CUDA_SEPARABLE_COMPILATION ON
-             INTERFACE_POSITION_INDEPENDENT_CODE ON
-             POSITION_INDEPENDENT_CODE ON
-)
-target_link_libraries(cuvs-cagra-search PRIVATE raft::raft)
-target_include_directories(
-  cuvs-cagra-search PRIVATE "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-)
-target_compile_options(
-  cuvs-cagra-search PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
-                            "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
-)
+  file(GLOB_RECURSE compute_distance_sources "src/neighbors/detail/cagra/compute_distance_*.cu")
+  set_source_files_properties(${compute_distance_sources} PROPERTIES COMPILE_FLAGS -maxrregcount=64)
 
-if(BUILD_MG_ALGOS)
-  set(CUVS_MG_ALGOS
-      src/neighbors/mg/mg_flat_float_int64_t.cu
-      src/neighbors/mg/mg_flat_int8_t_int64_t.cu
-      src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
-      src/neighbors/mg/mg_pq_float_int64_t.cu
-      src/neighbors/mg/mg_pq_half_int64_t.cu
-      src/neighbors/mg/mg_pq_int8_t_int64_t.cu
-      src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
-      src/neighbors/mg/mg_cagra_float_uint32_t.cu
-      src/neighbors/mg/mg_cagra_half_uint32_t.cu
-      src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
-      src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
-      src/neighbors/mg/omp_checks.cpp
-      src/neighbors/mg/nccl_comm.cpp
+  set_target_properties(
+    cuvs-cagra-search
+    PROPERTIES BUILD_RPATH "\$ORIGIN"
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+               CUDA_SEPARABLE_COMPILATION ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+               POSITION_INDEPENDENT_CODE ON
+  )
+  target_link_libraries(cuvs-cagra-search PRIVATE raft::raft)
+  target_include_directories(
+    cuvs-cagra-search PRIVATE "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+  )
+  target_compile_options(
+    cuvs-cagra-search PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
+                              "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
-endif()
-
-add_library(
-  cuvs_objs OBJECT
-  src/cluster/kmeans_balanced_fit_float.cu
-  src/cluster/kmeans_fit_mg_float.cu
-  src/cluster/kmeans_fit_mg_double.cu
-  src/cluster/kmeans_fit_double.cu
-  src/cluster/kmeans_fit_float.cu
-  src/cluster/kmeans_auto_find_k_float.cu
-  src/cluster/kmeans_fit_predict_double.cu
-  src/cluster/kmeans_fit_predict_float.cu
-  src/cluster/kmeans_predict_double.cu
-  src/cluster/kmeans_predict_float.cu
-  src/cluster/kmeans_balanced_fit_float.cu
-  src/cluster/kmeans_balanced_fit_predict_float.cu
-  src/cluster/kmeans_balanced_predict_float.cu
-  src/cluster/kmeans_balanced_fit_int8.cu
-  src/cluster/kmeans_balanced_fit_predict_int8.cu
-  src/cluster/kmeans_balanced_predict_int8.cu
-  src/cluster/kmeans_transform_double.cu
-  src/cluster/kmeans_transform_float.cu
-  src/cluster/single_linkage_float.cu
-  src/core/bitset.cu
-  src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_correlation_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_cosine_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l1_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l_inf_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
-  src/distance/detail/pairwise_matrix/dispatch_rbf.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu
-  src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu
-  src/distance/detail/fused_distance_nn.cu
-  src/distance/distance.cu
-  src/distance/pairwise_distance.cu
-  src/neighbors/brute_force.cu
-  src/neighbors/cagra_build_float.cu
-  src/neighbors/cagra_build_half.cu
-  src/neighbors/cagra_build_int8.cu
-  src/neighbors/cagra_build_uint8.cu
-  src/neighbors/cagra_extend_float.cu
-  src/neighbors/cagra_extend_int8.cu
-  src/neighbors/cagra_extend_uint8.cu
-  src/neighbors/cagra_optimize.cu
-  src/neighbors/cagra_serialize_float.cu
-  src/neighbors/cagra_serialize_half.cu
-  src/neighbors/cagra_serialize_int8.cu
-  src/neighbors/cagra_serialize_uint8.cu
-  src/neighbors/iface/iface_cagra_float_uint32_t.cu
-  src/neighbors/iface/iface_cagra_half_uint32_t.cu
-  src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
-  src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
-  src/neighbors/iface/iface_flat_float_int64_t.cu
-  src/neighbors/iface/iface_flat_int8_t_int64_t.cu
-  src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
-  src/neighbors/iface/iface_pq_float_int64_t.cu
-  src/neighbors/iface/iface_pq_half_int64_t.cu
-  src/neighbors/iface/iface_pq_int8_t_int64_t.cu
-  src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
-  src/neighbors/detail/cagra/cagra_build.cpp
-  src/neighbors/detail/cagra/topk_for_cagra/topk.cu
-  $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
-  src/neighbors/ivf_flat_index.cpp
-  src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_helpers.cu
-  src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
-  src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
-  src/neighbors/ivf_pq_index.cpp
-  src/neighbors/ivf_pq/ivf_pq_build_common.cu
-  src/neighbors/ivf_pq/ivf_pq_serialize.cu
-  src/neighbors/ivf_pq/ivf_pq_deserialize.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_half_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
-  src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
-  src/neighbors/nn_descent.cu
-  src/neighbors/nn_descent_float.cu
-  src/neighbors/nn_descent_half.cu
-  src/neighbors/nn_descent_int8.cu
-  src/neighbors/nn_descent_uint8.cu
-  src/neighbors/reachability.cu
-  src/neighbors/refine/detail/refine_device_float_float.cu
-  src/neighbors/refine/detail/refine_device_half_float.cu
-  src/neighbors/refine/detail/refine_device_int8_t_float.cu
-  src/neighbors/refine/detail/refine_device_uint8_t_float.cu
-  src/neighbors/refine/detail/refine_host_float_float.cpp
-  src/neighbors/refine/detail/refine_host_half_float.cpp
-  src/neighbors/refine/detail/refine_host_int8_t_float.cpp
-  src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
-  src/neighbors/sample_filter.cu
-  src/neighbors/vamana_build_float.cu
-  src/neighbors/vamana_build_uint8.cu
-  src/neighbors/vamana_build_int8.cu
-  src/neighbors/vamana_serialize_float.cu
-  src/neighbors/vamana_serialize_uint8.cu
-  src/neighbors/vamana_serialize_int8.cu
-  src/selection/select_k_float_int64_t.cu
-  src/selection/select_k_float_int32_t.cu
-  src/selection/select_k_float_uint32_t.cu
-  src/selection/select_k_half_uint32_t.cu
-  src/stats/silhouette_score.cu
-  src/stats/trustworthiness_score.cu
-  ${CUVS_MG_ALGOS}
-)
 
-set_target_properties(
-  cuvs_objs
-  PROPERTIES CXX_STANDARD 17
-             CXX_STANDARD_REQUIRED ON
-             CUDA_STANDARD 17
-             CUDA_STANDARD_REQUIRED ON
-             POSITION_INDEPENDENT_CODE ON
-)
-target_compile_options(
-  cuvs_objs PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
-                    "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
-)
-target_link_libraries(
-  cuvs_objs PUBLIC raft::raft rmm::rmm ${CUVS_CTK_MATH_DEPENDENCIES}
-                   $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-)
+  if(BUILD_MG_ALGOS)
+    set(CUVS_MG_ALGOS
+        src/neighbors/mg/mg_flat_float_int64_t.cu
+        src/neighbors/mg/mg_flat_int8_t_int64_t.cu
+        src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
+        src/neighbors/mg/mg_pq_float_int64_t.cu
+        src/neighbors/mg/mg_pq_half_int64_t.cu
+        src/neighbors/mg/mg_pq_int8_t_int64_t.cu
+        src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
+        src/neighbors/mg/mg_cagra_float_uint32_t.cu
+        src/neighbors/mg/mg_cagra_half_uint32_t.cu
+        src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
+        src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
+        src/neighbors/mg/omp_checks.cpp
+        src/neighbors/mg/nccl_comm.cpp
+    )
+  endif()
 
-add_library(cuvs SHARED $<TARGET_OBJECTS:cuvs_objs>)
-add_library(cuvs_static STATIC $<TARGET_OBJECTS:cuvs_objs>)
+  add_library(
+    cuvs_objs OBJECT
+    src/cluster/kmeans_balanced_fit_float.cu
+    src/cluster/kmeans_fit_mg_float.cu
+    src/cluster/kmeans_fit_mg_double.cu
+    src/cluster/kmeans_fit_double.cu
+    src/cluster/kmeans_fit_float.cu
+    src/cluster/kmeans_auto_find_k_float.cu
+    src/cluster/kmeans_fit_predict_double.cu
+    src/cluster/kmeans_fit_predict_float.cu
+    src/cluster/kmeans_predict_double.cu
+    src/cluster/kmeans_predict_float.cu
+    src/cluster/kmeans_balanced_fit_float.cu
+    src/cluster/kmeans_balanced_fit_predict_float.cu
+    src/cluster/kmeans_balanced_predict_float.cu
+    src/cluster/kmeans_balanced_fit_int8.cu
+    src/cluster/kmeans_balanced_fit_predict_int8.cu
+    src/cluster/kmeans_balanced_predict_int8.cu
+    src/cluster/kmeans_transform_double.cu
+    src/cluster/kmeans_transform_float.cu
+    src/cluster/single_linkage_float.cu
+    src/core/bitset.cu
+    src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_correlation_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_correlation_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_correlation_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_cosine_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_cosine_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_cosine_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_hamming_unexpanded_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_hellinger_expanded_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_jensen_shannon_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_kl_divergence_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_kl_divergence_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_kl_divergence_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l1_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l1_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l1_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_expanded_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_unexpanded_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l_inf_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l_inf_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_l_inf_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_lp_unexpanded_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_russel_rao_float_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_russel_rao_half_float_float_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_russel_rao_double_double_double_int.cu
+    src/distance/detail/pairwise_matrix/dispatch_rbf.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu
+    src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu
+    src/distance/detail/fused_distance_nn.cu
+    src/distance/distance.cu
+    src/distance/pairwise_distance.cu
+    src/neighbors/brute_force.cu
+    src/neighbors/cagra_build_float.cu
+    src/neighbors/cagra_build_half.cu
+    src/neighbors/cagra_build_int8.cu
+    src/neighbors/cagra_build_uint8.cu
+    src/neighbors/cagra_extend_float.cu
+    src/neighbors/cagra_extend_int8.cu
+    src/neighbors/cagra_extend_uint8.cu
+    src/neighbors/cagra_optimize.cu
+    src/neighbors/cagra_serialize_float.cu
+    src/neighbors/cagra_serialize_half.cu
+    src/neighbors/cagra_serialize_int8.cu
+    src/neighbors/cagra_serialize_uint8.cu
+    src/neighbors/iface/iface_cagra_float_uint32_t.cu
+    src/neighbors/iface/iface_cagra_half_uint32_t.cu
+    src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
+    src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
+    src/neighbors/iface/iface_flat_float_int64_t.cu
+    src/neighbors/iface/iface_flat_int8_t_int64_t.cu
+    src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
+    src/neighbors/iface/iface_pq_float_int64_t.cu
+    src/neighbors/iface/iface_pq_half_int64_t.cu
+    src/neighbors/iface/iface_pq_int8_t_int64_t.cu
+    src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
+    src/neighbors/detail/cagra/cagra_build.cpp
+    src/neighbors/detail/cagra/topk_for_cagra/topk.cu
+    $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
+    src/neighbors/ivf_flat_index.cpp
+    src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_helpers.cu
+    src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
+    src/neighbors/ivf_pq_index.cpp
+    src/neighbors/ivf_pq/ivf_pq_build_common.cu
+    src/neighbors/ivf_pq/ivf_pq_serialize.cu
+    src/neighbors/ivf_pq/ivf_pq_deserialize.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_build_extend_float_int64_t.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_build_extend_half_int64_t.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_build_extend_int8_t_int64_t.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_build_extend_uint8_t_int64_t.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
+    src/neighbors/nn_descent.cu
+    src/neighbors/nn_descent_float.cu
+    src/neighbors/nn_descent_half.cu
+    src/neighbors/nn_descent_int8.cu
+    src/neighbors/nn_descent_uint8.cu
+    src/neighbors/reachability.cu
+    src/neighbors/refine/detail/refine_device_float_float.cu
+    src/neighbors/refine/detail/refine_device_half_float.cu
+    src/neighbors/refine/detail/refine_device_int8_t_float.cu
+    src/neighbors/refine/detail/refine_device_uint8_t_float.cu
+    src/neighbors/refine/detail/refine_host_float_float.cpp
+    src/neighbors/refine/detail/refine_host_half_float.cpp
+    src/neighbors/refine/detail/refine_host_int8_t_float.cpp
+    src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
+    src/neighbors/sample_filter.cu
+    src/neighbors/vamana_build_float.cu
+    src/neighbors/vamana_build_uint8.cu
+    src/neighbors/vamana_build_int8.cu
+    src/neighbors/vamana_serialize_float.cu
+    src/neighbors/vamana_serialize_uint8.cu
+    src/neighbors/vamana_serialize_int8.cu
+    src/selection/select_k_float_int64_t.cu
+    src/selection/select_k_float_int32_t.cu
+    src/selection/select_k_float_uint32_t.cu
+    src/selection/select_k_half_uint32_t.cu
+    src/stats/silhouette_score.cu
+    src/stats/trustworthiness_score.cu
+    ${CUVS_MG_ALGOS}
+  )
 
-target_compile_options(
-  cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
-                 --expt-relaxed-constexpr>
-)
+  set_target_properties(
+    cuvs_objs
+    PROPERTIES CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
+  )
+  target_compile_options(
+    cuvs_objs PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
+                      "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
+  )
+  target_link_libraries(
+    cuvs_objs PUBLIC raft::raft rmm::rmm ${CUVS_CTK_MATH_DEPENDENCIES}
+                     $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+  )
 
-add_library(cuvs::cuvs ALIAS cuvs)
-add_library(cuvs::cuvs_static ALIAS cuvs_static)
-
-set_target_properties(
-  cuvs_static
-  PROPERTIES BUILD_RPATH "\$ORIGIN"
-             INSTALL_RPATH "\$ORIGIN"
-             CXX_STANDARD 17
-             CXX_STANDARD_REQUIRED ON
-             POSITION_INDEPENDENT_CODE ON
-             INTERFACE_POSITION_INDEPENDENT_CODE ON
-             EXPORT_NAME cuvs_static
-)
+  add_library(cuvs SHARED $<TARGET_OBJECTS:cuvs_objs>)
+  add_library(cuvs_static STATIC $<TARGET_OBJECTS:cuvs_objs>)
 
-target_compile_options(cuvs_static PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
+  target_compile_options(
+    cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
+                   --expt-relaxed-constexpr>
+  )
 
-target_include_directories(
-  cuvs_objs
-  PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-         "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-  INTERFACE "$<INSTALL_INTERFACE:include>"
-)
+  add_library(cuvs::cuvs ALIAS cuvs)
+  add_library(cuvs::cuvs_static ALIAS cuvs_static)
 
-target_include_directories(
-  cuvs_static
-  PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-  INTERFACE "$<INSTALL_INTERFACE:include>"
-)
+  set_target_properties(
+    cuvs_static
+    PROPERTIES BUILD_RPATH "\$ORIGIN"
+               INSTALL_RPATH "\$ORIGIN"
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+               EXPORT_NAME cuvs_static
+  )
 
-# ensure CUDA symbols aren't relocated to the middle of the debug build binaries
-target_link_options(cuvs_static PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
+  target_compile_options(cuvs_static PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
 
-target_include_directories(
-  cuvs_static PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-                     "$<INSTALL_INTERFACE:include>"
-)
+  target_include_directories(
+    cuvs_objs
+    PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+           "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+    INTERFACE "$<INSTALL_INTERFACE:include>"
+  )
 
-target_include_directories(
-  cuvs PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-              "$<INSTALL_INTERFACE:include>"
-)
+  target_include_directories(
+    cuvs_static
+    PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+    INTERFACE "$<INSTALL_INTERFACE:include>"
+  )
 
-rapids_find_package(
-  OpenMP REQUIRED
-  BUILD_EXPORT_SET cuvs-exports
-  INSTALL_EXPORT_SET cuvs-exports
-)
+  # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
+  target_link_options(cuvs_static PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
 
-if(NOT BUILD_CPU_ONLY)
+  target_include_directories(
+    cuvs_static PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+                       "$<INSTALL_INTERFACE:include>"
+  )
 
-  set(CUVS_CUSOLVER_DEPENDENCY CUDA::cusolver${_ctk_static_suffix})
-  set(CUVS_CUBLAS_DEPENDENCY CUDA::cublas${_ctk_static_suffix})
-  set(CUVS_CURAND_DEPENDENCY CUDA::curand${_ctk_static_suffix})
-  set(CUVS_CUSPARSE_DEPENDENCY CUDA::cusparse${_ctk_static_suffix})
+  target_include_directories(
+    cuvs PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+                "$<INSTALL_INTERFACE:include>"
+  )
 
-  set(CUVS_CTK_MATH_DEPENDENCIES ${CUVS_CUBLAS_DEPENDENCY} ${CUVS_CUSOLVER_DEPENDENCY}
-                                 ${CUVS_CUSPARSE_DEPENDENCY} ${CUVS_CURAND_DEPENDENCY}
+  rapids_find_package(
+    OpenMP REQUIRED
+    BUILD_EXPORT_SET cuvs-exports
+    INSTALL_EXPORT_SET cuvs-exports
   )
 
-  if(BUILD_MG_ALGOS)
-    set(CUVS_COMMS_DEPENDENCY nccl)
-  endif()
+  if(NOT BUILD_CPU_ONLY)
 
-  # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
-  target_link_libraries(
-    cuvs
-    PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
-    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX> cuvs-cagra-search
-            ${CUVS_COMMS_DEPENDENCY}
-  )
+    set(CUVS_CUSOLVER_DEPENDENCY CUDA::cusolver${_ctk_static_suffix})
+    set(CUVS_CUBLAS_DEPENDENCY CUDA::cublas${_ctk_static_suffix})
+    set(CUVS_CURAND_DEPENDENCY CUDA::curand${_ctk_static_suffix})
+    set(CUVS_CUSPARSE_DEPENDENCY CUDA::cusparse${_ctk_static_suffix})
 
-  target_link_libraries(
-    cuvs_static
-    PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
-    PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-  )
-endif()
+    set(CUVS_CTK_MATH_DEPENDENCIES ${CUVS_CUBLAS_DEPENDENCY} ${CUVS_CUSOLVER_DEPENDENCY}
+                                   ${CUVS_CUSPARSE_DEPENDENCY} ${CUVS_CURAND_DEPENDENCY}
+    )
 
-if(BUILD_MG_ALGOS)
-  target_compile_definitions(cuvs PUBLIC CUVS_BUILD_MG_ALGOS)
-  target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_MG_ALGOS)
-endif()
+    if(BUILD_MG_ALGOS)
+      set(CUVS_COMMS_DEPENDENCY nccl)
+    endif()
 
-if(BUILD_CAGRA_HNSWLIB)
-  target_link_libraries(cuvs_objs PRIVATE hnswlib::hnswlib)
-  target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
-endif()
+    # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
+    target_link_libraries(
+      cuvs
+      PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
+      PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+              cuvs-cagra-search ${CUVS_COMMS_DEPENDENCY}
+    )
 
-# Endian detection
-include(TestBigEndian)
-test_big_endian(BIG_ENDIAN)
-if(BIG_ENDIAN)
-  target_compile_definitions(cuvs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=0)
-else()
-  target_compile_definitions(cuvs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=1)
-endif()
+    target_link_libraries(
+      cuvs_static
+      PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
+      PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+    )
+  endif()
 
-file(
-  WRITE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld"
-  [=[
+  if(BUILD_MG_ALGOS)
+    target_compile_definitions(cuvs PUBLIC CUVS_BUILD_MG_ALGOS)
+    target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_MG_ALGOS)
+  endif()
+
+  if(BUILD_CAGRA_HNSWLIB)
+    target_link_libraries(cuvs_objs PRIVATE hnswlib::hnswlib)
+    target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
+  endif()
+
+  # Endian detection
+  include(TestBigEndian)
+  test_big_endian(BIG_ENDIAN)
+  if(BIG_ENDIAN)
+    target_compile_definitions(cuvs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=0)
+  else()
+    target_compile_definitions(cuvs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=1)
+  endif()
+
+  file(
+    WRITE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld"
+    [=[
 SECTIONS
 {
 .nvFatBinSegment : { *(.nvFatBinSegment) }
 .nv_fatbin : { *(.nv_fatbin) }
 }
 ]=]
-)
-
-# ##################################################################################################
-# * NVTX support in cuvs -----------------------------------------------------
-
-if(CUVS_NVTX)
-  # This enables NVTX within the project with no option to disable it downstream.
-  target_link_libraries(cuvs PUBLIC CUDA::nvtx3)
-  target_compile_definitions(cuvs PUBLIC NVTX_ENABLED)
-else()
-  # Allow enable NVTX downstream if not set here. This creates a new option at build/install time,
-  # which is set by default to OFF, but can be enabled in the dependent project.
-  get_property(
-    nvtx_option_help_string
-    CACHE CUVS_NVTX
-    PROPERTY HELPSTRING
   )
-  string(
-    CONCAT
-      nvtx_export_string
-      "option(CUVS_NVTX \""
-      ${nvtx_option_help_string}
-      "\" OFF)"
-      [=[
+
+  # ################################################################################################
+  # * NVTX support in cuvs -----------------------------------------------------
+
+  if(CUVS_NVTX)
+    # This enables NVTX within the project with no option to disable it downstream.
+    target_link_libraries(cuvs PUBLIC CUDA::nvtx3)
+    target_compile_definitions(cuvs PUBLIC NVTX_ENABLED)
+  else()
+    # Allow enable NVTX downstream if not set here. This creates a new option at build/install time,
+    # which is set by default to OFF, but can be enabled in the dependent project.
+    get_property(
+      nvtx_option_help_string
+      CACHE CUVS_NVTX
+      PROPERTY HELPSTRING
+    )
+    string(
+      CONCAT
+        nvtx_export_string
+        "option(CUVS_NVTX \""
+        ${nvtx_option_help_string}
+        "\" OFF)"
+        [=[
 
 target_link_libraries(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:CUDA::nvtx3>)
 target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENABLED>)
 
   ]=]
-  )
-endif()
-
-set_target_properties(
-  cuvs
-  PROPERTIES BUILD_RPATH "\$ORIGIN"
-             INSTALL_RPATH "\$ORIGIN"
-             CXX_STANDARD 17
-             CXX_STANDARD_REQUIRED ON
-             CUDA_STANDARD 17
-             CUDA_STANDARD_REQUIRED ON
-             INTERFACE_POSITION_INDEPENDENT_CODE ON
-             POSITION_INDEPENDENT_CODE ON
-)
-
-target_compile_options(
-  cuvs PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
-               "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
-)
-# ensure CUDA symbols aren't relocated to the middle of the debug build binaries
-target_link_options(cuvs PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
-
-# ##################################################################################################
-# * cuvs_c -------------------------------------------------------------------------------
-if(BUILD_C_LIBRARY)
-  add_library(
-    cuvs_c SHARED
-    src/core/c_api.cpp
-    src/neighbors/brute_force_c.cpp
-    src/neighbors/ivf_flat_c.cpp
-    src/neighbors/ivf_pq_c.cpp
-    src/neighbors/cagra_c.cpp
-    $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
-    src/neighbors/refine/refine_c.cpp
-    src/distance/pairwise_distance_c.cpp
-  )
-
-  if(BUILD_CAGRA_HNSWLIB)
-    target_link_libraries(cuvs_c PRIVATE hnswlib::hnswlib)
-    target_compile_definitions(cuvs_c PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
+    )
   endif()
 
-  add_library(cuvs::c_api ALIAS cuvs_c)
-
   set_target_properties(
-    cuvs_c
+    cuvs
     PROPERTIES BUILD_RPATH "\$ORIGIN"
                INSTALL_RPATH "\$ORIGIN"
                CXX_STANDARD 17
                CXX_STANDARD_REQUIRED ON
-               POSITION_INDEPENDENT_CODE ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
                INTERFACE_POSITION_INDEPENDENT_CODE ON
-               EXPORT_NAME c_api
+               POSITION_INDEPENDENT_CODE ON
   )
 
-  target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
-
-  target_include_directories(
-    cuvs_c
-    PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-    INTERFACE "$<INSTALL_INTERFACE:include>"
+  target_compile_options(
+    cuvs PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
+                 "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
+  # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
+  target_link_options(cuvs PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
+
+  # ################################################################################################
+  # * cuvs_c -------------------------------------------------------------------------------
+  if(BUILD_C_LIBRARY)
+    add_library(
+      cuvs_c SHARED
+      src/core/c_api.cpp
+      src/neighbors/brute_force_c.cpp
+      src/neighbors/ivf_flat_c.cpp
+      src/neighbors/ivf_pq_c.cpp
+      src/neighbors/cagra_c.cpp
+      $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
+      src/neighbors/refine/refine_c.cpp
+      src/distance/pairwise_distance_c.cpp
+    )
 
-  target_link_libraries(
-    cuvs_c
-    PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
-    PRIVATE raft::raft
-  )
+    if(BUILD_CAGRA_HNSWLIB)
+      target_link_libraries(cuvs_c PRIVATE hnswlib::hnswlib)
+      target_compile_definitions(cuvs_c PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
+    endif()
+
+    add_library(cuvs::c_api ALIAS cuvs_c)
+
+    set_target_properties(
+      cuvs_c
+      PROPERTIES BUILD_RPATH "\$ORIGIN"
+                 INSTALL_RPATH "\$ORIGIN"
+                 CXX_STANDARD 17
+                 CXX_STANDARD_REQUIRED ON
+                 POSITION_INDEPENDENT_CODE ON
+                 INTERFACE_POSITION_INDEPENDENT_CODE ON
+                 EXPORT_NAME c_api
+    )
 
-  # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
-  target_link_options(cuvs_c PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
-endif()
+    target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
 
-# ##################################################################################################
-# * install targets-----------------------------------------------------------
-rapids_cmake_install_lib_dir(lib_dir)
-include(GNUInstallDirs)
-include(CPack)
-
-install(
-  TARGETS cuvs cuvs_static
-  DESTINATION ${lib_dir}
-  COMPONENT cuvs
-  EXPORT cuvs-exports
-)
+    target_include_directories(
+      cuvs_c
+      PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+      INTERFACE "$<INSTALL_INTERFACE:include>"
+    )
 
-install(
-  DIRECTORY include/cuvs
-  COMPONENT cuvs
-  DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
-)
+    target_link_libraries(
+      cuvs_c
+      PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
+      PRIVATE raft::raft
+    )
+
+    # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
+    target_link_options(cuvs_c PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
+  endif()
+
+  # ################################################################################################
+  # * install targets-----------------------------------------------------------
+  rapids_cmake_install_lib_dir(lib_dir)
+  include(GNUInstallDirs)
+  include(CPack)
 
-if(BUILD_C_LIBRARY)
   install(
-    TARGETS cuvs_c
+    TARGETS cuvs cuvs_static
     DESTINATION ${lib_dir}
-    COMPONENT c_api
-    EXPORT cuvs-c-exports
+    COMPONENT cuvs
+    EXPORT cuvs-exports
   )
-endif()
 
-install(
-  FILES ${CMAKE_CURRENT_BINARY_DIR}/include/cuvs/version_config.hpp
-  COMPONENT cuvs
-  DESTINATION include/cuvs
-)
+  install(
+    DIRECTORY include/cuvs
+    COMPONENT cuvs
+    DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+  )
 
-if(TARGET cuvs_c)
-  list(APPEND cuvs_components c_api)
-  list(APPEND cuvs_export_sets cuvs-c-exports)
-  set(CUVS_C_TARGET cuvs_c)
-endif()
+  if(BUILD_C_LIBRARY)
+    install(
+      TARGETS cuvs_c
+      DESTINATION ${lib_dir}
+      COMPONENT c_api
+      EXPORT cuvs-c-exports
+    )
+  endif()
 
-# Use `rapids_export` for 22.04 as it will have COMPONENT support
-rapids_export(
-  INSTALL cuvs
-  EXPORT_SET cuvs-exports
-  COMPONENTS ${cuvs_components}
-  COMPONENTS_EXPORT_SET ${cuvs_export_sets}
-  GLOBAL_TARGETS cuvs ${CUVS_C_TARGET}
-  NAMESPACE cuvs::
-)
+  install(
+    FILES ${CMAKE_CURRENT_BINARY_DIR}/include/cuvs/version_config.hpp
+    COMPONENT cuvs
+    DESTINATION include/cuvs
+  )
 
-# ##################################################################################################
-# * build export -------------------------------------------------------------
-rapids_export(
-  BUILD cuvs
-  EXPORT_SET cuvs-exports
-  COMPONENTS ${cuvs_components}
-  COMPONENTS_EXPORT_SET ${cuvs_export_sets}
-  GLOBAL_TARGETS cuvs ${CUVS_C_TARGET}
-  NAMESPACE cuvs::
-)
+  if(TARGET cuvs_c)
+    list(APPEND cuvs_components c_api)
+    list(APPEND cuvs_export_sets cuvs-c-exports)
+    set(CUVS_C_TARGET cuvs_c)
+  endif()
+
+  # Use `rapids_export` for 22.04 as it will have COMPONENT support
+  rapids_export(
+    INSTALL cuvs
+    EXPORT_SET cuvs-exports
+    COMPONENTS ${cuvs_components}
+    COMPONENTS_EXPORT_SET ${cuvs_export_sets}
+    GLOBAL_TARGETS cuvs ${CUVS_C_TARGET}
+    NAMESPACE cuvs::
+  )
+
+  # ################################################################################################
+  # * build export -------------------------------------------------------------
+  rapids_export(
+    BUILD cuvs
+    EXPORT_SET cuvs-exports
+    COMPONENTS ${cuvs_components}
+    COMPONENTS_EXPORT_SET ${cuvs_export_sets}
+    GLOBAL_TARGETS cuvs ${CUVS_C_TARGET}
+    NAMESPACE cuvs::
+  )
+endif()
 
 # ##################################################################################################
 # * build test executable ----------------------------------------------------
diff --git a/cpp/cmake/thirdparty/get_cuvs.cmake b/cpp/cmake/thirdparty/get_cuvs.cmake
new file mode 100644
index 0000000000..c21cccbcc0
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_cuvs.cmake
@@ -0,0 +1,64 @@
+# =============================================================================
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+
+# Use RAPIDS_VERSION_MAJOR_MINOR from rapids_config.cmake
+set(CUVS_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
+set(CUVS_FORK "rapidsai")
+set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
+
+function(find_and_configure_cuvs)
+    set(oneValueArgs VERSION FORK PINNED_TAG ENABLE_NVTX CLONE_ON_PIN BUILD_CPU_ONLY BUILD_SHARED_LIBS)
+    cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+            "${multiValueArgs}" ${ARGN} )
+
+    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "branch-${CUVS_VERSION}")
+        message(STATUS "cuVS: pinned tag found: ${PKG_PINNED_TAG}. Cloning cuVS locally.")
+        set(CPM_DOWNLOAD_cuvs ON)
+    endif()
+
+    #-----------------------------------------------------
+    # Invoke CPM find_package()
+    #-----------------------------------------------------
+    rapids_cpm_find(cuvs ${PKG_VERSION}
+            GLOBAL_TARGETS      cuvs::cuvs
+            BUILD_EXPORT_SET    cuvs-bench-exports
+            INSTALL_EXPORT_SET  cuvs-bench-exports
+            COMPONENTS          cuvs
+            CPM_ARGS
+              GIT_REPOSITORY        https://github.com/${PKG_FORK}/cuvs.git
+              GIT_TAG               ${PKG_PINNED_TAG}
+              SOURCE_SUBDIR         cpp
+              OPTIONS
+              "BUILD_SHARED_LIBS ${PKG_BUILD_SHARED_LIBS}"
+              "BUILD_CPU_ONLY ${PKG_BUILD_CPU_ONLY}"
+              "BUILD_TESTS OFF"
+              "BUILD_CAGRA_HNSWLIB OFF"
+              "CUVS_CLONE_ON_PIN ${PKG_CLONE_ON_PIN}"
+            )
+endfunction()
+
+
+# Change pinned tag here to test a commit in CI
+# To use a different cuVS locally, set the CMake variable
+# CPM_cuvs_SOURCE=/path/to/local/cuvs
+find_and_configure_cuvs(VERSION  ${CUVS_VERSION}.00
+        FORK                     ${CUVS_FORK}
+        PINNED_TAG               ${CUVS_PINNED_TAG}
+        ENABLE_NVTX              OFF
+        # When PINNED_TAG above doesn't match the default rapids branch,
+        # force local cuvs clone in build directory
+        # even if it's already installed.
+        CLONE_ON_PIN     ${CUVS_CLONE_ON_PIN}
+        BUILD_CPU_ONLY ${BUILD_CPU_ONLY}
+        BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS}
+)
diff --git a/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
new file mode 100644
index 0000000000..2b4213016e
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
@@ -0,0 +1,241 @@
+#!/usr/bin/env python
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import argparse
+import os
+import sys
+
+import cupy as cp
+import numpy as np
+import rmm
+from pylibraft.common import DeviceResources
+from rmm.allocators.cupy import rmm_cupy_allocator
+
+from cuvs.neighbors.brute_force import knn
+
+from .utils import memmap_bin_file, suffix_from_dtype, write_bin
+
+
+def generate_random_queries(n_queries, n_features, dtype=np.float32):
+    print("Generating random queries")
+    if np.issubdtype(dtype, np.integer):
+        queries = cp.random.randint(
+            0, 255, size=(n_queries, n_features), dtype=dtype
+        )
+    else:
+        queries = cp.random.uniform(size=(n_queries, n_features)).astype(dtype)
+    return queries
+
+
+def choose_random_queries(dataset, n_queries):
+    print("Choosing random vector from dataset as query vectors")
+    query_idx = np.random.choice(
+        dataset.shape[0], size=(n_queries,), replace=False
+    )
+    return dataset[query_idx, :]
+
+
+def calc_truth(dataset, queries, k, metric="sqeuclidean"):
+    handle = DeviceResources()
+    n_samples = dataset.shape[0]
+    n = 500000  # batch size for processing neighbors
+    i = 0
+    indices = None
+    distances = None
+    queries = cp.asarray(queries, dtype=cp.float32)
+
+    while i < n_samples:
+        print("Step {0}/{1}:".format(i // n, n_samples // n))
+        n_batch = n if i + n <= n_samples else n_samples - i
+
+        X = cp.asarray(dataset[i : i + n_batch, :], cp.float32)
+
+        D, Ind = knn(X, queries, k, metric=metric, handle=handle)
+        handle.sync()
+
+        D, Ind = cp.asarray(D), cp.asarray(Ind)
+        Ind += i  # shift neighbor index by offset i
+
+        if distances is None:
+            distances = D
+            indices = Ind
+        else:
+            distances = cp.concatenate([distances, D], axis=1)
+            indices = cp.concatenate([indices, Ind], axis=1)
+            idx = cp.argsort(distances, axis=1)[:, :k]
+            distances = cp.take_along_axis(distances, idx, axis=1)
+            indices = cp.take_along_axis(indices, idx, axis=1)
+
+        i += n_batch
+
+    return distances, indices
+
+
+def main():
+    pool = rmm.mr.PoolMemoryResource(
+        rmm.mr.CudaMemoryResource(), initial_pool_size=2**30
+    )
+    rmm.mr.set_current_device_resource(pool)
+    cp.cuda.set_allocator(rmm_cupy_allocator)
+
+    parser = argparse.ArgumentParser(
+        prog="generate_groundtruth",
+        description="Generate true neighbors using exact NN search. "
+        "The input and output files are in big-ann-benchmark's binary format.",
+        epilog="""Example usage
+    # With existing query file
+    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.\
+fbin --output=groundtruth_dir --queries=/dataset/query.public.10K.fbin
+
+    # With randomly generated queries
+    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.\
+fbin --output=groundtruth_dir --queries=random --n_queries=10000
+
+    # Using only a subset of the dataset. Define queries by randomly
+    # selecting vectors from the (subset of the) dataset.
+    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.\
+fbin --nrows=2000000 --cols=128 --output=groundtruth_dir \
+--queries=random-choice --n_queries=10000
+    """,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+
+    parser.add_argument("dataset", type=str, help="input dataset file name")
+    parser.add_argument(
+        "--queries",
+        type=str,
+        default="random",
+        help="Queries file name, or one of 'random-choice' or 'random' "
+        "(default). 'random-choice': select n_queries vectors from the input "
+        "dataset. 'random': generate n_queries as uniform random numbers.",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="",
+        help="output directory name (default current dir)",
+    )
+
+    parser.add_argument(
+        "--n_queries",
+        type=int,
+        default=10000,
+        help="Number of quries to generate (if no query file is given). "
+        "Default: 10000.",
+    )
+
+    parser.add_argument(
+        "-N",
+        "--rows",
+        default=None,
+        type=int,
+        help="use only first N rows from dataset, by default the whole "
+        "dataset is used",
+    )
+    parser.add_argument(
+        "-D",
+        "--cols",
+        default=None,
+        type=int,
+        help="number of features (dataset columns). "
+        "Default: read from dataset file.",
+    )
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        help="Dataset dtype. When not specified, then derived from extension."
+        " Supported types: 'float32', 'float16', 'uint8', 'int8'",
+    )
+
+    parser.add_argument(
+        "-k",
+        type=int,
+        default=100,
+        help="Number of neighbors (per query) to calculate",
+    )
+    parser.add_argument(
+        "--metric",
+        type=str,
+        default="sqeuclidean",
+        help="Metric to use while calculating distances. Valid metrics are "
+        "those that are accepted by cuvs.neighbors.brute_force.knn. Most"
+        " commonly used with cuVS are 'sqeuclidean' and 'inner_product'",
+    )
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+    args = parser.parse_args()
+
+    if args.rows is not None:
+        print("Reading subset of the data, nrows=", args.rows)
+    else:
+        print("Reading whole dataset")
+
+    # Load input data
+    dataset = memmap_bin_file(
+        args.dataset, args.dtype, shape=(args.rows, args.cols)
+    )
+    n_features = dataset.shape[1]
+    dtype = dataset.dtype
+
+    print(
+        "Dataset size {:6.1f} GB, shape {}, dtype {}".format(
+            dataset.size * dataset.dtype.itemsize / 1e9,
+            dataset.shape,
+            np.dtype(dtype),
+        )
+    )
+
+    if len(args.output) > 0:
+        os.makedirs(args.output, exist_ok=True)
+
+    if args.queries == "random" or args.queries == "random-choice":
+        if args.n_queries is None:
+            raise RuntimeError(
+                "n_queries must be given to generate random queries"
+            )
+        if args.queries == "random":
+            queries = generate_random_queries(
+                args.n_queries, n_features, dtype
+            )
+        elif args.queries == "random-choice":
+            queries = choose_random_queries(dataset, args.n_queries)
+
+        queries_filename = os.path.join(
+            args.output, "queries" + suffix_from_dtype(dtype)
+        )
+        print("Writing queries file", queries_filename)
+        write_bin(queries_filename, queries)
+    else:
+        print("Reading queries from file", args.queries)
+        queries = memmap_bin_file(args.queries, dtype)
+
+    print("Calculating true nearest neighbors")
+    distances, indices = calc_truth(dataset, queries, args.k, args.metric)
+
+    write_bin(
+        os.path.join(args.output, "groundtruth.neighbors.ibin"),
+        indices.astype(np.uint32),
+    )
+    write_bin(
+        os.path.join(args.output, "groundtruth.distances.fbin"),
+        distances.astype(np.float32),
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/cuvs_bench/cuvs_bench/generate_groundtruth/utils.py b/python/cuvs_bench/cuvs_bench/generate_groundtruth/utils.py
new file mode 100644
index 0000000000..a969b3d895
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/generate_groundtruth/utils.py
@@ -0,0 +1,101 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+
+import numpy as np
+
+
+def dtype_from_filename(filename):
+    ext = os.path.splitext(filename)[1]
+    if ext == ".fbin":
+        return np.float32
+    if ext == ".hbin":
+        return np.float16
+    elif ext == ".ibin":
+        return np.int32
+    elif ext == ".u8bin":
+        return np.ubyte
+    elif ext == ".i8bin":
+        return np.byte
+    else:
+        raise RuntimeError("Not supported file extension" + ext)
+
+
+def suffix_from_dtype(dtype):
+    if dtype == np.float32:
+        return ".fbin"
+    if dtype == np.float16:
+        return ".hbin"
+    elif dtype == np.int32:
+        return ".ibin"
+    elif dtype == np.ubyte:
+        return ".u8bin"
+    elif dtype == np.byte:
+        return ".i8bin"
+    else:
+        raise RuntimeError("Not supported dtype extension" + dtype)
+
+
+def memmap_bin_file(
+    bin_file, dtype, shape=None, mode="r", size_dtype=np.uint32
+):
+    extent_itemsize = np.dtype(size_dtype).itemsize
+    offset = int(extent_itemsize) * 2
+    if bin_file is None:
+        return None
+    if dtype is None:
+        dtype = dtype_from_filename(bin_file)
+
+    if mode[0] == "r":
+        a = np.memmap(bin_file, mode=mode, dtype=size_dtype, shape=(2,))
+        if shape is None:
+            shape = (a[0], a[1])
+        else:
+            shape = tuple(
+                [
+                    aval if sval is None else sval
+                    for aval, sval in zip(a, shape)
+                ]
+            )
+
+        return np.memmap(
+            bin_file, mode=mode, dtype=dtype, offset=offset, shape=shape
+        )
+    elif mode[0] == "w":
+        if shape is None:
+            raise ValueError("Need to specify shape to map file in write mode")
+
+        print("creating file", bin_file)
+        dirname = os.path.dirname(bin_file)
+        if len(dirname) > 0:
+            os.makedirs(dirname, exist_ok=True)
+        a = np.memmap(bin_file, mode=mode, dtype=size_dtype, shape=(2,))
+        a[0] = shape[0]
+        a[1] = shape[1]
+        a.flush()
+        del a
+        fp = np.memmap(
+            bin_file, mode="r+", dtype=dtype, offset=offset, shape=shape
+        )
+        return fp
+
+
+def write_bin(fname, data):
+    print("writing", fname, data.shape, data.dtype, "...")
+    with open(fname, "wb") as f:
+        np.asarray(data.shape, dtype=np.uint32).tofile(f)
+        data.tofile(f)
diff --git a/python/cuvs_bench/cuvs_bench/get_dataset/__main__.py b/python/cuvs_bench/cuvs_bench/get_dataset/__main__.py
new file mode 100644
index 0000000000..a6b154ef21
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/get_dataset/__main__.py
@@ -0,0 +1,115 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+import subprocess
+import sys
+from urllib.request import urlretrieve
+
+
+def get_dataset_path(name, ann_bench_data_path):
+    if not os.path.exists(ann_bench_data_path):
+        os.mkdir(ann_bench_data_path)
+    return os.path.join(ann_bench_data_path, f"{name}.hdf5")
+
+
+def download_dataset(url, path):
+    if not os.path.exists(path):
+        print(f"downloading {url} -> {path}...")
+        urlretrieve(url, path)
+
+
+def convert_hdf5_to_fbin(path, normalize):
+    scripts_path = os.path.dirname(os.path.realpath(__file__))
+    ann_bench_scripts_path = os.path.join(scripts_path, "hdf5_to_fbin.py")
+    print(f"calling script {ann_bench_scripts_path}")
+    if normalize and "angular" in path:
+        subprocess.run(
+            ["python", ann_bench_scripts_path, "-n", "%s" % path], check=True
+        )
+    else:
+        subprocess.run(
+            ["python", ann_bench_scripts_path, "%s" % path], check=True
+        )
+
+
+def move(name, ann_bench_data_path):
+    if "angular" in name:
+        new_name = name.replace("angular", "inner")
+    else:
+        new_name = name
+    new_path = os.path.join(ann_bench_data_path, new_name)
+    if not os.path.exists(new_path):
+        os.mkdir(new_path)
+    for bin_name in [
+        "base.fbin",
+        "query.fbin",
+        "groundtruth.neighbors.ibin",
+        "groundtruth.distances.fbin",
+    ]:
+        os.rename(
+            f"{ann_bench_data_path}/{name}.{bin_name}",
+            f"{new_path}/{bin_name}",
+        )
+
+
+def download(name, normalize, ann_bench_data_path):
+    path = get_dataset_path(name, ann_bench_data_path)
+    try:
+        url = f"http://ann-benchmarks.com/{name}.hdf5"
+        download_dataset(url, path)
+
+        convert_hdf5_to_fbin(path, normalize)
+
+        move(name, ann_bench_data_path)
+    except Exception:
+        print(f"Cannot download {url}")
+        raise
+
+
+def main():
+    call_path = os.getcwd()
+    if "RAPIDS_DATASET_ROOT_DIR" in os.environ:
+        default_dataset_path = os.getenv("RAPIDS_DATASET_ROOT_DIR")
+    else:
+        default_dataset_path = os.path.join(call_path, "datasets/")
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--dataset", help="dataset to download", default="glove-100-angular"
+    )
+    parser.add_argument(
+        "--dataset-path",
+        help="path to download dataset",
+        default=default_dataset_path,
+    )
+    parser.add_argument(
+        "--normalize",
+        help="normalize cosine distance to inner product",
+        action="store_true",
+    )
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+    args = parser.parse_args()
+
+    download(args.dataset, args.normalize, args.dataset_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/cuvs_bench/cuvs_bench/get_dataset/fbin_to_f16bin.py b/python/cuvs_bench/cuvs_bench/get_dataset/fbin_to_f16bin.py
new file mode 100644
index 0000000000..1255e42dc5
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/get_dataset/fbin_to_f16bin.py
@@ -0,0 +1,49 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from __future__ import absolute_import, division, print_function
+
+import sys
+
+import numpy as np
+
+
+def read_fbin(fname):
+    shape = np.fromfile(fname, dtype=np.uint32, count=2)
+    if float(shape[0]) * shape[1] * 4 > 2_000_000_000:
+        data = np.memmap(fname, dtype=np.float32, offset=8, mode="r").reshape(
+            shape
+        )
+    else:
+        data = np.fromfile(fname, dtype=np.float32, offset=8).reshape(shape)
+    return data
+
+
+def write_bin(fname, data):
+    with open(fname, "wb") as f:
+        np.asarray(data.shape, dtype=np.uint32).tofile(f)
+        data.tofile(f)
+
+
+if len(sys.argv) != 3:
+    print(
+        "usage: %s input.fbin output.f16bin" % (sys.argv[0]),
+        file=sys.stderr,
+    )
+    sys.exit(-1)
+
+data = read_fbin(sys.argv[1]).astype(np.float16)
+write_bin(sys.argv[2], data)
diff --git a/python/cuvs_bench/cuvs_bench/get_dataset/hdf5_to_fbin.py b/python/cuvs_bench/cuvs_bench/get_dataset/hdf5_to_fbin.py
new file mode 100644
index 0000000000..317051aa22
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/get_dataset/hdf5_to_fbin.py
@@ -0,0 +1,90 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import sys
+
+import h5py
+import numpy as np
+
+
+def normalize(x):
+    norm = np.linalg.norm(x, axis=1)
+    return (x.T / norm).T
+
+
+def write_bin(fname, data):
+    with open(fname, "wb") as f:
+        np.asarray(data.shape, dtype=np.uint32).tofile(f)
+        data.tofile(f)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2 and len(sys.argv) != 3:
+        print(
+            "usage: %s [-n] <input>.hdf5\n" % (sys.argv[0]),
+            "  -n: normalize base/query set\n",
+            "outputs: <input>.base.fbin\n",
+            "         <input>.query.fbin\n",
+            "         <input>.groundtruth.neighbors.ibin\n",
+            "         <input>.groundtruth.distances.fbin",
+            file=sys.stderr,
+        )
+        sys.exit(-1)
+
+    need_normalize = False
+    if len(sys.argv) == 3:
+        assert sys.argv[1] == "-n"
+        need_normalize = True
+    fname_prefix = sys.argv[-1]
+    assert fname_prefix.endswith(".hdf5")
+    fname_prefix = fname_prefix[:-5]
+
+    hdf5 = h5py.File(sys.argv[-1], "r")
+    assert (
+        hdf5.attrs["distance"] == "angular"
+        or hdf5.attrs["distance"] == "euclidean"
+    )
+    assert hdf5["train"].dtype == np.float32
+    assert hdf5["test"].dtype == np.float32
+    assert hdf5["neighbors"].dtype == np.int32
+    assert hdf5["distances"].dtype == np.float32
+
+    base = hdf5["train"][:]
+    query = hdf5["test"][:]
+    if need_normalize:
+        base = normalize(base)
+        query = normalize(query)
+    elif hdf5.attrs["distance"] == "angular":
+        print(
+            "warning: input has angular distance, ",
+            "specify -n to normalize base/query set!\n",
+        )
+
+    output_fname = fname_prefix + ".base.fbin"
+    print("writing", output_fname, "...")
+    write_bin(output_fname, base)
+
+    output_fname = fname_prefix + ".query.fbin"
+    print("writing", output_fname, "...")
+    write_bin(output_fname, query)
+
+    output_fname = fname_prefix + ".groundtruth.neighbors.ibin"
+    print("writing", output_fname, "...")
+    write_bin(output_fname, hdf5["neighbors"][:])
+
+    output_fname = fname_prefix + ".groundtruth.distances.fbin"
+    print("writing", output_fname, "...")
+    write_bin(output_fname, hdf5["distances"][:])
diff --git a/python/cuvs_bench/cuvs_bench/run/__main__.py b/python/cuvs_bench/cuvs_bench/run/__main__.py
index bf9f8586dc..58fc5291b1 100644
--- a/python/cuvs_bench/cuvs_bench/run/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/run/__main__.py
@@ -19,8 +19,9 @@
 from typing import Optional
 
 import click
-from data_export import convert_json_to_csv_build, convert_json_to_csv_search
-from run import run_benchmark
+
+from .data_export import convert_json_to_csv_build, convert_json_to_csv_search
+from .run import run_benchmark
 
 
 @click.command()
diff --git a/python/cuvs_bench/cuvs_bench/run/run.py b/python/cuvs_bench/cuvs_bench/run/run.py
index a65d4b5fea..0159d2c19e 100644
--- a/python/cuvs_bench/cuvs_bench/run/run.py
+++ b/python/cuvs_bench/cuvs_bench/run/run.py
@@ -21,7 +21,8 @@
 from typing import Any, Dict, Optional, Tuple
 
 import yaml
-from runners import cuvs_bench_cpp
+
+from .runners import cuvs_bench_cpp
 
 
 def rmm_present() -> bool:
diff --git a/python/cuvs_bench/cuvs_bench/split_groundtruth/__main__.py b/python/cuvs_bench/cuvs_bench/split_groundtruth/__main__.py
new file mode 100644
index 0000000000..7fee30e429
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/split_groundtruth/__main__.py
@@ -0,0 +1,57 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+import subprocess
+import sys
+
+
+def split_groundtruth(groundtruth_filepath):
+    ann_bench_scripts_path = os.path.join(
+        os.path.dirname(os.path.realpath(__file__)), "split_groundtruth.pl"
+    )
+    pwd = os.getcwd()
+    path_to_groundtruth = os.path.normpath(groundtruth_filepath).split(os.sep)
+    if len(path_to_groundtruth) > 1:
+        os.chdir(os.path.join(*path_to_groundtruth[:-1]))
+    groundtruth_filename = path_to_groundtruth[-1]
+    subprocess.run(
+        [ann_bench_scripts_path, groundtruth_filename, "groundtruth"],
+        check=True,
+    )
+    os.chdir(pwd)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--groundtruth",
+        help="Path to billion-scale dataset groundtruth file",
+        required=True,
+    )
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+    args = parser.parse_args()
+
+    split_groundtruth(args.groundtruth)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/cuvs_bench/cuvs_bench/split_groundtruth/split_groundtruth.pl b/python/cuvs_bench/cuvs_bench/split_groundtruth/split_groundtruth.pl
new file mode 100644
index 0000000000..b0a59f806c
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/split_groundtruth/split_groundtruth.pl
@@ -0,0 +1,45 @@
+#!/usr/bin/perl
+
+# =============================================================================
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+
+use warnings;
+use strict;
+use autodie qw(open close);
+
+
+@ARGV == 2
+  or die "usage: $0 input output_prefix\n";
+
+open my $fh, '<:raw', $ARGV[0];
+
+my $raw;
+read($fh, $raw, 8);
+my ($nrows, $dim) = unpack('LL', $raw);
+
+my $expected_size = 8 + $nrows * $dim * (4 + 4);
+my $size = (stat($fh))[7];
+$size == $expected_size
+  or die("error: expected size is $expected_size, but actual size is $size\n");
+
+
+open my $fh_out1, '>:raw', "$ARGV[1].neighbors.ibin";
+open my $fh_out2, '>:raw', "$ARGV[1].distances.fbin";
+
+print {$fh_out1} $raw;
+print {$fh_out2} $raw;
+
+read($fh, $raw, $nrows * $dim * 4);
+print {$fh_out1} $raw;
+read($fh, $raw, $nrows * $dim * 4);
+print {$fh_out2} $raw;

From 5adf2644e7c262b81575d516379ce27fc25dbf1f Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 4 Oct 2024 05:54:44 -0700
Subject: [PATCH 179/623] fix 404 in documentation link in readme (#395)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/395
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 38933b4c88..70e04ebf46 100755
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 
 ## Useful Resources
 
-- [Documentation](https://docs.rapids.ai/api/cuvs/): Library documentation.
+- [Documentation](https://docs.rapids.ai/api/cuvs/nightly/): Library documentation.
 - [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build): Instructions for installing and building cuVS.
 - [Getting Started Guide](https://docs.rapids.ai/api/cuvs/nightly/getting_started): Guide to getting started with cuVS.
 - [Code Examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples): Self-contained Code Examples.

From 397e56e0df4a430edc7f8e16e572fc8a03a0e0c0 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Fri, 4 Oct 2024 13:21:33 -0400
Subject: [PATCH 180/623] Prune workflows based on changed files (#392)

Contributes to https://github.com/rapidsai/build-planning/issues/94

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/392
---
 .github/workflows/pr.yaml | 46 ++++++++++++++++++++++++++++++++++++---
 1 file changed, 43 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 4e3fb600a7..e18e82df08 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -12,6 +12,7 @@ concurrency:
 jobs:
   pr-builder:
     needs:
+      - changed-files
       - checks
       - conda-cpp-build
       - conda-cpp-tests
@@ -25,6 +26,42 @@ jobs:
       - devcontainer
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.12
+    if: always()
+    with:
+      needs: ${{ toJSON(needs) }}
+  changed-files:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-24.12
+    with:
+      files_yaml: |
+        test_cpp:
+          - '**'
+          - '!.devcontainer/**'
+          - '!.pre-commit-config.yaml'
+          - '!README.md'
+          - '!docs/**'
+          - '!img/**'
+          - '!notebooks/**'
+          - '!python/**'
+          - '!rust/**'
+          - '!thirdparty/LICENSES/**'
+        test_notebooks:
+          - '**'
+          - '!.devcontainer/**'
+          - '!.pre-commit-config.yaml'
+          - '!README.md'
+          - '!rust/**'
+          - '!thirdparty/LICENSES/**'
+        test_python:
+          - '**'
+          - '!.devcontainer/**'
+          - '!.pre-commit-config.yaml'
+          - '!README.md'
+          - '!docs/**'
+          - '!img/**'
+          - '!notebooks/**'
+          - '!rust/**'
+          - '!thirdparty/LICENSES/**'
   checks:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.12
@@ -38,9 +75,10 @@ jobs:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
-    needs: conda-cpp-build
+    needs: [conda-cpp-build, changed-files]
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.12
+    if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
@@ -58,9 +96,10 @@ jobs:
     with:
       build_type: pull-request
   conda-python-tests:
-    needs: conda-python-build
+    needs: [conda-python-build, changed-files]
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.12
+    if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
@@ -91,9 +130,10 @@ jobs:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
-    needs: wheel-build-cuvs
+    needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.12
+    if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh

From c14b87928881179943d6249de9c2292efe1a9dd9 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Fri, 4 Oct 2024 13:43:27 -0400
Subject: [PATCH 181/623] More doc updates for 24.10 (#396)

This PR adds some critical doc fixes for 24.10-

1. Since we're working on the Nvidia developer landing page for cuVS,
our README content needs to be aligned.
2. The cuvs-bench documentation was missing the datasets guide and table
of contents.
3. The cuvs-bench docs weren't linked into the main docs.
4. cuvs-bench was missing several of the mg enabled algorithms, their
constraints, and parameter ranges.

---------

Co-authored-by: Micka <mide@nvidia.com>
Co-authored-by: viclafargue <viclafargue@nvidia.com>
---
 README.md                                     | 41 +++++++++
 docs/source/build.rst                         |  5 --
 docs/source/cuvs_bench/datasets.rst           | 62 ++++++++++++++
 docs/source/cuvs_bench/index.rst              | 84 +++++++++++++++++--
 docs/source/index.rst                         |  1 +
 .../cuvs_bench/config/algorithms.yaml         |  9 ++
 .../config/algos/cuvs_mg_cagra.yaml           | 13 +++
 .../config/algos/cuvs_mg_ivf_flat.yaml        |  9 ++
 .../config/algos/cuvs_mg_ivf_pq.yaml          | 41 +++++++++
 9 files changed, 252 insertions(+), 13 deletions(-)
 create mode 100644 docs/source/cuvs_bench/datasets.rst
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml

diff --git a/README.md b/README.md
index 70e04ebf46..213fde6322 100755
--- a/README.md
+++ b/README.md
@@ -27,6 +27,47 @@
 
 cuVS contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.
 
+Vector search is an information retrieval method that has been growing in popularity over the past few  years, partly because of the rising importance of multimedia embeddings created from unstructured data and the need to perform semantic search on the embeddings to find items which are semantically similar to each other.
+
+Vector search is also used in _data mining and machine learning_ tasks and comprises an important step in many _clustering_ and _visualization_ algorithms like [UMAP](https://arxiv.org/abs/2008.00325), [t-SNE](https://lvdmaaten.github.io/tsne/), K-means, and [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html). 
+
+Finally, faster vector search enables interactions between dense vectors and graphs. Converting a pile of dense vectors into nearest neighbors graphs unlocks the entire world of graph analysis algorithms, such as those found in [GraphBLAS](https://graphblas.org/) and [cuGraph](https://github.com/rapidsai/cugraph). 
+
+Below are some common use-cases for vector search
+
+- ### Semantic search
+  - Generative AI & Retrieval augmented generation (RAG)
+  - Recommender systems
+  - Computer vision
+  - Image search
+  - Text search
+  - Audio search
+  - Molecular search
+  - Model training
+  
+
+- ### Data mining
+  - Clustering algorithms
+  - Visualization algorithms
+  - Sampling algorithms
+  - Class balancing
+  - Ensemble methods
+  - k-NN graph construction
+
+## Why cuVS?
+
+There are several benefits to using cuVS and GPUs for vector search, including
+
+1. Fast index build
+2. Latency critical and high throughput search
+3. Parameter tuning
+4. Cost savings
+5. Interoperability (build on GPU, deploy on CPU)
+6. Multiple language support
+7. Building blocks for composing new or accelerating existing algorithms
+
+In addition to the items above, cuVS takes on the burden of keeping non-trivial accelerated code up to date as new NVIDIA architectures and CUDA versions are released. This provides a deslightful development experimence, guaranteeing that any libraries, databases, or applications built on top of it will always be getting the best performance and scale. 
+
 ## Installing cuVS
 
 cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python) and [pip](https://pip.pypa.io/en/stable/). Different packages are available for the different languages supported by cuVS:
diff --git a/docs/source/build.rst b/docs/source/build.rst
index f4e1aab6d2..a6b8ccd13c 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -9,8 +9,6 @@ The cuVS software development kit provides APIs for C, C++, Python, and Rust lan
 
   * `Python through Pip`_
 
-  * `Rust through crates.io`_
-
 - `Build from source`_
 
   * `Prerequisites`_
@@ -73,9 +71,6 @@ And CUDA 12 packages:
 
 Note: these packages statically link the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code.
 
-Rust through crates.io
-^^^^^^^^^^^^^^^^^^^^^^
-
 Build from source
 -----------------
 
diff --git a/docs/source/cuvs_bench/datasets.rst b/docs/source/cuvs_bench/datasets.rst
new file mode 100644
index 0000000000..0f6539e560
--- /dev/null
+++ b/docs/source/cuvs_bench/datasets.rst
@@ -0,0 +1,62 @@
+~~~~~~~~~~~~~~~~~~~
+cuVS Bench Datasets
+~~~~~~~~~~~~~~~~~~~
+
+A dataset usually has 4 binary files containing database vectors, query vectors, ground truth neighbors and their corresponding distances. For example, Glove-100 dataset has files `base.fbin` (database vectors), `query.fbin` (query vectors), `groundtruth.neighbors.ibin` (ground truth neighbors), and `groundtruth.distances.fbin` (ground truth distances). The first two files are for index building and searching, while the other two are associated with a particular distance and are used for evaluation.
+
+The file suffixes `.fbin`, `.f16bin`, `.ibin`, `.u8bin`, and `.i8bin` denote that the data type of vectors stored in the file are `float32`, `float16`(a.k.a `half`), `int`, `uint8`, and `int8`, respectively.
+These binary files are little-endian and the format is: the first 8 bytes are `num_vectors` (`uint32_t`) and `num_dimensions` (`uint32_t`), and the following `num_vectors * num_dimensions * sizeof(type)` bytes are vectors stored in row-major order.
+
+Some implementation can take `float16` database and query vectors as inputs and will have better performance. Use `python/cuvs_bench/cuvs_bench/get_dataset/fbin_to_f16bin.py` to transform dataset from `float32` to `float16` type.
+
+Commonly used datasets can be downloaded from two websites:
+#. Million-scale datasets can be found at the `Data sets <https://github.com/erikbern/ann-benchmarks#data-sets>`_ section of `ann-benchmarks <https://github.com/erikbern/ann-benchmarks>`_.
+
+    However, these datasets are in HDF5 format. Use `python/cuvs_bench/cuvs_bench/get_dataset/hdf5_to_fbin.py` to transform the format. The usage of this script is:
+
+    .. code-block:: bash
+
+        $ python/cuvs_bench/cuvs_bench/get_dataset/hdf5_to_fbin.py
+        usage: hdf5_to_fbin.py [-n] <input>.hdf5
+           -n: normalize base/query set
+         outputs: <input>.base.fbin
+                  <input>.query.fbin
+                  <input>.groundtruth.neighbors.ibin
+                  <input>.groundtruth.distances.fbin
+
+    So for an input `.hdf5` file, four output binary files will be produced. See previous section for an example of prepossessing GloVe dataset.
+
+    Most datasets provided by `ann-benchmarks` use `Angular` or `Euclidean` distance. `Angular` denotes cosine distance. However, computing cosine distance reduces to computing inner product by normalizing vectors beforehand. In practice, we can always do the normalization to decrease computation cost, so it's better to measure the performance of inner product rather than cosine distance. The `-n` option of `hdf5_to_fbin.py` can be used to normalize the dataset.
+
+#. Billion-scale datasets can be found at `big-ann-benchmarks <http://big-ann-benchmarks.com>`_. The ground truth file contains both neighbors and distances, thus should be split. A script is provided for this:
+
+    Take Deep-1B dataset as an example:
+
+    .. code-block:: bash
+
+        mkdir -p data/deep-1B && cd data/deep-1B
+
+        # download manually "Ground Truth" file of "Yandex DEEP"
+        # suppose the file name is deep_new_groundtruth.public.10K.bin
+        python -m cuvs_bench.split_groundtruth deep_new_groundtruth.public.10K.bin groundtruth
+
+        # two files 'groundtruth.neighbors.ibin' and 'groundtruth.distances.fbin' should be produced
+
+    Besides ground truth files for the whole billion-scale datasets, this site also provides ground truth files for the first 10M or 100M vectors of the base sets. This mean we can use these billion-scale datasets as million-scale datasets. To facilitate this, an optional parameter `subset_size` for dataset can be used. See the next step for further explanation.
+
+Generate ground truth
+=====================
+
+If you have a dataset, but no corresponding ground truth file, then you can generate ground trunth using the `generate_groundtruth` utility. Example usage:
+
+.. code-block:: bash
+
+    # With existing query file
+    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.fbin --output=groundtruth_dir --queries=/dataset/query.public.10K.fbin
+
+    # With randomly generated queries
+    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.fbin --output=groundtruth_dir --queries=random --n_queries=10000
+
+    # Using only a subset of the dataset. Define queries by randomly
+    # selecting vectors from the (subset of the) dataset.
+    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.fbin --nrows=2000000 --output=groundtruth_dir --queries=random-choice --n_queries=10000
\ No newline at end of file
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index a1e31b98f2..61ac622d24 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -2,7 +2,21 @@
 cuVS Bench
 ~~~~~~~~~~
 
-cuVS bench provides a benchmarking tool for various ANN search implementations. It's especially suitable for comparing GPU implementations as well as comparing GPU against CPU.
+cuVS bench provides a reproducible benchmarking tool for various ANN search implementations. It's especially suitable for comparing GPU implementations as well as comparing GPU against CPU. One of the primary goals of cuVS is to capture ideal index configurations for a variety of important usage patterns so the results can be reproduced easily on different hardware environments, such as on-prem and cloud.
+
+This tool offers several benefits, including
+
+#. Making fair comparisons of index build times
+
+#. Making fair comparisons of index search throughput and/or latency
+
+#. Finding the optimal parameter settings for a range of recall buckets
+
+#. Easily generating consistently styled plots for index build and search
+
+#. Profiling blind spots and potential for algorithm optimization
+
+#. Investigating the relationship between different parameter settings, index build times, and search performance.
 
 - `Installing the benchmarks`_
 
@@ -10,6 +24,39 @@ cuVS bench provides a benchmarking tool for various ANN search implementations.
 
   * `Docker`_
 
+- `How to run the benchmarks`_
+
+  * `Step 1: Prepare the dataset`_
+
+  * `Step 2: Build and search index`_
+
+  * `Step 3: Data export`_
+
+  * `Step 4: Plot the results`_
+
+- `Running the benchmarks`_
+
+  * `End-to-end: smaller-scale benchmarks (<1M to 10M)`_
+
+  * `End-to-end: large-scale benchmarks (>10M vectors)`_
+
+  * `Running with Docker containers`_
+
+    * `End-to-end run on GPU`_
+
+    * `Manually run the scripts inside the container`_
+
+  * `Evaluating the results`_
+
+- `Creating and customizing dataset configurations`_
+
+  * `Multi-GPU benchmarks`_
+
+- `Adding a new index algorithm`_
+
+  * `Implementation and configuration`_
+
+  * `Adding a Cmake target`_
 
 Installing the benchmarks
 =========================
@@ -35,7 +82,7 @@ Conda
 
 The channel `rapidsai` can easily be substituted `rapidsai-nightly` if nightly benchmarks are desired. The CPU package currently allows to run the HNSW benchmarks.
 
-Please see the :doc:`build instructions <../build>` to build the benchmarks from source.
+Please see the :doc:`build instructions <build>` to build the benchmarks from source.
 
 Docker
 ------
@@ -77,7 +124,7 @@ We provide a collection of lightweight Python scripts to run the benchmarks. The
 Step 1: Prepare the dataset
 ---------------------------
 
-The script `cuvs_bench.get_dataset` will download and unpack the dataset in directory that the user provides. As of now, only million-scale datasets are supported by this script. For more information on :doc:`datasets and formats <ann_benchmarks_dataset>`.
+The script `cuvs_bench.get_dataset` will download and unpack the dataset in directory that the user provides. As of now, only million-scale datasets are supported by this script. For more information on :doc:`datasets and formats <datasets>`.
 
 The usage of this script is:
 
@@ -190,8 +237,8 @@ and index search statistics CSV file in `<dataset-path/<dataset>/result/search/<
 #. `throughput`: Pareto frontier of throughput results is exported
 #. `latency`: Pareto frontier of latency results is exported
 
-Step 4: Plot results
---------------------
+Step 4: Plot the results
+------------------------
 
 The script `cuvs_bench.plot` will plot results for all algorithms found in index search statistics CSV files `<dataset-path/<dataset>/result/search/*.csv`.
 
@@ -562,7 +609,7 @@ There config above has 2 fields:
 1. `name` - define the name of the algorithm for which the parameters are being specified.
 2. `groups` - define a run group which has a particular set of parameters. Each group helps create a cross-product of all hyper-parameter fields for `build` and `search`.
 
-The table below contains all algorithms supported by cuVS. Each unique algorithm will have its own set of `build` and `search` settings. The :doc:`ANN Algorithm Parameter Tuning Guide <ann_benchmarks_param_tuning>` contains detailed instructions on choosing build and search parameters for each supported algorithm.
+The table below contains all algorithms supported by cuVS. Each unique algorithm will have its own set of `build` and `search` settings. The :doc:`ANN Algorithm Parameter Tuning Guide <param_tuning>` contains detailed instructions on choosing build and search parameters for each supported algorithm.
 
 .. list-table::
 
@@ -584,8 +631,29 @@ The table below contains all algorithms supported by cuVS. Each unique algorithm
  * - cuVS
    - `cuvs_brute_force`, `cuvs_cagra`, `cuvs_ivf_flat`, `cuvs_ivf_pq`, `cuvs_cagra_hnswlib`
 
-Adding a new ANN algorithm
-==========================
+
+Multi-GPU benchmarks
+--------------------
+
+cuVS implements single node multi-GPU versions of IVF-Flat, IVF-PQ and CAGRA indexes.
+
+.. list-table::
+
+ * - Index type
+   - Multi-GPU algo name
+
+ * - IVF-Flat
+   - `cuvs_mg_ivf_flat`
+
+ * - IVF-PQ
+   - `cuvs_mg_ivf_pq`
+
+ * - CAGRA
+   - `cuvs_mg_cagra`
+
+
+Adding a new index algorithm
+============================
 
 Implementation and configuration
 --------------------------------
diff --git a/docs/source/index.rst b/docs/source/index.rst
index f2d57866f6..647061ae5a 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -35,6 +35,7 @@ Contents
    build.rst
    getting_started.rst
    integrations.rst
+   cuvs_bench/index.rst
    api_docs.rst
    contributing.md
 
diff --git a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
index dc1127fbc5..3575179335 100644
--- a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
@@ -31,6 +31,15 @@ cuvs_cagra:
 cuvs_brute_force:
   executable: CUVS_BRUTE_FORCE_ANN_BENCH
   requires_gpu: true
+cuvs_mg_ivf_flat:
+  executable: CUVS_MG_ANN_BENCH
+  requires_gpu: true
+cuvs_mg_ivf_pq:
+  executable: CUVS_MG_ANN_BENCH
+  requires_gpu: true
+cuvs_mg_cagra:
+  executable: CUVS_MG_ANN_BENCH
+  requires_gpu: true
 ggnn:
   executable: GGNN_ANN_BENCH
   requires_gpu: true
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml
new file mode 100644
index 0000000000..d730e33fb5
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml
@@ -0,0 +1,13 @@
+name: cuvs_mg_cagra
+constraints:
+  build: cuvs_bench.config.algos.constraints.cuvs_cagra_build
+  search: cuvs_bench.config.algos.constraints.cuvs_cagra_search
+groups:
+  base:
+    build:
+      graph_degree: [32, 64, 96, 128]
+      intermediate_graph_degree: [32, 64, 96, 128]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      itopk: [32, 64, 128, 256, 512]
+      search_width: [1, 2, 4, 8, 16, 32, 64]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml
new file mode 100644
index 0000000000..317e615e5b
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml
@@ -0,0 +1,9 @@
+name: cuvs_mg_ivf_flat
+groups:
+  base:
+    build:
+      nlist: [1024, 2048, 4096, 8192, 16384, 32000, 64000]
+      ratio: [1, 2, 4]
+      niter: [20, 25]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200, 500, 1000, 2000]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml
new file mode 100644
index 0000000000..7213968f4f
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml
@@ -0,0 +1,41 @@
+name: cuvs_mg_ivf_pq
+constraints:
+  build: cuvs_bench.config.algos.constraints.cuvs_ivf_pq_build
+  search: cuvs_bench.config.algos.constraints.cuvs_ivf_pq_search
+groups:
+  base:
+    build:
+      nlist: [1024, 2048, 4096, 8192]
+      pq_dim: [64, 32, 16]
+      pq_bits: [8, 6, 5, 4]
+      ratio: [10]
+      niter: [25]
+    search:
+      nprobe: [1, 5, 10, 50, 100, 200]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1, 2, 4]
+  large:
+    build:
+      nlist: [8192, 16384, 32768, 65536]
+      pq_dim: [48, 32, 16]
+      pq_bits: [8, 6, 5, 4]
+      ratio: [4]
+      niter: [20]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1, 2, 4]
+  100M:
+    build:
+      nlist: [50000]
+      pq_dim: [48]
+      pq_bits: [8, 6, 5, 4]
+      ratio: [10]
+      niter: [10]
+    search:
+      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1]

From 7debf51ae3bd9817143544b4f6593688fcb159f2 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 8 Oct 2024 22:19:51 +0200
Subject: [PATCH 182/623] Fix NVTX annotations (#400)

1) Replace the domain name from `raft` to `cuvs` to avoid confusion when using tools such as NSYS to analyze the program timeline.
2) Use C++17 feature `__has_include` instead of a CMake script to find out if NVTX available in the benchmark executable. It turns out our CMake check has been not reliable due to not finding include directories correctly.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/400
---
 cpp/bench/ann/CMakeLists.txt                  | 25 +++----------------
 cpp/bench/ann/src/common/util.hpp             |  3 ++-
 cpp/src/cluster/detail/kmeans.cuh             | 18 ++++++-------
 cpp/src/cluster/detail/kmeans_balanced.cuh    | 10 ++++----
 .../neighbors/detail/cagra/cagra_build.cuh    |  3 ++-
 .../neighbors/detail/cagra/cagra_search.cuh   |  4 +--
 .../detail/cagra/cagra_serialize.cuh          |  8 +++---
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |  6 +++++
 8 files changed, 33 insertions(+), 44 deletions(-)

diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 8cbf8c8b37..ac1301221f 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -87,21 +87,6 @@ if(CUVS_ANN_BENCH_USE_FAISS)
   include(cmake/thirdparty/get_faiss)
 endif()
 
-# ##################################################################################################
-# * Enable NVTX if available
-
-# Note: ANN_BENCH wrappers have extra NVTX code not related to raft::nvtx.They track gbench
-# benchmark cases and iterations. This is to make limited NVTX available to all algos, not just
-# raft/cuVS.
-if(TARGET CUDA::nvtx3)
-  set(_CMAKE_REQUIRED_INCLUDES_ORIG ${CMAKE_REQUIRED_INCLUDES})
-  get_target_property(CMAKE_REQUIRED_INCLUDES CUDA::nvtx3 INTERFACE_INCLUDE_DIRECTORIES)
-  unset(NVTX3_HEADERS_FOUND CACHE)
-  # Check the headers explicitly to make sure the cpu-only build succeeds
-  CHECK_INCLUDE_FILE_CXX(nvtx3/nvToolsExt.h NVTX3_HEADERS_FOUND)
-  set(CMAKE_REQUIRED_INCLUDES ${_CMAKE_REQUIRED_INCLUDES_ORIG})
-endif()
-
 # ##################################################################################################
 # * Target function -------------------------------------------------------------
 
@@ -127,12 +112,9 @@ function(ConfigureAnnBench)
     add_dependencies(${BENCH_NAME} ANN_BENCH)
   else()
     add_executable(${BENCH_NAME} ${ConfigureAnnBench_PATH})
-    target_compile_definitions(
-      ${BENCH_NAME} PRIVATE ANN_BENCH_BUILD_MAIN
-                            $<$<BOOL:${NVTX3_HEADERS_FOUND}>:ANN_BENCH_NVTX3_HEADERS_FOUND>
-    )
+    target_compile_definitions(${BENCH_NAME} PRIVATE ANN_BENCH_BUILD_MAIN>)
     target_link_libraries(
-      ${BENCH_NAME} PRIVATE benchmark::benchmark $<$<BOOL:${NVTX3_HEADERS_FOUND}>:CUDA::nvtx3>
+      ${BENCH_NAME} PRIVATE benchmark::benchmark $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3>
     )
   endif()
 
@@ -300,7 +282,7 @@ if(CUVS_ANN_BENCH_SINGLE_EXE)
   target_link_libraries(
     ANN_BENCH
     PRIVATE raft::raft nlohmann_json::nlohmann_json benchmark::benchmark dl fmt::fmt-header-only
-            spdlog::spdlog_header_only $<$<BOOL:${NVTX3_HEADERS_FOUND}>:CUDA::nvtx3>
+            spdlog::spdlog_header_only $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3>
   )
   set_target_properties(
     ANN_BENCH
@@ -318,7 +300,6 @@ if(CUVS_ANN_BENCH_SINGLE_EXE)
     ANN_BENCH
     PRIVATE
       $<$<BOOL:${CUDAToolkit_FOUND}>:ANN_BENCH_LINK_CUDART="libcudart.so.${CUDAToolkit_VERSION_MAJOR}.${CUDAToolkit_VERSION_MINOR}.${CUDAToolkit_VERSION_PATCH}">
-      $<$<BOOL:${NVTX3_HEADERS_FOUND}>:ANN_BENCH_NVTX3_HEADERS_FOUND>
   )
 
   target_link_options(ANN_BENCH PRIVATE -export-dynamic)
diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
index c3db2bb4bf..dbde74ccc6 100644
--- a/cpp/bench/ann/src/common/util.hpp
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -18,7 +18,8 @@
 #include "ann_types.hpp"
 #include "cuda_stub.hpp"  // cuda-related utils
 
-#ifdef ANN_BENCH_NVTX3_HEADERS_FOUND
+#if __has_include(<nvtx3/nvToolsExt.h>)
+#define ANN_BENCH_NVTX3_HEADERS_FOUND
 #include <nvtx3/nvToolsExt.h>
 #endif
 
diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
index 9b673bca34..3d054f0fd9 100644
--- a/cpp/src/cluster/detail/kmeans.cuh
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -15,12 +15,12 @@
  */
 #pragma once
 
+#include "../../core/nvtx.hpp"
 #include "kmeans_common.cuh"
 
 #include <cuvs/cluster/kmeans.hpp>
 #include <cuvs/distance/distance.hpp>
 
-#include <raft/common/nvtx.hpp>
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/host_mdarray.hpp>
@@ -71,7 +71,7 @@ void initRandom(raft::resources const& handle,
                 raft::device_matrix_view<const DataT, IndexT> X,
                 raft::device_matrix_view<DataT, IndexT> centroids)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("initRandom");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("initRandom");
   auto n_clusters = params.n_clusters;
   cuvs::cluster::kmeans::detail::shuffleAndGather<DataT, IndexT>(
     handle, X, centroids, n_clusters, params.rng_state.seed);
@@ -98,7 +98,7 @@ void kmeansPlusPlus(raft::resources const& handle,
                     raft::device_matrix_view<DataT, IndexT> centroidsRawData,
                     rmm::device_uvector<char>& workspace)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeansPlusPlus");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeansPlusPlus");
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   auto n_samples      = X.extent(0);
   auto n_features     = X.extent(1);
@@ -372,7 +372,7 @@ void kmeans_fit_main(raft::resources const& handle,
                      raft::host_scalar_view<IndexT> n_iter,
                      rmm::device_uvector<char>& workspace)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_fit_main");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeans_fit_main");
   raft::logger::get(RAFT_NAME).set_level(params.verbosity);
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   auto n_samples      = X.extent(0);
@@ -590,7 +590,7 @@ void initScalableKMeansPlusPlus(raft::resources const& handle,
                                 raft::device_matrix_view<DataT, IndexT> centroidsRawData,
                                 rmm::device_uvector<char>& workspace)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "initScalableKMeansPlusPlus");
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   auto n_samples      = X.extent(0);
@@ -841,7 +841,7 @@ void kmeans_fit(raft::resources const& handle,
                 raft::host_scalar_view<DataT> inertia,
                 raft::host_scalar_view<IndexT> n_iter)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_fit");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeans_fit");
   auto n_samples      = X.extent(0);
   auto n_features     = X.extent(1);
   auto n_clusters     = pams.n_clusters;
@@ -1009,7 +1009,7 @@ void kmeans_predict(raft::resources const& handle,
                     bool normalize_weight,
                     raft::host_scalar_view<DataT> inertia)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_predict");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeans_predict");
   auto n_samples      = X.extent(0);
   auto n_features     = X.extent(1);
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
@@ -1153,7 +1153,7 @@ void kmeans_fit_predict(raft::resources const& handle,
                         raft::host_scalar_view<DataT> inertia,
                         raft::host_scalar_view<IndexT> n_iter)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_fit_predict");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeans_fit_predict");
   if (!centroids.has_value()) {
     auto n_features = X.extent(1);
     auto centroids_matrix =
@@ -1217,7 +1217,7 @@ void kmeans_transform(raft::resources const& handle,
                       raft::device_matrix_view<const DataT> centroids,
                       raft::device_matrix_view<DataT> X_new)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("kmeans_transform");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeans_transform");
   raft::logger::get(RAFT_NAME).set_level(pams.verbosity);
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   auto n_samples      = X.extent(0);
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index 34bb22e85b..3f1ad2334a 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -20,10 +20,10 @@
 #include "kmeans_common.cuh"
 #include <cuvs/cluster/kmeans.hpp>
 
+#include "../../core/nvtx.hpp"
 #include "../../distance/distance.cuh"
 
 #include <cuvs/distance/distance.hpp>
-#include <raft/common/nvtx.hpp>
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/logger-ext.hpp>
 #include <raft/core/operators.hpp>
@@ -378,7 +378,7 @@ void compute_norm(const raft::resources& handle,
                   FinOpT norm_fin_op,
                   std::optional<rmm::device_async_resource_ref> mr = std::nullopt)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("compute_norm");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("compute_norm");
   auto stream = raft::resource::get_cuda_stream(handle);
   rmm::device_uvector<MathT> mapped_dataset(
     0, stream, mr.value_or(raft::resource::get_workspace_resource(handle)));
@@ -434,7 +434,7 @@ void predict(const raft::resources& handle,
              const MathT* dataset_norm                        = nullptr)
 {
   auto stream = raft::resource::get_cuda_stream(handle);
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "predict(%zu, %u)", static_cast<size_t>(n_rows), n_clusters);
   auto mem_res = mr.value_or(raft::resource::get_workspace_resource(handle));
   auto [max_minibatch_size, _mem_per_row] =
@@ -603,7 +603,7 @@ auto adjust_centers(MathT* centers,
                     rmm::cuda_stream_view stream,
                     rmm::device_async_resource_ref device_memory) -> bool
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "adjust_centers(%zu, %u)", static_cast<size_t>(n_rows), n_clusters);
   if (n_clusters == 0) { return false; }
   constexpr static std::array kPrimes{29,   71,   113,  173,  229,  281,  349,  409,  463,  541,
@@ -1036,7 +1036,7 @@ void build_hierarchical(const raft::resources& handle,
   auto stream  = raft::resource::get_cuda_stream(handle);
   using LabelT = uint32_t;
 
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "build_hierarchical(%zu, %u)", static_cast<size_t>(n_rows), n_clusters);
 
   IdxT n_mesoclusters = std::min(n_clusters, static_cast<IdxT>(std::sqrt(n_clusters) + 0.5));
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index e5495dc3e8..9e4d453e3a 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -15,6 +15,7 @@
  */
 #pragma once
 
+#include "../../../core/nvtx.hpp"
 #include "../../vpq_dataset.cuh"
 #include "graph_core.cuh"
 #include <cuvs/neighbors/cagra.hpp>
@@ -130,7 +131,7 @@ void build_knn_graph(
                "Currently only L2Expanded or InnerProduct metric are supported");
 
   uint32_t node_degree = knn_graph.extent(1);
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "cagra::build_graph(%zu, %zu, %u)",
     size_t(dataset.extent(0)),
     size_t(dataset.extent(1)),
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index 4c15b8e14b..95c158675e 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include "../../../core/nvtx.hpp"
 #include "factory.cuh"
 #include "sample_filter_utils.cuh"
 #include "search_plan.cuh"
@@ -23,7 +24,6 @@
 
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_mdspan.hpp>
-#include <raft/core/nvtx.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 
@@ -66,7 +66,7 @@ void search_main_core(raft::resources const& res,
     params.max_queries = std::min<size_t>(queries.extent(0), deviceProp.maxGridSize[1]);
   }
 
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope(
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "cagra::search(max_queries = %u, k = %u, dim = %zu)",
     params.max_queries,
     topk,
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index f86ed9ef62..4c3fe5e81f 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -21,10 +21,10 @@
 #include <raft/core/logger-ext.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/mdspan_types.hpp>
-#include <raft/core/nvtx.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/serialize.hpp>
 
+#include "../../../core/nvtx.hpp"
 #include "../dataset_serialize.hpp"
 
 #include <cstddef>
@@ -53,7 +53,7 @@ void serialize(raft::resources const& res,
                const index<T, IdxT>& index_,
                bool include_dataset)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("cagra::serialize");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("cagra::serialize");
 
   RAFT_LOG_DEBUG(
     "Saving CAGRA index, size %zu, dim %u", static_cast<size_t>(index_.size()), index_.dim());
@@ -103,7 +103,7 @@ void serialize_to_hnswlib(raft::resources const& res,
 {
   // static_assert(std::is_same_v<IdxT, int> or std::is_same_v<IdxT, uint32_t>,
   //               "An hnswlib index can only be trained with int32 or uint32 IdxT");
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("cagra::serialize");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("cagra::serialize");
   RAFT_LOG_DEBUG("Saving CAGRA index to hnswlib format, size %zu, dim %u",
                  static_cast<size_t>(index_.size()),
                  index_.dim());
@@ -234,7 +234,7 @@ void serialize_to_hnswlib(raft::resources const& res,
 template <typename T, typename IdxT>
 void deserialize(raft::resources const& res, std::istream& is, index<T, IdxT>* index_)
 {
-  raft::common::nvtx::range<raft::common::nvtx::domain::raft> fun_scope("cagra::deserialize");
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("cagra::deserialize");
 
   char dtype_string[4];
   is.read(dtype_string, 4);
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index c65ea81082..f0f464950a 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1729,6 +1729,12 @@ auto build(raft::resources const& handle,
     if constexpr (std::is_same_v<T, float>) {
       raft::matrix::sample_rows<T, int64_t>(handle, random_state, dataset, trainset.view());
     } else {
+      raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+        "   ivf_pq::build(%zu, %zu)/sample rows with tmp trainset (%zu rows).",
+        size_t(n_rows),
+        size_t(dim),
+        size_t(n_rows_train));
+
       // TODO(tfeher): Enable codebook generation with any type T, and then remove trainset tmp.
       auto trainset_tmp = raft::make_device_mdarray<T>(
         handle, big_memory_resource, raft::make_extents<int64_t>(n_rows_train, dim));

From 5836a81cf5b0a332330084c007b3871a5ced41da Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Wed, 9 Oct 2024 09:39:00 -0400
Subject: [PATCH 183/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 73 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1330990c6d..7ce4a14c3e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,76 @@
+# cuvs 24.10.00 (9 Oct 2024)
+
+## 🐛 Bug Fixes
+
+- Use 64 bit types for dataset size calculation in CAGRA graph optimizer ([#380](https://github.com/rapidsai/cuvs/pull/380)) [@tfeher](https://github.com/tfeher)
+- Remove EXPLICIT_INSTANTIATE_ONLY macros ([#358](https://github.com/rapidsai/cuvs/pull/358)) [@achirkin](https://github.com/achirkin)
+- Fix order of operations for cosine IVF Flat ([#329](https://github.com/rapidsai/cuvs/pull/329)) [@lowener](https://github.com/lowener)
+- Exclude any kernel symbol that uses cutlass ([#314](https://github.com/rapidsai/cuvs/pull/314)) [@benfred](https://github.com/benfred)
+- [Fix] pin raft dependent to rapidsai ([#299](https://github.com/rapidsai/cuvs/pull/299)) [@rhdong](https://github.com/rhdong)
+- Fix dataset dimension in IVF-PQ C wrappers ([#292](https://github.com/rapidsai/cuvs/pull/292)) [@tfeher](https://github.com/tfeher)
+- Fix python ivf-pq for int8/uint8 dtypes ([#271](https://github.com/rapidsai/cuvs/pull/271)) [@benfred](https://github.com/benfred)
+- FP16 API for CAGRA and IVF-PQ ([#264](https://github.com/rapidsai/cuvs/pull/264)) [@tfeher](https://github.com/tfeher)
+
+## 📖 Documentation
+
+- More doc updates for 24.10 ([#396](https://github.com/rapidsai/cuvs/pull/396)) [@cjnolet](https://github.com/cjnolet)
+- fix 404 in documentation link in readme ([#395](https://github.com/rapidsai/cuvs/pull/395)) [@benfred](https://github.com/benfred)
+- Improving getting started materials ([#342](https://github.com/rapidsai/cuvs/pull/342)) [@cjnolet](https://github.com/cjnolet)
+- Fix broken examples link in README. ([#326](https://github.com/rapidsai/cuvs/pull/326)) [@bdice](https://github.com/bdice)
+- Recommend `miniforge` for conda install ([#325](https://github.com/rapidsai/cuvs/pull/325)) [@bdice](https://github.com/bdice)
+
+## 🚀 New Features
+
+- Port remaining scripts to `cuvs_bench` ([#368](https://github.com/rapidsai/cuvs/pull/368)) [@divyegala](https://github.com/divyegala)
+- [Feat] Relative change with `bitset` API feature #2439 in raft ([#350](https://github.com/rapidsai/cuvs/pull/350)) [@rhdong](https://github.com/rhdong)
+- cuvs_bench plotting functions ([#347](https://github.com/rapidsai/cuvs/pull/347)) [@dantegd](https://github.com/dantegd)
+- CosineExpanded Metric for IVF-PQ (normalize inputs) ([#346](https://github.com/rapidsai/cuvs/pull/346)) [@tarang-jain](https://github.com/tarang-jain)
+- Python API for CAGRA+HNSW ([#246](https://github.com/rapidsai/cuvs/pull/246)) [@divyegala](https://github.com/divyegala)
+- C API for CAGRA+HNSW ([#240](https://github.com/rapidsai/cuvs/pull/240)) [@divyegala](https://github.com/divyegala)
+- SNMG ANN ([#231](https://github.com/rapidsai/cuvs/pull/231)) [@viclafargue](https://github.com/viclafargue)
+- [FEA] Support for half-float mixed precise in brute-force ([#225](https://github.com/rapidsai/cuvs/pull/225)) [@rhdong](https://github.com/rhdong)
+
+## 🛠️ Improvements
+
+- Remove cuvs-cagra-search from cuvs_static link ([#388](https://github.com/rapidsai/cuvs/pull/388)) [@benfred](https://github.com/benfred)
+- Add a static library for cuvs ([#382](https://github.com/rapidsai/cuvs/pull/382)) [@benfred](https://github.com/benfred)
+- Put the ann-bench large_workspace_resource in managed memory ([#372](https://github.com/rapidsai/cuvs/pull/372)) [@achirkin](https://github.com/achirkin)
+- Add multigpu kmeans fit function ([#348](https://github.com/rapidsai/cuvs/pull/348)) [@benfred](https://github.com/benfred)
+- Update update-version.sh to use packaging lib ([#344](https://github.com/rapidsai/cuvs/pull/344)) [@AyodeAwe](https://github.com/AyodeAwe)
+- remove NCCL pins in build and test environments ([#341](https://github.com/rapidsai/cuvs/pull/341)) [@jameslamb](https://github.com/jameslamb)
+- Vamana/DiskANN index build ([#339](https://github.com/rapidsai/cuvs/pull/339)) [@bkarsin](https://github.com/bkarsin)
+- Use CI workflow branch &#39;branch-24.10&#39; again ([#331](https://github.com/rapidsai/cuvs/pull/331)) [@jameslamb](https://github.com/jameslamb)
+- fix style checks on Python 3.12 ([#328](https://github.com/rapidsai/cuvs/pull/328)) [@jameslamb](https://github.com/jameslamb)
+- Update flake8 to 7.1.1. ([#327](https://github.com/rapidsai/cuvs/pull/327)) [@bdice](https://github.com/bdice)
+- Add function for calculating the mutual_reachability_graph ([#323](https://github.com/rapidsai/cuvs/pull/323)) [@benfred](https://github.com/benfred)
+- Simplify libcuvs conda recipe. ([#322](https://github.com/rapidsai/cuvs/pull/322)) [@bdice](https://github.com/bdice)
+- Refactor dependencies.yaml to use depends-on pattern. ([#321](https://github.com/rapidsai/cuvs/pull/321)) [@bdice](https://github.com/bdice)
+- Update Python versions in cuvs_bench pyproject.toml. ([#318](https://github.com/rapidsai/cuvs/pull/318)) [@bdice](https://github.com/bdice)
+- Brute force knn tile size heuristic ([#316](https://github.com/rapidsai/cuvs/pull/316)) [@mfoerste4](https://github.com/mfoerste4)
+- Euclidean distance example ([#315](https://github.com/rapidsai/cuvs/pull/315)) [@abner-ma](https://github.com/abner-ma)
+- Migrate trustworthiness and silhouette_score stats from RAFT ([#313](https://github.com/rapidsai/cuvs/pull/313)) [@benfred](https://github.com/benfred)
+- Add support for Python 3.12 ([#312](https://github.com/rapidsai/cuvs/pull/312)) [@jameslamb](https://github.com/jameslamb)
+- Add `managed` option for RMM Pool memory resource to C API ([#305](https://github.com/rapidsai/cuvs/pull/305)) [@ajit283](https://github.com/ajit283)
+- Update rapidsai/pre-commit-hooks ([#303](https://github.com/rapidsai/cuvs/pull/303)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Expose search function with pre-filter for ANN ([#302](https://github.com/rapidsai/cuvs/pull/302)) [@lowener](https://github.com/lowener)
+- Drop Python 3.9 support ([#301](https://github.com/rapidsai/cuvs/pull/301)) [@jameslamb](https://github.com/jameslamb)
+- Use CUDA math wheels ([#298](https://github.com/rapidsai/cuvs/pull/298)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Remove NumPy &lt;2 pin ([#297](https://github.com/rapidsai/cuvs/pull/297)) [@seberg](https://github.com/seberg)
+- CAGRA - separable compilation for distance computation ([#296](https://github.com/rapidsai/cuvs/pull/296)) [@achirkin](https://github.com/achirkin)
+- Updating example notebooks ([#294](https://github.com/rapidsai/cuvs/pull/294)) [@cjnolet](https://github.com/cjnolet)
+- Add RMM Pool memory resource to C API ([#285](https://github.com/rapidsai/cuvs/pull/285)) [@ajit283](https://github.com/ajit283)
+- Update pre-commit hooks ([#283](https://github.com/rapidsai/cuvs/pull/283)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Improve update-version.sh ([#282](https://github.com/rapidsai/cuvs/pull/282)) [@bdice](https://github.com/bdice)
+- Use tool.scikit-build.cmake.version, set scikit-build-core minimum-version ([#280](https://github.com/rapidsai/cuvs/pull/280)) [@jameslamb](https://github.com/jameslamb)
+- Add cuvs_bench.run python code and build ([#279](https://github.com/rapidsai/cuvs/pull/279)) [@dantegd](https://github.com/dantegd)
+- Add cuvs-bench to dependencies and conda environments ([#275](https://github.com/rapidsai/cuvs/pull/275)) [@dantegd](https://github.com/dantegd)
+- Update pip devcontainers to UCX v1.17.0 ([#262](https://github.com/rapidsai/cuvs/pull/262)) [@jameslamb](https://github.com/jameslamb)
+- Adding example for tuning build and search params using Optuna ([#257](https://github.com/rapidsai/cuvs/pull/257)) [@dpadmanabhan03](https://github.com/dpadmanabhan03)
+- Fixed link to build docs and corrected ivf_flat_example ([#255](https://github.com/rapidsai/cuvs/pull/255)) [@mmccarty](https://github.com/mmccarty)
+- Merge branch-24.08 into branch-24.10 ([#254](https://github.com/rapidsai/cuvs/pull/254)) [@jameslamb](https://github.com/jameslamb)
+- Persistent CAGRA kernel ([#215](https://github.com/rapidsai/cuvs/pull/215)) [@achirkin](https://github.com/achirkin)
+- [FEA] Support for Cosine distance in IVF-Flat ([#179](https://github.com/rapidsai/cuvs/pull/179)) [@lowener](https://github.com/lowener)
+
 # cuvs 24.08.00 (7 Aug 2024)
 
 ## 🚨 Breaking Changes

From e55e655e1ac6fb10ba846e808d3003ce20c580f5 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 11 Oct 2024 15:08:21 -0500
Subject: [PATCH 184/623] make conda installs in CI stricter (#406)

Contributes to https://github.com/rapidsai/build-planning/issues/106

Proposes specifying the RAPIDS version in `conda install` calls that install CI artifacts, to reduce the risk of CI jobs picking up artifacts from other releases.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Mike Sarahan (https://github.com/msarahan)

URL: https://github.com/rapidsai/cuvs/pull/406
---
 ci/build_docs.sh  | 11 ++++++-----
 ci/build_rust.sh  |  6 ++++--
 ci/test_cpp.sh    |  5 ++++-
 ci/test_python.sh |  5 ++++-
 4 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index 460cc38996..bce93c6054 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -6,6 +6,9 @@ set -euo pipefail
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
 
+RAPIDS_VERSION="$(rapids-version)"
+export RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
+
 rapids-dependency-file-generator \
   --output conda \
   --file-key docs \
@@ -28,11 +31,9 @@ PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
   --channel "${PYTHON_CHANNEL}" \
-  libcuvs cuvs
+  "libcuvs=${RAPIDS_VERSION}" \
+  "cuvs=${RAPIDS_VERSION}"
 
-export RAPIDS_VERSION="$(rapids-version)"
-export RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
-export RAPIDS_VERSION_NUMBER="$RAPIDS_VERSION_MAJOR_MINOR"
 export RAPIDS_DOCS_DIR="$(mktemp -d)"
 
 rapids-logger "Build CPP docs"
@@ -54,4 +55,4 @@ mkdir -p "${RAPIDS_DOCS_DIR}/cuvs/"html
 mv _html/* "${RAPIDS_DOCS_DIR}/cuvs/html"
 popd
 
-rapids-upload-docs
+RAPIDS_VERSION_NUMBER="${RAPIDS_VERSION_MAJOR_MINOR}" rapids-upload-docs
diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index 31d0de053f..309501c32b 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -6,6 +6,8 @@ set -euo pipefail
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
 
+RAPIDS_VERSION="$(rapids-version)"
+
 rapids-dependency-file-generator \
   --output conda \
   --file-key rust \
@@ -32,7 +34,7 @@ CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
 # installing libcuvs/libraft will speed up the rust build substantially
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
-  libcuvs  \
-  libraft
+  "libcuvs=${RAPIDS_VERSION}" \
+  "libraft=${RAPIDS_VERSION}"
 
 bash ./build.sh rust
diff --git a/ci/test_cpp.sh b/ci/test_cpp.sh
index 6dfc2cf714..134dc44212 100755
--- a/ci/test_cpp.sh
+++ b/ci/test_cpp.sh
@@ -5,6 +5,8 @@ set -euo pipefail
 
 . /opt/conda/etc/profile.d/conda.sh
 
+RAPIDS_VERSION="$(rapids-version)"
+
 rapids-logger "Generate C++ testing dependencies"
 rapids-dependency-file-generator \
   --output conda \
@@ -26,7 +28,8 @@ rapids-print-env
 
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
-  libcuvs libcuvs-tests
+  "libcuvs=${RAPIDS_VERSION}" \
+  "libcuvs-tests=${RAPIDS_VERSION}"
 
 rapids-logger "Check GPU usage"
 nvidia-smi
diff --git a/ci/test_python.sh b/ci/test_python.sh
index 93bc597cf4..b9c394062e 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -5,6 +5,8 @@ set -euo pipefail
 
 . /opt/conda/etc/profile.d/conda.sh
 
+RAPIDS_VERSION="$(rapids-version)"
+
 rapids-logger "Generate Python testing dependencies"
 rapids-dependency-file-generator \
   --output conda \
@@ -31,7 +33,8 @@ rapids-print-env
 rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
   --channel "${PYTHON_CHANNEL}" \
-  libcuvs cuvs
+  "libcuvs=${RAPIDS_VERSION}" \
+  "cuvs=${RAPIDS_VERSION}"
 
 rapids-logger "Check GPU usage"
 nvidia-smi

From f62b217f97c9e14b340f11bcbfe556fcad9ed816 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 15 Oct 2024 10:07:41 +0200
Subject: [PATCH 185/623] Add --no-lap-sync cmd option to ann-bench (#405)

Add a command-line option to disable the CUDA event/stream synchronization on each iteration. Only one sync is done per benchmark loop in this case instead. As a result, the measured QPS is observed due to:
1) A small `cudaEventSynchronize` is removed from the benchmark loop;
2) If a GPU algorithm has little to no sync between the GPU and CPU, the kernel launch latency and other CPU overheads are completely hidden.

The new option is experimental and disabled by default.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/405
---
 cpp/bench/ann/src/common/benchmark.hpp | 161 +++++++++++++++----------
 1 file changed, 98 insertions(+), 63 deletions(-)

diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index db3e533e07..06e1e27af4 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -119,7 +119,8 @@ template <typename T>
 void bench_build(::benchmark::State& state,
                  std::shared_ptr<const dataset<T>> dataset,
                  configuration::index index,
-                 bool force_overwrite)
+                 bool force_overwrite,
+                 bool no_lap_sync)
 {
   // NB: these two thread-local vars can be used within algo wrappers
   cuvs::bench::benchmark_thread_id = state.thread_index();
@@ -149,9 +150,22 @@ void bench_build(::benchmark::State& state,
   cuda_timer gpu_timer{algo};
   {
     nvtx_case nvtx{state.name()};
+    /* Note: GPU timing
+
+    The GPU time is measured between construction and destruction of `cuda_lap` objects (`gpu_all`
+    and `gpu_lap` variables) and added to the `gpu_timer` object.
+
+    We sync with the GPU (cudaEventSynchronize) either each iteration (lifetime of the `gpu_lap`
+    variable) or once per benchmark loop (lifetime of the `gpu_all` variable). The decision is
+
+    controlled by the `no_lap_sync` argument. In either case, we need at least one sync throughout
+    the benchmark loop to make sure the GPU has finished its work before we measure the total run
+    time.
+    */
+    [[maybe_unused]] auto gpu_all = gpu_timer.lap(no_lap_sync);
     for (auto _ : state) {
       [[maybe_unused]] auto ntx_lap = nvtx.lap();
-      [[maybe_unused]] auto gpu_lap = gpu_timer.lap();
+      [[maybe_unused]] auto gpu_lap = gpu_timer.lap(!no_lap_sync);
       try {
         algo->build(base_set, index_size);
       } catch (const std::exception& e) {
@@ -173,7 +187,8 @@ template <typename T>
 void bench_search(::benchmark::State& state,
                   configuration::index index,
                   std::size_t search_param_ix,
-                  std::shared_ptr<const dataset<T>> dataset)
+                  std::shared_ptr<const dataset<T>> dataset,
+                  bool no_lap_sync)
 {
   // NB: these two thread-local vars can be used within algo wrappers
   cuvs::bench::benchmark_thread_id = state.thread_index();
@@ -300,25 +315,29 @@ void bench_search(::benchmark::State& state,
     // Initialize with algo, so that the timer.lap() object can sync with algo::get_sync_stream()
     cuda_timer gpu_timer{a};
     auto start = std::chrono::high_resolution_clock::now();
-    for (auto _ : state) {
-      [[maybe_unused]] auto ntx_lap = nvtx.lap();
-      [[maybe_unused]] auto gpu_lap = gpu_timer.lap();
-      try {
-        a->search(query_set + batch_offset * dataset->dim(),
-                  n_queries,
-                  k,
-                  neighbors_ptr + out_offset * k,
-                  distances_ptr + out_offset * k);
-      } catch (const std::exception& e) {
-        state.SkipWithError("Benchmark loop: " + std::string(e.what()));
-        break;
-      }
+    {
+      /* See the note above: GPU timing */
+      [[maybe_unused]] auto gpu_all = gpu_timer.lap(no_lap_sync);
+      for (auto _ : state) {
+        [[maybe_unused]] auto ntx_lap = nvtx.lap();
+        [[maybe_unused]] auto gpu_lap = gpu_timer.lap(!no_lap_sync);
+        try {
+          a->search(query_set + batch_offset * dataset->dim(),
+                    n_queries,
+                    k,
+                    neighbors_ptr + out_offset * k,
+                    distances_ptr + out_offset * k);
+        } catch (const std::exception& e) {
+          state.SkipWithError("Benchmark loop: " + std::string(e.what()));
+          break;
+        }
 
-      // advance to the next batch
-      batch_offset = (batch_offset + queries_stride) % query_set_size;
-      out_offset   = (out_offset + n_queries) % query_set_size;
+        // advance to the next batch
+        batch_offset = (batch_offset + queries_stride) % query_set_size;
+        out_offset   = (out_offset + n_queries) % query_set_size;
 
-      queries_processed += n_queries;
+        queries_processed += n_queries;
+      }
     }
     auto end      = std::chrono::high_resolution_clock::now();
     auto duration = std::chrono::duration_cast<std::chrono::duration<double>>(end - start).count();
@@ -379,44 +398,51 @@ void bench_search(::benchmark::State& state,
 inline void printf_usage()
 {
   ::benchmark::PrintDefaultHelp();
-  fprintf(stdout,
-          "          [--build|--search] \n"
-          "          [--force]\n"
-          "          [--data_prefix=<prefix>]\n"
-          "          [--index_prefix=<prefix>]\n"
-          "          [--override_kv=<key:value1:value2:...:valueN>]\n"
-          "          [--mode=<latency|throughput>\n"
-          "          [--threads=min[:max]]\n"
-          "          <conf>.json\n"
-          "\n"
-          "Note the non-standard benchmark parameters:\n"
-          "  --build: build mode, will build index\n"
-          "  --search: search mode, will search using the built index\n"
-          "            one and only one of --build and --search should be specified\n"
-          "  --force: force overwriting existing index files\n"
-          "  --data_prefix=<prefix>:"
-          " prepend <prefix> to dataset file paths specified in the <conf>.json (default = "
-          "'data/').\n"
-          "  --index_prefix=<prefix>:"
-          " prepend <prefix> to index file paths specified in the <conf>.json (default = "
-          "'index/').\n"
-          "  --override_kv=<key:value1:value2:...:valueN>:"
-          " override a build/search key one or more times multiplying the number of configurations;"
-          " you can use this parameter multiple times to get the Cartesian product of benchmark"
-          " configs.\n"
-          "  --mode=<latency|throughput>"
-          " run the benchmarks in latency (accumulate times spent in each batch) or "
-          " throughput (pipeline batches and measure end-to-end) mode\n"
-          "  --threads=min[:max] specify the number threads to use for throughput benchmark."
-          " Power of 2 values between 'min' and 'max' will be used. If only 'min' is specified,"
-          " then a single test is run with 'min' threads. By default min=1, max=<num hyper"
-          " threads>.\n");
+  fprintf(
+    stdout,
+    "          [--build|--search] \n"
+    "          [--force]\n"
+    "          [--data_prefix=<prefix>]\n"
+    "          [--index_prefix=<prefix>]\n"
+    "          [--override_kv=<key:value1:value2:...:valueN>]\n"
+    "          [--mode=<latency|throughput>\n"
+    "          [--threads=min[:max]]\n"
+    "          [--no-lap-sync]\n"
+    "          <conf>.json\n"
+    "\n"
+    "Note the non-standard benchmark parameters:\n"
+    "  --build: build mode, will build index\n"
+    "  --search: search mode, will search using the built index\n"
+    "            one and only one of --build and --search should be specified\n"
+    "  --force: force overwriting existing index files\n"
+    "  --data_prefix=<prefix>:"
+    " prepend <prefix> to dataset file paths specified in the <conf>.json (default = "
+    "'data/').\n"
+    "  --index_prefix=<prefix>:"
+    " prepend <prefix> to index file paths specified in the <conf>.json (default = "
+    "'index/').\n"
+    "  --override_kv=<key:value1:value2:...:valueN>:"
+    " override a build/search key one or more times multiplying the number of configurations;"
+    " you can use this parameter multiple times to get the Cartesian product of benchmark"
+    " configs.\n"
+    "  --mode=<latency|throughput>"
+    " run the benchmarks in latency (accumulate times spent in each batch) or "
+    " throughput (pipeline batches and measure end-to-end) mode\n"
+    "  --threads=min[:max] specify the number threads to use for throughput benchmark."
+    " Power of 2 values between 'min' and 'max' will be used. If only 'min' is specified,"
+    " then a single test is run with 'min' threads. By default min=1, max=<num hyper"
+    " threads>.\n"
+    "  --no-lap-sync disable CUDA event synchronization between benchmark iterations. If a GPU"
+    " algorithm has no sync with CPU, this can make the GPU processing significantly lag behind the"
+    " CPU scheduling. Then this also hides the scheduling latencies and thus improves the measured"
+    " throughput (QPS). Note there's a sync at the end of the benchmark loop in any case.\n");
 }
 
 template <typename T>
 void register_build(std::shared_ptr<const dataset<T>> dataset,
                     std::vector<configuration::index> indices,
-                    bool force_overwrite)
+                    bool force_overwrite,
+                    bool no_lap_sync)
 {
   for (auto index : indices) {
     auto suf      = static_cast<std::string>(index.build_param["override_suffix"]);
@@ -425,7 +451,7 @@ void register_build(std::shared_ptr<const dataset<T>> dataset,
     std::replace(file_suf.begin(), file_suf.end(), '/', '-');
     index.file += file_suf;
     auto* b = ::benchmark::RegisterBenchmark(
-      index.name + suf, bench_build<T>, dataset, index, force_overwrite);
+      index.name + suf, bench_build<T>, dataset, index, force_overwrite, no_lap_sync);
     b->Unit(benchmark::kSecond);
     b->MeasureProcessCPUTime();
     b->UseRealTime();
@@ -436,14 +462,16 @@ template <typename T>
 void register_search(std::shared_ptr<const dataset<T>> dataset,
                      std::vector<configuration::index> indices,
                      Mode metric_objective,
-                     const std::vector<int>& threads)
+                     const std::vector<int>& threads,
+                     bool no_lap_sync)
 {
   for (auto index : indices) {
     for (std::size_t i = 0; i < index.search_params.size(); i++) {
       auto suf = static_cast<std::string>(index.search_params[i]["override_suffix"]);
       index.search_params[i].erase("override_suffix");
 
-      auto* b = ::benchmark::RegisterBenchmark(index.name + suf, bench_search<T>, index, i, dataset)
+      auto* b = ::benchmark::RegisterBenchmark(
+                  index.name + suf, bench_search<T>, index, i, dataset, no_lap_sync)
                   ->Unit(benchmark::kMillisecond)
                   /**
                    * The following are important for getting accuracy QPS measurements on both CPU
@@ -470,7 +498,8 @@ void dispatch_benchmark(std::string cmdline,
                         std::string index_prefix,
                         kv_series override_kv,
                         Mode metric_objective,
-                        const std::vector<int>& threads)
+                        const std::vector<int>& threads,
+                        bool no_lap_sync)
 {
   ::benchmark::AddCustomContext("command_line", cmdline);
   for (auto [key, value] : host_info()) {
@@ -514,7 +543,7 @@ void dispatch_benchmark(std::string cmdline,
         more_indices.push_back(modified_index);
       }
     }
-    register_build<T>(dataset, more_indices, force_overwrite);
+    register_build<T>(dataset, more_indices, force_overwrite, no_lap_sync);
   } else if (search_mode) {
     if (file_exists(query_file)) {
       log_info("Using the query file '%s'", query_file.c_str());
@@ -543,7 +572,7 @@ void dispatch_benchmark(std::string cmdline,
       index.search_params = apply_overrides(index.search_params, override_kv);
       index.file          = combine_path(index_prefix, index.file);
     }
-    register_search<T>(dataset, indices, metric_objective, threads);
+    register_search<T>(dataset, indices, metric_objective, threads, no_lap_sync);
   }
 }
 
@@ -571,6 +600,7 @@ inline auto run_main(int argc, char** argv) -> int
   bool force_overwrite                = false;
   bool build_mode                     = false;
   bool search_mode                    = false;
+  bool no_lap_sync                    = false;
   std::string data_prefix             = "data";
   std::string index_prefix            = "index";
   std::string new_override_kv         = "";
@@ -604,6 +634,7 @@ inline auto run_main(int argc, char** argv) -> int
     if (parse_bool_flag(argv[i], "--force", force_overwrite) ||
         parse_bool_flag(argv[i], "--build", build_mode) ||
         parse_bool_flag(argv[i], "--search", search_mode) ||
+        parse_bool_flag(argv[i], "--no-lap-sync", no_lap_sync) ||
         parse_string_flag(argv[i], "--data_prefix", data_prefix) ||
         parse_string_flag(argv[i], "--index_prefix", index_prefix) ||
         parse_string_flag(argv[i], "--mode", mode) ||
@@ -686,7 +717,8 @@ inline auto run_main(int argc, char** argv) -> int
                               index_prefix,
                               override_kv,
                               metric_objective,
-                              threads);
+                              threads,
+                              no_lap_sync);
   } else if (dtype == "half") {
     dispatch_benchmark<half>(cmdline,
                              conf,
@@ -697,7 +729,8 @@ inline auto run_main(int argc, char** argv) -> int
                              index_prefix,
                              override_kv,
                              metric_objective,
-                             threads);
+                             threads,
+                             no_lap_sync);
   } else if (dtype == "uint8") {
     dispatch_benchmark<std::uint8_t>(cmdline,
                                      conf,
@@ -708,7 +741,8 @@ inline auto run_main(int argc, char** argv) -> int
                                      index_prefix,
                                      override_kv,
                                      metric_objective,
-                                     threads);
+                                     threads,
+                                     no_lap_sync);
   } else if (dtype == "int8") {
     dispatch_benchmark<std::int8_t>(cmdline,
                                     conf,
@@ -719,7 +753,8 @@ inline auto run_main(int argc, char** argv) -> int
                                     index_prefix,
                                     override_kv,
                                     metric_objective,
-                                    threads);
+                                    threads,
+                                    no_lap_sync);
   } else {
     log_error("datatype '%s' is not supported", dtype.c_str());
     return -1;

From c86e74d6d12cd3396fdf1ed9fa3b96d858b1fa5f Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Thu, 17 Oct 2024 11:38:13 -0400
Subject: [PATCH 186/623] Add `click` package to `cuvs-bench` conda recipe
 (#408)

This package is available in `dependencies.yaml`, but due to an oversight was not added to conda metas.

Authors:
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/408
---
 conda/recipes/cuvs_bench/meta.yaml     | 1 +
 conda/recipes/cuvs_bench_cpu/meta.yaml | 1 +
 2 files changed, 2 insertions(+)

diff --git a/conda/recipes/cuvs_bench/meta.yaml b/conda/recipes/cuvs_bench/meta.yaml
index 9ecbf82bb0..67d66efce6 100644
--- a/conda/recipes/cuvs_bench/meta.yaml
+++ b/conda/recipes/cuvs_bench/meta.yaml
@@ -82,6 +82,7 @@ requirements:
 
   run:
     - benchmark
+    - click
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major == "11" %}
     - cudatoolkit
diff --git a/conda/recipes/cuvs_bench_cpu/meta.yaml b/conda/recipes/cuvs_bench_cpu/meta.yaml
index 0ce5db7449..95bea92ef3 100644
--- a/conda/recipes/cuvs_bench_cpu/meta.yaml
+++ b/conda/recipes/cuvs_bench_cpu/meta.yaml
@@ -55,6 +55,7 @@ requirements:
 
   run:
     - benchmark
+    - click
     - glog {{ glog_version }}
     - h5py {{ h5py_version }}
     - matplotlib

From f708fe388ee206105fd7894388283995e88ab7f9 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Thu, 17 Oct 2024 14:13:24 -0400
Subject: [PATCH 187/623] We need to enable the c_api by default (#416)

Remove a collection of unneccesarily complex CMake logic.

Major change is that we explicitly opt-in to building the C API bindings by default since it is a hard requirement for our python bindings, and the project has numerous conditions to disable it.

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/416
---
 cpp/CMakeLists.txt      | 18 ++++--------------
 cpp/test/CMakeLists.txt | 38 +++++++++++++++++++++++++++++---------
 2 files changed, 33 insertions(+), 23 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 3e98a247e7..7462457918 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -53,8 +53,7 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 option(BUILD_SHARED_LIBS "Build cuvs shared libraries" ON)
 option(BUILD_TESTS "Build cuvs unit-tests" ON)
-option(BUILD_C_LIBRARY "Build cuVS C API library" OFF)
-option(BUILD_C_TESTS "Build cuVS C API tests" OFF)
+option(BUILD_C_LIBRARY "Build cuVS C API library" ON)
 option(BUILD_CUVS_BENCH "Build cuVS ann benchmarks" OFF)
 option(BUILD_CAGRA_HNSWLIB "Build CAGRA+hnswlib interface" ON)
 option(BUILD_MG_ALGOS "Build with multi-GPU support" ON)
@@ -72,21 +71,12 @@ option(DISABLE_OPENMP "Disable OpenMP" OFF)
 option(CUVS_NVTX "Enable nvtx markers" OFF)
 option(CUVS_RAFT_CLONE_ON_PIN "Explicitly clone RAFT branch when pinned to non-feature branch" ON)
 
-if((BUILD_TESTS OR BUILD_C_LIBRARY) AND NOT BUILD_CPU_ONLY)
-
-endif()
-
 if(BUILD_CPU_ONLY)
   set(BUILD_SHARED_LIBS OFF)
   set(BUILD_TESTS OFF)
   set(BUILD_C_LIBRARY OFF)
-endif()
-
-if(NOT BUILD_C_LIBRARY)
-  set(BUILD_C_TESTS OFF)
-endif()
-
-if(NOT BUILD_SHARED_LIBS)
+  set(BUILD_CAGRA_HNSWLIB OFF)
+elseif(NOT BUILD_SHARED_LIBS)
   set(BUILD_TESTS OFF)
   set(BUILD_C_LIBRARY OFF)
   set(BUILD_CAGRA_HNSWLIB OFF)
@@ -771,7 +761,7 @@ endif()
 # ##################################################################################################
 # * build test executable ----------------------------------------------------
 
-if(BUILD_TESTS OR BUILD_C_TESTS)
+if(BUILD_TESTS)
   add_subdirectory(internal)
   add_subdirectory(test)
 endif()
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index f4d35e4383..60007825cf 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -215,7 +215,9 @@ if(BUILD_TESTS)
   )
 endif()
 
-if(BUILD_C_TESTS)
+if(TARGET cuvs::c_api)
+  enable_language(C)
+
   ConfigureTest(NAME INTEROP_TEST PATH core/interop.cu C_LIB)
   ConfigureTest(
     NAME DISTANCE_C_TEST PATH distance/run_pairwise_distance_c.c distance/pairwise_distance_c.cu
@@ -239,19 +241,37 @@ if(BUILD_C_TESTS)
     target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
     target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()
-endif()
 
-# ##################################################################################################
-# Install tests ####################################################################################
-# ##################################################################################################
-rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcuvs)
-
-if(BUILD_C_TESTS)
-  enable_language(C)
 
   add_executable(cuvs_c_test core/c_api.c)
   target_link_libraries(cuvs_c_test PUBLIC cuvs::c_api)
 
   add_executable(cuvs_c_neighbors_test neighbors/c_api.c)
   target_link_libraries(cuvs_c_neighbors_test PUBLIC cuvs::c_api)
+
+  set_target_properties(
+    cuvs_c_test cuvs_c_neighbors_test
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUVS_BINARY_DIR}/gtests>"
+               INSTALL_RPATH "\$ORIGIN/../../../lib"
+  )
+
+  rapids_test_add(
+    NAME cuvs_c_test
+    COMMAND cuvs_c_test
+    GPUS 1
+    PERCENT 100
+    INSTALL_COMPONENT_SET testing
+  )
+  rapids_test_add(
+    NAME cuvs_c_neighbors_test
+    COMMAND cuvs_c_neighbors_test
+    GPUS 1
+    PERCENT 100
+    INSTALL_COMPONENT_SET testing
+  )
 endif()
+
+# ##################################################################################################
+# Install tests ####################################################################################
+# ##################################################################################################
+rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcuvs)

From 801945fb1acfe4ca12b4d6dd30592f824166a389 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Fri, 18 Oct 2024 12:33:14 -0500
Subject: [PATCH 188/623] Use dashes in cuvs-bench package name. (#417)

This attempts to rename `cuvs_bench` to `cuvs-bench` and similarly for the CPU package. This follows from this thread: https://github.com/rapidsai/docker/pull/715#discussion_r1805232894

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: https://github.com/rapidsai/cuvs/pull/417
---
 build.sh                                             |  2 +-
 ci/build_python.sh                                   |  8 ++++----
 .../{cuvs_bench_cpu => cuvs-bench-cpu}/build.sh      |  0
 .../conda_build_config.yaml                          |  0
 .../{cuvs_bench_cpu => cuvs-bench-cpu}/meta.yaml     |  2 +-
 conda/recipes/{cuvs_bench => cuvs-bench}/build.sh    |  0
 .../conda_build_config.yaml                          |  0
 conda/recipes/{cuvs_bench => cuvs-bench}/meta.yaml   |  2 +-
 docs/source/cuvs_bench/index.rst                     | 12 ++++++------
 9 files changed, 13 insertions(+), 13 deletions(-)
 rename conda/recipes/{cuvs_bench_cpu => cuvs-bench-cpu}/build.sh (100%)
 rename conda/recipes/{cuvs_bench_cpu => cuvs-bench-cpu}/conda_build_config.yaml (100%)
 rename conda/recipes/{cuvs_bench_cpu => cuvs-bench-cpu}/meta.yaml (98%)
 rename conda/recipes/{cuvs_bench => cuvs-bench}/build.sh (100%)
 rename conda/recipes/{cuvs_bench => cuvs-bench}/conda_build_config.yaml (100%)
 rename conda/recipes/{cuvs_bench => cuvs-bench}/meta.yaml (99%)

diff --git a/build.sh b/build.sh
index b787d3a41f..29e8fe7c67 100755
--- a/build.sh
+++ b/build.sh
@@ -447,7 +447,7 @@ if (( ${NUMARGS} == 0 )) || hasArg python; then
         python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs
 fi
 
-# Build and (optionally) install the cuvs_bench Python package
+# Build and (optionally) install the cuvs-bench Python package
 if (( ${NUMARGS} == 0 )) || hasArg bench-ann; then
     python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs_bench
 fi
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 7b0c639afd..deb67e91c3 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -31,14 +31,14 @@ rapids-conda-retry mambabuild \
   --channel "${CPP_CHANNEL}" \
   conda/recipes/cuvs
 
-# Build cuvs_bench for each cuda and python version
+# Build cuvs-bench for each cuda and python version
 rapids-conda-retry mambabuild \
   --no-test \
   --channel "${CPP_CHANNEL}" \
   --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
-  conda/recipes/cuvs_bench
+  conda/recipes/cuvs-bench
 
-# Build cuvs_bench_cpu only in CUDA 12 jobs since it only depends on python
+# Build cuvs-bench-cpu only in CUDA 12 jobs since it only depends on python
 # version
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
@@ -46,7 +46,7 @@ if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
   --no-test \
   --channel "${CPP_CHANNEL}" \
   --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
-  conda/recipes/cuvs_bench_cpu
+  conda/recipes/cuvs-bench-cpu
 fi
 
 rapids-upload-conda-to-s3 python
diff --git a/conda/recipes/cuvs_bench_cpu/build.sh b/conda/recipes/cuvs-bench-cpu/build.sh
similarity index 100%
rename from conda/recipes/cuvs_bench_cpu/build.sh
rename to conda/recipes/cuvs-bench-cpu/build.sh
diff --git a/conda/recipes/cuvs_bench_cpu/conda_build_config.yaml b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
similarity index 100%
rename from conda/recipes/cuvs_bench_cpu/conda_build_config.yaml
rename to conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
diff --git a/conda/recipes/cuvs_bench_cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
similarity index 98%
rename from conda/recipes/cuvs_bench_cpu/meta.yaml
rename to conda/recipes/cuvs-bench-cpu/meta.yaml
index 95bea92ef3..02c11346fb 100644
--- a/conda/recipes/cuvs_bench_cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -8,7 +8,7 @@
 {% set date_string = environ['RAPIDS_DATE_STRING'] %}
 
 package:
-  name: cuvs_bench_cpu
+  name: cuvs-bench-cpu
   version: {{ version }}
   script: build.sh
 
diff --git a/conda/recipes/cuvs_bench/build.sh b/conda/recipes/cuvs-bench/build.sh
similarity index 100%
rename from conda/recipes/cuvs_bench/build.sh
rename to conda/recipes/cuvs-bench/build.sh
diff --git a/conda/recipes/cuvs_bench/conda_build_config.yaml b/conda/recipes/cuvs-bench/conda_build_config.yaml
similarity index 100%
rename from conda/recipes/cuvs_bench/conda_build_config.yaml
rename to conda/recipes/cuvs-bench/conda_build_config.yaml
diff --git a/conda/recipes/cuvs_bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
similarity index 99%
rename from conda/recipes/cuvs_bench/meta.yaml
rename to conda/recipes/cuvs-bench/meta.yaml
index 67d66efce6..3e81edc58f 100644
--- a/conda/recipes/cuvs_bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -10,7 +10,7 @@
 {% set date_string = environ['RAPIDS_DATE_STRING'] %}
 
 package:
-  name: cuvs_bench
+  name: cuvs-bench
   version: {{ version }}
   script: build.sh
 
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 61ac622d24..81fb7537c2 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -93,20 +93,20 @@ We provide images for GPU enabled systems, as well as systems without a GPU. The
 - `cuvs-bench-datasets`: Contains the GPU and CPU benchmarks with million-scale datasets already included in the container. Best suited for users that want to run multiple million scale datasets already included in the image.
 - `cuvs-bench-cpu`: Contains only CPU benchmarks with minimal size. Best suited for users that want the smallest containers to reproduce benchmarks on systems without a GPU.
 
-Nightly images are located in `dockerhub <https://hub.docker.com/r/rapidsai/cuvs-ann-bench/tags>`_, meanwhile release (stable) versions are located in `NGC <https://hub.docker.com/r/rapidsai/cuvs_bench>`_, starting with release 24.10.
+Nightly images are located in `dockerhub <https://hub.docker.com/r/rapidsai/cuvs-bench/tags>`_, meanwhile release (stable) versions are located in `NGC <https://hub.docker.com/r/rapidsai/cuvs-bench>`_, starting with release 24.10.
 
-The following command pulls the nightly container for python version 10, cuda version 12, and CUVS version 23.10:
+The following command pulls the nightly container for Python version 3.10, CUDA version 12.0, and cuVS version 24.10:
 
 .. code-block:: bash
 
-   docker pull rapidsai/cuvs_bench:24.10a-cuda12.0-py3.10 #substitute cuvs_bench for the exact desired container.
+   docker pull rapidsai/cuvs-bench:24.10a-cuda12.0-py3.10 #substitute cuvs-bench for the exact desired container.
 
 The CUDA and python versions can be changed for the supported values:
 - Supported CUDA versions: 11.4 and 12.x
 - Supported Python versions: 3.9 and 3.10.
 
 You can see the exact versions as well in the dockerhub site:
-- `cuVS bench images <https://hub.docker.com/r/rapidsai/cuvs_bench/tags>`_
+- `cuVS bench images <https://hub.docker.com/r/rapidsai/cuvs-bench/tags>`_
 - `cuVS bench with datasets preloaded images <https://hub.docker.com/r/rapidsai/cuvs-bench-cpu/tags>`_
 - `cuVS bench CPU only images <https://hub.docker.com/r/rapidsai/cuvs-bench-datasets/tags>`_
 
@@ -583,7 +583,7 @@ A default `datasets.yaml` is provided by CUVS in `${CUVS_HOME}/python/cuvs-ann-b
       dims: 128
       distance: euclidean
 
-Configuration files for ANN algorithms supported by `cuvs-bench` are provided in `${CUVS_HOME}/python/cuvs-bench/src/cuvs_bench/run/conf`. `cuvs_cagra` algorithm configuration looks like:
+Configuration files for ANN algorithms supported by `cuvs-bench` are provided in `${CUVS_HOME}/python/cuvs_bench/cuvs_bench/config/algos`. `cuvs_cagra` algorithm configuration looks like:
 
 .. code-block:: yaml
 
@@ -767,4 +767,4 @@ Add a new entry to `algos.yaml` to map the name of the algorithm to its binary e
       requires_gpu: true
 
 `executable` : specifies the name of the binary that will build/search the index. It is assumed to be available in `cuvs/cpp/build/`.
-`requires_gpu` : denotes whether an algorithm requires GPU to run.
\ No newline at end of file
+`requires_gpu` : denotes whether an algorithm requires GPU to run.

From 009bb8de03ce9708d4d797166187250f77a59a36 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Mon, 21 Oct 2024 14:24:34 -0500
Subject: [PATCH 189/623] Use Python for sccache hit rate computation. (#420)

Fixes an issue in CI computations of sccache hit rates. See
https://github.com/rapidsai/cuvs/pull/414 for details.
---
 build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/build.sh b/build.sh
index 29e8fe7c67..9503dff216 100755
--- a/build.sh
+++ b/build.sh
@@ -410,14 +410,14 @@ if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || has
           if [[ ${CACHE_TOOL} == "sccache" && -x "$(command -v sccache)" ]]; then
               COMPILE_REQUESTS=$(sccache -s | grep "Compile requests \+ [0-9]\+$" | awk '{ print $NF }')
               CACHE_HITS=$(sccache -s | grep "Cache hits \+ [0-9]\+$" | awk '{ print $NF }')
-              HIT_RATE=$(echo - | awk "{printf \"%.2f\n\", $CACHE_HITS / $COMPILE_REQUESTS * 100}")
+              HIT_RATE=$(python3 -c "print(f'{${CACHE_HITS} / ${COMPILE_REQUESTS}:.2f}' if ${COMPILE_REQUESTS} else 'nan')")
               MSG="${MSG}<br/>cache hit rate ${HIT_RATE} %"
           elif [[ ${CACHE_TOOL} == "ccache" && -x "$(command -v ccache)" ]]; then
               CACHE_STATS_LINE=$(ccache -s | grep "Hits: \+ [0-9]\+ / [0-9]\+" | tail -n1)
               if [[ ! -z "$CACHE_STATS_LINE" ]]; then
                   CACHE_HITS=$(echo "$CACHE_STATS_LINE" - | awk '{ print $2 }')
                   COMPILE_REQUESTS=$(echo "$CACHE_STATS_LINE" - | awk '{ print $4 }')
-                  HIT_RATE=$(echo - | awk "{printf \"%.2f\n\", $CACHE_HITS / $COMPILE_REQUESTS * 100}")
+                  HIT_RATE=$(python3 -c "print(f'{${CACHE_HITS} / ${COMPILE_REQUESTS}:.2f}' if ${COMPILE_REQUESTS} else 'nan')")
                   MSG="${MSG}<br/>cache hit rate ${HIT_RATE} %"
               fi
           fi

From e7f1085b71c340b9600f5f38f7f0059a5c7aa806 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 22 Oct 2024 07:57:08 -0500
Subject: [PATCH 190/623] Use environment variables in cache hit rate
 computation. (#422)

Follow-up PR to address feedback: https://github.com/rapidsai/raft/pull/2474#discussion_r1809398110

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/422
---
 build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/build.sh b/build.sh
index 9503dff216..c08c2900e7 100755
--- a/build.sh
+++ b/build.sh
@@ -410,14 +410,14 @@ if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || has
           if [[ ${CACHE_TOOL} == "sccache" && -x "$(command -v sccache)" ]]; then
               COMPILE_REQUESTS=$(sccache -s | grep "Compile requests \+ [0-9]\+$" | awk '{ print $NF }')
               CACHE_HITS=$(sccache -s | grep "Cache hits \+ [0-9]\+$" | awk '{ print $NF }')
-              HIT_RATE=$(python3 -c "print(f'{${CACHE_HITS} / ${COMPILE_REQUESTS}:.2f}' if ${COMPILE_REQUESTS} else 'nan')")
+              HIT_RATE=$(COMPILE_REQUESTS="${COMPILE_REQUESTS}" CACHE_HITS="${CACHE_HITS}" python3 -c "import os; print(f'{int(os.getenv(\"CACHE_HITS\")) / int(os.getenv(\"COMPILE_REQUESTS\")):.2f}' if int(os.getenv(\"COMPILE_REQUESTS\")) else 'nan')")
               MSG="${MSG}<br/>cache hit rate ${HIT_RATE} %"
           elif [[ ${CACHE_TOOL} == "ccache" && -x "$(command -v ccache)" ]]; then
               CACHE_STATS_LINE=$(ccache -s | grep "Hits: \+ [0-9]\+ / [0-9]\+" | tail -n1)
               if [[ ! -z "$CACHE_STATS_LINE" ]]; then
                   CACHE_HITS=$(echo "$CACHE_STATS_LINE" - | awk '{ print $2 }')
                   COMPILE_REQUESTS=$(echo "$CACHE_STATS_LINE" - | awk '{ print $4 }')
-                  HIT_RATE=$(python3 -c "print(f'{${CACHE_HITS} / ${COMPILE_REQUESTS}:.2f}' if ${COMPILE_REQUESTS} else 'nan')")
+                  HIT_RATE=$(COMPILE_REQUESTS="${COMPILE_REQUESTS}" CACHE_HITS="${CACHE_HITS}" python3 -c "import os; print(f'{int(os.getenv(\"CACHE_HITS\")) / int(os.getenv(\"COMPILE_REQUESTS\")):.2f}' if int(os.getenv(\"COMPILE_REQUESTS\")) else 'nan')")
                   MSG="${MSG}<br/>cache hit rate ${HIT_RATE} %"
               fi
           fi

From 12b10e88e8ea6e944e91dee8a0380c89999b3b21 Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Mon, 28 Oct 2024 11:56:53 -0500
Subject: [PATCH 191/623] Fix correct call to brute force in generate
 groundtruth of cuvs-bench (#427)

Fixes issue with helper script for generating ground truthset in cuvs-bench, which was using the old RAFT NN API.

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/427
---
 .../cuvs_bench/generate_groundtruth/__main__.py          | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
index 2b4213016e..dbee6cd363 100644
--- a/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
@@ -24,7 +24,7 @@
 from pylibraft.common import DeviceResources
 from rmm.allocators.cupy import rmm_cupy_allocator
 
-from cuvs.neighbors.brute_force import knn
+from cuvs.neighbors.brute_force import build, search
 
 from .utils import memmap_bin_file, suffix_from_dtype, write_bin
 
@@ -49,7 +49,7 @@ def choose_random_queries(dataset, n_queries):
 
 
 def calc_truth(dataset, queries, k, metric="sqeuclidean"):
-    handle = DeviceResources()
+    resources = DeviceResources()
     n_samples = dataset.shape[0]
     n = 500000  # batch size for processing neighbors
     i = 0
@@ -63,8 +63,9 @@ def calc_truth(dataset, queries, k, metric="sqeuclidean"):
 
         X = cp.asarray(dataset[i : i + n_batch, :], cp.float32)
 
-        D, Ind = knn(X, queries, k, metric=metric, handle=handle)
-        handle.sync()
+        index = build(X, metric=metric, resources=resources)
+        D, Ind = search(index, queries, k, resources=resources)
+        resources.sync()
 
         D, Ind = cp.asarray(D), cp.asarray(Ind)
         Ind += i  # shift neighbor index by offset i

From 9f035d8e0e44c8eabdf6983049dfe58f9f1ef807 Mon Sep 17 00:00:00 2001
From: abner-ma <969023674@qq.com>
Date: Tue, 29 Oct 2024 05:25:13 +0800
Subject: [PATCH 192/623] Ivf c example (#404)

Add examples of ivf-flat and ivf-pq in C language

Authors:
  - https://github.com/abner-ma

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/404
---
 examples/c/CMakeLists.txt           |   8 +
 examples/c/src/common.h             | 109 ++++++++++++
 examples/c/src/ivf_flat_c_example.c | 259 ++++++++++++++++++++++++++++
 examples/c/src/ivf_pq_c_example.c   | 189 ++++++++++++++++++++
 4 files changed, 565 insertions(+)
 create mode 100644 examples/c/src/common.h
 create mode 100644 examples/c/src/ivf_flat_c_example.c
 create mode 100644 examples/c/src/ivf_pq_c_example.c

diff --git a/examples/c/CMakeLists.txt b/examples/c/CMakeLists.txt
index ec8ca827a5..2a7e70522d 100644
--- a/examples/c/CMakeLists.txt
+++ b/examples/c/CMakeLists.txt
@@ -42,3 +42,11 @@ target_link_libraries(CAGRA_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXIST
 add_executable(L2_C_EXAMPLE src/L2_c_example.c)
 target_include_directories(L2_C_EXAMPLE PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
 target_link_libraries(L2_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env>)
+
+add_executable(IVF_FLAT_C_EXAMPLE src/ivf_flat_c_example.c)
+target_include_directories(IVF_FLAT_C_EXAMPLE PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
+target_link_libraries(IVF_FLAT_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env>)
+
+add_executable(IVF_PQ_C_EXAMPLE src/ivf_pq_c_example.c)
+target_include_directories(IVF_PQ_C_EXAMPLE PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
+target_link_libraries(IVF_PQ_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/c/src/common.h b/examples/c/src/common.h
new file mode 100644
index 0000000000..60b9b73cf6
--- /dev/null
+++ b/examples/c/src/common.h
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <dlpack/dlpack.h>
+
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <time.h>
+#include <string.h>
+
+/**
+ * @brief Initialize Tensor for kDLFloat.
+ *
+ * @param[in] t_d Pointer to a vector
+ * @param[in] t_shape[] Two-dimensional array, which stores the number of rows and columns of vectors.
+ * @param[out] t_tensor Stores the initialized DLManagedTensor.
+ */
+void float_tensor_initialize(float* t_d, int64_t t_shape[2], DLManagedTensor* t_tensor) {
+  t_tensor->dl_tensor.data = t_d;
+  t_tensor->dl_tensor.device.device_type = kDLCUDA;
+  t_tensor->dl_tensor.ndim = 2;
+  t_tensor->dl_tensor.dtype.code = kDLFloat;
+  t_tensor->dl_tensor.dtype.bits = 32;
+  t_tensor->dl_tensor.dtype.lanes = 1;
+  t_tensor->dl_tensor.shape = t_shape;
+  t_tensor->dl_tensor.strides = NULL;
+}
+
+/**
+ * @brief Initialize Tensor for kDLInt.
+ *
+ * @param[in] t_d Pointer to a vector
+ * @param[in] t_shape[] Two-dimensional array, which stores the number of rows and columns of vectors.
+ * @param[out] t_tensor Stores the initialized DLManagedTensor.
+ */
+void int_tensor_initialize(int64_t* t_d, int64_t t_shape[], DLManagedTensor* t_tensor) {
+  t_tensor->dl_tensor.data = t_d;
+  t_tensor->dl_tensor.device.device_type = kDLCUDA;
+  t_tensor->dl_tensor.ndim = 2;
+  t_tensor->dl_tensor.dtype.code = kDLInt;
+  t_tensor->dl_tensor.dtype.bits = 64;
+  t_tensor->dl_tensor.dtype.lanes = 1;
+  t_tensor->dl_tensor.shape = t_shape;
+  t_tensor->dl_tensor.strides = NULL;
+}
+
+/**
+ * @brief Fill a vector with random values.
+ *
+ * @param[out] Vec Pointer to a vector
+ * @param[in] n_rows the number of rows in the matrix.
+ * @param[in] n_cols the number of columns in the matrix.
+ * @param[in] min Minimum value among random values.
+ * @param[in] max Maximum value among random values.
+ */
+void generate_dataset(float * Vec,int n_rows, int n_cols, float min, float max) {
+    float scale;
+    float * ptr = Vec;
+    srand((unsigned int)time(NULL));
+    for (int i = 0; i < n_rows; i++) {
+        for (int j = 0; j < n_cols; j++) {
+            scale = rand()/(float)RAND_MAX;
+            ptr = Vec + i * n_cols + j;
+            *ptr = min + scale * (max - min);
+        }
+    }
+}
+
+/**
+ * @brief print the result.
+ *
+ * @param[in] neighbor Pointer to a neighbor vector
+ * @param[in] distances Pointer to a distances vector.
+ * @param[in] n_rows the number of rows in the matrix.
+ * @param[in] n_cols the number of columns in the matrix.
+ */
+void print_results(int64_t * neighbor, float* distances,int n_rows, int n_cols) {
+    int64_t * pn = neighbor;
+    float * pd = distances;
+    for (int i = 0; i < n_rows; ++i) {
+        printf("Query %d neighbor indices: =[", i);
+        for (int j = 0; j < n_cols; ++j) {
+            pn = neighbor + i * n_cols + j;
+            printf(" %ld", *pn);
+        }
+        printf("]\n");
+        printf("Query %d neighbor distances: =[", i);
+        for (int j = 0; j < n_cols; ++j) {
+            pd = distances + i * n_cols + j;
+            printf(" %f", *pd);
+        }
+        printf("]\n");
+    }
+}
+
diff --git a/examples/c/src/ivf_flat_c_example.c b/examples/c/src/ivf_flat_c_example.c
new file mode 100644
index 0000000000..c068d04f82
--- /dev/null
+++ b/examples/c/src/ivf_flat_c_example.c
@@ -0,0 +1,259 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/ivf_flat.h>
+
+#include <cuda_runtime.h>
+#include "common.h"
+
+void ivf_flat_build_search_simple(cuvsResources_t *res, DLManagedTensor * dataset_tensor, DLManagedTensor * queries_tensor) {
+    // Create default index params
+    cuvsIvfFlatIndexParams_t index_params;
+    cuvsIvfFlatIndexParamsCreate(&index_params);
+    index_params->n_lists                  = 1024; // default value
+    index_params->kmeans_n_iters = 20; // default value
+    index_params->kmeans_trainset_fraction = 0.1;
+    //index_params->metric default is L2Expanded
+
+    // Create IVF-Flat index
+    cuvsIvfFlatIndex_t index;
+    cuvsIvfFlatIndexCreate(&index);
+
+    printf("Building IVF-Flat index\n");
+    // Build the IVF-Flat Index
+    cuvsError_t build_status = cuvsIvfFlatBuild(*res, index_params, dataset_tensor, index);
+    if (build_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+        cuvsIvfFlatIndexDestroy(index);
+        cuvsIvfFlatIndexParamsDestroy(index_params);
+        return;
+    }
+
+    // Create output arrays.
+    int64_t topk      = 10;
+    int64_t n_queries = queries_tensor->dl_tensor.shape[0];
+
+    //Allocate memory for `neighbors` and `distances` output
+    int64_t *neighbors_d;
+    float *distances_d;
+    cuvsRMMAlloc(*res, (void**) &neighbors_d, sizeof(int64_t) * n_queries * topk);
+    cuvsRMMAlloc(*res, (void**) &distances_d, sizeof(float) * n_queries * topk);
+
+    DLManagedTensor neighbors_tensor;
+    int64_t neighbors_shape[2] = {n_queries, topk};
+    int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
+    
+    DLManagedTensor distances_tensor;
+    int64_t distances_shape[2] = {n_queries, topk};
+    float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
+
+    // Create default search params
+    cuvsIvfFlatSearchParams_t search_params;
+    cuvsIvfFlatSearchParamsCreate(&search_params);
+    search_params->n_probes = 50;
+
+    // Search the `index` built using `ivfFlatBuild`
+    cuvsError_t search_status = cuvsIvfFlatSearch(*res, search_params, index,
+     queries_tensor, &neighbors_tensor, &distances_tensor);
+    if (build_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+    }
+
+    int64_t *neighbors = (int64_t *)malloc(n_queries * topk * sizeof(int64_t));
+    float *distances = (float *)malloc(n_queries * topk * sizeof(float));
+    memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
+    memset(distances, 0, n_queries * topk * sizeof(float));
+
+    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
+    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
+
+    print_results(neighbors, distances, 2, topk);
+
+    free(distances);
+    free(neighbors);
+
+    cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk);
+    cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk);
+
+    cuvsIvfFlatSearchParamsDestroy(search_params);
+    cuvsIvfFlatIndexDestroy(index);
+    cuvsIvfFlatIndexParamsDestroy(index_params);  
+}
+
+void ivf_flat_build_extend_search(cuvsResources_t *res, DLManagedTensor * trainset_tensor, DLManagedTensor * dataset_tensor, DLManagedTensor * queries_tensor) {
+    int64_t *data_indices_d;
+    int64_t n_dataset = dataset_tensor->dl_tensor.shape[0];
+    cuvsRMMAlloc(*res, (void**) &data_indices_d, sizeof(int64_t) * n_dataset);
+    DLManagedTensor data_indices_tensor;
+    int64_t data_indices_shape[1] = {n_dataset};
+    int_tensor_initialize(data_indices_d, data_indices_shape, &data_indices_tensor);
+    data_indices_tensor.dl_tensor.ndim = 1;
+    
+    printf("\nRun k-means clustering using the training set\n");
+
+    int64_t *data_indices = (int64_t *)malloc(n_dataset * sizeof(int64_t));
+    int64_t * ptr = data_indices;
+    for (int i = 0; i < n_dataset; i++) {
+        *ptr = i;
+        ptr++;
+    }
+    ptr = NULL;
+    cudaMemcpy(data_indices_d, data_indices, sizeof(int64_t) * n_dataset, cudaMemcpyDefault);
+
+    // Create default index params
+    cuvsIvfFlatIndexParams_t index_params;
+    cuvsIvfFlatIndexParamsCreate(&index_params);
+    index_params->n_lists                  = 100;
+    index_params->add_data_on_build = false;
+    //index_params->metric default is L2Expanded
+
+    // Create IVF-Flat index
+    cuvsIvfFlatIndex_t index;
+    cuvsIvfFlatIndexCreate(&index);
+
+    // Build the IVF-Flat Index
+    cuvsError_t build_status = cuvsIvfFlatBuild(*res, index_params, trainset_tensor, index);
+    if (build_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+        cuvsIvfFlatIndexDestroy(index);
+        cuvsIvfFlatIndexParamsDestroy(index_params);
+        return;
+    }
+
+    printf("Filling index with the dataset vectors\n");
+    cuvsError_t extend_status = cuvsIvfFlatExtend(*res, dataset_tensor, &data_indices_tensor, index);
+    if (extend_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+        return;
+    }
+
+    // Create output arrays.
+    int64_t topk      = 10;
+    int64_t n_queries = queries_tensor->dl_tensor.shape[0];
+
+    //Allocate memory for `neighbors` and `distances` output
+    int64_t *neighbors_d;
+    float *distances_d;
+    cuvsRMMAlloc(*res, (void**) &neighbors_d, sizeof(int64_t) * n_queries * topk);
+    cuvsRMMAlloc(*res, (void**) &distances_d, sizeof(float) * n_queries * topk);
+
+    DLManagedTensor neighbors_tensor;
+    int64_t neighbors_shape[2] = {n_queries, topk};
+    int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
+    
+    DLManagedTensor distances_tensor;
+    int64_t distances_shape[2] = {n_queries, topk};
+    float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
+    
+    // Create default search params
+    cuvsIvfFlatSearchParams_t search_params;
+    cuvsIvfFlatSearchParamsCreate(&search_params);
+    search_params->n_probes = 10;
+
+    // Search the `index` built using `ivfFlatBuild`
+    cuvsError_t search_status = cuvsIvfFlatSearch(*res, search_params, index,
+     queries_tensor, &neighbors_tensor, &distances_tensor);
+    if (search_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+        exit(-1);
+    }
+
+    int64_t *neighbors = (int64_t *)malloc(n_queries * topk * sizeof(int64_t));
+    float *distances = (float *)malloc(n_queries * topk * sizeof(float));
+    memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
+    memset(distances, 0, n_queries * topk * sizeof(float));
+
+    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
+    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
+
+    print_results(neighbors, distances, 2, topk);
+
+    free(distances);
+    free(neighbors);
+    free(data_indices);
+    cuvsRMMFree(*res, data_indices_d, sizeof(int64_t) * n_dataset);
+    cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk);
+    cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk);
+
+    cuvsIvfFlatSearchParamsDestroy(search_params);
+    cuvsIvfFlatIndexDestroy(index);
+    cuvsIvfFlatIndexParamsDestroy(index_params);
+}
+
+int main() {
+    // Create input arrays.
+    int64_t n_samples = 10000;
+    int64_t n_dim     = 3;
+    int64_t n_queries = 10;
+    float *dataset = (float *)malloc(n_samples * n_dim * sizeof(float));
+    float *queries = (float *)malloc(n_queries * n_dim * sizeof(float));
+    generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
+    generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
+    
+    // Create a cuvsResources_t object
+    cuvsResources_t res;
+    cuvsResourcesCreate(&res);
+
+    // Allocate memory for `queries`
+    float *dataset_d;
+    cuvsRMMAlloc(res, (void**) &dataset_d, sizeof(float) * n_samples * n_dim);
+    // Use DLPack to represent `dataset_d` as a tensor
+    cudaMemcpy(dataset_d, dataset, sizeof(float) * n_samples * n_dim, cudaMemcpyDefault);
+
+    DLManagedTensor dataset_tensor;
+    int64_t dataset_shape[2] = {n_samples,n_dim};
+    float_tensor_initialize(dataset_d, dataset_shape, &dataset_tensor);
+
+    // Allocate memory for `queries`
+    float *queries_d;
+    cuvsRMMAlloc(res, (void**) &queries_d, sizeof(float) * n_queries * n_dim);
+
+    // Use DLPack to represent `queries` as tensors
+    cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * n_dim, cudaMemcpyDefault);
+
+    DLManagedTensor queries_tensor;
+    int64_t queries_shape[2] = {n_queries, n_dim};
+    float_tensor_initialize(queries_d, queries_shape, &queries_tensor);
+
+    // Simple build and search example.
+    ivf_flat_build_search_simple(&res, &dataset_tensor, &queries_tensor);
+
+    float *trainset_d;
+    int64_t n_trainset = n_samples * 0.1;
+    float *trainset = (float *)malloc(n_trainset * n_dim * sizeof(float));
+    for (int i = 0; i < n_trainset; i++) {
+        for (int j = 0; j < n_dim; j++) {
+            *(trainset + i * n_dim + j)  = *(dataset + i * n_dim + j);
+        }
+    }
+    cuvsRMMAlloc(res, (void**) &trainset_d, sizeof(float) * n_trainset * n_dim);
+    cudaMemcpy(trainset_d, trainset, sizeof(float) * n_trainset * n_dim, cudaMemcpyDefault);
+    DLManagedTensor trainset_tensor;
+    int64_t trainset_shape[2] = {n_trainset, n_dim};
+    float_tensor_initialize(trainset_d, trainset_shape, &trainset_tensor);
+    
+    // Build and extend example.
+    ivf_flat_build_extend_search(&res, &trainset_tensor, &dataset_tensor, &queries_tensor);
+
+    cuvsRMMFree(res, trainset_d, sizeof(float) * n_trainset * n_dim);
+    cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_dim);
+    cuvsRMMFree(res, dataset_d, sizeof(float) * n_samples * n_dim);
+    cuvsResourcesDestroy(res);
+    free(trainset);
+    free(dataset);
+    free(queries);
+}
diff --git a/examples/c/src/ivf_pq_c_example.c b/examples/c/src/ivf_pq_c_example.c
new file mode 100644
index 0000000000..b6d6b485b2
--- /dev/null
+++ b/examples/c/src/ivf_pq_c_example.c
@@ -0,0 +1,189 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/refine.h>
+
+#include <cuda_runtime.h>
+#include "common.h"
+
+void ivf_pq_build_search(cuvsResources_t *res, DLManagedTensor * dataset_tensor, DLManagedTensor * queries_tensor) {
+    // Create default index params
+    cuvsIvfPqIndexParams_t index_params;
+    cuvsIvfPqIndexParamsCreate(&index_params);
+    index_params->n_lists                  = 1024; // default value
+    index_params->kmeans_trainset_fraction = 0.1;
+    //index_params->metric default is L2Expanded
+    index_params->pq_bits = 8;
+    index_params->pq_dim = 2;
+
+    // Create IVF-PQ index
+    cuvsIvfPqIndex_t index;
+    cuvsIvfPqIndexCreate(&index);
+
+    printf("Building IVF-PQ index\n");
+
+    // Build the IVF-PQ Index
+    cuvsError_t build_status = cuvsIvfPqBuild(*res, index_params, dataset_tensor, index);
+    if (build_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+        cuvsIvfPqIndexDestroy(index);
+        cuvsIvfPqIndexParamsDestroy(index_params);
+        return;
+    }
+    
+    // Create output arrays.
+    int64_t topk      = 10;
+    int64_t n_queries = queries_tensor->dl_tensor.shape[0];
+
+    //Allocate memory for `neighbors` and `distances` output
+    int64_t *neighbors_d;
+    float *distances_d;
+    cuvsRMMAlloc(*res, (void**) &neighbors_d, sizeof(int64_t) * n_queries * topk);
+    cuvsRMMAlloc(*res, (void**) &distances_d, sizeof(float) * n_queries * topk);
+
+    DLManagedTensor neighbors_tensor;
+    int64_t neighbors_shape[2] = {n_queries, topk};
+    int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
+
+    DLManagedTensor distances_tensor;
+    int64_t distances_shape[2] = {n_queries, topk};
+    float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
+
+    // Create default search params
+    cuvsIvfPqSearchParams_t search_params;
+    cuvsIvfPqSearchParamsCreate(&search_params);
+    search_params->n_probes = 50;
+    search_params->internal_distance_dtype = CUDA_R_16F;
+    search_params->lut_dtype = CUDA_R_16F;
+
+    // Search the `index` built using `cuvsIvfPqBuild`
+    cuvsError_t search_status = cuvsIvfPqSearch(*res, search_params, index,
+     queries_tensor, &neighbors_tensor, &distances_tensor);
+    if (search_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+        exit(-1);
+    }
+
+    int64_t *neighbors = (int64_t *)malloc(n_queries * topk * sizeof(int64_t));
+    float *distances = (float *)malloc(n_queries * topk * sizeof(float));
+    memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
+    memset(distances, 0, n_queries * topk * sizeof(float));
+
+    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
+    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
+
+    printf("\nOriginal results:\n");
+    print_results(neighbors, distances, 2, topk);
+    
+    // Re-ranking operation: refine the initial search results by computing exact distances
+    int64_t topk_refined = 7;
+    int64_t *neighbors_refined_d;
+    float *distances_refined_d;
+    cuvsRMMAlloc(*res, (void**) &neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined);
+    cuvsRMMAlloc(*res, (void**) &distances_refined_d, sizeof(float) * n_queries * topk_refined);
+
+    DLManagedTensor neighbors_refined_tensor;
+    int64_t neighbors_refined_shape[2] = {n_queries, topk_refined};
+    int_tensor_initialize(neighbors_refined_d, neighbors_refined_shape, &neighbors_refined_tensor);
+    
+    DLManagedTensor distances_refined_tensor;
+    int64_t distances_refined_shape[2] = {n_queries, topk_refined};
+    float_tensor_initialize(distances_refined_d, distances_refined_shape, &distances_refined_tensor);
+    
+    // Note, refinement requires the original dataset and the queries.
+    // Don't forget to specify the same distance metric as used by the index.
+    cuvsError_t refine_status = cuvsRefine(*res, dataset_tensor, queries_tensor,
+                       &neighbors_tensor, index_params->metric,
+                       &neighbors_refined_tensor, &distances_refined_tensor);
+    if (refine_status != CUVS_SUCCESS) {
+        printf("%s.\n", cuvsGetLastErrorText());
+        exit(-1);
+    }
+
+    int64_t *neighbors_refine = (int64_t *)malloc(n_queries * topk_refined * sizeof(int64_t));
+    float *distances_refine = (float *)malloc(n_queries * topk_refined * sizeof(float));
+    memset(neighbors_refine, 0, n_queries * topk_refined * sizeof(int64_t));
+    memset(distances_refine, 0, n_queries * topk_refined * sizeof(float));
+
+    cudaMemcpy(neighbors_refine, neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined, cudaMemcpyDefault);
+    cudaMemcpy(distances_refine, distances_refined_d, sizeof(float) * n_queries * topk_refined, cudaMemcpyDefault);
+
+    printf("\nRefined results:\n");
+    print_results(neighbors, distances, 2, topk_refined);
+
+    free(distances_refine);
+    free(neighbors_refine);
+
+    free(distances);
+    free(neighbors);
+
+    cuvsRMMFree(*res, neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined);
+    cuvsRMMFree(*res, distances_refined_d, sizeof(float) * n_queries * topk_refined);
+
+    cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk);
+    cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk);
+
+    cuvsIvfPqSearchParamsDestroy(search_params);
+    cuvsIvfPqIndexDestroy(index);
+    cuvsIvfPqIndexParamsDestroy(index_params);  
+}
+
+int main() {
+    // Create input arrays.
+    int64_t n_samples = 10000;
+    int64_t n_dim     = 3;
+    int64_t n_queries = 10;
+    float *dataset = (float *)malloc(n_samples * n_dim * sizeof(float));
+    float *queries = (float *)malloc(n_queries * n_dim * sizeof(float));
+    generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
+    generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
+    
+    // Create a cuvsResources_t object
+    cuvsResources_t res;
+    cuvsResourcesCreate(&res);
+
+    // Allocate memory for `queries`
+    float *dataset_d;
+    cuvsRMMAlloc(res, (void**) &dataset_d, sizeof(float) * n_samples * n_dim);
+    // Use DLPack to represent `dataset_d` as a tensor
+    cudaMemcpy(dataset_d, dataset, sizeof(float) * n_samples * n_dim, cudaMemcpyDefault);
+
+    DLManagedTensor dataset_tensor;
+    int64_t dataset_shape[2] = {n_samples,n_dim};
+    float_tensor_initialize(dataset_d, dataset_shape, &dataset_tensor);
+    
+    // Allocate memory for `queries`
+    float *queries_d;
+    cuvsRMMAlloc(res, (void**) &queries_d, sizeof(float) * n_queries * n_dim);
+
+    // Use DLPack to represent `queries` as tensors
+    cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * n_dim, cudaMemcpyDefault);
+
+    DLManagedTensor queries_tensor;
+    int64_t queries_shape[2] = {n_queries, n_dim};
+    float_tensor_initialize(queries_d, queries_shape, &queries_tensor);
+    
+    // Simple build and search example.
+    ivf_pq_build_search(&res, &dataset_tensor, &queries_tensor);
+
+    cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_dim);
+    cuvsRMMFree(res, dataset_d, sizeof(float) * n_samples * n_dim);
+    cuvsResourcesDestroy(res);
+    free(dataset);
+    free(queries);
+}

From d296d811e3d0f9917068c9d5d2ef04fccacdcd08 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 29 Oct 2024 18:03:51 -0500
Subject: [PATCH 193/623] print sccache stats in builds (#413)

Contributes to https://github.com/rapidsai/build-planning/issues/111

Proposes some small packaging/CI changes, matching similar changes being made across RAPIDS.

* printing `sccache` stats to CI logs
* reducing `pip`'s verbosity in wheel building scripts
* updating to the latest `rapids-dependency-file-generator` (v1.16.0)
* always explicitly specifying `cpp` / `python` in calls to `rapids-upload-wheels-to-s3`
* modifying `dependencies.yaml` to match RAPIDS-wide naming conventions

## Notes for Reviewers

This originally also ran wheel builds with `--no-build-isolation`, but I reverted that based on https://github.com/rapidsai/build-planning/issues/108#issuecomment-2436764212.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/413
---
 .pre-commit-config.yaml |  2 +-
 ci/build_cpp.sh         |  4 ++++
 ci/build_python.sh      | 10 ++++++++++
 ci/build_wheel.sh       | 16 +++++++++++++---
 dependencies.yaml       | 10 +++++-----
 5 files changed, 33 insertions(+), 9 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 439b429592..f4fdf202eb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -113,7 +113,7 @@ repos:
                   cpp/cmake/modules/FindAVX\.cmake|
           - id: verify-alpha-spec
       - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.13.11
+        rev: v1.16.0
         hooks:
             - id: rapids-dependency-file-generator
               args: ["--clean"]
diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index 7bc0be5a75..db4c496cc5 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -15,6 +15,10 @@ rapids-print-env
 
 rapids-logger "Begin cpp build"
 
+sccache --zero-stats
+
 RAPIDS_PACKAGE_VERSION=$(rapids-generate-version) rapids-conda-retry mambabuild conda/recipes/libcuvs
 
+sccache --show-adv-stats
+
 rapids-upload-conda-to-s3 cpp
diff --git a/ci/build_python.sh b/ci/build_python.sh
index deb67e91c3..3241a2c2b0 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -24,6 +24,8 @@ version=$(rapids-generate-version)
 export RAPIDS_PACKAGE_VERSION=${version}
 echo "${version}" > VERSION
 
+sccache --zero-stats
+
 # TODO: Remove `--no-test` flags once importing on a CPU
 # node works correctly
 rapids-conda-retry mambabuild \
@@ -31,6 +33,9 @@ rapids-conda-retry mambabuild \
   --channel "${CPP_CHANNEL}" \
   conda/recipes/cuvs
 
+sccache --show-adv-stats
+sccache --zero-stats
+
 # Build cuvs-bench for each cuda and python version
 rapids-conda-retry mambabuild \
   --no-test \
@@ -38,6 +43,9 @@ rapids-conda-retry mambabuild \
   --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
   conda/recipes/cuvs-bench
 
+sccache --show-adv-stats
+sccache --zero-stats
+
 # Build cuvs-bench-cpu only in CUDA 12 jobs since it only depends on python
 # version
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
@@ -47,6 +55,8 @@ if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
   --channel "${CPP_CHANNEL}" \
   --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
   conda/recipes/cuvs-bench-cpu
+
+  sccache --show-adv-stats
 fi
 
 rapids-upload-conda-to-s3 python
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index d1030276f9..4994374a80 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -32,10 +32,20 @@ case "${RAPIDS_CUDA_VERSION}" in
   ;;
 esac
 
-# Hardcode the output dir
-python -m pip wheel . -w dist -vvv --no-deps --disable-pip-version-check
+rapids-logger "Building '${package_name}' wheel"
+
+sccache --zero-stats
+
+python -m pip wheel \
+    -w dist \
+    -v \
+    --no-deps \
+    --disable-pip-version-check \
+    .
+
+sccache --show-adv-stats
 
 mkdir -p final_dist
 python -m auditwheel repair -w final_dist "${EXCLUDE_ARGS[@]}" dist/*
 
-RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 final_dist
+RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 python final_dist
diff --git a/dependencies.yaml b/dependencies.yaml
index a68a550bb9..cf9b68c8ad 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -74,14 +74,14 @@ files:
       - rapids_build
       - cuda
       - rust
-  py_build_py_cuvs:
+  py_build_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
     extras:
       table: build-system
     includes:
       - build
-  py_rapids_build_py_cuvs:
+  py_rapids_build_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
     extras:
@@ -90,7 +90,7 @@ files:
     includes:
       - rapids_build
       - build_py_cuvs
-  py_run_py_cuvs:
+  py_run_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
     extras:
@@ -99,7 +99,7 @@ files:
       - cuda_wheels
       - run_py_cuvs
       - depends_on_pylibraft
-  py_test_py_cuvs:
+  py_test_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
     extras:
@@ -116,7 +116,7 @@ files:
       table: build-system
     includes:
       - rapids_build_setuptools
-  py_rapids_build_py_cuvs_bench:
+  py_rapids_build_cuvs_bench:
     output: pyproject
     pyproject_dir: python/cuvs_bench
     extras:

From b422cbeec92fe925ee59f5f966ac9834440200e2 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Wed, 30 Oct 2024 11:20:31 -0400
Subject: [PATCH 194/623] Add ci run_ scripts needed for build infra (#434)

These `run_*` scripts are needed by the build infra team and bring the cuvs project in line with the rest of RAPIDS

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/434
---
 ci/run_ctests.sh       | 9 +++++++++
 ci/run_cuvs_pytests.sh | 9 +++++++++
 2 files changed, 18 insertions(+)
 create mode 100755 ci/run_ctests.sh
 create mode 100755 ci/run_cuvs_pytests.sh

diff --git a/ci/run_ctests.sh b/ci/run_ctests.sh
new file mode 100755
index 0000000000..6bf83961b9
--- /dev/null
+++ b/ci/run_ctests.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+# Support customizing the ctests' install location
+cd "${INSTALL_PREFIX:-${CONDA_PREFIX:-/usr}}/bin/gtests/libcuvs/"
+
+ctest --output-on-failure --no-tests=error "$@"
diff --git a/ci/run_cuvs_pytests.sh b/ci/run_cuvs_pytests.sh
new file mode 100755
index 0000000000..4de8927b17
--- /dev/null
+++ b/ci/run_cuvs_pytests.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+# Support invoking run_pytests.sh outside the script directory
+cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/cuvs/cuvs
+
+pytest --cache-clear --verbose "$@" tests

From 6041a81ce8e534ac79f5b27c595c9231b88d1d10 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 31 Oct 2024 17:25:10 +0100
Subject: [PATCH 195/623] Enable NVTX in cuvs-cagra-search component (#439)

Since parts of CAGRA code have been separated into a static library component `cuvs-cagra-search` (to selectively enable CUDA separable compilation on them), the NVTX flags are not passed to the affected sources anymore. This PR fixes that.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/439
---
 cpp/CMakeLists.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 7462457918..e56e213831 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -603,6 +603,9 @@ SECTIONS
     # This enables NVTX within the project with no option to disable it downstream.
     target_link_libraries(cuvs PUBLIC CUDA::nvtx3)
     target_compile_definitions(cuvs PUBLIC NVTX_ENABLED)
+
+    target_link_libraries(cuvs-cagra-search PUBLIC CUDA::nvtx3)
+    target_compile_definitions(cuvs-cagra-search PUBLIC NVTX_ENABLED)
   else()
     # Allow enable NVTX downstream if not set here. This creates a new option at build/install time,
     # which is set by default to OFF, but can be enabled in the dependent project.

From 71deb26c457bbf398c9af0142740aefadf83220a Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 1 Nov 2024 16:18:32 +0100
Subject: [PATCH 196/623] Fix index overflow in edge cases of CAGRA graph
 optimize (#435)

Force `input_graph_degree`, `output_graph_degree`, and `graph_size` variables to `uint64_t`.
Before the PR, they've been `uint32_t`, and the product of them could overflow. This would lead to `cudaMemsetAsync` not filling in a large fraction of the graph.

It's not known whether this bug has surfaced for anyone until now, but it's better to be safe than sorry.
The change shouldn't have any impact on performance.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/435
---
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 23 ++++++++++---------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 43bf1ba2b2..4253cb781d 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -1076,11 +1076,11 @@ void optimize(
                "Each input array is expected to have the same number of rows");
   RAFT_EXPECTS(new_graph.extent(1) <= knn_graph.extent(1),
                "output graph cannot have more columns than input graph");
-  const uint32_t input_graph_degree  = knn_graph.extent(1);
-  const uint32_t output_graph_degree = new_graph.extent(1);
+  const uint64_t input_graph_degree  = knn_graph.extent(1);
+  const uint64_t output_graph_degree = new_graph.extent(1);
+  const uint64_t graph_size          = new_graph.extent(0);
   auto input_graph_ptr               = knn_graph.data_handle();
   auto output_graph_ptr              = new_graph.data_handle();
-  const IdxT graph_size              = new_graph.extent(0);
 
   // MST optimization
   auto mst_graph_num_edges     = raft::make_host_vector<uint32_t, int64_t>(graph_size);
@@ -1148,7 +1148,7 @@ void optimize(
     constexpr int MAX_DEGREE = 1024;
     if (input_graph_degree > MAX_DEGREE) {
       RAFT_FAIL(
-        "The degree of input knn graph is too large (%u). "
+        "The degree of input knn graph is too large (%zu). "
         "It must be equal to or smaller than %d.",
         input_graph_degree,
         1024);
@@ -1217,11 +1217,12 @@ void optimize(
         assert(next_num_detour != std::numeric_limits<uint32_t>::max());
         num_detour = next_num_detour;
       }
-      RAFT_EXPECTS(pk == output_graph_degree,
-                   "Couldn't find the output_graph_degree (%u) smallest detourable count nodes for "
-                   "node %lu in the rank-based node reranking process",
-                   output_graph_degree,
-                   static_cast<uint64_t>(i));
+      RAFT_EXPECTS(
+        pk == output_graph_degree,
+        "Couldn't find the output_graph_degree (%lu) smallest detourable count nodes for "
+        "node %lu in the rank-based node reranking process",
+        output_graph_degree,
+        i);
     }
 
     const double time_prune_end = cur_time();
@@ -1317,7 +1318,7 @@ void optimize(
       uint32_t kf       = 0;
       uint32_t k        = mst_graph_num_edges_ptr[i];
 
-      const uint64_t num_protected_edges = max(k, output_graph_degree / 2);
+      const auto num_protected_edges = std::max<uint64_t>(k, output_graph_degree / 2);
       assert(num_protected_edges <= output_graph_degree);
       if (num_protected_edges == output_graph_degree) continue;
 
@@ -1342,7 +1343,7 @@ void optimize(
       assert(kf <= output_graph_degree);
 
       // Replace some edges of the output graph with edges of the reverse graph.
-      uint32_t kr = std::min(rev_graph_count.data_handle()[i], output_graph_degree);
+      auto kr = std::min<uint32_t>(rev_graph_count.data_handle()[i], output_graph_degree);
       while (kr) {
         kr -= 1;
         if (my_rev_graph[kr] < graph_size) {

From 9bea21585ae121194c4df49e2ad4ce1bd16e3408 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 4 Nov 2024 14:12:05 -0500
Subject: [PATCH 197/623] call `enable_testing` in root CMakeLists.txt (#437)

Required to allow `ctest` to be called in the root of the build directory

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/437
---
 cpp/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index e56e213831..c493af488e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -765,6 +765,7 @@ endif()
 # * build test executable ----------------------------------------------------
 
 if(BUILD_TESTS)
+  enable_testing()
   add_subdirectory(internal)
   add_subdirectory(test)
 endif()

From 3ac206364afdd9f413de2175763cc37fdefd58b3 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 4 Nov 2024 14:24:59 -0500
Subject: [PATCH 198/623] Don't presume pointers location infers usability.
 (#441)

Here is the results of looking at the cudaPointerGetAttributes of different allocation types on Grace + Hopper. Allocations of `malloc` are still usable on the GPU.
```
ccudaPointerGetAttributes attributes malloc ptr
  is_dev_ptr  -> 1
  is_host_ptr -> 1
  memory loc  -> unregistered

cudaPointerGetAttributes attributes cudaMalloc ptr
  is_dev_ptr  -> 1
  is_host_ptr -> 0
  memory loc  -> device

cudaPointerGetAttributes attributes cudaMallocManaged cudaMemAttachGlobal ptr
  is_dev_ptr  -> 1
  is_host_ptr -> 1
  memory loc  -> managed

```

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/441
---
 cpp/src/neighbors/detail/ann_utils.cuh | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index 29f790ec51..652d41c853 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -63,14 +63,9 @@ struct pointer_residency_count<Type, Types...> {
     auto [on_device, on_host] = pointer_residency_count<Types...>::run(ptrs...);
     cudaPointerAttributes attr;
     RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, ptr));
-    switch (attr.type) {
-      case cudaMemoryTypeUnregistered: return std::make_tuple(on_device, on_host + 1);
-      case cudaMemoryTypeHost:
-        return std::make_tuple(on_device + int(attr.devicePointer == ptr), on_host + 1);
-      case cudaMemoryTypeDevice: return std::make_tuple(on_device + 1, on_host);
-      case cudaMemoryTypeManaged: return std::make_tuple(on_device + 1, on_host + 1);
-      default: return std::make_tuple(on_device, on_host);
-    }
+    if (attr.devicePointer || attr.type == cudaMemoryTypeDevice) { ++on_device; }
+    if (attr.hostPointer || attr.type == cudaMemoryTypeUnregistered) { ++on_host; }
+    return std::make_tuple(on_device, on_host);
   }
 };
 

From eff2cc5ccd83ba25083436d74f8ae3a3d6836f97 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Mon, 4 Nov 2024 20:49:04 +0100
Subject: [PATCH 199/623] BUG: CAGRA multi-cta illegal access with bad queries
 (#438)

CAGRA search kernel errors with `cudaErrorIllegalAddress` in some conditions, specified in the new test case.

According to compute-sanitizer, the illegal access happens in [compute_distance_to_child_nodes(...) function](https://github.com/rapidsai/cuvs/blob/b422cbeec92fe925ee59f5f966ac9834440200e2/cpp/src/neighbors/detail/cagra/device_common.hpp#L185) accessing the graph.
The `parent_id` variable in that function sometimes appears to be out-of-bounds (larger than the graph size / number of records); it's invalid and seems to be the same reported by multiple threads, yet it's not an `invalid_index`, neither `index_msb_1_mask`, neither any derivative of the two.
Further observations:
  - I've checked the graph just before calling the search kernel; it does not contain any invalid indices.
  - One should disable any fancy pool memory resources to make it easier to reproduce the error (so that `parent_id` does not hit other user allocations in the pool)
  - It seems important that the query yields infinite distance to the dataset.
  - Running the search with a newly created `raft::resources` seems to increase the chance to hit the error
  - Even with all conditions satisfied, the error does not reproduce every time...

### Reproducer
```
./build.sh -n tests --limit-tests=NEIGHBORS_ANN_CAGRA_TEST && ./cpp/build/gtests/NEIGHBORS_ANN_CAGRA_TEST --gtest_filter=AnnCagraBugMultiCTACrash*
```

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/438
---
 .../neighbors/detail/cagra/device_common.hpp  |   5 +-
 cpp/test/CMakeLists.txt                       |   2 +-
 .../ann_cagra/bug_multi_cta_crash.cu          | 108 ++++++++++++++++++
 3 files changed, 112 insertions(+), 3 deletions(-)
 create mode 100644 cpp/test/neighbors/ann_cagra/bug_multi_cta_crash.cu

diff --git a/cpp/src/neighbors/detail/cagra/device_common.hpp b/cpp/src/neighbors/detail/cagra/device_common.hpp
index b7cb9c42df..7ec3d4d9eb 100644
--- a/cpp/src/neighbors/detail/cagra/device_common.hpp
+++ b/cpp/src/neighbors/detail/cagra/device_common.hpp
@@ -120,7 +120,7 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_random_nodes(
   for (uint32_t i = threadIdx.x >> team_size_bits; i < max_i; i += (blockDim.x >> team_size_bits)) {
     const bool valid_i = (i < num_pickup);
 
-    IndexT best_index_team_local;
+    IndexT best_index_team_local    = raft::upper_bound<IndexT>();
     DistanceT best_norm2_team_local = raft::upper_bound<DistanceT>();
     for (uint32_t j = 0; j < num_distilation; j++) {
       // Select a node randomly and compute the distance to it
@@ -145,7 +145,8 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_random_nodes(
 
     const unsigned lane_id = threadIdx.x & ((1u << team_size_bits) - 1u);
     if (valid_i && lane_id == 0) {
-      if (hashmap::insert(visited_hash_ptr, hash_bitlen, best_index_team_local)) {
+      if (best_index_team_local != raft::upper_bound<IndexT>() &&
+          hashmap::insert(visited_hash_ptr, hash_bitlen, best_index_team_local)) {
         result_distances_ptr[i] = best_norm2_team_local;
         result_indices_ptr[i]   = best_index_team_local;
       } else {
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 60007825cf..1ed8466b3c 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -137,6 +137,7 @@ if(BUILD_TESTS)
     NAME
     NEIGHBORS_ANN_CAGRA_TEST
     PATH
+    neighbors/ann_cagra/bug_multi_cta_crash.cu
     neighbors/ann_cagra/test_float_uint32_t.cu
     neighbors/ann_cagra/test_half_uint32_t.cu
     neighbors/ann_cagra/test_int8_t_uint32_t.cu
@@ -242,7 +243,6 @@ if(TARGET cuvs::c_api)
     target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()
 
-
   add_executable(cuvs_c_test core/c_api.c)
   target_link_libraries(cuvs_c_test PUBLIC cuvs::c_api)
 
diff --git a/cpp/test/neighbors/ann_cagra/bug_multi_cta_crash.cu b/cpp/test/neighbors/ann_cagra/bug_multi_cta_crash.cu
new file mode 100644
index 0000000000..6f4aa059ef
--- /dev/null
+++ b/cpp/test/neighbors/ann_cagra/bug_multi_cta_crash.cu
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_cagra.cuh"
+
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::cagra {
+
+class AnnCagraBugMultiCTACrash : public ::testing::TestWithParam<cagra::search_algo> {
+ public:
+  using data_type = half;
+
+ protected:
+  void run()
+  {
+    cagra::index_params cagra_index_params;
+    cagra_index_params.graph_degree              = 32;
+    cagra_index_params.intermediate_graph_degree = 48;
+
+    auto cagra_index =
+      cagra::build(res, cagra_index_params, raft::make_const_mdspan(dataset->view()));
+    raft::resource::sync_stream(res);
+
+    cagra::search_params cagra_search_params;
+    cagra_search_params.itopk_size        = 32;
+    cagra_search_params.thread_block_size = 256;
+    cagra_search_params.search_width      = 1;
+    cagra_search_params.max_iterations    = 0;
+    cagra_search_params.algo = ::testing::TestWithParam<cagra::search_algo>::GetParam();
+
+    // NOTE: when using one resource/stream for everything, the bug is NOT reproducible
+    raft::resources res_search;
+    cagra::search(res_search,
+                  cagra_search_params,
+                  cagra_index,
+                  raft::make_const_mdspan(queries->view()),
+                  neighbors->view(),
+                  distances->view());
+
+    raft::resource::sync_stream(res_search);
+  }
+
+  void SetUp() override
+  {
+    dataset.emplace(raft::make_device_matrix<data_type, int64_t>(res, n_samples, n_dim));
+    queries.emplace(raft::make_device_matrix<data_type, int64_t>(res, n_queries, n_dim));
+    neighbors.emplace(raft::make_device_matrix<uint32_t, int64_t>(res, n_queries, k));
+    distances.emplace(raft::make_device_matrix<float, int64_t>(res, n_queries, k));
+    raft::random::RngState r(1234ULL);
+    InitDataset(res, dataset->data_handle(), n_samples, n_dim, metric, r);
+    // NOTE: when initializing queries with "normal" data, the bug is NOT reproducible
+    raft::linalg::map(
+      res, queries->view(), raft::const_op<data_type>{raft::upper_bound<data_type>()});
+    // InitDataset(res, queries->data_handle(), n_queries, n_dim, metric, r);
+    raft::resource::sync_stream(res);
+  }
+
+  void TearDown() override
+  {
+    dataset.reset();
+    queries.reset();
+    neighbors.reset();
+    distances.reset();
+    raft::resource::sync_stream(res);
+  }
+
+ private:
+  raft::resources res;
+  std::optional<raft::device_matrix<data_type, int64_t>> dataset  = std::nullopt;
+  std::optional<raft::device_matrix<data_type, int64_t>> queries  = std::nullopt;
+  std::optional<raft::device_matrix<uint32_t, int64_t>> neighbors = std::nullopt;
+  std::optional<raft::device_matrix<float, int64_t>> distances    = std::nullopt;
+
+  constexpr static int64_t n_samples                   = 1183514;
+  constexpr static int64_t n_dim                       = 100;
+  constexpr static int64_t n_queries                   = 30;
+  constexpr static int64_t k                           = 10;
+  constexpr static cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
+};
+
+TEST_P(AnnCagraBugMultiCTACrash, AnnCagraBugMultiCTACrash) { this->run(); }
+
+INSTANTIATE_TEST_CASE_P(AnnCagraBugMultiCTACrashReproducer,
+                        AnnCagraBugMultiCTACrash,
+                        ::testing::Values(cagra::search_algo::MULTI_CTA));
+
+}  // namespace cuvs::neighbors::cagra

From 6b35b65923933e6396ae61322ce2e9b0772eea4a Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 6 Nov 2024 08:24:44 +0100
Subject: [PATCH 200/623] CAGRA tech debt: distance descriptor and workspace
 memory (#436)

This PR introduces two changes:

1.  Refactor `dataset_descriptor_host` to pass and cache it by value while keeping the state in a thread-safe object in a shared pointers. Before this, the descriptor host itself was kept in shared pointer in LRU cache and was passed by reference; as a result, it could in theory die due to cache eviction while still being used via references to it.
2. Adjust the temporary buffers to always use the workspace resource in all CAGRA algo implementations (as of now, only SINGLE_CTA algo does this; the PR expands the change to MULTI_CTA and MULTI_KERNEL).

Both of the changes are required for effective use of stream-ordered dynamic batching https://github.com/rapidsai/cuvs/pull/261 (1. fixes crashes and 2. fixes thread-blocking behavior).

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/436
---
 .../neighbors/detail/cagra/cagra_search.cuh   |  4 +-
 .../detail/cagra/compute_distance.hpp         | 77 +++++++++++++------
 cpp/src/neighbors/detail/cagra/factory.cuh    | 20 ++---
 .../detail/cagra/search_multi_cta.cuh         | 12 +--
 .../detail/cagra/search_multi_kernel.cuh      | 53 +++++++------
 .../neighbors/detail/cagra/search_plan.cuh    |  2 +-
 6 files changed, 100 insertions(+), 68 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index 95c158675e..5778d85a6f 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -151,7 +151,7 @@ void search_main(raft::resources const& res,
   if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index.data());
       strided_dset != nullptr) {
     // Search using a plain (strided) row-major dataset
-    auto& desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
+    auto desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
       res, params, *strided_dset, index.metric());
     search_main_core<T, InternalIdxT, DistanceT, CagraSampleFilterT>(
       res, params, desc, graph_internal, queries, neighbors, distances, sample_filter);
@@ -161,7 +161,7 @@ void search_main(raft::resources const& res,
     RAFT_FAIL("FP32 VPQ dataset support is coming soon");
   } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<half, ds_idx_type>*>(&index.data());
              vpq_dset != nullptr) {
-    auto& desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
+    auto desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
       res, params, *vpq_dset, index.metric());
     search_main_core<T, InternalIdxT, DistanceT, CagraSampleFilterT>(
       res, params, desc, graph_internal, queries, neighbors, distances, sample_filter);
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.hpp b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
index 297eb1f559..7eb7984592 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
@@ -31,8 +31,10 @@
 #include <raft/util/device_loads_stores.cuh>
 #include <raft/util/vectorized.cuh>
 
+#include <atomic>
 #include <functional>
 #include <memory>
+#include <mutex>
 #include <type_traits>
 #include <variant>
 
@@ -232,52 +234,77 @@ struct alignas(device::LOAD_128BIT_T) dataset_descriptor_base_t {
  */
 template <typename DataT, typename IndexT, typename DistanceT>
 struct dataset_descriptor_host {
-  using dev_descriptor_t = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
-  using dd_ptr_t         = std::shared_ptr<dev_descriptor_t>;
-  using init_f =
-    std::tuple<std::function<void(dev_descriptor_t*, rmm::cuda_stream_view stream)>, size_t>;
+  using dev_descriptor_t         = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
   uint32_t smem_ws_size_in_bytes = 0;
   uint32_t team_size             = 0;
 
+  struct state {
+    using ready_t = std::tuple<dev_descriptor_t*, rmm::cuda_stream_view>;
+    using init_f =
+      std::tuple<std::function<void(dev_descriptor_t*, rmm::cuda_stream_view)>, size_t>;
+
+    std::mutex mutex;
+    std::atomic<bool> ready;  // Not sure if std::holds_alternative is thread-safe
+    std::variant<ready_t, init_f> value;
+
+    template <typename InitF>
+    state(InitF init, size_t size) : ready{false}, value{std::make_tuple(init, size)}
+    {
+    }
+
+    ~state() noexcept
+    {
+      if (std::holds_alternative<ready_t>(value)) {
+        auto& [ptr, stream] = std::get<ready_t>(value);
+        RAFT_CUDA_TRY_NO_THROW(cudaFreeAsync(ptr, stream));
+      }
+    }
+
+    void eval(rmm::cuda_stream_view stream)
+    {
+      std::lock_guard<std::mutex> lock(mutex);
+      if (std::holds_alternative<init_f>(value)) {
+        auto& [fun, size]     = std::get<init_f>(value);
+        dev_descriptor_t* ptr = nullptr;
+        RAFT_CUDA_TRY(cudaMallocAsync(&ptr, size, stream));
+        fun(ptr, stream);
+        value = std::make_tuple(ptr, stream);
+        ready.store(true, std::memory_order_release);
+      }
+    }
+
+    auto get(rmm::cuda_stream_view stream) -> dev_descriptor_t*
+    {
+      if (!ready.load(std::memory_order_acquire)) { eval(stream); }
+      return std::get<0>(std::get<ready_t>(value));
+    }
+  };
+
   template <typename DescriptorImpl, typename InitF>
   dataset_descriptor_host(const DescriptorImpl& dd_host, InitF init)
-    : value_{std::make_tuple(init, sizeof(DescriptorImpl))},
+    : value_{std::make_shared<state>(init, sizeof(DescriptorImpl))},
       smem_ws_size_in_bytes{dd_host.smem_ws_size_in_bytes()},
       team_size{dd_host.team_size()}
   {
   }
 
+  dataset_descriptor_host() = default;
+
   /**
    * Return the device pointer, possibly evaluating it in the given thread.
    */
   [[nodiscard]] auto dev_ptr(rmm::cuda_stream_view stream) const -> const dev_descriptor_t*
   {
-    if (std::holds_alternative<init_f>(value_)) { value_ = eval(std::get<init_f>(value_), stream); }
-    return std::get<dd_ptr_t>(value_).get();
+    return value_->get(stream);
   }
+
   [[nodiscard]] auto dev_ptr(rmm::cuda_stream_view stream) -> dev_descriptor_t*
   {
-    if (std::holds_alternative<init_f>(value_)) { value_ = eval(std::get<init_f>(value_), stream); }
-    return std::get<dd_ptr_t>(value_).get();
+    return value_->get(stream);
   }
 
  private:
-  mutable std::variant<dd_ptr_t, init_f> value_;
-
-  static auto eval(init_f init, rmm::cuda_stream_view stream) -> dd_ptr_t
-  {
-    using raft::RAFT_NAME;
-    auto& [fun, size] = init;
-    dd_ptr_t dev_ptr{
-      [stream, s = size]() {
-        dev_descriptor_t* p;
-        RAFT_CUDA_TRY(cudaMallocAsync(&p, s, stream));
-        return p;
-      }(),
-      [stream](dev_descriptor_t* p) { RAFT_CUDA_TRY_NO_THROW(cudaFreeAsync(p, stream)); }};
-    fun(dev_ptr.get(), stream);
-    return dev_ptr;
-  }
+  mutable std::shared_ptr<state> value_;
 };
 
 /**
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index abc907da5e..e6e7ff64fd 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -135,11 +135,9 @@ template <typename DataT, typename IndexT, typename DistanceT>
 struct store {
   /** Number of descriptors to cache. */
   static constexpr size_t kDefaultSize = 100;
-  raft::cache::lru<key,
-                   key_hash,
-                   std::equal_to<>,
-                   std::shared_ptr<dataset_descriptor_host<DataT, IndexT, DistanceT>>>
-    value{kDefaultSize};
+  raft::cache::
+    lru<key, key_hash, std::equal_to<>, dataset_descriptor_host<DataT, IndexT, DistanceT>>
+      value{kDefaultSize};
 };
 
 }  // namespace descriptor_cache
@@ -159,20 +157,18 @@ auto dataset_descriptor_init_with_cache(const raft::resources& res,
                                         const cagra::search_params& params,
                                         const DatasetT& dataset,
                                         cuvs::distance::DistanceType metric)
-  -> const dataset_descriptor_host<DataT, IndexT, DistanceT>&
+  -> dataset_descriptor_host<DataT, IndexT, DistanceT>
 {
-  using desc_t = dataset_descriptor_host<DataT, IndexT, DistanceT>;
-  auto key     = descriptor_cache::make_key(params, dataset, metric);
+  auto key = descriptor_cache::make_key(params, dataset, metric);
   auto& cache =
     raft::resource::get_custom_resource<descriptor_cache::store<DataT, IndexT, DistanceT>>(res)
       ->value;
-  std::shared_ptr<desc_t> desc{nullptr};
+  dataset_descriptor_host<DataT, IndexT, DistanceT> desc;
   if (!cache.get(key, &desc)) {
-    desc = std::make_shared<desc_t>(
-      std::move(dataset_descriptor_init<DataT, IndexT, DistanceT>(params, dataset, metric)));
+    desc = dataset_descriptor_init<DataT, IndexT, DistanceT>(params, dataset, metric);
     cache.set(key, desc);
   }
-  return *desc;
+  return desc;
 }
 
 };  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index 0003f24952..ecfd856f13 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -93,10 +93,10 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
   using base_type::num_seeds;
 
   uint32_t num_cta_per_query;
-  rmm::device_uvector<INDEX_T> intermediate_indices;
-  rmm::device_uvector<float> intermediate_distances;
+  lightweight_uvector<INDEX_T> intermediate_indices;
+  lightweight_uvector<float> intermediate_distances;
   size_t topk_workspace_size;
-  rmm::device_uvector<uint32_t> topk_workspace;
+  lightweight_uvector<uint32_t> topk_workspace;
 
   search(raft::resources const& res,
          search_params params,
@@ -105,9 +105,9 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
          int64_t graph_degree,
          uint32_t topk)
     : base_type(res, params, dataset_desc, dim, graph_degree, topk),
-      intermediate_indices(0, raft::resource::get_cuda_stream(res)),
-      intermediate_distances(0, raft::resource::get_cuda_stream(res)),
-      topk_workspace(0, raft::resource::get_cuda_stream(res))
+      intermediate_indices(res),
+      intermediate_distances(res),
+      topk_workspace(res)
 
   {
     set_params(res, params);
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index 9c22134a60..c6fe216429 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -91,6 +91,15 @@ void get_value(T* const host_ptr, const T* const dev_ptr, cudaStream_t cuda_stre
   get_value_kernel<T><<<1, 1, 0, cuda_stream>>>(host_ptr, dev_ptr);
 }
 
+template <class T>
+auto get_value(const T* const dev_ptr, cudaStream_t stream) -> T
+{
+  T value;
+  RAFT_CUDA_TRY(cudaMemcpyAsync(&value, dev_ptr, sizeof(value), cudaMemcpyDefault, stream));
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+  return value;
+}
+
 // MAX_DATASET_DIM : must equal to or greater than dataset_dim
 template <class DATASET_DESCRIPTOR_T>
 RAFT_KERNEL random_pickup_kernel(
@@ -609,18 +618,18 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
   using base_type::num_seeds;
 
   size_t result_buffer_allocation_size;
-  rmm::device_uvector<INDEX_T> result_indices;       // results_indices_buffer
-  rmm::device_uvector<DISTANCE_T> result_distances;  // result_distances_buffer
-  rmm::device_uvector<INDEX_T> parent_node_list;
-  rmm::device_uvector<uint32_t> topk_hint;
-  rmm::device_scalar<uint32_t> terminate_flag;  // dev_terminate_flag, host_terminate_flag.;
-  rmm::device_uvector<uint32_t> topk_workspace;
+  lightweight_uvector<INDEX_T> result_indices;       // results_indices_buffer
+  lightweight_uvector<DISTANCE_T> result_distances;  // result_distances_buffer
+  lightweight_uvector<INDEX_T> parent_node_list;
+  lightweight_uvector<uint32_t> topk_hint;
+  lightweight_uvector<uint32_t> terminate_flag;  // dev_terminate_flag, host_terminate_flag.;
+  lightweight_uvector<uint32_t> topk_workspace;
 
   // temporary storage for _find_topk
-  rmm::device_uvector<float> input_keys_storage;
-  rmm::device_uvector<float> output_keys_storage;
-  rmm::device_uvector<INDEX_T> input_values_storage;
-  rmm::device_uvector<INDEX_T> output_values_storage;
+  lightweight_uvector<float> input_keys_storage;
+  lightweight_uvector<float> output_keys_storage;
+  lightweight_uvector<INDEX_T> input_values_storage;
+  lightweight_uvector<INDEX_T> output_values_storage;
 
   search(raft::resources const& res,
          search_params params,
@@ -629,16 +638,16 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
          int64_t graph_degree,
          uint32_t topk)
     : base_type(res, params, dataset_desc, dim, graph_degree, topk),
-      result_indices(0, raft::resource::get_cuda_stream(res)),
-      result_distances(0, raft::resource::get_cuda_stream(res)),
-      parent_node_list(0, raft::resource::get_cuda_stream(res)),
-      topk_hint(0, raft::resource::get_cuda_stream(res)),
-      topk_workspace(0, raft::resource::get_cuda_stream(res)),
-      terminate_flag(raft::resource::get_cuda_stream(res)),
-      input_keys_storage(0, raft::resource::get_cuda_stream(res)),
-      output_keys_storage(0, raft::resource::get_cuda_stream(res)),
-      input_values_storage(0, raft::resource::get_cuda_stream(res)),
-      output_values_storage(0, raft::resource::get_cuda_stream(res))
+      result_indices(res),
+      result_distances(res),
+      parent_node_list(res),
+      topk_hint(res),
+      topk_workspace(res),
+      terminate_flag(res),
+      input_keys_storage(res),
+      output_keys_storage(res),
+      input_values_storage(res),
+      output_values_storage(res)
   {
     set_params(res);
   }
@@ -662,7 +671,7 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
       itopk_size, max_queries, result_buffer_size, utils::get_cuda_data_type<DATA_T>());
     RAFT_LOG_DEBUG("# topk_workspace_size: %lu", topk_workspace_size);
     topk_workspace.resize(topk_workspace_size, raft::resource::get_cuda_stream(res));
-
+    terminate_flag.resize(1, raft::resource::get_cuda_stream(res));
     hashmap.resize(hashmap_size, raft::resource::get_cuda_stream(res));
   }
 
@@ -847,7 +856,7 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
                           stream);
 
       // termination (2)
-      if (iter + 1 >= min_iterations && terminate_flag.value(stream)) {
+      if (iter + 1 >= min_iterations && get_value(terminate_flag.data(), stream)) {
         iter++;
         break;
       }
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index f23b966318..99254aa501 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -151,7 +151,7 @@ struct search_plan_impl : public search_plan_impl_base {
   lightweight_uvector<INDEX_T> hashmap;
   lightweight_uvector<uint32_t> num_executed_iterations;  // device or managed?
   lightweight_uvector<INDEX_T> dev_seed;
-  const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc;
+  dataset_descriptor_host<DataT, IndexT, DistanceT> dataset_desc;
 
   search_plan_impl(raft::resources const& res,
                    search_params params,

From 2d4afb515e3b509152adc652e3a9d97816b7bc3b Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 7 Nov 2024 11:23:15 -0500
Subject: [PATCH 201/623] Put a ceiling on cuda-python (#445)

This project is incompatible with newer versions of `cuda-python`. This puts ceilings of `<=11.8.3` (CUDA 11) and `<=12.6.0` (CUDA 12) on that library.

Those ceilings should be removed and replaced with `!=` constraints once new releases of `cuda-python` are up that this project is compatible with.

See https://github.com/rapidsai/build-planning/issues/116 for more information.

Authors:
  - Bradley Dice (https://github.com/bdice)
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/445
---
 conda/environments/all_cuda-118_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml        | 2 +-
 conda/environments/all_cuda-125_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-125_arch-x86_64.yaml        | 2 +-
 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/bench_ann_cuda-125_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-125_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs/meta.yaml                            | 8 +++++---
 cpp/test/neighbors/ann_ivf_flat.cuh                     | 1 +
 dependencies.yaml                                       | 4 ++--
 python/cuvs/pyproject.toml                              | 1 +
 12 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index aa12b4ed61..80bfb0c24d 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.7.1,<12.0a0,<=11.8.3
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 494ec394d1..07937726ce 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.7.1,<12.0a0,<=11.8.3
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index f4f03cceed..b7fd6fcfa9 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.0,<13.0a0,<=12.6.0
 - cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index a295e93f4a..83a457465a 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.0,<13.0a0,<=12.6.0
 - cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index a73839457c..21cb981807 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.7.1,<12.0a0,<=11.8.3
 - cuda-version=11.8
 - cudatoolkit
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 3f869da9a4..432509bcb3 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.7.1,<12.0a0,<=11.8.3
 - cuda-version=11.8
 - cudatoolkit
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 407fb60589..0c5043ac27 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.0,<13.0a0,<=12.6.0
 - cuda-version=12.5
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index 81943b1842..cbb22333c8 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.0,<13.0a0,<=12.6.0
 - cuda-version=12.5
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index e7e2daf0c6..560c95feb1 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -26,6 +26,7 @@ build:
     - {{ compiler('cuda') }}
     - cuda-cudart-dev
     {% endif %}
+    - cuda-python
 
 requirements:
   build:
@@ -42,10 +43,10 @@ requirements:
     - {{ stdlib("c") }}
   host:
     {% if cuda_major == "11" %}
-    - cuda-python >=11.7.1,<12.0a0
+    - cuda-python >=11.7.1,<12.0a0,<=11.8.3
     - cudatoolkit
     {% else %}
-    - cuda-python >=12.0,<13.0a0
+    - cuda-python >=12.0,<13.0a0,<=12.6.0
     - cuda-cudart-dev
     {% endif %}
     - cuda-version ={{ cuda_version }}
@@ -60,13 +61,14 @@ requirements:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major == "11" %}
     - cudatoolkit
+    - cuda-python >=11.7.1,<12.0a0,<=11.8.3
     {% else %}
     - cuda-cudart
+    - cuda-python >=12.0,<13.0a0,<=12.6.0
     {% endif %}
     - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
     - python x.x
-    - cuda-python
     - numpy >=1.23,<3.0a0
 
 tests:
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/test/neighbors/ann_ivf_flat.cuh
index 8cc46b2f7e..23d84ca98f 100644
--- a/cpp/test/neighbors/ann_ivf_flat.cuh
+++ b/cpp/test/neighbors/ann_ivf_flat.cuh
@@ -24,6 +24,7 @@
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <raft/linalg/normalize.cuh>
 #include <raft/stats/mean.cuh>
+#include <thrust/reduce.h>
 #include <thrust/sequence.h>
 
 #include <raft/core/resource/cuda_stream_pool.hpp>
diff --git a/dependencies.yaml b/dependencies.yaml
index cf9b68c8ad..e909ad0dc0 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -213,11 +213,11 @@ dependencies:
           - matrix:
               cuda: "12.*"
             packages:
-              - &cuda_python12 cuda-python>=12.0,<13.0a0
+              - &cuda_python12 cuda-python>=12.0,<13.0a0,<=12.6.0
           - matrix:
               cuda: "11.*"
             packages:
-              - &cuda_python11 cuda-python>=11.7.1,<12.0a0
+              - &cuda_python11 cuda-python>=11.7.1,<12.0a0,<=11.8.3
           - matrix:
             packages:
               - &cuda_python cuda-python
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index bf62f5adf4..30d784c673 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -136,4 +136,5 @@ matrix-entry = "cuda_suffixed=true;use_cuda_wheels=true"
 [tool.pytest.ini_options]
 filterwarnings = [
     "error",
+    "ignore:.*cuda..* module is deprecated.*:DeprecationWarning"
 ]

From e559d581acec030d8e71833aee1295fe442facb3 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 7 Nov 2024 21:04:57 -0500
Subject: [PATCH 202/623] Adding tech stack to docs (#448)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/448
---
 README.md             |   9 ++++++
 docs/source/index.rst |  72 +++++++++++++++++++++++++++++++++++-------
 img/tech_stack.png    | Bin 0 -> 125904 bytes
 3 files changed, 70 insertions(+), 11 deletions(-)
 create mode 100644 img/tech_stack.png

diff --git a/README.md b/README.md
index c1b74a9e87..572e8d0982 100755
--- a/README.md
+++ b/README.md
@@ -35,6 +35,7 @@ Finally, faster vector search enables interactions between dense vectors and gra
 
 Below are some common use-cases for vector search
 
+
 - ### Semantic search
   - Generative AI & Retrieval augmented generation (RAG)
   - Recommender systems
@@ -68,6 +69,14 @@ There are several benefits to using cuVS and GPUs for vector search, including
 
 In addition to the items above, cuVS takes on the burden of keeping non-trivial accelerated code up to date as new NVIDIA architectures and CUDA versions are released. This provides a deslightful development experimence, guaranteeing that any libraries, databases, or applications built on top of it will always be getting the best performance and scale. 
 
+## cuVS Technology Stack
+
+cuVS is built on top of the RAPIDS RAFT library of high performance machine learning primitives and provides all the necessary routines for vector search and clustering on the GPU. 
+
+![cuVS is built on top of low-level CUDA libraries and provides many important routines that enable vector search and clustering on the GPU](img/tech_stack.png "cuVS Technology Stack")
+
+
+
 ## Installing cuVS
 
 cuVS comes with pre-built packages that can be installed through [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-python) and [pip](https://pip.pypa.io/en/stable/). Different packages are available for the different languages supported by cuVS:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 647061ae5a..286836c186 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,19 +1,8 @@
 cuVS: Vector Search and Clustering on the GPU
 =============================================
 
-
 Welcome to cuVS, the premier library for GPU-accelerated vector search and clustering! cuVS provides several core building blocks for constructing new algorithms, as well as end-to-end vector search and clustering algorithms for use either standalone or through a growing list of :doc:`integrations <integrations>`.
 
-There are several benefits to using cuVS and GPUs for vector search, including
-
-#. Fast index build
-#. Latency critical and high throughput search
-#. Parameter tuning
-#. Cost savings
-#. Interoperability (build on GPU, deploy on CPU)
-#. Multiple language support
-#. Building blocks for composing new or accelerating existing algorithms
-
 Useful Resources
 ################
 
@@ -26,6 +15,67 @@ Useful Resources
 - `Issue tracker <https://github.com/rapidsai/cuvs/issues>`_: Report issues or request features.
 
 
+
+What is cuVS?
+#############
+
+cuVS contains state-of-the-art implementations of several algorithms for running approximate and exact nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.
+
+Vector search is an information retrieval method that has been growing in popularity over the past few  years, partly because of the rising importance of multimedia embeddings created from unstructured data and the need to perform semantic search on the embeddings to find items which are semantically similar to each other.
+
+Vector search is also used in *data mining and machine learning* tasks and comprises an important step in many *clustering* and *visualization* algorithms like `UMAP <https://arxiv.org/abs/2008.00325>`_, `t-SNE <https://lvdmaaten.github.io/tsne/>`_, K-means, and `HDBSCAN <https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html>`_.
+
+Finally, faster vector search enables interactions between dense vectors and graphs. Converting a pile of dense vectors into nearest neighbors graphs unlocks the entire world of graph analysis algorithms, such as those found in `GraphBLAS <https://graphblas.org/>`_ and `cuGraph <https://github.com/rapidsai/cugraph>`_.
+
+Below are some common use-cases for vector search
+
+Semantic search
+~~~~~~~~~~~~~~~
+- Generative AI & Retrieval augmented generation (RAG)
+- Recommender systems
+- Computer vision
+- Image search
+- Text search
+- Audio search
+- Molecular search
+- Model training
+
+
+Data mining
+~~~~~~~~~~~
+- Clustering algorithms
+- Visualization algorithms
+- Sampling algorithms
+- Class balancing
+- Ensemble methods
+- k-NN graph construction
+
+Why cuVS?
+#########
+
+There are several benefits to using cuVS and GPUs for vector search, including
+
+1. Fast index build
+2. Latency critical and high throughput search
+3. Parameter tuning
+4. Cost savings
+5. Interoperability (build on GPU, deploy on CPU)
+6. Multiple language support
+7. Building blocks for composing new or accelerating existing algorithms
+
+In addition to the items above, cuVS shoulders the responsibility of keeping non-trivial accelerated code up to date as new NVIDIA architectures and CUDA versions are released. This provides a deslightful development experimence, guaranteeing that any libraries, databases, or applications built on top of it will always be receiving the best performance and scale.
+
+cuVS Technology Stack
+#####################
+
+cuVS is built on top of the RAPIDS RAFT library of high performance machine learning primitives and provides all the necessary routines for vector search and clustering on the GPU.
+
+.. image:: ../../img/tech_stack.png
+  :width: 600
+  :alt: cuVS is built on top of low-level CUDA libraries and provides many important routines that enable vector search and clustering on the GPU
+
+
+
 Contents
 ########
 
diff --git a/img/tech_stack.png b/img/tech_stack.png
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From 29c0f5b8693bc8ca6e534e054cc91102f2bcf8f9 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Fri, 8 Nov 2024 00:59:05 -0500
Subject: [PATCH 203/623] Migrate feature diff for NN Descent from RAFT to cuVS
 (#421)

This PR is an amalgamation of the diff of 3 PRs in RAFT:

1. https://github.com/rapidsai/raft/pull/2345
2. https://github.com/rapidsai/raft/pull/2380
3. https://github.com/rapidsai/raft/pull/2403

This PR also addresses part 1 and 2 of #419, closes https://github.com/rapidsai/cuvs/issues/391 and makes CAGRA use the compiled headers of NN Descent, which seemed to have been a pending TODO https://github.com/rapidsai/cuvs/blob/009bb8de03ce9708d4d797166187250f77a59a36/cpp/src/neighbors/detail/cagra/cagra_build.cuh#L36-L37

Also, batch tests are disabled in this PR due to issue https://github.com/rapidsai/raft/issues/2450. PR https://github.com/rapidsai/cuvs/pull/424 will attempt to re-enable them.

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/421
---
 cpp/include/cuvs/neighbors/nn_descent.hpp     |  92 ++-
 .../neighbors/detail/cagra/cagra_build.cuh    |   8 +-
 cpp/src/neighbors/detail/nn_descent.cuh       | 294 ++++---
 cpp/src/neighbors/detail/nn_descent_batch.cuh | 736 ++++++++++++++++++
 cpp/src/neighbors/nn_descent.cuh              |  45 +-
 cpp/src/neighbors/nn_descent_float.cu         |  47 +-
 cpp/src/neighbors/nn_descent_half.cu          |  48 +-
 cpp/src/neighbors/nn_descent_int8.cu          |  48 +-
 cpp/src/neighbors/nn_descent_uint8.cu         |  48 +-
 cpp/test/neighbors/ann_nn_descent.cuh         | 191 ++++-
 .../ann_nn_descent/test_float_uint32_t.cu     |   6 +
 cpp/test/neighbors/ann_utils.cuh              |  20 +-
 python/cuvs/cuvs/test/test_hnsw.py            |   2 +-
 13 files changed, 1361 insertions(+), 224 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/nn_descent_batch.cuh

diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index 347ccf8894..bd41d1ff78 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -55,6 +55,8 @@ struct index_params : cuvs::neighbors::index_params {
   size_t intermediate_graph_degree = 128;     // Degree of input graph for pruning.
   size_t max_iterations            = 20;      // Number of nn-descent iterations.
   float termination_threshold      = 0.0001;  // Termination threshold of nn-descent.
+  bool return_distances            = true;    // return distances if true
+  size_t n_clusters                = 1;       // defaults to not using any batching
 
   /** @brief Construct NN descent parameters for a specific kNN graph degree
    *
@@ -100,14 +102,20 @@ struct index : cuvs::neighbors::index {
    * @param res raft::resources is an object mangaging resources
    * @param n_rows number of rows in knn-graph
    * @param n_cols number of cols in knn-graph
+   * @param return_distances whether to return distances
    */
-  index(raft::resources const& res, int64_t n_rows, int64_t n_cols)
+  index(raft::resources const& res, int64_t n_rows, int64_t n_cols, bool return_distances = false)
     : cuvs::neighbors::index(),
       res_{res},
       metric_{cuvs::distance::DistanceType::L2Expanded},
       graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(n_rows, n_cols)},
-      graph_view_{graph_.view()}
+      graph_view_{graph_.view()},
+      return_distances_{return_distances}
   {
+    if (return_distances) {
+      distances_      = raft::make_device_matrix<float, int64_t>(res_, n_rows, n_cols);
+      distances_view_ = distances_.value().view();
+    }
   }
 
   /**
@@ -119,14 +127,20 @@ struct index : cuvs::neighbors::index {
    *
    * @param res raft::resources is an object mangaging resources
    * @param graph_view raft::host_matrix_view<IdxT, int64_t, raft::row_major> for storing knn-graph
+   * @param distances_view optional raft::device_matrix_view<float, int64_t, row_major> for storing
+   * distances
    */
   index(raft::resources const& res,
-        raft::host_matrix_view<IdxT, int64_t, raft::row_major> graph_view)
+        raft::host_matrix_view<IdxT, int64_t, raft::row_major> graph_view,
+        std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances_view =
+          std::nullopt)
     : cuvs::neighbors::index(),
       res_{res},
       metric_{cuvs::distance::DistanceType::L2Expanded},
       graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(0, 0)},
-      graph_view_{graph_view}
+      graph_view_{graph_view},
+      distances_view_{distances_view},
+      return_distances_{distances_view.has_value()}
   {
   }
 
@@ -155,6 +169,13 @@ struct index : cuvs::neighbors::index {
     return graph_view_;
   }
 
+  /** neighborhood graph distances [size, graph-degree] */
+  [[nodiscard]] inline auto distances() noexcept
+    -> std::optional<device_matrix_view<float, int64_t, row_major>>
+  {
+    return distances_view_;
+  }
+
   // Don't allow copying the index for performance reasons (try avoiding copying data)
   index(const index&)                    = delete;
   index(index&&)                         = default;
@@ -166,8 +187,11 @@ struct index : cuvs::neighbors::index {
   raft::resources const& res_;
   cuvs::distance::DistanceType metric_;
   raft::host_matrix<IdxT, int64_t, raft::row_major> graph_;  // graph to return for non-int IdxT
+  std::optional<raft::device_matrix<float, int64_t, row_major>> distances_;
   raft::host_matrix_view<IdxT, int64_t, raft::row_major>
     graph_view_;  // view of graph for user provided matrix
+  std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances_view_;
+  bool return_distances_;
 };
 
 /** @} */
@@ -200,12 +224,15 @@ struct index : cuvs::neighbors::index {
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::device_matrix_view input dataset expected to be located
  *                in device memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
  * @brief Build nn-descent Index with dataset in host memory
@@ -232,12 +259,15 @@ auto build(raft::resources const& res,
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::host_matrix_view input dataset expected to be located
  *                in host memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
  * @brief Build nn-descent Index with dataset in device memory
@@ -262,12 +292,15 @@ auto build(raft::resources const& res,
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::device_matrix_view input dataset expected to be located
  *                in device memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
  * @brief Build nn-descent Index with dataset in host memory
@@ -294,12 +327,15 @@ auto build(raft::resources const& res,
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::host_matrix_view input dataset expected to be located
  *                in host memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
  * @brief Build nn-descent Index with dataset in device memory
@@ -324,12 +360,15 @@ auto build(raft::resources const& res,
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::device_matrix_view input dataset expected to be located
  *                in device memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+           raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
  * @brief Build nn-descent Index with dataset in host memory
@@ -356,12 +395,15 @@ auto build(raft::resources const& res,
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::host_matrix_view input dataset expected to be located
  *                in host memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+           raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
  * @brief Build nn-descent Index with dataset in device memory
@@ -386,14 +428,15 @@ auto build(raft::resources const& res,
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::device_matrix_view input dataset expected to be located
  *                in device memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
-
-/** @} */
+           raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
 /**
  * @brief Build nn-descent Index with dataset in host memory
@@ -420,12 +463,17 @@ auto build(raft::resources const& res,
  *               to run the nn-descent algorithm
  * @param[in] dataset raft::host_matrix_view input dataset expected to be located
  *                in host memory
+ * @param[in] graph optional raft::host_matrix_view<uint32_t, int64_t, raft::row_major> for owning
+ * the output graph
  * @return index<IdxT> index containing all-neighbors knn graph in host memory
  */
 auto build(raft::resources const& res,
            index_params const& params,
-           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::nn_descent::index<uint32_t>;
+           raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset,
+           std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
+             std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
+
+/** @} */
 
 /**
  * @brief Test if we have enough GPU memory to run NN descent algorithm.
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 9e4d453e3a..6209ff8196 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -33,8 +33,7 @@
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/refine.hpp>
 
-// TODO: Fixme- this needs to be migrated
-#include "../../nn_descent.cuh"
+#include <cuvs/neighbors/nn_descent.hpp>
 
 // TODO: This shouldn't be calling spatial/knn APIs
 #include "../ann_utils.cuh"
@@ -356,8 +355,8 @@ void build_knn_graph(
   raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
   cuvs::neighbors::nn_descent::index_params build_params)
 {
-  auto nn_descent_idx = cuvs::neighbors::nn_descent::index<IdxT>(res, knn_graph);
-  cuvs::neighbors::nn_descent::build<DataT, IdxT>(res, build_params, dataset, nn_descent_idx);
+  std::optional<raft::host_matrix_view<IdxT, int64_t, row_major>> graph_view = knn_graph;
+  auto nn_descent_idx = cuvs::neighbors::nn_descent::build(res, build_params, dataset, graph_view);
 
   using internal_IdxT = typename std::make_unsigned<IdxT>::type;
   using g_accessor    = typename decltype(nn_descent_idx.graph())::accessor_type;
@@ -471,6 +470,7 @@ index<T, IdxT> build(
     }
 
     // Use nn-descent to build CAGRA knn graph
+    nn_descent_params.return_distances = false;
     build_knn_graph<T, IdxT>(res, dataset, knn_graph->view(), nn_descent_params);
   }
 
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 8c5767c50c..883d82d76a 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -16,42 +16,41 @@
 
 #pragma once
 
-#include <cuvs/neighbors/nn_descent.hpp>
-
 #include "ann_utils.cuh"
 #include "cagra/device_common.hpp"
+
+#include <cuvs/neighbors/nn_descent.hpp>
+
 #include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
 #include <raft/core/error.hpp>
 #include <raft/core/host_mdarray.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/pinned_mdarray.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
-
+#include <raft/matrix/init.cuh>
+#include <raft/matrix/slice.cuh>
 #include <raft/util/arch.cuh>  // raft::util::arch::SM_*
 #include <raft/util/cuda_dev_essentials.cuh>
 #include <raft/util/cuda_rt_essentials.hpp>
 #include <raft/util/cudart_utils.hpp>
 #include <raft/util/pow2_utils.cuh>
 
-#include <cub/cub.cuh>
+#include <rmm/device_uvector.hpp>
+
 #include <cuda_runtime.h>
-#include <thrust/execution_policy.h>
-#include <thrust/fill.h>
-#include <thrust/host_vector.h>
-#include <thrust/mr/allocator.h>
-#include <thrust/mr/device_memory_resource.h>
 
 #include <mma.h>
 #include <omp.h>
 
 #include <limits>
+#include <optional>
 #include <queue>
 #include <random>
 
 namespace cuvs::neighbors::nn_descent::detail {
-static const std::string RAFT_NAME = "raft";
-using pinned_memory_resource       = thrust::universal_host_pinned_memory_resource;
-template <typename T>
-using pinned_memory_allocator = thrust::mr::stateless_resource_allocator<T, pinned_memory_resource>;
 
 using DistData_t = float;
 constexpr int DEGREE_ON_DEVICE{32};
@@ -216,6 +215,7 @@ struct BuildConfig {
   // If internal_node_degree == 0, the value of node_degree will be assigned to it
   size_t max_iterations{50};
   float termination_threshold{0.0001};
+  size_t output_graph_degree{32};
 };
 
 template <typename Index_t>
@@ -300,6 +300,7 @@ class BloomFilter {
 
 template <typename Index_t>
 struct GnndGraph {
+  raft::resources const& res;
   static constexpr int segment_size = 32;
   InternalID_t<Index_t>* h_graph;
 
@@ -310,16 +311,17 @@ struct GnndGraph {
 
   raft::host_matrix<DistData_t, size_t, raft::row_major> h_dists;
 
-  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_graph_new;
-  thrust::host_vector<int2, pinned_memory_allocator<int2>> h_list_sizes_new;
+  raft::pinned_matrix<Index_t, size_t> h_graph_new;
+  raft::pinned_vector<int2, size_t> h_list_sizes_new;
 
-  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_graph_old;
-  thrust::host_vector<int2, pinned_memory_allocator<int2>> h_list_sizes_old;
+  raft::pinned_matrix<Index_t, size_t> h_graph_old;
+  raft::pinned_vector<int2, size_t> h_list_sizes_old;
   BloomFilter<Index_t> bloom_filter;
 
   GnndGraph(const GnndGraph&)            = delete;
   GnndGraph& operator=(const GnndGraph&) = delete;
-  GnndGraph(const size_t nrow,
+  GnndGraph(raft::resources const& res,
+            const size_t nrow,
             const size_t node_degree,
             const size_t internal_node_degree,
             const size_t num_samples);
@@ -344,9 +346,14 @@ class GNND {
   GNND(const GNND&)            = delete;
   GNND& operator=(const GNND&) = delete;
 
-  void build(Data_t* data, const Index_t nrow, Index_t* output_graph);
+  void build(Data_t* data,
+             const Index_t nrow,
+             Index_t* output_graph,
+             bool return_distances,
+             DistData_t* output_distances);
   ~GNND()    = default;
   using ID_t = InternalID_t<Index_t>;
+  void reset(raft::resources const& res);
 
  private:
   void add_reverse_edges(Index_t* graph_ptr,
@@ -371,15 +378,14 @@ class GNND {
   raft::device_matrix<ID_t, size_t, raft::row_major> graph_buffer_;
   raft::device_matrix<DistData_t, size_t, raft::row_major> dists_buffer_;
 
-  // TODO: Investigate using RMM/RAFT types https://github.com/rapidsai/raft/issues/1827
-  thrust::host_vector<ID_t, pinned_memory_allocator<ID_t>> graph_host_buffer_;
-  thrust::host_vector<DistData_t, pinned_memory_allocator<DistData_t>> dists_host_buffer_;
+  raft::pinned_matrix<ID_t, size_t> graph_host_buffer_;
+  raft::pinned_matrix<DistData_t, size_t> dists_host_buffer_;
 
   raft::device_vector<int, size_t> d_locks_;
 
-  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_rev_graph_new_;
-  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_graph_old_;
-  thrust::host_vector<Index_t, pinned_memory_allocator<Index_t>> h_rev_graph_old_;
+  raft::pinned_matrix<Index_t, size_t> h_rev_graph_new_;
+  raft::pinned_matrix<Index_t, size_t> h_graph_old_;
+  raft::pinned_matrix<Index_t, size_t> h_rev_graph_old_;
   // int2.x is the number of forward edges, int2.y is the number of reverse edges
 
   raft::device_vector<int2, size_t> d_list_sizes_new_;
@@ -971,19 +977,21 @@ int insert_to_ordered_list(InternalID_t<Index_t>* list,
 }  // namespace
 
 template <typename Index_t>
-GnndGraph<Index_t>::GnndGraph(const size_t nrow,
+GnndGraph<Index_t>::GnndGraph(raft::resources const& res,
+                              const size_t nrow,
                               const size_t node_degree,
                               const size_t internal_node_degree,
                               const size_t num_samples)
-  : nrow(nrow),
+  : res(res),
+    nrow(nrow),
     node_degree(node_degree),
     num_samples(num_samples),
     bloom_filter(nrow, internal_node_degree / segment_size, 3),
     h_dists{raft::make_host_matrix<DistData_t, size_t, raft::row_major>(nrow, node_degree)},
-    h_graph_new(nrow * num_samples),
-    h_list_sizes_new(nrow),
-    h_graph_old(nrow * num_samples),
-    h_list_sizes_old{nrow}
+    h_graph_new{raft::make_pinned_matrix<Index_t, size_t, raft::row_major>(res, nrow, num_samples)},
+    h_list_sizes_new{raft::make_pinned_vector<int2, size_t>(res, nrow)},
+    h_graph_old{raft::make_pinned_matrix<Index_t, size_t, raft::row_major>(res, nrow, num_samples)},
+    h_list_sizes_old{raft::make_pinned_vector<int2, size_t>(res, nrow)}
 {
   // node_degree must be a multiple of segment_size;
   assert(node_degree % segment_size == 0);
@@ -1001,9 +1009,9 @@ void GnndGraph<Index_t>::sample_graph_new(InternalID_t<Index_t>* new_neighbors,
 {
 #pragma omp parallel for
   for (size_t i = 0; i < nrow; i++) {
-    auto list_new         = h_graph_new.data() + i * num_samples;
-    h_list_sizes_new[i].x = 0;
-    h_list_sizes_new[i].y = 0;
+    auto list_new                       = h_graph_new.data_handle() + i * num_samples;
+    h_list_sizes_new.data_handle()[i].x = 0;
+    h_list_sizes_new.data_handle()[i].y = 0;
 
     for (size_t j = 0; j < width; j++) {
       auto new_neighb_id = new_neighbors[i * width + j].id();
@@ -1011,8 +1019,8 @@ void GnndGraph<Index_t>::sample_graph_new(InternalID_t<Index_t>* new_neighbors,
       if (bloom_filter.check(i, new_neighb_id)) { continue; }
       bloom_filter.add(i, new_neighb_id);
       new_neighbors[i * width + j].mark_old();
-      list_new[h_list_sizes_new[i].x++] = new_neighb_id;
-      if (h_list_sizes_new[i].x == num_samples) break;
+      list_new[h_list_sizes_new.data_handle()[i].x++] = new_neighb_id;
+      if (h_list_sizes_new.data_handle()[i].x == num_samples) break;
     }
   }
 }
@@ -1051,31 +1059,37 @@ void GnndGraph<Index_t>::sample_graph(bool sample_new)
 {
 #pragma omp parallel for
   for (size_t i = 0; i < nrow; i++) {
-    h_list_sizes_old[i].x = 0;
-    h_list_sizes_old[i].y = 0;
-    h_list_sizes_new[i].x = 0;
-    h_list_sizes_new[i].y = 0;
+    h_list_sizes_old.data_handle()[i].x = 0;
+    h_list_sizes_old.data_handle()[i].y = 0;
+    h_list_sizes_new.data_handle()[i].x = 0;
+    h_list_sizes_new.data_handle()[i].y = 0;
 
     auto list     = h_graph + i * node_degree;
-    auto list_old = h_graph_old.data() + i * num_samples;
-    auto list_new = h_graph_new.data() + i * num_samples;
+    auto list_old = h_graph_old.data_handle() + i * num_samples;
+    auto list_new = h_graph_new.data_handle() + i * num_samples;
     for (int j = 0; j < segment_size; j++) {
       for (int k = 0; k < num_segments; k++) {
         auto neighbor = list[k * segment_size + j];
         if ((size_t)neighbor.id() >= nrow) continue;
         if (!neighbor.is_new()) {
-          if (h_list_sizes_old[i].x < num_samples) {
-            list_old[h_list_sizes_old[i].x++] = neighbor.id();
+          if (h_list_sizes_old.data_handle()[i].x < num_samples) {
+            list_old[h_list_sizes_old.data_handle()[i].x++] = neighbor.id();
           }
         } else if (sample_new) {
-          if (h_list_sizes_new[i].x < num_samples) {
+          if (h_list_sizes_new.data_handle()[i].x < num_samples) {
             list[k * segment_size + j].mark_old();
-            list_new[h_list_sizes_new[i].x++] = neighbor.id();
+            list_new[h_list_sizes_new.data_handle()[i].x++] = neighbor.id();
           }
         }
-        if (h_list_sizes_old[i].x == num_samples && h_list_sizes_new[i].x == num_samples) { break; }
+        if (h_list_sizes_old.data_handle()[i].x == num_samples &&
+            h_list_sizes_new.data_handle()[i].x == num_samples) {
+          break;
+        }
+      }
+      if (h_list_sizes_old.data_handle()[i].x == num_samples &&
+          h_list_sizes_new.data_handle()[i].x == num_samples) {
+        break;
       }
-      if (h_list_sizes_old[i].x == num_samples && h_list_sizes_new[i].x == num_samples) { break; }
     }
   }
 }
@@ -1137,7 +1151,8 @@ template <typename Data_t, typename Index_t>
 GNND<Data_t, Index_t>::GNND(raft::resources const& res, const BuildConfig& build_config)
   : res(res),
     build_config_(build_config),
-    graph_(build_config.max_dataset_size,
+    graph_(res,
+           build_config.max_dataset_size,
            align32::roundUp(build_config.node_degree),
            align32::roundUp(build_config.internal_node_degree ? build_config.internal_node_degree
                                                               : build_config.node_degree),
@@ -1151,28 +1166,38 @@ GNND<Data_t, Index_t>::GNND(raft::resources const& res, const BuildConfig& build
       raft::make_device_matrix<ID_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
     dists_buffer_{
       raft::make_device_matrix<DistData_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
-    graph_host_buffer_(nrow_ * DEGREE_ON_DEVICE),
-    dists_host_buffer_(nrow_ * DEGREE_ON_DEVICE),
+    graph_host_buffer_{
+      raft::make_pinned_matrix<ID_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
+    dists_host_buffer_{
+      raft::make_pinned_matrix<DistData_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
     d_locks_{raft::make_device_vector<int, size_t>(res, nrow_)},
-    h_rev_graph_new_(nrow_ * NUM_SAMPLES),
-    h_graph_old_(nrow_ * NUM_SAMPLES),
-    h_rev_graph_old_(nrow_ * NUM_SAMPLES),
+    h_rev_graph_new_{
+      raft::make_pinned_matrix<Index_t, size_t, raft::row_major>(res, nrow_, NUM_SAMPLES)},
+    h_graph_old_(
+      raft::make_pinned_matrix<Index_t, size_t, raft::row_major>(res, nrow_, NUM_SAMPLES)),
+    h_rev_graph_old_{
+      raft::make_pinned_matrix<Index_t, size_t, raft::row_major>(res, nrow_, NUM_SAMPLES)},
     d_list_sizes_new_{raft::make_device_vector<int2, size_t>(res, nrow_)},
     d_list_sizes_old_{raft::make_device_vector<int2, size_t>(res, nrow_)}
 {
   static_assert(NUM_SAMPLES <= 32);
-
-  thrust::fill(thrust::device,
-               dists_buffer_.data_handle(),
-               dists_buffer_.data_handle() + dists_buffer_.size(),
-               std::numeric_limits<float>::max());
-  thrust::fill(thrust::device,
-               reinterpret_cast<Index_t*>(graph_buffer_.data_handle()),
-               reinterpret_cast<Index_t*>(graph_buffer_.data_handle()) + graph_buffer_.size(),
-               std::numeric_limits<Index_t>::max());
-  thrust::fill(thrust::device, d_locks_.data_handle(), d_locks_.data_handle() + d_locks_.size(), 0);
+  raft::matrix::fill(res, dists_buffer_.view(), std::numeric_limits<float>::max());
+  auto graph_buffer_view = raft::make_device_matrix_view<Index_t, int64_t>(
+    reinterpret_cast<Index_t*>(graph_buffer_.data_handle()), nrow_, DEGREE_ON_DEVICE);
+  raft::matrix::fill(res, graph_buffer_view, std::numeric_limits<Index_t>::max());
+  raft::matrix::fill(res, d_locks_.view(), 0);
 };
 
+template <typename Data_t, typename Index_t>
+void GNND<Data_t, Index_t>::reset(raft::resources const& res)
+{
+  raft::matrix::fill(res, dists_buffer_.view(), std::numeric_limits<float>::max());
+  auto graph_buffer_view = raft::make_device_matrix_view<Index_t, int64_t>(
+    reinterpret_cast<Index_t*>(graph_buffer_.data_handle()), nrow_, DEGREE_ON_DEVICE);
+  raft::matrix::fill(res, graph_buffer_view, std::numeric_limits<Index_t>::max());
+  raft::matrix::fill(res, d_locks_.view(), 0);
+}
+
 template <typename Data_t, typename Index_t>
 void GNND<Data_t, Index_t>::add_reverse_edges(Index_t* graph_ptr,
                                               Index_t* h_rev_graph_ptr,
@@ -1189,34 +1214,35 @@ void GNND<Data_t, Index_t>::add_reverse_edges(Index_t* graph_ptr,
 template <typename Data_t, typename Index_t>
 void GNND<Data_t, Index_t>::local_join(cudaStream_t stream)
 {
-  thrust::fill(thrust::device.on(stream),
-               dists_buffer_.data_handle(),
-               dists_buffer_.data_handle() + dists_buffer_.size(),
-               std::numeric_limits<float>::max());
-  local_join_kernel<<<nrow_, BLOCK_SIZE, 0, stream>>>(
-    thrust::raw_pointer_cast(graph_.h_graph_new.data()),
-    thrust::raw_pointer_cast(h_rev_graph_new_.data()),
-    d_list_sizes_new_.data_handle(),
-    thrust::raw_pointer_cast(h_graph_old_.data()),
-    thrust::raw_pointer_cast(h_rev_graph_old_.data()),
-    d_list_sizes_old_.data_handle(),
-    NUM_SAMPLES,
-    d_data_.data_handle(),
-    ndim_,
-    graph_buffer_.data_handle(),
-    dists_buffer_.data_handle(),
-    DEGREE_ON_DEVICE,
-    d_locks_.data_handle(),
-    l2_norms_.data_handle());
+  raft::matrix::fill(res, dists_buffer_.view(), std::numeric_limits<float>::max());
+  local_join_kernel<<<nrow_, BLOCK_SIZE, 0, stream>>>(graph_.h_graph_new.data_handle(),
+                                                      h_rev_graph_new_.data_handle(),
+                                                      d_list_sizes_new_.data_handle(),
+                                                      h_graph_old_.data_handle(),
+                                                      h_rev_graph_old_.data_handle(),
+                                                      d_list_sizes_old_.data_handle(),
+                                                      NUM_SAMPLES,
+                                                      d_data_.data_handle(),
+                                                      ndim_,
+                                                      graph_buffer_.data_handle(),
+                                                      dists_buffer_.data_handle(),
+                                                      DEGREE_ON_DEVICE,
+                                                      d_locks_.data_handle(),
+                                                      l2_norms_.data_handle());
 }
 
 template <typename Data_t, typename Index_t>
-void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* output_graph)
+void GNND<Data_t, Index_t>::build(Data_t* data,
+                                  const Index_t nrow,
+                                  Index_t* output_graph,
+                                  bool return_distances,
+                                  DistData_t* output_distances)
 {
   using input_t = typename std::remove_const<Data_t>::type;
 
   cudaStream_t stream = raft::resource::get_cuda_stream(res);
   nrow_               = nrow;
+  graph_.nrow         = nrow;
   graph_.h_graph      = (InternalID_t<Index_t>*)output_graph;
 
   cudaPointerAttributes data_ptr_attr;
@@ -1226,24 +1252,18 @@ void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* out
   cuvs::spatial::knn::detail::utils::batch_load_iterator vec_batches{
     data, static_cast<size_t>(nrow_), build_config_.dataset_dim, batch_size, stream};
   for (auto const& batch : vec_batches) {
-    preprocess_data_kernel<<<batch.size(),
-                             raft::warp_size(),
-                             sizeof(Data_t) *
-                               raft::ceildiv(build_config_.dataset_dim,
-                                             static_cast<size_t>(raft::warp_size())) *
-                               raft::warp_size(),
-                             stream>>>(batch.data(),
-                                       d_data_.data_handle(),
-                                       build_config_.dataset_dim,
-                                       l2_norms_.data_handle(),
-                                       batch.offset());
+    preprocess_data_kernel<<<
+      batch.size(),
+      raft::warp_size(),
+      sizeof(Data_t) * ceildiv(build_config_.dataset_dim, static_cast<size_t>(raft::warp_size())) *
+        raft::warp_size(),
+      stream>>>(batch.data(),
+                d_data_.data_handle(),
+                build_config_.dataset_dim,
+                l2_norms_.data_handle(),
+                batch.offset());
   }
 
-  thrust::fill(thrust::device.on(stream),
-               (Index_t*)graph_buffer_.data_handle(),
-               (Index_t*)graph_buffer_.data_handle() + graph_buffer_.size(),
-               std::numeric_limits<Index_t>::max());
-
   graph_.clear();
   graph_.init_random_graph();
   graph_.sample_graph(true);
@@ -1251,8 +1271,8 @@ void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* out
   auto update_and_sample = [&](bool update_graph) {
     if (update_graph) {
       update_counter_ = 0;
-      graph_.update_graph(thrust::raw_pointer_cast(graph_host_buffer_.data()),
-                          thrust::raw_pointer_cast(dists_host_buffer_.data()),
+      graph_.update_graph(graph_host_buffer_.data_handle(),
+                          dists_host_buffer_.data_handle(),
                           DEGREE_ON_DEVICE,
                           update_counter_);
       if (update_counter_ < build_config_.termination_threshold * nrow_ *
@@ -1265,15 +1285,15 @@ void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* out
 
   for (size_t it = 0; it < build_config_.max_iterations; it++) {
     raft::copy(d_list_sizes_new_.data_handle(),
-               thrust::raw_pointer_cast(graph_.h_list_sizes_new.data()),
+               graph_.h_list_sizes_new.data_handle(),
                nrow_,
                raft::resource::get_cuda_stream(res));
-    raft::copy(thrust::raw_pointer_cast(h_graph_old_.data()),
-               thrust::raw_pointer_cast(graph_.h_graph_old.data()),
+    raft::copy(h_graph_old_.data_handle(),
+               graph_.h_graph_old.data_handle(),
                nrow_ * NUM_SAMPLES,
                raft::resource::get_cuda_stream(res));
     raft::copy(d_list_sizes_old_.data_handle(),
-               thrust::raw_pointer_cast(graph_.h_list_sizes_old.data()),
+               graph_.h_list_sizes_old.data_handle(),
                nrow_,
                raft::resource::get_cuda_stream(res));
     raft::resource::sync_stream(res);
@@ -1286,13 +1306,13 @@ void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* out
     // contains some information for local_join.
     static_assert(DEGREE_ON_DEVICE * sizeof(*(dists_buffer_.data_handle())) >=
                   NUM_SAMPLES * sizeof(*(graph_buffer_.data_handle())));
-    add_reverse_edges(thrust::raw_pointer_cast(graph_.h_graph_new.data()),
-                      thrust::raw_pointer_cast(h_rev_graph_new_.data()),
+    add_reverse_edges(graph_.h_graph_new.data_handle(),
+                      h_rev_graph_new_.data_handle(),
                       (Index_t*)dists_buffer_.data_handle(),
                       d_list_sizes_new_.data_handle(),
                       stream);
-    add_reverse_edges(thrust::raw_pointer_cast(h_graph_old_.data()),
-                      thrust::raw_pointer_cast(h_rev_graph_old_.data()),
+    add_reverse_edges(h_graph_old_.data_handle(),
+                      h_rev_graph_old_.data_handle(),
                       (Index_t*)dists_buffer_.data_handle(),
                       d_list_sizes_old_.data_handle(),
                       stream);
@@ -1316,21 +1336,21 @@ void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* out
     update_and_sample_thread.join();
 
     if (update_counter_ == -1) { break; }
-    raft::copy(thrust::raw_pointer_cast(graph_host_buffer_.data()),
+    raft::copy(graph_host_buffer_.data_handle(),
                graph_buffer_.data_handle(),
                nrow_ * DEGREE_ON_DEVICE,
                raft::resource::get_cuda_stream(res));
     raft::resource::sync_stream(res);
-    raft::copy(thrust::raw_pointer_cast(dists_host_buffer_.data()),
+    raft::copy(dists_host_buffer_.data_handle(),
                dists_buffer_.data_handle(),
                nrow_ * DEGREE_ON_DEVICE,
                raft::resource::get_cuda_stream(res));
 
-    graph_.sample_graph_new(thrust::raw_pointer_cast(graph_host_buffer_.data()), DEGREE_ON_DEVICE);
+    graph_.sample_graph_new(graph_host_buffer_.data_handle(), DEGREE_ON_DEVICE);
   }
 
-  graph_.update_graph(thrust::raw_pointer_cast(graph_host_buffer_.data()),
-                      thrust::raw_pointer_cast(dists_host_buffer_.data()),
+  graph_.update_graph(graph_host_buffer_.data_handle(),
+                      dists_host_buffer_.data_handle(),
                       DEGREE_ON_DEVICE,
                       update_counter_);
   raft::resource::sync_stream(res);
@@ -1338,6 +1358,27 @@ void GNND<Data_t, Index_t>::build(Data_t* data, const Index_t nrow, Index_t* out
 
   // Reuse graph_.h_dists as the buffer for shrink the lists in graph
   static_assert(sizeof(decltype(*(graph_.h_dists.data_handle()))) >= sizeof(Index_t));
+
+  if (return_distances) {
+    auto graph_d_dists = raft::make_device_matrix<DistData_t, int64_t, raft::row_major>(
+      res, nrow_, build_config_.node_degree);
+    raft::copy(graph_d_dists.data_handle(),
+               graph_.h_dists.data_handle(),
+               nrow_ * build_config_.node_degree,
+               raft::resource::get_cuda_stream(res));
+
+    auto output_dist_view = raft::make_device_matrix_view<DistData_t, int64_t, raft::row_major>(
+      output_distances, nrow_, build_config_.output_graph_degree);
+
+    raft::matrix::slice_coordinates coords{static_cast<int64_t>(0),
+                                           static_cast<int64_t>(0),
+                                           static_cast<int64_t>(nrow_),
+                                           static_cast<int64_t>(build_config_.output_graph_degree)};
+    raft::matrix::slice<DistData_t, int64_t, raft::row_major>(
+      res, raft::make_const_mdspan(graph_d_dists.view()), output_dist_view, coords);
+    raft::resource::sync_stream(res);
+  }
+
   Index_t* graph_shrink_buffer = (Index_t*)graph_.h_dists.data_handle();
 
 #pragma omp parallel for
@@ -1410,10 +1451,24 @@ void build(raft::resources const& res,
                            .node_degree           = extended_graph_degree,
                            .internal_node_degree  = extended_intermediate_degree,
                            .max_iterations        = params.max_iterations,
-                           .termination_threshold = params.termination_threshold};
+                           .termination_threshold = params.termination_threshold,
+                           .output_graph_degree   = params.graph_degree};
 
   GNND<const T, int> nnd(res, build_config);
-  nnd.build(dataset.data_handle(), dataset.extent(0), int_graph.data_handle());
+
+  if (idx.distances().has_value() || !params.return_distances) {
+    nnd.build(dataset.data_handle(),
+              dataset.extent(0),
+              int_graph.data_handle(),
+              params.return_distances,
+              idx.distances()
+                .value_or(raft::make_device_matrix<float, int64_t>(res, 0, 0).view())
+                .data_handle());
+  } else {
+    RAFT_EXPECTS(!params.return_distances,
+                 "Distance view not allocated. Using return_distances set to true requires "
+                 "distance view to be allocated.");
+  }
 
 #pragma omp parallel for
   for (size_t i = 0; i < static_cast<size_t>(dataset.extent(0)); i++) {
@@ -1445,11 +1500,12 @@ index<IdxT> build(
     graph_degree = intermediate_degree;
   }
 
-  index<IdxT> idx{res, dataset.extent(0), static_cast<int64_t>(graph_degree)};
+  index<IdxT> idx{
+    res, dataset.extent(0), static_cast<int64_t>(graph_degree), params.return_distances};
 
   build(res, params, dataset, idx);
 
   return idx;
 }
 
-}  // namespace  cuvs::neighbors::nn_descent::detail
+}  // namespace cuvs::neighbors::nn_descent::detail
diff --git a/cpp/src/neighbors/detail/nn_descent_batch.cuh b/cpp/src/neighbors/detail/nn_descent_batch.cuh
new file mode 100644
index 0000000000..842dbe7887
--- /dev/null
+++ b/cpp/src/neighbors/detail/nn_descent_batch.cuh
@@ -0,0 +1,736 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/core/resources.hpp>
+#include <raft/util/cudart_utils.hpp>
+#include <sys/types.h>
+#undef RAFT_EXPLICIT_INSTANTIATE_ONLY
+
+#include "nn_descent.cuh"
+#include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/managed_mdarray.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/matrix/detail/gather_inplace.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/matrix/sample_rows.cuh>
+
+#include <thrust/copy.h>
+
+#include <vector_types.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <optional>
+#include <random>
+#include <type_traits>
+
+namespace cuvs::neighbors::nn_descent::detail::experimental {
+
+//
+// Run balanced kmeans on a subsample of the dataset to get centroids
+//
+template <typename T,
+          typename IdxT = uint32_t,
+          typename Accessor =
+            host_device_accessor<std::experimental::default_accessor<T>, memory_type::host>>
+void get_balanced_kmeans_centroids(
+  raft::resources const& res,
+  cuvs::distance::DistanceType metric,
+  mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset,
+  raft::device_matrix_view<T, IdxT> centroids)
+{
+  size_t num_rows   = static_cast<size_t>(dataset.extent(0));
+  size_t num_cols   = static_cast<size_t>(dataset.extent(1));
+  size_t n_clusters = centroids.extent(0);
+  size_t num_subsamples =
+    std::min(static_cast<size_t>(num_rows / n_clusters), static_cast<size_t>(num_rows * 0.1));
+
+  auto d_subsample_dataset =
+    raft::make_device_matrix<T, int64_t, raft::row_major>(res, num_subsamples, num_cols);
+  raft::matrix::sample_rows<T, int64_t, Accessor>(
+    res, raft::random::RngState{0}, dataset, d_subsample_dataset.view());
+
+  cuvs::cluster::kmeans::balanced_params kmeans_params;
+  kmeans_params.metric = metric;
+
+  auto d_subsample_dataset_const_view =
+    raft::make_device_matrix_view<const T, int, raft::row_major>(
+      d_subsample_dataset.data_handle(), num_subsamples, num_cols);
+  auto centroids_view = raft::make_device_matrix_view<T, int, raft::row_major>(
+    centroids.data_handle(), n_clusters, num_cols);
+  cuvs::cluster::kmeans::fit(res, kmeans_params, d_subsample_dataset_const_view, centroids_view);
+}
+
+//
+// Get the top k closest centroid indices for each data point
+// Loads the data in batches onto device if data is on host for memory efficiency
+//
+template <typename T, typename IdxT = uint32_t>
+void get_global_nearest_k(
+  raft::resources const& res,
+  size_t k,
+  size_t num_rows,
+  size_t n_clusters,
+  const T* dataset,
+  raft::host_matrix_view<IdxT, IdxT, raft::row_major> global_nearest_cluster,
+  raft::device_matrix_view<T, IdxT, raft::row_major> centroids,
+  cuvs::distance::DistanceType metric)
+{
+  size_t num_cols     = centroids.extent(1);
+  auto centroids_view = raft::make_device_matrix_view<const T, int64_t, raft::row_major>(
+    centroids.data_handle(), n_clusters, num_cols);
+
+  cudaPointerAttributes attr;
+  RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, dataset));
+  float* ptr = reinterpret_cast<float*>(attr.devicePointer);
+
+  size_t num_batches = n_clusters;
+  size_t batch_size  = (num_rows + n_clusters) / n_clusters;
+  if (ptr == nullptr) {  // data on host
+
+    auto d_dataset_batch =
+      raft::make_device_matrix<T, int64_t, raft::row_major>(res, batch_size, num_cols);
+
+    auto nearest_clusters_idx =
+      raft::make_device_matrix<int64_t, int64_t, raft::row_major>(res, batch_size, k);
+    auto nearest_clusters_idxt =
+      raft::make_device_matrix<IdxT, int64_t, raft::row_major>(res, batch_size, k);
+    auto nearest_clusters_dist =
+      raft::make_device_matrix<T, int64_t, raft::row_major>(res, batch_size, k);
+
+    for (size_t i = 0; i < num_batches; i++) {
+      size_t batch_size_ = batch_size;
+
+      if (i == num_batches - 1) { batch_size_ = num_rows - batch_size * i; }
+      raft::copy(d_dataset_batch.data_handle(),
+                 dataset + i * batch_size * num_cols,
+                 batch_size_ * num_cols,
+                 resource::get_cuda_stream(res));
+
+      std::optional<raft::device_vector_view<const T, int64_t>> norms_view;
+      cuvs::neighbors::brute_force::index<T> brute_force_index(
+        res, centroids_view, norms_view, metric);
+      cuvs::neighbors::brute_force::search(res,
+                                           brute_force_index,
+                                           raft::make_const_mdspan(d_dataset_batch.view()),
+                                           nearest_clusters_idx.view(),
+                                           nearest_clusters_dist.view());
+
+      thrust::copy(raft::resource::get_thrust_policy(res),
+                   nearest_clusters_idx.data_handle(),
+                   nearest_clusters_idx.data_handle() + nearest_clusters_idx.size(),
+                   nearest_clusters_idxt.data_handle());
+      raft::copy(global_nearest_cluster.data_handle() + i * batch_size * k,
+                 nearest_clusters_idxt.data_handle(),
+                 batch_size_ * k,
+                 resource::get_cuda_stream(res));
+    }
+  } else {  // data on device
+    auto nearest_clusters_idx =
+      raft::make_device_matrix<int64_t, int64_t, raft::row_major>(res, num_rows, k);
+    auto nearest_clusters_dist =
+      raft::make_device_matrix<T, int64_t, raft::row_major>(res, num_rows, k);
+
+    std::optional<raft::device_vector_view<const T, int64_t>> norms_view;
+    cuvs::neighbors::brute_force::index<T> brute_force_index(
+      res, centroids_view, norms_view, metric);
+    auto dataset_view =
+      raft::make_device_matrix_view<const T, int64_t, raft::row_major>(dataset, num_rows, num_cols);
+    cuvs::neighbors::brute_force::search(res,
+                                         brute_force_index,
+                                         dataset_view,
+                                         nearest_clusters_idx.view(),
+                                         nearest_clusters_dist.view());
+
+    auto nearest_clusters_idxt =
+      raft::make_device_matrix<IdxT, int64_t, raft::row_major>(res, batch_size, k);
+    for (size_t i = 0; i < num_batches; i++) {
+      size_t batch_size_ = batch_size;
+
+      if (i == num_batches - 1) { batch_size_ = num_rows - batch_size * i; }
+      thrust::copy(raft::resource::get_thrust_policy(res),
+                   nearest_clusters_idx.data_handle() + i * batch_size_ * k,
+                   nearest_clusters_idx.data_handle() + (i + 1) * batch_size_ * k,
+                   nearest_clusters_idxt.data_handle());
+      raft::copy(global_nearest_cluster.data_handle() + i * batch_size_ * k,
+                 nearest_clusters_idxt.data_handle(),
+                 batch_size_ * k,
+                 resource::get_cuda_stream(res));
+    }
+  }
+}
+
+//
+// global_nearest_cluster [num_rows X k=2] : top 2 closest clusters for each data point
+// inverted_indices [num_rows x k vector] : sparse vector for data indices for each cluster
+// cluster_size [n_cluster] : cluster size for each cluster
+// offset [n_cluster] : offset in inverted_indices for each cluster
+// Loads the data in batches onto device if data is on host for memory efficiency
+//
+template <typename IdxT = uint32_t>
+void get_inverted_indices(raft::resources const& res,
+                          size_t n_clusters,
+                          size_t& max_cluster_size,
+                          size_t& min_cluster_size,
+                          raft::host_matrix_view<IdxT, IdxT> global_nearest_cluster,
+                          raft::host_vector_view<IdxT, IdxT> inverted_indices,
+                          raft::host_vector_view<IdxT, IdxT> cluster_size,
+                          raft::host_vector_view<IdxT, IdxT> offset)
+{
+  // build sparse inverted indices and get number of data points for each cluster
+  size_t num_rows = global_nearest_cluster.extent(0);
+  size_t k        = global_nearest_cluster.extent(1);
+
+  auto local_offset = raft::make_host_vector<IdxT>(n_clusters);
+
+  max_cluster_size = 0;
+  min_cluster_size = std::numeric_limits<size_t>::max();
+
+  std::fill(cluster_size.data_handle(), cluster_size.data_handle() + n_clusters, 0);
+  std::fill(local_offset.data_handle(), local_offset.data_handle() + n_clusters, 0);
+
+  // TODO: this part isn't really a bottleneck but maybe worth trying omp parallel
+  // for with atomic add
+  for (size_t i = 0; i < num_rows; i++) {
+    for (size_t j = 0; j < k; j++) {
+      IdxT cluster_id = global_nearest_cluster(i, j);
+      cluster_size(cluster_id) += 1;
+    }
+  }
+
+  offset(0) = 0;
+  for (size_t i = 1; i < n_clusters; i++) {
+    offset(i) = offset(i - 1) + cluster_size(i - 1);
+  }
+  for (size_t i = 0; i < num_rows; i++) {
+    for (size_t j = 0; j < k; j++) {
+      IdxT cluster_id = global_nearest_cluster(i, j);
+      inverted_indices(offset(cluster_id) + local_offset(cluster_id)) = i;
+      local_offset(cluster_id) += 1;
+    }
+  }
+
+  max_cluster_size = static_cast<size_t>(
+    *std::max_element(cluster_size.data_handle(), cluster_size.data_handle() + n_clusters));
+  min_cluster_size = static_cast<size_t>(
+    *std::min_element(cluster_size.data_handle(), cluster_size.data_handle() + n_clusters));
+}
+
+template <typename KeyType, typename ValueType>
+struct KeyValuePair {
+  KeyType key;
+  ValueType value;
+};
+
+template <typename KeyType, typename ValueType>
+struct CustomKeyComparator {
+  __device__ bool operator()(const KeyValuePair<KeyType, ValueType>& a,
+                             const KeyValuePair<KeyType, ValueType>& b) const
+  {
+    if (a.key == b.key) { return a.value < b.value; }
+    return a.key < b.key;
+  }
+};
+
+template <typename IdxT, int BLOCK_SIZE, int ITEMS_PER_THREAD>
+RAFT_KERNEL merge_subgraphs(IdxT* cluster_data_indices,
+                            size_t graph_degree,
+                            size_t num_cluster_in_batch,
+                            float* global_distances,
+                            float* batch_distances,
+                            IdxT* global_indices,
+                            IdxT* batch_indices)
+{
+  size_t batch_row = blockIdx.x;
+  typedef cub::BlockMergeSort<KeyValuePair<float, IdxT>, BLOCK_SIZE, ITEMS_PER_THREAD>
+    BlockMergeSortType;
+  __shared__ typename cub::BlockMergeSort<KeyValuePair<float, IdxT>, BLOCK_SIZE, ITEMS_PER_THREAD>::
+    TempStorage tmpSmem;
+
+  extern __shared__ char sharedMem[];
+  float* blockKeys  = reinterpret_cast<float*>(sharedMem);
+  IdxT* blockValues = reinterpret_cast<IdxT*>(&sharedMem[graph_degree * 2 * sizeof(float)]);
+  int16_t* uniqueMask =
+    reinterpret_cast<int16_t*>(&sharedMem[graph_degree * 2 * (sizeof(float) + sizeof(IdxT))]);
+
+  if (batch_row < num_cluster_in_batch) {
+    // load batch or global depending on threadIdx
+    size_t global_row = cluster_data_indices[batch_row];
+
+    KeyValuePair<float, IdxT> threadKeyValuePair[ITEMS_PER_THREAD];
+
+    size_t halfway   = BLOCK_SIZE / 2;
+    size_t do_global = threadIdx.x < halfway;
+
+    float* distances;
+    IdxT* indices;
+
+    if (do_global) {
+      distances = global_distances;
+      indices   = global_indices;
+    } else {
+      distances = batch_distances;
+      indices   = batch_indices;
+    }
+
+    size_t idxBase = (threadIdx.x * do_global + (threadIdx.x - halfway) * (1lu - do_global)) *
+                     static_cast<size_t>(ITEMS_PER_THREAD);
+    size_t arrIdxBase = (global_row * do_global + batch_row * (1lu - do_global)) * graph_degree;
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId < graph_degree) {
+        threadKeyValuePair[i].key   = distances[arrIdxBase + colId];
+        threadKeyValuePair[i].value = indices[arrIdxBase + colId];
+      } else {
+        threadKeyValuePair[i].key   = std::numeric_limits<float>::max();
+        threadKeyValuePair[i].value = std::numeric_limits<IdxT>::max();
+      }
+    }
+
+    __syncthreads();
+
+    BlockMergeSortType(tmpSmem).Sort(threadKeyValuePair, CustomKeyComparator<float, IdxT>{});
+
+    // load sorted result into shared memory to get unique values
+    idxBase = threadIdx.x * ITEMS_PER_THREAD;
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId < 2 * graph_degree) {
+        blockKeys[colId]   = threadKeyValuePair[i].key;
+        blockValues[colId] = threadKeyValuePair[i].value;
+      }
+    }
+
+    __syncthreads();
+
+    // get unique mask
+    if (threadIdx.x == 0) { uniqueMask[0] = 1; }
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId > 0 && colId < 2 * graph_degree) {
+        uniqueMask[colId] = static_cast<int16_t>(blockValues[colId] != blockValues[colId - 1]);
+      }
+    }
+
+    __syncthreads();
+
+    // prefix sum
+    if (threadIdx.x == 0) {
+      for (int i = 1; i < 2 * graph_degree; i++) {
+        uniqueMask[i] += uniqueMask[i - 1];
+      }
+    }
+
+    __syncthreads();
+    // load unique values to global memory
+    if (threadIdx.x == 0) {
+      global_distances[global_row * graph_degree] = blockKeys[0];
+      global_indices[global_row * graph_degree]   = blockValues[0];
+    }
+
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId > 0 && colId < 2 * graph_degree) {
+        bool is_unique       = uniqueMask[colId] != uniqueMask[colId - 1];
+        int16_t global_colId = uniqueMask[colId] - 1;
+        if (is_unique && static_cast<size_t>(global_colId) < graph_degree) {
+          global_distances[global_row * graph_degree + global_colId] = blockKeys[colId];
+          global_indices[global_row * graph_degree + global_colId]   = blockValues[colId];
+        }
+      }
+    }
+  }
+}
+
+//
+// builds knn graph using NN Descent and merge with global graph
+//
+template <typename T,
+          typename IdxT = uint32_t,
+          typename Accessor =
+            host_device_accessor<std::experimental::default_accessor<T>, memory_type::host>>
+void build_and_merge(raft::resources const& res,
+                     const index_params& params,
+                     size_t num_data_in_cluster,
+                     size_t graph_degree,
+                     size_t int_graph_node_degree,
+                     T* cluster_data,
+                     IdxT* cluster_data_indices,
+                     int* int_graph,
+                     IdxT* inverted_indices,
+                     IdxT* global_indices_d,
+                     float* global_distances_d,
+                     IdxT* batch_indices_h,
+                     IdxT* batch_indices_d,
+                     float* batch_distances_d,
+                     GNND<const T, int>& nnd)
+{
+  nnd.build(cluster_data, num_data_in_cluster, int_graph, true, batch_distances_d);
+
+  // remap indices
+#pragma omp parallel for
+  for (size_t i = 0; i < num_data_in_cluster; i++) {
+    for (size_t j = 0; j < graph_degree; j++) {
+      size_t local_idx                      = int_graph[i * int_graph_node_degree + j];
+      batch_indices_h[i * graph_degree + j] = inverted_indices[local_idx];
+    }
+  }
+
+  raft::copy(batch_indices_d,
+             batch_indices_h,
+             num_data_in_cluster * graph_degree,
+             raft::resource::get_cuda_stream(res));
+
+  size_t num_elems     = graph_degree * 2;
+  size_t sharedMemSize = num_elems * (sizeof(float) + sizeof(IdxT) + sizeof(int16_t));
+
+  if (num_elems <= 128) {
+    merge_subgraphs<IdxT, 32, 4>
+      <<<num_data_in_cluster, 32, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+        cluster_data_indices,
+        graph_degree,
+        num_data_in_cluster,
+        global_distances_d,
+        batch_distances_d,
+        global_indices_d,
+        batch_indices_d);
+  } else if (num_elems <= 512) {
+    merge_subgraphs<IdxT, 128, 4>
+      <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+        cluster_data_indices,
+        graph_degree,
+        num_data_in_cluster,
+        global_distances_d,
+        batch_distances_d,
+        global_indices_d,
+        batch_indices_d);
+  } else if (num_elems <= 1024) {
+    merge_subgraphs<IdxT, 128, 8>
+      <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+        cluster_data_indices,
+        graph_degree,
+        num_data_in_cluster,
+        global_distances_d,
+        batch_distances_d,
+        global_indices_d,
+        batch_indices_d);
+  } else if (num_elems <= 2048) {
+    merge_subgraphs<IdxT, 256, 8>
+      <<<num_data_in_cluster, 256, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+        cluster_data_indices,
+        graph_degree,
+        num_data_in_cluster,
+        global_distances_d,
+        batch_distances_d,
+        global_indices_d,
+        batch_indices_d);
+  } else {
+    // this is as far as we can get due to the shared mem usage of cub::BlockMergeSort
+    RAFT_FAIL("The degree of knn is too large (%lu). It must be smaller than 1024", graph_degree);
+  }
+  raft::resource::sync_stream(res);
+}
+
+//
+// For each cluster, gather the data samples that belong to that cluster, and
+// call build_and_merge
+//
+template <typename T, typename IdxT = uint32_t>
+void cluster_nnd(raft::resources const& res,
+                 const index_params& params,
+                 size_t graph_degree,
+                 size_t extended_graph_degree,
+                 size_t max_cluster_size,
+                 raft::host_matrix_view<const T, int64_t> dataset,
+                 IdxT* offsets,
+                 IdxT* cluster_size,
+                 IdxT* cluster_data_indices,
+                 int* int_graph,
+                 IdxT* inverted_indices,
+                 IdxT* global_indices_h,
+                 float* global_distances_h,
+                 IdxT* batch_indices_h,
+                 IdxT* batch_indices_d,
+                 float* batch_distances_d,
+                 const BuildConfig& build_config)
+{
+  size_t num_rows = dataset.extent(0);
+  size_t num_cols = dataset.extent(1);
+
+  GNND<const T, int> nnd(res, build_config);
+
+  auto cluster_data_matrix =
+    raft::make_host_matrix<T, int64_t, row_major>(max_cluster_size, num_cols);
+
+  for (size_t cluster_id = 0; cluster_id < params.n_clusters; cluster_id++) {
+    RAFT_LOG_DEBUG(
+      "# Data on host. Running clusters: %lu / %lu", cluster_id + 1, params.n_clusters);
+    size_t num_data_in_cluster = cluster_size[cluster_id];
+    size_t offset              = offsets[cluster_id];
+
+#pragma omp parallel for
+    for (size_t i = 0; i < num_data_in_cluster; i++) {
+      for (size_t j = 0; j < num_cols; j++) {
+        size_t global_row         = (inverted_indices + offset)[i];
+        cluster_data_matrix(i, j) = dataset(global_row, j);
+      }
+    }
+
+    build_and_merge<T, IdxT>(res,
+                             params,
+                             num_data_in_cluster,
+                             graph_degree,
+                             extended_graph_degree,
+                             cluster_data_matrix.data_handle(),
+                             cluster_data_indices + offset,
+                             int_graph,
+                             inverted_indices + offset,
+                             global_indices_h,
+                             global_distances_h,
+                             batch_indices_h,
+                             batch_indices_d,
+                             batch_distances_d,
+                             nnd);
+    nnd.reset(res);
+  }
+}
+
+template <typename T, typename IdxT = uint32_t>
+void cluster_nnd(raft::resources const& res,
+                 const index_params& params,
+                 size_t graph_degree,
+                 size_t extended_graph_degree,
+                 size_t max_cluster_size,
+                 raft::device_matrix_view<const T, int64_t> dataset,
+                 IdxT* offsets,
+                 IdxT* cluster_size,
+                 IdxT* cluster_data_indices,
+                 int* int_graph,
+                 IdxT* inverted_indices,
+                 IdxT* global_indices_h,
+                 float* global_distances_h,
+                 IdxT* batch_indices_h,
+                 IdxT* batch_indices_d,
+                 float* batch_distances_d,
+                 const BuildConfig& build_config)
+{
+  size_t num_rows = dataset.extent(0);
+  size_t num_cols = dataset.extent(1);
+
+  GNND<const T, int> nnd(res, build_config);
+
+  auto cluster_data_matrix =
+    raft::make_device_matrix<T, int64_t, row_major>(res, max_cluster_size, num_cols);
+
+  for (size_t cluster_id = 0; cluster_id < params.n_clusters; cluster_id++) {
+    RAFT_LOG_DEBUG(
+      "# Data on device. Running clusters: %lu / %lu", cluster_id + 1, params.n_clusters);
+    size_t num_data_in_cluster = cluster_size[cluster_id];
+    size_t offset              = offsets[cluster_id];
+
+    auto cluster_data_view = raft::make_device_matrix_view<T, IdxT>(
+      cluster_data_matrix.data_handle(), num_data_in_cluster, num_cols);
+    auto cluster_data_indices_view = raft::make_device_vector_view<const IdxT, IdxT>(
+      cluster_data_indices + offset, num_data_in_cluster);
+
+    auto dataset_IdxT =
+      raft::make_device_matrix_view<const T, IdxT>(dataset.data_handle(), num_rows, num_cols);
+    raft::matrix::gather(res, dataset_IdxT, cluster_data_indices_view, cluster_data_view);
+
+    build_and_merge<T, IdxT>(res,
+                             params,
+                             num_data_in_cluster,
+                             graph_degree,
+                             extended_graph_degree,
+                             cluster_data_view.data_handle(),
+                             cluster_data_indices + offset,
+                             int_graph,
+                             inverted_indices + offset,
+                             global_indices_h,
+                             global_distances_h,
+                             batch_indices_h,
+                             batch_indices_d,
+                             batch_distances_d,
+                             nnd);
+    nnd.reset(res);
+  }
+}
+
+template <typename T,
+          typename IdxT = uint32_t,
+          typename Accessor =
+            host_device_accessor<std::experimental::default_accessor<float>, memory_type::host>>
+void batch_build(raft::resources const& res,
+                 const index_params& params,
+                 mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset,
+                 index<IdxT>& global_idx)
+{
+  size_t graph_degree        = params.graph_degree;
+  size_t intermediate_degree = params.intermediate_graph_degree;
+
+  size_t num_rows = static_cast<size_t>(dataset.extent(0));
+  size_t num_cols = static_cast<size_t>(dataset.extent(1));
+
+  auto centroids =
+    raft::make_device_matrix<T, IdxT, raft::row_major>(res, params.n_clusters, num_cols);
+  get_balanced_kmeans_centroids<T, IdxT>(res, params.metric, dataset, centroids.view());
+
+  size_t k                    = 2;
+  auto global_nearest_cluster = raft::make_host_matrix<IdxT, IdxT, raft::row_major>(num_rows, k);
+  get_global_nearest_k<T, IdxT>(res,
+                                k,
+                                num_rows,
+                                params.n_clusters,
+                                dataset.data_handle(),
+                                global_nearest_cluster.view(),
+                                centroids.view(),
+                                params.metric);
+
+  auto inverted_indices = raft::make_host_vector<IdxT, IdxT, raft::row_major>(num_rows * k);
+  auto cluster_size     = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
+  auto offset           = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
+
+  size_t max_cluster_size, min_cluster_size;
+  get_inverted_indices(res,
+                       params.n_clusters,
+                       max_cluster_size,
+                       min_cluster_size,
+                       global_nearest_cluster.view(),
+                       inverted_indices.view(),
+                       cluster_size.view(),
+                       offset.view());
+
+  if (intermediate_degree >= min_cluster_size) {
+    RAFT_LOG_WARN(
+      "Intermediate graph degree cannot be larger than minimum cluster size, reducing it to %lu",
+      dataset.extent(0));
+    intermediate_degree = min_cluster_size - 1;
+  }
+  if (intermediate_degree < graph_degree) {
+    RAFT_LOG_WARN(
+      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
+      "graph_degree.",
+      graph_degree,
+      intermediate_degree);
+    graph_degree = intermediate_degree;
+  }
+
+  size_t extended_graph_degree =
+    align32::roundUp(static_cast<size_t>(graph_degree * (graph_degree <= 32 ? 1.0 : 1.3)));
+  size_t extended_intermediate_degree = align32::roundUp(
+    static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
+
+  auto int_graph = raft::make_host_matrix<int, int64_t, row_major>(
+    max_cluster_size, static_cast<int64_t>(extended_graph_degree));
+
+  BuildConfig build_config{.max_dataset_size      = max_cluster_size,
+                           .dataset_dim           = num_cols,
+                           .node_degree           = extended_graph_degree,
+                           .internal_node_degree  = extended_intermediate_degree,
+                           .max_iterations        = params.max_iterations,
+                           .termination_threshold = params.termination_threshold,
+                           .output_graph_degree   = graph_degree};
+
+  auto global_indices_h   = raft::make_managed_matrix<IdxT, int64_t>(res, num_rows, graph_degree);
+  auto global_distances_h = raft::make_managed_matrix<float, int64_t>(res, num_rows, graph_degree);
+
+  std::fill(global_indices_h.data_handle(),
+            global_indices_h.data_handle() + num_rows * graph_degree,
+            std::numeric_limits<IdxT>::max());
+  std::fill(global_distances_h.data_handle(),
+            global_distances_h.data_handle() + num_rows * graph_degree,
+            std::numeric_limits<float>::max());
+
+  auto batch_indices_h =
+    raft::make_host_matrix<IdxT, int64_t, row_major>(max_cluster_size, graph_degree);
+  auto batch_indices_d =
+    raft::make_device_matrix<IdxT, int64_t, row_major>(res, max_cluster_size, graph_degree);
+  auto batch_distances_d =
+    raft::make_device_matrix<float, int64_t, row_major>(res, max_cluster_size, graph_degree);
+
+  auto cluster_data_indices = raft::make_device_vector<IdxT, IdxT>(res, num_rows * k);
+  raft::copy(cluster_data_indices.data_handle(),
+             inverted_indices.data_handle(),
+             num_rows * k,
+             resource::get_cuda_stream(res));
+
+  cluster_nnd<T, IdxT>(res,
+                       params,
+                       graph_degree,
+                       extended_graph_degree,
+                       max_cluster_size,
+                       dataset,
+                       offset.data_handle(),
+                       cluster_size.data_handle(),
+                       cluster_data_indices.data_handle(),
+                       int_graph.data_handle(),
+                       inverted_indices.data_handle(),
+                       global_indices_h.data_handle(),
+                       global_distances_h.data_handle(),
+                       batch_indices_h.data_handle(),
+                       batch_indices_d.data_handle(),
+                       batch_distances_d.data_handle(),
+                       build_config);
+
+  raft::copy(global_idx.graph().data_handle(),
+             global_indices_h.data_handle(),
+             num_rows * graph_degree,
+             raft::resource::get_cuda_stream(res));
+  if (params.return_distances && global_idx.distances().has_value()) {
+    raft::copy(global_idx.distances().value().data_handle(),
+               global_distances_h.data_handle(),
+               num_rows * graph_degree,
+               raft::resource::get_cuda_stream(res));
+  }
+}
+
+template <typename T,
+          typename IdxT = uint32_t,
+          typename Accessor =
+            host_device_accessor<std::experimental::default_accessor<float>, memory_type::host>>
+index<IdxT> batch_build(raft::resources const& res,
+                        const index_params& params,
+                        mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset)
+{
+  size_t intermediate_degree = params.intermediate_graph_degree;
+  size_t graph_degree        = params.graph_degree;
+
+  if (intermediate_degree < graph_degree) {
+    RAFT_LOG_WARN(
+      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
+      "graph_degree.",
+      graph_degree,
+      intermediate_degree);
+    graph_degree = intermediate_degree;
+  }
+
+  index<IdxT> idx{
+    res, dataset.extent(0), static_cast<int64_t>(graph_degree), params.return_distances};
+
+  batch_build(res, params, dataset, idx);
+
+  return idx;
+}
+
+}  // namespace cuvs::neighbors::nn_descent::detail::experimental
diff --git a/cpp/src/neighbors/nn_descent.cuh b/cpp/src/neighbors/nn_descent.cuh
index 582da72c12..ed91dac919 100644
--- a/cpp/src/neighbors/nn_descent.cuh
+++ b/cpp/src/neighbors/nn_descent.cuh
@@ -17,9 +17,14 @@
 #pragma once
 
 #include "detail/nn_descent.cuh"
+#include "detail/nn_descent_batch.cuh"
+
+#include <cmath>
+#include <cstdint>
 #include <cuvs/neighbors/nn_descent.hpp>
 
 #include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
 #include <raft/core/host_mdspan.hpp>
 
 namespace cuvs::neighbors::nn_descent {
@@ -61,7 +66,15 @@ auto build(raft::resources const& res,
            index_params const& params,
            raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
 {
-  return detail::build<T, IdxT>(res, params, dataset);
+  if (params.n_clusters > 1) {
+    if constexpr (std::is_same_v<T, float>) {
+      return detail::experimental::batch_build<T, IdxT>(res, params, dataset);
+    } else {
+      RAFT_FAIL("Batched nn-descent is only supported for float precision");
+    }
+  } else {
+    return detail::build<T, IdxT>(res, params, dataset);
+  }
 }
 
 /**
@@ -100,7 +113,15 @@ void build(raft::resources const& res,
            raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
            index<IdxT>& idx)
 {
-  detail::build<T, IdxT>(res, params, dataset, idx);
+  if (params.n_clusters > 1) {
+    if constexpr (std::is_same_v<T, float>) {
+      detail::experimental::batch_build<T, IdxT>(res, params, dataset, idx);
+    } else {
+      RAFT_FAIL("Batched nn-descent is only supported for float precision");
+    }
+  } else {
+    detail::build<T, IdxT>(res, params, dataset, idx);
+  }
 }
 
 /**
@@ -135,7 +156,15 @@ auto build(raft::resources const& res,
            index_params const& params,
            raft::host_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
 {
-  return detail::build<T, IdxT>(res, params, dataset);
+  if (params.n_clusters > 1) {
+    if constexpr (std::is_same_v<T, float>) {
+      return detail::experimental::batch_build<T, IdxT>(res, params, dataset);
+    } else {
+      RAFT_FAIL("Batched nn-descent is only supported for float precision");
+    }
+  } else {
+    return detail::build<T, IdxT>(res, params, dataset);
+  }
 }
 
 /**
@@ -174,7 +203,15 @@ void build(raft::resources const& res,
            raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
            index<IdxT>& idx)
 {
-  detail::build<T, IdxT>(res, params, dataset, idx);
+  if (params.n_clusters > 1) {
+    if constexpr (std::is_same_v<T, float>) {
+      detail::experimental::batch_build<T, IdxT>(res, params, dataset, idx);
+    } else {
+      RAFT_FAIL("Batched nn-descent is only supported for float precision");
+    }
+  } else {
+    detail::build<T, IdxT>(res, params, dataset, idx);
+  }
 }
 
 /** @} */  // end group nn-descent
diff --git a/cpp/src/neighbors/nn_descent_float.cu b/cpp/src/neighbors/nn_descent_float.cu
index c6d3566713..fa85db1279 100644
--- a/cpp/src/neighbors/nn_descent_float.cu
+++ b/cpp/src/neighbors/nn_descent_float.cu
@@ -19,21 +19,38 @@
 
 namespace cuvs::neighbors::nn_descent {
 
-#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
-  };                                                                              \
-                                                                                  \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                                   \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    };                                                                                        \
+  }                                                                                           \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    }                                                                                         \
   };
 
 CUVS_INST_NN_DESCENT_BUILD(float, uint32_t);
diff --git a/cpp/src/neighbors/nn_descent_half.cu b/cpp/src/neighbors/nn_descent_half.cu
index 5879930315..2ee45d4352 100644
--- a/cpp/src/neighbors/nn_descent_half.cu
+++ b/cpp/src/neighbors/nn_descent_half.cu
@@ -19,21 +19,39 @@
 
 namespace cuvs::neighbors::nn_descent {
 
-#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
-  };                                                                              \
-                                                                                  \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                                   \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    }                                                                                         \
+  };                                                                                          \
+                                                                                              \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    }                                                                                         \
   };
 
 CUVS_INST_NN_DESCENT_BUILD(half, uint32_t);
diff --git a/cpp/src/neighbors/nn_descent_int8.cu b/cpp/src/neighbors/nn_descent_int8.cu
index 813a01746b..e150f511ba 100644
--- a/cpp/src/neighbors/nn_descent_int8.cu
+++ b/cpp/src/neighbors/nn_descent_int8.cu
@@ -19,21 +19,39 @@
 
 namespace cuvs::neighbors::nn_descent {
 
-#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
-  };                                                                              \
-                                                                                  \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                                   \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    }                                                                                         \
+  };                                                                                          \
+                                                                                              \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    }                                                                                         \
   };
 
 CUVS_INST_NN_DESCENT_BUILD(int8_t, uint32_t);
diff --git a/cpp/src/neighbors/nn_descent_uint8.cu b/cpp/src/neighbors/nn_descent_uint8.cu
index 9d73dd90ff..d8657777b9 100644
--- a/cpp/src/neighbors/nn_descent_uint8.cu
+++ b/cpp/src/neighbors/nn_descent_uint8.cu
@@ -19,21 +19,39 @@
 
 namespace cuvs::neighbors::nn_descent {
 
-#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                       \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
-  };                                                                              \
-                                                                                  \
-  auto build(raft::resources const& handle,                                       \
-             const cuvs::neighbors::nn_descent::index_params& params,             \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                    \
-  {                                                                               \
-    return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);  \
+#define CUVS_INST_NN_DESCENT_BUILD(T, IdxT)                                                   \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    }                                                                                         \
+  };                                                                                          \
+                                                                                              \
+  auto build(raft::resources const& handle,                                                   \
+             const cuvs::neighbors::nn_descent::index_params& params,                         \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
+             std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
+    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+  {                                                                                           \
+    if (!graph.has_value()) {                                                                 \
+      return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
+    } else {                                                                                  \
+      std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
+        std::nullopt;                                                                         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
+      return idx;                                                                             \
+    }                                                                                         \
   };
 
 CUVS_INST_NN_DESCENT_BUILD(uint8_t, uint32_t);
diff --git a/cpp/test/neighbors/ann_nn_descent.cuh b/cpp/test/neighbors/ann_nn_descent.cuh
index bce0f98996..7d2575c2bd 100644
--- a/cpp/test/neighbors/ann_nn_descent.cuh
+++ b/cpp/test/neighbors/ann_nn_descent.cuh
@@ -18,9 +18,13 @@
 #include "../test_utils.cuh"
 #include "ann_utils.cuh"
 
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
+
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cudart_utils.hpp>
 #include <raft/util/itertools.hpp>
+#include <rmm/device_uvector.hpp>
 
 #include "naive_knn.cuh"
 
@@ -42,6 +46,15 @@ struct AnnNNDescentInputs {
   double min_recall;
 };
 
+struct AnnNNDescentBatchInputs {
+  std::pair<double, size_t> recall_cluster;
+  int n_rows;
+  int dim;
+  int graph_degree;
+  cuvs::distance::DistanceType metric;
+  bool host_dataset;
+};
+
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnNNDescentInputs& p)
 {
   os << "dataset shape=" << p.n_rows << "x" << p.dim << ", graph_degree=" << p.graph_degree
@@ -50,6 +63,14 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AnnNNDescentInputs&
   return os;
 }
 
+inline ::std::ostream& operator<<(::std::ostream& os, const AnnNNDescentBatchInputs& p)
+{
+  os << "dataset shape=" << p.n_rows << "x" << p.dim << ", graph_degree=" << p.graph_degree
+     << ", metric=" << static_cast<int>(p.metric) << (p.host_dataset ? ", host" : ", device")
+     << ", clusters=" << p.recall_cluster.second << std::endl;
+  return os;
+}
+
 template <typename DistanceT, typename DataT, typename IdxT>
 class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
  public:
@@ -65,7 +86,9 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
   {
     size_t queries_size = ps.n_rows * ps.graph_degree;
     std::vector<IdxT> indices_NNDescent(queries_size);
+    std::vector<DistanceT> distances_NNDescent(queries_size);
     std::vector<IdxT> indices_naive(queries_size);
+    std::vector<DistanceT> distances_naive(queries_size);
 
     {
       rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
@@ -81,16 +104,18 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
                                         ps.graph_degree,
                                         ps.metric);
       raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
       raft::resource::sync_stream(handle_);
     }
 
     {
       {
-        cuvs::neighbors::nn_descent::index_params index_params;
+        nn_descent::index_params index_params;
         index_params.metric                    = ps.metric;
         index_params.graph_degree              = ps.graph_degree;
         index_params.intermediate_graph_degree = 2 * ps.graph_degree;
         index_params.max_iterations            = 100;
+        index_params.return_distances          = true;
 
         auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
           (const DataT*)database.data(), ps.n_rows, ps.dim);
@@ -101,22 +126,40 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
             raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
             auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
               (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
-            auto index =
-              cuvs::neighbors::nn_descent::build(handle_, index_params, database_host_view);
-            raft::update_host(
+            auto index = nn_descent::build(handle_, index_params, database_host_view);
+            raft::copy(
               indices_NNDescent.data(), index.graph().data_handle(), queries_size, stream_);
+            if (index.distances().has_value()) {
+              raft::copy(distances_NNDescent.data(),
+                         index.distances().value().data_handle(),
+                         queries_size,
+                         stream_);
+            }
+
           } else {
-            auto index = cuvs::neighbors::nn_descent::build(handle_, index_params, database_view);
-            raft::update_host(
+            auto index = nn_descent::build(handle_, index_params, database_view);
+            raft::copy(
               indices_NNDescent.data(), index.graph().data_handle(), queries_size, stream_);
+            if (index.distances().has_value()) {
+              raft::copy(distances_NNDescent.data(),
+                         index.distances().value().data_handle(),
+                         queries_size,
+                         stream_);
+            }
           };
         }
         raft::resource::sync_stream(handle_);
       }
 
       double min_recall = ps.min_recall;
-      EXPECT_TRUE(eval_recall(
-        indices_naive, indices_NNDescent, ps.n_rows, ps.graph_degree, 0.001, min_recall));
+      EXPECT_TRUE(eval_neighbours(indices_naive,
+                                  indices_NNDescent,
+                                  distances_naive,
+                                  distances_NNDescent,
+                                  ps.n_rows,
+                                  ps.graph_degree,
+                                  0.001,
+                                  min_recall));
     }
   }
 
@@ -146,6 +189,125 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
   rmm::device_uvector<DataT> database;
 };
 
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchInputs> {
+ public:
+  AnnNNDescentBatchTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnNNDescentBatchInputs>::GetParam()),
+      database(0, stream_)
+  {
+  }
+
+  void testNNDescentBatch()
+  {
+    size_t queries_size = ps.n_rows * ps.graph_degree;
+    std::vector<IdxT> indices_NNDescent(queries_size);
+    std::vector<DistanceT> distances_NNDescent(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<DistanceT> distances_naive(queries_size);
+
+    {
+      rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+      naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                        distances_naive_dev.data(),
+                                        indices_naive_dev.data(),
+                                        database.data(),
+                                        database.data(),
+                                        ps.n_rows,
+                                        ps.n_rows,
+                                        ps.dim,
+                                        ps.graph_degree,
+                                        ps.metric);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    {
+      {
+        nn_descent::index_params index_params;
+        index_params.metric                    = ps.metric;
+        index_params.graph_degree              = ps.graph_degree;
+        index_params.intermediate_graph_degree = 2 * ps.graph_degree;
+        index_params.max_iterations            = 10;
+        index_params.return_distances          = true;
+        index_params.n_clusters                = ps.recall_cluster.second;
+
+        auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          (const DataT*)database.data(), ps.n_rows, ps.dim);
+
+        {
+          if (ps.host_dataset) {
+            auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+            raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+            auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+              (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+            auto index = nn_descent::build(handle_, index_params, database_host_view);
+            raft::copy(
+              indices_NNDescent.data(), index.graph().data_handle(), queries_size, stream_);
+            if (index.distances().has_value()) {
+              raft::copy(distances_NNDescent.data(),
+                         index.distances().value().data_handle(),
+                         queries_size,
+                         stream_);
+            }
+
+          } else {
+            auto index = nn_descent::build(handle_, index_params, database_view);
+            raft::copy(
+              indices_NNDescent.data(), index.graph().data_handle(), queries_size, stream_);
+            if (index.distances().has_value()) {
+              raft::copy(distances_NNDescent.data(),
+                         index.distances().value().data_handle(),
+                         queries_size,
+                         stream_);
+            }
+          };
+        }
+        raft::resource::sync_stream(handle_);
+      }
+      double min_recall = ps.recall_cluster.first;
+      EXPECT_TRUE(eval_neighbours(indices_naive,
+                                  indices_NNDescent,
+                                  distances_naive,
+                                  distances_NNDescent,
+                                  ps.n_rows,
+                                  ps.graph_degree,
+                                  0.01,
+                                  min_recall,
+                                  true,
+                                  static_cast<size_t>(ps.graph_degree * 0.1)));
+    }
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(20));
+    }
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnNNDescentBatchInputs ps;
+  rmm::device_uvector<DataT> database;
+};
+
 const std::vector<AnnNNDescentInputs> inputs = raft::util::itertools::product<AnnNNDescentInputs>(
   {1000, 2000},                                              // n_rows
   {3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // dim
@@ -154,4 +316,15 @@ const std::vector<AnnNNDescentInputs> inputs = raft::util::itertools::product<An
   {false, true},
   {0.90});
 
-}  // namespace  cuvs::neighbors::nn_descent
+// TODO : Investigate why this test is failing Reference issue https
+// :  // github.com/rapidsai/raft/issues/2450
+const std::vector<AnnNNDescentBatchInputs> inputsBatch =
+  raft::util::itertools::product<AnnNNDescentBatchInputs>(
+    {std::make_pair(0.9, 3lu), std::make_pair(0.9, 2lu)},  // min_recall, n_clusters
+    {4000, 5000},                                          // n_rows
+    {192, 512},                                            // dim
+    {32, 64},                                              // graph_degree
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false, true});
+
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu b/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu
index 64c0e0291a..7a24f96a17 100644
--- a/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu
+++ b/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu
@@ -23,6 +23,12 @@ namespace cuvs::neighbors::nn_descent {
 typedef AnnNNDescentTest<float, float, std::uint32_t> AnnNNDescentTestF_U32;
 TEST_P(AnnNNDescentTestF_U32, AnnNNDescent) { this->testNNDescent(); }
 
+// typedef AnnNNDescentBatchTest<float, float, std::uint32_t> AnnNNDescentBatchTestF_U32;
+// TEST_P(AnnNNDescentBatchTestF_U32, AnnNNDescentBatch) { this->testNNDescentBatch(); }
+
 INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestF_U32, ::testing::ValuesIn(inputs));
+// INSTANTIATE_TEST_CASE_P(AnnNNDescentBatchTest,
+//                         AnnNNDescentBatchTestF_U32,
+//                         ::testing::ValuesIn(inputsBatch));
 
 }  // namespace   cuvs::neighbors::nn_descent
diff --git a/cpp/test/neighbors/ann_utils.cuh b/cpp/test/neighbors/ann_utils.cuh
index b08e1d7256..94bccade29 100644
--- a/cpp/test/neighbors/ann_utils.cuh
+++ b/cpp/test/neighbors/ann_utils.cuh
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include <cstddef>
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdarray.hpp>  // raft::make_device_matrix
 #include <raft/core/resource/cuda_stream.hpp>
@@ -165,9 +166,14 @@ auto calc_recall(const std::vector<T>& expected_idx,
 /** check uniqueness of indices
  */
 template <typename T>
-auto check_unique_indices(const std::vector<T>& actual_idx, size_t rows, size_t cols)
+auto check_unique_indices(const std::vector<T>& actual_idx,
+                          size_t rows,
+                          size_t cols,
+                          size_t max_duplicates = 0)
 {
   size_t max_count;
+  size_t dup_count = 0lu;
+
   std::set<T> unique_indices;
   for (size_t i = 0; i < rows; ++i) {
     unique_indices.clear();
@@ -180,8 +186,11 @@ auto check_unique_indices(const std::vector<T>& actual_idx, size_t rows, size_t
       } else if (unique_indices.find(act_idx) == unique_indices.end()) {
         unique_indices.insert(act_idx);
       } else {
-        return testing::AssertionFailure()
-               << "Duplicated index " << act_idx << " at k " << k << " for query " << i << "! ";
+        dup_count++;
+        if (dup_count > max_duplicates) {
+          return testing::AssertionFailure()
+                 << "Duplicated index " << act_idx << " at k " << k << " for query " << i << "! ";
+        }
       }
     }
   }
@@ -264,7 +273,8 @@ auto eval_neighbours(const std::vector<T>& expected_idx,
                      size_t cols,
                      double eps,
                      double min_recall,
-                     bool test_unique = true) -> testing::AssertionResult
+                     bool test_unique      = true,
+                     size_t max_duplicates = 0) -> testing::AssertionResult
 {
   auto [actual_recall, match_count, total_count] =
     calc_recall(expected_idx, actual_idx, expected_dist, actual_dist, rows, cols, eps);
@@ -284,7 +294,7 @@ auto eval_neighbours(const std::vector<T>& expected_idx,
            << min_recall << "); eps = " << eps << ". ";
   }
   if (test_unique)
-    return check_unique_indices(actual_idx, rows, cols);
+    return check_unique_indices(actual_idx, rows, cols, max_duplicates);
   else
     return testing::AssertionSuccess();
 }
diff --git a/python/cuvs/cuvs/test/test_hnsw.py b/python/cuvs/cuvs/test/test_hnsw.py
index 0ae97266b9..8bd2e8b76c 100644
--- a/python/cuvs/cuvs/test/test_hnsw.py
+++ b/python/cuvs/cuvs/test/test_hnsw.py
@@ -23,7 +23,7 @@
 
 
 def run_hnsw_build_search_test(
-    n_rows=1000,
+    n_rows=10000,
     n_cols=10,
     n_queries=100,
     k=10,

From fdb118002a482e878ec48fcaa7f11a15efd59140 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Wed, 13 Nov 2024 21:32:29 -0600
Subject: [PATCH 204/623] enforce wheel size limits, README formatting in CI
 (#464)

Contributes to https://github.com/rapidsai/build-planning/issues/110

Proposes adding 2 types of validation on wheels in CI, to ensure we continue to produce wheels that are suitable for PyPI.

* checks on wheel size (compressed),
  - *to be sure they're under PyPI limits*
  - *and to prompt discussion on PRs that significantly increase wheel sizes*
* checks on README formatting
  - *to ensure they'll render properly as the PyPI project homepages*
  - *e.g. like how https://github.com/scikit-learn/scikit-learn/blob/main/README.rst becomes https://pypi.org/project/scikit-learn/*

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/464
---
 ci/build_wheel_cuvs.sh     |  5 ++++-
 ci/validate_wheel.sh       | 21 +++++++++++++++++++++
 python/cuvs/pyproject.toml |  8 ++++++++
 3 files changed, 33 insertions(+), 1 deletion(-)
 create mode 100755 ci/validate_wheel.sh

diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index e03da9f19d..444657cc06 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -3,6 +3,8 @@
 
 set -euo pipefail
 
+package_dir="python/cuvs"
+
 case "${RAPIDS_CUDA_VERSION}" in
   12.*)
     EXTRA_CMAKE_ARGS=";-DUSE_CUDA_MATH_WHEELS=ON"
@@ -15,4 +17,5 @@ esac
 # Set up skbuild options. Enable sccache in skbuild config options
 export SKBUILD_CMAKE_ARGS="-DDETECT_CONDA_ENV=OFF;-DFIND_CUVS_CPP=OFF${EXTRA_CMAKE_ARGS}"
 
-ci/build_wheel.sh cuvs python/cuvs
+ci/build_wheel.sh cuvs ${package_dir}
+ci/validate_wheel.sh ${package_dir} final_dist
diff --git a/ci/validate_wheel.sh b/ci/validate_wheel.sh
new file mode 100755
index 0000000000..5910a5c59f
--- /dev/null
+++ b/ci/validate_wheel.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+package_dir=$1
+wheel_dir_relative_path=$2
+
+cd "${package_dir}"
+
+rapids-logger "validate packages with 'pydistcheck'"
+
+pydistcheck \
+    --inspect \
+    "$(echo ${wheel_dir_relative_path}/*.whl)"
+
+rapids-logger "validate packages with 'twine'"
+
+twine check \
+    --strict \
+    "$(echo ${wheel_dir_relative_path}/*.whl)"
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 30d784c673..d400267760 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -133,6 +133,14 @@ build-backend = "scikit_build_core.build"
 dependencies-file = "../../dependencies.yaml"
 matrix-entry = "cuda_suffixed=true;use_cuda_wheels=true"
 
+[tool.pydistcheck]
+select = [
+    "distro-too-large-compressed",
+]
+
+# detect when package size grows significantly
+max_allowed_size_compressed = '1.4G'
+
 [tool.pytest.ini_options]
 filterwarnings = [
     "error",

From bb9c669500cf0401114f4a5810d0f3a0ea1db6b3 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 14 Nov 2024 21:25:58 +0100
Subject: [PATCH 205/623] Fix include errors, header, and unsafe locks in
 iface.hpp (#467)

Fix a few issues with the internal header `neighbors/iface/iface.hpp`  leading to compile time errors and dangerous runtime behavior:

  - Add missing includes
  - Use `std::lock_guard` to avoid a deadlock on exception
  - Add NVIDIA header
  - Avoid an extra stream sync during search.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/467
---
 cpp/src/neighbors/cagra_c.cpp     |  2 ++
 cpp/src/neighbors/iface/iface.hpp | 53 +++++++++++++++++--------------
 cpp/src/neighbors/ivf_flat_c.cpp  |  2 ++
 cpp/src/neighbors/mg/mg.cuh       |  2 ++
 examples/cpp/src/common.cuh       |  4 +++
 5 files changed, 39 insertions(+), 24 deletions(-)
 mode change 100755 => 100644 cpp/src/neighbors/ivf_flat_c.cpp

diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 6985ff0948..326a896650 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -29,6 +29,8 @@
 #include <cuvs/neighbors/cagra.h>
 #include <cuvs/neighbors/cagra.hpp>
 
+#include <fstream>
+
 namespace {
 
 template <typename T>
diff --git a/cpp/src/neighbors/iface/iface.hpp b/cpp/src/neighbors/iface/iface.hpp
index a329db4298..9b3da75a47 100644
--- a/cpp/src/neighbors/iface/iface.hpp
+++ b/cpp/src/neighbors/iface/iface.hpp
@@ -1,4 +1,20 @@
-#include <mutex>
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
 
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.hpp>
@@ -6,6 +22,9 @@
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/device_resources.hpp>
 
+#include <fstream>
+#include <mutex>
+
 namespace cuvs::neighbors {
 
 using namespace raft;
@@ -16,7 +35,7 @@ void build(const raft::device_resources& handle,
            const cuvs::neighbors::index_params* index_params,
            raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> index_dataset)
 {
-  interface.mutex_->lock();
+  std::lock_guard(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     auto idx = cuvs::neighbors::ivf_flat::build(
@@ -32,8 +51,6 @@ void build(const raft::device_resources& handle,
     interface.index_.emplace(std::move(idx));
   }
   resource::sync_stream(handle);
-
-  interface.mutex_->unlock();
 }
 
 template <typename AnnIndexType, typename T, typename IdxT, typename Accessor1, typename Accessor2>
@@ -44,7 +61,7 @@ void extend(
   std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, Accessor2>>
     new_indices)
 {
-  interface.mutex_->lock();
+  std::lock_guard(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     auto idx =
@@ -58,8 +75,6 @@ void extend(
     RAFT_FAIL("CAGRA does not implement the extend method");
   }
   resource::sync_stream(handle);
-
-  interface.mutex_->unlock();
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
@@ -70,7 +85,7 @@ void search(const raft::device_resources& handle,
             raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,
             raft::device_matrix_view<float, int64_t, row_major> distances)
 {
-  // interface.mutex_->lock();
+  // std::lock_guard(*interface.mutex_);
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, int64_t>>::value) {
     cuvs::neighbors::ivf_flat::search(
       handle,
@@ -94,9 +109,7 @@ void search(const raft::device_resources& handle,
                                    neighbors,
                                    distances);
   }
-  resource::sync_stream(handle);
-
-  // interface.mutex_->unlock();
+  // resource::sync_stream(handle);
 }
 
 // for MG ANN only
@@ -108,7 +121,7 @@ void search(const raft::device_resources& handle,
             raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,
             raft::device_matrix_view<float, int64_t, row_major> d_distances)
 {
-  // interface.mutex_->lock();
+  // std::lock_guard(*interface.mutex_);
 
   int64_t n_rows = h_queries.extent(0);
   int64_t n_dims = h_queries.extent(1);
@@ -120,8 +133,6 @@ void search(const raft::device_resources& handle,
   auto d_query_view = raft::make_const_mdspan(d_queries.view());
 
   search(handle, interface, search_params, d_query_view, d_neighbors, d_distances);
-
-  // interface.mutex_->unlock();
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
@@ -129,7 +140,7 @@ void serialize(const raft::device_resources& handle,
                const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                std::ostream& os)
 {
-  interface.mutex_->lock();
+  std::lock_guard(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     ivf_flat::serialize(handle, os, interface.index_.value());
@@ -138,8 +149,6 @@ void serialize(const raft::device_resources& handle,
   } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
     cagra::serialize(handle, os, interface.index_.value(), true);
   }
-
-  interface.mutex_->unlock();
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
@@ -147,7 +156,7 @@ void deserialize(const raft::device_resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  std::istream& is)
 {
-  interface.mutex_->lock();
+  std::lock_guard(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     ivf_flat::index<T, IdxT> idx(handle);
@@ -162,8 +171,6 @@ void deserialize(const raft::device_resources& handle,
     cagra::deserialize(handle, is, &idx);
     interface.index_.emplace(std::move(idx));
   }
-
-  interface.mutex_->unlock();
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
@@ -171,7 +178,7 @@ void deserialize(const raft::device_resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  const std::string& filename)
 {
-  interface.mutex_->lock();
+  std::lock_guard(*interface.mutex_);
 
   std::ifstream is(filename, std::ios::in | std::ios::binary);
   if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
@@ -191,8 +198,6 @@ void deserialize(const raft::device_resources& handle,
   }
 
   is.close();
-
-  interface.mutex_->unlock();
 }
 
-};  // namespace cuvs::neighbors
\ No newline at end of file
+};  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
old mode 100755
new mode 100644
index c14c1edc0c..2acc6b6788
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -29,6 +29,8 @@
 #include <cuvs/neighbors/ivf_flat.h>
 #include <cuvs/neighbors/ivf_flat.hpp>
 
+#include <fstream>
+
 namespace {
 
 template <typename T, typename IdxT>
diff --git a/cpp/src/neighbors/mg/mg.cuh b/cpp/src/neighbors/mg/mg.cuh
index d3f635bc40..e9cdc30f6b 100644
--- a/cpp/src/neighbors/mg/mg.cuh
+++ b/cpp/src/neighbors/mg/mg.cuh
@@ -25,6 +25,8 @@
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/mg.hpp>
 
+#include <fstream>
+
 namespace cuvs::neighbors {
 using namespace raft;
 
diff --git a/examples/cpp/src/common.cuh b/examples/cpp/src/common.cuh
index 1c93dec0e8..8e109a7644 100644
--- a/examples/cpp/src/common.cuh
+++ b/examples/cpp/src/common.cuh
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#pragma once
+
 #include <cstdint>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_resources.hpp>
@@ -28,6 +30,8 @@
 #include <thrust/device_ptr.h>
 #include <thrust/iterator/counting_iterator.h>
 
+#include <fstream>
+
 // Fill dataset and queries with synthetic data.
 void generate_dataset(raft::device_resources const &dev_resources,
                       raft::device_matrix_view<float, int64_t> dataset,

From 6f2db2069dc9be70e3b393b07babc0134b43d71d Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Fri, 15 Nov 2024 09:31:54 -0500
Subject: [PATCH 206/623] DOC v25.02 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda11.8-pip/devcontainer.json  |  8 +++---
 .../cuda12.5-conda/devcontainer.json          |  6 ++---
 .devcontainer/cuda12.5-pip/devcontainer.json  |  8 +++---
 .github/workflows/build.yaml                  | 14 +++++-----
 .github/workflows/pr.yaml                     | 26 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++---
 README.md                                     |  2 +-
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 +--
 .../all_cuda-118_arch-x86_64.yaml             |  4 +--
 .../all_cuda-125_arch-aarch64.yaml            |  4 +--
 .../all_cuda-125_arch-x86_64.yaml             |  4 +--
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  4 +--
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  4 +--
 .../bench_ann_cuda-125_arch-aarch64.yaml      |  4 +--
 .../bench_ann_cuda-125_arch-x86_64.yaml       |  4 +--
 dependencies.yaml                             | 12 ++++-----
 docs/source/developer_guide.md                |  6 ++---
 examples/cmake/thirdparty/fetch_rapids.cmake  |  2 +-
 python/cuvs/pyproject.toml                    |  2 +-
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 23 files changed, 69 insertions(+), 69 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index 05f11c005e..f03ec7b19f 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.12-cpp-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda11.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index b4c507f867..a59c499d3c 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.12-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda11.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.12": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.2": {
       "version": "11.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.5-conda/devcontainer.json b/.devcontainer/cuda12.5-conda/devcontainer.json
index 4f8d628c2b..39852cec1c 100644
--- a/.devcontainer/cuda12.5-conda/devcontainer.json
+++ b/.devcontainer/cuda12.5-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:24.12-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.02-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda12.5-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.5-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.5-pip/devcontainer.json b/.devcontainer/cuda12.5-pip/devcontainer.json
index 8e6ba4de8c..d849666561 100644
--- a/.devcontainer/cuda12.5-pip/devcontainer.json
+++ b/.devcontainer/cuda12.5-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:24.12-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-24.12-cuda12.5-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.5-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:24.12": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.2": {
       "version": "12.5",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.12": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 7ac02e3655..e93b7a694c 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -92,7 +92,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index e18e82df08..a62b4e00a7 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -25,13 +25,13 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.02
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
   changed-files:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.02
     with:
       files_yaml: |
         test_cpp:
@@ -64,27 +64,27 @@ jobs:
           - '!thirdparty/LICENSES/**'
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.02
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -92,20 +92,20 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -115,7 +115,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -125,21 +125,21 @@ jobs:
   wheel-build-cuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.02
     with:
       arch: '["amd64"]'
       cuda: '["12.5"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 5f60c0a348..2645e5d5dd 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/README.md b/README.md
index 572e8d0982..8b7e529afc 100755
--- a/README.md
+++ b/README.md
@@ -109,7 +109,7 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=24.12
+conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.02
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
diff --git a/VERSION b/VERSION
index af28c42b52..72eefaf7c7 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-24.12.00
+25.02.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 80bfb0c24d..1daf668b1f 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -45,7 +45,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 07937726ce..0981563970 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -45,7 +45,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index b7fd6fcfa9..b94b447490 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 83a457465a..10e30a8c23 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - openblas
 - pre-commit
 - pydata-sphinx-theme
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 21cb981807..2a1d80aaa1 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -33,7 +33,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pandas
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 432509bcb3..6507f55cc9 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -33,7 +33,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
@@ -41,7 +41,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pandas
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 0c5043ac27..e53606a066 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -30,14 +30,14 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index cbb22333c8..e37c507c7d 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -30,14 +30,14 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==24.12.*,>=0.0.0a0
+- librmm==25.2.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==24.12.*,>=0.0.0a0
+- pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/dependencies.yaml b/dependencies.yaml
index e909ad0dc0..a7be191d6b 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -488,7 +488,7 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - &librmm_unsuffixed librmm==24.12.*,>=0.0.0a0
+          - &librmm_unsuffixed librmm==25.2.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -501,18 +501,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu12==24.12.*,>=0.0.0a0
+              - librmm-cu12==25.2.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu11==24.12.*,>=0.0.0a0
+              - librmm-cu11==25.2.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
       - output_types: conda
         packages:
-          - &pylibraft_unsuffixed pylibraft==24.12.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==25.2.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -525,10 +525,10 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu12==24.12.*,>=0.0.0a0
+              - pylibraft-cu12==25.2.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu11==24.12.*,>=0.0.0a0
+              - pylibraft-cu11==25.2.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 7702f80b3b..4fdd6405e9 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-24.12/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.02/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-24.12/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.02/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
@@ -230,7 +230,7 @@ Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY`
 ## Logging
 
 ### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-24.12/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
+Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-25.02/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
 
 ### Usage
 ```cpp
diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
index 6f4c627ed4..23c8490b40 100644
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and RAFT versions
-set(RAPIDS_VERSION "24.12")
+set(RAPIDS_VERSION "25.02")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index d400267760..894b8820f3 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -37,7 +37,7 @@ dependencies = [
     "nvidia-curand",
     "nvidia-cusolver",
     "nvidia-cusparse",
-    "pylibraft==24.12.*,>=0.0.0a0",
+    "pylibraft==25.2.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index 79aa5756a1..ddb8b32cd8 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "24.12.0"
+version = "25.2.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index 13cc658e36..1095b1fea3 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "24.12.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.2.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From 7ab2bfdd250613137a5622471212dab528319306 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Fri, 15 Nov 2024 12:16:17 -0500
Subject: [PATCH 207/623] Add `InnerProduct` and `CosineExpanded` metric
 support in NN Descent (#177)

Closes #171

Authors:
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/177
---
 cpp/CMakeLists.txt                            |  1 +
 cpp/include/cuvs/neighbors/nn_descent.hpp     | 24 ++---
 .../neighbors/detail/cagra/cagra_build.cuh    | 12 ++-
 cpp/src/neighbors/detail/nn_descent.cuh       | 87 +++++++++++++------
 cpp/src/neighbors/nn_descent_index.cpp        | 29 +++++++
 cpp/test/neighbors/ann_cagra.cuh              | 10 +--
 cpp/test/neighbors/ann_nn_descent.cuh         | 32 ++++---
 python/cuvs/cuvs/test/test_cagra.py           |  4 +-
 python/cuvs/cuvs/test/test_hnsw.py            |  4 +-
 9 files changed, 139 insertions(+), 64 deletions(-)
 create mode 100644 cpp/src/neighbors/nn_descent_index.cpp

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index c493af488e..81b82aa7bb 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -436,6 +436,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/nn_descent.cu
     src/neighbors/nn_descent_float.cu
     src/neighbors/nn_descent_half.cu
+    src/neighbors/nn_descent_index.cpp
     src/neighbors/nn_descent_int8.cu
     src/neighbors/nn_descent_uint8.cu
     src/neighbors/reachability.cu
diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index bd41d1ff78..9cd8192b59 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -61,11 +61,10 @@ struct index_params : cuvs::neighbors::index_params {
   /** @brief Construct NN descent parameters for a specific kNN graph degree
    *
    * @param graph_degree output graph degree
+   * @param metric distance metric to use
    */
-  index_params(size_t graph_degree = 64)
-    : graph_degree(graph_degree), intermediate_graph_degree(1.5 * graph_degree)
-  {
-  }
+  index_params(size_t graph_degree                 = 64,
+               cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded);
 };
 
 /**
@@ -103,11 +102,16 @@ struct index : cuvs::neighbors::index {
    * @param n_rows number of rows in knn-graph
    * @param n_cols number of cols in knn-graph
    * @param return_distances whether to return distances
+   * @param metric distance metric to use
    */
-  index(raft::resources const& res, int64_t n_rows, int64_t n_cols, bool return_distances = false)
+  index(raft::resources const& res,
+        int64_t n_rows,
+        int64_t n_cols,
+        bool return_distances               = false,
+        cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
     : cuvs::neighbors::index(),
       res_{res},
-      metric_{cuvs::distance::DistanceType::L2Expanded},
+      metric_{metric},
       graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(n_rows, n_cols)},
       graph_view_{graph_.view()},
       return_distances_{return_distances}
@@ -129,14 +133,16 @@ struct index : cuvs::neighbors::index {
    * @param graph_view raft::host_matrix_view<IdxT, int64_t, raft::row_major> for storing knn-graph
    * @param distances_view optional raft::device_matrix_view<float, int64_t, row_major> for storing
    * distances
+   * @param metric distance metric to use
    */
   index(raft::resources const& res,
         raft::host_matrix_view<IdxT, int64_t, raft::row_major> graph_view,
         std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances_view =
-          std::nullopt)
+          std::nullopt,
+        cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
     : cuvs::neighbors::index(),
       res_{res},
-      metric_{cuvs::distance::DistanceType::L2Expanded},
+      metric_{metric},
       graph_{raft::make_host_matrix<IdxT, int64_t, raft::row_major>(0, 0)},
       graph_view_{graph_view},
       distances_view_{distances_view},
@@ -473,8 +479,6 @@ auto build(raft::resources const& res,
            std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
              std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
-/** @} */
-
 /**
  * @brief Test if we have enough GPU memory to run NN descent algorithm.
  *
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 6209ff8196..b7fec724ba 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -436,11 +436,11 @@ index<T, IdxT> build(
   auto knn_build_params = params.graph_build_params;
   if (std::holds_alternative<std::monostate>(params.graph_build_params)) {
     // Heuristic to decide default build algo and its params.
-    if (params.metric == cuvs::distance::DistanceType::L2Expanded &&
-        cuvs::neighbors::nn_descent::has_enough_device_memory(
+    if (cuvs::neighbors::nn_descent::has_enough_device_memory(
           res, dataset.extents(), sizeof(IdxT))) {
       RAFT_LOG_DEBUG("NN descent solver");
-      knn_build_params = cagra::graph_build_params::nn_descent_params(intermediate_degree);
+      knn_build_params =
+        cagra::graph_build_params::nn_descent_params(intermediate_degree, params.metric);
     } else {
       RAFT_LOG_DEBUG("Selecting IVF-PQ solver");
       knn_build_params = cagra::graph_build_params::ivf_pq_params(dataset.extents(), params.metric);
@@ -453,9 +453,6 @@ index<T, IdxT> build(
       std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(knn_build_params);
     build_knn_graph(res, dataset, knn_graph->view(), ivf_pq_params);
   } else {
-    RAFT_EXPECTS(
-      params.metric == cuvs::distance::DistanceType::L2Expanded,
-      "L2Expanded is the only distance metrics supported for CAGRA build with nn_descent");
     auto nn_descent_params =
       std::get<cagra::graph_build_params::nn_descent_params>(knn_build_params);
 
@@ -466,7 +463,8 @@ index<T, IdxT> build(
         "nn-descent graph_degree.",
         nn_descent_params.graph_degree,
         intermediate_degree);
-      nn_descent_params = cagra::graph_build_params::nn_descent_params(intermediate_degree);
+      nn_descent_params =
+        cagra::graph_build_params::nn_descent_params(intermediate_degree, params.metric);
     }
 
     // Use nn-descent to build CAGRA knn graph
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 883d82d76a..c62a525407 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -19,6 +19,7 @@
 #include "ann_utils.cuh"
 #include "cagra/device_common.hpp"
 
+#include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 
 #include <raft/core/device_mdarray.hpp>
@@ -216,6 +217,7 @@ struct BuildConfig {
   size_t max_iterations{50};
   float termination_threshold{0.0001};
   size_t output_graph_degree{32};
+  cuvs::distance::DistanceType metric{cuvs::distance::DistanceType::L2Expanded};
 };
 
 template <typename Index_t>
@@ -454,11 +456,13 @@ __device__ __forceinline__ void load_vec(Data_t* vec_buffer,
 // TODO: Replace with RAFT utilities https://github.com/rapidsai/raft/issues/1827
 /** Calculate L2 norm, and cast data to __half */
 template <typename Data_t>
-RAFT_KERNEL preprocess_data_kernel(const Data_t* input_data,
-                                   __half* output_data,
-                                   int dim,
-                                   DistData_t* l2_norms,
-                                   size_t list_offset = 0)
+RAFT_KERNEL preprocess_data_kernel(
+  const Data_t* input_data,
+  __half* output_data,
+  int dim,
+  DistData_t* l2_norms,
+  size_t list_offset                  = 0,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
 {
   extern __shared__ char buffer[];
   __shared__ float l2_norm;
@@ -468,26 +472,32 @@ RAFT_KERNEL preprocess_data_kernel(const Data_t* input_data,
   load_vec(s_vec, input_data + blockIdx.x * dim, dim, dim, threadIdx.x % raft::warp_size());
   if (threadIdx.x == 0) { l2_norm = 0; }
   __syncthreads();
-  int lane_id = threadIdx.x % raft::warp_size();
-  for (int step = 0; step < raft::ceildiv(dim, raft::warp_size()); step++) {
-    int idx         = step * raft::warp_size() + lane_id;
-    float part_dist = 0;
-    if (idx < dim) {
-      part_dist = s_vec[idx];
-      part_dist = part_dist * part_dist;
-    }
-    __syncwarp();
-    for (int offset = raft::warp_size() >> 1; offset >= 1; offset >>= 1) {
-      part_dist += __shfl_down_sync(raft::warp_full_mask(), part_dist, offset);
+
+  if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::CosineExpanded) {
+    int lane_id = threadIdx.x % raft::warp_size();
+    for (int step = 0; step < raft::ceildiv(dim, raft::warp_size()); step++) {
+      int idx         = step * raft::warp_size() + lane_id;
+      float part_dist = 0;
+      if (idx < dim) {
+        part_dist = s_vec[idx];
+        part_dist = part_dist * part_dist;
+      }
+      __syncwarp();
+      for (int offset = raft::warp_size() >> 1; offset >= 1; offset >>= 1) {
+        part_dist += __shfl_down_sync(raft::warp_full_mask(), part_dist, offset);
+      }
+      if (lane_id == 0) { l2_norm += part_dist; }
+      __syncwarp();
     }
-    if (lane_id == 0) { l2_norm += part_dist; }
-    __syncwarp();
   }
 
   for (int step = 0; step < raft::ceildiv(dim, raft::warp_size()); step++) {
     int idx = step * raft::warp_size() + threadIdx.x;
     if (idx < dim) {
-      if (l2_norms == nullptr) {
+      if (metric == cuvs::distance::DistanceType::InnerProduct) {
+        output_data[list_id * dim + idx] = input_data[(size_t)blockIdx.x * dim + idx];
+      } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         output_data[list_id * dim + idx] =
           (float)input_data[(size_t)blockIdx.x * dim + idx] / sqrt(l2_norm);
       } else {
@@ -715,7 +725,8 @@ __launch_bounds__(BLOCK_SIZE, 4)
                     DistData_t* dists,
                     int graph_width,
                     int* locks,
-                    DistData_t* l2_norms)
+                    DistData_t* l2_norms,
+                    cuvs::distance::DistanceType metric)
 {
 #if (__CUDA_ARCH__ >= 700)
   using namespace nvcuda;
@@ -827,8 +838,10 @@ __launch_bounds__(BLOCK_SIZE, 4)
   for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
     if (i % SKEWED_MAX_NUM_BI_SAMPLES < list_new_size &&
         i / SKEWED_MAX_NUM_BI_SAMPLES < list_new_size) {
-      if (l2_norms == nullptr) {
+      if (metric == cuvs::distance::DistanceType::InnerProduct) {
         s_distances[i] = -s_distances[i];
+      } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+        s_distances[i] = 1.0 - s_distances[i];
       } else {
         s_distances[i] = l2_norms[new_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
                          l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
@@ -906,8 +919,10 @@ __launch_bounds__(BLOCK_SIZE, 4)
   for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
     if (i % SKEWED_MAX_NUM_BI_SAMPLES < list_old_size &&
         i / SKEWED_MAX_NUM_BI_SAMPLES < list_new_size) {
-      if (l2_norms == nullptr) {
+      if (metric == cuvs::distance::DistanceType::InnerProduct) {
         s_distances[i] = -s_distances[i];
+      } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+        s_distances[i] = 1.0 - s_distances[i];
       } else {
         s_distances[i] = l2_norms[old_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
                          l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
@@ -1161,7 +1176,7 @@ GNND<Data_t, Index_t>::GNND(raft::resources const& res, const BuildConfig& build
     ndim_(build_config.dataset_dim),
     d_data_{raft::make_device_matrix<__half, size_t, raft::row_major>(
       res, nrow_, build_config.dataset_dim)},
-    l2_norms_{raft::make_device_vector<DistData_t, size_t>(res, nrow_)},
+    l2_norms_{raft::make_device_vector<DistData_t, size_t>(res, 0)},
     graph_buffer_{
       raft::make_device_matrix<ID_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
     dists_buffer_{
@@ -1181,11 +1196,16 @@ GNND<Data_t, Index_t>::GNND(raft::resources const& res, const BuildConfig& build
     d_list_sizes_old_{raft::make_device_vector<int2, size_t>(res, nrow_)}
 {
   static_assert(NUM_SAMPLES <= 32);
+
   raft::matrix::fill(res, dists_buffer_.view(), std::numeric_limits<float>::max());
   auto graph_buffer_view = raft::make_device_matrix_view<Index_t, int64_t>(
     reinterpret_cast<Index_t*>(graph_buffer_.data_handle()), nrow_, DEGREE_ON_DEVICE);
   raft::matrix::fill(res, graph_buffer_view, std::numeric_limits<Index_t>::max());
   raft::matrix::fill(res, d_locks_.view(), 0);
+
+  if (build_config.metric == cuvs::distance::DistanceType::L2Expanded) {
+    l2_norms_ = raft::make_device_vector<DistData_t, size_t>(res, nrow_);
+  }
 };
 
 template <typename Data_t, typename Index_t>
@@ -1228,7 +1248,8 @@ void GNND<Data_t, Index_t>::local_join(cudaStream_t stream)
                                                       dists_buffer_.data_handle(),
                                                       DEGREE_ON_DEVICE,
                                                       d_locks_.data_handle(),
-                                                      l2_norms_.data_handle());
+                                                      l2_norms_.data_handle(),
+                                                      build_config_.metric);
 }
 
 template <typename Data_t, typename Index_t>
@@ -1261,7 +1282,8 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
                 d_data_.data_handle(),
                 build_config_.dataset_dim,
                 l2_norms_.data_handle(),
-                batch.offset());
+                batch.offset(),
+                build_config_.metric);
   }
 
   graph_.clear();
@@ -1417,6 +1439,11 @@ void build(raft::resources const& res,
   RAFT_EXPECTS(dataset.extent(0) < std::numeric_limits<int>::max() - 1,
                "The dataset size for GNND should be less than %d",
                std::numeric_limits<int>::max() - 1);
+  auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                         params.metric == cuvs::distance::DistanceType::CosineExpanded ||
+                         params.metric == cuvs::distance::DistanceType::InnerProduct;
+  RAFT_EXPECTS(allowed_metrics && idx.metric() == params.metric,
+               "The metric for NN Descent should be L2Expanded, CosineExpanded or InnerProduct");
   size_t intermediate_degree = params.intermediate_graph_degree;
   size_t graph_degree        = params.graph_degree;
 
@@ -1452,7 +1479,8 @@ void build(raft::resources const& res,
                            .internal_node_degree  = extended_intermediate_degree,
                            .max_iterations        = params.max_iterations,
                            .termination_threshold = params.termination_threshold,
-                           .output_graph_degree   = params.graph_degree};
+                           .output_graph_degree   = params.graph_degree,
+                           .metric                = params.metric};
 
   GNND<const T, int> nnd(res, build_config);
 
@@ -1500,8 +1528,11 @@ index<IdxT> build(
     graph_degree = intermediate_degree;
   }
 
-  index<IdxT> idx{
-    res, dataset.extent(0), static_cast<int64_t>(graph_degree), params.return_distances};
+  index<IdxT> idx{res,
+                  dataset.extent(0),
+                  static_cast<int64_t>(graph_degree),
+                  params.return_distances,
+                  params.metric};
 
   build(res, params, dataset, idx);
 
diff --git a/cpp/src/neighbors/nn_descent_index.cpp b/cpp/src/neighbors/nn_descent_index.cpp
new file mode 100644
index 0000000000..25d5b6af84
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent_index.cpp
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstddef>
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+
+namespace cuvs::neighbors::nn_descent {
+
+index_params::index_params(size_t graph_degree, cuvs::distance::DistanceType metric)
+{
+  this->graph_degree              = graph_degree;
+  this->intermediate_graph_degree = 1.5 * graph_degree;
+  this->metric                    = metric;
+}
+}  // namespace cuvs::neighbors::nn_descent
\ No newline at end of file
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 37d42dd1d3..660246c67e 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -361,8 +361,8 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
                                           // not used for knn_graph building.
         switch (ps.build_algo) {
           case graph_build_algo::IVF_PQ:
-            index_params.graph_build_params =
-              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            index_params.graph_build_params = graph_build_params::ivf_pq_params(
+              raft::matrix_extent<int64_t>(ps.n_rows, ps.dim), index_params.metric);
             if (ps.ivf_pq_search_refine_ratio) {
               std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
                 index_params.graph_build_params)
@@ -370,8 +370,8 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
             }
             break;
           case graph_build_algo::NN_DESCENT: {
-            index_params.graph_build_params =
-              graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
+            index_params.graph_build_params = graph_build_params::nn_descent_params(
+              index_params.intermediate_graph_degree, index_params.metric);
             break;
           }
           case graph_build_algo::AUTO:
@@ -389,7 +389,7 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
           (const DataT*)database.data(), ps.n_rows, ps.dim);
 
         {
-          cagra::index<DataT, IdxT> index(handle_);
+          cagra::index<DataT, IdxT> index(handle_, index_params.metric);
           if (ps.host_dataset) {
             auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
             raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
diff --git a/cpp/test/neighbors/ann_nn_descent.cuh b/cpp/test/neighbors/ann_nn_descent.cuh
index 7d2575c2bd..09861a2194 100644
--- a/cpp/test/neighbors/ann_nn_descent.cuh
+++ b/cpp/test/neighbors/ann_nn_descent.cuh
@@ -27,6 +27,7 @@
 #include <rmm/device_uvector.hpp>
 
 #include "naive_knn.cuh"
+#include <cuvs/distance/distance.hpp>
 
 #include <gtest/gtest.h>
 
@@ -107,7 +108,6 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
       raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
       raft::resource::sync_stream(handle_);
     }
-
     {
       {
         nn_descent::index_params index_params;
@@ -124,6 +124,7 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
           if (ps.host_dataset) {
             auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
             raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+            raft::resource::sync_stream(handle_);
             auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
               (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
             auto index = nn_descent::build(handle_, index_params, database_host_view);
@@ -151,6 +152,13 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
         raft::resource::sync_stream(handle_);
       }
 
+      if (ps.metric == cuvs::distance::DistanceType::InnerProduct) {
+        std::transform(
+          distances_naive.begin(), distances_naive.end(), distances_naive.begin(), [](auto x) {
+            return -x;
+          });
+      }
+
       double min_recall = ps.min_recall;
       EXPECT_TRUE(eval_neighbours(indices_naive,
                                   indices_NNDescent,
@@ -169,9 +177,11 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
     raft::random::RngState r(1234ULL);
     if constexpr (std::is_same<DataT, float>{}) {
       raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
-    } else {
+    } else if constexpr (std::is_same<DataT, int8_t>{}) {
       raft::random::uniformInt(
-        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(20));
+        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(-5), DataT(5));
+    } else {
+      raft::random::uniformInt(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0), DataT(5));
     }
     raft::resource::sync_stream(handle_);
   }
@@ -308,13 +318,15 @@ class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchI
   rmm::device_uvector<DataT> database;
 };
 
-const std::vector<AnnNNDescentInputs> inputs = raft::util::itertools::product<AnnNNDescentInputs>(
-  {1000, 2000},                                              // n_rows
-  {3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // dim
-  {32, 64},                                                  // graph_degree
-  {cuvs::distance::DistanceType::L2Expanded},
-  {false, true},
-  {0.90});
+const std::vector<AnnNNDescentInputs> inputs =
+  raft::util::itertools::product<AnnNNDescentInputs>({2000, 4000},            // n_rows
+                                                     {4, 16, 64, 256, 1024},  // dim
+                                                     {32, 64},                // graph_degree
+                                                     {cuvs::distance::DistanceType::L2Expanded,
+                                                      cuvs::distance::DistanceType::InnerProduct,
+                                                      cuvs::distance::DistanceType::CosineExpanded},
+                                                     {false, true},
+                                                     {0.90});
 
 // TODO : Investigate why this test is failing Reference issue https
 // :  // github.com/rapidsai/raft/issues/2450
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
index 92b88f0139..56e132c23c 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -122,8 +122,9 @@ def run_cagra_build_search_test(
 @pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
 @pytest.mark.parametrize("array_type", ["device", "host"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
+@pytest.mark.parametrize("metric", ["euclidean"])
 def test_cagra_dataset_dtype_host_device(
-    dtype, array_type, inplace, build_algo
+    dtype, array_type, inplace, build_algo, metric
 ):
     # Note that inner_product tests use normalized input which we cannot
     # represent in int8, therefore we test only sqeuclidean metric here.
@@ -132,6 +133,7 @@ def test_cagra_dataset_dtype_host_device(
         inplace=inplace,
         array_type=array_type,
         build_algo=build_algo,
+        metric=metric,
     )
 
 
diff --git a/python/cuvs/cuvs/test/test_hnsw.py b/python/cuvs/cuvs/test/test_hnsw.py
index 8bd2e8b76c..20a35401eb 100644
--- a/python/cuvs/cuvs/test/test_hnsw.py
+++ b/python/cuvs/cuvs/test/test_hnsw.py
@@ -41,8 +41,6 @@ def run_hnsw_build_search_test(
             pytest.skip(
                 "inner_product metric is not supported for int8/uint8 data"
             )
-        if build_algo == "nn_descent":
-            pytest.skip("inner_product metric is not supported for nn_descent")
 
     build_params = cagra.IndexParams(
         metric=metric,
@@ -83,7 +81,7 @@ def run_hnsw_build_search_test(
 @pytest.mark.parametrize("k", [10, 20])
 @pytest.mark.parametrize("ef", [30, 40])
 @pytest.mark.parametrize("num_threads", [2, 4])
-@pytest.mark.parametrize("metric", ["sqeuclidean"])
+@pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
 def test_hnsw(dtype, k, ef, num_threads, metric, build_algo):
     # Note that inner_product tests use normalized input which we cannot

From 7b879116684501f36ca5a19a74c01fcecb52e962 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 15 Nov 2024 16:12:42 -0600
Subject: [PATCH 208/623] use different wheel-size thresholds based on CUDA
 version (#469)

`cuvs-cu11` wheels are significantly larger than `cuvs-cu12` wheels, because (among other reasons) they are not able to dynamically link to CUDA math library wheels.

In #464, I proposed a size limit for CI checks of "max CUDA 11 wheel size + a buffer".

This PR proposes using different thresholds based on CUDA major version, following these discussions:

* https://github.com/rapidsai/cugraph/pull/4754#discussion_r1842526907
* https://github.com/rapidsai/cuml/pull/6136#discussion_r1841774811

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Mike Sarahan (https://github.com/msarahan)

URL: https://github.com/rapidsai/cuvs/pull/469
---
 ci/validate_wheel.sh       | 14 ++++++++++++++
 python/cuvs/pyproject.toml |  4 +---
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/ci/validate_wheel.sh b/ci/validate_wheel.sh
index 5910a5c59f..f2b235765c 100755
--- a/ci/validate_wheel.sh
+++ b/ci/validate_wheel.sh
@@ -6,12 +6,26 @@ set -euo pipefail
 package_dir=$1
 wheel_dir_relative_path=$2
 
+RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
+
+# some packages are much larger on CUDA 11 than on CUDA 12
+if [[ "${RAPIDS_CUDA_MAJOR}" == "11" ]]; then
+    PYDISTCHECK_ARGS=(
+        --max-allowed-size-compressed '1.4G'
+    )
+else
+    PYDISTCHECK_ARGS=(
+        --max-allowed-size-compressed '950M'
+    )
+fi
+
 cd "${package_dir}"
 
 rapids-logger "validate packages with 'pydistcheck'"
 
 pydistcheck \
     --inspect \
+    "${PYDISTCHECK_ARGS[@]}" \
     "$(echo ${wheel_dir_relative_path}/*.whl)"
 
 rapids-logger "validate packages with 'twine'"
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index d400267760..92e4993c76 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -135,12 +135,10 @@ matrix-entry = "cuda_suffixed=true;use_cuda_wheels=true"
 
 [tool.pydistcheck]
 select = [
+    # NOTE: size threshold is managed via CLI args in CI scripts
     "distro-too-large-compressed",
 ]
 
-# detect when package size grows significantly
-max_allowed_size_compressed = '1.4G'
-
 [tool.pytest.ini_options]
 filterwarnings = [
     "error",

From 27d45533d91f13ce00eabed409468a2b47452f4d Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Mon, 18 Nov 2024 14:55:13 -0800
Subject: [PATCH 209/623] Move check_input_array from pylibraft (#474)

With the changes in https://github.com/rapidsai/raft/pull/2498 we no longer have a pylibraft.neighbors module - but were still using a utility function `_check_input_array` from it in cuvs. Move this over to cuvs to unblock ci

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/474
---
 .../neighbors/brute_force/brute_force.pyx     |  2 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  3 +-
 python/cuvs/cuvs/neighbors/common.py          | 36 +++++++++++++++++++
 .../cuvs/cuvs/neighbors/filters/filters.pyx   |  2 +-
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx      |  2 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |  2 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  |  2 +-
 python/cuvs/cuvs/neighbors/refine.pyx         |  2 +-
 8 files changed, 44 insertions(+), 7 deletions(-)
 create mode 100644 python/cuvs/cuvs/neighbors/common.py

diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 559302ccc2..9d1d24eaee 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -31,9 +31,9 @@ from cuvs.distance_type cimport cuvsDistanceType
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.neighbors.common import _check_input_array
 
 from cuvs.distance import DISTANCE_TYPES
+from cuvs.neighbors.common import _check_input_array
 
 from cuvs.common.c_api cimport cuvsResources_t
 
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 95209dbeb9..752aef7415 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -32,7 +32,8 @@ from cuvs.common cimport cydlpack
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.neighbors.common import _check_input_array
+
+from cuvs.neighbors.common import _check_input_array
 
 from libc.stdint cimport (
     int8_t,
diff --git a/python/cuvs/cuvs/neighbors/common.py b/python/cuvs/cuvs/neighbors/common.py
new file mode 100644
index 0000000000..c14b9f8c9c
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/common.py
@@ -0,0 +1,36 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def _check_input_array(cai, exp_dt, exp_rows=None, exp_cols=None):
+    if cai.dtype not in exp_dt:
+        raise TypeError("dtype %s not supported" % cai.dtype)
+
+    if not cai.c_contiguous:
+        raise ValueError("Row major input is expected")
+
+    if exp_cols is not None and cai.shape[1] != exp_cols:
+        raise ValueError(
+            "Incorrect number of columns, expected {} got {}".format(
+                exp_cols, cai.shape[1]
+            )
+        )
+
+    if exp_rows is not None and cai.shape[0] != exp_rows:
+        raise ValueError(
+            "Incorrect number of rows, expected {} , got {}".format(
+                exp_rows, cai.shape[0]
+            )
+        )
diff --git a/python/cuvs/cuvs/neighbors/filters/filters.pyx b/python/cuvs/cuvs/neighbors/filters/filters.pyx
index 3a81cb7867..9bc2a905ce 100644
--- a/python/cuvs/cuvs/neighbors/filters/filters.pyx
+++ b/python/cuvs/cuvs/neighbors/filters/filters.pyx
@@ -20,11 +20,11 @@ import numpy as np
 from libc.stdint cimport uintptr_t
 
 from cuvs.common cimport cydlpack
+from cuvs.neighbors.common import _check_input_array
 
 from .filters cimport BITMAP, NO_FILTER, cuvsFilter
 
 from pylibraft.common.cai_wrapper import wrap_array
-from pylibraft.neighbors.common import _check_input_array
 
 
 cdef class Prefilter:
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
index 018fcfef93..bcfaf167ef 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
@@ -21,6 +21,7 @@ from libcpp.string cimport string
 
 from cuvs.common.exceptions import check_cuvs
 from cuvs.common.resources import auto_sync_resources
+from cuvs.neighbors.common import _check_input_array
 
 from cuvs.common cimport cydlpack
 
@@ -36,7 +37,6 @@ import uuid
 from pylibraft.common import auto_convert_output
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.neighbors.common import _check_input_array
 
 
 cdef class SearchParams:
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 25b9b2aeeb..7a169e1a0f 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -31,9 +31,9 @@ from cuvs.distance_type cimport cuvsDistanceType
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.neighbors.common import _check_input_array
 
 from cuvs.distance import DISTANCE_TYPES
+from cuvs.neighbors.common import _check_input_array
 
 from libc.stdint cimport (
     int8_t,
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 3add1df75f..531302ee62 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -31,9 +31,9 @@ from cuvs.distance_type cimport cuvsDistanceType
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.neighbors.common import _check_input_array
 
 from cuvs.distance import DISTANCE_TYPES
+from cuvs.neighbors.common import _check_input_array
 
 from libc.stdint cimport (
     int8_t,
diff --git a/python/cuvs/cuvs/neighbors/refine.pyx b/python/cuvs/cuvs/neighbors/refine.pyx
index 0eccc4108e..b7aa35dca0 100644
--- a/python/cuvs/cuvs/neighbors/refine.pyx
+++ b/python/cuvs/cuvs/neighbors/refine.pyx
@@ -31,13 +31,13 @@ from cuvs.distance_type cimport cuvsDistanceType
 from pylibraft.common import auto_convert_output, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
-from pylibraft.neighbors.common import _check_input_array
 
 from cuvs.distance import DISTANCE_TYPES
 
 from cuvs.common.c_api cimport cuvsResources_t
 
 from cuvs.common.exceptions import check_cuvs
+from cuvs.neighbors.common import _check_input_array
 
 
 @auto_sync_resources

From f127b06b83e3c9e3c3034fdc902441edbf841b90 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 19 Nov 2024 14:01:22 +0100
Subject: [PATCH 210/623] Fix an OOB error in device-side
 cuvs::neighbors::refine and CAGRA kern_prune (#460)

IVF-Flat index expects all valid indices during build, which may not be the case in the context of refinement.
At the same time, `cagra::detail::graph::kern_prune` fails with OOB error if some indices are invalid.

This PR tweaks both kernels to avoid touching the input data with an invalid index.

Fixes https://github.com/rapidsai/cuvs/issues/337

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/460
---
 cpp/src/neighbors/detail/cagra/graph_core.cuh |  1 +
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |  8 +-
 cpp/test/CMakeLists.txt                       |  1 +
 .../ann_cagra/bug_extreme_inputs_oob.cu       | 73 +++++++++++++++++++
 4 files changed, 81 insertions(+), 2 deletions(-)
 create mode 100644 cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu

diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 4253cb781d..daeac82b95 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -156,6 +156,7 @@ __global__ void kern_prune(const IdxT* const knn_graph,  // [graph_chunk_size, g
   // count number of detours (A->D->B)
   for (uint32_t kAD = 0; kAD < graph_degree - 1; kAD++) {
     const uint64_t iD = knn_graph[kAD + (graph_degree * iA)];
+    if (iD >= graph_size) { continue; }
     for (uint32_t kDB = threadIdx.x; kDB < graph_degree; kDB += blockDim.x) {
       const uint64_t iB_candidate = knn_graph[kDB + ((uint64_t)graph_degree * iD)];
       for (uint32_t kAB = kAD + 1; kAB < graph_degree; kAB++) {
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index fb110d8104..d6ffc12187 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -132,6 +132,10 @@ RAFT_KERNEL build_index_kernel(const LabelT* labels,
 {
   const IdxT i = IdxT(blockDim.x) * IdxT(blockIdx.x) + threadIdx.x;
   if (i >= n_rows) { return; }
+  auto source_ix = source_ixs == nullptr ? i + batch_offset : source_ixs[i];
+  // In the context of refinement, some indices may be invalid (the generating NN algorithm does
+  // not return enough valid items). Do not add the item to the index in this case.
+  if (source_ix == ivf::kInvalidRecord<IdxT> || source_ix == raft::upper_bound<IdxT>()) { return; }
 
   auto list_id     = labels[i];
   auto inlist_id   = atomicAdd(list_sizes_ptr + list_id, 1);
@@ -139,7 +143,7 @@ RAFT_KERNEL build_index_kernel(const LabelT* labels,
   auto* list_data  = list_data_ptrs[list_id];
 
   // Record the source vector id in the index
-  list_index[inlist_id] = source_ixs == nullptr ? i + batch_offset : source_ixs[i];
+  list_index[inlist_id] = source_ix;
 
   // The data is written in interleaved groups of `index::kGroupSize` vectors
   using interleaved_group = raft::Pow2<kIndexGroupSize>;
@@ -151,7 +155,7 @@ RAFT_KERNEL build_index_kernel(const LabelT* labels,
 
   // Point to the source vector
   if constexpr (gather_src) {
-    source_vecs += source_ixs[i] * dim;
+    source_vecs += source_ix * dim;
   } else {
     source_vecs += i * dim;
   }
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 1ed8466b3c..7754a50432 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -137,6 +137,7 @@ if(BUILD_TESTS)
     NAME
     NEIGHBORS_ANN_CAGRA_TEST
     PATH
+    neighbors/ann_cagra/bug_extreme_inputs_oob.cu
     neighbors/ann_cagra/bug_multi_cta_crash.cu
     neighbors/ann_cagra/test_float_uint32_t.cu
     neighbors/ann_cagra/test_half_uint32_t.cu
diff --git a/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu b/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
new file mode 100644
index 0000000000..e21a54e9e6
--- /dev/null
+++ b/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/random/rng.cuh>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::cagra {
+
+class cagra_extreme_inputs_oob_test : public ::testing::Test {
+ public:
+  using data_type = float;
+
+ protected:
+  void run()
+  {
+    cagra::index_params ix_ps;
+    graph_build_params::ivf_pq_params gb_params{};
+    gb_params.refinement_rate       = 2;
+    ix_ps.graph_build_params        = gb_params;
+    ix_ps.graph_degree              = 64;
+    ix_ps.intermediate_graph_degree = 128;
+
+    [[maybe_unused]] auto ix = cagra::build(res, ix_ps, raft::make_const_mdspan(dataset->view()));
+    raft::resource::sync_stream(res);
+  }
+
+  void SetUp() override
+  {
+    dataset.emplace(raft::make_device_matrix<data_type, int64_t>(res, n_samples, n_dim));
+    raft::random::RngState r(1234ULL);
+    raft::random::normal(
+      res, r, dataset->data_handle(), n_samples * n_dim, data_type(0), data_type(1e20));
+    raft::resource::sync_stream(res);
+  }
+
+  void TearDown() override
+  {
+    dataset.reset();
+    raft::resource::sync_stream(res);
+  }
+
+ private:
+  raft::resources res;
+  std::optional<raft::device_matrix<data_type, int64_t>> dataset = std::nullopt;
+
+  constexpr static int64_t n_samples                   = 100000;
+  constexpr static int64_t n_dim                       = 200;
+  constexpr static cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
+};
+
+TEST_F(cagra_extreme_inputs_oob_test, cagra_extreme_inputs_oob_test) { this->run(); }
+
+}  // namespace cuvs::neighbors::cagra

From 06afd5bd27d07ad6e58544c06f920d570b7df983 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 20 Nov 2024 15:26:05 -0800
Subject: [PATCH 211/623] Migrate sparse knn and distances code from raft
 (#457)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/457
---
 cpp/CMakeLists.txt                            |   2 +
 cpp/include/cuvs/distance/distance.hpp        |  81 ++
 cpp/include/cuvs/neighbors/brute_force.hpp    | 104 +++
 .../distance/detail/sparse/bin_distance.cuh   | 231 +++++
 cpp/src/distance/detail/sparse/common.hpp     |  59 ++
 cpp/src/distance/detail/sparse/coo_spmv.cuh   | 211 +++++
 .../detail/sparse/coo_spmv_kernel.cuh         | 229 +++++
 .../coo_spmv_strategies/base_strategy.cuh     | 149 +++
 .../coo_mask_row_iterators.cuh                | 234 +++++
 .../dense_smem_strategy.cuh                   | 121 +++
 .../coo_spmv_strategies/hash_strategy.cuh     | 296 ++++++
 .../distance/detail/sparse/ip_distance.cuh    |  89 ++
 .../distance/detail/sparse/l2_distance.cuh    | 502 +++++++++++
 .../distance/detail/sparse/lp_distance.cuh    | 333 +++++++
 cpp/src/distance/detail/sparse/utils.cuh      | 171 ++++
 cpp/src/distance/sparse_distance.cu           |  85 ++
 cpp/src/distance/sparse_distance.cuh          | 115 +++
 cpp/src/neighbors/detail/sparse_knn.cuh       | 437 +++++++++
 cpp/src/neighbors/sparse_brute_force.cu       |  72 ++
 cpp/test/CMakeLists.txt                       |   3 +-
 cpp/test/distance/sparse_distance.cu          | 850 ++++++++++++++++++
 cpp/test/neighbors/sparse_brute_force.cu      | 175 ++++
 22 files changed, 4548 insertions(+), 1 deletion(-)
 create mode 100644 cpp/src/distance/detail/sparse/bin_distance.cuh
 create mode 100644 cpp/src/distance/detail/sparse/common.hpp
 create mode 100644 cpp/src/distance/detail/sparse/coo_spmv.cuh
 create mode 100644 cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh
 create mode 100644 cpp/src/distance/detail/sparse/coo_spmv_strategies/base_strategy.cuh
 create mode 100644 cpp/src/distance/detail/sparse/coo_spmv_strategies/coo_mask_row_iterators.cuh
 create mode 100644 cpp/src/distance/detail/sparse/coo_spmv_strategies/dense_smem_strategy.cuh
 create mode 100644 cpp/src/distance/detail/sparse/coo_spmv_strategies/hash_strategy.cuh
 create mode 100644 cpp/src/distance/detail/sparse/ip_distance.cuh
 create mode 100644 cpp/src/distance/detail/sparse/l2_distance.cuh
 create mode 100644 cpp/src/distance/detail/sparse/lp_distance.cuh
 create mode 100644 cpp/src/distance/detail/sparse/utils.cuh
 create mode 100644 cpp/src/distance/sparse_distance.cu
 create mode 100644 cpp/src/distance/sparse_distance.cuh
 create mode 100644 cpp/src/neighbors/detail/sparse_knn.cuh
 create mode 100644 cpp/src/neighbors/sparse_brute_force.cu
 create mode 100644 cpp/test/distance/sparse_distance.cu
 create mode 100644 cpp/test/neighbors/sparse_brute_force.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 81b82aa7bb..32093776c3 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -369,6 +369,7 @@ if(BUILD_SHARED_LIBS)
     src/distance/detail/fused_distance_nn.cu
     src/distance/distance.cu
     src/distance/pairwise_distance.cu
+    src/distance/sparse_distance.cu
     src/neighbors/brute_force.cu
     src/neighbors/cagra_build_float.cu
     src/neighbors/cagra_build_half.cu
@@ -449,6 +450,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/refine/detail/refine_host_int8_t_float.cpp
     src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
     src/neighbors/sample_filter.cu
+    src/neighbors/sparse_brute_force.cu
     src/neighbors/vamana_build_float.cu
     src/neighbors/vamana_build_uint8.cu
     src/neighbors/vamana_build_int8.cu
diff --git a/cpp/include/cuvs/distance/distance.hpp b/cpp/include/cuvs/distance/distance.hpp
index def72641ec..42c574e581 100644
--- a/cpp/include/cuvs/distance/distance.hpp
+++ b/cpp/include/cuvs/distance/distance.hpp
@@ -20,6 +20,7 @@
 
 #include <cstdint>
 #include <cuda_fp16.h>
+#include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
 
@@ -331,6 +332,86 @@ void pairwise_distance(
   cuvs::distance::DistanceType metric,
   float metric_arg = 2.0f);
 
+/**
+ * @brief Compute sparse pairwise distances between x and y, using the provided
+ * input configuration and distance function.
+ *
+ * @code{.cpp}
+ * #include <raft/core/device_resources.hpp>
+ * #include <raft/core/device_csr_matrix.hpp>
+ * #include <raft/core/device_mdspan.hpp>
+ *
+ * int x_n_rows = 100000;
+ * int y_n_rows = 50000;
+ * int n_cols = 10000;
+ *
+ * raft::device_resources handle;
+ * auto x = raft::make_device_csr_matrix<float>(handle, x_n_rows, n_cols);
+ * auto y = raft::make_device_csr_matrix<float>(handle, y_n_rows, n_cols);
+ *
+ * ...
+ * // populate data
+ * ...
+ *
+ * auto out = raft::make_device_matrix<float>(handle, x_nrows, y_nrows);
+ * auto metric = cuvs::distance::DistanceType::L2Expanded;
+ * raft::sparse::distance::pairwise_distance(handle, x.view(), y.view(), out, metric);
+ * @endcode
+ *
+ * @param[in] handle raft::resources
+ * @param[in] x raft::device_csr_matrix_view
+ * @param[in] y raft::device_csr_matrix_view
+ * @param[out] dist raft::device_matrix_view dense matrix
+ * @param[in] metric distance metric to use
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(raft::resources const& handle,
+                       raft::device_csr_matrix_view<const float, int, int, int> x,
+                       raft::device_csr_matrix_view<const float, int, int, int> y,
+                       raft::device_matrix_view<float, int, raft::row_major> dist,
+                       cuvs::distance::DistanceType metric,
+                       float metric_arg = 2.0f);
+
+/**
+ * @brief Compute sparse pairwise distances between x and y, using the provided
+ * input configuration and distance function.
+ *
+ * @code{.cpp}
+ * #include <raft/core/device_resources.hpp>
+ * #include <raft/core/device_csr_matrix.hpp>
+ * #include <raft/core/device_mdspan.hpp>
+ *
+ * int x_n_rows = 100000;
+ * int y_n_rows = 50000;
+ * int n_cols = 10000;
+ *
+ * raft::device_resources handle;
+ * auto x = raft::make_device_csr_matrix<double>(handle, x_n_rows, n_cols);
+ * auto y = raft::make_device_csr_matrix<double>(handle, y_n_rows, n_cols);
+ *
+ * ...
+ * // populate data
+ * ...
+ *
+ * auto out = raft::make_device_matrix<double>(handle, x_nrows, y_nrows);
+ * auto metric = cuvs::distance::DistanceType::L2Expanded;
+ * raft::sparse::distance::pairwise_distance(handle, x.view(), y.view(), out, metric);
+ * @endcode
+ *
+ * @param[in] handle raft::resources
+ * @param[in] x raft::device_csr_matrix_view
+ * @param[in] y raft::device_csr_matrix_view
+ * @param[out] dist raft::device_matrix_view dense matrix
+ * @param[in] metric distance metric to use
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+void pairwise_distance(raft::resources const& handle,
+                       raft::device_csr_matrix_view<const double, int, int, int> x,
+                       raft::device_csr_matrix_view<const double, int, int, int> y,
+                       raft::device_matrix_view<double, int, raft::row_major> dist,
+                       cuvs::distance::DistanceType metric,
+                       float metric_arg = 2.0f);
+
 /** @} */  // end group pairwise_distance_runtime
 
 };  // namespace cuvs::distance
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 428fa592aa..ba67797ee3 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -18,6 +18,7 @@
 
 #include "common.hpp"
 #include <cuvs/neighbors/common.hpp>
+#include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/handle.hpp>
@@ -375,4 +376,107 @@ void search(raft::resources const& handle,
  * @}
  */
 
+/**
+ * @defgroup sparse_bruteforce_cpp_index Sparse Brute Force index
+ * @{
+ */
+/**
+ * @brief Sparse Brute Force index.
+ *
+ * @tparam T Data element type
+ * @tparam IdxT Index element type
+ */
+template <typename T, typename IdxT>
+struct sparse_index {
+ public:
+  sparse_index(const sparse_index&)            = delete;
+  sparse_index(sparse_index&&)                 = default;
+  sparse_index& operator=(const sparse_index&) = delete;
+  sparse_index& operator=(sparse_index&&)      = default;
+  ~sparse_index()                              = default;
+
+  /** Construct a sparse brute force sparse_index from dataset */
+  sparse_index(raft::resources const& res,
+               raft::device_csr_matrix_view<const T, IdxT, IdxT, IdxT> dataset,
+               cuvs::distance::DistanceType metric,
+               T metric_arg);
+
+  /** Distance metric used for retrieval */
+  cuvs::distance::DistanceType metric() const noexcept { return metric_; }
+
+  /** Metric argument */
+  T metric_arg() const noexcept { return metric_arg_; }
+
+  raft::device_csr_matrix_view<const T, IdxT, IdxT, IdxT> dataset() const noexcept
+  {
+    return dataset_;
+  }
+
+ private:
+  raft::device_csr_matrix_view<const T, IdxT, IdxT, IdxT> dataset_;
+  cuvs::distance::DistanceType metric_;
+  T metric_arg_;
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup sparse_bruteforce_cpp_index_build Sparse Brute Force index build
+ * @{
+ */
+
+/*
+ * @brief Build the Sparse index from the dataset
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // create and fill the index from a CSR dataset
+ *   auto index = brute_force::build(handle, dataset, metric);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] dataset A sparse CSR matrix in device memory to search against
+ * @param[in] metric cuvs::distance::DistanceType
+ * @param[in] metric_arg metric argument
+ *
+ * @return the constructed Sparse brute-force index
+ */
+auto build(raft::resources const& handle,
+           raft::device_csr_matrix_view<const float, int, int, int> dataset,
+           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+           float metric_arg = 0) -> cuvs::neighbors::brute_force::sparse_index<float, int>;
+/**
+ * @}
+ */
+
+/**
+ * @defgroup sparse_bruteforce_cpp_index_search Sparse Brute Force index search
+ * @{
+ */
+struct sparse_search_params {
+  int batch_size_index = 2 << 14;
+  int batch_size_query = 2 << 14;
+};
+
+/*
+ * @brief Search the sparse bruteforce index for nearest neighbors
+ *
+ * @param[in] handle
+ * @param[in] index Sparse brute-force constructed index
+ * @param[in] queries a sparse CSR matrix on the device to query
+ * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
+ */
+void search(raft::resources const& handle,
+            const sparse_search_params& params,
+            const sparse_index<float, int>& index,
+            raft::device_csr_matrix_view<const float, int, int, int> dataset,
+            raft::device_matrix_view<int, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+/**
+ * @}
+ */
 }  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/src/distance/detail/sparse/bin_distance.cuh b/cpp/src/distance/detail/sparse/bin_distance.cuh
new file mode 100644
index 0000000000..1a63a8eb9f
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/bin_distance.cuh
@@ -0,0 +1,231 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "common.hpp"
+#include "ip_distance.cuh"
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/sparse/detail/utils.h>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <limits.h>
+
+#include <nvfunctional>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+// @TODO: Move this into sparse prims (coo_norm)
+template <typename value_idx, typename value_t>
+RAFT_KERNEL compute_binary_row_norm_kernel(value_t* out,
+                                           const value_idx* __restrict__ coo_rows,
+                                           const value_t* __restrict__ data,
+                                           value_idx nnz)
+{
+  value_idx i = blockDim.x * blockIdx.x + threadIdx.x;
+  if (i < nnz) {
+    // We do conditional here only because it's
+    // possible there could be some stray zeros in
+    // the sparse structure and removing them would be
+    // more expensive.
+    atomicAdd(&out[coo_rows[i]], data[i] == 1.0);
+  }
+}
+
+template <typename value_idx, typename value_t, typename expansion_f>
+RAFT_KERNEL compute_binary_warp_kernel(value_t* __restrict__ C,
+                                       const value_t* __restrict__ Q_norms,
+                                       const value_t* __restrict__ R_norms,
+                                       value_idx n_rows,
+                                       value_idx n_cols,
+                                       expansion_f expansion_func)
+{
+  std::size_t tid = blockDim.x * blockIdx.x + threadIdx.x;
+  value_idx i     = tid / n_cols;
+  value_idx j     = tid % n_cols;
+
+  if (i >= n_rows || j >= n_cols) return;
+
+  value_t q_norm            = Q_norms[i];
+  value_t r_norm            = R_norms[j];
+  value_t dot               = C[(size_t)i * n_cols + j];
+  C[(size_t)i * n_cols + j] = expansion_func(dot, q_norm, r_norm);
+}
+
+template <typename value_idx, typename value_t, typename expansion_f, int tpb = 1024>
+void compute_binary(value_t* C,
+                    const value_t* Q_norms,
+                    const value_t* R_norms,
+                    value_idx n_rows,
+                    value_idx n_cols,
+                    expansion_f expansion_func,
+                    cudaStream_t stream)
+{
+  int blocks = raft::ceildiv<size_t>((size_t)n_rows * n_cols, tpb);
+  compute_binary_warp_kernel<<<blocks, tpb, 0, stream>>>(
+    C, Q_norms, R_norms, n_rows, n_cols, expansion_func);
+}
+
+template <typename value_idx, typename value_t, typename expansion_f, int tpb = 1024>
+void compute_bin_distance(value_t* out,
+                          const value_idx* Q_coo_rows,
+                          const value_t* Q_data,
+                          value_idx Q_nnz,
+                          const value_idx* R_coo_rows,
+                          const value_t* R_data,
+                          value_idx R_nnz,
+                          value_idx m,
+                          value_idx n,
+                          cudaStream_t stream,
+                          expansion_f expansion_func)
+{
+  rmm::device_uvector<value_t> Q_norms(m, stream);
+  rmm::device_uvector<value_t> R_norms(n, stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(Q_norms.data(), 0, Q_norms.size() * sizeof(value_t)));
+  RAFT_CUDA_TRY(cudaMemsetAsync(R_norms.data(), 0, R_norms.size() * sizeof(value_t)));
+
+  compute_binary_row_norm_kernel<<<raft::ceildiv(Q_nnz, tpb), tpb, 0, stream>>>(
+    Q_norms.data(), Q_coo_rows, Q_data, Q_nnz);
+  compute_binary_row_norm_kernel<<<raft::ceildiv(R_nnz, tpb), tpb, 0, stream>>>(
+    R_norms.data(), R_coo_rows, R_data, R_nnz);
+
+  compute_binary(out, Q_norms.data(), R_norms.data(), m, n, expansion_func, stream);
+}
+
+/**
+ * Jaccard distance using the expanded form:
+ * 1 - (sum(x_k * y_k) / ((sum(x_k) + sum(y_k)) - sum(x_k * y_k))
+ */
+template <typename value_idx = int, typename value_t = float>
+class jaccard_expanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit jaccard_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config),
+      workspace(0, raft::resource::get_cuda_stream(config.handle)),
+      ip_dists(config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    ip_dists.compute(out_dists);
+
+    value_idx* b_indices = ip_dists.b_rows_coo();
+    value_t* b_data      = ip_dists.b_data_coo();
+
+    rmm::device_uvector<value_idx> search_coo_rows(
+      config_->a_nnz, raft::resource::get_cuda_stream(config_->handle));
+    raft::sparse::convert::csr_to_coo(config_->a_indptr,
+                                      config_->a_nrows,
+                                      search_coo_rows.data(),
+                                      config_->a_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+
+    compute_bin_distance(out_dists,
+                         search_coo_rows.data(),
+                         config_->a_data,
+                         config_->a_nnz,
+                         b_indices,
+                         b_data,
+                         config_->b_nnz,
+                         config_->a_nrows,
+                         config_->b_nrows,
+                         raft::resource::get_cuda_stream(config_->handle),
+                         [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) {
+                           value_t q_r_union = q_norm + r_norm;
+                           value_t denom     = q_r_union - dot;
+
+                           value_t jacc = ((denom != 0) * dot) / ((denom == 0) + denom);
+
+                           // flip the similarity when both rows are 0
+                           bool both_empty = q_r_union == 0;
+                           return 1 - ((!both_empty * jacc) + both_empty);
+                         });
+  }
+
+  ~jaccard_expanded_distances_t() = default;
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+  rmm::device_uvector<char> workspace;
+  ip_distances_t<value_idx, value_t> ip_dists;
+};
+
+/**
+ * Dice distance using the expanded form:
+ * 1 - ((2 * sum(x_k * y_k)) / (sum(x_k) + sum(y_k)))
+ */
+template <typename value_idx = int, typename value_t = float>
+class dice_expanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit dice_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config),
+      workspace(0, raft::resource::get_cuda_stream(config.handle)),
+      ip_dists(config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    ip_dists.compute(out_dists);
+
+    value_idx* b_indices = ip_dists.b_rows_coo();
+    value_t* b_data      = ip_dists.b_data_coo();
+
+    rmm::device_uvector<value_idx> search_coo_rows(
+      config_->a_nnz, raft::resource::get_cuda_stream(config_->handle));
+    raft::sparse::convert::csr_to_coo(config_->a_indptr,
+                                      config_->a_nrows,
+                                      search_coo_rows.data(),
+                                      config_->a_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+
+    compute_bin_distance(out_dists,
+                         search_coo_rows.data(),
+                         config_->a_data,
+                         config_->a_nnz,
+                         b_indices,
+                         b_data,
+                         config_->b_nnz,
+                         config_->a_nrows,
+                         config_->b_nrows,
+                         raft::resource::get_cuda_stream(config_->handle),
+                         [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) {
+                           value_t q_r_union = q_norm + r_norm;
+                           value_t dice      = (2 * dot) / q_r_union;
+                           bool both_empty   = q_r_union == 0;
+                           return 1 - ((!both_empty * dice) + both_empty);
+                         });
+  }
+
+  ~dice_expanded_distances_t() = default;
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+  rmm::device_uvector<char> workspace;
+  ip_distances_t<value_idx, value_t> ip_dists;
+};
+
+}  // END namespace sparse
+}  // END namespace detail
+}  // END namespace distance
+}  // END namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/common.hpp b/cpp/src/distance/detail/sparse/common.hpp
new file mode 100644
index 0000000000..803dabe560
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/common.hpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/resources.hpp>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx, typename value_t>
+struct distances_config_t {
+  distances_config_t(raft::resources const& handle_) : handle(handle_) {}
+
+  // left side
+  value_idx a_nrows;
+  value_idx a_ncols;
+  value_idx a_nnz;
+  value_idx* a_indptr;
+  value_idx* a_indices;
+  value_t* a_data;
+
+  // right side
+  value_idx b_nrows;
+  value_idx b_ncols;
+  value_idx b_nnz;
+  value_idx* b_indptr;
+  value_idx* b_indices;
+  value_t* b_data;
+
+  raft::resources const& handle;
+};
+
+template <typename value_t>
+class distances_t {
+ public:
+  virtual void compute(value_t* out) {}
+  virtual ~distances_t() = default;
+};
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/coo_spmv.cuh b/cpp/src/distance/detail/sparse/coo_spmv.cuh
new file mode 100644
index 0000000000..181b531f75
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/coo_spmv.cuh
@@ -0,0 +1,211 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "common.hpp"
+#include "coo_spmv_strategies/dense_smem_strategy.cuh"
+#include "coo_spmv_strategies/hash_strategy.cuh"
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/sparse/csr.hpp>
+#include <raft/sparse/detail/cusparse_wrappers.h>
+#include <raft/sparse/detail/utils.h>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <cusparse_v2.h>
+#include <limits.h>
+
+#include <nvfunctional>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx,
+          typename value_t,
+          int threads_per_block = 1024,
+          typename product_f,
+          typename accum_f,
+          typename write_f,
+          typename strategy_t>
+inline void balanced_coo_pairwise_generalized_spmv(
+  value_t* out_dists,
+  const distances_config_t<value_idx, value_t>& config_,
+  value_idx* coo_rows_b,
+  product_f product_func,
+  accum_f accum_func,
+  write_f write_func,
+  strategy_t strategy,
+  int chunk_size = 500000)
+{
+  uint64_t n = (uint64_t)sizeof(value_t) * (uint64_t)config_.a_nrows * (uint64_t)config_.b_nrows;
+  RAFT_CUDA_TRY(cudaMemsetAsync(out_dists, 0, n, raft::resource::get_cuda_stream(config_.handle)));
+
+  strategy.dispatch(out_dists, coo_rows_b, product_func, accum_func, write_func, chunk_size);
+};
+
+/**
+ * Performs generalized sparse-matrix-sparse-matrix multiplication via a
+ * sparse-matrix-sparse-vector layout `out=A*B` where generalized product()
+ * and sum() operations can be used in place of the standard sum and product:
+ *
+ * out_ij = sum_k(product(A_ik, B_ik)) The sum goes through values of
+ * k=0..n_cols-1 where B_kj is nonzero.
+ *
+ * The product and sum operations shall form a semiring algebra with the
+ * following properties:
+ * 1. {+, 0} is a commutative sum reduction monoid with identity element 0
+ * 2. {*, 1} is a product monoid with identity element 1
+ * 3. Multiplication by 0 annihilates x. e.g. product(x, 0) = 0
+ *
+ * Each vector of A is loaded into shared memory in dense form and the
+ * non-zeros of B load balanced across the threads of each block.
+ * @tparam value_idx index type
+ * @tparam value_t value type
+ * @tparam threads_per_block block size
+ * @tparam product_f semiring product() function
+ * @tparam accum_f semiring sum() function
+ * @tparam write_f atomic semiring sum() function
+ * @param[out] out_dists dense array of out distances of size m * n in row-major
+ *             format.
+ * @param[in] config_ distance config object
+ * @param[in] coo_rows_b coo row array for B
+ * @param[in] product_func semiring product() function
+ * @param[in] accum_func semiring sum() function
+ * @param[in] write_func atomic semiring sum() function
+ * @param[in] chunk_size number of nonzeros of B to process for each row of A
+ *            this value was found through profiling and represents a reasonable
+ *            setting for both large and small densities
+ */
+template <typename value_idx,
+          typename value_t,
+          int threads_per_block = 1024,
+          typename product_f,
+          typename accum_f,
+          typename write_f>
+inline void balanced_coo_pairwise_generalized_spmv(
+  value_t* out_dists,
+  const distances_config_t<value_idx, value_t>& config_,
+  value_idx* coo_rows_b,
+  product_f product_func,
+  accum_f accum_func,
+  write_f write_func,
+  int chunk_size = 500000)
+{
+  uint64_t n = (uint64_t)sizeof(value_t) * (uint64_t)config_.a_nrows * (uint64_t)config_.b_nrows;
+  RAFT_CUDA_TRY(cudaMemsetAsync(out_dists, 0, n, raft::resource::get_cuda_stream(config_.handle)));
+
+  int max_cols = max_cols_per_block<value_idx, value_t>();
+
+  if (max_cols > config_.a_ncols) {
+    dense_smem_strategy<value_idx, value_t, threads_per_block> strategy(config_);
+    strategy.dispatch(out_dists, coo_rows_b, product_func, accum_func, write_func, chunk_size);
+  } else {
+    hash_strategy<value_idx, value_t, threads_per_block> strategy(config_);
+    strategy.dispatch(out_dists, coo_rows_b, product_func, accum_func, write_func, chunk_size);
+  }
+};
+
+template <typename value_idx,
+          typename value_t,
+          int threads_per_block = 1024,
+          typename product_f,
+          typename accum_f,
+          typename write_f,
+          typename strategy_t>
+inline void balanced_coo_pairwise_generalized_spmv_rev(
+  value_t* out_dists,
+  const distances_config_t<value_idx, value_t>& config_,
+  value_idx* coo_rows_a,
+  product_f product_func,
+  accum_f accum_func,
+  write_f write_func,
+  strategy_t strategy,
+  int chunk_size = 500000)
+{
+  strategy.dispatch_rev(out_dists, coo_rows_a, product_func, accum_func, write_func, chunk_size);
+};
+
+/**
+ * Used for computing distances where the reduction (e.g. product()) function
+ * requires an implicit union (product(x, 0) = x) to capture the difference A-B.
+ * This is necessary in some applications because the standard semiring algebra
+ * endowed with the default multiplication product monoid will only
+ * compute the intersection & B-A.
+ *
+ * This particular function is meant to accompany the function
+ * `balanced_coo_pairwise_generalized_spmv` and executes the product operation
+ * on only those columns that exist in B and not A.
+ *
+ * The product and sum operations shall enable the computation of a
+ * non-annihilating semiring algebra with the following properties:
+ * 1. {+, 0} is a commutative sum reduction monoid with identity element 0
+ * 2. {*, 0} is a product monoid with identity element 0
+ * 3. Multiplication by 0 does not annihilate x. e.g. product(x, 0) = x
+ *
+ * Manattan distance sum(abs(x_k-y_k)) is a great example of when this type of
+ * execution pattern is necessary.
+ *
+ * @tparam value_idx index type
+ * @tparam value_t value type
+ * @tparam threads_per_block block size
+ * @tparam product_f semiring product() function
+ * @tparam accum_f semiring sum() function
+ * @tparam write_f atomic semiring sum() function
+ * @param[out] out_dists dense array of out distances of size m * n
+ * @param[in] config_ distance config object
+ * @param[in] coo_rows_a coo row array for A
+ * @param[in] product_func semiring product() function
+ * @param[in] accum_func semiring sum() function
+ * @param[in] write_func atomic semiring sum() function
+ * @param[in] chunk_size number of nonzeros of B to process for each row of A
+ *            this value was found through profiling and represents a reasonable
+ *            setting for both large and small densities
+ */
+template <typename value_idx,
+          typename value_t,
+          int threads_per_block = 1024,
+          typename product_f,
+          typename accum_f,
+          typename write_f>
+inline void balanced_coo_pairwise_generalized_spmv_rev(
+  value_t* out_dists,
+  const distances_config_t<value_idx, value_t>& config_,
+  value_idx* coo_rows_a,
+  product_f product_func,
+  accum_f accum_func,
+  write_f write_func,
+  int chunk_size = 500000)
+{
+  // try dense first
+  int max_cols = max_cols_per_block<value_idx, value_t>();
+
+  if (max_cols > config_.b_ncols) {
+    dense_smem_strategy<value_idx, value_t, threads_per_block> strategy(config_);
+    strategy.dispatch_rev(out_dists, coo_rows_a, product_func, accum_func, write_func, chunk_size);
+  } else {
+    hash_strategy<value_idx, value_t, threads_per_block> strategy(config_);
+    strategy.dispatch_rev(out_dists, coo_rows_a, product_func, accum_func, write_func, chunk_size);
+  }
+};
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh b/cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh
new file mode 100644
index 0000000000..1f4b19af44
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh
@@ -0,0 +1,229 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cub/block/block_load.cuh>
+#include <cub/block/block_radix_sort.cuh>
+#include <cub/block/block_store.cuh>
+#include <cub/cub.cuh>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+__device__ __inline__ unsigned int get_lowest_peer(unsigned int peer_group)
+{
+  return __ffs(peer_group) - 1;
+}
+
+/**
+ * Load-balanced sparse-matrix-sparse-matrix multiplication (SPMM) kernel with
+ * sparse-matrix-sparse-vector multiplication layout (SPMV).
+ * This is intended to be scheduled n_chunks_b times for each row of a.
+ * The steps are as follows:
+ *
+ * 1. Load row from A into dense vector in shared memory.
+ *    This can be further chunked in the future if necessary to support larger
+ *    column sizes.
+ * 2. Threads of block all step through chunks of B in parallel.
+ *    When a new row is encountered in row_indices_b, a segmented
+ *    reduction is performed across the warps and then across the
+ *    block and the final value written out to host memory.
+ *
+ * Reference: https://www.icl.utk.edu/files/publications/2020/icl-utk-1421-2020.pdf
+ *
+ * @tparam value_idx index type
+ * @tparam value_t value type
+ * @tparam tpb threads per block configured on launch
+ * @tparam rev if this is true, the reduce/accumulate functions are only
+ *         executed when A[col] == 0.0. when executed before/after !rev
+ *         and A & B are reversed, this allows the full symmetric difference
+ *         and intersection to be computed.
+ * @tparam kv_t data type stored in shared mem cache
+ * @tparam product_f reduce function type (semiring product() function).
+ *                  accepts two arguments of value_t and returns a value_t
+ * @tparam accum_f accumulation function type (semiring sum() function).
+ *                 accepts two arguments of value_t and returns a value_t
+ * @tparam write_f function to write value out. this should be mathematically
+ *                 equivalent to the accumulate function but implemented as
+ *                 an atomic operation on global memory. Accepts two arguments
+ *                 of value_t* and value_t and updates the value given by the
+ *                 pointer.
+ * @param[in] indptrA column pointer array for A
+ * @param[in] indicesA column indices array for A
+ * @param[in] dataA data array for A
+ * @param[in] rowsB coo row array for B
+ * @param[in] indicesB column indices array for B
+ * @param[in] dataB data array for B
+ * @param[in] m number of rows in A
+ * @param[in] n number of rows in B
+ * @param[in] dim number of features
+ * @param[in] nnz_b number of nonzeros in B
+ * @param[out] out array of size m*n
+ * @param[in] n_blocks_per_row number of blocks of B per row of A
+ * @param[in] chunk_size number of nnz for B to use for each row of A
+ * @param[in] buffer_size amount of smem to use for each row of A
+ * @param[in] product_func semiring product() function
+ * @param[in] accum_func semiring sum() function
+ * @param[in] write_func atomic semiring sum() function
+ */
+template <typename strategy_t,
+          typename indptr_it,
+          typename value_idx,
+          typename value_t,
+          bool rev,
+          int tpb,
+          typename product_f,
+          typename accum_f,
+          typename write_f>
+RAFT_KERNEL balanced_coo_generalized_spmv_kernel(strategy_t strategy,
+                                                 indptr_it indptrA,
+                                                 value_idx* indicesA,
+                                                 value_t* dataA,
+                                                 value_idx nnz_a,
+                                                 value_idx* rowsB,
+                                                 value_idx* indicesB,
+                                                 value_t* dataB,
+                                                 value_idx m,
+                                                 value_idx n,
+                                                 int dim,
+                                                 value_idx nnz_b,
+                                                 value_t* out,
+                                                 int n_blocks_per_row,
+                                                 int chunk_size,
+                                                 value_idx b_ncols,
+                                                 product_f product_func,
+                                                 accum_f accum_func,
+                                                 write_f write_func)
+{
+  typedef cub::WarpReduce<value_t> warp_reduce;
+
+  value_idx cur_row_a        = indptrA.get_row_idx(n_blocks_per_row);
+  value_idx cur_chunk_offset = blockIdx.x % n_blocks_per_row;
+
+  // chunk starting offset
+  value_idx ind_offset = cur_chunk_offset * chunk_size * tpb;
+  // how many total cols will be processed by this block (should be <= chunk_size * n_threads)
+  value_idx active_chunk_size = min(chunk_size * tpb, nnz_b - ind_offset);
+
+  int tid     = threadIdx.x;
+  int warp_id = tid / raft::warp_size();
+
+  // compute id relative to current warp
+  unsigned int lane_id = tid & (raft::warp_size() - 1);
+  value_idx ind        = ind_offset + threadIdx.x;
+
+  extern __shared__ char smem[];
+
+  typename strategy_t::smem_type A                = (typename strategy_t::smem_type)(smem);
+  typename warp_reduce::TempStorage* temp_storage = (typename warp_reduce::TempStorage*)(A + dim);
+
+  auto inserter = strategy.init_insert(A, dim);
+
+  __syncthreads();
+
+  value_idx start_offset_a, stop_offset_a;
+  bool first_a_chunk, last_a_chunk;
+  indptrA.get_row_offsets(
+    cur_row_a, start_offset_a, stop_offset_a, n_blocks_per_row, first_a_chunk, last_a_chunk);
+
+  // Convert current row vector in A to dense
+  for (int i = tid; i <= (stop_offset_a - start_offset_a); i += blockDim.x) {
+    strategy.insert(inserter, indicesA[start_offset_a + i], dataA[start_offset_a + i]);
+  }
+
+  __syncthreads();
+
+  auto finder = strategy.init_find(A, dim);
+
+  if (cur_row_a > m || cur_chunk_offset > n_blocks_per_row) return;
+  if (ind >= nnz_b) return;
+
+  value_idx start_index_a = 0, stop_index_a = b_ncols - 1;
+  indptrA.get_indices_boundary(indicesA,
+                               cur_row_a,
+                               start_offset_a,
+                               stop_offset_a,
+                               start_index_a,
+                               stop_index_a,
+                               first_a_chunk,
+                               last_a_chunk);
+
+  value_idx cur_row_b = -1;
+  value_t c           = 0.0;
+
+  auto warp_red = warp_reduce(*(temp_storage + warp_id));
+
+  if (tid < active_chunk_size) {
+    cur_row_b = rowsB[ind];
+
+    auto index_b   = indicesB[ind];
+    auto in_bounds = indptrA.check_indices_bounds(start_index_a, stop_index_a, index_b);
+
+    if (in_bounds) {
+      value_t a_col = strategy.find(finder, index_b);
+      if (!rev || a_col == 0.0) { c = product_func(a_col, dataB[ind]); }
+    }
+  }
+
+  // loop through chunks in parallel, reducing when a new row is
+  // encountered by each thread
+  for (int i = tid; i < active_chunk_size; i += blockDim.x) {
+    value_idx ind_next   = ind + blockDim.x;
+    value_idx next_row_b = -1;
+
+    if (i + blockDim.x < active_chunk_size) next_row_b = rowsB[ind_next];
+
+    bool diff_rows = next_row_b != cur_row_b;
+
+    if (__any_sync(0xffffffff, diff_rows)) {
+      // grab the threads currently participating in loops.
+      // because any other threads should have returned already.
+      unsigned int peer_group = __match_any_sync(0xffffffff, cur_row_b);
+      bool is_leader          = get_lowest_peer(peer_group) == lane_id;
+      value_t v               = warp_red.HeadSegmentedReduce(c, is_leader, accum_func);
+
+      // thread with lowest lane id among peers writes out
+      if (is_leader && v != 0.0) {
+        // this conditional should be uniform, since rev is constant
+        size_t idx = !rev ? (size_t)cur_row_a * n + cur_row_b : (size_t)cur_row_b * m + cur_row_a;
+        write_func(out + idx, v);
+      }
+
+      c = 0.0;
+    }
+
+    if (next_row_b != -1) {
+      ind = ind_next;
+
+      auto index_b   = indicesB[ind];
+      auto in_bounds = indptrA.check_indices_bounds(start_index_a, stop_index_a, index_b);
+      if (in_bounds) {
+        value_t a_col = strategy.find(finder, index_b);
+
+        if (!rev || a_col == 0.0) { c = accum_func(c, product_func(a_col, dataB[ind])); }
+      }
+
+      cur_row_b = next_row_b;
+    }
+  }
+}
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/coo_spmv_strategies/base_strategy.cuh b/cpp/src/distance/detail/sparse/coo_spmv_strategies/base_strategy.cuh
new file mode 100644
index 0000000000..457b25eeac
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/coo_spmv_strategies/base_strategy.cuh
@@ -0,0 +1,149 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../common.hpp"
+#include "../coo_spmv_kernel.cuh"
+#include "../utils.cuh"
+#include "coo_mask_row_iterators.cuh"
+
+#include <raft/core/resource/cuda_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx, typename value_t, int tpb>
+class coo_spmv_strategy {
+ public:
+  coo_spmv_strategy(const distances_config_t<value_idx, value_t>& config_) : config(config_)
+  {
+    smem = raft::getSharedMemPerBlock();
+  }
+
+  template <typename strategy_t,
+            typename indptr_it,
+            typename product_f,
+            typename accum_f,
+            typename write_f>
+  void _dispatch_base(strategy_t& strategy,
+                      int smem_dim,
+                      indptr_it& a_indptr,
+                      value_t* out_dists,
+                      value_idx* coo_rows_b,
+                      product_f product_func,
+                      accum_f accum_func,
+                      write_f write_func,
+                      int chunk_size,
+                      int n_blocks,
+                      int n_blocks_per_row)
+  {
+    RAFT_CUDA_TRY(cudaFuncSetCacheConfig(balanced_coo_generalized_spmv_kernel<strategy_t,
+                                                                              indptr_it,
+                                                                              value_idx,
+                                                                              value_t,
+                                                                              false,
+                                                                              tpb,
+                                                                              product_f,
+                                                                              accum_f,
+                                                                              write_f>,
+                                         cudaFuncCachePreferShared));
+
+    balanced_coo_generalized_spmv_kernel<strategy_t, indptr_it, value_idx, value_t, false, tpb>
+      <<<n_blocks, tpb, smem, raft::resource::get_cuda_stream(config.handle)>>>(strategy,
+                                                                                a_indptr,
+                                                                                config.a_indices,
+                                                                                config.a_data,
+                                                                                config.a_nnz,
+                                                                                coo_rows_b,
+                                                                                config.b_indices,
+                                                                                config.b_data,
+                                                                                config.a_nrows,
+                                                                                config.b_nrows,
+                                                                                smem_dim,
+                                                                                config.b_nnz,
+                                                                                out_dists,
+                                                                                n_blocks_per_row,
+                                                                                chunk_size,
+                                                                                config.b_ncols,
+                                                                                product_func,
+                                                                                accum_func,
+                                                                                write_func);
+  }
+
+  template <typename strategy_t,
+            typename indptr_it,
+            typename product_f,
+            typename accum_f,
+            typename write_f>
+  void _dispatch_base_rev(strategy_t& strategy,
+                          int smem_dim,
+                          indptr_it& b_indptr,
+                          value_t* out_dists,
+                          value_idx* coo_rows_a,
+                          product_f product_func,
+                          accum_f accum_func,
+                          write_f write_func,
+                          int chunk_size,
+                          int n_blocks,
+                          int n_blocks_per_row)
+  {
+    RAFT_CUDA_TRY(cudaFuncSetCacheConfig(balanced_coo_generalized_spmv_kernel<strategy_t,
+                                                                              indptr_it,
+                                                                              value_idx,
+                                                                              value_t,
+                                                                              true,
+                                                                              tpb,
+                                                                              product_f,
+                                                                              accum_f,
+                                                                              write_f>,
+                                         cudaFuncCachePreferShared));
+
+    balanced_coo_generalized_spmv_kernel<strategy_t, indptr_it, value_idx, value_t, true, tpb>
+      <<<n_blocks, tpb, smem, raft::resource::get_cuda_stream(config.handle)>>>(strategy,
+                                                                                b_indptr,
+                                                                                config.b_indices,
+                                                                                config.b_data,
+                                                                                config.b_nnz,
+                                                                                coo_rows_a,
+                                                                                config.a_indices,
+                                                                                config.a_data,
+                                                                                config.b_nrows,
+                                                                                config.a_nrows,
+                                                                                smem_dim,
+                                                                                config.a_nnz,
+                                                                                out_dists,
+                                                                                n_blocks_per_row,
+                                                                                chunk_size,
+                                                                                config.a_ncols,
+                                                                                product_func,
+                                                                                accum_func,
+                                                                                write_func);
+  }
+
+ protected:
+  int smem;
+  const distances_config_t<value_idx, value_t>& config;
+};
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/coo_spmv_strategies/coo_mask_row_iterators.cuh b/cpp/src/distance/detail/sparse/coo_spmv_strategies/coo_mask_row_iterators.cuh
new file mode 100644
index 0000000000..a9040e1d82
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/coo_spmv_strategies/coo_mask_row_iterators.cuh
@@ -0,0 +1,234 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../common.hpp"
+#include "../utils.cuh"
+
+#include <raft/util/cuda_dev_essentials.cuh>  // raft::ceildiv
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx>
+class mask_row_it {
+ public:
+  mask_row_it(const value_idx* full_indptr_,
+              const value_idx& n_rows_,
+              value_idx* mask_row_idx_ = NULL)
+    : full_indptr(full_indptr_), mask_row_idx(mask_row_idx_), n_rows(n_rows_)
+  {
+  }
+
+  __device__ inline value_idx get_row_idx(const int& n_blocks_nnz_b)
+  {
+    if (mask_row_idx != NULL) {
+      return mask_row_idx[blockIdx.x / n_blocks_nnz_b];
+    } else {
+      return blockIdx.x / n_blocks_nnz_b;
+    }
+  }
+
+  __device__ inline void get_row_offsets(const value_idx& row_idx,
+                                         value_idx& start_offset,
+                                         value_idx& stop_offset,
+                                         const value_idx& n_blocks_nnz_b,
+                                         bool& first_a_chunk,
+                                         bool& last_a_chunk)
+  {
+    start_offset = full_indptr[row_idx];
+    stop_offset  = full_indptr[row_idx + 1] - 1;
+  }
+
+  __device__ constexpr inline void get_indices_boundary(const value_idx* indices,
+                                                        value_idx& indices_len,
+                                                        value_idx& start_offset,
+                                                        value_idx& stop_offset,
+                                                        value_idx& start_index,
+                                                        value_idx& stop_index,
+                                                        bool& first_a_chunk,
+                                                        bool& last_a_chunk)
+  {
+    // do nothing;
+  }
+
+  __device__ constexpr inline bool check_indices_bounds(value_idx& start_index_a,
+                                                        value_idx& stop_index_a,
+                                                        value_idx& index_b)
+  {
+    return true;
+  }
+
+  const value_idx *full_indptr, &n_rows;
+  value_idx* mask_row_idx;
+};
+
+template <typename value_idx>
+RAFT_KERNEL fill_chunk_indices_kernel(value_idx* n_chunks_per_row,
+                                      value_idx* chunk_indices,
+                                      value_idx n_rows)
+{
+  auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid < n_rows) {
+    auto start = n_chunks_per_row[tid];
+    auto end   = n_chunks_per_row[tid + 1];
+
+#pragma unroll
+    for (int i = start; i < end; i++) {
+      chunk_indices[i] = tid;
+    }
+  }
+}
+
+template <typename value_idx>
+class chunked_mask_row_it : public mask_row_it<value_idx> {
+ public:
+  chunked_mask_row_it(const value_idx* full_indptr_,
+                      const value_idx& n_rows_,
+                      value_idx* mask_row_idx_,
+                      int row_chunk_size_,
+                      const value_idx* n_chunks_per_row_,
+                      const value_idx* chunk_indices_,
+                      const cudaStream_t stream_)
+    : mask_row_it<value_idx>(full_indptr_, n_rows_, mask_row_idx_),
+      row_chunk_size(row_chunk_size_),
+      n_chunks_per_row(n_chunks_per_row_),
+      chunk_indices(chunk_indices_),
+      stream(stream_)
+  {
+  }
+
+  static void init(const value_idx* indptr,
+                   const value_idx* mask_row_idx,
+                   const value_idx& n_rows,
+                   const int row_chunk_size,
+                   rmm::device_uvector<value_idx>& n_chunks_per_row,
+                   rmm::device_uvector<value_idx>& chunk_indices,
+                   cudaStream_t stream)
+  {
+    auto policy = rmm::exec_policy(stream);
+
+    constexpr value_idx first_element = 0;
+    n_chunks_per_row.set_element_async(0, first_element, stream);
+    n_chunks_per_row_functor chunk_functor(indptr, row_chunk_size);
+    thrust::transform(
+      policy, mask_row_idx, mask_row_idx + n_rows, n_chunks_per_row.begin() + 1, chunk_functor);
+
+    thrust::inclusive_scan(
+      policy, n_chunks_per_row.begin() + 1, n_chunks_per_row.end(), n_chunks_per_row.begin() + 1);
+
+    raft::update_host(&total_row_blocks, n_chunks_per_row.data() + n_rows, 1, stream);
+
+    fill_chunk_indices(n_rows, n_chunks_per_row, chunk_indices, stream);
+  }
+
+  __device__ inline value_idx get_row_idx(const int& n_blocks_nnz_b)
+  {
+    return this->mask_row_idx[chunk_indices[blockIdx.x / n_blocks_nnz_b]];
+  }
+
+  __device__ inline void get_row_offsets(const value_idx& row_idx,
+                                         value_idx& start_offset,
+                                         value_idx& stop_offset,
+                                         const int& n_blocks_nnz_b,
+                                         bool& first_a_chunk,
+                                         bool& last_a_chunk)
+  {
+    auto chunk_index    = blockIdx.x / n_blocks_nnz_b;
+    auto chunk_val      = chunk_indices[chunk_index];
+    auto prev_n_chunks  = n_chunks_per_row[chunk_val];
+    auto relative_chunk = chunk_index - prev_n_chunks;
+    first_a_chunk       = relative_chunk == 0;
+
+    start_offset = this->full_indptr[row_idx] + relative_chunk * row_chunk_size;
+    stop_offset  = start_offset + row_chunk_size;
+
+    auto final_stop_offset = this->full_indptr[row_idx + 1];
+
+    last_a_chunk = stop_offset >= final_stop_offset;
+    stop_offset  = last_a_chunk ? final_stop_offset - 1 : stop_offset - 1;
+  }
+
+  __device__ inline void get_indices_boundary(const value_idx* indices,
+                                              value_idx& row_idx,
+                                              value_idx& start_offset,
+                                              value_idx& stop_offset,
+                                              value_idx& start_index,
+                                              value_idx& stop_index,
+                                              bool& first_a_chunk,
+                                              bool& last_a_chunk)
+  {
+    start_index = first_a_chunk ? start_index : indices[start_offset - 1] + 1;
+    stop_index  = last_a_chunk ? stop_index : indices[stop_offset];
+  }
+
+  __device__ inline bool check_indices_bounds(value_idx& start_index_a,
+                                              value_idx& stop_index_a,
+                                              value_idx& index_b)
+  {
+    return (index_b >= start_index_a && index_b <= stop_index_a);
+  }
+
+  inline static value_idx total_row_blocks = 0;
+  const cudaStream_t stream;
+  const value_idx *n_chunks_per_row, *chunk_indices;
+  value_idx row_chunk_size;
+
+  struct n_chunks_per_row_functor {
+   public:
+    n_chunks_per_row_functor(const value_idx* indptr_, value_idx row_chunk_size_)
+      : indptr(indptr_), row_chunk_size(row_chunk_size_)
+    {
+    }
+
+    __host__ __device__ value_idx operator()(const value_idx& i)
+    {
+      auto degree = indptr[i + 1] - indptr[i];
+      return raft::ceildiv(degree, (value_idx)row_chunk_size);
+    }
+
+    const value_idx* indptr;
+    value_idx row_chunk_size;
+  };
+
+ private:
+  static void fill_chunk_indices(const value_idx& n_rows,
+                                 rmm::device_uvector<value_idx>& n_chunks_per_row,
+                                 rmm::device_uvector<value_idx>& chunk_indices,
+                                 cudaStream_t stream)
+  {
+    auto n_threads = std::min(n_rows, 256);
+    auto n_blocks  = raft::ceildiv(n_rows, (value_idx)n_threads);
+
+    chunk_indices.resize(total_row_blocks, stream);
+
+    fill_chunk_indices_kernel<value_idx>
+      <<<n_blocks, n_threads, 0, stream>>>(n_chunks_per_row.data(), chunk_indices.data(), n_rows);
+  }
+};
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/coo_spmv_strategies/dense_smem_strategy.cuh b/cpp/src/distance/detail/sparse/coo_spmv_strategies/dense_smem_strategy.cuh
new file mode 100644
index 0000000000..baa913a6c0
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/coo_spmv_strategies/dense_smem_strategy.cuh
@@ -0,0 +1,121 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "base_strategy.cuh"
+
+#include <raft/util/cuda_dev_essentials.cuh>  // raft::ceildiv
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx, typename value_t, int tpb>
+class dense_smem_strategy : public coo_spmv_strategy<value_idx, value_t, tpb> {
+ public:
+  using smem_type   = value_t*;
+  using insert_type = smem_type;
+  using find_type   = smem_type;
+
+  dense_smem_strategy(const distances_config_t<value_idx, value_t>& config_)
+    : coo_spmv_strategy<value_idx, value_t, tpb>(config_)
+  {
+  }
+
+  inline static int smem_per_block(int n_cols)
+  {
+    return (n_cols * sizeof(value_t)) + ((1024 / raft::warp_size()) * sizeof(value_t));
+  }
+
+  template <typename product_f, typename accum_f, typename write_f>
+  void dispatch(value_t* out_dists,
+                value_idx* coo_rows_b,
+                product_f product_func,
+                accum_f accum_func,
+                write_f write_func,
+                int chunk_size)
+  {
+    auto n_blocks_per_row = raft::ceildiv(this->config.b_nnz, chunk_size * 1024);
+    auto n_blocks         = this->config.a_nrows * n_blocks_per_row;
+
+    mask_row_it<value_idx> a_indptr(this->config.a_indptr, this->config.a_nrows);
+
+    this->_dispatch_base(*this,
+                         this->config.b_ncols,
+                         a_indptr,
+                         out_dists,
+                         coo_rows_b,
+                         product_func,
+                         accum_func,
+                         write_func,
+                         chunk_size,
+                         n_blocks,
+                         n_blocks_per_row);
+  }
+
+  template <typename product_f, typename accum_f, typename write_f>
+  void dispatch_rev(value_t* out_dists,
+                    value_idx* coo_rows_a,
+                    product_f product_func,
+                    accum_f accum_func,
+                    write_f write_func,
+                    int chunk_size)
+  {
+    auto n_blocks_per_row = raft::ceildiv(this->config.a_nnz, chunk_size * 1024);
+    auto n_blocks         = this->config.b_nrows * n_blocks_per_row;
+
+    mask_row_it<value_idx> b_indptr(this->config.b_indptr, this->config.b_nrows);
+
+    this->_dispatch_base_rev(*this,
+                             this->config.a_ncols,
+                             b_indptr,
+                             out_dists,
+                             coo_rows_a,
+                             product_func,
+                             accum_func,
+                             write_func,
+                             chunk_size,
+                             n_blocks,
+                             n_blocks_per_row);
+  }
+
+  __device__ inline insert_type init_insert(smem_type cache, const value_idx& cache_size)
+  {
+    for (int k = threadIdx.x; k < cache_size; k += blockDim.x) {
+      cache[k] = 0.0;
+    }
+    return cache;
+  }
+
+  __device__ inline void insert(insert_type cache, const value_idx& key, const value_t& value)
+  {
+    cache[key] = value;
+  }
+
+  __device__ inline find_type init_find(smem_type cache, const value_idx& cache_size)
+  {
+    return cache;
+  }
+
+  __device__ inline value_t find(find_type cache, const value_idx& key) { return cache[key]; }
+};
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/coo_spmv_strategies/hash_strategy.cuh b/cpp/src/distance/detail/sparse/coo_spmv_strategies/hash_strategy.cuh
new file mode 100644
index 0000000000..cf212076b1
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/coo_spmv_strategies/hash_strategy.cuh
@@ -0,0 +1,296 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "base_strategy.cuh"
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+
+#include <cuco/static_map.cuh>
+#include <thrust/copy.h>
+#include <thrust/iterator/counting_iterator.h>
+
+// this is needed by cuco as key, value must be bitwise comparable.
+// compilers don't declare float/double as bitwise comparable
+// but that is too strict
+// for example, the following is true (or 0):
+// float a = 5;
+// float b = 5;
+// memcmp(&a, &b, sizeof(float));
+CUCO_DECLARE_BITWISE_COMPARABLE(float);
+CUCO_DECLARE_BITWISE_COMPARABLE(double);
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx, typename value_t, int tpb>
+class hash_strategy : public coo_spmv_strategy<value_idx, value_t, tpb> {
+ public:
+  using insert_type = typename cuco::legacy::
+    static_map<value_idx, value_t, cuda::thread_scope_block>::device_mutable_view;
+  using smem_type = typename insert_type::slot_type*;
+  using find_type =
+    typename cuco::legacy::static_map<value_idx, value_t, cuda::thread_scope_block>::device_view;
+
+  hash_strategy(const distances_config_t<value_idx, value_t>& config_,
+                float capacity_threshold_ = 0.5,
+                int map_size_             = get_map_size())
+    : coo_spmv_strategy<value_idx, value_t, tpb>(config_),
+      capacity_threshold(capacity_threshold_),
+      map_size(map_size_)
+  {
+  }
+
+  void chunking_needed(const value_idx* indptr,
+                       const value_idx n_rows,
+                       rmm::device_uvector<value_idx>& mask_indptr,
+                       std::tuple<value_idx, value_idx>& n_rows_divided,
+                       cudaStream_t stream)
+  {
+    auto policy = raft::resource::get_thrust_policy(this->config.handle);
+
+    auto less                   = thrust::copy_if(policy,
+                                thrust::make_counting_iterator(value_idx(0)),
+                                thrust::make_counting_iterator(n_rows),
+                                mask_indptr.data(),
+                                fits_in_hash_table(indptr, 0, capacity_threshold * map_size));
+    std::get<0>(n_rows_divided) = less - mask_indptr.data();
+
+    auto more = thrust::copy_if(
+      policy,
+      thrust::make_counting_iterator(value_idx(0)),
+      thrust::make_counting_iterator(n_rows),
+      less,
+      fits_in_hash_table(
+        indptr, capacity_threshold * map_size, std::numeric_limits<value_idx>::max()));
+    std::get<1>(n_rows_divided) = more - less;
+  }
+
+  template <typename product_f, typename accum_f, typename write_f>
+  void dispatch(value_t* out_dists,
+                value_idx* coo_rows_b,
+                product_f product_func,
+                accum_f accum_func,
+                write_f write_func,
+                int chunk_size)
+  {
+    auto n_blocks_per_row = raft::ceildiv(this->config.b_nnz, chunk_size * tpb);
+    rmm::device_uvector<value_idx> mask_indptr(
+      this->config.a_nrows, raft::resource::get_cuda_stream(this->config.handle));
+    std::tuple<value_idx, value_idx> n_rows_divided;
+
+    chunking_needed(this->config.a_indptr,
+                    this->config.a_nrows,
+                    mask_indptr,
+                    n_rows_divided,
+                    raft::resource::get_cuda_stream(this->config.handle));
+
+    auto less_rows = std::get<0>(n_rows_divided);
+    if (less_rows > 0) {
+      mask_row_it<value_idx> less(this->config.a_indptr, less_rows, mask_indptr.data());
+
+      auto n_less_blocks = less_rows * n_blocks_per_row;
+      this->_dispatch_base(*this,
+                           map_size,
+                           less,
+                           out_dists,
+                           coo_rows_b,
+                           product_func,
+                           accum_func,
+                           write_func,
+                           chunk_size,
+                           n_less_blocks,
+                           n_blocks_per_row);
+    }
+
+    auto more_rows = std::get<1>(n_rows_divided);
+    if (more_rows > 0) {
+      rmm::device_uvector<value_idx> n_chunks_per_row(
+        more_rows + 1, raft::resource::get_cuda_stream(this->config.handle));
+      rmm::device_uvector<value_idx> chunk_indices(
+        0, raft::resource::get_cuda_stream(this->config.handle));
+      chunked_mask_row_it<value_idx>::init(this->config.a_indptr,
+                                           mask_indptr.data() + less_rows,
+                                           more_rows,
+                                           capacity_threshold * map_size,
+                                           n_chunks_per_row,
+                                           chunk_indices,
+                                           raft::resource::get_cuda_stream(this->config.handle));
+
+      chunked_mask_row_it<value_idx> more(this->config.a_indptr,
+                                          more_rows,
+                                          mask_indptr.data() + less_rows,
+                                          capacity_threshold * map_size,
+                                          n_chunks_per_row.data(),
+                                          chunk_indices.data(),
+                                          raft::resource::get_cuda_stream(this->config.handle));
+
+      auto n_more_blocks = more.total_row_blocks * n_blocks_per_row;
+      this->_dispatch_base(*this,
+                           map_size,
+                           more,
+                           out_dists,
+                           coo_rows_b,
+                           product_func,
+                           accum_func,
+                           write_func,
+                           chunk_size,
+                           n_more_blocks,
+                           n_blocks_per_row);
+    }
+  }
+
+  template <typename product_f, typename accum_f, typename write_f>
+  void dispatch_rev(value_t* out_dists,
+                    value_idx* coo_rows_a,
+                    product_f product_func,
+                    accum_f accum_func,
+                    write_f write_func,
+                    int chunk_size)
+  {
+    auto n_blocks_per_row = raft::ceildiv(this->config.a_nnz, chunk_size * tpb);
+    rmm::device_uvector<value_idx> mask_indptr(
+      this->config.b_nrows, raft::resource::get_cuda_stream(this->config.handle));
+    std::tuple<value_idx, value_idx> n_rows_divided;
+
+    chunking_needed(this->config.b_indptr,
+                    this->config.b_nrows,
+                    mask_indptr,
+                    n_rows_divided,
+                    raft::resource::get_cuda_stream(this->config.handle));
+
+    auto less_rows = std::get<0>(n_rows_divided);
+    if (less_rows > 0) {
+      mask_row_it<value_idx> less(this->config.b_indptr, less_rows, mask_indptr.data());
+
+      auto n_less_blocks = less_rows * n_blocks_per_row;
+      this->_dispatch_base_rev(*this,
+                               map_size,
+                               less,
+                               out_dists,
+                               coo_rows_a,
+                               product_func,
+                               accum_func,
+                               write_func,
+                               chunk_size,
+                               n_less_blocks,
+                               n_blocks_per_row);
+    }
+
+    auto more_rows = std::get<1>(n_rows_divided);
+    if (more_rows > 0) {
+      rmm::device_uvector<value_idx> n_chunks_per_row(
+        more_rows + 1, raft::resource::get_cuda_stream(this->config.handle));
+      rmm::device_uvector<value_idx> chunk_indices(
+        0, raft::resource::get_cuda_stream(this->config.handle));
+      chunked_mask_row_it<value_idx>::init(this->config.b_indptr,
+                                           mask_indptr.data() + less_rows,
+                                           more_rows,
+                                           capacity_threshold * map_size,
+                                           n_chunks_per_row,
+                                           chunk_indices,
+                                           raft::resource::get_cuda_stream(this->config.handle));
+
+      chunked_mask_row_it<value_idx> more(this->config.b_indptr,
+                                          more_rows,
+                                          mask_indptr.data() + less_rows,
+                                          capacity_threshold * map_size,
+                                          n_chunks_per_row.data(),
+                                          chunk_indices.data(),
+                                          raft::resource::get_cuda_stream(this->config.handle));
+
+      auto n_more_blocks = more.total_row_blocks * n_blocks_per_row;
+      this->_dispatch_base_rev(*this,
+                               map_size,
+                               more,
+                               out_dists,
+                               coo_rows_a,
+                               product_func,
+                               accum_func,
+                               write_func,
+                               chunk_size,
+                               n_more_blocks,
+                               n_blocks_per_row);
+    }
+  }
+
+  __device__ inline insert_type init_insert(smem_type cache, const value_idx& cache_size)
+  {
+    return insert_type::make_from_uninitialized_slots(cooperative_groups::this_thread_block(),
+                                                      cache,
+                                                      cache_size,
+                                                      cuco::empty_key{value_idx{-1}},
+                                                      cuco::empty_value{value_t{0}});
+  }
+
+  __device__ inline void insert(insert_type cache, const value_idx& key, const value_t& value)
+  {
+    auto success = cache.insert(cuco::pair<value_idx, value_t>(key, value));
+  }
+
+  __device__ inline find_type init_find(smem_type cache, const value_idx& cache_size)
+  {
+    return find_type(
+      cache, cache_size, cuco::empty_key{value_idx{-1}}, cuco::empty_value{value_t{0}});
+  }
+
+  __device__ inline value_t find(find_type cache, const value_idx& key)
+  {
+    auto a_pair = cache.find(key);
+
+    value_t a_col = 0.0;
+    if (a_pair != cache.end()) { a_col = a_pair->second; }
+    return a_col;
+  }
+
+  struct fits_in_hash_table {
+   public:
+    fits_in_hash_table(const value_idx* indptr_, value_idx degree_l_, value_idx degree_r_)
+      : indptr(indptr_), degree_l(degree_l_), degree_r(degree_r_)
+    {
+    }
+
+    __host__ __device__ bool operator()(const value_idx& i)
+    {
+      auto degree = indptr[i + 1] - indptr[i];
+
+      return degree >= degree_l && degree < degree_r;
+    }
+
+   private:
+    const value_idx* indptr;
+    const value_idx degree_l, degree_r;
+  };
+
+  inline static int get_map_size()
+  {
+    return (raft::getSharedMemPerBlock() - ((tpb / raft::warp_size()) * sizeof(value_t))) /
+           sizeof(typename insert_type::slot_type);
+  }
+
+ private:
+  float capacity_threshold;
+  int map_size;
+};
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/ip_distance.cuh b/cpp/src/distance/detail/sparse/ip_distance.cuh
new file mode 100644
index 0000000000..3a11d4e990
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/ip_distance.cuh
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "common.hpp"
+#include "coo_spmv.cuh"
+
+#include <raft/core/operators.cuh>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/sparse/convert/coo.cuh>
+#include <raft/sparse/detail/cusparse_wrappers.h>
+#include <raft/sparse/detail/utils.h>
+#include <raft/sparse/linalg/transpose.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <limits.h>
+
+#include <nvfunctional>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx, typename value_t>
+class ip_distances_t : public distances_t<value_t> {
+ public:
+  /**
+   * Computes simple sparse inner product distances as sum(x_y * y_k)
+   * @param[in] config specifies inputs, outputs, and sizes
+   */
+  ip_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config), coo_rows_b(config.b_nnz, raft::resource::get_cuda_stream(config.handle))
+  {
+    raft::sparse::convert::csr_to_coo(config_->b_indptr,
+                                      config_->b_nrows,
+                                      coo_rows_b.data(),
+                                      config_->b_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+  }
+
+  /**
+   * Performs pairwise distance computation and computes output distances
+   * @param out_distances dense output matrix (size a_nrows * b_nrows)
+   */
+  void compute(value_t* out_distances)
+  {
+    /**
+     * Compute pairwise distances and return dense matrix in row-major format
+     */
+    balanced_coo_pairwise_generalized_spmv<value_idx, value_t>(out_distances,
+                                                               *config_,
+                                                               coo_rows_b.data(),
+                                                               raft::mul_op(),
+                                                               raft::add_op(),
+                                                               raft::atomic_add_op());
+  }
+
+  value_idx* b_rows_coo() { return coo_rows_b.data(); }
+
+  value_t* b_data_coo() { return config_->b_data; }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+  rmm::device_uvector<value_idx> coo_rows_b;
+};
+
+}  // END namespace sparse
+}  // END namespace detail
+}  // END namespace distance
+}  // END namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/l2_distance.cuh b/cpp/src/distance/detail/sparse/l2_distance.cuh
new file mode 100644
index 0000000000..40e7070fcb
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/l2_distance.cuh
@@ -0,0 +1,502 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "common.hpp"
+#include "ip_distance.cuh"
+#include <cuvs/distance/distance.hpp>
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/sparse/csr.hpp>
+#include <raft/sparse/detail/cusparse_wrappers.h>
+#include <raft/sparse/detail/utils.h>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <nvfunctional>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+// @TODO: Move this into sparse prims (coo_norm)
+template <typename value_idx, typename value_t>
+RAFT_KERNEL compute_row_norm_kernel(value_t* out,
+                                    const value_idx* __restrict__ coo_rows,
+                                    const value_t* __restrict__ data,
+                                    value_idx nnz)
+{
+  value_idx i = blockDim.x * blockIdx.x + threadIdx.x;
+  if (i < nnz) { atomicAdd(&out[coo_rows[i]], data[i] * data[i]); }
+}
+
+template <typename value_idx, typename value_t>
+RAFT_KERNEL compute_row_sum_kernel(value_t* out,
+                                   const value_idx* __restrict__ coo_rows,
+                                   const value_t* __restrict__ data,
+                                   value_idx nnz)
+{
+  value_idx i = blockDim.x * blockIdx.x + threadIdx.x;
+  if (i < nnz) { atomicAdd(&out[coo_rows[i]], data[i]); }
+}
+
+template <typename value_idx, typename value_t, typename expansion_f>
+RAFT_KERNEL compute_euclidean_warp_kernel(value_t* __restrict__ C,
+                                          const value_t* __restrict__ Q_sq_norms,
+                                          const value_t* __restrict__ R_sq_norms,
+                                          value_idx n_rows,
+                                          value_idx n_cols,
+                                          expansion_f expansion_func)
+{
+  std::size_t tid = blockDim.x * blockIdx.x + threadIdx.x;
+  value_idx i     = tid / n_cols;
+  value_idx j     = tid % n_cols;
+
+  if (i >= n_rows || j >= n_cols) return;
+
+  value_t dot = C[(size_t)i * n_cols + j];
+
+  // e.g. Euclidean expansion func = -2.0 * dot + q_norm + r_norm
+  value_t val = expansion_func(dot, Q_sq_norms[i], R_sq_norms[j]);
+
+  // correct for small instabilities
+  C[(size_t)i * n_cols + j] = val * (fabs(val) >= 0.0001);
+}
+
+template <typename value_idx, typename value_t>
+RAFT_KERNEL compute_correlation_warp_kernel(value_t* __restrict__ C,
+                                            const value_t* __restrict__ Q_sq_norms,
+                                            const value_t* __restrict__ R_sq_norms,
+                                            const value_t* __restrict__ Q_norms,
+                                            const value_t* __restrict__ R_norms,
+                                            value_idx n_rows,
+                                            value_idx n_cols,
+                                            value_idx n)
+{
+  std::size_t tid = blockDim.x * blockIdx.x + threadIdx.x;
+  value_idx i     = tid / n_cols;
+  value_idx j     = tid % n_cols;
+
+  if (i >= n_rows || j >= n_cols) return;
+
+  value_t dot  = C[(size_t)i * n_cols + j];
+  value_t Q_l1 = Q_norms[i];
+  value_t R_l1 = R_norms[j];
+
+  value_t Q_l2 = Q_sq_norms[i];
+  value_t R_l2 = R_sq_norms[j];
+
+  value_t numer   = n * dot - (Q_l1 * R_l1);
+  value_t Q_denom = n * Q_l2 - (Q_l1 * Q_l1);
+  value_t R_denom = n * R_l2 - (R_l1 * R_l1);
+
+  value_t val = 1 - (numer / raft::sqrt(Q_denom * R_denom));
+
+  // correct for small instabilities
+  C[(size_t)i * n_cols + j] = val * (fabs(val) >= 0.0001);
+}
+
+template <typename value_idx, typename value_t, int tpb = 256, typename expansion_f>
+void compute_euclidean(value_t* C,
+                       const value_t* Q_sq_norms,
+                       const value_t* R_sq_norms,
+                       value_idx n_rows,
+                       value_idx n_cols,
+                       cudaStream_t stream,
+                       expansion_f expansion_func)
+{
+  int blocks = raft::ceildiv<size_t>((size_t)n_rows * n_cols, tpb);
+  compute_euclidean_warp_kernel<<<blocks, tpb, 0, stream>>>(
+    C, Q_sq_norms, R_sq_norms, n_rows, n_cols, expansion_func);
+}
+
+template <typename value_idx, typename value_t, int tpb = 256, typename expansion_f>
+void compute_l2(value_t* out,
+                const value_idx* Q_coo_rows,
+                const value_t* Q_data,
+                value_idx Q_nnz,
+                const value_idx* R_coo_rows,
+                const value_t* R_data,
+                value_idx R_nnz,
+                value_idx m,
+                value_idx n,
+                cudaStream_t stream,
+                expansion_f expansion_func)
+{
+  rmm::device_uvector<value_t> Q_sq_norms(m, stream);
+  rmm::device_uvector<value_t> R_sq_norms(n, stream);
+  RAFT_CUDA_TRY(cudaMemsetAsync(Q_sq_norms.data(), 0, Q_sq_norms.size() * sizeof(value_t)));
+  RAFT_CUDA_TRY(cudaMemsetAsync(R_sq_norms.data(), 0, R_sq_norms.size() * sizeof(value_t)));
+
+  compute_row_norm_kernel<<<raft::ceildiv(Q_nnz, tpb), tpb, 0, stream>>>(
+    Q_sq_norms.data(), Q_coo_rows, Q_data, Q_nnz);
+  compute_row_norm_kernel<<<raft::ceildiv(R_nnz, tpb), tpb, 0, stream>>>(
+    R_sq_norms.data(), R_coo_rows, R_data, R_nnz);
+
+  compute_euclidean(out, Q_sq_norms.data(), R_sq_norms.data(), m, n, stream, expansion_func);
+}
+
+template <typename value_idx, typename value_t, int tpb = 256>
+void compute_correlation(value_t* C,
+                         const value_t* Q_sq_norms,
+                         const value_t* R_sq_norms,
+                         const value_t* Q_norms,
+                         const value_t* R_norms,
+                         value_idx n_rows,
+                         value_idx n_cols,
+                         value_idx n,
+                         cudaStream_t stream)
+{
+  int blocks = raft::ceildiv<size_t>((size_t)n_rows * n_cols, tpb);
+  compute_correlation_warp_kernel<<<blocks, tpb, 0, stream>>>(
+    C, Q_sq_norms, R_sq_norms, Q_norms, R_norms, n_rows, n_cols, n);
+}
+
+template <typename value_idx, typename value_t, int tpb = 256>
+void compute_corr(value_t* out,
+                  const value_idx* Q_coo_rows,
+                  const value_t* Q_data,
+                  value_idx Q_nnz,
+                  const value_idx* R_coo_rows,
+                  const value_t* R_data,
+                  value_idx R_nnz,
+                  value_idx m,
+                  value_idx n,
+                  value_idx n_cols,
+                  cudaStream_t stream)
+{
+  // sum_sq for std dev
+  rmm::device_uvector<value_t> Q_sq_norms(m, stream);
+  rmm::device_uvector<value_t> R_sq_norms(n, stream);
+
+  // sum for mean
+  rmm::device_uvector<value_t> Q_norms(m, stream);
+  rmm::device_uvector<value_t> R_norms(n, stream);
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(Q_sq_norms.data(), 0, Q_sq_norms.size() * sizeof(value_t)));
+  RAFT_CUDA_TRY(cudaMemsetAsync(R_sq_norms.data(), 0, R_sq_norms.size() * sizeof(value_t)));
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(Q_norms.data(), 0, Q_norms.size() * sizeof(value_t)));
+  RAFT_CUDA_TRY(cudaMemsetAsync(R_norms.data(), 0, R_norms.size() * sizeof(value_t)));
+
+  compute_row_norm_kernel<<<raft::ceildiv(Q_nnz, tpb), tpb, 0, stream>>>(
+    Q_sq_norms.data(), Q_coo_rows, Q_data, Q_nnz);
+  compute_row_norm_kernel<<<raft::ceildiv(R_nnz, tpb), tpb, 0, stream>>>(
+    R_sq_norms.data(), R_coo_rows, R_data, R_nnz);
+
+  compute_row_sum_kernel<<<raft::ceildiv(Q_nnz, tpb), tpb, 0, stream>>>(
+    Q_norms.data(), Q_coo_rows, Q_data, Q_nnz);
+  compute_row_sum_kernel<<<raft::ceildiv(R_nnz, tpb), tpb, 0, stream>>>(
+    R_norms.data(), R_coo_rows, R_data, R_nnz);
+
+  compute_correlation(out,
+                      Q_sq_norms.data(),
+                      R_sq_norms.data(),
+                      Q_norms.data(),
+                      R_norms.data(),
+                      m,
+                      n,
+                      n_cols,
+                      stream);
+}
+
+/**
+ * L2 distance using the expanded form: sum(x_k)^2 + sum(y_k)^2 - 2 * sum(x_k * y_k)
+ * The expanded form is more efficient for sparse data.
+ */
+template <typename value_idx = int, typename value_t = float>
+class l2_expanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit l2_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config), ip_dists(config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    ip_dists.compute(out_dists);
+
+    value_idx* b_indices = ip_dists.b_rows_coo();
+    value_t* b_data      = ip_dists.b_data_coo();
+
+    rmm::device_uvector<value_idx> search_coo_rows(
+      config_->a_nnz, raft::resource::get_cuda_stream(config_->handle));
+    raft::sparse::convert::csr_to_coo(config_->a_indptr,
+                                      config_->a_nrows,
+                                      search_coo_rows.data(),
+                                      config_->a_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+
+    compute_l2(out_dists,
+               search_coo_rows.data(),
+               config_->a_data,
+               config_->a_nnz,
+               b_indices,
+               b_data,
+               config_->b_nnz,
+               config_->a_nrows,
+               config_->b_nrows,
+               raft::resource::get_cuda_stream(config_->handle),
+               [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) {
+                 return -2 * dot + q_norm + r_norm;
+               });
+  }
+
+  ~l2_expanded_distances_t() = default;
+
+ protected:
+  const distances_config_t<value_idx, value_t>* config_;
+  ip_distances_t<value_idx, value_t> ip_dists;
+};
+
+/**
+ * L2 sqrt distance performing the sqrt operation after the distance computation
+ * The expanded form is more efficient for sparse data.
+ */
+template <typename value_idx = int, typename value_t = float>
+class l2_sqrt_expanded_distances_t : public l2_expanded_distances_t<value_idx, value_t> {
+ public:
+  explicit l2_sqrt_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : l2_expanded_distances_t<value_idx, value_t>(config)
+  {
+  }
+
+  void compute(value_t* out_dists) override
+  {
+    l2_expanded_distances_t<value_idx, value_t>::compute(out_dists);
+    // Sqrt Post-processing
+    raft::linalg::unaryOp<value_t>(
+      out_dists,
+      out_dists,
+      this->config_->a_nrows * this->config_->b_nrows,
+      [] __device__(value_t input) {
+        int neg = input < 0 ? -1 : 1;
+        return raft::sqrt(abs(input) * neg);
+      },
+      raft::resource::get_cuda_stream(this->config_->handle));
+  }
+
+  ~l2_sqrt_expanded_distances_t() = default;
+};
+
+template <typename value_idx, typename value_t>
+class correlation_expanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit correlation_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config), ip_dists(config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    ip_dists.compute(out_dists);
+
+    value_idx* b_indices = ip_dists.b_rows_coo();
+    value_t* b_data      = ip_dists.b_data_coo();
+
+    rmm::device_uvector<value_idx> search_coo_rows(
+      config_->a_nnz, raft::resource::get_cuda_stream(config_->handle));
+    raft::sparse::convert::csr_to_coo(config_->a_indptr,
+                                      config_->a_nrows,
+                                      search_coo_rows.data(),
+                                      config_->a_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+
+    compute_corr(out_dists,
+                 search_coo_rows.data(),
+                 config_->a_data,
+                 config_->a_nnz,
+                 b_indices,
+                 b_data,
+                 config_->b_nnz,
+                 config_->a_nrows,
+                 config_->b_nrows,
+                 config_->b_ncols,
+                 raft::resource::get_cuda_stream(config_->handle));
+  }
+
+  ~correlation_expanded_distances_t() = default;
+
+ protected:
+  const distances_config_t<value_idx, value_t>* config_;
+  ip_distances_t<value_idx, value_t> ip_dists;
+};
+
+/**
+ * Cosine distance using the expanded form: 1 - ( sum(x_k * y_k) / (sqrt(sum(x_k)^2) *
+ * sqrt(sum(y_k)^2))) The expanded form is more efficient for sparse data.
+ */
+template <typename value_idx = int, typename value_t = float>
+class cosine_expanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit cosine_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config),
+      workspace(0, raft::resource::get_cuda_stream(config.handle)),
+      ip_dists(config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    ip_dists.compute(out_dists);
+
+    value_idx* b_indices = ip_dists.b_rows_coo();
+    value_t* b_data      = ip_dists.b_data_coo();
+
+    rmm::device_uvector<value_idx> search_coo_rows(
+      config_->a_nnz, raft::resource::get_cuda_stream(config_->handle));
+    raft::sparse::convert::csr_to_coo(config_->a_indptr,
+                                      config_->a_nrows,
+                                      search_coo_rows.data(),
+                                      config_->a_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+
+    compute_l2(out_dists,
+               search_coo_rows.data(),
+               config_->a_data,
+               config_->a_nnz,
+               b_indices,
+               b_data,
+               config_->b_nnz,
+               config_->a_nrows,
+               config_->b_nrows,
+               raft::resource::get_cuda_stream(config_->handle),
+               [] __device__ __host__(value_t dot, value_t q_norm, value_t r_norm) {
+                 value_t norms = raft::sqrt(q_norm) * raft::sqrt(r_norm);
+                 // deal with potential for 0 in denominator by forcing 0/1 instead
+                 value_t cos = ((norms != 0) * dot) / ((norms == 0) + norms);
+
+                 // flip the similarity when both rows are 0
+                 bool both_empty = (q_norm == 0) && (r_norm == 0);
+                 return 1 - ((!both_empty * cos) + both_empty);
+               });
+  }
+
+  ~cosine_expanded_distances_t() = default;
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+  rmm::device_uvector<char> workspace;
+  ip_distances_t<value_idx, value_t> ip_dists;
+};
+
+/**
+ * Hellinger distance using the expanded form: sqrt(1 - sum(sqrt(x_k) * sqrt(y_k)))
+ * The expanded form is more efficient for sparse data.
+ *
+ * This distance computation modifies A and B by computing a sqrt
+ * and then performing a `pow(x, 2)` to convert it back. Because of this,
+ * it is possible that the values in A and B might differ slightly
+ * after this is invoked.
+ */
+template <typename value_idx = int, typename value_t = float>
+class hellinger_expanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit hellinger_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config), workspace(0, raft::resource::get_cuda_stream(config.handle))
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    rmm::device_uvector<value_idx> coo_rows(std::max(config_->b_nnz, config_->a_nnz),
+                                            raft::resource::get_cuda_stream(config_->handle));
+
+    raft::sparse::convert::csr_to_coo(config_->b_indptr,
+                                      config_->b_nrows,
+                                      coo_rows.data(),
+                                      config_->b_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+
+    balanced_coo_pairwise_generalized_spmv<value_idx, value_t>(
+      out_dists,
+      *config_,
+      coo_rows.data(),
+      [] __device__(value_t a, value_t b) { return raft::sqrt(a) * raft::sqrt(b); },
+      raft::add_op(),
+      raft::atomic_add_op());
+
+    raft::linalg::unaryOp<value_t>(
+      out_dists,
+      out_dists,
+      config_->a_nrows * config_->b_nrows,
+      [=] __device__(value_t input) {
+        // Adjust to replace NaN in sqrt with 0 if input to sqrt is negative
+        bool rectifier = (1 - input) > 0;
+        return raft::sqrt(rectifier * (1 - input));
+      },
+      raft::resource::get_cuda_stream(config_->handle));
+  }
+
+  ~hellinger_expanded_distances_t() = default;
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+  rmm::device_uvector<char> workspace;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class russelrao_expanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit russelrao_expanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config),
+      workspace(0, raft::resource::get_cuda_stream(config.handle)),
+      ip_dists(config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    ip_dists.compute(out_dists);
+
+    value_t n_cols     = config_->a_ncols;
+    value_t n_cols_inv = 1.0 / n_cols;
+    raft::linalg::unaryOp<value_t>(
+      out_dists,
+      out_dists,
+      config_->a_nrows * config_->b_nrows,
+      [=] __device__(value_t input) { return (n_cols - input) * n_cols_inv; },
+      raft::resource::get_cuda_stream(config_->handle));
+
+    auto exec_policy  = rmm::exec_policy(raft::resource::get_cuda_stream(config_->handle));
+    auto diags        = thrust::counting_iterator<value_idx>(0);
+    value_idx b_nrows = config_->b_nrows;
+    thrust::for_each(exec_policy, diags, diags + config_->a_nrows, [=] __device__(value_idx input) {
+      out_dists[input * b_nrows + input] = 0.0;
+    });
+  }
+
+  ~russelrao_expanded_distances_t() = default;
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+  rmm::device_uvector<char> workspace;
+  ip_distances_t<value_idx, value_t> ip_dists;
+};
+
+}  // END namespace sparse
+}  // END namespace detail
+}  // END namespace distance
+}  // END namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/lp_distance.cuh b/cpp/src/distance/detail/sparse/lp_distance.cuh
new file mode 100644
index 0000000000..18e7b04e4b
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/lp_distance.cuh
@@ -0,0 +1,333 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "common.hpp"
+
+#include <raft/core/operators.cuh>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/distance/distance_types.hpp>
+#include <raft/sparse/convert/coo.cuh>
+#include <raft/sparse/csr.hpp>
+#include <raft/sparse/detail/utils.h>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <limits.h>
+
+#include <algorithm>
+#include <nvfunctional>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+template <typename value_idx = int,
+          typename value_t   = float,
+          typename product_f,
+          typename accum_f,
+          typename write_f>
+void unexpanded_lp_distances(value_t* out_dists,
+                             const distances_config_t<value_idx, value_t>* config_,
+                             product_f product_func,
+                             accum_f accum_func,
+                             write_f write_func)
+{
+  rmm::device_uvector<value_idx> coo_rows(std::max(config_->b_nnz, config_->a_nnz),
+                                          raft::resource::get_cuda_stream(config_->handle));
+
+  raft::sparse::convert::csr_to_coo(config_->b_indptr,
+                                    config_->b_nrows,
+                                    coo_rows.data(),
+                                    config_->b_nnz,
+                                    raft::resource::get_cuda_stream(config_->handle));
+
+  balanced_coo_pairwise_generalized_spmv<value_idx, value_t>(
+    out_dists, *config_, coo_rows.data(), product_func, accum_func, write_func);
+
+  raft::sparse::convert::csr_to_coo(config_->a_indptr,
+                                    config_->a_nrows,
+                                    coo_rows.data(),
+                                    config_->a_nnz,
+                                    raft::resource::get_cuda_stream(config_->handle));
+
+  balanced_coo_pairwise_generalized_spmv_rev<value_idx, value_t>(
+    out_dists, *config_, coo_rows.data(), product_func, accum_func, write_func);
+}
+
+/**
+ * Computes L1 distances for sparse input. This does not have
+ * an equivalent expanded form, so it is only executed in
+ * an unexpanded form.
+ * @tparam value_idx
+ * @tparam value_t
+ */
+template <typename value_idx = int, typename value_t = float>
+class l1_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  l1_unexpanded_distances_t(const distances_config_t<value_idx, value_t>& config) : config_(&config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    unexpanded_lp_distances<value_idx, value_t>(
+      out_dists, config_, raft::absdiff_op(), raft::add_op(), raft::atomic_add_op());
+  }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class l2_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  l2_unexpanded_distances_t(const distances_config_t<value_idx, value_t>& config) : config_(&config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    unexpanded_lp_distances<value_idx, value_t>(
+      out_dists, config_, raft::sqdiff_op(), raft::add_op(), raft::atomic_add_op());
+  }
+
+ protected:
+  const distances_config_t<value_idx, value_t>* config_;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class l2_sqrt_unexpanded_distances_t : public l2_unexpanded_distances_t<value_idx, value_t> {
+ public:
+  l2_sqrt_unexpanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : l2_unexpanded_distances_t<value_idx, value_t>(config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    l2_unexpanded_distances_t<value_idx, value_t>::compute(out_dists);
+
+    uint64_t n = (uint64_t)this->config_->a_nrows * (uint64_t)this->config_->b_nrows;
+    // Sqrt Post-processing
+    raft::linalg::unaryOp<value_t>(
+      out_dists,
+      out_dists,
+      n,
+      [] __device__(value_t input) {
+        int neg = input < 0 ? -1 : 1;
+        return raft::sqrt(abs(input) * neg);
+      },
+      raft::resource::get_cuda_stream(this->config_->handle));
+  }
+};
+
+template <typename value_idx = int, typename value_t = float>
+class linf_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit linf_unexpanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    unexpanded_lp_distances<value_idx, value_t>(
+      out_dists, config_, raft::absdiff_op(), raft::max_op(), raft::atomic_max_op());
+  }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class canberra_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit canberra_unexpanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    unexpanded_lp_distances<value_idx, value_t>(
+      out_dists,
+      config_,
+      [] __device__(value_t a, value_t b) {
+        value_t d = fabs(a) + fabs(b);
+
+        // deal with potential for 0 in denominator by
+        // forcing 1/0 instead
+        return ((d != 0) * fabs(a - b)) / (d + (d == 0));
+      },
+      raft::add_op(),
+      raft::atomic_add_op());
+  }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class lp_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit lp_unexpanded_distances_t(const distances_config_t<value_idx, value_t>& config,
+                                     value_t p_)
+    : config_(&config), p(p_)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    unexpanded_lp_distances<value_idx, value_t>(
+      out_dists,
+      config_,
+      raft::compose_op(raft::pow_const_op<value_t>(p), raft::sub_op()),
+      raft::add_op(),
+      raft::atomic_add_op());
+
+    uint64_t n         = (uint64_t)this->config_->a_nrows * (uint64_t)this->config_->b_nrows;
+    value_t one_over_p = value_t{1} / p;
+    raft::linalg::unaryOp<value_t>(out_dists,
+                                   out_dists,
+                                   n,
+                                   raft::pow_const_op<value_t>(one_over_p),
+                                   raft::resource::get_cuda_stream(config_->handle));
+  }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+  value_t p;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class hamming_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit hamming_unexpanded_distances_t(const distances_config_t<value_idx, value_t>& config)
+    : config_(&config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    unexpanded_lp_distances<value_idx, value_t>(
+      out_dists, config_, raft::notequal_op(), raft::add_op(), raft::atomic_add_op());
+
+    uint64_t n     = (uint64_t)config_->a_nrows * (uint64_t)config_->b_nrows;
+    value_t n_cols = 1.0 / config_->a_ncols;
+    raft::linalg::unaryOp<value_t>(out_dists,
+                                   out_dists,
+                                   n,
+                                   raft::mul_const_op<value_t>(n_cols),
+                                   raft::resource::get_cuda_stream(config_->handle));
+  }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class jensen_shannon_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit jensen_shannon_unexpanded_distances_t(
+    const distances_config_t<value_idx, value_t>& config)
+    : config_(&config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    unexpanded_lp_distances<value_idx, value_t>(
+      out_dists,
+      config_,
+      [] __device__(value_t a, value_t b) {
+        value_t m   = 0.5f * (a + b);
+        bool a_zero = a == 0;
+        bool b_zero = b == 0;
+
+        value_t x = (!a_zero * m) / (a_zero + a);
+        value_t y = (!b_zero * m) / (b_zero + b);
+
+        bool x_zero = x == 0;
+        bool y_zero = y == 0;
+
+        return (-a * (!x_zero * log(x + x_zero))) + (-b * (!y_zero * log(y + y_zero)));
+      },
+      raft::add_op(),
+      raft::atomic_add_op());
+
+    uint64_t n = (uint64_t)this->config_->a_nrows * (uint64_t)this->config_->b_nrows;
+    raft::linalg::unaryOp<value_t>(
+      out_dists,
+      out_dists,
+      n,
+      [=] __device__(value_t input) { return raft::sqrt(0.5 * input); },
+      raft::resource::get_cuda_stream(config_->handle));
+  }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+};
+
+template <typename value_idx = int, typename value_t = float>
+class kl_divergence_unexpanded_distances_t : public distances_t<value_t> {
+ public:
+  explicit kl_divergence_unexpanded_distances_t(
+    const distances_config_t<value_idx, value_t>& config)
+    : config_(&config)
+  {
+  }
+
+  void compute(value_t* out_dists)
+  {
+    rmm::device_uvector<value_idx> coo_rows(std::max(config_->b_nnz, config_->a_nnz),
+                                            raft::resource::get_cuda_stream(config_->handle));
+
+    raft::sparse::convert::csr_to_coo(config_->b_indptr,
+                                      config_->b_nrows,
+                                      coo_rows.data(),
+                                      config_->b_nnz,
+                                      raft::resource::get_cuda_stream(config_->handle));
+
+    balanced_coo_pairwise_generalized_spmv<value_idx, value_t>(
+      out_dists,
+      *config_,
+      coo_rows.data(),
+      [] __device__(value_t a, value_t b) { return a * log(a / b); },
+      raft::add_op(),
+      raft::atomic_add_op());
+
+    uint64_t n = (uint64_t)this->config_->a_nrows * (uint64_t)this->config_->b_nrows;
+    raft::linalg::unaryOp<value_t>(out_dists,
+                                   out_dists,
+                                   n,
+                                   raft::mul_const_op<value_t>(0.5),
+                                   raft::resource::get_cuda_stream(config_->handle));
+  }
+
+ private:
+  const distances_config_t<value_idx, value_t>* config_;
+};
+
+}  // END namespace sparse
+}  // END namespace detail
+}  // END namespace distance
+}  // END namespace cuvs
diff --git a/cpp/src/distance/detail/sparse/utils.cuh b/cpp/src/distance/detail/sparse/utils.cuh
new file mode 100644
index 0000000000..dc7ae6df6d
--- /dev/null
+++ b/cpp/src/distance/detail/sparse/utils.cuh
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/math.hpp>
+
+#include <cub/cub.cuh>
+#include <cuda_fp16.h>
+#include <cuda_pipeline.h>
+
+namespace cuvs {
+namespace distance {
+namespace detail {
+namespace sparse {
+
+/**
+ * Computes the maximum number of columns that can be stored
+ * in shared memory in dense form with the given block size
+ * and precision.
+ * @return the maximum number of columns that can be stored in smem
+ */
+template <typename value_idx, typename value_t, int tpb = 1024>
+inline int max_cols_per_block()
+{
+  // max cols = (total smem available - cub reduction smem)
+  return (raft::getSharedMemPerBlock() - ((tpb / raft::warp_size()) * sizeof(value_t))) /
+         sizeof(value_t);
+}
+
+template <typename value_idx, typename value_t, typename dot_t = value_t>
+RAFT_KERNEL faster_dot_on_csr_kernel(dot_t* __restrict__ dot,
+                                     const value_idx* __restrict__ indptr,
+                                     const value_idx* __restrict__ cols,
+                                     const value_t* __restrict__ A,
+                                     const value_t* __restrict__ B,
+                                     const value_idx nnz,
+                                     const value_idx n_rows,
+                                     const value_idx dim)
+{
+  auto vec_id  = threadIdx.x;
+  auto lane_id = threadIdx.x & 0x1f;
+
+  extern __shared__ char smem[];
+  value_t* s_A      = (value_t*)smem;
+  value_idx cur_row = -1;
+
+  for (int row = blockIdx.x; row < n_rows; row += gridDim.x) {
+    for (int dot_id = blockIdx.y + indptr[row]; dot_id < indptr[row + 1]; dot_id += gridDim.y) {
+      if (dot_id >= nnz) { return; }
+      const value_idx col               = cols[dot_id] * dim;
+      const value_t* __restrict__ B_col = B + col;
+
+      if (threadIdx.x == 0) { dot[dot_id] = 0.0; }
+      __syncthreads();
+
+      if (cur_row != row) {
+        for (value_idx k = vec_id; k < dim; k += blockDim.x) {
+          s_A[k] = A[row * dim + k];
+        }
+        cur_row = row;
+      }
+
+      dot_t l_dot_ = 0.0;
+      for (value_idx k = vec_id; k < dim; k += blockDim.x) {
+        asm("prefetch.global.L2 [%0];" ::"l"(B_col + k + blockDim.x));
+        if constexpr ((std::is_same_v<dot_t, float> && std::is_same_v<value_t, half>)) {
+          l_dot_ += __half2float(s_A[k]) * __half2float(__ldcg(B_col + k));
+        } else {
+          l_dot_ += s_A[k] * __ldcg(B_col + k);
+        }
+      }
+
+      typedef cub::WarpReduce<dot_t> WarpReduce;
+      __shared__ typename WarpReduce::TempStorage temp_storage;
+      dot_t warp_sum = WarpReduce(temp_storage).Sum(l_dot_);
+
+      if (lane_id == 0) { atomicAdd_block(dot + dot_id, warp_sum); }
+    }
+  }
+}
+
+template <typename value_idx, typename value_t, typename dot_t = value_t>
+void faster_dot_on_csr(raft::resources const& handle,
+                       dot_t* dot,
+                       const value_idx nnz,
+                       const value_idx* indptr,
+                       const value_idx* cols,
+                       const value_t* A,
+                       const value_t* B,
+                       const value_idx n_rows,
+                       const value_idx dim)
+{
+  if (nnz == 0 || n_rows == 0) return;
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  constexpr value_idx MAX_ROW_PER_ITER = 500;
+  int dev_id, sm_count, blocks_per_sm;
+
+  const int smem_size = dim * sizeof(value_t);
+  cudaGetDevice(&dev_id);
+  cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev_id);
+
+  if (dim < 128) {
+    constexpr int tpb = 64;
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+      &blocks_per_sm, faster_dot_on_csr_kernel<value_idx, value_t, dot_t>, tpb, smem_size);
+    auto block_x = std::min(n_rows, MAX_ROW_PER_ITER);
+    auto block_y =
+      (std::min(value_idx(blocks_per_sm * sm_count * 16), nnz) + block_x - 1) / block_x;
+    dim3 blocks(block_x, block_y, 1);
+
+    faster_dot_on_csr_kernel<value_idx, value_t, dot_t>
+      <<<blocks, tpb, smem_size, stream>>>(dot, indptr, cols, A, B, nnz, n_rows, dim);
+
+  } else if (dim < 256) {
+    constexpr int tpb = 128;
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+      &blocks_per_sm, faster_dot_on_csr_kernel<value_idx, value_t, dot_t>, tpb, smem_size);
+    auto block_x = std::min(n_rows, MAX_ROW_PER_ITER);
+    auto block_y =
+      (std::min(value_idx(blocks_per_sm * sm_count * 16), nnz) + block_x - 1) / block_x;
+    dim3 blocks(block_x, block_y, 1);
+
+    faster_dot_on_csr_kernel<value_idx, value_t, dot_t>
+      <<<blocks, tpb, smem_size, stream>>>(dot, indptr, cols, A, B, nnz, n_rows, dim);
+  } else if (dim < 512) {
+    constexpr int tpb = 256;
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+      &blocks_per_sm, faster_dot_on_csr_kernel<value_idx, value_t, dot_t>, tpb, smem_size);
+    auto block_x = std::min(n_rows, MAX_ROW_PER_ITER);
+    auto block_y =
+      (std::min(value_idx(blocks_per_sm * sm_count * 16), nnz) + block_x - 1) / block_x;
+    dim3 blocks(block_x, block_y, 1);
+
+    faster_dot_on_csr_kernel<value_idx, value_t, dot_t>
+      <<<blocks, tpb, smem_size, stream>>>(dot, indptr, cols, A, B, nnz, n_rows, dim);
+  } else {
+    constexpr int tpb = 512;
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+      &blocks_per_sm, faster_dot_on_csr_kernel<value_idx, value_t, dot_t>, tpb, smem_size);
+    auto block_x = std::min(n_rows, MAX_ROW_PER_ITER);
+    auto block_y =
+      (std::min(value_idx(blocks_per_sm * sm_count * 16), nnz) + block_x - 1) / block_x;
+    dim3 blocks(block_x, block_y, 1);
+
+    faster_dot_on_csr_kernel<value_idx, value_t, dot_t>
+      <<<blocks, tpb, smem_size, stream>>>(dot, indptr, cols, A, B, nnz, n_rows, dim);
+  }
+
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+}  // namespace sparse
+}  // namespace detail
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/sparse_distance.cu b/cpp/src/distance/sparse_distance.cu
new file mode 100644
index 0000000000..338c4e908d
--- /dev/null
+++ b/cpp/src/distance/sparse_distance.cu
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include "sparse_distance.cuh"
+
+namespace cuvs {
+namespace distance {
+
+template <typename ElementType, typename IndexType>
+void pairwise_distance(
+  raft::resources const& handle,
+  raft::device_csr_matrix_view<const ElementType, IndexType, IndexType, IndexType> x,
+  raft::device_csr_matrix_view<const ElementType, IndexType, IndexType, IndexType> y,
+  raft::device_matrix_view<ElementType, IndexType, raft::row_major> dist,
+  cuvs::distance::DistanceType metric,
+  float metric_arg = 2.0f)
+{
+  auto x_structure = x.structure_view();
+  auto y_structure = y.structure_view();
+
+  RAFT_EXPECTS(x_structure.get_n_cols() == y_structure.get_n_cols(),
+               "Number of columns must be equal");
+
+  RAFT_EXPECTS(dist.extent(0) == x_structure.get_n_rows(),
+               "Number of rows in output must be equal to "
+               "number of rows in X");
+  RAFT_EXPECTS(dist.extent(1) == y_structure.get_n_rows(),
+               "Number of columns in output must be equal to "
+               "number of rows in Y");
+
+  detail::sparse::distances_config_t<IndexType, ElementType> input_config(handle);
+  input_config.a_nrows   = x_structure.get_n_rows();
+  input_config.a_ncols   = x_structure.get_n_cols();
+  input_config.a_nnz     = x_structure.get_nnz();
+  input_config.a_indptr  = const_cast<IndexType*>(x_structure.get_indptr().data());
+  input_config.a_indices = const_cast<IndexType*>(x_structure.get_indices().data());
+  input_config.a_data    = const_cast<ElementType*>(x.get_elements().data());
+
+  input_config.b_nrows   = y_structure.get_n_rows();
+  input_config.b_ncols   = y_structure.get_n_cols();
+  input_config.b_nnz     = y_structure.get_nnz();
+  input_config.b_indptr  = const_cast<IndexType*>(y_structure.get_indptr().data());
+  input_config.b_indices = const_cast<IndexType*>(y_structure.get_indices().data());
+  input_config.b_data    = const_cast<ElementType*>(y.get_elements().data());
+
+  pairwiseDistance(dist.data_handle(), input_config, metric, metric_arg);
+}
+
+void pairwise_distance(raft::resources const& handle,
+                       raft::device_csr_matrix_view<const float, int, int, int> x,
+                       raft::device_csr_matrix_view<const float, int, int, int> y,
+                       raft::device_matrix_view<float, int, raft::row_major> dist,
+                       cuvs::distance::DistanceType metric,
+                       float metric_arg)
+{
+  pairwise_distance<float, int>(handle, x, y, dist, metric, metric_arg);
+}
+
+void pairwise_distance(raft::resources const& handle,
+                       raft::device_csr_matrix_view<const double, int, int, int> x,
+                       raft::device_csr_matrix_view<const double, int, int, int> y,
+                       raft::device_matrix_view<double, int, raft::row_major> dist,
+                       cuvs::distance::DistanceType metric,
+                       float metric_arg)
+{
+  pairwise_distance<double, int>(handle, x, y, dist, metric, metric_arg);
+}
+}  // namespace distance
+}  // namespace cuvs
diff --git a/cpp/src/distance/sparse_distance.cuh b/cpp/src/distance/sparse_distance.cuh
new file mode 100644
index 0000000000..0d6dc0e6fb
--- /dev/null
+++ b/cpp/src/distance/sparse_distance.cuh
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/sparse/bin_distance.cuh"
+#include "detail/sparse/common.hpp"
+#include "detail/sparse/ip_distance.cuh"
+#include "detail/sparse/l2_distance.cuh"
+#include "detail/sparse/lp_distance.cuh"
+
+#include <cuvs/distance/distance.hpp>
+
+#include <raft/core/device_csr_matrix.hpp>
+
+#include <unordered_set>
+
+namespace cuvs {
+namespace distance {
+/**
+ * Compute pairwise distances between A and B, using the provided
+ * input configuration and distance function.
+ *
+ * @tparam value_idx index type
+ * @tparam value_t value type
+ * @param[out] out dense output array (size A.nrows * B.nrows)
+ * @param[in] input_config input argument configuration
+ * @param[in] metric distance metric to use
+ * @param[in] metric_arg metric argument (used for Minkowski distance)
+ */
+template <typename value_idx = int, typename value_t = float>
+void pairwiseDistance(value_t* out,
+                      detail::sparse::distances_config_t<value_idx, value_t> input_config,
+                      cuvs::distance::DistanceType metric,
+                      float metric_arg)
+{
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2Expanded:
+      detail::sparse::l2_expanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+      detail::sparse::l2_sqrt_expanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::InnerProduct:
+      detail::sparse::ip_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::L2Unexpanded:
+      detail::sparse::l2_unexpanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::L2SqrtUnexpanded:
+      detail::sparse::l2_sqrt_unexpanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::L1:
+      detail::sparse::l1_unexpanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::LpUnexpanded:
+      detail::sparse::lp_unexpanded_distances_t<value_idx, value_t>(input_config, metric_arg)
+        .compute(out);
+      break;
+    case cuvs::distance::DistanceType::Linf:
+      detail::sparse::linf_unexpanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::Canberra:
+      detail::sparse::canberra_unexpanded_distances_t<value_idx, value_t>(input_config)
+        .compute(out);
+      break;
+    case cuvs::distance::DistanceType::JaccardExpanded:
+      detail::sparse::jaccard_expanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::CosineExpanded:
+      detail::sparse::cosine_expanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::HellingerExpanded:
+      detail::sparse::hellinger_expanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::DiceExpanded:
+      detail::sparse::dice_expanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::CorrelationExpanded:
+      detail::sparse::correlation_expanded_distances_t<value_idx, value_t>(input_config)
+        .compute(out);
+      break;
+    case cuvs::distance::DistanceType::RusselRaoExpanded:
+      detail::sparse::russelrao_expanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::HammingUnexpanded:
+      detail::sparse::hamming_unexpanded_distances_t<value_idx, value_t>(input_config).compute(out);
+      break;
+    case cuvs::distance::DistanceType::JensenShannon:
+      detail::sparse::jensen_shannon_unexpanded_distances_t<value_idx, value_t>(input_config)
+        .compute(out);
+      break;
+    case cuvs::distance::DistanceType::KLDivergence:
+      detail::sparse::kl_divergence_unexpanded_distances_t<value_idx, value_t>(input_config)
+        .compute(out);
+      break;
+
+    default: THROW("Unsupported distance: %d", metric);
+  }
+}
+};  // namespace distance
+};  // namespace cuvs
diff --git a/cpp/src/neighbors/detail/sparse_knn.cuh b/cpp/src/neighbors/detail/sparse_knn.cuh
new file mode 100644
index 0000000000..9c8e971b90
--- /dev/null
+++ b/cpp/src/neighbors/detail/sparse_knn.cuh
@@ -0,0 +1,437 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "../../distance/sparse_distance.cuh"
+#include "knn_merge_parts.cuh"
+#include <cuvs/distance/distance.hpp>
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/unary_op.cuh>
+
+#include <cuvs/selection/select_k.hpp>
+
+#include <raft/sparse/coo.hpp>
+#include <raft/sparse/csr.hpp>
+#include <raft/sparse/detail/utils.h>
+#include <raft/sparse/op/slice.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <algorithm>
+
+namespace cuvs::neighbors::detail {
+
+template <typename value_idx, typename value_t>
+struct csr_batcher_t {
+  csr_batcher_t(value_idx batch_size,
+                value_idx n_rows,
+                const value_idx* csr_indptr,
+                const value_idx* csr_indices,
+                const value_t* csr_data)
+    : batch_start_(0),
+      batch_stop_(0),
+      batch_rows_(0),
+      total_rows_(n_rows),
+      batch_size_(batch_size),
+      csr_indptr_(csr_indptr),
+      csr_indices_(csr_indices),
+      csr_data_(csr_data),
+      batch_csr_start_offset_(0),
+      batch_csr_stop_offset_(0)
+  {
+  }
+
+  void set_batch(int batch_num)
+  {
+    batch_start_ = batch_num * batch_size_;
+    batch_stop_  = batch_start_ + batch_size_ - 1;  // zero-based indexing
+
+    if (batch_stop_ >= total_rows_) batch_stop_ = total_rows_ - 1;  // zero-based indexing
+
+    batch_rows_ = (batch_stop_ - batch_start_) + 1;
+  }
+
+  value_idx get_batch_csr_indptr_nnz(value_idx* batch_indptr, cudaStream_t stream)
+  {
+    raft::sparse::op::csr_row_slice_indptr(batch_start_,
+                                           batch_stop_,
+                                           csr_indptr_,
+                                           batch_indptr,
+                                           &batch_csr_start_offset_,
+                                           &batch_csr_stop_offset_,
+                                           stream);
+
+    return batch_csr_stop_offset_ - batch_csr_start_offset_;
+  }
+
+  void get_batch_csr_indices_data(value_idx* csr_indices, value_t* csr_data, cudaStream_t stream)
+  {
+    raft::sparse::op::csr_row_slice_populate(batch_csr_start_offset_,
+                                             batch_csr_stop_offset_,
+                                             csr_indices_,
+                                             csr_data_,
+                                             csr_indices,
+                                             csr_data,
+                                             stream);
+  }
+
+  value_idx batch_rows() const { return batch_rows_; }
+
+  value_idx batch_start() const { return batch_start_; }
+
+  value_idx batch_stop() const { return batch_stop_; }
+
+ private:
+  value_idx batch_size_;
+  value_idx batch_start_;
+  value_idx batch_stop_;
+  value_idx batch_rows_;
+
+  value_idx total_rows_;
+
+  const value_idx* csr_indptr_;
+  const value_idx* csr_indices_;
+  const value_t* csr_data_;
+
+  value_idx batch_csr_start_offset_;
+  value_idx batch_csr_stop_offset_;
+};
+
+template <typename value_idx, typename value_t>
+class sparse_knn_t {
+ public:
+  sparse_knn_t(const value_idx* idxIndptr_,
+               const value_idx* idxIndices_,
+               const value_t* idxData_,
+               size_t idxNNZ_,
+               int n_idx_rows_,
+               int n_idx_cols_,
+               const value_idx* queryIndptr_,
+               const value_idx* queryIndices_,
+               const value_t* queryData_,
+               size_t queryNNZ_,
+               int n_query_rows_,
+               int n_query_cols_,
+               value_idx* output_indices_,
+               value_t* output_dists_,
+               int k_,
+               raft::resources const& handle_,
+               size_t batch_size_index_             = 2 << 14,  // approx 1M
+               size_t batch_size_query_             = 2 << 14,
+               cuvs::distance::DistanceType metric_ = cuvs::distance::DistanceType::L2Expanded,
+               float metricArg_                     = 0)
+    : idxIndptr(idxIndptr_),
+      idxIndices(idxIndices_),
+      idxData(idxData_),
+      idxNNZ(idxNNZ_),
+      n_idx_rows(n_idx_rows_),
+      n_idx_cols(n_idx_cols_),
+      queryIndptr(queryIndptr_),
+      queryIndices(queryIndices_),
+      queryData(queryData_),
+      queryNNZ(queryNNZ_),
+      n_query_rows(n_query_rows_),
+      n_query_cols(n_query_cols_),
+      output_indices(output_indices_),
+      output_dists(output_dists_),
+      k(k_),
+      handle(handle_),
+      batch_size_index(batch_size_index_),
+      batch_size_query(batch_size_query_),
+      metric(metric_),
+      metricArg(metricArg_)
+  {
+  }
+
+  void run()
+  {
+    using namespace raft::sparse;
+
+    int n_batches_query = raft::ceildiv((size_t)n_query_rows, batch_size_query);
+    csr_batcher_t<value_idx, value_t> query_batcher(
+      batch_size_query, n_query_rows, queryIndptr, queryIndices, queryData);
+
+    size_t rows_processed = 0;
+
+    for (int i = 0; i < n_batches_query; i++) {
+      /**
+       * Compute index batch info
+       */
+      query_batcher.set_batch(i);
+
+      /**
+       * Slice CSR to rows in batch
+       */
+
+      rmm::device_uvector<value_idx> query_batch_indptr(query_batcher.batch_rows() + 1,
+                                                        raft::resource::get_cuda_stream(handle));
+
+      value_idx n_query_batch_nnz = query_batcher.get_batch_csr_indptr_nnz(
+        query_batch_indptr.data(), raft::resource::get_cuda_stream(handle));
+
+      rmm::device_uvector<value_idx> query_batch_indices(n_query_batch_nnz,
+                                                         raft::resource::get_cuda_stream(handle));
+      rmm::device_uvector<value_t> query_batch_data(n_query_batch_nnz,
+                                                    raft::resource::get_cuda_stream(handle));
+
+      query_batcher.get_batch_csr_indices_data(query_batch_indices.data(),
+                                               query_batch_data.data(),
+                                               raft::resource::get_cuda_stream(handle));
+
+      // A 3-partition temporary merge space to scale the batching. 2 parts for subsequent
+      // batches and 1 space for the results of the merge, which get copied back to the top
+      rmm::device_uvector<value_idx> merge_buffer_indices(0,
+                                                          raft::resource::get_cuda_stream(handle));
+      rmm::device_uvector<value_t> merge_buffer_dists(0, raft::resource::get_cuda_stream(handle));
+
+      value_t* dists_merge_buffer_ptr;
+      value_idx* indices_merge_buffer_ptr;
+
+      int n_batches_idx = raft::ceildiv((size_t)n_idx_rows, batch_size_index);
+      csr_batcher_t<value_idx, value_t> idx_batcher(
+        batch_size_index, n_idx_rows, idxIndptr, idxIndices, idxData);
+
+      for (int j = 0; j < n_batches_idx; j++) {
+        idx_batcher.set_batch(j);
+
+        merge_buffer_indices.resize(query_batcher.batch_rows() * k * 3,
+                                    raft::resource::get_cuda_stream(handle));
+        merge_buffer_dists.resize(query_batcher.batch_rows() * k * 3,
+                                  raft::resource::get_cuda_stream(handle));
+
+        /**
+         * Slice CSR to rows in batch
+         */
+        rmm::device_uvector<value_idx> idx_batch_indptr(idx_batcher.batch_rows() + 1,
+                                                        raft::resource::get_cuda_stream(handle));
+        rmm::device_uvector<value_idx> idx_batch_indices(0,
+                                                         raft::resource::get_cuda_stream(handle));
+        rmm::device_uvector<value_t> idx_batch_data(0, raft::resource::get_cuda_stream(handle));
+
+        value_idx idx_batch_nnz = idx_batcher.get_batch_csr_indptr_nnz(
+          idx_batch_indptr.data(), raft::resource::get_cuda_stream(handle));
+
+        idx_batch_indices.resize(idx_batch_nnz, raft::resource::get_cuda_stream(handle));
+        idx_batch_data.resize(idx_batch_nnz, raft::resource::get_cuda_stream(handle));
+
+        idx_batcher.get_batch_csr_indices_data(
+          idx_batch_indices.data(), idx_batch_data.data(), raft::resource::get_cuda_stream(handle));
+
+        /**
+         * Compute distances
+         */
+        uint64_t dense_size =
+          (uint64_t)idx_batcher.batch_rows() * (uint64_t)query_batcher.batch_rows();
+        rmm::device_uvector<value_t> batch_dists(dense_size,
+                                                 raft::resource::get_cuda_stream(handle));
+
+        RAFT_CUDA_TRY(cudaMemset(batch_dists.data(), 0, batch_dists.size() * sizeof(value_t)));
+
+        compute_distances(idx_batcher,
+                          query_batcher,
+                          idx_batch_nnz,
+                          n_query_batch_nnz,
+                          idx_batch_indptr.data(),
+                          idx_batch_indices.data(),
+                          idx_batch_data.data(),
+                          query_batch_indptr.data(),
+                          query_batch_indices.data(),
+                          query_batch_data.data(),
+                          batch_dists.data());
+
+        // Build batch indices array
+        rmm::device_uvector<value_idx> batch_indices(batch_dists.size(),
+                                                     raft::resource::get_cuda_stream(handle));
+
+        // populate batch indices array
+        value_idx batch_rows = query_batcher.batch_rows(), batch_cols = idx_batcher.batch_rows();
+
+        iota_fill(
+          batch_indices.data(), batch_rows, batch_cols, raft::resource::get_cuda_stream(handle));
+
+        /**
+         * Perform k-selection on batch & merge with other k-selections
+         */
+        size_t merge_buffer_offset = batch_rows * k;
+        dists_merge_buffer_ptr     = merge_buffer_dists.data() + merge_buffer_offset;
+        indices_merge_buffer_ptr   = merge_buffer_indices.data() + merge_buffer_offset;
+
+        perform_k_selection(idx_batcher,
+                            query_batcher,
+                            batch_dists.data(),
+                            batch_indices.data(),
+                            dists_merge_buffer_ptr,
+                            indices_merge_buffer_ptr);
+
+        value_t* dists_merge_buffer_tmp_ptr     = dists_merge_buffer_ptr;
+        value_idx* indices_merge_buffer_tmp_ptr = indices_merge_buffer_ptr;
+
+        // Merge results of difference batches if necessary
+        if (idx_batcher.batch_start() > 0) {
+          size_t merge_buffer_tmp_out  = batch_rows * k * 2;
+          dists_merge_buffer_tmp_ptr   = merge_buffer_dists.data() + merge_buffer_tmp_out;
+          indices_merge_buffer_tmp_ptr = merge_buffer_indices.data() + merge_buffer_tmp_out;
+
+          merge_batches(idx_batcher,
+                        query_batcher,
+                        merge_buffer_dists.data(),
+                        merge_buffer_indices.data(),
+                        dists_merge_buffer_tmp_ptr,
+                        indices_merge_buffer_tmp_ptr);
+        }
+
+        // copy merged output back into merge buffer partition for next iteration
+        raft::copy_async<value_idx>(merge_buffer_indices.data(),
+                                    indices_merge_buffer_tmp_ptr,
+                                    batch_rows * k,
+                                    raft::resource::get_cuda_stream(handle));
+        raft::copy_async<value_t>(merge_buffer_dists.data(),
+                                  dists_merge_buffer_tmp_ptr,
+                                  batch_rows * k,
+                                  raft::resource::get_cuda_stream(handle));
+      }
+
+      // Copy final merged batch to output array
+      raft::copy_async<value_idx>(output_indices + (rows_processed * k),
+                                  merge_buffer_indices.data(),
+                                  query_batcher.batch_rows() * k,
+                                  raft::resource::get_cuda_stream(handle));
+      raft::copy_async<value_t>(output_dists + (rows_processed * k),
+                                merge_buffer_dists.data(),
+                                query_batcher.batch_rows() * k,
+                                raft::resource::get_cuda_stream(handle));
+
+      rows_processed += query_batcher.batch_rows();
+    }
+  }
+
+ private:
+  void merge_batches(csr_batcher_t<value_idx, value_t>& idx_batcher,
+                     csr_batcher_t<value_idx, value_t>& query_batcher,
+                     value_t* merge_buffer_dists,
+                     value_idx* merge_buffer_indices,
+                     value_t* out_dists,
+                     value_idx* out_indices)
+  {
+    // build translation buffer to shift resulting indices by the batch
+    std::vector<value_idx> id_ranges;
+    id_ranges.push_back(0);
+    id_ranges.push_back(idx_batcher.batch_start());
+
+    rmm::device_uvector<value_idx> trans(id_ranges.size(), raft::resource::get_cuda_stream(handle));
+    raft::update_device(
+      trans.data(), id_ranges.data(), id_ranges.size(), raft::resource::get_cuda_stream(handle));
+
+    // combine merge buffers only if there's more than 1 partition to combine
+    cuvs::neighbors::detail::knn_merge_parts(merge_buffer_dists,
+                                             merge_buffer_indices,
+                                             out_dists,
+                                             out_indices,
+                                             query_batcher.batch_rows(),
+                                             2,
+                                             k,
+                                             raft::resource::get_cuda_stream(handle),
+                                             trans.data());
+  }
+
+  void perform_k_selection(csr_batcher_t<value_idx, value_t> idx_batcher,
+                           csr_batcher_t<value_idx, value_t> query_batcher,
+                           value_t* batch_dists,
+                           value_idx* batch_indices,
+                           value_t* out_dists,
+                           value_idx* out_indices)
+  {
+    // populate batch indices array
+    value_idx batch_rows = query_batcher.batch_rows(), batch_cols = idx_batcher.batch_rows();
+
+    // build translation buffer to shift resulting indices by the batch
+    std::vector<value_idx> id_ranges;
+    id_ranges.push_back(0);
+    id_ranges.push_back(idx_batcher.batch_start());
+
+    // in the case where the number of idx rows in the batch is < k, we
+    // want to adjust k.
+    value_idx n_neighbors = std::min(static_cast<value_idx>(k), batch_cols);
+
+    bool ascending = cuvs::distance::is_min_close(metric);
+
+    // kernel to slice first (min) k cols and copy into batched merge buffer
+    cuvs::selection::select_k(
+      handle,
+      raft::make_device_matrix_view<const value_t, int64_t>(batch_dists, batch_rows, batch_cols),
+      raft::make_device_matrix_view<const value_idx, int64_t>(
+        batch_indices, batch_rows, batch_cols),
+      raft::make_device_matrix_view<value_t, int64_t>(out_dists, batch_rows, n_neighbors),
+      raft::make_device_matrix_view<value_idx, int64_t>(out_indices, batch_rows, n_neighbors),
+      ascending,
+      true);
+  }
+
+  void compute_distances(csr_batcher_t<value_idx, value_t>& idx_batcher,
+                         csr_batcher_t<value_idx, value_t>& query_batcher,
+                         size_t idx_batch_nnz,
+                         size_t query_batch_nnz,
+                         value_idx* idx_batch_indptr,
+                         value_idx* idx_batch_indices,
+                         value_t* idx_batch_data,
+                         value_idx* query_batch_indptr,
+                         value_idx* query_batch_indices,
+                         value_t* query_batch_data,
+                         value_t* batch_dists)
+  {
+    /**
+     * Compute distances
+     */
+    cuvs::distance::detail::sparse::distances_config_t<value_idx, value_t> dist_config(handle);
+    dist_config.b_nrows = idx_batcher.batch_rows();
+    dist_config.b_ncols = n_idx_cols;
+    dist_config.b_nnz   = idx_batch_nnz;
+
+    dist_config.b_indptr  = idx_batch_indptr;
+    dist_config.b_indices = idx_batch_indices;
+    dist_config.b_data    = idx_batch_data;
+
+    dist_config.a_nrows = query_batcher.batch_rows();
+    dist_config.a_ncols = n_query_cols;
+    dist_config.a_nnz   = query_batch_nnz;
+
+    dist_config.a_indptr  = query_batch_indptr;
+    dist_config.a_indices = query_batch_indices;
+    dist_config.a_data    = query_batch_data;
+
+    cuvs::distance::pairwiseDistance(batch_dists, dist_config, metric, metricArg);
+  }
+
+  const value_idx *idxIndptr, *idxIndices, *queryIndptr, *queryIndices;
+  value_idx* output_indices;
+  const value_t *idxData, *queryData;
+  value_t* output_dists;
+
+  size_t idxNNZ, queryNNZ, batch_size_index, batch_size_query;
+
+  cuvs::distance::DistanceType metric;
+
+  float metricArg;
+
+  int n_idx_rows, n_idx_cols, n_query_rows, n_query_cols, k;
+
+  raft::resources const& handle;
+};
+
+};  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/sparse_brute_force.cu b/cpp/src/neighbors/sparse_brute_force.cu
new file mode 100644
index 0000000000..e277961ec7
--- /dev/null
+++ b/cpp/src/neighbors/sparse_brute_force.cu
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include "detail/sparse_knn.cuh"
+
+namespace cuvs::neighbors::brute_force {
+template <typename T, typename IdxT>
+sparse_index<T, IdxT>::sparse_index(raft::resources const& res,
+                                    raft::device_csr_matrix_view<const T, IdxT, IdxT, IdxT> dataset,
+                                    cuvs::distance::DistanceType metric,
+                                    T metric_arg)
+  : dataset_(dataset), metric_(metric), metric_arg_(metric_arg)
+{
+}
+
+auto build(raft::resources const& handle,
+           raft::device_csr_matrix_view<const float, int, int, int> dataset,
+           cuvs::distance::DistanceType metric,
+           float metric_arg) -> cuvs::neighbors::brute_force::sparse_index<float, int>
+{
+  return sparse_index<float, int>(handle, dataset, metric, metric_arg);
+}
+
+void search(raft::resources const& handle,
+            const sparse_search_params& params,
+            const sparse_index<float, int>& index,
+            raft::device_csr_matrix_view<const float, int, int, int> query,
+            raft::device_matrix_view<int, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances)
+{
+  auto idx_structure   = index.dataset().structure_view();
+  auto query_structure = query.structure_view();
+  int k                = neighbors.extent(1);
+
+  detail::sparse_knn_t<int, float>(idx_structure.get_indptr().data(),
+                                   idx_structure.get_indices().data(),
+                                   index.dataset().get_elements().data(),
+                                   idx_structure.get_nnz(),
+                                   idx_structure.get_n_rows(),
+                                   idx_structure.get_n_cols(),
+                                   query_structure.get_indptr().data(),
+                                   query_structure.get_indices().data(),
+                                   query.get_elements().data(),
+                                   query_structure.get_nnz(),
+                                   query_structure.get_n_rows(),
+                                   query_structure.get_n_cols(),
+                                   neighbors.data_handle(),
+                                   distances.data_handle(),
+                                   k,
+                                   handle,
+                                   params.batch_size_index,
+                                   params.batch_size_query,
+                                   index.metric(),
+                                   index.metric_arg())
+    .run();
+}
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 7754a50432..286d721d7f 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -94,7 +94,7 @@ endfunction()
 if(BUILD_TESTS)
   ConfigureTest(
     NAME NEIGHBORS_TEST PATH neighbors/brute_force.cu neighbors/brute_force_prefiltered.cu
-    neighbors/refine.cu GPUS 1 PERCENT 100
+    neighbors/sparse_brute_force.cu neighbors/refine.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
@@ -206,6 +206,7 @@ if(BUILD_TESTS)
     distance/dist_lp_unexp.cu
     distance/dist_russell_rao.cu
     distance/masked_nn.cu
+    distance/sparse_distance.cu
     sparse/neighbors/cross_component_nn.cu
     GPUS
     1
diff --git a/cpp/test/distance/sparse_distance.cu b/cpp/test/distance/sparse_distance.cu
new file mode 100644
index 0000000000..f954874147
--- /dev/null
+++ b/cpp/test/distance/sparse_distance.cu
@@ -0,0 +1,850 @@
+/*
+ * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+
+#include <raft/core/device_csr_matrix.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/sparse/detail/cusparse_wrappers.h>
+#include <raft/util/cudart_utils.hpp>
+
+#include <cuvs/distance/distance.hpp>
+
+#include <cusparse_v2.h>
+#include <gtest/gtest.h>
+
+namespace cuvs {
+namespace distance {
+
+using namespace raft;
+using namespace raft::sparse;
+
+template <typename value_idx, typename value_t>
+struct SparseDistanceInputs {
+  value_idx n_cols;
+
+  std::vector<value_idx> indptr_h;
+  std::vector<value_idx> indices_h;
+  std::vector<value_t> data_h;
+
+  std::vector<value_t> out_dists_ref_h;
+
+  cuvs::distance::DistanceType metric;
+
+  float metric_arg = 0.0;
+};
+
+template <typename value_idx, typename value_t>
+::std::ostream& operator<<(::std::ostream& os, const SparseDistanceInputs<value_idx, value_t>& dims)
+{
+  return os;
+}
+
+template <typename value_idx, typename value_t>
+class SparseDistanceTest
+  : public ::testing::TestWithParam<SparseDistanceInputs<value_idx, value_t>> {
+ public:
+  SparseDistanceTest()
+    : params(::testing::TestWithParam<SparseDistanceInputs<value_idx, value_t>>::GetParam()),
+      indptr(0, resource::get_cuda_stream(handle)),
+      indices(0, resource::get_cuda_stream(handle)),
+      data(0, resource::get_cuda_stream(handle)),
+      out_dists(0, resource::get_cuda_stream(handle)),
+      out_dists_ref(0, resource::get_cuda_stream(handle))
+  {
+  }
+
+  void SetUp() override
+  {
+    make_data();
+
+    int out_size = static_cast<value_idx>(params.indptr_h.size() - 1) *
+                   static_cast<value_idx>(params.indptr_h.size() - 1);
+
+    out_dists.resize(out_size, resource::get_cuda_stream(handle));
+
+    auto out = raft::make_device_matrix_view<value_t, value_idx>(
+      out_dists.data(),
+      static_cast<value_idx>(params.indptr_h.size() - 1),
+      static_cast<value_idx>(params.indptr_h.size() - 1));
+
+    auto x_structure = raft::make_device_compressed_structure_view<value_idx, value_idx, value_idx>(
+      indptr.data(),
+      indices.data(),
+      static_cast<value_idx>(params.indptr_h.size() - 1),
+      params.n_cols,
+      static_cast<value_idx>(params.indices_h.size()));
+    auto x = raft::make_device_csr_matrix_view<const value_t>(data.data(), x_structure);
+
+    cuvs::distance::pairwise_distance(handle, x, x, out, params.metric, params.metric_arg);
+
+    RAFT_CUDA_TRY(cudaStreamSynchronize(resource::get_cuda_stream(handle)));
+  }
+
+  void compare()
+  {
+    ASSERT_TRUE(devArrMatch(out_dists_ref.data(),
+                            out_dists.data(),
+                            params.out_dists_ref_h.size(),
+                            CompareApprox<value_t>(1e-3)));
+  }
+
+ protected:
+  void make_data()
+  {
+    std::vector<value_idx> indptr_h  = params.indptr_h;
+    std::vector<value_idx> indices_h = params.indices_h;
+    std::vector<value_t> data_h      = params.data_h;
+
+    auto stream = resource::get_cuda_stream(handle);
+    indptr.resize(indptr_h.size(), stream);
+    indices.resize(indices_h.size(), stream);
+    data.resize(data_h.size(), stream);
+
+    update_device(indptr.data(), indptr_h.data(), indptr_h.size(), stream);
+    update_device(indices.data(), indices_h.data(), indices_h.size(), stream);
+    update_device(data.data(), data_h.data(), data_h.size(), stream);
+
+    std::vector<value_t> out_dists_ref_h = params.out_dists_ref_h;
+
+    out_dists_ref.resize((indptr_h.size() - 1) * (indptr_h.size() - 1), stream);
+
+    update_device(out_dists_ref.data(),
+                  out_dists_ref_h.data(),
+                  out_dists_ref_h.size(),
+                  resource::get_cuda_stream(handle));
+  }
+
+  raft::resources handle;
+
+  // input data
+  rmm::device_uvector<value_idx> indptr, indices;
+  rmm::device_uvector<value_t> data;
+
+  // output data
+  rmm::device_uvector<value_t> out_dists, out_dists_ref;
+
+  SparseDistanceInputs<value_idx, value_t> params;
+};
+
+const std::vector<SparseDistanceInputs<int, float>> inputs_i32_f = {
+  {5,
+   {0, 0, 1, 2},
+
+   {1, 2},
+   {0.5, 0.5},
+   {0, 1, 1, 1, 0, 1, 1, 1, 0},
+   cuvs::distance::DistanceType::CosineExpanded,
+   0.0},
+  {5,
+   {0, 0, 1, 2},
+
+   {1, 2},
+   {1.0, 1.0},
+   {0, 1, 1, 1, 0, 1, 1, 1, 0},
+   cuvs::distance::DistanceType::JaccardExpanded,
+   0.0},
+  {2,
+   {0, 2, 4, 6, 8},
+   {0, 1, 0, 1, 0, 1, 0, 1},  // indices
+   {1.0f, 3.0f, 1.0f, 5.0f, 50.0f, 28.0f, 16.0f, 2.0f},
+   {
+     // dense output
+     0.0,
+     4.0,
+     3026.0,
+     226.0,
+     4.0,
+     0.0,
+     2930.0,
+     234.0,
+     3026.0,
+     2930.0,
+     0.0,
+     1832.0,
+     226.0,
+     234.0,
+     1832.0,
+     0.0,
+   },
+   cuvs::distance::DistanceType::L2Expanded,
+   0.0},
+  {2,
+   {0, 2, 4, 6, 8},
+   {0, 1, 0, 1, 0, 1, 0, 1},
+   {1.0f, 2.0f, 1.0f, 2.0f, 1.0f, 2.0f, 1.0f, 2.0f},
+   {5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0},
+   cuvs::distance::DistanceType::InnerProduct,
+   0.0},
+  {2,
+   {0, 2, 4, 6, 8},
+   {0, 1, 0, 1, 0, 1, 0, 1},  // indices
+   {1.0f, 3.0f, 1.0f, 5.0f, 50.0f, 28.0f, 16.0f, 2.0f},
+   {
+     // dense output
+     0.0,
+     4.0,
+     3026.0,
+     226.0,
+     4.0,
+     0.0,
+     2930.0,
+     234.0,
+     3026.0,
+     2930.0,
+     0.0,
+     1832.0,
+     226.0,
+     234.0,
+     1832.0,
+     0.0,
+   },
+   cuvs::distance::DistanceType::L2Unexpanded,
+   0.0},
+
+  {10,
+   {0, 5, 11, 15, 20, 27, 32, 36, 43, 47, 50},
+   {0, 1, 3, 6, 8, 0, 1, 2, 3, 5, 6, 1, 2, 4, 8, 0, 2, 3, 4, 7, 0, 1, 2, 3, 4,
+    6, 8, 0, 1, 2, 5, 7, 1, 5, 8, 9, 0, 1, 2, 5, 6, 8, 9, 2, 4, 5, 7, 0, 3, 9},  // indices
+   {0.5438, 0.2695, 0.4377, 0.7174, 0.9251, 0.7648, 0.3322, 0.7279, 0.4131, 0.5167,
+    0.8655, 0.0730, 0.0291, 0.9036, 0.7988, 0.5019, 0.7663, 0.2190, 0.8206, 0.3625,
+    0.0411, 0.3995, 0.5688, 0.7028, 0.8706, 0.3199, 0.4431, 0.0535, 0.2225, 0.8853,
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+    0.2325, 0.2331, 0.3018, 0.6231, 0.2645, 0.8429, 0.6625, 0.0797, 0.2724, 0.4218},
+   {0.,         0.39419924, 0.54823225, 0.79593037, 0.45658883, 0.93634219, 0.58146987, 0.44940102,
+    1.,         0.76978799, 0.39419924, 0.,         0.97577154, 0.48904013, 0.48300801, 0.45087445,
+    0.73323749, 0.21050481, 0.54847744, 0.78021386, 0.54823225, 0.97577154, 0.,         0.51413997,
+    0.31195441, 0.96546343, 0.67534399, 0.81665436, 0.8321819,  1.,         0.79593037, 0.48904013,
+    0.51413997, 0.,         0.28605559, 0.35772784, 1.,         0.60889396, 0.43324829, 0.84923694,
+    0.45658883, 0.48300801, 0.31195441, 0.28605559, 0.,         0.58623212, 0.6745457,  0.60287165,
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+    0.77917274, 0.48390993, 0.24558392, 0.99166225, 0.58146987, 0.73323749, 0.67534399, 1.,
+    0.6745457,  0.77917274, 0.,         0.27605686, 0.76064776, 0.61547536, 0.44940102, 0.21050481,
+    0.81665436, 0.60889396, 0.60287165, 0.48390993, 0.27605686, 0.,         0.51360432, 0.68185144,
+    1.,         0.54847744, 0.8321819,  0.43324829, 0.67676228, 0.24558392, 0.76064776, 0.51360432,
+    0.,         1.,         0.76978799, 0.78021386, 1.,         0.84923694, 0.73155632, 0.99166225,
+    0.61547536, 0.68185144, 1.,         0.},
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+  {10,
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+    6, 8, 0, 1, 2, 5, 7, 1, 5, 8, 9, 0, 1, 2, 5, 6, 8, 9, 2, 4, 5, 7, 0, 3, 9},  // indices
+   {1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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+    6, 8, 0, 1, 2, 5, 7, 1, 5, 8, 9, 0, 1, 2, 5, 6, 8, 9, 2, 4, 5, 7, 0, 3, 9},  // indices
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+    6, 8, 0, 1, 2, 5, 7, 1, 5, 8, 9, 0, 1, 2, 5, 6, 8, 9, 2, 4, 5, 7, 0, 3, 9},  // indices
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+    6, 8, 0, 1, 2, 5, 7, 1, 5, 8, 9, 0, 1, 2, 5, 6, 8, 9, 2, 4, 5, 7, 0, 3, 9},  // indices
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+   {1.05367121e-08, 8.35309089e-01, 1.00000000e+00, 9.24116813e-01,
+    9.90039274e-01, 7.97613546e-01, 8.91271059e-01, 1.00000000e+00,
+    6.64669302e-01, 8.59439512e-01, 8.35309089e-01, 1.05367121e-08,
+    1.00000000e+00, 7.33151506e-01, 1.00000000e+00, 9.86880955e-01,
+    9.19154851e-01, 5.38849774e-01, 1.00000000e+00, 8.98332369e-01,
+    1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 8.03303970e-01,
+    6.64465915e-01, 8.69374690e-01, 1.00000000e+00, 1.00000000e+00,
+    1.00000000e+00, 1.00000000e+00, 9.24116813e-01, 7.33151506e-01,
+    8.03303970e-01, 0.00000000e+00, 8.16225843e-01, 9.39818306e-01,
+    7.27700415e-01, 7.30155528e-01, 8.89451011e-01, 8.05419635e-01,
+    9.90039274e-01, 1.00000000e+00, 6.64465915e-01, 8.16225843e-01,
+    0.00000000e+00, 6.38804490e-01, 1.00000000e+00, 1.00000000e+00,
+    9.52559809e-01, 9.53789212e-01, 7.97613546e-01, 9.86880955e-01,
+    8.69374690e-01, 9.39818306e-01, 6.38804490e-01, 0.0,
+    1.00000000e+00, 9.72569112e-01, 8.24907516e-01, 8.07933016e-01,
+    8.91271059e-01, 9.19154851e-01, 1.00000000e+00, 7.27700415e-01,
+    1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 7.63596268e-01,
+    8.40131263e-01, 7.40428532e-01, 1.00000000e+00, 5.38849774e-01,
+    1.00000000e+00, 7.30155528e-01, 1.00000000e+00, 9.72569112e-01,
+    7.63596268e-01, 0.00000000e+00, 1.00000000e+00, 7.95485011e-01,
+    6.64669302e-01, 1.00000000e+00, 1.00000000e+00, 8.89451011e-01,
+    9.52559809e-01, 8.24907516e-01, 8.40131263e-01, 1.00000000e+00,
+    0.00000000e+00, 8.51370877e-01, 8.59439512e-01, 8.98332369e-01,
+    1.00000000e+00, 8.05419635e-01, 9.53789212e-01, 8.07933016e-01,
+    7.40428532e-01, 7.95485011e-01, 8.51370877e-01, 1.49011612e-08},
+   // Dataset is L1 normalized into pdfs
+   cuvs::distance::DistanceType::HellingerExpanded,
+   0.0},
+
+  {4,
+   {0, 1, 1, 2, 4},
+   {3, 2, 0, 1},  // indices
+   {0.99296, 0.42180, 0.11687, 0.305869},
+   {
+     // dense output
+     0.0,
+     0.99296,
+     1.41476,
+     1.415707,
+     0.99296,
+     0.0,
+     0.42180,
+     0.42274,
+     1.41476,
+     0.42180,
+     0.0,
+     0.84454,
+     1.41570,
+     0.42274,
+     0.84454,
+     0.0,
+   },
+   cuvs::distance::DistanceType::L1,
+   0.0},
+  {5,
+   {0, 3, 8, 12, 16, 20, 25, 30, 35, 40, 45},
+   {0, 3, 4, 0, 1, 2, 3, 4, 1, 2, 3, 4, 0, 2, 3, 4, 0, 1, 3, 4, 0, 1, 2,
+    3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4},
+   {0.70862347, 0.8232774,  0.12108795, 0.84527547, 0.94937088, 0.03258545, 0.99584118, 0.76835667,
+    0.34426657, 0.2357925,  0.01274851, 0.11422017, 0.3437756,  0.31967718, 0.5956055,  0.31610373,
+    0.04147273, 0.03724415, 0.21515727, 0.04751052, 0.50283183, 0.99957274, 0.01395933, 0.96032529,
+    0.88438711, 0.46095378, 0.27432481, 0.54294211, 0.54280225, 0.59503329, 0.61364678, 0.22837736,
+    0.56609561, 0.29809423, 0.76736686, 0.56460608, 0.98165371, 0.02140123, 0.19881268, 0.26057815,
+    0.31648823, 0.89874295, 0.27366735, 0.5119944,  0.11416134},
+   {// dense output
+    0.,         0.48769777, 1.88014197, 0.26127048, 0.26657011, 0.7874794,  0.76962708, 1.122858,
+    1.1232498,  1.08166081, 0.48769777, 0.,         1.31332116, 0.98318907, 0.42661815, 0.09279052,
+    1.35187836, 1.38429055, 0.40658897, 0.56136388, 1.88014197, 1.31332116, 0.,         1.82943642,
+    1.54826077, 1.05918884, 1.59360067, 1.34698954, 0.60215168, 0.46993848, 0.26127048, 0.98318907,
+    1.82943642, 0.,         0.29945563, 1.08494093, 0.22934281, 0.82801925, 1.74288748, 1.50610116,
+    0.26657011, 0.42661815, 1.54826077, 0.29945563, 0.,         0.45060069, 0.77814948, 1.45245711,
+    1.18328348, 0.82486987, 0.7874794,  0.09279052, 1.05918884, 1.08494093, 0.45060069, 0.,
+    1.29899154, 1.40683824, 0.48505269, 0.53862363, 0.76962708, 1.35187836, 1.59360067, 0.22934281,
+    0.77814948, 1.29899154, 0.,         0.33202426, 1.92108999, 1.88812175, 1.122858,   1.38429055,
+    1.34698954, 0.82801925, 1.45245711, 1.40683824, 0.33202426, 0.,         1.47318624, 1.92660889,
+    1.1232498,  0.40658897, 0.60215168, 1.74288748, 1.18328348, 0.48505269, 1.92108999, 1.47318624,
+    0.,         0.24992619, 1.08166081, 0.56136388, 0.46993848, 1.50610116, 0.82486987, 0.53862363,
+    1.88812175, 1.92660889, 0.24992619, 0.},
+   cuvs::distance::DistanceType::CorrelationExpanded,
+   0.0},
+  {5,
+   {0, 1, 2, 4, 4, 5, 6, 7, 9, 9, 10},
+   {1, 4, 0, 4, 1, 3, 0, 1, 3, 0},
+   {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.},
+   {// dense output
+    0.,  1.,  1.,  1., 0.8, 1., 1.,  0.8, 1., 1.,  1.,  0., 0.8, 1., 1.,  1.,  1.,  1.,  1., 1.,
+    1.,  0.8, 0.,  1., 1.,  1., 0.8, 1.,  1., 0.8, 1.,  1., 1.,  0., 1.,  1.,  1.,  1.,  1., 1.,
+    0.8, 1.,  1.,  1., 0.,  1., 1.,  0.8, 1., 1.,  1.,  1., 1.,  1., 1.,  0.,  1.,  0.8, 1., 1.,
+    1.,  1.,  0.8, 1., 1.,  1., 0.,  1.,  1., 0.8, 0.8, 1., 1.,  1., 0.8, 0.8, 1.,  0.,  1., 1.,
+    1.,  1.,  1.,  1., 1.,  1., 1.,  1.,  0., 1.,  1.,  1., 0.8, 1., 1.,  1.,  0.8, 1.,  1., 0.},
+   cuvs::distance::DistanceType::RusselRaoExpanded,
+   0.0},
+  {5,
+   {0, 1, 1, 3, 3, 4, 4, 6, 9, 10, 10},
+   {0, 3, 4, 4, 2, 3, 0, 2, 3, 2},
+   {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.},
+   {// dense output
+    0.,  0.2, 0.6, 0.2, 0.4, 0.2, 0.6, 0.4, 0.4, 0.2, 0.2, 0.,  0.4, 0.,  0.2, 0.,  0.4,
+    0.6, 0.2, 0.,  0.6, 0.4, 0.,  0.4, 0.2, 0.4, 0.4, 0.6, 0.6, 0.4, 0.2, 0.,  0.4, 0.,
+    0.2, 0.,  0.4, 0.6, 0.2, 0.,  0.4, 0.2, 0.2, 0.2, 0.,  0.2, 0.6, 0.8, 0.4, 0.2, 0.2,
+    0.,  0.4, 0.,  0.2, 0.,  0.4, 0.6, 0.2, 0.,  0.6, 0.4, 0.4, 0.4, 0.6, 0.4, 0.,  0.2,
+    0.2, 0.4, 0.4, 0.6, 0.6, 0.6, 0.8, 0.6, 0.2, 0.,  0.4, 0.6, 0.4, 0.2, 0.6, 0.2, 0.4,
+    0.2, 0.2, 0.4, 0.,  0.2, 0.2, 0.,  0.4, 0.,  0.2, 0.,  0.4, 0.6, 0.2, 0.},
+   cuvs::distance::DistanceType::HammingUnexpanded,
+   0.0},
+  {3,
+   {0, 1, 2},
+   {0, 1},
+   {1.0, 1.0},
+   {0.0, 0.83255, 0.83255, 0.0},
+   cuvs::distance::DistanceType::JensenShannon,
+   0.0},
+  {2,
+   {0, 1, 3},
+   {0, 0, 1},
+   {1.0, 0.5, 0.5},
+   {0, 0.4645014, 0.4645014, 0},
+   cuvs::distance::DistanceType::JensenShannon,
+   0.0},
+  {3,
+   {0, 1, 2},
+   {0, 0},
+   {1.0, 1.0},
+   {0.0, 0.0, 0.0, 0.0},
+   cuvs::distance::DistanceType::JensenShannon,
+   0.0},
+
+  {3,
+   {0, 1, 2},
+   {0, 1},
+   {1.0, 1.0},
+   {0.0, 1.0, 1.0, 0.0},
+   cuvs::distance::DistanceType::DiceExpanded,
+   0.0},
+  {3,
+   {0, 1, 3},
+   {0, 0, 1},
+   {1.0, 1.0, 1.0},
+   {0, 0.333333, 0.333333, 0},
+   cuvs::distance::DistanceType::DiceExpanded,
+   0.0},
+
+};
+
+typedef SparseDistanceTest<int, float> SparseDistanceTestF;
+TEST_P(SparseDistanceTestF, Result) { compare(); }
+INSTANTIATE_TEST_CASE_P(SparseDistanceTests,
+                        SparseDistanceTestF,
+                        ::testing::ValuesIn(inputs_i32_f));
+
+}  // end namespace distance
+}  // end namespace cuvs
diff --git a/cpp/test/neighbors/sparse_brute_force.cu b/cpp/test/neighbors/sparse_brute_force.cu
new file mode 100644
index 0000000000..cb68989d4f
--- /dev/null
+++ b/cpp/test/neighbors/sparse_brute_force.cu
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <cusparse_v2.h>
+#include <gtest/gtest.h>
+
+namespace cuvs {
+namespace neighbors {
+
+using namespace raft;
+using namespace raft::sparse;
+
+template <typename value_idx, typename value_t>
+struct SparseKNNInputs {
+  value_idx n_cols;
+
+  std::vector<value_idx> indptr_h;
+  std::vector<value_idx> indices_h;
+  std::vector<value_t> data_h;
+
+  std::vector<value_t> out_dists_ref_h;
+  std::vector<value_idx> out_indices_ref_h;
+
+  int k;
+
+  int batch_size_index = 2;
+  int batch_size_query = 2;
+
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+};
+
+template <typename value_idx, typename value_t>
+::std::ostream& operator<<(::std::ostream& os, const SparseKNNInputs<value_idx, value_t>& dims)
+{
+  return os;
+}
+
+template <typename value_idx, typename value_t>
+class SparseKNNTest : public ::testing::TestWithParam<SparseKNNInputs<value_idx, value_t>> {
+ public:
+  SparseKNNTest()
+    : params(::testing::TestWithParam<SparseKNNInputs<value_idx, value_t>>::GetParam()),
+      indptr(0, resource::get_cuda_stream(handle)),
+      indices(0, resource::get_cuda_stream(handle)),
+      data(0, resource::get_cuda_stream(handle)),
+      out_indices(0, resource::get_cuda_stream(handle)),
+      out_dists(0, resource::get_cuda_stream(handle)),
+      out_indices_ref(0, resource::get_cuda_stream(handle)),
+      out_dists_ref(0, resource::get_cuda_stream(handle))
+  {
+  }
+
+ protected:
+  void SetUp() override
+  {
+    n_rows = params.indptr_h.size() - 1;
+    nnz    = params.indices_h.size();
+    k      = params.k;
+
+    make_data();
+
+    auto index_structure =
+      raft::make_device_compressed_structure_view<value_idx, value_idx, value_idx>(
+        indptr.data(), indices.data(), n_rows, params.n_cols, nnz);
+    auto index_csr = raft::make_device_csr_matrix_view<const value_t>(data.data(), index_structure);
+
+    auto index = cuvs::neighbors::brute_force::build(handle, index_csr, params.metric);
+
+    cuvs::neighbors::brute_force::sparse_search_params search_params;
+    search_params.batch_size_index = params.batch_size_index;
+    search_params.batch_size_query = params.batch_size_query;
+
+    cuvs::neighbors::brute_force::search(
+      handle,
+      search_params,
+      index,
+      index_csr,
+      raft::make_device_matrix_view<value_idx, int64_t>(out_indices.data(), n_rows, k),
+      raft::make_device_matrix_view<value_t, int64_t>(out_dists.data(), n_rows, k));
+
+    RAFT_CUDA_TRY(cudaStreamSynchronize(resource::get_cuda_stream(handle)));
+  }
+
+  void compare()
+  {
+    ASSERT_TRUE(devArrMatch(
+      out_dists_ref.data(), out_dists.data(), n_rows * k, CompareApprox<value_t>(1e-4)));
+    ASSERT_TRUE(
+      devArrMatch(out_indices_ref.data(), out_indices.data(), n_rows * k, Compare<value_idx>()));
+  }
+
+ protected:
+  void make_data()
+  {
+    std::vector<value_idx> indptr_h  = params.indptr_h;
+    std::vector<value_idx> indices_h = params.indices_h;
+    std::vector<value_t> data_h      = params.data_h;
+
+    auto stream = resource::get_cuda_stream(handle);
+    indptr.resize(indptr_h.size(), stream);
+    indices.resize(indices_h.size(), stream);
+    data.resize(data_h.size(), stream);
+
+    update_device(indptr.data(), indptr_h.data(), indptr_h.size(), stream);
+    update_device(indices.data(), indices_h.data(), indices_h.size(), stream);
+    update_device(data.data(), data_h.data(), data_h.size(), stream);
+
+    std::vector<value_t> out_dists_ref_h     = params.out_dists_ref_h;
+    std::vector<value_idx> out_indices_ref_h = params.out_indices_ref_h;
+
+    out_indices_ref.resize(out_indices_ref_h.size(), stream);
+    out_dists_ref.resize(out_dists_ref_h.size(), stream);
+
+    update_device(
+      out_indices_ref.data(), out_indices_ref_h.data(), out_indices_ref_h.size(), stream);
+    update_device(out_dists_ref.data(), out_dists_ref_h.data(), out_dists_ref_h.size(), stream);
+
+    out_dists.resize(n_rows * k, stream);
+    out_indices.resize(n_rows * k, stream);
+  }
+
+  raft::resources handle;
+
+  int n_rows, nnz, k;
+
+  // input data
+  rmm::device_uvector<value_idx> indptr, indices;
+  rmm::device_uvector<value_t> data;
+
+  // output data
+  rmm::device_uvector<value_idx> out_indices;
+  rmm::device_uvector<value_t> out_dists;
+
+  rmm::device_uvector<value_idx> out_indices_ref;
+  rmm::device_uvector<value_t> out_dists_ref;
+
+  SparseKNNInputs<value_idx, value_t> params;
+};
+
+const std::vector<SparseKNNInputs<int, float>> inputs_i32_f = {
+  {9,                                                 // ncols
+   {0, 2, 4, 6, 8},                                   // indptr
+   {0, 4, 0, 3, 0, 2, 0, 8},                          // indices
+   {0.0f, 1.0f, 5.0f, 6.0f, 5.0f, 6.0f, 0.0f, 1.0f},  // data
+   {0, 1.41421, 0, 7.87401, 0, 7.87401, 0, 1.41421},  // dists
+   {0, 3, 1, 0, 2, 0, 3, 0},                          // inds
+   2,
+   2,
+   2,
+   cuvs::distance::DistanceType::L2SqrtExpanded}};
+typedef SparseKNNTest<int, float> SparseKNNTestF;
+TEST_P(SparseKNNTestF, Result) { compare(); }
+INSTANTIATE_TEST_CASE_P(SparseKNNTest, SparseKNNTestF, ::testing::ValuesIn(inputs_i32_f));
+
+};  // end namespace neighbors
+};  // end namespace cuvs

From 710e9f5a541c518deffb91f75a87cd4fe1372a8a Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Fri, 22 Nov 2024 09:25:27 -0500
Subject: [PATCH 212/623] Add `kIsSingleSource` to
 `PairwiseDistanceEpilogueElementwise` (#485)

With raft having recently migrated to cutlass 3.5.1, this field is now required.

Also remove `raft_cutlass` from symbol exclusions.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Vyas Ramasubramani (https://github.com/vyasr)
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/485
---
 .github/workflows/pr.yaml                                       | 2 +-
 .github/workflows/test.yaml                                     | 2 +-
 .../distance/detail/pairwise_distance_epilogue_elementwise.h    | 1 +
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index e18e82df08..78648235f9 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -88,7 +88,7 @@ jobs:
     with:
       build_type: pull-request
       enable_check_symbols: true
-      symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
+      symbol_exclusions: (void (thrust::|cub::))
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 5f60c0a348..27dc99a110 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -23,7 +23,7 @@ jobs:
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
       enable_check_symbols: true
-      symbol_exclusions: (void (thrust::|cub::)|raft_cutlass)
+      symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.12
diff --git a/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h b/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
index f9955334de..f4a7feabab 100644
--- a/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
+++ b/cpp/src/distance/detail/pairwise_distance_epilogue_elementwise.h
@@ -61,6 +61,7 @@ class PairwiseDistanceEpilogueElementwise {
   using ElementT                      = ElementT_;
   static int const kElementsPerAccess = ElementsPerAccess;
   static int const kCount             = kElementsPerAccess;
+  static bool const kIsSingleSource   = true;
 
   using DistanceOp = DistanceOp_;
   using FinalOp    = FinalOp_;

From 96d98b12df0030bc21c8588e8905df9cdc00784e Mon Sep 17 00:00:00 2001
From: Azurethi <Github@Azurethi.me>
Date: Sat, 23 Nov 2024 11:02:30 -0500
Subject: [PATCH 213/623] Fix broken link in README.md references (#473)

Fixed the broken link for "Top-K Algorithms on GPU: A Comprehensive Study and New Methods"

Authors:
  - https://github.com/Azurethi

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/473
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 572e8d0982..23759f5981 100755
--- a/README.md
+++ b/README.md
@@ -242,7 +242,7 @@ If you are interested in contributing to the cuVS library, please read our [Cont
 
 For the interested reader, many of the accelerated implementations in cuVS are also based on research papers which can provide a lot more background. We also ask you to please cite the corresponding algorithms by referencing them in your own research. 
 - [CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search](https://arxiv.org/abs/2308.15136)
-- [Top-K Algorithms on GPU: A Comprehensive Study and New Methods](https://dl.acm.org/doi/10.1145/3581784.3607062>)
+- [Top-K Algorithms on GPU: A Comprehensive Study and New Methods](https://dl.acm.org/doi/10.1145/3581784.3607062)
 - [Fast K-NN Graph Construction by GPU Based NN-Descent](https://dl.acm.org/doi/abs/10.1145/3459637.3482344?casa_token=O_nan1B1F5cAAAAA:QHWDEhh0wmd6UUTLY9_Gv6c3XI-5DXM9mXVaUXOYeStlpxTPmV3nKvABRfoivZAaQ3n8FWyrkWw>)
 - [cuSLINK: Single-linkage Agglomerative Clustering on the GPU](https://arxiv.org/abs/2306.16354)
 - [GPU Semiring Primitives for Sparse Neighborhood Methods](https://arxiv.org/abs/2104.06357)

From e1359e1a36ee48d2474a03a3b05c67b6610b220c Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Mon, 25 Nov 2024 21:09:26 +0100
Subject: [PATCH 214/623] Add serialization API to brute-force (#461)

I noticed it was missing while switching Milvus to cuVS

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/461
---
 .gitignore                                    |   1 +
 cpp/CMakeLists.txt                            |   1 +
 cpp/include/cuvs/neighbors/brute_force.h      |  60 +++++
 cpp/include/cuvs/neighbors/brute_force.hpp    | 243 ++++++++++++++++++
 cpp/src/neighbors/brute_force.cu              |  15 ++
 cpp/src/neighbors/brute_force_c.cpp           |  55 +++-
 cpp/src/neighbors/brute_force_serialize.cu    | 169 ++++++++++++
 cpp/test/neighbors/ann_brute_force.cuh        |  18 +-
 docs/source/c_api/neighbors_bruteforce_c.rst  |   8 +
 docs/source/c_api/neighbors_hnsw_c.rst        |   4 +-
 docs/source/c_api/neighbors_ivf_flat_c.rst    |   8 +
 docs/source/c_api/neighbors_ivf_pq_c.rst      |   8 +
 docs/source/cpp_api/neighbors_bruteforce.rst  |   8 +
 .../python_api/neighbors_brute_force.rst      |  10 +
 docs/source/python_api/neighbors_cagra.rst    |  10 +
 docs/source/python_api/neighbors_hnsw.rst     |  10 +
 docs/source/python_api/neighbors_ivf_flat.rst |  10 +
 docs/source/python_api/neighbors_ivf_pq.rst   |  10 +
 .../cuvs/neighbors/brute_force/__init__.py    |   4 +-
 .../neighbors/brute_force/brute_force.pxd     |   8 +
 .../neighbors/brute_force/brute_force.pyx     |  86 +++++++
 python/cuvs/cuvs/test/test_serialization.py   |  38 ++-
 22 files changed, 767 insertions(+), 17 deletions(-)
 create mode 100644 cpp/src/neighbors/brute_force_serialize.cu

diff --git a/.gitignore b/.gitignore
index 97eab287de..da6eb07f64 100644
--- a/.gitignore
+++ b/.gitignore
@@ -75,6 +75,7 @@ compile_commands.json
 .clangd/
 
 # serialized ann indexes
+brute_force_index
 cagra_index
 ivf_flat_index
 ivf_pq_index
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 32093776c3..eb2e7c7a4c 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -371,6 +371,7 @@ if(BUILD_SHARED_LIBS)
     src/distance/pairwise_distance.cu
     src/distance/sparse_distance.cu
     src/neighbors/brute_force.cu
+    src/neighbors/brute_force_serialize.cu
     src/neighbors/cagra_build_float.cu
     src/neighbors/cagra_build_half.cu
     src/neighbors/cagra_build_int8.cu
diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
index c9e172f629..33b92f11b9 100644
--- a/cpp/include/cuvs/neighbors/brute_force.h
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -166,6 +166,66 @@ cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
  * @}
  */
 
+/**
+ * @defgroup bruteforce_c_serialize BRUTEFORCE C-API serialize functions
+ * @{
+ */
+/**
+ * Save the index to file.
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.c}
+ * #include <cuvs/neighbors/brute_force.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create an index with `cuvsBruteforceBuild`
+ * cuvsBruteForceSerialize(res, "/path/to/index", index);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index BRUTEFORCE index
+ *
+ */
+cuvsError_t cuvsBruteForceSerialize(cuvsResources_t res,
+                                    const char* filename,
+                                    cuvsBruteForceIndex_t index);
+
+/**
+ * Load index from file.
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.c}
+ * #include <cuvs/neighbors/brute_force.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Deserialize an index previously built with `cuvsBruteforceBuild`
+ * cuvsBruteForceIndex_t index;
+ * cuvsBruteForceIndexCreate(&index);
+ * cuvsBruteForceDeserialize(res, "/path/to/index", index);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index BRUTEFORCE index loaded disk
+ */
+cuvsError_t cuvsBruteForceDeserialize(cuvsResources_t res,
+                                      const char* filename,
+                                      cuvsBruteForceIndex_t index);
+
+/**
+ * @}
+ */
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index ba67797ee3..d040e03dbd 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -48,6 +48,14 @@ struct index : cuvs::neighbors::index {
   index& operator=(index&&)      = default;
   ~index()                       = default;
 
+  /**
+   * @brief Construct an empty index.
+   *
+   * Constructs an empty index. This index will either need to be trained with `build`
+   * or loaded from a saved copy with `deserialize`
+   */
+  index(raft::resources const& handle);
+
   /** Construct a brute force index from dataset
    *
    * Constructs a brute force index from a dataset. This lets us precompute norms for
@@ -479,4 +487,239 @@ void search(raft::resources const& handle,
 /**
  * @}
  */
+
+/**
+ * @defgroup bruteforce_cpp_index_serialize Bruteforce index serialize functions
+ * @{
+ */
+/**
+ * Save the index to file.
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = brute_force::build(...);`
+ * cuvs::neighbors::brute_force::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index brute force index
+ * @param[in] include_dataset whether to include the dataset in the serialized
+ * output
+ */
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::brute_force::index<half, float>& index,
+               bool include_dataset = true);
+/**
+ * Save the index to file.
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = brute_force::build(...);`
+ * cuvs::neighbors::brute_force::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index brute force index
+ * @param[in] include_dataset whether to include the dataset in the serialized
+ * output
+ *
+ */
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::brute_force::index<float, float>& index,
+               bool include_dataset = true);
+
+/**
+ * Write the index to an output stream
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = cuvs::neighbors::brute_force::build(...);`
+ * cuvs::neighbors::brute_force::serialize(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index brute force index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const cuvs::neighbors::brute_force::index<half, float>& index,
+               bool include_dataset = true);
+
+/**
+ * Write the index to an output stream
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = cuvs::neighbors::brute_force::build(...);`
+ * cuvs::neighbors::brute_force::serialize(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index brute force index
+ * @param[in] include_dataset Whether or not to write out the dataset to the file.
+ */
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const cuvs::neighbors::brute_force::index<float, float>& index,
+               bool include_dataset = true);
+
+/**
+ * Load index from file.
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using T    = half; // data element type
+ * brute_force::index<T, float> index(handle);
+ * cuvs::neighbors::brute_force::deserialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index brute force index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::brute_force::index<half, float>* index);
+/**
+ * Load index from file.
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using T    = float; // data element type
+ * brute_force::index<T, float> index(handle);
+ * cuvs::neighbors::brute_force::deserialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[out] index brute force index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::brute_force::index<float, float>* index);
+/**
+ * Load index from input stream
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * using T    = half; // data element type
+ * brute_force::index<T, float> index(handle);
+ * cuvs::neighbors::brute_force::deserialize(handle, is, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ * @param[out] index brute force index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 std::istream& is,
+                 cuvs::neighbors::brute_force::index<half, float>* index);
+/**
+ * Load index from input stream
+ * The serialization format can be subject to changes, therefore loading
+ * an index saved with a previous version of cuvs is not guaranteed
+ * to work.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/brute_force.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * using T    = float; // data element type
+ * brute_force::index<T, float> index(handle);
+ * cuvs::neighbors::brute_force::deserialize(handle, is, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ * @param[out] index brute force index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 std::istream& is,
+                 cuvs::neighbors::brute_force::index<float, float>* index);
+/**
+ * @}
+ */
+
 }  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index b0f87e9acd..d534676e39 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -21,6 +21,21 @@
 #include <raft/core/copy.hpp>
 
 namespace cuvs::neighbors::brute_force {
+
+template <typename T, typename DistT>
+index<T, DistT>::index(raft::resources const& res)
+  // this constructor is just for a temporary index, for use in the deserialization
+  // api. all the parameters here will get replaced with loaded values - that aren't
+  // necessarily known ahead of time before deserialization.
+  // TODO: do we even need a handle here - could just construct one?
+  : cuvs::neighbors::index(),
+    metric_(cuvs::distance::DistanceType::L2Expanded),
+    dataset_(raft::make_device_matrix<T, int64_t>(res, 0, 0)),
+    norms_(std::nullopt),
+    metric_arg_(0)
+{
+}
+
 template <typename T, typename DistT>
 index<T, DistT>::index(raft::resources const& res,
                        raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index eda79aa319..f1a8c995d2 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -17,10 +17,12 @@
 
 #include <cstdint>
 #include <dlpack/dlpack.h>
+#include <fstream>
 
 #include <raft/core/error.hpp>
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/core/exceptions.hpp>
@@ -91,6 +93,22 @@ void _search(cuvsResources_t res,
   }
 }
 
+template <typename T>
+void _serialize(cuvsResources_t res, const char* filename, cuvsBruteForceIndex index)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T>*>(index.addr);
+  cuvs::neighbors::brute_force::serialize(*res_ptr, std::string(filename), *index_ptr);
+}
+
+template <typename T>
+void* _deserialize(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto index   = new cuvs::neighbors::brute_force::index<T>(*res_ptr);
+  cuvs::neighbors::brute_force::deserialize(*res_ptr, std::string(filename), index);
+  return index;
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsBruteForceIndexCreate(cuvsBruteForceIndex_t* index)
@@ -129,7 +147,7 @@ extern "C" cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<float>(res, dataset_tensor, metric, metric_arg));
-      index->dtype.code = kDLFloat;
+      index->dtype = dataset.dtype;
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
@@ -174,3 +192,38 @@ extern "C" cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
     }
   });
 }
+
+extern "C" cuvsError_t cuvsBruteForceDeserialize(cuvsResources_t res,
+                                                 const char* filename,
+                                                 cuvsBruteForceIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // read the numpy dtype from the beginning of the file
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename));
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsBruteForceSerialize(cuvsResources_t res,
+                                               const char* filename,
+                                               cuvsBruteForceIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _serialize<float>(res, filename, *index);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
\ No newline at end of file
diff --git a/cpp/src/neighbors/brute_force_serialize.cu b/cpp/src/neighbors/brute_force_serialize.cu
new file mode 100644
index 0000000000..1b5b5111e9
--- /dev/null
+++ b/cpp/src/neighbors/brute_force_serialize.cu
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/core/copy.cuh>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <fstream>
+
+namespace cuvs::neighbors::brute_force {
+
+int constexpr serialization_version = 0;
+
+template <typename T, typename DistT>
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const index<T, DistT>& index,
+               bool include_dataset = true)
+{
+  RAFT_LOG_DEBUG(
+    "Saving brute force index, size %zu, dim %u", static_cast<size_t>(index.size()), index.dim());
+
+  auto dtype_string = raft::detail::numpy_serializer::get_numpy_dtype<T>().to_string();
+  dtype_string.resize(4);
+  os << dtype_string;
+
+  raft::serialize_scalar(handle, os, serialization_version);
+  raft::serialize_scalar(handle, os, index.size());
+  raft::serialize_scalar(handle, os, index.dim());
+  raft::serialize_scalar(handle, os, index.metric());
+  raft::serialize_scalar(handle, os, index.metric_arg());
+  raft::serialize_scalar(handle, os, include_dataset);
+  if (include_dataset) { raft::serialize_mdspan(handle, os, index.dataset()); }
+  auto has_norms = index.has_norms();
+  raft::serialize_scalar(handle, os, has_norms);
+  if (has_norms) { raft::serialize_mdspan(handle, os, index.norms()); }
+  raft::resource::sync_stream(handle);
+}
+
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const index<half, float>& index,
+               bool include_dataset)
+{
+  auto os = std::ofstream{filename, std::ios::out | std::ios::binary};
+  RAFT_EXPECTS(os, "Cannot open file %s", filename.c_str());
+  serialize<half, float>(handle, os, index, include_dataset);
+}
+
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const index<float, float>& index,
+               bool include_dataset)
+{
+  auto os = std::ofstream{filename, std::ios::out | std::ios::binary};
+  RAFT_EXPECTS(os, "Cannot open file %s", filename.c_str());
+  serialize<float, float>(handle, os, index, include_dataset);
+}
+
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const index<half, float>& index,
+               bool include_dataset)
+{
+  serialize<half, float>(handle, os, index, include_dataset);
+}
+
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const index<float, float>& index,
+               bool include_dataset)
+{
+  serialize<float, float>(handle, os, index, include_dataset);
+}
+
+template <typename T, typename DistT>
+auto deserialize(raft::resources const& handle, std::istream& is)
+{
+  auto dtype_string = std::array<char, 4>{};
+  is.read(dtype_string.data(), 4);
+
+  auto ver = raft::deserialize_scalar<int>(handle, is);
+  if (ver != serialization_version) {
+    RAFT_FAIL("serialization version mismatch, expected %d, got %d ", serialization_version, ver);
+  }
+  std::int64_t rows = raft::deserialize_scalar<size_t>(handle, is);
+  std::int64_t dim  = raft::deserialize_scalar<size_t>(handle, is);
+  auto metric       = raft::deserialize_scalar<cuvs::distance::DistanceType>(handle, is);
+  auto metric_arg   = raft::deserialize_scalar<DistT>(handle, is);
+
+  auto dataset_storage = raft::make_host_matrix<T>(std::int64_t{}, std::int64_t{});
+  auto include_dataset = raft::deserialize_scalar<bool>(handle, is);
+  if (include_dataset) {
+    dataset_storage = raft::make_host_matrix<T>(rows, dim);
+    raft::deserialize_mdspan(handle, is, dataset_storage.view());
+  }
+
+  auto has_norms     = raft::deserialize_scalar<bool>(handle, is);
+  auto norms_storage = has_norms ? std::optional{raft::make_host_vector<DistT, std::int64_t>(rows)}
+                                 : std::optional<raft::host_vector<DistT, std::int64_t>>{};
+  // TODO(wphicks): Use mdbuffer here when available
+  auto norms_storage_dev =
+    has_norms ? std::optional{raft::make_device_vector<DistT, std::int64_t>(handle, rows)}
+              : std::optional<raft::device_vector<DistT, std::int64_t>>{};
+  if (has_norms) {
+    raft::deserialize_mdspan(handle, is, norms_storage->view());
+    raft::copy(handle, norms_storage_dev->view(), norms_storage->view());
+  }
+
+  auto result = index<T, DistT>(handle,
+                                raft::make_const_mdspan(dataset_storage.view()),
+                                std::move(norms_storage_dev),
+                                metric,
+                                metric_arg);
+  raft::resource::sync_stream(handle);
+
+  return result;
+}
+
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::brute_force::index<half, float>* index)
+{
+  auto is = std::ifstream{filename, std::ios::in | std::ios::binary};
+  RAFT_EXPECTS(is, "Cannot open file %s", filename.c_str());
+
+  *index = deserialize<half, float>(handle, is);
+}
+
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::brute_force::index<float, float>* index)
+{
+  auto is = std::ifstream{filename, std::ios::in | std::ios::binary};
+  RAFT_EXPECTS(is, "Cannot open file %s", filename.c_str());
+
+  *index = deserialize<float, float>(handle, is);
+}
+
+void deserialize(raft::resources const& handle,
+                 std::istream& is,
+                 cuvs::neighbors::brute_force::index<half, float>* index)
+{
+  *index = deserialize<half, float>(handle, is);
+}
+
+void deserialize(raft::resources const& handle,
+                 std::istream& is,
+                 cuvs::neighbors::brute_force::index<float, float>* index)
+{
+  *index = deserialize<float, float>(handle, is);
+}
+
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/test/neighbors/ann_brute_force.cuh b/cpp/test/neighbors/ann_brute_force.cuh
index c2afa4e8b3..03d6e820cd 100644
--- a/cpp/test/neighbors/ann_brute_force.cuh
+++ b/cpp/test/neighbors/ann_brute_force.cuh
@@ -114,12 +114,28 @@ class AnnBruteForceTest : public ::testing::TestWithParam<AnnBruteForceInputs<Id
                                                       0.001f,
                                                       stream_,
                                                       true));
+
+      brute_force::serialize(handle_, std::string{"brute_force_index"}, idx, true);
+      auto index_loaded = brute_force::index<DataT, T>(handle_);
+      brute_force::deserialize(handle_, std::string{"brute_force_index"}, &index_loaded);
+
       brute_force::search(handle_,
-                          idx,
+                          index_loaded,
                           search_queries_view,
                           indices_out_view,
                           dists_out_view,
                           cuvs::neighbors::filtering::none_sample_filter{});
+      raft::resource::sync_stream(handle_);
+
+      ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(indices_naive_dev.data(),
+                                                      indices_bruteforce_dev.data(),
+                                                      distances_naive_dev.data(),
+                                                      distances_bruteforce_dev.data(),
+                                                      ps.num_queries,
+                                                      ps.k,
+                                                      0.001f,
+                                                      stream_,
+                                                      true));
     }
   }
 
diff --git a/docs/source/c_api/neighbors_bruteforce_c.rst b/docs/source/c_api/neighbors_bruteforce_c.rst
index af0356eee4..a121752096 100644
--- a/docs/source/c_api/neighbors_bruteforce_c.rst
+++ b/docs/source/c_api/neighbors_bruteforce_c.rst
@@ -32,3 +32,11 @@ Index search
     :project: cuvs
     :members:
     :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: bruteforce_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/c_api/neighbors_hnsw_c.rst b/docs/source/c_api/neighbors_hnsw_c.rst
index 4d83cd3e38..988e5b6f33 100644
--- a/docs/source/c_api/neighbors_hnsw_c.rst
+++ b/docs/source/c_api/neighbors_hnsw_c.rst
@@ -29,13 +29,13 @@ Index
 Index search
 ------------
 
-.. doxygengroup:: cagra_c_index_search
+.. doxygengroup:: hnsw_c_index_search
     :project: cuvs
     :members:
     :content-only:
 
 Index serialize
-------------
+---------------
 
 .. doxygengroup:: hnsw_c_index_serialize
     :project: cuvs
diff --git a/docs/source/c_api/neighbors_ivf_flat_c.rst b/docs/source/c_api/neighbors_ivf_flat_c.rst
index 9e1ccc0d1d..1254d70ef8 100644
--- a/docs/source/c_api/neighbors_ivf_flat_c.rst
+++ b/docs/source/c_api/neighbors_ivf_flat_c.rst
@@ -48,3 +48,11 @@ Index search
     :project: cuvs
     :members:
     :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: ivf_flat_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/c_api/neighbors_ivf_pq_c.rst b/docs/source/c_api/neighbors_ivf_pq_c.rst
index 0707196098..260057b8cf 100644
--- a/docs/source/c_api/neighbors_ivf_pq_c.rst
+++ b/docs/source/c_api/neighbors_ivf_pq_c.rst
@@ -48,3 +48,11 @@ Index search
     :project: cuvs
     :members:
     :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: ivf_pq_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cpp_api/neighbors_bruteforce.rst b/docs/source/cpp_api/neighbors_bruteforce.rst
index 3adcb01c52..f75e26b3ca 100644
--- a/docs/source/cpp_api/neighbors_bruteforce.rst
+++ b/docs/source/cpp_api/neighbors_bruteforce.rst
@@ -34,3 +34,11 @@ Index search
     :project: cuvs
     :members:
     :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: bruteforce_cpp_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/python_api/neighbors_brute_force.rst b/docs/source/python_api/neighbors_brute_force.rst
index 5fdc3658fe..d756a6c802 100644
--- a/docs/source/python_api/neighbors_brute_force.rst
+++ b/docs/source/python_api/neighbors_brute_force.rst
@@ -20,3 +20,13 @@ Index search
 ############
 
 .. autofunction:: cuvs.neighbors.brute_force.search
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.brute_force.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.brute_force.load
diff --git a/docs/source/python_api/neighbors_cagra.rst b/docs/source/python_api/neighbors_cagra.rst
index 09b2e2694c..e7155efb83 100644
--- a/docs/source/python_api/neighbors_cagra.rst
+++ b/docs/source/python_api/neighbors_cagra.rst
@@ -34,3 +34,13 @@ Index search
 ############
 
 .. autofunction:: cuvs.neighbors.cagra.search
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.cagra.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.cagra.load
diff --git a/docs/source/python_api/neighbors_hnsw.rst b/docs/source/python_api/neighbors_hnsw.rst
index 9922805b36..64fe5493ba 100644
--- a/docs/source/python_api/neighbors_hnsw.rst
+++ b/docs/source/python_api/neighbors_hnsw.rst
@@ -28,3 +28,13 @@ Index search
 ############
 
 .. autofunction:: cuvs.neighbors.hnsw.search
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.hnsw.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.hnsw.load
diff --git a/docs/source/python_api/neighbors_ivf_flat.rst b/docs/source/python_api/neighbors_ivf_flat.rst
index 5514e5e43f..f2c21e68a1 100644
--- a/docs/source/python_api/neighbors_ivf_flat.rst
+++ b/docs/source/python_api/neighbors_ivf_flat.rst
@@ -32,3 +32,13 @@ Index search
 ############
 
 .. autofunction:: cuvs.neighbors.ivf_flat.search
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.ivf_flat.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.ivf_flat.load
diff --git a/docs/source/python_api/neighbors_ivf_pq.rst b/docs/source/python_api/neighbors_ivf_pq.rst
index e3625ba676..57668fbc33 100644
--- a/docs/source/python_api/neighbors_ivf_pq.rst
+++ b/docs/source/python_api/neighbors_ivf_pq.rst
@@ -32,3 +32,13 @@ Index search
 ############
 
 .. autofunction:: cuvs.neighbors.ivf_pq.search
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.ivf_pq.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.ivf_pq.load
diff --git a/python/cuvs/cuvs/neighbors/brute_force/__init__.py b/python/cuvs/cuvs/neighbors/brute_force/__init__.py
index b88c4b4648..6aa0e4bb2b 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/__init__.py
+++ b/python/cuvs/cuvs/neighbors/brute_force/__init__.py
@@ -13,6 +13,6 @@
 # limitations under the License.
 
 
-from .brute_force import Index, build, search
+from .brute_force import Index, build, load, save, search
 
-__all__ = ["Index", "build", "search"]
+__all__ = ["Index", "build", "search", "save", "load"]
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
index 1838279169..f1fc14ba76 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pxd
@@ -47,3 +47,11 @@ cdef extern from "cuvs/neighbors/brute_force.h" nogil:
                                      DLManagedTensor* neighbors,
                                      DLManagedTensor* distances,
                                      cuvsFilter filter) except +
+
+    cuvsError_t cuvsBruteForceSerialize(cuvsResources_t res,
+                                        const char * filename,
+                                        cuvsBruteForceIndex_t index) except +
+
+    cuvsError_t cuvsBruteForceDeserialize(cuvsResources_t res,
+                                          const char * filename,
+                                          cuvsBruteForceIndex_t index) except +
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 9d1d24eaee..9d43bfb290 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -24,6 +24,7 @@ from cuvs.common.resources import auto_sync_resources
 from cython.operator cimport dereference as deref
 from libc.stdint cimport uint32_t
 from libcpp cimport bool
+from libcpp.string cimport string
 
 from cuvs.common cimport cydlpack
 from cuvs.distance_type cimport cuvsDistanceType
@@ -256,3 +257,88 @@ def search(Index index,
         ))
 
     return (distances, neighbors)
+
+
+@auto_sync_resources
+def save(filename, Index index, bool include_dataset=True, resources=None):
+    """
+    Saves the index to a file.
+
+    The serialization format can be subject to changes, therefore loading
+    an index saved with a previous version of cuvs is not guaranteed
+    to work.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    index : Index
+        Trained Brute Force index.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import brute_force
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = brute_force.build(dataset)
+    >>> # Serialize and deserialize the brute_force index built
+    >>> brute_force.save("my_index.bin", index)
+    >>> index_loaded = brute_force.load("my_index.bin")
+    """
+    cdef string c_filename = filename.encode('utf-8')
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    check_cuvs(cuvsBruteForceSerialize(res,
+                                       c_filename.c_str(),
+                                       index.index))
+
+
+@auto_sync_resources
+def load(filename, resources=None):
+    """
+    Loads index from file.
+
+    The serialization format can be subject to changes, therefore loading
+    an index saved with a previous version of cuvs is not guaranteed
+    to work.
+
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import brute_force
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = brute_force.build(dataset)
+    >>> # Serialize and deserialize the brute_force index built
+    >>> brute_force.save("my_index.bin", index)
+    >>> index_loaded = brute_force.load("my_index.bin")
+    """
+    cdef Index idx = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    cdef string c_filename = filename.encode('utf-8')
+
+    check_cuvs(cuvsBruteForceDeserialize(
+        res,
+        c_filename.c_str(),
+        idx.index
+    ))
+    idx.trained = True
+    return idx
diff --git a/python/cuvs/cuvs/test/test_serialization.py b/python/cuvs/cuvs/test/test_serialization.py
index 4ffccf1214..1f4a54e87a 100644
--- a/python/cuvs/cuvs/test/test_serialization.py
+++ b/python/cuvs/cuvs/test/test_serialization.py
@@ -17,7 +17,7 @@
 import pytest
 from pylibraft.common import device_ndarray
 
-from cuvs.neighbors import cagra, ivf_flat, ivf_pq
+from cuvs.neighbors import brute_force, cagra, ivf_flat, ivf_pq
 from cuvs.test.ann_utils import generate_data
 
 
@@ -35,6 +35,10 @@ def test_save_load_ivf_pq():
     run_save_load(ivf_pq, np.float32)
 
 
+def test_save_load_brute_force():
+    run_save_load(brute_force, np.float32)
+
+
 def run_save_load(ann_module, dtype):
     n_rows = 10000
     n_cols = 50
@@ -43,8 +47,11 @@ def run_save_load(ann_module, dtype):
     dataset = generate_data((n_rows, n_cols), dtype)
     dataset_device = device_ndarray(dataset)
 
-    build_params = ann_module.IndexParams()
-    index = ann_module.build(build_params, dataset_device)
+    if ann_module == brute_force:
+        index = ann_module.build(dataset_device)
+    else:
+        build_params = ann_module.IndexParams()
+        index = ann_module.build(build_params, dataset_device)
 
     assert index.trained
     filename = "my_index.bin"
@@ -54,20 +61,29 @@ def run_save_load(ann_module, dtype):
     queries = generate_data((n_queries, n_cols), dtype)
 
     queries_device = device_ndarray(queries)
-    search_params = ann_module.SearchParams()
     k = 10
-
-    distance_dev, neighbors_dev = ann_module.search(
-        search_params, index, queries_device, k
-    )
+    if ann_module == brute_force:
+        distance_dev, neighbors_dev = ann_module.search(
+            index, queries_device, k
+        )
+    else:
+        search_params = ann_module.SearchParams()
+        distance_dev, neighbors_dev = ann_module.search(
+            search_params, index, queries_device, k
+        )
 
     neighbors = neighbors_dev.copy_to_host()
     dist = distance_dev.copy_to_host()
     del index
 
-    distance_dev, neighbors_dev = ann_module.search(
-        search_params, loaded_index, queries_device, k
-    )
+    if ann_module == brute_force:
+        distance_dev, neighbors_dev = ann_module.search(
+            loaded_index, queries_device, k
+        )
+    else:
+        distance_dev, neighbors_dev = ann_module.search(
+            search_params, loaded_index, queries_device, k
+        )
 
     neighbors2 = neighbors_dev.copy_to_host()
     dist2 = distance_dev.copy_to_host()

From 5062594138a40231475299c7bac61083b0669fd1 Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Tue, 26 Nov 2024 10:50:41 +0900
Subject: [PATCH 215/623] [Doc] Fix CAGRA search sample code (#484)

`.view()` is required

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/484
---
 cpp/include/cuvs/neighbors/cagra.hpp | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index e480507564..5ceb3010e0 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -363,7 +363,7 @@ struct index : cuvs::neighbors::index {
    *   // search K nearest neighbours
    *   auto neighbors = raft::make_device_matrix<uint32_t, int64_t>(res, n_queries, k);
    *   auto distances = raft::make_device_matrix<float, int64_t>(res, n_queries, k);
-   *   cagra::search(res, search_params, index, queries, neighbors, distances);
+   *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
    * @endcode
    *   In the above example, we have passed a host dataset to build. The returned index will own a
    * device copy of the dataset and the knn_graph. In contrast, if we pass the dataset as a
@@ -530,7 +530,7 @@ struct index : cuvs::neighbors::index {
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res
@@ -567,7 +567,7 @@ auto build(raft::resources const& res,
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res
@@ -604,7 +604,7 @@ auto build(raft::resources const& res,
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res
@@ -640,7 +640,7 @@ auto build(raft::resources const& res,
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res
@@ -676,7 +676,7 @@ auto build(raft::resources const& res,
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res
@@ -713,7 +713,7 @@ auto build(raft::resources const& res,
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res
@@ -750,7 +750,7 @@ auto build(raft::resources const& res,
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res
@@ -787,7 +787,7 @@ auto build(raft::resources const& res,
  *   // search K nearest neighbours
  *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
  *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
- *   cagra::search(res, search_params, index, queries, neighbors, distances);
+ *   cagra::search(res, search_params, index, queries, neighbors.view(), distances.view());
  * @endcode
  *
  * @param[in] res

From aba3fa7bb38690d394082aff65531c5858836450 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 26 Nov 2024 08:55:39 -0600
Subject: [PATCH 216/623] Update example code fetching rapids-cmake to use CUVS
 instead of RAFT (#493)

Small update to CMake example code to use cuVS instead of RAFT.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/493
---
 examples/cmake/thirdparty/fetch_rapids.cmake | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
index 23c8490b40..3c5510b8b6 100644
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -11,11 +11,11 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-# Use this variable to update RAPIDS and RAFT versions
+# Use this variable to update RAPIDS and cuVS versions
 set(RAPIDS_VERSION "25.02")
 
-if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
+if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
-            ${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
+            ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
 endif()
-include(${CMAKE_CURRENT_BINARY_DIR}/RAFT_RAPIDS.cmake)
+include(${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)

From 6e5c0c8b0ce57b4b2069cbe5255619f210420792 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 26 Nov 2024 11:24:01 -0600
Subject: [PATCH 217/623] Remove RAFT BUILD_ANN_BENCH option (#497)

This cleans up a reference to RAFT's `BUILD_ANN_BENCH` CMake option which no longer exists.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/497
---
 cpp/cmake/thirdparty/get_raft.cmake | 1 -
 1 file changed, 1 deletion(-)

diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 7640fbfa6e..5def74f4b3 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -50,7 +50,6 @@ function(find_and_configure_raft)
               OPTIONS
               "BUILD_TESTS OFF"
               "BUILD_PRIMS_BENCH OFF"
-              "BUILD_ANN_BENCH OFF"
               "RAFT_NVTX ${PKG_ENABLE_NVTX}"
               "RAFT_COMPILE_LIBRARY OFF"
             )

From bd0620df0c143711352c9f5e312268aaa1801cbd Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Tue, 26 Nov 2024 15:28:58 -0600
Subject: [PATCH 218/623] Add breaking change workflow trigger (#442)

Adds a workflow that triggers a second workflow which sends a
notification to a designated Slack channel on every PR labelled with
breaking, whenever any of the following events are triggered on the PR:

- closed
- reopened
- labeled
- unlabeled

Depends on https://github.com/rapidsai/shared-workflows/pull/257
---
 .../trigger-breaking-change-alert.yaml        | 26 +++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 .github/workflows/trigger-breaking-change-alert.yaml

diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
new file mode 100644
index 0000000000..3b972f31ca
--- /dev/null
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -0,0 +1,26 @@
+name: Trigger Breaking Change Notifications
+
+on:
+  pull_request_target:
+    types:
+      - closed
+      - reopened
+      - labeled
+      - unlabeled
+
+jobs:
+  trigger-notifier:
+    if: contains(github.event.pull_request.labels.*.name, 'breaking')
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-24.12
+    with:
+      sender_login: ${{ github.event.sender.login }}
+      sender_avatar: ${{ github.event.sender.avatar_url }}
+      repo: ${{ github.repository }}
+      pr_number: ${{ github.event.pull_request.number }}
+      pr_title: "${{ github.event.pull_request.title }}"
+      pr_body: "${{ github.event.pull_request.body || '_Empty PR description_' }}"
+      pr_base_ref: ${{ github.event.pull_request.base.ref }}
+      pr_author: ${{ github.event.pull_request.user.login }}
+      event_action: ${{ github.event.action }}
+      pr_merged: ${{ github.event.pull_request.merged }}

From ae6816c9be622fe18f1d80f3b32b70ac9f5566fe Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 26 Nov 2024 16:32:11 -0600
Subject: [PATCH 219/623] Require approval to run CI on draft PRs (#498)

By default, CI runs on draft PRs. This leads to many CI runs that may be unnecessary.

With this PR's change to `.github/copy-pr-bot.yaml`, an `/ok to test` comment from a trusted user is required to trigger CI on draft PRs. Non-draft PRs will run CI by default, assuming that all commits are signed by trusted users. Otherwise an `/ok to test` is required (as before) -- see the `copy-pr-bot` docs at https://docs.gha-runners.nvidia.com/apps/copy-pr-bot/ for more information.

Part of https://github.com/rapidsai/build-planning/issues/123.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/498
---
 .github/copy-pr-bot.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/copy-pr-bot.yaml b/.github/copy-pr-bot.yaml
index 895ba83ee5..e0ea775aad 100644
--- a/.github/copy-pr-bot.yaml
+++ b/.github/copy-pr-bot.yaml
@@ -2,3 +2,4 @@
 # https://docs.gha-runners.nvidia.com/apps/copy-pr-bot/
 
 enabled: true
+auto_sync_draft: false

From 31c59ce0cae2505c89e8e4cdd8d77fd29256df4a Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Fri, 29 Nov 2024 16:42:44 -0800
Subject: [PATCH 220/623] Adapt to rmm logger changes (#499)

This PR adapts to breaking changes in rmm in https://github.com/rapidsai/rmm/pull/1722.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/499
---
 cpp/CMakeLists.txt                                  |  9 +++++----
 cpp/bench/ann/CMakeLists.txt                        |  8 +++++++-
 cpp/test/CMakeLists.txt                             |  4 ++++
 examples/cpp/CMakeLists.txt                         | 13 ++++++++-----
 python/cuvs/CMakeLists.txt                          |  3 +++
 python/cuvs/cuvs/common/CMakeLists.txt              |  4 ++++
 python/cuvs/cuvs/distance/CMakeLists.txt            |  4 ++++
 python/cuvs/cuvs/neighbors/CMakeLists.txt           |  4 ++++
 .../cuvs/cuvs/neighbors/brute_force/CMakeLists.txt  |  4 ++++
 python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt     |  4 ++++
 python/cuvs/cuvs/neighbors/filters/CMakeLists.txt   |  4 ++++
 python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt      |  4 ++++
 python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt  |  4 ++++
 python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt    |  4 ++++
 python/cuvs/cuvs/test/conftest.py                   |  5 +++++
 15 files changed, 68 insertions(+), 10 deletions(-)
 create mode 100644 python/cuvs/cuvs/test/conftest.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index eb2e7c7a4c..3c8ef69fd9 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -480,12 +480,13 @@ if(BUILD_SHARED_LIBS)
                       "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
   target_link_libraries(
-    cuvs_objs PUBLIC raft::raft rmm::rmm ${CUVS_CTK_MATH_DEPENDENCIES}
+    cuvs_objs PUBLIC raft::raft rmm::rmm rmm::rmm_logger ${CUVS_CTK_MATH_DEPENDENCIES}
                      $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+              PRIVATE rmm::rmm_logger_impl
   )
 
-  add_library(cuvs SHARED $<TARGET_OBJECTS:cuvs_objs>)
-  add_library(cuvs_static STATIC $<TARGET_OBJECTS:cuvs_objs>)
+  add_library(cuvs SHARED $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
+  add_library(cuvs_static STATIC  $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
 
   target_compile_options(
     cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
@@ -696,7 +697,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
     target_link_libraries(
       cuvs_c
       PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
-      PRIVATE raft::raft
+      PRIVATE raft::raft rmm::rmm_logger_impl
     )
 
     # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 0f6b42ae9a..572b792a73 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -129,6 +129,7 @@ function(ConfigureAnnBench)
             $<$<BOOL:${GPU_BUILD}>:CUDA::cudart_static>
             $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
             $<TARGET_NAME_IF_EXISTS:conda_env>
+            $<TARGET_NAME_IF_EXISTS:cuvs_bench_rmm_logger>
   )
 
   set_target_properties(
@@ -174,6 +175,11 @@ function(ConfigureAnnBench)
   add_dependencies(CUVS_ANN_BENCH_ALL ${BENCH_NAME})
 endfunction()
 
+if(CUVS_FAISS_ENABLE_GPU)
+  add_library(cuvs_bench_rmm_logger OBJECT)
+  target_link_libraries(cuvs_bench_rmm_logger PRIVATE rmm::rmm_logger_impl)
+endif()
+
 # ##################################################################################################
 # * Configure benchmark targets -------------------------------------------------------------
 
@@ -300,7 +306,7 @@ if(CUVS_ANN_BENCH_SINGLE_EXE)
   target_link_libraries(
     ANN_BENCH
     PRIVATE raft::raft nlohmann_json::nlohmann_json benchmark::benchmark dl fmt::fmt-header-only
-            spdlog::spdlog_header_only $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3>
+            spdlog::spdlog_header_only $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3> rmm::rmm_logger_impl
   )
   set_target_properties(
     ANN_BENCH
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 286d721d7f..16663ba08b 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -49,6 +49,7 @@ function(ConfigureTest)
     PRIVATE cuvs
             cuvs::cuvs
             raft::raft
+            test_rmm_logger
             GTest::gtest
             GTest::gtest_main
             Threads::Threads
@@ -87,6 +88,9 @@ function(ConfigureTest)
   )
 endfunction()
 
+add_library(test_rmm_logger OBJECT)
+target_link_libraries(test_rmm_logger PRIVATE rmm::rmm_logger_impl)
+
 # ##################################################################################################
 # test sources ##################################################################################
 # ##################################################################################################
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 092b65ed98..48815b8701 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -44,10 +44,13 @@ add_executable(VAMANA_EXAMPLE src/vamana_example.cu)
 
 # `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets are
 # installed in a conda environment, if one exists
-target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+add_library(rmm_logger OBJECT)
+target_link_libraries(rmm_logger PRIVATE rmm::rmm_logger_impl)
+
+target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
 target_link_libraries(
-  CAGRA_PERSISTENT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
+  CAGRA_PERSISTENT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads rmm_logger
 )
-target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
-target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
-target_link_libraries(VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
+target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
+target_link_libraries(VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index feb3bd58c4..c0990995f4 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -110,6 +110,9 @@ endif()
 
 rapids_cython_init()
 
+add_library(cuvs_rmm_logger OBJECT)
+target_link_libraries(cuvs_rmm_logger PRIVATE rmm::rmm_logger_impl)
+
 add_subdirectory(cuvs/common)
 add_subdirectory(cuvs/distance)
 add_subdirectory(cuvs/neighbors)
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index 202919e012..361f2fafcc 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -22,3 +22,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX common_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/distance/CMakeLists.txt b/python/cuvs/cuvs/distance/CMakeLists.txt
index 363778a9ca..514b08c436 100644
--- a/python/cuvs/cuvs/distance/CMakeLists.txt
+++ b/python/cuvs/cuvs/distance/CMakeLists.txt
@@ -22,3 +22,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX distance_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index f68bbea533..031fd485ea 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -29,3 +29,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_refine_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
index 4806fb9fc0..61eda649cd 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
@@ -23,3 +23,7 @@ rapids_cython_create_modules(
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
                    neighbors_brute_force_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
index 87e6597fe1..1f40daab2c 100644
--- a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
@@ -22,3 +22,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_cagra_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
index c90615feb4..a678852d99 100644
--- a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
@@ -22,3 +22,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_prefilter_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
index 1f9c422ca5..8351916e6c 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
@@ -22,3 +22,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_hnsw_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
index 09bd8f4227..f5663cdaa2 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
@@ -22,3 +22,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_ivf_flat_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
index 97c3a18249..a24320ded8 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
@@ -22,3 +22,7 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_pq_
 )
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/test/conftest.py b/python/cuvs/cuvs/test/conftest.py
new file mode 100644
index 0000000000..d84de5d211
--- /dev/null
+++ b/python/cuvs/cuvs/test/conftest.py
@@ -0,0 +1,5 @@
+# arm tests sporadically run into
+# https://bugzilla.redhat.com/show_bug.cgi?id=1722181.
+# This is a workaround to ensure that OpenMP gets the TLS that it needs.
+
+import sklearn  # noqa: F401

From 121588259b3f48381c6c154556e78e46b3119eb4 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Mon, 2 Dec 2024 14:18:23 -0600
Subject: [PATCH 221/623] prefer system install of UCX in devcontainers (#501)

Contributes to https://github.com/rapidsai/build-planning/issues/118

Proposes the following changes for pip devcontainers:

* prefer system installation of ucx to the one provided by the `libucx-cu{11,12}` wheels (ref: https://github.com/rapidsai/devcontainers/pull/421#issuecomment-2502324982)

And some other related changes noticed while doing that:

* update lingering `24.*` references to `25.02`

## Notes for Reviewers

### How I tested this

Relying on CI for most things. Double-checked that `update-version.sh` would have caught the one lingering `24.12` reference like this:

```shell
./ci/release/update-version.sh '25.02.00'
git grep -E '24\.'
```

Similar to https://github.com/rapidsai/cuml/pull/6149

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/501
---
 .devcontainer/Dockerfile                             | 1 +
 .github/workflows/trigger-breaking-change-alert.yaml | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 594ba8c3c4..77b90fa205 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -13,6 +13,7 @@ RUN apt update -y \
  && rm -rf /tmp/* /var/tmp/* /var/cache/apt/* /var/lib/apt/lists/*;
 
 ENV DEFAULT_VIRTUAL_ENV=rapids
+ENV RAPIDS_LIBUCX_PREFER_SYSTEM_LIBRARY=true
 
 FROM ${BASE} as conda-base
 
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 3b972f31ca..01dd2436be 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-24.12
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.02
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}

From 441d2f1bcceb8f653a0fdaec5658c54c5201155b Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Mon, 2 Dec 2024 17:34:08 -0500
Subject: [PATCH 222/623] HNSW CPU Hierarchy (#465)

This PR adds an option to build the full HNSW hierarchy on the CPU when converting a CAGRA index to an hnswlib index. This lets us enable an `extend()` API.

For hnswlib:
1. Update to `v0.7.0`
2. Remove dependency as symbols are compiled within DSO

Authors:
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/465
---
 .../bench_ann_cuda-118_arch-aarch64.yaml      |   1 -
 .../bench_ann_cuda-118_arch-x86_64.yaml       |   1 -
 .../bench_ann_cuda-125_arch-aarch64.yaml      |   1 -
 .../bench_ann_cuda-125_arch-x86_64.yaml       |   1 -
 cpp/CMakeLists.txt                            |   1 +
 cpp/bench/ann/CMakeLists.txt                  |   4 +-
 cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu  |  34 +-
 .../ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h |  57 ++-
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |   2 +
 .../ann/src/hnswlib/hnswlib_benchmark.cpp     |   4 +-
 cpp/cmake/modules/ConfigureCUDA.cmake         |   8 +-
 cpp/cmake/patches/hnswlib.diff                | 327 ++++++-------
 cpp/cmake/patches/hnswlib_override.json       |  28 +-
 cpp/cmake/thirdparty/get_hnswlib.cmake        |   1 +
 cpp/include/cuvs/neighbors/hnsw.h             | 279 ++++++++++-
 cpp/include/cuvs/neighbors/hnsw.hpp           | 440 +++++++++++++++---
 cpp/src/neighbors/detail/hnsw.hpp             | 218 ++++++++-
 cpp/src/neighbors/hnsw.cpp                    |  57 ++-
 cpp/src/neighbors/hnsw_c.cpp                  | 157 ++++++-
 cpp/src/neighbors/iface/iface.hpp             |   1 +
 cpp/test/neighbors/ann_hnsw_c.cu              |   4 +-
 cpp/test/neighbors/hnsw.cu                    |   3 +-
 dependencies.yaml                             |   1 -
 docs/source/c_api/neighbors_hnsw_c.rst        |  22 +
 docs/source/cpp_api/neighbors_hnsw.rst        |  23 +-
 python/cuvs/cuvs/neighbors/hnsw/__init__.py   |  15 +-
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd      |  49 +-
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx      | 276 ++++++++---
 python/cuvs/cuvs/test/test_hnsw.py            |  89 +++-
 .../config/algos/cuvs_cagra_hnswlib.yaml      |   5 +-
 30 files changed, 1683 insertions(+), 426 deletions(-)

diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 21cb981807..1e602ccf1a 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -24,7 +24,6 @@ dependencies:
 - gcc_linux-aarch64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
-- hnswlib=0.6.2
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 432509bcb3..b060e78c20 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -24,7 +24,6 @@ dependencies:
 - gcc_linux-64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
-- hnswlib=0.6.2
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 0c5043ac27..4851222735 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -25,7 +25,6 @@ dependencies:
 - gcc_linux-aarch64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
-- hnswlib=0.6.2
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index cbb22333c8..d5f48dadb5 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -25,7 +25,6 @@ dependencies:
 - gcc_linux-64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
-- hnswlib=0.6.2
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index eb2e7c7a4c..34b7cb8983 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -577,6 +577,7 @@ if(BUILD_SHARED_LIBS)
 
   if(BUILD_CAGRA_HNSWLIB)
     target_link_libraries(cuvs_objs PRIVATE hnswlib::hnswlib)
+    target_compile_definitions(cuvs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
     target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()
 
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 0f6b42ae9a..c161a68bce 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -225,9 +225,7 @@ if(CUVS_ANN_BENCH_USE_CUVS_CAGRA)
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
-  ConfigureAnnBench(
-    NAME CUVS_CAGRA_HNSWLIB PATH src/cuvs/cuvs_cagra_hnswlib.cu LINKS cuvs hnswlib::hnswlib
-  )
+  ConfigureAnnBench(NAME CUVS_CAGRA_HNSWLIB PATH src/cuvs/cuvs_cagra_hnswlib.cu LINKS cuvs)
 endif()
 
 if(CUVS_ANN_BENCH_USE_CUVS_MG)
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
index 558ba01e0a..e45a3bd5a8 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
@@ -24,12 +24,35 @@
 
 namespace cuvs::bench {
 
+template <typename T, typename IdxT>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::cuvs_cagra_hnswlib<T, IdxT>::build_param& param)
+{
+  if (conf.contains("hierarchy")) {
+    if (conf.at("hierarchy") == "none") {
+      param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::NONE;
+    } else if (conf.at("hierarchy") == "cpu") {
+      param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::CPU;
+    } else {
+      THROW("Invalid value for hierarchy: %s", conf.at("hierarchy").get<std::string>().c_str());
+    }
+  }
+  if (conf.contains("ef_construction")) {
+    param.hnsw_index_params.ef_construction = conf.at("ef_construction");
+  }
+  if (conf.contains("num_threads")) {
+    param.hnsw_index_params.num_threads = conf.at("num_threads");
+  }
+}
+
 template <typename T, typename IdxT>
 void parse_search_param(const nlohmann::json& conf,
                         typename cuvs::bench::cuvs_cagra_hnswlib<T, IdxT>::search_param& param)
 {
-  param.ef = conf.at("ef");
-  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+  param.hnsw_search_param.ef = conf.at("ef");
+  if (conf.contains("num_threads")) {
+    param.hnsw_search_param.num_threads = conf.at("num_threads");
+  }
 }
 
 template <typename T>
@@ -43,9 +66,10 @@ auto create_algo(const std::string& algo_name,
 
   if constexpr (std::is_same_v<T, float> or std::is_same_v<T, std::uint8_t>) {
     if (algo_name == "raft_cagra_hnswlib" || algo_name == "cuvs_cagra_hnswlib") {
-      typename cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>::build_param param;
-      parse_build_param<T, uint32_t>(conf, param);
-      a = std::make_unique<cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>>(metric, dim, param);
+      typename cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>::build_param bparam;
+      ::parse_build_param<T, uint32_t>(conf, bparam.cagra_build_param);
+      parse_build_param<T, uint32_t>(conf, bparam);
+      a = std::make_unique<cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>>(metric, dim, bparam);
     }
   }
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
index 875fe0bba5..e4169f6f8c 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
@@ -15,8 +15,8 @@
  */
 #pragma once
 
-#include "../hnswlib/hnswlib_wrapper.h"
 #include "cuvs_cagra_wrapper.h"
+#include <cuvs/neighbors/hnsw.hpp>
 
 #include <memory>
 
@@ -26,14 +26,20 @@ template <typename T, typename IdxT>
 class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
  public:
   using search_param_base = typename algo<T>::search_param;
-  using build_param       = typename cuvs_cagra<T, IdxT>::build_param;
-  using search_param      = typename hnsw_lib<T>::search_param;
+
+  struct build_param {
+    typename cuvs_cagra<T, IdxT>::build_param cagra_build_param;
+    cuvs::neighbors::hnsw::index_params hnsw_index_params;
+  };
+
+  struct search_param : public search_param_base {
+    cuvs::neighbors::hnsw::search_params hnsw_search_param;
+  };
 
   cuvs_cagra_hnswlib(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
     : algo<T>(metric, dim),
-      cagra_build_{metric, dim, param, concurrent_searches},
-      // hnsw_lib param values don't matter since we don't build with hnsw_lib
-      hnswlib_search_{metric, dim, typename hnsw_lib<T>::build_param{50, 100}}
+      build_param_{param},
+      cagra_build_{metric, dim, param.cagra_build_param, concurrent_searches}
   {
   }
 
@@ -69,40 +75,67 @@ class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
   }
 
  private:
+  raft::resources handle_{};
+  build_param build_param_;
+  search_param search_param_;
   cuvs_cagra<T, IdxT> cagra_build_;
-  hnsw_lib<T> hnswlib_search_;
+  std::shared_ptr<cuvs::neighbors::hnsw::index<T>> hnsw_index_;
 };
 
 template <typename T, typename IdxT>
 void cuvs_cagra_hnswlib<T, IdxT>::build(const T* dataset, size_t nrow)
 {
   cagra_build_.build(dataset, nrow);
+  auto* cagra_index      = cagra_build_.get_index();
+  auto host_dataset_view = raft::make_host_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
+  auto opt_dataset_view =
+    std::optional<raft::host_matrix_view<const T, int64_t>>(std::move(host_dataset_view));
+  hnsw_index_ = cuvs::neighbors::hnsw::from_cagra(
+    handle_, build_param_.hnsw_index_params, *cagra_index, opt_dataset_view);
 }
 
 template <typename T, typename IdxT>
 void cuvs_cagra_hnswlib<T, IdxT>::set_search_param(const search_param_base& param_)
 {
-  hnswlib_search_.set_search_param(param_);
+  search_param_ = dynamic_cast<const search_param&>(param_);
 }
 
 template <typename T, typename IdxT>
 void cuvs_cagra_hnswlib<T, IdxT>::save(const std::string& file) const
 {
-  cagra_build_.save_to_hnswlib(file);
+  cuvs::neighbors::hnsw::serialize(handle_, file, *(hnsw_index_.get()));
 }
 
 template <typename T, typename IdxT>
 void cuvs_cagra_hnswlib<T, IdxT>::load(const std::string& file)
 {
-  hnswlib_search_.load(file);
-  hnswlib_search_.set_base_layer_only();
+  cuvs::neighbors::hnsw::index<T>* idx = nullptr;
+  cuvs::neighbors::hnsw::deserialize(handle_,
+                                     build_param_.hnsw_index_params,
+                                     file,
+                                     this->dim_,
+                                     parse_metric_type(this->metric_),
+                                     &idx);
+  hnsw_index_ = std::shared_ptr<cuvs::neighbors::hnsw::index<T>>(idx);
 }
 
 template <typename T, typename IdxT>
 void cuvs_cagra_hnswlib<T, IdxT>::search(
   const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
 {
-  hnswlib_search_.search(queries, batch_size, k, neighbors, distances);
+  // Only Latency mode is supported for now
+  auto queries_view =
+    raft::make_host_matrix_view<const T, int64_t>(queries, batch_size, this->dim_);
+  auto neighbors_view = raft::make_host_matrix_view<uint64_t, int64_t>(
+    reinterpret_cast<uint64_t*>(neighbors), batch_size, k);
+  auto distances_view = raft::make_host_matrix_view<float, int64_t>(distances, batch_size, k);
+
+  cuvs::neighbors::hnsw::search(handle_,
+                                search_param_.hnsw_search_param,
+                                *(hnsw_index_.get()),
+                                queries_view,
+                                neighbors_view,
+                                distances_view);
 }
 
 }  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index b2ba35eeec..f6d3d60fc1 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -154,6 +154,8 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
   void save_to_hnswlib(const std::string& file) const;
   std::unique_ptr<algo<T>> copy() override;
 
+  auto get_index() const -> const cuvs::neighbors::cagra::index<T, IdxT>* { return index_.get(); }
+
  private:
   // handle_ must go first to make sure it dies last and all memory allocated in pool
   configured_raft_resources handle_{};
diff --git a/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp b/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
index 755c7c8d69..6e219d2a77 100644
--- a/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
+++ b/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
@@ -33,7 +33,7 @@ void parse_build_param(const nlohmann::json& conf,
 {
   param.ef_construction = conf.at("efConstruction");
   param.m               = conf.at("M");
-  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+  if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
 }
 
 template <typename T>
@@ -41,7 +41,7 @@ void parse_search_param(const nlohmann::json& conf,
                         typename cuvs::bench::hnsw_lib<T>::search_param& param)
 {
   param.ef = conf.at("ef");
-  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+  if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
 }
 
 template <typename T, template <typename> class Algo>
diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index 74da256604..3e91d99950 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -22,8 +22,12 @@ endif()
 # Be very strict when compiling with GCC as host compiler (and thus more lenient when compiling with
 # clang)
 if(CMAKE_COMPILER_IS_GNUCXX)
-  list(APPEND CUVS_CXX_FLAGS -Wall -Werror -Wno-unknown-pragmas -Wno-error=deprecated-declarations)
-  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wall,-Werror,-Wno-error=deprecated-declarations)
+  list(APPEND CUVS_CXX_FLAGS -Wall -Werror -Wno-unknown-pragmas -Wno-error=deprecated-declarations
+       -Wno-reorder
+  )
+  list(APPEND CUVS_CUDA_FLAGS
+       -Xcompiler=-Wall,-Werror,-Wno-error=deprecated-declarations,-Wno-reorder
+  )
 
   # set warnings as errors
   if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 11.2.0)
diff --git a/cpp/cmake/patches/hnswlib.diff b/cpp/cmake/patches/hnswlib.diff
index e7f89a8cc9..f20c27d919 100644
--- a/cpp/cmake/patches/hnswlib.diff
+++ b/cpp/cmake/patches/hnswlib.diff
@@ -1,188 +1,159 @@
+diff --git a/hnswlib/hnswalg.h b/hnswlib/hnswalg.h
+index bef0017..0ee7931 100644
 --- a/hnswlib/hnswalg.h
 +++ b/hnswlib/hnswalg.h
-@@ -3,6 +3,7 @@
- #include "visited_list_pool.h"
- #include "hnswlib.h"
- #include <atomic>
-+#include <limits>
- #include <random>
- #include <stdlib.h>
- #include <assert.h>
-@@ -16,6 +17,8 @@ namespace hnswlib {
-     template<typename dist_t>
-     class HierarchicalNSW : public AlgorithmInterface<dist_t> {
-     public:
-+        bool base_layer_only{false};
-+        int num_seeds=32;
-         static const tableint max_update_element_locks = 65536;
-         HierarchicalNSW(SpaceInterface<dist_t> *s) {
-         }
-@@ -56,7 +59,7 @@ namespace hnswlib {
-             visited_list_pool_ = new VisitedListPool(1, max_elements);
- 
-             //initializations for special treatment of the first node
--            enterpoint_node_ = -1;
-+            enterpoint_node_ = std::numeric_limits<tableint>::max();
-             maxlevel_ = -1;
- 
-             linkLists_ = (char **) malloc(sizeof(void *) * max_elements_);
-@@ -527,7 +530,7 @@ namespace hnswlib {
-                     tableint *datal = (tableint *) (data + 1);
-                     for (int i = 0; i < size; i++) {
-                         tableint cand = datal[i];
--                        if (cand < 0 || cand > max_elements_)
-+                        if (cand > max_elements_)
-                             throw std::runtime_error("cand error");
-                         dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
- 
-@@ -1067,7 +1070,7 @@ namespace hnswlib {
-                             tableint *datal = (tableint *) (data + 1);
-                             for (int i = 0; i < size; i++) {
-                                 tableint cand = datal[i];
--                                if (cand < 0 || cand > max_elements_)
-+                                if (cand > max_elements_)
-                                     throw std::runtime_error("cand error");
-                                 dist_t d = fstdistfunc_(data_point, getDataByInternalId(cand), dist_func_param_);
-                                 if (d < curdist) {
-@@ -1119,28 +1122,41 @@ namespace hnswlib {
-             tableint currObj = enterpoint_node_;
-             dist_t curdist = fstdistfunc_(query_data, getDataByInternalId(enterpoint_node_), dist_func_param_);
- 
--            for (int level = maxlevel_; level > 0; level--) {
--                bool changed = true;
--                while (changed) {
--                    changed = false;
--                    unsigned int *data;
-+            if (base_layer_only) {
-+                // You can increase the number of seeds when testing large-scale dataset, num_seeds = 48 for 100M-scale
-+                for (int i = 0; i < num_seeds; i++) {
-+                    tableint obj = i * (max_elements_ / num_seeds);
-+                    dist_t dist = fstdistfunc_(query_data, getDataByInternalId(obj), dist_func_param_);
-+                    if (dist < curdist) {
-+                        curdist = dist;
-+                        currObj = obj;
-+                    }
+@@ -16,6 +16,9 @@ typedef unsigned int linklistsizeint;
+ template<typename dist_t>
+ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
+  public:
++    bool base_layer_only = false;
++    int num_seeds = 32;
++    bool base_layer_init = true;
+     static const tableint MAX_LABEL_OPERATION_LOCKS = 65536;
+     static const unsigned char DELETE_MARK = 0x01;
+ 
+@@ -1098,7 +1101,7 @@ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
+ 
+         std::unique_lock <std::mutex> lock_el(link_list_locks_[cur_c]);
+         int curlevel = getRandomLevel(mult_);
+-        if (level > 0)
++        if (level > -1)
+             curlevel = level;
+ 
+         element_levels_[cur_c] = curlevel;
+@@ -1116,6 +1119,9 @@ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
+         memcpy(getExternalLabeLp(cur_c), &label, sizeof(labeltype));
+         memcpy(getDataByInternalId(cur_c), data_point, data_size_);
+ 
++        if (!base_layer_init && curlevel == 0)
++            return cur_c;
++
+         if (curlevel) {
+             linkLists_[cur_c] = (char *) malloc(size_links_per_element_ * curlevel + 1);
+             if (linkLists_[cur_c] == nullptr)
+@@ -1138,7 +1144,7 @@ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
+                         tableint *datal = (tableint *) (data + 1);
+                         for (int i = 0; i < size; i++) {
+                             tableint cand = datal[i];
+-                            if (cand < 0 || cand > max_elements_)
++                            if (static_cast<int>(cand) < 0 || cand > max_elements_)
+                                 throw std::runtime_error("cand error");
+                             dist_t d = fstdistfunc_(data_point, getDataByInternalId(cand), dist_func_param_);
+                             if (d < curdist) {
+@@ -1188,28 +1194,41 @@ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
+         tableint currObj = enterpoint_node_;
+         dist_t curdist = fstdistfunc_(query_data, getDataByInternalId(enterpoint_node_), dist_func_param_);
+ 
+-        for (int level = maxlevel_; level > 0; level--) {
+-            bool changed = true;
+-            while (changed) {
+-                changed = false;
+-                unsigned int *data;
++        if (base_layer_only) {
++            // You can increase the number of seeds when testing large-scale dataset, num_seeds = 48 for 100M-scale
++            for (int i = 0; i < num_seeds; i++) {
++                tableint obj = i * (max_elements_ / num_seeds);
++                dist_t dist = fstdistfunc_(query_data, getDataByInternalId(obj), dist_func_param_);
++                if (dist < curdist) {
++                    curdist = dist;
++                    currObj = obj;
 +                }
 +            }
-+            else{
-+                for (int level = maxlevel_; level > 0; level--) {
-+                    bool changed = true;
-+                    while (changed) {
-+                        changed = false;
-+                        unsigned int *data;
- 
--                    data = (unsigned int *) get_linklist(currObj, level);
--                    int size = getListCount(data);
--                    metric_hops++;
--                    metric_distance_computations+=size;
-+                        data = (unsigned int *) get_linklist(currObj, level);
-+                        int size = getListCount(data);
-+                        metric_hops++;
-+                        metric_distance_computations+=size;
- 
--                    tableint *datal = (tableint *) (data + 1);
--                    for (int i = 0; i < size; i++) {
--                        tableint cand = datal[i];
--                        if (cand < 0 || cand > max_elements_)
--                            throw std::runtime_error("cand error");
--                        dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
-+                        tableint *datal = (tableint *) (data + 1);
-+                        for (int i = 0; i < size; i++) {
-+                            tableint cand = datal[i];
-+                            if (cand > max_elements_)
-+                                throw std::runtime_error("cand error");
-+                            dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
- 
--                        if (d < curdist) {
--                            curdist = d;
--                            currObj = cand;
--                            changed = true;
-+                            if (d < curdist) {
-+                                curdist = d;
-+                                currObj = cand;
-+                                changed = true;
-+                            }
-                         }
++        }
++        else {
++            for (int level = maxlevel_; level > 0; level--) {
++                bool changed = true;
++                while (changed) {
++                    changed = false;
++                    unsigned int *data;
+ 
+-                data = (unsigned int *) get_linklist(currObj, level);
+-                int size = getListCount(data);
+-                metric_hops++;
+-                metric_distance_computations+=size;
++                    data = (unsigned int *) get_linklist(currObj, level);
++                    int size = getListCount(data);
++                    metric_hops++;
++                    metric_distance_computations+=size;
++
++                    tableint *datal = (tableint *) (data + 1);
++                    for (int i = 0; i < size; i++) {
++                        tableint cand = datal[i];
++                        if (static_cast<int>(cand) < 0 || cand > max_elements_)
++                            throw std::runtime_error("cand error");
++                        dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
+ 
+-                tableint *datal = (tableint *) (data + 1);
+-                for (int i = 0; i < size; i++) {
+-                    tableint cand = datal[i];
+-                    if (cand < 0 || cand > max_elements_)
+-                        throw std::runtime_error("cand error");
+-                    dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
+-
+-                    if (d < curdist) {
+-                        curdist = d;
+-                        currObj = cand;
+-                        changed = true;
++                        if (d < curdist) {
++                            curdist = d;
++                            currObj = cand;
++                            changed = true;
++                        }
                      }
                  }
+             }
 diff --git a/hnswlib/space_l2.h b/hnswlib/space_l2.h
-index 4413537..c3240f3 100644
+index 834d19f..0c0af26 100644
 --- a/hnswlib/space_l2.h
 +++ b/hnswlib/space_l2.h
-@@ -252,13 +252,14 @@ namespace hnswlib {
-         ~L2Space() {}
-     };
- 
-+    template <typename T>
-     static int
-     L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const void *__restrict qty_ptr) {
- 
-         size_t qty = *((size_t *) qty_ptr);
-         int res = 0;
--        unsigned char *a = (unsigned char *) pVect1;
--        unsigned char *b = (unsigned char *) pVect2;
-+        T *a = (T *) pVect1;
-+        T *b = (T *) pVect2;
- 
-         qty = qty >> 2;
-         for (size_t i = 0; i < qty; i++) {
-@@ -279,11 +280,12 @@ namespace hnswlib {
-         return (res);
-     }
- 
-+    template <typename T>
-     static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2, const void* __restrict qty_ptr) {
-         size_t qty = *((size_t*)qty_ptr);
-         int res = 0;
--        unsigned char* a = (unsigned char*)pVect1;
--        unsigned char* b = (unsigned char*)pVect2;
-+        T* a = (T*)pVect1;
-+        T* b = (T*)pVect2;
- 
-         for(size_t i = 0; i < qty; i++)
-         {
-@@ -294,6 +296,7 @@ namespace hnswlib {
-         return (res);
-     }
- 
-+    template <typename T>
-     class L2SpaceI : public SpaceInterface<int> {
- 
-         DISTFUNC<int> fstdistfunc_;
-@@ -302,10 +305,10 @@ namespace hnswlib {
-     public:
-         L2SpaceI(size_t dim) {
-             if(dim % 4 == 0) {
--                fstdistfunc_ = L2SqrI4x;
-+                fstdistfunc_ = L2SqrI4x<T>;
-             }
-             else {
--                fstdistfunc_ = L2SqrI;
-+                fstdistfunc_ = L2SqrI<T>;
-             }
-             dim_ = dim;
-             data_size_ = dim * sizeof(unsigned char);
-diff --git a/hnswlib/visited_list_pool.h b/hnswlib/visited_list_pool.h
-index 5e1a4a5..4195ebd 100644
---- a/hnswlib/visited_list_pool.h
-+++ b/hnswlib/visited_list_pool.h
-@@ -3,6 +3,7 @@
- #include <mutex>
- #include <string.h>
- #include <deque>
-+#include <limits>
- 
- namespace hnswlib {
-     typedef unsigned short int vl_type;
-@@ -14,7 +15,7 @@ namespace hnswlib {
-         unsigned int numelements;
- 
-         VisitedList(int numelements1) {
--            curV = -1;
-+            curV = std::numeric_limits<vl_type>::max();
-             numelements = numelements1;
-             mass = new vl_type[numelements];
+@@ -252,12 +252,13 @@ class L2Space : public SpaceInterface<float> {
+     ~L2Space() {}
+ };
+ 
++template <typename T>
+ static int
+ L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const void *__restrict qty_ptr) {
+     size_t qty = *((size_t *) qty_ptr);
+     int res = 0;
+-    unsigned char *a = (unsigned char *) pVect1;
+-    unsigned char *b = (unsigned char *) pVect2;
++    T *a = (T *) pVect1;
++    T *b = (T *) pVect2;
+ 
+     qty = qty >> 2;
+     for (size_t i = 0; i < qty; i++) {
+@@ -277,11 +278,12 @@ L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const voi
+     return (res);
+ }
+ 
++template <typename T>
+ static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2, const void* __restrict qty_ptr) {
+     size_t qty = *((size_t*)qty_ptr);
+     int res = 0;
+-    unsigned char* a = (unsigned char*)pVect1;
+-    unsigned char* b = (unsigned char*)pVect2;
++    T* a = (T*)pVect1;
++    T* b = (T*)pVect2;
+ 
+     for (size_t i = 0; i < qty; i++) {
+         res += ((*a) - (*b)) * ((*a) - (*b));
+@@ -291,6 +293,7 @@ static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2,
+     return (res);
+ }
+ 
++template <typename T>
+ class L2SpaceI : public SpaceInterface<int> {
+     DISTFUNC<int> fstdistfunc_;
+     size_t data_size_;
+@@ -299,9 +302,9 @@ class L2SpaceI : public SpaceInterface<int> {
+  public:
+     L2SpaceI(size_t dim) {
+         if (dim % 4 == 0) {
+-            fstdistfunc_ = L2SqrI4x;
++            fstdistfunc_ = L2SqrI4x<T>;
+         } else {
+-            fstdistfunc_ = L2SqrI;
++            fstdistfunc_ = L2SqrI<T>;
          }
--- 
-2.43.0
-
+         dim_ = dim;
+         data_size_ = dim * sizeof(unsigned char);
diff --git a/cpp/cmake/patches/hnswlib_override.json b/cpp/cmake/patches/hnswlib_override.json
index aef2da772c..c50220e248 100644
--- a/cpp/cmake/patches/hnswlib_override.json
+++ b/cpp/cmake/patches/hnswlib_override.json
@@ -1,16 +1,16 @@
 {
-    "packages" : {
-      "hnswlib" : {
-        "version": "0.6.2",
-        "git_url": "https://github.com/nmslib/hnswlib.git",
-        "git_tag": "v${version}",
-        "patches" : [
-          {
-            "file" : "${current_json_dir}/hnswlib.diff",
-            "issue" : "Correct compilation issues",
-            "fixed_in" : ""
-          }
-        ]
-      }
+  "packages": {
+    "hnswlib": {
+      "version": "0.7.0",
+      "git_url": "https://github.com/nmslib/hnswlib.git",
+      "git_tag": "v${version}",
+      "patches": [
+        {
+          "file": "${current_json_dir}/hnswlib.diff",
+          "issue": "Correct compilation issues",
+          "fixed_in": ""
+        }
+      ]
     }
-  }
\ No newline at end of file
+  }
+}
\ No newline at end of file
diff --git a/cpp/cmake/thirdparty/get_hnswlib.cmake b/cpp/cmake/thirdparty/get_hnswlib.cmake
index 2e6c895e5f..5b4d89aa2a 100644
--- a/cpp/cmake/thirdparty/get_hnswlib.cmake
+++ b/cpp/cmake/thirdparty/get_hnswlib.cmake
@@ -15,6 +15,7 @@
 #=============================================================================
 
 function(find_and_configure_hnswlib)
+  message(STATUS "Finding or building hnswlib")
   set(oneValueArgs)
 
   include(${rapids-cmake-dir}/cpm/package_override.cmake)
diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
index 0495c574a6..b7eda54b87 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -16,6 +16,8 @@
 
 #pragma once
 
+#include "cagra.h"
+
 #include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.h>
 #include <dlpack/dlpack.h>
@@ -27,32 +29,51 @@ extern "C" {
 #endif
 
 /**
- * @defgroup hnsw_c_search_params C API for hnswlib wrapper search params
+ * @defgroup hnsw_c_index_params C API for HNSW index params
  * @{
  */
 
-struct cuvsHnswSearchParams {
-  int32_t ef;
-  int32_t numThreads;
+/**
+ * @brief Hierarchy for HNSW index when converting from CAGRA index
+ *
+ * NOTE: When the value is `NONE`, the HNSW index is built as a base-layer-only index.
+ */
+enum cuvsHnswHierarchy {
+  /* Flat hierarchy, search is base-layer only */
+  NONE,
+  /* Full hierarchy is built using the CPU */
+  CPU
 };
 
-typedef struct cuvsHnswSearchParams* cuvsHnswSearchParams_t;
+struct cuvsHnswIndexParams {
+  /* hierarchy of the hnsw index */
+  cuvsHnswHierarchy hierarchy;
+  /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
+  int ef_construction;
+  /** Number of host threads to use to construct hierarchy when hierarchy is `CPU`
+  NOTE: Constructing the hierarchy when converting from a CAGRA graph is highly sensitive
+  to parallelism, and increasing the number of threads can reduce the quality of the index.
+   */
+  int num_threads;
+};
+
+typedef struct cuvsHnswIndexParams* cuvsHnswIndexParams_t;
 
 /**
- * @brief Allocate HNSW search params, and populate with default values
+ * @brief Allocate HNSW Index params, and populate with default values
  *
- * @param[in] params cuvsHnswSearchParams_t to allocate
+ * @param[in] params cuvsHnswIndexParams_t to allocate
  * @return cuvsError_t
  */
-cuvsError_t cuvsHnswSearchParamsCreate(cuvsHnswSearchParams_t* params);
+cuvsError_t cuvsHnswIndexParamsCreate(cuvsHnswIndexParams_t* params);
 
 /**
- * @brief De-allocate HNSW search params
+ * @brief De-allocate HNSW Index params
  *
- * @param[in] params cuvsHnswSearchParams_t to de-allocate
+ * @param[in] params
  * @return cuvsError_t
  */
-cuvsError_t cuvsHnswSearchParamsDestroy(cuvsHnswSearchParams_t params);
+cuvsError_t cuvsHnswIndexParamsDestroy(cuvsHnswIndexParams_t params);
 
 /**
  * @}
@@ -90,6 +111,184 @@ cuvsError_t cuvsHnswIndexCreate(cuvsHnswIndex_t* index);
  */
 cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index);
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup hnsw_c_extend_params Parameters for extending HNSW index
+ * @{
+ */
+
+struct cuvsHnswExtendParams {
+  /** Number of CPU threads used to extend additional vectors */
+  int num_threads;
+};
+
+typedef struct cuvsHnswExtendParams* cuvsHnswExtendParams_t;
+
+/**
+ * @brief Allocate HNSW extend params, and populate with default values
+ *
+ * @param[in] params cuvsHnswExtendParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsHnswExtendParamsCreate(cuvsHnswExtendParams_t* params);
+
+/**
+ * @brief De-allocate HNSW extend params
+ *
+ * @param[in] params cuvsHnswExtendParams_t to de-allocate
+ * @return cuvsError_t
+ */
+
+cuvsError_t cuvsHnswExtendParamsDestroy(cuvsHnswExtendParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup hnsw_c_index_load Load CAGRA index as hnswlib index
+ * @{
+ */
+
+/**
+ * @brief Convert a CAGRA Index to an HNSW index.
+ * NOTE: When hierarchy is:
+ *       1. `NONE`: This method uses the filesystem to write the CAGRA index in
+ * `/tmp/<random_number>.bin` before reading it as an hnswlib index, then deleting the temporary
+ * file. The returned index is immutable and can only be searched by the hnswlib wrapper in cuVS, as
+ * the format is not compatible with the original hnswlib.
+ *       2. `CPU`: The returned index is mutable and can be extended with additional vectors. The
+ * serialized index is also compatible with the original hnswlib library.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsHnswIndexParams_t used to load Hnsw index
+ * @param[in] cagra_index cuvsCagraIndex_t to convert to HNSW index
+ * @param[out] hnsw_index cuvsHnswIndex_t to return the HNSW index
+ *
+ * @return cuvsError_t
+ *
+ * @code{.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/cagra.h>
+ * #include <cuvs/neighbors/hnsw.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create a CAGRA index with `cuvsCagraBuild`
+ *
+ * // Convert the CAGRA index to an HNSW index
+ * cuvsHnswIndex_t hnsw_index;
+ * cuvsHnswIndexCreate(&hnsw_index);
+ * cuvsHnswIndexParams_t hnsw_params;
+ * cuvsHnswIndexParamsCreate(&hnsw_params);
+ * cuvsHnswFromCagra(res, hnsw_params, cagra_index, hnsw_index);
+ *
+ * // de-allocate `hnsw_params`, `hnsw_index` and `res`
+ * cuvsError_t hnsw_params_destroy_status = cuvsHnswIndexParamsDestroy(hnsw_params);
+ * cuvsError_t hnsw_index_destroy_status = cuvsHnswIndexDestroy(hnsw_index);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ */
+cuvsError_t cuvsHnswFromCagra(cuvsResources_t res,
+                              cuvsHnswIndexParams_t params,
+                              cuvsCagraIndex_t cagra_index,
+                              cuvsHnswIndex_t hnsw_index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup hnsw_c_index_extend Extend HNSW index with additional vectors
+ * @{
+ */
+
+/**
+ * @brief Add new vectors to an HNSW index
+ * NOTE: The HNSW index can only be extended when the hierarchy is `CPU`
+ *       when converting from a CAGRA index.
+
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsHnswExtendParams_t used to extend Hnsw index
+ * @param[in] additional_dataset DLManagedTensor* additional dataset to extend the index
+ * @param[inout] index cuvsHnswIndex_t to extend
+  *
+  * @return cuvsError_t
+  *
+  * @code{.c}
+  * #include <cuvs/core/c_api.h>
+  * #include <cuvs/neighbors/cagra.h>
+  * #include <cuvs/neighbors/hnsw.h>
+  *
+  * // Create cuvsResources_t
+  * cuvsResources_t res;
+  * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+  *
+  * // create an index with `cuvsCagraBuild`
+  *
+  * // Convert the CAGRA index to an HNSW index
+  * cuvsHnswIndex_t hnsw_index;
+  * cuvsHnswIndexCreate(&hnsw_index);
+  * cuvsHnswIndexParams_t hnsw_params;
+  * cuvsHnswIndexParamsCreate(&hnsw_params);
+  * cuvsHnswFromCagra(res, hnsw_params, cagra_index, hnsw_index);
+  *
+  * // Extend the HNSW index with additional vectors
+  * DLManagedTensor additional_dataset;
+  * cuvsHnswExtendParams_t extend_params;
+  * cuvsHnswExtendParamsCreate(&extend_params);
+  * cuvsHnswExtend(res, extend_params, additional_dataset, hnsw_index);
+  *
+  * // de-allocate `hnsw_params`, `hnsw_index`, `extend_params` and `res`
+  * cuvsError_t hnsw_params_destroy_status = cuvsHnswIndexParamsDestroy(hnsw_params);
+  * cuvsError_t hnsw_index_destroy_status = cuvsHnswIndexDestroy(hnsw_index);
+  * cuvsError_t extend_params_destroy_status = cuvsHnswExtendParamsDestroy(extend_params);
+  * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+  * @endcode
+  */
+
+cuvsError_t cuvsHnswExtend(cuvsResources_t res,
+                           cuvsHnswExtendParams_t params,
+                           DLManagedTensor* additional_dataset,
+                           cuvsHnswIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup hnsw_c_search_params C API for hnswlib wrapper search params
+ * @{
+ */
+
+struct cuvsHnswSearchParams {
+  int32_t ef;
+  int32_t num_threads;
+};
+
+typedef struct cuvsHnswSearchParams* cuvsHnswSearchParams_t;
+
+/**
+ * @brief Allocate HNSW search params, and populate with default values
+ *
+ * @param[in] params cuvsHnswSearchParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsHnswSearchParamsCreate(cuvsHnswSearchParams_t* params);
+
+/**
+ * @brief De-allocate HNSW search params
+ *
+ * @param[in] params cuvsHnswSearchParams_t to de-allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsHnswSearchParamsDestroy(cuvsHnswSearchParams_t params);
+
 /**
  * @}
  */
@@ -111,8 +310,8 @@ cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index);
  *          c. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 64`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
- * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
- *       as the format is not compatible with the original hnswlib.
+ * NOTE: When hierarchy is `NONE`, the HNSW index can only be searched by the hnswlib wrapper in
+ * cuVS, as the format is not compatible with the original hnswlib.
  *
  * @code {.c}
  * #include <cuvs/core/c_api.h>
@@ -131,7 +330,7 @@ cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index);
  * cuvsHnswSearchParams_t params;
  * cuvsError_t params_create_status = cuvsHnswSearchParamsCreate(&params);
  *
- * // Search the `index` built using `cuvsHnswBuild`
+ * // Search the `index` built using `cuvsHnswFromCagra`
  * cuvsError_t search_status = cuvsHnswSearch(res, params, index, &queries, &neighbors,
  * &distances);
  *
@@ -142,7 +341,7 @@ cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index);
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] params cuvsHnswSearchParams_t used to search Hnsw index
- * @param[in] index cuvsHnswIndex which has been returned by `cuvsHnswBuild`
+ * @param[in] index cuvsHnswIndex which has been returned by `cuvsHnswFromCagra`
  * @param[in] queries DLManagedTensor* queries dataset to search
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
@@ -163,9 +362,50 @@ cuvsError_t cuvsHnswSearch(cuvsResources_t res,
  * @{
  */
 
+/**
+ * @brief Serialize a CAGRA index to a file as an hnswlib index
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the name of the file to save the index
+ * @param[in] index cuvsHnswIndex_t to serialize
+ * @return cuvsError_t
+ *
+ * @code{.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/cagra.h>
+ * #include <cuvs/neighbors/hnsw.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // create an index with `cuvsCagraBuild`
+ *
+ * // Convert the CAGRA index to an HNSW index
+ * cuvsHnswIndex_t hnsw_index;
+ * cuvsHnswIndexCreate(&hnsw_index);
+ * cuvsHnswIndexParams_t hnsw_params;
+ * cuvsHnswIndexParamsCreate(&hnsw_params);
+ * cuvsHnswFromCagra(res, hnsw_params, cagra_index, hnsw_index);
+ *
+ * // Serialize the HNSW index
+ * cuvsHnswSerialize(res, "/path/to/index", hnsw_index);
+ *
+ * // de-allocate `hnsw_params`, `hnsw_index` and `res`
+ * cuvsError_t hnsw_params_destroy_status = cuvsHnswIndexParamsDestroy(hnsw_params);
+ * cuvsError_t hnsw_index_destroy_status = cuvsHnswIndexDestroy(hnsw_index);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ */
+cuvsError_t cuvsHnswSerialize(cuvsResources_t res, const char* filename, cuvsHnswIndex_t index);
+
 /**
  * Load hnswlib index from file which was serialized from a HNSW index.
- * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * NOTE: When hierarchy is `NONE`, the loaded hnswlib index is immutable, and only be read by the
  * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
  * Experimental, both the API and the serialization format are subject to change.
  *
@@ -185,17 +425,22 @@ cuvsError_t cuvsHnswSearch(cuvsResources_t res,
  * // The index should have the same dtype as the one used to build CAGRA the index
  * cuvsHnswIndex_t hnsw_index;
  * cuvsHnswIndexCreate(&hnsw_index);
+ * cuvsHnsWIndexParams_t hnsw_params;
+ * cuvsHnswIndexParamsCreate(&hnsw_params);
+ * hnsw_params->hierarchy = NONE;
  * hnsw_index->dtype = index->dtype;
- * cuvsCagraDeserialize(res, "/path/to/index", hnsw_index);
+ * cuvsHnswDeserialize(res, hnsw_params, "/path/to/index", dim, metric hnsw_index);
  * @endcode
  *
  * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsHnswIndexParams_t used to load Hnsw index
  * @param[in] filename the name of the file that stores the index
  * @param[in] dim the dimension of the vectors in the index
  * @param[in] metric the distance metric used to build the index
  * @param[out] index HNSW index loaded disk
  */
 cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
+                                cuvsHnswIndexParams_t params,
                                 const char* filename,
                                 int dim,
                                 cuvsDistanceType metric,
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index d5abd6d559..f0b433d8e2 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -34,14 +34,30 @@
 namespace cuvs::neighbors::hnsw {
 
 /**
- * @defgroup hnsw_cpp_search_params Build CAGRA index and search with hnswlib
+ * @defgroup hnsw_cpp_index_params hnswlib index wrapper params
  * @{
  */
 
-struct search_params : cuvs::neighbors::search_params {
-  int ef;               // size of the candidate list
-  int num_threads = 0;  // number of host threads to use for concurrent searches. Value of 0
-                        // automatically maximizes parallelism
+/**
+ * @brief Hierarchy for HNSW index when converting from CAGRA index
+ *
+ * NOTE: When the value is `NONE`, the HNSW index is built as a base-layer-only index.
+ */
+enum class HnswHierarchy {
+  NONE,  // base-layer-only index
+  CPU    // full index with CPU-built hierarchy
+};
+
+struct index_params : cuvs::neighbors::index_params {
+  /** Hierarchy build type for HNSW index when converting from CAGRA index */
+  HnswHierarchy hierarchy = HnswHierarchy::NONE;
+  /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
+  int ef_construction = 200;
+  /** Number of host threads to use to construct hierarchy when hierarchy is `CPU`
+  NOTE: Constructing the hierarchy when converting from a CAGRA graph is highly sensitive
+  to parallelism, and increasing the number of threads can reduce the quality of the index.
+   */
+  int num_threads = 2;
 };
 
 /**@}*/
@@ -62,8 +78,12 @@ struct index : cuvs::neighbors::index {
    *
    * @param[in] dim dimensions of the training dataset
    * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+   * @param[in] hierarchy hierarchy used for upper HNSW layers
    */
-  index(int dim, cuvs::distance::DistanceType metric) : dim_{dim}, metric_{metric} {}
+  index(int dim, cuvs::distance::DistanceType metric, HnswHierarchy hierarchy = HnswHierarchy::NONE)
+    : dim_{dim}, metric_{metric}, hierarchy_{hierarchy}
+  {
+  }
 
   virtual ~index() {}
 
@@ -76,6 +96,8 @@ struct index : cuvs::neighbors::index {
 
   auto metric() const -> cuvs::distance::DistanceType { return metric_; }
 
+  auto hierarchy() const -> HnswHierarchy { return hierarchy_; }
+
   /**
   @brief Set ef for search
   */
@@ -84,24 +106,41 @@ struct index : cuvs::neighbors::index {
  private:
   int dim_;
   cuvs::distance::DistanceType metric_;
+  HnswHierarchy hierarchy_;
 };
 
 /**@}*/
 
+/**
+ * @defgroup hnsw_cpp_extend_params HNSW index extend parameters
+ * @{
+ */
+
+struct extend_params {
+  /** Number of host threads to use to add additional vectors to the index.
+  Value of 0 automatically maximizes parallelism. */
+  int num_threads = 0;
+};
+
 /**
  * @defgroup hnsw_cpp_index_load Load CAGRA index as hnswlib index
  * @{
  */
 
 /**
- * @brief Construct an immutable hnswlib base-layer-only index from a CAGRA index
- * NOTE: This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
- * before reading it as an hnswlib index, then deleting the temporary file. The returned index
- * is immutable and can only be searched by the hnswlib wrapper in cuVS, as the format is not
- * compatible with the original hnswlib.
+ * @brief Construct an hnswlib index from a CAGRA index
+ * NOTE: When `hnsw::index_params.hierarchy` is:
+ *       1. `NONE`: This method uses the filesystem to write the CAGRA index in
+ * `/tmp/<random_number>.bin` before reading it as an hnswlib index, then deleting the temporary
+ * file. The returned index is immutable and can only be searched by the hnswlib wrapper in cuVS, as
+ * the format is not compatible with the original hnswlib.
+ *       2. `CPU`: The returned index is mutable and can be extended with additional vectors. The
+ * serialized index is also compatible with the original hnswlib library.
  *
  * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
  * @param[in] cagra_index cagra index
+ * @param[in] dataset optional dataset to avoid extra memory copy when hierarchy is `CPU`
  *
  * Usage example:
  * @code{.cpp}
@@ -110,24 +149,34 @@ struct index : cuvs::neighbors::index {
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<float, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // Load CAGRA index as base-layer-only hnswlib index
- *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  * @endcode
  */
 std::unique_ptr<index<float>> from_cagra(
-  raft::resources const& res, const cuvs::neighbors::cagra::index<float, uint32_t>& cagra_index);
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<float, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const float, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
- * @brief Construct an immutable hnswlib base-layer-only index from a CAGRA index
- * NOTE: This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
- * before reading it as an hnswlib index, then deleting the temporary file.  The returned index
- * is immutable and can only be searched by the hnswlib wrapper in cuVS, as the format is not
- * compatible with the original hnswlib.
+ * @brief Construct an hnswlib index from a CAGRA index
+ * NOTE: When `hnsw::index_params.hierarchy` is:
+ *       1. `NONE`: This method uses the filesystem to write the CAGRA index in
+ * `/tmp/<random_number>.bin` before reading it as an hnswlib index, then deleting the temporary
+ * file. The returned index is immutable and can only be searched by the hnswlib wrapper in cuVS, as
+ * the format is not compatible with the original hnswlib.
+ *       2. `CPU`: The returned index is mutable and can be extended with additional vectors. The
+ * serialized index is also compatible with the original hnswlib library.
  *
  * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
  * @param[in] cagra_index cagra index
+ * @param[in] dataset optional dataset to avoid extra memory copy when hierarchy is `CPU`
  *
  * Usage example:
  * @code{.cpp}
@@ -136,24 +185,34 @@ std::unique_ptr<index<float>> from_cagra(
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<uint8_t, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // Load CAGRA index as base-layer-only hnswlib index
- *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  * @endcode
  */
 std::unique_ptr<index<uint8_t>> from_cagra(
-  raft::resources const& res, const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& cagra_index);
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const uint8_t, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
- * @brief Construct an immutable hnswlib base-layer-only index from a CAGRA index
- * NOTE: This method uses the filesystem to write the CAGRA index in `/tmp/<random_number>.bin`
- * before reading it as an hnswlib index, then deleting the temporary file.  The returned index
- * is immutable and can only be searched by the hnswlib wrapper in cuVS, as the format is not
- * compatible with the original hnswlib.
+ * @brief Construct an hnswlib index from a CAGRA index
+ * NOTE: When `hnsw::index_params.hierarchy` is:
+ *       1. `NONE`: This method uses the filesystem to write the CAGRA index in
+ * `/tmp/<random_number>.bin` before reading it as an hnswlib index, then deleting the temporary
+ * file. The returned index is immutable and can only be searched by the hnswlib wrapper in cuVS, as
+ * the format is not compatible with the original hnswlib.
+ *       2. `CPU`: The returned index is mutable and can be extended with additional vectors. The
+ * serialized index is also compatible with the original hnswlib library.
  *
  * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
  * @param[in] cagra_index cagra index
+ * @param[in] dataset optional dataset to avoid extra memory copy when hierarchy is `CPU`
  *
  * Usage example:
  * @code{.cpp}
@@ -162,14 +221,138 @@ std::unique_ptr<index<uint8_t>> from_cagra(
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<int8_t, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // Load CAGRA index as base-layer-only hnswlib index
- *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  * @endcode
  */
 std::unique_ptr<index<int8_t>> from_cagra(
-  raft::resources const& res, const cuvs::neighbors::cagra::index<int8_t, uint32_t>& cagra_index);
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<int8_t, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const int8_t, int64_t, raft::row_major>> dataset =
+    std::nullopt);
+
+/**@}*/
+
+/**
+ * @defgroup hnsw_cpp_index_extend Extend HNSW index with additional vectors
+ * @{
+ */
+
+/**
+ * @brief Add new vectors to an HNSW index
+ * NOTE: The HNSW index can only be extended when the `hnsw::index_params.hierarchy` is `CPU`
+ *       when converting from a CAGRA index.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the extend
+ * @param[in] additional_dataset a host matrix view to a row-major matrix [n_rows, index->dim()]
+ * @param[inout] idx HNSW index to extend
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   hnsw_params.hierarchy = hnsw::HnswHierarchy::CPU;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *
+ *   // Extend the HNSW index with additional vectors
+ *   auto additional_dataset = raft::make_host_matrix<float>(res, add_size, index->dim());
+ *   hnsw::extend_params extend_params;
+ *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ */
+void extend(raft::resources const& res,
+            const extend_params& params,
+            raft::host_matrix_view<const float, int64_t, raft::row_major> additional_dataset,
+            index<float>& idx);
+
+/**
+ * @brief Add new vectors to an HNSW index
+ * NOTE: The HNSW index can only be extended when the `hnsw::index_params.hierarchy` is `CPU`
+ *       when converting from a CAGRA index.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the extend
+ * @param[in] additional_dataset a host matrix view to a row-major matrix [n_rows, index->dim()]
+ * @param[inout] idx HNSW index to extend
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   hnsw_params.hierarchy = hnsw::HnswHierarchy::CPU;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *
+ *   // Extend the HNSW index with additional vectors
+ *   auto additional_dataset = raft::make_host_matrix<float>(res, add_size, index->dim());
+ *   hnsw::extend_params extend_params;
+ *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ */
+void extend(raft::resources const& res,
+            const extend_params& params,
+            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> additional_dataset,
+            index<uint8_t>& idx);
+
+/**
+ * @brief Add new vectors to an HNSW index
+ * NOTE: The HNSW index can only be extended when the `hnsw::index_params.hierarchy` is `CPU`
+ *       when converting from a CAGRA index.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the extend
+ * @param[in] additional_dataset a host matrix view to a row-major matrix [n_rows, index->dim()]
+ * @param[inout] idx HNSW index to extend
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   hnsw_params.hierarchy = hnsw::HnswHierarchy::CPU;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *
+ *   // Extend the HNSW index with additional vectors
+ *   auto additional_dataset = raft::make_host_matrix<float>(res, add_size, index->dim());
+ *   hnsw::extend_params extend_params;
+ *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ */
+void extend(raft::resources const& res,
+            const extend_params& params,
+            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> additional_dataset,
+            index<int8_t>& idx);
+
+/**@} */
+
+/**
+ * @defgroup hnsw_cpp_search_params Build CAGRA index and search with hnswlib
+ * @{
+ */
+
+struct search_params : cuvs::neighbors::search_params {
+  int ef;               // size of the candidate list
+  int num_threads = 0;  // number of host threads to use for concurrent searches. Value of 0
+                        // automatically maximizes parallelism
+};
 
 /**@}*/
 
@@ -181,9 +364,9 @@ std::unique_ptr<index<int8_t>> from_cagra(
  */
 
 /**
- * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
- * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
- *       as the format is not compatible with the original hnswlib.
+ * @brief Search HNSW index constructed from a CAGRA index
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS when the hierarchy is
+ * `NONE`, as the format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -201,10 +384,11 @@ std::unique_ptr<index<int8_t>> from_cagra(
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<float, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // Load CAGRA index as a base-layer HNSW index using the filesystem
- *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  *
  *   // Search K nearest neighbors as an hnswlib index
  *   // using host threads for concurrency
@@ -224,9 +408,9 @@ void search(raft::resources const& res,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances);
 
 /**
- * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
- * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
- *       as the format is not compatible with the original hnswlib.
+ * @brief Search HNSWindex constructed from a CAGRA index
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS when the hierarchy is
+ * `NONE`, as the format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -244,10 +428,11 @@ void search(raft::resources const& res,
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<uint8_t, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // Load CAGRA index as a base-layer HNSW index using the filesystem
- *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  *
  *   // Search K nearest neighbors as an hnswlib index
  *   // using host threads for concurrency
@@ -267,9 +452,9 @@ void search(raft::resources const& res,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances);
 
 /**
- * @brief Search hnswlib base-layer-only index constructed from a CAGRA index
- * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS,
- *       as the format is not compatible with the original hnswlib.
+ * @brief Search HNSW index constructed from a CAGRA index
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS when the hierarchy is
+ * `NONE`, as the format is not compatible with the original hnswlib.
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -287,10 +472,11 @@ void search(raft::resources const& res,
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<int8_t, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // Load CAGRA index as a base-layer HNSW index using the filesystem
- *   auto hnsw_index = hnsw::from_cagra(res, index);
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  *
  *   // Search K nearest neighbors as an hnswlib index
  *   // using host threads for concurrency
@@ -312,16 +498,106 @@ void search(raft::resources const& res,
 /**@}*/
 
 /**
- * @defgroup hnsw_cpp_index_deserialize Deserialize CAGRA index as hnswlib index
+ * @defgroup hnsw_cpp_index_serialize Deserialize CAGRA index as hnswlib index
  * @{
  */
 
+/**
+ * @brief Serialize a CAGRA index to a file as an hnswlib index
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
+ *
+ * @param[in] res raft resources
+ * @param[in] filename path to the file to save the serialized CAGRA index
+ * @param[in] idx cagra index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *   // Save the index
+ *   hnsw::serialize(res, "index.bin", index);
+ * @endcode
+ */
+void serialize(raft::resources const& res, const std::string& filename, const index<float>& idx);
+
+/**
+ * @brief Serialize a CAGRA index to a file as an hnswlib index
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
+ *
+ * @param[in] res raft resources
+ * @param[in] filename path to the file to save the serialized CAGRA index
+ * @param[in] idx cagra index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *   // Save the index
+ *   hnsw::serialize(res, "index.bin", index);
+ * @endcode
+ */
+void serialize(raft::resources const& res, const std::string& filename, const index<uint8_t>& idx);
+
+/**
+ * @brief Serialize a CAGRA index to a file as an hnswlib index
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
+ *
+ * @param[in] res raft resources
+ * @param[in] filename path to the file to save the serialized CAGRA index
+ * @param[in] idx cagra index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *   // Save the index
+ *   hnsw::serialize(res, "index.bin", index);
+ * @endcode
+ */
+void serialize(raft::resources const& res, const std::string& filename, const index<int8_t>& idx);
+
 /**
  * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
- * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
  * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
  *
  * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
  * @param[in] filename path to the file containing the serialized CAGRA index
  * @param[in] dim dimensions of the training dataset
  * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
@@ -334,19 +610,23 @@ void search(raft::resources const& res,
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<float, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // save a CAGRA index to a file
- *   cagra::serialize(res, index, "index.bin");
- *   // De-serialize a CAGRA index as a base-layer HNSW index using the filesystem
- *   index<float>* hnsw_index = nullptr;
- *   hnsw::deserialize(res, "index.bin", index->dim(), index->metric(), &hnsw_index);
+ *   // Load CAGRA index as an HNSW index
+ *  hnsw::index_params hnsw_params;
+ *  auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *  // save HNSW index to a file
+ *  hnsw::serialize(res, "index.bin", hnsw_index);
+ *  // De-serialize the HNSW index
+ *  index<float>* hnsw_index = nullptr;
+ *  hnsw::deserialize(res, hnsw_params, "index.bin", index->dim(), index->metric(), &hnsw_index);
  *
  *   // Delete index after use
  *   delete hnsw_index;
  * @endcode
  */
 void deserialize(raft::resources const& res,
+                 const index_params& params,
                  const std::string& filename,
                  int dim,
                  cuvs::distance::DistanceType metric,
@@ -354,10 +634,13 @@ void deserialize(raft::resources const& res,
 
 /**
  * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
- * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
  * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
  *
  * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
  * @param[in] filename path to the file containing the serialized CAGRA index
  * @param[in] dim dimensions of the training dataset
  * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
@@ -370,19 +653,23 @@ void deserialize(raft::resources const& res,
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<uint8_t, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // save a CAGRA index to a file
- *   cagra::serialize(res, index, "index.bin");
- *   // De-serialize a CAGRA index as a base-layer HNSW index using the filesystem
- *   index<uint8_t>* hnsw_index = nullptr;
- *   hnsw::deserialize(res, "index.bin", index->dim(), index->metric(), &hnsw_index);
+ *   // Load CAGRA index as an HNSW index
+ *  hnsw::index_params hnsw_params;
+ *  auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *  // save HNSW index to a file
+ *  hnsw::serialize(res, "index.bin", hnsw_index);
+ *  // De-serialize the HNSW index
+ *  index<uint8_t>* hnsw_index = nullptr;
+ *  hnsw::deserialize(res, hnsw_params, "index.bin", index->dim(), index->metric(), &hnsw_index);
  *
  *   // Delete index after use
  *   delete hnsw_index;
  * @endcode
  */
 void deserialize(raft::resources const& res,
+                 const index_params& params,
                  const std::string& filename,
                  int dim,
                  cuvs::distance::DistanceType metric,
@@ -390,10 +677,13 @@ void deserialize(raft::resources const& res,
 
 /**
  * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
- * NOTE: The loaded hnswlib index is immutable, and only be read by the
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
  * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
  *
  * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
  * @param[in] filename path to the file containing the serialized CAGRA index
  * @param[in] dim dimensions of the training dataset
  * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
@@ -406,19 +696,23 @@ void deserialize(raft::resources const& res,
  *   // use default index parameters
  *   cagra::index_params index_params;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = cagra::build<int8_t, uint32_t>(res, index_params, dataset);
+ *   auto index = cagra::build(res, index_params, dataset);
  *
- *   // save a CAGRA index to a file
- *   cagra::serialize(res, index, "index.bin");
- *   // De-serialize a CAGRA index as a base-layer HNSW index using the filesystem
- *   index<int8_t>* hnsw_index = nullptr;
- *   hnsw::deserialize(res, "index.bin", index->dim(), index->metric(), &hnsw_index);
+ *   // Load CAGRA index as an HNSW index
+ *  hnsw::index_params hnsw_params;
+ *  auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *  // save HNSW index to a file
+ *  hnsw::serialize(res, "index.bin", hnsw_index);
+ *  // De-serialize the HNSW index
+ *  index<int8_t>* hnsw_index = nullptr;
+ *  hnsw::deserialize(res, hnsw_params, "index.bin", index->dim(), index->metric(), &hnsw_index);
  *
  *   // Delete index after use
  *   delete hnsw_index;
  * @endcode
  */
 void deserialize(raft::resources const& res,
+                 const index_params& params,
                  const std::string& filename,
                  int dim,
                  cuvs::distance::DistanceType metric,
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index ce1e032648..e129d23e87 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -22,9 +22,63 @@
 #include <hnswlib/hnswlib.h>
 #include <memory>
 #include <random>
+#include <thread>
 
 namespace cuvs::neighbors::hnsw::detail {
 
+// Multithreaded executor
+// The helper function is copied from the hnswlib repository
+// as for some reason, adding vectors to the hnswlib index does not
+// work well with omp parallel for
+template <class Function>
+inline void ParallelFor(size_t start, size_t end, size_t numThreads, Function fn)
+{
+  if (numThreads <= 0) { numThreads = std::thread::hardware_concurrency(); }
+
+  if (numThreads == 1) {
+    for (size_t id = start; id < end; id++) {
+      fn(id, 0);
+    }
+  } else {
+    std::vector<std::thread> threads;
+    std::atomic<size_t> current(start);
+
+    // keep track of exceptions in threads
+    // https://stackoverflow.com/a/32428427/1713196
+    std::exception_ptr lastException = nullptr;
+    std::mutex lastExceptMutex;
+
+    for (size_t threadId = 0; threadId < numThreads; ++threadId) {
+      threads.push_back(std::thread([&, threadId] {
+        while (true) {
+          size_t id = current.fetch_add(1);
+
+          if (id >= end) { break; }
+
+          try {
+            fn(id, threadId);
+          } catch (...) {
+            std::unique_lock<std::mutex> lastExcepLock(lastExceptMutex);
+            lastException = std::current_exception();
+            /*
+             * This will work even when current is the largest value that
+             * size_t can fit, because fetch_add returns the previous value
+             * before the increment (what will result in overflow
+             * and produce 0 instead of current + 1).
+             */
+            current = end;
+            break;
+          }
+        }
+      }));
+    }
+    for (auto& thread : threads) {
+      thread.join();
+    }
+    if (lastException) { std::rethrow_exception(lastException); }
+  }
+}
+
 template <typename T>
 struct hnsw_dist_t {
   using type = void;
@@ -54,9 +108,10 @@ struct index_impl : index<T> {
    * @param[in] filepath path to the index
    * @param[in] dim dimensions of the training dataset
    * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+   * @param[in] hierarchy hierarchy used for upper HNSW layers
    */
-  index_impl(const std::string& filepath, int dim, cuvs::distance::DistanceType metric)
-    : index<T>{dim, metric}
+  index_impl(int dim, cuvs::distance::DistanceType metric, HnswHierarchy hierarchy)
+    : index<T>{dim, metric, hierarchy}
   {
     if constexpr (std::is_same_v<T, float>) {
       if (metric == cuvs::distance::DistanceType::L2Expanded) {
@@ -71,11 +126,6 @@ struct index_impl : index<T> {
     }
 
     RAFT_EXPECTS(space_ != nullptr, "Unsupported metric type was used");
-
-    appr_alg_ = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
-      space_.get(), filepath);
-
-    appr_alg_->base_layer_only = true;
   }
 
   /**
@@ -88,14 +138,32 @@ struct index_impl : index<T> {
   */
   void set_ef(int ef) const override { appr_alg_->ef_ = ef; }
 
+  /**
+  @brief Set index
+   */
+  void set_index(std::unique_ptr<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>&& index)
+  {
+    appr_alg_ = std::move(index);
+  }
+
+  /**
+  @brief Get space
+   */
+  auto get_space() const -> hnswlib::SpaceInterface<typename hnsw_dist_t<T>::type>*
+  {
+    return space_.get();
+  }
+
  private:
   std::unique_ptr<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>> appr_alg_;
   std::unique_ptr<hnswlib::SpaceInterface<typename hnsw_dist_t<T>::type>> space_;
 };
 
-template <typename T>
-std::unique_ptr<index<T>> from_cagra(raft::resources const& res,
-                                     const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index)
+template <typename T, HnswHierarchy hierarchy>
+std::enable_if_t<hierarchy == HnswHierarchy::NONE, std::unique_ptr<index<T>>> from_cagra(
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index)
 {
   std::random_device dev;
   std::mt19937 rng(dev());
@@ -103,13 +171,125 @@ std::unique_ptr<index<T>> from_cagra(raft::resources const& res,
   auto uuid            = std::to_string(dist(rng));
   std::string filepath = "/tmp/" + uuid + ".bin";
   cuvs::neighbors::cagra::serialize_to_hnswlib(res, filepath, cagra_index);
+
   index<T>* hnsw_index = nullptr;
   cuvs::neighbors::hnsw::deserialize(
-    res, filepath, cagra_index.dim(), cagra_index.metric(), &hnsw_index);
+    res, params, filepath, cagra_index.dim(), cagra_index.metric(), &hnsw_index);
   std::filesystem::remove(filepath);
   return std::unique_ptr<index<T>>(hnsw_index);
 }
 
+template <typename T, HnswHierarchy hierarchy>
+std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> from_cagra(
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
+{
+  // auto host_dataset = raft::make_host_matrix<T, int64_t>(dataset.extent(0), dataset.extent(1));
+  auto host_dataset = raft::make_host_matrix<T, int64_t>(0, 0);
+  raft::host_matrix_view<const T, int64_t, raft::row_major> host_dataset_view(
+    host_dataset.data_handle(), host_dataset.extent(0), host_dataset.extent(1));
+  if (dataset.has_value()) {
+    host_dataset_view = dataset.value();
+  } else {
+    // move dataset to host, remove padding
+    auto cagra_dataset = cagra_index.dataset();
+    host_dataset =
+      raft::make_host_matrix<T, int64_t>(cagra_dataset.extent(0), cagra_dataset.extent(1));
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_dataset.data_handle(),
+                                    sizeof(T) * host_dataset.extent(1),
+                                    cagra_dataset.data_handle(),
+                                    sizeof(T) * cagra_dataset.stride(0),
+                                    sizeof(T) * host_dataset.extent(1),
+                                    cagra_dataset.extent(0),
+                                    cudaMemcpyDefault,
+                                    raft::resource::get_cuda_stream(res)));
+    raft::resource::sync_stream(res);
+    host_dataset_view = host_dataset.view();
+  }
+  // build upper layers of hnsw index
+  auto hnsw_index =
+    std::make_unique<index_impl<T>>(cagra_index.dim(), cagra_index.metric(), hierarchy);
+  auto appr_algo = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+    hnsw_index->get_space(),
+    host_dataset_view.extent(0),
+    cagra_index.graph().extent(1) / 2,
+    params.ef_construction);
+  appr_algo->base_layer_init = false;  // tell hnswlib to build upper layers only
+  ParallelFor(0, host_dataset_view.extent(0), params.num_threads, [&](size_t i, size_t threadId) {
+    appr_algo->addPoint((void*)(host_dataset_view.data_handle() + i * host_dataset_view.extent(1)),
+                        i);
+  });
+  appr_algo->base_layer_init = true;  // reset to true to allow addition of new points
+
+  // move cagra graph to host
+  auto graph = cagra_index.graph();
+  auto host_graph =
+    raft::make_host_matrix<uint32_t, int64_t, raft::row_major>(graph.extent(0), graph.extent(1));
+  raft::copy(host_graph.data_handle(),
+             graph.data_handle(),
+             graph.size(),
+             raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+
+// copy cagra graph to hnswlib base layer
+#pragma omp parallel for
+  for (size_t i = 0; i < static_cast<size_t>(host_graph.extent(0)); ++i) {
+    auto ll_i = appr_algo->get_linklist0(i);
+    appr_algo->setListCount(ll_i, host_graph.extent(1));
+    auto* data = (uint32_t*)(ll_i + 1);
+    for (size_t j = 0; j < static_cast<size_t>(host_graph.extent(1)); ++j) {
+      data[j] = host_graph(i, j);
+    }
+  }
+
+  hnsw_index->set_index(std::move(appr_algo));
+  return hnsw_index;
+}
+
+template <typename T>
+std::unique_ptr<index<T>> from_cagra(
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
+{
+  if (params.hierarchy == HnswHierarchy::NONE) {
+    return from_cagra<T, HnswHierarchy::NONE>(res, params, cagra_index);
+  } else if (params.hierarchy == HnswHierarchy::CPU) {
+    return from_cagra<T, HnswHierarchy::CPU>(res, params, cagra_index, dataset);
+  }
+  {
+    RAFT_FAIL("Unsupported hierarchy type");
+  }
+}
+
+template <typename T>
+void extend(raft::resources const& res,
+            const extend_params& params,
+            raft::host_matrix_view<const T, int64_t, raft::row_major> additional_dataset,
+            index<T>& idx)
+{
+  auto* hnswlib_index = reinterpret_cast<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>*>(
+    const_cast<void*>(idx.get_index()));
+  auto current_element_count = hnswlib_index->getCurrentElementCount();
+  auto new_element_count     = additional_dataset.extent(0);
+  auto num_threads           = params.num_threads == 0 ? std::thread::hardware_concurrency()
+                                                       : static_cast<size_t>(params.num_threads);
+
+  hnswlib_index->resizeIndex(current_element_count + new_element_count);
+  ParallelFor(current_element_count,
+              current_element_count + new_element_count,
+              num_threads,
+              [&](size_t i, size_t threadId) {
+                hnswlib_index->addPoint(
+                  (void*)(additional_dataset.data_handle() +
+                          (i - current_element_count) * additional_dataset.extent(1)),
+                  i);
+              });
+}
+
 template <typename T>
 void get_search_knn_results(hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type> const* idx,
                             const T* query,
@@ -171,14 +351,28 @@ void search(raft::resources const& res,
   }
 }
 
+template <typename T>
+void serialize(raft::resources const& res, const std::string& filename, const index<T>& idx)
+{
+  auto* hnswlib_index = reinterpret_cast<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>*>(
+    const_cast<void*>(idx.get_index()));
+  hnswlib_index->saveIndex(filename);
+}
+
 template <typename T>
 void deserialize(raft::resources const& res,
+                 const index_params& params,
                  const std::string& filename,
                  int dim,
                  cuvs::distance::DistanceType metric,
                  index<T>** idx)
 {
-  *idx = new detail::index_impl<T>(filename, dim, metric);
+  auto hnsw_index = std::make_unique<index_impl<T>>(dim, metric, params.hierarchy);
+  auto appr_algo  = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+    hnsw_index->get_space(), filename);
+  if (params.hierarchy == HnswHierarchy::NONE) { appr_algo->base_layer_only = true; }
+  hnsw_index->set_index(std::move(appr_algo));
+  *idx = hnsw_index.release();
 }
 
 }  // namespace cuvs::neighbors::hnsw::detail
diff --git a/cpp/src/neighbors/hnsw.cpp b/cpp/src/neighbors/hnsw.cpp
index e6f3fbcc7a..f165176ece 100644
--- a/cpp/src/neighbors/hnsw.cpp
+++ b/cpp/src/neighbors/hnsw.cpp
@@ -21,11 +21,14 @@
 
 namespace cuvs::neighbors::hnsw {
 
-#define CUVS_INST_HNSW_FROM_CAGRA(T)                                                           \
-  std::unique_ptr<index<T>> from_cagra(                                                        \
-    raft::resources const& res, const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index) \
-  {                                                                                            \
-    return detail::from_cagra<T>(res, cagra_index);                                            \
+#define CUVS_INST_HNSW_FROM_CAGRA(T)                                                  \
+  std::unique_ptr<index<T>> from_cagra(                                               \
+    raft::resources const& res,                                                       \
+    const index_params& params,                                                       \
+    const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,                    \
+    std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset) \
+  {                                                                                   \
+    return detail::from_cagra<T>(res, params, cagra_index, dataset);                  \
   }
 
 CUVS_INST_HNSW_FROM_CAGRA(float);
@@ -34,6 +37,21 @@ CUVS_INST_HNSW_FROM_CAGRA(int8_t);
 
 #undef CUVS_INST_HNSW_FROM_CAGRA
 
+#define CUVS_INST_HNSW_EXTEND(T)                                                            \
+  void extend(raft::resources const& res,                                                   \
+              const extend_params& params,                                                  \
+              raft::host_matrix_view<const T, int64_t, raft::row_major> additional_dataset, \
+              index<T>& idx)                                                                \
+  {                                                                                         \
+    detail::extend<T>(res, params, additional_dataset, idx);                                \
+  }
+
+CUVS_INST_HNSW_EXTEND(float);
+CUVS_INST_HNSW_EXTEND(uint8_t);
+CUVS_INST_HNSW_EXTEND(int8_t);
+
+#undef CUVS_INST_HNSW_EXTEND
+
 #define CUVS_INST_HNSW_SEARCH(T)                                                    \
   void search(raft::resources const& res,                                           \
               const search_params& params,                                          \
@@ -51,20 +69,25 @@ CUVS_INST_HNSW_SEARCH(int8_t);
 
 #undef CUVS_INST_HNSW_SEARCH
 
-#define CUVS_INST_HNSW_DESERIALIZE(T)                        \
-  void deserialize(raft::resources const& res,               \
-                   const std::string& filename,              \
-                   int dim,                                  \
-                   cuvs::distance::DistanceType metric,      \
-                   index<T>** idx)                           \
-  {                                                          \
-    detail::deserialize<T>(res, filename, dim, metric, idx); \
+#define CUVS_INST_HNSW_SERIALIZE(T)                                                            \
+  void serialize(raft::resources const& res, const std::string& filename, const index<T>& idx) \
+  {                                                                                            \
+    detail::serialize<T>(res, filename, idx);                                                  \
+  }                                                                                            \
+  void deserialize(raft::resources const& res,                                                 \
+                   const index_params& params,                                                 \
+                   const std::string& filename,                                                \
+                   int dim,                                                                    \
+                   cuvs::distance::DistanceType metric,                                        \
+                   index<T>** idx)                                                             \
+  {                                                                                            \
+    detail::deserialize<T>(res, params, filename, dim, metric, idx);                           \
   }
 
-CUVS_INST_HNSW_DESERIALIZE(float);
-CUVS_INST_HNSW_DESERIALIZE(uint8_t);
-CUVS_INST_HNSW_DESERIALIZE(int8_t);
+CUVS_INST_HNSW_SERIALIZE(float);
+CUVS_INST_HNSW_SERIALIZE(uint8_t);
+CUVS_INST_HNSW_SERIALIZE(int8_t);
 
-#undef CUVS_INST_HNSW_DESERIALIZE
+#undef CUVS_INST_HNSW_SERIALIZE
 
 }  // namespace cuvs::neighbors::hnsw
diff --git a/cpp/src/neighbors/hnsw_c.cpp b/cpp/src/neighbors/hnsw_c.cpp
index a198756417..0233a510a1 100644
--- a/cpp/src/neighbors/hnsw_c.cpp
+++ b/cpp/src/neighbors/hnsw_c.cpp
@@ -31,6 +31,44 @@
 #include <cuvs/neighbors/hnsw.hpp>
 
 namespace {
+
+template <typename T>
+void _from_cagra(cuvsResources_t res,
+                 cuvsHnswIndexParams_t params,
+                 cuvsCagraIndex_t cagra_index,
+                 cuvsHnswIndex_t hnsw_index)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto index   = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(cagra_index->addr);
+  auto cpp_params            = cuvs::neighbors::hnsw::index_params();
+  cpp_params.hierarchy       = static_cast<cuvs::neighbors::hnsw::HnswHierarchy>(params->hierarchy);
+  cpp_params.ef_construction = params->ef_construction;
+  cpp_params.num_threads     = params->num_threads;
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset = std::nullopt;
+
+  auto hnsw_index_unique_ptr =
+    cuvs::neighbors::hnsw::from_cagra(*res_ptr, cpp_params, *index, dataset);
+  auto hnsw_index_ptr = hnsw_index_unique_ptr.release();
+  hnsw_index->addr    = reinterpret_cast<uintptr_t>(hnsw_index_ptr);
+}
+
+template <typename T>
+void _extend(cuvsResources_t res,
+             cuvsHnswExtendParams_t params,
+             DLManagedTensor* additional_dataset,
+             cuvsHnswIndex index)
+{
+  auto res_ptr           = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr         = reinterpret_cast<cuvs::neighbors::hnsw::index<T>*>(index.addr);
+  auto cpp_params        = cuvs::neighbors::hnsw::extend_params();
+  cpp_params.num_threads = params->num_threads;
+
+  using additional_dataset_mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+  auto additional_dataset_mds =
+    cuvs::core::from_dlpack<additional_dataset_mdspan_type>(additional_dataset);
+  cuvs::neighbors::hnsw::extend(*res_ptr, cpp_params, additional_dataset_mds, *index_ptr);
+}
+
 template <typename T>
 void _search(cuvsResources_t res,
              cuvsHnswSearchParams params,
@@ -44,7 +82,7 @@ void _search(cuvsResources_t res,
 
   auto search_params        = cuvs::neighbors::hnsw::search_params();
   search_params.ef          = params.ef;
-  search_params.num_threads = params.numThreads;
+  search_params.num_threads = params.num_threads;
 
   using queries_mdspan_type   = raft::host_matrix_view<T const, int64_t, raft::row_major>;
   using neighbors_mdspan_type = raft::host_matrix_view<uint64_t, int64_t, raft::row_major>;
@@ -57,26 +95,42 @@ void _search(cuvsResources_t res,
 }
 
 template <typename T>
-void* _deserialize(cuvsResources_t res, const char* filename, int dim, cuvsDistanceType metric)
+void _serialize(cuvsResources_t res, const char* filename, cuvsHnswIndex index)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<T>*>(index.addr);
+  cuvs::neighbors::hnsw::serialize(*res_ptr, std::string(filename), *index_ptr);
+}
+
+template <typename T>
+void* _deserialize(cuvsResources_t res,
+                   cuvsHnswIndexParams_t params,
+                   const char* filename,
+                   int dim,
+                   cuvsDistanceType metric)
 {
   auto res_ptr                           = reinterpret_cast<raft::resources*>(res);
   cuvs::neighbors::hnsw::index<T>* index = nullptr;
-  cuvs::neighbors::hnsw::deserialize(*res_ptr, std::string(filename), dim, metric, &index);
+  auto cpp_params                        = cuvs::neighbors::hnsw::index_params();
+  cpp_params.hierarchy = static_cast<cuvs::neighbors::hnsw::HnswHierarchy>(params->hierarchy);
+  cuvs::neighbors::hnsw::deserialize(
+    *res_ptr, cpp_params, std::string(filename), dim, metric, &index);
   return index;
 }
 }  // namespace
 
-extern "C" cuvsError_t cuvsHnswSearchParamsCreate(cuvsHnswSearchParams_t* params)
+extern "C" cuvsError_t cuvsHnswIndexParamsCreate(cuvsHnswIndexParams_t* params)
 {
-  return cuvs::core::translate_exceptions(
-    [=] { *params = new cuvsHnswSearchParams{.ef = 200, .numThreads = 0}; });
+  return cuvs::core::translate_exceptions([=] {
+    *params = new cuvsHnswIndexParams{
+      .hierarchy = cuvsHnswHierarchy::NONE, .ef_construction = 200, .num_threads = 2};
+  });
 }
 
-extern "C" cuvsError_t cuvsHnswSearchParamsDestroy(cuvsHnswSearchParams_t params)
+extern "C" cuvsError_t cuvsHnswIndexParamsDestroy(cuvsHnswIndexParams_t params)
 {
   return cuvs::core::translate_exceptions([=] { delete params; });
 }
-
 extern "C" cuvsError_t cuvsHnswIndexCreate(cuvsHnswIndex_t* index)
 {
   return cuvs::core::translate_exceptions([=] { *index = new cuvsHnswIndex{}; });
@@ -101,6 +155,66 @@ extern "C" cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index_c_ptr)
   });
 }
 
+extern "C" cuvsError_t cuvsHnswExtendParamsCreate(cuvsHnswExtendParams_t* params)
+{
+  return cuvs::core::translate_exceptions(
+    [=] { *params = new cuvsHnswExtendParams{.num_threads = 0}; });
+}
+
+extern "C" cuvsError_t cuvsHnswExtendParamsDestroy(cuvsHnswExtendParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsHnswFromCagra(cuvsResources_t res,
+                                         cuvsHnswIndexParams_t params,
+                                         cuvsCagraIndex_t cagra_index,
+                                         cuvsHnswIndex_t hnsw_index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index        = *cagra_index;
+    hnsw_index->dtype = index.dtype;
+    if (index.dtype.code == kDLFloat) {
+      _from_cagra<float>(res, params, cagra_index, hnsw_index);
+    } else if (index.dtype.code == kDLUInt) {
+      _from_cagra<uint8_t>(res, params, cagra_index, hnsw_index);
+    } else if (index.dtype.code == kDLInt) {
+      _from_cagra<int8_t>(res, params, cagra_index, hnsw_index);
+    } else {
+      RAFT_FAIL("Unsupported dtype: %d", index.dtype.code);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsHnswExtend(cuvsResources_t res,
+                                      cuvsHnswExtendParams_t params,
+                                      DLManagedTensor* additional_dataset,
+                                      cuvsHnswIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat) {
+      _extend<float>(res, params, additional_dataset, *index);
+    } else if (index->dtype.code == kDLUInt) {
+      _extend<uint8_t>(res, params, additional_dataset, *index);
+    } else if (index->dtype.code == kDLInt) {
+      _extend<int8_t>(res, params, additional_dataset, *index);
+    } else {
+      RAFT_FAIL("Unsupported dtype: %d", index->dtype.code);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsHnswSearchParamsCreate(cuvsHnswSearchParams_t* params)
+{
+  return cuvs::core::translate_exceptions(
+    [=] { *params = new cuvsHnswSearchParams{.ef = 200, .num_threads = 0}; });
+}
+
+extern "C" cuvsError_t cuvsHnswSearchParamsDestroy(cuvsHnswSearchParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
 extern "C" cuvsError_t cuvsHnswSearch(cuvsResources_t res,
                                       cuvsHnswSearchParams_t params,
                                       cuvsHnswIndex_t index_c_ptr,
@@ -140,7 +254,25 @@ extern "C" cuvsError_t cuvsHnswSearch(cuvsResources_t res,
   });
 }
 
+extern "C" cuvsError_t cuvsHnswSerialize(cuvsResources_t res,
+                                         const char* filename,
+                                         cuvsHnswIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat) {
+      _serialize<float>(res, filename, *index);
+    } else if (index->dtype.code == kDLInt) {
+      _serialize<int8_t>(res, filename, *index);
+    } else if (index->dtype.code == kDLUInt) {
+      _serialize<uint8_t>(res, filename, *index);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
 extern "C" cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
+                                           cuvsHnswIndexParams_t params,
                                            const char* filename,
                                            int dim,
                                            cuvsDistanceType metric,
@@ -148,11 +280,14 @@ extern "C" cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
 {
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
-      index->addr = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename, dim, metric));
+      index->addr =
+        reinterpret_cast<uintptr_t>(_deserialize<float>(res, params, filename, dim, metric));
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
-      index->addr = reinterpret_cast<uintptr_t>(_deserialize<uint8_t>(res, filename, dim, metric));
+      index->addr =
+        reinterpret_cast<uintptr_t>(_deserialize<uint8_t>(res, params, filename, dim, metric));
     } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
-      index->addr = reinterpret_cast<uintptr_t>(_deserialize<int8_t>(res, filename, dim, metric));
+      index->addr =
+        reinterpret_cast<uintptr_t>(_deserialize<int8_t>(res, params, filename, dim, metric));
     } else {
       RAFT_FAIL("Unsupported dtype in file %s", filename);
     }
diff --git a/cpp/src/neighbors/iface/iface.hpp b/cpp/src/neighbors/iface/iface.hpp
index 9b3da75a47..98ef3fdd38 100644
--- a/cpp/src/neighbors/iface/iface.hpp
+++ b/cpp/src/neighbors/iface/iface.hpp
@@ -20,6 +20,7 @@
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <fstream>
 #include <raft/core/device_resources.hpp>
 
 #include <fstream>
diff --git a/cpp/test/neighbors/ann_hnsw_c.cu b/cpp/test/neighbors/ann_hnsw_c.cu
index fc740b924c..2a6401b1dc 100644
--- a/cpp/test/neighbors/ann_hnsw_c.cu
+++ b/cpp/test/neighbors/ann_hnsw_c.cu
@@ -111,7 +111,9 @@ TEST(CagraHnswC, BuildSearch)
   cuvsHnswIndex_t hnsw_index;
   cuvsHnswIndexCreate(&hnsw_index);
   hnsw_index->dtype = index->dtype;
-  cuvsHnswDeserialize(res, "/tmp/cagra_hnswlib.index", 2, L2Expanded, hnsw_index);
+  cuvsHnswIndexParams_t hnsw_params;
+  cuvsHnswIndexParamsCreate(&hnsw_params);
+  cuvsHnswDeserialize(res, hnsw_params, "/tmp/cagra_hnswlib.index", 2, L2Expanded, hnsw_index);
 
   // search index
   cuvsHnswSearchParams_t search_params;
diff --git a/cpp/test/neighbors/hnsw.cu b/cpp/test/neighbors/hnsw.cu
index 9fb88be05b..20ee83a114 100644
--- a/cpp/test/neighbors/hnsw.cu
+++ b/cpp/test/neighbors/hnsw.cu
@@ -108,7 +108,8 @@ class AnnHNSWTest : public ::testing::TestWithParam<AnnHNSWInputs> {
 
       cuvs::neighbors::hnsw::search_params search_params;
       search_params.ef = ps.ef;
-      auto hnsw_index  = cuvs::neighbors::hnsw::from_cagra(handle_, index);
+      cuvs::neighbors::hnsw::index_params hnsw_params;
+      auto hnsw_index = cuvs::neighbors::hnsw::from_cagra(handle_, hnsw_params, index);
       auto queries_HNSW_view =
         raft::make_host_matrix_view<DataT, int64_t>(queries_h.data(), ps.n_queries, ps.dim);
       auto indices_HNSW_view =
diff --git a/dependencies.yaml b/dependencies.yaml
index e909ad0dc0..80a7d20241 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -470,7 +470,6 @@ dependencies:
     common:
       - output_types: [conda, pyproject, requirements]
         packages:
-          - hnswlib=0.6.2
           - nlohmann_json>=3.11.2
           - glog>=0.6.0
           - h5py>=3.8.0
diff --git a/docs/source/c_api/neighbors_hnsw_c.rst b/docs/source/c_api/neighbors_hnsw_c.rst
index 988e5b6f33..22ffc236d0 100644
--- a/docs/source/c_api/neighbors_hnsw_c.rst
+++ b/docs/source/c_api/neighbors_hnsw_c.rst
@@ -26,6 +26,28 @@ Index
     :members:
     :content-only:
 
+Index extend parameters
+-----------------------
+
+.. doxygengroup:: hnsw_c_extend_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index extend
+------------
+.. doxygengroup:: hnsw_c_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index load
+----------
+.. doxygengroup:: hnsw_c_index_load
+    :project: cuvs
+    :members:
+    :content-only:
+
 Index search
 ------------
 
diff --git a/docs/source/cpp_api/neighbors_hnsw.rst b/docs/source/cpp_api/neighbors_hnsw.rst
index b0af88af00..00dd3a213c 100644
--- a/docs/source/cpp_api/neighbors_hnsw.rst
+++ b/docs/source/cpp_api/neighbors_hnsw.rst
@@ -27,10 +27,25 @@ Index
     :members:
     :content-only:
 
-Index load
+Index extend parameters
+-----------------------
+
+.. doxygengroup:: hnsw_cpp_extend_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index extend
 ------------
+.. doxygengroup:: hnsw_cpp_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
 
-.. doxygengroup:: hnsw_cpp_index_search
+Index load
+----------
+
+.. doxygengroup:: hnsw_cpp_index_load
     :project: cuvs
     :members:
     :content-only:
@@ -43,10 +58,10 @@ Index search
     :members:
     :content-only:
 
-Index deserialize
+Index serialize
 ---------------
 
-.. doxygengroup:: hnsw_cpp_index_deserialize
+.. doxygengroup:: hnsw_cpp_index_serialize
     :project: cuvs
     :members:
     :content-only:
diff --git a/python/cuvs/cuvs/neighbors/hnsw/__init__.py b/python/cuvs/cuvs/neighbors/hnsw/__init__.py
index 5efcdf68b2..fafff7d034 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/__init__.py
+++ b/python/cuvs/cuvs/neighbors/hnsw/__init__.py
@@ -13,10 +13,23 @@
 # limitations under the License.
 
 
-from .hnsw import Index, SearchParams, from_cagra, load, save, search
+from .hnsw import (
+    ExtendParams,
+    Index,
+    IndexParams,
+    SearchParams,
+    extend,
+    from_cagra,
+    load,
+    save,
+    search,
+)
 
 __all__ = [
+    "IndexParams",
     "Index",
+    "ExtendParams",
+    "extend",
     "SearchParams",
     "load",
     "save",
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
index 1cdc974064..e0c5179331 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
@@ -20,14 +20,25 @@ from libc.stdint cimport int32_t, uintptr_t
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
 from cuvs.distance_type cimport cuvsDistanceType
+from cuvs.neighbors.cagra.cagra cimport cuvsCagraIndex_t
 
 
 cdef extern from "cuvs/neighbors/hnsw.h" nogil:
-    ctypedef struct cuvsHnswSearchParams:
-        int32_t ef
-        int32_t numThreads
 
-    ctypedef cuvsHnswSearchParams* cuvsHnswSearchParams_t
+    ctypedef enum cuvsHnswHierarchy:
+        NONE
+        CPU
+
+    ctypedef struct cuvsHnswIndexParams:
+        cuvsHnswHierarchy hierarchy
+        int32_t ef_construction
+        int32_t num_threads
+
+    ctypedef cuvsHnswIndexParams* cuvsHnswIndexParams_t
+
+    cuvsError_t cuvsHnswIndexParamsCreate(cuvsHnswIndexParams_t* params)
+
+    cuvsError_t cuvsHnswIndexParamsDestroy(cuvsHnswIndexParams_t params)
 
     ctypedef struct cuvsHnswIndex:
         uintptr_t addr
@@ -39,6 +50,31 @@ cdef extern from "cuvs/neighbors/hnsw.h" nogil:
 
     cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index)
 
+    ctypedef struct cuvsHnswExtendParams:
+        int32_t num_threads
+
+    ctypedef cuvsHnswExtendParams* cuvsHnswExtendParams_t
+
+    cuvsError_t cuvsHnswExtendParamsCreate(cuvsHnswExtendParams_t* params)
+
+    cuvsError_t cuvsHnswExtendParamsDestroy(cuvsHnswExtendParams_t params)
+
+    cuvsError_t cuvsHnswFromCagra(cuvsResources_t res,
+                                  cuvsHnswIndexParams_t params,
+                                  cuvsCagraIndex_t cagra_index,
+                                  cuvsHnswIndex_t hnsw_index) except +
+
+    cuvsError_t cuvsHnswExtend(cuvsResources_t res,
+                               cuvsHnswExtendParams_t params,
+                               DLManagedTensor* data,
+                               cuvsHnswIndex_t index) except +
+
+    ctypedef struct cuvsHnswSearchParams:
+        int32_t ef
+        int32_t num_threads
+
+    ctypedef cuvsHnswSearchParams* cuvsHnswSearchParams_t
+
     cuvsError_t cuvsHnswSearch(cuvsResources_t res,
                                cuvsHnswSearchParams* params,
                                cuvsHnswIndex_t index,
@@ -46,7 +82,12 @@ cdef extern from "cuvs/neighbors/hnsw.h" nogil:
                                DLManagedTensor* neighbors,
                                DLManagedTensor* distances) except +
 
+    cuvsError_t cuvsHnswSerialize(cuvsResources_t res,
+                                  const char * filename,
+                                  cuvsHnswIndex_t index) except +
+
     cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
+                                    cuvsHnswIndexParams_t params,
                                     const char * filename,
                                     int32_t dim,
                                     cuvsDistanceType metric,
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
index bcfaf167ef..4c44350e83 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
@@ -39,41 +39,63 @@ from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
 
-cdef class SearchParams:
+cdef class IndexParams:
     """
-    HNSW search parameters
+    Parameters to build index for HNSW nearest neighbor search
 
     Parameters
     ----------
-    ef: int, default = 200
-        Maximum number of candidate list size used during search.
-    num_threads: int, default = 0
-        Number of CPU threads used to increase search parallelism.
-        When set to 0, the number of threads is automatically determined
-        using OpenMP's `omp_get_max_threads()`.
+    hierarchy : string, default = "none" (optional)
+        The hierarchy of the HNSW index. Valid values are ["none", "cpu"].
+        - "none": No hierarchy is built.
+        - "cpu": Hierarchy is built using CPU.
+    ef_construction : int, default = 200 (optional)
+        Maximum number of candidate list size used during construction
+        when hierarchy is `cpu`.
+    num_threads : int, default = 2 (optional)
+        Number of CPU threads used to increase construction parallelism
+        when hierarchy is `cpu`.
+        NOTE: Constructing the hierarchy when converting from a CAGRA graph
+        is highly sensitive to parallelism, and increasing the number of
+        threads can reduce the quality of the index.
     """
 
-    cdef cuvsHnswSearchParams params
+    cdef cuvsHnswIndexParams* params
+
+    def __cinit__(self):
+        check_cuvs(cuvsHnswIndexParamsCreate(&self.params))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsHnswIndexParamsDestroy(self.params))
 
     def __init__(self, *,
-                 ef=200,
-                 num_threads=0):
-        self.params.ef = ef
-        self.params.numThreads = num_threads
+                 hierarchy="none",
+                 ef_construction=200,
+                 num_threads=2):
+        if hierarchy == "none":
+            self.params.hierarchy = cuvsHnswHierarchy.NONE
+        elif hierarchy == "cpu":
+            self.params.hierarchy = cuvsHnswHierarchy.CPU
+        else:
+            raise ValueError("Invalid hierarchy type."
+                             " Valid values are 'none' and 'cpu'.")
+        self.params.ef_construction = ef_construction
+        self.params.num_threads = num_threads
 
-    def __repr__(self):
-        attr_str = [attr + "=" + str(getattr(self, attr))
-                    for attr in [
-                        "ef", "num_threads"]]
-        return "SearchParams(type=HNSW, " + (", ".join(attr_str)) + ")"
+    @property
+    def hierarchy(self):
+        if self.params.hierarchy == cuvsHnswHierarchy.NONE:
+            return "none"
+        elif self.params.hierarchy == cuvsHnswHierarchy.CPU:
+            return "cpu"
 
     @property
-    def ef(self):
-        return self.params.ef
+    def ef_construction(self):
+        return self.params.ef_construction
 
     @property
     def num_threads(self):
-        return self.params.numThreads
+        return self.params.num_threads
 
 
 cdef class Index:
@@ -103,13 +125,44 @@ cdef class Index:
         return "Index(type=HNSW, metric=L2" + (", ".join(attr_str)) + ")"
 
 
+cdef class ExtendParams:
+    """
+    Parameters to extend the HNSW index with new data
+
+    Parameters
+    ----------
+    num_threads : int, default = 0 (optional)
+        Number of CPU threads used to increase construction parallelism.
+        When set to 0, the number of threads is automatically determined.
+    """
+
+    cdef cuvsHnswExtendParams* params
+
+    def __cinit__(self):
+        check_cuvs(cuvsHnswExtendParamsCreate(&self.params))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsHnswExtendParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 num_threads=0):
+        self.params.num_threads = num_threads
+
+    @property
+    def num_threads(self):
+        return self.params.num_threads
+
+
 @auto_sync_resources
-def save(filename, cagra.Index index, resources=None):
+def save(filename, Index index, resources=None):
     """
     Saves the CAGRA index to a file as an hnswlib index.
-    The saved index is immutable and can only be searched by the hnswlib
-    wrapper in cuVS, as the format is not compatible with the original
-    hnswlib.
+    If the index was constructed with `hnsw.IndexParams(hierarchy="none")`,
+    then the saved index is immutable and can only be searched by the hnswlib
+    wrapper in cuVS, as the format is not compatible with the original hnswlib.
+    However, if the index was constructed with
+    `hnsw.IndexParams(hierarchy="cpu")`, then the saved index is mutable and
+    compatible with the original hnswlib.
 
     Saving / loading the index is experimental. The serialization format is
     subject to change.
@@ -119,7 +172,7 @@ def save(filename, cagra.Index index, resources=None):
     filename : string
         Name of the file.
     index : Index
-        Trained CAGRA index.
+        Trained HNSW index.
     {resources_docstring}
 
     Examples
@@ -131,23 +184,28 @@ def save(filename, cagra.Index index, resources=None):
     >>> dataset = cp.random.random_sample((n_samples, n_features),
     ...                                   dtype=cp.float32)
     >>> # Build index
-    >>> index = cagra.build(cagra.IndexParams(), dataset)
+    >>> cagra_index = cagra.build(cagra.IndexParams(), dataset)
     >>> # Serialize and deserialize the cagra index built
-    >>> hnsw.save("my_index.bin", index)
+    >>> hnsw_index = hnsw.from_cagra(hnsw.IndexParams(), cagra_index)
+    >>> hnsw.save("my_index.bin", hnsw_index)
     """
     cdef string c_filename = filename.encode('utf-8')
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
-    check_cuvs(cagra.cuvsCagraSerializeToHnswlib(res,
-                                                 c_filename.c_str(),
-                                                 index.index))
+    check_cuvs(cuvsHnswSerialize(res,
+                                 c_filename.c_str(),
+                                 index.index))
 
 
 @auto_sync_resources
-def load(filename, dim, dtype, metric="sqeuclidean", resources=None):
+def load(IndexParams index_params, filename, dim, dtype, metric="sqeuclidean",
+         resources=None):
     """
-    Loads base-layer-only hnswlib index from file, which was originally
-    saved as a built CAGRA index. The loaded index is immutable and can only
-    be searched by the hnswlib wrapper in cuVS, as the format is not
+    Loads an HNSW index.
+    If the index was constructed with `hnsw.IndexParams(hierarchy="none")`,
+    then the loaded index is immutable and can only be searched by the hnswlib
+    wrapper in cuVS, as the format is not compatible with the original hnswlib.
+    However, if the index was constructed with
+    `hnsw.IndexParams(hierarchy="cpu")`, then the loaded index is mutable and
     compatible with the original hnswlib.
 
     Saving / loading the index is experimental. The serialization format is
@@ -156,6 +214,8 @@ def load(filename, dim, dtype, metric="sqeuclidean", resources=None):
 
     Parameters
     ----------
+    index_params : IndexParams
+        Parameters that were used to convert CAGRA index to HNSW index.
     filename : string
         Name of the file.
     dim : int
@@ -214,6 +274,7 @@ def load(filename, dim, dtype, metric="sqeuclidean", resources=None):
 
     check_cuvs(cuvsHnswDeserialize(
         res,
+        index_params.params,
         c_filename.c_str(),
         dim,
         distance_type,
@@ -224,26 +285,30 @@ def load(filename, dim, dtype, metric="sqeuclidean", resources=None):
 
 
 @auto_sync_resources
-def from_cagra(cagra.Index index, temporary_index_path=None, resources=None):
+def from_cagra(IndexParams index_params, cagra.Index cagra_index,
+               temporary_index_path=None, resources=None):
     """
-    Returns an hnsw base-layer-only index from a CAGRA index.
-
-    NOTE: This method uses the filesystem to write the CAGRA index in
-          `/tmp/<random_number>.bin` or the parameter `temporary_index_path`
-          if not None before reading it as an hnsw index,
-          then deleting the temporary file. The returned index is immutable
-          and can only be searched by the hnsw wrapper in cuVS, as the
-          format is not compatible with the original hnswlib library.
-          By `base_layer_only`, we mean that the hnsw index is created
-          without the additional layers that are used for the hierarchical
-          search in hnswlib. Instead, the base layer is used for the search.
+    Returns an HNSW index from a CAGRA index.
+
+    NOTE: When `index_params.hierarchy` is:
+          1. `NONE`: This method uses the filesystem to write the CAGRA index
+                     in `/tmp/<random_number>.bin` before reading it as an
+                     hnswlib index, then deleting the temporary file. The
+                     returned index is immutable and can only be searched by
+                     the hnswlib wrapper in cuVS, as the format is not
+                    compatible with the original hnswlib.
+          2. `CPU`: The returned index is mutable and can be extended with
+                    additional vectors. The serialized index is also compatible
+                    with the original hnswlib library.
 
     Saving / loading the index is experimental. The serialization format is
     subject to change.
 
     Parameters
     ----------
-    index : Index
+    index_params : IndexParams
+        Parameters to convert the CAGRA index to HNSW index.
+    cagra_index : cagra.Index
         Trained CAGRA index.
     temporary_index_path : string, default = None
         Path to save the temporary index file. If None, the temporary file
@@ -262,18 +327,107 @@ def from_cagra(cagra.Index index, temporary_index_path=None, resources=None):
     >>> # Build index
     >>> index = cagra.build(cagra.IndexParams(), dataset)
     >>> # Serialize the CAGRA index to hnswlib base layer only index format
-    >>> hnsw_index = hnsw.from_cagra(index)
+    >>> hnsw_index = hnsw.from_cagra(hnsw.IndexParams(), index)
     """
-    uuid_num = uuid.uuid4()
-    filename = temporary_index_path if temporary_index_path else \
-        f"/tmp/{uuid_num}.bin"
-    save(filename, index, resources=resources)
-    hnsw_index = load(filename, index.dim, np.dtype(index.active_index_type),
-                      "sqeuclidean", resources=resources)
-    os.remove(filename)
+
+    cdef Index hnsw_index = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    check_cuvs(cuvsHnswFromCagra(
+        res,
+        index_params.params,
+        cagra_index.index,
+        hnsw_index.index
+    ))
+
+    hnsw_index.trained = True
     return hnsw_index
 
 
+@auto_sync_resources
+def extend(ExtendParams extend_params, Index index, data, resources=None):
+    """
+    Extends the HNSW index with new data.
+
+    Parameters
+    ----------
+    extend_params : ExtendParams
+    index : Index
+        Trained HNSW index.
+    data : Host array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32, int8, uint8]
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from cuvs.neighbors import hnsw, cagra
+    >>>
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = np.random.random_sample((n_samples, n_features))
+    >>>
+    >>> # Build index
+    >>> index = cagra.build(hnsw.IndexParams(), dataset)
+    >>> # Load index
+    >>> hnsw_index = hnsw.from_cagra(hnsw.IndexParams(hierarchy="cpu"), index)
+    >>> # Extend the index with new data
+    >>> new_data = np.random.random_sample((n_samples, n_features))
+    >>> hnsw.extend(hnsw.ExtendParams(), hnsw_index, new_data)
+    """
+
+    data_ai = wrap_array(data)
+    _check_input_array(data_ai, [np.dtype('float32'),
+                                 np.dtype('uint8'),
+                                 np.dtype('int8')])
+
+    cdef cydlpack.DLManagedTensor* data_dlpack = cydlpack.dlpack_c(data_ai)
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    check_cuvs(cuvsHnswExtend(
+        res,
+        extend_params.params,
+        data_dlpack,
+        index.index
+    ))
+
+
+cdef class SearchParams:
+    """
+    HNSW search parameters
+
+    Parameters
+    ----------
+    ef: int, default = 200
+        Maximum number of candidate list size used during search.
+    num_threads: int, default = 0
+        Number of CPU threads used to increase search parallelism.
+        When set to 0, the number of threads is automatically determined
+        using OpenMP's `omp_get_max_threads()`.
+    """
+
+    cdef cuvsHnswSearchParams params
+
+    def __init__(self, *,
+                 ef=200,
+                 num_threads=0):
+        self.params.ef = ef
+        self.params.num_threads = num_threads
+
+    def __repr__(self):
+        attr_str = [attr + "=" + str(getattr(self, attr))
+                    for attr in [
+                        "ef", "num_threads"]]
+        return "SearchParams(type=HNSW, " + (", ".join(attr_str)) + ")"
+
+    @property
+    def ef(self):
+        return self.params.ef
+
+    @property
+    def num_threads(self):
+        return self.params.num_threads
+
+
 @auto_sync_resources
 @auto_convert_output
 def search(SearchParams search_params,
@@ -290,15 +444,15 @@ def search(SearchParams search_params,
     ----------
     search_params : SearchParams
     index : Index
-        Trained CAGRA index.
-    queries : CUDA array interface compliant matrix shape (n_samples, dim)
+        Trained HNSW index.
+    queries : CPU array interface compliant matrix shape (n_samples, dim)
         Supported dtype [float, int]
     k : int
         The number of neighbors.
-    neighbors : Optional CUDA array interface compliant matrix shape
+    neighbors : Optional CPU array interface compliant matrix shape
                 (n_queries, k), dtype uint64_t. If supplied, neighbor
                 indices will be written here in-place. (default None)
-    distances : Optional CUDA array interface compliant matrix shape
+    distances : Optional CPU array interface compliant matrix shape
                 (n_queries, k) If supplied, the distances to the
                 neighbors will be written here in-place. (default None)
     {resources_docstring}
@@ -323,7 +477,7 @@ def search(SearchParams search_params,
     ...     num_threads=0
     ... )
     >>> # Convert CAGRA index to HNSW
-    >>> hnsw_index = hnsw.from_cagra(index)
+    >>> hnsw_index = hnsw.from_cagra(hnsw.IndexParams(), index)
     >>> # Using a pooling allocator reduces overhead of temporary array
     >>> # creation during search. This is useful if multiple searches
     >>> # are performed with same query size.
diff --git a/python/cuvs/cuvs/test/test_hnsw.py b/python/cuvs/cuvs/test/test_hnsw.py
index 20a35401eb..20f583ae81 100644
--- a/python/cuvs/cuvs/test/test_hnsw.py
+++ b/python/cuvs/cuvs/test/test_hnsw.py
@@ -32,6 +32,7 @@ def run_hnsw_build_search_test(
     build_algo="ivf_pq",
     intermediate_graph_degree=128,
     graph_degree=64,
+    hierarchy="none",
     search_params={},
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
@@ -53,7 +54,8 @@ def run_hnsw_build_search_test(
 
     assert index.trained
 
-    hnsw_index = hnsw.from_cagra(index)
+    hnsw_params = hnsw.IndexParams(hierarchy=hierarchy, num_threads=1)
+    hnsw_index = hnsw.from_cagra(hnsw_params, index)
 
     queries = generate_data((n_queries, n_cols), dtype)
 
@@ -83,10 +85,93 @@ def run_hnsw_build_search_test(
 @pytest.mark.parametrize("num_threads", [2, 4])
 @pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
-def test_hnsw(dtype, k, ef, num_threads, metric, build_algo):
+@pytest.mark.parametrize("hierarchy", ["none", "cpu"])
+def test_hnsw(dtype, k, ef, num_threads, metric, build_algo, hierarchy):
     # Note that inner_product tests use normalized input which we cannot
     # represent in int8, therefore we test only sqeuclidean metric here.
     run_hnsw_build_search_test(
+        dtype=dtype,
+        k=k,
+        metric=metric,
+        build_algo=build_algo,
+        hierarchy=hierarchy,
+        search_params={"ef": ef, "num_threads": num_threads},
+    )
+
+
+def run_hnsw_extend_test(
+    n_rows=10000,
+    add_rows=2000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    metric="sqeuclidean",
+    build_algo="ivf_pq",
+    intermediate_graph_degree=128,
+    graph_degree=64,
+    search_params={},
+):
+    dataset = generate_data((n_rows, n_cols), dtype)
+    add_dataset = generate_data((add_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+        add_dataset = normalize(add_dataset, norm="l2", axis=1)
+        if dtype in [np.int8, np.uint8]:
+            pytest.skip(
+                "inner_product metric is not supported for int8/uint8 data"
+            )
+        if build_algo == "nn_descent":
+            pytest.skip("inner_product metric is not supported for nn_descent")
+
+    build_params = cagra.IndexParams(
+        metric=metric,
+        intermediate_graph_degree=intermediate_graph_degree,
+        graph_degree=graph_degree,
+        build_algo=build_algo,
+    )
+
+    index = cagra.build(build_params, dataset)
+
+    assert index.trained
+
+    hnsw_params = hnsw.IndexParams(hierarchy="cpu", num_threads=1)
+    hnsw_index = hnsw.from_cagra(hnsw_params, index)
+    hnsw.extend(hnsw.ExtendParams(), hnsw_index, add_dataset)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+
+    search_params = hnsw.SearchParams(**search_params)
+
+    out_dist, out_idx = hnsw.search(search_params, hnsw_index, queries, k)
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(np.vstack([dataset, add_dataset]))
+    skl_dist, skl_idx = nn_skl.kneighbors(queries, return_distance=True)
+
+    recall = calc_recall(out_idx, skl_idx)
+    print(recall)
+    assert recall > 0.95
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("k", [10, 20])
+@pytest.mark.parametrize("ef", [30, 40])
+@pytest.mark.parametrize("num_threads", [2, 4])
+@pytest.mark.parametrize("metric", ["sqeuclidean"])
+@pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
+def test_hnsw_extend(dtype, k, ef, num_threads, metric, build_algo):
+    # Note that inner_product tests use normalized input which we cannot
+    # represent in int8, therefore we test only sqeuclidean metric here.
+    run_hnsw_extend_test(
         dtype=dtype,
         k=k,
         metric=metric,
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
index f1a7f272cb..90a561bca4 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
@@ -4,8 +4,11 @@ constraints:
 groups:
   base:
     build:
-      graph_degree: [32, 64, 128, 256]
+      graph_degree: [32, 64, 96, 128]
       intermediate_graph_degree: [32, 64, 96, 128]
       graph_build_algo: ["NN_DESCENT"]
+      hierarchy: ["none", "cpu"]
+      ef_construction: [64, 128, 256, 512]
+      num_threads: [2, 5, 10]
     search:
       ef: [10, 20, 40, 60, 80, 120, 200, 400, 600, 800]

From e0aebfd0c499189585319f5a5fbf46dfb9ce04f9 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 3 Dec 2024 14:41:47 -0600
Subject: [PATCH 223/623] add a README for wheels (#504)

Wheel-building CI jobs are failing like this:

> Checking
final_dist/cuvs_cu12-25.2.0a26-cp310-cp310-manylinux_2_28_aarch64.whl:
FAILED due to warnings
> WARNING `long_description` missing.
> Error: Process completed with exit code 1.

([build
link](https://github.com/rapidsai/cuvs/actions/runs/12133882036))

Looks like the root cause is a combination of the following:

* there was a new `twine` release (6.x) 3 days ago:
https://pypi.org/project/twine/#history
* it contains https://github.com/pypa/twine/pull/1168, which makes
`twine check --strict` fail if the wheel's `long_description` is empty
* the `cuvs` wheel README (used as the wheel `long_description`) is
empty

This proposes adding a small README, with just 2 sentences copied from
the project's root-level README, to get past that check.

## Notes for Reviewers

The `long_description` becomes the project homepage when a project is
hosted on PyPI. The wheels produced from this repo aren't currently
being published to pypi.org so this change won't be seen there, but a
more user-friendly README should be added if/when we decide to publish
`cuvs-cu{11,12}` to pypi.org.

ref: https://github.com/rapidsai/build-planning/issues/70
---
 python/cuvs/README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/cuvs/README.md b/python/cuvs/README.md
index e69de29bb2..27b494811d 100644
--- a/python/cuvs/README.md
+++ b/python/cuvs/README.md
@@ -0,0 +1,3 @@
+# cuVS
+
+cuVS contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

From fbbca0570db27d476b500ef021c03482b0d989e2 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Wed, 4 Dec 2024 00:57:52 +0100
Subject: [PATCH 224/623] Add Question Retrieval notebook using Milvus (#451)

This notebook is adapting the Question Retrieval nb to use Milvus.
It can serve as a good example on how to do Bulk ingest, how to use cuVS, and especially CAGRA+HNSW on Milvus

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/451
---
 ...ectorSearch_QuestionRetrieval_Milvus.ipynb | 732 ++++++++++++++++++
 1 file changed, 732 insertions(+)
 create mode 100644 notebooks/VectorSearch_QuestionRetrieval_Milvus.ipynb

diff --git a/notebooks/VectorSearch_QuestionRetrieval_Milvus.ipynb b/notebooks/VectorSearch_QuestionRetrieval_Milvus.ipynb
new file mode 100644
index 0000000000..09a6cca43b
--- /dev/null
+++ b/notebooks/VectorSearch_QuestionRetrieval_Milvus.ipynb
@@ -0,0 +1,732 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5499b54",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Similar Questions Retrieval - Milvus - CAGRA-HNSW\n",
+    "\n",
+    "This notebook is inspired by the [similar search example of Sentence-Transformers](https://www.sbert.net/examples/applications/semantic-search/README.html#similar-questions-retrieval), and adapted to be used with [Milvus](https://milvus.io) and [cuVS](https://rapids.ai/cuvs/).\n",
+    "\n",
+    "The model was pre-trained on the [Natural Questions dataset](https://ai.google.com/research/NaturalQuestions). It consists of about 100k real Google search queries, together with an annotated passage from Wikipedia that provides the answer. It is an example of an asymmetric search task. As corpus, we use the smaller [Simple English Wikipedia](http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz) so that it fits easily into memory.\n",
+    "\n",
+    "The steps to install the latest Milvus package are available in the [Milvus documentation](https://milvus.io/docs/quickstart.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8d55ede",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:47:21.149465Z",
+     "iopub.status.busy": "2024-11-08T14:47:21.149218Z",
+     "iopub.status.idle": "2024-11-08T14:47:23.440275Z",
+     "shell.execute_reply": "2024-11-08T14:47:23.439436Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip install sentence_transformers torch pymilvus pymilvus[bulk_writer] dask dask[distributed]\n",
+    "\n",
+    "# Note: if you have a Hopper based GPU, like an H100, use these to install:\n",
+    "# pip install torch --index-url https://download.pytorch.org/whl/cu118\n",
+    "# pip install sentence_transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb1e81c3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:47:23.444058Z",
+     "iopub.status.busy": "2024-11-08T14:47:23.443683Z",
+     "iopub.status.idle": "2024-11-08T14:47:24.219903Z",
+     "shell.execute_reply": "2024-11-08T14:47:24.219228Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee4c5cc0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:47:24.223131Z",
+     "iopub.status.busy": "2024-11-08T14:47:24.222874Z",
+     "iopub.status.idle": "2024-11-08T14:47:34.024085Z",
+     "shell.execute_reply": "2024-11-08T14:47:34.023435Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import dask.array as da\n",
+    "import gzip\n",
+    "import json\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pymilvus\n",
+    "import time\n",
+    "import torch\n",
+    "\n",
+    "from minio import Minio\n",
+    "from multiprocessing import Process\n",
+    "from sentence_transformers import SentenceTransformer, CrossEncoder, util\n",
+    "from typing import List\n",
+    "\n",
+    "\n",
+    "from pymilvus import (\n",
+    "    connections, utility\n",
+    ")\n",
+    "from pymilvus.bulk_writer import LocalBulkWriter, BulkFileType  # pip install pymilvus[bulk_writer]\n",
+    "\n",
+    "if not torch.cuda.is_available():\n",
+    "  print(\"Warning: No GPU found. Please add GPU to your notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47cabaca",
+   "metadata": {},
+   "source": [
+    "# Setup Milvus Collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fcd259c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:47:34.027677Z",
+     "iopub.status.busy": "2024-11-08T14:47:34.027288Z",
+     "iopub.status.idle": "2024-11-08T14:47:34.109212Z",
+     "shell.execute_reply": "2024-11-08T14:47:34.108609Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "DIM = 768\n",
+    "MILVUS_PORT = 30004\n",
+    "MILVUS_HOST = f\"http://localhost:{MILVUS_PORT}\"\n",
+    "ID_FIELD=\"id\"\n",
+    "EMBEDDING_FIELD=\"embedding\"\n",
+    "\n",
+    "collection_name = \"simple_wiki\"\n",
+    "\n",
+    "def get_milvus_client():\n",
+    "    return pymilvus.MilvusClient(uri=MILVUS_HOST)\n",
+    "\n",
+    "client = get_milvus_client()\n",
+    "\n",
+    "fields = [\n",
+    "    pymilvus.FieldSchema(name=ID_FIELD, dtype=pymilvus.DataType.INT64, is_primary=True),\n",
+    "    pymilvus.FieldSchema(name=EMBEDDING_FIELD, dtype=pymilvus.DataType.FLOAT_VECTOR, dim=DIM)\n",
+    "]\n",
+    "\n",
+    "schema = pymilvus.CollectionSchema(fields)\n",
+    "schema.verify()\n",
+    "\n",
+    "if collection_name in client.list_collections():\n",
+    "    print(f\"Collection '{collection_name}' already exists. Deleting collection...\")\n",
+    "    client.drop_collection(collection_name)\n",
+    "\n",
+    "client.create_collection(collection_name, schema=schema, dimension=DIM, vector_field_name=EMBEDDING_FIELD)\n",
+    "collection = pymilvus.Collection(name=collection_name, using=client._using)\n",
+    "collection.release()\n",
+    "collection.drop_index()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00bd20f5",
+   "metadata": {},
+   "source": [
+    "# Setup Sentence Transformer model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a1a6307",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:47:34.111782Z",
+     "iopub.status.busy": "2024-11-08T14:47:34.111556Z",
+     "iopub.status.idle": "2024-11-08T14:47:39.654323Z",
+     "shell.execute_reply": "2024-11-08T14:47:39.653386Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# We use the Bi-Encoder to encode all passages, so that we can use it with semantic search\n",
+    "model_name = 'nq-distilbert-base-v1'\n",
+    "bi_encoder = SentenceTransformer(model_name)\n",
+    "\n",
+    "# As dataset, we use Simple English Wikipedia. Compared to the full English wikipedia, it has only\n",
+    "# about 170k articles. We split these articles into paragraphs and encode them with the bi-encoder\n",
+    "\n",
+    "wikipedia_filepath = 'data/simplewiki-2020-11-01.jsonl.gz'\n",
+    "\n",
+    "if not os.path.exists(wikipedia_filepath):\n",
+    "    util.http_get('http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz', wikipedia_filepath)\n",
+    "\n",
+    "passages = []\n",
+    "with gzip.open(wikipedia_filepath, 'rt', encoding='utf8') as fIn:\n",
+    "    for line in fIn:\n",
+    "        data = json.loads(line.strip())\n",
+    "        for paragraph in data['paragraphs']:\n",
+    "            # We encode the passages as [title, text]\n",
+    "            passages.append([data['title'], paragraph])\n",
+    "\n",
+    "# If you like, you can also limit the number of passages you want to use\n",
+    "print(\"Passages:\", len(passages))\n",
+    "\n",
+    "# To speed things up, pre-computed embeddings are downloaded.\n",
+    "# The provided file encoded the passages with the model 'nq-distilbert-base-v1'\n",
+    "if model_name == 'nq-distilbert-base-v1':\n",
+    "    embeddings_filepath = 'simplewiki-2020-11-01-nq-distilbert-base-v1.pt'\n",
+    "    if not os.path.exists(embeddings_filepath):\n",
+    "        util.http_get('http://sbert.net/datasets/simplewiki-2020-11-01-nq-distilbert-base-v1.pt', embeddings_filepath)\n",
+    "\n",
+    "    corpus_embeddings = torch.load(embeddings_filepath, map_location='cpu', weights_only=True).float()  # Convert embedding file to float\n",
+    "    #if torch.cuda.is_available():\n",
+    "    #    corpus_embeddings = corpus_embeddings.to('cuda')\n",
+    "else:  # Here, we compute the corpus_embeddings from scratch (which can take a while depending on the GPU)\n",
+    "    corpus_embeddings = bi_encoder.encode(passages, convert_to_tensor=True, show_progress_bar=True).to('cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f4e9b9d",
+   "metadata": {},
+   "source": [
+    "# Vector Search using Milvus and RAPIDS cuVS \n",
+    "Now that our embeddings are ready to be indexed and that the model has been loaded, we can use Milvus and RAPIDS cuVS to do our vector search.\n",
+    "\n",
+    "This is done in 3 steps: First we ingest all the vectors in the Milvus collection, then we build the Milvus index, to finally search it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "563751c1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:47:39.658832Z",
+     "iopub.status.busy": "2024-11-08T14:47:39.658374Z",
+     "iopub.status.idle": "2024-11-08T14:49:47.244768Z",
+     "shell.execute_reply": "2024-11-08T14:49:47.244162Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# minio\n",
+    "MINIO_PORT = 30009\n",
+    "MINIO_URL = f\"localhost:{MINIO_PORT}\"\n",
+    "MINIO_SECRET_KEY = \"minioadmin\"\n",
+    "MINIO_ACCESS_KEY = \"minioadmin\"\n",
+    "\n",
+    "def upload_to_minio(file_paths: List[List[str]], remote_paths: List[List[str]], bucket_name=\"milvus-bucket\"):\n",
+    "    minio_client = Minio(endpoint=MINIO_URL, access_key=MINIO_ACCESS_KEY, secret_key=MINIO_SECRET_KEY, secure=False)\n",
+    "    if not minio_client.bucket_exists(bucket_name):\n",
+    "        minio_client.make_bucket(bucket_name)\n",
+    "\n",
+    "    for local_batch, remote_batch in zip(file_paths, remote_paths):\n",
+    "        for local_file, remote_file in zip(local_batch, remote_batch):\n",
+    "            minio_client.fput_object(bucket_name, \n",
+    "                                     object_name=remote_file,\n",
+    "                                     file_path=local_file,\n",
+    "                                     part_size=512 * 1024 * 1024,\n",
+    "                                     num_parallel_uploads=5)\n",
+    "     \n",
+    "    \n",
+    "def ingest_data_bulk(collection_name, vectors, schema: pymilvus.CollectionSchema, log_times=True, bulk_writer_type=\"milvus\", debug=False):\n",
+    "    print(f\"-  Ingesting {len(vectors) // 1000}k vectors, Bulk\")\n",
+    "    tic = time.perf_counter()\n",
+    "    collection = pymilvus.Collection(collection_name, using=get_milvus_client()._using)\n",
+    "    remote_path = None\n",
+    "\n",
+    "    if bulk_writer_type == 'milvus':\n",
+    "        # # Prepare source data for faster ingestion\n",
+    "        writer = LocalBulkWriter(\n",
+    "            schema=schema,\n",
+    "            local_path='bulk_data',\n",
+    "            segment_size=512 * 1024 * 1024, # Default value\n",
+    "            file_type=BulkFileType.NPY\n",
+    "        )\n",
+    "        for id, vec in enumerate(vectors):\n",
+    "            writer.append_row({ID_FIELD: id, EMBEDDING_FIELD: vec})\n",
+    "\n",
+    "        if debug:\n",
+    "            print(writer.batch_files)\n",
+    "        def callback(file_list):\n",
+    "            if debug:\n",
+    "                print(f\"  -  Commit successful\")\n",
+    "                print(file_list)\n",
+    "        writer.commit(call_back=callback)\n",
+    "        files_to_upload = writer.batch_files\n",
+    "    elif bulk_writer_type == 'dask':\n",
+    "        # Prepare source data for faster ingestion\n",
+    "        if not os.path.isdir(\"bulk_data\"):\n",
+    "            os.mkdir(\"bulk_data\")\n",
+    "\n",
+    "        from dask.distributed import Client, LocalCluster\n",
+    "        cluster = LocalCluster(n_workers=1, threads_per_worker=1)\n",
+    "        client = Client(cluster)\n",
+    "\n",
+    "        chunk_size = 100000\n",
+    "        da_vectors = da.from_array(vectors, chunks=(chunk_size, vectors.shape[1]))\n",
+    "        da_ids = da.arange(len(vectors), chunks=(chunk_size,))\n",
+    "        da.to_npy_stack(\"bulk_data/da_embedding/\", da_vectors)\n",
+    "        da.to_npy_stack(\"bulk_data/da_id/\", da_ids)\n",
+    "        files_to_upload = []\n",
+    "        remote_path = []\n",
+    "        for chunk_nb in range(math.ceil(len(vectors) / chunk_size)):\n",
+    "            files_to_upload.append([f\"bulk_data/da_embedding/{chunk_nb}.npy\", f\"bulk_data/da_id/{chunk_nb}.npy\"])\n",
+    "            remote_path.append([f\"bulk_data/da_{chunk_nb}/embedding.npy\", f\"bulk_data/da__{chunk_nb}/id.npy\"])\n",
+    "\n",
+    "    elif bulk_writer_type == 'numpy':\n",
+    "        # Directly save NPY files\n",
+    "        np.save(\"bulk_data/embedding.npy\", vectors)\n",
+    "        np.save(\"bulk_data/id.npy\", np.arange(len(vectors)))\n",
+    "        files_to_upload = [[\"bulk_data/embedding.npy\", \"bulk_data/id.npy\"]]\n",
+    "    else:\n",
+    "        raise ValueError(\"Invalid bulk writer type\")\n",
+    "    \n",
+    "    toc = time.perf_counter()\n",
+    "    if log_times:\n",
+    "        print(f\"  -  File save time: {toc - tic:.2f} seconds\")\n",
+    "    # Import data\n",
+    "    if remote_path is None:\n",
+    "        remote_path = files_to_upload\n",
+    "    upload_to_minio(files_to_upload, remote_path)\n",
+    "    \n",
+    "    job_ids = [utility.do_bulk_insert(collection_name, batch, using=get_milvus_client()._using) for batch in remote_path]\n",
+    "\n",
+    "    while True:\n",
+    "        tasks = [utility.get_bulk_insert_state(job_id, using=get_milvus_client()._using) for job_id in job_ids]\n",
+    "        success = all(task.state_name == \"Completed\" for task in tasks)\n",
+    "        failure = any(task.state_name == \"Failed\" for task in tasks)\n",
+    "        for i in range(len(tasks)):\n",
+    "            task = tasks[i]\n",
+    "            if debug:\n",
+    "                print(f\"  -  Task {i}/{len(tasks)} state: {task.state_name}, Progress percent: {task.infos['progress_percent']}, Imported row count: {task.row_count}\")\n",
+    "            if task.state_name == \"Failed\":\n",
+    "                print(task)\n",
+    "        if success or failure:\n",
+    "            break\n",
+    "        time.sleep(2)\n",
+    "\n",
+    "    added_entities = str(sum([task.row_count for task in tasks]))\n",
+    "    failure = failure or added_entities != str(len(vectors))\n",
+    "    if failure:\n",
+    "        print(f\"-  Ingestion failed. Added entities: {added_entities}\")\n",
+    "    toc = time.perf_counter()\n",
+    "    if log_times:\n",
+    "        datasize = vectors.nbytes / 1024 / 1024\n",
+    "        print(f\"-  Ingestion time: {toc - tic:.2f} seconds. ({(datasize / (toc-tic)):.2f}MB/s)\")\n",
+    "\n",
+    "ingest_data_bulk(collection_name, np.array(corpus_embeddings), schema, bulk_writer_type='dask', log_times=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad90b4be",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:49:47.247498Z",
+     "iopub.status.busy": "2024-11-08T14:49:47.247268Z",
+     "iopub.status.idle": "2024-11-08T14:50:00.737502Z",
+     "shell.execute_reply": "2024-11-08T14:50:00.736808Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Setups the IVFPQ index\n",
+    "\n",
+    "index_params = dict(\n",
+    "    index_type=\"GPU_IVF_PQ\",\n",
+    "    metric_type=\"L2\",\n",
+    "    params={\"nlist\": 150, # Number of clusters\n",
+    "            \"m\": 96})      # Product Quantization dimension\n",
+    "\n",
+    "# Drop the index if it exists\n",
+    "if collection.has_index():\n",
+    "    collection.release()\n",
+    "    collection.drop_index()\n",
+    "\n",
+    "# Create the index\n",
+    "tic = time.perf_counter()\n",
+    "collection.create_index(field_name=EMBEDDING_FIELD, index_params=index_params)\n",
+    "collection.load()\n",
+    "toc = time.perf_counter()\n",
+    "print(f\"-  Index creation time: {toc - tic:.4f} seconds. ({index_params})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c75acea7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:00.740443Z",
+     "iopub.status.busy": "2024-11-08T14:50:00.740142Z",
+     "iopub.status.idle": "2024-11-08T14:50:00.745403Z",
+     "shell.execute_reply": "2024-11-08T14:50:00.744672Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Search the index\n",
+    "def search_cuvs_pq(query, top_k = 5, n_probe = 30):\n",
+    "    # Encode the query using the bi-encoder and find potentially relevant passages\n",
+    "    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n",
+    "\n",
+    "    search_params = {\"nprobe\": n_probe}\n",
+    "    tic = time.perf_counter()\n",
+    "    hits = collection.search(\n",
+    "                data=np.array(question_embedding[None].cpu()), anns_field=EMBEDDING_FIELD, param=search_params, limit=top_k\n",
+    "            )\n",
+    "    toc = time.perf_counter()\n",
+    "\n",
+    "    # Output of top-k hits\n",
+    "    print(\"Input question:\", query)\n",
+    "    print(\"Results (after {:.3f} ms):\".format((toc - tic)*1000))\n",
+    "    for k in range(top_k):\n",
+    "        print(\"\\t{:.3f}\\t{}\".format(hits[0][k].distance, passages[hits[0][k].id]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07935bca",
+   "metadata": {},
+   "source": [
+    "The ideal use-case for the IVF-PQ algorithm is when there is a need to reduce the memory footprint while keeping a good accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c27d4715",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:00.748001Z",
+     "iopub.status.busy": "2024-11-08T14:50:00.747783Z",
+     "iopub.status.idle": "2024-11-08T14:50:01.785914Z",
+     "shell.execute_reply": "2024-11-08T14:50:01.785223Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_pq(query=\"Who was Grace Hopper?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc375518",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:01.788877Z",
+     "iopub.status.busy": "2024-11-08T14:50:01.788640Z",
+     "iopub.status.idle": "2024-11-08T14:50:01.813820Z",
+     "shell.execute_reply": "2024-11-08T14:50:01.813153Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_pq(query=\"Who was Alan Turing?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab154181",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:01.816625Z",
+     "iopub.status.busy": "2024-11-08T14:50:01.816362Z",
+     "iopub.status.idle": "2024-11-08T14:50:01.839593Z",
+     "shell.execute_reply": "2024-11-08T14:50:01.838986Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_pq(query = \"What is creating tides?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "836344ec",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:01.842319Z",
+     "iopub.status.busy": "2024-11-08T14:50:01.842022Z",
+     "iopub.status.idle": "2024-11-08T14:50:15.969324Z",
+     "shell.execute_reply": "2024-11-08T14:50:15.968562Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Drop the current index if it exists\n",
+    "if collection.has_index():\n",
+    "    collection.release()\n",
+    "    collection.drop_index()\n",
+    "\n",
+    "# Create the IVF Flat index\n",
+    "index_params = dict(\n",
+    "    index_type=\"GPU_IVF_FLAT\",\n",
+    "    metric_type=\"L2\",\n",
+    "    params={\"nlist\": 150}) # Number of clusters)\n",
+    "tic = time.perf_counter()\n",
+    "collection.create_index(field_name=EMBEDDING_FIELD, index_params=index_params)\n",
+    "collection.load()\n",
+    "toc = time.perf_counter()\n",
+    "print(f\"-  Index creation time: {toc - tic:.4f} seconds. ({index_params})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2d6017ed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:15.972764Z",
+     "iopub.status.busy": "2024-11-08T14:50:15.972368Z",
+     "iopub.status.idle": "2024-11-08T14:50:15.977806Z",
+     "shell.execute_reply": "2024-11-08T14:50:15.977064Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def search_cuvs_flat(query, top_k = 5, n_probe = 30):\n",
+    "    # Encode the query using the bi-encoder and find potentially relevant passages\n",
+    "    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n",
+    "    \n",
+    "    search_params = {\"nprobe\": n_probe}\n",
+    "    tic = time.perf_counter()\n",
+    "    hits = collection.search(\n",
+    "                data=np.array(question_embedding[None].cpu()), anns_field=EMBEDDING_FIELD, param=search_params, limit=top_k\n",
+    "            )\n",
+    "    toc = time.perf_counter()\n",
+    "\n",
+    "    # Output of top-k hits\n",
+    "    print(\"Input question:\", query)\n",
+    "    print(\"Results (after {:.3f} ms):\".format((toc - tic)*1000))\n",
+    "    for k in range(top_k):\n",
+    "        print(\"\\t{:.3f}\\t{}\".format(hits[0][k].distance, passages[hits[0][k].id]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5cfb644",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:15.980796Z",
+     "iopub.status.busy": "2024-11-08T14:50:15.980408Z",
+     "iopub.status.idle": "2024-11-08T14:50:16.009271Z",
+     "shell.execute_reply": "2024-11-08T14:50:16.008579Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_flat(query=\"Who was Grace Hopper?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5694d00",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:16.012253Z",
+     "iopub.status.busy": "2024-11-08T14:50:16.011924Z",
+     "iopub.status.idle": "2024-11-08T14:50:16.043432Z",
+     "shell.execute_reply": "2024-11-08T14:50:16.042751Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_flat(query=\"Who was Alan Turing?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcfc3c5b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:16.046439Z",
+     "iopub.status.busy": "2024-11-08T14:50:16.046093Z",
+     "iopub.status.idle": "2024-11-08T14:50:16.071322Z",
+     "shell.execute_reply": "2024-11-08T14:50:16.070614Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_flat(query = \"What is creating tides?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a59d7b32-0832-4c3a-864e-aeb2e6e7fe1f",
+   "metadata": {},
+   "source": [
+    "## Using CAGRA: Hybrid GPU-CPU graph-based Vector Search\n",
+    "\n",
+    "CAGRA is a graph-based nearest neighbors implementation with state-of-the art performance for both small- and large-batch sized vector searches. \n",
+    "\n",
+    "CAGRA follows the same steps as IVF-FLAT and IVF-PQ in Milvus, but is also able to be adapted for querying on CPU.\n",
+    "This means that CAGRA is able to profit from a high training speed on GPU, as well as a low inference time on CPU, that minimize latency even on the smallest queries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5ce4dab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:16.074449Z",
+     "iopub.status.busy": "2024-11-08T14:50:16.074128Z",
+     "iopub.status.idle": "2024-11-08T14:50:30.479027Z",
+     "shell.execute_reply": "2024-11-08T14:50:30.478265Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Drop the current index if it exists\n",
+    "if collection.has_index():\n",
+    "    collection.release()\n",
+    "    collection.drop_index()\n",
+    "\n",
+    "# Create the IVF Flat index\n",
+    "index_params = dict(\n",
+    "    index_type=\"GPU_CAGRA\",\n",
+    "    metric_type=\"L2\",\n",
+    "    params={\"graph_degree\": 64, \"intermediate_graph_degree\": 128, \"build_algo\": \"NN_DESCENT\", \"adapt_for_cpu\": True})\n",
+    "tic = time.perf_counter()\n",
+    "collection.create_index(field_name=EMBEDDING_FIELD, index_params=index_params)\n",
+    "collection.load()\n",
+    "toc = time.perf_counter()\n",
+    "print(f\"-  Index creation time: {toc - tic:.4f} seconds. ({index_params})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "df229e21-f6b6-4d6c-ad54-2724f8738934",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:30.481748Z",
+     "iopub.status.busy": "2024-11-08T14:50:30.481474Z",
+     "iopub.status.idle": "2024-11-08T14:50:30.486324Z",
+     "shell.execute_reply": "2024-11-08T14:50:30.485696Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def search_cuvs_cagra(query, top_k = 5, itopk = 32):\n",
+    "    # Encode the query using the bi-encoder and find potentially relevant passages\n",
+    "    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n",
+    "\n",
+    "    search_params = {\"params\": {\"itopk\": itopk, \"ef\": 35}}\n",
+    "    tic = time.perf_counter()\n",
+    "    hits = collection.search(\n",
+    "                data=np.array(question_embedding[None].cpu()), anns_field=EMBEDDING_FIELD, param=search_params, limit=top_k\n",
+    "            )\n",
+    "    toc = time.perf_counter()\n",
+    "\n",
+    "    # Output of top-k hits\n",
+    "    print(\"Input question:\", query)\n",
+    "    print(\"Results (after {:.3f} ms):\".format((toc - tic)*1000))\n",
+    "    for k in range(top_k):\n",
+    "        print(\"\\t{:.3f}\\t{}\".format(hits[0][k].distance, passages[hits[0][k].id]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5e862fd-b7e5-4423-8fbf-36918f02c8f3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:30.489077Z",
+     "iopub.status.busy": "2024-11-08T14:50:30.488790Z",
+     "iopub.status.idle": "2024-11-08T14:50:30.513998Z",
+     "shell.execute_reply": "2024-11-08T14:50:30.513319Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_cagra(query=\"Who was Grace Hopper?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb8a5b7b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:30.516748Z",
+     "iopub.status.busy": "2024-11-08T14:50:30.516521Z",
+     "iopub.status.idle": "2024-11-08T14:50:30.538982Z",
+     "shell.execute_reply": "2024-11-08T14:50:30.538269Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_cagra(query=\"Who was Alan Turing?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c89810a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-08T14:50:30.541508Z",
+     "iopub.status.busy": "2024-11-08T14:50:30.541287Z",
+     "iopub.status.idle": "2024-11-08T14:50:30.562722Z",
+     "shell.execute_reply": "2024-11-08T14:50:30.562085Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "search_cuvs_cagra(query=\"What is creating tides?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From acbd097ed15afe186367b5a46a5d4b366ac9d804 Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Wed, 4 Dec 2024 18:06:17 +0900
Subject: [PATCH 225/623] [BUG] Fix CAGRA filter (#489)

Ref : https://github.com/rapidsai/cuvs/issues/472

## The cause of the bug
The bitonic sort was used on an array that was not a power of 2 long. In the current search implementation, the bitonic sort is used to move the invalid elements to the end of the buffer as:
https://github.com/rapidsai/cuvs/blob/5062594138a40231475299c7bac61083b0669fd1/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh#L758-L763
https://github.com/rapidsai/cuvs/blob/5062594138a40231475299c7bac61083b0669fd1/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh#L644-L649

The problem is that the (max) array length (=`MAX_ITOPK + MAX_CANDIDATES`) is not always the power of two.
These bitonic sorts are called even if no elements are filtered out unless `cuvs::neighbors::filtering::none_sample_filter` is specified as the filter, so #472 occurs.

## Fix
This PR changes the filtering process so that the bitonic sort is not used to move the invalid elements to the end of the buffer.

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/489
---
 .../detail/cagra/search_single_cta.cuh        |  16 +-
 .../cagra/search_single_cta_kernel-inl.cuh    | 182 +++++++++++++-----
 cpp/test/neighbors/ann_cagra.cuh              |   6 +-
 3 files changed, 153 insertions(+), 51 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index 2bed190090..fa71dbaf95 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -129,17 +129,27 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
       (sizeof(INDEX_T) + sizeof(DISTANCE_T)) * result_buffer_size_32 +
       sizeof(INDEX_T) * hashmap::get_size(small_hash_bitlen) + sizeof(INDEX_T) * search_width +
       sizeof(std::uint32_t) * topk_ws_size + sizeof(std::uint32_t);
-    smem_size = base_smem_size;
+
+    std::uint32_t additional_smem_size = 0;
     if (num_itopk_candidates > 256) {
       // Tentatively calculate the required share memory size when radix
       // sort based topk is used, assuming the block size is the maximum.
       if (itopk_size <= 256) {
-        smem_size += topk_by_radix_sort<256, INDEX_T>::smem_size * sizeof(std::uint32_t);
+        additional_smem_size += topk_by_radix_sort<256, INDEX_T>::smem_size * sizeof(std::uint32_t);
       } else {
-        smem_size += topk_by_radix_sort<512, INDEX_T>::smem_size * sizeof(std::uint32_t);
+        additional_smem_size += topk_by_radix_sort<512, INDEX_T>::smem_size * sizeof(std::uint32_t);
       }
     }
 
+    if (!std::is_same_v<SAMPLE_FILTER_T, cuvs::neighbors::filtering::none_sample_filter>) {
+      // For filtering postprocess
+      using scan_op_t = cub::WarpScan<unsigned>;
+      additional_smem_size =
+        std::max<std::uint32_t>(additional_smem_size, sizeof(scan_op_t::TempStorage));
+    }
+
+    smem_size = base_smem_size + additional_smem_size;
+
     uint32_t block_size = thread_block_size;
     if (block_size == 0) {
       block_size = min_block_size;
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index 79cb6bc101..678ed0cb45 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -111,7 +111,7 @@ RAFT_DEVICE_INLINE_FUNCTION void pickup_next_parents(std::uint32_t* const termin
 }
 
 template <unsigned MAX_CANDIDATES, class IdxT = void>
-RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_1st(
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_and_full(
   float* candidate_distances,  // [num_candidates]
   IdxT* candidate_indices,     // [num_candidates]
   const std::uint32_t num_candidates,
@@ -215,7 +215,7 @@ RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_1st(
 }
 
 template <unsigned MAX_ITOPK, class IdxT = void>
-RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_2nd(
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_and_merge(
   float* itopk_distances,  // [num_itopk]
   IdxT* itopk_indices,     // [num_itopk]
   const std::uint32_t num_itopk,
@@ -424,7 +424,7 @@ RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_2nd(
 template <unsigned MAX_ITOPK,
           unsigned MAX_CANDIDATES,
           class IdxT>
-RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort(
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort_and_merge(
   float* itopk_distances,  // [num_itopk]
   IdxT* itopk_indices,     // [num_itopk]
   const std::uint32_t num_itopk,
@@ -437,20 +437,62 @@ RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort(
   const unsigned MULTI_WARPS_2)
 {
   // The results in candidate_distances/indices are sorted by bitonic sort.
-  topk_by_bitonic_sort_1st<MAX_CANDIDATES, IdxT>(
+  topk_by_bitonic_sort_and_full<MAX_CANDIDATES, IdxT>(
     candidate_distances, candidate_indices, num_candidates, num_itopk, MULTI_WARPS_1);
 
   // The results sorted above are merged with the internal intermediate top-k
   // results so far using bitonic merge.
-  topk_by_bitonic_sort_2nd<MAX_ITOPK, IdxT>(itopk_distances,
-                                            itopk_indices,
-                                            num_itopk,
-                                            candidate_distances,
-                                            candidate_indices,
-                                            num_candidates,
-                                            work_buf,
-                                            first,
-                                            MULTI_WARPS_2);
+  topk_by_bitonic_sort_and_merge<MAX_ITOPK, IdxT>(itopk_distances,
+                                                  itopk_indices,
+                                                  num_itopk,
+                                                  candidate_distances,
+                                                  candidate_indices,
+                                                  num_candidates,
+                                                  work_buf,
+                                                  first,
+                                                  MULTI_WARPS_2);
+}
+
+// This function move the invalid index element to the end of the itopk list.
+// Require : array_length % 32 == 0 && The invalid entry is only one.
+template <class IdxT>
+RAFT_DEVICE_INLINE_FUNCTION void move_invalid_to_end_of_list(IdxT* const index_array,
+                                                             float* const distance_array,
+                                                             const std::uint32_t array_length)
+{
+  constexpr std::uint32_t warp_size     = 32;
+  constexpr std::uint32_t invalid_index = utils::get_max_value<IdxT>();
+  const std::uint32_t lane_id           = threadIdx.x % warp_size;
+
+  if (threadIdx.x >= warp_size) { return; }
+
+  bool found_invalid = false;
+  if (array_length % warp_size == 0) {
+    for (std::uint32_t i = lane_id; i < array_length; i += warp_size) {
+      const auto index    = index_array[i];
+      const auto distance = distance_array[i];
+
+      if (found_invalid) {
+        index_array[i - 1]    = index;
+        distance_array[i - 1] = distance;
+      } else {
+        // Check if the index is invalid
+        const auto I_found_invalid = (index == invalid_index);
+        const auto who_has_invalid = raft::ballot(I_found_invalid);
+        // if a value that is loaded by a smaller lane id thread, shift the array
+        if (who_has_invalid << (warp_size - lane_id)) {
+          index_array[i - 1]    = index;
+          distance_array[i - 1] = distance;
+        }
+
+        found_invalid = who_has_invalid;
+      }
+    }
+  }
+  if (lane_id == 0) {
+    index_array[array_length - 1]    = invalid_index;
+    distance_array[array_length - 1] = utils::get_max_value<float>();
+  }
 }
 
 template <class INDEX_T>
@@ -589,10 +631,10 @@ __device__ void search_core(
     // sort
     if constexpr (TOPK_BY_BITONIC_SORT) {
       // [Notice]
-      // It is good to use multiple warps in topk_by_bitonic_sort() when
+      // It is good to use multiple warps in topk_by_bitonic_sort_and_merge() when
       // batch size is small (short-latency), but it might not be always good
       // when batch size is large (high-throughput).
-      // topk_by_bitonic_sort() consists of two operations:
+      // topk_by_bitonic_sort_and_merge() consists of two operations:
       // if MAX_CANDIDATES is greater than 128, the first operation uses two warps;
       // if MAX_ITOPK is greater than 256, the second operation used two warps.
       const unsigned multi_warps_1 = ((blockDim.x >= 64) && (MAX_CANDIDATES > 128)) ? 1 : 0;
@@ -601,9 +643,9 @@ __device__ void search_core(
       // reset small-hash table.
       if ((iter + 1) % small_hash_reset_interval == 0) {
         // Depending on the block size and the number of warps used in
-        // topk_by_bitonic_sort(), determine which warps are used to reset
+        // topk_by_bitonic_sort_and_merge(), determine which warps are used to reset
         // the small hash and whether they are performed in overlap with
-        // topk_by_bitonic_sort().
+        // topk_by_bitonic_sort_and_merge().
         _CLK_START();
         unsigned hash_start_tid;
         if (blockDim.x == 32) {
@@ -627,28 +669,28 @@ __device__ void search_core(
 
       // topk with bitonic sort
       _CLK_START();
-      if (std::is_same<SAMPLE_FILTER_T, cuvs::neighbors::filtering::none_sample_filter>::value ||
-          *filter_flag == 0) {
-        topk_by_bitonic_sort<MAX_ITOPK, MAX_CANDIDATES>(result_distances_buffer,
-                                                        result_indices_buffer,
-                                                        internal_topk,
-                                                        result_distances_buffer + internal_topk,
-                                                        result_indices_buffer + internal_topk,
-                                                        search_width * graph_degree,
-                                                        topk_ws,
-                                                        (iter == 0),
-                                                        multi_warps_1,
-                                                        multi_warps_2);
-        __syncthreads();
-      } else {
-        topk_by_bitonic_sort_1st<MAX_ITOPK + MAX_CANDIDATES>(
-          result_distances_buffer,
-          result_indices_buffer,
-          internal_topk + search_width * graph_degree,
-          internal_topk,
-          false);
+      if (!(std::is_same<SAMPLE_FILTER_T, cuvs::neighbors::filtering::none_sample_filter>::value ||
+            *filter_flag == 0)) {
+        // Move the filtered out index to the end of the itopk list
+        for (unsigned i = 0; i < search_width; i++) {
+          move_invalid_to_end_of_list(
+            result_indices_buffer, result_distances_buffer, internal_topk);
+        }
+
         if (threadIdx.x == 0) { *terminate_flag = 0; }
       }
+      topk_by_bitonic_sort_and_merge<MAX_ITOPK, MAX_CANDIDATES>(
+        result_distances_buffer,
+        result_indices_buffer,
+        internal_topk,
+        result_distances_buffer + internal_topk,
+        result_indices_buffer + internal_topk,
+        search_width * graph_degree,
+        topk_ws,
+        (iter == 0),
+        multi_warps_1,
+        multi_warps_2);
+      __syncthreads();
       _CLK_REC(clk_topk);
     } else {
       _CLK_START();
@@ -755,12 +797,66 @@ __device__ void search_core(
     }
 
     __syncthreads();
-    topk_by_bitonic_sort_1st<MAX_ITOPK + MAX_CANDIDATES>(
-      result_distances_buffer,
-      result_indices_buffer,
-      internal_topk + search_width * graph_degree,
-      top_k,
-      false);
+    // Move invalid index items to the end of the buffer without sorting the entire buffer
+    using scan_op_t    = cub::WarpScan<unsigned>;
+    auto& temp_storage = *reinterpret_cast<typename scan_op_t::TempStorage*>(smem_work_ptr);
+
+    constexpr std::uint32_t warp_size = 32;
+    if (threadIdx.x < warp_size) {
+      std::uint32_t num_found_valid = 0;
+      for (std::uint32_t buffer_offset = 0; buffer_offset < internal_topk;
+           buffer_offset += warp_size) {
+        // Calculate the new buffer index
+        const auto src_position = buffer_offset + threadIdx.x;
+        const std::uint32_t is_valid_index =
+          (result_indices_buffer[src_position] & (~index_msb_1_mask)) == invalid_index ? 0 : 1;
+        std::uint32_t new_position;
+        scan_op_t(temp_storage).InclusiveSum(is_valid_index, new_position);
+        if (is_valid_index) {
+          const auto dst_position               = num_found_valid + (new_position - 1);
+          result_indices_buffer[dst_position]   = result_indices_buffer[src_position];
+          result_distances_buffer[dst_position] = result_distances_buffer[src_position];
+        }
+
+        // Calculate the largest valid position within a warp and bcast it for the next iteration
+        num_found_valid += new_position;
+        for (std::uint32_t offset = (warp_size >> 1); offset > 0; offset >>= 1) {
+          const auto v = raft::shfl_xor(num_found_valid, offset);
+          if ((threadIdx.x & offset) == 0) { num_found_valid = v; }
+        }
+
+        // If the enough number of items are found, do early termination
+        if (num_found_valid >= top_k) { break; }
+      }
+
+      if (num_found_valid < top_k) {
+        // Fill the remaining buffer with invalid values so that `topk_by_bitonic_sort_and_merge` is
+        // usable in the next step
+        for (std::uint32_t i = num_found_valid + threadIdx.x; i < internal_topk; i += warp_size) {
+          result_indices_buffer[i]   = invalid_index;
+          result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+        }
+      }
+    }
+
+    // If the sufficient number of valid indexes are not in the internal topk, pick up from the
+    // candidate list.
+    if (top_k > internal_topk || result_indices_buffer[top_k - 1] == invalid_index) {
+      __syncthreads();
+      const unsigned multi_warps_1 = ((blockDim.x >= 64) && (MAX_CANDIDATES > 128)) ? 1 : 0;
+      const unsigned multi_warps_2 = ((blockDim.x >= 64) && (MAX_ITOPK > 256)) ? 1 : 0;
+      topk_by_bitonic_sort_and_merge<MAX_ITOPK, MAX_CANDIDATES>(
+        result_distances_buffer,
+        result_indices_buffer,
+        internal_topk,
+        result_distances_buffer + internal_topk,
+        result_indices_buffer + internal_topk,
+        search_width * graph_degree,
+        topk_ws,
+        (iter == 0),
+        multi_warps_1,
+        multi_warps_2);
+    }
     __syncthreads();
   }
 
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 660246c67e..8d57014395 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -758,11 +758,7 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
         search_params.algo        = ps.algo;
         search_params.max_queries = ps.max_queries;
         search_params.team_size   = ps.team_size;
-
-        // TODO: setting search_params.itopk_size here breaks the filter tests, but is required for
-        // k>1024 skip these tests until fixed
-        if (ps.k >= 1024) { GTEST_SKIP(); }
-        // search_params.itopk_size   = ps.itopk_size;
+        search_params.itopk_size  = ps.itopk_size;
 
         auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
           (const DataT*)database.data(), ps.n_rows, ps.dim);

From a96b72086320ff1dab7b843c67a3c96352a7563d Mon Sep 17 00:00:00 2001
From: Ajit Mistry <55892788+ajit283@users.noreply.github.com>
Date: Wed, 4 Dec 2024 17:18:45 +0100
Subject: [PATCH 226/623] [WIP] Add pinned memory resource to C API (#311)

Let me know if this is out of scope for cuVS!

Authors:
  - Ajit Mistry (https://github.com/ajit283)
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/311
---
 cpp/include/cuvs/core/c_api.h | 16 ++++++++++++++++
 cpp/src/core/c_api.cpp        | 16 ++++++++++++++++
 cpp/test/core/c_api.c         |  9 +++++++++
 3 files changed, 41 insertions(+)

diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index c8c8d3934a..400d162ad7 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -151,6 +151,22 @@ cuvsError_t cuvsRMMPoolMemoryResourceEnable(int initial_pool_size_percent,
  */
 cuvsError_t cuvsRMMMemoryResourceReset();
 
+/**
+ * @brief Allocates pinned memory on the host using RMM
+ * @param[out] ptr Pointer to allocated host memory
+ * @param[in] bytes Size in bytes to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsRMMHostAlloc(void** ptr, size_t bytes);
+
+/**
+ * @brief Deallocates pinned memory on the host using RMM
+ * @param[in] ptr Pointer to allocated host memory to free
+ * @param[in] bytes Size in bytes to deallocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsRMMHostFree(void* ptr, size_t bytes);
+
 /** @} */
 
 #ifdef __cplusplus
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index cfbeed2d55..4333bff0cd 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -26,6 +26,7 @@
 #include <rmm/mr/device/owning_wrapper.hpp>
 #include <rmm/mr/device/per_device_resource.hpp>
 #include <rmm/mr/device/pool_memory_resource.hpp>
+#include <rmm/mr/host/pinned_memory_resource.hpp>
 #include <thread>
 
 extern "C" cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
@@ -130,6 +131,21 @@ extern "C" cuvsError_t cuvsRMMMemoryResourceReset()
   });
 }
 
+thread_local std::unique_ptr<rmm::mr::pinned_memory_resource> pinned_mr;
+
+extern "C" cuvsError_t cuvsRMMHostAlloc(void** ptr, size_t bytes)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (pinned_mr == nullptr) { pinned_mr = std::make_unique<rmm::mr::pinned_memory_resource>(); }
+    *ptr = pinned_mr->allocate(bytes);
+  });
+}
+
+extern "C" cuvsError_t cuvsRMMHostFree(void* ptr, size_t bytes)
+{
+  return cuvs::core::translate_exceptions([=] { pinned_mr->deallocate(ptr, bytes); });
+}
+
 thread_local std::string last_error_text = "";
 
 extern "C" const char* cuvsGetLastErrorText()
diff --git a/cpp/test/core/c_api.c b/cpp/test/core/c_api.c
index a3dae60044..a51824d2b9 100644
--- a/cpp/test/core/c_api.c
+++ b/cpp/test/core/c_api.c
@@ -73,6 +73,15 @@ int main()
   error = cuvsRMMMemoryResourceReset();
   if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
+  // Alloc memory on host (pinned)
+  void* ptr3;
+  cuvsError_t alloc_error_pinned = cuvsRMMHostAlloc(&ptr3, 1024);
+  if (alloc_error_pinned == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
+  // Free memory
+  cuvsError_t free_error_pinned = cuvsRMMHostFree(ptr3, 1024);
+  if (free_error_pinned == CUVS_ERROR) { exit(EXIT_FAILURE); }
+
   // Destroy resources
   error = cuvsResourcesDestroy(res);
   if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }

From 9fb21adc82e625deb7cc0f20b68c0f42902246f1 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 4 Dec 2024 17:38:05 +0100
Subject: [PATCH 227/623] Dynamic Batching (#261)

Non-blocking / stream-ordered dynamic batching as a new index type.

## API

This PR implements dynamic batching as a new index type, mirroring the API of other indices.

  * [_building is wrapping_] Building the index means creating a lightweight wrapper on top of an existing index and initializing necessary components, such as IO batch buffers and synchronization primitives.
  * [_type erasure_] The underlying/upstream index type is erased once the dynamic_batching wrapper is created, i.e. there's no way to recover the original search index type or parameters.
  * [_explicit control over batching_] To allow multiple user requests group into a dynamic batch request, the users must use copies of the same dynamic batching index (the user-facing index type is a thin wrapper on top of a shared pointer, hence the copy is shallow and cheap). The search function is thread-safe.

## Feature:  stream-ordered dynamic batching

Non-blocking / stream-ordered dynamic batching means the batching does not involve synchronizing with a GPU stream. The control is returned to the user as soon as the necessary work is submitted to the GPU. This entails a few good-to-know features:

1. The dynamic batching index has the same blocking properties as the upstream index: if the upstream index does not involve stream sync during search, that the dynamic batching index does not involve it as well (otherwise, the dynamic batching search obviously waits till the upstream search synchronizes under the hood).
2. It's responsibility of the user to synchronize the stream before getting the results back - even if the upstream index search does not need it (the batch results are scattered back to the request threads in a post-processing kernel).
3. If the upstream index does not synchronize during search, the dynamic batching index can group the queries even in a single-threaded application (_try it with --no-lap-sync option in the ann-bench benchmarks_).

Overall, stream-ordered dynamic batching makes it easy to modify existing cuVS indexes, because the wrapped index has the same execution behavior as the upstream index.

## Work-in-progress TODO

- [x] Add dynamic batching option to more indices in ann-bench
- [x] Add tests
- [x] **(postponed to 25.02)** Do proper benchmarking and possibly fine-tune the inter-thread communication
- [x] Review the API side (`cpp/include/cuvs/neighbors/dynamic_batching.hpp`) [ready for review CC @cjnolet]
- [x] Review the algorithm side (`cpp/src/neighbors/detail/dynamic_batching.cuh`) [ready for preliminary review: requests for algorithm docsting/clarifications are especially welcome]

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/261
---
 cpp/CMakeLists.txt                            |    1 +
 .../src/cuvs/cuvs_ann_bench_param_parser.h    |   26 +
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |   97 +-
 cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h  |   40 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |    4 +
 .../cuvs/neighbors/dynamic_batching.hpp       |  290 ++++
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |    4 +
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |    3 +
 cpp/src/neighbors/detail/dynamic_batching.cuh | 1197 +++++++++++++++++
 cpp/src/neighbors/dynamic_batching.cu         |   91 ++
 cpp/test/CMakeLists.txt                       |   13 +
 cpp/test/neighbors/dynamic_batching.cuh       |  292 ++++
 .../neighbors/dynamic_batching/test_cagra.cu  |   84 ++
 .../dynamic_batching/test_ivf_flat.cu         |   44 +
 .../neighbors/dynamic_batching/test_ivf_pq.cu |   41 +
 docs/source/cpp_api/neighbors.rst             |    1 +
 .../cpp_api/neighbors_dynamic_batching.rst    |   45 +
 examples/cpp/CMakeLists.txt                   |    4 +
 examples/cpp/src/dynamic_batching_example.cu  |  282 ++++
 19 files changed, 2539 insertions(+), 20 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/dynamic_batching.hpp
 create mode 100644 cpp/src/neighbors/detail/dynamic_batching.cuh
 create mode 100644 cpp/src/neighbors/dynamic_batching.cu
 create mode 100644 cpp/test/neighbors/dynamic_batching.cuh
 create mode 100644 cpp/test/neighbors/dynamic_batching/test_cagra.cu
 create mode 100644 cpp/test/neighbors/dynamic_batching/test_ivf_flat.cu
 create mode 100644 cpp/test/neighbors/dynamic_batching/test_ivf_pq.cu
 create mode 100644 docs/source/cpp_api/neighbors_dynamic_batching.rst
 create mode 100644 examples/cpp/src/dynamic_batching_example.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 34b7cb8983..6af423bd58 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -397,6 +397,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
     src/neighbors/detail/cagra/cagra_build.cpp
     src/neighbors/detail/cagra/topk_for_cagra/topk.cu
+    src/neighbors/dynamic_batching.cu
     $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
     src/neighbors/ivf_flat_index.cpp
     src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 57d5b19106..7617bfa66f 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -56,6 +56,26 @@ extern template class cuvs::bench::cuvs_cagra<int8_t, uint32_t>;
 #include "cuvs_mg_cagra_wrapper.h"
 #endif
 
+template <typename ParamT>
+void parse_dynamic_batching_params(const nlohmann::json& conf, ParamT& param)
+{
+  if (!conf.value("dynamic_batching", false)) { return; }
+  param.dynamic_batching = true;
+  if (conf.contains("dynamic_batching_max_batch_size")) {
+    param.dynamic_batching_max_batch_size = conf.at("dynamic_batching_max_batch_size");
+  }
+  param.dynamic_batching_conservative_dispatch =
+    conf.value("dynamic_batching_conservative_dispatch", false);
+  if (conf.contains("dynamic_batching_dispatch_timeout_ms")) {
+    param.dynamic_batching_dispatch_timeout_ms = conf.at("dynamic_batching_dispatch_timeout_ms");
+  }
+  if (conf.contains("dynamic_batching_n_queues")) {
+    param.dynamic_batching_n_queues = conf.at("dynamic_batching_n_queues");
+  }
+  param.dynamic_batching_k =
+    uint32_t(uint32_t(conf.at("k")) * float(conf.value("refine_ratio", 1.0f)));
+}
+
 #if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT) || defined(CUVS_ANN_BENCH_USE_CUVS_MG)
 template <typename T, typename IdxT>
 void parse_build_param(const nlohmann::json& conf,
@@ -138,6 +158,9 @@ void parse_search_param(const nlohmann::json& conf,
     param.refine_ratio = conf.at("refine_ratio");
     if (param.refine_ratio < 1.0f) { throw std::runtime_error("refine_ratio should be >= 1.0"); }
   }
+
+  // enable dynamic batching
+  parse_dynamic_batching_params(conf, param);
 }
 #endif
 
@@ -291,5 +314,8 @@ void parse_search_param(const nlohmann::json& conf,
   }
   // Same ratio as in IVF-PQ
   param.refine_ratio = conf.value("refine_ratio", 1.0f);
+
+  // enable dynamic batching
+  parse_dynamic_batching_params(conf, param);
 }
 #endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index f6d3d60fc1..8c9cb2d4f3 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -24,6 +24,7 @@
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/dynamic_batching.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 #include <raft/core/device_mdspan.hpp>
@@ -63,6 +64,13 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
     AllocatorType graph_mem   = AllocatorType::kDevice;
     AllocatorType dataset_mem = AllocatorType::kDevice;
     [[nodiscard]] auto needs_dataset() const -> bool override { return true; }
+    /* Dynamic batching */
+    bool dynamic_batching = false;
+    int64_t dynamic_batching_k;
+    int64_t dynamic_batching_max_batch_size     = 4;
+    double dynamic_batching_dispatch_timeout_ms = 0.01;
+    size_t dynamic_batching_n_queues            = 8;
+    bool dynamic_batching_conservative_dispatch = false;
   };
 
   struct build_param {
@@ -173,6 +181,12 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
   std::shared_ptr<raft::device_matrix<T, int64_t, raft::row_major>> dataset_;
   std::shared_ptr<raft::device_matrix_view<const T, int64_t, raft::row_major>> input_dataset_v_;
 
+  std::shared_ptr<cuvs::neighbors::dynamic_batching::index<T, IdxT>> dynamic_batcher_;
+  cuvs::neighbors::dynamic_batching::search_params dynamic_batcher_sp_{};
+  int64_t dynamic_batching_max_batch_size_;
+  size_t dynamic_batching_n_queues_;
+  bool dynamic_batching_conservative_dispatch_;
+
   inline rmm::device_async_resource_ref get_mr(AllocatorType mem_type)
   {
     switch (mem_type) {
@@ -216,26 +230,33 @@ inline auto allocator_to_string(AllocatorType mem_type) -> std::string
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param)
 {
-  auto sp        = dynamic_cast<const search_param&>(param);
-  search_params_ = sp.p;
-  refine_ratio_  = sp.refine_ratio;
+  auto sp = dynamic_cast<const search_param&>(param);
+  bool needs_dynamic_batcher_update =
+    (dynamic_batching_max_batch_size_ != sp.dynamic_batching_max_batch_size) ||
+    (dynamic_batching_n_queues_ != sp.dynamic_batching_n_queues) ||
+    (dynamic_batching_conservative_dispatch_ != sp.dynamic_batching_conservative_dispatch);
+  dynamic_batching_max_batch_size_        = sp.dynamic_batching_max_batch_size;
+  dynamic_batching_n_queues_              = sp.dynamic_batching_n_queues;
+  dynamic_batching_conservative_dispatch_ = sp.dynamic_batching_conservative_dispatch;
+  search_params_                          = sp.p;
+  refine_ratio_                           = sp.refine_ratio;
   if (sp.graph_mem != graph_mem_) {
     // Move graph to correct memory space
     graph_mem_ = sp.graph_mem;
     RAFT_LOG_DEBUG("moving graph to new memory space: %s", allocator_to_string(graph_mem_).c_str());
     // We create a new graph and copy to it from existing graph
-    auto mr        = get_mr(graph_mem_);
-    auto new_graph = raft::make_device_mdarray<IdxT, int64_t>(
+    auto mr = get_mr(graph_mem_);
+    *graph_ = raft::make_device_mdarray<IdxT, int64_t>(
       handle_, mr, raft::make_extents<int64_t>(index_->graph().extent(0), index_->graph_degree()));
 
-    raft::copy(new_graph.data_handle(),
+    raft::copy(graph_->data_handle(),
                index_->graph().data_handle(),
                index_->graph().size(),
                raft::resource::get_cuda_stream(handle_));
 
-    index_->update_graph(handle_, make_const_mdspan(new_graph.view()));
-    // update_graph() only stores a view in the index. We need to keep the graph object alive.
-    *graph_ = std::move(new_graph);
+    // NB: update_graph() only stores a view in the index. We need to keep the graph object alive.
+    index_->update_graph(handle_, make_const_mdspan(graph_->view()));
+    needs_dynamic_batcher_update = true;
   }
 
   if (sp.dataset_mem != dataset_mem_ || need_dataset_update_) {
@@ -256,7 +277,26 @@ void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param)
       dataset_->data_handle(), dataset_->extent(0), this->dim_, dataset_->extent(1));
     index_->update_dataset(handle_, dataset_view);
 
-    need_dataset_update_ = false;
+    need_dataset_update_         = false;
+    needs_dynamic_batcher_update = true;
+  }
+
+  // dynamic batching
+  if (sp.dynamic_batching) {
+    if (!dynamic_batcher_ || needs_dynamic_batcher_update) {
+      dynamic_batcher_ = std::make_shared<cuvs::neighbors::dynamic_batching::index<T, IdxT>>(
+        handle_,
+        cuvs::neighbors::dynamic_batching::index_params{{},
+                                                        sp.dynamic_batching_k,
+                                                        sp.dynamic_batching_max_batch_size,
+                                                        sp.dynamic_batching_n_queues,
+                                                        sp.dynamic_batching_conservative_dispatch},
+        *index_,
+        search_params_);
+    }
+    dynamic_batcher_sp_.dispatch_timeout_ms = sp.dynamic_batching_dispatch_timeout_ms;
+  } else {
+    if (dynamic_batcher_) { dynamic_batcher_.reset(); }
   }
 }
 
@@ -306,7 +346,7 @@ void cuvs_cagra<T, IdxT>::load(const std::string& file)
 template <typename T, typename IdxT>
 std::unique_ptr<algo<T>> cuvs_cagra<T, IdxT>::copy()
 {
-  return std::make_unique<cuvs_cagra<T, IdxT>>(*this);  // use copy constructor
+  return std::make_unique<cuvs_cagra<T, IdxT>>(std::cref(*this));  // use copy constructor
 }
 
 template <typename T, typename IdxT>
@@ -330,8 +370,17 @@ void cuvs_cagra<T, IdxT>::search_base(const T* queries,
     raft::make_device_matrix_view<IdxT, int64_t>(neighbors_idx_t, batch_size, k);
   auto distances_view = raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k);
 
-  cuvs::neighbors::cagra::search(
-    handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+  if (dynamic_batcher_) {
+    cuvs::neighbors::dynamic_batching::search(handle_,
+                                              dynamic_batcher_sp_,
+                                              *dynamic_batcher_,
+                                              queries_view,
+                                              neighbors_view,
+                                              distances_view);
+  } else {
+    cuvs::neighbors::cagra::search(
+      handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+  }
 
   if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
     if (raft::get_device_for_address(neighbors) < 0 &&
@@ -367,11 +416,23 @@ void cuvs_cagra<T, IdxT>::search(
   const raft::resources& res    = handle_;
   auto mem_type =
     raft::get_device_for_address(neighbors) >= 0 ? MemoryType::kDevice : MemoryType::kHostPinned;
-  auto& tmp_buf = get_tmp_buffer_from_global_pool(
-    ((disable_refinement ? 0 : (sizeof(float) + sizeof(algo_base::index_type))) +
-     (kNeedsIoMapping ? sizeof(IdxT) : 0)) *
-    batch_size * k0);
-  auto* candidates_ptr = reinterpret_cast<algo_base::index_type*>(tmp_buf.data(mem_type));
+
+  // If dynamic batching is used and there's no sync between benchmark laps, multiple sequential
+  // requests can group together. The data is copied asynchronously, and if the same intermediate
+  // buffer is used for multiple requests, they can override each other's data. Hence, we need to
+  // allocate as much space as required by the maximum number of sequential requests.
+  auto max_dyn_grouping = dynamic_batcher_ ? raft::div_rounding_up_safe<int64_t>(
+                                               dynamic_batching_max_batch_size_, batch_size) *
+                                               dynamic_batching_n_queues_
+                                           : 1;
+  auto tmp_buf_size = ((disable_refinement ? 0 : (sizeof(float) + sizeof(algo_base::index_type))) +
+                       (kNeedsIoMapping ? sizeof(IdxT) : 0)) *
+                      batch_size * k0;
+  auto& tmp_buf = get_tmp_buffer_from_global_pool(tmp_buf_size * max_dyn_grouping);
+  thread_local static int64_t group_id = 0;
+  auto* candidates_ptr                 = reinterpret_cast<algo_base::index_type*>(
+    reinterpret_cast<uint8_t*>(tmp_buf.data(mem_type)) + tmp_buf_size * group_id);
+  group_id = (group_id + 1) % max_dyn_grouping;
   auto* candidate_dists_ptr =
     reinterpret_cast<float*>(candidates_ptr + (disable_refinement ? 0 : batch_size * k0));
   auto* neighbors_idx_t =
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
index 4c8a91f23d..dac7666697 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
@@ -19,7 +19,9 @@
 #include "cuvs_ann_bench_utils.h"
 
 #include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/dynamic_batching.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
+
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/device_resources.hpp>
@@ -46,6 +48,13 @@ class cuvs_ivf_pq : public algo<T>, public algo_gpu {
     cuvs::neighbors::ivf_pq::search_params pq_param;
     float refine_ratio = 1.0f;
     [[nodiscard]] auto needs_dataset() const -> bool override { return refine_ratio > 1.0f; }
+    /* Dynamic batching */
+    bool dynamic_batching = false;
+    int64_t dynamic_batching_k;
+    int64_t dynamic_batching_max_batch_size     = 128;
+    double dynamic_batching_dispatch_timeout_ms = 0.01;
+    size_t dynamic_batching_n_queues            = 3;
+    bool dynamic_batching_conservative_dispatch = true;
   };
 
   using build_param = cuvs::neighbors::ivf_pq::index_params;
@@ -98,6 +107,9 @@ class cuvs_ivf_pq : public algo<T>, public algo_gpu {
   int dimension_;
   float refine_ratio_ = 1.0;
   raft::device_matrix_view<const T, IdxT> dataset_;
+
+  std::shared_ptr<cuvs::neighbors::dynamic_batching::index<T, IdxT>> dynamic_batcher_;
+  cuvs::neighbors::dynamic_batching::search_params dynamic_batcher_sp_{};
 };
 
 template <typename T, typename IdxT>
@@ -138,6 +150,21 @@ void cuvs_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param)
   search_params_ = sp.pq_param;
   refine_ratio_  = sp.refine_ratio;
   assert(search_params_.n_probes <= index_params_.n_lists);
+
+  if (sp.dynamic_batching) {
+    dynamic_batcher_ = std::make_shared<cuvs::neighbors::dynamic_batching::index<T, IdxT>>(
+      handle_,
+      cuvs::neighbors::dynamic_batching::index_params{{},
+                                                      sp.dynamic_batching_k,
+                                                      sp.dynamic_batching_max_batch_size,
+                                                      sp.dynamic_batching_n_queues,
+                                                      sp.dynamic_batching_conservative_dispatch},
+      *index_,
+      search_params_);
+    dynamic_batcher_sp_.dispatch_timeout_ms = sp.dynamic_batching_dispatch_timeout_ms;
+  } else {
+    dynamic_batcher_.reset();
+  }
 }
 
 template <typename T, typename IdxT>
@@ -168,8 +195,17 @@ void cuvs_ivf_pq<T, IdxT>::search_base(
     raft::make_device_matrix_view<IdxT, uint32_t>(neighbors_idx_t, batch_size, k);
   auto distances_view = raft::make_device_matrix_view<float, uint32_t>(distances, batch_size, k);
 
-  cuvs::neighbors::ivf_pq::search(
-    handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+  if (dynamic_batcher_) {
+    cuvs::neighbors::dynamic_batching::search(handle_,
+                                              dynamic_batcher_sp_,
+                                              *dynamic_batcher_,
+                                              queries_view,
+                                              neighbors_view,
+                                              distances_view);
+  } else {
+    cuvs::neighbors::ivf_pq::search(
+      handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+  }
 
   if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
     raft::linalg::unaryOp(neighbors,
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 5ceb3010e0..a4684ce267 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -272,6 +272,10 @@ static_assert(std::is_aggregate_v<search_params>);
  */
 template <typename T, typename IdxT>
 struct index : cuvs::neighbors::index {
+  using index_params_type  = cagra::index_params;
+  using search_params_type = cagra::search_params;
+  using index_type         = IdxT;
+  using value_type         = T;
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
diff --git a/cpp/include/cuvs/neighbors/dynamic_batching.hpp b/cpp/include/cuvs/neighbors/dynamic_batching.hpp
new file mode 100644
index 0000000000..4108003575
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/dynamic_batching.hpp
@@ -0,0 +1,290 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/common.hpp>
+
+namespace cuvs::neighbors::dynamic_batching {
+
+namespace detail {
+template <typename T, typename IdxT>
+class batch_runner;
+}
+
+/**
+ * @defgroup dynamic_batching_cpp_index_params Dynamic Batching index parameters
+ * @{
+ */
+struct index_params : cuvs::neighbors::index_params {
+  /** The number of neighbors to search is fixed at construction time. */
+  int64_t k;
+  /** Maximum size of the batch to submit to the upstream index. */
+  int64_t max_batch_size = 100;
+  /**
+   * The number of independent request queues.
+   *
+   * Each queue is associated with a unique CUDA stream and IO device buffers. If the number of
+   * concurrent requests is high, using multiple queues allows to fill-in data and prepare the batch
+   * while the other queue is busy. Moreover, the queues are submitted concurrently; this allows to
+   * better utilize the GPU by hiding the kernel launch latencies, which helps to improve the
+   * throughput.
+   */
+  size_t n_queues = 3;
+  /**
+   * By default (`conservative_dispatch = false`) the first CPU thread to commit a query to a batch
+   * dispatches the upstream search function as soon as possible (before the batch is full). In that
+   * case, it does not know the final batch size at the time of calling the upstream search and thus
+   * runs the upstream search with the maximum batch size every time, even if only one valid query
+   * is present in the batch. This reduces the latency at the cost of wasted GPU resources.
+   *
+   * The alternative behavaior (`conservative_dispatch = true`) is more conservative: the dispatcher
+   * thread starts the kernel that gathers input queries, but waits till the batch is full or the
+   * waiting time is exceeded. Only then it acquires the actual batch size and launches the upstream
+   * search. As a result, less GPU resources are wasted at the cost of exposing upstream search
+   * latency.
+   *
+   * *Rule of Thumb*:
+   *    for a large `max_batch_size` set `conservative_dispatch = true`, otherwise keep it disabled.
+   */
+  bool conservative_dispatch = false;
+};
+/** @} */
+
+/**
+ * @defgroup dynamic_batching_cpp_search_params Dynamic Batching search parameters
+ * @{
+ */
+struct search_params : cuvs::neighbors::search_params {
+  /**
+   * How long a request can stay in the queue (milliseconds).
+   * Note, this only affects the dispatch time and does not reflect full request latency;
+   * the latter depends on the upstream search parameters and the batch size.
+   */
+  double dispatch_timeout_ms = 1.0;
+};
+/** @} */
+
+/**
+ * @defgroup dynamic_batching_cpp_index Dynamic Batching index type
+ * @{
+ */
+
+/**
+ * @brief Lightweight dynamic batching index wrapper
+ *
+ * @tparam T data type
+ * @tparam IdxT index type
+ *
+ * One lightweight dynamic batching index manages a single index and a single search parameter set.
+ * This structure should be shared among multiple users via copy semantics: access to the
+ * underlying implementation is managed via a shared pointer, and concurrent search among the
+ * participants is thread-safe.
+ *
+ * __Usage example__
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // When creating a dynamic batching index, k parameter has to be passed explicitly.
+ *   // The first empty braces default-initialize the parent `neighbors::index_params` (unused).
+ *   dynamic_batching::index_params dynb_index_params{{}, k};
+ *   // Construct the index by wrapping the upstream index and search parameters.
+ *   dynamic_batching::index<float, uint32_t> index{
+ *       res, dynb_index_params, upstream_index, upstream_search_params
+ *   };
+ *   // Use default search parameters
+ *   dynamic_batching::search_params search_params;
+ *   // Search K nearest neighbours
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   dynamic_batching::search(
+ *       res, search_params, index, queries, neighbors.view(), distances.view()
+ *   );
+ * @endcode
+ *
+ *
+ * __Priority queues__
+ *
+ * The dynamic batching index has a limited support for prioritizing individual requests.
+ * There's only one pool of queues in the batcher and no functionality to prioritize one bach over
+ * the other. The `search_params::dispatch_timeout_ms` parameters passed in each request are
+ * aggregated internally and the batch is dispatched no later than any of the timeouts is exceeded.
+ * In this logic, a high-priority request can never be processed earlier than any lower-priority
+ * requests submitted earlier.
+ *
+ * However, dynamic batching indexes are lightweight and do not contain any global or static state.
+ * This means it's easy to combine multiple batchers.
+ * As an example, you can construct one batching index per priority class:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // Large batch size (128), couple queues (2),
+ *   //   enabled conservative dispatch - all for better throughput
+ *   dynamic_batching::index_params low_priority_params{{}, k, 128, 2, true};
+ *   // Small batch size (16), more queues (4),
+ *   //   disabled conservative dispatch - to minimize latency with reasonable throughput
+ *   dynamic_batching::index_params high_priority_params{{}, k, 16, 4, false};
+ *   // Construct the indexes by wrapping the upstream index and search parameters.
+ *   dynamic_batching::index<float, uint32_t> low_priority_index{
+ *       res, low_priority_params, upstream_index, upstream_search_params
+ *   };
+ *   dynamic_batching::index<float, uint32_t> high_priority_index{
+ *       res, high_priority_params, upstream_index, upstream_search_params
+ *   };
+ *   // Define a combined search function with priority selection
+ *   double high_priority_threshold_ms = 0.1;
+ *   auto search_function =
+ *      [low_priority_index, high_priority_index, high_priority_threshold_ms](
+ *        raft::resources const &res,
+ *        dynamic_batching::search_params search_params,
+ *        raft::device_matrix_view<const float, int64_t> queries,
+ *        raft::device_matrix_view<uint32_t, int64_t> neighbors,
+ *        raft::device_matrix_view<float, int64_t> distances) {
+ *      dynamic_batching::search(
+ *          res,
+ *          search_params,
+ *          search_params.dispatch_timeout_ms < high_priority_threshold_ms
+ *            ? high_priority_index : low_priority_index,
+ *          queries,
+ *          neighbors,
+ *          distances
+ *      );
+ *   };
+ * @endcode
+ */
+template <typename T, typename IdxT>
+struct index : cuvs::neighbors::index {
+  std::shared_ptr<detail::batch_runner<T, IdxT>> runner;
+
+  /**
+   * @brief Construct a dynamic batching index by wrapping the upstream index.
+   *
+   * @tparam Upstream the upstream index type
+   *
+   * @param[in] res raft resources
+   * @param[in] params dynamic batching parameters
+   * @param[in] upstream_index the original index to perform the search
+   *     (the reference must be alive for the lifetime of the dynamic batching index)
+   * @param[in] upstream_params the original index search parameters for all queries in a batch
+   *     (the parameters are captured by value for the lifetime of the dynamic batching index)
+   * @param[in] sample_filter
+   *     filtering function, if any, must be the same for all requests in a batch
+   *     (the pointer must be alive for the lifetime of the dynamic batching index)
+   */
+  template <typename Upstream>
+  index(const raft::resources& res,
+        const cuvs::neighbors::dynamic_batching::index_params& params,
+        const Upstream& upstream_index,
+        const typename Upstream::search_params_type& upstream_params,
+        const cuvs::neighbors::filtering::base_filter* sample_filter = nullptr);
+};
+/** @} */
+
+/**
+ *
+ * @defgroup dynamic_batching_cpp_search Dynamic Batching search
+ *
+ * @{
+ */
+
+/**
+ * @brief Search ANN using a dynamic batching index.
+ *
+ * The search parameters of the upstream index and the optional filtering function are configured at
+ * the dynamic batching index construction time.
+ *
+ * Like with many other indexes, the dynamic batching search has the stream-ordered semantics: the
+ * host function may return the control before the results are ready. Synchronize with the main CUDA
+ * stream in the given resource object to wait for arrival of the search results.
+ *
+ * Dynamic batching search is thread-safe: call the search function with copies of the same index in
+ * multiple threads to increase the occupancy of the batches.
+ *
+ * @param[in] res
+ * @param[in] params query-specific batching parameters, such as the maximum waiting time
+ * @param[in] index a dynamic batching index
+ * @param[in] queries a device matrix view to a row-major matrix
+ *               [n_queries, dim]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ *               [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors
+ *               [n_queries, k]
+ *
+ */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<float, uint32_t> const& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<half, uint32_t> const& index,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<int8_t, uint32_t> const& index,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<uint8_t, uint32_t> const& index,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<float, int64_t> const& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<half, int64_t> const& index,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<int8_t, int64_t> const& index,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            cuvs::neighbors::dynamic_batching::search_params const& params,
+            dynamic_batching::index<uint8_t, int64_t> const& index,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances);
+
+/** @} */
+
+}  // namespace cuvs::neighbors::dynamic_batching
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index 7f852d635d..e017946d9c 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -138,6 +138,10 @@ using list_data = ivf::list<list_spec, SizeT, ValueT, IdxT>;
  */
 template <typename T, typename IdxT>
 struct index : cuvs::neighbors::index {
+  using index_params_type  = ivf_flat::index_params;
+  using search_params_type = ivf_flat::search_params;
+  using index_type         = IdxT;
+  using value_type         = T;
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index ae543c9e9a..d85753b7f2 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -319,6 +319,9 @@ using list_data = ivf::list<list_spec, SizeT, IdxT>;
  */
 template <typename IdxT>
 struct index : cuvs::neighbors::index {
+  using index_params_type  = ivf_pq::index_params;
+  using search_params_type = ivf_pq::search_params;
+  using index_type         = IdxT;
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
diff --git a/cpp/src/neighbors/detail/dynamic_batching.cuh b/cpp/src/neighbors/detail/dynamic_batching.cuh
new file mode 100644
index 0000000000..5c6b1654e2
--- /dev/null
+++ b/cpp/src/neighbors/detail/dynamic_batching.cuh
@@ -0,0 +1,1197 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../sample_filter.cuh"
+
+#include <cuvs/neighbors/dynamic_batching.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/pinned_mdarray.hpp>
+#include <raft/core/pinned_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cooperative_groups.h>
+#include <cuda/atomic>
+#include <cuda/std/atomic>
+#include <rmm/mr/pinned_host_memory_resource.hpp>
+#include <rmm/resource_ref.hpp>
+
+#include <chrono>
+#include <limits>
+#include <memory>
+#include <variant>
+#include <vector>
+
+#ifndef CUVS_SYSTEM_LITTLE_ENDIAN
+#if defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
+#define CUVS_SYSTEM_LITTLE_ENDIAN 0
+#else
+#define CUVS_SYSTEM_LITTLE_ENDIAN 1
+#endif
+#endif
+
+namespace cuvs::neighbors::dynamic_batching::detail {
+
+using raft::RAFT_NAME;  // TODO: a workaround for RAFT_LOG_XXX macros
+
+/**
+ * A helper to make the requester threads more cooperative when busy-spinning.
+ * It is used in the wait loops across this file to reduce the CPU usage.
+ *
+ * Ideally, we should be using atomics notify/wait feature, but that is not always possible
+ * (e.g. waiting on multiple things or waiting on GPU volatile stores).
+ */
+struct local_waiter {
+  static constexpr inline int64_t kNonSleepIterations = 10;
+
+  explicit local_waiter(std::chrono::nanoseconds base_sleep_time,
+                        int64_t start_iteration = 0) noexcept
+    : base_sleep_time_{base_sleep_time}, iteration_{start_iteration}
+  {
+  }
+
+  inline void wait() noexcept
+  {
+    if (iteration_ < 2) {
+      // Don't wait for the first few iterations:
+      // maybe there's a weak CAS op in the loop, or something else that could return quickly
+    } else if (iteration_ < kNonSleepIterations) {
+      std::this_thread::yield();
+    } else {
+      auto k = iteration_ + 1 - kNonSleepIterations;
+      std::this_thread::sleep_for(base_sleep_time_ * k);
+    }
+    ++iteration_;
+  }
+
+  inline void reset(int64_t start_iteration = 0) noexcept { iteration_ = start_iteration; }
+
+ private:
+  std::chrono::nanoseconds base_sleep_time_;
+  int64_t iteration_;
+};
+
+class cuda_event {
+ public:
+  cuda_event(cuda_event&&)            = default;
+  cuda_event& operator=(cuda_event&&) = default;
+  ~cuda_event()                       = default;
+  cuda_event(cuda_event const&)       = delete;  // Copying disallowed: one event one owner
+  cuda_event& operator=(cuda_event&)  = delete;
+
+  cuda_event()
+    : event_{[]() {
+               cudaEvent_t* e = new cudaEvent_t;
+               RAFT_CUDA_TRY(cudaEventCreateWithFlags(e, cudaEventDisableTiming));
+               return e;
+             }(),
+             [](cudaEvent_t* e) {
+               RAFT_CUDA_TRY_NO_THROW(cudaEventDestroy(*e));
+               delete e;
+             }}
+  {
+  }
+
+  cudaEvent_t value() const { return *event_; }
+
+ private:
+  std::unique_ptr<cudaEvent_t, std::function<void(cudaEvent_t*)>> event_;
+};
+
+template <typename MdSpanOrArray>
+struct get_accessor_type_t {
+  using type = typename MdSpanOrArray::accessor_type;
+};
+
+template <typename ElementType, typename Extents, typename LayoutPolicy, typename ContainerPolicy>
+struct get_accessor_type_t<raft::mdarray<ElementType, Extents, LayoutPolicy, ContainerPolicy>> {
+  using mdarray_type = raft::mdarray<ElementType, Extents, LayoutPolicy, ContainerPolicy>;
+  using view_type    = typename mdarray_type::view_type;
+  using type         = typename view_type::accessor_type;
+};
+
+template <typename MdSpanOrArray>
+using get_accessor_type = typename get_accessor_type_t<MdSpanOrArray>::type;
+
+template <typename Source3DT>
+constexpr inline auto slice_3d(typename Source3DT::index_type i,
+                               const Source3DT& source3d,
+                               typename Source3DT::index_type n_rows = 0)
+{
+  using element_type  = typename Source3DT::element_type;
+  using index_type    = typename Source3DT::index_type;
+  using layout_type   = typename Source3DT::layout_type;
+  using accessor_type = get_accessor_type<Source3DT>;
+  auto extent2d =
+    raft::make_extents<index_type>(n_rows == 0 ? source3d.extent(1) : n_rows, source3d.extent(2));
+  auto stride = uint64_t(source3d.extent(1)) * uint64_t(source3d.extent(2));
+  return raft::mdspan<element_type, decltype(extent2d), layout_type, accessor_type>{
+    const_cast<element_type*>(source3d.data_handle()) + stride * i, extent2d};
+}
+
+template <typename Source2DT>
+constexpr inline auto slice_2d(typename Source2DT::index_type i, const Source2DT& source2d)
+{
+  using element_type  = typename Source2DT::element_type;
+  using index_type    = typename Source2DT::index_type;
+  using layout_type   = typename Source2DT::layout_type;
+  using accessor_type = get_accessor_type<Source2DT>;
+  auto extent1d       = raft::make_extents<index_type>(source2d.extent(1));
+  auto stride         = uint64_t(extent1d.extent(0));
+  return raft::mdspan<element_type, decltype(extent1d), layout_type, accessor_type>{
+    const_cast<element_type*>(source2d.data_handle()) + stride * i, extent1d};
+}
+
+// ---------------------------------------------
+
+constexpr size_t kCacheLineBytes = 64;
+
+template <typename Upstream, typename T, typename IdxT>
+using upstream_search_type_const = void(raft::resources const&,
+                                        typename Upstream::search_params_type const&,
+                                        Upstream const&,
+                                        raft::device_matrix_view<const T, int64_t, raft::row_major>,
+                                        raft::device_matrix_view<IdxT, int64_t, raft::row_major>,
+                                        raft::device_matrix_view<float, int64_t, raft::row_major>,
+                                        const cuvs::neighbors::filtering::base_filter&);
+
+template <typename Upstream, typename T, typename IdxT>
+using upstream_search_type = void(raft::resources const&,
+                                  typename Upstream::search_params_type const&,
+                                  Upstream&,
+                                  raft::device_matrix_view<const T, int64_t, raft::row_major>,
+                                  raft::device_matrix_view<IdxT, int64_t, raft::row_major>,
+                                  raft::device_matrix_view<float, int64_t, raft::row_major>,
+                                  const cuvs::neighbors::filtering::base_filter&);
+
+template <typename T, typename IdxT>
+using function_search_type = void(raft::resources const&,
+                                  raft::device_matrix_view<const T, int64_t, raft::row_major>,
+                                  raft::device_matrix_view<IdxT, int64_t, raft::row_major>,
+                                  raft::device_matrix_view<float, int64_t, raft::row_major>);
+
+/**
+ * State of the batch token slot.
+ *
+ * In a nutshell, there are only two batch slot states that matter: empty or full.
+ * Initially, all slots are empty. The host threads can commit (i.e. subscribe) to a batch slot even
+ * if it's empty (when they know it will be filled-in at some point in future). With this logic, we
+ * smooth out the bottleneck that occurs when many threads try to submit their work using a single
+ * atomic counter (the batch queue head).
+ *
+ * Once a GPU IO buffer is available, its owner returns the buffer to the queue by marking a slot as
+ * full. By that time, it may be partially or fully committed (i.e. several host threads are
+ * committed to submit a certain number of queries).
+ *
+ * If we had an infinite buffer, these two states would suffice. However, we have a finite ring
+ * buffer, so the used-up slots must be emptied again, so that they are usable in the following
+ * rounds through the ring buffer.
+ *
+ * The slot state depends not only on the value stored in it, but on the accessing thread as well
+ * (see `batch_queue_t::batch_status` below). The accessing thread may be ahead or behind the others
+ * (as defined by the sequential order id below). Depending on the accessor state, it may view the
+ * slot as being emptied/filled in the future, current, or previous rounds. This affects the
+ * decision whether the slot can be used and whether the thread has the right to advance tail or
+ * head counters of the batch queue.
+ *
+ */
+enum struct slot_state : int32_t {
+  /** The slot is empty, cleared-up in this round (hence the head should be past it). */
+  kEmptyPast = 1025,
+  /** The slot is empty, cleared-up in previous round. */
+  kEmpty = 1024,
+  /** The slot is empty, cleared-up two round ago and cannot be used yet (due to be filled). */
+  kEmptyBusy = 1023,
+  /** The current thread has been sleeping for too long and is way behind the others. */
+  kFullPast = 1,
+  /** The slot is full, filled-in in this round. */
+  kFull = 0,
+  /** This state is considered full, filled-in in previous round.  */
+  kFullBusy = -1
+  /** The rest of the values are impossible states indicating an error in the algo. */
+};
+
+/**
+ * Identifies the batch and its job-commit state.
+ * Should be in the pinned memory for fast shared access on CPU and GPU side.
+ *
+ * The batch token packs the IO buffer address (id) and a number of committed queries in a single
+ * 64-bit atomic. This is to allow conflict-free atomic updates of both values.
+ *
+ */
+struct batch_token {
+  uint64_t value = 0;
+
+  constexpr inline batch_token() {}
+  explicit constexpr inline batch_token(uint32_t buffer_id) { id() = buffer_id; }
+
+  /**
+   * Sequential id of the batch in the array of batches.
+   *
+   * The `id` field, in practice, stores not only the IO buffer address, but also an extra
+   * sequential "round" id. The latter identifies how many rounds through the batch ring buffer has
+   * already been done (computed from the the `seq_order_id` counter in the batch queue) and is used
+   * by `batch_queue_t::batch_status` below to compute the `slot_state`. This is to avoid the ABA
+   * atomic updates problem when using the ring buffer.
+   *
+   * There cannot be more IO buffers than the size of the ring buffer. The size of the ring buffer
+   * is always a power-of-two. Hence the IO buffer address needs only `log2(Size)` bits, and the
+   * rest is used for the ring buffer round id (see `batch_queue_t::make_seq_batch_id`).
+   *
+   */
+  RAFT_INLINE_FUNCTION auto id() noexcept -> uint32_t&
+  {
+    return *(reinterpret_cast<uint32_t*>(&value) + kOffsetOfId);
+  }
+  /**
+   * How many queries are promised by the participating CPU threads (requesters).
+   *
+   * The CPU threads atomically increment this counter until its size reaches `max_batch_size`.
+   *
+   * Any (CPU or GPU thread) may atomically write to the highest byte of this value, which indicates
+   * that no one can commit to this batch anymore (e.g. the wait timeout is exceeded).
+   * Hence, the actual number of committed queries is `size_committed % 0x00ffffff`.
+   *
+   * The gather kernel cannot finish while `size_committed < max_batch_size`.
+   *
+   * NB: we use the trick of writing to the highest byte to allow GPU write atomically to the pinned
+   * host memory. This way, we don't need to use device RMW atomics on host memory, which are not
+   * available on a broad class of GPUs. If not this workaround, we could simply do atomic add/or
+   * with value 0x01000000.
+   */
+  RAFT_INLINE_FUNCTION auto size_committed() noexcept -> uint32_t&
+  {
+    return *(reinterpret_cast<uint32_t*>(&value) + kOffsetOfSC);
+  }
+
+ private:
+  /** Offset of the `id()` value in the token if it's interpreted as uint32_t[2]. */
+  static constexpr inline uint32_t kOffsetOfId = CUVS_SYSTEM_LITTLE_ENDIAN;
+  /** Offset of the `size_committed()` value in the token if it's interpreted as uint32_t[2]. */
+  static constexpr inline uint32_t kOffsetOfSC = 1 - kOffsetOfId;
+};
+static_assert(sizeof(batch_token) == sizeof(uint64_t));
+static_assert(cuda::std::atomic<batch_token>::is_always_lock_free);
+
+/**
+ * The batch queue consists of several ring buffers and two counters determining where are the head
+ * and the tail of the queue in those buffers.
+ *
+ * There is an internal sequentially consistent order in the queue, defined by `seq_order_id`
+ * counter. The head and tail members define where the participants should look for full and
+ * empty slots in the queue respectively.
+ *
+ * The slots in the queue have their own states (see `slot_state` above). The states are updated
+ * concurrently in many threads, so the head and tail counters do not always accurately represent
+ * the actual compound state of the queue.
+ *
+ * `.head()` is where a host thread starts looking for a batch token. All slots earlier than
+ * returned by this method are not usable anymore (they batches are either "fully committed",
+ * dispatched, or emptied earlier). If a host thread determines that the current slot is not usable
+ * anymore, it increments the counter by calling `.pop()`.
+ *
+ * The tail is where a host thread reserves an empty slot to be filled-in by a GPU worker thread
+ * once it releases the owned IO buffer. There's no `.tail()` method, but `.push()` method returns
+ * the tail position (before advancing it). `.push()` blocks the host thread until it knows the slot
+ * isn't used by any other threads anymore (i.e. cleaned-up from the previous round).
+ *
+ * There's no strict relation between the head and the tail.
+ * Normally there is a single batch in the ring buffer being partially filled. It is followed by
+ * contiguous list of empty idle batches and reserved empty slots. The head and the tail loosely
+ * correspond to the beginning and the end of this sequence.
+ *
+ * Sometimes, the head can go further than the tail. This means all batches are busy and there are
+ * more threads committed to the slots that are not populated with the batches (and not even
+ * reserved for filling-in yet).
+ *
+ *
+ */
+template <uint32_t Size>
+struct batch_queue_t {
+  static constexpr uint32_t kSize        = Size;
+  static constexpr uint32_t kMinElemSize = sizeof(uint32_t);
+  static_assert(cuda::std::atomic<batch_token>::is_always_lock_free,
+                "The value type must be lock-free.");
+  static_assert(cuda::std::atomic<uint32_t>::is_always_lock_free,
+                "The value type must be lock-free.");
+  static_assert(cuda::std::atomic<int32_t>::is_always_lock_free,
+                "The value type must be lock-free.");
+  static_assert(raft::is_a_power_of_two(kSize), "The size must be a power-of-two for efficiency.");
+
+  static constexpr auto kMemOrder = cuda::std::memory_order_relaxed;
+
+  /** Type-safe synonym for the internal head & tail counters. */
+  struct seq_order_id {
+    uint32_t value;
+  };
+
+  explicit batch_queue_t(const raft::resources& res, bool use_batch_sizes) noexcept
+    : tokens_{raft::make_pinned_vector<cuda::atomic<batch_token, cuda::thread_scope_system>,
+                                       uint32_t>(res, kSize)},
+      rem_time_us_{
+        raft::make_pinned_vector<cuda::atomic<int32_t, cuda::thread_scope_system>, uint32_t>(
+          res, kSize)},
+      dispatch_sequence_id_(kSize),
+      batch_sizes_{
+        use_batch_sizes
+          ? std::make_optional(
+              raft::make_pinned_vector<cuda::atomic<uint32_t, cuda::thread_scope_system>, uint32_t>(
+                res, kSize))
+          : std::nullopt}
+  {
+    tail_.store(0, kMemOrder);
+    head_.store(0, kMemOrder);
+    auto past_seq_id = seq_order_id{static_cast<uint32_t>(-1)};
+    for (uint32_t i = 0; i < kSize; i++) {
+      rem_time_us_(i).store(std::numeric_limits<int32_t>::max(), kMemOrder);
+      if (batch_sizes_.has_value()) { batch_sizes_.value()(i).store(0, kMemOrder); }
+      dispatch_sequence_id_[i].store(past_seq_id.value, kMemOrder);
+      tokens_(i).store(make_empty_token(past_seq_id), kMemOrder);
+    }
+  }
+
+  /**
+   * Advance the tail position, ensure the slot is empty, and return the reference to the new slot.
+   * The calling side is responsible for filling-in the slot with an actual value at a later time.
+   *
+   * Conceptually, this method reserves a ring buffer slot on the host side, so that the GPU worker
+   * thread can return the IO buffer (filling the token slot) asynchronously.
+   */
+  inline auto push() -> seq_order_id
+  {
+    seq_order_id seq_id{tail_.fetch_add(1, kMemOrder)};
+    auto& loc = token(seq_id);
+    auto ss   = batch_status(loc.load(kMemOrder), seq_id);
+    /* [Note: very small waiting time]
+
+    Only a few (dispatcher) threads are going to call this function at the same time as opposed to
+    potentially any number of threads waiting on new batches to arrive.
+    This is a performance-critical code path.
+
+    Hence the small base sleep time.
+    */
+    local_waiter till_empty{std::chrono::nanoseconds{1000}};
+    while (ss == slot_state::kFull || ss == slot_state::kFullBusy || ss == slot_state::kEmptyBusy) {
+      // Wait till the slot becomes empty (doesn't matter future or past).
+      // The batch id is only ever updated in the scatter/gather kernels, which are the only source
+      // of truth whether a batch buffer is currently used by the GPU.
+      till_empty.wait();
+      ss = batch_status(loc.load(kMemOrder), seq_id);
+    }
+    return seq_id;
+  }
+
+  /**
+   * Return the offset of the given w.r.t. the tail of the queue.
+   * Negative value means the given slot is in the body of the queue and should be dispatched soon.
+   * Positive value means the given slot is ahead of the queue and should wait longer.
+   *
+   * That is the lower the value the higher the priority.
+   */
+  [[nodiscard]] inline auto niceness(seq_order_id id) const noexcept -> int32_t
+  {
+    return static_cast<int32_t>(id.value - tail_.load(kMemOrder));
+  }
+
+  /** Get the reference to the first element in the queue. */
+  inline auto head() noexcept -> seq_order_id
+  {
+    auto h = head_.load(kMemOrder);
+    // The head cannot go ahead of the tail by more than the queue buffer size.
+    // If the head is ahead by not more than kSize elements though, everything is fine;
+    // the slots too far ahead are protected by busy tokens.
+    local_waiter for_tail(std::chrono::nanoseconds{100000});
+    while (static_cast<int32_t>(h - tail_.load(kMemOrder)) >= static_cast<int32_t>(kSize)) {
+      for_tail.wait();
+      h = head_.load(kMemOrder);
+    }
+    return seq_order_id{h};
+  }
+
+  /** Batch commit state and IO buffer id (see `batch_token`) */
+  inline auto token(seq_order_id id) -> cuda::atomic<batch_token, cuda::thread_scope_system>&
+  {
+    return tokens_(cache_friendly_idx(id.value));
+  }
+
+  /**
+   * How much time has this batch left for waiting.
+   * It is an approximate value by design - to minimize the synchronization between CPU and GPU.
+   *
+   * The clocks on GPU and CPU may have different values, so the running kernel and the CPU thread
+   * have different ideas on how much time is left. Rather than trying to synchronize the clocks, we
+   * maintain independent timers and accept the uncertainty.
+   *
+   * Access pattern: CPU write-only (producer); GPU read-only (consumer).
+   */
+  inline auto rem_time_us(seq_order_id id) -> cuda::atomic<int32_t, cuda::thread_scope_system>&
+  {
+    return rem_time_us_(cache_friendly_idx(id.value));
+  }
+
+  /**
+   * The actual batch size - the final number of committed queries.
+   * This is only used if `conservative_dispatch = true`.
+   */
+  inline auto batch_size(seq_order_id id) noexcept
+    -> cuda::atomic<uint32_t, cuda::thread_scope_system>*
+  {
+    if (batch_sizes_.has_value()) { return &batch_sizes_.value()(cache_friendly_idx(id.value)); }
+    return nullptr;
+  }
+
+  /**
+   * This value is updated by the host thread after it submits the job completion event to indicate
+   * to other threads can wait on the event to get the results back.
+   * Other threads get the value from the batch queue and compare that value against this atomic.
+   *
+   * Access pattern: CPU-only; dispatching thread writes the id once, other threads wait on it.
+   */
+  inline auto dispatch_sequence_id(seq_order_id id) -> cuda::std::atomic<uint32_t>&
+  {
+    return dispatch_sequence_id_[cache_friendly_idx(id.value)];
+  }
+
+  /**
+   * An `atomicMax` on the queue head in disguise.
+   * This makes the given batch slot and all prior slots unreachable (not possible to commit).
+   */
+  inline void pop(seq_order_id id) noexcept
+  {
+    const auto desired = id.value + 1;
+    auto observed      = id.value;
+    while (observed < desired &&
+           !head_.compare_exchange_weak(observed, desired, kMemOrder, kMemOrder)) {}
+  }
+
+  static constexpr inline auto batch_id(batch_token token) noexcept -> uint32_t
+  {
+    return token.id() & kCounterLocMask;
+  }
+
+  /**
+   * Construct a token that is interpreted as having been emptied in the current round
+   * (the round is derived from seq_id).
+   *
+   * NB: "round" is the number of times the queue counters went over the whole ring buffer.
+   *     It's used to avoid the ABA problem for atomic token updates.
+   */
+  static constexpr inline auto make_empty_token(seq_order_id seq_id) noexcept -> batch_token
+  {
+    // Modify the seq_id to identify that the token slot is empty
+    auto empty_round    = static_cast<uint32_t>(slot_state::kEmptyPast) * kSize;
+    auto empty_round_id = seq_order_id{seq_id.value + empty_round};
+    // Id of empty slot is ignored and can be anything
+    auto empty_id = kCounterLocMask;
+    return batch_token{make_seq_batch_id(empty_round_id, empty_id)};
+  }
+
+  /**
+   * Construct a sequential batch id by combining the current round and the real batch id.
+   *
+   * The "round" part gives a hint when the token slot was filled-in to avoid the ABA problem
+   *  (see above).
+   */
+  static constexpr inline auto make_seq_batch_id(seq_order_id seq_id, uint32_t batch_id) noexcept
+    -> uint32_t
+  {
+    return seq_round(seq_id) | batch_id;
+  }
+
+  /**
+   * Get the state of the batch slot w.r.t. the given seq_order_id counter.
+   * This gives the information whether the slot is emptied/filled by another thread and whether
+   * that thread is ahead or behind the current thread.
+   * By introducing these future/past flavours of states we solve the ABA problem for atomic updates
+   * of the ring buffer slots.
+   */
+  static inline auto batch_status(batch_token token, seq_order_id seq_id) -> slot_state
+  {
+    /*
+    The "round" part of the id is just a seq_id without the low bits.
+    Essentially, we comparing here seq_ids of two threads: the one that wrote to the slot in the
+    past and the one reads from it now.
+
+    `kSize` determines the number of bits we use for the IO buffer id and for the round id.
+      */
+    auto v =
+      static_cast<int32_t>(seq_round(token) - seq_round(seq_id)) / static_cast<int32_t>(kSize);
+    if (v < static_cast<int32_t>(slot_state::kFullBusy)) { RAFT_FAIL("Invalid batch state %d", v); }
+    if (v < static_cast<int32_t>(slot_state::kEmptyBusy)) {
+      return static_cast<slot_state>(std::min(v, static_cast<int32_t>(slot_state::kFullPast)));
+    }
+    return static_cast<slot_state>(std::min(v, static_cast<int32_t>(slot_state::kEmptyPast)));
+  }
+
+ private:
+  alignas(kCacheLineBytes) cuda::std::atomic<uint32_t> tail_{};
+  alignas(kCacheLineBytes) cuda::std::atomic<uint32_t> head_{};
+
+  alignas(kCacheLineBytes)
+    raft::pinned_vector<cuda::atomic<batch_token, cuda::thread_scope_system>, uint32_t> tokens_;
+  raft::pinned_vector<cuda::atomic<int32_t, cuda::thread_scope_system>, uint32_t> rem_time_us_;
+  std::vector<cuda::std::atomic<uint32_t>> dispatch_sequence_id_;
+  std::optional<raft::pinned_vector<cuda::atomic<uint32_t, cuda::thread_scope_system>, uint32_t>>
+    batch_sizes_;
+
+  /* [Note: cache-friendly indexing]
+     To avoid false sharing, the queue pushes and pops values not sequentially, but with an
+     increment that is larger than the cache line size.
+     Hence we introduce the `kCounterIncrement > kCacheLineBytes`.
+     However, to make sure all indices are used, we choose the increment to be coprime with the
+     buffer size. We also require that the buffer size is a power-of-two for two reasons:
+       1) Fast modulus operation - reduces to binary `and` (with `kCounterLocMask`).
+       2) Easy to ensure GCD(kCounterIncrement, kSize) == 1 by construction
+          (see the definition below).
+   */
+  static constexpr uint32_t kElemsPerCacheLine =
+    raft::div_rounding_up_safe<uint32_t>(kCacheLineBytes, kMinElemSize);
+  static constexpr uint32_t kCounterIncrement = raft::bound_by_power_of_two(kElemsPerCacheLine) + 1;
+  static constexpr uint32_t kCounterLocMask   = kSize - 1;
+  // These props hold by design, but we add them here as a documentation and a sanity check.
+  static_assert(
+    kCounterIncrement * kMinElemSize >= kCacheLineBytes,
+    "The counter increment should be larger than the cache line size to avoid false sharing.");
+  static_assert(
+    std::gcd(kCounterIncrement, kSize) == 1,
+    "The counter increment and the size must be coprime to allow using all of the queue slots.");
+  /** Map the sequential index onto cache-friendly strided index. */
+  static constexpr inline auto cache_friendly_idx(uint32_t source_idx) noexcept -> uint32_t
+  {
+    return (source_idx * kCounterIncrement) & kCounterLocMask;
+  }
+
+  /** The "round": the number of times the queue counter went over the whole ring buffer. */
+  static constexpr inline auto seq_round(seq_order_id id) noexcept -> uint32_t
+  {
+    return id.value & ~kCounterLocMask;
+  }
+
+  /** The "round": the number of times the queue counter went over the whole ring buffer. */
+  static constexpr inline auto seq_round(batch_token token) noexcept -> uint32_t
+  {
+    return token.id() & ~kCounterLocMask;
+  }
+};
+
+template <typename T, typename IdxT>
+struct alignas(kCacheLineBytes) request_pointers {
+  /**
+   * A pointer to `dim` values of a single query (input).
+   *
+   * Serves as a synchronization point between the CPU thread (producer) and a GPU block in the
+   * `gather_inputs` kernel (consumer).
+   */
+  cuda::atomic<const T*, cuda::thread_scope_system> query{nullptr};
+  /** A pointer to `k` nearest neighbors (output) */
+  IdxT* neighbors{nullptr};
+  /** A pointer to distances of `k` nearest neighbors (output) */
+  float* distances{nullptr};
+};
+
+/**
+ * Check the current timestamp at the moment of construction and repeatedly compare the elapsed time
+ * to the timeout value provided by the host (passed via an atomic).
+ *
+ * This is used in the gather inputs kernel to make it stop waiting for new queries in a batch
+ * once the deadline is reached.
+ */
+struct gpu_time_keeper {
+  /**
+   * @param[in] cpu_provided_remaining_time_us
+   *   a pointer to a shared atomic, represent the remaining waiting time in microseconds.
+   *   Note, the remaining time is updated atomically by each participating host thread in their
+   *   "private coordinate systems". That's ok, we don't expect a single reference time for all host
+   *   and device threads.
+   *   We tolerate the errors coming from the time difference between the host thread writing their
+   *   remaining waiting time and the GPU thread reading that value.
+   */
+  RAFT_DEVICE_INLINE_FUNCTION explicit gpu_time_keeper(
+    cuda::atomic<int32_t, cuda::thread_scope_system>* cpu_provided_remaining_time_us)
+    : cpu_provided_remaining_time_us_{cpu_provided_remaining_time_us}
+  {
+    update_timestamp();
+  }
+
+  /**
+   * Check whether the deadline is not reached yet:
+   * 1) Compare the internal clock against the last-read deadline value
+   * 2) Read the deadline value from the host-visible atomic and check the internal clock again.
+   */
+  RAFT_DEVICE_INLINE_FUNCTION auto has_time() noexcept -> bool
+  {
+    if (timeout) { return false; }
+    update_local_remaining_time();
+    if (local_remaining_time_us_ <= 0) {
+      timeout = true;
+      return false;
+    }
+    update_cpu_provided_remaining_time();
+    if (local_remaining_time_us_ <= 0) {
+      timeout = true;
+      return false;
+    }
+    return true;
+  }
+
+ private:
+  cuda::atomic<int32_t, cuda::thread_scope_system>* cpu_provided_remaining_time_us_;
+  uint64_t timestamp_ns_           = 0;
+  int32_t local_remaining_time_us_ = std::numeric_limits<int32_t>::max();
+  bool timeout                     = false;
+
+  RAFT_DEVICE_INLINE_FUNCTION void update_timestamp() noexcept
+  {
+    asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(timestamp_ns_));
+  }
+
+  RAFT_DEVICE_INLINE_FUNCTION void update_local_remaining_time() noexcept
+  {
+    auto prev_timestamp = timestamp_ns_;
+    update_timestamp();
+    // subtract the time passed since the last check
+    // (assuming local time is updated every time timestamp is read)
+    local_remaining_time_us_ -= static_cast<int32_t>((timestamp_ns_ - prev_timestamp) / 1000ull);
+  }
+
+  RAFT_DEVICE_INLINE_FUNCTION void update_cpu_provided_remaining_time() noexcept
+  {
+    local_remaining_time_us_ =
+      std::min<int32_t>(local_remaining_time_us_,
+                        cpu_provided_remaining_time_us_->load(cuda::std::memory_order_relaxed));
+  }
+};
+
+/**
+ * Copy the queries from the submitted pointers to the batch store, one query per block.
+ * Upon completion of this kernel, the submitted queries are all in the contiguous buffer
+ * `batch_queries`.
+ *
+ * Block size: (n, 1, 1) any number of threads copying a single row of data.
+ * Grid size: (max_batch_size, 1, 1) - one block per query
+ *
+ * Note, we view the incoming queries and the batch as going through multiple stages:
+ *   1) A host thread "commits" a query: it reserves a slot for the query in the batch and promises
+ *      to fill-in the corresponding query pointer.
+ *   2) A host thread "submits" the query: it fills-in the pointer to the query data in the reserved
+ *      slot.
+ *   3) This kernel copies the query data to the contiguous query buffer owned by the batch.
+ *
+ * The batch is "fully committed" when the number of committed queries reaches the maximum batch
+ * size (all slots are reserved). Committing, submitting, and copying of the queries is somewhat
+ * overlapped among multiple host and device threads. Only the copying happens in a CUDA stream in
+ * this kernel, and the upstream search is dispatched right after this kernel (in the same stream).
+ *
+ */
+template <typename T, typename IdxT>
+RAFT_KERNEL gather_inputs(
+  raft::device_matrix_view<T, uint32_t, raft::row_major> batch_queries,
+  raft::pinned_vector_view<request_pointers<T, IdxT>, uint32_t> request_ptrs,
+  /* The remaining time may be updated on the host side: a thread with a tighter deadline may reduce
+     it (but not increase). */
+  cuda::atomic<int32_t, cuda::thread_scope_system>* remaining_time_us,
+  /* The token contains the current number of queries committed and is cleared in this kernel. */
+  cuda::atomic<batch_token, cuda::thread_scope_system>* batch_token_ptr,
+  /* The host-visible batch size counter (used in `conservative_dispatch`). */
+  cuda::atomic<uint32_t, cuda::thread_scope_system>* batch_size_out,
+  /**
+   * The token value considered empty depends on the round over the ring buffer
+   * (which is defined by the seq_order_id)
+   */
+  batch_token empty_token_value,
+  /**
+   * The counter is used to find the last CTA to finish and to share the batch size with the
+   * scatter_inputs kernel.
+   */
+  cuda::atomic<uint32_t, cuda::std::thread_scope_device>* kernel_progress_counter)
+{
+  const uint32_t query_id = blockIdx.x;
+  __shared__ const T* query_ptr;
+
+  if (threadIdx.x == 0) {
+    query_ptr = nullptr;
+
+    // NB: we have to read/write to `batch_token_ptr`, `bs_committed`, and `batch_fully_committed`
+    // using volatile assembly ops, because otherwise the compiler seems to fail to understand that
+    // this is the same location in memory. The order of reads in writes here is extremely
+    // important, as it involves multiple host and device threads (the host threads do RMW atomic
+    // increments on the commit counter).
+    volatile uint32_t* bs_committed =
+      reinterpret_cast<volatile uint32_t*>(batch_token_ptr) + 1 - CUVS_SYSTEM_LITTLE_ENDIAN;
+    volatile uint8_t* batch_fully_committed =
+      reinterpret_cast<volatile uint8_t*>(bs_committed) + (CUVS_SYSTEM_LITTLE_ENDIAN * 3);
+
+    gpu_time_keeper runtime{remaining_time_us};
+    bool committed          = false;  // if the query is committed, we have to wait for it to arrive
+    auto& request_query_ptr = request_ptrs(query_id).query;
+    while (true) {
+      query_ptr = request_query_ptr.load(cuda::std::memory_order_acquire);
+      if (query_ptr != nullptr) {
+        // The query is submitted to this block's slot; erase the pointer buffer for future use and
+        // exit the loop.
+        request_query_ptr.store(nullptr, cuda::std::memory_order_relaxed);
+        break;
+      }
+      // The query hasn't been submitted, but is already committed; other checks may be skipped
+      if (committed) { continue; }
+      // Check if the query is committed
+      uint32_t committed_count;
+      asm volatile("ld.volatile.global.u32 %0, [%1];"
+                   : "=r"(committed_count)
+                   : "l"(bs_committed)
+                   : "memory");
+      committed = (committed_count & 0x00ffffff) > query_id;
+      if (committed) { continue; }
+      // If the query is not committed, but the batch is past the deadline, we exit without copying
+      // the query
+      if (committed_count > 0x00ffffff) { break; }
+      // The query hasn't been submitted yet; check if we're past the deadline
+      if (runtime.has_time()) { continue; }
+      // Otherwise, let the others know time is out
+      // Set the highest byte of the commit counter to 1 (thus avoiding RMW atomic)
+      // This prevents any more CPU threads from committing to this batch.
+      asm volatile("st.volatile.global.u8 [%0], %1;"
+                   :
+                   : "l"(batch_fully_committed), "r"(1)
+                   : "memory");
+      asm volatile("ld.volatile.global.u32 %0, [%1];"
+                   : "=r"(committed_count)
+                   : "l"(bs_committed)
+                   : "memory");
+      committed = (committed_count & 0x00ffffff) > query_id;
+      if (committed) { continue; }
+      break;
+    }
+    auto progress = kernel_progress_counter->fetch_add(1, cuda::std::memory_order_acq_rel) + 1;
+    if (progress >= gridDim.x) {
+      // read the last value of the committed count to know the batch size for sure
+      uint32_t committed_count;
+      asm volatile("ld.volatile.global.u32 %0, [%1];"
+                   : "=r"(committed_count)
+                   : "l"(bs_committed)
+                   : "memory");
+      committed_count &= 0x00ffffff;  // Clear the timeout bit
+      if (batch_size_out != nullptr) {
+        // Inform the dispatcher about the final batch size if `conservative_dispatch` is enabled
+        batch_size_out->store(committed_count, cuda::std::memory_order_relaxed);
+      }
+      // store the batch size in the progress counter, so we can read it in the scatter kernel
+      kernel_progress_counter->store(committed_count, cuda::std::memory_order_relaxed);
+      // Clear the batch token slot, so it can be re-used by others
+      asm volatile("st.volatile.global.u64 [%0], %1;"
+                   :
+                   : "l"(reinterpret_cast<uint64_t*>(batch_token_ptr)),
+                     "l"(reinterpret_cast<uint64_t&>(empty_token_value))
+                   : "memory");
+    }
+  }
+  // The block waits till the leading thread gets the query pointer
+  cooperative_groups::this_thread_block().sync();
+  auto query_ptr_local = query_ptr;
+  if (query_ptr_local == nullptr) { return; }
+  // block-wide copy input query
+  auto dim = batch_queries.extent(1);
+  for (uint32_t i = threadIdx.x; i < dim; i += blockDim.x) {
+    batch_queries(query_id, i) = query_ptr_local[i];
+  }
+}
+
+/** Copy the results of the search back to the requesters. */
+template <typename T, typename IdxT>
+RAFT_KERNEL scatter_outputs(
+  raft::pinned_vector_view<request_pointers<T, IdxT>, uint32_t> request_ptrs,
+  raft::device_matrix_view<const IdxT, uint32_t> batch_neighbors,
+  raft::device_matrix_view<const float, uint32_t> batch_distances,
+  cuda::atomic<uint32_t, cuda::std::thread_scope_device>* kernel_progress_counter,
+  cuda::atomic<batch_token, cuda::thread_scope_system>* next_token,
+  uint32_t batch_id)
+{
+  __shared__ uint32_t batch_size;
+  if (threadIdx.x == 0 && threadIdx.y == 0) {
+    batch_size = kernel_progress_counter->exchange(0, cuda::std::memory_order_relaxed);
+  }
+  // Copy output
+  cooperative_groups::this_thread_block().sync();
+  auto k = batch_neighbors.extent(1);
+  for (uint32_t i = threadIdx.y; i < batch_size; i += blockDim.y) {
+    auto* request_neighbors = request_ptrs(i).neighbors;
+    auto* request_distances = request_ptrs(i).distances;
+    for (uint32_t j = threadIdx.x; j < k; j += blockDim.x) {
+      request_neighbors[j] = batch_neighbors(i, j);
+      request_distances[j] = batch_distances(i, j);
+    }
+  }
+  // Clear the batch state after all threads copied the data, so the batch can be reused
+  cuda::atomic_thread_fence(cuda::std::memory_order_release, cuda::thread_scope_system);
+  cooperative_groups::this_thread_block().sync();
+  if (threadIdx.x != 0 || threadIdx.y != 0) { return; }
+  reinterpret_cast<cuda::atomic<uint32_t, cuda::thread_scope_system>*>(
+    &reinterpret_cast<batch_token*>(next_token)->id())
+    ->store(batch_id, cuda::std::memory_order_relaxed);
+}
+
+/**
+ * Batch runner is shared among the users of the `dynamic_batching::index` (i.e. the index can be
+ * copied, but the copies hold shared pointers to a single batch runner).
+ *
+ * Constructor and destructor of this class do not need to be thread-safe, as their execution is
+ * guaranteed to happen in one thread by the holding shared pointer.
+ *
+ * The search function must be thread-safe. We only have to pay attention to the `mutable` members
+ * though, because the function is marked const.
+ */
+template <typename T, typename IdxT>
+class batch_runner {
+ public:
+  constexpr static uint32_t kMaxNumQueues = 256;
+
+  using batch_queue  = batch_queue_t<kMaxNumQueues>;
+  using seq_order_id = typename batch_queue::seq_order_id;
+
+  // Save the parameters and the upstream batched search function to invoke
+  template <typename Upstream>
+  batch_runner(const raft::resources& res,
+               const dynamic_batching::index_params& params,
+               const Upstream& upstream_index,
+               const typename Upstream::search_params_type& upstream_params,
+               upstream_search_type_const<Upstream, T, IdxT>* upstream_search,
+               const cuvs::neighbors::filtering::base_filter* sample_filter)
+    : res_{res},
+      upstream_search_{[&upstream_index, upstream_search, upstream_params, sample_filter](
+                         raft::resources const& res,
+                         raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+                         raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
+                         raft::device_matrix_view<float, int64_t, raft::row_major> distances) {
+        /* Note: passing sample_filter by pointer
+
+        Ideally, dynamic batching would capture the filter by value. Unfortunately, one cannot use
+        the copy constructor of the `base_filter` (it would erase the actual filter type).
+        Therefore, we can only pass the filter by pointer or reference and require the user to keep
+        the filter alive for the lifetime of the dynamic batching index.
+        This, however, may lead to a segfault when the user doesn't provide the filter argument and
+        the argument is passed by reference: the lifetime of the none_sample_filter default argument
+        is limited to the search function call, so it is destroyed while the dynamic batching index
+        is still alive.
+        Hence the solution is to pass the filter by pointer and default it to nullptr.
+        */
+        if (sample_filter == nullptr) {
+          using base_filter_type = cuvs::neighbors::filtering::base_filter;
+          const auto none_filter = cuvs::neighbors::filtering::none_sample_filter{};
+          return upstream_search(res,
+                                 upstream_params,
+                                 upstream_index,
+                                 queries,
+                                 neighbors,
+                                 distances,
+                                 static_cast<const base_filter_type&>(none_filter));
+
+        } else {
+          return upstream_search(
+            res, upstream_params, upstream_index, queries, neighbors, distances, *sample_filter);
+        }
+      }},
+      k_{uint32_t(params.k)},
+      dim_{uint32_t(upstream_index.dim())},
+      max_batch_size_{uint32_t(params.max_batch_size)},
+      n_queues_{uint32_t(params.n_queues)},
+      batch_queue_{res_, params.conservative_dispatch},
+      completion_events_(n_queues_),
+      input_extents_{n_queues_, max_batch_size_, dim_},
+      output_extents_{n_queues_, max_batch_size_, k_},
+      queries_{raft::make_device_mdarray<T>(res_, input_extents_)},
+      neighbors_{raft::make_device_mdarray<IdxT>(res_, output_extents_)},
+      distances_{raft::make_device_mdarray<float>(res_, output_extents_)},
+      kernel_progress_counters_{
+        raft::make_device_vector<cuda::atomic<uint32_t, cuda::std::thread_scope_device>>(
+          res_, n_queues_)},
+      request_ptrs_{raft::make_pinned_matrix<request_pointers<T, IdxT>, uint32_t>(
+        res_, n_queues_, max_batch_size_)}
+  {
+    RAFT_CUDA_TRY(cudaMemsetAsync(
+      kernel_progress_counters_.data_handle(),
+      0,
+      sizeof(*kernel_progress_counters_.data_handle()) * kernel_progress_counters_.size(),
+      raft::resource::get_cuda_stream(res_)));
+    // Make sure to initialize the atomic values in the batch_state structs.
+    for (uint32_t i = 0; i < n_queues_; i++) {
+      auto seq_id = batch_queue_.push();
+      batch_queue_.token(seq_id).store(batch_token{batch_queue::make_seq_batch_id(seq_id, i)});
+      // Make sure to initialize query pointers, because they are used for synchronization
+      for (uint32_t j = 0; j < max_batch_size_; j++) {
+        new (&request_ptrs_(i, j)) request_pointers<T, IdxT>{};
+      }
+    }
+  }
+
+  // A workaround for algos, which have non-const `index` type in their arguments
+  template <typename Upstream>
+  batch_runner(const raft::resources& res,
+               const dynamic_batching::index_params& params,
+               const Upstream& upstream_index,
+               const typename Upstream::search_params_type& upstream_params,
+               upstream_search_type<Upstream, T, IdxT>* upstream_search,
+               const cuvs::neighbors::filtering::base_filter* sample_filter)
+    : batch_runner{
+        res,
+        params,
+        upstream_index,
+        upstream_params,
+        reinterpret_cast<upstream_search_type_const<Upstream, T, IdxT>*>(upstream_search),
+        sample_filter}
+  {
+  }
+
+  void search(raft::resources const& res,
+              cuvs::neighbors::dynamic_batching::search_params const& params,
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances) const
+  {
+    uint32_t n_queries = queries.extent(0);
+    if (n_queries >= max_batch_size_) {
+      return upstream_search_(res, queries, neighbors, distances);
+    }
+
+    if (neighbors.extent(1) != int64_t(k_)) {
+      // TODO: the check can be relaxed to `neighbors.extent(1) > int64_t(k_)`;
+      //       this, however, would require an extra bounds check per-query in the scatter kernel.
+      RAFT_LOG_WARN(
+        "The requested number of neighbors (%zd) doesn't match the configured "
+        "dynamic_batching::index_params::k (%u); dynamic batching is disabled for the request.",
+        neighbors.extent(1),
+        k_);
+      return upstream_search_(res, queries, neighbors, distances);
+    }
+
+    auto deadline = std::chrono::system_clock::now() +
+                    std::chrono::nanoseconds(size_t(params.dispatch_timeout_ms * 1000000.0));
+
+    int64_t local_io_offset = 0;
+    batch_token batch_token_observed{0};
+    local_waiter to_commit{std::chrono::nanoseconds(size_t(params.dispatch_timeout_ms * 3e5)),
+                           local_waiter::kNonSleepIterations};
+    while (true) {
+      const auto seq_id        = batch_queue_.head();
+      const auto commit_result = try_commit(seq_id, n_queries);
+      // The bool (busy or not) returned if no queries were committed:
+      if (std::holds_alternative<bool>(commit_result)) {
+        // Pause if the system is busy
+        // (otherwise the progress is guaranteed due to update of the head counter)
+        if (std::get<bool>(commit_result)) { to_commit.wait(); }
+        continue;  // Try to get a new batch token
+      }
+      batch_token_observed           = std::get<batch_token>(std::get<0>(commit_result));
+      const auto queries_committed   = std::get<uint32_t>(std::get<0>(commit_result));
+      const auto batch_offset        = batch_token_observed.size_committed();
+      auto& batch_token_ref          = batch_queue_.token(seq_id);
+      auto& rem_time_us_ref          = batch_queue_.rem_time_us(seq_id);
+      auto& dispatch_sequence_id_ref = batch_queue_.dispatch_sequence_id(seq_id);
+      auto* batch_size_ptr           = batch_queue_.batch_size(seq_id);
+      // sleep for 1/10 of deadline time or more
+      //   (if couldn't get the value in the first few iterations).
+      local_waiter till_full{std::chrono::nanoseconds(size_t(params.dispatch_timeout_ms * 1e5)),
+                             batch_queue_.niceness(seq_id)};
+      while (batch_queue::batch_status(batch_token_observed, seq_id) != slot_state::kFull) {
+        /* Note: waiting for batch IO buffers
+        The CPU threads can commit to the incoming batches in the queue in advance (this happens in
+        try_commit).
+        In this loop, a thread waits for the batch IO buffer to be released by a running search on
+        the GPU side (scatter_outputs kernel). Hence, this loop is engaged only if all buffers are
+        currently used, which suggests that the GPU is busy (or there's not enough IO buffers).
+        This also means the current search is not likely to meet the deadline set by the user.
+
+        The scatter kernel returns its buffer id into an acquired slot in the batch queue; in this
+        loop we wait for that id to arrive.
+
+        Generally, we want to waste as little as possible CPU cycles here to let other threads wait
+        on dispatch_sequence_id_ref below more efficiently. At the same time, we shouldn't use
+        `.wait()` here, because `.notify_all()` would have to come from GPU.
+        */
+        till_full.wait();
+        batch_token_observed = batch_token_ref.load(cuda::std::memory_order_acquire);
+      }
+      // Whether this thread is responsible for dispatching the batch.
+      bool is_dispatcher = batch_offset == 0;
+      auto stream        = raft::resource::get_cuda_stream(res);
+      auto batch_id      = batch_queue::batch_id(batch_token_observed);
+      auto request_ptrs  = slice_2d(batch_id, request_ptrs_);
+
+      if (is_dispatcher) {
+        // Conservatively initialize the remaining time
+        // TODO (achirkin): this initialization may happen after the other requesters update the
+        //                  time and thus erase their deadlines.
+        rem_time_us_ref.store(static_cast<int32_t>(params.dispatch_timeout_ms * 1000),
+                              cuda::std::memory_order_relaxed);
+        // run the gather kernel before submitting the data to reduce the latency
+        gather_inputs<T, IdxT><<<max_batch_size_, 32, 0, stream>>>(
+          slice_3d(batch_id, queries_),
+          request_ptrs,
+          &rem_time_us_ref,
+          &batch_token_ref,
+          batch_size_ptr,
+          // This indicates the empty token slot, which can only be used in the following round
+          batch_queue::make_empty_token(seq_id),
+          kernel_progress_counters_.data_handle() + batch_id);
+      }
+
+      // *** Set the pointers to queries, neighbors, distances - query-by-query
+      for (uint32_t i = 0; i < queries_committed; i++) {
+        const auto o   = local_io_offset + i;
+        auto& ptrs     = request_ptrs(batch_offset + i);
+        ptrs.neighbors = neighbors.data_handle() + o * k_;
+        ptrs.distances = distances.data_handle() + o * k_;
+        ptrs.query.store(queries.data_handle() + o * dim_, cuda::std::memory_order_release);
+      }
+
+      // Submit estimated remaining time
+      {
+        auto rem_time_us = static_cast<int32_t>(
+          std::max<int64_t>(0, (deadline - std::chrono::system_clock::now()).count()) / 1000);
+        rem_time_us_ref.fetch_min(rem_time_us, cuda::std::memory_order_relaxed);
+      }
+
+      if (is_dispatcher) {
+        uint32_t batch_size = max_batch_size_;
+        if (batch_size_ptr != nullptr) {
+          // Block until the real batch size is available if conservative dispatch is used.
+          local_waiter for_dispatch{
+            std::chrono::nanoseconds(size_t(params.dispatch_timeout_ms * 1e5))};
+          batch_size = batch_size_ptr->load(cuda::std::memory_order_relaxed);
+          while (batch_size == 0) {
+            for_dispatch.wait();
+            batch_size = batch_size_ptr->load(cuda::std::memory_order_relaxed);
+          }
+          batch_size_ptr->store(0, cuda::std::memory_order_relaxed);
+        }
+        auto batch_neighbors = slice_3d(batch_id, neighbors_, batch_size);
+        auto batch_distances = slice_3d(batch_id, distances_, batch_size);
+        upstream_search_(
+          res, slice_3d(batch_id, queries_, batch_size), batch_neighbors, batch_distances);
+        auto next_seq_id     = batch_queue_.push();
+        auto& next_token_ref = batch_queue_.token(next_seq_id);
+        // next_batch_token);
+        auto bs = dim3(128, 8, 1);
+        scatter_outputs<T, IdxT>
+          <<<1, bs, 0, stream>>>(request_ptrs,
+                                 batch_neighbors,
+                                 batch_distances,
+                                 kernel_progress_counters_.data_handle() + batch_id,
+                                 &next_token_ref,
+                                 batch_queue::make_seq_batch_id(next_seq_id, batch_id));
+        RAFT_CUDA_TRY(cudaEventRecord(completion_events_[batch_id].value(), stream));
+        dispatch_sequence_id_ref.store(seq_id.value, cuda::std::memory_order_release);
+        dispatch_sequence_id_ref.notify_all();
+
+      } else {
+        // Wait till the dispatch_sequence_id counter is updated, which means the event is recorded
+        auto dispatched_id_observed =
+          dispatch_sequence_id_ref.load(cuda::std::memory_order_acquire);
+        while (static_cast<int32_t>(seq_id.value - dispatched_id_observed) > 0) {
+          dispatch_sequence_id_ref.wait(dispatched_id_observed, cuda::std::memory_order_relaxed);
+          dispatched_id_observed = dispatch_sequence_id_ref.load(cuda::std::memory_order_acquire);
+        }
+        // Now we can safely record the event
+        RAFT_CUDA_TRY(cudaStreamWaitEvent(stream, completion_events_[batch_id].value()));
+      }
+
+      n_queries -= queries_committed;
+
+      if (n_queries == 0) { return; }
+      // If not all queries were committed, continue in the loop.
+      // TODO: it could potentially be more efficient to first commit everything and only then
+      //        submit the work/wait for the event
+      local_io_offset += queries_committed;
+      to_commit.reset(
+        local_waiter::kNonSleepIterations);  // reset the waiter for the next iteration.
+    }
+  }
+
+ private:
+  raft::resources res_;  // Sic! Store by value to copy the resource.
+  std::function<function_search_type<T, IdxT>> upstream_search_;
+  uint32_t k_;
+  uint32_t dim_;
+  uint32_t max_batch_size_;
+  uint32_t n_queues_;
+
+  mutable batch_queue batch_queue_;
+  std::vector<cuda_event> completion_events_;
+
+  using batch_extents = raft::extent_3d<uint32_t>;
+  batch_extents input_extents_;
+  batch_extents output_extents_;
+
+  mutable raft::device_mdarray<T, batch_extents, raft::row_major> queries_;
+  mutable raft::device_mdarray<IdxT, batch_extents, raft::row_major> neighbors_;
+  mutable raft::device_mdarray<float, batch_extents, raft::row_major> distances_;
+  mutable raft::device_vector<cuda::atomic<uint32_t, cuda::std::thread_scope_device>>
+    kernel_progress_counters_;
+
+  mutable raft::pinned_matrix<request_pointers<T, IdxT>, uint32_t, raft::row_major> request_ptrs_;
+
+  /**
+   * Try to commit n_queries at most; returns the last observed batch_token (where `size_committed`
+   * represents offset at which new queries are committed if successful), the number of committed
+   * queries, or whether the ring buffer appears to be busy (on unsuccessful commit).
+   */
+  auto try_commit(seq_order_id seq_id, uint32_t n_queries) const
+    -> std::variant<std::tuple<batch_token, uint32_t>, bool>
+  {
+    auto& batch_token_ref            = batch_queue_.token(seq_id);
+    batch_token batch_token_observed = batch_token_ref.load(cuda::std::memory_order_relaxed);
+    batch_token batch_token_updated;
+    slot_state token_status;
+    do {
+      // The interpretation of the token status depends on the current seq_order_id and a similar
+      // counter in the token. This is to prevent conflicts when too many parallel requests wrap
+      // over the whole ring buffer (batch_queue_t).
+      token_status = batch_queue::batch_status(batch_token_observed, seq_id);
+      // Busy status means the current thread is a whole ring buffer ahead of the token.
+      // The thread should wait for the rest of the system.
+      if (token_status == slot_state::kFullBusy || token_status == slot_state::kEmptyBusy) {
+        return true;
+      }
+      // This branch checks if the token was recently filled or dispatched.
+      // This means the head counter of the ring buffer is slightly outdated.
+      if (token_status == slot_state::kEmptyPast || token_status == slot_state::kFullPast ||
+          batch_token_observed.size_committed() >= max_batch_size_) {
+        batch_queue_.pop(seq_id);
+        return false;
+      }
+      batch_token_updated = batch_token_observed;
+      batch_token_updated.size_committed() =
+        std::min(batch_token_observed.size_committed() + n_queries, max_batch_size_);
+    } while (!batch_token_ref.compare_exchange_weak(batch_token_observed,
+                                                    batch_token_updated,
+                                                    cuda::std::memory_order_acq_rel,
+                                                    cuda::std::memory_order_relaxed));
+    if (batch_token_updated.size_committed() >= max_batch_size_) {
+      // The batch is already full, let's try to pop it from the queue
+      //                                 (if nobody has done so already)
+      batch_queue_.pop(seq_id);
+    }
+    return std::make_tuple(
+      batch_token_observed,
+      batch_token_updated.size_committed() - batch_token_observed.size_committed());
+  }
+};
+
+}  // namespace cuvs::neighbors::dynamic_batching::detail
diff --git a/cpp/src/neighbors/dynamic_batching.cu b/cpp/src/neighbors/dynamic_batching.cu
new file mode 100644
index 0000000000..6be70353ba
--- /dev/null
+++ b/cpp/src/neighbors/dynamic_batching.cu
@@ -0,0 +1,91 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/dynamic_batching.cuh"
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::neighbors::dynamic_batching {
+
+// NB: the (template) index parameter should be the last; it may contain the spaces and so split
+//       into multiple preprocessor token. Then it is consumed as __VA_ARGS__
+//
+#define CUVS_INST_DYNAMIC_BATCHING_INDEX(T, IdxT, Namespace, ...)                         \
+  template <>                                                                             \
+  template <>                                                                             \
+  index<T, IdxT>::index<Namespace ::__VA_ARGS__>(                                         \
+    const raft::resources& res,                                                           \
+    const cuvs::neighbors::dynamic_batching::index_params& params,                        \
+    const Namespace ::__VA_ARGS__& upstream_index,                                        \
+    const typename Namespace ::__VA_ARGS__::search_params_type& upstream_params,          \
+    const cuvs::neighbors::filtering::base_filter* sample_filter)                         \
+    : runner{new detail::batch_runner<T, IdxT>(                                           \
+        res, params, upstream_index, upstream_params, Namespace ::search, sample_filter)} \
+  {                                                                                       \
+  }
+
+#define CUVS_INST_DYNAMIC_BATCHING_SEARCH(T, IdxT)                                 \
+  void search(raft::resources const& res,                                          \
+              cuvs::neighbors::dynamic_batching::search_params const& params,      \
+              cuvs::neighbors::dynamic_batching::index<T, IdxT> const& index,      \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances) \
+  {                                                                                \
+    return index.runner->search(res, params, queries, neighbors, distances);       \
+  }
+
+CUVS_INST_DYNAMIC_BATCHING_INDEX(float, uint32_t, cuvs::neighbors::cagra, index<float, uint32_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(half, uint32_t, cuvs::neighbors::cagra, index<half, uint32_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t, uint32_t, cuvs::neighbors::cagra, index<int8_t, uint32_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(uint8_t,
+                                 uint32_t,
+                                 cuvs::neighbors::cagra,
+                                 index<uint8_t, uint32_t>);
+
+CUVS_INST_DYNAMIC_BATCHING_INDEX(float, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(half, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(uint8_t, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
+
+CUVS_INST_DYNAMIC_BATCHING_INDEX(float, int64_t, cuvs::neighbors::ivf_flat, index<float, int64_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t,
+                                 int64_t,
+                                 cuvs::neighbors::ivf_flat,
+                                 index<int8_t, int64_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(uint8_t,
+                                 int64_t,
+                                 cuvs::neighbors::ivf_flat,
+                                 index<uint8_t, int64_t>);
+
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(float, int64_t);
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(half, int64_t);
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(int8_t, int64_t);
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(uint8_t, int64_t);
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(float, uint32_t);  // uint32_t index type is needed for CAGRA
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(half, uint32_t);
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(int8_t, uint32_t);
+CUVS_INST_DYNAMIC_BATCHING_SEARCH(uint8_t, uint32_t);
+
+#undef CUVS_INST_DYNAMIC_BATCHING_INDEX
+#undef CUVS_INST_DYNAMIC_BATCHING_SEARCH
+
+}  // namespace cuvs::neighbors::dynamic_batching
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 286d721d7f..1c8de2ad0c 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -175,6 +175,19 @@ if(BUILD_TESTS)
     100
   )
 
+  ConfigureTest(
+    NAME
+    NEIGHBORS_DYNAMIC_BATCHING_TEST
+    PATH
+    neighbors/dynamic_batching/test_cagra.cu
+    neighbors/dynamic_batching/test_ivf_flat.cu
+    neighbors/dynamic_batching/test_ivf_pq.cu
+    GPUS
+    1
+    PERCENT
+    100
+  )
+
   if(BUILD_CAGRA_HNSWLIB)
     ConfigureTest(NAME NEIGHBORS_HNSW_TEST PATH neighbors/hnsw.cu GPUS 1 PERCENT 100)
     target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
diff --git a/cpp/test/neighbors/dynamic_batching.cuh b/cpp/test/neighbors/dynamic_batching.cuh
new file mode 100644
index 0000000000..b64c5b01e4
--- /dev/null
+++ b/cpp/test/neighbors/dynamic_batching.cuh
@@ -0,0 +1,292 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "ann_utils.cuh"
+
+#include <gtest/gtest.h>
+
+#include <cuvs/neighbors/dynamic_batching.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/random/rng.cuh>
+
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <cstdint>
+#include <future>
+#include <vector>
+
+namespace cuvs::neighbors::dynamic_batching {
+
+struct dynamic_batching_spec {
+  int64_t n_queries                   = 1000;
+  int64_t n_rows                      = 100000;
+  int64_t dim                         = 128;
+  int64_t k                           = 10;
+  int64_t max_batch_size              = 64;
+  size_t n_queues                     = 3;
+  bool conservative_dispatch          = false;
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
+  int64_t max_concurrent_threads      = 128;
+};
+
+inline ::std::ostream& operator<<(::std::ostream& os, const dynamic_batching_spec& p)
+{
+  os << "{n_queries=" << p.n_queries;
+  os << ", dataset shape=" << p.n_rows << "x" << p.dim;
+  os << ", metric=" << print_metric{p.metric};
+  os << ", k=" << p.k;
+  os << ", max_batch_size=" << p.max_batch_size;
+  os << ", n_queues=" << p.n_queues;
+  os << ", conservative_dispatch=" << p.conservative_dispatch;
+  os << '}' << std::endl;
+  return os;
+}
+
+template <typename DataT, typename IdxT, typename UpstreamT>
+using build_function = UpstreamT(const raft::resources&,
+                                 const typename UpstreamT::index_params_type&,
+                                 raft::device_matrix_view<const DataT, int64_t, raft::row_major>);
+
+template <typename DataT, typename IdxT, typename UpstreamT>
+using search_function = void(const raft::resources&,
+                             const typename UpstreamT::search_params_type& params,
+                             const UpstreamT& index,
+                             raft::device_matrix_view<const DataT, int64_t, raft::row_major>,
+                             raft::device_matrix_view<IdxT, int64_t, raft::row_major>,
+                             raft::device_matrix_view<float, int64_t, raft::row_major>,
+                             const cuvs::neighbors::filtering::base_filter&);
+
+template <typename DataT,
+          typename IdxT,
+          typename UpstreamT,
+          build_function<DataT, IdxT, UpstreamT> UpstreamBuildF,
+          search_function<DataT, IdxT, UpstreamT> UpstreamSearchF>
+struct dynamic_batching_test : public ::testing::TestWithParam<dynamic_batching_spec> {
+  using distance_type = float;
+  using data_type     = DataT;
+  using index_type    = IdxT;
+  using upstream_type = UpstreamT;
+
+  dynamic_batching_spec ps = ::testing::TestWithParam<dynamic_batching_spec>::GetParam();
+  raft::resources res;
+
+  // input data
+  std::optional<raft::device_matrix<data_type, int64_t>> dataset            = std::nullopt;
+  std::optional<raft::device_matrix<data_type, int64_t>> queries            = std::nullopt;
+  std::optional<raft::device_matrix<index_type, int64_t>> neighbors_upsm    = std::nullopt;
+  std::optional<raft::device_matrix<index_type, int64_t>> neighbors_dynb    = std::nullopt;
+  std::optional<raft::device_matrix<distance_type, int64_t>> distances_upsm = std::nullopt;
+  std::optional<raft::device_matrix<distance_type, int64_t>> distances_dynb = std::nullopt;
+
+  // build parameters
+  cuvs::neighbors::index_params build_params_base{ps.metric};
+  typename upstream_type::index_params_type build_params_upsm{build_params_base};
+  dynamic_batching::index_params build_params_dynb{
+    build_params_base, ps.k, ps.max_batch_size, ps.n_queues, ps.conservative_dispatch};
+
+  // search parameters
+  typename upstream_type::search_params_type search_params_upsm{};
+  dynamic_batching::search_params search_params_dynb{};
+
+  // indexes
+  std::optional<upstream_type> index_upsm                                  = std::nullopt;
+  std::optional<dynamic_batching::index<data_type, index_type>> index_dynb = std::nullopt;
+
+  void build_all()
+  {
+    index_dynb.reset();
+    index_upsm.reset();
+    index_upsm = UpstreamBuildF(res, build_params_upsm, dataset->view());
+    index_dynb.emplace(res, build_params_dynb, index_upsm.value(), search_params_upsm);
+  }
+
+  void search_all()
+  {
+    // Search using upstream index - all queries at once
+    UpstreamSearchF(res,
+                    search_params_upsm,
+                    index_upsm.value(),
+                    queries->view(),
+                    neighbors_upsm->view(),
+                    distances_upsm->view(),
+                    filtering::none_sample_filter{});
+    raft::resource::sync_stream(res);
+
+    // Search with dynamic batching
+    // Streaming scenario: prepare concurrent resources
+    rmm::cuda_stream_pool worker_streams(ps.max_concurrent_threads);
+    std::vector<std::future<void>> futures(ps.max_concurrent_threads);
+    std::vector<raft::resources> resource_pool(0);
+    for (int64_t i = 0; i < ps.max_concurrent_threads; i++) {
+      resource_pool.push_back(res);  // copies the resource
+      raft::resource::set_cuda_stream(resource_pool[i], worker_streams.get_stream(i));
+    }
+
+    // Try multiple batch sizes in a round-robin to improve test coverage
+    std::vector<int64_t> minibatch_sizes{1, 3, 7, 10};
+    auto get_bs = [&minibatch_sizes](auto i) {
+      return minibatch_sizes[i % minibatch_sizes.size()];
+    };
+    int64_t i = 0;
+    for (int64_t offset = 0; offset < ps.n_queries; offset += get_bs(i++)) {
+      auto bs = std::min<int64_t>(get_bs(i), ps.n_queries - offset);
+      auto j  = i % ps.max_concurrent_threads;
+      // wait for previous job in the same slot to finish
+      if (i >= ps.max_concurrent_threads) { futures[j].wait(); }
+      // submit a new job
+      futures[j] = std::async(
+        std::launch::async,
+        [&res       = resource_pool[j],
+         &params    = search_params_dynb,
+         index      = index_dynb.value(),
+         query_view = raft::make_device_matrix_view<data_type, int64_t>(
+           queries->data_handle() + offset * ps.dim, bs, ps.dim),
+         neighbors_view = raft::make_device_matrix_view<index_type, int64_t>(
+           neighbors_dynb->data_handle() + offset * ps.k, bs, ps.k),
+         distances_view = raft::make_device_matrix_view<distance_type, int64_t>(
+           distances_dynb->data_handle() + offset * ps.k, bs, ps.k)]() {
+          dynamic_batching::search(res, params, index, query_view, neighbors_view, distances_view);
+        });
+    }
+
+    // finalize all resources
+    for (int64_t j = 0; j < ps.max_concurrent_threads && j < i; j++) {
+      futures[j].wait();
+      raft::resource::sync_stream(resource_pool[j]);
+    }
+    raft::resource::sync_stream(res);
+  }
+
+  /*
+    Check the dynamic batching generated neighbors against the upstream index. They both may be
+    imperfect w.r.t. the ground truth, but they shouldn't differ too much.
+   */
+  void check_neighbors()
+  {
+    auto stream         = raft::resource::get_cuda_stream(res);
+    size_t queries_size = ps.n_queries * ps.k;
+    std::vector<index_type> neighbors_upsm_host(queries_size);
+    std::vector<index_type> neighbors_dynb_host(queries_size);
+    std::vector<distance_type> distances_upsm_host(queries_size);
+    std::vector<distance_type> distances_dynb_host(queries_size);
+    raft::copy(neighbors_upsm_host.data(), neighbors_upsm->data_handle(), queries_size, stream);
+    raft::copy(neighbors_dynb_host.data(), neighbors_dynb->data_handle(), queries_size, stream);
+    raft::copy(distances_upsm_host.data(), distances_upsm->data_handle(), queries_size, stream);
+    raft::copy(distances_dynb_host.data(), distances_dynb->data_handle(), queries_size, stream);
+    raft::resource::sync_stream(res);
+    ASSERT_TRUE(eval_neighbours(neighbors_upsm_host,
+                                neighbors_dynb_host,
+                                distances_upsm_host,
+                                distances_dynb_host,
+                                ps.n_queries,
+                                ps.k,
+                                0.001,
+                                0.9))
+      << ps;
+  }
+
+  void SetUp() override
+  {
+    dataset.emplace(raft::make_device_matrix<data_type, int64_t>(res, ps.n_rows, ps.dim));
+    queries.emplace(raft::make_device_matrix<data_type, int64_t>(res, ps.n_queries, ps.dim));
+    neighbors_upsm.emplace(raft::make_device_matrix<index_type, int64_t>(res, ps.n_queries, ps.k));
+    neighbors_dynb.emplace(raft::make_device_matrix<index_type, int64_t>(res, ps.n_queries, ps.k));
+    distances_upsm.emplace(
+      raft::make_device_matrix<distance_type, int64_t>(res, ps.n_queries, ps.k));
+    distances_dynb.emplace(
+      raft::make_device_matrix<distance_type, int64_t>(res, ps.n_queries, ps.k));
+
+    raft::random::RngState rng(666ULL);
+    if constexpr (std::is_same_v<data_type, float> || std::is_same_v<data_type, half>) {
+      raft::random::uniform(
+        res, rng, dataset->data_handle(), dataset->size(), data_type(0.1), data_type(2.0));
+      raft::random::uniform(
+        res, rng, queries->data_handle(), queries->size(), data_type(0.1), data_type(2.0));
+    } else {
+      raft::random::uniformInt(
+        res, rng, dataset->data_handle(), dataset->size(), data_type(1), data_type(20));
+      raft::random::uniformInt(
+        res, rng, queries->data_handle(), queries->size(), data_type(1), data_type(20));
+    }
+    raft::resource::sync_stream(res);
+  }
+
+  void TearDown() override
+  {
+    index_dynb.reset();
+    index_upsm.reset();
+    dataset.reset();
+    queries.reset();
+    neighbors_upsm.reset();
+    neighbors_dynb.reset();
+    distances_upsm.reset();
+    distances_dynb.reset();
+    raft::resource::sync_stream(res);
+  }
+};
+
+inline std::vector<dynamic_batching_spec> generate_inputs()
+{
+  std::vector<dynamic_batching_spec> inputs{dynamic_batching_spec{}};
+
+  for (auto alt_n_queries : {10, 50, 100}) {
+    dynamic_batching_spec input{};
+    input.n_queries = alt_n_queries;
+    inputs.push_back(input);
+  }
+
+  for (auto alt_k : {100, 200}) {
+    dynamic_batching_spec input{};
+    input.k = alt_k;
+    inputs.push_back(input);
+  }
+
+  for (auto alt_max_batch_size : {4, 16, 128, 256, 512, 1024}) {
+    dynamic_batching_spec input{};
+    input.max_batch_size = alt_max_batch_size;
+    inputs.push_back(input);
+  }
+
+  for (auto alt_n_queues : {1, 2, 16, 32}) {
+    dynamic_batching_spec input{};
+    input.n_queues = alt_n_queues;
+    inputs.push_back(input);
+  }
+
+  for (auto alt_max_concurrent_threads : {1, 2, 16, 32}) {
+    dynamic_batching_spec input{};
+    input.max_concurrent_threads = alt_max_concurrent_threads;
+    inputs.push_back(input);
+  }
+
+  {
+    auto n = inputs.size();
+    for (size_t i = 0; i < n; i++) {
+      auto input                  = inputs[i];
+      input.conservative_dispatch = !input.conservative_dispatch;
+      inputs.push_back(input);
+    }
+  }
+
+  return inputs;
+}
+
+const std::vector<dynamic_batching_spec> inputs = generate_inputs();
+
+}  // namespace cuvs::neighbors::dynamic_batching
diff --git a/cpp/test/neighbors/dynamic_batching/test_cagra.cu b/cpp/test/neighbors/dynamic_batching/test_cagra.cu
new file mode 100644
index 0000000000..604fc29cf3
--- /dev/null
+++ b/cpp/test/neighbors/dynamic_batching/test_cagra.cu
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../dynamic_batching.cuh"
+
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::dynamic_batching {
+
+using cagra_F32 = dynamic_batching_test<float,
+                                        uint32_t,
+                                        cagra::index<float, uint32_t>,
+                                        cagra::build,
+                                        cagra::search>;
+
+using cagra_U8 = dynamic_batching_test<uint8_t,
+                                       uint32_t,
+                                       cagra::index<uint8_t, uint32_t>,
+                                       cagra::build,
+                                       cagra::search>;
+
+template <typename fixture>
+static void set_default_cagra_params(fixture& that)
+{
+  that.build_params_upsm.intermediate_graph_degree = 128;
+  that.build_params_upsm.graph_degree              = 64;
+  that.search_params_upsm.itopk_size =
+    std::clamp<int64_t>(raft::bound_by_power_of_two(that.ps.k) * 16, 128, 512);
+}
+
+TEST_P(cagra_F32, single_cta)
+{
+  set_default_cagra_params(*this);
+  search_params_upsm.algo = cagra::search_algo::SINGLE_CTA;
+  build_all();
+  search_all();
+  check_neighbors();
+}
+
+TEST_P(cagra_F32, multi_cta)
+{
+  set_default_cagra_params(*this);
+  search_params_upsm.algo = cagra::search_algo::MULTI_CTA;
+  build_all();
+  search_all();
+  check_neighbors();
+}
+
+TEST_P(cagra_F32, multi_kernel)
+{
+  set_default_cagra_params(*this);
+  search_params_upsm.algo = cagra::search_algo::MULTI_KERNEL;
+  build_all();
+  search_all();
+  check_neighbors();
+}
+
+TEST_P(cagra_U8, defaults)
+{
+  set_default_cagra_params(*this);
+  build_all();
+  search_all();
+  check_neighbors();
+}
+
+INSTANTIATE_TEST_CASE_P(dynamic_batching, cagra_F32, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(dynamic_batching, cagra_U8, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::dynamic_batching
diff --git a/cpp/test/neighbors/dynamic_batching/test_ivf_flat.cu b/cpp/test/neighbors/dynamic_batching/test_ivf_flat.cu
new file mode 100644
index 0000000000..4922cffa34
--- /dev/null
+++ b/cpp/test/neighbors/dynamic_batching/test_ivf_flat.cu
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../dynamic_batching.cuh"
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::dynamic_batching {
+
+using ivf_flat_i8 = dynamic_batching_test<uint8_t,
+                                          int64_t,
+                                          ivf_flat::index<uint8_t, int64_t>,
+                                          ivf_flat::build,
+                                          ivf_flat::search>;
+
+TEST_P(ivf_flat_i8, defaults)
+{
+  build_params_upsm.n_lists = std::round(std::sqrt(ps.n_rows));
+  search_params_upsm.n_probes =
+    std::max<uint32_t>(std::min<uint32_t>(build_params_upsm.n_lists, 10),
+                       raft::div_rounding_up_safe<uint32_t>(build_params_upsm.n_lists, 50));
+  build_all();
+  search_all();
+  check_neighbors();
+}
+
+INSTANTIATE_TEST_CASE_P(dynamic_batching, ivf_flat_i8, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::dynamic_batching
diff --git a/cpp/test/neighbors/dynamic_batching/test_ivf_pq.cu b/cpp/test/neighbors/dynamic_batching/test_ivf_pq.cu
new file mode 100644
index 0000000000..ec57e0b571
--- /dev/null
+++ b/cpp/test/neighbors/dynamic_batching/test_ivf_pq.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../dynamic_batching.cuh"
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::dynamic_batching {
+
+using ivf_pq_f16 =
+  dynamic_batching_test<half, int64_t, ivf_pq::index<int64_t>, ivf_pq::build, ivf_pq::search>;
+
+TEST_P(ivf_pq_f16, defaults)
+{
+  build_params_upsm.n_lists = std::round(std::sqrt(ps.n_rows));
+  search_params_upsm.n_probes =
+    std::max<uint32_t>(std::min<uint32_t>(build_params_upsm.n_lists, 10),
+                       raft::div_rounding_up_safe<uint32_t>(build_params_upsm.n_lists, 50));
+  build_all();
+  search_all();
+  check_neighbors();
+}
+
+INSTANTIATE_TEST_CASE_P(dynamic_batching, ivf_pq_f16, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::dynamic_batching
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index d55d58eb0a..ab810ab531 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -11,6 +11,7 @@ Nearest Neighbors
 
    neighbors_bruteforce.rst
    neighbors_cagra.rst
+   neighbors_dynamic_batching.rst
    neighbors_hnsw.rst
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
diff --git a/docs/source/cpp_api/neighbors_dynamic_batching.rst b/docs/source/cpp_api/neighbors_dynamic_batching.rst
new file mode 100644
index 0000000000..adc5cb56aa
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_dynamic_batching.rst
@@ -0,0 +1,45 @@
+Dynamic Batching
+================
+
+Dynamic Batching allows grouping small search requests into batches to increase the device occupancy and throughput while keeping the latency within limits.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/dynamic_batching.hpp>``
+
+namespace *cuvs::neighbors::dynamic_batching*
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: dynamic_batching_cpp_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index search parameters
+-----------------------
+
+.. doxygengroup:: dynamic_batching_cpp_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: dynamic_batching_cpp_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+
+Index search
+------------
+
+.. doxygengroup:: dynamic_batching_cpp_search
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 092b65ed98..951e0ad0c4 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -38,6 +38,7 @@ include(../cmake/thirdparty/get_cuvs.cmake)
 # -------------- compile tasks ----------------- #
 add_executable(CAGRA_EXAMPLE src/cagra_example.cu)
 add_executable(CAGRA_PERSISTENT_EXAMPLE src/cagra_persistent_example.cu)
+add_executable(DYNAMIC_BATCHING_EXAMPLE src/dynamic_batching_example.cu)
 add_executable(IVF_FLAT_EXAMPLE src/ivf_flat_example.cu)
 add_executable(IVF_PQ_EXAMPLE src/ivf_pq_example.cu)
 add_executable(VAMANA_EXAMPLE src/vamana_example.cu)
@@ -48,6 +49,9 @@ target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:c
 target_link_libraries(
   CAGRA_PERSISTENT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
 )
+target_link_libraries(
+  DYNAMIC_BATCHING_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
+)
 target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/cpp/src/dynamic_batching_example.cu b/examples/cpp/src/dynamic_batching_example.cu
new file mode 100644
index 0000000000..95f66a454a
--- /dev/null
+++ b/examples/cpp/src/dynamic_batching_example.cu
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.cuh"
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/dynamic_batching.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/cuda_stream_pool.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <rmm/cuda_stream_pool.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <array>
+#include <chrono>
+#include <cstdint>
+#include <future>
+
+// A helper to split the dataset into chunks
+template <typename DeviceMatrixOrView>
+auto slice_matrix(const DeviceMatrixOrView &source,
+                  typename DeviceMatrixOrView::index_type offset_rows,
+                  typename DeviceMatrixOrView::index_type count_rows) {
+  auto n_cols = source.extent(1);
+  return raft::make_device_matrix_view<
+      typename DeviceMatrixOrView::element_type,
+      typename DeviceMatrixOrView::index_type>(
+      const_cast<typename DeviceMatrixOrView::element_type *>(
+          source.data_handle()) +
+          offset_rows * n_cols,
+      count_rows, n_cols);
+}
+
+// A helper to measure the execution time of a function
+template <typename F, typename... Args>
+void time_it(std::string label, F f, Args &&...xs) {
+  auto start = std::chrono::system_clock::now();
+  f(std::forward<Args>(xs)...);
+  auto end = std::chrono::system_clock::now();
+  auto t = std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+  auto t_ms = double(t.count()) / 1000.0;
+  std::cout << "[" << label << "] execution time: " << t_ms << " ms"
+            << std::endl;
+}
+
+/**
+ * Wrap waiting on a stream work into an async C++ future object.
+ * This is similar to recording and waiting on CUDA events, but in C++11 API.
+ */
+struct cuda_work_completion_promise {
+
+  cuda_work_completion_promise(const raft::resources &res) {
+    auto *promise = new std::promise<void>;
+    RAFT_CUDA_TRY(cudaLaunchHostFunc(raft::resource::get_cuda_stream(res),
+                                     completion_callback,
+                                     reinterpret_cast<void *>(promise)));
+    value_ = promise->get_future();
+  }
+
+  /**
+   * Waiting on the produced `future` object has the same effect as
+   * cudaEventSynchronize if an event was recorded at the time of creation of
+   * this promise object.
+   */
+  auto get_future() -> std::future<void> && { return std::move(value_); }
+
+private:
+  std::future<void> value_;
+
+  static void completion_callback(void *ptr) {
+    auto *promise = reinterpret_cast<std::promise<void> *>(ptr);
+    promise->set_value();
+    delete promise;
+  }
+};
+
+void dynamic_batching_example(
+    raft::resources const &res,
+    raft::device_matrix_view<const float, int64_t> dataset,
+    raft::device_matrix_view<const float, int64_t> queries) {
+  using namespace cuvs::neighbors;
+
+  // Number of neighbors to search
+  int64_t topk = 100;
+
+  // Streaming scenario: maximum number of requests in-flight
+  constexpr int64_t kMaxJobs = 1000;
+  // Streaming scenario: number of concurrent CUDA streams
+  constexpr int64_t kNumWorkerStreams = 5;
+
+  // Split the queries into two subsets to run every experiment twice and thus
+  // surface any initialization overheads.
+  int64_t n_queries_a = queries.extent(0) / 2;
+  int64_t n_queries_b = queries.extent(0) - n_queries_a;
+
+  auto queries_a = slice_matrix(queries, 0, n_queries_a);
+  auto queries_b = slice_matrix(queries, n_queries_a, n_queries_b);
+
+  // create output arrays
+  auto neighbors =
+      raft::make_device_matrix<uint32_t>(res, queries.extent(0), topk);
+  auto distances =
+      raft::make_device_matrix<float>(res, queries.extent(0), topk);
+  // slice them same as queries
+  auto neighbors_a = slice_matrix(neighbors, 0, n_queries_a);
+  auto distances_a = slice_matrix(distances, 0, n_queries_a);
+  auto neighbors_b = slice_matrix(neighbors, n_queries_a, n_queries_b);
+  auto distances_b = slice_matrix(distances, n_queries_a, n_queries_b);
+
+  // use default index parameters
+  cagra::index_params orig_index_params;
+
+  std::cout << "Building CAGRA index (search graph)" << std::endl;
+  auto orig_index = cagra::build(res, orig_index_params, dataset);
+
+  std::cout << "CAGRA index has " << orig_index.size() << " vectors"
+            << std::endl;
+  std::cout << "CAGRA graph has degree " << orig_index.graph_degree()
+            << ", graph size [" << orig_index.graph().extent(0) << ", "
+            << orig_index.graph().extent(1) << "]" << std::endl;
+
+  // use default search parameters
+  cagra::search_params orig_search_params;
+  // get a decent recall by increasing the internal topk list
+  orig_search_params.itopk_size = 512;
+  orig_search_params.algo = cagra::search_algo::SINGLE_CTA;
+
+  // Set up dynamic batching parameters
+  dynamic_batching::index_params dynb_index_params{
+      /* default-initializing the parent `neighbors::index_params`
+         (not used anyway) */
+      {},
+      /* Set the K in advance (the batcher needs to allocate buffers) */
+      topk,
+      /* Configure the number and the size of IO buffers */
+      64,
+      kNumWorkerStreams};
+
+  // "build" the index (it's a low-cost index wrapping),
+  //  that is we need to pass the original index and its search params here
+  dynamic_batching::index<float, uint32_t> dynb_index(
+      res, dynb_index_params, orig_index, orig_search_params);
+
+  // You can implement job priorities by varying the deadlines of individual
+  // requests
+  dynamic_batching::search_params dynb_search_params;
+  dynb_search_params.dispatch_timeout_ms = 0.1;
+
+  // Define the big-batch setting as a baseline for measuring the throughput.
+  auto search_batch_orig =
+      [&res, &orig_index, &orig_search_params](
+          raft::device_matrix_view<const float, int64_t> queries,
+          raft::device_matrix_view<uint32_t, int64_t> neighbors,
+          raft::device_matrix_view<float, int64_t> distances) {
+        cagra::search(res, orig_search_params, orig_index, queries, neighbors,
+                      distances);
+        raft::resource::sync_stream(res);
+      };
+
+  // Launch the baseline search: check the big-batch performance
+  time_it("standard/batch A", search_batch_orig, queries_a, neighbors_a,
+          distances_a);
+  time_it("standard/batch B", search_batch_orig, queries_b, neighbors_b,
+          distances_b);
+
+  // Streaming scenario: prepare concurrent resources
+  rmm::cuda_stream_pool worker_streams{kNumWorkerStreams};
+  std::vector<raft::resources> resource_pool(0);
+  for (int64_t i = 0; i < kNumWorkerStreams; i++) {
+    resource_pool.push_back(res);
+    raft::resource::set_cuda_stream(resource_pool[i],
+                                    worker_streams.get_stream(i));
+  }
+
+  // Streaming scenario:
+  // send queries one-by-one, with a maximum kMaxJobs in-flight
+  auto search_async_orig =
+      [&resource_pool, &orig_index, &orig_search_params](
+          raft::device_matrix_view<const float, int64_t> queries,
+          raft::device_matrix_view<uint32_t, int64_t> neighbors,
+          raft::device_matrix_view<float, int64_t> distances) {
+        auto work_size = queries.extent(0);
+        std::array<std::future<void>, kMaxJobs> futures;
+        for (int64_t i = 0; i < work_size + kMaxJobs; i++) {
+          // wait for previous job in the same slot to finish
+          if (i >= kMaxJobs) {
+            futures[i % kMaxJobs].wait();
+          }
+          // submit a new job
+          if (i < work_size) {
+            auto &res = resource_pool[i % kNumWorkerStreams];
+            cagra::search(res, orig_search_params, orig_index,
+                          slice_matrix(queries, i, 1),
+                          slice_matrix(neighbors, i, 1),
+                          slice_matrix(distances, i, 1));
+            futures[i % kMaxJobs] =
+                cuda_work_completion_promise(res).get_future();
+          }
+        }
+      };
+
+  // Streaming scenario with dynamic batching:
+  // send queries one-by-one, with a maximum kMaxJobs in-flight,
+  // yet allow grouping the sequential requests (subject to deadlines)
+  auto search_async_dynb =
+      [&resource_pool, &dynb_index, &dynb_search_params](
+          raft::device_matrix_view<const float, int64_t> queries,
+          raft::device_matrix_view<uint32_t, int64_t> neighbors,
+          raft::device_matrix_view<float, int64_t> distances) {
+        auto work_size = queries.extent(0);
+        std::array<std::future<void>, kMaxJobs> futures;
+        for (int64_t i = 0; i < work_size + kMaxJobs; i++) {
+          // wait for previous job in the same slot to finish
+          if (i >= kMaxJobs) {
+            futures[i % kMaxJobs].wait();
+          }
+          // submit a new job
+          if (i < work_size) {
+            auto &res = resource_pool[i % kNumWorkerStreams];
+            dynamic_batching::search(res, dynb_search_params, dynb_index,
+                                     slice_matrix(queries, i, 1),
+                                     slice_matrix(neighbors, i, 1),
+                                     slice_matrix(distances, i, 1));
+            futures[i % kMaxJobs] =
+                cuda_work_completion_promise(res).get_future();
+          }
+        }
+      };
+
+  // Try to handle the same amount of work in the async setting using the
+  // standard implementation.
+  time_it("standard/async A", search_async_orig, queries_a, neighbors_a,
+          distances_a);
+  time_it("standard/async B", search_async_orig, queries_b, neighbors_b,
+          distances_b);
+
+  // Do the same using dynamic batching
+  time_it("dynamic_batching/async A", search_async_dynb, queries_a, neighbors_a,
+          distances_a);
+  time_it("dynamic_batching/async B", search_async_dynb, queries_b, neighbors_b,
+          distances_b);
+}
+
+int main() {
+  raft::device_resources res;
+
+  // Set the raft resource to use a pool for internal memory allocations
+  // (workspace) and limit the available workspace size.
+  raft::resource::set_workspace_to_pool_resource(res,
+                                                 12ull * 1024 * 1024 * 1024ull);
+
+  // Create input arrays.
+  int64_t n_samples = 1000000;
+  int64_t n_dim = 128;
+  int64_t n_queries = 10000;
+  auto dataset =
+      raft::make_device_matrix<float, int64_t>(res, n_samples, n_dim);
+  auto queries =
+      raft::make_device_matrix<float, int64_t>(res, n_queries, n_dim);
+  generate_dataset(res, dataset.view(), queries.view());
+
+  // run the interesting part of the program
+  dynamic_batching_example(res, raft::make_const_mdspan(dataset.view()),
+                           raft::make_const_mdspan(queries.view()));
+}

From 69199c2297e5a6012dd0f26a491550066cecdd4b Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 4 Dec 2024 18:19:19 -0600
Subject: [PATCH 228/623] Remove upper bounds on cuda-python to allow 12.6.2
 and 11.8.5 (#508)

Now that some upstream bugs have been fixed, we can allow cuda-python 12.6.2 and 11.8.5.

See https://github.com/NVIDIA/cuda-python/issues/226#issuecomment-2472355738 for more information.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/508
---
 conda/environments/all_cuda-118_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml        | 2 +-
 conda/environments/all_cuda-125_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-125_arch-x86_64.yaml        | 2 +-
 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/bench_ann_cuda-125_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-125_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs/meta.yaml                            | 8 ++++----
 dependencies.yaml                                       | 4 ++--
 10 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 1daf668b1f..b27e9d3410 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0,<=11.8.3
+- cuda-python>=11.7.1,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 0981563970..2a2791824b 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0,<=11.8.3
+- cuda-python>=11.7.1,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index b94b447490..800d8c5cc1 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0,<=12.6.0
+- cuda-python>=12.0,<13.0a0
 - cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 10e30a8c23..15addf9da4 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0,<=12.6.0
+- cuda-python>=12.0,<13.0a0
 - cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index dd7499c785..ced5176e96 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0,<=11.8.3
+- cuda-python>=11.7.1,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index f12e01c60d..d8e49519f9 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0,<=11.8.3
+- cuda-python>=11.7.1,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 89134093cc..5e6373ad11 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0,<=12.6.0
+- cuda-python>=12.0,<13.0a0
 - cuda-version=12.5
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index 88b4c859a8..fece589d5a 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0,<=12.6.0
+- cuda-python>=12.0,<13.0a0
 - cuda-version=12.5
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index 560c95feb1..f799d9b0be 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -43,10 +43,10 @@ requirements:
     - {{ stdlib("c") }}
   host:
     {% if cuda_major == "11" %}
-    - cuda-python >=11.7.1,<12.0a0,<=11.8.3
+    - cuda-python >=11.7.1,<12.0a0
     - cudatoolkit
     {% else %}
-    - cuda-python >=12.0,<13.0a0,<=12.6.0
+    - cuda-python >=12.0,<13.0a0
     - cuda-cudart-dev
     {% endif %}
     - cuda-version ={{ cuda_version }}
@@ -61,10 +61,10 @@ requirements:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major == "11" %}
     - cudatoolkit
-    - cuda-python >=11.7.1,<12.0a0,<=11.8.3
+    - cuda-python >=11.7.1,<12.0a0
     {% else %}
     - cuda-cudart
-    - cuda-python >=12.0,<13.0a0,<=12.6.0
+    - cuda-python >=12.0,<13.0a0
     {% endif %}
     - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
diff --git a/dependencies.yaml b/dependencies.yaml
index ee51554897..28ded36716 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -213,11 +213,11 @@ dependencies:
           - matrix:
               cuda: "12.*"
             packages:
-              - &cuda_python12 cuda-python>=12.0,<13.0a0,<=12.6.0
+              - &cuda_python12 cuda-python>=12.0,<13.0a0
           - matrix:
               cuda: "11.*"
             packages:
-              - &cuda_python11 cuda-python>=11.7.1,<12.0a0,<=11.8.3
+              - &cuda_python11 cuda-python>=11.7.1,<12.0a0
           - matrix:
             packages:
               - &cuda_python cuda-python

From b051f805129fab36ee5da7299ed0fb98850fa44c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20F=C3=B6rster?=
 <97973773+mfoerste4@users.noreply.github.com>
Date: Thu, 5 Dec 2024 06:27:33 +0100
Subject: [PATCH 229/623] Add C++ API scalar quantization (#494)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

First draft for scalar quantization.

WIP status:
* only int8_t target type
* quantile computation inefficient (via sampling & sorting)

Authors:
  - Malte Förster (https://github.com/mfoerste4)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/494
---
 cpp/CMakeLists.txt                            |   1 +
 .../cuvs/preprocessing/quantize/scalar.hpp    | 489 ++++++++++++++++++
 .../preprocessing/quantize/detail/scalar.cuh  | 227 ++++++++
 cpp/src/preprocessing/quantize/scalar.cu      |  74 +++
 cpp/test/CMakeLists.txt                       |   5 +
 cpp/test/preprocessing/scalar_quantization.cu | 291 +++++++++++
 docs/source/cpp_api.rst                       |   1 +
 docs/source/cpp_api/preprocessing.rst         |  12 +
 .../source/cpp_api/preprocessing_quantize.rst |  20 +
 9 files changed, 1120 insertions(+)
 create mode 100644 cpp/include/cuvs/preprocessing/quantize/scalar.hpp
 create mode 100644 cpp/src/preprocessing/quantize/detail/scalar.cuh
 create mode 100644 cpp/src/preprocessing/quantize/scalar.cu
 create mode 100644 cpp/test/preprocessing/scalar_quantization.cu
 create mode 100644 docs/source/cpp_api/preprocessing.rst
 create mode 100644 docs/source/cpp_api/preprocessing_quantize.rst

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 6af423bd58..199bb232d5 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -459,6 +459,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/vamana_serialize_float.cu
     src/neighbors/vamana_serialize_uint8.cu
     src/neighbors/vamana_serialize_int8.cu
+    src/preprocessing/quantize/scalar.cu
     src/selection/select_k_float_int64_t.cu
     src/selection/select_k_float_int32_t.cu
     src/selection/select_k_float_uint32_t.cu
diff --git a/cpp/include/cuvs/preprocessing/quantize/scalar.hpp b/cpp/include/cuvs/preprocessing/quantize/scalar.hpp
new file mode 100644
index 0000000000..49b4bb7a67
--- /dev/null
+++ b/cpp/include/cuvs/preprocessing/quantize/scalar.hpp
@@ -0,0 +1,489 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+
+#include <cuda_fp16.h>
+
+namespace cuvs::preprocessing::quantize::scalar {
+
+/**
+ * @defgroup scalar Scalar quantizer utilities
+ * @{
+ */
+
+/**
+ * @brief quantizer parameters.
+ */
+struct params {
+  /*
+   * specifies how many outliers at top & bottom will be ignored
+   * needs to be within range of (0, 1]
+   */
+  float quantile = 0.99;
+};
+
+/**
+ * @brief Defines and stores scalar for quantisation upon training
+ *
+ * The quantization is performed by a linear mapping of an interval in the
+ * float data type to the full range of the quantized int type.
+ *
+ * @tparam T data element type
+ *
+ */
+template <typename T>
+struct quantizer {
+  T min_;
+  T max_;
+};
+
+/**
+ * @brief Initializes a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
+ * dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure scalar quantizer, e.g. quantile
+ * @param[in] dataset a row-major matrix view on device
+ *
+ * @return quantizer
+ */
+quantizer<double> train(raft::resources const& res,
+                        const params params,
+                        raft::device_matrix_view<const double, int64_t> dataset);
+
+/**
+ * @brief Initializes a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
+ * dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure scalar quantizer, e.g. quantile
+ * @param[in] dataset a row-major matrix view on host
+ *
+ * @return quantizer
+ */
+quantizer<double> train(raft::resources const& res,
+                        const params params,
+                        raft::host_matrix_view<const double, int64_t> dataset);
+
+/**
+ * @brief Applies quantization transform to given dataset
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
+ * dataset); auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples,
+ * features); cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void transform(raft::resources const& res,
+               const quantizer<double>& quantizer,
+               raft::device_matrix_view<const double, int64_t> dataset,
+               raft::device_matrix_view<int8_t, int64_t> out);
+
+/**
+ * @brief Applies quantization transform to given dataset
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
+ * dataset); auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void transform(raft::resources const& res,
+               const quantizer<double>& quantizer,
+               raft::host_matrix_view<const double, int64_t> dataset,
+               raft::host_matrix_view<int8_t, int64_t> out);
+
+/**
+ * @brief Perform inverse quantization step on previously quantized dataset
+ *
+ * Note that depending on the chosen data types train dataset the conversion is
+ * not lossless.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view()); auto dataset_revert = raft::make_device_matrix<double,
+ * int64_t>(handle, samples, features);
+ * cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
+ * dataset_revert.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void inverse_transform(raft::resources const& res,
+                       const quantizer<double>& quantizer,
+                       raft::device_matrix_view<const int8_t, int64_t> dataset,
+                       raft::device_matrix_view<double, int64_t> out);
+
+/**
+ * @brief Perform inverse quantization step on previously quantized dataset
+ *
+ * Note that depending on the chosen data types train dataset the conversion is
+ * not lossless.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view()); auto dataset_revert = raft::make_host_matrix<double, int64_t>(samples,
+ * features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
+ * dataset_revert.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void inverse_transform(raft::resources const& res,
+                       const quantizer<double>& quantizer,
+                       raft::host_matrix_view<const int8_t, int64_t> dataset,
+                       raft::host_matrix_view<double, int64_t> out);
+
+/**
+ * @brief Initializes a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
+ * dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure scalar quantizer, e.g. quantile
+ * @param[in] dataset a row-major matrix view on device
+ *
+ * @return quantizer
+ */
+quantizer<float> train(raft::resources const& res,
+                       const params params,
+                       raft::device_matrix_view<const float, int64_t> dataset);
+
+/**
+ * @brief Initializes a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
+ * dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure scalar quantizer, e.g. quantile
+ * @param[in] dataset a row-major matrix view on host
+ *
+ * @return quantizer
+ */
+quantizer<float> train(raft::resources const& res,
+                       const params params,
+                       raft::host_matrix_view<const float, int64_t> dataset);
+
+/**
+ * @brief Applies quantization transform to given dataset
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
+ * dataset); auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples,
+ * features); cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void transform(raft::resources const& res,
+               const quantizer<float>& quantizer,
+               raft::device_matrix_view<const float, int64_t> dataset,
+               raft::device_matrix_view<int8_t, int64_t> out);
+
+/**
+ * @brief Applies quantization transform to given dataset
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
+ * dataset); auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void transform(raft::resources const& res,
+               const quantizer<float>& quantizer,
+               raft::host_matrix_view<const float, int64_t> dataset,
+               raft::host_matrix_view<int8_t, int64_t> out);
+
+/**
+ * @brief Perform inverse quantization step on previously quantized dataset
+ *
+ * Note that depending on the chosen data types train dataset the conversion is
+ * not lossless.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view()); auto dataset_revert = raft::make_device_matrix<float, int64_t>(handle,
+ * samples, features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
+ * dataset_revert.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void inverse_transform(raft::resources const& res,
+                       const quantizer<float>& quantizer,
+                       raft::device_matrix_view<const int8_t, int64_t> dataset,
+                       raft::device_matrix_view<float, int64_t> out);
+
+/**
+ * @brief Perform inverse quantization step on previously quantized dataset
+ *
+ * Note that depending on the chosen data types train dataset the conversion is
+ * not lossless.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view()); auto dataset_revert = raft::make_host_matrix<float, int64_t>(samples,
+ * features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
+ * dataset_revert.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void inverse_transform(raft::resources const& res,
+                       const quantizer<float>& quantizer,
+                       raft::host_matrix_view<const int8_t, int64_t> dataset,
+                       raft::host_matrix_view<float, int64_t> out);
+
+/**
+ * @brief Initializes a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
+ * dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure scalar quantizer, e.g. quantile
+ * @param[in] dataset a row-major matrix view on device
+ *
+ * @return quantizer
+ */
+quantizer<half> train(raft::resources const& res,
+                      const params params,
+                      raft::device_matrix_view<const half, int64_t> dataset);
+
+/**
+ * @brief Initializes a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
+ * dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure scalar quantizer, e.g. quantile
+ * @param[in] dataset a row-major matrix view on host
+ *
+ * @return quantizer
+ */
+quantizer<half> train(raft::resources const& res,
+                      const params params,
+                      raft::host_matrix_view<const half, int64_t> dataset);
+
+/**
+ * @brief Applies quantization transform to given dataset
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
+ * dataset); auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples,
+ * features); cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void transform(raft::resources const& res,
+               const quantizer<half>& quantizer,
+               raft::device_matrix_view<const half, int64_t> dataset,
+               raft::device_matrix_view<int8_t, int64_t> out);
+
+/**
+ * @brief Applies quantization transform to given dataset
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::scalar::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
+ * dataset); auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void transform(raft::resources const& res,
+               const quantizer<half>& quantizer,
+               raft::host_matrix_view<const half, int64_t> dataset,
+               raft::host_matrix_view<int8_t, int64_t> out);
+
+/**
+ * @brief Perform inverse quantization step on previously quantized dataset
+ *
+ * Note that depending on the chosen data types train dataset the conversion is
+ * not lossless.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * auto quantized_dataset = raft::make_device_matrix<int8_t, int64_t>(handle, samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view()); auto dataset_revert = raft::make_device_matrix<half, int64_t>(handle,
+ * samples, features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
+ * dataset_revert.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void inverse_transform(raft::resources const& res,
+                       const quantizer<half>& quantizer,
+                       raft::device_matrix_view<const int8_t, int64_t> dataset,
+                       raft::device_matrix_view<half, int64_t> out);
+
+/**
+ * @brief Perform inverse quantization step on previously quantized dataset
+ *
+ * Note that depending on the chosen data types train dataset the conversion is
+ * not lossless.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * auto quantized_dataset = raft::make_host_matrix<int8_t, int64_t>(samples, features);
+ * cuvs::preprocessing::quantize::scalar::transform(handle, quantizer, dataset,
+ * quantized_dataset.view()); auto dataset_revert = raft::make_host_matrix<half, int64_t>(samples,
+ * features); cuvs::preprocessing::quantize::scalar::inverse_transform(handle, quantizer,
+ * dataset_revert.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void inverse_transform(raft::resources const& res,
+                       const quantizer<half>& quantizer,
+                       raft::host_matrix_view<const int8_t, int64_t> dataset,
+                       raft::host_matrix_view<half, int64_t> out);
+
+/** @} */  // end of group scalar
+
+}  // namespace cuvs::preprocessing::quantize::scalar
diff --git a/cpp/src/preprocessing/quantize/detail/scalar.cuh b/cpp/src/preprocessing/quantize/detail/scalar.cuh
new file mode 100644
index 0000000000..fc132eb7f8
--- /dev/null
+++ b/cpp/src/preprocessing/quantize/detail/scalar.cuh
@@ -0,0 +1,227 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/preprocessing/quantize/scalar.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/random/sample_without_replacement.cuh>
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+#include <thrust/system/omp/execution_policy.h>
+
+namespace cuvs::preprocessing::quantize::detail {
+
+template <class T>
+_RAFT_HOST_DEVICE bool fp_lt(const T& a, const T& b)
+{
+  return a < b;
+}
+
+template <>
+_RAFT_HOST_DEVICE bool fp_lt(const half& a, const half& b)
+{
+  return static_cast<float>(a) < static_cast<float>(b);
+}
+
+template <typename T, typename QuantI, typename TempT = double>
+struct quantize_op {
+  const T min_;
+  const T max_;
+  const QuantI q_type_min_ = std::numeric_limits<QuantI>::min();
+  const QuantI q_type_max_ = std::numeric_limits<QuantI>::max();
+  const TempT scalar_;
+  const TempT offset_;
+
+  constexpr explicit quantize_op(T min, T max)
+    : min_(min),
+      max_(max),
+      scalar_(static_cast<TempT>(max_) > static_cast<TempT>(min_)
+                ? ((static_cast<TempT>(q_type_max_) - static_cast<TempT>(q_type_min_)) /
+                   (static_cast<TempT>(max_) - static_cast<TempT>(min_)))
+                : static_cast<TempT>(1)),
+      offset_(static_cast<TempT>(q_type_min_) - static_cast<TempT>(min_) * scalar_)
+  {
+  }
+
+  constexpr RAFT_INLINE_FUNCTION QuantI operator()(const T& x) const
+  {
+    if (!fp_lt(min_, x)) return q_type_min_;
+    if (!fp_lt(x, max_)) return q_type_max_;
+    return static_cast<QuantI>(lroundf(scalar_ * static_cast<TempT>(x) + offset_));
+  }
+
+  constexpr RAFT_INLINE_FUNCTION T operator()(const QuantI& x) const
+  {
+    return static_cast<T>((static_cast<TempT>(x) - offset_) / scalar_);
+  }
+};
+
+template <typename T>
+std::tuple<T, T> quantile_min_max(raft::resources const& res,
+                                  raft::device_matrix_view<const T, int64_t> dataset,
+                                  double quantile)
+{
+  // settings for quantile approximation
+  constexpr size_t max_num_samples = 1000000;
+  constexpr int seed               = 137;
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(res);
+
+  // select subsample
+  raft::random::RngState rng(seed);
+  size_t n_elements  = dataset.extent(0) * dataset.extent(1);
+  size_t subset_size = std::min(max_num_samples, n_elements);
+  auto subset        = raft::make_device_vector<T>(res, subset_size);
+  auto dataset_view  = raft::make_device_vector_view<const T>(dataset.data_handle(), n_elements);
+  raft::random::sample_without_replacement(
+    res, rng, dataset_view, std::nullopt, subset.view(), std::nullopt);
+
+  // quantile / sort and pick for now
+  thrust::sort(raft::resource::get_thrust_policy(res),
+               subset.data_handle(),
+               subset.data_handle() + subset_size);
+
+  double half_quantile_pos = (0.5 + 0.5 * quantile) * subset_size;
+  int pos_max              = std::ceil(half_quantile_pos) - 1;
+  int pos_min              = subset_size - pos_max - 1;
+
+  T minmax_h[2];
+  raft::update_host(&(minmax_h[0]), subset.data_handle() + pos_min, 1, stream);
+  raft::update_host(&(minmax_h[1]), subset.data_handle() + pos_max, 1, stream);
+  raft::resource::sync_stream(res);
+
+  return {minmax_h[0], minmax_h[1]};
+}
+
+template <typename T>
+std::tuple<T, T> quantile_min_max(raft::resources const& res,
+                                  raft::host_matrix_view<const T, int64_t> dataset,
+                                  double quantile)
+{
+  // settings for quantile approximation
+  constexpr size_t max_num_samples = 1000000;
+  constexpr int seed               = 137;
+
+  // select subsample
+  std::mt19937 rng(seed);
+  size_t n_elements  = dataset.extent(0) * dataset.extent(1);
+  size_t subset_size = std::min(max_num_samples, n_elements);
+  std::vector<T> subset;
+  std::sample(dataset.data_handle(),
+              dataset.data_handle() + n_elements,
+              std::back_inserter(subset),
+              subset_size,
+              rng);
+
+  // quantile / sort and pick for now
+  thrust::sort(thrust::omp::par, subset.data(), subset.data() + subset_size, fp_lt<T>);
+  double half_quantile_pos = (0.5 + 0.5 * quantile) * subset_size;
+  int pos_max              = std::ceil(half_quantile_pos) - 1;
+  int pos_min              = subset_size - pos_max - 1;
+
+  return {subset[pos_min], subset[pos_max]};
+}
+
+template <typename T>
+cuvs::preprocessing::quantize::scalar::quantizer<T> train(
+  raft::resources const& res,
+  const cuvs::preprocessing::quantize::scalar::params params,
+  raft::device_matrix_view<const T, int64_t> dataset)
+{
+  RAFT_EXPECTS(params.quantile > 0.0 && params.quantile <= 1.0,
+               "quantile for scalar quantization needs to be within (0, 1] but is %f",
+               params.quantile);
+
+  auto [min, max] = detail::quantile_min_max(res, dataset, params.quantile);
+
+  RAFT_LOG_DEBUG("quantizer train min=%lf max=%lf.", double(min), double(max));
+
+  return cuvs::preprocessing::quantize::scalar::quantizer<T>{min, max};
+}
+
+template <typename T>
+cuvs::preprocessing::quantize::scalar::quantizer<T> train(
+  raft::resources const& res,
+  const cuvs::preprocessing::quantize::scalar::params params,
+  raft::host_matrix_view<const T, int64_t> dataset)
+{
+  RAFT_EXPECTS(params.quantile > 0.0 && params.quantile <= 1.0,
+               "quantile for scalar quantization needs to be within (0, 1] but is %f",
+               params.quantile);
+
+  auto [min, max] = detail::quantile_min_max(res, dataset, params.quantile);
+
+  RAFT_LOG_DEBUG("quantizer train min=%lf max=%lf.", double(min), double(max));
+
+  return cuvs::preprocessing::quantize::scalar::quantizer<T>{min, max};
+}
+
+template <typename T, typename QuantI = int8_t>
+void transform(raft::resources const& res,
+               const cuvs::preprocessing::quantize::scalar::quantizer<T>& quantizer,
+               raft::device_matrix_view<const T, int64_t> dataset,
+               raft::device_matrix_view<QuantI, int64_t> out)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(res);
+
+  raft::linalg::map(res, out, quantize_op<T, QuantI>(quantizer.min_, quantizer.max_), dataset);
+}
+
+template <typename T, typename QuantI = int8_t>
+void transform(raft::resources const& res,
+               const cuvs::preprocessing::quantize::scalar::quantizer<T>& quantizer,
+               raft::host_matrix_view<const T, int64_t> dataset,
+               raft::host_matrix_view<QuantI, int64_t> out)
+{
+  auto main_op      = quantize_op<T, QuantI>(quantizer.min_, quantizer.max_);
+  size_t n_elements = dataset.extent(0) * dataset.extent(1);
+
+#pragma omp parallel for
+  for (size_t i = 0; i < n_elements; ++i) {
+    out.data_handle()[i] = main_op(dataset.data_handle()[i]);
+  }
+}
+
+template <typename T, typename QuantI = int8_t>
+void inverse_transform(raft::resources const& res,
+                       const cuvs::preprocessing::quantize::scalar::quantizer<T>& quantizer,
+                       raft::device_matrix_view<const QuantI, int64_t> dataset,
+                       raft::device_matrix_view<T, int64_t> out)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(res);
+
+  raft::linalg::map(res, out, quantize_op<T, QuantI>(quantizer.min_, quantizer.max_), dataset);
+}
+
+template <typename T, typename QuantI = int8_t>
+void inverse_transform(raft::resources const& res,
+                       const cuvs::preprocessing::quantize::scalar::quantizer<T>& quantizer,
+                       raft::host_matrix_view<const QuantI, int64_t> dataset,
+                       raft::host_matrix_view<T, int64_t> out)
+{
+  auto main_op      = quantize_op<T, QuantI>(quantizer.min_, quantizer.max_);
+  size_t n_elements = dataset.extent(0) * dataset.extent(1);
+
+#pragma omp parallel for
+  for (size_t i = 0; i < n_elements; ++i) {
+    out.data_handle()[i] = main_op(dataset.data_handle()[i]);
+  }
+}
+
+}  // namespace cuvs::preprocessing::quantize::detail
diff --git a/cpp/src/preprocessing/quantize/scalar.cu b/cpp/src/preprocessing/quantize/scalar.cu
new file mode 100644
index 0000000000..9624ad4fe8
--- /dev/null
+++ b/cpp/src/preprocessing/quantize/scalar.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./detail/scalar.cuh"
+
+#include <cuvs/preprocessing/quantize/scalar.hpp>
+
+namespace cuvs::preprocessing::quantize::scalar {
+
+#define CUVS_INST_QUANTIZATION(T, QuantI)                                         \
+  auto train(raft::resources const& res,                                          \
+             const params params,                                                 \
+             raft::device_matrix_view<const T, int64_t> dataset)                  \
+    ->quantizer<T>                                                                \
+  {                                                                               \
+    return detail::train(res, params, dataset);                                   \
+  }                                                                               \
+  auto train(raft::resources const& res,                                          \
+             const params params,                                                 \
+             raft::host_matrix_view<const T, int64_t> dataset)                    \
+    ->quantizer<T>                                                                \
+  {                                                                               \
+    return detail::train(res, params, dataset);                                   \
+  }                                                                               \
+  void transform(raft::resources const& res,                                      \
+                 const quantizer<T>& quantizer,                                   \
+                 raft::device_matrix_view<const T, int64_t> dataset,              \
+                 raft::device_matrix_view<QuantI, int64_t> out)                   \
+  {                                                                               \
+    detail::transform(res, quantizer, dataset, out);                              \
+  }                                                                               \
+  void transform(raft::resources const& res,                                      \
+                 const quantizer<T>& quantizer,                                   \
+                 raft::host_matrix_view<const T, int64_t> dataset,                \
+                 raft::host_matrix_view<QuantI, int64_t> out)                     \
+  {                                                                               \
+    detail::transform(res, quantizer, dataset, out);                              \
+  }                                                                               \
+  void inverse_transform(raft::resources const& res,                              \
+                         const quantizer<T>& quantizer,                           \
+                         raft::device_matrix_view<const QuantI, int64_t> dataset, \
+                         raft::device_matrix_view<T, int64_t> out)                \
+  {                                                                               \
+    detail::inverse_transform(res, quantizer, dataset, out);                      \
+  }                                                                               \
+  void inverse_transform(raft::resources const& res,                              \
+                         const quantizer<T>& quantizer,                           \
+                         raft::host_matrix_view<const QuantI, int64_t> dataset,   \
+                         raft::host_matrix_view<T, int64_t> out)                  \
+  {                                                                               \
+    detail::inverse_transform(res, quantizer, dataset, out);                      \
+  }                                                                               \
+  template struct quantizer<T>;
+
+CUVS_INST_QUANTIZATION(double, int8_t);
+CUVS_INST_QUANTIZATION(float, int8_t);
+CUVS_INST_QUANTIZATION(half, int8_t);
+
+#undef CUVS_INST_QUANTIZATION
+
+}  // namespace cuvs::preprocessing::quantize::scalar
\ No newline at end of file
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 1c8de2ad0c..0ecac6ec20 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -226,6 +226,11 @@ if(BUILD_TESTS)
     PERCENT
     100
   )
+
+  ConfigureTest(
+    NAME PREPROCESSING_TEST PATH preprocessing/scalar_quantization.cu GPUS 1 PERCENT 100
+  )
+
   ConfigureTest(
     NAME STATS_TEST PATH stats/trustworthiness.cu stats/silhouette_score.cu GPUS 1 PERCENT 100
   )
diff --git a/cpp/test/preprocessing/scalar_quantization.cu b/cpp/test/preprocessing/scalar_quantization.cu
new file mode 100644
index 0000000000..2fdfe7555b
--- /dev/null
+++ b/cpp/test/preprocessing/scalar_quantization.cu
@@ -0,0 +1,291 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include <cuvs/preprocessing/quantize/scalar.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/transpose.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/stats/stddev.cuh>
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+
+namespace cuvs::preprocessing::quantize::scalar {
+
+template <typename T>
+struct QuantizationInputs {
+  cuvs::preprocessing::quantize::scalar::params quantization_params;
+  int rows;
+  int cols;
+  T min            = T(-1.0);
+  T max            = T(1.0);
+  double threshold = 2e-2;
+};
+
+template <typename T>
+std::ostream& operator<<(std::ostream& os, const QuantizationInputs<T>& inputs)
+{
+  return os << "quantization_quantile:<" << inputs.quantization_params.quantile
+            << "> rows:" << inputs.rows << " cols:" << inputs.cols << " min:" << (double)inputs.min
+            << " max:" << (double)inputs.max;
+}
+
+template <typename T, typename QuantI>
+class QuantizationTest : public ::testing::TestWithParam<QuantizationInputs<T>> {
+ public:
+  QuantizationTest()
+    : params_(::testing::TestWithParam<QuantizationInputs<T>>::GetParam()),
+      stream(raft::resource::get_cuda_stream(handle)),
+      input_(0, stream)
+  {
+  }
+
+  double getRelativeErrorStddev(const T* array_a, const T* array_b, size_t size, float quantile)
+  {
+    // relative error elementwise
+    rmm::device_uvector<double> relative_error(size, stream);
+    raft::linalg::binaryOp(
+      relative_error.data(),
+      array_a,
+      array_b,
+      size,
+      [] __device__(double a, double b) {
+        return a != b ? (raft::abs(a - b) / raft::max(raft::abs(a), raft::abs(b))) : 0;
+      },
+      stream);
+
+    // sort by size --> remove largest errors to account for quantile chosen
+    thrust::sort(raft::resource::get_thrust_policy(handle),
+                 relative_error.data(),
+                 relative_error.data() + size);
+    int elements_to_consider =
+      std::ceil(double(params_.quantization_params.quantile) * double(size));
+
+    rmm::device_uvector<double> mu(1, stream);
+    RAFT_CUDA_TRY(cudaMemsetAsync(mu.data(), 0, sizeof(double), stream));
+
+    rmm::device_uvector<double> error_stddev(1, stream);
+    raft::stats::stddev(error_stddev.data(),
+                        relative_error.data(),
+                        mu.data(),
+                        1,
+                        elements_to_consider,
+                        false,
+                        true,
+                        stream);
+
+    double error_stddev_h;
+    raft::update_host(&error_stddev_h, error_stddev.data(), 1, stream);
+    raft::resource::sync_stream(handle, stream);
+    return error_stddev_h;
+  }
+
+ protected:
+  void testScalarQuantization()
+  {
+    // dataset identical on host / device
+    auto dataset = raft::make_device_matrix_view<const T, int64_t, raft::row_major>(
+      (const T*)(input_.data()), rows_, cols_);
+    auto dataset_h = raft::make_host_matrix_view<const T, int64_t, raft::row_major>(
+      (const T*)(host_input_.data()), rows_, cols_);
+
+    size_t print_size = std::min(input_.size(), 20ul);
+
+    // train quantizer_1 on device
+    auto quantizer_1 =
+      cuvs::preprocessing::quantize::scalar::train(handle, params_.quantization_params, dataset);
+    std::cerr << "Q1: min = " << (double)quantizer_1.min_ << ", max = " << (double)quantizer_1.max_
+              << std::endl;
+
+    {
+      auto quantized_input_h = raft::make_host_matrix<QuantI, int64_t>(rows_, cols_);
+      auto quantized_input_d = raft::make_device_matrix<QuantI, int64_t>(handle, rows_, cols_);
+      cuvs::preprocessing::quantize::scalar::transform(
+        handle, quantizer_1, dataset, quantized_input_d.view());
+      cuvs::preprocessing::quantize::scalar::transform(
+        handle, quantizer_1, dataset_h, quantized_input_h.view());
+
+      {
+        raft::print_device_vector("Input array: ", input_.data(), print_size, std::cerr);
+
+        rmm::device_uvector<int> quantization_for_print(print_size, stream);
+        raft::linalg::unaryOp(quantization_for_print.data(),
+                              quantized_input_d.data_handle(),
+                              print_size,
+                              raft::cast_op<int>{},
+                              stream);
+        raft::resource::sync_stream(handle, stream);
+        raft::print_device_vector(
+          "Quantized array 1: ", quantization_for_print.data(), print_size, std::cerr);
+      }
+
+      // test (inverse) transform host/device equal
+      ASSERT_TRUE(devArrMatchHost(quantized_input_h.data_handle(),
+                                  quantized_input_d.data_handle(),
+                                  input_.size(),
+                                  cuvs::Compare<QuantI>(),
+                                  stream));
+
+      auto quantized_input_h_const_view = raft::make_host_matrix_view<const QuantI, int64_t>(
+        quantized_input_h.data_handle(), rows_, cols_);
+      auto re_transformed_input_h = raft::make_host_matrix<T, int64_t>(rows_, cols_);
+      cuvs::preprocessing::quantize::scalar::inverse_transform(
+        handle, quantizer_1, quantized_input_h_const_view, re_transformed_input_h.view());
+
+      auto quantized_input_d_const_view = raft::make_device_matrix_view<const QuantI, int64_t>(
+        quantized_input_d.data_handle(), rows_, cols_);
+      auto re_transformed_input_d = raft::make_device_matrix<T, int64_t>(handle, rows_, cols_);
+      cuvs::preprocessing::quantize::scalar::inverse_transform(
+        handle, quantizer_1, quantized_input_d_const_view, re_transformed_input_d.view());
+      raft::print_device_vector(
+        "re-transformed array: ", re_transformed_input_d.data_handle(), print_size, std::cerr);
+
+      {
+        double l2_error = getRelativeErrorStddev(dataset.data_handle(),
+                                                 re_transformed_input_d.data_handle(),
+                                                 input_.size(),
+                                                 params_.quantization_params.quantile);
+        std::cerr << "error stddev = " << l2_error << ", threshold = " << params_.threshold
+                  << std::endl;
+        // test (inverse) transform close to original dataset
+        ASSERT_TRUE(l2_error < params_.threshold);
+      }
+    }
+
+    // train quantizer_2 on host
+    auto quantizer_2 =
+      cuvs::preprocessing::quantize::scalar::train(handle, params_.quantization_params, dataset_h);
+    std::cerr << "Q2: min = " << (double)quantizer_2.min_ << ", max = " << (double)quantizer_2.max_
+              << std::endl;
+
+    // check both quantizers are the same (valid if sampling is identical)
+    if (input_.size() <= 1000000) {
+      ASSERT_TRUE((double)quantizer_1.min_ == (double)quantizer_2.min_);
+      ASSERT_TRUE((double)quantizer_1.max_ == (double)quantizer_2.max_);
+    }
+
+    {
+      // test transform host/device equal
+      auto quantized_input_h = raft::make_host_matrix<QuantI, int64_t>(rows_, cols_);
+      auto quantized_input_d = raft::make_device_matrix<QuantI, int64_t>(handle, rows_, cols_);
+      cuvs::preprocessing::quantize::scalar::transform(
+        handle, quantizer_2, dataset, quantized_input_d.view());
+      cuvs::preprocessing::quantize::scalar::transform(
+        handle, quantizer_2, dataset_h, quantized_input_h.view());
+
+      {
+        rmm::device_uvector<int> quantization_for_print(print_size, stream);
+        raft::linalg::unaryOp(quantization_for_print.data(),
+                              quantized_input_d.data_handle(),
+                              print_size,
+                              raft::cast_op<int>{},
+                              stream);
+        raft::resource::sync_stream(handle, stream);
+        raft::print_device_vector(
+          "Quantized array 2: ", quantization_for_print.data(), print_size, std::cerr);
+      }
+
+      ASSERT_TRUE(devArrMatchHost(quantized_input_h.data_handle(),
+                                  quantized_input_d.data_handle(),
+                                  input_.size(),
+                                  cuvs::Compare<QuantI>(),
+                                  stream));
+    }
+
+    // sort_by_key (input, quantization) -- check <= on result
+    {
+      auto quantized_input = raft::make_device_matrix<QuantI, int64_t>(handle, rows_, cols_);
+      cuvs::preprocessing::quantize::scalar::transform(
+        handle, quantizer_1, dataset, quantized_input.view());
+      thrust::sort_by_key(raft::resource::get_thrust_policy(handle),
+                          input_.data(),
+                          input_.data() + input_.size(),
+                          quantized_input.data_handle());
+      std::vector<QuantI> quantized_input_sorted_host(input_.size());
+      raft::update_host(
+        quantized_input_sorted_host.data(), quantized_input.data_handle(), input_.size(), stream);
+      raft::resource::sync_stream(handle, stream);
+
+      for (size_t i = 0; i < input_.size() - 1; ++i) {
+        ASSERT_TRUE(quantized_input_sorted_host[i] <= quantized_input_sorted_host[i + 1]);
+      }
+    }
+  }
+
+  void SetUp() override
+  {
+    rows_ = params_.rows;
+    cols_ = params_.cols;
+
+    int n_elements = rows_ * cols_;
+    input_.resize(n_elements, stream);
+    host_input_.resize(n_elements);
+
+    // random input
+    unsigned long long int seed = 1234ULL;
+    raft::random::RngState r(seed);
+    uniform(handle, r, input_.data(), input_.size(), params_.min, params_.max);
+
+    raft::update_host(host_input_.data(), input_.data(), input_.size(), stream);
+
+    raft::resource::sync_stream(handle, stream);
+  }
+
+ private:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  QuantizationInputs<T> params_;
+  int rows_;
+  int cols_;
+  rmm::device_uvector<T> input_;
+  std::vector<T> host_input_;
+};
+
+template <typename T>
+const std::vector<QuantizationInputs<T>> inputs = {
+  {{1.0}, 5, 5, T(0.0), T(1.0)},
+  {{0.98}, 10, 20, T(0.0), T(1.0)},
+  {{0.90}, 1000, 1500, T(-500.0), T(100.0)},
+  {{0.59}, 100, 200},
+  {{0.1}, 1, 1, T(0.0), T(1.0)},
+  {{0.01}, 50, 50, T(0.0), T(1.0)},
+  {{0.94}, 10, 20, T(-1.0), T(0.0)},
+  {{0.95}, 10, 2, T(50.0), T(100.0)},
+  {{0.95}, 10, 20, T(-500.0), T(-100.0)},
+  {{0.95}, 10, 20, T(5.0), T(5.0)},
+};
+
+typedef QuantizationTest<float, int8_t> QuantizationTest_float_int8t;
+TEST_P(QuantizationTest_float_int8t, ScalarQuantizationTest) { this->testScalarQuantization(); }
+
+typedef QuantizationTest<double, int8_t> QuantizationTest_double_int8t;
+TEST_P(QuantizationTest_double_int8t, ScalarQuantizationTest) { this->testScalarQuantization(); }
+
+typedef QuantizationTest<half, int8_t> QuantizationTest_half_int8t;
+TEST_P(QuantizationTest_half_int8t, ScalarQuantizationTest) { this->testScalarQuantization(); }
+
+INSTANTIATE_TEST_CASE_P(QuantizationTest,
+                        QuantizationTest_float_int8t,
+                        ::testing::ValuesIn(inputs<float>));
+INSTANTIATE_TEST_CASE_P(QuantizationTest,
+                        QuantizationTest_double_int8t,
+                        ::testing::ValuesIn(inputs<double>));
+INSTANTIATE_TEST_CASE_P(QuantizationTest,
+                        QuantizationTest_half_int8t,
+                        ::testing::ValuesIn(inputs<half>));
+
+}  // namespace cuvs::preprocessing::quantize::scalar
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index 49732dc92f..34f48a88f6 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -10,5 +10,6 @@ C++ API Documentation
    cpp_api/cluster.rst
    cpp_api/distance.rst
    cpp_api/neighbors.rst
+   cpp_api/preprocessing.rst
    cpp_api/selection.rst
    cpp_api/stats.rst
diff --git a/docs/source/cpp_api/preprocessing.rst b/docs/source/cpp_api/preprocessing.rst
new file mode 100644
index 0000000000..1c2b0f0513
--- /dev/null
+++ b/docs/source/cpp_api/preprocessing.rst
@@ -0,0 +1,12 @@
+Preprocessing
+=============
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   preprocessing_quantize.rst
diff --git a/docs/source/cpp_api/preprocessing_quantize.rst b/docs/source/cpp_api/preprocessing_quantize.rst
new file mode 100644
index 0000000000..b660c61c51
--- /dev/null
+++ b/docs/source/cpp_api/preprocessing_quantize.rst
@@ -0,0 +1,20 @@
+Quantize
+========
+
+This page provides C++ class references for the publicly-exposed elements of the
+`cuvs/preprocessing/quantize` package. 
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+Scalar
+------
+
+``#include <cuvs/preprocessing/quantize/scalar.hpp>``
+
+namespace *cuvs::preprocessing::quantize::scalar*
+
+.. doxygengroup:: scalar
+   :project: cuvs
+

From c9b38623932039722214caf02a516ce12883c9a4 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 5 Dec 2024 09:44:33 +0100
Subject: [PATCH 230/623] Skip IVF-PQ packing test for lists with not enough
 data (#512)

Skip some checks involving hard-coded offsets into the data when the number of records in the checked PQ list is smaller than needed.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/512
---
 cpp/test/neighbors/ann_ivf_pq.cuh | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index fd4e330db2..3a92b5e3d6 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -379,7 +379,14 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     // Pack a few vectors back to the list.
     int row_offset = 5;
     int n_vec      = 3;
-    ASSERT_TRUE(row_offset + n_vec < n_rows);
+    if (static_cast<decltype(n_rows)>(row_offset + n_vec) > n_rows) {
+      RAFT_LOG_INFO(
+        "Skipping IVF-PQ check_packing/pack test for label %u due to insufficient data (%u "
+        "records)",
+        label,
+        uint32_t(n_rows));
+      return;
+    }
     size_t offset      = row_offset * index->pq_dim();
     auto codes_to_pack = raft::make_device_matrix_view<const uint8_t, uint32_t>(
       codes.data_handle() + offset, n_vec, index->pq_dim());
@@ -393,7 +400,14 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     // Another test with the API that take list_data directly
     [[maybe_unused]] auto list_data = index->lists()[label]->data.view();
     uint32_t n_take                 = 4;
-    ASSERT_TRUE(row_offset + n_take < n_rows);
+    if (static_cast<decltype(n_rows)>(row_offset + n_take) > n_rows) {
+      RAFT_LOG_INFO(
+        "Skipping IVF-PQ check_packing/take test for label %u due to insufficient data (%u "
+        "records)",
+        label,
+        uint32_t(n_rows));
+      return;
+    }
     auto codes2 = raft::make_device_matrix<uint8_t>(handle_, n_take, index->pq_dim());
     ivf_pq::helpers::codepacker::unpack(
       handle_, list_data, index->pq_bits(), row_offset, codes2.view());

From c5e03f2eaf5e30053a248a866428249909e99180 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Thu, 5 Dec 2024 07:51:00 -0800
Subject: [PATCH 231/623] Update cuvs to match raft's cutlass changes (#516)

Due to the tight integration between cuvs and raft, we need to ensure that cuvs is updated for rapidsai/raft#2503 or builds of cuvs that rely on cloning raft will get an incompatible version of cutlass due to raft's update.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/516
---
 .pre-commit-config.yaml                      |  5 ++-
 cpp/cmake/patches/cutlass/build-export.patch | 27 +++++++++++++++
 cpp/cmake/patches/cutlass_override.json      | 16 +++++++++
 cpp/cmake/thirdparty/get_cutlass.cmake       | 35 ++++++++++----------
 4 files changed, 64 insertions(+), 19 deletions(-)
 create mode 100644 cpp/cmake/patches/cutlass/build-export.patch
 create mode 100644 cpp/cmake/patches/cutlass_override.json

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f4fdf202eb..5e53abd92a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -91,7 +91,10 @@ repos:
               - id: codespell
                 additional_dependencies: [tomli]
                 args: ["--toml", "pyproject.toml"]
-                exclude: (?x)^(^CHANGELOG.md$)
+                exclude: |
+                  (?x)
+                    ^CHANGELOG[.]md$|
+                    ^cpp/cmake/patches/cutlass/build-export[.]patch$
       - repo: https://github.com/pre-commit/pre-commit-hooks
         rev: v4.5.0
         hooks:
diff --git a/cpp/cmake/patches/cutlass/build-export.patch b/cpp/cmake/patches/cutlass/build-export.patch
new file mode 100644
index 0000000000..a6423e9c08
--- /dev/null
+++ b/cpp/cmake/patches/cutlass/build-export.patch
@@ -0,0 +1,27 @@
+From e0a9597946257a01ae8444200f836ee51d5597ba Mon Sep 17 00:00:00 2001
+From: Kyle Edwards <kyedwards@nvidia.com>
+Date: Wed, 20 Nov 2024 16:37:38 -0500
+Subject: [PATCH] Remove erroneous include directories
+
+These directories are left over from when CuTe was a separate
+CMake project. Remove them.
+---
+ CMakeLists.txt | 2 --
+ 1 file changed, 2 deletions(-)
+
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 7419bdf5e..545384d82 100755
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -665,8 +665,6 @@ target_include_directories(
+   $<INSTALL_INTERFACE:include>
+   $<BUILD_INTERFACE:${CUTLASS_INCLUDE_DIR}>
+   $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>
+-  $<BUILD_INTERFACE:${cute_SOURCE_DIR}/include>
+-  $<BUILD_INTERFACE:${cute_SOURCE_DIR}/examples>
+   )
+ 
+ # Mark CTK headers as system to supress warnings from them
+-- 
+2.34.1
+
diff --git a/cpp/cmake/patches/cutlass_override.json b/cpp/cmake/patches/cutlass_override.json
new file mode 100644
index 0000000000..7bf818987f
--- /dev/null
+++ b/cpp/cmake/patches/cutlass_override.json
@@ -0,0 +1,16 @@
+{
+  "packages" : {
+    "cutlass" : {
+      "version": "3.5.1",
+      "git_url": "https://github.com/NVIDIA/cutlass.git",
+      "git_tag": "v${version}",
+      "patches" : [
+        {
+          "file" : "${current_json_dir}/cutlass/build-export.patch",
+          "issue" : "Fix build directory export",
+          "fixed_in" : ""
+        }
+      ]
+    }
+  }
+}
diff --git a/cpp/cmake/thirdparty/get_cutlass.cmake b/cpp/cmake/thirdparty/get_cutlass.cmake
index 61065318ba..71bd2d26c9 100644
--- a/cpp/cmake/thirdparty/get_cutlass.cmake
+++ b/cpp/cmake/thirdparty/get_cutlass.cmake
@@ -13,10 +13,11 @@
 # =============================================================================
 
 function(find_and_configure_cutlass)
-  set(oneValueArgs VERSION REPOSITORY PINNED_TAG)
+  set(options)
+  set(oneValueArgs)
+  set(multiValueArgs)
   cmake_parse_arguments(PKG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
-  # if(RAFT_ENABLE_DIST_DEPENDENCIES OR RAFT_COMPILE_LIBRARIES)
   set(CUTLASS_ENABLE_HEADERS_ONLY
       ON
       CACHE BOOL "Enable only the header library"
@@ -34,13 +35,22 @@ function(find_and_configure_cutlass)
     set(CUDART_LIBRARY "${CUDA_cudart_static_LIBRARY}" CACHE FILEPATH "fixing cutlass cmake code" FORCE)
   endif()
 
+  include("${rapids-cmake-dir}/cpm/package_override.cmake")
+  rapids_cpm_package_override("${CMAKE_CURRENT_FUNCTION_LIST_DIR}/../patches/cutlass_override.json")
+
+  include("${rapids-cmake-dir}/cpm/detail/package_details.cmake")
+  rapids_cpm_package_details(cutlass version repository tag shallow exclude)
+
+  include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
+  rapids_cpm_generate_patch_command(cutlass ${version} patch_command)
+
   rapids_cpm_find(
-    NvidiaCutlass ${PKG_VERSION}
+    NvidiaCutlass ${version}
     GLOBAL_TARGETS nvidia::cutlass::cutlass
     CPM_ARGS
-    GIT_REPOSITORY ${PKG_REPOSITORY}
-    GIT_TAG ${PKG_PINNED_TAG}
-    GIT_SHALLOW TRUE
+    GIT_REPOSITORY ${repository}
+    GIT_TAG ${tag}
+    GIT_SHALLOW ${shallow} ${patch_command}
     OPTIONS "CUDAToolkit_ROOT ${CUDAToolkit_LIBRARY_DIR}"
   )
 
@@ -56,7 +66,6 @@ function(find_and_configure_cutlass)
       NAMESPACE nvidia::cutlass::
     )
   endif()
-  # endif()
 
   # We generate the cutlass-config files when we built cutlass locally, so always do
   # `find_dependency`
@@ -79,14 +88,4 @@ function(find_and_configure_cutlass)
   )
 endfunction()
 
-if(NOT RAFT_CUTLASS_GIT_TAG)
-  set(RAFT_CUTLASS_GIT_TAG v2.10.0)
-endif()
-
-if(NOT RAFT_CUTLASS_GIT_REPOSITORY)
-  set(RAFT_CUTLASS_GIT_REPOSITORY https://github.com/NVIDIA/cutlass.git)
-endif()
-
-find_and_configure_cutlass(
-  VERSION 2.10.0 REPOSITORY ${RAFT_CUTLASS_GIT_REPOSITORY} PINNED_TAG ${RAFT_CUTLASS_GIT_TAG}
-)
+find_and_configure_cutlass()

From 007c3d2c9efddec6dc46549a5aa2e9f48d4d1612 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Thu, 5 Dec 2024 15:48:41 -0500
Subject: [PATCH 232/623] Moving spectral embedding and kernel gramm APIs to
 cuVS (#463)

Partially addresses #455

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/463
---
 build.sh                                      |  12 +-
 cpp/CMakeLists.txt                            |   4 +
 cpp/include/cuvs/cluster/agglomerative.hpp    |   1 +
 cpp/include/cuvs/distance/grammian.hpp        | 665 +++++++++++++++
 cpp/include/cuvs/embed/spectral.hpp           |  40 +
 .../distance/detail/kernels/gram_matrix.cu    | 481 +++++++++++
 .../distance/detail/kernels/gram_matrix.cuh   | 488 -----------
 .../distance/detail/kernels/kernel_factory.cu |  61 ++
 .../detail/kernels/kernel_factory.cuh         |  65 --
 .../detail/kernels/kernel_matrices.cu         | 726 ++++++++++++++++
 .../detail/kernels/kernel_matrices.cuh        | 777 ------------------
 .../distance/detail/kernels/rbf_fin_op.cuh    |   4 +-
 .../detail/pairwise_matrix/dispatch-ext.cuh   |   4 +-
 .../detail/pairwise_matrix/dispatch_rbf.cu    |   6 +-
 cpp/src/distance/distance-ext.cuh             |   4 +-
 cpp/src/distance/distance.cu                  |   4 +-
 cpp/src/embed/spectral.cu                     |  53 ++
 cpp/src/sparse/cluster/cluster_solvers.cuh    | 100 +++
 cpp/src/sparse/cluster/detail/spectral.cuh    | 111 +++
 .../spectral/modularity_maximization.hpp      | 176 ++++
 .../cluster/detail/spectral/partition.hpp     | 188 +++++
 .../cluster/detail/spectral/spectral_util.cuh | 181 ++++
 cpp/src/sparse/cluster/eigen_solvers.cuh      | 107 +++
 .../cluster/modularity_maximization.cuh       |  86 ++
 cpp/src/sparse/cluster/partition.cuh          |  95 +++
 cpp/test/CMakeLists.txt                       |   6 +
 cpp/test/distance/gram.cu                     | 174 ++++
 cpp/test/distance/gram_base.cuh               |  91 ++
 cpp/test/sparse/cluster/cluster_solvers.cu    | 105 +++
 cpp/test/sparse/cluster/eigen_solvers.cu      | 119 +++
 cpp/test/sparse/cluster/spectral.cu           | 109 +++
 cpp/test/sparse/cluster/spectral_matrix.cu    |  84 ++
 cpp/test/sparse/gram.cu                       | 330 ++++++++
 33 files changed, 4105 insertions(+), 1352 deletions(-)
 create mode 100644 cpp/include/cuvs/distance/grammian.hpp
 create mode 100644 cpp/include/cuvs/embed/spectral.hpp
 create mode 100644 cpp/src/distance/detail/kernels/gram_matrix.cu
 delete mode 100644 cpp/src/distance/detail/kernels/gram_matrix.cuh
 create mode 100644 cpp/src/distance/detail/kernels/kernel_factory.cu
 delete mode 100644 cpp/src/distance/detail/kernels/kernel_factory.cuh
 create mode 100644 cpp/src/distance/detail/kernels/kernel_matrices.cu
 delete mode 100644 cpp/src/distance/detail/kernels/kernel_matrices.cuh
 create mode 100644 cpp/src/embed/spectral.cu
 create mode 100644 cpp/src/sparse/cluster/cluster_solvers.cuh
 create mode 100644 cpp/src/sparse/cluster/detail/spectral.cuh
 create mode 100644 cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
 create mode 100644 cpp/src/sparse/cluster/detail/spectral/partition.hpp
 create mode 100644 cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh
 create mode 100644 cpp/src/sparse/cluster/eigen_solvers.cuh
 create mode 100644 cpp/src/sparse/cluster/modularity_maximization.cuh
 create mode 100644 cpp/src/sparse/cluster/partition.cuh
 create mode 100644 cpp/test/distance/gram.cu
 create mode 100644 cpp/test/distance/gram_base.cuh
 create mode 100644 cpp/test/sparse/cluster/cluster_solvers.cu
 create mode 100644 cpp/test/sparse/cluster/eigen_solvers.cu
 create mode 100644 cpp/test/sparse/cluster/spectral.cu
 create mode 100644 cpp/test/sparse/cluster/spectral_matrix.cu
 create mode 100644 cpp/test/sparse/gram.cu

diff --git a/build.sh b/build.sh
index c08c2900e7..bd5fa649b0 100755
--- a/build.sh
+++ b/build.sh
@@ -76,8 +76,8 @@ BUILD_REPORT_METRICS=""
 BUILD_REPORT_INCL_CACHE_STATS=OFF
 BUILD_SHARED_LIBS=ON
 
-TEST_TARGETS="NEIGHBORS_ANN_CAGRA_TEST"
-ANN_BENCH_TARGETS="CUVS_ANN_BENCH_ALL"
+TEST_TARGETS=""
+ANN_BENCH_TARGETS=""
 
 CACHE_ARGS=""
 NVTX=ON
@@ -273,14 +273,6 @@ fi
 if hasArg tests || (( ${NUMARGS} == 0 )); then
     BUILD_TESTS=ON
     CMAKE_TARGET="${CMAKE_TARGET};${TEST_TARGETS}"
-
-    # Force compile library when needed test targets are specified
-    if [[ $CMAKE_TARGET == *"CAGRA_C_TEST"* || \
-          $CMAKE_TARGET == *"INTEROP_TEST"* || \
-          $CMAKE_TARGET == *"NEIGHBORS_ANN_CAGRA_TEST"* ]]; then
-      echo "-- Enabling compiled lib for gtests"
-      COMPILE_LIBRARY=ON
-    fi
 fi
 
 if hasArg bench-ann || (( ${NUMARGS} == 0 )); then
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 199bb232d5..95fb7e63bc 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -324,6 +324,9 @@ if(BUILD_SHARED_LIBS)
     src/cluster/kmeans_transform_float.cu
     src/cluster/single_linkage_float.cu
     src/core/bitset.cu
+    src/distance/detail/kernels/gram_matrix.cu
+    src/distance/detail/kernels/kernel_factory.cu
+    src/distance/detail/kernels/kernel_matrices.cu
     src/distance/detail/pairwise_matrix/dispatch_canberra_float_float_float_int.cu
     src/distance/detail/pairwise_matrix/dispatch_canberra_half_float_float_int.cu
     src/distance/detail/pairwise_matrix/dispatch_canberra_double_double_double_int.cu
@@ -370,6 +373,7 @@ if(BUILD_SHARED_LIBS)
     src/distance/distance.cu
     src/distance/pairwise_distance.cu
     src/distance/sparse_distance.cu
+    src/embed/spectral.cu
     src/neighbors/brute_force.cu
     src/neighbors/brute_force_serialize.cu
     src/neighbors/cagra_build_float.cu
diff --git a/cpp/include/cuvs/cluster/agglomerative.hpp b/cpp/include/cuvs/cluster/agglomerative.hpp
index e1da040854..8f7e8675aa 100644
--- a/cpp/include/cuvs/cluster/agglomerative.hpp
+++ b/cpp/include/cuvs/cluster/agglomerative.hpp
@@ -18,6 +18,7 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <optional>
+
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
 
diff --git a/cpp/include/cuvs/distance/grammian.hpp b/cpp/include/cuvs/distance/grammian.hpp
new file mode 100644
index 0000000000..0c904d493c
--- /dev/null
+++ b/cpp/include/cuvs/distance/grammian.hpp
@@ -0,0 +1,665 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cublas_v2.h>
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/device_csr_matrix.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::distance::kernels {
+
+template <typename math_t>
+using dense_input_matrix_view_t = raft::device_matrix_view<const math_t, int, raft::layout_stride>;
+template <typename math_t>
+using dense_output_matrix_view_t = raft::device_matrix_view<math_t, int, raft::layout_stride>;
+template <typename math_t>
+using csr_input_matrix_view_t = raft::device_csr_matrix_view<const math_t, int, int, int>;
+
+/**
+ * Base class for general Gram matrices
+ * A Gram matrix is the Hermitian matrix of inner probucts G_ik = <x_i, x_k>
+ * Here, the  inner product is evaluated for all elements from vectors sets X1,
+ * and X2.
+ *
+ * To be more precise, on exit the output buffer will store:
+ * - if is_row_major == true: out[j+k*n1] = <x1_j, x2_k>,
+ * - if is_row_major == false: out[j*n2 + k] = <x1_j, x2_k>,
+ * where x1_j is the j-th vector from the x1 set and x2_k is the k-th vector
+ * from the x2 set.
+ */
+template <typename math_t>
+class GramMatrixBase {
+ protected:
+  cublasHandle_t cublas_handle;
+  bool legacy_interface;
+
+ public:
+  GramMatrixBase() : legacy_interface(false){};
+  [[deprecated]] GramMatrixBase(cublasHandle_t cublas_handle)
+    : cublas_handle(cublas_handle), legacy_interface(true){};
+
+  virtual ~GramMatrixBase(){};
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *  Vector sets are provided in Matrix format
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void operator()(raft::resources const& handle,
+                  dense_input_matrix_view_t<math_t> x1,
+                  dense_input_matrix_view_t<math_t> x2,
+                  dense_output_matrix_view_t<math_t> out,
+                  math_t* norm_x1 = nullptr,
+                  math_t* norm_x2 = nullptr);
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *  Vector sets are provided in Matrix format
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void operator()(raft::resources const& handle,
+                  csr_input_matrix_view_t<math_t> x1,
+                  dense_input_matrix_view_t<math_t> x2,
+                  dense_output_matrix_view_t<math_t> out,
+                  math_t* norm_x1 = nullptr,
+                  math_t* norm_x2 = nullptr);
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *  Vector sets are provided in Matrix format
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void operator()(raft::resources const& handle,
+                  csr_input_matrix_view_t<math_t> x1,
+                  csr_input_matrix_view_t<math_t> x2,
+                  dense_output_matrix_view_t<math_t> out,
+                  math_t* norm_x1 = nullptr,
+                  math_t* norm_x2 = nullptr);
+
+  // unfortunately, 'evaluate' cannot be templatized as it needs to be virtual
+
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  virtual void evaluate(raft::resources const& handle,
+                        dense_input_matrix_view_t<math_t> x1,
+                        dense_input_matrix_view_t<math_t> x2,
+                        dense_output_matrix_view_t<math_t> out,
+                        math_t* norm_x1,
+                        math_t* norm_x2);
+
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  virtual void evaluate(raft::resources const& handle,
+                        csr_input_matrix_view_t<math_t> x1,
+                        dense_input_matrix_view_t<math_t> x2,
+                        dense_output_matrix_view_t<math_t> out,
+                        math_t* norm_x1,
+                        math_t* norm_x2);
+
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  virtual void evaluate(raft::resources const& handle,
+                        csr_input_matrix_view_t<math_t> x1,
+                        csr_input_matrix_view_t<math_t> x2,
+                        dense_output_matrix_view_t<math_t> out,
+                        math_t* norm_x1,
+                        math_t* norm_x2);
+
+  /** Evaluate the Gram matrix for two vector sets using simple dot product.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] virtual void evaluate(const math_t* x1,
+                                       int n1,
+                                       int n_cols,
+                                       const math_t* x2,
+                                       int n2,
+                                       math_t* out,
+                                       bool is_row_major,
+                                       cudaStream_t stream,
+                                       int ld1,
+                                       int ld2,
+                                       int ld_out);
+
+  /** Convenience function to evaluate the Gram matrix for two vector sets.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1
+   * @param ld2 leading dimension of x2
+   * @param ld_out leading dimension of out
+   */
+  [[deprecated]] void operator()(const math_t* x1,
+                                 int n1,
+                                 int n_cols,
+                                 const math_t* x2,
+                                 int n2,
+                                 math_t* out,
+                                 bool is_row_major,
+                                 cudaStream_t stream,
+                                 int ld1    = 0,
+                                 int ld2    = 0,
+                                 int ld_out = 0);
+
+ protected:
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1
+   * @param ld2 leading dimension of x2
+   * @param ld_out leading dimension of out
+   */
+  [[deprecated]] void linear(const math_t* x1,
+                             int n1,
+                             int n_cols,
+                             const math_t* x2,
+                             int n2,
+                             math_t* out,
+                             bool is_row_major,
+                             cudaStream_t stream,
+                             int ld1,
+                             int ld2,
+                             int ld_out);
+
+ protected:
+  bool get_is_row_major(dense_output_matrix_view_t<math_t> matrix);
+  bool get_is_row_major(dense_input_matrix_view_t<math_t> matrix);
+  bool get_is_col_major(dense_output_matrix_view_t<math_t> matrix);
+  bool get_is_col_major(dense_input_matrix_view_t<math_t> matrix);
+
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   */
+  void linear(raft::resources const& handle,
+              dense_input_matrix_view_t<math_t> x1,
+              dense_input_matrix_view_t<math_t> x2,
+              dense_output_matrix_view_t<math_t> out);
+
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   */
+  void linear(raft::resources const& handle,
+              csr_input_matrix_view_t<math_t> x1,
+              dense_input_matrix_view_t<math_t> x2,
+              dense_output_matrix_view_t<math_t> out);
+
+  /** Calculates the Gram matrix using simple dot product between vector sets.
+   *
+   * out = x1 * x2
+   *
+   * Can be used as a building block for more complex kernel functions.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   */
+  void linear(raft::resources const& handle,
+              csr_input_matrix_view_t<math_t> x1,
+              csr_input_matrix_view_t<math_t> x2,
+              dense_output_matrix_view_t<math_t> out);
+};
+
+template <typename math_t>
+class KernelFactory {
+ public:
+  static GramMatrixBase<math_t>* create(KernelParams params);
+  [[deprecated]] static GramMatrixBase<math_t>* create(KernelParams params, cublasHandle_t handle);
+};
+
+/**
+ * Create a kernel matrix using polynomial kernel function.
+ */
+template <typename math_t, typename exp_t>
+class PolynomialKernel : public GramMatrixBase<math_t> {
+  exp_t exponent;
+  math_t gain;
+  math_t offset;
+
+  void applyKernel(
+    math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream);
+
+ public:
+  /**
+   * Constructs a polynomial kernel object.
+   * It evaluates the kernel matrix using the following formula:
+   * K_ij = (gain*<x1_i, x2_k> + offset)^exponent
+   *
+   * @tparam math_t floating point type
+   * @tparam exp_t type of exponent
+   * @param exponent
+   * @param gain
+   * @param offset
+   */
+  PolynomialKernel(exp_t exponent, math_t gain, math_t offset)
+    : GramMatrixBase<math_t>(), exponent(exponent), gain(gain), offset(offset){};
+
+  [[deprecated]] PolynomialKernel(exp_t exponent, math_t gain, math_t offset, cublasHandle_t handle)
+    : GramMatrixBase<math_t>(handle), exponent(exponent), gain(gain), offset(offset){};
+
+  /** Evaluate kernel matrix using polynomial kernel.
+   *
+   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                dense_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate kernel matrix using polynomial kernel.
+   *
+   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate kernel matrix using polynomial kernel.
+   *
+   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                csr_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate the Gram matrix using the legacy interface.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] void evaluate(const math_t* x1,
+                               int n1,
+                               int n_cols,
+                               const math_t* x2,
+                               int n2,
+                               math_t* out,
+                               bool is_row_major,
+                               cudaStream_t stream,
+                               int ld1,
+                               int ld2,
+                               int ld_out);
+};
+
+/**
+ * Create a kernel matrix using tanh kernel function.
+ */
+template <typename math_t>
+class TanhKernel : public GramMatrixBase<math_t> {
+  math_t gain, offset;
+
+  void applyKernel(
+    math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream);
+
+ public:
+  /**
+   * Constructs a tanh kernel object.
+   * It evaluates the kernel matrix using the following formula:
+   * K_ij = tanh(gain*<x1_i, x2_k> + offset)
+   *
+   * @tparam math_t floating point type
+   * @param gain
+   * @param offset
+   */
+  TanhKernel(math_t gain, math_t offset) : GramMatrixBase<math_t>(), gain(gain), offset(offset) {}
+
+  [[deprecated]] TanhKernel(math_t gain, math_t offset, cublasHandle_t handle)
+    : GramMatrixBase<math_t>(handle), gain(gain), offset(offset){};
+
+  /** Evaluate kernel matrix using tanh kernel.
+   *
+   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                dense_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate kernel matrix using tanh kernel.
+   *
+   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate kernel matrix using tanh kernel.
+   *
+   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and < , > denotes dot product.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 unused.
+   * @param norm_x2 unused.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                csr_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate the Gram matrix using the legacy interface.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] void evaluate(const math_t* x1,
+                               int n1,
+                               int n_cols,
+                               const math_t* x2,
+                               int n2,
+                               math_t* out,
+                               bool is_row_major,
+                               cudaStream_t stream,
+                               int ld1,
+                               int ld2,
+                               int ld_out);
+};
+
+/**
+ * Create a kernel matrix using RBF kernel function.
+ */
+template <typename math_t>
+class RBFKernel : public GramMatrixBase<math_t> {
+  math_t gain;
+
+  void applyKernel(math_t* inout,
+                   int ld,
+                   int rows,
+                   int cols,
+                   math_t* norm_x1,
+                   math_t* norm_x2,
+                   bool is_row_major,
+                   cudaStream_t stream);
+
+ public:
+  /**
+   * Constructs a RBF kernel object.
+   * It evaluates the kernel matrix using the following formula:
+   * K_ij = exp(-gain*|x1_i- x2_k|^2)
+   *
+   * @tparam math_t floating point type
+   * @param gain
+   */
+  RBFKernel(math_t gain) : GramMatrixBase<math_t>(), gain(gain){};
+
+  [[deprecated]] RBFKernel(math_t gain, cublasHandle_t handle)
+    : GramMatrixBase<math_t>(handle), gain(gain){};
+
+  void matrixRowNormL2(raft::resources const& handle,
+                       dense_input_matrix_view_t<math_t> matrix,
+                       math_t* target);
+
+  void matrixRowNormL2(raft::resources const& handle,
+                       csr_input_matrix_view_t<math_t> matrix,
+                       math_t* target);
+
+  /** Evaluate kernel matrix using RBF kernel.
+   *
+   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and | | euclidean distance.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 dense device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void evaluate(raft::resources const& handle,
+                dense_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate kernel matrix using RBF kernel.
+   *
+   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and | | euclidean distance.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 dense device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                dense_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate kernel matrix using RBF kernel.
+   *
+   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+   * in the x2 set, and | | euclidean distance.
+   *
+   * @param [in] handle raft handle
+   * @param [in] x1 csr device matrix view, size [n1*n_cols]
+   * @param [in] x2 csr device matrix view, size [n2*n_cols]
+   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+   */
+  void evaluate(raft::resources const& handle,
+                csr_input_matrix_view_t<math_t> x1,
+                csr_input_matrix_view_t<math_t> x2,
+                dense_output_matrix_view_t<math_t> out,
+                math_t* norm_x1,
+                math_t* norm_x2);
+
+  /** Evaluate the Gram matrix using the legacy interface.
+   *
+   * @param [in] x1 device array of vectors, size [n1*n_cols]
+   * @param [in] n1 number vectors in x1
+   * @param [in] n_cols number of columns (features) in x1 and x2
+   * @param [in] x2 device array of vectors, size [n2*n_cols]
+   * @param [in] n2 number vectors in x2
+   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+   * @param [in] is_row_major whether the input and output matrices are in row
+   *        major format
+   * @param [in] stream cuda stream
+   * @param ld1 leading dimension of x1 (usually it is n1)
+   * @param ld2 leading dimension of x2 (usually it is n2)
+   * @param ld_out leading dimension of out (usually it is n1)
+   */
+  [[deprecated]] void evaluate(const math_t* x1,
+                               int n1,
+                               int n_cols,
+                               const math_t* x2,
+                               int n2,
+                               math_t* out,
+                               bool is_row_major,
+                               cudaStream_t stream,
+                               int ld1,
+                               int ld2,
+                               int ld_out);
+};
+};  // end namespace cuvs::distance::kernels
diff --git a/cpp/include/cuvs/embed/spectral.hpp b/cpp/include/cuvs/embed/spectral.hpp
new file mode 100644
index 0000000000..1a8fed96a1
--- /dev/null
+++ b/cpp/include/cuvs/embed/spectral.hpp
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/core/device_coo_matrix.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::embed::spectral {
+
+/**
+ * Given a COO formatted (symmetric) knn graph, this function computes the spectral embeddings
+ * (lowest n_components eigenvectors), using Lanczos min cut algorithm. Please note that this
+ * algorithm does not compute a full laplacian eigenmap, as the laplacian eigenmap would embed each
+ * connected component. Laplacian eigenmaps can be built from this algorithm by running it on the
+ * vectors for each connected component.
+
+ * @param[in] handle
+ * @param[in] knn_graph KNN Graph
+ * @param[in] n_components the number of components to project into
+ * @param[out] out output array for embedding (size n*n_comonents)
+ * @param[in] seed
+ */
+void fit(const raft::resources& handle,
+         raft::device_coo_matrix_view<float, int, int, int> knn_graph,
+         int n_components,
+         raft::device_matrix_view<float, int> out,
+         unsigned long long seed = 0L);
+};  // namespace cuvs::embed::spectral
diff --git a/cpp/src/distance/detail/kernels/gram_matrix.cu b/cpp/src/distance/detail/kernels/gram_matrix.cu
new file mode 100644
index 0000000000..0e4f3e639d
--- /dev/null
+++ b/cpp/src/distance/detail/kernels/gram_matrix.cu
@@ -0,0 +1,481 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../../distance.cuh"
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/distance/grammian.hpp>
+#include <raft/core/device_csr_matrix.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/detail/cublas_wrappers.hpp>
+#include <raft/linalg/gemm.cuh>
+#include <raft/sparse/linalg/spmm.hpp>
+
+namespace cuvs::distance::kernels {
+
+/**
+ * Base class for general Gram matrices
+ * A Gram matrix is the Hermitian matrix of inner probucts G_ik = <x_i, x_k>
+ * Here, the  inner product is evaluated for all elements from vectors sets X1,
+ * and X2.
+ *
+ * To be more precise, on exit the output buffer will store:
+ * - if is_row_major == true: out[j+k*n1] = <x1_j, x2_k>,
+ * - if is_row_major == false: out[j*n2 + k] = <x1_j, x2_k>,
+ * where x1_j is the j-th vector from the x1 set and x2_k is the k-th vector
+ * from the x2 set.
+ */
+
+/** Convenience function to evaluate the Gram matrix for two vector sets.
+ *  Vector sets are provided in Matrix format
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 dense device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+ * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::operator()(raft::resources const& handle,
+                                        dense_input_matrix_view_t<math_t> x1,
+                                        dense_input_matrix_view_t<math_t> x2,
+                                        dense_output_matrix_view_t<math_t> out,
+                                        math_t* norm_x1,
+                                        math_t* norm_x2)
+{
+  evaluate(handle, x1, x2, out, norm_x1, norm_x2);
+}
+
+/** Convenience function to evaluate the Gram matrix for two vector sets.
+ *  Vector sets are provided in Matrix format
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+ * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::operator()(raft::resources const& handle,
+                                        csr_input_matrix_view_t<math_t> x1,
+                                        dense_input_matrix_view_t<math_t> x2,
+                                        dense_output_matrix_view_t<math_t> out,
+                                        math_t* norm_x1,
+                                        math_t* norm_x2)
+{
+  evaluate(handle, x1, x2, out, norm_x1, norm_x2);
+}
+
+/** Convenience function to evaluate the Gram matrix for two vector sets.
+ *  Vector sets are provided in Matrix format
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 csr device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+ * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::operator()(raft::resources const& handle,
+                                        csr_input_matrix_view_t<math_t> x1,
+                                        csr_input_matrix_view_t<math_t> x2,
+                                        dense_output_matrix_view_t<math_t> out,
+                                        math_t* norm_x1,
+                                        math_t* norm_x2)
+{
+  evaluate(handle, x1, x2, out, norm_x1, norm_x2);
+}
+
+// unfortunately, 'evaluate' cannot be templatized as it needs to be virtual
+
+/** Evaluate the Gram matrix for two vector sets using simple dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 dense device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::evaluate(raft::resources const& handle,
+                                      dense_input_matrix_view_t<math_t> x1,
+                                      dense_input_matrix_view_t<math_t> x2,
+                                      dense_output_matrix_view_t<math_t> out,
+                                      math_t* norm_x1,
+                                      math_t* norm_x2)
+{
+  linear(handle, x1, x2, out);
+}
+/** Evaluate the Gram matrix for two vector sets using simple dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::evaluate(raft::resources const& handle,
+                                      csr_input_matrix_view_t<math_t> x1,
+                                      dense_input_matrix_view_t<math_t> x2,
+                                      dense_output_matrix_view_t<math_t> out,
+                                      math_t* norm_x1,
+                                      math_t* norm_x2)
+{
+  linear(handle, x1, x2, out);
+}
+/** Evaluate the Gram matrix for two vector sets using simple dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 csr device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::evaluate(raft::resources const& handle,
+                                      csr_input_matrix_view_t<math_t> x1,
+                                      csr_input_matrix_view_t<math_t> x2,
+                                      dense_output_matrix_view_t<math_t> out,
+                                      math_t* norm_x1,
+                                      math_t* norm_x2)
+{
+  linear(handle, x1, x2, out);
+}
+
+/** Evaluate the Gram matrix for two vector sets using simple dot product.
+ *
+ * @param [in] x1 device array of vectors, size [n1*n_cols]
+ * @param [in] n1 number vectors in x1
+ * @param [in] n_cols number of columns (features) in x1 and x2
+ * @param [in] x2 device array of vectors, size [n2*n_cols]
+ * @param [in] n2 number vectors in x2
+ * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+ * @param [in] is_row_major whether the input and output matrices are in row
+ *        major format
+ * @param [in] stream cuda stream
+ * @param ld1 leading dimension of x1 (usually it is n1)
+ * @param ld2 leading dimension of x2 (usually it is n2)
+ * @param ld_out leading dimension of out (usually it is n1)
+ */
+template <typename math_t>
+[[deprecated]] void GramMatrixBase<math_t>::evaluate(const math_t* x1,
+                                                     int n1,
+                                                     int n_cols,
+                                                     const math_t* x2,
+                                                     int n2,
+                                                     math_t* out,
+                                                     bool is_row_major,
+                                                     cudaStream_t stream,
+                                                     int ld1,
+                                                     int ld2,
+                                                     int ld_out)
+{
+  linear(x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+}
+
+/** Convenience function to evaluate the Gram matrix for two vector sets.
+ *
+ * @param [in] x1 device array of vectors, size [n1*n_cols]
+ * @param [in] n1 number vectors in x1
+ * @param [in] n_cols number of columns (features) in x1 and x2
+ * @param [in] x2 device array of vectors, size [n2*n_cols]
+ * @param [in] n2 number vectors in x2
+ * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+ * @param [in] is_row_major whether the input and output matrices are in row
+ *        major format
+ * @param [in] stream cuda stream
+ * @param ld1 leading dimension of x1
+ * @param ld2 leading dimension of x2
+ * @param ld_out leading dimension of out
+ */
+template <typename math_t>
+[[deprecated]] void GramMatrixBase<math_t>::operator()(const math_t* x1,
+                                                       int n1,
+                                                       int n_cols,
+                                                       const math_t* x2,
+                                                       int n2,
+                                                       math_t* out,
+                                                       bool is_row_major,
+                                                       cudaStream_t stream,
+                                                       int ld1,
+                                                       int ld2,
+                                                       int ld_out)
+{
+  ASSERT(legacy_interface, "Legacy interface can only be used with legacy ctor.");
+  if (ld1 <= 0) { ld1 = is_row_major ? n_cols : n1; }
+  if (ld2 <= 0) { ld2 = is_row_major ? n_cols : n2; }
+  if (ld_out <= 0) { ld_out = is_row_major ? n2 : n1; }
+  evaluate(x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+}
+
+/** Calculates the Gram matrix using simple dot product between vector sets.
+ *
+ * out = x1 * x2
+ *
+ * Can be used as a building block for more complex kernel functions.
+ *
+ * @param [in] x1 device array of vectors, size [n1*n_cols]
+ * @param [in] n1 number vectors in x1
+ * @param [in] n_cols number of columns (features) in x1 and x2
+ * @param [in] x2 device array of vectors, size [n2*n_cols]
+ * @param [in] n2 number vectors in x2
+ * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+ * @param [in] is_row_major whether the input and output matrices are in row
+ *        major format
+ * @param [in] stream cuda stream
+ * @param ld1 leading dimension of x1
+ * @param ld2 leading dimension of x2
+ * @param ld_out leading dimension of out
+ */
+template <typename math_t>
+[[deprecated]] void GramMatrixBase<math_t>::linear(const math_t* x1,
+                                                   int n1,
+                                                   int n_cols,
+                                                   const math_t* x2,
+                                                   int n2,
+                                                   math_t* out,
+                                                   bool is_row_major,
+                                                   cudaStream_t stream,
+                                                   int ld1,
+                                                   int ld2,
+                                                   int ld_out)
+{
+  math_t alpha = 1.0;
+  math_t beta  = 0.0;
+  if (is_row_major) {
+    // #TODO: Call from public API when ready
+    RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(cublas_handle,
+                                                     CUBLAS_OP_T,
+                                                     CUBLAS_OP_N,
+                                                     n2,
+                                                     n1,
+                                                     n_cols,
+                                                     &alpha,
+                                                     x2,
+                                                     ld2,
+                                                     x1,
+                                                     ld1,
+                                                     &beta,
+                                                     out,
+                                                     ld_out,
+                                                     stream));
+  } else {
+    // #TODO: Call from public API when ready
+    RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(cublas_handle,
+                                                     CUBLAS_OP_N,
+                                                     CUBLAS_OP_T,
+                                                     n1,
+                                                     n2,
+                                                     n_cols,
+                                                     &alpha,
+                                                     x1,
+                                                     ld1,
+                                                     x2,
+                                                     ld2,
+                                                     &beta,
+                                                     out,
+                                                     ld_out,
+                                                     stream));
+  }
+}
+
+template <typename math_t>
+bool GramMatrixBase<math_t>::get_is_row_major(dense_output_matrix_view_t<math_t> matrix)
+{
+  return (matrix.stride(1) == 1);
+}
+template <typename math_t>
+bool GramMatrixBase<math_t>::get_is_row_major(dense_input_matrix_view_t<math_t> matrix)
+{
+  return (matrix.stride(1) == 1);
+}
+
+template <typename math_t>
+bool GramMatrixBase<math_t>::get_is_col_major(dense_output_matrix_view_t<math_t> matrix)
+{
+  return (matrix.stride(0) == 1);
+}
+
+template <typename math_t>
+bool GramMatrixBase<math_t>::get_is_col_major(dense_input_matrix_view_t<math_t> matrix)
+{
+  return (matrix.stride(0) == 1);
+}
+
+/** Calculates the Gram matrix using simple dot product between vector sets.
+ *
+ * out = x1 * x2
+ *
+ * Can be used as a building block for more complex kernel functions.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 dense device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::linear(raft::resources const& handle,
+                                    dense_input_matrix_view_t<math_t> x1,
+                                    dense_input_matrix_view_t<math_t> x2,
+                                    dense_output_matrix_view_t<math_t> out)
+{
+  // check is_row_major consistency
+  bool is_row_major = get_is_row_major(x1) && get_is_row_major(x2) && get_is_row_major(out);
+  bool is_col_major = get_is_col_major(x1) && get_is_col_major(x2) && get_is_col_major(out);
+  ASSERT(is_row_major || is_col_major,
+         "GramMatrix leading dimensions for x1, x2 and out do not match");
+
+  // check dimensions
+  int n1     = out.extent(0);
+  int n2     = out.extent(1);
+  int n_cols = x1.extent(1);
+  ASSERT(x1.extent(0) == n1, "GramMatrix input matrix dimensions for x1 and out do not match");
+  ASSERT(x2.extent(0) == n2, "GramMatrix input matrix dimensions for x2 and out do not match");
+  ASSERT(x2.extent(1) == n_cols, "GramMatrix input matrix dimensions for x1 and x2 do not match");
+
+  // extract major stride
+  int ld1    = is_row_major ? x1.stride(0) : x1.stride(1);
+  int ld2    = is_row_major ? x2.stride(0) : x2.stride(1);
+  int ld_out = is_row_major ? out.stride(0) : out.stride(1);
+
+  math_t alpha = 1.0;
+  math_t beta  = 0.0;
+  if (is_row_major) {
+    // #TODO: Use mdspan-based API when stride-capable
+    // https://github.com/rapidsai/raft/issues/875
+    raft::linalg::gemm(handle,
+                       true,
+                       false,
+                       n2,
+                       n1,
+                       n_cols,
+                       &alpha,
+                       x2.data_handle(),
+                       ld2,
+                       x1.data_handle(),
+                       ld1,
+                       &beta,
+                       out.data_handle(),
+                       ld_out,
+                       raft::resource::get_cuda_stream(handle));
+  } else {
+    // #TODO: Use mdspan-based API when stride-capable
+    // https://github.com/rapidsai/raft/issues/875
+    raft::linalg::gemm(handle,
+                       false,
+                       true,
+                       n1,
+                       n2,
+                       n_cols,
+                       &alpha,
+                       x1.data_handle(),
+                       ld1,
+                       x2.data_handle(),
+                       ld2,
+                       &beta,
+                       out.data_handle(),
+                       ld_out,
+                       raft::resource::get_cuda_stream(handle));
+  }
+}
+
+/** Calculates the Gram matrix using simple dot product between vector sets.
+ *
+ * out = x1 * x2
+ *
+ * Can be used as a building block for more complex kernel functions.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::linear(raft::resources const& handle,
+                                    csr_input_matrix_view_t<math_t> x1,
+                                    dense_input_matrix_view_t<math_t> x2,
+                                    dense_output_matrix_view_t<math_t> out)
+{
+  // check is_row_major consistency
+  bool is_row_major = get_is_row_major(x2) && get_is_row_major(out);
+  bool is_col_major = get_is_col_major(x2) && get_is_col_major(out);
+  ASSERT(is_row_major || is_col_major, "GramMatrix leading dimensions for x2 and out do not match");
+
+  // check dimensions
+  auto x1_structure = x1.structure_view();
+  ASSERT(x1_structure.get_n_rows() == out.extent(0),
+         "GramMatrix input matrix dimensions for x1 and out do not match");
+  ASSERT(x2.extent(0) == out.extent(1),
+         "GramMatrix input matrix dimensions for x2 and out do not match");
+  ASSERT(x2.extent(1) == x1_structure.get_n_cols(),
+         "GramMatrix input matrix dimensions for x1 and x2 do not match");
+
+  math_t alpha = 1.0;
+  math_t beta  = 0.0;
+
+  raft::sparse::linalg::spmm(handle, false, true, &alpha, x1, x2, &beta, out);
+}
+
+/** Calculates the Gram matrix using simple dot product between vector sets.
+ *
+ * out = x1 * x2
+ *
+ * Can be used as a building block for more complex kernel functions.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 csr device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ */
+template <typename math_t>
+void GramMatrixBase<math_t>::linear(raft::resources const& handle,
+                                    csr_input_matrix_view_t<math_t> x1,
+                                    csr_input_matrix_view_t<math_t> x2,
+                                    dense_output_matrix_view_t<math_t> out)
+{
+  // check layout consistency (w.r.t. strides a matrix might be both row & col major)
+  bool is_row_major_nopad = get_is_row_major(out) && out.stride(0) == out.extent(1);
+  bool is_col_major_nopad = get_is_col_major(out) && out.stride(1) == out.extent(0);
+
+  ASSERT(is_row_major_nopad || is_col_major_nopad,
+         "Sparse linear Kernel distance does not support ld_out parameter");
+
+  // switch a,b based on is_row_major
+  if (is_col_major_nopad) {
+    auto out_row_major = raft::make_device_matrix_view<math_t, int, raft::row_major>(
+      out.data_handle(), out.extent(1), out.extent(0));
+
+    cuvs::distance::pairwise_distance(
+      handle, x2, x1, out_row_major, cuvs::distance::DistanceType::InnerProduct, 0.0);
+  } else {
+    auto out_row_major = raft::make_device_matrix_view<math_t, int, raft::row_major>(
+      out.data_handle(), out.extent(0), out.extent(1));
+    cuvs::distance::pairwise_distance(
+      handle, x1, x2, out_row_major, cuvs::distance::DistanceType::InnerProduct, 0.0);
+  }
+}
+
+template class GramMatrixBase<float>;
+template class GramMatrixBase<double>;
+
+};  // namespace cuvs::distance::kernels
diff --git a/cpp/src/distance/detail/kernels/gram_matrix.cuh b/cpp/src/distance/detail/kernels/gram_matrix.cuh
deleted file mode 100644
index d435fb4d1d..0000000000
--- a/cpp/src/distance/detail/kernels/gram_matrix.cuh
+++ /dev/null
@@ -1,488 +0,0 @@
-/*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "../../distance.cuh"
-#include <cuvs/distance/distance.hpp>
-#include <raft/core/device_csr_matrix.hpp>
-#include <raft/core/resource/cuda_stream.hpp>
-#include <raft/core/resources.hpp>
-// #include <raft/sparse/detail/cusparse_wrappers.h>
-#include <raft/linalg/detail/cublas_wrappers.hpp>
-#include <raft/linalg/gemm.cuh>
-#include <raft/sparse/distance/distance.cuh>
-#include <raft/sparse/linalg/spmm.hpp>
-
-namespace cuvs::distance::kernels::detail {
-
-template <typename math_t>
-using dense_input_matrix_view_t = raft::device_matrix_view<const math_t, int, layout_stride>;
-template <typename math_t>
-using dense_output_matrix_view_t = raft::device_matrix_view<math_t, int, layout_stride>;
-template <typename math_t>
-using csr_input_matrix_view_t = raft::device_csr_matrix_view<const math_t, int, int, int>;
-
-/**
- * Base class for general Gram matrices
- * A Gram matrix is the Hermitian matrix of inner probucts G_ik = <x_i, x_k>
- * Here, the  inner product is evaluated for all elements from vectors sets X1,
- * and X2.
- *
- * To be more precise, on exit the output buffer will store:
- * - if is_row_major == true: out[j+k*n1] = <x1_j, x2_k>,
- * - if is_row_major == false: out[j*n2 + k] = <x1_j, x2_k>,
- * where x1_j is the j-th vector from the x1 set and x2_k is the k-th vector
- * from the x2 set.
- */
-template <typename math_t>
-class GramMatrixBase {
- protected:
-  cublasHandle_t cublas_handle;
-  bool legacy_interface;
-
- public:
-  GramMatrixBase() : legacy_interface(false){};
-  [[deprecated]] GramMatrixBase(cublasHandle_t cublas_handle)
-    : cublas_handle(cublas_handle), legacy_interface(true){};
-
-  virtual ~GramMatrixBase(){};
-
-  /** Convenience function to evaluate the Gram matrix for two vector sets.
-   *  Vector sets are provided in Matrix format
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 dense device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
-   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
-   */
-  void operator()(raft::resources const& handle,
-                  dense_input_matrix_view_t<math_t> x1,
-                  dense_input_matrix_view_t<math_t> x2,
-                  dense_output_matrix_view_t<math_t> out,
-                  math_t* norm_x1 = nullptr,
-                  math_t* norm_x2 = nullptr)
-  {
-    evaluate(handle, x1, x2, out, norm_x1, norm_x2);
-  }
-
-  /** Convenience function to evaluate the Gram matrix for two vector sets.
-   *  Vector sets are provided in Matrix format
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
-   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
-   */
-  void operator()(raft::resources const& handle,
-                  csr_input_matrix_view_t<math_t> x1,
-                  dense_input_matrix_view_t<math_t> x2,
-                  dense_output_matrix_view_t<math_t> out,
-                  math_t* norm_x1 = nullptr,
-                  math_t* norm_x2 = nullptr)
-  {
-    evaluate(handle, x1, x2, out, norm_x1, norm_x2);
-  }
-
-  /** Convenience function to evaluate the Gram matrix for two vector sets.
-   *  Vector sets are provided in Matrix format
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 csr device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
-   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
-   */
-  void operator()(raft::resources const& handle,
-                  csr_input_matrix_view_t<math_t> x1,
-                  csr_input_matrix_view_t<math_t> x2,
-                  dense_output_matrix_view_t<math_t> out,
-                  math_t* norm_x1 = nullptr,
-                  math_t* norm_x2 = nullptr)
-  {
-    evaluate(handle, x1, x2, out, norm_x1, norm_x2);
-  }
-
-  // unfortunately, 'evaluate' cannot be templatized as it needs to be virtual
-
-  /** Evaluate the Gram matrix for two vector sets using simple dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 dense device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  virtual void evaluate(raft::resources const& handle,
-                        dense_input_matrix_view_t<math_t> x1,
-                        dense_input_matrix_view_t<math_t> x2,
-                        dense_output_matrix_view_t<math_t> out,
-                        math_t* norm_x1,
-                        math_t* norm_x2)
-  {
-    linear(handle, x1, x2, out);
-  }
-  /** Evaluate the Gram matrix for two vector sets using simple dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  virtual void evaluate(raft::resources const& handle,
-                        csr_input_matrix_view_t<math_t> x1,
-                        dense_input_matrix_view_t<math_t> x2,
-                        dense_output_matrix_view_t<math_t> out,
-                        math_t* norm_x1,
-                        math_t* norm_x2)
-  {
-    linear(handle, x1, x2, out);
-  }
-  /** Evaluate the Gram matrix for two vector sets using simple dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 csr device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  virtual void evaluate(raft::resources const& handle,
-                        csr_input_matrix_view_t<math_t> x1,
-                        csr_input_matrix_view_t<math_t> x2,
-                        dense_output_matrix_view_t<math_t> out,
-                        math_t* norm_x1,
-                        math_t* norm_x2)
-  {
-    linear(handle, x1, x2, out);
-  }
-
-  /** Evaluate the Gram matrix for two vector sets using simple dot product.
-   *
-   * @param [in] x1 device array of vectors, size [n1*n_cols]
-   * @param [in] n1 number vectors in x1
-   * @param [in] n_cols number of columns (features) in x1 and x2
-   * @param [in] x2 device array of vectors, size [n2*n_cols]
-   * @param [in] n2 number vectors in x2
-   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
-   * @param [in] is_row_major whether the input and output matrices are in row
-   *        major format
-   * @param [in] stream cuda stream
-   * @param ld1 leading dimension of x1 (usually it is n1)
-   * @param ld2 leading dimension of x2 (usually it is n2)
-   * @param ld_out leading dimension of out (usually it is n1)
-   */
-  [[deprecated]] virtual void evaluate(const math_t* x1,
-                                       int n1,
-                                       int n_cols,
-                                       const math_t* x2,
-                                       int n2,
-                                       math_t* out,
-                                       bool is_row_major,
-                                       cudaStream_t stream,
-                                       int ld1,
-                                       int ld2,
-                                       int ld_out)
-  {
-    linear(x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
-  }
-
-  /** Convenience function to evaluate the Gram matrix for two vector sets.
-   *
-   * @param [in] x1 device array of vectors, size [n1*n_cols]
-   * @param [in] n1 number vectors in x1
-   * @param [in] n_cols number of columns (features) in x1 and x2
-   * @param [in] x2 device array of vectors, size [n2*n_cols]
-   * @param [in] n2 number vectors in x2
-   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
-   * @param [in] is_row_major whether the input and output matrices are in row
-   *        major format
-   * @param [in] stream cuda stream
-   * @param ld1 leading dimension of x1
-   * @param ld2 leading dimension of x2
-   * @param ld_out leading dimension of out
-   */
-  [[deprecated]] void operator()(const math_t* x1,
-                                 int n1,
-                                 int n_cols,
-                                 const math_t* x2,
-                                 int n2,
-                                 math_t* out,
-                                 bool is_row_major,
-                                 cudaStream_t stream,
-                                 int ld1    = 0,
-                                 int ld2    = 0,
-                                 int ld_out = 0)
-  {
-    ASSERT(legacy_interface, "Legacy interface can only be used with legacy ctor.");
-    if (ld1 <= 0) { ld1 = is_row_major ? n_cols : n1; }
-    if (ld2 <= 0) { ld2 = is_row_major ? n_cols : n2; }
-    if (ld_out <= 0) { ld_out = is_row_major ? n2 : n1; }
-    evaluate(x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
-  }
-
- protected:
-  /** Calculates the Gram matrix using simple dot product between vector sets.
-   *
-   * out = x1 * x2
-   *
-   * Can be used as a building block for more complex kernel functions.
-   *
-   * @param [in] x1 device array of vectors, size [n1*n_cols]
-   * @param [in] n1 number vectors in x1
-   * @param [in] n_cols number of columns (features) in x1 and x2
-   * @param [in] x2 device array of vectors, size [n2*n_cols]
-   * @param [in] n2 number vectors in x2
-   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
-   * @param [in] is_row_major whether the input and output matrices are in row
-   *        major format
-   * @param [in] stream cuda stream
-   * @param ld1 leading dimension of x1
-   * @param ld2 leading dimension of x2
-   * @param ld_out leading dimension of out
-   */
-  [[deprecated]] void linear(const math_t* x1,
-                             int n1,
-                             int n_cols,
-                             const math_t* x2,
-                             int n2,
-                             math_t* out,
-                             bool is_row_major,
-                             cudaStream_t stream,
-                             int ld1,
-                             int ld2,
-                             int ld_out)
-  {
-    math_t alpha = 1.0;
-    math_t beta  = 0.0;
-    if (is_row_major) {
-      // #TODO: Call from public API when ready
-      RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(cublas_handle,
-                                                       CUBLAS_OP_T,
-                                                       CUBLAS_OP_N,
-                                                       n2,
-                                                       n1,
-                                                       n_cols,
-                                                       &alpha,
-                                                       x2,
-                                                       ld2,
-                                                       x1,
-                                                       ld1,
-                                                       &beta,
-                                                       out,
-                                                       ld_out,
-                                                       stream));
-    } else {
-      // #TODO: Call from public API when ready
-      RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgemm(cublas_handle,
-                                                       CUBLAS_OP_N,
-                                                       CUBLAS_OP_T,
-                                                       n1,
-                                                       n2,
-                                                       n_cols,
-                                                       &alpha,
-                                                       x1,
-                                                       ld1,
-                                                       x2,
-                                                       ld2,
-                                                       &beta,
-                                                       out,
-                                                       ld_out,
-                                                       stream));
-    }
-  }
-
- protected:
-  bool get_is_row_major(dense_output_matrix_view_t<math_t> matrix)
-  {
-    return (matrix.stride(1) == 1);
-  }
-
-  bool get_is_row_major(dense_input_matrix_view_t<math_t> matrix)
-  {
-    return (matrix.stride(1) == 1);
-  }
-
-  bool get_is_col_major(dense_output_matrix_view_t<math_t> matrix)
-  {
-    return (matrix.stride(0) == 1);
-  }
-
-  bool get_is_col_major(dense_input_matrix_view_t<math_t> matrix)
-  {
-    return (matrix.stride(0) == 1);
-  }
-
-  /** Calculates the Gram matrix using simple dot product between vector sets.
-   *
-   * out = x1 * x2
-   *
-   * Can be used as a building block for more complex kernel functions.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 dense device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   */
-  void linear(raft::resources const& handle,
-              dense_input_matrix_view_t<math_t> x1,
-              dense_input_matrix_view_t<math_t> x2,
-              dense_output_matrix_view_t<math_t> out)
-  {
-    // check is_row_major consistency
-    bool is_row_major = get_is_row_major(x1) && get_is_row_major(x2) && get_is_row_major(out);
-    bool is_col_major = get_is_col_major(x1) && get_is_col_major(x2) && get_is_col_major(out);
-    ASSERT(is_row_major || is_col_major,
-           "GramMatrix leading dimensions for x1, x2 and out do not match");
-
-    // check dimensions
-    int n1     = out.extent(0);
-    int n2     = out.extent(1);
-    int n_cols = x1.extent(1);
-    ASSERT(x1.extent(0) == n1, "GramMatrix input matrix dimensions for x1 and out do not match");
-    ASSERT(x2.extent(0) == n2, "GramMatrix input matrix dimensions for x2 and out do not match");
-    ASSERT(x2.extent(1) == n_cols, "GramMatrix input matrix dimensions for x1 and x2 do not match");
-
-    // extract major stride
-    int ld1    = is_row_major ? x1.stride(0) : x1.stride(1);
-    int ld2    = is_row_major ? x2.stride(0) : x2.stride(1);
-    int ld_out = is_row_major ? out.stride(0) : out.stride(1);
-
-    math_t alpha = 1.0;
-    math_t beta  = 0.0;
-    if (is_row_major) {
-      // #TODO: Use mdspan-based API when stride-capable
-      // https://github.com/rapidsai/raft/issues/875
-      raft::linalg::gemm(handle,
-                         true,
-                         false,
-                         n2,
-                         n1,
-                         n_cols,
-                         &alpha,
-                         x2.data_handle(),
-                         ld2,
-                         x1.data_handle(),
-                         ld1,
-                         &beta,
-                         out.data_handle(),
-                         ld_out,
-                         resource::get_cuda_stream(handle));
-    } else {
-      // #TODO: Use mdspan-based API when stride-capable
-      // https://github.com/rapidsai/raft/issues/875
-      raft::linalg::gemm(handle,
-                         false,
-                         true,
-                         n1,
-                         n2,
-                         n_cols,
-                         &alpha,
-                         x1.data_handle(),
-                         ld1,
-                         x2.data_handle(),
-                         ld2,
-                         &beta,
-                         out.data_handle(),
-                         ld_out,
-                         resource::get_cuda_stream(handle));
-    }
-  }
-
-  /** Calculates the Gram matrix using simple dot product between vector sets.
-   *
-   * out = x1 * x2
-   *
-   * Can be used as a building block for more complex kernel functions.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   */
-  void linear(raft::resources const& handle,
-              csr_input_matrix_view_t<math_t> x1,
-              dense_input_matrix_view_t<math_t> x2,
-              dense_output_matrix_view_t<math_t> out)
-  {
-    // check is_row_major consistency
-    bool is_row_major = get_is_row_major(x2) && get_is_row_major(out);
-    bool is_col_major = get_is_col_major(x2) && get_is_col_major(out);
-    ASSERT(is_row_major || is_col_major,
-           "GramMatrix leading dimensions for x2 and out do not match");
-
-    // check dimensions
-    auto x1_structure = x1.structure_view();
-    ASSERT(x1_structure.get_n_rows() == out.extent(0),
-           "GramMatrix input matrix dimensions for x1 and out do not match");
-    ASSERT(x2.extent(0) == out.extent(1),
-           "GramMatrix input matrix dimensions for x2 and out do not match");
-    ASSERT(x2.extent(1) == x1_structure.get_n_cols(),
-           "GramMatrix input matrix dimensions for x1 and x2 do not match");
-
-    math_t alpha = 1.0;
-    math_t beta  = 0.0;
-
-    raft::sparse::linalg::spmm(handle, false, true, &alpha, x1, x2, &beta, out);
-  }
-
-  /** Calculates the Gram matrix using simple dot product between vector sets.
-   *
-   * out = x1 * x2
-   *
-   * Can be used as a building block for more complex kernel functions.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 csr device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   */
-  void linear(raft::resources const& handle,
-              csr_input_matrix_view_t<math_t> x1,
-              csr_input_matrix_view_t<math_t> x2,
-              dense_output_matrix_view_t<math_t> out)
-  {
-    // check layout consistency (w.r.t. strides a matrix might be both row & col major)
-    bool is_row_major_nopad = get_is_row_major(out) && out.stride(0) == out.extent(1);
-    bool is_col_major_nopad = get_is_col_major(out) && out.stride(1) == out.extent(0);
-
-    ASSERT(is_row_major_nopad || is_col_major_nopad,
-           "Sparse linear Kernel distance does not support ld_out parameter");
-
-    // switch a,b based on is_row_major
-    if (is_col_major_nopad) {
-      auto out_row_major = raft::make_device_matrix_view<math_t, int, raft::row_major>(
-        out.data_handle(), out.extent(1), out.extent(0));
-      raft::sparse::distance::pairwise_distance(
-        handle, x2, x1, out_row_major, cuvs::distance::DistanceType::InnerProduct, 0.0);
-    } else {
-      auto out_row_major = raft::make_device_matrix_view<math_t, int, raft::row_major>(
-        out.data_handle(), out.extent(0), out.extent(1));
-      raft::sparse::distance::pairwise_distance(
-        handle, x1, x2, out_row_major, cuvs::distance::DistanceType::InnerProduct, 0.0);
-    }
-  }
-};
-
-};  // end namespace cuvs::distance::kernels::detail
diff --git a/cpp/src/distance/detail/kernels/kernel_factory.cu b/cpp/src/distance/detail/kernels/kernel_factory.cu
new file mode 100644
index 0000000000..25f9e9b84b
--- /dev/null
+++ b/cpp/src/distance/detail/kernels/kernel_factory.cu
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/distance/grammian.hpp>
+
+namespace cuvs::distance::kernels {
+
+template <typename math_t>
+GramMatrixBase<math_t>* KernelFactory<math_t>::create(KernelParams params)
+{
+  GramMatrixBase<math_t>* res;
+  // KernelParams is not templated, we convert the parameters to math_t here:
+  math_t coef0 = params.coef0;
+  math_t gamma = params.gamma;
+  switch (params.kernel) {
+    case LINEAR: res = new GramMatrixBase<math_t>(); break;
+    case POLYNOMIAL: res = new PolynomialKernel<math_t, int>(params.degree, gamma, coef0); break;
+    case TANH: res = new TanhKernel<math_t>(gamma, coef0); break;
+    case RBF: res = new RBFKernel<math_t>(gamma); break;
+    default: throw raft::exception("Kernel not implemented");
+  }
+  return res;
+}
+
+template <typename math_t>
+[[deprecated]] GramMatrixBase<math_t>* KernelFactory<math_t>::create(KernelParams params,
+                                                                     cublasHandle_t handle)
+{
+  GramMatrixBase<math_t>* res;
+  // KernelParams is not templated, we convert the parameters to math_t here:
+  math_t coef0 = params.coef0;
+  math_t gamma = params.gamma;
+  switch (params.kernel) {
+    case LINEAR: res = new GramMatrixBase<math_t>(handle); break;
+    case POLYNOMIAL:
+      res = new PolynomialKernel<math_t, int>(params.degree, gamma, coef0, handle);
+      break;
+    case TANH: res = new TanhKernel<math_t>(gamma, coef0, handle); break;
+    case RBF: res = new RBFKernel<math_t>(gamma, handle); break;
+    default: throw raft::exception("Kernel not implemented");
+  }
+  return res;
+}
+
+template class KernelFactory<float>;
+template class KernelFactory<double>;
+
+};  // end namespace cuvs::distance::kernels
diff --git a/cpp/src/distance/detail/kernels/kernel_factory.cuh b/cpp/src/distance/detail/kernels/kernel_factory.cuh
deleted file mode 100644
index 5c50a95a33..0000000000
--- a/cpp/src/distance/detail/kernels/kernel_factory.cuh
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "gram_matrix.cuh"
-#include "kernel_matrices.cuh"
-
-#include <cuvs/distance/distance.hpp>
-#include <raft/util/cudart_utils.hpp>
-
-namespace cuvs::distance::kernels::detail {
-
-template <typename math_t>
-class KernelFactory {
- public:
-  static GramMatrixBase<math_t>* create(KernelParams params)
-  {
-    GramMatrixBase<math_t>* res;
-    // KernelParams is not templated, we convert the parameters to math_t here:
-    math_t coef0 = params.coef0;
-    math_t gamma = params.gamma;
-    switch (params.kernel) {
-      case LINEAR: res = new GramMatrixBase<math_t>(); break;
-      case POLYNOMIAL: res = new PolynomialKernel<math_t, int>(params.degree, gamma, coef0); break;
-      case TANH: res = new TanhKernel<math_t>(gamma, coef0); break;
-      case RBF: res = new RBFKernel<math_t>(gamma); break;
-      default: throw raft::exception("Kernel not implemented");
-    }
-    return res;
-  }
-
-  [[deprecated]] static GramMatrixBase<math_t>* create(KernelParams params, cublasHandle_t handle)
-  {
-    GramMatrixBase<math_t>* res;
-    // KernelParams is not templated, we convert the parameters to math_t here:
-    math_t coef0 = params.coef0;
-    math_t gamma = params.gamma;
-    switch (params.kernel) {
-      case LINEAR: res = new GramMatrixBase<math_t>(handle); break;
-      case POLYNOMIAL:
-        res = new PolynomialKernel<math_t, int>(params.degree, gamma, coef0, handle);
-        break;
-      case TANH: res = new TanhKernel<math_t>(gamma, coef0, handle); break;
-      case RBF: res = new RBFKernel<math_t>(gamma, handle); break;
-      default: throw raft::exception("Kernel not implemented");
-    }
-    return res;
-  }
-};
-
-};  // end namespace cuvs::distance::kernels::detail
diff --git a/cpp/src/distance/detail/kernels/kernel_matrices.cu b/cpp/src/distance/detail/kernels/kernel_matrices.cu
new file mode 100644
index 0000000000..526ca106ff
--- /dev/null
+++ b/cpp/src/distance/detail/kernels/kernel_matrices.cu
@@ -0,0 +1,726 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../../../distance/distance.cuh"
+#include <cuvs/distance/grammian.hpp>
+
+#include "rbf_fin_op.cuh"
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/gemm.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/sparse/linalg/norm.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+namespace cuvs::distance::kernels {
+
+/** Epiloge function for polynomial kernel without padding.
+ * Calculates output = (gain*in + offset)^exponent
+ * @param inout device vector in column major format, size [len]
+ * @param len array length
+ * @param exponent
+ * @param gain
+ * @param offset
+ */
+template <typename math_t, typename exp_t>
+RAFT_KERNEL polynomial_kernel_nopad(
+  math_t* inout, size_t len, exp_t exponent, math_t gain, math_t offset)
+{
+  for (size_t tid = threadIdx.x + blockIdx.x * blockDim.x; tid < len;
+       tid += blockDim.x * gridDim.x) {
+    inout[tid] = pow(gain * inout[tid] + offset, exponent);
+  }
+}
+
+/** Epiloge function for polynomial kernel with padding.
+ * Calculates output = (gain*input + offset)^exponent
+ * @param inout device vector in column major format, size [ld * cols]
+ * @param ld leading dimension of the inout buffer
+ * @param rows number of rows (rows <= ld)
+ * @param cols number of columns
+ * @param exponent
+ * @param gain
+ * @param offset
+ */
+template <typename math_t, typename exp_t>
+RAFT_KERNEL polynomial_kernel(
+  math_t* inout, int ld, int rows, int cols, exp_t exponent, math_t gain, math_t offset)
+{
+  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
+       tidy += blockDim.y * gridDim.y)
+    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
+         tidx += blockDim.x * gridDim.x) {
+      inout[tidx + tidy * ld] = pow(gain * inout[tidx + tidy * ld] + offset, exponent);
+    }
+}
+
+/** Epiloge function for tanh kernel without padding.
+ * Calculates output = tanh(gain*input + offset)
+ * @param inout device vector, size [len]
+ * @param len length of the input vector
+ * @param gain
+ * @param offset
+ */
+template <typename math_t>
+RAFT_KERNEL tanh_kernel_nopad(math_t* inout, size_t len, math_t gain, math_t offset)
+{
+  for (size_t tid = threadIdx.x + blockIdx.x * blockDim.x; tid < len;
+       tid += blockDim.x * gridDim.x) {
+    inout[tid] = tanh(gain * inout[tid] + offset);
+  }
+}
+
+/** Epiloge function for tanh kernel without padding.
+ * Calculates output = tanh(gain*input + offset)
+ * @param inout device vector in column major format, size [ld * cols]
+ * @param ld leading dimension of the inout buffer
+ * @param rows number of rows (rows <= ld)
+ * @param cols number of columns
+ * @param gain
+ * @param offset
+ */
+template <typename math_t>
+RAFT_KERNEL tanh_kernel(math_t* inout, int ld, int rows, int cols, math_t gain, math_t offset)
+{
+  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
+       tidy += blockDim.y * gridDim.y)
+    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
+         tidx += blockDim.x * gridDim.x) {
+      inout[tidx + tidy * ld] = tanh(gain * inout[tidx + tidy * ld] + offset);
+    }
+}
+
+/** Epiloge function for rbf kernel using expansion.
+ *
+ * Calculates output_ij = exp(-gain * (norm_x_i + norm_y_j - 2*input_ij));
+ *
+ * Intended usage
+ *   - input is the product of two matrices X and Y input_ij = sum_k X_ik * Y_jk
+ *   - norm_x_i = l2_norm(x_i), where x_i is the i-th row of matrix X
+ *   - norm_y_j = l2_norm(y_j), where y_j is the j-th row of matrix Y
+ *
+ * @param inout device vector in column major format, size [ld * cols]
+ * @param ld leading dimension of the inout buffer
+ * @param rows number of rows (rows <= ld)
+ * @param cols number of columns
+ * @param norm_x l2-norm of X's rows
+ * @param norm_y l2-norm of Y's rows
+ * @param gain
+ */
+template <typename math_t>
+RAFT_KERNEL rbf_kernel_expanded(
+  math_t* inout, int ld, int rows, int cols, math_t* norm_x, math_t* norm_y, math_t gain)
+{
+  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
+       tidy += blockDim.y * gridDim.y) {
+    math_t norm_y_val = norm_y[tidy];
+    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
+         tidx += blockDim.x * gridDim.x) {
+      inout[tidx + tidy * ld] =
+        exp(-1.0 * gain * (norm_x[tidx] + norm_y_val - inout[tidx + tidy * ld] * 2));
+    }
+  }
+}
+
+std::tuple<dim3, dim3> generateLaunchConfig2dElementwiseOp(int n1, int n2)
+{
+  dim3 block_shape       = dim3(32, 4);
+  const int num_blocks_x = raft::ceildiv(n1, 32);
+  const int num_blocks_y = std::min(raft::ceildiv(n2, 32), (1 << 16) - 1);
+  dim3 grid_shape        = dim3(num_blocks_x, num_blocks_y);
+  return std::make_tuple(grid_shape, block_shape);
+}
+
+/**
+ * Create a kernel matrix using polynomial kernel function.
+ */
+template <typename math_t, typename exp_t>
+void PolynomialKernel<math_t, exp_t>::applyKernel(
+  math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream)
+{
+  const int n_minor = is_row_major ? cols : rows;
+  if (ld == n_minor) {
+    polynomial_kernel_nopad<<<raft::ceildiv<size_t>((size_t)rows * cols, 128), 128, 0, stream>>>(
+      inout, rows * cols, exponent, gain, offset);
+  } else {
+    int n1                         = is_row_major ? cols : rows;
+    int n2                         = is_row_major ? rows : cols;
+    auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
+    polynomial_kernel<<<grid_shape, block_shape, 0, stream>>>(
+      inout, ld, n1, n2, exponent, gain, offset);
+  }
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/** Evaluate kernel matrix using polynomial kernel.
+ *
+ * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and < , > denotes dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 dense device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t, typename exp_t>
+void PolynomialKernel<math_t, exp_t>::evaluate(raft::resources const& handle,
+                                               dense_input_matrix_view_t<math_t> x1,
+                                               dense_input_matrix_view_t<math_t> x2,
+                                               dense_output_matrix_view_t<math_t> out,
+                                               math_t* norm_x1,
+                                               math_t* norm_x2)
+{
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate kernel matrix using polynomial kernel.
+ *
+ * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and < , > denotes dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t, typename exp_t>
+void PolynomialKernel<math_t, exp_t>::evaluate(raft::resources const& handle,
+                                               csr_input_matrix_view_t<math_t> x1,
+                                               dense_input_matrix_view_t<math_t> x2,
+                                               dense_output_matrix_view_t<math_t> out,
+                                               math_t* norm_x1,
+                                               math_t* norm_x2)
+{
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate kernel matrix using polynomial kernel.
+ *
+ * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and < , > denotes dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 csr device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t, typename exp_t>
+void PolynomialKernel<math_t, exp_t>::evaluate(raft::resources const& handle,
+                                               csr_input_matrix_view_t<math_t> x1,
+                                               csr_input_matrix_view_t<math_t> x2,
+                                               dense_output_matrix_view_t<math_t> out,
+                                               math_t* norm_x1,
+                                               math_t* norm_x2)
+{
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate the Gram matrix using the legacy interface.
+ *
+ * @param [in] x1 device array of vectors, size [n1*n_cols]
+ * @param [in] n1 number vectors in x1
+ * @param [in] n_cols number of columns (features) in x1 and x2
+ * @param [in] x2 device array of vectors, size [n2*n_cols]
+ * @param [in] n2 number vectors in x2
+ * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+ * @param [in] is_row_major whether the input and output matrices are in row
+ *        major format
+ * @param [in] stream cuda stream
+ * @param ld1 leading dimension of x1 (usually it is n1)
+ * @param ld2 leading dimension of x2 (usually it is n2)
+ * @param ld_out leading dimension of out (usually it is n1)
+ */
+template <typename math_t, typename exp_t>
+[[deprecated]] void PolynomialKernel<math_t, exp_t>::evaluate(const math_t* x1,
+                                                              int n1,
+                                                              int n_cols,
+                                                              const math_t* x2,
+                                                              int n2,
+                                                              math_t* out,
+                                                              bool is_row_major,
+                                                              cudaStream_t stream,
+                                                              int ld1,
+                                                              int ld2,
+                                                              int ld_out)
+{
+  ASSERT(GramMatrixBase<math_t>::legacy_interface,
+         "Legacy interface can only be used with legacy ctor.");
+  GramMatrixBase<math_t>::linear(
+    x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+  applyKernel(out, ld_out, n1, n2, is_row_major, stream);
+}
+
+/**
+ * Create a kernel matrix using tanh kernel function.
+ */
+template <typename math_t>
+void TanhKernel<math_t>::applyKernel(
+  math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream)
+{
+  const int n_minor = is_row_major ? cols : rows;
+  if (ld == n_minor) {
+    tanh_kernel_nopad<<<raft::ceildiv<size_t>((size_t)rows * cols, 128), 128, 0, stream>>>(
+      inout, rows * cols, gain, offset);
+  } else {
+    int n1                         = is_row_major ? cols : rows;
+    int n2                         = is_row_major ? rows : cols;
+    auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
+    tanh_kernel<<<grid_shape, block_shape, 0, stream>>>(inout, ld, n1, n2, gain, offset);
+  }
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/** Evaluate kernel matrix using tanh kernel.
+ *
+ * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and < , > denotes dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 dense device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t>
+void TanhKernel<math_t>::evaluate(raft::resources const& handle,
+                                  dense_input_matrix_view_t<math_t> x1,
+                                  dense_input_matrix_view_t<math_t> x2,
+                                  dense_output_matrix_view_t<math_t> out,
+                                  math_t* norm_x1,
+                                  math_t* norm_x2)
+{
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate kernel matrix using tanh kernel.
+ *
+ * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and < , > denotes dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t>
+void TanhKernel<math_t>::evaluate(raft::resources const& handle,
+                                  csr_input_matrix_view_t<math_t> x1,
+                                  dense_input_matrix_view_t<math_t> x2,
+                                  dense_output_matrix_view_t<math_t> out,
+                                  math_t* norm_x1,
+                                  math_t* norm_x2)
+{
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate kernel matrix using tanh kernel.
+ *
+ * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and < , > denotes dot product.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 csr device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 unused.
+ * @param norm_x2 unused.
+ */
+template <typename math_t>
+void TanhKernel<math_t>::evaluate(raft::resources const& handle,
+                                  csr_input_matrix_view_t<math_t> x1,
+                                  csr_input_matrix_view_t<math_t> x2,
+                                  dense_output_matrix_view_t<math_t> out,
+                                  math_t* norm_x1,
+                                  math_t* norm_x2)
+{
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate the Gram matrix using the legacy interface.
+ *
+ * @param [in] x1 device array of vectors, size [n1*n_cols]
+ * @param [in] n1 number vectors in x1
+ * @param [in] n_cols number of columns (features) in x1 and x2
+ * @param [in] x2 device array of vectors, size [n2*n_cols]
+ * @param [in] n2 number vectors in x2
+ * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+ * @param [in] is_row_major whether the input and output matrices are in row
+ *        major format
+ * @param [in] stream cuda stream
+ * @param ld1 leading dimension of x1 (usually it is n1)
+ * @param ld2 leading dimension of x2 (usually it is n2)
+ * @param ld_out leading dimension of out (usually it is n1)
+ */
+template <typename math_t>
+[[deprecated]] void TanhKernel<math_t>::evaluate(const math_t* x1,
+                                                 int n1,
+                                                 int n_cols,
+                                                 const math_t* x2,
+                                                 int n2,
+                                                 math_t* out,
+                                                 bool is_row_major,
+                                                 cudaStream_t stream,
+                                                 int ld1,
+                                                 int ld2,
+                                                 int ld_out)
+{
+  ASSERT(GramMatrixBase<math_t>::legacy_interface,
+         "Legacy interface can only be used with legacy ctor.");
+  GramMatrixBase<math_t>::linear(
+    x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
+  applyKernel(out, ld_out, n1, n2, is_row_major, stream);
+}
+
+/**
+ * Create a kernel matrix using RBF kernel function.
+ */
+template <typename math_t>
+void RBFKernel<math_t>::applyKernel(math_t* inout,
+                                    int ld,
+                                    int rows,
+                                    int cols,
+                                    math_t* norm_x1,
+                                    math_t* norm_x2,
+                                    bool is_row_major,
+                                    cudaStream_t stream)
+{
+  int n1                         = is_row_major ? cols : rows;
+  int n2                         = is_row_major ? rows : cols;
+  math_t* norm_n1                = is_row_major ? norm_x2 : norm_x1;
+  math_t* norm_n2                = is_row_major ? norm_x1 : norm_x2;
+  auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
+  rbf_kernel_expanded<<<grid_shape, block_shape, 0, stream>>>(
+    inout, ld, n1, n2, norm_n1, norm_n2, gain);
+}
+
+template <typename math_t>
+void RBFKernel<math_t>::matrixRowNormL2(raft::resources const& handle,
+                                        dense_input_matrix_view_t<math_t> matrix,
+                                        math_t* target)
+{
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(matrix);
+  int minor         = is_row_major ? matrix.extent(1) : matrix.extent(0);
+  int ld            = is_row_major ? matrix.stride(0) : matrix.stride(1);
+  ASSERT(ld == minor, "RBF Kernel lazy rowNorm compute does not support ld parameter");
+  raft::linalg::rowNorm(target,
+                        matrix.data_handle(),
+                        matrix.extent(1),
+                        matrix.extent(0),
+                        raft::linalg::NormType::L2Norm,
+                        is_row_major,
+                        raft::resource::get_cuda_stream(handle));
+}
+
+template <typename math_t>
+void RBFKernel<math_t>::matrixRowNormL2(raft::resources const& handle,
+                                        csr_input_matrix_view_t<math_t> matrix,
+                                        math_t* target)
+{
+  auto matrix_structure = matrix.structure_view();
+  raft::sparse::linalg::rowNormCsr(handle,
+                                   matrix_structure.get_indptr().data(),
+                                   matrix.get_elements().data(),
+                                   matrix_structure.get_nnz(),
+                                   matrix_structure.get_n_rows(),
+                                   target,
+                                   raft::linalg::NormType::L2Norm);
+}
+
+/** Evaluate kernel matrix using RBF kernel.
+ *
+ * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and | | euclidean distance.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 dense device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+ * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+ */
+template <typename math_t>
+void RBFKernel<math_t>::evaluate(raft::resources const& handle,
+                                 dense_input_matrix_view_t<math_t> x1,
+                                 dense_input_matrix_view_t<math_t> x2,
+                                 dense_output_matrix_view_t<math_t> out,
+                                 math_t* norm_x1,
+                                 math_t* norm_x2)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+  // lazy compute norms if not given
+  rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
+  rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
+  if (norm_x1 == nullptr) {
+    tmp_norm_x1.reserve(x1.extent(0), stream);
+    norm_x1 = tmp_norm_x1.data();
+    matrixRowNormL2(handle, x1, norm_x1);
+  }
+  if (norm_x2 == nullptr) {
+    tmp_norm_x2.reserve(x2.extent(0), stream);
+    norm_x2 = tmp_norm_x2.data();
+    matrixRowNormL2(handle, x2, norm_x2);
+  }
+
+  // compute L2expanded
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              norm_x1,
+              norm_x2,
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate kernel matrix using RBF kernel.
+ *
+ * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and | | euclidean distance.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 dense device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+ * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+ */
+template <typename math_t>
+void RBFKernel<math_t>::evaluate(raft::resources const& handle,
+                                 csr_input_matrix_view_t<math_t> x1,
+                                 dense_input_matrix_view_t<math_t> x2,
+                                 dense_output_matrix_view_t<math_t> out,
+                                 math_t* norm_x1,
+                                 math_t* norm_x2)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  // lazy compute norms if not given
+  rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
+  rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
+  if (norm_x1 == nullptr) {
+    tmp_norm_x1.reserve(x1.structure_view().get_n_rows(), stream);
+    norm_x1 = tmp_norm_x1.data();
+    matrixRowNormL2(handle, x1, norm_x1);
+  }
+  if (norm_x2 == nullptr) {
+    tmp_norm_x2.reserve(x2.extent(0), stream);
+    norm_x2 = tmp_norm_x2.data();
+    matrixRowNormL2(handle, x2, norm_x2);
+  }
+
+  // compute L2expanded
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              norm_x1,
+              norm_x2,
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate kernel matrix using RBF kernel.
+ *
+ * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
+ * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
+ * in the x2 set, and | | euclidean distance.
+ *
+ * @param [in] handle raft handle
+ * @param [in] x1 csr device matrix view, size [n1*n_cols]
+ * @param [in] x2 csr device matrix view, size [n2*n_cols]
+ * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
+ * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
+ * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
+ */
+template <typename math_t>
+void RBFKernel<math_t>::evaluate(raft::resources const& handle,
+                                 csr_input_matrix_view_t<math_t> x1,
+                                 csr_input_matrix_view_t<math_t> x2,
+                                 dense_output_matrix_view_t<math_t> out,
+                                 math_t* norm_x1,
+                                 math_t* norm_x2)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(handle);
+
+  // lazy compute norms if not given
+  rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
+  rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
+  if (norm_x1 == nullptr) {
+    tmp_norm_x1.reserve(x1.structure_view().get_n_rows(), stream);
+    norm_x1 = tmp_norm_x1.data();
+    matrixRowNormL2(handle, x1, norm_x1);
+  }
+  if (norm_x2 == nullptr) {
+    tmp_norm_x2.reserve(x2.structure_view().get_n_rows(), stream);
+    norm_x2 = tmp_norm_x2.data();
+    matrixRowNormL2(handle, x2, norm_x2);
+  }
+
+  // compute L2expanded
+  bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
+  int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
+  GramMatrixBase<math_t>::linear(handle, x1, x2, out);
+  applyKernel(out.data_handle(),
+              ld_out,
+              out.extent(0),
+              out.extent(1),
+              norm_x1,
+              norm_x2,
+              is_row_major,
+              raft::resource::get_cuda_stream(handle));
+}
+
+/** Evaluate the Gram matrix using the legacy interface.
+ *
+ * @param [in] x1 device array of vectors, size [n1*n_cols]
+ * @param [in] n1 number vectors in x1
+ * @param [in] n_cols number of columns (features) in x1 and x2
+ * @param [in] x2 device array of vectors, size [n2*n_cols]
+ * @param [in] n2 number vectors in x2
+ * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
+ * @param [in] is_row_major whether the input and output matrices are in row
+ *        major format
+ * @param [in] stream cuda stream
+ * @param ld1 leading dimension of x1 (usually it is n1)
+ * @param ld2 leading dimension of x2 (usually it is n2)
+ * @param ld_out leading dimension of out (usually it is n1)
+ */
+template <typename math_t>
+[[deprecated]] void RBFKernel<math_t>::evaluate(const math_t* x1,
+                                                int n1,
+                                                int n_cols,
+                                                const math_t* x2,
+                                                int n2,
+                                                math_t* out,
+                                                bool is_row_major,
+                                                cudaStream_t stream,
+                                                int ld1,
+                                                int ld2,
+                                                int ld_out)
+{
+  ASSERT(GramMatrixBase<math_t>::legacy_interface,
+         "Legacy interface can only be used with legacy ctor.");
+  int minor1    = is_row_major ? n_cols : n1;
+  int minor2    = is_row_major ? n_cols : n2;
+  int minor_out = is_row_major ? n2 : n1;
+  ASSERT(ld1 == minor1, "RBF Kernel distance does not support ld1 parameter");
+  ASSERT(ld2 == minor2, "RBF Kernel distance does not support ld2 parameter");
+  ASSERT(ld_out == minor_out, "RBF Kernel distance does not support ld_out parameter");
+
+  math_t gain   = this->gain;
+  using index_t = int64_t;
+
+  rbf_fin_op fin_op{gain};
+
+  raft::resources handle;
+  raft::resource::set_cuda_stream(handle, stream);
+
+  cuvs::distance::distance<cuvs::distance::DistanceType::L2Unexpanded,
+                           math_t,
+                           math_t,
+                           math_t,
+                           decltype(fin_op),
+                           index_t>(handle,
+                                    const_cast<math_t*>(x1),
+                                    const_cast<math_t*>(x2),
+                                    out,
+                                    n1,
+                                    n2,
+                                    n_cols,
+                                    NULL,
+                                    0,
+                                    fin_op,
+                                    is_row_major);
+}
+
+template class PolynomialKernel<float, int>;
+template class PolynomialKernel<double, int>;
+template class TanhKernel<float>;
+template class TanhKernel<double>;
+template class RBFKernel<float>;
+template class RBFKernel<double>;
+
+};  // end namespace cuvs::distance::kernels
diff --git a/cpp/src/distance/detail/kernels/kernel_matrices.cuh b/cpp/src/distance/detail/kernels/kernel_matrices.cuh
deleted file mode 100644
index bff5bda923..0000000000
--- a/cpp/src/distance/detail/kernels/kernel_matrices.cuh
+++ /dev/null
@@ -1,777 +0,0 @@
-/*
- * Copyright (c) 2019-2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include "gram_matrix.cuh"
-
-#include "../detail/kernels/rbf_fin_op.cuh"
-#include <cuvs/distance/distance.cuh>
-#include <raft/core/resource/cuda_stream.hpp>
-#include <raft/linalg/gemm.cuh>
-#include <raft/sparse/linalg/norm.cuh>
-#include <raft/util/cuda_utils.cuh>
-
-namespace cuvs::distance::kernels::detail {
-
-/** Epiloge function for polynomial kernel without padding.
- * Calculates output = (gain*in + offset)^exponent
- * @param inout device vector in column major format, size [len]
- * @param len array length
- * @param exponent
- * @param gain
- * @param offset
- */
-template <typename math_t, typename exp_t>
-RAFT_KERNEL polynomial_kernel_nopad(
-  math_t* inout, size_t len, exp_t exponent, math_t gain, math_t offset)
-{
-  for (size_t tid = threadIdx.x + blockIdx.x * blockDim.x; tid < len;
-       tid += blockDim.x * gridDim.x) {
-    inout[tid] = pow(gain * inout[tid] + offset, exponent);
-  }
-}
-
-/** Epiloge function for polynomial kernel with padding.
- * Calculates output = (gain*input + offset)^exponent
- * @param inout device vector in column major format, size [ld * cols]
- * @param ld leading dimension of the inout buffer
- * @param rows number of rows (rows <= ld)
- * @param cols number of columns
- * @param exponent
- * @param gain
- * @param offset
- */
-template <typename math_t, typename exp_t>
-RAFT_KERNEL polynomial_kernel(
-  math_t* inout, int ld, int rows, int cols, exp_t exponent, math_t gain, math_t offset)
-{
-  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
-       tidy += blockDim.y * gridDim.y)
-    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
-         tidx += blockDim.x * gridDim.x) {
-      inout[tidx + tidy * ld] = pow(gain * inout[tidx + tidy * ld] + offset, exponent);
-    }
-}
-
-/** Epiloge function for tanh kernel without padding.
- * Calculates output = tanh(gain*input + offset)
- * @param inout device vector, size [len]
- * @param len length of the input vector
- * @param gain
- * @param offset
- */
-template <typename math_t>
-RAFT_KERNEL tanh_kernel_nopad(math_t* inout, size_t len, math_t gain, math_t offset)
-{
-  for (size_t tid = threadIdx.x + blockIdx.x * blockDim.x; tid < len;
-       tid += blockDim.x * gridDim.x) {
-    inout[tid] = tanh(gain * inout[tid] + offset);
-  }
-}
-
-/** Epiloge function for tanh kernel without padding.
- * Calculates output = tanh(gain*input + offset)
- * @param inout device vector in column major format, size [ld * cols]
- * @param ld leading dimension of the inout buffer
- * @param rows number of rows (rows <= ld)
- * @param cols number of columns
- * @param gain
- * @param offset
- */
-template <typename math_t>
-RAFT_KERNEL tanh_kernel(math_t* inout, int ld, int rows, int cols, math_t gain, math_t offset)
-{
-  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
-       tidy += blockDim.y * gridDim.y)
-    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
-         tidx += blockDim.x * gridDim.x) {
-      inout[tidx + tidy * ld] = tanh(gain * inout[tidx + tidy * ld] + offset);
-    }
-}
-
-/** Epiloge function for rbf kernel using expansion.
- *
- * Calculates output_ij = exp(-gain * (norm_x_i + norm_y_j - 2*input_ij));
- *
- * Intended usage
- *   - input is the product of two matrices X and Y input_ij = sum_k X_ik * Y_jk
- *   - norm_x_i = l2_norm(x_i), where x_i is the i-th row of matrix X
- *   - norm_y_j = l2_norm(y_j), where y_j is the j-th row of matrix Y
- *
- * @param inout device vector in column major format, size [ld * cols]
- * @param ld leading dimension of the inout buffer
- * @param rows number of rows (rows <= ld)
- * @param cols number of columns
- * @param norm_x l2-norm of X's rows
- * @param norm_y l2-norm of Y's rows
- * @param gain
- */
-template <typename math_t>
-RAFT_KERNEL rbf_kernel_expanded(
-  math_t* inout, int ld, int rows, int cols, math_t* norm_x, math_t* norm_y, math_t gain)
-{
-  for (size_t tidy = threadIdx.y + blockIdx.y * blockDim.y; tidy < cols;
-       tidy += blockDim.y * gridDim.y) {
-    math_t norm_y_val = norm_y[tidy];
-    for (size_t tidx = threadIdx.x + blockIdx.x * blockDim.x; tidx < rows;
-         tidx += blockDim.x * gridDim.x) {
-      inout[tidx + tidy * ld] =
-        exp(-1.0 * gain * (norm_x[tidx] + norm_y_val - inout[tidx + tidy * ld] * 2));
-    }
-  }
-}
-
-namespace {
-std::tuple<dim3, dim3> generateLaunchConfig2dElementwiseOp(int n1, int n2)
-{
-  dim3 block_shape       = dim3(32, 4);
-  const int num_blocks_x = raft::ceildiv(n1, 32);
-  const int num_blocks_y = std::min(raft::ceildiv(n2, 32), (1 << 16) - 1);
-  dim3 grid_shape        = dim3(num_blocks_x, num_blocks_y);
-  return std::make_tuple(grid_shape, block_shape);
-}
-}  // namespace
-
-/**
- * Create a kernel matrix using polynomial kernel function.
- */
-template <typename math_t, typename exp_t>
-class PolynomialKernel : public GramMatrixBase<math_t> {
-  exp_t exponent;
-  math_t gain;
-  math_t offset;
-
-  void applyKernel(
-    math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream)
-  {
-    const int n_minor = is_row_major ? cols : rows;
-    if (ld == n_minor) {
-      polynomial_kernel_nopad<<<raft::ceildiv<size_t>((size_t)rows * cols, 128), 128, 0, stream>>>(
-        inout, rows * cols, exponent, gain, offset);
-    } else {
-      int n1                         = is_row_major ? cols : rows;
-      int n2                         = is_row_major ? rows : cols;
-      auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
-      polynomial_kernel<<<grid_shape, block_shape, 0, stream>>>(
-        inout, ld, n1, n2, exponent, gain, offset);
-    }
-    RAFT_CUDA_TRY(cudaPeekAtLastError());
-  }
-
- public:
-  /**
-   * Constructs a polynomial kernel object.
-   * It evaluates the kernel matrix using the following formula:
-   * K_ij = (gain*<x1_i, x2_k> + offset)^exponent
-   *
-   * @tparam math_t floating point type
-   * @tparam exp_t type of exponent
-   * @param exponent
-   * @param gain
-   * @param offset
-   */
-  PolynomialKernel(exp_t exponent, math_t gain, math_t offset)
-    : GramMatrixBase<math_t>(), exponent(exponent), gain(gain), offset(offset)
-  {
-  }
-
-  [[deprecated]] PolynomialKernel(exp_t exponent, math_t gain, math_t offset, cublasHandle_t handle)
-    : GramMatrixBase<math_t>(handle), exponent(exponent), gain(gain), offset(offset)
-  {
-  }
-
-  /** Evaluate kernel matrix using polynomial kernel.
-   *
-   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and < , > denotes dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 dense device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  void evaluate(raft::resources const& handle,
-                dense_input_matrix_view_t<math_t> x1,
-                dense_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate kernel matrix using polynomial kernel.
-   *
-   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and < , > denotes dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  void evaluate(raft::resources const& handle,
-                csr_input_matrix_view_t<math_t> x1,
-                dense_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate kernel matrix using polynomial kernel.
-   *
-   * output[i,k] = (gain*<x1_i, x2_k> + offset)^exponent,
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and < , > denotes dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 csr device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  void evaluate(raft::resources const& handle,
-                csr_input_matrix_view_t<math_t> x1,
-                csr_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate the Gram matrix using the legacy interface.
-   *
-   * @param [in] x1 device array of vectors, size [n1*n_cols]
-   * @param [in] n1 number vectors in x1
-   * @param [in] n_cols number of columns (features) in x1 and x2
-   * @param [in] x2 device array of vectors, size [n2*n_cols]
-   * @param [in] n2 number vectors in x2
-   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
-   * @param [in] is_row_major whether the input and output matrices are in row
-   *        major format
-   * @param [in] stream cuda stream
-   * @param ld1 leading dimension of x1 (usually it is n1)
-   * @param ld2 leading dimension of x2 (usually it is n2)
-   * @param ld_out leading dimension of out (usually it is n1)
-   */
-  [[deprecated]] void evaluate(const math_t* x1,
-                               int n1,
-                               int n_cols,
-                               const math_t* x2,
-                               int n2,
-                               math_t* out,
-                               bool is_row_major,
-                               cudaStream_t stream,
-                               int ld1,
-                               int ld2,
-                               int ld_out)
-  {
-    ASSERT(GramMatrixBase<math_t>::legacy_interface,
-           "Legacy interface can only be used with legacy ctor.");
-    GramMatrixBase<math_t>::linear(
-      x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
-    applyKernel(out, ld_out, n1, n2, is_row_major, stream);
-  }
-};
-
-/**
- * Create a kernel matrix using tanh kernel function.
- */
-template <typename math_t>
-class TanhKernel : public GramMatrixBase<math_t> {
-  math_t gain, offset;
-
-  void applyKernel(
-    math_t* inout, int ld, int rows, int cols, bool is_row_major, cudaStream_t stream)
-  {
-    const int n_minor = is_row_major ? cols : rows;
-    if (ld == n_minor) {
-      tanh_kernel_nopad<<<raft::ceildiv<size_t>((size_t)rows * cols, 128), 128, 0, stream>>>(
-        inout, rows * cols, gain, offset);
-    } else {
-      int n1                         = is_row_major ? cols : rows;
-      int n2                         = is_row_major ? rows : cols;
-      auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
-      tanh_kernel<<<grid_shape, block_shape, 0, stream>>>(inout, ld, n1, n2, gain, offset);
-    }
-    RAFT_CUDA_TRY(cudaPeekAtLastError());
-  }
-
- public:
-  /**
-   * Constructs a tanh kernel object.
-   * It evaluates the kernel matrix using the following formula:
-   * K_ij = tanh(gain*<x1_i, x2_k> + offset)
-   *
-   * @tparam math_t floating point type
-   * @param gain
-   * @param offset
-   */
-  TanhKernel(math_t gain, math_t offset) : GramMatrixBase<math_t>(), gain(gain), offset(offset) {}
-
-  [[deprecated]] TanhKernel(math_t gain, math_t offset, cublasHandle_t handle)
-    : GramMatrixBase<math_t>(handle), gain(gain), offset(offset)
-  {
-  }
-
-  /** Evaluate kernel matrix using tanh kernel.
-   *
-   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and < , > denotes dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 dense device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  void evaluate(raft::resources const& handle,
-                dense_input_matrix_view_t<math_t> x1,
-                dense_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate kernel matrix using tanh kernel.
-   *
-   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and < , > denotes dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  void evaluate(raft::resources const& handle,
-                csr_input_matrix_view_t<math_t> x1,
-                dense_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate kernel matrix using tanh kernel.
-   *
-   * output_[i + k*n1] = (gain*<x1_i, x2_k> + offset)^exponent,
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and < , > denotes dot product.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 csr device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 unused.
-   * @param norm_x2 unused.
-   */
-  void evaluate(raft::resources const& handle,
-                csr_input_matrix_view_t<math_t> x1,
-                csr_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate the Gram matrix using the legacy interface.
-   *
-   * @param [in] x1 device array of vectors, size [n1*n_cols]
-   * @param [in] n1 number vectors in x1
-   * @param [in] n_cols number of columns (features) in x1 and x2
-   * @param [in] x2 device array of vectors, size [n2*n_cols]
-   * @param [in] n2 number vectors in x2
-   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
-   * @param [in] is_row_major whether the input and output matrices are in row
-   *        major format
-   * @param [in] stream cuda stream
-   * @param ld1 leading dimension of x1 (usually it is n1)
-   * @param ld2 leading dimension of x2 (usually it is n2)
-   * @param ld_out leading dimension of out (usually it is n1)
-   */
-  [[deprecated]] void evaluate(const math_t* x1,
-                               int n1,
-                               int n_cols,
-                               const math_t* x2,
-                               int n2,
-                               math_t* out,
-                               bool is_row_major,
-                               cudaStream_t stream,
-                               int ld1,
-                               int ld2,
-                               int ld_out)
-  {
-    ASSERT(GramMatrixBase<math_t>::legacy_interface,
-           "Legacy interface can only be used with legacy ctor.");
-    GramMatrixBase<math_t>::linear(
-      x1, n1, n_cols, x2, n2, out, is_row_major, stream, ld1, ld2, ld_out);
-    applyKernel(out, ld_out, n1, n2, is_row_major, stream);
-  }
-};
-
-/**
- * Create a kernel matrix using RBF kernel function.
- */
-template <typename math_t>
-class RBFKernel : public GramMatrixBase<math_t> {
-  math_t gain;
-
-  void applyKernel(math_t* inout,
-                   int ld,
-                   int rows,
-                   int cols,
-                   math_t* norm_x1,
-                   math_t* norm_x2,
-                   bool is_row_major,
-                   cudaStream_t stream)
-  {
-    int n1                         = is_row_major ? cols : rows;
-    int n2                         = is_row_major ? rows : cols;
-    math_t* norm_n1                = is_row_major ? norm_x2 : norm_x1;
-    math_t* norm_n2                = is_row_major ? norm_x1 : norm_x2;
-    auto [grid_shape, block_shape] = generateLaunchConfig2dElementwiseOp(n1, n2);
-    rbf_kernel_expanded<<<grid_shape, block_shape, 0, stream>>>(
-      inout, ld, n1, n2, norm_n1, norm_n2, gain);
-  }
-
- public:
-  /**
-   * Constructs a RBF kernel object.
-   * It evaluates the kernel matrix using the following formula:
-   * K_ij = exp(-gain*|x1_i- x2_k|^2)
-   *
-   * @tparam math_t floating point type
-   * @param gain
-   */
-  RBFKernel(math_t gain) : GramMatrixBase<math_t>(), gain(gain) {}
-
-  [[deprecated]] RBFKernel(math_t gain, cublasHandle_t handle)
-    : GramMatrixBase<math_t>(handle), gain(gain)
-  {
-  }
-
-  void matrixRowNormL2(raft::resources const& handle,
-                       dense_input_matrix_view_t<math_t> matrix,
-                       math_t* target)
-  {
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(matrix);
-    int minor         = is_row_major ? matrix.extent(1) : matrix.extent(0);
-    int ld            = is_row_major ? matrix.stride(0) : matrix.stride(1);
-    ASSERT(ld == minor, "RBF Kernel lazy rowNorm compute does not support ld parameter");
-    raft::linalg::rowNorm(target,
-                          matrix.data_handle(),
-                          matrix.extent(1),
-                          matrix.extent(0),
-                          raft::linalg::NormType::L2Norm,
-                          is_row_major,
-                          resource::get_cuda_stream(handle));
-  }
-
-  void matrixRowNormL2(raft::resources const& handle,
-                       csr_input_matrix_view_t<math_t> matrix,
-                       math_t* target)
-  {
-    auto matrix_structure = matrix.structure_view();
-    raft::sparse::linalg::rowNormCsr(handle,
-                                     matrix_structure.get_indptr().data(),
-                                     matrix.get_elements().data(),
-                                     matrix_structure.get_nnz(),
-                                     matrix_structure.get_n_rows(),
-                                     target,
-                                     raft::linalg::NormType::L2Norm);
-  }
-
-  /** Evaluate kernel matrix using RBF kernel.
-   *
-   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and | | euclidean distance.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 dense device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
-   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
-   */
-  void evaluate(raft::resources const& handle,
-                dense_input_matrix_view_t<math_t> x1,
-                dense_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    cudaStream_t stream = resource::get_cuda_stream(handle);
-    // lazy compute norms if not given
-    rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
-    rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
-    if (norm_x1 == nullptr) {
-      tmp_norm_x1.reserve(x1.extent(0), stream);
-      norm_x1 = tmp_norm_x1.data();
-      matrixRowNormL2(handle, x1, norm_x1);
-    }
-    if (norm_x2 == nullptr) {
-      tmp_norm_x2.reserve(x2.extent(0), stream);
-      norm_x2 = tmp_norm_x2.data();
-      matrixRowNormL2(handle, x2, norm_x2);
-    }
-
-    // compute L2expanded
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                norm_x1,
-                norm_x2,
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate kernel matrix using RBF kernel.
-   *
-   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and | | euclidean distance.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 dense device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
-   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
-   */
-  void evaluate(raft::resources const& handle,
-                csr_input_matrix_view_t<math_t> x1,
-                dense_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    cudaStream_t stream = resource::get_cuda_stream(handle);
-
-    // lazy compute norms if not given
-    rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
-    rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
-    if (norm_x1 == nullptr) {
-      tmp_norm_x1.reserve(x1.structure_view().get_n_rows(), stream);
-      norm_x1 = tmp_norm_x1.data();
-      matrixRowNormL2(handle, x1, norm_x1);
-    }
-    if (norm_x2 == nullptr) {
-      tmp_norm_x2.reserve(x2.extent(0), stream);
-      norm_x2 = tmp_norm_x2.data();
-      matrixRowNormL2(handle, x2, norm_x2);
-    }
-
-    // compute L2expanded
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                norm_x1,
-                norm_x2,
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate kernel matrix using RBF kernel.
-   *
-   * output_[i + k*n1] = exp(-gain*|x1_i - x2_k|^2),
-   * where x1_i is the i-th vector from the x1 set, and x2_k is k-th vector
-   * in the x2 set, and | | euclidean distance.
-   *
-   * @param [in] handle raft handle
-   * @param [in] x1 csr device matrix view, size [n1*n_cols]
-   * @param [in] x2 csr device matrix view, size [n2*n_cols]
-   * @param [out] out dense device matrix view for the Gram matrix, size [n1*n2]
-   * @param norm_x1 optional L2-norm of x1's rows for computation within RBF.
-   * @param norm_x2 optional L2-norm of x2's rows for computation within RBF.
-   */
-  void evaluate(raft::resources const& handle,
-                csr_input_matrix_view_t<math_t> x1,
-                csr_input_matrix_view_t<math_t> x2,
-                dense_output_matrix_view_t<math_t> out,
-                math_t* norm_x1,
-                math_t* norm_x2)
-  {
-    cudaStream_t stream = resource::get_cuda_stream(handle);
-
-    // lazy compute norms if not given
-    rmm::device_uvector<math_t> tmp_norm_x1(0, stream);
-    rmm::device_uvector<math_t> tmp_norm_x2(0, stream);
-    if (norm_x1 == nullptr) {
-      tmp_norm_x1.reserve(x1.structure_view().get_n_rows(), stream);
-      norm_x1 = tmp_norm_x1.data();
-      matrixRowNormL2(handle, x1, norm_x1);
-    }
-    if (norm_x2 == nullptr) {
-      tmp_norm_x2.reserve(x2.structure_view().get_n_rows(), stream);
-      norm_x2 = tmp_norm_x2.data();
-      matrixRowNormL2(handle, x2, norm_x2);
-    }
-
-    // compute L2expanded
-    bool is_row_major = GramMatrixBase<math_t>::get_is_row_major(out);
-    int ld_out        = is_row_major ? out.stride(0) : out.stride(1);
-    GramMatrixBase<math_t>::linear(handle, x1, x2, out);
-    applyKernel(out.data_handle(),
-                ld_out,
-                out.extent(0),
-                out.extent(1),
-                norm_x1,
-                norm_x2,
-                is_row_major,
-                resource::get_cuda_stream(handle));
-  }
-
-  /** Evaluate the Gram matrix using the legacy interface.
-   *
-   * @param [in] x1 device array of vectors, size [n1*n_cols]
-   * @param [in] n1 number vectors in x1
-   * @param [in] n_cols number of columns (features) in x1 and x2
-   * @param [in] x2 device array of vectors, size [n2*n_cols]
-   * @param [in] n2 number vectors in x2
-   * @param [out] out device buffer to store the Gram matrix, size [n1*n2]
-   * @param [in] is_row_major whether the input and output matrices are in row
-   *        major format
-   * @param [in] stream cuda stream
-   * @param ld1 leading dimension of x1 (usually it is n1)
-   * @param ld2 leading dimension of x2 (usually it is n2)
-   * @param ld_out leading dimension of out (usually it is n1)
-   */
-  [[deprecated]] void evaluate(const math_t* x1,
-                               int n1,
-                               int n_cols,
-                               const math_t* x2,
-                               int n2,
-                               math_t* out,
-                               bool is_row_major,
-                               cudaStream_t stream,
-                               int ld1,
-                               int ld2,
-                               int ld_out)
-  {
-    ASSERT(GramMatrixBase<math_t>::legacy_interface,
-           "Legacy interface can only be used with legacy ctor.");
-    int minor1    = is_row_major ? n_cols : n1;
-    int minor2    = is_row_major ? n_cols : n2;
-    int minor_out = is_row_major ? n2 : n1;
-    ASSERT(ld1 == minor1, "RBF Kernel distance does not support ld1 parameter");
-    ASSERT(ld2 == minor2, "RBF Kernel distance does not support ld2 parameter");
-    ASSERT(ld_out == minor_out, "RBF Kernel distance does not support ld_out parameter");
-
-    math_t gain   = this->gain;
-    using index_t = int64_t;
-
-    rbf_fin_op fin_op{gain};
-
-    raft::resources handle;
-    resource::set_cuda_stream(handle, stream);
-
-    cuvs::distance::distance<cuvs::distance::DistanceType::L2Unexpanded,
-                             math_t,
-                             math_t,
-                             math_t,
-                             decltype(fin_op),
-                             index_t>(handle,
-                                      const_cast<math_t*>(x1),
-                                      const_cast<math_t*>(x2),
-                                      out,
-                                      n1,
-                                      n2,
-                                      n_cols,
-                                      NULL,
-                                      0,
-                                      fin_op,
-                                      is_row_major);
-  }
-};
-
-};  // end namespace cuvs::distance::kernels::detail
diff --git a/cpp/src/distance/detail/kernels/rbf_fin_op.cuh b/cpp/src/distance/detail/kernels/rbf_fin_op.cuh
index 73588baeae..53022368d9 100644
--- a/cpp/src/distance/detail/kernels/rbf_fin_op.cuh
+++ b/cpp/src/distance/detail/kernels/rbf_fin_op.cuh
@@ -28,7 +28,7 @@
 #include <raft/core/math.hpp>                 // raft::exp
 #include <raft/util/cuda_dev_essentials.cuh>  // HD
 
-namespace cuvs::distance::kernels::detail {
+namespace cuvs::distance::kernels {
 
 /** @brief: Final op for Gram matrix with RBF kernel.
  *
@@ -48,4 +48,4 @@ struct rbf_fin_op {
   }
 };  // struct rbf_fin_op
 
-}  // namespace cuvs::distance::kernels::detail
+}  // namespace cuvs::distance::kernels
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
index edfd7cf5f2..49497ab3a2 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch-ext.cuh
@@ -118,9 +118,7 @@ instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
 instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
   cuvs::distance::detail::ops::russel_rao_distance_op, int);
 instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo(
-  cuvs::distance::detail::ops::l2_unexp_distance_op,
-  int64_t,
-  cuvs::distance::kernels::detail::rbf_fin_op);
+  cuvs::distance::detail::ops::l2_unexp_distance_op, int64_t, cuvs::distance::kernels::rbf_fin_op);
 
 instantiate_cuvs_distance_detail_pairwise_matrix_dispatch_by_algo_default(
   cuvs::distance::detail::ops::l2_exp_distance_op, int64_t);
diff --git a/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
index 3c8f251097..a2e12b6df9 100644
--- a/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
+++ b/cpp/src/distance/detail/pairwise_matrix/dispatch_rbf.cu
@@ -50,7 +50,7 @@ instantiate_raft_distance_detail_pairwise_matrix_dispatch(
   float,
   float,
   float,
-  cuvs::distance::kernels::detail::rbf_fin_op<float>,
+  cuvs::distance::kernels::rbf_fin_op<float>,
   int64_t);
 
 instantiate_raft_distance_detail_pairwise_matrix_dispatch(
@@ -58,7 +58,7 @@ instantiate_raft_distance_detail_pairwise_matrix_dispatch(
   double,
   double,
   double,
-  cuvs::distance::kernels::detail::rbf_fin_op<double>,
+  cuvs::distance::kernels::rbf_fin_op<double>,
   int64_t);
 
 instantiate_raft_distance_detail_pairwise_matrix_dispatch(
@@ -66,7 +66,7 @@ instantiate_raft_distance_detail_pairwise_matrix_dispatch(
   half,
   float,
   float,
-  cuvs::distance::kernels::detail::rbf_fin_op<half>,
+  cuvs::distance::kernels::rbf_fin_op<half>,
   int64_t);
 
 #undef instantiate_raft_distance_detail_pairwise_matrix_dispatch
diff --git a/cpp/src/distance/distance-ext.cuh b/cpp/src/distance/distance-ext.cuh
index e623f76bac..a692a62a35 100644
--- a/cpp/src/distance/distance-ext.cuh
+++ b/cpp/src/distance/distance-ext.cuh
@@ -273,13 +273,13 @@ instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpan
                                          float,
                                          float,
                                          float,
-                                         cuvs::distance::kernels::detail::rbf_fin_op<float>,
+                                         cuvs::distance::kernels::rbf_fin_op<float>,
                                          int64_t);
 instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpanded,
                                          double,
                                          double,
                                          double,
-                                         cuvs::distance::kernels::detail::rbf_fin_op<double>,
+                                         cuvs::distance::kernels::rbf_fin_op<double>,
                                          int64_t);
 
 #undef instantiate_cuvs_distance_distance_extra
diff --git a/cpp/src/distance/distance.cu b/cpp/src/distance/distance.cu
index c1d39f360f..47e72460f0 100644
--- a/cpp/src/distance/distance.cu
+++ b/cpp/src/distance/distance.cu
@@ -139,13 +139,13 @@ instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpan
                                          float,
                                          float,
                                          float,
-                                         cuvs::distance::kernels::detail::rbf_fin_op<float>,
+                                         cuvs::distance::kernels::rbf_fin_op<float>,
                                          int64_t);
 instantiate_cuvs_distance_distance_extra(cuvs::distance::DistanceType::L2Unexpanded,
                                          double,
                                          double,
                                          double,
-                                         cuvs::distance::kernels::detail::rbf_fin_op<double>,
+                                         cuvs::distance::kernels::rbf_fin_op<double>,
                                          int64_t);
 
 #undef instantiate_cuvs_distance_distance_extra
diff --git a/cpp/src/embed/spectral.cu b/cpp/src/embed/spectral.cu
new file mode 100644
index 0000000000..c3d4e3fc79
--- /dev/null
+++ b/cpp/src/embed/spectral.cu
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../sparse/cluster/detail/spectral.cuh"
+#include <cuvs/embed/spectral.hpp>
+#include <raft/core/device_coo_matrix.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::embed::spectral {
+
+/**
+ * Given a COO formatted (symmetric) knn graph, this function computes the spectral embeddings
+ * (lowest n_components eigenvectors), using Lanczos min cut algorithm.
+ * @param rows source vertices of knn graph (size nnz)
+ * @param cols destination vertices of knn graph (size nnz)
+ * @param vals edge weights connecting vertices of knn graph (size nnz)
+ * @param nnz size of rows/cols/vals
+ * @param n number of samples in X
+ * @param n_neighbors the number of neighbors to query for knn graph construction
+ * @param n_components the number of components to project the X into
+ * @param out output array for embedding (size n*n_comonents)
+ */
+void fit(const raft::resources& handle,
+         raft::device_coo_matrix_view<float, int, int, int> knn_graph,
+         int n_components,
+         raft::device_matrix_view<float, int> out,
+         unsigned long long seed)
+{
+  cuvs::sparse::cluster::spectral::detail::fit_embedding(
+    handle,
+    knn_graph.structure_view().get_rows().data(),
+    knn_graph.structure_view().get_cols().data(),
+    knn_graph.get_elements().data(),
+    knn_graph.structure_view().get_nnz(),
+    knn_graph.structure_view().get_n_rows(),
+    n_components,
+    out.data_handle(),
+    seed);
+}
+};  // namespace cuvs::embed::spectral
diff --git a/cpp/src/sparse/cluster/cluster_solvers.cuh b/cpp/src/sparse/cluster/cluster_solvers.cuh
new file mode 100644
index 0000000000..7b4cf6ab34
--- /dev/null
+++ b/cpp/src/sparse/cluster/cluster_solvers.cuh
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __CLUSTER_SOLVERS_H
+#define __CLUSTER_SOLVERS_H
+
+#pragma once
+
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/spectral/matrix_wrappers.hpp>
+
+#include <utility>  // for std::pair
+
+namespace cuvs {
+namespace spectral {
+
+using namespace raft::spectral::matrix;
+
+// aggregate of control params for Eigen Solver:
+//
+template <typename index_type_t, typename value_type_t, typename size_type_t = index_type_t>
+struct cluster_solver_config_t {
+  size_type_t n_clusters;
+  size_type_t maxIter;
+
+  value_type_t tol;
+
+  unsigned long long seed{123456};
+};
+
+template <typename index_type_t, typename value_type_t, typename size_type_t = index_type_t>
+struct kmeans_solver_t {
+  explicit kmeans_solver_t(
+    cluster_solver_config_t<index_type_t, value_type_t, size_type_t> const& config)
+    : config_(config)
+  {
+  }
+
+  std::pair<value_type_t, index_type_t> solve(raft::resources const& handle,
+                                              size_type_t n_obs_vecs,
+                                              size_type_t dim,
+                                              value_type_t const* __restrict__ obs,
+                                              index_type_t* __restrict__ codes) const
+  {
+    RAFT_EXPECTS(obs != nullptr, "Null obs buffer.");
+    RAFT_EXPECTS(codes != nullptr, "Null codes buffer.");
+    value_type_t residual{};
+    index_type_t iters{};
+    cuvs::cluster::kmeans::params km_params;
+    km_params.n_clusters     = config_.n_clusters;
+    km_params.tol            = config_.tol;
+    km_params.max_iter       = config_.maxIter;
+    km_params.rng_state.seed = config_.seed;
+
+    auto X      = raft::make_device_matrix_view<const value_type_t>(obs, n_obs_vecs, dim);
+    auto labels = raft::make_device_vector_view<index_type_t>(codes, n_obs_vecs);
+    auto centroids =
+      raft::make_device_matrix<value_type_t, index_type_t>(handle, config_.n_clusters, dim);
+    auto weight = raft::make_device_vector<value_type_t, index_type_t>(handle, n_obs_vecs);
+    thrust::fill(raft::resource::get_thrust_policy(handle),
+                 weight.data_handle(),
+                 weight.data_handle() + n_obs_vecs,
+                 1);
+
+    auto sw = std::make_optional((raft::device_vector_view<const value_type_t>)weight.view());
+    cuvs::cluster::kmeans::fit_predict(handle,
+                                       km_params,
+                                       X,
+                                       sw,
+                                       centroids.view(),
+                                       labels,
+                                       raft::make_host_scalar_view(&residual),
+                                       raft::make_host_scalar_view(&iters));
+    return std::make_pair(residual, iters);
+  }
+
+  auto const& get_config(void) const { return config_; }
+
+ private:
+  cluster_solver_config_t<index_type_t, value_type_t, size_type_t> config_;
+};
+
+}  // namespace spectral
+}  // namespace cuvs
+
+#endif
\ No newline at end of file
diff --git a/cpp/src/sparse/cluster/detail/spectral.cuh b/cpp/src/sparse/cluster/detail/spectral.cuh
new file mode 100644
index 0000000000..571d92bf56
--- /dev/null
+++ b/cpp/src/sparse/cluster/detail/spectral.cuh
@@ -0,0 +1,111 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../cluster_solvers.cuh"
+#include "../eigen_solvers.cuh"
+#include "../partition.cuh"
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+namespace cuvs::sparse::cluster::spectral::detail {
+
+template <typename T>
+void fit_embedding(raft::resources const& handle,
+                   int* rows,
+                   int* cols,
+                   T* vals,
+                   int nnz,
+                   int n,
+                   int n_components,
+                   T* out,
+                   unsigned long long seed = 1234567)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+  rmm::device_uvector<int> src_offsets(n + 1, stream);
+  rmm::device_uvector<int> dst_cols(nnz, stream);
+  rmm::device_uvector<T> dst_vals(nnz, stream);
+  raft::sparse::convert::coo_to_csr(
+    handle, rows, cols, vals, nnz, n, src_offsets.data(), dst_cols.data(), dst_vals.data());
+
+  rmm::device_uvector<T> eigVals(n_components + 1, stream);
+  rmm::device_uvector<T> eigVecs(n * (n_components + 1), stream);
+  rmm::device_uvector<int> labels(n, stream);
+
+  raft::resource::sync_stream(handle, stream);
+
+  /**
+   * Raft spectral clustering
+   */
+  using index_type = int;
+  using value_type = T;
+
+  index_type* ro = src_offsets.data();
+  index_type* ci = dst_cols.data();
+  value_type* vs = dst_vals.data();
+
+  raft::spectral::matrix::sparse_matrix_t<index_type, value_type> const r_csr_m{
+    handle, ro, ci, vs, n, nnz};
+
+  index_type neigvs       = n_components + 1;
+  index_type maxiter      = 4000;  // default reset value (when set to 0);
+  value_type tol          = 0.01;
+  index_type restart_iter = 15 + neigvs;  // what cugraph is using
+
+  cuvs::spectral::eigen_solver_config_t<index_type, value_type> cfg{
+    neigvs, maxiter, restart_iter, tol};
+
+  cfg.seed = seed;
+
+  cuvs::spectral::lanczos_solver_t<index_type, value_type> eig_solver{cfg};
+
+  // cluster computation here is irrelevant,
+  // hence define a no-op such solver to
+  // feed partition():
+  //
+  struct no_op_cluster_solver_t {
+    using index_type_t = index_type;
+    using size_type_t  = index_type;
+    using value_type_t = value_type;
+
+    std::pair<value_type_t, index_type_t> solve(raft::resources const& handle,
+                                                size_type_t n_obs_vecs,
+                                                size_type_t dim,
+                                                value_type_t const* __restrict__ obs,
+                                                index_type_t* __restrict__ codes) const
+    {
+      return std::make_pair<value_type_t, index_type_t>(0, 0);
+    }
+  };
+
+  cuvs::spectral::partition(handle,
+                            r_csr_m,
+                            eig_solver,
+                            no_op_cluster_solver_t{},
+                            labels.data(),
+                            eigVals.data(),
+                            eigVecs.data());
+
+  raft::copy<T>(out, eigVecs.data() + n, n * n_components, stream);
+
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+};  // namespace cuvs::sparse::cluster::spectral::detail
\ No newline at end of file
diff --git a/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp b/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
new file mode 100644
index 0000000000..a42ad2dc11
--- /dev/null
+++ b/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
@@ -0,0 +1,176 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/resource/cublas_handle.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+// TODO: Expose needed wrappers in RAFT's public API so we don't need to call detail APIs in cuVS
+#include "../../cluster_solvers.cuh"
+#include "../../eigen_solvers.cuh"
+#include "spectral_util.cuh"
+#include <raft/linalg/detail/cublas_wrappers.hpp>
+#include <raft/linalg/normalize.cuh>
+#include <raft/spectral/matrix_wrappers.hpp>
+
+#include <cuda.h>
+#include <thrust/fill.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+
+#include <math.h>
+#include <stdio.h>
+
+#include <tuple>
+
+namespace cuvs {
+namespace spectral {
+namespace detail {
+
+// =========================================================
+// Spectral modularity_maximization
+// =========================================================
+
+/** Compute partition for a weighted undirected graph. This
+ *  partition attempts to minimize the cost function:
+ *    Cost = \sum_i (Edges cut by ith partition)/(Vertices in ith partition)
+ *
+ *  @param G Weighted graph in CSR format
+ *  @param nClusters Number of partitions.
+ *  @param nEigVecs Number of eigenvectors to compute.
+ *  @param maxIter_lanczos Maximum number of Lanczos iterations.
+ *  @param restartIter_lanczos Maximum size of Lanczos system before
+ *    implicit restart.
+ *  @param tol_lanczos Convergence tolerance for Lanczos method.
+ *  @param maxIter_kmeans Maximum number of k-means iterations.
+ *  @param tol_kmeans Convergence tolerance for k-means algorithm.
+ *  @param clusters (Output, device memory, n entries) Cluster
+ *    assignments.
+ *  @param iters_lanczos On exit, number of Lanczos iterations
+ *    performed.
+ *  @param iters_kmeans On exit, number of k-means iterations
+ *    performed.
+ *  @return error flag.
+ */
+template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  EigenSolver const& eigen_solver,
+  ClusterSolver const& cluster_solver,
+  vertex_t* __restrict__ clusters,
+  weight_t* eigVals,
+  weight_t* eigVecs)
+{
+  RAFT_EXPECTS(clusters != nullptr, "Null clusters buffer.");
+  RAFT_EXPECTS(eigVals != nullptr, "Null eigVals buffer.");
+  RAFT_EXPECTS(eigVecs != nullptr, "Null eigVecs buffer.");
+
+  auto stream   = raft::resource::get_cuda_stream(handle);
+  auto cublas_h = raft::resource::get_cublas_handle(handle);
+
+  std::tuple<vertex_t, weight_t, vertex_t>
+    stats;  // # iters eigen solver, cluster solver residual, # iters cluster solver
+
+  vertex_t n = csr_m.nrows_;
+
+  // Compute eigenvectors of Modularity Matrix
+
+  // Initialize Modularity Matrix
+  raft::spectral::matrix::modularity_matrix_t<vertex_t, weight_t> B{handle, csr_m};
+
+  auto eigen_config = eigen_solver.get_config();
+  auto nEigVecs     = eigen_config.n_eigVecs;
+
+  // Compute eigenvectors corresponding to largest eigenvalues
+  std::get<0>(stats) = eigen_solver.solve_largest_eigenvectors(handle, B, eigVals, eigVecs);
+
+  // Whiten eigenvector matrix
+  transform_eigen_matrix(handle, n, nEigVecs, eigVecs);
+
+  // notice that at this point the matrix has already been transposed, so we are scaling
+  // columns
+  auto dataset_view = raft::make_device_matrix_view(eigVecs, nEigVecs, n);
+  raft::linalg::row_normalize(
+    handle, raft::make_const_mdspan(dataset_view), dataset_view, raft::linalg::L2Norm);
+
+  // Find partition clustering
+  auto pair_cluster = cluster_solver.solve(handle, n, nEigVecs, eigVecs, clusters);
+
+  std::get<1>(stats) = pair_cluster.first;
+  std::get<2>(stats) = pair_cluster.second;
+
+  return stats;
+}
+//===================================================
+// Analysis of graph partition
+// =========================================================
+
+/// Compute modularity
+/** This function determines the modularity based on a graph and cluster assignments
+ *  @param G Weighted graph in CSR format
+ *  @param nClusters Number of clusters.
+ *  @param clusters (Input, device memory, n entries) Cluster assignments.
+ *  @param modularity On exit, modularity
+ */
+template <typename vertex_t, typename weight_t>
+void analyzeModularity(raft::resources const& handle,
+                       raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+                       vertex_t nClusters,
+                       vertex_t const* __restrict__ clusters,
+                       weight_t& modularity)
+{
+  RAFT_EXPECTS(clusters != nullptr, "Null clusters buffer.");
+
+  vertex_t i;
+  vertex_t n = csr_m.nrows_;
+  weight_t partModularity, clustersize;
+
+  auto cublas_h = raft::resource::get_cublas_handle(handle);
+  auto stream   = raft::resource::get_cuda_stream(handle);
+
+  // Device memory
+  raft::spectral::matrix::vector_t<weight_t> part_i(handle, n);
+  raft::spectral::matrix::vector_t<weight_t> Bx(handle, n);
+
+  // Initialize cuBLAS
+  RAFT_CUBLAS_TRY(
+    raft::linalg::detail::cublassetpointermode(cublas_h, CUBLAS_POINTER_MODE_HOST, stream));
+
+  // Initialize Modularity
+  raft::spectral::matrix::modularity_matrix_t<vertex_t, weight_t> B{handle, csr_m};
+
+  // Initialize output
+  modularity = 0;
+
+  // Iterate through partitions
+  for (i = 0; i < nClusters; ++i) {
+    if (!construct_indicator(handle, i, n, clustersize, partModularity, clusters, part_i, Bx, B)) {
+      WARNING("empty partition");
+      continue;
+    }
+
+    // Record results
+    modularity += partModularity;
+  }
+
+  modularity = modularity / B.diagonal_.nrm1();
+}
+
+}  // namespace detail
+}  // namespace spectral
+}  // namespace cuvs
diff --git a/cpp/src/sparse/cluster/detail/spectral/partition.hpp b/cpp/src/sparse/cluster/detail/spectral/partition.hpp
new file mode 100644
index 0000000000..77e83c17d3
--- /dev/null
+++ b/cpp/src/sparse/cluster/detail/spectral/partition.hpp
@@ -0,0 +1,188 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <raft/core/resource/cublas_handle.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+// TODO: Expose needed wrappers in RAFT's public API so we don't need to call detail APIs in cuVS
+#include <raft/linalg/detail/cublas_wrappers.hpp>
+
+#include "../../cluster_solvers.cuh"
+#include "../../eigen_solvers.cuh"
+#include "spectral_util.cuh"
+#include <raft/spectral/matrix_wrappers.hpp>
+
+#include <cuda.h>
+#include <thrust/fill.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+
+#include <math.h>
+#include <stdio.h>
+
+#include <tuple>
+
+namespace cuvs {
+namespace spectral {
+namespace detail {
+
+// =========================================================
+// Spectral partitioner
+// =========================================================
+
+/// Compute spectral graph partition
+/** Compute partition for a weighted undirected graph. This
+ *  partition attempts to minimize the cost function:
+ *    Cost = \sum_i (Edges cut by ith partition)/(Vertices in ith partition)
+ *
+ *  @param G Weighted graph in CSR format
+ *  @param nClusters Number of partitions.
+ *  @param nEigVecs Number of eigenvectors to compute.
+ *  @param maxIter_lanczos Maximum number of Lanczos iterations.
+ *  @param restartIter_lanczos Maximum size of Lanczos system before
+ *    implicit restart.
+ *  @param tol_lanczos Convergence tolerance for Lanczos method.
+ *  @param maxIter_kmeans Maximum number of k-means iterations.
+ *  @param tol_kmeans Convergence tolerance for k-means algorithm.
+ *  @param clusters (Output, device memory, n entries) Partition
+ *    assignments.
+ *  @param iters_lanczos On exit, number of Lanczos iterations
+ *    performed.
+ *  @param iters_kmeans On exit, number of k-means iterations
+ *    performed.
+ *  @return statistics: number of eigensolver iterations, .
+ */
+template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+std::tuple<vertex_t, weight_t, vertex_t> partition(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  EigenSolver const& eigen_solver,
+  ClusterSolver const& cluster_solver,
+  vertex_t* __restrict__ clusters,
+  weight_t* eigVals,
+  weight_t* eigVecs)
+{
+  RAFT_EXPECTS(clusters != nullptr, "Null clusters buffer.");
+  RAFT_EXPECTS(eigVals != nullptr, "Null eigVals buffer.");
+  RAFT_EXPECTS(eigVecs != nullptr, "Null eigVecs buffer.");
+
+  auto stream   = raft::resource::get_cuda_stream(handle);
+  auto cublas_h = raft::resource::get_cublas_handle(handle);
+
+  std::tuple<vertex_t, weight_t, vertex_t>
+    stats;  //{iters_eig_solver,residual_cluster,iters_cluster_solver} // # iters eigen solver,
+            // cluster solver residual, # iters cluster solver
+
+  vertex_t n = csr_m.nrows_;
+
+  // -------------------------------------------------------
+  // Spectral partitioner
+  // -------------------------------------------------------
+
+  // Compute eigenvectors of Laplacian
+
+  // Initialize Laplacian
+  /// sparse_matrix_t<vertex_t, weight_t> A{handle, graph};
+  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t> L{handle, csr_m};
+
+  auto eigen_config = eigen_solver.get_config();
+  auto nEigVecs     = eigen_config.n_eigVecs;
+
+  // Compute smallest eigenvalues and eigenvectors
+  std::get<0>(stats) = eigen_solver.solve_smallest_eigenvectors(handle, L, eigVals, eigVecs);
+
+  // Whiten eigenvector matrix
+  transform_eigen_matrix(handle, n, nEigVecs, eigVecs);
+
+  // Find partition clustering
+  auto pair_cluster = cluster_solver.solve(handle, n, nEigVecs, eigVecs, clusters);
+
+  std::get<1>(stats) = pair_cluster.first;
+  std::get<2>(stats) = pair_cluster.second;
+
+  return stats;
+}
+
+// =========================================================
+// Analysis of graph partition
+// =========================================================
+
+/// Compute cost function for partition
+/** This function determines the edges cut by a partition and a cost
+ *  function:
+ *    Cost = \sum_i (Edges cut by ith partition)/(Vertices in ith partition)
+ *  Graph is assumed to be weighted and undirected.
+ *
+ *  @param G Weighted graph in CSR format
+ *  @param nClusters Number of partitions.
+ *  @param clusters (Input, device memory, n entries) Partition
+ *    assignments.
+ *  @param edgeCut On exit, weight of edges cut by partition.
+ *  @param cost On exit, partition cost function.
+ *  @return error flag.
+ */
+template <typename vertex_t, typename weight_t>
+void analyzePartition(raft::resources const& handle,
+                      raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+                      vertex_t nClusters,
+                      const vertex_t* __restrict__ clusters,
+                      weight_t& edgeCut,
+                      weight_t& cost)
+{
+  RAFT_EXPECTS(clusters != nullptr, "Null clusters buffer.");
+
+  vertex_t i;
+  vertex_t n = csr_m.nrows_;
+
+  auto stream   = raft::resource::get_cuda_stream(handle);
+  auto cublas_h = raft::resource::get_cublas_handle(handle);
+
+  weight_t partEdgesCut, clustersize;
+
+  // Device memory
+  raft::spectral::matrix::vector_t<weight_t> part_i(handle, n);
+  raft::spectral::matrix::vector_t<weight_t> Lx(handle, n);
+
+  // Initialize cuBLAS
+  RAFT_CUBLAS_TRY(
+    raft::linalg::detail::cublassetpointermode(cublas_h, CUBLAS_POINTER_MODE_HOST, stream));
+
+  // Initialize Laplacian
+  /// sparse_matrix_t<vertex_t, weight_t> A{handle, graph};
+  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t> L{handle, csr_m};
+
+  // Initialize output
+  cost    = 0;
+  edgeCut = 0;
+
+  // Iterate through partitions
+  for (i = 0; i < nClusters; ++i) {
+    // Construct indicator vector for ith partition
+    if (!construct_indicator(handle, i, n, clustersize, partEdgesCut, clusters, part_i, Lx, L)) {
+      WARNING("empty partition");
+      continue;
+    }
+
+    // Record results
+    cost += partEdgesCut / clustersize;
+    edgeCut += partEdgesCut / 2;
+  }
+}
+
+}  // namespace detail
+}  // namespace spectral
+}  // namespace cuvs
diff --git a/cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh b/cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh
new file mode 100644
index 0000000000..1d2e58e2a5
--- /dev/null
+++ b/cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh
@@ -0,0 +1,181 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/resource/cublas_handle.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+
+// TODO: Expose needed wrappers in RAFT's public API so we don't need to call detail APIs in cuVS
+#include <raft/linalg/detail/cublas_wrappers.hpp>
+#include <raft/spectral/matrix_wrappers.hpp>
+
+#include <raft/util/cudart_utils.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <algorithm>
+
+namespace cuvs {
+namespace spectral {
+
+template <typename vertex_t, typename edge_t, typename weight_t>
+void transform_eigen_matrix(raft::resources const& handle,
+                            edge_t n,
+                            vertex_t nEigVecs,
+                            weight_t* eigVecs)
+{
+  auto stream             = raft::resource::get_cuda_stream(handle);
+  auto cublas_h           = raft::resource::get_cublas_handle(handle);
+  auto thrust_exec_policy = raft::resource::get_thrust_policy(handle);
+
+  const weight_t zero{0.0};
+  const weight_t one{1.0};
+
+  // Whiten eigenvector matrix
+  for (auto i = 0; i < nEigVecs; ++i) {
+    weight_t mean, std;
+
+    mean = thrust::reduce(thrust_exec_policy,
+                          thrust::device_pointer_cast(eigVecs + IDX(0, i, n)),
+                          thrust::device_pointer_cast(eigVecs + IDX(0, i + 1, n)));
+    RAFT_CHECK_CUDA(stream);
+    mean /= n;
+    thrust::transform(thrust_exec_policy,
+                      thrust::device_pointer_cast(eigVecs + IDX(0, i, n)),
+                      thrust::device_pointer_cast(eigVecs + IDX(0, i + 1, n)),
+                      thrust::make_constant_iterator(mean),
+                      thrust::device_pointer_cast(eigVecs + IDX(0, i, n)),
+                      thrust::minus<weight_t>());
+    RAFT_CHECK_CUDA(stream);
+
+    // TODO: Call from public API when ready
+    RAFT_CUBLAS_TRY(
+      raft::linalg::detail::cublasnrm2(cublas_h, n, eigVecs + IDX(0, i, n), 1, &std, stream));
+
+    std /= std::sqrt(static_cast<weight_t>(n));
+
+    thrust::transform(thrust_exec_policy,
+                      thrust::device_pointer_cast(eigVecs + IDX(0, i, n)),
+                      thrust::device_pointer_cast(eigVecs + IDX(0, i + 1, n)),
+                      thrust::make_constant_iterator(std),
+                      thrust::device_pointer_cast(eigVecs + IDX(0, i, n)),
+                      thrust::divides<weight_t>());
+    RAFT_CHECK_CUDA(stream);
+  }
+
+  // Transpose eigenvector matrix
+  //   TODO: in-place transpose
+  {
+    raft::spectral::matrix::vector_t<weight_t> work(handle, nEigVecs * n);
+    // TODO: Call from public API when ready
+    RAFT_CUBLAS_TRY(
+      raft::linalg::detail::cublassetpointermode(cublas_h, CUBLAS_POINTER_MODE_HOST, stream));
+
+    // TODO: Call from public API when ready
+    RAFT_CUBLAS_TRY(raft::linalg::detail::cublasgeam(cublas_h,
+                                                     CUBLAS_OP_T,
+                                                     CUBLAS_OP_N,
+                                                     nEigVecs,
+                                                     n,
+                                                     &one,
+                                                     eigVecs,
+                                                     n,
+                                                     &zero,
+                                                     (weight_t*)NULL,
+                                                     nEigVecs,
+                                                     work.raw(),
+                                                     nEigVecs,
+                                                     stream));
+
+    RAFT_CUDA_TRY(cudaMemcpyAsync(
+      eigVecs, work.raw(), nEigVecs * n * sizeof(weight_t), cudaMemcpyDeviceToDevice, stream));
+  }
+}
+
+namespace {
+/// Functor to generate indicator vectors
+/** For use in Thrust transform
+ */
+template <typename index_type_t, typename value_type_t>
+struct equal_to_i_op {
+  const index_type_t i;
+
+ public:
+  equal_to_i_op(index_type_t _i) : i(_i) {}
+  template <typename Tuple_>
+  __host__ __device__ void operator()(Tuple_ t)
+  {
+    thrust::get<1>(t) = (thrust::get<0>(t) == i) ? (value_type_t)1.0 : (value_type_t)0.0;
+  }
+};
+}  // namespace
+
+// Construct indicator vector for ith partition
+//
+template <typename vertex_t, typename edge_t, typename weight_t>
+bool construct_indicator(raft::resources const& handle,
+                         edge_t index,
+                         edge_t n,
+                         weight_t& clustersize,
+                         weight_t& partStats,
+                         vertex_t const* __restrict__ clusters,
+                         raft::spectral::matrix::vector_t<weight_t>& part_i,
+                         raft::spectral::matrix::vector_t<weight_t>& Bx,
+                         raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t> const& B)
+{
+  auto stream             = raft::resource::get_cuda_stream(handle);
+  auto cublas_h           = raft::resource::get_cublas_handle(handle);
+  auto thrust_exec_policy = raft::resource::get_thrust_policy(handle);
+
+  thrust::for_each(
+    thrust_exec_policy,
+    thrust::make_zip_iterator(thrust::make_tuple(thrust::device_pointer_cast(clusters),
+                                                 thrust::device_pointer_cast(part_i.raw()))),
+    thrust::make_zip_iterator(thrust::make_tuple(thrust::device_pointer_cast(clusters + n),
+                                                 thrust::device_pointer_cast(part_i.raw() + n))),
+    equal_to_i_op<vertex_t, weight_t>(index));
+  RAFT_CHECK_CUDA(stream);
+
+  // Compute size of ith partition
+  // TODO: Call from public API when ready
+  RAFT_CUBLAS_TRY(raft::linalg::detail::cublasdot(
+    cublas_h, n, part_i.raw(), 1, part_i.raw(), 1, &clustersize, stream));
+
+  clustersize = round(clustersize);
+  if (clustersize < 0.5) { return false; }
+
+  // Compute part stats
+  B.mv(1, part_i.raw(), 0, Bx.raw());
+  // TODO: Call from public API when ready
+  RAFT_CUBLAS_TRY(
+    raft::linalg::detail::cublasdot(cublas_h, n, Bx.raw(), 1, part_i.raw(), 1, &partStats, stream));
+
+  return true;
+}
+
+}  // namespace spectral
+}  // namespace cuvs
diff --git a/cpp/src/sparse/cluster/eigen_solvers.cuh b/cpp/src/sparse/cluster/eigen_solvers.cuh
new file mode 100644
index 0000000000..1b2501d68b
--- /dev/null
+++ b/cpp/src/sparse/cluster/eigen_solvers.cuh
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef __EIGEN_SOLVERS_H
+#define __EIGEN_SOLVERS_H
+
+#pragma once
+
+#include <raft/sparse/solver/lanczos.cuh>
+#include <raft/spectral/matrix_wrappers.hpp>
+
+namespace cuvs {
+namespace spectral {
+
+// aggregate of control params for Eigen Solver:
+//
+template <typename index_type_t, typename value_type_t, typename size_type_t = index_type_t>
+struct eigen_solver_config_t {
+  size_type_t n_eigVecs;
+  size_type_t maxIter;
+
+  size_type_t restartIter;
+  value_type_t tol;
+
+  bool reorthogonalize{false};
+  unsigned long long seed{
+    1234567};  // CAVEAT: this default value is now common to all instances of using seed in
+               // Lanczos; was not the case before: there were places where a default seed = 123456
+               // was used; this may trigger slightly different # solver iterations
+};
+
+template <typename index_type_t, typename value_type_t, typename size_type_t = index_type_t>
+struct lanczos_solver_t {
+  explicit lanczos_solver_t(
+    eigen_solver_config_t<index_type_t, value_type_t, size_type_t> const& config)
+    : config_(config)
+  {
+  }
+
+  index_type_t solve_smallest_eigenvectors(
+    raft::resources const& handle,
+    raft::spectral::matrix::sparse_matrix_t<index_type_t, value_type_t> const& A,
+    value_type_t* __restrict__ eigVals,
+    value_type_t* __restrict__ eigVecs) const
+  {
+    RAFT_EXPECTS(eigVals != nullptr, "Null eigVals buffer.");
+    RAFT_EXPECTS(eigVecs != nullptr, "Null eigVecs buffer.");
+    index_type_t iters{};
+    raft::sparse::solver::computeSmallestEigenvectors(handle,
+                                                      A,
+                                                      config_.n_eigVecs,
+                                                      config_.maxIter,
+                                                      config_.restartIter,
+                                                      config_.tol,
+                                                      config_.reorthogonalize,
+                                                      iters,
+                                                      eigVals,
+                                                      eigVecs,
+                                                      config_.seed);
+    return iters;
+  }
+
+  index_type_t solve_largest_eigenvectors(
+    raft::resources const& handle,
+    raft::spectral::matrix::sparse_matrix_t<index_type_t, value_type_t> const& A,
+    value_type_t* __restrict__ eigVals,
+    value_type_t* __restrict__ eigVecs) const
+  {
+    RAFT_EXPECTS(eigVals != nullptr, "Null eigVals buffer.");
+    RAFT_EXPECTS(eigVecs != nullptr, "Null eigVecs buffer.");
+    index_type_t iters{};
+    raft::sparse::solver::computeLargestEigenvectors(handle,
+                                                     A,
+                                                     config_.n_eigVecs,
+                                                     config_.maxIter,
+                                                     config_.restartIter,
+                                                     config_.tol,
+                                                     config_.reorthogonalize,
+                                                     iters,
+                                                     eigVals,
+                                                     eigVecs,
+                                                     config_.seed);
+    return iters;
+  }
+
+  auto const& get_config(void) const { return config_; }
+
+ private:
+  eigen_solver_config_t<index_type_t, value_type_t, size_type_t> config_;
+};
+
+}  // namespace spectral
+}  // namespace cuvs
+
+#endif
diff --git a/cpp/src/sparse/cluster/modularity_maximization.cuh b/cpp/src/sparse/cluster/modularity_maximization.cuh
new file mode 100644
index 0000000000..71cba69272
--- /dev/null
+++ b/cpp/src/sparse/cluster/modularity_maximization.cuh
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef __MODULARITY_MAXIMIZATION_H
+#define __MODULARITY_MAXIMIZATION_H
+
+#pragma once
+
+#include "detail/spectral/modularity_maximization.hpp"
+
+#include <tuple>
+
+namespace cuvs {
+namespace spectral {
+
+// =========================================================
+// Spectral modularity_maximization
+// =========================================================
+
+/** Compute partition for a weighted undirected graph. This
+ *  partition attempts to minimize the cost function:
+ *    Cost = \f$sum_i\f$ (Edges cut by ith partition)/(Vertices in ith partition)
+ *
+ *  @param handle raft handle for managing expensive resources
+ *  @param csr_m Weighted graph in CSR format
+ *  @param eigen_solver Eigensolver implementation
+ *  @param cluster_solver Cluster solver implementation
+ *  @param clusters (Output, device memory, n entries) Partition
+ *    assignments.
+ *  @param eigVals Output eigenvalue array pointer on device
+ *  @param eigVecs Output eigenvector array pointer on device
+ *  @return statistics: number of eigensolver iterations, .
+ */
+template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  EigenSolver const& eigen_solver,
+  ClusterSolver const& cluster_solver,
+  vertex_t* __restrict__ clusters,
+  weight_t* eigVals,
+  weight_t* eigVecs)
+{
+  return cuvs::spectral::detail::
+    modularity_maximization<vertex_t, weight_t, EigenSolver, ClusterSolver>(
+      handle, csr_m, eigen_solver, cluster_solver, clusters, eigVals, eigVecs);
+}
+//===================================================
+// Analysis of graph partition
+// =========================================================
+
+/// Compute modularity
+/** This function determines the modularity based on a graph and cluster assignments
+ *  @param handle raft handle for managing expensive resources
+ *  @param csr_m Weighted graph in CSR format
+ *  @param nClusters Number of clusters.
+ *  @param clusters (Input, device memory, n entries) Cluster assignments.
+ *  @param modularity On exit, modularity
+ */
+template <typename vertex_t, typename weight_t>
+void analyzeModularity(raft::resources const& handle,
+                       raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+                       vertex_t nClusters,
+                       vertex_t const* __restrict__ clusters,
+                       weight_t& modularity)
+{
+  cuvs::spectral::detail::analyzeModularity<vertex_t, weight_t>(
+    handle, csr_m, nClusters, clusters, modularity);
+}
+
+}  // namespace spectral
+}  // namespace cuvs
+
+#endif
\ No newline at end of file
diff --git a/cpp/src/sparse/cluster/partition.cuh b/cpp/src/sparse/cluster/partition.cuh
new file mode 100644
index 0000000000..df78a8a2d9
--- /dev/null
+++ b/cpp/src/sparse/cluster/partition.cuh
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __PARTITION_H
+#define __PARTITION_H
+
+#pragma once
+
+#include "detail/spectral/partition.hpp"
+
+#include <tuple>
+
+namespace cuvs {
+namespace spectral {
+
+// =========================================================
+// Spectral partitioner
+// =========================================================
+
+/// Compute spectral graph partition
+/** Compute partition for a weighted undirected graph. This
+ *  partition attempts to minimize the cost function:
+ *    Cost = \f$sum_i\f$ (Edges cut by ith partition)/(Vertices in ith partition)
+ *
+ *  @param handle raft handle for managing expensive resources
+ *  @param csr_m Weighted graph in CSR format
+ *  @param eigen_solver Eigensolver implementation
+ *  @param cluster_solver Cluster solver implementation
+ *  @param clusters (Output, device memory, n entries) Partition
+ *    assignments.
+ *  @param eigVals Output eigenvalue array pointer on device
+ *  @param eigVecs Output eigenvector array pointer on device
+ *  @return statistics: number of eigensolver iterations, .
+ */
+template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+std::tuple<vertex_t, weight_t, vertex_t> partition(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  EigenSolver const& eigen_solver,
+  ClusterSolver const& cluster_solver,
+  vertex_t* __restrict__ clusters,
+  weight_t* eigVals,
+  weight_t* eigVecs)
+{
+  return cuvs::spectral::detail::partition<vertex_t, weight_t, EigenSolver, ClusterSolver>(
+    handle, csr_m, eigen_solver, cluster_solver, clusters, eigVals, eigVecs);
+}
+
+// =========================================================
+// Analysis of graph partition
+// =========================================================
+
+/// Compute cost function for partition
+/** This function determines the edges cut by a partition and a cost
+ *  function:
+ *    Cost = \f$sum_i\f$ (Edges cut by ith partition)/(Vertices in ith partition)
+ *  Graph is assumed to be weighted and undirected.
+ *
+ *  @param handle raft handle for managing expensive resources
+ *  @param csr_m Weighted graph in CSR format
+ *  @param nClusters Number of partitions.
+ *  @param clusters (Input, device memory, n entries) Partition
+ *    assignments.
+ *  @param edgeCut On exit, weight of edges cut by partition.
+ *  @param cost On exit, partition cost function.
+ */
+template <typename vertex_t, typename weight_t>
+void analyzePartition(raft::resources const& handle,
+                      raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+                      vertex_t nClusters,
+                      const vertex_t* __restrict__ clusters,
+                      weight_t& edgeCut,
+                      weight_t& cost)
+{
+  cuvs::spectral::detail::analyzePartition<vertex_t, weight_t>(
+    handle, csr_m, nClusters, clusters, edgeCut, cost);
+}
+
+}  // namespace spectral
+}  // namespace cuvs
+
+#endif
\ No newline at end of file
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 0ecac6ec20..9224e88d81 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -218,6 +218,7 @@ if(BUILD_TESTS)
     distance/dist_l_inf.cu
     distance/dist_lp_unexp.cu
     distance/dist_russell_rao.cu
+    distance/gram.cu
     distance/masked_nn.cu
     distance/sparse_distance.cu
     sparse/neighbors/cross_component_nn.cu
@@ -227,6 +228,11 @@ if(BUILD_TESTS)
     100
   )
 
+  ConfigureTest(
+    NAME SPARSE_TEST PATH sparse/cluster/cluster_solvers.cu sparse/cluster/eigen_solvers.cu
+    sparse/cluster/spectral.cu GPUS 1 PERCENT 100
+  )
+  
   ConfigureTest(
     NAME PREPROCESSING_TEST PATH preprocessing/scalar_quantization.cu GPUS 1 PERCENT 100
   )
diff --git a/cpp/test/distance/gram.cu b/cpp/test/distance/gram.cu
new file mode 100644
index 0000000000..89b1525ea8
--- /dev/null
+++ b/cpp/test/distance/gram.cu
@@ -0,0 +1,174 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "gram_base.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/distance/grammian.hpp>
+#include <raft/random/rng.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <gtest/gtest.h>
+
+#include <iostream>
+#include <memory>
+
+namespace cuvs::distance::kernels {
+
+struct GramMatrixInputs {
+  int n1;      // feature vectors in matrix 1
+  int n2;      // featuer vectors in matrix 2
+  int n_cols;  // number of elements in a feature vector
+  bool is_row_major;
+  KernelParams kernel;
+  int ld1;
+  int ld2;
+  int ld_out;
+  // We will generate random input using the dimensions given here.
+  // The reference output is calculated by a custom kernel.
+};
+
+std::ostream& operator<<(std::ostream& os, const GramMatrixInputs& p)
+{
+  std::vector<std::string> kernel_names{"linear", "poly", "rbf", "tanh"};
+  os << "/" << p.n1 << "x" << p.n2 << "x" << p.n_cols << "/"
+     << (p.is_row_major ? "RowMajor/" : "ColMajor/") << kernel_names[p.kernel.kernel] << "/ld_"
+     << p.ld1 << "x" << p.ld2 << "x" << p.ld_out;
+  return os;
+}
+
+const std::vector<GramMatrixInputs> inputs = {
+  {42, 137, 2, false, {KernelType::LINEAR}},
+  {42, 137, 2, true, {KernelType::LINEAR}},
+  {42, 137, 2, false, {KernelType::LINEAR}, 64, 179, 181},
+  {42, 137, 2, true, {KernelType::LINEAR}, 64, 179, 181},
+  {137, 42, 2, false, {KernelType::POLYNOMIAL, 2, 0.5, 2.4}},
+  {137, 42, 2, true, {KernelType::POLYNOMIAL, 2, 0.5, 2.4}},
+  {137, 42, 2, false, {KernelType::POLYNOMIAL, 2, 0.5, 2.4}, 159, 73, 144},
+  {137, 42, 2, true, {KernelType::POLYNOMIAL, 2, 0.5, 2.4}, 159, 73, 144},
+  {42, 137, 2, false, {KernelType::TANH, 0, 0.5, 2.4}},
+  {42, 137, 2, true, {KernelType::TANH, 0, 0.5, 2.4}},
+  {42, 137, 2, false, {KernelType::TANH, 0, 0.5, 2.4}, 64, 155, 49},
+  {42, 137, 2, true, {KernelType::TANH, 0, 0.5, 2.4}, 64, 155, 143},
+  {3, 4, 2, false, {KernelType::RBF, 0, 0.5}},
+  {42, 137, 2, false, {KernelType::RBF, 0, 0.5}},
+  {42, 137, 2, true, {KernelType::RBF, 0, 0.5}},
+  // Distance kernel does not support LD parameter yet.
+  //{42, 137, 2, false, {KernelType::RBF, 0, 0.5}, 64, 155, 49},
+  // {42, 137, 2, true, {KernelType::RBF, 0, 0.5}, 64, 155, 143},
+};
+
+template <typename math_t>
+class GramMatrixTest : public ::testing::TestWithParam<GramMatrixInputs> {
+ protected:
+  GramMatrixTest()
+    : params(GetParam()),
+      handle(),
+      x1(0, raft::resource::get_cuda_stream(handle)),
+      x2(0, raft::resource::get_cuda_stream(handle)),
+      gram(0, raft::resource::get_cuda_stream(handle)),
+      gram_host(0)
+  {
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    if (params.ld1 == 0) { params.ld1 = params.is_row_major ? params.n_cols : params.n1; }
+    if (params.ld2 == 0) { params.ld2 = params.is_row_major ? params.n_cols : params.n2; }
+    if (params.ld_out == 0) { params.ld_out = params.is_row_major ? params.n2 : params.n1; }
+    // Derive the size of the output from the offset of the last element.
+    size_t size = get_offset(params.n1 - 1, params.n_cols - 1, params.ld1, params.is_row_major) + 1;
+    x1.resize(size, stream);
+    size = get_offset(params.n2 - 1, params.n_cols - 1, params.ld2, params.is_row_major) + 1;
+    x2.resize(size, stream);
+    size = get_offset(params.n1 - 1, params.n2 - 1, params.ld_out, params.is_row_major) + 1;
+
+    gram.resize(size, stream);
+    RAFT_CUDA_TRY(cudaMemsetAsync(gram.data(), 0, gram.size() * sizeof(math_t), stream));
+    gram_host.resize(gram.size());
+    std::fill(gram_host.begin(), gram_host.end(), 0);
+
+    raft::random::RngState rng(42137ULL);
+    raft::random::uniform(handle, rng, x1.data(), x1.size(), math_t(0), math_t(1));
+    raft::random::uniform(handle, rng, x2.data(), x2.size(), math_t(0), math_t(1));
+  }
+
+  ~GramMatrixTest() override {}
+
+  void runTest()
+  {
+    std::unique_ptr<GramMatrixBase<math_t>> kernel =
+      std::unique_ptr<GramMatrixBase<math_t>>(KernelFactory<math_t>::create(params.kernel));
+
+    auto x1_span =
+      params.is_row_major
+        ? raft::make_device_strided_matrix_view<const math_t, int, raft::layout_c_contiguous>(
+            x1.data(), params.n1, params.n_cols, params.ld1)
+        : raft::make_device_strided_matrix_view<const math_t, int, raft::layout_f_contiguous>(
+            x1.data(), params.n1, params.n_cols, params.ld1);
+    auto x2_span =
+      params.is_row_major
+        ? raft::make_device_strided_matrix_view<const math_t, int, raft::layout_c_contiguous>(
+            x2.data(), params.n2, params.n_cols, params.ld2)
+        : raft::make_device_strided_matrix_view<const math_t, int, raft::layout_f_contiguous>(
+            x2.data(), params.n2, params.n_cols, params.ld2);
+    auto out_span =
+      params.is_row_major
+        ? raft::make_device_strided_matrix_view<math_t, int, raft::layout_c_contiguous>(
+            gram.data(), params.n1, params.n2, params.ld_out)
+        : raft::make_device_strided_matrix_view<math_t, int, raft::layout_f_contiguous>(
+            gram.data(), params.n1, params.n2, params.ld_out);
+
+    (*kernel)(handle, x1_span, x2_span, out_span);
+
+    auto stream = raft::resource::get_cuda_stream(handle);
+    naiveGramMatrixKernel(params.n1,
+                          params.n2,
+                          params.n_cols,
+                          x1,
+                          x2,
+                          gram_host.data(),
+                          params.ld1,
+                          params.ld2,
+                          params.ld_out,
+                          params.is_row_major,
+                          params.kernel,
+                          stream,
+                          handle);
+
+    ASSERT_TRUE(cuvs::devArrMatchHost(
+      gram_host.data(), gram.data(), gram.size(), cuvs::CompareApprox<math_t>(1e-6f), stream));
+  }
+
+  GramMatrixInputs params;
+  raft::resources handle;
+
+  rmm::device_uvector<math_t> x1;
+  rmm::device_uvector<math_t> x2;
+  rmm::device_uvector<math_t> gram;
+
+  std::vector<math_t> gram_host;
+};
+
+typedef GramMatrixTest<float> GramMatrixTestFloat;
+typedef GramMatrixTest<double> GramMatrixTestDouble;
+
+TEST_P(GramMatrixTestFloat, Gram) { runTest(); }
+
+INSTANTIATE_TEST_SUITE_P(GramMatrixTests, GramMatrixTestFloat, ::testing::ValuesIn(inputs));
+};  // namespace cuvs::distance::kernels
\ No newline at end of file
diff --git a/cpp/test/distance/gram_base.cuh b/cpp/test/distance/gram_base.cuh
new file mode 100644
index 0000000000..326cdb4f86
--- /dev/null
+++ b/cpp/test/distance/gram_base.cuh
@@ -0,0 +1,91 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/distance/grammian.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <iostream>
+#include <memory>
+
+namespace cuvs {
+namespace distance {
+namespace kernels {
+
+// Get the offset of element [i,k].
+HDI int get_offset(int i, int k, int ld, bool is_row_major)
+{
+  return is_row_major ? i * ld + k : i + k * ld;
+}
+
+// Calculate the Gram matrix on the host.
+template <typename math_t>
+void naiveGramMatrixKernel(int n1,
+                           int n2,
+                           int n_cols,
+                           const rmm::device_uvector<math_t>& x1,
+                           const rmm::device_uvector<math_t>& x2,
+                           math_t* gram_host,
+                           int ld1,
+                           int ld2,
+                           int ld_out,
+                           bool is_row_major,
+                           KernelParams kernel,
+                           cudaStream_t stream,
+                           const raft::resources& handle)
+{
+  std::vector<math_t> x1_host(x1.size());
+  raft::update_host(x1_host.data(), x1.data(), x1.size(), stream);
+  std::vector<math_t> x2_host(x2.size());
+  raft::update_host(x2_host.data(), x2.data(), x2.size(), stream);
+  raft::resource::sync_stream(handle, stream);
+
+  for (int i = 0; i < n1; i++) {
+    for (int j = 0; j < n2; j++) {
+      float d = 0;
+      for (int k = 0; k < n_cols; k++) {
+        if (kernel.kernel == KernelType::RBF) {
+          math_t diff = x1_host[get_offset(i, k, ld1, is_row_major)] -
+                        x2_host[get_offset(j, k, ld2, is_row_major)];
+          d += diff * diff;
+        } else {
+          d += x1_host[get_offset(i, k, ld1, is_row_major)] *
+               x2_host[get_offset(j, k, ld2, is_row_major)];
+        }
+      }
+      int idx  = get_offset(i, j, ld_out, is_row_major);
+      math_t v = 0;
+      switch (kernel.kernel) {
+        case (KernelType::LINEAR): gram_host[idx] = d; break;
+        case (KernelType::POLYNOMIAL):
+          v              = kernel.gamma * d + kernel.coef0;
+          gram_host[idx] = std::pow(v, kernel.degree);
+          break;
+        case (KernelType::TANH): gram_host[idx] = std::tanh(kernel.gamma * d + kernel.coef0); break;
+        case (KernelType::RBF): gram_host[idx] = exp(-kernel.gamma * d); break;
+      }
+    }
+  }
+}
+
+}  // namespace kernels
+}  // namespace distance
+}  // namespace cuvs
\ No newline at end of file
diff --git a/cpp/test/sparse/cluster/cluster_solvers.cu b/cpp/test/sparse/cluster/cluster_solvers.cu
new file mode 100644
index 0000000000..c0b6c1a78c
--- /dev/null
+++ b/cpp/test/sparse/cluster/cluster_solvers.cu
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../../../src/sparse/cluster/cluster_solvers.cuh"
+#include "../../../src/sparse/cluster/eigen_solvers.cuh"
+#include "../../../src/sparse/cluster/modularity_maximization.cuh"
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_id.hpp>
+#include <raft/core/resources.hpp>
+
+#include <gtest/gtest.h>
+
+#include <iostream>
+#include <memory>
+
+namespace cuvs {
+namespace spectral {
+
+TEST(Raft, ClusterSolvers)
+{
+  using namespace raft::spectral::matrix;
+  using index_type = int;
+  using value_type = double;
+
+  raft::resources h;
+
+  index_type maxiter{100};
+  value_type tol{1.0e-10};
+  unsigned long long seed{100110021003};
+
+  auto stream = raft::resource::get_cuda_stream(h);
+
+  index_type n{100};
+  index_type d{10};
+  index_type k{5};
+
+  // nullptr expected to trigger exceptions:
+  //
+  value_type* eigvecs{nullptr};
+  index_type* codes{nullptr};
+
+  cluster_solver_config_t<index_type, value_type> cfg{k, maxiter, tol, seed};
+
+  kmeans_solver_t<index_type, value_type> cluster_solver{cfg};
+
+  EXPECT_ANY_THROW(cluster_solver.solve(h, n, d, eigvecs, codes));
+}
+
+TEST(Raft, ModularitySolvers)
+{
+  using namespace raft::spectral::matrix;
+  using index_type = int;
+  using value_type = double;
+
+  raft::resources h;
+  ASSERT_EQ(0, raft::resource::get_device_id(h));
+
+  index_type neigvs{10};
+  index_type maxiter{100};
+  index_type restart_iter{10};
+  value_type tol{1.0e-10};
+  bool reorthog{true};
+
+  // nullptr expected to trigger exceptions:
+  //
+  index_type* clusters{nullptr};
+  value_type* eigvals{nullptr};
+  value_type* eigvecs{nullptr};
+
+  unsigned long long seed{100110021003};
+
+  eigen_solver_config_t<index_type, value_type> eig_cfg{
+    neigvs, maxiter, restart_iter, tol, reorthog, seed};
+  lanczos_solver_t<index_type, value_type> eig_solver{eig_cfg};
+
+  index_type k{5};
+
+  cluster_solver_config_t<index_type, value_type> clust_cfg{k, maxiter, tol, seed};
+  kmeans_solver_t<index_type, value_type> cluster_solver{clust_cfg};
+
+  auto stream = raft::resource::get_cuda_stream(h);
+  sparse_matrix_t<index_type, value_type> sm{h, nullptr, nullptr, nullptr, 0, 0};
+
+  EXPECT_ANY_THROW(cuvs::spectral::modularity_maximization(
+    h, sm, eig_solver, cluster_solver, clusters, eigvals, eigvecs));
+
+  value_type modularity{0};
+  EXPECT_ANY_THROW(spectral::analyzeModularity(h, sm, k, clusters, modularity));
+}
+
+}  // namespace spectral
+}  // namespace cuvs
diff --git a/cpp/test/sparse/cluster/eigen_solvers.cu b/cpp/test/sparse/cluster/eigen_solvers.cu
new file mode 100644
index 0000000000..8de0b49e7d
--- /dev/null
+++ b/cpp/test/sparse/cluster/eigen_solvers.cu
@@ -0,0 +1,119 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../../../src/sparse/cluster/eigen_solvers.cuh"
+#include "../../../src/sparse/cluster/partition.cuh"
+#include <raft/core/nvtx.hpp>
+#include <raft/core/resource/device_id.hpp>
+#include <raft/core/resources.hpp>
+
+#include <gtest/gtest.h>
+
+#include <cstddef>
+#include <iostream>
+#include <memory>
+#include <type_traits>
+
+namespace cuvs {
+namespace spectral {
+
+TEST(Raft, EigenSolvers)
+{
+  raft::common::nvtx::range fun_scope("test::EigenSolvers");
+  using namespace raft::spectral::matrix;
+  using index_type = int;
+  using value_type = double;
+
+  raft::resources h;
+  ASSERT_EQ(0, raft::resource::get_device_id(h));
+
+  index_type* ro{nullptr};
+  index_type* ci{nullptr};
+  value_type* vs{nullptr};
+  index_type nnz   = 0;
+  index_type nrows = 0;
+
+  sparse_matrix_t<index_type, value_type> sm1{h, ro, ci, vs, nrows, nnz};
+  ASSERT_EQ(nullptr, sm1.row_offsets_);
+
+  index_type neigvs{10};
+  index_type maxiter{100};
+  index_type restart_iter{10};
+  value_type tol{1.0e-10};
+  bool reorthog{true};
+
+  // nullptr expected to trigger exceptions:
+  //
+  value_type* eigvals{nullptr};
+  value_type* eigvecs{nullptr};
+  std::uint64_t seed{100110021003};
+
+  eigen_solver_config_t<index_type, value_type> cfg{
+    neigvs, maxiter, restart_iter, tol, reorthog, seed};
+
+  lanczos_solver_t<index_type, value_type> eig_solver{cfg};
+
+  EXPECT_ANY_THROW(eig_solver.solve_smallest_eigenvectors(h, sm1, eigvals, eigvecs));
+
+  EXPECT_ANY_THROW(eig_solver.solve_largest_eigenvectors(h, sm1, eigvals, eigvecs));
+}
+
+TEST(Raft, SpectralSolvers)
+{
+  raft::common::nvtx::range fun_scope("test::SpectralSolvers");
+  using namespace raft::spectral::matrix;
+  using index_type = int;
+  using value_type = double;
+
+  raft::resources h;
+  ASSERT_EQ(0, raft::resource::get_device_id(h)
+
+  );
+
+  index_type neigvs{10};
+  index_type maxiter{100};
+  index_type restart_iter{10};
+  value_type tol{1.0e-10};
+  bool reorthog{true};
+
+  // nullptr expected to trigger exceptions:
+  //
+  index_type* clusters{nullptr};
+  value_type* eigvals{nullptr};
+  value_type* eigvecs{nullptr};
+
+  unsigned long long seed{100110021003};
+
+  eigen_solver_config_t<index_type, value_type> eig_cfg{
+    neigvs, maxiter, restart_iter, tol, reorthog, seed};
+  lanczos_solver_t<index_type, value_type> eig_solver{eig_cfg};
+
+  index_type k{5};
+
+  cluster_solver_config_t<index_type, value_type> clust_cfg{k, maxiter, tol, seed};
+  kmeans_solver_t<index_type, value_type> cluster_solver{clust_cfg};
+
+  sparse_matrix_t<index_type, value_type> sm{h, nullptr, nullptr, nullptr, 0, 0};
+  EXPECT_ANY_THROW(
+    spectral::partition(h, sm, eig_solver, cluster_solver, clusters, eigvals, eigvecs));
+
+  value_type edgeCut{0};
+  value_type cost{0};
+  EXPECT_ANY_THROW(spectral::analyzePartition(h, sm, k, clusters, edgeCut, cost));
+}
+
+}  // namespace spectral
+}  // namespace cuvs
diff --git a/cpp/test/sparse/cluster/spectral.cu b/cpp/test/sparse/cluster/spectral.cu
new file mode 100644
index 0000000000..7d0cdef9db
--- /dev/null
+++ b/cpp/test/sparse/cluster/spectral.cu
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../../test_utils.cuh"
+
+#include "../../../src/sparse/cluster/modularity_maximization.cuh"
+#include "../../../src/sparse/cluster/partition.cuh"
+#include <raft/core/handle.hpp>
+
+#include <gtest/gtest.h>
+
+#include <iostream>
+#include <memory>
+
+namespace cuvs {
+namespace cluster {
+
+/**
+ * Warning: There appears to be a CUDA 12.2 bug in cusparse that causes an
+ * alignment issue. We've fixed the bug in our code through a workaround
+ * (see raft/sparse/linalg/spmm.hpp for fix). This test is meant to fail
+ * in the case where the fix is accidentally reverted, so that it doesn't
+ * break any downstream libraries that depend on RAFT
+ */
+TEST(Raft, Spectral)
+{
+  raft::handle_t handle;
+
+  std::vector<int32_t> h_offsets({0, 2, 4, 7, 10, 12, 14});
+  std::vector<int32_t> h_indices({1, 2, 0, 2, 0, 1, 3, 2, 4, 5, 3, 5, 3, 4});
+  std::vector<float> h_values(
+    {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0});
+  std::vector<int32_t> expected_clustering({1, 1, 1, 0, 0, 0});
+
+  int32_t n_clusters{2};
+  int32_t n_eigenvectors{2};
+  int32_t evs_max_it{100};
+  int32_t kmean_max_it{100};
+  int32_t restartIter_lanczos = 15 + n_eigenvectors;
+  float evs_tol{0.001};
+  float kmean_tol{0.001};
+  unsigned long long seed1{1234567};
+  unsigned long long seed2{12345678};
+  bool reorthog{false};
+
+  rmm::device_uvector<int32_t> offsets(h_offsets.size(), handle.get_stream());
+  rmm::device_uvector<int32_t> indices(h_indices.size(), handle.get_stream());
+  rmm::device_uvector<float> values(h_indices.size(), handle.get_stream());
+  rmm::device_uvector<int32_t> clustering(expected_clustering.size(), handle.get_stream());
+  rmm::device_uvector<float> eigenvalues(n_eigenvectors, handle.get_stream());
+  rmm::device_uvector<float> eigenvectors(n_eigenvectors * expected_clustering.size(),
+                                          handle.get_stream());
+
+  rmm::device_uvector<int32_t> exp_dev(expected_clustering.size(), handle.get_stream());
+
+  raft::update_device(
+    exp_dev.data(), expected_clustering.data(), expected_clustering.size(), handle.get_stream());
+
+  raft::update_device(offsets.data(), h_offsets.data(), h_offsets.size(), handle.get_stream());
+  raft::update_device(indices.data(), h_indices.data(), h_indices.size(), handle.get_stream());
+  raft::update_device(values.data(), h_values.data(), h_values.size(), handle.get_stream());
+
+  raft::spectral::matrix::sparse_matrix_t<int32_t, float> const matrix{
+    handle,
+    offsets.data(),
+    indices.data(),
+    values.data(),
+    static_cast<int32_t>(offsets.size() - 1),
+    static_cast<int32_t>(indices.size())};
+
+  cuvs::spectral::eigen_solver_config_t<int32_t, float> eig_cfg{
+    n_eigenvectors, evs_max_it, restartIter_lanczos, evs_tol, reorthog, seed1};
+  cuvs::spectral::lanczos_solver_t<int32_t, float> eig_solver{eig_cfg};
+
+  cuvs::spectral::cluster_solver_config_t<int32_t, float> clust_cfg{
+    n_clusters, kmean_max_it, kmean_tol, seed2};
+  cuvs::spectral::kmeans_solver_t<int32_t, float> cluster_solver{clust_cfg};
+
+  cuvs::spectral::partition(handle,
+                            matrix,
+                            eig_solver,
+                            cluster_solver,
+                            clustering.data(),
+                            eigenvalues.data(),
+                            eigenvectors.data());
+
+  ASSERT_TRUE(devArrMatch(expected_clustering.data(),
+                          exp_dev.data(),
+                          exp_dev.size(),
+                          1,
+                          cuvs::Compare<int32_t>(),
+                          handle.get_stream()));
+}
+
+}  // namespace cluster
+}  // namespace cuvs
\ No newline at end of file
diff --git a/cpp/test/sparse/cluster/spectral_matrix.cu b/cpp/test/sparse/cluster/spectral_matrix.cu
new file mode 100644
index 0000000000..37a4202b8c
--- /dev/null
+++ b/cpp/test/sparse/cluster/spectral_matrix.cu
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_id.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/spectral/matrix_wrappers.hpp>
+
+#include <gtest/gtest.h>
+
+#include <iostream>
+#include <memory>
+
+namespace cuvs {
+namespace spectral {
+namespace matrix {
+namespace {
+template <typename index_type, typename value_type>
+struct csr_view_t {
+  index_type* offsets;
+  index_type* indices;
+  value_type* edge_data;
+  index_type number_of_vertices;
+  index_type number_of_edges;
+};
+}  // namespace
+TEST(Raft, SpectralMatrices)
+{
+  using index_type = int;
+  using value_type = double;
+
+  raft::resources h;
+  ASSERT_EQ(0, raft::resource::get_device_id(h));
+
+  csr_view_t<index_type, value_type> csr_v{nullptr, nullptr, nullptr, 0, 0};
+
+  int const sz = 10;
+  vector_t<index_type> d_v{h, sz};
+
+  index_type* ro{nullptr};
+  index_type* ci{nullptr};
+  value_type* vs{nullptr};
+  index_type nnz   = 0;
+  index_type nrows = 0;
+  sparse_matrix_t<index_type, value_type> sm1{h, ro, ci, vs, nrows, nnz};
+  sparse_matrix_t<index_type, value_type> sm2{h, csr_v};
+  ASSERT_EQ(nullptr, sm1.row_offsets_);
+  ASSERT_EQ(nullptr, sm2.row_offsets_);
+
+  auto stream = resource::get_cuda_stream(h);
+
+  auto cnstr_lm1 = [&h, ro, ci, vs, nrows, nnz](void) {
+    laplacian_matrix_t<index_type, value_type> lm1{h, ro, ci, vs, nrows, nnz};
+  };
+  EXPECT_ANY_THROW(cnstr_lm1());  // because of nullptr ptr args
+
+  auto cnstr_lm2 = [&h, &sm2](void) { laplacian_matrix_t<index_type, value_type> lm2{h, sm2}; };
+  EXPECT_ANY_THROW(cnstr_lm2());  // because of nullptr ptr args
+
+  auto cnstr_mm1 = [&h, ro, ci, vs, nrows, nnz](void) {
+    modularity_matrix_t<index_type, value_type> mm1{h, ro, ci, vs, nrows, nnz};
+  };
+  EXPECT_ANY_THROW(cnstr_mm1());  // because of nullptr ptr args
+
+  auto cnstr_mm2 = [&h, &sm2](void) { modularity_matrix_t<index_type, value_type> mm2{h, sm2}; };
+  EXPECT_ANY_THROW(cnstr_mm2());  // because of nullptr ptr args
+}
+
+}  // namespace matrix
+}  // namespace spectral
+}  // namespace cuvs
diff --git a/cpp/test/sparse/gram.cu b/cpp/test/sparse/gram.cu
new file mode 100644
index 0000000000..d7af30a1cb
--- /dev/null
+++ b/cpp/test/sparse/gram.cu
@@ -0,0 +1,330 @@
+/*
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/core/resource/cuda_stream.hpp>
+
+#include "../distance/gram_base.cuh"
+#include "../test_utils.cuh"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/distance/grammian.hpp>
+
+#include <raft/random/rng.cuh>
+#include <raft/sparse/convert/dense.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/itertools.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <gtest/gtest.h>
+
+#include <iostream>
+#include <memory>
+
+namespace cuvs::distance::kernels::sparse {
+
+/**
+ * Structure to describe structure of the input matrices:
+ *  - DENSE: dense, dense
+ *  - MIX: CSR, dense
+ *  - CSR: CSR, CSR
+ */
+enum SparseType { DENSE, MIX, CSR };
+
+struct GramMatrixInputs {
+  int n1;      // feature vectors in matrix 1
+  int n2;      // featuer vectors in matrix 2
+  int n_cols;  // number of elements in a feature vector
+  bool is_row_major;
+  SparseType sparse_input;
+  KernelParams kernel;
+  int ld1;
+  int ld2;
+  int ld_out;
+  // We will generate random input using the dimensions given here.
+  // The reference output is calculated by a custom kernel.
+};
+
+std::ostream& operator<<(std::ostream& os, const GramMatrixInputs& p)
+{
+  std::vector<std::string> kernel_names{"linear", "poly", "rbf", "tanh"};
+  os << "/" << p.n1 << "x" << p.n2 << "x" << p.n_cols << "/"
+     << (p.is_row_major ? "RowMajor/" : "ColMajor/")
+     << (p.sparse_input == SparseType::DENSE
+           ? "DenseDense/"
+           : (p.sparse_input == SparseType::MIX ? "CsrDense/" : "CsrCsr/"))
+     << kernel_names[p.kernel.kernel] << "/ld_" << p.ld1 << "x" << p.ld2 << "x" << p.ld_out;
+  return os;
+}
+
+/*struct KernelParams {
+  // Kernel function parameters
+  KernelType kernel;  //!< Type of the kernel function
+  int degree;         //!< Degree of polynomial kernel (ignored by others)
+  double gamma;       //!< multiplier in the
+  double coef0;       //!< additive constant in poly and tanh kernels
+};*/
+
+// const KernelParams linear_kernel_params{.kernel=KernelType::LINEAR};
+
+// {KernelType::POLYNOMIAL, 2, 0.5, 2.4}, {KernelType::TANH, 0, 0.5, 2.4}, {KernelType::RBF, 0, 0.5}
+const std::vector<GramMatrixInputs> inputs = raft::util::itertools::product<GramMatrixInputs>(
+  {42},
+  {137},
+  {2},
+  {true, false},
+  {SparseType::DENSE, SparseType::MIX, SparseType::CSR},
+  {KernelParams{KernelType::LINEAR},
+   KernelParams{KernelType::POLYNOMIAL, 2, 0.5, 2.4},
+   KernelParams{KernelType::TANH, 0, 0.5, 2.4},
+   KernelParams{KernelType::RBF, 0, 0.5}});
+
+// (ld_1, ld_2, ld_out) not supported by RBF and CSR
+const std::vector<GramMatrixInputs> inputs_ld = raft::util::itertools::product<GramMatrixInputs>(
+  {137},
+  {42},
+  {2},
+  {true, false},
+  {SparseType::DENSE, SparseType::MIX},
+  {KernelParams{KernelType::LINEAR},
+   KernelParams{KernelType::POLYNOMIAL, 2, 0.5, 2.4},
+   KernelParams{KernelType::TANH, 0, 0.5, 2.4}},
+  {159},
+  {73},
+  {144});
+
+// (ld_1, ld_2) are supported by CSR
+const std::vector<GramMatrixInputs> inputs_ld_csr =
+  raft::util::itertools::product<GramMatrixInputs>(
+    {42},
+    {137},
+    {2},
+    {true, false},
+    {SparseType::CSR, SparseType::MIX},
+    {KernelParams{KernelType::LINEAR},
+     KernelParams{KernelType::POLYNOMIAL, 2, 0.5, 2.4},
+     KernelParams{KernelType::TANH, 0, 0.5, 2.4}},
+    {64},
+    {155},
+    {0});
+
+template <typename math_t>
+class GramMatrixTest : public ::testing::TestWithParam<GramMatrixInputs> {
+ protected:
+  GramMatrixTest()
+    : params(GetParam()),
+      stream(raft::resource::get_cuda_stream(handle)),
+      x1(0, stream),
+      x2(0, stream),
+      x1_csr_indptr(0, stream),
+      x1_csr_indices(0, stream),
+      x1_csr_data(0, stream),
+      x2_csr_indptr(0, stream),
+      x2_csr_indices(0, stream),
+      x2_csr_data(0, stream),
+      gram(0, stream),
+      gram_host(0)
+  {
+    if (params.ld1 == 0) { params.ld1 = params.is_row_major ? params.n_cols : params.n1; }
+    if (params.ld2 == 0) { params.ld2 = params.is_row_major ? params.n_cols : params.n2; }
+    if (params.ld_out == 0) { params.ld_out = params.is_row_major ? params.n2 : params.n1; }
+    // Derive the size of the output from the offset of the last element.
+    size_t size = get_offset(params.n1 - 1, params.n_cols - 1, params.ld1, params.is_row_major) + 1;
+    x1.resize(size, stream);
+    size = get_offset(params.n2 - 1, params.n_cols - 1, params.ld2, params.is_row_major) + 1;
+    x2.resize(size, stream);
+    size = get_offset(params.n1 - 1, params.n2 - 1, params.ld_out, params.is_row_major) + 1;
+
+    gram.resize(size, stream);
+    RAFT_CUDA_TRY(cudaMemsetAsync(gram.data(), 0, gram.size() * sizeof(math_t), stream));
+    gram_host.resize(gram.size());
+    std::fill(gram_host.begin(), gram_host.end(), 0);
+
+    raft::random::RngState r(42137ULL);
+    raft::random::uniform(handle, r, x1.data(), x1.size(), math_t(0), math_t(1));
+    raft::random::uniform(handle, r, x2.data(), x2.size(), math_t(0), math_t(1));
+
+    RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+  }
+
+  ~GramMatrixTest() override {}
+
+  int prepareCsr(math_t* dense, int n_rows, int ld, int* indptr, int* indices, math_t* data)
+  {
+    int nnz           = 0;
+    double eps        = 1e-6;
+    int n_cols        = params.n_cols;
+    bool is_row_major = params.is_row_major;
+    size_t dense_size = get_offset(n_rows - 1, n_cols - 1, ld, is_row_major) + 1;
+
+    std::vector<math_t> dense_host(dense_size);
+    raft::update_host(dense_host.data(), dense, dense_size, stream);
+    raft::resource::sync_stream(handle, stream);
+
+    std::vector<int> indptr_host(n_rows + 1);
+    std::vector<int> indices_host(n_rows * n_cols);
+    std::vector<math_t> data_host(n_rows * n_cols);
+
+    // create csr matrix from dense (with threshold)
+    for (int i = 0; i < n_rows; ++i) {
+      indptr_host[i] = nnz;
+      for (int j = 0; j < n_cols; ++j) {
+        math_t value = dense_host[get_offset(i, j, ld, is_row_major)];
+        if (value > eps) {
+          indices_host[nnz] = j;
+          data_host[nnz]    = value;
+          nnz++;
+        }
+      }
+    }
+    indptr_host[n_rows] = nnz;
+
+    // fill back dense matrix from CSR
+    std::fill(dense_host.data(), dense_host.data() + dense_size, 0);
+    for (int i = 0; i < n_rows; ++i) {
+      for (int idx = indptr_host[i]; idx < indptr_host[i + 1]; ++idx) {
+        dense_host[get_offset(i, indices_host[idx], ld, is_row_major)] = data_host[idx];
+      }
+    }
+
+    raft::update_device(dense, dense_host.data(), dense_size, stream);
+    raft::update_device(indptr, indptr_host.data(), n_rows + 1, stream);
+    raft::update_device(indices, indices_host.data(), nnz, stream);
+    raft::update_device(data, data_host.data(), nnz, stream);
+    raft::resource::sync_stream(handle, stream);
+    return nnz;
+  }
+
+  void runTest()
+  {
+    std::unique_ptr<GramMatrixBase<math_t>> kernel =
+      std::unique_ptr<GramMatrixBase<math_t>>(KernelFactory<math_t>::create(params.kernel));
+
+    auto x1_span =
+      params.is_row_major
+        ? raft::make_device_strided_matrix_view<const math_t, int, raft::layout_c_contiguous>(
+            x1.data(), params.n1, params.n_cols, params.ld1)
+        : raft::make_device_strided_matrix_view<const math_t, int, raft::layout_f_contiguous>(
+            x1.data(), params.n1, params.n_cols, params.ld1);
+    auto x2_span =
+      params.is_row_major
+        ? raft::make_device_strided_matrix_view<const math_t, int, raft::layout_c_contiguous>(
+            x2.data(), params.n2, params.n_cols, params.ld2)
+        : raft::make_device_strided_matrix_view<const math_t, int, raft::layout_f_contiguous>(
+            x2.data(), params.n2, params.n_cols, params.ld2);
+    auto out_span =
+      params.is_row_major
+        ? raft::make_device_strided_matrix_view<math_t, int, raft::layout_c_contiguous>(
+            gram.data(), params.n1, params.n2, params.ld_out)
+        : raft::make_device_strided_matrix_view<math_t, int, raft::layout_f_contiguous>(
+            gram.data(), params.n1, params.n2, params.ld_out);
+
+    if (params.sparse_input == SparseType::DENSE) {
+      (*kernel)(handle, x1_span, x2_span, out_span);
+    } else {
+      x1_csr_indptr.reserve(params.n1 + 1, stream);
+      x1_csr_indices.reserve(params.n1 * params.n_cols, stream);
+      x1_csr_data.reserve(params.n1 * params.n_cols, stream);
+      int x1_nnz = prepareCsr(x1.data(),
+                              params.n1,
+                              params.ld1,
+                              x1_csr_indptr.data(),
+                              x1_csr_indices.data(),
+                              x1_csr_data.data());
+
+      auto x1_csr_structure = raft::make_device_compressed_structure_view<int, int, int>(
+        x1_csr_indptr.data(), x1_csr_indices.data(), params.n1, params.n_cols, x1_nnz);
+      auto x1_csr = raft::device_csr_matrix_view<const math_t, int, int, int>(
+        raft::device_span<const math_t>(x1_csr_data.data(), x1_csr_structure.get_nnz()),
+        x1_csr_structure);
+
+      if (params.sparse_input == SparseType::MIX) {
+        (*kernel)(handle, x1_csr, x2_span, out_span);
+      } else {
+        x2_csr_indptr.reserve(params.n2 + 1, stream);
+        x2_csr_indices.reserve(params.n2 * params.n_cols, stream);
+        x2_csr_data.reserve(params.n2 * params.n_cols, stream);
+        int x2_nnz = prepareCsr(x2.data(),
+                                params.n2,
+                                params.ld2,
+                                x2_csr_indptr.data(),
+                                x2_csr_indices.data(),
+                                x2_csr_data.data());
+
+        auto x2_csr_structure = raft::make_device_compressed_structure_view<int, int, int>(
+          x2_csr_indptr.data(), x2_csr_indices.data(), params.n2, params.n_cols, x2_nnz);
+        auto x2_csr = raft::device_csr_matrix_view<const math_t, int, int, int>(
+          raft::device_span<const math_t>(x2_csr_data.data(), x2_csr_structure.get_nnz()),
+          x2_csr_structure);
+
+        (*kernel)(handle, x1_csr, x2_csr, out_span);
+      }
+    }
+    // Something in gram is executing not on the 'stream' and therefore
+    // a full device sync is required
+    RAFT_CUDA_TRY(cudaDeviceSynchronize());
+    naiveGramMatrixKernel(params.n1,
+                          params.n2,
+                          params.n_cols,
+                          x1,
+                          x2,
+                          gram_host.data(),
+                          params.ld1,
+                          params.ld2,
+                          params.ld_out,
+                          params.is_row_major,
+                          params.kernel,
+                          stream,
+                          handle);
+    raft::resource::sync_stream(handle, stream);
+
+    ASSERT_TRUE(cuvs::devArrMatchHost(
+      gram_host.data(), gram.data(), gram.size(), cuvs::CompareApprox<math_t>(1e-6f), stream));
+  }
+
+  raft::resources handle;
+  cudaStream_t stream = 0;
+  GramMatrixInputs params;
+
+  rmm::device_uvector<math_t> x1;
+  rmm::device_uvector<math_t> x2;
+
+  rmm::device_uvector<int> x1_csr_indptr;
+  rmm::device_uvector<int> x1_csr_indices;
+  rmm::device_uvector<math_t> x1_csr_data;
+  rmm::device_uvector<int> x2_csr_indptr;
+  rmm::device_uvector<int> x2_csr_indices;
+  rmm::device_uvector<math_t> x2_csr_data;
+
+  rmm::device_uvector<math_t> gram;
+  std::vector<math_t> gram_host;
+};
+
+typedef GramMatrixTest<float> GramMatrixTestFloatStandard;
+typedef GramMatrixTest<float> GramMatrixTestFloatLd;
+typedef GramMatrixTest<float> GramMatrixTestFloatLdCsr;
+
+TEST_P(GramMatrixTestFloatStandard, Gram) { runTest(); }
+TEST_P(GramMatrixTestFloatLd, Gram) { runTest(); }
+TEST_P(GramMatrixTestFloatLdCsr, Gram) { runTest(); }
+
+INSTANTIATE_TEST_SUITE_P(GramMatrixTests, GramMatrixTestFloatStandard, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_SUITE_P(GramMatrixTests, GramMatrixTestFloatLd, ::testing::ValuesIn(inputs_ld));
+INSTANTIATE_TEST_SUITE_P(GramMatrixTests,
+                         GramMatrixTestFloatLdCsr,
+                         ::testing::ValuesIn(inputs_ld_csr));
+};  // namespace cuvs::distance::kernels::sparse
\ No newline at end of file

From fa8838a3a00b17c6b8284e094d22382d80f87247 Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Fri, 6 Dec 2024 09:42:21 -0600
Subject: [PATCH 233/623] Modify cuvs-bench to be able to generate ground truth
 in CPU systems (#466)

PR allows calculating ground truth for cuvs-bench on CPU systems. Current version uses a simple NumPy brute force, perhaps we should consider using faiss? cc @cjnolet @divyegala

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/466
---
 .../bench_ann_cuda-118_arch-aarch64.yaml      |   3 +
 .../bench_ann_cuda-118_arch-x86_64.yaml       |   3 +
 .../bench_ann_cuda-125_arch-aarch64.yaml      |   3 +
 .../bench_ann_cuda-125_arch-x86_64.yaml       |   3 +
 conda/recipes/cuvs-bench-cpu/meta.yaml        |   1 +
 conda/recipes/cuvs-bench/meta.yaml            |   3 +-
 dependencies.yaml                             |   3 +
 .../generate_groundtruth/__main__.py          | 204 +++++++++++++++---
 .../cuvs_bench/cuvs_bench/run/data_export.py  |  73 +++----
 python/cuvs_bench/pyproject.toml              |   1 +
 10 files changed, 220 insertions(+), 77 deletions(-)

diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 1e602ccf1a..59d471bda2 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -18,6 +18,8 @@ dependencies:
 - cuda-python>=11.7.1,<12.0a0,<=11.8.3
 - cuda-version=11.8
 - cudatoolkit
+- cupy>=12.0.0
+- cuvs==24.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,6 +34,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- libcuvs==24.12.*,>=0.0.0a0
 - librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index b060e78c20..31a416eb5e 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -18,6 +18,8 @@ dependencies:
 - cuda-python>=11.7.1,<12.0a0,<=11.8.3
 - cuda-version=11.8
 - cudatoolkit
+- cupy>=12.0.0
+- cuvs==24.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,6 +34,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
+- libcuvs==24.12.*,>=0.0.0a0
 - librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 4851222735..3efe9ebde8 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -19,6 +19,8 @@ dependencies:
 - cuda-profiler-api
 - cuda-python>=12.0,<13.0a0,<=12.6.0
 - cuda-version=12.5
+- cupy>=12.0.0
+- cuvs==24.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -29,6 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- libcuvs==24.12.*,>=0.0.0a0
 - librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index d5f48dadb5..7fbd773681 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -19,6 +19,8 @@ dependencies:
 - cuda-profiler-api
 - cuda-python>=12.0,<13.0a0,<=12.6.0
 - cuda-version=12.5
+- cupy>=12.0.0
+- cuvs==24.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -29,6 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
+- libcuvs==24.12.*,>=0.0.0a0
 - librmm==24.12.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
index 02c11346fb..016df56be1 100644
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -59,6 +59,7 @@ requirements:
     - glog {{ glog_version }}
     - h5py {{ h5py_version }}
     - matplotlib
+    - numpy >=1.23,<3.0a0
     - pandas
     - pyyaml
     - python
diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
index 3e81edc58f..0681a1038f 100644
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -88,10 +88,11 @@ requirements:
     - cudatoolkit
     {% else %}
     - cuda-cudart
+    - cupy>=12.0.0
     - libcublas
     {% endif %}
     - glog {{ glog_version }}
-    - libcuvs {{ version }}
+    - cuvs {{ version }}
     - h5py {{ h5py_version }}
     - matplotlib
     - pandas
diff --git a/dependencies.yaml b/dependencies.yaml
index 80a7d20241..98cac53002 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -39,6 +39,7 @@ files:
       - bench
       - bench_python
       - rapids_build_setuptools
+      - cupy
   test_cpp:
     output: none
     includes:
@@ -475,11 +476,13 @@ dependencies:
           - h5py>=3.8.0
           - benchmark>=1.8.2
           - openblas
+          - libcuvs==24.12.*,>=0.0.0a0
   bench_python:
     common:
       - output_types: [conda, pyproject, requirements]
         packages:
           - click
+          - cuvs==24.12.*,>=0.0.0a0
           - matplotlib
           - pandas
           - pyyaml
diff --git a/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
index dbee6cd363..88ec55dfa5 100644
--- a/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
@@ -15,70 +15,206 @@
 # limitations under the License.
 #
 import argparse
+import importlib
 import os
 import sys
+import warnings
 
-import cupy as cp
-import numpy as np
-import rmm
-from pylibraft.common import DeviceResources
-from rmm.allocators.cupy import rmm_cupy_allocator
+from .utils import memmap_bin_file, suffix_from_dtype, write_bin
 
-from cuvs.neighbors.brute_force import build, search
 
-from .utils import memmap_bin_file, suffix_from_dtype, write_bin
+def import_with_fallback(primary_lib, secondary_lib=None, alias=None):
+    """
+    Attempt to import a primary library, with an optional fallback to a
+    secondary library.
+    Optionally assigns the imported module to a global alias.
+
+    Parameters
+    ----------
+    primary_lib : str
+        Name of the primary library to import.
+    secondary_lib : str, optional
+        Name of the secondary library to use as a fallback. If `None`,
+        no fallback is attempted.
+    alias : str, optional
+        Alias to assign the imported module globally.
+
+    Returns
+    -------
+    module or None
+        The imported module if successful; otherwise, `None`.
+
+    Examples
+    --------
+    >>> xp = import_with_fallback('cupy', 'numpy')
+    >>> mod = import_with_fallback('nonexistent_lib')
+    >>> if mod is None:
+    ...     print("Library not found.")
+    """
+    try:
+        module = importlib.import_module(primary_lib)
+    except ImportError:
+        if secondary_lib is not None:
+            try:
+                module = importlib.import_module(secondary_lib)
+            except ImportError:
+                module = None
+        else:
+            module = None
+    if alias and module is not None:
+        globals()[alias] = module
+    return module
+
+
+xp = import_with_fallback("cupy", "numpy")
+rmm = import_with_fallback("rmm")
+gpu_system = False
+
 
+def force_fallback_to_numpy():
+    global xp, gpu_system
+    xp = import_with_fallback("numpy")
+    gpu_system = False
+    warnings.warn(
+        "Consider using a GPU-based system to greatly accelerate "
+        " generating groundtruths using cuVS."
+    )
+
+
+if rmm is not None:
+    gpu_system = True
+    try:
+        from pylibraft.common import DeviceResources
+        from rmm.allocators.cupy import rmm_cupy_allocator
 
-def generate_random_queries(n_queries, n_features, dtype=np.float32):
+        from cuvs.neighbors.brute_force import build, search
+    except ImportError:
+        # RMM is available, cupy is available, but cuVS is not
+        force_fallback_to_numpy()
+else:
+    # No RMM, no cuVS, but cupy is available
+    force_fallback_to_numpy()
+
+
+def generate_random_queries(n_queries, n_features, dtype=xp.float32):
     print("Generating random queries")
-    if np.issubdtype(dtype, np.integer):
-        queries = cp.random.randint(
+    if xp.issubdtype(dtype, xp.integer):
+        queries = xp.random.randint(
             0, 255, size=(n_queries, n_features), dtype=dtype
         )
     else:
-        queries = cp.random.uniform(size=(n_queries, n_features)).astype(dtype)
+        queries = xp.random.uniform(size=(n_queries, n_features)).astype(dtype)
     return queries
 
 
 def choose_random_queries(dataset, n_queries):
     print("Choosing random vector from dataset as query vectors")
-    query_idx = np.random.choice(
+    query_idx = xp.random.choice(
         dataset.shape[0], size=(n_queries,), replace=False
     )
     return dataset[query_idx, :]
 
 
+def cpu_search(dataset, queries, k, metric="squeclidean"):
+    """
+    Find the k nearest neighbors for each query point in the dataset using the
+    specified metric.
+
+    Parameters
+    ----------
+    dataset : numpy.ndarray
+        An array of shape (n_samples, n_features) representing the dataset.
+    queries : numpy.ndarray
+        An array of shape (n_queries, n_features) representing the query
+        points.
+    k : int
+        The number of nearest neighbors to find.
+    metric : str, optional
+        The distance metric to use. Can be 'squeclidean' or 'inner_product'.
+        Default is 'squeclidean'.
+
+    Returns
+    -------
+    distances : numpy.ndarray
+        An array of shape (n_queries, k) containing the distances
+        (for 'squeclidean') or similarities
+        (for 'inner_product') to the k nearest neighbors for each query.
+    indices : numpy.ndarray
+        An array of shape (n_queries, k) containing the indices of the
+        k nearest neighbors in the dataset for each query.
+
+    """
+    if metric == "squeclidean":
+        diff = queries[:, xp.newaxis, :] - dataset[xp.newaxis, :, :]
+        dist_sq = xp.sum(diff**2, axis=2)  # Shape: (n_queries, n_samples)
+
+        indices = xp.argpartition(dist_sq, kth=k - 1, axis=1)[:, :k]
+        distances = xp.take_along_axis(dist_sq, indices, axis=1)
+
+        sorted_idx = xp.argsort(distances, axis=1)
+        distances = xp.take_along_axis(distances, sorted_idx, axis=1)
+        indices = xp.take_along_axis(indices, sorted_idx, axis=1)
+
+    elif metric == "inner_product":
+        similarities = xp.dot(
+            queries, dataset.T
+        )  # Shape: (n_queries, n_samples)
+
+        neg_similarities = -similarities
+        indices = xp.argpartition(neg_similarities, kth=k - 1, axis=1)[:, :k]
+        distances = xp.take_along_axis(similarities, indices, axis=1)
+
+        sorted_idx = xp.argsort(-distances, axis=1)
+
+    else:
+        raise ValueError(
+            "Unsupported metric in cuvs-bench-cpu. "
+            "Use 'squeclidean' or 'inner_product' or use the GPU package"
+            "to use any distance supported by cuVS."
+        )
+
+    distances = xp.take_along_axis(distances, sorted_idx, axis=1)
+    indices = xp.take_along_axis(indices, sorted_idx, axis=1)
+
+    return distances, indices
+
+
 def calc_truth(dataset, queries, k, metric="sqeuclidean"):
-    resources = DeviceResources()
     n_samples = dataset.shape[0]
     n = 500000  # batch size for processing neighbors
     i = 0
     indices = None
     distances = None
-    queries = cp.asarray(queries, dtype=cp.float32)
+    queries = xp.asarray(queries, dtype=xp.float32)
+
+    if gpu_system:
+        resources = DeviceResources()
 
     while i < n_samples:
         print("Step {0}/{1}:".format(i // n, n_samples // n))
         n_batch = n if i + n <= n_samples else n_samples - i
 
-        X = cp.asarray(dataset[i : i + n_batch, :], cp.float32)
+        X = xp.asarray(dataset[i : i + n_batch, :], xp.float32)
 
-        index = build(X, metric=metric, resources=resources)
-        D, Ind = search(index, queries, k, resources=resources)
-        resources.sync()
+        if gpu_system:
+            index = build(X, metric=metric, resources=resources)
+            D, Ind = search(index, queries, k, resources=resources)
+            resources.sync()
+        else:
+            D, Ind = cpu_search(X, queries, metric=metric)
 
-        D, Ind = cp.asarray(D), cp.asarray(Ind)
+        D, Ind = xp.asarray(D), xp.asarray(Ind)
         Ind += i  # shift neighbor index by offset i
 
         if distances is None:
             distances = D
             indices = Ind
         else:
-            distances = cp.concatenate([distances, D], axis=1)
-            indices = cp.concatenate([indices, Ind], axis=1)
-            idx = cp.argsort(distances, axis=1)[:, :k]
-            distances = cp.take_along_axis(distances, idx, axis=1)
-            indices = cp.take_along_axis(indices, idx, axis=1)
+            distances = xp.concatenate([distances, D], axis=1)
+            indices = xp.concatenate([indices, Ind], axis=1)
+            idx = xp.argsort(distances, axis=1)[:, :k]
+            distances = xp.take_along_axis(distances, idx, axis=1)
+            indices = xp.take_along_axis(indices, idx, axis=1)
 
         i += n_batch
 
@@ -86,11 +222,15 @@ def calc_truth(dataset, queries, k, metric="sqeuclidean"):
 
 
 def main():
-    pool = rmm.mr.PoolMemoryResource(
-        rmm.mr.CudaMemoryResource(), initial_pool_size=2**30
-    )
-    rmm.mr.set_current_device_resource(pool)
-    cp.cuda.set_allocator(rmm_cupy_allocator)
+    if gpu_system and xp.__name__ == "cupy":
+        pool = rmm.mr.PoolMemoryResource(
+            rmm.mr.CudaMemoryResource(), initial_pool_size=2**30
+        )
+        rmm.mr.set_current_device_resource(pool)
+        xp.cuda.set_allocator(rmm_cupy_allocator)
+    else:
+        # RMM is available, but cupy is not
+        force_fallback_to_numpy()
 
     parser = argparse.ArgumentParser(
         prog="generate_groundtruth",
@@ -197,7 +337,7 @@ def main():
         "Dataset size {:6.1f} GB, shape {}, dtype {}".format(
             dataset.size * dataset.dtype.itemsize / 1e9,
             dataset.shape,
-            np.dtype(dtype),
+            xp.dtype(dtype),
         )
     )
 
@@ -230,11 +370,11 @@ def main():
 
     write_bin(
         os.path.join(args.output, "groundtruth.neighbors.ibin"),
-        indices.astype(np.uint32),
+        indices.astype(xp.uint32),
     )
     write_bin(
         os.path.join(args.output, "groundtruth.distances.fbin"),
-        distances.astype(np.float32),
+        distances.astype(xp.float32),
     )
 
 
diff --git a/python/cuvs_bench/cuvs_bench/run/data_export.py b/python/cuvs_bench/cuvs_bench/run/data_export.py
index 997dab5001..1d0ac40a01 100644
--- a/python/cuvs_bench/cuvs_bench/run/data_export.py
+++ b/python/cuvs_bench/cuvs_bench/run/data_export.py
@@ -17,7 +17,6 @@
 import json
 import os
 import traceback
-import warnings
 
 import pandas as pd
 
@@ -170,44 +169,6 @@ def convert_json_to_csv_build(dataset, dataset_path):
             traceback.print_exc()
 
 
-def append_build_data(write, build_file):
-    """
-    Append build data to the search DataFrame.
-
-    Parameters
-    ----------
-    write : pandas.DataFrame
-        The DataFrame containing the search data to which build
-        data will be appended.
-    build_file : str
-        The file path to the build CSV file.
-    """
-    if os.path.exists(build_file):
-        build_df = pd.read_csv(build_file)
-        write_ncols = len(write.columns)
-        # Initialize columns for build data
-        build_columns = [
-            "build time",
-            "build threads",
-            "build cpu_time",
-            "build GPU",
-        ]
-        write = write.assign(**{col: None for col in build_columns})
-        # Append additional columns if available
-        for col_name in build_df.columns[6:]:
-            write[col_name] = None
-        # Match build rows with search rows by index_name
-        for s_index, search_row in write.iterrows():
-            for b_index, build_row in build_df.iterrows():
-                if search_row["index_name"] == build_row["index_name"]:
-                    write.iloc[s_index, write_ncols:] = build_row[2:].values
-                    break
-    else:
-        warnings.warn(
-            f"Build CSV not found for {build_file}, build params not appended."
-        )
-
-
 def convert_json_to_csv_search(dataset, dataset_path):
     """
     Convert search JSON files to CSV format.
@@ -232,7 +193,7 @@ def convert_json_to_csv_search(dataset, dataset_path):
             )
             algo_name = clean_algo_name(algo_name)
             df["name"] = df["name"].str.split("/").str[0]
-            write_data = pd.DataFrame(
+            write = pd.DataFrame(
                 {
                     "algo_name": [algo_name] * len(df),
                     "index_name": df["name"],
@@ -242,11 +203,35 @@ def convert_json_to_csv_search(dataset, dataset_path):
                 }
             )
             # Append build data
-            append_build_data(write_data, build_file)
+            for name in df:
+                if name not in skip_search_cols:
+                    write[name] = df[name]
+            if os.path.exists(build_file):
+                build_df = pd.read_csv(build_file)
+                write_ncols = len(write.columns)
+                write["build time"] = None
+                write["build threads"] = None
+                write["build cpu_time"] = None
+                write["build GPU"] = None
+
+                for col_idx in range(6, len(build_df.columns)):
+                    col_name = build_df.columns[col_idx]
+                    write[col_name] = None
+
+                for s_index, search_row in write.iterrows():
+                    for b_index, build_row in build_df.iterrows():
+                        if search_row["index_name"] == build_row["index_name"]:
+                            write.iloc[s_index, write_ncols] = build_df.iloc[
+                                b_index, 2
+                            ]
+                            write.iloc[
+                                s_index, write_ncols + 1 :
+                            ] = build_df.iloc[b_index, 3:]
+                            break
             # Write search data and compute frontiers
-            write_data.to_csv(file.replace(".json", ",raw.csv"), index=False)
-            write_frontier(file, write_data, "throughput")
-            write_frontier(file, write_data, "latency")
+            write.to_csv(file.replace(".json", ",raw.csv"), index=False)
+            write_frontier(file, write, "throughput")
+            write_frontier(file, write, "latency")
         except Exception as e:
             print(f"Error processing search file {file}: {e}. Skipping...")
             traceback.print_exc()
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 41ebad1165..5b17f7228a 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -19,6 +19,7 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "click",
+    "cuvs==24.12.*,>=0.0.0a0",
     "matplotlib",
     "pandas",
     "pyyaml",

From e1a57084a61aeffdd5b45e35dcff11e418527c1d Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Sat, 7 Dec 2024 00:34:59 -0600
Subject: [PATCH 234/623] Update cuda-python lower bounds to 12.6.2 / 11.8.5
 (#524)

We require a newer cuda-python lower bound for new features and to use the new layout.
This will fix a number of errors observed when the runtime version of cuda-python is older than the version used to build packages using Cython features from cuda-python.

See https://github.com/rapidsai/build-planning/issues/117#issuecomment-2524250915 for details.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/524
---
 conda/environments/all_cuda-118_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml        | 2 +-
 conda/environments/all_cuda-125_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-125_arch-x86_64.yaml        | 2 +-
 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/bench_ann_cuda-125_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-125_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs/meta.yaml                            | 8 ++++----
 dependencies.yaml                                       | 4 ++--
 10 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index b27e9d3410..50aa3fe7e7 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.8.5,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 2a2791824b..8f15b6164a 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.8.5,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index 800d8c5cc1..f194c01a30 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 15addf9da4..912d1629b3 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.5
 - cupy>=12.0.0
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index ced5176e96..bb85af66f1 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.8.5,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index d8e49519f9..225340fbb2 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
-- cuda-python>=11.7.1,<12.0a0
+- cuda-python>=11.8.5,<12.0a0
 - cuda-version=11.8
 - cudatoolkit
 - cxx-compiler
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 5e6373ad11..a541db0ca5 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.5
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index fece589d5a..25de384436 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.0,<13.0a0
+- cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.5
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index f799d9b0be..ad7ffe7563 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -43,10 +43,10 @@ requirements:
     - {{ stdlib("c") }}
   host:
     {% if cuda_major == "11" %}
-    - cuda-python >=11.7.1,<12.0a0
+    - cuda-python >=11.8.5,<12.0a0
     - cudatoolkit
     {% else %}
-    - cuda-python >=12.0,<13.0a0
+    - cuda-python >=12.6.2,<13.0a0
     - cuda-cudart-dev
     {% endif %}
     - cuda-version ={{ cuda_version }}
@@ -61,10 +61,10 @@ requirements:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major == "11" %}
     - cudatoolkit
-    - cuda-python >=11.7.1,<12.0a0
+    - cuda-python >=11.8.5,<12.0a0
     {% else %}
     - cuda-cudart
-    - cuda-python >=12.0,<13.0a0
+    - cuda-python >=12.6.2,<13.0a0
     {% endif %}
     - pylibraft {{ minor_version }}
     - libcuvs {{ version }}
diff --git a/dependencies.yaml b/dependencies.yaml
index 28ded36716..7adb38d2a7 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -213,11 +213,11 @@ dependencies:
           - matrix:
               cuda: "12.*"
             packages:
-              - &cuda_python12 cuda-python>=12.0,<13.0a0
+              - &cuda_python12 cuda-python>=12.6.2,<13.0a0
           - matrix:
               cuda: "11.*"
             packages:
-              - &cuda_python11 cuda-python>=11.7.1,<12.0a0
+              - &cuda_python11 cuda-python>=11.8.5,<12.0a0
           - matrix:
             packages:
               - &cuda_python cuda-python

From cf2885c9d0b8a5d839378939a29154a4d165fefe Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Wed, 11 Dec 2024 13:11:32 -0500
Subject: [PATCH 235/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7ce4a14c3e..ed9429d55d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,67 @@
+# cuvs 24.12.00 (11 Dec 2024)
+
+## 🚨 Breaking Changes
+
+- HNSW CPU Hierarchy ([#465](https://github.com/rapidsai/cuvs/pull/465)) [@divyegala](https://github.com/divyegala)
+- Use dashes in cuvs-bench package name. ([#417](https://github.com/rapidsai/cuvs/pull/417)) [@bdice](https://github.com/bdice)
+
+## 🐛 Bug Fixes
+
+- Skip IVF-PQ packing test for lists with not enough data ([#512](https://github.com/rapidsai/cuvs/pull/512)) [@achirkin](https://github.com/achirkin)
+- [BUG] Fix CAGRA filter ([#489](https://github.com/rapidsai/cuvs/pull/489)) [@enp1s0](https://github.com/enp1s0)
+- Add `kIsSingleSource` to `PairwiseDistanceEpilogueElementwise` ([#485](https://github.com/rapidsai/cuvs/pull/485)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Fix include errors, header, and unsafe locks in iface.hpp ([#467](https://github.com/rapidsai/cuvs/pull/467)) [@achirkin](https://github.com/achirkin)
+- Fix an OOB error in device-side cuvs::neighbors::refine and CAGRA kern_prune ([#460](https://github.com/rapidsai/cuvs/pull/460)) [@achirkin](https://github.com/achirkin)
+- Put a ceiling on cuda-python ([#445](https://github.com/rapidsai/cuvs/pull/445)) [@bdice](https://github.com/bdice)
+- Enable NVTX in cuvs-cagra-search component ([#439](https://github.com/rapidsai/cuvs/pull/439)) [@achirkin](https://github.com/achirkin)
+- BUG: CAGRA multi-cta illegal access with bad queries ([#438](https://github.com/rapidsai/cuvs/pull/438)) [@achirkin](https://github.com/achirkin)
+- Fix index overflow in edge cases of CAGRA graph optimize ([#435](https://github.com/rapidsai/cuvs/pull/435)) [@achirkin](https://github.com/achirkin)
+- Fix correct call to brute force in generate groundtruth of cuvs-bench ([#427](https://github.com/rapidsai/cuvs/pull/427)) [@dantegd](https://github.com/dantegd)
+- Use Python for sccache hit rate computation. ([#420](https://github.com/rapidsai/cuvs/pull/420)) [@bdice](https://github.com/bdice)
+- Add `click` package to `cuvs-bench` conda recipe ([#408](https://github.com/rapidsai/cuvs/pull/408)) [@divyegala](https://github.com/divyegala)
+- Fix NVTX annotations ([#400](https://github.com/rapidsai/cuvs/pull/400)) [@achirkin](https://github.com/achirkin)
+
+## 📖 Documentation
+
+- [Doc] Fix CAGRA search sample code ([#484](https://github.com/rapidsai/cuvs/pull/484)) [@enp1s0](https://github.com/enp1s0)
+- Fix broken link in README.md references ([#473](https://github.com/rapidsai/cuvs/pull/473)) [@Azurethi](https://github.com/Azurethi)
+- Adding tech stack to docs ([#448](https://github.com/rapidsai/cuvs/pull/448)) [@cjnolet](https://github.com/cjnolet)
+- Fix Question Retrieval notebook ([#352](https://github.com/rapidsai/cuvs/pull/352)) [@lowener](https://github.com/lowener)
+
+## 🚀 New Features
+
+- Add C++ API scalar quantization ([#494](https://github.com/rapidsai/cuvs/pull/494)) [@mfoerste4](https://github.com/mfoerste4)
+- HNSW CPU Hierarchy ([#465](https://github.com/rapidsai/cuvs/pull/465)) [@divyegala](https://github.com/divyegala)
+- Add serialization API to brute-force ([#461](https://github.com/rapidsai/cuvs/pull/461)) [@lowener](https://github.com/lowener)
+- Add Question Retrieval notebook using Milvus ([#451](https://github.com/rapidsai/cuvs/pull/451)) [@lowener](https://github.com/lowener)
+- Migrate feature diff for NN Descent from RAFT to cuVS ([#421](https://github.com/rapidsai/cuvs/pull/421)) [@divyegala](https://github.com/divyegala)
+- Add --no-lap-sync cmd option to ann-bench ([#405](https://github.com/rapidsai/cuvs/pull/405)) [@achirkin](https://github.com/achirkin)
+- Add `InnerProduct` and `CosineExpanded` metric support in NN Descent ([#177](https://github.com/rapidsai/cuvs/pull/177)) [@divyegala](https://github.com/divyegala)
+
+## 🛠️ Improvements
+
+- Update cuvs to match raft&#39;s cutlass changes ([#516](https://github.com/rapidsai/cuvs/pull/516)) [@vyasr](https://github.com/vyasr)
+- add a README for wheels ([#504](https://github.com/rapidsai/cuvs/pull/504)) [@jameslamb](https://github.com/jameslamb)
+- Move check_input_array from pylibraft ([#474](https://github.com/rapidsai/cuvs/pull/474)) [@benfred](https://github.com/benfred)
+- use different wheel-size thresholds based on CUDA version ([#469](https://github.com/rapidsai/cuvs/pull/469)) [@jameslamb](https://github.com/jameslamb)
+- Modify cuvs-bench to be able to generate ground truth in CPU systems ([#466](https://github.com/rapidsai/cuvs/pull/466)) [@dantegd](https://github.com/dantegd)
+- enforce wheel size limits, README formatting in CI ([#464](https://github.com/rapidsai/cuvs/pull/464)) [@jameslamb](https://github.com/jameslamb)
+- Moving spectral embedding and kernel gramm APIs to cuVS ([#463](https://github.com/rapidsai/cuvs/pull/463)) [@cjnolet](https://github.com/cjnolet)
+- Migrate sparse knn and distances code from raft ([#457](https://github.com/rapidsai/cuvs/pull/457)) [@benfred](https://github.com/benfred)
+- Don&#39;t presume pointers location infers usability. ([#441](https://github.com/rapidsai/cuvs/pull/441)) [@robertmaynard](https://github.com/robertmaynard)
+- call `enable_testing` in root CMakeLists.txt ([#437](https://github.com/rapidsai/cuvs/pull/437)) [@robertmaynard](https://github.com/robertmaynard)
+- CAGRA tech debt: distance descriptor and workspace memory ([#436](https://github.com/rapidsai/cuvs/pull/436)) [@achirkin](https://github.com/achirkin)
+- Add ci run_ scripts needed for build infra ([#434](https://github.com/rapidsai/cuvs/pull/434)) [@robertmaynard](https://github.com/robertmaynard)
+- Use environment variables in cache hit rate computation. ([#422](https://github.com/rapidsai/cuvs/pull/422)) [@bdice](https://github.com/bdice)
+- Use dashes in cuvs-bench package name. ([#417](https://github.com/rapidsai/cuvs/pull/417)) [@bdice](https://github.com/bdice)
+- We need to enable the c_api by default ([#416](https://github.com/rapidsai/cuvs/pull/416)) [@robertmaynard](https://github.com/robertmaynard)
+- print sccache stats in builds ([#413](https://github.com/rapidsai/cuvs/pull/413)) [@jameslamb](https://github.com/jameslamb)
+- make conda installs in CI stricter ([#406](https://github.com/rapidsai/cuvs/pull/406)) [@jameslamb](https://github.com/jameslamb)
+- Ivf c example ([#404](https://github.com/rapidsai/cuvs/pull/404)) [@abner-ma](https://github.com/abner-ma)
+- Prune workflows based on changed files ([#392](https://github.com/rapidsai/cuvs/pull/392)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- [WIP] Add pinned memory resource to C API ([#311](https://github.com/rapidsai/cuvs/pull/311)) [@ajit283](https://github.com/ajit283)
+- Dynamic Batching ([#261](https://github.com/rapidsai/cuvs/pull/261)) [@achirkin](https://github.com/achirkin)
+
 # cuvs 24.10.00 (9 Oct 2024)
 
 ## 🐛 Bug Fixes

From ef16a9e7fa7af418019e8cc7bcdd33828aee9f67 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 12 Dec 2024 00:04:37 +0100
Subject: [PATCH 236/623] Fix Grace-specific issues in CAGRA (#527)

Fix Grace-specific test failures:
  1. Add stream syncs at the places where host-allocated memory may be destructed while still being accessed by GPU to avoid relying on stream-ordered semantics of memory allocations.
  2. A bug in tests: CAGRA index produced by `cagra::build` is not guaranteed to be owning. The tests assumed otherwise; when the host dataset is passed, and it's accessible on the device (it's the case with Grace), the created index ended up non-owning. The lifetime of the host dataset in the tests was smaller than of the index, which led to invalid host accesses from the device.
  3. A bug in `dataset_deserialize.hpp`: `deserialize_strided` function constructed a non-owning strided dataset, because the host data was accessible by the GPU. The current fix is to add a move-semantics overload of `make_strided_dataset` that always owns the passed data (either via moving the mdarray or by copying the data).

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/527
---
 cpp/include/cuvs/neighbors/common.hpp         | 78 ++++++++++++++++++-
 cpp/src/neighbors/detail/ann_utils.cuh        | 11 +++
 cpp/src/neighbors/detail/cagra/utils.hpp      | 16 +++-
 .../neighbors/detail/dataset_serialize.hpp    |  2 +-
 cpp/test/neighbors/ann_cagra.cuh              | 23 +++---
 5 files changed, 115 insertions(+), 15 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 60b8cc1222..bd9ea48346 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -264,6 +264,77 @@ auto make_strided_dataset(const raft::resources& res, const SrcT& src, uint32_t
   return std::make_unique<out_owning_type>(std::move(out_array), out_layout);
 }
 
+/**
+ * @brief Contstruct a strided matrix from any mdarray.
+ *
+ * This function constructs an owning device matrix and copies the data.
+ * When the data is copied, padding elements are filled with zeroes.
+ *
+ * @tparam DataT
+ * @tparam IdxT
+ * @tparam LayoutPolicy
+ * @tparam ContainerPolicy
+ *
+ * @param[in] res raft resources handle
+ * @param[in] src the source mdarray or mdspan
+ * @param[in] required_stride the leading dimension (in elements)
+ * @return owning current-device-accessible strided matrix
+ */
+template <typename DataT, typename IdxT, typename LayoutPolicy, typename ContainerPolicy>
+auto make_strided_dataset(
+  const raft::resources& res,
+  raft::mdarray<DataT, raft::matrix_extent<IdxT>, LayoutPolicy, ContainerPolicy>&& src,
+  uint32_t required_stride) -> std::unique_ptr<strided_dataset<DataT, IdxT>>
+{
+  using value_type            = DataT;
+  using index_type            = IdxT;
+  using layout_type           = LayoutPolicy;
+  using container_policy_type = ContainerPolicy;
+  static_assert(std::is_same_v<layout_type, raft::layout_right> ||
+                  std::is_same_v<layout_type, raft::layout_right_padded<value_type>> ||
+                  std::is_same_v<layout_type, raft::layout_stride>,
+                "The input must be row-major");
+  RAFT_EXPECTS(src.extent(1) <= required_stride,
+               "The input row length must be not larger than the desired stride.");
+  const uint32_t src_stride = src.stride(0) > 0 ? src.stride(0) : src.extent(1);
+  const bool stride_matches = required_stride == src_stride;
+
+  auto out_layout =
+    raft::make_strided_layout(src.extents(), std::array<index_type, 2>{required_stride, 1});
+
+  using out_mdarray_type          = raft::device_matrix<value_type, index_type>;
+  using out_layout_type           = typename out_mdarray_type::layout_type;
+  using out_container_policy_type = typename out_mdarray_type::container_policy_type;
+  using out_owning_type =
+    owning_dataset<value_type, index_type, out_layout_type, out_container_policy_type>;
+
+  if constexpr (std::is_same_v<layout_type, out_layout_type> &&
+                std::is_same_v<container_policy_type, out_container_policy_type>) {
+    if (stride_matches) {
+      // Everything matches, we can own the mdarray
+      return std::make_unique<out_owning_type>(std::move(src), out_layout);
+    }
+  }
+  // Something is wrong: have to make a copy and produce an owning dataset
+  auto out_array =
+    raft::make_device_matrix<value_type, index_type>(res, src.extent(0), required_stride);
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(out_array.data_handle(),
+                                0,
+                                out_array.size() * sizeof(value_type),
+                                raft::resource::get_cuda_stream(res)));
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(out_array.data_handle(),
+                                  sizeof(value_type) * required_stride,
+                                  src.data_handle(),
+                                  sizeof(value_type) * src_stride,
+                                  sizeof(value_type) * src.extent(1),
+                                  src.extent(0),
+                                  cudaMemcpyDefault,
+                                  raft::resource::get_cuda_stream(res)));
+
+  return std::make_unique<out_owning_type>(std::move(out_array), out_layout);
+}
+
 /**
  * @brief Contstruct a strided matrix from any mdarray or mdspan.
  *
@@ -278,14 +349,15 @@ auto make_strided_dataset(const raft::resources& res, const SrcT& src, uint32_t
  * @return maybe owning current-device-accessible strided matrix
  */
 template <typename SrcT>
-auto make_aligned_dataset(const raft::resources& res, const SrcT& src, uint32_t align_bytes = 16)
+auto make_aligned_dataset(const raft::resources& res, SrcT src, uint32_t align_bytes = 16)
   -> std::unique_ptr<strided_dataset<typename SrcT::value_type, typename SrcT::index_type>>
 {
-  using value_type       = typename SrcT::value_type;
+  using source_type      = std::remove_cv_t<std::remove_reference_t<SrcT>>;
+  using value_type       = typename source_type::value_type;
   constexpr size_t kSize = sizeof(value_type);
   uint32_t required_stride =
     raft::round_up_safe<size_t>(src.extent(1) * kSize, std::lcm(align_bytes, kSize)) / kSize;
-  return make_strided_dataset(res, src, required_stride);
+  return make_strided_dataset(res, std::forward<SrcT>(src), required_stride);
 }
 /**
  * @brief VPQ compressed dataset.
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index 652d41c853..5293563514 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -403,6 +403,17 @@ struct batch_load_iterator {
 
   /** A single batch of data residing in device memory. */
   struct batch {
+    ~batch() noexcept
+    {
+      /*
+      If there's no copy, there's no allocation owned by the batch.
+      If there's no allocation, there's no guarantee that the device pointer is stream-ordered.
+      If there's no stream order guarantee, we must synchronize with the stream before the batch is
+      destroyed to make sure all GPU operations in that stream finish earlier.
+      */
+      if (!does_copy()) { RAFT_CUDA_TRY_NO_THROW(cudaStreamSynchronize(stream_)); }
+    }
+
     /** Logical width of a single row in a batch, in elements of type `T`. */
     [[nodiscard]] auto row_width() const -> size_type { return row_width_; }
     /** Logical offset of the batch, in rows (`row_width()`) */
diff --git a/cpp/src/neighbors/detail/cagra/utils.hpp b/cpp/src/neighbors/detail/cagra/utils.hpp
index 0f83093282..9f95c5b1c7 100644
--- a/cpp/src/neighbors/detail/cagra/utils.hpp
+++ b/cpp/src/neighbors/detail/cagra/utils.hpp
@@ -179,7 +179,7 @@ class device_matrix_view_from_host {
  public:
   device_matrix_view_from_host(raft::resources const& res,
                                raft::host_matrix_view<T, IdxT> host_view)
-    : host_view_(host_view)
+    : res_(res), host_view_(host_view)
   {
     cudaPointerAttributes attr;
     RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, host_view.data_handle()));
@@ -199,6 +199,17 @@ class device_matrix_view_from_host {
     }
   }
 
+  ~device_matrix_view_from_host() noexcept
+  {
+    /*
+    If there's no copy, there's no allocation owned by this struct.
+    If there's no allocation, there's no guarantee that the device pointer is stream-ordered.
+    If there's no stream order guarantee, we must synchronize with the stream before the struct is
+    destroyed to make sure all GPU operations in that stream finish earlier.
+    */
+    if (!allocated_memory()) { raft::resource::sync_stream(res_); }
+  }
+
   raft::device_matrix_view<T, IdxT> view()
   {
     return raft::make_device_matrix_view<T, IdxT>(
@@ -207,9 +218,10 @@ class device_matrix_view_from_host {
 
   T* data_handle() { return device_ptr; }
 
-  bool allocated_memory() const { return device_mem_.has_value(); }
+  [[nodiscard]] bool allocated_memory() const { return device_mem_.has_value(); }
 
  private:
+  const raft::resources& res_;
   std::optional<raft::device_matrix<T, IdxT>> device_mem_;
   raft::host_matrix_view<T, IdxT> host_view_;
   T* device_ptr;
diff --git a/cpp/src/neighbors/detail/dataset_serialize.hpp b/cpp/src/neighbors/detail/dataset_serialize.hpp
index 40d9df930f..0ecc2cf5d8 100644
--- a/cpp/src/neighbors/detail/dataset_serialize.hpp
+++ b/cpp/src/neighbors/detail/dataset_serialize.hpp
@@ -140,7 +140,7 @@ auto deserialize_strided(raft::resources const& res, std::istream& is)
   auto stride     = raft::deserialize_scalar<uint32_t>(res, is);
   auto host_array = raft::make_host_matrix<DataT, IdxT>(n_rows, dim);
   raft::deserialize_mdspan(res, is, host_array.view());
-  return make_strided_dataset(res, host_array, stride);
+  return make_strided_dataset(res, std::move(host_array), stride);
 }
 
 template <typename MathT, typename IdxT>
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index 8d57014395..c1cd3ca090 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -389,12 +389,13 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
           (const DataT*)database.data(), ps.n_rows, ps.dim);
 
         {
+          std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
           cagra::index<DataT, IdxT> index(handle_, index_params.metric);
           if (ps.host_dataset) {
-            auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
-            raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+            database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+            raft::copy(database_host->data_handle(), database.data(), database.size(), stream_);
             auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
-              (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+              (const DataT*)database_host->data_handle(), ps.n_rows, ps.dim);
 
             index = cagra::build(handle_, index_params, database_host_view);
           } else {
@@ -567,13 +568,16 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
         auto initial_database_view = raft::make_device_matrix_view<const DataT, int64_t>(
           (const DataT*)database.data(), initial_database_size, ps.dim);
 
+        std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
         cagra::index<DataT, IdxT> index(handle_);
         if (ps.host_dataset) {
-          auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+          database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
           raft::copy(
-            database_host.data_handle(), database.data(), initial_database_view.size(), stream_);
+            database_host->data_handle(), database.data(), initial_database_view.size(), stream_);
           auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
-            (const DataT*)database_host.data_handle(), initial_database_size, ps.dim);
+            (const DataT*)database_host->data_handle(), initial_database_size, ps.dim);
+          // NB: database_host must live no less than the index, because the index _may_be_
+          //     non-onwning
           index = cagra::build(handle_, index_params, database_host_view);
         } else {
           index = cagra::build(handle_, index_params, initial_database_view);
@@ -763,12 +767,13 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
         auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
           (const DataT*)database.data(), ps.n_rows, ps.dim);
 
+        std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
         cagra::index<DataT, IdxT> index(handle_);
         if (ps.host_dataset) {
-          auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
-          raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+          database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+          raft::copy(database_host->data_handle(), database.data(), database.size(), stream_);
           auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
-            (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+            (const DataT*)database_host->data_handle(), ps.n_rows, ps.dim);
           index = cagra::build(handle_, index_params, database_host_view);
         } else {
           index = cagra::build(handle_, index_params, database_view);

From b859bc5edd41009604219416356cb6c96b189c2f Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Fri, 13 Dec 2024 15:30:32 +0900
Subject: [PATCH 237/623] Fix cagra::extend error message (#532)

When extending a CAGRA index that is built with `index_param.attach_dataset_on_build = false`, an error message "Only uncompressed dataset is supported" is displayed even if the dataset used to build the graph is not compressed. This problem occurs since the extend function does not check whether the dataset is empty. This PR fixes it.

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/532
---
 cpp/src/neighbors/detail/cagra/add_nodes.cuh | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
index b03b8214b9..952039130f 100644
--- a/cpp/src/neighbors/detail/cagra/add_nodes.cuh
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -432,8 +432,14 @@ void extend_core(
     } else {
       index.update_graph(handle, raft::make_const_mdspan(updated_graph.view()));
     }
+  } else if (dynamic_cast<const cuvs::neighbors::empty_dataset<int64_t>*>(&index.data()) !=
+             nullptr) {
+    RAFT_FAIL(
+      "cagra::extend only supports an index to which the dataset is attached. Please check if the "
+      "index was built with index_param.attach_dataset_on_build = true, or if a dataset was "
+      "attached after the build.");
   } else {
-    RAFT_FAIL("Only uncompressed dataset is supported");
+    RAFT_FAIL("cagra::extend only supports an uncompressed dataset index");
   }
 }
 }  // namespace cuvs::neighbors::cagra

From b3ce774d39e149d4e34c401068f24136eac44e13 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 17 Dec 2024 17:45:35 -0800
Subject: [PATCH 238/623] Fix CI for python cuvs_bench (#541)

I'm seeing CI failures due to a missing 'setuptools' on a recent PR https://github.com/rapidsai/cuvs/actions/runs/12363479650/job/34545223389?pr=536 in building the cuvs_bench code.

This seems to be because we were missing some requires in dependencies.yaml. Fix

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/541
---
 conda/recipes/cuvs-bench/meta.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
index 0681a1038f..d77aee8cee 100644
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -79,6 +79,7 @@ requirements:
     - python
     - rapids-build-backend>=0.3.0,<0.4.0.dev0
     - rmm ={{ minor_version }}
+    - setuptools>=64.0.0
 
   run:
     - benchmark

From 660a2caa64f864e38e0e7bd19df86556d25aa7db Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Thu, 19 Dec 2024 13:29:55 -0800
Subject: [PATCH 239/623] Additional Distances for CAGRA C and Python API
 (#546)

Add InnerProduct metric to CAGRA C and Python API + updates to CAGRA pytests.
Closes https://github.com/rapidsai/cuvs/issues/545

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/546
---
 cpp/include/cuvs/neighbors/cagra.h         |  3 +++
 cpp/src/neighbors/cagra_c.cpp              |  6 ++++--
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd |  2 ++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx | 20 ++++++++++++--------
 python/cuvs/cuvs/test/test_cagra.py        |  8 +++++---
 5 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 14331ebbca..f7f58a19c6 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -17,6 +17,7 @@
 #pragma once
 
 #include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
 #include <dlpack/dlpack.h>
 #include <stdbool.h>
 #include <stdint.h>
@@ -87,6 +88,8 @@ typedef struct cuvsCagraCompressionParams* cuvsCagraCompressionParams_t;
  *
  */
 struct cuvsCagraIndexParams {
+  /** Distance type. */
+  cuvsDistanceType metric;
   /** Degree of input graph for pruning. */
   size_t intermediate_graph_degree;
   /** Degree of output graph. */
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 326a896650..02b7a566ef 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -41,7 +41,8 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
   auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
 
-  auto index_params                      = cuvs::neighbors::cagra::index_params();
+  auto index_params   = cuvs::neighbors::cagra::index_params();
+  index_params.metric = static_cast<cuvs::distance::DistanceType>((int)params.metric),
   index_params.intermediate_graph_degree = params.intermediate_graph_degree;
   index_params.graph_degree              = params.graph_degree;
 
@@ -252,7 +253,8 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
 extern "C" cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
-    *params = new cuvsCagraIndexParams{.intermediate_graph_degree = 128,
+    *params = new cuvsCagraIndexParams{.metric                    = L2Expanded,
+                                       .intermediate_graph_degree = 128,
                                        .graph_degree              = 64,
                                        .build_algo                = IVF_PQ,
                                        .nn_descent_niter          = 20};
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index bba5a91a8e..a0f8114805 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -28,6 +28,7 @@ from libcpp cimport bool
 
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
 
 
 cdef extern from "cuvs/neighbors/cagra.h" nogil:
@@ -47,6 +48,7 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
     ctypedef cuvsCagraCompressionParams* cuvsCagraCompressionParams_t
 
     ctypedef struct cuvsCagraIndexParams:
+        cuvsDistanceType metric
         size_t intermediate_graph_degree
         size_t graph_degree
         cuvsCagraGraphBuildAlgo build_algo
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 752aef7415..fd55905cf5 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -28,11 +28,13 @@ from libcpp cimport bool, cast
 from libcpp.string cimport string
 
 from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
 
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
+from cuvs.distance import DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
 
 from libc.stdint cimport (
@@ -131,9 +133,11 @@ cdef class IndexParams:
     Parameters
     ----------
     metric : string denoting the metric type, default="sqeuclidean"
-        Valid values for metric: ["sqeuclidean"], where
+        Valid values for metric: ["sqeuclidean", "inner_product"], where
             - sqeuclidean is the euclidean distance without the square root
               operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2
+            - inner_product distance is defined as
+              distance(a, b) = \\sum_i a_i * b_i.
     intermediate_graph_degree : int, default = 128
 
     graph_degree : int, default = 64
@@ -151,6 +155,7 @@ cdef class IndexParams:
     """
 
     cdef cuvsCagraIndexParams* params
+    cdef object _metric
 
     # hold on to a reference to the compression, to keep from being GC'ed
     cdef public object compression
@@ -170,10 +175,8 @@ cdef class IndexParams:
                  nn_descent_niter=20,
                  compression=None):
 
-        # todo (dgd): enable once other metrics are present
-        # and exposed in cuVS C API
-        # self.params.metric = _get_metric(metric)
-        # self.params.metric_arg = 0
+        self._metric = metric
+        self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
         self.params.intermediate_graph_degree = intermediate_graph_degree
         self.params.graph_degree = graph_degree
         if build_algo == "ivf_pq":
@@ -186,9 +189,9 @@ cdef class IndexParams:
             self.params.compression = \
                 <cuvsCagraCompressionParams_t><size_t>compression.get_handle()
 
-    # @property
-    # def metric(self):
-        # return self.params.metric
+    @property
+    def metric(self):
+        return self._metric
 
     @property
     def intermediate_graph_degree(self):
@@ -247,6 +250,7 @@ def build(IndexParams index_params, dataset, resources=None):
 
     The following distance metrics are supported:
         - L2
+        - InnerProduct
 
     Parameters
     ----------
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
index 56e132c23c..d3b03a5d03 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -29,7 +29,7 @@ def run_cagra_build_search_test(
     n_queries=100,
     k=10,
     dtype=np.float32,
-    metric="euclidean",
+    metric="sqeuclidean",
     intermediate_graph_degree=128,
     graph_degree=64,
     build_algo="ivf_pq",
@@ -42,6 +42,8 @@ def run_cagra_build_search_test(
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     if metric == "inner_product":
+        if dtype in [np.int8, np.uint8]:
+            pytest.skip("skip normalization for int8/uint8 data")
         dataset = normalize(dataset, norm="l2", axis=1)
     dataset_device = device_ndarray(dataset)
 
@@ -122,7 +124,7 @@ def run_cagra_build_search_test(
 @pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
 @pytest.mark.parametrize("array_type", ["device", "host"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
-@pytest.mark.parametrize("metric", ["euclidean"])
+@pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
 def test_cagra_dataset_dtype_host_device(
     dtype, array_type, inplace, build_algo, metric
 ):
@@ -145,7 +147,7 @@ def test_cagra_dataset_dtype_host_device(
             "graph_degree": 32,
             "add_data_on_build": True,
             "k": 1,
-            "metric": "euclidean",
+            "metric": "sqeuclidean",
             "build_algo": "ivf_pq",
         },
         {

From 89ebf15150223f4bce4a08bf3a6a4089380a1d0a Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 19 Dec 2024 19:34:09 -0800
Subject: [PATCH 240/623] Use nvidia-sphinx-theme for docs (#528)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/528
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 6 ++++--
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 6 ++++--
 conda/environments/all_cuda-125_arch-aarch64.yaml | 6 ++++--
 conda/environments/all_cuda-125_arch-x86_64.yaml  | 6 ++++--
 dependencies.yaml                                 | 8 +++++---
 docs/source/conf.py                               | 2 +-
 6 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 50aa3fe7e7..a6d98ea3bd 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -7,7 +7,6 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
-- breathe
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -44,7 +43,6 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pre-commit
-- pydata-sphinx-theme
 - pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
@@ -55,5 +53,9 @@ dependencies:
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
+- sphinx>=8.0.0
 - sysroot_linux-aarch64==2.17
+- pip:
+  - breathe>=4.35.0
+  - nvidia-sphinx-theme
 name: all_cuda-118_arch-aarch64
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 8f15b6164a..1063e4d6cd 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -7,7 +7,6 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
-- breathe
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -44,7 +43,6 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pre-commit
-- pydata-sphinx-theme
 - pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
@@ -55,5 +53,9 @@ dependencies:
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
+- sphinx>=8.0.0
 - sysroot_linux-64==2.17
+- pip:
+  - breathe>=4.35.0
+  - nvidia-sphinx-theme
 name: all_cuda-118_arch-x86_64
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index f194c01a30..ee7b376953 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -7,7 +7,6 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
-- breathe
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -40,7 +39,6 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pydata-sphinx-theme
 - pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
@@ -51,5 +49,9 @@ dependencies:
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
+- sphinx>=8.0.0
 - sysroot_linux-aarch64==2.17
+- pip:
+  - breathe>=4.35.0
+  - nvidia-sphinx-theme
 name: all_cuda-125_arch-aarch64
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 912d1629b3..7c8e1fd998 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -7,7 +7,6 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
-- breathe
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -40,7 +39,6 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pydata-sphinx-theme
 - pylibraft==25.2.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
@@ -51,5 +49,9 @@ dependencies:
 - scikit-learn
 - sphinx-copybutton
 - sphinx-markdown-tables
+- sphinx>=8.0.0
 - sysroot_linux-64==2.17
+- pip:
+  - breathe>=4.35.0
+  - nvidia-sphinx-theme
 name: all_cuda-125_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index eca97d2f58..a73fe7b8fc 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -394,22 +394,24 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
-          - breathe
           - doxygen>=1.8.20
           - graphviz
           - ipython
           - numpydoc
-          - pydata-sphinx-theme
           - recommonmark
+          - sphinx>=8.0.0
           - sphinx-copybutton
           - sphinx-markdown-tables
+          - pip:
+            - nvidia-sphinx-theme
+            - breathe>=4.35.0
   rust:
     common:
       - output_types: [conda]
         packages:
           - make
           - rust
-          # clang/liblclang only needed for bindgen support
+          # clang/libclang only needed for bindgen support
           - clang
           - libclang
   build_wheels:
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0d667833ad..c149195685 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -99,7 +99,7 @@
 # a list of builtin themes.
 #
 
-html_theme = "pydata_sphinx_theme"
+html_theme = "nvidia_sphinx_theme"
 
 
 # Theme options are theme-specific and customize the look and feel of a theme

From f48e9aab593232b72f74fd79ad256ed51b997b43 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 19 Dec 2024 19:39:29 -0800
Subject: [PATCH 241/623] Add support for float16 to the python pairwise
 distance api (#547)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/547
---
 cpp/src/distance/pairwise_distance_c.cpp | 13 +++++++++----
 python/cuvs/cuvs/distance/distance.pyx   |  7 +++++--
 python/cuvs/cuvs/test/test_distance.py   | 13 ++++++++++---
 3 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/cpp/src/distance/pairwise_distance_c.cpp b/cpp/src/distance/pairwise_distance_c.cpp
index d457198a29..061adaa2c2 100644
--- a/cpp/src/distance/pairwise_distance_c.cpp
+++ b/cpp/src/distance/pairwise_distance_c.cpp
@@ -29,7 +29,7 @@
 
 namespace {
 
-template <typename T>
+template <typename T, typename DistT>
 void _pairwise_distance(cuvsResources_t res,
                         DLManagedTensor* x_tensor,
                         DLManagedTensor* y_tensor,
@@ -40,7 +40,7 @@ void _pairwise_distance(cuvsResources_t res,
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
 
   using mdspan_type           = raft::device_matrix_view<T const, int64_t, raft::row_major>;
-  using distances_mdspan_type = raft::device_matrix_view<T, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::device_matrix_view<DistT, int64_t, raft::row_major>;
 
   auto x_mds         = cuvs::core::from_dlpack<mdspan_type>(x_tensor);
   auto y_mds         = cuvs::core::from_dlpack<mdspan_type>(y_tensor);
@@ -71,9 +71,14 @@ extern "C" cuvsError_t cuvsPairwiseDistance(cuvsResources_t res,
     }
 
     if (x_dt.bits == 32) {
-      _pairwise_distance<float>(res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      _pairwise_distance<float, float>(
+        res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+    } else if (x_dt.bits == 16) {
+      _pairwise_distance<half, float>(
+        res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
     } else if (x_dt.bits == 64) {
-      _pairwise_distance<double>(res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      _pairwise_distance<double, double>(
+        res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
     } else {
       RAFT_FAIL("Unsupported DLtensor dtype: %d and bits: %d", x_dt.code, x_dt.bits);
     }
diff --git a/python/cuvs/cuvs/distance/distance.pyx b/python/cuvs/cuvs/distance/distance.pyx
index eb34366e43..187532bfeb 100644
--- a/python/cuvs/cuvs/distance/distance.pyx
+++ b/python/cuvs/cuvs/distance/distance.pyx
@@ -100,7 +100,10 @@ def pairwise_distance(X, Y, out=None, metric="euclidean", metric_arg=2.0,
     n = y_cai.shape[0]
 
     if out is None:
-        out = device_ndarray.empty((m, n), dtype=y_cai.dtype)
+        output_dtype = y_cai.dtype
+        if np.issubdtype(y_cai.dtype, np.float16):
+            output_dtype = np.float32
+        out = device_ndarray.empty((m, n), dtype=output_dtype)
     out_cai = wrap_array(out)
 
     x_k = x_cai.shape[1]
@@ -119,7 +122,7 @@ def pairwise_distance(X, Y, out=None, metric="euclidean", metric_arg=2.0,
     y_dt = y_cai.dtype
     d_dt = out_cai.dtype
 
-    if x_dt != y_dt or x_dt != d_dt:
+    if x_dt != y_dt:
         raise ValueError("Inputs must have the same dtypes")
 
     cdef cydlpack.DLManagedTensor* x_dlpack = \
diff --git a/python/cuvs/cuvs/test/test_distance.py b/python/cuvs/cuvs/test/test_distance.py
index 681217fc81..f466c27439 100644
--- a/python/cuvs/cuvs/test/test_distance.py
+++ b/python/cuvs/cuvs/test/test_distance.py
@@ -40,7 +40,7 @@
     ],
 )
 @pytest.mark.parametrize("inplace", [True, False])
-@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
 def test_distance(n_rows, n_cols, inplace, metric, dtype):
     input1 = np.random.random_sample((n_rows, n_cols))
     input1 = np.asarray(input1).astype(dtype)
@@ -55,7 +55,10 @@ def test_distance(n_rows, n_cols, inplace, metric, dtype):
         norm = np.sum(input1, axis=1)
         input1 = (input1.T / norm).T
 
-    output = np.zeros((n_rows, n_rows), dtype=dtype)
+    output_dtype = dtype
+    if np.issubdtype(dtype, np.float16):
+        output_dtype = np.float32
+    output = np.zeros((n_rows, n_rows), dtype=output_dtype)
 
     if metric == "inner_product":
         expected = np.matmul(input1, input1.T)
@@ -76,4 +79,8 @@ def test_distance(n_rows, n_cols, inplace, metric, dtype):
 
     actual = output_device.copy_to_host()
 
-    assert np.allclose(expected, actual, atol=1e-3, rtol=1e-3)
+    tol = 1e-3
+    if np.issubdtype(dtype, np.float16):
+        tol = 1e-1
+
+    assert np.allclose(expected, actual, atol=tol, rtol=tol)

From ac49c414254cb448efce02d7a7b08190e43584e8 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Mon, 30 Dec 2024 11:44:25 -0800
Subject: [PATCH 242/623] Check if nightlies have succeeded recently enough
 (#548)

Contributes to https://github.com/rapidsai/build-planning/issues/127

This PR cannot be merged unless nightly CI has passed within the past 7 days, so if it remains unmerged that will itself be an indication that nightly CI needs fixing.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/548
---
 .github/workflows/pr.yaml | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 4c3b4d06aa..91f51bd90d 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -12,6 +12,7 @@ concurrency:
 jobs:
   pr-builder:
     needs:
+      - check-nightly-ci
       - changed-files
       - checks
       - conda-cpp-build
@@ -29,6 +30,18 @@ jobs:
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
+  check-nightly-ci:
+    # Switch to ubuntu-latest once it defaults to a version of Ubuntu that
+    # provides at least Python 3.11 (see
+    # https://docs.python.org/3/library/datetime.html#datetime.date.fromisoformat)
+    runs-on: ubuntu-24.04
+    env:
+      RAPIDS_GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - name: Check if nightly CI is passing
+        uses: rapidsai/shared-actions/check_nightly_success/dispatch@main
+        with:
+          repo: cuvs
   changed-files:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.02

From a57227310a54b42481e20aaece72d0879f4c5b96 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Mon, 30 Dec 2024 16:09:03 -0800
Subject: [PATCH 243/623] Update for raft logger changes (#540)

This PR updates cuvs to use raft's updated logger implementation using [rapids-logger](https://github.com/rapidsai/rapids-logger). It is a breaking change because it changes the kmeans `base_params` verbosity type from an int to a `raft::level_enum`.

This PR requires https://github.com/rapidsai/raft/pull/2530.

Contributes to https://github.com/rapidsai/build-planning/issues/104

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/540
---
 cpp/CMakeLists.txt                            | 11 ++++---
 cpp/bench/ann/CMakeLists.txt                  | 21 +++++++++----
 cpp/bench/ann/src/common/benchmark.hpp        | 31 +++++++------------
 cpp/include/cuvs/cluster/kmeans.hpp           |  3 +-
 cpp/src/cluster/detail/kmeans.cuh             | 12 +++----
 cpp/src/cluster/detail/kmeans_auto_find_k.cuh |  4 +--
 cpp/src/cluster/detail/kmeans_balanced.cuh    |  4 +--
 cpp/src/cluster/detail/kmeans_common.cuh      |  2 +-
 .../detail/sparse/coo_spmv_kernel.cuh         |  2 ++
 cpp/src/neighbors/detail/ann_utils.cuh        |  2 +-
 cpp/src/neighbors/detail/cagra/add_nodes.cuh  |  2 --
 .../neighbors/detail/cagra/cagra_build.cuh    |  4 +--
 .../detail/cagra/cagra_serialize.cuh          |  4 +--
 .../detail/cagra/compute_distance.hpp         |  2 +-
 .../detail/cagra/search_multi_cta.cuh         |  2 +-
 .../cagra/search_multi_cta_kernel-inl.cuh     |  2 +-
 .../detail/cagra/search_multi_kernel.cuh      |  2 +-
 .../detail/cagra/search_single_cta.cuh        |  2 +-
 .../cagra/search_single_cta_kernel-inl.cuh    |  3 +-
 .../neighbors/detail/dataset_serialize.hpp    |  2 +-
 cpp/src/neighbors/detail/dynamic_batching.cuh |  2 --
 .../neighbors/detail/vamana/vamana_build.cuh  |  4 +--
 .../detail/vamana/vamana_serialize.cuh        |  2 +-
 .../detail/vamana/vamana_structs.cuh          |  2 +-
 cpp/src/neighbors/detail/vpq_dataset.cuh      |  2 +-
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |  3 +-
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    |  2 +-
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |  3 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |  3 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh   |  2 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |  2 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh |  2 +-
 cpp/src/neighbors/mg/omp_checks.cpp           |  1 -
 cpp/test/CMakeLists.txt                       |  4 +--
 cpp/test/neighbors/ann_ivf_pq.cuh             |  2 --
 cpp/test/neighbors/ann_utils.cuh              |  2 --
 cpp/test/neighbors/brute_force.cu             |  2 --
 37 files changed, 71 insertions(+), 86 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 79e50c1c19..26c0b82d3a 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -486,13 +486,14 @@ if(BUILD_SHARED_LIBS)
                       "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
   target_link_libraries(
-    cuvs_objs PUBLIC raft::raft rmm::rmm rmm::rmm_logger ${CUVS_CTK_MATH_DEPENDENCIES}
-                     $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-              PRIVATE rmm::rmm_logger_impl
+    cuvs_objs
+    PUBLIC raft::raft rmm::rmm rmm::rmm_logger ${CUVS_CTK_MATH_DEPENDENCIES}
+           $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+    PRIVATE rmm::rmm_logger_impl raft::raft_logger_impl
   )
 
   add_library(cuvs SHARED $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
-  add_library(cuvs_static STATIC  $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
+  add_library(cuvs_static STATIC $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
 
   target_compile_options(
     cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
@@ -704,7 +705,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
     target_link_libraries(
       cuvs_c
       PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
-      PRIVATE raft::raft rmm::rmm_logger_impl
+      PRIVATE raft::raft rmm::rmm_logger_impl raft::raft_logger_impl
     )
 
     # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 144cd30481..200b52ab34 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -126,10 +126,11 @@ function(ConfigureAnnBench)
     PRIVATE ${ConfigureAnnBench_LINKS}
             nlohmann_json::nlohmann_json
             Threads::Threads
+            $<TARGET_NAME_IF_EXISTS:raft::raft_logger>
             $<$<BOOL:${GPU_BUILD}>:CUDA::cudart_static>
             $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
             $<TARGET_NAME_IF_EXISTS:conda_env>
-            $<TARGET_NAME_IF_EXISTS:cuvs_bench_rmm_logger>
+            $<TARGET_NAME_IF_EXISTS:cuvs_bench_logger>
   )
 
   set_target_properties(
@@ -175,9 +176,11 @@ function(ConfigureAnnBench)
   add_dependencies(CUVS_ANN_BENCH_ALL ${BENCH_NAME})
 endfunction()
 
-if(CUVS_FAISS_ENABLE_GPU)
-  add_library(cuvs_bench_rmm_logger OBJECT)
-  target_link_libraries(cuvs_bench_rmm_logger PRIVATE rmm::rmm_logger_impl)
+if(CUVS_FAISS_ENABLE_GPU OR CUVS_ANN_BENCH_SINGLE_EXE)
+  add_library(cuvs_bench_logger OBJECT)
+  target_link_libraries(
+    cuvs_bench_logger PRIVATE rmm::rmm_logger_impl $<TARGET_NAME_IF_EXISTS:raft::raft_logger>
+  )
 endif()
 
 # ##################################################################################################
@@ -303,8 +306,14 @@ if(CUVS_ANN_BENCH_SINGLE_EXE)
 
   target_link_libraries(
     ANN_BENCH
-    PRIVATE raft::raft nlohmann_json::nlohmann_json benchmark::benchmark dl fmt::fmt-header-only
-            spdlog::spdlog_header_only $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3> rmm::rmm_logger_impl
+    PRIVATE raft::raft
+            nlohmann_json::nlohmann_json
+            benchmark::benchmark
+            dl
+            fmt::fmt-header-only
+            spdlog::spdlog_header_only
+            $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3>
+            cuvs_bench_logger
   )
   set_target_properties(
     ANN_BENCH
diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index 06e1e27af4..49be786737 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -597,18 +597,16 @@ inline auto parse_string_flag(const char* arg, const char* pat, std::string& res
 
 inline auto run_main(int argc, char** argv) -> int
 {
-  bool force_overwrite                = false;
-  bool build_mode                     = false;
-  bool search_mode                    = false;
-  bool no_lap_sync                    = false;
-  std::string data_prefix             = "data";
-  std::string index_prefix            = "index";
-  std::string new_override_kv         = "";
-  std::string mode                    = "latency";
-  std::string threads_arg_txt         = "";
-  std::vector<int> threads            = {1, -1};  // min_thread, max_thread
-  std::string log_level_str           = "";
-  [[maybe_unused]] int raft_log_level = 0;  // raft::logger::get(RAFT_NAME).get_level();
+  bool force_overwrite        = false;
+  bool build_mode             = false;
+  bool search_mode            = false;
+  bool no_lap_sync            = false;
+  std::string data_prefix     = "data";
+  std::string index_prefix    = "index";
+  std::string new_override_kv = "";
+  std::string mode            = "latency";
+  std::string threads_arg_txt = "";
+  std::vector<int> threads    = {1, -1};  // min_thread, max_thread
   kv_series override_kv{};
 
   char arg0_default[] = "benchmark";  // NOLINT
@@ -639,12 +637,7 @@ inline auto run_main(int argc, char** argv) -> int
         parse_string_flag(argv[i], "--index_prefix", index_prefix) ||
         parse_string_flag(argv[i], "--mode", mode) ||
         parse_string_flag(argv[i], "--override_kv", new_override_kv) ||
-        parse_string_flag(argv[i], "--threads", threads_arg_txt) ||
-        parse_string_flag(argv[i], "--raft_log_level", log_level_str)) {
-      if (!log_level_str.empty()) {
-        raft_log_level = std::stoi(log_level_str);
-        log_level_str  = "";
-      }
+        parse_string_flag(argv[i], "--threads", threads_arg_txt)) {
       if (!threads_arg_txt.empty()) {
         auto threads_arg = split(threads_arg_txt, ':');
         threads[0]       = std::stoi(threads_arg[0]);
@@ -673,8 +666,6 @@ inline auto run_main(int argc, char** argv) -> int
     }
   }
 
-  // raft::logger::get(RAFT_NAME).set_level(raft_log_level);
-
   Mode metric_objective = Mode::kLatency;
   if (mode == "throughput") { metric_objective = Mode::kThroughput; }
 
diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index 89b3acc24f..cb8d36b10b 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -17,6 +17,7 @@
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/random/rng_state.hpp>
 
@@ -85,7 +86,7 @@ struct params : base_params {
   /**
    * verbosity level.
    */
-  int verbosity = RAFT_LEVEL_INFO;
+  raft::level_enum verbosity = raft::level_enum::info;
 
   /**
    * Seed to the random number generator.
diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
index 3d054f0fd9..e943b8afca 100644
--- a/cpp/src/cluster/detail/kmeans.cuh
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -25,7 +25,7 @@
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/kvp.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
@@ -56,8 +56,6 @@
 
 namespace cuvs::cluster::kmeans::detail {
 
-// TODO(cjnolet): RAFT_NAME needs to be removed and the raft::logger fixed to not require it
-static const std::string RAFT_NAME = "raft";
 static const std::string CUVS_NAME = "cuvs";
 
 // =========================================================
@@ -373,7 +371,7 @@ void kmeans_fit_main(raft::resources const& handle,
                      rmm::device_uvector<char>& workspace)
 {
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeans_fit_main");
-  raft::logger::get(RAFT_NAME).set_level(params.verbosity);
+  raft::default_logger().set_level(params.verbosity);
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   auto n_samples      = X.extent(0);
   auto n_features     = X.extent(1);
@@ -879,7 +877,7 @@ void kmeans_fit(raft::resources const& handle,
       pams.n_clusters);
   }
 
-  raft::logger::get(RAFT_NAME).set_level(pams.verbosity);
+  raft::default_logger().set_level(pams.verbosity);
 
   // Allocate memory
   rmm::device_uvector<char> workspace(0, stream);
@@ -1025,7 +1023,7 @@ void kmeans_predict(raft::resources const& handle,
   RAFT_EXPECTS(centroids.extent(1) == n_features,
                "invalid parameter (centroids.extent(1) != n_features)");
 
-  raft::logger::get(RAFT_NAME).set_level(pams.verbosity);
+  raft::default_logger().set_level(pams.verbosity);
   auto metric = pams.metric;
 
   // Allocate memory
@@ -1218,7 +1216,7 @@ void kmeans_transform(raft::resources const& handle,
                       raft::device_matrix_view<DataT> X_new)
 {
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("kmeans_transform");
-  raft::logger::get(RAFT_NAME).set_level(pams.verbosity);
+  raft::default_logger().set_level(pams.verbosity);
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
   auto n_samples      = X.extent(0);
   auto n_features     = X.extent(1);
diff --git a/cpp/src/cluster/detail/kmeans_auto_find_k.cuh b/cpp/src/cluster/detail/kmeans_auto_find_k.cuh
index 6441f7ad5b..797b33bca9 100644
--- a/cpp/src/cluster/detail/kmeans_auto_find_k.cuh
+++ b/cpp/src/cluster/detail/kmeans_auto_find_k.cuh
@@ -21,7 +21,7 @@
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/error.hpp>
 #include <raft/core/host_mdarray.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/stats/dispersion.cuh>
@@ -230,4 +230,4 @@ void find_k(raft::resources const& handle,
       n_iter);
   }
 }
-}  // namespace  cuvs::cluster::kmeans::detail
\ No newline at end of file
+}  // namespace  cuvs::cluster::kmeans::detail
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index 3f1ad2334a..ba4cabbded 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -25,7 +25,8 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/cudart_utils.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger-macros.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_memory_resource.hpp>
@@ -59,7 +60,6 @@
 
 namespace cuvs::cluster::kmeans::detail {
 
-static const std::string RAFT_NAME                 = "raft";
 constexpr static inline float kAdjustCentersWeight = 7.0f;
 
 /**
diff --git a/cpp/src/cluster/detail/kmeans_common.cuh b/cpp/src/cluster/detail/kmeans_common.cuh
index eec71b5d2b..03db08bd10 100644
--- a/cpp/src/cluster/detail/kmeans_common.cuh
+++ b/cpp/src/cluster/detail/kmeans_common.cuh
@@ -24,7 +24,7 @@
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/kvp.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
diff --git a/cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh b/cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh
index 1f4b19af44..e44edc68a0 100644
--- a/cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh
+++ b/cpp/src/distance/detail/sparse/coo_spmv_kernel.cuh
@@ -16,6 +16,8 @@
 
 #pragma once
 
+#include <raft/core/detail/macros.hpp>
+
 #include <cub/block/block_load.cuh>
 #include <cub/block/block_radix_sort.cuh>
 #include <cub/block/block_store.cuh>
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index 5293563514..149eea3f1e 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -18,7 +18,7 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <raft/common/nvtx.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
 #include <raft/util/integer_utils.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
index 952039130f..358b7643e4 100644
--- a/cpp/src/neighbors/detail/cagra/add_nodes.cuh
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -31,8 +31,6 @@
 
 namespace cuvs::neighbors::cagra {
 
-static const std::string RAFT_NAME = "raft";
-
 template <class T, class IdxT, class Accessor>
 void add_node_core(
   raft::resources const& handle,
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index b7fec724ba..3409864487 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -26,7 +26,7 @@
 #include <raft/core/host_device_accessor.hpp>
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/host_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 
 #include <cuvs/distance/distance.hpp>
@@ -46,8 +46,6 @@
 
 namespace cuvs::neighbors::cagra::detail {
 
-static const std::string RAFT_NAME = "raft";
-
 template <typename IdxT>
 void write_to_graph(raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
                     raft::host_matrix_view<int64_t, int64_t, raft::row_major> neighbors_host_view,
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index 0f6cf852f9..c83da7bb10 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -18,7 +18,7 @@
 
 #include <cuvs/neighbors/cagra.hpp>
 #include <raft/core/host_mdarray.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
@@ -34,8 +34,6 @@
 
 namespace cuvs::neighbors::cagra::detail {
 
-static const std::string RAFT_NAME = "raft";
-
 constexpr int serialization_version = 4;
 
 /**
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.hpp b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
index 7eb7984592..2227e4f9ea 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
@@ -22,7 +22,7 @@
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.hpp>
-#include <raft/core/logger-macros.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/operators.hpp>
 
 // TODO: This shouldn't be invoking spatial/knn
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index ecfd856f13..9cb432bcbd 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -26,7 +26,7 @@
 
 #include <raft/core/detail/macros.hpp>
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 9fa9d58944..7535ff217a 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -26,7 +26,7 @@
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index c6fe216429..469c80a08e 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -23,7 +23,7 @@
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index fa71dbaf95..161aa8c4a3 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -26,7 +26,7 @@
 #include "utils.hpp"
 
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index 678ed0cb45..188862fbbb 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -28,7 +28,7 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_properties.hpp>
 #include <raft/core/resources.hpp>
@@ -64,7 +64,6 @@
 
 namespace cuvs::neighbors::cagra::detail {
 namespace single_cta_search {
-using raft::RAFT_NAME;  // TODO: this is required for RAFT_LOG_XXX messages.
 
 // #define _CLK_BREAKDOWN
 
diff --git a/cpp/src/neighbors/detail/dataset_serialize.hpp b/cpp/src/neighbors/detail/dataset_serialize.hpp
index 0ecc2cf5d8..ba3090b59c 100644
--- a/cpp/src/neighbors/detail/dataset_serialize.hpp
+++ b/cpp/src/neighbors/detail/dataset_serialize.hpp
@@ -21,7 +21,7 @@
 #include <raft/core/resources.hpp>
 #include <raft/core/serialize.hpp>
 
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 
 #include <cuda_fp16.h>
 
diff --git a/cpp/src/neighbors/detail/dynamic_batching.cuh b/cpp/src/neighbors/detail/dynamic_batching.cuh
index 5c6b1654e2..cb8e08ef51 100644
--- a/cpp/src/neighbors/detail/dynamic_batching.cuh
+++ b/cpp/src/neighbors/detail/dynamic_batching.cuh
@@ -50,8 +50,6 @@
 
 namespace cuvs::neighbors::dynamic_batching::detail {
 
-using raft::RAFT_NAME;  // TODO: a workaround for RAFT_LOG_XXX macros
-
 /**
  * A helper to make the requester threads more cooperative when busy-spinning.
  * It is used in the wait loops across this file to reduce the CPU usage.
diff --git a/cpp/src/neighbors/detail/vamana/vamana_build.cuh b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
index da24decb36..ec75c99c11 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_build.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
@@ -29,7 +29,7 @@
 #include <raft/core/host_device_accessor.hpp>
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/host_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/matrix/copy.cuh>
 #include <raft/matrix/init.cuh>
@@ -52,8 +52,6 @@ namespace cuvs::neighbors::experimental::vamana::detail {
  * @{
  */
 
-static const std::string RAFT_NAME = "raft";
-
 static const int blockD    = 32;
 static const int maxBlocks = 10000;
 
diff --git a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
index a554464f6c..c360ae19a5 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
@@ -20,7 +20,7 @@
 
 #include <cuvs/neighbors/vamana.hpp>
 #include <raft/core/host_mdarray.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/nvtx.hpp>
diff --git a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
index 86cb4e1f8f..f6f0279f7c 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
@@ -29,7 +29,7 @@
 #include <raft/core/host_device_accessor.hpp>
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/host_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 
 #include <cuvs/distance/distance.hpp>
diff --git a/cpp/src/neighbors/detail/vpq_dataset.cuh b/cpp/src/neighbors/detail/vpq_dataset.cuh
index d85bad9205..0d7882b4b0 100644
--- a/cpp/src/neighbors/detail/vpq_dataset.cuh
+++ b/cpp/src/neighbors/detail/vpq_dataset.cuh
@@ -25,7 +25,7 @@
 
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/linalg/map.cuh>
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index d6ffc12187..f594343c76 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -27,7 +27,8 @@
 #include "../../cluster/kmeans_balanced.cuh"
 #include "../detail/ann_utils.cuh"
 #include <cuvs/distance/distance.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger-macros.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index f5a4267cd8..79b4f1a188 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -23,7 +23,7 @@
 
 #include "../detail/ann_utils.cuh"
 #include <cuvs/distance/distance.hpp>
-#include <raft/core/logger-ext.hpp>  // RAFT_LOG_TRACE
+#include <raft/core/logger.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/matrix/detail/select_warpsort.cuh>
 #include <raft/util/cuda_rt_essentials.hpp>  // RAFT_CUDA_TRY
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index 032b6a8ff0..2df6f4f0ef 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -27,7 +27,8 @@
 #include <cuvs/distance/distance.hpp>   // is_min_close, DistanceType
 #include <cuvs/selection/select_k.hpp>  // cuvs::selection::select_k
 #include <raft/core/error.hpp>
-#include <raft/core/logger-ext.hpp>  // RAFT_LOG_TRACE
+#include <raft/core/logger-macros.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>   // raft::resources
 #include <raft/linalg/gemm.cuh>      // raft::linalg::gemm
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 1d4acea1ec..44a1b11fa4 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -30,7 +30,7 @@
 #include "../../cluster/kmeans_balanced.cuh"
 
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/mdspan.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
@@ -68,7 +68,6 @@
 #include <variant>
 
 namespace cuvs::neighbors::ivf_pq::detail {
-using raft::RAFT_NAME;                       // TODO: this is required for RAFT_LOG_XXX messages.
 using namespace cuvs::spatial::knn::detail;  // NOLINT
 
 using internal_extents_t = int64_t;  // The default mdspan extent type used internally.
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
index 5b41e5f3d0..1b098ac5c1 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_fp_8bit.cuh
@@ -20,7 +20,7 @@
 
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/nvtx.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index db8f9fbd3b..05bb993533 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -28,7 +28,7 @@
 #include <cuvs/selection/select_k.hpp>
 #include <raft/core/cudart_utils.hpp>
 #include <raft/core/device_mdarray.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/custom_resource.hpp>
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
index 5eaebe69d7..4af9dbb8e8 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_serialize.cuh
@@ -21,7 +21,7 @@
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/host_mdarray.hpp>
-#include <raft/core/logger-ext.hpp>
+#include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/core/serialize.hpp>
diff --git a/cpp/src/neighbors/mg/omp_checks.cpp b/cpp/src/neighbors/mg/omp_checks.cpp
index e09182dfe8..c8cc274141 100644
--- a/cpp/src/neighbors/mg/omp_checks.cpp
+++ b/cpp/src/neighbors/mg/omp_checks.cpp
@@ -18,7 +18,6 @@
 #include <raft/core/logger.hpp>
 
 namespace cuvs::neighbors::mg {
-using raft::RAFT_NAME;
 
 void check_omp_threads(const int requirements)
 {
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 4d13daaedb..cca0614553 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -89,7 +89,7 @@ function(ConfigureTest)
 endfunction()
 
 add_library(test_rmm_logger OBJECT)
-target_link_libraries(test_rmm_logger PRIVATE rmm::rmm_logger_impl)
+target_link_libraries(test_rmm_logger PRIVATE rmm::rmm_logger_impl raft::raft_logger_impl)
 
 # ##################################################################################################
 # test sources ##################################################################################
@@ -236,7 +236,7 @@ if(BUILD_TESTS)
     NAME SPARSE_TEST PATH sparse/cluster/cluster_solvers.cu sparse/cluster/eigen_solvers.cu
     sparse/cluster/spectral.cu GPUS 1 PERCENT 100
   )
-  
+
   ConfigureTest(
     NAME PREPROCESSING_TEST PATH preprocessing/scalar_quantization.cu GPUS 1 PERCENT 100
   )
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index 3a92b5e3d6..01efd804e2 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -31,8 +31,6 @@
 
 namespace cuvs::neighbors::ivf_pq {
 
-using raft::RAFT_NAME;  // For logging
-
 struct test_ivf_sample_filter {
   static constexpr unsigned offset = 300;
 };
diff --git a/cpp/test/neighbors/ann_utils.cuh b/cpp/test/neighbors/ann_utils.cuh
index 94bccade29..ded8cb5af8 100644
--- a/cpp/test/neighbors/ann_utils.cuh
+++ b/cpp/test/neighbors/ann_utils.cuh
@@ -38,8 +38,6 @@
 
 namespace cuvs::neighbors {
 
-using raft::RAFT_NAME;  // For logging
-
 struct print_dtype {
   cudaDataType_t value;
 };
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index 8c354baa96..2cefb10986 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -76,11 +76,9 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs<T>> {
  protected:
   void testBruteForce()
   {
-    // #if (RAFT_ACTIVE_LEVEL >= RAFT_LEVEL_DEBUG)
     raft::print_device_vector("Input array: ", input_.data(), rows_ * cols_, std::cout);
     std::cout << "K: " << k_ << std::endl;
     raft::print_device_vector("Labels array: ", search_labels_.data(), rows_, std::cout);
-    // #endif
 
     auto index = raft::make_device_matrix_view<const T, IdxT, raft::row_major>(
       (const T*)(input_.data()), rows_, cols_);

From 55c5a7f0f9c3e103a33264a913dbd17b059eff78 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Mon, 30 Dec 2024 18:48:13 -0800
Subject: [PATCH 244/623] Get Breathe from conda again (#554)

As part of https://github.com/rapidsai/cuvs/pull/528 cuvs's doc builds were modified to pull Breathe from pip. That was necessary because the nvidia-sphinx-theme requires Sphinx 8 but [the conda-forge Breathe package was not compatible with that Sphinx version](https://github.com/conda-forge/breathe-feedstock/issues/63). I fixed that in https://github.com/conda-forge/breathe-feedstock/pull/64, so now we can go back to using Breathe from conda to avoid mixing pip and conda for dependency management in the same environment.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/554
---
 conda/environments/all_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/all_cuda-125_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-125_arch-x86_64.yaml  | 2 +-
 dependencies.yaml                                 | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index a6d98ea3bd..01853da84a 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -7,6 +7,7 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
+- breathe>=4.35.0
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -56,6 +57,5 @@ dependencies:
 - sphinx>=8.0.0
 - sysroot_linux-aarch64==2.17
 - pip:
-  - breathe>=4.35.0
   - nvidia-sphinx-theme
 name: all_cuda-118_arch-aarch64
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 1063e4d6cd..a1ad68d7fd 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -7,6 +7,7 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
+- breathe>=4.35.0
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -56,6 +57,5 @@ dependencies:
 - sphinx>=8.0.0
 - sysroot_linux-64==2.17
 - pip:
-  - breathe>=4.35.0
   - nvidia-sphinx-theme
 name: all_cuda-118_arch-x86_64
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index ee7b376953..ee0213fff9 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -7,6 +7,7 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
+- breathe>=4.35.0
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -52,6 +53,5 @@ dependencies:
 - sphinx>=8.0.0
 - sysroot_linux-aarch64==2.17
 - pip:
-  - breathe>=4.35.0
   - nvidia-sphinx-theme
 name: all_cuda-125_arch-aarch64
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 7c8e1fd998..d93dcaf7a1 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -7,6 +7,7 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
+- breathe>=4.35.0
 - c-compiler
 - clang
 - clang-tools=16.0.6
@@ -52,6 +53,5 @@ dependencies:
 - sphinx>=8.0.0
 - sysroot_linux-64==2.17
 - pip:
-  - breathe>=4.35.0
   - nvidia-sphinx-theme
 name: all_cuda-125_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index a73fe7b8fc..a11e59e312 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -394,6 +394,7 @@ dependencies:
     common:
       - output_types: [conda]
         packages:
+          - breathe>=4.35.0
           - doxygen>=1.8.20
           - graphviz
           - ipython
@@ -404,7 +405,6 @@ dependencies:
           - sphinx-markdown-tables
           - pip:
             - nvidia-sphinx-theme
-            - breathe>=4.35.0
   rust:
     common:
       - output_types: [conda]

From 0e735ea025f8e1e24e8e9b3d3f2ac502711f5387 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 7 Jan 2025 12:32:02 -0600
Subject: [PATCH 245/623] remove setup.cfg files, other packaging cleanup
 (#544)

Similar to https://github.com/rapidsai/raft/pull/2532, this proposes some small packaging cleanup.

* removes `setup.cfg` files
   - *these are currently being ignored by tools, in favor of identical configuration in `pyproject.toml` and `.flake8` files*
   - e.g. https://github.com/rapidsai/cuvs/blob/b3ce774d39e149d4e34c401068f24136eac44e13/.pre-commit-config.yaml#L31-L35
* alphabetizes dependency lists in `dependencies.yaml`
* changes `cupy:` group in `dependencies.yaml` to `depends_on_cupy:` (for consistency with other dependencies)

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/544
---
 .pre-commit-config.yaml |  3 +--
 dependencies.yaml       | 40 +++++++++++++++---------------
 pyproject.toml          |  4 +--
 python/cuvs/setup.cfg   | 39 -----------------------------
 setup.cfg               | 55 -----------------------------------------
 5 files changed, 23 insertions(+), 118 deletions(-)
 delete mode 100644 python/cuvs/setup.cfg
 delete mode 100644 setup.cfg

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5e53abd92a..fcfc7e1fa4 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -108,8 +108,7 @@ repos:
                   [.](cmake|cpp|cu|cuh|h|hpp|sh|pxd|py|pyx|rs)$|
                   CMakeLists[.]txt$|
                   CMakeLists_standalone[.]txt$|
-                  meta[.]yaml$|
-                  setup[.]cfg$
+                  meta[.]yaml$
             exclude: |
               (?x)
                   docs/source/sphinxext/github_link\.py|
diff --git a/dependencies.yaml b/dependencies.yaml
index a11e59e312..fbd1d83727 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -7,39 +7,39 @@ files:
       arch: [x86_64, aarch64]
     includes:
       - build
-      - rapids_build
       - build_py_cuvs
+      - build_wheels
+      - checks
       - cuda
       - cuda_version
-      - depends_on_pylibraft
+      - depends_on_cupy
       - depends_on_librmm
+      - depends_on_pylibraft
       - develop
-      - checks
-      - build_wheels
-      - test_libcuvs
       - docs
+      - rapids_build
       - run_py_cuvs
+      - rust
+      - test_libcuvs
       - test_python_common
       - test_py_cuvs
-      - cupy
-      - rust
   bench_ann:
     output: conda
     matrix:
       cuda: ["11.8", "12.5"]
       arch: [x86_64, aarch64]
     includes:
-      - rapids_build
+      - bench
+      - bench_python
       - build_py_cuvs
       - cuda
       - cuda_version
+      - depends_on_cupy
       - depends_on_pylibraft
       - depends_on_librmm
       - develop
-      - bench
-      - bench_python
+      - rapids_build
       - rapids_build_setuptools
-      - cupy
   test_cpp:
     output: none
     includes:
@@ -49,10 +49,10 @@ files:
     output: none
     includes:
       - cuda_version
+      - depends_on_cupy
       - py_version
       - test_python_common
       - test_py_cuvs
-      - cupy
   checks:
     output: none
     includes:
@@ -61,19 +61,19 @@ files:
   docs:
     output: none
     includes:
+      - cuda
       - cuda_version
-      - cupy
+      - depends_on_cupy
       - docs
       - py_version
-      - rust
       - rapids_build
-      - cuda
+      - rust
   rust:
     output: none
     includes:
+      - cuda
       - cuda_version
       - rapids_build
-      - cuda
       - rust
   py_build_cuvs:
     output: pyproject
@@ -89,8 +89,8 @@ files:
       table: tool.rapids-build-backend
       key: requires
     includes:
-      - rapids_build
       - build_py_cuvs
+      - rapids_build
   py_run_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
@@ -98,8 +98,8 @@ files:
       table: project
     includes:
       - cuda_wheels
-      - run_py_cuvs
       - depends_on_pylibraft
+      - run_py_cuvs
   py_test_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
@@ -107,9 +107,9 @@ files:
       table: project.optional-dependencies
       key: test
     includes:
+      - depends_on_cupy
       - test_python_common
       - test_py_cuvs
-      - cupy
   py_build_cuvs_bench:
     output: pyproject
     pyproject_dir: python/cuvs_bench
@@ -368,7 +368,7 @@ dependencies:
               - nvidia-cusolver
               - nvidia-cusparse
 
-  cupy:
+  depends_on_cupy:
     common:
       - output_types: conda
         packages:
diff --git a/pyproject.toml b/pyproject.toml
index fbf4cf41fe..4175144663 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,7 +25,7 @@ force-exclude = '''
 # unlike the match option above this match-dir will have no effect when
 # pydocstyle is invoked from pre-commit. Therefore this exclusion list must
 # also be maintained in the pre-commit config file.
-match-dir = "^(?!(ci|cpp|conda|docs)).*$"
+match-dir = "^(?!(ci|cpp|conda|docs|notebooks)).*$"
 select = "D201, D204, D206, D207, D208, D209, D210, D211, D214, D215, D300, D301, D302, D403, D405, D406, D407, D408, D409, D410, D411, D412, D414, D418"
     # Would like to enable the following rules in the future:
     # D200, D202, D205, D400
@@ -42,6 +42,6 @@ follow_imports = "skip"
 skip = "./.git,./.github,./cpp/build,.*egg-info.*,./.mypy_cache,.*_skbuild"
 # ignore short words, and typename parameters like OffsetT
 ignore-regex = "\\b(.{1,4}|[A-Z]\\w*T)\\b"
-ignore-words-list = "inout,numer"
+ignore-words-list = "inout,unparseable,numer"
 builtin = "clear"
 quiet-level = 3
diff --git a/python/cuvs/setup.cfg b/python/cuvs/setup.cfg
deleted file mode 100644
index 57b4954bc6..0000000000
--- a/python/cuvs/setup.cfg
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
-
-[isort]
-line_length=79
-multi_line_output=3
-include_trailing_comma=True
-force_grid_wrap=0
-combine_as_imports=True
-order_by_type=True
-known_dask=
-    dask
-    distributed
-    dask_cuda
-known_rapids=
-    cuvs
-    nvtext
-    cudf
-    cuml
-    raft
-    cugraph
-    dask_cudf
-    rmm
-known_first_party=
-    cuvs
-default_section=THIRDPARTY
-sections=FUTURE,STDLIB,THIRDPARTY,DASK,RAPIDS,FIRSTPARTY,LOCALFOLDER
-skip=
-    thirdparty
-    .eggs
-    .git
-    .hg
-    .mypy_cache
-    .tox
-    .venv
-    _build
-    buck-out
-    build
-    dist
-    __init__.py
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index e64641d05b..0000000000
--- a/setup.cfg
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
-
-[flake8]
-filename = *.py, *.pyx, *.pxd, *.pxi
-exclude = __init__.py, *.egg, build, docs, .git
-force-check = True
-ignore =
-    # line break before binary operator
-    W503,
-    # whitespace before :
-    E203
-per-file-ignores =
-    # Rules ignored only in Cython:
-    # E211: whitespace before '(' (used in multi-line imports)
-    # E225: Missing whitespace around operators (breaks cython casting syntax like <int>)
-    # E226: Missing whitespace around arithmetic operators (breaks cython pointer syntax like int*)
-    # E227: Missing whitespace around bitwise or shift operator (Can also break casting syntax)
-    # E275: Missing whitespace after keyword (Doesn't work with Cython except?)
-    # E402: invalid syntax (works for Python, not Cython)
-    # E999: invalid syntax (works for Python, not Cython)
-    # W504: line break after binary operator (breaks lines that end with a pointer)
-    *.pyx: E211, E225, E226, E227, E275, E402, E999, W504
-    *.pxd: E211, E225, E226, E227, E275, E402, E999, W504
-    *.pxi: E211, E225, E226, E227, E275, E402, E999, W504
-
-[pydocstyle]
-# Due to https://github.com/PyCQA/pydocstyle/issues/363, we must exclude rather
-# than include using match-dir. Note that as discussed in
-# https://stackoverflow.com/questions/65478393/how-to-filter-directories-using-the-match-dir-flag-for-pydocstyle,
-# unlike the match option above this match-dir will have no effect when
-# pydocstyle is invoked from pre-commit. Therefore this exclusion list must
-# also be maintained in the pre-commit config file.
-match-dir = ^(?!(ci|cpp|conda|docs|java|notebooks)).*$
-# Allow missing docstrings for docutils
-ignore-decorators = .*(docutils|doc_apply|copy_docstring).*
-select =
-    D201, D204, D206, D207, D208, D209, D210, D211, D214, D215, D300, D301, D302, D403, D405, D406, D407, D408, D409, D410, D411, D412, D414, D418
-    # Would like to enable the following rules in the future:
-    # D200, D202, D205, D400
-
-[mypy]
-ignore_missing_imports = True
-# If we don't specify this, then mypy will check excluded files if
-# they are imported by a checked file.
-follow_imports = skip
-
-[codespell]
-# note: pre-commit passes explicit lists of files here, which this skip file list doesn't override -
-# this is only to allow you to run codespell interactively
-skip = ./.git,./.github,./cpp/build,.*egg-info.*,./.mypy_cache,.*_skbuild
-# ignore short words, and typename parameters like OffsetT
-ignore-regex = \b(.{1,4}|[A-Z]\w*T)\b
-ignore-words-list = inout,unparseable,numer
-builtin = clear
-quiet-level = 3

From e3244123a3021f52f1374fdafedbd8f37546d112 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Tue, 7 Jan 2025 11:55:06 -0800
Subject: [PATCH 246/623] Support raft's logger targets (#557)

rapidsai/raft#2530 added new targets that we need to make global in cuvs's CMake as well.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/557
---
 cpp/cmake/thirdparty/get_raft.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 5def74f4b3..2e57df84e5 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -39,7 +39,7 @@ function(find_and_configure_raft)
     # Invoke CPM find_package()
     #-----------------------------------------------------
     rapids_cpm_find(raft ${PKG_VERSION}
-            GLOBAL_TARGETS      raft::raft
+            GLOBAL_TARGETS      raft::raft raft::raft_logger raft::raft_logger_impl
             BUILD_EXPORT_SET    cuvs-exports
             INSTALL_EXPORT_SET  cuvs-exports
             COMPONENTS          ${RAFT_COMPONENTS}

From 2a10353fcaa1e1da8429267a542f2c5a6e3412b1 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 7 Jan 2025 16:45:22 -0800
Subject: [PATCH 247/623] Change brute_force api to match ivf*/cagra (#536)

This changes the brute_force knn api to match that of ivf-* and cagra , by adding a search_params and index_params structure to the relevant calls.

This allows us to use the dynamic batching code on brute_force knn, as well as provide a more standardized API for our users.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/536
---
 cpp/include/cuvs/neighbors/brute_force.hpp    | 139 +++++++++++++-----
 cpp/src/neighbors/brute_force.cu              | 116 ++++++++++-----
 cpp/src/neighbors/brute_force_c.cpp           |  15 +-
 cpp/src/neighbors/dynamic_batching.cu         |   3 +
 cpp/test/CMakeLists.txt                       |   1 +
 cpp/test/neighbors/brute_force.cu             |  34 +++--
 .../dynamic_batching/test_brute_force.cu      |  40 +++++
 7 files changed, 259 insertions(+), 89 deletions(-)
 create mode 100644 cpp/test/neighbors/dynamic_batching/test_brute_force.cu

diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index d040e03dbd..8fca9da834 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -16,7 +16,6 @@
 
 #pragma once
 
-#include "common.hpp"
 #include <cuvs/neighbors/common.hpp>
 #include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/device_mdarray.hpp>
@@ -28,6 +27,10 @@
 
 namespace cuvs::neighbors::brute_force {
 
+struct index_params : cuvs::neighbors::index_params {};
+
+struct search_params : cuvs::neighbors::search_params {};
+
 /**
  * @defgroup bruteforce_cpp_index Bruteforce index
  * @{
@@ -41,6 +44,11 @@ namespace cuvs::neighbors::brute_force {
  */
 template <typename T, typename DistT = T>
 struct index : cuvs::neighbors::index {
+  using index_params_type  = brute_force::index_params;
+  using search_params_type = brute_force::search_params;
+  using index_type         = int64_t;
+  using value_type         = T;
+
  public:
   index(const index&)            = delete;
   index(index&&)                 = default;
@@ -181,20 +189,26 @@ struct index : cuvs::neighbors::index {
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = brute_force::build(handle, dataset, metric);
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
  * @endcode
  *
  * @param[in] handle
+ * @param[in] index_params parameters such as the distance metric to use
  * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
- * @param[in] metric cuvs::distance::DistanceType
- * @param[in] metric_arg metric argument
  *
  * @return the constructed brute-force index
  */
 auto build(raft::resources const& handle,
-           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
-           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
-           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<float, float>;
+           const cuvs::neighbors::brute_force::index_params& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::brute_force::index<float, float>;
+
+[[deprecated]] auto build(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+  float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<float, float>;
 /**
  * @brief Build the index from the dataset for efficient search.
  *
@@ -202,62 +216,78 @@ auto build(raft::resources const& handle,
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
  *   // create and fill the index from a [N, D] dataset
- *   auto index = brute_force::build(handle, dataset, metric);
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
  * @endcode
  *
  * @param[in] handle
+ * @param[in] index_params parameters such as the distance metric to use
  * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
- * @param[in] metric cuvs::distance::DistanceType
- * @param[in] metric_arg metric argument
  *
- * @return the constructed ivf-flat index
+ * @return the constructed brute force index
  */
 auto build(raft::resources const& handle,
-           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
-           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
-           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<half, float>;
+           const cuvs::neighbors::brute_force::index_params& index_params,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::brute_force::index<half, float>;
+
+[[deprecated]] auto build(
+  raft::resources const& handle,
+  raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+  float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<half, float>;
+
 /**
  * @brief Build the index from the dataset for efficient search.
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors;
- *   // create and fill the index from a [N, D] dataset
- *   auto index = brute_force::build(handle, dataset, metric);
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
  * @endcode
  *
  * @param[in] handle
- * @param[in] dataset a device pointer to a col-major matrix [n_rows, dim]
- * @param[in] metric cuvs::distance::DistanceType
- * @param[in] metric_arg metric argument
+ * @param[in] index_params parameters such as the distance metric to use
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
  *
- * @return the constructed bruteforce index
+ * @return the constructed brute force index
  */
 auto build(raft::resources const& handle,
-           raft::device_matrix_view<const float, int64_t, raft::col_major> dataset,
-           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
-           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<float, float>;
+           const cuvs::neighbors::brute_force::index_params& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::col_major> dataset)
+  -> cuvs::neighbors::brute_force::index<float, float>;
+
+[[deprecated]] auto build(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::col_major> dataset,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+  float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<float, float>;
+
 /**
  * @brief Build the index from the dataset for efficient search.
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors;
- *   // create and fill the index from a [N, D] dataset
- *   auto index = brute_force::build(handle, dataset, metric);
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
  * @endcode
  *
  * @param[in] handle
- * @param[in] dataset a device pointer to a col-major matrix [n_rows, dim]
- * @param[in] metric cuvs::distance::DistanceType
- * @param[in] metric_arg metric argument
+ * @param[in] index_params parameters such as the distance metric to use
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
  *
- * @return the constructed bruteforce index
+ * @return the constructed brute force index
  */
 auto build(raft::resources const& handle,
-           raft::device_matrix_view<const half, int64_t, raft::col_major> dataset,
-           cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
-           float metric_arg = 0) -> cuvs::neighbors::brute_force::index<half, float>;
+           const cuvs::neighbors::brute_force::index_params& index_params,
+           raft::device_matrix_view<const half, int64_t, raft::col_major> dataset)
+  -> cuvs::neighbors::brute_force::index<half, float>;
+
+[[deprecated]] auto build(
+  raft::resources const& handle,
+  raft::device_matrix_view<const half, int64_t, raft::col_major> dataset,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded,
+  float metric_arg                    = 0) -> cuvs::neighbors::brute_force::index<half, float>;
 /**
  * @}
  */
@@ -286,6 +316,7 @@ auto build(raft::resources const& handle,
  * @endcode
  *
  * @param[in] handle
+ * @param[in] params parameters configuring the search
  * @param[in] index brute-force constructed index
  * @param[in] queries a device pointer to a row-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
@@ -296,6 +327,7 @@ auto build(raft::resources const& handle,
  * `index->size()` bits to indicate whether queries[0] should compute the distance with dataset.
  */
 void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::search_params& params,
             const cuvs::neighbors::brute_force::index<float, float>& index,
             raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
@@ -303,6 +335,14 @@ void search(raft::resources const& handle,
             const cuvs::neighbors::filtering::base_filter& sample_filter =
               cuvs::neighbors::filtering::none_sample_filter{});
 
+[[deprecated]] void search(raft::resources const& handle,
+                           const cuvs::neighbors::brute_force::index<float, float>& index,
+                           raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+                           raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+                           raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+                           const cuvs::neighbors::filtering::base_filter& sample_filter =
+                             cuvs::neighbors::filtering::none_sample_filter{});
+
 /**
  * @brief Search ANN using the constructed index.
  *
@@ -323,6 +363,7 @@ void search(raft::resources const& handle,
  * @endcode
  *
  * @param[in] handle
+ * @param[in] params parameters configuring the search
  * @param[in] index ivf-flat constructed index
  * @param[in] queries a device pointer to a row-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
@@ -332,18 +373,28 @@ void search(raft::resources const& handle,
  * given
  */
 void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::search_params& params,
             const cuvs::neighbors::brute_force::index<half, float>& index,
             raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             const cuvs::neighbors::filtering::base_filter& sample_filter =
               cuvs::neighbors::filtering::none_sample_filter{});
+
+[[deprecated]] void search(raft::resources const& handle,
+                           const cuvs::neighbors::brute_force::index<half, float>& index,
+                           raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+                           raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+                           raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+                           const cuvs::neighbors::filtering::base_filter& sample_filter =
+                             cuvs::neighbors::filtering::none_sample_filter{});
 /**
  * @brief Search ANN using the constructed index.
  *
  * See the [brute_force::build](#brute_force::build) documentation for a usage example.
  *
  * @param[in] handle
+ * @param[in] params parameters configuring the search
  * @param[in] index bruteforce constructed index
  * @param[in] queries a device pointer to a col-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
@@ -353,18 +404,28 @@ void search(raft::resources const& handle,
  * given query
  */
 void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::search_params& params,
             const cuvs::neighbors::brute_force::index<float, float>& index,
             raft::device_matrix_view<const float, int64_t, raft::col_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             const cuvs::neighbors::filtering::base_filter& sample_filter =
               cuvs::neighbors::filtering::none_sample_filter{});
+
+[[deprecated]] void search(raft::resources const& handle,
+                           const cuvs::neighbors::brute_force::index<float, float>& index,
+                           raft::device_matrix_view<const float, int64_t, raft::col_major> queries,
+                           raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+                           raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+                           const cuvs::neighbors::filtering::base_filter& sample_filter =
+                             cuvs::neighbors::filtering::none_sample_filter{});
 /**
  * @brief Search ANN using the constructed index.
  *
  * See the [brute_force::build](#brute_force::build) documentation for a usage example.
  *
  * @param[in] handle
+ * @param[in] params parameters configuring the search
  * @param[in] index bruteforce constructed index
  * @param[in] queries a device pointer to a col-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
@@ -374,12 +435,21 @@ void search(raft::resources const& handle,
  * given query
  */
 void search(raft::resources const& handle,
+            const cuvs::neighbors::brute_force::search_params& params,
             const cuvs::neighbors::brute_force::index<half, float>& index,
             raft::device_matrix_view<const half, int64_t, raft::col_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             const cuvs::neighbors::filtering::base_filter& sample_filter =
               cuvs::neighbors::filtering::none_sample_filter{});
+
+[[deprecated]] void search(raft::resources const& handle,
+                           const cuvs::neighbors::brute_force::index<half, float>& index,
+                           raft::device_matrix_view<const half, int64_t, raft::col_major> queries,
+                           raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+                           raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+                           const cuvs::neighbors::filtering::base_filter& sample_filter =
+                             cuvs::neighbors::filtering::none_sample_filter{});
 /**
  * @}
  */
@@ -472,6 +542,7 @@ struct sparse_search_params {
  * @brief Search the sparse bruteforce index for nearest neighbors
  *
  * @param[in] handle
+ * @param[in] params parameters configuring the search
  * @param[in] index Sparse brute-force constructed index
  * @param[in] queries a sparse CSR matrix on the device to query
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index d534676e39..a9980a390a 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -160,45 +160,81 @@ void index<T, DistT>::update_dataset(
   dataset_view_ = raft::make_const_mdspan(dataset_.view());
 }
 
-#define CUVS_INST_BFKNN(T, DistT)                                                    \
-  auto build(raft::resources const& res,                                             \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,    \
-             cuvs::distance::DistanceType metric,                                    \
-             DistT metric_arg)                                                       \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                  \
-  {                                                                                  \
-    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                \
-  }                                                                                  \
-  auto build(raft::resources const& res,                                             \
-             raft::device_matrix_view<const T, int64_t, raft::col_major> dataset,    \
-             cuvs::distance::DistanceType metric,                                    \
-             DistT metric_arg)                                                       \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                  \
-  {                                                                                  \
-    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                \
-  }                                                                                  \
-                                                                                     \
-  void search(raft::resources const& res,                                            \
-              const cuvs::neighbors::brute_force::index<T, DistT>& idx,              \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,   \
-              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors, \
-              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,   \
-              const cuvs::neighbors::filtering::base_filter& sample_filter)          \
-  {                                                                                  \
-    detail::search<T, int64_t, DistT, raft::row_major>(                              \
-      res, idx, queries, neighbors, distances, sample_filter);                       \
-  }                                                                                  \
-  void search(raft::resources const& res,                                            \
-              const cuvs::neighbors::brute_force::index<T, DistT>& idx,              \
-              raft::device_matrix_view<const T, int64_t, raft::col_major> queries,   \
-              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors, \
-              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,   \
-              const cuvs::neighbors::filtering::base_filter& sample_filter)          \
-  {                                                                                  \
-    detail::search<T, int64_t, DistT, raft::col_major>(                              \
-      res, idx, queries, neighbors, distances, sample_filter);                       \
-  }                                                                                  \
-                                                                                     \
+#define CUVS_INST_BFKNN(T, DistT)                                                               \
+  auto build(raft::resources const& res,                                                        \
+             const cuvs::neighbors::brute_force::index_params& index_params,                    \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)               \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+  {                                                                                             \
+    return detail::build<T, DistT>(res, dataset, index_params.metric, index_params.metric_arg); \
+  }                                                                                             \
+  auto build(raft::resources const& res,                                                        \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,               \
+             cuvs::distance::DistanceType metric,                                               \
+             DistT metric_arg)                                                                  \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+  {                                                                                             \
+    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                           \
+  }                                                                                             \
+  auto build(raft::resources const& res,                                                        \
+             const cuvs::neighbors::brute_force::index_params& index_params,                    \
+             raft::device_matrix_view<const T, int64_t, raft::col_major> dataset)               \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+  {                                                                                             \
+    return detail::build<T, DistT>(res, dataset, index_params.metric, index_params.metric_arg); \
+  }                                                                                             \
+  auto build(raft::resources const& res,                                                        \
+             raft::device_matrix_view<const T, int64_t, raft::col_major> dataset,               \
+             cuvs::distance::DistanceType metric,                                               \
+             DistT metric_arg)                                                                  \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+  {                                                                                             \
+    return detail::build<T, DistT>(res, dataset, metric, metric_arg);                           \
+  }                                                                                             \
+                                                                                                \
+  void search(raft::resources const& res,                                                       \
+              const cuvs::neighbors::brute_force::search_params& params,                        \
+              const cuvs::neighbors::brute_force::index<T, DistT>& idx,                         \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,              \
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,            \
+              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,              \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)                     \
+  {                                                                                             \
+    detail::search<T, int64_t, DistT, raft::row_major>(                                         \
+      res, idx, queries, neighbors, distances, sample_filter);                                  \
+  }                                                                                             \
+  void search(raft::resources const& res,                                                       \
+              const cuvs::neighbors::brute_force::index<T, DistT>& idx,                         \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,              \
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,            \
+              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,              \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)                     \
+  {                                                                                             \
+    detail::search<T, int64_t, DistT, raft::row_major>(                                         \
+      res, idx, queries, neighbors, distances, sample_filter);                                  \
+  }                                                                                             \
+  void search(raft::resources const& res,                                                       \
+              const cuvs::neighbors::brute_force::search_params& params,                        \
+              const cuvs::neighbors::brute_force::index<T, DistT>& idx,                         \
+              raft::device_matrix_view<const T, int64_t, raft::col_major> queries,              \
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,            \
+              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,              \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)                     \
+  {                                                                                             \
+    detail::search<T, int64_t, DistT, raft::col_major>(                                         \
+      res, idx, queries, neighbors, distances, sample_filter);                                  \
+  }                                                                                             \
+  void search(raft::resources const& res,                                                       \
+              const cuvs::neighbors::brute_force::index<T, DistT>& idx,                         \
+              raft::device_matrix_view<const T, int64_t, raft::col_major> queries,              \
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,            \
+              raft::device_matrix_view<DistT, int64_t, raft::row_major> distances,              \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)                     \
+  {                                                                                             \
+    detail::search<T, int64_t, DistT, raft::col_major>(                                         \
+      res, idx, queries, neighbors, distances, sample_filter);                                  \
+  }                                                                                             \
+                                                                                                \
   template struct cuvs::neighbors::brute_force::index<T, DistT>;
 
 CUVS_INST_BFKNN(float, float);
@@ -206,4 +242,4 @@ CUVS_INST_BFKNN(half, float);
 
 #undef CUVS_INST_BFKNN
 
-}  // namespace cuvs::neighbors::brute_force
\ No newline at end of file
+}  // namespace cuvs::neighbors::brute_force
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index f1a8c995d2..2b8980863e 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -44,10 +44,12 @@ void* _build(cuvsResources_t res,
   using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
   auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
 
-  auto index_on_stack = cuvs::neighbors::brute_force::build(
-    *res_ptr, mds, static_cast<cuvs::distance::DistanceType>((int)metric), metric_arg);
-  auto index_on_heap = new cuvs::neighbors::brute_force::index<T>(std::move(index_on_stack));
+  cuvs::neighbors::brute_force::index_params params;
+  params.metric     = metric;
+  params.metric_arg = metric_arg;
 
+  auto index_on_stack = cuvs::neighbors::brute_force::build(*res_ptr, params, mds);
+  auto index_on_heap  = new cuvs::neighbors::brute_force::index<T>(std::move(index_on_stack));
   return index_on_heap;
 }
 
@@ -72,8 +74,11 @@ void _search(cuvsResources_t res,
   auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
   auto distances_mds = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
 
+  cuvs::neighbors::brute_force::search_params params;
+
   if (prefilter.type == NO_FILTER) {
     cuvs::neighbors::brute_force::search(*res_ptr,
+                                         params,
                                          *index_ptr,
                                          queries_mds,
                                          neighbors_mds,
@@ -87,7 +92,7 @@ void _search(cuvsResources_t res,
                          queries_mds.extent(0),
                          index_ptr->dataset().extent(0)));
     cuvs::neighbors::brute_force::search(
-      *res_ptr, *index_ptr, queries_mds, neighbors_mds, distances_mds, prefilter_view);
+      *res_ptr, params, *index_ptr, queries_mds, neighbors_mds, distances_mds, prefilter_view);
   } else {
     RAFT_FAIL("Unsupported prefilter type: BITSET");
   }
@@ -226,4 +231,4 @@ extern "C" cuvsError_t cuvsBruteForceSerialize(cuvsResources_t res,
       RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
     }
   });
-}
\ No newline at end of file
+}
diff --git a/cpp/src/neighbors/dynamic_batching.cu b/cpp/src/neighbors/dynamic_batching.cu
index 6be70353ba..84c8a2cf1d 100644
--- a/cpp/src/neighbors/dynamic_batching.cu
+++ b/cpp/src/neighbors/dynamic_batching.cu
@@ -16,6 +16,7 @@
 
 #include "detail/dynamic_batching.cuh"
 
+#include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
@@ -53,6 +54,8 @@ namespace cuvs::neighbors::dynamic_batching {
     return index.runner->search(res, params, queries, neighbors, distances);       \
   }
 
+CUVS_INST_DYNAMIC_BATCHING_INDEX(float, int64_t, cuvs::neighbors::brute_force, index<float, float>);
+
 CUVS_INST_DYNAMIC_BATCHING_INDEX(float, uint32_t, cuvs::neighbors::cagra, index<float, uint32_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(half, uint32_t, cuvs::neighbors::cagra, index<half, uint32_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t, uint32_t, cuvs::neighbors::cagra, index<int8_t, uint32_t>);
diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index cca0614553..9aa596a6e0 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -183,6 +183,7 @@ if(BUILD_TESTS)
     NAME
     NEIGHBORS_DYNAMIC_BATCHING_TEST
     PATH
+    neighbors/dynamic_batching/test_brute_force.cu
     neighbors/dynamic_batching/test_cagra.cu
     neighbors/dynamic_batching/test_ivf_flat.cu
     neighbors/dynamic_batching/test_ivf_pq.cu
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index 2cefb10986..b1c819a26e 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -89,10 +89,18 @@ class KNNTest : public ::testing::TestWithParam<KNNInputs<T>> {
     auto distances =
       raft::make_device_matrix_view<DistT, IdxT, raft::row_major>(distances_.data(), rows_, k_);
 
-    auto metric = cuvs::distance::DistanceType::L2Unexpanded;
-    auto idx    = cuvs::neighbors::brute_force::build(handle, index, metric);
-    cuvs::neighbors::brute_force::search(
-      handle, idx, search, indices, distances, cuvs::neighbors::filtering::none_sample_filter{});
+    cuvs::neighbors::brute_force::index_params index_params;
+    index_params.metric = cuvs::distance::DistanceType::L2Unexpanded;
+
+    auto idx = cuvs::neighbors::brute_force::build(handle, index_params, index);
+    cuvs::neighbors::brute_force::search_params search_params;
+    cuvs::neighbors::brute_force::search(handle,
+                                         search_params,
+                                         idx,
+                                         search,
+                                         indices,
+                                         distances,
+                                         cuvs::neighbors::filtering::none_sample_filter{});
 
     build_actual_output<<<raft::ceildiv(rows_ * k_, 32), 32, 0, stream>>>(
       actual_labels_.data(), rows_, k_, search_labels_.data(), indices_.data());
@@ -385,16 +393,22 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
     auto distances = raft::make_device_matrix_view<DistT, int64_t, raft::row_major>(
       cuvs_distances_.data(), params_.num_queries, params_.k);
 
+    cuvs::neighbors::brute_force::index_params index_params;
+    index_params.metric     = metric;
+    index_params.metric_arg = metric_arg;
+
+    cuvs::neighbors::brute_force::search_params search_params;
+
     if (params_.row_major) {
       auto idx =
         cuvs::neighbors::brute_force::build(handle_,
+                                            index_params,
                                             raft::make_device_matrix_view<const T, int64_t>(
-                                              database.data(), params_.num_db_vecs, params_.dim),
-                                            metric,
-                                            metric_arg);
+                                              database.data(), params_.num_db_vecs, params_.dim));
 
       cuvs::neighbors::brute_force::search(
         handle_,
+        search_params,
         idx,
         raft::make_device_matrix_view<const T, int64_t>(
           search_queries.data(), params_.num_queries, params_.dim),
@@ -404,13 +418,13 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
     } else {
       auto idx = cuvs::neighbors::brute_force::build(
         handle_,
+        index_params,
         raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
-          database.data(), params_.num_db_vecs, params_.dim),
-        metric,
-        metric_arg);
+          database.data(), params_.num_db_vecs, params_.dim));
 
       cuvs::neighbors::brute_force::search(
         handle_,
+        search_params,
         idx,
         raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
           search_queries.data(), params_.num_queries, params_.dim),
diff --git a/cpp/test/neighbors/dynamic_batching/test_brute_force.cu b/cpp/test/neighbors/dynamic_batching/test_brute_force.cu
new file mode 100644
index 0000000000..11f4683745
--- /dev/null
+++ b/cpp/test/neighbors/dynamic_batching/test_brute_force.cu
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../dynamic_batching.cuh"
+
+#include <cuvs/neighbors/brute_force.hpp>
+
+namespace cuvs::neighbors::dynamic_batching {
+
+using brute_force_float32 = dynamic_batching_test<float,
+                                                  int64_t,
+                                                  brute_force::index<float, float>,
+                                                  brute_force::build,
+                                                  brute_force::search>;
+
+TEST_P(brute_force_float32, defaults)
+{
+  build_all();
+  search_all();
+  check_neighbors();
+}
+
+INSTANTIATE_TEST_CASE_P(dynamic_batching, brute_force_float32, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::dynamic_batching

From 1e548f8c3a773452ce69556f4db72fc712efae02 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 10 Jan 2025 07:38:42 -0800
Subject: [PATCH 248/623] Allow brute_force::build to work on host matrix
 dataset (#562)

Closes #538

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/562
---
 cpp/include/cuvs/neighbors/brute_force.hpp   |  28 ++++
 cpp/src/neighbors/brute_force.cu             |   7 +
 cpp/src/neighbors/detail/knn_brute_force.cuh |  22 ++-
 cpp/test/neighbors/brute_force.cu            | 142 ++++++++++++-------
 4 files changed, 140 insertions(+), 59 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 8fca9da834..99581469f7 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -204,6 +204,20 @@ auto build(raft::resources const& handle,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::brute_force::index<float, float>;
 
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * @param[in] handle
+ * @param[in] index_params parameters such as the distance metric to use
+ * @param[in] dataset a host pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed brute-force index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::brute_force::index_params& index_params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::brute_force::index<float, float>;
+
 [[deprecated]] auto build(
   raft::resources const& handle,
   raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
@@ -231,6 +245,20 @@ auto build(raft::resources const& handle,
            raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::brute_force::index<half, float>;
 
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * @param[in] handle
+ * @param[in] index_params parameters such as the distance metric to use
+ * @param[in] dataset a host pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed brute-force index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::brute_force::index_params& index_params,
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::brute_force::index<half, float>;
+
 [[deprecated]] auto build(
   raft::resources const& handle,
   raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index a9980a390a..d54a758791 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -168,6 +168,13 @@ void index<T, DistT>::update_dataset(
   {                                                                                             \
     return detail::build<T, DistT>(res, dataset, index_params.metric, index_params.metric_arg); \
   }                                                                                             \
+  auto build(raft::resources const& res,                                                        \
+             const cuvs::neighbors::brute_force::index_params& index_params,                    \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)                 \
+    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+  {                                                                                             \
+    return detail::build<T, DistT>(res, dataset, index_params.metric, index_params.metric_arg); \
+  }                                                                                             \
   auto build(raft::resources const& res,                                                        \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,               \
              cuvs::distance::DistanceType metric,                                               \
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index e5eeecbc96..f1976e0029 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -28,6 +28,7 @@
 #include "./knn_utils.cuh"
 
 #include <raft/core/bitmap.cuh>
+#include <raft/core/copy.cuh>
 #include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
@@ -750,10 +751,10 @@ void search(raft::resources const& res,
   }
 }
 
-template <typename T, typename DistT, typename LayoutT = raft::row_major>
+template <typename T, typename DistT, typename AccessorT, typename LayoutT = raft::row_major>
 cuvs::neighbors::brute_force::index<T, DistT> build(
   raft::resources const& res,
-  raft::device_matrix_view<const T, int64_t, LayoutT> dataset,
+  mdspan<const T, matrix_extent<int64_t>, LayoutT, AccessorT> dataset,
   cuvs::distance::DistanceType metric,
   DistT metric_arg)
 {
@@ -764,18 +765,31 @@ cuvs::neighbors::brute_force::index<T, DistT> build(
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
       metric == cuvs::distance::DistanceType::CosineExpanded) {
+    auto dataset_storage = std::optional<device_matrix<T, int64_t, LayoutT>>{};
+    auto dataset_view    = [&res, &dataset_storage, dataset]() {
+      if constexpr (std::is_same_v<decltype(dataset),
+                                   raft::device_matrix_view<const T, int64_t, row_major>>) {
+        return dataset;
+      } else {
+        dataset_storage =
+          make_device_matrix<T, int64_t, LayoutT>(res, dataset.extent(0), dataset.extent(1));
+        raft::copy(res, dataset_storage->view(), dataset);
+        return raft::make_const_mdspan(dataset_storage->view());
+      }
+    }();
+
     norms = raft::make_device_vector<DistT, int64_t>(res, dataset.extent(0));
     // cosine needs the l2norm, where as l2 distances needs the squared norm
     if (metric == cuvs::distance::DistanceType::CosineExpanded) {
       raft::linalg::norm(res,
-                         dataset,
+                         dataset_view,
                          norms->view(),
                          raft::linalg::NormType::L2Norm,
                          raft::linalg::Apply::ALONG_ROWS,
                          raft::sqrt_op{});
     } else {
       raft::linalg::norm(res,
-                         dataset,
+                         dataset_view,
                          norms->view(),
                          raft::linalg::NormType::L2Norm,
                          raft::linalg::Apply::ALONG_ROWS);
diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/test/neighbors/brute_force.cu
index b1c819a26e..a9ad4bf1c9 100644
--- a/cpp/test/neighbors/brute_force.cu
+++ b/cpp/test/neighbors/brute_force.cu
@@ -21,6 +21,7 @@
 #include <cuvs/selection/select_k.hpp>
 
 #include <cuvs/neighbors/brute_force.hpp>
+#include <raft/core/host_mdarray.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/linalg/transpose.cuh>
 #include <raft/matrix/init.cuh>
@@ -210,6 +211,7 @@ struct RandomKNNInputs {
   int k;
   cuvs::distance::DistanceType metric;
   bool row_major;
+  bool host_dataset;
 };
 
 std::ostream& operator<<(std::ostream& os, const RandomKNNInputs& input)
@@ -217,7 +219,7 @@ std::ostream& operator<<(std::ostream& os, const RandomKNNInputs& input)
   return os << "num_queries:" << input.num_queries << " num_vecs:" << input.num_db_vecs
             << " dim:" << input.dim << " k:" << input.k
             << " metric:" << cuvs::neighbors::print_metric{input.metric}
-            << " row_major:" << input.row_major;
+            << " row_major:" << input.row_major << " host_dataset:" << input.host_dataset;
 }
 
 template <typename T, typename DistT = T>
@@ -399,12 +401,15 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
 
     cuvs::neighbors::brute_force::search_params search_params;
 
-    if (params_.row_major) {
-      auto idx =
-        cuvs::neighbors::brute_force::build(handle_,
-                                            index_params,
-                                            raft::make_device_matrix_view<const T, int64_t>(
-                                              database.data(), params_.num_db_vecs, params_.dim));
+    if (params_.host_dataset) {
+      // test building from a dataset in host memory
+      auto host_database =
+        raft::make_host_matrix<T, int64_t, raft::row_major>(params_.num_db_vecs, params_.dim);
+      raft::copy(
+        host_database.data_handle(), database.data(), params_.num_db_vecs * params_.dim, stream_);
+
+      auto idx = cuvs::neighbors::brute_force::build(
+        handle_, index_params, raft::make_const_mdspan(host_database.view()));
 
       cuvs::neighbors::brute_force::search(
         handle_,
@@ -416,21 +421,39 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
         distances,
         cuvs::neighbors::filtering::none_sample_filter{});
     } else {
-      auto idx = cuvs::neighbors::brute_force::build(
-        handle_,
-        index_params,
-        raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
-          database.data(), params_.num_db_vecs, params_.dim));
+      if (params_.row_major) {
+        auto idx =
+          cuvs::neighbors::brute_force::build(handle_,
+                                              index_params,
+                                              raft::make_device_matrix_view<const T, int64_t>(
+                                                database.data(), params_.num_db_vecs, params_.dim));
 
-      cuvs::neighbors::brute_force::search(
-        handle_,
-        search_params,
-        idx,
-        raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
-          search_queries.data(), params_.num_queries, params_.dim),
-        indices,
-        distances,
-        cuvs::neighbors::filtering::none_sample_filter{});
+        cuvs::neighbors::brute_force::search(
+          handle_,
+          search_params,
+          idx,
+          raft::make_device_matrix_view<const T, int64_t>(
+            search_queries.data(), params_.num_queries, params_.dim),
+          indices,
+          distances,
+          cuvs::neighbors::filtering::none_sample_filter{});
+      } else {
+        auto idx = cuvs::neighbors::brute_force::build(
+          handle_,
+          index_params,
+          raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+            database.data(), params_.num_db_vecs, params_.dim));
+
+        cuvs::neighbors::brute_force::search(
+          handle_,
+          search_params,
+          idx,
+          raft::make_device_matrix_view<const T, int64_t, raft::col_major>(
+            search_queries.data(), params_.num_queries, params_.dim),
+          indices,
+          distances,
+          cuvs::neighbors::filtering::none_sample_filter{});
+      }
     }
 
     ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(ref_indices_.data(),
@@ -480,42 +503,51 @@ class RandomBruteForceKNNTest : public ::testing::TestWithParam<RandomKNNInputs>
 
 const std::vector<RandomKNNInputs> random_inputs = {
   // test each distance metric on a small-ish input, with row-major inputs
-  {100, 256, 2, 65, cuvs::distance::DistanceType::L2Expanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L1, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::Linf, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::CorrelationExpanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::CosineExpanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::LpUnexpanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::JensenShannon, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::Canberra, true},
+  {100, 256, 2, 65, cuvs::distance::DistanceType::L2Expanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L1, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Linf, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CorrelationExpanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CosineExpanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::LpUnexpanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::JensenShannon, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Canberra, true, false},
   // test each distance metric with col-major inputs
-  {256, 512, 16, 7, cuvs::distance::DistanceType::L2Expanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L1, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::Linf, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::CorrelationExpanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::CosineExpanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::LpUnexpanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::JensenShannon, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, false},
-  {256, 512, 16, 8, cuvs::distance::DistanceType::Canberra, false},
+  {256, 512, 16, 7, cuvs::distance::DistanceType::L2Expanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Unexpanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L1, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Linf, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CorrelationExpanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::CosineExpanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::LpUnexpanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::JensenShannon, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2SqrtExpanded, false, false},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Canberra, false, false},
   // larger tests on different sized data / k values
-  {10000, 40000, 32, 30, cuvs::distance::DistanceType::L2Expanded, false},
-  {345, 1023, 16, 128, cuvs::distance::DistanceType::CosineExpanded, true},
-  {789, 20516, 64, 256, cuvs::distance::DistanceType::L2SqrtExpanded, false},
-  {1000, 200000, 128, 128, cuvs::distance::DistanceType::L2Expanded, true},
-  {1000, 200000, 128, 128, cuvs::distance::DistanceType::L2Expanded, false},
-  {1000, 5000, 128, 128, cuvs::distance::DistanceType::LpUnexpanded, true},
-  {1000, 5000, 128, 128, cuvs::distance::DistanceType::L2SqrtExpanded, false},
-  {1000, 5000, 128, 128, cuvs::distance::DistanceType::InnerProduct, false}};
+  {10000, 40000, 32, 30, cuvs::distance::DistanceType::L2Expanded, false, false},
+  {345, 1023, 16, 128, cuvs::distance::DistanceType::CosineExpanded, true, false},
+  {789, 20516, 64, 256, cuvs::distance::DistanceType::L2SqrtExpanded, false, false},
+  {1000, 200000, 128, 128, cuvs::distance::DistanceType::L2Expanded, true, false},
+  {1000, 200000, 128, 128, cuvs::distance::DistanceType::L2Expanded, false, false},
+  {1000, 5000, 128, 128, cuvs::distance::DistanceType::LpUnexpanded, true, false},
+  {1000, 5000, 128, 128, cuvs::distance::DistanceType::L2SqrtExpanded, false, false},
+  {1000, 5000, 128, 128, cuvs::distance::DistanceType::InnerProduct, false, false},
+  // test with datasets on host memory
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L2Expanded, true, true},
+  {256, 512, 32, 16, cuvs::distance::DistanceType::L2Unexpanded, true, true},
+  {256, 512, 8, 8, cuvs::distance::DistanceType::L2SqrtExpanded, true, true},
+  {256, 128, 32, 8, cuvs::distance::DistanceType::L2SqrtUnexpanded, true, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::L1, true, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::Linf, true, true},
+  {256, 512, 16, 8, cuvs::distance::DistanceType::InnerProduct, true, true},
+  {256, 512, 16, 7, cuvs::distance::DistanceType::L2Expanded, true, true}};
 
 typedef RandomBruteForceKNNTest<float, float> RandomBruteForceKNNTestF;
 TEST_P(RandomBruteForceKNNTestF, BruteForce) { this->testBruteForce(); }

From 28d9990821e26b9bef7b452d9f797bec2972a92e Mon Sep 17 00:00:00 2001
From: Micka <ide.mickael@gmail.com>
Date: Mon, 13 Jan 2025 20:38:03 +0100
Subject: [PATCH 249/623] Add support for refinement with `uint32_t` index type
 (#563)

Closes #537.
Needed change for the transition from Raft to cuVS.

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/563
---
 cpp/include/cuvs/neighbors/refine.hpp         | 45 +++++++++++++++++++
 cpp/src/neighbors/ivf_flat_index.cpp          |  1 +
 .../detail/refine_device_float_float.cu       |  1 +
 cpp/src/neighbors/refine/refine_device.cuh    | 13 +++---
 4 files changed, 54 insertions(+), 6 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/refine.hpp b/cpp/include/cuvs/neighbors/refine.hpp
index 19fbd30bb7..5e60ff5379 100644
--- a/cpp/include/cuvs/neighbors/refine.hpp
+++ b/cpp/include/cuvs/neighbors/refine.hpp
@@ -76,6 +76,51 @@ void refine(raft::resources const& handle,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
 
+/**
+ * @brief Refine nearest neighbor search.
+ *
+ * Refinement is an operation that follows an approximate NN search. The approximate search has
+ * already selected n_candidates neighbor candidates for each query. We narrow it down to k
+ * neighbors. For each query, we calculate the exact distance between the query and its
+ * n_candidates neighbor candidate, and select the k nearest ones.
+ *
+ * The k nearest neighbors and distances are returned.
+ *
+ * Example usage
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_pq::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_pq::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   ivf_pq::search_params search_params;
+ *   // search m = 4 * k nearest neighbours for each of the N queries
+ *   ivf_pq::search(handle, search_params, index, queries, neighbor_candidates,
+ *                  out_dists_tmp);
+ *   // refine it to the k nearest one
+ *   refine(handle, dataset, queries, neighbor_candidates, out_indices, out_dists,
+ *           index.metric());
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] dataset device matrix that stores the dataset [n_rows, dims]
+ * @param[in] queries device matrix of the queries [n_queris, dims]
+ * @param[in] neighbor_candidates indices of candidate vectors [n_queries, n_candidates], where
+ *   n_candidates >= k
+ * @param[out] indices device matrix that stores the refined indices [n_queries, k]
+ * @param[out] distances device matrix that stores the refined distances [n_queries, k]
+ * @param[in] metric distance metric to use. Euclidean (L2) is used by default
+ */
+void refine(raft::resources const& handle,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<const uint32_t, int64_t, raft::row_major> neighbor_candidates,
+            raft::device_matrix_view<uint32_t, int64_t, raft::row_major> indices,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
 /**
  * @brief Refine nearest neighbor search.
  *
diff --git a/cpp/src/neighbors/ivf_flat_index.cpp b/cpp/src/neighbors/ivf_flat_index.cpp
index 6f7d11e500..c16dc47aa9 100644
--- a/cpp/src/neighbors/ivf_flat_index.cpp
+++ b/cpp/src/neighbors/ivf_flat_index.cpp
@@ -226,6 +226,7 @@ void index<T, IdxT>::check_consistency()
     "inconsistent number of lists (clusters)");
 }
 
+template struct index<float, uint32_t>;  // Used for refine function
 template struct index<float, int64_t>;
 template struct index<half, int64_t>;
 template struct index<int8_t, int64_t>;
diff --git a/cpp/src/neighbors/refine/detail/refine_device_float_float.cu b/cpp/src/neighbors/refine/detail/refine_device_float_float.cu
index 25bad201b1..76b792d1cc 100644
--- a/cpp/src/neighbors/refine/detail/refine_device_float_float.cu
+++ b/cpp/src/neighbors/refine/detail/refine_device_float_float.cu
@@ -43,5 +43,6 @@
   }
 
 instantiate_cuvs_neighbors_refine_d(int64_t, float, float, int64_t);
+instantiate_cuvs_neighbors_refine_d(uint32_t, float, float, int64_t);
 
 #undef instantiate_cuvs_neighbors_refine_d
diff --git a/cpp/src/neighbors/refine/refine_device.cuh b/cpp/src/neighbors/refine/refine_device.cuh
index 6184e540b7..a5491be0da 100644
--- a/cpp/src/neighbors/refine/refine_device.cuh
+++ b/cpp/src/neighbors/refine/refine_device.cuh
@@ -84,12 +84,13 @@ void refine_device(
   cuvs::neighbors::ivf_flat::index<data_t, idx_t> refinement_index(
     handle, cuvs::distance::DistanceType(metric), n_queries, false, true, dim);
 
-  cuvs::neighbors::ivf_flat::detail::fill_refinement_index(handle,
-                                                           &refinement_index,
-                                                           dataset.data_handle(),
-                                                           neighbor_candidates.data_handle(),
-                                                           n_queries,
-                                                           n_candidates);
+  cuvs::neighbors::ivf_flat::detail::fill_refinement_index<data_t, idx_t>(
+    handle,
+    &refinement_index,
+    dataset.data_handle(),
+    neighbor_candidates.data_handle(),
+    static_cast<idx_t>(n_queries),
+    static_cast<uint32_t>(n_candidates));
   uint32_t grid_dim_x = 1;
 
   // the neighbor ids will be computed in uint32_t as offset

From 898ccfb588cb3013cd4de90fccefd406668e7dce Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 15 Jan 2025 08:46:50 -0800
Subject: [PATCH 250/623] Expose col-major pairwise distances to python (#572)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/572
---
 cpp/include/cuvs/core/detail/interop.hpp | 33 ++++++++++-
 cpp/include/cuvs/core/interop.hpp        | 18 +++++-
 cpp/src/distance/pairwise_distance_c.cpp | 73 +++++++++++++++++++-----
 python/cuvs/cuvs/common/cydlpack.pyx     | 13 ++++-
 python/cuvs/cuvs/distance/distance.pyx   |  4 +-
 python/cuvs/cuvs/test/test_distance.py   |  7 ++-
 6 files changed, 128 insertions(+), 20 deletions(-)

diff --git a/cpp/include/cuvs/core/detail/interop.hpp b/cpp/include/cuvs/core/detail/interop.hpp
index 2ed0b330de..19e4a922cf 100644
--- a/cpp/include/cuvs/core/detail/interop.hpp
+++ b/cpp/include/cuvs/core/detail/interop.hpp
@@ -86,7 +86,6 @@ inline MdspanType from_dlpack(DLManagedTensor* managed_tensor)
   RAFT_EXPECTS(to_data_type.lanes == tensor.dtype.lanes,
                "lanes mismatch between return mdspan and DLTensor");
   RAFT_EXPECTS(tensor.dtype.lanes == 1, "More than 1 DLTensor lanes not supported");
-  RAFT_EXPECTS(tensor.strides == nullptr, "Strided memory layout for DLTensor not supported");
 
   auto to_device = accessor_type_to_DLDevice<typename MdspanType::accessor_type>();
   if (to_device.device_type == kDLCUDA) {
@@ -110,4 +109,36 @@ inline MdspanType from_dlpack(DLManagedTensor* managed_tensor)
   return MdspanType{reinterpret_cast<typename MdspanType::data_handle_type>(tensor.data), exts};
 }
 
+inline bool is_f_contiguous(DLManagedTensor* managed_tensor)
+{
+  auto tensor = managed_tensor->dl_tensor;
+
+  if (!tensor.strides) { return false; }
+  int64_t expected_stride = 1;
+  for (int64_t i = 0; i < tensor.ndim; ++i) {
+    if (tensor.strides[i] != expected_stride) { return false; }
+    expected_stride *= tensor.shape[i];
+  }
+
+  return true;
+}
+
+inline bool is_c_contiguous(DLManagedTensor* managed_tensor)
+{
+  auto tensor = managed_tensor->dl_tensor;
+
+  if (!tensor.strides) {
+    // no stride information indicates a row-major tensor according to the dlpack spec
+    return true;
+  }
+
+  int64_t expected_stride = 1;
+  for (int64_t i = tensor.ndim - 1; i >= 0; --i) {
+    if (tensor.strides[i] != expected_stride) { return false; }
+    expected_stride *= tensor.shape[i];
+  }
+
+  return true;
+}
+
 }  // namespace cuvs::core::detail
diff --git a/cpp/include/cuvs/core/interop.hpp b/cpp/include/cuvs/core/interop.hpp
index 2462f02ec0..096885f2f7 100644
--- a/cpp/include/cuvs/core/interop.hpp
+++ b/cpp/include/cuvs/core/interop.hpp
@@ -51,9 +51,25 @@ inline bool is_dlpack_host_compatible(DLTensor tensor)
   return detail::is_dlpack_host_compatible(tensor);
 }
 
+/**
+ * @brief Check if DLManagedTensor has a row-major (c-contiguous) layout
+ *
+ * @param tensor DLManagedTensor object to check
+ * @return bool
+ */
+inline bool is_c_contiguous(DLManagedTensor* tensor) { return detail::is_c_contiguous(tensor); }
+
+/**
+ * @brief Check if DLManagedTensor has a col-major (f-contiguous) layout
+ *
+ * @param tensor DLManagedTensor object to check
+ * @return bool
+ */
+inline bool is_f_contiguous(DLManagedTensor* tensor) { return detail::is_f_contiguous(tensor); }
+
 /**
  * @brief Convert a DLManagedTensor to an mdspan
- * NOTE: This function only supports compact row-major layouts.
+ * NOTE: This function only supports compact row-major and col-major layouts.
  *
  * @code {.cpp}
  * #include <raft/core/device_mdspan.hpp>
diff --git a/cpp/src/distance/pairwise_distance_c.cpp b/cpp/src/distance/pairwise_distance_c.cpp
index 061adaa2c2..5344a554ca 100644
--- a/cpp/src/distance/pairwise_distance_c.cpp
+++ b/cpp/src/distance/pairwise_distance_c.cpp
@@ -29,7 +29,7 @@
 
 namespace {
 
-template <typename T, typename DistT>
+template <typename T, typename DistT, typename LayoutT = raft::row_major>
 void _pairwise_distance(cuvsResources_t res,
                         DLManagedTensor* x_tensor,
                         DLManagedTensor* y_tensor,
@@ -39,8 +39,8 @@ void _pairwise_distance(cuvsResources_t res,
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
 
-  using mdspan_type           = raft::device_matrix_view<T const, int64_t, raft::row_major>;
-  using distances_mdspan_type = raft::device_matrix_view<DistT, int64_t, raft::row_major>;
+  using mdspan_type           = raft::device_matrix_view<T const, int64_t, LayoutT>;
+  using distances_mdspan_type = raft::device_matrix_view<DistT, int64_t, LayoutT>;
 
   auto x_mds         = cuvs::core::from_dlpack<mdspan_type>(x_tensor);
   auto y_mds         = cuvs::core::from_dlpack<mdspan_type>(y_tensor);
@@ -70,17 +70,64 @@ extern "C" cuvsError_t cuvsPairwiseDistance(cuvsResources_t res,
       RAFT_FAIL("Inputs to cuvsPairwiseDistance must all have the same dtype");
     }
 
-    if (x_dt.bits == 32) {
-      _pairwise_distance<float, float>(
-        res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
-    } else if (x_dt.bits == 16) {
-      _pairwise_distance<half, float>(
-        res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
-    } else if (x_dt.bits == 64) {
-      _pairwise_distance<double, double>(
-        res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+    bool x_row_major;
+    if (cuvs::core::is_c_contiguous(x_tensor)) {
+      x_row_major = true;
+    } else if (cuvs::core::is_f_contiguous(x_tensor)) {
+      x_row_major = false;
     } else {
-      RAFT_FAIL("Unsupported DLtensor dtype: %d and bits: %d", x_dt.code, x_dt.bits);
+      RAFT_FAIL("X input to cuvsPairwiseDistance must be contiguous (non-strided)");
+    }
+
+    bool y_row_major;
+    if (cuvs::core::is_c_contiguous(y_tensor)) {
+      y_row_major = true;
+    } else if (cuvs::core::is_f_contiguous(y_tensor)) {
+      y_row_major = false;
+    } else {
+      RAFT_FAIL("Y input to cuvsPairwiseDistance must be contiguous (non-strided)");
+    }
+
+    bool distances_row_major;
+    if (cuvs::core::is_c_contiguous(distances_tensor)) {
+      distances_row_major = true;
+    } else if (cuvs::core::is_f_contiguous(distances_tensor)) {
+      distances_row_major = false;
+    } else {
+      RAFT_FAIL("distances input to cuvsPairwiseDistance must be contiguous (non-strided)");
+    }
+
+    if ((x_row_major != y_row_major) || (x_row_major != distances_row_major)) {
+      RAFT_FAIL(
+        "Inputs to cuvsPairwiseDistance must all have the same layout (row-major or col-major");
+    }
+
+    if (x_row_major) {
+      if (x_dt.bits == 32) {
+        _pairwise_distance<float, float>(
+          res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      } else if (x_dt.bits == 16) {
+        _pairwise_distance<half, float>(
+          res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      } else if (x_dt.bits == 64) {
+        _pairwise_distance<double, double>(
+          res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      } else {
+        RAFT_FAIL("Unsupported DLtensor dtype: %d and bits: %d", x_dt.code, x_dt.bits);
+      }
+    } else {
+      if (x_dt.bits == 32) {
+        _pairwise_distance<float, float, raft::col_major>(
+          res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      } else if (x_dt.bits == 16) {
+        _pairwise_distance<half, float, raft::col_major>(
+          res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      } else if (x_dt.bits == 64) {
+        _pairwise_distance<double, double, raft::col_major>(
+          res, x_tensor, y_tensor, distances_tensor, metric, metric_arg);
+      } else {
+        RAFT_FAIL("Unsupported DLtensor dtype: %d and bits: %d", x_dt.code, x_dt.bits);
+      }
     }
   });
 }
diff --git a/python/cuvs/cuvs/common/cydlpack.pyx b/python/cuvs/cuvs/common/cydlpack.pyx
index 79f88cddc9..bee8d9afa7 100644
--- a/python/cuvs/cuvs/common/cydlpack.pyx
+++ b/python/cuvs/cuvs/common/cydlpack.pyx
@@ -25,6 +25,8 @@ cdef void deleter(DLManagedTensor* tensor) noexcept:
     if tensor.manager_ctx is NULL:
         return
     stdlib.free(tensor.dl_tensor.shape)
+    if tensor.dl_tensor.strides is not NULL:
+        stdlib.free(tensor.dl_tensor.strides)
     tensor.manager_ctx = NULL
     stdlib.free(tensor)
 
@@ -95,11 +97,20 @@ cdef DLManagedTensor* dlpack_c(ary):
     tensor.data = <void*> tensor_ptr
     tensor.device = dev
     tensor.dtype = dtype
-    tensor.strides = NULL
     tensor.ndim = ndim
     tensor.shape = shape
     tensor.byte_offset = 0
 
+    if ary.c_contiguous:
+        tensor.strides = NULL
+    elif ary.f_contiguous:
+        tensor.strides = <int64_t*>stdlib.malloc(ndim * sizeof(int64_t))
+        tensor.strides[0] = 1
+        for i in range(1, ndim):
+            tensor.strides[i] = tensor.strides[i-1] * tensor.shape[i-1]
+    else:
+        raise ValueError("Input data must be contiguous")
+
     dlm.dl_tensor = tensor
     dlm.manager_ctx = NULL
     dlm.deleter = deleter
diff --git a/python/cuvs/cuvs/distance/distance.pyx b/python/cuvs/cuvs/distance/distance.pyx
index 187532bfeb..d00e6b1b1f 100644
--- a/python/cuvs/cuvs/distance/distance.pyx
+++ b/python/cuvs/cuvs/distance/distance.pyx
@@ -103,7 +103,9 @@ def pairwise_distance(X, Y, out=None, metric="euclidean", metric_arg=2.0,
         output_dtype = y_cai.dtype
         if np.issubdtype(y_cai.dtype, np.float16):
             output_dtype = np.float32
-        out = device_ndarray.empty((m, n), dtype=output_dtype)
+
+        order = "C" if getattr(X, "flags", X).c_contiguous else "F"
+        out = device_ndarray.empty((m, n), dtype=output_dtype, order=order)
     out_cai = wrap_array(out)
 
     x_k = x_cai.shape[1]
diff --git a/python/cuvs/cuvs/test/test_distance.py b/python/cuvs/cuvs/test/test_distance.py
index f466c27439..9f206064c6 100644
--- a/python/cuvs/cuvs/test/test_distance.py
+++ b/python/cuvs/cuvs/test/test_distance.py
@@ -40,10 +40,11 @@
     ],
 )
 @pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("order", ["F", "C"])
 @pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
-def test_distance(n_rows, n_cols, inplace, metric, dtype):
+def test_distance(n_rows, n_cols, inplace, order, metric, dtype):
     input1 = np.random.random_sample((n_rows, n_cols))
-    input1 = np.asarray(input1).astype(dtype)
+    input1 = np.asarray(input1, order=order).astype(dtype)
 
     # RussellRao expects boolean arrays
     if metric == "russellrao":
@@ -58,7 +59,7 @@ def test_distance(n_rows, n_cols, inplace, metric, dtype):
     output_dtype = dtype
     if np.issubdtype(dtype, np.float16):
         output_dtype = np.float32
-    output = np.zeros((n_rows, n_rows), dtype=output_dtype)
+    output = np.zeros((n_rows, n_rows), dtype=output_dtype, order=order)
 
     if metric == "inner_product":
         expected = np.matmul(input1, input1.T)

From 47d71c391453f002f4070512056075e37c8fcd3e Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 16 Jan 2025 05:24:14 +0100
Subject: [PATCH 251/623] Reduce the recall threshold for IVF-PQ low-precision
 LUT inner product  tests (#573)

IVF-PQ allows to use low-precision for the lookup table during search to improve QPS. When used for the inner product distance, this has extra tall on recall. This PR reduces our expectation of the recall in this case as an answer to occasional test failures in CI.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/573
---
 cpp/test/neighbors/ann_ivf_pq.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/test/neighbors/ann_ivf_pq.cuh
index 01efd804e2..6c0fdc6088 100644
--- a/cpp/test/neighbors/ann_ivf_pq.cuh
+++ b/cpp/test/neighbors/ann_ivf_pq.cuh
@@ -879,7 +879,7 @@ inline auto enum_variety_ip() -> test_cases_t
         // InnerProduct score is signed,
         // thus we're forced to used signed 8-bit representation,
         // thus we have one bit less precision
-        y.min_recall = y.min_recall.value() * 0.90;
+        y.min_recall = y.min_recall.value() * 0.88;
       } else {
         // In other cases it seems to perform a little bit better, still worse than L2
         y.min_recall = y.min_recall.value() * 0.94;

From c49ba7bf9b26633f08b254968ae33bb74039a104 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 15 Jan 2025 20:26:05 -0800
Subject: [PATCH 252/623] expose col-major bfknn to python (#575)

Follow on to #572 -

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/575
---
 cpp/src/distance/pairwise_distance_c.cpp      |  2 +-
 cpp/src/neighbors/brute_force_c.cpp           | 28 ++++++++++++++-----
 .../neighbors/brute_force/brute_force.pyx     |  4 +--
 python/cuvs/cuvs/neighbors/common.py          |  6 ++--
 python/cuvs/cuvs/test/test_brute_force.py     |  9 ++++--
 5 files changed, 34 insertions(+), 15 deletions(-)

diff --git a/cpp/src/distance/pairwise_distance_c.cpp b/cpp/src/distance/pairwise_distance_c.cpp
index 5344a554ca..121574880b 100644
--- a/cpp/src/distance/pairwise_distance_c.cpp
+++ b/cpp/src/distance/pairwise_distance_c.cpp
@@ -99,7 +99,7 @@ extern "C" cuvsError_t cuvsPairwiseDistance(cuvsResources_t res,
 
     if ((x_row_major != y_row_major) || (x_row_major != distances_row_major)) {
       RAFT_FAIL(
-        "Inputs to cuvsPairwiseDistance must all have the same layout (row-major or col-major");
+        "Inputs to cuvsPairwiseDistance must all have the same layout (row-major or col-major)");
     }
 
     if (x_row_major) {
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index 2b8980863e..1693ac930b 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -33,7 +33,7 @@
 
 namespace {
 
-template <typename T>
+template <typename T, typename LayoutT = raft::row_major>
 void* _build(cuvsResources_t res,
              DLManagedTensor* dataset_tensor,
              cuvsDistanceType metric,
@@ -41,7 +41,7 @@ void* _build(cuvsResources_t res,
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
 
-  using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+  using mdspan_type = raft::device_matrix_view<T const, int64_t, LayoutT>;
   auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
 
   cuvs::neighbors::brute_force::index_params params;
@@ -53,7 +53,7 @@ void* _build(cuvsResources_t res,
   return index_on_heap;
 }
 
-template <typename T>
+template <typename T, typename QueriesLayoutT = raft::row_major>
 void _search(cuvsResources_t res,
              cuvsBruteForceIndex index,
              DLManagedTensor* queries_tensor,
@@ -64,7 +64,7 @@ void _search(cuvsResources_t res,
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T>*>(index.addr);
 
-  using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+  using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, QueriesLayoutT>;
   using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
   using distances_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
   using prefilter_mds_type    = raft::device_vector_view<const uint32_t, int64_t>;
@@ -150,8 +150,15 @@ extern "C" cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
     auto dataset = dataset_tensor->dl_tensor;
 
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
-      index->addr =
-        reinterpret_cast<uintptr_t>(_build<float>(res, dataset_tensor, metric, metric_arg));
+      if (cuvs::core::is_c_contiguous(dataset_tensor)) {
+        index->addr =
+          reinterpret_cast<uintptr_t>(_build<float>(res, dataset_tensor, metric, metric_arg));
+      } else if (cuvs::core::is_f_contiguous(dataset_tensor)) {
+        index->addr = reinterpret_cast<uintptr_t>(
+          _build<float, raft::col_major>(res, dataset_tensor, metric, metric_arg));
+      } else {
+        RAFT_FAIL("dataset input to cuvsBruteForceBuild must be contiguous (non-strided)");
+      }
       index->dtype = dataset.dtype;
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
@@ -189,7 +196,14 @@ extern "C" cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
     RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
 
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
-      _search<float>(res, index, queries_tensor, neighbors_tensor, distances_tensor, prefilter);
+      if (cuvs::core::is_c_contiguous(queries_tensor)) {
+        _search<float>(res, index, queries_tensor, neighbors_tensor, distances_tensor, prefilter);
+      } else if (cuvs::core::is_f_contiguous(queries_tensor)) {
+        _search<float, raft::col_major>(
+          res, index, queries_tensor, neighbors_tensor, distances_tensor, prefilter);
+      } else {
+        RAFT_FAIL("queries input to cuvsBruteForceSearch must be contiguous (non-strided)");
+      }
     } else {
       RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
                 queries.dtype.code,
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 9d43bfb290..f71acd0864 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -102,7 +102,7 @@ def build(dataset, metric="sqeuclidean", metric_arg=2.0, resources=None):
     """
 
     dataset_ai = wrap_array(dataset)
-    _check_input_array(dataset_ai, [np.dtype('float32')])
+    _check_input_array(dataset_ai, [np.dtype('float32')], exp_row_major=False)
 
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
@@ -218,7 +218,7 @@ def search(Index index,
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
     queries_cai = wrap_array(queries)
-    _check_input_array(queries_cai, [np.dtype('float32')])
+    _check_input_array(queries_cai, [np.dtype('float32')], exp_row_major=False)
 
     cdef uint32_t n_queries = queries_cai.shape[0]
 
diff --git a/python/cuvs/cuvs/neighbors/common.py b/python/cuvs/cuvs/neighbors/common.py
index c14b9f8c9c..f49d9eb1f0 100644
--- a/python/cuvs/cuvs/neighbors/common.py
+++ b/python/cuvs/cuvs/neighbors/common.py
@@ -14,11 +14,13 @@
 # limitations under the License.
 
 
-def _check_input_array(cai, exp_dt, exp_rows=None, exp_cols=None):
+def _check_input_array(
+    cai, exp_dt, exp_rows=None, exp_cols=None, exp_row_major=True
+):
     if cai.dtype not in exp_dt:
         raise TypeError("dtype %s not supported" % cai.dtype)
 
-    if not cai.c_contiguous:
+    if exp_row_major and not cai.c_contiguous:
         raise ValueError("Row major input is expected")
 
     if exp_cols is not None and cai.shape[1] != exp_cols:
diff --git a/python/cuvs/cuvs/test/test_brute_force.py b/python/cuvs/cuvs/test/test_brute_force.py
index acf347ec35..0b37ad885d 100644
--- a/python/cuvs/cuvs/test/test_brute_force.py
+++ b/python/cuvs/cuvs/test/test_brute_force.py
@@ -40,12 +40,15 @@
     ],
 )
 @pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("order", ["F", "C"])
 @pytest.mark.parametrize("dtype", [np.float32])
 def test_brute_force_knn(
-    n_index_rows, n_query_rows, n_cols, k, inplace, metric, dtype
+    n_index_rows, n_query_rows, n_cols, k, inplace, order, metric, dtype
 ):
-    index = np.random.random_sample((n_index_rows, n_cols)).astype(dtype)
-    queries = np.random.random_sample((n_query_rows, n_cols)).astype(dtype)
+    index = np.random.random_sample((n_index_rows, n_cols))
+    index = np.asarray(index, order=order).astype(dtype)
+    queries = np.random.random_sample((n_query_rows, n_cols))
+    queries = np.asarray(queries, order=order).astype(dtype)
 
     # RussellRao expects boolean arrays
     if metric == "russellrao":

From 8aae7069141ffe413f03cd4cd036860394fc0c44 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Wed, 15 Jan 2025 23:32:54 -0500
Subject: [PATCH 253/623] Small fixes to docs and pairwise distances (#570)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/570
---
 docs/source/cuvs_bench/index.rst       | 24 ++++++++++++++----------
 python/cuvs/cuvs/distance/distance.pyx |  6 +++---
 python/cuvs/cuvs/test/test_distance.py |  6 ++----
 3 files changed, 19 insertions(+), 17 deletions(-)

diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 81fb7537c2..820c44c4f4 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -24,7 +24,7 @@ This tool offers several benefits, including
 
   * `Docker`_
 
-- `How to run the benchmarks`_
+- `How benchmarks are run`_
 
   * `Step 1: Prepare the dataset`_
 
@@ -93,32 +93,36 @@ We provide images for GPU enabled systems, as well as systems without a GPU. The
 - `cuvs-bench-datasets`: Contains the GPU and CPU benchmarks with million-scale datasets already included in the container. Best suited for users that want to run multiple million scale datasets already included in the image.
 - `cuvs-bench-cpu`: Contains only CPU benchmarks with minimal size. Best suited for users that want the smallest containers to reproduce benchmarks on systems without a GPU.
 
-Nightly images are located in `dockerhub <https://hub.docker.com/r/rapidsai/cuvs-bench/tags>`_, meanwhile release (stable) versions are located in `NGC <https://hub.docker.com/r/rapidsai/cuvs-bench>`_, starting with release 24.10.
+Nightly images are located in `dockerhub <https://hub.docker.com/r/rapidsai/cuvs-bench/tags>`_.
 
-The following command pulls the nightly container for Python version 3.10, CUDA version 12.0, and cuVS version 24.10:
+The following command pulls the nightly container for Python version 3.10, CUDA version 12.5, and cuVS version 24.12:
 
 .. code-block:: bash
 
-   docker pull rapidsai/cuvs-bench:24.10a-cuda12.0-py3.10 #substitute cuvs-bench for the exact desired container.
+   docker pull rapidsai/cuvs-bench:24.12a-cuda12.5-py3.10 #substitute cuvs-bench for the exact desired container.
 
 The CUDA and python versions can be changed for the supported values:
-- Supported CUDA versions: 11.4 and 12.x
-- Supported Python versions: 3.9 and 3.10.
+- Supported CUDA versions: 11.8 and 12.5
+- Supported Python versions: 3.10 and 3.11.
 
 You can see the exact versions as well in the dockerhub site:
 - `cuVS bench images <https://hub.docker.com/r/rapidsai/cuvs-bench/tags>`_
-- `cuVS bench with datasets preloaded images <https://hub.docker.com/r/rapidsai/cuvs-bench-cpu/tags>`_
+- `cuVS bench with pre-loaded million-scale datasets images <https://hub.docker.com/r/rapidsai/cuvs-bench-cpu/tags>`_
 - `cuVS bench CPU only images <https://hub.docker.com/r/rapidsai/cuvs-bench-datasets/tags>`_
 
 **Note:** GPU containers use the CUDA toolkit from inside the container, the only requirement is a driver installed on the host machine that supports that version. So, for example, CUDA 11.8 containers can run in systems with a CUDA 12.x capable driver. Please also note that the Nvidia-Docker runtime from the `Nvidia Container Toolkit <https://github.com/NVIDIA/nvidia-docker>`_ is required to use GPUs inside docker containers.
 
-How to run the benchmarks
-=========================
+How benchmarks are run
+======================
+
+The `cuvs-bench` package contains lightweight Python scripts to run the benchmarks. There are 4 general steps to running the benchmarks and visualizing the results.
 
-We provide a collection of lightweight Python scripts to run the benchmarks. There are 4 general steps to running the benchmarks and visualizing the results.
 #. Prepare Dataset
+
 #. Build Index and Search Index
+
 #. Data Export
+
 #. Plot Results
 
 Step 1: Prepare the dataset
diff --git a/python/cuvs/cuvs/distance/distance.pyx b/python/cuvs/cuvs/distance/distance.pyx
index d00e6b1b1f..d50fc152f4 100644
--- a/python/cuvs/cuvs/distance/distance.pyx
+++ b/python/cuvs/cuvs/distance/distance.pyx
@@ -56,7 +56,7 @@ SUPPORTED_DISTANCES = ["euclidean", "l1", "cityblock", "l2", "inner_product",
 
 @auto_sync_resources
 @auto_convert_output
-def pairwise_distance(X, Y, out=None, metric="euclidean", metric_arg=2.0,
+def pairwise_distance(X, Y, out=None, metric="euclidean", p=2.0,
                       resources=None):
     """
     Compute pairwise distances between X and Y
@@ -74,7 +74,7 @@ def pairwise_distance(X, Y, out=None, metric="euclidean", metric_arg=2.0,
     Y : CUDA array interface compliant matrix shape (n, k)
     out : Optional writable CUDA array interface matrix shape (m, n)
     metric : string denoting the metric type (default="euclidean")
-    metric_arg : metric parameter (currently used only for "minkowski")
+    p : metric parameter (currently used only for "minkowski")
     {resources_docstring}
 
     Examples
@@ -139,6 +139,6 @@ def pairwise_distance(X, Y, out=None, metric="euclidean", metric_arg=2.0,
                                     y_dlpack,
                                     out_dlpack,
                                     distance_type,
-                                    metric_arg))
+                                    p))
 
     return out
diff --git a/python/cuvs/cuvs/test/test_distance.py b/python/cuvs/cuvs/test/test_distance.py
index 9f206064c6..483d5d2011 100644
--- a/python/cuvs/cuvs/test/test_distance.py
+++ b/python/cuvs/cuvs/test/test_distance.py
@@ -35,6 +35,7 @@
         "jensenshannon",
         "russellrao",
         "cosine",
+        "minkowski",
         "sqeuclidean",
         "inner_product",
     ],
@@ -70,10 +71,7 @@ def test_distance(n_rows, n_cols, inplace, order, metric, dtype):
     output_device = device_ndarray(output) if inplace else None
 
     ret_output = pairwise_distance(
-        input1_device,
-        input1_device,
-        output_device,
-        metric,
+        input1_device, input1_device, output_device, metric, p=2.0
     )
 
     output_device = ret_output if not inplace else output_device

From 3ffb29ff06ff0b8fffa140780750cf1999626f7e Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Thu, 16 Jan 2025 07:14:47 +0100
Subject: [PATCH 254/623] Fix broken link to python doc (#564)

Apply the same change as https://github.com/rapidsai/cuml/pull/6202 to fix Python links to source code.
Closes #533

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/564
---
 docs/source/conf.py                  |  2 +-
 docs/source/sphinxext/github_link.py | 22 ++++++++++++++++++----
 2 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index c149195685..ca7330279d 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -198,7 +198,7 @@ def setup(app):
 linkcode_resolve = make_linkcode_resolve(
     "cuvs",
     "https://github.com/rapidsai/cuvs/"
-    "blob/{revision}/python/cuvs/cuvs/"
+    "blob/{revision}/python/cuvs/"
     "{package}/{path}#L{lineno}",
 )
 
diff --git a/docs/source/sphinxext/github_link.py b/docs/source/sphinxext/github_link.py
index 2c52488caa..75acfbd6e1 100644
--- a/docs/source/sphinxext/github_link.py
+++ b/docs/source/sphinxext/github_link.py
@@ -1,5 +1,20 @@
 # This contains code with copyright by the scikit-learn project, subject to the
 # license in /thirdparty/LICENSES/LICENSE.scikit_learn
+#
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
 
 import inspect
 import os
@@ -101,10 +116,9 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision):
         else:
             return
     else:
-        # Test if we are absolute or not (pyx are relative)
-        if (not os.path.isabs(fn)):
-            # Should be relative to docs right now
-            fn = os.path.abspath(os.path.join("..", "python", fn))
+        if fn.endswith(".pyx"):
+            sp_path = next(x for x in sys.path if re.match(".*site-packages$", x))
+            fn = fn.replace("/opt/conda/conda-bld/work/python/cuvs", sp_path)
 
         # Convert to relative from module root
         fn = os.path.relpath(fn,

From 86b4ee8c6244b9ee2f1a718dad8dea0b1476ed15 Mon Sep 17 00:00:00 2001
From: Nathan VanBenschoten <nvanbenschoten@gmail.com>
Date: Wed, 15 Jan 2025 23:15:31 -0700
Subject: [PATCH 255/623] Fix typos in README (#543)

Spotted while learning about the project.

Authors:
  - Nathan VanBenschoten (https://github.com/nvanbenschoten)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/543
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 47f094039a..dac71c881f 100755
--- a/README.md
+++ b/README.md
@@ -67,7 +67,7 @@ There are several benefits to using cuVS and GPUs for vector search, including
 6. Multiple language support
 7. Building blocks for composing new or accelerating existing algorithms
 
-In addition to the items above, cuVS takes on the burden of keeping non-trivial accelerated code up to date as new NVIDIA architectures and CUDA versions are released. This provides a deslightful development experimence, guaranteeing that any libraries, databases, or applications built on top of it will always be getting the best performance and scale. 
+In addition to the items above, cuVS takes on the burden of keeping non-trivial accelerated code up to date as new NVIDIA architectures and CUDA versions are released. This provides a delightful development experience, guaranteeing that any libraries, databases, or applications built on top of it will always be getting the best performance and scale.
 
 ## cuVS Technology Stack
 

From b9f71fe346af926de55223b313979a5c85a01b1b Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 16 Jan 2025 15:40:52 +0100
Subject: [PATCH 256/623] Fix the use of constexpr in the dynamic batching
 header (#582)

Remove the `constexpr` in a function that is non-constexpr according to the C++17 rules.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/582
---
 cpp/src/neighbors/detail/dynamic_batching.cuh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/cpp/src/neighbors/detail/dynamic_batching.cuh b/cpp/src/neighbors/detail/dynamic_batching.cuh
index cb8e08ef51..23c5c07f6a 100644
--- a/cpp/src/neighbors/detail/dynamic_batching.cuh
+++ b/cpp/src/neighbors/detail/dynamic_batching.cuh
@@ -238,8 +238,8 @@ enum struct slot_state : int32_t {
 struct batch_token {
   uint64_t value = 0;
 
-  constexpr inline batch_token() {}
-  explicit constexpr inline batch_token(uint32_t buffer_id) { id() = buffer_id; }
+  constexpr inline batch_token() = default;
+  RAFT_INLINE_FUNCTION explicit batch_token(uint32_t buffer_id) { id() = buffer_id; }
 
   /**
    * Sequential id of the batch in the array of batches.
@@ -492,7 +492,7 @@ struct batch_queue_t {
    * NB: "round" is the number of times the queue counters went over the whole ring buffer.
    *     It's used to avoid the ABA problem for atomic token updates.
    */
-  static constexpr inline auto make_empty_token(seq_order_id seq_id) noexcept -> batch_token
+  static inline auto make_empty_token(seq_order_id seq_id) noexcept -> batch_token
   {
     // Modify the seq_id to identify that the token slot is empty
     auto empty_round    = static_cast<uint32_t>(slot_state::kEmptyPast) * kSize;

From 6371aa32e957b6e8df0cae3347b94ef5604e6e7b Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Thu, 16 Jan 2025 20:00:54 -0500
Subject: [PATCH 257/623] run_cuvs_pytests.sh uses proper test dir (#584)

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/584
---
 ci/run_cuvs_pytests.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/run_cuvs_pytests.sh b/ci/run_cuvs_pytests.sh
index 4de8927b17..57df9af940 100755
--- a/ci/run_cuvs_pytests.sh
+++ b/ci/run_cuvs_pytests.sh
@@ -6,4 +6,4 @@ set -euo pipefail
 # Support invoking run_pytests.sh outside the script directory
 cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/cuvs/cuvs
 
-pytest --cache-clear --verbose "$@" tests
+pytest --cache-clear --verbose "$@" test

From bd603a97ea7c095dd109c802387b33dc1d591b54 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Fri, 17 Jan 2025 13:16:16 -0500
Subject: [PATCH 258/623] Fixing small typo in cuvs bench docs (#586)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/586
---
 docs/source/cuvs_bench/index.rst | 195 +------------------------------
 1 file changed, 3 insertions(+), 192 deletions(-)

diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 820c44c4f4..c15aa41c10 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -24,16 +24,6 @@ This tool offers several benefits, including
 
   * `Docker`_
 
-- `How benchmarks are run`_
-
-  * `Step 1: Prepare the dataset`_
-
-  * `Step 2: Build and search index`_
-
-  * `Step 3: Data export`_
-
-  * `Step 4: Plot the results`_
-
 - `Running the benchmarks`_
 
   * `End-to-end: smaller-scale benchmarks (<1M to 10M)`_
@@ -75,7 +65,7 @@ Conda
    conda activate cuvs_benchmarks
 
    # to install GPU package:
-   conda install -c rapidsai -c conda-forge -c nvidia cuvs-ann-bench=<rapids_version> cuda-version=11.8*
+   conda install -c rapidsai -c conda-forge -c nvidia cuvs-bench=<rapids_version> cuda-version=11.8*
 
    # to install CPU package for usage in CPU-only systems:
    conda install -c rapidsai -c conda-forge  cuvs-bench-cpu
@@ -99,7 +89,7 @@ The following command pulls the nightly container for Python version 3.10, CUDA
 
 .. code-block:: bash
 
-   docker pull rapidsai/cuvs-bench:24.12a-cuda12.5-py3.10 #substitute cuvs-bench for the exact desired container.
+   docker pull rapidsai/cuvs-bench:24.12a-cuda12.5-py3.10 # substitute cuvs-bench for the exact desired container.
 
 The CUDA and python versions can be changed for the supported values:
 - Supported CUDA versions: 11.8 and 12.5
@@ -112,185 +102,6 @@ You can see the exact versions as well in the dockerhub site:
 
 **Note:** GPU containers use the CUDA toolkit from inside the container, the only requirement is a driver installed on the host machine that supports that version. So, for example, CUDA 11.8 containers can run in systems with a CUDA 12.x capable driver. Please also note that the Nvidia-Docker runtime from the `Nvidia Container Toolkit <https://github.com/NVIDIA/nvidia-docker>`_ is required to use GPUs inside docker containers.
 
-How benchmarks are run
-======================
-
-The `cuvs-bench` package contains lightweight Python scripts to run the benchmarks. There are 4 general steps to running the benchmarks and visualizing the results.
-
-#. Prepare Dataset
-
-#. Build Index and Search Index
-
-#. Data Export
-
-#. Plot Results
-
-Step 1: Prepare the dataset
----------------------------
-
-The script `cuvs_bench.get_dataset` will download and unpack the dataset in directory that the user provides. As of now, only million-scale datasets are supported by this script. For more information on :doc:`datasets and formats <datasets>`.
-
-The usage of this script is:
-
-.. code-block:: bash
-
-    usage: get_dataset.py [-h] [--name NAME] [--dataset-path DATASET_PATH] [--normalize]
-
-    options:
-      -h, --help            show this help message and exit
-      --dataset DATASET     dataset to download (default: glove-100-angular)
-      --dataset-path DATASET_PATH
-                            path to download dataset (default: ${RAPIDS_DATASET_ROOT_DIR})
-      --normalize           normalize cosine distance to inner product (default: False)
-
-When option `normalize` is provided to the script, any dataset that has cosine distances
-will be normalized to inner product. So, for example, the dataset `glove-100-angular`
-will be written at location `datasets/glove-100-inner/`.
-
-Step 2: Build and search index
-------------------------------
-
-The script `cuvs_bench.run` will build and search indices for a given dataset and its
-specified configuration.
-
-The usage of the script `cuvs_bench.run` is:
-
-.. code-block:: bash
-
-    usage: __main__.py [-h] [--subset-size SUBSET_SIZE] [-k COUNT] [-bs BATCH_SIZE] [--dataset-configuration DATASET_CONFIGURATION] [--configuration CONFIGURATION] [--dataset DATASET]
-                       [--dataset-path DATASET_PATH] [--build] [--search] [--algorithms ALGORITHMS] [--groups GROUPS] [--algo-groups ALGO_GROUPS] [-f] [-m SEARCH_MODE]
-
-    options:
-      -h, --help            show this help message and exit
-      --subset-size SUBSET_SIZE
-                            the number of subset rows of the dataset to build the index (default: None)
-      -k COUNT, --count COUNT
-                            the number of nearest neighbors to search for (default: 10)
-      -bs BATCH_SIZE, --batch-size BATCH_SIZE
-                            number of query vectors to use in each query trial (default: 10000)
-      --dataset-configuration DATASET_CONFIGURATION
-                            path to YAML configuration file for datasets (default: None)
-      --configuration CONFIGURATION
-                            path to YAML configuration file or directory for algorithms Any run groups found in the specified file/directory will automatically override groups of the same name
-                            present in the default configurations, including `base` (default: None)
-      --dataset DATASET     name of dataset (default: glove-100-inner)
-      --dataset-path DATASET_PATH
-                            path to dataset folder, by default will look in RAPIDS_DATASET_ROOT_DIR if defined, otherwise a datasets subdirectory from the calling directory (default:
-                            os.getcwd()/datasets/)
-      --build
-      --search
-      --algorithms ALGORITHMS
-                            run only comma separated list of named algorithms. If parameters `groups` and `algo-groups` are both undefined, then group `base` is run by default (default: None)
-      --groups GROUPS       run only comma separated groups of parameters (default: base)
-      --algo-groups ALGO_GROUPS
-                            add comma separated <algorithm>.<group> to run. Example usage: "--algo-groups=cuvs_cagra.large,hnswlib.large" (default: None)
-      -f, --force           re-run algorithms even if their results already exist (default: False)
-      -m SEARCH_MODE, --search-mode SEARCH_MODE
-                            run search in 'latency' (measure individual batches) or 'throughput' (pipeline batches and measure end-to-end) mode (default: throughput)
-      -t SEARCH_THREADS, --search-threads SEARCH_THREADS
-                            specify the number threads to use for throughput benchmark. Single value or a pair of min and max separated by ':'. Example --search-threads=1:4. Power of 2 values between 'min' and 'max' will be used. If only 'min' is
-                            specified, then a single test is run with 'min' threads. By default min=1, max=<num hyper threads>. (default: None)
-      -r, --dry-run         dry-run mode will convert the yaml config for the specified algorithms and datasets to the json format that's consumed by the lower-level c++ binaries and then print the command to run execute the benchmarks but
-                            will not actually execute the command. (default: False)
-
-`dataset`: name of the dataset to be searched in `datasets.yaml`_
-
-`dataset-configuration`: optional filepath to custom dataset YAML config which has an entry for arg `dataset`
-
-`configuration`: optional filepath to YAML configuration for an algorithm or to directory that contains YAML configurations for several algorithms. Refer to `Dataset.yaml config`_ for more info.
-
-`algorithms`: runs all algorithms that it can find in YAML configs found by `configuration`. By default, only `base` group will be run.
-
-`groups`: run only specific groups of parameters configurations for an algorithm. Groups are defined in YAML configs (see `configuration`), and by default run `base` group
-
-`algo-groups`: this parameter is helpful to append any specific algorithm+group combination to run the benchmark for in addition to all the arguments from `algorithms` and `groups`. It is of the format `<algorithm>.<group>`, or for example, `cuvs_cagra.large`
-
-For every algorithm run by this script, it outputs an index build statistics JSON file in `<dataset-path/<dataset>/result/build/<{algo},{group}.json>`
-and an index search statistics JSON file in `<dataset-path/<dataset>/result/search/<{algo},{group},k{k},bs{batch_size}.json>`. NOTE: The filenames will not have ",{group}" if `group = "base"`.
-
-For every algorithm run by this script, it outputs an index build statistics JSON file in `<dataset-path/<dataset>/result/build/<{algo},{group}.json>`
-and an index search statistics JSON file in `<dataset-path/<dataset>/result/search/<{algo},{group},k{k},bs{batch_size}.json>`. NOTE: The filenames will not have ",{group}" if `group = "base"`.
-
-`dataset-path` :
-#. data is read from `<dataset-path>/<dataset>`
-#. indices are built in `<dataset-path>/<dataset>/index`
-#. build/search results are stored in `<dataset-path>/<dataset>/result`
-
-`build` and `search` : if both parameters are not supplied to the script then it is assumed both are `True`.
-
-`indices` and `algorithms` : these parameters ensure that the algorithm specified for an index is available in `algos.yaml` and not disabled, as well as having an associated executable.
-
-Step 3: Data export
--------------------
-
-The script `cuvs_bench.data_export` will convert the intermediate JSON outputs produced by `cuvs_bench.run` to more easily readable CSV files, which are needed to build charts made by `cuvs_bench.plot`.
-
-.. code-block:: bash
-
-    usage: data_export.py [-h] [--dataset DATASET] [--dataset-path DATASET_PATH]
-
-    options:
-      -h, --help            show this help message and exit
-      --dataset DATASET     dataset to download (default: glove-100-inner)
-      --dataset-path DATASET_PATH
-                            path to dataset folder (default: ${RAPIDS_DATASET_ROOT_DIR})
-
-Build statistics CSV file is stored in `<dataset-path/<dataset>/result/build/<{algo},{group}.csv>`
-and index search statistics CSV file in `<dataset-path/<dataset>/result/search/<{algo},{group},k{k},bs{batch_size},{suffix}.csv>`, where suffix has three values:
-#. `raw`: All search results are exported
-#. `throughput`: Pareto frontier of throughput results is exported
-#. `latency`: Pareto frontier of latency results is exported
-
-Step 4: Plot the results
-------------------------
-
-The script `cuvs_bench.plot` will plot results for all algorithms found in index search statistics CSV files `<dataset-path/<dataset>/result/search/*.csv`.
-
-The usage of this script is:
-
-.. code-block:: bash
-
-    usage:  [-h] [--dataset DATASET] [--dataset-path DATASET_PATH] [--output-filepath OUTPUT_FILEPATH] [--algorithms ALGORITHMS] [--groups GROUPS] [--algo-groups ALGO_GROUPS]
-            [-k COUNT] [-bs BATCH_SIZE] [--build] [--search] [--x-scale X_SCALE] [--y-scale {linear,log,symlog,logit}] [--x-start X_START] [--mode {throughput,latency}]
-            [--time-unit {s,ms,us}] [--raw]
-
-    options:
-      -h, --help            show this help message and exit
-      --dataset DATASET     dataset to plot (default: glove-100-inner)
-      --dataset-path DATASET_PATH
-                            path to dataset folder (default: /home/coder/cuvs/datasets/)
-      --output-filepath OUTPUT_FILEPATH
-                            directory for PNG to be saved (default: /home/coder/cuvs)
-      --algorithms ALGORITHMS
-                            plot only comma separated list of named algorithms. If parameters `groups` and `algo-groups are both undefined, then group `base` is plot by default
-                            (default: None)
-      --groups GROUPS       plot only comma separated groups of parameters (default: base)
-      --algo-groups ALGO_GROUPS, --algo-groups ALGO_GROUPS
-                            add comma separated <algorithm>.<group> to plot. Example usage: "--algo-groups=cuvs_cagra.large,hnswlib.large" (default: None)
-      -k COUNT, --count COUNT
-                            the number of nearest neighbors to search for (default: 10)
-      -bs BATCH_SIZE, --batch-size BATCH_SIZE
-                            number of query vectors to use in each query trial (default: 10000)
-      --build
-      --search
-      --x-scale X_SCALE     Scale to use when drawing the X-axis. Typically linear, logit or a2 (default: linear)
-      --y-scale {linear,log,symlog,logit}
-                            Scale to use when drawing the Y-axis (default: linear)
-      --x-start X_START     Recall values to start the x-axis from (default: 0.8)
-      --mode {throughput,latency}
-                            search mode whose Pareto frontier is used on the y-axis (default: throughput)
-      --time-unit {s,ms,us}
-                            time unit to plot when mode is latency (default: ms)
-      --raw                 Show raw results (not just Pareto frontier) of mode arg (default: False)
-
-`mode`: plots pareto frontier of `throughput` or `latency` results exported in the previous step
-
-`algorithms`: plots all algorithms that it can find results for the specified `dataset`. By default, only `base` group will be plotted.
-
-`groups`: plot only specific groups of parameters configurations for an algorithm. Groups are defined in YAML configs (see `configuration`), and by default run `base` group
-
-`algo-groups`: this parameter is helpful to append any specific algorithm+group combination to plot results for in addition to all the arguments from `algorithms` and `groups`. It is of the format `<algorithm>.<group>`, or for example, `cuvs_cagra.large`
-
 Running the benchmarks
 ======================
 
@@ -576,7 +387,7 @@ Creating and customizing dataset configurations
 
 A single configuration will often define a set of algorithms, with associated index and search parameters, that can be generalize across datasets. We use YAML to define dataset specific and algorithm specific configurations.
 
-A default `datasets.yaml` is provided by CUVS in `${CUVS_HOME}/python/cuvs-ann-bench/src/cuvs_bench/run/conf` with configurations available for several datasets. Here's a simple example entry for the `sift-128-euclidean` dataset:
+A default `datasets.yaml` is provided by CUVS in `${CUVS_HOME}/python/cuvs_bench/src/cuvs_bench/run/conf` with configurations available for several datasets. Here's a simple example entry for the `sift-128-euclidean` dataset:
 
 .. code-block:: yaml
 

From f1de1b287f776bcb03d20765fad85da3555969d6 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 21 Jan 2025 11:30:32 -0600
Subject: [PATCH 259/623] Use GCC 13 in CUDA 12 conda builds. (#567)

## Description
conda-forge is using GCC 13 for CUDA 12 builds. This PR updates CUDA 12
conda builds to use GCC 13, for alignment.

These PRs should be merged in a specific order, see
https://github.com/rapidsai/build-planning/issues/129 for details.

Closes https://github.com/rapidsai/build-planning/issues/129.
---
 .../all_cuda-118_arch-aarch64.yaml            |  2 +-
 .../all_cuda-118_arch-x86_64.yaml             |  2 +-
 .../all_cuda-125_arch-aarch64.yaml            |  4 ++--
 .../all_cuda-125_arch-x86_64.yaml             |  4 ++--
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  2 +-
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  2 +-
 .../bench_ann_cuda-125_arch-aarch64.yaml      |  4 ++--
 .../bench_ann_cuda-125_arch-x86_64.yaml       |  4 ++--
 .../cuvs-bench-cpu/conda_build_config.yaml    |  8 ++++---
 .../cuvs-bench/conda_build_config.yaml        | 14 +++++------
 conda/recipes/cuvs-bench/meta.yaml            |  6 ++---
 conda/recipes/cuvs/conda_build_config.yaml    | 14 +++++------
 conda/recipes/cuvs/meta.yaml                  |  6 ++---
 conda/recipes/libcuvs/conda_build_config.yaml | 14 +++++------
 conda/recipes/libcuvs/meta.yaml               | 24 +++++++------------
 cpp/test/distance/masked_nn.cu                |  4 ++--
 dependencies.yaml                             | 18 ++++++++++++--
 17 files changed, 68 insertions(+), 64 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 01853da84a..4c464ef4ee 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -55,7 +55,7 @@ dependencies:
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sphinx>=8.0.0
-- sysroot_linux-aarch64==2.17
+- sysroot_linux-aarch64==2.28
 - pip:
   - nvidia-sphinx-theme
 name: all_cuda-118_arch-aarch64
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index a1ad68d7fd..71cbeeaf3e 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -55,7 +55,7 @@ dependencies:
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sphinx>=8.0.0
-- sysroot_linux-64==2.17
+- sysroot_linux-64==2.28
 - pip:
   - nvidia-sphinx-theme
 name: all_cuda-118_arch-x86_64
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index ee0213fff9..4fd08fa97b 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -24,7 +24,7 @@ dependencies:
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
-- gcc_linux-aarch64=11.*
+- gcc_linux-aarch64=13.*
 - graphviz
 - ipython
 - libclang
@@ -51,7 +51,7 @@ dependencies:
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sphinx>=8.0.0
-- sysroot_linux-aarch64==2.17
+- sysroot_linux-aarch64==2.28
 - pip:
   - nvidia-sphinx-theme
 name: all_cuda-125_arch-aarch64
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index d93dcaf7a1..de5f341fa1 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -24,7 +24,7 @@ dependencies:
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
-- gcc_linux-64=11.*
+- gcc_linux-64=13.*
 - graphviz
 - ipython
 - libclang
@@ -51,7 +51,7 @@ dependencies:
 - sphinx-copybutton
 - sphinx-markdown-tables
 - sphinx>=8.0.0
-- sysroot_linux-64==2.17
+- sysroot_linux-64==2.28
 - pip:
   - nvidia-sphinx-theme
 name: all_cuda-125_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index a90dc03e77..fb69ac251f 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -47,6 +47,6 @@ dependencies:
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
-- sysroot_linux-aarch64==2.17
+- sysroot_linux-aarch64==2.28
 - wheel
 name: bench_ann_cuda-118_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index b7344c8228..123033b08c 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -47,6 +47,6 @@ dependencies:
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
-- sysroot_linux-64==2.17
+- sysroot_linux-64==2.28
 - wheel
 name: bench_ann_cuda-118_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index da7229004e..fa2ae79556 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -24,7 +24,7 @@ dependencies:
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
-- gcc_linux-aarch64=11.*
+- gcc_linux-aarch64=13.*
 - glog>=0.6.0
 - h5py>=3.8.0
 - libcublas-dev
@@ -43,6 +43,6 @@ dependencies:
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
-- sysroot_linux-aarch64==2.17
+- sysroot_linux-aarch64==2.28
 - wheel
 name: bench_ann_cuda-125_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index 5d1dd8fc75..76b005e3cf 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -24,7 +24,7 @@ dependencies:
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
-- gcc_linux-64=11.*
+- gcc_linux-64=13.*
 - glog>=0.6.0
 - h5py>=3.8.0
 - libcublas-dev
@@ -43,6 +43,6 @@ dependencies:
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
-- sysroot_linux-64==2.17
+- sysroot_linux-64==2.28
 - wheel
 name: bench_ann_cuda-125_arch-x86_64
diff --git a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
index ed6f708e14..5407d7c17f 100644
--- a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
@@ -1,14 +1,16 @@
 c_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 cxx_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 c_stdlib:
   - sysroot
 
 c_stdlib_version:
-  - "2.17"
+  - "2.28"
 
 cmake_version:
   - ">=3.26.4,!=3.30.0"
diff --git a/conda/recipes/cuvs-bench/conda_build_config.yaml b/conda/recipes/cuvs-bench/conda_build_config.yaml
index 47bd730daf..ccd7341d17 100644
--- a/conda/recipes/cuvs-bench/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench/conda_build_config.yaml
@@ -1,20 +1,20 @@
 c_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 cxx_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 cuda_compiler:
-  - cuda-nvcc
-
-cuda11_compiler:
-  - nvcc
+  - cuda-nvcc  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - nvcc  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 c_stdlib:
   - sysroot
 
 c_stdlib_version:
-  - "2.17"
+  - "2.28"
 
 cmake_version:
   - ">=3.26.4,!=3.30.0"
diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
index d77aee8cee..33b1745ecb 100644
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -37,10 +37,8 @@ build:
   number: {{ GIT_DESCRIBE_NUMBER }}
   string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
   ignore_run_exports_from:
-    {% if cuda_major == "11" %}
-    - {{ compiler('cuda11') }}
-    {% else %}
     - {{ compiler('cuda') }}
+    {% if cuda_major != "11" %}
     - cuda-cudart-dev
     - libcublas-dev
     {% endif %}
@@ -50,7 +48,7 @@ requirements:
     - {{ compiler('c') }}
     - {{ compiler('cxx') }}
     {% if cuda_major == "11" %}
-    - {{ compiler('cuda11') }} ={{ cuda_version }}
+    - {{ compiler('cuda') }} ={{ cuda_version }}
     {% else %}
     - {{ compiler('cuda') }}
     {% endif %}
diff --git a/conda/recipes/cuvs/conda_build_config.yaml b/conda/recipes/cuvs/conda_build_config.yaml
index 001878ff25..83f5ebcb15 100644
--- a/conda/recipes/cuvs/conda_build_config.yaml
+++ b/conda/recipes/cuvs/conda_build_config.yaml
@@ -1,20 +1,20 @@
 c_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 cxx_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 cuda_compiler:
-  - cuda-nvcc
-
-cuda11_compiler:
-  - nvcc
+  - cuda-nvcc  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - nvcc  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 c_stdlib:
   - sysroot
 
 c_stdlib_version:
-  - "2.17"
+  - "2.28"
 
 cmake_version:
   - ">=3.26.4,!=3.30.0"
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
index ad7ffe7563..25fc204a85 100644
--- a/conda/recipes/cuvs/meta.yaml
+++ b/conda/recipes/cuvs/meta.yaml
@@ -20,10 +20,8 @@ build:
   number: {{ GIT_DESCRIBE_NUMBER }}
   string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
   ignore_run_exports_from:
-    {% if cuda_major == "11" %}
-    - {{ compiler('cuda11') }}
-    {% else %}
     - {{ compiler('cuda') }}
+    {% if cuda_major != "11" %}
     - cuda-cudart-dev
     {% endif %}
     - cuda-python
@@ -33,7 +31,7 @@ requirements:
     - {{ compiler('c') }}
     - {{ compiler('cxx') }}
     {% if cuda_major == "11" %}
-    - {{ compiler('cuda11') }} ={{ cuda_version }}
+    - {{ compiler('cuda') }} ={{ cuda_version }}
     {% else %}
     - {{ compiler('cuda') }}
     {% endif %}
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index b8c49943eb..72cc4415d4 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -1,20 +1,20 @@
 c_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 cxx_compiler_version:
-  - 11
+  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 cuda_compiler:
-  - cuda-nvcc
-
-cuda11_compiler:
-  - nvcc
+  - cuda-nvcc  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - nvcc  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
 
 c_stdlib:
   - sysroot
 
 c_stdlib_version:
-  - "2.17"
+  - "2.28"
 
 cmake_version:
   - ">=3.26.4,!=3.30.0"
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
index 46552c3975..fd466cd225 100644
--- a/conda/recipes/libcuvs/meta.yaml
+++ b/conda/recipes/libcuvs/meta.yaml
@@ -39,10 +39,8 @@ outputs:
       number: {{ GIT_DESCRIBE_NUMBER }}
       string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
       ignore_run_exports_from:
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }}
-        {% else %}
         - {{ compiler('cuda') }}
+        {% if cuda_major != "11" %}
         - cuda-cudart-dev
         - libcublas-dev
         - libcurand-dev
@@ -54,7 +52,7 @@ outputs:
         - {{ compiler('c') }}
         - {{ compiler('cxx') }}
         {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }} ={{ cuda_version }}
+        - {{ compiler('cuda') }} ={{ cuda_version }}
         {% else %}
         - {{ compiler('cuda') }}
         {% endif %}
@@ -106,10 +104,8 @@ outputs:
       number: {{ GIT_DESCRIBE_NUMBER }}
       string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
       ignore_run_exports_from:
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }}
-        {% else %}
         - {{ compiler('cuda') }}
+        {% if cuda_major != "11" %}
         - cuda-cudart-dev
         - libcublas-dev
         - libcurand-dev
@@ -121,7 +117,7 @@ outputs:
         - {{ compiler('c') }}
         - {{ compiler('cxx') }}
         {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }} ={{ cuda_version }}
+        - {{ compiler('cuda') }} ={{ cuda_version }}
         {% else %}
         - {{ compiler('cuda') }}
         {% endif %}
@@ -174,10 +170,8 @@ outputs:
       number: {{ GIT_DESCRIBE_NUMBER }}
       string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
       ignore_run_exports_from:
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }}
-        {% else %}
         - {{ compiler('cuda') }}
+        {% if cuda_major != "11" %}
         - cuda-cudart-dev
         - libcublas-dev
         - libcurand-dev
@@ -189,7 +183,7 @@ outputs:
         - {{ compiler('c') }}
         - {{ compiler('cxx') }}
         {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }} ={{ cuda_version }}
+        - {{ compiler('cuda') }} ={{ cuda_version }}
         {% else %}
         - {{ compiler('cuda') }}
         {% endif %}
@@ -246,10 +240,8 @@ outputs:
       number: {{ GIT_DESCRIBE_NUMBER }}
       string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
       ignore_run_exports_from:
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }}
-        {% else %}
         - {{ compiler('cuda') }}
+        {% if cuda_major != "11" %}
         - cuda-cudart-dev
         - libcublas-dev
         - libcurand-dev
@@ -261,7 +253,7 @@ outputs:
         - {{ compiler('c') }}
         - {{ compiler('cxx') }}
         {% if cuda_major == "11" %}
-        - {{ compiler('cuda11') }} ={{ cuda_version }}
+        - {{ compiler('cuda') }} ={{ cuda_version }}
         {% else %}
         - {{ compiler('cuda') }}
         {% endif %}
diff --git a/cpp/test/distance/masked_nn.cu b/cpp/test/distance/masked_nn.cu
index a8f2f5163f..a1c7846693 100644
--- a/cpp/test/distance/masked_nn.cu
+++ b/cpp/test/distance/masked_nn.cu
@@ -314,8 +314,8 @@ template <typename K, typename V, typename L>
                                        cudaStream_t stream = 0)
 {
   typedef typename raft::KeyValuePair<K, V> KVP;
-  std::shared_ptr<KVP> exp_h(new KVP[size]);
-  std::shared_ptr<KVP> act_h(new KVP[size]);
+  std::shared_ptr<KVP[]> exp_h(new KVP[size]);
+  std::shared_ptr<KVP[]> act_h(new KVP[size]);
   raft::update_host<KVP>(exp_h.get(), expected, size, stream);
   raft::update_host<KVP>(act_h.get(), actual, size, stream);
   RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
diff --git a/dependencies.yaml b/dependencies.yaml
index fbd1d83727..d23c118c00 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -166,14 +166,28 @@ dependencies:
         matrices:
           - matrix:
               arch: x86_64
+              cuda: "11.*"
             packages:
               - gcc_linux-64=11.*
-              - sysroot_linux-64==2.17
+              - sysroot_linux-64==2.28
           - matrix:
               arch: aarch64
+              cuda: "11.*"
             packages:
               - gcc_linux-aarch64=11.*
-              - sysroot_linux-aarch64==2.17
+              - sysroot_linux-aarch64==2.28
+          - matrix:
+              arch: x86_64
+              cuda: "12.*"
+            packages:
+              - gcc_linux-64=13.*
+              - sysroot_linux-64==2.28
+          - matrix:
+              arch: aarch64
+              cuda: "12.*"
+            packages:
+              - gcc_linux-aarch64=13.*
+              - sysroot_linux-aarch64==2.28
       - output_types: conda
         matrices:
           - matrix: {cuda: "12.*"}

From 9b7bb975249f6863f72aa04147bf423d130a25c3 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 22 Jan 2025 00:41:17 -0600
Subject: [PATCH 260/623] Temporarily skip CUDA 11 wheel CI (#599)

Due to some failures coming from libraft C++ wheels, CUDA 11 wheel CI will not pass. This PR temporarily disables CUDA 11 wheel tests until those issues can be resolved.

See https://github.com/rapidsai/build-planning/issues/137.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/599
---
 .github/workflows/pr.yaml   | 3 +++
 .github/workflows/test.yaml | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 91f51bd90d..ca85c5c2e3 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -150,6 +150,9 @@ jobs:
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
+      # CUDA 11 wheel CI is disabled until
+      # https://github.com/rapidsai/build-planning/issues/137 is resolved.
+      matrix_filter: map(select(.CUDA_VER | startswith("11") | not))
   devcontainer:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.02
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index e3bf5d16f2..cf081d5797 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -49,3 +49,6 @@ jobs:
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
       script: ci/test_wheel_cuvs.sh
+      # CUDA 11 wheel CI is disabled until
+      # https://github.com/rapidsai/build-planning/issues/137 is resolved.
+      matrix_filter: map(select(.CUDA_VER | startswith("11") | not))

From d6476f122e57d20e54cf1df6eb3a5b2baeefb175 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 22 Jan 2025 11:05:14 -0600
Subject: [PATCH 261/623] Use cuda.bindings layout. (#588)

This PR updates cuVS to use the new cuda-python `cuda.bindings` layout. See https://github.com/rapidsai/build-planning/issues/117.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cuvs/pull/588
---
 python/cuvs/cuvs/common/c_api.pxd     | 2 +-
 python/cuvs/cuvs/common/resources.pyx | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/cuvs/cuvs/common/c_api.pxd b/python/cuvs/cuvs/common/c_api.pxd
index f99fd5348e..dae93d7503 100644
--- a/python/cuvs/cuvs/common/c_api.pxd
+++ b/python/cuvs/cuvs/common/c_api.pxd
@@ -16,7 +16,7 @@
 # cython: language_level=3
 
 
-from cuda.ccudart cimport cudaStream_t
+from cuda.bindings.cyruntime cimport cudaStream_t
 from libc.stdint cimport uintptr_t
 
 
diff --git a/python/cuvs/cuvs/common/resources.pyx b/python/cuvs/cuvs/common/resources.pyx
index c0b72ae348..0edf53fc1d 100644
--- a/python/cuvs/cuvs/common/resources.pyx
+++ b/python/cuvs/cuvs/common/resources.pyx
@@ -17,7 +17,7 @@
 
 import functools
 
-from cuda.ccudart cimport cudaStream_t
+from cuda.bindings.cyruntime cimport cudaStream_t
 
 from cuvs.common.c_api cimport (
     cuvsResources_t,

From 43969ca29e054fb94820f518c047e8446e6730a0 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 22 Jan 2025 14:31:23 -0600
Subject: [PATCH 262/623] Revert "Temporarily skip CUDA 11 wheel CI" (#601)

Reverts rapidsai/cuvs#599 now that https://github.com/rapidsai/raft/pull/2548 has landed.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/601
---
 .github/workflows/pr.yaml   | 3 ---
 .github/workflows/test.yaml | 3 ---
 2 files changed, 6 deletions(-)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index ca85c5c2e3..91f51bd90d 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -150,9 +150,6 @@ jobs:
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
-      # CUDA 11 wheel CI is disabled until
-      # https://github.com/rapidsai/build-planning/issues/137 is resolved.
-      matrix_filter: map(select(.CUDA_VER | startswith("11") | not))
   devcontainer:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.02
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index cf081d5797..e3bf5d16f2 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -49,6 +49,3 @@ jobs:
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
       script: ci/test_wheel_cuvs.sh
-      # CUDA 11 wheel CI is disabled until
-      # https://github.com/rapidsai/build-planning/issues/137 is resolved.
-      matrix_filter: map(select(.CUDA_VER | startswith("11") | not))

From 1c91e1f6bf38321fb1f57250bc32980ea0674138 Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Wed, 22 Jan 2025 13:33:10 -0800
Subject: [PATCH 263/623] [Fix] l2_exp random fail in half-float32 mixed
 precision on self-neighboring (#596)

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/596
---
 .../distance/detail/distance_ops/l2_exp.cuh   | 34 ++++++++++++-------
 python/cuvs/cuvs/test/test_distance.py        |  5 ++-
 2 files changed, 23 insertions(+), 16 deletions(-)

diff --git a/cpp/src/distance/detail/distance_ops/l2_exp.cuh b/cpp/src/distance/detail/distance_ops/l2_exp.cuh
index 04817aa8b1..f49771605e 100644
--- a/cpp/src/distance/detail/distance_ops/l2_exp.cuh
+++ b/cpp/src/distance/detail/distance_ops/l2_exp.cuh
@@ -28,14 +28,14 @@ namespace cuvs::distance::detail::ops {
  * for round-off error tolerance.
  * @tparam DataT
  */
-template <typename DataT>
-__device__ constexpr DataT get_clamp_precision()
+template <typename DataT, typename AccT>
+__device__ constexpr AccT get_clamp_precision()
 {
   switch (sizeof(DataT)) {
-    case 2: return 1e-3;
-    case 4: return 1e-6;
-    case 8: return 1e-15;
-    default: return 0;
+    case 2: return AccT{1e-3};
+    case 4: return AccT{1e-6};
+    case 8: return AccT{1e-15};
+    default: return AccT{0};
   }
 }
 
@@ -46,19 +46,27 @@ struct l2_exp_cutlass_op {
 
   __device__ l2_exp_cutlass_op() noexcept : sqrt(false) {}
   __device__ l2_exp_cutlass_op(bool isSqrt) noexcept : sqrt(isSqrt) {}
-  inline __device__ AccT operator()(DataT aNorm, DataT bNorm, DataT accVal) const noexcept
+  inline __device__ AccT operator()(AccT aNorm, AccT bNorm, AccT accVal) const noexcept
   {
-    AccT outVal = aNorm + bNorm - DataT(2.0) * accVal;
+    AccT outVal = aNorm + bNorm - AccT(2.0) * accVal;
 
     /**
      * Self-neighboring points should have (aNorm == bNorm) == accVal and the dot product (accVal)
      * can sometimes have round-off errors, which will cause (aNorm == bNorm) ~ accVal instead.
      */
-    outVal = outVal * AccT(!((outVal * outVal < get_clamp_precision<AccT>()) * (aNorm == bNorm)));
+    outVal =
+      outVal * AccT(!((outVal * outVal < get_clamp_precision<DataT, AccT>()) * (aNorm == bNorm)));
     return sqrt ? raft::sqrt(outVal * static_cast<AccT>(outVal > AccT(0))) : outVal;
   }
 
-  __device__ AccT operator()(DataT aData) const noexcept { return aData; }
+  __device__ AccT operator()(DataT aData) const noexcept
+  {
+    if constexpr (std::is_same_v<DataT, half> && std::is_same_v<AccT, float>) {
+      return __half2float(aData);
+    } else {
+      return aData;
+    }
+  }
 };
 
 /**
@@ -121,9 +129,9 @@ struct l2_exp_distance_op {
          * (accVal) can sometimes have round-off errors, which will cause (aNorm == bNorm) ~ accVal
          * instead.
          */
-        acc[i][j] =
-          val * static_cast<AccT>((val > AccT(0))) *
-          static_cast<AccT>(!((val * val < get_clamp_precision<AccT>()) * (regxn[i] == regyn[j])));
+        acc[i][j] = val * static_cast<AccT>((val > AccT(0))) *
+                    static_cast<AccT>(
+                      !((val * val < get_clamp_precision<DataT, AccT>()) * (regxn[i] == regyn[j])));
       }
     }
     if (sqrt) {
diff --git a/python/cuvs/cuvs/test/test_distance.py b/python/cuvs/cuvs/test/test_distance.py
index 483d5d2011..370dd773a0 100644
--- a/python/cuvs/cuvs/test/test_distance.py
+++ b/python/cuvs/cuvs/test/test_distance.py
@@ -21,6 +21,7 @@
 from cuvs.distance import pairwise_distance
 
 
+@pytest.mark.parametrize("times", range(20))
 @pytest.mark.parametrize("n_rows", [50, 100])
 @pytest.mark.parametrize("n_cols", [10, 50])
 @pytest.mark.parametrize(
@@ -43,7 +44,7 @@
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("order", ["F", "C"])
 @pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
-def test_distance(n_rows, n_cols, inplace, order, metric, dtype):
+def test_distance(n_rows, n_cols, inplace, order, metric, dtype, times):
     input1 = np.random.random_sample((n_rows, n_cols))
     input1 = np.asarray(input1, order=order).astype(dtype)
 
@@ -79,7 +80,5 @@ def test_distance(n_rows, n_cols, inplace, order, metric, dtype):
     actual = output_device.copy_to_host()
 
     tol = 1e-3
-    if np.issubdtype(dtype, np.float16):
-        tol = 1e-1
 
     assert np.allclose(expected, actual, atol=tol, rtol=tol)

From b62b11aa73c5b39fe5ccd7328ccdcf468c4c2323 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Wed, 22 Jan 2025 21:53:22 -0600
Subject: [PATCH 264/623] introduce libcuvs wheels (#594)

Contributes to https://github.com/rapidsai/build-planning/issues/33.

Proposes packaging `libcuvs` as a wheel, which is then re-used by `cuvs-cu{11,12}`.

Similar changes were recently made in RAFT: https://github.com/rapidsai/raft/pull/2531

As part of this, also proposes:

* introducing a new CMake option, `CUVS_COMPILE_DYNAMIC_ONLY`, to allow building/installing only the dynamic shared library (i.e. skipping the static library)
* enforcing `rapids-cmake`'s preferred CMake style (similar https://github.com/rapidsai/raft/pull/2531#discussion_r1917039870)
* standardizing `clang` pins across the project, and pinning to `clang` 16 for Rust builds (https://github.com/rapidsai/cuvs/pull/594#discussion_r1924484839)

## Notes for Reviewers

### Benefits of these changes

* smaller wheels for projects that depend on cuVS (they can dynamically link against `libcuvs` instead of statically linking in the pieces they need)
* fewer CI resources used for cuVS wheels (no more re-compiling for every Python minor version)
* faster, cheaper cuML wheel builds (https://github.com/rapidsai/cuml/pull/6199#discussion_r1920830933)
* other benefits mentioned in https://github.com/rapidsai/build-planning/issues/33

### Wheel contents

`libcuvs`:

* `libcuvs.so` and `libcuvs_c.so` (shared library)
* cuVS headers
* vendored dependencies (hnswlib)

`cuvs`:

* `cuvs` Python / Cython code and compiled Cython extensions

### Size changes (CUDA 12, Python 3.12, x86_64)

| wheel                | num files (before) | num files (this PR) | size (before)  | size (this PR) |
|:---------------:|------------------:|-----------------:|--------------:|---------------:|
| `libcuvs`          |   ---                        |  67                        | ---                   | 843M               |
| `cuvs`              | 88                          |   84                       |845M               | 2M                    |
|**TOTAL**          |   **88**                |   **131**                 | **845M**       | **845M**         |

*NOTES: size = compressed, "before" = 2025-01-22 nightlies*

<details><summary>how I calculated those (click me)</summary>

* nightly commit = https://github.com/rapidsai/cuml/commit/7c715c494dff71274d0fdec774bdee12a7e78827
* PR = this PR

```shell
docker run \
    --rm \
    --network host \
    --env RAPIDS_NIGHTLY_DATE=2025-01-22 \
    --env CUVS_NIGHTLY_SHA=f1de1b2 \
    --env CUVS_PR="pull-request/594" \
    --env CUVS_PR_SHA="97c56178cd0e07e4b6b138bb0904af78379f1bb3" \
    --env RAPIDS_PY_CUDA_SUFFIX=cu12 \
    --env WHEEL_DIR_BEFORE=/tmp/wheels-before \
    --env WHEEL_DIR_AFTER=/tmp/wheels-after \
    -it rapidsai/ci-wheel:cuda12.5.1-rockylinux8-py3.12 \
    bash

# --- nightly wheels --- #
mkdir -p ./wheels-before

export RAPIDS_BUILD_TYPE=branch
export RAPIDS_REF_NAME="branch-25.02"

# cuvs
RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" \
RAPIDS_REPOSITORY=rapidsai/cuvs \
RAPIDS_SHA=${CUVS_NIGHTLY_SHA} \
    rapids-download-wheels-from-s3 python ./wheels-before

# --- wheels from CI --- #
mkdir -p ./wheels-after

export RAPIDS_BUILD_TYPE="pull-request"

# libcuvs
RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" \
RAPIDS_REPOSITORY=rapidsai/cuvs \
RAPIDS_REF_NAME="${CUVS_PR}" \
RAPIDS_SHA="${CUVS_PR_SHA}" \
    rapids-download-wheels-from-s3 cpp ./wheels-after

# cuvs
RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" \
RAPIDS_REPOSITORY=rapidsai/cuvs \
RAPIDS_REF_NAME="${CUVS_PR}" \
RAPIDS_SHA="${CUVS_PR_SHA}" \
    rapids-download-wheels-from-s3 python ./wheels-after

pip install pydistcheck
pydistcheck \
    --inspect \
    --select 'distro-too-large-compressed' \
    ./wheels-before/*.whl \
| grep -E '^checking|files: | compressed' \
> ./before.txt

# get more exact sizes
du -sh ./wheels-before/*

pydistcheck \
    --inspect \
    --select 'distro-too-large-compressed' \
    ./wheels-after/*.whl \
| grep -E '^checking|files: | compressed' \
> ./after.txt

# get more exact sizes
du -sh ./wheels-after/*
```

</details>

### How I tested this

* https://github.com/rapidsai/devcontainers/pull/440
* https://github.com/rapidsai/cuml/pull/6199

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/594
---
 .github/workflows/build.yaml                  |  24 ++++
 .github/workflows/pr.yaml                     |  12 +-
 .pre-commit-config.yaml                       |   2 +-
 build.sh                                      |   8 +-
 ci/build_wheel.sh                             |  34 +++---
 ci/build_wheel_cuvs.sh                        |  23 ++--
 ci/build_wheel_libcuvs.sh                     |  32 +++++
 ci/check_style.sh                             |   7 ++
 ci/release/update-version.sh                  |   4 +-
 ci/test_wheel_cuvs.sh                         |   9 +-
 ci/validate_wheel.sh                          |  12 --
 .../all_cuda-118_arch-aarch64.yaml            |   5 +-
 .../all_cuda-118_arch-x86_64.yaml             |   5 +-
 .../all_cuda-125_arch-aarch64.yaml            |   5 +-
 .../all_cuda-125_arch-x86_64.yaml             |   5 +-
 .../bench_ann_cuda-118_arch-aarch64.yaml      |   3 +-
 .../bench_ann_cuda-118_arch-x86_64.yaml       |   3 +-
 .../bench_ann_cuda-125_arch-aarch64.yaml      |   3 +-
 .../bench_ann_cuda-125_arch-x86_64.yaml       |   3 +-
 cpp/CMakeLists.txt                            |  77 +++++++-----
 cpp/cmake/thirdparty/get_raft.cmake           |   1 +
 dependencies.yaml                             | 110 +++++++++++++++---
 examples/cpp/CMakeLists.txt                   |  22 +++-
 python/cuvs/CMakeLists.txt                    |  70 +----------
 python/cuvs/cuvs/__init__.py                  |  11 ++
 python/cuvs/cuvs/common/CMakeLists.txt        |   2 +-
 python/cuvs/cuvs/distance/CMakeLists.txt      |   2 +-
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   2 +-
 .../cuvs/neighbors/brute_force/CMakeLists.txt |   3 +-
 .../cuvs/cuvs/neighbors/cagra/CMakeLists.txt  |   2 +-
 .../cuvs/neighbors/filters/CMakeLists.txt     |   2 +-
 .../cuvs/cuvs/neighbors/hnsw/CMakeLists.txt   |   2 +-
 .../cuvs/neighbors/ivf_flat/CMakeLists.txt    |   2 +-
 .../cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt |   2 +-
 python/cuvs/pyproject.toml                    |  19 ++-
 python/libcuvs/CMakeLists.txt                 |  69 +++++++++++
 python/libcuvs/LICENSE                        |   1 +
 python/libcuvs/README.md                      |   1 +
 python/libcuvs/libcuvs/VERSION                |   1 +
 python/libcuvs/libcuvs/__init__.py            |  16 +++
 python/libcuvs/libcuvs/_version.py            |  33 ++++++
 python/libcuvs/libcuvs/load.py                | 100 ++++++++++++++++
 python/libcuvs/pyproject.toml                 | 108 +++++++++++++++++
 43 files changed, 653 insertions(+), 204 deletions(-)
 create mode 100755 ci/build_wheel_libcuvs.sh
 create mode 100644 python/libcuvs/CMakeLists.txt
 create mode 120000 python/libcuvs/LICENSE
 create mode 120000 python/libcuvs/README.md
 create mode 120000 python/libcuvs/libcuvs/VERSION
 create mode 100644 python/libcuvs/libcuvs/__init__.py
 create mode 100644 python/libcuvs/libcuvs/_version.py
 create mode 100644 python/libcuvs/libcuvs/load.py
 create mode 100644 python/libcuvs/pyproject.toml

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index e93b7a694c..59b8e00de1 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -80,7 +80,30 @@ jobs:
       node_type: "gpu-v100-latest-1"
       run_script: "ci/build_docs.sh"
       sha: ${{ inputs.sha }}
+  wheel-build-libcuvs:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      script: ci/build_wheel_libcuvs.sh
+      # build for every combination of arch and CUDA version, but only for the latest Python
+      matrix_filter: group_by([.ARCH, (.CUDA_VER|split(".")|map(tonumber)|.[0])]) | map(max_by(.PY_VER|split(".")|map(tonumber)))
+  wheel-publish-libcuvs:
+    needs: wheel-build-libcuvs
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      package-name: libcuvs
+      package-type: cpp
   wheel-build-cuvs:
+    needs: wheel-build-libcuvs
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
@@ -99,3 +122,4 @@ jobs:
       sha: ${{ inputs.sha }}
       date: ${{ inputs.date }}
       package-name: cuvs
+      package-type: python
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 91f51bd90d..843439f26d 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -22,6 +22,7 @@ jobs:
       - conda-python-tests
       - docs-build
       - rust-build
+      - wheel-build-libcuvs
       - wheel-build-cuvs
       - wheel-tests-cuvs
       - devcontainer
@@ -135,10 +136,19 @@ jobs:
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
       run_script: "ci/build_rust.sh"
-  wheel-build-cuvs:
+  wheel-build-libcuvs:
     needs: checks
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    with:
+      build_type: pull-request
+      script: ci/build_wheel_libcuvs.sh
+      # build for every combination of arch and CUDA version, but only for the latest Python
+      matrix_filter: group_by([.ARCH, (.CUDA_VER|split(".")|map(tonumber)|.[0])]) | map(max_by(.PY_VER|split(".")|map(tonumber)))
+  wheel-build-cuvs:
+    needs: wheel-build-libcuvs
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index fcfc7e1fa4..240f82be68 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -115,7 +115,7 @@ repos:
                   cpp/cmake/modules/FindAVX\.cmake|
           - id: verify-alpha-spec
       - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.16.0
+        rev: v1.17.0
         hooks:
             - id: rapids-dependency-file-generator
               args: ["--clean"]
diff --git a/build.sh b/build.sh
index bd5fa649b0..3b9a9a3a8f 100755
--- a/build.sh
+++ b/build.sh
@@ -313,12 +313,6 @@ if [[ ${CMAKE_TARGET} == "" ]]; then
     CMAKE_TARGET="all"
 fi
 
-
-SKBUILD_EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS}"
-if [[ "${EXTRA_CMAKE_ARGS}" != *"DFIND_CUVS_CPP"* ]]; then
-    SKBUILD_EXTRA_CMAKE_ARGS="${SKBUILD_EXTRA_CMAKE_ARGS};-DFIND_CUVS_CPP=ON"
-fi
-
 # If clean given, run it prior to any other steps
 if (( ${CLEAN} == 1 )); then
     # If the dirs to clean are mounted dirs in a container, the
@@ -434,7 +428,7 @@ fi
 
 # Build and (optionally) install the cuvs Python package
 if (( ${NUMARGS} == 0 )) || hasArg python; then
-    SKBUILD_CMAKE_ARGS="${SKBUILD_EXTRA_CMAKE_ARGS}" \
+    SKBUILD_CMAKE_ARGS="${EXTRA_CMAKE_ARGS}" \
         SKBUILD_BUILD_OPTIONS="-j${PARALLEL_LEVEL}" \
         python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs
 fi
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 4994374a80..c6f1232b37 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -1,10 +1,11 @@
 #!/bin/bash
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
 package_name=$1
 package_dir=$2
+package_type=$3
 underscore_package_name=$(echo "${package_name}" | tr "-" "_")
 
 source rapids-configure-sccache
@@ -16,21 +17,22 @@ rapids-generate-version > ./VERSION
 
 cd "${package_dir}"
 
-case "${RAPIDS_CUDA_VERSION}" in
-  12.*)
-    EXCLUDE_ARGS=(
-      --exclude "libcublas.so.12"
-      --exclude "libcublasLt.so.12"
-      --exclude "libcurand.so.10"
-      --exclude "libcusolver.so.11"
-      --exclude "libcusparse.so.12"
-      --exclude "libnvJitLink.so.12"
+EXCLUDE_ARGS=(
+  --exclude "libraft.so"
+  --exclude "libcublas.so.*"
+  --exclude "libcublasLt.so.*"
+  --exclude "libcurand.so.*"
+  --exclude "libcusolver.so.*"
+  --exclude "libcusparse.so.*"
+  --exclude "libnvJitLink.so.*"
+)
+
+if [[ "${package_dir}" != "python/libcuvs" ]]; then
+    EXCLUDE_ARGS+=(
+      --exclude "libcuvs_c.so"
+      --exclude "libcuvs.so"
     )
-  ;;
-  11.*)
-    EXCLUDE_ARGS=()
-  ;;
-esac
+fi
 
 rapids-logger "Building '${package_name}' wheel"
 
@@ -48,4 +50,4 @@ sccache --show-adv-stats
 mkdir -p final_dist
 python -m auditwheel repair -w final_dist "${EXCLUDE_ARGS[@]}" dist/*
 
-RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 python final_dist
+RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 ${package_type} final_dist
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index 444657cc06..fb40d14593 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -1,21 +1,20 @@
 #!/bin/bash
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
 package_dir="python/cuvs"
 
-case "${RAPIDS_CUDA_VERSION}" in
-  12.*)
-    EXTRA_CMAKE_ARGS=";-DUSE_CUDA_MATH_WHEELS=ON"
-  ;;
-  11.*)
-    EXTRA_CMAKE_ARGS=";-DUSE_CUDA_MATH_WHEELS=OFF"
-  ;;
-esac
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
 
-# Set up skbuild options. Enable sccache in skbuild config options
-export SKBUILD_CMAKE_ARGS="-DDETECT_CONDA_ENV=OFF;-DFIND_CUVS_CPP=OFF${EXTRA_CMAKE_ARGS}"
+# Downloads libcuvs wheels from this current build,
+# then ensures 'cuvs' wheel builds always use the 'libcuvs' just built in the same CI run.
+#
+# Using env variable PIP_CONSTRAINT is necessary to ensure the constraints
+# are used when creating the isolated build environment.
+RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 cpp /tmp/libcuvs_dist
+echo "libcuvs-${RAPIDS_PY_CUDA_SUFFIX} @ file://$(echo /tmp/libcuvs_dist/libcuvs_*.whl)" > /tmp/constraints.txt
+export PIP_CONSTRAINT="/tmp/constraints.txt"
 
-ci/build_wheel.sh cuvs ${package_dir}
+ci/build_wheel.sh cuvs ${package_dir} python
 ci/validate_wheel.sh ${package_dir} final_dist
diff --git a/ci/build_wheel_libcuvs.sh b/ci/build_wheel_libcuvs.sh
new file mode 100755
index 0000000000..148be89a27
--- /dev/null
+++ b/ci/build_wheel_libcuvs.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+package_name="libcuvs"
+package_dir="python/libcuvs"
+
+rapids-logger "Generating build requirements"
+matrix_selectors="cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION};cuda_suffixed=true"
+
+rapids-dependency-file-generator \
+  --output requirements \
+  --file-key "py_build_${package_name}" \
+  --file-key "py_rapids_build_${package_name}" \
+  --matrix "${matrix_selectors}" \
+| tee /tmp/requirements-build.txt
+
+rapids-logger "Installing build requirements"
+python -m pip install \
+    -v \
+    --prefer-binary \
+    -r /tmp/requirements-build.txt
+
+# build with '--no-build-isolation', for better sccache hit rate
+# 0 really means "add --no-build-isolation" (ref: https://github.com/pypa/pip/issues/5735)
+export PIP_NO_BUILD_ISOLATION=0
+
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+
+ci/build_wheel.sh libcuvs ${package_dir} cpp
+ci/validate_wheel.sh ${package_dir} final_dist libcuvs
diff --git a/ci/check_style.sh b/ci/check_style.sh
index c22f3f9f0a..952e94bf1c 100755
--- a/ci/check_style.sh
+++ b/ci/check_style.sh
@@ -14,5 +14,12 @@ rapids-dependency-file-generator \
 rapids-mamba-retry env create --yes -f env.yaml -n checks
 conda activate checks
 
+# get config for cmake-format checks
+RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
+FORMAT_FILE_URL="https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION_MAJOR_MINOR}/cmake-format-rapids-cmake.json"
+export RAPIDS_CMAKE_FORMAT_FILE=/tmp/rapids_cmake_ci/cmake-formats-rapids-cmake.json
+mkdir -p $(dirname ${RAPIDS_CMAKE_FORMAT_FILE})
+wget -O ${RAPIDS_CMAKE_FORMAT_FILE} ${FORMAT_FILE_URL}
+
 # Run pre-commit checks
 pre-commit run --all-files --show-diff-on-failure
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 4cf1f06175..7562035a95 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -44,8 +44,10 @@ echo "${NEXT_FULL_TAG}" > VERSION
 DEPENDENCIES=(
   dask-cuda
   cuvs
-  pylibraft
+  libcuvs
+  libraft
   librmm
+  pylibraft
   rmm
   rapids-dask-dependency
 )
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 7033003e95..862c69a3aa 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -1,13 +1,16 @@
 #!/bin/bash
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
 mkdir -p ./dist
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
-RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./dist
+RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 cpp ./local-libcuvs-dep
+RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 python ./dist
 
 # echo to expand wildcard before adding `[extra]` requires for pip
-python -m pip install $(echo ./dist/cuvs*.whl)[test]
+python -m pip install \
+    ./local-libcuvs-dep/libcuvs*.whl \
+    "$(echo ./dist/cuvs*.whl)[test]"
 
 python -m pytest ./python/cuvs/cuvs/test
diff --git a/ci/validate_wheel.sh b/ci/validate_wheel.sh
index f2b235765c..19d413fa20 100755
--- a/ci/validate_wheel.sh
+++ b/ci/validate_wheel.sh
@@ -8,24 +8,12 @@ wheel_dir_relative_path=$2
 
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 
-# some packages are much larger on CUDA 11 than on CUDA 12
-if [[ "${RAPIDS_CUDA_MAJOR}" == "11" ]]; then
-    PYDISTCHECK_ARGS=(
-        --max-allowed-size-compressed '1.4G'
-    )
-else
-    PYDISTCHECK_ARGS=(
-        --max-allowed-size-compressed '950M'
-    )
-fi
-
 cd "${package_dir}"
 
 rapids-logger "validate packages with 'pydistcheck'"
 
 pydistcheck \
     --inspect \
-    "${PYDISTCHECK_ARGS[@]}" \
     "$(echo ${wheel_dir_relative_path}/*.whl)"
 
 rapids-logger "validate packages with 'twine'"
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 4c464ef4ee..123acb4219 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -9,8 +9,7 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
@@ -26,7 +25,7 @@ dependencies:
 - gcc_linux-aarch64=11.*
 - graphviz
 - ipython
-- libclang
+- libclang==16.0.6
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 71cbeeaf3e..c6a65e6848 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -9,8 +9,7 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - cmake>=3.26.4,!=3.30.0
 - cuda-nvtx=11.8
@@ -26,7 +25,7 @@ dependencies:
 - gcc_linux-64=11.*
 - graphviz
 - ipython
-- libclang
+- libclang==16.0.6
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index 4fd08fa97b..b71f5ed438 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -9,8 +9,7 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - cmake>=3.26.4,!=3.30.0
 - cuda-cudart-dev
@@ -27,7 +26,7 @@ dependencies:
 - gcc_linux-aarch64=13.*
 - graphviz
 - ipython
-- libclang
+- libclang==16.0.6
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index de5f341fa1..16cd595d33 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -9,8 +9,7 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - cmake>=3.26.4,!=3.30.0
 - cuda-cudart-dev
@@ -27,7 +26,7 @@ dependencies:
 - gcc_linux-64=13.*
 - graphviz
 - ipython
-- libclang
+- libclang==16.0.6
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index fb69ac251f..2e2ad84468 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -9,7 +9,7 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - click
 - cmake>=3.26.4,!=3.30.0
@@ -26,6 +26,7 @@ dependencies:
 - gcc_linux-aarch64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
+- libclang==16.0.6
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 123033b08c..90243415c1 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -9,7 +9,7 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - click
 - cmake>=3.26.4,!=3.30.0
@@ -26,6 +26,7 @@ dependencies:
 - gcc_linux-64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
+- libclang==16.0.6
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index fa2ae79556..34e01aeea8 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -9,7 +9,7 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - click
 - cmake>=3.26.4,!=3.30.0
@@ -27,6 +27,7 @@ dependencies:
 - gcc_linux-aarch64=13.*
 - glog>=0.6.0
 - h5py>=3.8.0
+- libclang==16.0.6
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index 76b005e3cf..dcfb54a222 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -9,7 +9,7 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools=16.0.6
+- clang-tools==16.0.6
 - clang==16.0.6
 - click
 - cmake>=3.26.4,!=3.30.0
@@ -27,6 +27,7 @@ dependencies:
 - gcc_linux-64=13.*
 - glog>=0.6.0
 - h5py>=3.8.0
+- libclang==16.0.6
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 26c0b82d3a..11f21db44b 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -68,6 +68,7 @@ option(CUDA_LOG_COMPILE_TIME "Write a log of compilation times to nvcc_compile_l
 option(DETECT_CONDA_ENV "Enable detection of conda environment for dependencies" ON)
 option(DISABLE_DEPRECATION_WARNINGS "Disable deprecaction warnings " ON)
 option(DISABLE_OPENMP "Disable OpenMP" OFF)
+option(CUVS_COMPILE_DYNAMIC_ONLY "Only build the shared library and skip the static library." OFF)
 option(CUVS_NVTX "Enable nvtx markers" OFF)
 option(CUVS_RAFT_CLONE_ON_PIN "Explicitly clone RAFT branch when pinned to non-feature branch" ON)
 
@@ -94,6 +95,7 @@ include(CMakeDependentOption)
 message(VERBOSE "cuVS: Build cuVS unit-tests: ${BUILD_TESTS}")
 message(VERBOSE "cuVS: Build CPU only components: ${BUILD_CPU_ONLY}")
 message(VERBOSE "cuVS: Build ANN benchmarks: ${BUILD_CUVS_BENCH}")
+message(VERBOSE "cuVS: Build only the shared library: ${CUVS_COMPILE_DYNAMIC_ONLY}")
 message(VERBOSE "cuVS: Enable detection of conda environment for dependencies: ${DETECT_CONDA_ENV}")
 message(VERBOSE "cuVS: Disable depreaction warnings " ${DISABLE_DEPRECATION_WARNINGS})
 message(VERBOSE "cuVS: Disable OpenMP: ${DISABLE_OPENMP}")
@@ -493,7 +495,10 @@ if(BUILD_SHARED_LIBS)
   )
 
   add_library(cuvs SHARED $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
-  add_library(cuvs_static STATIC $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
+
+  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
+    add_library(cuvs_static STATIC $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
+  endif()
 
   target_compile_options(
     cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
@@ -501,20 +506,23 @@ if(BUILD_SHARED_LIBS)
   )
 
   add_library(cuvs::cuvs ALIAS cuvs)
-  add_library(cuvs::cuvs_static ALIAS cuvs_static)
 
-  set_target_properties(
-    cuvs_static
-    PROPERTIES BUILD_RPATH "\$ORIGIN"
-               INSTALL_RPATH "\$ORIGIN"
-               CXX_STANDARD 17
-               CXX_STANDARD_REQUIRED ON
-               POSITION_INDEPENDENT_CODE ON
-               INTERFACE_POSITION_INDEPENDENT_CODE ON
-               EXPORT_NAME cuvs_static
-  )
+  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
+    add_library(cuvs::cuvs_static ALIAS cuvs_static)
 
-  target_compile_options(cuvs_static PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
+    set_target_properties(
+      cuvs_static
+      PROPERTIES BUILD_RPATH "\$ORIGIN"
+                 INSTALL_RPATH "\$ORIGIN"
+                 CXX_STANDARD 17
+                 CXX_STANDARD_REQUIRED ON
+                 POSITION_INDEPENDENT_CODE ON
+                 INTERFACE_POSITION_INDEPENDENT_CODE ON
+                 EXPORT_NAME cuvs_static
+    )
+
+    target_compile_options(cuvs_static PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
+  endif()
 
   target_include_directories(
     cuvs_objs
@@ -523,19 +531,21 @@ if(BUILD_SHARED_LIBS)
     INTERFACE "$<INSTALL_INTERFACE:include>"
   )
 
-  target_include_directories(
-    cuvs_static
-    PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-    INTERFACE "$<INSTALL_INTERFACE:include>"
-  )
+  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
+    target_include_directories(
+      cuvs_static
+      PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+      INTERFACE "$<INSTALL_INTERFACE:include>"
+    )
 
-  # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
-  target_link_options(cuvs_static PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
+    # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
+    target_link_options(cuvs_static PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
 
-  target_include_directories(
-    cuvs_static PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-                       "$<INSTALL_INTERFACE:include>"
-  )
+    target_include_directories(
+      cuvs_static PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+                         "$<INSTALL_INTERFACE:include>"
+    )
+  endif()
 
   target_include_directories(
     cuvs PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
@@ -571,11 +581,13 @@ if(BUILD_SHARED_LIBS)
               cuvs-cagra-search ${CUVS_COMMS_DEPENDENCY}
     )
 
-    target_link_libraries(
-      cuvs_static
-      PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
-      PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-    )
+    if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
+      target_link_libraries(
+        cuvs_static
+        PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
+        PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+      )
+    endif()
   endif()
 
   if(BUILD_MG_ALGOS)
@@ -718,8 +730,13 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
   include(GNUInstallDirs)
   include(CPack)
 
+  set(_cuvs_lib_targets cuvs)
+  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
+    list(APPEND _cuvs_lib_targets cuvs_static)
+  endif()
+
   install(
-    TARGETS cuvs cuvs_static
+    TARGETS ${_cuvs_lib_targets}
     DESTINATION ${lib_dir}
     COMPONENT cuvs
     EXPORT cuvs-exports
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 2e57df84e5..845c7a8337 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -44,6 +44,7 @@ function(find_and_configure_raft)
             INSTALL_EXPORT_SET  cuvs-exports
             COMPONENTS          ${RAFT_COMPONENTS}
             CPM_ARGS
+              EXCLUDE_FROM_ALL TRUE  
               GIT_REPOSITORY        https://github.com/${PKG_FORK}/raft.git
               GIT_TAG               ${PKG_PINNED_TAG}
               SOURCE_SUBDIR         cpp
diff --git a/dependencies.yaml b/dependencies.yaml
index d23c118c00..478b2acc27 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -7,15 +7,16 @@ files:
       arch: [x86_64, aarch64]
     includes:
       - build
+      - build_cython
       - build_py_cuvs
       - build_wheels
       - checks
+      - clang
       - cuda
       - cuda_version
       - depends_on_cupy
       - depends_on_librmm
       - depends_on_pylibraft
-      - develop
       - docs
       - rapids_build
       - run_py_cuvs
@@ -31,13 +32,15 @@ files:
     includes:
       - bench
       - bench_python
+      - build_cython
       - build_py_cuvs
+      - clang
       - cuda
       - cuda_version
       - depends_on_cupy
       - depends_on_pylibraft
+      - depends_on_libcuvs
       - depends_on_librmm
-      - develop
       - rapids_build
       - rapids_build_setuptools
   test_cpp:
@@ -61,6 +64,7 @@ files:
   docs:
     output: none
     includes:
+      - clang
       - cuda
       - cuda_version
       - depends_on_cupy
@@ -71,10 +75,37 @@ files:
   rust:
     output: none
     includes:
+      # clang/libclang only needed for bindgen support
+      - clang
       - cuda
       - cuda_version
       - rapids_build
       - rust
+  py_build_libcuvs:
+    output: pyproject
+    pyproject_dir: python/libcuvs
+    extras:
+      table: build-system
+    includes:
+      - build
+  py_rapids_build_libcuvs:
+    output: pyproject
+    pyproject_dir: python/libcuvs
+    extras:
+      table: tool.rapids-build-backend
+      key: requires
+    includes:
+      - depends_on_libraft
+      - depends_on_librmm
+      - rapids_build
+  py_run_libcuvs:
+    output: pyproject
+    pyproject_dir: python/libcuvs
+    extras:
+      table: project
+    includes:
+      - cuda_wheels
+      - depends_on_libraft
   py_build_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
@@ -89,7 +120,11 @@ files:
       table: tool.rapids-build-backend
       key: requires
     includes:
+      - build_cython
       - build_py_cuvs
+      - depends_on_libcuvs
+      - depends_on_libraft
+      - depends_on_librmm
       - rapids_build
   py_run_cuvs:
     output: pyproject
@@ -97,7 +132,6 @@ files:
     extras:
       table: project
     includes:
-      - cuda_wheels
       - depends_on_pylibraft
       - run_py_cuvs
   py_test_cuvs:
@@ -149,12 +183,16 @@ dependencies:
       - output_types: [requirements, pyproject]
         packages:
           - scikit-build-core[pyproject]>=0.10.0
+  build_cython:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - cython>=3.0.0
   rapids_build:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
           - &cmake_ver cmake>=3.26.4,!=3.30.0
-          - cython>=3.0.0
           - ninja
       - output_types: [conda]
         packages:
@@ -241,12 +279,13 @@ dependencies:
       - output_types: [conda, requirements]
         packages:
           - pre-commit
-  develop:
+  clang:
     common:
       - output_types: conda
         packages:
           - clang==16.0.6
-          - clang-tools=16.0.6
+          - clang-tools==16.0.6
+          - libclang==16.0.6
   cuda_version:
     specific:
       - output_types: conda
@@ -363,13 +402,14 @@ dependencies:
               - nvidia-curand-cu12
               - nvidia-cusolver-cu12
               - nvidia-cusparse-cu12
-          # CUDA 11 does not provide wheels, so use the system libraries instead
           - matrix:
               cuda: "11.*"
               use_cuda_wheels: "true"
             packages:
-          # if use_cuda_wheels=false is provided, do not add dependencies on any CUDA wheels
-          # (e.g. for DLFW and pip devcontainers)
+              - nvidia-cublas-cu11
+              - nvidia-curand-cu11
+              - nvidia-cusolver-cu11
+              - nvidia-cusparse-cu11
           - matrix:
               use_cuda_wheels: "false"
             packages:
@@ -425,9 +465,6 @@ dependencies:
         packages:
           - make
           - rust
-          # clang/libclang only needed for bindgen support
-          - clang
-          - libclang
   build_wheels:
     common:
       - output_types: [requirements, pyproject]
@@ -492,7 +529,6 @@ dependencies:
           - h5py>=3.8.0
           - benchmark>=1.8.2
           - openblas
-          - libcuvs==25.2.*,>=0.0.0a0
   bench_python:
     common:
       - output_types: [conda, pyproject, requirements]
@@ -502,6 +538,54 @@ dependencies:
           - matplotlib
           - pandas
           - pyyaml
+  depends_on_libcuvs:
+    common:
+      - output_types: conda
+        packages:
+          - &libcuvs_unsuffixed libcuvs==25.2.*,>=0.0.0a0
+      - output_types: requirements
+        packages:
+          # pip recognizes the index as a global option for the requirements.txt file
+          - --extra-index-url=https://pypi.nvidia.com
+          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
+    specific:
+      - output_types: [requirements, pyproject]
+        matrices:
+          - matrix:
+              cuda: "12.*"
+              cuda_suffixed: "true"
+            packages:
+              - libcuvs-cu12==25.2.*,>=0.0.0a0
+          - matrix:
+              cuda: "11.*"
+              cuda_suffixed: "true"
+            packages:
+              - libcuvs-cu11==25.2.*,>=0.0.0a0
+          - {matrix: null, packages: [*libcuvs_unsuffixed]}
+  depends_on_libraft:
+    common:
+      - output_types: conda
+        packages:
+          - &libraft_unsuffixed libraft==25.2.*,>=0.0.0a0
+      - output_types: requirements
+        packages:
+          # pip recognizes the index as a global option for the requirements.txt file
+          - --extra-index-url=https://pypi.nvidia.com
+          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
+    specific:
+      - output_types: [requirements, pyproject]
+        matrices:
+          - matrix:
+              cuda: "12.*"
+              cuda_suffixed: "true"
+            packages:
+              - libraft-cu12==25.2.*,>=0.0.0a0
+          - matrix:
+              cuda: "11.*"
+              cuda_suffixed: "true"
+            packages:
+              - libraft-cu11==25.2.*,>=0.0.0a0
+          - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
       - output_types: conda
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 9554207bb0..b0d0ae9ee2 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -48,13 +48,23 @@ add_executable(VAMANA_EXAMPLE src/vamana_example.cu)
 add_library(rmm_logger OBJECT)
 target_link_libraries(rmm_logger PRIVATE rmm::rmm_logger_impl)
 
-target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
 target_link_libraries(
-  CAGRA_PERSISTENT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads rmm_logger
+  CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
 )
 target_link_libraries(
-  DYNAMIC_BATCHING_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads rmm_logger
+  CAGRA_PERSISTENT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
+                                   rmm_logger
+)
+target_link_libraries(
+  DYNAMIC_BATCHING_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
+                                   rmm_logger
+)
+target_link_libraries(
+  IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
+)
+target_link_libraries(
+  IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
+)
+target_link_libraries(
+  VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
 )
-target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
-target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
-target_link_libraries(VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger)
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index c0990995f4..f3feae9a7c 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -31,18 +31,6 @@ project(
             C CXX CUDA
 )
 
-# ##################################################################################################
-# * User Options  --------------------------------------------------------------
-
-option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulting to local files"
-       OFF
-)
-option(USE_CUDA_MATH_WHEELS "Use the CUDA math wheels instead of the system libraries" OFF)
-
-message(
-  "CUVS_PY: Searching for existing cuVS C/C++ installations before defaulting to local files: ${FIND_CUVS_CPP}"
-)
-
 # ##################################################################################################
 # * Process User Options  ------------------------------------------------------
 
@@ -54,56 +42,14 @@ include(rapids-find)
 
 rapids_cpm_init()
 
-# If the user requested it we attempt to find CUVS.
-if(FIND_CUVS_CPP)
-  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS c_api)
-  include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
-else()
-  set(cuvs_FOUND OFF)
-endif()
+# --- cuVS ---#
+find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS c_api)
 
-if(NOT cuvs_FOUND)
-  find_package(CUDAToolkit REQUIRED)
+# --- dlpack ---#
+include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
 
-  set(BUILD_TESTS OFF)
-  set(BUILD_C_LIBRARY ON)
-
-  # Statically link dependencies if building wheels
-  set(CUDA_STATIC_RUNTIME ON)
-  set(CUDA_STATIC_MATH_LIBRARIES ON)
-  set(CUVS_USE_RAFT_STATIC ON)
-
-  if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 12.0)
-    set(CUDA_STATIC_MATH_LIBRARIES OFF)
-  elseif(USE_CUDA_MATH_WHEELS)
-    message(FATAL_ERROR "Cannot use CUDA math wheels with CUDA < 12.0")
-  endif()
-
-  add_subdirectory(../../cpp cuvs-cpp EXCLUDE_FROM_ALL)
-
-  if(NOT CUDA_STATIC_MATH_LIBRARIES AND USE_CUDA_MATH_WHEELS)
-    set(rpaths
-        "$ORIGIN/../nvidia/cublas/lib"
-        "$ORIGIN/../nvidia/curand/lib"
-        "$ORIGIN/../nvidia/cusolver/lib"
-        "$ORIGIN/../nvidia/cusparse/lib"
-        "$ORIGIN/../nvidia/nvjitlink/lib"
-    )
-    set_property(
-      TARGET cuvs
-      PROPERTY INSTALL_RPATH ${rpaths}
-      APPEND
-    )
-    set_property(
-      TARGET cuvs_c
-      PROPERTY INSTALL_RPATH ${rpaths}
-      APPEND
-    )
-  endif()
-
-  set(cython_lib_dir cuvs)
-  install(TARGETS cuvs cuvs_c DESTINATION ${cython_lib_dir})
-endif()
+# ensure Cython targets can find dlpack headers (these do not come installed with with cuVS)
+target_include_directories(cuvs::cuvs INTERFACE "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
 
 # ##################################################################################################
 # * Build Cython artifacts -----------------------------------------------------
@@ -116,7 +62,3 @@ target_link_libraries(cuvs_rmm_logger PRIVATE rmm::rmm_logger_impl)
 add_subdirectory(cuvs/common)
 add_subdirectory(cuvs/distance)
 add_subdirectory(cuvs/neighbors)
-
-if(DEFINED cython_lib_dir)
-  rapids_cython_add_rpath_entries(TARGET cuvs PATHS "${cython_lib_dir}")
-endif()
diff --git a/python/cuvs/cuvs/__init__.py b/python/cuvs/cuvs/__init__.py
index 9f0481cb70..1a41f0d76d 100644
--- a/python/cuvs/cuvs/__init__.py
+++ b/python/cuvs/cuvs/__init__.py
@@ -13,4 +13,15 @@
 # limitations under the License.
 #
 
+# If libcuvs was installed as a wheel, we must request it to load the library
+# symbols. Otherwise, we assume that the library was installed in a system path that ld
+# can find.
+try:
+    import libcuvs
+except ModuleNotFoundError:
+    pass
+else:
+    libcuvs.load_library()
+    del libcuvs
+
 from cuvs._version import __git_commit__, __version__
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index 361f2fafcc..b0e1cb335a 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -20,7 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX common_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX common_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/distance/CMakeLists.txt b/python/cuvs/cuvs/distance/CMakeLists.txt
index 514b08c436..ded07395cf 100644
--- a/python/cuvs/cuvs/distance/CMakeLists.txt
+++ b/python/cuvs/cuvs/distance/CMakeLists.txt
@@ -20,7 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX distance_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX distance_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 031fd485ea..b9161eefc6 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -27,7 +27,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_refine_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_refine_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
index 61eda649cd..3c646f4986 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
@@ -20,8 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
-                   neighbors_brute_force_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_brute_force_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
index 1f40daab2c..6cf0956a2d 100644
--- a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
@@ -20,7 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_cagra_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_cagra_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
index a678852d99..43e0083635 100644
--- a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
@@ -20,7 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_prefilter_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_prefilter_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
index 8351916e6c..c33313c3c1 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
@@ -20,7 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_hnsw_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_hnsw_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
index f5663cdaa2..eadb8934c9 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
@@ -20,7 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_ivf_flat_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_ivf_flat_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
index a24320ded8..df61793b89 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
@@ -20,7 +20,7 @@ set(linked_libraries cuvs::cuvs cuvs::c_api)
 rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
-  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX neighbors_pq_
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_pq_
 )
 
 foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 155e454a8c..30658623ba 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -33,10 +33,6 @@ requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
     "numpy>=1.23,<3.0a0",
-    "nvidia-cublas",
-    "nvidia-curand",
-    "nvidia-cusolver",
-    "nvidia-cusparse",
     "pylibraft==25.2.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
@@ -59,12 +55,6 @@ test = [
 Homepage = "https://github.com/rapidsai/cuvs"
 Documentation = "https://docs.rapids.ai/api/cuvs/stable/"
 
-[tool.setuptools]
-license-files = ["LICENSE"]
-
-[tool.setuptools.dynamic]
-version = {file = "cuvs/VERSION"}
-
 [tool.isort]
 line_length = 79
 multi_line_output = 3
@@ -127,18 +117,23 @@ requires = [
     "cmake>=3.26.4,!=3.30.0",
     "cuda-python",
     "cython>=3.0.0",
+    "libcuvs==25.2.*,>=0.0.0a0",
+    "libraft==25.2.*,>=0.0.0a0",
+    "librmm==25.2.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
 dependencies-file = "../../dependencies.yaml"
-matrix-entry = "cuda_suffixed=true;use_cuda_wheels=true"
+matrix-entry = "cuda_suffixed=true"
 
 [tool.pydistcheck]
 select = [
-    # NOTE: size threshold is managed via CLI args in CI scripts
     "distro-too-large-compressed",
 ]
 
+# PyPI limit is 100 MiB, fail CI before we get too close to that
+max_allowed_size_compressed = '75M'
+
 [tool.pytest.ini_options]
 filterwarnings = [
     "error",
diff --git a/python/libcuvs/CMakeLists.txt b/python/libcuvs/CMakeLists.txt
new file mode 100644
index 0000000000..569652b711
--- /dev/null
+++ b/python/libcuvs/CMakeLists.txt
@@ -0,0 +1,69 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+include(../../rapids_config.cmake)
+
+include(rapids-cuda)
+rapids_cuda_init_architectures(libcuvs-python)
+
+project(
+  libcuvs-python
+  VERSION "${RAPIDS_VERSION}"
+  LANGUAGES CXX CUDA
+)
+
+# Check if cuVS is already available. If so, it is the user's responsibility to ensure that the
+# CMake package is also available at build time of the Python cuvs package.
+find_package(cuvs "${RAPIDS_VERSION}")
+
+if(cuvs_FOUND)
+  return()
+endif()
+
+unset(cuvs_FOUND)
+
+# --- CUDA --- #
+set(CUDA_STATIC_RUNTIME ON)
+set(CUDA_STATIC_MATH_LIBRARIES OFF)
+
+# --- RAFT ---#
+set(CUVS_USE_RAFT_STATIC OFF)
+
+# --- cuVS ---#
+set(BUILD_TESTS OFF)
+set(BUILD_C_LIBRARY ON)
+set(CUVS_COMPILE_DYNAMIC_ONLY ON)
+
+add_subdirectory(../../cpp cuvs-cpp)
+
+# assumes libcuvs.so is installed 2 levels deep, e.g. site-packages/libcuvs/lib64/libcuvs.so
+set(rpaths
+    "$ORIGIN/../../nvidia/cublas/lib"
+    "$ORIGIN/../../nvidia/curand/lib"
+    "$ORIGIN/../../nvidia/cusolver/lib"
+    "$ORIGIN/../../nvidia/cusparse/lib"
+    "$ORIGIN/../../nvidia/nvjitlink/lib"
+)
+set_property(
+  TARGET cuvs
+  PROPERTY INSTALL_RPATH ${rpaths}
+  APPEND
+)
+set_property(
+  TARGET cuvs_c
+  PROPERTY INSTALL_RPATH ${rpaths}
+  APPEND
+)
diff --git a/python/libcuvs/LICENSE b/python/libcuvs/LICENSE
new file mode 120000
index 0000000000..30cff7403d
--- /dev/null
+++ b/python/libcuvs/LICENSE
@@ -0,0 +1 @@
+../../LICENSE
\ No newline at end of file
diff --git a/python/libcuvs/README.md b/python/libcuvs/README.md
new file mode 120000
index 0000000000..fe84005413
--- /dev/null
+++ b/python/libcuvs/README.md
@@ -0,0 +1 @@
+../../README.md
\ No newline at end of file
diff --git a/python/libcuvs/libcuvs/VERSION b/python/libcuvs/libcuvs/VERSION
new file mode 120000
index 0000000000..d62dc733ef
--- /dev/null
+++ b/python/libcuvs/libcuvs/VERSION
@@ -0,0 +1 @@
+../../../VERSION
\ No newline at end of file
diff --git a/python/libcuvs/libcuvs/__init__.py b/python/libcuvs/libcuvs/__init__.py
new file mode 100644
index 0000000000..2d3a860155
--- /dev/null
+++ b/python/libcuvs/libcuvs/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from libcuvs._version import __git_commit__, __version__
+from libcuvs.load import load_library
diff --git a/python/libcuvs/libcuvs/_version.py b/python/libcuvs/libcuvs/_version.py
new file mode 100644
index 0000000000..530bf8bea6
--- /dev/null
+++ b/python/libcuvs/libcuvs/_version.py
@@ -0,0 +1,33 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib.resources
+
+__version__ = (
+    importlib.resources.files(__package__)
+    .joinpath("VERSION")
+    .read_text()
+    .strip()
+)
+try:
+    __git_commit__ = (
+        importlib.resources.files(__package__)
+        .joinpath("GIT_COMMIT")
+        .read_text()
+        .strip()
+    )
+except FileNotFoundError:
+    __git_commit__ = ""
+
+__all__ = ["__git_commit__", "__version__"]
diff --git a/python/libcuvs/libcuvs/load.py b/python/libcuvs/libcuvs/load.py
new file mode 100644
index 0000000000..a9c6a93250
--- /dev/null
+++ b/python/libcuvs/libcuvs/load.py
@@ -0,0 +1,100 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import ctypes
+import os
+
+# Loading with RTLD_LOCAL adds the library itself to the loader's
+# loaded library cache without loading any symbols into the global
+# namespace. This allows libraries that express a dependency on
+# this library to be loaded later and successfully satisfy this dependency
+# without polluting the global symbol table with symbols from
+# libcuvs that could conflict with symbols from other DSOs.
+PREFERRED_LOAD_FLAG = ctypes.RTLD_LOCAL
+
+
+def _load_system_installation(soname: str):
+    """Try to dlopen() the library indicated by ``soname``
+    Raises ``OSError`` if library cannot be loaded.
+    """
+    return ctypes.CDLL(soname, PREFERRED_LOAD_FLAG)
+
+
+def _load_wheel_installation(soname: str):
+    """Try to dlopen() the library indicated by ``soname``
+    Returns ``None`` if the library cannot be loaded.
+    """
+    if os.path.isfile(
+        lib := os.path.join(os.path.dirname(__file__), "lib64", soname)
+    ):
+        return ctypes.CDLL(lib, PREFERRED_LOAD_FLAG)
+    return None
+
+
+def load_library():
+    """Dynamically load libcuvs.so and its dependencies"""
+    try:
+        # libraft must be loaded before libcuvs because libcuvs
+        # references its symbols
+        import libraft
+
+        libraft.load_library()
+    except ModuleNotFoundError:
+        # 'libcuvs' has a runtime dependency on 'libraft'. However,
+        # that dependency might be satisfied by the 'libraft' conda package
+        # (which does not have any Python modules), instead of the
+        # 'libraft' wheel.
+        #
+        # In that situation, assume that 'libraft.so' is in a place where
+        # the loader can find it.
+        pass
+
+    prefer_system_installation = (
+        os.getenv("RAPIDS_LIBCUVS_PREFER_SYSTEM_LIBRARY", "false").lower()
+        != "false"
+    )
+
+    libs_to_return = []
+    for soname in ["libcuvs.so", "libcuvs_c.so"]:
+        libcuvs_lib = None
+        if prefer_system_installation:
+            # Prefer a system library if one is present to
+            # avoid clobbering symbols that other packages might expect,
+            # but if no other library is present use the one in the wheel.
+            try:
+                libcuvs_lib = _load_system_installation(soname)
+            except OSError:
+                libcuvs_lib = _load_wheel_installation(soname)
+        else:
+            # Prefer the libraries bundled in this package. If they aren't
+            # found (which might be the case in builds where the library was
+            # prebuilt before packaging the wheel), look for a system
+            # installation.
+            try:
+                libcuvs_lib = _load_wheel_installation(soname)
+                if libcuvs_lib is None:
+                    libcuvs_lib = _load_system_installation(soname)
+            except OSError:
+                # If none of the searches above succeed, just silently return
+                # None and rely on other mechanisms (like RPATHs on other DSOs)
+                # to help the loader find the library.
+                pass
+        if libcuvs_lib:
+            libs_to_return.append(libcuvs_lib)
+
+    # The caller almost never needs to do anything with this library, but no
+    # harm in offering the option since this object at least provides a handle
+    # to inspect where libcuvs was loaded from.
+    return libs_to_return
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
new file mode 100644
index 0000000000..28443b7820
--- /dev/null
+++ b/python/libcuvs/pyproject.toml
@@ -0,0 +1,108 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+[build-system]
+requires = [
+    "rapids-build-backend>=0.3.0,<0.4.0.dev0",
+    "scikit-build-core[pyproject]>=0.10.0",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+build-backend = "rapids_build_backend.build"
+
+[project]
+name = "libcuvs"
+dynamic = ["version"]
+description = "cuVS: Vector Search on the GPU (C++)"
+readme = { file = "README.md", content-type = "text/markdown" }
+authors = [
+    { name = "NVIDIA Corporation" },
+]
+license = { text = "Apache 2.0" }
+requires-python = ">=3.10"
+dependencies = [
+    "libraft==25.2.*,>=0.0.0a0",
+    "nvidia-cublas",
+    "nvidia-curand",
+    "nvidia-cusolver",
+    "nvidia-cusparse",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+classifiers = [
+    "Intended Audience :: Developers",
+]
+
+[project.urls]
+Homepage = "https://github.com/rapidsai/cuvs"
+Documentation = "https://docs.rapids.ai/api/cuvs/stable/"
+
+[project.entry-points."cmake.prefix"]
+libcuvs = "libcuvs"
+
+[tool.isort]
+line_length = 79
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+combine_as_imports = true
+order_by_type = true
+known_first_party = [
+    "libcuvs",
+]
+skip = [
+    "thirdparty",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".tox",
+    ".venv",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "__init__.py",
+]
+
+[tool.scikit-build]
+build-dir = "build/{wheel_tag}"
+cmake.build-type = "Release"
+cmake.version = "CMakeLists.txt"
+minimum-version = "build-system.requires"
+ninja.make-fallback = true
+sdist.reproducible = true
+wheel.install-dir = "libcuvs"
+wheel.packages = ["libcuvs"]
+wheel.py-api = "py3"
+
+[tool.scikit-build.metadata.version]
+provider = "scikit_build_core.metadata.regex"
+input = "libcuvs/VERSION"
+regex = "(?P<value>.*)"
+
+[tool.rapids-build-backend]
+build-backend = "scikit_build_core.build"
+requires = [
+    "cmake>=3.26.4,!=3.30.0",
+    "libraft==25.2.*,>=0.0.0a0",
+    "librmm==25.2.*,>=0.0.0a0",
+    "ninja",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+dependencies-file = "../../dependencies.yaml"
+matrix-entry = "cuda_suffixed=true;use_cuda_wheels=true"
+
+[tool.pydistcheck]
+select = [
+    "distro-too-large-compressed",
+]
+
+# detect when package size grows significantly
+max_allowed_size_compressed = '1.1G'

From 80370a1220af0928d69ca96a66e6b6ff1220bab7 Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Fri, 24 Jan 2025 02:35:08 +0900
Subject: [PATCH 265/623] Improve the performance of CAGRA new vector addition
 with the default params (#569)

This PR updates the default chunk size of the CAGRA graph extension and also adds a knob to control the batch size of the CAGRA searches run inside for better throughput.

The default chunk size was set to 1 in the current implementation because there is a potential problem with low recall when the chunk size is large, because no edges are made within nodes in the same chunk. However, as I have investigated, the low recall problem rarely occurs with large chunk sizes.

# Search performance

The performance was measured after applying a bugfix https://github.com/rapidsai/cuvs/pull/565

## degree = 32


![extend-ir0 9-degree32](https://github.com/user-attachments/assets/a5bb2fb6-8c12-49ad-b96a-1b384d79a96b)


(I don't know the reason the performance is unstable in NYTimes.)

## degree = 64
![extend-ir0 9-degree64](https://github.com/user-attachments/assets/8e926e1c-d772-4682-9419-9cc027f09d3f)

So I increase the default chunk size to the size of the new dataset vectors for better throughput in this PR. I also make public a knob to control the search batch size in the `extend' function to control the balance between throughput and memory consumption.

Authors:
  - tsuki (https://github.com/enp1s0)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/569
---
 cpp/src/neighbors/detail/cagra/add_nodes.cuh | 34 ++++++++++++++------
 1 file changed, 24 insertions(+), 10 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
index 358b7643e4..4539289922 100644
--- a/cpp/src/neighbors/detail/cagra/add_nodes.cuh
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -37,7 +37,8 @@ void add_node_core(
   const cuvs::neighbors::cagra::index<T, IdxT>& idx,
   raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::layout_stride, Accessor>
     additional_dataset_view,
-  raft::host_matrix_view<IdxT, std::int64_t> updated_graph)
+  raft::host_matrix_view<IdxT, std::int64_t> updated_graph,
+  const cuvs::neighbors::cagra::extend_params& extend_params)
 {
   using DistanceT                 = float;
   const std::size_t degree        = idx.graph_degree();
@@ -68,7 +69,19 @@ void add_node_core(
              new_size,
              raft::resource::get_cuda_stream(handle));
 
-  const std::size_t max_chunk_size = 1024;
+  std::size_t data_size_per_vector =
+    sizeof(IdxT) * base_degree + sizeof(DistanceT) * base_degree + sizeof(T) * dim;
+  cudaPointerAttributes attr;
+  RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, additional_dataset_view.data_handle()));
+  if (attr.devicePointer == nullptr) {
+    // for batch_load_iterator
+    data_size_per_vector += sizeof(T) * dim;
+  }
+
+  const std::size_t max_search_batch_size =
+    std::min(std::max(1lu, raft::resource::get_workspace_free_bytes(handle) / data_size_per_vector),
+             num_add);
+  RAFT_EXPECTS(max_search_batch_size > 0, "No enough working memory space is left.");
 
   cuvs::neighbors::cagra::search_params params;
   params.itopk_size = std::max(base_degree * 2lu, 256lu);
@@ -77,24 +90,24 @@ void add_node_core(
   auto mr = raft::resource::get_workspace_resource(handle);
 
   auto neighbor_indices = raft::make_device_mdarray<IdxT, std::int64_t>(
-    handle, mr, raft::make_extents<std::int64_t>(max_chunk_size, base_degree));
+    handle, mr, raft::make_extents<std::int64_t>(max_search_batch_size, base_degree));
 
   auto neighbor_distances = raft::make_device_mdarray<DistanceT, std::int64_t>(
-    handle, mr, raft::make_extents<std::int64_t>(max_chunk_size, base_degree));
+    handle, mr, raft::make_extents<std::int64_t>(max_search_batch_size, base_degree));
 
   auto queries = raft::make_device_mdarray<T, std::int64_t>(
-    handle, mr, raft::make_extents<std::int64_t>(max_chunk_size, dim));
+    handle, mr, raft::make_extents<std::int64_t>(max_search_batch_size, dim));
 
   auto host_neighbor_indices =
-    raft::make_host_matrix<IdxT, std::int64_t>(max_chunk_size, base_degree);
+    raft::make_host_matrix<IdxT, std::int64_t>(max_search_batch_size, base_degree);
 
   cuvs::spatial::knn::detail::utils::batch_load_iterator<T> additional_dataset_batch(
     additional_dataset_view.data_handle(),
     num_add,
     additional_dataset_view.stride(0),
-    max_chunk_size,
+    max_search_batch_size,
     raft::resource::get_cuda_stream(handle),
-    raft::resource::get_workspace_resource(handle));
+    mr);
   for (const auto& batch : additional_dataset_batch) {
     // Step 1: Obtain K (=base_degree) nearest neighbors of the new vectors by CAGRA search
     // Create queries
@@ -254,7 +267,8 @@ void add_graph_nodes(
   const std::size_t degree               = index.graph_degree();
   const std::size_t dim                  = index.dim();
   const std::size_t stride               = input_updated_dataset_view.stride(0);
-  const std::size_t max_chunk_size_      = params.max_chunk_size == 0 ? 1 : params.max_chunk_size;
+  const std::size_t max_chunk_size_ =
+    params.max_chunk_size == 0 ? new_dataset_size : params.max_chunk_size;
 
   raft::copy(updated_graph_view.data_handle(),
              index.graph().data_handle(),
@@ -298,7 +312,7 @@ void add_graph_nodes(
       stride);
 
     neighbors::cagra::add_node_core<T, IdxT>(
-      handle, internal_index, additional_dataset_view, updated_graph);
+      handle, internal_index, additional_dataset_view, updated_graph, params);
     raft::resource::sync_stream(handle);
   }
 }

From 51873ee3392c889efbbe24c91ce1b5988b39a450 Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Thu, 23 Jan 2025 15:07:14 -0500
Subject: [PATCH 266/623] DOC v25.04 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  6 ++--
 .devcontainer/cuda11.8-pip/devcontainer.json  |  8 +++---
 .../cuda12.5-conda/devcontainer.json          |  6 ++--
 .devcontainer/cuda12.5-pip/devcontainer.json  |  8 +++---
 .github/workflows/build.yaml                  | 18 ++++++------
 .github/workflows/pr.yaml                     | 28 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 README.md                                     |  2 +-
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 +--
 .../all_cuda-118_arch-x86_64.yaml             |  4 +--
 .../all_cuda-125_arch-aarch64.yaml            |  4 +--
 .../all_cuda-125_arch-x86_64.yaml             |  4 +--
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  8 +++---
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  8 +++---
 .../bench_ann_cuda-125_arch-aarch64.yaml      |  8 +++---
 .../bench_ann_cuda-125_arch-x86_64.yaml       |  8 +++---
 dependencies.yaml                             | 26 ++++++++---------
 docs/source/developer_guide.md                |  6 ++--
 examples/cmake/thirdparty/fetch_rapids.cmake  |  2 +-
 python/cuvs/pyproject.toml                    |  8 +++---
 python/cuvs_bench/pyproject.toml              |  2 +-
 python/libcuvs/pyproject.toml                 |  6 ++--
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 26 files changed, 95 insertions(+), 95 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index f03ec7b19f..d93877bfa8 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda11.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index a59c499d3c..531debcf8f 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda11.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.2": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.4": {
       "version": "11.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.5-conda/devcontainer.json b/.devcontainer/cuda12.5-conda/devcontainer.json
index 39852cec1c..1453630092 100644
--- a/.devcontainer/cuda12.5-conda/devcontainer.json
+++ b/.devcontainer/cuda12.5-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.02-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.04-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.5-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.5-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.5-pip/devcontainer.json b/.devcontainer/cuda12.5-pip/devcontainer.json
index d849666561..572fb431ab 100644
--- a/.devcontainer/cuda12.5-pip/devcontainer.json
+++ b/.devcontainer/cuda12.5-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.5-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.5-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.2": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.4": {
       "version": "12.5",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.2": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 59b8e00de1..7cbc6802f3 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -94,7 +94,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -105,7 +105,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -115,7 +115,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 843439f26d..a4d10ef467 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.04
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -45,7 +45,7 @@ jobs:
           repo: cuvs
   changed-files:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.04
     with:
       files_yaml: |
         test_cpp:
@@ -78,27 +78,27 @@ jobs:
           - '!thirdparty/LICENSES/**'
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.04
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -106,20 +106,20 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -129,7 +129,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -139,7 +139,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -148,21 +148,21 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.04
     with:
       arch: '["amd64"]'
       cuda: '["12.5"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index e3bf5d16f2..ccb3c39a98 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 01dd2436be..9764c62c15 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.04
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/README.md b/README.md
index dac71c881f..7ee0875dd9 100755
--- a/README.md
+++ b/README.md
@@ -109,7 +109,7 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.02
+conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.04
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
diff --git a/VERSION b/VERSION
index 72eefaf7c7..b922658ff3 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-25.02.00
+25.04.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 123acb4219..ac2533d6cd 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -34,7 +34,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==25.2.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -43,7 +43,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pre-commit
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index c6a65e6848..53a773eea0 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -34,7 +34,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==25.2.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -43,7 +43,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pre-commit
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-125_arch-aarch64.yaml
index b71f5ed438..15a170012b 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-125_arch-aarch64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.2.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-125_arch-x86_64.yaml
index 16cd595d33..c35920bdbd 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-125_arch-x86_64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.2.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 2e2ad84468..42d845d60b 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -19,7 +19,7 @@ dependencies:
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
-- cuvs==25.2.*,>=0.0.0a0
+- cuvs==25.4.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -35,8 +35,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.2.*,>=0.0.0a0
-- librmm==25.2.*,>=0.0.0a0
+- libcuvs==25.4.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pandas
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 90243415c1..d6553cacec 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -19,7 +19,7 @@ dependencies:
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
-- cuvs==25.2.*,>=0.0.0a0
+- cuvs==25.4.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -35,8 +35,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.2.*,>=0.0.0a0
-- librmm==25.2.*,>=0.0.0a0
+- libcuvs==25.4.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pandas
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
index 34e01aeea8..e43e5b656a 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
@@ -20,7 +20,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.5
 - cupy>=12.0.0
-- cuvs==25.2.*,>=0.0.0a0
+- cuvs==25.4.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,15 +32,15 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.2.*,>=0.0.0a0
-- librmm==25.2.*,>=0.0.0a0
+- libcuvs==25.4.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
index dcfb54a222..cbdc364441 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
@@ -20,7 +20,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.5
 - cupy>=12.0.0
-- cuvs==25.2.*,>=0.0.0a0
+- cuvs==25.4.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,15 +32,15 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.2.*,>=0.0.0a0
-- librmm==25.2.*,>=0.0.0a0
+- libcuvs==25.4.*,>=0.0.0a0
+- librmm==25.4.*,>=0.0.0a0
 - matplotlib
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.2.*,>=0.0.0a0
+- pylibraft==25.4.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - setuptools
diff --git a/dependencies.yaml b/dependencies.yaml
index 478b2acc27..8ba4b743fd 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -534,7 +534,7 @@ dependencies:
       - output_types: [conda, pyproject, requirements]
         packages:
           - click
-          - cuvs==25.2.*,>=0.0.0a0
+          - cuvs==25.4.*,>=0.0.0a0
           - matplotlib
           - pandas
           - pyyaml
@@ -542,7 +542,7 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - &libcuvs_unsuffixed libcuvs==25.2.*,>=0.0.0a0
+          - &libcuvs_unsuffixed libcuvs==25.4.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -555,18 +555,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu12==25.2.*,>=0.0.0a0
+              - libcuvs-cu12==25.4.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu11==25.2.*,>=0.0.0a0
+              - libcuvs-cu11==25.4.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
   depends_on_libraft:
     common:
       - output_types: conda
         packages:
-          - &libraft_unsuffixed libraft==25.2.*,>=0.0.0a0
+          - &libraft_unsuffixed libraft==25.4.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -579,18 +579,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu12==25.2.*,>=0.0.0a0
+              - libraft-cu12==25.4.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu11==25.2.*,>=0.0.0a0
+              - libraft-cu11==25.4.*,>=0.0.0a0
           - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
       - output_types: conda
         packages:
-          - &librmm_unsuffixed librmm==25.2.*,>=0.0.0a0
+          - &librmm_unsuffixed librmm==25.4.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -603,18 +603,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu12==25.2.*,>=0.0.0a0
+              - librmm-cu12==25.4.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu11==25.2.*,>=0.0.0a0
+              - librmm-cu11==25.4.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
       - output_types: conda
         packages:
-          - &pylibraft_unsuffixed pylibraft==25.2.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==25.4.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -627,10 +627,10 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu12==25.2.*,>=0.0.0a0
+              - pylibraft-cu12==25.4.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu11==25.2.*,>=0.0.0a0
+              - pylibraft-cu11==25.4.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 4fdd6405e9..51ac1aa601 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.02/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.04/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.02/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.04/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
@@ -230,7 +230,7 @@ Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY`
 ## Logging
 
 ### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-25.02/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
+Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-25.04/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
 
 ### Usage
 ```cpp
diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
index 3c5510b8b6..07fe407653 100644
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and cuVS versions
-set(RAPIDS_VERSION "25.02")
+set(RAPIDS_VERSION "25.04")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 30658623ba..91a8cde1aa 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -33,7 +33,7 @@ requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
     "numpy>=1.23,<3.0a0",
-    "pylibraft==25.2.*,>=0.0.0a0",
+    "pylibraft==25.4.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -117,9 +117,9 @@ requires = [
     "cmake>=3.26.4,!=3.30.0",
     "cuda-python",
     "cython>=3.0.0",
-    "libcuvs==25.2.*,>=0.0.0a0",
-    "libraft==25.2.*,>=0.0.0a0",
-    "librmm==25.2.*,>=0.0.0a0",
+    "libcuvs==25.4.*,>=0.0.0a0",
+    "libraft==25.4.*,>=0.0.0a0",
+    "librmm==25.4.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 75e5406d4b..920be5b9f7 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -19,7 +19,7 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "click",
-    "cuvs==25.2.*,>=0.0.0a0",
+    "cuvs==25.4.*,>=0.0.0a0",
     "matplotlib",
     "pandas",
     "pyyaml",
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 28443b7820..94d3e81d89 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -30,7 +30,7 @@ authors = [
 license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
-    "libraft==25.2.*,>=0.0.0a0",
+    "libraft==25.4.*,>=0.0.0a0",
     "nvidia-cublas",
     "nvidia-curand",
     "nvidia-cusolver",
@@ -92,8 +92,8 @@ regex = "(?P<value>.*)"
 build-backend = "scikit_build_core.build"
 requires = [
     "cmake>=3.26.4,!=3.30.0",
-    "libraft==25.2.*,>=0.0.0a0",
-    "librmm==25.2.*,>=0.0.0a0",
+    "libraft==25.4.*,>=0.0.0a0",
+    "librmm==25.4.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 dependencies-file = "../../dependencies.yaml"
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index ddb8b32cd8..6aa9a1e10b 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "25.2.0"
+version = "25.4.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index 1095b1fea3..187a53c09d 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.2.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.4.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From 2f18645dde2d4d909bb0a7538953725d0f280930 Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Fri, 24 Jan 2025 09:25:59 +0900
Subject: [PATCH 267/623] Add CAGRA InnerProduct test and fix a bug (#595)

There is no CAGRA InnerProduct test in cuVS while there was in RAFT. This PR adds the test.

Also, this PR
- fixes an error that occurs when building a CAGRA graph with NN Descent + InnerProduct (`cpp/src/neighbors/nn_descent_*.cu`)
  - Could not pass the `idx.metric() == params.metric` validation in the `nn_descent::detail::build` function.
- updates `RAFT_EXPECT` in the `nn_descent::detail::build` function for better error understanding
- fixes the NN Descent API sample codes

Authors:
  - tsuki (https://github.com/enp1s0)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/595
---
 cpp/include/cuvs/neighbors/nn_descent.hpp | 18 +++---
 cpp/src/neighbors/detail/nn_descent.cuh   |  5 +-
 cpp/src/neighbors/nn_descent_float.cu     |  6 +-
 cpp/src/neighbors/nn_descent_half.cu      |  6 +-
 cpp/src/neighbors/nn_descent_int8.cu      |  6 +-
 cpp/src/neighbors/nn_descent_uint8.cu     |  6 +-
 cpp/test/neighbors/ann_cagra.cuh          | 72 +++++++++++------------
 7 files changed, 65 insertions(+), 54 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index 9cd8192b59..94300e2737 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -248,11 +248,11 @@ auto build(raft::resources const& res,
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors::experimental;
+ *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   nn_descent::index_params index_params;
  *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
- *   auto index = cagra::build(res, index_params, dataset);
+ *   auto index = nn_descent::build(res, index_params, dataset);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
@@ -316,11 +316,11 @@ auto build(raft::resources const& res,
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors::experimental;
+ *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   nn_descent::index_params index_params;
  *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
- *   auto index = cagra::build(res, index_params, dataset);
+ *   auto index = nn_descent::build(res, index_params, dataset);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
@@ -384,11 +384,11 @@ auto build(raft::resources const& res,
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors::experimental;
+ *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   nn_descent::index_params index_params;
  *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
- *   auto index = cagra::build(res, index_params, dataset);
+ *   auto index = nn_descent::build(res, index_params, dataset);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
@@ -452,11 +452,11 @@ auto build(raft::resources const& res,
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors::experimental;
+ *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   nn_descent::index_params index_params;
  *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
- *   auto index = cagra::build(res, index_params, dataset);
+ *   auto index = nn_descent::build(res, index_params, dataset);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
@@ -492,4 +492,4 @@ bool has_enough_device_memory(raft::resources const& res,
                               raft::matrix_extent<int64_t> dataset,
                               size_t idx_size = 4);
 
-}  // namespace cuvs::neighbors::nn_descent
\ No newline at end of file
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index c62a525407..4c577a4d2e 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -1442,8 +1442,11 @@ void build(raft::resources const& res,
   auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
                          params.metric == cuvs::distance::DistanceType::CosineExpanded ||
                          params.metric == cuvs::distance::DistanceType::InnerProduct;
-  RAFT_EXPECTS(allowed_metrics && idx.metric() == params.metric,
+  RAFT_EXPECTS(allowed_metrics,
                "The metric for NN Descent should be L2Expanded, CosineExpanded or InnerProduct");
+  RAFT_EXPECTS(
+    idx.metric() == params.metric,
+    "The metrics set in nn_descent::index_params and nn_descent::index are inconsistent");
   size_t intermediate_degree = params.intermediate_graph_degree;
   size_t graph_degree        = params.graph_degree;
 
diff --git a/cpp/src/neighbors/nn_descent_float.cu b/cpp/src/neighbors/nn_descent_float.cu
index fa85db1279..9af1c5c697 100644
--- a/cpp/src/neighbors/nn_descent_float.cu
+++ b/cpp/src/neighbors/nn_descent_float.cu
@@ -31,7 +31,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     };                                                                                        \
@@ -47,7 +48,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
diff --git a/cpp/src/neighbors/nn_descent_half.cu b/cpp/src/neighbors/nn_descent_half.cu
index 2ee45d4352..0b6ba74b18 100644
--- a/cpp/src/neighbors/nn_descent_half.cu
+++ b/cpp/src/neighbors/nn_descent_half.cu
@@ -31,7 +31,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
@@ -48,7 +49,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
diff --git a/cpp/src/neighbors/nn_descent_int8.cu b/cpp/src/neighbors/nn_descent_int8.cu
index e150f511ba..a43e68d4ed 100644
--- a/cpp/src/neighbors/nn_descent_int8.cu
+++ b/cpp/src/neighbors/nn_descent_int8.cu
@@ -31,7 +31,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
@@ -48,7 +49,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
diff --git a/cpp/src/neighbors/nn_descent_uint8.cu b/cpp/src/neighbors/nn_descent_uint8.cu
index d8657777b9..6b565c8342 100644
--- a/cpp/src/neighbors/nn_descent_uint8.cu
+++ b/cpp/src/neighbors/nn_descent_uint8.cu
@@ -31,7 +31,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
@@ -48,7 +49,8 @@ namespace cuvs::neighbors::nn_descent {
     } else {                                                                                  \
       std::optional<raft::device_matrix_view<float, int64_t, raft::row_major>> distances =    \
         std::nullopt;                                                                         \
-      cuvs::neighbors::nn_descent::index<IdxT> idx{handle, graph.value(), distances};         \
+      cuvs::neighbors::nn_descent::index<IdxT> idx{                                           \
+        handle, graph.value(), distances, params.metric};                                     \
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index c1cd3ca090..bbafae6d18 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -887,7 +887,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0},
     {256},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {true},
     {0.995});
@@ -903,7 +903,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0},
     {64},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {true},
     {0.995});
@@ -919,7 +919,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0, 8, 16, 32},  // team_size
     {64},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {false},
     {0.995});
@@ -936,27 +936,27 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0},  // team_size
     {32, 64, 128, 256, 512, 768},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {true},
     {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
-  inputs2 =
-    raft::util::itertools::product<AnnCagraInputs>({100},
-                                                   {10000, 20000},
-                                                   {32},
-                                                   {10},
-                                                   {graph_build_algo::AUTO},
-                                                   {search_algo::AUTO},
-                                                   {10},
-                                                   {0},  // team_size
-                                                   {64},
-                                                   {1},
-                                                   {cuvs::distance::DistanceType::L2Expanded},
-                                                   {false, true},
-                                                   {false},
-                                                   {0.985});
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {10000, 20000},
+    {32},
+    {10},
+    {graph_build_algo::AUTO},
+    {search_algo::AUTO},
+    {10},
+    {0},  // team_size
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false, true},
+    {false},
+    {0.985});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // a few PQ configurations
@@ -988,22 +988,22 @@ inline std::vector<AnnCagraInputs> generate_inputs()
   }
 
   // refinement options
-  inputs2 =
-    raft::util::itertools::product<AnnCagraInputs>({100},
-                                                   {5000},
-                                                   {32, 64},
-                                                   {16},
-                                                   {graph_build_algo::IVF_PQ},
-                                                   {search_algo::AUTO},
-                                                   {10},
-                                                   {0},  // team_size
-                                                   {64},
-                                                   {1},
-                                                   {cuvs::distance::DistanceType::L2Expanded},
-                                                   {false, true},
-                                                   {false},
-                                                   {0.99},
-                                                   {1.0f, 2.0f, 3.0f});
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {5000},
+    {32, 64},
+    {16},
+    {graph_build_algo::IVF_PQ},
+    {search_algo::AUTO},
+    {10},
+    {0},  // team_size
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false, true},
+    {false},
+    {0.99},
+    {1.0f, 2.0f, 3.0f});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
@@ -1017,7 +1017,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0},  // team_size
     {64},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {false},
     {0.995});

From b4b3d49708b485dde96673756b9d27aa4ce6dd95 Mon Sep 17 00:00:00 2001
From: Yinzuo Jiang <jiangyinzuo@foxmail.com>
Date: Fri, 24 Jan 2025 08:26:17 +0800
Subject: [PATCH 268/623] fix create_pointset for throughput mode (#589)

The columns of raw.csv file are
`algo_name,index_name,recall,throughput,latency,...`

`col 4` is latency and `col 3` is throughput.

Authors:
  - Yinzuo Jiang (https://github.com/jiangyinzuo)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/589
---
 python/cuvs_bench/cuvs_bench/run/data_export.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/cuvs_bench/cuvs_bench/run/data_export.py b/python/cuvs_bench/cuvs_bench/run/data_export.py
index 1d0ac40a01..3b03995d47 100644
--- a/python/cuvs_bench/cuvs_bench/run/data_export.py
+++ b/python/cuvs_bench/cuvs_bench/run/data_export.py
@@ -256,11 +256,13 @@ def create_pointset(data, xn, yn):
         Filtered list of data points sorted by x and y metrics.
     """
     xm, ym = metrics[xn], metrics[yn]
+    y_col = 4 if yn == "latency" else 3
+
     rev_x, rev_y = (-1 if xm["worst"] < 0 else 1), (
         -1 if ym["worst"] < 0 else 1
     )
     # Sort data based on x and y metrics
-    data.sort(key=lambda t: (rev_y * t[4], rev_x * t[2]))
+    data.sort(key=lambda t: (rev_y * t[y_col], rev_x * t[2]))
     lines = []
     last_x = xm["worst"]
     comparator = (

From dc00f801de265e91f7c5454dc9ae6e10b915d64c Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Fri, 24 Jan 2025 09:27:46 +0900
Subject: [PATCH 269/623] [BUG] Fix CAGRA graph optimization bug (#565)

The merging process of the pruned and revedge graphs is inappropriate, resulting in neighbor index duplication. This PR fixes the issue and adds the validation of graph indices at the end of the `graph::optimze` function.

The simplest solution to fix this issue is 1151511b5e0c11675986dfe314fa525777d7849b, but it ignores the memory space optimization by https://github.com/rapidsai/raft/commit/12480cf795196cf487dda25aa77599ac5683627f. The code changes in this PR apply the memory space optimization to the simplest solution to remove the memory space for the pruned graph (`pruned_graph`).

This PR also fixes a problem that throwing an exception from an OMP loop does not work as expected.

This is the search performance comparison between the graph optimization that has a bug (branch-25.02) and fixed (fix-cagra-graph-optimization-bug). Each point represents the search performance for itopk=32, 64, ... 512. By this PR, the recall becomes slightly higher when searching with a small itopk size. Because the number of duplicated nodes by this bug is not so large (typically less than 100 in a 1M dataset), the performances are almost the same when searching with a large itopk size and traversing the graph sufficiently.
![fix-cagra-graph-optimization-bug](https://github.com/user-attachments/assets/4cacb300-ac6f-4cb5-aefd-77878c4b7f04)

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/565
---
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 157 ++++++++++++++----
 .../ann_cagra/bug_extreme_inputs_oob.cu       |  10 +-
 2 files changed, 129 insertions(+), 38 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index daeac82b95..4adaa7a936 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -35,7 +35,6 @@
 #include <omp.h>
 #include <sys/time.h>
 
-#include <cassert>
 #include <climits>
 #include <iostream>
 #include <memory>
@@ -994,14 +993,18 @@ void mst_optimization(raft::resources const& res,
         total_incoming_edges += incoming_num_edges_ptr[i];
       }
 
+      bool check_num_mst_edges = true;
 #pragma omp parallel for
       for (uint64_t i = 0; i < graph_size; i++) {
         if (outgoing_num_edges_ptr[i] < outgoing_max_edges_ptr[i]) continue;
         if (outgoing_num_edges_ptr[i] + incoming_num_edges_ptr[i] == mst_graph_degree) continue;
-        assert(outgoing_num_edges_ptr[i] + incoming_num_edges_ptr[i] < mst_graph_degree);
+        if (outgoing_num_edges_ptr[i] + incoming_num_edges_ptr[i] > mst_graph_degree) {
+          check_num_mst_edges = false;
+        }
         outgoing_max_edges_ptr[i] += 1;
         incoming_max_edges_ptr[i] = mst_graph_degree - outgoing_max_edges_ptr[i];
       }
+      RAFT_EXPECTS(check_num_mst_edges, "Some nodes have too many MST graph edges.");
     }
 
     // 6. Show stats
@@ -1018,8 +1021,9 @@ void mst_optimization(raft::resources const& res,
       }
       RAFT_LOG_DEBUG("%s", msg.c_str());
     }
-    assert(num_clusters > 0);
-    assert(total_outgoing_edges == total_incoming_edges);
+    RAFT_EXPECTS(num_clusters > 0, "No clusters could not be created in MST optimization.");
+    RAFT_EXPECTS(total_outgoing_edges == total_incoming_edges,
+                 "The numbers of incoming and outcoming edges are mismatch.");
     if (num_clusters == 1) { break; }
     num_clusters_pre = num_clusters;
   }
@@ -1084,6 +1088,7 @@ void optimize(
   auto output_graph_ptr              = new_graph.data_handle();
 
   // MST optimization
+  auto mst_graph               = raft::make_host_matrix<IdxT, int64_t, raft::row_major>(0, 0);
   auto mst_graph_num_edges     = raft::make_host_vector<uint32_t, int64_t>(graph_size);
   auto mst_graph_num_edges_ptr = mst_graph_num_edges.data_handle();
 #pragma omp parallel for
@@ -1091,9 +1096,11 @@ void optimize(
     mst_graph_num_edges_ptr[i] = 0;
   }
   if (guarantee_connectivity) {
+    mst_graph =
+      raft::make_host_matrix<IdxT, int64_t, raft::row_major>(graph_size, output_graph_degree);
     RAFT_LOG_INFO("MST optimization is used to guarantee graph connectivity.");
     constexpr bool use_gpu = true;
-    mst_optimization(res, knn_graph, new_graph, mst_graph_num_edges.view(), use_gpu);
+    mst_optimization(res, knn_graph, mst_graph.view(), mst_graph_num_edges.view(), use_gpu);
 
     for (uint64_t i = 0; i < graph_size; i++) {
       if (i < 8 || i >= graph_size - 8) {
@@ -1102,7 +1109,6 @@ void optimize(
     }
   }
 
-  auto pruned_graph = raft::make_host_matrix<uint32_t, int64_t>(graph_size, output_graph_degree);
   {
     //
     // Prune kNN graph
@@ -1191,13 +1197,14 @@ void optimize(
     const auto num_full = host_stats.data_handle()[1];
 
     // Create pruned kNN graph
+    bool invalid_neighbor_list = false;
 #pragma omp parallel for
     for (uint64_t i = 0; i < graph_size; i++) {
       // Find the `output_graph_degree` smallest detourable count nodes by checking the detourable
       // count of the neighbors while increasing the target detourable count from zero.
       uint64_t pk         = 0;
       uint32_t num_detour = 0;
-      while (pk < output_graph_degree) {
+      for (uint32_t l = 0; l < input_graph_degree && pk < output_graph_degree; l++) {
         uint32_t next_num_detour = std::numeric_limits<uint32_t>::max();
         for (uint64_t k = 0; k < input_graph_degree; k++) {
           const auto num_detour_k = detour_count.data_handle()[k + (input_graph_degree * i)];
@@ -1208,23 +1215,45 @@ void optimize(
 
           // Store the neighbor index if its detourable count is equal to `num_detour`.
           if (num_detour_k != num_detour) { continue; }
-          output_graph_ptr[pk + (output_graph_degree * i)] =
-            input_graph_ptr[k + (input_graph_degree * i)];
-          pk += 1;
+
+          // Check duplication and append
+          const auto candidate_node = input_graph_ptr[k + (input_graph_degree * i)];
+          bool dup                  = false;
+          for (uint32_t dk = 0; dk < pk; dk++) {
+            if (candidate_node == output_graph_ptr[i * output_graph_degree + dk]) {
+              dup = true;
+              break;
+            }
+          }
+          if (!dup && candidate_node < graph_size) {
+            output_graph_ptr[i * output_graph_degree + pk] = candidate_node;
+            pk += 1;
+          }
           if (pk >= output_graph_degree) break;
         }
         if (pk >= output_graph_degree) break;
 
-        assert(next_num_detour != std::numeric_limits<uint32_t>::max());
+        if (next_num_detour == std::numeric_limits<uint32_t>::max()) {
+          // There are no valid edges enough in the initial kNN graph. Break the loop here and catch
+          // the error at the next validation (pk != output_graph_degree).
+          break;
+        }
         num_detour = next_num_detour;
       }
-      RAFT_EXPECTS(
-        pk == output_graph_degree,
-        "Couldn't find the output_graph_degree (%lu) smallest detourable count nodes for "
-        "node %lu in the rank-based node reranking process",
-        output_graph_degree,
-        i);
+      if (pk != output_graph_degree) {
+        RAFT_LOG_DEBUG(
+          "Couldn't find the output_graph_degree (%lu) smallest detourable count nodes for "
+          "node %lu in the rank-based node reranking process",
+          output_graph_degree,
+          i);
+        invalid_neighbor_list = true;
+      }
     }
+    RAFT_EXPECTS(
+      !invalid_neighbor_list,
+      "Could not generate an intermediate CAGRA graph because the initial kNN graph contains too "
+      "many invalid or duplicated neighbor nodes. This error can occur, for example, if too many "
+      "overflows occur during the norm computation between the dataset vectors.");
 
     const double time_prune_end = cur_time();
     RAFT_LOG_DEBUG(
@@ -1311,37 +1340,51 @@ void optimize(
     //
     const double time_replace_start = cur_time();
 
+    bool check_num_protected_edges = true;
 #pragma omp parallel for
     for (uint64_t i = 0; i < graph_size; i++) {
-      auto my_fwd_graph = pruned_graph.data_handle() + (output_graph_degree * i);
       auto my_rev_graph = rev_graph.data_handle() + (output_graph_degree * i);
       auto my_out_graph = output_graph_ptr + (output_graph_degree * i);
-      uint32_t kf       = 0;
-      uint32_t k        = mst_graph_num_edges_ptr[i];
 
-      const auto num_protected_edges = std::max<uint64_t>(k, output_graph_degree / 2);
-      assert(num_protected_edges <= output_graph_degree);
-      if (num_protected_edges == output_graph_degree) continue;
+      // If guarantee_connectivity == true, use a temporal list to merge the neighbor lists of the
+      // graphs.
+      std::vector<IdxT> temp_output_neighbor_list;
+      if (guarantee_connectivity) {
+        temp_output_neighbor_list.resize(output_graph_degree);
+        my_out_graph                   = temp_output_neighbor_list.data();
+        const auto mst_graph_num_edges = mst_graph_num_edges_ptr[i];
+
+        // Set MST graph edges
+        for (uint32_t j = 0; j < mst_graph_num_edges; j++) {
+          my_out_graph[j] = mst_graph(i, j);
+        }
 
-      // Append edges from the pruned graph to output graph
-      while (k < output_graph_degree && kf < output_graph_degree) {
-        if (my_fwd_graph[kf] < graph_size) {
-          auto flag_match = false;
-          for (uint32_t kk = 0; kk < k; kk++) {
-            if (my_out_graph[kk] == my_fwd_graph[kf]) {
-              flag_match = true;
+        // Set pruned graph edges
+        for (uint32_t pruned_j = 0, output_j = mst_graph_num_edges;
+             (pruned_j < output_graph_degree) && (output_j < output_graph_degree);
+             pruned_j++) {
+          const auto v = output_graph_ptr[output_graph_degree * i + pruned_j];
+
+          // duplication check
+          bool dup = false;
+          for (uint32_t m = 0; m < output_j; m++) {
+            if (v == my_out_graph[m]) {
+              dup = true;
               break;
             }
           }
-          if (!flag_match) {
-            my_out_graph[k] = my_fwd_graph[kf];
-            k += 1;
+
+          if (!dup) {
+            my_out_graph[output_j] = v;
+            output_j++;
           }
         }
-        kf += 1;
       }
-      assert(k == output_graph_degree);
-      assert(kf <= output_graph_degree);
+
+      const auto num_protected_edges =
+        std::max<uint64_t>(mst_graph_num_edges_ptr[i], output_graph_degree / 2);
+      if (num_protected_edges > output_graph_degree) { check_num_protected_edges = false; }
+      if (num_protected_edges == output_graph_degree) continue;
 
       // Replace some edges of the output graph with edges of the reverse graph.
       auto kr = std::min<uint32_t>(rev_graph_count.data_handle()[i], output_graph_degree);
@@ -1358,7 +1401,19 @@ void optimize(
           my_out_graph[num_protected_edges] = my_rev_graph[kr];
         }
       }
+
+      // If guarantee_connectivity == true, move the output neighbor list from the temporal list to
+      // the output list. If false, the copy is not needed because my_out_graph is a pointer to the
+      // output buffer.
+      if (guarantee_connectivity) {
+        for (uint32_t j = 0; j < output_graph_degree; j++) {
+          output_graph_ptr[(output_graph_degree * i) + j] = my_out_graph[j];
+        }
+      }
     }
+    RAFT_EXPECTS(check_num_protected_edges,
+                 "Failed to merge the MST, pruned, and reverse edge graphs. Some nodes have too "
+                 "many MST optimization edges.");
 
     const double time_replace_end = cur_time();
     RAFT_LOG_DEBUG("# Replacing edges time: %.1lf sec", time_replace_end - time_replace_start);
@@ -1419,6 +1474,36 @@ void optimize(
                      (double)sum_hist / graph_size);
     }
   }
+
+  // Check duplication and out-of-range indices
+  {
+    uint64_t num_dup = 0;
+    uint64_t num_oor = 0;
+#pragma omp parallel for reduction(+ : num_dup) reduction(+ : num_oor)
+    for (uint64_t i = 0; i < graph_size; i++) {
+      auto my_out_graph = output_graph_ptr + (output_graph_degree * i);
+      for (uint32_t j = 0; j < output_graph_degree; j++) {
+        const auto neighbor_a = my_out_graph[j];
+
+        // Check oor
+        if (neighbor_a > graph_size) {
+          num_oor++;
+          continue;
+        }
+
+        // Check duplication
+        for (uint32_t k = j + 1; k < output_graph_degree; k++) {
+          const auto neighbor_b = my_out_graph[k];
+          if (neighbor_a == neighbor_b) { num_dup++; }
+        }
+      }
+    }
+    RAFT_EXPECTS(
+      num_dup == 0, "%lu duplicated node(s) are found in the generated CAGRA graph", num_dup);
+    RAFT_EXPECTS(num_oor == 0,
+                 "%lu out-of-range index node(s) are found in the generated CAGRA graph",
+                 num_oor);
+  }
 }
 
 }  // namespace cuvs::neighbors::cagra::detail::graph
diff --git a/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu b/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
index e21a54e9e6..e2071b1d39 100644
--- a/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
+++ b/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
@@ -40,8 +40,14 @@ class cagra_extreme_inputs_oob_test : public ::testing::Test {
     ix_ps.graph_degree              = 64;
     ix_ps.intermediate_graph_degree = 128;
 
-    [[maybe_unused]] auto ix = cagra::build(res, ix_ps, raft::make_const_mdspan(dataset->view()));
-    raft::resource::sync_stream(res);
+    try {
+      [[maybe_unused]] auto ix = cagra::build(res, ix_ps, raft::make_const_mdspan(dataset->view()));
+      raft::resource::sync_stream(res);
+    } catch (const std::exception&) {
+      SUCCEED();
+      return;
+    }
+    FAIL();
   }
 
   void SetUp() override

From 44dafe6fa5bfa8ed51a84194ea29258b86fa6472 Mon Sep 17 00:00:00 2001
From: Yinzuo Jiang <jiangyinzuo@foxmail.com>
Date: Fri, 24 Jan 2025 08:28:20 +0800
Subject: [PATCH 270/623] fix cuvs_bench.run --groups options (#592)

issue: #585

Authors:
  - Yinzuo Jiang (https://github.com/jiangyinzuo)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: https://github.com/rapidsai/cuvs/pull/592
---
 python/cuvs_bench/cuvs_bench/run/run.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/python/cuvs_bench/cuvs_bench/run/run.py b/python/cuvs_bench/cuvs_bench/run/run.py
index 0159d2c19e..d7827a096e 100644
--- a/python/cuvs_bench/cuvs_bench/run/run.py
+++ b/python/cuvs_bench/cuvs_bench/run/run.py
@@ -158,6 +158,7 @@ def gather_algorithm_configs(
 def load_algorithms_conf(
     algos_conf_fs: list,
     allowed_algos: Optional[list],
+    allowed_groups: Optional[list],
     allowed_algo_groups: Optional[tuple],
 ) -> dict:
     """
@@ -187,8 +188,11 @@ def load_algorithms_conf(
             continue
         if allowed_algos and algo["name"] not in allowed_algos:
             continue
+        groups = algo.get("groups", {})
+        if allowed_groups:
+            groups = {k: v for k, v in groups.items() if k in allowed_groups}
         algos_conf[algo["name"]] = {
-            "groups": algo.get("groups", {}),
+            "groups": groups,
             "constraints": algo.get("constraints", {}),
         }
         if allowed_algo_groups and algo["name"] in allowed_algo_groups[0]:
@@ -643,6 +647,7 @@ def run_benchmark(
     algos_conf_fs = gather_algorithm_configs(scripts_path, configuration)
 
     allowed_algos = algorithms.split(",") if algorithms else None
+    allowed_groups = groups.split(",") if groups else None
     allowed_algo_groups = (
         [algo_group.split(".") for algo_group in algo_groups.split(",")]
         if algo_groups
@@ -651,7 +656,8 @@ def run_benchmark(
     algos_conf = load_algorithms_conf(
         algos_conf_fs,
         allowed_algos,
-        list(zip(*allowed_algo_groups)) if allowed_algo_groups else None,
+        allowed_groups,
+        tuple(zip(*allowed_algo_groups)) if allowed_algo_groups else None,
     )
 
     executables_to_run = prepare_executables(

From 21e0f0ead374c7718974019a3ef9d9cdc2eb03d9 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Fri, 24 Jan 2025 01:22:24 -0500
Subject: [PATCH 271/623] Add cuda 12.8 support (#605)

CUDA 12.8 introduces sm_120 that requires a reduced number of threads per sm.

We also need to pass -static-global-template-stub=false when building with 12.8 as we violate CUDA ODR kernel rules

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cuvs/pull/605
---
 cpp/cmake/modules/ConfigureCUDA.cmake   | 7 ++++++-
 cpp/src/neighbors/detail/nn_descent.cuh | 5 +++--
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index 3e91d99950..e99a530497 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2018-2024, NVIDIA CORPORATION.
+# Copyright (c) 2018-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -35,6 +35,11 @@ if(CMAKE_COMPILER_IS_GNUCXX)
   endif()
 endif()
 
+# Allow invalid CUDA kernels in the short term
+if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
+  list(APPEND CUVS_CUDA_FLAGS -static-global-template-stub=false)
+endif()
+
 if(CUDA_LOG_COMPILE_TIME)
   list(APPEND CUVS_CUDA_FLAGS "--time=nvcc_compile_log.csv")
 endif()
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 4c577a4d2e..310d4e7a67 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -706,7 +706,8 @@ __device__ __forceinline__ void remove_duplicates(
 template <typename Index_t, typename ID_t = InternalID_t<Index_t>>
 RAFT_KERNEL
 #ifdef __CUDA_ARCH__
-#if (__CUDA_ARCH__) == 750 || ((__CUDA_ARCH__) >= 860 && (__CUDA_ARCH__) <= 890)
+#if (__CUDA_ARCH__) == 750 || ((__CUDA_ARCH__) >= 860 && (__CUDA_ARCH__) <= 890) || \
+  (__CUDA_ARCH__) == 1200
 __launch_bounds__(BLOCK_SIZE)
 #else
 __launch_bounds__(BLOCK_SIZE, 4)

From e9983e17408e6bec6f2558f9df49be97a7255417 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 24 Jan 2025 13:13:13 -0600
Subject: [PATCH 272/623] update pip devcontainers to UCX 1.18, small
 update-version.sh fixes (#604)

Contributes to https://github.com/rapidsai/build-planning/issues/138

Updates to using UCX 1.18 in pip devcontainers here.

Also fixes some small `update-version.sh` issues, and updates references that were outdated as a result of those issues.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Gil Forsyth (https://github.com/gforsyth)
  - https://github.com/jakirkham
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/604
---
 .devcontainer/cuda11.8-pip/devcontainer.json  | 2 +-
 .devcontainer/cuda12.5-pip/devcontainer.json  | 2 +-
 README.md                                     | 4 ++--
 ci/release/update-version.sh                  | 4 ++++
 python/cuvs_bench/cuvs_bench/plot/__main__.py | 2 +-
 5 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index a59c499d3c..42cada9c22 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda11.8-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda11.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
diff --git a/.devcontainer/cuda12.5-pip/devcontainer.json b/.devcontainer/cuda12.5-pip/devcontainer.json
index d849666561..f9aa4a7a94 100644
--- a/.devcontainer/cuda12.5-pip/devcontainer.json
+++ b/.devcontainer/cuda12.5-pip/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "12.5",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda12.5-ucx1.17.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda12.5-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
diff --git a/README.md b/README.md
index dac71c881f..aded8813cb 100755
--- a/README.md
+++ b/README.md
@@ -169,7 +169,7 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
-For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-24.10/examples/c)
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.02/examples/c)
 
 ### Rust API
 
@@ -232,7 +232,7 @@ fn cagra_example() -> Result<()> {
 }
 ```
 
-For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-24.10/examples/rust).
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.02/examples/rust).
 
 ## Contributing
 
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 7562035a95..bfa3672bff 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -78,6 +78,10 @@ done
 sed_runner "/rapidsai\/raft/ s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/developer_guide.md
 
 sed_runner "s|=[0-9][0-9].[0-9][0-9]|=${NEXT_SHORT_TAG}|g" README.md
+sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" README.md
+
+# references to license files
+sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" python/cuvs_bench/cuvs_bench/plot/__main__.py
 
 # rust can't handle leading 0's in the major/minor/patch version - remove
 NEXT_FULL_RUST_TAG=$(printf "%d.%d.%d" $((10#$NEXT_MAJOR)) $((10#$NEXT_MINOR)) $((10#$NEXT_PATCH)))
diff --git a/python/cuvs_bench/cuvs_bench/plot/__main__.py b/python/cuvs_bench/cuvs_bench/plot/__main__.py
index 93deb69c78..7d5e3ae83f 100644
--- a/python/cuvs_bench/cuvs_bench/plot/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/plot/__main__.py
@@ -17,7 +17,7 @@
 # 1: https://github.com/erikbern/ann-benchmarks/blob/main/plot.py
 # 2: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/utils.py  # noqa: E501
 # 3: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/metrics.py  # noqa: E501
-# License: https://github.com/rapidsai/cuvs/blob/branch-24.10/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
+# License: https://github.com/rapidsai/cuvs/blob/branch-25.02/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
 
 import itertools
 import os

From fc6a862fce33200abe1bb07faf9c445189e82e0e Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 24 Jan 2025 17:31:40 -0600
Subject: [PATCH 273/623] add runtime dependency on libcuvs in cuvs wheels
 (#615)

`cuvs-cu{11,12}` wheels don't currently have a runtime dependency on `libcuvs-cu{11,12}`. They need one, for library-loading:

https://github.com/rapidsai/cuvs/blob/e9983e17408e6bec6f2558f9df49be97a7255417/python/cuvs/cuvs/__init__.py#L19-L25

This was missed in #594. This PR adds it.

## Notes for Reviewers

Adding for searchability... this bug can result in issues like this at runtime when using `cuvs` installed from wheels:

> ImportError: libcuvs_c.so: cannot open shared object file: No such file or directory

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: https://github.com/rapidsai/cuvs/pull/615
---
 dependencies.yaml          | 1 +
 python/cuvs/pyproject.toml | 1 +
 2 files changed, 2 insertions(+)

diff --git a/dependencies.yaml b/dependencies.yaml
index 478b2acc27..c998710f6d 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -132,6 +132,7 @@ files:
     extras:
       table: project
     includes:
+      - depends_on_libcuvs
       - depends_on_pylibraft
       - run_py_cuvs
   py_test_cuvs:
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 30658623ba..df3b83faad 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -32,6 +32,7 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
+    "libcuvs==25.2.*,>=0.0.0a0",
     "numpy>=1.23,<3.0a0",
     "pylibraft==25.2.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.

From 8ae81e49d9a4e670852b1cd04c7062db0b3d9e34 Mon Sep 17 00:00:00 2001
From: Ajit Mistry <55892788+ajit283@users.noreply.github.com>
Date: Sat, 25 Jan 2025 02:43:37 +0100
Subject: [PATCH 274/623] Add filtering for CAGRA to C API (#452)

Adds the CAGRA filtering feature to the C API using DLPack Tensor as blocklist

Authors:
  - Ajit Mistry (https://github.com/ajit283)
  - Ben Frederickson (https://github.com/benfred)
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Micka (https://github.com/lowener)
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/452
---
 cpp/include/cuvs/neighbors/cagra.h            |   6 +-
 cpp/src/neighbors/cagra_c.cpp                 |  38 +++++-
 cpp/test/neighbors/ann_cagra_c.cu             | 122 +++++++++++++++++-
 examples/c/src/cagra_c_example.c              |  12 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |   4 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  13 +-
 .../cuvs/cuvs/neighbors/filters/__init__.py   |   4 +-
 .../cuvs/cuvs/neighbors/filters/filters.pyx   |  51 +++++++-
 python/cuvs/cuvs/test/test_cagra.py           |  95 +++++++++++++-
 rust/cuvs/src/cagra/index.rs                  |   9 +-
 10 files changed, 333 insertions(+), 21 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index f7f58a19c6..3e17f1d0fe 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -18,6 +18,7 @@
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.h>
+#include <cuvs/neighbors/common.h>
 #include <dlpack/dlpack.h>
 #include <stdbool.h>
 #include <stdint.h>
@@ -388,13 +389,16 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  * @param[in] queries DLManagedTensor* queries dataset to search
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
+ * @param[in] filter cuvsFilter input filter that can be used
+              to filter queries and neighbors based on the given bitset.
  */
 cuvsError_t cuvsCagraSearch(cuvsResources_t res,
                             cuvsCagraSearchParams_t params,
                             cuvsCagraIndex_t index,
                             DLManagedTensor* queries,
                             DLManagedTensor* neighbors,
-                            DLManagedTensor* distances);
+                            DLManagedTensor* distances,
+                            cuvsFilter filter);
 
 /**
  * @}
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 02b7a566ef..20cbae7d08 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -28,6 +28,7 @@
 #include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/cagra.h>
 #include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/common.h>
 
 #include <fstream>
 
@@ -92,7 +93,8 @@ void _search(cuvsResources_t res,
              cuvsCagraIndex index,
              DLManagedTensor* queries_tensor,
              DLManagedTensor* neighbors_tensor,
-             DLManagedTensor* distances_tensor)
+             DLManagedTensor* distances_tensor,
+             cuvsFilter filter)
 {
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index.addr);
@@ -118,8 +120,26 @@ void _search(cuvsResources_t res,
   auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
   auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
   auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
-  cuvs::neighbors::cagra::search(
-    *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+  if (filter.type == NO_FILTER) {
+    cuvs::neighbors::cagra::search(
+      *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+  } else if (filter.type == BITSET) {
+    using filter_mdspan_type    = raft::device_vector_view<std::uint32_t, int64_t, raft::row_major>;
+    auto removed_indices_tensor = reinterpret_cast<DLManagedTensor*>(filter.addr);
+    auto removed_indices = cuvs::core::from_dlpack<filter_mdspan_type>(removed_indices_tensor);
+    cuvs::core::bitset_view<std::uint32_t, int64_t> removed_indices_bitset(
+      removed_indices, index_ptr->dataset().extent(0));
+    auto bitset_filter_obj = cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset);
+    cuvs::neighbors::cagra::search(*res_ptr,
+                                   search_params,
+                                   *index_ptr,
+                                   queries_mds,
+                                   neighbors_mds,
+                                   distances_mds,
+                                   bitset_filter_obj);
+  } else {
+    RAFT_FAIL("Unsupported filter type: BITMAP");
+  }
 }
 
 template <typename T>
@@ -214,7 +234,8 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
                                        cuvsCagraIndex_t index_c_ptr,
                                        DLManagedTensor* queries_tensor,
                                        DLManagedTensor* neighbors_tensor,
-                                       DLManagedTensor* distances_tensor)
+                                       DLManagedTensor* distances_tensor,
+                                       cuvsFilter filter)
 {
   return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
@@ -237,11 +258,14 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
     RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
 
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
-      _search<float>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+      _search<float>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
-      _search<int8_t>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+      _search<int8_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
-      _search<uint8_t>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+      _search<uint8_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else {
       RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
                 queries.dtype.code,
diff --git a/cpp/test/neighbors/ann_cagra_c.cu b/cpp/test/neighbors/ann_cagra_c.cu
index 599d2d8428..7315813cc9 100644
--- a/cpp/test/neighbors/ann_cagra_c.cu
+++ b/cpp/test/neighbors/ann_cagra_c.cu
@@ -34,9 +34,14 @@ float queries[4][2] = {{0.48216683, 0.0428398},
                        {0.51260436, 0.2643005},
                        {0.05198065, 0.5789965}};
 
+uint32_t filter[1] = {0b1001};  // index 1 and 2 are removed
+
 uint32_t neighbors_exp[4] = {3, 0, 3, 1};
 float distances_exp[4]    = {0.03878258, 0.12472608, 0.04776672, 0.15224178};
 
+uint32_t neighbors_exp_filtered[4] = {3, 0, 3, 0};
+float distances_exp_filtered[4]    = {0.03878258, 0.12472608, 0.04776672, 0.59063464};
+
 TEST(CagraC, BuildSearch)
 {
   // create cuvsResources_t
@@ -109,10 +114,15 @@ TEST(CagraC, BuildSearch)
   distances_tensor.dl_tensor.shape              = distances_shape;
   distances_tensor.dl_tensor.strides            = nullptr;
 
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+
   // search index
   cuvsCagraSearchParams_t search_params;
   cuvsCagraSearchParamsCreate(&search_params);
-  cuvsCagraSearch(res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  cuvsCagraSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor, filter);
 
   // verify output
   ASSERT_TRUE(
@@ -126,3 +136,113 @@ TEST(CagraC, BuildSearch)
   cuvsCagraIndexDestroy(index);
   cuvsResourcesDestroy(res);
 }
+
+TEST(CagraC, BuildSearchFiltered)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+  cudaStream_t stream;
+  cuvsStreamGet(res, &stream);
+
+  // create dataset DLTensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = dataset;
+  dataset_tensor.dl_tensor.device.device_type = kDLCPU;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {4, 2};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = nullptr;
+
+  // create index
+  cuvsCagraIndex_t index;
+  cuvsCagraIndexCreate(&index);
+
+  // build index
+  cuvsCagraIndexParams_t build_params;
+  cuvsCagraIndexParamsCreate(&build_params);
+  cuvsCagraBuild(res, build_params, &dataset_tensor, index);
+
+  // create queries DLTensor
+  rmm::device_uvector<float> queries_d(4 * 2, stream);
+  raft::copy(queries_d.data(), (float*)queries, 4 * 2, stream);
+
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = queries_d.data();
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {4, 2};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = nullptr;
+
+  // create neighbors DLTensor
+  rmm::device_uvector<uint32_t> neighbors_d(4, stream);
+
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = neighbors_d.data();
+  neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLUInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 32;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {4, 1};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = nullptr;
+
+  // create distances DLTensor
+  rmm::device_uvector<float> distances_d(4, stream);
+
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = distances_d.data();
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {4, 1};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = nullptr;
+
+  // create filter DLTensor
+  rmm::device_uvector<uint32_t> filter_d(1, stream);
+  raft::copy(filter_d.data(), filter, 1, stream);
+
+  cuvsFilter filter;
+
+  DLManagedTensor filter_tensor;
+  filter_tensor.dl_tensor.data               = filter_d.data();
+  filter_tensor.dl_tensor.device.device_type = kDLCUDA;
+  filter_tensor.dl_tensor.ndim               = 1;
+  filter_tensor.dl_tensor.dtype.code         = kDLUInt;
+  filter_tensor.dl_tensor.dtype.bits         = 32;
+  filter_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t filter_shape[1]                    = {1};
+  filter_tensor.dl_tensor.shape              = filter_shape;
+  filter_tensor.dl_tensor.strides            = nullptr;
+
+  filter.type = BITSET;
+  filter.addr = (uintptr_t)&filter_tensor;
+
+  // search index
+  cuvsCagraSearchParams_t search_params;
+  cuvsCagraSearchParamsCreate(&search_params);
+  cuvsCagraSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor, filter);
+  // verify output
+  ASSERT_TRUE(cuvs::devArrMatchHost(
+    neighbors_exp_filtered, neighbors_d.data(), 4, cuvs::Compare<uint32_t>()));
+  ASSERT_TRUE(cuvs::devArrMatchHost(
+    distances_exp_filtered, distances_d.data(), 4, cuvs::CompareApprox<float>(0.001f)));
+
+  // de-allocate index and res
+  cuvsCagraSearchParamsDestroy(search_params);
+  cuvsCagraIndexParamsDestroy(build_params);
+  cuvsCagraIndexDestroy(index);
+  cuvsResourcesDestroy(res);
+}
diff --git a/examples/c/src/cagra_c_example.c b/examples/c/src/cagra_c_example.c
index fdcbbf5712..83957aabca 100644
--- a/examples/c/src/cagra_c_example.c
+++ b/examples/c/src/cagra_c_example.c
@@ -67,9 +67,9 @@ void cagra_build_search_simple() {
   // Allocate memory for `queries`, `neighbors` and `distances` output
   uint32_t *neighbors;
   float *distances, *queries_d;
-  cuvsRMMAlloc(res, (void**) &queries_d, sizeof(float) * n_queries * n_cols);
-  cuvsRMMAlloc(res, (void**) &neighbors, sizeof(uint32_t) * n_queries * topk);
-  cuvsRMMAlloc(res, (void**) &distances, sizeof(float) * n_queries * topk);
+  cuvsRMMAlloc(res, (void **)&queries_d, sizeof(float) * n_queries * n_cols);
+  cuvsRMMAlloc(res, (void **)&neighbors, sizeof(uint32_t) * n_queries * topk);
+  cuvsRMMAlloc(res, (void **)&distances, sizeof(float) * n_queries * topk);
 
   // Use DLPack to represent `queries`, `neighbors` and `distances` as tensors
   cudaMemcpy(queries_d, queries, sizeof(float) * 4 * 2, cudaMemcpyDefault);
@@ -111,8 +111,12 @@ void cagra_build_search_simple() {
   cuvsCagraSearchParams_t search_params;
   cuvsCagraSearchParamsCreate(&search_params);
 
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+
   cuvsCagraSearch(res, search_params, index, &queries_tensor, &neighbors_tensor,
-                  &distances_tensor);
+                  &distances_tensor, filter);
 
   // print results
   uint32_t *neighbors_h =
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index a0f8114805..fba7e3d1ee 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -29,6 +29,7 @@ from libcpp cimport bool
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
 from cuvs.distance_type cimport cuvsDistanceType
+from cuvs.neighbors.filters.filters cimport cuvsFilter
 
 
 cdef extern from "cuvs/neighbors/cagra.h" nogil:
@@ -115,7 +116,8 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
                                 cuvsCagraIndex_t index,
                                 DLManagedTensor* queries,
                                 DLManagedTensor* neighbors,
-                                DLManagedTensor* distances) except +
+                                DLManagedTensor* distances,
+                                cuvsFilter filter) except +
 
     cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
                                    const char * filename,
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index fd55905cf5..f62563f610 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -48,6 +48,7 @@ from libc.stdint cimport (
 )
 
 from cuvs.common.exceptions import check_cuvs
+from cuvs.neighbors.filters import no_filter
 
 
 cdef class CompressionParams:
@@ -484,7 +485,8 @@ def search(SearchParams search_params,
            k,
            neighbors=None,
            distances=None,
-           resources=None):
+           resources=None,
+           filter=None):
     """
     Find the k nearest neighbors for each query.
 
@@ -503,6 +505,9 @@ def search(SearchParams search_params,
     distances : Optional CUDA array interface compliant matrix shape
                 (n_queries, k) If supplied, the distances to the
                 neighbors will be written here in-place. (default None)
+    filter:     Optional cuvs.neighbors.cuvsFilter can be used to filter
+                neighbors based on a given bitset.
+        (default None)
     {resources_docstring}
 
     Examples
@@ -557,6 +562,9 @@ def search(SearchParams search_params,
     _check_input_array(distances_cai, [np.dtype('float32')],
                        exp_rows=n_queries, exp_cols=k)
 
+    if filter is None:
+        filter = no_filter()
+
     cdef cuvsCagraSearchParams* params = &search_params.params
     cdef cydlpack.DLManagedTensor* queries_dlpack = \
         cydlpack.dlpack_c(queries_cai)
@@ -573,7 +581,8 @@ def search(SearchParams search_params,
             index.index,
             queries_dlpack,
             neighbors_dlpack,
-            distances_dlpack
+            distances_dlpack,
+            filter.prefilter
         ))
 
     return (distances, neighbors)
diff --git a/python/cuvs/cuvs/neighbors/filters/__init__.py b/python/cuvs/cuvs/neighbors/filters/__init__.py
index 2ad118965a..0ddf809c96 100644
--- a/python/cuvs/cuvs/neighbors/filters/__init__.py
+++ b/python/cuvs/cuvs/neighbors/filters/__init__.py
@@ -13,6 +13,6 @@
 # limitations under the License.
 
 
-from .filters import Prefilter, from_bitmap, no_filter
+from .filters import Prefilter, from_bitmap, from_bitset, no_filter
 
-__all__ = ["no_filter", "from_bitmap", "Prefilter"]
+__all__ = ["no_filter", "from_bitmap", "from_bitset", "Prefilter"]
diff --git a/python/cuvs/cuvs/neighbors/filters/filters.pyx b/python/cuvs/cuvs/neighbors/filters/filters.pyx
index 9bc2a905ce..16042e9662 100644
--- a/python/cuvs/cuvs/neighbors/filters/filters.pyx
+++ b/python/cuvs/cuvs/neighbors/filters/filters.pyx
@@ -22,7 +22,7 @@ from libc.stdint cimport uintptr_t
 from cuvs.common cimport cydlpack
 from cuvs.neighbors.common import _check_input_array
 
-from .filters cimport BITMAP, NO_FILTER, cuvsFilter
+from .filters cimport BITMAP, BITSET, NO_FILTER, cuvsFilter
 
 from pylibraft.common.cai_wrapper import wrap_array
 
@@ -95,3 +95,52 @@ def from_bitmap(bitmap):
     filter.addr = <uintptr_t> bitmap_dlpack
 
     return Prefilter(filter, parent=bitmap)
+
+
+def from_bitset(bitset):
+    """
+    Create a pre-filter from an array with type of uint32.
+
+    Parameters
+    ----------
+    bitset : numpy.ndarray
+        An array with type of `uint32` where each bit in the array
+        corresponds to if a sample is greenlit (not filtered) or filtered.
+        Each bit in a `uint32` element represents a different sample of
+        the dataset.
+
+        - Bit value of 1: The sample is greenlit (allowed).
+        - Bit value of 0: The sample pair is filtered.
+
+    Returns
+    -------
+    filter : cuvs.neighbors.filters.Prefilter
+        An instance of `Prefilter` that can be used to filter neighbors
+        based on the given bitset.
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> import numpy as np
+    >>> from cuvs.neighbors import filters
+    >>>
+    >>> n_samples = 50000
+    >>> n_queries = 1000
+    >>>
+    >>> n_bitset = np.ceil(n_samples / 32).astype(int)
+    >>> bitset = cp.random.randint(1, 100, size=(n_bitset,), dtype=cp.uint32)
+    >>> prefilter = filters.from_bitset(bitset)
+    """
+    bitset_cai = wrap_array(bitset)
+    _check_input_array(bitset_cai, [np.dtype('uint32')])
+
+    cdef cydlpack.DLManagedTensor* bitset_dlpack = \
+        cydlpack.dlpack_c(bitset_cai)
+
+    cdef cuvsFilter filter
+    filter.type = BITSET
+    filter.addr = <uintptr_t> bitset_dlpack
+
+    return Prefilter(filter, parent=bitset)
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/test/test_cagra.py
index d3b03a5d03..9efd489233 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/test/test_cagra.py
@@ -19,7 +19,7 @@
 from sklearn.neighbors import NearestNeighbors
 from sklearn.preprocessing import normalize
 
-from cuvs.neighbors import cagra
+from cuvs.neighbors import cagra, filters
 from cuvs.test.ann_utils import calc_recall, generate_data
 
 
@@ -139,6 +139,99 @@ def test_cagra_dataset_dtype_host_device(
     )
 
 
+def create_sparse_bitset(n_size, sparsity):
+    bits_per_uint32 = 32
+    num_bits = n_size
+    num_uint32s = (num_bits + bits_per_uint32 - 1) // bits_per_uint32
+    num_ones = int(num_bits * sparsity)
+
+    array = np.zeros(num_uint32s, dtype=np.uint32)
+    indices = np.random.choice(num_bits, num_ones, replace=False)
+
+    for index in indices:
+        i = index // bits_per_uint32
+        bit_position = index % bits_per_uint32
+        array[i] |= 1 << bit_position
+
+    return array
+
+
+@pytest.mark.parametrize("sparsity", [0.2, 0.5, 0.7, 1.0])
+def test_filtered_cagra(
+    sparsity,
+    n_rows=10000,
+    n_cols=10,
+    n_queries=10,
+    k=10,
+):
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    bitset = create_sparse_bitset(n_rows, sparsity)
+
+    dataset_device = device_ndarray(dataset)
+    queries_device = device_ndarray(queries)
+    bitset_device = device_ndarray(bitset)
+
+    build_params = cagra.IndexParams()
+    index = cagra.build(build_params, dataset_device)
+
+    filter_ = filters.from_bitset(bitset_device)
+
+    out_idx = np.zeros((n_queries, k), dtype=np.uint32)
+    out_dist = np.zeros((n_queries, k), dtype=np.float32)
+    out_idx_device = device_ndarray(out_idx)
+    out_dist_device = device_ndarray(out_dist)
+
+    search_params = cagra.SearchParams()
+    ret_distances, ret_indices = cagra.search(
+        search_params,
+        index,
+        queries_device,
+        k,
+        neighbors=out_idx_device,
+        distances=out_dist_device,
+        filter=filter_,
+    )
+
+    # Convert bitset to bool array for validation
+    bitset_as_uint8 = bitset.view(np.uint8)
+    bool_filter = np.unpackbits(bitset_as_uint8)
+    bool_filter = bool_filter.reshape(-1, 4, 8)
+    bool_filter = np.flip(bool_filter, axis=2)
+    bool_filter = bool_filter.reshape(-1)[:n_rows]
+    bool_filter = np.logical_not(bool_filter)  # Flip so True means filtered
+
+    # Get filtered dataset for reference calculation
+    non_filtered_mask = ~bool_filter
+    filtered_dataset = dataset[non_filtered_mask]
+
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric="euclidean"
+    )
+    nn_skl.fit(filtered_dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    actual_indices = out_idx_device.copy_to_host()
+
+    filtered_idx_map = (
+        np.cumsum(~bool_filter) - 1
+    )  # -1 because cumsum starts at 1
+
+    # Map CAGRA indices to filtered space
+    mapped_actual_indices = np.take(
+        filtered_idx_map, actual_indices, mode="clip"
+    )
+
+    filtered_indices = np.where(bool_filter)[0]
+    for i in range(n_queries):
+        assert not np.intersect1d(filtered_indices, actual_indices[i]).size
+
+    recall = calc_recall(mapped_actual_indices, skl_idx)
+
+    assert recall > 0.7
+
+
 @pytest.mark.parametrize(
     "params",
     [
diff --git a/rust/cuvs/src/cagra/index.rs b/rust/cuvs/src/cagra/index.rs
index 959959f608..bf316b4d7a 100644
--- a/rust/cuvs/src/cagra/index.rs
+++ b/rust/cuvs/src/cagra/index.rs
@@ -78,6 +78,11 @@ impl Index {
         distances: &ManagedTensor,
     ) -> Result<()> {
         unsafe {
+            let prefilter = ffi::cuvsFilter {
+                addr: 0,
+                type_: ffi::cuvsFilterType::NO_FILTER,
+            };
+
             check_cuvs(ffi::cuvsCagraSearch(
                 res.0,
                 params.0,
@@ -85,6 +90,7 @@ impl Index {
                 queries.as_ptr(),
                 neighbors.as_ptr(),
                 distances.as_ptr(),
+                prefilter,
             ))
         }
     }
@@ -167,7 +173,8 @@ mod tests {
     #[test]
     fn test_cagra_compression() {
         use crate::cagra::CompressionParams;
-        let build_params = IndexParams::new().unwrap()
+        let build_params = IndexParams::new()
+            .unwrap()
             .set_compression(CompressionParams::new().unwrap());
         test_cagra(build_params);
     }

From c01c7ca300f8de9cd0586ac7f0b47c84a1daaab5 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Fri, 24 Jan 2025 22:27:25 -0600
Subject: [PATCH 275/623] Normalize whitespace (#593)

This PR applies `pre-commit` hooks to normalize whitespace (trimming trailing whitespace and enforcing consistent end-of-file newlines).

These rules are already applied to most other RAPIDS repos, so this PR aligns with the norm in RAPIDS.

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/593
---
 .pre-commit-config.yaml                       |  7 +-
 README.md                                     | 12 +--
 cpp/.clang-format                             |  2 +-
 .../ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h   |  2 +-
 .../ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h     |  2 +-
 cpp/cmake/config.json                         |  2 +-
 cpp/cmake/patches/cutlass/build-export.patch  |  5 +-
 cpp/cmake/patches/faiss_override.json         |  2 +-
 cpp/cmake/patches/ggnn.diff                   | 79 +++++++++----------
 cpp/cmake/patches/ggnn_override.json          |  2 +-
 cpp/cmake/patches/hnswlib.diff                | 24 +++---
 cpp/cmake/patches/hnswlib_override.json       |  2 +-
 cpp/cmake/thirdparty/get_faiss.cmake          |  4 +-
 cpp/cmake/thirdparty/get_ggnn.cmake           |  2 +-
 cpp/cmake/thirdparty/get_raft.cmake           |  2 +-
 cpp/doxygen/Doxyfile                          |  2 +-
 cpp/doxygen/main_page.md                      |  2 +-
 cpp/include/cuvs/neighbors/hnsw.hpp           |  2 +-
 cpp/include/cuvs/neighbors/refine.hpp         |  2 +-
 cpp/scripts/run-clang-compile.py              |  4 +-
 cpp/scripts/run-clang-tidy.py                 |  4 +-
 cpp/scripts/run-cmake-format.sh               |  2 +-
 cpp/src/cluster/detail/mst.cuh                |  2 +-
 cpp/src/cluster/detail/single_linkage.cuh     |  2 +-
 .../distance/detail/fused_distance_nn/gemm.h  |  2 +-
 cpp/src/neighbors/cagra_optimize.cu           |  2 +-
 .../neighbors/detail/cagra/cagra_build.cpp    |  2 +-
 .../neighbors/ivf_flat/generate_ivf_flat.py   |  2 +-
 cpp/src/neighbors/ivf_list.cuh                |  2 +-
 cpp/src/neighbors/nn_descent_index.cpp        |  2 +-
 .../refine/detail/refine_host_half_float.cpp  |  2 +-
 cpp/src/neighbors/refine/refine_device.cuh    |  2 +-
 cpp/src/neighbors/refine/refine_host.hpp      |  2 +-
 cpp/src/preprocessing/quantize/scalar.cu      |  2 +-
 cpp/src/sparse/cluster/cluster_solvers.cuh    |  2 +-
 cpp/src/sparse/cluster/detail/spectral.cuh    |  2 +-
 .../cluster/modularity_maximization.cuh       |  2 +-
 cpp/src/sparse/cluster/partition.cuh          |  2 +-
 .../sparse/neighbors/cross_component_nn.cuh   |  2 +-
 cpp/test/distance/gram.cu                     |  2 +-
 cpp/test/distance/gram_base.cuh               |  2 +-
 cpp/test/sparse/cluster/spectral.cu           |  2 +-
 cpp/test/sparse/gram.cu                       |  2 +-
 cpp/test/test_utils.cuh                       |  2 +-
 docs/README.md                                |  2 +-
 docs/source/_static/references.css            |  2 +-
 docs/source/api_basics.rst                    |  2 +-
 docs/source/api_docs.rst                      |  2 +-
 docs/source/api_interoperability.rst          |  4 +-
 docs/source/comparing_indexes.rst             |  2 +-
 docs/source/contributing.md                   |  2 -
 docs/source/cpp_api/cluster.rst               |  1 -
 docs/source/cpp_api/cluster_kmeans.rst        |  2 +-
 docs/source/cpp_api/neighbors_nn_descent.rst  |  2 +-
 docs/source/cpp_api/neighbors_refine.rst      |  2 +-
 .../source/cpp_api/preprocessing_quantize.rst |  3 +-
 docs/source/cpp_api/stats.rst                 |  1 -
 docs/source/cuvs_bench/build.rst              |  2 +-
 docs/source/cuvs_bench/datasets.rst           |  2 +-
 docs/source/cuvs_bench/param_tuning.rst       |  2 +-
 docs/source/cuvs_bench/wiki_all_dataset.rst   |  2 +-
 docs/source/getting_started.rst               |  2 +-
 docs/source/index.rst                         |  1 -
 docs/source/indexes/cagra.rst                 |  2 -
 docs/source/indexes/indexes.rst               |  2 +-
 docs/source/indexes/ivfflat.rst               |  1 -
 docs/source/indexes/ivfpq.rst                 |  5 --
 docs/source/python_api.rst                    |  2 +-
 .../vector_databases_vs_vector_search.rst     |  2 +-
 examples/README.md                            |  8 +-
 examples/c/src/L2_c_example.c                 | 10 +--
 examples/c/src/common.h                       |  1 -
 examples/c/src/ivf_flat_c_example.c           | 14 ++--
 examples/c/src/ivf_pq_c_example.c             | 16 ++--
 examples/cpp/src/common.cuh                   |  3 +-
 python/cuvs/.coveragerc                       |  2 +-
 .../config/algos/faiss_cpu_ivf_flat.yaml      |  2 +-
 .../config/algos/faiss_gpu_ivf_flat.yaml      |  2 +-
 rust/Cargo.toml                               |  1 -
 rust/cuvs/src/distance/mod.rs                 |  2 +-
 rust/cuvs/src/ivf_flat/mod.rs                 |  4 +-
 thirdparty/LICENSES/LICENSE.ann-benchmark     |  2 +-
 thirdparty/LICENSES/LICENSE.faiss             |  2 +-
 thirdparty/LICENSES/LICENSE.pytorch           |  2 +-
 84 files changed, 160 insertions(+), 174 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 240f82be68..2f064ebb61 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,11 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 repos:
+      - repo: https://github.com/pre-commit/pre-commit-hooks
+        rev: v5.0.0
+        hooks:
+              - id: trailing-whitespace
+              - id: end-of-file-fixer
       - repo: https://github.com/PyCQA/isort
         rev: 5.12.0
         hooks:
diff --git a/README.md b/README.md
index aded8813cb..3107f6d0cb 100755
--- a/README.md
+++ b/README.md
@@ -29,9 +29,9 @@ cuVS contains state-of-the-art implementations of several algorithms for running
 
 Vector search is an information retrieval method that has been growing in popularity over the past few  years, partly because of the rising importance of multimedia embeddings created from unstructured data and the need to perform semantic search on the embeddings to find items which are semantically similar to each other.
 
-Vector search is also used in _data mining and machine learning_ tasks and comprises an important step in many _clustering_ and _visualization_ algorithms like [UMAP](https://arxiv.org/abs/2008.00325), [t-SNE](https://lvdmaaten.github.io/tsne/), K-means, and [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html). 
+Vector search is also used in _data mining and machine learning_ tasks and comprises an important step in many _clustering_ and _visualization_ algorithms like [UMAP](https://arxiv.org/abs/2008.00325), [t-SNE](https://lvdmaaten.github.io/tsne/), K-means, and [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html).
 
-Finally, faster vector search enables interactions between dense vectors and graphs. Converting a pile of dense vectors into nearest neighbors graphs unlocks the entire world of graph analysis algorithms, such as those found in [GraphBLAS](https://graphblas.org/) and [cuGraph](https://github.com/rapidsai/cugraph). 
+Finally, faster vector search enables interactions between dense vectors and graphs. Converting a pile of dense vectors into nearest neighbors graphs unlocks the entire world of graph analysis algorithms, such as those found in [GraphBLAS](https://graphblas.org/) and [cuGraph](https://github.com/rapidsai/cugraph).
 
 Below are some common use-cases for vector search
 
@@ -45,7 +45,7 @@ Below are some common use-cases for vector search
   - Audio search
   - Molecular search
   - Model training
-  
+
 
 - ### Data mining
   - Clustering algorithms
@@ -71,7 +71,7 @@ In addition to the items above, cuVS takes on the burden of keeping non-trivial
 
 ## cuVS Technology Stack
 
-cuVS is built on top of the RAPIDS RAFT library of high performance machine learning primitives and provides all the necessary routines for vector search and clustering on the GPU. 
+cuVS is built on top of the RAPIDS RAFT library of high performance machine learning primitives and provides all the necessary routines for vector search and clustering on the GPU.
 
 ![cuVS is built on top of low-level CUDA libraries and provides many important routines that enable vector search and clustering on the GPU](img/tech_stack.png "cuVS Technology Stack")
 
@@ -103,7 +103,7 @@ pip install cuvs-cu11 --extra-index-url=https://pypi.nvidia.com
 And CUDA 12 packages:
 ```bash
 pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
-```    
+```
 
 ### Nightlies
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
@@ -240,7 +240,7 @@ If you are interested in contributing to the cuVS library, please read our [Cont
 
 ## References
 
-For the interested reader, many of the accelerated implementations in cuVS are also based on research papers which can provide a lot more background. We also ask you to please cite the corresponding algorithms by referencing them in your own research. 
+For the interested reader, many of the accelerated implementations in cuVS are also based on research papers which can provide a lot more background. We also ask you to please cite the corresponding algorithms by referencing them in your own research.
 - [CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search](https://arxiv.org/abs/2308.15136)
 - [Top-K Algorithms on GPU: A Comprehensive Study and New Methods](https://dl.acm.org/doi/10.1145/3581784.3607062)
 - [Fast K-NN Graph Construction by GPU Based NN-Descent](https://dl.acm.org/doi/abs/10.1145/3459637.3482344?casa_token=O_nan1B1F5cAAAAA:QHWDEhh0wmd6UUTLY9_Gv6c3XI-5DXM9mXVaUXOYeStlpxTPmV3nKvABRfoivZAaQ3n8FWyrkWw>)
diff --git a/cpp/.clang-format b/cpp/.clang-format
index 18f376d660..26b9a5bf4c 100644
--- a/cpp/.clang-format
+++ b/cpp/.clang-format
@@ -27,7 +27,7 @@ AlwaysBreakAfterDefinitionReturnType: None
 AlwaysBreakAfterReturnType: None
 AlwaysBreakBeforeMultilineStrings: true
 AlwaysBreakTemplateDeclarations: Yes
-BinPackArguments:  false       
+BinPackArguments:  false
 BinPackParameters: false
 BraceWrapping:
   AfterClass:            false
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
index 54a0d2facb..0540edc8f0 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
@@ -137,4 +137,4 @@ void cuvs_mg_ivf_flat<T, IdxT>::search(
     handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
 }
 
-}  // namespace cuvs::bench
\ No newline at end of file
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
index 84aea7d4a3..65ca1bb11e 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
@@ -136,4 +136,4 @@ void cuvs_mg_ivf_pq<T, IdxT>::search(
     handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
 }
 
-}  // namespace cuvs::bench
\ No newline at end of file
+}  // namespace cuvs::bench
diff --git a/cpp/cmake/config.json b/cpp/cmake/config.json
index f7cc50e513..3c568d9766 100644
--- a/cpp/cmake/config.json
+++ b/cpp/cmake/config.json
@@ -9,7 +9,7 @@
           "VERSION": "?",
           "GIT_SHALLOW": "?",
           "OPTIONS": "*",
-          "FIND_PACKAGE_ARGUMENTS": "*" 
+          "FIND_PACKAGE_ARGUMENTS": "*"
         }
       },
       "ConfigureTest": {
diff --git a/cpp/cmake/patches/cutlass/build-export.patch b/cpp/cmake/patches/cutlass/build-export.patch
index a6423e9c08..31bbd25102 100644
--- a/cpp/cmake/patches/cutlass/build-export.patch
+++ b/cpp/cmake/patches/cutlass/build-export.patch
@@ -20,8 +20,7 @@ index 7419bdf5e..545384d82 100755
 -  $<BUILD_INTERFACE:${cute_SOURCE_DIR}/include>
 -  $<BUILD_INTERFACE:${cute_SOURCE_DIR}/examples>
    )
- 
+
  # Mark CTK headers as system to supress warnings from them
--- 
+--
 2.34.1
-
diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
index c39abdc2b7..18542fcf5f 100644
--- a/cpp/cmake/patches/faiss_override.json
+++ b/cpp/cmake/patches/faiss_override.json
@@ -6,4 +6,4 @@
         "git_tag": "main"
       }
     }
-  }
\ No newline at end of file
+  }
diff --git a/cpp/cmake/patches/ggnn.diff b/cpp/cmake/patches/ggnn.diff
index fc45298803..c2413110b6 100644
--- a/cpp/cmake/patches/ggnn.diff
+++ b/cpp/cmake/patches/ggnn.diff
@@ -3,11 +3,11 @@
 @@ -62,7 +62,7 @@ struct SimpleKNNSymCache {
                                                  const ValueT dist_half)
          : dist_query(dist_query), dist_half(dist_half) {}
- 
+
 -    __device__ __forceinline__ DistQueryAndHalf() {}
 +    DistQueryAndHalf() = default;
    };
- 
+
    struct DistanceAndNorm {
 @@ -98,8 +98,7 @@ struct SimpleKNNSymCache {
      KeyT cache;
@@ -17,7 +17,7 @@
 -    __device__ __forceinline__ SyncTempStorage() {}
 +    SyncTempStorage() = default;
    };
- 
+
   public:
 diff --git a/include/ggnn/cuda_knn_ggnn_gpu_instance.cuh b/include/ggnn/cuda_knn_ggnn_gpu_instance.cuh
 index 8cbaf0d..6eb72ac 100644
@@ -28,23 +28,23 @@ index 8cbaf0d..6eb72ac 100644
  #include "ggnn/utils/cuda_knn_utils.cuh"
  #include "ggnn/utils/cuda_knn_constants.cuh"
 -#include "ggnn/utils/cuda_knn_dataset.cuh"
- 
+
  template <typename ValueT>
  __global__ void divide(ValueT* res, ValueT* input, ValueT N) {
 @@ -98,9 +97,7 @@ struct GGNNGPUInstance {
    typedef GGNNGraphDevice<KeyT, BaseT, ValueT> GGNNGraphDevice;
    typedef GGNNGraphHost<KeyT, BaseT, ValueT> GGNNGraphHost;
- 
+
 -  const Dataset<KeyT, BaseT, BAddrT>* dataset;
    GGNNGraphBuffer<KeyT, ValueT>* ggnn_buffer {nullptr};
 -  GGNNQuery<KeyT, ValueT, BaseT> ggnn_query;
- 
+
    // Graph Shards resident on the GPU
    std::vector<GGNNGraphDevice> ggnn_shards;
 @@ -117,13 +114,12 @@ struct GGNNGPUInstance {
    // number of shards that need to be processed by this instance
    const int num_parts;
- 
+
 -  GGNNGPUInstance(const int gpu_id, const Dataset<KeyT, BaseT, BAddrT>* dataset,
 +  GGNNGPUInstance(const int gpu_id,
              const int N_shard, const int L,
@@ -60,14 +60,14 @@ index 8cbaf0d..6eb72ac 100644
 @@ -135,7 +131,6 @@ struct GGNNGPUInstance {
        CHECK_EQ(current_gpu_id, gpu_id) << "cudaSetDevice() needs to be called in advance!";
      }
- 
+
 -    ggnn_query.loadQueriesAsync(dataset->h_query, 0);
- 
+
      computeGraphParameters();
- 
+
 @@ -186,7 +181,7 @@ struct GGNNGPUInstance {
    }
- 
+
    GGNNGPUInstance(const GGNNGPUInstance& other)
 -   : dataset{nullptr}, ggnn_query{0, D, KQuery},
 +   :
@@ -75,9 +75,9 @@ index 8cbaf0d..6eb72ac 100644
      // this exists to allow using vector::emplace_back
      // when it triggers a reallocation, this code will be called.
 @@ -305,6 +300,7 @@ struct GGNNGPUInstance {
- 
+
    // io
- 
+
 +  /*
    void waitForDiskIO(const int shard_id) {
      auto& cpu_buffer = ggnn_cpu_buffers[shard_id%ggnn_cpu_buffers.size()];
@@ -87,41 +87,41 @@ index 8cbaf0d..6eb72ac 100644
      CHECK_CUDA(cudaPeekAtLastError());
    }
 +  */
- 
+
    // graph operations
- 
+
    template <int BLOCK_DIM_X = 32, int MAX_ITERATIONS = 400, int CACHE_SIZE = 512, int SORTED_SIZE = 256, bool DIST_STATS = false>
 -  void queryLayer(const int shard_id = 0) const {
 +  void queryLayer(const BaseT* d_query, int batch_size, KeyT* d_query_result_ids, ValueT* d_query_result_dists, const int shard_id = 0) const {
      CHECK_CUDA(cudaSetDevice(gpu_id));
      const auto& shard = ggnn_shards.at(shard_id%ggnn_shards.size());
- 
+
 @@ -482,21 +479,21 @@ struct GGNNGPUInstance {
- 
+
      int* m_dist_statistics = nullptr;
      if (DIST_STATS)
 -      cudaMallocManaged(&m_dist_statistics, dataset->N_query * sizeof(int));
 +      cudaMallocManaged(&m_dist_statistics, batch_size * sizeof(int));
- 
+
      QueryKernel query_kernel;
      query_kernel.d_base = shard.d_base;
 -    query_kernel.d_query = ggnn_query.d_query;
 +    query_kernel.d_query = d_query;
- 
+
      query_kernel.d_graph = shard.d_graph;
 -    query_kernel.d_query_results = ggnn_query.d_query_result_ids;
 -    query_kernel.d_query_results_dists = ggnn_query.d_query_result_dists;
 +    query_kernel.d_query_results = d_query_result_ids;
 +    query_kernel.d_query_results_dists = d_query_result_dists;
- 
+
      query_kernel.d_translation = shard.d_translation;
- 
+
      query_kernel.d_nn1_stats = shard.d_nn1_stats;
- 
+
 -    query_kernel.N = dataset->N_query;
 +    query_kernel.N = batch_size;
      query_kernel.N_offset = 0;
- 
+
      query_kernel.d_dist_stats = m_dist_statistics;
 @@ -771,6 +768,16 @@ struct GGNNGPUInstance {
        sym(layer, shard_id);
@@ -138,7 +138,7 @@ index 8cbaf0d..6eb72ac 100644
 +    ggnn_shards.at(0).d_base = dataset;
 +  }
  };
- 
+
  #endif  // INCLUDE_GGNN_CUDA_KNN_GGNN_GPU_INSTANCE_CUH_
 diff --git a/include/ggnn/graph/cuda_knn_ggnn_graph_device.cuh b/include/ggnn/graph/cuda_knn_ggnn_graph_device.cuh
 index c94a8f1..781226d 100644
@@ -146,11 +146,11 @@ index c94a8f1..781226d 100644
 +++ b/include/ggnn/graph/cuda_knn_ggnn_graph_device.cuh
 @@ -50,7 +50,7 @@ struct GGNNGraphDevice {
    ValueT* d_nn1_stats;
- 
+
    /// base data pointer for the shard.
 -  BaseT* d_base;
 +  const BaseT* d_base;
- 
+
    /// combined memory pool
    char* d_memory;
 @@ -69,7 +69,9 @@ struct GGNNGraphDevice {
@@ -161,12 +161,12 @@ index c94a8f1..781226d 100644
 +    // base_size = align8(static_cast<size_t>(N) * D * sizeof(BaseT));
 +    (void) N;
 +    (void) D;
- 
+
      const size_t total_size = base_size+total_graph_size;
- 
+
 @@ -86,8 +88,7 @@ struct GGNNGraphDevice {
      CHECK_CUDA(cudaMalloc(&d_memory, total_size));
- 
+
      size_t pos = 0;
 -    d_base = reinterpret_cast<BaseT*>(d_memory+pos);
 -    pos += base_size;
@@ -175,17 +175,17 @@ index c94a8f1..781226d 100644
      pos += graph_size;
      d_translation = reinterpret_cast<KeyT*>(d_memory+pos);
 @@ -99,14 +100,14 @@ struct GGNNGraphDevice {
- 
+
      CHECK_EQ(pos, total_size);
- 
+
 -    CHECK_CUDA(cudaStreamCreate(&stream));
 +    // CHECK_CUDA(cudaStreamCreate(&stream));
- 
+
      CHECK_CUDA(cudaPeekAtLastError());
      CHECK_CUDA(cudaDeviceSynchronize());
      CHECK_CUDA(cudaPeekAtLastError());
    }
- 
+
 -  GGNNGraphDevice(const GGNNGraphDevice& other) {
 +  GGNNGraphDevice(const GGNNGraphDevice&) {
      // this exists to allow using vector::emplace_back
@@ -194,12 +194,12 @@ index c94a8f1..781226d 100644
 @@ -116,7 +117,7 @@ struct GGNNGraphDevice {
    ~GGNNGraphDevice() {
      cudaFree(d_memory);
- 
+
 -    CHECK_CUDA(cudaStreamDestroy(stream));
 +    // CHECK_CUDA(cudaStreamDestroy(stream));
    }
  };
- 
+
 diff --git a/include/ggnn/graph/cuda_knn_ggnn_graph_host.cuh b/include/ggnn/graph/cuda_knn_ggnn_graph_host.cuh
 index 2055f9e..ef5843a 100644
 --- a/include/ggnn/graph/cuda_knn_ggnn_graph_host.cuh
@@ -207,7 +207,7 @@ index 2055f9e..ef5843a 100644
 @@ -92,7 +92,7 @@ struct GGNNGraphHost {
      CHECK_CUDA(cudaPeekAtLastError());
    }
- 
+
 -  GGNNGraphHost(const GGNNGraphHost& other) {
 +  GGNNGraphHost(const GGNNGraphHost&) {
      // this exists to allow using vector::emplace_back
@@ -220,11 +220,10 @@ index 49d76a1..eef69e6 100644
 @@ -22,7 +22,6 @@ limitations under the License.
  #include <cuda.h>
  #include <cuda_runtime.h>
- 
+
 -#include <gflags/gflags.h>
  #include <cub/cub.cuh>
- 
+
  #include "ggnn/utils/cuda_knn_constants.cuh"
--- 
+--
 2.43.0
-
diff --git a/cpp/cmake/patches/ggnn_override.json b/cpp/cmake/patches/ggnn_override.json
index c9a1b69782..f1bd8c8a4b 100644
--- a/cpp/cmake/patches/ggnn_override.json
+++ b/cpp/cmake/patches/ggnn_override.json
@@ -13,4 +13,4 @@
         ]
       }
     }
-  }
\ No newline at end of file
+  }
diff --git a/cpp/cmake/patches/hnswlib.diff b/cpp/cmake/patches/hnswlib.diff
index f20c27d919..96be2b2380 100644
--- a/cpp/cmake/patches/hnswlib.diff
+++ b/cpp/cmake/patches/hnswlib.diff
@@ -11,20 +11,20 @@ index bef0017..0ee7931 100644
 +    bool base_layer_init = true;
      static const tableint MAX_LABEL_OPERATION_LOCKS = 65536;
      static const unsigned char DELETE_MARK = 0x01;
- 
+
 @@ -1098,7 +1101,7 @@ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
- 
+
          std::unique_lock <std::mutex> lock_el(link_list_locks_[cur_c]);
          int curlevel = getRandomLevel(mult_);
 -        if (level > 0)
 +        if (level > -1)
              curlevel = level;
- 
+
          element_levels_[cur_c] = curlevel;
 @@ -1116,6 +1119,9 @@ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
          memcpy(getExternalLabeLp(cur_c), &label, sizeof(labeltype));
          memcpy(getDataByInternalId(cur_c), data_point, data_size_);
- 
+
 +        if (!base_layer_init && curlevel == 0)
 +            return cur_c;
 +
@@ -43,7 +43,7 @@ index bef0017..0ee7931 100644
 @@ -1188,28 +1194,41 @@ class HierarchicalNSW : public AlgorithmInterface<dist_t> {
          tableint currObj = enterpoint_node_;
          dist_t curdist = fstdistfunc_(query_data, getDataByInternalId(enterpoint_node_), dist_func_param_);
- 
+
 -        for (int level = maxlevel_; level > 0; level--) {
 -            bool changed = true;
 -            while (changed) {
@@ -66,7 +66,7 @@ index bef0017..0ee7931 100644
 +                while (changed) {
 +                    changed = false;
 +                    unsigned int *data;
- 
+
 -                data = (unsigned int *) get_linklist(currObj, level);
 -                int size = getListCount(data);
 -                metric_hops++;
@@ -82,7 +82,7 @@ index bef0017..0ee7931 100644
 +                        if (static_cast<int>(cand) < 0 || cand > max_elements_)
 +                            throw std::runtime_error("cand error");
 +                        dist_t d = fstdistfunc_(query_data, getDataByInternalId(cand), dist_func_param_);
- 
+
 -                tableint *datal = (tableint *) (data + 1);
 -                for (int i = 0; i < size; i++) {
 -                    tableint cand = datal[i];
@@ -109,7 +109,7 @@ index 834d19f..0c0af26 100644
 @@ -252,12 +252,13 @@ class L2Space : public SpaceInterface<float> {
      ~L2Space() {}
  };
- 
+
 +template <typename T>
  static int
  L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const void *__restrict qty_ptr) {
@@ -119,13 +119,13 @@ index 834d19f..0c0af26 100644
 -    unsigned char *b = (unsigned char *) pVect2;
 +    T *a = (T *) pVect1;
 +    T *b = (T *) pVect2;
- 
+
      qty = qty >> 2;
      for (size_t i = 0; i < qty; i++) {
 @@ -277,11 +278,12 @@ L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const voi
      return (res);
  }
- 
+
 +template <typename T>
  static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2, const void* __restrict qty_ptr) {
      size_t qty = *((size_t*)qty_ptr);
@@ -134,13 +134,13 @@ index 834d19f..0c0af26 100644
 -    unsigned char* b = (unsigned char*)pVect2;
 +    T* a = (T*)pVect1;
 +    T* b = (T*)pVect2;
- 
+
      for (size_t i = 0; i < qty; i++) {
          res += ((*a) - (*b)) * ((*a) - (*b));
 @@ -291,6 +293,7 @@ static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2,
      return (res);
  }
- 
+
 +template <typename T>
  class L2SpaceI : public SpaceInterface<int> {
      DISTFUNC<int> fstdistfunc_;
diff --git a/cpp/cmake/patches/hnswlib_override.json b/cpp/cmake/patches/hnswlib_override.json
index c50220e248..812af74bc1 100644
--- a/cpp/cmake/patches/hnswlib_override.json
+++ b/cpp/cmake/patches/hnswlib_override.json
@@ -13,4 +13,4 @@
       ]
     }
   }
-}
\ No newline at end of file
+}
diff --git a/cpp/cmake/thirdparty/get_faiss.cmake b/cpp/cmake/thirdparty/get_faiss.cmake
index d6261d2484..90d53a4d8b 100644
--- a/cpp/cmake/thirdparty/get_faiss.cmake
+++ b/cpp/cmake/thirdparty/get_faiss.cmake
@@ -23,7 +23,7 @@ function(find_and_configure_faiss)
     HEADER_NAMES  faiss/IndexFlat.h
     LIBRARY_NAMES faiss
     )
-  
+
   set(patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/../patches")
   rapids_cpm_package_override("${patch_dir}/faiss_override.json")
 
@@ -62,7 +62,7 @@ function(find_and_configure_faiss)
     "CMAKE_MESSAGE_LOG_LEVEL VERBOSE"
     )
 
-  
+
   include("${rapids-cmake-dir}/cpm/detail/display_patch_status.cmake")
   rapids_cpm_display_patch_status(faiss)
 
diff --git a/cpp/cmake/thirdparty/get_ggnn.cmake b/cpp/cmake/thirdparty/get_ggnn.cmake
index 2ccfbc64df..8ee5bfd1d3 100644
--- a/cpp/cmake/thirdparty/get_ggnn.cmake
+++ b/cpp/cmake/thirdparty/get_ggnn.cmake
@@ -38,7 +38,7 @@ function(find_and_configure_ggnn)
 
   include("${rapids-cmake-dir}/cpm/detail/display_patch_status.cmake")
   rapids_cpm_display_patch_status(ggnn)
-  
+
   if(NOT TARGET ggnn::ggnn)
     add_library(ggnn INTERFACE)
     target_include_directories(ggnn INTERFACE "$<BUILD_INTERFACE:${ggnn_SOURCE_DIR}/include>")
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 845c7a8337..3c352b6936 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -44,7 +44,7 @@ function(find_and_configure_raft)
             INSTALL_EXPORT_SET  cuvs-exports
             COMPONENTS          ${RAFT_COMPONENTS}
             CPM_ARGS
-              EXCLUDE_FROM_ALL TRUE  
+              EXCLUDE_FROM_ALL TRUE
               GIT_REPOSITORY        https://github.com/${PKG_FORK}/raft.git
               GIT_TAG               ${PKG_PINNED_TAG}
               SOURCE_SUBDIR         cpp
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index e28572457b..ce6741c1f2 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -880,7 +880,7 @@ RECURSIVE              = YES
 # Note that relative paths are relative to the directory from which doxygen is
 # run.
 
-EXCLUDE                = ../include/cuvs/sparse/selection 
+EXCLUDE                = ../include/cuvs/sparse/selection
 
 # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
 # directories that are symbolic links (a Unix file system feature) are excluded
diff --git a/cpp/doxygen/main_page.md b/cpp/doxygen/main_page.md
index d002df0017..81757f5dbd 100644
--- a/cpp/doxygen/main_page.md
+++ b/cpp/doxygen/main_page.md
@@ -1,3 +1,3 @@
 # libcuvs
 
-cuVS is a library for vector search on the GPU
\ No newline at end of file
+cuVS is a library for vector search on the GPU
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index f0b433d8e2..81a823493c 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -724,4 +724,4 @@ void deserialize(raft::resources const& res,
 
 #else
 #error "This header is only available if cuVS CMake option `BUILD_CAGRA_HNSWLIB=ON"
-#endif
\ No newline at end of file
+#endif
diff --git a/cpp/include/cuvs/neighbors/refine.hpp b/cpp/include/cuvs/neighbors/refine.hpp
index 5e60ff5379..e03ffb5632 100644
--- a/cpp/include/cuvs/neighbors/refine.hpp
+++ b/cpp/include/cuvs/neighbors/refine.hpp
@@ -481,4 +481,4 @@ void refine(raft::resources const& handle,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances,
             cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
 
-}  // namespace cuvs::neighbors
\ No newline at end of file
+}  // namespace cuvs::neighbors
diff --git a/cpp/scripts/run-clang-compile.py b/cpp/scripts/run-clang-compile.py
index eae52f7310..71b5db568b 100644
--- a/cpp/scripts/run-clang-compile.py
+++ b/cpp/scripts/run-clang-compile.py
@@ -253,12 +253,12 @@ def run_clang_command(clang_cmd, cwd):
 class LockContext(object):
     def __init__(self, lock=None) -> None:
         self._lock = lock
-    
+
     def __enter__(self):
         if self._lock:
             self._lock.acquire()
         return self
-    
+
     def __exit__(self, _, __, ___):
         if self._lock:
             self._lock.release()
diff --git a/cpp/scripts/run-clang-tidy.py b/cpp/scripts/run-clang-tidy.py
index 3d8bbcec4a..cad08ca551 100644
--- a/cpp/scripts/run-clang-tidy.py
+++ b/cpp/scripts/run-clang-tidy.py
@@ -296,12 +296,12 @@ def run_clang_tidy_command(tidy_cmd, cwd):
 class LockContext(object):
     def __init__(self, lock=None) -> None:
         self._lock = lock
-    
+
     def __enter__(self):
         if self._lock:
             self._lock.acquire()
         return self
-    
+
     def __exit__(self, _, __, ___):
         if self._lock:
             self._lock.release()
diff --git a/cpp/scripts/run-cmake-format.sh b/cpp/scripts/run-cmake-format.sh
index 1f7ae359f1..7e30258fcb 100755
--- a/cpp/scripts/run-cmake-format.sh
+++ b/cpp/scripts/run-cmake-format.sh
@@ -17,7 +17,7 @@
 # and exits gracefully if the file is not found. If a user wishes to specify a
 # config file at a nonstandard location, they may do so by setting the
 # environment variable RAPIDS_CMAKE_FORMAT_FILE.
-# 
+#
 # This script can be invoked directly anywhere within the project repository.
 # Alternatively, it may be invoked as a pre-commit hook via
 # `pre-commit run (cmake-format)|(cmake-lint)`.
diff --git a/cpp/src/cluster/detail/mst.cuh b/cpp/src/cluster/detail/mst.cuh
index 5804b8b5d9..2929262d21 100644
--- a/cpp/src/cluster/detail/mst.cuh
+++ b/cpp/src/cluster/detail/mst.cuh
@@ -204,4 +204,4 @@ void build_sorted_mst(
   raft::copy_async(mst_weight, mst_coo.weights.data(), mst_coo.n_edges, stream);
 }
 
-};  // namespace  cuvs::cluster::agglomerative::detail
\ No newline at end of file
+};  // namespace  cuvs::cluster::agglomerative::detail
diff --git a/cpp/src/cluster/detail/single_linkage.cuh b/cpp/src/cluster/detail/single_linkage.cuh
index 8b90336b71..d1660f9ff3 100644
--- a/cpp/src/cluster/detail/single_linkage.cuh
+++ b/cpp/src/cluster/detail/single_linkage.cuh
@@ -122,4 +122,4 @@ void single_linkage(raft::resources const& handle,
   out->n_leaves               = m;
   out->n_connected_components = 1;
 }
-};  // namespace  cuvs::cluster::agglomerative::detail
\ No newline at end of file
+};  // namespace  cuvs::cluster::agglomerative::detail
diff --git a/cpp/src/distance/detail/fused_distance_nn/gemm.h b/cpp/src/distance/detail/fused_distance_nn/gemm.h
index 6c8d718d0e..789878a736 100644
--- a/cpp/src/distance/detail/fused_distance_nn/gemm.h
+++ b/cpp/src/distance/detail/fused_distance_nn/gemm.h
@@ -409,4 +409,4 @@ struct FusedDistanceNNGemm<double,
 
 }  // namespace kernel
 }  // namespace gemm
-}  // namespace cuvs
\ No newline at end of file
+}  // namespace cuvs
diff --git a/cpp/src/neighbors/cagra_optimize.cu b/cpp/src/neighbors/cagra_optimize.cu
index cba66a5e9b..436d5d321b 100644
--- a/cpp/src/neighbors/cagra_optimize.cu
+++ b/cpp/src/neighbors/cagra_optimize.cu
@@ -38,4 +38,4 @@ void optimize(raft::resources const& handle,
                                raft::memory_type::host>>(handle, knn_graph, new_graph);
 }
 
-}  // namespace cuvs::neighbors::cagra
\ No newline at end of file
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cpp b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
index 574a020977..490dc0f304 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cpp
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
@@ -32,4 +32,4 @@ ivf_pq_params::ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
 
   refinement_rate = 2;
 }
-}  // namespace cuvs::neighbors::cagra::graph_build_params
\ No newline at end of file
+}  // namespace cuvs::neighbors::cagra::graph_build_params
diff --git a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
index c435cc6d95..39baeeafae 100644
--- a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
+++ b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
@@ -136,7 +136,7 @@
   {                                                                                  \\
     cuvs::neighbors::ivf_flat::detail::extend(                                       \\
       handle, new_vectors, new_indices, idx);                                        \\
-  }                    
+  }
 """
 
 search_macro = """
diff --git a/cpp/src/neighbors/ivf_list.cuh b/cpp/src/neighbors/ivf_list.cuh
index c6335b1142..9d8aef5033 100644
--- a/cpp/src/neighbors/ivf_list.cuh
+++ b/cpp/src/neighbors/ivf_list.cuh
@@ -192,4 +192,4 @@ enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
   // Make sure the data is copied from host to device before the host arrays get out of the scope.
   raft::resource::sync_stream(handle);
 }
-}  // namespace cuvs::neighbors::ivf
\ No newline at end of file
+}  // namespace cuvs::neighbors::ivf
diff --git a/cpp/src/neighbors/nn_descent_index.cpp b/cpp/src/neighbors/nn_descent_index.cpp
index 25d5b6af84..3fe69e7754 100644
--- a/cpp/src/neighbors/nn_descent_index.cpp
+++ b/cpp/src/neighbors/nn_descent_index.cpp
@@ -26,4 +26,4 @@ index_params::index_params(size_t graph_degree, cuvs::distance::DistanceType met
   this->intermediate_graph_degree = 1.5 * graph_degree;
   this->metric                    = metric;
 }
-}  // namespace cuvs::neighbors::nn_descent
\ No newline at end of file
+}  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/src/neighbors/refine/detail/refine_host_half_float.cpp b/cpp/src/neighbors/refine/detail/refine_host_half_float.cpp
index ed9c412089..4c9378dfc1 100644
--- a/cpp/src/neighbors/refine/detail/refine_host_half_float.cpp
+++ b/cpp/src/neighbors/refine/detail/refine_host_half_float.cpp
@@ -34,4 +34,4 @@
 
 instantiate_cuvs_neighbors_refine(int64_t, half, float, int64_t);
 
-#undef instantiate_cuvs_neighbors_refine
\ No newline at end of file
+#undef instantiate_cuvs_neighbors_refine
diff --git a/cpp/src/neighbors/refine/refine_device.cuh b/cpp/src/neighbors/refine/refine_device.cuh
index a5491be0da..606ba58e39 100644
--- a/cpp/src/neighbors/refine/refine_device.cuh
+++ b/cpp/src/neighbors/refine/refine_device.cuh
@@ -159,4 +159,4 @@ void refine_impl(
 {
   detail::refine_device(handle, dataset, queries, neighbor_candidates, indices, distances, metric);
 }
-}  // namespace cuvs::neighbors
\ No newline at end of file
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/refine/refine_host.hpp b/cpp/src/neighbors/refine/refine_host.hpp
index dcaccd5a87..105023d590 100644
--- a/cpp/src/neighbors/refine/refine_host.hpp
+++ b/cpp/src/neighbors/refine/refine_host.hpp
@@ -211,4 +211,4 @@ void refine_impl(
   detail::refine_host(dataset, queries, neighbor_candidates, indices, distances, metric);
 }
 
-}  // namespace cuvs::neighbors
\ No newline at end of file
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/preprocessing/quantize/scalar.cu b/cpp/src/preprocessing/quantize/scalar.cu
index 9624ad4fe8..68cba4cc8d 100644
--- a/cpp/src/preprocessing/quantize/scalar.cu
+++ b/cpp/src/preprocessing/quantize/scalar.cu
@@ -71,4 +71,4 @@ CUVS_INST_QUANTIZATION(half, int8_t);
 
 #undef CUVS_INST_QUANTIZATION
 
-}  // namespace cuvs::preprocessing::quantize::scalar
\ No newline at end of file
+}  // namespace cuvs::preprocessing::quantize::scalar
diff --git a/cpp/src/sparse/cluster/cluster_solvers.cuh b/cpp/src/sparse/cluster/cluster_solvers.cuh
index 7b4cf6ab34..737dfa5f8a 100644
--- a/cpp/src/sparse/cluster/cluster_solvers.cuh
+++ b/cpp/src/sparse/cluster/cluster_solvers.cuh
@@ -97,4 +97,4 @@ struct kmeans_solver_t {
 }  // namespace spectral
 }  // namespace cuvs
 
-#endif
\ No newline at end of file
+#endif
diff --git a/cpp/src/sparse/cluster/detail/spectral.cuh b/cpp/src/sparse/cluster/detail/spectral.cuh
index 571d92bf56..3d44bd4a20 100644
--- a/cpp/src/sparse/cluster/detail/spectral.cuh
+++ b/cpp/src/sparse/cluster/detail/spectral.cuh
@@ -108,4 +108,4 @@ void fit_embedding(raft::resources const& handle,
   RAFT_CUDA_TRY(cudaGetLastError());
 }
 
-};  // namespace cuvs::sparse::cluster::spectral::detail
\ No newline at end of file
+};  // namespace cuvs::sparse::cluster::spectral::detail
diff --git a/cpp/src/sparse/cluster/modularity_maximization.cuh b/cpp/src/sparse/cluster/modularity_maximization.cuh
index 71cba69272..ce60116d13 100644
--- a/cpp/src/sparse/cluster/modularity_maximization.cuh
+++ b/cpp/src/sparse/cluster/modularity_maximization.cuh
@@ -83,4 +83,4 @@ void analyzeModularity(raft::resources const& handle,
 }  // namespace spectral
 }  // namespace cuvs
 
-#endif
\ No newline at end of file
+#endif
diff --git a/cpp/src/sparse/cluster/partition.cuh b/cpp/src/sparse/cluster/partition.cuh
index df78a8a2d9..111decadf3 100644
--- a/cpp/src/sparse/cluster/partition.cuh
+++ b/cpp/src/sparse/cluster/partition.cuh
@@ -92,4 +92,4 @@ void analyzePartition(raft::resources const& handle,
 }  // namespace spectral
 }  // namespace cuvs
 
-#endif
\ No newline at end of file
+#endif
diff --git a/cpp/src/sparse/neighbors/cross_component_nn.cuh b/cpp/src/sparse/neighbors/cross_component_nn.cuh
index 36a0c79f59..02ce240c4a 100644
--- a/cpp/src/sparse/neighbors/cross_component_nn.cuh
+++ b/cpp/src/sparse/neighbors/cross_component_nn.cuh
@@ -96,4 +96,4 @@ void cross_component_nn(
                              metric);
 }
 
-};  // end namespace cuvs::sparse::neighbors
\ No newline at end of file
+};  // end namespace cuvs::sparse::neighbors
diff --git a/cpp/test/distance/gram.cu b/cpp/test/distance/gram.cu
index 89b1525ea8..3d14da84da 100644
--- a/cpp/test/distance/gram.cu
+++ b/cpp/test/distance/gram.cu
@@ -171,4 +171,4 @@ typedef GramMatrixTest<double> GramMatrixTestDouble;
 TEST_P(GramMatrixTestFloat, Gram) { runTest(); }
 
 INSTANTIATE_TEST_SUITE_P(GramMatrixTests, GramMatrixTestFloat, ::testing::ValuesIn(inputs));
-};  // namespace cuvs::distance::kernels
\ No newline at end of file
+};  // namespace cuvs::distance::kernels
diff --git a/cpp/test/distance/gram_base.cuh b/cpp/test/distance/gram_base.cuh
index 326cdb4f86..6cfb1807b0 100644
--- a/cpp/test/distance/gram_base.cuh
+++ b/cpp/test/distance/gram_base.cuh
@@ -88,4 +88,4 @@ void naiveGramMatrixKernel(int n1,
 
 }  // namespace kernels
 }  // namespace distance
-}  // namespace cuvs
\ No newline at end of file
+}  // namespace cuvs
diff --git a/cpp/test/sparse/cluster/spectral.cu b/cpp/test/sparse/cluster/spectral.cu
index 7d0cdef9db..98b963451f 100644
--- a/cpp/test/sparse/cluster/spectral.cu
+++ b/cpp/test/sparse/cluster/spectral.cu
@@ -106,4 +106,4 @@ TEST(Raft, Spectral)
 }
 
 }  // namespace cluster
-}  // namespace cuvs
\ No newline at end of file
+}  // namespace cuvs
diff --git a/cpp/test/sparse/gram.cu b/cpp/test/sparse/gram.cu
index d7af30a1cb..c3ce99817e 100644
--- a/cpp/test/sparse/gram.cu
+++ b/cpp/test/sparse/gram.cu
@@ -327,4 +327,4 @@ INSTANTIATE_TEST_SUITE_P(GramMatrixTests, GramMatrixTestFloatLd, ::testing::Valu
 INSTANTIATE_TEST_SUITE_P(GramMatrixTests,
                          GramMatrixTestFloatLdCsr,
                          ::testing::ValuesIn(inputs_ld_csr));
-};  // namespace cuvs::distance::kernels::sparse
\ No newline at end of file
+};  // namespace cuvs::distance::kernels::sparse
diff --git a/cpp/test/test_utils.cuh b/cpp/test/test_utils.cuh
index d0b2d6bdac..022202ffb2 100644
--- a/cpp/test/test_utils.cuh
+++ b/cpp/test/test_utils.cuh
@@ -327,4 +327,4 @@ inline std::vector<float> read_csv(std::string filename, bool skip_first_n_colum
   return result;
 }
 
-};  // end namespace cuvs
\ No newline at end of file
+};  // end namespace cuvs
diff --git a/docs/README.md b/docs/README.md
index cd0fe0a149..639961ea37 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -11,4 +11,4 @@ bash build.sh docs
 #### Once the process finishes, documentation can be found in build/html
 ```shell script
 xdg-open build/html/index.html`
-```
\ No newline at end of file
+```
diff --git a/docs/source/_static/references.css b/docs/source/_static/references.css
index 225cf13ba9..d1f647233a 100644
--- a/docs/source/_static/references.css
+++ b/docs/source/_static/references.css
@@ -20,4 +20,4 @@ dl.citation > dt.label > span::before {
 /* Add closing bracket */
 dl.citation > dt.label > span::after {
   content: "]";
-}
\ No newline at end of file
+}
diff --git a/docs/source/api_basics.rst b/docs/source/api_basics.rst
index 4842e27889..d3e2c8a6ff 100644
--- a/docs/source/api_basics.rst
+++ b/docs/source/api_basics.rst
@@ -87,4 +87,4 @@ Rust
 
 .. code-block:: rust
 
-    let res = cuvs::Resources::new()?;
\ No newline at end of file
+    let res = cuvs::Resources::new()?;
diff --git a/docs/source/api_docs.rst b/docs/source/api_docs.rst
index f4deef5064..68d184c72c 100644
--- a/docs/source/api_docs.rst
+++ b/docs/source/api_docs.rst
@@ -10,4 +10,4 @@ API Reference
    rust_api/index.rst
 
 * :ref:`genindex`
-* :ref:`search`
\ No newline at end of file
+* :ref:`search`
diff --git a/docs/source/api_interoperability.rst b/docs/source/api_interoperability.rst
index 4dfe1e09af..519660036f 100644
--- a/docs/source/api_interoperability.rst
+++ b/docs/source/api_interoperability.rst
@@ -48,10 +48,10 @@ The `mdarray` is an owning object that forms a convenience layer over RMM and ca
 .. code-block:: c++
 
     #include <raft/core/device_mdarray.hpp>
-    
+
     int n_rows = 10;
     int n_cols = 10;
-    
+
     auto scalar = raft::make_device_scalar<float>(handle, 1.0);
     auto vector = raft::make_device_vector<float>(handle, n_cols);
     auto matrix = raft::make_device_matrix<float>(handle, n_rows, n_cols);
diff --git a/docs/source/comparing_indexes.rst b/docs/source/comparing_indexes.rst
index 221aab6d75..167aa2e072 100644
--- a/docs/source/comparing_indexes.rst
+++ b/docs/source/comparing_indexes.rst
@@ -57,4 +57,4 @@ It turns out that most vector databases, like Milvus for example, make many smal
 
 Please note, however, that there are often caps on the size of each of these smaller indexes, and that needs to be taken into consideration when choosing the size of the sub sample to tune.
 
-Please see :doc:`this guide <tuning_guide>` for more information on the steps one would take to do this subsampling and tuning process.
\ No newline at end of file
+Please see :doc:`this guide <tuning_guide>` for more information on the steps one would take to do this subsampling and tuning process.
diff --git a/docs/source/contributing.md b/docs/source/contributing.md
index c426ce5340..1a58da4d75 100755
--- a/docs/source/contributing.md
+++ b/docs/source/contributing.md
@@ -89,5 +89,3 @@ implementation of the issue, ask them in the issue instead of the PR.
 
 ## Attribution
 Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md
-
-
diff --git a/docs/source/cpp_api/cluster.rst b/docs/source/cpp_api/cluster.rst
index 3e3e627f75..3de663a21b 100644
--- a/docs/source/cpp_api/cluster.rst
+++ b/docs/source/cpp_api/cluster.rst
@@ -11,4 +11,3 @@ Cluster
 
    cluster_kmeans.rst
    cluster_agglomerative.rst
-
diff --git a/docs/source/cpp_api/cluster_kmeans.rst b/docs/source/cpp_api/cluster_kmeans.rst
index 3ff8a03e5a..6c342baabd 100644
--- a/docs/source/cpp_api/cluster_kmeans.rst
+++ b/docs/source/cpp_api/cluster_kmeans.rst
@@ -41,4 +41,4 @@ namespace *cuvs::cluster::kmeans::helpers*
 .. doxygengroup:: kmeans_helpers
     :project: cuvs
     :members:
-    :content-only:
\ No newline at end of file
+    :content-only:
diff --git a/docs/source/cpp_api/neighbors_nn_descent.rst b/docs/source/cpp_api/neighbors_nn_descent.rst
index edecdfa8df..c21a1003db 100644
--- a/docs/source/cpp_api/neighbors_nn_descent.rst
+++ b/docs/source/cpp_api/neighbors_nn_descent.rst
@@ -34,4 +34,4 @@ Index build
 .. doxygengroup:: nn_descent_cpp_index_build
     :project: cuvs
     :members:
-    :content-only:
\ No newline at end of file
+    :content-only:
diff --git a/docs/source/cpp_api/neighbors_refine.rst b/docs/source/cpp_api/neighbors_refine.rst
index d53c8087cc..4a90ee9959 100644
--- a/docs/source/cpp_api/neighbors_refine.rst
+++ b/docs/source/cpp_api/neighbors_refine.rst
@@ -17,4 +17,4 @@ Index
 .. doxygengroup:: ann_refine
     :project: cuvs
     :members:
-    :content-only:
\ No newline at end of file
+    :content-only:
diff --git a/docs/source/cpp_api/preprocessing_quantize.rst b/docs/source/cpp_api/preprocessing_quantize.rst
index b660c61c51..8a08fb98ba 100644
--- a/docs/source/cpp_api/preprocessing_quantize.rst
+++ b/docs/source/cpp_api/preprocessing_quantize.rst
@@ -2,7 +2,7 @@ Quantize
 ========
 
 This page provides C++ class references for the publicly-exposed elements of the
-`cuvs/preprocessing/quantize` package. 
+`cuvs/preprocessing/quantize` package.
 
 .. role:: py(code)
    :language: c++
@@ -17,4 +17,3 @@ namespace *cuvs::preprocessing::quantize::scalar*
 
 .. doxygengroup:: scalar
    :project: cuvs
-
diff --git a/docs/source/cpp_api/stats.rst b/docs/source/cpp_api/stats.rst
index 80d6c65fc9..988ba05dfc 100644
--- a/docs/source/cpp_api/stats.rst
+++ b/docs/source/cpp_api/stats.rst
@@ -32,4 +32,3 @@ namespace *cuvs::stats*
     :project: cuvs
     :members:
     :content-only:
-
diff --git a/docs/source/cuvs_bench/build.rst b/docs/source/cuvs_bench/build.rst
index de84326915..0c2dc98b8c 100644
--- a/docs/source/cuvs_bench/build.rst
+++ b/docs/source/cuvs_bench/build.rst
@@ -56,4 +56,4 @@ Available targets to use with `--limit-bench-ann` are:
 - CUVS_IVF_PQ_ANN_BENCH
 - CUVS_IVF_FLAT_ANN_BENCH
 
-By default, the `*_ANN_BENCH` executables program infer the dataset's datatype from the filename's extension. For example, an extension of `fbin` uses a `float` datatype, `f16bin` uses a `float16` datatype, extension of `i8bin` uses `int8_t` datatype, and `u8bin` uses `uint8_t` type. Currently, only `float`, `float16`, int8_t`, and `unit8_t` are supported.
\ No newline at end of file
+By default, the `*_ANN_BENCH` executables program infer the dataset's datatype from the filename's extension. For example, an extension of `fbin` uses a `float` datatype, `f16bin` uses a `float16` datatype, extension of `i8bin` uses `int8_t` datatype, and `u8bin` uses `uint8_t` type. Currently, only `float`, `float16`, int8_t`, and `unit8_t` are supported.
diff --git a/docs/source/cuvs_bench/datasets.rst b/docs/source/cuvs_bench/datasets.rst
index 0f6539e560..e6a53ca82b 100644
--- a/docs/source/cuvs_bench/datasets.rst
+++ b/docs/source/cuvs_bench/datasets.rst
@@ -59,4 +59,4 @@ If you have a dataset, but no corresponding ground truth file, then you can gene
 
     # Using only a subset of the dataset. Define queries by randomly
     # selecting vectors from the (subset of the) dataset.
-    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.fbin --nrows=2000000 --output=groundtruth_dir --queries=random-choice --n_queries=10000
\ No newline at end of file
+    python -m cuvs_bench.generate_groundtruth --dataset /dataset/base.fbin --nrows=2000000 --output=groundtruth_dir --queries=random-choice --n_queries=10000
diff --git a/docs/source/cuvs_bench/param_tuning.rst b/docs/source/cuvs_bench/param_tuning.rst
index faffa9daff..c85504e967 100644
--- a/docs/source/cuvs_bench/param_tuning.rst
+++ b/docs/source/cuvs_bench/param_tuning.rst
@@ -671,4 +671,4 @@ hnswlib
    - 1
    - Number of threads to use for queries.
 
-Please refer to `HNSW algorithm parameters guide <https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md>`_ from `hnswlib` to learn more about these arguments.
\ No newline at end of file
+Please refer to `HNSW algorithm parameters guide <https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md>`_ from `hnswlib` to learn more about these arguments.
diff --git a/docs/source/cuvs_bench/wiki_all_dataset.rst b/docs/source/cuvs_bench/wiki_all_dataset.rst
index 04ac7d9a4b..cedeeb93f5 100644
--- a/docs/source/cuvs_bench/wiki_all_dataset.rst
+++ b/docs/source/cuvs_bench/wiki_all_dataset.rst
@@ -52,4 +52,4 @@ After the dataset is downloaded and extracted to the `wiki_all_88M` directory (o
 License info
 ============
 
-The English wiki texts available on Kaggle come with the `CC BY-NCSA 4.0 <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_ license and the Cohere wikipedia data set comes with the `Apache 2.0 <https://choosealicense.com/licenses/apache-2.0/>`_ license.
\ No newline at end of file
+The English wiki texts available on Kaggle come with the `CC BY-NCSA 4.0 <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_ license and the Cohere wikipedia data set comes with the `Apache 2.0 <https://choosealicense.com/licenses/apache-2.0/>`_ license.
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
index fa38c45ee0..b9cfdaca2e 100644
--- a/docs/source/getting_started.rst
+++ b/docs/source/getting_started.rst
@@ -117,4 +117,4 @@ We always welcome patches for new features and bug fixes. Please read our `contr
    comparing_indexes.rst
    indexes/indexes.rst
    api_basics.rst
-   api_interoperability.rst
\ No newline at end of file
+   api_interoperability.rst
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 286836c186..4dcf59112d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -88,4 +88,3 @@ Contents
    cuvs_bench/index.rst
    api_docs.rst
    contributing.md
-
diff --git a/docs/source/indexes/cagra.rst b/docs/source/indexes/cagra.rst
index de8821e745..551eca8f72 100644
--- a/docs/source/indexes/cagra.rst
+++ b/docs/source/indexes/cagra.rst
@@ -145,5 +145,3 @@ IVF-PQ Search (max batch size 1024 vectors on device at a time):
    [n_vectors * (pq_dim * pq_bits / 8 + sizeof(int64_t)) + O(n_clusters)]
    + [batch_size * n_dim * sizeof(float)] + [batch_size * intermediate_degree * sizeof(uint32_t)] +
    [batch_size * intermediate_degree * sizeof(float)]
-
-
diff --git a/docs/source/indexes/indexes.rst b/docs/source/indexes/indexes.rst
index a2fb1434a5..4a18f793a7 100644
--- a/docs/source/indexes/indexes.rst
+++ b/docs/source/indexes/indexes.rst
@@ -16,4 +16,4 @@ Indices and tables
 
 * :ref:`genindex`
 * :ref:`modindex`
-* :ref:`search`
\ No newline at end of file
+* :ref:`search`
diff --git a/docs/source/indexes/ivfflat.rst b/docs/source/indexes/ivfflat.rst
index 14dd1798c5..89a9fb6e64 100644
--- a/docs/source/indexes/ivfflat.rst
+++ b/docs/source/indexes/ivfflat.rst
@@ -113,4 +113,3 @@ Peak device memory usage for index build:
 :math:`workspace = min(1GB, n\_queries * [(n\_lists + 1 + n\_probes * (k + 1)) * sizeof_{float}) + n\_probes * k * sizeof_{idx}])`
 
 :math:`index\_size + workspace`
-
diff --git a/docs/source/indexes/ivfpq.rst b/docs/source/indexes/ivfpq.rst
index e4bd81395b..0474452a01 100644
--- a/docs/source/indexes/ivfpq.rst
+++ b/docs/source/indexes/ivfpq.rst
@@ -133,8 +133,3 @@ Build peak memory usage (device):
    + n\_clusters * dim * sizeof_{float}
 
 Note, if there’s not enough space left in the workspace memory resource, IVF-PQ build automatically switches to the managed memory for the training set and labels.
-
-
-
-
-
diff --git a/docs/source/python_api.rst b/docs/source/python_api.rst
index 69bcd300b6..9d7a583fea 100644
--- a/docs/source/python_api.rst
+++ b/docs/source/python_api.rst
@@ -8,4 +8,4 @@ Python API Documentation
    :maxdepth: 4
 
    python_api/distance.rst
-   python_api/neighbors.rst
\ No newline at end of file
+   python_api/neighbors.rst
diff --git a/docs/source/vector_databases_vs_vector_search.rst b/docs/source/vector_databases_vs_vector_search.rst
index 446737c11c..5c43ee5508 100644
--- a/docs/source/vector_databases_vs_vector_search.rst
+++ b/docs/source/vector_databases_vs_vector_search.rst
@@ -54,4 +54,4 @@ Unfortunately, for large datasets, doing a hyper-parameter optimization on the w
 
 Full hyper-parameter optimization may also not always be necessary- for example, once you have built a ground truth dataset on a subset, many times you can start by building an index with the default build parameters and then playing around with different search parameters until you get the desired quality and search performance.  For massive indexes that might be multiple terabytes, you could also take this subsampling of, say, 10M vectors, train an index and then tune the search parameters from there. While there might be a small margin of error, the chosen build/search parameters should generalize fairly well for the databases that build locally partitioned indexes.
 
-Refer to our :doc:`tuning guide <tuning_guide>` for more information and examples on how to efficiently and automatically tune your vector search indexes based on your needs.
\ No newline at end of file
+Refer to our :doc:`tuning guide <tuning_guide>` for more information and examples on how to efficiently and automatically tune your vector search indexes based on your needs.
diff --git a/examples/README.md b/examples/README.md
index 701fd0deee..f5a606ee35 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -1,17 +1,17 @@
 # cuVS C and C++ Examples
 
-This template project provides a drop-in sample to either start building a new application with, or using CUVS in an existing CMake project. 
+This template project provides a drop-in sample to either start building a new application with, or using CUVS in an existing CMake project.
 
 First, please refer to our [installation docs](https://docs.rapids.ai/api/cuvs/stable/build.html#cuda-gpu-requirements) for the minimum requirements to use cuVS.
 
-Once the minimum requirements are satisfied, this example template application can be built with the provided `build.sh` script. This is a bash script that calls the appropriate CMake commands, so you can look into it to see the typical CMake based build workflow.  
+Once the minimum requirements are satisfied, this example template application can be built with the provided `build.sh` script. This is a bash script that calls the appropriate CMake commands, so you can look into it to see the typical CMake based build workflow.
 
 The directories (`CUVS_SOURCE/examples/c`) or (`CUVS_SOURCE/examples/cpp`) can be copied directly in order to build a new application with cuVS.
 
-cuVS can be integrated into an existing CMake project by copying the contents in the `configure rapids-cmake` and `configure cuvs` sections of the provided `CMakeLists.txt` into your project, along with `cmake/thirdparty/get_cuvs.cmake`. 
+cuVS can be integrated into an existing CMake project by copying the contents in the `configure rapids-cmake` and `configure cuvs` sections of the provided `CMakeLists.txt` into your project, along with `cmake/thirdparty/get_cuvs.cmake`.
 
 Make sure to link against the appropriate CMake targets. Use `cuvs::c_api` and `cuvs::cuvs` to use the C and C++ shared libraries respectively.
 
 ```cmake
 target_link_libraries(your_app_target PRIVATE cuvs::cuvs)
-```
\ No newline at end of file
+```
diff --git a/examples/c/src/L2_c_example.c b/examples/c/src/L2_c_example.c
index 73ddf61039..0b621798e3 100644
--- a/examples/c/src/L2_c_example.c
+++ b/examples/c/src/L2_c_example.c
@@ -84,7 +84,7 @@ void l2_distance_calc(int64_t n_cols,float x[], float y[], float *ret) {
   DLManagedTensor y_tensor;
   int64_t y_shape[2] = {N_ROWS, n_cols};
   tensor_initialize(y_d, y_shape, &y_tensor);
-  
+
   DLManagedTensor dist_tensor;
   int64_t distances_shape[2] = {N_ROWS, N_ROWS};
   tensor_initialize(distance_d, distances_shape, &dist_tensor);
@@ -93,7 +93,7 @@ void l2_distance_calc(int64_t n_cols,float x[], float y[], float *ret) {
   cuvsPairwiseDistance(res, &x_tensor, &y_tensor, &dist_tensor, L2SqrtUnexpanded, 2.0);
 
   cudaMemcpy(ret, distance_d, sizeof(float) * N_ROWS * N_ROWS, cudaMemcpyDefault);
-  
+
   cuvsRMMFree(res, distance_d, sizeof(float) * N_ROWS * N_ROWS);
   cuvsRMMFree(res, x_d, sizeof(float) * N_ROWS * n_cols);
   cuvsRMMFree(res, y_d, sizeof(float) * N_ROWS * n_cols);
@@ -106,12 +106,12 @@ int euclidean_distance_calculation_example() {
 
   outputVector((float *)PointA);
   outputVector((float *)PointB);
-  
+
   float ret;
-  
+
   l2_distance_calc(DIM, (float *)PointA, (float *)PointB, &ret);
   printf("L2 distance is %f.\n", ret);
-  
+
   cuvsResourcesDestroy(res);
 
   return 0;
diff --git a/examples/c/src/common.h b/examples/c/src/common.h
index 60b9b73cf6..e5b8d01768 100644
--- a/examples/c/src/common.h
+++ b/examples/c/src/common.h
@@ -106,4 +106,3 @@ void print_results(int64_t * neighbor, float* distances,int n_rows, int n_cols)
         printf("]\n");
     }
 }
-
diff --git a/examples/c/src/ivf_flat_c_example.c b/examples/c/src/ivf_flat_c_example.c
index c068d04f82..2121ca35e5 100644
--- a/examples/c/src/ivf_flat_c_example.c
+++ b/examples/c/src/ivf_flat_c_example.c
@@ -56,7 +56,7 @@ void ivf_flat_build_search_simple(cuvsResources_t *res, DLManagedTensor * datase
     DLManagedTensor neighbors_tensor;
     int64_t neighbors_shape[2] = {n_queries, topk};
     int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
-    
+
     DLManagedTensor distances_tensor;
     int64_t distances_shape[2] = {n_queries, topk};
     float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
@@ -91,7 +91,7 @@ void ivf_flat_build_search_simple(cuvsResources_t *res, DLManagedTensor * datase
 
     cuvsIvfFlatSearchParamsDestroy(search_params);
     cuvsIvfFlatIndexDestroy(index);
-    cuvsIvfFlatIndexParamsDestroy(index_params);  
+    cuvsIvfFlatIndexParamsDestroy(index_params);
 }
 
 void ivf_flat_build_extend_search(cuvsResources_t *res, DLManagedTensor * trainset_tensor, DLManagedTensor * dataset_tensor, DLManagedTensor * queries_tensor) {
@@ -102,7 +102,7 @@ void ivf_flat_build_extend_search(cuvsResources_t *res, DLManagedTensor * trains
     int64_t data_indices_shape[1] = {n_dataset};
     int_tensor_initialize(data_indices_d, data_indices_shape, &data_indices_tensor);
     data_indices_tensor.dl_tensor.ndim = 1;
-    
+
     printf("\nRun k-means clustering using the training set\n");
 
     int64_t *data_indices = (int64_t *)malloc(n_dataset * sizeof(int64_t));
@@ -154,11 +154,11 @@ void ivf_flat_build_extend_search(cuvsResources_t *res, DLManagedTensor * trains
     DLManagedTensor neighbors_tensor;
     int64_t neighbors_shape[2] = {n_queries, topk};
     int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
-    
+
     DLManagedTensor distances_tensor;
     int64_t distances_shape[2] = {n_queries, topk};
     float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
-    
+
     // Create default search params
     cuvsIvfFlatSearchParams_t search_params;
     cuvsIvfFlatSearchParamsCreate(&search_params);
@@ -203,7 +203,7 @@ int main() {
     float *queries = (float *)malloc(n_queries * n_dim * sizeof(float));
     generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
     generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
-    
+
     // Create a cuvsResources_t object
     cuvsResources_t res;
     cuvsResourcesCreate(&res);
@@ -245,7 +245,7 @@ int main() {
     DLManagedTensor trainset_tensor;
     int64_t trainset_shape[2] = {n_trainset, n_dim};
     float_tensor_initialize(trainset_d, trainset_shape, &trainset_tensor);
-    
+
     // Build and extend example.
     ivf_flat_build_extend_search(&res, &trainset_tensor, &dataset_tensor, &queries_tensor);
 
diff --git a/examples/c/src/ivf_pq_c_example.c b/examples/c/src/ivf_pq_c_example.c
index b6d6b485b2..c3235f2d45 100644
--- a/examples/c/src/ivf_pq_c_example.c
+++ b/examples/c/src/ivf_pq_c_example.c
@@ -45,7 +45,7 @@ void ivf_pq_build_search(cuvsResources_t *res, DLManagedTensor * dataset_tensor,
         cuvsIvfPqIndexParamsDestroy(index_params);
         return;
     }
-    
+
     // Create output arrays.
     int64_t topk      = 10;
     int64_t n_queries = queries_tensor->dl_tensor.shape[0];
@@ -89,7 +89,7 @@ void ivf_pq_build_search(cuvsResources_t *res, DLManagedTensor * dataset_tensor,
 
     printf("\nOriginal results:\n");
     print_results(neighbors, distances, 2, topk);
-    
+
     // Re-ranking operation: refine the initial search results by computing exact distances
     int64_t topk_refined = 7;
     int64_t *neighbors_refined_d;
@@ -100,11 +100,11 @@ void ivf_pq_build_search(cuvsResources_t *res, DLManagedTensor * dataset_tensor,
     DLManagedTensor neighbors_refined_tensor;
     int64_t neighbors_refined_shape[2] = {n_queries, topk_refined};
     int_tensor_initialize(neighbors_refined_d, neighbors_refined_shape, &neighbors_refined_tensor);
-    
+
     DLManagedTensor distances_refined_tensor;
     int64_t distances_refined_shape[2] = {n_queries, topk_refined};
     float_tensor_initialize(distances_refined_d, distances_refined_shape, &distances_refined_tensor);
-    
+
     // Note, refinement requires the original dataset and the queries.
     // Don't forget to specify the same distance metric as used by the index.
     cuvsError_t refine_status = cuvsRefine(*res, dataset_tensor, queries_tensor,
@@ -140,7 +140,7 @@ void ivf_pq_build_search(cuvsResources_t *res, DLManagedTensor * dataset_tensor,
 
     cuvsIvfPqSearchParamsDestroy(search_params);
     cuvsIvfPqIndexDestroy(index);
-    cuvsIvfPqIndexParamsDestroy(index_params);  
+    cuvsIvfPqIndexParamsDestroy(index_params);
 }
 
 int main() {
@@ -152,7 +152,7 @@ int main() {
     float *queries = (float *)malloc(n_queries * n_dim * sizeof(float));
     generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
     generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
-    
+
     // Create a cuvsResources_t object
     cuvsResources_t res;
     cuvsResourcesCreate(&res);
@@ -166,7 +166,7 @@ int main() {
     DLManagedTensor dataset_tensor;
     int64_t dataset_shape[2] = {n_samples,n_dim};
     float_tensor_initialize(dataset_d, dataset_shape, &dataset_tensor);
-    
+
     // Allocate memory for `queries`
     float *queries_d;
     cuvsRMMAlloc(res, (void**) &queries_d, sizeof(float) * n_queries * n_dim);
@@ -177,7 +177,7 @@ int main() {
     DLManagedTensor queries_tensor;
     int64_t queries_shape[2] = {n_queries, n_dim};
     float_tensor_initialize(queries_d, queries_shape, &queries_tensor);
-    
+
     // Simple build and search example.
     ivf_pq_build_search(&res, &dataset_tensor, &queries_tensor);
 
diff --git a/examples/cpp/src/common.cuh b/examples/cpp/src/common.cuh
index 8e109a7644..a743ff802d 100644
--- a/examples/cpp/src/common.cuh
+++ b/examples/cpp/src/common.cuh
@@ -124,7 +124,6 @@ raft::device_matrix<T,idxT> read_bin_dataset(raft::device_resources const &dev_r
 
   auto dataset = raft::make_device_matrix<T, idxT>(dev_resources, N, dim);
   raft::copy(dataset.data_handle(), data.data(), data.size(), raft::resource::get_cuda_stream(dev_resources));
- 
+
   return dataset;
 }
-
diff --git a/python/cuvs/.coveragerc b/python/cuvs/.coveragerc
index fc087fb9c5..3269e10b8a 100644
--- a/python/cuvs/.coveragerc
+++ b/python/cuvs/.coveragerc
@@ -1,3 +1,3 @@
 # Configuration file for Python coverage tests
 [run]
-source = pylibraft
\ No newline at end of file
+source = pylibraft
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
index 29c145f86d..aa7409dce8 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
@@ -7,4 +7,4 @@ groups:
       useFloat16: [False]
     search:
       nprobe: [1, 5, 10, 50, 100, 200]
-      refine_ratio: [1]
\ No newline at end of file
+      refine_ratio: [1]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
index e4abc35f5c..ba1dcbab9b 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
@@ -18,4 +18,4 @@ groups:
       use_raft: [True]
     search:
       nprobe: [1, 5, 10, 50, 100, 200]
-      refine_ratio: [1]
\ No newline at end of file
+      refine_ratio: [1]
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index ddb8b32cd8..37aa837f60 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -13,4 +13,3 @@ homepage = "https://github.com/rapidsai/cuvs"
 description = "RAPIDS vector search library"
 authors = ["NVIDIA Corporation"]
 license = "Apache-2.0"
-
diff --git a/rust/cuvs/src/distance/mod.rs b/rust/cuvs/src/distance/mod.rs
index 566bf0129e..f6911c6836 100644
--- a/rust/cuvs/src/distance/mod.rs
+++ b/rust/cuvs/src/distance/mod.rs
@@ -71,7 +71,7 @@ mod tests {
         let distances = ManagedTensor::from(&distances_host)
             .to_device(&res)
             .unwrap();
-    
+
         pairwise_distance(&res, &dataset_device, &dataset_device, &distances, DistanceType::L2Expanded,
         None).unwrap();
 
diff --git a/rust/cuvs/src/ivf_flat/mod.rs b/rust/cuvs/src/ivf_flat/mod.rs
index 32aa707845..cee4e9454c 100644
--- a/rust/cuvs/src/ivf_flat/mod.rs
+++ b/rust/cuvs/src/ivf_flat/mod.rs
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-//! The IVF-Flat method is an ANN algorithm. It uses an inverted file index (IVF) with 
-//! unmodified (that is, flat) vectors. This algorithm provides simple knobs to reduce 
+//! The IVF-Flat method is an ANN algorithm. It uses an inverted file index (IVF) with
+//! unmodified (that is, flat) vectors. This algorithm provides simple knobs to reduce
 //! the overall search space and to trade-off accuracy for speed.
 //!
 //! Example:
diff --git a/thirdparty/LICENSES/LICENSE.ann-benchmark b/thirdparty/LICENSES/LICENSE.ann-benchmark
index 9f8e4222f6..4d04745ab4 100644
--- a/thirdparty/LICENSES/LICENSE.ann-benchmark
+++ b/thirdparty/LICENSES/LICENSE.ann-benchmark
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
\ No newline at end of file
+SOFTWARE.
diff --git a/thirdparty/LICENSES/LICENSE.faiss b/thirdparty/LICENSES/LICENSE.faiss
index 87cbf536c6..b96dcb0480 100644
--- a/thirdparty/LICENSES/LICENSE.faiss
+++ b/thirdparty/LICENSES/LICENSE.faiss
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
\ No newline at end of file
+SOFTWARE.
diff --git a/thirdparty/LICENSES/LICENSE.pytorch b/thirdparty/LICENSES/LICENSE.pytorch
index 7ad3d737a5..04f9ad1105 100644
--- a/thirdparty/LICENSES/LICENSE.pytorch
+++ b/thirdparty/LICENSES/LICENSE.pytorch
@@ -74,4 +74,4 @@ SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
+POSSIBILITY OF SUCH DAMAGE.

From 11ecbb2a263e3f6591d0a2390453ca0356b845c0 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Sat, 25 Jan 2025 00:22:37 -0800
Subject: [PATCH 276/623] Add Scalar Quantization to the c and python apis
 (#617)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/617
---
 cpp/CMakeLists.txt                            |   1 +
 .../cuvs/preprocessing/quantize/scalar.h      | 130 ++++++++++
 cpp/src/preprocessing/quantize/scalar_c.cpp   | 209 ++++++++++++++++
 docs/source/python_api.rst                    |   1 +
 docs/source/python_api/distance.rst           |   2 +-
 docs/source/python_api/preprocessing.rst      |  11 +
 python/cuvs/CMakeLists.txt                    |   1 +
 python/cuvs/cuvs/preprocessing/CMakeLists.txt |  15 ++
 python/cuvs/cuvs/preprocessing/__init__.py    |   0
 .../cuvs/preprocessing/quantize/__init__.py   |   0
 .../quantize/scalar/CMakeLists.txt            |  28 +++
 .../quantize/scalar/__init__.pxd              |   0
 .../preprocessing/quantize/scalar/__init__.py |  21 ++
 .../preprocessing/quantize/scalar/scalar.pxd  |  59 +++++
 .../preprocessing/quantize/scalar/scalar.pyx  | 236 ++++++++++++++++++
 python/cuvs/cuvs/test/test_doctests.py        |   2 +
 .../cuvs/cuvs/test/test_scalar_quantizer.py   |  53 ++++
 17 files changed, 768 insertions(+), 1 deletion(-)
 create mode 100644 cpp/include/cuvs/preprocessing/quantize/scalar.h
 create mode 100644 cpp/src/preprocessing/quantize/scalar_c.cpp
 create mode 100644 docs/source/python_api/preprocessing.rst
 create mode 100644 python/cuvs/cuvs/preprocessing/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/preprocessing/__init__.py
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/__init__.py
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.pxd
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pxd
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx
 create mode 100644 python/cuvs/cuvs/test/test_scalar_quantizer.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 11f21db44b..f88ca1af51 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -685,6 +685,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       src/neighbors/cagra_c.cpp
       $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
       src/neighbors/refine/refine_c.cpp
+      src/preprocessing/quantize/scalar_c.cpp
       src/distance/pairwise_distance_c.cpp
     )
 
diff --git a/cpp/include/cuvs/preprocessing/quantize/scalar.h b/cpp/include/cuvs/preprocessing/quantize/scalar.h
new file mode 100644
index 0000000000..a8dbcb78be
--- /dev/null
+++ b/cpp/include/cuvs/preprocessing/quantize/scalar.h
@@ -0,0 +1,130 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Scalar quantizer parameters.
+ */
+struct cuvsScalarQuantizerParams {
+  /*
+   * specifies how many outliers at top & bottom will be ignored
+   * needs to be within range of (0, 1]
+   */
+  float quantile;
+};
+
+typedef struct cuvsScalarQuantizerParams* cuvsScalarQuantizerParams_t;
+
+/**
+ * @brief Allocate Scalar Quantizer params, and populate with default values
+ *
+ * @param[in] params cuvsScalarQuantizerParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsScalarQuantizerParamsCreate(cuvsScalarQuantizerParams_t* params);
+
+/**
+ * @brief De-allocate Scalar Quantizer params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsScalarQuantizerParamsDestroy(cuvsScalarQuantizerParams_t params);
+
+/**
+ * @brief Defines and stores scalar for quantisation upon training
+ *
+ * The quantization is performed by a linear mapping of an interval in the
+ * float data type to the full range of the quantized int type.
+ */
+typedef struct {
+  double min_;
+  double max_;
+} cuvsScalarQuantizer;
+
+typedef cuvsScalarQuantizer* cuvsScalarQuantizer_t;
+
+/**
+ * @brief Allocate Scalar Quantizer and populate with default values
+ *
+ * @param[in] quantizer cuvsScalarQuantizer_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsScalarQuantizerCreate(cuvsScalarQuantizer_t* quantizer);
+
+/**
+ * @brief De-allocate Scalar Quantizer
+ *
+ * @param[in] quantizer
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsScalarQuantizerDestroy(cuvsScalarQuantizer_t quantizer);
+
+/**
+ * @brief Trains a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure scalar quantizer, e.g. quantile
+ * @param[in] dataset a row-major host or device matrix
+ * @param[out] quantizer trained scalar quantizer
+ */
+cuvsError_t cuvsScalarQuantizerTrain(cuvsResources_t res,
+                                     cuvsScalarQuantizerParams_t params,
+                                     DLManagedTensor* dataset,
+                                     cuvsScalarQuantizer_t quantizer);
+
+/**
+ * @brief Applies quantization transform to given dataset
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major host or device matrix to transform
+ * @param[out] out a row-major host or device matrix to store transformed data
+ */
+cuvsError_t cuvsScalarQuantizerTransform(cuvsResources_t res,
+                                         cuvsScalarQuantizer_t quantizer,
+                                         DLManagedTensor* dataset,
+                                         DLManagedTensor* out);
+
+/**
+ * @brief Perform inverse quantization step on previously quantized dataset
+ *
+ * Note that depending on the chosen data types train dataset the conversion is
+ * not lossless.
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer a scalar quantizer
+ * @param[in] dataset a row-major host or device matrix
+ * @param[out] out a row-major host or device matrix
+ *
+ */
+cuvsError_t cuvsScalarQuantizerInverseTransform(cuvsResources_t res,
+                                                cuvsScalarQuantizer_t quantizer,
+                                                DLManagedTensor* dataset,
+                                                DLManagedTensor* out);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/preprocessing/quantize/scalar_c.cpp b/cpp/src/preprocessing/quantize/scalar_c.cpp
new file mode 100644
index 0000000000..99880e4264
--- /dev/null
+++ b/cpp/src/preprocessing/quantize/scalar_c.cpp
@@ -0,0 +1,209 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/preprocessing/quantize/scalar.h>
+#include <cuvs/preprocessing/quantize/scalar.hpp>
+
+namespace {
+
+template <typename T>
+void _train(cuvsResources_t res,
+            cuvsScalarQuantizerParams params,
+            DLManagedTensor* dataset_tensor,
+            cuvsScalarQuantizer_t quantizer)
+{
+  auto dataset = dataset_tensor->dl_tensor;
+
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto quantizer_params     = cuvs::preprocessing::quantize::scalar::params();
+  quantizer_params.quantile = params.quantile;
+
+  cuvs::preprocessing::quantize::scalar::quantizer<T> ret;
+
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    ret = cuvs::preprocessing::quantize::scalar::train(*res_ptr, quantizer_params, mds);
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    ret = cuvs::preprocessing::quantize::scalar::train(*res_ptr, quantizer_params, mds);
+  } else {
+    RAFT_FAIL("dataset must be accessible on host or device memory");
+  }
+
+  quantizer->min_ = ret.min_;
+  quantizer->max_ = ret.max_;
+}
+
+template <typename T, typename OutputT = int8_t>
+void _transform(cuvsResources_t res,
+                cuvsScalarQuantizer_t quantizer_,
+                DLManagedTensor* dataset_tensor,
+                DLManagedTensor* out_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  cuvs::preprocessing::quantize::scalar::quantizer<T> quantizer;
+  quantizer.min_ = quantizer_->min_;
+  quantizer.max_ = quantizer_->max_;
+
+  auto dataset = dataset_tensor->dl_tensor;
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type     = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    using out_mdspan_type = raft::device_matrix_view<OutputT, int64_t, raft::row_major>;
+
+    cuvs::preprocessing::quantize::scalar::transform(
+      *res_ptr,
+      quantizer,
+      cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
+      cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
+
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type     = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    using out_mdspan_type = raft::host_matrix_view<OutputT, int64_t, raft::row_major>;
+
+    cuvs::preprocessing::quantize::scalar::transform(
+      *res_ptr,
+      quantizer,
+      cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
+      cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
+  } else {
+    RAFT_FAIL("dataset must be accessible on host or device memory");
+  }
+}
+
+template <typename OutputT, typename InputT = int8_t>
+void _inverse_transform(cuvsResources_t res,
+                        cuvsScalarQuantizer_t quantizer_,
+                        DLManagedTensor* dataset_tensor,
+                        DLManagedTensor* out_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  cuvs::preprocessing::quantize::scalar::quantizer<OutputT> quantizer;
+  quantizer.min_ = quantizer_->min_;
+  quantizer.max_ = quantizer_->max_;
+
+  auto dataset = dataset_tensor->dl_tensor;
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type     = raft::device_matrix_view<InputT const, int64_t, raft::row_major>;
+    using out_mdspan_type = raft::device_matrix_view<OutputT, int64_t, raft::row_major>;
+
+    cuvs::preprocessing::quantize::scalar::inverse_transform(
+      *res_ptr,
+      quantizer,
+      cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
+      cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
+
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type     = raft::host_matrix_view<InputT const, int64_t, raft::row_major>;
+    using out_mdspan_type = raft::host_matrix_view<OutputT, int64_t, raft::row_major>;
+
+    cuvs::preprocessing::quantize::scalar::inverse_transform(
+      *res_ptr,
+      quantizer,
+      cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
+      cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
+  } else {
+    RAFT_FAIL("dataset must be accessible on host or device memory");
+  }
+}
+}  // namespace
+
+extern "C" cuvsError_t cuvsScalarQuantizerParamsCreate(cuvsScalarQuantizerParams_t* params)
+{
+  return cuvs::core::translate_exceptions(
+    [=] { *params = new cuvsScalarQuantizerParams{.quantile = 0.99}; });
+}
+
+extern "C" cuvsError_t cuvsScalarQuantizerParamsDestroy(cuvsScalarQuantizerParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsScalarQuantizerCreate(cuvsScalarQuantizer_t* quantizer)
+{
+  return cuvs::core::translate_exceptions([=] { *quantizer = new cuvsScalarQuantizer{}; });
+}
+
+extern "C" cuvsError_t cuvsScalarQuantizerDestroy(cuvsScalarQuantizer_t quantizer)
+{
+  return cuvs::core::translate_exceptions([=] { delete quantizer; });
+}
+
+extern "C" cuvsError_t cuvsScalarQuantizerTrain(cuvsResources_t res,
+                                                cuvsScalarQuantizerParams_t params,
+                                                DLManagedTensor* dataset_tensor,
+                                                cuvsScalarQuantizer_t quantizer)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = dataset_tensor->dl_tensor;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      _train<float>(res, *params, dataset_tensor, quantizer);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
+      _train<double>(res, *params, dataset_tensor, quantizer);
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsScalarQuantizerTransform(cuvsResources_t res,
+                                                    cuvsScalarQuantizer_t quantizer,
+                                                    DLManagedTensor* dataset_tensor,
+                                                    DLManagedTensor* out_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = dataset_tensor->dl_tensor;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      _transform<float>(res, quantizer, dataset_tensor, out_tensor);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
+      _transform<double>(res, quantizer, dataset_tensor, out_tensor);
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+cuvsError_t cuvsScalarQuantizerInverseTransform(cuvsResources_t res,
+                                                cuvsScalarQuantizer_t quantizer,
+                                                DLManagedTensor* dataset,
+                                                DLManagedTensor* out)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dtype = out->dl_tensor.dtype;
+    if (dtype.code == kDLFloat && dtype.bits == 32) {
+      _inverse_transform<float>(res, quantizer, dataset, out);
+    } else if (dtype.code == kDLFloat && dtype.bits == 64) {
+      _inverse_transform<double>(res, quantizer, dataset, out);
+    } else {
+      RAFT_FAIL(
+        "Unsupported output dataset DLtensor dtype: %d and bits: %d", dtype.code, dtype.bits);
+    }
+  });
+}
diff --git a/docs/source/python_api.rst b/docs/source/python_api.rst
index 9d7a583fea..aa75160c1f 100644
--- a/docs/source/python_api.rst
+++ b/docs/source/python_api.rst
@@ -9,3 +9,4 @@ Python API Documentation
 
    python_api/distance.rst
    python_api/neighbors.rst
+   python_api/preprocessing.rst
diff --git a/docs/source/python_api/distance.rst b/docs/source/python_api/distance.rst
index fd9efa853e..debd82953c 100644
--- a/docs/source/python_api/distance.rst
+++ b/docs/source/python_api/distance.rst
@@ -7,6 +7,6 @@ Distance
 
 
 Pairwise Distance
-##################
+#################
 
 .. autofunction:: cuvs.distance.pairwise_distance
diff --git a/docs/source/python_api/preprocessing.rst b/docs/source/python_api/preprocessing.rst
new file mode 100644
index 0000000000..679eabc16a
--- /dev/null
+++ b/docs/source/python_api/preprocessing.rst
@@ -0,0 +1,11 @@
+Preprocessing
+=============
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Scalar Quantizer
+################
+
+.. autofunction:: cuvs.preprocessing.quantize.scalar
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index f3feae9a7c..465a2ba8ad 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -62,3 +62,4 @@ target_link_libraries(cuvs_rmm_logger PRIVATE rmm::rmm_logger_impl)
 add_subdirectory(cuvs/common)
 add_subdirectory(cuvs/distance)
 add_subdirectory(cuvs/neighbors)
+add_subdirectory(cuvs/preprocessing)
diff --git a/python/cuvs/cuvs/preprocessing/CMakeLists.txt b/python/cuvs/cuvs/preprocessing/CMakeLists.txt
new file mode 100644
index 0000000000..be31760f93
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/CMakeLists.txt
@@ -0,0 +1,15 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+add_subdirectory(quantize/scalar)
diff --git a/python/cuvs/cuvs/preprocessing/__init__.py b/python/cuvs/cuvs/preprocessing/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/preprocessing/quantize/__init__.py b/python/cuvs/cuvs/preprocessing/quantize/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt b/python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt
new file mode 100644
index 0000000000..dc55edbf04
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt
@@ -0,0 +1,28 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources scalar.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
+                   preprocessing_quantize_scalar_
+)
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.pxd b/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py b/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py
new file mode 100644
index 0000000000..f24510bab6
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .scalar import (
+    Quantizer,
+    QuantizerParams,
+    inverse_transform,
+    train,
+    transform,
+)
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pxd b/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pxd
new file mode 100644
index 0000000000..f195c4b0de
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pxd
@@ -0,0 +1,59 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+
+
+cdef extern from "cuvs/preprocessing/quantize/scalar.h" nogil:
+    ctypedef struct cuvsScalarQuantizerParams:
+        float quantile
+
+    ctypedef cuvsScalarQuantizerParams* cuvsScalarQuantizerParams_t
+
+    cuvsError_t cuvsScalarQuantizerParamsCreate(
+        cuvsScalarQuantizerParams_t* params)
+
+    cuvsError_t cuvsScalarQuantizerParamsDestroy(
+        cuvsScalarQuantizerParams_t params)
+
+    ctypedef struct cuvsScalarQuantizer:
+        double min_
+        double max_
+
+    ctypedef cuvsScalarQuantizer* cuvsScalarQuantizer_t
+
+    cuvsError_t cuvsScalarQuantizerCreate(
+        cuvsScalarQuantizer_t* quantizer)
+
+    cuvsError_t cuvsScalarQuantizerDestroy(
+        cuvsScalarQuantizer_t quantizer)
+
+    cuvsError_t cuvsScalarQuantizerTrain(cuvsResources_t res,
+                                         cuvsScalarQuantizerParams_t params,
+                                         DLManagedTensor* dataset,
+                                         cuvsScalarQuantizer_t quantizer)
+
+    cuvsError_t cuvsScalarQuantizerTransform(cuvsResources_t res,
+                                             cuvsScalarQuantizer_t quantizer,
+                                             DLManagedTensor* dataset,
+                                             DLManagedTensor* out)
+
+    cuvsError_t cuvsScalarQuantizerInverseTransform(cuvsResources_t res,
+                                                    cuvsScalarQuantizer_t q,
+                                                    DLManagedTensor* dataset,
+                                                    DLManagedTensor* out)
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx b/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx
new file mode 100644
index 0000000000..c4cc53f25e
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx
@@ -0,0 +1,236 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from cuvs.common cimport cydlpack
+
+from pylibraft.common import auto_convert_output, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.resources import auto_sync_resources
+from cuvs.neighbors.common import _check_input_array
+
+
+cdef class QuantizerParams:
+    """
+    Parameters for scalar quantization
+
+    Parameters
+    ----------
+    quantile: float
+        specifies how many outliers at top & bottom will be ignored
+        needs to be within range of (0, 1]
+    """
+
+    cdef cuvsScalarQuantizerParams * params
+
+    def __cinit__(self):
+        check_cuvs(cuvsScalarQuantizerParamsCreate(&self.params))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsScalarQuantizerParamsDestroy(self.params))
+
+    def __init__(self, *, quantile=None):
+        if quantile is not None:
+            self.params.quantile = quantile
+
+    @property
+    def quantile(self):
+        return self.params.quantile
+
+
+cdef class Quantizer:
+    """
+    Defines and stores scalar for quantisation upon training
+
+    The quantization is performed by a linear mapping of an interval in the
+    float data type to the full range of the quantized int type.
+    """
+    cdef cuvsScalarQuantizer * quantizer
+
+    def __cinit__(self):
+        check_cuvs(cuvsScalarQuantizerCreate(&self.quantizer))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsScalarQuantizerDestroy(self.quantizer))
+
+    @property
+    def min(self):
+        return self.quantizer.min_
+
+    @property
+    def max(self):
+        return self.quantizer.max_
+
+    def __repr__(self):
+        return f"scalar.Quantizer(min={self.min}, max={self.max})"
+
+
+@auto_sync_resources
+def train(QuantizerParams params, dataset, resources=None):
+    """
+    Initializes a scalar quantizer to be used later for quantizing the dataset.
+
+    Parameters
+    ----------
+    params : QuantizerParams object
+    dataset : row major host or device dataset
+    {resources_docstring}
+
+    Returns
+    -------
+    quantizer: cuvs.preprocessing.quantize.scalar.Quantizer
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.preprocessing.quantize import scalar
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> params = scalar.QuantizerParams(quantile=0.99)
+    >>> quantizer = scalar.train(params, dataset)
+    >>> transformed = scalar.transform(quantizer, dataset)
+    """
+    dataset_ai = wrap_array(dataset)
+
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+
+    _check_input_array(dataset_ai, [np.dtype("float32"), np.dtype("float64")])
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    cdef Quantizer ret = Quantizer()
+
+    check_cuvs(cuvsScalarQuantizerTrain(res,
+                                        params.params,
+                                        dataset_dlpack,
+                                        ret.quantizer))
+
+    return ret
+
+
+@auto_sync_resources
+@auto_convert_output
+def transform(Quantizer quantizer, dataset, output=None, resources=None):
+    """
+    Applies quantization transform to given dataset
+
+    Parameters
+    ----------
+    quantizer : trained Quantizer object
+    dataset : row major host or device dataset to transform
+    output : optional preallocated output memory, on host or device memory
+    {resources_docstring}
+
+    Returns
+    -------
+    output : transformed dataset quantized into a int8
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.preprocessing.quantize import scalar
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> params = scalar.QuantizerParams(quantile=0.99)
+    >>> quantizer = scalar.train(params, dataset)
+    >>> transformed = scalar.transform(quantizer, dataset)
+    """
+
+    dataset_ai = wrap_array(dataset)
+
+    _check_input_array(dataset_ai, [np.dtype("float32"), np.dtype("float64")])
+
+    if output is None:
+        on_device = hasattr(dataset, "__cuda_array_interface__")
+        ndarray = device_ndarray if on_device else np
+        output = ndarray.empty((dataset_ai.shape[0],
+                                dataset_ai.shape[1]), dtype="int8")
+
+    output_ai = wrap_array(output)
+    _check_input_array(output_ai, [np.dtype("int8")])
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cydlpack.DLManagedTensor* output_dlpack = \
+        cydlpack.dlpack_c(output_ai)
+
+    check_cuvs(cuvsScalarQuantizerTransform(res,
+                                            quantizer.quantizer,
+                                            dataset_dlpack,
+                                            output_dlpack))
+
+    return output
+
+
+@auto_sync_resources
+@auto_convert_output
+def inverse_transform(Quantizer quantizer, dataset, output=None,
+                      resources=None):
+    """
+    Perform inverse quantization step on previously quantized dataset
+
+    Note that depending on the chosen data types train dataset the conversion
+    is not lossless.
+
+    Parameters
+    ----------
+    quantizer : trained Quantizer object
+    dataset : row major host or device dataset to transform
+    output : optional preallocated output memory, on host or device
+    {resources_docstring}
+
+    Returns
+    -------
+    output : transformed dataset with scalar quantization reversed
+    """
+
+    dataset_ai = wrap_array(dataset)
+
+    _check_input_array(dataset_ai, [np.dtype("int8")])
+
+    if output is None:
+        on_device = hasattr(dataset, "__cuda_array_interface__")
+        ndarray = device_ndarray if on_device else np
+        output = ndarray.empty((dataset_ai.shape[0],
+                                dataset_ai.shape[1]), dtype="float32")
+
+    output_ai = wrap_array(output)
+    _check_input_array(output_ai, [np.dtype("float32"), np.dtype("float64")])
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cydlpack.DLManagedTensor* output_dlpack = \
+        cydlpack.dlpack_c(output_ai)
+
+    check_cuvs(cuvsScalarQuantizerInverseTransform(res,
+                                                   quantizer.quantizer,
+                                                   dataset_dlpack,
+                                                   output_dlpack))
+
+    return output
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/test/test_doctests.py
index 4407bda526..381a077fbd 100644
--- a/python/cuvs/cuvs/test/test_doctests.py
+++ b/python/cuvs/cuvs/test/test_doctests.py
@@ -23,6 +23,7 @@
 
 import cuvs.distance
 import cuvs.neighbors
+import cuvs.preprocessing.quantize.scalar
 
 # Code adapted from https://github.com/rapidsai/cudf/blob/branch-23.02/python/cudf/cudf/tests/test_doctests.py  # noqa
 
@@ -96,6 +97,7 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.ivf_flat))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.common))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.distance))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.preprocessing.quantize.scalar))
 
 
 def _test_name_from_docstring(docstring):
diff --git a/python/cuvs/cuvs/test/test_scalar_quantizer.py b/python/cuvs/cuvs/test/test_scalar_quantizer.py
new file mode 100644
index 0000000000..0dbcada859
--- /dev/null
+++ b/python/cuvs/cuvs/test/test_scalar_quantizer.py
@@ -0,0 +1,53 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+
+from cuvs.preprocessing.quantize import scalar
+
+
+@pytest.mark.parametrize("n_rows", [50, 100])
+@pytest.mark.parametrize("n_cols", [10, 50])
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("device_memory", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+def test_scalar_quantizer(n_rows, n_cols, inplace, device_memory, dtype):
+    input1 = np.random.random_sample((n_rows, n_cols)).astype(dtype)
+    output = np.zeros((n_rows, n_cols), dtype="int8") if inplace else None
+
+    input1_device = device_ndarray(input1)
+    output_device = device_ndarray(output) if inplace else None
+
+    params = scalar.QuantizerParams(quantile=0.99)
+    quantizer = scalar.train(
+        params, input1_device if device_memory else input1
+    )
+    transformed = scalar.transform(
+        quantizer,
+        input1_device if device_memory else input1,
+        output=output_device if device_memory else output,
+    )
+    if device_memory:
+        actual = transformed if not inplace else output_device
+        actual = actual.copy_to_host()
+    else:
+        actual = transformed if not inplace else output
+
+    # naive sq quantization
+    start, end = quantizer.min, quantizer.max
+    expected = np.int8(255 * (input1 - start) / (end - start) - 128)
+    assert np.allclose(expected, actual, atol=2, rtol=2)

From 5527cdf8e983fecc361887fcf280f2f7f9978fe5 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Mon, 27 Jan 2025 05:58:45 -0600
Subject: [PATCH 277/623] Reduce CAGRA test runtime (#602)

Currently, running `NEIGHBORS_ANN_CAGRA_TEST` takes:
[0.96 hours on CUDA 11.8, V100 (x86)](https://github.com/rapidsai/cuvs/actions/runs/12913409417/job/36012418022?pr=596#step:8:1718)
[1.59 hours on CUDA 12.5, V100 (x86)](https://github.com/rapidsai/cuvs/actions/runs/12913409417/job/36012418329?pr=596#step:8:492)
[0.28 hours on CUDA 12.0, A100 (ARM)](https://github.com/rapidsai/cuvs/actions/runs/12913409417/job/36012418741?pr=596#step:8:1729)

Individual tests should be able to complete in less than an hour. Ideally, less than 10 minutes.

This PR proposes some changes to CAGRA tests:
- Each CAGRA type is now its own test executable (e.g. `NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST`)
- Some parameter combinations were trimmed by ~50%

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/602
---
 cpp/test/CMakeLists.txt                       |  35 ++--
 cpp/test/neighbors/ann_cagra.cuh              | 184 +++++++++++++++++-
 .../ann_cagra/test_float_uint32_t.cu          |   6 +-
 .../ann_cagra/test_int8_t_uint32_t.cu         |   6 +-
 .../ann_cagra/test_uint8_t_uint32_t.cu        |   6 +-
 5 files changed, 208 insertions(+), 29 deletions(-)

diff --git a/cpp/test/CMakeLists.txt b/cpp/test/CMakeLists.txt
index 9aa596a6e0..fdca48d71d 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/test/CMakeLists.txt
@@ -138,19 +138,28 @@ if(BUILD_TESTS)
   )
 
   ConfigureTest(
-    NAME
-    NEIGHBORS_ANN_CAGRA_TEST
-    PATH
-    neighbors/ann_cagra/bug_extreme_inputs_oob.cu
-    neighbors/ann_cagra/bug_multi_cta_crash.cu
-    neighbors/ann_cagra/test_float_uint32_t.cu
-    neighbors/ann_cagra/test_half_uint32_t.cu
-    neighbors/ann_cagra/test_int8_t_uint32_t.cu
-    neighbors/ann_cagra/test_uint8_t_uint32_t.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_ANN_CAGRA_TEST_BUGS PATH neighbors/ann_cagra/bug_extreme_inputs_oob.cu
+    neighbors/ann_cagra/bug_multi_cta_crash.cu GPUS 1 PERCENT 100
+  )
+
+  ConfigureTest(
+    NAME NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST PATH neighbors/ann_cagra/test_float_uint32_t.cu GPUS
+    1 PERCENT 100
+  )
+
+  ConfigureTest(
+    NAME NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST PATH neighbors/ann_cagra/test_half_uint32_t.cu GPUS 1
+    PERCENT 100
+  )
+
+  ConfigureTest(
+    NAME NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST PATH neighbors/ann_cagra/test_int8_t_uint32_t.cu GPUS
+    1 PERCENT 100
+  )
+
+  ConfigureTest(
+    NAME NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST PATH neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+    GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/test/neighbors/ann_cagra.cuh
index bbafae6d18..cab91c6982 100644
--- a/cpp/test/neighbors/ann_cagra.cuh
+++ b/cpp/test/neighbors/ann_cagra.cuh
@@ -876,14 +876,15 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
 inline std::vector<AnnCagraInputs> generate_inputs()
 {
   // TODO(tfeher): test MULTI_CTA kernel with search_width > 1 to allow multiple CTA per queries
+  // Charge graph dim, search algo and max_query parameter
   std::vector<AnnCagraInputs> inputs = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
     {1, 8, 17},
-    {1, 16},  // k
-    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
+    {16},  // k
+    {graph_build_algo::NN_DESCENT},
     {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
-    {0, 1, 10, 100},  // query size
+    {0, 10},  // query size
     {0},
     {256},
     {1},
@@ -892,11 +893,30 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {true},
     {0.995});
 
+  // Fixed dim, and changing neighbors and query size (output matrix size)
   auto inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {1, 100},
+    {1000},
+    {8},
+    {1, 16},  // k
+    {graph_build_algo::NN_DESCENT},
+    {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
+    {0},  // query size
+    {0},
+    {256},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false},
+    {true},
+    {0.995});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  // Varying dim and build algo.
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
-    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // dim
-    {16},                                                         // k
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 1024},  // dim
+    {16},                                                    // k
     {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
     {search_algo::AUTO},
     {10},
@@ -908,6 +928,8 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {true},
     {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  // Varying team_size, graph_build_algo
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
@@ -925,6 +947,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
+  // Varying graph_build_algo, itopk_size
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
@@ -942,9 +965,10 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
+  // Varying n_rows, host_dataset
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
-    {10000, 20000},
+    {10000},
     {32},
     {10},
     {graph_build_algo::AUTO},
@@ -959,7 +983,8 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0.985});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
-  // a few PQ configurations
+  // A few PQ configurations.
+  // Varying dim, vq_n_centers
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {10000},
@@ -987,7 +1012,8 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     }
   }
 
-  // refinement options
+  // Refinement options
+  // Varying host_dataset, ivf_pq_search_refine_ratio
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {5000},
@@ -1006,10 +1032,11 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {1.0f, 2.0f, 3.0f});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
+  // Varying dim, adding non_owning_memory_buffer_flag
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
-    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // dim
+    {1, 5, 8, 64, 137, 256, 619, 1024},  // dim
     {10},
     {graph_build_algo::IVF_PQ},
     {search_algo::AUTO},
@@ -1029,6 +1056,143 @@ inline std::vector<AnnCagraInputs> generate_inputs()
   return inputs;
 }
 
-const std::vector<AnnCagraInputs> inputs = generate_inputs();
+inline std::vector<AnnCagraInputs> generate_addnode_inputs()
+{
+  // changing dim
+  std::vector<AnnCagraInputs> inputs = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {1000},
+    {1, 8, 17, 64, 128, 137, 512, 1024},  // dim
+    {16},                                 // k
+    {graph_build_algo::NN_DESCENT},
+    {search_algo::AUTO},
+    {10},
+    {0},
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false},
+    {true},
+    {0.995});
+
+  // testing host and device datasets
+  auto inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {10000},
+    {32},
+    {10},
+    {graph_build_algo::AUTO},
+    {search_algo::AUTO},
+    {10},
+    {0},  // team_size
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false, true},
+    {false},
+    {0.985});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  // a few PQ configurations
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {10000},
+    {192, 1024},  // dim
+    {16},         // k
+    {graph_build_algo::IVF_PQ},
+    {search_algo::AUTO},
+    {10},
+    {0},
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {true},
+    {0.6});                      // don't demand high recall without refinement
+  for (uint32_t pq_len : {2}) {  // for now, only pq_len = 2 is supported, more options coming soon
+    for (uint32_t vq_n_centers : {100}) {
+      for (auto input : inputs2) {
+        vpq_params ps{};
+        ps.pq_dim       = input.dim / pq_len;
+        ps.vq_n_centers = vq_n_centers;
+        input.compression.emplace(ps);
+        inputs.push_back(input);
+      }
+    }
+  }
+
+  return inputs;
+}
+
+inline std::vector<AnnCagraInputs> generate_filtering_inputs()
+{
+  // Charge graph dim, search algo
+  std::vector<AnnCagraInputs> inputs = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {1000},
+    {1, 8, 17},
+    {16},  // k
+    {graph_build_algo::NN_DESCENT},
+    {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
+    {0},  // query size
+    {0},
+    {256},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false},
+    {true},
+    {0.995});
+
+  // Fixed dim, and changing neighbors and query size (output matrix size)
+  auto inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {1, 100},
+    {1000},
+    {8},
+    {1, 16},  // k
+    {graph_build_algo::NN_DESCENT},
+    {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
+    {0},  // query size
+    {0},
+    {256},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {false},
+    {true},
+    {0.995});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  // a few PQ configurations
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {10000},
+    {256},  // dim
+    {16},   // k
+    {graph_build_algo::IVF_PQ},
+    {search_algo::AUTO},
+    {10},
+    {0},
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {true},
+    {0.6});                      // don't demand high recall without refinement
+  for (uint32_t pq_len : {2}) {  // for now, only pq_len = 2 is supported, more options coming soon
+    for (uint32_t vq_n_centers : {100}) {
+      for (auto input : inputs2) {
+        vpq_params ps{};
+        ps.pq_dim       = input.dim / pq_len;
+        ps.vq_n_centers = vq_n_centers;
+        input.compression.emplace(ps);
+        inputs.push_back(input);
+      }
+    }
+  }
+
+  return inputs;
+}
+const std::vector<AnnCagraInputs> inputs           = generate_inputs();
+const std::vector<AnnCagraInputs> inputs_addnode   = generate_addnode_inputs();
+const std::vector<AnnCagraInputs> inputs_filtering = generate_filtering_inputs();
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
index ca188d1320..640657ccb8 100644
--- a/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
@@ -32,7 +32,9 @@ TEST_P(AnnCagraFilterTestF_U32, AnnCagra) { this->testCagra(); }
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
                         AnnCagraAddNodesTestF_U32,
-                        ::testing::ValuesIn(inputs));
-INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest, AnnCagraFilterTestF_U32, ::testing::ValuesIn(inputs));
+                        ::testing::ValuesIn(inputs_addnode));
+INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest,
+                        AnnCagraFilterTestF_U32,
+                        ::testing::ValuesIn(inputs_filtering));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
index 4aa03afd58..32f6289fe5 100644
--- a/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
@@ -30,7 +30,9 @@ TEST_P(AnnCagraFilterTestI8_U32, AnnCagra) { this->testCagra(); }
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestI8_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
                         AnnCagraAddNodesTestI8_U32,
-                        ::testing::ValuesIn(inputs));
-INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest, AnnCagraFilterTestI8_U32, ::testing::ValuesIn(inputs));
+                        ::testing::ValuesIn(inputs_addnode));
+INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest,
+                        AnnCagraFilterTestI8_U32,
+                        ::testing::ValuesIn(inputs_filtering));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu b/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
index b8e2a6b770..53f804be6e 100644
--- a/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+++ b/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
@@ -30,7 +30,9 @@ TEST_P(AnnCagraFilterTestU8_U32, AnnCagra) { this->testCagra(); }
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestU8_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
                         AnnCagraAddNodesTestU8_U32,
-                        ::testing::ValuesIn(inputs));
-INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest, AnnCagraFilterTestU8_U32, ::testing::ValuesIn(inputs));
+                        ::testing::ValuesIn(inputs_addnode));
+INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest,
+                        AnnCagraFilterTestU8_U32,
+                        ::testing::ValuesIn(inputs_filtering));
 
 }  // namespace cuvs::neighbors::cagra

From a1e3b511ee0d6ae8b272b55d5445f0248c022432 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Mon, 27 Jan 2025 22:35:58 -0600
Subject: [PATCH 278/623] Rename test to tests. (#590)

Renames `test` directories to `tests` for alignment with the rest of RAPIDS.

Closes https://github.com/rapidsai/build-planning/issues/140.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Ray Douglass (https://github.com/raydouglass)
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/590
---
 .pre-commit-config.yaml                                         | 2 +-
 ci/run_cuvs_pytests.sh                                          | 2 +-
 ci/test_python.sh                                               | 2 +-
 ci/test_wheel_cuvs.sh                                           | 2 +-
 cpp/CMakeLists.txt                                              | 2 +-
 cpp/{test => tests}/CMakeLists.txt                              | 2 +-
 cpp/{test => tests}/cluster/kmeans.cu                           | 0
 cpp/{test => tests}/cluster/kmeans_balanced.cu                  | 0
 cpp/{test => tests}/cluster/kmeans_find_k.cu                    | 0
 cpp/{test => tests}/cluster/kmeans_mg.cu                        | 0
 cpp/{test => tests}/cluster/linkage.cu                          | 0
 cpp/{test => tests}/core/c_api.c                                | 0
 cpp/{test => tests}/core/interop.cu                             | 0
 cpp/{test => tests}/distance/dist_canberra.cu                   | 0
 cpp/{test => tests}/distance/dist_correlation.cu                | 0
 cpp/{test => tests}/distance/dist_cos.cu                        | 0
 cpp/{test => tests}/distance/dist_hamming.cu                    | 0
 cpp/{test => tests}/distance/dist_hellinger.cu                  | 0
 cpp/{test => tests}/distance/dist_inner_product.cu              | 0
 cpp/{test => tests}/distance/dist_jensen_shannon.cu             | 0
 cpp/{test => tests}/distance/dist_kl_divergence.cu              | 0
 cpp/{test => tests}/distance/dist_l1.cu                         | 0
 cpp/{test => tests}/distance/dist_l2_exp.cu                     | 0
 cpp/{test => tests}/distance/dist_l2_sqrt_exp.cu                | 0
 cpp/{test => tests}/distance/dist_l2_unexp.cu                   | 0
 cpp/{test => tests}/distance/dist_l_inf.cu                      | 0
 cpp/{test => tests}/distance/dist_lp_unexp.cu                   | 0
 cpp/{test => tests}/distance/dist_russell_rao.cu                | 0
 cpp/{test => tests}/distance/distance_base.cuh                  | 0
 cpp/{test => tests}/distance/gram.cu                            | 0
 cpp/{test => tests}/distance/gram_base.cuh                      | 0
 cpp/{test => tests}/distance/masked_nn.cu                       | 0
 cpp/{test => tests}/distance/pairwise_distance_c.cu             | 0
 cpp/{test => tests}/distance/run_pairwise_distance_c.c          | 0
 cpp/{test => tests}/distance/sparse_distance.cu                 | 0
 cpp/{test => tests}/neighbors/ann_brute_force.cuh               | 0
 cpp/{test => tests}/neighbors/ann_brute_force/test_float.cu     | 0
 cpp/{test => tests}/neighbors/ann_brute_force/test_half.cu      | 0
 cpp/{test => tests}/neighbors/ann_cagra.cuh                     | 0
 .../neighbors/ann_cagra/bug_extreme_inputs_oob.cu               | 0
 cpp/{test => tests}/neighbors/ann_cagra/bug_multi_cta_crash.cu  | 0
 cpp/{test => tests}/neighbors/ann_cagra/test_float_uint32_t.cu  | 0
 cpp/{test => tests}/neighbors/ann_cagra/test_half_uint32_t.cu   | 0
 cpp/{test => tests}/neighbors/ann_cagra/test_int8_t_uint32_t.cu | 0
 .../neighbors/ann_cagra/test_uint8_t_uint32_t.cu                | 0
 cpp/{test => tests}/neighbors/ann_cagra_c.cu                    | 0
 cpp/{test => tests}/neighbors/ann_hnsw_c.cu                     | 0
 cpp/{test => tests}/neighbors/ann_ivf_flat.cuh                  | 0
 .../neighbors/ann_ivf_flat/test_float_int64_t.cu                | 0
 .../neighbors/ann_ivf_flat/test_int8_t_int64_t.cu               | 0
 .../neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu              | 0
 cpp/{test => tests}/neighbors/ann_ivf_flat_c.cu                 | 0
 cpp/{test => tests}/neighbors/ann_ivf_pq.cuh                    | 0
 cpp/{test => tests}/neighbors/ann_ivf_pq/test_float_int64_t.cu  | 0
 cpp/{test => tests}/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu | 0
 .../neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu                | 0
 cpp/{test => tests}/neighbors/ann_ivf_pq_c.cu                   | 0
 cpp/{test => tests}/neighbors/ann_nn_descent.cuh                | 0
 .../neighbors/ann_nn_descent/test_float_uint32_t.cu             | 0
 .../neighbors/ann_nn_descent/test_int8_t_uint32_t.cu            | 0
 .../neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu           | 0
 cpp/{test => tests}/neighbors/ann_utils.cuh                     | 0
 cpp/{test => tests}/neighbors/ann_vamana.cuh                    | 0
 cpp/{test => tests}/neighbors/ann_vamana/test_float_uint32_t.cu | 0
 .../neighbors/ann_vamana/test_int8_t_uint32_t.cu                | 0
 .../neighbors/ann_vamana/test_uint8_t_uint32_t.cu               | 0
 cpp/{test => tests}/neighbors/brute_force.cu                    | 0
 cpp/{test => tests}/neighbors/brute_force_c.cu                  | 0
 cpp/{test => tests}/neighbors/brute_force_prefiltered.cu        | 0
 cpp/{test => tests}/neighbors/c_api.c                           | 0
 cpp/{test => tests}/neighbors/dynamic_batching.cuh              | 0
 .../neighbors/dynamic_batching/test_brute_force.cu              | 0
 cpp/{test => tests}/neighbors/dynamic_batching/test_cagra.cu    | 0
 cpp/{test => tests}/neighbors/dynamic_batching/test_ivf_flat.cu | 0
 cpp/{test => tests}/neighbors/dynamic_batching/test_ivf_pq.cu   | 0
 cpp/{test => tests}/neighbors/hnsw.cu                           | 0
 cpp/{test => tests}/neighbors/knn_utils.cuh                     | 0
 cpp/{test => tests}/neighbors/mg.cuh                            | 0
 cpp/{test => tests}/neighbors/mg/test_float.cu                  | 0
 cpp/{test => tests}/neighbors/naive_knn.cuh                     | 0
 cpp/{test => tests}/neighbors/refine.cu                         | 0
 cpp/{test => tests}/neighbors/refine_helper.cuh                 | 0
 cpp/{test => tests}/neighbors/run_brute_force_c.c               | 0
 cpp/{test => tests}/neighbors/run_ivf_flat_c.c                  | 0
 cpp/{test => tests}/neighbors/run_ivf_pq_c.c                    | 0
 cpp/{test => tests}/neighbors/sparse_brute_force.cu             | 0
 cpp/{test => tests}/preprocessing/scalar_quantization.cu        | 0
 cpp/{test => tests}/sparse/cluster/cluster_solvers.cu           | 0
 cpp/{test => tests}/sparse/cluster/eigen_solvers.cu             | 0
 cpp/{test => tests}/sparse/cluster/spectral.cu                  | 0
 cpp/{test => tests}/sparse/cluster/spectral_matrix.cu           | 0
 cpp/{test => tests}/sparse/gram.cu                              | 0
 cpp/{test => tests}/sparse/neighbors/cross_component_nn.cu      | 0
 cpp/{test => tests}/stats/silhouette_score.cu                   | 0
 cpp/{test => tests}/stats/trustworthiness.cu                    | 0
 cpp/{test => tests}/test.cpp                                    | 0
 cpp/{test => tests}/test_utils.cuh                              | 0
 cpp/{test => tests}/test_utils.h                                | 0
 docs/source/developer_guide.md                                  | 2 +-
 python/cuvs/cuvs/{test => tests}/__init__py                     | 0
 python/cuvs/cuvs/{test => tests}/ann_utils.py                   | 0
 python/cuvs/cuvs/{test => tests}/conftest.py                    | 0
 python/cuvs/cuvs/{test => tests}/test_brute_force.py            | 0
 python/cuvs/cuvs/{test => tests}/test_cagra.py                  | 2 +-
 python/cuvs/cuvs/{test => tests}/test_distance.py               | 0
 python/cuvs/cuvs/{test => tests}/test_doctests.py               | 0
 python/cuvs/cuvs/{test => tests}/test_hnsw.py                   | 2 +-
 python/cuvs/cuvs/{test => tests}/test_ivf_flat.py               | 2 +-
 python/cuvs/cuvs/{test => tests}/test_ivf_pq.py                 | 2 +-
 python/cuvs/cuvs/{test => tests}/test_refine.py                 | 0
 python/cuvs/cuvs/{test => tests}/test_scalar_quantizer.py       | 0
 python/cuvs/cuvs/{test => tests}/test_serialization.py          | 2 +-
 python/cuvs/cuvs/{test => tests}/test_version.py                | 0
 113 files changed, 12 insertions(+), 12 deletions(-)
 rename cpp/{test => tests}/CMakeLists.txt (99%)
 rename cpp/{test => tests}/cluster/kmeans.cu (100%)
 rename cpp/{test => tests}/cluster/kmeans_balanced.cu (100%)
 rename cpp/{test => tests}/cluster/kmeans_find_k.cu (100%)
 rename cpp/{test => tests}/cluster/kmeans_mg.cu (100%)
 rename cpp/{test => tests}/cluster/linkage.cu (100%)
 rename cpp/{test => tests}/core/c_api.c (100%)
 rename cpp/{test => tests}/core/interop.cu (100%)
 rename cpp/{test => tests}/distance/dist_canberra.cu (100%)
 rename cpp/{test => tests}/distance/dist_correlation.cu (100%)
 rename cpp/{test => tests}/distance/dist_cos.cu (100%)
 rename cpp/{test => tests}/distance/dist_hamming.cu (100%)
 rename cpp/{test => tests}/distance/dist_hellinger.cu (100%)
 rename cpp/{test => tests}/distance/dist_inner_product.cu (100%)
 rename cpp/{test => tests}/distance/dist_jensen_shannon.cu (100%)
 rename cpp/{test => tests}/distance/dist_kl_divergence.cu (100%)
 rename cpp/{test => tests}/distance/dist_l1.cu (100%)
 rename cpp/{test => tests}/distance/dist_l2_exp.cu (100%)
 rename cpp/{test => tests}/distance/dist_l2_sqrt_exp.cu (100%)
 rename cpp/{test => tests}/distance/dist_l2_unexp.cu (100%)
 rename cpp/{test => tests}/distance/dist_l_inf.cu (100%)
 rename cpp/{test => tests}/distance/dist_lp_unexp.cu (100%)
 rename cpp/{test => tests}/distance/dist_russell_rao.cu (100%)
 rename cpp/{test => tests}/distance/distance_base.cuh (100%)
 rename cpp/{test => tests}/distance/gram.cu (100%)
 rename cpp/{test => tests}/distance/gram_base.cuh (100%)
 rename cpp/{test => tests}/distance/masked_nn.cu (100%)
 rename cpp/{test => tests}/distance/pairwise_distance_c.cu (100%)
 rename cpp/{test => tests}/distance/run_pairwise_distance_c.c (100%)
 rename cpp/{test => tests}/distance/sparse_distance.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_brute_force.cuh (100%)
 rename cpp/{test => tests}/neighbors/ann_brute_force/test_float.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_brute_force/test_half.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra.cuh (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra/bug_extreme_inputs_oob.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra/bug_multi_cta_crash.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra/test_float_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra/test_half_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra/test_int8_t_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra/test_uint8_t_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_cagra_c.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_hnsw_c.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_flat.cuh (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_flat/test_float_int64_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_flat_c.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_pq.cuh (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_pq/test_float_int64_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_ivf_pq_c.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_nn_descent.cuh (100%)
 rename cpp/{test => tests}/neighbors/ann_nn_descent/test_float_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_utils.cuh (100%)
 rename cpp/{test => tests}/neighbors/ann_vamana.cuh (100%)
 rename cpp/{test => tests}/neighbors/ann_vamana/test_float_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_vamana/test_int8_t_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/ann_vamana/test_uint8_t_uint32_t.cu (100%)
 rename cpp/{test => tests}/neighbors/brute_force.cu (100%)
 rename cpp/{test => tests}/neighbors/brute_force_c.cu (100%)
 rename cpp/{test => tests}/neighbors/brute_force_prefiltered.cu (100%)
 rename cpp/{test => tests}/neighbors/c_api.c (100%)
 rename cpp/{test => tests}/neighbors/dynamic_batching.cuh (100%)
 rename cpp/{test => tests}/neighbors/dynamic_batching/test_brute_force.cu (100%)
 rename cpp/{test => tests}/neighbors/dynamic_batching/test_cagra.cu (100%)
 rename cpp/{test => tests}/neighbors/dynamic_batching/test_ivf_flat.cu (100%)
 rename cpp/{test => tests}/neighbors/dynamic_batching/test_ivf_pq.cu (100%)
 rename cpp/{test => tests}/neighbors/hnsw.cu (100%)
 rename cpp/{test => tests}/neighbors/knn_utils.cuh (100%)
 rename cpp/{test => tests}/neighbors/mg.cuh (100%)
 rename cpp/{test => tests}/neighbors/mg/test_float.cu (100%)
 rename cpp/{test => tests}/neighbors/naive_knn.cuh (100%)
 rename cpp/{test => tests}/neighbors/refine.cu (100%)
 rename cpp/{test => tests}/neighbors/refine_helper.cuh (100%)
 rename cpp/{test => tests}/neighbors/run_brute_force_c.c (100%)
 rename cpp/{test => tests}/neighbors/run_ivf_flat_c.c (100%)
 rename cpp/{test => tests}/neighbors/run_ivf_pq_c.c (100%)
 rename cpp/{test => tests}/neighbors/sparse_brute_force.cu (100%)
 rename cpp/{test => tests}/preprocessing/scalar_quantization.cu (100%)
 rename cpp/{test => tests}/sparse/cluster/cluster_solvers.cu (100%)
 rename cpp/{test => tests}/sparse/cluster/eigen_solvers.cu (100%)
 rename cpp/{test => tests}/sparse/cluster/spectral.cu (100%)
 rename cpp/{test => tests}/sparse/cluster/spectral_matrix.cu (100%)
 rename cpp/{test => tests}/sparse/gram.cu (100%)
 rename cpp/{test => tests}/sparse/neighbors/cross_component_nn.cu (100%)
 rename cpp/{test => tests}/stats/silhouette_score.cu (100%)
 rename cpp/{test => tests}/stats/trustworthiness.cu (100%)
 rename cpp/{test => tests}/test.cpp (100%)
 rename cpp/{test => tests}/test_utils.cuh (100%)
 rename cpp/{test => tests}/test_utils.h (100%)
 rename python/cuvs/cuvs/{test => tests}/__init__py (100%)
 rename python/cuvs/cuvs/{test => tests}/ann_utils.py (100%)
 rename python/cuvs/cuvs/{test => tests}/conftest.py (100%)
 rename python/cuvs/cuvs/{test => tests}/test_brute_force.py (100%)
 rename python/cuvs/cuvs/{test => tests}/test_cagra.py (99%)
 rename python/cuvs/cuvs/{test => tests}/test_distance.py (100%)
 rename python/cuvs/cuvs/{test => tests}/test_doctests.py (100%)
 rename python/cuvs/cuvs/{test => tests}/test_hnsw.py (98%)
 rename python/cuvs/cuvs/{test => tests}/test_ivf_flat.py (98%)
 rename python/cuvs/cuvs/{test => tests}/test_ivf_pq.py (98%)
 rename python/cuvs/cuvs/{test => tests}/test_refine.py (100%)
 rename python/cuvs/cuvs/{test => tests}/test_scalar_quantizer.py (100%)
 rename python/cuvs/cuvs/{test => tests}/test_serialization.py (98%)
 rename python/cuvs/cuvs/{test => tests}/test_version.py (100%)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 2f064ebb61..1124d3814e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -86,7 +86,7 @@ repos:
                 exclude: .*/thirdparty/.*
               - id: include-check
                 name: include-check
-                entry: python ./cpp/scripts/include_checker.py cpp/bench cpp/include cpp/test
+                entry: python ./cpp/scripts/include_checker.py cpp/bench cpp/include cpp/tests
                 pass_filenames: false
                 language: python
                 additional_dependencies: [gitpython]
diff --git a/ci/run_cuvs_pytests.sh b/ci/run_cuvs_pytests.sh
index 57df9af940..4de8927b17 100755
--- a/ci/run_cuvs_pytests.sh
+++ b/ci/run_cuvs_pytests.sh
@@ -6,4 +6,4 @@ set -euo pipefail
 # Support invoking run_pytests.sh outside the script directory
 cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/cuvs/cuvs
 
-pytest --cache-clear --verbose "$@" test
+pytest --cache-clear --verbose "$@" tests
diff --git a/ci/test_python.sh b/ci/test_python.sh
index b9c394062e..e8749fe798 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -52,7 +52,7 @@ pytest \
  --cov=cuvs \
  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cuvs-coverage.xml" \
  --cov-report=term \
- test
+ tests
 
 rapids-logger "Test script exiting with value: $EXITCODE"
 exit ${EXITCODE}
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 862c69a3aa..cb74c52064 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -13,4 +13,4 @@ python -m pip install \
     ./local-libcuvs-dep/libcuvs*.whl \
     "$(echo ./dist/cuvs*.whl)[test]"
 
-python -m pytest ./python/cuvs/cuvs/test
+python -m pytest ./python/cuvs/cuvs/tests
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index f88ca1af51..63e57965be 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -798,7 +798,7 @@ endif()
 if(BUILD_TESTS)
   enable_testing()
   add_subdirectory(internal)
-  add_subdirectory(test)
+  add_subdirectory(tests)
 endif()
 
 # ##################################################################################################
diff --git a/cpp/test/CMakeLists.txt b/cpp/tests/CMakeLists.txt
similarity index 99%
rename from cpp/test/CMakeLists.txt
rename to cpp/tests/CMakeLists.txt
index fdca48d71d..3ed37175d5 100644
--- a/cpp/test/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -77,7 +77,7 @@ function(ConfigureTest)
     target_compile_definitions(${TEST_NAME} PRIVATE "CUVS_DISABLE_CUDA")
   endif()
 
-  target_include_directories(${TEST_NAME} PUBLIC "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/test>")
+  target_include_directories(${TEST_NAME} PUBLIC "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/tests>")
 
   rapids_test_add(
     NAME ${TEST_NAME}
diff --git a/cpp/test/cluster/kmeans.cu b/cpp/tests/cluster/kmeans.cu
similarity index 100%
rename from cpp/test/cluster/kmeans.cu
rename to cpp/tests/cluster/kmeans.cu
diff --git a/cpp/test/cluster/kmeans_balanced.cu b/cpp/tests/cluster/kmeans_balanced.cu
similarity index 100%
rename from cpp/test/cluster/kmeans_balanced.cu
rename to cpp/tests/cluster/kmeans_balanced.cu
diff --git a/cpp/test/cluster/kmeans_find_k.cu b/cpp/tests/cluster/kmeans_find_k.cu
similarity index 100%
rename from cpp/test/cluster/kmeans_find_k.cu
rename to cpp/tests/cluster/kmeans_find_k.cu
diff --git a/cpp/test/cluster/kmeans_mg.cu b/cpp/tests/cluster/kmeans_mg.cu
similarity index 100%
rename from cpp/test/cluster/kmeans_mg.cu
rename to cpp/tests/cluster/kmeans_mg.cu
diff --git a/cpp/test/cluster/linkage.cu b/cpp/tests/cluster/linkage.cu
similarity index 100%
rename from cpp/test/cluster/linkage.cu
rename to cpp/tests/cluster/linkage.cu
diff --git a/cpp/test/core/c_api.c b/cpp/tests/core/c_api.c
similarity index 100%
rename from cpp/test/core/c_api.c
rename to cpp/tests/core/c_api.c
diff --git a/cpp/test/core/interop.cu b/cpp/tests/core/interop.cu
similarity index 100%
rename from cpp/test/core/interop.cu
rename to cpp/tests/core/interop.cu
diff --git a/cpp/test/distance/dist_canberra.cu b/cpp/tests/distance/dist_canberra.cu
similarity index 100%
rename from cpp/test/distance/dist_canberra.cu
rename to cpp/tests/distance/dist_canberra.cu
diff --git a/cpp/test/distance/dist_correlation.cu b/cpp/tests/distance/dist_correlation.cu
similarity index 100%
rename from cpp/test/distance/dist_correlation.cu
rename to cpp/tests/distance/dist_correlation.cu
diff --git a/cpp/test/distance/dist_cos.cu b/cpp/tests/distance/dist_cos.cu
similarity index 100%
rename from cpp/test/distance/dist_cos.cu
rename to cpp/tests/distance/dist_cos.cu
diff --git a/cpp/test/distance/dist_hamming.cu b/cpp/tests/distance/dist_hamming.cu
similarity index 100%
rename from cpp/test/distance/dist_hamming.cu
rename to cpp/tests/distance/dist_hamming.cu
diff --git a/cpp/test/distance/dist_hellinger.cu b/cpp/tests/distance/dist_hellinger.cu
similarity index 100%
rename from cpp/test/distance/dist_hellinger.cu
rename to cpp/tests/distance/dist_hellinger.cu
diff --git a/cpp/test/distance/dist_inner_product.cu b/cpp/tests/distance/dist_inner_product.cu
similarity index 100%
rename from cpp/test/distance/dist_inner_product.cu
rename to cpp/tests/distance/dist_inner_product.cu
diff --git a/cpp/test/distance/dist_jensen_shannon.cu b/cpp/tests/distance/dist_jensen_shannon.cu
similarity index 100%
rename from cpp/test/distance/dist_jensen_shannon.cu
rename to cpp/tests/distance/dist_jensen_shannon.cu
diff --git a/cpp/test/distance/dist_kl_divergence.cu b/cpp/tests/distance/dist_kl_divergence.cu
similarity index 100%
rename from cpp/test/distance/dist_kl_divergence.cu
rename to cpp/tests/distance/dist_kl_divergence.cu
diff --git a/cpp/test/distance/dist_l1.cu b/cpp/tests/distance/dist_l1.cu
similarity index 100%
rename from cpp/test/distance/dist_l1.cu
rename to cpp/tests/distance/dist_l1.cu
diff --git a/cpp/test/distance/dist_l2_exp.cu b/cpp/tests/distance/dist_l2_exp.cu
similarity index 100%
rename from cpp/test/distance/dist_l2_exp.cu
rename to cpp/tests/distance/dist_l2_exp.cu
diff --git a/cpp/test/distance/dist_l2_sqrt_exp.cu b/cpp/tests/distance/dist_l2_sqrt_exp.cu
similarity index 100%
rename from cpp/test/distance/dist_l2_sqrt_exp.cu
rename to cpp/tests/distance/dist_l2_sqrt_exp.cu
diff --git a/cpp/test/distance/dist_l2_unexp.cu b/cpp/tests/distance/dist_l2_unexp.cu
similarity index 100%
rename from cpp/test/distance/dist_l2_unexp.cu
rename to cpp/tests/distance/dist_l2_unexp.cu
diff --git a/cpp/test/distance/dist_l_inf.cu b/cpp/tests/distance/dist_l_inf.cu
similarity index 100%
rename from cpp/test/distance/dist_l_inf.cu
rename to cpp/tests/distance/dist_l_inf.cu
diff --git a/cpp/test/distance/dist_lp_unexp.cu b/cpp/tests/distance/dist_lp_unexp.cu
similarity index 100%
rename from cpp/test/distance/dist_lp_unexp.cu
rename to cpp/tests/distance/dist_lp_unexp.cu
diff --git a/cpp/test/distance/dist_russell_rao.cu b/cpp/tests/distance/dist_russell_rao.cu
similarity index 100%
rename from cpp/test/distance/dist_russell_rao.cu
rename to cpp/tests/distance/dist_russell_rao.cu
diff --git a/cpp/test/distance/distance_base.cuh b/cpp/tests/distance/distance_base.cuh
similarity index 100%
rename from cpp/test/distance/distance_base.cuh
rename to cpp/tests/distance/distance_base.cuh
diff --git a/cpp/test/distance/gram.cu b/cpp/tests/distance/gram.cu
similarity index 100%
rename from cpp/test/distance/gram.cu
rename to cpp/tests/distance/gram.cu
diff --git a/cpp/test/distance/gram_base.cuh b/cpp/tests/distance/gram_base.cuh
similarity index 100%
rename from cpp/test/distance/gram_base.cuh
rename to cpp/tests/distance/gram_base.cuh
diff --git a/cpp/test/distance/masked_nn.cu b/cpp/tests/distance/masked_nn.cu
similarity index 100%
rename from cpp/test/distance/masked_nn.cu
rename to cpp/tests/distance/masked_nn.cu
diff --git a/cpp/test/distance/pairwise_distance_c.cu b/cpp/tests/distance/pairwise_distance_c.cu
similarity index 100%
rename from cpp/test/distance/pairwise_distance_c.cu
rename to cpp/tests/distance/pairwise_distance_c.cu
diff --git a/cpp/test/distance/run_pairwise_distance_c.c b/cpp/tests/distance/run_pairwise_distance_c.c
similarity index 100%
rename from cpp/test/distance/run_pairwise_distance_c.c
rename to cpp/tests/distance/run_pairwise_distance_c.c
diff --git a/cpp/test/distance/sparse_distance.cu b/cpp/tests/distance/sparse_distance.cu
similarity index 100%
rename from cpp/test/distance/sparse_distance.cu
rename to cpp/tests/distance/sparse_distance.cu
diff --git a/cpp/test/neighbors/ann_brute_force.cuh b/cpp/tests/neighbors/ann_brute_force.cuh
similarity index 100%
rename from cpp/test/neighbors/ann_brute_force.cuh
rename to cpp/tests/neighbors/ann_brute_force.cuh
diff --git a/cpp/test/neighbors/ann_brute_force/test_float.cu b/cpp/tests/neighbors/ann_brute_force/test_float.cu
similarity index 100%
rename from cpp/test/neighbors/ann_brute_force/test_float.cu
rename to cpp/tests/neighbors/ann_brute_force/test_float.cu
diff --git a/cpp/test/neighbors/ann_brute_force/test_half.cu b/cpp/tests/neighbors/ann_brute_force/test_half.cu
similarity index 100%
rename from cpp/test/neighbors/ann_brute_force/test_half.cu
rename to cpp/tests/neighbors/ann_brute_force/test_half.cu
diff --git a/cpp/test/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
similarity index 100%
rename from cpp/test/neighbors/ann_cagra.cuh
rename to cpp/tests/neighbors/ann_cagra.cuh
diff --git a/cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu b/cpp/tests/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
similarity index 100%
rename from cpp/test/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
rename to cpp/tests/neighbors/ann_cagra/bug_extreme_inputs_oob.cu
diff --git a/cpp/test/neighbors/ann_cagra/bug_multi_cta_crash.cu b/cpp/tests/neighbors/ann_cagra/bug_multi_cta_crash.cu
similarity index 100%
rename from cpp/test/neighbors/ann_cagra/bug_multi_cta_crash.cu
rename to cpp/tests/neighbors/ann_cagra/bug_multi_cta_crash.cu
diff --git a/cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_cagra/test_float_uint32_t.cu
rename to cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
diff --git a/cpp/test/neighbors/ann_cagra/test_half_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_cagra/test_half_uint32_t.cu
rename to cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
diff --git a/cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_int8_t_uint32_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_cagra/test_int8_t_uint32_t.cu
rename to cpp/tests/neighbors/ann_cagra/test_int8_t_uint32_t.cu
diff --git a/cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
rename to cpp/tests/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
diff --git a/cpp/test/neighbors/ann_cagra_c.cu b/cpp/tests/neighbors/ann_cagra_c.cu
similarity index 100%
rename from cpp/test/neighbors/ann_cagra_c.cu
rename to cpp/tests/neighbors/ann_cagra_c.cu
diff --git a/cpp/test/neighbors/ann_hnsw_c.cu b/cpp/tests/neighbors/ann_hnsw_c.cu
similarity index 100%
rename from cpp/test/neighbors/ann_hnsw_c.cu
rename to cpp/tests/neighbors/ann_hnsw_c.cu
diff --git a/cpp/test/neighbors/ann_ivf_flat.cuh b/cpp/tests/neighbors/ann_ivf_flat.cuh
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_flat.cuh
rename to cpp/tests/neighbors/ann_ivf_flat.cuh
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu b/cpp/tests/neighbors/ann_ivf_flat/test_float_int64_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_flat/test_float_int64_t.cu
rename to cpp/tests/neighbors/ann_ivf_flat/test_float_int64_t.cu
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu b/cpp/tests/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
rename to cpp/tests/neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
diff --git a/cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu b/cpp/tests/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
rename to cpp/tests/neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
diff --git a/cpp/test/neighbors/ann_ivf_flat_c.cu b/cpp/tests/neighbors/ann_ivf_flat_c.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_flat_c.cu
rename to cpp/tests/neighbors/ann_ivf_flat_c.cu
diff --git a/cpp/test/neighbors/ann_ivf_pq.cuh b/cpp/tests/neighbors/ann_ivf_pq.cuh
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_pq.cuh
rename to cpp/tests/neighbors/ann_ivf_pq.cuh
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu b/cpp/tests/neighbors/ann_ivf_pq/test_float_int64_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_pq/test_float_int64_t.cu
rename to cpp/tests/neighbors/ann_ivf_pq/test_float_int64_t.cu
diff --git a/cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu b/cpp/tests/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
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diff --git a/cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu b/cpp/tests/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
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diff --git a/cpp/test/neighbors/ann_ivf_pq_c.cu b/cpp/tests/neighbors/ann_ivf_pq_c.cu
similarity index 100%
rename from cpp/test/neighbors/ann_ivf_pq_c.cu
rename to cpp/tests/neighbors/ann_ivf_pq_c.cu
diff --git a/cpp/test/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
similarity index 100%
rename from cpp/test/neighbors/ann_nn_descent.cuh
rename to cpp/tests/neighbors/ann_nn_descent.cuh
diff --git a/cpp/test/neighbors/ann_nn_descent/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
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diff --git a/cpp/test/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
similarity index 100%
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diff --git a/cpp/test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
similarity index 100%
rename from cpp/test/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
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diff --git a/cpp/test/neighbors/ann_utils.cuh b/cpp/tests/neighbors/ann_utils.cuh
similarity index 100%
rename from cpp/test/neighbors/ann_utils.cuh
rename to cpp/tests/neighbors/ann_utils.cuh
diff --git a/cpp/test/neighbors/ann_vamana.cuh b/cpp/tests/neighbors/ann_vamana.cuh
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rename from cpp/test/neighbors/ann_vamana.cuh
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diff --git a/cpp/test/neighbors/ann_vamana/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_vamana/test_float_uint32_t.cu
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diff --git a/cpp/test/neighbors/ann_vamana/test_int8_t_uint32_t.cu b/cpp/tests/neighbors/ann_vamana/test_int8_t_uint32_t.cu
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diff --git a/cpp/test/neighbors/ann_vamana/test_uint8_t_uint32_t.cu b/cpp/tests/neighbors/ann_vamana/test_uint8_t_uint32_t.cu
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diff --git a/cpp/test/neighbors/brute_force.cu b/cpp/tests/neighbors/brute_force.cu
similarity index 100%
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rename to cpp/tests/neighbors/brute_force.cu
diff --git a/cpp/test/neighbors/brute_force_c.cu b/cpp/tests/neighbors/brute_force_c.cu
similarity index 100%
rename from cpp/test/neighbors/brute_force_c.cu
rename to cpp/tests/neighbors/brute_force_c.cu
diff --git a/cpp/test/neighbors/brute_force_prefiltered.cu b/cpp/tests/neighbors/brute_force_prefiltered.cu
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diff --git a/cpp/test/neighbors/c_api.c b/cpp/tests/neighbors/c_api.c
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diff --git a/cpp/test/neighbors/dynamic_batching.cuh b/cpp/tests/neighbors/dynamic_batching.cuh
similarity index 100%
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diff --git a/cpp/test/neighbors/dynamic_batching/test_brute_force.cu b/cpp/tests/neighbors/dynamic_batching/test_brute_force.cu
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diff --git a/cpp/test/neighbors/dynamic_batching/test_cagra.cu b/cpp/tests/neighbors/dynamic_batching/test_cagra.cu
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diff --git a/cpp/test/neighbors/dynamic_batching/test_ivf_flat.cu b/cpp/tests/neighbors/dynamic_batching/test_ivf_flat.cu
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diff --git a/cpp/test/neighbors/dynamic_batching/test_ivf_pq.cu b/cpp/tests/neighbors/dynamic_batching/test_ivf_pq.cu
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diff --git a/cpp/test/neighbors/hnsw.cu b/cpp/tests/neighbors/hnsw.cu
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diff --git a/cpp/test/neighbors/knn_utils.cuh b/cpp/tests/neighbors/knn_utils.cuh
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diff --git a/cpp/test/neighbors/mg.cuh b/cpp/tests/neighbors/mg.cuh
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diff --git a/cpp/test/neighbors/mg/test_float.cu b/cpp/tests/neighbors/mg/test_float.cu
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diff --git a/cpp/test/neighbors/naive_knn.cuh b/cpp/tests/neighbors/naive_knn.cuh
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diff --git a/cpp/test/neighbors/refine.cu b/cpp/tests/neighbors/refine.cu
similarity index 100%
rename from cpp/test/neighbors/refine.cu
rename to cpp/tests/neighbors/refine.cu
diff --git a/cpp/test/neighbors/refine_helper.cuh b/cpp/tests/neighbors/refine_helper.cuh
similarity index 100%
rename from cpp/test/neighbors/refine_helper.cuh
rename to cpp/tests/neighbors/refine_helper.cuh
diff --git a/cpp/test/neighbors/run_brute_force_c.c b/cpp/tests/neighbors/run_brute_force_c.c
similarity index 100%
rename from cpp/test/neighbors/run_brute_force_c.c
rename to cpp/tests/neighbors/run_brute_force_c.c
diff --git a/cpp/test/neighbors/run_ivf_flat_c.c b/cpp/tests/neighbors/run_ivf_flat_c.c
similarity index 100%
rename from cpp/test/neighbors/run_ivf_flat_c.c
rename to cpp/tests/neighbors/run_ivf_flat_c.c
diff --git a/cpp/test/neighbors/run_ivf_pq_c.c b/cpp/tests/neighbors/run_ivf_pq_c.c
similarity index 100%
rename from cpp/test/neighbors/run_ivf_pq_c.c
rename to cpp/tests/neighbors/run_ivf_pq_c.c
diff --git a/cpp/test/neighbors/sparse_brute_force.cu b/cpp/tests/neighbors/sparse_brute_force.cu
similarity index 100%
rename from cpp/test/neighbors/sparse_brute_force.cu
rename to cpp/tests/neighbors/sparse_brute_force.cu
diff --git a/cpp/test/preprocessing/scalar_quantization.cu b/cpp/tests/preprocessing/scalar_quantization.cu
similarity index 100%
rename from cpp/test/preprocessing/scalar_quantization.cu
rename to cpp/tests/preprocessing/scalar_quantization.cu
diff --git a/cpp/test/sparse/cluster/cluster_solvers.cu b/cpp/tests/sparse/cluster/cluster_solvers.cu
similarity index 100%
rename from cpp/test/sparse/cluster/cluster_solvers.cu
rename to cpp/tests/sparse/cluster/cluster_solvers.cu
diff --git a/cpp/test/sparse/cluster/eigen_solvers.cu b/cpp/tests/sparse/cluster/eigen_solvers.cu
similarity index 100%
rename from cpp/test/sparse/cluster/eigen_solvers.cu
rename to cpp/tests/sparse/cluster/eigen_solvers.cu
diff --git a/cpp/test/sparse/cluster/spectral.cu b/cpp/tests/sparse/cluster/spectral.cu
similarity index 100%
rename from cpp/test/sparse/cluster/spectral.cu
rename to cpp/tests/sparse/cluster/spectral.cu
diff --git a/cpp/test/sparse/cluster/spectral_matrix.cu b/cpp/tests/sparse/cluster/spectral_matrix.cu
similarity index 100%
rename from cpp/test/sparse/cluster/spectral_matrix.cu
rename to cpp/tests/sparse/cluster/spectral_matrix.cu
diff --git a/cpp/test/sparse/gram.cu b/cpp/tests/sparse/gram.cu
similarity index 100%
rename from cpp/test/sparse/gram.cu
rename to cpp/tests/sparse/gram.cu
diff --git a/cpp/test/sparse/neighbors/cross_component_nn.cu b/cpp/tests/sparse/neighbors/cross_component_nn.cu
similarity index 100%
rename from cpp/test/sparse/neighbors/cross_component_nn.cu
rename to cpp/tests/sparse/neighbors/cross_component_nn.cu
diff --git a/cpp/test/stats/silhouette_score.cu b/cpp/tests/stats/silhouette_score.cu
similarity index 100%
rename from cpp/test/stats/silhouette_score.cu
rename to cpp/tests/stats/silhouette_score.cu
diff --git a/cpp/test/stats/trustworthiness.cu b/cpp/tests/stats/trustworthiness.cu
similarity index 100%
rename from cpp/test/stats/trustworthiness.cu
rename to cpp/tests/stats/trustworthiness.cu
diff --git a/cpp/test/test.cpp b/cpp/tests/test.cpp
similarity index 100%
rename from cpp/test/test.cpp
rename to cpp/tests/test.cpp
diff --git a/cpp/test/test_utils.cuh b/cpp/tests/test_utils.cuh
similarity index 100%
rename from cpp/test/test_utils.cuh
rename to cpp/tests/test_utils.cuh
diff --git a/cpp/test/test_utils.h b/cpp/tests/test_utils.h
similarity index 100%
rename from cpp/test/test_utils.h
rename to cpp/tests/test_utils.h
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 4fdd6405e9..daba99b93a 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -211,7 +211,7 @@ This will bring up an interactive prompt to select which spelling fixes to apply
 
 Manually, run the following to bulk-fix include style issues:
 ```bash
-python ./cpp/scripts/include_checker.py --inplace [cpp/include cpp/test ... list of folders which you want to fix]
+python ./cpp/scripts/include_checker.py --inplace [cpp/include cpp/tests ... list of folders which you want to fix]
 ```
 
 ### Copyright header
diff --git a/python/cuvs/cuvs/test/__init__py b/python/cuvs/cuvs/tests/__init__py
similarity index 100%
rename from python/cuvs/cuvs/test/__init__py
rename to python/cuvs/cuvs/tests/__init__py
diff --git a/python/cuvs/cuvs/test/ann_utils.py b/python/cuvs/cuvs/tests/ann_utils.py
similarity index 100%
rename from python/cuvs/cuvs/test/ann_utils.py
rename to python/cuvs/cuvs/tests/ann_utils.py
diff --git a/python/cuvs/cuvs/test/conftest.py b/python/cuvs/cuvs/tests/conftest.py
similarity index 100%
rename from python/cuvs/cuvs/test/conftest.py
rename to python/cuvs/cuvs/tests/conftest.py
diff --git a/python/cuvs/cuvs/test/test_brute_force.py b/python/cuvs/cuvs/tests/test_brute_force.py
similarity index 100%
rename from python/cuvs/cuvs/test/test_brute_force.py
rename to python/cuvs/cuvs/tests/test_brute_force.py
diff --git a/python/cuvs/cuvs/test/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
similarity index 99%
rename from python/cuvs/cuvs/test/test_cagra.py
rename to python/cuvs/cuvs/tests/test_cagra.py
index 9efd489233..ab3ac4a37b 100644
--- a/python/cuvs/cuvs/test/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -20,7 +20,7 @@
 from sklearn.preprocessing import normalize
 
 from cuvs.neighbors import cagra, filters
-from cuvs.test.ann_utils import calc_recall, generate_data
+from cuvs.tests.ann_utils import calc_recall, generate_data
 
 
 def run_cagra_build_search_test(
diff --git a/python/cuvs/cuvs/test/test_distance.py b/python/cuvs/cuvs/tests/test_distance.py
similarity index 100%
rename from python/cuvs/cuvs/test/test_distance.py
rename to python/cuvs/cuvs/tests/test_distance.py
diff --git a/python/cuvs/cuvs/test/test_doctests.py b/python/cuvs/cuvs/tests/test_doctests.py
similarity index 100%
rename from python/cuvs/cuvs/test/test_doctests.py
rename to python/cuvs/cuvs/tests/test_doctests.py
diff --git a/python/cuvs/cuvs/test/test_hnsw.py b/python/cuvs/cuvs/tests/test_hnsw.py
similarity index 98%
rename from python/cuvs/cuvs/test/test_hnsw.py
rename to python/cuvs/cuvs/tests/test_hnsw.py
index 20f583ae81..e00b88bb7e 100644
--- a/python/cuvs/cuvs/test/test_hnsw.py
+++ b/python/cuvs/cuvs/tests/test_hnsw.py
@@ -19,7 +19,7 @@
 from sklearn.preprocessing import normalize
 
 from cuvs.neighbors import cagra, hnsw
-from cuvs.test.ann_utils import calc_recall, generate_data
+from cuvs.tests.ann_utils import calc_recall, generate_data
 
 
 def run_hnsw_build_search_test(
diff --git a/python/cuvs/cuvs/test/test_ivf_flat.py b/python/cuvs/cuvs/tests/test_ivf_flat.py
similarity index 98%
rename from python/cuvs/cuvs/test/test_ivf_flat.py
rename to python/cuvs/cuvs/tests/test_ivf_flat.py
index 9dd4097dcb..c3ec0252ae 100644
--- a/python/cuvs/cuvs/test/test_ivf_flat.py
+++ b/python/cuvs/cuvs/tests/test_ivf_flat.py
@@ -20,7 +20,7 @@
 from sklearn.preprocessing import normalize
 
 from cuvs.neighbors import ivf_flat
-from cuvs.test.ann_utils import calc_recall, generate_data
+from cuvs.tests.ann_utils import calc_recall, generate_data
 
 
 def run_ivf_flat_build_search_test(
diff --git a/python/cuvs/cuvs/test/test_ivf_pq.py b/python/cuvs/cuvs/tests/test_ivf_pq.py
similarity index 98%
rename from python/cuvs/cuvs/test/test_ivf_pq.py
rename to python/cuvs/cuvs/tests/test_ivf_pq.py
index dff65b18cd..6661be7de7 100644
--- a/python/cuvs/cuvs/test/test_ivf_pq.py
+++ b/python/cuvs/cuvs/tests/test_ivf_pq.py
@@ -20,7 +20,7 @@
 from sklearn.preprocessing import normalize
 
 from cuvs.neighbors import ivf_pq
-from cuvs.test.ann_utils import calc_recall, generate_data
+from cuvs.tests.ann_utils import calc_recall, generate_data
 
 
 def run_ivf_pq_build_search_test(
diff --git a/python/cuvs/cuvs/test/test_refine.py b/python/cuvs/cuvs/tests/test_refine.py
similarity index 100%
rename from python/cuvs/cuvs/test/test_refine.py
rename to python/cuvs/cuvs/tests/test_refine.py
diff --git a/python/cuvs/cuvs/test/test_scalar_quantizer.py b/python/cuvs/cuvs/tests/test_scalar_quantizer.py
similarity index 100%
rename from python/cuvs/cuvs/test/test_scalar_quantizer.py
rename to python/cuvs/cuvs/tests/test_scalar_quantizer.py
diff --git a/python/cuvs/cuvs/test/test_serialization.py b/python/cuvs/cuvs/tests/test_serialization.py
similarity index 98%
rename from python/cuvs/cuvs/test/test_serialization.py
rename to python/cuvs/cuvs/tests/test_serialization.py
index 1f4a54e87a..dafaed4c78 100644
--- a/python/cuvs/cuvs/test/test_serialization.py
+++ b/python/cuvs/cuvs/tests/test_serialization.py
@@ -18,7 +18,7 @@
 from pylibraft.common import device_ndarray
 
 from cuvs.neighbors import brute_force, cagra, ivf_flat, ivf_pq
-from cuvs.test.ann_utils import generate_data
+from cuvs.tests.ann_utils import generate_data
 
 
 @pytest.mark.parametrize("dtype", [np.float32, np.int8, np.ubyte])
diff --git a/python/cuvs/cuvs/test/test_version.py b/python/cuvs/cuvs/tests/test_version.py
similarity index 100%
rename from python/cuvs/cuvs/test/test_version.py
rename to python/cuvs/cuvs/tests/test_version.py

From a84788db8baa0ffb8f5baadb60df2ec16c99da04 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Wed, 29 Jan 2025 07:00:41 +0530
Subject: [PATCH 279/623] FAISS with cuVS enabled in cuvs-bench (#561)

Mostly adapted from https://github.com/rapidsai/raft/pull/2026

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/561
---
 cpp/bench/ann/CMakeLists.txt                  |  11 ++
 .../ann/src/faiss/faiss_gpu_benchmark.cu      |  64 ++++++-
 cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h   | 178 +++++++++++++++---
 cpp/cmake/thirdparty/get_faiss.cmake          |   1 +
 .../cuvs_bench/config/algorithms.yaml         |   3 +
 .../config/algos/constraints/__init__.py      |   4 +-
 .../config/algos/faiss_gpu_cagra.yaml         |  13 ++
 .../config/algos/faiss_gpu_ivf_flat.yaml      |  33 ++--
 .../config/algos/faiss_gpu_ivf_pq.yaml        |  75 ++++----
 python/cuvs_bench/cuvs_bench/run/__main__.py  |  13 --
 python/cuvs_bench/cuvs_bench/run/run.py       |   4 -
 python/cuvs_bench/cuvs_bench/run/runners.py   |   5 -
 12 files changed, 301 insertions(+), 103 deletions(-)
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml

diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 200b52ab34..1ec9eeb9f3 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -20,6 +20,7 @@ list(APPEND CMAKE_MODULE_PATH "${CUVS_SOURCE_DIR}")
 option(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT "Include faiss' brute-force knn algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT "Include faiss' ivf flat algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ "Include faiss' ivf pq algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_FAISS_GPU_CAGRA "Include faiss' cagra algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT "Include faiss' cpu brute-force algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT "Include faiss' cpu ivf flat algorithm in benchmark"
        ON
@@ -275,18 +276,28 @@ endif()
 if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
     NAME FAISS_GPU_IVF_FLAT PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
+    raft::raft
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
     NAME FAISS_GPU_IVF_PQ PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
+    raft::raft
   )
 endif()
 
 if(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
     NAME FAISS_GPU_FLAT PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
+    raft::raft
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_CAGRA AND CUVS_FAISS_ENABLE_GPU)
+  ConfigureAnnBench(
+    NAME FAISS_GPU_CAGRA PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
+    raft::raft
   )
 endif()
 
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
index 2d92716395..8483e52d81 100644
--- a/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
@@ -42,6 +42,11 @@ void parse_build_param(const nlohmann::json& conf,
                        typename cuvs::bench::faiss_gpu_ivf_flat<T>::build_param& param)
 {
   parse_base_build_param<T>(conf, param);
+  if (conf.contains("use_cuvs")) {
+    param.use_cuvs = conf.at("use_cuvs");
+  } else {
+    param.use_cuvs = false;
+  }
 }
 
 template <typename T>
@@ -60,6 +65,16 @@ void parse_build_param(const nlohmann::json& conf,
   } else {
     param.use_float16 = false;
   }
+  if (conf.contains("use_cuvs")) {
+    param.use_cuvs = conf.at("use_cuvs");
+  } else {
+    param.use_cuvs = false;
+  }
+  if (conf.contains("bitsPerCode")) {
+    param.bitsPerCode = conf.at("bitsPerCode");
+  } else {
+    param.bitsPerCode = 8;
+  }
 }
 
 template <typename T>
@@ -70,11 +85,52 @@ void parse_build_param(const nlohmann::json& conf,
   param.quantizer_type = conf.at("quantizer_type");
 }
 
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_gpu_cagra<T>::build_param& param)
+{
+  if (conf.contains("graph_degree")) {
+    param.graph_degree = conf.at("graph_degree");
+  } else {
+    param.graph_degree = 64;
+  }
+  if (conf.contains("intermediate_graph_degree")) {
+    param.intermediate_graph_degree = conf.at("intermediate_graph_degree");
+  } else {
+    param.intermediate_graph_degree = 128;
+  }
+  if (conf.contains("cagra_build_algo")) { param.cagra_build_algo = conf.at("cagra_build_algo"); }
+}
+
 template <typename T>
 void parse_search_param(const nlohmann::json& conf,
                         typename cuvs::bench::faiss_gpu<T>::search_param& param)
 {
-  param.nprobe = conf.at("nprobe");
+  if (conf.contains("nprobe")) { param.nprobe = conf.at("nprobe"); }
+  if (conf.contains("refine_ratio")) { param.refine_ratio = conf.at("refine_ratio"); }
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::faiss_gpu_cagra<T>::search_param& param)
+{
+  if (conf.contains("itopk")) { param.p.itopk_size = conf.at("itopk"); }
+  if (conf.contains("search_width")) { param.p.search_width = conf.at("search_width"); }
+  if (conf.contains("max_iterations")) { param.p.max_iterations = conf.at("max_iterations"); }
+  if (conf.contains("algo")) {
+    if (conf.at("algo") == "single_cta") {
+      param.p.algo = faiss::gpu::search_algo::SINGLE_CTA;
+    } else if (conf.at("algo") == "multi_cta") {
+      param.p.algo = faiss::gpu::search_algo::MULTI_CTA;
+    } else if (conf.at("algo") == "multi_kernel") {
+      param.p.algo = faiss::gpu::search_algo::MULTI_KERNEL;
+    } else if (conf.at("algo") == "auto") {
+      param.p.algo = faiss::gpu::search_algo::AUTO;
+    } else {
+      std::string tmp = conf.at("algo");
+      THROW("Invalid value for algo: %s", tmp.c_str());
+    }
+  }
   if (conf.contains("refine_ratio")) { param.refine_ratio = conf.at("refine_ratio"); }
 }
 
@@ -105,6 +161,8 @@ auto create_algo(const std::string& algo_name,
       a = make_algo<T, cuvs::bench::faiss_gpu_ivfsq>(metric, dim, conf);
     } else if (algo_name == "faiss_gpu_flat") {
       a = std::make_unique<cuvs::bench::faiss_gpu_flat<T>>(metric, dim);
+    } else if (algo_name == "faiss_gpu_cagra") {
+      a = make_algo<T, cuvs::bench::faiss_gpu_cagra>(metric, dim, conf);
     }
   }
 
@@ -125,6 +183,10 @@ auto create_search_param(const std::string& algo_name, const nlohmann::json& con
   } else if (algo_name == "faiss_gpu_flat") {
     auto param = std::make_unique<typename cuvs::bench::faiss_gpu<T>::search_param>();
     return param;
+  } else if (algo_name == "faiss_gpu_cagra") {
+    auto param = std::make_unique<typename cuvs::bench::faiss_gpu_cagra<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
   }
   // else
   throw std::runtime_error("invalid algo: '" + algo_name + "'");
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
index f935e365fe..6cf1fe4b0b 100644
--- a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
@@ -17,12 +17,16 @@
 
 #include "../common/ann_types.hpp"
 #include "../common/util.hpp"
+#include "../cuvs/cuvs_ann_bench_utils.h"
+#include <cuvs/neighbors/refine.hpp>
 
 #include <faiss/IndexFlat.h>
+#include <faiss/IndexHNSW.h>
 #include <faiss/IndexIVFFlat.h>
 #include <faiss/IndexIVFPQ.h>
 #include <faiss/IndexRefine.h>
 #include <faiss/IndexScalarQuantizer.h>
+#include <faiss/gpu/GpuIndexCagra.h>
 #include <faiss/gpu/GpuIndexFlat.h>
 #include <faiss/gpu/GpuIndexIVFFlat.h>
 #include <faiss/gpu/GpuIndexIVFPQ.h>
@@ -32,6 +36,10 @@
 #include <faiss/index_io.h>
 #include <omp.h>
 
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/core/host_mdarray.hpp>
+
 #include <cassert>
 #include <iostream>
 #include <memory>
@@ -41,7 +49,7 @@
 
 namespace {
 
-auto parse_metric_type(cuvs::bench::Metric metric) -> faiss::MetricType
+auto parse_metric_faiss(cuvs::bench::Metric metric) -> faiss::MetricType
 {
   if (metric == cuvs::bench::Metric::kInnerProduct) {
     return faiss::METRIC_INNER_PRODUCT;
@@ -80,7 +88,7 @@ class faiss_gpu : public algo<T>, public algo_gpu {
  public:
   using search_param_base = typename algo<T>::search_param;
   struct search_param : public search_param_base {
-    int nprobe;
+    int nprobe         = 1;
     float refine_ratio = 1.0;
     [[nodiscard]] auto needs_dataset() const -> bool override { return refine_ratio > 1.0f; }
   };
@@ -93,7 +101,7 @@ class faiss_gpu : public algo<T>, public algo_gpu {
   faiss_gpu(Metric metric, int dim, const build_param& param)
     : algo<T>(metric, dim),
       gpu_resource_{std::make_shared<faiss::gpu::StandardGpuResources>()},
-      metric_type_(parse_metric_type(metric)),
+      metric_type_(parse_metric_faiss(metric)),
       nlist_{param.nlist},
       training_sample_fraction_{1.0 / double(param.ratio)}
   {
@@ -152,6 +160,9 @@ class faiss_gpu : public algo<T>, public algo_gpu {
    * faiss::gpu::StandardGpuResources are thread-safe.
    *
    */
+
+  // simply owning a configured_raft_resource object takes care of setting the pool memory resource
+  configured_raft_resources handle_{};
   mutable std::shared_ptr<faiss::gpu::StandardGpuResources> gpu_resource_;
   std::shared_ptr<faiss::gpu::GpuIndex> index_;
   std::shared_ptr<faiss::IndexRefineFlat> index_refine_{nullptr};
@@ -160,6 +171,7 @@ class faiss_gpu : public algo<T>, public algo_gpu {
   int device_;
   double training_sample_fraction_;
   std::shared_ptr<faiss::SearchParameters> search_params_;
+  std::shared_ptr<faiss::IndexRefineSearchParameters> refine_search_params_{nullptr};
   const T* dataset_;
   float refine_ratio_ = 1.0;
 };
@@ -192,26 +204,75 @@ void faiss_gpu<T>::build(const T* dataset, size_t nrow)
   }
   index_->train(nrow, dataset);  // faiss::gpu::GpuIndexFlat::train() will do nothing
   assert(index_->is_trained);
-  index_->add(nrow, dataset);
+  auto index_cagra = dynamic_cast<faiss::gpu::GpuIndexCagra*>(index_.get());
+  if (index_cagra == nullptr) { index_->add(nrow, dataset); }
 }
 
 template <typename T>
 void faiss_gpu<T>::search(
   const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
 {
+  ASSERT(
+    cuvs::bench::benchmark_n_threads == 1,
+    "Throughput mode disabled. Underlying StandardGpuResources object might not be thread-safe.");
+  using IdxT = faiss::idx_t;
   static_assert(sizeof(size_t) == sizeof(faiss::idx_t),
                 "sizes of size_t and faiss::idx_t are different");
 
-  if (this->refine_ratio_ > 1.0) {
-    // TODO(snanditale): FAISS changed their search APIs to accept the search parameters as a struct
-    // object but their refine API doesn't allow the struct to be passed in. Once this is fixed, we
-    // need to re-enable refinement below
-    // index_refine_->search(batch_size, queries, k, distances,
-    // reinterpret_cast<faiss::idx_t*>(neighbors), this->search_params_.get()); Related FAISS issue:
-    // https://github.com/facebookresearch/faiss/issues/3118
-    throw std::runtime_error(
-      "FAISS doesn't support refinement in their new APIs so this feature is disabled in the "
-      "benchmarks for the time being.");
+  if (refine_ratio_ > 1.0) {
+    if (raft::get_device_for_address(queries) >= 0) {
+      uint32_t k0        = static_cast<uint32_t>(refine_ratio_ * k);
+      auto distances_tmp = raft::make_device_matrix<float, IdxT>(
+        gpu_resource_->getRaftHandle(device_), batch_size, k0);
+      auto candidates =
+        raft::make_device_matrix<IdxT, IdxT>(gpu_resource_->getRaftHandle(device_), batch_size, k0);
+      index_->search(batch_size,
+                     queries,
+                     k0,
+                     distances_tmp.data_handle(),
+                     candidates.data_handle(),
+                     this->search_params_.get());
+      gpu_resource_->getRaftHandle(device_).sync_stream();
+
+      auto queries_host    = raft::make_host_matrix<T, IdxT>(batch_size, index_->d);
+      auto candidates_host = raft::make_host_matrix<IdxT, IdxT>(batch_size, k0);
+      auto neighbors_host  = raft::make_host_matrix<IdxT, IdxT>(batch_size, k);
+      auto distances_host  = raft::make_host_matrix<float, IdxT>(batch_size, k);
+      auto dataset_v       = raft::make_host_matrix_view<const T, faiss::idx_t>(
+        this->dataset_, index_->ntotal, index_->d);
+
+      raft::device_resources handle_ = gpu_resource_->getRaftHandle(device_);
+
+      raft::copy(queries_host.data_handle(), queries, queries_host.size(), handle_.get_stream());
+      raft::copy(candidates_host.data_handle(),
+                 candidates.data_handle(),
+                 candidates_host.size(),
+                 handle_.get_stream());
+
+      // wait for the queries to copy to host in 'stream`
+      handle_.sync_stream();
+
+      cuvs::neighbors::refine(handle_,
+                              dataset_v,
+                              queries_host.view(),
+                              candidates_host.view(),
+                              neighbors_host.view(),
+                              distances_host.view(),
+                              parse_metric_type(this->metric_));
+      handle_.sync_stream();
+
+      raft::copy(
+        neighbors, neighbors_host.data_handle(), neighbors_host.size(), handle_.get_stream());
+      raft::copy(
+        distances, distances_host.data_handle(), distances_host.size(), handle_.get_stream());
+    } else {
+      index_refine_->search(batch_size,
+                            queries,
+                            k,
+                            distances,
+                            reinterpret_cast<faiss::idx_t*>(neighbors),
+                            this->refine_search_params_.get());
+    }
   } else {
     index_->search(batch_size,
                    queries,
@@ -253,15 +314,18 @@ void faiss_gpu<T>::load_(const std::string& file)
 template <typename T>
 class faiss_gpu_ivf_flat : public faiss_gpu<T> {
  public:
-  using typename faiss_gpu<T>::build_param;
+  struct build_param : public faiss_gpu<T>::build_param {
+    bool use_cuvs;
+  };
   using typename faiss_gpu<T>::search_param_base;
 
   faiss_gpu_ivf_flat(Metric metric, int dim, const build_param& param)
     : faiss_gpu<T>(metric, dim, param)
   {
     faiss::gpu::GpuIndexIVFFlatConfig config;
-    config.device = this->device_;
-    this->index_  = std::make_shared<faiss::gpu::GpuIndexIVFFlat>(
+    config.device   = this->device_;
+    config.use_cuvs = param.use_cuvs;
+    this->index_    = std::make_shared<faiss::gpu::GpuIndexIVFFlat>(
       this->gpu_resource_.get(), dim, param.nlist, this->metric_type_, config);
   }
 
@@ -298,6 +362,8 @@ class faiss_gpu_ivfpq : public faiss_gpu<T> {
     int m;
     bool use_float16;
     bool use_precomputed;
+    bool use_cuvs;
+    int bitsPerCode;
   };
   using typename faiss_gpu<T>::search_param_base;
 
@@ -307,16 +373,17 @@ class faiss_gpu_ivfpq : public faiss_gpu<T> {
     faiss::gpu::GpuIndexIVFPQConfig config;
     config.useFloat16LookupTables = param.use_float16;
     config.usePrecomputedTables   = param.use_precomputed;
-    config.device                 = this->device_;
-
-    this->index_ =
-      std::make_shared<faiss::gpu::GpuIndexIVFPQ>(this->gpu_resource_.get(),
-                                                  dim,
-                                                  param.nlist,
-                                                  param.m,
-                                                  8,  // FAISS only supports bitsPerCode=8
-                                                  this->metric_type_,
-                                                  config);
+    config.use_cuvs               = param.use_cuvs;
+    if (param.use_cuvs) { config.interleavedLayout = param.use_cuvs; }
+    config.device = this->device_;
+
+    this->index_ = std::make_shared<faiss::gpu::GpuIndexIVFPQ>(this->gpu_resource_.get(),
+                                                               dim,
+                                                               param.nlist,
+                                                               param.m,
+                                                               param.bitsPerCode,
+                                                               this->metric_type_,
+                                                               config);
   }
 
   void set_search_param(const search_param_base& param) override
@@ -334,6 +401,11 @@ class faiss_gpu_ivfpq : public faiss_gpu<T> {
       this->index_refine_ =
         std::make_shared<faiss::IndexRefineFlat>(this->index_.get(), this->dataset_);
       this->index_refine_.get()->k_factor = sp.refine_ratio;
+      faiss::IndexRefineSearchParameters faiss_refine_search_params;
+      faiss_refine_search_params.k_factor          = this->index_refine_.get()->k_factor;
+      faiss_refine_search_params.base_index_params = this->search_params_.get();
+      this->refine_search_params_ =
+        std::make_unique<faiss::IndexRefineSearchParameters>(faiss_refine_search_params);
     }
   }
 
@@ -441,4 +513,56 @@ class faiss_gpu_flat : public faiss_gpu<T> {
   std::unique_ptr<algo<T>> copy() override { return std::make_unique<faiss_gpu_flat<T>>(*this); };
 };
 
+template <typename T>
+class faiss_gpu_cagra : public faiss_gpu<T> {
+ public:
+  struct build_param : public faiss_gpu<T>::build_param {
+    size_t intermediate_graph_degree;
+    /// Degree of output graph.
+    size_t graph_degree;
+    /// ANN algorithm to build knn graph.
+    std::string cagra_build_algo;
+    /// Number of Iterations to run if building with NN_DESCENT
+    size_t nn_descent_niter;
+  };
+  using typename faiss_gpu<T>::search_param_base;
+  struct search_param : public faiss_gpu<T>::search_param {
+    faiss::gpu::SearchParametersCagra p;
+  };
+
+  faiss_gpu_cagra(Metric metric, int dim, const build_param& param)
+    : faiss_gpu<T>(metric, dim, param)
+  {
+    faiss::gpu::GpuIndexCagraConfig config;
+    config.graph_degree              = param.graph_degree;
+    config.intermediate_graph_degree = param.intermediate_graph_degree;
+    config.device                    = this->device_;
+    if (param.cagra_build_algo == "IVF_PQ") {
+      config.build_algo = faiss::gpu::graph_build_algo::IVF_PQ;
+    } else {
+      config.build_algo = faiss::gpu::graph_build_algo::NN_DESCENT;
+    }
+    config.nn_descent_niter = param.nn_descent_niter;
+
+    this->index_ = std::make_shared<faiss::gpu::GpuIndexCagra>(
+      this->gpu_resource_.get(), dim, parse_metric_faiss(this->metric_), config);
+  }
+
+  void set_search_param(const search_param_base& param) override
+  {
+    auto sp              = static_cast<const typename faiss_gpu_cagra<T>::search_param&>(param);
+    this->search_params_ = std::make_shared<faiss::gpu::SearchParametersCagra>(sp.p);
+  }
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::gpu::GpuIndexCagra, faiss::IndexHNSWCagra>(file);
+  }
+  void load(const std::string& file) override
+  {
+    this->template load_<faiss::gpu::GpuIndexCagra, faiss::IndexHNSWCagra>(file);
+  }
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<faiss_gpu_cagra<T>>(*this); };
+};
+
 }  // namespace cuvs::bench
diff --git a/cpp/cmake/thirdparty/get_faiss.cmake b/cpp/cmake/thirdparty/get_faiss.cmake
index 90d53a4d8b..bc5b0ad1bc 100644
--- a/cpp/cmake/thirdparty/get_faiss.cmake
+++ b/cpp/cmake/thirdparty/get_faiss.cmake
@@ -55,6 +55,7 @@ function(find_and_configure_faiss)
     EXCLUDE_FROM_ALL ${exclude}
     OPTIONS
     "FAISS_ENABLE_GPU ${PKG_ENABLE_GPU}"
+    "FAISS_ENABLE_CUVS ${PKG_ENABLE_GPU}"
     "FAISS_ENABLE_PYTHON OFF"
     "FAISS_OPT_LEVEL ${CUVS_FAISS_OPT_LEVEL}"
     "FAISS_USE_CUDA_TOOLKIT_STATIC ${CUDA_STATIC_RUNTIME}"
diff --git a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
index 3575179335..609a8378e6 100644
--- a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
@@ -10,6 +10,9 @@ faiss_gpu_ivf_pq:
 faiss_gpu_ivf_sq:
   executable: FAISS_GPU_IVF_PQ_ANN_BENCH
   requires_gpu: true
+faiss_gpu_cagra:
+  executable: FAISS_GPU_CAGRA_ANN_BENCH
+  requires_gpu: true
 faiss_cpu_flat:
   executable: FAISS_CPU_FLAT_ANN_BENCH
   requires_gpu: false
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
index de05bd752b..71a105bc85 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
+++ b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
@@ -72,9 +72,7 @@ def faiss_gpu_ivf_pq_build(params, dims):
     ret = params["M"] <= dims and dims % params["M"] == 0
     if "use_cuvs" in params and params["use_cuvs"]:
         return ret
-    pq_bits = 8
-    if "bitsPerCode" in params:
-        pq_bits = params["bitsPerCode"]
+    pq_bits = params.get("bitsPerCode", 8)
     lookup_table_size = 4
     if "useFloat16" in params and params["useFloat16"]:
         lookup_table_size = 2
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
new file mode 100644
index 0000000000..5788850968
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
@@ -0,0 +1,13 @@
+name: faiss_gpu_cagra
+constraints:
+  build: cuvs_bench.config.algos.constraints.cuvs_cagra_build
+  search: cuvs_bench.config.algos.constraints.cuvs_cagra_search
+groups:
+  base:
+    build:
+      graph_degree: [32, 64, 96, 128]
+      intermediate_graph_degree: [32, 64, 96, 128]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      itopk: [32, 64, 128, 256, 512]
+      search_width: [1, 2, 4, 8, 16, 32, 64]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
index ba1dcbab9b..20329e60d5 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
@@ -2,20 +2,29 @@ name: faiss_gpu_ivf_flat
 groups:
   base:
     build:
-      nlist: [2048]
-      ratio: [10]
-      useFloat16: [False, True]
-      use_raft: [False]
+      nlist: [1024, 2048, 4096]
+      ratio: [4]
+      use_cuvs: [False]
     search:
       nprobe: [1, 5, 10, 50, 100, 200]
-      refine_ratio: [1]
-groups:
-  baseraft:
+  basecuvs:
     build:
-      nlist: [2048]
-      ratio: [10]
-      useFloat16: [False, True]
-      use_raft: [True]
+      nlist: [1024, 2048, 4096]
+      ratio: [4]
+      use_cuvs: [True]
     search:
       nprobe: [1, 5, 10, 50, 100, 200]
-      refine_ratio: [1]
+  large:
+    build:
+      nlist: [8192, 16384, 32768]
+      ratio: [4]
+      use_cuvs: [False]
+    search:
+      nprobe: [10, 20, 30, 50, 100, 200, 500, 1000]
+  largecuvs:
+    build:
+      nlist: [8192, 16384, 32768]
+      ratio: [4]
+      use_cuvs: [True]
+    search:
+      nprobe: [10, 20, 30, 50, 100, 200, 500, 1000]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
index 782f3aed12..d6cfe0569a 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
@@ -5,73 +5,72 @@ constraints:
 groups:
   base:
     build:
-      nlist: [1024, 2048, 4096, 8192]
-      M: [64, 32, 16]
-      ratio: [10]
-      usePrecomputed: [False, True]
-      useFloat16: [False, True]
-      use_raft: [False]
+      nlist: [1024, 2048, 4096]
+      M: [96, 64]
+      ratio: [4]
+      usePrecomputed: [True]
+      useFloat16: [True]
+      use_cuvs: [False]
       bitsPerCode: [8]
     search:
-      nprobe: [1, 5, 10, 50, 100, 200]
+      nprobe: [10, 20, 50, 100, 200]
       refine_ratio: [1, 2, 4]
-  baseraft:
+  basecuvs:
     build:
-      nlist: [1024, 2048, 4096, 8192]
-      M: [64, 32, 16]
-      ratio: [10]
+      nlist: [1024, 2048, 4096]
+      M: [96, 64]
+      ratio: [4]
       usePrecomputed: [False]
-      useFloat16: [False, True]
-      use_raft: [True]
+      useFloat16: [False]
+      use_cuvs: [True]
       bitsPerCode: [8, 6, 5, 4]
     search:
-      nprobe: [1, 5, 10, 50, 100, 200]
+      nprobe: [10, 20, 50, 100, 200]
       refine_ratio: [1, 2, 4]
   large:
     build:
-      nlist: [8192, 16384, 32768, 65536]
-      M: [48, 32, 16]
+      nlist: [8192, 16384, 32768]
+      M: [96, 48, 32]
       ratio: [4]
-      usePrecomputed: [False, True]
+      usePrecomputed: [False]
       useFloat16: [False, True]
-      use_raft: [False]
+      use_cuvs: [False]
       bitsPerCode: [8]
     search:
-      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      nprobe: [20, 30, 50, 100, 200, 500, 1000]
       refine_ratio: [1, 2, 4]
-  largeraft:
+  largecuvs:
     build:
-      nlist: [8192, 16384, 32768, 65536]
-      M: [48, 32, 16]
+      nlist: [8192, 16384, 32768]
+      M: [96, 48, 32]
       ratio: [4]
       usePrecomputed: [False]
-      useFloat16: [False, True]
-      use_raft: [True]
+      useFloat16: [False]
+      use_cuvs: [True]
       bitsPerCode: [8, 6, 5, 4]
     search:
-      nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+      nprobe: [20, 30, 50, 100, 200, 500, 1000]
       refine_ratio: [1, 2, 4]
   100M:
     build:
-      nlist: [50000]
-      M: [48]
-      ratio: [10]
+      nlist: [100000]
+      M: [96, 64]
+      ratio: [4]
       usePrecomputed: [False, True]
-      useFloat16: [False, True]
-      use_raft: [False]
+      useFloat16: [True]
+      use_cuvs: [False]
       bitsPerCode: [8]
     search:
       nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
-      refine_ratio: [1]
-  100Mraft:
+      refine_ratio: [1, 2, 4]
+  100Mcuvs:
     build:
-      nlist: [50000]
-      M: [48]
-      ratio: [10]
-      usePrecomputed: [False, True]
+      nlist: [100000]
+      M: [96, 64]
+      ratio: [4]
       useFloat16: [False, True]
-      use_raft: [True]
+      use_cuvs: [True]
       bitsPerCode: [8, 6, 5, 4]
     search:
       nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
-      refine_ratio: [1]
+      refine_ratio: [1, 2, 4]
diff --git a/python/cuvs_bench/cuvs_bench/run/__main__.py b/python/cuvs_bench/cuvs_bench/run/__main__.py
index 58fc5291b1..3246eb9914 100644
--- a/python/cuvs_bench/cuvs_bench/run/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/run/__main__.py
@@ -150,16 +150,6 @@
     "were interrupted, use this option to convert those intermediate "
     "files manually.",
 )
-@click.option(
-    "--raft-log-level",
-    default="info",
-    show_default=True,
-    prompt="Enter the log level",
-    help="Log level, possible values are [off, error, warn, info, debug, "
-    "trace]. Default: 'info'. Note that 'debug' or more detailed "
-    "logging level requires that the library is compiled with "
-    "-DRAFT_ACTIVE_LEVEL=<L> where <L> >= <requested log level>.",
-)
 def main(
     subset_size: Optional[int],
     count: int,
@@ -178,7 +168,6 @@ def main(
     search_threads: Optional[str],
     dry_run: bool,
     data_export: bool,
-    raft_log_level: str,
 ) -> None:
     """
     Main function to run the benchmark with the provided options.
@@ -217,8 +206,6 @@ def main(
         The number of threads to use for throughput benchmark.
     dry_run : bool
         Whether to perform a dry run without actual execution.
-    raft_log_level : str
-        The logging level for the RAFT library.
 
     """
 
diff --git a/python/cuvs_bench/cuvs_bench/run/run.py b/python/cuvs_bench/cuvs_bench/run/run.py
index d7827a096e..a16f01b94b 100644
--- a/python/cuvs_bench/cuvs_bench/run/run.py
+++ b/python/cuvs_bench/cuvs_bench/run/run.py
@@ -586,7 +586,6 @@ def run_benchmark(
     search_threads: int,
     dry_run: bool,
     data_export: bool,
-    raft_log_level: int,
 ) -> None:
     """
     Runs a benchmarking process based on the provided configurations.
@@ -625,8 +624,6 @@ def run_benchmark(
         The number of threads to use for searching.
     dry_run : bool
         Whether to perform a dry run without actual execution.
-    raft_log_level : int
-        The logging level for the RAFT library.
 
     Returns
     -------
@@ -689,5 +686,4 @@ def run_benchmark(
         batch_size,
         search_threads,
         search_mode,
-        raft_log_level,
     )
diff --git a/python/cuvs_bench/cuvs_bench/run/runners.py b/python/cuvs_bench/cuvs_bench/run/runners.py
index 5a540d2e5e..522636e6c7 100644
--- a/python/cuvs_bench/cuvs_bench/run/runners.py
+++ b/python/cuvs_bench/cuvs_bench/run/runners.py
@@ -37,7 +37,6 @@ def cuvs_bench_cpp(
     batch_size: int,
     search_threads: Optional[int],
     mode: str = "throughput",
-    raft_log_level: str = "info",
 ) -> None:
     """
     Run the CUVS benchmarking tool with the provided configuration.
@@ -72,8 +71,6 @@ def cuvs_bench_cpp(
     mode : str, optional
         The mode of search to perform ('latency' or 'throughput'),
         by default 'throughput'.
-    raft_log_level : str, optional
-        The logging level for the RAFT library, by default 'info'.
 
     Returns
     -------
@@ -117,7 +114,6 @@ def cuvs_bench_cpp(
                 "--benchmark_out_format=json",
                 "--benchmark_counters_tabular=true",
                 f"--benchmark_out={os.path.join(benchmark_out)}",
-                f"--raft_log_level={parse_log_level(raft_log_level)}",
             ]
             if force:
                 cmd.append("--force")
@@ -156,7 +152,6 @@ def cuvs_bench_cpp(
                 "--benchmark_out_format=json",
                 f"--mode={mode}",
                 f"--benchmark_out={os.path.join(search_folder, search_file)}",
-                f"--raft_log_level={parse_log_level(raft_log_level)}",
             ]
             if force:
                 cmd.append("--force")

From 9489d0c1daa601b951e55ffa8dba9eacd108f881 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 30 Jan 2025 01:50:02 -0600
Subject: [PATCH 280/623] Build and test with CUDA 12.8.0 (#621)

This PR uses CUDA 12.8.0 to build and test.

xref: https://github.com/rapidsai/build-planning/issues/139

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: https://github.com/rapidsai/cuvs/pull/621
---
 .../devcontainer.json                         |  8 ++---
 .../devcontainer.json                         | 12 +++----
 .github/workflows/build.yaml                  | 18 +++++------
 .github/workflows/pr.yaml                     | 32 +++++++++----------
 .github/workflows/test.yaml                   |  8 ++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 ...64.yaml => all_cuda-128_arch-aarch64.yaml} |  4 +--
 ..._64.yaml => all_cuda-128_arch-x86_64.yaml} |  4 +--
 ...l => bench_ann_cuda-128_arch-aarch64.yaml} |  4 +--
 ...ml => bench_ann_cuda-128_arch-x86_64.yaml} |  4 +--
 cpp/CMakeLists.txt                            |  3 ++
 dependencies.yaml                             |  8 +++--
 docs/source/build.rst                         |  6 ++--
 13 files changed, 60 insertions(+), 53 deletions(-)
 rename .devcontainer/{cuda12.5-conda => cuda12.8-conda}/devcontainer.json (91%)
 rename .devcontainer/{cuda12.5-pip => cuda12.8-pip}/devcontainer.json (88%)
 rename conda/environments/{all_cuda-125_arch-aarch64.yaml => all_cuda-128_arch-aarch64.yaml} (95%)
 rename conda/environments/{all_cuda-125_arch-x86_64.yaml => all_cuda-128_arch-x86_64.yaml} (95%)
 rename conda/environments/{bench_ann_cuda-125_arch-aarch64.yaml => bench_ann_cuda-128_arch-aarch64.yaml} (94%)
 rename conda/environments/{bench_ann_cuda-125_arch-x86_64.yaml => bench_ann_cuda-128_arch-x86_64.yaml} (94%)

diff --git a/.devcontainer/cuda12.5-conda/devcontainer.json b/.devcontainer/cuda12.8-conda/devcontainer.json
similarity index 91%
rename from .devcontainer/cuda12.5-conda/devcontainer.json
rename to .devcontainer/cuda12.8-conda/devcontainer.json
index 39852cec1c..54af23072d 100644
--- a/.devcontainer/cuda12.5-conda/devcontainer.json
+++ b/.devcontainer/cuda12.8-conda/devcontainer.json
@@ -3,7 +3,7 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.5",
+      "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
       "BASE": "rapidsai/devcontainers:25.02-cpp-mambaforge-ubuntu22.04"
     }
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.5-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
@@ -20,7 +20,7 @@
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.5-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.8-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -29,7 +29,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.5-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.8-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.devcontainer/cuda12.5-pip/devcontainer.json b/.devcontainer/cuda12.8-pip/devcontainer.json
similarity index 88%
rename from .devcontainer/cuda12.5-pip/devcontainer.json
rename to .devcontainer/cuda12.8-pip/devcontainer.json
index f9aa4a7a94..1a6a5520c2 100644
--- a/.devcontainer/cuda12.5-pip/devcontainer.json
+++ b/.devcontainer/cuda12.8-pip/devcontainer.json
@@ -3,20 +3,20 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.5",
+      "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda12.5-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.02-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.5-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.02-cuda12.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/cuda:25.2": {
-      "version": "12.5",
+      "version": "12.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
@@ -29,7 +29,7 @@
     "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.5-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -37,7 +37,7 @@
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.5-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 59b8e00de1..ca6d48fe6b 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -94,7 +94,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -105,7 +105,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -115,7 +115,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.8.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 843439f26d..9f8b1b84bc 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda-12.8.0
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -45,7 +45,7 @@ jobs:
           repo: cuvs
   changed-files:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@cuda-12.8.0
     with:
       files_yaml: |
         test_cpp:
@@ -78,27 +78,27 @@ jobs:
           - '!thirdparty/LICENSES/**'
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda-12.8.0
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.8.0
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.8.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.8.0
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -106,30 +106,30 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.8.0
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.8.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:cuda12.8.0-ubuntu24.04-py3.12"
       run_script: "ci/build_docs.sh"
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -139,7 +139,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -148,24 +148,24 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.8.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-12.8.0
     with:
       arch: '["amd64"]'
-      cuda: '["12.5"]'
+      cuda: '["12.8"]'
       build_command: |
         sccache -z;
         build-all --verbose;
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index e3bf5d16f2..ac2277e32e 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.8.0
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.8.0
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.8.0
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.8.0
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 01dd2436be..07f0f83cc9 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@cuda-12.8.0
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/conda/environments/all_cuda-125_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
similarity index 95%
rename from conda/environments/all_cuda-125_arch-aarch64.yaml
rename to conda/environments/all_cuda-128_arch-aarch64.yaml
index b71f5ed438..c508a9dc19 100644
--- a/conda/environments/all_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.5
+- cuda-version=12.8
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -53,4 +53,4 @@ dependencies:
 - sysroot_linux-aarch64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-125_arch-aarch64
+name: all_cuda-128_arch-aarch64
diff --git a/conda/environments/all_cuda-125_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
similarity index 95%
rename from conda/environments/all_cuda-125_arch-x86_64.yaml
rename to conda/environments/all_cuda-128_arch-x86_64.yaml
index 16cd595d33..f043e544bb 100644
--- a/conda/environments/all_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.5
+- cuda-version=12.8
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -53,4 +53,4 @@ dependencies:
 - sysroot_linux-64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-125_arch-x86_64
+name: all_cuda-128_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
rename to conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index 34e01aeea8..43d3ca8211 100644
--- a/conda/environments/bench_ann_cuda-125_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.5
+- cuda-version=12.8
 - cupy>=12.0.0
 - cuvs==25.2.*,>=0.0.0a0
 - cxx-compiler
@@ -46,4 +46,4 @@ dependencies:
 - setuptools
 - sysroot_linux-aarch64==2.28
 - wheel
-name: bench_ann_cuda-125_arch-aarch64
+name: bench_ann_cuda-128_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
rename to conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index dcfb54a222..54588287ae 100644
--- a/conda/environments/bench_ann_cuda-125_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.5
+- cuda-version=12.8
 - cupy>=12.0.0
 - cuvs==25.2.*,>=0.0.0a0
 - cxx-compiler
@@ -46,4 +46,4 @@ dependencies:
 - setuptools
 - sysroot_linux-64==2.28
 - wheel
-name: bench_ann_cuda-125_arch-x86_64
+name: bench_ann_cuda-128_arch-x86_64
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 63e57965be..3ed3227e0d 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -173,6 +173,9 @@ include(cmake/modules/ConfigureCUDA.cmake)
 rapids_cpm_init()
 
 if(NOT BUILD_CPU_ONLY)
+  # We must find CCCL ourselves before raft so that we get the right version.
+  include(${rapids-cmake-dir}/cpm/cccl.cmake)
+  rapids_cpm_cccl(BUILD_EXPORT_SET cuvs-exports INSTALL_EXPORT_SET cuvs-exports)
   include(cmake/thirdparty/get_raft.cmake)
   include(cmake/thirdparty/get_cutlass.cmake)
 endif()
diff --git a/dependencies.yaml b/dependencies.yaml
index c998710f6d..7a50c0d469 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,7 +3,7 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["11.8", "12.5"]
+      cuda: ["11.8", "12.8"]
       arch: [x86_64, aarch64]
     includes:
       - build
@@ -27,7 +27,7 @@ files:
   bench_ann:
     output: conda
     matrix:
-      cuda: ["11.8", "12.5"]
+      cuda: ["11.8", "12.8"]
       arch: [x86_64, aarch64]
     includes:
       - bench
@@ -319,6 +319,10 @@ dependencies:
               cuda: "12.5"
             packages:
               - cuda-version=12.5
+          - matrix:
+              cuda: "12.8"
+            packages:
+              - cuda-version=12.8
   cuda:
     specific:
       - output_types: conda
diff --git a/docs/source/build.rst b/docs/source/build.rst
index a6b8ccd13c..5d1b8e354d 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -43,14 +43,14 @@ C/C++ Package
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
+   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.8
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.5
+   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.8
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
@@ -92,7 +92,7 @@ Conda environment scripts are provided for installing the necessary dependencies
 
 .. code-block:: bash
 
-    conda env create --name cuvs -f conda/environments/all_cuda-125_arch-x86_64.yaml
+    conda env create --name cuvs -f conda/environments/all_cuda-128_arch-x86_64.yaml
     conda activate cuvs
 
 The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment

From 836183e60acac3e58a025026f6526522ca9c4b58 Mon Sep 17 00:00:00 2001
From: Akira Naruse <anaruse@nvidia.com>
Date: Thu, 30 Jan 2025 19:50:15 +0900
Subject: [PATCH 281/623] Improve multi-CTA algorithm (#492)

It has been reported that when the number of search results is large, for example 100, using the multi-CTA algorithm can cause a decrease in recall. This PR is intended to alleviate this low recall issue.

close #208

Authors:
  - Akira Naruse (https://github.com/anaruse)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Artem M. Chirkin (https://github.com/achirkin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - tsuki (https://github.com/enp1s0)
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/492
---
 cpp/src/neighbors/detail/cagra/add_nodes.cuh  |  62 +++-
 .../neighbors/detail/cagra/cagra_search.cuh   |   2 +-
 .../neighbors/detail/cagra/device_common.hpp  |  64 +++-
 cpp/src/neighbors/detail/cagra/factory.cuh    |   9 +-
 cpp/src/neighbors/detail/cagra/hashmap.hpp    |  87 ++++-
 .../detail/cagra/search_multi_cta.cuh         |  24 +-
 .../detail/cagra/search_multi_cta_inst.cuh    |   1 +
 .../cagra/search_multi_cta_kernel-inl.cuh     | 348 +++++++++++-------
 .../detail/cagra/search_multi_cta_kernel.cuh  |   5 +-
 .../detail/cagra/search_multi_kernel.cuh      |   3 +-
 .../neighbors/detail/cagra/search_plan.cuh    | 175 +++++----
 .../detail/cagra/search_single_cta.cuh        |   3 +-
 .../cagra/search_single_cta_kernel-inl.cuh    |   6 +-
 13 files changed, 532 insertions(+), 257 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
index 4539289922..913094e2af 100644
--- a/cpp/src/neighbors/detail/cagra/add_nodes.cuh
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -137,6 +137,31 @@ void add_node_core(
                raft::resource::get_cuda_stream(handle));
     raft::resource::sync_stream(handle);
 
+    // Check search results
+    constexpr int max_warnings = 3;
+    int num_warnings           = 0;
+    for (std::size_t vec_i = 0; vec_i < batch.size(); vec_i++) {
+      std::uint32_t invalid_edges = 0;
+      for (std::uint32_t i = 0; i < base_degree; i++) {
+        if (host_neighbor_indices(vec_i, i) >= old_size) { invalid_edges++; }
+      }
+      if (invalid_edges > 0) {
+        if (num_warnings < max_warnings) {
+          RAFT_LOG_WARN(
+            "Invalid edges found in search results "
+            "(vec_i:%lu, invalid_edges:%lu, degree:%lu, base_degree:%lu)",
+            (uint64_t)vec_i,
+            (uint64_t)invalid_edges,
+            (uint64_t)degree,
+            (uint64_t)base_degree);
+        }
+        num_warnings += 1;
+      }
+    }
+    if (num_warnings > max_warnings) {
+      RAFT_LOG_WARN("The number of queries that contain invalid search results: %d", num_warnings);
+    }
+
     // Step 2: rank-based reordering
 #pragma omp parallel
     {
@@ -147,9 +172,16 @@ void add_node_core(
         for (std::uint32_t i = 0; i < base_degree; i++) {
           std::uint32_t detourable_node_count = 0;
           const auto a_id                     = host_neighbor_indices(vec_i, i);
+          if (a_id >= idx.size()) {
+            // If the node ID is not valid, the number of detours is increased
+            // to a value greater than the maximum, so that the edge to that
+            // node is not selected as much as possible.
+            detourable_node_count_list[i] = std::make_pair(a_id, base_degree + 1);
+            continue;
+          }
           for (std::uint32_t j = 0; j < i; j++) {
             const auto b0_id = host_neighbor_indices(vec_i, j);
-            assert(b0_id < idx.size());
+            if (b0_id >= idx.size()) { continue; }
             for (std::uint32_t k = 0; k < degree; k++) {
               const auto b1_id = updated_graph(b0_id, k);
               if (a_id == b1_id) {
@@ -160,6 +192,7 @@ void add_node_core(
           }
           detourable_node_count_list[i] = std::make_pair(a_id, detourable_node_count);
         }
+
         std::sort(detourable_node_count_list.begin(),
                   detourable_node_count_list.end(),
                   [&](const std::pair<IdxT, std::size_t> a, const std::pair<IdxT, std::size_t> b) {
@@ -181,13 +214,18 @@ void add_node_core(
       const auto target_new_node_id = old_size + batch.offset() + vec_i;
       for (std::size_t i = 0; i < num_rev_edges; i++) {
         const auto target_node_id = updated_graph(old_size + batch.offset() + vec_i, i);
-
+        if (target_node_id >= new_size) {
+          RAFT_FAIL("Invalid node ID found in updated_graph (%u)\n", target_node_id);
+        }
         IdxT replace_id                        = new_size;
         IdxT replace_id_j                      = 0;
         std::size_t replace_num_incoming_edges = 0;
         for (std::int32_t j = degree - 1; j >= static_cast<std::int32_t>(rev_edge_search_range);
              j--) {
-          const auto neighbor_id               = updated_graph(target_node_id, j);
+          const auto neighbor_id = updated_graph(target_node_id, j);
+          if (neighbor_id >= new_size) {
+            RAFT_FAIL("Invalid node ID found in updated_graph (%u)\n", neighbor_id);
+          }
           const std::size_t num_incoming_edges = host_num_incoming_edges(neighbor_id);
           if (num_incoming_edges > replace_num_incoming_edges) {
             // Check duplication
@@ -206,10 +244,6 @@ void add_node_core(
             replace_id_j               = j;
           }
         }
-        if (replace_id >= new_size) {
-          std::fprintf(stderr, "Invalid rev edge index (%u)\n", replace_id);
-          return;
-        }
         updated_graph(target_node_id, replace_id_j) = target_new_node_id;
         rev_edges[i]                                = replace_id;
       }
@@ -221,13 +255,15 @@ void add_node_core(
       const auto rank_based_list_ptr =
         updated_graph.data_handle() + (old_size + batch.offset() + vec_i) * degree;
       const auto rev_edges_return_list_ptr = rev_edges.data();
-      while (num_add < degree) {
+      while ((num_add < degree) &&
+             ((rank_base_i < degree) || (rev_edges_return_i < num_rev_edges))) {
         const auto node_list_ptr =
           interleave_switch == 0 ? rank_based_list_ptr : rev_edges_return_list_ptr;
         auto& node_list_index          = interleave_switch == 0 ? rank_base_i : rev_edges_return_i;
         const auto max_node_list_index = interleave_switch == 0 ? degree : num_rev_edges;
         for (; node_list_index < max_node_list_index; node_list_index++) {
           const auto candidate = node_list_ptr[node_list_index];
+          if (candidate >= new_size) { continue; }
           // Check duplication
           bool dup = false;
           for (std::uint32_t j = 0; j < num_add; j++) {
@@ -244,6 +280,12 @@ void add_node_core(
         }
         interleave_switch = 1 - interleave_switch;
       }
+      if (num_add < degree) {
+        RAFT_FAIL("Number of edges is not enough (target_new_node_id:%lu, num_add:%lu, degree:%lu)",
+                  (uint64_t)target_new_node_id,
+                  (uint64_t)num_add,
+                  (uint64_t)degree);
+      }
       for (std::uint32_t i = 0; i < degree; i++) {
         updated_graph(target_new_node_id, i) = temp[i];
       }
@@ -259,7 +301,9 @@ void add_graph_nodes(
   raft::host_matrix_view<IdxT, std::int64_t> updated_graph_view,
   const cagra::extend_params& params)
 {
-  assert(input_updated_dataset_view.extent(0) >= index.size());
+  if (input_updated_dataset_view.extent(0) < index.size()) {
+    RAFT_FAIL("Updated dataset must be not smaller than the previous index state.");
+  }
 
   const std::size_t initial_dataset_size = index.size();
   const std::size_t new_dataset_size     = input_updated_dataset_view.extent(0);
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index 5778d85a6f..b4f701819d 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -75,7 +75,7 @@ void search_main_core(raft::resources const& res,
   using CagraSampleFilterT_s = typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type;
   std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT_s>> plan =
     factory<DataT, IndexT, DistanceT, CagraSampleFilterT_s>::create(
-      res, params, dataset_desc, queries.extent(1), graph.extent(1), topk);
+      res, params, dataset_desc, queries.extent(1), graph.extent(0), graph.extent(1), topk);
 
   plan->check(topk);
 
diff --git a/cpp/src/neighbors/detail/cagra/device_common.hpp b/cpp/src/neighbors/detail/cagra/device_common.hpp
index 7ec3d4d9eb..e5886582df 100644
--- a/cpp/src/neighbors/detail/cagra/device_common.hpp
+++ b/cpp/src/neighbors/detail/cagra/device_common.hpp
@@ -109,7 +109,9 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_random_nodes(
   const IndexT* __restrict__ seed_ptr,  // [num_seeds]
   const uint32_t num_seeds,
   IndexT* __restrict__ visited_hash_ptr,
-  const uint32_t hash_bitlen,
+  const uint32_t visited_hash_bitlen,
+  IndexT* __restrict__ traversed_hash_ptr,
+  const uint32_t traversed_hash_bitlen,
   const uint32_t block_id   = 0,
   const uint32_t num_blocks = 1)
 {
@@ -145,19 +147,29 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_random_nodes(
 
     const unsigned lane_id = threadIdx.x & ((1u << team_size_bits) - 1u);
     if (valid_i && lane_id == 0) {
-      if (best_index_team_local != raft::upper_bound<IndexT>() &&
-          hashmap::insert(visited_hash_ptr, hash_bitlen, best_index_team_local)) {
-        result_distances_ptr[i] = best_norm2_team_local;
-        result_indices_ptr[i]   = best_index_team_local;
-      } else {
-        result_distances_ptr[i] = raft::upper_bound<DistanceT>();
-        result_indices_ptr[i]   = raft::upper_bound<IndexT>();
+      if (best_index_team_local != raft::upper_bound<IndexT>()) {
+        if (hashmap::insert(visited_hash_ptr, visited_hash_bitlen, best_index_team_local) == 0) {
+          // Deactivate this entry as insertion into visited hash table has failed.
+          best_norm2_team_local = raft::upper_bound<DistanceT>();
+          best_index_team_local = raft::upper_bound<IndexT>();
+        } else if ((traversed_hash_ptr != nullptr) &&
+                   hashmap::search<IndexT, 1>(
+                     traversed_hash_ptr, traversed_hash_bitlen, best_index_team_local)) {
+          // Deactivate this entry as it has been already used by others.
+          best_norm2_team_local = raft::upper_bound<DistanceT>();
+          best_index_team_local = raft::upper_bound<IndexT>();
+        }
       }
+      result_distances_ptr[i] = best_norm2_team_local;
+      result_indices_ptr[i]   = best_index_team_local;
     }
   }
 }
 
-template <typename IndexT, typename DistanceT, typename DATASET_DESCRIPTOR_T>
+template <typename IndexT,
+          typename DistanceT,
+          typename DATASET_DESCRIPTOR_T,
+          int STATIC_RESULT_POSITION = 1>
 RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_child_nodes(
   IndexT* __restrict__ result_child_indices_ptr,
   DistanceT* __restrict__ result_child_distances_ptr,
@@ -168,13 +180,17 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_child_nodes(
   const uint32_t knn_k,
   // hashmap
   IndexT* __restrict__ visited_hashmap_ptr,
-  const uint32_t hash_bitlen,
+  const uint32_t visited_hash_bitlen,
+  IndexT* __restrict__ traversed_hashmap_ptr,
+  const uint32_t traversed_hash_bitlen,
   const IndexT* __restrict__ parent_indices,
   const IndexT* __restrict__ internal_topk_list,
-  const uint32_t search_width)
+  const uint32_t search_width,
+  int* __restrict__ result_position = nullptr,
+  const int max_result_position     = 0)
 {
   constexpr IndexT index_msb_1_mask = utils::gen_index_msb_1_mask<IndexT>::value;
-  constexpr IndexT invalid_index    = raft::upper_bound<IndexT>();
+  constexpr IndexT invalid_index    = ~static_cast<IndexT>(0);
 
   // Read child indices of parents from knn graph and check if the distance
   // computaiton is necessary.
@@ -186,11 +202,22 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_child_nodes(
       child_id             = knn_graph[(i % knn_k) + (static_cast<int64_t>(knn_k) * parent_id)];
     }
     if (child_id != invalid_index) {
-      if (hashmap::insert(visited_hashmap_ptr, hash_bitlen, child_id) == 0) {
+      if (hashmap::insert(visited_hashmap_ptr, visited_hash_bitlen, child_id) == 0) {
+        // Deactivate this entry as insertion into visited hash table has failed.
+        child_id = invalid_index;
+      } else if ((traversed_hashmap_ptr != nullptr) &&
+                 hashmap::search<IndexT, 1>(
+                   traversed_hashmap_ptr, traversed_hash_bitlen, child_id)) {
+        // Deactivate this entry as this has been already used by others.
         child_id = invalid_index;
       }
     }
-    result_child_indices_ptr[i] = child_id;
+    if (STATIC_RESULT_POSITION) {
+      result_child_indices_ptr[i] = child_id;
+    } else if (child_id != invalid_index) {
+      int j                       = atomicSub(result_position, 1) - 1;
+      result_child_indices_ptr[j] = child_id;
+    }
   }
   __syncthreads();
 
@@ -201,9 +228,11 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_child_nodes(
   const auto compute_distance = dataset_desc.compute_distance_impl;
   const auto args             = dataset_desc.args.load();
   const bool lead_lane        = (threadIdx.x & ((1u << team_size_bits) - 1u)) == 0;
+  const uint32_t ofst         = STATIC_RESULT_POSITION ? 0 : result_position[0];
   for (uint32_t i = threadIdx.x >> team_size_bits; i < max_i; i += blockDim.x >> team_size_bits) {
-    const bool valid_i  = i < num_k;
-    const auto child_id = valid_i ? result_child_indices_ptr[i] : invalid_index;
+    const auto j        = i + ofst;
+    const bool valid_i  = STATIC_RESULT_POSITION ? (j < num_k) : (j < max_result_position);
+    const auto child_id = valid_i ? result_child_indices_ptr[j] : invalid_index;
 
     // We should be calling `dataset_desc.compute_distance(..)` here as follows:
     // > const auto child_dist = dataset_desc.compute_distance(child_id, child_id != invalid_index);
@@ -213,9 +242,10 @@ RAFT_DEVICE_INLINE_FUNCTION void compute_distance_to_child_nodes(
       (child_id != invalid_index) ? compute_distance(args, child_id)
                                   : (lead_lane ? raft::upper_bound<DistanceT>() : 0),
       team_size_bits);
+    __syncwarp();
 
     // Store the distance
-    if (valid_i && lead_lane) { result_child_distances_ptr[i] = child_dist; }
+    if (valid_i && lead_lane) { result_child_distances_ptr[j] = child_dist; }
   }
 }
 
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index e6e7ff64fd..d2ae5c55b7 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -40,10 +40,11 @@ class factory {
     search_params const& params,
     const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
     int64_t dim,
+    int64_t dataset_size,
     int64_t graph_degree,
     uint32_t topk)
   {
-    search_plan_impl_base plan(params, dim, graph_degree, topk);
+    search_plan_impl_base plan(params, dim, dataset_size, graph_degree, topk);
     return dispatch_kernel(res, plan, dataset_desc);
   }
 
@@ -56,15 +57,15 @@ class factory {
     if (plan.algo == search_algo::SINGLE_CTA) {
       return std::make_unique<
         single_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
-        res, plan, dataset_desc, plan.dim, plan.graph_degree, plan.topk);
+        res, plan, dataset_desc, plan.dim, plan.dataset_size, plan.graph_degree, plan.topk);
     } else if (plan.algo == search_algo::MULTI_CTA) {
       return std::make_unique<
         multi_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
-        res, plan, dataset_desc, plan.dim, plan.graph_degree, plan.topk);
+        res, plan, dataset_desc, plan.dim, plan.dataset_size, plan.graph_degree, plan.topk);
     } else {
       return std::make_unique<
         multi_kernel_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
-        res, plan, dataset_desc, plan.dim, plan.graph_degree, plan.topk);
+        res, plan, dataset_desc, plan.dim, plan.dataset_size, plan.graph_degree, plan.topk);
     }
   }
 };
diff --git a/cpp/src/neighbors/detail/cagra/hashmap.hpp b/cpp/src/neighbors/detail/cagra/hashmap.hpp
index 2c62dda90e..652e1db228 100644
--- a/cpp/src/neighbors/detail/cagra/hashmap.hpp
+++ b/cpp/src/neighbors/detail/cagra/hashmap.hpp
@@ -23,6 +23,8 @@
 
 #include <cstdint>
 
+#define HASHMAP_LINEAR_PROBING
+
 // #pragma GCC diagnostic push
 // #pragma GCC diagnostic ignored
 // #pragma GCC diagnostic pop
@@ -38,11 +40,11 @@ RAFT_DEVICE_INLINE_FUNCTION void init(IdxT* const table,
 {
   if (threadIdx.x < FIRST_TID) return;
   for (unsigned i = threadIdx.x - FIRST_TID; i < get_size(bitlen); i += blockDim.x - FIRST_TID) {
-    table[i] = utils::get_max_value<IdxT>();
+    table[i] = ~static_cast<IdxT>(0);
   }
 }
 
-template <class IdxT>
+template <class IdxT, unsigned SUPPORT_REMOVE = 0>
 RAFT_DEVICE_INLINE_FUNCTION uint32_t insert(IdxT* const table,
                                             const uint32_t bitlen,
                                             const IdxT key)
@@ -50,7 +52,7 @@ RAFT_DEVICE_INLINE_FUNCTION uint32_t insert(IdxT* const table,
   // Open addressing is used for collision resolution
   const uint32_t size     = get_size(bitlen);
   const uint32_t bit_mask = size - 1;
-#if 1
+#ifdef HASHMAP_LINEAR_PROBING
   // Linear probing
   IdxT index                = (key ^ (key >> bitlen)) & bit_mask;
   constexpr uint32_t stride = 1;
@@ -59,32 +61,89 @@ RAFT_DEVICE_INLINE_FUNCTION uint32_t insert(IdxT* const table,
   uint32_t index        = key & bit_mask;
   const uint32_t stride = (key >> bitlen) * 2 + 1;
 #endif
+  constexpr IdxT hashval_empty = ~static_cast<IdxT>(0);
+  const IdxT removed_key       = key | utils::gen_index_msb_1_mask<IdxT>::value;
   for (unsigned i = 0; i < size; i++) {
-    const IdxT old = atomicCAS(&table[index], ~static_cast<IdxT>(0), key);
-    if (old == ~static_cast<IdxT>(0)) {
+    const IdxT old = atomicCAS(&table[index], hashval_empty, key);
+    if (old == hashval_empty) {
       return 1;
     } else if (old == key) {
       return 0;
+    } else if (SUPPORT_REMOVE) {
+      // Checks if this key has been removed before.
+      const uint32_t old = atomicCAS(&table[index], removed_key, key);
+      if (old == removed_key) {
+        return 1;
+      } else if (old == key) {
+        return 0;
+      }
     }
     index = (index + stride) & bit_mask;
   }
   return 0;
 }
 
-template <unsigned TEAM_SIZE, class IdxT>
-RAFT_DEVICE_INLINE_FUNCTION uint32_t insert(IdxT* const table,
-                                            const uint32_t bitlen,
-                                            const IdxT key)
+template <class IdxT, unsigned SUPPORT_REMOVE = 0>
+RAFT_DEVICE_INLINE_FUNCTION uint32_t search(IdxT* table, const uint32_t bitlen, const IdxT key)
 {
-  IdxT ret = 0;
-  if (threadIdx.x % TEAM_SIZE == 0) { ret = insert(table, bitlen, key); }
-  for (unsigned offset = 1; offset < TEAM_SIZE; offset *= 2) {
-    ret |= __shfl_xor_sync(0xffffffff, ret, offset);
+  const uint32_t size     = get_size(bitlen);
+  const uint32_t bit_mask = size - 1;
+#ifdef HASHMAP_LINEAR_PROBING
+  // Linear probing
+  IdxT index                = (key ^ (key >> bitlen)) & bit_mask;
+  constexpr uint32_t stride = 1;
+#else
+  // Double hashing
+  IdxT index            = key & bit_mask;
+  const uint32_t stride = (key >> bitlen) * 2 + 1;
+#endif
+  constexpr IdxT hashval_empty = ~static_cast<IdxT>(0);
+  const IdxT removed_key       = key | utils::gen_index_msb_1_mask<IdxT>::value;
+  for (unsigned i = 0; i < size; i++) {
+    const IdxT val = table[index];
+    if (val == key) {
+      return 1;
+    } else if (val == hashval_empty) {
+      return 0;
+    } else if (SUPPORT_REMOVE) {
+      // Check if this key has been removed.
+      if (val == removed_key) { return 0; }
+    }
+    index = (index + stride) & bit_mask;
   }
-  return ret;
+  return 0;
 }
 
 template <class IdxT>
+RAFT_DEVICE_INLINE_FUNCTION uint32_t remove(IdxT* table, const uint32_t bitlen, const IdxT key)
+{
+  const uint32_t size     = get_size(bitlen);
+  const uint32_t bit_mask = size - 1;
+#ifdef HASHMAP_LINEAR_PROBING
+  // Linear probing
+  IdxT index                = (key ^ (key >> bitlen)) & bit_mask;
+  constexpr uint32_t stride = 1;
+#else
+  // Double hashing
+  IdxT index            = key & bit_mask;
+  const uint32_t stride = (key >> bitlen) * 2 + 1;
+#endif
+  constexpr IdxT hashval_empty = ~static_cast<IdxT>(0);
+  const IdxT removed_key       = key | utils::gen_index_msb_1_mask<IdxT>::value;
+  for (unsigned i = 0; i < size; i++) {
+    // To remove a key, set the MSB to 1.
+    const uint32_t old = atomicCAS(&table[index], key, removed_key);
+    if (old == key) {
+      return 1;
+    } else if (old == hashval_empty) {
+      return 0;
+    }
+    index = (index + stride) & bit_mask;
+  }
+  return 0;
+}
+
+template <class IdxT, unsigned SUPPORT_REMOVE = 0>
 RAFT_DEVICE_INLINE_FUNCTION uint32_t
 insert(unsigned team_size, IdxT* const table, const uint32_t bitlen, const IdxT key)
 {
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index 9cb432bcbd..2f1c0332a8 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -102,33 +102,36 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
          search_params params,
          const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
          int64_t dim,
+         int64_t dataset_size,
          int64_t graph_degree,
          uint32_t topk)
-    : base_type(res, params, dataset_desc, dim, graph_degree, topk),
+    : base_type(res, params, dataset_desc, dim, dataset_size, graph_degree, topk),
       intermediate_indices(res),
       intermediate_distances(res),
       topk_workspace(res)
-
   {
     set_params(res, params);
   }
 
   void set_params(raft::resources const& res, const search_params& params)
   {
-    constexpr unsigned muti_cta_itopk_size = 32;
-    this->itopk_size                       = muti_cta_itopk_size;
-    search_width                           = 1;
+    constexpr unsigned multi_cta_itopk_size = 32;
+    this->itopk_size                        = multi_cta_itopk_size;
+    search_width                            = 1;
     num_cta_per_query =
-      max(params.search_width, raft::ceildiv(params.itopk_size, (size_t)muti_cta_itopk_size));
-    result_buffer_size = itopk_size + search_width * graph_degree;
+      max(params.search_width, raft::ceildiv(params.itopk_size, (size_t)multi_cta_itopk_size));
+    result_buffer_size = itopk_size + (search_width * graph_degree);
     typedef raft::Pow2<32> AlignBytes;
     unsigned result_buffer_size_32 = AlignBytes::roundUp(result_buffer_size);
     // constexpr unsigned max_result_buffer_size = 256;
     RAFT_EXPECTS(result_buffer_size_32 <= 256, "Result buffer size cannot exceed 256");
 
-    smem_size = dataset_desc.smem_ws_size_in_bytes +
-                (sizeof(INDEX_T) + sizeof(DISTANCE_T)) * result_buffer_size_32 +
-                sizeof(uint32_t) * search_width + sizeof(uint32_t);
+    smem_size =
+      dataset_desc.smem_ws_size_in_bytes +
+      (sizeof(INDEX_T) + sizeof(DISTANCE_T)) * (result_buffer_size_32) +
+      sizeof(INDEX_T) * hashmap::get_size(small_hash_bitlen) +  // local_visited_hashmap_ptr
+      sizeof(INDEX_T) * search_width +                          // parent_indices_buffer
+      sizeof(int);                                              // result_position
     RAFT_LOG_DEBUG("# smem_size: %u", smem_size);
 
     //
@@ -222,6 +225,7 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
                    thread_block_size,
                    result_buffer_size,
                    smem_size,
+                   small_hash_bitlen,
                    hash_bitlen,
                    hashmap.data(),
                    num_cta_per_query,
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
index 8d34ab0d68..60dcbab09b 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_inst.cuh
@@ -36,6 +36,7 @@ namespace cuvs::neighbors::cagra::detail::multi_cta_search {
     uint32_t block_size,                                                      \
     uint32_t result_buffer_size,                                              \
     uint32_t smem_size,                                                       \
+    uint32_t small_hash_bitlen,                                               \
     int64_t hash_bitlen,                                                      \
     IndexT* hashmap_ptr,                                                      \
     uint32_t num_cta_per_query,                                               \
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
index 7535ff217a..ea738b137b 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel-inl.cuh
@@ -54,54 +54,76 @@ namespace multi_cta_search {
 
 // #define _CLK_BREAKDOWN
 
-template <class INDEX_T>
-RAFT_DEVICE_INLINE_FUNCTION void pickup_next_parents(
-  INDEX_T* const next_parent_indices,  // [search_width]
-  const uint32_t search_width,
-  INDEX_T* const itopk_indices,  // [num_itopk]
-  const size_t num_itopk,
-  uint32_t* const terminate_flag)
+template <class INDEX_T, class DISTANCE_T>
+RAFT_DEVICE_INLINE_FUNCTION void pickup_next_parent(
+  INDEX_T* const next_parent_indices,
+  INDEX_T* const itopk_indices,       // [itopk_size * 2]
+  DISTANCE_T* const itopk_distances,  // [itopk_size * 2]
+  INDEX_T* const hash_ptr,
+  const uint32_t hash_bitlen)
 {
+  constexpr uint32_t itopk_size      = 32;
   constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
-  const unsigned warp_id             = threadIdx.x / 32;
+  constexpr INDEX_T invalid_index    = ~static_cast<INDEX_T>(0);
+
+  const unsigned warp_id = threadIdx.x / 32;
   if (warp_id > 0) { return; }
-  const unsigned lane_id = threadIdx.x % 32;
-  for (uint32_t i = lane_id; i < search_width; i += 32) {
-    next_parent_indices[i] = utils::get_max_value<INDEX_T>();
-  }
-  uint32_t max_itopk = num_itopk;
-  if (max_itopk % 32) { max_itopk += 32 - (max_itopk % 32); }
-  uint32_t num_new_parents = 0;
-  for (uint32_t j = lane_id; j < max_itopk; j += 32) {
-    INDEX_T index;
-    int new_parent = 0;
-    if (j < num_itopk) {
-      index = itopk_indices[j];
-      if ((index & index_msb_1_mask) == 0) {  // check if most significant bit is set
-        new_parent = 1;
-      }
+  if (threadIdx.x == 0) { next_parent_indices[0] = invalid_index; }
+  __syncwarp();
+
+  int j = -1;
+  for (unsigned i = threadIdx.x; i < itopk_size * 2; i += 32) {
+    INDEX_T index    = itopk_indices[i];
+    int is_invalid   = 0;
+    int is_candidate = 0;
+    if (index == invalid_index) {
+      is_invalid = 1;
+    } else if (index & index_msb_1_mask) {
+    } else {
+      is_candidate = 1;
     }
-    const uint32_t ballot_mask = __ballot_sync(0xffffffff, new_parent);
-    if (new_parent) {
-      const auto i = __popc(ballot_mask & ((1 << lane_id) - 1)) + num_new_parents;
-      if (i < search_width) {
-        next_parent_indices[i] = j;
-        itopk_indices[j] |= index_msb_1_mask;  // set most significant bit as used node
+
+    const auto ballot_mask  = __ballot_sync(0xffffffff, is_candidate);
+    const auto candidate_id = __popc(ballot_mask & ((1 << threadIdx.x) - 1));
+    for (int k = 0; k < __popc(ballot_mask); k++) {
+      int flag_done = 0;
+      if (is_candidate && candidate_id == k) {
+        is_candidate = 0;
+        if (hashmap::insert<INDEX_T, 1>(hash_ptr, hash_bitlen, index)) {
+          // Use this candidate as next parent
+          index |= index_msb_1_mask;  // set most significant bit as used node
+          if (i < itopk_size) {
+            next_parent_indices[0] = i;
+            itopk_indices[i]       = index;
+          } else {
+            next_parent_indices[0] = j;
+            // Move the next parent node from i-th position to j-th position
+            itopk_indices[j]   = index;
+            itopk_distances[j] = itopk_distances[i];
+            itopk_indices[i]   = invalid_index;
+            itopk_distances[i] = utils::get_max_value<DISTANCE_T>();
+          }
+          flag_done = 1;
+        } else {
+          // Deactivate the node since it has been used by other CTA.
+          itopk_indices[i]   = invalid_index;
+          itopk_distances[i] = utils::get_max_value<DISTANCE_T>();
+          is_invalid         = 1;
+        }
       }
+      if (__any_sync(0xffffffff, (flag_done > 0))) { return; }
+    }
+    if (i < itopk_size) {
+      j = 31 - __clz(__ballot_sync(0xffffffff, is_invalid));
+      if (j < 0) { return; }
     }
-    num_new_parents += __popc(ballot_mask);
-    if (num_new_parents >= search_width) { break; }
   }
-  if (threadIdx.x == 0 && (num_new_parents == 0)) { *terminate_flag = 1; }
 }
 
 template <unsigned MAX_ELEMENTS, class INDEX_T>
-RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort(
-  float* distances,  // [num_elements]
-  INDEX_T* indices,  // [num_elements]
-  const uint32_t num_elements,
-  const uint32_t num_itopk  // num_itopk <= num_elements
-)
+RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort(float* distances,  // [num_elements]
+                                                      INDEX_T* indices,  // [num_elements]
+                                                      const uint32_t num_elements)
 {
   const unsigned warp_id = threadIdx.x / 32;
   if (warp_id > 0) { return; }
@@ -116,15 +138,15 @@ RAFT_DEVICE_INLINE_FUNCTION void topk_by_bitonic_sort(
       val[i] = indices[j];
     } else {
       key[i] = utils::get_max_value<float>();
-      val[i] = utils::get_max_value<INDEX_T>();
+      val[i] = ~static_cast<INDEX_T>(0);
     }
   }
   /* Warp Sort */
   bitonic::warp_sort<float, INDEX_T, N>(key, val);
-  /* Store itopk sorted results */
+  /* Store sorted results */
   for (unsigned i = 0; i < N; i++) {
     unsigned j = (N * lane_id) + i;
-    if (j < num_itopk) {
+    if (j < num_elements) {
       distances[j] = key[i];
       indices[j]   = val[i];
     }
@@ -148,11 +170,11 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   const uint64_t rand_xor_mask,
   const typename DATASET_DESCRIPTOR_T::INDEX_T* seed_ptr,  // [num_queries, num_seeds]
   const uint32_t num_seeds,
+  const uint32_t visited_hash_bitlen,
   typename DATASET_DESCRIPTOR_T::INDEX_T* const
-    visited_hashmap_ptr,  // [num_queries, 1 << hash_bitlen]
-  const uint32_t hash_bitlen,
+    traversed_hashmap_ptr,  // [num_queries, 1 << traversed_hash_bitlen]
+  const uint32_t traversed_hash_bitlen,
   const uint32_t itopk_size,
-  const uint32_t search_width,
   const uint32_t min_iteration,
   const uint32_t max_iteration,
   uint32_t* const num_executed_iterations, /* stats */
@@ -185,12 +207,12 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   extern __shared__ uint8_t smem[];
 
   // Layout of result_buffer
-  // +----------------+------------------------------+---------+
-  // | internal_top_k | neighbors of parent nodes    | padding |
-  // | <itopk_size>   | <search_width * graph_degree> | upto 32 |
-  // +----------------+------------------------------+---------+
-  // |<---          result_buffer_size           --->|
-  const auto result_buffer_size    = itopk_size + (search_width * graph_degree);
+  // +----------------+---------+---------------------------+
+  // | internal_top_k | padding | neighbors of parent nodes |
+  // | <itopk_size>   | upto 32 | <graph_degree>            |
+  // +----------------+---------+---------------------------+
+  // |<---        result_buffer_size_32                 --->|
+  const auto result_buffer_size    = itopk_size + graph_degree;
   const auto result_buffer_size_32 = raft::round_up_safe<uint32_t>(result_buffer_size, 32);
   assert(result_buffer_size_32 <= MAX_ELEMENTS);
 
@@ -201,22 +223,23 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
     reinterpret_cast<INDEX_T*>(smem + dataset_desc->smem_ws_size_in_bytes());
   auto* __restrict__ result_distances_buffer =
     reinterpret_cast<DISTANCE_T*>(result_indices_buffer + result_buffer_size_32);
-  auto* __restrict__ parent_indices_buffer =
+  auto* __restrict__ local_visited_hashmap_ptr =
     reinterpret_cast<INDEX_T*>(result_distances_buffer + result_buffer_size_32);
-  auto* __restrict__ terminate_flag =
-    reinterpret_cast<uint32_t*>(parent_indices_buffer + search_width);
+  auto* __restrict__ parent_indices_buffer =
+    reinterpret_cast<INDEX_T*>(local_visited_hashmap_ptr + hashmap::get_size(visited_hash_bitlen));
+  auto* __restrict__ result_position = reinterpret_cast<int*>(parent_indices_buffer + 1);
 
-#if 0
-    /* debug */
-    for (unsigned i = threadIdx.x; i < result_buffer_size_32; i += blockDim.x) {
-        result_indices_buffer[i] = utils::get_max_value<INDEX_T>();
-        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
-    }
-#endif
+  INDEX_T* const local_traversed_hashmap_ptr =
+    traversed_hashmap_ptr + (hashmap::get_size(traversed_hash_bitlen) * query_id);
 
-  if (threadIdx.x == 0) { terminate_flag[0] = 0; }
-  INDEX_T* const local_visited_hashmap_ptr =
-    visited_hashmap_ptr + (hashmap::get_size(hash_bitlen) * query_id);
+  constexpr INDEX_T invalid_index    = ~static_cast<INDEX_T>(0);
+  constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
+
+  for (unsigned i = threadIdx.x; i < result_buffer_size_32; i += blockDim.x) {
+    result_indices_buffer[i]   = invalid_index;
+    result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+  }
+  hashmap::init<INDEX_T>(local_visited_hashmap_ptr, visited_hash_bitlen);
   __syncthreads();
   _CLK_REC(clk_init);
 
@@ -229,13 +252,15 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
   device::compute_distance_to_random_nodes(result_indices_buffer,
                                            result_distances_buffer,
                                            *dataset_desc,
-                                           result_buffer_size,
+                                           graph_degree,
                                            num_distilation,
                                            rand_xor_mask,
                                            local_seed_ptr,
                                            num_seeds,
                                            local_visited_hashmap_ptr,
-                                           hash_bitlen,
+                                           visited_hash_bitlen,
+                                           local_traversed_hashmap_ptr,
+                                           traversed_hash_bitlen,
                                            block_id,
                                            num_blocks);
   __syncthreads();
@@ -243,50 +268,90 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
 
   uint32_t iter = 0;
   while (1) {
-    // topk with bitonic sort
     _CLK_START();
-    topk_by_bitonic_sort<MAX_ELEMENTS, INDEX_T>(result_distances_buffer,
-                                                result_indices_buffer,
-                                                itopk_size + (search_width * graph_degree),
-                                                itopk_size);
+    if (threadIdx.x < 32) {
+      // [1st warp] Topk with bitonic sort
+      topk_by_bitonic_sort<MAX_ELEMENTS, INDEX_T>(
+        result_distances_buffer, result_indices_buffer, result_buffer_size_32);
+    }
+    __syncthreads();
     _CLK_REC(clk_topk);
 
-    if (iter + 1 == max_iteration) {
-      __syncthreads();
-      break;
-    }
+    if (iter + 1 >= max_iteration) { break; }
 
-    // pick up next parents
     _CLK_START();
-    pickup_next_parents<INDEX_T>(
-      parent_indices_buffer, search_width, result_indices_buffer, itopk_size, terminate_flag);
+    if (threadIdx.x < 32) {
+      // [1st warp] Pick up a next parent
+      pickup_next_parent<INDEX_T, DISTANCE_T>(parent_indices_buffer,
+                                              result_indices_buffer,
+                                              result_distances_buffer,
+                                              local_traversed_hashmap_ptr,
+                                              traversed_hash_bitlen);
+    } else {
+      // [Other warps] Reset visited hashmap
+      hashmap::init<INDEX_T>(local_visited_hashmap_ptr, visited_hash_bitlen, 32);
+    }
+    __syncthreads();
     _CLK_REC(clk_pickup_parents);
 
-    __syncthreads();
-    if (*terminate_flag && iter >= min_iteration) { break; }
+    if ((parent_indices_buffer[0] == invalid_index) && (iter >= min_iteration)) { break; }
 
-    // compute the norms between child nodes and query node
     _CLK_START();
-    device::compute_distance_to_child_nodes(result_indices_buffer + itopk_size,
-                                            result_distances_buffer + itopk_size,
-                                            *dataset_desc,
-                                            knn_graph,
-                                            graph_degree,
-                                            local_visited_hashmap_ptr,
-                                            hash_bitlen,
-                                            parent_indices_buffer,
-                                            result_indices_buffer,
-                                            search_width);
-    _CLK_REC(clk_compute_distance);
+    for (unsigned i = threadIdx.x; i < result_buffer_size_32; i += blockDim.x) {
+      INDEX_T index = result_indices_buffer[i];
+      if (index == invalid_index) { continue; }
+      if ((i >= itopk_size) && (index & index_msb_1_mask)) {
+        // Remove nodes kicked out of the itopk list from the traversed hash table.
+        hashmap::remove<INDEX_T>(
+          local_traversed_hashmap_ptr, traversed_hash_bitlen, index & ~index_msb_1_mask);
+        result_indices_buffer[i]   = invalid_index;
+        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+      } else {
+        // Restore visited hashmap by putting nodes on result buffer in it.
+        index &= ~index_msb_1_mask;
+        hashmap::insert(local_visited_hashmap_ptr, visited_hash_bitlen, index);
+      }
+    }
+    // Initialize buffer for compute_distance_to_child_nodes.
+    if (threadIdx.x == blockDim.x - 1) { result_position[0] = result_buffer_size_32; }
+    __syncthreads();
+
+    // Compute the norms between child nodes and query node
+    device::compute_distance_to_child_nodes<INDEX_T, DISTANCE_T, DATASET_DESCRIPTOR_T, 0>(
+      result_indices_buffer,
+      result_distances_buffer,
+      *dataset_desc,
+      knn_graph,
+      graph_degree,
+      local_visited_hashmap_ptr,
+      visited_hash_bitlen,
+      local_traversed_hashmap_ptr,
+      traversed_hash_bitlen,
+      parent_indices_buffer,
+      result_indices_buffer,
+      1,
+      result_position,
+      result_buffer_size_32);
+    // __syncthreads();
+
+    // Check the state of the nodes in the result buffer which were not updated
+    // by the compute_distance_to_child_nodes above, and if it cannot be used as
+    // a parent node, it is deactivated.
+    for (uint32_t i = threadIdx.x; i < result_position[0]; i += blockDim.x) {
+      INDEX_T index = result_indices_buffer[i];
+      if (index == invalid_index || index & index_msb_1_mask) { continue; }
+      if (hashmap::search<INDEX_T, 1>(local_traversed_hashmap_ptr, traversed_hash_bitlen, index)) {
+        result_indices_buffer[i]   = invalid_index;
+        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+      }
+    }
     __syncthreads();
+    _CLK_REC(clk_compute_distance);
 
     // Filtering
     if constexpr (!std::is_same<SAMPLE_FILTER_T,
                                 cuvs::neighbors::filtering::none_sample_filter>::value) {
-      constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
-      const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
-
-      for (unsigned p = threadIdx.x; p < search_width; p += blockDim.x) {
+      for (unsigned p = threadIdx.x; p < 1; p += blockDim.x) {
         if (parent_indices_buffer[p] != invalid_index) {
           const auto parent_id =
             result_indices_buffer[parent_indices_buffer[p]] & ~index_msb_1_mask;
@@ -303,36 +368,64 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
     iter++;
   }
 
-  // Post process for filtering
+  // Filtering
   if constexpr (!std::is_same<SAMPLE_FILTER_T,
                               cuvs::neighbors::filtering::none_sample_filter>::value) {
-    constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
-    const INDEX_T invalid_index        = utils::get_max_value<INDEX_T>();
-
-    for (unsigned i = threadIdx.x; i < itopk_size + search_width * graph_degree; i += blockDim.x) {
-      const auto node_id = result_indices_buffer[i] & ~index_msb_1_mask;
-      if (node_id != (invalid_index & ~index_msb_1_mask) && !sample_filter(query_id, node_id)) {
-        // If the parent must not be in the resulting top-k list, remove from the parent list
-        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
+    for (uint32_t i = threadIdx.x; i < result_buffer_size_32; i += blockDim.x) {
+      INDEX_T index = result_indices_buffer[i];
+      if (index == invalid_index) { continue; }
+      index &= ~index_msb_1_mask;
+      if (!sample_filter(query_id, index)) {
         result_indices_buffer[i]   = invalid_index;
+        result_distances_buffer[i] = utils::get_max_value<DISTANCE_T>();
       }
     }
-
-    __syncthreads();
-    topk_by_bitonic_sort<MAX_ELEMENTS, INDEX_T>(result_distances_buffer,
-                                                result_indices_buffer,
-                                                itopk_size + (search_width * graph_degree),
-                                                itopk_size);
     __syncthreads();
   }
 
-  for (uint32_t i = threadIdx.x; i < itopk_size; i += blockDim.x) {
-    uint32_t j = i + (itopk_size * (cta_id + (num_cta_per_query * query_id)));
-    if (result_distances_ptr != nullptr) { result_distances_ptr[j] = result_distances_buffer[i]; }
-    constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
-
-    result_indices_ptr[j] =
-      result_indices_buffer[i] & ~index_msb_1_mask;  // clear most significant bit
+  // Output search results (1st warp only).
+  if (threadIdx.x < 32) {
+    uint32_t offset = 0;
+    for (uint32_t i = threadIdx.x; i < result_buffer_size_32; i += 32) {
+      INDEX_T index = result_indices_buffer[i];
+      bool is_valid = false;
+      if (index != invalid_index) {
+        if (index & index_msb_1_mask) {
+          is_valid = true;
+          index &= ~index_msb_1_mask;
+        } else if ((offset < itopk_size) &&
+                   hashmap::insert<INDEX_T, 1>(
+                     local_traversed_hashmap_ptr, traversed_hash_bitlen, index)) {
+          // If a node that is not used as a parent can be inserted into
+          // the traversed hash table, it is considered a valid result.
+          is_valid = true;
+        }
+      }
+      const auto mask = __ballot_sync(0xffffffff, is_valid);
+      if (is_valid) {
+        const auto j = offset + __popc(mask & ((1 << threadIdx.x) - 1));
+        if (j < itopk_size) {
+          uint32_t k            = j + (itopk_size * (cta_id + (num_cta_per_query * query_id)));
+          result_indices_ptr[k] = index & ~index_msb_1_mask;
+          if (result_distances_ptr != nullptr) {
+            result_distances_ptr[k] = result_distances_buffer[i];
+          }
+        } else {
+          // If it is valid and registered in the traversed hash table but is
+          // not output as a result, it is removed from the hash table.
+          hashmap::remove<INDEX_T>(local_traversed_hashmap_ptr, traversed_hash_bitlen, index);
+        }
+      }
+      offset += __popc(mask);
+    }
+    // If the number of outputs is insufficient, fill in with invalid results.
+    for (uint32_t i = offset + threadIdx.x; i < itopk_size; i += 32) {
+      uint32_t k            = i + (itopk_size * (cta_id + (num_cta_per_query * query_id)));
+      result_indices_ptr[k] = invalid_index;
+      if (result_distances_ptr != nullptr) {
+        result_distances_ptr[k] = utils::get_max_value<DISTANCE_T>();
+      }
+    }
   }
 
   if (threadIdx.x == 0 && cta_id == 0 && num_executed_iterations != nullptr) {
@@ -427,8 +520,9 @@ void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dat
                     uint32_t block_size,  //
                     uint32_t result_buffer_size,
                     uint32_t smem_size,
-                    int64_t hash_bitlen,
-                    IndexT* hashmap_ptr,
+                    uint32_t visited_hash_bitlen,
+                    int64_t traversed_hash_bitlen,
+                    IndexT* traversed_hashmap_ptr,
                     uint32_t num_cta_per_query,
                     uint32_t num_seeds,
                     SampleFilterT sample_filter,
@@ -441,9 +535,13 @@ void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dat
   RAFT_CUDA_TRY(
     cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
   // Initialize hash table
-  const uint32_t hash_size = hashmap::get_size(hash_bitlen);
-  set_value_batch(
-    hashmap_ptr, hash_size, utils::get_max_value<IndexT>(), hash_size, num_queries, stream);
+  const uint32_t traversed_hash_size = hashmap::get_size(traversed_hash_bitlen);
+  set_value_batch(traversed_hashmap_ptr,
+                  traversed_hash_size,
+                  ~static_cast<IndexT>(0),
+                  traversed_hash_size,
+                  num_queries,
+                  stream);
 
   dim3 block_dims(block_size, 1, 1);
   dim3 grid_dims(num_cta_per_query, num_queries, 1);
@@ -463,10 +561,10 @@ void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dat
                                                        ps.rand_xor_mask,
                                                        dev_seed_ptr,
                                                        num_seeds,
-                                                       hashmap_ptr,
-                                                       hash_bitlen,
+                                                       visited_hash_bitlen,
+                                                       traversed_hashmap_ptr,
+                                                       traversed_hash_bitlen,
                                                        ps.itopk_size,
-                                                       ps.search_width,
                                                        ps.min_iterations,
                                                        ps.max_iterations,
                                                        num_executed_iterations,
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
index 1a1dcd579a..e5dc29f271 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta_kernel.cuh
@@ -36,8 +36,9 @@ void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dat
                     uint32_t block_size,  //
                     uint32_t result_buffer_size,
                     uint32_t smem_size,
-                    int64_t hash_bitlen,
-                    IndexT* hashmap_ptr,
+                    uint32_t visited_hash_bitlen,
+                    int64_t traversed_hash_bitlen,
+                    IndexT* traversed_hashmap_ptr,
                     uint32_t num_cta_per_query,
                     uint32_t num_seeds,
                     SampleFilterT sample_filter,
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index 469c80a08e..cb3e819c96 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -635,9 +635,10 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
          search_params params,
          const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
          int64_t dim,
+         int64_t dataset_size,
          int64_t graph_degree,
          uint32_t topk)
-    : base_type(res, params, dataset_desc, dim, graph_degree, topk),
+    : base_type(res, params, dataset_desc, dim, dataset_size, graph_degree, topk),
       result_indices(res),
       result_distances(res),
       parent_node_list(res),
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index 99254aa501..5fe5b0903a 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -108,11 +108,17 @@ struct lightweight_uvector {
 };
 
 struct search_plan_impl_base : public search_params {
+  int64_t dataset_size;
   int64_t dim;
   int64_t graph_degree;
   uint32_t topk;
-  search_plan_impl_base(search_params params, int64_t dim, int64_t graph_degree, uint32_t topk)
-    : search_params(params), dim(dim), graph_degree(graph_degree), topk(topk)
+  search_plan_impl_base(
+    search_params params, int64_t dim, int64_t dataset_size, int64_t graph_degree, uint32_t topk)
+    : search_params(params),
+      dim(dim),
+      dataset_size(dataset_size),
+      graph_degree(graph_degree),
+      topk(topk)
   {
     if (algo == search_algo::AUTO) {
       const size_t num_sm = raft::getMultiProcessorCount();
@@ -141,7 +147,6 @@ struct search_plan_impl : public search_plan_impl_base {
   size_t small_hash_bitlen;
   size_t small_hash_reset_interval;
   size_t hashmap_size;
-  uint32_t dataset_size;
   uint32_t result_buffer_size;
 
   uint32_t smem_size;
@@ -157,9 +162,10 @@ struct search_plan_impl : public search_plan_impl_base {
                    search_params params,
                    const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                    int64_t dim,
+                   int64_t dataset_size,
                    int64_t graph_degree,
                    uint32_t topk)
-    : search_plan_impl_base(params, dim, graph_degree, topk),
+    : search_plan_impl_base(params, dim, dataset_size, graph_degree, topk),
       hashmap(res),
       num_executed_iterations(res),
       dev_seed(res),
@@ -193,10 +199,16 @@ struct search_plan_impl : public search_plan_impl_base {
     uint32_t _max_iterations = max_iterations;
     if (max_iterations == 0) {
       if (algo == search_algo::MULTI_CTA) {
-        _max_iterations = 1 + std::min(32 * 1.1, 32 + 10.0);  // TODO(anaruse)
+        constexpr uint32_t mc_itopk_size   = 32;
+        constexpr uint32_t mc_search_width = 1;
+        _max_iterations                    = mc_itopk_size / mc_search_width;
       } else {
-        _max_iterations =
-          1 + std::min((itopk_size / search_width) * 1.1, (itopk_size / search_width) + 10.0);
+        _max_iterations = itopk_size / search_width;
+      }
+      int64_t num_reachable_nodes = 1;
+      while (num_reachable_nodes < dataset_size) {
+        num_reachable_nodes *= max((int64_t)2, graph_degree / 2);
+        _max_iterations += 1;
       }
     }
     if (max_iterations < min_iterations) { _max_iterations = min_iterations; }
@@ -219,88 +231,107 @@ struct search_plan_impl : public search_plan_impl_base {
   // defines hash_bitlen, small_hash_bitlen, small_hash_reset interval, hash_size
   inline void calc_hashmap_params(raft::resources const& res)
   {
-    // for multiple CTA search
-    uint32_t mc_num_cta_per_query = 0;
-    uint32_t mc_search_width      = 0;
-    uint32_t mc_itopk_size        = 0;
-    if (algo == search_algo::MULTI_CTA) {
-      mc_itopk_size        = 32;
-      mc_search_width      = 1;
-      mc_num_cta_per_query = max(search_width, raft::ceildiv(itopk_size, (size_t)32));
-      RAFT_LOG_DEBUG("# mc_itopk_size: %u", mc_itopk_size);
-      RAFT_LOG_DEBUG("# mc_search_width: %u", mc_search_width);
-      RAFT_LOG_DEBUG("# mc_num_cta_per_query: %u", mc_num_cta_per_query);
-    }
-
     // Determine hash size (bit length)
     hashmap_size              = 0;
     hash_bitlen               = 0;
     small_hash_bitlen         = 0;
     small_hash_reset_interval = 1024 * 1024;
     float max_fill_rate       = hashmap_max_fill_rate;
-    while (hashmap_mode == hash_mode::AUTO || hashmap_mode == hash_mode::SMALL) {
-      //
-      // The small-hash reduces hash table size by initializing the hash table
-      // for each iteration and re-registering only the nodes that should not be
-      // re-visited in that iteration. Therefore, the size of small-hash should
-      // be determined based on the internal topk size and the number of nodes
-      // visited per iteration.
-      //
-      const auto max_visited_nodes = itopk_size + (search_width * graph_degree * 1);
-      unsigned min_bitlen          = 8;   // 256
-      unsigned max_bitlen          = 13;  // 8K
-      if (min_bitlen < hashmap_min_bitlen) { min_bitlen = hashmap_min_bitlen; }
-      hash_bitlen = min_bitlen;
-      while (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) {
-        hash_bitlen += 1;
-      }
-      if (hash_bitlen > max_bitlen) {
-        // Switch to normal hash if hashmap_mode is AUTO, otherwise exit.
-        if (hashmap_mode == hash_mode::AUTO) {
-          hash_bitlen = 0;
-          break;
-        } else {
-          RAFT_FAIL(
-            "small-hash cannot be used because the required hash size exceeds the limit (%u)",
-            hashmap::get_size(max_bitlen));
-        }
-      }
-      small_hash_bitlen = hash_bitlen;
+    if (algo == search_algo::MULTI_CTA) {
+      const uint32_t mc_itopk_size = 32;
+      const uint32_t mc_num_cta_per_query =
+        max(search_width, raft::ceildiv(itopk_size, (size_t)mc_itopk_size));
+      RAFT_LOG_DEBUG("# mc_itopk_size: %u", mc_itopk_size);
+      RAFT_LOG_DEBUG("# mc_num_cta_per_query: %u", mc_num_cta_per_query);
       //
-      // Sincc the hash table size is limited to a power of 2, the requirement,
-      // the maximum fill rate, may be satisfied even if the frequency of hash
-      // table reset is reduced to once every 2 or more iterations without
-      // changing the hash table size. In that case, reduce the reset frequency.
+      // [visited_hash_table]
+      // In the multi CTA algo, which node has been visited is managed in a hash
+      // table that each CTA has in the shared memory. This hash table is not
+      // shared among CTAs. This hash table is reset and restored in each iteration.
       //
-      small_hash_reset_interval = 1;
-      while (1) {
-        const auto max_visited_nodes =
-          itopk_size + (search_width * graph_degree * (small_hash_reset_interval + 1));
-        if (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) { break; }
-        small_hash_reset_interval += 1;
+      const uint32_t max_visited_nodes = mc_itopk_size + (graph_degree * 2);
+      small_hash_bitlen                = 8;  // 256
+      while (max_visited_nodes > hashmap::get_size(small_hash_bitlen) * max_fill_rate) {
+        small_hash_bitlen += 1;
       }
-      break;
-    }
-    if (hash_bitlen == 0) {
+      RAFT_EXPECTS(small_hash_bitlen <= 14, "small_hash_bitlen cannot be largen than 14 (16K)");
       //
-      // The size of hash table is determined based on the maximum number of
-      // nodes that may be visited before the search is completed and the
-      // maximum fill rate of the hash table.
+      // [traversed_hash_table]
+      // Whether a node has ever been used as the starting point for a traversal
+      // in each iteration is managed in a separate hash table, which is shared
+      // among the CTAs.
       //
-      uint32_t max_visited_nodes = itopk_size + (search_width * graph_degree * max_iterations);
-      if (algo == search_algo::MULTI_CTA) {
-        max_visited_nodes = mc_itopk_size + (mc_search_width * graph_degree * max_iterations);
-        max_visited_nodes *= mc_num_cta_per_query;
-      }
+      const auto max_traversed_nodes =
+        mc_num_cta_per_query * max((size_t)mc_itopk_size, max_iterations);
       unsigned min_bitlen = 11;  // 2K
       if (min_bitlen < hashmap_min_bitlen) { min_bitlen = hashmap_min_bitlen; }
       hash_bitlen = min_bitlen;
-      while (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) {
+      while (max_traversed_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) {
         hash_bitlen += 1;
       }
-      RAFT_EXPECTS(hash_bitlen <= 20, "hash_bitlen cannot be largen than 20 (1M)");
+      RAFT_EXPECTS(hash_bitlen <= 25, "hash_bitlen cannot be largen than 25 (32M)");
+    } else {
+      while (hashmap_mode == hash_mode::AUTO || hashmap_mode == hash_mode::SMALL) {
+        //
+        // The small-hash reduces hash table size by initializing the hash table
+        // for each iteration and re-registering only the nodes that should not be
+        // re-visited in that iteration. Therefore, the size of small-hash should
+        // be determined based on the internal topk size and the number of nodes
+        // visited per iteration.
+        //
+        const auto max_visited_nodes = itopk_size + (search_width * graph_degree * 1);
+        unsigned min_bitlen          = 8;   // 256
+        unsigned max_bitlen          = 13;  // 8K
+        if (min_bitlen < hashmap_min_bitlen) { min_bitlen = hashmap_min_bitlen; }
+        hash_bitlen = min_bitlen;
+        while (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) {
+          hash_bitlen += 1;
+        }
+        if (hash_bitlen > max_bitlen) {
+          // Switch to normal hash if hashmap_mode is AUTO, otherwise exit.
+          if (hashmap_mode == hash_mode::AUTO) {
+            hash_bitlen = 0;
+            break;
+          } else {
+            RAFT_FAIL(
+              "small-hash cannot be used because the required hash size exceeds the limit (%u)",
+              hashmap::get_size(max_bitlen));
+          }
+        }
+        small_hash_bitlen = hash_bitlen;
+        //
+        // Sincc the hash table size is limited to a power of 2, the requirement,
+        // the maximum fill rate, may be satisfied even if the frequency of hash
+        // table reset is reduced to once every 2 or more iterations without
+        // changing the hash table size. In that case, reduce the reset frequency.
+        //
+        small_hash_reset_interval = 1;
+        while (1) {
+          const auto max_visited_nodes =
+            itopk_size + (search_width * graph_degree * (small_hash_reset_interval + 1));
+          if (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) { break; }
+          small_hash_reset_interval += 1;
+        }
+        break;
+      }
+      if (hash_bitlen == 0) {
+        //
+        // The size of hash table is determined based on the maximum number of
+        // nodes that may be visited before the search is completed and the
+        // maximum fill rate of the hash table.
+        //
+        uint32_t max_visited_nodes = itopk_size + (search_width * graph_degree * max_iterations);
+        unsigned min_bitlen        = 11;  // 2K
+        if (min_bitlen < hashmap_min_bitlen) { min_bitlen = hashmap_min_bitlen; }
+        hash_bitlen = min_bitlen;
+        while (max_visited_nodes > hashmap::get_size(hash_bitlen) * max_fill_rate) {
+          hash_bitlen += 1;
+        }
+        RAFT_EXPECTS(hash_bitlen <= 20,
+                     "hash_bitlen cannot be largen than 20 (1M). You can decrease itopk_size, "
+                     "search_width or max_iterations to reduce the required hashmap size.");
+      }
     }
-
     RAFT_LOG_DEBUG("# internal topK = %lu", itopk_size);
     RAFT_LOG_DEBUG("# parent size = %lu", search_width);
     RAFT_LOG_DEBUG("# min_iterations = %lu", min_iterations);
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index 161aa8c4a3..20070487bf 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -94,9 +94,10 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
          search_params params,
          const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
          int64_t dim,
+         int64_t dataset_size,
          int64_t graph_degree,
          uint32_t topk)
-    : base_type(res, params, dataset_desc, dim, graph_degree, topk)
+    : base_type(res, params, dataset_desc, dim, dataset_size, graph_degree, topk)
   {
     set_params(res);
   }
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index 188862fbbb..1e072f5406 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -621,7 +621,9 @@ __device__ void search_core(
                                            local_seed_ptr,
                                            num_seeds,
                                            local_visited_hashmap_ptr,
-                                           hash_bitlen);
+                                           hash_bitlen,
+                                           (INDEX_T*)nullptr,
+                                           0);
   __syncthreads();
   _CLK_REC(clk_compute_1st_distance);
 
@@ -748,6 +750,8 @@ __device__ void search_core(
                                             graph_degree,
                                             local_visited_hashmap_ptr,
                                             hash_bitlen,
+                                            (INDEX_T*)nullptr,
+                                            0,
                                             parent_list_buffer,
                                             result_indices_buffer,
                                             search_width);

From 0dd7bde8c16d3eb8da9a2fd546f540fbf7e40c81 Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Thu, 30 Jan 2025 17:04:48 +0100
Subject: [PATCH 282/623] Fix cagra_hnsw serialization when dataset is not part
 of index (#591)

After calling `build()`, ideally the CAGRA index contains both the dataset and the graph. But when we do not have sufficient device memory, then only the graph is returned. In such case we need to pass the dataset explicitly to the serialization routines.

For serialization in HNSW format, in case we have flat hierarchy, the dataset was not passed. This PR fixes this problem by adding an optional `dataset` argument to `cagra::serialize_to_hnswlib`.

Furthermore, to improve execution time, we change from writing a single element to writing a single row of the graph and dataset at time.

Additionally, debug messages for tracking data saving time are added.

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/591
---
 .../ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h |   9 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |  66 ++++++++---
 cpp/src/neighbors/cagra_serialize.cuh         |  95 ++++++++--------
 .../detail/cagra/cagra_serialize.cuh          | 105 ++++++++++++------
 cpp/src/neighbors/detail/hnsw.hpp             |  18 ++-
 5 files changed, 191 insertions(+), 102 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
index e4169f6f8c..6670ed8926 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
@@ -17,7 +17,9 @@
 
 #include "cuvs_cagra_wrapper.h"
 #include <cuvs/neighbors/hnsw.hpp>
+#include <raft/core/logger.hpp>
 
+#include <chrono>
 #include <memory>
 
 namespace cuvs::bench {
@@ -90,8 +92,13 @@ void cuvs_cagra_hnswlib<T, IdxT>::build(const T* dataset, size_t nrow)
   auto host_dataset_view = raft::make_host_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
   auto opt_dataset_view =
     std::optional<raft::host_matrix_view<const T, int64_t>>(std::move(host_dataset_view));
-  hnsw_index_ = cuvs::neighbors::hnsw::from_cagra(
+  const auto start_clock = std::chrono::system_clock::now();
+  hnsw_index_            = cuvs::neighbors::hnsw::from_cagra(
     handle_, build_param_.hnsw_index_params, *cagra_index, opt_dataset_view);
+  int time =
+    std::chrono::duration_cast<std::chrono::seconds>(std::chrono::system_clock::now() - start_clock)
+      .count();
+  RAFT_LOG_DEBUG("Graph saved to HNSW format in %d:%d min", time / 60, time % 60);
 }
 
 template <typename T, typename IdxT>
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index a4684ce267..597e186d7c 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1599,11 +1599,16 @@ void deserialize(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] os output stream
  * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
  *
  */
-void serialize_to_hnswlib(raft::resources const& handle,
-                          std::ostream& os,
-                          const cuvs::neighbors::cagra::index<float, uint32_t>& index);
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  std::ostream& os,
+  const cuvs::neighbors::cagra::index<float, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const float, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
  * Save a CAGRA build index in hnswlib base-layer-only serialized format
@@ -1628,11 +1633,16 @@ void serialize_to_hnswlib(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] filename the file name for saving the index
  * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
  *
  */
-void serialize_to_hnswlib(raft::resources const& handle,
-                          const std::string& filename,
-                          const cuvs::neighbors::cagra::index<float, uint32_t>& index);
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  const std::string& filename,
+  const cuvs::neighbors::cagra::index<float, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const float, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
  * Write the CAGRA built index as a base layer HNSW index to an output stream
@@ -1656,11 +1666,16 @@ void serialize_to_hnswlib(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] os output stream
  * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
  *
  */
-void serialize_to_hnswlib(raft::resources const& handle,
-                          std::ostream& os,
-                          const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index);
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  std::ostream& os,
+  const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const int8_t, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
  * Save a CAGRA build index in hnswlib base-layer-only serialized format
@@ -1685,11 +1700,16 @@ void serialize_to_hnswlib(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] filename the file name for saving the index
  * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
  *
  */
-void serialize_to_hnswlib(raft::resources const& handle,
-                          const std::string& filename,
-                          const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index);
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  const std::string& filename,
+  const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const int8_t, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
  * Write the CAGRA built index as a base layer HNSW index to an output stream
@@ -1713,11 +1733,16 @@ void serialize_to_hnswlib(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] os output stream
  * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
  *
  */
-void serialize_to_hnswlib(raft::resources const& handle,
-                          std::ostream& os,
-                          const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index);
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  std::ostream& os,
+  const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const uint8_t, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
  * Save a CAGRA build index in hnswlib base-layer-only serialized format
@@ -1742,11 +1767,16 @@ void serialize_to_hnswlib(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] filename the file name for saving the index
  * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
  *
  */
-void serialize_to_hnswlib(raft::resources const& handle,
-                          const std::string& filename,
-                          const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index);
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  const std::string& filename,
+  const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const uint8_t, int64_t, raft::row_major>> dataset =
+    std::nullopt);
 
 /**
  * @}
diff --git a/cpp/src/neighbors/cagra_serialize.cuh b/cpp/src/neighbors/cagra_serialize.cuh
index e193c06304..1b153b2ce3 100644
--- a/cpp/src/neighbors/cagra_serialize.cuh
+++ b/cpp/src/neighbors/cagra_serialize.cuh
@@ -20,51 +20,56 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                      \
-  void serialize(raft::resources const& handle,                                               \
-                 const std::string& filename,                                                 \
-                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                 \
-                 bool include_dataset)                                                        \
-  {                                                                                           \
-    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                               \
-      handle, filename, index, include_dataset);                                              \
-  };                                                                                          \
-                                                                                              \
-  void deserialize(raft::resources const& handle,                                             \
-                   const std::string& filename,                                               \
-                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                     \
-  {                                                                                           \
-    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, filename, index);    \
-  };                                                                                          \
-  void serialize(raft::resources const& handle,                                               \
-                 std::ostream& os,                                                            \
-                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                 \
-                 bool include_dataset)                                                        \
-  {                                                                                           \
-    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                               \
-      handle, os, index, include_dataset);                                                    \
-  }                                                                                           \
-                                                                                              \
-  void deserialize(raft::resources const& handle,                                             \
-                   std::istream& is,                                                          \
-                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                     \
-  {                                                                                           \
-    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, is, index);          \
-  }                                                                                           \
-                                                                                              \
-  void serialize_to_hnswlib(raft::resources const& handle,                                    \
-                            std::ostream& os,                                                 \
-                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)      \
-  {                                                                                           \
-    cuvs::neighbors::cagra::detail::serialize_to_hnswlib<DTYPE, uint32_t>(handle, os, index); \
-  }                                                                                           \
-                                                                                              \
-  void serialize_to_hnswlib(raft::resources const& handle,                                    \
-                            const std::string& filename,                                      \
-                            const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index)      \
-  {                                                                                           \
-    cuvs::neighbors::cagra::detail::serialize_to_hnswlib<DTYPE, uint32_t>(                    \
-      handle, filename, index);                                                               \
+#define CUVS_INST_CAGRA_SERIALIZE(DTYPE)                                                   \
+  void serialize(raft::resources const& handle,                                            \
+                 const std::string& filename,                                              \
+                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,              \
+                 bool include_dataset)                                                     \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                            \
+      handle, filename, index, include_dataset);                                           \
+  };                                                                                       \
+                                                                                           \
+  void deserialize(raft::resources const& handle,                                          \
+                   const std::string& filename,                                            \
+                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                  \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, filename, index); \
+  };                                                                                       \
+  void serialize(raft::resources const& handle,                                            \
+                 std::ostream& os,                                                         \
+                 const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,              \
+                 bool include_dataset)                                                     \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::serialize<DTYPE, uint32_t>(                            \
+      handle, os, index, include_dataset);                                                 \
+  }                                                                                        \
+                                                                                           \
+  void deserialize(raft::resources const& handle,                                          \
+                   std::istream& is,                                                       \
+                   cuvs::neighbors::cagra::index<DTYPE, uint32_t>* index)                  \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::deserialize<DTYPE, uint32_t>(handle, is, index);       \
+  }                                                                                        \
+                                                                                           \
+  void serialize_to_hnswlib(                                                               \
+    raft::resources const& handle,                                                         \
+    std::ostream& os,                                                                      \
+    const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                           \
+    std::optional<raft::host_matrix_view<const DTYPE, int64_t, raft::row_major>> dataset)  \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::serialize_to_hnswlib<DTYPE, uint32_t>(                 \
+      handle, os, index, dataset);                                                         \
+  }                                                                                        \
+                                                                                           \
+  void serialize_to_hnswlib(                                                               \
+    raft::resources const& handle,                                                         \
+    const std::string& filename,                                                           \
+    const cuvs::neighbors::cagra::index<DTYPE, uint32_t>& index,                           \
+    std::optional<raft::host_matrix_view<const DTYPE, int64_t, raft::row_major>> dataset)  \
+  {                                                                                        \
+    cuvs::neighbors::cagra::detail::serialize_to_hnswlib<DTYPE, uint32_t>(                 \
+      handle, filename, index, dataset);                                                   \
   }
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index c83da7bb10..4bd761dc60 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -30,6 +30,7 @@
 #include <cstddef>
 #include <cstdint>
 #include <fstream>
+#include <optional>
 #include <type_traits>
 
 namespace cuvs::neighbors::cagra::detail {
@@ -96,16 +97,19 @@ void serialize(raft::resources const& res,
 }
 
 template <typename T, typename IdxT>
-void serialize_to_hnswlib(raft::resources const& res,
-                          std::ostream& os,
-                          const cuvs::neighbors::cagra::index<T, IdxT>& index_)
+void serialize_to_hnswlib(
+  raft::resources const& res,
+  std::ostream& os,
+  const cuvs::neighbors::cagra::index<T, IdxT>& index_,
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
   // static_assert(std::is_same_v<IdxT, int> or std::is_same_v<IdxT, uint32_t>,
   //               "An hnswlib index can only be trained with int32 or uint32 IdxT");
+  int dim = (dataset) ? dataset->extent(1) : index_.dim();
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("cagra::serialize");
   RAFT_LOG_DEBUG("Saving CAGRA index to hnswlib format, size %zu, dim %u",
                  static_cast<size_t>(index_.size()),
-                 index_.dim());
+                 dim);
 
   // offset_level_0
   std::size_t offset_level_0 = 0;
@@ -119,8 +123,8 @@ void serialize_to_hnswlib(raft::resources const& res,
   // Example:M: 16, dim = 128, data_t = float, index_t = uint32_t, list_size_type = uint32_t,
   // labeltype: size_t size_data_per_element_ = M * 2 * sizeof(index_t) + sizeof(list_size_type) +
   // dim * sizeof(T) + sizeof(labeltype)
-  auto size_data_per_element = static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4 +
-                                                        index_.dim() * sizeof(T) + 8);
+  auto size_data_per_element =
+    static_cast<std::size_t>(index_.graph_degree() * sizeof(IdxT) + 4 + dim * sizeof(T) + 8);
   os.write(reinterpret_cast<char*>(&size_data_per_element), sizeof(std::size_t));
   // label_offset
   std::size_t label_offset = size_data_per_element - 8;
@@ -150,19 +154,29 @@ void serialize_to_hnswlib(raft::resources const& res,
   std::size_t efConstruction = 500;
   os.write(reinterpret_cast<char*>(&efConstruction), sizeof(std::size_t));
 
-  auto dataset = index_.dataset();
   // Remove padding before saving the dataset
-  auto host_dataset = raft::make_host_matrix<T, int64_t>(dataset.extent(0), dataset.extent(1));
-  RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_dataset.data_handle(),
-                                  sizeof(T) * host_dataset.extent(1),
-                                  dataset.data_handle(),
-                                  sizeof(T) * dataset.stride(0),
-                                  sizeof(T) * host_dataset.extent(1),
-                                  dataset.extent(0),
-                                  cudaMemcpyDefault,
-                                  raft::resource::get_cuda_stream(res)));
-  raft::resource::sync_stream(res);
-
+  raft::host_matrix<T, int64_t> host_dataset = raft::make_host_matrix<T, int64_t>(0, 0);
+  raft::host_matrix_view<const T, int64_t> host_dataset_view;
+  if (dataset) {
+    host_dataset_view = *dataset;
+  } else {
+    auto dataset = index_.dataset();
+    RAFT_EXPECTS(dataset.size() > 0,
+                 "Invalid CAGRA dataset of size 0 during serialization, shape %zux%zu",
+                 static_cast<size_t>(dataset.extent(0)),
+                 static_cast<size_t>(dataset.extent(1)));
+    host_dataset = raft::make_host_matrix<T, int64_t>(dataset.extent(0), dataset.extent(1));
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_dataset.data_handle(),
+                                    sizeof(T) * host_dataset.extent(1),
+                                    dataset.data_handle(),
+                                    sizeof(T) * dataset.stride(0),
+                                    sizeof(T) * host_dataset.extent(1),
+                                    dataset.extent(0),
+                                    cudaMemcpyDefault,
+                                    raft::resource::get_cuda_stream(res)));
+    raft::resource::sync_stream(res);
+    host_dataset_view = raft::make_const_mdspan(host_dataset.view());
+  }
   auto graph = index_.graph();
   auto host_graph =
     raft::make_host_matrix<IdxT, int64_t, raft::row_major>(graph.extent(0), graph.extent(1));
@@ -172,23 +186,48 @@ void serialize_to_hnswlib(raft::resources const& res,
              raft::resource::get_cuda_stream(res));
   raft::resource::sync_stream(res);
 
+  size_t d_report_offset    = index_.size() / 10;  // Report progress in 10% steps.
+  size_t next_report_offset = d_report_offset;
+  const auto start_clock    = std::chrono::system_clock::now();
   // Write one dataset and graph row at a time
+  RAFT_EXPECTS(host_graph.stride(1) == 1, "serialize_to_hnswlib expects row_major graph");
+  RAFT_EXPECTS(host_dataset_view.stride(1) == 1, "serialize_to_hnswlib expects row_major dataset");
+
+  size_t bytes_written = 0;
+  float GiB            = 1 << 30;
   for (std::size_t i = 0; i < index_.size(); i++) {
     auto graph_degree = static_cast<int>(index_.graph_degree());
     os.write(reinterpret_cast<char*>(&graph_degree), sizeof(int));
 
-    for (std::size_t j = 0; j < index_.graph_degree(); ++j) {
-      auto graph_elem = host_graph(i, j);
-      os.write(reinterpret_cast<char*>(&graph_elem), sizeof(IdxT));
-    }
-
-    auto data_row = host_dataset.data_handle() + (index_.dim() * i);
-    for (std::size_t j = 0; j < index_.dim(); ++j) {
-      auto data_elem = static_cast<T>(host_dataset(i, j));
-      os.write(reinterpret_cast<char*>(&data_elem), sizeof(T));
-    }
+    IdxT* graph_row = &host_graph(i, 0);
+    os.write(reinterpret_cast<char*>(graph_row), sizeof(IdxT) * index_.graph_degree());
 
+    const T* data_row = &host_dataset_view(i, 0);
+    os.write(reinterpret_cast<const char*>(data_row), sizeof(T) * dim);
     os.write(reinterpret_cast<char*>(&i), sizeof(std::size_t));
+
+    bytes_written +=
+      dim * sizeof(T) + index_.graph_degree() * sizeof(IdxT) + sizeof(int) + sizeof(size_t);
+    const auto end_clock = std::chrono::system_clock::now();
+    if (!os.good()) { RAFT_FAIL("Error writing HNSW file, row %zu", i); }
+    if (i > next_report_offset) {
+      next_report_offset += d_report_offset;
+      const auto time =
+        std::chrono::duration_cast<std::chrono::microseconds>(end_clock - start_clock).count() *
+        1e-6;
+      float throughput      = bytes_written / GiB / time;
+      float rows_throughput = i / time;
+      float ETA             = (index_.size() - i) / rows_throughput;
+      RAFT_LOG_DEBUG(
+        "# Writing rows %12lu / %12lu (%3.2f %%), %3.2f GiB/sec, ETA %d:%3.1f, written %3.2f GiB\r",
+        i,
+        index_.size(),
+        i / static_cast<double>(index_.size()) * 100,
+        throughput,
+        int(ETA / 60),
+        std::fmod(ETA, 60.0f),
+        bytes_written / GiB);
+    }
   }
 
   for (std::size_t i = 0; i < index_.size(); i++) {
@@ -199,14 +238,16 @@ void serialize_to_hnswlib(raft::resources const& res,
 }
 
 template <typename T, typename IdxT>
-void serialize_to_hnswlib(raft::resources const& res,
-                          const std::string& filename,
-                          const cuvs::neighbors::cagra::index<T, IdxT>& index_)
+void serialize_to_hnswlib(
+  raft::resources const& res,
+  const std::string& filename,
+  const cuvs::neighbors::cagra::index<T, IdxT>& index_,
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
   std::ofstream of(filename, std::ios::out | std::ios::binary);
   if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
 
-  detail::serialize_to_hnswlib<T, IdxT>(res, of, index_);
+  detail::serialize_to_hnswlib<T, IdxT>(res, of, index_, dataset);
 
   of.close();
   if (!of) { RAFT_FAIL("Error writing output %s", filename.c_str()); }
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index e129d23e87..5447ae07a6 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -21,6 +21,7 @@
 #include <hnswlib/hnswalg.h>
 #include <hnswlib/hnswlib.h>
 #include <memory>
+#include <raft/core/logger.hpp>
 #include <random>
 #include <thread>
 
@@ -163,14 +164,15 @@ template <typename T, HnswHierarchy hierarchy>
 std::enable_if_t<hierarchy == HnswHierarchy::NONE, std::unique_ptr<index<T>>> from_cagra(
   raft::resources const& res,
   const index_params& params,
-  const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index)
+  const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
   std::random_device dev;
   std::mt19937 rng(dev());
   std::uniform_int_distribution<std::mt19937::result_type> dist(0);
   auto uuid            = std::to_string(dist(rng));
   std::string filepath = "/tmp/" + uuid + ".bin";
-  cuvs::neighbors::cagra::serialize_to_hnswlib(res, filepath, cagra_index);
+  cuvs::neighbors::cagra::serialize_to_hnswlib(res, filepath, cagra_index, dataset);
 
   index<T>* hnsw_index = nullptr;
   cuvs::neighbors::hnsw::deserialize(
@@ -195,6 +197,10 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
   } else {
     // move dataset to host, remove padding
     auto cagra_dataset = cagra_index.dataset();
+    RAFT_EXPECTS(cagra_dataset.size() > 0,
+                 "Invalid CAGRA dataset of size 0, shape %zux%zu",
+                 static_cast<size_t>(cagra_dataset.extent(0)),
+                 static_cast<size_t>(cagra_dataset.extent(1)));
     host_dataset =
       raft::make_host_matrix<T, int64_t>(cagra_dataset.extent(0), cagra_dataset.extent(1));
     RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_dataset.data_handle(),
@@ -209,9 +215,9 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
     host_dataset_view = host_dataset.view();
   }
   // build upper layers of hnsw index
-  auto hnsw_index =
-    std::make_unique<index_impl<T>>(cagra_index.dim(), cagra_index.metric(), hierarchy);
-  auto appr_algo = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+  int dim         = host_dataset_view.extent(1);
+  auto hnsw_index = std::make_unique<index_impl<T>>(dim, cagra_index.metric(), hierarchy);
+  auto appr_algo  = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
     hnsw_index->get_space(),
     host_dataset_view.extent(0),
     cagra_index.graph().extent(1) / 2,
@@ -256,7 +262,7 @@ std::unique_ptr<index<T>> from_cagra(
   std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
   if (params.hierarchy == HnswHierarchy::NONE) {
-    return from_cagra<T, HnswHierarchy::NONE>(res, params, cagra_index);
+    return from_cagra<T, HnswHierarchy::NONE>(res, params, cagra_index, dataset);
   } else if (params.hierarchy == HnswHierarchy::CPU) {
     return from_cagra<T, HnswHierarchy::CPU>(res, params, cagra_index, dataset);
   }

From 4ca47c92b03856e7d4ae328aabf6ee3e853a137e Mon Sep 17 00:00:00 2001
From: Ishan Chattopadhyaya <ichattopadhyaya@gmail.com>
Date: Thu, 30 Jan 2025 23:12:55 +0530
Subject: [PATCH 283/623] Initial cut for a cuVS Java API (#450)

A Java API for cuVS for easy integration into Apache Lucene or other Java based projects.

Try:
```
./build.sh libcuvs
./build.sh java
```

For generating docs, ```mvn javadoc:javadoc```

Prerequisites:
* JDK 22
* Maven 3.9.6+

Todo:
* Generate project panama classes using jextract on every build
* Algorithms other than Cagra
* Prefiltering in cagra

Authors:
  - Ishan Chattopadhyaya (https://github.com/chatman)
  - Vivek Narang (https://github.com/narangvivek10)
  - Chris Hegarty (https://github.com/ChrisHegarty)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Mike Sarahan (https://github.com/msarahan)

URL: https://github.com/rapidsai/cuvs/pull/450
---
 .gitignore                                    |    3 +
 build.sh                                      |   15 +-
 ci/release/update-version.sh                  |    8 +
 cpp/include/cuvs/neighbors/hnsw.h             |    2 +-
 java/README.md                                |   14 +
 java/build.sh                                 |   14 +
 java/cuvs-java/.gitignore                     |    1 +
 java/cuvs-java/pom.xml                        |  159 +
 .../java/com/nvidia/cuvs/BruteForceIndex.java |  364 ++
 .../nvidia/cuvs/BruteForceIndexParams.java    |   71 +
 .../java/com/nvidia/cuvs/BruteForceQuery.java |  156 +
 .../nvidia/cuvs/BruteForceSearchResults.java  |   61 +
 .../nvidia/cuvs/CagraCompressionParams.java   |  266 ++
 .../main/java/com/nvidia/cuvs/CagraIndex.java |  517 +++
 .../com/nvidia/cuvs/CagraIndexParams.java     |  344 ++
 .../main/java/com/nvidia/cuvs/CagraQuery.java |  165 +
 .../com/nvidia/cuvs/CagraSearchParams.java    |  505 +++
 .../com/nvidia/cuvs/CagraSearchResults.java   |   61 +
 .../java/com/nvidia/cuvs/CuVSResources.java   |  152 +
 .../main/java/com/nvidia/cuvs/GPUInfo.java    |   56 +
 .../main/java/com/nvidia/cuvs/HnswIndex.java  |  263 ++
 .../java/com/nvidia/cuvs/HnswIndexParams.java |  211 ++
 .../main/java/com/nvidia/cuvs/HnswQuery.java  |  156 +
 .../com/nvidia/cuvs/HnswSearchParams.java     |  137 +
 .../com/nvidia/cuvs/HnswSearchResults.java    |   61 +
 .../com/nvidia/cuvs/LibraryException.java     |   18 +
 .../com/nvidia/cuvs/common/SearchResults.java |   53 +
 .../java/com/nvidia/cuvs/common/Util.java     |  265 ++
 .../com/nvidia/cuvs/panama/BruteForceH.java   | 1912 ++++++++++
 .../java/com/nvidia/cuvs/panama/CagraH.java   | 2298 ++++++++++++
 .../cuvs/panama/CuVSBruteForceIndex.java      |  183 +
 .../panama/CuVSCagraCompressionParams.java    |  352 ++
 .../nvidia/cuvs/panama/CuVSCagraIndex.java    |  183 +
 .../cuvs/panama/CuVSCagraIndexParams.java     |  354 ++
 .../cuvs/panama/CuVSCagraSearchParams.java    |  644 ++++
 .../com/nvidia/cuvs/panama/CuVSFilter.java    |  188 +
 .../cuvs/panama/CuVSHnswExtendParams.java     |  142 +
 .../com/nvidia/cuvs/panama/CuVSHnswIndex.java |  183 +
 .../cuvs/panama/CuVSHnswIndexParams.java      |  224 ++
 .../cuvs/panama/CuVSHnswSearchParams.java     |  183 +
 .../com/nvidia/cuvs/panama/DLDataType.java    |  233 ++
 .../java/com/nvidia/cuvs/panama/DLDevice.java |  186 +
 .../nvidia/cuvs/panama/DLManagedTensor.java   |  288 ++
 .../cuvs/panama/DLManagedTensorVersioned.java |  381 ++
 .../com/nvidia/cuvs/panama/DLPackVersion.java |  186 +
 .../java/com/nvidia/cuvs/panama/DLTensor.java |  418 +++
 .../com/nvidia/cuvs/panama/DistanceH.java     |  274 ++
 .../java/com/nvidia/cuvs/panama/DlpackH.java  | 1898 ++++++++++
 .../java/com/nvidia/cuvs/panama/Fsidt.java    |  175 +
 .../java/com/nvidia/cuvs/panama/GpuInfo.java  |  342 ++
 .../java/com/nvidia/cuvs/panama/GpuInfoH.java |   86 +
 .../java/com/nvidia/cuvs/panama/HnswH.java    | 2350 ++++++++++++
 .../java/com/nvidia/cuvs/panama/IvfFlatH.java | 2845 +++++++++++++++
 .../java/com/nvidia/cuvs/panama/IvfPqH.java   | 3182 +++++++++++++++++
 .../com/nvidia/cuvs/panama/MaxAlignT.java     |  142 +
 java/cuvs-java/src/main/java/module-info.java |   19 +
 .../nvidia/cuvs/BruteForceAndSearchTest.java  |  129 +
 .../nvidia/cuvs/BruteForceRandomizedTest.java |   99 +
 .../nvidia/cuvs/CagraBuildAndSearchTest.java  |  137 +
 .../com/nvidia/cuvs/CagraRandomizedTest.java  |  101 +
 .../java/com/nvidia/cuvs/CuVSTestCase.java    |   88 +
 .../nvidia/cuvs/HnswBuildAndSearchTest.java   |  132 +
 .../com/nvidia/cuvs/HnswRandomizedTest.java   |  136 +
 java/examples/.gitignore                      |    1 +
 java/examples/README.md                       |    8 +
 java/examples/pom.xml                         |  105 +
 .../cuvs/examples/BruteForceExample.java      |   90 +
 .../nvidia/cuvs/examples/CagraExample.java    |  100 +
 .../com/nvidia/cuvs/examples/HnswExample.java |   99 +
 java/examples/src/main/resources/log4j2.xml   |   14 +
 java/internal/CMakeLists.txt                  |   62 +
 java/internal/VERSION                         |    1 +
 java/internal/get_dlpack.cmake                |    1 +
 java/internal/rapids_config.cmake             |    1 +
 java/internal/src/cuvs_java.c                 |  464 +++
 75 files changed, 25714 insertions(+), 3 deletions(-)
 create mode 100644 java/README.md
 create mode 100755 java/build.sh
 create mode 100644 java/cuvs-java/.gitignore
 create mode 100644 java/cuvs-java/pom.xml
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceSearchResults.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchResults.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchResults.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/common/SearchResults.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/BruteForceH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CagraH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSBruteForceIndex.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraCompressionParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndex.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndexParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraSearchParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSFilter.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswExtendParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndex.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndexParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswSearchParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDataType.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDevice.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensor.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensorVersioned.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLPackVersion.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLTensor.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DistanceH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DlpackH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/Fsidt.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfo.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfoH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/HnswH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfFlatH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfPqH.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/MaxAlignT.java
 create mode 100644 java/cuvs-java/src/main/java/module-info.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchTest.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedTest.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchTest.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedTest.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchTest.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedTest.java
 create mode 100644 java/examples/.gitignore
 create mode 100644 java/examples/README.md
 create mode 100644 java/examples/pom.xml
 create mode 100644 java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
 create mode 100644 java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
 create mode 100644 java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
 create mode 100644 java/examples/src/main/resources/log4j2.xml
 create mode 100644 java/internal/CMakeLists.txt
 create mode 120000 java/internal/VERSION
 create mode 120000 java/internal/get_dlpack.cmake
 create mode 120000 java/internal/rapids_config.cmake
 create mode 100644 java/internal/src/cuvs_java.c

diff --git a/.gitignore b/.gitignore
index da6eb07f64..a70ca197ff 100644
--- a/.gitignore
+++ b/.gitignore
@@ -83,3 +83,6 @@ ivf_pq_index
 # cuvs_bench
 datasets/
 /*.json
+
+# java
+.classpath
diff --git a/build.sh b/build.sh
index 3b9a9a3a8f..89e1b5a337 100755
--- a/build.sh
+++ b/build.sh
@@ -18,7 +18,7 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python rust docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python rust java docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
@@ -26,6 +26,7 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
                       around the C++ code.
    python           - build the cuvs Python package
    rust             - build the cuvs Rust bindings
+   java             - build the cuvs Java bindings
    docs             - build the documentation
    tests            - build the tests
    bench-ann        - build end-to-end ann benchmarks
@@ -61,7 +62,8 @@ SPHINX_BUILD_DIR=${REPODIR}/docs
 DOXYGEN_BUILD_DIR=${REPODIR}/cpp/doxygen
 PYTHON_BUILD_DIR=${REPODIR}/python/cuvs/_skbuild
 RUST_BUILD_DIR=${REPODIR}/rust/target
-BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR} ${RUST_BUILD_DIR}"
+JAVA_BUILD_DIR=${REPODIR}/java/cuvs-java/target
+BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR} ${RUST_BUILD_DIR} ${JAVA_BUILD_DIR}"
 
 # Set defaults for vars modified by flags to this script
 CMAKE_LOG_LEVEL=""
@@ -445,6 +447,15 @@ if (( ${NUMARGS} == 0 )) || hasArg rust; then
     cargo test
 fi
 
+# Build the cuvs Java bindings
+if (( ${NUMARGS} == 0 )) || hasArg java; then
+    if ! hasArg libcuvs; then
+        echo "Please add 'libcuvs' to this script's arguments (ex. './build.sh libcuvs java') if libcuvs libraries are not already built"
+    fi
+    cd ${REPODIR}/java
+    ./build.sh
+fi
+
 export RAPIDS_VERSION="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2.\3/' "${REPODIR}/VERSION")"
 export RAPIDS_VERSION_MAJOR_MINOR="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2/' "${REPODIR}/VERSION")"
 
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index bfa3672bff..3385d4b61c 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -28,6 +28,7 @@ NEXT_UCXX_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/${NEXT_SHORT_TAG
 # Need to distutils-normalize the original version
 NEXT_SHORT_TAG_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_SHORT_TAG}'))")
 NEXT_UCXX_SHORT_TAG_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_UCXX_SHORT_TAG}'))")
+PATCH_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_PATCH}'))")
 
 echo "Preparing release $CURRENT_TAG => $NEXT_FULL_TAG"
 
@@ -96,3 +97,10 @@ find .devcontainer/ -type f -name devcontainer.json -print0 | while IFS= read -r
     sed_runner "s@rapidsai/devcontainers/features/rapids-build-utils:[0-9.]*@rapidsai/devcontainers/features/rapids-build-utils:${NEXT_SHORT_TAG_PEP440}@" "${filename}"
     sed_runner "s@rapids-\${localWorkspaceFolderBasename}-${CURRENT_SHORT_TAG}@rapids-\${localWorkspaceFolderBasename}-${NEXT_SHORT_TAG}@g" "${filename}"
 done
+
+# Update Java API version
+NEXT_FULL_JAVA_TAG="${NEXT_SHORT_TAG}.${PATCH_PEP440}"
+sed_runner "s/VERSION=\".*\"/VERSION=\"${NEXT_FULL_JAVA_TAG}\"/g" java/build.sh
+for FILE in java/*/pom.xml; do
+  sed_runner "/<!--CUVS_JAVA#VERSION_UPDATE_MARKER_START-->.*<!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->/s//<!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>${NEXT_FULL_JAVA_TAG}<\/version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->/g" "${FILE}"
+done
diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
index b7eda54b87..a7597a939c 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -47,7 +47,7 @@ enum cuvsHnswHierarchy {
 
 struct cuvsHnswIndexParams {
   /* hierarchy of the hnsw index */
-  cuvsHnswHierarchy hierarchy;
+  enum cuvsHnswHierarchy hierarchy;
   /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
   int ef_construction;
   /** Number of host threads to use to construct hierarchy when hierarchy is `CPU`
diff --git a/java/README.md b/java/README.md
new file mode 100644
index 0000000000..e5676146a2
--- /dev/null
+++ b/java/README.md
@@ -0,0 +1,14 @@
+Prerequisites
+-------------
+
+* JDK 22
+* Maven 3.9.6 or later
+
+To build this API, please do `./build.sh java` in the top level directory. Since this API is dependent on `libcuvs` it must be noted that `libcuvs` gets built automatically before building this API.
+
+Alternatively, please build libcuvs (`./build.sh libcuvs` from top level directory) before building the Java API with `./build.sh` from this directory.
+
+Building
+--------
+
+`./build.sh` will generate the `libcuvs_java.so` file in the `internal/` directory, and then build the final jar file for the cuVS Java API in the `cuvs-java/` directory.
diff --git a/java/build.sh b/java/build.sh
new file mode 100755
index 0000000000..05b47de778
--- /dev/null
+++ b/java/build.sh
@@ -0,0 +1,14 @@
+VERSION="25.02.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
+GROUP_ID="com.nvidia.cuvs"
+SO_FILE_PATH="./internal"
+
+if [ -z "$CMAKE_PREFIX_PATH" ]; then
+    export CMAKE_PREFIX_PATH=`pwd`/../cpp/build
+fi
+
+cd internal && cmake . && cmake --build . \
+  && cd .. \
+  && mvn install:install-file -DgroupId=$GROUP_ID -DartifactId=cuvs-java-internal -Dversion=$VERSION -Dpackaging=so -Dfile=$SO_FILE_PATH/libcuvs_java.so \
+  && cd cuvs-java \
+  && mvn package \
+  && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar
diff --git a/java/cuvs-java/.gitignore b/java/cuvs-java/.gitignore
new file mode 100644
index 0000000000..b83d22266a
--- /dev/null
+++ b/java/cuvs-java/.gitignore
@@ -0,0 +1 @@
+/target/
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
new file mode 100644
index 0000000000..c8d31638ce
--- /dev/null
+++ b/java/cuvs-java/pom.xml
@@ -0,0 +1,159 @@
+<!--
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <groupId>com.nvidia.cuvs</groupId>
+    <artifactId>cuvs-java</artifactId>
+    <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.02.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <name>cuvs-java</name>
+    <packaging>jar</packaging>
+
+    <properties>
+        <maven.compiler.target>22</maven.compiler.target>
+        <maven.compiler.source>22</maven.compiler.source>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
+    </properties>
+
+    <dependencies>
+
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+            <version>1.7.36</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-simple</artifactId>
+            <version>1.7.36</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>com.carrotsearch.randomizedtesting</groupId>
+            <artifactId>randomizedtesting-runner</artifactId>
+            <version>2.8.2</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+            <version>4.13.1</version>
+            <scope>test</scope>
+        </dependency>
+
+    </dependencies>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-surefire-plugin</artifactId>
+                <version>3.5.2</version>
+                <configuration>
+                    <parallel>suites</parallel>
+                    <threadCountSuites>1</threadCountSuites>
+                    <perCoreThreadCount>false</perCoreThreadCount>
+                    <systemPropertyVariables>
+                        <java.library.path>${project.build.directory}/classes</java.library.path>
+                    </systemPropertyVariables>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-dependency-plugin</artifactId>
+                <version>2.10</version>
+                <executions>
+                    <execution>
+                        <id>copy</id>
+                        <phase>compile</phase>
+                        <goals>
+                            <goal>copy</goal>
+                        </goals>
+                        <configuration>
+                            <artifactItems>
+                                <artifactItem>
+                                    <groupId>com.nvidia.cuvs</groupId>
+                                    <artifactId>cuvs-java-internal</artifactId>
+                                    <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
+                                    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.02.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+                                    <type>so</type>
+                                    <overWrite>false</overWrite>
+                                    <outputDirectory>
+                                        ${project.build.directory}/classes</outputDirectory>
+                                    <destFileName>libcuvs_java.so</destFileName>
+                                </artifactItem>
+                            </artifactItems>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-assembly-plugin</artifactId>
+                <version>3.4.2</version>
+                <configuration>
+                    <descriptorRefs>
+                        <descriptorRef>jar-with-dependencies</descriptorRef>
+                    </descriptorRefs>
+                    <archiverConfig>
+                        <duplicateBehavior>add</duplicateBehavior>
+                    </archiverConfig>
+                </configuration>
+                <executions>
+                    <execution>
+                        <id>assemble-all</id>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>single</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-jar-plugin</artifactId>
+                <version>2.2</version>
+                <configuration>
+                    <archive>
+                        <manifest>
+                            <addClasspath>true</addClasspath>
+                            <mainClass>
+                                com.nvidia.cuvs.examples.CagraExample</mainClass>
+                        </manifest>
+                    </archive>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-javadoc-plugin</artifactId>
+                <version>3.6.2</version>
+                <configuration>
+                    <excludePackageNames>com.nvidia.cuvs.examples,com.nvidia.cuvs.panama</excludePackageNames>
+                    <reportOutputDirectory>${project.build.directory}</reportOutputDirectory>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+</project>
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
new file mode 100644
index 0000000000..30ffca1cd1
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
@@ -0,0 +1,364 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.UUID;
+
+import com.nvidia.cuvs.common.Util;
+import com.nvidia.cuvs.panama.CuVSBruteForceIndex;
+
+/**
+ *
+ * {@link BruteForceIndex} encapsulates a BRUTEFORCE index, along with methods
+ * to interact with it.
+ *
+ * @since 25.02
+ */
+public class BruteForceIndex {
+
+  private final float[][] dataset;
+  private final CuVSResources resources;
+  private MethodHandle indexMethodHandle;
+  private MethodHandle searchMethodHandle;
+  private MethodHandle destroyIndexMethodHandle;
+  private MethodHandle serializeMethodHandle;
+  private MethodHandle deserializeMethodHandle;
+  private IndexReference bruteForceIndexReference;
+  private BruteForceIndexParams bruteForceIndexParams;
+  private MemoryLayout longMemoryLayout;
+  private MemoryLayout intMemoryLayout;
+  private MemoryLayout floatMemoryLayout;
+
+  /**
+   * Constructor for building the index using specified dataset
+   *
+   * @param dataset               the dataset used for creating the BRUTEFORCE
+   *                              index
+   * @param resources             an instance of {@link CuVSResources}
+   * @param bruteForceIndexParams an instance of {@link BruteForceIndexParams}
+   *                              holding the index parameters
+   */
+  private BruteForceIndex(float[][] dataset, CuVSResources resources, BruteForceIndexParams bruteForceIndexParams)
+      throws Throwable {
+    this.dataset = dataset;
+    this.resources = resources;
+    this.bruteForceIndexParams = bruteForceIndexParams;
+
+    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
+    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
+    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
+
+    initializeMethodHandles();
+    this.bruteForceIndexReference = build();
+  }
+
+  /**
+   * Constructor for loading the index from an {@link InputStream}
+   *
+   * @param inputStream an instance of stream to read the index bytes from
+   * @param resources   an instance of {@link CuVSResources}
+   */
+  private BruteForceIndex(InputStream inputStream, CuVSResources resources) throws Throwable {
+    this.bruteForceIndexParams = null;
+    this.dataset = null;
+    this.resources = resources;
+
+    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
+    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
+    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
+
+    initializeMethodHandles();
+    this.bruteForceIndexReference = deserialize(inputStream);
+  }
+
+  /**
+   * Initializes the {@link MethodHandles} for invoking native methods.
+   *
+   * @throws IOException @{@link IOException} is unable to load the native library
+   */
+  private void initializeMethodHandles() throws IOException {
+    indexMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("build_brute_force_index").get(),
+        FunctionDescriptor.of(ValueLayout.ADDRESS, ValueLayout.ADDRESS, longMemoryLayout, longMemoryLayout,
+            ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout));
+
+    searchMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("search_brute_force_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout, longMemoryLayout,
+            intMemoryLayout, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS,
+            ValueLayout.ADDRESS, longMemoryLayout, longMemoryLayout));
+
+    destroyIndexMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("destroy_brute_force_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+    serializeMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("serialize_brute_force_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+    deserializeMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("deserialize_brute_force_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+  }
+
+  /**
+   * Invokes the native destroy_brute_force_index function to de-allocate
+   * BRUTEFORCE index
+   */
+  public void destroyIndex() throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    destroyIndexMethodHandle.invokeExact(bruteForceIndexReference.getMemorySegment(), returnValueMemorySegment);
+  }
+
+  /**
+   * Invokes the native build_brute_force_index function via the Panama API to
+   * build the {@link BruteForceIndex}
+   *
+   * @return an instance of {@link IndexReference} that holds the pointer to the
+   *         index
+   */
+  private IndexReference build() throws Throwable {
+    long rows = dataset.length;
+    long cols = rows > 0 ? dataset[0].length : 0;
+
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+
+    IndexReference indexReference = new IndexReference((MemorySegment) indexMethodHandle.invokeExact(
+        Util.buildMemorySegment(resources.linker, resources.arena, dataset), rows, cols, resources.getMemorySegment(),
+        returnValueMemorySegment, bruteForceIndexParams.getNumWriterThreads()));
+
+    return indexReference;
+  }
+
+  /**
+   * Invokes the native search_brute_force_index via the Panama API for searching
+   * a BRUTEFORCE index.
+   *
+   * @param cuvsQuery an instance of {@link BruteForceQuery} holding the query
+   *                  vectors and other parameters
+   * @return an instance of {@link BruteForceSearchResults} containing the results
+   */
+  public BruteForceSearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
+    long numQueries = cuvsQuery.getQueryVectors().length;
+    long numBlocks = cuvsQuery.getTopK() * numQueries;
+    int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
+    long prefilterDataLength = cuvsQuery.getPrefilter() != null ? cuvsQuery.getPrefilter().length : 0;
+    long numRows = dataset != null ? dataset.length : 0;
+
+    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, longMemoryLayout);
+    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, floatMemoryLayout);
+    MemorySegment neighborsMemorySegment = resources.arena.allocate(neighborsSequenceLayout);
+    MemorySegment distancesMemorySegment = resources.arena.allocate(distancesSequenceLayout);
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    MemorySegment prefilterDataMemorySegment = cuvsQuery.getPrefilter() != null
+        ? Util.buildMemorySegment(resources.linker, resources.arena, cuvsQuery.getPrefilter())
+        : MemorySegment.NULL;
+
+    searchMethodHandle.invokeExact(bruteForceIndexReference.getMemorySegment(),
+        Util.buildMemorySegment(resources.linker, resources.arena, cuvsQuery.getQueryVectors()), cuvsQuery.getTopK(),
+        numQueries, vectorDimension, resources.getMemorySegment(), neighborsMemorySegment, distancesMemorySegment,
+        returnValueMemorySegment, prefilterDataMemorySegment, prefilterDataLength, numRows);
+
+    return new BruteForceSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+        distancesMemorySegment, cuvsQuery.getTopK(), cuvsQuery.getMapping(), numQueries);
+  }
+
+  /**
+   * A method to persist a BRUTEFORCE index using an instance of
+   * {@link OutputStream} for writing index bytes.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes into
+   */
+  public void serialize(OutputStream outputStream) throws Throwable {
+    serialize(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".bf"));
+  }
+
+  /**
+   * A method to persist a BRUTEFORCE index using an instance of
+   * {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param tempFile     an intermediate {@link File} where BRUTEFORCE index is
+   *                     written temporarily
+   */
+  public void serialize(OutputStream outputStream, File tempFile) throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    serializeMethodHandle.invokeExact(resources.getMemorySegment(), bruteForceIndexReference.getMemorySegment(),
+        returnValueMemorySegment,
+        Util.buildMemorySegment(resources.linker, resources.arena, tempFile.getAbsolutePath()));
+    FileInputStream fileInputStream = new FileInputStream(tempFile);
+    byte[] chunk = new byte[1024]; // TODO: Make this configurable
+    int chunkLength = 0;
+    while ((chunkLength = fileInputStream.read(chunk)) != -1) {
+      outputStream.write(chunk, 0, chunkLength);
+    }
+    fileInputStream.close();
+    tempFile.delete();
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a BRUTEFORCE
+   * index using an {@link InputStream}.
+   *
+   * @param inputStream an instance of {@link InputStream}
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream) throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    String tmpIndexFile = "/tmp/" + UUID.randomUUID().toString() + ".bf";
+    IndexReference indexReference = new IndexReference(resources);
+
+    File tempFile = new File(tmpIndexFile);
+    FileOutputStream fileOutputStream = new FileOutputStream(tempFile);
+    byte[] chunk = new byte[1024];
+    int chunkLength = 0;
+    while ((chunkLength = inputStream.read(chunk)) != -1) {
+      fileOutputStream.write(chunk, 0, chunkLength);
+    }
+    deserializeMethodHandle.invokeExact(resources.getMemorySegment(), indexReference.getMemorySegment(),
+        returnValueMemorySegment, Util.buildMemorySegment(resources.linker, resources.arena, tmpIndexFile));
+
+    inputStream.close();
+    fileOutputStream.close();
+    tempFile.delete();
+
+    return indexReference;
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link BruteForceIndex}.
+   */
+  public static class Builder {
+
+    private float[][] dataset;
+    private CuVSResources cuvsResources;
+    private BruteForceIndexParams bruteForceIndexParams;
+    private InputStream inputStream;
+
+    /**
+     * Constructs this Builder with an instance of {@link CuVSResources}.
+     *
+     * @param cuvsResources an instance of {@link CuVSResources}
+     */
+    public Builder(CuVSResources cuvsResources) {
+      this.cuvsResources = cuvsResources;
+    }
+
+    /**
+     * Registers an instance of configured {@link BruteForceIndexParams} with this
+     * Builder.
+     *
+     * @param bruteForceIndexParams An instance of BruteForceIndexParams
+     * @return An instance of this Builder
+     */
+    public Builder withIndexParams(BruteForceIndexParams bruteForceIndexParams) {
+      this.bruteForceIndexParams = bruteForceIndexParams;
+      return this;
+    }
+
+    /**
+     * Sets an instance of InputStream typically used when index deserialization is
+     * needed.
+     *
+     * @param inputStream an instance of {@link InputStream}
+     * @return an instance of this Builder
+     */
+    public Builder from(InputStream inputStream) {
+      this.inputStream = inputStream;
+      return this;
+    }
+
+    /**
+     * Sets the dataset for building the {@link BruteForceIndex}.
+     *
+     * @param dataset a two-dimensional float array
+     * @return an instance of this Builder
+     */
+    public Builder withDataset(float[][] dataset) {
+      this.dataset = dataset;
+      return this;
+    }
+
+    /**
+     * Builds and returns an instance of {@link BruteForceIndex}.
+     *
+     * @return an instance of {@link BruteForceIndex}
+     */
+    public BruteForceIndex build() throws Throwable {
+      if (inputStream != null) {
+        return new BruteForceIndex(inputStream, cuvsResources);
+      } else {
+        return new BruteForceIndex(dataset, cuvsResources, bruteForceIndexParams);
+      }
+    }
+  }
+
+  /**
+   * Holds the memory reference to a BRUTEFORCE index.
+   */
+  protected static class IndexReference {
+
+    private final MemorySegment memorySegment;
+
+    /**
+     * Constructs CagraIndexReference and allocate the MemorySegment.
+     */
+    protected IndexReference(CuVSResources resources) {
+      memorySegment = CuVSBruteForceIndex.allocate(resources.arena);
+    }
+
+    /**
+     * Constructs BruteForceIndexReference with an instance of MemorySegment passed
+     * as a parameter.
+     *
+     * @param indexMemorySegment the MemorySegment instance to use for containing
+     *                           index reference
+     */
+    protected IndexReference(MemorySegment indexMemorySegment) {
+      this.memorySegment = indexMemorySegment;
+    }
+
+    /**
+     * Gets the instance of index MemorySegment.
+     *
+     * @return index MemorySegment
+     */
+    protected MemorySegment getMemorySegment() {
+      return memorySegment;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java
new file mode 100644
index 0000000000..832edf51a1
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+/**
+ * Supplemental parameters to build BRUTEFORCE index.
+ *
+ * @since 25.02
+ */
+public class BruteForceIndexParams {
+
+  private final int numWriterThreads;
+
+  private BruteForceIndexParams(int writerThreads) {
+    this.numWriterThreads = writerThreads;
+  }
+
+  @Override
+  public String toString() {
+    return "BruteForceIndexParams [numWriterThreads=" + numWriterThreads + "]";
+  }
+
+  /**
+   * Gets the number of threads used to build the index.
+   */
+  public int getNumWriterThreads() {
+    return numWriterThreads;
+  }
+
+  /**
+   * Builder configures and creates an instance of {@link BruteForceIndexParams}.
+   */
+  public static class Builder {
+
+    private int numWriterThreads = 2;
+
+    /**
+     * Sets the number of writer threads to use for indexing.
+     *
+     * @param numWriterThreads number of writer threads to use
+     * @return an instance of Builder
+     */
+    public Builder withNumWriterThreads(int numWriterThreads) {
+      this.numWriterThreads = numWriterThreads;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link BruteForceIndexParams}.
+     *
+     * @return an instance of {@link BruteForceIndexParams}
+     */
+    public BruteForceIndexParams build() {
+      return new BruteForceIndexParams(numWriterThreads);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
new file mode 100644
index 0000000000..7febc3ba37
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
@@ -0,0 +1,156 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * BruteForceQuery holds the query vectors to be used while invoking search.
+ *
+ * @since 25.02
+ */
+public class BruteForceQuery {
+
+  private List<Integer> mapping;
+  private float[][] queryVectors;
+  private long[] prefilter;
+  private int topK;
+
+  /**
+   * Constructs an instance of {@link BruteForceQuery} using queryVectors,
+   * mapping, and topK.
+   *
+   * @param queryVectors 2D float query vector array
+   * @param mapping      an instance of ID mapping
+   * @param topK         the top k results to return
+   * @param prefilter    the prefilter data to use while searching the BRUTEFORCE
+   *                     index
+   */
+  public BruteForceQuery(float[][] queryVectors, List<Integer> mapping, int topK, long[] prefilter) {
+    this.queryVectors = queryVectors;
+    this.mapping = mapping;
+    this.topK = topK;
+    this.prefilter = prefilter;
+  }
+
+  /**
+   * Gets the query vector 2D float array.
+   *
+   * @return 2D float array
+   */
+  public float[][] getQueryVectors() {
+    return queryVectors;
+  }
+
+  /**
+   * Gets the passed map instance.
+   *
+   * @return a map of ID mappings
+   */
+  public List<Integer> getMapping() {
+    return mapping;
+  }
+
+  /**
+   * Gets the topK value.
+   *
+   * @return an integer
+   */
+  public int getTopK() {
+    return topK;
+  }
+
+  /**
+   * Gets the prefilter long array
+   *
+   * @return a long array
+   */
+  public long[] getPrefilter() {
+    return prefilter;
+  }
+
+  @Override
+  public String toString() {
+    return "BruteForceQuery [mapping=" + mapping + ", queryVectors=" + Arrays.toString(queryVectors) + ", prefilter="
+        + Arrays.toString(prefilter) + ", topK=" + topK + "]";
+  }
+
+  /**
+   * Builder helps configure and create an instance of BruteForceQuery.
+   */
+  public static class Builder {
+
+    private float[][] queryVectors;
+    private long[] prefilter;
+    private List<Integer> mapping;
+    private int topK = 2;
+
+    /**
+     * Registers the query vectors to be passed in the search call.
+     *
+     * @param queryVectors 2D float query vector array
+     * @return an instance of this Builder
+     */
+    public Builder withQueryVectors(float[][] queryVectors) {
+      this.queryVectors = queryVectors;
+      return this;
+    }
+
+    /**
+     * Sets the instance of mapping to be used for ID mapping.
+     *
+     * @param mapping the ID mapping instance
+     * @return an instance of this Builder
+     */
+    public Builder withMapping(List<Integer> mapping) {
+      this.mapping = mapping;
+      return this;
+    }
+
+    /**
+     * Registers the topK value.
+     *
+     * @param topK the topK value used to retrieve the topK results
+     * @return an instance of this Builder
+     */
+    public Builder withTopK(int topK) {
+      this.topK = topK;
+      return this;
+    }
+
+    /**
+     * Sets the prefilter data for building the {@link BruteForceQuery}.
+     *
+     * @param prefilter a one-dimensional long array
+     * @return an instance of this Builder
+     */
+    public Builder withPrefilter(long[] prefilter) {
+      this.prefilter = prefilter;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link BruteForceQuery}
+     *
+     * @return an instance of {@link BruteForceQuery}
+     */
+    public BruteForceQuery build() {
+      return new BruteForceQuery(queryVectors, mapping, topK, prefilter);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceSearchResults.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceSearchResults.java
new file mode 100644
index 0000000000..b87e1f601e
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceSearchResults.java
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+
+import com.nvidia.cuvs.common.SearchResults;
+
+/**
+ * SearchResult encapsulates the logic for reading and holding search results.
+ *
+ * @since 25.02
+ */
+public class BruteForceSearchResults extends SearchResults {
+
+  protected BruteForceSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+      long numberOfQueries) {
+    super(neighboursSequenceLayout, distancesSequenceLayout, neighboursMemorySegment, distancesMemorySegment, topK,
+        mapping, numberOfQueries);
+    readResultMemorySegments();
+  }
+
+  /**
+   * Reads neighbors and distances {@link MemorySegment} and loads the values
+   * internally
+   */
+  protected void readResultMemorySegments() {
+    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<Integer, Float>();
+    int count = 0;
+    for (long i = 0; i < topK * numberOfQueries; i++) {
+      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
+      float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
+      intermediateResultMap.put(mapping != null ? mapping.get((int) id) : (int) id, dst);
+      count += 1;
+      if (count == topK) {
+        results.add(intermediateResultMap);
+        intermediateResultMap = new LinkedHashMap<Integer, Float>();
+        count = 0;
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
new file mode 100644
index 0000000000..09f6bae8bf
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
@@ -0,0 +1,266 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+
+import com.nvidia.cuvs.panama.CuVSCagraCompressionParams;
+
+/**
+ * Supplemental compression parameters to build CAGRA Index.
+ *
+ * @since 25.02
+ */
+public class CagraCompressionParams {
+
+  private final MemorySegment memorySegment;
+  private CuVSResources resources;
+  private final int pqBits;
+  private final int pqDim;
+  private final int vqNCenters;
+  private final int kmeansNIters;
+  private final double vqKmeansTrainsetFraction;
+  private final double pqKmeansTrainsetFraction;
+
+  /**
+   * Constructs an instance of CagraCompressionParams with passed search
+   * parameters.
+   *
+   * @param resources                the resources instance to use
+   * @param pqBits                   the bit length of the vector element after
+   *                                 compression by PQ
+   * @param pqDim                    the dimensionality of the vector after
+   *                                 compression by PQ
+   * @param vqNCenters               the vector quantization (VQ) codebook size -
+   *                                 number of “coarse cluster centers”
+   * @param kmeansNIters             the number of iterations searching for kmeans
+   *                                 centers (both VQ and PQ phases)
+   * @param vqKmeansTrainsetFraction the fraction of data to use during iterative
+   *                                 kmeans building (VQ phase)
+   * @param pqKmeansTrainsetFraction the fraction of data to use during iterative
+   *                                 kmeans building (PQ phase)
+   */
+  private CagraCompressionParams(CuVSResources resources, int pqBits, int pqDim, int vqNCenters, int kmeansNIters,
+      double vqKmeansTrainsetFraction, double pqKmeansTrainsetFraction) {
+    this.resources = resources;
+    this.pqBits = pqBits;
+    this.pqDim = pqDim;
+    this.vqNCenters = vqNCenters;
+    this.kmeansNIters = kmeansNIters;
+    this.vqKmeansTrainsetFraction = vqKmeansTrainsetFraction;
+    this.pqKmeansTrainsetFraction = pqKmeansTrainsetFraction;
+    this.memorySegment = initMemorySegment();
+  }
+
+  /**
+   * Allocates the configured compression parameters in the MemorySegment.
+   */
+  private MemorySegment initMemorySegment() {
+    MemorySegment compressionParamsMemorySegment = CuVSCagraCompressionParams.allocate(resources.arena);
+    CuVSCagraCompressionParams.pq_bits(compressionParamsMemorySegment, pqBits);
+    CuVSCagraCompressionParams.pq_dim(compressionParamsMemorySegment, pqDim);
+    CuVSCagraCompressionParams.vq_n_centers(compressionParamsMemorySegment, vqNCenters);
+    CuVSCagraCompressionParams.kmeans_n_iters(compressionParamsMemorySegment, kmeansNIters);
+    CuVSCagraCompressionParams.vq_kmeans_trainset_fraction(compressionParamsMemorySegment, vqKmeansTrainsetFraction);
+    CuVSCagraCompressionParams.pq_kmeans_trainset_fraction(compressionParamsMemorySegment, pqKmeansTrainsetFraction);
+    return compressionParamsMemorySegment;
+  }
+
+  /**
+   * Gets the memory segment instance containing the compression parameters.
+   *
+   * @return the memory segment instance containing the compression parameters.
+   */
+  protected MemorySegment getMemorySegment() {
+    return memorySegment;
+  }
+
+  /**
+   * Gets the bit length of the vector element after compression by PQ.
+   *
+   * @return the bit length of the vector element after compression by PQ.
+   */
+  public int getPqBits() {
+    return pqBits;
+  }
+
+  /**
+   * Gets the dimensionality of the vector after compression by PQ.
+   *
+   * @return the dimensionality of the vector after compression by PQ.
+   */
+  public int getPqDim() {
+    return pqDim;
+  }
+
+  /**
+   * Gets the vector quantization (VQ) codebook size - number of “coarse cluster
+   * centers”.
+   *
+   * @return the vector quantization (VQ) codebook size - number of “coarse
+   *         cluster centers”.
+   */
+  public int getVqNCenters() {
+    return vqNCenters;
+  }
+
+  /**
+   * Gets the number of iterations searching for kmeans centers (both VQ and PQ
+   * phases).
+   *
+   * @return the number of iterations searching for kmeans centers (both VQ and PQ
+   *         phases).
+   */
+  public int getKmeansNIters() {
+    return kmeansNIters;
+  }
+
+  /**
+   * Gets the fraction of data to use during iterative kmeans building (VQ phase).
+   *
+   * @return the fraction of data to use during iterative kmeans building (VQ
+   *         phase).
+   */
+  public double getVqKmeansTrainsetFraction() {
+    return vqKmeansTrainsetFraction;
+  }
+
+  /**
+   * Gets the fraction of data to use during iterative kmeans building (PQ phase).
+   *
+   * @return the fraction of data to use during iterative kmeans building (PQ
+   *         phase).
+   */
+  public double getPqKmeansTrainsetFraction() {
+    return pqKmeansTrainsetFraction;
+  }
+
+  @Override
+  public String toString() {
+    return "CagraCompressionParams [pqBits=" + pqBits + ", pqDim=" + pqDim + ", vqNCenters=" + vqNCenters
+        + ", kmeansNIters=" + kmeansNIters + ", vqKmeansTrainsetFraction=" + vqKmeansTrainsetFraction
+        + ", pqKmeansTrainsetFraction=" + pqKmeansTrainsetFraction + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of {@link CagraCompressionParams}.
+   */
+  public static class Builder {
+
+    private CuVSResources resources;
+    private int pqBits = 8;
+    private int pqDim = 0;
+    private int vqNCenters = 0;
+    private int kmeansNIters = 25;
+    private double vqKmeansTrainsetFraction = 0;
+    private double pqKmeansTrainsetFraction = 0;
+
+    public Builder(CuVSResources resources) {
+      this.resources = resources;
+    }
+
+    /**
+     * Sets the bit length of the vector element after compression by PQ.
+     *
+     * Possible values: [4, 5, 6, 7, 8]. Hint: the smaller the ‘pq_bits’, the
+     * smaller the index size and the better the search performance, but the lower
+     * the recall.
+     *
+     * @param pqBits
+     * @return an instance of Builder
+     */
+    public Builder withPqBits(int pqBits) {
+      this.pqBits = pqBits;
+      return this;
+    }
+
+    /**
+     * Sets the dimensionality of the vector after compression by PQ.
+     *
+     * When zero, an optimal value is selected using a heuristic.
+     *
+     * @param pqDim
+     * @return an instance of Builder
+     */
+    public Builder withPqDim(int pqDim) {
+      this.pqDim = pqDim;
+      return this;
+    }
+
+    /**
+     * Sets the vector quantization (VQ) codebook size - number of “coarse cluster
+     * centers”.
+     *
+     * When zero, an optimal value is selected using a heuristic.
+     *
+     * @param vqNCenters
+     * @return an instance of Builder
+     */
+    public Builder withVqNCenters(int vqNCenters) {
+      this.vqNCenters = vqNCenters;
+      return this;
+    }
+
+    /**
+     * Sets the number of iterations searching for kmeans centers (both VQ and PQ
+     * phases).
+     *
+     * @param kmeansNIters
+     * @return an instance of Builder
+     */
+    public Builder withKmeansNIters(int kmeansNIters) {
+      this.kmeansNIters = kmeansNIters;
+      return this;
+    }
+
+    /**
+     * Sets the fraction of data to use during iterative kmeans building (VQ phase).
+     *
+     * When zero, an optimal value is selected using a heuristic.
+     *
+     * @param vqKmeansTrainsetFraction
+     * @return an instance of Builder
+     */
+    public Builder withVqKmeansTrainsetFraction(double vqKmeansTrainsetFraction) {
+      this.vqKmeansTrainsetFraction = vqKmeansTrainsetFraction;
+      return this;
+    }
+
+    /**
+     * Sets the fraction of data to use during iterative kmeans building (PQ phase).
+     *
+     * When zero, an optimal value is selected using a heuristic.
+     *
+     * @param pqKmeansTrainsetFraction
+     * @return an instance of Builder
+     */
+    public Builder withPqKmeansTrainsetFraction(double pqKmeansTrainsetFraction) {
+      this.pqKmeansTrainsetFraction = pqKmeansTrainsetFraction;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link CagraCompressionParams}.
+     *
+     * @return an instance of {@link CagraCompressionParams}
+     */
+    public CagraCompressionParams build() {
+      return new CagraCompressionParams(resources, pqBits, pqDim, vqNCenters, kmeansNIters, vqKmeansTrainsetFraction,
+          pqKmeansTrainsetFraction);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
new file mode 100644
index 0000000000..21eea97838
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -0,0 +1,517 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.UUID;
+
+import com.nvidia.cuvs.common.Util;
+import com.nvidia.cuvs.panama.CuVSCagraIndex;
+
+/**
+ * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
+ * with it.
+ * <p>
+ * CAGRA is a graph-based nearest neighbors algorithm that was built from the
+ * ground up for GPU acceleration. CAGRA demonstrates state-of-the art index
+ * build and query performance for both small and large-batch sized search. Know
+ * more about this algorithm
+ * <a href="https://arxiv.org/abs/2308.15136" target="_blank">here</a>
+ *
+ * @since 25.02
+ */
+public class CagraIndex {
+
+  private final float[][] dataset;
+  private final CuVSResources resources;
+  private MethodHandle indexMethodHandle;
+  private MethodHandle searchMethodHandle;
+  private MethodHandle serializeMethodHandle;
+  private MethodHandle deserializeMethodHandle;
+  private MethodHandle destroyIndexMethodHandle;
+  private MethodHandle serializeCAGRAIndexToHNSWMethodHandle;
+  private CagraIndexParams cagraIndexParameters;
+  private CagraCompressionParams cagraCompressionParams;
+  private IndexReference cagraIndexReference;
+  private MemoryLayout longMemoryLayout;
+  private MemoryLayout intMemoryLayout;
+  private MemoryLayout floatMemoryLayout;
+
+  /**
+   * Constructor for building the index using specified dataset
+   *
+   * @param indexParameters        an instance of {@link CagraIndexParams} holding
+   *                               the index parameters
+   * @param cagraCompressionParams an instance of {@link CagraCompressionParams}
+   *                               holding the compression parameters
+   * @param dataset                the dataset for indexing
+   * @param resources              an instance of {@link CuVSResources}
+   */
+  private CagraIndex(CagraIndexParams indexParameters, CagraCompressionParams cagraCompressionParams, float[][] dataset,
+      CuVSResources resources) throws Throwable {
+    this.cagraIndexParameters = indexParameters;
+    this.cagraCompressionParams = cagraCompressionParams;
+    this.dataset = dataset;
+    this.resources = resources;
+
+    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
+    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
+    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
+
+    initializeMethodHandles();
+    this.cagraIndexReference = build();
+  }
+
+  /**
+   * Constructor for loading the index from an {@link InputStream}
+   *
+   * @param inputStream an instance of stream to read the index bytes from
+   * @param resources   an instance of {@link CuVSResources}
+   */
+  private CagraIndex(InputStream inputStream, CuVSResources resources) throws Throwable {
+    this.cagraIndexParameters = null;
+    this.cagraCompressionParams = null;
+    this.dataset = null;
+    this.resources = resources;
+
+    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
+    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
+    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
+
+    initializeMethodHandles();
+    this.cagraIndexReference = deserialize(inputStream);
+  }
+
+  /**
+   * Initializes the {@link MethodHandles} for invoking native methods.
+   *
+   * @throws IOException @{@link IOException} is unable to load the native library
+   */
+  private void initializeMethodHandles() throws IOException {
+    indexMethodHandle = resources.linker.downcallHandle(resources.getSymbolLookup().find("build_cagra_index").get(),
+        FunctionDescriptor.of(ValueLayout.ADDRESS, ValueLayout.ADDRESS, longMemoryLayout, longMemoryLayout,
+            ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout));
+
+    searchMethodHandle = resources.linker.downcallHandle(resources.getSymbolLookup().find("search_cagra_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout, longMemoryLayout,
+            intMemoryLayout, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS,
+            ValueLayout.ADDRESS));
+
+    serializeMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("serialize_cagra_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+    deserializeMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("deserialize_cagra_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+    destroyIndexMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("destroy_cagra_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+    serializeCAGRAIndexToHNSWMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("serialize_cagra_index_to_hnsw").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+  }
+
+  /**
+   * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
+   */
+  public void destroyIndex() throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    destroyIndexMethodHandle.invokeExact(cagraIndexReference.getMemorySegment(), returnValueMemorySegment);
+  }
+
+  /**
+   * Invokes the native build_cagra_index function via the Panama API to build the
+   * {@link CagraIndex}
+   *
+   * @return an instance of {@link IndexReference} that holds the pointer to the
+   *         index
+   */
+  private IndexReference build() throws Throwable {
+    long rows = dataset.length;
+    long cols = rows > 0 ? dataset[0].length : 0;
+
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+
+    MemorySegment indexParamsMemorySegment = cagraIndexParameters != null ? cagraIndexParameters.getMemorySegment()
+        : MemorySegment.NULL;
+
+    int numWriterThreads = cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
+
+    MemorySegment compressionParamsMemorySegment = cagraCompressionParams != null
+        ? cagraCompressionParams.getMemorySegment()
+        : MemorySegment.NULL;
+
+    IndexReference indexReference = new IndexReference((MemorySegment) indexMethodHandle.invokeExact(
+        Util.buildMemorySegment(resources.linker, resources.arena, dataset), rows, cols, resources.getMemorySegment(),
+        returnValueMemorySegment, indexParamsMemorySegment, compressionParamsMemorySegment, numWriterThreads));
+
+    return indexReference;
+  }
+
+  /**
+   * Invokes the native search_cagra_index via the Panama API for searching a
+   * CAGRA index.
+   *
+   * @param query an instance of {@link CagraQuery} holding the query vectors and
+   *              other parameters
+   * @return an instance of {@link CagraSearchResults} containing the results
+   */
+  public CagraSearchResults search(CagraQuery query) throws Throwable {
+    int topK = query.getMapping() != null ? Math.min(query.getMapping().size(), query.getTopK()) : query.getTopK();
+    long numQueries = query.getQueryVectors().length;
+    long numBlocks = topK * numQueries;
+    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
+
+    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, intMemoryLayout);
+    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, floatMemoryLayout);
+    MemorySegment neighborsMemorySegment = resources.arena.allocate(neighborsSequenceLayout);
+    MemorySegment distancesMemorySegment = resources.arena.allocate(distancesSequenceLayout);
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+
+    searchMethodHandle.invokeExact(cagraIndexReference.getMemorySegment(),
+        Util.buildMemorySegment(resources.linker, resources.arena, query.getQueryVectors()), topK, numQueries,
+        vectorDimension, resources.getMemorySegment(), neighborsMemorySegment, distancesMemorySegment,
+        returnValueMemorySegment, query.getCagraSearchParameters().getMemorySegment());
+
+    return new CagraSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+        distancesMemorySegment, topK, query.getMapping(), numQueries);
+  }
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * for writing index bytes.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes into
+   */
+  public void serialize(OutputStream outputStream) throws Throwable {
+    serialize(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".cag"), 1024);
+  }
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * for writing index bytes.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes into
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  public void serialize(OutputStream outputStream, int bufferLength) throws Throwable {
+    serialize(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".cag"), bufferLength);
+  }
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * for writing index bytes.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes into
+   * @param tempFile     an intermediate {@link File} where CAGRA index is written
+   *                     temporarily
+   */
+  public void serialize(OutputStream outputStream, File tempFile) throws Throwable {
+    serialize(outputStream, tempFile, 1024);
+  }
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param tempFile     an intermediate {@link File} where CAGRA index is written
+   *                     temporarily
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  public void serialize(OutputStream outputStream, File tempFile, int bufferLength) throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    serializeMethodHandle.invokeExact(resources.getMemorySegment(), cagraIndexReference.getMemorySegment(),
+        returnValueMemorySegment,
+        Util.buildMemorySegment(resources.linker, resources.arena, tempFile.getAbsolutePath()));
+    FileInputStream fileInputStream = new FileInputStream(tempFile);
+    byte[] chunk = new byte[bufferLength];
+    int chunkLength = 0;
+    while ((chunkLength = fileInputStream.read(chunk)) != -1) {
+      outputStream.write(chunk, 0, chunkLength);
+    }
+    fileInputStream.close();
+    tempFile.delete();
+  }
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   */
+  public void serializeToHNSW(OutputStream outputStream) throws Throwable {
+    serializeToHNSW(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".hnsw"), 1024);
+  }
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  public void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable {
+    serializeToHNSW(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".hnsw"), bufferLength);
+  }
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param tempFile     an intermediate {@link File} where CAGRA index is written
+   *                     temporarily
+   */
+  public void serializeToHNSW(OutputStream outputStream, File tempFile) throws Throwable {
+    serializeToHNSW(outputStream, tempFile, 1024);
+  }
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param tempFile     an intermediate {@link File} where CAGRA index is written
+   *                     temporarily
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  public void serializeToHNSW(OutputStream outputStream, File tempFile, int bufferLength) throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    serializeCAGRAIndexToHNSWMethodHandle.invokeExact(resources.getMemorySegment(),
+        Util.buildMemorySegment(resources.linker, resources.arena, tempFile.getAbsolutePath()),
+        cagraIndexReference.getMemorySegment(), returnValueMemorySegment);
+    FileInputStream fileInputStream = new FileInputStream(tempFile);
+    byte[] chunk = new byte[bufferLength];
+    int chunkLength = 0;
+    while ((chunkLength = fileInputStream.read(chunk)) != -1) {
+      outputStream.write(chunk, 0, chunkLength);
+    }
+    fileInputStream.close();
+    tempFile.delete();
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream an instance of {@link InputStream}
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream) throws Throwable {
+    return deserialize(inputStream, 1024);
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream  an instance of {@link InputStream}
+   * @param bufferLength the length of the buffer to use while reading the bytes
+   *                     from the stream. Default value is 1024.
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    String tmpIndexFile = "/tmp/" + UUID.randomUUID().toString() + ".cag";
+    IndexReference indexReference = new IndexReference(resources);
+
+    File tempFile = new File(tmpIndexFile);
+    FileOutputStream fileOutputStream = new FileOutputStream(tempFile);
+    byte[] chunk = new byte[bufferLength];
+    int chunkLength = 0;
+    while ((chunkLength = inputStream.read(chunk)) != -1) {
+      fileOutputStream.write(chunk, 0, chunkLength);
+    }
+    deserializeMethodHandle.invokeExact(resources.getMemorySegment(), indexReference.getMemorySegment(),
+        returnValueMemorySegment, Util.buildMemorySegment(resources.linker, resources.arena, tmpIndexFile));
+
+    inputStream.close();
+    fileOutputStream.close();
+    tempFile.delete();
+
+    return indexReference;
+  }
+
+  /**
+   * Gets an instance of {@link CagraIndexParams}
+   *
+   * @return an instance of {@link CagraIndexParams}
+   */
+  public CagraIndexParams getCagraIndexParameters() {
+    return cagraIndexParameters;
+  }
+
+  /**
+   * Gets an instance of {@link CuVSResources}
+   *
+   * @return an instance of {@link CuVSResources}
+   */
+  public CuVSResources getCuVSResources() {
+    return resources;
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link CagraIndex}.
+   */
+  public static class Builder {
+
+    private float[][] dataset;
+    private CagraIndexParams cagraIndexParams;
+    private CagraCompressionParams cagraCompressionParams;
+    private CuVSResources cuvsResources;
+    private InputStream inputStream;
+
+    /**
+     * Constructs this Builder with an instance of {@link CuVSResources}.
+     *
+     * @param cuvsResources an instance of {@link CuVSResources}
+     */
+    public Builder(CuVSResources cuvsResources) {
+      this.cuvsResources = cuvsResources;
+    }
+
+    /**
+     * Sets an instance of InputStream typically used when index deserialization is
+     * needed.
+     *
+     * @param inputStream an instance of {@link InputStream}
+     * @return an instance of this Builder
+     */
+    public Builder from(InputStream inputStream) {
+      this.inputStream = inputStream;
+      return this;
+    }
+
+    /**
+     * Sets the dataset for building the {@link CagraIndex}.
+     *
+     * @param dataset a two-dimensional float array
+     * @return an instance of this Builder
+     */
+    public Builder withDataset(float[][] dataset) {
+      this.dataset = dataset;
+      return this;
+    }
+
+    /**
+     * Registers an instance of configured {@link CagraIndexParams} with this
+     * Builder.
+     *
+     * @param cagraIndexParameters An instance of CagraIndexParams.
+     * @return An instance of this Builder.
+     */
+    public Builder withIndexParams(CagraIndexParams cagraIndexParameters) {
+      this.cagraIndexParams = cagraIndexParameters;
+      return this;
+    }
+
+    /**
+     * Registers an instance of configured {@link CagraCompressionParams} with this
+     * Builder.
+     *
+     * @param cagraCompressionParams An instance of CagraCompressionParams.
+     * @return An instance of this Builder.
+     */
+    public Builder withCompressionParams(CagraCompressionParams cagraCompressionParams) {
+      this.cagraCompressionParams = cagraCompressionParams;
+      return this;
+    }
+
+    /**
+     * Builds and returns an instance of CagraIndex.
+     *
+     * @return an instance of CagraIndex
+     */
+    public CagraIndex build() throws Throwable {
+      if (inputStream != null) {
+        return new CagraIndex(inputStream, cuvsResources);
+      } else {
+        return new CagraIndex(cagraIndexParams, cagraCompressionParams, dataset, cuvsResources);
+      }
+    }
+  }
+
+  /**
+   * Holds the memory reference to a CAGRA index.
+   */
+  protected static class IndexReference {
+
+    private final MemorySegment memorySegment;
+
+    /**
+     * Constructs CagraIndexReference and allocate the MemorySegment.
+     */
+    protected IndexReference(CuVSResources resources) {
+      memorySegment = CuVSCagraIndex.allocate(resources.arena);
+    }
+
+    /**
+     * Constructs CagraIndexReference with an instance of MemorySegment passed as a
+     * parameter.
+     *
+     * @param indexMemorySegment the MemorySegment instance to use for containing
+     *                           index reference
+     */
+    protected IndexReference(MemorySegment indexMemorySegment) {
+      this.memorySegment = indexMemorySegment;
+    }
+
+    /**
+     * Gets the instance of index MemorySegment.
+     *
+     * @return index MemorySegment
+     */
+    protected MemorySegment getMemorySegment() {
+      return memorySegment;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
new file mode 100644
index 0000000000..767ee281be
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
@@ -0,0 +1,344 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+
+import com.nvidia.cuvs.panama.CuVSCagraIndexParams;
+
+/**
+ * Supplemental parameters to build CAGRA Index.
+ *
+ * @since 25.02
+ */
+public class CagraIndexParams {
+
+  private final CagraGraphBuildAlgo cuvsCagraGraphBuildAlgo;
+  private final CuvsDistanceType cuvsDistanceType;
+  private final MemorySegment memorySegment;
+  private CuVSResources resources;
+  private final int intermediateGraphDegree;
+  private final int graphDegree;
+  private final int nnDescentNiter;
+  private final int numWriterThreads;
+
+  /**
+   * Enum that denotes which ANN algorithm is used to build CAGRA graph.
+   */
+  public enum CagraGraphBuildAlgo {
+    /**
+     * Select build algorithm automatically
+     */
+    AUTO_SELECT(0),
+    /**
+     * Use IVF-PQ to build all-neighbors knn graph
+     */
+    IVF_PQ(1),
+    /**
+     * Experimental, use NN-Descent to build all-neighbors knn graph
+     */
+    NN_DESCENT(2);
+
+    /**
+     * The value for the enum choice.
+     */
+    public final int value;
+
+    private CagraGraphBuildAlgo(int value) {
+      this.value = value;
+    }
+  }
+
+  /**
+   * Enum that denotes how to compute distance.
+   */
+  public enum CuvsDistanceType {
+
+    /**
+     * evaluate as dist_ij = sum(x_ik^2) + sum(y_ij)^2 - 2*sum(x_ik * y_jk)
+     */
+    L2Expanded(0),
+    /**
+     * same as above, but inside the epilogue, perform square root operation
+     */
+    L2SqrtExpanded(1),
+    /**
+     * cosine distance
+     */
+    CosineExpanded(2),
+    /**
+     * L1 distance *
+     */
+    L1(3),
+    /**
+     * evaluate as dist_ij += (x_ik - y-jk)^2 *
+     */
+    L2Unexpanded(4),
+    /**
+     * same as above, but inside the epilogue, perform square root operation
+     */
+    L2SqrtUnexpanded(5),
+    /**
+     * basic inner product
+     */
+    InnerProduct(6),
+    /**
+     * Chebyshev (Linf) distance
+     */
+    Linf(7),
+    /**
+     * Canberra distance
+     */
+    Canberra(8),
+    /**
+     * Generalized Minkowski distance
+     */
+    LpUnexpanded(9),
+    /**
+     * Correlation distance
+     */
+    CorrelationExpanded(10),
+    /**
+     * Jaccard distance
+     */
+    JaccardExpanded(11),
+    /**
+     * Hellinger distance
+     */
+    HellingerExpanded(12),
+    /**
+     * Haversine distance
+     */
+    Haversine(13),
+    /**
+     * Bray-Curtis distance
+     */
+    BrayCurtis(14),
+    /**
+     * Jensen-Shannon distance
+     */
+    JensenShannon(15),
+    /**
+     * Hamming distance
+     */
+    HammingUnexpanded(16),
+    /**
+     * KLDivergence
+     */
+    KLDivergence(17),
+    /**
+     * RusselRao
+     */
+    RusselRaoExpanded(18),
+    /**
+     * Dice-Sorensen distance
+     */
+    DiceExpanded(19),
+    /**
+     * Precomputed (special value)
+     */
+    Precomputed(100);
+
+    /**
+     * The value for the enum choice.
+     */
+    public final int value;
+
+    private CuvsDistanceType(int value) {
+      this.value = value;
+    }
+
+  }
+
+  private CagraIndexParams(CuVSResources resources, int intermediateGraphDegree, int graphDegree,
+      CagraGraphBuildAlgo CuvsCagraGraphBuildAlgo, int nnDescentNiter, int writerThreads,
+      CuvsDistanceType cuvsDistanceType) {
+    this.resources = resources;
+    this.intermediateGraphDegree = intermediateGraphDegree;
+    this.graphDegree = graphDegree;
+    this.cuvsCagraGraphBuildAlgo = CuvsCagraGraphBuildAlgo;
+    this.nnDescentNiter = nnDescentNiter;
+    this.numWriterThreads = writerThreads;
+    this.cuvsDistanceType = cuvsDistanceType;
+
+    this.memorySegment = initMemorySegment();
+  }
+
+  private MemorySegment initMemorySegment() {
+    MemorySegment indexParamsMemorySegment = CuVSCagraIndexParams.allocate(resources.arena);
+    CuVSCagraIndexParams.intermediate_graph_degree(indexParamsMemorySegment, intermediateGraphDegree);
+    CuVSCagraIndexParams.graph_degree(indexParamsMemorySegment, graphDegree);
+    CuVSCagraIndexParams.build_algo(indexParamsMemorySegment, cuvsCagraGraphBuildAlgo.value);
+    CuVSCagraIndexParams.nn_descent_niter(indexParamsMemorySegment, nnDescentNiter);
+    CuVSCagraIndexParams.metric(indexParamsMemorySegment, cuvsDistanceType.value);
+    return indexParamsMemorySegment;
+  }
+
+  /**
+   * Gets the degree of input graph for pruning.
+   *
+   * @return the degree of input graph
+   */
+  public int getIntermediateGraphDegree() {
+    return intermediateGraphDegree;
+  }
+
+  /**
+   * Gets the degree of output graph.
+   *
+   * @return the degree of output graph
+   */
+  public int getGraphDegree() {
+    return graphDegree;
+  }
+
+  /**
+   * Gets the {@link CagraGraphBuildAlgo} used to build the index.
+   */
+  public CagraGraphBuildAlgo getCagraGraphBuildAlgo() {
+    return cuvsCagraGraphBuildAlgo;
+  }
+
+  /**
+   * Gets the number of iterations to run if building with
+   * {@link CagraGraphBuildAlgo#NN_DESCENT}
+   */
+  public int getNNDescentNumIterations() {
+    return nnDescentNiter;
+  }
+
+  protected MemorySegment getMemorySegment() {
+    return memorySegment;
+  }
+
+  /**
+   * Gets the {@link CuvsDistanceType} used to build the index.
+   */
+  public CuvsDistanceType getCuvsDistanceType() {
+    return cuvsDistanceType;
+  }
+
+  /**
+   * Gets the number of threads used to build the index.
+   */
+  public int getNumWriterThreads() {
+    return numWriterThreads;
+  }
+
+  @Override
+  public String toString() {
+    return "CagraIndexParams [cuvsCagraGraphBuildAlgo=" + cuvsCagraGraphBuildAlgo + ", cuvsDistanceType="
+        + cuvsDistanceType + ", intermediateGraphDegree=" + intermediateGraphDegree + ", graphDegree=" + graphDegree
+        + ", nnDescentNiter=" + nnDescentNiter + ", numWriterThreads=" + numWriterThreads + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of {@link CagraIndexParams}.
+   */
+  public static class Builder {
+
+    private CuVSResources resources;
+    private CagraGraphBuildAlgo cuvsCagraGraphBuildAlgo = CagraGraphBuildAlgo.NN_DESCENT;
+    private CuvsDistanceType cuvsDistanceType = CuvsDistanceType.L2Expanded;
+    private int intermediateGraphDegree = 128;
+    private int graphDegree = 64;
+    private int nnDescentNumIterations = 20;
+    private int numWriterThreads = 2;
+
+    public Builder(CuVSResources resources) {
+      this.resources = resources;
+    }
+
+    /**
+     * Sets the degree of input graph for pruning.
+     *
+     * @param intermediateGraphDegree degree of input graph for pruning
+     * @return an instance of Builder
+     */
+    public Builder withIntermediateGraphDegree(int intermediateGraphDegree) {
+      this.intermediateGraphDegree = intermediateGraphDegree;
+      return this;
+    }
+
+    /**
+     * Sets the degree of output graph.
+     *
+     * @param graphDegree degree of output graph
+     * @return an instance to Builder
+     */
+    public Builder withGraphDegree(int graphDegree) {
+      this.graphDegree = graphDegree;
+      return this;
+    }
+
+    /**
+     * Sets the CuvsCagraGraphBuildAlgo to use.
+     *
+     * @param cuvsCagraGraphBuildAlgo the CuvsCagraGraphBuildAlgo to use
+     * @return an instance of Builder
+     */
+    public Builder withCagraGraphBuildAlgo(CagraGraphBuildAlgo cuvsCagraGraphBuildAlgo) {
+      this.cuvsCagraGraphBuildAlgo = cuvsCagraGraphBuildAlgo;
+      return this;
+    }
+
+    /**
+     * Sets the metric to use.
+     *
+     * @param cuvsDistanceType the {@link CuvsDistanceType} to use
+     * @return an instance of Builder
+     */
+    public Builder withMetric(CuvsDistanceType cuvsDistanceType) {
+      this.cuvsDistanceType = cuvsDistanceType;
+      return this;
+    }
+
+    /**
+     * Sets the Number of Iterations to run if building with
+     * {@link CagraGraphBuildAlgo#NN_DESCENT}.
+     *
+     * @param nnDescentNiter number of Iterations to run if building with
+     *                       {@link CagraGraphBuildAlgo#NN_DESCENT}
+     * @return an instance of Builder
+     */
+    public Builder withNNDescentNumIterations(int nnDescentNiter) {
+      this.nnDescentNumIterations = nnDescentNiter;
+      return this;
+    }
+
+    /**
+     * Sets the number of writer threads to use for indexing.
+     *
+     * @param numWriterThreads number of writer threads to use
+     * @return an instance of Builder
+     */
+    public Builder withNumWriterThreads(int numWriterThreads) {
+      this.numWriterThreads = numWriterThreads;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link CagraIndexParams}.
+     *
+     * @return an instance of {@link CagraIndexParams}
+     */
+    public CagraIndexParams build() {
+      return new CagraIndexParams(resources, intermediateGraphDegree, graphDegree, cuvsCagraGraphBuildAlgo,
+          nnDescentNumIterations, numWriterThreads, cuvsDistanceType);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
new file mode 100644
index 0000000000..de2fc3f417
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -0,0 +1,165 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * CagraQuery holds the CagraSearchParams and the query vectors to be used while
+ * invoking search.
+ *
+ * @since 25.02
+ */
+public class CagraQuery {
+
+  private CagraSearchParams cagraSearchParameters;
+  private List<Integer> mapping;
+  private float[][] queryVectors;
+  private int topK;
+
+  /**
+   * Constructs an instance of {@link CagraQuery} using cagraSearchParameters,
+   * preFilter, queryVectors, mapping, and topK.
+   *
+   * @param cagraSearchParameters an instance of {@link CagraSearchParams} holding
+   *                              the search parameters
+   * @param queryVectors          2D float query vector array
+   * @param mapping               an instance of ID mapping
+   * @param topK                  the top k results to return
+   */
+  public CagraQuery(CagraSearchParams cagraSearchParameters, float[][] queryVectors, List<Integer> mapping, int topK) {
+    super();
+    this.cagraSearchParameters = cagraSearchParameters;
+    this.queryVectors = queryVectors;
+    this.mapping = mapping;
+    this.topK = topK;
+  }
+
+  /**
+   * Gets the instance of CagraSearchParams initially set.
+   *
+   * @return an instance CagraSearchParams
+   */
+  public CagraSearchParams getCagraSearchParameters() {
+    return cagraSearchParameters;
+  }
+
+  /**
+   * Gets the query vector 2D float array.
+   *
+   * @return 2D float array
+   */
+  public float[][] getQueryVectors() {
+    return queryVectors;
+  }
+
+  /**
+   * Gets the passed map instance.
+   *
+   * @return a map of ID mappings
+   */
+  public List<Integer> getMapping() {
+    return mapping;
+  }
+
+  /**
+   * Gets the topK value.
+   *
+   * @return the topK value
+   */
+  public int getTopK() {
+    return topK;
+  }
+
+  @Override
+  public String toString() {
+    return "CuVSQuery [cagraSearchParameters=" + cagraSearchParameters + ", queryVectors="
+        + Arrays.toString(queryVectors) + ", mapping=" + mapping + ", topK=" + topK + "]";
+  }
+
+  /**
+   * Builder helps configure and create an instance of CagraQuery.
+   */
+  public static class Builder {
+
+    private CagraSearchParams cagraSearchParams;
+    private float[][] queryVectors;
+    private List<Integer> mapping;
+    private int topK = 2;
+
+    /**
+     * Default constructor.
+     */
+    public Builder() {
+    }
+
+    /**
+     * Sets the instance of configured CagraSearchParams to be passed for search.
+     *
+     * @param cagraSearchParams an instance of the configured CagraSearchParams to
+     *                          be used for this query
+     * @return an instance of this Builder
+     */
+    public Builder withSearchParams(CagraSearchParams cagraSearchParams) {
+      this.cagraSearchParams = cagraSearchParams;
+      return this;
+    }
+
+    /**
+     * Registers the query vectors to be passed in the search call.
+     *
+     * @param queryVectors 2D float query vector array
+     * @return an instance of this Builder
+     */
+    public Builder withQueryVectors(float[][] queryVectors) {
+      this.queryVectors = queryVectors;
+      return this;
+    }
+
+    /**
+     * Sets the instance of mapping to be used for ID mapping.
+     *
+     * @param mapping the ID mapping instance
+     * @return an instance of this Builder
+     */
+    public Builder withMapping(List<Integer> mapping) {
+      this.mapping = mapping;
+      return this;
+    }
+
+    /**
+     * Registers the topK value.
+     *
+     * @param topK the topK value used to retrieve the topK results
+     * @return an instance of this Builder
+     */
+    public Builder withTopK(int topK) {
+      this.topK = topK;
+      return this;
+    }
+
+    /**
+     * Builds an instance of CuVSQuery.
+     *
+     * @return an instance of CuVSQuery
+     */
+    public CagraQuery build() {
+      return new CagraQuery(cagraSearchParams, queryVectors, mapping, topK);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
new file mode 100644
index 0000000000..54dbb548e5
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
@@ -0,0 +1,505 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+
+import com.nvidia.cuvs.panama.CuVSCagraSearchParams;
+
+/**
+ * CagraSearchParams encapsulates the logic for configuring and holding search
+ * parameters.
+ *
+ * @since 25.02
+ */
+public class CagraSearchParams {
+
+  private CuVSResources resources;
+  private int maxQueries;
+  private int iTopKSize;
+  private int maxIterations;
+  private int teamSize;
+  private int searchWidth;
+  private int minIterations;
+  private int threadBlockSize;
+  private int hashmapMinBitlen;
+  private int numRandomSamplings;
+  private float hashMapMaxFillRate;
+  private long randXORMask;
+  private MemorySegment memorySegment;
+  private SearchAlgo searchAlgo;
+  private HashMapMode hashMapMode;
+
+  /**
+   * Enum to denote algorithm used to search CAGRA Index.
+   */
+  public enum SearchAlgo {
+    /**
+     * for large batch sizes
+     */
+    SINGLE_CTA(0),
+    /**
+     * for small batch sizes
+     */
+    MULTI_CTA(1),
+    /**
+     * MULTI_KERNEL
+     */
+    MULTI_KERNEL(2),
+    /**
+     * AUTO
+     */
+    AUTO(3);
+
+    /**
+     * The value for the enum choice.
+     */
+    public final int value;
+
+    private SearchAlgo(int value) {
+      this.value = value;
+    }
+  }
+
+  /**
+   * Enum to denote Hash Mode used while searching CAGRA index.
+   */
+  public enum HashMapMode {
+    /**
+     * HASH
+     */
+    HASH(0),
+    /**
+     * SMALL
+     */
+    SMALL(1),
+    /**
+     * AUTO_HASH
+     */
+    AUTO_HASH(2);
+
+    /**
+     * The value for the enum choice.
+     */
+    public final int value;
+
+    private HashMapMode(int value) {
+      this.value = value;
+    }
+  }
+
+  /**
+   * Constructs an instance of CagraSearchParams with passed search parameters.
+   *
+   * @param resources          the resources instance to use
+   * @param maxQueries         the maximum number of queries to search at the same
+   *                           time (batch size)
+   * @param iTopKSize          the number of intermediate search results retained
+   *                           during the search
+   * @param maxIterations      the upper limit of search iterations
+   * @param searchAlgo         the search implementation is configured
+   * @param teamSize           the number of threads used to calculate a single
+   *                           distance
+   * @param searchWidth        the number of graph nodes to select as the starting
+   *                           point for the search in each iteration
+   * @param minIterations      the lower limit of search iterations
+   * @param threadBlockSize    the thread block size
+   * @param hashmapMode        the hash map type configured
+   * @param hashmapMinBitlen   the lower limit of hash map bit length
+   * @param hashmapMaxFillRate the upper limit of hash map fill rate
+   * @param numRandomSamplings the number of iterations of initial random seed
+   *                           node selection
+   * @param randXORMask        the bit mask used for initial random seed node
+   *                           selection
+   */
+  private CagraSearchParams(CuVSResources resources, int maxQueries, int iTopKSize, int maxIterations,
+      SearchAlgo searchAlgo, int teamSize, int searchWidth, int minIterations, int threadBlockSize,
+      HashMapMode hashmapMode, int hashmapMinBitlen, float hashmapMaxFillRate, int numRandomSamplings,
+      long randXORMask) {
+    this.maxQueries = maxQueries;
+    this.iTopKSize = iTopKSize;
+    this.maxIterations = maxIterations;
+    this.searchAlgo = searchAlgo;
+    this.teamSize = teamSize;
+    this.searchWidth = searchWidth;
+    this.minIterations = minIterations;
+    this.threadBlockSize = threadBlockSize;
+    this.hashMapMode = hashmapMode;
+    this.hashmapMinBitlen = hashmapMinBitlen;
+    this.hashMapMaxFillRate = hashmapMaxFillRate;
+    this.numRandomSamplings = numRandomSamplings;
+    this.randXORMask = randXORMask;
+    this.resources = resources;
+
+    this.memorySegment = allocateMemorySegment();
+  }
+
+  /**
+   * Allocates the configured search parameters in the MemorySegment.
+   */
+  private MemorySegment allocateMemorySegment() {
+    MemorySegment memorySegment = CuVSCagraSearchParams.allocate(resources.arena);
+    CuVSCagraSearchParams.max_queries(memorySegment, maxQueries);
+    CuVSCagraSearchParams.itopk_size(memorySegment, iTopKSize);
+    CuVSCagraSearchParams.max_iterations(memorySegment, maxIterations);
+    if (searchAlgo != null) {
+      CuVSCagraSearchParams.algo(memorySegment, searchAlgo.value);
+    }
+    CuVSCagraSearchParams.team_size(memorySegment, teamSize);
+    CuVSCagraSearchParams.search_width(memorySegment, searchWidth);
+    CuVSCagraSearchParams.min_iterations(memorySegment, minIterations);
+    CuVSCagraSearchParams.thread_block_size(memorySegment, threadBlockSize);
+    if (hashMapMode != null) {
+      CuVSCagraSearchParams.hashmap_mode(memorySegment, hashMapMode.value);
+    }
+    CuVSCagraSearchParams.hashmap_min_bitlen(memorySegment, hashmapMinBitlen);
+    CuVSCagraSearchParams.hashmap_max_fill_rate(memorySegment, hashMapMaxFillRate);
+    CuVSCagraSearchParams.num_random_samplings(memorySegment, numRandomSamplings);
+    CuVSCagraSearchParams.rand_xor_mask(memorySegment, randXORMask);
+    return memorySegment;
+  }
+
+  /**
+   * Gets the maximum number of queries to search at the same time (batch size).
+   *
+   * @return the maximum number of queries
+   */
+  public int getMaxQueries() {
+    return maxQueries;
+  }
+
+  /**
+   * Gets the number of intermediate search results retained during the search.
+   *
+   * @return the number of intermediate search results
+   */
+  public int getITopKSize() {
+    return iTopKSize;
+  }
+
+  /**
+   * Gets the upper limit of search iterations.
+   *
+   * @return the upper limit value
+   */
+  public int getMaxIterations() {
+    return maxIterations;
+  }
+
+  /**
+   * Gets the number of threads used to calculate a single distance.
+   *
+   * @return the number of threads configured
+   */
+  public int getTeamSize() {
+    return teamSize;
+  }
+
+  /**
+   * Gets the number of graph nodes to select as the starting point for the search
+   * in each iteration.
+   *
+   * @return the number of graph nodes
+   */
+  public int getSearchWidth() {
+    return searchWidth;
+  }
+
+  /**
+   * Gets the lower limit of search iterations.
+   *
+   * @return the lower limit value
+   */
+  public int getMinIterations() {
+    return minIterations;
+  }
+
+  /**
+   * Gets the thread block size.
+   *
+   * @return the thread block size
+   */
+  public int getThreadBlockSize() {
+    return threadBlockSize;
+  }
+
+  /**
+   * Gets the lower limit of hash map bit length.
+   *
+   * @return the lower limit value
+   */
+  public int getHashmapMinBitlen() {
+    return hashmapMinBitlen;
+  }
+
+  /**
+   * Gets the number of iterations of initial random seed node selection.
+   *
+   * @return the number of iterations
+   */
+  public int getNumRandomSamplings() {
+    return numRandomSamplings;
+  }
+
+  /**
+   * Gets the upper limit of hash map fill rate.
+   *
+   * @return the upper limit of hash map fill rate
+   */
+  public float getHashMapMaxFillRate() {
+    return hashMapMaxFillRate;
+  }
+
+  /**
+   * Gets the bit mask used for initial random seed node selection.
+   *
+   * @return the bit mask value
+   */
+  public long getRandXORMask() {
+    return randXORMask;
+  }
+
+  /**
+   * Gets the MemorySegment holding CagraSearchParams.
+   *
+   * @return the MemorySegment holding CagraSearchParams
+   */
+  protected MemorySegment getMemorySegment() {
+    return memorySegment;
+  }
+
+  /**
+   * Gets which search implementation is configured.
+   *
+   * @return the configured {@link SearchAlgo}
+   */
+  public SearchAlgo getCagraSearchAlgo() {
+    return searchAlgo;
+  }
+
+  /**
+   * Gets the hash map mode configured.
+   *
+   * @return the configured {@link HashMapMode}
+   */
+  public HashMapMode getHashMapMode() {
+    return hashMapMode;
+  }
+
+  @Override
+  public String toString() {
+    return "CagraSearchParams [resources=" + resources + ", maxQueries=" + maxQueries + ", itopkSize=" + iTopKSize
+        + ", maxIterations=" + maxIterations + ", cuvsCagraSearchAlgo=" + searchAlgo + ", teamSize=" + teamSize
+        + ", searchWidth=" + searchWidth + ", minIterations=" + minIterations + ", threadBlockSize=" + threadBlockSize
+        + ", hashMapMode=" + hashMapMode + ", hashMapMinBitlen=" + hashmapMinBitlen + ", hashMapMaxFillRate="
+        + hashMapMaxFillRate + ", numRandomSamplings=" + numRandomSamplings + ", randXORMask=" + randXORMask
+        + ", memorySegment=" + memorySegment + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of CagraSearchParams.
+   */
+  public static class Builder {
+
+    private CuVSResources resources;
+    private int maxQueries;
+    private int iTopKSize = 64;
+    private int maxIterations;
+    private int teamSize;
+    private int searchWidth = 1;
+    private int minIterations;
+    private int threadBlockSize;
+    private int hashMapMinBitlen;
+    private int numRandomSamplings = 1;
+    private float hashMapMaxFillRate = 0.5f;
+    private long randXORMask = 0x128394;
+    private SearchAlgo searchAlgo;
+    private HashMapMode hashMapMode;
+
+    /**
+     * Constructs this Builder with an instance of Arena.
+     *
+     * @param resources the {@link CuVSResources} instance to use
+     */
+    public Builder(CuVSResources resources) {
+      this.resources = resources;
+    }
+
+    /**
+     * Sets the maximum number of queries to search at the same time (batch size).
+     * Auto select when 0.
+     *
+     * @param maxQueries the maximum number of queries
+     * @return an instance of this Builder
+     */
+    public Builder withMaxQueries(int maxQueries) {
+      this.maxQueries = maxQueries;
+      return this;
+    }
+
+    /**
+     * Sets the number of intermediate search results retained during the search.
+     * This is the main knob to adjust trade off between accuracy and search speed.
+     * Higher values improve the search accuracy.
+     *
+     * @param iTopKSize the number of intermediate search results
+     * @return an instance of this Builder
+     */
+    public Builder withItopkSize(int iTopKSize) {
+      this.iTopKSize = iTopKSize;
+      return this;
+    }
+
+    /**
+     * Sets the upper limit of search iterations. Auto select when 0.
+     *
+     * @param maxIterations the upper limit of search iterations
+     * @return an instance of this Builder
+     */
+    public Builder withMaxIterations(int maxIterations) {
+      this.maxIterations = maxIterations;
+      return this;
+    }
+
+    /**
+     * Sets which search implementation to use.
+     *
+     * @param cuvsCagraSearchAlgo the {@link SearchAlgo} to use
+     * @return an instance of this Builder
+     */
+    public Builder withAlgo(SearchAlgo cuvsCagraSearchAlgo) {
+      this.searchAlgo = cuvsCagraSearchAlgo;
+      return this;
+    }
+
+    /**
+     * Sets the number of threads used to calculate a single distance. 4, 8, 16, or
+     * 32.
+     *
+     * @param teamSize the number of threads used to calculate a single distance
+     * @return an instance of this Builder
+     */
+    public Builder withTeamSize(int teamSize) {
+      this.teamSize = teamSize;
+      return this;
+    }
+
+    /**
+     * Sets the number of graph nodes to select as the starting point for the search
+     * in each iteration.
+     *
+     * @param searchWidth the number of graph nodes to select
+     * @return an instance of this Builder
+     */
+    public Builder withSearchWidth(int searchWidth) {
+      this.searchWidth = searchWidth;
+      return this;
+    }
+
+    /**
+     * Sets the lower limit of search iterations.
+     *
+     * @param minIterations the lower limit of search iterations
+     * @return an instance of this Builder
+     */
+    public Builder withMinIterations(int minIterations) {
+      this.minIterations = minIterations;
+      return this;
+    }
+
+    /**
+     * Sets the thread block size. 0, 64, 128, 256, 512, 1024. Auto selection when
+     * 0.
+     *
+     * @param threadBlockSize the thread block size
+     * @return an instance of this Builder
+     */
+    public Builder withThreadBlockSize(int threadBlockSize) {
+      this.threadBlockSize = threadBlockSize;
+      return this;
+    }
+
+    /**
+     * Sets the hash map type. Auto selection when AUTO.
+     *
+     * @param hashMapMode the {@link HashMapMode}
+     * @return an instance of this Builder
+     */
+    public Builder withHashMapMode(HashMapMode hashMapMode) {
+      this.hashMapMode = hashMapMode;
+      return this;
+    }
+
+    /**
+     * Sets the lower limit of hash map bit length. More than 8.
+     *
+     * @param hashMapMinBitlen the lower limit of hash map bit length
+     * @return an instance of this Builder
+     */
+    public Builder withHashMapMinBitlen(int hashMapMinBitlen) {
+      this.hashMapMinBitlen = hashMapMinBitlen;
+      return this;
+    }
+
+    /**
+     * Sets the upper limit of hash map fill rate. More than 0.1, less than 0.9.
+     *
+     * @param hashMapMaxFillRate the upper limit of hash map fill rate
+     * @return an instance of this Builder
+     */
+    public Builder withHashMapMaxFillRate(float hashMapMaxFillRate) {
+      this.hashMapMaxFillRate = hashMapMaxFillRate;
+      return this;
+    }
+
+    /**
+     * Sets the number of iterations of initial random seed node selection. 1 or
+     * more.
+     *
+     * @param numRandomSamplings the number of iterations of initial random seed
+     *                           node selection
+     * @return an instance of this Builder
+     */
+    public Builder withNumRandomSamplings(int numRandomSamplings) {
+      this.numRandomSamplings = numRandomSamplings;
+      return this;
+    }
+
+    /**
+     * Sets the bit mask used for initial random seed node selection.
+     *
+     * @param randXORMask the bit mask used for initial random seed node selection
+     * @return an instance of this Builder
+     */
+    public Builder withRandXorMask(long randXORMask) {
+      this.randXORMask = randXORMask;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link CagraSearchParams} with passed search
+     * parameters.
+     *
+     * @return an instance of CagraSearchParams
+     */
+    public CagraSearchParams build() {
+      return new CagraSearchParams(resources, maxQueries, iTopKSize, maxIterations, searchAlgo, teamSize, searchWidth,
+          minIterations, threadBlockSize, hashMapMode, hashMapMinBitlen, hashMapMaxFillRate, numRandomSamplings,
+          randXORMask);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchResults.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchResults.java
new file mode 100644
index 0000000000..3473faccef
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchResults.java
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+
+import com.nvidia.cuvs.common.SearchResults;
+
+/**
+ * SearchResult encapsulates the logic for reading and holding search results.
+ *
+ * @since 25.02
+ */
+public class CagraSearchResults extends SearchResults {
+
+  protected CagraSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+      long numberOfQueries) {
+    super(neighboursSequenceLayout, distancesSequenceLayout, neighboursMemorySegment, distancesMemorySegment, topK,
+        mapping, numberOfQueries);
+    readResultMemorySegments();
+  }
+
+  /**
+   * Reads neighbors and distances {@link MemorySegment} and loads the values
+   * internally
+   */
+  protected void readResultMemorySegments() {
+    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<Integer, Float>();
+    int count = 0;
+    for (long i = 0; i < topK * numberOfQueries; i++) {
+      int id = (int) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
+      float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
+      intermediateResultMap.put(mapping != null ? mapping.get(id) : id, dst);
+      count += 1;
+      if (count == topK) {
+        results.add(intermediateResultMap);
+        intermediateResultMap = new LinkedHashMap<Integer, Float>();
+        count = 0;
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
new file mode 100644
index 0000000000..dbaba3ebb0
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.io.File;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+
+import com.nvidia.cuvs.common.Util;
+
+/**
+ * Used for allocating resources for cuVS
+ *
+ * @since 25.02
+ */
+public class CuVSResources implements AutoCloseable {
+
+  public final Arena arena;
+  public final Linker linker;
+  public final SymbolLookup symbolLookup;
+  protected File nativeLibrary;
+  private final MethodHandle createResourcesMethodHandle;
+  private final MethodHandle destroyResourcesMethodHandle;
+  private MemorySegment resourcesMemorySegment;
+  private MemoryLayout intMemoryLayout;
+
+  /**
+   * Constructor that allocates the resources needed for cuVS
+   *
+   * @throws Throwable exception thrown when native function is invoked
+   */
+  public CuVSResources() throws Throwable {
+    linker = Linker.nativeLinker();
+    arena = Arena.ofShared();
+
+    nativeLibrary = Util.loadNativeLibrary();
+    symbolLookup = SymbolLookup.libraryLookup(nativeLibrary.getAbsolutePath(), arena);
+    intMemoryLayout = linker.canonicalLayouts().get("int");
+
+    createResourcesMethodHandle = linker.downcallHandle(symbolLookup.find("create_resources").get(),
+        FunctionDescriptor.of(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+    destroyResourcesMethodHandle = linker.downcallHandle(symbolLookup.find("destroy_resources").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+
+    createResources();
+  }
+
+  /**
+   * Creates the resources used internally and returns its reference.
+   *
+   * @throws Throwable exception thrown when native function is invoked
+   */
+  public void createResources() throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = arena.allocate(returnValueMemoryLayout);
+    resourcesMemorySegment = (MemorySegment) createResourcesMethodHandle.invokeExact(returnValueMemorySegment);
+  }
+
+  @Override
+  public void close() {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = arena.allocate(returnValueMemoryLayout);
+    try {
+      destroyResourcesMethodHandle.invokeExact(resourcesMemorySegment, returnValueMemorySegment);
+    } catch (Throwable e) {
+      e.printStackTrace();
+    }
+    if (!arena.scope().isAlive()) {
+      arena.close();
+    }
+    nativeLibrary.delete();
+  }
+
+  /**
+   * Gets the reference to the cuvsResources MemorySegment.
+   *
+   * @return cuvsResources MemorySegment
+   */
+  protected MemorySegment getMemorySegment() {
+    return resourcesMemorySegment;
+  }
+
+  /**
+   * Returns the loaded libcuvs_java_cagra.so as a {@link SymbolLookup}
+   */
+  protected SymbolLookup getSymbolLookup() {
+    return symbolLookup;
+  }
+
+  /**
+   * Container for GPU information
+   */
+  public class GPUInfo {
+
+    private final int gpuId;
+    private final long freeMemory;
+    private final long totalMemory;
+    private final float computeCapability;
+
+    public GPUInfo(int gpuId, long freeMemory, long totalMemory, float computeCapability) {
+      super();
+      this.gpuId = gpuId;
+      this.freeMemory = freeMemory;
+      this.totalMemory = totalMemory;
+      this.computeCapability = computeCapability;
+    }
+
+    public int getGpuId() {
+      return gpuId;
+    }
+
+    public long getFreeMemory() {
+      return freeMemory;
+    }
+
+    public long getTotalMemory() {
+      return totalMemory;
+    }
+
+    public float getComputeCapability() {
+      return computeCapability;
+    }
+
+    @Override
+    public String toString() {
+      return "GPUInfo [gpuId=" + gpuId + ", freeMemory=" + freeMemory + ", totalMemory=" + totalMemory
+          + ", computeCapability=" + computeCapability + "]";
+    }
+
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
new file mode 100644
index 0000000000..48cade179b
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
@@ -0,0 +1,56 @@
+package com.nvidia.cuvs;
+
+/**
+ * Contains GPU information
+ */
+public class GPUInfo {
+  private final int gpuId;
+  private final String name;
+  private final long freeMemory;
+  private final long totalMemory;
+  private final float computeCapability;
+
+  /**
+   * Constructor for GPUInfo
+   *
+   * @param gpuId             id of the GPU starting from 0
+   * @param name              ASCII string identifying device
+   * @param freeMemory        returned free memory in bytes
+   * @param totalMemory       returned total memory in bytes
+   * @param computeCapability the compute capability of the device
+   */
+  public GPUInfo(int gpuId, String name, long freeMemory, long totalMemory, float computeCapability) {
+    super();
+    this.gpuId = gpuId;
+    this.name = name;
+    this.freeMemory = freeMemory;
+    this.totalMemory = totalMemory;
+    this.computeCapability = computeCapability;
+  }
+
+  public int getGpuId() {
+    return gpuId;
+  }
+
+  public String getName() {
+    return name;
+  }
+
+  public long getFreeMemory() {
+    return freeMemory;
+  }
+
+  public long getTotalMemory() {
+    return totalMemory;
+  }
+
+  public float getComputeCapability() {
+    return computeCapability;
+  }
+
+  @Override
+  public String toString() {
+    return "GPUInfo [gpuId=" + gpuId + ", name=" + name + ", freeMemory=" + freeMemory + ", totalMemory=" + totalMemory
+        + ", computeCapability=" + computeCapability + "]";
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
new file mode 100644
index 0000000000..d55308a1b4
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
@@ -0,0 +1,263 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.UUID;
+
+import com.nvidia.cuvs.common.Util;
+import com.nvidia.cuvs.panama.CuVSHnswIndex;
+
+/**
+ * {@link HnswIndex} encapsulates a HNSW index, along with methods to interact
+ * with it.
+ *
+ * @since 25.02
+ */
+public class HnswIndex {
+
+  private final CuVSResources resources;
+  private MethodHandle deserializeHnswIndexMethodHandle;
+  private MethodHandle searchHnswIndexMethodHandle;
+  private MethodHandle destroyHnswIndexMethodHandle;
+  private HnswIndexParams hnswIndexParams;
+  private IndexReference hnswIndexReference;
+  private MemoryLayout longMemoryLayout;
+  private MemoryLayout intMemoryLayout;
+  private MemoryLayout floatMemoryLayout;
+
+  /**
+   * Constructor for loading the index from an {@link InputStream}
+   *
+   * @param inputStream an instance of stream to read the index bytes from
+   * @param resources   an instance of {@link CuVSResources}
+   */
+  private HnswIndex(InputStream inputStream, CuVSResources resources, HnswIndexParams hnswIndexParams)
+      throws Throwable {
+    this.hnswIndexParams = hnswIndexParams;
+    this.resources = resources;
+
+    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
+    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
+    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
+
+    initializeMethodHandles();
+    this.hnswIndexReference = deserialize(inputStream);
+  }
+
+  /**
+   * Initializes the {@link MethodHandles} for invoking native methods.
+   *
+   * @throws IOException @{@link IOException} is unable to load the native library
+   */
+  private void initializeMethodHandles() throws IOException {
+    deserializeHnswIndexMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("deserialize_hnsw_index").get(), FunctionDescriptor.of(ValueLayout.ADDRESS,
+            ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout));
+
+    searchHnswIndexMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("search_hnsw_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS,
+            ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout, intMemoryLayout,
+            longMemoryLayout));
+
+    destroyHnswIndexMethodHandle = resources.linker.downcallHandle(
+        resources.getSymbolLookup().find("destroy_hnsw_index").get(),
+        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+  }
+
+  /**
+   * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
+   */
+  public void destroyIndex() throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    destroyHnswIndexMethodHandle.invokeExact(hnswIndexReference.getMemorySegment(), returnValueMemorySegment);
+  }
+
+  /**
+   * Invokes the native search_hnsw_index via the Panama API for searching a HNSW
+   * index.
+   *
+   * @param query an instance of {@link HnswQuery} holding the query vectors and
+   *              other parameters
+   * @return an instance of {@link HnswSearchResults} containing the results
+   */
+  public HnswSearchResults search(HnswQuery query) throws Throwable {
+    long numQueries = query.getQueryVectors().length;
+    long numBlocks = query.getTopK() * numQueries;
+    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
+
+    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, longMemoryLayout);
+    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, floatMemoryLayout);
+    MemorySegment neighborsMemorySegment = resources.arena.allocate(neighborsSequenceLayout);
+    MemorySegment distancesMemorySegment = resources.arena.allocate(distancesSequenceLayout);
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+
+    searchHnswIndexMethodHandle.invokeExact(resources.getMemorySegment(), hnswIndexReference.getMemorySegment(),
+        query.getHnswSearchParams().getHnswSearchParamsMemorySegment(), returnValueMemorySegment,
+        neighborsMemorySegment, distancesMemorySegment,
+        Util.buildMemorySegment(resources.linker, resources.arena, query.getQueryVectors()), query.getTopK(),
+        vectorDimension, numQueries);
+
+    return new HnswSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+        distancesMemorySegment, query.getTopK(), query.getMapping(), numQueries);
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a HNSW index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream an instance of {@link InputStream}
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream) throws Throwable {
+    return deserialize(inputStream, 1024);
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a HNSW index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream  an instance of {@link InputStream}
+   * @param bufferLength the length of the buffer to use while reading the bytes
+   *                     from the stream. Default value is 1024.
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
+    String tmpIndexFile = "/tmp/" + UUID.randomUUID().toString() + ".hnsw";
+
+    File tempFile = new File(tmpIndexFile);
+    FileOutputStream fileOutputStream = new FileOutputStream(tempFile);
+    byte[] chunk = new byte[bufferLength];
+    int chunkLength = 0;
+    while ((chunkLength = inputStream.read(chunk)) != -1) {
+      fileOutputStream.write(chunk, 0, chunkLength);
+    }
+
+    IndexReference indexReference = new IndexReference((MemorySegment) deserializeHnswIndexMethodHandle.invokeExact(
+        resources.getMemorySegment(), Util.buildMemorySegment(resources.linker, resources.arena, tmpIndexFile),
+        hnswIndexParams.getHnswIndexParamsMemorySegment(), returnValueMemorySegment,
+        hnswIndexParams.getVectorDimension()));
+
+    inputStream.close();
+    fileOutputStream.close();
+    tempFile.delete();
+
+    return indexReference;
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link HnswIndex}.
+   */
+  public static class Builder {
+
+    private CuVSResources cuvsResources;
+    private InputStream inputStream;
+    private HnswIndexParams hnswIndexParams;
+
+    /**
+     * Constructs this Builder with an instance of {@link CuVSResources}.
+     *
+     * @param cuvsResources an instance of {@link CuVSResources}
+     */
+    public Builder(CuVSResources cuvsResources) {
+      this.cuvsResources = cuvsResources;
+    }
+
+    /**
+     * Sets an instance of InputStream typically used when index deserialization is
+     * needed.
+     *
+     * @param inputStream an instance of {@link InputStream}
+     * @return an instance of this Builder
+     */
+    public Builder from(InputStream inputStream) {
+      this.inputStream = inputStream;
+      return this;
+    }
+
+    /**
+     * Registers an instance of configured {@link HnswIndexParams} with this
+     * Builder.
+     *
+     * @param hnswIndexParameters An instance of HnswIndexParams.
+     * @return An instance of this Builder.
+     */
+    public Builder withIndexParams(HnswIndexParams hnswIndexParameters) {
+      this.hnswIndexParams = hnswIndexParameters;
+      return this;
+    }
+
+    /**
+     * Builds and returns an instance of CagraIndex.
+     *
+     * @return an instance of CagraIndex
+     */
+    public HnswIndex build() throws Throwable {
+      return new HnswIndex(inputStream, cuvsResources, hnswIndexParams);
+    }
+  }
+
+  /**
+   * Holds the memory reference to a HNSW index.
+   */
+  protected static class IndexReference {
+
+    private final MemorySegment memorySegment;
+
+    /**
+     * Constructs CagraIndexReference and allocate the MemorySegment.
+     */
+    protected IndexReference(CuVSResources resources) {
+      memorySegment = CuVSHnswIndex.allocate(resources.arena);
+    }
+
+    /**
+     * Constructs CagraIndexReference with an instance of MemorySegment passed as a
+     * parameter.
+     *
+     * @param indexMemorySegment the MemorySegment instance to use for containing
+     *                           index reference
+     */
+    protected IndexReference(MemorySegment indexMemorySegment) {
+      this.memorySegment = indexMemorySegment;
+    }
+
+    /**
+     * Gets the instance of index MemorySegment.
+     *
+     * @return index MemorySegment
+     */
+    protected MemorySegment getMemorySegment() {
+      return memorySegment;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
new file mode 100644
index 0000000000..ef06adf619
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
@@ -0,0 +1,211 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+
+import com.nvidia.cuvs.panama.CuVSHnswIndexParams;
+
+/**
+ * Supplemental parameters to build HNSW index.
+ *
+ * @since 25.02
+ */
+public class HnswIndexParams {
+
+  /**
+   * Hierarchy for HNSW index when converting from CAGRA index
+   *
+   * NOTE: When the value is `NONE`, the HNSW index is built as a base-layer-only
+   * index.
+   */
+  public enum CuvsHnswHierarchy {
+
+    /**
+     * Flat hierarchy, search is base-layer only
+     */
+    NONE(0),
+
+    /**
+     * Full hierarchy is built using the CPU
+     */
+    CPU(1);
+
+    /**
+     * The value for the enum choice.
+     */
+    public final int value;
+
+    private CuvsHnswHierarchy(int value) {
+      this.value = value;
+    }
+  };
+
+  private CuVSResources resources;
+  private MemorySegment memorySegment;
+  private CuvsHnswHierarchy hierarchy = CuvsHnswHierarchy.NONE;
+  private int efConstruction = 200;
+  private int numThreads = 2;
+  private int vectorDimension;
+
+  private HnswIndexParams(CuVSResources resources, CuvsHnswHierarchy hierarchy, int efConstruction, int numThreads,
+      int vectorDimension) {
+    this.resources = resources;
+    this.hierarchy = hierarchy;
+    this.efConstruction = efConstruction;
+    this.numThreads = numThreads;
+    this.vectorDimension = vectorDimension;
+    this.memorySegment = allocateMemorySegment();
+  }
+
+  /**
+   * Allocates the configured search parameters in the MemorySegment.
+   */
+  private MemorySegment allocateMemorySegment() {
+    MemorySegment memorySegment = CuVSHnswIndexParams.allocate(resources.arena);
+    CuVSHnswIndexParams.ef_construction(memorySegment, efConstruction);
+    CuVSHnswIndexParams.num_threads(memorySegment, numThreads);
+    return memorySegment;
+  }
+
+  public MemorySegment getHnswIndexParamsMemorySegment() {
+    return memorySegment;
+  }
+
+  /**
+   *
+   * @return
+   */
+  public CuvsHnswHierarchy getHierarchy() {
+    return hierarchy;
+  }
+
+  /**
+   *
+   * @return
+   */
+  public int getEfConstruction() {
+    return efConstruction;
+  }
+
+  /**
+   *
+   * @return
+   */
+  public int getNumThreads() {
+    return numThreads;
+  }
+
+  /**
+   *
+   * @return
+   */
+  public int getVectorDimension() {
+    return vectorDimension;
+  }
+
+  public CuVSResources getResources() {
+    return resources;
+  }
+
+  @Override
+  public String toString() {
+    return "HnswIndexParams [hierarchy=" + hierarchy + ", efConstruction=" + efConstruction + ", numThreads="
+        + numThreads + ", vectorDimension=" + vectorDimension + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of {@link HnswIndexParams}.
+   */
+  public static class Builder {
+
+    private CuVSResources resources;
+    private CuvsHnswHierarchy hierarchy = CuvsHnswHierarchy.NONE;
+    private int efConstruction = 200;
+    private int numThreads = 2;
+    private int vectorDimension;
+
+    /**
+     * Constructs this Builder with an instance of Arena.
+     *
+     * @param resources the {@link CuVSResources} instance to use
+     */
+    public Builder(CuVSResources resources) {
+      this.resources = resources;
+    }
+
+    /**
+     * Sets the hierarchy for HNSW index when converting from CAGRA index.
+     *
+     * NOTE: When the value is `NONE`, the HNSW index is built as a base-layer-only
+     * index.
+     *
+     * @param hierarchy the hierarchy for HNSW index when converting from CAGRA
+     *                  index
+     * @return an instance of Builder
+     */
+    public Builder withHierarchy(CuvsHnswHierarchy hierarchy) {
+      this.hierarchy = hierarchy;
+      return this;
+    }
+
+    /**
+     * Sets the size of the candidate list during hierarchy construction when
+     * hierarchy is `CPU`.
+     *
+     * @param efConstruction the size of the candidate list during hierarchy
+     *                       construction when hierarchy is `CPU`
+     * @return an instance of Builder
+     */
+    public Builder withEfConstruction(int efConstruction) {
+      this.efConstruction = efConstruction;
+      return this;
+    }
+
+    /**
+     * Sets the number of host threads to use to construct hierarchy when hierarchy
+     * is `CPU`.
+     *
+     * @param numThreads the number of threads
+     * @return an instance of Builder
+     */
+    public Builder withNumThreads(int numThreads) {
+      this.numThreads = numThreads;
+      return this;
+    }
+
+    /**
+     * Sets the vector dimension
+     *
+     * @param vectorDimension the vector dimension
+     * @return an instance of Builder
+     */
+    public Builder withVectorDimension(int vectorDimension) {
+      this.vectorDimension = vectorDimension;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link HnswIndexParams}.
+     *
+     * @return an instance of {@link HnswIndexParams}
+     */
+    public HnswIndexParams build() {
+      return new HnswIndexParams(resources, hierarchy, efConstruction, numThreads, vectorDimension);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
new file mode 100644
index 0000000000..2575bbde40
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
@@ -0,0 +1,156 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * HnswQuery holds the query vectors to be used while invoking search on the
+ * HNSW index.
+ *
+ * @since 25.02
+ */
+public class HnswQuery {
+
+  private HnswSearchParams hnswSearchParams;
+  private List<Integer> mapping;
+  private float[][] queryVectors;
+  private int topK;
+
+  /**
+   * Constructs an instance of {@link HnswQuery} using queryVectors, mapping, and
+   * topK.
+   *
+   * @param hnswSearchParams the search parameters to use
+   * @param queryVectors     2D float query vector array
+   * @param mapping          an instance of ID mapping
+   * @param topK             the top k results to return
+   */
+  private HnswQuery(HnswSearchParams hnswSearchParams, float[][] queryVectors, List<Integer> mapping, int topK) {
+    this.hnswSearchParams = hnswSearchParams;
+    this.queryVectors = queryVectors;
+    this.mapping = mapping;
+    this.topK = topK;
+  }
+
+  /**
+   * Gets the instance of HnswSearchParams.
+   *
+   * @return the instance of {@link HnswSearchParams}
+   */
+  public HnswSearchParams getHnswSearchParams() {
+    return hnswSearchParams;
+  }
+
+  /**
+   * Gets the query vector 2D float array.
+   *
+   * @return 2D float array
+   */
+  public float[][] getQueryVectors() {
+    return queryVectors;
+  }
+
+  /**
+   * Gets the passed map instance.
+   *
+   * @return a map of ID mappings
+   */
+  public List<Integer> getMapping() {
+    return mapping;
+  }
+
+  /**
+   * Gets the topK value.
+   *
+   * @return an integer
+   */
+  public int getTopK() {
+    return topK;
+  }
+
+  @Override
+  public String toString() {
+    return "HnswQuery [mapping=" + mapping + ", queryVectors=" + Arrays.toString(queryVectors) + ", topK=" + topK + "]";
+  }
+
+  /**
+   * Builder helps configure and create an instance of BruteForceQuery.
+   */
+  public static class Builder {
+
+    private HnswSearchParams hnswSearchParams;
+    private float[][] queryVectors;
+    private List<Integer> mapping;
+    private int topK = 2;
+
+    /**
+     * Sets the instance of configured HnswSearchParams to be passed for search.
+     *
+     * @param hnswSearchParams an instance of the configured HnswSearchParams to be
+     *                         used for this query
+     * @return an instance of this Builder
+     */
+    public Builder withSearchParams(HnswSearchParams hnswSearchParams) {
+      this.hnswSearchParams = hnswSearchParams;
+      return this;
+    }
+
+    /**
+     * Registers the query vectors to be passed in the search call.
+     *
+     * @param queryVectors 2D float query vector array
+     * @return an instance of this Builder
+     */
+    public Builder withQueryVectors(float[][] queryVectors) {
+      this.queryVectors = queryVectors;
+      return this;
+    }
+
+    /**
+     * Sets the instance of mapping to be used for ID mapping.
+     *
+     * @param mapping the ID mapping instance
+     * @return an instance of this Builder
+     */
+    public Builder withMapping(List<Integer> mapping) {
+      this.mapping = mapping;
+      return this;
+    }
+
+    /**
+     * Registers the topK value.
+     *
+     * @param topK the topK value used to retrieve the topK results
+     * @return an instance of this Builder
+     */
+    public Builder withTopK(int topK) {
+      this.topK = topK;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link HnswQuery}
+     *
+     * @return an instance of {@link HnswQuery}
+     */
+    public HnswQuery build() {
+      return new HnswQuery(hnswSearchParams, queryVectors, mapping, topK);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
new file mode 100644
index 0000000000..a2725e6dea
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+
+import com.nvidia.cuvs.panama.CuVSHnswSearchParams;
+
+/**
+ * HnswSearchParams encapsulates the logic for configuring and holding search
+ * parameters for HNSW index.
+ *
+ * @since 25.02
+ */
+public class HnswSearchParams {
+
+  private CuVSResources resources;
+  private MemorySegment memorySegment;
+  private int ef = 200;
+  private int numThreads = 0;
+
+  /**
+   * Constructs an instance of HnswSearchParams with passed search parameters.
+   *
+   * @param resources  the resources instance to use
+   * @param ef         the ef value
+   * @param numThreads the number of threads
+   *
+   */
+  private HnswSearchParams(CuVSResources resources, int ef, int numThreads) {
+    this.resources = resources;
+    this.ef = ef;
+    this.numThreads = numThreads;
+    this.memorySegment = allocateMemorySegment();
+  }
+
+  /**
+   * Allocates the configured search parameters in the MemorySegment.
+   */
+  private MemorySegment allocateMemorySegment() {
+    MemorySegment memorySegment = CuVSHnswSearchParams.allocate(resources.arena);
+    CuVSHnswSearchParams.ef(memorySegment, ef);
+    CuVSHnswSearchParams.num_threads(memorySegment, numThreads);
+    return memorySegment;
+  }
+
+  public MemorySegment getHnswSearchParamsMemorySegment() {
+    return memorySegment;
+  }
+
+  /**
+   * Gets the ef value
+   *
+   * @return the integer ef value
+   */
+  public int getEf() {
+    return ef;
+  }
+
+  /**
+   * Gets the number of threads
+   *
+   * @return the number of threads
+   */
+  public int getNumThreads() {
+    return numThreads;
+  }
+
+  @Override
+  public String toString() {
+    return "HnswSearchParams [ef=" + ef + ", numThreads=" + numThreads + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of HnswSearchParams.
+   */
+  public static class Builder {
+
+    private CuVSResources resources;
+    private int ef = 200;
+    private int numThreads = 0;
+
+    /**
+     * Constructs this Builder with an instance of Arena.
+     *
+     * @param resources the {@link CuVSResources} instance to use
+     */
+    public Builder(CuVSResources resources) {
+      this.resources = resources;
+    }
+
+    /**
+     * Sets the ef value
+     *
+     * @param ef the ef value
+     * @return an instance of this Builder
+     */
+    public Builder withEF(int ef) {
+      this.ef = ef;
+      return this;
+    }
+
+    /**
+     * Sets the number of threads
+     *
+     * @param numThreads the number of threads
+     * @return an instance of this Builder
+     */
+    public Builder withNumThreads(int numThreads) {
+      this.numThreads = numThreads;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link HnswSearchParams} with passed search parameters.
+     *
+     * @return an instance of HnswSearchParams
+     */
+    public HnswSearchParams build() {
+      return new HnswSearchParams(resources, ef, numThreads);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchResults.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchResults.java
new file mode 100644
index 0000000000..8cb4d89e03
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchResults.java
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+
+import com.nvidia.cuvs.common.SearchResults;
+
+/**
+ * SearchResult encapsulates the logic for reading and holding search results.
+ *
+ * @since 25.02
+ */
+public class HnswSearchResults extends SearchResults {
+
+  protected HnswSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+      long numberOfQueries) {
+    super(neighboursSequenceLayout, distancesSequenceLayout, neighboursMemorySegment, distancesMemorySegment, topK,
+        mapping, numberOfQueries);
+    readResultMemorySegments();
+  }
+
+  /**
+   * Reads neighbors and distances {@link MemorySegment} and loads the values
+   * internally
+   */
+  protected void readResultMemorySegments() {
+    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<Integer, Float>();
+    int count = 0;
+    for (long i = 0; i < topK * numberOfQueries; i++) {
+      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
+      float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
+      intermediateResultMap.put(mapping != null ? mapping.get((int) id) : (int) id, dst);
+      count += 1;
+      if (count == topK) {
+        results.add(intermediateResultMap);
+        intermediateResultMap = new LinkedHashMap<Integer, Float>();
+        count = 0;
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
new file mode 100644
index 0000000000..40018be922
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
@@ -0,0 +1,18 @@
+package com.nvidia.cuvs;
+
+public class LibraryException extends RuntimeException {
+
+  private static final long serialVersionUID = -2311171907713571455L;
+
+  public LibraryException(Exception ex) {
+    super(ex);
+  }
+
+  public LibraryException(String message) {
+    super(message);
+  }
+
+  public LibraryException(String message, Exception e) {
+    super(message, e);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/SearchResults.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/SearchResults.java
new file mode 100644
index 0000000000..83e98cb8e9
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/SearchResults.java
@@ -0,0 +1,53 @@
+package com.nvidia.cuvs.common;
+
+import java.lang.foreign.MemoryLayout.PathElement;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.invoke.VarHandle;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+
+public abstract class SearchResults {
+
+  protected final List<Map<Integer, Float>> results;
+  protected final List<Integer> mapping; // TODO: Is this performant in a user application?
+  protected final SequenceLayout neighboursSequenceLayout;
+  protected final SequenceLayout distancesSequenceLayout;
+  protected final MemorySegment neighboursMemorySegment;
+  protected final MemorySegment distancesMemorySegment;
+  protected final int topK;
+  protected final long numberOfQueries;
+  protected final VarHandle neighboursVarHandle;
+  protected final VarHandle distancesVarHandle;
+
+  protected SearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+      long numberOfQueries) {
+    this.topK = topK;
+    this.numberOfQueries = numberOfQueries;
+    this.neighboursSequenceLayout = neighboursSequenceLayout;
+    this.distancesSequenceLayout = distancesSequenceLayout;
+    this.neighboursMemorySegment = neighboursMemorySegment;
+    this.distancesMemorySegment = distancesMemorySegment;
+    this.mapping = mapping;
+    results = new LinkedList<Map<Integer, Float>>();
+    neighboursVarHandle = neighboursSequenceLayout.varHandle(PathElement.sequenceElement());
+    distancesVarHandle = distancesSequenceLayout.varHandle(PathElement.sequenceElement());
+  }
+
+  /**
+   * Reads neighbors and distances {@link MemorySegment} and loads the values
+   * internally
+   */
+  protected abstract void readResultMemorySegments();
+
+  /**
+   * Gets a list results as a map of neighbor IDs to distances.
+   *
+   * @return a list of results for each query as a map of neighbor IDs to distance
+   */
+  public List<Map<Integer, Float>> getResults() {
+    return results;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java
new file mode 100644
index 0000000000..163ef3a846
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java
@@ -0,0 +1,265 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.common;
+
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemoryLayout.PathElement;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.VarHandle;
+import java.util.ArrayList;
+import java.util.List;
+
+import com.nvidia.cuvs.GPUInfo;
+import com.nvidia.cuvs.LibraryException;
+import com.nvidia.cuvs.panama.GpuInfo;
+
+public class Util {
+
+  private static Arena arena = null;
+  private static Linker linker = null;
+  private static SymbolLookup symbolLookup = null;
+  private static MemoryLayout intMemoryLayout;
+  private static MethodHandle getGpuInfoMethodHandle = null;
+  protected static File nativeLibrary;
+
+  static {
+    try {
+      linker = Linker.nativeLinker();
+      arena = Arena.ofShared();
+      nativeLibrary = Util.loadLibraryFromJar("/libcuvs_java.so");
+      symbolLookup = SymbolLookup.libraryLookup(nativeLibrary.getAbsolutePath(), arena);
+      intMemoryLayout = linker.canonicalLayouts().get("int");
+      getGpuInfoMethodHandle = linker.downcallHandle(symbolLookup.find("get_gpu_info").get(),
+          FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
+    } catch (Exception e) {
+      throw new LibraryException("LibCuVS Java Library Not Loaded", e);
+    }
+  }
+
+  /**
+   * Get the list of compatible GPUs based on compute capability >= 7.0 and total
+   * memory >= 8GB
+   *
+   * @return a list of compatible GPUs. See {@link GPUInfo}
+   */
+  public static List<GPUInfo> compatibleGPUs() throws Throwable {
+    return compatibleGPUs(7.0, 8192);
+  }
+
+  /**
+   * Get the list of compatible GPUs based on given compute capability and total
+   * memory
+   *
+   * @param minComputeCapability the minimum compute capability
+   * @param minDeviceMemoryMB    the minimum total available memory in MB
+   * @return a list of compatible GPUs. See {@link GPUInfo}
+   */
+  public static List<GPUInfo> compatibleGPUs(double minComputeCapability, int minDeviceMemoryMB) throws Throwable {
+    List<GPUInfo> compatibleGPUs = new ArrayList<GPUInfo>();
+    double minDeviceMemoryB = Math.pow(2, 20) * minDeviceMemoryMB;
+    for (GPUInfo gpuInfo : availableGPUs()) {
+      if (gpuInfo.getComputeCapability() >= minComputeCapability && gpuInfo.getTotalMemory() >= minDeviceMemoryB) {
+        compatibleGPUs.add(gpuInfo);
+      }
+    }
+    return compatibleGPUs;
+  }
+
+  /**
+   * Gets all the available GPUs
+   *
+   * @return a list of {@link GPUInfo} objects with GPU details
+   */
+  public static List<GPUInfo> availableGPUs() throws Throwable {
+    List<GPUInfo> results = new ArrayList<GPUInfo>();
+    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
+    MemorySegment returnValueMemorySegment = arena.allocate(returnValueMemoryLayout);
+    MemoryLayout numGpuMemoryLayout = intMemoryLayout;
+    MemorySegment numGpuMemorySegment = arena.allocate(numGpuMemoryLayout);
+
+    /*
+     * Setting a value of 1024 because we cannot predict how much memory to allocate
+     * before the function is invoked as cudaGetDeviceCount is inside the
+     * get_gpu_info function.
+     */
+    MemorySegment GpuInfoArrayMemorySegment = GpuInfo.allocateArray(1024, arena);
+    getGpuInfoMethodHandle.invokeExact(returnValueMemorySegment, numGpuMemorySegment, GpuInfoArrayMemorySegment);
+    int numGPUs = numGpuMemorySegment.get(ValueLayout.JAVA_INT, 0);
+    MemoryLayout ml = MemoryLayout.sequenceLayout(numGPUs, GpuInfo.layout());
+    for (int i = 0; i < numGPUs; i++) {
+      VarHandle gpuIdVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("gpu_id"));
+      VarHandle freeMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
+          PathElement.groupElement("free_memory"));
+      VarHandle totalMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
+          PathElement.groupElement("total_memory"));
+      VarHandle ComputeCapabilityVarHandle = ml.varHandle(PathElement.sequenceElement(i),
+          PathElement.groupElement("compute_capability"));
+      StringBuilder gpuName = new StringBuilder();
+      char b = 1;
+      int p = 0;
+      while (b != 0x00) {
+        VarHandle gpuNameVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("name"),
+            PathElement.sequenceElement(p++));
+        b = (char) (byte) gpuNameVarHandle.get(GpuInfoArrayMemorySegment, 0L);
+        gpuName.append(b);
+      }
+      results.add(new GPUInfo((int) gpuIdVarHandle.get(GpuInfoArrayMemorySegment, 0L), gpuName.toString().trim(),
+          (long) freeMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
+          (long) totalMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
+          (float) ComputeCapabilityVarHandle.get(GpuInfoArrayMemorySegment, 0L)));
+    }
+    return results;
+  }
+
+  /**
+   * A utility method for getting an instance of {@link MemorySegment} for a
+   * {@link String}.
+   *
+   * @param str the string for the expected {@link MemorySegment}
+   * @return an instance of {@link MemorySegment}
+   */
+  public static MemorySegment buildMemorySegment(Linker linker, Arena arena, String str) {
+    MemoryLayout charMemoryLayout = linker.canonicalLayouts().get("char");
+    StringBuilder sb = new StringBuilder(str).append('\0');
+    MemoryLayout stringMemoryLayout = MemoryLayout.sequenceLayout(sb.length(), charMemoryLayout);
+    MemorySegment stringMemorySegment = arena.allocate(stringMemoryLayout);
+
+    for (int i = 0; i < sb.length(); i++) {
+      VarHandle varHandle = stringMemoryLayout.varHandle(PathElement.sequenceElement(i));
+      varHandle.set(stringMemorySegment, 0L, (byte) sb.charAt(i));
+    }
+    return stringMemorySegment;
+  }
+
+  /**
+   * A utility method for building a {@link MemorySegment} for a 1D long array.
+   *
+   * @param data The 1D long array for which the {@link MemorySegment} is needed
+   * @return an instance of {@link MemorySegment}
+   */
+  public static MemorySegment buildMemorySegment(Linker linker, Arena arena, long[] data) {
+    int cells = data.length;
+    MemoryLayout longMemoryLayout = linker.canonicalLayouts().get("long");
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(cells, longMemoryLayout);
+    MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+    MemorySegment.copy(data, 0, dataMemorySegment, (ValueLayout) longMemoryLayout, 0, cells);
+    return dataMemorySegment;
+  }
+
+  /**
+   * A utility method for building a {@link MemorySegment} for a 2D float array.
+   *
+   * @param data The 2D float array for which the {@link MemorySegment} is needed
+   * @return an instance of {@link MemorySegment}
+   */
+  public static MemorySegment buildMemorySegment(Linker linker, Arena arena, float[][] data) {
+    long rows = data.length;
+    long cols = rows > 0 ? data[0].length : 0;
+    MemoryLayout floatMemoryLayout = linker.canonicalLayouts().get("float");
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(rows * cols, floatMemoryLayout);
+    MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+    long floatByteSize = floatMemoryLayout.byteSize();
+
+    for (int r = 0; r < rows; r++) {
+      MemorySegment.copy(data[r], 0, dataMemorySegment, (ValueLayout) floatMemoryLayout, (r * cols * floatByteSize),
+          (int) cols);
+    }
+
+    return dataMemorySegment;
+  }
+
+  /**
+   * Load the CuVS .so file from environment variable CUVS_JAVA_SO_PATH. If not
+   * found there, try to load it from the classpath to a temporary file.
+   */
+  public static File loadNativeLibrary() throws IOException {
+    String libraryPathFromEnvironment = System.getenv("CUVS_JAVA_SO_PATH");
+    if (libraryPathFromEnvironment != null) {
+      File file = new File(libraryPathFromEnvironment);
+      if (!file.exists())
+        throw new RuntimeException(
+            "Environment variable CUVS_JAVA_SO_PATH points to non-existent file: " + libraryPathFromEnvironment);
+      return file;
+    }
+    return loadLibraryFromJar("/libcuvs_java.so");
+  }
+
+  private static File loadLibraryFromJar(String path) throws IOException {
+    if (!path.startsWith("/")) {
+      throw new IllegalArgumentException("The path has to be absolute (start with '/').");
+    }
+    // Obtain filename from path
+    String[] parts = path.split("/");
+    String filename = (parts.length > 1) ? parts[parts.length - 1] : null;
+
+    // Split filename to prefix and suffix (extension)
+    String prefix = "";
+    String suffix = null;
+    if (filename != null) {
+      parts = filename.split("\\.", 2);
+      prefix = parts[0];
+      suffix = (parts.length > 1) ? "." + parts[parts.length - 1] : null;
+    }
+    // Prepare temporary file
+    File temp = File.createTempFile(prefix, suffix);
+    InputStream libraryStream = Util.class.getModule().getResourceAsStream(path); // Util.class.getResourceAsStream(path);
+    streamCopy(libraryStream, new FileOutputStream(temp));
+
+    return temp;
+  }
+
+  private static void streamCopy(InputStream is, OutputStream os) throws LibraryException {
+    if (is == null) {
+      throw new LibraryException("CuVS Library Not Found in ClassPath");
+    }
+    byte[] buffer = new byte[1024];
+    int readBytes;
+
+    try {
+      while ((readBytes = is.read(buffer)) != -1) {
+        os.write(buffer, 0, readBytes);
+      }
+    } catch (IOException e) {
+      throw new LibraryException(e);
+    } finally {
+      // If read/write fails, close streams safely before throwing an exception
+      if (os != null)
+        try {
+          os.close();
+        } catch (IOException e) {
+          e.printStackTrace();
+        }
+      if (is != null)
+        try {
+          is.close();
+        } catch (IOException e) {
+          e.printStackTrace();
+        }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/BruteForceH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/BruteForceH.java
new file mode 100644
index 0000000000..16603606bd
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/BruteForceH.java
@@ -0,0 +1,1912 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.ValueLayout.JAVA_BYTE;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.PaddingLayout;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.StructLayout;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.foreign.ValueLayout.OfByte;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.foreign.ValueLayout.OfShort;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.stream.Collectors;
+
+public class BruteForceH {
+
+  BruteForceH() {
+    // Should not be called directly
+  }
+
+  static final Arena LIBRARY_ARENA = Arena.ofAuto();
+  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+  static void traceDowncall(String name, Object... args) {
+    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
+    System.out.printf("%s(%s)\n", name, traceArgs);
+  }
+
+  static MemorySegment findOrThrow(String symbol) {
+    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+  }
+
+  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+    try {
+      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+    } catch (ReflectiveOperationException ex) {
+      throw new AssertionError(ex);
+    }
+  }
+
+  static MemoryLayout align(MemoryLayout layout, long align) {
+    return switch (layout) {
+    case PaddingLayout p -> p;
+    case ValueLayout v -> v.withByteAlignment(align);
+    case GroupLayout g -> {
+      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
+          : MemoryLayout.unionLayout(alignedMembers);
+    }
+    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+    };
+  }
+
+  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
+
+  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+  private static final int DLPACK_VERSION = (int) 80L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_VERSION 80
+   * }
+   */
+  public static int DLPACK_VERSION() {
+    return DLPACK_VERSION;
+  }
+
+  private static final int DLPACK_ABI_VERSION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_ABI_VERSION 1
+   * }
+   */
+  public static int DLPACK_ABI_VERSION() {
+    return DLPACK_ABI_VERSION;
+  }
+
+  private static final int _STDINT_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDINT_H 1
+   * }
+   */
+  public static int _STDINT_H() {
+    return _STDINT_H;
+  }
+
+  private static final int _FEATURES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _FEATURES_H 1
+   * }
+   */
+  public static int _FEATURES_H() {
+    return _FEATURES_H;
+  }
+
+  private static final int _DEFAULT_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
+   * }
+   */
+  public static int _DEFAULT_SOURCE() {
+    return _DEFAULT_SOURCE;
+  }
+
+  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
+   * }
+   */
+  public static int __GLIBC_USE_ISOC2X() {
+    return __GLIBC_USE_ISOC2X;
+  }
+
+  private static final int __USE_ISOC11 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC11 1
+   * }
+   */
+  public static int __USE_ISOC11() {
+    return __USE_ISOC11;
+  }
+
+  private static final int __USE_ISOC99 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC99 1
+   * }
+   */
+  public static int __USE_ISOC99() {
+    return __USE_ISOC99;
+  }
+
+  private static final int __USE_ISOC95 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC95 1
+   * }
+   */
+  public static int __USE_ISOC95() {
+    return __USE_ISOC95;
+  }
+
+  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
+   * }
+   */
+  public static int __USE_POSIX_IMPLICITLY() {
+    return __USE_POSIX_IMPLICITLY;
+  }
+
+  private static final int _POSIX_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_SOURCE 1
+   * }
+   */
+  public static int _POSIX_SOURCE() {
+    return _POSIX_SOURCE;
+  }
+
+  private static final int __USE_POSIX = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX 1
+   * }
+   */
+  public static int __USE_POSIX() {
+    return __USE_POSIX;
+  }
+
+  private static final int __USE_POSIX2 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX2 1
+   * }
+   */
+  public static int __USE_POSIX2() {
+    return __USE_POSIX2;
+  }
+
+  private static final int __USE_POSIX199309 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199309 1
+   * }
+   */
+  public static int __USE_POSIX199309() {
+    return __USE_POSIX199309;
+  }
+
+  private static final int __USE_POSIX199506 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199506 1
+   * }
+   */
+  public static int __USE_POSIX199506() {
+    return __USE_POSIX199506;
+  }
+
+  private static final int __USE_XOPEN2K = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K 1
+   * }
+   */
+  public static int __USE_XOPEN2K() {
+    return __USE_XOPEN2K;
+  }
+
+  private static final int __USE_XOPEN2K8 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
+   * }
+   */
+  public static int __USE_XOPEN2K8() {
+    return __USE_XOPEN2K8;
+  }
+
+  private static final int _ATFILE_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
+   * }
+   */
+  public static int _ATFILE_SOURCE() {
+    return _ATFILE_SOURCE;
+  }
+
+  private static final int __WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE 64
+   * }
+   */
+  public static int __WORDSIZE() {
+    return __WORDSIZE;
+  }
+
+  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
+   * }
+   */
+  public static int __WORDSIZE_TIME64_COMPAT32() {
+    return __WORDSIZE_TIME64_COMPAT32;
+  }
+
+  private static final int __SYSCALL_WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
+   * }
+   */
+  public static int __SYSCALL_WORDSIZE() {
+    return __SYSCALL_WORDSIZE;
+  }
+
+  private static final int __USE_MISC = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_MISC 1
+   * }
+   */
+  public static int __USE_MISC() {
+    return __USE_MISC;
+  }
+
+  private static final int __USE_ATFILE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ATFILE 1
+   * }
+   */
+  public static int __USE_ATFILE() {
+    return __USE_ATFILE;
+  }
+
+  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
+   * }
+   */
+  public static int __USE_FORTIFY_LEVEL() {
+    return __USE_FORTIFY_LEVEL;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_GETS() {
+    return __GLIBC_USE_DEPRECATED_GETS;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_SCANF() {
+    return __GLIBC_USE_DEPRECATED_SCANF;
+  }
+
+  private static final int _STDC_PREDEF_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
+   * }
+   */
+  public static int _STDC_PREDEF_H() {
+    return _STDC_PREDEF_H;
+  }
+
+  private static final int __STDC_IEC_559__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
+   * }
+   */
+  public static int __STDC_IEC_559__() {
+    return __STDC_IEC_559__;
+  }
+
+  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
+   * }
+   */
+  public static int __STDC_IEC_559_COMPLEX__() {
+    return __STDC_IEC_559_COMPLEX__;
+  }
+
+  private static final int __GNU_LIBRARY__ = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
+   * }
+   */
+  public static int __GNU_LIBRARY__() {
+    return __GNU_LIBRARY__;
+  }
+
+  private static final int __GLIBC__ = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC__ 2
+   * }
+   */
+  public static int __GLIBC__() {
+    return __GLIBC__;
+  }
+
+  private static final int __GLIBC_MINOR__ = (int) 35L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
+   * }
+   */
+  public static int __GLIBC_MINOR__() {
+    return __GLIBC_MINOR__;
+  }
+
+  private static final int _SYS_CDEFS_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
+   * }
+   */
+  public static int _SYS_CDEFS_H() {
+    return _SYS_CDEFS_H;
+  }
+
+  private static final int __glibc_c99_flexarr_available = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
+   * }
+   */
+  public static int __glibc_c99_flexarr_available() {
+    return __glibc_c99_flexarr_available;
+  }
+
+  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
+   * }
+   */
+  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
+    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
+  }
+
+  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
+   * }
+   */
+  public static int __HAVE_GENERIC_SELECTION() {
+    return __HAVE_GENERIC_SELECTION;
+  }
+
+  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
+   * }
+   */
+  public static int __GLIBC_USE_LIB_EXT2() {
+    return __GLIBC_USE_LIB_EXT2;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_EXT() {
+    return __GLIBC_USE_IEC_60559_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
+    return __GLIBC_USE_IEC_60559_TYPES_EXT;
+  }
+
+  private static final int _BITS_TYPES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPES_H 1
+   * }
+   */
+  public static int _BITS_TYPES_H() {
+    return _BITS_TYPES_H;
+  }
+
+  private static final int _BITS_TYPESIZES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
+   * }
+   */
+  public static int _BITS_TYPESIZES_H() {
+    return _BITS_TYPESIZES_H;
+  }
+
+  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
+   * }
+   */
+  public static int __OFF_T_MATCHES_OFF64_T() {
+    return __OFF_T_MATCHES_OFF64_T;
+  }
+
+  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
+   * }
+   */
+  public static int __INO_T_MATCHES_INO64_T() {
+    return __INO_T_MATCHES_INO64_T;
+  }
+
+  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
+   * }
+   */
+  public static int __RLIM_T_MATCHES_RLIM64_T() {
+    return __RLIM_T_MATCHES_RLIM64_T;
+  }
+
+  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
+   * }
+   */
+  public static int __STATFS_MATCHES_STATFS64() {
+    return __STATFS_MATCHES_STATFS64;
+  }
+
+  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
+   * }
+   */
+  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
+    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
+  }
+
+  private static final int __FD_SETSIZE = (int) 1024L;
+
+  /**
+   * {@snippet lang = c : * #define __FD_SETSIZE 1024
+   * }
+   */
+  public static int __FD_SETSIZE() {
+    return __FD_SETSIZE;
+  }
+
+  private static final int _BITS_TIME64_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TIME64_H 1
+   * }
+   */
+  public static int _BITS_TIME64_H() {
+    return _BITS_TIME64_H;
+  }
+
+  private static final int _BITS_WCHAR_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
+   * }
+   */
+  public static int _BITS_WCHAR_H() {
+    return _BITS_WCHAR_H;
+  }
+
+  private static final int _BITS_STDINT_INTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_INTN_H() {
+    return _BITS_STDINT_INTN_H;
+  }
+
+  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_UINTN_H() {
+    return _BITS_STDINT_UINTN_H;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef unsigned char __u_char
+   * }
+   */
+  public static final OfByte __u_char = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __u_short
+   * }
+   */
+  public static final OfShort __u_short = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __u_int
+   * }
+   */
+  public static final OfInt __u_int = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_long
+   * }
+   */
+  public static final OfLong __u_long = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char __int8_t
+   * }
+   */
+  public static final OfByte __int8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned char __uint8_t
+   * }
+   */
+  public static final OfByte __uint8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef short __int16_t
+   * }
+   */
+  public static final OfShort __int16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __uint16_t
+   * }
+   */
+  public static final OfShort __uint16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef int __int32_t
+   * }
+   */
+  public static final OfInt __int32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uint32_t
+   * }
+   */
+  public static final OfInt __uint32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __int64_t
+   * }
+   */
+  public static final OfLong __int64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uint64_t
+   * }
+   */
+  public static final OfLong __uint64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int8_t __int_least8_t
+   * }
+   */
+  public static final OfByte __int_least8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
+   * }
+   */
+  public static final OfByte __uint_least8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t __int_least16_t
+   * }
+   */
+  public static final OfShort __int_least16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
+   * }
+   */
+  public static final OfShort __uint_least16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t __int_least32_t
+   * }
+   */
+  public static final OfInt __int_least32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
+   * }
+   */
+  public static final OfInt __uint_least32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t __int_least64_t
+   * }
+   */
+  public static final OfLong __int_least64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
+   * }
+   */
+  public static final OfLong __uint_least64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __quad_t
+   * }
+   */
+  public static final OfLong __quad_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_quad_t
+   * }
+   */
+  public static final OfLong __u_quad_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __intmax_t
+   * }
+   */
+  public static final OfLong __intmax_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uintmax_t
+   * }
+   */
+  public static final OfLong __uintmax_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __dev_t
+   * }
+   */
+  public static final OfLong __dev_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uid_t
+   * }
+   */
+  public static final OfInt __uid_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __gid_t
+   * }
+   */
+  public static final OfInt __gid_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino_t
+   * }
+   */
+  public static final OfLong __ino_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino64_t
+   * }
+   */
+  public static final OfLong __ino64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __mode_t
+   * }
+   */
+  public static final OfInt __mode_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __nlink_t
+   * }
+   */
+  public static final OfLong __nlink_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off_t
+   * }
+   */
+  public static final OfLong __off_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off64_t
+   * }
+   */
+  public static final OfLong __off64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __pid_t
+   * }
+   */
+  public static final OfInt __pid_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __clock_t
+   * }
+   */
+  public static final OfLong __clock_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim_t
+   * }
+   */
+  public static final OfLong __rlim_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim64_t
+   * }
+   */
+  public static final OfLong __rlim64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __id_t
+   * }
+   */
+  public static final OfInt __id_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __time_t
+   * }
+   */
+  public static final OfLong __time_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __useconds_t
+   * }
+   */
+  public static final OfInt __useconds_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds_t
+   * }
+   */
+  public static final OfLong __suseconds_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds64_t
+   * }
+   */
+  public static final OfLong __suseconds64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __daddr_t
+   * }
+   */
+  public static final OfInt __daddr_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __key_t
+   * }
+   */
+  public static final OfInt __key_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __clockid_t
+   * }
+   */
+  public static final OfInt __clockid_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef void *__timer_t
+   * }
+   */
+  public static final AddressLayout __timer_t = BruteForceH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __blksize_t
+   * }
+   */
+  public static final OfLong __blksize_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt_t
+   * }
+   */
+  public static final OfLong __blkcnt_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt64_t
+   * }
+   */
+  public static final OfLong __blkcnt64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
+   * }
+   */
+  public static final OfLong __fsblkcnt_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
+   * }
+   */
+  public static final OfLong __fsblkcnt64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
+   * }
+   */
+  public static final OfLong __fsfilcnt_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
+   * }
+   */
+  public static final OfLong __fsfilcnt64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __fsword_t
+   * }
+   */
+  public static final OfLong __fsword_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __ssize_t
+   * }
+   */
+  public static final OfLong __ssize_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __syscall_slong_t
+   * }
+   */
+  public static final OfLong __syscall_slong_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
+   * }
+   */
+  public static final OfLong __syscall_ulong_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __off64_t __loff_t
+   * }
+   */
+  public static final OfLong __loff_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef char *__caddr_t
+   * }
+   */
+  public static final AddressLayout __caddr_t = BruteForceH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __intptr_t
+   * }
+   */
+  public static final OfLong __intptr_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __socklen_t
+   * }
+   */
+  public static final OfInt __socklen_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __sig_atomic_t
+   * }
+   */
+  public static final OfInt __sig_atomic_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int8_t int8_t
+   * }
+   */
+  public static final OfByte int8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t int16_t
+   * }
+   */
+  public static final OfShort int16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t int32_t
+   * }
+   */
+  public static final OfInt int32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t int64_t
+   * }
+   */
+  public static final OfLong int64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t uint8_t
+   * }
+   */
+  public static final OfByte uint8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t uint16_t
+   * }
+   */
+  public static final OfShort uint16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t uint32_t
+   * }
+   */
+  public static final OfInt uint32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t uint64_t
+   * }
+   */
+  public static final OfLong uint64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
+   * }
+   */
+  public static final OfByte int_least8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
+   * }
+   */
+  public static final OfShort int_least16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
+   * }
+   */
+  public static final OfInt int_least32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
+   * }
+   */
+  public static final OfLong int_least64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
+   * }
+   */
+  public static final OfByte uint_least8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
+   * }
+   */
+  public static final OfShort uint_least16_t = BruteForceH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
+   * }
+   */
+  public static final OfInt uint_least32_t = BruteForceH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
+   * }
+   */
+  public static final OfLong uint_least64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char int_fast8_t
+   * }
+   */
+  public static final OfByte int_fast8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef long int_fast16_t
+   * }
+   */
+  public static final OfLong int_fast16_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast32_t
+   * }
+   */
+  public static final OfLong int_fast32_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast64_t
+   * }
+   */
+  public static final OfLong int_fast64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
+   * }
+   */
+  public static final OfByte uint_fast8_t = BruteForceH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
+   * }
+   */
+  public static final OfLong uint_fast16_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
+   * }
+   */
+  public static final OfLong uint_fast32_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
+   * }
+   */
+  public static final OfLong uint_fast64_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long intptr_t
+   * }
+   */
+  public static final OfLong intptr_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uintptr_t
+   * }
+   */
+  public static final OfLong uintptr_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __intmax_t intmax_t
+   * }
+   */
+  public static final OfLong intmax_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
+   * }
+   */
+  public static final OfLong uintmax_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long ptrdiff_t
+   * }
+   */
+  public static final OfLong ptrdiff_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long size_t
+   * }
+   */
+  public static final OfLong size_t = BruteForceH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int wchar_t
+   * }
+   */
+  public static final OfInt wchar_t = BruteForceH.C_INT;
+  private static final int kDLCPU = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
+   * }
+   */
+  public static int kDLCPU() {
+    return kDLCPU;
+  }
+
+  private static final int kDLCUDA = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
+   * }
+   */
+  public static int kDLCUDA() {
+    return kDLCUDA;
+  }
+
+  private static final int kDLCUDAHost = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
+   * }
+   */
+  public static int kDLCUDAHost() {
+    return kDLCUDAHost;
+  }
+
+  private static final int kDLOpenCL = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
+   * }
+   */
+  public static int kDLOpenCL() {
+    return kDLOpenCL;
+  }
+
+  private static final int kDLVulkan = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
+   * }
+   */
+  public static int kDLVulkan() {
+    return kDLVulkan;
+  }
+
+  private static final int kDLMetal = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
+   * }
+   */
+  public static int kDLMetal() {
+    return kDLMetal;
+  }
+
+  private static final int kDLVPI = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
+   * }
+   */
+  public static int kDLVPI() {
+    return kDLVPI;
+  }
+
+  private static final int kDLROCM = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
+   * }
+   */
+  public static int kDLROCM() {
+    return kDLROCM;
+  }
+
+  private static final int kDLROCMHost = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
+   * }
+   */
+  public static int kDLROCMHost() {
+    return kDLROCMHost;
+  }
+
+  private static final int kDLExtDev = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
+   * }
+   */
+  public static int kDLExtDev() {
+    return kDLExtDev;
+  }
+
+  private static final int kDLCUDAManaged = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
+   * }
+   */
+  public static int kDLCUDAManaged() {
+    return kDLCUDAManaged;
+  }
+
+  private static final int kDLOneAPI = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
+   * }
+   */
+  public static int kDLOneAPI() {
+    return kDLOneAPI;
+  }
+
+  private static final int kDLWebGPU = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
+   * }
+   */
+  public static int kDLWebGPU() {
+    return kDLWebGPU;
+  }
+
+  private static final int kDLHexagon = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
+   * }
+   */
+  public static int kDLHexagon() {
+    return kDLHexagon;
+  }
+
+  private static final int kDLInt = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
+   * }
+   */
+  public static int kDLInt() {
+    return kDLInt;
+  }
+
+  private static final int kDLUInt = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
+   * }
+   */
+  public static int kDLUInt() {
+    return kDLUInt;
+  }
+
+  private static final int kDLFloat = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
+   * }
+   */
+  public static int kDLFloat() {
+    return kDLFloat;
+  }
+
+  private static final int kDLOpaqueHandle = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
+   * }
+   */
+  public static int kDLOpaqueHandle() {
+    return kDLOpaqueHandle;
+  }
+
+  private static final int kDLBfloat = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
+   * }
+   */
+  public static int kDLBfloat() {
+    return kDLBfloat;
+  }
+
+  private static final int kDLComplex = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
+   * }
+   */
+  public static int kDLComplex() {
+    return kDLComplex;
+  }
+
+  private static final int kDLBool = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
+   * }
+   */
+  public static int kDLBool() {
+    return kDLBool;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef cuvsBruteForceIndex *cuvsBruteForceIndex_t
+   * }
+   */
+  public static final AddressLayout cuvsBruteForceIndex_t = BruteForceH.C_POINTER;
+  private static final long _POSIX_C_SOURCE = 200809L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
+   * }
+   */
+  public static long _POSIX_C_SOURCE() {
+    return _POSIX_C_SOURCE;
+  }
+
+  private static final int __TIMESIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __TIMESIZE 64
+   * }
+   */
+  public static int __TIMESIZE() {
+    return __TIMESIZE;
+  }
+
+  private static final long __STDC_IEC_60559_BFP__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_BFP__() {
+    return __STDC_IEC_60559_BFP__;
+  }
+
+  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_COMPLEX__() {
+    return __STDC_IEC_60559_COMPLEX__;
+  }
+
+  private static final long __STDC_ISO_10646__ = 201706L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
+   * }
+   */
+  public static long __STDC_ISO_10646__() {
+    return __STDC_ISO_10646__;
+  }
+
+  private static final int __WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
+   * }
+   */
+  public static int __WCHAR_MAX() {
+    return __WCHAR_MAX;
+  }
+
+  private static final int __WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
+   * }
+   */
+  public static int __WCHAR_MIN() {
+    return __WCHAR_MIN;
+  }
+
+  private static final int INT8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MIN -128
+   * }
+   */
+  public static int INT8_MIN() {
+    return INT8_MIN;
+  }
+
+  private static final int INT16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MIN -32768
+   * }
+   */
+  public static int INT16_MIN() {
+    return INT16_MIN;
+  }
+
+  private static final int INT32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MIN -2147483648
+   * }
+   */
+  public static int INT32_MIN() {
+    return INT32_MIN;
+  }
+
+  private static final long INT64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT64_MIN() {
+    return INT64_MIN;
+  }
+
+  private static final int INT8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MAX 127
+   * }
+   */
+  public static int INT8_MAX() {
+    return INT8_MAX;
+  }
+
+  private static final int INT16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MAX 32767
+   * }
+   */
+  public static int INT16_MAX() {
+    return INT16_MAX;
+  }
+
+  private static final int INT32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MAX 2147483647
+   * }
+   */
+  public static int INT32_MAX() {
+    return INT32_MAX;
+  }
+
+  private static final long INT64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT64_MAX() {
+    return INT64_MAX;
+  }
+
+  private static final int UINT8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT8_MAX 255
+   * }
+   */
+  public static int UINT8_MAX() {
+    return UINT8_MAX;
+  }
+
+  private static final int UINT16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT16_MAX 65535
+   * }
+   */
+  public static int UINT16_MAX() {
+    return UINT16_MAX;
+  }
+
+  private static final int UINT32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT32_MAX 4294967295
+   * }
+   */
+  public static int UINT32_MAX() {
+    return UINT32_MAX;
+  }
+
+  private static final long UINT64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT64_MAX -1
+   * }
+   */
+  public static long UINT64_MAX() {
+    return UINT64_MAX;
+  }
+
+  private static final int INT_LEAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
+   * }
+   */
+  public static int INT_LEAST8_MIN() {
+    return INT_LEAST8_MIN;
+  }
+
+  private static final int INT_LEAST16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
+   * }
+   */
+  public static int INT_LEAST16_MIN() {
+    return INT_LEAST16_MIN;
+  }
+
+  private static final int INT_LEAST32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
+   * }
+   */
+  public static int INT_LEAST32_MIN() {
+    return INT_LEAST32_MIN;
+  }
+
+  private static final long INT_LEAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_LEAST64_MIN() {
+    return INT_LEAST64_MIN;
+  }
+
+  private static final int INT_LEAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
+   * }
+   */
+  public static int INT_LEAST8_MAX() {
+    return INT_LEAST8_MAX;
+  }
+
+  private static final int INT_LEAST16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
+   * }
+   */
+  public static int INT_LEAST16_MAX() {
+    return INT_LEAST16_MAX;
+  }
+
+  private static final int INT_LEAST32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
+   * }
+   */
+  public static int INT_LEAST32_MAX() {
+    return INT_LEAST32_MAX;
+  }
+
+  private static final long INT_LEAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_LEAST64_MAX() {
+    return INT_LEAST64_MAX;
+  }
+
+  private static final int UINT_LEAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
+   * }
+   */
+  public static int UINT_LEAST8_MAX() {
+    return UINT_LEAST8_MAX;
+  }
+
+  private static final int UINT_LEAST16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
+   * }
+   */
+  public static int UINT_LEAST16_MAX() {
+    return UINT_LEAST16_MAX;
+  }
+
+  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
+   * }
+   */
+  public static int UINT_LEAST32_MAX() {
+    return UINT_LEAST32_MAX;
+  }
+
+  private static final long UINT_LEAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
+   * }
+   */
+  public static long UINT_LEAST64_MAX() {
+    return UINT_LEAST64_MAX;
+  }
+
+  private static final int INT_FAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MIN -128
+   * }
+   */
+  public static int INT_FAST8_MIN() {
+    return INT_FAST8_MIN;
+  }
+
+  private static final long INT_FAST16_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST16_MIN() {
+    return INT_FAST16_MIN;
+  }
+
+  private static final long INT_FAST32_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST32_MIN() {
+    return INT_FAST32_MIN;
+  }
+
+  private static final long INT_FAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST64_MIN() {
+    return INT_FAST64_MIN;
+  }
+
+  private static final int INT_FAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MAX 127
+   * }
+   */
+  public static int INT_FAST8_MAX() {
+    return INT_FAST8_MAX;
+  }
+
+  private static final long INT_FAST16_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST16_MAX() {
+    return INT_FAST16_MAX;
+  }
+
+  private static final long INT_FAST32_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST32_MAX() {
+    return INT_FAST32_MAX;
+  }
+
+  private static final long INT_FAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST64_MAX() {
+    return INT_FAST64_MAX;
+  }
+
+  private static final int UINT_FAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
+   * }
+   */
+  public static int UINT_FAST8_MAX() {
+    return UINT_FAST8_MAX;
+  }
+
+  private static final long UINT_FAST16_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
+   * }
+   */
+  public static long UINT_FAST16_MAX() {
+    return UINT_FAST16_MAX;
+  }
+
+  private static final long UINT_FAST32_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
+   * }
+   */
+  public static long UINT_FAST32_MAX() {
+    return UINT_FAST32_MAX;
+  }
+
+  private static final long UINT_FAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
+   * }
+   */
+  public static long UINT_FAST64_MAX() {
+    return UINT_FAST64_MAX;
+  }
+
+  private static final long INTPTR_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
+   * }
+   */
+  public static long INTPTR_MIN() {
+    return INTPTR_MIN;
+  }
+
+  private static final long INTPTR_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
+   * }
+   */
+  public static long INTPTR_MAX() {
+    return INTPTR_MAX;
+  }
+
+  private static final long UINTPTR_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTPTR_MAX -1
+   * }
+   */
+  public static long UINTPTR_MAX() {
+    return UINTPTR_MAX;
+  }
+
+  private static final long INTMAX_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
+   * }
+   */
+  public static long INTMAX_MIN() {
+    return INTMAX_MIN;
+  }
+
+  private static final long INTMAX_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
+   * }
+   */
+  public static long INTMAX_MAX() {
+    return INTMAX_MAX;
+  }
+
+  private static final long UINTMAX_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTMAX_MAX -1
+   * }
+   */
+  public static long UINTMAX_MAX() {
+    return UINTMAX_MAX;
+  }
+
+  private static final long PTRDIFF_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
+   * }
+   */
+  public static long PTRDIFF_MIN() {
+    return PTRDIFF_MIN;
+  }
+
+  private static final long PTRDIFF_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
+   * }
+   */
+  public static long PTRDIFF_MAX() {
+    return PTRDIFF_MAX;
+  }
+
+  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
+   * }
+   */
+  public static int SIG_ATOMIC_MIN() {
+    return SIG_ATOMIC_MIN;
+  }
+
+  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
+   * }
+   */
+  public static int SIG_ATOMIC_MAX() {
+    return SIG_ATOMIC_MAX;
+  }
+
+  private static final long SIZE_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define SIZE_MAX -1
+   * }
+   */
+  public static long SIZE_MAX() {
+    return SIZE_MAX;
+  }
+
+  private static final int WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
+   * }
+   */
+  public static int WCHAR_MIN() {
+    return WCHAR_MIN;
+  }
+
+  private static final int WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
+   * }
+   */
+  public static int WCHAR_MAX() {
+    return WCHAR_MAX;
+  }
+
+  private static final int WINT_MIN = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MIN 0
+   * }
+   */
+  public static int WINT_MIN() {
+    return WINT_MIN;
+  }
+
+  private static final int WINT_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MAX 4294967295
+   * }
+   */
+  public static int WINT_MAX() {
+    return WINT_MAX;
+  }
+
+  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
+
+  /**
+   * {@snippet lang = c : * #define NULL (void*) 0
+   * }
+   */
+  public static MemorySegment NULL() {
+    return NULL;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CagraH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CagraH.java
new file mode 100644
index 0000000000..88ddeb6feb
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CagraH.java
@@ -0,0 +1,2298 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.ValueLayout.JAVA_BYTE;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.PaddingLayout;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.StructLayout;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.foreign.ValueLayout.OfByte;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.foreign.ValueLayout.OfShort;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.stream.Collectors;
+
+public class CagraH {
+
+  CagraH() {
+    // Should not be called directly
+  }
+
+  static final Arena LIBRARY_ARENA = Arena.ofAuto();
+  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+  static void traceDowncall(String name, Object... args) {
+    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
+    System.out.printf("%s(%s)\n", name, traceArgs);
+  }
+
+  static MemorySegment findOrThrow(String symbol) {
+    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+  }
+
+  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+    try {
+      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+    } catch (ReflectiveOperationException ex) {
+      throw new AssertionError(ex);
+    }
+  }
+
+  static MemoryLayout align(MemoryLayout layout, long align) {
+    return switch (layout) {
+    case PaddingLayout p -> p;
+    case ValueLayout v -> v.withByteAlignment(align);
+    case GroupLayout g -> {
+      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
+          : MemoryLayout.unionLayout(alignedMembers);
+    }
+    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+    };
+  }
+
+  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
+
+  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+  private static final int DLPACK_VERSION = (int) 80L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_VERSION 80
+   * }
+   */
+  public static int DLPACK_VERSION() {
+    return DLPACK_VERSION;
+  }
+
+  private static final int DLPACK_ABI_VERSION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_ABI_VERSION 1
+   * }
+   */
+  public static int DLPACK_ABI_VERSION() {
+    return DLPACK_ABI_VERSION;
+  }
+
+  private static final int _STDINT_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDINT_H 1
+   * }
+   */
+  public static int _STDINT_H() {
+    return _STDINT_H;
+  }
+
+  private static final int _FEATURES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _FEATURES_H 1
+   * }
+   */
+  public static int _FEATURES_H() {
+    return _FEATURES_H;
+  }
+
+  private static final int _DEFAULT_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
+   * }
+   */
+  public static int _DEFAULT_SOURCE() {
+    return _DEFAULT_SOURCE;
+  }
+
+  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
+   * }
+   */
+  public static int __GLIBC_USE_ISOC2X() {
+    return __GLIBC_USE_ISOC2X;
+  }
+
+  private static final int __USE_ISOC11 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC11 1
+   * }
+   */
+  public static int __USE_ISOC11() {
+    return __USE_ISOC11;
+  }
+
+  private static final int __USE_ISOC99 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC99 1
+   * }
+   */
+  public static int __USE_ISOC99() {
+    return __USE_ISOC99;
+  }
+
+  private static final int __USE_ISOC95 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC95 1
+   * }
+   */
+  public static int __USE_ISOC95() {
+    return __USE_ISOC95;
+  }
+
+  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
+   * }
+   */
+  public static int __USE_POSIX_IMPLICITLY() {
+    return __USE_POSIX_IMPLICITLY;
+  }
+
+  private static final int _POSIX_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_SOURCE 1
+   * }
+   */
+  public static int _POSIX_SOURCE() {
+    return _POSIX_SOURCE;
+  }
+
+  private static final int __USE_POSIX = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX 1
+   * }
+   */
+  public static int __USE_POSIX() {
+    return __USE_POSIX;
+  }
+
+  private static final int __USE_POSIX2 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX2 1
+   * }
+   */
+  public static int __USE_POSIX2() {
+    return __USE_POSIX2;
+  }
+
+  private static final int __USE_POSIX199309 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199309 1
+   * }
+   */
+  public static int __USE_POSIX199309() {
+    return __USE_POSIX199309;
+  }
+
+  private static final int __USE_POSIX199506 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199506 1
+   * }
+   */
+  public static int __USE_POSIX199506() {
+    return __USE_POSIX199506;
+  }
+
+  private static final int __USE_XOPEN2K = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K 1
+   * }
+   */
+  public static int __USE_XOPEN2K() {
+    return __USE_XOPEN2K;
+  }
+
+  private static final int __USE_XOPEN2K8 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
+   * }
+   */
+  public static int __USE_XOPEN2K8() {
+    return __USE_XOPEN2K8;
+  }
+
+  private static final int _ATFILE_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
+   * }
+   */
+  public static int _ATFILE_SOURCE() {
+    return _ATFILE_SOURCE;
+  }
+
+  private static final int __WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE 64
+   * }
+   */
+  public static int __WORDSIZE() {
+    return __WORDSIZE;
+  }
+
+  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
+   * }
+   */
+  public static int __WORDSIZE_TIME64_COMPAT32() {
+    return __WORDSIZE_TIME64_COMPAT32;
+  }
+
+  private static final int __SYSCALL_WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
+   * }
+   */
+  public static int __SYSCALL_WORDSIZE() {
+    return __SYSCALL_WORDSIZE;
+  }
+
+  private static final int __USE_MISC = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_MISC 1
+   * }
+   */
+  public static int __USE_MISC() {
+    return __USE_MISC;
+  }
+
+  private static final int __USE_ATFILE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ATFILE 1
+   * }
+   */
+  public static int __USE_ATFILE() {
+    return __USE_ATFILE;
+  }
+
+  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
+   * }
+   */
+  public static int __USE_FORTIFY_LEVEL() {
+    return __USE_FORTIFY_LEVEL;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_GETS() {
+    return __GLIBC_USE_DEPRECATED_GETS;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_SCANF() {
+    return __GLIBC_USE_DEPRECATED_SCANF;
+  }
+
+  private static final int _STDC_PREDEF_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
+   * }
+   */
+  public static int _STDC_PREDEF_H() {
+    return _STDC_PREDEF_H;
+  }
+
+  private static final int __STDC_IEC_559__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
+   * }
+   */
+  public static int __STDC_IEC_559__() {
+    return __STDC_IEC_559__;
+  }
+
+  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
+   * }
+   */
+  public static int __STDC_IEC_559_COMPLEX__() {
+    return __STDC_IEC_559_COMPLEX__;
+  }
+
+  private static final int __GNU_LIBRARY__ = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
+   * }
+   */
+  public static int __GNU_LIBRARY__() {
+    return __GNU_LIBRARY__;
+  }
+
+  private static final int __GLIBC__ = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC__ 2
+   * }
+   */
+  public static int __GLIBC__() {
+    return __GLIBC__;
+  }
+
+  private static final int __GLIBC_MINOR__ = (int) 35L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
+   * }
+   */
+  public static int __GLIBC_MINOR__() {
+    return __GLIBC_MINOR__;
+  }
+
+  private static final int _SYS_CDEFS_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
+   * }
+   */
+  public static int _SYS_CDEFS_H() {
+    return _SYS_CDEFS_H;
+  }
+
+  private static final int __glibc_c99_flexarr_available = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
+   * }
+   */
+  public static int __glibc_c99_flexarr_available() {
+    return __glibc_c99_flexarr_available;
+  }
+
+  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
+   * }
+   */
+  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
+    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
+  }
+
+  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
+   * }
+   */
+  public static int __HAVE_GENERIC_SELECTION() {
+    return __HAVE_GENERIC_SELECTION;
+  }
+
+  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
+   * }
+   */
+  public static int __GLIBC_USE_LIB_EXT2() {
+    return __GLIBC_USE_LIB_EXT2;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_EXT() {
+    return __GLIBC_USE_IEC_60559_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
+    return __GLIBC_USE_IEC_60559_TYPES_EXT;
+  }
+
+  private static final int _BITS_TYPES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPES_H 1
+   * }
+   */
+  public static int _BITS_TYPES_H() {
+    return _BITS_TYPES_H;
+  }
+
+  private static final int _BITS_TYPESIZES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
+   * }
+   */
+  public static int _BITS_TYPESIZES_H() {
+    return _BITS_TYPESIZES_H;
+  }
+
+  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
+   * }
+   */
+  public static int __OFF_T_MATCHES_OFF64_T() {
+    return __OFF_T_MATCHES_OFF64_T;
+  }
+
+  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
+   * }
+   */
+  public static int __INO_T_MATCHES_INO64_T() {
+    return __INO_T_MATCHES_INO64_T;
+  }
+
+  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
+   * }
+   */
+  public static int __RLIM_T_MATCHES_RLIM64_T() {
+    return __RLIM_T_MATCHES_RLIM64_T;
+  }
+
+  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
+   * }
+   */
+  public static int __STATFS_MATCHES_STATFS64() {
+    return __STATFS_MATCHES_STATFS64;
+  }
+
+  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
+   * }
+   */
+  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
+    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
+  }
+
+  private static final int __FD_SETSIZE = (int) 1024L;
+
+  /**
+   * {@snippet lang = c : * #define __FD_SETSIZE 1024
+   * }
+   */
+  public static int __FD_SETSIZE() {
+    return __FD_SETSIZE;
+  }
+
+  private static final int _BITS_TIME64_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TIME64_H 1
+   * }
+   */
+  public static int _BITS_TIME64_H() {
+    return _BITS_TIME64_H;
+  }
+
+  private static final int _BITS_WCHAR_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
+   * }
+   */
+  public static int _BITS_WCHAR_H() {
+    return _BITS_WCHAR_H;
+  }
+
+  private static final int _BITS_STDINT_INTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_INTN_H() {
+    return _BITS_STDINT_INTN_H;
+  }
+
+  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_UINTN_H() {
+    return _BITS_STDINT_UINTN_H;
+  }
+
+  private static final int true_ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define true 1
+   * }
+   */
+  public static int true_() {
+    return true_;
+  }
+
+  private static final int false_ = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define false 0
+   * }
+   */
+  public static int false_() {
+    return false_;
+  }
+
+  private static final int __bool_true_false_are_defined = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
+   * }
+   */
+  public static int __bool_true_false_are_defined() {
+    return __bool_true_false_are_defined;
+  }
+
+  private static final int L2Expanded = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
+   * }
+   */
+  public static int L2Expanded() {
+    return L2Expanded;
+  }
+
+  private static final int L2SqrtExpanded = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
+   * }
+   */
+  public static int L2SqrtExpanded() {
+    return L2SqrtExpanded;
+  }
+
+  private static final int CosineExpanded = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
+   * }
+   */
+  public static int CosineExpanded() {
+    return CosineExpanded;
+  }
+
+  private static final int L1 = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L1 = 3
+   * }
+   */
+  public static int L1() {
+    return L1;
+  }
+
+  private static final int L2Unexpanded = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
+   * }
+   */
+  public static int L2Unexpanded() {
+    return L2Unexpanded;
+  }
+
+  private static final int L2SqrtUnexpanded = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
+   * }
+   */
+  public static int L2SqrtUnexpanded() {
+    return L2SqrtUnexpanded;
+  }
+
+  private static final int InnerProduct = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
+   * }
+   */
+  public static int InnerProduct() {
+    return InnerProduct;
+  }
+
+  private static final int Linf = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Linf = 7
+   * }
+   */
+  public static int Linf() {
+    return Linf;
+  }
+
+  private static final int Canberra = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
+   * }
+   */
+  public static int Canberra() {
+    return Canberra;
+  }
+
+  private static final int LpUnexpanded = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
+   * }
+   */
+  public static int LpUnexpanded() {
+    return LpUnexpanded;
+  }
+
+  private static final int CorrelationExpanded = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
+   * }
+   */
+  public static int CorrelationExpanded() {
+    return CorrelationExpanded;
+  }
+
+  private static final int JaccardExpanded = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
+   * }
+   */
+  public static int JaccardExpanded() {
+    return JaccardExpanded;
+  }
+
+  private static final int HellingerExpanded = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
+   * }
+   */
+  public static int HellingerExpanded() {
+    return HellingerExpanded;
+  }
+
+  private static final int Haversine = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
+   * }
+   */
+  public static int Haversine() {
+    return Haversine;
+  }
+
+  private static final int BrayCurtis = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
+   * }
+   */
+  public static int BrayCurtis() {
+    return BrayCurtis;
+  }
+
+  private static final int JensenShannon = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
+   * }
+   */
+  public static int JensenShannon() {
+    return JensenShannon;
+  }
+
+  private static final int HammingUnexpanded = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
+   * }
+   */
+  public static int HammingUnexpanded() {
+    return HammingUnexpanded;
+  }
+
+  private static final int KLDivergence = (int) 17L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
+   * }
+   */
+  public static int KLDivergence() {
+    return KLDivergence;
+  }
+
+  private static final int RusselRaoExpanded = (int) 18L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
+   * }
+   */
+  public static int RusselRaoExpanded() {
+    return RusselRaoExpanded;
+  }
+
+  private static final int DiceExpanded = (int) 19L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
+   * }
+   */
+  public static int DiceExpanded() {
+    return DiceExpanded;
+  }
+
+  private static final int Precomputed = (int) 100L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
+   * }
+   */
+  public static int Precomputed() {
+    return Precomputed;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef unsigned char __u_char
+   * }
+   */
+  public static final OfByte __u_char = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __u_short
+   * }
+   */
+  public static final OfShort __u_short = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __u_int
+   * }
+   */
+  public static final OfInt __u_int = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_long
+   * }
+   */
+  public static final OfLong __u_long = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char __int8_t
+   * }
+   */
+  public static final OfByte __int8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned char __uint8_t
+   * }
+   */
+  public static final OfByte __uint8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef short __int16_t
+   * }
+   */
+  public static final OfShort __int16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __uint16_t
+   * }
+   */
+  public static final OfShort __uint16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef int __int32_t
+   * }
+   */
+  public static final OfInt __int32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uint32_t
+   * }
+   */
+  public static final OfInt __uint32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __int64_t
+   * }
+   */
+  public static final OfLong __int64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uint64_t
+   * }
+   */
+  public static final OfLong __uint64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int8_t __int_least8_t
+   * }
+   */
+  public static final OfByte __int_least8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
+   * }
+   */
+  public static final OfByte __uint_least8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t __int_least16_t
+   * }
+   */
+  public static final OfShort __int_least16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
+   * }
+   */
+  public static final OfShort __uint_least16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t __int_least32_t
+   * }
+   */
+  public static final OfInt __int_least32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
+   * }
+   */
+  public static final OfInt __uint_least32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t __int_least64_t
+   * }
+   */
+  public static final OfLong __int_least64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
+   * }
+   */
+  public static final OfLong __uint_least64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __quad_t
+   * }
+   */
+  public static final OfLong __quad_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_quad_t
+   * }
+   */
+  public static final OfLong __u_quad_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __intmax_t
+   * }
+   */
+  public static final OfLong __intmax_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uintmax_t
+   * }
+   */
+  public static final OfLong __uintmax_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __dev_t
+   * }
+   */
+  public static final OfLong __dev_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uid_t
+   * }
+   */
+  public static final OfInt __uid_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __gid_t
+   * }
+   */
+  public static final OfInt __gid_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino_t
+   * }
+   */
+  public static final OfLong __ino_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino64_t
+   * }
+   */
+  public static final OfLong __ino64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __mode_t
+   * }
+   */
+  public static final OfInt __mode_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __nlink_t
+   * }
+   */
+  public static final OfLong __nlink_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off_t
+   * }
+   */
+  public static final OfLong __off_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off64_t
+   * }
+   */
+  public static final OfLong __off64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __pid_t
+   * }
+   */
+  public static final OfInt __pid_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __clock_t
+   * }
+   */
+  public static final OfLong __clock_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim_t
+   * }
+   */
+  public static final OfLong __rlim_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim64_t
+   * }
+   */
+  public static final OfLong __rlim64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __id_t
+   * }
+   */
+  public static final OfInt __id_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __time_t
+   * }
+   */
+  public static final OfLong __time_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __useconds_t
+   * }
+   */
+  public static final OfInt __useconds_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds_t
+   * }
+   */
+  public static final OfLong __suseconds_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds64_t
+   * }
+   */
+  public static final OfLong __suseconds64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __daddr_t
+   * }
+   */
+  public static final OfInt __daddr_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __key_t
+   * }
+   */
+  public static final OfInt __key_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __clockid_t
+   * }
+   */
+  public static final OfInt __clockid_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef void *__timer_t
+   * }
+   */
+  public static final AddressLayout __timer_t = CagraH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __blksize_t
+   * }
+   */
+  public static final OfLong __blksize_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt_t
+   * }
+   */
+  public static final OfLong __blkcnt_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt64_t
+   * }
+   */
+  public static final OfLong __blkcnt64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
+   * }
+   */
+  public static final OfLong __fsblkcnt_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
+   * }
+   */
+  public static final OfLong __fsblkcnt64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
+   * }
+   */
+  public static final OfLong __fsfilcnt_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
+   * }
+   */
+  public static final OfLong __fsfilcnt64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __fsword_t
+   * }
+   */
+  public static final OfLong __fsword_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __ssize_t
+   * }
+   */
+  public static final OfLong __ssize_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __syscall_slong_t
+   * }
+   */
+  public static final OfLong __syscall_slong_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
+   * }
+   */
+  public static final OfLong __syscall_ulong_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __off64_t __loff_t
+   * }
+   */
+  public static final OfLong __loff_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef char *__caddr_t
+   * }
+   */
+  public static final AddressLayout __caddr_t = CagraH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __intptr_t
+   * }
+   */
+  public static final OfLong __intptr_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __socklen_t
+   * }
+   */
+  public static final OfInt __socklen_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __sig_atomic_t
+   * }
+   */
+  public static final OfInt __sig_atomic_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int8_t int8_t
+   * }
+   */
+  public static final OfByte int8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t int16_t
+   * }
+   */
+  public static final OfShort int16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t int32_t
+   * }
+   */
+  public static final OfInt int32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t int64_t
+   * }
+   */
+  public static final OfLong int64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t uint8_t
+   * }
+   */
+  public static final OfByte uint8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t uint16_t
+   * }
+   */
+  public static final OfShort uint16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t uint32_t
+   * }
+   */
+  public static final OfInt uint32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t uint64_t
+   * }
+   */
+  public static final OfLong uint64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
+   * }
+   */
+  public static final OfByte int_least8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
+   * }
+   */
+  public static final OfShort int_least16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
+   * }
+   */
+  public static final OfInt int_least32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
+   * }
+   */
+  public static final OfLong int_least64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
+   * }
+   */
+  public static final OfByte uint_least8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
+   * }
+   */
+  public static final OfShort uint_least16_t = CagraH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
+   * }
+   */
+  public static final OfInt uint_least32_t = CagraH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
+   * }
+   */
+  public static final OfLong uint_least64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char int_fast8_t
+   * }
+   */
+  public static final OfByte int_fast8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef long int_fast16_t
+   * }
+   */
+  public static final OfLong int_fast16_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast32_t
+   * }
+   */
+  public static final OfLong int_fast32_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast64_t
+   * }
+   */
+  public static final OfLong int_fast64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
+   * }
+   */
+  public static final OfByte uint_fast8_t = CagraH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
+   * }
+   */
+  public static final OfLong uint_fast16_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
+   * }
+   */
+  public static final OfLong uint_fast32_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
+   * }
+   */
+  public static final OfLong uint_fast64_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long intptr_t
+   * }
+   */
+  public static final OfLong intptr_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uintptr_t
+   * }
+   */
+  public static final OfLong uintptr_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __intmax_t intmax_t
+   * }
+   */
+  public static final OfLong intmax_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
+   * }
+   */
+  public static final OfLong uintmax_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long ptrdiff_t
+   * }
+   */
+  public static final OfLong ptrdiff_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long size_t
+   * }
+   */
+  public static final OfLong size_t = CagraH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int wchar_t
+   * }
+   */
+  public static final OfInt wchar_t = CagraH.C_INT;
+  private static final int kDLCPU = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
+   * }
+   */
+  public static int kDLCPU() {
+    return kDLCPU;
+  }
+
+  private static final int kDLCUDA = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
+   * }
+   */
+  public static int kDLCUDA() {
+    return kDLCUDA;
+  }
+
+  private static final int kDLCUDAHost = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
+   * }
+   */
+  public static int kDLCUDAHost() {
+    return kDLCUDAHost;
+  }
+
+  private static final int kDLOpenCL = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
+   * }
+   */
+  public static int kDLOpenCL() {
+    return kDLOpenCL;
+  }
+
+  private static final int kDLVulkan = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
+   * }
+   */
+  public static int kDLVulkan() {
+    return kDLVulkan;
+  }
+
+  private static final int kDLMetal = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
+   * }
+   */
+  public static int kDLMetal() {
+    return kDLMetal;
+  }
+
+  private static final int kDLVPI = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
+   * }
+   */
+  public static int kDLVPI() {
+    return kDLVPI;
+  }
+
+  private static final int kDLROCM = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
+   * }
+   */
+  public static int kDLROCM() {
+    return kDLROCM;
+  }
+
+  private static final int kDLROCMHost = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
+   * }
+   */
+  public static int kDLROCMHost() {
+    return kDLROCMHost;
+  }
+
+  private static final int kDLExtDev = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
+   * }
+   */
+  public static int kDLExtDev() {
+    return kDLExtDev;
+  }
+
+  private static final int kDLCUDAManaged = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
+   * }
+   */
+  public static int kDLCUDAManaged() {
+    return kDLCUDAManaged;
+  }
+
+  private static final int kDLOneAPI = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
+   * }
+   */
+  public static int kDLOneAPI() {
+    return kDLOneAPI;
+  }
+
+  private static final int kDLWebGPU = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
+   * }
+   */
+  public static int kDLWebGPU() {
+    return kDLWebGPU;
+  }
+
+  private static final int kDLHexagon = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
+   * }
+   */
+  public static int kDLHexagon() {
+    return kDLHexagon;
+  }
+
+  private static final int kDLInt = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
+   * }
+   */
+  public static int kDLInt() {
+    return kDLInt;
+  }
+
+  private static final int kDLUInt = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
+   * }
+   */
+  public static int kDLUInt() {
+    return kDLUInt;
+  }
+
+  private static final int kDLFloat = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
+   * }
+   */
+  public static int kDLFloat() {
+    return kDLFloat;
+  }
+
+  private static final int kDLOpaqueHandle = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
+   * }
+   */
+  public static int kDLOpaqueHandle() {
+    return kDLOpaqueHandle;
+  }
+
+  private static final int kDLBfloat = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
+   * }
+   */
+  public static int kDLBfloat() {
+    return kDLBfloat;
+  }
+
+  private static final int kDLComplex = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
+   * }
+   */
+  public static int kDLComplex() {
+    return kDLComplex;
+  }
+
+  private static final int kDLBool = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
+   * }
+   */
+  public static int kDLBool() {
+    return kDLBool;
+  }
+
+  private static final int AUTO_SELECT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.AUTO_SELECT = 0
+   * }
+   */
+  public static int AUTO_SELECT() {
+    return AUTO_SELECT;
+  }
+
+  private static final int IVF_PQ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.IVF_PQ = 1
+   * }
+   */
+  public static int IVF_PQ() {
+    return IVF_PQ;
+  }
+
+  private static final int NN_DESCENT = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.NN_DESCENT = 2
+   * }
+   */
+  public static int NN_DESCENT() {
+    return NN_DESCENT;
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsCagraCompressionParams {
+   *     uint32_t pq_bits;
+   *     uint32_t pq_dim;
+   *     uint32_t vq_n_centers;
+   *     uint32_t kmeans_n_iters;
+   *     double vq_kmeans_trainset_fraction;
+   *     double pq_kmeans_trainset_fraction;
+   * } *cuvsCagraCompressionParams_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraCompressionParams_t = CagraH.C_POINTER;
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsCagraIndexParams {
+   *     cuvsDistanceType metric;
+   *     long intermediate_graph_degree;
+   *     long graph_degree;
+   *     enum cuvsCagraGraphBuildAlgo build_algo;
+   *     long nn_descent_niter;
+   *     cuvsCagraCompressionParams_t compression;
+   * } *cuvsCagraIndexParams_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraIndexParams_t = CagraH.C_POINTER;
+  private static final int SINGLE_CTA = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.SINGLE_CTA = 0
+   * }
+   */
+  public static int SINGLE_CTA() {
+    return SINGLE_CTA;
+  }
+
+  private static final int MULTI_CTA = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_CTA = 1
+   * }
+   */
+  public static int MULTI_CTA() {
+    return MULTI_CTA;
+  }
+
+  private static final int MULTI_KERNEL = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_KERNEL = 2
+   * }
+   */
+  public static int MULTI_KERNEL() {
+    return MULTI_KERNEL;
+  }
+
+  private static final int AUTO = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.AUTO = 3
+   * }
+   */
+  public static int AUTO() {
+    return AUTO;
+  }
+
+  private static final int HASH = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraHashMode.HASH = 0
+   * }
+   */
+  public static int HASH() {
+    return HASH;
+  }
+
+  private static final int SMALL = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraHashMode.SMALL = 1
+   * }
+   */
+  public static int SMALL() {
+    return SMALL;
+  }
+
+  private static final int AUTO_HASH = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraHashMode.AUTO_HASH = 2
+   * }
+   */
+  public static int AUTO_HASH() {
+    return AUTO_HASH;
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsCagraSearchParams {
+   *     long max_queries;
+   *     long itopk_size;
+   *     long max_iterations;
+   *     enum cuvsCagraSearchAlgo algo;
+   *     long team_size;
+   *     long search_width;
+   *     long min_iterations;
+   *     long thread_block_size;
+   *     enum cuvsCagraHashMode hashmap_mode;
+   *     long hashmap_min_bitlen;
+   *     float hashmap_max_fill_rate;
+   *     uint32_t num_random_samplings;
+   *     uint64_t rand_xor_mask;
+   * } *cuvsCagraSearchParams_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraSearchParams_t = CagraH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef cuvsCagraIndex *cuvsCagraIndex_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraIndex_t = CagraH.C_POINTER;
+  private static final long _POSIX_C_SOURCE = 200809L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
+   * }
+   */
+  public static long _POSIX_C_SOURCE() {
+    return _POSIX_C_SOURCE;
+  }
+
+  private static final int __TIMESIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __TIMESIZE 64
+   * }
+   */
+  public static int __TIMESIZE() {
+    return __TIMESIZE;
+  }
+
+  private static final long __STDC_IEC_60559_BFP__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_BFP__() {
+    return __STDC_IEC_60559_BFP__;
+  }
+
+  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_COMPLEX__() {
+    return __STDC_IEC_60559_COMPLEX__;
+  }
+
+  private static final long __STDC_ISO_10646__ = 201706L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
+   * }
+   */
+  public static long __STDC_ISO_10646__() {
+    return __STDC_ISO_10646__;
+  }
+
+  private static final int __WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
+   * }
+   */
+  public static int __WCHAR_MAX() {
+    return __WCHAR_MAX;
+  }
+
+  private static final int __WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
+   * }
+   */
+  public static int __WCHAR_MIN() {
+    return __WCHAR_MIN;
+  }
+
+  private static final int INT8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MIN -128
+   * }
+   */
+  public static int INT8_MIN() {
+    return INT8_MIN;
+  }
+
+  private static final int INT16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MIN -32768
+   * }
+   */
+  public static int INT16_MIN() {
+    return INT16_MIN;
+  }
+
+  private static final int INT32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MIN -2147483648
+   * }
+   */
+  public static int INT32_MIN() {
+    return INT32_MIN;
+  }
+
+  private static final long INT64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT64_MIN() {
+    return INT64_MIN;
+  }
+
+  private static final int INT8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MAX 127
+   * }
+   */
+  public static int INT8_MAX() {
+    return INT8_MAX;
+  }
+
+  private static final int INT16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MAX 32767
+   * }
+   */
+  public static int INT16_MAX() {
+    return INT16_MAX;
+  }
+
+  private static final int INT32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MAX 2147483647
+   * }
+   */
+  public static int INT32_MAX() {
+    return INT32_MAX;
+  }
+
+  private static final long INT64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT64_MAX() {
+    return INT64_MAX;
+  }
+
+  private static final int UINT8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT8_MAX 255
+   * }
+   */
+  public static int UINT8_MAX() {
+    return UINT8_MAX;
+  }
+
+  private static final int UINT16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT16_MAX 65535
+   * }
+   */
+  public static int UINT16_MAX() {
+    return UINT16_MAX;
+  }
+
+  private static final int UINT32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT32_MAX 4294967295
+   * }
+   */
+  public static int UINT32_MAX() {
+    return UINT32_MAX;
+  }
+
+  private static final long UINT64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT64_MAX -1
+   * }
+   */
+  public static long UINT64_MAX() {
+    return UINT64_MAX;
+  }
+
+  private static final int INT_LEAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
+   * }
+   */
+  public static int INT_LEAST8_MIN() {
+    return INT_LEAST8_MIN;
+  }
+
+  private static final int INT_LEAST16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
+   * }
+   */
+  public static int INT_LEAST16_MIN() {
+    return INT_LEAST16_MIN;
+  }
+
+  private static final int INT_LEAST32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
+   * }
+   */
+  public static int INT_LEAST32_MIN() {
+    return INT_LEAST32_MIN;
+  }
+
+  private static final long INT_LEAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_LEAST64_MIN() {
+    return INT_LEAST64_MIN;
+  }
+
+  private static final int INT_LEAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
+   * }
+   */
+  public static int INT_LEAST8_MAX() {
+    return INT_LEAST8_MAX;
+  }
+
+  private static final int INT_LEAST16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
+   * }
+   */
+  public static int INT_LEAST16_MAX() {
+    return INT_LEAST16_MAX;
+  }
+
+  private static final int INT_LEAST32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
+   * }
+   */
+  public static int INT_LEAST32_MAX() {
+    return INT_LEAST32_MAX;
+  }
+
+  private static final long INT_LEAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_LEAST64_MAX() {
+    return INT_LEAST64_MAX;
+  }
+
+  private static final int UINT_LEAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
+   * }
+   */
+  public static int UINT_LEAST8_MAX() {
+    return UINT_LEAST8_MAX;
+  }
+
+  private static final int UINT_LEAST16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
+   * }
+   */
+  public static int UINT_LEAST16_MAX() {
+    return UINT_LEAST16_MAX;
+  }
+
+  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
+   * }
+   */
+  public static int UINT_LEAST32_MAX() {
+    return UINT_LEAST32_MAX;
+  }
+
+  private static final long UINT_LEAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
+   * }
+   */
+  public static long UINT_LEAST64_MAX() {
+    return UINT_LEAST64_MAX;
+  }
+
+  private static final int INT_FAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MIN -128
+   * }
+   */
+  public static int INT_FAST8_MIN() {
+    return INT_FAST8_MIN;
+  }
+
+  private static final long INT_FAST16_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST16_MIN() {
+    return INT_FAST16_MIN;
+  }
+
+  private static final long INT_FAST32_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST32_MIN() {
+    return INT_FAST32_MIN;
+  }
+
+  private static final long INT_FAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST64_MIN() {
+    return INT_FAST64_MIN;
+  }
+
+  private static final int INT_FAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MAX 127
+   * }
+   */
+  public static int INT_FAST8_MAX() {
+    return INT_FAST8_MAX;
+  }
+
+  private static final long INT_FAST16_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST16_MAX() {
+    return INT_FAST16_MAX;
+  }
+
+  private static final long INT_FAST32_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST32_MAX() {
+    return INT_FAST32_MAX;
+  }
+
+  private static final long INT_FAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST64_MAX() {
+    return INT_FAST64_MAX;
+  }
+
+  private static final int UINT_FAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
+   * }
+   */
+  public static int UINT_FAST8_MAX() {
+    return UINT_FAST8_MAX;
+  }
+
+  private static final long UINT_FAST16_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
+   * }
+   */
+  public static long UINT_FAST16_MAX() {
+    return UINT_FAST16_MAX;
+  }
+
+  private static final long UINT_FAST32_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
+   * }
+   */
+  public static long UINT_FAST32_MAX() {
+    return UINT_FAST32_MAX;
+  }
+
+  private static final long UINT_FAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
+   * }
+   */
+  public static long UINT_FAST64_MAX() {
+    return UINT_FAST64_MAX;
+  }
+
+  private static final long INTPTR_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
+   * }
+   */
+  public static long INTPTR_MIN() {
+    return INTPTR_MIN;
+  }
+
+  private static final long INTPTR_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
+   * }
+   */
+  public static long INTPTR_MAX() {
+    return INTPTR_MAX;
+  }
+
+  private static final long UINTPTR_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTPTR_MAX -1
+   * }
+   */
+  public static long UINTPTR_MAX() {
+    return UINTPTR_MAX;
+  }
+
+  private static final long INTMAX_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
+   * }
+   */
+  public static long INTMAX_MIN() {
+    return INTMAX_MIN;
+  }
+
+  private static final long INTMAX_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
+   * }
+   */
+  public static long INTMAX_MAX() {
+    return INTMAX_MAX;
+  }
+
+  private static final long UINTMAX_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTMAX_MAX -1
+   * }
+   */
+  public static long UINTMAX_MAX() {
+    return UINTMAX_MAX;
+  }
+
+  private static final long PTRDIFF_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
+   * }
+   */
+  public static long PTRDIFF_MIN() {
+    return PTRDIFF_MIN;
+  }
+
+  private static final long PTRDIFF_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
+   * }
+   */
+  public static long PTRDIFF_MAX() {
+    return PTRDIFF_MAX;
+  }
+
+  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
+   * }
+   */
+  public static int SIG_ATOMIC_MIN() {
+    return SIG_ATOMIC_MIN;
+  }
+
+  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
+   * }
+   */
+  public static int SIG_ATOMIC_MAX() {
+    return SIG_ATOMIC_MAX;
+  }
+
+  private static final long SIZE_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define SIZE_MAX -1
+   * }
+   */
+  public static long SIZE_MAX() {
+    return SIZE_MAX;
+  }
+
+  private static final int WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
+   * }
+   */
+  public static int WCHAR_MIN() {
+    return WCHAR_MIN;
+  }
+
+  private static final int WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
+   * }
+   */
+  public static int WCHAR_MAX() {
+    return WCHAR_MAX;
+  }
+
+  private static final int WINT_MIN = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MIN 0
+   * }
+   */
+  public static int WINT_MIN() {
+    return WINT_MIN;
+  }
+
+  private static final int WINT_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MAX 4294967295
+   * }
+   */
+  public static int WINT_MAX() {
+    return WINT_MAX;
+  }
+
+  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
+
+  /**
+   * {@snippet lang = c : * #define NULL (void*) 0
+   * }
+   */
+  public static MemorySegment NULL() {
+    return NULL;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSBruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSBruteForceIndex.java
new file mode 100644
index 0000000000..8bf29027ec
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSBruteForceIndex.java
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct {
+ *     uintptr_t addr;
+ *     DLDataType dtype;
+ * }
+ * }
+ */
+public class CuVSBruteForceIndex {
+
+  CuVSBruteForceIndex() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(BruteForceH.C_LONG.withName("addr"),
+      DLDataType.layout().withName("dtype"), MemoryLayout.paddingLayout(4)).withName("$anon$22:9");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfLong addr$LAYOUT = (OfLong) $LAYOUT.select(groupElement("addr"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static final OfLong addr$layout() {
+    return addr$LAYOUT;
+  }
+
+  private static final long addr$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static final long addr$offset() {
+    return addr$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static long addr(MemorySegment struct) {
+    return struct.get(addr$LAYOUT, addr$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static void addr(MemorySegment struct, long fieldValue) {
+    struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
+  }
+
+  private static final GroupLayout dtype$LAYOUT = (GroupLayout) $LAYOUT.select(groupElement("dtype"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static final GroupLayout dtype$layout() {
+    return dtype$LAYOUT;
+  }
+
+  private static final long dtype$OFFSET = 8;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static final long dtype$offset() {
+    return dtype$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static MemorySegment dtype(MemorySegment struct) {
+    return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
+    MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraCompressionParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraCompressionParams.java
new file mode 100644
index 0000000000..1fe8eca769
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraCompressionParams.java
@@ -0,0 +1,352 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfDouble;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct cuvsCagraCompressionParams {
+ *     uint32_t pq_bits;
+ *     uint32_t pq_dim;
+ *     uint32_t vq_n_centers;
+ *     uint32_t kmeans_n_iters;
+ *     double vq_kmeans_trainset_fraction;
+ *     double pq_kmeans_trainset_fraction;
+ * }
+ * }
+ */
+public class CuVSCagraCompressionParams {
+
+  CuVSCagraCompressionParams() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(CagraH.C_INT.withName("pq_bits"),
+      CagraH.C_INT.withName("pq_dim"), CagraH.C_INT.withName("vq_n_centers"),
+      CagraH.C_INT.withName("kmeans_n_iters"), CagraH.C_DOUBLE.withName("vq_kmeans_trainset_fraction"),
+      CagraH.C_DOUBLE.withName("pq_kmeans_trainset_fraction")).withName("cuvsCagraCompressionParams");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfInt pq_bits$LAYOUT = (OfInt) $LAYOUT.select(groupElement("pq_bits"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uint32_t pq_bits
+   * }
+   */
+  public static final OfInt pq_bits$layout() {
+    return pq_bits$LAYOUT;
+  }
+
+  private static final long pq_bits$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uint32_t pq_bits
+   * }
+   */
+  public static final long pq_bits$offset() {
+    return pq_bits$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uint32_t pq_bits
+   * }
+   */
+  public static int pq_bits(MemorySegment struct) {
+    return struct.get(pq_bits$LAYOUT, pq_bits$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uint32_t pq_bits
+   * }
+   */
+  public static void pq_bits(MemorySegment struct, int fieldValue) {
+    struct.set(pq_bits$LAYOUT, pq_bits$OFFSET, fieldValue);
+  }
+
+  private static final OfInt pq_dim$LAYOUT = (OfInt) $LAYOUT.select(groupElement("pq_dim"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uint32_t pq_dim
+   * }
+   */
+  public static final OfInt pq_dim$layout() {
+    return pq_dim$LAYOUT;
+  }
+
+  private static final long pq_dim$OFFSET = 4;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uint32_t pq_dim
+   * }
+   */
+  public static final long pq_dim$offset() {
+    return pq_dim$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uint32_t pq_dim
+   * }
+   */
+  public static int pq_dim(MemorySegment struct) {
+    return struct.get(pq_dim$LAYOUT, pq_dim$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uint32_t pq_dim
+   * }
+   */
+  public static void pq_dim(MemorySegment struct, int fieldValue) {
+    struct.set(pq_dim$LAYOUT, pq_dim$OFFSET, fieldValue);
+  }
+
+  private static final OfInt vq_n_centers$LAYOUT = (OfInt) $LAYOUT.select(groupElement("vq_n_centers"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uint32_t vq_n_centers
+   * }
+   */
+  public static final OfInt vq_n_centers$layout() {
+    return vq_n_centers$LAYOUT;
+  }
+
+  private static final long vq_n_centers$OFFSET = 8;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uint32_t vq_n_centers
+   * }
+   */
+  public static final long vq_n_centers$offset() {
+    return vq_n_centers$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uint32_t vq_n_centers
+   * }
+   */
+  public static int vq_n_centers(MemorySegment struct) {
+    return struct.get(vq_n_centers$LAYOUT, vq_n_centers$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uint32_t vq_n_centers
+   * }
+   */
+  public static void vq_n_centers(MemorySegment struct, int fieldValue) {
+    struct.set(vq_n_centers$LAYOUT, vq_n_centers$OFFSET, fieldValue);
+  }
+
+  private static final OfInt kmeans_n_iters$LAYOUT = (OfInt) $LAYOUT.select(groupElement("kmeans_n_iters"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uint32_t kmeans_n_iters
+   * }
+   */
+  public static final OfInt kmeans_n_iters$layout() {
+    return kmeans_n_iters$LAYOUT;
+  }
+
+  private static final long kmeans_n_iters$OFFSET = 12;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uint32_t kmeans_n_iters
+   * }
+   */
+  public static final long kmeans_n_iters$offset() {
+    return kmeans_n_iters$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uint32_t kmeans_n_iters
+   * }
+   */
+  public static int kmeans_n_iters(MemorySegment struct) {
+    return struct.get(kmeans_n_iters$LAYOUT, kmeans_n_iters$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uint32_t kmeans_n_iters
+   * }
+   */
+  public static void kmeans_n_iters(MemorySegment struct, int fieldValue) {
+    struct.set(kmeans_n_iters$LAYOUT, kmeans_n_iters$OFFSET, fieldValue);
+  }
+
+  private static final OfDouble vq_kmeans_trainset_fraction$LAYOUT = (OfDouble) $LAYOUT
+      .select(groupElement("vq_kmeans_trainset_fraction"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
+   * }
+   */
+  public static final OfDouble vq_kmeans_trainset_fraction$layout() {
+    return vq_kmeans_trainset_fraction$LAYOUT;
+  }
+
+  private static final long vq_kmeans_trainset_fraction$OFFSET = 16;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
+   * }
+   */
+  public static final long vq_kmeans_trainset_fraction$offset() {
+    return vq_kmeans_trainset_fraction$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
+   * }
+   */
+  public static double vq_kmeans_trainset_fraction(MemorySegment struct) {
+    return struct.get(vq_kmeans_trainset_fraction$LAYOUT, vq_kmeans_trainset_fraction$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
+   * }
+   */
+  public static void vq_kmeans_trainset_fraction(MemorySegment struct, double fieldValue) {
+    struct.set(vq_kmeans_trainset_fraction$LAYOUT, vq_kmeans_trainset_fraction$OFFSET, fieldValue);
+  }
+
+  private static final OfDouble pq_kmeans_trainset_fraction$LAYOUT = (OfDouble) $LAYOUT
+      .select(groupElement("pq_kmeans_trainset_fraction"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
+   * }
+   */
+  public static final OfDouble pq_kmeans_trainset_fraction$layout() {
+    return pq_kmeans_trainset_fraction$LAYOUT;
+  }
+
+  private static final long pq_kmeans_trainset_fraction$OFFSET = 24;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
+   * }
+   */
+  public static final long pq_kmeans_trainset_fraction$offset() {
+    return pq_kmeans_trainset_fraction$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
+   * }
+   */
+  public static double pq_kmeans_trainset_fraction(MemorySegment struct) {
+    return struct.get(pq_kmeans_trainset_fraction$LAYOUT, pq_kmeans_trainset_fraction$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
+   * }
+   */
+  public static void pq_kmeans_trainset_fraction(MemorySegment struct, double fieldValue) {
+    struct.set(pq_kmeans_trainset_fraction$LAYOUT, pq_kmeans_trainset_fraction$OFFSET, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndex.java
new file mode 100644
index 0000000000..ec15f6729b
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndex.java
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct {
+ *     uintptr_t addr;
+ *     DLDataType dtype;
+ * }
+ * }
+ */
+public class CuVSCagraIndex {
+
+  CuVSCagraIndex() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(CagraH.C_LONG.withName("addr"),
+      DLDataType.layout().withName("dtype"), MemoryLayout.paddingLayout(4)).withName("$anon$175:9");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfLong addr$LAYOUT = (OfLong) $LAYOUT.select(groupElement("addr"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static final OfLong addr$layout() {
+    return addr$LAYOUT;
+  }
+
+  private static final long addr$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static final long addr$offset() {
+    return addr$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static long addr(MemorySegment struct) {
+    return struct.get(addr$LAYOUT, addr$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static void addr(MemorySegment struct, long fieldValue) {
+    struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
+  }
+
+  private static final GroupLayout dtype$LAYOUT = (GroupLayout) $LAYOUT.select(groupElement("dtype"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static final GroupLayout dtype$layout() {
+    return dtype$LAYOUT;
+  }
+
+  private static final long dtype$OFFSET = 8;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static final long dtype$offset() {
+    return dtype$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static MemorySegment dtype(MemorySegment struct) {
+    return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
+    MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndexParams.java
new file mode 100644
index 0000000000..1823eacfee
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndexParams.java
@@ -0,0 +1,354 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct cuvsCagraIndexParams {
+ *     cuvsDistanceType metric;
+ *     long intermediate_graph_degree;
+ *     long graph_degree;
+ *     enum cuvsCagraGraphBuildAlgo build_algo;
+ *     long nn_descent_niter;
+ *     cuvsCagraCompressionParams_t compression;
+ * }
+ * }
+ */
+public class CuVSCagraIndexParams {
+
+  CuVSCagraIndexParams() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout
+      .structLayout(CagraH.C_INT.withName("metric"), MemoryLayout.paddingLayout(4),
+          CagraH.C_LONG.withName("intermediate_graph_degree"), CagraH.C_LONG.withName("graph_degree"),
+          CagraH.C_INT.withName("build_algo"), MemoryLayout.paddingLayout(4),
+          CagraH.C_LONG.withName("nn_descent_niter"), CagraH.C_POINTER.withName("compression"))
+      .withName("cuvsCagraIndexParams");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfInt metric$LAYOUT = (OfInt) $LAYOUT.select(groupElement("metric"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * cuvsDistanceType metric
+   * }
+   */
+  public static final OfInt metric$layout() {
+    return metric$LAYOUT;
+  }
+
+  private static final long metric$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * cuvsDistanceType metric
+   * }
+   */
+  public static final long metric$offset() {
+    return metric$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * cuvsDistanceType metric
+   * }
+   */
+  public static int metric(MemorySegment struct) {
+    return struct.get(metric$LAYOUT, metric$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * cuvsDistanceType metric
+   * }
+   */
+  public static void metric(MemorySegment struct, int fieldValue) {
+    struct.set(metric$LAYOUT, metric$OFFSET, fieldValue);
+  }
+
+  private static final OfLong intermediate_graph_degree$LAYOUT = (OfLong) $LAYOUT
+      .select(groupElement("intermediate_graph_degree"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long intermediate_graph_degree
+   * }
+   */
+  public static final OfLong intermediate_graph_degree$layout() {
+    return intermediate_graph_degree$LAYOUT;
+  }
+
+  private static final long intermediate_graph_degree$OFFSET = 8;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long intermediate_graph_degree
+   * }
+   */
+  public static final long intermediate_graph_degree$offset() {
+    return intermediate_graph_degree$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long intermediate_graph_degree
+   * }
+   */
+  public static long intermediate_graph_degree(MemorySegment struct) {
+    return struct.get(intermediate_graph_degree$LAYOUT, intermediate_graph_degree$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long intermediate_graph_degree
+   * }
+   */
+  public static void intermediate_graph_degree(MemorySegment struct, long fieldValue) {
+    struct.set(intermediate_graph_degree$LAYOUT, intermediate_graph_degree$OFFSET, fieldValue);
+  }
+
+  private static final OfLong graph_degree$LAYOUT = (OfLong) $LAYOUT.select(groupElement("graph_degree"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long graph_degree
+   * }
+   */
+  public static final OfLong graph_degree$layout() {
+    return graph_degree$LAYOUT;
+  }
+
+  private static final long graph_degree$OFFSET = 16;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long graph_degree
+   * }
+   */
+  public static final long graph_degree$offset() {
+    return graph_degree$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long graph_degree
+   * }
+   */
+  public static long graph_degree(MemorySegment struct) {
+    return struct.get(graph_degree$LAYOUT, graph_degree$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long graph_degree
+   * }
+   */
+  public static void graph_degree(MemorySegment struct, long fieldValue) {
+    struct.set(graph_degree$LAYOUT, graph_degree$OFFSET, fieldValue);
+  }
+
+  private static final OfInt build_algo$LAYOUT = (OfInt) $LAYOUT.select(groupElement("build_algo"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
+   * }
+   */
+  public static final OfInt build_algo$layout() {
+    return build_algo$LAYOUT;
+  }
+
+  private static final long build_algo$OFFSET = 24;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
+   * }
+   */
+  public static final long build_algo$offset() {
+    return build_algo$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
+   * }
+   */
+  public static int build_algo(MemorySegment struct) {
+    return struct.get(build_algo$LAYOUT, build_algo$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
+   * }
+   */
+  public static void build_algo(MemorySegment struct, int fieldValue) {
+    struct.set(build_algo$LAYOUT, build_algo$OFFSET, fieldValue);
+  }
+
+  private static final OfLong nn_descent_niter$LAYOUT = (OfLong) $LAYOUT.select(groupElement("nn_descent_niter"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long nn_descent_niter
+   * }
+   */
+  public static final OfLong nn_descent_niter$layout() {
+    return nn_descent_niter$LAYOUT;
+  }
+
+  private static final long nn_descent_niter$OFFSET = 32;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long nn_descent_niter
+   * }
+   */
+  public static final long nn_descent_niter$offset() {
+    return nn_descent_niter$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long nn_descent_niter
+   * }
+   */
+  public static long nn_descent_niter(MemorySegment struct) {
+    return struct.get(nn_descent_niter$LAYOUT, nn_descent_niter$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long nn_descent_niter
+   * }
+   */
+  public static void nn_descent_niter(MemorySegment struct, long fieldValue) {
+    struct.set(nn_descent_niter$LAYOUT, nn_descent_niter$OFFSET, fieldValue);
+  }
+
+  private static final AddressLayout compression$LAYOUT = (AddressLayout) $LAYOUT.select(groupElement("compression"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
+   * }
+   */
+  public static final AddressLayout compression$layout() {
+    return compression$LAYOUT;
+  }
+
+  private static final long compression$OFFSET = 40;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
+   * }
+   */
+  public static final long compression$offset() {
+    return compression$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
+   * }
+   */
+  public static MemorySegment compression(MemorySegment struct) {
+    return struct.get(compression$LAYOUT, compression$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
+   * }
+   */
+  public static void compression(MemorySegment struct, MemorySegment fieldValue) {
+    struct.set(compression$LAYOUT, compression$OFFSET, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraSearchParams.java
new file mode 100644
index 0000000000..ec59284d9a
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraSearchParams.java
@@ -0,0 +1,644 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfFloat;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct cuvsCagraSearchParams {
+ *     long max_queries;
+ *     long itopk_size;
+ *     long max_iterations;
+ *     enum cuvsCagraSearchAlgo algo;
+ *     long team_size;
+ *     long search_width;
+ *     long min_iterations;
+ *     long thread_block_size;
+ *     enum cuvsCagraHashMode hashmap_mode;
+ *     long hashmap_min_bitlen;
+ *     float hashmap_max_fill_rate;
+ *     uint32_t num_random_samplings;
+ *     uint64_t rand_xor_mask;
+ * }
+ * }
+ */
+public class CuVSCagraSearchParams {
+
+  CuVSCagraSearchParams() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout
+      .structLayout(CagraH.C_LONG.withName("max_queries"), CagraH.C_LONG.withName("itopk_size"),
+          CagraH.C_LONG.withName("max_iterations"), CagraH.C_INT.withName("algo"), MemoryLayout.paddingLayout(4),
+          CagraH.C_LONG.withName("team_size"), CagraH.C_LONG.withName("search_width"),
+          CagraH.C_LONG.withName("min_iterations"), CagraH.C_LONG.withName("thread_block_size"),
+          CagraH.C_INT.withName("hashmap_mode"), MemoryLayout.paddingLayout(4),
+          CagraH.C_LONG.withName("hashmap_min_bitlen"), CagraH.C_FLOAT.withName("hashmap_max_fill_rate"),
+          CagraH.C_INT.withName("num_random_samplings"), CagraH.C_LONG.withName("rand_xor_mask"))
+      .withName("cuvsCagraSearchParams");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfLong max_queries$LAYOUT = (OfLong) $LAYOUT.select(groupElement("max_queries"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long max_queries
+   * }
+   */
+  public static final OfLong max_queries$layout() {
+    return max_queries$LAYOUT;
+  }
+
+  private static final long max_queries$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long max_queries
+   * }
+   */
+  public static final long max_queries$offset() {
+    return max_queries$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long max_queries
+   * }
+   */
+  public static long max_queries(MemorySegment struct) {
+    return struct.get(max_queries$LAYOUT, max_queries$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long max_queries
+   * }
+   */
+  public static void max_queries(MemorySegment struct, long fieldValue) {
+    struct.set(max_queries$LAYOUT, max_queries$OFFSET, fieldValue);
+  }
+
+  private static final OfLong itopk_size$LAYOUT = (OfLong) $LAYOUT.select(groupElement("itopk_size"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long itopk_size
+   * }
+   */
+  public static final OfLong itopk_size$layout() {
+    return itopk_size$LAYOUT;
+  }
+
+  private static final long itopk_size$OFFSET = 8;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long itopk_size
+   * }
+   */
+  public static final long itopk_size$offset() {
+    return itopk_size$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long itopk_size
+   * }
+   */
+  public static long itopk_size(MemorySegment struct) {
+    return struct.get(itopk_size$LAYOUT, itopk_size$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long itopk_size
+   * }
+   */
+  public static void itopk_size(MemorySegment struct, long fieldValue) {
+    struct.set(itopk_size$LAYOUT, itopk_size$OFFSET, fieldValue);
+  }
+
+  private static final OfLong max_iterations$LAYOUT = (OfLong) $LAYOUT.select(groupElement("max_iterations"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long max_iterations
+   * }
+   */
+  public static final OfLong max_iterations$layout() {
+    return max_iterations$LAYOUT;
+  }
+
+  private static final long max_iterations$OFFSET = 16;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long max_iterations
+   * }
+   */
+  public static final long max_iterations$offset() {
+    return max_iterations$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long max_iterations
+   * }
+   */
+  public static long max_iterations(MemorySegment struct) {
+    return struct.get(max_iterations$LAYOUT, max_iterations$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long max_iterations
+   * }
+   */
+  public static void max_iterations(MemorySegment struct, long fieldValue) {
+    struct.set(max_iterations$LAYOUT, max_iterations$OFFSET, fieldValue);
+  }
+
+  private static final OfInt algo$LAYOUT = (OfInt) $LAYOUT.select(groupElement("algo"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
+   * }
+   */
+  public static final OfInt algo$layout() {
+    return algo$LAYOUT;
+  }
+
+  private static final long algo$OFFSET = 24;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
+   * }
+   */
+  public static final long algo$offset() {
+    return algo$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
+   * }
+   */
+  public static int algo(MemorySegment struct) {
+    return struct.get(algo$LAYOUT, algo$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
+   * }
+   */
+  public static void algo(MemorySegment struct, int fieldValue) {
+    struct.set(algo$LAYOUT, algo$OFFSET, fieldValue);
+  }
+
+  private static final OfLong team_size$LAYOUT = (OfLong) $LAYOUT.select(groupElement("team_size"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long team_size
+   * }
+   */
+  public static final OfLong team_size$layout() {
+    return team_size$LAYOUT;
+  }
+
+  private static final long team_size$OFFSET = 32;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long team_size
+   * }
+   */
+  public static final long team_size$offset() {
+    return team_size$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long team_size
+   * }
+   */
+  public static long team_size(MemorySegment struct) {
+    return struct.get(team_size$LAYOUT, team_size$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long team_size
+   * }
+   */
+  public static void team_size(MemorySegment struct, long fieldValue) {
+    struct.set(team_size$LAYOUT, team_size$OFFSET, fieldValue);
+  }
+
+  private static final OfLong search_width$LAYOUT = (OfLong) $LAYOUT.select(groupElement("search_width"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long search_width
+   * }
+   */
+  public static final OfLong search_width$layout() {
+    return search_width$LAYOUT;
+  }
+
+  private static final long search_width$OFFSET = 40;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long search_width
+   * }
+   */
+  public static final long search_width$offset() {
+    return search_width$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long search_width
+   * }
+   */
+  public static long search_width(MemorySegment struct) {
+    return struct.get(search_width$LAYOUT, search_width$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long search_width
+   * }
+   */
+  public static void search_width(MemorySegment struct, long fieldValue) {
+    struct.set(search_width$LAYOUT, search_width$OFFSET, fieldValue);
+  }
+
+  private static final OfLong min_iterations$LAYOUT = (OfLong) $LAYOUT.select(groupElement("min_iterations"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long min_iterations
+   * }
+   */
+  public static final OfLong min_iterations$layout() {
+    return min_iterations$LAYOUT;
+  }
+
+  private static final long min_iterations$OFFSET = 48;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long min_iterations
+   * }
+   */
+  public static final long min_iterations$offset() {
+    return min_iterations$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long min_iterations
+   * }
+   */
+  public static long min_iterations(MemorySegment struct) {
+    return struct.get(min_iterations$LAYOUT, min_iterations$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long min_iterations
+   * }
+   */
+  public static void min_iterations(MemorySegment struct, long fieldValue) {
+    struct.set(min_iterations$LAYOUT, min_iterations$OFFSET, fieldValue);
+  }
+
+  private static final OfLong thread_block_size$LAYOUT = (OfLong) $LAYOUT.select(groupElement("thread_block_size"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long thread_block_size
+   * }
+   */
+  public static final OfLong thread_block_size$layout() {
+    return thread_block_size$LAYOUT;
+  }
+
+  private static final long thread_block_size$OFFSET = 56;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long thread_block_size
+   * }
+   */
+  public static final long thread_block_size$offset() {
+    return thread_block_size$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long thread_block_size
+   * }
+   */
+  public static long thread_block_size(MemorySegment struct) {
+    return struct.get(thread_block_size$LAYOUT, thread_block_size$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long thread_block_size
+   * }
+   */
+  public static void thread_block_size(MemorySegment struct, long fieldValue) {
+    struct.set(thread_block_size$LAYOUT, thread_block_size$OFFSET, fieldValue);
+  }
+
+  private static final OfInt hashmap_mode$LAYOUT = (OfInt) $LAYOUT.select(groupElement("hashmap_mode"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
+   * }
+   */
+  public static final OfInt hashmap_mode$layout() {
+    return hashmap_mode$LAYOUT;
+  }
+
+  private static final long hashmap_mode$OFFSET = 64;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
+   * }
+   */
+  public static final long hashmap_mode$offset() {
+    return hashmap_mode$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
+   * }
+   */
+  public static int hashmap_mode(MemorySegment struct) {
+    return struct.get(hashmap_mode$LAYOUT, hashmap_mode$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
+   * }
+   */
+  public static void hashmap_mode(MemorySegment struct, int fieldValue) {
+    struct.set(hashmap_mode$LAYOUT, hashmap_mode$OFFSET, fieldValue);
+  }
+
+  private static final OfLong hashmap_min_bitlen$LAYOUT = (OfLong) $LAYOUT.select(groupElement("hashmap_min_bitlen"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long hashmap_min_bitlen
+   * }
+   */
+  public static final OfLong hashmap_min_bitlen$layout() {
+    return hashmap_min_bitlen$LAYOUT;
+  }
+
+  private static final long hashmap_min_bitlen$OFFSET = 72;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long hashmap_min_bitlen
+   * }
+   */
+  public static final long hashmap_min_bitlen$offset() {
+    return hashmap_min_bitlen$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long hashmap_min_bitlen
+   * }
+   */
+  public static long hashmap_min_bitlen(MemorySegment struct) {
+    return struct.get(hashmap_min_bitlen$LAYOUT, hashmap_min_bitlen$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long hashmap_min_bitlen
+   * }
+   */
+  public static void hashmap_min_bitlen(MemorySegment struct, long fieldValue) {
+    struct.set(hashmap_min_bitlen$LAYOUT, hashmap_min_bitlen$OFFSET, fieldValue);
+  }
+
+  private static final OfFloat hashmap_max_fill_rate$LAYOUT = (OfFloat) $LAYOUT
+      .select(groupElement("hashmap_max_fill_rate"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * float hashmap_max_fill_rate
+   * }
+   */
+  public static final OfFloat hashmap_max_fill_rate$layout() {
+    return hashmap_max_fill_rate$LAYOUT;
+  }
+
+  private static final long hashmap_max_fill_rate$OFFSET = 80;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * float hashmap_max_fill_rate
+   * }
+   */
+  public static final long hashmap_max_fill_rate$offset() {
+    return hashmap_max_fill_rate$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * float hashmap_max_fill_rate
+   * }
+   */
+  public static float hashmap_max_fill_rate(MemorySegment struct) {
+    return struct.get(hashmap_max_fill_rate$LAYOUT, hashmap_max_fill_rate$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * float hashmap_max_fill_rate
+   * }
+   */
+  public static void hashmap_max_fill_rate(MemorySegment struct, float fieldValue) {
+    struct.set(hashmap_max_fill_rate$LAYOUT, hashmap_max_fill_rate$OFFSET, fieldValue);
+  }
+
+  private static final OfInt num_random_samplings$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_random_samplings"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uint32_t num_random_samplings
+   * }
+   */
+  public static final OfInt num_random_samplings$layout() {
+    return num_random_samplings$LAYOUT;
+  }
+
+  private static final long num_random_samplings$OFFSET = 84;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uint32_t num_random_samplings
+   * }
+   */
+  public static final long num_random_samplings$offset() {
+    return num_random_samplings$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uint32_t num_random_samplings
+   * }
+   */
+  public static int num_random_samplings(MemorySegment struct) {
+    return struct.get(num_random_samplings$LAYOUT, num_random_samplings$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uint32_t num_random_samplings
+   * }
+   */
+  public static void num_random_samplings(MemorySegment struct, int fieldValue) {
+    struct.set(num_random_samplings$LAYOUT, num_random_samplings$OFFSET, fieldValue);
+  }
+
+  private static final OfLong rand_xor_mask$LAYOUT = (OfLong) $LAYOUT.select(groupElement("rand_xor_mask"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uint64_t rand_xor_mask
+   * }
+   */
+  public static final OfLong rand_xor_mask$layout() {
+    return rand_xor_mask$LAYOUT;
+  }
+
+  private static final long rand_xor_mask$OFFSET = 88;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uint64_t rand_xor_mask
+   * }
+   */
+  public static final long rand_xor_mask$offset() {
+    return rand_xor_mask$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uint64_t rand_xor_mask
+   * }
+   */
+  public static long rand_xor_mask(MemorySegment struct) {
+    return struct.get(rand_xor_mask$LAYOUT, rand_xor_mask$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uint64_t rand_xor_mask
+   * }
+   */
+  public static void rand_xor_mask(MemorySegment struct, long fieldValue) {
+    struct.set(rand_xor_mask$LAYOUT, rand_xor_mask$OFFSET, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSFilter.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSFilter.java
new file mode 100644
index 0000000000..9385660dc5
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSFilter.java
@@ -0,0 +1,188 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct {
+ *     uintptr_t addr;
+ *     enum cuvsFilterType type;
+ * }
+ * }
+ */
+public class CuVSFilter {
+
+    CuVSFilter() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        BruteForceH.C_LONG.withName("addr"),
+        BruteForceH.C_INT.withName("type"),
+        MemoryLayout.paddingLayout(4)
+    ).withName("$anon$50:9");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final OfLong addr$LAYOUT = (OfLong)$LAYOUT.select(groupElement("addr"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uintptr_t addr
+     * }
+     */
+    public static final OfLong addr$layout() {
+        return addr$LAYOUT;
+    }
+
+    private static final long addr$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uintptr_t addr
+     * }
+     */
+    public static final long addr$offset() {
+        return addr$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uintptr_t addr
+     * }
+     */
+    public static long addr(MemorySegment struct) {
+        return struct.get(addr$LAYOUT, addr$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uintptr_t addr
+     * }
+     */
+    public static void addr(MemorySegment struct, long fieldValue) {
+        struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
+    }
+
+    private static final OfInt type$LAYOUT = (OfInt)$LAYOUT.select(groupElement("type"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * enum cuvsFilterType type
+     * }
+     */
+    public static final OfInt type$layout() {
+        return type$LAYOUT;
+    }
+
+    private static final long type$OFFSET = 8;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * enum cuvsFilterType type
+     * }
+     */
+    public static final long type$offset() {
+        return type$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * enum cuvsFilterType type
+     * }
+     */
+    public static int type(MemorySegment struct) {
+        return struct.get(type$LAYOUT, type$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * enum cuvsFilterType type
+     * }
+     */
+    public static void type(MemorySegment struct, int fieldValue) {
+        struct.set(type$LAYOUT, type$OFFSET, fieldValue);
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswExtendParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswExtendParams.java
new file mode 100644
index 0000000000..8d750d02e7
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswExtendParams.java
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct cuvsHnswExtendParams {
+ *     int num_threads;
+ * }
+ * }
+ */
+public class CuVSHnswExtendParams {
+
+  CuVSHnswExtendParams() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(HnswH.C_INT.withName("num_threads"))
+      .withName("cuvsHnswExtendParams");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfInt num_threads$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_threads"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static final OfInt num_threads$layout() {
+    return num_threads$LAYOUT;
+  }
+
+  private static final long num_threads$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static final long num_threads$offset() {
+    return num_threads$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static int num_threads(MemorySegment struct) {
+    return struct.get(num_threads$LAYOUT, num_threads$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static void num_threads(MemorySegment struct, int fieldValue) {
+    struct.set(num_threads$LAYOUT, num_threads$OFFSET, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndex.java
new file mode 100644
index 0000000000..1f879462be
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndex.java
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct {
+ *     uintptr_t addr;
+ *     DLDataType dtype;
+ * }
+ * }
+ */
+public class CuVSHnswIndex {
+
+  CuVSHnswIndex() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(HnswH.C_LONG.withName("addr"),
+      DLDataType.layout().withName("dtype"), MemoryLayout.paddingLayout(4)).withName("$anon$66:9");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfLong addr$LAYOUT = (OfLong) $LAYOUT.select(groupElement("addr"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static final OfLong addr$layout() {
+    return addr$LAYOUT;
+  }
+
+  private static final long addr$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static final long addr$offset() {
+    return addr$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static long addr(MemorySegment struct) {
+    return struct.get(addr$LAYOUT, addr$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * uintptr_t addr
+   * }
+   */
+  public static void addr(MemorySegment struct, long fieldValue) {
+    struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
+  }
+
+  private static final GroupLayout dtype$LAYOUT = (GroupLayout) $LAYOUT.select(groupElement("dtype"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static final GroupLayout dtype$layout() {
+    return dtype$LAYOUT;
+  }
+
+  private static final long dtype$OFFSET = 8;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static final long dtype$offset() {
+    return dtype$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static MemorySegment dtype(MemorySegment struct) {
+    return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * DLDataType dtype
+   * }
+   */
+  public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
+    MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndexParams.java
new file mode 100644
index 0000000000..3e6e45a099
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndexParams.java
@@ -0,0 +1,224 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct cuvsHnswIndexParams {
+ *     cuvsHnswHierarchy hierarchy;
+ *     int ef_construction;
+ *     int num_threads;
+ * }
+ * }
+ */
+public class CuVSHnswIndexParams {
+
+  CuVSHnswIndexParams() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(HnswH.C_INT.withName("hierarchy"),
+      HnswH.C_INT.withName("ef_construction"), HnswH.C_INT.withName("num_threads")).withName("cuvsHnswIndexParams");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfInt hierarchy$LAYOUT = (OfInt) $LAYOUT.select(groupElement("hierarchy"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
+   * }
+   */
+  public static final OfInt hierarchy$layout() {
+    return hierarchy$LAYOUT;
+  }
+
+  private static final long hierarchy$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
+   * }
+   */
+  public static final long hierarchy$offset() {
+    return hierarchy$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
+   * }
+   */
+  public static int hierarchy(MemorySegment struct) {
+    return struct.get(hierarchy$LAYOUT, hierarchy$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
+   * }
+   */
+  public static void hierarchy(MemorySegment struct, int fieldValue) {
+    struct.set(hierarchy$LAYOUT, hierarchy$OFFSET, fieldValue);
+  }
+
+  private static final OfInt ef_construction$LAYOUT = (OfInt) $LAYOUT.select(groupElement("ef_construction"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * int ef_construction
+   * }
+   */
+  public static final OfInt ef_construction$layout() {
+    return ef_construction$LAYOUT;
+  }
+
+  private static final long ef_construction$OFFSET = 4;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * int ef_construction
+   * }
+   */
+  public static final long ef_construction$offset() {
+    return ef_construction$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * int ef_construction
+   * }
+   */
+  public static int ef_construction(MemorySegment struct) {
+    return struct.get(ef_construction$LAYOUT, ef_construction$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * int ef_construction
+   * }
+   */
+  public static void ef_construction(MemorySegment struct, int fieldValue) {
+    struct.set(ef_construction$LAYOUT, ef_construction$OFFSET, fieldValue);
+  }
+
+  private static final OfInt num_threads$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_threads"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static final OfInt num_threads$layout() {
+    return num_threads$LAYOUT;
+  }
+
+  private static final long num_threads$OFFSET = 8;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static final long num_threads$offset() {
+    return num_threads$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static int num_threads(MemorySegment struct) {
+    return struct.get(num_threads$LAYOUT, num_threads$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * int num_threads
+   * }
+   */
+  public static void num_threads(MemorySegment struct, int fieldValue) {
+    struct.set(num_threads$LAYOUT, num_threads$OFFSET, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswSearchParams.java
new file mode 100644
index 0000000000..4a5941a3cf
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswSearchParams.java
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct cuvsHnswSearchParams {
+ *     int32_t ef;
+ *     int32_t num_threads;
+ * }
+ * }
+ */
+public class CuVSHnswSearchParams {
+
+  CuVSHnswSearchParams() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout
+      .structLayout(HnswH.C_INT.withName("ef"), HnswH.C_INT.withName("num_threads")).withName("cuvsHnswSearchParams");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfInt ef$LAYOUT = (OfInt) $LAYOUT.select(groupElement("ef"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * int32_t ef
+   * }
+   */
+  public static final OfInt ef$layout() {
+    return ef$LAYOUT;
+  }
+
+  private static final long ef$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * int32_t ef
+   * }
+   */
+  public static final long ef$offset() {
+    return ef$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * int32_t ef
+   * }
+   */
+  public static int ef(MemorySegment struct) {
+    return struct.get(ef$LAYOUT, ef$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * int32_t ef
+   * }
+   */
+  public static void ef(MemorySegment struct, int fieldValue) {
+    struct.set(ef$LAYOUT, ef$OFFSET, fieldValue);
+  }
+
+  private static final OfInt num_threads$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_threads"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * int32_t num_threads
+   * }
+   */
+  public static final OfInt num_threads$layout() {
+    return num_threads$LAYOUT;
+  }
+
+  private static final long num_threads$OFFSET = 4;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * int32_t num_threads
+   * }
+   */
+  public static final long num_threads$offset() {
+    return num_threads$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * int32_t num_threads
+   * }
+   */
+  public static int num_threads(MemorySegment struct) {
+    return struct.get(num_threads$LAYOUT, num_threads$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * int32_t num_threads
+   * }
+   */
+  public static void num_threads(MemorySegment struct, int fieldValue) {
+    struct.set(num_threads$LAYOUT, num_threads$OFFSET, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDataType.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDataType.java
new file mode 100644
index 0000000000..c7c588676f
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDataType.java
@@ -0,0 +1,233 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfByte;
+import java.lang.foreign.ValueLayout.OfShort;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct {
+ *     uint8_t code;
+ *     uint8_t bits;
+ *     uint16_t lanes;
+ * }
+ * }
+ */
+public class DLDataType {
+
+    DLDataType() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        DlpackH.C_CHAR.withName("code"),
+        DlpackH.C_CHAR.withName("bits"),
+        DlpackH.C_SHORT.withName("lanes")
+    ).withName("$anon$174:9");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final OfByte code$LAYOUT = (OfByte)$LAYOUT.select(groupElement("code"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uint8_t code
+     * }
+     */
+    public static final OfByte code$layout() {
+        return code$LAYOUT;
+    }
+
+    private static final long code$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uint8_t code
+     * }
+     */
+    public static final long code$offset() {
+        return code$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uint8_t code
+     * }
+     */
+    public static byte code(MemorySegment struct) {
+        return struct.get(code$LAYOUT, code$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uint8_t code
+     * }
+     */
+    public static void code(MemorySegment struct, byte fieldValue) {
+        struct.set(code$LAYOUT, code$OFFSET, fieldValue);
+    }
+
+    private static final OfByte bits$LAYOUT = (OfByte)$LAYOUT.select(groupElement("bits"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uint8_t bits
+     * }
+     */
+    public static final OfByte bits$layout() {
+        return bits$LAYOUT;
+    }
+
+    private static final long bits$OFFSET = 1;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uint8_t bits
+     * }
+     */
+    public static final long bits$offset() {
+        return bits$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uint8_t bits
+     * }
+     */
+    public static byte bits(MemorySegment struct) {
+        return struct.get(bits$LAYOUT, bits$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uint8_t bits
+     * }
+     */
+    public static void bits(MemorySegment struct, byte fieldValue) {
+        struct.set(bits$LAYOUT, bits$OFFSET, fieldValue);
+    }
+
+    private static final OfShort lanes$LAYOUT = (OfShort)$LAYOUT.select(groupElement("lanes"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uint16_t lanes
+     * }
+     */
+    public static final OfShort lanes$layout() {
+        return lanes$LAYOUT;
+    }
+
+    private static final long lanes$OFFSET = 2;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uint16_t lanes
+     * }
+     */
+    public static final long lanes$offset() {
+        return lanes$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uint16_t lanes
+     * }
+     */
+    public static short lanes(MemorySegment struct) {
+        return struct.get(lanes$LAYOUT, lanes$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uint16_t lanes
+     * }
+     */
+    public static void lanes(MemorySegment struct, short fieldValue) {
+        struct.set(lanes$LAYOUT, lanes$OFFSET, fieldValue);
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDevice.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDevice.java
new file mode 100644
index 0000000000..a0d5d89ea4
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDevice.java
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct {
+ *     DLDeviceType device_type;
+ *     int32_t device_id;
+ * }
+ * }
+ */
+public class DLDevice {
+
+    DLDevice() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        DlpackH.C_INT.withName("device_type"),
+        DlpackH.C_INT.withName("device_id")
+    ).withName("$anon$126:9");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final OfInt device_type$LAYOUT = (OfInt)$LAYOUT.select(groupElement("device_type"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * DLDeviceType device_type
+     * }
+     */
+    public static final OfInt device_type$layout() {
+        return device_type$LAYOUT;
+    }
+
+    private static final long device_type$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * DLDeviceType device_type
+     * }
+     */
+    public static final long device_type$offset() {
+        return device_type$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * DLDeviceType device_type
+     * }
+     */
+    public static int device_type(MemorySegment struct) {
+        return struct.get(device_type$LAYOUT, device_type$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * DLDeviceType device_type
+     * }
+     */
+    public static void device_type(MemorySegment struct, int fieldValue) {
+        struct.set(device_type$LAYOUT, device_type$OFFSET, fieldValue);
+    }
+
+    private static final OfInt device_id$LAYOUT = (OfInt)$LAYOUT.select(groupElement("device_id"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * int32_t device_id
+     * }
+     */
+    public static final OfInt device_id$layout() {
+        return device_id$LAYOUT;
+    }
+
+    private static final long device_id$OFFSET = 4;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * int32_t device_id
+     * }
+     */
+    public static final long device_id$offset() {
+        return device_id$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * int32_t device_id
+     * }
+     */
+    public static int device_id(MemorySegment struct) {
+        return struct.get(device_id$LAYOUT, device_id$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * int32_t device_id
+     * }
+     */
+    public static void device_id(MemorySegment struct, int fieldValue) {
+        struct.set(device_id$LAYOUT, device_id$OFFSET, fieldValue);
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensor.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensor.java
new file mode 100644
index 0000000000..71bba9fe5f
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensor.java
@@ -0,0 +1,288 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.invoke.MethodHandle;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct DLManagedTensor {
+ *     DLTensor dl_tensor;
+ *     void *manager_ctx;
+ *     void (*deleter)(struct DLManagedTensor *);
+ * }
+ * }
+ */
+public class DLManagedTensor {
+
+    DLManagedTensor() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        DLTensor.layout().withName("dl_tensor"),
+        DlpackH.C_POINTER.withName("manager_ctx"),
+        DlpackH.C_POINTER.withName("deleter")
+    ).withName("DLManagedTensor");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final GroupLayout dl_tensor$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("dl_tensor"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static final GroupLayout dl_tensor$layout() {
+        return dl_tensor$LAYOUT;
+    }
+
+    private static final long dl_tensor$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static final long dl_tensor$offset() {
+        return dl_tensor$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static MemorySegment dl_tensor(MemorySegment struct) {
+        return struct.asSlice(dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static void dl_tensor(MemorySegment struct, MemorySegment fieldValue) {
+        MemorySegment.copy(fieldValue, 0L, struct, dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
+    }
+
+    private static final AddressLayout manager_ctx$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("manager_ctx"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static final AddressLayout manager_ctx$layout() {
+        return manager_ctx$LAYOUT;
+    }
+
+    private static final long manager_ctx$OFFSET = 48;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static final long manager_ctx$offset() {
+        return manager_ctx$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static MemorySegment manager_ctx(MemorySegment struct) {
+        return struct.get(manager_ctx$LAYOUT, manager_ctx$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static void manager_ctx(MemorySegment struct, MemorySegment fieldValue) {
+        struct.set(manager_ctx$LAYOUT, manager_ctx$OFFSET, fieldValue);
+    }
+
+    /**
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensor *)
+     * }
+     */
+    public static class deleter {
+
+        deleter() {
+            // Should not be called directly
+        }
+
+        /**
+         * The function pointer signature, expressed as a functional interface
+         */
+        public interface Function {
+            void apply(MemorySegment _x0);
+        }
+
+        private static final FunctionDescriptor $DESC = FunctionDescriptor.ofVoid(
+            DlpackH.C_POINTER
+        );
+
+        /**
+         * The descriptor of this function pointer
+         */
+        public static FunctionDescriptor descriptor() {
+            return $DESC;
+        }
+
+        private static final MethodHandle UP$MH = DlpackH.upcallHandle(deleter.Function.class, "apply", $DESC);
+
+        /**
+         * Allocates a new upcall stub, whose implementation is defined by {@code fi}.
+         * The lifetime of the returned segment is managed by {@code arena}
+         */
+        public static MemorySegment allocate(deleter.Function fi, Arena arena) {
+            return Linker.nativeLinker().upcallStub(UP$MH.bindTo(fi), $DESC, arena);
+        }
+
+        private static final MethodHandle DOWN$MH = Linker.nativeLinker().downcallHandle($DESC);
+
+        /**
+         * Invoke the upcall stub {@code funcPtr}, with given parameters
+         */
+        public static void invoke(MemorySegment funcPtr,MemorySegment _x0) {
+            try {
+                 DOWN$MH.invokeExact(funcPtr, _x0);
+            } catch (Throwable ex$) {
+                throw new AssertionError("should not reach here", ex$);
+            }
+        }
+    }
+
+    private static final AddressLayout deleter$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("deleter"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensor *)
+     * }
+     */
+    public static final AddressLayout deleter$layout() {
+        return deleter$LAYOUT;
+    }
+
+    private static final long deleter$OFFSET = 56;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensor *)
+     * }
+     */
+    public static final long deleter$offset() {
+        return deleter$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensor *)
+     * }
+     */
+    public static MemorySegment deleter(MemorySegment struct) {
+        return struct.get(deleter$LAYOUT, deleter$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensor *)
+     * }
+     */
+    public static void deleter(MemorySegment struct, MemorySegment fieldValue) {
+        struct.set(deleter$LAYOUT, deleter$OFFSET, fieldValue);
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensorVersioned.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensorVersioned.java
new file mode 100644
index 0000000000..efdcf90439
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensorVersioned.java
@@ -0,0 +1,381 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.invoke.MethodHandle;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct DLManagedTensorVersioned {
+ *     DLPackVersion version;
+ *     void *manager_ctx;
+ *     void (*deleter)(struct DLManagedTensorVersioned *);
+ *     uint64_t flags;
+ *     DLTensor dl_tensor;
+ * }
+ * }
+ */
+public class DLManagedTensorVersioned {
+
+    DLManagedTensorVersioned() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        DLPackVersion.layout().withName("version"),
+        DlpackH.C_POINTER.withName("manager_ctx"),
+        DlpackH.C_POINTER.withName("deleter"),
+        DlpackH.C_LONG.withName("flags"),
+        DLTensor.layout().withName("dl_tensor")
+    ).withName("DLManagedTensorVersioned");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final GroupLayout version$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("version"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * DLPackVersion version
+     * }
+     */
+    public static final GroupLayout version$layout() {
+        return version$LAYOUT;
+    }
+
+    private static final long version$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * DLPackVersion version
+     * }
+     */
+    public static final long version$offset() {
+        return version$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * DLPackVersion version
+     * }
+     */
+    public static MemorySegment version(MemorySegment struct) {
+        return struct.asSlice(version$OFFSET, version$LAYOUT.byteSize());
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * DLPackVersion version
+     * }
+     */
+    public static void version(MemorySegment struct, MemorySegment fieldValue) {
+        MemorySegment.copy(fieldValue, 0L, struct, version$OFFSET, version$LAYOUT.byteSize());
+    }
+
+    private static final AddressLayout manager_ctx$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("manager_ctx"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static final AddressLayout manager_ctx$layout() {
+        return manager_ctx$LAYOUT;
+    }
+
+    private static final long manager_ctx$OFFSET = 8;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static final long manager_ctx$offset() {
+        return manager_ctx$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static MemorySegment manager_ctx(MemorySegment struct) {
+        return struct.get(manager_ctx$LAYOUT, manager_ctx$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * void *manager_ctx
+     * }
+     */
+    public static void manager_ctx(MemorySegment struct, MemorySegment fieldValue) {
+        struct.set(manager_ctx$LAYOUT, manager_ctx$OFFSET, fieldValue);
+    }
+
+    /**
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensorVersioned *)
+     * }
+     */
+    public static class deleter {
+
+        deleter() {
+            // Should not be called directly
+        }
+
+        /**
+         * The function pointer signature, expressed as a functional interface
+         */
+        public interface Function {
+            void apply(MemorySegment _x0);
+        }
+
+        private static final FunctionDescriptor $DESC = FunctionDescriptor.ofVoid(
+            DlpackH.C_POINTER
+        );
+
+        /**
+         * The descriptor of this function pointer
+         */
+        public static FunctionDescriptor descriptor() {
+            return $DESC;
+        }
+
+        private static final MethodHandle UP$MH = DlpackH.upcallHandle(deleter.Function.class, "apply", $DESC);
+
+        /**
+         * Allocates a new upcall stub, whose implementation is defined by {@code fi}.
+         * The lifetime of the returned segment is managed by {@code arena}
+         */
+        public static MemorySegment allocate(deleter.Function fi, Arena arena) {
+            return Linker.nativeLinker().upcallStub(UP$MH.bindTo(fi), $DESC, arena);
+        }
+
+        private static final MethodHandle DOWN$MH = Linker.nativeLinker().downcallHandle($DESC);
+
+        /**
+         * Invoke the upcall stub {@code funcPtr}, with given parameters
+         */
+        public static void invoke(MemorySegment funcPtr,MemorySegment _x0) {
+            try {
+                 DOWN$MH.invokeExact(funcPtr, _x0);
+            } catch (Throwable ex$) {
+                throw new AssertionError("should not reach here", ex$);
+            }
+        }
+    }
+
+    private static final AddressLayout deleter$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("deleter"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensorVersioned *)
+     * }
+     */
+    public static final AddressLayout deleter$layout() {
+        return deleter$LAYOUT;
+    }
+
+    private static final long deleter$OFFSET = 16;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensorVersioned *)
+     * }
+     */
+    public static final long deleter$offset() {
+        return deleter$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensorVersioned *)
+     * }
+     */
+    public static MemorySegment deleter(MemorySegment struct) {
+        return struct.get(deleter$LAYOUT, deleter$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * void (*deleter)(struct DLManagedTensorVersioned *)
+     * }
+     */
+    public static void deleter(MemorySegment struct, MemorySegment fieldValue) {
+        struct.set(deleter$LAYOUT, deleter$OFFSET, fieldValue);
+    }
+
+    private static final OfLong flags$LAYOUT = (OfLong)$LAYOUT.select(groupElement("flags"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uint64_t flags
+     * }
+     */
+    public static final OfLong flags$layout() {
+        return flags$LAYOUT;
+    }
+
+    private static final long flags$OFFSET = 24;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uint64_t flags
+     * }
+     */
+    public static final long flags$offset() {
+        return flags$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uint64_t flags
+     * }
+     */
+    public static long flags(MemorySegment struct) {
+        return struct.get(flags$LAYOUT, flags$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uint64_t flags
+     * }
+     */
+    public static void flags(MemorySegment struct, long fieldValue) {
+        struct.set(flags$LAYOUT, flags$OFFSET, fieldValue);
+    }
+
+    private static final GroupLayout dl_tensor$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("dl_tensor"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static final GroupLayout dl_tensor$layout() {
+        return dl_tensor$LAYOUT;
+    }
+
+    private static final long dl_tensor$OFFSET = 32;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static final long dl_tensor$offset() {
+        return dl_tensor$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static MemorySegment dl_tensor(MemorySegment struct) {
+        return struct.asSlice(dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * DLTensor dl_tensor
+     * }
+     */
+    public static void dl_tensor(MemorySegment struct, MemorySegment fieldValue) {
+        MemorySegment.copy(fieldValue, 0L, struct, dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLPackVersion.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLPackVersion.java
new file mode 100644
index 0000000000..bc80507669
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLPackVersion.java
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct {
+ *     uint32_t major;
+ *     uint32_t minor;
+ * }
+ * }
+ */
+public class DLPackVersion {
+
+    DLPackVersion() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        DlpackH.C_INT.withName("major"),
+        DlpackH.C_INT.withName("minor")
+    ).withName("$anon$61:9");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final OfInt major$LAYOUT = (OfInt)$LAYOUT.select(groupElement("major"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uint32_t major
+     * }
+     */
+    public static final OfInt major$layout() {
+        return major$LAYOUT;
+    }
+
+    private static final long major$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uint32_t major
+     * }
+     */
+    public static final long major$offset() {
+        return major$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uint32_t major
+     * }
+     */
+    public static int major(MemorySegment struct) {
+        return struct.get(major$LAYOUT, major$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uint32_t major
+     * }
+     */
+    public static void major(MemorySegment struct, int fieldValue) {
+        struct.set(major$LAYOUT, major$OFFSET, fieldValue);
+    }
+
+    private static final OfInt minor$LAYOUT = (OfInt)$LAYOUT.select(groupElement("minor"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uint32_t minor
+     * }
+     */
+    public static final OfInt minor$layout() {
+        return minor$LAYOUT;
+    }
+
+    private static final long minor$OFFSET = 4;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uint32_t minor
+     * }
+     */
+    public static final long minor$offset() {
+        return minor$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uint32_t minor
+     * }
+     */
+    public static int minor(MemorySegment struct) {
+        return struct.get(minor$LAYOUT, minor$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uint32_t minor
+     * }
+     */
+    public static void minor(MemorySegment struct, int fieldValue) {
+        struct.set(minor$LAYOUT, minor$OFFSET, fieldValue);
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLTensor.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLTensor.java
new file mode 100644
index 0000000000..63082b35a9
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLTensor.java
@@ -0,0 +1,418 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct {
+ *     void *data;
+ *     DLDevice device;
+ *     int32_t ndim;
+ *     DLDataType dtype;
+ *     int64_t *shape;
+ *     int64_t *strides;
+ *     uint64_t byte_offset;
+ * }
+ * }
+ */
+public class DLTensor {
+
+    DLTensor() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        DlpackH.C_POINTER.withName("data"),
+        DLDevice.layout().withName("device"),
+        DlpackH.C_INT.withName("ndim"),
+        DLDataType.layout().withName("dtype"),
+        DlpackH.C_POINTER.withName("shape"),
+        DlpackH.C_POINTER.withName("strides"),
+        DlpackH.C_LONG.withName("byte_offset")
+    ).withName("$anon$192:9");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final AddressLayout data$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("data"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * void *data
+     * }
+     */
+    public static final AddressLayout data$layout() {
+        return data$LAYOUT;
+    }
+
+    private static final long data$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * void *data
+     * }
+     */
+    public static final long data$offset() {
+        return data$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * void *data
+     * }
+     */
+    public static MemorySegment data(MemorySegment struct) {
+        return struct.get(data$LAYOUT, data$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * void *data
+     * }
+     */
+    public static void data(MemorySegment struct, MemorySegment fieldValue) {
+        struct.set(data$LAYOUT, data$OFFSET, fieldValue);
+    }
+
+    private static final GroupLayout device$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("device"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * DLDevice device
+     * }
+     */
+    public static final GroupLayout device$layout() {
+        return device$LAYOUT;
+    }
+
+    private static final long device$OFFSET = 8;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * DLDevice device
+     * }
+     */
+    public static final long device$offset() {
+        return device$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * DLDevice device
+     * }
+     */
+    public static MemorySegment device(MemorySegment struct) {
+        return struct.asSlice(device$OFFSET, device$LAYOUT.byteSize());
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * DLDevice device
+     * }
+     */
+    public static void device(MemorySegment struct, MemorySegment fieldValue) {
+        MemorySegment.copy(fieldValue, 0L, struct, device$OFFSET, device$LAYOUT.byteSize());
+    }
+
+    private static final OfInt ndim$LAYOUT = (OfInt)$LAYOUT.select(groupElement("ndim"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * int32_t ndim
+     * }
+     */
+    public static final OfInt ndim$layout() {
+        return ndim$LAYOUT;
+    }
+
+    private static final long ndim$OFFSET = 16;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * int32_t ndim
+     * }
+     */
+    public static final long ndim$offset() {
+        return ndim$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * int32_t ndim
+     * }
+     */
+    public static int ndim(MemorySegment struct) {
+        return struct.get(ndim$LAYOUT, ndim$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * int32_t ndim
+     * }
+     */
+    public static void ndim(MemorySegment struct, int fieldValue) {
+        struct.set(ndim$LAYOUT, ndim$OFFSET, fieldValue);
+    }
+
+    private static final GroupLayout dtype$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("dtype"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * DLDataType dtype
+     * }
+     */
+    public static final GroupLayout dtype$layout() {
+        return dtype$LAYOUT;
+    }
+
+    private static final long dtype$OFFSET = 20;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * DLDataType dtype
+     * }
+     */
+    public static final long dtype$offset() {
+        return dtype$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * DLDataType dtype
+     * }
+     */
+    public static MemorySegment dtype(MemorySegment struct) {
+        return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * DLDataType dtype
+     * }
+     */
+    public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
+        MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
+    }
+
+    private static final AddressLayout shape$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("shape"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * int64_t *shape
+     * }
+     */
+    public static final AddressLayout shape$layout() {
+        return shape$LAYOUT;
+    }
+
+    private static final long shape$OFFSET = 24;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * int64_t *shape
+     * }
+     */
+    public static final long shape$offset() {
+        return shape$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * int64_t *shape
+     * }
+     */
+    public static MemorySegment shape(MemorySegment struct) {
+        return struct.get(shape$LAYOUT, shape$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * int64_t *shape
+     * }
+     */
+    public static void shape(MemorySegment struct, MemorySegment fieldValue) {
+        struct.set(shape$LAYOUT, shape$OFFSET, fieldValue);
+    }
+
+    private static final AddressLayout strides$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("strides"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * int64_t *strides
+     * }
+     */
+    public static final AddressLayout strides$layout() {
+        return strides$LAYOUT;
+    }
+
+    private static final long strides$OFFSET = 32;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * int64_t *strides
+     * }
+     */
+    public static final long strides$offset() {
+        return strides$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * int64_t *strides
+     * }
+     */
+    public static MemorySegment strides(MemorySegment struct) {
+        return struct.get(strides$LAYOUT, strides$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * int64_t *strides
+     * }
+     */
+    public static void strides(MemorySegment struct, MemorySegment fieldValue) {
+        struct.set(strides$LAYOUT, strides$OFFSET, fieldValue);
+    }
+
+    private static final OfLong byte_offset$LAYOUT = (OfLong)$LAYOUT.select(groupElement("byte_offset"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * uint64_t byte_offset
+     * }
+     */
+    public static final OfLong byte_offset$layout() {
+        return byte_offset$LAYOUT;
+    }
+
+    private static final long byte_offset$OFFSET = 40;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * uint64_t byte_offset
+     * }
+     */
+    public static final long byte_offset$offset() {
+        return byte_offset$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * uint64_t byte_offset
+     * }
+     */
+    public static long byte_offset(MemorySegment struct) {
+        return struct.get(byte_offset$LAYOUT, byte_offset$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * uint64_t byte_offset
+     * }
+     */
+    public static void byte_offset(MemorySegment struct, long fieldValue) {
+        struct.set(byte_offset$LAYOUT, byte_offset$OFFSET, fieldValue);
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DistanceH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DistanceH.java
new file mode 100644
index 0000000000..3949e10899
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DistanceH.java
@@ -0,0 +1,274 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import java.lang.invoke.*;
+import java.lang.foreign.*;
+import java.nio.ByteOrder;
+import java.util.*;
+import java.util.function.*;
+import java.util.stream.*;
+
+import static java.lang.foreign.ValueLayout.*;
+import static java.lang.foreign.MemoryLayout.PathElement.*;
+
+public class DistanceH {
+
+    DistanceH() {
+        // Should not be called directly
+    }
+
+    static final Arena LIBRARY_ARENA = Arena.ofAuto();
+    static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+    static void traceDowncall(String name, Object... args) {
+         String traceArgs = Arrays.stream(args)
+                       .map(Object::toString)
+                       .collect(Collectors.joining(", "));
+         System.out.printf("%s(%s)\n", name, traceArgs);
+    }
+
+    static MemorySegment findOrThrow(String symbol) {
+        return SYMBOL_LOOKUP.find(symbol)
+            .orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+    }
+
+    static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+        try {
+            return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+        } catch (ReflectiveOperationException ex) {
+            throw new AssertionError(ex);
+        }
+    }
+
+    static MemoryLayout align(MemoryLayout layout, long align) {
+        return switch (layout) {
+            case PaddingLayout p -> p;
+            case ValueLayout v -> v.withByteAlignment(align);
+            case GroupLayout g -> {
+                MemoryLayout[] alignedMembers = g.memberLayouts().stream()
+                        .map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+                yield g instanceof StructLayout ?
+                        MemoryLayout.structLayout(alignedMembers) : MemoryLayout.unionLayout(alignedMembers);
+            }
+            case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+        };
+    }
+
+    static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup()
+            .or(Linker.nativeLinker().defaultLookup());
+
+    public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+    public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+    public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+    public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+    public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+    public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+    public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+    public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+            .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+    public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+    private static final int L2Expanded = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.L2Expanded = 0
+     * }
+     */
+    public static int L2Expanded() {
+        return L2Expanded;
+    }
+    private static final int L2SqrtExpanded = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.L2SqrtExpanded = 1
+     * }
+     */
+    public static int L2SqrtExpanded() {
+        return L2SqrtExpanded;
+    }
+    private static final int CosineExpanded = (int)2L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.CosineExpanded = 2
+     * }
+     */
+    public static int CosineExpanded() {
+        return CosineExpanded;
+    }
+    private static final int L1 = (int)3L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.L1 = 3
+     * }
+     */
+    public static int L1() {
+        return L1;
+    }
+    private static final int L2Unexpanded = (int)4L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.L2Unexpanded = 4
+     * }
+     */
+    public static int L2Unexpanded() {
+        return L2Unexpanded;
+    }
+    private static final int L2SqrtUnexpanded = (int)5L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.L2SqrtUnexpanded = 5
+     * }
+     */
+    public static int L2SqrtUnexpanded() {
+        return L2SqrtUnexpanded;
+    }
+    private static final int InnerProduct = (int)6L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.InnerProduct = 6
+     * }
+     */
+    public static int InnerProduct() {
+        return InnerProduct;
+    }
+    private static final int Linf = (int)7L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.Linf = 7
+     * }
+     */
+    public static int Linf() {
+        return Linf;
+    }
+    private static final int Canberra = (int)8L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.Canberra = 8
+     * }
+     */
+    public static int Canberra() {
+        return Canberra;
+    }
+    private static final int LpUnexpanded = (int)9L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.LpUnexpanded = 9
+     * }
+     */
+    public static int LpUnexpanded() {
+        return LpUnexpanded;
+    }
+    private static final int CorrelationExpanded = (int)10L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.CorrelationExpanded = 10
+     * }
+     */
+    public static int CorrelationExpanded() {
+        return CorrelationExpanded;
+    }
+    private static final int JaccardExpanded = (int)11L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.JaccardExpanded = 11
+     * }
+     */
+    public static int JaccardExpanded() {
+        return JaccardExpanded;
+    }
+    private static final int HellingerExpanded = (int)12L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.HellingerExpanded = 12
+     * }
+     */
+    public static int HellingerExpanded() {
+        return HellingerExpanded;
+    }
+    private static final int Haversine = (int)13L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.Haversine = 13
+     * }
+     */
+    public static int Haversine() {
+        return Haversine;
+    }
+    private static final int BrayCurtis = (int)14L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.BrayCurtis = 14
+     * }
+     */
+    public static int BrayCurtis() {
+        return BrayCurtis;
+    }
+    private static final int JensenShannon = (int)15L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.JensenShannon = 15
+     * }
+     */
+    public static int JensenShannon() {
+        return JensenShannon;
+    }
+    private static final int HammingUnexpanded = (int)16L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.HammingUnexpanded = 16
+     * }
+     */
+    public static int HammingUnexpanded() {
+        return HammingUnexpanded;
+    }
+    private static final int KLDivergence = (int)17L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.KLDivergence = 17
+     * }
+     */
+    public static int KLDivergence() {
+        return KLDivergence;
+    }
+    private static final int RusselRaoExpanded = (int)18L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.RusselRaoExpanded = 18
+     * }
+     */
+    public static int RusselRaoExpanded() {
+        return RusselRaoExpanded;
+    }
+    private static final int DiceExpanded = (int)19L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.DiceExpanded = 19
+     * }
+     */
+    public static int DiceExpanded() {
+        return DiceExpanded;
+    }
+    private static final int Precomputed = (int)100L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.Precomputed = 100
+     * }
+     */
+    public static int Precomputed() {
+        return Precomputed;
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DlpackH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DlpackH.java
new file mode 100644
index 0000000000..d459dd3bf6
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DlpackH.java
@@ -0,0 +1,1898 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.ValueLayout.JAVA_BYTE;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.PaddingLayout;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.StructLayout;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.foreign.ValueLayout.OfByte;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.foreign.ValueLayout.OfShort;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.stream.Collectors;
+
+public class DlpackH {
+
+    DlpackH() {
+        // Should not be called directly
+    }
+
+    static final Arena LIBRARY_ARENA = Arena.ofAuto();
+    static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+    static void traceDowncall(String name, Object... args) {
+         String traceArgs = Arrays.stream(args)
+                       .map(Object::toString)
+                       .collect(Collectors.joining(", "));
+         System.out.printf("%s(%s)\n", name, traceArgs);
+    }
+
+    static MemorySegment findOrThrow(String symbol) {
+        return SYMBOL_LOOKUP.find(symbol)
+            .orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+    }
+
+    static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+        try {
+            return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+        } catch (ReflectiveOperationException ex) {
+            throw new AssertionError(ex);
+        }
+    }
+
+    static MemoryLayout align(MemoryLayout layout, long align) {
+        return switch (layout) {
+            case PaddingLayout p -> p;
+            case ValueLayout v -> v.withByteAlignment(align);
+            case GroupLayout g -> {
+                MemoryLayout[] alignedMembers = g.memberLayouts().stream()
+                        .map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+                yield g instanceof StructLayout ?
+                        MemoryLayout.structLayout(alignedMembers) : MemoryLayout.unionLayout(alignedMembers);
+            }
+            case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+        };
+    }
+
+    static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup()
+            .or(Linker.nativeLinker().defaultLookup());
+
+    public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+    public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+    public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+    public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+    public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+    public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+    public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+    public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+            .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+    public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+    private static final int DLPACK_MAJOR_VERSION = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define DLPACK_MAJOR_VERSION 1
+     * }
+     */
+    public static int DLPACK_MAJOR_VERSION() {
+        return DLPACK_MAJOR_VERSION;
+    }
+    private static final int DLPACK_MINOR_VERSION = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define DLPACK_MINOR_VERSION 0
+     * }
+     */
+    public static int DLPACK_MINOR_VERSION() {
+        return DLPACK_MINOR_VERSION;
+    }
+    private static final int _STDINT_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _STDINT_H 1
+     * }
+     */
+    public static int _STDINT_H() {
+        return _STDINT_H;
+    }
+    private static final int _FEATURES_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _FEATURES_H 1
+     * }
+     */
+    public static int _FEATURES_H() {
+        return _FEATURES_H;
+    }
+    private static final int _DEFAULT_SOURCE = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _DEFAULT_SOURCE 1
+     * }
+     */
+    public static int _DEFAULT_SOURCE() {
+        return _DEFAULT_SOURCE;
+    }
+    private static final int __GLIBC_USE_ISOC2X = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_ISOC2X 0
+     * }
+     */
+    public static int __GLIBC_USE_ISOC2X() {
+        return __GLIBC_USE_ISOC2X;
+    }
+    private static final int __USE_ISOC11 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_ISOC11 1
+     * }
+     */
+    public static int __USE_ISOC11() {
+        return __USE_ISOC11;
+    }
+    private static final int __USE_ISOC99 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_ISOC99 1
+     * }
+     */
+    public static int __USE_ISOC99() {
+        return __USE_ISOC99;
+    }
+    private static final int __USE_ISOC95 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_ISOC95 1
+     * }
+     */
+    public static int __USE_ISOC95() {
+        return __USE_ISOC95;
+    }
+    private static final int __USE_POSIX_IMPLICITLY = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_POSIX_IMPLICITLY 1
+     * }
+     */
+    public static int __USE_POSIX_IMPLICITLY() {
+        return __USE_POSIX_IMPLICITLY;
+    }
+    private static final int _POSIX_SOURCE = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _POSIX_SOURCE 1
+     * }
+     */
+    public static int _POSIX_SOURCE() {
+        return _POSIX_SOURCE;
+    }
+    private static final int __USE_POSIX = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_POSIX 1
+     * }
+     */
+    public static int __USE_POSIX() {
+        return __USE_POSIX;
+    }
+    private static final int __USE_POSIX2 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_POSIX2 1
+     * }
+     */
+    public static int __USE_POSIX2() {
+        return __USE_POSIX2;
+    }
+    private static final int __USE_POSIX199309 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_POSIX199309 1
+     * }
+     */
+    public static int __USE_POSIX199309() {
+        return __USE_POSIX199309;
+    }
+    private static final int __USE_POSIX199506 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_POSIX199506 1
+     * }
+     */
+    public static int __USE_POSIX199506() {
+        return __USE_POSIX199506;
+    }
+    private static final int __USE_XOPEN2K = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_XOPEN2K 1
+     * }
+     */
+    public static int __USE_XOPEN2K() {
+        return __USE_XOPEN2K;
+    }
+    private static final int __USE_XOPEN2K8 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_XOPEN2K8 1
+     * }
+     */
+    public static int __USE_XOPEN2K8() {
+        return __USE_XOPEN2K8;
+    }
+    private static final int _ATFILE_SOURCE = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _ATFILE_SOURCE 1
+     * }
+     */
+    public static int _ATFILE_SOURCE() {
+        return _ATFILE_SOURCE;
+    }
+    private static final int __WORDSIZE = (int)64L;
+    /**
+     * {@snippet lang=c :
+     * #define __WORDSIZE 64
+     * }
+     */
+    public static int __WORDSIZE() {
+        return __WORDSIZE;
+    }
+    private static final int __WORDSIZE_TIME64_COMPAT32 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __WORDSIZE_TIME64_COMPAT32 1
+     * }
+     */
+    public static int __WORDSIZE_TIME64_COMPAT32() {
+        return __WORDSIZE_TIME64_COMPAT32;
+    }
+    private static final int __SYSCALL_WORDSIZE = (int)64L;
+    /**
+     * {@snippet lang=c :
+     * #define __SYSCALL_WORDSIZE 64
+     * }
+     */
+    public static int __SYSCALL_WORDSIZE() {
+        return __SYSCALL_WORDSIZE;
+    }
+    private static final int __USE_MISC = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_MISC 1
+     * }
+     */
+    public static int __USE_MISC() {
+        return __USE_MISC;
+    }
+    private static final int __USE_ATFILE = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_ATFILE 1
+     * }
+     */
+    public static int __USE_ATFILE() {
+        return __USE_ATFILE;
+    }
+    private static final int __USE_FORTIFY_LEVEL = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __USE_FORTIFY_LEVEL 0
+     * }
+     */
+    public static int __USE_FORTIFY_LEVEL() {
+        return __USE_FORTIFY_LEVEL;
+    }
+    private static final int __GLIBC_USE_DEPRECATED_GETS = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_DEPRECATED_GETS 0
+     * }
+     */
+    public static int __GLIBC_USE_DEPRECATED_GETS() {
+        return __GLIBC_USE_DEPRECATED_GETS;
+    }
+    private static final int __GLIBC_USE_DEPRECATED_SCANF = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_DEPRECATED_SCANF 0
+     * }
+     */
+    public static int __GLIBC_USE_DEPRECATED_SCANF() {
+        return __GLIBC_USE_DEPRECATED_SCANF;
+    }
+    private static final int _STDC_PREDEF_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _STDC_PREDEF_H 1
+     * }
+     */
+    public static int _STDC_PREDEF_H() {
+        return _STDC_PREDEF_H;
+    }
+    private static final int __STDC_IEC_559__ = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __STDC_IEC_559__ 1
+     * }
+     */
+    public static int __STDC_IEC_559__() {
+        return __STDC_IEC_559__;
+    }
+    private static final int __STDC_IEC_559_COMPLEX__ = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __STDC_IEC_559_COMPLEX__ 1
+     * }
+     */
+    public static int __STDC_IEC_559_COMPLEX__() {
+        return __STDC_IEC_559_COMPLEX__;
+    }
+    private static final int __GNU_LIBRARY__ = (int)6L;
+    /**
+     * {@snippet lang=c :
+     * #define __GNU_LIBRARY__ 6
+     * }
+     */
+    public static int __GNU_LIBRARY__() {
+        return __GNU_LIBRARY__;
+    }
+    private static final int __GLIBC__ = (int)2L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC__ 2
+     * }
+     */
+    public static int __GLIBC__() {
+        return __GLIBC__;
+    }
+    private static final int __GLIBC_MINOR__ = (int)35L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_MINOR__ 35
+     * }
+     */
+    public static int __GLIBC_MINOR__() {
+        return __GLIBC_MINOR__;
+    }
+    private static final int _SYS_CDEFS_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _SYS_CDEFS_H 1
+     * }
+     */
+    public static int _SYS_CDEFS_H() {
+        return _SYS_CDEFS_H;
+    }
+    private static final int __glibc_c99_flexarr_available = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __glibc_c99_flexarr_available 1
+     * }
+     */
+    public static int __glibc_c99_flexarr_available() {
+        return __glibc_c99_flexarr_available;
+    }
+    private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
+     * }
+     */
+    public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
+        return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
+    }
+    private static final int __HAVE_GENERIC_SELECTION = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __HAVE_GENERIC_SELECTION 1
+     * }
+     */
+    public static int __HAVE_GENERIC_SELECTION() {
+        return __HAVE_GENERIC_SELECTION;
+    }
+    private static final int __GLIBC_USE_LIB_EXT2 = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_LIB_EXT2 0
+     * }
+     */
+    public static int __GLIBC_USE_LIB_EXT2() {
+        return __GLIBC_USE_LIB_EXT2;
+    }
+    private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
+     * }
+     */
+    public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
+        return __GLIBC_USE_IEC_60559_BFP_EXT;
+    }
+    private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
+     * }
+     */
+    public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
+        return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
+    }
+    private static final int __GLIBC_USE_IEC_60559_EXT = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_IEC_60559_EXT 0
+     * }
+     */
+    public static int __GLIBC_USE_IEC_60559_EXT() {
+        return __GLIBC_USE_IEC_60559_EXT;
+    }
+    private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
+     * }
+     */
+    public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
+        return __GLIBC_USE_IEC_60559_FUNCS_EXT;
+    }
+    private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
+     * }
+     */
+    public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
+        return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
+    }
+    private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
+     * }
+     */
+    public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
+        return __GLIBC_USE_IEC_60559_TYPES_EXT;
+    }
+    private static final int _BITS_TYPES_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _BITS_TYPES_H 1
+     * }
+     */
+    public static int _BITS_TYPES_H() {
+        return _BITS_TYPES_H;
+    }
+    private static final int _BITS_TYPESIZES_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _BITS_TYPESIZES_H 1
+     * }
+     */
+    public static int _BITS_TYPESIZES_H() {
+        return _BITS_TYPESIZES_H;
+    }
+    private static final int __OFF_T_MATCHES_OFF64_T = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __OFF_T_MATCHES_OFF64_T 1
+     * }
+     */
+    public static int __OFF_T_MATCHES_OFF64_T() {
+        return __OFF_T_MATCHES_OFF64_T;
+    }
+    private static final int __INO_T_MATCHES_INO64_T = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __INO_T_MATCHES_INO64_T 1
+     * }
+     */
+    public static int __INO_T_MATCHES_INO64_T() {
+        return __INO_T_MATCHES_INO64_T;
+    }
+    private static final int __RLIM_T_MATCHES_RLIM64_T = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __RLIM_T_MATCHES_RLIM64_T 1
+     * }
+     */
+    public static int __RLIM_T_MATCHES_RLIM64_T() {
+        return __RLIM_T_MATCHES_RLIM64_T;
+    }
+    private static final int __STATFS_MATCHES_STATFS64 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __STATFS_MATCHES_STATFS64 1
+     * }
+     */
+    public static int __STATFS_MATCHES_STATFS64() {
+        return __STATFS_MATCHES_STATFS64;
+    }
+    private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
+     * }
+     */
+    public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
+        return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
+    }
+    private static final int __FD_SETSIZE = (int)1024L;
+    /**
+     * {@snippet lang=c :
+     * #define __FD_SETSIZE 1024
+     * }
+     */
+    public static int __FD_SETSIZE() {
+        return __FD_SETSIZE;
+    }
+    private static final int _BITS_TIME64_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _BITS_TIME64_H 1
+     * }
+     */
+    public static int _BITS_TIME64_H() {
+        return _BITS_TIME64_H;
+    }
+    private static final int _BITS_WCHAR_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _BITS_WCHAR_H 1
+     * }
+     */
+    public static int _BITS_WCHAR_H() {
+        return _BITS_WCHAR_H;
+    }
+    private static final int _BITS_STDINT_INTN_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _BITS_STDINT_INTN_H 1
+     * }
+     */
+    public static int _BITS_STDINT_INTN_H() {
+        return _BITS_STDINT_INTN_H;
+    }
+    private static final int _BITS_STDINT_UINTN_H = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * #define _BITS_STDINT_UINTN_H 1
+     * }
+     */
+    public static int _BITS_STDINT_UINTN_H() {
+        return _BITS_STDINT_UINTN_H;
+    }
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned char __u_char
+     * }
+     */
+    public static final OfByte __u_char = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned short __u_short
+     * }
+     */
+    public static final OfShort __u_short = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __u_int
+     * }
+     */
+    public static final OfInt __u_int = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __u_long
+     * }
+     */
+    public static final OfLong __u_long = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef signed char __int8_t
+     * }
+     */
+    public static final OfByte __int8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned char __uint8_t
+     * }
+     */
+    public static final OfByte __uint8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef short __int16_t
+     * }
+     */
+    public static final OfShort __int16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned short __uint16_t
+     * }
+     */
+    public static final OfShort __uint16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef int __int32_t
+     * }
+     */
+    public static final OfInt __int32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __uint32_t
+     * }
+     */
+    public static final OfInt __uint32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef long __int64_t
+     * }
+     */
+    public static final OfLong __int64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __uint64_t
+     * }
+     */
+    public static final OfLong __uint64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __int8_t __int_least8_t
+     * }
+     */
+    public static final OfByte __int_least8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint8_t __uint_least8_t
+     * }
+     */
+    public static final OfByte __uint_least8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef __int16_t __int_least16_t
+     * }
+     */
+    public static final OfShort __int_least16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint16_t __uint_least16_t
+     * }
+     */
+    public static final OfShort __uint_least16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef __int32_t __int_least32_t
+     * }
+     */
+    public static final OfInt __int_least32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint32_t __uint_least32_t
+     * }
+     */
+    public static final OfInt __uint_least32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef __int64_t __int_least64_t
+     * }
+     */
+    public static final OfLong __int_least64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint64_t __uint_least64_t
+     * }
+     */
+    public static final OfLong __uint_least64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __quad_t
+     * }
+     */
+    public static final OfLong __quad_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __u_quad_t
+     * }
+     */
+    public static final OfLong __u_quad_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __intmax_t
+     * }
+     */
+    public static final OfLong __intmax_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __uintmax_t
+     * }
+     */
+    public static final OfLong __uintmax_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __dev_t
+     * }
+     */
+    public static final OfLong __dev_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __uid_t
+     * }
+     */
+    public static final OfInt __uid_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __gid_t
+     * }
+     */
+    public static final OfInt __gid_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __ino_t
+     * }
+     */
+    public static final OfLong __ino_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __ino64_t
+     * }
+     */
+    public static final OfLong __ino64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __mode_t
+     * }
+     */
+    public static final OfInt __mode_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __nlink_t
+     * }
+     */
+    public static final OfLong __nlink_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __off_t
+     * }
+     */
+    public static final OfLong __off_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __off64_t
+     * }
+     */
+    public static final OfLong __off64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef int __pid_t
+     * }
+     */
+    public static final OfInt __pid_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef long __clock_t
+     * }
+     */
+    public static final OfLong __clock_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __rlim_t
+     * }
+     */
+    public static final OfLong __rlim_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __rlim64_t
+     * }
+     */
+    public static final OfLong __rlim64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __id_t
+     * }
+     */
+    public static final OfInt __id_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef long __time_t
+     * }
+     */
+    public static final OfLong __time_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __useconds_t
+     * }
+     */
+    public static final OfInt __useconds_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef long __suseconds_t
+     * }
+     */
+    public static final OfLong __suseconds_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __suseconds64_t
+     * }
+     */
+    public static final OfLong __suseconds64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef int __daddr_t
+     * }
+     */
+    public static final OfInt __daddr_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef int __key_t
+     * }
+     */
+    public static final OfInt __key_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef int __clockid_t
+     * }
+     */
+    public static final OfInt __clockid_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef void *__timer_t
+     * }
+     */
+    public static final AddressLayout __timer_t = DlpackH.C_POINTER;
+    /**
+     * {@snippet lang=c :
+     * typedef long __blksize_t
+     * }
+     */
+    public static final OfLong __blksize_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __blkcnt_t
+     * }
+     */
+    public static final OfLong __blkcnt_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __blkcnt64_t
+     * }
+     */
+    public static final OfLong __blkcnt64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __fsblkcnt_t
+     * }
+     */
+    public static final OfLong __fsblkcnt_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __fsblkcnt64_t
+     * }
+     */
+    public static final OfLong __fsblkcnt64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __fsfilcnt_t
+     * }
+     */
+    public static final OfLong __fsfilcnt_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __fsfilcnt64_t
+     * }
+     */
+    public static final OfLong __fsfilcnt64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __fsword_t
+     * }
+     */
+    public static final OfLong __fsword_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __ssize_t
+     * }
+     */
+    public static final OfLong __ssize_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long __syscall_slong_t
+     * }
+     */
+    public static final OfLong __syscall_slong_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long __syscall_ulong_t
+     * }
+     */
+    public static final OfLong __syscall_ulong_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __off64_t __loff_t
+     * }
+     */
+    public static final OfLong __loff_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef char *__caddr_t
+     * }
+     */
+    public static final AddressLayout __caddr_t = DlpackH.C_POINTER;
+    /**
+     * {@snippet lang=c :
+     * typedef long __intptr_t
+     * }
+     */
+    public static final OfLong __intptr_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned int __socklen_t
+     * }
+     */
+    public static final OfInt __socklen_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef int __sig_atomic_t
+     * }
+     */
+    public static final OfInt __sig_atomic_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef __int8_t int8_t
+     * }
+     */
+    public static final OfByte int8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef __int16_t int16_t
+     * }
+     */
+    public static final OfShort int16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef __int32_t int32_t
+     * }
+     */
+    public static final OfInt int32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef __int64_t int64_t
+     * }
+     */
+    public static final OfLong int64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint8_t uint8_t
+     * }
+     */
+    public static final OfByte uint8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint16_t uint16_t
+     * }
+     */
+    public static final OfShort uint16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint32_t uint32_t
+     * }
+     */
+    public static final OfInt uint32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint64_t uint64_t
+     * }
+     */
+    public static final OfLong uint64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __int_least8_t int_least8_t
+     * }
+     */
+    public static final OfByte int_least8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef __int_least16_t int_least16_t
+     * }
+     */
+    public static final OfShort int_least16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef __int_least32_t int_least32_t
+     * }
+     */
+    public static final OfInt int_least32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef __int_least64_t int_least64_t
+     * }
+     */
+    public static final OfLong int_least64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint_least8_t uint_least8_t
+     * }
+     */
+    public static final OfByte uint_least8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint_least16_t uint_least16_t
+     * }
+     */
+    public static final OfShort uint_least16_t = DlpackH.C_SHORT;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint_least32_t uint_least32_t
+     * }
+     */
+    public static final OfInt uint_least32_t = DlpackH.C_INT;
+    /**
+     * {@snippet lang=c :
+     * typedef __uint_least64_t uint_least64_t
+     * }
+     */
+    public static final OfLong uint_least64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef signed char int_fast8_t
+     * }
+     */
+    public static final OfByte int_fast8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef long int_fast16_t
+     * }
+     */
+    public static final OfLong int_fast16_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long int_fast32_t
+     * }
+     */
+    public static final OfLong int_fast32_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long int_fast64_t
+     * }
+     */
+    public static final OfLong int_fast64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned char uint_fast8_t
+     * }
+     */
+    public static final OfByte uint_fast8_t = DlpackH.C_CHAR;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long uint_fast16_t
+     * }
+     */
+    public static final OfLong uint_fast16_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long uint_fast32_t
+     * }
+     */
+    public static final OfLong uint_fast32_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long uint_fast64_t
+     * }
+     */
+    public static final OfLong uint_fast64_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long intptr_t
+     * }
+     */
+    public static final OfLong intptr_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long uintptr_t
+     * }
+     */
+    public static final OfLong uintptr_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __intmax_t intmax_t
+     * }
+     */
+    public static final OfLong intmax_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef __uintmax_t uintmax_t
+     * }
+     */
+    public static final OfLong uintmax_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef long ptrdiff_t
+     * }
+     */
+    public static final OfLong ptrdiff_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef unsigned long size_t
+     * }
+     */
+    public static final OfLong size_t = DlpackH.C_LONG;
+    /**
+     * {@snippet lang=c :
+     * typedef int wchar_t
+     * }
+     */
+    public static final OfInt wchar_t = DlpackH.C_INT;
+    private static final int kDLCPU = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLCPU = 1
+     * }
+     */
+    public static int kDLCPU() {
+        return kDLCPU;
+    }
+    private static final int kDLCUDA = (int)2L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLCUDA = 2
+     * }
+     */
+    public static int kDLCUDA() {
+        return kDLCUDA;
+    }
+    private static final int kDLCUDAHost = (int)3L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLCUDAHost = 3
+     * }
+     */
+    public static int kDLCUDAHost() {
+        return kDLCUDAHost;
+    }
+    private static final int kDLOpenCL = (int)4L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLOpenCL = 4
+     * }
+     */
+    public static int kDLOpenCL() {
+        return kDLOpenCL;
+    }
+    private static final int kDLVulkan = (int)7L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLVulkan = 7
+     * }
+     */
+    public static int kDLVulkan() {
+        return kDLVulkan;
+    }
+    private static final int kDLMetal = (int)8L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLMetal = 8
+     * }
+     */
+    public static int kDLMetal() {
+        return kDLMetal;
+    }
+    private static final int kDLVPI = (int)9L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLVPI = 9
+     * }
+     */
+    public static int kDLVPI() {
+        return kDLVPI;
+    }
+    private static final int kDLROCM = (int)10L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLROCM = 10
+     * }
+     */
+    public static int kDLROCM() {
+        return kDLROCM;
+    }
+    private static final int kDLROCMHost = (int)11L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLROCMHost = 11
+     * }
+     */
+    public static int kDLROCMHost() {
+        return kDLROCMHost;
+    }
+    private static final int kDLExtDev = (int)12L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLExtDev = 12
+     * }
+     */
+    public static int kDLExtDev() {
+        return kDLExtDev;
+    }
+    private static final int kDLCUDAManaged = (int)13L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLCUDAManaged = 13
+     * }
+     */
+    public static int kDLCUDAManaged() {
+        return kDLCUDAManaged;
+    }
+    private static final int kDLOneAPI = (int)14L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLOneAPI = 14
+     * }
+     */
+    public static int kDLOneAPI() {
+        return kDLOneAPI;
+    }
+    private static final int kDLWebGPU = (int)15L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLWebGPU = 15
+     * }
+     */
+    public static int kDLWebGPU() {
+        return kDLWebGPU;
+    }
+    private static final int kDLHexagon = (int)16L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLHexagon = 16
+     * }
+     */
+    public static int kDLHexagon() {
+        return kDLHexagon;
+    }
+    private static final int kDLMAIA = (int)17L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLMAIA = 17
+     * }
+     */
+    public static int kDLMAIA() {
+        return kDLMAIA;
+    }
+    private static final int kDLInt = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLInt = 0
+     * }
+     */
+    public static int kDLInt() {
+        return kDLInt;
+    }
+    private static final int kDLUInt = (int)1L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLUInt = 1
+     * }
+     */
+    public static int kDLUInt() {
+        return kDLUInt;
+    }
+    private static final int kDLFloat = (int)2L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLFloat = 2
+     * }
+     */
+    public static int kDLFloat() {
+        return kDLFloat;
+    }
+    private static final int kDLOpaqueHandle = (int)3L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLOpaqueHandle = 3
+     * }
+     */
+    public static int kDLOpaqueHandle() {
+        return kDLOpaqueHandle;
+    }
+    private static final int kDLBfloat = (int)4L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLBfloat = 4
+     * }
+     */
+    public static int kDLBfloat() {
+        return kDLBfloat;
+    }
+    private static final int kDLComplex = (int)5L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLComplex = 5
+     * }
+     */
+    public static int kDLComplex() {
+        return kDLComplex;
+    }
+    private static final int kDLBool = (int)6L;
+    /**
+     * {@snippet lang=c :
+     * enum <anonymous>.kDLBool = 6
+     * }
+     */
+    public static int kDLBool() {
+        return kDLBool;
+    }
+    private static final long _POSIX_C_SOURCE = 200809L;
+    /**
+     * {@snippet lang=c :
+     * #define _POSIX_C_SOURCE 200809
+     * }
+     */
+    public static long _POSIX_C_SOURCE() {
+        return _POSIX_C_SOURCE;
+    }
+    private static final int __TIMESIZE = (int)64L;
+    /**
+     * {@snippet lang=c :
+     * #define __TIMESIZE 64
+     * }
+     */
+    public static int __TIMESIZE() {
+        return __TIMESIZE;
+    }
+    private static final long __STDC_IEC_60559_BFP__ = 201404L;
+    /**
+     * {@snippet lang=c :
+     * #define __STDC_IEC_60559_BFP__ 201404
+     * }
+     */
+    public static long __STDC_IEC_60559_BFP__() {
+        return __STDC_IEC_60559_BFP__;
+    }
+    private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
+    /**
+     * {@snippet lang=c :
+     * #define __STDC_IEC_60559_COMPLEX__ 201404
+     * }
+     */
+    public static long __STDC_IEC_60559_COMPLEX__() {
+        return __STDC_IEC_60559_COMPLEX__;
+    }
+    private static final long __STDC_ISO_10646__ = 201706L;
+    /**
+     * {@snippet lang=c :
+     * #define __STDC_ISO_10646__ 201706
+     * }
+     */
+    public static long __STDC_ISO_10646__() {
+        return __STDC_ISO_10646__;
+    }
+    private static final int __WCHAR_MAX = (int)2147483647L;
+    /**
+     * {@snippet lang=c :
+     * #define __WCHAR_MAX 2147483647
+     * }
+     */
+    public static int __WCHAR_MAX() {
+        return __WCHAR_MAX;
+    }
+    private static final int __WCHAR_MIN = (int)-2147483648L;
+    /**
+     * {@snippet lang=c :
+     * #define __WCHAR_MIN -2147483648
+     * }
+     */
+    public static int __WCHAR_MIN() {
+        return __WCHAR_MIN;
+    }
+    private static final int INT8_MIN = (int)-128L;
+    /**
+     * {@snippet lang=c :
+     * #define INT8_MIN -128
+     * }
+     */
+    public static int INT8_MIN() {
+        return INT8_MIN;
+    }
+    private static final int INT16_MIN = (int)-32768L;
+    /**
+     * {@snippet lang=c :
+     * #define INT16_MIN -32768
+     * }
+     */
+    public static int INT16_MIN() {
+        return INT16_MIN;
+    }
+    private static final int INT32_MIN = (int)-2147483648L;
+    /**
+     * {@snippet lang=c :
+     * #define INT32_MIN -2147483648
+     * }
+     */
+    public static int INT32_MIN() {
+        return INT32_MIN;
+    }
+    private static final long INT64_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define INT64_MIN -9223372036854775808
+     * }
+     */
+    public static long INT64_MIN() {
+        return INT64_MIN;
+    }
+    private static final int INT8_MAX = (int)127L;
+    /**
+     * {@snippet lang=c :
+     * #define INT8_MAX 127
+     * }
+     */
+    public static int INT8_MAX() {
+        return INT8_MAX;
+    }
+    private static final int INT16_MAX = (int)32767L;
+    /**
+     * {@snippet lang=c :
+     * #define INT16_MAX 32767
+     * }
+     */
+    public static int INT16_MAX() {
+        return INT16_MAX;
+    }
+    private static final int INT32_MAX = (int)2147483647L;
+    /**
+     * {@snippet lang=c :
+     * #define INT32_MAX 2147483647
+     * }
+     */
+    public static int INT32_MAX() {
+        return INT32_MAX;
+    }
+    private static final long INT64_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define INT64_MAX 9223372036854775807
+     * }
+     */
+    public static long INT64_MAX() {
+        return INT64_MAX;
+    }
+    private static final int UINT8_MAX = (int)255L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT8_MAX 255
+     * }
+     */
+    public static int UINT8_MAX() {
+        return UINT8_MAX;
+    }
+    private static final int UINT16_MAX = (int)65535L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT16_MAX 65535
+     * }
+     */
+    public static int UINT16_MAX() {
+        return UINT16_MAX;
+    }
+    private static final int UINT32_MAX = (int)4294967295L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT32_MAX 4294967295
+     * }
+     */
+    public static int UINT32_MAX() {
+        return UINT32_MAX;
+    }
+    private static final long UINT64_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT64_MAX -1
+     * }
+     */
+    public static long UINT64_MAX() {
+        return UINT64_MAX;
+    }
+    private static final int INT_LEAST8_MIN = (int)-128L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST8_MIN -128
+     * }
+     */
+    public static int INT_LEAST8_MIN() {
+        return INT_LEAST8_MIN;
+    }
+    private static final int INT_LEAST16_MIN = (int)-32768L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST16_MIN -32768
+     * }
+     */
+    public static int INT_LEAST16_MIN() {
+        return INT_LEAST16_MIN;
+    }
+    private static final int INT_LEAST32_MIN = (int)-2147483648L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST32_MIN -2147483648
+     * }
+     */
+    public static int INT_LEAST32_MIN() {
+        return INT_LEAST32_MIN;
+    }
+    private static final long INT_LEAST64_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST64_MIN -9223372036854775808
+     * }
+     */
+    public static long INT_LEAST64_MIN() {
+        return INT_LEAST64_MIN;
+    }
+    private static final int INT_LEAST8_MAX = (int)127L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST8_MAX 127
+     * }
+     */
+    public static int INT_LEAST8_MAX() {
+        return INT_LEAST8_MAX;
+    }
+    private static final int INT_LEAST16_MAX = (int)32767L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST16_MAX 32767
+     * }
+     */
+    public static int INT_LEAST16_MAX() {
+        return INT_LEAST16_MAX;
+    }
+    private static final int INT_LEAST32_MAX = (int)2147483647L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST32_MAX 2147483647
+     * }
+     */
+    public static int INT_LEAST32_MAX() {
+        return INT_LEAST32_MAX;
+    }
+    private static final long INT_LEAST64_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_LEAST64_MAX 9223372036854775807
+     * }
+     */
+    public static long INT_LEAST64_MAX() {
+        return INT_LEAST64_MAX;
+    }
+    private static final int UINT_LEAST8_MAX = (int)255L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_LEAST8_MAX 255
+     * }
+     */
+    public static int UINT_LEAST8_MAX() {
+        return UINT_LEAST8_MAX;
+    }
+    private static final int UINT_LEAST16_MAX = (int)65535L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_LEAST16_MAX 65535
+     * }
+     */
+    public static int UINT_LEAST16_MAX() {
+        return UINT_LEAST16_MAX;
+    }
+    private static final int UINT_LEAST32_MAX = (int)4294967295L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_LEAST32_MAX 4294967295
+     * }
+     */
+    public static int UINT_LEAST32_MAX() {
+        return UINT_LEAST32_MAX;
+    }
+    private static final long UINT_LEAST64_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_LEAST64_MAX -1
+     * }
+     */
+    public static long UINT_LEAST64_MAX() {
+        return UINT_LEAST64_MAX;
+    }
+    private static final int INT_FAST8_MIN = (int)-128L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST8_MIN -128
+     * }
+     */
+    public static int INT_FAST8_MIN() {
+        return INT_FAST8_MIN;
+    }
+    private static final long INT_FAST16_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST16_MIN -9223372036854775808
+     * }
+     */
+    public static long INT_FAST16_MIN() {
+        return INT_FAST16_MIN;
+    }
+    private static final long INT_FAST32_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST32_MIN -9223372036854775808
+     * }
+     */
+    public static long INT_FAST32_MIN() {
+        return INT_FAST32_MIN;
+    }
+    private static final long INT_FAST64_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST64_MIN -9223372036854775808
+     * }
+     */
+    public static long INT_FAST64_MIN() {
+        return INT_FAST64_MIN;
+    }
+    private static final int INT_FAST8_MAX = (int)127L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST8_MAX 127
+     * }
+     */
+    public static int INT_FAST8_MAX() {
+        return INT_FAST8_MAX;
+    }
+    private static final long INT_FAST16_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST16_MAX 9223372036854775807
+     * }
+     */
+    public static long INT_FAST16_MAX() {
+        return INT_FAST16_MAX;
+    }
+    private static final long INT_FAST32_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST32_MAX 9223372036854775807
+     * }
+     */
+    public static long INT_FAST32_MAX() {
+        return INT_FAST32_MAX;
+    }
+    private static final long INT_FAST64_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define INT_FAST64_MAX 9223372036854775807
+     * }
+     */
+    public static long INT_FAST64_MAX() {
+        return INT_FAST64_MAX;
+    }
+    private static final int UINT_FAST8_MAX = (int)255L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_FAST8_MAX 255
+     * }
+     */
+    public static int UINT_FAST8_MAX() {
+        return UINT_FAST8_MAX;
+    }
+    private static final long UINT_FAST16_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_FAST16_MAX -1
+     * }
+     */
+    public static long UINT_FAST16_MAX() {
+        return UINT_FAST16_MAX;
+    }
+    private static final long UINT_FAST32_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_FAST32_MAX -1
+     * }
+     */
+    public static long UINT_FAST32_MAX() {
+        return UINT_FAST32_MAX;
+    }
+    private static final long UINT_FAST64_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define UINT_FAST64_MAX -1
+     * }
+     */
+    public static long UINT_FAST64_MAX() {
+        return UINT_FAST64_MAX;
+    }
+    private static final long INTPTR_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define INTPTR_MIN -9223372036854775808
+     * }
+     */
+    public static long INTPTR_MIN() {
+        return INTPTR_MIN;
+    }
+    private static final long INTPTR_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define INTPTR_MAX 9223372036854775807
+     * }
+     */
+    public static long INTPTR_MAX() {
+        return INTPTR_MAX;
+    }
+    private static final long UINTPTR_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define UINTPTR_MAX -1
+     * }
+     */
+    public static long UINTPTR_MAX() {
+        return UINTPTR_MAX;
+    }
+    private static final long INTMAX_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define INTMAX_MIN -9223372036854775808
+     * }
+     */
+    public static long INTMAX_MIN() {
+        return INTMAX_MIN;
+    }
+    private static final long INTMAX_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define INTMAX_MAX 9223372036854775807
+     * }
+     */
+    public static long INTMAX_MAX() {
+        return INTMAX_MAX;
+    }
+    private static final long UINTMAX_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define UINTMAX_MAX -1
+     * }
+     */
+    public static long UINTMAX_MAX() {
+        return UINTMAX_MAX;
+    }
+    private static final long PTRDIFF_MIN = -9223372036854775808L;
+    /**
+     * {@snippet lang=c :
+     * #define PTRDIFF_MIN -9223372036854775808
+     * }
+     */
+    public static long PTRDIFF_MIN() {
+        return PTRDIFF_MIN;
+    }
+    private static final long PTRDIFF_MAX = 9223372036854775807L;
+    /**
+     * {@snippet lang=c :
+     * #define PTRDIFF_MAX 9223372036854775807
+     * }
+     */
+    public static long PTRDIFF_MAX() {
+        return PTRDIFF_MAX;
+    }
+    private static final int SIG_ATOMIC_MIN = (int)-2147483648L;
+    /**
+     * {@snippet lang=c :
+     * #define SIG_ATOMIC_MIN -2147483648
+     * }
+     */
+    public static int SIG_ATOMIC_MIN() {
+        return SIG_ATOMIC_MIN;
+    }
+    private static final int SIG_ATOMIC_MAX = (int)2147483647L;
+    /**
+     * {@snippet lang=c :
+     * #define SIG_ATOMIC_MAX 2147483647
+     * }
+     */
+    public static int SIG_ATOMIC_MAX() {
+        return SIG_ATOMIC_MAX;
+    }
+    private static final long SIZE_MAX = -1L;
+    /**
+     * {@snippet lang=c :
+     * #define SIZE_MAX -1
+     * }
+     */
+    public static long SIZE_MAX() {
+        return SIZE_MAX;
+    }
+    private static final int WCHAR_MIN = (int)-2147483648L;
+    /**
+     * {@snippet lang=c :
+     * #define WCHAR_MIN -2147483648
+     * }
+     */
+    public static int WCHAR_MIN() {
+        return WCHAR_MIN;
+    }
+    private static final int WCHAR_MAX = (int)2147483647L;
+    /**
+     * {@snippet lang=c :
+     * #define WCHAR_MAX 2147483647
+     * }
+     */
+    public static int WCHAR_MAX() {
+        return WCHAR_MAX;
+    }
+    private static final int WINT_MIN = (int)0L;
+    /**
+     * {@snippet lang=c :
+     * #define WINT_MIN 0
+     * }
+     */
+    public static int WINT_MIN() {
+        return WINT_MIN;
+    }
+    private static final int WINT_MAX = (int)4294967295L;
+    /**
+     * {@snippet lang=c :
+     * #define WINT_MAX 4294967295
+     * }
+     */
+    public static int WINT_MAX() {
+        return WINT_MAX;
+    }
+    private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
+    /**
+     * {@snippet lang=c :
+     * #define NULL (void*) 0
+     * }
+     */
+    public static MemorySegment NULL() {
+        return NULL;
+    }
+    private static final long DLPACK_FLAG_BITMASK_READ_ONLY = 1L;
+    /**
+     * {@snippet lang=c :
+     * #define DLPACK_FLAG_BITMASK_READ_ONLY 1
+     * }
+     */
+    public static long DLPACK_FLAG_BITMASK_READ_ONLY() {
+        return DLPACK_FLAG_BITMASK_READ_ONLY;
+    }
+    private static final long DLPACK_FLAG_BITMASK_IS_COPIED = 2L;
+    /**
+     * {@snippet lang=c :
+     * #define DLPACK_FLAG_BITMASK_IS_COPIED 2
+     * }
+     */
+    public static long DLPACK_FLAG_BITMASK_IS_COPIED() {
+        return DLPACK_FLAG_BITMASK_IS_COPIED;
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/Fsidt.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/Fsidt.java
new file mode 100644
index 0000000000..07f2849a51
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/Fsidt.java
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+import static java.lang.foreign.MemoryLayout.PathElement.sequenceElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.SequenceLayout;
+import java.lang.invoke.VarHandle;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct {
+ *     int __val[2];
+ * }
+ * }
+ */
+public class Fsidt {
+
+  Fsidt() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout
+      .structLayout(MemoryLayout.sequenceLayout(2, CagraH.C_INT).withName("__val")).withName("$anon$155:12");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final SequenceLayout __val$LAYOUT = (SequenceLayout) $LAYOUT.select(groupElement("__val"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * int __val[2]
+   * }
+   */
+  public static final SequenceLayout __val$layout() {
+    return __val$LAYOUT;
+  }
+
+  private static final long __val$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * int __val[2]
+   * }
+   */
+  public static final long __val$offset() {
+    return __val$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * int __val[2]
+   * }
+   */
+  public static MemorySegment __val(MemorySegment struct) {
+    return struct.asSlice(__val$OFFSET, __val$LAYOUT.byteSize());
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * int __val[2]
+   * }
+   */
+  public static void __val(MemorySegment struct, MemorySegment fieldValue) {
+    MemorySegment.copy(fieldValue, 0L, struct, __val$OFFSET, __val$LAYOUT.byteSize());
+  }
+
+  private static long[] __val$DIMS = { 2 };
+
+  /**
+   * Dimensions for array field:
+   * {@snippet lang = c : * int __val[2]
+   * }
+   */
+  public static long[] __val$dimensions() {
+    return __val$DIMS;
+  }
+
+  private static final VarHandle __val$ELEM_HANDLE = __val$LAYOUT.varHandle(sequenceElement());
+
+  /**
+   * Indexed getter for field:
+   * {@snippet lang = c : * int __val[2]
+   * }
+   */
+  public static int __val(MemorySegment struct, long index0) {
+    return (int) __val$ELEM_HANDLE.get(struct, 0L, index0);
+  }
+
+  /**
+   * Indexed setter for field:
+   * {@snippet lang = c : * int __val[2]
+   * }
+   */
+  public static void __val(MemorySegment struct, long index0, int fieldValue) {
+    __val$ELEM_HANDLE.set(struct, 0L, index0, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfo.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfo.java
new file mode 100644
index 0000000000..c1d13575ca
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfo.java
@@ -0,0 +1,342 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+import static java.lang.foreign.MemoryLayout.PathElement.sequenceElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout.OfFloat;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.invoke.VarHandle;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang = c :
+ * struct gpuInfo {
+ *     int gpu_id;
+ *     char name[256];
+ *     long free_memory;
+ *     long total_memory;
+ *     float compute_capability;
+ * }
+ * }
+ */
+public class GpuInfo {
+  GpuInfo() {
+    // Should not be called directly
+  }
+
+  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(GpuInfoH.C_INT.withName("gpu_id"),
+      MemoryLayout.sequenceLayout(256, GpuInfoH.C_CHAR).withName("name"), MemoryLayout.paddingLayout(4),
+      GpuInfoH.C_LONG.withName("free_memory"), GpuInfoH.C_LONG.withName("total_memory"),
+      GpuInfoH.C_FLOAT.withName("compute_capability"), MemoryLayout.paddingLayout(4)).withName("gpuInfo");
+
+  /**
+   * The layout of this struct
+   */
+  public static final GroupLayout layout() {
+    return $LAYOUT;
+  }
+
+  private static final OfInt gpu_id$LAYOUT = (OfInt) $LAYOUT.select(groupElement("gpu_id"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * int gpu_id
+   * }
+   */
+  public static final OfInt gpu_id$layout() {
+    return gpu_id$LAYOUT;
+  }
+
+  private static final long gpu_id$OFFSET = 0;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * int gpu_id
+   * }
+   */
+  public static final long gpu_id$offset() {
+    return gpu_id$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * int gpu_id
+   * }
+   */
+  public static int gpu_id(MemorySegment struct) {
+    return struct.get(gpu_id$LAYOUT, gpu_id$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * int gpu_id
+   * }
+   */
+  public static void gpu_id(MemorySegment struct, int fieldValue) {
+    struct.set(gpu_id$LAYOUT, gpu_id$OFFSET, fieldValue);
+  }
+
+  private static final SequenceLayout name$LAYOUT = (SequenceLayout) $LAYOUT.select(groupElement("name"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * char name[256]
+   * }
+   */
+  public static final SequenceLayout name$layout() {
+    return name$LAYOUT;
+  }
+
+  private static final long name$OFFSET = 4;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * char name[256]
+   * }
+   */
+  public static final long name$offset() {
+    return name$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * char name[256]
+   * }
+   */
+  public static MemorySegment name(MemorySegment struct) {
+    return struct.asSlice(name$OFFSET, name$LAYOUT.byteSize());
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * char name[256]
+   * }
+   */
+  public static void name(MemorySegment struct, MemorySegment fieldValue) {
+    MemorySegment.copy(fieldValue, 0L, struct, name$OFFSET, name$LAYOUT.byteSize());
+  }
+
+  private static long[] name$DIMS = { 256 };
+
+  /**
+   * Dimensions for array field:
+   * {@snippet lang = c : * char name[256]
+   * }
+   */
+  public static long[] name$dimensions() {
+    return name$DIMS;
+  }
+
+  private static final VarHandle name$ELEM_HANDLE = name$LAYOUT.varHandle(sequenceElement());
+
+  /**
+   * Indexed getter for field:
+   * {@snippet lang = c : * char name[256]
+   * }
+   */
+  public static byte name(MemorySegment struct, long index0) {
+    return (byte) name$ELEM_HANDLE.get(struct, 0L, index0);
+  }
+
+  /**
+   * Indexed setter for field:
+   * {@snippet lang = c : * char name[256]
+   * }
+   */
+  public static void name(MemorySegment struct, long index0, byte fieldValue) {
+    name$ELEM_HANDLE.set(struct, 0L, index0, fieldValue);
+  }
+
+  private static final OfLong free_memory$LAYOUT = (OfLong) $LAYOUT.select(groupElement("free_memory"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long free_memory
+   * }
+   */
+  public static final OfLong free_memory$layout() {
+    return free_memory$LAYOUT;
+  }
+
+  private static final long free_memory$OFFSET = 264;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long free_memory
+   * }
+   */
+  public static final long free_memory$offset() {
+    return free_memory$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long free_memory
+   * }
+   */
+  public static long free_memory(MemorySegment struct) {
+    return struct.get(free_memory$LAYOUT, free_memory$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long free_memory
+   * }
+   */
+  public static void free_memory(MemorySegment struct, long fieldValue) {
+    struct.set(free_memory$LAYOUT, free_memory$OFFSET, fieldValue);
+  }
+
+  private static final OfLong total_memory$LAYOUT = (OfLong) $LAYOUT.select(groupElement("total_memory"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * long total_memory
+   * }
+   */
+  public static final OfLong total_memory$layout() {
+    return total_memory$LAYOUT;
+  }
+
+  private static final long total_memory$OFFSET = 272;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * long total_memory
+   * }
+   */
+  public static final long total_memory$offset() {
+    return total_memory$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * long total_memory
+   * }
+   */
+  public static long total_memory(MemorySegment struct) {
+    return struct.get(total_memory$LAYOUT, total_memory$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * long total_memory
+   * }
+   */
+  public static void total_memory(MemorySegment struct, long fieldValue) {
+    struct.set(total_memory$LAYOUT, total_memory$OFFSET, fieldValue);
+  }
+
+  private static final OfFloat compute_capability$LAYOUT = (OfFloat) $LAYOUT.select(groupElement("compute_capability"));
+
+  /**
+   * Layout for field:
+   * {@snippet lang = c : * float compute_capability
+   * }
+   */
+  public static final OfFloat compute_capability$layout() {
+    return compute_capability$LAYOUT;
+  }
+
+  private static final long compute_capability$OFFSET = 280;
+
+  /**
+   * Offset for field:
+   * {@snippet lang = c : * float compute_capability
+   * }
+   */
+  public static final long compute_capability$offset() {
+    return compute_capability$OFFSET;
+  }
+
+  /**
+   * Getter for field:
+   * {@snippet lang = c : * float compute_capability
+   * }
+   */
+  public static float compute_capability(MemorySegment struct) {
+    return struct.get(compute_capability$LAYOUT, compute_capability$OFFSET);
+  }
+
+  /**
+   * Setter for field:
+   * {@snippet lang = c : * float compute_capability
+   * }
+   */
+  public static void compute_capability(MemorySegment struct, float fieldValue) {
+    struct.set(compute_capability$LAYOUT, compute_capability$OFFSET, fieldValue);
+  }
+
+  /**
+   * Obtains a slice of {@code arrayParam} which selects the array element at
+   * {@code index}. The returned segment has address
+   * {@code arrayParam.address() + index * layout().byteSize()}
+   */
+  public static MemorySegment asSlice(MemorySegment array, long index) {
+    return array.asSlice(layout().byteSize() * index);
+  }
+
+  /**
+   * The size (in bytes) of this struct
+   */
+  public static long sizeof() {
+    return layout().byteSize();
+  }
+
+  /**
+   * Allocate a segment of size {@code layout().byteSize()} using
+   * {@code allocator}
+   */
+  public static MemorySegment allocate(SegmentAllocator allocator) {
+    return allocator.allocate(layout());
+  }
+
+  /**
+   * Allocate an array of size {@code elementCount} using {@code allocator}. The
+   * returned segment has size {@code elementCount * layout().byteSize()}.
+   */
+  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+    return reinterpret(addr, 1, arena, cleanup);
+  }
+
+  /**
+   * Reinterprets {@code addr} using target {@code arena} and
+   * {@code cleanupAction} (if any). The returned segment has size
+   * {@code elementCount * layout().byteSize()}
+   */
+  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
+      Consumer<MemorySegment> cleanup) {
+    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfoH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfoH.java
new file mode 100644
index 0000000000..ad37e37ecf
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfoH.java
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.ValueLayout.JAVA_BYTE;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.PaddingLayout;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.StructLayout;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.stream.Collectors;
+
+public class GpuInfoH {
+  GpuInfoH() {
+    // Should not be called directly
+  }
+
+  static final Arena LIBRARY_ARENA = Arena.ofAuto();
+  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+  static void traceDowncall(String name, Object... args) {
+    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
+    System.out.printf("%s(%s)\n", name, traceArgs);
+  }
+
+  static MemorySegment findOrThrow(String symbol) {
+    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+  }
+
+  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+    try {
+      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+    } catch (ReflectiveOperationException ex) {
+      throw new AssertionError(ex);
+    }
+  }
+
+  static MemoryLayout align(MemoryLayout layout, long align) {
+    return switch (layout) {
+    case PaddingLayout p -> p;
+    case ValueLayout v -> v.withByteAlignment(align);
+    case GroupLayout g -> {
+      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
+          : MemoryLayout.unionLayout(alignedMembers);
+    }
+    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+    };
+  }
+
+  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
+  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/HnswH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/HnswH.java
new file mode 100644
index 0000000000..32945c432d
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/HnswH.java
@@ -0,0 +1,2350 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.ValueLayout.JAVA_BYTE;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.PaddingLayout;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.StructLayout;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.foreign.ValueLayout.OfByte;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.foreign.ValueLayout.OfShort;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.stream.Collectors;
+
+public class HnswH {
+
+  HnswH() {
+    // Should not be called directly
+  }
+
+  static final Arena LIBRARY_ARENA = Arena.ofAuto();
+  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+  static void traceDowncall(String name, Object... args) {
+    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
+    System.out.printf("%s(%s)\n", name, traceArgs);
+  }
+
+  static MemorySegment findOrThrow(String symbol) {
+    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+  }
+
+  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+    try {
+      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+    } catch (ReflectiveOperationException ex) {
+      throw new AssertionError(ex);
+    }
+  }
+
+  static MemoryLayout align(MemoryLayout layout, long align) {
+    return switch (layout) {
+    case PaddingLayout p -> p;
+    case ValueLayout v -> v.withByteAlignment(align);
+    case GroupLayout g -> {
+      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
+          : MemoryLayout.unionLayout(alignedMembers);
+    }
+    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+    };
+  }
+
+  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
+
+  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+  private static final int DLPACK_VERSION = (int) 80L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_VERSION 80
+   * }
+   */
+  public static int DLPACK_VERSION() {
+    return DLPACK_VERSION;
+  }
+
+  private static final int DLPACK_ABI_VERSION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_ABI_VERSION 1
+   * }
+   */
+  public static int DLPACK_ABI_VERSION() {
+    return DLPACK_ABI_VERSION;
+  }
+
+  private static final int _STDINT_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDINT_H 1
+   * }
+   */
+  public static int _STDINT_H() {
+    return _STDINT_H;
+  }
+
+  private static final int _FEATURES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _FEATURES_H 1
+   * }
+   */
+  public static int _FEATURES_H() {
+    return _FEATURES_H;
+  }
+
+  private static final int _DEFAULT_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
+   * }
+   */
+  public static int _DEFAULT_SOURCE() {
+    return _DEFAULT_SOURCE;
+  }
+
+  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
+   * }
+   */
+  public static int __GLIBC_USE_ISOC2X() {
+    return __GLIBC_USE_ISOC2X;
+  }
+
+  private static final int __USE_ISOC11 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC11 1
+   * }
+   */
+  public static int __USE_ISOC11() {
+    return __USE_ISOC11;
+  }
+
+  private static final int __USE_ISOC99 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC99 1
+   * }
+   */
+  public static int __USE_ISOC99() {
+    return __USE_ISOC99;
+  }
+
+  private static final int __USE_ISOC95 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC95 1
+   * }
+   */
+  public static int __USE_ISOC95() {
+    return __USE_ISOC95;
+  }
+
+  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
+   * }
+   */
+  public static int __USE_POSIX_IMPLICITLY() {
+    return __USE_POSIX_IMPLICITLY;
+  }
+
+  private static final int _POSIX_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_SOURCE 1
+   * }
+   */
+  public static int _POSIX_SOURCE() {
+    return _POSIX_SOURCE;
+  }
+
+  private static final int __USE_POSIX = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX 1
+   * }
+   */
+  public static int __USE_POSIX() {
+    return __USE_POSIX;
+  }
+
+  private static final int __USE_POSIX2 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX2 1
+   * }
+   */
+  public static int __USE_POSIX2() {
+    return __USE_POSIX2;
+  }
+
+  private static final int __USE_POSIX199309 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199309 1
+   * }
+   */
+  public static int __USE_POSIX199309() {
+    return __USE_POSIX199309;
+  }
+
+  private static final int __USE_POSIX199506 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199506 1
+   * }
+   */
+  public static int __USE_POSIX199506() {
+    return __USE_POSIX199506;
+  }
+
+  private static final int __USE_XOPEN2K = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K 1
+   * }
+   */
+  public static int __USE_XOPEN2K() {
+    return __USE_XOPEN2K;
+  }
+
+  private static final int __USE_XOPEN2K8 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
+   * }
+   */
+  public static int __USE_XOPEN2K8() {
+    return __USE_XOPEN2K8;
+  }
+
+  private static final int _ATFILE_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
+   * }
+   */
+  public static int _ATFILE_SOURCE() {
+    return _ATFILE_SOURCE;
+  }
+
+  private static final int __WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE 64
+   * }
+   */
+  public static int __WORDSIZE() {
+    return __WORDSIZE;
+  }
+
+  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
+   * }
+   */
+  public static int __WORDSIZE_TIME64_COMPAT32() {
+    return __WORDSIZE_TIME64_COMPAT32;
+  }
+
+  private static final int __SYSCALL_WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
+   * }
+   */
+  public static int __SYSCALL_WORDSIZE() {
+    return __SYSCALL_WORDSIZE;
+  }
+
+  private static final int __USE_MISC = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_MISC 1
+   * }
+   */
+  public static int __USE_MISC() {
+    return __USE_MISC;
+  }
+
+  private static final int __USE_ATFILE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ATFILE 1
+   * }
+   */
+  public static int __USE_ATFILE() {
+    return __USE_ATFILE;
+  }
+
+  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
+   * }
+   */
+  public static int __USE_FORTIFY_LEVEL() {
+    return __USE_FORTIFY_LEVEL;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_GETS() {
+    return __GLIBC_USE_DEPRECATED_GETS;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_SCANF() {
+    return __GLIBC_USE_DEPRECATED_SCANF;
+  }
+
+  private static final int _STDC_PREDEF_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
+   * }
+   */
+  public static int _STDC_PREDEF_H() {
+    return _STDC_PREDEF_H;
+  }
+
+  private static final int __STDC_IEC_559__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
+   * }
+   */
+  public static int __STDC_IEC_559__() {
+    return __STDC_IEC_559__;
+  }
+
+  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
+   * }
+   */
+  public static int __STDC_IEC_559_COMPLEX__() {
+    return __STDC_IEC_559_COMPLEX__;
+  }
+
+  private static final int __GNU_LIBRARY__ = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
+   * }
+   */
+  public static int __GNU_LIBRARY__() {
+    return __GNU_LIBRARY__;
+  }
+
+  private static final int __GLIBC__ = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC__ 2
+   * }
+   */
+  public static int __GLIBC__() {
+    return __GLIBC__;
+  }
+
+  private static final int __GLIBC_MINOR__ = (int) 35L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
+   * }
+   */
+  public static int __GLIBC_MINOR__() {
+    return __GLIBC_MINOR__;
+  }
+
+  private static final int _SYS_CDEFS_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
+   * }
+   */
+  public static int _SYS_CDEFS_H() {
+    return _SYS_CDEFS_H;
+  }
+
+  private static final int __glibc_c99_flexarr_available = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
+   * }
+   */
+  public static int __glibc_c99_flexarr_available() {
+    return __glibc_c99_flexarr_available;
+  }
+
+  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
+   * }
+   */
+  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
+    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
+  }
+
+  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
+   * }
+   */
+  public static int __HAVE_GENERIC_SELECTION() {
+    return __HAVE_GENERIC_SELECTION;
+  }
+
+  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
+   * }
+   */
+  public static int __GLIBC_USE_LIB_EXT2() {
+    return __GLIBC_USE_LIB_EXT2;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_EXT() {
+    return __GLIBC_USE_IEC_60559_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
+    return __GLIBC_USE_IEC_60559_TYPES_EXT;
+  }
+
+  private static final int _BITS_TYPES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPES_H 1
+   * }
+   */
+  public static int _BITS_TYPES_H() {
+    return _BITS_TYPES_H;
+  }
+
+  private static final int _BITS_TYPESIZES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
+   * }
+   */
+  public static int _BITS_TYPESIZES_H() {
+    return _BITS_TYPESIZES_H;
+  }
+
+  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
+   * }
+   */
+  public static int __OFF_T_MATCHES_OFF64_T() {
+    return __OFF_T_MATCHES_OFF64_T;
+  }
+
+  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
+   * }
+   */
+  public static int __INO_T_MATCHES_INO64_T() {
+    return __INO_T_MATCHES_INO64_T;
+  }
+
+  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
+   * }
+   */
+  public static int __RLIM_T_MATCHES_RLIM64_T() {
+    return __RLIM_T_MATCHES_RLIM64_T;
+  }
+
+  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
+   * }
+   */
+  public static int __STATFS_MATCHES_STATFS64() {
+    return __STATFS_MATCHES_STATFS64;
+  }
+
+  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
+   * }
+   */
+  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
+    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
+  }
+
+  private static final int __FD_SETSIZE = (int) 1024L;
+
+  /**
+   * {@snippet lang = c : * #define __FD_SETSIZE 1024
+   * }
+   */
+  public static int __FD_SETSIZE() {
+    return __FD_SETSIZE;
+  }
+
+  private static final int _BITS_TIME64_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TIME64_H 1
+   * }
+   */
+  public static int _BITS_TIME64_H() {
+    return _BITS_TIME64_H;
+  }
+
+  private static final int _BITS_WCHAR_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
+   * }
+   */
+  public static int _BITS_WCHAR_H() {
+    return _BITS_WCHAR_H;
+  }
+
+  private static final int _BITS_STDINT_INTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_INTN_H() {
+    return _BITS_STDINT_INTN_H;
+  }
+
+  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_UINTN_H() {
+    return _BITS_STDINT_UINTN_H;
+  }
+
+  private static final int true_ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define true 1
+   * }
+   */
+  public static int true_() {
+    return true_;
+  }
+
+  private static final int false_ = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define false 0
+   * }
+   */
+  public static int false_() {
+    return false_;
+  }
+
+  private static final int __bool_true_false_are_defined = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
+   * }
+   */
+  public static int __bool_true_false_are_defined() {
+    return __bool_true_false_are_defined;
+  }
+
+  private static final int L2Expanded = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
+   * }
+   */
+  public static int L2Expanded() {
+    return L2Expanded;
+  }
+
+  private static final int L2SqrtExpanded = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
+   * }
+   */
+  public static int L2SqrtExpanded() {
+    return L2SqrtExpanded;
+  }
+
+  private static final int CosineExpanded = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
+   * }
+   */
+  public static int CosineExpanded() {
+    return CosineExpanded;
+  }
+
+  private static final int L1 = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L1 = 3
+   * }
+   */
+  public static int L1() {
+    return L1;
+  }
+
+  private static final int L2Unexpanded = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
+   * }
+   */
+  public static int L2Unexpanded() {
+    return L2Unexpanded;
+  }
+
+  private static final int L2SqrtUnexpanded = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
+   * }
+   */
+  public static int L2SqrtUnexpanded() {
+    return L2SqrtUnexpanded;
+  }
+
+  private static final int InnerProduct = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
+   * }
+   */
+  public static int InnerProduct() {
+    return InnerProduct;
+  }
+
+  private static final int Linf = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Linf = 7
+   * }
+   */
+  public static int Linf() {
+    return Linf;
+  }
+
+  private static final int Canberra = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
+   * }
+   */
+  public static int Canberra() {
+    return Canberra;
+  }
+
+  private static final int LpUnexpanded = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
+   * }
+   */
+  public static int LpUnexpanded() {
+    return LpUnexpanded;
+  }
+
+  private static final int CorrelationExpanded = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
+   * }
+   */
+  public static int CorrelationExpanded() {
+    return CorrelationExpanded;
+  }
+
+  private static final int JaccardExpanded = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
+   * }
+   */
+  public static int JaccardExpanded() {
+    return JaccardExpanded;
+  }
+
+  private static final int HellingerExpanded = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
+   * }
+   */
+  public static int HellingerExpanded() {
+    return HellingerExpanded;
+  }
+
+  private static final int Haversine = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
+   * }
+   */
+  public static int Haversine() {
+    return Haversine;
+  }
+
+  private static final int BrayCurtis = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
+   * }
+   */
+  public static int BrayCurtis() {
+    return BrayCurtis;
+  }
+
+  private static final int JensenShannon = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
+   * }
+   */
+  public static int JensenShannon() {
+    return JensenShannon;
+  }
+
+  private static final int HammingUnexpanded = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
+   * }
+   */
+  public static int HammingUnexpanded() {
+    return HammingUnexpanded;
+  }
+
+  private static final int KLDivergence = (int) 17L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
+   * }
+   */
+  public static int KLDivergence() {
+    return KLDivergence;
+  }
+
+  private static final int RusselRaoExpanded = (int) 18L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
+   * }
+   */
+  public static int RusselRaoExpanded() {
+    return RusselRaoExpanded;
+  }
+
+  private static final int DiceExpanded = (int) 19L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
+   * }
+   */
+  public static int DiceExpanded() {
+    return DiceExpanded;
+  }
+
+  private static final int Precomputed = (int) 100L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
+   * }
+   */
+  public static int Precomputed() {
+    return Precomputed;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef unsigned char __u_char
+   * }
+   */
+  public static final OfByte __u_char = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __u_short
+   * }
+   */
+  public static final OfShort __u_short = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __u_int
+   * }
+   */
+  public static final OfInt __u_int = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_long
+   * }
+   */
+  public static final OfLong __u_long = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char __int8_t
+   * }
+   */
+  public static final OfByte __int8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned char __uint8_t
+   * }
+   */
+  public static final OfByte __uint8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef short __int16_t
+   * }
+   */
+  public static final OfShort __int16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __uint16_t
+   * }
+   */
+  public static final OfShort __uint16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef int __int32_t
+   * }
+   */
+  public static final OfInt __int32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uint32_t
+   * }
+   */
+  public static final OfInt __uint32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __int64_t
+   * }
+   */
+  public static final OfLong __int64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uint64_t
+   * }
+   */
+  public static final OfLong __uint64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int8_t __int_least8_t
+   * }
+   */
+  public static final OfByte __int_least8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
+   * }
+   */
+  public static final OfByte __uint_least8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t __int_least16_t
+   * }
+   */
+  public static final OfShort __int_least16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
+   * }
+   */
+  public static final OfShort __uint_least16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t __int_least32_t
+   * }
+   */
+  public static final OfInt __int_least32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
+   * }
+   */
+  public static final OfInt __uint_least32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t __int_least64_t
+   * }
+   */
+  public static final OfLong __int_least64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
+   * }
+   */
+  public static final OfLong __uint_least64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __quad_t
+   * }
+   */
+  public static final OfLong __quad_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_quad_t
+   * }
+   */
+  public static final OfLong __u_quad_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __intmax_t
+   * }
+   */
+  public static final OfLong __intmax_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uintmax_t
+   * }
+   */
+  public static final OfLong __uintmax_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __dev_t
+   * }
+   */
+  public static final OfLong __dev_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uid_t
+   * }
+   */
+  public static final OfInt __uid_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __gid_t
+   * }
+   */
+  public static final OfInt __gid_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino_t
+   * }
+   */
+  public static final OfLong __ino_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino64_t
+   * }
+   */
+  public static final OfLong __ino64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __mode_t
+   * }
+   */
+  public static final OfInt __mode_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __nlink_t
+   * }
+   */
+  public static final OfLong __nlink_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off_t
+   * }
+   */
+  public static final OfLong __off_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off64_t
+   * }
+   */
+  public static final OfLong __off64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __pid_t
+   * }
+   */
+  public static final OfInt __pid_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __clock_t
+   * }
+   */
+  public static final OfLong __clock_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim_t
+   * }
+   */
+  public static final OfLong __rlim_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim64_t
+   * }
+   */
+  public static final OfLong __rlim64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __id_t
+   * }
+   */
+  public static final OfInt __id_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __time_t
+   * }
+   */
+  public static final OfLong __time_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __useconds_t
+   * }
+   */
+  public static final OfInt __useconds_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds_t
+   * }
+   */
+  public static final OfLong __suseconds_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds64_t
+   * }
+   */
+  public static final OfLong __suseconds64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __daddr_t
+   * }
+   */
+  public static final OfInt __daddr_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __key_t
+   * }
+   */
+  public static final OfInt __key_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __clockid_t
+   * }
+   */
+  public static final OfInt __clockid_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef void *__timer_t
+   * }
+   */
+  public static final AddressLayout __timer_t = HnswH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __blksize_t
+   * }
+   */
+  public static final OfLong __blksize_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt_t
+   * }
+   */
+  public static final OfLong __blkcnt_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt64_t
+   * }
+   */
+  public static final OfLong __blkcnt64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
+   * }
+   */
+  public static final OfLong __fsblkcnt_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
+   * }
+   */
+  public static final OfLong __fsblkcnt64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
+   * }
+   */
+  public static final OfLong __fsfilcnt_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
+   * }
+   */
+  public static final OfLong __fsfilcnt64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __fsword_t
+   * }
+   */
+  public static final OfLong __fsword_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __ssize_t
+   * }
+   */
+  public static final OfLong __ssize_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __syscall_slong_t
+   * }
+   */
+  public static final OfLong __syscall_slong_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
+   * }
+   */
+  public static final OfLong __syscall_ulong_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __off64_t __loff_t
+   * }
+   */
+  public static final OfLong __loff_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef char *__caddr_t
+   * }
+   */
+  public static final AddressLayout __caddr_t = HnswH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __intptr_t
+   * }
+   */
+  public static final OfLong __intptr_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __socklen_t
+   * }
+   */
+  public static final OfInt __socklen_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __sig_atomic_t
+   * }
+   */
+  public static final OfInt __sig_atomic_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int8_t int8_t
+   * }
+   */
+  public static final OfByte int8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t int16_t
+   * }
+   */
+  public static final OfShort int16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t int32_t
+   * }
+   */
+  public static final OfInt int32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t int64_t
+   * }
+   */
+  public static final OfLong int64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t uint8_t
+   * }
+   */
+  public static final OfByte uint8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t uint16_t
+   * }
+   */
+  public static final OfShort uint16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t uint32_t
+   * }
+   */
+  public static final OfInt uint32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t uint64_t
+   * }
+   */
+  public static final OfLong uint64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
+   * }
+   */
+  public static final OfByte int_least8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
+   * }
+   */
+  public static final OfShort int_least16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
+   * }
+   */
+  public static final OfInt int_least32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
+   * }
+   */
+  public static final OfLong int_least64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
+   * }
+   */
+  public static final OfByte uint_least8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
+   * }
+   */
+  public static final OfShort uint_least16_t = HnswH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
+   * }
+   */
+  public static final OfInt uint_least32_t = HnswH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
+   * }
+   */
+  public static final OfLong uint_least64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char int_fast8_t
+   * }
+   */
+  public static final OfByte int_fast8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef long int_fast16_t
+   * }
+   */
+  public static final OfLong int_fast16_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast32_t
+   * }
+   */
+  public static final OfLong int_fast32_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast64_t
+   * }
+   */
+  public static final OfLong int_fast64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
+   * }
+   */
+  public static final OfByte uint_fast8_t = HnswH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
+   * }
+   */
+  public static final OfLong uint_fast16_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
+   * }
+   */
+  public static final OfLong uint_fast32_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
+   * }
+   */
+  public static final OfLong uint_fast64_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long intptr_t
+   * }
+   */
+  public static final OfLong intptr_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uintptr_t
+   * }
+   */
+  public static final OfLong uintptr_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __intmax_t intmax_t
+   * }
+   */
+  public static final OfLong intmax_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
+   * }
+   */
+  public static final OfLong uintmax_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long ptrdiff_t
+   * }
+   */
+  public static final OfLong ptrdiff_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long size_t
+   * }
+   */
+  public static final OfLong size_t = HnswH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int wchar_t
+   * }
+   */
+  public static final OfInt wchar_t = HnswH.C_INT;
+  private static final int kDLCPU = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
+   * }
+   */
+  public static int kDLCPU() {
+    return kDLCPU;
+  }
+
+  private static final int kDLCUDA = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
+   * }
+   */
+  public static int kDLCUDA() {
+    return kDLCUDA;
+  }
+
+  private static final int kDLCUDAHost = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
+   * }
+   */
+  public static int kDLCUDAHost() {
+    return kDLCUDAHost;
+  }
+
+  private static final int kDLOpenCL = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
+   * }
+   */
+  public static int kDLOpenCL() {
+    return kDLOpenCL;
+  }
+
+  private static final int kDLVulkan = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
+   * }
+   */
+  public static int kDLVulkan() {
+    return kDLVulkan;
+  }
+
+  private static final int kDLMetal = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
+   * }
+   */
+  public static int kDLMetal() {
+    return kDLMetal;
+  }
+
+  private static final int kDLVPI = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
+   * }
+   */
+  public static int kDLVPI() {
+    return kDLVPI;
+  }
+
+  private static final int kDLROCM = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
+   * }
+   */
+  public static int kDLROCM() {
+    return kDLROCM;
+  }
+
+  private static final int kDLROCMHost = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
+   * }
+   */
+  public static int kDLROCMHost() {
+    return kDLROCMHost;
+  }
+
+  private static final int kDLExtDev = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
+   * }
+   */
+  public static int kDLExtDev() {
+    return kDLExtDev;
+  }
+
+  private static final int kDLCUDAManaged = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
+   * }
+   */
+  public static int kDLCUDAManaged() {
+    return kDLCUDAManaged;
+  }
+
+  private static final int kDLOneAPI = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
+   * }
+   */
+  public static int kDLOneAPI() {
+    return kDLOneAPI;
+  }
+
+  private static final int kDLWebGPU = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
+   * }
+   */
+  public static int kDLWebGPU() {
+    return kDLWebGPU;
+  }
+
+  private static final int kDLHexagon = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
+   * }
+   */
+  public static int kDLHexagon() {
+    return kDLHexagon;
+  }
+
+  private static final int kDLInt = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
+   * }
+   */
+  public static int kDLInt() {
+    return kDLInt;
+  }
+
+  private static final int kDLUInt = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
+   * }
+   */
+  public static int kDLUInt() {
+    return kDLUInt;
+  }
+
+  private static final int kDLFloat = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
+   * }
+   */
+  public static int kDLFloat() {
+    return kDLFloat;
+  }
+
+  private static final int kDLOpaqueHandle = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
+   * }
+   */
+  public static int kDLOpaqueHandle() {
+    return kDLOpaqueHandle;
+  }
+
+  private static final int kDLBfloat = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
+   * }
+   */
+  public static int kDLBfloat() {
+    return kDLBfloat;
+  }
+
+  private static final int kDLComplex = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
+   * }
+   */
+  public static int kDLComplex() {
+    return kDLComplex;
+  }
+
+  private static final int kDLBool = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
+   * }
+   */
+  public static int kDLBool() {
+    return kDLBool;
+  }
+
+  private static final int AUTO_SELECT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.AUTO_SELECT = 0
+   * }
+   */
+  public static int AUTO_SELECT() {
+    return AUTO_SELECT;
+  }
+
+  private static final int IVF_PQ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.IVF_PQ = 1
+   * }
+   */
+  public static int IVF_PQ() {
+    return IVF_PQ;
+  }
+
+  private static final int NN_DESCENT = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.NN_DESCENT = 2
+   * }
+   */
+  public static int NN_DESCENT() {
+    return NN_DESCENT;
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsCagraCompressionParams {
+   *     uint32_t pq_bits;
+   *     uint32_t pq_dim;
+   *     uint32_t vq_n_centers;
+   *     uint32_t kmeans_n_iters;
+   *     double vq_kmeans_trainset_fraction;
+   *     double pq_kmeans_trainset_fraction;
+   * } *cuvsCagraCompressionParams_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraCompressionParams_t = HnswH.C_POINTER;
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsCagraIndexParams {
+   *     cuvsDistanceType metric;
+   *     long intermediate_graph_degree;
+   *     long graph_degree;
+   *     enum cuvsCagraGraphBuildAlgo build_algo;
+   *     long nn_descent_niter;
+   *     cuvsCagraCompressionParams_t compression;
+   * } *cuvsCagraIndexParams_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraIndexParams_t = HnswH.C_POINTER;
+  private static final int SINGLE_CTA = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.SINGLE_CTA = 0
+   * }
+   */
+  public static int SINGLE_CTA() {
+    return SINGLE_CTA;
+  }
+
+  private static final int MULTI_CTA = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_CTA = 1
+   * }
+   */
+  public static int MULTI_CTA() {
+    return MULTI_CTA;
+  }
+
+  private static final int MULTI_KERNEL = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_KERNEL = 2
+   * }
+   */
+  public static int MULTI_KERNEL() {
+    return MULTI_KERNEL;
+  }
+
+  private static final int AUTO = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.AUTO = 3
+   * }
+   */
+  public static int AUTO() {
+    return AUTO;
+  }
+
+  private static final int HASH = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraHashMode.HASH = 0
+   * }
+   */
+  public static int HASH() {
+    return HASH;
+  }
+
+  private static final int SMALL = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraHashMode.SMALL = 1
+   * }
+   */
+  public static int SMALL() {
+    return SMALL;
+  }
+
+  private static final int AUTO_HASH = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsCagraHashMode.AUTO_HASH = 2
+   * }
+   */
+  public static int AUTO_HASH() {
+    return AUTO_HASH;
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsCagraSearchParams {
+   *     long max_queries;
+   *     long itopk_size;
+   *     long max_iterations;
+   *     enum cuvsCagraSearchAlgo algo;
+   *     long team_size;
+   *     long search_width;
+   *     long min_iterations;
+   *     long thread_block_size;
+   *     enum cuvsCagraHashMode hashmap_mode;
+   *     long hashmap_min_bitlen;
+   *     float hashmap_max_fill_rate;
+   *     uint32_t num_random_samplings;
+   *     uint64_t rand_xor_mask;
+   * } *cuvsCagraSearchParams_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraSearchParams_t = HnswH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef cuvsCagraIndex *cuvsCagraIndex_t
+   * }
+   */
+  public static final AddressLayout cuvsCagraIndex_t = HnswH.C_POINTER;
+  private static final int NONE = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsHnswHierarchy.NONE = 0
+   * }
+   */
+  public static int NONE() {
+    return NONE;
+  }
+
+  private static final int CPU = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cuvsHnswHierarchy.CPU = 1
+   * }
+   */
+  public static int CPU() {
+    return CPU;
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsHnswIndexParams {
+   *     cuvsHnswHierarchy hierarchy;
+   *     int ef_construction;
+   *     int num_threads;
+   * } *cuvsHnswIndexParams_t
+   * }
+   */
+  public static final AddressLayout cuvsHnswIndexParams_t = HnswH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef cuvsHnswIndex *cuvsHnswIndex_t
+   * }
+   */
+  public static final AddressLayout cuvsHnswIndex_t = HnswH.C_POINTER;
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsHnswExtendParams {
+   *     int num_threads;
+   * } *cuvsHnswExtendParams_t
+   * }
+   */
+  public static final AddressLayout cuvsHnswExtendParams_t = HnswH.C_POINTER;
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsHnswSearchParams {
+   *     int32_t ef;
+   *     int32_t num_threads;
+   * } *cuvsHnswSearchParams_t
+   * }
+   */
+  public static final AddressLayout cuvsHnswSearchParams_t = HnswH.C_POINTER;
+  private static final long _POSIX_C_SOURCE = 200809L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
+   * }
+   */
+  public static long _POSIX_C_SOURCE() {
+    return _POSIX_C_SOURCE;
+  }
+
+  private static final int __TIMESIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __TIMESIZE 64
+   * }
+   */
+  public static int __TIMESIZE() {
+    return __TIMESIZE;
+  }
+
+  private static final long __STDC_IEC_60559_BFP__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_BFP__() {
+    return __STDC_IEC_60559_BFP__;
+  }
+
+  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_COMPLEX__() {
+    return __STDC_IEC_60559_COMPLEX__;
+  }
+
+  private static final long __STDC_ISO_10646__ = 201706L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
+   * }
+   */
+  public static long __STDC_ISO_10646__() {
+    return __STDC_ISO_10646__;
+  }
+
+  private static final int __WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
+   * }
+   */
+  public static int __WCHAR_MAX() {
+    return __WCHAR_MAX;
+  }
+
+  private static final int __WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
+   * }
+   */
+  public static int __WCHAR_MIN() {
+    return __WCHAR_MIN;
+  }
+
+  private static final int INT8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MIN -128
+   * }
+   */
+  public static int INT8_MIN() {
+    return INT8_MIN;
+  }
+
+  private static final int INT16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MIN -32768
+   * }
+   */
+  public static int INT16_MIN() {
+    return INT16_MIN;
+  }
+
+  private static final int INT32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MIN -2147483648
+   * }
+   */
+  public static int INT32_MIN() {
+    return INT32_MIN;
+  }
+
+  private static final long INT64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT64_MIN() {
+    return INT64_MIN;
+  }
+
+  private static final int INT8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MAX 127
+   * }
+   */
+  public static int INT8_MAX() {
+    return INT8_MAX;
+  }
+
+  private static final int INT16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MAX 32767
+   * }
+   */
+  public static int INT16_MAX() {
+    return INT16_MAX;
+  }
+
+  private static final int INT32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MAX 2147483647
+   * }
+   */
+  public static int INT32_MAX() {
+    return INT32_MAX;
+  }
+
+  private static final long INT64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT64_MAX() {
+    return INT64_MAX;
+  }
+
+  private static final int UINT8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT8_MAX 255
+   * }
+   */
+  public static int UINT8_MAX() {
+    return UINT8_MAX;
+  }
+
+  private static final int UINT16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT16_MAX 65535
+   * }
+   */
+  public static int UINT16_MAX() {
+    return UINT16_MAX;
+  }
+
+  private static final int UINT32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT32_MAX 4294967295
+   * }
+   */
+  public static int UINT32_MAX() {
+    return UINT32_MAX;
+  }
+
+  private static final long UINT64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT64_MAX -1
+   * }
+   */
+  public static long UINT64_MAX() {
+    return UINT64_MAX;
+  }
+
+  private static final int INT_LEAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
+   * }
+   */
+  public static int INT_LEAST8_MIN() {
+    return INT_LEAST8_MIN;
+  }
+
+  private static final int INT_LEAST16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
+   * }
+   */
+  public static int INT_LEAST16_MIN() {
+    return INT_LEAST16_MIN;
+  }
+
+  private static final int INT_LEAST32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
+   * }
+   */
+  public static int INT_LEAST32_MIN() {
+    return INT_LEAST32_MIN;
+  }
+
+  private static final long INT_LEAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_LEAST64_MIN() {
+    return INT_LEAST64_MIN;
+  }
+
+  private static final int INT_LEAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
+   * }
+   */
+  public static int INT_LEAST8_MAX() {
+    return INT_LEAST8_MAX;
+  }
+
+  private static final int INT_LEAST16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
+   * }
+   */
+  public static int INT_LEAST16_MAX() {
+    return INT_LEAST16_MAX;
+  }
+
+  private static final int INT_LEAST32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
+   * }
+   */
+  public static int INT_LEAST32_MAX() {
+    return INT_LEAST32_MAX;
+  }
+
+  private static final long INT_LEAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_LEAST64_MAX() {
+    return INT_LEAST64_MAX;
+  }
+
+  private static final int UINT_LEAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
+   * }
+   */
+  public static int UINT_LEAST8_MAX() {
+    return UINT_LEAST8_MAX;
+  }
+
+  private static final int UINT_LEAST16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
+   * }
+   */
+  public static int UINT_LEAST16_MAX() {
+    return UINT_LEAST16_MAX;
+  }
+
+  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
+   * }
+   */
+  public static int UINT_LEAST32_MAX() {
+    return UINT_LEAST32_MAX;
+  }
+
+  private static final long UINT_LEAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
+   * }
+   */
+  public static long UINT_LEAST64_MAX() {
+    return UINT_LEAST64_MAX;
+  }
+
+  private static final int INT_FAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MIN -128
+   * }
+   */
+  public static int INT_FAST8_MIN() {
+    return INT_FAST8_MIN;
+  }
+
+  private static final long INT_FAST16_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST16_MIN() {
+    return INT_FAST16_MIN;
+  }
+
+  private static final long INT_FAST32_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST32_MIN() {
+    return INT_FAST32_MIN;
+  }
+
+  private static final long INT_FAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST64_MIN() {
+    return INT_FAST64_MIN;
+  }
+
+  private static final int INT_FAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MAX 127
+   * }
+   */
+  public static int INT_FAST8_MAX() {
+    return INT_FAST8_MAX;
+  }
+
+  private static final long INT_FAST16_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST16_MAX() {
+    return INT_FAST16_MAX;
+  }
+
+  private static final long INT_FAST32_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST32_MAX() {
+    return INT_FAST32_MAX;
+  }
+
+  private static final long INT_FAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST64_MAX() {
+    return INT_FAST64_MAX;
+  }
+
+  private static final int UINT_FAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
+   * }
+   */
+  public static int UINT_FAST8_MAX() {
+    return UINT_FAST8_MAX;
+  }
+
+  private static final long UINT_FAST16_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
+   * }
+   */
+  public static long UINT_FAST16_MAX() {
+    return UINT_FAST16_MAX;
+  }
+
+  private static final long UINT_FAST32_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
+   * }
+   */
+  public static long UINT_FAST32_MAX() {
+    return UINT_FAST32_MAX;
+  }
+
+  private static final long UINT_FAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
+   * }
+   */
+  public static long UINT_FAST64_MAX() {
+    return UINT_FAST64_MAX;
+  }
+
+  private static final long INTPTR_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
+   * }
+   */
+  public static long INTPTR_MIN() {
+    return INTPTR_MIN;
+  }
+
+  private static final long INTPTR_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
+   * }
+   */
+  public static long INTPTR_MAX() {
+    return INTPTR_MAX;
+  }
+
+  private static final long UINTPTR_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTPTR_MAX -1
+   * }
+   */
+  public static long UINTPTR_MAX() {
+    return UINTPTR_MAX;
+  }
+
+  private static final long INTMAX_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
+   * }
+   */
+  public static long INTMAX_MIN() {
+    return INTMAX_MIN;
+  }
+
+  private static final long INTMAX_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
+   * }
+   */
+  public static long INTMAX_MAX() {
+    return INTMAX_MAX;
+  }
+
+  private static final long UINTMAX_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTMAX_MAX -1
+   * }
+   */
+  public static long UINTMAX_MAX() {
+    return UINTMAX_MAX;
+  }
+
+  private static final long PTRDIFF_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
+   * }
+   */
+  public static long PTRDIFF_MIN() {
+    return PTRDIFF_MIN;
+  }
+
+  private static final long PTRDIFF_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
+   * }
+   */
+  public static long PTRDIFF_MAX() {
+    return PTRDIFF_MAX;
+  }
+
+  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
+   * }
+   */
+  public static int SIG_ATOMIC_MIN() {
+    return SIG_ATOMIC_MIN;
+  }
+
+  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
+   * }
+   */
+  public static int SIG_ATOMIC_MAX() {
+    return SIG_ATOMIC_MAX;
+  }
+
+  private static final long SIZE_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define SIZE_MAX -1
+   * }
+   */
+  public static long SIZE_MAX() {
+    return SIZE_MAX;
+  }
+
+  private static final int WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
+   * }
+   */
+  public static int WCHAR_MIN() {
+    return WCHAR_MIN;
+  }
+
+  private static final int WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
+   * }
+   */
+  public static int WCHAR_MAX() {
+    return WCHAR_MAX;
+  }
+
+  private static final int WINT_MIN = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MIN 0
+   * }
+   */
+  public static int WINT_MIN() {
+    return WINT_MIN;
+  }
+
+  private static final int WINT_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MAX 4294967295
+   * }
+   */
+  public static int WINT_MAX() {
+    return WINT_MAX;
+  }
+
+  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
+
+  /**
+   * {@snippet lang = c : * #define NULL (void*) 0
+   * }
+   */
+  public static MemorySegment NULL() {
+    return NULL;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfFlatH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfFlatH.java
new file mode 100644
index 0000000000..47353cc9e2
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfFlatH.java
@@ -0,0 +1,2845 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.ValueLayout.JAVA_BYTE;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.PaddingLayout;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.StructLayout;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.foreign.ValueLayout.OfByte;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.foreign.ValueLayout.OfShort;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.stream.Collectors;
+
+public class IvfFlatH {
+
+  IvfFlatH() {
+    // Should not be called directly
+  }
+
+  static final Arena LIBRARY_ARENA = Arena.ofAuto();
+  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+  static void traceDowncall(String name, Object... args) {
+    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
+    System.out.printf("%s(%s)\n", name, traceArgs);
+  }
+
+  static MemorySegment findOrThrow(String symbol) {
+    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+  }
+
+  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+    try {
+      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+    } catch (ReflectiveOperationException ex) {
+      throw new AssertionError(ex);
+    }
+  }
+
+  static MemoryLayout align(MemoryLayout layout, long align) {
+    return switch (layout) {
+    case PaddingLayout p -> p;
+    case ValueLayout v -> v.withByteAlignment(align);
+    case GroupLayout g -> {
+      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
+          : MemoryLayout.unionLayout(alignedMembers);
+    }
+    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+    };
+  }
+
+  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
+
+  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+  private static final int _STDINT_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDINT_H 1
+   * }
+   */
+  public static int _STDINT_H() {
+    return _STDINT_H;
+  }
+
+  private static final int _FEATURES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _FEATURES_H 1
+   * }
+   */
+  public static int _FEATURES_H() {
+    return _FEATURES_H;
+  }
+
+  private static final int _DEFAULT_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
+   * }
+   */
+  public static int _DEFAULT_SOURCE() {
+    return _DEFAULT_SOURCE;
+  }
+
+  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
+   * }
+   */
+  public static int __GLIBC_USE_ISOC2X() {
+    return __GLIBC_USE_ISOC2X;
+  }
+
+  private static final int __USE_ISOC11 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC11 1
+   * }
+   */
+  public static int __USE_ISOC11() {
+    return __USE_ISOC11;
+  }
+
+  private static final int __USE_ISOC99 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC99 1
+   * }
+   */
+  public static int __USE_ISOC99() {
+    return __USE_ISOC99;
+  }
+
+  private static final int __USE_ISOC95 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC95 1
+   * }
+   */
+  public static int __USE_ISOC95() {
+    return __USE_ISOC95;
+  }
+
+  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
+   * }
+   */
+  public static int __USE_POSIX_IMPLICITLY() {
+    return __USE_POSIX_IMPLICITLY;
+  }
+
+  private static final int _POSIX_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_SOURCE 1
+   * }
+   */
+  public static int _POSIX_SOURCE() {
+    return _POSIX_SOURCE;
+  }
+
+  private static final int __USE_POSIX = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX 1
+   * }
+   */
+  public static int __USE_POSIX() {
+    return __USE_POSIX;
+  }
+
+  private static final int __USE_POSIX2 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX2 1
+   * }
+   */
+  public static int __USE_POSIX2() {
+    return __USE_POSIX2;
+  }
+
+  private static final int __USE_POSIX199309 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199309 1
+   * }
+   */
+  public static int __USE_POSIX199309() {
+    return __USE_POSIX199309;
+  }
+
+  private static final int __USE_POSIX199506 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199506 1
+   * }
+   */
+  public static int __USE_POSIX199506() {
+    return __USE_POSIX199506;
+  }
+
+  private static final int __USE_XOPEN2K = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K 1
+   * }
+   */
+  public static int __USE_XOPEN2K() {
+    return __USE_XOPEN2K;
+  }
+
+  private static final int __USE_XOPEN2K8 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
+   * }
+   */
+  public static int __USE_XOPEN2K8() {
+    return __USE_XOPEN2K8;
+  }
+
+  private static final int _ATFILE_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
+   * }
+   */
+  public static int _ATFILE_SOURCE() {
+    return _ATFILE_SOURCE;
+  }
+
+  private static final int __WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE 64
+   * }
+   */
+  public static int __WORDSIZE() {
+    return __WORDSIZE;
+  }
+
+  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
+   * }
+   */
+  public static int __WORDSIZE_TIME64_COMPAT32() {
+    return __WORDSIZE_TIME64_COMPAT32;
+  }
+
+  private static final int __SYSCALL_WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
+   * }
+   */
+  public static int __SYSCALL_WORDSIZE() {
+    return __SYSCALL_WORDSIZE;
+  }
+
+  private static final int __USE_MISC = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_MISC 1
+   * }
+   */
+  public static int __USE_MISC() {
+    return __USE_MISC;
+  }
+
+  private static final int __USE_ATFILE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ATFILE 1
+   * }
+   */
+  public static int __USE_ATFILE() {
+    return __USE_ATFILE;
+  }
+
+  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
+   * }
+   */
+  public static int __USE_FORTIFY_LEVEL() {
+    return __USE_FORTIFY_LEVEL;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_GETS() {
+    return __GLIBC_USE_DEPRECATED_GETS;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_SCANF() {
+    return __GLIBC_USE_DEPRECATED_SCANF;
+  }
+
+  private static final int _STDC_PREDEF_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
+   * }
+   */
+  public static int _STDC_PREDEF_H() {
+    return _STDC_PREDEF_H;
+  }
+
+  private static final int __STDC_IEC_559__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
+   * }
+   */
+  public static int __STDC_IEC_559__() {
+    return __STDC_IEC_559__;
+  }
+
+  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
+   * }
+   */
+  public static int __STDC_IEC_559_COMPLEX__() {
+    return __STDC_IEC_559_COMPLEX__;
+  }
+
+  private static final int __GNU_LIBRARY__ = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
+   * }
+   */
+  public static int __GNU_LIBRARY__() {
+    return __GNU_LIBRARY__;
+  }
+
+  private static final int __GLIBC__ = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC__ 2
+   * }
+   */
+  public static int __GLIBC__() {
+    return __GLIBC__;
+  }
+
+  private static final int __GLIBC_MINOR__ = (int) 35L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
+   * }
+   */
+  public static int __GLIBC_MINOR__() {
+    return __GLIBC_MINOR__;
+  }
+
+  private static final int _SYS_CDEFS_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
+   * }
+   */
+  public static int _SYS_CDEFS_H() {
+    return _SYS_CDEFS_H;
+  }
+
+  private static final int __glibc_c99_flexarr_available = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
+   * }
+   */
+  public static int __glibc_c99_flexarr_available() {
+    return __glibc_c99_flexarr_available;
+  }
+
+  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
+   * }
+   */
+  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
+    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
+  }
+
+  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
+   * }
+   */
+  public static int __HAVE_GENERIC_SELECTION() {
+    return __HAVE_GENERIC_SELECTION;
+  }
+
+  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
+   * }
+   */
+  public static int __GLIBC_USE_LIB_EXT2() {
+    return __GLIBC_USE_LIB_EXT2;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_EXT() {
+    return __GLIBC_USE_IEC_60559_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
+    return __GLIBC_USE_IEC_60559_TYPES_EXT;
+  }
+
+  private static final int _BITS_TYPES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPES_H 1
+   * }
+   */
+  public static int _BITS_TYPES_H() {
+    return _BITS_TYPES_H;
+  }
+
+  private static final int _BITS_TYPESIZES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
+   * }
+   */
+  public static int _BITS_TYPESIZES_H() {
+    return _BITS_TYPESIZES_H;
+  }
+
+  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
+   * }
+   */
+  public static int __OFF_T_MATCHES_OFF64_T() {
+    return __OFF_T_MATCHES_OFF64_T;
+  }
+
+  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
+   * }
+   */
+  public static int __INO_T_MATCHES_INO64_T() {
+    return __INO_T_MATCHES_INO64_T;
+  }
+
+  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
+   * }
+   */
+  public static int __RLIM_T_MATCHES_RLIM64_T() {
+    return __RLIM_T_MATCHES_RLIM64_T;
+  }
+
+  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
+   * }
+   */
+  public static int __STATFS_MATCHES_STATFS64() {
+    return __STATFS_MATCHES_STATFS64;
+  }
+
+  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
+   * }
+   */
+  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
+    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
+  }
+
+  private static final int __FD_SETSIZE = (int) 1024L;
+
+  /**
+   * {@snippet lang = c : * #define __FD_SETSIZE 1024
+   * }
+   */
+  public static int __FD_SETSIZE() {
+    return __FD_SETSIZE;
+  }
+
+  private static final int _BITS_TIME64_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TIME64_H 1
+   * }
+   */
+  public static int _BITS_TIME64_H() {
+    return _BITS_TIME64_H;
+  }
+
+  private static final int _BITS_WCHAR_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
+   * }
+   */
+  public static int _BITS_WCHAR_H() {
+    return _BITS_WCHAR_H;
+  }
+
+  private static final int _BITS_STDINT_INTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_INTN_H() {
+    return _BITS_STDINT_INTN_H;
+  }
+
+  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_UINTN_H() {
+    return _BITS_STDINT_UINTN_H;
+  }
+
+  private static final int DLPACK_MAJOR_VERSION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_MAJOR_VERSION 1
+   * }
+   */
+  public static int DLPACK_MAJOR_VERSION() {
+    return DLPACK_MAJOR_VERSION;
+  }
+
+  private static final int DLPACK_MINOR_VERSION = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_MINOR_VERSION 0
+   * }
+   */
+  public static int DLPACK_MINOR_VERSION() {
+    return DLPACK_MINOR_VERSION;
+  }
+
+  private static final int true_ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define true 1
+   * }
+   */
+  public static int true_() {
+    return true_;
+  }
+
+  private static final int false_ = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define false 0
+   * }
+   */
+  public static int false_() {
+    return false_;
+  }
+
+  private static final int __bool_true_false_are_defined = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
+   * }
+   */
+  public static int __bool_true_false_are_defined() {
+    return __bool_true_false_are_defined;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef unsigned char __u_char
+   * }
+   */
+  public static final OfByte __u_char = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __u_short
+   * }
+   */
+  public static final OfShort __u_short = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __u_int
+   * }
+   */
+  public static final OfInt __u_int = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_long
+   * }
+   */
+  public static final OfLong __u_long = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char __int8_t
+   * }
+   */
+  public static final OfByte __int8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned char __uint8_t
+   * }
+   */
+  public static final OfByte __uint8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef short __int16_t
+   * }
+   */
+  public static final OfShort __int16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __uint16_t
+   * }
+   */
+  public static final OfShort __uint16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef int __int32_t
+   * }
+   */
+  public static final OfInt __int32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uint32_t
+   * }
+   */
+  public static final OfInt __uint32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __int64_t
+   * }
+   */
+  public static final OfLong __int64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uint64_t
+   * }
+   */
+  public static final OfLong __uint64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int8_t __int_least8_t
+   * }
+   */
+  public static final OfByte __int_least8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
+   * }
+   */
+  public static final OfByte __uint_least8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t __int_least16_t
+   * }
+   */
+  public static final OfShort __int_least16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
+   * }
+   */
+  public static final OfShort __uint_least16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t __int_least32_t
+   * }
+   */
+  public static final OfInt __int_least32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
+   * }
+   */
+  public static final OfInt __uint_least32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t __int_least64_t
+   * }
+   */
+  public static final OfLong __int_least64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
+   * }
+   */
+  public static final OfLong __uint_least64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __quad_t
+   * }
+   */
+  public static final OfLong __quad_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_quad_t
+   * }
+   */
+  public static final OfLong __u_quad_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __intmax_t
+   * }
+   */
+  public static final OfLong __intmax_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uintmax_t
+   * }
+   */
+  public static final OfLong __uintmax_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __dev_t
+   * }
+   */
+  public static final OfLong __dev_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uid_t
+   * }
+   */
+  public static final OfInt __uid_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __gid_t
+   * }
+   */
+  public static final OfInt __gid_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino_t
+   * }
+   */
+  public static final OfLong __ino_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino64_t
+   * }
+   */
+  public static final OfLong __ino64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __mode_t
+   * }
+   */
+  public static final OfInt __mode_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __nlink_t
+   * }
+   */
+  public static final OfLong __nlink_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off_t
+   * }
+   */
+  public static final OfLong __off_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off64_t
+   * }
+   */
+  public static final OfLong __off64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __pid_t
+   * }
+   */
+  public static final OfInt __pid_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __clock_t
+   * }
+   */
+  public static final OfLong __clock_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim_t
+   * }
+   */
+  public static final OfLong __rlim_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim64_t
+   * }
+   */
+  public static final OfLong __rlim64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __id_t
+   * }
+   */
+  public static final OfInt __id_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __time_t
+   * }
+   */
+  public static final OfLong __time_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __useconds_t
+   * }
+   */
+  public static final OfInt __useconds_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds_t
+   * }
+   */
+  public static final OfLong __suseconds_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds64_t
+   * }
+   */
+  public static final OfLong __suseconds64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __daddr_t
+   * }
+   */
+  public static final OfInt __daddr_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __key_t
+   * }
+   */
+  public static final OfInt __key_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __clockid_t
+   * }
+   */
+  public static final OfInt __clockid_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef void *__timer_t
+   * }
+   */
+  public static final AddressLayout __timer_t = IvfFlatH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __blksize_t
+   * }
+   */
+  public static final OfLong __blksize_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt_t
+   * }
+   */
+  public static final OfLong __blkcnt_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt64_t
+   * }
+   */
+  public static final OfLong __blkcnt64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
+   * }
+   */
+  public static final OfLong __fsblkcnt_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
+   * }
+   */
+  public static final OfLong __fsblkcnt64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
+   * }
+   */
+  public static final OfLong __fsfilcnt_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
+   * }
+   */
+  public static final OfLong __fsfilcnt64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __fsword_t
+   * }
+   */
+  public static final OfLong __fsword_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __ssize_t
+   * }
+   */
+  public static final OfLong __ssize_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __syscall_slong_t
+   * }
+   */
+  public static final OfLong __syscall_slong_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
+   * }
+   */
+  public static final OfLong __syscall_ulong_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __off64_t __loff_t
+   * }
+   */
+  public static final OfLong __loff_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef char *__caddr_t
+   * }
+   */
+  public static final AddressLayout __caddr_t = IvfFlatH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __intptr_t
+   * }
+   */
+  public static final OfLong __intptr_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __socklen_t
+   * }
+   */
+  public static final OfInt __socklen_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __sig_atomic_t
+   * }
+   */
+  public static final OfInt __sig_atomic_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int8_t int8_t
+   * }
+   */
+  public static final OfByte int8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t int16_t
+   * }
+   */
+  public static final OfShort int16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t int32_t
+   * }
+   */
+  public static final OfInt int32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t int64_t
+   * }
+   */
+  public static final OfLong int64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t uint8_t
+   * }
+   */
+  public static final OfByte uint8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t uint16_t
+   * }
+   */
+  public static final OfShort uint16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t uint32_t
+   * }
+   */
+  public static final OfInt uint32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t uint64_t
+   * }
+   */
+  public static final OfLong uint64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
+   * }
+   */
+  public static final OfByte int_least8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
+   * }
+   */
+  public static final OfShort int_least16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
+   * }
+   */
+  public static final OfInt int_least32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
+   * }
+   */
+  public static final OfLong int_least64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
+   * }
+   */
+  public static final OfByte uint_least8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
+   * }
+   */
+  public static final OfShort uint_least16_t = IvfFlatH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
+   * }
+   */
+  public static final OfInt uint_least32_t = IvfFlatH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
+   * }
+   */
+  public static final OfLong uint_least64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char int_fast8_t
+   * }
+   */
+  public static final OfByte int_fast8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef long int_fast16_t
+   * }
+   */
+  public static final OfLong int_fast16_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast32_t
+   * }
+   */
+  public static final OfLong int_fast32_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast64_t
+   * }
+   */
+  public static final OfLong int_fast64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
+   * }
+   */
+  public static final OfByte uint_fast8_t = IvfFlatH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
+   * }
+   */
+  public static final OfLong uint_fast16_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
+   * }
+   */
+  public static final OfLong uint_fast32_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
+   * }
+   */
+  public static final OfLong uint_fast64_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long intptr_t
+   * }
+   */
+  public static final OfLong intptr_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uintptr_t
+   * }
+   */
+  public static final OfLong uintptr_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __intmax_t intmax_t
+   * }
+   */
+  public static final OfLong intmax_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
+   * }
+   */
+  public static final OfLong uintmax_t = IvfFlatH.C_LONG;
+  private static final int CUVS_ERROR = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CUVS_ERROR = 0
+   * }
+   */
+  public static int CUVS_ERROR() {
+    return CUVS_ERROR;
+  }
+
+  private static final int CUVS_SUCCESS = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CUVS_SUCCESS = 1
+   * }
+   */
+  public static int CUVS_SUCCESS() {
+    return CUVS_SUCCESS;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef uintptr_t cuvsResources_t
+   * }
+   */
+  public static final OfLong cuvsResources_t = IvfFlatH.C_LONG;
+  private static final int L2Expanded = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
+   * }
+   */
+  public static int L2Expanded() {
+    return L2Expanded;
+  }
+
+  private static final int L2SqrtExpanded = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
+   * }
+   */
+  public static int L2SqrtExpanded() {
+    return L2SqrtExpanded;
+  }
+
+  private static final int CosineExpanded = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
+   * }
+   */
+  public static int CosineExpanded() {
+    return CosineExpanded;
+  }
+
+  private static final int L1 = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L1 = 3
+   * }
+   */
+  public static int L1() {
+    return L1;
+  }
+
+  private static final int L2Unexpanded = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
+   * }
+   */
+  public static int L2Unexpanded() {
+    return L2Unexpanded;
+  }
+
+  private static final int L2SqrtUnexpanded = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
+   * }
+   */
+  public static int L2SqrtUnexpanded() {
+    return L2SqrtUnexpanded;
+  }
+
+  private static final int InnerProduct = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
+   * }
+   */
+  public static int InnerProduct() {
+    return InnerProduct;
+  }
+
+  private static final int Linf = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Linf = 7
+   * }
+   */
+  public static int Linf() {
+    return Linf;
+  }
+
+  private static final int Canberra = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
+   * }
+   */
+  public static int Canberra() {
+    return Canberra;
+  }
+
+  private static final int LpUnexpanded = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
+   * }
+   */
+  public static int LpUnexpanded() {
+    return LpUnexpanded;
+  }
+
+  private static final int CorrelationExpanded = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
+   * }
+   */
+  public static int CorrelationExpanded() {
+    return CorrelationExpanded;
+  }
+
+  private static final int JaccardExpanded = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
+   * }
+   */
+  public static int JaccardExpanded() {
+    return JaccardExpanded;
+  }
+
+  private static final int HellingerExpanded = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
+   * }
+   */
+  public static int HellingerExpanded() {
+    return HellingerExpanded;
+  }
+
+  private static final int Haversine = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
+   * }
+   */
+  public static int Haversine() {
+    return Haversine;
+  }
+
+  private static final int BrayCurtis = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
+   * }
+   */
+  public static int BrayCurtis() {
+    return BrayCurtis;
+  }
+
+  private static final int JensenShannon = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
+   * }
+   */
+  public static int JensenShannon() {
+    return JensenShannon;
+  }
+
+  private static final int HammingUnexpanded = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
+   * }
+   */
+  public static int HammingUnexpanded() {
+    return HammingUnexpanded;
+  }
+
+  private static final int KLDivergence = (int) 17L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
+   * }
+   */
+  public static int KLDivergence() {
+    return KLDivergence;
+  }
+
+  private static final int RusselRaoExpanded = (int) 18L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
+   * }
+   */
+  public static int RusselRaoExpanded() {
+    return RusselRaoExpanded;
+  }
+
+  private static final int DiceExpanded = (int) 19L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
+   * }
+   */
+  public static int DiceExpanded() {
+    return DiceExpanded;
+  }
+
+  private static final int Precomputed = (int) 100L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
+   * }
+   */
+  public static int Precomputed() {
+    return Precomputed;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef long ptrdiff_t
+   * }
+   */
+  public static final OfLong ptrdiff_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long size_t
+   * }
+   */
+  public static final OfLong size_t = IvfFlatH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int wchar_t
+   * }
+   */
+  public static final OfInt wchar_t = IvfFlatH.C_INT;
+  private static final int kDLCPU = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
+   * }
+   */
+  public static int kDLCPU() {
+    return kDLCPU;
+  }
+
+  private static final int kDLCUDA = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
+   * }
+   */
+  public static int kDLCUDA() {
+    return kDLCUDA;
+  }
+
+  private static final int kDLCUDAHost = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
+   * }
+   */
+  public static int kDLCUDAHost() {
+    return kDLCUDAHost;
+  }
+
+  private static final int kDLOpenCL = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
+   * }
+   */
+  public static int kDLOpenCL() {
+    return kDLOpenCL;
+  }
+
+  private static final int kDLVulkan = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
+   * }
+   */
+  public static int kDLVulkan() {
+    return kDLVulkan;
+  }
+
+  private static final int kDLMetal = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
+   * }
+   */
+  public static int kDLMetal() {
+    return kDLMetal;
+  }
+
+  private static final int kDLVPI = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
+   * }
+   */
+  public static int kDLVPI() {
+    return kDLVPI;
+  }
+
+  private static final int kDLROCM = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
+   * }
+   */
+  public static int kDLROCM() {
+    return kDLROCM;
+  }
+
+  private static final int kDLROCMHost = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
+   * }
+   */
+  public static int kDLROCMHost() {
+    return kDLROCMHost;
+  }
+
+  private static final int kDLExtDev = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
+   * }
+   */
+  public static int kDLExtDev() {
+    return kDLExtDev;
+  }
+
+  private static final int kDLCUDAManaged = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
+   * }
+   */
+  public static int kDLCUDAManaged() {
+    return kDLCUDAManaged;
+  }
+
+  private static final int kDLOneAPI = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
+   * }
+   */
+  public static int kDLOneAPI() {
+    return kDLOneAPI;
+  }
+
+  private static final int kDLWebGPU = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
+   * }
+   */
+  public static int kDLWebGPU() {
+    return kDLWebGPU;
+  }
+
+  private static final int kDLHexagon = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
+   * }
+   */
+  public static int kDLHexagon() {
+    return kDLHexagon;
+  }
+
+  private static final int kDLMAIA = (int) 17L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLMAIA = 17
+   * }
+   */
+  public static int kDLMAIA() {
+    return kDLMAIA;
+  }
+
+  private static final int kDLInt = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
+   * }
+   */
+  public static int kDLInt() {
+    return kDLInt;
+  }
+
+  private static final int kDLUInt = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
+   * }
+   */
+  public static int kDLUInt() {
+    return kDLUInt;
+  }
+
+  private static final int kDLFloat = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
+   * }
+   */
+  public static int kDLFloat() {
+    return kDLFloat;
+  }
+
+  private static final int kDLOpaqueHandle = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
+   * }
+   */
+  public static int kDLOpaqueHandle() {
+    return kDLOpaqueHandle;
+  }
+
+  private static final int kDLBfloat = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
+   * }
+   */
+  public static int kDLBfloat() {
+    return kDLBfloat;
+  }
+
+  private static final int kDLComplex = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
+   * }
+   */
+  public static int kDLComplex() {
+    return kDLComplex;
+  }
+
+  private static final int kDLBool = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
+   * }
+   */
+  public static int kDLBool() {
+    return kDLBool;
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsIvfFlatIndexParams {
+   *     cuvsDistanceType metric;
+   *     float metric_arg;
+   *     _Bool add_data_on_build;
+   *     uint32_t n_lists;
+   *     uint32_t kmeans_n_iters;
+   *     double kmeans_trainset_fraction;
+   *     _Bool adaptive_centers;
+   *     _Bool conservative_memory_allocation;
+   * } *cuvsIvfFlatIndexParams_t
+   * }
+   */
+  public static final AddressLayout cuvsIvfFlatIndexParams_t = IvfFlatH.C_POINTER;
+
+  private static class cuvsIvfFlatIndexParamsCreate {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexParamsCreate");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatIndexParamsCreate$descriptor() {
+    return cuvsIvfFlatIndexParamsCreate.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatIndexParamsCreate$handle() {
+    return cuvsIvfFlatIndexParamsCreate.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatIndexParamsCreate$address() {
+    return cuvsIvfFlatIndexParamsCreate.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
+   * }
+   */
+  public static int cuvsIvfFlatIndexParamsCreate(MemorySegment index_params) {
+    var mh$ = cuvsIvfFlatIndexParamsCreate.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatIndexParamsCreate", index_params);
+      }
+      return (int) mh$.invokeExact(index_params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatIndexParamsDestroy {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexParamsDestroy");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatIndexParamsDestroy$descriptor() {
+    return cuvsIvfFlatIndexParamsDestroy.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatIndexParamsDestroy$handle() {
+    return cuvsIvfFlatIndexParamsDestroy.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatIndexParamsDestroy$address() {
+    return cuvsIvfFlatIndexParamsDestroy.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
+   * }
+   */
+  public static int cuvsIvfFlatIndexParamsDestroy(MemorySegment index_params) {
+    var mh$ = cuvsIvfFlatIndexParamsDestroy.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatIndexParamsDestroy", index_params);
+      }
+      return (int) mh$.invokeExact(index_params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsIvfFlatSearchParams {
+   *     uint32_t n_probes;
+   * } *cuvsIvfFlatSearchParams_t
+   * }
+   */
+  public static final AddressLayout cuvsIvfFlatSearchParams_t = IvfFlatH.C_POINTER;
+
+  private static class cuvsIvfFlatSearchParamsCreate {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSearchParamsCreate");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatSearchParamsCreate$descriptor() {
+    return cuvsIvfFlatSearchParamsCreate.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatSearchParamsCreate$handle() {
+    return cuvsIvfFlatSearchParamsCreate.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatSearchParamsCreate$address() {
+    return cuvsIvfFlatSearchParamsCreate.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
+   * }
+   */
+  public static int cuvsIvfFlatSearchParamsCreate(MemorySegment params) {
+    var mh$ = cuvsIvfFlatSearchParamsCreate.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatSearchParamsCreate", params);
+      }
+      return (int) mh$.invokeExact(params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatSearchParamsDestroy {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSearchParamsDestroy");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatSearchParamsDestroy$descriptor() {
+    return cuvsIvfFlatSearchParamsDestroy.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatSearchParamsDestroy$handle() {
+    return cuvsIvfFlatSearchParamsDestroy.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatSearchParamsDestroy$address() {
+    return cuvsIvfFlatSearchParamsDestroy.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
+   * }
+   */
+  public static int cuvsIvfFlatSearchParamsDestroy(MemorySegment params) {
+    var mh$ = cuvsIvfFlatSearchParamsDestroy.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatSearchParamsDestroy", params);
+      }
+      return (int) mh$.invokeExact(params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  /**
+   * {@snippet lang = c : * typedef cuvsIvfFlatIndex *cuvsIvfFlatIndex_t
+   * }
+   */
+  public static final AddressLayout cuvsIvfFlatIndex_t = IvfFlatH.C_POINTER;
+
+  private static class cuvsIvfFlatIndexCreate {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexCreate");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatIndexCreate$descriptor() {
+    return cuvsIvfFlatIndexCreate.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatIndexCreate$handle() {
+    return cuvsIvfFlatIndexCreate.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatIndexCreate$address() {
+    return cuvsIvfFlatIndexCreate.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
+   * }
+   */
+  public static int cuvsIvfFlatIndexCreate(MemorySegment index) {
+    var mh$ = cuvsIvfFlatIndexCreate.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatIndexCreate", index);
+      }
+      return (int) mh$.invokeExact(index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatIndexDestroy {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexDestroy");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatIndexDestroy$descriptor() {
+    return cuvsIvfFlatIndexDestroy.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatIndexDestroy$handle() {
+    return cuvsIvfFlatIndexDestroy.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatIndexDestroy$address() {
+    return cuvsIvfFlatIndexDestroy.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static int cuvsIvfFlatIndexDestroy(MemorySegment index) {
+    var mh$ = cuvsIvfFlatIndexDestroy.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatIndexDestroy", index);
+      }
+      return (int) mh$.invokeExact(index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatBuild {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
+        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatBuild");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatBuild$descriptor() {
+    return cuvsIvfFlatBuild.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatBuild$handle() {
+    return cuvsIvfFlatBuild.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatBuild$address() {
+    return cuvsIvfFlatBuild.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static int cuvsIvfFlatBuild(long res, MemorySegment index_params, MemorySegment dataset, MemorySegment index) {
+    var mh$ = cuvsIvfFlatBuild.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatBuild", res, index_params, dataset, index);
+      }
+      return (int) mh$.invokeExact(res, index_params, dataset, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatSearch {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
+        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSearch");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatSearch$descriptor() {
+    return cuvsIvfFlatSearch.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatSearch$handle() {
+    return cuvsIvfFlatSearch.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatSearch$address() {
+    return cuvsIvfFlatSearch.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static int cuvsIvfFlatSearch(long res, MemorySegment search_params, MemorySegment index, MemorySegment queries,
+      MemorySegment neighbors, MemorySegment distances) {
+    var mh$ = cuvsIvfFlatSearch.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatSearch", res, search_params, index, queries, neighbors, distances);
+      }
+      return (int) mh$.invokeExact(res, search_params, index, queries, neighbors, distances);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatSerialize {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
+        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSerialize");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatSerialize$descriptor() {
+    return cuvsIvfFlatSerialize.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatSerialize$handle() {
+    return cuvsIvfFlatSerialize.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatSerialize$address() {
+    return cuvsIvfFlatSerialize.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static int cuvsIvfFlatSerialize(long res, MemorySegment filename, MemorySegment index) {
+    var mh$ = cuvsIvfFlatSerialize.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatSerialize", res, filename, index);
+      }
+      return (int) mh$.invokeExact(res, filename, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatDeserialize {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
+        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatDeserialize");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatDeserialize$descriptor() {
+    return cuvsIvfFlatDeserialize.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatDeserialize$handle() {
+    return cuvsIvfFlatDeserialize.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatDeserialize$address() {
+    return cuvsIvfFlatDeserialize.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static int cuvsIvfFlatDeserialize(long res, MemorySegment filename, MemorySegment index) {
+    var mh$ = cuvsIvfFlatDeserialize.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatDeserialize", res, filename, index);
+      }
+      return (int) mh$.invokeExact(res, filename, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfFlatExtend {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
+        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatExtend");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfFlatExtend$descriptor() {
+    return cuvsIvfFlatExtend.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfFlatExtend$handle() {
+    return cuvsIvfFlatExtend.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfFlatExtend$address() {
+    return cuvsIvfFlatExtend.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
+   * }
+   */
+  public static int cuvsIvfFlatExtend(long res, MemorySegment new_vectors, MemorySegment new_indices,
+      MemorySegment index) {
+    var mh$ = cuvsIvfFlatExtend.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfFlatExtend", res, new_vectors, new_indices, index);
+      }
+      return (int) mh$.invokeExact(res, new_vectors, new_indices, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static final long _POSIX_C_SOURCE = 200809L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
+   * }
+   */
+  public static long _POSIX_C_SOURCE() {
+    return _POSIX_C_SOURCE;
+  }
+
+  private static final int __TIMESIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __TIMESIZE 64
+   * }
+   */
+  public static int __TIMESIZE() {
+    return __TIMESIZE;
+  }
+
+  private static final long __STDC_IEC_60559_BFP__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_BFP__() {
+    return __STDC_IEC_60559_BFP__;
+  }
+
+  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_COMPLEX__() {
+    return __STDC_IEC_60559_COMPLEX__;
+  }
+
+  private static final long __STDC_ISO_10646__ = 201706L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
+   * }
+   */
+  public static long __STDC_ISO_10646__() {
+    return __STDC_ISO_10646__;
+  }
+
+  private static final int __WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
+   * }
+   */
+  public static int __WCHAR_MAX() {
+    return __WCHAR_MAX;
+  }
+
+  private static final int __WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
+   * }
+   */
+  public static int __WCHAR_MIN() {
+    return __WCHAR_MIN;
+  }
+
+  private static final int INT8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MIN -128
+   * }
+   */
+  public static int INT8_MIN() {
+    return INT8_MIN;
+  }
+
+  private static final int INT16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MIN -32768
+   * }
+   */
+  public static int INT16_MIN() {
+    return INT16_MIN;
+  }
+
+  private static final int INT32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MIN -2147483648
+   * }
+   */
+  public static int INT32_MIN() {
+    return INT32_MIN;
+  }
+
+  private static final long INT64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT64_MIN() {
+    return INT64_MIN;
+  }
+
+  private static final int INT8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MAX 127
+   * }
+   */
+  public static int INT8_MAX() {
+    return INT8_MAX;
+  }
+
+  private static final int INT16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MAX 32767
+   * }
+   */
+  public static int INT16_MAX() {
+    return INT16_MAX;
+  }
+
+  private static final int INT32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MAX 2147483647
+   * }
+   */
+  public static int INT32_MAX() {
+    return INT32_MAX;
+  }
+
+  private static final long INT64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT64_MAX() {
+    return INT64_MAX;
+  }
+
+  private static final int UINT8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT8_MAX 255
+   * }
+   */
+  public static int UINT8_MAX() {
+    return UINT8_MAX;
+  }
+
+  private static final int UINT16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT16_MAX 65535
+   * }
+   */
+  public static int UINT16_MAX() {
+    return UINT16_MAX;
+  }
+
+  private static final int UINT32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT32_MAX 4294967295
+   * }
+   */
+  public static int UINT32_MAX() {
+    return UINT32_MAX;
+  }
+
+  private static final long UINT64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT64_MAX -1
+   * }
+   */
+  public static long UINT64_MAX() {
+    return UINT64_MAX;
+  }
+
+  private static final int INT_LEAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
+   * }
+   */
+  public static int INT_LEAST8_MIN() {
+    return INT_LEAST8_MIN;
+  }
+
+  private static final int INT_LEAST16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
+   * }
+   */
+  public static int INT_LEAST16_MIN() {
+    return INT_LEAST16_MIN;
+  }
+
+  private static final int INT_LEAST32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
+   * }
+   */
+  public static int INT_LEAST32_MIN() {
+    return INT_LEAST32_MIN;
+  }
+
+  private static final long INT_LEAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_LEAST64_MIN() {
+    return INT_LEAST64_MIN;
+  }
+
+  private static final int INT_LEAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
+   * }
+   */
+  public static int INT_LEAST8_MAX() {
+    return INT_LEAST8_MAX;
+  }
+
+  private static final int INT_LEAST16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
+   * }
+   */
+  public static int INT_LEAST16_MAX() {
+    return INT_LEAST16_MAX;
+  }
+
+  private static final int INT_LEAST32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
+   * }
+   */
+  public static int INT_LEAST32_MAX() {
+    return INT_LEAST32_MAX;
+  }
+
+  private static final long INT_LEAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_LEAST64_MAX() {
+    return INT_LEAST64_MAX;
+  }
+
+  private static final int UINT_LEAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
+   * }
+   */
+  public static int UINT_LEAST8_MAX() {
+    return UINT_LEAST8_MAX;
+  }
+
+  private static final int UINT_LEAST16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
+   * }
+   */
+  public static int UINT_LEAST16_MAX() {
+    return UINT_LEAST16_MAX;
+  }
+
+  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
+   * }
+   */
+  public static int UINT_LEAST32_MAX() {
+    return UINT_LEAST32_MAX;
+  }
+
+  private static final long UINT_LEAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
+   * }
+   */
+  public static long UINT_LEAST64_MAX() {
+    return UINT_LEAST64_MAX;
+  }
+
+  private static final int INT_FAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MIN -128
+   * }
+   */
+  public static int INT_FAST8_MIN() {
+    return INT_FAST8_MIN;
+  }
+
+  private static final long INT_FAST16_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST16_MIN() {
+    return INT_FAST16_MIN;
+  }
+
+  private static final long INT_FAST32_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST32_MIN() {
+    return INT_FAST32_MIN;
+  }
+
+  private static final long INT_FAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST64_MIN() {
+    return INT_FAST64_MIN;
+  }
+
+  private static final int INT_FAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MAX 127
+   * }
+   */
+  public static int INT_FAST8_MAX() {
+    return INT_FAST8_MAX;
+  }
+
+  private static final long INT_FAST16_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST16_MAX() {
+    return INT_FAST16_MAX;
+  }
+
+  private static final long INT_FAST32_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST32_MAX() {
+    return INT_FAST32_MAX;
+  }
+
+  private static final long INT_FAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST64_MAX() {
+    return INT_FAST64_MAX;
+  }
+
+  private static final int UINT_FAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
+   * }
+   */
+  public static int UINT_FAST8_MAX() {
+    return UINT_FAST8_MAX;
+  }
+
+  private static final long UINT_FAST16_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
+   * }
+   */
+  public static long UINT_FAST16_MAX() {
+    return UINT_FAST16_MAX;
+  }
+
+  private static final long UINT_FAST32_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
+   * }
+   */
+  public static long UINT_FAST32_MAX() {
+    return UINT_FAST32_MAX;
+  }
+
+  private static final long UINT_FAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
+   * }
+   */
+  public static long UINT_FAST64_MAX() {
+    return UINT_FAST64_MAX;
+  }
+
+  private static final long INTPTR_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
+   * }
+   */
+  public static long INTPTR_MIN() {
+    return INTPTR_MIN;
+  }
+
+  private static final long INTPTR_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
+   * }
+   */
+  public static long INTPTR_MAX() {
+    return INTPTR_MAX;
+  }
+
+  private static final long UINTPTR_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTPTR_MAX -1
+   * }
+   */
+  public static long UINTPTR_MAX() {
+    return UINTPTR_MAX;
+  }
+
+  private static final long INTMAX_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
+   * }
+   */
+  public static long INTMAX_MIN() {
+    return INTMAX_MIN;
+  }
+
+  private static final long INTMAX_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
+   * }
+   */
+  public static long INTMAX_MAX() {
+    return INTMAX_MAX;
+  }
+
+  private static final long UINTMAX_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTMAX_MAX -1
+   * }
+   */
+  public static long UINTMAX_MAX() {
+    return UINTMAX_MAX;
+  }
+
+  private static final long PTRDIFF_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
+   * }
+   */
+  public static long PTRDIFF_MIN() {
+    return PTRDIFF_MIN;
+  }
+
+  private static final long PTRDIFF_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
+   * }
+   */
+  public static long PTRDIFF_MAX() {
+    return PTRDIFF_MAX;
+  }
+
+  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
+   * }
+   */
+  public static int SIG_ATOMIC_MIN() {
+    return SIG_ATOMIC_MIN;
+  }
+
+  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
+   * }
+   */
+  public static int SIG_ATOMIC_MAX() {
+    return SIG_ATOMIC_MAX;
+  }
+
+  private static final long SIZE_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define SIZE_MAX -1
+   * }
+   */
+  public static long SIZE_MAX() {
+    return SIZE_MAX;
+  }
+
+  private static final int WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
+   * }
+   */
+  public static int WCHAR_MIN() {
+    return WCHAR_MIN;
+  }
+
+  private static final int WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
+   * }
+   */
+  public static int WCHAR_MAX() {
+    return WCHAR_MAX;
+  }
+
+  private static final int WINT_MIN = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MIN 0
+   * }
+   */
+  public static int WINT_MIN() {
+    return WINT_MIN;
+  }
+
+  private static final int WINT_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MAX 4294967295
+   * }
+   */
+  public static int WINT_MAX() {
+    return WINT_MAX;
+  }
+
+  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
+
+  /**
+   * {@snippet lang = c : * #define NULL (void*) 0
+   * }
+   */
+  public static MemorySegment NULL() {
+    return NULL;
+  }
+
+  private static final long DLPACK_FLAG_BITMASK_READ_ONLY = 1L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_READ_ONLY 1
+   * }
+   */
+  public static long DLPACK_FLAG_BITMASK_READ_ONLY() {
+    return DLPACK_FLAG_BITMASK_READ_ONLY;
+  }
+
+  private static final long DLPACK_FLAG_BITMASK_IS_COPIED = 2L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_IS_COPIED 2
+   * }
+   */
+  public static long DLPACK_FLAG_BITMASK_IS_COPIED() {
+    return DLPACK_FLAG_BITMASK_IS_COPIED;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfPqH.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfPqH.java
new file mode 100644
index 0000000000..d5f1e3cee4
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfPqH.java
@@ -0,0 +1,3182 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.ValueLayout.JAVA_BYTE;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.PaddingLayout;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.StructLayout;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.foreign.ValueLayout.OfByte;
+import java.lang.foreign.ValueLayout.OfInt;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.lang.foreign.ValueLayout.OfShort;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.stream.Collectors;
+
+public class IvfPqH {
+
+  IvfPqH() {
+    // Should not be called directly
+  }
+
+  static final Arena LIBRARY_ARENA = Arena.ofAuto();
+  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
+
+  static void traceDowncall(String name, Object... args) {
+    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
+    System.out.printf("%s(%s)\n", name, traceArgs);
+  }
+
+  static MemorySegment findOrThrow(String symbol) {
+    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
+  }
+
+  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
+    try {
+      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
+    } catch (ReflectiveOperationException ex) {
+      throw new AssertionError(ex);
+    }
+  }
+
+  static MemoryLayout align(MemoryLayout layout, long align) {
+    return switch (layout) {
+    case PaddingLayout p -> p;
+    case ValueLayout v -> v.withByteAlignment(align);
+    case GroupLayout g -> {
+      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
+      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
+          : MemoryLayout.unionLayout(alignedMembers);
+    }
+    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
+    };
+  }
+
+  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
+
+  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
+  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
+  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
+  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
+  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
+  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
+  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
+  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
+      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
+  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
+  private static final int _STDINT_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDINT_H 1
+   * }
+   */
+  public static int _STDINT_H() {
+    return _STDINT_H;
+  }
+
+  private static final int _FEATURES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _FEATURES_H 1
+   * }
+   */
+  public static int _FEATURES_H() {
+    return _FEATURES_H;
+  }
+
+  private static final int _DEFAULT_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
+   * }
+   */
+  public static int _DEFAULT_SOURCE() {
+    return _DEFAULT_SOURCE;
+  }
+
+  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
+   * }
+   */
+  public static int __GLIBC_USE_ISOC2X() {
+    return __GLIBC_USE_ISOC2X;
+  }
+
+  private static final int __USE_ISOC11 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC11 1
+   * }
+   */
+  public static int __USE_ISOC11() {
+    return __USE_ISOC11;
+  }
+
+  private static final int __USE_ISOC99 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC99 1
+   * }
+   */
+  public static int __USE_ISOC99() {
+    return __USE_ISOC99;
+  }
+
+  private static final int __USE_ISOC95 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ISOC95 1
+   * }
+   */
+  public static int __USE_ISOC95() {
+    return __USE_ISOC95;
+  }
+
+  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
+   * }
+   */
+  public static int __USE_POSIX_IMPLICITLY() {
+    return __USE_POSIX_IMPLICITLY;
+  }
+
+  private static final int _POSIX_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_SOURCE 1
+   * }
+   */
+  public static int _POSIX_SOURCE() {
+    return _POSIX_SOURCE;
+  }
+
+  private static final int __USE_POSIX = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX 1
+   * }
+   */
+  public static int __USE_POSIX() {
+    return __USE_POSIX;
+  }
+
+  private static final int __USE_POSIX2 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX2 1
+   * }
+   */
+  public static int __USE_POSIX2() {
+    return __USE_POSIX2;
+  }
+
+  private static final int __USE_POSIX199309 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199309 1
+   * }
+   */
+  public static int __USE_POSIX199309() {
+    return __USE_POSIX199309;
+  }
+
+  private static final int __USE_POSIX199506 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_POSIX199506 1
+   * }
+   */
+  public static int __USE_POSIX199506() {
+    return __USE_POSIX199506;
+  }
+
+  private static final int __USE_XOPEN2K = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K 1
+   * }
+   */
+  public static int __USE_XOPEN2K() {
+    return __USE_XOPEN2K;
+  }
+
+  private static final int __USE_XOPEN2K8 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
+   * }
+   */
+  public static int __USE_XOPEN2K8() {
+    return __USE_XOPEN2K8;
+  }
+
+  private static final int _ATFILE_SOURCE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
+   * }
+   */
+  public static int _ATFILE_SOURCE() {
+    return _ATFILE_SOURCE;
+  }
+
+  private static final int __WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE 64
+   * }
+   */
+  public static int __WORDSIZE() {
+    return __WORDSIZE;
+  }
+
+  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
+   * }
+   */
+  public static int __WORDSIZE_TIME64_COMPAT32() {
+    return __WORDSIZE_TIME64_COMPAT32;
+  }
+
+  private static final int __SYSCALL_WORDSIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
+   * }
+   */
+  public static int __SYSCALL_WORDSIZE() {
+    return __SYSCALL_WORDSIZE;
+  }
+
+  private static final int __USE_MISC = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_MISC 1
+   * }
+   */
+  public static int __USE_MISC() {
+    return __USE_MISC;
+  }
+
+  private static final int __USE_ATFILE = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_ATFILE 1
+   * }
+   */
+  public static int __USE_ATFILE() {
+    return __USE_ATFILE;
+  }
+
+  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
+   * }
+   */
+  public static int __USE_FORTIFY_LEVEL() {
+    return __USE_FORTIFY_LEVEL;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_GETS() {
+    return __GLIBC_USE_DEPRECATED_GETS;
+  }
+
+  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
+   * }
+   */
+  public static int __GLIBC_USE_DEPRECATED_SCANF() {
+    return __GLIBC_USE_DEPRECATED_SCANF;
+  }
+
+  private static final int _STDC_PREDEF_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
+   * }
+   */
+  public static int _STDC_PREDEF_H() {
+    return _STDC_PREDEF_H;
+  }
+
+  private static final int __STDC_IEC_559__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
+   * }
+   */
+  public static int __STDC_IEC_559__() {
+    return __STDC_IEC_559__;
+  }
+
+  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
+   * }
+   */
+  public static int __STDC_IEC_559_COMPLEX__() {
+    return __STDC_IEC_559_COMPLEX__;
+  }
+
+  private static final int __GNU_LIBRARY__ = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
+   * }
+   */
+  public static int __GNU_LIBRARY__() {
+    return __GNU_LIBRARY__;
+  }
+
+  private static final int __GLIBC__ = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC__ 2
+   * }
+   */
+  public static int __GLIBC__() {
+    return __GLIBC__;
+  }
+
+  private static final int __GLIBC_MINOR__ = (int) 35L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
+   * }
+   */
+  public static int __GLIBC_MINOR__() {
+    return __GLIBC_MINOR__;
+  }
+
+  private static final int _SYS_CDEFS_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
+   * }
+   */
+  public static int _SYS_CDEFS_H() {
+    return _SYS_CDEFS_H;
+  }
+
+  private static final int __glibc_c99_flexarr_available = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
+   * }
+   */
+  public static int __glibc_c99_flexarr_available() {
+    return __glibc_c99_flexarr_available;
+  }
+
+  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
+   * }
+   */
+  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
+    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
+  }
+
+  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
+   * }
+   */
+  public static int __HAVE_GENERIC_SELECTION() {
+    return __HAVE_GENERIC_SELECTION;
+  }
+
+  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
+   * }
+   */
+  public static int __GLIBC_USE_LIB_EXT2() {
+    return __GLIBC_USE_LIB_EXT2;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_EXT() {
+    return __GLIBC_USE_IEC_60559_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
+    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
+  }
+
+  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
+   * }
+   */
+  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
+    return __GLIBC_USE_IEC_60559_TYPES_EXT;
+  }
+
+  private static final int _BITS_TYPES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPES_H 1
+   * }
+   */
+  public static int _BITS_TYPES_H() {
+    return _BITS_TYPES_H;
+  }
+
+  private static final int _BITS_TYPESIZES_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
+   * }
+   */
+  public static int _BITS_TYPESIZES_H() {
+    return _BITS_TYPESIZES_H;
+  }
+
+  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
+   * }
+   */
+  public static int __OFF_T_MATCHES_OFF64_T() {
+    return __OFF_T_MATCHES_OFF64_T;
+  }
+
+  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
+   * }
+   */
+  public static int __INO_T_MATCHES_INO64_T() {
+    return __INO_T_MATCHES_INO64_T;
+  }
+
+  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
+   * }
+   */
+  public static int __RLIM_T_MATCHES_RLIM64_T() {
+    return __RLIM_T_MATCHES_RLIM64_T;
+  }
+
+  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
+   * }
+   */
+  public static int __STATFS_MATCHES_STATFS64() {
+    return __STATFS_MATCHES_STATFS64;
+  }
+
+  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
+   * }
+   */
+  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
+    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
+  }
+
+  private static final int __FD_SETSIZE = (int) 1024L;
+
+  /**
+   * {@snippet lang = c : * #define __FD_SETSIZE 1024
+   * }
+   */
+  public static int __FD_SETSIZE() {
+    return __FD_SETSIZE;
+  }
+
+  private static final int _BITS_TIME64_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_TIME64_H 1
+   * }
+   */
+  public static int _BITS_TIME64_H() {
+    return _BITS_TIME64_H;
+  }
+
+  private static final int _BITS_WCHAR_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
+   * }
+   */
+  public static int _BITS_WCHAR_H() {
+    return _BITS_WCHAR_H;
+  }
+
+  private static final int _BITS_STDINT_INTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_INTN_H() {
+    return _BITS_STDINT_INTN_H;
+  }
+
+  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
+   * }
+   */
+  public static int _BITS_STDINT_UINTN_H() {
+    return _BITS_STDINT_UINTN_H;
+  }
+
+  private static final int DLPACK_MAJOR_VERSION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_MAJOR_VERSION 1
+   * }
+   */
+  public static int DLPACK_MAJOR_VERSION() {
+    return DLPACK_MAJOR_VERSION;
+  }
+
+  private static final int DLPACK_MINOR_VERSION = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_MINOR_VERSION 0
+   * }
+   */
+  public static int DLPACK_MINOR_VERSION() {
+    return DLPACK_MINOR_VERSION;
+  }
+
+  private static final int true_ = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define true 1
+   * }
+   */
+  public static int true_() {
+    return true_;
+  }
+
+  private static final int false_ = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define false 0
+   * }
+   */
+  public static int false_() {
+    return false_;
+  }
+
+  private static final int __bool_true_false_are_defined = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
+   * }
+   */
+  public static int __bool_true_false_are_defined() {
+    return __bool_true_false_are_defined;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef unsigned char __u_char
+   * }
+   */
+  public static final OfByte __u_char = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __u_short
+   * }
+   */
+  public static final OfShort __u_short = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __u_int
+   * }
+   */
+  public static final OfInt __u_int = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_long
+   * }
+   */
+  public static final OfLong __u_long = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char __int8_t
+   * }
+   */
+  public static final OfByte __int8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned char __uint8_t
+   * }
+   */
+  public static final OfByte __uint8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef short __int16_t
+   * }
+   */
+  public static final OfShort __int16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef unsigned short __uint16_t
+   * }
+   */
+  public static final OfShort __uint16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef int __int32_t
+   * }
+   */
+  public static final OfInt __int32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uint32_t
+   * }
+   */
+  public static final OfInt __uint32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __int64_t
+   * }
+   */
+  public static final OfLong __int64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uint64_t
+   * }
+   */
+  public static final OfLong __uint64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int8_t __int_least8_t
+   * }
+   */
+  public static final OfByte __int_least8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
+   * }
+   */
+  public static final OfByte __uint_least8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t __int_least16_t
+   * }
+   */
+  public static final OfShort __int_least16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
+   * }
+   */
+  public static final OfShort __uint_least16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t __int_least32_t
+   * }
+   */
+  public static final OfInt __int_least32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
+   * }
+   */
+  public static final OfInt __uint_least32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t __int_least64_t
+   * }
+   */
+  public static final OfLong __int_least64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
+   * }
+   */
+  public static final OfLong __uint_least64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __quad_t
+   * }
+   */
+  public static final OfLong __quad_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __u_quad_t
+   * }
+   */
+  public static final OfLong __u_quad_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __intmax_t
+   * }
+   */
+  public static final OfLong __intmax_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __uintmax_t
+   * }
+   */
+  public static final OfLong __uintmax_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __dev_t
+   * }
+   */
+  public static final OfLong __dev_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __uid_t
+   * }
+   */
+  public static final OfInt __uid_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __gid_t
+   * }
+   */
+  public static final OfInt __gid_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino_t
+   * }
+   */
+  public static final OfLong __ino_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __ino64_t
+   * }
+   */
+  public static final OfLong __ino64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __mode_t
+   * }
+   */
+  public static final OfInt __mode_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __nlink_t
+   * }
+   */
+  public static final OfLong __nlink_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off_t
+   * }
+   */
+  public static final OfLong __off_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __off64_t
+   * }
+   */
+  public static final OfLong __off64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __pid_t
+   * }
+   */
+  public static final OfInt __pid_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __clock_t
+   * }
+   */
+  public static final OfLong __clock_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim_t
+   * }
+   */
+  public static final OfLong __rlim_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __rlim64_t
+   * }
+   */
+  public static final OfLong __rlim64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __id_t
+   * }
+   */
+  public static final OfInt __id_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __time_t
+   * }
+   */
+  public static final OfLong __time_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __useconds_t
+   * }
+   */
+  public static final OfInt __useconds_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds_t
+   * }
+   */
+  public static final OfLong __suseconds_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __suseconds64_t
+   * }
+   */
+  public static final OfLong __suseconds64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int __daddr_t
+   * }
+   */
+  public static final OfInt __daddr_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __key_t
+   * }
+   */
+  public static final OfInt __key_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __clockid_t
+   * }
+   */
+  public static final OfInt __clockid_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef void *__timer_t
+   * }
+   */
+  public static final AddressLayout __timer_t = IvfPqH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __blksize_t
+   * }
+   */
+  public static final OfLong __blksize_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt_t
+   * }
+   */
+  public static final OfLong __blkcnt_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __blkcnt64_t
+   * }
+   */
+  public static final OfLong __blkcnt64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
+   * }
+   */
+  public static final OfLong __fsblkcnt_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
+   * }
+   */
+  public static final OfLong __fsblkcnt64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
+   * }
+   */
+  public static final OfLong __fsfilcnt_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
+   * }
+   */
+  public static final OfLong __fsfilcnt64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __fsword_t
+   * }
+   */
+  public static final OfLong __fsword_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __ssize_t
+   * }
+   */
+  public static final OfLong __ssize_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long __syscall_slong_t
+   * }
+   */
+  public static final OfLong __syscall_slong_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
+   * }
+   */
+  public static final OfLong __syscall_ulong_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __off64_t __loff_t
+   * }
+   */
+  public static final OfLong __loff_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef char *__caddr_t
+   * }
+   */
+  public static final AddressLayout __caddr_t = IvfPqH.C_POINTER;
+  /**
+   * {@snippet lang = c : * typedef long __intptr_t
+   * }
+   */
+  public static final OfLong __intptr_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned int __socklen_t
+   * }
+   */
+  public static final OfInt __socklen_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef int __sig_atomic_t
+   * }
+   */
+  public static final OfInt __sig_atomic_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int8_t int8_t
+   * }
+   */
+  public static final OfByte int8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int16_t int16_t
+   * }
+   */
+  public static final OfShort int16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int32_t int32_t
+   * }
+   */
+  public static final OfInt int32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int64_t int64_t
+   * }
+   */
+  public static final OfLong int64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint8_t uint8_t
+   * }
+   */
+  public static final OfByte uint8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint16_t uint16_t
+   * }
+   */
+  public static final OfShort uint16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint32_t uint32_t
+   * }
+   */
+  public static final OfInt uint32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint64_t uint64_t
+   * }
+   */
+  public static final OfLong uint64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
+   * }
+   */
+  public static final OfByte int_least8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
+   * }
+   */
+  public static final OfShort int_least16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
+   * }
+   */
+  public static final OfInt int_least32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
+   * }
+   */
+  public static final OfLong int_least64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
+   * }
+   */
+  public static final OfByte uint_least8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
+   * }
+   */
+  public static final OfShort uint_least16_t = IvfPqH.C_SHORT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
+   * }
+   */
+  public static final OfInt uint_least32_t = IvfPqH.C_INT;
+  /**
+   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
+   * }
+   */
+  public static final OfLong uint_least64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef signed char int_fast8_t
+   * }
+   */
+  public static final OfByte int_fast8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef long int_fast16_t
+   * }
+   */
+  public static final OfLong int_fast16_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast32_t
+   * }
+   */
+  public static final OfLong int_fast32_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long int_fast64_t
+   * }
+   */
+  public static final OfLong int_fast64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
+   * }
+   */
+  public static final OfByte uint_fast8_t = IvfPqH.C_CHAR;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
+   * }
+   */
+  public static final OfLong uint_fast16_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
+   * }
+   */
+  public static final OfLong uint_fast32_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
+   * }
+   */
+  public static final OfLong uint_fast64_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef long intptr_t
+   * }
+   */
+  public static final OfLong intptr_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long uintptr_t
+   * }
+   */
+  public static final OfLong uintptr_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __intmax_t intmax_t
+   * }
+   */
+  public static final OfLong intmax_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
+   * }
+   */
+  public static final OfLong uintmax_t = IvfPqH.C_LONG;
+  private static final int CUVS_ERROR = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CUVS_ERROR = 0
+   * }
+   */
+  public static int CUVS_ERROR() {
+    return CUVS_ERROR;
+  }
+
+  private static final int CUVS_SUCCESS = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CUVS_SUCCESS = 1
+   * }
+   */
+  public static int CUVS_SUCCESS() {
+    return CUVS_SUCCESS;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef uintptr_t cuvsResources_t
+   * }
+   */
+  public static final OfLong cuvsResources_t = IvfPqH.C_LONG;
+  private static final int L2Expanded = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
+   * }
+   */
+  public static int L2Expanded() {
+    return L2Expanded;
+  }
+
+  private static final int L2SqrtExpanded = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
+   * }
+   */
+  public static int L2SqrtExpanded() {
+    return L2SqrtExpanded;
+  }
+
+  private static final int CosineExpanded = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
+   * }
+   */
+  public static int CosineExpanded() {
+    return CosineExpanded;
+  }
+
+  private static final int L1 = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L1 = 3
+   * }
+   */
+  public static int L1() {
+    return L1;
+  }
+
+  private static final int L2Unexpanded = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
+   * }
+   */
+  public static int L2Unexpanded() {
+    return L2Unexpanded;
+  }
+
+  private static final int L2SqrtUnexpanded = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
+   * }
+   */
+  public static int L2SqrtUnexpanded() {
+    return L2SqrtUnexpanded;
+  }
+
+  private static final int InnerProduct = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
+   * }
+   */
+  public static int InnerProduct() {
+    return InnerProduct;
+  }
+
+  private static final int Linf = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Linf = 7
+   * }
+   */
+  public static int Linf() {
+    return Linf;
+  }
+
+  private static final int Canberra = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
+   * }
+   */
+  public static int Canberra() {
+    return Canberra;
+  }
+
+  private static final int LpUnexpanded = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
+   * }
+   */
+  public static int LpUnexpanded() {
+    return LpUnexpanded;
+  }
+
+  private static final int CorrelationExpanded = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
+   * }
+   */
+  public static int CorrelationExpanded() {
+    return CorrelationExpanded;
+  }
+
+  private static final int JaccardExpanded = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
+   * }
+   */
+  public static int JaccardExpanded() {
+    return JaccardExpanded;
+  }
+
+  private static final int HellingerExpanded = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
+   * }
+   */
+  public static int HellingerExpanded() {
+    return HellingerExpanded;
+  }
+
+  private static final int Haversine = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
+   * }
+   */
+  public static int Haversine() {
+    return Haversine;
+  }
+
+  private static final int BrayCurtis = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
+   * }
+   */
+  public static int BrayCurtis() {
+    return BrayCurtis;
+  }
+
+  private static final int JensenShannon = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
+   * }
+   */
+  public static int JensenShannon() {
+    return JensenShannon;
+  }
+
+  private static final int HammingUnexpanded = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
+   * }
+   */
+  public static int HammingUnexpanded() {
+    return HammingUnexpanded;
+  }
+
+  private static final int KLDivergence = (int) 17L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
+   * }
+   */
+  public static int KLDivergence() {
+    return KLDivergence;
+  }
+
+  private static final int RusselRaoExpanded = (int) 18L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
+   * }
+   */
+  public static int RusselRaoExpanded() {
+    return RusselRaoExpanded;
+  }
+
+  private static final int DiceExpanded = (int) 19L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
+   * }
+   */
+  public static int DiceExpanded() {
+    return DiceExpanded;
+  }
+
+  private static final int Precomputed = (int) 100L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
+   * }
+   */
+  public static int Precomputed() {
+    return Precomputed;
+  }
+
+  /**
+   * {@snippet lang = c : * typedef long ptrdiff_t
+   * }
+   */
+  public static final OfLong ptrdiff_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef unsigned long size_t
+   * }
+   */
+  public static final OfLong size_t = IvfPqH.C_LONG;
+  /**
+   * {@snippet lang = c : * typedef int wchar_t
+   * }
+   */
+  public static final OfInt wchar_t = IvfPqH.C_INT;
+  private static final int kDLCPU = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
+   * }
+   */
+  public static int kDLCPU() {
+    return kDLCPU;
+  }
+
+  private static final int kDLCUDA = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
+   * }
+   */
+  public static int kDLCUDA() {
+    return kDLCUDA;
+  }
+
+  private static final int kDLCUDAHost = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
+   * }
+   */
+  public static int kDLCUDAHost() {
+    return kDLCUDAHost;
+  }
+
+  private static final int kDLOpenCL = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
+   * }
+   */
+  public static int kDLOpenCL() {
+    return kDLOpenCL;
+  }
+
+  private static final int kDLVulkan = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
+   * }
+   */
+  public static int kDLVulkan() {
+    return kDLVulkan;
+  }
+
+  private static final int kDLMetal = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
+   * }
+   */
+  public static int kDLMetal() {
+    return kDLMetal;
+  }
+
+  private static final int kDLVPI = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
+   * }
+   */
+  public static int kDLVPI() {
+    return kDLVPI;
+  }
+
+  private static final int kDLROCM = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
+   * }
+   */
+  public static int kDLROCM() {
+    return kDLROCM;
+  }
+
+  private static final int kDLROCMHost = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
+   * }
+   */
+  public static int kDLROCMHost() {
+    return kDLROCMHost;
+  }
+
+  private static final int kDLExtDev = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
+   * }
+   */
+  public static int kDLExtDev() {
+    return kDLExtDev;
+  }
+
+  private static final int kDLCUDAManaged = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
+   * }
+   */
+  public static int kDLCUDAManaged() {
+    return kDLCUDAManaged;
+  }
+
+  private static final int kDLOneAPI = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
+   * }
+   */
+  public static int kDLOneAPI() {
+    return kDLOneAPI;
+  }
+
+  private static final int kDLWebGPU = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
+   * }
+   */
+  public static int kDLWebGPU() {
+    return kDLWebGPU;
+  }
+
+  private static final int kDLHexagon = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
+   * }
+   */
+  public static int kDLHexagon() {
+    return kDLHexagon;
+  }
+
+  private static final int kDLMAIA = (int) 17L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLMAIA = 17
+   * }
+   */
+  public static int kDLMAIA() {
+    return kDLMAIA;
+  }
+
+  private static final int kDLInt = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
+   * }
+   */
+  public static int kDLInt() {
+    return kDLInt;
+  }
+
+  private static final int kDLUInt = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
+   * }
+   */
+  public static int kDLUInt() {
+    return kDLUInt;
+  }
+
+  private static final int kDLFloat = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
+   * }
+   */
+  public static int kDLFloat() {
+    return kDLFloat;
+  }
+
+  private static final int kDLOpaqueHandle = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
+   * }
+   */
+  public static int kDLOpaqueHandle() {
+    return kDLOpaqueHandle;
+  }
+
+  private static final int kDLBfloat = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
+   * }
+   */
+  public static int kDLBfloat() {
+    return kDLBfloat;
+  }
+
+  private static final int kDLComplex = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
+   * }
+   */
+  public static int kDLComplex() {
+    return kDLComplex;
+  }
+
+  private static final int kDLBool = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
+   * }
+   */
+  public static int kDLBool() {
+    return kDLBool;
+  }
+
+  private static final int CUDA_R_16F = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16F = 2
+   * }
+   */
+  public static int CUDA_R_16F() {
+    return CUDA_R_16F;
+  }
+
+  private static final int CUDA_C_16F = (int) 6L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16F = 6
+   * }
+   */
+  public static int CUDA_C_16F() {
+    return CUDA_C_16F;
+  }
+
+  private static final int CUDA_R_16BF = (int) 14L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16BF = 14
+   * }
+   */
+  public static int CUDA_R_16BF() {
+    return CUDA_R_16BF;
+  }
+
+  private static final int CUDA_C_16BF = (int) 15L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16BF = 15
+   * }
+   */
+  public static int CUDA_C_16BF() {
+    return CUDA_C_16BF;
+  }
+
+  private static final int CUDA_R_32F = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_32F = 0
+   * }
+   */
+  public static int CUDA_R_32F() {
+    return CUDA_R_32F;
+  }
+
+  private static final int CUDA_C_32F = (int) 4L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_32F = 4
+   * }
+   */
+  public static int CUDA_C_32F() {
+    return CUDA_C_32F;
+  }
+
+  private static final int CUDA_R_64F = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_64F = 1
+   * }
+   */
+  public static int CUDA_R_64F() {
+    return CUDA_R_64F;
+  }
+
+  private static final int CUDA_C_64F = (int) 5L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_64F = 5
+   * }
+   */
+  public static int CUDA_C_64F() {
+    return CUDA_C_64F;
+  }
+
+  private static final int CUDA_R_4I = (int) 16L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_4I = 16
+   * }
+   */
+  public static int CUDA_R_4I() {
+    return CUDA_R_4I;
+  }
+
+  private static final int CUDA_C_4I = (int) 17L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_4I = 17
+   * }
+   */
+  public static int CUDA_C_4I() {
+    return CUDA_C_4I;
+  }
+
+  private static final int CUDA_R_4U = (int) 18L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_4U = 18
+   * }
+   */
+  public static int CUDA_R_4U() {
+    return CUDA_R_4U;
+  }
+
+  private static final int CUDA_C_4U = (int) 19L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_4U = 19
+   * }
+   */
+  public static int CUDA_C_4U() {
+    return CUDA_C_4U;
+  }
+
+  private static final int CUDA_R_8I = (int) 3L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_8I = 3
+   * }
+   */
+  public static int CUDA_R_8I() {
+    return CUDA_R_8I;
+  }
+
+  private static final int CUDA_C_8I = (int) 7L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_8I = 7
+   * }
+   */
+  public static int CUDA_C_8I() {
+    return CUDA_C_8I;
+  }
+
+  private static final int CUDA_R_8U = (int) 8L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_8U = 8
+   * }
+   */
+  public static int CUDA_R_8U() {
+    return CUDA_R_8U;
+  }
+
+  private static final int CUDA_C_8U = (int) 9L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_8U = 9
+   * }
+   */
+  public static int CUDA_C_8U() {
+    return CUDA_C_8U;
+  }
+
+  private static final int CUDA_R_16I = (int) 20L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16I = 20
+   * }
+   */
+  public static int CUDA_R_16I() {
+    return CUDA_R_16I;
+  }
+
+  private static final int CUDA_C_16I = (int) 21L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16I = 21
+   * }
+   */
+  public static int CUDA_C_16I() {
+    return CUDA_C_16I;
+  }
+
+  private static final int CUDA_R_16U = (int) 22L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16U = 22
+   * }
+   */
+  public static int CUDA_R_16U() {
+    return CUDA_R_16U;
+  }
+
+  private static final int CUDA_C_16U = (int) 23L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16U = 23
+   * }
+   */
+  public static int CUDA_C_16U() {
+    return CUDA_C_16U;
+  }
+
+  private static final int CUDA_R_32I = (int) 10L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_32I = 10
+   * }
+   */
+  public static int CUDA_R_32I() {
+    return CUDA_R_32I;
+  }
+
+  private static final int CUDA_C_32I = (int) 11L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_32I = 11
+   * }
+   */
+  public static int CUDA_C_32I() {
+    return CUDA_C_32I;
+  }
+
+  private static final int CUDA_R_32U = (int) 12L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_32U = 12
+   * }
+   */
+  public static int CUDA_R_32U() {
+    return CUDA_R_32U;
+  }
+
+  private static final int CUDA_C_32U = (int) 13L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_32U = 13
+   * }
+   */
+  public static int CUDA_C_32U() {
+    return CUDA_C_32U;
+  }
+
+  private static final int CUDA_R_64I = (int) 24L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_64I = 24
+   * }
+   */
+  public static int CUDA_R_64I() {
+    return CUDA_R_64I;
+  }
+
+  private static final int CUDA_C_64I = (int) 25L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_64I = 25
+   * }
+   */
+  public static int CUDA_C_64I() {
+    return CUDA_C_64I;
+  }
+
+  private static final int CUDA_R_64U = (int) 26L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_64U = 26
+   * }
+   */
+  public static int CUDA_R_64U() {
+    return CUDA_R_64U;
+  }
+
+  private static final int CUDA_C_64U = (int) 27L;
+
+  /**
+   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_64U = 27
+   * }
+   */
+  public static int CUDA_C_64U() {
+    return CUDA_C_64U;
+  }
+
+  private static final int MAJOR_VERSION = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum libraryPropertyType_t.MAJOR_VERSION = 0
+   * }
+   */
+  public static int MAJOR_VERSION() {
+    return MAJOR_VERSION;
+  }
+
+  private static final int MINOR_VERSION = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum libraryPropertyType_t.MINOR_VERSION = 1
+   * }
+   */
+  public static int MINOR_VERSION() {
+    return MINOR_VERSION;
+  }
+
+  private static final int PATCH_LEVEL = (int) 2L;
+
+  /**
+   * {@snippet lang = c : * enum libraryPropertyType_t.PATCH_LEVEL = 2
+   * }
+   */
+  public static int PATCH_LEVEL() {
+    return PATCH_LEVEL;
+  }
+
+  private static final int PER_SUBSPACE = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * enum codebook_gen.PER_SUBSPACE = 0
+   * }
+   */
+  public static int PER_SUBSPACE() {
+    return PER_SUBSPACE;
+  }
+
+  private static final int PER_CLUSTER = (int) 1L;
+
+  /**
+   * {@snippet lang = c : * enum codebook_gen.PER_CLUSTER = 1
+   * }
+   */
+  public static int PER_CLUSTER() {
+    return PER_CLUSTER;
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsIvfPqIndexParams {
+   *     cuvsDistanceType metric;
+   *     float metric_arg;
+   *     _Bool add_data_on_build;
+   *     uint32_t n_lists;
+   *     uint32_t kmeans_n_iters;
+   *     double kmeans_trainset_fraction;
+   *     uint32_t pq_bits;
+   *     uint32_t pq_dim;
+   *     enum codebook_gen codebook_kind;
+   *     _Bool force_random_rotation;
+   *     _Bool conservative_memory_allocation;
+   *     uint32_t max_train_points_per_pq_code;
+   * } *cuvsIvfPqIndexParams_t
+   * }
+   */
+  public static final AddressLayout cuvsIvfPqIndexParams_t = IvfPqH.C_POINTER;
+
+  private static class cuvsIvfPqIndexParamsCreate {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexParamsCreate");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqIndexParamsCreate$descriptor() {
+    return cuvsIvfPqIndexParamsCreate.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqIndexParamsCreate$handle() {
+    return cuvsIvfPqIndexParamsCreate.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqIndexParamsCreate$address() {
+    return cuvsIvfPqIndexParamsCreate.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
+   * }
+   */
+  public static int cuvsIvfPqIndexParamsCreate(MemorySegment index_params) {
+    var mh$ = cuvsIvfPqIndexParamsCreate.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqIndexParamsCreate", index_params);
+      }
+      return (int) mh$.invokeExact(index_params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqIndexParamsDestroy {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexParamsDestroy");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqIndexParamsDestroy$descriptor() {
+    return cuvsIvfPqIndexParamsDestroy.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqIndexParamsDestroy$handle() {
+    return cuvsIvfPqIndexParamsDestroy.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqIndexParamsDestroy$address() {
+    return cuvsIvfPqIndexParamsDestroy.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
+   * }
+   */
+  public static int cuvsIvfPqIndexParamsDestroy(MemorySegment index_params) {
+    var mh$ = cuvsIvfPqIndexParamsDestroy.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqIndexParamsDestroy", index_params);
+      }
+      return (int) mh$.invokeExact(index_params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  /**
+   * {@snippet lang = c :
+   * typedef struct cuvsIvfPqSearchParams {
+   *     uint32_t n_probes;
+   *     cudaDataType_t lut_dtype;
+   *     cudaDataType_t internal_distance_dtype;
+   *     double preferred_shmem_carveout;
+   * } *cuvsIvfPqSearchParams_t
+   * }
+   */
+  public static final AddressLayout cuvsIvfPqSearchParams_t = IvfPqH.C_POINTER;
+
+  private static class cuvsIvfPqSearchParamsCreate {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSearchParamsCreate");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqSearchParamsCreate$descriptor() {
+    return cuvsIvfPqSearchParamsCreate.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqSearchParamsCreate$handle() {
+    return cuvsIvfPqSearchParamsCreate.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqSearchParamsCreate$address() {
+    return cuvsIvfPqSearchParamsCreate.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
+   * }
+   */
+  public static int cuvsIvfPqSearchParamsCreate(MemorySegment params) {
+    var mh$ = cuvsIvfPqSearchParamsCreate.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqSearchParamsCreate", params);
+      }
+      return (int) mh$.invokeExact(params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqSearchParamsDestroy {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSearchParamsDestroy");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqSearchParamsDestroy$descriptor() {
+    return cuvsIvfPqSearchParamsDestroy.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqSearchParamsDestroy$handle() {
+    return cuvsIvfPqSearchParamsDestroy.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqSearchParamsDestroy$address() {
+    return cuvsIvfPqSearchParamsDestroy.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
+   * }
+   */
+  public static int cuvsIvfPqSearchParamsDestroy(MemorySegment params) {
+    var mh$ = cuvsIvfPqSearchParamsDestroy.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqSearchParamsDestroy", params);
+      }
+      return (int) mh$.invokeExact(params);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  /**
+   * {@snippet lang = c : * typedef cuvsIvfPq *cuvsIvfPqIndex_t
+   * }
+   */
+  public static final AddressLayout cuvsIvfPqIndex_t = IvfPqH.C_POINTER;
+
+  private static class cuvsIvfPqIndexCreate {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexCreate");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqIndexCreate$descriptor() {
+    return cuvsIvfPqIndexCreate.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqIndexCreate$handle() {
+    return cuvsIvfPqIndexCreate.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqIndexCreate$address() {
+    return cuvsIvfPqIndexCreate.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
+   * }
+   */
+  public static int cuvsIvfPqIndexCreate(MemorySegment index) {
+    var mh$ = cuvsIvfPqIndexCreate.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqIndexCreate", index);
+      }
+      return (int) mh$.invokeExact(index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqIndexDestroy {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexDestroy");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqIndexDestroy$descriptor() {
+    return cuvsIvfPqIndexDestroy.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqIndexDestroy$handle() {
+    return cuvsIvfPqIndexDestroy.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqIndexDestroy$address() {
+    return cuvsIvfPqIndexDestroy.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static int cuvsIvfPqIndexDestroy(MemorySegment index) {
+    var mh$ = cuvsIvfPqIndexDestroy.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqIndexDestroy", index);
+      }
+      return (int) mh$.invokeExact(index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqBuild {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
+        IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqBuild");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqBuild$descriptor() {
+    return cuvsIvfPqBuild.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqBuild$handle() {
+    return cuvsIvfPqBuild.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqBuild$address() {
+    return cuvsIvfPqBuild.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static int cuvsIvfPqBuild(long res, MemorySegment params, MemorySegment dataset, MemorySegment index) {
+    var mh$ = cuvsIvfPqBuild.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqBuild", res, params, dataset, index);
+      }
+      return (int) mh$.invokeExact(res, params, dataset, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqSearch {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
+        IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSearch");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqSearch$descriptor() {
+    return cuvsIvfPqSearch.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqSearch$handle() {
+    return cuvsIvfPqSearch.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqSearch$address() {
+    return cuvsIvfPqSearch.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
+   * }
+   */
+  public static int cuvsIvfPqSearch(long res, MemorySegment search_params, MemorySegment index, MemorySegment queries,
+      MemorySegment neighbors, MemorySegment distances) {
+    var mh$ = cuvsIvfPqSearch.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqSearch", res, search_params, index, queries, neighbors, distances);
+      }
+      return (int) mh$.invokeExact(res, search_params, index, queries, neighbors, distances);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqSerialize {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
+        IvfPqH.C_POINTER, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSerialize");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqSerialize$descriptor() {
+    return cuvsIvfPqSerialize.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqSerialize$handle() {
+    return cuvsIvfPqSerialize.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqSerialize$address() {
+    return cuvsIvfPqSerialize.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static int cuvsIvfPqSerialize(long res, MemorySegment filename, MemorySegment index) {
+    var mh$ = cuvsIvfPqSerialize.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqSerialize", res, filename, index);
+      }
+      return (int) mh$.invokeExact(res, filename, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqDeserialize {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
+        IvfPqH.C_POINTER, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqDeserialize");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqDeserialize$descriptor() {
+    return cuvsIvfPqDeserialize.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqDeserialize$handle() {
+    return cuvsIvfPqDeserialize.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqDeserialize$address() {
+    return cuvsIvfPqDeserialize.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static int cuvsIvfPqDeserialize(long res, MemorySegment filename, MemorySegment index) {
+    var mh$ = cuvsIvfPqDeserialize.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqDeserialize", res, filename, index);
+      }
+      return (int) mh$.invokeExact(res, filename, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static class cuvsIvfPqExtend {
+    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
+        IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER);
+
+    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqExtend");
+
+    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
+  }
+
+  /**
+   * Function descriptor for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static FunctionDescriptor cuvsIvfPqExtend$descriptor() {
+    return cuvsIvfPqExtend.DESC;
+  }
+
+  /**
+   * Downcall method handle for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MethodHandle cuvsIvfPqExtend$handle() {
+    return cuvsIvfPqExtend.HANDLE;
+  }
+
+  /**
+   * Address for:
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static MemorySegment cuvsIvfPqExtend$address() {
+    return cuvsIvfPqExtend.ADDR;
+  }
+
+  /**
+   * {@snippet lang = c
+   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
+   * }
+   */
+  public static int cuvsIvfPqExtend(long res, MemorySegment new_vectors, MemorySegment new_indices,
+      MemorySegment index) {
+    var mh$ = cuvsIvfPqExtend.HANDLE;
+    try {
+      if (TRACE_DOWNCALLS) {
+        traceDowncall("cuvsIvfPqExtend", res, new_vectors, new_indices, index);
+      }
+      return (int) mh$.invokeExact(res, new_vectors, new_indices, index);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  private static final long _POSIX_C_SOURCE = 200809L;
+
+  /**
+   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
+   * }
+   */
+  public static long _POSIX_C_SOURCE() {
+    return _POSIX_C_SOURCE;
+  }
+
+  private static final int __TIMESIZE = (int) 64L;
+
+  /**
+   * {@snippet lang = c : * #define __TIMESIZE 64
+   * }
+   */
+  public static int __TIMESIZE() {
+    return __TIMESIZE;
+  }
+
+  private static final long __STDC_IEC_60559_BFP__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_BFP__() {
+    return __STDC_IEC_60559_BFP__;
+  }
+
+  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
+   * }
+   */
+  public static long __STDC_IEC_60559_COMPLEX__() {
+    return __STDC_IEC_60559_COMPLEX__;
+  }
+
+  private static final long __STDC_ISO_10646__ = 201706L;
+
+  /**
+   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
+   * }
+   */
+  public static long __STDC_ISO_10646__() {
+    return __STDC_ISO_10646__;
+  }
+
+  private static final int __WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
+   * }
+   */
+  public static int __WCHAR_MAX() {
+    return __WCHAR_MAX;
+  }
+
+  private static final int __WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
+   * }
+   */
+  public static int __WCHAR_MIN() {
+    return __WCHAR_MIN;
+  }
+
+  private static final int INT8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MIN -128
+   * }
+   */
+  public static int INT8_MIN() {
+    return INT8_MIN;
+  }
+
+  private static final int INT16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MIN -32768
+   * }
+   */
+  public static int INT16_MIN() {
+    return INT16_MIN;
+  }
+
+  private static final int INT32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MIN -2147483648
+   * }
+   */
+  public static int INT32_MIN() {
+    return INT32_MIN;
+  }
+
+  private static final long INT64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT64_MIN() {
+    return INT64_MIN;
+  }
+
+  private static final int INT8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT8_MAX 127
+   * }
+   */
+  public static int INT8_MAX() {
+    return INT8_MAX;
+  }
+
+  private static final int INT16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT16_MAX 32767
+   * }
+   */
+  public static int INT16_MAX() {
+    return INT16_MAX;
+  }
+
+  private static final int INT32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT32_MAX 2147483647
+   * }
+   */
+  public static int INT32_MAX() {
+    return INT32_MAX;
+  }
+
+  private static final long INT64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT64_MAX() {
+    return INT64_MAX;
+  }
+
+  private static final int UINT8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT8_MAX 255
+   * }
+   */
+  public static int UINT8_MAX() {
+    return UINT8_MAX;
+  }
+
+  private static final int UINT16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT16_MAX 65535
+   * }
+   */
+  public static int UINT16_MAX() {
+    return UINT16_MAX;
+  }
+
+  private static final int UINT32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT32_MAX 4294967295
+   * }
+   */
+  public static int UINT32_MAX() {
+    return UINT32_MAX;
+  }
+
+  private static final long UINT64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT64_MAX -1
+   * }
+   */
+  public static long UINT64_MAX() {
+    return UINT64_MAX;
+  }
+
+  private static final int INT_LEAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
+   * }
+   */
+  public static int INT_LEAST8_MIN() {
+    return INT_LEAST8_MIN;
+  }
+
+  private static final int INT_LEAST16_MIN = (int) -32768L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
+   * }
+   */
+  public static int INT_LEAST16_MIN() {
+    return INT_LEAST16_MIN;
+  }
+
+  private static final int INT_LEAST32_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
+   * }
+   */
+  public static int INT_LEAST32_MIN() {
+    return INT_LEAST32_MIN;
+  }
+
+  private static final long INT_LEAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_LEAST64_MIN() {
+    return INT_LEAST64_MIN;
+  }
+
+  private static final int INT_LEAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
+   * }
+   */
+  public static int INT_LEAST8_MAX() {
+    return INT_LEAST8_MAX;
+  }
+
+  private static final int INT_LEAST16_MAX = (int) 32767L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
+   * }
+   */
+  public static int INT_LEAST16_MAX() {
+    return INT_LEAST16_MAX;
+  }
+
+  private static final int INT_LEAST32_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
+   * }
+   */
+  public static int INT_LEAST32_MAX() {
+    return INT_LEAST32_MAX;
+  }
+
+  private static final long INT_LEAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_LEAST64_MAX() {
+    return INT_LEAST64_MAX;
+  }
+
+  private static final int UINT_LEAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
+   * }
+   */
+  public static int UINT_LEAST8_MAX() {
+    return UINT_LEAST8_MAX;
+  }
+
+  private static final int UINT_LEAST16_MAX = (int) 65535L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
+   * }
+   */
+  public static int UINT_LEAST16_MAX() {
+    return UINT_LEAST16_MAX;
+  }
+
+  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
+   * }
+   */
+  public static int UINT_LEAST32_MAX() {
+    return UINT_LEAST32_MAX;
+  }
+
+  private static final long UINT_LEAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
+   * }
+   */
+  public static long UINT_LEAST64_MAX() {
+    return UINT_LEAST64_MAX;
+  }
+
+  private static final int INT_FAST8_MIN = (int) -128L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MIN -128
+   * }
+   */
+  public static int INT_FAST8_MIN() {
+    return INT_FAST8_MIN;
+  }
+
+  private static final long INT_FAST16_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST16_MIN() {
+    return INT_FAST16_MIN;
+  }
+
+  private static final long INT_FAST32_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST32_MIN() {
+    return INT_FAST32_MIN;
+  }
+
+  private static final long INT_FAST64_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
+   * }
+   */
+  public static long INT_FAST64_MIN() {
+    return INT_FAST64_MIN;
+  }
+
+  private static final int INT_FAST8_MAX = (int) 127L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST8_MAX 127
+   * }
+   */
+  public static int INT_FAST8_MAX() {
+    return INT_FAST8_MAX;
+  }
+
+  private static final long INT_FAST16_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST16_MAX() {
+    return INT_FAST16_MAX;
+  }
+
+  private static final long INT_FAST32_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST32_MAX() {
+    return INT_FAST32_MAX;
+  }
+
+  private static final long INT_FAST64_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
+   * }
+   */
+  public static long INT_FAST64_MAX() {
+    return INT_FAST64_MAX;
+  }
+
+  private static final int UINT_FAST8_MAX = (int) 255L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
+   * }
+   */
+  public static int UINT_FAST8_MAX() {
+    return UINT_FAST8_MAX;
+  }
+
+  private static final long UINT_FAST16_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
+   * }
+   */
+  public static long UINT_FAST16_MAX() {
+    return UINT_FAST16_MAX;
+  }
+
+  private static final long UINT_FAST32_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
+   * }
+   */
+  public static long UINT_FAST32_MAX() {
+    return UINT_FAST32_MAX;
+  }
+
+  private static final long UINT_FAST64_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
+   * }
+   */
+  public static long UINT_FAST64_MAX() {
+    return UINT_FAST64_MAX;
+  }
+
+  private static final long INTPTR_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
+   * }
+   */
+  public static long INTPTR_MIN() {
+    return INTPTR_MIN;
+  }
+
+  private static final long INTPTR_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
+   * }
+   */
+  public static long INTPTR_MAX() {
+    return INTPTR_MAX;
+  }
+
+  private static final long UINTPTR_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTPTR_MAX -1
+   * }
+   */
+  public static long UINTPTR_MAX() {
+    return UINTPTR_MAX;
+  }
+
+  private static final long INTMAX_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
+   * }
+   */
+  public static long INTMAX_MIN() {
+    return INTMAX_MIN;
+  }
+
+  private static final long INTMAX_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
+   * }
+   */
+  public static long INTMAX_MAX() {
+    return INTMAX_MAX;
+  }
+
+  private static final long UINTMAX_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define UINTMAX_MAX -1
+   * }
+   */
+  public static long UINTMAX_MAX() {
+    return UINTMAX_MAX;
+  }
+
+  private static final long PTRDIFF_MIN = -9223372036854775808L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
+   * }
+   */
+  public static long PTRDIFF_MIN() {
+    return PTRDIFF_MIN;
+  }
+
+  private static final long PTRDIFF_MAX = 9223372036854775807L;
+
+  /**
+   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
+   * }
+   */
+  public static long PTRDIFF_MAX() {
+    return PTRDIFF_MAX;
+  }
+
+  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
+   * }
+   */
+  public static int SIG_ATOMIC_MIN() {
+    return SIG_ATOMIC_MIN;
+  }
+
+  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
+   * }
+   */
+  public static int SIG_ATOMIC_MAX() {
+    return SIG_ATOMIC_MAX;
+  }
+
+  private static final long SIZE_MAX = -1L;
+
+  /**
+   * {@snippet lang = c : * #define SIZE_MAX -1
+   * }
+   */
+  public static long SIZE_MAX() {
+    return SIZE_MAX;
+  }
+
+  private static final int WCHAR_MIN = (int) -2147483648L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
+   * }
+   */
+  public static int WCHAR_MIN() {
+    return WCHAR_MIN;
+  }
+
+  private static final int WCHAR_MAX = (int) 2147483647L;
+
+  /**
+   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
+   * }
+   */
+  public static int WCHAR_MAX() {
+    return WCHAR_MAX;
+  }
+
+  private static final int WINT_MIN = (int) 0L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MIN 0
+   * }
+   */
+  public static int WINT_MIN() {
+    return WINT_MIN;
+  }
+
+  private static final int WINT_MAX = (int) 4294967295L;
+
+  /**
+   * {@snippet lang = c : * #define WINT_MAX 4294967295
+   * }
+   */
+  public static int WINT_MAX() {
+    return WINT_MAX;
+  }
+
+  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
+
+  /**
+   * {@snippet lang = c : * #define NULL (void*) 0
+   * }
+   */
+  public static MemorySegment NULL() {
+    return NULL;
+  }
+
+  private static final long DLPACK_FLAG_BITMASK_READ_ONLY = 1L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_READ_ONLY 1
+   * }
+   */
+  public static long DLPACK_FLAG_BITMASK_READ_ONLY() {
+    return DLPACK_FLAG_BITMASK_READ_ONLY;
+  }
+
+  private static final long DLPACK_FLAG_BITMASK_IS_COPIED = 2L;
+
+  /**
+   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_IS_COPIED 2
+   * }
+   */
+  public static long DLPACK_FLAG_BITMASK_IS_COPIED() {
+    return DLPACK_FLAG_BITMASK_IS_COPIED;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/MaxAlignT.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/MaxAlignT.java
new file mode 100644
index 0000000000..1216df6f7a
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/MaxAlignT.java
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.panama;
+
+import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.GroupLayout;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SegmentAllocator;
+import java.lang.foreign.ValueLayout.OfLong;
+import java.util.function.Consumer;
+
+/**
+ * {@snippet lang=c :
+ * struct {
+ *     long long __clang_max_align_nonce1;
+ *     long double __clang_max_align_nonce2;
+ * }
+ * }
+ */
+public class MaxAlignT {
+
+    MaxAlignT() {
+        // Should not be called directly
+    }
+
+    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
+        DlpackH.C_LONG_LONG.withName("__clang_max_align_nonce1"),
+        MemoryLayout.paddingLayout(24)
+    ).withName("$anon$19:9");
+
+    /**
+     * The layout of this struct
+     */
+    public static final GroupLayout layout() {
+        return $LAYOUT;
+    }
+
+    private static final OfLong __clang_max_align_nonce1$LAYOUT = (OfLong)$LAYOUT.select(groupElement("__clang_max_align_nonce1"));
+
+    /**
+     * Layout for field:
+     * {@snippet lang=c :
+     * long long __clang_max_align_nonce1
+     * }
+     */
+    public static final OfLong __clang_max_align_nonce1$layout() {
+        return __clang_max_align_nonce1$LAYOUT;
+    }
+
+    private static final long __clang_max_align_nonce1$OFFSET = 0;
+
+    /**
+     * Offset for field:
+     * {@snippet lang=c :
+     * long long __clang_max_align_nonce1
+     * }
+     */
+    public static final long __clang_max_align_nonce1$offset() {
+        return __clang_max_align_nonce1$OFFSET;
+    }
+
+    /**
+     * Getter for field:
+     * {@snippet lang=c :
+     * long long __clang_max_align_nonce1
+     * }
+     */
+    public static long __clang_max_align_nonce1(MemorySegment struct) {
+        return struct.get(__clang_max_align_nonce1$LAYOUT, __clang_max_align_nonce1$OFFSET);
+    }
+
+    /**
+     * Setter for field:
+     * {@snippet lang=c :
+     * long long __clang_max_align_nonce1
+     * }
+     */
+    public static void __clang_max_align_nonce1(MemorySegment struct, long fieldValue) {
+        struct.set(__clang_max_align_nonce1$LAYOUT, __clang_max_align_nonce1$OFFSET, fieldValue);
+    }
+
+    /**
+     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
+     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
+     */
+    public static MemorySegment asSlice(MemorySegment array, long index) {
+        return array.asSlice(layout().byteSize() * index);
+    }
+
+    /**
+     * The size (in bytes) of this struct
+     */
+    public static long sizeof() { return layout().byteSize(); }
+
+    /**
+     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
+     */
+    public static MemorySegment allocate(SegmentAllocator allocator) {
+        return allocator.allocate(layout());
+    }
+
+    /**
+     * Allocate an array of size {@code elementCount} using {@code allocator}.
+     * The returned segment has size {@code elementCount * layout().byteSize()}.
+     */
+    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
+        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
+        return reinterpret(addr, 1, arena, cleanup);
+    }
+
+    /**
+     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
+     * The returned segment has size {@code elementCount * layout().byteSize()}
+     */
+    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
+        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
+    }
+}
diff --git a/java/cuvs-java/src/main/java/module-info.java b/java/cuvs-java/src/main/java/module-info.java
new file mode 100644
index 0000000000..468252f22e
--- /dev/null
+++ b/java/cuvs-java/src/main/java/module-info.java
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+module com.nvidia.cuvs {
+  exports com.nvidia.cuvs;
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchTest.java
new file mode 100644
index 0000000000..91e6825bc4
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchTest.java
@@ -0,0 +1,129 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import static org.junit.Assert.assertEquals;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import java.util.UUID;
+
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.nvidia.cuvs.common.SearchResults;
+
+public class BruteForceAndSearchTest {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  /**
+   * A basic test that checks the whole flow - from indexing to search.
+   *
+   * @throws Throwable
+   */
+  @Test
+  public void testIndexingAndSearchingFlow() throws Throwable {
+
+    // Sample data and query
+    float[][] dataset = {
+        { 0.74021935f, 0.9209938f },
+        { 0.03902049f, 0.9689629f },
+        { 0.92514056f, 0.4463501f },
+        { 0.6673192f, 0.10993068f }
+      };
+    List<Integer> map = List.of(0, 1, 2, 3);
+    float[][] queries = {
+        { 0.48216683f, 0.0428398f },
+        { 0.5084142f, 0.6545497f },
+        { 0.51260436f, 0.2643005f },
+        { 0.05198065f, 0.5789965f }
+      };
+
+    // Expected search results
+    List<Map<Integer, Float>> expectedResults = Arrays.asList(
+        Map.of(3, 0.038782537f, 2, 0.35904616f, 0, 0.83774555f),
+        Map.of(0, 0.12472606f, 2, 0.21700788f, 1, 0.3191862f),
+        Map.of(3, 0.047766685f, 2, 0.20332813f, 0, 0.48305476f),
+        Map.of(1, 0.15224183f, 0, 0.5906347f, 3, 0.5986643f)
+      );
+
+    for (int j = 0; j < 10; j++) {
+
+      try (CuVSResources resources = new CuVSResources()) {
+
+        // Create a query object with the query vectors
+        BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
+            .withTopK(3)
+            .withQueryVectors(queries)
+            .withMapping(map)
+            .build();
+
+        // Set index parameters
+        BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
+            .withNumWriterThreads(32)
+            .build();
+
+        // Create the index with the dataset
+        BruteForceIndex index = new BruteForceIndex.Builder(resources)
+            .withDataset(dataset)
+            .withIndexParams(indexParams)
+            .build();
+
+        // Saving the index on to the disk.
+        String indexFileName = UUID.randomUUID().toString() + ".bf";
+        index.serialize(new FileOutputStream(indexFileName));
+
+        // Loading a BRUTEFORCE index from disk.
+        File indexFile = new File(indexFileName);
+        InputStream inputStream = new FileInputStream(indexFile);
+        BruteForceIndex loadedIndex = new BruteForceIndex.Builder(resources)
+            .from(inputStream)
+            .build();
+
+        // Perform the search
+        SearchResults resultsFromLoadedIndex = loadedIndex.search(cuvsQuery);
+
+        // Check results
+        log.info(resultsFromLoadedIndex.getResults().toString());
+        assertEquals(expectedResults, resultsFromLoadedIndex.getResults());
+
+        // Perform the search
+        SearchResults results = index.search(cuvsQuery);
+
+        // Check results
+        log.info(results.getResults().toString());
+        assertEquals(expectedResults, results.getResults());
+
+        // Cleanup
+        index.destroyIndex();
+        loadedIndex.destroyIndex();
+
+        if (indexFile.exists()) {
+          indexFile.delete();
+        }
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedTest.java
new file mode 100644
index 0000000000..64d1eb86ec
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedTest.java
@@ -0,0 +1,99 @@
+package com.nvidia.cuvs;
+
+import java.lang.invoke.MethodHandles;
+import java.util.List;
+
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+
+@RunWith(RandomizedRunner.class)
+public class BruteForceRandomizedTest extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  @Before
+  public void setup() {
+    initializeRandom();
+    log.info("Random context initialized for test.");
+  }
+
+  @Test
+  public void testResultsTopKWithRandomValues() throws Throwable {
+    for (int i = 0; i < 10; i++) {
+      tmpResultsTopKWithRandomValues();
+    }
+  }
+
+  private void tmpResultsTopKWithRandomValues() throws Throwable {
+    int DATASET_SIZE_LIMIT = 10_000;
+    int DIMENSIONS_LIMIT = 2048;
+    int NUM_QUERIES_LIMIT = 10;
+    int TOP_K_LIMIT = 64; // nocommit This fails beyond 64
+
+    int datasetSize = random.nextInt(DATASET_SIZE_LIMIT) + 1;
+    int dimensions = random.nextInt(DIMENSIONS_LIMIT) + 1;
+    int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
+    int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
+
+    if (datasetSize < topK)
+      datasetSize = topK;
+
+    // Generate a random dataset
+    float[][] dataset = generateData(random, datasetSize, dimensions);
+
+    // Generate random query vectors
+    float[][] queries = generateData(random, numQueries, dimensions);
+
+    log.info("Dataset size: {}x{}", datasetSize, dimensions);
+    log.info("Query size: {}x{}", numQueries, dimensions);
+    log.info("TopK: {}", topK);
+
+    // Debugging: Log dataset and queries
+    if (log.isDebugEnabled()) {
+      log.debug("Dataset:");
+      for (float[] row : dataset) {
+        log.debug(java.util.Arrays.toString(row));
+      }
+      log.debug("Queries:");
+      for (float[] query : queries) {
+        log.debug(java.util.Arrays.toString(query));
+      }
+    }
+    // Sanity checks
+    assert dataset.length > 0 : "Dataset is empty.";
+    assert queries.length > 0 : "Queries are empty.";
+    assert dimensions > 0 : "Invalid dimensions.";
+    assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
+
+    // Generate expected results using brute force
+    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
+
+    // Create CuVS index and query
+    try (CuVSResources resources = new CuVSResources()) {
+
+      BruteForceQuery query = new BruteForceQuery.Builder()
+          .withTopK(topK)
+          .withQueryVectors(queries)
+          .build();
+
+      BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
+          .withNumWriterThreads(32)
+          .build();
+
+      BruteForceIndex index = new BruteForceIndex.Builder(resources)
+          .withDataset(dataset)
+          .withIndexParams(indexParams)
+          .build();
+
+      log.info("Index built successfully. Executing search...");
+      BruteForceSearchResults results = index.search(query);
+
+      compareResults(results, expected, topK, datasetSize, numQueries);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchTest.java
new file mode 100644
index 0000000000..934e60b1c7
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchTest.java
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import static org.junit.Assert.assertEquals;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import java.util.UUID;
+
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import com.nvidia.cuvs.common.SearchResults;
+
+public class CagraBuildAndSearchTest {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  /**
+   * A basic test that checks the whole flow - from indexing to search.
+   *
+   * @throws Throwable
+   */
+  @Test
+  public void testIndexingAndSearchingFlow() throws Throwable {
+
+    // Sample data and query
+    float[][] dataset = {
+        { 0.74021935f, 0.9209938f },
+        { 0.03902049f, 0.9689629f },
+        { 0.92514056f, 0.4463501f },
+        { 0.6673192f, 0.10993068f }
+      };
+    List<Integer> map = List.of(0, 1, 2, 3);
+    float[][] queries = {
+        { 0.48216683f, 0.0428398f },
+        { 0.5084142f, 0.6545497f },
+        { 0.51260436f, 0.2643005f },
+        { 0.05198065f, 0.5789965f }
+      };
+
+    // Expected search results
+    List<Map<Integer, Float>> expectedResults = Arrays.asList(
+        Map.of(3, 0.038782578f, 2, 0.3590463f, 0, 0.83774555f),
+        Map.of(0, 0.12472608f, 2, 0.21700792f, 1, 0.31918612f),
+        Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
+        Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.5986642f));
+
+    for (int j = 0; j < 10; j++) {
+
+      try (CuVSResources resources = new CuVSResources()) {
+
+        // Configure index parameters
+        CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+            .withGraphDegree(1)
+            .withIntermediateGraphDegree(2)
+            .withNumWriterThreads(32)
+            .withMetric(CuvsDistanceType.L2Expanded)
+            .build();
+
+        // Create the index with the dataset
+        CagraIndex index = new CagraIndex.Builder(resources)
+            .withDataset(dataset)
+            .withIndexParams(indexParams)
+            .build();
+
+        // Saving the index on to the disk.
+        String indexFileName = UUID.randomUUID().toString() + ".cag";
+        index.serialize(new FileOutputStream(indexFileName));
+
+        // Loading a CAGRA index from disk.
+        File indexFile = new File(indexFileName);
+        InputStream inputStream = new FileInputStream(indexFile);
+        CagraIndex loadedIndex = new CagraIndex.Builder(resources)
+            .from(inputStream)
+            .build();
+
+        // Configure search parameters
+        CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+            .build();
+
+        // Create a query object with the query vectors
+        CagraQuery cuvsQuery = new CagraQuery.Builder()
+            .withTopK(3)
+            .withSearchParams(searchParams)
+            .withQueryVectors(queries)
+            .withMapping(map)
+            .build();
+
+        // Perform the search
+        SearchResults results = index.search(cuvsQuery);
+
+        // Check results
+        log.info(results.getResults().toString());
+        assertEquals(expectedResults, results.getResults());
+
+        // Search from deserialized index
+        results = loadedIndex.search(cuvsQuery);
+
+        // Check results
+        log.info(results.getResults().toString());
+        assertEquals(expectedResults, results.getResults());
+
+        // Cleanup
+        if (indexFile.exists()) {
+          indexFile.delete();
+        }
+        index.destroyIndex();
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedTest.java
new file mode 100644
index 0000000000..fc7570133a
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedTest.java
@@ -0,0 +1,101 @@
+package com.nvidia.cuvs;
+
+import java.lang.invoke.MethodHandles;
+import java.util.List;
+
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+
+@RunWith(RandomizedRunner.class)
+public class CagraRandomizedTest extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  @Before
+  public void setup() {
+    initializeRandom();
+    log.info("Random context initialized for test.");
+  }
+
+  @Test
+  public void testResultsTopKWithRandomValues() throws Throwable {
+    for (int i = 0; i < 10; i++) {
+      tmpResultsTopKWithRandomValues();
+    }
+  }
+
+  private void tmpResultsTopKWithRandomValues() throws Throwable {
+    int DATASET_SIZE_LIMIT = 10_000;
+    int DIMENSIONS_LIMIT = 2048;
+    int NUM_QUERIES_LIMIT = 10;
+    int TOP_K_LIMIT = 64; // nocommit This fails beyond 64
+
+    int datasetSize = random.nextInt(DATASET_SIZE_LIMIT) + 1;
+    int dimensions = random.nextInt(DIMENSIONS_LIMIT) + 1;
+    int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
+    int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
+
+    if (datasetSize < topK)
+      datasetSize = topK;
+
+    // Generate a random dataset
+    float[][] dataset = generateData(random, datasetSize, dimensions);
+
+    // Generate random query vectors
+    float[][] queries = generateData(random, numQueries, dimensions);
+
+    log.info("Dataset size: {}x{}", datasetSize, dimensions);
+    log.info("Query size: {}x{}", numQueries, dimensions);
+    log.info("TopK: {}", topK);
+
+    // Debugging: Log dataset and queries
+    if (log.isDebugEnabled()) {
+      log.debug("Dataset:");
+      for (float[] row : dataset) {
+        log.debug(java.util.Arrays.toString(row));
+      }
+      log.debug("Queries:");
+      for (float[] query : queries) {
+        log.debug(java.util.Arrays.toString(query));
+      }
+    }
+    // Sanity checks
+    assert dataset.length > 0 : "Dataset is empty.";
+    assert queries.length > 0 : "Queries are empty.";
+    assert dimensions > 0 : "Invalid dimensions.";
+    assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
+
+    // Generate expected results using brute force
+    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
+
+    // Create CuVS index and query
+    try (CuVSResources resources = new CuVSResources()) {
+      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+          .build();
+      CagraIndex index = new CagraIndex.Builder(resources)
+          .withDataset(dataset)
+          .withIndexParams(indexParams)
+          .build();
+      log.info("Index built successfully.");
+
+      // Execute search and retrieve results
+      CagraQuery query = new CagraQuery.Builder()
+          .withQueryVectors(queries)
+          .withTopK(topK)
+          .withSearchParams(new CagraSearchParams.Builder(resources)
+              .build())
+          .build();
+      log.info("Query built successfully. Executing search...");
+      CagraSearchResults results = index.search(query);
+
+      compareResults(results, expected, topK, datasetSize, numQueries);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
new file mode 100644
index 0000000000..bd50dc759d
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
@@ -0,0 +1,88 @@
+package com.nvidia.cuvs;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+import java.lang.invoke.MethodHandles;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+import java.util.TreeMap;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.carrotsearch.randomizedtesting.RandomizedContext;
+import com.nvidia.cuvs.common.SearchResults;
+
+public abstract class CuVSTestCase {
+  protected Random random;
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  protected void initializeRandom() {
+    random = RandomizedContext.current().getRandom();
+    log.info("Test seed: " + RandomizedContext.current().getRunnerSeedAsString());
+  }
+
+  protected float[][] generateData(Random random, int rows, int cols) {
+    float[][] data = new float[rows][cols];
+    for (int i = 0; i < rows; i++) {
+      for (int j = 0; j < cols; j++) {
+        data[i][j] = random.nextFloat() * 100;
+      }
+    }
+    return data;
+  }
+
+  protected List<List<Integer>> generateExpectedResults(int topK, float[][] dataset, float[][] queries, Logger log) {
+    List<List<Integer>> neighborsResult = new ArrayList<>();
+    int dimensions = dataset[0].length;
+
+    for (float[] query : queries) {
+      Map<Integer, Double> distances = new TreeMap<>();
+      for (int j = 0; j < dataset.length; j++) {
+        double distance = 0;
+        for (int k = 0; k < dimensions; k++) {
+          distance += (query[k] - dataset[j][k]) * (query[k] - dataset[j][k]);
+        }
+        distances.put(j, Math.sqrt(distance));
+      }
+
+      // Sort by distance and select the topK nearest neighbors
+      List<Integer> neighbors = distances.entrySet().stream().sorted(Map.Entry.comparingByValue())
+          .map(Map.Entry::getKey).toList();
+      neighborsResult.add(neighbors.subList(0, Math.min(topK * 2, dataset.length)));
+    }
+
+    log.info("Expected results generated successfully.");
+    return neighborsResult;
+  }
+
+  protected void compareResults(SearchResults results, List<List<Integer>> expected, int topK, int datasetSize,
+      int numQueries) {
+
+    for (int i = 0; i < numQueries; i++) {
+      log.info("Results returned for query " + i + ": " + results.getResults().get(i).keySet());
+      log.info("Expected results for query " + i + ": " + expected.get(i).subList(0, Math.min(topK, datasetSize)));
+    }
+
+    // actual vs. expected results
+    for (int i = 0; i < results.getResults().size(); i++) {
+      Map<Integer, Float> result = results.getResults().get(i);
+      assertEquals("TopK mismatch for query.", Math.min(topK, datasetSize), result.size());
+
+      // Sort result by values (distances) and extract keys
+      List<Integer> sortedResultKeys = result.entrySet().stream().sorted(Map.Entry.comparingByValue())
+          .map(Map.Entry::getKey) // Extract sorted keys
+          .toList();
+
+      // just make sure that the first 5 results are in the expected list (which
+      // comprises of 2*topK results)
+      for (int j = 0; j < Math.min(5, sortedResultKeys.size()); j++) {
+        assertTrue("Not found in expected list: " + sortedResultKeys.get(j),
+            expected.get(i).contains(sortedResultKeys.get(j)));
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchTest.java
new file mode 100644
index 0000000000..712e7edf98
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchTest.java
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import static org.junit.Assert.assertEquals;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import java.util.UUID;
+
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+
+public class HnswBuildAndSearchTest {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  /**
+   * A basic test that checks the whole flow - from indexing to search.
+   *
+   * @throws Throwable
+   */
+  @Test
+  public void testIndexingAndSearchingFlow() throws Throwable {
+
+    // Sample data and query
+    float[][] dataset = {
+        { 0.74021935f, 0.9209938f },
+        { 0.03902049f, 0.9689629f },
+        { 0.92514056f, 0.4463501f },
+        { 0.6673192f, 0.10993068f }
+      };
+    List<Integer> map = List.of(0, 1, 2, 3);
+    float[][] queries = {
+        { 0.48216683f, 0.0428398f },
+        { 0.5084142f, 0.6545497f },
+        { 0.51260436f, 0.2643005f },
+        { 0.05198065f, 0.5789965f }
+      };
+
+    // Expected search results
+    List<Map<Integer, Float>> expectedResults = Arrays.asList(
+        Map.of(3, 0.038782578f, 2, 0.35904628f, 0, 0.8377455f),
+        Map.of(0, 0.12472608f, 2, 0.21700794f, 1, 0.31918612f),
+        Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
+        Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.59866416f)
+      );
+
+    for (int j = 0; j < 10; j++) {
+
+      try (CuVSResources resources = new CuVSResources()) {
+
+        // Configure index parameters
+        CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
+            .withGraphDegree(64)
+            .withIntermediateGraphDegree(128)
+            .withNumWriterThreads(32)
+            .withMetric(CuvsDistanceType.L2Expanded)
+            .build();
+
+        // Create the index with the dataset
+        CagraIndex index = new CagraIndex.Builder(resources)
+            .withDataset(dataset)
+            .withIndexParams(indexParams)
+            .build();
+
+        // Saving the HNSW index on to the disk.
+        String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
+        index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
+
+        HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder(resources)
+            .withVectorDimension(2)
+            .build();
+        InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
+        File hnswIndexFile = new File(hnswIndexFileName);
+
+        HnswIndex hnswIndex = new HnswIndex.Builder(resources)
+            .from(inputStreamHNSW)
+            .withIndexParams(hnswIndexParams)
+            .build();
+
+        HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder(resources)
+            .build();
+
+        HnswQuery hnswQuery = new HnswQuery.Builder()
+            .withMapping(map)
+            .withQueryVectors(queries)
+            .withSearchParams(hnswSearchParams)
+            .withTopK(3)
+            .build();
+
+        HnswSearchResults results = hnswIndex.search(hnswQuery);
+
+        // Check results
+        log.info(results.getResults().toString());
+        assertEquals(expectedResults, results.getResults());
+
+        // Cleanup
+        if (hnswIndexFile.exists()) {
+          hnswIndexFile.delete();
+        }
+        index.destroyIndex();
+        hnswIndex.destroyIndex();
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedTest.java
new file mode 100644
index 0000000000..c292309f7c
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedTest.java
@@ -0,0 +1,136 @@
+package com.nvidia.cuvs;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.invoke.MethodHandles;
+import java.util.List;
+import java.util.UUID;
+
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+
+@RunWith(RandomizedRunner.class)
+public class HnswRandomizedTest extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  @Before
+  public void setup() {
+    initializeRandom();
+    log.info("Random context initialized for test.");
+  }
+
+  @Test
+  public void testResultsTopKWithRandomValues() throws Throwable {
+    for (int i = 0; i < 10; i++) {
+      tmpResultsTopKWithRandomValues();
+    }
+  }
+
+  private void tmpResultsTopKWithRandomValues() throws Throwable {
+    int DATASET_SIZE_LIMIT = 10_000;
+    int DIMENSIONS_LIMIT = 2048;
+    int NUM_QUERIES_LIMIT = 10;
+    int TOP_K_LIMIT = 64; // nocommit This fails beyond 64
+
+    int datasetSize = random.nextInt(DATASET_SIZE_LIMIT) + 1;
+    int dimensions = random.nextInt(DIMENSIONS_LIMIT) + 1;
+    int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
+    int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
+
+    if (datasetSize < topK)
+      datasetSize = topK;
+
+    // Generate a random dataset
+    float[][] dataset = generateData(random, datasetSize, dimensions);
+
+    // Generate random query vectors
+    float[][] queries = generateData(random, numQueries, dimensions);
+
+    log.info("Dataset size: {}x{}", datasetSize, dimensions);
+    log.info("Query size: {}x{}", numQueries, dimensions);
+    log.info("TopK: {}", topK);
+
+    // Debugging: Log dataset and queries
+    if (log.isDebugEnabled()) {
+      log.debug("Dataset:");
+      for (float[] row : dataset) {
+        log.debug(java.util.Arrays.toString(row));
+      }
+      log.debug("Queries:");
+      for (float[] query : queries) {
+        log.debug(java.util.Arrays.toString(query));
+      }
+    }
+    // Sanity checks
+    assert dataset.length > 0 : "Dataset is empty.";
+    assert queries.length > 0 : "Queries are empty.";
+    assert dimensions > 0 : "Invalid dimensions.";
+    assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
+
+    // Generate expected results using brute force
+    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
+
+    // Create CuVS index and query
+    try (CuVSResources resources = new CuVSResources()) {
+
+      // Configure index parameters
+      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+          .withGraphDegree(64)
+          .withIntermediateGraphDegree(128)
+          .withNumWriterThreads(32)
+          .withMetric(CuvsDistanceType.L2Expanded)
+          .build();
+
+      // Create the index with the dataset
+      CagraIndex index = new CagraIndex.Builder(resources)
+          .withDataset(dataset)
+          .withIndexParams(indexParams)
+          .build();
+
+      // Saving the HNSW index on to the disk.
+      String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
+      index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
+
+      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder(resources)
+          .withVectorDimension(dimensions)
+          .build();
+      InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
+      File hnswIndexFile = new File(hnswIndexFileName);
+
+      HnswIndex hnswIndex = new HnswIndex.Builder(resources)
+          .from(inputStreamHNSW)
+          .withIndexParams(hnswIndexParams)
+          .build();
+
+      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder(resources)
+          .withNumThreads(32)
+          .build();
+
+      HnswQuery hnswQuery = new HnswQuery.Builder()
+          .withQueryVectors(queries)
+          .withSearchParams(hnswSearchParams)
+          .withTopK(topK)
+          .build();
+
+      log.info("Index built successfully. Executing search...");
+      HnswSearchResults results = hnswIndex.search(hnswQuery);
+
+      if (hnswIndexFile.exists()) {
+        hnswIndexFile.delete();
+      }
+
+      compareResults(results, expected, topK, datasetSize, numQueries);
+    }
+  }
+}
diff --git a/java/examples/.gitignore b/java/examples/.gitignore
new file mode 100644
index 0000000000..b83d22266a
--- /dev/null
+++ b/java/examples/.gitignore
@@ -0,0 +1 @@
+/target/
diff --git a/java/examples/README.md b/java/examples/README.md
new file mode 100644
index 0000000000..d05d7b9117
--- /dev/null
+++ b/java/examples/README.md
@@ -0,0 +1,8 @@
+Building and Running
+--------------------
+
+Make sure to have JDK 22 and Maven 3.9.6+.
+
+    mvn clean compile assembly:single
+
+    java --enable-native-access=ALL-UNNAMED -jar ./target/cagra-sample-1.0-SNAPSHOT-jar-with-dependencies.jar
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
new file mode 100644
index 0000000000..58ebef84e0
--- /dev/null
+++ b/java/examples/pom.xml
@@ -0,0 +1,105 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <groupId>com.nvidia.cuvs.examples</groupId>
+  <artifactId>cuvs-java-examples</artifactId>
+  <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
+  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.02.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+  <name>cuvs-java-examples</name>
+
+  <properties>
+    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    <maven.compiler.release>22</maven.compiler.release>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>com.nvidia.cuvs</groupId>
+      <artifactId>cuvs-java</artifactId>
+      <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
+      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.02.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <version>2.0.13</version>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-simple</artifactId>
+      <version>2.0.13</version>
+      <scope>runtime</scope>
+   </dependency>
+  </dependencies>
+
+  <build>
+    <pluginManagement><!-- lock down plugins versions to avoid using Maven defaults (may be moved to parent pom) -->
+      <plugins>
+        <!-- clean lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#clean_Lifecycle -->
+        <plugin>
+          <artifactId>maven-clean-plugin</artifactId>
+          <version>3.4.0</version>
+        </plugin>
+        <!-- default lifecycle, jar packaging: see https://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging -->
+        <plugin>
+          <artifactId>maven-resources-plugin</artifactId>
+          <version>3.3.1</version>
+        </plugin>
+        <plugin>
+          <artifactId>maven-compiler-plugin</artifactId>
+          <version>3.13.0</version>
+        </plugin>
+        <plugin>
+          <artifactId>maven-surefire-plugin</artifactId>
+          <version>3.3.0</version>
+        </plugin>
+        <plugin>
+          <artifactId>maven-jar-plugin</artifactId>
+          <version>3.4.2</version>
+        </plugin>
+        <plugin>
+          <artifactId>maven-install-plugin</artifactId>
+          <version>3.1.2</version>
+        </plugin>
+        <plugin>
+          <artifactId>maven-deploy-plugin</artifactId>
+          <version>3.1.2</version>
+        </plugin>
+        <!-- site lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#site_Lifecycle -->
+        <plugin>
+          <artifactId>maven-site-plugin</artifactId>
+          <version>3.12.1</version>
+        </plugin>
+        <plugin>
+          <artifactId>maven-project-info-reports-plugin</artifactId>
+          <version>3.6.1</version>
+        </plugin>
+        <plugin>
+            <artifactId>maven-assembly-plugin</artifactId>
+            <executions>
+               <execution>
+                  <id>make-jar-with-dependencies</id>
+                  <!-- this is used for inheritance merges -->
+                  <phase>package</phase>
+                  <!-- bind to the packaging phase -->
+                  <goals>
+                     <goal>single</goal>
+                  </goals>
+               </execution>
+            </executions>
+            <configuration>
+               <archive>
+                  <manifest>
+                     <mainClass>com.nvidia.cuvs.examples.CagraExample</mainClass>
+                  </manifest>
+               </archive>
+               <descriptorRefs>
+                  <descriptorRef>jar-with-dependencies</descriptorRef>
+               </descriptorRefs>
+            </configuration>
+        </plugin>
+      </plugins>
+    </pluginManagement>
+  </build>
+</project>
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
new file mode 100644
index 0000000000..5f72d92fc7
--- /dev/null
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
@@ -0,0 +1,90 @@
+package com.nvidia.cuvs.examples;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.invoke.MethodHandles;
+import java.util.UUID;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.nvidia.cuvs.BruteForceIndex;
+import com.nvidia.cuvs.BruteForceIndexParams;
+import com.nvidia.cuvs.BruteForceQuery;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.common.SearchResults;
+
+public class BruteForceExample {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  public static void main(String[] args) throws Throwable {
+
+    // Sample data and query
+    float[][] dataset = {
+        { 0.74021935f, 0.9209938f },
+        { 0.03902049f, 0.9689629f },
+        { 0.92514056f, 0.4463501f },
+        { 0.6673192f, 0.10993068f }
+       };
+
+    float[][] queries = {
+        { 0.48216683f, 0.0428398f },
+        { 0.5084142f, 0.6545497f },
+        { 0.51260436f, 0.2643005f },
+        { 0.05198065f, 0.5789965f }
+       };
+
+    try (CuVSResources resources = new CuVSResources()) {
+
+      // Create a query object with the query vectors
+      BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
+          .withTopK(3)
+          .withQueryVectors(queries)
+          .build();
+
+      // Set index parameters
+      BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
+          .build();
+
+      // Create the index with the dataset
+      BruteForceIndex index = new BruteForceIndex.Builder(resources)
+          .withDataset(dataset)
+          .withIndexParams(indexParams)
+          .build();
+
+      // Saving the index on to the disk.
+      String indexFileName = UUID.randomUUID().toString() + ".bf";
+      index.serialize(new FileOutputStream(indexFileName));
+
+      // Loading a BRUTEFORCE index from disk.
+      File indexFile = new File(indexFileName);
+      InputStream inputStream = new FileInputStream(indexFile);
+      BruteForceIndex loadedIndex = new BruteForceIndex.Builder(resources)
+          .from(inputStream)
+          .build();
+
+      // Perform the search
+      SearchResults resultsFromLoadedIndex = loadedIndex.search(cuvsQuery);
+
+      // Check results
+      log.info(resultsFromLoadedIndex.getResults().toString());
+
+      // Perform the search
+      SearchResults results = index.search(cuvsQuery);
+
+      // Check results
+      log.info(results.getResults().toString());
+
+      // Cleanup
+      index.destroyIndex();
+      loadedIndex.destroyIndex();
+
+      if (indexFile.exists()) {
+        indexFile.delete();
+      }
+    }
+  }
+}
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
new file mode 100644
index 0000000000..f561ce69d4
--- /dev/null
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
@@ -0,0 +1,100 @@
+package com.nvidia.cuvs.examples;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.invoke.MethodHandles;
+import java.util.UUID;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CagraIndexParams;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import com.nvidia.cuvs.CagraQuery;
+import com.nvidia.cuvs.CagraSearchParams;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.common.SearchResults;
+
+public class CagraExample {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  public static void main(String[] args) throws Throwable {
+
+    // Sample data and query
+    float[][] dataset = {
+        { 0.74021935f, 0.9209938f },
+        { 0.03902049f, 0.9689629f },
+        { 0.92514056f, 0.4463501f },
+        { 0.6673192f, 0.10993068f }
+       };
+
+    float[][] queries = {
+        { 0.48216683f, 0.0428398f },
+        { 0.5084142f, 0.6545497f },
+        { 0.51260436f, 0.2643005f },
+        { 0.05198065f, 0.5789965f }
+       };
+
+    try (CuVSResources resources = new CuVSResources()) {
+
+      // Configure index parameters
+      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+          .withGraphDegree(1)
+          .withIntermediateGraphDegree(2)
+          .withMetric(CuvsDistanceType.L2Expanded)
+          .build();
+
+      // Create the index with the dataset
+      CagraIndex index = new CagraIndex.Builder(resources)
+          .withDataset(dataset)
+          .withIndexParams(indexParams)
+          .build();
+
+      // Saving the index on to the disk.
+      String indexFileName = UUID.randomUUID().toString() + ".cag";
+      index.serialize(new FileOutputStream(indexFileName));
+
+      // Loading a CAGRA index from disk.
+      File indexFile = new File(indexFileName);
+      InputStream inputStream = new FileInputStream(indexFile);
+      CagraIndex loadedIndex = new CagraIndex.Builder(resources)
+          .from(inputStream)
+          .build();
+
+      // Configure search parameters
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+          .build();
+
+      // Create a query object with the query vectors
+      CagraQuery cuvsQuery = new CagraQuery.Builder()
+          .withTopK(3)
+          .withSearchParams(searchParams)
+          .withQueryVectors(queries)
+          .build();
+
+      // Perform the search
+      SearchResults results = index.search(cuvsQuery);
+
+      // Check results
+      log.info(results.getResults().toString());
+
+      // Search from deserialized index
+      results = loadedIndex.search(cuvsQuery);
+
+      // Check results
+      log.info(results.getResults().toString());
+
+      // Cleanup
+      if (indexFile.exists()) {
+        indexFile.delete();
+      }
+      index.destroyIndex();
+    }
+  }
+}
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
new file mode 100644
index 0000000000..0f13be3bc3
--- /dev/null
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
@@ -0,0 +1,99 @@
+package com.nvidia.cuvs.examples;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.invoke.MethodHandles;
+import java.util.UUID;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CagraIndexParams;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.HnswIndexParams;
+import com.nvidia.cuvs.HnswQuery;
+import com.nvidia.cuvs.HnswSearchParams;
+import com.nvidia.cuvs.HnswSearchResults;
+
+public class HnswExample {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  public static void main(String[] args) throws Throwable {
+
+    // Sample data and query
+    float[][] dataset = {
+        { 0.74021935f, 0.9209938f },
+        { 0.03902049f, 0.9689629f },
+        { 0.92514056f, 0.4463501f },
+        { 0.6673192f, 0.10993068f }
+       };
+
+    float[][] queries = {
+        { 0.48216683f, 0.0428398f },
+        { 0.5084142f, 0.6545497f },
+        { 0.51260436f, 0.2643005f },
+        { 0.05198065f, 0.5789965f }
+       };
+
+    try (CuVSResources resources = new CuVSResources()) {
+
+      // Configure index parameters
+      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
+          .withGraphDegree(64)
+          .withIntermediateGraphDegree(128)
+          .withNumWriterThreads(32)
+          .withMetric(CuvsDistanceType.L2Expanded)
+          .build();
+
+      // Create the index with the dataset
+      CagraIndex index = new CagraIndex.Builder(resources)
+          .withDataset(dataset)
+          .withIndexParams(indexParams)
+          .build();
+
+      // Saving the HNSW index on to the disk.
+      String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
+      index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
+
+      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder(resources)
+          .withVectorDimension(2)
+          .build();
+      InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
+      File hnswIndexFile = new File(hnswIndexFileName);
+
+      HnswIndex hnswIndex = new HnswIndex.Builder(resources)
+          .from(inputStreamHNSW)
+          .withIndexParams(hnswIndexParams)
+          .build();
+
+      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder(resources)
+          .build();
+
+      HnswQuery hnswQuery = new HnswQuery.Builder()
+          .withQueryVectors(queries)
+          .withSearchParams(hnswSearchParams)
+          .withTopK(3)
+          .build();
+
+      HnswSearchResults results = hnswIndex.search(hnswQuery);
+
+      // Check results
+      log.info(results.getResults().toString());
+
+      // Cleanup
+      if (hnswIndexFile.exists()) {
+        hnswIndexFile.delete();
+      }
+      index.destroyIndex();
+      hnswIndex.destroyIndex();
+    }
+  }
+}
diff --git a/java/examples/src/main/resources/log4j2.xml b/java/examples/src/main/resources/log4j2.xml
new file mode 100644
index 0000000000..bf0eb598c1
--- /dev/null
+++ b/java/examples/src/main/resources/log4j2.xml
@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Configuration status="INFO">
+    <Appenders>
+        <Console name="Console" target="SYSTEM_OUT">
+            <PatternLayout pattern="%d{HH:mm:ss.SSS} [%t] %-5level %logger{36} - %msg%n"/>
+        </Console>
+    </Appenders>
+
+    <Loggers>
+        <Root level="debug">
+            <AppenderRef ref="Console"/>
+        </Root>
+    </Loggers>
+</Configuration>
diff --git a/java/internal/CMakeLists.txt b/java/internal/CMakeLists.txt
new file mode 100644
index 0000000000..3bd316e2d9
--- /dev/null
+++ b/java/internal/CMakeLists.txt
@@ -0,0 +1,62 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+include(rapids_config.cmake)
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-export)
+include(rapids-find)
+rapids_cpm_init()
+
+project(
+  cuvs-java
+  VERSION "${RAPIDS_VERSION}"
+  LANGUAGES CXX C
+)
+
+find_package(OpenMP)
+find_package(Threads)
+
+option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulting to local files"
+       ON
+)
+
+if(FIND_CUVS_CPP)
+  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS c_api)
+  if(NOT TARGET cuvs::c_api)
+    message(
+      FATAL_ERROR
+        "Building against a preexisting libcuvs library requires the compiled libcuvs to have been built!"
+    )
+  endif()
+else()
+  set(cuvs_FOUND OFF)
+endif()
+
+if(NOT cuvs_FOUND)
+  set(BUILD_TESTS OFF)
+  set(BUILD_C_LIBRARY ON)
+  add_subdirectory(../../cpp cuvs-cpp EXCLUDE_FROM_ALL)
+endif()
+
+include(get_dlpack.cmake)
+
+ADD_LIBRARY(cuvs_java SHARED src/cuvs_java.c)
+target_include_directories(cuvs_java PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
+target_link_libraries(
+  cuvs_java PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env> OpenMP::OpenMP_CXX
+                    Threads::Threads
+)
diff --git a/java/internal/VERSION b/java/internal/VERSION
new file mode 120000
index 0000000000..558194c5a5
--- /dev/null
+++ b/java/internal/VERSION
@@ -0,0 +1 @@
+../../VERSION
\ No newline at end of file
diff --git a/java/internal/get_dlpack.cmake b/java/internal/get_dlpack.cmake
new file mode 120000
index 0000000000..93a54ca4d7
--- /dev/null
+++ b/java/internal/get_dlpack.cmake
@@ -0,0 +1 @@
+../../cpp/cmake/thirdparty/get_dlpack.cmake
\ No newline at end of file
diff --git a/java/internal/rapids_config.cmake b/java/internal/rapids_config.cmake
new file mode 120000
index 0000000000..398eea52da
--- /dev/null
+++ b/java/internal/rapids_config.cmake
@@ -0,0 +1 @@
+../../rapids_config.cmake
\ No newline at end of file
diff --git a/java/internal/src/cuvs_java.c b/java/internal/src/cuvs_java.c
new file mode 100644
index 0000000000..febde8463b
--- /dev/null
+++ b/java/internal/src/cuvs_java.c
@@ -0,0 +1,464 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/brute_force.h>
+#include <cuvs/neighbors/hnsw.h>
+#include <dlpack/dlpack.h>
+#include <cuda_runtime.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <omp.h>
+#include <string.h>
+
+#define try bool __HadError=false;
+#define catch(x) ExitJmp:if(__HadError)
+#define throw(x) {__HadError=true;goto ExitJmp;}
+
+/**
+ * @brief Create an Initialized opaque C handle
+ *
+ * @param[out] return_value return value for cuvsResourcesCreate function call
+ * @return cuvsResources_t
+ */
+cuvsResources_t create_resources(int *return_value) {
+  cuvsResources_t cuvs_resources;
+  *return_value = cuvsResourcesCreate(&cuvs_resources);
+  return cuvs_resources;
+}
+
+/**
+ * @brief Destroy and de-allocate opaque C handle
+ *
+ * @param[in] cuvs_resources an opaque C handle
+ * @param[out] return_value return value for cuvsResourcesDestroy function call
+ */
+void destroy_resources(cuvsResources_t cuvs_resources, int *return_value) {
+  *return_value = cuvsResourcesDestroy(cuvs_resources);
+}
+
+/**
+ * @brief Helper function for creating DLManagedTensor instance
+ *
+ * @param[in] data the data pointer points to the allocated data
+ * @param[in] shape the shape of the tensor
+ * @param[in] code the type code of base types
+ * @param[in] bits the shape of the tensor
+ * @param[in] ndim the number of dimensions
+ * @return DLManagedTensor
+ */
+DLManagedTensor prepare_tensor(void *data, int64_t shape[], DLDataTypeCode code, int bits, int ndim, DLDeviceType device_type) {
+  DLManagedTensor tensor;
+
+  tensor.dl_tensor.data = data;
+  tensor.dl_tensor.device.device_type = device_type; //kDLCUDA;
+  tensor.dl_tensor.ndim = ndim;
+  tensor.dl_tensor.dtype.code = code;
+  tensor.dl_tensor.dtype.bits = bits;
+  tensor.dl_tensor.dtype.lanes = 1;
+  tensor.dl_tensor.shape = shape;
+  tensor.dl_tensor.strides = NULL;
+
+  return tensor;
+}
+
+/**
+ * @brief Function for building CAGRA index
+ *
+ * @param[in] dataset index dataset
+ * @param[in] rows number of dataset rows
+ * @param[in] dimensions vector dimension of the dataset
+ * @param[in] cuvs_resources reference of the underlying opaque C handle
+ * @param[out] return_value return value for cuvsCagraBuild function call
+ * @param[in] index_params a reference to the index parameters
+ * @param[in] compression_params a reference to the compression parameters
+ * @param[in] n_writer_threads number of omp threads to use
+ * @return cuvsCagraIndex_t
+ */
+cuvsCagraIndex_t build_cagra_index(float *dataset, long rows, long dimensions, cuvsResources_t cuvs_resources, int *return_value,
+    cuvsCagraIndexParams_t index_params, cuvsCagraCompressionParams_t compression_params, int n_writer_threads) {
+
+  cudaStream_t stream;
+  cuvsStreamGet(cuvs_resources, &stream);
+
+  omp_set_num_threads(n_writer_threads);
+  cuvsRMMPoolMemoryResourceEnable(95, 95, false);
+
+  int64_t dataset_shape[2] = {rows, dimensions};
+  DLManagedTensor dataset_tensor = prepare_tensor(dataset, dataset_shape, kDLFloat, 32, 2, kDLCUDA);
+
+  cuvsCagraIndex_t index;
+  cuvsCagraIndexCreate(&index);
+
+  index_params->compression = compression_params;
+  cuvsStreamSync(cuvs_resources);
+  *return_value = cuvsCagraBuild(cuvs_resources, index_params, &dataset_tensor, index);
+
+  omp_set_num_threads(1);
+
+  return index;
+}
+
+/**
+ * @brief A function to de-allocate CAGRA index
+ *
+ * @param[in] index cuvsCagraIndex_t to de-allocate
+ * @param[out] return_value return value for cuvsCagraIndexDestroy function call
+ */
+void destroy_cagra_index(cuvsCagraIndex_t index, int *return_value) {
+  *return_value = cuvsCagraIndexDestroy(index);
+}
+
+/**
+ * @brief A function to serialize a CAGRA index
+ *
+ * @param[in] cuvs_resources reference of the underlying opaque C handle
+ * @param[in] index cuvsCagraIndex_t reference
+ * @param[out] return_value return value for cuvsCagraSerialize function call
+ * @param[in] filename the filename of the index file
+ */
+void serialize_cagra_index(cuvsResources_t cuvs_resources, cuvsCagraIndex_t index, int *return_value, char* filename) {
+  *return_value = cuvsCagraSerialize(cuvs_resources, filename, index, true);
+}
+
+/**
+ * @brief A function to de-serialize a CAGRA index
+ *
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[in] index cuvsCagraIndex_t reference
+ * @param[out] return_value return value for cuvsCagraDeserialize function call
+ * @param[in] filename the filename of the index file
+ */
+void deserialize_cagra_index(cuvsResources_t cuvs_resources, cuvsCagraIndex_t index, int *return_value, char* filename) {
+  *return_value = cuvsCagraDeserialize(cuvs_resources, filename, index);
+}
+
+/**
+ * @brief A function to search a CAGRA index and return results
+ *
+ * @param[in] index reference to a CAGRA index to search on
+ * @param[in] queries query vectors
+ * @param[in] topk topK results to return
+ * @param[in] n_queries number of queries
+ * @param[in] dimensions vector dimension
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[out] neighbors_h reference to the neighbor results on the host memory
+ * @param[out] distances_h reference to the distance results on the host memory
+ * @param[out] return_value return value for cuvsCagraSearch function call
+ * @param[in] search_params reference to cuvsCagraSearchParams_t holding the search parameters
+ */
+void search_cagra_index(cuvsCagraIndex_t index, float *queries, int topk, long n_queries, int dimensions,
+    cuvsResources_t cuvs_resources, int *neighbors_h, float *distances_h, int *return_value, cuvsCagraSearchParams_t search_params) {
+
+  cudaStream_t stream;
+  cuvsStreamGet(cuvs_resources, &stream);
+
+  uint32_t *neighbors;
+  float *distances, *queries_d;
+  cuvsRMMAlloc(cuvs_resources, (void**) &queries_d, sizeof(float) * n_queries * dimensions);
+  cuvsRMMAlloc(cuvs_resources, (void**) &neighbors, sizeof(uint32_t) * n_queries * topk);
+  cuvsRMMAlloc(cuvs_resources, (void**) &distances, sizeof(float) * n_queries * topk);
+
+  cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * dimensions, cudaMemcpyDefault);
+
+  int64_t queries_shape[2] = {n_queries, dimensions};
+  DLManagedTensor queries_tensor = prepare_tensor(queries_d, queries_shape, kDLFloat, 32, 2, kDLCUDA);
+
+  int64_t neighbors_shape[2] = {n_queries, topk};
+  DLManagedTensor neighbors_tensor = prepare_tensor(neighbors, neighbors_shape, kDLUInt, 32, 2, kDLCUDA);
+
+  int64_t distances_shape[2] = {n_queries, topk};
+  DLManagedTensor distances_tensor = prepare_tensor(distances, distances_shape, kDLFloat, 32, 2, kDLCUDA);
+
+  cuvsStreamSync(cuvs_resources);
+
+  cuvsFilter filter; // TODO: Implement Cagra Pre-Filtering, but leave it as no-op for now
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+
+  *return_value = cuvsCagraSearch(cuvs_resources, search_params, index, &queries_tensor, &neighbors_tensor,
+                  &distances_tensor, filter);
+
+  cudaMemcpy(neighbors_h, neighbors, sizeof(uint32_t) * n_queries * topk, cudaMemcpyDefault);
+  cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
+
+  cuvsRMMFree(cuvs_resources, distances, sizeof(float) * n_queries * topk);
+  cuvsRMMFree(cuvs_resources, neighbors, sizeof(uint32_t) * n_queries * topk);
+  cuvsRMMFree(cuvs_resources, queries_d, sizeof(float) * n_queries * dimensions);
+}
+
+/**
+ * @brief De-allocate BRUTEFORCE index
+ *
+ * @param[in] index reference to BRUTEFORCE index
+ * @param[out] return_value return value for cuvsBruteForceIndexDestroy function call
+ */
+void destroy_brute_force_index(cuvsBruteForceIndex_t index, int *return_value) {
+  *return_value = cuvsBruteForceIndexDestroy(index);
+}
+
+/**
+ * @brief A function to build BRUTEFORCE index
+ *
+ * @param[in] dataset the dataset to be indexed
+ * @param[in] rows the number of rows in the dataset
+ * @param[in] dimensions the vector dimension
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[out] return_value return value for cuvsBruteForceBuild function call
+ * @param[in] n_writer_threads number of threads to use while indexing
+ * @return cuvsBruteForceIndex_t
+ */
+cuvsBruteForceIndex_t build_brute_force_index(float *dataset, long rows, long dimensions, cuvsResources_t cuvs_resources,
+  int *return_value, int n_writer_threads) {
+
+  omp_set_num_threads(n_writer_threads);
+  cuvsRMMPoolMemoryResourceEnable(95, 95, false);
+
+  cudaStream_t stream;
+  cuvsStreamGet(cuvs_resources, &stream);
+
+  float *dataset_d;
+  cuvsRMMAlloc(cuvs_resources, (void**) &dataset_d, sizeof(float) * rows * dimensions);
+  cudaMemcpy(dataset_d, dataset, sizeof(float) * rows * dimensions, cudaMemcpyDefault);
+
+  int64_t dataset_shape[2] = {rows, dimensions};
+  DLManagedTensor dataset_tensor = prepare_tensor(dataset_d, dataset_shape, kDLFloat, 32, 2, kDLCUDA);
+
+  cuvsBruteForceIndex_t index;
+  cuvsError_t index_create_status = cuvsBruteForceIndexCreate(&index);
+
+  cuvsStreamSync(cuvs_resources);
+  *return_value = cuvsBruteForceBuild(cuvs_resources, &dataset_tensor, L2Expanded, 0.0f, index);
+
+  omp_set_num_threads(1);
+
+  return index;
+}
+
+/**
+ * @brief A function to search the BRUTEFORCE index
+ *
+ * @param[in] index reference to a BRUTEFORCE index to search on
+ * @param[in] queries reference to query vectors
+ * @param[in] topk the top k results to return
+ * @param[in] n_queries number of queries
+ * @param[in] dimensions vector dimension
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[out] neighbors_h reference to the neighbor results on the host memory
+ * @param[out] distances_h reference to the distance results on the host memory
+ * @param[out] return_value return value for cuvsBruteForceSearch function call
+ * @param[in] prefilter_data cuvsFilter input prefilter that can be used to filter queries and neighbors based on the given bitmap
+ * @param[in] prefilter_data_length prefilter length input
+ * @param[in] n_rows number of rows in the dataset
+ */
+void search_brute_force_index(cuvsBruteForceIndex_t index, float *queries, int topk, long n_queries, int dimensions,
+    cuvsResources_t cuvs_resources, int64_t *neighbors_h, float *distances_h, int *return_value, long *prefilter_data,
+    long prefilter_data_length, long n_rows) {
+
+  cudaStream_t stream;
+  cuvsStreamGet(cuvs_resources, &stream);
+
+  int64_t *neighbors;
+  float *distances, *queries_d;
+  long *prefilter_data_d;
+
+  long prefilter_data_32_size = sizeof(uint32_t) * prefilter_data_length * 2;
+  uint32_t *prefilter_data_32 = (uint32_t *)malloc(prefilter_data_32_size);
+
+  cuvsRMMAlloc(cuvs_resources, (void**) &queries_d, sizeof(float) * n_queries * dimensions);
+  cuvsRMMAlloc(cuvs_resources, (void**) &neighbors, sizeof(int64_t) * n_queries * topk);
+  cuvsRMMAlloc(cuvs_resources, (void**) &distances, sizeof(float) * n_queries * topk);
+  cuvsRMMAlloc(cuvs_resources, (void**) &prefilter_data_d, prefilter_data_32_size);
+
+  cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * dimensions, cudaMemcpyDefault);
+  cudaMemcpy(prefilter_data_d, prefilter_data_32, prefilter_data_32_size, cudaMemcpyDefault);
+
+  int64_t queries_shape[2] = {n_queries, dimensions};
+  DLManagedTensor queries_tensor = prepare_tensor(queries_d, queries_shape, kDLFloat, 32, 2, kDLCUDA);
+
+  int64_t neighbors_shape[2] = {n_queries, topk};
+  DLManagedTensor neighbors_tensor = prepare_tensor(neighbors, neighbors_shape, kDLInt, 64, 2, kDLCUDA);
+
+  int64_t distances_shape[2] = {n_queries, topk};
+  DLManagedTensor distances_tensor = prepare_tensor(distances, distances_shape, kDLFloat, 32, 2, kDLCUDA);
+
+  // unpack the incoming long into two 32bit ints
+  for (long i = 0; i < prefilter_data_length; i++) {
+    *(prefilter_data_32 + (2 * i)) = (int)(*(prefilter_data + i) >> 32);
+    *(prefilter_data_32 + ((2 * i) + 1)) = (int)*(prefilter_data + i);
+    //long l = (((long)*(prefilter_data_32 + (2 * i))) << 32) | (*(prefilter_data_32 + ((2 * i) + 1)) & 0xffffffffL);
+  }
+
+  cuvsFilter prefilter;
+  if (prefilter_data == NULL) {
+    prefilter.type = NO_FILTER;
+    prefilter.addr = (uintptr_t)NULL;
+  } else {
+    int64_t prefilter_shape[1] = {(n_queries * n_rows + 31) / 32};
+    DLManagedTensor prefilter_tensor = prepare_tensor(prefilter_data_d, prefilter_shape, kDLUInt, 32, 1, kDLCUDA);
+    prefilter.type = BITMAP;
+    prefilter.addr = (uintptr_t)&prefilter_tensor;
+  }
+
+  cuvsStreamSync(cuvs_resources);
+  *return_value = cuvsBruteForceSearch(cuvs_resources, index, &queries_tensor, &neighbors_tensor, &distances_tensor, prefilter);
+
+  cudaMemcpy(neighbors_h, neighbors, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
+  cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
+
+  cuvsRMMFree(cuvs_resources, neighbors, sizeof(int64_t) * n_queries * topk);
+  cuvsRMMFree(cuvs_resources, distances, sizeof(float) * n_queries * topk);
+  cuvsRMMFree(cuvs_resources, queries_d, sizeof(float) * n_queries * dimensions);
+}
+
+/**
+ * @brief A function to serialize a BRUTEFORCE index
+ *
+ * @param[in] cuvs_resources reference of the underlying opaque C handle
+ * @param[in] index cuvsBruteForceIndex_t reference
+ * @param[out] return_value return value for cuvsBruteForceSerialize function call
+ * @param[in] filename the filename of the index file
+ */
+void serialize_brute_force_index(cuvsResources_t cuvs_resources, cuvsBruteForceIndex_t index, int *return_value, char* filename) {
+  *return_value = cuvsBruteForceSerialize(cuvs_resources, filename, index);
+}
+
+/**
+ * @brief A function to de-serialize a BRUTEFORCE index
+ *
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[in] index cuvsBruteForceIndex_t reference
+ * @param[out] return_value return value for cuvsBruteForceDeserialize function call
+ * @param[in] filename the filename of the index file
+ */
+void deserialize_brute_force_index(cuvsResources_t cuvs_resources, cuvsBruteForceIndex_t index, int *return_value, char* filename) {
+  *return_value = cuvsBruteForceDeserialize(cuvs_resources, filename, index);
+}
+
+/**
+ * @brief A function to create and serialize an HNSW index from CAGRA index
+ *
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[in] file_path the path to the file of the created HNSW index
+ * @param[in] index cuvsCagraIndex_t reference to the existing CAGRA index
+ * @param[out] return_value return value for cuvsCagraSerializeToHnswlib function call
+ */
+void serialize_cagra_index_to_hnsw(cuvsResources_t cuvs_resources, char *file_path, cuvsCagraIndex_t index, int *return_value) {
+  *return_value = cuvsCagraSerializeToHnswlib(cuvs_resources, file_path, index);
+}
+
+/**
+ * @brief A function to deserialize the persisted HNSW index
+ *
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[in] file_path the path to the persisted HNSW index file
+ * @param[in] hnsw_params reference to the HNSW index params
+ * @param[out] return_value return value for cuvsHnswDeserialize function call
+ * @param[in] vector_dimension the dimension of the vectors in the HNSW index
+ * @returns cuvsHnswIndex_t reference to the created HNSW index
+ */
+cuvsHnswIndex_t deserialize_hnsw_index(cuvsResources_t cuvs_resources, char *file_path,
+  cuvsHnswIndexParams_t hnsw_params, int *return_value, int vector_dimension) {
+  cuvsHnswIndex_t hnsw_index;
+  cuvsError_t rv = cuvsHnswIndexCreate(&hnsw_index);
+  hnsw_index->dtype.bits = 32;
+  hnsw_index->dtype.code = kDLFloat;
+  hnsw_index->dtype.lanes = 1;
+  *return_value = cuvsHnswDeserialize(cuvs_resources, hnsw_params, file_path, vector_dimension, L2Expanded, hnsw_index);
+  return hnsw_index;
+}
+
+/**
+ * @brief A Function to search in the HNSW index
+ *
+ * @param[in] cuvs_resources reference to the underlying opaque C handle
+ * @param[in] hnsw_index the HNSW index reference
+ * @param[in] search_params reference to the HNSW search parameters
+ * @param[out] return_value return value for cuvsHnswSearch function call
+ * @param[out] neighbors_h result container on host holding the neighbor ids
+ * @param[out] distances_h result container on host holding the distances
+ * @param[in] queries reference to the queries
+ * @param[in] topk the top k results to return
+ * @param[in] query_dimension the dimension of the query vectors
+ * @param[in] n_queries the number of queries passed to the function
+ */
+void search_hnsw_index(cuvsResources_t cuvs_resources, cuvsHnswIndex_t hnsw_index, cuvsHnswSearchParams_t search_params,
+  int *return_value, uint64_t *neighbors_h, float *distances_h, float *queries, int topk, int query_dimension, int n_queries) {
+
+  int64_t queries_shape[2] = {n_queries, query_dimension};
+  DLManagedTensor queries_tensor = prepare_tensor(queries, queries_shape, kDLFloat, 32, 2, kDLCPU);
+
+  int64_t neighbors_shape[2] = {n_queries, topk};
+  DLManagedTensor neighbors_tensor = prepare_tensor(neighbors_h, neighbors_shape, kDLUInt, 64, 2, kDLCPU);
+
+  int64_t distances_shape[2] = {n_queries, topk};
+  DLManagedTensor distances_tensor = prepare_tensor(distances_h, distances_shape, kDLFloat, 32, 2, kDLCPU);
+
+  *return_value = cuvsHnswSearch(
+    cuvs_resources, search_params, hnsw_index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+}
+
+/**
+ * @brief A function to destroy the HNSW index
+ *
+ * @param[in] hnsw_index the HNSW index reference
+ * @param[out] return_value return value for cuvsHnswIndexDestroy function call
+ */
+void destroy_hnsw_index(cuvsHnswIndex_t hnsw_index, int *return_value) {
+  *return_value = cuvsHnswIndexDestroy(hnsw_index);
+}
+
+/**
+ * @brief struct for containing gpu information
+ */
+typedef struct gpuInfo {
+  int gpu_id;
+  char name[256];
+  long free_memory;
+  long total_memory;
+  float compute_capability;
+} gpuInfo;
+
+/**
+ * @brief A function to get GPU details
+ *
+ * @param[out] return_value return value for cudaMemGetInfo function call
+ * @param[out] num_gpus the number of devices found
+ * @param[out] gpu_info_arr reference to the array of gpuInfo objects
+ */
+void get_gpu_info(int *return_value, int *num_gpus, gpuInfo *gpu_info_arr) {
+  cudaGetDeviceCount(num_gpus);
+  // Limiting the num_gpus to 1024. For more details please see comments in Util.availableGPUs()
+  *num_gpus = (*num_gpus > 1024) ? 1024 : *num_gpus;
+  struct gpuInfo gpuInfos[*num_gpus];
+  size_t free, total;
+  // https://docs.nvidia.com/cuda/cuda-runtime-api/structcudaDeviceProp.html#structcudaDeviceProp
+  struct cudaDeviceProp deviceProp;
+  for (int i = 0; i < *num_gpus; i++) {
+    cudaSetDevice(i);
+    cudaGetDeviceProperties(&deviceProp, i);
+    char buffer[10];
+    sprintf(buffer, "%d.%d", deviceProp.major, deviceProp.minor);
+    *return_value = cudaMemGetInfo(&free, &total);
+    gpuInfos[i].gpu_id = i;
+    strcpy(gpuInfos[i].name, deviceProp.name);
+    gpuInfos[i].free_memory = free;
+    gpuInfos[i].total_memory = total;
+    gpuInfos[i].compute_capability = atof(buffer);
+    *(gpu_info_arr + i) = gpuInfos[i];
+  }
+}

From 7609d18cc7423558c03fc7e2dfcf6df487725c36 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Thu, 30 Jan 2025 17:54:55 -0500
Subject: [PATCH 284/623] Fix indexing bug when using parallelism to build CPU
 hierarchy in HNSW (#620)

hnswlib uses an internal indexing system which assigns an ID to points, atomically, in-order that they are added to the index. When using parallelism to add points to the index, the internal ID may be different than the "label" of the point (label, for us, is just the index of the row in the dataset) as a consequence of adding points in-parallel in no deterministic order.

The bug was that I was using the label itself to write out the CPU hierarchy, when I should have been using hnswlib's internal ID for the point associated with that label.

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/620
---
 cpp/include/cuvs/neighbors/hnsw.h             | 22 ++---
 cpp/include/cuvs/neighbors/hnsw.hpp           |  6 +-
 cpp/src/neighbors/detail/hnsw.hpp             | 84 ++++---------------
 cpp/src/neighbors/hnsw_c.cpp                  |  2 +-
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx      | 10 +--
 python/cuvs/cuvs/tests/test_hnsw.py           |  5 +-
 .../config/algos/cuvs_cagra_hnswlib.yaml      |  2 +-
 7 files changed, 38 insertions(+), 93 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
index a7597a939c..d88fd3b4ed 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -51,9 +51,9 @@ struct cuvsHnswIndexParams {
   /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
   int ef_construction;
   /** Number of host threads to use to construct hierarchy when hierarchy is `CPU`
-  NOTE: Constructing the hierarchy when converting from a CAGRA graph is highly sensitive
-  to parallelism, and increasing the number of threads can reduce the quality of the index.
-   */
+      When the value is 0, the number of threads is automatically determined to the maximum
+      number of threads available.
+  */
   int num_threads;
 };
 
@@ -158,8 +158,8 @@ cuvsError_t cuvsHnswExtendParamsDestroy(cuvsHnswExtendParams_t params);
  * NOTE: When hierarchy is:
  *       1. `NONE`: This method uses the filesystem to write the CAGRA index in
  * `/tmp/<random_number>.bin` before reading it as an hnswlib index, then deleting the temporary
- * file. The returned index is immutable and can only be searched by the hnswlib wrapper in cuVS, as
- * the format is not compatible with the original hnswlib.
+ * file. The returned index is immutable and can only be searched by the hnswlib wrapper in cuVS,
+ * as the format is not compatible with the original hnswlib.
  *       2. `CPU`: The returned index is mutable and can be extended with additional vectors. The
  * serialized index is also compatible with the original hnswlib library.
  *
@@ -364,10 +364,10 @@ cuvsError_t cuvsHnswSearch(cuvsResources_t res,
 
 /**
  * @brief Serialize a CAGRA index to a file as an hnswlib index
- * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
- * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
- * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
- * library.
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by
+ * the hnswlib wrapper in cuVS, as the serialization format is not compatible with the original
+ * hnswlib. However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the
+ * original hnswlib library.
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] filename the name of the file to save the index
@@ -406,8 +406,8 @@ cuvsError_t cuvsHnswSerialize(cuvsResources_t res, const char* filename, cuvsHns
 /**
  * Load hnswlib index from file which was serialized from a HNSW index.
  * NOTE: When hierarchy is `NONE`, the loaded hnswlib index is immutable, and only be read by the
- * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
- * Experimental, both the API and the serialization format are subject to change.
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original
+ * hnswlib. Experimental, both the API and the serialization format are subject to change.
  *
  * @code{.c}
  * #include <cuvs/core/c_api.h>
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 81a823493c..750f1f87fc 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -54,10 +54,10 @@ struct index_params : cuvs::neighbors::index_params {
   /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
   int ef_construction = 200;
   /** Number of host threads to use to construct hierarchy when hierarchy is `CPU`
-  NOTE: Constructing the hierarchy when converting from a CAGRA graph is highly sensitive
-  to parallelism, and increasing the number of threads can reduce the quality of the index.
+      When the value is 0, the number of threads is automatically determined to the
+      maximum number of threads available.
    */
-  int num_threads = 2;
+  int num_threads = 0;
 };
 
 /**@}*/
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index 5447ae07a6..07e0123495 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -21,65 +21,13 @@
 #include <hnswlib/hnswalg.h>
 #include <hnswlib/hnswlib.h>
 #include <memory>
+#include <omp.h>
 #include <raft/core/logger.hpp>
 #include <random>
 #include <thread>
 
 namespace cuvs::neighbors::hnsw::detail {
 
-// Multithreaded executor
-// The helper function is copied from the hnswlib repository
-// as for some reason, adding vectors to the hnswlib index does not
-// work well with omp parallel for
-template <class Function>
-inline void ParallelFor(size_t start, size_t end, size_t numThreads, Function fn)
-{
-  if (numThreads <= 0) { numThreads = std::thread::hardware_concurrency(); }
-
-  if (numThreads == 1) {
-    for (size_t id = start; id < end; id++) {
-      fn(id, 0);
-    }
-  } else {
-    std::vector<std::thread> threads;
-    std::atomic<size_t> current(start);
-
-    // keep track of exceptions in threads
-    // https://stackoverflow.com/a/32428427/1713196
-    std::exception_ptr lastException = nullptr;
-    std::mutex lastExceptMutex;
-
-    for (size_t threadId = 0; threadId < numThreads; ++threadId) {
-      threads.push_back(std::thread([&, threadId] {
-        while (true) {
-          size_t id = current.fetch_add(1);
-
-          if (id >= end) { break; }
-
-          try {
-            fn(id, threadId);
-          } catch (...) {
-            std::unique_lock<std::mutex> lastExcepLock(lastExceptMutex);
-            lastException = std::current_exception();
-            /*
-             * This will work even when current is the largest value that
-             * size_t can fit, because fetch_add returns the previous value
-             * before the increment (what will result in overflow
-             * and produce 0 instead of current + 1).
-             */
-            current = end;
-            break;
-          }
-        }
-      }));
-    }
-    for (auto& thread : threads) {
-      thread.join();
-    }
-    if (lastException) { std::rethrow_exception(lastException); }
-  }
-}
-
 template <typename T>
 struct hnsw_dist_t {
   using type = void;
@@ -223,10 +171,12 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
     cagra_index.graph().extent(1) / 2,
     params.ef_construction);
   appr_algo->base_layer_init = false;  // tell hnswlib to build upper layers only
-  ParallelFor(0, host_dataset_view.extent(0), params.num_threads, [&](size_t i, size_t threadId) {
+  auto num_threads           = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+#pragma omp parallel for num_threads(num_threads)
+  for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
     appr_algo->addPoint((void*)(host_dataset_view.data_handle() + i * host_dataset_view.extent(1)),
                         i);
-  });
+  }
   appr_algo->base_layer_init = true;  // reset to true to allow addition of new points
 
   // move cagra graph to host
@@ -242,11 +192,13 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
 // copy cagra graph to hnswlib base layer
 #pragma omp parallel for
   for (size_t i = 0; i < static_cast<size_t>(host_graph.extent(0)); ++i) {
-    auto ll_i = appr_algo->get_linklist0(i);
+    auto hnsw_internal_id = appr_algo->label_lookup_.find(i)->second;
+    auto ll_i             = appr_algo->get_linklist0(hnsw_internal_id);
     appr_algo->setListCount(ll_i, host_graph.extent(1));
     auto* data = (uint32_t*)(ll_i + 1);
     for (size_t j = 0; j < static_cast<size_t>(host_graph.extent(1)); ++j) {
-      data[j] = host_graph(i, j);
+      auto neighbor_internal_id = appr_algo->label_lookup_.find(host_graph(i, j))->second;
+      data[j]                   = neighbor_internal_id;
     }
   }
 
@@ -281,19 +233,15 @@ void extend(raft::resources const& res,
     const_cast<void*>(idx.get_index()));
   auto current_element_count = hnswlib_index->getCurrentElementCount();
   auto new_element_count     = additional_dataset.extent(0);
-  auto num_threads           = params.num_threads == 0 ? std::thread::hardware_concurrency()
-                                                       : static_cast<size_t>(params.num_threads);
+  auto num_threads           = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
 
   hnswlib_index->resizeIndex(current_element_count + new_element_count);
-  ParallelFor(current_element_count,
-              current_element_count + new_element_count,
-              num_threads,
-              [&](size_t i, size_t threadId) {
-                hnswlib_index->addPoint(
-                  (void*)(additional_dataset.data_handle() +
-                          (i - current_element_count) * additional_dataset.extent(1)),
-                  i);
-              });
+#pragma omp parallel for num_threads(num_threads)
+  for (int64_t i = 0; i < additional_dataset.extent(0); i++) {
+    hnswlib_index->addPoint(
+      (void*)(additional_dataset.data_handle() + i * additional_dataset.extent(1)),
+      current_element_count + i);
+  }
 }
 
 template <typename T>
diff --git a/cpp/src/neighbors/hnsw_c.cpp b/cpp/src/neighbors/hnsw_c.cpp
index 0233a510a1..628d87e007 100644
--- a/cpp/src/neighbors/hnsw_c.cpp
+++ b/cpp/src/neighbors/hnsw_c.cpp
@@ -123,7 +123,7 @@ extern "C" cuvsError_t cuvsHnswIndexParamsCreate(cuvsHnswIndexParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
     *params = new cuvsHnswIndexParams{
-      .hierarchy = cuvsHnswHierarchy::NONE, .ef_construction = 200, .num_threads = 2};
+      .hierarchy = cuvsHnswHierarchy::NONE, .ef_construction = 200, .num_threads = 0};
   });
 }
 
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
index 4c44350e83..72a3617bd0 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
@@ -52,12 +52,10 @@ cdef class IndexParams:
     ef_construction : int, default = 200 (optional)
         Maximum number of candidate list size used during construction
         when hierarchy is `cpu`.
-    num_threads : int, default = 2 (optional)
+    num_threads : int, default = 0 (optional)
         Number of CPU threads used to increase construction parallelism
-        when hierarchy is `cpu`.
-        NOTE: Constructing the hierarchy when converting from a CAGRA graph
-        is highly sensitive to parallelism, and increasing the number of
-        threads can reduce the quality of the index.
+        when hierarchy is `cpu`. When the value is 0, the number of threads is
+        automatically determined to the maximum number of threads available.
     """
 
     cdef cuvsHnswIndexParams* params
@@ -71,7 +69,7 @@ cdef class IndexParams:
     def __init__(self, *,
                  hierarchy="none",
                  ef_construction=200,
-                 num_threads=2):
+                 num_threads=0):
         if hierarchy == "none":
             self.params.hierarchy = cuvsHnswHierarchy.NONE
         elif hierarchy == "cpu":
diff --git a/python/cuvs/cuvs/tests/test_hnsw.py b/python/cuvs/cuvs/tests/test_hnsw.py
index e00b88bb7e..23a0920efb 100644
--- a/python/cuvs/cuvs/tests/test_hnsw.py
+++ b/python/cuvs/cuvs/tests/test_hnsw.py
@@ -54,7 +54,7 @@ def run_hnsw_build_search_test(
 
     assert index.trained
 
-    hnsw_params = hnsw.IndexParams(hierarchy=hierarchy, num_threads=1)
+    hnsw_params = hnsw.IndexParams(hierarchy=hierarchy)
     hnsw_index = hnsw.from_cagra(hnsw_params, index)
 
     queries = generate_data((n_queries, n_cols), dtype)
@@ -135,7 +135,7 @@ def run_hnsw_extend_test(
 
     assert index.trained
 
-    hnsw_params = hnsw.IndexParams(hierarchy="cpu", num_threads=1)
+    hnsw_params = hnsw.IndexParams(hierarchy="cpu")
     hnsw_index = hnsw.from_cagra(hnsw_params, index)
     hnsw.extend(hnsw.ExtendParams(), hnsw_index, add_dataset)
 
@@ -158,7 +158,6 @@ def run_hnsw_extend_test(
     skl_dist, skl_idx = nn_skl.kneighbors(queries, return_distance=True)
 
     recall = calc_recall(out_idx, skl_idx)
-    print(recall)
     assert recall > 0.95
 
 
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
index 90a561bca4..630dc94ff3 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
@@ -1,5 +1,6 @@
 name: cuvs_cagra_hnswlib
 constraints:
+  build: cuvs_bench.config.algos.constraints.cuvs_cagra_build
   search: cuvs_bench.config.algos.constraints.hnswlib_search
 groups:
   base:
@@ -9,6 +10,5 @@ groups:
       graph_build_algo: ["NN_DESCENT"]
       hierarchy: ["none", "cpu"]
       ef_construction: [64, 128, 256, 512]
-      num_threads: [2, 5, 10]
     search:
       ef: [10, 20, 40, 60, 80, 120, 200, 400, 600, 800]

From 833f28c4d630d8c9057b1179440909501dab8022 Mon Sep 17 00:00:00 2001
From: Ben Karsin <bkarsin@nvidia.com>
Date: Thu, 30 Jan 2025 12:56:27 -1000
Subject: [PATCH 285/623] Vamana build improvement and added docs (#558)

Includes several fixes and improvements to Vamana, primarily:
- Edge case and bug fixes for Vamana index build (details below)
- Documentation added for Vamana
- experimental namespace removed
- Reduce device memory usage by splitting reverse edge work into batches

The edge case fix adds padding to all shared memory size and offset calculations so any dataset dimension is supported (tests added that verify this). A bug was also fixed with the L2 distance metric causing incorrect results in some rare cases.

This PR addresses the most pressing items in #393 and stabilize the index construction sufficiently to remove the experimental namespace.

Authors:
  - Ben Karsin (https://github.com/bkarsin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/558
---
 cpp/include/cuvs/neighbors/vamana.hpp         | 299 ++++++++++++++++--
 .../neighbors/detail/vamana/greedy_search.cuh |   8 +-
 cpp/src/neighbors/detail/vamana/macros.cuh    |   4 +-
 .../detail/vamana/priority_queue.cuh          |   4 +-
 .../neighbors/detail/vamana/robust_prune.cuh  |  14 +-
 .../neighbors/detail/vamana/vamana_build.cuh  | 197 +++++++-----
 .../detail/vamana/vamana_serialize.cuh        |   4 +-
 .../detail/vamana/vamana_structs.cuh          |  25 +-
 cpp/src/neighbors/vamana.cuh                  |   9 +-
 cpp/src/neighbors/vamana_build_float.cu       |  34 +-
 cpp/src/neighbors/vamana_build_int8.cu        |  34 +-
 cpp/src/neighbors/vamana_build_uint8.cu       |  34 +-
 cpp/src/neighbors/vamana_serialize.cuh        |   9 +-
 cpp/src/neighbors/vamana_serialize_float.cu   |   4 +-
 cpp/src/neighbors/vamana_serialize_int8.cu    |   4 +-
 cpp/src/neighbors/vamana_serialize_uint8.cu   |   4 +-
 cpp/tests/neighbors/ann_vamana.cuh            | 116 +++----
 .../ann_vamana/test_float_uint32_t.cu         |   4 +-
 .../ann_vamana/test_int8_t_uint32_t.cu        |   4 +-
 .../ann_vamana/test_uint8_t_uint32_t.cu       |   4 +-
 docs/source/cpp_api/neighbors.rst             |   1 +
 docs/source/cpp_api/neighbors_vamana.rst      |  44 +++
 docs/source/indexes/indexes.rst               |   1 +
 docs/source/indexes/vamana.rst                |  75 +++++
 examples/cpp/src/vamana_example.cu            |  60 ++--
 25 files changed, 703 insertions(+), 293 deletions(-)
 create mode 100644 docs/source/cpp_api/neighbors_vamana.rst
 create mode 100644 docs/source/indexes/vamana.rst

diff --git a/cpp/include/cuvs/neighbors/vamana.hpp b/cpp/include/cuvs/neighbors/vamana.hpp
index bec17937f9..bc205a6f4f 100644
--- a/cpp/include/cuvs/neighbors/vamana.hpp
+++ b/cpp/include/cuvs/neighbors/vamana.hpp
@@ -31,16 +31,27 @@
 #include <optional>
 #include <variant>
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
+
 /**
  * @defgroup vamana_cpp_index_params Vamana index build parameters
  * @{
  */
 
 /**
- * @brief ANN parameters used by VAMANA to build index
+ * @brief Parameters used to build DiskANN index
  *
+ * `graph_degree`: Maximum degree of graph; correspods to the R parameter of
+ * Vamana algorithm in the literature.
+ * `visited_size`: Maximum number of visited nodes per search during Vamana algorithm.
+ * Loosely corresponds to the L parameter in the literature.
+ * `vamana_iters`: The number of times all vectors are inserted into the graph. If > 1,
+ * all vectors are re-inserted to improve graph quality.
+ * `max_fraction`: The maximum batch size is this fraction of the total dataset size. Larger
+ * gives faster build but lower graph quality.
+ * `alpha`: Used to determine how aggressive the pruning will be.
  */
+
 struct index_params : cuvs::neighbors::index_params {
   /** Maximum degree of output graph corresponds to the R parameter in the original Vamana
    * literature. */
@@ -55,10 +66,12 @@ struct index_params : cuvs::neighbors::index_params {
   /** Maximum fraction of dataset inserted per batch.              *
    * Larger max batch decreases graph quality, but improves speed */
   float max_fraction = 0.06;
-  /** Base of growth rate of batch sies **/
+  /** Base of growth rate of batch sizes **/
   float batch_base = 2;
   /** Size of candidate queue structure - should be (2^x)-1 */
   uint32_t queue_size = 127;
+  /** Max batchsize of reverse edge processing (reduces memory footprint) */
+  uint32_t reverse_batchsize = 1000000;
 };
 
 /**
@@ -215,61 +228,295 @@ struct index : cuvs::neighbors::index {
  * @{
  */
 /**
- * @brief Build the index from the dataset for efficient search.
+ * @brief Build the index from the dataset for efficient DiskANN search.
+ *
+ * The build utilities the Vamana insertion-based algorithm to create the graph. The algorithm
+ * starts with an empty graph and iteratively iserts batches of nodes. Each batch involves
+ * performing a greedy search for each vector to be inserted, and inserting it with edges to
+ * all nodes traversed during the search. Reverse edges are also inserted and robustPrune is applied
+ * to improve graph quality. The index_params struct controls the degree of the final graph.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters;
+ *   vamana::index_params index_params;
+ *   // create and fill index from a [N, D] dataset;
+ *   auto index = vamana::build(res, index_params, dataset);
+ *   // write index to file to be used by CPU-based DiskANN search (cuVS does not yet support
+ * search) vamana::serialize(res, filename, index);
+ * @endcode
  *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed vamana index
  */
-auto build(raft::resources const& handle,
-           const cuvs::neighbors::experimental::vamana::index_params& params,
+auto build(raft::resources const& res,
+           const cuvs::neighbors::vamana::index_params& params,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::experimental::vamana::index<float, uint32_t>;
+  -> cuvs::neighbors::vamana::index<float, uint32_t>;
 
-auto build(raft::resources const& handle,
-           const cuvs::neighbors::experimental::vamana::index_params& params,
+/**
+ * @brief Build the index from the dataset for efficient DiskANN search.
+ *
+ * The build utilities the Vamana insertion-based algorithm to create the graph. The algorithm
+ * starts with an empty graph and iteratively iserts batches of nodes. Each batch involves
+ * performing a greedy search for each vector to be inserted, and inserting it with edges to
+ * all nodes traversed during the search. Reverse edges are also inserted and robustPrune is applied
+ * to improve graph quality. The index_params struct controls the degree of the final graph.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters;
+ *   vamana::index_params index_params;
+ *   // create and fill index from a [N, D] dataset;
+ *   auto index = vamana::build(res, index_params, dataset);
+ *   // write index to file to be used by CPU-based DiskANN search (cuVS does not yet support
+ * search) vamana::serialize(res, filename, index);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed vamana index
+ */
+auto build(raft::resources const& res,
+           const cuvs::neighbors::vamana::index_params& params,
            raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::experimental::vamana::index<float, uint32_t>;
+  -> cuvs::neighbors::vamana::index<float, uint32_t>;
 
-auto build(raft::resources const& handle,
-           const cuvs::neighbors::experimental::vamana::index_params& params,
+/**
+ * @brief Build the index from the dataset for efficient DiskANN search.
+ *
+ * The build utilities the Vamana insertion-based algorithm to create the graph. The algorithm
+ * starts with an empty graph and iteratively iserts batches of nodes. Each batch involves
+ * performing a greedy search for each vector to be inserted, and inserting it with edges to
+ * all nodes traversed during the search. Reverse edges are also inserted and robustPrune is applied
+ * to improve graph quality. The index_params struct controls the degree of the final graph.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters;
+ *   vamana::index_params index_params;
+ *   // create and fill index from a [N, D] dataset;
+ *   auto index = vamana::build(res, index_params, dataset);
+ *   // write index to file to be used by CPU-based DiskANN search (cuVS does not yet support
+ * search) vamana::serialize(res, filename, index);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed vamana index
+ */
+auto build(raft::resources const& res,
+           const cuvs::neighbors::vamana::index_params& params,
            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::experimental::vamana::index<int8_t, uint32_t>;
+  -> cuvs::neighbors::vamana::index<int8_t, uint32_t>;
 
-auto build(raft::resources const& handle,
-           const cuvs::neighbors::experimental::vamana::index_params& params,
+/**
+ * @brief Build the index from the dataset for efficient DiskANN search.
+ *
+ * The build utilities the Vamana insertion-based algorithm to create the graph. The algorithm
+ * starts with an empty graph and iteratively iserts batches of nodes. Each batch involves
+ * performing a greedy search for each vector to be inserted, and inserting it with edges to
+ * all nodes traversed during the search. Reverse edges are also inserted and robustPrune is applied
+ * to improve graph quality. The index_params struct controls the degree of the final graph.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters;
+ *   vamana::index_params index_params;
+ *   // create and fill index from a [N, D] dataset;
+ *   auto index = vamana::build(res, index_params, dataset);
+ *   // write index to file to be used by CPU-based DiskANN search (cuVS does not yet support
+ * search) vamana::serialize(res, filename, index);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed vamana index
+ */
+auto build(raft::resources const& res,
+           const cuvs::neighbors::vamana::index_params& params,
            raft::host_matrix_view<const int8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::experimental::vamana::index<int8_t, uint32_t>;
+  -> cuvs::neighbors::vamana::index<int8_t, uint32_t>;
 
-auto build(raft::resources const& handle,
-           const cuvs::neighbors::experimental::vamana::index_params& params,
+/**
+ * @brief Build the index from the dataset for efficient DiskANN search.
+ *
+ * The build utilities the Vamana insertion-based algorithm to create the graph. The algorithm
+ * starts with an empty graph and iteratively iserts batches of nodes. Each batch involves
+ * performing a greedy search for each vector to be inserted, and inserting it with edges to
+ * all nodes traversed during the search. Reverse edges are also inserted and robustPrune is applied
+ * to improve graph quality. The index_params struct controls the degree of the final graph.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters;
+ *   vamana::index_params index_params;
+ *   // create and fill index from a [N, D] dataset;
+ *   auto index = vamana::build(res, index_params, dataset);
+ *   // write index to file to be used by CPU-based DiskANN search (cuVS does not yet support
+ * search) vamana::serialize(res, filename, index);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed vamana index
+ */
+auto build(raft::resources const& res,
+           const cuvs::neighbors::vamana::index_params& params,
            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::experimental::vamana::index<uint8_t, uint32_t>;
+  -> cuvs::neighbors::vamana::index<uint8_t, uint32_t>;
 
-auto build(raft::resources const& handle,
-           const cuvs::neighbors::experimental::vamana::index_params& params,
+/**
+ * @brief Build the index from the dataset for efficient DiskANN search.
+ *
+ * The build utilities the Vamana insertion-based algorithm to create the graph. The algorithm
+ * starts with an empty graph and iteratively iserts batches of nodes. Each batch involves
+ * performing a greedy search for each vector to be inserted, and inserting it with edges to
+ * all nodes traversed during the search. Reverse edges are also inserted and robustPrune is applied
+ * to improve graph quality. The index_params struct controls the degree of the final graph.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters;
+ *   vamana::index_params index_params;
+ *   // create and fill index from a [N, D] dataset;
+ *   auto index = vamana::build(res, index_params, dataset);
+ *   // write index to file to be used by CPU-based DiskANN search (cuVS does not yet support
+ * search) vamana::serialize(res, filename, index);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed vamana index
+ */
+auto build(raft::resources const& res,
+           const cuvs::neighbors::vamana::index_params& params,
            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
-  -> cuvs::neighbors::experimental::vamana::index<uint8_t, uint32_t>;
+  -> cuvs::neighbors::vamana::index<uint8_t, uint32_t>;
 
 /**
  * @defgroup vamana_cpp_serialize Vamana serialize functions
  * @{
  */
+
 /**
  * Save the index to file.
+ *
+ * Matches the file format used by the DiskANN open-source repository, allowing cross-compatibility.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/neighbors/vamana.hpp>
+ *
+ *   raft::resources handle;
+ *
+ *   // create a string with a filepath
+ *   std::string file_prefix("/path/to/index/prefix");
+ *   // create an index with `auto index = cuvs::neighbors::vamana::build(...);`
+ *   cuvs::neighbors::vamana::serialize(handle, file_prefix, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] file_prefix prefix of path and name of index files
+ * @param[in] index Vamana index
+ *
  */
 
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
-               const cuvs::neighbors::experimental::vamana::index<float, uint32_t>& index);
+               const cuvs::neighbors::vamana::index<float, uint32_t>& index);
 
+/**
+ * Save the index to file.
+ *
+ * Matches the file format used by the DiskANN open-source repository, allowing cross-compatibility.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/neighbors/vamana.hpp>
+ *
+ *   raft::resources handle;
+ *
+ *   // create a string with a filepath
+ *   std::string file_prefix("/path/to/index/prefix");
+ *   // create an index with `auto index = cuvs::neighbors::vamana::build(...);`
+ *   cuvs::neighbors::vamana::serialize(handle, file_prefix, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] file_prefix prefix of path and name of index files
+ * @param[in] index Vamana index
+ *
+ */
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
-               const cuvs::neighbors::experimental::vamana::index<int8_t, uint32_t>& index);
+               const cuvs::neighbors::vamana::index<int8_t, uint32_t>& index);
 
+/**
+ * Save the index to file.
+ *
+ * Matches the file format used by the DiskANN open-source repository, allowing cross-compatibility.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/neighbors/vamana.hpp>
+ *
+ *   raft::resources handle;
+ *
+ *   // create a string with a filepath
+ *   std::string file_prefix("/path/to/index/prefix");
+ *   // create an index with `auto index = cuvs::neighbors::vamana::build(...);`
+ *   cuvs::neighbors::vamana::serialize(handle, file_prefix, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] file_prefix prefix of path and name of index files
+ * @param[in] index Vamana index
+ *
+ */
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
-               const cuvs::neighbors::experimental::vamana::index<uint8_t, uint32_t>& index);
+               const cuvs::neighbors::vamana::index<uint8_t, uint32_t>& index);
 
 /**
  * @}
  */
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/detail/vamana/greedy_search.cuh b/cpp/src/neighbors/detail/vamana/greedy_search.cuh
index f51c6c91bf..4d94bbaa77 100644
--- a/cpp/src/neighbors/detail/vamana/greedy_search.cuh
+++ b/cpp/src/neighbors/detail/vamana/greedy_search.cuh
@@ -30,7 +30,7 @@
 #include <cstdio>
 #include <vector>
 
-namespace cuvs::neighbors::experimental::vamana::detail {
+namespace cuvs::neighbors::vamana::detail {
 
 /* @defgroup greedy_search_detail greedy search
  * @{
@@ -112,13 +112,15 @@ __global__ void GreedySearchKernel(
     DistPair<IdxT, accT> candidate_queue;
   };
 
+  int align_padding = (((dim - 1) / alignof(ShmemLayout)) + 1) * alignof(ShmemLayout) - dim;
+
   // Dynamic shared memory used for blocksort, temp vector storage, and neighborhood list
   extern __shared__ __align__(alignof(ShmemLayout)) char smem[];
 
   size_t smem_offset = sort_smem_size;  // temp sorting memory takes first chunk
 
   T* s_coords = reinterpret_cast<T*>(&smem[smem_offset]);
-  smem_offset += dim * sizeof(T);
+  smem_offset += (dim + align_padding) * sizeof(T);
 
   Node<accT>* topk_pq = reinterpret_cast<Node<accT>*>(&smem[smem_offset]);
   smem_offset += topk * sizeof(Node<accT>);
@@ -283,4 +285,4 @@ __global__ void GreedySearchKernel(
  * @}
  */
 
-}  // namespace cuvs::neighbors::experimental::vamana::detail
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/macros.cuh b/cpp/src/neighbors/detail/vamana/macros.cuh
index 5692650a0e..c290413a20 100644
--- a/cpp/src/neighbors/detail/vamana/macros.cuh
+++ b/cpp/src/neighbors/detail/vamana/macros.cuh
@@ -16,7 +16,7 @@
 
 #pragma once
 
-namespace cuvs::neighbors::experimental::vamana::detail {
+namespace cuvs::neighbors::vamana::detail {
 
 /* Macros to compute the shared memory requirements for CUB primitives used by search and prune */
 #define COMPUTE_SMEM_SIZES(degree, visited_size, DEG, CANDS)                                     \
@@ -79,4 +79,4 @@ namespace cuvs::neighbors::experimental::vamana::detail {
   SEARCH_CALL_SORT(topk, 512);   \
   SEARCH_CALL_SORT(topk, 1024);
 
-}  // namespace cuvs::neighbors::experimental::vamana::detail
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/priority_queue.cuh b/cpp/src/neighbors/detail/vamana/priority_queue.cuh
index 4b3bd84661..6dc1dc94a1 100644
--- a/cpp/src/neighbors/detail/vamana/priority_queue.cuh
+++ b/cpp/src/neighbors/detail/vamana/priority_queue.cuh
@@ -20,7 +20,7 @@
 #include <raft/util/warp_primitives.cuh>
 #include <stdio.h>
 
-namespace cuvs::neighbors::experimental::vamana::detail {
+namespace cuvs::neighbors::vamana::detail {
 
 /***************************************************************************************
 ***************************************************************************************/
@@ -326,4 +326,4 @@ __forceinline__ __device__ void enqueue_all_neighbors(int num_neighbors,
   }
 }
 
-}  // namespace cuvs::neighbors::experimental::vamana::detail
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/robust_prune.cuh b/cpp/src/neighbors/detail/vamana/robust_prune.cuh
index 8446ac136d..182d20c887 100644
--- a/cpp/src/neighbors/detail/vamana/robust_prune.cuh
+++ b/cpp/src/neighbors/detail/vamana/robust_prune.cuh
@@ -19,10 +19,12 @@
 #include <cub/cub.cuh>
 #include <thrust/sort.h>
 
+#include <raft/util/cuda_dev_essentials.cuh>
+
 #include "macros.cuh"
 #include "vamana_structs.cuh"
 
-namespace cuvs::neighbors::experimental::vamana::detail {
+namespace cuvs::neighbors::vamana::detail {
 
 // Load candidates (from query) and previous edges (from nbh_list) into registers (tmp) spanning
 // warp
@@ -145,9 +147,11 @@ __global__ void RobustPruneKernel(
   // Dynamic shared memory used for blocksort, temp vector storage, and neighborhood list
   extern __shared__ __align__(alignof(ShmemLayout)) char smem[];
 
-  T* s_coords = reinterpret_cast<T*>(&smem[sort_smem_size]);
-  DistPair<IdxT, accT>* new_nbh_list =
-    reinterpret_cast<DistPair<IdxT, accT>*>(&smem[dim * sizeof(T) + sort_smem_size]);
+  int align_padding = raft::alignTo<int>(dim, alignof(ShmemLayout)) - dim;
+
+  T* s_coords                        = reinterpret_cast<T*>(&smem[sort_smem_size]);
+  DistPair<IdxT, accT>* new_nbh_list = reinterpret_cast<DistPair<IdxT, accT>*>(
+    &smem[(dim + align_padding) * sizeof(T) + sort_smem_size]);
 
   static __shared__ Point<T, accT> s_query;
   s_query.coords = s_coords;
@@ -245,4 +249,4 @@ __global__ void RobustPruneKernel(
 
 }  // namespace
 
-}  // namespace cuvs::neighbors::experimental::vamana::detail
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_build.cuh b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
index ec75c99c11..184b024f86 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_build.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
@@ -46,7 +46,7 @@
 #include <cstdio>
 #include <vector>
 
-namespace cuvs::neighbors::experimental::vamana::detail {
+namespace cuvs::neighbors::vamana::detail {
 
 /* @defgroup vamana_build_detail vamana build
  * @{
@@ -104,11 +104,12 @@ void batched_insert_vamana(
       "to 1.0");
     max_batchsize = (int)dataset.extent(0);
   }
-  int insert_iters = (int)(params.vamana_iters);
-  double base      = (double)(params.batch_base);
-  float alpha      = (float)(params.alpha);
-  int visited_size = params.visited_size;
-  int queue_size   = params.queue_size;
+  int insert_iters  = (int)(params.vamana_iters);
+  double base       = (double)(params.batch_base);
+  float alpha       = (float)(params.alpha);
+  int visited_size  = params.visited_size;
+  int queue_size    = params.queue_size;
+  int reverse_batch = params.reverse_batchsize;
 
   if ((visited_size & (visited_size - 1)) != 0) {
     RAFT_LOG_WARN("visited_size must be a power of 2, rounding up.");
@@ -152,36 +153,20 @@ void batched_insert_vamana(
   std::vector<IdxT> insert_order;
   create_insert_permutation<IdxT>(insert_order, (uint32_t)N);
 
-  // Memory needed to sort reverse edges - potentially large memory footprint
-  auto edge_dest =
-    raft::make_device_mdarray<IdxT>(res,
-                                    raft::resource::get_large_workspace_resource(res),
-                                    raft::make_extents<int64_t>(max_batchsize, degree));
-  auto edge_src =
-    raft::make_device_mdarray<IdxT>(res,
-                                    raft::resource::get_large_workspace_resource(res),
-                                    raft::make_extents<int64_t>(max_batchsize, degree));
-
-  size_t temp_storage_bytes = max_batchsize * degree * (2 * sizeof(IdxT));
-  RAFT_LOG_DEBUG("Temp storage needed for sorting (bytes): %lu", temp_storage_bytes);
-  auto temp_sort_storage =
-    raft::make_device_mdarray<IdxT>(res,
-                                    raft::resource::get_large_workspace_resource(res),
-                                    raft::make_extents<int64_t>(2 * max_batchsize, degree));
-
   // Calculate the shared memory sizes of each kernel
   int search_smem_sort_size = 0;
   int prune_smem_sort_size  = 0;
   SELECT_SMEM_SIZES(degree, visited_size);  // Sets above 2 variables to appropriate sizes
 
   // Total dynamic shared memory used by GreedySearch
-  int search_smem_total_size =
-    static_cast<int>(search_smem_sort_size + dim * sizeof(T) + visited_size * sizeof(Node<accT>) +
-                     degree * sizeof(int) + queue_size * sizeof(DistPair<IdxT, accT>));
+  int align_padding          = raft::alignTo(dim, 16) - dim;
+  int search_smem_total_size = static_cast<int>(
+    search_smem_sort_size + (dim + align_padding) * sizeof(T) + visited_size * sizeof(Node<accT>) +
+    degree * sizeof(int) + queue_size * sizeof(DistPair<IdxT, accT>));
 
   // Total dynamic shared memory size needed by both RobustPrune calls
-  int prune_smem_total_size =
-    prune_smem_sort_size + dim * sizeof(T) + (degree + visited_size) * sizeof(DistPair<IdxT, accT>);
+  int prune_smem_total_size = prune_smem_sort_size + (dim + align_padding) * sizeof(T) +
+                              (degree + visited_size) * sizeof(DistPair<IdxT, accT>);
 
   RAFT_LOG_DEBUG("Dynamic shared memory usage (bytes): GreedySearch: %d, RobustPrune: %d",
                  search_smem_total_size,
@@ -228,7 +213,6 @@ void batched_insert_vamana(
                                                                  metric,
                                                                  queue_size,
                                                                  search_smem_sort_size);
-
       // Run on candidates of vectors being inserted
       RobustPruneKernel<T, accT, IdxT>
         <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
@@ -252,6 +236,16 @@ void batched_insert_vamana(
 
       int total_edges;
       raft::copy(&total_edges, d_total_edges.data_handle(), 1, stream);
+      RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+
+      auto edge_dest =
+        raft::make_device_mdarray<IdxT>(res,
+                                        raft::resource::get_large_workspace_resource(res),
+                                        raft::make_extents<int64_t>(total_edges));
+      auto edge_src =
+        raft::make_device_mdarray<IdxT>(res,
+                                        raft::resource::get_large_workspace_resource(res),
+                                        raft::make_extents<int64_t>(total_edges));
 
       // Create reverse edge list
       create_reverse_edge_list<accT, IdxT>
@@ -261,6 +255,24 @@ void batched_insert_vamana(
                                             edge_src.data_handle(),
                                             edge_dest.data_handle());
 
+      void* d_temp_storage      = nullptr;
+      size_t temp_storage_bytes = 0;
+
+      cub::DeviceMergeSort::SortPairs(d_temp_storage,
+                                      temp_storage_bytes,
+                                      edge_dest.data_handle(),
+                                      edge_src.data_handle(),
+                                      total_edges,
+                                      CmpEdge<IdxT>(),
+                                      stream);
+
+      RAFT_LOG_DEBUG("Temp storage needed for sorting (bytes): %lu", temp_storage_bytes);
+
+      auto temp_sort_storage = raft::make_device_mdarray<IdxT>(
+        res,
+        raft::resource::get_large_workspace_resource(res),
+        raft::make_extents<int64_t>(temp_storage_bytes / sizeof(IdxT)));
+
       // Sort to group reverse edges by destination
       cub::DeviceMergeSort::SortPairs(temp_sort_storage.data_handle(),
                                       temp_storage_bytes,
@@ -279,64 +291,77 @@ void batched_insert_vamana(
                                                 edge_dest.data_handle() + total_edges);
       auto unique_indices = raft::make_device_vector<int>(res, total_edges);
       raft::linalg::map_offset(res, unique_indices.view(), raft::identity_op{});
+
       thrust::unique_by_key(
         edge_dest_vec.begin(), edge_dest_vec.end(), unique_indices.data_handle());
 
-      // Allocate reverse QueryCandidate list based on number of unique destinations
-      // TODO - Do this in batches to reduce memory footprint / support larger datasets
-      auto reverse_list_ptr = raft::make_device_mdarray<QueryCandidates<IdxT, accT>>(
-        res,
-        raft::resource::get_large_workspace_resource(res),
-        raft::make_extents<int64_t>(unique_dests));
-      auto rev_ids =
-        raft::make_device_mdarray<IdxT>(res,
-                                        raft::resource::get_large_workspace_resource(res),
-                                        raft::make_extents<int64_t>(unique_dests, visited_size));
-      auto rev_dists =
-        raft::make_device_mdarray<accT>(res,
-                                        raft::resource::get_large_workspace_resource(res),
-                                        raft::make_extents<int64_t>(unique_dests, visited_size));
-
-      QueryCandidates<IdxT, accT>* reverse_list =
-        static_cast<QueryCandidates<IdxT, accT>*>(reverse_list_ptr.data_handle());
-
-      init_query_candidate_list<IdxT, accT><<<256, blockD, 0, stream>>>(reverse_list,
-                                                                        rev_ids.data_handle(),
-                                                                        rev_dists.data_handle(),
-                                                                        (int)unique_dests,
-                                                                        visited_size);
-
-      // May need more blocks for reverse list
-      num_blocks = min(maxBlocks, unique_dests);
-
-      // Populate reverse list ids and candidate lists from edge_src and edge_dest
-      populate_reverse_list_struct<T, accT, IdxT>
-        <<<num_blocks, blockD, 0, stream>>>(reverse_list,
-                                            edge_src.data_handle(),
-                                            edge_dest.data_handle(),
-                                            unique_indices.data_handle(),
-                                            unique_dests,
-                                            total_edges,
-                                            dataset.extent(0));
-
-      // Recompute distances (avoided keeping it during sorting)
-      recompute_reverse_dists<T, accT, IdxT>
-        <<<num_blocks, blockD, 0, stream>>>(reverse_list, dataset, unique_dests, metric);
-
-      // Call 2nd RobustPrune on reverse query_list
-      RobustPruneKernel<T, accT, IdxT>
-        <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
-                                                                raft::make_const_mdspan(dataset),
-                                                                reverse_list_ptr.data_handle(),
-                                                                unique_dests,
-                                                                visited_size,
-                                                                metric,
-                                                                alpha,
-                                                                prune_smem_sort_size);
-
-      // Write new edge lists to graph
-      write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
-        d_graph.view(), reverse_list_ptr.data_handle(), degree, unique_dests);
+      edge_dest_vec.clear();
+      edge_dest_vec.shrink_to_fit();
+
+      // Batch execution of reverse edge creation/application
+      reverse_batch = params.reverse_batchsize;
+      for (int rev_start = 0; rev_start < (int)unique_dests; rev_start += reverse_batch) {
+        if (rev_start + reverse_batch > (int)unique_dests) {
+          reverse_batch = (int)unique_dests - rev_start;
+        }
+
+        // Allocate reverse QueryCandidate list based on number of unique destinations
+        auto reverse_list_ptr = raft::make_device_mdarray<QueryCandidates<IdxT, accT>>(
+          res,
+          raft::resource::get_large_workspace_resource(res),
+          raft::make_extents<int64_t>(reverse_batch));
+        auto rev_ids =
+          raft::make_device_mdarray<IdxT>(res,
+                                          raft::resource::get_large_workspace_resource(res),
+                                          raft::make_extents<int64_t>(reverse_batch, visited_size));
+        auto rev_dists =
+          raft::make_device_mdarray<accT>(res,
+                                          raft::resource::get_large_workspace_resource(res),
+                                          raft::make_extents<int64_t>(reverse_batch, visited_size));
+
+        QueryCandidates<IdxT, accT>* reverse_list =
+          static_cast<QueryCandidates<IdxT, accT>*>(reverse_list_ptr.data_handle());
+
+        init_query_candidate_list<IdxT, accT><<<256, blockD, 0, stream>>>(reverse_list,
+                                                                          rev_ids.data_handle(),
+                                                                          rev_dists.data_handle(),
+                                                                          (int)reverse_batch,
+                                                                          visited_size);
+
+        // May need more blocks for reverse list
+        num_blocks = min(maxBlocks, reverse_batch);
+
+        // Populate reverse list ids and candidate lists from edge_src and edge_dest
+        populate_reverse_list_struct<T, accT, IdxT>
+          <<<num_blocks, blockD, 0, stream>>>(reverse_list,
+                                              edge_src.data_handle(),
+                                              edge_dest.data_handle(),
+                                              unique_indices.data_handle(),
+                                              unique_dests,
+                                              total_edges,
+                                              dataset.extent(0),
+                                              rev_start,
+                                              reverse_batch);
+
+        // Recompute distances (avoided keeping it during sorting)
+        recompute_reverse_dists<T, accT, IdxT>
+          <<<num_blocks, blockD, 0, stream>>>(reverse_list, dataset, reverse_batch, metric);
+
+        // Call 2nd RobustPrune on reverse query_list
+        RobustPruneKernel<T, accT, IdxT>
+          <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
+                                                                  raft::make_const_mdspan(dataset),
+                                                                  reverse_list_ptr.data_handle(),
+                                                                  reverse_batch,
+                                                                  visited_size,
+                                                                  metric,
+                                                                  alpha,
+                                                                  prune_smem_sort_size);
+
+        // Write new edge lists to graph
+        write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
+          d_graph.view(), reverse_list_ptr.data_handle(), degree, reverse_batch);
+      }
 
       start += step_size;
       step_size *= base;
@@ -371,8 +396,6 @@ index<T, IdxT> build(
   RAFT_EXPECTS(params.visited_size > graph_degree, "visited_size must be > graph_degree");
 
   int dim = dataset.extent(1);
-  // TODO - Fix issue with alignment when dataset dimension is odd
-  RAFT_EXPECTS(dim % 2 == 0, "Datasets with an odd number of dimensions not currently supported");
 
   RAFT_LOG_DEBUG("Creating empty graph structure");
   auto vamana_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
@@ -405,4 +428,4 @@ index<T, IdxT> build(
  * @}
  */
 
-}  // namespace cuvs::neighbors::experimental::vamana::detail
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
index c360ae19a5..27a17205e6 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
@@ -34,7 +34,7 @@
 #include <fstream>
 #include <type_traits>
 
-namespace cuvs::neighbors::experimental::vamana::detail {
+namespace cuvs::neighbors::vamana::detail {
 
 /**
  * Save the index to file.
@@ -117,4 +117,4 @@ void serialize(raft::resources const& res,
   if (!index_of) { RAFT_FAIL("Error writing output %s", file_name.c_str()); }
 }
 
-}  // namespace cuvs::neighbors::experimental::vamana::detail
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
index f6f0279f7c..22678c1962 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
@@ -34,7 +34,7 @@
 
 #include <cuvs/distance/distance.hpp>
 
-namespace cuvs::neighbors::experimental::vamana::detail {
+namespace cuvs::neighbors::vamana::detail {
 
 /* @defgroup vamana_structures vamana structures
  * @{
@@ -170,7 +170,7 @@ __device__ SUMTYPE l2_ILP4(Point<T, SUMTYPE>* src_vec, Point<T, SUMTYPE>* dst_ve
     temp_dst[0] = dst_vec->coords[i];
     if (i + 32 < src_vec->Dim) temp_dst[1] = dst_vec->coords[i + 32];
     if (i + 64 < src_vec->Dim) temp_dst[2] = dst_vec->coords[i + 64];
-    if (i + 92 < src_vec->Dim) temp_dst[3] = dst_vec->coords[i + 96];
+    if (i + 96 < src_vec->Dim) temp_dst[3] = dst_vec->coords[i + 96];
 
     partial_sum[0] = fmaf(
       (src_vec[0].coords[i] - temp_dst[0]), (src_vec[0].coords[i] - temp_dst[0]), partial_sum[0]);
@@ -182,7 +182,7 @@ __device__ SUMTYPE l2_ILP4(Point<T, SUMTYPE>* src_vec, Point<T, SUMTYPE>* dst_ve
       partial_sum[2] = fmaf((src_vec[0].coords[i + 64] - temp_dst[2]),
                             (src_vec[0].coords[i + 64] - temp_dst[2]),
                             partial_sum[2]);
-    if (i + 92 < src_vec->Dim)
+    if (i + 96 < src_vec->Dim)
       partial_sum[3] = fmaf((src_vec[0].coords[i + 96] - temp_dst[3]),
                             (src_vec[0].coords[i + 96] - temp_dst[3]),
                             partial_sum[3]);
@@ -192,6 +192,7 @@ __device__ SUMTYPE l2_ILP4(Point<T, SUMTYPE>* src_vec, Point<T, SUMTYPE>* dst_ve
   for (int offset = 16; offset > 0; offset /= 2) {
     partial_sum[0] += __shfl_down_sync(FULL_BITMASK, partial_sum[0], offset);
   }
+
   return partial_sum[0];
 }
 
@@ -419,22 +420,24 @@ __global__ void populate_reverse_list_struct(QueryCandidates<IdxT, accT>* revers
                                              int* unique_indices,
                                              int unique_dests,
                                              int total_edges,
-                                             int N)
+                                             int N,
+                                             int rev_start,
+                                             int reverse_batch)
 {
-  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < unique_dests;
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < reverse_batch;
        i += blockDim.x * gridDim.x) {
-    reverse_list[i].queryId = edge_dest[unique_indices[i]];
-    if (i == unique_dests - 1) {
-      reverse_list[i].size = total_edges - unique_indices[i];
+    reverse_list[i].queryId = edge_dest[unique_indices[i + rev_start]];
+    if (rev_start + i == unique_dests - 1) {
+      reverse_list[i].size = total_edges - unique_indices[i + rev_start];
     } else {
-      reverse_list[i].size = unique_indices[i + 1] - unique_indices[i];
+      reverse_list[i].size = unique_indices[i + rev_start + 1] - unique_indices[i + rev_start];
     }
     if (reverse_list[i].size > reverse_list[i].maxSize) {
       reverse_list[i].size = reverse_list[i].maxSize;
     }
 
     for (int j = 0; j < reverse_list[i].size; j++) {
-      reverse_list[i].ids[j] = edge_src[unique_indices[i] + j];
+      reverse_list[i].ids[j] = edge_src[unique_indices[i + rev_start] + j];
     }
     for (int j = reverse_list[i].size; j < reverse_list[i].maxSize; j++) {
       reverse_list[i].ids[j]   = raft::upper_bound<IdxT>();
@@ -475,4 +478,4 @@ __global__ void recompute_reverse_dists(
  * @}
  */
 
-}  // namespace cuvs::neighbors::experimental::vamana::detail
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/vamana.cuh b/cpp/src/neighbors/vamana.cuh
index 9b9e8d271d..964d7a9a09 100644
--- a/cpp/src/neighbors/vamana.cuh
+++ b/cpp/src/neighbors/vamana.cuh
@@ -31,7 +31,7 @@
 
 #include <rmm/cuda_stream_view.hpp>
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 /**
  * @defgroup VAMANA ANN Graph-based nearest neighbor search
@@ -85,8 +85,7 @@ index<T, IdxT> build(
   const index_params& params,
   raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
 {
-  return cuvs::neighbors::experimental::vamana::detail::build<T, IdxT, Accessor>(
-    res, params, dataset);
+  return cuvs::neighbors::vamana::detail::build<T, IdxT, Accessor>(res, params, dataset);
 }
 
 template <typename T, typename IdxT>
@@ -94,9 +93,9 @@ void serialize(raft::resources const& res,
                const std::string& file_prefix,
                const index<T, IdxT>& index_)
 {
-  cuvs::neighbors::experimental::vamana::detail::build<T, IdxT>(res, file_prefix, index_);
+  cuvs::neighbors::vamana::detail::build<T, IdxT>(res, file_prefix, index_);
 }
 
 /** @} */  // end group vamana
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_build_float.cu b/cpp/src/neighbors/vamana_build_float.cu
index b83af61220..0e09d63994 100644
--- a/cpp/src/neighbors/vamana_build_float.cu
+++ b/cpp/src/neighbors/vamana_build_float.cu
@@ -17,27 +17,27 @@
 #include "vamana.cuh"
 #include <cuvs/neighbors/vamana.hpp>
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
-#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                                    \
-  auto build(raft::resources const& handle,                                                \
-             const cuvs::neighbors::experimental::vamana::index_params& params,            \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)          \
-    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
-  {                                                                                        \
-    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
-  }                                                                                        \
-                                                                                           \
-  auto build(raft::resources const& handle,                                                \
-             const cuvs::neighbors::experimental::vamana::index_params& params,            \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)            \
-    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
-  {                                                                                        \
-    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                           \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::vamana::index_params& params,                 \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+  {                                                                               \
+    return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
+  }                                                                               \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::vamana::index_params& params,                 \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+  {                                                                               \
+    return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }
 
 RAFT_INST_VAMANA_BUILD(float, uint32_t);
 
 #undef RAFT_INST_VAMANA_BUILD
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_build_int8.cu b/cpp/src/neighbors/vamana_build_int8.cu
index 91d2cf0280..f70b9ea276 100644
--- a/cpp/src/neighbors/vamana_build_int8.cu
+++ b/cpp/src/neighbors/vamana_build_int8.cu
@@ -17,27 +17,27 @@
 #include "vamana.cuh"
 #include <cuvs/neighbors/vamana.hpp>
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
-#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                                    \
-  auto build(raft::resources const& handle,                                                \
-             const cuvs::neighbors::experimental::vamana::index_params& params,            \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)          \
-    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
-  {                                                                                        \
-    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
-  }                                                                                        \
-                                                                                           \
-  auto build(raft::resources const& handle,                                                \
-             const cuvs::neighbors::experimental::vamana::index_params& params,            \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)            \
-    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
-  {                                                                                        \
-    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                           \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::vamana::index_params& params,                 \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+  {                                                                               \
+    return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
+  }                                                                               \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::vamana::index_params& params,                 \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+  {                                                                               \
+    return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }
 
 RAFT_INST_VAMANA_BUILD(int8_t, uint32_t);
 
 #undef RAFT_INST_VAMANA_BUILD
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_build_uint8.cu b/cpp/src/neighbors/vamana_build_uint8.cu
index bba93e7f45..8daf0c065c 100644
--- a/cpp/src/neighbors/vamana_build_uint8.cu
+++ b/cpp/src/neighbors/vamana_build_uint8.cu
@@ -17,27 +17,27 @@
 #include "vamana.cuh"
 #include <cuvs/neighbors/vamana.hpp>
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
-#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                                    \
-  auto build(raft::resources const& handle,                                                \
-             const cuvs::neighbors::experimental::vamana::index_params& params,            \
-             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)          \
-    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
-  {                                                                                        \
-    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
-  }                                                                                        \
-                                                                                           \
-  auto build(raft::resources const& handle,                                                \
-             const cuvs::neighbors::experimental::vamana::index_params& params,            \
-             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)            \
-    ->cuvs::neighbors::experimental::vamana::index<T, IdxT>                                \
-  {                                                                                        \
-    return cuvs::neighbors::experimental::vamana::build<T, IdxT>(handle, params, dataset); \
+#define RAFT_INST_VAMANA_BUILD(T, IdxT)                                           \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::vamana::index_params& params,                 \
+             raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
+    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+  {                                                                               \
+    return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
+  }                                                                               \
+                                                                                  \
+  auto build(raft::resources const& handle,                                       \
+             const cuvs::neighbors::vamana::index_params& params,                 \
+             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
+    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+  {                                                                               \
+    return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }
 
 RAFT_INST_VAMANA_BUILD(uint8_t, uint32_t);
 
 #undef RAFT_INST_VAMANA_BUILD
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_serialize.cuh b/cpp/src/neighbors/vamana_serialize.cuh
index a49d267b3a..b8cb580a82 100644
--- a/cpp/src/neighbors/vamana_serialize.cuh
+++ b/cpp/src/neighbors/vamana_serialize.cuh
@@ -18,7 +18,7 @@
 
 #include "detail/vamana/vamana_serialize.cuh"
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 /**
  * @defgroup VAMANA graph serialize/derserialize
@@ -28,12 +28,11 @@ namespace cuvs::neighbors::experimental::vamana {
 #define CUVS_INST_VAMANA_SERIALIZE(DTYPE)                                                     \
   void serialize(raft::resources const& handle,                                               \
                  const std::string& file_prefix,                                              \
-                 const cuvs::neighbors::experimental::vamana::index<DTYPE, uint32_t>& index_) \
+                 const cuvs::neighbors::vamana::index<DTYPE, uint32_t>& index_)               \
   {                                                                                           \
-    cuvs::neighbors::experimental::vamana::detail::serialize<DTYPE, uint32_t>(                \
-      handle, file_prefix, index_);                                                           \
+    cuvs::neighbors::vamana::detail::serialize<DTYPE, uint32_t>(handle, file_prefix, index_); \
   };
 
 /** @} */  // end group vamana
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_serialize_float.cu b/cpp/src/neighbors/vamana_serialize_float.cu
index f253693687..8bf7ceb1e1 100644
--- a/cpp/src/neighbors/vamana_serialize_float.cu
+++ b/cpp/src/neighbors/vamana_serialize_float.cu
@@ -16,8 +16,8 @@
 
 #include "vamana_serialize.cuh"
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 CUVS_INST_VAMANA_SERIALIZE(float);
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_serialize_int8.cu b/cpp/src/neighbors/vamana_serialize_int8.cu
index 1cd54b1983..0f87f67ce1 100644
--- a/cpp/src/neighbors/vamana_serialize_int8.cu
+++ b/cpp/src/neighbors/vamana_serialize_int8.cu
@@ -16,8 +16,8 @@
 
 #include "vamana_serialize.cuh"
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 CUVS_INST_VAMANA_SERIALIZE(int8_t);
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_serialize_uint8.cu b/cpp/src/neighbors/vamana_serialize_uint8.cu
index 3e6d945b80..871c305069 100644
--- a/cpp/src/neighbors/vamana_serialize_uint8.cu
+++ b/cpp/src/neighbors/vamana_serialize_uint8.cu
@@ -16,8 +16,8 @@
 
 #include "vamana_serialize.cuh"
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 CUVS_INST_VAMANA_SERIALIZE(uint8_t);
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/tests/neighbors/ann_vamana.cuh b/cpp/tests/neighbors/ann_vamana.cuh
index 9d9df44703..9fe0324d78 100644
--- a/cpp/tests/neighbors/ann_vamana.cuh
+++ b/cpp/tests/neighbors/ann_vamana.cuh
@@ -46,7 +46,7 @@
 #include <string>
 #include <vector>
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 struct edge_op {
   template <typename Type, typename... UnusedArgs>
@@ -64,6 +64,7 @@ struct AnnVamanaInputs {
   double max_fraction;
   cuvs::distance::DistanceType metric;
   bool host_dataset;
+  int reverse_batchsize;
 
   // cagra search params
   int n_queries;
@@ -131,10 +132,11 @@ class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
   void testVamana()
   {
     vamana::index_params index_params;
-    index_params.metric       = ps.metric;
-    index_params.graph_degree = ps.graph_degree;
-    index_params.visited_size = ps.visited_size;
-    index_params.max_fraction = ps.max_fraction;
+    index_params.metric            = ps.metric;
+    index_params.graph_degree      = ps.graph_degree;
+    index_params.visited_size      = ps.visited_size;
+    index_params.max_fraction      = ps.max_fraction;
+    index_params.reverse_batchsize = ps.reverse_batchsize;
 
     auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
       (const DataT*)database.data(), ps.n_rows, ps.dim);
@@ -264,14 +266,13 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
 {
   std::vector<AnnVamanaInputs> inputs = raft::util::itertools::product<AnnVamanaInputs>(
     {1000},
-    //    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},  // TODO - fix alignment
-    //    issue for odd dims
-    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
-    {32},                                    // graph degree
-    {64, 128, 256},                          // visited_size
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {32},       // graph degree
+    {64, 256},  // visited_size
     {0.06, 0.1},
     {cuvs::distance::DistanceType::L2Expanded},
     {false},
+    {100, 1000000},
     {100},
     {10},
     {cagra::search_algo::AUTO},
@@ -280,55 +281,58 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
     {1},
     {0.2});
 
-  std::vector<AnnVamanaInputs> inputs2 =
-    raft::util::itertools::product<AnnVamanaInputs>({1000},
-                                                    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
-                                                    {64},             // graph degree
-                                                    {128, 256, 512},  // visited_size
-                                                    {0.06, 0.1},
-                                                    {cuvs::distance::DistanceType::L2Expanded},
-                                                    {false},
-                                                    {100},
-                                                    {10},
-                                                    {cagra::search_algo::AUTO},
-                                                    {10},
-                                                    {32},
-                                                    {1},
-                                                    {0.2});
+  std::vector<AnnVamanaInputs> inputs2 = raft::util::itertools::product<AnnVamanaInputs>(
+    {1000},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {64},        // graph degree
+    {128, 512},  // visited_size
+    {0.06},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {1000000},
+    {100},
+    {10},
+    {cagra::search_algo::AUTO},
+    {10},
+    {32},
+    {1},
+    {0.2});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
-  inputs2 =
-    raft::util::itertools::product<AnnVamanaInputs>({1000},
-                                                    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
-                                                    {128},       // graph degree
-                                                    {256, 512},  // visited_size
-                                                    {0.06, 0.1},
-                                                    {cuvs::distance::DistanceType::L2Expanded},
-                                                    {false},
-                                                    {100},
-                                                    {10},
-                                                    {cagra::search_algo::AUTO},
-                                                    {10},
-                                                    {64},
-                                                    {1},
-                                                    {0.2});
+  inputs2 = raft::util::itertools::product<AnnVamanaInputs>(
+    {1000},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {128},  // graph degree
+    {256},  // visited_size
+    {0.06},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {1000000},
+    {100},
+    {10},
+    {cagra::search_algo::AUTO},
+    {10},
+    {64},
+    {1},
+    {0.2});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
-  inputs2 =
-    raft::util::itertools::product<AnnVamanaInputs>({1000},
-                                                    {16, 32, 64, 128, 192, 256, 512, 1024},  // dim
-                                                    {256},        // graph degree
-                                                    {512, 1024},  // visited_size
-                                                    {0.06, 0.1},
-                                                    {cuvs::distance::DistanceType::L2Expanded},
-                                                    {false},
-                                                    {100},
-                                                    {10},
-                                                    {cagra::search_algo::AUTO},
-                                                    {10},
-                                                    {64},
-                                                    {1},
-                                                    {0.2});
+  inputs2 = raft::util::itertools::product<AnnVamanaInputs>(
+    {1000},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {256},        // graph degree
+    {512, 1024},  // visited_size
+    {0.06},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {1000000},
+    {100},
+    {10},
+    {cagra::search_algo::AUTO},
+    {10},
+    {64},
+    {1},
+    {0.2});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   return inputs;
@@ -336,4 +340,4 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
 
 const std::vector<AnnVamanaInputs> inputs = generate_inputs();
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/tests/neighbors/ann_vamana/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_vamana/test_float_uint32_t.cu
index 9aa9da1b8c..7b89b65448 100644
--- a/cpp/tests/neighbors/ann_vamana/test_float_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_vamana/test_float_uint32_t.cu
@@ -18,11 +18,11 @@
 
 #include "../ann_vamana.cuh"
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 typedef AnnVamanaTest<float, float, std::uint32_t> AnnVamanaTestF_U32;
 TEST_P(AnnVamanaTestF_U32, AnnVamana) { this->testVamana(); }
 
 INSTANTIATE_TEST_CASE_P(AnnVamanaTest, AnnVamanaTestF_U32, ::testing::ValuesIn(inputs));
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/tests/neighbors/ann_vamana/test_int8_t_uint32_t.cu b/cpp/tests/neighbors/ann_vamana/test_int8_t_uint32_t.cu
index 0a6b563b2c..843d2274a8 100644
--- a/cpp/tests/neighbors/ann_vamana/test_int8_t_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_vamana/test_int8_t_uint32_t.cu
@@ -18,11 +18,11 @@
 
 #include "../ann_vamana.cuh"
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 typedef AnnVamanaTest<float, int8_t, std::uint32_t> AnnVamanaTestI8_U32;
 TEST_P(AnnVamanaTestI8_U32, AnnVamana) { this->testVamana(); }
 
 INSTANTIATE_TEST_CASE_P(AnnVamanaTest, AnnVamanaTestI8_U32, ::testing::ValuesIn(inputs));
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/tests/neighbors/ann_vamana/test_uint8_t_uint32_t.cu b/cpp/tests/neighbors/ann_vamana/test_uint8_t_uint32_t.cu
index c0680dc188..f08db0c49f 100644
--- a/cpp/tests/neighbors/ann_vamana/test_uint8_t_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_vamana/test_uint8_t_uint32_t.cu
@@ -18,11 +18,11 @@
 
 #include "../ann_vamana.cuh"
 
-namespace cuvs::neighbors::experimental::vamana {
+namespace cuvs::neighbors::vamana {
 
 typedef AnnVamanaTest<float, uint8_t, std::uint32_t> AnnVamanaTestU8_U32;
 TEST_P(AnnVamanaTestU8_U32, AnnVamana) { this->testVamana(); }
 
 INSTANTIATE_TEST_CASE_P(AnnVamanaTest, AnnVamanaTestU8_U32, ::testing::ValuesIn(inputs));
 
-}  // namespace cuvs::neighbors::experimental::vamana
+}  // namespace cuvs::neighbors::vamana
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index ab810ab531..95359558f5 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -18,3 +18,4 @@ Nearest Neighbors
    neighbors_nn_descent.rst
    neighbors_refine.rst
    neighbors_mg.rst
+   neighbors_vamana.rst
diff --git a/docs/source/cpp_api/neighbors_vamana.rst b/docs/source/cpp_api/neighbors_vamana.rst
new file mode 100644
index 0000000000..25447efce1
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_vamana.rst
@@ -0,0 +1,44 @@
+Vamana
+======
+
+Vamana is the graph construction algorithm behind the well-known DiskANN vector search solution. The cuVS implementation of Vamana/DiskANN is a custom GPU-acceleration version of the algorithm that aims to reduce index construction time using NVIDIA GPUs.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/vamana.hpp>``
+
+namespace *cuvs::neighbors::vamana*
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: vamana_cpp_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: vamana_cpp_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: vamana_cpp_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: vamana_cpp_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/indexes/indexes.rst b/docs/source/indexes/indexes.rst
index 4a18f793a7..8746b84acc 100644
--- a/docs/source/indexes/indexes.rst
+++ b/docs/source/indexes/indexes.rst
@@ -9,6 +9,7 @@ Nearest Neighbor Indexes
    cagra.rst
    ivfflat.rst
    ivfpq.rst
+   vamana.rst
 
 
 Indices and tables
diff --git a/docs/source/indexes/vamana.rst b/docs/source/indexes/vamana.rst
new file mode 100644
index 0000000000..7e0a79fb84
--- /dev/null
+++ b/docs/source/indexes/vamana.rst
@@ -0,0 +1,75 @@
+Vamana
+======
+
+VAMANA is the underlying graph construction algorithm used to construct indexes for the DiskANN vector search solution. DiskANN and the Vamana algorithm are described in detail in the `published paper <https://papers.nips.cc/paper/9527-rand-nsg-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.pdf>`, and a highly optimized `open-source repository <https://github.com/microsoft/DiskANN>`  includes many features for index construction and search. In cuVS, we provide a version of the Vamana algorithm optimized for GPU architectures to accelreate graph construction to build DiskANN idnexes. At a high level, the Vamana algorithm operates as follows:
+
+* 1. Starting with an empty graph, select a medoid vector from the D-dimension vector dataset and insert it into the graph.
+* 2. Iteratively insert batches of dataset vectors into the graph, connecting each inserted vector to neighbors based on a graph traversal.
+* 3. For each batch, create reverse edges and prune unnecessary edges.
+
+There are many algorithmic details that are outlined in the `paper <https://papers.nips.cc/paper/9527-rand-nsg-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.pdf>`, and many GPU-specific optimizations are included in this implementation.
+
+The current implementation of DiskANN in cuVS only includes the 'in-memory' graph construction and a serialization step that writes the index to a file. This index file can be then used by the `open-source DiskANN <https://github.com/microsoft/DiskANN>` library to perform efficient search. Additional DiskANN functionality, including GPU-accelerated search and 'ssd' index build are planned for future cuVS releases.
+
+[ :doc:`C++ API <../cpp_api/neighbors_vamana>` | :doc:`Python API <../python_api/neighbors_vamana>` ]
+
+Interoperability with CPU DiskANN
+---------------------------------
+
+The 'vamana::serialize' API calls writes the index to a file with a format that is compatible with the `open-source DiskANN repositoriy <https://github.com/microsoft/DiskANN>`. This allows cuVS to be used to accelerate index construction while leveraging the efficient CPU-based search currently available.
+
+Configuration parameters
+------------------------
+
+Build parameters
+~~~~~~~~~~~~~~~~
+
+.. list-table::
+   :widths: 25 25 50
+   :header-rows: 1
+
+   * - Name
+     - Default
+     - Description
+   * - graph_degree
+     - 32
+     - The maximum degre of the final Vamana graph. The internal representation of the graph includes this many edges for every node, but serialize will compress the graph into a 'CSR' format with, potentially, fewer edges.
+   * - visited_size
+     - 64
+     - Maximum number of visited nodes saved during each traversal to insert a new node. This corresponds to the 'L' parameter in the paper.
+   * - vamana_iters
+     - 1
+     - Number of iterations ran to improve the graph. Each iteration involves inserting every vector in the dataset.
+   * - alpha
+     - 1.2
+     - Alpha parameter that defines how aggressively to prune edges.
+   * - max_fraction
+     - 0.06
+     - Maximum fraction of the dataset that will be inserted as a single batch. Larger max batch size decreases graph quality but improves speed.
+   * - batch_base
+     - 2
+     - Base of growth rate of batch sizes. Insertion batch sizes increase exponentially based on this parameter until max_fraction is reached.
+   * - queue_size
+     - 127
+     - Size of the candidate queue structure used during graph traversal. Must be (2^x)-1 for some x, and must be > visited_size.
+
+Tuning Considerations
+---------------------
+
+The 2 hyper-parameters that are most often tuned are `graph_degree` and `visited_size`. The time needed to create a graph increases dramatically when increasing `graph_degree`, in particular. However, larger graphs may be needed to achieve very high recall search, especially for large datasets.
+
+Memory footprint
+----------------
+
+Vamana builds a graph that is stored in device memory. However, in order to serialize the index and write it to a file for later use, it must be moved into host memory. If the `include_dataset` parameter is also set, then the dataset must be resident in host memory when calling serialize as well.
+
+Device memory usage
+~~~~~~~~~~~~~~~~~~~
+
+The built index represents the graph as fixed degree, storing a total of :math:`graph\_degree * n\_index\_vectors` edges. Graph construction also requires the dataset be in device memory (or it copies it to device during build). In addition, device memory is used during construction to sort and create the reverse edges. Thus, the amount of device memory needed depends on the dataset itself, but it is bounded by a maximum sum of:
+
+- vector dataset: :math:`n\_index\_vectors * n\_dims * sizeof(T)`
+- output graph: :math:`graph\_degree * n\_index\_vectors * sizeof(IdxT)`
+- scratch memory: :math:`n\_index\_vectors * max\_fraction * (2 + graph\_degree) * sizeof(IdxT)`
+
+Reduction in scratch device memory requirements are planned for upcoming releases of cuVS.
diff --git a/examples/cpp/src/vamana_example.cu b/examples/cpp/src/vamana_example.cu
index 60bf14d56a..9e5201d31c 100644
--- a/examples/cpp/src/vamana_example.cu
+++ b/examples/cpp/src/vamana_example.cu
@@ -29,11 +29,15 @@
 #include "common.cuh"
 
 template <typename T>
-void vamana_build_and_write(raft::device_resources const &dev_resources,
+void vamana_build_and_write(raft::device_resources const& dev_resources,
                             raft::device_matrix_view<const T, int64_t> dataset,
-                            std::string out_fname, int degree, int visited_size,
-                            float max_fraction, int iters) {
-  using namespace cuvs::neighbors::experimental;
+                            std::string out_fname,
+                            int degree,
+                            int visited_size,
+                            float max_fraction,
+                            int iters)
+{
+  using namespace cuvs::neighbors;
 
   // use default index parameters
   vamana::index_params index_params;
@@ -46,13 +50,12 @@ void vamana_build_and_write(raft::device_resources const &dev_resources,
 
   auto start = std::chrono::system_clock::now();
   auto index = vamana::build(dev_resources, index_params, dataset);
-  auto end = std::chrono::system_clock::now();
+  auto end   = std::chrono::system_clock::now();
   std::chrono::duration<double> elapsed_seconds = end - start;
 
   std::cout << "Vamana index has " << index.size() << " vectors" << std::endl;
-  std::cout << "Vamana graph has degree " << index.graph_degree()
-            << ", graph size [" << index.graph().extent(0) << ", "
-            << index.graph().extent(1) << "]" << std::endl;
+  std::cout << "Vamana graph has degree " << index.graph_degree() << ", graph size ["
+            << index.graph().extent(0) << ", " << index.graph().extent(1) << "]" << std::endl;
 
   std::cout << "Time to build index: " << elapsed_seconds.count() << "s\n";
 
@@ -60,9 +63,11 @@ void vamana_build_and_write(raft::device_resources const &dev_resources,
   serialize(dev_resources, out_fname, index);
 }
 
-void usage() {
-  printf("Usage: ./vamana_example <data filename> <output filename> <graph "
-         "degree> <visited_size> <max_fraction> <iterations> \n");
+void usage()
+{
+  printf(
+    "Usage: ./vamana_example <data filename> <output filename> <graph "
+    "degree> <visited_size> <max_fraction> <iterations> \n");
   printf("Input file expected to be binary file of fp32 vectors.\n");
   printf("Graph degree sizes supported: 32, 64, 128, 256\n");
   printf("Visited_size must be > degree and a power of 2.\n");
@@ -71,13 +76,14 @@ void usage() {
   exit(1);
 }
 
-int main(int argc, char *argv[]) {
+int main(int argc, char* argv[])
+{
   raft::device_resources dev_resources;
 
   // Set pool memory resource with 1 GiB initial pool size. All allocations use
   // the same pool.
   rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
-      rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+    rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
   rmm::mr::set_current_device_resource(&pool_mr);
 
   // Alternatively, one could define a pool allocator for temporary arrays (used
@@ -87,22 +93,24 @@ int main(int argc, char *argv[]) {
   // limit. raft::resource::set_workspace_to_pool_resource(dev_resources, 2 *
   // 1024 * 1024 * 1024ull);
 
-  if (argc != 7)
-    usage();
+  if (argc != 7) usage();
 
-  std::string data_fname = (std::string)(argv[1]); // Input filename
-  std::string out_fname = (std::string)argv[2];    // Output index filename
-  int degree = atoi(argv[3]);
-  int max_visited = atoi(argv[4]);
-  float max_fraction = atof(argv[5]);
-  int iters = atoi(argv[6]);
+  std::string data_fname = (std::string)(argv[1]);  // Input filename
+  std::string out_fname  = (std::string)argv[2];    // Output index filename
+  int degree             = atoi(argv[3]);
+  int max_visited        = atoi(argv[4]);
+  float max_fraction     = atof(argv[5]);
+  int iters              = atoi(argv[6]);
 
   // Read in binary dataset file
-  auto dataset =
-      read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
+  auto dataset = read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
 
   // Simple build example to create graph and write to a file
-  vamana_build_and_write<uint8_t>(
-      dev_resources, raft::make_const_mdspan(dataset.view()), out_fname, degree,
-      max_visited, max_fraction, iters);
+  vamana_build_and_write<uint8_t>(dev_resources,
+                                  raft::make_const_mdspan(dataset.view()),
+                                  out_fname,
+                                  degree,
+                                  max_visited,
+                                  max_fraction,
+                                  iters);
 }

From c778c88118d829404630bbe71d139d688a591bcb Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Thu, 30 Jan 2025 16:29:15 -0800
Subject: [PATCH 286/623] [Feat] Support `bitset` filter for Brute Force (#560)

Authors:
  - rhdong (https://github.com/rhdong)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/560
---
 cpp/include/cuvs/neighbors/brute_force.hpp    | 165 +++++-
 cpp/include/cuvs/neighbors/common.hpp         |  34 +-
 cpp/src/neighbors/brute_force_c.cpp           |  14 +-
 cpp/src/neighbors/detail/knn_brute_force.cuh  | 122 +++--
 cpp/src/neighbors/sample_filter.cuh           |  16 +
 .../neighbors/brute_force_prefiltered.cu      | 496 +++++++++++++++++-
 python/cuvs/cuvs/tests/test_brute_force.py    |   2 +-
 7 files changed, 750 insertions(+), 99 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 99581469f7..72a5cac121 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -332,15 +332,28 @@ auto build(raft::resources const& handle,
  * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
  * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
  * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`:
+ * eliminate entirely allocations happening within `search`.
+ *
+ * Usage example:
  * @code{.cpp}
- *   ...
- *   // Use the same allocator across multiple searches to reduce the number of
- *   // cuda memory allocations
- *   brute_force::search(handle, index, queries1, out_inds1, out_dists1);
- *   brute_force::search(handle, index, queries2, out_inds2, out_dists2);
- *   brute_force::search(handle, index, queries3, out_inds3, out_dists3);
- *   ...
+ *   using namespace cuvs::neighbors;
+ *
+ *   // use default index parameters
+ *   brute_force::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   brute_force::search_params search_params;
+ *   // create a bitset to filter the search
+ *   auto removed_indices = raft::make_device_vector<int64_t, int64_t>(res, n_removed_indices);
+ *   raft::core::bitset<std::uint32_t, int64_t> removed_indices_bitset(
+ *     res, removed_indices.view(), dataset.extent(0));
+ *   // search K nearest neighbours according to a bitset
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   auto filter    = filtering::bitset_filter(removed_indices_bitset.view());
+ *   brute_force::search(res, search_params, index, queries, neighbors, distances, filter);
  * @endcode
  *
  * @param[in] handle
@@ -350,9 +363,17 @@ auto build(raft::resources const& handle,
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
- * @param[in] sample_filter An optional device bitmap filter function with a `row-major` layout and
- * the shape of [n_queries, index->size()], which means the filter will use the first
- * `index->size()` bits to indicate whether queries[0] should compute the distance with dataset.
+ * @param[in] sample_filter An optional device filter that restricts which dataset elements should
+ * be considered for each query.
+ *
+ * - Supports two types of filters:
+ *   1. **Bitset Filter**: A shared filter where each bit corresponds to a dataset element.
+ *      All queries share the same filter, with a logical shape of `[1, index->size()]`.
+ *   2. **Bitmap Filter**: A per-query filter with a logical shape of `[n_queries, index->size()]`,
+ *      where each bit indicates whether a specific dataset element should be considered for a
+ *      particular query. (1 for inclusion, 0 for exclusion).
+ *
+ * - The default value is `none_sample_filter`, which applies no filtering.
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::brute_force::search_params& params,
@@ -379,15 +400,28 @@ void search(raft::resources const& handle,
  * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
  * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
  * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
- * eliminate entirely allocations happening within `search`:
+ * eliminate entirely allocations happening within `search`.
+ *
+ * Usage example:
  * @code{.cpp}
- *   ...
- *   // Use the same allocator across multiple searches to reduce the number of
- *   // cuda memory allocations
- *   brute_force::search(handle, index, queries1, out_inds1, out_dists1);
- *   brute_force::search(handle, index, queries2, out_inds2, out_dists2);
- *   brute_force::search(handle, index, queries3, out_inds3, out_dists3);
- *   ...
+ *   using namespace cuvs::neighbors;
+ *
+ *   // use default index parameters
+ *   brute_force::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   brute_force::search_params search_params;
+ *   // create a bitset to filter the search
+ *   auto removed_indices = raft::make_device_vector<int64_t, int64_t>(res, n_removed_indices);
+ *   raft::core::bitset<std::uint32_t, int64_t> removed_indices_bitset(
+ *     res, removed_indices.view(), dataset.extent(0));
+ *   // search K nearest neighbours according to a bitset
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<half>(res, n_queries, k);
+ *   auto filter    = filtering::bitset_filter(removed_indices_bitset.view());
+ *   brute_force::search(res, search_params, index, queries, neighbors, distances, filter);
  * @endcode
  *
  * @param[in] handle
@@ -397,8 +431,17 @@ void search(raft::resources const& handle,
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
- * @param[in] sample_filter a optional device bitmap filter function that greenlights samples for a
- * given
+ * @param[in] sample_filter An optional device filter that restricts which dataset elements should
+ * be considered for each query.
+ *
+ * - Supports two types of filters:
+ *   1. **Bitset Filter**: A shared filter where each bit corresponds to a dataset element.
+ *      All queries share the same filter, with a logical shape of `[1, index->size()]`.
+ *   2. **Bitmap Filter**: A per-query filter with a logical shape of `[n_queries, index->size()]`,
+ *      where each bit indicates whether a specific dataset element should be considered for a
+ *      particular query. (1 for inclusion, 0 for exclusion).
+ *
+ * - The default value is `none_sample_filter`, which applies no filtering.
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::brute_force::search_params& params,
@@ -421,6 +464,33 @@ void search(raft::resources const& handle,
  *
  * See the [brute_force::build](#brute_force::build) documentation for a usage example.
  *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *
+ *   // use default index parameters
+ *   brute_force::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   brute_force::search_params search_params;
+ *   // create a bitset to filter the search
+ *   auto removed_indices = raft::make_device_vector<int64_t, int64_t>(res, n_removed_indices);
+ *   raft::core::bitset<std::uint32_t, int64_t> removed_indices_bitset(
+ *     res, removed_indices.view(), dataset.extent(0));
+ *   // search K nearest neighbours according to a bitset
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<float>(res, n_queries, k);
+ *   auto filter    = filtering::bitset_filter(removed_indices_bitset.view());
+ *   brute_force::search(res, search_params, index, queries, neighbors, distances, filter);
+ * @endcode
+ *
  * @param[in] handle
  * @param[in] params parameters configuring the search
  * @param[in] index bruteforce constructed index
@@ -428,8 +498,17 @@ void search(raft::resources const& handle,
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
- * @param[in] sample_filter an optional device bitmap filter function that greenlights samples for a
- * given query
+ * @param[in] sample_filter An optional device filter that restricts which dataset elements should
+ * be considered for each query.
+ *
+ * - Supports two types of filters:
+ *   1. **Bitset Filter**: A shared filter where each bit corresponds to a dataset element.
+ *      All queries share the same filter, with a logical shape of `[1, index->size()]`.
+ *   2. **Bitmap Filter**: A per-query filter with a logical shape of `[n_queries, index->size()]`,
+ *      where each bit indicates whether a specific dataset element should be considered for a
+ *      particular query. (1 for inclusion, 0 for exclusion).
+ *
+ * - The default value is `none_sample_filter`, which applies no filtering.
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::brute_force::search_params& params,
@@ -452,6 +531,33 @@ void search(raft::resources const& handle,
  *
  * See the [brute_force::build](#brute_force::build) documentation for a usage example.
  *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *
+ *   // use default index parameters
+ *   brute_force::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   brute_force::index_params index_params;
+ *   auto index = brute_force::build(handle, index_params, dataset);
+ *   // use default search parameters
+ *   brute_force::search_params search_params;
+ *   // create a bitset to filter the search
+ *   auto removed_indices = raft::make_device_vector<int64_t, int64_t>(res, n_removed_indices);
+ *   raft::core::bitset<std::uint32_t, int64_t> removed_indices_bitset(
+ *     res, removed_indices.view(), dataset.extent(0));
+ *   // search K nearest neighbours according to a bitset
+ *   auto neighbors = raft::make_device_matrix<uint32_t>(res, n_queries, k);
+ *   auto distances = raft::make_device_matrix<half>(res, n_queries, k);
+ *   auto filter    = filtering::bitset_filter(removed_indices_bitset.view());
+ *   brute_force::search(res, search_params, index, queries, neighbors, distances, filter);
+ * @endcode
+ *
  * @param[in] handle
  * @param[in] params parameters configuring the search
  * @param[in] index bruteforce constructed index
@@ -459,8 +565,17 @@ void search(raft::resources const& handle,
  * @param[out] neighbors a device pointer to the indices of the neighbors in the source dataset
  * [n_queries, k]
  * @param[out] distances a device pointer to the distances to the selected neighbors [n_queries, k]
- * @param[in] sample_filter an optional device bitmap filter function that greenlights samples for a
- * given query
+ * @param[in] sample_filter An optional device filter that restricts which dataset elements should
+ * be considered for each query.
+ *
+ * - Supports two types of filters:
+ *   1. **Bitset Filter**: A shared filter where each bit corresponds to a dataset element.
+ *      All queries share the same filter, with a logical shape of `[1, index->size()]`.
+ *   2. **Bitmap Filter**: A per-query filter with a logical shape of `[n_queries, index->size()]`,
+ *      where each bit indicates whether a specific dataset element should be considered for a
+ *      particular query. (1 for inclusion, 0 for exclusion).
+ *
+ * - The default value is `none_sample_filter`, which applies no filtering.
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::brute_force::search_params& params,
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index bd9ea48346..5dc99a4e87 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -18,6 +18,7 @@
 
 #include <cstdint>
 #include <cuvs/distance/distance.hpp>
+#include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_resources.hpp>
 #include <raft/core/host_mdspan.hpp>
@@ -456,8 +457,11 @@ inline constexpr bool is_vpq_dataset_v = is_vpq_dataset<DatasetT>::value;
 
 namespace filtering {
 
+enum class FilterType { None, Bitmap, Bitset };
+
 struct base_filter {
-  virtual ~base_filter() = default;
+  virtual ~base_filter()                     = default;
+  virtual FilterType get_filter_type() const = 0;
 };
 
 /* A filter that filters nothing. This is the default behavior. */
@@ -475,6 +479,8 @@ struct none_sample_filter : public base_filter {
     const uint32_t query_ix,
     // the index of the current sample
     const uint32_t sample_ix) const;
+
+  FilterType get_filter_type() const override { return FilterType::None; }
 };
 
 /**
@@ -513,15 +519,24 @@ struct ivf_to_sample_filter {
  */
 template <typename bitmap_t, typename index_t>
 struct bitmap_filter : public base_filter {
+  using view_t = cuvs::core::bitmap_view<bitmap_t, index_t>;
+
   // View of the bitset to use as a filter
-  const cuvs::core::bitmap_view<bitmap_t, index_t> bitmap_view_;
+  const view_t bitmap_view_;
 
-  bitmap_filter(const cuvs::core::bitmap_view<bitmap_t, index_t> bitmap_for_filtering);
+  bitmap_filter(const view_t bitmap_for_filtering);
   inline _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
     // the index of the current sample
     const uint32_t sample_ix) const;
+
+  FilterType get_filter_type() const override { return FilterType::Bitmap; }
+
+  view_t view() const { return bitmap_view_; }
+
+  template <typename csr_matrix_t>
+  void to_csr(raft::resources const& handle, csr_matrix_t& csr);
 };
 
 /**
@@ -532,15 +547,24 @@ struct bitmap_filter : public base_filter {
  */
 template <typename bitset_t, typename index_t>
 struct bitset_filter : public base_filter {
+  using view_t = cuvs::core::bitset_view<bitset_t, index_t>;
+
   // View of the bitset to use as a filter
-  const cuvs::core::bitset_view<bitset_t, index_t> bitset_view_;
+  const view_t bitset_view_;
 
-  bitset_filter(const cuvs::core::bitset_view<bitset_t, index_t> bitset_for_filtering);
+  bitset_filter(const view_t bitset_for_filtering);
   inline _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
     // the index of the current sample
     const uint32_t sample_ix) const;
+
+  FilterType get_filter_type() const override { return FilterType::Bitset; }
+
+  view_t view() const { return bitset_view_; }
+
+  template <typename csr_matrix_t>
+  void to_csr(raft::resources const& handle, csr_matrix_t& csr);
 };
 
 /**
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index 1693ac930b..98c74e2852 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -67,8 +67,8 @@ void _search(cuvsResources_t res,
   using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, QueriesLayoutT>;
   using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
   using distances_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
-  using prefilter_mds_type    = raft::device_vector_view<const uint32_t, int64_t>;
-  using prefilter_bmp_type    = cuvs::core::bitmap_view<const uint32_t, int64_t>;
+  using prefilter_mds_type    = raft::device_vector_view<uint32_t, int64_t>;
+  using prefilter_bmp_type    = cuvs::core::bitmap_view<uint32_t, int64_t>;
 
   auto queries_mds   = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
   auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
@@ -85,14 +85,14 @@ void _search(cuvsResources_t res,
                                          distances_mds,
                                          cuvs::neighbors::filtering::none_sample_filter{});
   } else if (prefilter.type == BITMAP) {
-    auto prefilter_ptr  = reinterpret_cast<DLManagedTensor*>(prefilter.addr);
-    auto prefilter_mds  = cuvs::core::from_dlpack<prefilter_mds_type>(prefilter_ptr);
-    auto prefilter_view = cuvs::neighbors::filtering::bitmap_filter(
-      prefilter_bmp_type((const uint32_t*)prefilter_mds.data_handle(),
+    auto prefilter_ptr   = reinterpret_cast<DLManagedTensor*>(prefilter.addr);
+    auto prefilter_mds   = cuvs::core::from_dlpack<prefilter_mds_type>(prefilter_ptr);
+    const auto prefilter = cuvs::neighbors::filtering::bitmap_filter(
+      prefilter_bmp_type((uint32_t*)prefilter_mds.data_handle(),
                          queries_mds.extent(0),
                          index_ptr->dataset().extent(0)));
     cuvs::neighbors::brute_force::search(
-      *res_ptr, params, *index_ptr, queries_mds, neighbors_mds, distances_mds, prefilter_view);
+      *res_ptr, params, *index_ptr, queries_mds, neighbors_mds, distances_mds, prefilter);
   } else {
     RAFT_FAIL("Unsupported prefilter type: BITSET");
   }
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index f1976e0029..5caf84cc79 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -56,9 +56,12 @@
 
 #include <cstdint>
 #include <iostream>
+#include <optional>
 #include <set>
+#include <variant>
 
 namespace cuvs::neighbors::detail {
+
 /**
  * Calculates brute force knn, using a fixed memory budget
  * by tiling over both the rows and columns of pairwise_distances
@@ -82,8 +85,10 @@ void tiled_brute_force_knn(const raft::resources& handle,
                            size_t max_col_tile_size                  = 0,
                            const DistanceT* precomputed_index_norms  = nullptr,
                            const DistanceT* precomputed_search_norms = nullptr,
-                           const uint32_t* filter_bitmap             = nullptr,
-                           DistanceEpilogue distance_epilogue        = raft::identity_op())
+                           const uint32_t* filter_bits               = nullptr,
+                           DistanceEpilogue distance_epilogue        = raft::identity_op(),
+                           cuvs::neighbors::filtering::FilterType filter_type =
+                             cuvs::neighbors::filtering::FilterType::Bitmap)
 {
   // Figure out the number of rows/cols to tile for
   size_t tile_rows = 0;
@@ -245,21 +250,23 @@ void tiled_brute_force_knn(const raft::resources& handle,
         }
       }
 
-      if (filter_bitmap != nullptr) {
-        auto distances_ptr        = temp_distances.data();
-        auto count                = thrust::make_counting_iterator<IndexType>(0);
-        DistanceT masked_distance = select_min ? std::numeric_limits<DistanceT>::infinity()
-                                               : std::numeric_limits<DistanceT>::lowest();
+      auto distances_ptr        = temp_distances.data();
+      auto count                = thrust::make_counting_iterator<IndexType>(0);
+      DistanceT masked_distance = select_min ? std::numeric_limits<DistanceT>::infinity()
+                                             : std::numeric_limits<DistanceT>::lowest();
+
+      if (filter_bits != nullptr) {
+        size_t n_cols = filter_type == cuvs::neighbors::filtering::FilterType::Bitmap ? n : 0;
         thrust::for_each(raft::resource::get_thrust_policy(handle),
                          count,
                          count + current_query_size * current_centroid_size,
                          [=] __device__(IndexType idx) {
                            IndexType row      = i + (idx / current_centroid_size);
                            IndexType col      = j + (idx % current_centroid_size);
-                           IndexType g_idx    = row * n + col;
+                           IndexType g_idx    = row * n_cols + col;
                            IndexType item_idx = (g_idx) >> 5;
                            uint32_t bit_idx   = (g_idx)&31;
-                           uint32_t filter    = filter_bitmap[item_idx];
+                           uint32_t filter    = filter_bits[item_idx];
                            if ((filter & (uint32_t(1) << bit_idx)) == 0) {
                              distances_ptr[idx] = masked_distance;
                            }
@@ -575,12 +582,12 @@ void brute_force_search(
     query_norms ? query_norms->data_handle() : nullptr);
 }
 
-template <typename T, typename IdxT, typename BitmapT, typename DistanceT = float>
+template <typename T, typename IdxT, typename BitsT, typename DistanceT = float>
 void brute_force_search_filtered(
   raft::resources const& res,
   const cuvs::neighbors::brute_force::index<T, DistanceT>& idx,
   raft::device_matrix_view<const T, IdxT, raft::row_major> queries,
-  cuvs::core::bitmap_view<const BitmapT, IdxT> filter,
+  const cuvs::neighbors::filtering::base_filter* filter,
   raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,
   raft::device_matrix_view<DistanceT, IdxT, raft::row_major> distances,
   std::optional<raft::device_vector_view<const DistanceT, IdxT>> query_norms = std::nullopt)
@@ -601,29 +608,42 @@ void brute_force_search_filtered(
                                     metric == cuvs::distance::DistanceType::CosineExpanded),
                "Index must has norms when using Euclidean, IP, and Cosine!");
 
-  IdxT n_queries = queries.extent(0);
-  IdxT n_dataset = idx.dataset().extent(0);
-  IdxT dim       = idx.dataset().extent(1);
-  IdxT k         = neighbors.extent(1);
+  IdxT n_queries                                     = queries.extent(0);
+  IdxT n_dataset                                     = idx.dataset().extent(0);
+  IdxT dim                                           = idx.dataset().extent(1);
+  IdxT k                                             = neighbors.extent(1);
+  cuvs::neighbors::filtering::FilterType filter_type = filter->get_filter_type();
 
   auto stream = raft::resource::get_cuda_stream(res);
 
-  // calc nnz
-  IdxT nnz_h = 0;
-  rmm::device_scalar<IdxT> nnz(0, stream);
-  auto nnz_view = raft::make_device_scalar_view<IdxT>(nnz.data());
-  auto filter_view =
-    raft::make_device_vector_view<const BitmapT, IdxT>(filter.data(), filter.n_elements());
-  IdxT size_h    = n_queries * n_dataset;
-  auto size_view = raft::make_host_scalar_view<const IdxT, IdxT>(&size_h);
-
-  raft::popc(res, filter_view, size_view, nnz_view);
-  raft::copy(&nnz_h, nnz.data(), 1, stream);
+  std::optional<std::variant<const cuvs::core::bitmap_view<BitsT, IdxT>,
+                             const cuvs::core::bitset_view<BitsT, IdxT>>>
+    filter_view;
+
+  IdxT nnz_h     = 0;
+  float sparsity = 0.0f;
+
+  const BitsT* filter_data = nullptr;
+
+  if (filter_type == cuvs::neighbors::filtering::FilterType::Bitmap) {
+    auto actual_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::bitmap_filter<BitsT, int64_t>*>(filter);
+    filter_view.emplace(actual_filter->view());
+    nnz_h    = actual_filter->view().count(res);
+    sparsity = 1.0 - nnz_h / (1.0 * n_queries * n_dataset);
+  } else if (filter_type == cuvs::neighbors::filtering::FilterType::Bitset) {
+    auto actual_filter =
+      dynamic_cast<const cuvs::neighbors::filtering::bitset_filter<BitsT, int64_t>*>(filter);
+    filter_view.emplace(actual_filter->view());
+    nnz_h    = n_queries * actual_filter->view().count(res);
+    sparsity = 1.0 - nnz_h / (1.0 * n_queries * n_dataset);
+  } else {
+    RAFT_FAIL("Unsupported sample filter type");
+  }
 
-  raft::resource::sync_stream(res, stream);
-  float sparsity = (1.0f * nnz_h / (1.0f * n_queries * n_dataset));
+  std::visit([&](const auto& actual_view) { filter_data = actual_view.data(); }, *filter_view);
 
-  if (sparsity > 0.01f) {
+  if (sparsity < 0.9f) {
     raft::resources stream_pool_handle(res);
     raft::resource::set_cuda_stream(stream_pool_handle, stream);
     auto idx_norm = idx.has_norms() ? const_cast<DistanceT*>(idx.norms().data_handle()) : nullptr;
@@ -643,12 +663,12 @@ void brute_force_search_filtered(
                                               0,
                                               idx_norm,
                                               nullptr,
-                                              filter.data());
+                                              filter_data,
+                                              raft::identity_op(),
+                                              filter_type);
   } else {
     auto csr = raft::make_device_csr_matrix<DistanceT, IdxT>(res, n_queries, n_dataset, nnz_h);
-
-    // fill csr
-    raft::sparse::convert::bitmap_to_csr(res, filter, csr);
+    std::visit([&](const auto& actual_view) { actual_view.to_csr(res, csr); }, *filter_view);
 
     // create filter csr view
     auto compressed_csr_view = csr.structure_view();
@@ -664,7 +684,11 @@ void brute_force_search_filtered(
     auto csr_view = raft::make_device_csr_matrix_view<DistanceT, IdxT, IdxT, IdxT>(
       csr.get_elements().data(), compressed_csr_view);
 
-    raft::sparse::linalg::masked_matmul(res, queries, dataset_view, filter, csr_view);
+    std::visit(
+      [&](const auto& actual_view) {
+        raft::sparse::linalg::masked_matmul(res, queries, dataset_view, actual_view, csr_view);
+      },
+      *filter_view);
 
     // post process
     std::optional<raft::device_vector<DistanceT, IdxT>> query_norms_;
@@ -733,21 +757,27 @@ void search(raft::resources const& res,
     return brute_force_search<T, int64_t, DistT>(res, idx, queries, neighbors, distances);
   } catch (const std::bad_cast&) {
   }
+  if constexpr (std::is_same_v<LayoutT, raft::col_major>) {
+    RAFT_FAIL("filtered search isn't available with col_major queries yet");
+  } else {
+    try {
+      auto& sample_filter =
+        dynamic_cast<const cuvs::neighbors::filtering::bitmap_filter<uint32_t, int64_t>&>(
+          sample_filter_ref);
+      return brute_force_search_filtered<T, int64_t, uint32_t, DistT>(
+        res, idx, queries, &sample_filter, neighbors, distances);
+    } catch (const std::bad_cast&) {
+    }
 
-  try {
-    auto& sample_filter =
-      dynamic_cast<const cuvs::neighbors::filtering::bitmap_filter<const uint32_t, int64_t>&>(
-        sample_filter_ref);
-    if constexpr (std::is_same_v<LayoutT, raft::col_major>) {
-      RAFT_FAIL("filtered search isn't available with col_major queries yet");
-    } else {
-      cuvs::core::bitmap_view<const uint32_t, int64_t> sample_filter_view =
-        sample_filter.bitmap_view_;
+    try {
+      auto& sample_filter =
+        dynamic_cast<const cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t>&>(
+          sample_filter_ref);
       return brute_force_search_filtered<T, int64_t, uint32_t, DistT>(
-        res, idx, queries, sample_filter_view, neighbors, distances);
+        res, idx, queries, &sample_filter, neighbors, distances);
+    } catch (const std::bad_cast&) {
+      RAFT_FAIL("Unsupported sample filter type");
     }
-  } catch (const std::bad_cast&) {
-    RAFT_FAIL("Unsupported sample filter type");
   }
 }
 
diff --git a/cpp/src/neighbors/sample_filter.cuh b/cpp/src/neighbors/sample_filter.cuh
index 258116ed36..b0c61f924b 100644
--- a/cpp/src/neighbors/sample_filter.cuh
+++ b/cpp/src/neighbors/sample_filter.cuh
@@ -20,6 +20,7 @@
 #include <raft/core/bitmap.cuh>
 #include <raft/core/bitset.cuh>
 #include <raft/core/detail/macros.hpp>
+#include <raft/sparse/convert/csr.cuh>
 
 #include <cstddef>
 #include <cstdint>
@@ -108,6 +109,13 @@ inline _RAFT_HOST_DEVICE bool bitset_filter<bitset_t, index_t>::operator()(
   return bitset_view_.test(sample_ix);
 }
 
+template <typename bitset_t, typename index_t>
+template <typename csr_matrix_t>
+void bitset_filter<bitset_t, index_t>::to_csr(raft::resources const& handle, csr_matrix_t& csr)
+{
+  raft::sparse::convert::bitset_to_csr(handle, bitset_view_, csr);
+}
+
 template <typename bitmap_t, typename index_t>
 bitmap_filter<bitmap_t, index_t>::bitmap_filter(
   const cuvs::core::bitmap_view<bitmap_t, index_t> bitmap_for_filtering)
@@ -124,4 +132,12 @@ inline _RAFT_HOST_DEVICE bool bitmap_filter<bitmap_t, index_t>::operator()(
 {
   return bitmap_view_.test(query_ix, sample_ix);
 }
+
+template <typename bitmap_t, typename index_t>
+template <typename csr_matrix_t>
+void bitmap_filter<bitmap_t, index_t>::to_csr(raft::resources const& handle, csr_matrix_t& csr)
+{
+  raft::sparse::convert::bitmap_to_csr(handle, bitmap_view_, csr);
+}
+
 }  // namespace cuvs::neighbors::filtering
diff --git a/cpp/tests/neighbors/brute_force_prefiltered.cu b/cpp/tests/neighbors/brute_force_prefiltered.cu
index 12b1c529e2..bf7dce7eec 100644
--- a/cpp/tests/neighbors/brute_force_prefiltered.cu
+++ b/cpp/tests/neighbors/brute_force_prefiltered.cu
@@ -28,6 +28,7 @@
 #include <raft/random/rng_state.hpp>
 #include <raft/util/popc.cuh>
 
+#include <cusparse.h>
 #include <gtest/gtest.h>
 
 #include <cuda_fp16.h>
@@ -146,11 +147,27 @@ void set_bitmap(const index_t* src,
   RAFT_CUDA_TRY(cudaGetLastError());
 }
 
+bool isCuSparseVersionGreaterThan_12_0_1()
+{
+  int version;
+  cusparseHandle_t handle;
+  cusparseCreate(&handle);
+  cusparseGetVersion(handle, &version);
+
+  int major = version / 1000;
+  int minor = (version % 1000) / 100;
+  int patch = version % 100;
+
+  cusparseDestroy(handle);
+
+  return (major > 12) || (major == 12 && minor > 0) || (major == 12 && minor == 0 && patch >= 2);
+}
+
 template <typename value_t, typename dist_t, typename index_t, typename bitmap_t = uint32_t>
-class PrefilteredBruteForceTest
+class PrefilteredBruteForceOnBitmapTest
   : public ::testing::TestWithParam<PrefilteredBruteForceInputs<index_t>> {
  public:
-  PrefilteredBruteForceTest()
+  PrefilteredBruteForceOnBitmapTest()
     : stream(raft::resource::get_cuda_stream(handle)),
       params(::testing::TestWithParam<PrefilteredBruteForceInputs<index_t>>::GetParam()),
       filter_d(0, stream),
@@ -352,6 +369,9 @@ class PrefilteredBruteForceTest
 
   void SetUp() override
   {
+    if (std::is_same_v<value_t, half> && !isCuSparseVersionGreaterThan_12_0_1()) {
+      GTEST_SKIP() << "Skipping all tests for half-float as cuSparse doesn't support it.";
+    }
     index_t element =
       raft::ceildiv(params.n_queries * params.n_dataset, index_t(sizeof(bitmap_t) * 8));
     std::vector<bitmap_t> filter_h(element);
@@ -476,8 +496,6 @@ class PrefilteredBruteForceTest
     out_val_expected_d.resize(params.n_queries * params.top_k, stream);
     out_idx_expected_d.resize(params.n_queries * params.top_k, stream);
 
-    // dump_vector(out_val_h.data(), out_val_h.size(), "out_val_h");
-
     raft::update_device(out_val_expected_d.data(), out_val_h.data(), out_val_h.size(), stream);
     raft::update_device(out_idx_expected_d.data(), out_idx_h.data(), out_idx_h.size(), stream);
 
@@ -494,8 +512,8 @@ class PrefilteredBruteForceTest
 
     auto dataset = brute_force::build(handle, dataset_raw, params.metric);
 
-    auto filter = cuvs::core::bitmap_view<const bitmap_t, index_t>(
-      (const bitmap_t*)filter_d.data(), params.n_queries, params.n_dataset);
+    auto filter = cuvs::core::bitmap_view<bitmap_t, index_t>(
+      (bitmap_t*)filter_d.data(), params.n_queries, params.n_dataset);
 
     auto out_val = raft::make_device_matrix_view<dist_t, index_t, raft::row_major>(
       out_val_d.data(), params.n_queries, params.top_k);
@@ -544,11 +562,451 @@ class PrefilteredBruteForceTest
   rmm::device_uvector<index_t> out_idx_expected_d;
 };
 
-using PrefilteredBruteForceTest_float_int64 = PrefilteredBruteForceTest<float, float, int64_t>;
-TEST_P(PrefilteredBruteForceTest_float_int64, Result) { Run(); }
+template <typename value_t, typename dist_t, typename index_t, typename bitset_t = uint32_t>
+class PrefilteredBruteForceOnBitsetTest
+  : public ::testing::TestWithParam<PrefilteredBruteForceInputs<index_t>> {
+ public:
+  PrefilteredBruteForceOnBitsetTest()
+    : stream(raft::resource::get_cuda_stream(handle)),
+      params(::testing::TestWithParam<PrefilteredBruteForceInputs<index_t>>::GetParam()),
+      filter_d(0, stream),
+      dataset_d(0, stream),
+      queries_d(0, stream),
+      out_val_d(0, stream),
+      out_val_expected_d(0, stream),
+      out_idx_d(0, stream),
+      out_idx_expected_d(0, stream)
+  {
+  }
+
+ protected:
+  void repeat_cpu_bitset(std::vector<bitset_t>& input,
+                         size_t input_bits,
+                         size_t repeat,
+                         std::vector<bitset_t>& output)
+  {
+    const size_t output_bits  = input_bits * repeat;
+    const size_t output_units = (output_bits + sizeof(bitset_t) * 8 - 1) / (sizeof(bitset_t) * 8);
+
+    std::memset(output.data(), 0, output_units * sizeof(bitset_t));
+
+    size_t output_bit_index = 0;
+
+    for (size_t r = 0; r < repeat; ++r) {
+      for (size_t i = 0; i < input_bits; ++i) {
+        size_t input_unit_index = i / (sizeof(bitset_t) * 8);
+        size_t input_bit_offset = i % (sizeof(bitset_t) * 8);
+        bool bit                = (input[input_unit_index] >> input_bit_offset) & 1;
+
+        size_t output_unit_index = output_bit_index / (sizeof(bitset_t) * 8);
+        size_t output_bit_offset = output_bit_index % (sizeof(bitset_t) * 8);
+
+        output[output_unit_index] |= (static_cast<bitset_t>(bit) << output_bit_offset);
+
+        ++output_bit_index;
+      }
+    }
+  }
+
+  index_t create_sparse_matrix_with_rmat(index_t m,
+                                         index_t n,
+                                         float sparsity,
+                                         rmm::device_uvector<bitset_t>& filter_d)
+  {
+    index_t r_scale   = (index_t)std::log2(m);
+    index_t c_scale   = (index_t)std::log2(n);
+    index_t n_edges   = (index_t)(m * n * 1.0f * sparsity);
+    index_t max_scale = std::max(r_scale, c_scale);
+
+    rmm::device_uvector<index_t> out_src{(unsigned long)n_edges, stream};
+    rmm::device_uvector<index_t> out_dst{(unsigned long)n_edges, stream};
+    rmm::device_uvector<float> theta{(unsigned long)(4 * max_scale), stream};
+
+    raft::random::RngState state{2024ULL, raft::random::GeneratorType::GenPC};
+
+    raft::random::uniform<float>(handle, state, theta.data(), theta.size(), 0.0f, 1.0f);
+    normalize<float, float>(
+      theta.data(), theta.data(), max_scale, r_scale, c_scale, r_scale != c_scale, true, stream);
+    raft::random::rmat_rectangular_gen((index_t*)nullptr,
+                                       out_src.data(),
+                                       out_dst.data(),
+                                       theta.data(),
+                                       r_scale,
+                                       c_scale,
+                                       n_edges,
+                                       stream,
+                                       state);
+
+    index_t nnz_h = 0;
+    {
+      auto src    = out_src.data();
+      auto dst    = out_dst.data();
+      auto bitset = filter_d.data();
+      rmm::device_scalar<index_t> nnz(0, stream);
+      auto nnz_view = raft::make_device_scalar_view<index_t>(nnz.data());
+      auto filter_view =
+        raft::make_device_vector_view<const uint32_t, index_t>(filter_d.data(), filter_d.size());
+      index_t size_h = m * n;
+      auto size_view = raft::make_host_scalar_view<const index_t, index_t>(&size_h);
+
+      set_bitmap(src, dst, bitset, n_edges, n, stream);
+
+      raft::popc(handle, filter_view, size_view, nnz_view);
+      raft::copy(&nnz_h, nnz.data(), 1, stream);
+
+      raft::resource::sync_stream(handle, stream);
+    }
+
+    return nnz_h;
+  }
+
+  void cpu_convert_to_csr(std::vector<bitset_t>& bitset,
+                          index_t rows,
+                          index_t cols,
+                          std::vector<index_t>& indices,
+                          std::vector<index_t>& indptr)
+  {
+    index_t offset_indptr   = 0;
+    index_t offset_values   = 0;
+    indptr[offset_indptr++] = 0;
+
+    index_t index        = 0;
+    bitset_t element     = 0;
+    index_t bit_position = 0;
+
+    for (index_t i = 0; i < rows; ++i) {
+      for (index_t j = 0; j < cols; ++j) {
+        index        = i * cols + j;
+        element      = bitset[index / (8 * sizeof(bitset_t))];
+        bit_position = index % (8 * sizeof(bitset_t));
+
+        if (((element >> bit_position) & 1)) {
+          indices[offset_values] = static_cast<index_t>(j);
+          offset_values++;
+        }
+      }
+      indptr[offset_indptr++] = static_cast<index_t>(offset_values);
+    }
+  }
+
+  void cpu_sddmm(const std::vector<dist_t>& A,
+                 const std::vector<dist_t>& B,
+                 std::vector<dist_t>& vals,
+                 const std::vector<index_t>& cols,
+                 const std::vector<index_t>& row_ptrs,
+                 bool is_row_major_A,
+                 bool is_row_major_B,
+                 dist_t alpha = 1.0,
+                 dist_t beta  = 0.0)
+  {
+    if (params.n_queries * params.dim != static_cast<index_t>(A.size()) ||
+        params.dim * params.n_dataset != static_cast<index_t>(B.size())) {
+      std::cerr << "Matrix dimensions and vector size do not match!" << std::endl;
+      return;
+    }
 
-using PrefilteredBruteForceTest_half_int64 = PrefilteredBruteForceTest<half, float, int64_t>;
-TEST_P(PrefilteredBruteForceTest_half_int64, Result) { Run(); }
+    bool trans_a = is_row_major_A;
+    bool trans_b = is_row_major_B;
+
+    for (index_t i = 0; i < params.n_queries; ++i) {
+      for (index_t j = row_ptrs[i]; j < row_ptrs[i + 1]; ++j) {
+        dist_t sum     = 0;
+        dist_t norms_A = 0;
+        dist_t norms_B = 0;
+
+        for (index_t l = 0; l < params.dim; ++l) {
+          index_t a_index = trans_a ? i * params.dim + l : l * params.n_queries + i;
+          index_t b_index = trans_b ? l * params.n_dataset + cols[j] : cols[j] * params.dim + l;
+          dist_t A_v;
+          dist_t B_v;
+          if constexpr (sizeof(value_t) == 2) {
+            A_v = __half2float(__float2half(A[a_index]));
+            B_v = __half2float(__float2half(B[b_index]));
+          } else {
+            A_v = A[a_index];
+            B_v = B[b_index];
+          }
+
+          sum += A_v * B_v;
+
+          norms_A += A_v * A_v;
+          norms_B += B_v * B_v;
+        }
+        vals[j] = alpha * sum + beta * vals[j];
+        if (params.metric == cuvs::distance::DistanceType::L2Expanded) {
+          vals[j] = dist_t(-2.0) * vals[j] + norms_A + norms_B;
+        } else if (params.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+          vals[j] = std::sqrt(dist_t(-2.0) * vals[j] + norms_A + norms_B);
+        } else if (params.metric == cuvs::distance::DistanceType::CosineExpanded) {
+          vals[j] = dist_t(1.0) - vals[j] / std::sqrt(norms_A * norms_B);
+        }
+      }
+    }
+  }
+
+  void cpu_select_k(const std::vector<index_t>& indptr_h,
+                    const std::vector<index_t>& indices_h,
+                    const std::vector<dist_t>& values_h,
+                    std::optional<std::vector<index_t>>& in_idx_h,
+                    index_t n_queries,
+                    index_t n_dataset,
+                    index_t top_k,
+                    std::vector<dist_t>& out_values_h,
+                    std::vector<index_t>& out_indices_h,
+                    bool select_min = true)
+  {
+    auto comp = [select_min](const std::pair<dist_t, index_t>& a,
+                             const std::pair<dist_t, index_t>& b) {
+      return select_min ? a.first < b.first : a.first >= b.first;
+    };
+
+    for (index_t row = 0; row < n_queries; ++row) {
+      std::priority_queue<std::pair<dist_t, index_t>,
+                          std::vector<std::pair<dist_t, index_t>>,
+                          decltype(comp)>
+        pq(comp);
+      for (index_t idx = indptr_h[row]; idx < indptr_h[row + 1]; ++idx) {
+        pq.push({values_h[idx], (in_idx_h.has_value()) ? (*in_idx_h)[idx] : indices_h[idx]});
+        if (pq.size() > size_t(top_k)) { pq.pop(); }
+      }
+
+      std::vector<std::pair<dist_t, index_t>> row_pairs;
+      while (!pq.empty()) {
+        row_pairs.push_back(pq.top());
+        pq.pop();
+      }
+
+      if (select_min) {
+        std::sort(row_pairs.begin(), row_pairs.end(), [](const auto& a, const auto& b) {
+          return a.first <= b.first;
+        });
+      } else {
+        std::sort(row_pairs.begin(), row_pairs.end(), [](const auto& a, const auto& b) {
+          return a.first >= b.first;
+        });
+      }
+      for (index_t col = 0; col < top_k; col++) {
+        if (col < index_t(row_pairs.size())) {
+          out_values_h[row * top_k + col]  = row_pairs[col].first;
+          out_indices_h[row * top_k + col] = row_pairs[col].second;
+        }
+      }
+    }
+  }
+
+  void SetUp() override
+  {
+    if (std::is_same_v<value_t, half> && !isCuSparseVersionGreaterThan_12_0_1()) {
+      GTEST_SKIP() << "Skipping all tests for half-float as cuSparse doesn't support it.";
+    }
+    index_t element = raft::ceildiv(1 * params.n_dataset, index_t(sizeof(bitset_t) * 8));
+    std::vector<bitset_t> filter_h(element);
+    std::vector<bitset_t> filter_repeat_h(element * params.n_queries);
+
+    filter_d.resize(element, stream);
+
+    nnz = create_sparse_matrix_with_rmat(1, params.n_dataset, params.sparsity, filter_d);
+    raft::update_host(filter_h.data(), filter_d.data(), filter_d.size(), stream);
+    raft::resource::sync_stream(handle, stream);
+
+    repeat_cpu_bitset(
+      filter_h, size_t(params.n_dataset), size_t(params.n_queries), filter_repeat_h);
+    nnz *= params.n_queries;
+
+    index_t dataset_size = params.n_dataset * params.dim;
+    index_t queries_size = params.n_queries * params.dim;
+
+    std::vector<dist_t> dataset_h(dataset_size);
+    std::vector<dist_t> queries_h(queries_size);
+
+    dataset_d.resize(dataset_size, stream);
+    queries_d.resize(queries_size, stream);
+
+    auto blobs_in_val =
+      raft::make_device_matrix<dist_t, index_t>(handle, 1, dataset_size + queries_size);
+    auto labels = raft::make_device_vector<index_t, index_t>(handle, 1);
+
+    if constexpr (!std::is_same_v<value_t, half>) {
+      raft::random::make_blobs<value_t, index_t>(blobs_in_val.data_handle(),
+                                                 labels.data_handle(),
+                                                 1,
+                                                 dataset_size + queries_size,
+                                                 1,
+                                                 stream,
+                                                 false,
+                                                 nullptr,
+                                                 nullptr,
+                                                 value_t(1.0),
+                                                 false,
+                                                 value_t(-1.0f),
+                                                 value_t(1.0f),
+                                                 uint64_t(2024));
+    } else {
+      raft::random::make_blobs<dist_t, index_t>(blobs_in_val.data_handle(),
+                                                labels.data_handle(),
+                                                1,
+                                                dataset_size + queries_size,
+                                                1,
+                                                stream,
+                                                false,
+                                                nullptr,
+                                                nullptr,
+                                                dist_t(1.0),
+                                                false,
+                                                dist_t(-1.0f),
+                                                dist_t(1.0f),
+                                                uint64_t(2024));
+    }
+
+    raft::copy(dataset_h.data(), blobs_in_val.data_handle(), dataset_size, stream);
+
+    if constexpr (std::is_same_v<value_t, half>) {
+      thrust::device_ptr<dist_t> d_output_ptr =
+        thrust::device_pointer_cast(blobs_in_val.data_handle());
+      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(dataset_d.data());
+      thrust::transform(thrust::cuda::par.on(stream),
+                        d_output_ptr,
+                        d_output_ptr + dataset_size,
+                        d_value_ptr,
+                        float_to_half());
+    } else {
+      raft::copy(dataset_d.data(), blobs_in_val.data_handle(), dataset_size, stream);
+    }
+
+    raft::copy(queries_h.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
+    if constexpr (std::is_same_v<value_t, half>) {
+      thrust::device_ptr<dist_t> d_output_ptr =
+        thrust::device_pointer_cast(blobs_in_val.data_handle() + dataset_size);
+      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(queries_d.data());
+      thrust::transform(thrust::cuda::par.on(stream),
+                        d_output_ptr,
+                        d_output_ptr + queries_size,
+                        d_value_ptr,
+                        float_to_half());
+    } else {
+      raft::copy(queries_d.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
+    }
+
+    raft::resource::sync_stream(handle);
+
+    std::vector<dist_t> values_h(nnz);
+    std::vector<index_t> indices_h(nnz);
+    std::vector<index_t> indptr_h(params.n_queries + 1);
+
+    cpu_convert_to_csr(filter_repeat_h, params.n_queries, params.n_dataset, indices_h, indptr_h);
+
+    cpu_sddmm(queries_h, dataset_h, values_h, indices_h, indptr_h, true, false);
+
+    bool select_min = cuvs::distance::is_min_close(params.metric);
+
+    std::vector<dist_t> out_val_h(
+      params.n_queries * params.top_k,
+      select_min ? std::numeric_limits<dist_t>::infinity() : std::numeric_limits<dist_t>::lowest());
+    std::vector<index_t> out_idx_h(params.n_queries * params.top_k, static_cast<index_t>(0));
+
+    out_val_d.resize(params.n_queries * params.top_k, stream);
+    out_idx_d.resize(params.n_queries * params.top_k, stream);
+
+    raft::update_device(out_val_d.data(), out_val_h.data(), out_val_h.size(), stream);
+    raft::update_device(out_idx_d.data(), out_idx_h.data(), out_idx_h.size(), stream);
+
+    raft::resource::sync_stream(handle);
+
+    std::optional<std::vector<index_t>> optional_indices_h = std::nullopt;
+    cpu_select_k(indptr_h,
+                 indices_h,
+                 values_h,
+                 optional_indices_h,
+                 params.n_queries,
+                 params.n_dataset,
+                 params.top_k,
+                 out_val_h,
+                 out_idx_h,
+                 select_min);
+    out_val_expected_d.resize(params.n_queries * params.top_k, stream);
+    out_idx_expected_d.resize(params.n_queries * params.top_k, stream);
+
+    raft::update_device(out_val_expected_d.data(), out_val_h.data(), out_val_h.size(), stream);
+    raft::update_device(out_idx_expected_d.data(), out_idx_h.data(), out_idx_h.size(), stream);
+
+    raft::resource::sync_stream(handle);
+  }
+
+  void Run()
+  {
+    auto dataset_raw = raft::make_device_matrix_view<const value_t, index_t, raft::row_major>(
+      (const value_t*)dataset_d.data(), params.n_dataset, params.dim);
+
+    auto queries = raft::make_device_matrix_view<const value_t, index_t, raft::row_major>(
+      (const value_t*)queries_d.data(), params.n_queries, params.dim);
+
+    auto dataset = brute_force::build(handle, dataset_raw, params.metric);
+
+    auto filter =
+      cuvs::core::bitset_view<bitset_t, index_t>((bitset_t*)filter_d.data(), params.n_dataset);
+
+    auto out_val = raft::make_device_matrix_view<dist_t, index_t, raft::row_major>(
+      out_val_d.data(), params.n_queries, params.top_k);
+    auto out_idx = raft::make_device_matrix_view<index_t, index_t, raft::row_major>(
+      out_idx_d.data(), params.n_queries, params.top_k);
+
+    brute_force::search(handle,
+                        dataset,
+                        queries,
+                        out_idx,
+                        out_val,
+                        cuvs::neighbors::filtering::bitset_filter(filter));
+    std::vector<dist_t> out_val_h(params.n_queries * params.top_k,
+                                  std::numeric_limits<dist_t>::infinity());
+
+    raft::update_host(out_val_h.data(), out_val_d.data(), out_val_h.size(), stream);
+    raft::resource::sync_stream(handle);
+
+    ASSERT_TRUE(cuvs::neighbors::devArrMatchKnnPair(out_idx_expected_d.data(),
+                                                    out_idx.data_handle(),
+                                                    out_val_expected_d.data(),
+                                                    out_val.data_handle(),
+                                                    params.n_queries,
+                                                    params.top_k,
+                                                    0.001f,
+                                                    stream,
+                                                    true));
+  }
+
+ protected:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  PrefilteredBruteForceInputs<index_t> params;
+
+  index_t nnz;
+
+  rmm::device_uvector<value_t> dataset_d;
+  rmm::device_uvector<value_t> queries_d;
+  rmm::device_uvector<bitset_t> filter_d;
+
+  rmm::device_uvector<dist_t> out_val_d;
+  rmm::device_uvector<dist_t> out_val_expected_d;
+
+  rmm::device_uvector<index_t> out_idx_d;
+  rmm::device_uvector<index_t> out_idx_expected_d;
+};
+
+using PrefilteredBruteForceTestOnBitmap_float_int64 =
+  PrefilteredBruteForceOnBitmapTest<float, float, int64_t>;
+TEST_P(PrefilteredBruteForceTestOnBitmap_float_int64, Result) { Run(); }
+
+using PrefilteredBruteForceTestOnBitmap_half_int64 =
+  PrefilteredBruteForceOnBitmapTest<half, float, int64_t>;
+TEST_P(PrefilteredBruteForceTestOnBitmap_half_int64, Result) { Run(); }
+
+using PrefilteredBruteForceTestOnBitset_float_int64 =
+  PrefilteredBruteForceOnBitsetTest<float, float, int64_t>;
+TEST_P(PrefilteredBruteForceTestOnBitset_float_int64, Result) { Run(); }
+
+using PrefilteredBruteForceTestOnBitset_half_int64 =
+  PrefilteredBruteForceOnBitsetTest<half, float, int64_t>;
+TEST_P(PrefilteredBruteForceTestOnBitset_half_int64, Result) { Run(); }
 
 template <typename index_t>
 const std::vector<PrefilteredBruteForceInputs<index_t>> selectk_inputs = {
@@ -570,7 +1028,7 @@ const std::vector<PrefilteredBruteForceInputs<index_t>> selectk_inputs = {
   {1024, 8192, 5, 0, 0.1, cuvs::distance::DistanceType::L2SqrtExpanded},
   {1024, 8192, 8, 0, 0.1, cuvs::distance::DistanceType::CosineExpanded},
 
-  {1024, 8192, 1, 1, 0.1, cuvs::distance::DistanceType::L2Expanded},  //--
+  {1024, 8192, 1, 1, 0.1, cuvs::distance::DistanceType::L2Expanded},
   {1024, 8192, 3, 1, 0.1, cuvs::distance::DistanceType::InnerProduct},
   {1024, 8192, 5, 1, 0.1, cuvs::distance::DistanceType::L2SqrtExpanded},
   {1024, 8192, 8, 1, 0.1, cuvs::distance::DistanceType::CosineExpanded},
@@ -599,12 +1057,20 @@ const std::vector<PrefilteredBruteForceInputs<index_t>> selectk_inputs = {
   {1024, 8192, 5, 16, 0.5, cuvs::distance::DistanceType::CosineExpanded},
   {1024, 8192, 8, 16, 0.2, cuvs::distance::DistanceType::CosineExpanded}};
 
-INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceTest,
-                        PrefilteredBruteForceTest_float_int64,
+INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceOnBitmapTest,
+                        PrefilteredBruteForceTestOnBitmap_float_int64,
+                        ::testing::ValuesIn(selectk_inputs<int64_t>));
+
+INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceOnBitmapTest,
+                        PrefilteredBruteForceTestOnBitmap_half_int64,
+                        ::testing::ValuesIn(selectk_inputs<int64_t>));
+
+INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceOnBitsetTest,
+                        PrefilteredBruteForceTestOnBitset_float_int64,
                         ::testing::ValuesIn(selectk_inputs<int64_t>));
 
-INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceTest,
-                        PrefilteredBruteForceTest_half_int64,
+INSTANTIATE_TEST_CASE_P(PrefilteredBruteForceOnBitsetTest,
+                        PrefilteredBruteForceTestOnBitset_half_int64,
                         ::testing::ValuesIn(selectk_inputs<int64_t>));
 
 }  // namespace cuvs::neighbors::brute_force
diff --git a/python/cuvs/cuvs/tests/test_brute_force.py b/python/cuvs/cuvs/tests/test_brute_force.py
index 0b37ad885d..a234794f90 100644
--- a/python/cuvs/cuvs/tests/test_brute_force.py
+++ b/python/cuvs/cuvs/tests/test_brute_force.py
@@ -134,7 +134,7 @@ def test_prefiltered_brute_force_knn(
     index = np.random.random_sample((n_index_rows, n_cols)).astype(dtype)
     queries = np.random.random_sample((n_query_rows, n_cols)).astype(dtype)
     bitmap = create_sparse_array(
-        (np.ceil(n_query_rows * n_index_rows / 32).astype(int)), sparsity
+        (np.ceil(n_query_rows * n_index_rows / 32).astype(np.uint32)), sparsity
     )
 
     is_min = metric != "inner_product"

From 8eca5247782abca479543ec69b1d479beb6009a6 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Fri, 31 Jan 2025 05:52:55 +0100
Subject: [PATCH 287/623] Improve filtering documentation (#568)

This PR add a dedicated documentation page for filtering in the `Getting started` tab, and add the `cuvs::neighbors::filtering` namespace to the C++ documentation

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/568
---
 cpp/include/cuvs/neighbors/common.hpp        |   7 ++
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh    |   2 +-
 docs/source/cpp_api/neighbors.rst            |   1 +
 docs/source/cpp_api/neighbors_bruteforce.rst |   2 +-
 docs/source/cpp_api/neighbors_filter.rst     |  18 +++
 docs/source/filtering.rst                    | 116 +++++++++++++++++++
 docs/source/getting_started.rst              |   2 +
 docs/source/indexes/bruteforce.rst           |   6 +-
 docs/source/indexes/cagra.rst                |  24 ++--
 docs/source/indexes/ivfflat.rst              |   4 +-
 docs/source/indexes/ivfpq.rst                |  12 +-
 examples/cpp/CMakeLists.txt                  |   4 +
 examples/cpp/src/brute_force_bitmap.cu       |  84 ++++++++++++++
 13 files changed, 259 insertions(+), 23 deletions(-)
 create mode 100644 docs/source/cpp_api/neighbors_filter.rst
 create mode 100644 docs/source/filtering.rst
 create mode 100644 examples/cpp/src/brute_force_bitmap.cu

diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 5dc99a4e87..038b6b1da5 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -457,6 +457,11 @@ inline constexpr bool is_vpq_dataset_v = is_vpq_dataset<DatasetT>::value;
 
 namespace filtering {
 
+/**
+ * @defgroup neighbors_filtering Filtering for ANN Types
+ * @{
+ */
+
 enum class FilterType { None, Bitmap, Bitset };
 
 struct base_filter {
@@ -567,6 +572,8 @@ struct bitset_filter : public base_filter {
   void to_csr(raft::resources const& handle, csr_matrix_t& csr);
 };
 
+/** @} */  // end group neighbors_filtering
+
 /**
  * If the filtering depends on the index of a sample, then the following
  * filter template can be used:
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 44a1b11fa4..0e492da6c4 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1696,7 +1696,7 @@ auto build(raft::resources const& handle,
                 "Unsupported data type");
 
   std::cout << "using ivf_pq::index_params nrows " << (int)dataset.extent(0) << ", dim "
-            << (int)dataset.extent(1) << ", n_lits " << (int)params.n_lists << ", pq_dim "
+            << (int)dataset.extent(1) << ", n_lists " << (int)params.n_lists << ", pq_dim "
             << (int)params.pq_dim << std::endl;
   RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
   RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index 95359558f5..ff1566e6ca 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -12,6 +12,7 @@ Nearest Neighbors
    neighbors_bruteforce.rst
    neighbors_cagra.rst
    neighbors_dynamic_batching.rst
+   neighbors_filter.rst
    neighbors_hnsw.rst
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
diff --git a/docs/source/cpp_api/neighbors_bruteforce.rst b/docs/source/cpp_api/neighbors_bruteforce.rst
index f75e26b3ca..1a3f2f7154 100644
--- a/docs/source/cpp_api/neighbors_bruteforce.rst
+++ b/docs/source/cpp_api/neighbors_bruteforce.rst
@@ -7,7 +7,7 @@ The bruteforce method is running the KNN algorithm. It performs an extensive sea
    :language: c++
    :class: highlight
 
-``#include <cuvs/neighbors/bruteforce.hpp>``
+``#include <cuvs/neighbors/brute_force.hpp>``
 
 namespace *cuvs::neighbors::bruteforce*
 
diff --git a/docs/source/cpp_api/neighbors_filter.rst b/docs/source/cpp_api/neighbors_filter.rst
new file mode 100644
index 0000000000..aba1d348fe
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_filter.rst
@@ -0,0 +1,18 @@
+Filtering
+==========
+
+All nearest neighbors search methods support filtering. Filtering is a method to reduce the number
+of candidates that are considered for the nearest neighbors search.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/common.hpp>``
+
+namespace *cuvs::neighbors*
+
+.. doxygengroup:: neighbors_filtering
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/filtering.rst b/docs/source/filtering.rst
new file mode 100644
index 0000000000..35805c5def
--- /dev/null
+++ b/docs/source/filtering.rst
@@ -0,0 +1,116 @@
+.. _filtering:
+
+~~~~~~~~~~~~~~~~~~~~~~~~
+Filtering vector indexes
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+cuVS supports different type of filtering depending on the vector index being used. The main method used in all of the vector indexes
+is pre-filtering, which is a technique that will into account the filtering of the vectors before computing it's closest neighbors, saving
+some computation from calculating distances.
+
+Bitset
+======
+
+A bitset is an array of bits where each bit can have two possible values: `0` and `1`, which signify in the context of filtering whether
+a sample should be filtered or not. `0` means that the corresponding vector will be filtered, and will therefore not be present in the results of the search.
+This mechanism is optimized to take as little memory space as possible, and is available through the RAFT library
+(check out RAFT's `bitset API documentation <https://docs.rapids.ai/api/raft/stable/cpp_api/core_bitset/>`). When calling a search function of an ANN index, the
+bitset length should match the number of vectors present in the database.
+
+Bitmap
+======
+
+A bitmap is based on the same principle as a bitset, but in two dimensions. This allows users to provide a different bitset for each query
+being searched. Check out RAFT's `bitmap API documentation <https://docs.rapids.ai/api/raft/stable/cpp_api/core_bitmap/>`.
+
+Examples
+=======
+
+Using a Bitset filter on a CAGRA index
+--------------------------------------
+
+.. code-block:: c++
+
+    #include <cuvs/neighbors/cagra.hpp>
+    #include <cuvs/core/bitset.hpp>
+
+    using namespace cuvs::neighbors;
+    cagra::index index;
+
+    // ... build index ...
+
+    cagra::search_params search_params;
+    raft::device_resources res;
+    raft::device_matrix_view<float> queries = load_queries();
+    raft::device_matrix_view<uint32_t> neighbors = make_device_matrix_view<uint32_t>(n_queries, k);
+    raft::device_matrix_view<float> distances = make_device_matrix_view<float>(n_queries, k);
+
+    // Load a list of all the samples that will get filtered
+    std::vector<uint32_t> removed_indices_host = get_invalid_indices();
+    auto removed_indices_device =
+          raft::make_device_vector<uint32_t, uint32_t>(res, removed_indices_host.size());
+    // Copy this list to device
+    raft::copy(removed_indices_device.data_handle(), removed_indices_host.data(),
+               removed_indices_host.size(), raft::resource::get_cuda_stream(res));
+
+    // Create a bitset with the list of samples to filter.
+    cuvs::core::bitset<uint32_t, uint32_t> removed_indices_bitset(
+        res, removed_indices_device.view(), index.size());
+    // Use a `bitset_filter` in the `cagra::search` function call.
+    auto bitset_filter =
+          cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset.view());
+    cagra::search(res,
+                  search_params,
+                  index,
+                  queries,
+                  neighbors,
+                  distances,
+                  bitset_filter);
+
+
+Using a Bitmap filter on a Brute-force index
+--------------------------------------------
+
+.. code-block:: c++
+
+    #include <cuvs/neighbors/brute_force.hpp>
+    #include <cuvs/core/bitmap.hpp>
+
+    using namespace cuvs::neighbors;
+    using indexing_dtype = int64_t;
+
+    // ... build index ...
+    brute_force::index_params index_params;
+    brute_force::search_params search_params;
+    raft::device_resources res;
+    raft::device_matrix_view<float, indexing_dtype> dataset = load_dataset(n_vectors, dim);
+    raft::device_matrix_view<float, indexing_dtype> queries = load_queries(n_queries, dim);
+    auto index = brute_force::build(res, index_params, raft::make_const_mdspan(dataset.view()));
+
+    // Load a list of all the samples that will get filtered
+    std::vector<uint32_t> removed_indices_host = get_invalid_indices();
+    auto removed_indices_device =
+          raft::make_device_vector<uint32_t, uint32_t>(res, removed_indices_host.size());
+    // Copy this list to device
+    raft::copy(removed_indices_device.data_handle(), removed_indices_host.data(),
+               removed_indices_host.size(), raft::resource::get_cuda_stream(res));
+
+    // Create a bitmap with the list of samples to filter.
+    cuvs::core::bitset<uint32_t, indexing_dtype> removed_indices_bitset(
+      res, removed_indices_device.view(), n_queries * n_vectors);
+    cuvs::core::bitmap_view<const uint32_t, indexing_dtype> removed_indices_bitmap(
+        removed_indices_bitset.data(), n_queries, n_vectors);
+
+    // Use a `bitmap_filter` in the `brute_force::search` function call.
+    auto bitmap_filter =
+          cuvs::neighbors::filtering::bitmap_filter(removed_indices_bitmap);
+
+    auto neighbors = raft::make_device_matrix_view<uint32_t, indexing_dtype>(n_queries, k);
+    auto distances = raft::make_device_matrix_view<float, indexing_dtype>(n_queries, k);
+    brute_force::search(res,
+                        search_params,
+                        index,
+                        raft::make_const_mdspan(queries.view()),
+                        neighbors.view(),
+                        distances.view(),
+                        bitmap_filter);
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
index b9cfdaca2e..c4706e5101 100644
--- a/docs/source/getting_started.rst
+++ b/docs/source/getting_started.rst
@@ -118,3 +118,5 @@ We always welcome patches for new features and bug fixes. Please read our `contr
    indexes/indexes.rst
    api_basics.rst
    api_interoperability.rst
+   working_with_ann_indexes.rst
+   filtering.rst
diff --git a/docs/source/indexes/bruteforce.rst b/docs/source/indexes/bruteforce.rst
index 0bd17dbf15..3dc1155073 100644
--- a/docs/source/indexes/bruteforce.rst
+++ b/docs/source/indexes/bruteforce.rst
@@ -12,7 +12,7 @@ Brute-force can also be a good choice for heavily filtered queries where other a
 when filtering out 90%-95% of the vectors from a search, the IVF methods could struggle to return anything at all with smaller number of probes and
 graph-based algorithms with limited hash table memory could end up skipping over important unfiltered entries.
 
-[ :doc:`C API <../c_api/neighbors_bruteforce_c>` | :doc:`C++ API <../cpp_api/neighbors_bruteforce>` | :doc:`Python API <../python_api/neighbors_bruteforce>` | :doc:`Rust API <../rust_api/index>` ]
+[ :doc:`C API <../c_api/neighbors_bruteforce_c>` | :doc:`C++ API <../cpp_api/neighbors_bruteforce>` | :doc:`Python API <../python_api/neighbors_brute_force>` | :doc:`Rust API <../rust_api/index>` ]
 
 Filtering considerations
 ------------------------
@@ -57,6 +57,6 @@ Memory footprint
 Index footprint
 ~~~~~~~~~~~~~~~
 
-Raw vectors: :math:`n_vectors * n_dimensions * precision`
+Raw vectors: :math:`n\_vectors * n\_dimensions * precision`
 
-Vector norms (for distances which require them): :math:`n_vectors * precision`
+Vector norms (for distances which require them): :math:`n\_vectors * precision`
diff --git a/docs/source/indexes/cagra.rst b/docs/source/indexes/cagra.rst
index 551eca8f72..14d6c65027 100644
--- a/docs/source/indexes/cagra.rst
+++ b/docs/source/indexes/cagra.rst
@@ -108,14 +108,14 @@ IVFPQ or NN-DESCENT can be used to build the graph (additions to the peak memory
 Dataset on device (graph on host):
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Index memory footprint (device): :math:`n_index_vectors * n_dims * sizeof(T)`
+Index memory footprint (device): :math:`n\_index\_vectors * n\_dims * sizeof(T)`
 
-Index memory footprint (host): :math:`graph_degree * n_index_vectors * sizeof(T)``
+Index memory footprint (host): :math:`graph\_degree * n\_index\_vectors * sizeof(T)``
 
 Dataset on host (graph on host):
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Index memory footprint (host): :math:`n_index_vectors * n_dims * sizeof(T) + graph_degree * n_index_vectors * sizeof(T)`
+Index memory footprint (host): :math:`n\_index\_vectors * n\_dims * sizeof(T) + graph\_degree * n\_index\_vectors * sizeof(T)`
 
 Build peak memory usage:
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -123,7 +123,7 @@ Build peak memory usage:
 When built using NN-descent / IVF-PQ, the build process consists of two phases: (1) building an initial/(intermediate) graph and then (2) optimizing the graph. Key input parameters are n_vectors, intermediate_graph_degree, graph_degree.
 The memory usage in the first phase (building) depends on the chosen method. The biggest allocation is the graph (n_vectors*intermediate_graph_degree), but it’s stored in the host memory.
 Usually, the second phase (optimize) uses the most device memory. The peak memory usage is achieved during the pruning step (graph_core.cuh/optimize)
-Optimize: formula for peak memory usage (device): :math:`n_vectors * (4 + (sizeof(IdxT) + 1) * intermediate_degree)``
+Optimize: formula for peak memory usage (device): :math:`n\_vectors * (4 + (sizeof(IdxT) + 1) * intermediate_degree)``
 
 Build with out-of-core IVF-PQ peak memory usage:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -134,14 +134,18 @@ IVF-PQ Build:
 
 .. math::
 
-   n_vectors / train_set_ratio * dim * sizeof(float)   // trainset, may be in managed mem
-   + n_vectors / train_set_ratio * sizeof(uint32_t)    // labels, may be in managed mem
-   + n_clusters * n_dim * sizeof(float)                // cluster centers
+   n\_vectors / train\_set\_ratio * dim * sizeof_{float}   // trainset, may be in managed mem
+
+   + n\_vectors / train\_set\_ratio * sizeof(uint32_t)    // labels, may be in managed mem
+
+   + n\_clusters * n\_dim * sizeof_{float}                // cluster centers
 
 IVF-PQ Search (max batch size 1024 vectors on device at a time):
 
 .. math::
 
-   [n_vectors * (pq_dim * pq_bits / 8 + sizeof(int64_t)) + O(n_clusters)]
-   + [batch_size * n_dim * sizeof(float)] + [batch_size * intermediate_degree * sizeof(uint32_t)] +
-   [batch_size * intermediate_degree * sizeof(float)]
+   [n\_vectors * (pq\_dim * pq\_bits / 8 + sizeof_{int64\_t}) + O(n\_clusters)]
+
+   + [batch\_size * n\_dim * sizeof_{float}] + [batch\_size * intermediate\_degree * sizeof_{uint32\_t}]
+
+   + [batch\_size * intermediate\_degree * sizeof_{float}]
diff --git a/docs/source/indexes/ivfflat.rst b/docs/source/indexes/ivfflat.rst
index 89a9fb6e64..7154db0375 100644
--- a/docs/source/indexes/ivfflat.rst
+++ b/docs/source/indexes/ivfflat.rst
@@ -86,7 +86,7 @@ Memory footprint
 ----------------
 
 Each cluster is padded to at least 32 vectors (but potentially up to 1024). Assuming uniform random distribution of vectors/list, we would have
-:math:`cluster\_overhead = (conservative\_memory\_allocation ? 16 : 512 ) * dim * sizeof_{float})`
+:math:`cluster\_overhead = (conservative\_memory\_allocation ? 16 : 512 ) * dim * sizeof_{float}`
 
 Note that each cluster is allocated as a separate allocation. If we use a `cuda_memory_resource`, that would grab memory in 1 MiB chunks, so on average we might have 0.5 MiB overhead per cluster. If we us 10s of thousands of clusters, it becomes essential to use pool allocator to avoid this overhead.
 
@@ -110,6 +110,6 @@ Index (device memory):
 Peak device memory usage for index build:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:math:`workspace = min(1GB, n\_queries * [(n\_lists + 1 + n\_probes * (k + 1)) * sizeof_{float}) + n\_probes * k * sizeof_{idx}])`
+:math:`workspace = min(1GB, n\_queries * [(n\_lists + 1 + n\_probes * (k + 1)) * sizeof_{float} + n\_probes * k * sizeof_{idx}])`
 
 :math:`index\_size + workspace`
diff --git a/docs/source/indexes/ivfpq.rst b/docs/source/indexes/ivfpq.rst
index 0474452a01..ad973adf28 100644
--- a/docs/source/indexes/ivfpq.rst
+++ b/docs/source/indexes/ivfpq.rst
@@ -97,22 +97,22 @@ Simple approximate formula: :math:`n\_vectors * (pq\_dim * \frac{pq\_bits}{8} +
 
 The IVF lists end up being represented by a sparse data structure that stores the pointers to each list, an indices array that contains the indexes of each vector in each list, and an array with the encoded (and interleaved) data for each list.
 
-IVF list pointers: :math:`n\_clusters * sizeof_{uint32_t}`
+IVF list pointers: :math:`n\_clusters * sizeof_{uint32\_t}`
 
-Indices: :math:`n\_vectors * sizeof_{idx}``
+Indices: :math:`n\_vectors * sizeof_{idx}`
 
 Encoded data (interleaved): :math:`n\_vectors * pq\_dim * \frac{pq\_bits}{8}`
 
-Per subspace method: :math:`4 * pq\_dim * pq\_len * 2^pq\_bits`
+Per subspace method: :math:`4 * pq\_dim * pq\_len * 2^{pq\_bits}`
 
-Per cluster method: :math:`4 * n\_clusters * pq\_len * 2^pq\_bits`
+Per cluster method: :math:`4 * n\_clusters * pq\_len * 2^{pq\_bits}`
 
 Extras: :math:`n\_clusters * (20 + 8 * dim)`
 
 Index (host memory):
 ~~~~~~~~~~~~~~~~~~~~
 
-When refinement is used with the dataset on host, the original raw vectors are needed: :math:`n\_vectors * dims * sizeof_{Tloat}`
+When refinement is used with the dataset on host, the original raw vectors are needed: :math:`n\_vectors * dims * sizeof_{float}`
 
 Search peak memory usage (device);
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -128,7 +128,7 @@ Build peak memory usage (device):
 
    \frac{n\_vectors}{trainset\_ratio * dims * sizeof_{float}}
 
-   + \frac{n\_vectors}{trainset\_ratio * sizeof_{uint32_t}}
+   + \frac{n\_vectors}{trainset\_ratio * sizeof_{uint32\_t}}
 
    + n\_clusters * dim * sizeof_{float}
 
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index b0d0ae9ee2..6bf8f34080 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -36,6 +36,7 @@ set(BUILD_CUVS_C_LIBRARY OFF)
 include(../cmake/thirdparty/get_cuvs.cmake)
 
 # -------------- compile tasks ----------------- #
+add_executable(BRUTE_FORCE_EXAMPLE src/brute_force_bitmap.cu)
 add_executable(CAGRA_EXAMPLE src/cagra_example.cu)
 add_executable(CAGRA_PERSISTENT_EXAMPLE src/cagra_persistent_example.cu)
 add_executable(DYNAMIC_BATCHING_EXAMPLE src/dynamic_batching_example.cu)
@@ -48,6 +49,9 @@ add_executable(VAMANA_EXAMPLE src/vamana_example.cu)
 add_library(rmm_logger OBJECT)
 target_link_libraries(rmm_logger PRIVATE rmm::rmm_logger_impl)
 
+target_link_libraries(
+  BRUTE_FORCE_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
+)
 target_link_libraries(
   CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
 )
diff --git a/examples/cpp/src/brute_force_bitmap.cu b/examples/cpp/src/brute_force_bitmap.cu
new file mode 100644
index 0000000000..69e4df60b8
--- /dev/null
+++ b/examples/cpp/src/brute_force_bitmap.cu
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/core/bitmap.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/random/make_blobs.cuh>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <iostream>
+
+void load_dataset(const raft::device_resources& res, float* data_ptr, int n_vectors, int dim)
+{
+  raft::random::RngState rng(1234ULL);
+  raft::random::uniform(
+    res, rng, data_ptr, n_vectors * dim, 0.1f, 2.0f);
+}
+
+int main()
+{
+  using namespace cuvs::neighbors;
+  using dataset_dtype = float;
+  using indexing_dtype = int64_t;
+  auto dim = 128;
+  auto n_vectors = 90;
+  auto n_queries = 100;
+  auto k = 5;
+
+  // ... build index ...
+  raft::device_resources res;
+  brute_force::index_params index_params;
+  brute_force::search_params search_params;
+  auto dataset = raft::make_device_matrix<dataset_dtype, indexing_dtype>(res, n_vectors, dim);
+  auto queries = raft::make_device_matrix<dataset_dtype, indexing_dtype>(res, n_queries, dim);
+
+  load_dataset(res, dataset.data_handle(), n_vectors, dim);
+  load_dataset(res, queries.data_handle(), n_queries, dim);
+  auto index = brute_force::build(res, index_params, raft::make_const_mdspan(dataset.view()));
+
+  // Load a list of all the samples that will get filtered
+  std::vector<indexing_dtype> removed_indices_host = {2, 13, 21, 8};
+  auto removed_indices_device =
+        raft::make_device_vector<indexing_dtype, indexing_dtype>(res, removed_indices_host.size());
+  // Copy this list to device
+  raft::copy(removed_indices_device.data_handle(), removed_indices_host.data(),
+             removed_indices_host.size(), raft::resource::get_cuda_stream(res));
+
+  // Create a bitmap with the list of samples to filter.
+  cuvs::core::bitset<uint32_t, indexing_dtype> removed_indices_bitset(
+      res, removed_indices_device.view(), n_queries * n_vectors);
+  cuvs::core::bitmap_view<const uint32_t, indexing_dtype> removed_indices_bitmap(
+      removed_indices_bitset.data(), n_queries, n_vectors);
+
+  // Use a `bitmap_filter` in the `brute_force::search` function call.
+  auto bitmap_filter = cuvs::neighbors::filtering::bitmap_filter(removed_indices_bitmap);
+
+  auto neighbors = raft::make_device_matrix<indexing_dtype, indexing_dtype>(res, n_queries, k);
+  auto distances = raft::make_device_matrix<dataset_dtype, indexing_dtype>(res, n_queries, k);
+  std::cout << "Searching..." << std::endl;
+  brute_force::search(res,
+                      search_params,
+                      index,
+                      raft::make_const_mdspan(queries.view()),
+                      neighbors.view(),
+                      distances.view(),
+                      bitmap_filter);
+  std::cout << "Success!" << std::endl;
+  return 0;
+}

From e6e09260b487bc6ec05fafc9cfb22878f9f3fd45 Mon Sep 17 00:00:00 2001
From: Ajit Mistry <55892788+ajit283@users.noreply.github.com>
Date: Fri, 31 Jan 2025 07:50:03 +0100
Subject: [PATCH 288/623] Expose `extend()` in C API (#276)

Add functionality to add additional vectors after build to C API

Authors:
  - Ajit Mistry (https://github.com/ajit283)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)
  - Micka (https://github.com/lowener)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/276
---
 cpp/include/cuvs/neighbors/cagra.h |  69 +++++++++
 cpp/src/neighbors/cagra_c.cpp      |  72 +++++++++
 cpp/tests/neighbors/ann_cagra_c.cu | 230 +++++++++++++++++++++++++++++
 3 files changed, 371 insertions(+)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 3e17f1d0fe..207f3f21b4 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -141,6 +141,45 @@ cuvsError_t cuvsCagraCompressionParamsCreate(cuvsCagraCompressionParams_t* param
  */
 cuvsError_t cuvsCagraCompressionParamsDestroy(cuvsCagraCompressionParams_t params);
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_c_extend_params C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+/**
+ * @brief Supplemental parameters to extend CAGRA Index
+ *
+ */
+struct cuvsCagraExtendParams {
+  /** The additional dataset is divided into chunks and added to the graph. This is the knob to
+   * adjust the tradeoff between the recall and operation throughput. Large chunk sizes can result
+   * in high throughput, but use more working memory (O(max_chunk_size*degree^2)). This can also
+   * degrade recall because no edges are added between the nodes in the same chunk. Auto select when
+   * 0. */
+  uint32_t max_chunk_size;
+};
+
+typedef struct cuvsCagraExtendParams* cuvsCagraExtendParams_t;
+
+/**
+ * @brief Allocate CAGRA Extend params, and populate with default values
+ *
+ * @param[in] params cuvsCagraExtendParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraExtendParamsCreate(cuvsCagraExtendParams_t* params);
+
+/**
+ * @brief De-allocate CAGRA Extend params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraExtendParamsDestroy(cuvsCagraExtendParams_t params);
+
 /**
  * @}
  */
@@ -340,6 +379,36 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  * @}
  */
 
+/**
+ * @defgroup cagra_c_extend_params C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
+/**
+ * @brief Extend a CAGRA index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
+ *        or `kDLCPU`. Also, acceptable underlying types are:
+ *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsCagraExtendParams_t used to extend CAGRA index
+ * @param[in] additional_dataset DLManagedTensor* additional dataset
+ * @param[in,out] index cuvsCagraIndex_t CAGRA index
+ * @param[out] return_dataset DLManagedTensor* extended dataset
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraExtend(cuvsResources_t res,
+                            cuvsCagraExtendParams_t params,
+                            DLManagedTensor* additional_dataset,
+                            cuvsCagraIndex_t index,
+                            DLManagedTensor* return_dataset);
+
+/**
+ * @}
+ */
+
 /**
  * @defgroup cagra_c_index_search C API for CUDA ANN Graph-based nearest neighbor search
  * @{
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 20cbae7d08..333fffb428 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -87,6 +87,43 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   return index;
 }
 
+template <typename T>
+void _extend(cuvsResources_t res,
+             cuvsCagraExtendParams params,
+             cuvsCagraIndex index,
+             DLManagedTensor* additional_dataset_tensor,
+             DLManagedTensor* return_tensor)
+{
+  auto dataset          = additional_dataset_tensor->dl_tensor;
+  auto return_dl_tensor = return_tensor->dl_tensor;
+  auto index_ptr        = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index.addr);
+  auto res_ptr          = reinterpret_cast<raft::resources*>(res);
+
+  // TODO: use C struct here (see issue #487)
+  auto extend_params           = cuvs::neighbors::cagra::extend_params();
+  extend_params.max_chunk_size = params.max_chunk_size;
+
+  if (cuvs::core::is_dlpack_device_compatible(dataset) &&
+      cuvs::core::is_dlpack_device_compatible(return_dl_tensor)) {
+    using mdspan_type        = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    using mdspan_return_type = raft::device_matrix_view<T, int64_t, raft::row_major>;
+    auto mds                 = cuvs::core::from_dlpack<mdspan_type>(additional_dataset_tensor);
+    auto return_mds          = cuvs::core::from_dlpack<mdspan_return_type>(return_tensor);
+    cuvs::neighbors::cagra::extend(*res_ptr, extend_params, mds, *index_ptr, return_mds);
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset) &&
+             cuvs::core::is_dlpack_host_compatible(return_dl_tensor)) {
+    using mdspan_type        = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    using mdspan_return_type = raft::device_matrix_view<T, int64_t, raft::row_major>;
+    auto mds                 = cuvs::core::from_dlpack<mdspan_type>(additional_dataset_tensor);
+    auto return_mds          = cuvs::core::from_dlpack<mdspan_return_type>(return_tensor);
+    cuvs::neighbors::cagra::extend(*res_ptr, extend_params, mds, *index_ptr, return_mds);
+  } else {
+    RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+              dataset.dtype.code,
+              dataset.dtype.bits);
+  }
+}
+
 template <typename T>
 void _search(cuvsResources_t res,
              cuvsCagraSearchParams params,
@@ -229,6 +266,30 @@ extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
   });
 }
 
+extern "C" cuvsError_t cuvsCagraExtend(cuvsResources_t res,
+                                       cuvsCagraExtendParams_t params,
+                                       DLManagedTensor* additional_dataset_tensor,
+                                       cuvsCagraIndex_t index_c_ptr,
+                                       DLManagedTensor* return_dataset_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = additional_dataset_tensor->dl_tensor;
+    auto index   = *index_c_ptr;
+
+    if ((dataset.dtype.code == kDLFloat) && (dataset.dtype.bits == 32)) {
+      _extend<float>(res, *params, index, additional_dataset_tensor, return_dataset_tensor);
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      _extend<int8_t>(res, *params, index, additional_dataset_tensor, return_dataset_tensor);
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      _extend<uint8_t>(res, *params, index, additional_dataset_tensor, return_dataset_tensor);
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
 extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
                                        cuvsCagraSearchParams_t params,
                                        cuvsCagraIndex_t index_c_ptr,
@@ -309,6 +370,17 @@ extern "C" cuvsError_t cuvsCagraCompressionParamsDestroy(cuvsCagraCompressionPar
   return cuvs::core::translate_exceptions([=] { delete params; });
 }
 
+extern "C" cuvsError_t cuvsCagraExtendParamsCreate(cuvsCagraExtendParams_t* params)
+{
+  return cuvs::core::translate_exceptions(
+    [=] { *params = new cuvsCagraExtendParams{.max_chunk_size = 0}; });
+}
+
+extern "C" cuvsError_t cuvsCagraExtendParamsDestroy(cuvsCagraExtendParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
 extern "C" cuvsError_t cuvsCagraSearchParamsCreate(cuvsCagraSearchParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
diff --git a/cpp/tests/neighbors/ann_cagra_c.cu b/cpp/tests/neighbors/ann_cagra_c.cu
index 7315813cc9..9e0890c34d 100644
--- a/cpp/tests/neighbors/ann_cagra_c.cu
+++ b/cpp/tests/neighbors/ann_cagra_c.cu
@@ -15,7 +15,9 @@
  */
 
 #include "../test_utils.cuh"
+#include <cstddef>
 #include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.hpp>
 #include <dlpack/dlpack.h>
 
 #include <cstdint>
@@ -23,8 +25,19 @@
 
 #include <cuda_runtime.h>
 #include <gtest/gtest.h>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/math.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/matrix/argmin.cuh>
+#include <raft/matrix/linewise_op.cuh>
 #include <sys/types.h>
 
+#include <raft/random/make_blobs.cuh>
+
 float dataset[4][2] = {{0.74021935, 0.9209938},
                        {0.03902049, 0.9689629},
                        {0.92514056, 0.4463501},
@@ -137,6 +150,223 @@ TEST(CagraC, BuildSearch)
   cuvsResourcesDestroy(res);
 }
 
+TEST(CagraC, BuildExtendSearch)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+  cudaStream_t stream;
+  cuvsStreamGet(res, &stream);
+
+  raft::resources handle;
+
+  const int32_t dimensions = 16;
+  // main_data_size needs to be >= 128 (see issue #486)
+  const int32_t main_data_size       = 1024;
+  const int32_t additional_data_size = 64;
+  const int32_t num_queries          = 4;
+
+  // create random data for datasets and queries
+  rmm::device_uvector<float> random_data_d(
+    (main_data_size + additional_data_size + num_queries) * dimensions, stream);
+  rmm::device_uvector<int32_t> random_labels_d(
+    (main_data_size + additional_data_size + num_queries) * dimensions, stream);
+  raft::random::make_blobs(random_data_d.data(),
+                           random_labels_d.data(),
+                           main_data_size + additional_data_size + num_queries,
+                           dimensions,
+                           10,
+                           stream);
+
+  // create  dataset DLTensor
+  rmm::device_uvector<float> main_d(main_data_size * dimensions, stream);
+  rmm::device_uvector<int32_t> main_labels_d(main_data_size, stream);
+  raft::copy(main_d.data(), random_data_d.data(), main_data_size * dimensions, stream);
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = main_d.data();
+  dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {main_data_size, dimensions};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = nullptr;
+
+  // create additional dataset DLTensor
+  rmm::device_uvector<float> additional_d(additional_data_size * dimensions, stream);
+  raft::copy(additional_d.data(),
+             random_data_d.data() + main_d.size(),
+             additional_data_size * dimensions,
+             stream);
+  DLManagedTensor additional_dataset_tensor;
+  additional_dataset_tensor.dl_tensor.data               = additional_d.data();
+  additional_dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  additional_dataset_tensor.dl_tensor.ndim               = 2;
+  additional_dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  additional_dataset_tensor.dl_tensor.dtype.bits         = 32;
+  additional_dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t additional_dataset_shape[2]                    = {additional_data_size, dimensions};
+  additional_dataset_tensor.dl_tensor.shape              = additional_dataset_shape;
+  additional_dataset_tensor.dl_tensor.strides            = nullptr;
+
+  // create tensor for that points to the extended dataset
+  rmm::device_uvector<float> extend_return_d((additional_data_size + main_data_size) * dimensions,
+                                             stream);
+  DLManagedTensor additional_dataset_return_tensor;
+  additional_dataset_return_tensor.dl_tensor.data               = extend_return_d.data();
+  additional_dataset_return_tensor.dl_tensor.device.device_type = kDLCUDA;
+  additional_dataset_return_tensor.dl_tensor.ndim               = 2;
+  additional_dataset_return_tensor.dl_tensor.dtype.code         = kDLFloat;
+  additional_dataset_return_tensor.dl_tensor.dtype.bits         = 32;
+  additional_dataset_return_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t additional_return_dataset_shape[2] = {additional_data_size + main_data_size, dimensions};
+  additional_dataset_return_tensor.dl_tensor.shape   = additional_return_dataset_shape;
+  additional_dataset_return_tensor.dl_tensor.strides = nullptr;
+
+  // create index
+  cuvsCagraIndex_t index;
+  cuvsCagraIndexCreate(&index);
+
+  // build index
+  cuvsCagraIndexParams_t build_params;
+  cuvsCagraIndexParamsCreate(&build_params);
+  cuvsCagraBuild(res, build_params, &dataset_tensor, index);
+
+  cuvsStreamSync(res);
+
+  // extend index
+  cuvsCagraExtendParams_t extend_params;
+  cuvsCagraExtendParamsCreate(&extend_params);
+  cuvsCagraExtend(
+    res, extend_params, &additional_dataset_tensor, index, &additional_dataset_return_tensor);
+
+  // create queries DLTensor
+  rmm::device_uvector<float> queries_d(num_queries * dimensions, stream);
+  raft::copy(queries_d.data(),
+             random_data_d.data() + (main_data_size + additional_data_size) * dimensions,
+             num_queries * dimensions,
+             stream);
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = queries_d.data();
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {4, dimensions};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = nullptr;
+
+  // create pairwise distance matrix for dataset and queries
+  auto pairwise_distance_dataset_input =
+    raft::make_device_matrix<float>(handle, main_data_size + additional_data_size, dimensions);
+
+  raft::copy(pairwise_distance_dataset_input.data_handle(), main_d.data(), main_d.size(), stream);
+  raft::copy(pairwise_distance_dataset_input.data_handle() + main_d.size(),
+             additional_d.data(),
+             additional_d.size(),
+             stream);
+
+  auto pairwise_distance_queries_input =
+    raft::make_device_matrix<float>(handle, num_queries, dimensions);
+
+  raft::copy(pairwise_distance_queries_input.data_handle(),
+             (float*)queries_d.data(),
+             num_queries * dimensions,
+             stream);
+
+  auto pairwise_distances =
+    raft::make_device_matrix<float>(handle, num_queries, (main_data_size + additional_data_size));
+  auto metric = cuvs::distance::DistanceType::L2Expanded;
+
+  cuvs::distance::pairwise_distance(handle,
+                                    pairwise_distance_queries_input.view(),
+                                    pairwise_distance_dataset_input.view(),
+
+                                    pairwise_distances.view(),
+                                    metric);
+
+  auto min_cols =
+    raft::make_device_vector<uint32_t, uint32_t>(handle, pairwise_distances.extent(0));
+
+  auto distances_const_view = raft::make_device_matrix_view<const float, uint32_t>(
+    pairwise_distances.data_handle(), pairwise_distances.extent(0), pairwise_distances.extent(1));
+
+  raft::matrix::argmin(handle, distances_const_view, min_cols.view());
+
+  float min_cols_distances[num_queries];
+
+  for (uint32_t i = 0; i < min_cols.extent(0); i++) {
+    uint32_t mc           = min_cols(i);
+    min_cols_distances[i] = pairwise_distances(i, mc);
+  }
+
+  // create neighbors DLTensor
+  rmm::device_uvector<uint32_t> neighbors_d(4, stream);
+
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = neighbors_d.data();
+  neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLUInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 32;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {num_queries, 1};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = nullptr;
+
+  // create distances DLTensor
+  rmm::device_uvector<float> distances_d(4, stream);
+
+  distances_d.resize(4, stream);
+
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = distances_d.data();
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {num_queries, 1};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = nullptr;
+
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+
+  // search index
+  cuvsCagraSearchParams_t search_params;
+  cuvsCagraSearchParamsCreate(&search_params);
+  cuvsCagraSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor, filter);
+
+  // make sure that extend_return_d points to the extended dataset
+  ASSERT_TRUE(cuvs::devArrMatch(
+    main_d.data(), extend_return_d.data(), main_d.size(), cuvs::Compare<float>()));
+
+  ASSERT_TRUE(cuvs::devArrMatch(additional_d.data(),
+                                extend_return_d.data() + main_d.size(),
+                                additional_d.size(),
+                                cuvs::Compare<float>()));
+
+  // check neighbors
+  ASSERT_TRUE(
+    cuvs::devArrMatch(min_cols.data_handle(), neighbors_d.data(), 4, cuvs::Compare<uint32_t>()));
+
+  // check distances
+  ASSERT_TRUE(cuvs::devArrMatchHost(
+    min_cols_distances, distances_d.data(), 4, cuvs::CompareApprox<float>(0.001f)));
+
+  // de-allocate index and res
+  cuvsCagraSearchParamsDestroy(search_params);
+  cuvsCagraExtendParamsDestroy(extend_params);
+  cuvsCagraIndexParamsDestroy(build_params);
+  cuvsCagraIndexDestroy(index);
+  cuvsResourcesDestroy(res);
+}
+
 TEST(CagraC, BuildSearchFiltered)
 {
   // create cuvsResources_t

From 5d2b6c7a4e6900061883c3ea0ea7f09b9f52bebc Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Fri, 31 Jan 2025 06:24:11 -0800
Subject: [PATCH 289/623] Revert CUDA 12.8 shared workflow branch changes
 (#630)

This PR points the shared workflow branches back to the default 25.02
branches.

xref: https://github.com/rapidsai/build-planning/issues/139
---
 .github/workflows/build.yaml                  | 18 ++++++------
 .github/workflows/pr.yaml                     | 28 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 4 files changed, 28 insertions(+), 28 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index ca6d48fe6b..59b8e00de1 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -94,7 +94,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -105,7 +105,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -115,7 +115,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.02
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 9f8b1b84bc..9197060c7a 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.02
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -45,7 +45,7 @@ jobs:
           repo: cuvs
   changed-files:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.02
     with:
       files_yaml: |
         test_cpp:
@@ -78,27 +78,27 @@ jobs:
           - '!thirdparty/LICENSES/**'
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.02
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.02
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -106,20 +106,20 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.02
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -129,7 +129,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -139,7 +139,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -148,21 +148,21 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.02
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.02
     with:
       arch: '["amd64"]'
       cuda: '["12.8"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index ac2277e32e..e3bf5d16f2 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.02
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 07f0f83cc9..01dd2436be 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@cuda-12.8.0
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.02
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}

From bfd546be248d49a811f7d55307e18bf7f72125fb Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Fri, 31 Jan 2025 16:30:53 -0600
Subject: [PATCH 290/623] Migrate to NVKS for amd64 CI runners (#627)

This migrates amd64 CI jobs (PRs and nightlies) to use L4 GPUs from the NVKS cluster.

xref: https://github.com/rapidsai/build-infra/issues/184

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Jake Awe (https://github.com/AyodeAwe)

URL: https://github.com/rapidsai/cuvs/pull/627
---
 .github/workflows/build.yaml                  | 22 ++++++-------
 .github/workflows/pr.yaml                     | 32 +++++++++----------
 .github/workflows/test.yaml                   |  8 ++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 4 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 7cbc6802f3..479b2d66cb 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,20 +37,20 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
       container_image: "rapidsai/ci-conda:latest"
-      node_type: "gpu-v100-latest-1"
+      node_type: "gpu-l4-latest-1"
       run_script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,19 +70,19 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
       build_type: ${{ inputs.build_type || 'branch' }}
       container_image: "rapidsai/ci-conda:latest"
       date: ${{ inputs.date }}
-      node_type: "gpu-v100-latest-1"
+      node_type: "gpu-l4-latest-1"
       run_script: "ci/build_docs.sh"
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -94,7 +94,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -105,7 +105,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -115,7 +115,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@nvks-runners
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index d7e27e65d9..211eb2bb50 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@nvks-runners
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -45,7 +45,7 @@ jobs:
           repo: cuvs
   changed-files:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@nvks-runners
     with:
       files_yaml: |
         test_cpp:
@@ -78,27 +78,27 @@ jobs:
           - '!thirdparty/LICENSES/**'
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@nvks-runners
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@nvks-runners
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@nvks-runners
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@nvks-runners
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -106,40 +106,40 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@nvks-runners
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@nvks-runners
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
     with:
       build_type: pull-request
-      node_type: "gpu-v100-latest-1"
+      node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:cuda12.8.0-ubuntu24.04-py3.12"
       run_script: "ci/build_docs.sh"
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
     with:
       build_type: pull-request
-      node_type: "gpu-v100-latest-1"
+      node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
       run_script: "ci/build_rust.sh"
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -148,21 +148,21 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@nvks-runners
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@nvks-runners
     with:
       arch: '["amd64"]'
       cuda: '["12.8"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index ccb3c39a98..82f95cb205 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -16,7 +16,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@nvks-runners
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -26,7 +26,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@nvks-runners
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@nvks-runners
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
@@ -42,7 +42,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@nvks-runners
     with:
       build_type: nightly
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 9764c62c15..7b5b4810fb 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@nvks-runners
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}

From 888a34f1d2ad3472fa7bac632466abae30b9af1a Mon Sep 17 00:00:00 2001
From: Akira Naruse <anaruse@nvidia.com>
Date: Sat, 1 Feb 2025 14:50:16 +0900
Subject: [PATCH 291/623] Automatic adjustment of itopk size according to
 filtering rate (#509)

This PR is based on #492.

The new multi-CTA algorithm proposed in #492 can be used to obtain good recall even with high filtering rates. However, good recall cannot be obtained unless the number of search iterations, or itopk size, one of CAGRA's search parameters, is appropriately increased according to the filtering rate. Therefore, users need to find the appropriate itopk size according to the filtering rate by trial and error, which is a pain.

This PR is intended to alleviate this problem by internally calculating the filtering rate and automatically adjusting the itopk size accordingly.

Authors:
  - Akira Naruse (https://github.com/anaruse)
  - Tamas Bela Feher (https://github.com/tfeher)
  - Artem M. Chirkin (https://github.com/achirkin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/509
---
 cpp/include/cuvs/neighbors/cagra.hpp          |  7 +++++++
 cpp/src/neighbors/cagra.cuh                   | 19 ++++++++++++++----
 .../detail/cagra/search_multi_cta.cuh         |  5 ++++-
 .../neighbors/detail/cagra/search_plan.cuh    | 20 ++++++++++++++-----
 docs/source/indexes/cagra.rst                 |  5 ++---
 5 files changed, 43 insertions(+), 13 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 597e186d7c..4544fcaf8a 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -229,6 +229,13 @@ struct search_params : cuvs::neighbors::search_params {
    * impact on the throughput.
    */
   float persistent_device_usage = 1.0;
+
+  /**
+   * A parameter indicating the rate of nodes to be filtered-out, when filtering is used.
+   * The value must be equal to or greater than 0.0 and less than 1.0. Default value is
+   * negative, in which case the filtering rate is automatically calculated.
+   */
+  float filtering_rate = -1.0;
 };
 
 /**
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index dacfd6f630..f294c9b449 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -336,11 +336,13 @@ void search(raft::resources const& res,
             const cuvs::neighbors::filtering::base_filter& sample_filter_ref)
 {
   try {
-    using none_filter_type  = cuvs::neighbors::filtering::none_sample_filter;
-    auto& sample_filter     = dynamic_cast<const none_filter_type&>(sample_filter_ref);
+    using none_filter_type    = cuvs::neighbors::filtering::none_sample_filter;
+    auto& sample_filter       = dynamic_cast<const none_filter_type&>(sample_filter_ref);
+    search_params params_copy = params;
+    if (params.filtering_rate < 0.0) { params_copy.filtering_rate = 0.0; }
     auto sample_filter_copy = sample_filter;
     return search_with_filtering<T, IdxT, none_filter_type>(
-      res, params, idx, queries, neighbors, distances, sample_filter_copy);
+      res, params_copy, idx, queries, neighbors, distances, sample_filter_copy);
     return;
   } catch (const std::bad_cast&) {
   }
@@ -349,9 +351,18 @@ void search(raft::resources const& res,
     auto& sample_filter =
       dynamic_cast<const cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t>&>(
         sample_filter_ref);
+    search_params params_copy = params;
+    if (params.filtering_rate < 0.0) {
+      const auto num_set_bits = sample_filter.bitset_view_.count(res);
+      auto filtering_rate     = (float)(idx.data().n_rows() - num_set_bits) / idx.data().n_rows();
+      const float min_filtering_rate = 0.0;
+      const float max_filtering_rate = 0.999;
+      params_copy.filtering_rate =
+        std::min(std::max(filtering_rate, min_filtering_rate), max_filtering_rate);
+    }
     auto sample_filter_copy = sample_filter;
     return search_with_filtering<T, IdxT, decltype(sample_filter_copy)>(
-      res, params, idx, queries, neighbors, distances, sample_filter_copy);
+      res, params_copy, idx, queries, neighbors, distances, sample_filter_copy);
   } catch (const std::bad_cast&) {
     RAFT_FAIL("Unsupported sample filter type");
   }
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index 2f1c0332a8..19839f7b6e 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -115,11 +115,14 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
 
   void set_params(raft::resources const& res, const search_params& params)
   {
+    size_t global_itopk_size                = itopk_size;
     constexpr unsigned multi_cta_itopk_size = 32;
     this->itopk_size                        = multi_cta_itopk_size;
     search_width                            = 1;
+    RAFT_LOG_DEBUG("params.itopk_size: %lu", (uint64_t)params.itopk_size);
+    RAFT_LOG_DEBUG("global_itopk_size: %lu", (uint64_t)global_itopk_size);
     num_cta_per_query =
-      max(params.search_width, raft::ceildiv(params.itopk_size, (size_t)multi_cta_itopk_size));
+      max(params.search_width, raft::ceildiv(global_itopk_size, (size_t)multi_cta_itopk_size));
     result_buffer_size = itopk_size + (search_width * graph_degree);
     typedef raft::Pow2<32> AlignBytes;
     unsigned result_buffer_size_32 = AlignBytes::roundUp(result_buffer_size);
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index 5fe5b0903a..adbedd8409 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -125,12 +125,9 @@ struct search_plan_impl_base : public search_params {
       if (itopk_size <= 512 && search_params::max_queries >= num_sm * 2lu) {
         algo = search_algo::SINGLE_CTA;
         RAFT_LOG_DEBUG("Auto strategy: selecting single-cta");
-      } else if (topk <= 1024) {
+      } else {
         algo = search_algo::MULTI_CTA;
         RAFT_LOG_DEBUG("Auto strategy: selecting multi-cta");
-      } else {
-        algo = search_algo::MULTI_KERNEL;
-        RAFT_LOG_DEBUG("Auto strategy: selecting multi kernel");
       }
     }
   }
@@ -150,7 +147,6 @@ struct search_plan_impl : public search_plan_impl_base {
   uint32_t result_buffer_size;
 
   uint32_t smem_size;
-  uint32_t topk;
   uint32_t num_seeds;
 
   lightweight_uvector<INDEX_T> hashmap;
@@ -217,6 +213,20 @@ struct search_plan_impl : public search_plan_impl_base {
         "# max_iterations is increased from %lu to %u.", max_iterations, _max_iterations);
       max_iterations = _max_iterations;
     }
+    if (algo == search_algo::MULTI_CTA && (0.0 < filtering_rate && filtering_rate < 1.0)) {
+      size_t adjusted_itopk_size =
+        (size_t)((float)topk / (1.0 - filtering_rate) +
+                 (float)(itopk_size - topk) / std::sqrt(1.0 - filtering_rate));
+      if (adjusted_itopk_size % 32) { adjusted_itopk_size += 32 - (adjusted_itopk_size % 32); }
+      if (itopk_size < adjusted_itopk_size) {
+        RAFT_LOG_DEBUG(
+          "# internal_topk is increased from %lu to %lu, considering fintering rate %f.",
+          itopk_size,
+          adjusted_itopk_size,
+          filtering_rate);
+        itopk_size = adjusted_itopk_size;
+      }
+    }
     if (itopk_size % 32) {
       uint32_t itopk32 = itopk_size;
       itopk32 += 32 - (itopk_size % 32);
diff --git a/docs/source/indexes/cagra.rst b/docs/source/indexes/cagra.rst
index 14d6c65027..651ffc7dd9 100644
--- a/docs/source/indexes/cagra.rst
+++ b/docs/source/indexes/cagra.rst
@@ -24,10 +24,9 @@ while the CPU can be leveraged for search.
 Filtering considerations
 ------------------------
 
-CAGRA supports filtered search which can work well for moderately small filters (such as filtering out only a small percentage of the vectors in the index (e.g. <<50%).
+CAGRA supports filtered search and has improved multi-CTA algorithm in branch-25.02 to provide reasonable recall and performance for filtering rate as high as 90% or more.
 
-When a filter is expected to remove 80%-99% of the vectors in the index, it is preferred to use brute-force with pre-filtering instead, as that will compute only those distances
-between the vectors not being filtered out. By default, CAGRA will pass the filter to the pre-filtered brute-force when the number of vevtors being filtered out is >90% of the vectors in the index.
+To obtain an appropriate recall in fitered search, it is necessary to set search parameters according to the filtering rate, but since it is difficult for users to to this, CAGRA automatically adjusts `itopk_size` internally according to the filtering rate on a heuristic basis. If you want to disable this automatic adjustment, set `filtering_rate`, one of the search parameters, to `0.0`, and `itopk_size` will not be adjusted automatically.
 
 Configuration parameters
 ------------------------

From 88f0dfcd515b49ba80f43841715a533fd053f391 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Sat, 1 Feb 2025 07:13:18 +0100
Subject: [PATCH 292/623] ANN_BENCH enhanced dataset support (#624)

Refactor the dataset module in the benchmark utility to add missing functionality:
  - [x] Data in managed memory (in addition to host/device/mmap/pinned)
  - [x] Basic filtering support: randomly generated bitset by setting 'filtering_rate' in the dataset config
  - [x] Partial support within CUVS algorithms (bitset only)
  - [ ] Support in all algorithms
  - [ ] Using files for bitset filtering
  - [x] Adjusting ground truth to account for the filtered data
  - [ ] Fine-grained control over where the bitset is located (like there is for the base set and query set)
  - [ ] Expose 2MB huge-pages support via config/cmd arguments
  - [ ] Add quantization as a dataset property

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/624
---
 cpp/bench/ann/src/common/ann_types.hpp        |  17 +-
 cpp/bench/ann/src/common/benchmark.hpp        |  59 +-
 cpp/bench/ann/src/common/blob.hpp             | 889 ++++++++++++++++++
 cpp/bench/ann/src/common/conf.hpp             |   9 +-
 cpp/bench/ann/src/common/dataset.hpp          | 605 +++---------
 cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h |  19 +
 .../ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h |   6 +-
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |  13 +-
 .../ann/src/cuvs/cuvs_ivf_flat_wrapper.h      |  11 +-
 cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h  |  13 +-
 .../ann/src/cuvs/cuvs_mg_cagra_wrapper.h      |   6 +-
 .../ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h   |   6 +-
 .../ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h     |   6 +-
 cpp/bench/ann/src/cuvs/cuvs_wrapper.h         |  16 +-
 cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h   |   9 +-
 cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h   |  17 +-
 cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh       |  11 +-
 cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h   |   5 +-
 18 files changed, 1174 insertions(+), 543 deletions(-)
 create mode 100644 cpp/bench/ann/src/common/blob.hpp

diff --git a/cpp/bench/ann/src/common/ann_types.hpp b/cpp/bench/ann/src/common/ann_types.hpp
index c2f85e5396..eed18272cd 100644
--- a/cpp/bench/ann/src/common/ann_types.hpp
+++ b/cpp/bench/ann/src/common/ann_types.hpp
@@ -37,6 +37,19 @@ enum class MemoryType {
   kHostMmap,
   kHostPinned,
   kDevice,
+  kManaged,
+};
+
+/** Request 2MB huge pages support for an allocation */
+enum class HugePages {
+  /** Don't use huge pages if possible. */
+  kDisable = 0,
+  /** Enable huge pages if possible, ignore otherwise. */
+  kAsk = 1,
+  /** Enable huge pages if possible, warn the user otherwise. */
+  kRequire = 2,
+  /** Force enable huge pages, throw an exception if not possible. */
+  kDemand = 3
 };
 
 enum class Metric {
@@ -65,6 +78,8 @@ inline auto parse_memory_type(const std::string& memory_type) -> MemoryType
     return MemoryType::kHostPinned;
   } else if (memory_type == "device") {
     return MemoryType::kDevice;
+  } else if (memory_type == "managed") {
+    return MemoryType::kManaged;
   } else {
     throw std::runtime_error("invalid memory type: '" + memory_type + "'");
   }
@@ -130,7 +145,7 @@ class algo : public algo_base {
 
   virtual void build(const T* dataset, size_t nrow) = 0;
 
-  virtual void set_search_param(const search_param& param) = 0;
+  virtual void set_search_param(const search_param& param, const void* filter_bitset) = 0;
   // TODO(snanditale): this assumes that an algorithm can always return k results.
   // This is not always possible.
   virtual void search(const T* queries,
diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index 49be786737..8d9c30cb18 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -271,7 +271,8 @@ void bench_search(::benchmark::State& state,
       }
     }
     try {
-      a->set_search_param(*search_param);
+      a->set_search_param(*search_param,
+                          dataset->filter_bitset(current_algo_props->dataset_memory_type));
     } catch (const std::exception& ex) {
       state.SkipWithError("An error occurred setting search parameters: " + std::string(ex.what()));
       return;
@@ -359,13 +360,19 @@ void bench_search(::benchmark::State& state,
   // Each thread calculates recall on their partition of queries.
   // evaluate recall
   if (dataset->max_k() >= k) {
-    const std::int32_t* gt    = dataset->gt_set();
+    const std::int32_t* gt             = dataset->gt_set();
+    const std::uint32_t* filter_bitset = dataset->filter_bitset(MemoryType::kHostMmap);
+    auto filter                        = [filter_bitset](std::int32_t i) -> bool {
+      if (filter_bitset == nullptr) { return true; }
+      auto word = filter_bitset[i >> 5];
+      return word & (1 << (i & 31));
+    };
     const std::uint32_t max_k = dataset->max_k();
     result_buf.transfer_data(MemoryType::kHost, current_algo_props->query_memory_type);
     auto* neighbors_host    = reinterpret_cast<index_type*>(result_buf.data(MemoryType::kHost));
     std::size_t rows        = std::min(queries_processed, query_set_size);
     std::size_t match_count = 0;
-    std::size_t total_count = rows * static_cast<size_t>(k);
+    std::size_t total_count = 0;
 
     // We go through the groundtruth with same stride as the benchmark loop.
     size_t out_offset   = 0;
@@ -375,22 +382,44 @@ void bench_search(::benchmark::State& state,
         size_t i_orig_idx = batch_offset + i;
         size_t i_out_idx  = out_offset + i;
         if (i_out_idx < rows) {
-          for (std::uint32_t j = 0; j < k; j++) {
-            auto act_idx = static_cast<std::int32_t>(neighbors_host[i_out_idx * k + j]);
-            for (std::uint32_t l = 0; l < k; l++) {
-              auto exp_idx = gt[i_orig_idx * max_k + l];
+          /* NOTE: recall correctness & filtering
+
+          In the loop below, we filter the ground truth values on-the-fly.
+          We need enough ground truth values to compute recall correctly though.
+          But the ground truth file only contains `max_k` values per row; if there are less valid
+          values than k among them, we overestimate the recall. Essentially, we compare the first
+          `filter_pass_count` values of the algorithm output, and this counter can be less than `k`.
+          In the extreme case of very high filtering rate, we may be bypassing entire rows of
+          results. However, this is still better than no recall estimate at all.
+
+          TODO: consider generating the filtered ground truth on-the-fly
+          */
+          uint32_t filter_pass_count = 0;
+          for (std::uint32_t l = 0; l < max_k && filter_pass_count < k; l++) {
+            auto exp_idx = gt[i_orig_idx * max_k + l];
+            if (!filter(exp_idx)) { continue; }
+            filter_pass_count++;
+            for (std::uint32_t j = 0; j < k; j++) {
+              auto act_idx = static_cast<std::int32_t>(neighbors_host[i_out_idx * k + j]);
               if (act_idx == exp_idx) {
                 match_count++;
                 break;
               }
             }
           }
+          total_count += filter_pass_count;
         }
       }
       out_offset += n_queries;
       batch_offset = (batch_offset + queries_stride) % query_set_size;
     }
     double actual_recall = static_cast<double>(match_count) / static_cast<double>(total_count);
+    /* NOTE: recall in the throughput mode & filtering
+
+    When filtering is enabled, `total_count` may vary between individual threads, but we still take
+    the simple average across in-thread recalls. Strictly speaking, this is incorrect, but it's good
+    enough under assumption that the filtering is more-or-less uniform.
+    */
     state.counters.insert({"Recall", {actual_recall, benchmark::Counter::kAvgThreads}});
   }
 }
@@ -515,13 +544,15 @@ void dispatch_benchmark(std::string cmdline,
   auto query_file         = combine_path(data_prefix, dataset_conf.query_file);
   auto gt_file            = dataset_conf.groundtruth_neighbors_file;
   if (gt_file.has_value()) { gt_file.emplace(combine_path(data_prefix, gt_file.value())); }
-  auto dataset = std::make_shared<bin_dataset<T>>(dataset_conf.name,
-                                                  base_file,
-                                                  dataset_conf.subset_first_row,
-                                                  dataset_conf.subset_size,
-                                                  query_file,
-                                                  dataset_conf.distance,
-                                                  gt_file);
+  auto dataset =
+    std::make_shared<bench::dataset<T>>(dataset_conf.name,
+                                        base_file,
+                                        dataset_conf.subset_first_row,
+                                        dataset_conf.subset_size,
+                                        query_file,
+                                        dataset_conf.distance,
+                                        gt_file,
+                                        search_mode ? dataset_conf.filtering_rate : std::nullopt);
   ::benchmark::AddCustomContext("dataset", dataset_conf.name);
   ::benchmark::AddCustomContext("distance", dataset_conf.distance);
   std::vector<configuration::index> indices = conf.get_indices();
diff --git a/cpp/bench/ann/src/common/blob.hpp b/cpp/bench/ann/src/common/blob.hpp
new file mode 100644
index 0000000000..81310ae0b8
--- /dev/null
+++ b/cpp/bench/ann/src/common/blob.hpp
@@ -0,0 +1,889 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "ann_types.hpp"
+#include "util.hpp"
+
+#include <cerrno>
+#include <linux/mman.h>
+#include <sys/mman.h>
+#include <sys/stat.h>
+
+#include <array>
+#include <cassert>
+#include <cstdint>
+#include <cstdio>
+#include <optional>
+#include <stdexcept>
+#include <string>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+#include <variant>
+
+namespace cuvs::bench {
+
+/** RAII wrapper for a file descriptor. */
+struct file_descriptor {
+  explicit file_descriptor(std::string file_name) : fd_{std::fopen(file_name.c_str(), "r")}
+  {
+    if (fd_ == nullptr) {
+      throw std::runtime_error(
+        "cuvs::bench::file_descriptor: failed to open a file (std::fopen): errno = " +
+        std::to_string(errno) + ", " + std::string(strerror(errno)) + ". File name: " + file_name);
+    }
+  }
+
+  explicit file_descriptor(size_t file_size_bytes) : fd_{std::tmpfile()}
+  {
+    if (fd_ == nullptr) {
+      throw std::runtime_error(
+        "cuvs::bench::file_descriptor: failed to open a temporary file (std::tmpfile): errno = " +
+        std::to_string(errno) + ", " + std::string(strerror(errno)));
+    }
+    if (ftruncate(fileno(fd_), file_size_bytes) == -1) {
+      throw std::runtime_error(
+        "cuvs::bench::file_descriptor: failed to call `ftruncate` to allocate memory for a "
+        "temporary file.");
+    }
+  }
+
+  // No copies for owning struct
+  file_descriptor(const file_descriptor& res)                      = delete;
+  auto operator=(const file_descriptor& other) -> file_descriptor& = delete;
+  // Moving is fine
+  file_descriptor(file_descriptor&& other) : fd_{std::exchange(other.fd_, nullptr)} {}
+  auto operator=(file_descriptor&& other) -> file_descriptor&
+  {
+    std::swap(this->fd_, other.fd_);
+    return *this;
+  }
+
+  ~file_descriptor() noexcept
+  {
+    if (fd_ != nullptr) { std::fclose(fd_); }
+  }
+
+  [[nodiscard]] auto value() const -> FILE* { return fd_; }
+
+ private:
+  FILE* fd_ = nullptr;
+};
+
+class mmap_error : public std::runtime_error {
+ private:
+  int errno_;
+
+ public:
+  mmap_error(std::string extra_msg)
+    : std::runtime_error("cuvs::bench::mmap_owner: `mmap` error: Value of errno " +
+                         std::to_string(errno) + ", " + std::string(strerror(errno)) + ". " +
+                         extra_msg),
+      errno_(errno)
+  {
+  }
+
+  [[nodiscard]] auto code() const noexcept { return errno_; }
+};
+
+/** RAII wrapper for a mmap/munmap. */
+struct mmap_owner {
+  /** Map a file */
+  mmap_owner(
+    const file_descriptor& descriptor, size_t offset, size_t size, int flags, bool writable = false)
+    : ptr_{mmap_verbose(size,
+                        writable ? PROT_READ | PROT_WRITE : PROT_READ,
+                        flags,
+                        fileno(descriptor.value()),
+                        offset)},
+      size_{size}
+  {
+  }
+
+  /** Allocate a new memory (not backed by a file). */
+  mmap_owner(size_t size, int flags)
+    : ptr_{mmap_verbose(size, PROT_READ | PROT_WRITE, flags, -1, 0)}, size_{size}
+  {
+  }
+
+  ~mmap_owner() noexcept
+  {
+    if (ptr_ != nullptr) { munmap(ptr_, size_); }
+  }
+
+  // No copies for owning struct
+  mmap_owner(const mmap_owner& res)                      = delete;
+  auto operator=(const mmap_owner& other) -> mmap_owner& = delete;
+  // Moving is fine
+  mmap_owner(mmap_owner&& other)
+    : ptr_{std::exchange(other.ptr_, nullptr)}, size_{std::exchange(other.size_, 0)}
+  {
+  }
+  auto operator=(mmap_owner&& other) -> mmap_owner&
+  {
+    std::swap(this->ptr_, other.ptr_);
+    std::swap(this->size_, other.size_);
+    return *this;
+  }
+
+  [[nodiscard]] auto data() const -> void* { return ptr_; }
+  [[nodiscard]] auto size() const -> size_t { return size_; }
+
+ private:
+  void* ptr_;
+  size_t size_;
+
+  static inline auto mmap_verbose(size_t length, int prot, int flags, int fd, off_t offset) -> void*
+  {
+    auto ptr = mmap(nullptr, length, prot, flags, fd, offset);
+    if (ptr == MAP_FAILED) {
+      std::array<char, 256> buf;
+      snprintf(buf.data(),
+               sizeof(buf),
+               "Failed call: cuvs::bench::mmap_owner:mmap(nullptr, %zu, 0x%08x, 0x%08x, %d, %zd)",
+               length,
+               prot,
+               flags,
+               fd,
+               offset);
+      throw mmap_error{std::string(buf.data())};
+    }
+    return ptr;
+  }
+};
+
+/** RAII wrapper for managed memory. */
+struct managed_mem_owner {
+  explicit managed_mem_owner(size_t size) : size_{size}
+  {
+#ifndef BUILD_CPU_ONLY
+    auto err_code = cudaMallocManaged(&ptr_, size_);
+    if (err_code != cudaSuccess) {
+      ptr_ = nullptr;
+      throw std::runtime_error{
+        "cuvs::bench::managed_mem_owner: call to cudaMallocManaged failed with code " +
+        std::to_string(err_code)};
+    }
+#else
+    throw std::runtime_error{
+      "Device functions are not available when built with BUILD_CPU_ONLY flag."};
+#endif
+  }
+
+  ~managed_mem_owner() noexcept
+  {
+    if (ptr_ != nullptr) {
+#ifndef BUILD_CPU_ONLY
+      cudaFree(ptr_);
+#endif
+    }
+  }
+
+  // No copies for owning struct
+  managed_mem_owner(const managed_mem_owner& res)                      = delete;
+  auto operator=(const managed_mem_owner& other) -> managed_mem_owner& = delete;
+  // Moving is fine
+  managed_mem_owner(managed_mem_owner&& other)
+    : ptr_{std::exchange(other.ptr_, nullptr)}, size_{std::exchange(other.size_, 0)}
+  {
+  }
+  auto operator=(managed_mem_owner&& other) -> managed_mem_owner&
+  {
+    std::swap(this->ptr_, other.ptr_);
+    std::swap(this->size_, other.size_);
+    return *this;
+  }
+
+  [[nodiscard]] auto data() const -> void* { return ptr_; }
+  [[nodiscard]] auto size() const -> size_t { return size_; }
+
+ private:
+  void* ptr_ = nullptr;
+  size_t size_;
+};
+
+/** RAII wrapper for device memory. */
+struct device_mem_owner {
+  explicit device_mem_owner(size_t size) : size_{size}
+  {
+#ifndef BUILD_CPU_ONLY
+    auto err_code = cudaMalloc(&ptr_, size_);
+    if (err_code != cudaSuccess) {
+      ptr_ = nullptr;
+      throw std::runtime_error{
+        "cuvs::bench::device_mem_owner: call to cudaMalloc failed with code " +
+        std::to_string(err_code)};
+    }
+#else
+    throw std::runtime_error{
+      "Device functions are not available when built with BUILD_CPU_ONLY flag."};
+#endif
+  }
+  ~device_mem_owner() noexcept
+  {
+    if (ptr_ != nullptr) {
+#ifndef BUILD_CPU_ONLY
+      cudaFree(ptr_);
+#endif
+    }
+  }
+  // No copies for owning struct
+  device_mem_owner(const device_mem_owner& res)                      = delete;
+  auto operator=(const device_mem_owner& other) -> device_mem_owner& = delete;
+  // Moving is fine
+  device_mem_owner(device_mem_owner&& other)
+    : ptr_{std::exchange(other.ptr_, nullptr)}, size_{std::exchange(other.size_, 0)}
+  {
+  }
+  auto operator=(device_mem_owner&& other) -> device_mem_owner&
+  {
+    std::swap(this->ptr_, other.ptr_);
+    std::swap(this->size_, other.size_);
+    return *this;
+  }
+
+  [[nodiscard]] auto data() const -> void* { return ptr_; }
+  [[nodiscard]] auto size() const -> size_t { return size_; }
+
+ private:
+  void* ptr_ = nullptr;
+  size_t size_;
+};
+
+/** Lazy-initialized file handle. */
+struct file {
+  explicit file(std::string file_name) : reqsize_or_name_{std::move(file_name)} {}
+  explicit file(size_t tmp_size_bytes) : reqsize_or_name_{tmp_size_bytes} {}
+
+  // this shouldn't be necessary, but adds extra safety (make sure descriptors are not copied)
+  file(const file&)                    = delete;
+  auto operator=(const file&) -> file& = delete;
+  file(file&&);
+  auto operator=(file&&) -> file&;
+
+  [[nodiscard]] auto descriptor() const -> const file_descriptor&
+  {
+    if (!descriptor_.has_value()) {
+      std::visit([&d = descriptor_](auto&& x) { d.emplace(x); }, reqsize_or_name_);
+    }
+    return descriptor_.value();
+  }
+
+  [[nodiscard]] auto path() const -> std::string
+  {
+    return std::holds_alternative<std::string>(reqsize_or_name_)
+             ? std::get<std::string>(reqsize_or_name_)
+             : "<temporary>";
+  }
+
+  [[nodiscard]] auto size() const -> size_t
+  {
+    if (!size_.has_value()) {
+      size_.emplace(std::visit(
+        [&s = size_](auto&& x) {
+          if constexpr (std::is_same_v<std::decay_t<decltype(x)>, size_t>) {
+            return x;
+          } else {
+            struct stat statbuf;
+            if (stat(x.c_str(), &statbuf) != 0) {
+              throw std::runtime_error{"cuvs::bench::file::size() error: `stat` failed: " + x};
+            }
+            return static_cast<size_t>(statbuf.st_size);
+          }
+        },
+        reqsize_or_name_));
+    }
+    return size_.value();
+  }
+
+  [[nodiscard]] auto is_temporary() const -> bool
+  {
+    return std::holds_alternative<size_t>(reqsize_or_name_);
+  }
+
+  void reset_lazy_state() const
+  {
+    descriptor_.reset();
+    size_.reset();
+  }
+
+ private:
+  std::variant<size_t, std::string> reqsize_or_name_;
+  mutable std::optional<file_descriptor> descriptor_ = std::nullopt;
+  mutable std::optional<size_t> size_                = std::nullopt;
+};
+
+// declare the move constructors explicitly outside of the class declaration to make sure if
+// anything is wrong, we catch that at compile time.
+inline file::file(file&&)                    = default;
+inline auto file::operator=(file&&) -> file& = default;
+
+/**
+ * Lazy-initialized file handle with the size information provided by our .bin format:
+ * The file always starts with two uint32_t values: [n_rows, n_cols].
+ */
+template <typename T>
+struct blob_file : public file {
+  explicit blob_file(std::string file_name, uint32_t rows_offset = 0, uint32_t rows_limit = 0)
+    : file{std::move(file_name)}, rows_offset_{rows_offset}, rows_limit_{rows_limit}
+  {
+  }
+
+  explicit blob_file(uint32_t n_rows, uint32_t n_cols)
+    : file{sizeof(T) * n_rows * n_cols + 2 * sizeof(uint32_t)}, rows_offset_{0}, rows_limit_{0}
+  {
+    // NB: this forces the file descriptor, thus breaking lazy-initialization. Not sure if it's
+    // worth refactoring in this case.
+    std::array<uint32_t, 2> h{n_rows, n_cols};
+    if (std::fwrite(h.data(), sizeof(uint32_t), h.size(), descriptor().value()) != 2) {
+      throw std::runtime_error{
+        "cuvs::bench::blob_file `fwrite` failed when initializing a tmp file."};
+    }
+    if (std::fflush(descriptor().value()) != 0) {
+      throw std::runtime_error{
+        "cuvs::bench::blob_file `fflush` failed with non-zero code when initializing a tmp file."};
+    }
+  }
+
+  blob_file(const blob_file<T>&)                    = delete;
+  auto operator=(const blob_file<T>&) -> blob_file& = delete;
+  blob_file(blob_file<T>&&);
+  auto operator=(blob_file<T>&&) -> blob_file<T>&;
+
+  [[nodiscard]] auto n_rows() const -> uint32_t { return header()[0]; }
+  [[nodiscard]] auto n_cols() const -> uint32_t { return header()[1]; }
+
+  [[nodiscard]] auto rows_offset() const -> uint32_t { return rows_offset_; }
+  [[nodiscard]] auto rows_limit() const -> uint32_t
+  {
+    auto rows_max = n_rows() - std::min(rows_offset(), n_rows());          // available rows
+    return rows_limit_ == 0 ? rows_max : std::min(rows_limit_, rows_max);  // limited rows
+  }
+
+  void reset_lazy_state() const
+  {
+    file::reset_lazy_state();
+    header_.reset();
+  }
+
+ private:
+  mutable std::optional<std::array<uint32_t, 2>> header_ = std::nullopt;
+  uint32_t rows_offset_;
+  uint32_t rows_limit_;
+
+  [[nodiscard]] auto header() const -> const std::array<uint32_t, 2>&
+  {
+    if (!header_.has_value()) {
+      std::array<uint32_t, 2> h;
+      std::rewind(descriptor().value());
+      if (std::fread(h.data(), sizeof(uint32_t), h.size(), descriptor().value()) != 2) {
+        throw std::runtime_error{"cuvs::bench::blob_file read header of bin file failed: " +
+                                 path()};
+      }
+      header_.emplace(h);
+    }
+    return header_.value();
+  }
+};
+
+// declare the move constructors explicitly outside of the class declaration to make sure if
+// anything is wrong, we catch that at compile time.
+template <typename T>
+inline blob_file<T>::blob_file(blob_file<T>&&) = default;
+template <typename T>
+inline auto blob_file<T>::operator=(blob_file<T>&&) -> blob_file<T>& = default;
+
+/** Lazily map or copy the file content onto host memory. */
+template <typename T>
+struct blob_mmap {
+  explicit blob_mmap(blob_file<T>&& file,
+                     bool copy_in_memory     = false,
+                     HugePages hugepages_2mb = HugePages::kDisable)
+    : file_{std::move(file)},
+      copy_in_memory_{copy_in_memory},
+      hugepages_2mb_requested_{hugepages_2mb},
+      hugepages_2mb_actual_{hugepages_2mb > HugePages::kDisable}
+  {
+  }
+  explicit blob_mmap(std::string file_name,
+                     uint32_t rows_offset    = 0,
+                     uint32_t rows_limit     = 0,
+                     bool copy_in_memory     = false,
+                     HugePages hugepages_2mb = HugePages::kDisable)
+    : blob_mmap{
+        blob_file<T>{std::move(file_name), rows_offset, rows_limit}, copy_in_memory, hugepages_2mb}
+  {
+  }
+
+ private:
+  blob_file<T> file_;
+  bool copy_in_memory_;
+  HugePages hugepages_2mb_requested_;
+  mutable bool hugepages_2mb_actual_;
+
+  mutable std::optional<std::tuple<mmap_owner, ptrdiff_t>> handle_;
+
+  [[nodiscard]] auto handle() const -> const std::tuple<mmap_owner, ptrdiff_t>&
+  {
+    if (!handle_.has_value()) {
+      size_t page_size = hugepages_2mb_actual_ ? 1024ull * 1024ull * 2ull : sysconf(_SC_PAGE_SIZE);
+      int flags        = 0;
+      if (hugepages_2mb_actual_) { flags |= MAP_HUGETLB | MAP_HUGE_2MB; }
+      size_t data_start = sizeof(T) * file_.rows_offset() * file_.n_cols() + sizeof(uint32_t) * 2;
+      size_t data_end   = sizeof(T) * file_.rows_limit() * file_.n_cols() + data_start;
+
+      try {
+        if (copy_in_memory_) {
+          // Copy the content in-memory
+          flags |= MAP_ANONYMOUS | MAP_PRIVATE;
+          size_t size = data_end - data_start;
+          mmap_owner owner{size, flags};
+          std::fseek(file_.descriptor().value(), data_start, SEEK_SET);
+          size_t n_elems = file_.rows_limit() * file_.n_cols();
+          if (std::fread(owner.data(), sizeof(T), n_elems, file_.descriptor().value()) != n_elems) {
+            throw std::runtime_error{"cuvs::bench::blob_mmap() fread " + file_.path() + " failed"};
+          }
+          handle_.emplace(std::move(owner), 0);
+        } else {
+          // Map the file
+          // If this is a temporary file, we're supposed to write to it, hence MAP_SHARED.
+          flags |= file_.is_temporary() ? MAP_SHARED : MAP_PRIVATE;
+          size_t mmap_start = (data_start / page_size) * page_size;
+          size_t mmap_size  = data_end - mmap_start;
+          handle_.emplace(
+            mmap_owner{file_.descriptor(), mmap_start, mmap_size, flags, file_.is_temporary()},
+            data_start - mmap_start);
+        }
+      } catch (const mmap_error& e) {
+        bool hugepages_2mb_asked = hugepages_2mb_requested_ == HugePages::kAsk ||
+                                   hugepages_2mb_requested_ == HugePages::kRequire;
+        if (e.code() == EPERM && hugepages_2mb_asked && hugepages_2mb_actual_) {
+          if (hugepages_2mb_requested_ == HugePages::kRequire) {
+            log_warn(
+              "cuvs::bench::blob_mmap: `mmap` failed to map due to EPERM, which is likely caused "
+              "by the permissions issue. You either need a CAP_IPC_LOCK capability or run the "
+              "program with sudo. We will try again without huge pages.");
+          }
+          hugepages_2mb_actual_ = false;
+          return handle();
+        }
+        if (e.code() == EINVAL && hugepages_2mb_asked && hugepages_2mb_actual_ &&
+            !copy_in_memory_) {
+          if (hugepages_2mb_requested_ == HugePages::kRequire) {
+            log_warn(
+              "cuvs::bench::blob_mmap: `mmap` failed to map due to EINVAL, which is likely caused "
+              "by the file system not supporting huge pages. We will try again without huge "
+              "pages.");
+          }
+          hugepages_2mb_actual_ = false;
+          return handle();
+        }
+        throw;  // The error is not due to huge pages or otherwise unrecoverable
+      }
+    }
+    return handle_.value();
+  }
+
+ public:
+  [[nodiscard]] auto data() const -> T*
+  {
+    auto& [owner, offset] = handle();
+    return reinterpret_cast<T*>(reinterpret_cast<uint8_t*>(owner.data()) + offset);
+  }
+
+  [[nodiscard]] auto is_in_memory() const -> bool { return copy_in_memory_; }
+  [[nodiscard]] auto is_hugepage() const -> bool { return hugepages_2mb_actual_; }
+  /**
+   * Enabling hugepages is not always possible. For convenience, we may silently disable it in some
+   * cases. This function helps to decide whether the current setting is compatible with the
+   * request.R
+   */
+  [[nodiscard]] auto hugepage_compliant(HugePages request) const -> bool
+  {
+    if (request == hugepages_2mb_requested_) {
+      // whatever the actual state is, the result would be the same if we recreated
+      // the mapping.
+      return true;
+    }
+    bool hp_enabled    = hugepages_2mb_actual_ && request > HugePages::kDisable;
+    bool hp_disabled   = !hugepages_2mb_actual_ && request == HugePages::kDisable;
+    bool hp_not_forced = !hugepages_2mb_actual_ && request == HugePages::kAsk;
+    return hp_enabled || hp_disabled || hp_not_forced;
+  }
+  [[nodiscard]] auto n_rows() const -> uint32_t { return file_.rows_limit(); }
+  [[nodiscard]] auto n_cols() const -> uint32_t { return file_.n_cols(); }
+  [[nodiscard]] auto size() const -> size_t { return sizeof(T) * n_rows() * n_cols(); }
+  [[nodiscard]] auto unmap() && noexcept -> blob_file<T>
+  {
+    // If we used mmap on a temporary file, then it was writable.
+    // Then there's a chance the user wrote something to the mmap.
+    // Then we must ensure the changes are visible in the file before migrating it.
+    bool flush_writes = file_.is_temporary() && handle_.has_value();
+    if (flush_writes) {
+      auto& [owner, _] = handle();
+      msync(owner.data(), owner.size(), MS_SYNC | MS_INVALIDATE);
+    }
+    handle_.reset();
+    if (flush_writes) { std::fflush(file_.descriptor().value()); }
+    return blob_file<T>{std::move(file_)};
+  }
+  [[nodiscard]] auto release() && noexcept -> blob_file<T> { return std::move(*this).unmap(); }
+
+  void reset_lazy_state() const
+  {
+    file_.reset_lazy_state();
+    hugepages_2mb_actual_ = hugepages_2mb_requested_ > HugePages::kDisable;
+  }
+};
+
+template <typename T>
+struct blob_pinned {
+ private:
+  mutable blob_mmap<T> blob_;
+  mutable void* ptr_ = nullptr;
+
+ public:
+  explicit blob_pinned(blob_mmap<T>&& blob) : blob_{std::move(blob)} {}
+  // First map the file and then register to CUDA.
+  // NB: as per docs, huge pages are not supported.
+  explicit blob_pinned(blob_file<T>&& blob, bool copy_in_memory = true)
+    : blob_{std::move(blob), copy_in_memory, false}
+  {
+  }
+  explicit blob_pinned(std::string file_name,
+                       uint32_t rows_offset = 0,
+                       uint32_t rows_limit  = 0,
+                       bool copy_in_memory  = true)
+    : blob_pinned{blob_file<T>{file_name, rows_offset, rows_limit}, copy_in_memory}
+  {
+  }
+
+  ~blob_pinned() noexcept { reset_lazy_state(); }
+
+  // No copies for owning struct
+  blob_pinned(const blob_pinned& res)                      = delete;
+  auto operator=(const blob_pinned& other) -> blob_pinned& = delete;
+  // Moving is fine
+  blob_pinned(blob_pinned&& other) : blob_{std::move(other.blob_)}, ptr_{other.ptr_}
+  {
+    other.ptr_ = nullptr;
+  }
+  auto operator=(blob_pinned&& other) -> blob_pinned&
+  {
+    std::swap(this->blob_, other.blob_);
+    std::swap(this->ptr_, other.ptr_);
+    return *this;
+  }
+
+  [[nodiscard]] auto data() const -> T*
+  {
+    if (ptr_ == nullptr) {
+      void* ptr = reinterpret_cast<void*>(blob_.data());
+#ifndef BUILD_CPU_ONLY
+      int flags       = cudaHostRegisterDefault;
+      auto error_code = cudaSuccess;
+      if (!blob_.is_in_memory()) {
+        flags      = cudaHostRegisterIoMemory | cudaHostRegisterReadOnly;
+        error_code = cudaHostRegister(ptr, blob_.size(), flags);
+        if (error_code == cudaErrorNotSupported) {
+          // Sometimes read-only is not supported
+          flags      = cudaHostRegisterIoMemory;
+          error_code = cudaHostRegister(ptr, blob_.size(), error_code);
+        }
+      } else {
+        error_code = cudaHostRegister(ptr, blob_.size(), cudaHostRegisterDefault);
+      }
+      if (error_code == cudaErrorInvalidValue && (!blob_.is_in_memory() || blob_.is_hugepage())) {
+        auto hugepage =
+          (blob_.is_hugepage() && blob_.is_in_memory()) ? HugePages::kAsk : HugePages::kDisable;
+        auto file = std::move(blob_).release();
+        blob_     = blob_mmap<T>{std::move(file), true, hugepage};
+        return data();
+      }
+      if (error_code != cudaSuccess) {
+        log_error(
+          "cuvs::bench::blob_pinned: cudaHostRegister(%p, %zu, %d)", ptr, blob_.size(), flags);
+        throw std::runtime_error{
+          "cuvs::bench::blob_pinned: call to cudaHostRegister failed with code " +
+          std::to_string(error_code)};
+      }
+#endif
+      ptr_ = ptr;
+    }
+    return reinterpret_cast<T*>(ptr_);
+  }
+
+  [[nodiscard]] auto unpin() && noexcept -> blob_mmap<T>
+  {
+    // unregister the memory before passing it to a third party
+    if (ptr_ != nullptr) {
+#ifndef BUILD_CPU_ONLY
+      cudaHostUnregister(ptr_);
+#endif
+      ptr_ = nullptr;
+    }
+    return blob_mmap<T>{std::move(blob_)};
+  }
+  void reset_lazy_state() const
+  {
+    if (ptr_ != nullptr) {
+#ifndef BUILD_CPU_ONLY
+      cudaHostUnregister(ptr_);
+#endif
+      ptr_ = nullptr;
+    }
+    blob_.reset_lazy_state();
+  }
+
+  [[nodiscard]] auto is_in_memory() const noexcept -> bool { return true; }
+  [[nodiscard]] auto is_hugepage() const noexcept -> bool { return blob_.is_hugepage(); }
+  [[nodiscard]] auto hugepage_compliant(HugePages request) const -> bool
+  {
+    return blob_.hugepage_compliant(request);
+  }
+  [[nodiscard]] auto n_rows() const -> uint32_t { return blob_.n_rows(); }
+  [[nodiscard]] auto n_cols() const -> uint32_t { return blob_.n_cols(); }
+  [[nodiscard]] auto size() const -> size_t { return blob_.size(); }
+  [[nodiscard]] auto release() && noexcept -> blob_file<T>
+  {
+    return std::move(*this).unpin().release();
+  }
+};
+
+template <typename OwnerType, typename T>
+struct blob_copying {
+ private:
+  blob_mmap<T> blob_;
+  mutable std::optional<OwnerType> mem_ = std::nullopt;
+
+ public:
+  explicit blob_copying(blob_mmap<T>&& blob) : blob_{std::move(blob)} {}
+  // First map the file and then copy it to device; use huge pages for faster copy
+  explicit blob_copying(blob_file<T>&& blob) : blob_{std::move(blob), false, HugePages::kAsk} {}
+  explicit blob_copying(std::string file_name, uint32_t rows_offset = 0, uint32_t rows_limit = 0)
+    : blob_copying{blob_file<T>{file_name, rows_offset, rows_limit}}
+  {
+  }
+
+  [[nodiscard]] auto data() const -> T*
+  {
+    if (!mem_.has_value()) {
+      mem_.emplace(blob_.size());
+#ifndef BUILD_CPU_ONLY
+      auto error_code = cudaMemcpy(mem_->data(), blob_.data(), blob_.size(), cudaMemcpyDefault);
+      if (error_code != cudaSuccess) {
+        throw std::runtime_error{"cuvs::bench::blob_device: call to cudaMemcpy failed with code " +
+                                 std::to_string(error_code)};
+      }
+#endif
+    }
+    return reinterpret_cast<T*>(mem_->data());
+  }
+
+  [[nodiscard]] auto free() && noexcept -> blob_mmap<T>
+  {
+    mem_.reset();
+    return blob_mmap<T>{std::move(blob_)};
+  }
+  void reset_lazy_state() const
+  {
+    mem_.reset();
+    blob_.reset_lazy_state();
+  }
+
+  [[nodiscard]] auto is_in_memory() const noexcept -> bool { return true; }
+  [[nodiscard]] auto is_hugepage() const noexcept -> bool { return blob_.is_hugepage(); }
+  // For copying CUDA allocation, the hugepage setting is not relevant at all.
+  [[nodiscard]] auto hugepage_compliant(HugePages request) const -> bool { return true; }
+  [[nodiscard]] auto n_rows() const -> uint32_t { return blob_.n_rows(); }
+  [[nodiscard]] auto n_cols() const -> uint32_t { return blob_.n_cols(); }
+  [[nodiscard]] auto size() const -> size_t { return blob_.size(); }
+  [[nodiscard]] auto release() && noexcept -> blob_file<T>
+  {
+    return std::move(*this).free().release();
+  }
+};
+
+template <typename T>
+using blob_device = blob_copying<device_mem_owner, T>;
+template <typename T>
+using blob_managed = blob_copying<managed_mem_owner, T>;
+
+/**
+ * @brief A blob is a single contiguous piece of data.
+ *
+ * It can reside in host, managed, or device memory, it can be pinned in physical memory or backed
+ * by a filesystem. You can also control whether to use huge pages (2MB) when it is in host memory.
+ *
+ * The blob data is a one- or two-dimensional typed array (it has n_rows and n_cols properties).
+ *
+ * The blob tries to be lazy accessing the data. It reads or copies the data only when it is
+ * requested. This allows the benchmarking executable be more resilient; for example, a blob can
+ * point to a non-existent query dataset when the benchmark is in the index build mode.
+ *
+ * @tparam T the data type.
+ */
+template <typename T>
+struct blob {
+ private:
+  using blob_type = std::variant<blob_mmap<T>, blob_pinned<T>, blob_device<T>, blob_managed<T>>;
+  mutable blob_type value_;
+
+  [[nodiscard]] auto data_mmap(bool in_memory, HugePages request_hugepages_2mb) const -> T*
+  {
+    if (auto* v = std::get_if<blob_mmap<T>>(&value_)) {
+      if (v->is_in_memory() == in_memory && v->hugepage_compliant(request_hugepages_2mb)) {
+        return v->data();
+      }
+    }
+    blob_type tmp{std::move(value_)};
+    value_ = std::visit(
+      [in_memory, request_hugepages_2mb](auto&& val) {
+        return blob_mmap<T>{std::move(val).release(), in_memory, request_hugepages_2mb};
+      },
+      std::move(tmp));
+
+    return data();
+  }
+
+  [[nodiscard]] auto data_pinned(HugePages request_hugepages_2mb) const -> T*
+  {
+    // The requested type is there
+    if (auto* v = std::get_if<blob_pinned<T>>(&value_)) {
+      if (v->hugepage_compliant(request_hugepages_2mb)) { return v->data(); }
+    }
+    // If there's already an mmap allocation, we just need to pin it.
+    if (auto* v = std::get_if<blob_mmap<T>>(&value_)) {
+      if (v->hugepage_compliant(request_hugepages_2mb)) {
+        blob_mmap<T> tmp{std::move(*v)};
+        return value_.template emplace<blob_pinned<T>>(std::move(tmp)).data();
+      }
+    }
+    // otherwise do full reset
+    blob_type tmp{std::move(value_)};
+    value_ = std::visit(
+      [request_hugepages_2mb](auto&& val) {
+        blob_mmap<T> tmp{std::move(val).release(), true, request_hugepages_2mb};
+        return blob_pinned<T>{std::move(tmp)};
+      },
+      std::move(value_));
+
+    return data();
+  }
+
+  [[nodiscard]] auto data_device() const -> T*
+  {
+    // The requested type is there
+    if (auto* v = std::get_if<blob_device<T>>(&value_)) { return v->data(); }
+    // otherwise do full reset
+    blob_type tmp{std::move(value_)};
+    value_ = std::visit([](auto&& val) { return blob_device<T>{std::move(val).release()}; },
+                        std::move(tmp));
+    return data();
+  }
+
+  [[nodiscard]] auto data_managed() const -> T*
+  {
+    // The requested type is there
+    if (auto* v = std::get_if<blob_managed<T>>(&value_)) { return v->data(); }
+    // otherwise do full reset
+    blob_type tmp{std::move(value_)};
+    value_ = std::visit([](auto&& val) { return blob_managed<T>{std::move(val).release()}; },
+                        std::move(tmp));
+    return data();
+  }
+
+ public:
+  explicit blob(std::string file_name,
+                uint32_t rows_offset    = 0,
+                uint32_t rows_limit     = 0,
+                bool copy_in_memory     = false,
+                HugePages hugepages_2mb = HugePages::kDisable)
+    : value_{std::in_place_type<blob_mmap<T>>,
+             std::move(file_name),
+             rows_offset,
+             rows_limit,
+             copy_in_memory,
+             hugepages_2mb}
+  {
+  }
+
+  template <typename VariantT>
+  explicit blob(VariantT&& blob_variant) : value_{std::move(blob_variant)}
+  {
+  }
+
+  [[nodiscard]] auto data() const -> T*
+  {
+    return std::visit([](auto&& val) { return val.data(); }, value_);
+  }
+
+  [[nodiscard]] auto data(MemoryType memory_type,
+                          HugePages request_hugepages_2mb = HugePages::kDisable) const -> T*
+  {
+    switch (memory_type) {
+      case MemoryType::kHost: return data_mmap(true, request_hugepages_2mb);
+      case MemoryType::kHostMmap: return data_mmap(false, request_hugepages_2mb);
+      case MemoryType::kHostPinned:
+        if (request_hugepages_2mb > HugePages::kDisable) {
+          log_error(
+            "cuvs::bench::blob::data(): huge pages are requested but not supported by "
+            "cudaHostRegister at the moment. We will try nevertheless...");
+        }
+        return data_pinned(request_hugepages_2mb);
+      case MemoryType::kDevice: return data_device();    // hugepages are not relevant here
+      case MemoryType::kManaged: return data_managed();  // hugepages are not relevant here
+      default:
+        throw std::runtime_error{"cuvs::bench::blob::data(): unexpected memory type " +
+                                 std::to_string(static_cast<int>(memory_type))};
+    }
+  }
+
+  [[nodiscard]] auto is_in_memory() const noexcept -> bool
+  {
+    return std::visit([](auto&& val) { return val.is_in_memory(); }, value_);
+  }
+  [[nodiscard]] auto is_hugepage() const noexcept -> bool
+  {
+    return std::visit([](auto&& val) { return val.is_hugepage(); }, value_);
+  }
+  [[nodiscard]] auto n_rows() const -> uint32_t
+  {
+    return std::visit([](auto&& val) { return val.n_rows(); }, value_);
+  }
+
+  [[nodiscard]] auto n_cols() const -> uint32_t
+  {
+    return std::visit([](auto&& val) { return val.n_cols(); }, value_);
+  }
+  /** Size of the blob content in bytes (doesn't include file header). */
+  [[nodiscard]] auto size() const -> size_t
+  {
+    return std::visit([](auto&& val) { return val.size(); }, value_);
+  }
+  /**
+   * Reset the hidden internal state, e.g. release the memory allocation or close a file descriptor.
+   * This is useful in case when accessing the blob resulted in an error/invalid state, which should
+   * be reproduced at a later point in time.
+   */
+  void reset_lazy_state() const
+  {
+    std::visit([](auto&& val) { val.reset_lazy_state(); }, value_);
+  }
+};
+
+}  // namespace  cuvs::bench
diff --git a/cpp/bench/ann/src/common/conf.hpp b/cpp/bench/ann/src/common/conf.hpp
index 1fc7327cb5..ac1361219e 100644
--- a/cpp/bench/ann/src/common/conf.hpp
+++ b/cpp/bench/ann/src/common/conf.hpp
@@ -45,14 +45,16 @@ class configuration {
     // the range of rows is [subset_first_row, subset_first_row + subset_size)
     // however, subset_size = 0 means using all rows after subset_first_row
     // that is, the subset is [subset_first_row, #rows in base_file)
-    size_t subset_first_row{0};
-    size_t subset_size{0};
+    uint32_t subset_first_row{0};
+    uint32_t subset_size{0};
     std::string query_file;
     std::string distance;
     std::optional<std::string> groundtruth_neighbors_file{std::nullopt};
 
     // data type of input dataset, possible values ["float", "int8", "uint8"]
     std::string dtype;
+
+    std::optional<double> filtering_rate{std::nullopt};
   };
 
   explicit inline configuration(std::istream& conf_stream)
@@ -74,6 +76,9 @@ class configuration {
     dataset_conf_.base_file  = conf.at("base_file");
     dataset_conf_.query_file = conf.at("query_file");
     dataset_conf_.distance   = conf.at("distance");
+    if (conf.contains("filtering_rate")) {
+      dataset_conf_.filtering_rate.emplace(conf.at("filtering_rate"));
+    }
 
     if (conf.contains("groundtruth_neighbors_file")) {
       dataset_conf_.groundtruth_neighbors_file = conf.at("groundtruth_neighbors_file");
diff --git a/cpp/bench/ann/src/common/dataset.hpp b/cpp/bench/ann/src/common/dataset.hpp
index 49020fe36e..ba801d1651 100644
--- a/cpp/bench/ann/src/common/dataset.hpp
+++ b/cpp/bench/ann/src/common/dataset.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,529 +16,166 @@
 #pragma once
 
 #include "ann_types.hpp"
-#include "util.hpp"
+#include "blob.hpp"
 
-#include <cerrno>
-#include <sys/mman.h>
-#include <sys/stat.h>
-
-#include <cassert>
 #include <cstdint>
 #include <cstdio>
 #include <optional>
-#include <stdexcept>
+#include <random>
 #include <string>
-#include <type_traits>
-#include <utility>
-#include <vector>
 
 namespace cuvs::bench {
 
-// http://big-algo-benchmarks.com/index.html:
-// binary format that starts with 8 bytes of data consisting of num_points(uint32_t)
-// num_dimensions(uint32) followed by num_pts x num_dimensions x sizeof(type) bytes of
-// data stored one vector after another.
-// Data files will have suffixes .fbin, .u8bin, and .i8bin to represent float32, uint8
-// and int8 type data.
-// As extensions for this benchmark, half and int data files will have suffixes .f16bin
-// and .ibin, respectively.
-template <typename T>
-class bin_file {
- public:
-  bin_file(std::string file,
-           const std::string& mode,
-           uint32_t subset_first_row = 0,
-           uint32_t subset_size      = 0);
-  ~bin_file()
-  {
-    if (mapped_ptr_ != nullptr) { unmap(); }
-    if (fp_ != nullptr) { fclose(fp_); }
-  }
-  bin_file(const bin_file&)                    = delete;
-  auto operator=(const bin_file&) -> bin_file& = delete;
-
-  void get_shape(size_t* nrows, int* ndims) const
-  {
-    assert(read_mode_);
-    if (!fp_) { open_file(); }
-    *nrows = nrows_;
-    *ndims = ndims_;
-  }
-
-  void read(T* data) const
-  {
-    assert(read_mode_);
-    if (!fp_) { open_file(); }
-    size_t total = static_cast<size_t>(nrows_) * ndims_;
-    if (fread(data, sizeof(T), total, fp_) != total) {
-      throw std::runtime_error{"fread() bin_file " + file_ + " failed"};
-    }
-  }
-
-  void write(const T* data, uint32_t nrows, uint32_t ndims)
-  {
-    assert(!read_mode_);
-    if (!fp_) { open_file(); }
-    if (fwrite(&nrows, sizeof(uint32_t), 1, fp_) != 1) {
-      throw std::runtime_error{"fwrite() bin_file " + file_ + " failed"};
-    }
-    if (fwrite(&ndims, sizeof(uint32_t), 1, fp_) != 1) {
-      throw std::runtime_error{"fwrite() bin_file " + file_ + " failed"};
-    }
-
-    size_t total = static_cast<size_t>(nrows) * ndims;
-    if (fwrite(data, sizeof(T), total, fp_) != total) {
-      throw std::runtime_error{"fwrite() bin_file " + file_ + " failed"};
-    }
-  }
-
-  auto map() const -> T*
-  {
-    assert(read_mode_);
-    if (!fp_) { open_file(); }
-    int fid     = fileno(fp_);
-    mapped_ptr_ = mmap(nullptr, file_size_, PROT_READ, MAP_PRIVATE, fid, 0);
-    if (mapped_ptr_ == MAP_FAILED) {
-      mapped_ptr_ = nullptr;
-      throw std::runtime_error{"mmap error: Value of errno " + std::to_string(errno) + ", " +
-                               std::string(strerror(errno))};
-    }
-    return reinterpret_cast<T*>(reinterpret_cast<uint8_t*>(mapped_ptr_) + 2 * sizeof(uint32_t) +
-                                subset_first_row_ * ndims_ * sizeof(T));
-  }
-
-  void unmap() const
-  {
-    if (munmap(mapped_ptr_, file_size_) == -1) {
-      throw std::runtime_error{"munmap error: " + std::string(strerror(errno))};
-    }
-  }
-
- private:
-  void check_suffix();
-  void open_file() const;
-
-  std::string file_;
-  bool read_mode_;
-  uint32_t subset_first_row_;
-  uint32_t subset_size_;
-
-  mutable FILE* fp_{nullptr};
-  mutable uint32_t nrows_;
-  mutable uint32_t ndims_;
-  mutable size_t file_size_;
-  mutable void* mapped_ptr_{nullptr};
-};
-
-template <typename T>
-bin_file<T>::bin_file(std::string file,
-                      const std::string& mode,
-                      uint32_t subset_first_row,
-                      uint32_t subset_size)
-  : file_(std::move(file)),
-    read_mode_(mode == "r"),
-    subset_first_row_(subset_first_row),
-    subset_size_(subset_size)
-
+template <typename CarrierT>
+void generate_bernoulli(CarrierT* data, size_t words, double p)
 {
-  check_suffix();
-
-  if (!read_mode_) {
-    if (mode == "w") {
-      if (subset_first_row != 0) {
-        throw std::runtime_error{"subset_first_row should be zero for write mode"};
-      }
-      if (subset_size != 0) {
-        throw std::runtime_error{"subset_size should be zero for write mode"};
-      }
-    } else {
-      throw std::runtime_error{"bin_file's mode must be either 'r' or 'w': " + file_};
+  constexpr size_t kBitsPerCarrierValue = sizeof(CarrierT) * 8;
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::bernoulli_distribution d(p);
+  for (size_t i = 0; i < words; i++) {
+    CarrierT word = 0;
+    for (size_t j = 0; j < kBitsPerCarrierValue; j++) {
+      word |= CarrierT{d(gen)} << j;
     }
+    data[i] = word;
   }
-}
-
-template <typename T>
-void bin_file<T>::open_file() const
-{
-  fp_ = fopen(file_.c_str(), read_mode_ ? "r" : "w");
-  if (!fp_) { throw std::runtime_error{"open bin_file failed: " + file_}; }
-
-  if (read_mode_) {
-    struct stat statbuf;
-    if (stat(file_.c_str(), &statbuf) != 0) { throw std::runtime_error{"stat() failed: " + file_}; }
-    file_size_ = statbuf.st_size;
+};
 
-    uint32_t header[2];
-    if (fread(header, sizeof(uint32_t), 2, fp_) != 2) {
-      throw std::runtime_error{"read header of bin_file failed: " + file_};
-    }
-    nrows_ = header[0];
-    ndims_ = header[1];
+template <typename DataT, typename IdxT = int32_t>
+struct dataset {
+ public:
+  using bitset_carrier_type                           = uint32_t;
+  static inline constexpr size_t kBitsPerCarrierValue = sizeof(bitset_carrier_type) * 8;
 
-    size_t expected_file_size =
-      2 * sizeof(uint32_t) + static_cast<size_t>(nrows_) * ndims_ * sizeof(T);
-    if (file_size_ != expected_file_size) {
-      throw std::runtime_error{"expected file size of " + file_ + " is " +
-                               std::to_string(expected_file_size) + ", however, actual size is " +
-                               std::to_string(file_size_)};
-    }
+ private:
+  std::string name_;
+  std::string distance_;
+  blob<DataT> base_set_;
+  blob<DataT> query_set_;
+  std::optional<blob<IdxT>> ground_truth_set_;
+  std::optional<blob<bitset_carrier_type>> filter_bitset_;
 
-    if (subset_first_row_ >= nrows_) {
-      throw std::runtime_error{file_ + ": subset_first_row (" + std::to_string(subset_first_row_) +
-                               ") >= nrows (" + std::to_string(nrows_) + ")"};
-    }
-    if (subset_first_row_ + subset_size_ > nrows_) {
-      throw std::runtime_error{file_ + ": subset_first_row (" + std::to_string(subset_first_row_) +
-                               ") + subset_size (" + std::to_string(subset_size_) + ") > nrows (" +
-                               std::to_string(nrows_) + ")"};
-    }
+  mutable bool base_set_accessed_  = false;
+  mutable bool query_set_accessed_ = false;
 
-    if (subset_first_row_) {
-      static_assert(sizeof(long) == 8, "fseek() don't support 64-bit offset");
-      if (fseek(fp_, sizeof(T) * subset_first_row_ * ndims_, SEEK_CUR) == -1) {
-        throw std::runtime_error{file_ + ": fseek failed"};
+ public:
+  dataset(std::string name,
+          std::string base_file,
+          uint32_t subset_first_row,
+          uint32_t subset_size,
+          std::string query_file,
+          std::string distance,
+          std::optional<std::string> groundtruth_neighbors_file,
+          std::optional<double> filtering_rate = std::nullopt)
+    : name_{std::move(name)},
+      distance_{std::move(distance)},
+      base_set_{base_file, subset_first_row, subset_size},
+      query_set_{query_file},
+      ground_truth_set_{groundtruth_neighbors_file.has_value()
+                          ? std::make_optional<blob<IdxT>>(groundtruth_neighbors_file.value())
+                          : std::nullopt}
+  {
+    if (filtering_rate.has_value()) {
+      // Generate a random bitset for filtering
+      auto n_rows = static_cast<size_t>(subset_size) + static_cast<size_t>(subset_first_row);
+      if (subset_size == 0) {
+        // Read the base set size as a last resort only - for better laziness
+        n_rows = base_set_size();
       }
-      nrows_ -= subset_first_row_;
+      auto bitset_size = (n_rows - 1) / kBitsPerCarrierValue + 1;
+      blob_file<bitset_carrier_type> bitset_blob_file{static_cast<uint32_t>(bitset_size), 1};
+      blob_mmap<bitset_carrier_type> bitset_blob{
+        std::move(bitset_blob_file), false, HugePages::kDisable};
+      generate_bernoulli(const_cast<bitset_carrier_type*>(bitset_blob.data()),
+                         bitset_size,
+                         1.0 - filtering_rate.value());
+      filter_bitset_.emplace(std::move(bitset_blob));
     }
-    if (subset_size_) { nrows_ = subset_size_; }
   }
-}
 
-template <typename T>
-void bin_file<T>::check_suffix()
-{
-  auto pos = file_.rfind('.');
-  if (pos == std::string::npos) {
-    throw std::runtime_error{"name of bin_file doesn't have a suffix: " + file_};
+  [[nodiscard]] auto name() const -> std::string { return name_; }
+  [[nodiscard]] auto distance() const -> std::string { return distance_; }
+  [[nodiscard]] auto dim() const -> int
+  {
+    // If any of base/query set are already accessed, use those
+    if (base_set_accessed_) { return static_cast<int>(base_set_.n_cols()); }
+    if (query_set_accessed_) { return static_cast<int>(query_set_.n_cols()); }
+    // Otherwise, try reading both (one of the two sets may be missing)
+    try {
+      query_set_accessed_ = true;
+      return static_cast<int>(query_set_.n_cols());
+    } catch (const std::runtime_error& e) {
+      // Any exception raised above will re-raise next time we try to access the query set.
+      query_set_accessed_ = false;
+      query_set_.reset_lazy_state();
+    }
+    base_set_accessed_ = true;
+    return static_cast<int>(base_set_.n_cols());
+  }
+  [[nodiscard]] auto max_k() const -> uint32_t
+  {
+    if (ground_truth_set_.has_value()) { return ground_truth_set_->n_cols(); }
+    return 0;
   }
-  std::string suffix = file_.substr(pos + 1);
-
-  if constexpr (std::is_same_v<T, float>) {
-    if (suffix != "fbin") {
-      throw std::runtime_error{"bin_file<float> should has .fbin suffix: " + file_};
-    }
-  } else if constexpr (std::is_same_v<T, half>) {
-    if (suffix != "f16bin" && suffix != "fbin") {
-      throw std::runtime_error{"bin_file<half> should has .f16bin suffix: " + file_};
-    }
-  } else if constexpr (std::is_same_v<T, int>) {
-    if (suffix != "ibin") {
-      throw std::runtime_error{"bin_file<int> should has .ibin suffix: " + file_};
-    }
-  } else if constexpr (std::is_same_v<T, uint8_t>) {
-    if (suffix != "u8bin") {
-      throw std::runtime_error{"bin_file<uint8_t> should has .u8bin suffix: " + file_};
-    }
-  } else if constexpr (std::is_same_v<T, int8_t>) {
-    if (suffix != "i8bin") {
-      throw std::runtime_error{"bin_file<int8_t> should has .i8bin suffix: " + file_};
-    }
-  } else {
-    throw std::runtime_error(
-      "T of bin_file<T> should be one of float, half, int, uint8_t, or int8_t");
+  [[nodiscard]] auto base_set_size() const -> size_t
+  {
+    base_set_accessed_ = true;
+    return base_set_.n_rows();
   }
-}
-
-template <typename T>
-class dataset {
- public:
-  explicit dataset(std::string name) : name_(std::move(name)) {}
-  dataset(std::string name, std::string distance)
-    : name_(std::move(name)), distance_(std::move(distance))
+  [[nodiscard]] auto query_set_size() const -> size_t
   {
+    query_set_accessed_ = true;
+    return query_set_.n_rows();
   }
-  dataset(const dataset&)                    = delete;
-  auto operator=(const dataset&) -> dataset& = delete;
-  virtual ~dataset();
-
-  auto name() const -> std::string { return name_; }
-  auto distance() const -> std::string { return distance_; }
-  virtual auto dim() const -> int               = 0;
-  virtual auto max_k() const -> uint32_t        = 0;
-  virtual auto base_set_size() const -> size_t  = 0;
-  virtual auto query_set_size() const -> size_t = 0;
 
-  // load data lazily, so don't pay the overhead of reading unneeded set
-  // e.g. don't load base set when searching
-  auto base_set() const -> const T*
+  [[nodiscard]] auto gt_set() const -> const IdxT*
   {
-    if (!base_set_) { load_base_set(); }
-    return base_set_;
+    if (ground_truth_set_.has_value()) { return ground_truth_set_->data(); }
+    return nullptr;
   }
 
-  auto query_set() const -> const T*
+  [[nodiscard]] auto query_set() const -> const DataT*
   {
-    if (!query_set_) { load_query_set(); }
-    return query_set_;
+    query_set_accessed_ = true;
+    return query_set_.data();
   }
-
-  auto gt_set() const -> const int32_t*
+  [[nodiscard]] auto query_set(MemoryType memory_type,
+                               HugePages request_hugepages_2mb = HugePages::kDisable) const
+    -> const DataT*
   {
-    if (!gt_set_) { load_gt_set(); }
-    return gt_set_;
+    query_set_accessed_ = true;
+    return query_set_.data(memory_type, request_hugepages_2mb);
   }
 
-  auto base_set_on_gpu() const -> const T*;
-  auto query_set_on_gpu() const -> const T*;
-  auto mapped_base_set() const -> const T*;
-
-  auto query_set(MemoryType memory_type) const -> const T*
+  [[nodiscard]] auto base_set() const -> const DataT*
   {
-    switch (memory_type) {
-      case MemoryType::kDevice: return query_set_on_gpu();
-      case MemoryType::kHost: {
-        auto r = query_set();
-#ifndef BUILD_CPU_ONLY
-        if (query_set_pinned_) {
-          cudaHostUnregister(const_cast<T*>(r));
-          query_set_pinned_ = false;
-        }
-#endif
-        return r;
-      }
-      case MemoryType::kHostPinned: {
-        auto r = query_set();
-#ifndef BUILD_CPU_ONLY
-        if (!query_set_pinned_) {
-          cudaHostRegister(
-            const_cast<T*>(r), query_set_size() * dim() * sizeof(T), cudaHostRegisterDefault);
-          query_set_pinned_ = true;
-        }
-#endif
-        return r;
-      }
-      default: return nullptr;
-    }
+    base_set_accessed_ = true;
+    return base_set_.data();
   }
-
-  auto base_set(MemoryType memory_type) const -> const T*
+  [[nodiscard]] auto base_set(MemoryType memory_type,
+                              HugePages request_hugepages_2mb = HugePages::kDisable) const
+    -> const DataT*
   {
-    switch (memory_type) {
-      case MemoryType::kDevice: return base_set_on_gpu();
-      case MemoryType::kHost: {
-        auto r = base_set();
-#ifndef BUILD_CPU_ONLY
-        if (base_set_pinned_) {
-          cudaHostUnregister(const_cast<T*>(r));
-          base_set_pinned_ = false;
-        }
-#endif
-        return r;
-      }
-      case MemoryType::kHostPinned: {
-        auto r = base_set();
-#ifndef BUILD_CPU_ONLY
-        if (!base_set_pinned_) {
-          cudaHostRegister(
-            const_cast<T*>(r), base_set_size() * dim() * sizeof(T), cudaHostRegisterDefault);
-          base_set_pinned_ = true;
-        }
-#endif
-        return r;
-      }
-      case MemoryType::kHostMmap: return mapped_base_set();
-      default: return nullptr;
-    }
+    base_set_accessed_ = true;
+    return base_set_.data(memory_type, request_hugepages_2mb);
   }
 
- protected:
-  virtual void load_base_set() const  = 0;
-  virtual void load_gt_set() const    = 0;
-  virtual void load_query_set() const = 0;
-  virtual void map_base_set() const   = 0;
-
-  std::string name_;
-  std::string distance_;
-
-  mutable T* base_set_        = nullptr;
-  mutable T* query_set_       = nullptr;
-  mutable T* d_base_set_      = nullptr;
-  mutable T* d_query_set_     = nullptr;
-  mutable T* mapped_base_set_ = nullptr;
-  mutable int32_t* gt_set_    = nullptr;
-
-  mutable bool base_set_pinned_  = false;
-  mutable bool query_set_pinned_ = false;
-};
-
-template <typename T>
-dataset<T>::~dataset()
-{
-#ifndef BUILD_CPU_ONLY
-  if (d_base_set_) { cudaFree(d_base_set_); }
-  if (d_query_set_) { cudaFree(d_query_set_); }
-  if (base_set_pinned_) { cudaHostUnregister(base_set_); }
-  if (query_set_pinned_) { cudaHostUnregister(query_set_); }
-#endif
-  delete[] base_set_;
-  delete[] query_set_;
-  delete[] gt_set_;
-}
-
-template <typename T>
-auto dataset<T>::base_set_on_gpu() const -> const T*
-{
-#ifndef BUILD_CPU_ONLY
-  if (!d_base_set_) {
-    base_set();
-    cudaMalloc(reinterpret_cast<void**>(&d_base_set_), base_set_size() * dim() * sizeof(T));
-    cudaMemcpy(d_base_set_, base_set_, base_set_size() * dim() * sizeof(T), cudaMemcpyHostToDevice);
+  [[nodiscard]] auto filter_bitset() const -> const bitset_carrier_type*
+  {
+    if (filter_bitset_.has_value()) { return filter_bitset_->data(); }
+    return nullptr;
   }
-#endif
-  return d_base_set_;
-}
 
-template <typename T>
-auto dataset<T>::query_set_on_gpu() const -> const T*
-{
-#ifndef BUILD_CPU_ONLY
-  if (!d_query_set_) {
-    query_set();
-    cudaMalloc(reinterpret_cast<void**>(&d_query_set_), query_set_size() * dim() * sizeof(T));
-    cudaMemcpy(
-      d_query_set_, query_set_, query_set_size() * dim() * sizeof(T), cudaMemcpyHostToDevice);
+  [[nodiscard]] auto filter_bitset(MemoryType memory_type,
+                                   HugePages request_hugepages_2mb = HugePages::kDisable) const
+    -> const bitset_carrier_type*
+  {
+    if (filter_bitset_.has_value()) {
+      return filter_bitset_->data(memory_type, request_hugepages_2mb);
+    }
+    return nullptr;
   }
-#endif
-  return d_query_set_;
-}
-
-template <typename T>
-auto dataset<T>::mapped_base_set() const -> const T*
-{
-  if (!mapped_base_set_) { map_base_set(); }
-  return mapped_base_set_;
-}
-
-template <typename T>
-class bin_dataset : public dataset<T> {
- public:
-  bin_dataset(const std::string& name,
-              const std::string& base_file,
-              size_t subset_first_row,
-              size_t subset_size,
-              const std::string& query_file,
-              const std::string& distance,
-              const std::optional<std::string>& groundtruth_neighbors_file);
-
-  auto dim() const -> int override;
-  auto max_k() const -> uint32_t override;
-  auto base_set_size() const -> size_t override;
-  auto query_set_size() const -> size_t override;
-
- private:
-  void load_base_set() const;
-  void load_query_set() const;
-  void load_gt_set() const;
-  void map_base_set() const;
-
-  mutable int dim_               = 0;
-  mutable uint32_t max_k_        = 0;
-  mutable size_t base_set_size_  = 0;
-  mutable size_t query_set_size_ = 0;
-
-  bin_file<T> base_file_;
-  bin_file<T> query_file_;
-  std::optional<bin_file<std::int32_t>> gt_file_{std::nullopt};
 };
 
-template <typename T>
-bin_dataset<T>::bin_dataset(const std::string& name,
-                            const std::string& base_file,
-                            size_t subset_first_row,
-                            size_t subset_size,
-                            const std::string& query_file,
-                            const std::string& distance,
-                            const std::optional<std::string>& groundtruth_neighbors_file)
-  : dataset<T>(name, distance),
-    base_file_(base_file, "r", subset_first_row, subset_size),
-    query_file_(query_file, "r")
-{
-  if (groundtruth_neighbors_file.has_value()) {
-    gt_file_.emplace(groundtruth_neighbors_file.value(), "r");
-  }
-}
-
-template <typename T>
-auto bin_dataset<T>::dim() const -> int
-{
-  if (dim_ > 0) { return dim_; }
-  if (base_set_size() > 0) { return dim_; }
-  if (query_set_size() > 0) { return dim_; }
-  return dim_;
-}
-
-template <typename T>
-auto bin_dataset<T>::max_k() const -> uint32_t
-{
-  if (!this->gt_set_) { load_gt_set(); }
-  return max_k_;
-}
-
-template <typename T>
-auto bin_dataset<T>::query_set_size() const -> size_t
-{
-  if (query_set_size_ > 0) { return query_set_size_; }
-  int dim;
-  query_file_.get_shape(&query_set_size_, &dim);
-  if (query_set_size_ == 0) { throw std::runtime_error{"Zero query set size"}; }
-  if (dim == 0) { throw std::runtime_error{"Zero query set dim"}; }
-  if (dim_ == 0) {
-    dim_ = dim;
-  } else if (dim_ != dim) {
-    throw std::runtime_error{"base set dim (" + std::to_string(dim_) + ") != query set dim (" +
-                             std::to_string(dim)};
-  }
-  return query_set_size_;
-}
-
-template <typename T>
-auto bin_dataset<T>::base_set_size() const -> size_t
-{
-  if (base_set_size_ > 0) { return base_set_size_; }
-  int dim;
-  base_file_.get_shape(&base_set_size_, &dim);
-  if (base_set_size_ == 0) { throw std::runtime_error{"Zero base set size"}; }
-  if (dim == 0) { throw std::runtime_error{"Zero base set dim"}; }
-  if (dim_ == 0) {
-    dim_ = dim;
-  } else if (dim_ != dim) {
-    throw std::runtime_error{"base set dim (" + std::to_string(dim) + ") != query set dim (" +
-                             std::to_string(dim_)};
-  }
-  return base_set_size_;
-}
-
-template <typename T>
-void bin_dataset<T>::load_base_set() const
-{
-  this->base_set_ = new T[base_set_size() * dim()];
-  base_file_.read(this->base_set_);
-}
-
-template <typename T>
-void bin_dataset<T>::load_query_set() const
-{
-  this->query_set_ = new T[query_set_size() * dim()];
-  query_file_.read(this->query_set_);
-}
-
-template <typename T>
-void bin_dataset<T>::load_gt_set() const
-{
-  if (gt_file_.has_value()) {
-    size_t queries;
-    int k;
-    gt_file_->get_shape(&queries, &k);
-    this->gt_set_ = new std::int32_t[queries * k];
-    gt_file_->read(this->gt_set_);
-    max_k_ = k;
-  }
-}
-
-template <typename T>
-void bin_dataset<T>::map_base_set() const
-{
-  this->mapped_base_set_ = base_file_.map();
-}
-
 }  // namespace  cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
index 11e0e4ad3f..441a1eafac 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
@@ -275,4 +275,23 @@ void refine_helper(const raft::resources& res,
   }
 }
 
+/**
+ * Construct a cuVS-compatible bitset filter object from a raw pointer to the bitset.
+ *
+ * @param[in] filter_bitset a pointer to a pre-generated bitset
+ * @param[in] n_rows the number of elements in the bitset / dataset.
+ * @return a shared pointer to the filter object (doesn't own the bitset data!)
+ */
+inline auto make_cuvs_filter(const void* filter_bitset, int64_t n_rows)
+  -> std::shared_ptr<cuvs::neighbors::filtering::base_filter>
+{
+  if (filter_bitset != nullptr) {
+    return std::make_shared<cuvs::neighbors::filtering::bitset_filter<uint32_t, int64_t>>(
+      raft::core::bitset_view<uint32_t, int64_t>(
+        const_cast<uint32_t*>(reinterpret_cast<const uint32_t*>(filter_bitset)), n_rows));
+  } else {
+    return std::make_shared<cuvs::neighbors::filtering::none_sample_filter>();
+  }
+}
+
 }  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
index 6670ed8926..af72dd02bb 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
@@ -47,7 +47,7 @@ class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) final;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
 
   void search(const T* queries,
               int batch_size,
@@ -102,8 +102,10 @@ void cuvs_cagra_hnswlib<T, IdxT>::build(const T* dataset, size_t nrow)
 }
 
 template <typename T, typename IdxT>
-void cuvs_cagra_hnswlib<T, IdxT>::set_search_param(const search_param_base& param_)
+void cuvs_cagra_hnswlib<T, IdxT>::set_search_param(const search_param_base& param_,
+                                                   const void* filter_bitset)
 {
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   search_param_ = dynamic_cast<const search_param&>(param_);
 }
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index 8c9cb2d4f3..f5f6097108 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -119,7 +119,7 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) final;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
 
   void set_search_dataset(const T* dataset, size_t nrow) override;
 
@@ -187,6 +187,8 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
   size_t dynamic_batching_n_queues_;
   bool dynamic_batching_conservative_dispatch_;
 
+  std::shared_ptr<cuvs::neighbors::filtering::base_filter> filter_;
+
   inline rmm::device_async_resource_ref get_mr(AllocatorType mem_type)
   {
     switch (mem_type) {
@@ -228,8 +230,10 @@ inline auto allocator_to_string(AllocatorType mem_type) -> std::string
 }
 
 template <typename T, typename IdxT>
-void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param)
+void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param,
+                                           const void* filter_bitset)
 {
+  filter_ = make_cuvs_filter(filter_bitset, index_->size());
   auto sp = dynamic_cast<const search_param&>(param);
   bool needs_dynamic_batcher_update =
     (dynamic_batching_max_batch_size_ != sp.dynamic_batching_max_batch_size) ||
@@ -292,7 +296,8 @@ void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param)
                                                         sp.dynamic_batching_n_queues,
                                                         sp.dynamic_batching_conservative_dispatch},
         *index_,
-        search_params_);
+        search_params_,
+        filter_.get());
     }
     dynamic_batcher_sp_.dispatch_timeout_ms = sp.dynamic_batching_dispatch_timeout_ms;
   } else {
@@ -379,7 +384,7 @@ void cuvs_cagra<T, IdxT>::search_base(const T* queries,
                                               distances_view);
   } else {
     cuvs::neighbors::cagra::search(
-      handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+      handle_, search_params_, *index_, queries_view, neighbors_view, distances_view, *filter_);
   }
 
   if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
index b7e335622d..a9dec026e4 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_flat_wrapper.h
@@ -59,7 +59,7 @@ class cuvs_ivf_flat : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) final;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
 
   void search(const T* queries,
               int batch_size,
@@ -92,6 +92,8 @@ class cuvs_ivf_flat : public algo<T>, public algo_gpu {
   std::shared_ptr<cuvs::neighbors::ivf_flat::index<T, IdxT>> index_;
   int device_;
   int dimension_;
+
+  std::shared_ptr<cuvs::neighbors::filtering::base_filter> filter_;
 };
 
 template <typename T, typename IdxT>
@@ -111,8 +113,10 @@ void cuvs_ivf_flat<T, IdxT>::build(const T* dataset, size_t nrow)
 }
 
 template <typename T, typename IdxT>
-void cuvs_ivf_flat<T, IdxT>::set_search_param(const search_param_base& param)
+void cuvs_ivf_flat<T, IdxT>::set_search_param(const search_param_base& param,
+                                              const void* filter_bitset)
 {
+  filter_        = make_cuvs_filter(filter_bitset, index_->size());
   auto sp        = dynamic_cast<const search_param&>(param);
   search_params_ = sp.ivf_flat_params;
   assert(search_params_.n_probes <= index_params_.n_lists);
@@ -162,7 +166,8 @@ void cuvs_ivf_flat<T, IdxT>::search(
     *index_,
     raft::make_device_matrix_view<const T, int64_t>(queries, batch_size, index_->dim()),
     raft::make_device_matrix_view<IdxT, int64_t>(neighbors_idx_t, batch_size, k),
-    raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k));
+    raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k),
+    *filter_);
   if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
     raft::linalg::unaryOp(neighbors,
                           neighbors_idx_t,
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
index dac7666697..5bef85b7e5 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ivf_pq_wrapper.h
@@ -67,7 +67,7 @@ class cuvs_ivf_pq : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) final;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
   void set_search_dataset(const T* dataset, size_t nrow) override;
 
   void search(const T* queries,
@@ -110,6 +110,8 @@ class cuvs_ivf_pq : public algo<T>, public algo_gpu {
 
   std::shared_ptr<cuvs::neighbors::dynamic_batching::index<T, IdxT>> dynamic_batcher_;
   cuvs::neighbors::dynamic_batching::search_params dynamic_batcher_sp_{};
+
+  std::shared_ptr<cuvs::neighbors::filtering::base_filter> filter_;
 };
 
 template <typename T, typename IdxT>
@@ -144,8 +146,10 @@ std::unique_ptr<algo<T>> cuvs_ivf_pq<T, IdxT>::copy()
 }
 
 template <typename T, typename IdxT>
-void cuvs_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param)
+void cuvs_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param,
+                                            const void* filter_bitset)
 {
+  filter_        = make_cuvs_filter(filter_bitset, index_->size());
   auto sp        = dynamic_cast<const search_param&>(param);
   search_params_ = sp.pq_param;
   refine_ratio_  = sp.refine_ratio;
@@ -160,7 +164,8 @@ void cuvs_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param)
                                                       sp.dynamic_batching_n_queues,
                                                       sp.dynamic_batching_conservative_dispatch},
       *index_,
-      search_params_);
+      search_params_,
+      filter_.get());
     dynamic_batcher_sp_.dispatch_timeout_ms = sp.dynamic_batching_dispatch_timeout_ms;
   } else {
     dynamic_batcher_.reset();
@@ -204,7 +209,7 @@ void cuvs_ivf_pq<T, IdxT>::search_base(
                                               distances_view);
   } else {
     cuvs::neighbors::ivf_pq::search(
-      handle_, search_params_, *index_, queries_view, neighbors_view, distances_view);
+      handle_, search_params_, *index_, queries_view, neighbors_view, distances_view, *filter_);
   }
 
   if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
index 50c1ff4dbf..cb2bbd9b5a 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
@@ -52,7 +52,7 @@ class cuvs_mg_cagra : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) final;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
 
   void set_search_dataset(const T* dataset, size_t nrow) override;
 
@@ -114,8 +114,10 @@ void cuvs_mg_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
 inline auto allocator_to_string(AllocatorType mem_type) -> std::string;
 
 template <typename T, typename IdxT>
-void cuvs_mg_cagra<T, IdxT>::set_search_param(const search_param_base& param)
+void cuvs_mg_cagra<T, IdxT>::set_search_param(const search_param_base& param,
+                                              const void* filter_bitset)
 {
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   auto sp = dynamic_cast<const search_param&>(param);
   // search_params_ = static_cast<mg::search_params<cagra::search_params>>(sp.p);
   cagra::search_params* search_params_ptr_ = static_cast<cagra::search_params*>(&search_params_);
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
index 0540edc8f0..b9f8b4b322 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
@@ -45,7 +45,7 @@ class cuvs_mg_ivf_flat : public algo<T>, public algo_gpu {
   }
 
   void build(const T* dataset, size_t nrow) final;
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
   void search(const T* queries,
               int batch_size,
               int k,
@@ -91,8 +91,10 @@ void cuvs_mg_ivf_flat<T, IdxT>::build(const T* dataset, size_t nrow)
 }
 
 template <typename T, typename IdxT>
-void cuvs_mg_ivf_flat<T, IdxT>::set_search_param(const search_param_base& param)
+void cuvs_mg_ivf_flat<T, IdxT>::set_search_param(const search_param_base& param,
+                                                 const void* filter_bitset)
 {
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   auto sp = dynamic_cast<const search_param&>(param);
   // search_params_ = sp.ivf_flat_params;
   ivf_flat::search_params* search_params_ptr_ =
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
index 65ca1bb11e..26781c522b 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
@@ -45,7 +45,7 @@ class cuvs_mg_ivf_pq : public algo<T>, public algo_gpu {
   }
 
   void build(const T* dataset, size_t nrow) final;
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
   void search(const T* queries,
               int batch_size,
               int k,
@@ -91,8 +91,10 @@ void cuvs_mg_ivf_pq<T, IdxT>::build(const T* dataset, size_t nrow)
 }
 
 template <typename T, typename IdxT>
-void cuvs_mg_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param)
+void cuvs_mg_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param,
+                                               const void* filter_bitset)
 {
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   auto sp = dynamic_cast<const search_param&>(param);
   // search_params_ = static_cast<mg::search_params<ivf_pq::search_params>>(sp.pq_param);
   ivf_pq::search_params* search_params_ptr_ = static_cast<ivf_pq::search_params*>(&search_params_);
diff --git a/cpp/bench/ann/src/cuvs/cuvs_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
index bf0fa59340..69bdaeb12e 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_wrapper.h
@@ -58,7 +58,7 @@ class cuvs_gpu : public algo<T>, public algo_gpu {
 
   void build(const T*, size_t) final;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
 
   void search(const T* queries,
               int batch_size,
@@ -91,6 +91,8 @@ class cuvs_gpu : public algo<T>, public algo_gpu {
   int device_;
   const T* dataset_;
   size_t nrow_;
+
+  std::shared_ptr<cuvs::neighbors::filtering::base_filter> filter_;
 };
 
 template <typename T>
@@ -111,9 +113,9 @@ void cuvs_gpu<T>::build(const T* dataset, size_t nrow)
 }
 
 template <typename T>
-void cuvs_gpu<T>::set_search_param(const search_param_base&)
+void cuvs_gpu<T>::set_search_param(const search_param_base&, const void* filter_bitset)
 {
-  // Nothing to set here as it is brute force implementation
+  filter_ = make_cuvs_filter(filter_bitset, index_->size());
 }
 
 template <typename T>
@@ -155,12 +157,8 @@ void cuvs_gpu<T>::search(
     raft::make_device_matrix_view<algo_base::index_type, int64_t>(neighbors, batch_size, k);
   auto distances_view = raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k);
 
-  cuvs::neighbors::brute_force::search(handle_,
-                                       *index_,
-                                       queries_view,
-                                       neighbors_view,
-                                       distances_view,
-                                       cuvs::neighbors::filtering::none_sample_filter{});
+  cuvs::neighbors::brute_force::search(
+    handle_, *index_, queries_view, neighbors_view, distances_view, *filter_);
 }
 
 template <typename T>
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
index 0cc40de37f..4cb630ed23 100644
--- a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
@@ -75,7 +75,7 @@ class faiss_cpu : public algo<T> {
 
   void build(const T* dataset, size_t nrow) final;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
 
   void init_quantizer(int dim)
   {
@@ -153,8 +153,9 @@ void faiss_cpu<T>::build(const T* dataset, size_t nrow)
 }
 
 template <typename T>
-void faiss_cpu<T>::set_search_param(const search_param_base& param)
+void faiss_cpu<T>::set_search_param(const search_param_base& param, const void* filter_bitset)
 {
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   auto sp    = dynamic_cast<const search_param&>(param);
   int nprobe = sp.nprobe;
   assert(nprobe <= nlist_);
@@ -303,8 +304,10 @@ class faiss_cpu_flat : public faiss_cpu<T> {
   }
 
   // class faiss_cpu is more like a IVF class, so need special treating here
-  void set_search_param(const typename algo<T>::search_param& param) override
+  void set_search_param(const typename algo<T>::search_param& param,
+                        const void* filter_bitset) override
   {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
     auto search_param = dynamic_cast<const typename faiss_cpu<T>::search_param&>(param);
     if (!this->thread_pool_ || this->num_threads_ != search_param.num_threads) {
       this->num_threads_ = search_param.num_threads;
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
index 6cf1fe4b0b..ed80917e85 100644
--- a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
@@ -111,7 +111,7 @@ class faiss_gpu : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) final;
 
-  virtual void set_search_param(const search_param_base& param) {}
+  virtual void set_search_param(const search_param_base& param, const void* filter_bitset) {}
 
   void set_search_dataset(const T* dataset, size_t nrow) override { dataset_ = dataset; }
 
@@ -329,8 +329,9 @@ class faiss_gpu_ivf_flat : public faiss_gpu<T> {
       this->gpu_resource_.get(), dim, param.nlist, this->metric_type_, config);
   }
 
-  void set_search_param(const search_param_base& param) override
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override
   {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
     auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
     int nprobe = sp.nprobe;
     assert(nprobe <= this->nlist_);
@@ -386,8 +387,9 @@ class faiss_gpu_ivfpq : public faiss_gpu<T> {
                                                                config);
   }
 
-  void set_search_param(const search_param_base& param) override
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override
   {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
     auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
     int nprobe = sp.nprobe;
     assert(nprobe <= this->nlist_);
@@ -449,8 +451,9 @@ class faiss_gpu_ivfsq : public faiss_gpu<T> {
       this->gpu_resource_.get(), dim, param.nlist, qtype, this->metric_type_, true, config);
   }
 
-  void set_search_param(const search_param_base& param) override
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override
   {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
     auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
     int nprobe = sp.nprobe;
     assert(nprobe <= this->nlist_);
@@ -493,8 +496,9 @@ class faiss_gpu_flat : public faiss_gpu<T> {
     this->index_  = std::make_shared<faiss::gpu::GpuIndexFlat>(
       this->gpu_resource_.get(), dim, this->metric_type_, config);
   }
-  void set_search_param(const search_param_base& param) override
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override
   {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
     auto sp    = dynamic_cast<const typename faiss_gpu<T>::search_param&>(param);
     int nprobe = sp.nprobe;
     assert(nprobe <= this->nlist_);
@@ -548,8 +552,9 @@ class faiss_gpu_cagra : public faiss_gpu<T> {
       this->gpu_resource_.get(), dim, parse_metric_faiss(this->metric_), config);
   }
 
-  void set_search_param(const search_param_base& param) override
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override
   {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
     auto sp              = static_cast<const typename faiss_gpu_cagra<T>::search_param&>(param);
     this->search_params_ = std::make_shared<faiss::gpu::SearchParametersCagra>(sp.p);
   }
diff --git a/cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh b/cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh
index e2ca18e223..b1c6c7b26d 100644
--- a/cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh
+++ b/cpp/bench/ann/src/ggnn/ggnn_wrapper.cuh
@@ -57,7 +57,10 @@ class ggnn : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) override { impl_->build(dataset, nrow); }
 
-  void set_search_param(const search_param_base& param) override { impl_->set_search_param(param); }
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override
+  {
+    impl_->set_search_param(param, filter_bitset);
+  }
   void search(const T* queries,
               int batch_size,
               int k,
@@ -128,7 +131,7 @@ class ggnn_impl : public algo<T>, public algo_gpu {
 
   void build(const T* dataset, size_t nrow) override;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
   void search(const T* queries,
               int batch_size,
               int k,
@@ -243,8 +246,10 @@ void ggnn_impl<T, measure, D, KBuild, KQuery, S>::set_search_dataset(const T* da
 }
 
 template <typename T, DistanceMeasure measure, int D, int KBuild, int KQuery, int S>
-void ggnn_impl<T, measure, D, KBuild, KQuery, S>::set_search_param(const search_param_base& param)
+void ggnn_impl<T, measure, D, KBuild, KQuery, S>::set_search_param(const search_param_base& param,
+                                                                   const void* filter_bitset)
 {
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   search_param_ = dynamic_cast<const typename ggnn<T>::search_param&>(param);
 }
 
diff --git a/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h b/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
index 9d643f12a1..d6870ae1c3 100644
--- a/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
+++ b/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
@@ -79,7 +79,7 @@ class hnsw_lib : public algo<T> {
 
   void build(const T* dataset, size_t nrow) override;
 
-  void set_search_param(const search_param_base& param) override;
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
   void search(const T* query,
               int batch_size,
               int k,
@@ -169,8 +169,9 @@ void hnsw_lib<T>::build(const T* dataset, size_t nrow)
 }
 
 template <typename T>
-void hnsw_lib<T>::set_search_param(const search_param_base& param_)
+void hnsw_lib<T>::set_search_param(const search_param_base& param_, const void* filter_bitset)
 {
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   auto param     = dynamic_cast<const search_param&>(param_);
   appr_alg_->ef_ = param.ef;
   num_threads_   = param.num_threads;

From 995b2441087722b52ef2081747b63a86938508bb Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Sun, 2 Feb 2025 02:57:48 +0900
Subject: [PATCH 293/623] Fix std::lock_guard use for gcc 14 support (#639)

The constructor of `std::lock_guard` in gcc 14 <mutex>, which is supported by CUDA 12.8, has the `nodiscard` attribute. This PR fixes the error occurring with gcc 14.

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/639
---
 cpp/bench/ann/src/common/thread_pool.hpp |  2 +-
 cpp/src/neighbors/iface/iface.hpp        | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/cpp/bench/ann/src/common/thread_pool.hpp b/cpp/bench/ann/src/common/thread_pool.hpp
index 9d0218606e..e2192daea1 100644
--- a/cpp/bench/ann/src/common/thread_pool.hpp
+++ b/cpp/bench/ann/src/common/thread_pool.hpp
@@ -61,7 +61,7 @@ class fixed_thread_pool {
     finished_.store(true, std::memory_order_relaxed);
     for (unsigned i = 0; i < threads_.size(); ++i) {
       auto& task = tasks_[i];
-      std::lock_guard<std::mutex>(task.mtx);
+      std::lock_guard lock(task.mtx);
 
       task.cv.notify_one();
       threads_[i].join();
diff --git a/cpp/src/neighbors/iface/iface.hpp b/cpp/src/neighbors/iface/iface.hpp
index 98ef3fdd38..59d3e12d0e 100644
--- a/cpp/src/neighbors/iface/iface.hpp
+++ b/cpp/src/neighbors/iface/iface.hpp
@@ -36,7 +36,7 @@ void build(const raft::device_resources& handle,
            const cuvs::neighbors::index_params* index_params,
            raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> index_dataset)
 {
-  std::lock_guard(*interface.mutex_);
+  std::lock_guard lock(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     auto idx = cuvs::neighbors::ivf_flat::build(
@@ -62,7 +62,7 @@ void extend(
   std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, Accessor2>>
     new_indices)
 {
-  std::lock_guard(*interface.mutex_);
+  std::lock_guard lock(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     auto idx =
@@ -141,7 +141,7 @@ void serialize(const raft::device_resources& handle,
                const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                std::ostream& os)
 {
-  std::lock_guard(*interface.mutex_);
+  std::lock_guard lock(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     ivf_flat::serialize(handle, os, interface.index_.value());
@@ -157,7 +157,7 @@ void deserialize(const raft::device_resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  std::istream& is)
 {
-  std::lock_guard(*interface.mutex_);
+  std::lock_guard lock(*interface.mutex_);
 
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     ivf_flat::index<T, IdxT> idx(handle);
@@ -179,7 +179,7 @@ void deserialize(const raft::device_resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  const std::string& filename)
 {
-  std::lock_guard(*interface.mutex_);
+  std::lock_guard lock(*interface.mutex_);
 
   std::ifstream is(filename, std::ios::in | std::ios::binary);
   if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }

From 2b7dce65abf8334664f00c7bb23076caaf603f6e Mon Sep 17 00:00:00 2001
From: Akira Naruse <anaruse@nvidia.com>
Date: Sun, 2 Feb 2025 16:56:00 +0900
Subject: [PATCH 294/623] Iteratively build graph index (#612)

This PR is about how CAGRA's search() and optimize() can be used to iteratively create and improve graph index.

Currently, IVFPQ and NND are used to create the initial kNN graph, which is then optimized to create the CAGRA search graph. So, for example, if you want to support a new data type in CAGRA, you need to create an initial kNN graph with that data type, and IVFPQ or NND must also support that new data type. This is a bit of hassle.

This PR is one solution to that problem. With functionality of this PR, once the CAGRA search supports the new data type, it can be used to create a graph index with it.

Authors:
  - Akira Naruse (https://github.com/anaruse)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/612
---
 cpp/include/cuvs/neighbors/cagra.hpp          |  17 +-
 .../neighbors/detail/cagra/cagra_build.cuh    | 224 +++++++++++++++---
 cpp/tests/neighbors/ann_cagra.cuh             |  30 ++-
 3 files changed, 234 insertions(+), 37 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 4544fcaf8a..d8edf742b0 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -72,6 +72,10 @@ struct ivf_pq_params {
 };
 
 using nn_descent_params = cuvs::neighbors::nn_descent::index_params;
+
+// **** Experimental ****
+using iterative_search_params = cuvs::neighbors::search_params;
+
 }  // namespace graph_build_params
 
 struct index_params : cuvs::neighbors::index_params {
@@ -87,9 +91,9 @@ struct index_params : cuvs::neighbors::index_params {
 
   /** Parameters for graph building.
    *
-   * Set ivf_pq_params or nn_descent_params to select the graph build algorithm and control their
-   * parameters. The default (std::monostate) is to use a heuristic to decide the algorithm and its
-   * parameters.
+   * Set ivf_pq_params, nn_descent_params, or iterative_search_params to select the graph build
+   * algorithm and control their parameters. The default (std::monostate) is to use a heuristic
+   *  to decide the algorithm and its parameters.
    *
    * @code{.cpp}
    * cagra::index_params params;
@@ -100,11 +104,16 @@ struct index_params : cuvs::neighbors::index_params {
    * // 2. Choose NN Descent algorithm for kNN graph construction
    * params.graph_build_params =
    * cagra::graph_build_params::nn_descent_params(params.intermediate_graph_degree);
+   *
+   * // 3. Choose iterative graph building using CAGRA's search() and optimize()  [Experimental]
+   * params.graph_build_params =
+   * cagra::graph_build_params::iterative_search_params();
    * @endcode
    */
   std::variant<std::monostate,
                graph_build_params::ivf_pq_params,
-               graph_build_params::nn_descent_params>
+               graph_build_params::nn_descent_params,
+               graph_build_params::iterative_search_params>
     graph_build_params;
 
   /**
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 3409864487..ee4b1444f9 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -401,6 +401,167 @@ void optimize(
     res, knn_graph_internal, new_graph_internal, guarantee_connectivity);
 }
 
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+auto iterative_build_graph(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
+{
+  size_t intermediate_degree = params.intermediate_graph_degree;
+  size_t graph_degree        = params.graph_degree;
+
+  auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(0, 0);
+
+  // Iteratively improve the accuracy of the graph by repeatedly running
+  // CAGRA's search() and optimize(). As for the size of the graph, instead
+  // of targeting all nodes from the beginning, the number of nodes is
+  // initially small, and the number of nodes is doubled with each iteration.
+  RAFT_LOG_INFO("Iteratively creating/improving graph index using CAGRA's search() and optimize()");
+
+  // If dataset is a host matrix, change it to a device matrix. Also, if the
+  // dimensionality of the dataset does not meet the alighnemt restriction,
+  // add extra dimensions and change it to a strided matrix.
+  std::unique_ptr<strided_dataset<T, int64_t>> dev_aligned_dataset;
+  try {
+    dev_aligned_dataset = make_aligned_dataset(res, dataset);
+  } catch (raft::logic_error& e) {
+    RAFT_LOG_ERROR("Iterative CAGRA graph build requires the dataset to fit GPU memory");
+    throw e;
+  }
+  auto dev_aligned_dataset_view = dev_aligned_dataset.get()->view();
+
+  // If the matrix stride and extent do no match, the extra dimensions are
+  // also as extent since it cannot be used as query matrix.
+  auto dev_dataset =
+    raft::make_device_matrix_view<const T, int64_t>(dev_aligned_dataset_view.data_handle(),
+                                                    dev_aligned_dataset_view.extent(0),
+                                                    dev_aligned_dataset_view.stride(0));
+
+  // Determine initial graph size.
+  uint64_t final_graph_size   = (uint64_t)dataset.extent(0);
+  uint64_t initial_graph_size = (final_graph_size + 1) / 2;
+  while (initial_graph_size > graph_degree * 64) {
+    initial_graph_size = (initial_graph_size + 1) / 2;
+  }
+  RAFT_LOG_DEBUG("# initial graph size = %lu", (uint64_t)initial_graph_size);
+
+  // Allocate memory for search results.
+  constexpr uint64_t max_chunk_size = 8192;
+  auto topk                         = intermediate_degree;
+  auto dev_neighbors = raft::make_device_matrix<IdxT, int64_t>(res, max_chunk_size, topk);
+  auto dev_distances = raft::make_device_matrix<float, int64_t>(res, max_chunk_size, topk);
+
+  // Determine graph degree and number of search results while increasing
+  // graph size.
+  auto small_graph_degree = std::max(graph_degree / 2, std::min(graph_degree, (uint64_t)32));
+  auto small_topk         = topk * small_graph_degree / graph_degree;
+  RAFT_LOG_DEBUG("# graph_degree = %lu", (uint64_t)graph_degree);
+  RAFT_LOG_DEBUG("# small_graph_degree = %lu", (uint64_t)small_graph_degree);
+  RAFT_LOG_DEBUG("# topk = %lu", (uint64_t)topk);
+  RAFT_LOG_DEBUG("# small_topk = %lu", (uint64_t)small_topk);
+
+  // Create an initial graph. The initial graph created here is not suitable for
+  // searching, but connectivity is guaranteed.
+  auto offset       = raft::make_host_vector<IdxT, int64_t>(small_graph_degree);
+  const double base = sqrt((double)2.0);
+  for (uint64_t j = 0; j < small_graph_degree; j++) {
+    if (j == 0) {
+      offset(j) = 1;
+    } else {
+      offset(j) = offset(j - 1) + 1;
+    }
+    IdxT ofst = initial_graph_size * pow(base, (double)j - small_graph_degree - 1);
+    if (offset(j) < ofst) { offset(j) = ofst; }
+    RAFT_LOG_DEBUG("# offset(%lu) = %lu\n", (uint64_t)j, (uint64_t)offset(j));
+  }
+  cagra_graph = raft::make_host_matrix<IdxT, int64_t>(initial_graph_size, small_graph_degree);
+  for (uint64_t i = 0; i < initial_graph_size; i++) {
+    for (uint64_t j = 0; j < small_graph_degree; j++) {
+      cagra_graph(i, j) = (i + offset(j)) % initial_graph_size;
+    }
+  }
+
+  auto curr_graph_size = initial_graph_size;
+  while (true) {
+    RAFT_LOG_DEBUG("# graph_size = %lu (%.3lf)",
+                   (uint64_t)curr_graph_size,
+                   (double)curr_graph_size / final_graph_size);
+
+    auto curr_query_size   = std::min(2 * curr_graph_size, final_graph_size);
+    auto curr_topk         = small_topk;
+    auto curr_itopk_size   = small_topk * 3 / 2;
+    auto curr_graph_degree = small_graph_degree;
+    if (curr_query_size == final_graph_size) {
+      curr_topk         = topk;
+      curr_itopk_size   = topk * 2;
+      curr_graph_degree = graph_degree;
+    }
+
+    cuvs::neighbors::cagra::search_params search_params;
+    search_params.algo        = cuvs::neighbors::cagra::search_algo::AUTO;
+    search_params.max_queries = max_chunk_size;
+    search_params.itopk_size  = curr_itopk_size;
+
+    // Create an index (idx), a query view (dev_query_view), and a mdarray for
+    // search results (neighbors).
+    auto dev_dataset_view = raft::make_device_matrix_view<const T, int64_t>(
+      dev_dataset.data_handle(), (int64_t)curr_graph_size, dev_dataset.extent(1));
+
+    auto idx = index<T, IdxT>(
+      res, params.metric, dev_dataset_view, raft::make_const_mdspan(cagra_graph.view()));
+
+    auto dev_query_view = raft::make_device_matrix_view<const T, int64_t>(
+      dev_dataset.data_handle(), (int64_t)curr_query_size, dev_dataset.extent(1));
+    auto neighbors = raft::make_host_matrix<IdxT, int64_t>(curr_query_size, curr_topk);
+
+    // Search.
+    // Since there are many queries, divide them into batches and search them.
+    cuvs::spatial::knn::detail::utils::batch_load_iterator<T> query_batch(
+      dev_query_view.data_handle(),
+      curr_query_size,
+      dev_query_view.extent(1),
+      max_chunk_size,
+      raft::resource::get_cuda_stream(res),
+      raft::resource::get_workspace_resource(res));
+    for (const auto& batch : query_batch) {
+      auto batch_dev_query_view = raft::make_device_matrix_view<const T, int64_t>(
+        batch.data(), batch.size(), dev_query_view.extent(1));
+      auto batch_dev_neighbors_view = raft::make_device_matrix_view<IdxT, int64_t>(
+        dev_neighbors.data_handle(), batch.size(), curr_topk);
+      auto batch_dev_distances_view = raft::make_device_matrix_view<float, int64_t>(
+        dev_distances.data_handle(), batch.size(), curr_topk);
+
+      cuvs::neighbors::cagra::search(res,
+                                     search_params,
+                                     idx,
+                                     batch_dev_query_view,
+                                     batch_dev_neighbors_view,
+                                     batch_dev_distances_view);
+
+      auto batch_neighbors_view = raft::make_host_matrix_view<IdxT, int64_t>(
+        neighbors.data_handle() + batch.offset() * curr_topk, batch.size(), curr_topk);
+      raft::copy(batch_neighbors_view.data_handle(),
+                 batch_dev_neighbors_view.data_handle(),
+                 batch_neighbors_view.size(),
+                 raft::resource::get_cuda_stream(res));
+    }
+
+    // Optimize graph
+    bool flag_last  = (curr_graph_size == final_graph_size);
+    curr_graph_size = curr_query_size;
+    cagra_graph     = raft::make_host_matrix<IdxT, int64_t>(0, 0);  // delete existing grahp
+    cagra_graph     = raft::make_host_matrix<IdxT, int64_t>(curr_graph_size, curr_graph_degree);
+    optimize<IdxT>(
+      res, neighbors.view(), cagra_graph.view(), flag_last ? params.guarantee_connectivity : 0);
+    if (flag_last) { break; }
+  }
+
+  return cagra_graph;
+}
+
 template <typename T,
           typename IdxT     = uint32_t,
           typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
@@ -427,9 +588,6 @@ index<T, IdxT> build(
     graph_degree = intermediate_degree;
   }
 
-  std::optional<raft::host_matrix<IdxT, int64_t>> knn_graph(
-    raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), intermediate_degree));
-
   // Set default value in case knn_build_params is not defined.
   auto knn_build_params = params.graph_build_params;
   if (std::holds_alternative<std::monostate>(params.graph_build_params)) {
@@ -445,38 +603,48 @@ index<T, IdxT> build(
     }
   }
 
+  auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(0, 0);
+
   // Dispatch based on graph_build_params
-  if (std::holds_alternative<cagra::graph_build_params::ivf_pq_params>(knn_build_params)) {
-    auto ivf_pq_params =
-      std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(knn_build_params);
-    build_knn_graph(res, dataset, knn_graph->view(), ivf_pq_params);
+  if (std::holds_alternative<cagra::graph_build_params::iterative_search_params>(
+        knn_build_params)) {
+    cagra_graph = iterative_build_graph<T, IdxT, Accessor>(res, params, dataset);
   } else {
-    auto nn_descent_params =
-      std::get<cagra::graph_build_params::nn_descent_params>(knn_build_params);
+    std::optional<raft::host_matrix<IdxT, int64_t>> knn_graph(
+      raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), intermediate_degree));
 
-    if (nn_descent_params.graph_degree != intermediate_degree) {
-      RAFT_LOG_WARN(
-        "Graph degree (%lu) for nn-descent needs to match cagra intermediate graph degree (%lu), "
-        "aligning "
-        "nn-descent graph_degree.",
-        nn_descent_params.graph_degree,
-        intermediate_degree);
-      nn_descent_params =
-        cagra::graph_build_params::nn_descent_params(intermediate_degree, params.metric);
-    }
+    if (std::holds_alternative<cagra::graph_build_params::ivf_pq_params>(knn_build_params)) {
+      auto ivf_pq_params =
+        std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(knn_build_params);
+      build_knn_graph(res, dataset, knn_graph->view(), ivf_pq_params);
+    } else {
+      auto nn_descent_params =
+        std::get<cagra::graph_build_params::nn_descent_params>(knn_build_params);
+
+      if (nn_descent_params.graph_degree != intermediate_degree) {
+        RAFT_LOG_WARN(
+          "Graph degree (%lu) for nn-descent needs to match cagra intermediate graph degree (%lu), "
+          "aligning "
+          "nn-descent graph_degree.",
+          nn_descent_params.graph_degree,
+          intermediate_degree);
+        nn_descent_params =
+          cagra::graph_build_params::nn_descent_params(intermediate_degree, params.metric);
+      }
 
-    // Use nn-descent to build CAGRA knn graph
-    nn_descent_params.return_distances = false;
-    build_knn_graph<T, IdxT>(res, dataset, knn_graph->view(), nn_descent_params);
-  }
+      // Use nn-descent to build CAGRA knn graph
+      nn_descent_params.return_distances = false;
+      build_knn_graph<T, IdxT>(res, dataset, knn_graph->view(), nn_descent_params);
+    }
 
-  auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
+    cagra_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
 
-  RAFT_LOG_INFO("optimizing graph");
-  optimize<IdxT>(res, knn_graph->view(), cagra_graph.view(), params.guarantee_connectivity);
+    RAFT_LOG_INFO("optimizing graph");
+    optimize<IdxT>(res, knn_graph->view(), cagra_graph.view(), params.guarantee_connectivity);
 
-  // free intermediate graph before trying to create the index
-  knn_graph.reset();
+    // free intermediate graph before trying to create the index
+    knn_graph.reset();
+  }
 
   RAFT_LOG_INFO("Graph optimized, creating index");
 
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index cab91c6982..99d1fd5ccd 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -254,6 +254,8 @@ enum class graph_build_algo {
   IVF_PQ,
   /* Experimental, use NN-Descent to build all-neighbors knn graph */
   NN_DESCENT,
+  /* Experimental, iteratively execute CAGRA's search() and optimize() */
+  ITERATIVE_CAGRA_SEARCH,
   /* Choose default automatically */
   AUTO
 };
@@ -294,7 +296,7 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
   };
 
   std::vector<std::string> algo       = {"single-cta", "multi_cta", "multi_kernel", "auto"};
-  std::vector<std::string> build_algo = {"IVF_PQ", "NN_DESCENT", "AUTO"};
+  std::vector<std::string> build_algo = {"IVF_PQ", "NN_DESCENT", "ITERATIVE_CAGRA_SEARCH", "AUTO"};
   os << "{n_queries=" << p.n_queries << ", dataset shape=" << p.n_rows << "x" << p.dim
      << ", k=" << p.k << ", " << algo.at((int)p.algo) << ", max_queries=" << p.max_queries
      << ", itopk_size=" << p.itopk_size << ", search_width=" << p.search_width
@@ -374,6 +376,10 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
               index_params.intermediate_graph_degree, index_params.metric);
             break;
           }
+          case graph_build_algo::ITERATIVE_CAGRA_SEARCH: {
+            index_params.graph_build_params = graph_build_params::iterative_search_params();
+            break;
+          }
           case graph_build_algo::AUTO:
             // do nothing
             break;
@@ -551,6 +557,10 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
               graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
             break;
           }
+          case graph_build_algo::ITERATIVE_CAGRA_SEARCH: {
+            index_params.graph_build_params = graph_build_params::iterative_search_params();
+            break;
+          }
           case graph_build_algo::AUTO:
             // do nothing
             break;
@@ -752,6 +762,10 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
               graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
             break;
           }
+          case graph_build_algo::ITERATIVE_CAGRA_SEARCH: {
+            index_params.graph_build_params = graph_build_params::iterative_search_params();
+            break;
+          }
           case graph_build_algo::AUTO:
             // do nothing
             break;
@@ -882,7 +896,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {1000},
     {1, 8, 17},
     {16},  // k
-    {graph_build_algo::NN_DESCENT},
+    {graph_build_algo::NN_DESCENT, graph_build_algo::ITERATIVE_CAGRA_SEARCH},
     {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
     {0, 10},  // query size
     {0},
@@ -917,7 +931,9 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {1000},
     {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 1024},  // dim
     {16},                                                    // k
-    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
+    {graph_build_algo::IVF_PQ,
+     graph_build_algo::NN_DESCENT,
+     graph_build_algo::ITERATIVE_CAGRA_SEARCH},
     {search_algo::AUTO},
     {10},
     {0},
@@ -935,7 +951,9 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {1000},
     {64},
     {16},
-    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
+    {graph_build_algo::IVF_PQ,
+     graph_build_algo::NN_DESCENT,
+     graph_build_algo::ITERATIVE_CAGRA_SEARCH},
     {search_algo::AUTO},
     {10},
     {0, 8, 16, 32},  // team_size
@@ -953,7 +971,9 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {1000},
     {64},
     {16},
-    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
+    {graph_build_algo::IVF_PQ,
+     graph_build_algo::NN_DESCENT,
+     graph_build_algo::ITERATIVE_CAGRA_SEARCH},
     {search_algo::AUTO},
     {10},
     {0},  // team_size

From df4ea06e87b4c1f4f758cc3f90d6d6ec3aeaa497 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Mon, 3 Feb 2025 09:13:32 -0600
Subject: [PATCH 295/623] disallow fallback to Make in Python builds (#636)

Contributes to https://github.com/rapidsai/build-planning/issues/146

Proposes:

* setting `[tool.scikit-build].ninja.make-fallback = false`, so `scikit-build-core` will not silently fallback to using GNU Make if `ninja` is not available
* including all groups in `dependencies.yaml` with `output_types: pyproject` in `output_types: requirements` as well.
  - *So that, for example, they show up in the solved environment in RAPIDS devcontainers (https://github.com/rapidsai/devcontainers)*

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/636
---
 dependencies.yaml             | 2 +-
 python/cuvs/pyproject.toml    | 2 +-
 python/libcuvs/pyproject.toml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/dependencies.yaml b/dependencies.yaml
index 186e148411..eabfd1ca6c 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -397,7 +397,7 @@ dependencies:
               - *libcusparse114
   cuda_wheels:
     specific:
-      - output_types: pyproject
+      - output_types: [requirements, pyproject]
         matrices:
           - matrix:
               cuda: "12.*"
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 2193f7dcba..d6847b3818 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -104,7 +104,7 @@ build-dir = "build/{wheel_tag}"
 cmake.build-type = "Release"
 cmake.version = "CMakeLists.txt"
 minimum-version = "build-system.requires"
-ninja.make-fallback = true
+ninja.make-fallback = false
 sdist.reproducible = true
 wheel.packages = ["cuvs"]
 
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 94d3e81d89..64dd54cfc3 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -77,7 +77,7 @@ build-dir = "build/{wheel_tag}"
 cmake.build-type = "Release"
 cmake.version = "CMakeLists.txt"
 minimum-version = "build-system.requires"
-ninja.make-fallback = true
+ninja.make-fallback = false
 sdist.reproducible = true
 wheel.install-dir = "libcuvs"
 wheel.packages = ["libcuvs"]

From 612bd161c5875db422f6519d676669e0ed09456a Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Mon, 3 Feb 2025 16:59:13 -0800
Subject: [PATCH 296/623] Revert docs builds to CI latest tag. (#643)

This cleans up one change from #621 that could not be reverted in #630. It is now safe to use `ci-imgs`' `latest` tag, because https://github.com/rapidsai/ci-imgs/pull/236 is merged.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Mike Sarahan (https://github.com/msarahan)

URL: https://github.com/rapidsai/cuvs/pull/643
---
 .github/workflows/pr.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 9197060c7a..a5981fe1f9 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -124,7 +124,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:cuda12.8.0-ubuntu24.04-py3.12"
+      container_image: "rapidsai/ci-conda:latest"
       run_script: "ci/build_docs.sh"
   rust-build:
     needs: conda-cpp-build

From 058eef24c9ef765291661475240354adc430889c Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Tue, 4 Feb 2025 12:52:00 +0900
Subject: [PATCH 297/623] CAGRA binary Hamming distance support (#610)

This PR adds the binary Hamming distance support to CAGRA.

dependency: https://github.com/rapidsai/cuvs/pull/612

TODO:
- [x] Add Hamming distance dist_op for CAGRA search
  - Add `DistanceType::BinaryHamming` (because `HammingUnexpanded` is not bitwise operation)
- [x] Support graph build
  - Want to use the iterative graph build method that uses the CAGRA search for graph build (https://github.com/anaruse/cuvs/commit/5a806595c2dca4ca5834071f391004272d920bad) because otherwise the binary Hamming distance support for IVFPQ or NN Descent is additionally required.
- [x] Add CI test
  - GT creation
  - Add test cases
- [x] Test on some benchmark datasets
- [x] Add `preprocessing::quantize::binary`

Authors:
  - tsuki (https://github.com/enp1s0)
  - Akira Naruse (https://github.com/anaruse)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/610
---
 cpp/CMakeLists.txt                            |   4 +
 cpp/include/cuvs/distance/distance.h          |   2 +
 .../cuvs/preprocessing/quantize/binary.hpp    | 170 ++++++++++++++++
 .../cuvs_internal/neighbors/naive_knn.cuh     |  16 +-
 .../neighbors/detail/cagra/cagra_build.cuh    |   5 +
 .../detail/cagra/compute_distance-ext.cuh     |  28 ++-
 .../detail/cagra/compute_distance.cu          |   5 +-
 .../cagra/compute_distance_00_generate.py     |  18 ++
 .../cagra/compute_distance_standard-impl.cuh  |  38 ++--
 ...dard_BitwiseHamming_u8_uint32_dim128_t8.cu |  38 ++++
 ...ard_BitwiseHamming_u8_uint32_dim256_t16.cu |  38 ++++
 ...ard_BitwiseHamming_u8_uint32_dim512_t32.cu |  38 ++++
 .../neighbors/detail/cagra/device_common.hpp  |   7 +
 cpp/src/neighbors/ivf_common.cuh              |   1 +
 cpp/src/preprocessing/quantize/binary.cu      |  43 ++++
 .../preprocessing/quantize/detail/binary.cuh  | 183 ++++++++++++++++++
 cpp/tests/CMakeLists.txt                      |   3 +-
 cpp/tests/neighbors/ann_cagra.cuh             | 111 +++++++----
 cpp/tests/neighbors/naive_knn.cuh             |  19 +-
 .../preprocessing/binary_quantization.cu      | 133 +++++++++++++
 20 files changed, 838 insertions(+), 62 deletions(-)
 create mode 100644 cpp/include/cuvs/preprocessing/quantize/binary.hpp
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/preprocessing/quantize/binary.cu
 create mode 100644 cpp/src/preprocessing/quantize/detail/binary.cuh
 create mode 100644 cpp/tests/preprocessing/binary_quantization.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 3ed3227e0d..65b1471f51 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -208,6 +208,9 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/cagra_search_int8.cu
     src/neighbors/cagra_search_uint8.cu
     src/neighbors/detail/cagra/compute_distance.cu
+    src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim512_t32.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
@@ -469,6 +472,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/vamana_serialize_uint8.cu
     src/neighbors/vamana_serialize_int8.cu
     src/preprocessing/quantize/scalar.cu
+    src/preprocessing/quantize/binary.cu
     src/selection/select_k_float_int64_t.cu
     src/selection/select_k_float_int32_t.cu
     src/selection/select_k_float_uint32_t.cu
diff --git a/cpp/include/cuvs/distance/distance.h b/cpp/include/cuvs/distance/distance.h
index 550221e8e6..8d76d5f6d9 100644
--- a/cpp/include/cuvs/distance/distance.h
+++ b/cpp/include/cuvs/distance/distance.h
@@ -62,6 +62,8 @@ typedef enum {
   RusselRaoExpanded = 18,
   /** Dice-Sorensen distance **/
   DiceExpanded = 19,
+  /** Bitstring Hamming distance **/
+  BitwiseHamming = 20,
   /** Precomputed (special value) **/
   Precomputed = 100
 } cuvsDistanceType;
diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.hpp b/cpp/include/cuvs/preprocessing/quantize/binary.hpp
new file mode 100644
index 0000000000..4f7d36048e
--- /dev/null
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.hpp
@@ -0,0 +1,170 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+
+#include <cuda_fp16.h>
+
+namespace cuvs::preprocessing::quantize::binary {
+
+/**
+ * @defgroup binary Binary quantizer utilities
+ * @{
+ */
+
+/**
+ * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
+ * set the corresponding bit to 1.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * auto quantized_dataset = raft::make_device_matrix<uint8_t, int64_t>(handle, samples,
+ * features); cuvs::preprocessing::quantize::binary::transform(handle, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void transform(raft::resources const& res,
+               raft::device_matrix_view<const double, int64_t> dataset,
+               raft::device_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
+ * set the corresponding bit to 1.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * auto quantized_dataset = raft::make_host_matrix<uint8_t, int64_t>(handle, samples,
+ * features); cuvs::preprocessing::quantize::binary::transform(handle, dataset,
+ * quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void transform(raft::resources const& res,
+               raft::host_matrix_view<const double, int64_t> dataset,
+               raft::host_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
+ * set the corresponding bit to 1.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * raft::device_matrix<float, uint64_t> dataset = read_dataset(filename);
+ * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
+ * auto quantized_dataset = raft::make_device_matrix<uint8_t, int64_t>(
+ *    handle, dataset.extent(0), quantized_dim);
+ *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void transform(raft::resources const& res,
+               raft::device_matrix_view<const float, int64_t> dataset,
+               raft::device_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
+ * set the corresponding bit to 1.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * raft::host_matrix<float, uint64_t> dataset = read_dataset(filename);
+ * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
+ * auto quantized_dataset = raft::make_host_matrix<uint8_t, int64_t>(
+ *    handle, dataset.extent(0), quantized_dim);
+ *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void transform(raft::resources const& res,
+               raft::host_matrix_view<const float, int64_t> dataset,
+               raft::host_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
+ * set the corresponding bit to 1.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * raft::device_matrix<half, uint64_t> dataset = read_dataset(filename);
+ * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
+ * auto quantized_dataset = raft::make_device_matrix<uint8_t, int64_t>(
+ *    handle, dataset.extent(0), quantized_dim);
+ *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+void transform(raft::resources const& res,
+               raft::device_matrix_view<const half, int64_t> dataset,
+               raft::device_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
+ * set the corresponding bit to 1.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * raft::host_matrix<half, uint64_t> dataset = read_dataset(filename);
+ * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
+ * auto quantized_dataset = raft::make_host_matrix<uint8_t, int64_t>(
+ *    handle, dataset.extent(0), quantized_dim);
+ *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+void transform(raft::resources const& res,
+               raft::host_matrix_view<const half, int64_t> dataset,
+               raft::host_matrix_view<uint8_t, int64_t> out);
+
+/** @} */  // end of group binary
+
+}  // namespace cuvs::preprocessing::quantize::binary
diff --git a/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh b/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh
index 1bde550ab2..5c9c01f54d 100644
--- a/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh
+++ b/cpp/internal/cuvs_internal/neighbors/naive_knn.cuh
@@ -45,19 +45,27 @@ RAFT_KERNEL naive_distance_kernel(EvalT* dist,
     for (IdxT i = 0; i < k; ++i) {
       IdxT xidx = i + midx * k;
       IdxT yidx = i + nidx * k;
-      auto xv   = EvalT(x[xidx]);
-      auto yv   = EvalT(y[yidx]);
+      auto xv   = x[xidx];
+      auto yv   = y[yidx];
       switch (metric) {
         case cuvs::distance::DistanceType::InnerProduct: {
-          acc += xv * yv;
+          acc += static_cast<EvalT>(xv) * static_cast<EvalT>(yv);
+        } break;
+        case cuvs::distance::DistanceType::CosineExpanded: {
+          acc += static_cast<EvalT>(xv) * static_cast<EvalT>(yv);
         } break;
         case cuvs::distance::DistanceType::L2SqrtExpanded:
         case cuvs::distance::DistanceType::L2SqrtUnexpanded:
         case cuvs::distance::DistanceType::L2Expanded:
         case cuvs::distance::DistanceType::L2Unexpanded: {
-          auto diff = xv - yv;
+          auto diff = static_cast<EvalT>(xv) - static_cast<EvalT>(yv);
           acc += diff * diff;
         } break;
+        case cuvs::distance::DistanceType::BitwiseHamming: {
+          if constexpr (std::is_same_v<uint8_t, DataT>) {
+            acc += __popc(static_cast<uint32_t>(xv ^ yv) & 0xff);
+          }
+        } break;
         default: break;
       }
     }
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index ee4b1444f9..4d559a6626 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -602,6 +602,11 @@ index<T, IdxT> build(
       knn_build_params = cagra::graph_build_params::ivf_pq_params(dataset.extents(), params.metric);
     }
   }
+  RAFT_EXPECTS(
+    params.metric != BitwiseHamming ||
+      std::holds_alternative<cagra::graph_build_params::iterative_search_params>(knn_build_params),
+    "IVF_PQ and NN_DESCENT for CAGRA graph build do not support BitwiseHamming as a metric. Please "
+    "use the iterative CAGRA search build.");
 
   auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(0, 0);
 
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
index df447d1961..faca960808 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
@@ -392,6 +392,24 @@ extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            uint8_t,
                                            uint32_t,
                                            float>;
+extern template struct standard_descriptor_spec<DistanceType::BitwiseHamming,
+                                                8,
+                                                128,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::BitwiseHamming,
+                                                16,
+                                                256,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
+extern template struct standard_descriptor_spec<DistanceType::BitwiseHamming,
+                                                32,
+                                                512,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
 
 extern template struct instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
@@ -441,7 +459,10 @@ extern template struct instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
-  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>>;
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 32, 512, uint8_t, uint32_t, float>>;
 
 using descriptor_instances = instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
@@ -491,7 +512,10 @@ using descriptor_instances = instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
-  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>>;
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 32, 512, uint8_t, uint32_t, float>>;
 
 template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
 auto dataset_descriptor_init(const cagra::search_params& params,
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.cu b/cpp/src/neighbors/detail/cagra/compute_distance.cu
index 45316e59bd..4158863465 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.cu
@@ -77,6 +77,9 @@ template struct instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
-  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>>;
+  vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::BitwiseHamming, 32, 512, uint8_t, uint32_t, float>>;
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
index aef31d1618..1c813f1a50 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
@@ -113,6 +113,24 @@
                             f.write(template.format(includes=includes, content=content))
                             cmake_list.append(f"  src/neighbors/detail/cagra/{path}")
 
+# CAGRA (Binary Hamming distance)
+for (mxdim, team) in mxdim_team:
+    metric = 'BitwiseHamming'
+    type_path = 'u8_uint32'
+    idx_t = 'uint32_t'
+    distance_t = 'float'
+    data_t = 'uint8_t'
+
+    path = f"compute_distance_standard_{metric}_{type_path}_dim{mxdim}_t{team}.cu"
+    includes = '#include "compute_distance_standard-impl.cuh"'
+    params = f"{metric_prefix}{metric}, {team}, {mxdim}, {data_t}, {idx_t}, {distance_t}"
+    spec = f"standard_descriptor_spec<{params}>"
+    content = f"""template struct {spec};"""
+    specs.append(spec)
+    with open(path, "w") as f:
+        f.write(template.format(includes=includes, content=content))
+        cmake_list.append(f"  src/neighbors/detail/cagra/{path}")
+
 with open("compute_distance-ext.cuh", "w") as f:
     includes = '''
 #pragma once
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
index 877d83fff7..fdc8731000 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
@@ -25,19 +25,30 @@
 
 namespace cuvs::neighbors::cagra::detail {
 namespace {
-template <typename T, cuvs::distance::DistanceType Metric>
-RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(T a, T b)
-  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::L2Expanded, T>
+template <typename DATA_T, typename DISTANCE_T, cuvs::distance::DistanceType Metric>
+RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(DATA_T a, DATA_T b)
+  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::L2Expanded, DISTANCE_T>
 {
-  T diff = a - b;
+  DISTANCE_T diff = a - b;
   return diff * diff;
 }
 
-template <typename T, cuvs::distance::DistanceType Metric>
-RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(T a, T b)
-  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::InnerProduct, T>
+template <typename DATA_T, typename DISTANCE_T, cuvs::distance::DistanceType Metric>
+RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(DATA_T a, DATA_T b)
+  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::InnerProduct, DISTANCE_T>
 {
-  return -a * b;
+  return -static_cast<DISTANCE_T>(a) * static_cast<DISTANCE_T>(b);
+}
+
+template <typename DATA_T, typename DISTANCE_T, cuvs::distance::DistanceType Metric>
+RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(DATA_T a, DATA_T b)
+  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::BitwiseHamming &&
+                        std::is_integral_v<DATA_T>,
+                      DISTANCE_T>
+{
+  // mask the result of xor for the integer promotion
+  const auto v = (a ^ b) & 0xffu;
+  return __popc(v);
 }
 }  // namespace
 
@@ -49,7 +60,8 @@ template <cuvs::distance::DistanceType Metric,
           typename DistanceT>
 struct standard_dataset_descriptor_t : public dataset_descriptor_base_t<DataT, IndexT, DistanceT> {
   using base_type = dataset_descriptor_base_t<DataT, IndexT, DistanceT>;
-  using QUERY_T   = float;
+  using QUERY_T   = typename std::
+    conditional_t<Metric == cuvs::distance::DistanceType::BitwiseHamming, DataT, float>;
   using base_type::args;
   using base_type::smem_ws_size_in_bytes;
   using typename base_type::args_t;
@@ -150,7 +162,7 @@ _RAFT_DEVICE __noinline__ auto setup_workspace_standard(
     if (i < dim) {
       buf[j] = cuvs::spatial::knn::detail::utils::mapping<QUERY_T>{}(queries_ptr[i]);
     } else {
-      buf[j] = 0.0;
+      buf[j] = 0;
     }
   }
 
@@ -194,13 +206,13 @@ RAFT_DEVICE_INLINE_FUNCTION auto compute_distance_standard_worker(
         // because:
         // - Above the last element (dataset_dim-1), the query array is filled with zeros.
         // - The data buffer has to be also padded with zeros.
-        DISTANCE_T d;
+        QUERY_T d;
         device::lds(
           d,
           query_smem_ptr +
             sizeof(QUERY_T) * device::swizzling<kDatasetBlockDim, vlen * kTeamSize>(k + v));
-        r += dist_op<DISTANCE_T, DescriptorT::kMetric>(
-          d, cuvs::spatial::knn::detail::utils::mapping<DISTANCE_T>{}(data[e][v]));
+        r += dist_op<QUERY_T, DISTANCE_T, DescriptorT::kMetric>(
+          d, cuvs::spatial::knn::detail::utils::mapping<QUERY_T>{}(data[e][v]));
       }
     }
   }
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..477ecc664a
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::BitwiseHamming,
+                                         8,
+                                         128,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..3dfc2538fe
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::BitwiseHamming,
+                                         16,
+                                         256,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..2c9f48db77
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_BitwiseHamming_u8_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::BitwiseHamming,
+                                         32,
+                                         512,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/device_common.hpp b/cpp/src/neighbors/detail/cagra/device_common.hpp
index e5886582df..882928add0 100644
--- a/cpp/src/neighbors/detail/cagra/device_common.hpp
+++ b/cpp/src/neighbors/detail/cagra/device_common.hpp
@@ -281,6 +281,13 @@ RAFT_DEVICE_INLINE_FUNCTION void lds(half (&x)[4], uint32_t addr)
                : "r"(addr));
 }
 
+RAFT_DEVICE_INLINE_FUNCTION void lds(uint8_t& x, uint32_t addr)
+{
+  uint32_t res;
+  asm volatile("ld.shared.u8 {%0}, [%1];" : "=r"(res) : "r"(addr));
+  x = static_cast<uint32_t>(res);
+}
+
 RAFT_DEVICE_INLINE_FUNCTION void lds(uint32_t& x, uint32_t addr)
 {
   asm volatile("ld.shared.u32 {%0}, [%1];" : "=r"(x) : "r"(addr));
diff --git a/cpp/src/neighbors/ivf_common.cuh b/cpp/src/neighbors/ivf_common.cuh
index fb73fb8a90..0c6f7c5ba9 100644
--- a/cpp/src/neighbors/ivf_common.cuh
+++ b/cpp/src/neighbors/ivf_common.cuh
@@ -268,6 +268,7 @@ void postprocess_distances(ScoreOutT* out,      // [n_queries, topk]
         raft::linalg::unaryOp(out, in, len, raft::cast_op<ScoreOutT>{}, stream);
       }
     } break;
+    case distance::DistanceType::BitwiseHamming: break;
     default: RAFT_FAIL("Unexpected metric.");
   }
 }
diff --git a/cpp/src/preprocessing/quantize/binary.cu b/cpp/src/preprocessing/quantize/binary.cu
new file mode 100644
index 0000000000..fd04c7fe49
--- /dev/null
+++ b/cpp/src/preprocessing/quantize/binary.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "./detail/binary.cuh"
+
+#include <cuvs/preprocessing/quantize/binary.hpp>
+
+namespace cuvs::preprocessing::quantize::binary {
+
+#define CUVS_INST_QUANTIZATION(T, QuantI)                            \
+  void transform(raft::resources const& res,                         \
+                 raft::device_matrix_view<const T, int64_t> dataset, \
+                 raft::device_matrix_view<QuantI, int64_t> out)      \
+  {                                                                  \
+    detail::transform(res, dataset, out);                            \
+  }                                                                  \
+  void transform(raft::resources const& res,                         \
+                 raft::host_matrix_view<const T, int64_t> dataset,   \
+                 raft::host_matrix_view<QuantI, int64_t> out)        \
+  {                                                                  \
+    detail::transform(res, dataset, out);                            \
+  }
+
+CUVS_INST_QUANTIZATION(double, uint8_t);
+CUVS_INST_QUANTIZATION(float, uint8_t);
+CUVS_INST_QUANTIZATION(half, uint8_t);
+
+#undef CUVS_INST_QUANTIZATION
+
+}  // namespace cuvs::preprocessing::quantize::binary
diff --git a/cpp/src/preprocessing/quantize/detail/binary.cuh b/cpp/src/preprocessing/quantize/detail/binary.cuh
new file mode 100644
index 0000000000..8ddc33c944
--- /dev/null
+++ b/cpp/src/preprocessing/quantize/detail/binary.cuh
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/preprocessing/quantize/scalar.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/random/sample_without_replacement.cuh>
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+#include <thrust/system/omp/execution_policy.h>
+
+namespace cuvs::preprocessing::quantize::detail {
+
+template <class T>
+_RAFT_HOST_DEVICE bool is_positive(const T& a)
+{
+  return a > 0;
+}
+
+template <>
+_RAFT_HOST_DEVICE bool is_positive<half>(const half& a)
+{
+  return is_positive(static_cast<float>(a));
+}
+
+template <class T, uint32_t block_size, class pack_t = uint8_t>
+RAFT_KERNEL binary_quantization_kernel(const T* const in_ptr,
+                                       const uint32_t ldi,
+                                       const size_t dataset_size,
+                                       const uint32_t dataset_dim,
+                                       pack_t* const out_ptr,
+                                       const uint32_t ldo)
+{
+  constexpr uint32_t warp_size = 32;
+  const uint32_t bits_per_pack = sizeof(pack_t) * 8;
+  const auto output_dim        = raft::div_rounding_up_safe(dataset_dim, bits_per_pack);
+
+  const auto vector_id = (blockDim.x * blockIdx.x + threadIdx.x) / warp_size;
+  if (vector_id >= dataset_size) { return; }
+
+  __shared__ pack_t smem[block_size];
+  auto local_smem = smem + (threadIdx.x / warp_size) * warp_size;
+
+  const auto lane_id = threadIdx.x % warp_size;
+  for (uint32_t j_offset = 0; j_offset < dataset_dim; j_offset += warp_size * bits_per_pack) {
+    // Load dataset vector elements with coalesce access. The mapping of the vector element position
+    // and the `pack` register is as follows:
+    //
+    // lane_id | LSB  (pack(u8))  MSB
+    //       0 |  0, 32, 64, ..., 224
+    //       1 |  1, 33, 65, ..., 225
+    //       ...
+    //      31 | 31, 63, 95, ..., 255
+    pack_t pack = 0;
+    for (uint32_t bit_offset = 0; bit_offset < bits_per_pack; bit_offset++) {
+      const auto j = j_offset + lane_id + bit_offset * warp_size;
+      if (j < dataset_dim) {
+        const auto v = in_ptr[vector_id * ldi + j];
+        if (is_positive(v)) { pack |= (1u << bit_offset); }
+      }
+    }
+
+    // Store the local result in smem so that the other threads in the same warp can read
+    local_smem[lane_id] = pack;
+
+    // Store the result with (a kind of) transposition so that the the coalesce access can be used.
+    // The mapping of the result `pack` register bit position and (smem_index, bit_position) is as
+    // follows:
+    //
+    // lane_id | LSB          (pack(u8))         MSB
+    //       0 | ( 0,0), ( 1,0), ( 2,0), ..., ( 7,0)
+    //       1 | ( 8,0), ( 9,0), (10,0), ..., (15,0)
+    //       ...
+    //       4 | ( 0,1), ( 1,1), ( 2,1), ..., ( 7,1)
+    //       ...
+    //      31 | (24,7), (25,7), (26,7), ..., (31,7)
+    //
+    // The `bit_position`-th bit of `local_smem[smem_index]` is copied to the corresponding `pack`
+    // bit. By this mapping, the quantization result of 8*i-th ~ (8*(i+1)-1)-th vector elements is
+    // stored by the lane_id=i thread.
+    pack                    = 0;
+    const auto smem_start_i = (lane_id % (warp_size / bits_per_pack)) * bits_per_pack;
+    const auto mask         = 1u << (lane_id / (warp_size / bits_per_pack));
+    for (uint32_t j = 0; j < bits_per_pack; j++) {
+      if (local_smem[smem_start_i + j] & mask) { pack |= (1u << j); }
+    }
+
+    const auto out_j = j_offset / bits_per_pack + lane_id;
+    if (out_j < output_dim) { out_ptr[vector_id * ldo + out_j] = pack; }
+  }
+}
+
+template <typename T, typename QuantI = uint8_t>
+void transform(raft::resources const& res,
+               raft::device_matrix_view<const T, int64_t> dataset,
+               raft::device_matrix_view<QuantI, int64_t> out)
+{
+  cudaStream_t stream            = raft::resource::get_cuda_stream(res);
+  const uint32_t bits_per_pack   = sizeof(QuantI) * 8;
+  const uint32_t dataset_dim     = dataset.extent(1);
+  const uint32_t out_dim         = out.extent(1);
+  const size_t dataset_size      = dataset.extent(0);
+  const size_t out_dataset_size  = out.extent(0);
+  const uint32_t minimul_out_dim = raft::div_rounding_up_safe(dataset_dim, bits_per_pack);
+  RAFT_EXPECTS(out_dim >= minimul_out_dim,
+               "The quantized dataset dimension must be larger or equal to "
+               "%u but is %u passed",
+               minimul_out_dim,
+               out_dim);
+  RAFT_EXPECTS(out_dataset_size >= dataset_size,
+               "The quantized dataset size must be larger or equal to "
+               "the input dataset size (%lu) but is %lu passed",
+               dataset_size,
+               out_dataset_size);
+
+  constexpr uint32_t warp_size    = 32;
+  constexpr uint32_t block_size   = 256;
+  constexpr uint32_t vecs_per_cta = block_size / warp_size;
+  const auto grid_size =
+    raft::div_rounding_up_safe(dataset_size, static_cast<size_t>(vecs_per_cta));
+
+  binary_quantization_kernel<T, block_size>
+    <<<grid_size, block_size, 0, stream>>>(dataset.data_handle(),
+                                           dataset.stride(0),
+                                           dataset_size,
+                                           dataset_dim,
+                                           out.data_handle(),
+                                           out.stride(0));
+}
+
+template <typename T, typename QuantI = uint8_t>
+void transform(raft::resources const& res,
+               raft::host_matrix_view<const T, int64_t> dataset,
+               raft::host_matrix_view<QuantI, int64_t> out)
+{
+  const uint32_t bits_per_pack   = sizeof(QuantI) * 8;
+  const uint32_t dataset_dim     = dataset.extent(1);
+  const uint32_t out_dim         = out.extent(1);
+  const size_t dataset_size      = dataset.extent(0);
+  const size_t out_dataset_size  = out.extent(0);
+  const uint32_t minimul_out_dim = raft::div_rounding_up_safe(dataset_dim, bits_per_pack);
+  RAFT_EXPECTS(out_dim >= minimul_out_dim,
+               "The quantized dataset dimension must be larger or equal to "
+               "%u but is %u passed",
+               minimul_out_dim,
+               out_dim);
+  RAFT_EXPECTS(out_dataset_size >= dataset_size,
+               "The quantized dataset size must be larger or equal to "
+               "the input dataset size (%lu) but is %lu passed",
+               dataset_size,
+               out_dataset_size);
+
+#pragma omp parallel for collapse(2)
+  for (size_t i = 0; i < dataset_size; ++i) {
+    for (uint32_t out_j = 0; out_j < minimul_out_dim; ++out_j) {
+      QuantI pack = 0;
+      for (uint32_t pack_j = 0; pack_j < bits_per_pack; ++pack_j) {
+        const uint32_t in_j = out_j * bits_per_pack + pack_j;
+        if (in_j < dataset_dim) {
+          if (is_positive(dataset(i, in_j))) { pack |= (1u << pack_j); }
+        }
+      }
+      out(i, out_j) = pack;
+    }
+  }
+}
+}  // namespace cuvs::preprocessing::quantize::detail
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index 3ed37175d5..cea4e0ce60 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -248,7 +248,8 @@ if(BUILD_TESTS)
   )
 
   ConfigureTest(
-    NAME PREPROCESSING_TEST PATH preprocessing/scalar_quantization.cu GPUS 1 PERCENT 100
+    NAME PREPROCESSING_TEST PATH preprocessing/scalar_quantization.cu
+    preprocessing/binary_quantization.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 99d1fd5ccd..aedb11543d 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -290,6 +290,7 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
     switch (dist) {
       case InnerProduct: return "InnerProduct";
       case L2Expanded: return "L2";
+      case BitwiseHamming: return "BitwiseHamming";
       default: break;
     }
     return "Unknown";
@@ -328,6 +329,18 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
  protected:
   void testCagra()
   {
+    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        ((!std::is_same_v<DataT, uint8_t>) ||
+         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+      GTEST_SKIP();
+    // If the dataset dimension is small and the dataset size is large, there can be a lot of
+    // dataset vectors that have the same distance to the query, especially in the binary Hamming
+    // distance, making it impossible to make a top-k ground truth.
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        (ps.k * ps.dim * 8 / 5 /*(=magic number)*/ < ps.n_rows))
+      GTEST_SKIP();
+
     size_t queries_size = ps.n_queries * ps.k;
     std::vector<IdxT> indices_Cagra(queries_size);
     std::vector<IdxT> indices_naive(queries_size);
@@ -507,6 +520,17 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
     // issue: https://github.com/rapidsai/raft/issues/2276
     if (ps.metric == InnerProduct && ps.build_algo == graph_build_algo::NN_DESCENT) GTEST_SKIP();
     if (ps.compression != std::nullopt) GTEST_SKIP();
+    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        ((!std::is_same_v<DataT, uint8_t>) ||
+         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+      GTEST_SKIP();
+    // If the dataset dimension is small and the dataset size is large, there can be a lot of
+    // dataset vectors that have the same distance to the query, especially in the binary Hamming
+    // distance, making it impossible to make a top-k ground truth.
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        (ps.k * ps.dim * 8 / 5 /*(=magic number)*/ < ps.n_rows))
+      GTEST_SKIP();
 
     size_t queries_size = ps.n_queries * ps.k;
     std::vector<IdxT> indices_Cagra(queries_size);
@@ -706,6 +730,17 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
     if (ps.metric == cuvs::distance::DistanceType::InnerProduct &&
         ps.build_algo == graph_build_algo::NN_DESCENT)
       GTEST_SKIP();
+    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        ((!std::is_same_v<DataT, uint8_t>) ||
+         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+      GTEST_SKIP();
+    // If the dataset dimension is small and the dataset size is large, there can be a lot of
+    // dataset vectors that have the same distance to the query, especially in the binary Hamming
+    // distance, making it impossible to make a top-k ground truth.
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        (ps.k * ps.dim * 8 / 5 /*(=magic number)*/ < ps.n_rows))
+      GTEST_SKIP();
 
     size_t queries_size = ps.n_queries * ps.k;
     std::vector<IdxT> indices_Cagra(queries_size);
@@ -896,7 +931,9 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {1000},
     {1, 8, 17},
     {16},  // k
-    {graph_build_algo::NN_DESCENT, graph_build_algo::ITERATIVE_CAGRA_SEARCH},
+    {graph_build_algo::NN_DESCENT,
+     graph_build_algo::ITERATIVE_CAGRA_SEARCH},  // build algo. ITERATIVE_CAGRA_SEARCH is needed to
+                                                 // test BitwiseHamming
     {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
     {0, 10},  // query size
     {0},
@@ -939,7 +976,9 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0},
     {64},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {cuvs::distance::DistanceType::L2Expanded,
+     cuvs::distance::DistanceType::InnerProduct,
+     cuvs::distance::DistanceType::BitwiseHamming},
     {false},
     {true},
     {0.995});
@@ -1053,21 +1092,21 @@ inline std::vector<AnnCagraInputs> generate_inputs()
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Varying dim, adding non_owning_memory_buffer_flag
-  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
-    {100},
-    {1000},
-    {1, 5, 8, 64, 137, 256, 619, 1024},  // dim
-    {10},
-    {graph_build_algo::IVF_PQ},
-    {search_algo::AUTO},
-    {10},
-    {0},  // team_size
-    {64},
-    {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
-    {false},
-    {false},
-    {0.995});
+  inputs2 =
+    raft::util::itertools::product<AnnCagraInputs>({100},
+                                                   {1000},
+                                                   {1, 5, 8, 64, 137, 256, 619, 1024},  // dim
+                                                   {10},
+                                                   {graph_build_algo::IVF_PQ},
+                                                   {search_algo::AUTO},
+                                                   {10},
+                                                   {0},  // team_size
+                                                   {64},
+                                                   {1},
+                                                   {cuvs::distance::DistanceType::L2Expanded},
+                                                   {false},
+                                                   {false},
+                                                   {0.995});
   for (auto input : inputs2) {
     input.non_owning_memory_buffer_flag = true;
     inputs.push_back(input);
@@ -1079,21 +1118,23 @@ inline std::vector<AnnCagraInputs> generate_inputs()
 inline std::vector<AnnCagraInputs> generate_addnode_inputs()
 {
   // changing dim
-  std::vector<AnnCagraInputs> inputs = raft::util::itertools::product<AnnCagraInputs>(
-    {100},
-    {1000},
-    {1, 8, 17, 64, 128, 137, 512, 1024},  // dim
-    {16},                                 // k
-    {graph_build_algo::NN_DESCENT},
-    {search_algo::AUTO},
-    {10},
-    {0},
-    {64},
-    {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
-    {false},
-    {true},
-    {0.995});
+  std::vector<AnnCagraInputs> inputs =
+    raft::util::itertools::product<AnnCagraInputs>({100},
+                                                   {1000},
+                                                   {1, 8, 17, 64, 128, 137, 512, 1024},  // dim
+                                                   {16},                                 // k
+                                                   {graph_build_algo::ITERATIVE_CAGRA_SEARCH},
+                                                   {search_algo::AUTO},
+                                                   {10},
+                                                   {0},
+                                                   {64},
+                                                   {1},
+                                                   {cuvs::distance::DistanceType::L2Expanded,
+                                                    cuvs::distance::DistanceType::InnerProduct,
+                                                    cuvs::distance::DistanceType::BitwiseHamming},
+                                                   {false},
+                                                   {true},
+                                                   {0.995});
 
   // testing host and device datasets
   auto inputs2 = raft::util::itertools::product<AnnCagraInputs>(
@@ -1150,7 +1191,7 @@ inline std::vector<AnnCagraInputs> generate_filtering_inputs()
   std::vector<AnnCagraInputs> inputs = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
-    {1, 8, 17},
+    {1, 8, 17, 102},
     {16},  // k
     {graph_build_algo::NN_DESCENT},
     {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
@@ -1158,7 +1199,9 @@ inline std::vector<AnnCagraInputs> generate_filtering_inputs()
     {0},
     {256},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {cuvs::distance::DistanceType::L2Expanded,
+     cuvs::distance::DistanceType::InnerProduct,
+     cuvs::distance::DistanceType::BitwiseHamming},
     {false},
     {true},
     {0.995});
diff --git a/cpp/tests/neighbors/naive_knn.cuh b/cpp/tests/neighbors/naive_knn.cuh
index 553e667aa1..484022c795 100644
--- a/cpp/tests/neighbors/naive_knn.cuh
+++ b/cpp/tests/neighbors/naive_knn.cuh
@@ -47,24 +47,29 @@ RAFT_KERNEL naive_distance_kernel(EvalT* dist,
     for (IdxT i = 0; i < k; ++i) {
       IdxT xidx = i + midx * k;
       IdxT yidx = i + nidx * k;
-      auto xv   = EvalT(x[xidx]);
-      auto yv   = EvalT(y[yidx]);
+      auto xv   = x[xidx];
+      auto yv   = y[yidx];
       switch (metric) {
         case cuvs::distance::DistanceType::InnerProduct: {
-          acc += xv * yv;
+          acc += static_cast<EvalT>(xv) * static_cast<EvalT>(yv);
         } break;
         case cuvs::distance::DistanceType::CosineExpanded: {
-          acc += xv * yv;
-          normX += xv * xv;
-          normY += yv * yv;
+          acc += static_cast<EvalT>(xv) * static_cast<EvalT>(yv);
+          normX += static_cast<EvalT>(xv) * static_cast<EvalT>(xv);
+          normY += static_cast<EvalT>(yv) * static_cast<EvalT>(yv);
         } break;
         case cuvs::distance::DistanceType::L2SqrtExpanded:
         case cuvs::distance::DistanceType::L2SqrtUnexpanded:
         case cuvs::distance::DistanceType::L2Expanded:
         case cuvs::distance::DistanceType::L2Unexpanded: {
-          auto diff = xv - yv;
+          auto diff = static_cast<EvalT>(xv) - static_cast<EvalT>(yv);
           acc += diff * diff;
         } break;
+        case cuvs::distance::DistanceType::BitwiseHamming: {
+          if constexpr (std::is_same_v<uint8_t, DataT>) {
+            acc += __popc(static_cast<uint32_t>(xv ^ yv) & 0xff);
+          }
+        } break;
         default: break;
       }
     }
diff --git a/cpp/tests/preprocessing/binary_quantization.cu b/cpp/tests/preprocessing/binary_quantization.cu
new file mode 100644
index 0000000000..af1cc07e8b
--- /dev/null
+++ b/cpp/tests/preprocessing/binary_quantization.cu
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include <cuvs/preprocessing/quantize/binary.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/transpose.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/stats/stddev.cuh>
+#include <thrust/reduce.h>
+
+namespace cuvs::preprocessing::quantize::binary {
+
+template <typename T>
+struct BinaryQuantizationInputs {
+  int rows;
+  int cols;
+};
+
+template <typename T>
+std::ostream& operator<<(std::ostream& os, const BinaryQuantizationInputs<T>& inputs)
+{
+  return os << "> rows:" << inputs.rows << " cols:" << inputs.cols;
+}
+
+template <typename T, typename QuantI>
+class BinaryQuantizationTest : public ::testing::TestWithParam<BinaryQuantizationInputs<T>> {
+ public:
+  BinaryQuantizationTest()
+    : params_(::testing::TestWithParam<BinaryQuantizationInputs<T>>::GetParam()),
+      stream(raft::resource::get_cuda_stream(handle)),
+      input_(0, stream)
+  {
+  }
+
+ protected:
+  void testBinaryQuantization()
+  {
+    // dataset identical on host / device
+    auto dataset = raft::make_device_matrix_view<const T, int64_t, raft::row_major>(
+      (const T*)(input_.data()), rows_, cols_);
+    auto dataset_h = raft::make_host_matrix_view<const T, int64_t, raft::row_major>(
+      (const T*)(host_input_.data()), rows_, cols_);
+
+    {
+      static_assert(std::is_same_v<QuantI, uint8_t>);
+
+      const auto col_quantized = raft::div_rounding_up_safe(cols_, 8);
+      auto quantized_input_h   = raft::make_host_matrix<QuantI, int64_t>(rows_, cols_);
+      auto quantized_input_d   = raft::make_device_matrix<QuantI, int64_t>(handle, rows_, cols_);
+      cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_input_d.view());
+      cuvs::preprocessing::quantize::binary::transform(handle, dataset_h, quantized_input_h.view());
+
+      ASSERT_TRUE(devArrMatchHost(quantized_input_h.data_handle(),
+                                  quantized_input_d.data_handle(),
+                                  input_.size(),
+                                  cuvs::Compare<QuantI>(),
+                                  stream));
+    }
+  }
+
+  void SetUp() override
+  {
+    rows_ = params_.rows;
+    cols_ = params_.cols;
+
+    int n_elements = rows_ * cols_;
+    input_.resize(n_elements, stream);
+    host_input_.resize(n_elements);
+
+    // random input
+    unsigned long long int seed = 1234ULL;
+    raft::random::RngState r(seed);
+    uniform(handle, r, input_.data(), input_.size(), static_cast<T>(-1), static_cast<T>(1));
+
+    raft::update_host(host_input_.data(), input_.data(), input_.size(), stream);
+
+    raft::resource::sync_stream(handle, stream);
+  }
+
+ private:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  BinaryQuantizationInputs<T> params_;
+  int rows_;
+  int cols_;
+  rmm::device_uvector<T> input_;
+  std::vector<T> host_input_;
+};
+
+template <typename T>
+const std::vector<BinaryQuantizationInputs<T>> inputs = {
+  {5, 5},
+  {100, 7},
+  {100, 128},
+  {100, 1999},
+  {1000, 1999},
+};
+
+typedef BinaryQuantizationTest<float, uint8_t> QuantizationTest_float_uint8t;
+TEST_P(QuantizationTest_float_uint8t, BinaryQuantizationTest) { this->testBinaryQuantization(); }
+
+typedef BinaryQuantizationTest<double, uint8_t> QuantizationTest_double_uint8t;
+TEST_P(QuantizationTest_double_uint8t, BinaryQuantizationTest) { this->testBinaryQuantization(); }
+
+typedef BinaryQuantizationTest<half, uint8_t> QuantizationTest_half_uint8t;
+TEST_P(QuantizationTest_half_uint8t, BinaryQuantizationTest) { this->testBinaryQuantization(); }
+
+INSTANTIATE_TEST_CASE_P(BinaryQuantizationTest,
+                        QuantizationTest_float_uint8t,
+                        ::testing::ValuesIn(inputs<float>));
+INSTANTIATE_TEST_CASE_P(BinaryQuantizationTest,
+                        QuantizationTest_double_uint8t,
+                        ::testing::ValuesIn(inputs<double>));
+INSTANTIATE_TEST_CASE_P(BinaryQuantizationTest,
+                        QuantizationTest_half_uint8t,
+                        ::testing::ValuesIn(inputs<half>));
+
+}  // namespace cuvs::preprocessing::quantize::binary

From ddec76238602d5028cbe76f04e2b494cefedc950 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 4 Feb 2025 18:48:05 +0100
Subject: [PATCH 298/623] Expose configuration singleton as a global context
 for ann-bench algos (#647)

Expose the `bench::configuration` as a global context (singleton), so that algorithms can inspect it during setup if the need arises.

This is a proposal for https://github.com/rapidsai/cuvs/pull/260 to avoid mixing algorithm-specific logic into the common benchmarking infrastructure.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/647
---
 cpp/bench/ann/src/common/benchmark.hpp | 28 ++++----------
 cpp/bench/ann/src/common/conf.hpp      | 52 +++++++++++++++++++-------
 2 files changed, 47 insertions(+), 33 deletions(-)

diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index 8d9c30cb18..9653a589b7 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -519,12 +519,10 @@ void register_search(std::shared_ptr<const dataset<T>> dataset,
 
 template <typename T>
 void dispatch_benchmark(std::string cmdline,
-                        const configuration& conf,
+                        configuration& conf,
                         bool force_overwrite,
                         bool build_mode,
                         bool search_mode,
-                        std::string data_prefix,
-                        std::string index_prefix,
                         kv_series override_kv,
                         Mode metric_objective,
                         const std::vector<int>& threads,
@@ -539,11 +537,10 @@ void dispatch_benchmark(std::string cmdline,
       ::benchmark::AddCustomContext(key, value);
     }
   }
-  const auto dataset_conf = conf.get_dataset_conf();
-  auto base_file          = combine_path(data_prefix, dataset_conf.base_file);
-  auto query_file         = combine_path(data_prefix, dataset_conf.query_file);
-  auto gt_file            = dataset_conf.groundtruth_neighbors_file;
-  if (gt_file.has_value()) { gt_file.emplace(combine_path(data_prefix, gt_file.value())); }
+  auto& dataset_conf = conf.get_dataset_conf();
+  auto base_file     = dataset_conf.base_file;
+  auto query_file    = dataset_conf.query_file;
+  auto gt_file       = dataset_conf.groundtruth_neighbors_file;
   auto dataset =
     std::make_shared<bench::dataset<T>>(dataset_conf.name,
                                         base_file,
@@ -555,7 +552,7 @@ void dispatch_benchmark(std::string cmdline,
                                         search_mode ? dataset_conf.filtering_rate : std::nullopt);
   ::benchmark::AddCustomContext("dataset", dataset_conf.name);
   ::benchmark::AddCustomContext("distance", dataset_conf.distance);
-  std::vector<configuration::index> indices = conf.get_indices();
+  std::vector<configuration::index>& indices = conf.get_indices();
   if (build_mode) {
     if (file_exists(base_file)) {
       log_info("Using the dataset file '%s'", base_file.c_str());
@@ -570,10 +567,10 @@ void dispatch_benchmark(std::string cmdline,
       for (auto param : apply_overrides(index.build_param, override_kv)) {
         auto modified_index        = index;
         modified_index.build_param = param;
-        modified_index.file        = combine_path(index_prefix, modified_index.file);
         more_indices.push_back(modified_index);
       }
     }
+    std::swap(more_indices, indices);  // update the config in case algorithms need to access it
     register_build<T>(dataset, more_indices, force_overwrite, no_lap_sync);
   } else if (search_mode) {
     if (file_exists(query_file)) {
@@ -601,7 +598,6 @@ void dispatch_benchmark(std::string cmdline,
     }
     for (auto& index : indices) {
       index.search_params = apply_overrides(index.search_params, override_kv);
-      index.file          = combine_path(index_prefix, index.file);
     }
     register_search<T>(dataset, indices, metric_objective, threads, no_lap_sync);
   }
@@ -726,7 +722,7 @@ inline auto run_main(int argc, char** argv) -> int
     log_warn("cudart library is not found, GPU-based indices won't work.");
   }
 
-  configuration conf(conf_stream);
+  auto& conf        = bench::configuration::initialize(conf_stream, data_prefix, index_prefix);
   std::string dtype = conf.get_dataset_conf().dtype;
 
   if (dtype == "float") {
@@ -735,8 +731,6 @@ inline auto run_main(int argc, char** argv) -> int
                               force_overwrite,
                               build_mode,
                               search_mode,
-                              data_prefix,
-                              index_prefix,
                               override_kv,
                               metric_objective,
                               threads,
@@ -747,8 +741,6 @@ inline auto run_main(int argc, char** argv) -> int
                              force_overwrite,
                              build_mode,
                              search_mode,
-                             data_prefix,
-                             index_prefix,
                              override_kv,
                              metric_objective,
                              threads,
@@ -759,8 +751,6 @@ inline auto run_main(int argc, char** argv) -> int
                                      force_overwrite,
                                      build_mode,
                                      search_mode,
-                                     data_prefix,
-                                     index_prefix,
                                      override_kv,
                                      metric_objective,
                                      threads,
@@ -771,8 +761,6 @@ inline auto run_main(int argc, char** argv) -> int
                                     force_overwrite,
                                     build_mode,
                                     search_mode,
-                                    data_prefix,
-                                    index_prefix,
                                     override_kv,
                                     metric_objective,
                                     threads,
diff --git a/cpp/bench/ann/src/common/conf.hpp b/cpp/bench/ann/src/common/conf.hpp
index ac1361219e..b19cad5fed 100644
--- a/cpp/bench/ann/src/common/conf.hpp
+++ b/cpp/bench/ann/src/common/conf.hpp
@@ -57,31 +57,53 @@ class configuration {
     std::optional<double> filtering_rate{std::nullopt};
   };
 
-  explicit inline configuration(std::istream& conf_stream)
+  [[nodiscard]] inline auto get_dataset_conf() const -> const dataset_conf&
+  {
+    return dataset_conf_;
+  }
+  [[nodiscard]] inline auto get_dataset_conf() -> dataset_conf& { return dataset_conf_; }
+  [[nodiscard]] inline auto get_indices() const -> const std::vector<index>& { return indices_; };
+  [[nodiscard]] inline auto get_indices() -> std::vector<index>& { return indices_; };
+
+  /** The benchmark initializes the configuration once and has a chance to modify it during the
+   * setup. */
+  static inline auto initialize(std::istream& conf_stream,
+                                std::string data_prefix,
+                                std::string index_prefix) -> configuration&
+  {
+    singleton_ =
+      std::unique_ptr<configuration>(new configuration{conf_stream, data_prefix, index_prefix});
+    return *singleton_;
+  }
+
+  /** Any algorithm can access the benchmark configuration as an immutable context. */
+  [[nodiscard]] static inline auto singleton() -> const configuration& { return *singleton_; }
+
+ private:
+  explicit inline configuration(std::istream& conf_stream,
+                                std::string data_prefix,
+                                std::string index_prefix)
   {
     // to enable comments in json
     auto conf = nlohmann::json::parse(conf_stream, nullptr, true, true);
 
-    parse_dataset(conf.at("dataset"));
-    parse_index(conf.at("index"), conf.at("search_basic_param"));
+    parse_dataset(conf.at("dataset"), data_prefix);
+    parse_index(conf.at("index"), conf.at("search_basic_param"), index_prefix);
   }
 
-  [[nodiscard]] inline auto get_dataset_conf() const -> dataset_conf { return dataset_conf_; }
-  [[nodiscard]] inline auto get_indices() const -> std::vector<index> { return indices_; };
-
- private:
-  inline void parse_dataset(const nlohmann::json& conf)
+  inline void parse_dataset(const nlohmann::json& conf, std::string data_prefix)
   {
     dataset_conf_.name       = conf.at("name");
-    dataset_conf_.base_file  = conf.at("base_file");
-    dataset_conf_.query_file = conf.at("query_file");
+    dataset_conf_.base_file  = combine_path(data_prefix, conf.at("base_file"));
+    dataset_conf_.query_file = combine_path(data_prefix, conf.at("query_file"));
     dataset_conf_.distance   = conf.at("distance");
     if (conf.contains("filtering_rate")) {
       dataset_conf_.filtering_rate.emplace(conf.at("filtering_rate"));
     }
 
     if (conf.contains("groundtruth_neighbors_file")) {
-      dataset_conf_.groundtruth_neighbors_file = conf.at("groundtruth_neighbors_file");
+      dataset_conf_.groundtruth_neighbors_file =
+        combine_path(data_prefix, conf.at("groundtruth_neighbors_file"));
     }
     if (conf.contains("subset_first_row")) {
       dataset_conf_.subset_first_row = conf.at("subset_first_row");
@@ -108,7 +130,9 @@ class configuration {
       }
     }
   }
-  inline void parse_index(const nlohmann::json& index_conf, const nlohmann::json& search_basic_conf)
+  inline void parse_index(const nlohmann::json& index_conf,
+                          const nlohmann::json& search_basic_conf,
+                          std::string index_prefix)
   {
     const int batch_size = search_basic_conf.at("batch_size");
     const int k          = search_basic_conf.at("k");
@@ -118,7 +142,7 @@ class configuration {
       index.name        = conf.at("name");
       index.algo        = conf.at("algo");
       index.build_param = conf.at("build_param");
-      index.file        = conf.at("file");
+      index.file        = combine_path(index_prefix, conf.at("file"));
       index.batch_size  = batch_size;
       index.k           = k;
 
@@ -147,6 +171,8 @@ class configuration {
 
   dataset_conf dataset_conf_;
   std::vector<index> indices_;
+
+  static inline std::unique_ptr<configuration> singleton_ = nullptr;
 };
 
 }  // namespace cuvs::bench

From 8c683b0dcacb61c33d4118b24df1d48cbf2b2287 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 4 Feb 2025 10:15:16 -0800
Subject: [PATCH 299/623] Add float16 support in python for
 cagra/brute_force/ivf_pq and scalar quantizer (#637)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/637
---
 cpp/include/cuvs/neighbors/brute_force.h      |  6 +-
 cpp/include/cuvs/neighbors/cagra.h            | 10 ++--
 cpp/include/cuvs/neighbors/ivf_pq.h           |  6 +-
 cpp/src/neighbors/brute_force_c.cpp           | 57 +++++++++++++------
 cpp/src/neighbors/cagra_c.cpp                 |  7 +++
 cpp/src/neighbors/ivf_pq_c.cpp                |  8 +++
 cpp/src/preprocessing/quantize/scalar_c.cpp   |  6 ++
 .../neighbors/brute_force/brute_force.pyx     | 12 ++--
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  8 ++-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  | 12 +++-
 .../preprocessing/quantize/scalar/scalar.pyx  | 15 ++++-
 python/cuvs/cuvs/tests/test_brute_force.py    |  4 +-
 python/cuvs/cuvs/tests/test_cagra.py          |  2 +-
 python/cuvs/cuvs/tests/test_ivf_pq.py         |  2 +-
 .../cuvs/cuvs/tests/test_scalar_quantizer.py  |  2 +-
 15 files changed, 114 insertions(+), 43 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
index 33b92f11b9..91893e7d93 100644
--- a/cpp/include/cuvs/neighbors/brute_force.h
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -68,8 +68,7 @@ cuvsError_t cuvsBruteForceIndexDestroy(cuvsBruteForceIndex_t index);
  *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
  *        or `kDLCPU`. Also, acceptable underlying types are:
  *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
- *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
- *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *        2. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 16`
  *
  * @code {.c}
  * #include <cuvs/core/c_api.h>
@@ -120,7 +119,8 @@ cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
  *        It is also important to note that the BRUTEFORCE index must have been built
  *        with the same type of `queries`, such that `index.dtype.code ==
  *        queries.dl_tensor.dtype.code` Types for input are:
- *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32` or
+ *          `kDLDataType.bits = 16`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 207f3f21b4..d6d757e0ee 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -333,8 +333,9 @@ cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim);
  *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
  *        or `kDLCPU`. Also, acceptable underlying types are:
  *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
- *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
- *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *        2. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 16`
+ *        3. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        4. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *
  * @code {.c}
  * #include <cuvs/core/c_api.h>
@@ -421,8 +422,9 @@ cuvsError_t cuvsCagraExtend(cuvsResources_t res,
  * queries.dl_tensor.dtype.code` Types for input are:
  *        1. `queries`:
  *          a. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
- *          b. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
- *          c. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *          b. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 16`
+ *          c. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *          d. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index d588fec415..08fe600ae8 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -258,8 +258,9 @@ cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index);
  *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
  *        or `kDLCPU`. Also, acceptable underlying types are:
  *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
- *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
- *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *        2. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 16`
+ *        3. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        4. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *
  * @code {.c}
  * #include <cuvs/core/c_api.h>
@@ -314,6 +315,7 @@ cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
  *        with the same type of `queries`, such that `index.dtype.code ==
  * queries.dl_tensor.dtype.code` Types for input are:
  *        1. `queries`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *            or `kDLDataType.bits = 16`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index 98c74e2852..01779ac6c1 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -33,7 +33,7 @@
 
 namespace {
 
-template <typename T, typename LayoutT = raft::row_major>
+template <typename T, typename LayoutT = raft::row_major, typename DistT = float>
 void* _build(cuvsResources_t res,
              DLManagedTensor* dataset_tensor,
              cuvsDistanceType metric,
@@ -49,11 +49,11 @@ void* _build(cuvsResources_t res,
   params.metric_arg = metric_arg;
 
   auto index_on_stack = cuvs::neighbors::brute_force::build(*res_ptr, params, mds);
-  auto index_on_heap  = new cuvs::neighbors::brute_force::index<T>(std::move(index_on_stack));
+  auto index_on_heap = new cuvs::neighbors::brute_force::index<T, DistT>(std::move(index_on_stack));
   return index_on_heap;
 }
 
-template <typename T, typename QueriesLayoutT = raft::row_major>
+template <typename T, typename QueriesLayoutT = raft::row_major, typename DistT = float>
 void _search(cuvsResources_t res,
              cuvsBruteForceIndex index,
              DLManagedTensor* queries_tensor,
@@ -62,11 +62,11 @@ void _search(cuvsResources_t res,
              cuvsFilter prefilter)
 {
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
-  auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T>*>(index.addr);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T, DistT>*>(index.addr);
 
   using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, QueriesLayoutT>;
   using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
-  using distances_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::device_matrix_view<DistT, int64_t, raft::row_major>;
   using prefilter_mds_type    = raft::device_vector_view<uint32_t, int64_t>;
   using prefilter_bmp_type    = cuvs::core::bitmap_view<uint32_t, int64_t>;
 
@@ -98,19 +98,19 @@ void _search(cuvsResources_t res,
   }
 }
 
-template <typename T>
+template <typename T, typename DistT = float>
 void _serialize(cuvsResources_t res, const char* filename, cuvsBruteForceIndex index)
 {
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
-  auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T>*>(index.addr);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<T, DistT>*>(index.addr);
   cuvs::neighbors::brute_force::serialize(*res_ptr, std::string(filename), *index_ptr);
 }
 
-template <typename T>
+template <typename T, typename DistT = float>
 void* _deserialize(cuvsResources_t res, const char* filename)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
-  auto index   = new cuvs::neighbors::brute_force::index<T>(*res_ptr);
+  auto index   = new cuvs::neighbors::brute_force::index<T, DistT>(*res_ptr);
   cuvs::neighbors::brute_force::deserialize(*res_ptr, std::string(filename), index);
   return index;
 }
@@ -126,14 +126,13 @@ extern "C" cuvsError_t cuvsBruteForceIndexDestroy(cuvsBruteForceIndex_t index_c_
   return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
-    if (index.dtype.code == kDLFloat) {
-      auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<float>*>(index.addr);
+    if ((index.dtype.code == kDLFloat) && index.dtype.bits == 32) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::brute_force::index<float, float>*>(index.addr);
       delete index_ptr;
-    } else if (index.dtype.code == kDLInt) {
-      auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<int8_t>*>(index.addr);
-      delete index_ptr;
-    } else if (index.dtype.code == kDLUInt) {
-      auto index_ptr = reinterpret_cast<cuvs::neighbors::brute_force::index<uint8_t>*>(index.addr);
+    } else if ((index.dtype.code == kDLFloat) && index.dtype.bits == 16) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::brute_force::index<half, float>*>(index.addr);
       delete index_ptr;
     }
     delete index_c_ptr;
@@ -148,6 +147,7 @@ extern "C" cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
 {
   return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
+    index->dtype = dataset.dtype;
 
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       if (cuvs::core::is_c_contiguous(dataset_tensor)) {
@@ -159,7 +159,16 @@ extern "C" cuvsError_t cuvsBruteForceBuild(cuvsResources_t res,
       } else {
         RAFT_FAIL("dataset input to cuvsBruteForceBuild must be contiguous (non-strided)");
       }
-      index->dtype = dataset.dtype;
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      if (cuvs::core::is_c_contiguous(dataset_tensor)) {
+        index->addr =
+          reinterpret_cast<uintptr_t>(_build<half>(res, dataset_tensor, metric, metric_arg));
+      } else if (cuvs::core::is_f_contiguous(dataset_tensor)) {
+        index->addr = reinterpret_cast<uintptr_t>(
+          _build<half, raft::col_major>(res, dataset_tensor, metric, metric_arg));
+      } else {
+        RAFT_FAIL("dataset input to cuvsBruteForceBuild must be contiguous (non-strided)");
+      }
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
@@ -204,6 +213,15 @@ extern "C" cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
       } else {
         RAFT_FAIL("queries input to cuvsBruteForceSearch must be contiguous (non-strided)");
       }
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      if (cuvs::core::is_c_contiguous(queries_tensor)) {
+        _search<half>(res, index, queries_tensor, neighbors_tensor, distances_tensor, prefilter);
+      } else if (cuvs::core::is_f_contiguous(queries_tensor)) {
+        _search<half, raft::col_major>(
+          res, index, queries_tensor, neighbors_tensor, distances_tensor, prefilter);
+      } else {
+        RAFT_FAIL("queries input to cuvsBruteForceSearch must be contiguous (non-strided)");
+      }
     } else {
       RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
                 queries.dtype.code,
@@ -228,6 +246,9 @@ extern "C" cuvsError_t cuvsBruteForceDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename));
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<half>(res, filename));
     } else {
       RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
     }
@@ -241,6 +262,8 @@ extern "C" cuvsError_t cuvsBruteForceSerialize(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       _serialize<float>(res, filename, *index);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _serialize<half>(res, filename, *index);
     } else {
       RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
     }
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 333fffb428..f2c09f904f 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -254,6 +254,8 @@ extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
     index->dtype = dataset.dtype;
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       index->addr = reinterpret_cast<uintptr_t>(_build<float>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      index->addr = reinterpret_cast<uintptr_t>(_build<half>(res, *params, dataset_tensor));
     } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
       index->addr = reinterpret_cast<uintptr_t>(_build<int8_t>(res, *params, dataset_tensor));
     } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
@@ -321,6 +323,9 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
       _search<float>(
         res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      _search<half>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
       _search<int8_t>(
         res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
@@ -433,6 +438,8 @@ extern "C" cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       _serialize<float>(res, filename, index, include_dataset);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _serialize<half>(res, filename, index, include_dataset);
     } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
       _serialize<int8_t>(res, filename, index, include_dataset);
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 2ecad6ada8..00ede91c09 100755
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -155,6 +155,9 @@ extern "C" cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<float, int64_t>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<half, int64_t>(res, *params, dataset_tensor));
     } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<int8_t, int64_t>(res, *params, dataset_tensor));
@@ -197,6 +200,9 @@ extern "C" cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
       _search<float, int64_t>(
         res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      _search<half, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
     } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
       _search<int8_t, int64_t>(
         res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
@@ -274,6 +280,8 @@ extern "C" cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
 
     if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
       _extend<float, int64_t>(res, new_vectors, new_indices, *index);
+    } else if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 16) {
+      _extend<half, int64_t>(res, new_vectors, new_indices, *index);
     } else if (vectors.dtype.code == kDLInt && vectors.dtype.bits == 8) {
       _extend<int8_t, int64_t>(res, new_vectors, new_indices, *index);
     } else if (vectors.dtype.code == kDLUInt && vectors.dtype.bits == 8) {
diff --git a/cpp/src/preprocessing/quantize/scalar_c.cpp b/cpp/src/preprocessing/quantize/scalar_c.cpp
index 99880e4264..21a80c3c85 100644
--- a/cpp/src/preprocessing/quantize/scalar_c.cpp
+++ b/cpp/src/preprocessing/quantize/scalar_c.cpp
@@ -161,6 +161,8 @@ extern "C" cuvsError_t cuvsScalarQuantizerTrain(cuvsResources_t res,
     auto dataset = dataset_tensor->dl_tensor;
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       _train<float>(res, *params, dataset_tensor, quantizer);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      _train<half>(res, *params, dataset_tensor, quantizer);
     } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
       _train<double>(res, *params, dataset_tensor, quantizer);
     } else {
@@ -180,6 +182,8 @@ extern "C" cuvsError_t cuvsScalarQuantizerTransform(cuvsResources_t res,
     auto dataset = dataset_tensor->dl_tensor;
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       _transform<float>(res, quantizer, dataset_tensor, out_tensor);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      _transform<half>(res, quantizer, dataset_tensor, out_tensor);
     } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
       _transform<double>(res, quantizer, dataset_tensor, out_tensor);
     } else {
@@ -199,6 +203,8 @@ cuvsError_t cuvsScalarQuantizerInverseTransform(cuvsResources_t res,
     auto dtype = out->dl_tensor.dtype;
     if (dtype.code == kDLFloat && dtype.bits == 32) {
       _inverse_transform<float>(res, quantizer, dataset, out);
+    } else if (dtype.code == kDLFloat && dtype.bits == 16) {
+      _inverse_transform<half>(res, quantizer, dataset, out);
     } else if (dtype.code == kDLFloat && dtype.bits == 64) {
       _inverse_transform<double>(res, quantizer, dataset, out);
     } else {
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index f71acd0864..2c80385728 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -75,7 +75,7 @@ def build(dataset, metric="sqeuclidean", metric_arg=2.0, resources=None):
     Parameters
     ----------
     dataset : CUDA array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float, int8, uint8]
+        Supported dtype [float32, float16]
     metric : Distance metric to use. Default is sqeuclidean
     metric_arg : value of 'p' for Minkowski distances
     {resources_docstring}
@@ -102,7 +102,9 @@ def build(dataset, metric="sqeuclidean", metric_arg=2.0, resources=None):
     """
 
     dataset_ai = wrap_array(dataset)
-    _check_input_array(dataset_ai, [np.dtype('float32')], exp_row_major=False)
+    _check_input_array(dataset_ai,
+                       [np.dtype('float32'), np.dtype('float16')],
+                       exp_row_major=False)
 
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
@@ -141,7 +143,7 @@ def search(Index index,
     index : Index
         Trained Brute Force index.
     queries : CUDA array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float, int8, uint8]
+        Supported dtype [float32, float16]
     k : int
         The number of neighbors.
     neighbors : Optional CUDA array interface compliant matrix shape
@@ -218,7 +220,9 @@ def search(Index index,
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
     queries_cai = wrap_array(queries)
-    _check_input_array(queries_cai, [np.dtype('float32')], exp_row_major=False)
+    _check_input_array(queries_cai,
+                       [np.dtype('float32'), np.dtype('float16')],
+                       exp_row_major=False)
 
     cdef uint32_t n_queries = queries_cai.shape[0]
 
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index f62563f610..1853292e91 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -287,7 +287,9 @@ def build(IndexParams index_params, dataset, resources=None):
     # todo(dgd): we can make the check of dtype a parameter of wrap_array
     # in RAFT to make this a single call
     dataset_ai = wrap_array(dataset)
-    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
+    _check_input_array(dataset_ai, [np.dtype('float32'),
+                                    np.dtype('float16'),
+                                    np.dtype('byte'),
                                     np.dtype('ubyte')])
 
     cdef Index idx = Index()
@@ -543,7 +545,9 @@ def search(SearchParams search_params,
     # todo(dgd): we can make the check of dtype a parameter of wrap_array
     # in RAFT to make this a single call
     queries_cai = wrap_array(queries)
-    _check_input_array(queries_cai, [np.dtype('float32'), np.dtype('byte'),
+    _check_input_array(queries_cai, [np.dtype('float32'),
+                                     np.dtype('float16'),
+                                     np.dtype('byte'),
                                      np.dtype('ubyte')])
 
     cdef uint32_t n_queries = queries_cai.shape[0]
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 531302ee62..36f56983a9 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -277,7 +277,9 @@ def build(IndexParams index_params, dataset, resources=None):
     """
 
     dataset_ai = wrap_array(dataset)
-    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
+    _check_input_array(dataset_ai, [np.dtype('float32'),
+                                    np.dtype('float16'),
+                                    np.dtype('byte'),
                                     np.dtype('ubyte')])
 
     cdef Index idx = Index()
@@ -415,7 +417,9 @@ def search(SearchParams search_params,
         raise ValueError("Index needs to be built before calling search.")
 
     queries_cai = wrap_array(queries)
-    _check_input_array(queries_cai, [np.dtype('float32'), np.dtype('byte'),
+    _check_input_array(queries_cai, [np.dtype('float32'),
+                                     np.dtype('float16'),
+                                     np.dtype('byte'),
                                      np.dtype('ubyte')])
 
     cdef uint32_t n_queries = queries_cai.shape[0]
@@ -575,7 +579,9 @@ def extend(Index index, new_vectors, new_indices, resources=None):
     """
 
     new_vectors_ai = wrap_array(new_vectors)
-    _check_input_array(new_vectors_ai, [np.dtype('float32'), np.dtype('byte'),
+    _check_input_array(new_vectors_ai, [np.dtype('float32'),
+                                        np.dtype('float16'),
+                                        np.dtype('byte'),
                                         np.dtype('ubyte')])
 
     new_indices_ai = wrap_array(new_indices)
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx b/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx
index c4cc53f25e..105ec69352 100644
--- a/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx
+++ b/python/cuvs/cuvs/preprocessing/quantize/scalar/scalar.pyx
@@ -115,7 +115,10 @@ def train(QuantizerParams params, dataset, resources=None):
     cdef cydlpack.DLManagedTensor* dataset_dlpack = \
         cydlpack.dlpack_c(dataset_ai)
 
-    _check_input_array(dataset_ai, [np.dtype("float32"), np.dtype("float64")])
+    _check_input_array(dataset_ai,
+                       [np.dtype("float32"),
+                        np.dtype("float64"),
+                        np.dtype("float16")])
 
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
     cdef Quantizer ret = Quantizer()
@@ -160,7 +163,10 @@ def transform(Quantizer quantizer, dataset, output=None, resources=None):
 
     dataset_ai = wrap_array(dataset)
 
-    _check_input_array(dataset_ai, [np.dtype("float32"), np.dtype("float64")])
+    _check_input_array(dataset_ai,
+                       [np.dtype("float32"),
+                        np.dtype("float64"),
+                        np.dtype("float16")])
 
     if output is None:
         on_device = hasattr(dataset, "__cuda_array_interface__")
@@ -219,7 +225,10 @@ def inverse_transform(Quantizer quantizer, dataset, output=None,
                                 dataset_ai.shape[1]), dtype="float32")
 
     output_ai = wrap_array(output)
-    _check_input_array(output_ai, [np.dtype("float32"), np.dtype("float64")])
+    _check_input_array(output_ai,
+                       [np.dtype("float32"),
+                        np.dtype("float64"),
+                        np.dtype("float16")])
 
     cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
diff --git a/python/cuvs/cuvs/tests/test_brute_force.py b/python/cuvs/cuvs/tests/test_brute_force.py
index a234794f90..0c39c601a8 100644
--- a/python/cuvs/cuvs/tests/test_brute_force.py
+++ b/python/cuvs/cuvs/tests/test_brute_force.py
@@ -41,7 +41,7 @@
 )
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("order", ["F", "C"])
-@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize("dtype", [np.float32, np.float16])
 def test_brute_force_knn(
     n_index_rows, n_query_rows, n_cols, k, inplace, order, metric, dtype
 ):
@@ -58,7 +58,7 @@ def test_brute_force_knn(
         queries[queries >= 0.5] = 1.0
 
     indices = np.zeros((n_query_rows, k), dtype="int64")
-    distances = np.zeros((n_query_rows, k), dtype=dtype)
+    distances = np.zeros((n_query_rows, k), dtype="float32")
 
     index_device = device_ndarray(index)
     queries_device = device_ndarray(queries)
diff --git a/python/cuvs/cuvs/tests/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
index ab3ac4a37b..831ef11f77 100644
--- a/python/cuvs/cuvs/tests/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -121,7 +121,7 @@ def run_cagra_build_search_test(
 
 
 @pytest.mark.parametrize("inplace", [True, False])
-@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
 @pytest.mark.parametrize("array_type", ["device", "host"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
 @pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
diff --git a/python/cuvs/cuvs/tests/test_ivf_pq.py b/python/cuvs/cuvs/tests/test_ivf_pq.py
index 6661be7de7..9e0c5572a2 100644
--- a/python/cuvs/cuvs/tests/test_ivf_pq.py
+++ b/python/cuvs/cuvs/tests/test_ivf_pq.py
@@ -195,7 +195,7 @@ def test_extend(dtype):
 
 
 @pytest.mark.parametrize("inplace", [True, False])
-@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
 def test_ivf_pq_dtype(inplace, dtype):
     run_ivf_pq_build_search_test(
         dtype=dtype,
diff --git a/python/cuvs/cuvs/tests/test_scalar_quantizer.py b/python/cuvs/cuvs/tests/test_scalar_quantizer.py
index 0dbcada859..ae51329ac3 100644
--- a/python/cuvs/cuvs/tests/test_scalar_quantizer.py
+++ b/python/cuvs/cuvs/tests/test_scalar_quantizer.py
@@ -24,7 +24,7 @@
 @pytest.mark.parametrize("n_cols", [10, 50])
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("device_memory", [True, False])
-@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
 def test_scalar_quantizer(n_rows, n_cols, inplace, device_memory, dtype):
     input1 = np.random.random_sample((n_rows, n_cols)).astype(dtype)
     output = np.zeros((n_rows, n_cols), dtype="int8") if inplace else None

From 45703bfc5b3a78740ce8bc67f3e89acb95f08ffc Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 4 Feb 2025 16:14:02 -0800
Subject: [PATCH 300/623] Expose NN-Descent to C and Python (#635)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/635
---
 cpp/CMakeLists.txt                            |   1 +
 cpp/include/cuvs/neighbors/nn_descent.h       | 181 +++++++++++++
 cpp/src/neighbors/nn_descent_c.cpp            | 167 ++++++++++++
 python/cuvs/cuvs/distance/__init__.py         |   4 +-
 python/cuvs/cuvs/distance/distance.pyx        |   2 +
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 python/cuvs/cuvs/neighbors/__init__.py        |  10 +-
 .../cuvs/neighbors/nn_descent/CMakeLists.txt  |  28 ++
 .../cuvs/neighbors/nn_descent/__init__.pxd    |   0
 .../cuvs/neighbors/nn_descent/__init__.py     |  22 ++
 .../cuvs/neighbors/nn_descent/nn_descent.pxd  |  63 +++++
 .../cuvs/neighbors/nn_descent/nn_descent.pyx  | 239 ++++++++++++++++++
 python/cuvs/cuvs/tests/test_nn_descent.py     |  53 ++++
 13 files changed, 768 insertions(+), 3 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/nn_descent.h
 create mode 100644 cpp/src/neighbors/nn_descent_c.cpp
 create mode 100644 python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/nn_descent/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/nn_descent/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
 create mode 100644 python/cuvs/cuvs/tests/test_nn_descent.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 65b1471f51..fb33c1ab7f 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -691,6 +691,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       src/neighbors/ivf_pq_c.cpp
       src/neighbors/cagra_c.cpp
       $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
+      src/neighbors/nn_descent_c.cpp
       src/neighbors/refine/refine_c.cpp
       src/preprocessing/quantize/scalar_c.cpp
       src/distance/pairwise_distance_c.cpp
diff --git a/cpp/include/cuvs/neighbors/nn_descent.h b/cpp/include/cuvs/neighbors/nn_descent.h
new file mode 100644
index 0000000000..81c162598f
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/nn_descent.h
@@ -0,0 +1,181 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <dlpack/dlpack.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup nn_descent_c_index_params The nn-descent algorithm parameters.
+ * @{
+ */
+/**
+ * @brief Parameters used to build an nn-descent index
+ *
+ * `metric`: The distance metric to use
+ * `metric_arg`: The argument used by distance metrics like Minkowskidistance
+ * `graph_degree`: For an input dataset of dimensions (N, D),
+ * determines the final dimensions of the all-neighbors knn graph
+ * which turns out to be of dimensions (N, graph_degree)
+ * `intermediate_graph_degree`: Internally, nn-descent builds an
+ * all-neighbors knn graph of dimensions (N, intermediate_graph_degree)
+ * before selecting the final `graph_degree` neighbors. It's recommended
+ * that `intermediate_graph_degree` >= 1.5 * graph_degree
+ * `max_iterations`: The number of iterations that nn-descent will refine
+ * the graph for. More iterations produce a better quality graph at cost of performance
+ * `termination_threshold`: The delta at which nn-descent will terminate its iterations
+ */
+struct cuvsNNDescentIndexParams {
+  cuvsDistanceType metric;
+  float metric_arg;
+  size_t graph_degree;
+  size_t intermediate_graph_degree;
+  size_t max_iterations;
+  float termination_threshold;
+  bool return_distances;
+  size_t n_clusters;
+};
+
+typedef struct cuvsNNDescentIndexParams* cuvsNNDescentIndexParams_t;
+
+/**
+ * @brief Allocate NN-Descent Index params, and populate with default values
+ *
+ * @param[in] index_params cuvsNNDescentIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsNNDescentIndexParamsCreate(cuvsNNDescentIndexParams_t* index_params);
+
+/**
+ * @brief De-allocate NN-Descent Index params
+ *
+ * @param[in] index_params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsNNDescentIndexParamsDestroy(cuvsNNDescentIndexParams_t index_params);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup nn_descent_c_index NN-Descent index
+ * @{
+ */
+/**
+ * @brief Struct to hold address of cuvs::neighbors::nn_descent::index and its active trained dtype
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} cuvsNNDescentIndex;
+
+typedef cuvsNNDescentIndex* cuvsNNDescentIndex_t;
+
+/**
+ * @brief Allocate NN-Descent index
+ *
+ * @param[in] index cuvsNNDescentIndex_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsNNDescentIndexCreate(cuvsNNDescentIndex_t* index);
+
+/**
+ * @brief De-allocate NN-Descent index
+ *
+ * @param[in] index cuvsNNDescentIndex_t to de-allocate
+ */
+cuvsError_t cuvsNNDescentIndexDestroy(cuvsNNDescentIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup nn_descent_c_index_build NN-Descent index build
+ * @{
+ */
+/**
+ * @brief Build a NN-Descent index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
+ *        or `kDLCPU`. Also, acceptable underlying types are:
+ *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 16`
+ *        3. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        4. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/nn_descent.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ *
+ * // Create default index params
+ * cuvsNNDescentIndexParams_t index_params;
+ * cuvsError_t params_create_status = cuvsNNDescentIndexParamsCreate(&index_params);
+ *
+ * // Create NN-Descent index
+ * cuvsNNDescentIndex_t index;
+ * cuvsError_t index_create_status = cuvsNNDescentIndexCreate(&index);
+ *
+ * // Build the NN-Descent Index
+ * cuvsError_t build_status = cuvsNNDescentBuild(res, index_params, &dataset, index);
+ *
+ * // de-allocate `index_params`, `index` and `res`
+ * cuvsError_t params_destroy_status = cuvsNNDescentIndexParamsDestroy(index_params);
+ * cuvsError_t index_destroy_status = cuvsNNDescentIndexDestroy(index);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index_params cuvsNNDescentIndexParams_t used to build NN-Descent index
+ * @param[in] dataset DLManagedTensor* training dataset on host or device memory
+ * @param[inout] graph Optional preallocated graph on host memory to store output
+ * @param[out] index cuvsNNDescentIndex_t Newly built NN-Descent index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsNNDescentBuild(cuvsResources_t res,
+                               cuvsNNDescentIndexParams_t index_params,
+                               DLManagedTensor* dataset,
+                               DLManagedTensor* graph,
+                               cuvsNNDescentIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @brief Get the KNN graph from a built NN-Descent index
+ *
+ * @param[in] index cuvsNNDescentIndex_t Built NN-Descent index
+ * @param[inout] graph Optional preallocated graph on host memory to store output
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsNNDescentIndexGetGraph(cuvsNNDescentIndex_t index, DLManagedTensor* graph);
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/neighbors/nn_descent_c.cpp b/cpp/src/neighbors/nn_descent_c.cpp
new file mode 100644
index 0000000000..727b332cb5
--- /dev/null
+++ b/cpp/src/neighbors/nn_descent_c.cpp
@@ -0,0 +1,167 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/copy.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/nn_descent.h>
+#include <cuvs/neighbors/nn_descent.hpp>
+
+#include <fstream>
+
+namespace {
+
+template <typename T, typename IdxT = uint32_t>
+void* _build(cuvsResources_t res,
+             cuvsNNDescentIndexParams params,
+             DLManagedTensor* dataset_tensor,
+             DLManagedTensor* graph_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto dataset = dataset_tensor->dl_tensor;
+
+  auto build_params         = cuvs::neighbors::nn_descent::index_params();
+  build_params.metric       = static_cast<cuvs::distance::DistanceType>((int)params.metric),
+  build_params.metric_arg   = params.metric_arg;
+  build_params.graph_degree = params.graph_degree;
+  build_params.intermediate_graph_degree = params.intermediate_graph_degree;
+  build_params.max_iterations            = params.max_iterations;
+  build_params.termination_threshold     = params.termination_threshold;
+  build_params.return_distances          = params.return_distances;
+  build_params.n_clusters                = params.n_clusters;
+
+  using graph_type = raft::host_matrix_view<IdxT, int64_t, raft::row_major>;
+  std::optional<graph_type> graph;
+  if (graph_tensor != NULL) { graph = cuvs::core::from_dlpack<graph_type>(graph_tensor); }
+
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using dataset_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    auto dataset       = cuvs::core::from_dlpack<dataset_type>(dataset_tensor);
+    auto index         = cuvs::neighbors::nn_descent::build(*res_ptr, build_params, dataset, graph);
+    return new cuvs::neighbors::nn_descent::index<IdxT>(std::move(index));
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using dataset_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    auto dataset       = cuvs::core::from_dlpack<dataset_type>(dataset_tensor);
+    auto index         = cuvs::neighbors::nn_descent::build(*res_ptr, build_params, dataset, graph);
+    return new cuvs::neighbors::nn_descent::index<IdxT>(std::move(index));
+  } else {
+    RAFT_FAIL("dataset must be accessible on host or device memory");
+  }
+}
+}  // namespace
+
+extern "C" cuvsError_t cuvsNNDescentIndexCreate(cuvsNNDescentIndex_t* index)
+{
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsNNDescentIndex{}; });
+}
+
+extern "C" cuvsError_t cuvsNNDescentIndexDestroy(cuvsNNDescentIndex_t index_c_ptr)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index = *index_c_ptr;
+    if ((index.dtype.code == kDLUInt) && (index.dtype.bits == 32)) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::nn_descent::index<uint32_t>*>(index.addr);
+      delete index_ptr;
+    } else {
+      RAFT_FAIL(
+        "Unsupported nn-descent index dtype: %d and bits: %d", index.dtype.code, index.dtype.bits);
+    }
+    delete index_c_ptr;
+  });
+}
+
+extern "C" cuvsError_t cuvsNNDescentBuild(cuvsResources_t res,
+                                          cuvsNNDescentIndexParams_t params,
+                                          DLManagedTensor* dataset_tensor,
+                                          DLManagedTensor* graph_tensor,
+                                          cuvsNNDescentIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    index->dtype.code = kDLUInt;
+    index->dtype.bits = 32;
+
+    auto dtype = dataset_tensor->dl_tensor.dtype;
+
+    if ((dtype.code == kDLFloat) && (dtype.bits == 32)) {
+      index->addr = reinterpret_cast<uintptr_t>(
+        _build<float, uint32_t>(res, *params, dataset_tensor, graph_tensor));
+    } else if ((dtype.code == kDLFloat) && (dtype.bits == 16)) {
+      index->addr = reinterpret_cast<uintptr_t>(
+        _build<half, uint32_t>(res, *params, dataset_tensor, graph_tensor));
+    } else if ((dtype.code == kDLInt) && (dtype.bits == 8)) {
+      index->addr = reinterpret_cast<uintptr_t>(
+        _build<int8_t, uint32_t>(res, *params, dataset_tensor, graph_tensor));
+    } else if ((dtype.code == kDLUInt) && (dtype.bits == 8)) {
+      index->addr = reinterpret_cast<uintptr_t>(
+        _build<uint8_t, uint32_t>(res, *params, dataset_tensor, graph_tensor));
+    } else {
+      RAFT_FAIL("Unsupported nn-descent dataset dtype: %d and bits: %d", dtype.code, dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsNNDescentIndexParamsCreate(cuvsNNDescentIndexParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // get defaults from cpp parameters struct
+    cuvs::neighbors::nn_descent::index_params cpp_params;
+
+    *params = new cuvsNNDescentIndexParams{
+      .metric                    = cpp_params.metric,
+      .metric_arg                = cpp_params.metric_arg,
+      .graph_degree              = cpp_params.graph_degree,
+      .intermediate_graph_degree = cpp_params.intermediate_graph_degree,
+      .max_iterations            = cpp_params.max_iterations,
+      .termination_threshold     = cpp_params.termination_threshold,
+      .return_distances          = cpp_params.return_distances,
+      .n_clusters                = cpp_params.n_clusters};
+  });
+}
+
+extern "C" cuvsError_t cuvsNNDescentIndexParamsDestroy(cuvsNNDescentIndexParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsNNDescentIndexGetGraph(cuvsNNDescentIndex_t index,
+                                                  DLManagedTensor* graph)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dtype = index->dtype;
+    if ((dtype.code == kDLUInt) && (dtype.bits == 32)) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::nn_descent::index<uint32_t>*>(index->addr);
+      using output_mdspan_type = raft::host_matrix_view<uint32_t, int64_t, raft::row_major>;
+      auto dst                 = cuvs::core::from_dlpack<output_mdspan_type>(graph);
+      auto src                 = index_ptr->graph();
+
+      RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output graph has incorrect number of rows");
+      RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output graph has incorrect number of cols");
+      std::copy(src.data_handle(), src.data_handle() + dst.size(), dst.data_handle());
+    } else {
+      RAFT_FAIL("Unsupported nn-descent index dtype: %d and bits: %d", dtype.code, dtype.bits);
+    }
+  });
+}
diff --git a/python/cuvs/cuvs/distance/__init__.py b/python/cuvs/cuvs/distance/__init__.py
index 5c985e7b19..024f2f2c03 100644
--- a/python/cuvs/cuvs/distance/__init__.py
+++ b/python/cuvs/cuvs/distance/__init__.py
@@ -12,6 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .distance import DISTANCE_TYPES, pairwise_distance
+from .distance import DISTANCE_NAMES, DISTANCE_TYPES, pairwise_distance
 
-__all__ = ["DISTANCE_TYPES", "pairwise_distance"]
+__all__ = ["DISTANCE_NAMES", "DISTANCE_TYPES", "pairwise_distance"]
diff --git a/python/cuvs/cuvs/distance/distance.pyx b/python/cuvs/cuvs/distance/distance.pyx
index d50fc152f4..6b80d43b2e 100644
--- a/python/cuvs/cuvs/distance/distance.pyx
+++ b/python/cuvs/cuvs/distance/distance.pyx
@@ -48,6 +48,8 @@ DISTANCE_TYPES = {
     "dice": cuvsDistanceType.DiceExpanded,
 }
 
+DISTANCE_NAMES = {v: k for k, v in DISTANCE_TYPES.items()}
+
 SUPPORTED_DISTANCES = ["euclidean", "l1", "cityblock", "l2", "inner_product",
                        "chebyshev", "minkowski", "canberra", "kl_divergence",
                        "correlation", "russellrao", "hellinger", "lp",
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index b9161eefc6..3ac426f2b2 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -18,6 +18,7 @@ add_subdirectory(hnsw)
 add_subdirectory(ivf_flat)
 add_subdirectory(ivf_pq)
 add_subdirectory(filters)
+add_subdirectory(nn_descent)
 
 # Set the list of Cython files to build
 set(cython_sources refine.pyx)
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index 52bb1eef84..1ba7f79ec8 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -13,7 +13,14 @@
 # limitations under the License.
 
 
-from cuvs.neighbors import brute_force, cagra, filters, ivf_flat, ivf_pq
+from cuvs.neighbors import (
+    brute_force,
+    cagra,
+    filters,
+    ivf_flat,
+    ivf_pq,
+    nn_descent,
+)
 
 from .refine import refine
 
@@ -23,5 +30,6 @@
     "filters",
     "ivf_flat",
     "ivf_pq",
+    "nn_descent",
     "refine",
 ]
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt b/python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt
new file mode 100644
index 0000000000..20f37bc238
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt
@@ -0,0 +1,28 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources nn_descent.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_nn_descent_
+)
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/__init__.pxd b/python/cuvs/cuvs/neighbors/nn_descent/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/__init__.py b/python/cuvs/cuvs/neighbors/nn_descent/__init__.py
new file mode 100644
index 0000000000..312bc735df
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/nn_descent/__init__.py
@@ -0,0 +1,22 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .nn_descent import Index, IndexParams, build
+
+__all__ = [
+    "Index",
+    "IndexParams",
+    "build",
+]
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
new file mode 100644
index 0000000000..6d0edb1f30
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
@@ -0,0 +1,63 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uint32_t, uintptr_t
+from libcpp cimport bool
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "cuvs/neighbors/nn_descent.h" nogil:
+
+    ctypedef struct cuvsNNDescentIndexParams:
+        cuvsDistanceType metric
+        float metric_arg
+        size_t graph_degree
+        size_t intermediate_graph_degree
+        size_t max_iterations
+        float termination_threshold
+        bool return_distances
+        size_t n_clusters
+
+    ctypedef cuvsNNDescentIndexParams* cuvsNNDescentIndexParams_t
+
+    ctypedef struct cuvsNNDescentIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsNNDescentIndex* cuvsNNDescentIndex_t
+
+    cuvsError_t cuvsNNDescentIndexParamsCreate(
+        cuvsNNDescentIndexParams_t* params)
+
+    cuvsError_t cuvsNNDescentIndexParamsDestroy(
+        cuvsNNDescentIndexParams_t index)
+
+    cuvsError_t cuvsNNDescentIndexCreate(cuvsNNDescentIndex_t* index)
+
+    cuvsError_t cuvsNNDescentIndexDestroy(cuvsNNDescentIndex_t index)
+
+    cuvsError_t cuvsNNDescentIndexGetGraph(cuvsNNDescentIndex_t index,
+                                           DLManagedTensor * output)
+
+    cuvsError_t cuvsNNDescentBuild(cuvsResources_t res,
+                                   cuvsNNDescentIndexParams* params,
+                                   DLManagedTensor* dataset,
+                                   DLManagedTensor* graph,
+                                   cuvsNNDescentIndex_t index) except +
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
new file mode 100644
index 0000000000..9dd14c27e7
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
@@ -0,0 +1,239 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libcpp cimport bool, cast
+from libcpp.string cimport string
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
+from cuvs.neighbors.common import _check_input_array
+
+from libc.stdint cimport (
+    int8_t,
+    int64_t,
+    uint8_t,
+    uint32_t,
+    uint64_t,
+    uintptr_t,
+)
+
+from cuvs.common.exceptions import check_cuvs
+
+
+cdef class IndexParams:
+    """
+    Parameters to build NN-Descent Index
+
+    Parameters
+    ----------
+    metric : str, default = "sqeuclidean"
+        String denoting the metric type.
+        distribution of the newly added data.
+    graph_degree :  int
+        For an input dataset of dimensions (N, D), determines the final
+        dimensions of the all-neighbors knn graph which turns out to be of
+        dimensions (N, graph_degree)
+    intermediate_graph_degree : int
+        Internally, nn-descent builds an all-neighbors knn graph of dimensions
+        (N, intermediate_graph_degree) before selecting the final
+        `graph_degree` neighbors. It's recommended that
+        `intermediate_graph_degree` >= 1.5 * graph_degree
+    max_iterations : int
+        The number of iterations that nn-descent will refine the graph for.
+        More iterations produce a better quality graph at cost of performance
+    termination_threshold : float
+        The delta at which nn-descent will terminate its iterations
+    """
+
+    cdef cuvsNNDescentIndexParams* params
+    cdef object _metric
+
+    def __cinit__(self):
+        cuvsNNDescentIndexParamsCreate(&self.params)
+
+    def __dealloc__(self):
+        check_cuvs(cuvsNNDescentIndexParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 metric=None,
+                 metric_arg=None,
+                 graph_degree=None,
+                 intermediate_graph_degree=None,
+                 max_iterations=None,
+                 termination_threshold=None,
+                 n_clusters=None
+                 ):
+        if metric is not None:
+            self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+        if graph_degree is not None:
+            self.params.graph_degree = graph_degree
+        if intermediate_graph_degree is not None:
+            self.params.intermediate_graph_degree = intermediate_graph_degree
+        if max_iterations is not None:
+            self.params.max_iterations = max_iterations
+        if termination_threshold is not None:
+            self.params.termination_threshold = termination_threshold
+        if n_clusters is not None:
+            self.params.n_clusters = n_clusters
+
+        # setting this parameter to true will cause an exception in the c++
+        # api (`Using return_distances set to true requires distance view to
+        # be allocated.`) - so instead force to be false here
+        self.params.return_distances = False
+
+    @property
+    def metric(self):
+        return DISTANCE_NAMES[self.params.metric]
+
+    @property
+    def metric_arg(self):
+        return self.params.metric_arg
+
+    @property
+    def graph_degree(self):
+        return self.params.graph_degree
+
+    @property
+    def intermediate_graph_degree(self):
+        return self.params.intermediate_graph_degree
+
+    @property
+    def max_iterations(self):
+        return self.params.max_iterations
+
+    @property
+    def termination_threshold(self):
+        return self.params.termination_threshold
+
+    @property
+    def n_clusters(self):
+        return self.params.n_clusters
+
+cdef class Index:
+    """
+    NN-Descent index object. This object stores the trained NN-Descent index,
+    which can be used to get the NN-Descent graph and distances after
+    building
+    """
+
+    cdef cuvsNNDescentIndex_t index
+    cdef bool trained
+    cdef int64_t num_rows
+    cdef size_t graph_degree
+
+    def __cinit__(self):
+        self.trained = False
+        self.num_rows = 0
+        self.graph_degree = 0
+        check_cuvs(cuvsNNDescentIndexCreate(&self.index))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsNNDescentIndexDestroy(self.index))
+
+    @property
+    def trained(self):
+        return self.trained
+
+    @property
+    def graph(self):
+        if not self.trained:
+            raise ValueError("Index needs to be built before getting graph")
+
+        output = np.empty((self.num_rows, self.graph_degree), dtype='uint32')
+        ai = wrap_array(output)
+        cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
+        check_cuvs(cuvsNNDescentIndexGetGraph(self.index, output_dlpack))
+        return output
+
+    def __repr__(self):
+        return "Index(type=NNDescent)"
+
+
+@auto_sync_resources
+def build(IndexParams index_params, dataset, graph=None, resources=None):
+    """
+    Build KNN graph from the dataset
+
+    Parameters
+    ----------
+    index_params : :py:class:`cuvs.neighbors.nn_descent.IndexParams`
+    dataset : Array interface compliant matrix, on either host or device memory
+        Supported dtype [float, int8, uint8]
+    graph : Optional host matrix for storing output graph
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.nn_descent.Index`
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import nn_descent
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> build_params = nn_descent.IndexParams(metric="sqeuclidean")
+    >>> index = nn_descent.build(build_params, dataset)
+    >>> graph = index.graph
+    """
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
+                                    np.dtype('ubyte')])
+
+    cdef Index idx = Index()
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cuvsNNDescentIndexParams* params = index_params.params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* graph_dlpack = NULL
+    if graph is not None:
+        graph_ai = wrap_array(graph)
+        graph_dlpack = cydlpack.dlpack_c(graph_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsNNDescentBuild(
+            res,
+            params,
+            dataset_dlpack,
+            graph_dlpack,
+            idx.index
+        ))
+        idx.trained = True
+        idx.num_rows = dataset_ai.shape[0]
+        idx.graph_degree = params.graph_degree
+
+    return idx
diff --git a/python/cuvs/cuvs/tests/test_nn_descent.py b/python/cuvs/cuvs/tests/test_nn_descent.py
new file mode 100644
index 0000000000..e2cc5555f4
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_nn_descent.py
@@ -0,0 +1,53 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+
+from cuvs.neighbors import brute_force, nn_descent
+from cuvs.tests.ann_utils import calc_recall
+
+
+@pytest.mark.parametrize("n_rows", [1024, 2048])
+@pytest.mark.parametrize("n_cols", [32, 64])
+@pytest.mark.parametrize("device_memory", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_nn_descent(n_rows, n_cols, device_memory, dtype, inplace):
+    metric = "sqeuclidean"
+    graph_degree = 64
+
+    input1 = np.random.random_sample((n_rows, n_cols)).astype(dtype)
+    input1_device = device_ndarray(input1)
+    graph = np.zeros((n_rows, graph_degree), dtype="uint32")
+
+    params = nn_descent.IndexParams(metric=metric, graph_degree=graph_degree)
+    index = nn_descent.build(
+        params,
+        input1_device if device_memory else input1,
+        graph=graph if inplace else None,
+    )
+
+    if not inplace:
+        graph = index.graph
+
+    bfknn_index = brute_force.build(input1_device, metric=metric)
+    _, bfknn_graph = brute_force.search(
+        bfknn_index, input1_device, k=graph_degree
+    )
+    bfknn_graph = bfknn_graph.copy_to_host()
+
+    assert calc_recall(graph, bfknn_graph) > 0.9

From ddf1c8f0b9ba75954f4fdefb800c2dd41ef1781c Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Wed, 5 Feb 2025 13:27:38 +0530
Subject: [PATCH 301/623] cuvs-bench Fixes (#654)

- Simple bug fix for seg fault while running cuvs-bench
- Pin the correct FAISS version for cuvs-bench

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/654
---
 cpp/bench/ann/src/common/benchmark.hpp | 2 +-
 cpp/cmake/patches/faiss_override.json  | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index 9653a589b7..d2b3f89ff9 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -571,7 +571,7 @@ void dispatch_benchmark(std::string cmdline,
       }
     }
     std::swap(more_indices, indices);  // update the config in case algorithms need to access it
-    register_build<T>(dataset, more_indices, force_overwrite, no_lap_sync);
+    register_build<T>(dataset, indices, force_overwrite, no_lap_sync);
   } else if (search_mode) {
     if (file_exists(query_file)) {
       log_info("Using the query file '%s'", query_file.c_str());
diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
index 18542fcf5f..c211f4455f 100644
--- a/cpp/cmake/patches/faiss_override.json
+++ b/cpp/cmake/patches/faiss_override.json
@@ -1,9 +1,9 @@
 {
     "packages" : {
       "faiss" : {
-        "version": "1.7.4",
+        "version": "1.10.0",
         "git_url": "https://github.com/facebookresearch/faiss.git",
-        "git_tag": "main"
+        "git_tag": "v1.10.0"
       }
     }
   }

From 550d3bcb9ca2188f2c47a73f0c95a89d188084cf Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Wed, 5 Feb 2025 08:00:35 -0500
Subject: [PATCH 302/623] Use `rapids-pip-retry` in CI jobs that might need
 retries (#648)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Uses a retry wrapper for `pip` commands to try to alleviate CI failures due to hash mismatches that result from network hiccups

xref rapidsai/gha-tools#132

This will retry failures that show up in CI like:

```
   Collecting nvidia-cusolver-cu12 (from libraft-cu12==25.2.*,>=0.0.0a0)
    Downloading https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.2.55-py3-none-manylinux_2_27_aarch64.whl (260.1 MB)
       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━      226.5/260.1 MB 70.2 MB/s eta 0:00:01
  ERROR: THESE PACKAGES DO NOT MATCH THE HASHES FROM THE REQUIREMENTS FILE. If you have updated the package versions, please update the hashes. Otherwise, examine the package contents carefully; someone may have tampered with them.
      nvidia-cusolver-cu12 from https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.2.55-py3-none-manylinux_2_27_aarch64.whl#sha256=0fd9e98246f43c15bee5561147ad235dfdf2d037f5d07c9d41af3f7f72feb7cc (from libraft-cu12==25.2.*,>=0.0.0a0):
          Expected sha256 0fd9e98246f43c15bee5561147ad235dfdf2d037f5d07c9d41af3f7f72feb7cc
               Got        5ab0571003be61a02d3cbf59307e987c3e18a755fa472475a7f5178e77e12012

  error: subprocess-exited-with-error

  × pip subprocess to install backend dependencies did not run successfully.
  │ exit code: 1
  ╰─> See above for output.
```

ref: https://github.com/rapidsai/cuvs/actions/runs/13115119921/job/36588587993?pr=643#step:10:128

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/648
---
 ci/build_wheel.sh         | 2 +-
 ci/build_wheel_libcuvs.sh | 2 +-
 ci/test_wheel_cuvs.sh     | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index c6f1232b37..91cb50a502 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -38,7 +38,7 @@ rapids-logger "Building '${package_name}' wheel"
 
 sccache --zero-stats
 
-python -m pip wheel \
+rapids-pip-retry wheel \
     -w dist \
     -v \
     --no-deps \
diff --git a/ci/build_wheel_libcuvs.sh b/ci/build_wheel_libcuvs.sh
index 148be89a27..3bceed423f 100755
--- a/ci/build_wheel_libcuvs.sh
+++ b/ci/build_wheel_libcuvs.sh
@@ -17,7 +17,7 @@ rapids-dependency-file-generator \
 | tee /tmp/requirements-build.txt
 
 rapids-logger "Installing build requirements"
-python -m pip install \
+rapids-pip-retry install \
     -v \
     --prefer-binary \
     -r /tmp/requirements-build.txt
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index cb74c52064..c2d02a30e0 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -9,7 +9,7 @@ RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-f
 RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 python ./dist
 
 # echo to expand wildcard before adding `[extra]` requires for pip
-python -m pip install \
+rapids-pip-retry install \
     ./local-libcuvs-dep/libcuvs*.whl \
     "$(echo ./dist/cuvs*.whl)[test]"
 

From d7c258e1649a90fd8df2606fc170b5829140150a Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Wed, 5 Feb 2025 08:51:01 -0500
Subject: [PATCH 303/623] Add telemetry (#652)

Enables telemetry during CUVS CI runs.  This is done by parsing GitHub Actions run log metadata and should have no impact on build or test times.

xref rapidsai/build-infra#139

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Mike Sarahan (https://github.com/msarahan)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/652
---
 .github/workflows/pr.yaml | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index a5981fe1f9..233b82f94b 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -26,12 +26,23 @@ jobs:
       - wheel-build-cuvs
       - wheel-tests-cuvs
       - devcontainer
+      - telemetry-setup
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.02
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
+  telemetry-setup:
+    continue-on-error: true
+    runs-on: ubuntu-latest
+    env:
+      OTEL_SERVICE_NAME: 'pr-cuvs'
+    steps:
+      - name: Telemetry setup
+        if: ${{ vars.TELEMETRY_ENABLED == 'true' }}
+        uses: rapidsai/shared-actions/telemetry-dispatch-stash-base-env-vars@main
   check-nightly-ci:
+    needs: telemetry-setup
     # Switch to ubuntu-latest once it defaults to a version of Ubuntu that
     # provides at least Python 3.11 (see
     # https://docs.python.org/3/library/datetime.html#datetime.date.fromisoformat)
@@ -44,6 +55,7 @@ jobs:
         with:
           repo: cuvs
   changed-files:
+    needs: telemetry-setup
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.02
     with:
@@ -77,10 +89,12 @@ jobs:
           - '!rust/**'
           - '!thirdparty/LICENSES/**'
   checks:
+    needs: telemetry-setup
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.02
     with:
       enable_check_generated_files: false
+      ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
@@ -162,6 +176,7 @@ jobs:
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
+    needs: telemetry-setup
     uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.02
     with:
       arch: '["amd64"]'
@@ -170,3 +185,13 @@ jobs:
         sccache -z;
         build-all --verbose;
         sccache -s;
+
+  telemetry-summarize:
+    # This job must use a self-hosted runner to record telemetry traces.
+    runs-on: linux-amd64-cpu4
+    needs: pr-builder
+    if: ${{ vars.TELEMETRY_ENABLED == 'true' && !cancelled() }}
+    continue-on-error: true
+    steps:
+      - name: Telemetry summarize
+        uses: rapidsai/shared-actions/telemetry-dispatch-summarize@main

From 228d949bcb8d4625710a8780a04c89c199e4164e Mon Sep 17 00:00:00 2001
From: Chris Hegarty <62058229+ChrisHegarty@users.noreply.github.com>
Date: Thu, 6 Feb 2025 02:48:55 +0000
Subject: [PATCH 304/623] cuvs-java: Rework the api to be Java 21 friendly
 (#628)

This change reworks the api to allow it to be used with Java 21. The implementation is moved to an internal package, compiled with JDK 22, and packaged as an mrjar. The benefit of this structure is that the api can be used in environments that compile to a minimum of Java 21, but run on more recent JDKs like 22 and 23 - which is exactly what Elasticsearch and Lucene do.  In fact, a minimum compilation target of Java 21 is common, since 21, at the time of writing, is the most recent LTS Java release.

The most significant change is that the non-trivial api types are now, for the most part, interfaces. Instance can be created by one of the factory methods, which lookup an spi to find the implementation. If on a release greater than Java 21, then a functioning implementation is returned. Otherwise, a no-op implementation is returned. This is a reasonably standard way for a Java api to behave, and allows the developer to handle the case where the platform does not have a functioning implementation.

This change also refactors the native downcall method handles so that they are static final constants - which optimise better by the JVM. It's also the generally accepted pattern, where the handles are tied to the lifetime of class which effectively mediates access - by virtue of reachability.

Another thing that I added is the ability to programmatically set the temporary directory used for intermediate operations - this is important to how both Lucene and Elasticsearch work - since they commonly only have permission to write to certain parts of the disk.

Additionally,
1. the error codes from native calls are plumbed in and checked. As well as `cuvsGetLastErrorText`.
2. a state is added to any classes that hold a reference to native resources that could be released.
3. a local arena is used for memory allocation only needed per downcall invocation, e.g. the return value.
4. I moved the tests to be integration tests, since they need to run on the jar (rather than the exploded classes). They can be run by any of; `mvn verify`, or `mvn integration-test`, or `mvn -Dit.test="*Hnsw*" verify`
5. I refactored the entry-points to the api to be static methods and added an `spi` layer. You can see the minimal impact on the tests.
6. Move the native library out of the top-level directory in the jar and into an os/arch position in the META-INF.
7. add service provider support for custom implementations.

Authors:
  - Chris Hegarty (https://github.com/ChrisHegarty)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/628
---
 java/build.sh                                 |   2 +-
 java/cuvs-java/pom.xml                        |  61 +-
 .../java/com/nvidia/cuvs/BruteForceIndex.java | 288 +--------
 .../nvidia/cuvs/CagraCompressionParams.java   |  29 -
 .../main/java/com/nvidia/cuvs/CagraIndex.java | 579 +++++-------------
 .../com/nvidia/cuvs/CagraIndexParams.java     |  32 +-
 .../com/nvidia/cuvs/CagraSearchParams.java    |  47 +-
 .../java/com/nvidia/cuvs/CuVSResources.java   | 134 +---
 .../main/java/com/nvidia/cuvs/GPUInfo.java    |  74 +--
 .../main/java/com/nvidia/cuvs/HnswIndex.java  | 263 ++------
 .../java/com/nvidia/cuvs/HnswIndexParams.java |  37 +-
 .../com/nvidia/cuvs/HnswSearchParams.java     |  90 +--
 .../com/nvidia/cuvs/LibraryException.java     |  16 +
 .../java/com/nvidia/cuvs/SearchResults.java   |  30 +
 .../java/com/nvidia/cuvs/common/Util.java     | 265 --------
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  69 +++
 .../nvidia/cuvs/spi/CuVSServiceProvider.java  |  68 ++
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |  50 ++
 java/cuvs-java/src/main/java/module-info.java |   3 +
 .../cuvs/internal/BruteForceIndexImpl.java    | 380 ++++++++++++
 .../internal}/BruteForceSearchResults.java    |   8 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 515 ++++++++++++++++
 .../cuvs/internal}/CagraSearchResults.java    |   6 +-
 .../cuvs/internal/CuVSResourcesImpl.java      | 154 +++++
 .../nvidia/cuvs/internal/HnswIndexImpl.java   | 303 +++++++++
 .../cuvs/internal}/HnswSearchResults.java     |   8 +-
 .../cuvs/internal/common/LinkerHelper.java    |  64 ++
 .../cuvs/internal/common/LoaderUtils.java     | 100 +++
 .../internal/common/SearchResultsImpl.java}   |  14 +-
 .../com/nvidia/cuvs/internal/common/Util.java | 204 ++++++
 .../cuvs/internal}/panama/BruteForceH.java    |   2 +-
 .../nvidia/cuvs/internal}/panama/CagraH.java  |   2 +-
 .../internal}/panama/CuVSBruteForceIndex.java |   2 +-
 .../panama/CuVSCagraCompressionParams.java    |   2 +-
 .../cuvs/internal}/panama/CuVSCagraIndex.java |   2 +-
 .../panama/CuVSCagraIndexParams.java          |   2 +-
 .../panama/CuVSCagraSearchParams.java         |   2 +-
 .../cuvs/internal}/panama/CuVSFilter.java     |   2 +-
 .../panama/CuVSHnswExtendParams.java          |   2 +-
 .../cuvs/internal}/panama/CuVSHnswIndex.java  |   2 +-
 .../internal}/panama/CuVSHnswIndexParams.java |   2 +-
 .../panama/CuVSHnswSearchParams.java          |   2 +-
 .../cuvs/internal}/panama/DLDataType.java     |   2 +-
 .../cuvs/internal}/panama/DLDevice.java       |   2 +-
 .../internal}/panama/DLManagedTensor.java     |   2 +-
 .../panama/DLManagedTensorVersioned.java      |   2 +-
 .../cuvs/internal}/panama/DLPackVersion.java  |   2 +-
 .../cuvs/internal}/panama/DLTensor.java       |   2 +-
 .../cuvs/internal}/panama/DistanceH.java      |   2 +-
 .../nvidia/cuvs/internal}/panama/DlpackH.java |   2 +-
 .../nvidia/cuvs/internal}/panama/Fsidt.java   |   2 +-
 .../nvidia/cuvs/internal}/panama/GpuInfo.java |   2 +-
 .../cuvs/internal}/panama/GpuInfoH.java       |   2 +-
 .../nvidia/cuvs/internal}/panama/HnswH.java   |   2 +-
 .../cuvs/internal}/panama/IvfFlatH.java       |   2 +-
 .../nvidia/cuvs/internal}/panama/IvfPqH.java  |   2 +-
 .../cuvs/internal}/panama/MaxAlignT.java      |   2 +-
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  60 ++
 ...chTest.java => BruteForceAndSearchIT.java} |  19 +-
 ...dTest.java => BruteForceRandomizedIT.java} |  27 +-
 ...chTest.java => CagraBuildAndSearchIT.java} |  18 +-
 ...omizedTest.java => CagraRandomizedIT.java} |  53 +-
 .../java/com/nvidia/cuvs/CuVSTestCase.java    |  22 +-
 ...rchTest.java => HnswBuildAndSearchIT.java} |  23 +-
 ...domizedTest.java => HnswRandomizedIT.java} |  37 +-
 .../nvidia/cuvs/internal/common/UtilIT.java   |  37 ++
 java/examples/pom.xml                         |   8 +-
 .../cuvs/examples/BruteForceExample.java      |   8 +-
 .../nvidia/cuvs/examples/CagraExample.java    |  10 +-
 .../com/nvidia/cuvs/examples/HnswExample.java |  16 +-
 70 files changed, 2600 insertions(+), 1685 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
 delete mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/BruteForceSearchResults.java (93%)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/CagraSearchResults.java (93%)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/HnswSearchResults.java (93%)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs/common/SearchResults.java => java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java} (80%)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/BruteForceH.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CagraH.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSBruteForceIndex.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSCagraCompressionParams.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSCagraIndex.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSCagraIndexParams.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSCagraSearchParams.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSFilter.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSHnswExtendParams.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSHnswIndex.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSHnswIndexParams.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/CuVSHnswSearchParams.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DLDataType.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DLDevice.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DLManagedTensor.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DLManagedTensorVersioned.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DLPackVersion.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DLTensor.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DistanceH.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/DlpackH.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/Fsidt.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/GpuInfo.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/GpuInfoH.java (98%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/HnswH.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/IvfFlatH.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/IvfPqH.java (99%)
 rename java/cuvs-java/src/main/{java/com/nvidia/cuvs => java22/com/nvidia/cuvs/internal}/panama/MaxAlignT.java (99%)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
 rename java/cuvs-java/src/test/java/com/nvidia/cuvs/{BruteForceAndSearchTest.java => BruteForceAndSearchIT.java} (88%)
 rename java/cuvs-java/src/test/java/com/nvidia/cuvs/{BruteForceRandomizedTest.java => BruteForceRandomizedIT.java} (75%)
 rename java/cuvs-java/src/test/java/com/nvidia/cuvs/{CagraBuildAndSearchTest.java => CagraBuildAndSearchIT.java} (89%)
 rename java/cuvs-java/src/test/java/com/nvidia/cuvs/{CagraRandomizedTest.java => CagraRandomizedIT.java} (64%)
 rename java/cuvs-java/src/test/java/com/nvidia/cuvs/{HnswBuildAndSearchTest.java => HnswBuildAndSearchIT.java} (87%)
 rename java/cuvs-java/src/test/java/com/nvidia/cuvs/{HnswRandomizedTest.java => HnswRandomizedIT.java} (79%)
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java

diff --git a/java/build.sh b/java/build.sh
index 05b47de778..3483c544e8 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -10,5 +10,5 @@ cd internal && cmake . && cmake --build . \
   && cd .. \
   && mvn install:install-file -DgroupId=$GROUP_ID -DartifactId=cuvs-java-internal -Dversion=$VERSION -Dpackaging=so -Dfile=$SO_FILE_PATH/libcuvs_java.so \
   && cd cuvs-java \
-  && mvn package \
+  && mvn verify \
   && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index c8d31638ce..c036c6c66e 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -69,16 +69,53 @@
         <plugins>
             <plugin>
                 <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-surefire-plugin</artifactId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>3.13.0</version>
+                <executions>
+                    <execution>
+                        <id>default-compile</id>
+                        <goals>
+                            <goal>compile</goal>
+                        </goals>
+                        <configuration>
+                            <release>21</release>
+                            <compileSourceRoots>
+                                <compileSourceRoot>${project.basedir}/src/main/java</compileSourceRoot>
+                            </compileSourceRoots>
+                        </configuration>
+                    </execution>
+                    <execution>
+                        <id>compile-java-22</id>
+                        <phase>compile</phase>
+                        <goals>
+                            <goal>compile</goal>
+                        </goals>
+                        <configuration>
+                            <release>22</release>
+                            <compileSourceRoots>
+                                <compileSourceRoot>${project.basedir}/src/main/java22</compileSourceRoot>
+                            </compileSourceRoots>
+                            <multiReleaseOutput>true</multiReleaseOutput>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-failsafe-plugin</artifactId>
                 <version>3.5.2</version>
                 <configuration>
-                    <parallel>suites</parallel>
-                    <threadCountSuites>1</threadCountSuites>
-                    <perCoreThreadCount>false</perCoreThreadCount>
-                    <systemPropertyVariables>
-                        <java.library.path>${project.build.directory}/classes</java.library.path>
-                    </systemPropertyVariables>
+                    <forkCount>1</forkCount>
+                    <argLine>--enable-native-access=com.nvidia.cuvs</argLine>
                 </configuration>
+                <executions>
+                    <execution>
+                        <goals>
+                            <goal>integration-test</goal>
+                            <goal>verify</goal>
+                        </goals>
+                    </execution>
+                </executions>
             </plugin>
             <plugin>
                 <groupId>org.apache.maven.plugins</groupId>
@@ -101,7 +138,7 @@
                                     <type>so</type>
                                     <overWrite>false</overWrite>
                                     <outputDirectory>
-                                        ${project.build.directory}/classes</outputDirectory>
+                                        ${project.build.directory}/classes/META-INF/native/linux_x64</outputDirectory>
                                     <destFileName>libcuvs_java.so</destFileName>
                                 </artifactItem>
                             </artifactItems>
@@ -117,6 +154,7 @@
                     <descriptorRefs>
                         <descriptorRef>jar-with-dependencies</descriptorRef>
                     </descriptorRefs>
+                    <mergeManifestMode>merge</mergeManifestMode>
                     <archiverConfig>
                         <duplicateBehavior>add</duplicateBehavior>
                     </archiverConfig>
@@ -134,14 +172,15 @@
             <plugin>
                 <groupId>org.apache.maven.plugins</groupId>
                 <artifactId>maven-jar-plugin</artifactId>
-                <version>2.2</version>
+                <version>3.4.2</version>
                 <configuration>
                     <archive>
-                        <manifest>
+                        <manifestEntries>
+                            <Multi-Release>true</Multi-Release>
                             <addClasspath>true</addClasspath>
                             <mainClass>
                                 com.nvidia.cuvs.examples.CagraExample</mainClass>
-                        </manifest>
+                        </manifestEntries>
                     </archive>
                 </configuration>
             </plugin>
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
index 30ffca1cd1..e09c0228fb 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
@@ -16,24 +16,14 @@
 
 package com.nvidia.cuvs;
 
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.IOException;
+import com.nvidia.cuvs.spi.CuVSProvider;
+
 import java.io.InputStream;
 import java.io.OutputStream;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
+import java.nio.file.Path;
+import java.util.Objects;
 import java.util.UUID;
 
-import com.nvidia.cuvs.common.Util;
-import com.nvidia.cuvs.panama.CuVSBruteForceIndex;
-
 /**
  *
  * {@link BruteForceIndex} encapsulates a BRUTEFORCE index, along with methods
@@ -41,123 +31,13 @@
  *
  * @since 25.02
  */
-public class BruteForceIndex {
-
-  private final float[][] dataset;
-  private final CuVSResources resources;
-  private MethodHandle indexMethodHandle;
-  private MethodHandle searchMethodHandle;
-  private MethodHandle destroyIndexMethodHandle;
-  private MethodHandle serializeMethodHandle;
-  private MethodHandle deserializeMethodHandle;
-  private IndexReference bruteForceIndexReference;
-  private BruteForceIndexParams bruteForceIndexParams;
-  private MemoryLayout longMemoryLayout;
-  private MemoryLayout intMemoryLayout;
-  private MemoryLayout floatMemoryLayout;
-
-  /**
-   * Constructor for building the index using specified dataset
-   *
-   * @param dataset               the dataset used for creating the BRUTEFORCE
-   *                              index
-   * @param resources             an instance of {@link CuVSResources}
-   * @param bruteForceIndexParams an instance of {@link BruteForceIndexParams}
-   *                              holding the index parameters
-   */
-  private BruteForceIndex(float[][] dataset, CuVSResources resources, BruteForceIndexParams bruteForceIndexParams)
-      throws Throwable {
-    this.dataset = dataset;
-    this.resources = resources;
-    this.bruteForceIndexParams = bruteForceIndexParams;
-
-    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
-    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
-    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
-
-    initializeMethodHandles();
-    this.bruteForceIndexReference = build();
-  }
-
-  /**
-   * Constructor for loading the index from an {@link InputStream}
-   *
-   * @param inputStream an instance of stream to read the index bytes from
-   * @param resources   an instance of {@link CuVSResources}
-   */
-  private BruteForceIndex(InputStream inputStream, CuVSResources resources) throws Throwable {
-    this.bruteForceIndexParams = null;
-    this.dataset = null;
-    this.resources = resources;
-
-    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
-    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
-    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
-
-    initializeMethodHandles();
-    this.bruteForceIndexReference = deserialize(inputStream);
-  }
-
-  /**
-   * Initializes the {@link MethodHandles} for invoking native methods.
-   *
-   * @throws IOException @{@link IOException} is unable to load the native library
-   */
-  private void initializeMethodHandles() throws IOException {
-    indexMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("build_brute_force_index").get(),
-        FunctionDescriptor.of(ValueLayout.ADDRESS, ValueLayout.ADDRESS, longMemoryLayout, longMemoryLayout,
-            ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout));
-
-    searchMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("search_brute_force_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout, longMemoryLayout,
-            intMemoryLayout, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS,
-            ValueLayout.ADDRESS, longMemoryLayout, longMemoryLayout));
-
-    destroyIndexMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("destroy_brute_force_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-    serializeMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("serialize_brute_force_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-    deserializeMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("deserialize_brute_force_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-  }
+public interface BruteForceIndex {
 
   /**
    * Invokes the native destroy_brute_force_index function to de-allocate
    * BRUTEFORCE index
    */
-  public void destroyIndex() throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    destroyIndexMethodHandle.invokeExact(bruteForceIndexReference.getMemorySegment(), returnValueMemorySegment);
-  }
-
-  /**
-   * Invokes the native build_brute_force_index function via the Panama API to
-   * build the {@link BruteForceIndex}
-   *
-   * @return an instance of {@link IndexReference} that holds the pointer to the
-   *         index
-   */
-  private IndexReference build() throws Throwable {
-    long rows = dataset.length;
-    long cols = rows > 0 ? dataset[0].length : 0;
-
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-
-    IndexReference indexReference = new IndexReference((MemorySegment) indexMethodHandle.invokeExact(
-        Util.buildMemorySegment(resources.linker, resources.arena, dataset), rows, cols, resources.getMemorySegment(),
-        returnValueMemorySegment, bruteForceIndexParams.getNumWriterThreads()));
-
-    return indexReference;
-  }
+  void destroyIndex() throws Throwable;
 
   /**
    * Invokes the native search_brute_force_index via the Panama API for searching
@@ -165,33 +45,9 @@ private IndexReference build() throws Throwable {
    *
    * @param cuvsQuery an instance of {@link BruteForceQuery} holding the query
    *                  vectors and other parameters
-   * @return an instance of {@link BruteForceSearchResults} containing the results
+   * @return an instance of {@link SearchResults} containing the results
    */
-  public BruteForceSearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
-    long numQueries = cuvsQuery.getQueryVectors().length;
-    long numBlocks = cuvsQuery.getTopK() * numQueries;
-    int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
-    long prefilterDataLength = cuvsQuery.getPrefilter() != null ? cuvsQuery.getPrefilter().length : 0;
-    long numRows = dataset != null ? dataset.length : 0;
-
-    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, longMemoryLayout);
-    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, floatMemoryLayout);
-    MemorySegment neighborsMemorySegment = resources.arena.allocate(neighborsSequenceLayout);
-    MemorySegment distancesMemorySegment = resources.arena.allocate(distancesSequenceLayout);
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    MemorySegment prefilterDataMemorySegment = cuvsQuery.getPrefilter() != null
-        ? Util.buildMemorySegment(resources.linker, resources.arena, cuvsQuery.getPrefilter())
-        : MemorySegment.NULL;
-
-    searchMethodHandle.invokeExact(bruteForceIndexReference.getMemorySegment(),
-        Util.buildMemorySegment(resources.linker, resources.arena, cuvsQuery.getQueryVectors()), cuvsQuery.getTopK(),
-        numQueries, vectorDimension, resources.getMemorySegment(), neighborsMemorySegment, distancesMemorySegment,
-        returnValueMemorySegment, prefilterDataMemorySegment, prefilterDataLength, numRows);
-
-    return new BruteForceSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-        distancesMemorySegment, cuvsQuery.getTopK(), cuvsQuery.getMapping(), numQueries);
-  }
+  SearchResults search(BruteForceQuery cuvsQuery) throws Throwable;
 
   /**
    * A method to persist a BRUTEFORCE index using an instance of
@@ -200,83 +56,33 @@ public BruteForceSearchResults search(BruteForceQuery cuvsQuery) throws Throwabl
    * @param outputStream an instance of {@link OutputStream} to write the index
    *                     bytes into
    */
-  public void serialize(OutputStream outputStream) throws Throwable {
-    serialize(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".bf"));
-  }
-
+  void serialize(OutputStream outputStream) throws Throwable;
   /**
    * A method to persist a BRUTEFORCE index using an instance of
    * {@link OutputStream} and path to the intermediate temporary file.
    *
    * @param outputStream an instance of {@link OutputStream} to write the index
    *                     bytes to
-   * @param tempFile     an intermediate {@link File} where BRUTEFORCE index is
+   * @param tempFile     an intermediate {@link Path} where BRUTEFORCE index is
    *                     written temporarily
    */
-  public void serialize(OutputStream outputStream, File tempFile) throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    serializeMethodHandle.invokeExact(resources.getMemorySegment(), bruteForceIndexReference.getMemorySegment(),
-        returnValueMemorySegment,
-        Util.buildMemorySegment(resources.linker, resources.arena, tempFile.getAbsolutePath()));
-    FileInputStream fileInputStream = new FileInputStream(tempFile);
-    byte[] chunk = new byte[1024]; // TODO: Make this configurable
-    int chunkLength = 0;
-    while ((chunkLength = fileInputStream.read(chunk)) != -1) {
-      outputStream.write(chunk, 0, chunkLength);
-    }
-    fileInputStream.close();
-    tempFile.delete();
-  }
+  void serialize(OutputStream outputStream, Path tempFile) throws Throwable;
 
   /**
-   * Gets an instance of {@link IndexReference} by deserializing a BRUTEFORCE
-   * index using an {@link InputStream}.
+   * Creates a new Builder with an instance of {@link CuVSResources}.
    *
-   * @param inputStream an instance of {@link InputStream}
-   * @return an instance of {@link IndexReference}.
+   * @param cuvsResources an instance of {@link CuVSResources}
+   * @throws UnsupportedOperationException if the provider does not cuvs
    */
-  private IndexReference deserialize(InputStream inputStream) throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    String tmpIndexFile = "/tmp/" + UUID.randomUUID().toString() + ".bf";
-    IndexReference indexReference = new IndexReference(resources);
-
-    File tempFile = new File(tmpIndexFile);
-    FileOutputStream fileOutputStream = new FileOutputStream(tempFile);
-    byte[] chunk = new byte[1024];
-    int chunkLength = 0;
-    while ((chunkLength = inputStream.read(chunk)) != -1) {
-      fileOutputStream.write(chunk, 0, chunkLength);
-    }
-    deserializeMethodHandle.invokeExact(resources.getMemorySegment(), indexReference.getMemorySegment(),
-        returnValueMemorySegment, Util.buildMemorySegment(resources.linker, resources.arena, tmpIndexFile));
-
-    inputStream.close();
-    fileOutputStream.close();
-    tempFile.delete();
-
-    return indexReference;
+  static Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    return CuVSProvider.provider().newBruteForceIndexBuilder(cuvsResources);
   }
 
   /**
    * Builder helps configure and create an instance of {@link BruteForceIndex}.
    */
-  public static class Builder {
-
-    private float[][] dataset;
-    private CuVSResources cuvsResources;
-    private BruteForceIndexParams bruteForceIndexParams;
-    private InputStream inputStream;
-
-    /**
-     * Constructs this Builder with an instance of {@link CuVSResources}.
-     *
-     * @param cuvsResources an instance of {@link CuVSResources}
-     */
-    public Builder(CuVSResources cuvsResources) {
-      this.cuvsResources = cuvsResources;
-    }
+  interface Builder {
 
     /**
      * Registers an instance of configured {@link BruteForceIndexParams} with this
@@ -285,10 +91,7 @@ public Builder(CuVSResources cuvsResources) {
      * @param bruteForceIndexParams An instance of BruteForceIndexParams
      * @return An instance of this Builder
      */
-    public Builder withIndexParams(BruteForceIndexParams bruteForceIndexParams) {
-      this.bruteForceIndexParams = bruteForceIndexParams;
-      return this;
-    }
+    Builder withIndexParams(BruteForceIndexParams bruteForceIndexParams);
 
     /**
      * Sets an instance of InputStream typically used when index deserialization is
@@ -297,10 +100,7 @@ public Builder withIndexParams(BruteForceIndexParams bruteForceIndexParams) {
      * @param inputStream an instance of {@link InputStream}
      * @return an instance of this Builder
      */
-    public Builder from(InputStream inputStream) {
-      this.inputStream = inputStream;
-      return this;
-    }
+    Builder from(InputStream inputStream);
 
     /**
      * Sets the dataset for building the {@link BruteForceIndex}.
@@ -308,57 +108,13 @@ public Builder from(InputStream inputStream) {
      * @param dataset a two-dimensional float array
      * @return an instance of this Builder
      */
-    public Builder withDataset(float[][] dataset) {
-      this.dataset = dataset;
-      return this;
-    }
+    Builder withDataset(float[][] dataset);
 
     /**
      * Builds and returns an instance of {@link BruteForceIndex}.
      *
      * @return an instance of {@link BruteForceIndex}
      */
-    public BruteForceIndex build() throws Throwable {
-      if (inputStream != null) {
-        return new BruteForceIndex(inputStream, cuvsResources);
-      } else {
-        return new BruteForceIndex(dataset, cuvsResources, bruteForceIndexParams);
-      }
-    }
-  }
-
-  /**
-   * Holds the memory reference to a BRUTEFORCE index.
-   */
-  protected static class IndexReference {
-
-    private final MemorySegment memorySegment;
-
-    /**
-     * Constructs CagraIndexReference and allocate the MemorySegment.
-     */
-    protected IndexReference(CuVSResources resources) {
-      memorySegment = CuVSBruteForceIndex.allocate(resources.arena);
-    }
-
-    /**
-     * Constructs BruteForceIndexReference with an instance of MemorySegment passed
-     * as a parameter.
-     *
-     * @param indexMemorySegment the MemorySegment instance to use for containing
-     *                           index reference
-     */
-    protected IndexReference(MemorySegment indexMemorySegment) {
-      this.memorySegment = indexMemorySegment;
-    }
-
-    /**
-     * Gets the instance of index MemorySegment.
-     *
-     * @return index MemorySegment
-     */
-    protected MemorySegment getMemorySegment() {
-      return memorySegment;
-    }
+    BruteForceIndex build() throws Throwable;
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
index 09f6bae8bf..83c9c9acb6 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
@@ -16,10 +16,6 @@
 
 package com.nvidia.cuvs;
 
-import java.lang.foreign.MemorySegment;
-
-import com.nvidia.cuvs.panama.CuVSCagraCompressionParams;
-
 /**
  * Supplemental compression parameters to build CAGRA Index.
  *
@@ -27,7 +23,6 @@
  */
 public class CagraCompressionParams {
 
-  private final MemorySegment memorySegment;
   private CuVSResources resources;
   private final int pqBits;
   private final int pqDim;
@@ -63,30 +58,6 @@ private CagraCompressionParams(CuVSResources resources, int pqBits, int pqDim, i
     this.kmeansNIters = kmeansNIters;
     this.vqKmeansTrainsetFraction = vqKmeansTrainsetFraction;
     this.pqKmeansTrainsetFraction = pqKmeansTrainsetFraction;
-    this.memorySegment = initMemorySegment();
-  }
-
-  /**
-   * Allocates the configured compression parameters in the MemorySegment.
-   */
-  private MemorySegment initMemorySegment() {
-    MemorySegment compressionParamsMemorySegment = CuVSCagraCompressionParams.allocate(resources.arena);
-    CuVSCagraCompressionParams.pq_bits(compressionParamsMemorySegment, pqBits);
-    CuVSCagraCompressionParams.pq_dim(compressionParamsMemorySegment, pqDim);
-    CuVSCagraCompressionParams.vq_n_centers(compressionParamsMemorySegment, vqNCenters);
-    CuVSCagraCompressionParams.kmeans_n_iters(compressionParamsMemorySegment, kmeansNIters);
-    CuVSCagraCompressionParams.vq_kmeans_trainset_fraction(compressionParamsMemorySegment, vqKmeansTrainsetFraction);
-    CuVSCagraCompressionParams.pq_kmeans_trainset_fraction(compressionParamsMemorySegment, pqKmeansTrainsetFraction);
-    return compressionParamsMemorySegment;
-  }
-
-  /**
-   * Gets the memory segment instance containing the compression parameters.
-   *
-   * @return the memory segment instance containing the compression parameters.
-   */
-  protected MemorySegment getMemorySegment() {
-    return memorySegment;
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index 21eea97838..f92d97edd5 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -16,23 +16,13 @@
 
 package com.nvidia.cuvs;
 
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.IOException;
 import java.io.InputStream;
 import java.io.OutputStream;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
+import java.nio.file.Path;
+import java.util.Objects;
 import java.util.UUID;
 
-import com.nvidia.cuvs.common.Util;
-import com.nvidia.cuvs.panama.CuVSCagraIndex;
+import com.nvidia.cuvs.spi.CuVSProvider;
 
 /**
  * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
@@ -46,472 +36,185 @@
  *
  * @since 25.02
  */
-public class CagraIndex {
+public interface CagraIndex {
 
-  private final float[][] dataset;
-  private final CuVSResources resources;
-  private MethodHandle indexMethodHandle;
-  private MethodHandle searchMethodHandle;
-  private MethodHandle serializeMethodHandle;
-  private MethodHandle deserializeMethodHandle;
-  private MethodHandle destroyIndexMethodHandle;
-  private MethodHandle serializeCAGRAIndexToHNSWMethodHandle;
-  private CagraIndexParams cagraIndexParameters;
-  private CagraCompressionParams cagraCompressionParams;
-  private IndexReference cagraIndexReference;
-  private MemoryLayout longMemoryLayout;
-  private MemoryLayout intMemoryLayout;
-  private MemoryLayout floatMemoryLayout;
-
-  /**
-   * Constructor for building the index using specified dataset
-   *
-   * @param indexParameters        an instance of {@link CagraIndexParams} holding
-   *                               the index parameters
-   * @param cagraCompressionParams an instance of {@link CagraCompressionParams}
-   *                               holding the compression parameters
-   * @param dataset                the dataset for indexing
-   * @param resources              an instance of {@link CuVSResources}
-   */
-  private CagraIndex(CagraIndexParams indexParameters, CagraCompressionParams cagraCompressionParams, float[][] dataset,
-      CuVSResources resources) throws Throwable {
-    this.cagraIndexParameters = indexParameters;
-    this.cagraCompressionParams = cagraCompressionParams;
-    this.dataset = dataset;
-    this.resources = resources;
-
-    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
-    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
-    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
-
-    initializeMethodHandles();
-    this.cagraIndexReference = build();
-  }
-
-  /**
-   * Constructor for loading the index from an {@link InputStream}
-   *
-   * @param inputStream an instance of stream to read the index bytes from
-   * @param resources   an instance of {@link CuVSResources}
-   */
-  private CagraIndex(InputStream inputStream, CuVSResources resources) throws Throwable {
-    this.cagraIndexParameters = null;
-    this.cagraCompressionParams = null;
-    this.dataset = null;
-    this.resources = resources;
-
-    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
-    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
-    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
-
-    initializeMethodHandles();
-    this.cagraIndexReference = deserialize(inputStream);
-  }
-
-  /**
-   * Initializes the {@link MethodHandles} for invoking native methods.
-   *
-   * @throws IOException @{@link IOException} is unable to load the native library
-   */
-  private void initializeMethodHandles() throws IOException {
-    indexMethodHandle = resources.linker.downcallHandle(resources.getSymbolLookup().find("build_cagra_index").get(),
-        FunctionDescriptor.of(ValueLayout.ADDRESS, ValueLayout.ADDRESS, longMemoryLayout, longMemoryLayout,
-            ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout));
-
-    searchMethodHandle = resources.linker.downcallHandle(resources.getSymbolLookup().find("search_cagra_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout, longMemoryLayout,
-            intMemoryLayout, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS,
-            ValueLayout.ADDRESS));
-
-    serializeMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("serialize_cagra_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-    deserializeMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("deserialize_cagra_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-    destroyIndexMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("destroy_cagra_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-    serializeCAGRAIndexToHNSWMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("serialize_cagra_index_to_hnsw").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-  }
-
-  /**
-   * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
-   */
-  public void destroyIndex() throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    destroyIndexMethodHandle.invokeExact(cagraIndexReference.getMemorySegment(), returnValueMemorySegment);
-  }
-
-  /**
-   * Invokes the native build_cagra_index function via the Panama API to build the
-   * {@link CagraIndex}
-   *
-   * @return an instance of {@link IndexReference} that holds the pointer to the
-   *         index
-   */
-  private IndexReference build() throws Throwable {
-    long rows = dataset.length;
-    long cols = rows > 0 ? dataset[0].length : 0;
-
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-
-    MemorySegment indexParamsMemorySegment = cagraIndexParameters != null ? cagraIndexParameters.getMemorySegment()
-        : MemorySegment.NULL;
-
-    int numWriterThreads = cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
-
-    MemorySegment compressionParamsMemorySegment = cagraCompressionParams != null
-        ? cagraCompressionParams.getMemorySegment()
-        : MemorySegment.NULL;
-
-    IndexReference indexReference = new IndexReference((MemorySegment) indexMethodHandle.invokeExact(
-        Util.buildMemorySegment(resources.linker, resources.arena, dataset), rows, cols, resources.getMemorySegment(),
-        returnValueMemorySegment, indexParamsMemorySegment, compressionParamsMemorySegment, numWriterThreads));
-
-    return indexReference;
-  }
-
-  /**
-   * Invokes the native search_cagra_index via the Panama API for searching a
-   * CAGRA index.
-   *
-   * @param query an instance of {@link CagraQuery} holding the query vectors and
-   *              other parameters
-   * @return an instance of {@link CagraSearchResults} containing the results
-   */
-  public CagraSearchResults search(CagraQuery query) throws Throwable {
-    int topK = query.getMapping() != null ? Math.min(query.getMapping().size(), query.getTopK()) : query.getTopK();
-    long numQueries = query.getQueryVectors().length;
-    long numBlocks = topK * numQueries;
-    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
-
-    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, intMemoryLayout);
-    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, floatMemoryLayout);
-    MemorySegment neighborsMemorySegment = resources.arena.allocate(neighborsSequenceLayout);
-    MemorySegment distancesMemorySegment = resources.arena.allocate(distancesSequenceLayout);
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-
-    searchMethodHandle.invokeExact(cagraIndexReference.getMemorySegment(),
-        Util.buildMemorySegment(resources.linker, resources.arena, query.getQueryVectors()), topK, numQueries,
-        vectorDimension, resources.getMemorySegment(), neighborsMemorySegment, distancesMemorySegment,
-        returnValueMemorySegment, query.getCagraSearchParameters().getMemorySegment());
-
-    return new CagraSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-        distancesMemorySegment, topK, query.getMapping(), numQueries);
-  }
-
-  /**
-   * A method to persist a CAGRA index using an instance of {@link OutputStream}
-   * for writing index bytes.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes into
-   */
-  public void serialize(OutputStream outputStream) throws Throwable {
-    serialize(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".cag"), 1024);
-  }
-
-  /**
-   * A method to persist a CAGRA index using an instance of {@link OutputStream}
-   * for writing index bytes.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes into
-   * @param bufferLength the length of buffer to use for writing bytes. Default
-   *                     value is 1024
-   */
-  public void serialize(OutputStream outputStream, int bufferLength) throws Throwable {
-    serialize(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".cag"), bufferLength);
-  }
-
-  /**
-   * A method to persist a CAGRA index using an instance of {@link OutputStream}
-   * for writing index bytes.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes into
-   * @param tempFile     an intermediate {@link File} where CAGRA index is written
-   *                     temporarily
-   */
-  public void serialize(OutputStream outputStream, File tempFile) throws Throwable {
-    serialize(outputStream, tempFile, 1024);
-  }
-
-  /**
-   * A method to persist a CAGRA index using an instance of {@link OutputStream}
-   * and path to the intermediate temporary file.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes to
-   * @param tempFile     an intermediate {@link File} where CAGRA index is written
-   *                     temporarily
-   * @param bufferLength the length of buffer to use for writing bytes. Default
-   *                     value is 1024
-   */
-  public void serialize(OutputStream outputStream, File tempFile, int bufferLength) throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    serializeMethodHandle.invokeExact(resources.getMemorySegment(), cagraIndexReference.getMemorySegment(),
-        returnValueMemorySegment,
-        Util.buildMemorySegment(resources.linker, resources.arena, tempFile.getAbsolutePath()));
-    FileInputStream fileInputStream = new FileInputStream(tempFile);
-    byte[] chunk = new byte[bufferLength];
-    int chunkLength = 0;
-    while ((chunkLength = fileInputStream.read(chunk)) != -1) {
-      outputStream.write(chunk, 0, chunkLength);
-    }
-    fileInputStream.close();
-    tempFile.delete();
-  }
-
-  /**
-   * A method to create and persist HNSW index from CAGRA index using an instance
-   * of {@link OutputStream} and path to the intermediate temporary file.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes to
-   */
-  public void serializeToHNSW(OutputStream outputStream) throws Throwable {
-    serializeToHNSW(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".hnsw"), 1024);
-  }
-
-  /**
-   * A method to create and persist HNSW index from CAGRA index using an instance
-   * of {@link OutputStream} and path to the intermediate temporary file.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes to
-   * @param bufferLength the length of buffer to use for writing bytes. Default
-   *                     value is 1024
-   */
-  public void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable {
-    serializeToHNSW(outputStream, File.createTempFile(UUID.randomUUID().toString(), ".hnsw"), bufferLength);
-  }
-
-  /**
-   * A method to create and persist HNSW index from CAGRA index using an instance
-   * of {@link OutputStream} and path to the intermediate temporary file.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes to
-   * @param tempFile     an intermediate {@link File} where CAGRA index is written
-   *                     temporarily
-   */
-  public void serializeToHNSW(OutputStream outputStream, File tempFile) throws Throwable {
-    serializeToHNSW(outputStream, tempFile, 1024);
-  }
-
-  /**
-   * A method to create and persist HNSW index from CAGRA index using an instance
-   * of {@link OutputStream} and path to the intermediate temporary file.
-   *
-   * @param outputStream an instance of {@link OutputStream} to write the index
-   *                     bytes to
-   * @param tempFile     an intermediate {@link File} where CAGRA index is written
-   *                     temporarily
-   * @param bufferLength the length of buffer to use for writing bytes. Default
-   *                     value is 1024
-   */
-  public void serializeToHNSW(OutputStream outputStream, File tempFile, int bufferLength) throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    serializeCAGRAIndexToHNSWMethodHandle.invokeExact(resources.getMemorySegment(),
-        Util.buildMemorySegment(resources.linker, resources.arena, tempFile.getAbsolutePath()),
-        cagraIndexReference.getMemorySegment(), returnValueMemorySegment);
-    FileInputStream fileInputStream = new FileInputStream(tempFile);
-    byte[] chunk = new byte[bufferLength];
-    int chunkLength = 0;
-    while ((chunkLength = fileInputStream.read(chunk)) != -1) {
-      outputStream.write(chunk, 0, chunkLength);
-    }
-    fileInputStream.close();
-    tempFile.delete();
-  }
-
-  /**
-   * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
-   * using an {@link InputStream}.
-   *
-   * @param inputStream an instance of {@link InputStream}
-   * @return an instance of {@link IndexReference}.
-   */
-  private IndexReference deserialize(InputStream inputStream) throws Throwable {
-    return deserialize(inputStream, 1024);
-  }
-
-  /**
-   * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
-   * using an {@link InputStream}.
-   *
-   * @param inputStream  an instance of {@link InputStream}
-   * @param bufferLength the length of the buffer to use while reading the bytes
-   *                     from the stream. Default value is 1024.
-   * @return an instance of {@link IndexReference}.
-   */
-  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    String tmpIndexFile = "/tmp/" + UUID.randomUUID().toString() + ".cag";
-    IndexReference indexReference = new IndexReference(resources);
-
-    File tempFile = new File(tmpIndexFile);
-    FileOutputStream fileOutputStream = new FileOutputStream(tempFile);
-    byte[] chunk = new byte[bufferLength];
-    int chunkLength = 0;
-    while ((chunkLength = inputStream.read(chunk)) != -1) {
-      fileOutputStream.write(chunk, 0, chunkLength);
-    }
-    deserializeMethodHandle.invokeExact(resources.getMemorySegment(), indexReference.getMemorySegment(),
-        returnValueMemorySegment, Util.buildMemorySegment(resources.linker, resources.arena, tmpIndexFile));
-
-    inputStream.close();
-    fileOutputStream.close();
-    tempFile.delete();
-
-    return indexReference;
-  }
-
-  /**
-   * Gets an instance of {@link CagraIndexParams}
-   *
-   * @return an instance of {@link CagraIndexParams}
-   */
-  public CagraIndexParams getCagraIndexParameters() {
-    return cagraIndexParameters;
-  }
-
-  /**
-   * Gets an instance of {@link CuVSResources}
-   *
-   * @return an instance of {@link CuVSResources}
-   */
-  public CuVSResources getCuVSResources() {
-    return resources;
-  }
+    /**
+     * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
+     */
+    void destroyIndex() throws Throwable;
 
-  /**
-   * Builder helps configure and create an instance of {@link CagraIndex}.
-   */
-  public static class Builder {
+    /**
+     * Invokes the native search_cagra_index via the Panama API for searching a
+     * CAGRA index.
+     *
+     * @param query an instance of {@link CagraQuery} holding the query vectors and
+     *              other parameters
+     * @return an instance of {@link SearchResults} containing the results
+     */
+    SearchResults search(CagraQuery query) throws Throwable;
 
-    private float[][] dataset;
-    private CagraIndexParams cagraIndexParams;
-    private CagraCompressionParams cagraCompressionParams;
-    private CuVSResources cuvsResources;
-    private InputStream inputStream;
+    /**
+     * A method to persist a CAGRA index using an instance of {@link OutputStream}
+     * for writing index bytes.
+     *
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes into
+     */
+    void serialize(OutputStream outputStream) throws Throwable;
 
     /**
-     * Constructs this Builder with an instance of {@link CuVSResources}.
+     * A method to persist a CAGRA index using an instance of {@link OutputStream}
+     * for writing index bytes.
      *
-     * @param cuvsResources an instance of {@link CuVSResources}
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes into
+     * @param bufferLength the length of buffer to use for writing bytes. Default
+     *                     value is 1024
      */
-    public Builder(CuVSResources cuvsResources) {
-      this.cuvsResources = cuvsResources;
-    }
+    void serialize(OutputStream outputStream, int bufferLength) throws Throwable;
 
     /**
-     * Sets an instance of InputStream typically used when index deserialization is
-     * needed.
+     * A method to persist a CAGRA index using an instance of {@link OutputStream}
+     * for writing index bytes.
      *
-     * @param inputStream an instance of {@link InputStream}
-     * @return an instance of this Builder
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes into
+     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+     *                     temporarily
      */
-    public Builder from(InputStream inputStream) {
-      this.inputStream = inputStream;
-      return this;
+    default void serialize(OutputStream outputStream, Path tempFile) throws Throwable {
+        serialize(outputStream, tempFile, 1024);
     }
 
     /**
-     * Sets the dataset for building the {@link CagraIndex}.
+     * A method to persist a CAGRA index using an instance of {@link OutputStream}
+     * and path to the intermediate temporary file.
      *
-     * @param dataset a two-dimensional float array
-     * @return an instance of this Builder
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes to
+     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+     *                     temporarily
+     * @param bufferLength the length of buffer to use for writing bytes. Default
+     *                     value is 1024
      */
-    public Builder withDataset(float[][] dataset) {
-      this.dataset = dataset;
-      return this;
-    }
+    void serialize(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable;
 
     /**
-     * Registers an instance of configured {@link CagraIndexParams} with this
-     * Builder.
+     * A method to create and persist HNSW index from CAGRA index using an instance
+     * of {@link OutputStream} and path to the intermediate temporary file.
      *
-     * @param cagraIndexParameters An instance of CagraIndexParams.
-     * @return An instance of this Builder.
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes to
      */
-    public Builder withIndexParams(CagraIndexParams cagraIndexParameters) {
-      this.cagraIndexParams = cagraIndexParameters;
-      return this;
-    }
+    void serializeToHNSW(OutputStream outputStream) throws Throwable;
 
     /**
-     * Registers an instance of configured {@link CagraCompressionParams} with this
-     * Builder.
+     * A method to create and persist HNSW index from CAGRA index using an instance
+     * of {@link OutputStream} and path to the intermediate temporary file.
      *
-     * @param cagraCompressionParams An instance of CagraCompressionParams.
-     * @return An instance of this Builder.
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes to
+     * @param bufferLength the length of buffer to use for writing bytes. Default
+     *                     value is 1024
      */
-    public Builder withCompressionParams(CagraCompressionParams cagraCompressionParams) {
-      this.cagraCompressionParams = cagraCompressionParams;
-      return this;
-    }
+    void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable;
 
     /**
-     * Builds and returns an instance of CagraIndex.
+     * A method to create and persist HNSW index from CAGRA index using an instance
+     * of {@link OutputStream} and path to the intermediate temporary file.
      *
-     * @return an instance of CagraIndex
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes to
+     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+     *                     temporarily
      */
-    public CagraIndex build() throws Throwable {
-      if (inputStream != null) {
-        return new CagraIndex(inputStream, cuvsResources);
-      } else {
-        return new CagraIndex(cagraIndexParams, cagraCompressionParams, dataset, cuvsResources);
-      }
+    default void serializeToHNSW(OutputStream outputStream, Path tempFile) throws Throwable {
+        serializeToHNSW(outputStream, tempFile, 1024);
     }
-  }
 
-  /**
-   * Holds the memory reference to a CAGRA index.
-   */
-  protected static class IndexReference {
+    /**
+     * A method to create and persist HNSW index from CAGRA index using an instance
+     * of {@link OutputStream} and path to the intermediate temporary file.
+     *
+     * @param outputStream an instance of {@link OutputStream} to write the index
+     *                     bytes to
+     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+     *                     temporarily
+     * @param bufferLength the length of buffer to use for writing bytes. Default
+     *                     value is 1024
+     */
+    void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable;
 
-    private final MemorySegment memorySegment;
+    /**
+     * Gets an instance of {@link CagraIndexParams}
+     *
+     * @return an instance of {@link CagraIndexParams}
+     */
+    CagraIndexParams getCagraIndexParameters();
 
     /**
-     * Constructs CagraIndexReference and allocate the MemorySegment.
+     * Gets an instance of {@link CuVSResources}
+     *
+     * @return an instance of {@link CuVSResources}
      */
-    protected IndexReference(CuVSResources resources) {
-      memorySegment = CuVSCagraIndex.allocate(resources.arena);
-    }
+    CuVSResources getCuVSResources();
 
     /**
-     * Constructs CagraIndexReference with an instance of MemorySegment passed as a
-     * parameter.
+     * Creates a new Builder with an instance of {@link CuVSResources}.
      *
-     * @param indexMemorySegment the MemorySegment instance to use for containing
-     *                           index reference
+     * @param cuvsResources an instance of {@link CuVSResources}
+     * @throws UnsupportedOperationException if the provider does not cuvs
      */
-    protected IndexReference(MemorySegment indexMemorySegment) {
-      this.memorySegment = indexMemorySegment;
+    static Builder newBuilder(CuVSResources cuvsResources) {
+        Objects.requireNonNull(cuvsResources);
+        return CuVSProvider.provider().newCagraIndexBuilder(cuvsResources);
     }
 
     /**
-     * Gets the instance of index MemorySegment.
-     *
-     * @return index MemorySegment
+     * Builder helps configure and create an instance of {@link CagraIndex}.
      */
-    protected MemorySegment getMemorySegment() {
-      return memorySegment;
+    interface Builder {
+
+        /**
+         * Sets an instance of InputStream typically used when index deserialization is
+         * needed.
+         *
+         * @param inputStream an instance of {@link InputStream}
+         * @return an instance of this Builder
+         */
+        Builder from(InputStream inputStream);
+
+        /**
+         * Sets the dataset for building the {@link CagraIndex}.
+         *
+         * @param dataset a two-dimensional float array
+         * @return an instance of this Builder
+         */
+        Builder withDataset(float[][] dataset);
+
+        /**
+         * Registers an instance of configured {@link CagraIndexParams} with this
+         * Builder.
+         *
+         * @param cagraIndexParameters An instance of CagraIndexParams.
+         * @return An instance of this Builder.
+         */
+        Builder withIndexParams(CagraIndexParams cagraIndexParameters);
+
+        /**
+         * Registers an instance of configured {@link CagraCompressionParams} with this
+         * Builder.
+         *
+         * @param cagraCompressionParams An instance of CagraCompressionParams.
+         * @return An instance of this Builder.
+         */
+        public Builder withCompressionParams(CagraCompressionParams cagraCompressionParams);
+
+        /**
+         * Builds and returns an instance of CagraIndex.
+         *
+         * @return an instance of CagraIndex
+         */
+         CagraIndex build() throws Throwable;
     }
-  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
index 767ee281be..11b4f4b90a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
@@ -16,10 +16,6 @@
 
 package com.nvidia.cuvs;
 
-import java.lang.foreign.MemorySegment;
-
-import com.nvidia.cuvs.panama.CuVSCagraIndexParams;
-
 /**
  * Supplemental parameters to build CAGRA Index.
  *
@@ -29,8 +25,6 @@ public class CagraIndexParams {
 
   private final CagraGraphBuildAlgo cuvsCagraGraphBuildAlgo;
   private final CuvsDistanceType cuvsDistanceType;
-  private final MemorySegment memorySegment;
-  private CuVSResources resources;
   private final int intermediateGraphDegree;
   private final int graphDegree;
   private final int nnDescentNiter;
@@ -164,28 +158,15 @@ private CuvsDistanceType(int value) {
 
   }
 
-  private CagraIndexParams(CuVSResources resources, int intermediateGraphDegree, int graphDegree,
+  private CagraIndexParams(int intermediateGraphDegree, int graphDegree,
       CagraGraphBuildAlgo CuvsCagraGraphBuildAlgo, int nnDescentNiter, int writerThreads,
       CuvsDistanceType cuvsDistanceType) {
-    this.resources = resources;
     this.intermediateGraphDegree = intermediateGraphDegree;
     this.graphDegree = graphDegree;
     this.cuvsCagraGraphBuildAlgo = CuvsCagraGraphBuildAlgo;
     this.nnDescentNiter = nnDescentNiter;
     this.numWriterThreads = writerThreads;
     this.cuvsDistanceType = cuvsDistanceType;
-
-    this.memorySegment = initMemorySegment();
-  }
-
-  private MemorySegment initMemorySegment() {
-    MemorySegment indexParamsMemorySegment = CuVSCagraIndexParams.allocate(resources.arena);
-    CuVSCagraIndexParams.intermediate_graph_degree(indexParamsMemorySegment, intermediateGraphDegree);
-    CuVSCagraIndexParams.graph_degree(indexParamsMemorySegment, graphDegree);
-    CuVSCagraIndexParams.build_algo(indexParamsMemorySegment, cuvsCagraGraphBuildAlgo.value);
-    CuVSCagraIndexParams.nn_descent_niter(indexParamsMemorySegment, nnDescentNiter);
-    CuVSCagraIndexParams.metric(indexParamsMemorySegment, cuvsDistanceType.value);
-    return indexParamsMemorySegment;
   }
 
   /**
@@ -221,10 +202,6 @@ public int getNNDescentNumIterations() {
     return nnDescentNiter;
   }
 
-  protected MemorySegment getMemorySegment() {
-    return memorySegment;
-  }
-
   /**
    * Gets the {@link CuvsDistanceType} used to build the index.
    */
@@ -251,7 +228,6 @@ public String toString() {
    */
   public static class Builder {
 
-    private CuVSResources resources;
     private CagraGraphBuildAlgo cuvsCagraGraphBuildAlgo = CagraGraphBuildAlgo.NN_DESCENT;
     private CuvsDistanceType cuvsDistanceType = CuvsDistanceType.L2Expanded;
     private int intermediateGraphDegree = 128;
@@ -259,9 +235,7 @@ public static class Builder {
     private int nnDescentNumIterations = 20;
     private int numWriterThreads = 2;
 
-    public Builder(CuVSResources resources) {
-      this.resources = resources;
-    }
+    public Builder() { }
 
     /**
      * Sets the degree of input graph for pruning.
@@ -337,7 +311,7 @@ public Builder withNumWriterThreads(int numWriterThreads) {
      * @return an instance of {@link CagraIndexParams}
      */
     public CagraIndexParams build() {
-      return new CagraIndexParams(resources, intermediateGraphDegree, graphDegree, cuvsCagraGraphBuildAlgo,
+      return new CagraIndexParams(intermediateGraphDegree, graphDegree, cuvsCagraGraphBuildAlgo,
           nnDescentNumIterations, numWriterThreads, cuvsDistanceType);
     }
   }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
index 54dbb548e5..74ce37c249 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
@@ -16,10 +16,6 @@
 
 package com.nvidia.cuvs;
 
-import java.lang.foreign.MemorySegment;
-
-import com.nvidia.cuvs.panama.CuVSCagraSearchParams;
-
 /**
  * CagraSearchParams encapsulates the logic for configuring and holding search
  * parameters.
@@ -28,7 +24,6 @@
  */
 public class CagraSearchParams {
 
-  private CuVSResources resources;
   private int maxQueries;
   private int iTopKSize;
   private int maxIterations;
@@ -40,7 +35,6 @@ public class CagraSearchParams {
   private int numRandomSamplings;
   private float hashMapMaxFillRate;
   private long randXORMask;
-  private MemorySegment memorySegment;
   private SearchAlgo searchAlgo;
   private HashMapMode hashMapMode;
 
@@ -143,34 +137,6 @@ private CagraSearchParams(CuVSResources resources, int maxQueries, int iTopKSize
     this.hashMapMaxFillRate = hashmapMaxFillRate;
     this.numRandomSamplings = numRandomSamplings;
     this.randXORMask = randXORMask;
-    this.resources = resources;
-
-    this.memorySegment = allocateMemorySegment();
-  }
-
-  /**
-   * Allocates the configured search parameters in the MemorySegment.
-   */
-  private MemorySegment allocateMemorySegment() {
-    MemorySegment memorySegment = CuVSCagraSearchParams.allocate(resources.arena);
-    CuVSCagraSearchParams.max_queries(memorySegment, maxQueries);
-    CuVSCagraSearchParams.itopk_size(memorySegment, iTopKSize);
-    CuVSCagraSearchParams.max_iterations(memorySegment, maxIterations);
-    if (searchAlgo != null) {
-      CuVSCagraSearchParams.algo(memorySegment, searchAlgo.value);
-    }
-    CuVSCagraSearchParams.team_size(memorySegment, teamSize);
-    CuVSCagraSearchParams.search_width(memorySegment, searchWidth);
-    CuVSCagraSearchParams.min_iterations(memorySegment, minIterations);
-    CuVSCagraSearchParams.thread_block_size(memorySegment, threadBlockSize);
-    if (hashMapMode != null) {
-      CuVSCagraSearchParams.hashmap_mode(memorySegment, hashMapMode.value);
-    }
-    CuVSCagraSearchParams.hashmap_min_bitlen(memorySegment, hashmapMinBitlen);
-    CuVSCagraSearchParams.hashmap_max_fill_rate(memorySegment, hashMapMaxFillRate);
-    CuVSCagraSearchParams.num_random_samplings(memorySegment, numRandomSamplings);
-    CuVSCagraSearchParams.rand_xor_mask(memorySegment, randXORMask);
-    return memorySegment;
   }
 
   /**
@@ -273,15 +239,6 @@ public long getRandXORMask() {
     return randXORMask;
   }
 
-  /**
-   * Gets the MemorySegment holding CagraSearchParams.
-   *
-   * @return the MemorySegment holding CagraSearchParams
-   */
-  protected MemorySegment getMemorySegment() {
-    return memorySegment;
-  }
-
   /**
    * Gets which search implementation is configured.
    *
@@ -302,12 +259,12 @@ public HashMapMode getHashMapMode() {
 
   @Override
   public String toString() {
-    return "CagraSearchParams [resources=" + resources + ", maxQueries=" + maxQueries + ", itopkSize=" + iTopKSize
+    return "CagraSearchParams [maxQueries=" + maxQueries + ", itopkSize=" + iTopKSize
         + ", maxIterations=" + maxIterations + ", cuvsCagraSearchAlgo=" + searchAlgo + ", teamSize=" + teamSize
         + ", searchWidth=" + searchWidth + ", minIterations=" + minIterations + ", threadBlockSize=" + threadBlockSize
         + ", hashMapMode=" + hashMapMode + ", hashMapMinBitlen=" + hashmapMinBitlen + ", hashMapMaxFillRate="
         + hashMapMaxFillRate + ", numRandomSamplings=" + numRandomSamplings + ", randXORMask=" + randXORMask
-        + ", memorySegment=" + memorySegment + "]";
+        + "]";
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
index dbaba3ebb0..371ff01cc4 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
@@ -16,137 +16,49 @@
 
 package com.nvidia.cuvs;
 
-import java.io.File;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
+import com.nvidia.cuvs.spi.CuVSProvider;
 
-import com.nvidia.cuvs.common.Util;
+import java.nio.file.Path;
 
 /**
  * Used for allocating resources for cuVS
  *
  * @since 25.02
  */
-public class CuVSResources implements AutoCloseable {
-
-  public final Arena arena;
-  public final Linker linker;
-  public final SymbolLookup symbolLookup;
-  protected File nativeLibrary;
-  private final MethodHandle createResourcesMethodHandle;
-  private final MethodHandle destroyResourcesMethodHandle;
-  private MemorySegment resourcesMemorySegment;
-  private MemoryLayout intMemoryLayout;
+public interface CuVSResources extends AutoCloseable {
 
   /**
-   * Constructor that allocates the resources needed for cuVS
-   *
-   * @throws Throwable exception thrown when native function is invoked
+   * Closes this resources and releases any resources associated with it.
    */
-  public CuVSResources() throws Throwable {
-    linker = Linker.nativeLinker();
-    arena = Arena.ofShared();
-
-    nativeLibrary = Util.loadNativeLibrary();
-    symbolLookup = SymbolLookup.libraryLookup(nativeLibrary.getAbsolutePath(), arena);
-    intMemoryLayout = linker.canonicalLayouts().get("int");
-
-    createResourcesMethodHandle = linker.downcallHandle(symbolLookup.find("create_resources").get(),
-        FunctionDescriptor.of(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-    destroyResourcesMethodHandle = linker.downcallHandle(symbolLookup.find("destroy_resources").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-
-    createResources();
-  }
-
-  /**
-   * Creates the resources used internally and returns its reference.
-   *
-   * @throws Throwable exception thrown when native function is invoked
-   */
-  public void createResources() throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = arena.allocate(returnValueMemoryLayout);
-    resourcesMemorySegment = (MemorySegment) createResourcesMethodHandle.invokeExact(returnValueMemorySegment);
-  }
-
   @Override
-  public void close() {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = arena.allocate(returnValueMemoryLayout);
-    try {
-      destroyResourcesMethodHandle.invokeExact(resourcesMemorySegment, returnValueMemorySegment);
-    } catch (Throwable e) {
-      e.printStackTrace();
-    }
-    if (!arena.scope().isAlive()) {
-      arena.close();
-    }
-    nativeLibrary.delete();
-  }
+  void close();
+
 
   /**
-   * Gets the reference to the cuvsResources MemorySegment.
-   *
-   * @return cuvsResources MemorySegment
+   * The temporary directory to use for intermediate operations.
+   * Defaults to {@systemProperty java.io.tmpdir}.
    */
-  protected MemorySegment getMemorySegment() {
-    return resourcesMemorySegment;
-  }
+  Path tempDirectory();
 
   /**
-   * Returns the loaded libcuvs_java_cagra.so as a {@link SymbolLookup}
+   * Creates a new resources.
+   * Equivalent to
+   * <pre>{@code
+   *   create(CuVSProvider.tempDirectory())
+   * }</pre>
    */
-  protected SymbolLookup getSymbolLookup() {
-    return symbolLookup;
+  static CuVSResources create() throws Throwable {
+    return create(CuVSProvider.tempDirectory());
   }
 
   /**
-   * Container for GPU information
+   * Creates a new resources.
+   *
+   * @param tempDirectory the temporary directory to use for intermediate operations
+   * @throws UnsupportedOperationException if the provider does not cuvs
+   * @throws LibraryException if the native library cannot be loaded
    */
-  public class GPUInfo {
-
-    private final int gpuId;
-    private final long freeMemory;
-    private final long totalMemory;
-    private final float computeCapability;
-
-    public GPUInfo(int gpuId, long freeMemory, long totalMemory, float computeCapability) {
-      super();
-      this.gpuId = gpuId;
-      this.freeMemory = freeMemory;
-      this.totalMemory = totalMemory;
-      this.computeCapability = computeCapability;
-    }
-
-    public int getGpuId() {
-      return gpuId;
-    }
-
-    public long getFreeMemory() {
-      return freeMemory;
-    }
-
-    public long getTotalMemory() {
-      return totalMemory;
-    }
-
-    public float getComputeCapability() {
-      return computeCapability;
-    }
-
-    @Override
-    public String toString() {
-      return "GPUInfo [gpuId=" + gpuId + ", freeMemory=" + freeMemory + ", totalMemory=" + totalMemory
-          + ", computeCapability=" + computeCapability + "]";
-    }
-
+  static CuVSResources create(Path tempDirectory) throws Throwable {
+    return CuVSProvider.provider().newCuVSResources(tempDirectory);
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
index 48cade179b..c9c3bd9f8f 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
@@ -1,56 +1,28 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package com.nvidia.cuvs;
 
 /**
  * Contains GPU information
+ *
+ * @param gpuId             id of the GPU starting from 0
+ * @param name              ASCII string identifying device
+ * @param freeMemory        returned free memory in bytes
+ * @param totalMemory       returned total memory in bytes
+ * @param computeCapability the compute capability of the device
  */
-public class GPUInfo {
-  private final int gpuId;
-  private final String name;
-  private final long freeMemory;
-  private final long totalMemory;
-  private final float computeCapability;
-
-  /**
-   * Constructor for GPUInfo
-   *
-   * @param gpuId             id of the GPU starting from 0
-   * @param name              ASCII string identifying device
-   * @param freeMemory        returned free memory in bytes
-   * @param totalMemory       returned total memory in bytes
-   * @param computeCapability the compute capability of the device
-   */
-  public GPUInfo(int gpuId, String name, long freeMemory, long totalMemory, float computeCapability) {
-    super();
-    this.gpuId = gpuId;
-    this.name = name;
-    this.freeMemory = freeMemory;
-    this.totalMemory = totalMemory;
-    this.computeCapability = computeCapability;
-  }
-
-  public int getGpuId() {
-    return gpuId;
-  }
-
-  public String getName() {
-    return name;
-  }
-
-  public long getFreeMemory() {
-    return freeMemory;
-  }
-
-  public long getTotalMemory() {
-    return totalMemory;
-  }
-
-  public float getComputeCapability() {
-    return computeCapability;
-  }
-
-  @Override
-  public String toString() {
-    return "GPUInfo [gpuId=" + gpuId + ", name=" + name + ", freeMemory=" + freeMemory + ", totalMemory=" + totalMemory
-        + ", computeCapability=" + computeCapability + "]";
-  }
-}
+public record GPUInfo(int gpuId, String name, long freeMemory, long totalMemory, float computeCapability) { }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
index d55308a1b4..ebd7776a83 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
@@ -16,21 +16,10 @@
 
 package com.nvidia.cuvs;
 
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.IOException;
 import java.io.InputStream;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.UUID;
+import java.util.Objects;
 
-import com.nvidia.cuvs.common.Util;
-import com.nvidia.cuvs.panama.CuVSHnswIndex;
+import com.nvidia.cuvs.spi.CuVSProvider;
 
 /**
  * {@link HnswIndex} encapsulates a HNSW index, along with methods to interact
@@ -38,226 +27,62 @@
  *
  * @since 25.02
  */
-public class HnswIndex {
-
-  private final CuVSResources resources;
-  private MethodHandle deserializeHnswIndexMethodHandle;
-  private MethodHandle searchHnswIndexMethodHandle;
-  private MethodHandle destroyHnswIndexMethodHandle;
-  private HnswIndexParams hnswIndexParams;
-  private IndexReference hnswIndexReference;
-  private MemoryLayout longMemoryLayout;
-  private MemoryLayout intMemoryLayout;
-  private MemoryLayout floatMemoryLayout;
-
-  /**
-   * Constructor for loading the index from an {@link InputStream}
-   *
-   * @param inputStream an instance of stream to read the index bytes from
-   * @param resources   an instance of {@link CuVSResources}
-   */
-  private HnswIndex(InputStream inputStream, CuVSResources resources, HnswIndexParams hnswIndexParams)
-      throws Throwable {
-    this.hnswIndexParams = hnswIndexParams;
-    this.resources = resources;
-
-    longMemoryLayout = resources.linker.canonicalLayouts().get("long");
-    intMemoryLayout = resources.linker.canonicalLayouts().get("int");
-    floatMemoryLayout = resources.linker.canonicalLayouts().get("float");
-
-    initializeMethodHandles();
-    this.hnswIndexReference = deserialize(inputStream);
-  }
-
-  /**
-   * Initializes the {@link MethodHandles} for invoking native methods.
-   *
-   * @throws IOException @{@link IOException} is unable to load the native library
-   */
-  private void initializeMethodHandles() throws IOException {
-    deserializeHnswIndexMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("deserialize_hnsw_index").get(), FunctionDescriptor.of(ValueLayout.ADDRESS,
-            ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout));
-
-    searchHnswIndexMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("search_hnsw_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS,
-            ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS, intMemoryLayout, intMemoryLayout,
-            longMemoryLayout));
-
-    destroyHnswIndexMethodHandle = resources.linker.downcallHandle(
-        resources.getSymbolLookup().find("destroy_hnsw_index").get(),
-        FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-  }
-
-  /**
-   * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
-   */
-  public void destroyIndex() throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    destroyHnswIndexMethodHandle.invokeExact(hnswIndexReference.getMemorySegment(), returnValueMemorySegment);
-  }
-
-  /**
-   * Invokes the native search_hnsw_index via the Panama API for searching a HNSW
-   * index.
-   *
-   * @param query an instance of {@link HnswQuery} holding the query vectors and
-   *              other parameters
-   * @return an instance of {@link HnswSearchResults} containing the results
-   */
-  public HnswSearchResults search(HnswQuery query) throws Throwable {
-    long numQueries = query.getQueryVectors().length;
-    long numBlocks = query.getTopK() * numQueries;
-    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
-
-    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, longMemoryLayout);
-    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, floatMemoryLayout);
-    MemorySegment neighborsMemorySegment = resources.arena.allocate(neighborsSequenceLayout);
-    MemorySegment distancesMemorySegment = resources.arena.allocate(distancesSequenceLayout);
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-
-    searchHnswIndexMethodHandle.invokeExact(resources.getMemorySegment(), hnswIndexReference.getMemorySegment(),
-        query.getHnswSearchParams().getHnswSearchParamsMemorySegment(), returnValueMemorySegment,
-        neighborsMemorySegment, distancesMemorySegment,
-        Util.buildMemorySegment(resources.linker, resources.arena, query.getQueryVectors()), query.getTopK(),
-        vectorDimension, numQueries);
-
-    return new HnswSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-        distancesMemorySegment, query.getTopK(), query.getMapping(), numQueries);
-  }
-
-  /**
-   * Gets an instance of {@link IndexReference} by deserializing a HNSW index
-   * using an {@link InputStream}.
-   *
-   * @param inputStream an instance of {@link InputStream}
-   * @return an instance of {@link IndexReference}.
-   */
-  private IndexReference deserialize(InputStream inputStream) throws Throwable {
-    return deserialize(inputStream, 1024);
-  }
-
-  /**
-   * Gets an instance of {@link IndexReference} by deserializing a HNSW index
-   * using an {@link InputStream}.
-   *
-   * @param inputStream  an instance of {@link InputStream}
-   * @param bufferLength the length of the buffer to use while reading the bytes
-   *                     from the stream. Default value is 1024.
-   * @return an instance of {@link IndexReference}.
-   */
-  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = resources.arena.allocate(returnValueMemoryLayout);
-    String tmpIndexFile = "/tmp/" + UUID.randomUUID().toString() + ".hnsw";
-
-    File tempFile = new File(tmpIndexFile);
-    FileOutputStream fileOutputStream = new FileOutputStream(tempFile);
-    byte[] chunk = new byte[bufferLength];
-    int chunkLength = 0;
-    while ((chunkLength = inputStream.read(chunk)) != -1) {
-      fileOutputStream.write(chunk, 0, chunkLength);
-    }
-
-    IndexReference indexReference = new IndexReference((MemorySegment) deserializeHnswIndexMethodHandle.invokeExact(
-        resources.getMemorySegment(), Util.buildMemorySegment(resources.linker, resources.arena, tmpIndexFile),
-        hnswIndexParams.getHnswIndexParamsMemorySegment(), returnValueMemorySegment,
-        hnswIndexParams.getVectorDimension()));
-
-    inputStream.close();
-    fileOutputStream.close();
-    tempFile.delete();
-
-    return indexReference;
-  }
-
-  /**
-   * Builder helps configure and create an instance of {@link HnswIndex}.
-   */
-  public static class Builder {
-
-    private CuVSResources cuvsResources;
-    private InputStream inputStream;
-    private HnswIndexParams hnswIndexParams;
+public interface HnswIndex {
 
     /**
-     * Constructs this Builder with an instance of {@link CuVSResources}.
-     *
-     * @param cuvsResources an instance of {@link CuVSResources}
+     * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
      */
-    public Builder(CuVSResources cuvsResources) {
-      this.cuvsResources = cuvsResources;
-    }
+    void destroyIndex() throws Throwable;
 
     /**
-     * Sets an instance of InputStream typically used when index deserialization is
-     * needed.
+     * Invokes the native search_hnsw_index via the Panama API for searching a HNSW
+     * index.
      *
-     * @param inputStream an instance of {@link InputStream}
-     * @return an instance of this Builder
+     * @param query an instance of {@link HnswQuery} holding the query vectors and
+     *              other parameters
+     * @return an instance of {@link SearchResults} containing the results
      */
-    public Builder from(InputStream inputStream) {
-      this.inputStream = inputStream;
-      return this;
-    }
+    SearchResults search(HnswQuery query) throws Throwable;
 
     /**
-     * Registers an instance of configured {@link HnswIndexParams} with this
-     * Builder.
+     * Creates a new Builder with an instance of {@link CuVSResources}.
      *
-     * @param hnswIndexParameters An instance of HnswIndexParams.
-     * @return An instance of this Builder.
-     */
-    public Builder withIndexParams(HnswIndexParams hnswIndexParameters) {
-      this.hnswIndexParams = hnswIndexParameters;
-      return this;
-    }
-
-    /**
-     * Builds and returns an instance of CagraIndex.
-     *
-     * @return an instance of CagraIndex
-     */
-    public HnswIndex build() throws Throwable {
-      return new HnswIndex(inputStream, cuvsResources, hnswIndexParams);
-    }
-  }
-
-  /**
-   * Holds the memory reference to a HNSW index.
-   */
-  protected static class IndexReference {
-
-    private final MemorySegment memorySegment;
-
-    /**
-     * Constructs CagraIndexReference and allocate the MemorySegment.
-     */
-    protected IndexReference(CuVSResources resources) {
-      memorySegment = CuVSHnswIndex.allocate(resources.arena);
-    }
-
-    /**
-     * Constructs CagraIndexReference with an instance of MemorySegment passed as a
-     * parameter.
-     *
-     * @param indexMemorySegment the MemorySegment instance to use for containing
-     *                           index reference
+     * @param cuvsResources an instance of {@link CuVSResources}
+     * @throws UnsupportedOperationException if the provider does not cuvs
      */
-    protected IndexReference(MemorySegment indexMemorySegment) {
-      this.memorySegment = indexMemorySegment;
+    static HnswIndex.Builder newBuilder(CuVSResources cuvsResources) {
+        Objects.requireNonNull(cuvsResources);
+        return CuVSProvider.provider().newHnswIndexBuilder(cuvsResources);
     }
 
     /**
-     * Gets the instance of index MemorySegment.
-     *
-     * @return index MemorySegment
+     * Builder helps configure and create an instance of {@link HnswIndex}.
      */
-    protected MemorySegment getMemorySegment() {
-      return memorySegment;
+    interface Builder {
+
+        /**
+         * Sets an instance of InputStream typically used when index deserialization is
+         * needed.
+         *
+         * @param inputStream an instance of {@link InputStream}
+         * @return an instance of this Builder
+         */
+        Builder from(InputStream inputStream);
+
+        /**
+         * Registers an instance of configured {@link HnswIndexParams} with this
+         * Builder.
+         *
+         * @param hnswIndexParameters An instance of HnswIndexParams.
+         * @return An instance of this Builder.
+         */
+        Builder withIndexParams(HnswIndexParams hnswIndexParameters);
+
+        /**
+         * Builds and returns an instance of CagraIndex.
+         *
+         * @return an instance of CagraIndex
+         */
+        HnswIndex build() throws Throwable;
     }
-  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
index ef06adf619..3960d5dae6 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
@@ -16,10 +16,6 @@
 
 package com.nvidia.cuvs;
 
-import java.lang.foreign.MemorySegment;
-
-import com.nvidia.cuvs.panama.CuVSHnswIndexParams;
-
 /**
  * Supplemental parameters to build HNSW index.
  *
@@ -55,35 +51,17 @@ private CuvsHnswHierarchy(int value) {
     }
   };
 
-  private CuVSResources resources;
-  private MemorySegment memorySegment;
   private CuvsHnswHierarchy hierarchy = CuvsHnswHierarchy.NONE;
   private int efConstruction = 200;
   private int numThreads = 2;
   private int vectorDimension;
 
-  private HnswIndexParams(CuVSResources resources, CuvsHnswHierarchy hierarchy, int efConstruction, int numThreads,
+  private HnswIndexParams(CuvsHnswHierarchy hierarchy, int efConstruction, int numThreads,
       int vectorDimension) {
-    this.resources = resources;
     this.hierarchy = hierarchy;
     this.efConstruction = efConstruction;
     this.numThreads = numThreads;
     this.vectorDimension = vectorDimension;
-    this.memorySegment = allocateMemorySegment();
-  }
-
-  /**
-   * Allocates the configured search parameters in the MemorySegment.
-   */
-  private MemorySegment allocateMemorySegment() {
-    MemorySegment memorySegment = CuVSHnswIndexParams.allocate(resources.arena);
-    CuVSHnswIndexParams.ef_construction(memorySegment, efConstruction);
-    CuVSHnswIndexParams.num_threads(memorySegment, numThreads);
-    return memorySegment;
-  }
-
-  public MemorySegment getHnswIndexParamsMemorySegment() {
-    return memorySegment;
   }
 
   /**
@@ -118,10 +96,6 @@ public int getVectorDimension() {
     return vectorDimension;
   }
 
-  public CuVSResources getResources() {
-    return resources;
-  }
-
   @Override
   public String toString() {
     return "HnswIndexParams [hierarchy=" + hierarchy + ", efConstruction=" + efConstruction + ", numThreads="
@@ -133,7 +107,6 @@ public String toString() {
    */
   public static class Builder {
 
-    private CuVSResources resources;
     private CuvsHnswHierarchy hierarchy = CuvsHnswHierarchy.NONE;
     private int efConstruction = 200;
     private int numThreads = 2;
@@ -141,12 +114,8 @@ public static class Builder {
 
     /**
      * Constructs this Builder with an instance of Arena.
-     *
-     * @param resources the {@link CuVSResources} instance to use
      */
-    public Builder(CuVSResources resources) {
-      this.resources = resources;
-    }
+    public Builder() { }
 
     /**
      * Sets the hierarchy for HNSW index when converting from CAGRA index.
@@ -205,7 +174,7 @@ public Builder withVectorDimension(int vectorDimension) {
      * @return an instance of {@link HnswIndexParams}
      */
     public HnswIndexParams build() {
-      return new HnswIndexParams(resources, hierarchy, efConstruction, numThreads, vectorDimension);
+      return new HnswIndexParams(hierarchy, efConstruction, numThreads, vectorDimension);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
index a2725e6dea..26b91a5a43 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
@@ -16,92 +16,40 @@
 
 package com.nvidia.cuvs;
 
-import java.lang.foreign.MemorySegment;
-
-import com.nvidia.cuvs.panama.CuVSHnswSearchParams;
-
 /**
  * HnswSearchParams encapsulates the logic for configuring and holding search
  * parameters for HNSW index.
  *
+ * @param ef the ef value
+ * @param numThreads the number of threads
  * @since 25.02
  */
-public class HnswSearchParams {
-
-  private CuVSResources resources;
-  private MemorySegment memorySegment;
-  private int ef = 200;
-  private int numThreads = 0;
-
-  /**
-   * Constructs an instance of HnswSearchParams with passed search parameters.
-   *
-   * @param resources  the resources instance to use
-   * @param ef         the ef value
-   * @param numThreads the number of threads
-   *
-   */
-  private HnswSearchParams(CuVSResources resources, int ef, int numThreads) {
-    this.resources = resources;
-    this.ef = ef;
-    this.numThreads = numThreads;
-    this.memorySegment = allocateMemorySegment();
-  }
-
-  /**
-   * Allocates the configured search parameters in the MemorySegment.
-   */
-  private MemorySegment allocateMemorySegment() {
-    MemorySegment memorySegment = CuVSHnswSearchParams.allocate(resources.arena);
-    CuVSHnswSearchParams.ef(memorySegment, ef);
-    CuVSHnswSearchParams.num_threads(memorySegment, numThreads);
-    return memorySegment;
-  }
-
-  public MemorySegment getHnswSearchParamsMemorySegment() {
-    return memorySegment;
-  }
-
-  /**
-   * Gets the ef value
-   *
-   * @return the integer ef value
-   */
-  public int getEf() {
-    return ef;
-  }
-
-  /**
-   * Gets the number of threads
-   *
-   * @return the number of threads
-   */
-  public int getNumThreads() {
-    return numThreads;
-  }
-
-  @Override
-  public String toString() {
-    return "HnswSearchParams [ef=" + ef + ", numThreads=" + numThreads + "]";
-  }
+public record HnswSearchParams (int ef, int numThreads) {
+
+    public HnswSearchParams {
+      if (ef < 0) {
+        throw new IllegalArgumentException();
+      }
+      if (numThreads < 0) {
+        throw new IllegalArgumentException();
+      }
+    }
 
   /**
    * Builder configures and creates an instance of HnswSearchParams.
    */
   public static class Builder {
 
-    private CuVSResources resources;
-    private int ef = 200;
-    private int numThreads = 0;
+    private static final int DEFAULT_EF_VALUE = 200;
+    private static final int DEFAULT_NUM_THREADS = 0;
+
+    private int ef = DEFAULT_EF_VALUE;
+    private int numThreads = DEFAULT_NUM_THREADS;
 
     /**
      * Constructs this Builder with an instance of Arena.
-     *
-     * @param resources the {@link CuVSResources} instance to use
      */
-    public Builder(CuVSResources resources) {
-      this.resources = resources;
-    }
+    public Builder() { }
 
     /**
      * Sets the ef value
@@ -131,7 +79,7 @@ public Builder withNumThreads(int numThreads) {
      * @return an instance of HnswSearchParams
      */
     public HnswSearchParams build() {
-      return new HnswSearchParams(resources, ef, numThreads);
+      return new HnswSearchParams(ef, numThreads);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
index 40018be922..ace825a95c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
@@ -1,3 +1,19 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package com.nvidia.cuvs;
 
 public class LibraryException extends RuntimeException {
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
new file mode 100644
index 0000000000..c01ea04a3c
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import java.util.List;
+import java.util.Map;
+
+public interface SearchResults {
+
+    /**
+     * Gets a list results as a map of neighbor IDs to distances.
+     *
+     * @return a list of results for each query as a map of neighbor IDs to distance
+     */
+    List<Map<Integer, Float>> getResults();
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java
deleted file mode 100644
index 163ef3a846..0000000000
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/Util.java
+++ /dev/null
@@ -1,265 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.common;
-
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.OutputStream;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemoryLayout.PathElement;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.VarHandle;
-import java.util.ArrayList;
-import java.util.List;
-
-import com.nvidia.cuvs.GPUInfo;
-import com.nvidia.cuvs.LibraryException;
-import com.nvidia.cuvs.panama.GpuInfo;
-
-public class Util {
-
-  private static Arena arena = null;
-  private static Linker linker = null;
-  private static SymbolLookup symbolLookup = null;
-  private static MemoryLayout intMemoryLayout;
-  private static MethodHandle getGpuInfoMethodHandle = null;
-  protected static File nativeLibrary;
-
-  static {
-    try {
-      linker = Linker.nativeLinker();
-      arena = Arena.ofShared();
-      nativeLibrary = Util.loadLibraryFromJar("/libcuvs_java.so");
-      symbolLookup = SymbolLookup.libraryLookup(nativeLibrary.getAbsolutePath(), arena);
-      intMemoryLayout = linker.canonicalLayouts().get("int");
-      getGpuInfoMethodHandle = linker.downcallHandle(symbolLookup.find("get_gpu_info").get(),
-          FunctionDescriptor.ofVoid(ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.ADDRESS));
-    } catch (Exception e) {
-      throw new LibraryException("LibCuVS Java Library Not Loaded", e);
-    }
-  }
-
-  /**
-   * Get the list of compatible GPUs based on compute capability >= 7.0 and total
-   * memory >= 8GB
-   *
-   * @return a list of compatible GPUs. See {@link GPUInfo}
-   */
-  public static List<GPUInfo> compatibleGPUs() throws Throwable {
-    return compatibleGPUs(7.0, 8192);
-  }
-
-  /**
-   * Get the list of compatible GPUs based on given compute capability and total
-   * memory
-   *
-   * @param minComputeCapability the minimum compute capability
-   * @param minDeviceMemoryMB    the minimum total available memory in MB
-   * @return a list of compatible GPUs. See {@link GPUInfo}
-   */
-  public static List<GPUInfo> compatibleGPUs(double minComputeCapability, int minDeviceMemoryMB) throws Throwable {
-    List<GPUInfo> compatibleGPUs = new ArrayList<GPUInfo>();
-    double minDeviceMemoryB = Math.pow(2, 20) * minDeviceMemoryMB;
-    for (GPUInfo gpuInfo : availableGPUs()) {
-      if (gpuInfo.getComputeCapability() >= minComputeCapability && gpuInfo.getTotalMemory() >= minDeviceMemoryB) {
-        compatibleGPUs.add(gpuInfo);
-      }
-    }
-    return compatibleGPUs;
-  }
-
-  /**
-   * Gets all the available GPUs
-   *
-   * @return a list of {@link GPUInfo} objects with GPU details
-   */
-  public static List<GPUInfo> availableGPUs() throws Throwable {
-    List<GPUInfo> results = new ArrayList<GPUInfo>();
-    MemoryLayout returnValueMemoryLayout = intMemoryLayout;
-    MemorySegment returnValueMemorySegment = arena.allocate(returnValueMemoryLayout);
-    MemoryLayout numGpuMemoryLayout = intMemoryLayout;
-    MemorySegment numGpuMemorySegment = arena.allocate(numGpuMemoryLayout);
-
-    /*
-     * Setting a value of 1024 because we cannot predict how much memory to allocate
-     * before the function is invoked as cudaGetDeviceCount is inside the
-     * get_gpu_info function.
-     */
-    MemorySegment GpuInfoArrayMemorySegment = GpuInfo.allocateArray(1024, arena);
-    getGpuInfoMethodHandle.invokeExact(returnValueMemorySegment, numGpuMemorySegment, GpuInfoArrayMemorySegment);
-    int numGPUs = numGpuMemorySegment.get(ValueLayout.JAVA_INT, 0);
-    MemoryLayout ml = MemoryLayout.sequenceLayout(numGPUs, GpuInfo.layout());
-    for (int i = 0; i < numGPUs; i++) {
-      VarHandle gpuIdVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("gpu_id"));
-      VarHandle freeMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-          PathElement.groupElement("free_memory"));
-      VarHandle totalMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-          PathElement.groupElement("total_memory"));
-      VarHandle ComputeCapabilityVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-          PathElement.groupElement("compute_capability"));
-      StringBuilder gpuName = new StringBuilder();
-      char b = 1;
-      int p = 0;
-      while (b != 0x00) {
-        VarHandle gpuNameVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("name"),
-            PathElement.sequenceElement(p++));
-        b = (char) (byte) gpuNameVarHandle.get(GpuInfoArrayMemorySegment, 0L);
-        gpuName.append(b);
-      }
-      results.add(new GPUInfo((int) gpuIdVarHandle.get(GpuInfoArrayMemorySegment, 0L), gpuName.toString().trim(),
-          (long) freeMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
-          (long) totalMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
-          (float) ComputeCapabilityVarHandle.get(GpuInfoArrayMemorySegment, 0L)));
-    }
-    return results;
-  }
-
-  /**
-   * A utility method for getting an instance of {@link MemorySegment} for a
-   * {@link String}.
-   *
-   * @param str the string for the expected {@link MemorySegment}
-   * @return an instance of {@link MemorySegment}
-   */
-  public static MemorySegment buildMemorySegment(Linker linker, Arena arena, String str) {
-    MemoryLayout charMemoryLayout = linker.canonicalLayouts().get("char");
-    StringBuilder sb = new StringBuilder(str).append('\0');
-    MemoryLayout stringMemoryLayout = MemoryLayout.sequenceLayout(sb.length(), charMemoryLayout);
-    MemorySegment stringMemorySegment = arena.allocate(stringMemoryLayout);
-
-    for (int i = 0; i < sb.length(); i++) {
-      VarHandle varHandle = stringMemoryLayout.varHandle(PathElement.sequenceElement(i));
-      varHandle.set(stringMemorySegment, 0L, (byte) sb.charAt(i));
-    }
-    return stringMemorySegment;
-  }
-
-  /**
-   * A utility method for building a {@link MemorySegment} for a 1D long array.
-   *
-   * @param data The 1D long array for which the {@link MemorySegment} is needed
-   * @return an instance of {@link MemorySegment}
-   */
-  public static MemorySegment buildMemorySegment(Linker linker, Arena arena, long[] data) {
-    int cells = data.length;
-    MemoryLayout longMemoryLayout = linker.canonicalLayouts().get("long");
-    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(cells, longMemoryLayout);
-    MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
-    MemorySegment.copy(data, 0, dataMemorySegment, (ValueLayout) longMemoryLayout, 0, cells);
-    return dataMemorySegment;
-  }
-
-  /**
-   * A utility method for building a {@link MemorySegment} for a 2D float array.
-   *
-   * @param data The 2D float array for which the {@link MemorySegment} is needed
-   * @return an instance of {@link MemorySegment}
-   */
-  public static MemorySegment buildMemorySegment(Linker linker, Arena arena, float[][] data) {
-    long rows = data.length;
-    long cols = rows > 0 ? data[0].length : 0;
-    MemoryLayout floatMemoryLayout = linker.canonicalLayouts().get("float");
-    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(rows * cols, floatMemoryLayout);
-    MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
-    long floatByteSize = floatMemoryLayout.byteSize();
-
-    for (int r = 0; r < rows; r++) {
-      MemorySegment.copy(data[r], 0, dataMemorySegment, (ValueLayout) floatMemoryLayout, (r * cols * floatByteSize),
-          (int) cols);
-    }
-
-    return dataMemorySegment;
-  }
-
-  /**
-   * Load the CuVS .so file from environment variable CUVS_JAVA_SO_PATH. If not
-   * found there, try to load it from the classpath to a temporary file.
-   */
-  public static File loadNativeLibrary() throws IOException {
-    String libraryPathFromEnvironment = System.getenv("CUVS_JAVA_SO_PATH");
-    if (libraryPathFromEnvironment != null) {
-      File file = new File(libraryPathFromEnvironment);
-      if (!file.exists())
-        throw new RuntimeException(
-            "Environment variable CUVS_JAVA_SO_PATH points to non-existent file: " + libraryPathFromEnvironment);
-      return file;
-    }
-    return loadLibraryFromJar("/libcuvs_java.so");
-  }
-
-  private static File loadLibraryFromJar(String path) throws IOException {
-    if (!path.startsWith("/")) {
-      throw new IllegalArgumentException("The path has to be absolute (start with '/').");
-    }
-    // Obtain filename from path
-    String[] parts = path.split("/");
-    String filename = (parts.length > 1) ? parts[parts.length - 1] : null;
-
-    // Split filename to prefix and suffix (extension)
-    String prefix = "";
-    String suffix = null;
-    if (filename != null) {
-      parts = filename.split("\\.", 2);
-      prefix = parts[0];
-      suffix = (parts.length > 1) ? "." + parts[parts.length - 1] : null;
-    }
-    // Prepare temporary file
-    File temp = File.createTempFile(prefix, suffix);
-    InputStream libraryStream = Util.class.getModule().getResourceAsStream(path); // Util.class.getResourceAsStream(path);
-    streamCopy(libraryStream, new FileOutputStream(temp));
-
-    return temp;
-  }
-
-  private static void streamCopy(InputStream is, OutputStream os) throws LibraryException {
-    if (is == null) {
-      throw new LibraryException("CuVS Library Not Found in ClassPath");
-    }
-    byte[] buffer = new byte[1024];
-    int readBytes;
-
-    try {
-      while ((readBytes = is.read(buffer)) != -1) {
-        os.write(buffer, 0, readBytes);
-      }
-    } catch (IOException e) {
-      throw new LibraryException(e);
-    } finally {
-      // If read/write fails, close streams safely before throwing an exception
-      if (os != null)
-        try {
-          os.close();
-        } catch (IOException e) {
-          e.printStackTrace();
-        }
-      if (is != null)
-        try {
-          is.close();
-        } catch (IOException e) {
-          e.printStackTrace();
-        }
-    }
-  }
-}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
new file mode 100644
index 0000000000..04bce4cc72
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.spi;
+
+import com.nvidia.cuvs.BruteForceIndex;
+import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.HnswIndex;
+
+import java.nio.file.Path;
+
+/**
+ * A provider of low-level cuvs resources and builders.
+ */
+public interface CuVSProvider {
+
+  Path TMPDIR = Path.of(System.getProperty("java.io.tmpdir"));
+
+  /**
+   * The temporary directory to use for intermediate operations.
+   * Defaults to {@systemProperty java.io.tmpdir}.
+   */
+  static Path tempDirectory() {
+    return TMPDIR;
+  }
+
+  /**
+   * The directory where to extract and install the native library.
+   * Defaults to {@systemProperty java.io.tmpdir}.
+   */
+  default Path nativeLibraryPath() {
+    return TMPDIR;
+  }
+
+  /** Creates a new CuVSResources. */
+  CuVSResources newCuVSResources(Path tempDirectory)
+      throws Throwable;
+
+  /** Creates a new BruteForceIndex Builder. */
+  BruteForceIndex.Builder newBruteForceIndexBuilder(CuVSResources cuVSResources)
+      throws UnsupportedOperationException;
+
+  /** Creates a new CagraIndex Builder. */
+  CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources)
+      throws UnsupportedOperationException;
+
+  /** Creates a new HnswIndex Builder. */
+  HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources)
+      throws UnsupportedOperationException;
+
+  /** Retrieves the system-wide provider. */
+  static CuVSProvider provider() {
+    return CuVSServiceProvider.Holder.INSTANCE;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
new file mode 100644
index 0000000000..e811263665
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.spi;
+
+import java.lang.invoke.MethodHandles;
+import java.lang.invoke.MethodType;
+import java.util.ServiceLoader;
+
+/**
+ * Service-provider class for {@linkplain CuVSProvider}.
+ */
+public abstract class CuVSServiceProvider {
+
+  /**
+   * Initialize and return an {@link CuVSProvider} provided by this provider.
+   * @param builtinProvider the built-in provider.
+   * @return the CuVSProvider provided by this provider
+   */
+  public abstract CuVSProvider get(CuVSProvider builtinProvider);
+
+  static class Holder {
+    static final CuVSProvider INSTANCE = loadProvider();
+
+    private static CuVSProvider loadProvider() {
+      var builtinProvider = builtinProvider();
+      return ServiceLoader.load(CuVSServiceProvider.class)
+              .findFirst()
+              .map(p -> p.get(builtinProvider))
+              .orElse(builtinProvider);
+    }
+
+    static CuVSProvider builtinProvider() {
+      if (Runtime.version().feature() > 21 && isLinuxAmd64()) {
+        try {
+          var cls = Class.forName("com.nvidia.cuvs.spi.JDKProvider");
+          var ctr = MethodHandles.lookup().findConstructor(cls, MethodType.methodType(void.class));
+          return (CuVSProvider) ctr.invoke();
+        } catch (Throwable e) {
+          throw new AssertionError(e);
+        }
+      }
+      return new UnsupportedProvider();
+    }
+
+    /**
+     * Returns true iff the architecture is x64 (amd64) and the OS Linux
+     * (the * OS we currently support for the native lib).
+     */
+    static boolean isLinuxAmd64() {
+      String name = System.getProperty("os.name");
+      return (name.startsWith("Linux")) && System.getProperty("os.arch").equals("amd64");
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
new file mode 100644
index 0000000000..7dc8207509
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.spi;
+
+import com.nvidia.cuvs.BruteForceIndex;
+import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.HnswIndex;
+
+import java.nio.file.Path;
+
+/**
+ * A provider that unconditionally throws UnsupportedOperationException.
+ */
+final class UnsupportedProvider implements CuVSProvider {
+
+  @Override
+  public CuVSResources newCuVSResources(Path tempDirectory) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public BruteForceIndex.Builder newBruteForceIndexBuilder(CuVSResources cuVSResources) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
+    throw new UnsupportedOperationException();
+  }
+}
diff --git a/java/cuvs-java/src/main/java/module-info.java b/java/cuvs-java/src/main/java/module-info.java
index 468252f22e..5bcc9990f3 100644
--- a/java/cuvs-java/src/main/java/module-info.java
+++ b/java/cuvs-java/src/main/java/module-info.java
@@ -16,4 +16,7 @@
 
 module com.nvidia.cuvs {
   exports com.nvidia.cuvs;
+  exports com.nvidia.cuvs.spi;
+
+  uses com.nvidia.cuvs.spi.CuVSServiceProvider;
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
new file mode 100644
index 0000000000..7ea2801f31
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -0,0 +1,380 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal;
+
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.invoke.MethodHandle;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Objects;
+import java.util.UUID;
+
+import com.nvidia.cuvs.BruteForceIndex;
+import com.nvidia.cuvs.BruteForceIndexParams;
+import com.nvidia.cuvs.BruteForceQuery;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.internal.common.Util;
+import com.nvidia.cuvs.internal.panama.CuVSBruteForceIndex;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.common.Util.checkError;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
+/**
+ *
+ * {@link BruteForceIndex} encapsulates a BRUTEFORCE index, along with methods
+ * to interact with it.
+ *
+ * @since 25.02
+ */
+public class BruteForceIndexImpl implements BruteForceIndex{
+
+  private static final MethodHandle indexMethodHandle = downcallHandle("build_brute_force_index",
+      FunctionDescriptor.of(ADDRESS, ADDRESS, C_LONG, C_LONG, ADDRESS, ADDRESS, C_INT));
+
+  private static final MethodHandle searchMethodHandle = downcallHandle("search_brute_force_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_LONG, C_LONG));
+
+  private static final MethodHandle destroyIndexMethodHandle = downcallHandle("destroy_brute_force_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS));
+
+  private static final MethodHandle serializeMethodHandle = downcallHandle("serialize_brute_force_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+
+  private static final MethodHandle deserializeMethodHandle = downcallHandle("deserialize_brute_force_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+
+  private final float[][] dataset;
+  private final CuVSResourcesImpl resources;
+  private final IndexReference bruteForceIndexReference;
+  private final BruteForceIndexParams bruteForceIndexParams;
+  private boolean destroyed;
+
+  /**
+   * Constructor for building the index using specified dataset
+   *
+   * @param dataset               the dataset used for creating the BRUTEFORCE
+   *                              index
+   * @param resources             an instance of {@link CuVSResourcesImpl}
+   * @param bruteForceIndexParams an instance of {@link BruteForceIndexParams}
+   *                              holding the index parameters
+   */
+  private BruteForceIndexImpl(float[][] dataset, CuVSResourcesImpl resources, BruteForceIndexParams bruteForceIndexParams)
+      throws Throwable {
+    this.dataset = dataset;
+    this.resources = resources;
+    this.bruteForceIndexParams = bruteForceIndexParams;
+    this.bruteForceIndexReference = build();
+  }
+
+  /**
+   * Constructor for loading the index from an {@link InputStream}
+   *
+   * @param inputStream an instance of stream to read the index bytes from
+   * @param resources   an instance of {@link CuVSResourcesImpl}
+   */
+  private BruteForceIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
+    this.bruteForceIndexParams = null;
+    this.dataset = null;
+    this.resources = resources;
+    this.bruteForceIndexReference = deserialize(inputStream);
+  }
+
+  private void checkNotDestroyed() {
+    if (destroyed) {
+      throw new IllegalStateException("destroyed");
+    }
+  }
+
+  /**
+   * Invokes the native destroy_brute_force_index function to de-allocate
+   * BRUTEFORCE index
+   */
+  @Override
+  public void destroyIndex() throws Throwable {
+    checkNotDestroyed();
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      destroyIndexMethodHandle.invokeExact(bruteForceIndexReference.getMemorySegment(), returnValue);
+      checkError(returnValue.get(C_INT, 0L), "destroyIndexMethodHandle");
+    } finally {
+      destroyed = true;
+    }
+  }
+
+  /**
+   * Invokes the native build_brute_force_index function via the Panama API to
+   * build the {@link BruteForceIndex}
+   *
+   * @return an instance of {@link IndexReference} that holds the pointer to the
+   *         index
+   */
+  private IndexReference build() throws Throwable {
+    long rows = dataset.length;
+    long cols = rows > 0 ? dataset[0].length : 0;
+
+    MemorySegment dataSeg = Util.buildMemorySegment(resources.getArena(), dataset);
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      MemorySegment indexSeg = (MemorySegment) indexMethodHandle.invokeExact(
+        dataSeg,
+        rows,
+        cols,
+        resources.getMemorySegment(),
+        returnValue,
+        bruteForceIndexParams.getNumWriterThreads()
+      );
+      checkError(returnValue.get(C_INT, 0L), "indexMethodHandle");
+      return new IndexReference(indexSeg);
+    }
+  }
+
+  /**
+   * Invokes the native search_brute_force_index via the Panama API for searching
+   * a BRUTEFORCE index.
+   *
+   * @param cuvsQuery an instance of {@link BruteForceQuery} holding the query
+   *                  vectors and other parameters
+   * @return an instance of {@link BruteForceSearchResults} containing the results
+   */
+  @Override
+  public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
+    checkNotDestroyed();
+    long numQueries = cuvsQuery.getQueryVectors().length;
+    long numBlocks = cuvsQuery.getTopK() * numQueries;
+    int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
+    long prefilterDataLength = cuvsQuery.getPrefilter() != null ? cuvsQuery.getPrefilter().length : 0;
+    long numRows = dataset != null ? dataset.length : 0;
+
+    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
+    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+    MemorySegment neighborsMemorySegment = resources.getArena().allocate(neighborsSequenceLayout);
+    MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
+    MemorySegment prefilterDataMemorySegment = cuvsQuery.getPrefilter() != null
+            ? Util.buildMemorySegment(resources.getArena(), cuvsQuery.getPrefilter())
+            : MemorySegment.NULL;
+    MemorySegment querySeg = Util.buildMemorySegment(resources.getArena(), cuvsQuery.getQueryVectors());
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      searchMethodHandle.invokeExact(
+        bruteForceIndexReference.getMemorySegment(),
+        querySeg,
+        cuvsQuery.getTopK(),
+        numQueries,
+        vectorDimension,
+        resources.getMemorySegment(),
+        neighborsMemorySegment,
+        distancesMemorySegment,
+        returnValue,
+        prefilterDataMemorySegment,
+        prefilterDataLength, numRows
+      );
+      checkError(returnValue.get(C_INT, 0L), "searchMethodHandle");
+    }
+    return new BruteForceSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+            distancesMemorySegment, cuvsQuery.getTopK(), cuvsQuery.getMapping(), numQueries);
+  }
+
+  @Override
+  public void serialize(OutputStream outputStream) throws Throwable {
+    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
+    serialize(outputStream, p);
+  }
+
+  @Override
+  public void serialize(OutputStream outputStream, Path tempFile) throws Throwable {
+    checkNotDestroyed();
+    tempFile = tempFile.toAbsolutePath();
+    MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tempFile.toString());
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      serializeMethodHandle.invokeExact(
+        resources.getMemorySegment(),
+        bruteForceIndexReference.getMemorySegment(),
+        returnValue,
+        pathSeg
+      );
+      checkError(returnValue.get(C_INT, 0L), "serializeMethodHandle");
+
+      try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
+        fileInputStream.transferTo(outputStream);
+      } finally {
+        Files.deleteIfExists(tempFile);
+      }
+    }
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a BRUTEFORCE
+   * index using an {@link InputStream}.
+   *
+   * @param inputStream an instance of {@link InputStream}
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream) throws Throwable {
+    checkNotDestroyed();
+    Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
+    tmpIndexFile = tmpIndexFile.toAbsolutePath();
+    IndexReference indexReference = new IndexReference(resources);
+
+    try (var in = inputStream;
+         FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+      in.transferTo(fileOutputStream);
+      MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tmpIndexFile.toString());
+      try (var localArena = Arena.ofConfined()) {
+        MemorySegment returnValue = localArena.allocate(C_INT);
+        deserializeMethodHandle.invokeExact(
+          resources.getMemorySegment(),
+          indexReference.getMemorySegment(),
+          returnValue,
+          pathSeg
+        );
+        checkError(returnValue.get(C_INT, 0L), "deserializeMethodHandle");
+      }
+    } finally {
+      Files.deleteIfExists(tmpIndexFile);
+    }
+    return indexReference;
+  }
+
+  public static BruteForceIndex.Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
+      throw new IllegalArgumentException("Unsupported " + cuvsResources);
+    }
+    return new Builder((CuVSResourcesImpl)cuvsResources);
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link BruteForceIndex}.
+   */
+  public static class Builder implements BruteForceIndex.Builder {
+
+    private float[][] dataset;
+    private final CuVSResourcesImpl cuvsResources;
+    private BruteForceIndexParams bruteForceIndexParams;
+    private InputStream inputStream;
+
+    /**
+     * Constructs this Builder with an instance of {@link CuVSResourcesImpl}.
+     *
+     * @param cuvsResources an instance of {@link CuVSResources}
+     */
+    public Builder(CuVSResourcesImpl cuvsResources) {
+      this.cuvsResources = cuvsResources;
+    }
+
+    /**
+     * Registers an instance of configured {@link BruteForceIndexParams} with this
+     * Builder.
+     *
+     * @param bruteForceIndexParams An instance of BruteForceIndexParams
+     * @return An instance of this Builder
+     */
+    @Override
+    public Builder withIndexParams(BruteForceIndexParams bruteForceIndexParams) {
+      this.bruteForceIndexParams = bruteForceIndexParams;
+      return this;
+    }
+
+    /**
+     * Sets an instance of InputStream typically used when index deserialization is
+     * needed.
+     *
+     * @param inputStream an instance of {@link InputStream}
+     * @return an instance of this Builder
+     */
+    @Override
+    public Builder from(InputStream inputStream) {
+      this.inputStream = inputStream;
+      return this;
+    }
+
+    /**
+     * Sets the dataset for building the {@link BruteForceIndex}.
+     *
+     * @param dataset a two-dimensional float array
+     * @return an instance of this Builder
+     */
+    @Override
+    public Builder withDataset(float[][] dataset) {
+      this.dataset = dataset;
+      return this;
+    }
+
+    /**
+     * Builds and returns an instance of {@link BruteForceIndex}.
+     *
+     * @return an instance of {@link BruteForceIndex}
+     */
+    @Override
+    public BruteForceIndexImpl build() throws Throwable {
+      if (inputStream != null) {
+        return new BruteForceIndexImpl(inputStream, cuvsResources);
+      } else {
+        return new BruteForceIndexImpl(dataset, cuvsResources, bruteForceIndexParams);
+      }
+    }
+  }
+
+  /**
+   * Holds the memory reference to a BRUTEFORCE index.
+   */
+  protected static class IndexReference {
+
+    private final MemorySegment memorySegment;
+
+    /**
+     * Constructs CagraIndexReference and allocate the MemorySegment.
+     */
+    protected IndexReference(CuVSResourcesImpl resources) {
+      memorySegment = CuVSBruteForceIndex.allocate(resources.getArena());
+    }
+
+    /**
+     * Constructs BruteForceIndexReference with an instance of MemorySegment passed
+     * as a parameter.
+     *
+     * @param indexMemorySegment the MemorySegment instance to use for containing
+     *                           index reference
+     */
+    protected IndexReference(MemorySegment indexMemorySegment) {
+      this.memorySegment = indexMemorySegment;
+    }
+
+    /**
+     * Gets the instance of index MemorySegment.
+     *
+     * @return index MemorySegment
+     */
+    protected MemorySegment getMemorySegment() {
+      return memorySegment;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
similarity index 93%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceSearchResults.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
index b87e1f601e..1b872708d9 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
@@ -14,7 +14,9 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs;
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.internal.common.SearchResultsImpl;
 
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
@@ -22,14 +24,12 @@
 import java.util.List;
 import java.util.Map;
 
-import com.nvidia.cuvs.common.SearchResults;
-
 /**
  * SearchResult encapsulates the logic for reading and holding search results.
  *
  * @since 25.02
  */
-public class BruteForceSearchResults extends SearchResults {
+public class BruteForceSearchResults extends SearchResultsImpl {
 
   protected BruteForceSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
       MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
new file mode 100644
index 0000000000..0577276d34
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -0,0 +1,515 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal;
+
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.invoke.MethodHandle;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Objects;
+import java.util.UUID;
+
+import com.nvidia.cuvs.CagraCompressionParams;
+import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CagraIndexParams;
+import com.nvidia.cuvs.CagraQuery;
+import com.nvidia.cuvs.CagraSearchParams;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.internal.common.Util;
+import com.nvidia.cuvs.internal.panama.CuVSCagraCompressionParams;
+import com.nvidia.cuvs.internal.panama.CuVSCagraIndex;
+import com.nvidia.cuvs.internal.panama.CuVSCagraIndexParams;
+import com.nvidia.cuvs.internal.panama.CuVSCagraSearchParams;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.common.Util.checkError;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
+/**
+ * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
+ * with it.
+ * <p>
+ * CAGRA is a graph-based nearest neighbors algorithm that was built from the
+ * ground up for GPU acceleration. CAGRA demonstrates state-of-the art index
+ * build and query performance for both small and large-batch sized search. Know
+ * more about this algorithm
+ * <a href="https://arxiv.org/abs/2308.15136" target="_blank">here</a>
+ *
+ * @since 25.02
+ */
+public class CagraIndexImpl implements CagraIndex {
+
+  private static final MethodHandle indexMethodHandle = downcallHandle("build_cagra_index",
+      FunctionDescriptor.of(ADDRESS, ADDRESS, C_LONG, C_LONG, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_INT));
+
+  private static final MethodHandle searchMethodHandle = downcallHandle("search_cagra_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+
+  private static final MethodHandle serializeMethodHandle = downcallHandle("serialize_cagra_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+
+  private static final MethodHandle deserializeMethodHandle = downcallHandle("deserialize_cagra_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+
+  private static final MethodHandle destroyIndexMethodHandle = downcallHandle("destroy_cagra_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS));
+
+  private static final MethodHandle serializeCAGRAIndexToHNSWMethodHandle = downcallHandle("serialize_cagra_index_to_hnsw",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+
+  private final float[][] dataset;
+  private final CuVSResourcesImpl resources;
+  private final CagraIndexParams cagraIndexParameters;
+  private final CagraCompressionParams cagraCompressionParams;
+  private final IndexReference cagraIndexReference;
+  private boolean destroyed;
+
+  /**
+   * Constructor for building the index using specified dataset
+   *
+   * @param indexParameters        an instance of {@link CagraIndexParams} holding
+   *                               the index parameters
+   * @param cagraCompressionParams an instance of {@link CagraCompressionParams}
+   *                               holding the compression parameters
+   * @param dataset                the dataset for indexing
+   * @param resources              an instance of {@link CuVSResources}
+   */
+  private CagraIndexImpl(CagraIndexParams indexParameters, CagraCompressionParams cagraCompressionParams, float[][] dataset,
+      CuVSResourcesImpl resources) throws Throwable {
+    this.cagraIndexParameters = indexParameters;
+    this.cagraCompressionParams = cagraCompressionParams;
+    this.dataset = dataset;
+    this.resources = resources;
+    this.cagraIndexReference = build();
+  }
+
+  /**
+   * Constructor for loading the index from an {@link InputStream}
+   *
+   * @param inputStream an instance of stream to read the index bytes from
+   * @param resources   an instance of {@link CuVSResources}
+   */
+  private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
+    this.cagraIndexParameters = null;
+    this.cagraCompressionParams = null;
+    this.dataset = null;
+    this.resources = resources;
+    this.cagraIndexReference = deserialize(inputStream);
+  }
+
+  private void checkNotDestroyed() {
+    if (destroyed) {
+      throw new IllegalStateException("destroyed");
+    }
+  }
+
+  /**
+   * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
+   */
+  @Override
+  public void destroyIndex() throws Throwable {
+    checkNotDestroyed();
+    try (var arena = Arena.ofConfined()) {
+      MemorySegment returnValue = arena.allocate(C_INT);
+      destroyIndexMethodHandle.invokeExact(cagraIndexReference.getMemorySegment(), returnValue);
+      checkError(returnValue.get(C_INT, 0L), "destroyIndexMethodHandle");
+    } finally {
+      destroyed = true;
+    }
+  }
+
+  /**
+   * Invokes the native build_cagra_index function via the Panama API to build the
+   * {@link CagraIndex}
+   *
+   * @return an instance of {@link IndexReference} that holds the pointer to the
+   *         index
+   */
+  private IndexReference build() throws Throwable {
+    long rows = dataset.length;
+    long cols = rows > 0 ? dataset[0].length : 0;
+
+    MemorySegment indexParamsMemorySegment = cagraIndexParameters != null
+        ? segmentFromIndexParams(cagraIndexParameters)
+        : MemorySegment.NULL;
+
+    int numWriterThreads = cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
+
+    MemorySegment compressionParamsMemorySegment = cagraCompressionParams != null
+        ? segmentFromCompressionParams(cagraCompressionParams)
+        : MemorySegment.NULL;
+
+    MemorySegment dataSeg = Util.buildMemorySegment(resources.getArena(), dataset);
+
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      var indexSeg = (MemorySegment) indexMethodHandle.invokeExact(
+        dataSeg,
+        rows,
+        cols,
+        resources.getMemorySegment(),
+        returnValue,
+        indexParamsMemorySegment,
+        compressionParamsMemorySegment,
+        numWriterThreads
+      );
+      checkError(returnValue.get(C_INT, 0L), "indexMethodHandle");
+      return new IndexReference(indexSeg);
+    }
+  }
+
+  /**
+   * Invokes the native search_cagra_index via the Panama API for searching a
+   * CAGRA index.
+   *
+   * @param query an instance of {@link CagraQuery} holding the query vectors and
+   *              other parameters
+   * @return an instance of {@link CagraSearchResults} containing the results
+   */
+  @Override
+  public SearchResults search(CagraQuery query) throws Throwable {
+    checkNotDestroyed();
+    int topK = query.getMapping() != null ? Math.min(query.getMapping().size(), query.getTopK()) : query.getTopK();
+    long numQueries = query.getQueryVectors().length;
+    long numBlocks = topK * numQueries;
+    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
+
+    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_INT);
+    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+    MemorySegment neighborsMemorySegment = resources.getArena().allocate(neighborsSequenceLayout);
+    MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
+    MemorySegment floatsSeg = Util.buildMemorySegment(resources.getArena(), query.getQueryVectors());
+
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      searchMethodHandle.invokeExact(
+        cagraIndexReference.getMemorySegment(),
+        floatsSeg,
+        topK,
+        numQueries,
+        vectorDimension,
+        resources.getMemorySegment(),
+        neighborsMemorySegment,
+        distancesMemorySegment,
+        returnValue,
+        segmentFromSearchParams(query.getCagraSearchParameters())
+      );
+      checkError(returnValue.get(C_INT, 0L), "searchMethodHandle");
+    }
+    return new CagraSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+        distancesMemorySegment, topK, query.getMapping(), numQueries);
+  }
+
+  @Override
+  public void serialize(OutputStream outputStream) throws Throwable {
+    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    serialize(outputStream, p, 1024);
+  }
+
+  @Override
+  public void serialize(OutputStream outputStream, int bufferLength) throws Throwable {
+    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    serialize(outputStream, p, bufferLength);
+  }
+
+  @Override
+  public void serialize(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable {
+    checkNotDestroyed();
+    tempFile = tempFile.toAbsolutePath();
+    MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tempFile.toString());
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      serializeMethodHandle.invokeExact(
+        resources.getMemorySegment(),
+        cagraIndexReference.getMemorySegment(),
+        returnValue,
+        pathSeg
+      );
+      checkError(returnValue.get(C_INT, 0L), "serializeMethodHandle");
+
+      try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
+        byte[] chunk = new byte[bufferLength];
+        int chunkLength = 0;
+        while ((chunkLength = fileInputStream.read(chunk)) != -1) {
+          outputStream.write(chunk, 0, chunkLength);
+        }
+      } finally {
+        Files.deleteIfExists(tempFile);
+      }
+    }
+  }
+
+  @Override
+  public void serializeToHNSW(OutputStream outputStream) throws Throwable {
+    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+    serializeToHNSW(outputStream, p, 1024);
+  }
+
+  @Override
+  public void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable {
+    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+    serializeToHNSW(outputStream, p, bufferLength);
+  }
+
+  @Override
+  public void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable {
+    checkNotDestroyed();
+    tempFile = tempFile.toAbsolutePath();
+    MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tempFile.toString());
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      serializeCAGRAIndexToHNSWMethodHandle.invokeExact(
+        resources.getMemorySegment(),
+        pathSeg,
+        cagraIndexReference.getMemorySegment(),
+        returnValue
+      );
+      checkError(returnValue.get(C_INT, 0L), "serializeCAGRAIndexToHNSWMethodHandle");
+
+      try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
+        byte[] chunk = new byte[bufferLength];
+        int chunkLength;
+        while ((chunkLength = fileInputStream.read(chunk)) != -1) {
+          outputStream.write(chunk, 0, chunkLength);
+        }
+      } finally {
+        Files.deleteIfExists(tempFile);
+      }
+    }
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream an instance of {@link InputStream}
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream) throws Throwable {
+    return deserialize(inputStream, 1024);
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream  an instance of {@link InputStream}
+   * @param bufferLength the length of the buffer to use while reading the bytes
+   *                     from the stream. Default value is 1024.
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
+    Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    tmpIndexFile = tmpIndexFile.toAbsolutePath();
+    IndexReference indexReference = new IndexReference(resources);
+
+    try (var in = inputStream;
+         FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+      in.transferTo(fileOutputStream);
+      MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tmpIndexFile.toString());
+      try (var localArena = Arena.ofConfined()) {
+        MemorySegment returnValue = localArena.allocate(C_INT);
+        deserializeMethodHandle.invokeExact(
+          resources.getMemorySegment(),
+          indexReference.getMemorySegment(),
+          returnValue,
+          pathSeg
+        );
+        checkError(returnValue.get(C_INT, 0L), "deserializeMethodHandle");
+      }
+    } finally {
+      Files.deleteIfExists(tmpIndexFile);
+    }
+    return indexReference;
+  }
+
+  /**
+   * Gets an instance of {@link CagraIndexParams}
+   *
+   * @return an instance of {@link CagraIndexParams}
+   */
+  @Override
+  public CagraIndexParams getCagraIndexParameters() {
+    return cagraIndexParameters;
+  }
+
+  /**
+   * Gets an instance of {@link CuVSResources}
+   *
+   * @return an instance of {@link CuVSResources}
+   */
+  @Override
+  public CuVSResourcesImpl getCuVSResources() {
+    return resources;
+  }
+
+
+  /**
+   * Allocates the configured compression parameters in the MemorySegment.
+   */
+  private MemorySegment segmentFromCompressionParams(CagraCompressionParams params) {
+    MemorySegment seg = CuVSCagraCompressionParams.allocate(resources.getArena());
+    CuVSCagraCompressionParams.pq_bits(seg, params.getPqBits());
+    CuVSCagraCompressionParams.pq_dim(seg, params.getPqDim());
+    CuVSCagraCompressionParams.vq_n_centers(seg, params.getVqNCenters());
+    CuVSCagraCompressionParams.kmeans_n_iters(seg, params.getKmeansNIters());
+    CuVSCagraCompressionParams.vq_kmeans_trainset_fraction(seg, params.getVqKmeansTrainsetFraction());
+    CuVSCagraCompressionParams.pq_kmeans_trainset_fraction(seg, params.getPqKmeansTrainsetFraction());
+    return seg;
+  }
+
+  /**
+   * Allocates the configured index parameters in the MemorySegment.
+   */
+  private MemorySegment segmentFromIndexParams(CagraIndexParams params) {
+    MemorySegment seg = CuVSCagraIndexParams.allocate(resources.getArena());
+    CuVSCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
+    CuVSCagraIndexParams.graph_degree(seg, params.getGraphDegree());
+    CuVSCagraIndexParams.build_algo(seg, params.getCagraGraphBuildAlgo().value);
+    CuVSCagraIndexParams.nn_descent_niter(seg, params.getNNDescentNumIterations());
+    CuVSCagraIndexParams.metric(seg, params.getCuvsDistanceType().value);
+    return seg;
+  }
+
+  /**
+   * Allocates the configured search parameters in the MemorySegment.
+   */
+  private MemorySegment segmentFromSearchParams(CagraSearchParams params) {
+    MemorySegment seg = CuVSCagraSearchParams.allocate(resources.getArena());
+    CuVSCagraSearchParams.max_queries(seg, params.getMaxQueries());
+    CuVSCagraSearchParams.itopk_size(seg, params.getITopKSize());
+    CuVSCagraSearchParams.max_iterations(seg, params.getMaxIterations());
+    if (params.getCagraSearchAlgo() != null) {
+      CuVSCagraSearchParams.algo(seg, params.getCagraSearchAlgo().value);
+    }
+    CuVSCagraSearchParams.team_size(seg, params.getTeamSize());
+    CuVSCagraSearchParams.search_width(seg, params.getSearchWidth());
+    CuVSCagraSearchParams.min_iterations(seg, params.getMinIterations());
+    CuVSCagraSearchParams.thread_block_size(seg, params.getThreadBlockSize());
+    if (params.getHashMapMode() != null) {
+      CuVSCagraSearchParams.hashmap_mode(seg, params.getHashMapMode().value);
+    }
+    CuVSCagraSearchParams.hashmap_max_fill_rate(seg, params.getHashMapMaxFillRate());
+    CuVSCagraSearchParams.num_random_samplings(seg, params.getNumRandomSamplings());
+    CuVSCagraSearchParams.rand_xor_mask(seg, params.getRandXORMask());
+    return seg;
+  }
+
+  public static CagraIndex.Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
+      throw new IllegalArgumentException("Unsupported " + cuvsResources);
+    }
+    return new CagraIndexImpl.Builder((CuVSResourcesImpl)cuvsResources);
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link CagraIndex}.
+   */
+  public static class Builder implements CagraIndex.Builder{
+
+    private float[][] dataset;
+    private CagraIndexParams cagraIndexParams;
+    private CagraCompressionParams cagraCompressionParams;
+    private CuVSResourcesImpl cuvsResources;
+    private InputStream inputStream;
+
+    public Builder(CuVSResourcesImpl cuvsResources) {
+      this.cuvsResources = cuvsResources;
+    }
+
+    @Override
+    public Builder from(InputStream inputStream) {
+      this.inputStream = inputStream;
+      return this;
+    }
+
+    @Override
+    public Builder withDataset(float[][] dataset) {
+      this.dataset = dataset;
+      return this;
+    }
+
+    @Override
+    public Builder withIndexParams(CagraIndexParams cagraIndexParameters) {
+      this.cagraIndexParams = cagraIndexParameters;
+      return this;
+    }
+
+    @Override
+    public Builder withCompressionParams(CagraCompressionParams cagraCompressionParams) {
+      this.cagraCompressionParams = cagraCompressionParams;
+      return this;
+    }
+
+    @Override
+    public CagraIndexImpl build() throws Throwable {
+      if (inputStream != null) {
+        return new CagraIndexImpl(inputStream, cuvsResources);
+      } else {
+        return new CagraIndexImpl(cagraIndexParams, cagraCompressionParams, dataset, cuvsResources);
+      }
+    }
+  }
+
+  /**
+   * Holds the memory reference to a CAGRA index.
+   */
+  protected static class IndexReference {
+
+    private final MemorySegment memorySegment;
+
+    /**
+     * Constructs CagraIndexReference and allocate the MemorySegment.
+     */
+    protected IndexReference(CuVSResourcesImpl resources) {
+      memorySegment = CuVSCagraIndex.allocate(resources.getArena());
+    }
+
+    /**
+     * Constructs CagraIndexReference with an instance of MemorySegment passed as a
+     * parameter.
+     *
+     * @param indexMemorySegment the MemorySegment instance to use for containing
+     *                           index reference
+     */
+    protected IndexReference(MemorySegment indexMemorySegment) {
+      this.memorySegment = indexMemorySegment;
+    }
+
+    /**
+     * Gets the instance of index MemorySegment.
+     *
+     * @return index MemorySegment
+     */
+    protected MemorySegment getMemorySegment() {
+      return memorySegment;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
similarity index 93%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchResults.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
index 3473faccef..4902013073 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs;
+package com.nvidia.cuvs.internal;
 
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
@@ -22,14 +22,14 @@
 import java.util.List;
 import java.util.Map;
 
-import com.nvidia.cuvs.common.SearchResults;
+import com.nvidia.cuvs.internal.common.SearchResultsImpl;
 
 /**
  * SearchResult encapsulates the logic for reading and holding search results.
  *
  * @since 25.02
  */
-public class CagraSearchResults extends SearchResults {
+public class CagraSearchResults extends SearchResultsImpl {
 
   protected CagraSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
       MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
new file mode 100644
index 0000000000..a44d7ede41
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -0,0 +1,154 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.CuVSResources;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemorySegment;
+import java.lang.invoke.MethodHandle;
+import java.nio.file.Path;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.common.Util.checkError;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
+/**
+ * Used for allocating resources for cuVS
+ *
+ * @since 25.02
+ */
+public class CuVSResourcesImpl implements CuVSResources {
+
+  static final MethodHandle createResourcesMethodHandle = downcallHandle(
+      "create_resources", FunctionDescriptor.of(ADDRESS, ADDRESS)
+  );
+
+  private static final MethodHandle destroyResourcesMethodHandle = downcallHandle(
+       "destroy_resources", FunctionDescriptor.ofVoid(ADDRESS, ADDRESS)
+  );
+
+  private final Path tempDirectory;
+  private final Arena arena;
+  private final MemorySegment resourcesMemorySegment;
+  private boolean destroyed;
+
+  /**
+   * Constructor that allocates the resources needed for cuVS
+   *
+   * @throws Throwable exception thrown when native function is invoked
+   */
+  public CuVSResourcesImpl(Path tempDirectory) throws Throwable {
+    this.tempDirectory = tempDirectory;
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      resourcesMemorySegment = (MemorySegment) createResourcesMethodHandle.invokeExact(returnValue);
+      checkError(returnValue.get(C_INT, 0L), "createResourcesMethodHandle");
+    }
+    arena = Arena.ofShared();
+  }
+
+  @Override
+  public void close() {
+    checkNotDestroyed();
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      destroyResourcesMethodHandle.invokeExact(resourcesMemorySegment, returnValue);
+      checkError(returnValue.get(C_INT, 0L), "destroyResourcesMethodHandle");
+    } catch (Throwable e) {
+      e.printStackTrace();
+    } finally {
+      destroyed = true;
+    }
+    if (!arena.scope().isAlive()) {
+      arena.close();
+    }
+  }
+
+  @Override
+  public Path tempDirectory() {
+    return tempDirectory;
+  }
+
+  private void checkNotDestroyed() {
+    if (destroyed) {
+      throw new IllegalStateException("destroyed");
+    }
+  }
+
+  /**
+   * Gets the reference to the cuvsResources MemorySegment.
+   *
+   * @return cuvsResources MemorySegment
+   */
+  protected MemorySegment getMemorySegment() {
+    checkNotDestroyed();
+    return resourcesMemorySegment;
+  }
+
+  /**
+   * The allocation arena used by this resources.
+   */
+  protected Arena getArena() {
+    checkNotDestroyed();
+    return arena;
+  }
+
+  /**
+   * Container for GPU information
+   */
+  public class GPUInfo {
+
+    private final int gpuId;
+    private final long freeMemory;
+    private final long totalMemory;
+    private final float computeCapability;
+
+    public GPUInfo(int gpuId, long freeMemory, long totalMemory, float computeCapability) {
+      super();
+      this.gpuId = gpuId;
+      this.freeMemory = freeMemory;
+      this.totalMemory = totalMemory;
+      this.computeCapability = computeCapability;
+    }
+
+    public int getGpuId() {
+      return gpuId;
+    }
+
+    public long getFreeMemory() {
+      return freeMemory;
+    }
+
+    public long getTotalMemory() {
+      return totalMemory;
+    }
+
+    public float getComputeCapability() {
+      return computeCapability;
+    }
+
+    @Override
+    public String toString() {
+      return "GPUInfo [gpuId=" + gpuId + ", freeMemory=" + freeMemory + ", totalMemory=" + totalMemory
+          + ", computeCapability=" + computeCapability + "]";
+    }
+
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
new file mode 100644
index 0000000000..bb9553e255
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -0,0 +1,303 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal;
+
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.invoke.MethodHandle;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Objects;
+import java.util.UUID;
+
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.HnswIndexParams;
+import com.nvidia.cuvs.HnswQuery;
+import com.nvidia.cuvs.HnswSearchParams;
+import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.internal.common.Util;
+import com.nvidia.cuvs.internal.panama.CuVSHnswIndex;
+import com.nvidia.cuvs.internal.panama.CuVSHnswIndexParams;
+import com.nvidia.cuvs.internal.panama.CuVSHnswSearchParams;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.common.Util.checkError;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
+/**
+ * {@link HnswIndex} encapsulates a HNSW index, along with methods to interact
+ * with it.
+ *
+ * @since 25.02
+ */
+public class HnswIndexImpl implements HnswIndex {
+
+  private static final MethodHandle deserializeHnswIndexMethodHandle = downcallHandle("deserialize_hnsw_index",
+      FunctionDescriptor.of(ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_INT));
+
+  private static final MethodHandle searchHnswIndexMethodHandle = downcallHandle("search_hnsw_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_INT, C_INT, C_LONG));
+
+  private static final MethodHandle destroyHnswIndexMethodHandle = downcallHandle("destroy_hnsw_index",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS));
+
+  private final CuVSResourcesImpl resources;
+  private final HnswIndexParams hnswIndexParams;
+  private final IndexReference hnswIndexReference;
+
+  /**
+   * Constructor for loading the index from an {@link InputStream}
+   *
+   * @param inputStream an instance of stream to read the index bytes from
+   * @param resources   an instance of {@link CuVSResourcesImpl}
+   */
+  private HnswIndexImpl(InputStream inputStream, CuVSResourcesImpl resources, HnswIndexParams hnswIndexParams)
+      throws Throwable {
+    this.hnswIndexParams = hnswIndexParams;
+    this.resources = resources;
+    this.hnswIndexReference = deserialize(inputStream);
+  }
+
+  /**
+   * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
+   */
+  @Override
+  public void destroyIndex() throws Throwable {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      destroyHnswIndexMethodHandle.invokeExact(hnswIndexReference.getMemorySegment(), returnValue);
+      checkError(returnValue.get(C_INT, 0L), "destroyHnswIndexMethodHandle");
+    }
+  }
+
+  /**
+   * Invokes the native search_hnsw_index via the Panama API for searching a HNSW
+   * index.
+   *
+   * @param query an instance of {@link HnswQuery} holding the query vectors and
+   *              other parameters
+   * @return an instance of {@link HnswSearchResults} containing the results
+   */
+  @Override
+  public SearchResults search(HnswQuery query) throws Throwable {
+    long numQueries = query.getQueryVectors().length;
+    long numBlocks = query.getTopK() * numQueries;
+    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
+
+    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
+    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+    MemorySegment neighborsMemorySegment = resources.getArena().allocate(neighborsSequenceLayout);
+    MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
+    MemorySegment querySeg = Util.buildMemorySegment(resources.getArena(), query.getQueryVectors());
+
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValue = localArena.allocate(C_INT);
+      searchHnswIndexMethodHandle.invokeExact(
+        resources.getMemorySegment(),
+        hnswIndexReference.getMemorySegment(),
+        segmentFromSearchParams(query.getHnswSearchParams()),
+        returnValue,
+        neighborsMemorySegment,
+        distancesMemorySegment,
+        querySeg,
+        query.getTopK(),
+        vectorDimension,
+        numQueries
+      );
+      checkError(returnValue.get(C_INT, 0L), "searchHnswIndexMethodHandle");
+    }
+    return new HnswSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+        distancesMemorySegment, query.getTopK(), query.getMapping(), numQueries);
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a HNSW index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream an instance of {@link InputStream}
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream) throws Throwable {
+    return deserialize(inputStream, 1024);
+  }
+
+  /**
+   * Gets an instance of {@link IndexReference} by deserializing a HNSW index
+   * using an {@link InputStream}.
+   *
+   * @param inputStream  an instance of {@link InputStream}
+   * @param bufferLength the length of the buffer to use while reading the bytes
+   *                     from the stream. Default value is 1024.
+   * @return an instance of {@link IndexReference}.
+   */
+  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
+    Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+    tmpIndexFile = tmpIndexFile.toAbsolutePath();
+
+    try (var in = inputStream;
+         FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+      byte[] chunk = new byte[bufferLength];
+      int chunkLength;
+      while ((chunkLength = in.read(chunk)) != -1) {
+        fileOutputStream.write(chunk, 0, chunkLength);
+      }
+
+      MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tmpIndexFile.toString());
+      try (var localArena = Arena.ofConfined()) {
+        MemorySegment returnValue = localArena.allocate(C_INT);
+        MemorySegment deserSeg = (MemorySegment) deserializeHnswIndexMethodHandle.invokeExact(
+          resources.getMemorySegment(),
+          pathSeg,
+          segmentFromIndexParams(hnswIndexParams),
+          returnValue,
+          hnswIndexParams.getVectorDimension()
+        );
+        checkError(returnValue.get(C_INT, 0L), "deserializeHnswIndexMethodHandle");
+        return new IndexReference(deserSeg);
+      }
+    } finally {
+      Files.deleteIfExists(tmpIndexFile);
+    }
+  }
+
+  /**
+   * Allocates the configured search parameters in the MemorySegment.
+   */
+  private MemorySegment segmentFromIndexParams(HnswIndexParams params) {
+    MemorySegment seg = CuVSHnswIndexParams.allocate(resources.getArena());
+    CuVSHnswIndexParams.ef_construction(seg, params.getEfConstruction());
+    CuVSHnswIndexParams.num_threads(seg, params.getNumThreads());
+    return seg;
+  }
+
+  /**
+   * Allocates the configured search parameters in the MemorySegment.
+   */
+  private MemorySegment segmentFromSearchParams(HnswSearchParams params) {
+    MemorySegment seg = CuVSHnswSearchParams.allocate(resources.getArena());
+    CuVSHnswSearchParams.ef(seg, params.ef());
+    CuVSHnswSearchParams.num_threads(seg, params.numThreads());
+    return seg;
+  }
+
+  public static HnswIndex.Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
+      throw new IllegalArgumentException("Unsupported " + cuvsResources);
+    }
+    return new HnswIndexImpl.Builder((CuVSResourcesImpl)cuvsResources);
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link HnswIndex}.
+   */
+  public static class Builder implements HnswIndex.Builder {
+
+    private final CuVSResourcesImpl cuvsResources;
+    private InputStream inputStream;
+    private HnswIndexParams hnswIndexParams;
+
+    /**
+     * Constructs this Builder with an instance of {@link CuVSResources}.
+     *
+     * @param cuvsResources an instance of {@link CuVSResources}
+     */
+    public Builder(CuVSResourcesImpl cuvsResources) {
+      this.cuvsResources = cuvsResources;
+    }
+
+    /**
+     * Sets an instance of InputStream typically used when index deserialization is
+     * needed.
+     *
+     * @param inputStream an instance of {@link InputStream}
+     * @return an instance of this Builder
+     */
+    @Override
+    public Builder from(InputStream inputStream) {
+      this.inputStream = inputStream;
+      return this;
+    }
+
+    /**
+     * Registers an instance of configured {@link HnswIndexParams} with this
+     * Builder.
+     *
+     * @param hnswIndexParameters An instance of HnswIndexParams.
+     * @return An instance of this Builder.
+     */
+    @Override
+    public Builder withIndexParams(HnswIndexParams hnswIndexParameters) {
+      this.hnswIndexParams = hnswIndexParameters;
+      return this;
+    }
+
+    /**
+     * Builds and returns an instance of CagraIndex.
+     *
+     * @return an instance of CagraIndex
+     */
+    @Override
+    public HnswIndexImpl build() throws Throwable {
+      return new HnswIndexImpl(inputStream, cuvsResources, hnswIndexParams);
+    }
+  }
+
+  /**
+   * Holds the memory reference to a HNSW index.
+   */
+  protected static class IndexReference {
+
+    private final MemorySegment memorySegment;
+
+    /**
+     * Constructs CagraIndexReference and allocate the MemorySegment.
+     */
+    protected IndexReference(CuVSResourcesImpl resources) {
+      memorySegment = CuVSHnswIndex.allocate(resources.getArena());
+    }
+
+    /**
+     * Constructs CagraIndexReference with an instance of MemorySegment passed as a
+     * parameter.
+     *
+     * @param indexMemorySegment the MemorySegment instance to use for containing
+     *                           index reference
+     */
+    protected IndexReference(MemorySegment indexMemorySegment) {
+      this.memorySegment = indexMemorySegment;
+    }
+
+    /**
+     * Gets the instance of index MemorySegment.
+     *
+     * @return index MemorySegment
+     */
+    protected MemorySegment getMemorySegment() {
+      return memorySegment;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
similarity index 93%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchResults.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
index 8cb4d89e03..d2920c7177 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
@@ -14,7 +14,9 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs;
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.internal.common.SearchResultsImpl;
 
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
@@ -22,14 +24,12 @@
 import java.util.List;
 import java.util.Map;
 
-import com.nvidia.cuvs.common.SearchResults;
-
 /**
  * SearchResult encapsulates the logic for reading and holding search results.
  *
  * @since 25.02
  */
-public class HnswSearchResults extends SearchResults {
+public class HnswSearchResults extends SearchResultsImpl {
 
   protected HnswSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
       MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
new file mode 100644
index 0000000000..176f4f90da
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal.common;
+
+import java.lang.foreign.AddressLayout;
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SymbolLookup;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+
+/**
+ * Utility methods for calling into the native linker.
+ */
+public class LinkerHelper {
+
+  private static final Linker LINKER = Linker.nativeLinker();
+  private static final SymbolLookup SYMBOL_LOOKUP;
+
+  public static final ValueLayout.OfByte C_CHAR = (ValueLayout.OfByte) LINKER.canonicalLayouts().get("char");
+
+  public static final ValueLayout.OfInt C_INT = (ValueLayout.OfInt) LINKER.canonicalLayouts().get("int");
+
+  public static final ValueLayout.OfLong C_LONG = (ValueLayout.OfLong) LINKER.canonicalLayouts().get("long");
+
+  public static final ValueLayout.OfFloat C_FLOAT = (ValueLayout.OfFloat) LINKER.canonicalLayouts().get("float");
+
+  public static final AddressLayout C_POINTER = ((AddressLayout) LINKER.canonicalLayouts().get("void*"))
+          .withTargetLayout(MemoryLayout.sequenceLayout(Long.MAX_VALUE, C_CHAR));
+
+  static {
+    var nativeLibrary = LoaderUtils.loadNativeLibrary();
+    // we use a global arena here, since the symbols obtained
+    // from the returned lookup are for the lifetime of the jvm
+    SYMBOL_LOOKUP = SymbolLookup.libraryLookup(nativeLibrary.toAbsolutePath(), Arena.global());
+  }
+
+  static MemorySegment functionAddress(String function) {
+    return SYMBOL_LOOKUP.find(function).orElseThrow(() -> new LinkageError("Native function " + function + " could not be found"));
+  }
+
+  public static MethodHandle downcallHandle(String function, FunctionDescriptor functionDescriptor, Linker.Option... options) {
+    return LINKER.downcallHandle(functionAddress(function), functionDescriptor, options);
+  }
+
+  private LinkerHelper() {}
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java
new file mode 100644
index 0000000000..f97696984a
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal.common;
+
+import com.nvidia.cuvs.LibraryException;
+import com.nvidia.cuvs.spi.CuVSProvider;
+
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Objects;
+
+class LoaderUtils {
+
+  private LoaderUtils() {}
+
+  /**
+   * Load the CuVS .so file from environment variable CUVS_JAVA_SO_PATH. If not
+   * found there, try to load it from the classpath to a temporary file.
+   */
+  static Path loadNativeLibrary() throws LibraryException {
+    String libraryPathFromEnvironment = System.getenv("CUVS_JAVA_SO_PATH");
+    if (libraryPathFromEnvironment != null) {
+      Path file = Path.of(libraryPathFromEnvironment).toAbsolutePath();
+      if (Files.notExists(file)) {
+        throw new LibraryException(
+          "Environment variable CUVS_JAVA_SO_PATH points to non-existent file: " + libraryPathFromEnvironment);
+      }
+      if (Files.isDirectory(file)) {
+        throw new LibraryException(
+          "Environment variable CUVS_JAVA_SO_PATH points to a directory: " + libraryPathFromEnvironment);
+      }
+      return file;
+    }
+    return loadLibraryFromJar("/META-INF/native/linux_x64/libcuvs_java.so");
+  }
+
+  static Path loadLibraryFromJar(String path) throws LibraryException {
+    if (!path.startsWith("/")) {
+      throw new IllegalArgumentException("The path has to be absolute (start with '/').");
+    }
+    // Obtain filename from path
+    String filename = path.substring(path.lastIndexOf("/") + 1);
+
+    // Split filename to prefix and suffix (extension)
+    String[] parts = filename.split("\\.", 2);
+    String prefix = parts[0];
+    String suffix = (parts.length > 1) ? "." + parts[parts.length - 1] : null;
+
+    // Prepare temporary file
+    try {
+      Path temp = Files.createTempFile(nativeLibraryPath(), prefix, suffix);
+      temp.toFile().deleteOnExit();
+      InputStream libraryStream = Util.class.getModule().getResourceAsStream(path);
+      if (libraryStream == null) {
+        throw new LibraryException("CuVS Library Not Found in ClassPath");
+      }
+      streamCopy(libraryStream, new FileOutputStream(temp.toFile()));
+      return temp;
+    } catch (IOException ioe) {
+      throw new LibraryException(ioe);
+    }
+  }
+
+  static void streamCopy(InputStream is, OutputStream os) throws IOException {
+    Objects.requireNonNull(is);
+    try (var in = is;
+         var out = os) {
+      in.transferTo(out);
+    }
+  }
+
+  static Path nativeLibraryPath() {
+    Path p = CuVSProvider.provider().nativeLibraryPath().toAbsolutePath();
+    if (Files.notExists(p)) {
+      throw new LibraryException("non-existent path: " + p);
+    }
+    if (!Files.isDirectory(p)) {
+      throw new LibraryException("not a directory: " + p);
+    }
+    return p;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/SearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
similarity index 80%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/common/SearchResults.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
index 83e98cb8e9..dfd82c004f 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/common/SearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
@@ -1,4 +1,6 @@
-package com.nvidia.cuvs.common;
+package com.nvidia.cuvs.internal.common;
+
+import com.nvidia.cuvs.SearchResults;
 
 import java.lang.foreign.MemoryLayout.PathElement;
 import java.lang.foreign.MemorySegment;
@@ -8,12 +10,11 @@
 import java.util.List;
 import java.util.Map;
 
-public abstract class SearchResults {
+public abstract class SearchResultsImpl implements SearchResults {
 
   protected final List<Map<Integer, Float>> results;
   protected final List<Integer> mapping; // TODO: Is this performant in a user application?
-  protected final SequenceLayout neighboursSequenceLayout;
-  protected final SequenceLayout distancesSequenceLayout;
+
   protected final MemorySegment neighboursMemorySegment;
   protected final MemorySegment distancesMemorySegment;
   protected final int topK;
@@ -21,13 +22,11 @@ public abstract class SearchResults {
   protected final VarHandle neighboursVarHandle;
   protected final VarHandle distancesVarHandle;
 
-  protected SearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
+  protected SearchResultsImpl(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
       MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
       long numberOfQueries) {
     this.topK = topK;
     this.numberOfQueries = numberOfQueries;
-    this.neighboursSequenceLayout = neighboursSequenceLayout;
-    this.distancesSequenceLayout = distancesSequenceLayout;
     this.neighboursMemorySegment = neighboursMemorySegment;
     this.distancesMemorySegment = distancesMemorySegment;
     this.mapping = mapping;
@@ -47,6 +46,7 @@ protected SearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout
    *
    * @return a list of results for each query as a map of neighbor IDs to distance
    */
+  @Override
   public List<Map<Integer, Float>> getResults() {
     return results;
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
new file mode 100644
index 0000000000..5bc695ae3b
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -0,0 +1,204 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal.common;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemoryLayout.PathElement;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.VarHandle;
+import java.util.ArrayList;
+import java.util.List;
+
+import com.nvidia.cuvs.GPUInfo;
+import com.nvidia.cuvs.internal.panama.GpuInfo;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
+public class Util {
+
+  public static final int CUVS_SUCCESS = 1;
+
+  private static final MethodHandle getGpuInfoMethodHandle = downcallHandle("get_gpu_info",
+          FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS));
+
+  private static final MethodHandle getLastErrorTextMethodHandle = downcallHandle("cuvsGetLastErrorText",
+          FunctionDescriptor.of(ADDRESS));
+
+  private Util() {}
+
+  /**
+   * Checks the result value of a native method handle call.
+   *
+   * @param value the return value
+   * @param caller the native method handle that was called
+   */
+  public static void checkError(int value, String caller) {
+    if (value != CUVS_SUCCESS) {
+      String errorMsg = getLastErrorText();
+      throw new RuntimeException(caller + " returned " + value + "[" + errorMsg + "]");
+    }
+  }
+
+  static final long MAX_ERROR_TEXT = 1_000_000L;
+
+  static String getLastErrorText() {
+    try {
+      MemorySegment seg = (MemorySegment) getLastErrorTextMethodHandle.invokeExact();
+      if (seg.equals(MemorySegment.NULL)) {
+        return "no last error text";
+      }
+      return seg.reinterpret(MAX_ERROR_TEXT).getString(0L);
+    } catch (Throwable t) {
+      throw new RuntimeException(t);
+    }
+  }
+
+  /**
+   * Get the list of compatible GPUs based on compute capability >= 7.0 and total
+   * memory >= 8GB
+   *
+   * @return a list of compatible GPUs. See {@link GPUInfo}
+   */
+  public static List<GPUInfo> compatibleGPUs() throws Throwable {
+    return compatibleGPUs(7.0, 8192);
+  }
+
+  /**
+   * Get the list of compatible GPUs based on given compute capability and total
+   * memory
+   *
+   * @param minComputeCapability the minimum compute capability
+   * @param minDeviceMemoryMB    the minimum total available memory in MB
+   * @return a list of compatible GPUs. See {@link GPUInfo}
+   */
+  public static List<GPUInfo> compatibleGPUs(double minComputeCapability, int minDeviceMemoryMB) throws Throwable {
+    List<GPUInfo> compatibleGPUs = new ArrayList<GPUInfo>();
+    double minDeviceMemoryB = Math.pow(2, 20) * minDeviceMemoryMB;
+    for (GPUInfo gpuInfo : availableGPUs()) {
+      if (gpuInfo.computeCapability() >= minComputeCapability && gpuInfo.totalMemory() >= minDeviceMemoryB) {
+        compatibleGPUs.add(gpuInfo);
+      }
+    }
+    return compatibleGPUs;
+  }
+
+  /**
+   * Gets all the available GPUs
+   *
+   * @return a list of {@link GPUInfo} objects with GPU details
+   */
+  public static List<GPUInfo> availableGPUs() throws Throwable {
+    List<GPUInfo> results = new ArrayList<>();
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment returnValueMemorySegment = localArena.allocate(C_INT);
+      MemorySegment numGpuMemorySegment = localArena.allocate(C_INT);
+
+      /*
+       * Setting a value of 1024 because we cannot predict how much memory to allocate
+       * before the function is invoked as cudaGetDeviceCount is inside the
+       * get_gpu_info function.
+       */
+      MemorySegment GpuInfoArrayMemorySegment = GpuInfo.allocateArray(1024, localArena);
+      getGpuInfoMethodHandle.invokeExact(returnValueMemorySegment, numGpuMemorySegment, GpuInfoArrayMemorySegment);
+      int numGPUs = numGpuMemorySegment.get(ValueLayout.JAVA_INT, 0);
+      MemoryLayout ml = MemoryLayout.sequenceLayout(numGPUs, GpuInfo.layout());
+      for (int i = 0; i < numGPUs; i++) {
+        VarHandle gpuIdVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("gpu_id"));
+        VarHandle freeMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
+                PathElement.groupElement("free_memory"));
+        VarHandle totalMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
+                PathElement.groupElement("total_memory"));
+        VarHandle ComputeCapabilityVarHandle = ml.varHandle(PathElement.sequenceElement(i),
+                PathElement.groupElement("compute_capability"));
+        StringBuilder gpuName = new StringBuilder();
+        char b = 1;
+        int p = 0;
+        while (b != 0x00) {
+          VarHandle gpuNameVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("name"),
+                  PathElement.sequenceElement(p++));
+          b = (char) (byte) gpuNameVarHandle.get(GpuInfoArrayMemorySegment, 0L);
+          gpuName.append(b);
+        }
+        results.add(new GPUInfo((int) gpuIdVarHandle.get(GpuInfoArrayMemorySegment, 0L), gpuName.toString().trim(),
+                (long) freeMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
+                (long) totalMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
+                (float) ComputeCapabilityVarHandle.get(GpuInfoArrayMemorySegment, 0L)));
+      }
+      return results;
+    }
+  }
+
+  /**
+   * A utility method for getting an instance of {@link MemorySegment} for a
+   * {@link String}.
+   *
+   * @param str the string for the expected {@link MemorySegment}
+   * @return an instance of {@link MemorySegment}
+   */
+  public static MemorySegment buildMemorySegment(Arena arena, String str) {
+    StringBuilder sb = new StringBuilder(str).append('\0');
+    MemoryLayout stringMemoryLayout = MemoryLayout.sequenceLayout(sb.length(), C_CHAR);
+    MemorySegment stringMemorySegment = arena.allocate(stringMemoryLayout);
+
+    for (int i = 0; i < sb.length(); i++) {
+      VarHandle varHandle = stringMemoryLayout.varHandle(PathElement.sequenceElement(i));
+      varHandle.set(stringMemorySegment, 0L, (byte) sb.charAt(i));
+    }
+    return stringMemorySegment;
+  }
+
+  /**
+   * A utility method for building a {@link MemorySegment} for a 1D long array.
+   *
+   * @param data The 1D long array for which the {@link MemorySegment} is needed
+   * @return an instance of {@link MemorySegment}
+   */
+  public static MemorySegment buildMemorySegment(Arena arena, long[] data) {
+    int cells = data.length;
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(cells, C_LONG);
+    MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+    MemorySegment.copy(data, 0, dataMemorySegment, C_LONG, 0, cells);
+    return dataMemorySegment;
+  }
+
+  /**
+   * A utility method for building a {@link MemorySegment} for a 2D float array.
+   *
+   * @param data The 2D float array for which the {@link MemorySegment} is needed
+   * @return an instance of {@link MemorySegment}
+   */
+  public static MemorySegment buildMemorySegment(Arena arena, float[][] data) {
+    long rows = data.length;
+    long cols = rows > 0 ? data[0].length : 0;
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(rows * cols, C_FLOAT);
+    MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+    for (int r = 0; r < rows; r++) {
+      MemorySegment.copy(data[r], 0, dataMemorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()),
+          (int) cols);
+    }
+    return dataMemorySegment;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/BruteForceH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/BruteForceH.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/BruteForceH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/BruteForceH.java
index 16603606bd..0e34eb2cff 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/BruteForceH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/BruteForceH.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.ValueLayout.JAVA_BYTE;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CagraH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CagraH.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CagraH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CagraH.java
index 88ddeb6feb..82ecbdc34a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CagraH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CagraH.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.ValueLayout.JAVA_BYTE;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSBruteForceIndex.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSBruteForceIndex.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSBruteForceIndex.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSBruteForceIndex.java
index 8bf29027ec..0a1ac29e22 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSBruteForceIndex.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSBruteForceIndex.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraCompressionParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraCompressionParams.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraCompressionParams.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraCompressionParams.java
index 1fe8eca769..923287fe40 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraCompressionParams.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraCompressionParams.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndex.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndex.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndex.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndex.java
index ec15f6729b..7ea435ba0f 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndex.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndex.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndexParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndexParams.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndexParams.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndexParams.java
index 1823eacfee..ffe88effaf 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraIndexParams.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndexParams.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraSearchParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraSearchParams.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraSearchParams.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraSearchParams.java
index ec59284d9a..2a1efbfd7f 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSCagraSearchParams.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraSearchParams.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSFilter.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSFilter.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSFilter.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSFilter.java
index 9385660dc5..dceaca994b 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSFilter.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSFilter.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswExtendParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswExtendParams.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswExtendParams.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswExtendParams.java
index 8d750d02e7..83358e2338 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswExtendParams.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswExtendParams.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndex.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndex.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndex.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndex.java
index 1f879462be..aa12e4400b 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndex.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndex.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndexParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndexParams.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndexParams.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndexParams.java
index 3e6e45a099..d6b06872f6 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswIndexParams.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndexParams.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswSearchParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswSearchParams.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswSearchParams.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswSearchParams.java
index 4a5941a3cf..ff8329a57c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/CuVSHnswSearchParams.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswSearchParams.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDataType.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDataType.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDataType.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDataType.java
index c7c588676f..aaa2f4d2dd 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDataType.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDataType.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDevice.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDevice.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDevice.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDevice.java
index a0d5d89ea4..ceadb6a698 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLDevice.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDevice.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensor.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensor.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensor.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensor.java
index 71bba9fe5f..c28dd71c37 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensor.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensor.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensorVersioned.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensorVersioned.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensorVersioned.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensorVersioned.java
index efdcf90439..f631140e23 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLManagedTensorVersioned.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensorVersioned.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLPackVersion.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLPackVersion.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLPackVersion.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLPackVersion.java
index bc80507669..c65fdd8871 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLPackVersion.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLPackVersion.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLTensor.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLTensor.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLTensor.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLTensor.java
index 63082b35a9..21d928905e 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DLTensor.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLTensor.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DistanceH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DistanceH.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DistanceH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DistanceH.java
index 3949e10899..b98f95ae23 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DistanceH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DistanceH.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import java.lang.invoke.*;
 import java.lang.foreign.*;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DlpackH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DlpackH.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DlpackH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DlpackH.java
index d459dd3bf6..6ef78336f7 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/DlpackH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DlpackH.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.ValueLayout.JAVA_BYTE;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/Fsidt.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/Fsidt.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/Fsidt.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/Fsidt.java
index 07f2849a51..fb5f82ed18 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/Fsidt.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/Fsidt.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 import static java.lang.foreign.MemoryLayout.PathElement.sequenceElement;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfo.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfo.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfo.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfo.java
index c1d13575ca..eb8aba2235 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfo.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfo.java
@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 import static java.lang.foreign.MemoryLayout.PathElement.sequenceElement;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfoH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfoH.java
similarity index 98%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfoH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfoH.java
index ad37e37ecf..0457d50be0 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/GpuInfoH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfoH.java
@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.ValueLayout.JAVA_BYTE;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/HnswH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/HnswH.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/HnswH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/HnswH.java
index 32945c432d..b6945030b4 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/HnswH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/HnswH.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.ValueLayout.JAVA_BYTE;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfFlatH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfFlatH.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfFlatH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfFlatH.java
index 47353cc9e2..32cdeb8132 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfFlatH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfFlatH.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.ValueLayout.JAVA_BYTE;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfPqH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfPqH.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfPqH.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfPqH.java
index d5f1e3cee4..66d0ba5acb 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/IvfPqH.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfPqH.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.ValueLayout.JAVA_BYTE;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/MaxAlignT.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/MaxAlignT.java
similarity index 99%
rename from java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/MaxAlignT.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/MaxAlignT.java
index 1216df6f7a..d07c524dbe 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/panama/MaxAlignT.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/MaxAlignT.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package com.nvidia.cuvs.panama;
+package com.nvidia.cuvs.internal.panama;
 
 import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
new file mode 100644
index 0000000000..32f0225de0
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.spi;
+
+import com.nvidia.cuvs.BruteForceIndex;
+import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.internal.BruteForceIndexImpl;
+import com.nvidia.cuvs.internal.CagraIndexImpl;
+import com.nvidia.cuvs.internal.CuVSResourcesImpl;
+import com.nvidia.cuvs.internal.HnswIndexImpl;
+
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Objects;
+
+final class JDKProvider implements CuVSProvider {
+
+  @Override
+  public CuVSResources newCuVSResources(Path tempDirectory) throws Throwable {
+    Objects.requireNonNull(tempDirectory);
+    if (Files.notExists(tempDirectory)) {
+      throw new IllegalArgumentException("does not exist:" + tempDirectory);
+    }
+    if (!Files.isDirectory(tempDirectory)) {
+      throw new IllegalArgumentException("not a directory:" + tempDirectory);
+    }
+    return new CuVSResourcesImpl(tempDirectory);
+  }
+
+  @Override
+  public BruteForceIndex.Builder newBruteForceIndexBuilder(CuVSResources cuVSResources) {
+    return BruteForceIndexImpl.newBuilder(Objects.requireNonNull(cuVSResources));
+  }
+
+  @Override
+  public CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources) {
+    return CagraIndexImpl.newBuilder(Objects.requireNonNull(cuVSResources));
+  }
+
+  @Override
+  public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
+    return HnswIndexImpl.newBuilder(Objects.requireNonNull(cuVSResources));
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
similarity index 88%
rename from java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchTest.java
rename to java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index 91e6825bc4..3a1ea25dd4 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchTest.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -16,8 +16,6 @@
 
 package com.nvidia.cuvs;
 
-import static org.junit.Assert.assertEquals;
-
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
@@ -28,16 +26,23 @@
 import java.util.Map;
 import java.util.UUID;
 
+import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import com.nvidia.cuvs.common.SearchResults;
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static org.junit.Assert.assertEquals;
 
-public class BruteForceAndSearchTest {
+public class BruteForceAndSearchIT extends CuVSTestCase{
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+  }
+
   /**
    * A basic test that checks the whole flow - from indexing to search.
    *
@@ -71,7 +76,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
 
     for (int j = 0; j < 10; j++) {
 
-      try (CuVSResources resources = new CuVSResources()) {
+      try (CuVSResources resources = CuVSResources.create()) {
 
         // Create a query object with the query vectors
         BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
@@ -86,7 +91,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             .build();
 
         // Create the index with the dataset
-        BruteForceIndex index = new BruteForceIndex.Builder(resources)
+        BruteForceIndex index = BruteForceIndex.newBuilder(resources)
             .withDataset(dataset)
             .withIndexParams(indexParams)
             .build();
@@ -98,7 +103,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         // Loading a BRUTEFORCE index from disk.
         File indexFile = new File(indexFileName);
         InputStream inputStream = new FileInputStream(indexFile);
-        BruteForceIndex loadedIndex = new BruteForceIndex.Builder(resources)
+        BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources)
             .from(inputStream)
             .build();
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
similarity index 75%
rename from java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedTest.java
rename to java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 64d1eb86ec..030b3b0224 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedTest.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -1,3 +1,19 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package com.nvidia.cuvs;
 
 import java.lang.invoke.MethodHandles;
@@ -11,13 +27,16 @@
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+
 @RunWith(RandomizedRunner.class)
-public class BruteForceRandomizedTest extends CuVSTestCase {
+public class BruteForceRandomizedIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
   @Before
   public void setup() {
+    assumeTrue(isLinuxAmd64());
     initializeRandom();
     log.info("Random context initialized for test.");
   }
@@ -74,7 +93,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
 
     // Create CuVS index and query
-    try (CuVSResources resources = new CuVSResources()) {
+    try (CuVSResources resources = CuVSResources.create()) {
 
       BruteForceQuery query = new BruteForceQuery.Builder()
           .withTopK(topK)
@@ -85,13 +104,13 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
           .withNumWriterThreads(32)
           .build();
 
-      BruteForceIndex index = new BruteForceIndex.Builder(resources)
+      BruteForceIndex index = BruteForceIndex.newBuilder(resources)
           .withDataset(dataset)
           .withIndexParams(indexParams)
           .build();
 
       log.info("Index built successfully. Executing search...");
-      BruteForceSearchResults results = index.search(query);
+      SearchResults results = index.search(query);
 
       compareResults(results, expected, topK, datasetSize, numQueries);
     }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
similarity index 89%
rename from java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchTest.java
rename to java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 934e60b1c7..874c3e525c 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchTest.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -16,6 +16,7 @@
 
 package com.nvidia.cuvs;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 import static org.junit.Assert.assertEquals;
 
 import java.io.File;
@@ -28,18 +29,23 @@
 import java.util.Map;
 import java.util.UUID;
 
+import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
-import com.nvidia.cuvs.common.SearchResults;
 
-public class CagraBuildAndSearchTest {
+public class CagraBuildAndSearchIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+  }
+
   /**
    * A basic test that checks the whole flow - from indexing to search.
    *
@@ -72,10 +78,10 @@ public void testIndexingAndSearchingFlow() throws Throwable {
 
     for (int j = 0; j < 10; j++) {
 
-      try (CuVSResources resources = new CuVSResources()) {
+      try (CuVSResources resources = CuVSResources.create()) {
 
         // Configure index parameters
-        CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+        CagraIndexParams indexParams = new CagraIndexParams.Builder()
             .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
             .withGraphDegree(1)
             .withIntermediateGraphDegree(2)
@@ -84,7 +90,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             .build();
 
         // Create the index with the dataset
-        CagraIndex index = new CagraIndex.Builder(resources)
+        CagraIndex index = CagraIndex.newBuilder(resources)
             .withDataset(dataset)
             .withIndexParams(indexParams)
             .build();
@@ -96,7 +102,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         // Loading a CAGRA index from disk.
         File indexFile = new File(indexFileName);
         InputStream inputStream = new FileInputStream(indexFile);
-        CagraIndex loadedIndex = new CagraIndex.Builder(resources)
+        CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
             .from(inputStream)
             .build();
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
similarity index 64%
rename from java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedTest.java
rename to java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index fc7570133a..811866dc61 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedTest.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -1,3 +1,19 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package com.nvidia.cuvs;
 
 import java.lang.invoke.MethodHandles;
@@ -12,13 +28,16 @@
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+
 @RunWith(RandomizedRunner.class)
-public class CagraRandomizedTest extends CuVSTestCase {
+public class CagraRandomizedIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
   @Before
   public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
     initializeRandom();
     log.info("Random context initialized for test.");
   }
@@ -75,27 +94,31 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
 
     // Create CuVS index and query
-    try (CuVSResources resources = new CuVSResources()) {
-      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
           .build();
-      CagraIndex index = new CagraIndex.Builder(resources)
+      CagraIndex index = CagraIndex.newBuilder(resources)
           .withDataset(dataset)
           .withIndexParams(indexParams)
           .build();
       log.info("Index built successfully.");
 
-      // Execute search and retrieve results
-      CagraQuery query = new CagraQuery.Builder()
-          .withQueryVectors(queries)
-          .withTopK(topK)
-          .withSearchParams(new CagraSearchParams.Builder(resources)
-              .build())
-          .build();
-      log.info("Query built successfully. Executing search...");
-      CagraSearchResults results = index.search(query);
-
-      compareResults(results, expected, topK, datasetSize, numQueries);
+      try {
+        // Execute search and retrieve results
+        CagraQuery query = new CagraQuery.Builder()
+                .withQueryVectors(queries)
+                .withTopK(topK)
+                .withSearchParams(new CagraSearchParams.Builder(resources)
+                        .build())
+                .build();
+        log.info("Query built successfully. Executing search...");
+        SearchResults results = index.search(query);
+
+        compareResults(results, expected, topK, datasetSize, numQueries);
+      } finally {
+        index.destroyIndex();
+      }
     }
   }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
index bd50dc759d..8a7d84a199 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
@@ -1,3 +1,19 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package com.nvidia.cuvs;
 
 import static org.junit.Assert.assertEquals;
@@ -14,7 +30,6 @@
 import org.slf4j.LoggerFactory;
 
 import com.carrotsearch.randomizedtesting.RandomizedContext;
-import com.nvidia.cuvs.common.SearchResults;
 
 public abstract class CuVSTestCase {
   protected Random random;
@@ -85,4 +100,9 @@ protected void compareResults(SearchResults results, List<List<Integer>> expecte
       }
     }
   }
+
+  protected static boolean isLinuxAmd64() {
+    String name = System.getProperty("os.name");
+    return (name.startsWith("Linux")) && System.getProperty("os.arch").equals("amd64");
+  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
similarity index 87%
rename from java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchTest.java
rename to java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index 712e7edf98..eb4933cf91 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchTest.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -16,6 +16,7 @@
 
 package com.nvidia.cuvs;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 import static org.junit.Assert.assertEquals;
 
 import java.io.File;
@@ -28,6 +29,7 @@
 import java.util.Map;
 import java.util.UUID;
 
+import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -35,10 +37,15 @@
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
 
-public class HnswBuildAndSearchTest {
+public class HnswBuildAndSearchIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+  }
+
   /**
    * A basic test that checks the whole flow - from indexing to search.
    *
@@ -72,10 +79,10 @@ public void testIndexingAndSearchingFlow() throws Throwable {
 
     for (int j = 0; j < 10; j++) {
 
-      try (CuVSResources resources = new CuVSResources()) {
+      try (CuVSResources resources = CuVSResources.create()) {
 
         // Configure index parameters
-        CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+        CagraIndexParams indexParams = new CagraIndexParams.Builder()
             .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
             .withGraphDegree(64)
             .withIntermediateGraphDegree(128)
@@ -84,7 +91,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             .build();
 
         // Create the index with the dataset
-        CagraIndex index = new CagraIndex.Builder(resources)
+        CagraIndex index = CagraIndex.newBuilder(resources)
             .withDataset(dataset)
             .withIndexParams(indexParams)
             .build();
@@ -93,18 +100,18 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
         index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
 
-        HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder(resources)
+        HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder()
             .withVectorDimension(2)
             .build();
         InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
         File hnswIndexFile = new File(hnswIndexFileName);
 
-        HnswIndex hnswIndex = new HnswIndex.Builder(resources)
+        HnswIndex hnswIndex = HnswIndex.newBuilder(resources)
             .from(inputStreamHNSW)
             .withIndexParams(hnswIndexParams)
             .build();
 
-        HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder(resources)
+        HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder()
             .build();
 
         HnswQuery hnswQuery = new HnswQuery.Builder()
@@ -114,7 +121,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             .withTopK(3)
             .build();
 
-        HnswSearchResults results = hnswIndex.search(hnswQuery);
+        SearchResults results = hnswIndex.search(hnswQuery);
 
         // Check results
         log.info(results.getResults().toString());
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedTest.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
similarity index 79%
rename from java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedTest.java
rename to java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index c292309f7c..6d367efc43 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedTest.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -1,3 +1,19 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 package com.nvidia.cuvs;
 
 import java.io.File;
@@ -18,13 +34,16 @@
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+
 @RunWith(RandomizedRunner.class)
-public class HnswRandomizedTest extends CuVSTestCase {
+public class HnswRandomizedIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
   @Before
   public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
     initializeRandom();
     log.info("Random context initialized for test.");
   }
@@ -81,10 +100,10 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
 
     // Create CuVS index and query
-    try (CuVSResources resources = new CuVSResources()) {
+    try (CuVSResources resources = CuVSResources.create()) {
 
       // Configure index parameters
-      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+      CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
           .withGraphDegree(64)
           .withIntermediateGraphDegree(128)
@@ -93,27 +112,27 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
           .build();
 
       // Create the index with the dataset
-      CagraIndex index = new CagraIndex.Builder(resources)
+      CagraIndex index = CagraIndex.newBuilder(resources)
           .withDataset(dataset)
           .withIndexParams(indexParams)
           .build();
 
       // Saving the HNSW index on to the disk.
       String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
-      index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
+      index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));   // fails here
 
-      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder(resources)
+      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder()
           .withVectorDimension(dimensions)
           .build();
       InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
       File hnswIndexFile = new File(hnswIndexFileName);
 
-      HnswIndex hnswIndex = new HnswIndex.Builder(resources)
+      HnswIndex hnswIndex = HnswIndex.newBuilder(resources)
           .from(inputStreamHNSW)
           .withIndexParams(hnswIndexParams)
           .build();
 
-      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder(resources)
+      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder()
           .withNumThreads(32)
           .build();
 
@@ -124,7 +143,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
           .build();
 
       log.info("Index built successfully. Executing search...");
-      HnswSearchResults results = hnswIndex.search(hnswQuery);
+      SearchResults results = hnswIndex.search(hnswQuery);
 
       if (hnswIndexFile.exists()) {
         hnswIndexFile.delete();
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java
new file mode 100644
index 0000000000..a86d1154c9
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java
@@ -0,0 +1,37 @@
+package com.nvidia.cuvs.internal.common;
+
+import com.nvidia.cuvs.CuVSTestCase;
+import org.junit.Before;
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.lang.invoke.MethodHandles;
+import java.lang.invoke.MethodType;
+
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static org.hamcrest.CoreMatchers.equalTo;
+import static org.hamcrest.MatcherAssert.assertThat;
+
+public class UtilIT extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(UtilIT.class);
+
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+  }
+
+  @Test
+  public void testGetLastErrorText() throws Throwable {
+    var cls = Class.forName("com.nvidia.cuvs.internal.common.Util");
+    var lookup = MethodHandles.lookup();
+    var mt = MethodType.methodType(String.class);
+    var mh = lookup.findStatic(cls, "getLastErrorText", mt);
+
+    // first, ensures that accessing the error text when there is none does not crash!
+    String errorText = (String) mh.invoke();
+    // second, ensures that the default test is returned
+    assertThat(errorText, equalTo("no last error text"));
+  }
+}
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
index 58ebef84e0..90b51743c1 100644
--- a/java/examples/pom.xml
+++ b/java/examples/pom.xml
@@ -77,6 +77,7 @@
         </plugin>
         <plugin>
             <artifactId>maven-assembly-plugin</artifactId>
+            <version>3.4.2</version>
             <executions>
                <execution>
                   <id>make-jar-with-dependencies</id>
@@ -90,9 +91,10 @@
             </executions>
             <configuration>
                <archive>
-                  <manifest>
-                     <mainClass>com.nvidia.cuvs.examples.CagraExample</mainClass>
-                  </manifest>
+                   <manifestEntries>
+                       <Multi-Release>true</Multi-Release>
+                       <Main-Class>com.nvidia.cuvs.examples.CagraExample</Main-Class>
+                   </manifestEntries>
                </archive>
                <descriptorRefs>
                   <descriptorRef>jar-with-dependencies</descriptorRef>
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
index 5f72d92fc7..c859129418 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
@@ -7,6 +7,7 @@
 import java.lang.invoke.MethodHandles;
 import java.util.UUID;
 
+import com.nvidia.cuvs.SearchResults;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -14,7 +15,6 @@
 import com.nvidia.cuvs.BruteForceIndexParams;
 import com.nvidia.cuvs.BruteForceQuery;
 import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.common.SearchResults;
 
 public class BruteForceExample {
 
@@ -37,7 +37,7 @@ public static void main(String[] args) throws Throwable {
         { 0.05198065f, 0.5789965f }
        };
 
-    try (CuVSResources resources = new CuVSResources()) {
+    try (CuVSResources resources = CuVSResources.create()) {
 
       // Create a query object with the query vectors
       BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
@@ -50,7 +50,7 @@ public static void main(String[] args) throws Throwable {
           .build();
 
       // Create the index with the dataset
-      BruteForceIndex index = new BruteForceIndex.Builder(resources)
+      BruteForceIndex index = BruteForceIndex.newBuilder(resources)
           .withDataset(dataset)
           .withIndexParams(indexParams)
           .build();
@@ -62,7 +62,7 @@ public static void main(String[] args) throws Throwable {
       // Loading a BRUTEFORCE index from disk.
       File indexFile = new File(indexFileName);
       InputStream inputStream = new FileInputStream(indexFile);
-      BruteForceIndex loadedIndex = new BruteForceIndex.Builder(resources)
+      BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources)
           .from(inputStream)
           .build();
 
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
index f561ce69d4..9dabcc6bca 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
@@ -7,6 +7,7 @@
 import java.lang.invoke.MethodHandles;
 import java.util.UUID;
 
+import com.nvidia.cuvs.SearchResults;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -17,7 +18,6 @@
 import com.nvidia.cuvs.CagraQuery;
 import com.nvidia.cuvs.CagraSearchParams;
 import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.common.SearchResults;
 
 public class CagraExample {
 
@@ -40,10 +40,10 @@ public static void main(String[] args) throws Throwable {
         { 0.05198065f, 0.5789965f }
        };
 
-    try (CuVSResources resources = new CuVSResources()) {
+    try (CuVSResources resources = CuVSResources.create()) {
 
       // Configure index parameters
-      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+      CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
           .withGraphDegree(1)
           .withIntermediateGraphDegree(2)
@@ -51,7 +51,7 @@ public static void main(String[] args) throws Throwable {
           .build();
 
       // Create the index with the dataset
-      CagraIndex index = new CagraIndex.Builder(resources)
+      CagraIndex index = CagraIndex.newBuilder(resources)
           .withDataset(dataset)
           .withIndexParams(indexParams)
           .build();
@@ -63,7 +63,7 @@ public static void main(String[] args) throws Throwable {
       // Loading a CAGRA index from disk.
       File indexFile = new File(indexFileName);
       InputStream inputStream = new FileInputStream(indexFile);
-      CagraIndex loadedIndex = new CagraIndex.Builder(resources)
+      CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
           .from(inputStream)
           .build();
 
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
index 0f13be3bc3..7fbbccf643 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
@@ -7,6 +7,7 @@
 import java.lang.invoke.MethodHandles;
 import java.util.UUID;
 
+import com.nvidia.cuvs.SearchResults;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -19,7 +20,6 @@
 import com.nvidia.cuvs.HnswIndexParams;
 import com.nvidia.cuvs.HnswQuery;
 import com.nvidia.cuvs.HnswSearchParams;
-import com.nvidia.cuvs.HnswSearchResults;
 
 public class HnswExample {
 
@@ -42,10 +42,10 @@ public static void main(String[] args) throws Throwable {
         { 0.05198065f, 0.5789965f }
        };
 
-    try (CuVSResources resources = new CuVSResources()) {
+    try (CuVSResources resources = CuVSResources.create()) {
 
       // Configure index parameters
-      CagraIndexParams indexParams = new CagraIndexParams.Builder(resources)
+      CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
           .withGraphDegree(64)
           .withIntermediateGraphDegree(128)
@@ -54,7 +54,7 @@ public static void main(String[] args) throws Throwable {
           .build();
 
       // Create the index with the dataset
-      CagraIndex index = new CagraIndex.Builder(resources)
+      CagraIndex index = CagraIndex.newBuilder(resources)
           .withDataset(dataset)
           .withIndexParams(indexParams)
           .build();
@@ -63,18 +63,18 @@ public static void main(String[] args) throws Throwable {
       String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
       index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
 
-      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder(resources)
+      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder()
           .withVectorDimension(2)
           .build();
       InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
       File hnswIndexFile = new File(hnswIndexFileName);
 
-      HnswIndex hnswIndex = new HnswIndex.Builder(resources)
+      HnswIndex hnswIndex = HnswIndex.newBuilder(resources)
           .from(inputStreamHNSW)
           .withIndexParams(hnswIndexParams)
           .build();
 
-      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder(resources)
+      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder()
           .build();
 
       HnswQuery hnswQuery = new HnswQuery.Builder()
@@ -83,7 +83,7 @@ public static void main(String[] args) throws Throwable {
           .withTopK(3)
           .build();
 
-      HnswSearchResults results = hnswIndex.search(hnswQuery);
+      SearchResults results = hnswIndex.search(hnswQuery);
 
       // Check results
       log.info(results.getResults().toString());

From bf86cde5000946e285c58f098dee2f2579f9c5fa Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 5 Feb 2025 18:54:51 -0800
Subject: [PATCH 305/623] Expose binary quantizer to C and Python (#660)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/660
---
 cpp/CMakeLists.txt                            |  1 +
 cpp/include/cuvs/neighbors/cagra.h            |  4 +-
 .../cuvs/preprocessing/quantize/binary.h      | 44 +++++++++
 cpp/src/neighbors/cagra_c.cpp                 |  8 +-
 cpp/src/preprocessing/quantize/binary_c.cpp   | 76 +++++++++++++++
 python/cuvs/cuvs/distance/distance.pyx        |  1 +
 python/cuvs/cuvs/distance_type.pxd            |  1 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |  1 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    | 11 ++-
 python/cuvs/cuvs/preprocessing/CMakeLists.txt |  1 +
 .../cuvs/preprocessing/quantize/__init__.py   | 18 ++++
 .../quantize/binary/CMakeLists.txt            | 28 ++++++
 .../quantize/binary/__init__.pxd              |  0
 .../preprocessing/quantize/binary/__init__.py | 17 ++++
 .../preprocessing/quantize/binary/binary.pxd  | 25 +++++
 .../preprocessing/quantize/binary/binary.pyx  | 94 +++++++++++++++++++
 .../preprocessing/quantize/scalar/__init__.py |  8 ++
 .../cuvs/cuvs/tests/test_binary_quantizer.py  | 52 ++++++++++
 python/cuvs/cuvs/tests/test_doctests.py       |  4 +-
 19 files changed, 389 insertions(+), 5 deletions(-)
 create mode 100644 cpp/include/cuvs/preprocessing/quantize/binary.h
 create mode 100644 cpp/src/preprocessing/quantize/binary_c.cpp
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/binary/__init__.pxd
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/binary/binary.pxd
 create mode 100644 python/cuvs/cuvs/preprocessing/quantize/binary/binary.pyx
 create mode 100644 python/cuvs/cuvs/tests/test_binary_quantizer.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index fb33c1ab7f..b324c6786f 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -693,6 +693,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
       src/neighbors/nn_descent_c.cpp
       src/neighbors/refine/refine_c.cpp
+      src/preprocessing/quantize/binary_c.cpp
       src/preprocessing/quantize/scalar_c.cpp
       src/distance/pairwise_distance_c.cpp
     )
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index d6d757e0ee..f3dc0d5c2f 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -42,7 +42,9 @@ enum cuvsCagraGraphBuildAlgo {
   /* Use IVF-PQ to build all-neighbors knn graph */
   IVF_PQ,
   /* Experimental, use NN-Descent to build all-neighbors knn graph */
-  NN_DESCENT
+  NN_DESCENT,
+  /* Experimental, use iterative cagra search and optimize to build the knn graph */
+  ITERATIVE_CAGRA_SEARCH
 };
 
 /** Parameters for VPQ compression. */
diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.h b/cpp/include/cuvs/preprocessing/quantize/binary.h
new file mode 100644
index 0000000000..28ef6f0a8e
--- /dev/null
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.h
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Applies binary quantization transform to the given dataset
+ *
+ * This applies binary quantization to a dataset, changing any positive
+ * values to a bitwise 1. This is useful for searching with the
+ * BitwiseHamming distance type.
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major host or device matrix to transform
+ * @param[out] out a row-major host or device matrix to store transformed data
+ */
+cuvsError_t cuvsBinaryQuantizerTransform(cuvsResources_t res,
+                                         DLManagedTensor* dataset,
+                                         DLManagedTensor* out);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index f2c09f904f..9b86072ef1 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -55,13 +55,19 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
         cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(dataset_extent);
       break;
     }
-    case cuvsCagraGraphBuildAlgo::NN_DESCENT:
+    case cuvsCagraGraphBuildAlgo::NN_DESCENT: {
       cuvs::neighbors::cagra::graph_build_params::nn_descent_params nn_descent_params{};
       nn_descent_params =
         cuvs::neighbors::nn_descent::index_params(index_params.intermediate_graph_degree);
       nn_descent_params.max_iterations = params.nn_descent_niter;
       index_params.graph_build_params  = nn_descent_params;
       break;
+    }
+    case cuvsCagraGraphBuildAlgo::ITERATIVE_CAGRA_SEARCH: {
+      cuvs::neighbors::cagra::graph_build_params::iterative_search_params p;
+      index_params.graph_build_params = p;
+      break;
+    }
   };
 
   if (auto* cparams = params.compression; cparams != nullptr) {
diff --git a/cpp/src/preprocessing/quantize/binary_c.cpp b/cpp/src/preprocessing/quantize/binary_c.cpp
new file mode 100644
index 0000000000..1045f06b31
--- /dev/null
+++ b/cpp/src/preprocessing/quantize/binary_c.cpp
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/preprocessing/quantize/binary.h>
+#include <cuvs/preprocessing/quantize/binary.hpp>
+
+namespace {
+
+template <typename T, typename OutputT = uint8_t>
+void _transform(cuvsResources_t res, DLManagedTensor* dataset_tensor, DLManagedTensor* out_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto dataset = dataset_tensor->dl_tensor;
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type     = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    using out_mdspan_type = raft::device_matrix_view<OutputT, int64_t, raft::row_major>;
+
+    cuvs::preprocessing::quantize::binary::transform(
+      *res_ptr,
+      cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
+      cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
+
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type     = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    using out_mdspan_type = raft::host_matrix_view<OutputT, int64_t, raft::row_major>;
+
+    cuvs::preprocessing::quantize::binary::transform(
+      *res_ptr,
+      cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
+      cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
+  } else {
+    RAFT_FAIL("dataset must be accessible on host or device memory");
+  }
+}
+
+}  // namespace
+
+extern "C" cuvsError_t cuvsBinaryQuantizerTransform(cuvsResources_t res,
+                                                    DLManagedTensor* dataset_tensor,
+                                                    DLManagedTensor* out_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = dataset_tensor->dl_tensor;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      _transform<float>(res, dataset_tensor, out_tensor);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      _transform<half>(res, dataset_tensor, out_tensor);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
+      _transform<double>(res, dataset_tensor, out_tensor);
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
diff --git a/python/cuvs/cuvs/distance/distance.pyx b/python/cuvs/cuvs/distance/distance.pyx
index 6b80d43b2e..d6222b59aa 100644
--- a/python/cuvs/cuvs/distance/distance.pyx
+++ b/python/cuvs/cuvs/distance/distance.pyx
@@ -46,6 +46,7 @@ DISTANCE_TYPES = {
     "minkowski": cuvsDistanceType.LpUnexpanded,
     "russellrao": cuvsDistanceType.RusselRaoExpanded,
     "dice": cuvsDistanceType.DiceExpanded,
+    "bitwise_hamming": cuvsDistanceType.BitwiseHamming
 }
 
 DISTANCE_NAMES = {v: k for k, v in DISTANCE_TYPES.items()}
diff --git a/python/cuvs/cuvs/distance_type.pxd b/python/cuvs/cuvs/distance_type.pxd
index e3cc545687..078745012d 100644
--- a/python/cuvs/cuvs/distance_type.pxd
+++ b/python/cuvs/cuvs/distance_type.pxd
@@ -38,4 +38,5 @@ cdef extern from "cuvs/distance/distance.h" nogil:
         KLDivergence
         RusselRaoExpanded
         DiceExpanded
+        BitwiseHamming
         Precomputed
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index fba7e3d1ee..41d74dbc7c 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -37,6 +37,7 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
     ctypedef enum cuvsCagraGraphBuildAlgo:
         IVF_PQ
         NN_DESCENT
+        ITERATIVE_CAGRA_SEARCH
 
     ctypedef struct cuvsCagraCompressionParams:
         uint32_t pq_bits
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 1853292e91..56a7c061bb 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -145,11 +145,14 @@ cdef class IndexParams:
 
     build_algo: string denoting the graph building algorithm to use, \
                 default = "ivf_pq"
-        Valid values for algo: ["ivf_pq", "nn_descent"], where
+        Valid values for algo: ["ivf_pq", "nn_descent",
+                                "iterative_cagra_search"], where
             - ivf_pq will use the IVF-PQ algorithm for building the knn graph
             - nn_descent (experimental) will use the NN-Descent algorithm for
               building the knn graph. It is expected to be generally
               faster than ivf_pq.
+            - iterative_cagra_search will iteratively build the knn graph using
+              CAGRA's search() and optimize()
     compression: CompressionParams, optional
         If compression is desired should be a CompressionParams object. If None
         compression will be disabled.
@@ -184,6 +187,12 @@ cdef class IndexParams:
             self.params.build_algo = cuvsCagraGraphBuildAlgo.IVF_PQ
         elif build_algo == "nn_descent":
             self.params.build_algo = cuvsCagraGraphBuildAlgo.NN_DESCENT
+        elif build_algo == "iterative_cagra_search":
+            self.params.build_algo = \
+                cuvsCagraGraphBuildAlgo.ITERATIVE_CAGRA_SEARCH
+        else:
+            raise ValueError(f"Unknown build_algo '{build_algo}'")
+
         self.params.nn_descent_niter = nn_descent_niter
         if compression is not None:
             self.compression = compression
diff --git a/python/cuvs/cuvs/preprocessing/CMakeLists.txt b/python/cuvs/cuvs/preprocessing/CMakeLists.txt
index be31760f93..403dab0cbb 100644
--- a/python/cuvs/cuvs/preprocessing/CMakeLists.txt
+++ b/python/cuvs/cuvs/preprocessing/CMakeLists.txt
@@ -12,4 +12,5 @@
 # the License.
 # =============================================================================
 
+add_subdirectory(quantize/binary)
 add_subdirectory(quantize/scalar)
diff --git a/python/cuvs/cuvs/preprocessing/quantize/__init__.py b/python/cuvs/cuvs/preprocessing/quantize/__init__.py
index e69de29bb2..1fa2cbcf0c 100644
--- a/python/cuvs/cuvs/preprocessing/quantize/__init__.py
+++ b/python/cuvs/cuvs/preprocessing/quantize/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from cuvs.preprocessing.quantize import binary, scalar
+
+__all__ = ["binary", "scalar"]
diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt b/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
new file mode 100644
index 0000000000..5e2d3bde34
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
@@ -0,0 +1,28 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources binary.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
+                   preprocessing_quantize_scalar_
+)
+
+foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
+  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
+endforeach()
diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.pxd b/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py b/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py
new file mode 100644
index 0000000000..ce1a9ffeeb
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .binary import transform
+
+__all__ = ["transform"]
diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/binary.pxd b/python/cuvs/cuvs/preprocessing/quantize/binary/binary.pxd
new file mode 100644
index 0000000000..f00b308f5f
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/binary/binary.pxd
@@ -0,0 +1,25 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+
+
+cdef extern from "cuvs/preprocessing/quantize/binary.h" nogil:
+    cuvsError_t cuvsBinaryQuantizerTransform(cuvsResources_t res,
+                                             DLManagedTensor* dataset,
+                                             DLManagedTensor* out)
diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/binary.pyx b/python/cuvs/cuvs/preprocessing/quantize/binary/binary.pyx
new file mode 100644
index 0000000000..eb776646f5
--- /dev/null
+++ b/python/cuvs/cuvs/preprocessing/quantize/binary/binary.pyx
@@ -0,0 +1,94 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from cuvs.common cimport cydlpack
+
+from pylibraft.common import auto_convert_output, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.resources import auto_sync_resources
+from cuvs.neighbors.common import _check_input_array
+
+
+@auto_sync_resources
+@auto_convert_output
+def transform(dataset, output=None, resources=None):
+    """
+    Applies binary quantization transform to given dataset
+
+    This applies binary quantization to a dataset, changing any positive
+    values to a bitwise 1. This is useful for searching with the
+    BitwiseHamming distance type.
+
+    Parameters
+    ----------
+    dataset : row major host or device dataset to transform
+    output : optional preallocated output memory, on host or device memory
+    {resources_docstring}
+
+    Returns
+    -------
+    output : transformed dataset quantized into a uint8
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.preprocessing.quantize import binary
+    >>> from cuvs.neighbors import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.standard_normal((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> transformed = binary.transform(dataset)
+    >>>
+    >>> # build a cagra index on the binarized data
+    >>> params = cagra.IndexParams(metric="bitwise_hamming",
+    ...                            build_algo="iterative_cagra_search")
+    >>> idx = cagra.build(params, transformed)
+    """
+
+    dataset_ai = wrap_array(dataset)
+
+    _check_input_array(dataset_ai,
+                       [np.dtype("float32"),
+                        np.dtype("float64"),
+                        np.dtype("float16")])
+
+    if output is None:
+        on_device = hasattr(dataset, "__cuda_array_interface__")
+        ndarray = device_ndarray if on_device else np
+        cols = int(np.ceil(dataset_ai.shape[1] / 8))
+        output = ndarray.empty((dataset_ai.shape[0], cols), dtype="uint8")
+
+    output_ai = wrap_array(output)
+    _check_input_array(output_ai, [np.dtype("uint8")])
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cydlpack.DLManagedTensor* output_dlpack = \
+        cydlpack.dlpack_c(output_ai)
+
+    check_cuvs(cuvsBinaryQuantizerTransform(res,
+                                            dataset_dlpack,
+                                            output_dlpack))
+
+    return output
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py b/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py
index f24510bab6..06b766c21e 100644
--- a/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py
+++ b/python/cuvs/cuvs/preprocessing/quantize/scalar/__init__.py
@@ -19,3 +19,11 @@
     train,
     transform,
 )
+
+__all__ = [
+    "Quantizer",
+    "QuantizerParams",
+    "inverse_transform",
+    "train",
+    "transform",
+]
diff --git a/python/cuvs/cuvs/tests/test_binary_quantizer.py b/python/cuvs/cuvs/tests/test_binary_quantizer.py
new file mode 100644
index 0000000000..e9bba0d3fc
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_binary_quantizer.py
@@ -0,0 +1,52 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+
+from cuvs.preprocessing.quantize import binary
+
+
+@pytest.mark.parametrize("n_rows", [50, 100])
+@pytest.mark.parametrize("n_cols", [10, 50])
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("device_memory", [True, False])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
+def test_binary_quantizer(n_rows, n_cols, inplace, device_memory, dtype):
+    input1 = np.random.random_sample((n_rows, n_cols)).astype(dtype)
+
+    output_cols = int(np.ceil(n_cols / 8))
+    output = (
+        np.zeros((n_rows, output_cols), dtype="uint8") if inplace else None
+    )
+
+    input1_device = device_ndarray(input1)
+    output_device = device_ndarray(output) if inplace else None
+
+    transformed = binary.transform(
+        input1_device if device_memory else input1,
+        output=(output_device if device_memory else output)
+        if inplace
+        else None,
+    )
+    if device_memory:
+        actual = transformed if not inplace else output_device
+        actual = actual.copy_to_host()
+    else:
+        actual = transformed if not inplace else output
+
+    expected = np.packbits(input1 > 0, axis=-1, bitorder="little")
+    assert np.all(actual == expected)
diff --git a/python/cuvs/cuvs/tests/test_doctests.py b/python/cuvs/cuvs/tests/test_doctests.py
index 381a077fbd..a35ef4eca7 100644
--- a/python/cuvs/cuvs/tests/test_doctests.py
+++ b/python/cuvs/cuvs/tests/test_doctests.py
@@ -23,7 +23,7 @@
 
 import cuvs.distance
 import cuvs.neighbors
-import cuvs.preprocessing.quantize.scalar
+import cuvs.preprocessing.quantize
 
 # Code adapted from https://github.com/rapidsai/cudf/blob/branch-23.02/python/cudf/cudf/tests/test_doctests.py  # noqa
 
@@ -97,7 +97,7 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.ivf_flat))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.common))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.distance))
-DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.preprocessing.quantize.scalar))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.preprocessing.quantize))
 
 
 def _test_name_from_docstring(docstring):

From 47f53686be9121c19a74e952b4fd9cc19a0204d4 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Thu, 6 Feb 2025 00:07:28 -0500
Subject: [PATCH 306/623] HNSW GPU hierarchy (#616)

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/616
---
 cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu |   2 +
 cpp/include/cuvs/neighbors/hnsw.h            |  13 +-
 cpp/include/cuvs/neighbors/hnsw.hpp          |   8 +-
 cpp/src/neighbors/detail/hnsw.hpp            | 245 +++++++++++++++++--
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd     |   1 +
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx     |  13 +-
 python/cuvs/cuvs/tests/test_hnsw.py          |   9 +-
 7 files changed, 261 insertions(+), 30 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
index e45a3bd5a8..ed6c7b20fe 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
@@ -33,6 +33,8 @@ void parse_build_param(const nlohmann::json& conf,
       param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::NONE;
     } else if (conf.at("hierarchy") == "cpu") {
       param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::CPU;
+    } else if (conf.at("hierarchy") == "gpu") {
+      param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::GPU;
     } else {
       THROW("Invalid value for hierarchy: %s", conf.at("hierarchy").get<std::string>().c_str());
     }
diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
index d88fd3b4ed..61426e9314 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -42,7 +42,9 @@ enum cuvsHnswHierarchy {
   /* Flat hierarchy, search is base-layer only */
   NONE,
   /* Full hierarchy is built using the CPU */
-  CPU
+  CPU,
+  /* Full hierarchy is built using the GPU */
+  GPU
 };
 
 struct cuvsHnswIndexParams {
@@ -50,9 +52,12 @@ struct cuvsHnswIndexParams {
   enum cuvsHnswHierarchy hierarchy;
   /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
   int ef_construction;
-  /** Number of host threads to use to construct hierarchy when hierarchy is `CPU`
-      When the value is 0, the number of threads is automatically determined to the maximum
-      number of threads available.
+  /** Number of host threads to use to construct hierarchy when hierarchy is `CPU` or `GPU`.
+      When the value is 0, the number of threads is automatically determined to the
+      maximum number of threads available.
+      NOTE: When hierarchy is `GPU`, while the majority of the work is done on the GPU,
+      initialization of the HNSW index itself and some other work
+      is parallelized with the help of CPU threads.
   */
   int num_threads;
 };
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 750f1f87fc..88cbf88b0c 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -45,7 +45,8 @@ namespace cuvs::neighbors::hnsw {
  */
 enum class HnswHierarchy {
   NONE,  // base-layer-only index
-  CPU    // full index with CPU-built hierarchy
+  CPU,   // full index with CPU-built hierarchy
+  GPU    // full index with GPU-built hierarchy
 };
 
 struct index_params : cuvs::neighbors::index_params {
@@ -53,9 +54,12 @@ struct index_params : cuvs::neighbors::index_params {
   HnswHierarchy hierarchy = HnswHierarchy::NONE;
   /** Size of the candidate list during hierarchy construction when hierarchy is `CPU`*/
   int ef_construction = 200;
-  /** Number of host threads to use to construct hierarchy when hierarchy is `CPU`
+  /** Number of host threads to use to construct hierarchy when hierarchy is `CPU` or `GPU`.
       When the value is 0, the number of threads is automatically determined to the
       maximum number of threads available.
+      NOTE: When hierarchy is `GPU`, while the majority of the work is done on the GPU,
+      initialization of the HNSW index itself and some other work
+      is parallelized with the help of CPU threads.
    */
   int num_threads = 0;
 };
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index 07e0123495..6ab8631d43 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/hnsw.hpp>
 #include <filesystem>
 #include <hnswlib/hnswalg.h>
@@ -28,6 +29,11 @@
 
 namespace cuvs::neighbors::hnsw::detail {
 
+// This is needed as hnswlib hardcodes the distance type to float
+// or int32_t in certain places. However, we can solve uint8 or int8
+// natively with the pacth cuVS applies. We could potentially remove
+// all the hardcodes and propagate templates throughout hnswlib, but
+// as of now it's not needed.
 template <typename T>
 struct hnsw_dist_t {
   using type = void;
@@ -136,7 +142,6 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
   const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
   std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
-  // auto host_dataset = raft::make_host_matrix<T, int64_t>(dataset.extent(0), dataset.extent(1));
   auto host_dataset = raft::make_host_matrix<T, int64_t>(0, 0);
   raft::host_matrix_view<const T, int64_t, raft::row_major> host_dataset_view(
     host_dataset.data_handle(), host_dataset.extent(0), host_dataset.extent(1));
@@ -179,24 +184,29 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
   }
   appr_algo->base_layer_init = true;  // reset to true to allow addition of new points
 
-  // move cagra graph to host
-  auto graph = cagra_index.graph();
-  auto host_graph =
-    raft::make_host_matrix<uint32_t, int64_t, raft::row_major>(graph.extent(0), graph.extent(1));
-  raft::copy(host_graph.data_handle(),
-             graph.data_handle(),
-             graph.size(),
-             raft::resource::get_cuda_stream(res));
-  raft::resource::sync_stream(res);
+  // move cagra graph to host or access it from host if available
+  auto host_graph_view = cagra_index.graph();
+  auto host_graph      = raft::make_host_matrix<uint32_t, int64_t>(0, 0);
+  if (!raft::is_host_accessible(raft::memory_type_from_pointer(host_graph_view.data_handle()))) {
+    // copy cagra graph to host
+    host_graph = raft::make_host_matrix<uint32_t, int64_t>(host_graph_view.extent(0),
+                                                           host_graph_view.extent(1));
+    raft::copy(host_graph.data_handle(),
+               host_graph_view.data_handle(),
+               host_graph_view.size(),
+               raft::resource::get_cuda_stream(res));
+    raft::resource::sync_stream(res);
+    host_graph_view = host_graph.view();
+  }
 
 // copy cagra graph to hnswlib base layer
-#pragma omp parallel for
-  for (size_t i = 0; i < static_cast<size_t>(host_graph.extent(0)); ++i) {
+#pragma omp parallel for num_threads(num_threads)
+  for (size_t i = 0; i < static_cast<size_t>(host_graph_view.extent(0)); ++i) {
     auto hnsw_internal_id = appr_algo->label_lookup_.find(i)->second;
     auto ll_i             = appr_algo->get_linklist0(hnsw_internal_id);
-    appr_algo->setListCount(ll_i, host_graph.extent(1));
+    appr_algo->setListCount(ll_i, host_graph_view.extent(1));
     auto* data = (uint32_t*)(ll_i + 1);
-    for (size_t j = 0; j < static_cast<size_t>(host_graph.extent(1)); ++j) {
+    for (size_t j = 0; j < static_cast<size_t>(host_graph_view.extent(1)); ++j) {
       auto neighbor_internal_id = appr_algo->label_lookup_.find(host_graph(i, j))->second;
       data[j]                   = neighbor_internal_id;
     }
@@ -206,6 +216,202 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
   return hnsw_index;
 }
 
+template <typename T, typename DistT>
+int initialize_point_in_hnsw(hnswlib::HierarchicalNSW<DistT>* appr_algo,
+                             raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
+                             int64_t real_index,
+                             int curlevel)
+{
+  auto cur_c                        = appr_algo->cur_element_count++;
+  appr_algo->element_levels_[cur_c] = curlevel;
+  memset(appr_algo->data_level0_memory_ + cur_c * appr_algo->size_data_per_element_ +
+           appr_algo->offsetLevel0_,
+         0,
+         appr_algo->size_data_per_element_);
+
+  // Initialisation of the data and label
+  memcpy(appr_algo->getExternalLabeLp(cur_c), &real_index, sizeof(hnswlib::labeltype));
+  memcpy(appr_algo->getDataByInternalId(cur_c),
+         dataset.data_handle() + real_index * dataset.extent(1),
+         appr_algo->data_size_);
+
+  if (curlevel) {
+    appr_algo->linkLists_[cur_c] = (char*)malloc(appr_algo->size_links_per_element_ * curlevel + 1);
+    if (appr_algo->linkLists_[cur_c] == nullptr)
+      throw std::runtime_error("Not enough memory: addPoint failed to allocate linklist");
+    memset(appr_algo->linkLists_[cur_c], 0, appr_algo->size_links_per_element_ * curlevel + 1);
+  }
+  return cur_c;
+}
+
+template <typename T>
+void all_neighbors_graph(raft::resources const& res,
+                         raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
+                         raft::host_matrix_view<uint32_t, int64_t, raft::row_major> neighbors,
+                         cuvs::distance::DistanceType metric)
+{
+  nn_descent::index_params nn_params;
+  nn_params.graph_degree              = neighbors.extent(1);
+  nn_params.intermediate_graph_degree = neighbors.extent(1) * 2;
+  nn_params.metric                    = metric;
+  nn_params.return_distances          = false;
+  auto nn_index                       = nn_descent::build(res, nn_params, dataset, neighbors);
+}
+
+template <typename T, HnswHierarchy hierarchy>
+std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> from_cagra(
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
+{
+  auto host_dataset = raft::make_host_matrix<T, int64_t>(0, 0);
+  raft::host_matrix_view<const T, int64_t, raft::row_major> host_dataset_view(
+    host_dataset.data_handle(), host_dataset.extent(0), host_dataset.extent(1));
+  if (dataset.has_value()) {
+    host_dataset_view = dataset.value();
+  } else {
+    // move dataset to host, remove padding
+    auto cagra_dataset = cagra_index.dataset();
+    host_dataset =
+      raft::make_host_matrix<T, int64_t>(cagra_dataset.extent(0), cagra_dataset.extent(1));
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_dataset.data_handle(),
+                                    sizeof(T) * host_dataset.extent(1),
+                                    cagra_dataset.data_handle(),
+                                    sizeof(T) * cagra_dataset.stride(0),
+                                    sizeof(T) * host_dataset.extent(1),
+                                    cagra_dataset.extent(0),
+                                    cudaMemcpyDefault,
+                                    raft::resource::get_cuda_stream(res)));
+    raft::resource::sync_stream(res);
+    host_dataset_view = host_dataset.view();
+  }
+
+  // initialize hnsw index
+  auto hnsw_index =
+    std::make_unique<index_impl<T>>(host_dataset_view.extent(1), cagra_index.metric(), hierarchy);
+  auto appr_algo = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+    hnsw_index->get_space(),
+    host_dataset_view.extent(0),
+    cagra_index.graph().extent(1) / 2,
+    params.ef_construction);
+
+  // assign a level to each point and initialize the points in hnsw
+  std::vector<size_t> levels(host_dataset_view.extent(0));
+  std::vector<uint32_t> hnsw_internal_ids(host_dataset_view.extent(0));
+
+  auto num_threads = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+#pragma omp parallel for num_threads(num_threads)
+  for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
+    levels[i] = appr_algo->getRandomLevel(appr_algo->mult_) + 1;
+    hnsw_internal_ids[i] =
+      initialize_point_in_hnsw(appr_algo.get(), host_dataset_view, i, levels[i] - 1);
+  }
+
+  // sort the points by levels
+  // build histogram
+  std::vector<size_t> hist;
+  std::vector<size_t> order(host_dataset_view.extent(0));
+  for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
+    auto pt_level = levels[i] - 1;
+    while (pt_level >= hist.size())
+      hist.push_back(0);
+    hist[pt_level]++;
+  }
+
+  // accumulate
+  std::vector<size_t> offsets(hist.size() + 1, 0);
+  for (size_t i = 0; i < hist.size() - 1; i++) {
+    offsets[i + 1] = offsets[i] + hist[i];
+  }
+
+  // bucket sort
+  for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
+    auto pt_level              = levels[i] - 1;
+    order[offsets[pt_level]++] = i;
+  }
+
+  // set last point of the highest level as the entry point
+  appr_algo->enterpoint_node_ = hnsw_internal_ids[order.back()];
+  appr_algo->maxlevel_        = hist.size() - 1;
+
+  // iterate over the points in the descending order of their levels
+  for (size_t pt_level = hist.size() - 1; pt_level >= 1; pt_level--) {
+    auto start_idx     = offsets[pt_level - 1];
+    auto end_idx       = offsets[hist.size() - 1];
+    auto num_pts       = end_idx - start_idx;
+    auto neighbor_size = num_pts > appr_algo->M_ ? appr_algo->M_ : num_pts - 1;
+    if (num_pts <= 1) {
+      // this means only 1 point in the level
+      continue;
+    }
+
+    // gather points from dataset to form query set on host
+    auto host_query_set = raft::make_host_matrix<T, int64_t>(num_pts, host_dataset_view.extent(1));
+    // TODO: Use `raft::matrix::gather` when available as a public API
+    // Issue: https://github.com/rapidsai/raft/issues/2572
+#pragma omp parallel for num_threads(num_threads)
+    for (auto i = start_idx; i < end_idx; i++) {
+      auto pt_id = order[i];
+      std::copy(&host_dataset_view(pt_id, 0),
+                &host_dataset_view(pt_id + 1, 0),
+                &host_query_set(i - start_idx, 0));
+    }
+
+    // find neighbors of the query set
+    auto host_neighbors = raft::make_host_matrix<uint32_t, int64_t>(num_pts, neighbor_size);
+    all_neighbors_graph(res,
+                        raft::make_const_mdspan(host_query_set.view()),
+                        host_neighbors.view(),
+                        cagra_index.metric());
+
+    // add points to the HNSW index upper layers
+#pragma omp parallel for num_threads(num_threads)
+    for (auto i = start_idx; i < end_idx; i++) {
+      auto pt_id       = order[i];
+      auto internal_id = hnsw_internal_ids[pt_id];
+      auto ll_cur      = appr_algo->get_linklist(internal_id, pt_level);
+      appr_algo->setListCount(ll_cur, host_neighbors.extent(1));
+      auto* data     = (uint32_t*)(ll_cur + 1);
+      auto neighbors = &host_neighbors(i - start_idx, 0);
+      for (auto j = 0; j < host_neighbors.extent(1); j++) {
+        auto neighbor_id          = order[neighbors[j] + start_idx];
+        auto neighbor_internal_id = hnsw_internal_ids[neighbor_id];
+        data[j]                   = neighbor_internal_id;
+      }
+    }
+  }
+
+  // move cagra graph to host or access it from host if available
+  auto host_graph_view = cagra_index.graph();
+  auto host_graph      = raft::make_host_matrix<uint32_t, int64_t>(0, 0);
+  if (!raft::is_host_accessible(raft::memory_type_from_pointer(host_graph_view.data_handle()))) {
+    // copy cagra graph to host
+    host_graph = raft::make_host_matrix<uint32_t, int64_t>(host_graph_view.extent(0),
+                                                           host_graph_view.extent(1));
+    raft::copy(host_graph.data_handle(),
+               host_graph_view.data_handle(),
+               host_graph_view.size(),
+               raft::resource::get_cuda_stream(res));
+    raft::resource::sync_stream(res);
+    host_graph_view = host_graph.view();
+  }
+
+// copy cagra graph to hnswlib base layer
+#pragma omp parallel for num_threads(num_threads)
+  for (size_t i = 0; i < static_cast<size_t>(host_graph_view.extent(0)); ++i) {
+    auto ll_i = appr_algo->get_linklist0(hnsw_internal_ids[i]);
+    appr_algo->setListCount(ll_i, host_graph_view.extent(1));
+    auto* data = (uint32_t*)(ll_i + 1);
+    for (size_t j = 0; j < static_cast<size_t>(host_graph_view.extent(1)); ++j) {
+      data[j] = hnsw_internal_ids[host_graph_view(i, j)];
+    }
+  }
+
+  hnsw_index->set_index(std::move(appr_algo));
+  return hnsw_index;
+}
+
 template <typename T>
 std::unique_ptr<index<T>> from_cagra(
   raft::resources const& res,
@@ -217,8 +423,9 @@ std::unique_ptr<index<T>> from_cagra(
     return from_cagra<T, HnswHierarchy::NONE>(res, params, cagra_index, dataset);
   } else if (params.hierarchy == HnswHierarchy::CPU) {
     return from_cagra<T, HnswHierarchy::CPU>(res, params, cagra_index, dataset);
-  }
-  {
+  } else if (params.hierarchy == HnswHierarchy::GPU) {
+    return from_cagra<T, HnswHierarchy::GPU>(res, params, cagra_index, dataset);
+  } else {
     RAFT_FAIL("Unsupported hierarchy type");
   }
 }
@@ -269,9 +476,9 @@ void search(raft::resources const& res,
             raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances)
 {
-  RAFT_EXPECTS(
-    queries.extent(0) == neighbors.extent(0) && queries.extent(0) == distances.extent(0),
-    "Number of rows in output neighbors and distances matrices must equal the number of queries.");
+  RAFT_EXPECTS(queries.extent(0) == neighbors.extent(0) && queries.extent(0) == distances.extent(0),
+               "Number of rows in output neighbors and distances matrices must equal the number of "
+               "queries.");
 
   RAFT_EXPECTS(neighbors.extent(1) == distances.extent(1),
                "Number of columns in output neighbors and distances matrices must equal k");
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
index e0c5179331..8e478bcd27 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pxd
@@ -28,6 +28,7 @@ cdef extern from "cuvs/neighbors/hnsw.h" nogil:
     ctypedef enum cuvsHnswHierarchy:
         NONE
         CPU
+        GPU
 
     ctypedef struct cuvsHnswIndexParams:
         cuvsHnswHierarchy hierarchy
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
index 72a3617bd0..789a3d93bf 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
@@ -49,13 +49,18 @@ cdef class IndexParams:
         The hierarchy of the HNSW index. Valid values are ["none", "cpu"].
         - "none": No hierarchy is built.
         - "cpu": Hierarchy is built using CPU.
+        - "gpu": Hierarchy is built using GPU.
     ef_construction : int, default = 200 (optional)
         Maximum number of candidate list size used during construction
         when hierarchy is `cpu`.
     num_threads : int, default = 0 (optional)
         Number of CPU threads used to increase construction parallelism
-        when hierarchy is `cpu`. When the value is 0, the number of threads is
-        automatically determined to the maximum number of threads available.
+        when hierarchy is `cpu` or `gpu`. When the value is 0, the number of
+        threads is automatically determined to the maximum number of threads
+        available.
+        NOTE: When hierarchy is `gpu`, while the majority of the work is done
+        on the GPU, initialization of the HNSW index itself and some other
+        work is parallelized with the help of CPU threads.
     """
 
     cdef cuvsHnswIndexParams* params
@@ -74,6 +79,8 @@ cdef class IndexParams:
             self.params.hierarchy = cuvsHnswHierarchy.NONE
         elif hierarchy == "cpu":
             self.params.hierarchy = cuvsHnswHierarchy.CPU
+        elif hierarchy == "gpu":
+            self.params.hierarchy = cuvsHnswHierarchy.GPU
         else:
             raise ValueError("Invalid hierarchy type."
                              " Valid values are 'none' and 'cpu'.")
@@ -86,6 +93,8 @@ cdef class IndexParams:
             return "none"
         elif self.params.hierarchy == cuvsHnswHierarchy.CPU:
             return "cpu"
+        elif self.params.hierarchy == cuvsHnswHierarchy.GPU:
+            return "gpu"
 
     @property
     def ef_construction(self):
diff --git a/python/cuvs/cuvs/tests/test_hnsw.py b/python/cuvs/cuvs/tests/test_hnsw.py
index 23a0920efb..c24cdf8a15 100644
--- a/python/cuvs/cuvs/tests/test_hnsw.py
+++ b/python/cuvs/cuvs/tests/test_hnsw.py
@@ -85,7 +85,7 @@ def run_hnsw_build_search_test(
 @pytest.mark.parametrize("num_threads", [2, 4])
 @pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
-@pytest.mark.parametrize("hierarchy", ["none", "cpu"])
+@pytest.mark.parametrize("hierarchy", ["none", "cpu", "gpu"])
 def test_hnsw(dtype, k, ef, num_threads, metric, build_algo, hierarchy):
     # Note that inner_product tests use normalized input which we cannot
     # represent in int8, therefore we test only sqeuclidean metric here.
@@ -111,6 +111,7 @@ def run_hnsw_extend_test(
     intermediate_graph_degree=128,
     graph_degree=64,
     search_params={},
+    hierarchy="cpu",
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     add_dataset = generate_data((add_rows, n_cols), dtype)
@@ -135,7 +136,7 @@ def run_hnsw_extend_test(
 
     assert index.trained
 
-    hnsw_params = hnsw.IndexParams(hierarchy="cpu")
+    hnsw_params = hnsw.IndexParams(hierarchy=hierarchy)
     hnsw_index = hnsw.from_cagra(hnsw_params, index)
     hnsw.extend(hnsw.ExtendParams(), hnsw_index, add_dataset)
 
@@ -167,7 +168,8 @@ def run_hnsw_extend_test(
 @pytest.mark.parametrize("num_threads", [2, 4])
 @pytest.mark.parametrize("metric", ["sqeuclidean"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
-def test_hnsw_extend(dtype, k, ef, num_threads, metric, build_algo):
+@pytest.mark.parametrize("hierarchy", ["cpu", "gpu"])
+def test_hnsw_extend(dtype, k, ef, num_threads, metric, build_algo, hierarchy):
     # Note that inner_product tests use normalized input which we cannot
     # represent in int8, therefore we test only sqeuclidean metric here.
     run_hnsw_extend_test(
@@ -176,4 +178,5 @@ def test_hnsw_extend(dtype, k, ef, num_threads, metric, build_algo):
         metric=metric,
         build_algo=build_algo,
         search_params={"ef": ef, "num_threads": num_threads},
+        hierarchy=hierarchy,
     )

From 97bd78d03dd8d47b0c44dff1872fbdc265050c9a Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 6 Feb 2025 15:17:05 +0100
Subject: [PATCH 307/623] Fix ann-bench deadlocking on HNSW destruction due to
 task locks (#667)

The ann-bench tool has been observed to deadlock on thread.join() due to unnecessary mutex lock.
The problem is that the destructor doesn't release the thread mutex and thus doesn't allow the thread to escape the condition_variable.wait() function.
The fix is to just remove the lock in the destructor (which doesn't modify the state of the task anyway).

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/667
---
 cpp/bench/ann/src/common/thread_pool.hpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/cpp/bench/ann/src/common/thread_pool.hpp b/cpp/bench/ann/src/common/thread_pool.hpp
index e2192daea1..287ac0a2dc 100644
--- a/cpp/bench/ann/src/common/thread_pool.hpp
+++ b/cpp/bench/ann/src/common/thread_pool.hpp
@@ -60,9 +60,8 @@ class fixed_thread_pool {
 
     finished_.store(true, std::memory_order_relaxed);
     for (unsigned i = 0; i < threads_.size(); ++i) {
+      // NB: don't lock the task mutex here, may deadlock on .join() otherwise
       auto& task = tasks_[i];
-      std::lock_guard lock(task.mtx);
-
       task.cv.notify_one();
       threads_[i].join();
     }
@@ -105,8 +104,7 @@ class fixed_thread_pool {
       IdxT start = i * items_per_thread;
       auto& task = tasks_[i];
       {
-        std::lock_guard lock(task.mtx);
-        (void)lock;  // stop nvcc warning
+        [[maybe_unused]] std::lock_guard lock(task.mtx);
         task.task = std::packaged_task<void()>([=] { wrapped_f(start, start + items_per_thread); });
         futures.push_back(task.task.get_future());
         task.has_task = true;

From 4377d489f4f4fe94d0ca7d418604a6daff60683e Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Thu, 6 Feb 2025 09:59:24 -0500
Subject: [PATCH 308/623] Add `shellcheck` to pre-commit and fix warnings
 (#662)

`shellcheck` is a fast, static analysis tool for shell scripts. It's good at
flagging up unused variables, unintentional glob expansions, and other potential
execution and security headaches that arise from the wonders of `bash` (and
other shlangs).

This PR adds a `pre-commit` hook to run `shellcheck` on all of the `sh-lang`
files in the `ci/` directory, and the changes requested by `shellcheck` to make
the existing files pass the check.

xref: rapidsai/build-planning#135

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/662
---
 .pre-commit-config.yaml      |  6 ++++++
 ci/build_docs.sh             |  9 ++++++---
 ci/build_python.sh           |  3 ---
 ci/build_rust.sh             |  3 ++-
 ci/build_wheel.sh            |  4 ++--
 ci/build_wheel_cuvs.sh       |  2 +-
 ci/build_wheel_libcuvs.sh    |  2 --
 ci/check_style.sh            |  4 ++--
 ci/checks/black_lists.sh     | 16 ++++++++--------
 ci/release/update-version.sh | 15 +++++++--------
 ci/test_wheel_cuvs.sh        |  2 +-
 ci/validate_wheel.sh         |  5 ++---
 12 files changed, 37 insertions(+), 34 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1124d3814e..bbfe734019 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -124,6 +124,12 @@ repos:
         hooks:
             - id: rapids-dependency-file-generator
               args: ["--clean"]
+      - repo: https://github.com/shellcheck-py/shellcheck-py
+        rev: v0.10.0.1
+        hooks:
+          - id: shellcheck
+            args: ["--severity=warning"]
+            files: ^ci/
 
 default_language_version:
       python: python3
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index bce93c6054..053e0200af 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -7,7 +7,8 @@ rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
 
 RAPIDS_VERSION="$(rapids-version)"
-export RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
+RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
+export RAPIDS_VERSION_MAJOR_MINOR
 
 rapids-dependency-file-generator \
   --output conda \
@@ -34,7 +35,8 @@ rapids-mamba-retry install \
   "libcuvs=${RAPIDS_VERSION}" \
   "cuvs=${RAPIDS_VERSION}"
 
-export RAPIDS_DOCS_DIR="$(mktemp -d)"
+RAPIDS_DOCS_DIR="$(mktemp -d)"
+export RAPIDS_DOCS_DIR
 
 rapids-logger "Build CPP docs"
 pushd cpp/doxygen
@@ -43,7 +45,8 @@ popd
 
 rapids-logger "Build Rust docs"
 pushd rust
-export LIBCLANG_PATH=$(dirname $(find /opt/conda -name libclang.so | head -n 1))
+LIBCLANG_PATH=$(dirname "$(find /opt/conda -name libclang.so | head -n 1)")
+export LIBCLANG_PATH
 cargo doc -p cuvs --no-deps
 popd
 
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 3241a2c2b0..d9ea64de91 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -15,9 +15,6 @@ rapids-print-env
 
 rapids-logger "Begin py build"
 
-package_name="cuvs"
-package_dir="python"
-
 CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
 
 version=$(rapids-generate-version)
diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index 309501c32b..0d0832031d 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -25,7 +25,8 @@ rapids-print-env
 
 # we need to set up LIBCLANG_PATH to allow rust bindgen to work,
 # grab it from the conda env
-export LIBCLANG_PATH=$(dirname $(find /opt/conda -name libclang.so | head -n 1))
+LIBCLANG_PATH=$(dirname "$(find /opt/conda -name libclang.so | head -n 1)")
+export LIBCLANG_PATH
 echo "LIBCLANG_PATH=$LIBCLANG_PATH"
 
 rapids-logger "Downloading artifacts from previous jobs"
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 91cb50a502..f3b68c0b10 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -11,7 +11,7 @@ underscore_package_name=$(echo "${package_name}" | tr "-" "_")
 source rapids-configure-sccache
 source rapids-date-string
 
-RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 
 rapids-generate-version > ./VERSION
 
@@ -50,4 +50,4 @@ sccache --show-adv-stats
 mkdir -p final_dist
 python -m auditwheel repair -w final_dist "${EXCLUDE_ARGS[@]}" dist/*
 
-RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 ${package_type} final_dist
+RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 "${package_type}" final_dist
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index fb40d14593..8557d65d11 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -5,7 +5,7 @@ set -euo pipefail
 
 package_dir="python/cuvs"
 
-RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 
 # Downloads libcuvs wheels from this current build,
 # then ensures 'cuvs' wheel builds always use the 'libcuvs' just built in the same CI run.
diff --git a/ci/build_wheel_libcuvs.sh b/ci/build_wheel_libcuvs.sh
index 3bceed423f..0d45a11b13 100755
--- a/ci/build_wheel_libcuvs.sh
+++ b/ci/build_wheel_libcuvs.sh
@@ -26,7 +26,5 @@ rapids-pip-retry install \
 # 0 really means "add --no-build-isolation" (ref: https://github.com/pypa/pip/issues/5735)
 export PIP_NO_BUILD_ISOLATION=0
 
-RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
-
 ci/build_wheel.sh libcuvs ${package_dir} cpp
 ci/validate_wheel.sh ${package_dir} final_dist libcuvs
diff --git a/ci/check_style.sh b/ci/check_style.sh
index 952e94bf1c..95f1e3742d 100755
--- a/ci/check_style.sh
+++ b/ci/check_style.sh
@@ -18,8 +18,8 @@ conda activate checks
 RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
 FORMAT_FILE_URL="https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION_MAJOR_MINOR}/cmake-format-rapids-cmake.json"
 export RAPIDS_CMAKE_FORMAT_FILE=/tmp/rapids_cmake_ci/cmake-formats-rapids-cmake.json
-mkdir -p $(dirname ${RAPIDS_CMAKE_FORMAT_FILE})
-wget -O ${RAPIDS_CMAKE_FORMAT_FILE} ${FORMAT_FILE_URL}
+mkdir -p "$(dirname "${RAPIDS_CMAKE_FORMAT_FILE}")"
+wget -O ${RAPIDS_CMAKE_FORMAT_FILE} "${FORMAT_FILE_URL}"
 
 # Run pre-commit checks
 pre-commit run --all-files --show-diff-on-failure
diff --git a/ci/checks/black_lists.sh b/ci/checks/black_lists.sh
index ed61527c34..ed9f3745b2 100755
--- a/ci/checks/black_lists.sh
+++ b/ci/checks/black_lists.sh
@@ -20,16 +20,16 @@ set +H
 RETVAL=0
 
 for black_listed in cudaDeviceSynchronize cudaMalloc cudaMallocManaged cudaFree cudaMallocHost cudaHostAlloc cudaFreeHost; do
-    TMP=`git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$black_listed" $PR_TARGET_BRANCH | grep '^+' | grep -v '^+++' | grep "$black_listed"`
+    TMP=$(git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$black_listed" "$PR_TARGET_BRANCH" | grep '^+' | grep -v '^+++' | grep "$black_listed")
     if [ "$TMP" != "" ]; then
-        for filename in `git --no-pager diff --ignore-submodules -w --minimal --name-only -S"$black_listed" $PR_TARGET_BRANCH`; do
+        for filename in $(git --no-pager diff --ignore-submodules -w --minimal --name-only -S"$black_listed" "$PR_TARGET_BRANCH"); do
             basefilename=$(basename -- "$filename")
             filext="${basefilename##*.}"
             if [ "$filext" != "md" ] && [ "$filext" != "sh" ]; then
-                TMP2=`git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$black_listed" $PR_TARGET_BRANCH -- $filename | grep '^+' | grep -v '^+++' | grep "$black_listed" | grep -vE "^\+[[:space:]]*/{2,}.*$black_listed"`
+                TMP2=$(git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$black_listed" "$PR_TARGET_BRANCH" -- "$filename" | grep '^+' | grep -v '^+++' | grep "$black_listed" | grep -vE "^\+[[:space:]]*/{2,}.*$black_listed")
                 if [ "$TMP2" != "" ]; then
                     echo "=== ERROR: black listed function call $black_listed added to $filename ==="
-                    git --no-pager diff --ignore-submodules -w --minimal -S"$black_listed" $PR_TARGET_BRANCH -- $filename
+                    git --no-pager diff --ignore-submodules -w --minimal -S"$black_listed" "$PR_TARGET_BRANCH" -- "$filename"
                     echo "=== END ERROR ==="
                     RETVAL=1
                 fi
@@ -39,17 +39,17 @@ for black_listed in cudaDeviceSynchronize cudaMalloc cudaMallocManaged cudaFree
 done
 
 for cond_black_listed in cudaMemcpy cudaMemset; do
-    TMP=`git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$cond_black_listed" $PR_TARGET_BRANCH | grep '^+' | grep -v '^+++' | grep -P "$cond_black_listed(?!Async)"`
+    TMP=$(git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$cond_black_listed" "$PR_TARGET_BRANCH" | grep '^+' | grep -v '^+++' | grep -P "$cond_black_listed(?!Async)")
 
     if [ "$TMP" != "" ]; then
-        for filename in `git --no-pager diff --ignore-submodules -w --minimal --name-only -S"$cond_black_listed" $PR_TARGET_BRANCH`; do
+        for filename in $(git --no-pager diff --ignore-submodules -w --minimal --name-only -S"$cond_black_listed" "$PR_TARGET_BRANCH"); do
             basefilename=$(basename -- "$filename")
             filext="${basefilename##*.}"
             if [ "$filext" != "md" ] && [ "$filext" != "sh" ]; then
-                TMP2=`git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$cond_black_listed" $PR_TARGET_BRANCH -- $filename | grep '^+' | grep -v '^+++' | grep -P "$cond_black_listed(?!Async)" | grep -vE "^\+[[:space:]]*/{2,}.*$cond_black_listed"`
+                TMP2=$(git --no-pager diff --ignore-submodules -w --minimal -U0 -S"$cond_black_listed" "$PR_TARGET_BRANCH" -- "$filename" | grep '^+' | grep -v '^+++' | grep -P "$cond_black_listed(?!Async)" | grep -vE "^\+[[:space:]]*/{2,}.*$cond_black_listed")
                 if [ "$TMP2" != "" ]; then
                     echo "=== ERROR: black listed function call $cond_black_listed added to $filename ==="
-                    git --no-pager diff --ignore-submodules -w --minimal -S"$cond_black_listed" $PR_TARGET_BRANCH -- $filename
+                    git --no-pager diff --ignore-submodules -w --minimal -S"$cond_black_listed" "$PR_TARGET_BRANCH" -- "$filename"
                     echo "=== END ERROR ==="
                     RETVAL=1
                 fi
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 3385d4b61c..9bc916a871 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -13,17 +13,16 @@ NEXT_FULL_TAG=$1
 
 # Get current version
 CURRENT_TAG=$(git tag --merged HEAD | grep -xE '^v.*' | sort --version-sort | tail -n 1 | tr -d 'v')
-CURRENT_MAJOR=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[1]}')
-CURRENT_MINOR=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[2]}')
-CURRENT_PATCH=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[3]}')
+CURRENT_MAJOR=$(echo "$CURRENT_TAG" | awk '{split($0, a, "."); print a[1]}')
+CURRENT_MINOR=$(echo "$CURRENT_TAG" | awk '{split($0, a, "."); print a[2]}')
 CURRENT_SHORT_TAG=${CURRENT_MAJOR}.${CURRENT_MINOR}
 
 # Get <major>.<minor> for next version
-NEXT_MAJOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[1]}')
-NEXT_MINOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[2]}')
-NEXT_PATCH=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[3]}')
+NEXT_MAJOR=$(echo "$NEXT_FULL_TAG" | awk '{split($0, a, "."); print a[1]}')
+NEXT_MINOR=$(echo "$NEXT_FULL_TAG" | awk '{split($0, a, "."); print a[2]}')
+NEXT_PATCH=$(echo "$NEXT_FULL_TAG" | awk '{split($0, a, "."); print a[3]}')
 NEXT_SHORT_TAG=${NEXT_MAJOR}.${NEXT_MINOR}
-NEXT_UCXX_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/${NEXT_SHORT_TAG})"
+NEXT_UCXX_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/"${NEXT_SHORT_TAG}")"
 
 # Need to distutils-normalize the original version
 NEXT_SHORT_TAG_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_SHORT_TAG}'))")
@@ -34,7 +33,7 @@ echo "Preparing release $CURRENT_TAG => $NEXT_FULL_TAG"
 
 # Inplace sed replace; workaround for Linux and Mac
 function sed_runner() {
-    sed -i.bak ''"$1"'' $2 && rm -f ${2}.bak
+    sed -i.bak ''"$1"'' "$2" && rm -f "${2}".bak
 }
 
 sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" examples/cmake/thirdparty/fetch_rapids.cmake
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index c2d02a30e0..382a42b2f9 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -4,7 +4,7 @@
 set -euo pipefail
 
 mkdir -p ./dist
-RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 cpp ./local-libcuvs-dep
 RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 python ./dist
 
diff --git a/ci/validate_wheel.sh b/ci/validate_wheel.sh
index 19d413fa20..5c2c5f2cf6 100755
--- a/ci/validate_wheel.sh
+++ b/ci/validate_wheel.sh
@@ -6,7 +6,6 @@ set -euo pipefail
 package_dir=$1
 wheel_dir_relative_path=$2
 
-RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 
 cd "${package_dir}"
 
@@ -14,10 +13,10 @@ rapids-logger "validate packages with 'pydistcheck'"
 
 pydistcheck \
     --inspect \
-    "$(echo ${wheel_dir_relative_path}/*.whl)"
+    "$(echo "${wheel_dir_relative_path}"/*.whl)"
 
 rapids-logger "validate packages with 'twine'"
 
 twine check \
     --strict \
-    "$(echo ${wheel_dir_relative_path}/*.whl)"
+    "$(echo "${wheel_dir_relative_path}"/*.whl)"

From 7fe75b8d3a0fe1b7436d3b552ae0337ec8a0c9f8 Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Thu, 6 Feb 2025 10:04:23 -0500
Subject: [PATCH 309/623] Add build_type input field for test.yaml (#661)

Exposes `build_type` as an input in `test.yaml` so that `test.yaml` can be
manually run against a specific branch/commit as needed.

The default value is still `nightly`, and without maintainer intervention, that
is what will run each night.

xref rapidsai/build-planning#147

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/661
---
 .github/workflows/test.yaml | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 82f95cb205..acfd766470 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -12,13 +12,16 @@ on:
       sha:
         required: true
         type: string
+      build_type:
+        type: string
+        default: nightly
 
 jobs:
   conda-cpp-checks:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@nvks-runners
     with:
-      build_type: nightly
+      build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
@@ -28,7 +31,7 @@ jobs:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@nvks-runners
     with:
-      build_type: nightly
+      build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
@@ -36,7 +39,7 @@ jobs:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@nvks-runners
     with:
-      build_type: nightly
+      build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
@@ -44,7 +47,7 @@ jobs:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@nvks-runners
     with:
-      build_type: nightly
+      build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}

From 961e60c34efd55159f764c33a0495bb370abde76 Mon Sep 17 00:00:00 2001
From: Ajit Mistry <55892788+ajit283@users.noreply.github.com>
Date: Thu, 6 Feb 2025 21:57:05 +0100
Subject: [PATCH 310/623] Go API - [WIP] (#212)

This PR adds a Go API. It's far from completion and still work in progress. Feel free to suggest improvements!

Authors:
  - Ajit Mistry (https://github.com/ajit283)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/212
---
 .github/workflows/build.yaml                  |  13 +
 .github/workflows/pr.yaml                     |  14 +
 build.sh                                      |  10 +-
 ci/build_go.sh                                |  39 ++
 .../all_cuda-118_arch-aarch64.yaml            |   1 +
 .../all_cuda-118_arch-x86_64.yaml             |   1 +
 .../all_cuda-128_arch-aarch64.yaml            |   1 +
 .../all_cuda-128_arch-x86_64.yaml             |   1 +
 dependencies.yaml                             |  15 +
 go/brute_force/brute_force.go                 |  83 ++++
 go/brute_force/brute_force_test.go            | 110 +++++
 go/cagra/cagra.go                             | 136 ++++++
 go/cagra/cagra_test.go                        | 327 ++++++++++++++
 go/cagra/extend_params.go                     |  46 ++
 go/cagra/index_params.go                      | 159 +++++++
 go/cagra/search_params.go                     | 163 +++++++
 go/distance.go                                |  70 +++
 go/distance_test.go                           |  49 ++
 go/dlpack.go                                  | 420 ++++++++++++++++++
 go/dlpack_test.go                             | 190 ++++++++
 go/exceptions.go                              |  23 +
 go/go.mod                                     |   3 +
 go/go.sum                                     |   4 +
 go/ivf_flat/index_params.go                   |  87 ++++
 go/ivf_flat/ivf_flat.go                       |  72 +++
 go/ivf_flat/ivf_flat_test.go                  | 123 +++++
 go/ivf_flat/search_params.go                  |  39 ++
 go/ivf_pq/index_params.go                     | 149 +++++++
 go/ivf_pq/ivf_pq.go                           |  73 +++
 go/ivf_pq/ivf_pq_test.go                      | 123 +++++
 go/ivf_pq/search_params.go                    | 107 +++++
 go/memory_resource.go                         |  93 ++++
 go/memory_resource_test.go                    |  19 +
 go/resources.go                               |  57 +++
 34 files changed, 2819 insertions(+), 1 deletion(-)
 create mode 100755 ci/build_go.sh
 create mode 100644 go/brute_force/brute_force.go
 create mode 100644 go/brute_force/brute_force_test.go
 create mode 100644 go/cagra/cagra.go
 create mode 100644 go/cagra/cagra_test.go
 create mode 100644 go/cagra/extend_params.go
 create mode 100644 go/cagra/index_params.go
 create mode 100644 go/cagra/search_params.go
 create mode 100644 go/distance.go
 create mode 100644 go/distance_test.go
 create mode 100644 go/dlpack.go
 create mode 100644 go/dlpack_test.go
 create mode 100644 go/exceptions.go
 create mode 100644 go/go.mod
 create mode 100644 go/go.sum
 create mode 100644 go/ivf_flat/index_params.go
 create mode 100644 go/ivf_flat/ivf_flat.go
 create mode 100644 go/ivf_flat/ivf_flat_test.go
 create mode 100644 go/ivf_flat/search_params.go
 create mode 100644 go/ivf_pq/index_params.go
 create mode 100644 go/ivf_pq/ivf_pq.go
 create mode 100644 go/ivf_pq/ivf_pq_test.go
 create mode 100644 go/ivf_pq/search_params.go
 create mode 100644 go/memory_resource.go
 create mode 100644 go/memory_resource_test.go
 create mode 100644 go/resources.go

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 59b8e00de1..d1f633248f 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -47,6 +47,19 @@ jobs:
       node_type: "gpu-v100-latest-1"
       run_script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
+  go-build:
+    needs: cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      arch: "amd64"
+      date: ${{ inputs.date }}
+      container_image: "rapidsai/ci-conda:latest"
+      node_type: "gpu-v100-latest-1"
+      run_script: "ci/build_go.sh"
+      sha: ${{ inputs.sha }}
   python-build:
     needs: [cpp-build]
     secrets: inherit
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 233b82f94b..832932d99e 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -22,6 +22,7 @@ jobs:
       - conda-python-tests
       - docs-build
       - rust-build
+      - go-build
       - wheel-build-libcuvs
       - wheel-build-cuvs
       - wheel-tests-cuvs
@@ -70,6 +71,7 @@ jobs:
           - '!notebooks/**'
           - '!python/**'
           - '!rust/**'
+          - '!go/**'
           - '!thirdparty/LICENSES/**'
         test_notebooks:
           - '**'
@@ -77,6 +79,7 @@ jobs:
           - '!.pre-commit-config.yaml'
           - '!README.md'
           - '!rust/**'
+          - '!go/**'
           - '!thirdparty/LICENSES/**'
         test_python:
           - '**'
@@ -87,6 +90,7 @@ jobs:
           - '!img/**'
           - '!notebooks/**'
           - '!rust/**'
+          - '!go/**'
           - '!thirdparty/LICENSES/**'
   checks:
     needs: telemetry-setup
@@ -150,6 +154,16 @@ jobs:
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
       run_script: "ci/build_rust.sh"
+  go-build:
+    needs: conda-cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/build_go.sh"
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
diff --git a/build.sh b/build.sh
index 89e1b5a337..4aaa5617c3 100755
--- a/build.sh
+++ b/build.sh
@@ -18,7 +18,7 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python rust java docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python rust go java docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
@@ -26,6 +26,7 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
                       around the C++ code.
    python           - build the cuvs Python package
    rust             - build the cuvs Rust bindings
+   go               - build the cuvs Go bindings
    java             - build the cuvs Java bindings
    docs             - build the documentation
    tests            - build the tests
@@ -447,6 +448,13 @@ if (( ${NUMARGS} == 0 )) || hasArg rust; then
     cargo test
 fi
 
+# Build the cuvs Go bindings
+if (( ${NUMARGS} == 0 )) || hasArg go; then
+    cd ${REPODIR}/go
+    go build ./...
+    go test ./...
+fi
+
 # Build the cuvs Java bindings
 if (( ${NUMARGS} == 0 )) || hasArg java; then
     if ! hasArg libcuvs; then
diff --git a/ci/build_go.sh b/ci/build_go.sh
new file mode 100755
index 0000000000..9496f74139
--- /dev/null
+++ b/ci/build_go.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+rapids-logger "Create test conda environment"
+. /opt/conda/etc/profile.d/conda.sh
+
+RAPIDS_VERSION="$(rapids-version)"
+
+rapids-dependency-file-generator \
+  --output conda \
+  --file-key go \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+
+rapids-mamba-retry env create --yes -f env.yaml -n go
+
+# seeing failures on activating the environment here on unbound locals
+# apply workaround from https://github.com/conda/conda/issues/8186#issuecomment-532874667
+set +eu
+conda activate go
+set -eu
+
+rapids-print-env
+
+export CGO_CFLAGS="-I${CONDA_PREFIX}/include"
+export CGO_LDFLAGS="-L${CONDA_PREFIX}/lib -lcudart -lcuvs -lcuvs_c"
+export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+export CC=clang
+
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  "libcuvs=${RAPIDS_VERSION}" \
+  "libraft=${RAPIDS_VERSION}"
+
+bash ./build.sh go
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 123acb4219..2e2036ccfa 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -23,6 +23,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-aarch64=11.*
+- go
 - graphviz
 - ipython
 - libclang==16.0.6
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index c6a65e6848..cf73f4b745 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -23,6 +23,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-64=11.*
+- go
 - graphviz
 - ipython
 - libclang==16.0.6
diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
index c508a9dc19..f161975614 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -24,6 +24,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-aarch64=13.*
+- go
 - graphviz
 - ipython
 - libclang==16.0.6
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
index f043e544bb..5056ef1854 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -24,6 +24,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
 - gcc_linux-64=13.*
+- go
 - graphviz
 - ipython
 - libclang==16.0.6
diff --git a/dependencies.yaml b/dependencies.yaml
index 7a50c0d469..cfa63250da 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -21,6 +21,7 @@ files:
       - rapids_build
       - run_py_cuvs
       - rust
+      - go
       - test_libcuvs
       - test_python_common
       - test_py_cuvs
@@ -81,6 +82,14 @@ files:
       - cuda_version
       - rapids_build
       - rust
+  go:
+    output: none
+    includes:
+      - clang
+      - cuda
+      - cuda_version
+      - rapids_build
+      - go
   py_build_libcuvs:
     output: pyproject
     pyproject_dir: python/libcuvs
@@ -470,6 +479,12 @@ dependencies:
         packages:
           - make
           - rust
+  go:
+    common:
+      - output_types: [conda]
+        packages:
+          - go
+          - dlpack>=0.8,<1.0
   build_wheels:
     common:
       - output_types: [requirements, pyproject]
diff --git a/go/brute_force/brute_force.go b/go/brute_force/brute_force.go
new file mode 100644
index 0000000000..29f1a5d958
--- /dev/null
+++ b/go/brute_force/brute_force.go
@@ -0,0 +1,83 @@
+package brute_force
+
+// #include <cuvs/neighbors/brute_force.h>
+import "C"
+
+import (
+	"errors"
+	"unsafe"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// Brute Force KNN Index
+type BruteForceIndex struct {
+	index   C.cuvsBruteForceIndex_t
+	trained bool
+}
+
+// Creates a new empty Brute Force KNN Index
+func CreateIndex() (*BruteForceIndex, error) {
+	var index C.cuvsBruteForceIndex_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsBruteForceIndexCreate(&index)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &BruteForceIndex{index: index, trained: false}, nil
+}
+
+// Destroys the Brute Force KNN Index
+func (index *BruteForceIndex) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsBruteForceIndexDestroy(index.index)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+// Builds a new Brute Force KNN Index from the dataset for efficient search.
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `Dataset` - A row-major matrix on either the host or device to index
+// * `metric` - Distance type to use for building the index
+// * `metric_arg` - Value of `p` for Minkowski distances - set to 2.0 if not applicable
+func BuildIndex[T any](Resources cuvs.Resource, Dataset *cuvs.Tensor[T], metric cuvs.Distance, metric_arg float32, index *BruteForceIndex) error {
+	CMetric, exists := cuvs.CDistances[metric]
+
+	if !exists {
+		return errors.New("cuvs: invalid distance metric")
+	}
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsBruteForceBuild(C.cuvsResources_t(Resources.Resource), (*C.DLManagedTensor)(unsafe.Pointer(Dataset.C_tensor)), C.cuvsDistanceType(CMetric), C.float(metric_arg), index.index)))
+	if err != nil {
+		return err
+	}
+	index.trained = true
+
+	return nil
+}
+
+// Perform a Nearest Neighbors search on the Index
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `queries` - Tensor in device memory to query for
+// * `neighbors` - Tensor in device memory that receives the indices of the nearest neighbors
+// * `distances` - Tensor in device memory that receives the distances of the nearest neighbors
+func SearchIndex[T any](resources cuvs.Resource, index BruteForceIndex, queries *cuvs.Tensor[T], neighbors *cuvs.Tensor[int64], distances *cuvs.Tensor[T]) error {
+	if !index.trained {
+		return errors.New("index needs to be built before calling search")
+	}
+
+	prefilter := C.cuvsFilter{
+		addr:  0,
+		_type: C.NO_FILTER,
+	}
+
+	return cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsBruteForceSearch(C.ulong(resources.Resource), index.index, (*C.DLManagedTensor)(unsafe.Pointer(queries.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(neighbors.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(distances.C_tensor)), prefilter)))
+}
diff --git a/go/brute_force/brute_force_test.go b/go/brute_force/brute_force_test.go
new file mode 100644
index 0000000000..151c48e6ac
--- /dev/null
+++ b/go/brute_force/brute_force_test.go
@@ -0,0 +1,110 @@
+package brute_force
+
+import (
+	"math/rand/v2"
+	"testing"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+func TestCagra(t *testing.T) {
+	const (
+		nDataPoints = 1024
+		nFeatures   = 16
+		nQueries    = 4
+		k           = 4
+		epsilon     = 0.001
+	)
+
+	resource, _ := cuvs.NewResource(nil)
+	defer resource.Close()
+
+	testDataset := make([][]float32, nDataPoints)
+	for i := range testDataset {
+		testDataset[i] = make([]float32, nFeatures)
+		for j := range testDataset[i] {
+			testDataset[i][j] = rand.Float32()
+		}
+	}
+
+	dataset, err := cuvs.NewTensor(testDataset)
+	if err != nil {
+		t.Fatalf("error creating dataset tensor: %v", err)
+	}
+	defer dataset.Close()
+
+	index, _ := CreateIndex()
+	defer index.Close()
+
+	// use the first 4 points from the dataset as queries : will test that we get them back
+	// as their own nearest neighbor
+	queries, _ := cuvs.NewTensor(testDataset[:nQueries])
+	defer queries.Close()
+
+	neighbors, err := cuvs.NewTensorOnDevice[int64](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating neighbors tensor: %v", err)
+	}
+	defer neighbors.Close()
+
+	distances, err := cuvs.NewTensorOnDevice[float32](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating distances tensor: %v", err)
+	}
+	defer distances.Close()
+
+	if _, err := dataset.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving dataset to device: %v", err)
+	}
+
+	if err := BuildIndex(resource, &dataset, cuvs.DistanceL2, 2.0, index); err != nil {
+		t.Fatalf("error building index: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	if _, err := queries.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving queries to device: %v", err)
+	}
+
+	err = SearchIndex(resource, *index, &queries, &neighbors, &distances)
+	if err != nil {
+		t.Fatalf("error searching index: %v", err)
+	}
+
+	if _, err := neighbors.ToHost(&resource); err != nil {
+		t.Fatalf("error moving neighbors to host: %v", err)
+	}
+
+	if _, err := distances.ToHost(&resource); err != nil {
+		t.Fatalf("error moving distances to host: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	neighborsSlice, err := neighbors.Slice()
+	if err != nil {
+		t.Fatalf("error getting neighbors slice: %v", err)
+	}
+
+	for i := range neighborsSlice {
+		if neighborsSlice[i][0] != int64(i) {
+			t.Error("wrong neighbor, expected", i, "got", neighborsSlice[i][0])
+		}
+	}
+
+	distancesSlice, err := distances.Slice()
+	if err != nil {
+		t.Fatalf("error getting distances slice: %v", err)
+	}
+
+	for i := range distancesSlice {
+		if distancesSlice[i][0] >= epsilon || distancesSlice[i][0] <= -epsilon {
+			t.Error("distance should be close to 0, got", distancesSlice[i][0])
+		}
+	}
+}
diff --git a/go/cagra/cagra.go b/go/cagra/cagra.go
new file mode 100644
index 0000000000..61b9e71d6e
--- /dev/null
+++ b/go/cagra/cagra.go
@@ -0,0 +1,136 @@
+package cagra
+
+// #include <cuvs/neighbors/cagra.h>
+import "C"
+
+import (
+	"errors"
+	"unsafe"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// Cagra ANN Index
+type CagraIndex struct {
+	index   C.cuvsCagraIndex_t
+	trained bool
+}
+
+// Creates a new empty Cagra Index
+func CreateIndex() (*CagraIndex, error) {
+	var index C.cuvsCagraIndex_t
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraIndexCreate(&index)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &CagraIndex{index: index}, nil
+}
+
+// Builds a new Index from the dataset for efficient search.
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `params` - Parameters for building the index
+// * `dataset` - A row-major Tensor on either the host or device to index
+// * `index` - CagraIndex to build
+func BuildIndex[T any](Resources cuvs.Resource, params *IndexParams, dataset *cuvs.Tensor[T], index *CagraIndex) error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraBuild(C.ulong(Resources.Resource), params.params, (*C.DLManagedTensor)(unsafe.Pointer(dataset.C_tensor)), index.index)))
+	if err != nil {
+		return err
+	}
+	index.trained = true
+	return nil
+}
+
+// Extends the index with additional data
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `params` - Parameters for extending the index
+// * `additional_dataset` - A row-major Tensor on the device to extend the index with
+// * `return_dataset` - A row-major Tensor on the device that will receive the extended dataset
+// * `index` - CagraIndex to extend
+func ExtendIndex[T any](Resources cuvs.Resource, params *ExtendParams, additional_dataset *cuvs.Tensor[T], return_dataset *cuvs.Tensor[T], index *CagraIndex) error {
+	if !index.trained {
+		return errors.New("index needs to be built before calling extend")
+	}
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraExtend(C.ulong(Resources.Resource), params.params, (*C.DLManagedTensor)(unsafe.Pointer(additional_dataset.C_tensor)), index.index, (*C.DLManagedTensor)(unsafe.Pointer(return_dataset.C_tensor)))))
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+// Destroys the Cagra Index
+func (index *CagraIndex) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraIndexDestroy(index.index)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+// Perform a Approximate Nearest Neighbors search on the Index
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `params` - Parameters to use in searching the index
+// * `queries` - A tensor in device memory to query for
+// * `neighbors` - Tensor in device memory that receives the indices of the nearest neighbors
+// * `distances` - Tensor in device memory that receives the distances of the nearest neighbors
+// * `allowList` - List of indices to allow in the search, if nil, no filtering is applied
+func SearchIndex[T any](Resources cuvs.Resource, params *SearchParams, index *CagraIndex, queries *cuvs.Tensor[T], neighbors *cuvs.Tensor[uint32], distances *cuvs.Tensor[T], allowList []uint32) error {
+	if !index.trained {
+		return errors.New("index needs to be built before calling search")
+	}
+
+	var filter C.cuvsFilter
+	bitset := createBitset(allowList)
+	allowListTensor, err := cuvs.NewVector[uint32](bitset)
+	if err != nil {
+		return err
+	}
+	defer allowListTensor.Close()
+	_, err = allowListTensor.ToDevice(&Resources)
+	if err != nil {
+		return err
+	}
+	if allowList == nil {
+		filter = C.cuvsFilter{
+			_type: C.NO_FILTER,
+			addr:  C.uintptr_t(0),
+		}
+	} else {
+		filter = C.cuvsFilter{
+			_type: C.BITSET,
+			addr:  C.uintptr_t(uintptr(unsafe.Pointer(allowListTensor.C_tensor))),
+		}
+	}
+	return cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraSearch(C.cuvsResources_t(Resources.Resource), params.params, index.index, (*C.DLManagedTensor)(unsafe.Pointer(queries.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(neighbors.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(distances.C_tensor)), filter)))
+}
+
+func createBitset(allowList []uint32) []uint32 {
+	// Calculate size needed for the bitset array
+	// Each uint32 handles 32 bits, so we divide the max ID by 32 (shift right by 5)
+	maxID := uint32(0)
+	for _, id := range allowList {
+		if id > maxID {
+			maxID = id
+		}
+	}
+	size := (maxID >> 5) + 1 // Division by 32, add 1 to handle remainder
+	bitset := make([]uint32, size)
+	for _, id := range allowList {
+		// Calculate which uint32 in our array (divide by 32)
+		arrayIndex := id >> 5
+		// Calculate bit position within that uint32 (mod 32)
+		bitPosition := id & 31 // equivalent to id % 32
+		// Set the bit
+		bitset[arrayIndex] |= 1 << bitPosition
+	}
+	return bitset
+}
diff --git a/go/cagra/cagra_test.go b/go/cagra/cagra_test.go
new file mode 100644
index 0000000000..86baf5e601
--- /dev/null
+++ b/go/cagra/cagra_test.go
@@ -0,0 +1,327 @@
+package cagra
+
+import (
+	"math/rand/v2"
+	"testing"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+func TestCagra(t *testing.T) {
+	const (
+		nDataPoints = 1024
+		nFeatures   = 16
+		nQueries    = 4
+		k           = 4
+		epsilon     = 0.001
+	)
+
+	resource, _ := cuvs.NewResource(nil)
+	defer resource.Close()
+
+	testDataset := make([][]float32, nDataPoints)
+	for i := range testDataset {
+		testDataset[i] = make([]float32, nFeatures)
+		for j := range testDataset[i] {
+			testDataset[i][j] = rand.Float32()
+		}
+	}
+
+	dataset, err := cuvs.NewTensor(testDataset)
+	if err != nil {
+		t.Fatalf("error creating dataset tensor: %v", err)
+	}
+	defer dataset.Close()
+
+	indexParams, err := CreateIndexParams()
+	if err != nil {
+		t.Fatalf("error creating index params: %v", err)
+	}
+	defer indexParams.Close()
+
+	index, _ := CreateIndex()
+	defer index.Close()
+
+	// use the first 4 points from the dataset as queries : will test that we get them back
+	// as their own nearest neighbor
+	queries, _ := cuvs.NewTensor(testDataset[:nQueries])
+	defer queries.Close()
+
+	neighbors, err := cuvs.NewTensorOnDevice[uint32](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating neighbors tensor: %v", err)
+	}
+	defer neighbors.Close()
+
+	distances, err := cuvs.NewTensorOnDevice[float32](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating distances tensor: %v", err)
+	}
+	defer distances.Close()
+
+	if _, err := dataset.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving dataset to device: %v", err)
+	}
+
+	if err := BuildIndex(resource, indexParams, &dataset, index); err != nil {
+		t.Fatalf("error building index: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	if _, err := queries.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving queries to device: %v", err)
+	}
+
+	SearchParams, err := CreateSearchParams()
+	if err != nil {
+		t.Fatalf("error creating search params: %v", err)
+	}
+	defer SearchParams.Close()
+
+	err = SearchIndex(resource, SearchParams, index, &queries, &neighbors, &distances, nil)
+	if err != nil {
+		t.Fatalf("error searching index: %v", err)
+	}
+
+	if _, err := neighbors.ToHost(&resource); err != nil {
+		t.Fatalf("error moving neighbors to host: %v", err)
+	}
+
+	if _, err := distances.ToHost(&resource); err != nil {
+		t.Fatalf("error moving distances to host: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	neighborsSlice, err := neighbors.Slice()
+	if err != nil {
+		t.Fatalf("error getting neighbors slice: %v", err)
+	}
+
+	for i := range neighborsSlice {
+		if neighborsSlice[i][0] != uint32(i) {
+			t.Error("wrong neighbor, expected", i, "got", neighborsSlice[i][0])
+		}
+	}
+
+	distancesSlice, err := distances.Slice()
+	if err != nil {
+		t.Fatalf("error getting distances slice: %v", err)
+	}
+
+	for i := range distancesSlice {
+		if distancesSlice[i][0] >= epsilon || distancesSlice[i][0] <= -epsilon {
+			t.Error("distance should be close to 0, got", distancesSlice[i][0])
+		}
+	}
+}
+
+func TestCagraFiltering(t *testing.T) {
+	const (
+		nDataPoints  = 1024
+		nFilteredOut = 512
+		nFeatures    = 16
+		nQueries     = 4
+		k            = 4
+		epsilon      = 0.001
+	)
+
+	resource, _ := cuvs.NewResource(nil)
+	defer resource.Close()
+
+	testDataset := make([][]float32, nDataPoints)
+	for i := range testDataset {
+		testDataset[i] = make([]float32, nFeatures)
+		for j := range testDataset[i] {
+			testDataset[i][j] = rand.Float32()
+		}
+	}
+
+	dataset, err := cuvs.NewTensor(testDataset)
+	if err != nil {
+		t.Fatalf("error creating dataset tensor: %v", err)
+	}
+	defer dataset.Close()
+
+	indexParams, err := CreateIndexParams()
+	if err != nil {
+		t.Fatalf("error creating index params: %v", err)
+	}
+	defer indexParams.Close()
+
+	index, _ := CreateIndex()
+	defer index.Close()
+
+	// Test queries: first 4 points (should be found without filter)
+	queries1, _ := cuvs.NewTensor(testDataset[:nQueries])
+	defer queries1.Close()
+
+	// Test queries: points 512-515 (should be found with filter, not found without)
+	queries2, _ := cuvs.NewTensor(testDataset[nFilteredOut:(nFilteredOut + nQueries)])
+	defer queries2.Close()
+
+	neighbors, err := cuvs.NewTensorOnDevice[uint32](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating neighbors tensor: %v", err)
+	}
+	defer neighbors.Close()
+
+	distances, err := cuvs.NewTensorOnDevice[float32](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating distances tensor: %v", err)
+	}
+	defer distances.Close()
+
+	if _, err := dataset.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving dataset to device: %v", err)
+	}
+
+	if err := BuildIndex(resource, indexParams, &dataset, index); err != nil {
+		t.Fatalf("error building index: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	SearchParams, err := CreateSearchParams()
+	if err != nil {
+		t.Fatalf("error creating search params: %v", err)
+	}
+	defer SearchParams.Close()
+
+	// Step 1: Search without filter - first 4 points should find themselves
+	if _, err := queries1.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving queries1 to device: %v", err)
+	}
+
+	err = SearchIndex(resource, SearchParams, index, &queries1, &neighbors, &distances, nil)
+	if err != nil {
+		t.Fatalf("error searching index without filter: %v", err)
+	}
+
+	if _, err := neighbors.ToHost(&resource); err != nil {
+		t.Fatalf("error moving neighbors to host: %v", err)
+	}
+
+	if _, err := distances.ToHost(&resource); err != nil {
+		t.Fatalf("error moving distances to host: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	// Verify first 4 points found themselves without filter
+	neighborsSlice, err := neighbors.Slice()
+	if err != nil {
+		t.Fatalf("error getting neighbors slice: %v", err)
+	}
+
+	for i := range neighborsSlice {
+		if neighborsSlice[i][0] != uint32(i) {
+			t.Error("without filter: wrong neighbor, expected", i, "got", neighborsSlice[i][0])
+		}
+	}
+
+	// Step 2: Search with filter excluding first half - first 4 points should not be found
+	allowList := make([]uint32, nDataPoints-nFilteredOut)
+	for i := range allowList {
+		allowList[i] = uint32(i + nFilteredOut)
+	}
+
+	if _, err := queries1.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving queries1 back to device: %v", err)
+	}
+
+	if _, err := neighbors.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving neighbors back to device: %v", err)
+	}
+
+	if _, err := distances.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving distances back to device: %v", err)
+	}
+
+	err = SearchIndex(resource, SearchParams, index, &queries1, &neighbors, &distances, allowList)
+	if err != nil {
+		t.Fatalf("error searching index with filter: %v", err)
+	}
+
+	if _, err := neighbors.ToHost(&resource); err != nil {
+		t.Fatalf("error moving neighbors to host: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	// Verify first 4 points are not in results when filtered
+	neighborsSlice, err = neighbors.Slice()
+	if err != nil {
+		t.Fatalf("error getting neighbors slice: %v", err)
+	}
+
+	for i := range neighborsSlice {
+		for j := range neighborsSlice[i] {
+			if neighborsSlice[i][j] < uint32(nFilteredOut) {
+				t.Error("with filter: found point that should be filtered out:", neighborsSlice[i][j])
+			}
+		}
+	}
+
+	// Step 3: Search points 512-515 with filter - they should find themselves
+	if _, err := queries2.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving queries2 to device: %v", err)
+	}
+
+	if _, err := neighbors.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving neighbors back to device: %v", err)
+	}
+
+	if _, err := distances.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving distances back to device: %v", err)
+	}
+
+	err = SearchIndex(resource, SearchParams, index, &queries2, &neighbors, &distances, allowList)
+	if err != nil {
+		t.Fatalf("error searching index with filter for second query set: %v", err)
+	}
+
+	if _, err := neighbors.ToHost(&resource); err != nil {
+		t.Fatalf("error moving neighbors to host: %v", err)
+	}
+
+	if _, err := distances.ToHost(&resource); err != nil {
+		t.Fatalf("error moving distances to host: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	neighborsSlice, err = neighbors.Slice()
+	if err != nil {
+		t.Fatalf("error getting neighbors slice: %v", err)
+	}
+
+	distancesSlice, err := distances.Slice()
+	if err != nil {
+		t.Fatalf("error getting distances slice: %v", err)
+	}
+
+	// Verify points 512-515 find themselves when filtered to second half
+	for i := range neighborsSlice {
+		expectedID := uint32(i + nFilteredOut)
+		if neighborsSlice[i][0] != expectedID {
+			t.Error("with filter: wrong neighbor for filtered query, expected", expectedID, "got", neighborsSlice[i][0])
+		}
+		if distancesSlice[i][0] >= epsilon || distancesSlice[i][0] <= -epsilon {
+			t.Error("with filter: distance should be close to 0 for filtered query, got", distancesSlice[i][0])
+		}
+	}
+}
diff --git a/go/cagra/extend_params.go b/go/cagra/extend_params.go
new file mode 100644
index 0000000000..7e173c0456
--- /dev/null
+++ b/go/cagra/extend_params.go
@@ -0,0 +1,46 @@
+package cagra
+
+// #include <cuvs/neighbors/cagra.h>
+import "C"
+
+import (
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// Parameters to extend CAGRA Index
+type ExtendParams struct {
+	params C.cuvsCagraExtendParams_t
+}
+
+// Creates a new ExtendParams
+func CreateExtendParams() (*ExtendParams, error) {
+	var params C.cuvsCagraExtendParams_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraExtendParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	ExtendParams := &ExtendParams{params: params}
+
+	return ExtendParams, nil
+}
+
+// The additional dataset is divided into chunks and added to the graph.
+// This is the knob to adjust the tradeoff between the recall and operation throughput.
+// Large chunk sizes can result in high throughput, but use more
+// working memory (O(max_chunk_size*degree^2)).
+// This can also degrade recall because no edges are added between the nodes in the same chunk.
+// Auto select when 0.
+func (p *ExtendParams) SetMaxChunkSize(max_chunk_size uint32) (*ExtendParams, error) {
+	p.params.max_chunk_size = C.uint32_t(max_chunk_size)
+	return p, nil
+}
+
+func (p *ExtendParams) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraExtendParamsDestroy(p.params)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
diff --git a/go/cagra/index_params.go b/go/cagra/index_params.go
new file mode 100644
index 0000000000..51fdb318fa
--- /dev/null
+++ b/go/cagra/index_params.go
@@ -0,0 +1,159 @@
+package cagra
+
+// #include <cuvs/neighbors/cagra.h>
+import "C"
+
+import (
+	"errors"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+type IndexParams struct {
+	params C.cuvsCagraIndexParams_t
+}
+
+// Supplemental parameters to build CAGRA Index
+type CompressionParams struct {
+	params C.cuvsCagraCompressionParams_t
+}
+
+type BuildAlgo int
+
+const (
+	IvfPq BuildAlgo = iota
+	NnDescent
+	AutoSelect
+)
+
+var cBuildAlgos = map[BuildAlgo]int{
+	IvfPq:      C.IVF_PQ,
+	NnDescent:  C.NN_DESCENT,
+	AutoSelect: C.AUTO_SELECT,
+}
+
+// Creates a new CompressionParams
+func CreateCompressionParams() (*CompressionParams, error) {
+	var params C.cuvsCagraCompressionParams_t
+
+	if params == nil {
+		return nil, errors.New("memory allocation failed")
+	}
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraCompressionParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &CompressionParams{params: params}, nil
+}
+
+// The bit length of the vector element after compression by PQ.
+func (p *CompressionParams) SetPQBits(pq_bits uint32) (*CompressionParams, error) {
+	p.params.pq_bits = C.uint32_t(pq_bits)
+
+	return p, nil
+}
+
+// The dimensionality of the vector after compression by PQ. When zero,
+// an optimal value is selected using a heuristic.
+func (p *CompressionParams) SetPQDim(pq_dim uint32) (*CompressionParams, error) {
+	p.params.pq_dim = C.uint32_t(pq_dim)
+
+	return p, nil
+}
+
+// Vector Quantization (VQ) codebook size - number of "coarse cluster
+// centers". When zero, an optimal value is selected using a heuristic.
+func (p *CompressionParams) SetVQNCenters(vq_n_centers uint32) (*CompressionParams, error) {
+	p.params.vq_n_centers = C.uint32_t(vq_n_centers)
+
+	return p, nil
+}
+
+// The number of iterations searching for kmeans centers (both VQ & PQ
+// phases).
+func (p *CompressionParams) SetKMeansNIters(kmeans_n_iters uint32) (*CompressionParams, error) {
+	p.params.kmeans_n_iters = C.uint32_t(kmeans_n_iters)
+
+	return p, nil
+}
+
+// The fraction of data to use during iterative kmeans building (VQ
+// phase). When zero, an optimal value is selected using a heuristic.
+func (p *CompressionParams) SetVQKMeansTrainsetFraction(vq_kmeans_trainset_fraction float64) (*CompressionParams, error) {
+	p.params.vq_kmeans_trainset_fraction = C.double(vq_kmeans_trainset_fraction)
+
+	return p, nil
+}
+
+// The fraction of data to use during iterative kmeans building (PQ
+// phase). When zero, an optimal value is selected using a heuristic.
+func (p *CompressionParams) SetPQKMeansTrainsetFraction(pq_kmeans_trainset_fraction float64) (*CompressionParams, error) {
+	p.params.pq_kmeans_trainset_fraction = C.double(pq_kmeans_trainset_fraction)
+
+	return p, nil
+}
+
+// Creates a new IndexParams
+func CreateIndexParams() (*IndexParams, error) {
+	var params C.cuvsCagraIndexParams_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraIndexParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	IndexParams := &IndexParams{params: params}
+
+	return IndexParams, nil
+}
+
+// Degree of input graph for pruning
+func (p *IndexParams) SetIntermediateGraphDegree(intermediate_graph_degree uintptr) (*IndexParams, error) {
+	p.params.intermediate_graph_degree = C.size_t(intermediate_graph_degree)
+	return p, nil
+}
+
+// Degree of output graph
+func (p *IndexParams) SetGraphDegree(intermediate_graph_degree uintptr) (*IndexParams, error) {
+	p.params.graph_degree = C.size_t(intermediate_graph_degree)
+
+	return p, nil
+}
+
+// ANN algorithm to build knn graph
+func (p *IndexParams) SetBuildAlgo(build_algo BuildAlgo) (*IndexParams, error) {
+	CBuildAlgo, exists := cBuildAlgos[build_algo]
+
+	if !exists {
+		return nil, errors.New("cuvs: invalid build_algo")
+	}
+	p.params.build_algo = uint32(CBuildAlgo)
+
+	return p, nil
+}
+
+// Number of iterations to run if building with NN_DESCENT
+func (p *IndexParams) SetNNDescentNiter(nn_descent_niter uint32) (*IndexParams, error) {
+	p.params.nn_descent_niter = C.ulong(nn_descent_niter)
+
+	return p, nil
+}
+
+// Compression parameters
+func (p *IndexParams) SetCompression(compression *CompressionParams) (*IndexParams, error) {
+	p.params.compression = C.cuvsCagraCompressionParams_t(compression.params)
+
+	return p, nil
+}
+
+// Destroys IndexParams
+func (p *IndexParams) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraIndexParamsDestroy(p.params)))
+	if err != nil {
+		return err
+	}
+
+	return nil
+}
diff --git a/go/cagra/search_params.go b/go/cagra/search_params.go
new file mode 100644
index 0000000000..94207d2881
--- /dev/null
+++ b/go/cagra/search_params.go
@@ -0,0 +1,163 @@
+package cagra
+
+// #include <cuvs/neighbors/cagra.h>
+import "C"
+
+import (
+	"errors"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// Supplemental parameters to search CAGRA Index
+type SearchParams struct {
+	params C.cuvsCagraSearchParams_t
+}
+
+type SearchAlgo int
+
+const (
+	SearchAlgoSingleCta SearchAlgo = iota
+	SearchAlgoMultiCta
+	SearchAlgoMultiKernel
+	SearchAlgoAuto
+)
+
+type HashmapMode int
+
+const (
+	HashmapModeHash HashmapMode = iota
+	HashmapModeSmall
+	HashmapModeAuto
+)
+
+// Creates a new SearchParams
+func CreateSearchParams() (*SearchParams, error) {
+	var params C.cuvsCagraSearchParams_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraSearchParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &SearchParams{params: params}, nil
+}
+
+// Maximum number of queries to search at the same time (batch size). Auto select when 0
+func (p *SearchParams) SetMaxQueries(max_queries uintptr) (*SearchParams, error) {
+	p.params.max_queries = C.size_t(max_queries)
+	return p, nil
+}
+
+// Number of intermediate search results retained during the search.
+// This is the main knob to adjust trade off between accuracy and search speed.
+// Higher values improve the search accuracy
+func (p *SearchParams) SetItopkSize(itopk_size uintptr) (*SearchParams, error) {
+	p.params.itopk_size = C.size_t(itopk_size)
+	return p, nil
+}
+
+// Upper limit of search iterations. Auto select when 0.
+func (p *SearchParams) SetMaxIterations(max_iterations uintptr) (*SearchParams, error) {
+	p.params.max_iterations = C.size_t(max_iterations)
+	return p, nil
+}
+
+// Which search implementation to use.
+func (p *SearchParams) SetAlgo(algo SearchAlgo) (*SearchParams, error) {
+	CAlgo := C.SINGLE_CTA
+
+	switch algo {
+	case SearchAlgoSingleCta:
+		CAlgo = C.SINGLE_CTA
+	case SearchAlgoMultiCta:
+		CAlgo = C.MULTI_CTA
+	case SearchAlgoMultiKernel:
+		CAlgo = C.MULTI_KERNEL
+	case SearchAlgoAuto:
+		CAlgo = C.AUTO
+	default:
+		return nil, errors.New("unsupported algo")
+	}
+
+	p.params.algo = uint32(CAlgo)
+
+	return p, nil
+}
+
+// Number of threads used to calculate a single distance. 4, 8, 16, or 32.
+func (p *SearchParams) SetTeamSize(team_size uintptr) (*SearchParams, error) {
+	p.params.team_size = C.size_t(team_size)
+	return p, nil
+}
+
+// Lower limit of search iterations.
+func (p *SearchParams) SetMinIterations(min_iterations uintptr) (*SearchParams, error) {
+	p.params.min_iterations = C.size_t(min_iterations)
+	return p, nil
+}
+
+// How many nodes to search at once. Auto select when 0.
+func (p *SearchParams) SetSearchWidth(search_width uintptr) (*SearchParams, error) {
+	p.params.search_width = C.size_t(search_width)
+	return p, nil
+}
+
+// Thread block size. 0, 64, 128, 256, 512, 1024. Auto selection when 0.
+func (p *SearchParams) SetThreadBlockSize(thread_block_size uintptr) (*SearchParams, error) {
+	p.params.thread_block_size = C.size_t(thread_block_size)
+	return p, nil
+}
+
+// Hashmap type. Auto selection when AUTO.
+func (p *SearchParams) SetHashmapMode(hashmap_mode HashmapMode) (*SearchParams, error) {
+	CHashMode := C.AUTO_HASH
+
+	switch hashmap_mode {
+	case HashmapModeHash:
+		CHashMode = C.HASH
+	case HashmapModeSmall:
+		CHashMode = C.SMALL
+	case HashmapModeAuto:
+		CHashMode = C.AUTO_HASH
+	default:
+		return nil, errors.New("unsupported hashmap_mode")
+	}
+
+	p.params.hashmap_mode = uint32(CHashMode)
+
+	return p, nil
+}
+
+// Lower limit of hashmap bit length. More than 8.
+func (p *SearchParams) SetHashmapMinBitlen(hashmap_min_bitlen uintptr) (*SearchParams, error) {
+	p.params.hashmap_min_bitlen = C.size_t(hashmap_min_bitlen)
+	return p, nil
+}
+
+// Upper limit of hashmap fill rate. More than 0.1, less than 0.9.
+func (p *SearchParams) SetHashmapMaxFillRate(hashmap_max_fill_rate float32) (*SearchParams, error) {
+	p.params.hashmap_max_fill_rate = C.float(hashmap_max_fill_rate)
+	return p, nil
+}
+
+// Number of iterations of initial random seed node selection. 1 or more.
+func (p *SearchParams) SetNumRandomSamplings(num_random_samplings uint32) (*SearchParams, error) {
+	p.params.num_random_samplings = C.uint32_t(num_random_samplings)
+	return p, nil
+}
+
+// Bit mask used for initial random seed node selection.
+func (p *SearchParams) SetRandXorMask(rand_xor_mask uint64) (*SearchParams, error) {
+	p.params.rand_xor_mask = C.uint64_t(rand_xor_mask)
+	return p, nil
+}
+
+// Destroys SearchParams
+func (p *SearchParams) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraSearchParamsDestroy(p.params)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
diff --git a/go/distance.go b/go/distance.go
new file mode 100644
index 0000000000..530e1d61b3
--- /dev/null
+++ b/go/distance.go
@@ -0,0 +1,70 @@
+package cuvs
+
+// #include <cuvs/distance/pairwise_distance.h>
+import "C"
+
+import (
+	"errors"
+	"unsafe"
+)
+
+type Distance int
+
+// Supported distance metrics
+const (
+	DistanceL2 Distance = iota
+	DistanceSQEuclidean
+	DistanceEuclidean
+	DistanceL1
+	DistanceCityblock
+	DistanceInnerProduct
+	DistanceChebyshev
+	DistanceCanberra
+	DistanceCosine
+	DistanceLp
+	DistanceCorrelation
+	DistanceJaccard
+	DistanceHellinger
+	DistanceBrayCurtis
+	DistanceJensenShannon
+	DistanceHamming
+	DistanceKLDivergence
+	DistanceMinkowski
+	DistanceRusselRao
+	DistanceDice
+)
+
+// Maps cuvs Go distances to C distances
+var CDistances = map[Distance]int{
+	DistanceL2:            C.L2SqrtExpanded,
+	DistanceSQEuclidean:   C.L2Expanded,
+	DistanceEuclidean:     C.L2SqrtExpanded,
+	DistanceL1:            C.L1,
+	DistanceCityblock:     C.L1,
+	DistanceInnerProduct:  C.InnerProduct,
+	DistanceChebyshev:     C.Linf,
+	DistanceCanberra:      C.Canberra,
+	DistanceCosine:        C.CosineExpanded,
+	DistanceLp:            C.LpUnexpanded,
+	DistanceCorrelation:   C.CorrelationExpanded,
+	DistanceJaccard:       C.JaccardExpanded,
+	DistanceHellinger:     C.HellingerExpanded,
+	DistanceBrayCurtis:    C.BrayCurtis,
+	DistanceJensenShannon: C.JensenShannon,
+	DistanceHamming:       C.HammingUnexpanded,
+	DistanceKLDivergence:  C.KLDivergence,
+	DistanceMinkowski:     C.LpUnexpanded,
+	DistanceRusselRao:     C.RusselRaoExpanded,
+	DistanceDice:          C.DiceExpanded,
+}
+
+// Computes the pairwise distance between two vectors.
+func PairwiseDistance[T any](Resources Resource, x *Tensor[T], y *Tensor[T], distances *Tensor[float32], metric Distance, metric_arg float32) error {
+	CMetric, exists := CDistances[metric]
+
+	if !exists {
+		return errors.New("cuvs: invalid distance metric")
+	}
+
+	return CheckCuvs(CuvsError(C.cuvsPairwiseDistance(C.cuvsResources_t(Resources.Resource), (*C.DLManagedTensor)(unsafe.Pointer(x.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(y.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(distances.C_tensor)), C.cuvsDistanceType(CMetric), C.float(metric_arg))))
+}
diff --git a/go/distance_test.go b/go/distance_test.go
new file mode 100644
index 0000000000..17f40a1ba2
--- /dev/null
+++ b/go/distance_test.go
@@ -0,0 +1,49 @@
+package cuvs_test
+
+import (
+	"math/rand"
+	"testing"
+	"time"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+func TestDistance(t *testing.T) {
+	resource, _ := cuvs.NewResource(nil)
+
+	rand.Seed(time.Now().UnixNano())
+
+	NDataPoints := 256
+	NFeatures := 16
+
+	TestDataset := make([][]float32, NDataPoints)
+	for i := range TestDataset {
+		TestDataset[i] = make([]float32, NFeatures)
+		for j := range TestDataset[i] {
+			TestDataset[i][j] = rand.Float32()
+		}
+	}
+
+	dataset, _ := cuvs.NewTensor(TestDataset)
+
+	DistancesDataset := make([][]float32, NDataPoints)
+	for i := range DistancesDataset {
+		DistancesDataset[i] = make([]float32, NDataPoints)
+	}
+
+	distances, _ := cuvs.NewTensor(DistancesDataset)
+
+	distances.ToDevice(&resource)
+	dataset.ToDevice(&resource)
+
+	cuvs.PairwiseDistance(resource, &dataset, &dataset, &distances, cuvs.DistanceL2, 0.0)
+
+	distances.ToHost(&resource)
+
+	resource.Sync()
+
+	arr, _ := distances.Slice()
+	if arr[0][0] != 0.0 {
+		t.Error("wrong distance, expected", 0.0, "got", arr[0][0])
+	}
+}
diff --git a/go/dlpack.go b/go/dlpack.go
new file mode 100644
index 0000000000..6fe619fd35
--- /dev/null
+++ b/go/dlpack.go
@@ -0,0 +1,420 @@
+package cuvs
+
+// #include <stdlib.h>
+// #include <dlpack/dlpack.h>
+// #include <cuvs/core/c_api.h>
+import "C"
+
+import (
+	"errors"
+	"strconv"
+	"unsafe"
+)
+
+type TensorNumberType interface {
+	int64 | uint32 | float32
+}
+
+// ManagedTensor is a wrapper around a dlpack DLManagedTensor object.
+// This lets you pass matrices in device or host memory into cuvs.
+type Tensor[T any] struct {
+	C_tensor *C.DLManagedTensor
+	shape    []int64
+}
+
+// Creates a new Tensor on the host and copies the data into it.
+func NewTensor[T TensorNumberType](data [][]T) (Tensor[T], error) {
+	if len(data) == 0 || len(data[0]) == 0 {
+		return Tensor[T]{}, errors.New("empty data")
+	}
+
+	dtype := getDLDataType[T]()
+
+	totalElements := len(data) * len(data[0])
+
+	dataPtr := C.malloc(C.size_t(totalElements * int(unsafe.Sizeof(T(0)))))
+	if dataPtr == nil {
+		return Tensor[T]{}, errors.New("data memory allocation failed")
+	}
+
+	dataSlice := unsafe.Slice((*T)(dataPtr), totalElements)
+	flattenData(data, dataSlice)
+
+	shapePtr := C.malloc(C.size_t(2 * int(unsafe.Sizeof(C.int64_t(0)))))
+	if shapePtr == nil {
+		C.free(dataPtr)
+		return Tensor[T]{}, errors.New("shape memory allocation failed")
+	}
+
+	shapeSlice := unsafe.Slice((*C.int64_t)(shapePtr), 2)
+	shapeSlice[0] = C.int64_t(len(data))
+	shapeSlice[1] = C.int64_t(len(data[0]))
+
+	// Create DLManagedTensor
+	dlm := (*C.DLManagedTensor)(C.malloc(C.size_t(unsafe.Sizeof(C.DLManagedTensor{}))))
+	if dlm == nil {
+		return Tensor[T]{}, errors.New("tensor allocation failed")
+	}
+
+	dlm.dl_tensor.data = dataPtr
+	dlm.dl_tensor.device = C.DLDevice{
+		device_type: C.DLDeviceType(C.kDLCPU),
+		device_id:   0,
+	}
+	dlm.dl_tensor.dtype = dtype
+	dlm.dl_tensor.ndim = 2
+	dlm.dl_tensor.shape = (*C.int64_t)(shapePtr)
+	dlm.dl_tensor.strides = nil
+	dlm.dl_tensor.byte_offset = 0
+	dlm.manager_ctx = nil
+	dlm.deleter = nil
+
+	return Tensor[T]{
+		C_tensor: dlm,
+		shape:    []int64{int64(len(data)), int64(len(data[0]))},
+	}, nil
+}
+
+func NewVector[T TensorNumberType](data []T) (Tensor[T], error) {
+	if len(data) == 0 {
+		return Tensor[T]{}, errors.New("empty data")
+	}
+
+	dtype := getDLDataType[T]()
+
+	totalElements := len(data)
+
+	dataPtr := C.malloc(C.size_t(totalElements * int(unsafe.Sizeof(T(0)))))
+	if dataPtr == nil {
+		return Tensor[T]{}, errors.New("data memory allocation failed")
+	}
+
+	dataSlice := unsafe.Slice((*T)(dataPtr), totalElements)
+	copy(dataSlice, data)
+
+	shapePtr := C.malloc(C.size_t(int(unsafe.Sizeof(C.int64_t(0)))))
+	if shapePtr == nil {
+		C.free(dataPtr)
+		return Tensor[T]{}, errors.New("shape memory allocation failed")
+	}
+
+	shapeSlice := unsafe.Slice((*C.int64_t)(shapePtr), 1)
+	shapeSlice[0] = C.int64_t(len(data))
+
+	// Create DLManagedTensor
+	dlm := (*C.DLManagedTensor)(C.malloc(C.size_t(unsafe.Sizeof(C.DLManagedTensor{}))))
+	if dlm == nil {
+		return Tensor[T]{}, errors.New("tensor allocation failed")
+	}
+
+	dlm.dl_tensor.data = dataPtr
+	dlm.dl_tensor.device = C.DLDevice{
+		device_type: C.DLDeviceType(C.kDLCPU),
+		device_id:   0,
+	}
+	dlm.dl_tensor.dtype = dtype
+	dlm.dl_tensor.ndim = 1
+	dlm.dl_tensor.shape = (*C.int64_t)(shapePtr)
+	dlm.dl_tensor.strides = nil
+	dlm.dl_tensor.byte_offset = 0
+	dlm.manager_ctx = nil
+	dlm.deleter = nil
+
+	return Tensor[T]{
+		C_tensor: dlm,
+		shape:    []int64{int64(len(data))},
+	}, nil
+}
+
+// Creates a new Tensor with uninitialized data on the current device.
+func NewTensorOnDevice[T TensorNumberType](res *Resource, shape []int64) (Tensor[T], error) {
+	if len(shape) < 2 {
+		return Tensor[T]{}, errors.New("shape must be at least 2")
+	}
+
+	shapePtr := C.malloc(C.size_t(len(shape) * int(unsafe.Sizeof(C.int64_t(0)))))
+	if shapePtr == nil {
+		return Tensor[T]{}, errors.New("shape memory allocation failed")
+	}
+
+	shapeSlice := unsafe.Slice((*C.int64_t)(shapePtr), len(shape))
+	for i, dim := range shape {
+		shapeSlice[i] = C.int64_t(dim)
+	}
+
+	dlm := (*C.DLManagedTensor)(C.malloc(C.size_t(unsafe.Sizeof(C.DLManagedTensor{}))))
+	if dlm == nil {
+		return Tensor[T]{}, errors.New("tensor allocation failed")
+	}
+	dtype := getDLDataType[T]()
+
+	var deviceDataPtr unsafe.Pointer
+	bytes := calculateBytes(shape, dtype)
+	err := CheckCuvs(CuvsError(C.cuvsRMMAlloc(res.Resource, &deviceDataPtr, C.size_t(bytes))))
+	if err != nil {
+		C.free(unsafe.Pointer(dlm))
+		C.free(unsafe.Pointer(shapePtr))
+		return Tensor[T]{}, err
+	}
+
+	dlm.dl_tensor.data = deviceDataPtr
+	dlm.dl_tensor.device = C.DLDevice{
+		device_type: C.DLDeviceType(C.kDLCUDA),
+		device_id:   0,
+	}
+	dlm.dl_tensor.dtype = dtype
+	dlm.dl_tensor.ndim = C.int(len(shape))
+	dlm.dl_tensor.shape = (*C.int64_t)(shapePtr)
+	dlm.dl_tensor.strides = nil
+	dlm.dl_tensor.byte_offset = 0
+	dlm.manager_ctx = nil
+	dlm.deleter = nil
+
+	shapeCopy := make([]int64, len(shape))
+	copy(shapeCopy, shape)
+
+	return Tensor[T]{
+		C_tensor: dlm,
+		shape:    shapeCopy,
+	}, nil
+}
+
+// Destroys Tensor, freeing the memory it was allocated on.
+func (t *Tensor[T]) Close() error {
+	if t.C_tensor.dl_tensor.device.device_type == C.kDLCUDA {
+		bytes := t.sizeInBytes()
+		res, err := NewResource(nil)
+		if err != nil {
+			return err
+		}
+		err = CheckCuvs(CuvsError(C.cuvsRMMFree(res.Resource, t.C_tensor.dl_tensor.data, C.size_t(bytes))))
+
+		return err
+	} else if t.C_tensor.dl_tensor.device.device_type == C.kDLCPU {
+		if t.C_tensor.dl_tensor.data != nil {
+			C.free(t.C_tensor.dl_tensor.data)
+			t.C_tensor.dl_tensor.data = nil
+		}
+	}
+
+	if t.C_tensor.dl_tensor.shape != nil {
+		C.free(unsafe.Pointer(t.C_tensor.dl_tensor.shape))
+		t.C_tensor.dl_tensor.shape = nil
+	}
+
+	if t.C_tensor != nil {
+		C.free(unsafe.Pointer(t.C_tensor))
+		t.C_tensor = nil
+	}
+
+	t.C_tensor = nil
+	return nil
+}
+
+// Transfers the data in the Tensor to the device.
+func (t *Tensor[T]) ToDevice(res *Resource) (*Tensor[T], error) {
+	bytes := t.sizeInBytes()
+
+	var DeviceDataPointer unsafe.Pointer
+
+	err := CheckCuvs(CuvsError(C.cuvsRMMAlloc(res.Resource, &DeviceDataPointer, C.size_t(bytes))))
+	if err != nil {
+		return nil, err
+	}
+
+	err = CheckCuda(
+		C.cudaMemcpy(
+			DeviceDataPointer,
+			t.C_tensor.dl_tensor.data,
+			C.size_t(bytes),
+			C.cudaMemcpyHostToDevice,
+		))
+	if err != nil {
+		C.cuvsRMMFree(res.Resource, DeviceDataPointer, C.size_t(bytes))
+		return nil, err
+	}
+	t.C_tensor.dl_tensor.device.device_type = C.kDLCUDA
+	t.C_tensor.dl_tensor.data = DeviceDataPointer
+
+	return t, nil
+}
+
+// Returns the shape of the Tensor.
+func (t *Tensor[T]) Shape() []int64 {
+	return t.shape
+}
+
+// Expands the Tensor by adding newData to the end of the current data.
+// The Tensor must be on the device.
+func (t *Tensor[T]) Expand(res *Resource, newData [][]T) (*Tensor[T], error) {
+	if t.C_tensor.dl_tensor.device.device_type != C.kDLCUDA {
+		return &Tensor[T]{}, errors.New("Tensor must be on GPU")
+	}
+
+	newShape := []int64{int64(len(newData)), int64(len(newData[0]))}
+
+	flatData := make([]T, len(newData)*len(newData[0]))
+	for i := range newData {
+		for j := range newData[i] {
+			flatData[i*len(newData[0])+j] = newData[i][j]
+		}
+	}
+
+	old_shape := unsafe.Slice((*int64)(unsafe.Pointer(t.C_tensor.dl_tensor.shape)), 2)
+
+	if old_shape[1] != newShape[1] {
+		return &Tensor[T]{}, errors.New("new shape must be same as old shape, old shape: " + strconv.Itoa(int(old_shape[1])) + ", new shape: " + strconv.Itoa(int(newShape[1])))
+	}
+
+	newDataSize := newShape[0] * newShape[1] * int64(t.C_tensor.dl_tensor.dtype.bits) / 8
+
+	bytes := t.sizeInBytes()
+
+	var NewDeviceDataPointer unsafe.Pointer
+
+	err := CheckCuvs(CuvsError(C.cuvsRMMAlloc(res.Resource, &NewDeviceDataPointer, C.size_t(bytes+newDataSize))))
+	if err != nil {
+		return nil, err
+	}
+
+	err = CheckCuda(
+		C.cudaMemcpy(
+			NewDeviceDataPointer,
+			t.C_tensor.dl_tensor.data,
+			C.size_t(bytes),
+			C.cudaMemcpyDeviceToDevice,
+		))
+	if err != nil {
+		C.cuvsRMMFree(res.Resource, NewDeviceDataPointer, C.size_t(bytes+newDataSize))
+		return nil, err
+	}
+
+	err = CheckCuda(
+		C.cudaMemcpy(
+			unsafe.Pointer(uintptr(NewDeviceDataPointer)+uintptr(bytes)),
+			unsafe.Pointer(&flatData[0]),
+			C.size_t(newDataSize),
+			C.cudaMemcpyHostToDevice,
+		))
+	if err != nil {
+		C.cuvsRMMFree(res.Resource, NewDeviceDataPointer, C.size_t(bytes+newDataSize))
+		return nil, err
+	}
+
+	err = CheckCuvs(CuvsError(
+		C.cuvsRMMFree(res.Resource, t.C_tensor.dl_tensor.data, C.size_t(bytes))))
+	if err != nil {
+		return nil, err
+	}
+
+	shape := make([]int64, 2)
+	shape[0] = int64(*t.C_tensor.dl_tensor.shape) + int64(len(newData))
+
+	shape[1] = newShape[1]
+
+	t.shape = shape
+
+	t.C_tensor.dl_tensor.data = NewDeviceDataPointer
+	t.C_tensor.dl_tensor.shape = (*C.int64_t)(unsafe.Pointer(&shape[0]))
+
+	return t, nil
+}
+
+// Transfers the data in the Tensor to the host.
+func (t *Tensor[T]) ToHost(res *Resource) (*Tensor[T], error) {
+	bytes := t.sizeInBytes()
+
+	addr := (C.malloc(C.size_t(bytes)))
+	if addr == nil {
+		return nil, errors.New("memory allocation failed")
+	}
+
+	err := CheckCuda(
+		C.cudaMemcpy(
+			addr,
+			t.C_tensor.dl_tensor.data,
+			C.size_t(bytes),
+			C.cudaMemcpyDeviceToHost,
+		))
+	if err != nil {
+		return nil, err
+	}
+
+	err = CheckCuvs(CuvsError(
+		C.cuvsRMMFree(res.Resource, t.C_tensor.dl_tensor.data, C.size_t(bytes))))
+	if err != nil {
+		return nil, err
+	}
+
+	t.C_tensor.dl_tensor.device.device_type = C.kDLCPU
+	t.C_tensor.dl_tensor.data = addr
+
+	return t, nil
+}
+
+// Returns a slice of the data in the Tensor.
+// The Tensor must be on the host.
+func (t *Tensor[T]) Slice() ([][]T, error) {
+	if t.C_tensor.dl_tensor.device.device_type != C.kDLCPU {
+		return nil, errors.New("Tensor must be on CPU")
+	}
+
+	flatData := unsafe.Slice((*T)(t.C_tensor.dl_tensor.data), t.shape[0]*t.shape[1])
+
+	data := make([][]T, t.shape[0])
+	for i := range data {
+		data[i] = make([]T, t.shape[1])
+		for j := range data[i] {
+			data[i][j] = flatData[i*int(t.shape[1])+j]
+		}
+	}
+
+	return data, nil
+}
+
+func getDLDataType[T TensorNumberType]() C.DLDataType {
+	var zero T
+	switch any(zero).(type) {
+	case int64:
+		return C.DLDataType{
+			bits:  C.uchar(64),
+			lanes: C.ushort(1),
+			code:  C.kDLInt,
+		}
+	case uint32:
+		return C.DLDataType{
+			bits:  C.uchar(32),
+			lanes: C.ushort(1),
+			code:  C.kDLUInt,
+		}
+	case float32:
+		return C.DLDataType{
+			bits:  C.uchar(32),
+			lanes: C.ushort(1),
+			code:  C.kDLFloat,
+		}
+	}
+	panic("unreachable") // Go compiler ensures this is unreachable
+}
+
+func flattenData[T TensorNumberType](data [][]T, dest []T) {
+	cols := len(data[0])
+	for i, row := range data {
+		copy(dest[i*cols:], row)
+	}
+}
+
+func (t *Tensor[T]) sizeInBytes() int64 {
+	return calculateBytes(t.shape, t.C_tensor.dl_tensor.dtype)
+}
+
+func calculateBytes(shape []int64, dtype C.DLDataType) int64 {
+	bytes := int64(1)
+	for dim := range shape {
+		bytes *= (shape[dim])
+	}
+
+	bytes *= int64(dtype.bits) / 8
+
+	return bytes
+}
diff --git a/go/dlpack_test.go b/go/dlpack_test.go
new file mode 100644
index 0000000000..135363f811
--- /dev/null
+++ b/go/dlpack_test.go
@@ -0,0 +1,190 @@
+package cuvs_test
+
+import (
+	"math/rand"
+	"reflect"
+	"testing"
+	"time"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+func TestDlPack(t *testing.T) {
+	resource, _ := cuvs.NewResource(nil)
+	rand.Seed(time.Now().UnixNano())
+	NDataPoints := 256
+	NFeatures := 16
+	TestDataset := make([][]float32, NDataPoints)
+	for i := range TestDataset {
+		TestDataset[i] = make([]float32, NFeatures)
+		for j := range TestDataset[i] {
+			TestDataset[i][j] = float32(i)
+		}
+	}
+
+	dataset, err := cuvs.NewTensor(TestDataset[:127])
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	_, err = dataset.ToDevice(&resource)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	_, err = dataset.Expand(&resource, TestDataset[127:])
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	_, err = dataset.ToHost(&resource)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	arr, err := dataset.Slice()
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	for i := range arr {
+		for j := range arr[i] {
+			if arr[i][j] != TestDataset[i][j] {
+				t.Errorf("slices don't match at [%d][%d], expected %f, got %f",
+					i, j, TestDataset[i][j], arr[i][j])
+			}
+		}
+	}
+}
+
+func TestShape(t *testing.T) {
+	// Test cases with different shapes
+	testCases := []struct {
+		rows int
+		cols int
+		name string
+	}{
+		{10, 5, "small matrix"},
+		{100, 20, "medium matrix"},
+		{1000, 50, "large matrix"},
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			// Create test data
+			data := make([][]float32, tc.rows)
+			for i := range data {
+				data[i] = make([]float32, tc.cols)
+				for j := range data[i] {
+					data[i][j] = float32(i * j)
+				}
+			}
+
+			// Create tensor
+			tensor, err := cuvs.NewTensor(data)
+			if err != nil {
+				t.Fatalf("failed to create tensor: %v", err)
+			}
+			defer tensor.Close()
+
+			// Check shape
+			shape := tensor.Shape()
+			expectedShape := []int64{int64(tc.rows), int64(tc.cols)}
+			if !reflect.DeepEqual(shape, expectedShape) {
+				t.Errorf("incorrect shape: got %v, want %v", shape, expectedShape)
+			}
+		})
+	}
+}
+
+func TestEmptyTensor(t *testing.T) {
+	// Test creating tensor with empty data
+	_, err := cuvs.NewTensor([][]float32{})
+	if err == nil {
+		t.Error("expected error when creating tensor with empty data, got nil")
+	}
+}
+
+func TestDeviceOperations(t *testing.T) {
+	resource, err := cuvs.NewResource(nil)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	// Create test data
+	data := make([][]float32, 10)
+	for i := range data {
+		data[i] = make([]float32, 5)
+		for j := range data[i] {
+			data[i][j] = float32(i * j)
+		}
+	}
+
+	// Test device transfer operations
+	t.Run("device transfer", func(t *testing.T) {
+		tensor, err := cuvs.NewTensor(data)
+		if err != nil {
+			t.Fatal(err)
+		}
+		defer tensor.Close()
+
+		// Transfer to device
+		deviceTensor, err := tensor.ToDevice(&resource)
+		if err != nil {
+			t.Fatalf("failed to transfer to device: %v", err)
+		}
+
+		// Transfer back to host
+		hostTensor, err := deviceTensor.ToHost(&resource)
+		if err != nil {
+			t.Fatalf("failed to transfer back to host: %v", err)
+		}
+
+		// Verify data
+		result, err := hostTensor.Slice()
+		if err != nil {
+			t.Fatalf("failed to slice tensor: %v", err)
+		}
+
+		for i := range data {
+			for j := range data[i] {
+				if result[i][j] != data[i][j] {
+					t.Errorf("data mismatch at [%d][%d]: got %f, want %f",
+						i, j, result[i][j], data[i][j])
+				}
+			}
+		}
+	})
+}
+
+func TestDifferentDataTypes(t *testing.T) {
+	// Test int64 tensor
+	t.Run("int64", func(t *testing.T) {
+		data := [][]int64{{1, 2}, {3, 4}}
+		tensor, err := cuvs.NewTensor(data)
+		if err != nil {
+			t.Fatal(err)
+		}
+		defer tensor.Close()
+
+		shape := tensor.Shape()
+		if !reflect.DeepEqual(shape, []int64{2, 2}) {
+			t.Errorf("incorrect shape for int64 tensor: got %v, want [2 2]", shape)
+		}
+	})
+
+	// Test uint32 tensor
+	t.Run("uint32", func(t *testing.T) {
+		data := [][]uint32{{1, 2}, {3, 4}}
+		tensor, err := cuvs.NewTensor(data)
+		if err != nil {
+			t.Fatal(err)
+		}
+		defer tensor.Close()
+
+		shape := tensor.Shape()
+		if !reflect.DeepEqual(shape, []int64{2, 2}) {
+			t.Errorf("incorrect shape for uint32 tensor: got %v, want [2 2]", shape)
+		}
+	})
+}
diff --git a/go/exceptions.go b/go/exceptions.go
new file mode 100644
index 0000000000..af73ca9911
--- /dev/null
+++ b/go/exceptions.go
@@ -0,0 +1,23 @@
+package cuvs
+
+// #include <cuvs/core/c_api.h>
+import "C"
+import "errors"
+
+type CuvsError C.cuvsError_t
+
+// Wrapper function to convert cuvs error to Go error
+func CheckCuvs(error CuvsError) error {
+	if error == C.CUVS_ERROR {
+		return errors.New(C.GoString(C.cuvsGetLastErrorText()))
+	}
+	return nil
+}
+
+// Wrapper function to convert cuda error to Go error
+func CheckCuda(error C.cudaError_t) error {
+	if error != C.cudaSuccess {
+		return errors.New(C.GoString(C.cudaGetErrorString(error)))
+	}
+	return nil
+}
diff --git a/go/go.mod b/go/go.mod
new file mode 100644
index 0000000000..4e1ab39a44
--- /dev/null
+++ b/go/go.mod
@@ -0,0 +1,3 @@
+module github.com/rapidsai/cuvs/go
+
+go 1.22.4
diff --git a/go/go.sum b/go/go.sum
new file mode 100644
index 0000000000..81937f7b14
--- /dev/null
+++ b/go/go.sum
@@ -0,0 +1,4 @@
+golang.org/x/mod v0.22.0 h1:D4nJWe9zXqHOmWqj4VMOJhvzj7bEZg4wEYa759z1pH4=
+golang.org/x/mod v0.22.0/go.mod h1:6SkKJ3Xj0I0BrPOZoBy3bdMptDDU9oJrpohJ3eWZ1fY=
+golang.org/x/tools v0.27.0 h1:qEKojBykQkQ4EynWy4S8Weg69NumxKdn40Fce3uc/8o=
+golang.org/x/tools v0.27.0/go.mod h1:sUi0ZgbwW9ZPAq26Ekut+weQPR5eIM6GQLQ1Yjm1H0Q=
diff --git a/go/ivf_flat/index_params.go b/go/ivf_flat/index_params.go
new file mode 100644
index 0000000000..f6cbe60fbf
--- /dev/null
+++ b/go/ivf_flat/index_params.go
@@ -0,0 +1,87 @@
+package ivf_flat
+
+// #include <cuvs/neighbors/ivf_flat.h>
+import "C"
+
+import (
+	"errors"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// Supplemental parameters to build IVF Flat Index
+type IndexParams struct {
+	params C.cuvsIvfFlatIndexParams_t
+}
+
+// Creates a new IndexParams
+func CreateIndexParams() (*IndexParams, error) {
+	var params C.cuvsIvfFlatIndexParams_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatIndexParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &IndexParams{params: params}, nil
+}
+
+// The number of clusters used in the coarse quantizer.
+func (p *IndexParams) SetNLists(n_lists uint32) (*IndexParams, error) {
+	p.params.n_lists = C.uint32_t(n_lists)
+	return p, nil
+}
+
+// Distance Type to use for building the index
+func (p *IndexParams) SetMetric(metric cuvs.Distance) (*IndexParams, error) {
+	CMetric, exists := cuvs.CDistances[metric]
+
+	if !exists {
+		return nil, errors.New("cuvs: invalid distance metric")
+	}
+	p.params.metric = C.cuvsDistanceType(CMetric)
+
+	return p, nil
+}
+
+// Metric argument for Minkowski distances - set to 2.0 if not applicable
+func (p *IndexParams) SetMetricArg(metric_arg float32) (*IndexParams, error) {
+	p.params.metric_arg = C.float(metric_arg)
+	return p, nil
+}
+
+// The number of iterations searching for kmeans centers during index building.
+func (p *IndexParams) SetKMeansNIters(kmeans_n_iters uint32) (*IndexParams, error) {
+	p.params.kmeans_n_iters = C.uint32_t(kmeans_n_iters)
+	return p, nil
+}
+
+// If kmeans_trainset_fraction is less than 1, then the dataset is
+// subsampled, and only n_samples * kmeans_trainset_fraction rows
+// are used for training.
+func (p *IndexParams) SetKMeansTrainsetFraction(kmeans_trainset_fraction float64) (*IndexParams, error) {
+	p.params.kmeans_trainset_fraction = C.double(kmeans_trainset_fraction)
+	return p, nil
+}
+
+// After training the coarse and fine quantizers, we will populate
+// the index with the dataset if add_data_on_build == true, otherwise
+// the index is left empty, and the extend method can be used
+// to add new vectors to the index.
+func (p *IndexParams) SetAddDataOnBuild(add_data_on_build bool) (*IndexParams, error) {
+	if add_data_on_build {
+		p.params.add_data_on_build = C._Bool(true)
+	} else {
+		p.params.add_data_on_build = C._Bool(false)
+	}
+	return p, nil
+}
+
+// Destroys IndexParams
+func (p *IndexParams) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatIndexParamsDestroy(p.params)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
diff --git a/go/ivf_flat/ivf_flat.go b/go/ivf_flat/ivf_flat.go
new file mode 100644
index 0000000000..3330eb95e2
--- /dev/null
+++ b/go/ivf_flat/ivf_flat.go
@@ -0,0 +1,72 @@
+package ivf_flat
+
+// #include <cuvs/neighbors/ivf_flat.h>
+import "C"
+
+import (
+	"errors"
+	"unsafe"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// IVF Flat Index
+type IvfFlatIndex struct {
+	index   C.cuvsIvfFlatIndex_t
+	trained bool
+}
+
+// Creates a new empty IvfFlatIndex
+func CreateIndex(params *IndexParams, dataset *cuvs.Tensor[float32]) (*IvfFlatIndex, error) {
+	var index C.cuvsIvfFlatIndex_t
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatIndexCreate(&index)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &IvfFlatIndex{index: index}, nil
+}
+
+// Builds an IvfFlatIndex from the dataset for efficient search.
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `params` - Parameters for building the index
+// * `dataset` - A row-major Tensor on either the host or device to index
+// * `index` - IvfFlatIndex to build
+func BuildIndex[T any](Resources cuvs.Resource, params *IndexParams, dataset *cuvs.Tensor[T], index *IvfFlatIndex) error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatBuild(C.ulong(Resources.Resource), params.params, (*C.DLManagedTensor)(unsafe.Pointer(dataset.C_tensor)), index.index)))
+	if err != nil {
+		return err
+	}
+	index.trained = true
+	return nil
+}
+
+// Destroys the IvfFlatIndex
+func (index *IvfFlatIndex) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatIndexDestroy(index.index)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+// Perform a Approximate Nearest Neighbors search on the Index
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `params` - Parameters to use in searching the index
+// * `index` - IvfFlatIndex to search
+// * `queries` - A tensor in device memory to query for
+// * `neighbors` - Tensor in device memory that receives the indices of the nearest neighbors
+// * `distances` - Tensor in device memory that receives the distances of the nearest neighbors
+func SearchIndex[T any](Resources cuvs.Resource, params *SearchParams, index *IvfFlatIndex, queries *cuvs.Tensor[T], neighbors *cuvs.Tensor[int64], distances *cuvs.Tensor[T]) error {
+	if !index.trained {
+		return errors.New("index needs to be built before calling search")
+	}
+
+	return cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatSearch(C.cuvsResources_t(Resources.Resource), params.params, index.index, (*C.DLManagedTensor)(unsafe.Pointer(queries.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(neighbors.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(distances.C_tensor)))))
+}
diff --git a/go/ivf_flat/ivf_flat_test.go b/go/ivf_flat/ivf_flat_test.go
new file mode 100644
index 0000000000..8bca965f89
--- /dev/null
+++ b/go/ivf_flat/ivf_flat_test.go
@@ -0,0 +1,123 @@
+package ivf_flat
+
+import (
+	"math/rand/v2"
+	"testing"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+func TestIvfFlat(t *testing.T) {
+	const (
+		nDataPoints = 1024
+		nFeatures   = 16
+		nQueries    = 4
+		k           = 4
+		epsilon     = 0.001
+	)
+
+	resource, _ := cuvs.NewResource(nil)
+	defer resource.Close()
+
+	testDataset := make([][]float32, nDataPoints)
+	for i := range testDataset {
+		testDataset[i] = make([]float32, nFeatures)
+		for j := range testDataset[i] {
+			testDataset[i][j] = rand.Float32()
+		}
+	}
+
+	dataset, err := cuvs.NewTensor(testDataset)
+	if err != nil {
+		t.Fatalf("error creating dataset tensor: %v", err)
+	}
+	defer dataset.Close()
+
+	indexParams, err := CreateIndexParams()
+	if err != nil {
+		t.Fatalf("error creating index params: %v", err)
+	}
+	defer indexParams.Close()
+
+	index, _ := CreateIndex(indexParams, &dataset)
+	defer index.Close()
+
+	// use the first 4 points from the dataset as queries : will test that we get them back
+	// as their own nearest neighbor
+	queries, _ := cuvs.NewTensor(testDataset[:nQueries])
+	defer queries.Close()
+
+	neighbors, err := cuvs.NewTensorOnDevice[int64](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating neighbors tensor: %v", err)
+	}
+	defer neighbors.Close()
+
+	distances, err := cuvs.NewTensorOnDevice[float32](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating distances tensor: %v", err)
+	}
+	defer distances.Close()
+
+	if _, err := dataset.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving dataset to device: %v", err)
+	}
+
+	if err := BuildIndex(resource, indexParams, &dataset, index); err != nil {
+		t.Fatalf("error building index: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	if _, err := queries.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving queries to device: %v", err)
+	}
+
+	SearchParams, err := CreateSearchParams()
+	if err != nil {
+		t.Fatalf("error creating search params: %v", err)
+	}
+	defer SearchParams.Close()
+
+	err = SearchIndex(resource, SearchParams, index, &queries, &neighbors, &distances)
+	if err != nil {
+		t.Fatalf("error searching index: %v", err)
+	}
+
+	if _, err := neighbors.ToHost(&resource); err != nil {
+		t.Fatalf("error moving neighbors to host: %v", err)
+	}
+
+	if _, err := distances.ToHost(&resource); err != nil {
+		t.Fatalf("error moving distances to host: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	neighborsSlice, err := neighbors.Slice()
+	if err != nil {
+		t.Fatalf("error getting neighbors slice: %v", err)
+	}
+
+	for i := range neighborsSlice {
+		println(neighborsSlice[i][0])
+		if neighborsSlice[i][0] != int64(i) {
+			t.Error("wrong neighbor, expected", i, "got", neighborsSlice[i][0])
+		}
+	}
+
+	distancesSlice, err := distances.Slice()
+	if err != nil {
+		t.Fatalf("error getting distances slice: %v", err)
+	}
+
+	for i := range distancesSlice {
+		if distancesSlice[i][0] >= epsilon || distancesSlice[i][0] <= -epsilon {
+			t.Error("distance should be close to 0, got", distancesSlice[i][0])
+		}
+	}
+}
diff --git a/go/ivf_flat/search_params.go b/go/ivf_flat/search_params.go
new file mode 100644
index 0000000000..3606e4662e
--- /dev/null
+++ b/go/ivf_flat/search_params.go
@@ -0,0 +1,39 @@
+package ivf_flat
+
+// #include <cuvs/neighbors/ivf_flat.h>
+import "C"
+
+import (
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+type SearchParams struct {
+	params C.cuvsIvfFlatSearchParams_t
+}
+
+// Creates a new SearchParams
+func CreateSearchParams() (*SearchParams, error) {
+	var params C.cuvsIvfFlatSearchParams_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatSearchParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &SearchParams{params: params}, nil
+}
+
+// The number of clusters to search.
+func (p *SearchParams) SetNProbes(n_probes uint32) (*SearchParams, error) {
+	p.params.n_probes = C.uint32_t(n_probes)
+	return p, nil
+}
+
+// Destroy SearchParams
+func (p *SearchParams) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatSearchParamsDestroy(p.params)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
diff --git a/go/ivf_pq/index_params.go b/go/ivf_pq/index_params.go
new file mode 100644
index 0000000000..d04b44621e
--- /dev/null
+++ b/go/ivf_pq/index_params.go
@@ -0,0 +1,149 @@
+package ivf_pq
+
+// #include <cuvs/neighbors/ivf_pq.h>
+import "C"
+
+import (
+	"errors"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+type IndexParams struct {
+	params C.cuvsIvfPqIndexParams_t
+}
+
+type codebookKind int
+
+const (
+	Subspace codebookKind = iota
+	Cluster
+)
+
+var cCodebookKinds = map[codebookKind]int{
+	Subspace: C.PER_SUBSPACE,
+	Cluster:  C.PER_CLUSTER,
+}
+
+// Creates a new IndexParams
+func CreateIndexParams() (*IndexParams, error) {
+	var params C.cuvsIvfPqIndexParams_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqIndexParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &IndexParams{params: params}, nil
+}
+
+// The number of clusters used in the coarse quantizer.
+func (p *IndexParams) SetNLists(n_lists uint32) (*IndexParams, error) {
+	p.params.n_lists = C.uint32_t(n_lists)
+	return p, nil
+}
+
+// Distance Type to use for building the index
+func (p *IndexParams) SetMetric(metric cuvs.Distance) (*IndexParams, error) {
+	CMetric, exists := cuvs.CDistances[metric]
+
+	if !exists {
+		return nil, errors.New("cuvs: invalid distance metric")
+	}
+	p.params.metric = C.cuvsDistanceType(CMetric)
+
+	return p, nil
+}
+
+// Metric argument for Minkowski distances - set to 2.0 if not applicable
+func (p *IndexParams) SetMetricArg(metric_arg float32) (*IndexParams, error) {
+	p.params.metric_arg = C.float(metric_arg)
+	return p, nil
+}
+
+// The number of iterations searching for kmeans centers during index building.
+func (p *IndexParams) SetKMeansNIters(kmeans_n_iters uint32) (*IndexParams, error) {
+	p.params.kmeans_n_iters = C.uint32_t(kmeans_n_iters)
+	return p, nil
+}
+
+// If kmeans_trainset_fraction is less than 1, then the dataset is
+// subsampled, and only n_samples * kmeans_trainset_fraction rows
+// are used for training.
+func (p *IndexParams) SetKMeansTrainsetFraction(kmeans_trainset_fraction float64) (*IndexParams, error) {
+	p.params.kmeans_trainset_fraction = C.double(kmeans_trainset_fraction)
+	return p, nil
+}
+
+// The bit length of the vector element after quantization.
+func (p *IndexParams) SetPQBits(pq_bits uint32) (*IndexParams, error) {
+	p.params.pq_bits = C.uint32_t(pq_bits)
+	return p, nil
+}
+
+// The dimensionality of a the vector after product quantization.
+// When zero, an optimal value is selected using a heuristic. Note
+// pq_dim * pq_bits must be a multiple of 8. Hint: a smaller 'pq_dim'
+// results in a smaller index size and better search performance, but
+// lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any number,
+// but multiple of 8 are desirable for good performance. If 'pq_bits'
+// is not 8, 'pq_dim' should be a multiple of 8. For good performance,
+// it is desirable that 'pq_dim' is a multiple of 32. Ideally,
+// 'pq_dim' should be also a divisor of the dataset dim.
+func (p *IndexParams) SetPQDim(pq_dim uint32) (*IndexParams, error) {
+	p.params.pq_dim = C.uint32_t(pq_dim)
+	return p, nil
+}
+
+func (p *IndexParams) SetCodebookKind(codebook_kind codebookKind) (*IndexParams, error) {
+	CCodebookKind, exists := cCodebookKinds[codebook_kind]
+
+	if !exists {
+		return nil, errors.New("cuvs: invalid codebook_kind")
+	}
+	p.params.codebook_kind = uint32(CCodebookKind)
+
+	return p, nil
+}
+
+// Apply a random rotation matrix on the input data and queries even
+// if `dim % pq_dim == 0`. Note: if `dim` is not multiple of `pq_dim`,
+// a random rotation is always applied to the input data and queries
+// to transform the working space from `dim` to `rot_dim`, which may
+// be slightly larger than the original space and and is a multiple
+// of `pq_dim` (`rot_dim % pq_dim == 0`). However, this transform is
+// not necessary when `dim` is multiple of `pq_dim` (`dim == rot_dim`,
+// hence no need in adding "extra" data columns / features). By
+// default, if `dim == rot_dim`, the rotation transform is
+// initialized with the identity matrix. When
+// `force_random_rotation == True`, a random orthogonal transform
+func (p *IndexParams) SetForceRandomRotation(force_random_rotation bool) (*IndexParams, error) {
+	if force_random_rotation {
+		p.params.force_random_rotation = C._Bool(true)
+	} else {
+		p.params.force_random_rotation = C._Bool(false)
+	}
+	return p, nil
+}
+
+// After training the coarse and fine quantizers, we will populate
+// the index with the dataset if add_data_on_build == true, otherwise
+// the index is left empty, and the extend method can be used
+// to add new vectors to the index.
+func (p *IndexParams) SetAddDataOnBuild(add_data_on_build bool) (*IndexParams, error) {
+	if add_data_on_build {
+		p.params.add_data_on_build = C._Bool(true)
+	} else {
+		p.params.add_data_on_build = C._Bool(false)
+	}
+	return p, nil
+}
+
+// Destroys IndexParams
+func (p *IndexParams) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqIndexParamsDestroy(p.params)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
diff --git a/go/ivf_pq/ivf_pq.go b/go/ivf_pq/ivf_pq.go
new file mode 100644
index 0000000000..cbbec629d1
--- /dev/null
+++ b/go/ivf_pq/ivf_pq.go
@@ -0,0 +1,73 @@
+package ivf_pq
+
+// #include <cuvs/neighbors/ivf_pq.h>
+import "C"
+
+import (
+	"errors"
+	"unsafe"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// IVF PQ Index
+type IvfPqIndex struct {
+	index   C.cuvsIvfPqIndex_t
+	trained bool
+}
+
+// Creates a new empty IvfPqIndex
+func CreateIndex(params *IndexParams, dataset *cuvs.Tensor[float32]) (*IvfPqIndex, error) {
+	var index C.cuvsIvfPqIndex_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqIndexCreate(&index)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &IvfPqIndex{index: index}, nil
+}
+
+// Builds an IvfPqIndex from the dataset for efficient search.
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `params` - Parameters for building the index
+// * `dataset` - A row-major Tensor on either the host or device to index
+// * `index` - IvfPqIndex to build
+func BuildIndex[T any](Resources cuvs.Resource, params *IndexParams, dataset *cuvs.Tensor[T], index *IvfPqIndex) error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqBuild(C.ulong(Resources.Resource), params.params, (*C.DLManagedTensor)(unsafe.Pointer(dataset.C_tensor)), index.index)))
+	if err != nil {
+		return err
+	}
+	index.trained = true
+	return nil
+}
+
+// Destroys the IvfPqIndex
+func (index *IvfPqIndex) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqIndexDestroy(index.index)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+// Perform a Approximate Nearest Neighbors search on the Index
+//
+// # Arguments
+//
+// * `Resources` - Resources to use
+// * `params` - Parameters to use in searching the index
+// * `index` - IvfPqIndex to search
+// * `queries` - A tensor in device memory to query for
+// * `neighbors` - Tensor in device memory that receives the indices of the nearest neighbors
+// * `distances` - Tensor in device memory that receives the distances of the nearest neighbors
+func SearchIndex[T any](Resources cuvs.Resource, params *SearchParams, index *IvfPqIndex, queries *cuvs.Tensor[T], neighbors *cuvs.Tensor[int64], distances *cuvs.Tensor[T]) error {
+	if !index.trained {
+		return errors.New("index needs to be built before calling search")
+	}
+
+	return cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqSearch(C.cuvsResources_t(Resources.Resource), params.params, index.index, (*C.DLManagedTensor)(unsafe.Pointer(queries.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(neighbors.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(distances.C_tensor)))))
+}
diff --git a/go/ivf_pq/ivf_pq_test.go b/go/ivf_pq/ivf_pq_test.go
new file mode 100644
index 0000000000..39b7727a3d
--- /dev/null
+++ b/go/ivf_pq/ivf_pq_test.go
@@ -0,0 +1,123 @@
+package ivf_pq
+
+import (
+	"math/rand/v2"
+	"testing"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+func TestIvfPq(t *testing.T) {
+	const (
+		nDataPoints = 1024
+		nFeatures   = 16
+		nQueries    = 4
+		k           = 4
+		epsilon     = 0.001
+	)
+
+	resource, _ := cuvs.NewResource(nil)
+	defer resource.Close()
+
+	testDataset := make([][]float32, nDataPoints)
+	for i := range testDataset {
+		testDataset[i] = make([]float32, nFeatures)
+		for j := range testDataset[i] {
+			testDataset[i][j] = rand.Float32()
+		}
+	}
+
+	dataset, err := cuvs.NewTensor(testDataset)
+	if err != nil {
+		t.Fatalf("error creating dataset tensor: %v", err)
+	}
+	defer dataset.Close()
+
+	indexParams, err := CreateIndexParams()
+	if err != nil {
+		t.Fatalf("error creating index params: %v", err)
+	}
+	defer indexParams.Close()
+
+	index, _ := CreateIndex(indexParams, &dataset)
+	defer index.Close()
+
+	// use the first 4 points from the dataset as queries : will test that we get them back
+	// as their own nearest neighbor
+	queries, _ := cuvs.NewTensor(testDataset[:nQueries])
+	defer queries.Close()
+
+	neighbors, err := cuvs.NewTensorOnDevice[int64](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating neighbors tensor: %v", err)
+	}
+	defer neighbors.Close()
+
+	distances, err := cuvs.NewTensorOnDevice[float32](&resource, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		t.Fatalf("error creating distances tensor: %v", err)
+	}
+	defer distances.Close()
+
+	if _, err := dataset.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving dataset to device: %v", err)
+	}
+
+	if err := BuildIndex(resource, indexParams, &dataset, index); err != nil {
+		t.Fatalf("error building index: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	if _, err := queries.ToDevice(&resource); err != nil {
+		t.Fatalf("error moving queries to device: %v", err)
+	}
+
+	SearchParams, err := CreateSearchParams()
+	if err != nil {
+		t.Fatalf("error creating search params: %v", err)
+	}
+	defer SearchParams.Close()
+
+	err = SearchIndex(resource, SearchParams, index, &queries, &neighbors, &distances)
+	if err != nil {
+		t.Fatalf("error searching index: %v", err)
+	}
+
+	if _, err := neighbors.ToHost(&resource); err != nil {
+		t.Fatalf("error moving neighbors to host: %v", err)
+	}
+
+	if _, err := distances.ToHost(&resource); err != nil {
+		t.Fatalf("error moving distances to host: %v", err)
+	}
+
+	if err := resource.Sync(); err != nil {
+		t.Fatalf("error syncing resource: %v", err)
+	}
+
+	neighborsSlice, err := neighbors.Slice()
+	if err != nil {
+		t.Fatalf("error getting neighbors slice: %v", err)
+	}
+
+	for i := range neighborsSlice {
+		println(neighborsSlice[i][0])
+		if neighborsSlice[i][0] != int64(i) {
+			t.Error("wrong neighbor, expected", i, "got", neighborsSlice[i][0])
+		}
+	}
+
+	distancesSlice, err := distances.Slice()
+	if err != nil {
+		t.Fatalf("error getting distances slice: %v", err)
+	}
+
+	for i := range distancesSlice {
+		if distancesSlice[i][0] >= epsilon || distancesSlice[i][0] <= -epsilon {
+			t.Error("distance should be close to 0, got", distancesSlice[i][0])
+		}
+	}
+}
diff --git a/go/ivf_pq/search_params.go b/go/ivf_pq/search_params.go
new file mode 100644
index 0000000000..190ca7036e
--- /dev/null
+++ b/go/ivf_pq/search_params.go
@@ -0,0 +1,107 @@
+package ivf_pq
+
+// #include <cuvs/neighbors/ivf_pq.h>
+import "C"
+
+import (
+	"errors"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+// Supplemental parameters to search IVF PQ Index
+type SearchParams struct {
+	params C.cuvsIvfPqSearchParams_t
+}
+
+type lutDtype int
+
+const (
+	Lut_Uint8 lutDtype = iota
+	Lut_Uint16
+	Lut_Uint32
+	Lut_Uint64
+	Lut_Int8
+	Lut_Int16
+	Lut_Int32
+	Lut_Int64
+)
+
+var cLutDtypes = map[lutDtype]int{
+	Lut_Uint8:  C.CUDA_R_8U,
+	Lut_Uint16: C.CUDA_R_16U,
+	Lut_Uint32: C.CUDA_R_32U,
+	Lut_Uint64: C.CUDA_R_64U,
+	Lut_Int8:   C.CUDA_R_8I,
+	Lut_Int16:  C.CUDA_R_16I,
+	Lut_Int32:  C.CUDA_R_32I,
+	Lut_Int64:  C.CUDA_R_64I,
+}
+
+type internalDistanceDtype int
+
+const (
+	InternalDistance_Float32 internalDistanceDtype = iota
+	InternalDistance_Float64
+)
+
+var CInternalDistanceDtypes = map[internalDistanceDtype]int{
+	InternalDistance_Float32: C.CUDA_R_32F,
+	InternalDistance_Float64: C.CUDA_R_64F,
+}
+
+// Creates a new SearchParams
+func CreateSearchParams() (*SearchParams, error) {
+	var params C.cuvsIvfPqSearchParams_t
+
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqSearchParamsCreate(&params)))
+	if err != nil {
+		return nil, err
+	}
+
+	return &SearchParams{params: params}, nil
+}
+
+// The number of clusters to search.
+func (p *SearchParams) SetNProbes(n_probes uint32) (*SearchParams, error) {
+	p.params.n_probes = C.uint32_t(n_probes)
+	return p, nil
+}
+
+// Data type of look up table to be created dynamically at search
+// time. The use of low-precision types reduces the amount of shared
+// memory required at search time, so fast shared memory kernels can
+// be used even for datasets with large dimansionality. Note that
+// the recall is slightly degraded when low-precision type is
+// selected.
+func (p *SearchParams) SetLutDtype(lut_dtype lutDtype) (*SearchParams, error) {
+	CLutDtype, exists := cLutDtypes[lutDtype(lut_dtype)]
+
+	if !exists {
+		return nil, errors.New("cuvs: invalid lut_dtype")
+	}
+	p.params.lut_dtype = C.cudaDataType_t(CLutDtype)
+
+	return p, nil
+}
+
+// Storage data type for distance/similarity computation.
+func (p *SearchParams) SetInternalDistanceDtype(internal_distance_dtype internalDistanceDtype) (*SearchParams, error) {
+	CInternalDistanceDtype, exists := CInternalDistanceDtypes[internalDistanceDtype(internal_distance_dtype)]
+
+	if !exists {
+		return nil, errors.New("cuvs: invalid internal_distance_dtype")
+	}
+	p.params.internal_distance_dtype = C.cudaDataType_t(CInternalDistanceDtype)
+
+	return p, nil
+}
+
+// Destroys SearchParams
+func (p *SearchParams) Close() error {
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfPqSearchParamsDestroy(p.params)))
+	if err != nil {
+		return err
+	}
+	return nil
+}
diff --git a/go/memory_resource.go b/go/memory_resource.go
new file mode 100644
index 0000000000..df042c821d
--- /dev/null
+++ b/go/memory_resource.go
@@ -0,0 +1,93 @@
+package cuvs
+
+// #include <cuvs/core/c_api.h>
+import "C"
+
+import (
+	"runtime"
+)
+
+type CuvsMemoryCommand int
+
+const (
+	CuvsMemoryNew = iota
+	CuvsMemoryRelease
+)
+
+type CuvsPoolMemory struct {
+	ch                        chan CuvsMemoryCommand
+	errCh                     chan error
+	initial_pool_size_percent int
+	max_pool_size_percent     int
+	managed                   bool
+}
+
+// Creates new CuvsPoolMemory struct
+// initial_pool_size_percent is the initial size of the pool in percent of total available device memory
+// max_pool_size_percent is the maximum size of the pool in percent of total available device memory
+// managed is whether to use CUDA managed memory
+func NewCuvsPoolMemory(initial_pool_size_percent int, max_pool_size_percent int, managed bool) (*CuvsPoolMemory, error) {
+	c := CuvsPoolMemory{
+		ch:                        make(chan CuvsMemoryCommand),
+		errCh:                     make(chan error),
+		initial_pool_size_percent: initial_pool_size_percent,
+		max_pool_size_percent:     max_pool_size_percent,
+		managed:                   managed,
+	}
+
+	c.start()
+	c.ch <- CuvsMemoryNew
+
+	if err := <-c.errCh; err != nil {
+		return nil, err
+	}
+
+	return &c, nil
+}
+
+// Enables pool memory
+func (m *CuvsPoolMemory) start() {
+	go func() {
+		runtime.LockOSThread()
+		defer runtime.UnlockOSThread()
+
+		for command := range m.ch {
+			var err error
+			switch command {
+			case CuvsMemoryNew:
+				err = CheckCuvs(CuvsError(C.cuvsRMMPoolMemoryResourceEnable(
+					C.int(m.initial_pool_size_percent),
+					C.int(m.max_pool_size_percent),
+					C._Bool(m.managed))))
+				m.errCh <- err
+
+			case CuvsMemoryRelease:
+				err = CheckCuvs(CuvsError(C.cuvsRMMMemoryResourceReset()))
+				m.errCh <- err
+			}
+		}
+	}()
+}
+
+// Disables pool memory
+func (m *CuvsPoolMemory) Close() error {
+	m.ch <- CuvsMemoryRelease
+	err := <-m.errCh
+	close(m.ch)
+	close(m.errCh)
+	return err
+}
+
+func Example() error {
+	mem, err := NewCuvsPoolMemory(60, 100, false)
+	if err != nil {
+		return err
+	}
+
+	err = mem.Close()
+	if err != nil {
+		return err
+	}
+
+	return nil
+}
diff --git a/go/memory_resource_test.go b/go/memory_resource_test.go
new file mode 100644
index 0000000000..e44c20485e
--- /dev/null
+++ b/go/memory_resource_test.go
@@ -0,0 +1,19 @@
+package cuvs_test
+
+import (
+	"testing"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+)
+
+func TestMemoryResource(t *testing.T) {
+	mem, err := cuvs.NewCuvsPoolMemory(60, 100, false)
+	if err != nil {
+		t.Fatal("Failed to create memory resource:", err)
+	}
+
+	err = mem.Close()
+	if err != nil {
+		t.Fatal("Failed to close memory resource:", err)
+	}
+}
diff --git a/go/resources.go b/go/resources.go
new file mode 100644
index 0000000000..562aa7b7fc
--- /dev/null
+++ b/go/resources.go
@@ -0,0 +1,57 @@
+package cuvs
+
+// #include <cuvs/core/c_api.h>
+import "C"
+
+type cuvsResource C.cuvsResources_t
+
+// Resources are objects that are shared between function calls,
+// and includes things like CUDA streams, cuBLAS handles and other
+// resources that are expensive to create.
+type Resource struct {
+	Resource C.cuvsResources_t
+}
+
+// Returns a new Resource object
+func NewResource(stream C.cudaStream_t) (Resource, error) {
+	res := C.cuvsResources_t(0)
+	err := CheckCuvs(CuvsError(C.cuvsResourcesCreate(&res)))
+	if err != nil {
+		return Resource{}, err
+	}
+
+	if stream != nil {
+		err := CheckCuvs(CuvsError(C.cuvsStreamSet(res, stream)))
+		if err != nil {
+			return Resource{}, err
+		}
+	}
+
+	return Resource{Resource: res}, nil
+}
+
+// Syncs the current cuda stream
+func (r Resource) Sync() error {
+	return CheckCuvs(CuvsError(C.cuvsStreamSync(r.Resource)))
+}
+
+// Gets the current cuda stream
+func (r Resource) GetCudaStream() (C.cudaStream_t, error) {
+	var stream C.cudaStream_t
+
+	err := CheckCuvs(CuvsError(C.cuvsStreamGet(r.Resource, &stream)))
+	if err != nil {
+		return C.cudaStream_t(nil), err
+	}
+
+	return stream, nil
+}
+
+func (r Resource) Close() error {
+	err := CheckCuvs(CuvsError(C.cuvsResourcesDestroy(r.Resource)))
+	if err != nil {
+		return err
+	}
+
+	return nil
+}

From 52dd92ccec83924b2045d645c4b48cdb59c741ea Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Thu, 6 Feb 2025 13:10:58 -0800
Subject: [PATCH 311/623] [Feat] Add Support for Index `merge` in CAGRA (#618)

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ishan Chattopadhyaya (https://github.com/chatman)

URL: https://github.com/rapidsai/cuvs/pull/618
---
 cpp/CMakeLists.txt                            |   4 +
 cpp/include/cuvs/neighbors/cagra.hpp          | 189 +++++++++++++++++
 cpp/src/neighbors/cagra.cuh                   |   9 +
 cpp/src/neighbors/cagra_merge_float.cu        |  35 +++
 cpp/src/neighbors/cagra_merge_half.cu         |  35 +++
 cpp/src/neighbors/cagra_merge_int8.cu         |  35 +++
 cpp/src/neighbors/cagra_merge_uint8.cu        |  35 +++
 .../neighbors/detail/cagra/cagra_merge.cuh    | 139 ++++++++++++
 cpp/tests/neighbors/ann_cagra.cuh             | 199 ++++++++++++++++++
 .../ann_cagra/test_float_uint32_t.cu          |   6 +
 .../neighbors/ann_cagra/test_half_uint32_t.cu |   6 +
 .../ann_cagra/test_int8_t_uint32_t.cu         |   5 +
 .../ann_cagra/test_uint8_t_uint32_t.cu        |   5 +
 python/libcuvs/pyproject.toml                 |   2 +-
 14 files changed, 703 insertions(+), 1 deletion(-)
 create mode 100644 cpp/src/neighbors/cagra_merge_float.cu
 create mode 100644 cpp/src/neighbors/cagra_merge_half.cu
 create mode 100644 cpp/src/neighbors/cagra_merge_int8.cu
 create mode 100644 cpp/src/neighbors/cagra_merge_uint8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/cagra_merge.cuh

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index b324c6786f..6cd87cbade 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -396,6 +396,10 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/cagra_serialize_half.cu
     src/neighbors/cagra_serialize_int8.cu
     src/neighbors/cagra_serialize_uint8.cu
+    src/neighbors/cagra_merge_float.cu
+    src/neighbors/cagra_merge_half.cu
+    src/neighbors/cagra_merge_int8.cu
+    src/neighbors/cagra_merge_uint8.cu
     src/neighbors/iface/iface_cagra_float_uint32_t.cu
     src/neighbors/iface/iface_cagra_half_uint32_t.cu
     src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index d8edf742b0..1d0acbe355 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -264,6 +264,51 @@ struct extend_params {
    * 0. */
   uint32_t max_chunk_size = 0;
 };
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_cpp_merge_params CAGRA index merge parameters
+ * @{
+ */
+
+/**
+ * @brief Determines the strategy for merging CAGRA graphs.
+ *
+ * @note Currently, only the PHYSICAL strategy is supported.
+ */
+enum MergeStrategy {
+  /**
+   * @brief Physical merge: Builds a new CAGRA graph from the union of dataset points
+   * in existing CAGRA graphs.
+   *
+   * This is expensive to build but does not impact search latency or quality.
+   * Preferred for many smaller CAGRA graphs.
+   *
+   * @note Currently, this is the only supported strategy.
+   */
+  PHYSICAL
+};
+
+/**
+ * @brief Parameters for merging CAGRA indexes.
+ */
+struct merge_params {
+  merge_params() = default;
+
+  /**
+   * @brief Constructs merge parameters with given index parameters.
+   * @param params Parameters for creating the output index.
+   */
+  explicit merge_params(const cagra::index_params& params) : output_index_params(params) {}
+
+  /// Parameters for creating the output index.
+  cagra::index_params output_index_params;
+
+  /// Strategy for merging. Defaults to `MergeStrategy::PHYSICAL`.
+  MergeStrategy strategy = MergeStrategy::PHYSICAL;
+};
 
 /**
  * @}
@@ -1794,6 +1839,150 @@ void serialize_to_hnswlib(
   std::optional<raft::host_matrix_view<const uint8_t, int64_t, raft::row_major>> dataset =
     std::nullopt);
 
+/**
+ * @defgroup cagra_cpp_index_merge CAGRA index build functions
+ * @{
+ */
+
+/** @brief Merge multiple CAGRA indices into a single index.
+ *
+ * This function merges multiple CAGRA indices into one, combining both the datasets and graph
+ * structures.
+ *
+ * @note: When device memory is sufficient, the dataset attached to the returned index is allocated
+ * in device memory by default; otherwise, host memory is used automatically.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto dataset0 = raft::make_host_matrix<float, int64_t>(handle, size0, dim);
+ *   auto dataset1 = raft::make_host_matrix<float, int64_t>(handle, size1, dim);
+ *
+ *   auto index0 = cagra::build(res, index_params, dataset0);
+ *   auto index1 = cagra::build(res, index_params, dataset1);
+ *
+ *   std::vector<cagra::index<float, uint32_t>*> indices{&index0, &index1};
+ *   cagra::merge_params params{index_params};
+ *
+ *   auto merged_index = cagra::merge(res, params, indices);
+ * @endcode
+ *
+ * @param[in] res RAFT resources used for the merge operation.
+ * @param[in] params Parameters that control the merging process.
+ * @param[in] indices A vector of pointers to the CAGRA indices to merge. All indices must:
+ *                    - Have attached datasets with the same dimension.
+ *
+ * @return A new CAGRA index containing the merged indices, graph, and dataset.
+ */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::merge_params& params,
+           std::vector<cuvs::neighbors::cagra::index<float, uint32_t>*>& indices)
+  -> cuvs::neighbors::cagra::index<float, uint32_t>;
+
+/** @brief Merge multiple CAGRA indices into a single index.
+ *
+ * This function merges multiple CAGRA indices into one, combining both the datasets and graph
+ * structures.
+ *
+ * @note: When device memory is sufficient, the dataset attached to the returned index is allocated
+ * in device memory by default; otherwise, host memory is used automatically.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto dataset0 = raft::make_host_matrix<half, int64_t>(handle, size0, dim);
+ *   auto dataset1 = raft::make_host_matrix<half, int64_t>(handle, size1, dim);
+ *
+ *   auto index0 = cagra::build(res, index_params, dataset0);
+ *   auto index1 = cagra::build(res, index_params, dataset1);
+ *
+ *   std::vector<cagra::index<half, uint32_t>*> indices{&index0, &index1};
+ *   cagra::merge_params params{index_params};
+ *
+ *   auto merged_index = cagra::merge(res, params, indices);
+ * @endcode
+ *
+ * @param[in] res RAFT resources used for the merge operation.
+ * @param[in] params Parameters that control the merging process.
+ * @param[in] indices A vector of pointers to the CAGRA indices to merge. All indices must:
+ *                    - Have attached datasets with the same dimension.
+ *
+ * @return A new CAGRA index containing the merged indices, graph, and dataset.
+ */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::merge_params& params,
+           std::vector<cuvs::neighbors::cagra::index<half, uint32_t>*>& indices)
+  -> cuvs::neighbors::cagra::index<half, uint32_t>;
+
+/** @brief Merge multiple CAGRA indices into a single index.
+ *
+ * This function merges multiple CAGRA indices into one, combining both the datasets and graph
+ * structures.
+ *
+ * @note: When device memory is sufficient, the dataset attached to the returned index is allocated
+ * in device memory by default; otherwise, host memory is used automatically.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto dataset0 = raft::make_host_matrix<int8_t, int64_t>(handle, size0, dim);
+ *   auto dataset1 = raft::make_host_matrix<int8_t, int64_t>(handle, size1, dim);
+ *
+ *   auto index0 = cagra::build(res, index_params, dataset0);
+ *   auto index1 = cagra::build(res, index_params, dataset1);
+ *
+ *   std::vector<cagra::index<int8_t, uint32_t>*> indices{&index0, &index1};
+ *   cagra::merge_params params{index_params};
+ *
+ *   auto merged_index = cagra::merge(res, params, indices);
+ * @endcode
+ *
+ * @param[in] res RAFT resources used for the merge operation.
+ * @param[in] params Parameters that control the merging process.
+ * @param[in] indices A vector of pointers to the CAGRA indices to merge. All indices must:
+ *                    - Have attached datasets with the same dimension.
+ *
+ * @return A new CAGRA index containing the merged indices, graph, and dataset.
+ */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::merge_params& params,
+           std::vector<cuvs::neighbors::cagra::index<int8_t, uint32_t>*>& indices)
+  -> cuvs::neighbors::cagra::index<int8_t, uint32_t>;
+
+/** @brief Merge multiple CAGRA indices into a single index.
+ *
+ * This function merges multiple CAGRA indices into one, combining both the datasets and graph
+ * structures.
+ *
+ * @note: When device memory is sufficient, the dataset attached to the returned index is allocated
+ * in device memory by default; otherwise, host memory is used automatically.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace raft::neighbors;
+ *   auto dataset0 = raft::make_host_matrix<uint8_t, int64_t>(handle, size0, dim);
+ *   auto dataset1 = raft::make_host_matrix<uint8_t, int64_t>(handle, size1, dim);
+ *
+ *   auto index0 = cagra::build(res, index_params, dataset0);
+ *   auto index1 = cagra::build(res, index_params, dataset1);
+ *
+ *   std::vector<cagra::index<uint8_t, uint32_t>*> indices{&index0, &index1};
+ *   cagra::merge_params params{index_params};
+ *
+ *   auto merged_index = cagra::merge(res, params, indices);
+ * @endcode
+ *
+ * @param[in] res RAFT resources used for the merge operation.
+ * @param[in] params Parameters that control the merging process.
+ * @param[in] indices A vector of pointers to the CAGRA indices to merge. All indices must:
+ *                    - Have attached datasets with the same dimension.
+ *
+ * @return A new CAGRA index containing the merged indices, graph, and dataset.
+ */
+auto merge(raft::resources const& res,
+           const cuvs::neighbors::cagra::merge_params& params,
+           std::vector<cuvs::neighbors::cagra::index<uint8_t, uint32_t>*>& indices)
+  -> cuvs::neighbors::cagra::index<uint8_t, uint32_t>;
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index f294c9b449..305788f7c4 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -18,6 +18,7 @@
 
 #include "detail/cagra/add_nodes.cuh"
 #include "detail/cagra/cagra_build.cuh"
+#include "detail/cagra/cagra_merge.cuh"
 #include "detail/cagra/cagra_search.cuh"
 #include "detail/cagra/graph_core.cuh"
 
@@ -380,6 +381,14 @@ void extend(
   cagra::extend_core<T, IdxT, Accessor>(handle, additional_dataset, index, params, ndv, ngv);
 }
 
+template <class T, class IdxT>
+index<T, IdxT> merge(raft::resources const& handle,
+                     const cagra::merge_params& params,
+                     std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices)
+{
+  return cagra::detail::merge<T, IdxT>(handle, params, indices);
+}
+
 /** @} */  // end group cagra
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_merge_float.cu b/cpp/src/neighbors/cagra_merge_float.cu
new file mode 100644
index 0000000000..951c0c5fe9
--- /dev/null
+++ b/cpp/src/neighbors/cagra_merge_float.cu
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define RAFT_INST_CAGRA_MERGE(T, IdxT)                                      \
+  auto merge(raft::resources const& handle,                                 \
+             const cuvs::neighbors::cagra::merge_params& params,            \
+             std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+  {                                                                         \
+    return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
+  }
+
+RAFT_INST_CAGRA_MERGE(float, uint32_t);
+
+#undef RAFT_INST_CAGRA_MERGE
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_merge_half.cu b/cpp/src/neighbors/cagra_merge_half.cu
new file mode 100644
index 0000000000..704a00f747
--- /dev/null
+++ b/cpp/src/neighbors/cagra_merge_half.cu
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define RAFT_INST_CAGRA_MERGE(T, IdxT)                                      \
+  auto merge(raft::resources const& handle,                                 \
+             const cuvs::neighbors::cagra::merge_params& params,            \
+             std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+  {                                                                         \
+    return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
+  }
+
+RAFT_INST_CAGRA_MERGE(half, uint32_t);
+
+#undef RAFT_INST_CAGRA_MERGE
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_merge_int8.cu b/cpp/src/neighbors/cagra_merge_int8.cu
new file mode 100644
index 0000000000..a7e9035626
--- /dev/null
+++ b/cpp/src/neighbors/cagra_merge_int8.cu
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define RAFT_INST_CAGRA_MERGE(T, IdxT)                                      \
+  auto merge(raft::resources const& handle,                                 \
+             const cuvs::neighbors::cagra::merge_params& params,            \
+             std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+  {                                                                         \
+    return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
+  }
+
+RAFT_INST_CAGRA_MERGE(int8_t, uint32_t);
+
+#undef RAFT_INST_CAGRA_MERGE
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/cagra_merge_uint8.cu b/cpp/src/neighbors/cagra_merge_uint8.cu
new file mode 100644
index 0000000000..a4fc7149c1
--- /dev/null
+++ b/cpp/src/neighbors/cagra_merge_uint8.cu
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra.cuh"
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+#define RAFT_INST_CAGRA_MERGE(T, IdxT)                                      \
+  auto merge(raft::resources const& handle,                                 \
+             const cuvs::neighbors::cagra::merge_params& params,            \
+             std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
+    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+  {                                                                         \
+    return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
+  }
+
+RAFT_INST_CAGRA_MERGE(uint8_t, uint32_t);
+
+#undef RAFT_INST_CAGRA_MERGE
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
new file mode 100644
index 0000000000..bc29cb2060
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuvs/neighbors/cagra.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/logger.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/refine.hpp>
+
+#include <rmm/resource_ref.hpp>
+
+#include <chrono>
+#include <cstdio>
+#include <vector>
+
+namespace cuvs::neighbors::cagra::detail {
+
+template <class T, class IdxT>
+index<T, IdxT> merge(raft::resources const& handle,
+                     const cagra::merge_params& params,
+                     std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices)
+{
+  std::size_t dim              = 0;
+  std::size_t new_dataset_size = 0;
+  int64_t stride               = -1;
+
+  for (auto index : indices) {
+    RAFT_EXPECTS(index != nullptr,
+                 "Null pointer detected in 'indices'. Ensure all elements are valid before usage.");
+    using ds_idx_type = decltype(index->data().n_rows());
+    if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index->data());
+        strided_dset != nullptr) {
+      if (dim == 0) {
+        dim    = index->dim();
+        stride = strided_dset->stride();
+      } else {
+        RAFT_EXPECTS(dim == index->dim(), "Dimension of datasets in indices must be equal.");
+      }
+      new_dataset_size += index->size();
+    } else if (dynamic_cast<const cuvs::neighbors::empty_dataset<int64_t>*>(&index->data()) !=
+               nullptr) {
+      RAFT_FAIL(
+        "cagra::merge only supports an index to which the dataset is attached. Please check if the "
+        "index was built with index_param.attach_dataset_on_build = true, or if a dataset was "
+        "attached after the build.");
+    } else {
+      RAFT_FAIL("cagra::merge only supports an uncompressed dataset index");
+    }
+  }
+
+  IdxT offset = 0;
+
+  auto merge_dataset = [&](T* dst) {
+    for (auto index : indices) {
+      using ds_idx_type  = decltype(index->data().n_rows());
+      auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index->data());
+
+      RAFT_CUDA_TRY(cudaMemcpy2DAsync(dst + offset * dim,
+                                      sizeof(T) * dim,
+                                      strided_dset->view().data_handle(),
+                                      sizeof(T) * stride,
+                                      sizeof(T) * dim,
+                                      strided_dset->n_rows(),
+                                      cudaMemcpyDefault,
+                                      raft::resource::get_cuda_stream(handle)));
+
+      offset += IdxT(index->data().n_rows());
+    }
+  };
+
+  cagra::index_params output_index_params = params.output_index_params;
+
+  try {
+    auto updated_dataset = raft::make_device_matrix<T, std::int64_t>(
+      handle, std::int64_t(new_dataset_size), std::int64_t(dim));
+
+    merge_dataset(updated_dataset.data_handle());
+
+    auto merged_index =
+      cagra::build(handle, output_index_params, raft::make_const_mdspan(updated_dataset.view()));
+    if (!merged_index.data().is_owning() && output_index_params.attach_dataset_on_build) {
+      using matrix_t           = decltype(updated_dataset);
+      using layout_t           = typename matrix_t::layout_type;
+      using container_policy_t = typename matrix_t::container_policy_type;
+      using owning_t           = owning_dataset<T, int64_t, layout_t, container_policy_t>;
+      auto out_layout          = raft::make_strided_layout(updated_dataset.view().extents(),
+                                                  std::array<int64_t, 2>{stride, 1});
+      merged_index.update_dataset(handle, owning_t{std::move(updated_dataset), out_layout});
+    }
+    RAFT_LOG_DEBUG("cagra merge: using device memory for merged dataset");
+    return merged_index;
+
+  } catch (std::bad_alloc& e) {
+    RAFT_LOG_DEBUG("cagra::merge: using host memory for merged dataset");
+
+    auto updated_dataset =
+      raft::make_host_matrix<T, std::int64_t>(std::int64_t(new_dataset_size), std::int64_t(dim));
+
+    merge_dataset(updated_dataset.data_handle());
+
+    auto merged_index =
+      cagra::build(handle, output_index_params, raft::make_const_mdspan(updated_dataset.view()));
+    if (!merged_index.data().is_owning() && output_index_params.attach_dataset_on_build) {
+      using matrix_t           = decltype(updated_dataset);
+      using layout_t           = typename matrix_t::layout_type;
+      using container_policy_t = typename matrix_t::container_policy_type;
+      using owning_t           = owning_dataset<T, int64_t, layout_t, container_policy_t>;
+      auto out_layout          = raft::make_strided_layout(updated_dataset.view().extents(),
+                                                  std::array<int64_t, 2>{stride, 1});
+      merged_index.update_dataset(handle, owning_t{std::move(updated_dataset), out_layout});
+    }
+    return merged_index;
+  }
+}
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index aedb11543d..1e695f9a8f 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -922,6 +922,205 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
   rmm::device_uvector<DataT> search_queries;
 };
 
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
+ public:
+  AnnCagraIndexMergeTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnCagraInputs>::GetParam()),
+      database(0, stream_),
+      search_queries(0, stream_)
+  {
+  }
+
+ protected:
+  void testCagra()
+  {
+    // TODO (tarang-jain): remove when NN Descent index building support InnerProduct. Reference
+    // issue: https://github.com/rapidsai/raft/issues/2276
+    if (ps.metric == InnerProduct && ps.build_algo == graph_build_algo::NN_DESCENT) GTEST_SKIP();
+    if (ps.compression != std::nullopt) GTEST_SKIP();
+    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        ((!std::is_same_v<DataT, uint8_t>) ||
+         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+      GTEST_SKIP();
+    // If the dataset dimension is small and the dataset size is large, there can be a lot of
+    // dataset vectors that have the same distance to the query, especially in the binary Hamming
+    // distance, making it impossible to make a top-k ground truth.
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        (ps.k * ps.dim * 8 / 5 /*(=magic number)*/ < ps.n_rows))
+      GTEST_SKIP();
+
+    size_t queries_size = ps.n_queries * ps.k;
+    std::vector<IdxT> indices_Cagra(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<DistanceT> distances_Cagra(queries_size);
+    std::vector<DistanceT> distances_naive(queries_size);
+
+    {
+      rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+
+      cuvs::neighbors::naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                                         distances_naive_dev.data(),
+                                                         indices_naive_dev.data(),
+                                                         search_queries.data(),
+                                                         database.data(),
+                                                         ps.n_queries,
+                                                         ps.n_rows,
+                                                         ps.dim,
+                                                         ps.k,
+                                                         ps.metric);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    {
+      rmm::device_uvector<DistanceT> distances_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_dev(queries_size, stream_);
+
+      {
+        cagra::index_params index_params;
+        index_params.metric = ps.metric;  // Note: currently ony the cagra::index_params metric is
+                                          // not used for knn_graph building.
+
+        switch (ps.build_algo) {
+          case graph_build_algo::IVF_PQ:
+            index_params.graph_build_params =
+              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            if (ps.ivf_pq_search_refine_ratio) {
+              std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
+                index_params.graph_build_params)
+                .refinement_rate = *ps.ivf_pq_search_refine_ratio;
+            }
+            break;
+          case graph_build_algo::NN_DESCENT: {
+            index_params.graph_build_params =
+              graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
+            break;
+          }
+          case graph_build_algo::ITERATIVE_CAGRA_SEARCH: {
+            index_params.graph_build_params = graph_build_params::iterative_search_params();
+            break;
+          }
+          case graph_build_algo::AUTO:
+            // do nothing
+            break;
+        };
+
+        const double splite_ratio        = 0.55;
+        const std::size_t database0_size = ps.n_rows * splite_ratio;
+        const std::size_t database1_size = ps.n_rows - database0_size;
+
+        auto database0_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          (const DataT*)database.data(), database0_size, ps.dim);
+
+        auto database1_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          (const DataT*)database.data() + database0_view.size(), database1_size, ps.dim);
+
+        cagra::index<DataT, IdxT> index0(handle_);
+        cagra::index<DataT, IdxT> index1(handle_);
+        if (ps.host_dataset) {
+          {
+            std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
+            database_host = raft::make_host_matrix<DataT, int64_t>(database0_size, ps.dim);
+            raft::copy(database_host->data_handle(),
+                       database0_view.data_handle(),
+                       database0_view.size(),
+                       stream_);
+            auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+              (const DataT*)database_host->data_handle(), database0_size, ps.dim);
+            index0 = cagra::build(handle_, index_params, database_host_view);
+          }
+          {
+            std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
+            database_host = raft::make_host_matrix<DataT, int64_t>(database1_size, ps.dim);
+            raft::copy(database_host->data_handle(),
+                       database1_view.data_handle(),
+                       database1_view.size(),
+                       stream_);
+            auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+              (const DataT*)database_host->data_handle(), database1_size, ps.dim);
+            index1 = cagra::build(handle_, index_params, database_host_view);
+          }
+        } else {
+          index0 = cagra::build(handle_, index_params, database0_view);
+          index1 = cagra::build(handle_, index_params, database1_view);
+        };
+        std::vector<cagra::index<DataT, IdxT>*> indices{&index0, &index1};
+        cagra::merge_params merge_params{index_params};
+        auto index = cagra::merge(handle_, merge_params, indices);
+
+        auto search_queries_view = raft::make_device_matrix_view<const DataT, int64_t>(
+          search_queries.data(), ps.n_queries, ps.dim);
+        auto indices_out_view =
+          raft::make_device_matrix_view<IdxT, int64_t>(indices_dev.data(), ps.n_queries, ps.k);
+        auto dists_out_view = raft::make_device_matrix_view<DistanceT, int64_t>(
+          distances_dev.data(), ps.n_queries, ps.k);
+
+        cagra::search_params search_params;
+        search_params.algo        = ps.algo;
+        search_params.max_queries = ps.max_queries;
+        search_params.team_size   = ps.team_size;
+        search_params.itopk_size  = ps.itopk_size;
+
+        cagra::search(
+          handle_, search_params, index, search_queries_view, indices_out_view, dists_out_view);
+        raft::update_host(distances_Cagra.data(), distances_dev.data(), queries_size, stream_);
+        raft::update_host(indices_Cagra.data(), indices_dev.data(), queries_size, stream_);
+        raft::resource::sync_stream(handle_);
+      }
+
+      double min_recall = ps.min_recall;
+      EXPECT_TRUE(eval_neighbours(indices_naive,
+                                  indices_Cagra,
+                                  distances_naive,
+                                  distances_Cagra,
+                                  ps.n_queries,
+                                  ps.k,
+                                  0.006,
+                                  min_recall));
+      EXPECT_TRUE(eval_distances(handle_,
+                                 database.data(),
+                                 search_queries.data(),
+                                 indices_dev.data(),
+                                 distances_dev.data(),
+                                 ps.n_rows,
+                                 ps.dim,
+                                 ps.n_queries,
+                                 ps.k,
+                                 ps.metric,
+                                 1.0e-4));
+    }
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    search_queries.resize(ps.n_queries * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    InitDataset(handle_, database.data(), ps.n_rows, ps.dim, ps.metric, r);
+    InitDataset(handle_, search_queries.data(), ps.n_queries, ps.dim, ps.metric, r);
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    search_queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnCagraInputs ps;
+  rmm::device_uvector<DataT> database;
+  rmm::device_uvector<DataT> search_queries;
+};
+
 inline std::vector<AnnCagraInputs> generate_inputs()
 {
   // TODO(tfeher): test MULTI_CTA kernel with search_width > 1 to allow multiple CTA per queries
diff --git a/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
index 640657ccb8..16c0d3d2a0 100644
--- a/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
@@ -29,6 +29,9 @@ TEST_P(AnnCagraAddNodesTestF_U32, AnnCagraAddNodes) { this->testCagra(); }
 typedef AnnCagraFilterTest<float, float, std::uint32_t> AnnCagraFilterTestF_U32;
 TEST_P(AnnCagraFilterTestF_U32, AnnCagra) { this->testCagra(); }
 
+typedef AnnCagraIndexMergeTest<float, float, std::uint32_t> AnnCagraIndexMergeTestF_U32;
+TEST_P(AnnCagraIndexMergeTestF_U32, AnnCagraIndexMerge) { this->testCagra(); }
+
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
                         AnnCagraAddNodesTestF_U32,
@@ -36,5 +39,8 @@ INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
 INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest,
                         AnnCagraFilterTestF_U32,
                         ::testing::ValuesIn(inputs_filtering));
+INSTANTIATE_TEST_CASE_P(AnnCagraIndexMergeTest,
+                        AnnCagraIndexMergeTestF_U32,
+                        ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
index f03de69d2c..de682cee49 100644
--- a/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
@@ -23,6 +23,12 @@ namespace cuvs::neighbors::cagra {
 typedef AnnCagraTest<float, half, std::uint32_t> AnnCagraTestF16_U32;
 TEST_P(AnnCagraTestF16_U32, AnnCagra) { this->testCagra(); }
 
+typedef AnnCagraIndexMergeTest<float, half, std::uint32_t> AnnCagraIndexMergeTestF16_U32;
+TEST_P(AnnCagraIndexMergeTestF16_U32, AnnCagraIndexMerge) { this->testCagra(); }
+
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF16_U32, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnCagraIndexMergeTest,
+                        AnnCagraIndexMergeTestF16_U32,
+                        ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/tests/neighbors/ann_cagra/test_int8_t_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_int8_t_uint32_t.cu
index 32f6289fe5..28be13c5b6 100644
--- a/cpp/tests/neighbors/ann_cagra/test_int8_t_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_int8_t_uint32_t.cu
@@ -26,6 +26,8 @@ typedef AnnCagraAddNodesTest<float, std::int8_t, std::uint32_t> AnnCagraAddNodes
 TEST_P(AnnCagraAddNodesTestI8_U32, AnnCagra) { this->testCagra(); }
 typedef AnnCagraFilterTest<float, std::int8_t, std::uint32_t> AnnCagraFilterTestI8_U32;
 TEST_P(AnnCagraFilterTestI8_U32, AnnCagra) { this->testCagra(); }
+typedef AnnCagraIndexMergeTest<float, std::int8_t, std::uint32_t> AnnCagraIndexMergeTestI8_U32;
+TEST_P(AnnCagraIndexMergeTestI8_U32, AnnCagra) { this->testCagra(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestI8_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
@@ -34,5 +36,8 @@ INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
 INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest,
                         AnnCagraFilterTestI8_U32,
                         ::testing::ValuesIn(inputs_filtering));
+INSTANTIATE_TEST_CASE_P(AnnCagraIndexMergeTest,
+                        AnnCagraIndexMergeTestI8_U32,
+                        ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/tests/neighbors/ann_cagra/test_uint8_t_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
index 53f804be6e..dd9ffd1ae8 100644
--- a/cpp/tests/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_uint8_t_uint32_t.cu
@@ -26,6 +26,8 @@ typedef AnnCagraAddNodesTest<float, std::uint8_t, std::uint32_t> AnnCagraAddNode
 TEST_P(AnnCagraAddNodesTestU8_U32, AnnCagra) { this->testCagra(); }
 typedef AnnCagraFilterTest<float, std::uint8_t, std::uint32_t> AnnCagraFilterTestU8_U32;
 TEST_P(AnnCagraFilterTestU8_U32, AnnCagra) { this->testCagra(); }
+typedef AnnCagraIndexMergeTest<float, std::uint8_t, std::uint32_t> AnnCagraIndexMergeTestU8_U32;
+TEST_P(AnnCagraIndexMergeTestU8_U32, AnnCagra) { this->testCagra(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestU8_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
@@ -34,5 +36,8 @@ INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
 INSTANTIATE_TEST_CASE_P(AnnCagraFilterTest,
                         AnnCagraFilterTestU8_U32,
                         ::testing::ValuesIn(inputs_filtering));
+INSTANTIATE_TEST_CASE_P(AnnCagraIndexMergeTest,
+                        AnnCagraIndexMergeTestU8_U32,
+                        ::testing::ValuesIn(inputs));
 
 }  // namespace cuvs::neighbors::cagra
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 28443b7820..3fd69b9899 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -105,4 +105,4 @@ select = [
 ]
 
 # detect when package size grows significantly
-max_allowed_size_compressed = '1.1G'
+max_allowed_size_compressed = '1.2G'

From 4b289a045483cceee0f33796f65aeaf4747579b2 Mon Sep 17 00:00:00 2001
From: Severin Dicks <37635888+Intron7@users.noreply.github.com>
Date: Thu, 6 Feb 2025 22:39:34 +0100
Subject: [PATCH 312/623] add docs for nn_descent (#668)

This PR adds docs for cuvs nn_descent

Authors:
  - Severin Dicks (https://github.com/Intron7)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/668
---
 docs/source/python_api/neighbors.rst          |  1 +
 .../source/python_api/neighbors_nn_decent.rst | 24 +++++++++++++++++++
 2 files changed, 25 insertions(+)
 create mode 100644 docs/source/python_api/neighbors_nn_decent.rst

diff --git a/docs/source/python_api/neighbors.rst b/docs/source/python_api/neighbors.rst
index cd4f2609cd..8e2bad9f14 100644
--- a/docs/source/python_api/neighbors.rst
+++ b/docs/source/python_api/neighbors.rst
@@ -14,3 +14,4 @@ Nearest Neighbors
    neighbors_hnsw.rst
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
+   neighbors_nn_decent.rst
diff --git a/docs/source/python_api/neighbors_nn_decent.rst b/docs/source/python_api/neighbors_nn_decent.rst
new file mode 100644
index 0000000000..df872d7e99
--- /dev/null
+++ b/docs/source/python_api/neighbors_nn_decent.rst
@@ -0,0 +1,24 @@
+NN-Descent
+======
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Index build parameters
+######################
+
+.. autoclass:: cuvs.neighbors.nn_descent.IndexParams
+    :members:
+
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.nn_descent.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.nn_descent.build

From f15c1ea93eb63b99a8f885d1e627f94ab7a74196 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 7 Feb 2025 15:24:29 +0100
Subject: [PATCH 313/623] Fix ann-bench dataset blob integer overflow leading
 to incorrect data copy beyond 4B elems (#671)

ann-bench keeps data dimensions as `uint32_t`. We use `std::fread` to copy the data from a file to the host memory and pass `n_rows * n_cols` there, which gets casted to size_t only after the multiplication. This leads to integer overflow for the datasets larger than 4B elements and a partial data copy.

This PR fixes the bug by casting the dimensions before the multiplication.
The bug only affects the benchmark cases where the data is requested in the host memory not backed by a file.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/671
---
 cpp/bench/ann/src/common/blob.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cpp/bench/ann/src/common/blob.hpp b/cpp/bench/ann/src/common/blob.hpp
index 81310ae0b8..3a51372cff 100644
--- a/cpp/bench/ann/src/common/blob.hpp
+++ b/cpp/bench/ann/src/common/blob.hpp
@@ -453,7 +453,8 @@ struct blob_mmap {
           size_t size = data_end - data_start;
           mmap_owner owner{size, flags};
           std::fseek(file_.descriptor().value(), data_start, SEEK_SET);
-          size_t n_elems = file_.rows_limit() * file_.n_cols();
+          auto n_elems =
+            static_cast<size_t>(file_.rows_limit()) * static_cast<size_t>(file_.n_cols());
           if (std::fread(owner.data(), sizeof(T), n_elems, file_.descriptor().value()) != n_elems) {
             throw std::runtime_error{"cuvs::bench::blob_mmap() fread " + file_.path() + " failed"};
           }

From 904051ee89af61235496c7bed444bb1a2f535941 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Fri, 7 Feb 2025 20:22:38 +0530
Subject: [PATCH 314/623] Add deep-100M to datasets.yaml for cuvs-bench (#670)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/670
---
 .../cuvs_bench/cuvs_bench/config/datasets/datasets.yaml   | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
index 188d24d20f..d64e232b7c 100644
--- a/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
@@ -125,3 +125,11 @@
   query_file: wiki_all_88M/queries.fbin
   groundtruth_neighbors_file: wiki_all_88M/groundtruth.88M.neighbors.ibin
   distance: euclidean
+
+- name: deep-100M
+  dims: 96
+  subset_size: 100000000
+  base_file: deep-100M/base.1B.fbin
+  query_file: deep-100M/query.public.10K.fbin
+  groundtruth_neighbors_file: deep-100M/groundtruth.public.10K.ibin
+  distance: euclidean

From ab597ecaae9335f9edd791574f68b40aef66e734 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 7 Feb 2025 09:39:55 -0800
Subject: [PATCH 315/623] Add filtering to python for ivf_flat (#664)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/664
---
 cpp/include/cuvs/neighbors/ivf_flat.h         |   7 +-
 cpp/src/neighbors/ivf_flat_c.cpp              |  36 +++++--
 cpp/tests/neighbors/run_ivf_flat_c.c          |   6 +-
 examples/c/src/ivf_flat_c_example.c           |  11 +-
 go/ivf_flat/ivf_flat.go                       |   6 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |   4 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |  12 ++-
 python/cuvs/cuvs/tests/ann_utils.py           |  92 +++++++++++++++-
 python/cuvs/cuvs/tests/test_cagra.py          | 100 ++----------------
 python/cuvs/cuvs/tests/test_ivf_flat.py       |  11 +-
 rust/cuvs/src/ivf_flat/index.rs               |   6 ++
 11 files changed, 182 insertions(+), 109 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 5c61620418..c46a9b2cdb 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -18,6 +18,7 @@
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.h>
+#include <cuvs/neighbors/common.h>
 #include <dlpack/dlpack.h>
 #include <stdbool.h>
 #include <stdint.h>
@@ -267,13 +268,17 @@ cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
  * @param[in] queries DLManagedTensor* queries dataset to search
  * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
  * @param[out] distances DLManagedTensor* output `k` distances for queries
+ * @param[in] filter cuvsFilter input filter that can be used
+              to filter queries and neighbors based on the given bitset.
  */
 cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
                               cuvsIvfFlatSearchParams_t search_params,
                               cuvsIvfFlatIndex_t index,
                               DLManagedTensor* queries,
                               DLManagedTensor* neighbors,
-                              DLManagedTensor* distances);
+                              DLManagedTensor* distances,
+                              cuvsFilter filter);
+
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index 2acc6b6788..b38f1808dd 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -67,7 +67,8 @@ void _search(cuvsResources_t res,
              cuvsIvfFlatIndex index,
              DLManagedTensor* queries_tensor,
              DLManagedTensor* neighbors_tensor,
-             DLManagedTensor* distances_tensor)
+             DLManagedTensor* distances_tensor,
+             cuvsFilter filter)
 {
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
@@ -82,8 +83,27 @@ void _search(cuvsResources_t res,
   auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
   auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
 
-  cuvs::neighbors::ivf_flat::search(
-    *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+  if (filter.type == NO_FILTER) {
+    cuvs::neighbors::ivf_flat::search(
+      *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+  } else if (filter.type == BITSET) {
+    using filter_mdspan_type    = raft::device_vector_view<std::uint32_t, int64_t, raft::row_major>;
+    auto removed_indices_tensor = reinterpret_cast<DLManagedTensor*>(filter.addr);
+    auto removed_indices = cuvs::core::from_dlpack<filter_mdspan_type>(removed_indices_tensor);
+    cuvs::core::bitset_view<std::uint32_t, int64_t> removed_indices_bitset(removed_indices,
+                                                                           index_ptr->size());
+    auto bitset_filter_obj = cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset);
+    cuvs::neighbors::ivf_flat::search(*res_ptr,
+                                      search_params,
+                                      *index_ptr,
+                                      queries_mds,
+                                      neighbors_mds,
+                                      distances_mds,
+                                      bitset_filter_obj);
+
+  } else {
+    RAFT_FAIL("Unsupported filter type: BITMAP");
+  }
 }
 
 template <typename T, typename IdxT>
@@ -179,7 +199,9 @@ extern "C" cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
                                          cuvsIvfFlatIndex_t index_c_ptr,
                                          DLManagedTensor* queries_tensor,
                                          DLManagedTensor* neighbors_tensor,
-                                         DLManagedTensor* distances_tensor)
+                                         DLManagedTensor* distances_tensor,
+                                         cuvsFilter filter)
+
 {
   return cuvs::core::translate_exceptions([=] {
     auto queries   = queries_tensor->dl_tensor;
@@ -203,13 +225,13 @@ extern "C" cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
 
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
       _search<float, int64_t>(
-        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
       _search<int8_t, int64_t>(
-        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
       _search<uint8_t, int64_t>(
-        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else {
       RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
                 queries.dtype.code,
diff --git a/cpp/tests/neighbors/run_ivf_flat_c.c b/cpp/tests/neighbors/run_ivf_flat_c.c
index 8cd79c91fa..d58f11bf3a 100644
--- a/cpp/tests/neighbors/run_ivf_flat_c.c
+++ b/cpp/tests/neighbors/run_ivf_flat_c.c
@@ -91,12 +91,16 @@ void run_ivf_flat(int64_t n_rows,
   distances_tensor.dl_tensor.shape              = distances_shape;
   distances_tensor.dl_tensor.strides            = NULL;
 
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+
   // search index
   cuvsIvfFlatSearchParams_t search_params;
   cuvsIvfFlatSearchParamsCreate(&search_params);
   search_params->n_probes = n_probes;
   cuvsIvfFlatSearch(
-    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor, filter);
 
   // de-allocate index and res
   cuvsIvfFlatSearchParamsDestroy(search_params);
diff --git a/examples/c/src/ivf_flat_c_example.c b/examples/c/src/ivf_flat_c_example.c
index 2121ca35e5..510d624f3f 100644
--- a/examples/c/src/ivf_flat_c_example.c
+++ b/examples/c/src/ivf_flat_c_example.c
@@ -67,8 +67,12 @@ void ivf_flat_build_search_simple(cuvsResources_t *res, DLManagedTensor * datase
     search_params->n_probes = 50;
 
     // Search the `index` built using `ivfFlatBuild`
+    cuvsFilter filter;
+    filter.type = NO_FILTER;
+    filter.addr = (uintptr_t)NULL;
+
     cuvsError_t search_status = cuvsIvfFlatSearch(*res, search_params, index,
-     queries_tensor, &neighbors_tensor, &distances_tensor);
+     queries_tensor, &neighbors_tensor, &distances_tensor, filter);
     if (build_status != CUVS_SUCCESS) {
         printf("%s.\n", cuvsGetLastErrorText());
     }
@@ -165,8 +169,11 @@ void ivf_flat_build_extend_search(cuvsResources_t *res, DLManagedTensor * trains
     search_params->n_probes = 10;
 
     // Search the `index` built using `ivfFlatBuild`
+    cuvsFilter filter;
+    filter.type = NO_FILTER;
+    filter.addr = (uintptr_t)NULL;
     cuvsError_t search_status = cuvsIvfFlatSearch(*res, search_params, index,
-     queries_tensor, &neighbors_tensor, &distances_tensor);
+     queries_tensor, &neighbors_tensor, &distances_tensor, filter);
     if (search_status != CUVS_SUCCESS) {
         printf("%s.\n", cuvsGetLastErrorText());
         exit(-1);
diff --git a/go/ivf_flat/ivf_flat.go b/go/ivf_flat/ivf_flat.go
index 3330eb95e2..61e17172ff 100644
--- a/go/ivf_flat/ivf_flat.go
+++ b/go/ivf_flat/ivf_flat.go
@@ -67,6 +67,10 @@ func SearchIndex[T any](Resources cuvs.Resource, params *SearchParams, index *Iv
 	if !index.trained {
 		return errors.New("index needs to be built before calling search")
 	}
+        prefilter := C.cuvsFilter{
+                addr:  0,
+                _type: C.NO_FILTER,
+        }
 
-	return cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatSearch(C.cuvsResources_t(Resources.Resource), params.params, index.index, (*C.DLManagedTensor)(unsafe.Pointer(queries.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(neighbors.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(distances.C_tensor)))))
+	return cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsIvfFlatSearch(C.cuvsResources_t(Resources.Resource), params.params, index.index, (*C.DLManagedTensor)(unsafe.Pointer(queries.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(neighbors.C_tensor)), (*C.DLManagedTensor)(unsafe.Pointer(distances.C_tensor)), prefilter)))
 }
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
index 96bc557e6a..f2bd6a9b18 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -21,6 +21,7 @@ from libcpp cimport bool
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
 from cuvs.distance_type cimport cuvsDistanceType
+from cuvs.neighbors.filters.filters cimport cuvsFilter
 
 
 cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
@@ -71,7 +72,8 @@ cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
                                   cuvsIvfFlatIndex_t index,
                                   DLManagedTensor* queries,
                                   DLManagedTensor* neighbors,
-                                  DLManagedTensor* distances) except +
+                                  DLManagedTensor* distances,
+                                  cuvsFilter filter) except +
 
     cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res,
                                      const char * filename,
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 7a169e1a0f..437499c1ea 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -34,6 +34,7 @@ from pylibraft.common.interruptible import cuda_interruptible
 
 from cuvs.distance import DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
+from cuvs.neighbors.filters import no_filter
 
 from libc.stdint cimport (
     int8_t,
@@ -274,7 +275,8 @@ def search(SearchParams search_params,
            k,
            neighbors=None,
            distances=None,
-           resources=None):
+           resources=None,
+           filter=None):
     """
     Find the k nearest neighbors for each query.
 
@@ -293,6 +295,8 @@ def search(SearchParams search_params,
     distances : Optional CUDA array interface compliant matrix shape
                 (n_queries, k) If supplied, the distances to the
                 neighbors will be written here in-place. (default None)
+    filter:     Optional cuvs.neighbors.cuvsFilter can be used to filter
+                neighbors based on a given bitset. (default None)
     {resources_docstring}
 
     Examples
@@ -339,6 +343,9 @@ def search(SearchParams search_params,
     _check_input_array(distances_cai, [np.dtype('float32')],
                        exp_rows=n_queries, exp_cols=k)
 
+    if filter is None:
+        filter = no_filter()
+
     cdef cuvsIvfFlatSearchParams* params = search_params.params
     cdef cuvsError_t search_status
     cdef cydlpack.DLManagedTensor* queries_dlpack = \
@@ -356,7 +363,8 @@ def search(SearchParams search_params,
             index.index,
             queries_dlpack,
             neighbors_dlpack,
-            distances_dlpack
+            distances_dlpack,
+            filter.prefilter
         ))
 
     return (distances, neighbors)
diff --git a/python/cuvs/cuvs/tests/ann_utils.py b/python/cuvs/cuvs/tests/ann_utils.py
index 60db7f3273..b8f5d0bb00 100644
--- a/python/cuvs/cuvs/tests/ann_utils.py
+++ b/python/cuvs/cuvs/tests/ann_utils.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,10 @@
 # limitations under the License.
 
 import numpy as np
+from pylibraft.common import device_ndarray
+from sklearn.neighbors import NearestNeighbors
+
+from cuvs.neighbors import filters
 
 
 def generate_data(shape, dtype):
@@ -33,3 +37,89 @@ def calc_recall(ann_idx, true_nn_idx):
         n += np.intersect1d(ann_idx[i, :], true_nn_idx[i, :]).size
     recall = n / ann_idx.size
     return recall
+
+
+def create_sparse_bitset(n_size, sparsity):
+    bits_per_uint32 = 32
+    num_bits = n_size
+    num_uint32s = (num_bits + bits_per_uint32 - 1) // bits_per_uint32
+    num_ones = int(num_bits * sparsity)
+
+    array = np.zeros(num_uint32s, dtype=np.uint32)
+    indices = np.random.choice(num_bits, num_ones, replace=False)
+
+    for index in indices:
+        i = index // bits_per_uint32
+        bit_position = index % bits_per_uint32
+        array[i] |= 1 << bit_position
+
+    return array
+
+
+def run_filtered_search_test(
+    search_module,
+    sparsity,
+    n_rows=10000,
+    n_cols=10,
+    n_queries=10,
+    k=10,
+):
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    bitset = create_sparse_bitset(n_rows, sparsity)
+
+    dataset_device = device_ndarray(dataset)
+    queries_device = device_ndarray(queries)
+    bitset_device = device_ndarray(bitset)
+
+    build_params = search_module.IndexParams()
+    index = search_module.build(build_params, dataset_device)
+
+    filter_ = filters.from_bitset(bitset_device)
+
+    search_params = search_module.SearchParams()
+    ret_distances, ret_indices = search_module.search(
+        search_params,
+        index,
+        queries_device,
+        k,
+        filter=filter_,
+    )
+
+    # Convert bitset to bool array for validation
+    bitset_as_uint8 = bitset.view(np.uint8)
+    bool_filter = np.unpackbits(bitset_as_uint8)
+    bool_filter = bool_filter.reshape(-1, 4, 8)
+    bool_filter = np.flip(bool_filter, axis=2)
+    bool_filter = bool_filter.reshape(-1)[:n_rows]
+    bool_filter = np.logical_not(bool_filter)  # Flip so True means filtered
+
+    # Get filtered dataset for reference calculation
+    non_filtered_mask = ~bool_filter
+    filtered_dataset = dataset[non_filtered_mask]
+
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric="euclidean"
+    )
+    nn_skl.fit(filtered_dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    actual_indices = ret_indices.copy_to_host()
+
+    filtered_idx_map = (
+        np.cumsum(~bool_filter) - 1
+    )  # -1 because cumsum starts at 1
+
+    # Map ANN indices to filtered space
+    mapped_actual_indices = np.take(
+        filtered_idx_map, actual_indices, mode="clip"
+    )
+
+    filtered_indices = np.where(bool_filter)[0]
+    for i in range(n_queries):
+        assert not np.intersect1d(filtered_indices, actual_indices[i]).size
+
+    recall = calc_recall(mapped_actual_indices, skl_idx)
+
+    assert recall > 0.7
diff --git a/python/cuvs/cuvs/tests/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
index 831ef11f77..f3de488dab 100644
--- a/python/cuvs/cuvs/tests/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -19,8 +19,12 @@
 from sklearn.neighbors import NearestNeighbors
 from sklearn.preprocessing import normalize
 
-from cuvs.neighbors import cagra, filters
-from cuvs.tests.ann_utils import calc_recall, generate_data
+from cuvs.neighbors import cagra
+from cuvs.tests.ann_utils import (
+    calc_recall,
+    generate_data,
+    run_filtered_search_test,
+)
 
 
 def run_cagra_build_search_test(
@@ -139,97 +143,9 @@ def test_cagra_dataset_dtype_host_device(
     )
 
 
-def create_sparse_bitset(n_size, sparsity):
-    bits_per_uint32 = 32
-    num_bits = n_size
-    num_uint32s = (num_bits + bits_per_uint32 - 1) // bits_per_uint32
-    num_ones = int(num_bits * sparsity)
-
-    array = np.zeros(num_uint32s, dtype=np.uint32)
-    indices = np.random.choice(num_bits, num_ones, replace=False)
-
-    for index in indices:
-        i = index // bits_per_uint32
-        bit_position = index % bits_per_uint32
-        array[i] |= 1 << bit_position
-
-    return array
-
-
 @pytest.mark.parametrize("sparsity", [0.2, 0.5, 0.7, 1.0])
-def test_filtered_cagra(
-    sparsity,
-    n_rows=10000,
-    n_cols=10,
-    n_queries=10,
-    k=10,
-):
-    dataset = generate_data((n_rows, n_cols), np.float32)
-    queries = generate_data((n_queries, n_cols), np.float32)
-
-    bitset = create_sparse_bitset(n_rows, sparsity)
-
-    dataset_device = device_ndarray(dataset)
-    queries_device = device_ndarray(queries)
-    bitset_device = device_ndarray(bitset)
-
-    build_params = cagra.IndexParams()
-    index = cagra.build(build_params, dataset_device)
-
-    filter_ = filters.from_bitset(bitset_device)
-
-    out_idx = np.zeros((n_queries, k), dtype=np.uint32)
-    out_dist = np.zeros((n_queries, k), dtype=np.float32)
-    out_idx_device = device_ndarray(out_idx)
-    out_dist_device = device_ndarray(out_dist)
-
-    search_params = cagra.SearchParams()
-    ret_distances, ret_indices = cagra.search(
-        search_params,
-        index,
-        queries_device,
-        k,
-        neighbors=out_idx_device,
-        distances=out_dist_device,
-        filter=filter_,
-    )
-
-    # Convert bitset to bool array for validation
-    bitset_as_uint8 = bitset.view(np.uint8)
-    bool_filter = np.unpackbits(bitset_as_uint8)
-    bool_filter = bool_filter.reshape(-1, 4, 8)
-    bool_filter = np.flip(bool_filter, axis=2)
-    bool_filter = bool_filter.reshape(-1)[:n_rows]
-    bool_filter = np.logical_not(bool_filter)  # Flip so True means filtered
-
-    # Get filtered dataset for reference calculation
-    non_filtered_mask = ~bool_filter
-    filtered_dataset = dataset[non_filtered_mask]
-
-    nn_skl = NearestNeighbors(
-        n_neighbors=k, algorithm="brute", metric="euclidean"
-    )
-    nn_skl.fit(filtered_dataset)
-    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
-
-    actual_indices = out_idx_device.copy_to_host()
-
-    filtered_idx_map = (
-        np.cumsum(~bool_filter) - 1
-    )  # -1 because cumsum starts at 1
-
-    # Map CAGRA indices to filtered space
-    mapped_actual_indices = np.take(
-        filtered_idx_map, actual_indices, mode="clip"
-    )
-
-    filtered_indices = np.where(bool_filter)[0]
-    for i in range(n_queries):
-        assert not np.intersect1d(filtered_indices, actual_indices[i]).size
-
-    recall = calc_recall(mapped_actual_indices, skl_idx)
-
-    assert recall > 0.7
+def test_filtered_cagra(sparsity):
+    run_filtered_search_test(cagra, sparsity)
 
 
 @pytest.mark.parametrize(
diff --git a/python/cuvs/cuvs/tests/test_ivf_flat.py b/python/cuvs/cuvs/tests/test_ivf_flat.py
index c3ec0252ae..6b89041fa8 100644
--- a/python/cuvs/cuvs/tests/test_ivf_flat.py
+++ b/python/cuvs/cuvs/tests/test_ivf_flat.py
@@ -20,7 +20,11 @@
 from sklearn.preprocessing import normalize
 
 from cuvs.neighbors import ivf_flat
-from cuvs.tests.ann_utils import calc_recall, generate_data
+from cuvs.tests.ann_utils import (
+    calc_recall,
+    generate_data,
+    run_filtered_search_test,
+)
 
 
 def run_ivf_flat_build_search_test(
@@ -129,3 +133,8 @@ def test_extend(dtype):
         dtype=dtype,
         add_data_on_build=False,
     )
+
+
+@pytest.mark.parametrize("sparsity", [0.5, 0.7, 1.0])
+def test_filtered_ivf_flat(sparsity):
+    run_filtered_search_test(ivf_flat, sparsity)
diff --git a/rust/cuvs/src/ivf_flat/index.rs b/rust/cuvs/src/ivf_flat/index.rs
index b1462a0e69..f66904142b 100644
--- a/rust/cuvs/src/ivf_flat/index.rs
+++ b/rust/cuvs/src/ivf_flat/index.rs
@@ -78,6 +78,11 @@ impl Index {
         distances: &ManagedTensor,
     ) -> Result<()> {
         unsafe {
+            let prefilter = ffi::cuvsFilter {
+                addr: 0,
+                type_: ffi::cuvsFilterType::NO_FILTER,
+            };
+
             check_cuvs(ffi::cuvsIvfFlatSearch(
                 res.0,
                 params.0,
@@ -85,6 +90,7 @@ impl Index {
                 queries.as_ptr(),
                 neighbors.as_ptr(),
                 distances.as_ptr(),
+                prefilter,
             ))
         }
     }

From d760d856fd12b4734b6e284099b3c416ee8d2c2a Mon Sep 17 00:00:00 2001
From: Dante Gama Dessavre <danteg@nvidia.com>
Date: Fri, 7 Feb 2025 13:16:55 -0600
Subject: [PATCH 316/623] Run cuvs-bench pytests and end-to-end tests in CI
 (#574)

PR does the following:

- [x] Modifies CI to run pytest and e2e test of cuvs-bench
    - [x] We need to test the additional time needed to run the tests. They should be fast, but if they are not, then we can add an additional job to run them in parallel.
- [x] Adds synthetic test-data generation so the CI jobs don't depend on downloading datasets, and users can have easy testing locally.
    - [ ] Few improvements to be done to docs, yaml and other things to make it easy for users.
- [x] Check in some additional pytests that hadn't been checked in before.

Authors:
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Micka (https://github.com/lowener)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/574
---
 ci/test_python.sh                             |  17 +-
 .../bench_ann_cuda-118_arch-aarch64.yaml      |   4 +-
 .../bench_ann_cuda-118_arch-x86_64.yaml       |   4 +-
 .../bench_ann_cuda-128_arch-aarch64.yaml      |   4 +-
 .../bench_ann_cuda-128_arch-x86_64.yaml       |   4 +-
 conda/recipes/cuvs-bench-cpu/meta.yaml        |   3 +-
 conda/recipes/cuvs-bench/meta.yaml            |   3 +-
 dependencies.yaml                             |   9 +-
 python/cuvs_bench/.coveragerc                 |   3 +
 .../cuvs_bench/config/algos/cuvs_cagra.yaml   |   8 +
 .../config/algos/cuvs_cagra_hnswlib.yaml      |   9 +
 .../config/algos/cuvs_ivf_flat.yaml           |   7 +
 .../cuvs_bench/config/algos/cuvs_ivf_pq.yaml  |  12 +
 .../config/algos/cuvs_mg_cagra.yaml           |   8 +
 .../config/algos/cuvs_mg_ivf_flat.yaml        |   7 +
 .../config/algos/cuvs_mg_ivf_pq.yaml          |  12 +
 .../config/algos/faiss_cpu_ivf_flat.yaml      |   8 +
 .../config/algos/faiss_cpu_ivf_pq.yaml        |   8 +
 .../config/algos/faiss_gpu_cagra.yaml         |   8 +
 .../config/algos/faiss_gpu_ivf_flat.yaml      |   7 +
 .../config/algos/faiss_gpu_ivf_pq.yaml        |  12 +
 .../cuvs_bench/config/algos/hnswlib.yaml      |   6 +
 .../config/datasets/bigann-100M.yaml          | 295 ------
 .../cuvs_bench/config/datasets/datasets.yaml  |   7 +
 .../cuvs_bench/config/datasets/deep-100M.yaml | 896 ------------------
 .../cuvs_bench/config/datasets/deep-1B.yaml   |  26 -
 .../config/datasets/deep-image-96-inner.yaml  | 712 --------------
 .../datasets/fashion-mnist-784-euclidean.yaml | 778 ---------------
 .../config/datasets/gist-960-euclidean.yaml   | 777 ---------------
 .../config/datasets/glove-100-angular.yaml    | 777 ---------------
 .../config/datasets/glove-100-inner.yaml      | 777 ---------------
 .../config/datasets/glove-50-angular.yaml     | 777 ---------------
 .../config/datasets/glove-50-inner.yaml       | 777 ---------------
 .../config/datasets/lastfm-65-angular.yaml    | 777 ---------------
 .../config/datasets/mnist-784-euclidean.yaml  | 778 ---------------
 .../config/datasets/nytimes-256-angular.yaml  | 778 ---------------
 .../config/datasets/nytimes-256-inner.yaml    | 778 ---------------
 .../config/datasets/sift-128-euclidean.yaml   | 562 -----------
 .../config/datasets/wiki_all_10M.yaml         | 357 -------
 .../config/datasets/wiki_all_1M.yaml          | 371 --------
 .../config/datasets/wiki_all_88M.yaml         | 357 -------
 .../cuvs_bench/get_dataset/__main__.py        | 170 +++-
 python/cuvs_bench/cuvs_bench/plot/__main__.py |   5 +-
 .../cuvs_bench/cuvs_bench/run/data_export.py  |   6 +-
 python/cuvs_bench/cuvs_bench/run/run.py       |  10 +-
 .../cuvs_bench/cuvs_bench/tests/test_cli.py   | 422 +++++++++
 .../cuvs_bench/cuvs_bench/tests/test_run.py   | 162 +++-
 python/cuvs_bench/pyproject.toml              |   2 +
 48 files changed, 869 insertions(+), 11428 deletions(-)
 create mode 100644 python/cuvs_bench/.coveragerc
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/bigann-100M.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/deep-100M.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/deep-1B.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/deep-image-96-inner.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/fashion-mnist-784-euclidean.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/gist-960-euclidean.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/glove-100-angular.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/glove-100-inner.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/glove-50-angular.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/glove-50-inner.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/lastfm-65-angular.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/mnist-784-euclidean.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-angular.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-inner.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/sift-128-euclidean.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_10M.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_1M.yaml
 delete mode 100644 python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_88M.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/tests/test_cli.py

diff --git a/ci/test_python.sh b/ci/test_python.sh
index e8749fe798..6ba36bc5a4 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
@@ -34,7 +34,8 @@ rapids-mamba-retry install \
   --channel "${CPP_CHANNEL}" \
   --channel "${PYTHON_CHANNEL}" \
   "libcuvs=${RAPIDS_VERSION}" \
-  "cuvs=${RAPIDS_VERSION}"
+  "cuvs=${RAPIDS_VERSION}" \
+  "cuvs-bench=${RAPIDS_VERSION}"
 
 rapids-logger "Check GPU usage"
 nvidia-smi
@@ -54,5 +55,17 @@ pytest \
  --cov-report=term \
  tests
 
+rapids-logger "pytest cuvs-bench"
+popd
+pushd python/cuvs_bench/cuvs_bench
+pytest \
+ --cache-clear \
+ --junitxml="${RAPIDS_TESTS_DIR}/junit-cuvs.xml" \
+ --cov-config=../.coveragerc \
+ --cov=cuvs \
+ --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cuvs-bench-coverage.xml" \
+ --cov-report=term \
+ tests
+
 rapids-logger "Test script exiting with value: $EXITCODE"
 exit ${EXITCODE}
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 2e2ad84468..e3da7c3760 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -37,7 +37,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - libcuvs==25.2.*,>=0.0.0a0
 - librmm==25.2.*,>=0.0.0a0
-- matplotlib
+- matplotlib-base
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
@@ -47,6 +47,8 @@ dependencies:
 - pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
+- requests
+- scikit-learn
 - setuptools
 - sysroot_linux-aarch64==2.28
 - wheel
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 90243415c1..1f8d61c2d3 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -37,7 +37,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - libcuvs==25.2.*,>=0.0.0a0
 - librmm==25.2.*,>=0.0.0a0
-- matplotlib
+- matplotlib-base
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
@@ -47,6 +47,8 @@ dependencies:
 - pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
+- requests
+- scikit-learn
 - setuptools
 - sysroot_linux-64==2.28
 - wheel
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index 43d3ca8211..330871a126 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -34,7 +34,7 @@ dependencies:
 - libcusparse-dev
 - libcuvs==25.2.*,>=0.0.0a0
 - librmm==25.2.*,>=0.0.0a0
-- matplotlib
+- matplotlib-base
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
@@ -43,6 +43,8 @@ dependencies:
 - pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
+- requests
+- scikit-learn
 - setuptools
 - sysroot_linux-aarch64==2.28
 - wheel
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index 54588287ae..8216bcd48a 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -34,7 +34,7 @@ dependencies:
 - libcusparse-dev
 - libcuvs==25.2.*,>=0.0.0a0
 - librmm==25.2.*,>=0.0.0a0
-- matplotlib
+- matplotlib-base
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
@@ -43,6 +43,8 @@ dependencies:
 - pylibraft==25.2.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
+- requests
+- scikit-learn
 - setuptools
 - sysroot_linux-64==2.28
 - wheel
diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
index 016df56be1..ddc13d568f 100644
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -58,11 +58,12 @@ requirements:
     - click
     - glog {{ glog_version }}
     - h5py {{ h5py_version }}
-    - matplotlib
+    - matplotlib-base
     - numpy >=1.23,<3.0a0
     - pandas
     - pyyaml
     - python
+    - requests
 about:
   home: https://rapids.ai/
   license: Apache-2.0
diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
index 33b1745ecb..1b151a16bc 100644
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -93,12 +93,13 @@ requirements:
     - glog {{ glog_version }}
     - cuvs {{ version }}
     - h5py {{ h5py_version }}
-    - matplotlib
+    - matplotlib-base
     - pandas
     - pyyaml
     # rmm is needed to determine if package is gpu-enabled
     - pylibraft ={{ minor_version }}
     - python
+    - requests
     - rmm ={{ minor_version }}
 about:
   home: https://rapids.ai/
diff --git a/dependencies.yaml b/dependencies.yaml
index cfa63250da..7109f4de5d 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -555,9 +555,16 @@ dependencies:
         packages:
           - click
           - cuvs==25.2.*,>=0.0.0a0
-          - matplotlib
           - pandas
           - pyyaml
+          - requests
+          - scikit-learn
+      - output_types: [conda]
+        packages:
+          - matplotlib-base
+      - output_types: [requirements, pyproject]
+        packages:
+          - matplotlib
   depends_on_libcuvs:
     common:
       - output_types: conda
diff --git a/python/cuvs_bench/.coveragerc b/python/cuvs_bench/.coveragerc
new file mode 100644
index 0000000000..2ee96a94d9
--- /dev/null
+++ b/python/cuvs_bench/.coveragerc
@@ -0,0 +1,3 @@
+# Configuration file for Python coverage tests
+[run]
+source = cuvs_bench
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
index edacb25b5f..240d2ac8ee 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra.yaml
@@ -11,3 +11,11 @@ groups:
     search:
       itopk: [32, 64, 128, 256, 512]
       search_width: [1, 2, 4, 8, 16, 32, 64]
+  test:
+    build:
+      graph_degree: [32]
+      intermediate_graph_degree: [32]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      itopk: [32]
+      search_width: [1, 2]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
index 630dc94ff3..0635022909 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_hnswlib.yaml
@@ -12,3 +12,12 @@ groups:
       ef_construction: [64, 128, 256, 512]
     search:
       ef: [10, 20, 40, 60, 80, 120, 200, 400, 600, 800]
+  test:
+    build:
+      graph_degree: [32]
+      intermediate_graph_degree: [32]
+      graph_build_algo: ["NN_DESCENT"]
+      hierarchy: ["none", "cpu"]
+      ef_construction: [64]
+    search:
+      ef: [10,20]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml
index 22afc79ab1..28e69c0abd 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_flat.yaml
@@ -7,3 +7,10 @@ groups:
       niter: [20, 25]
     search:
       nprobe: [1, 5, 10, 50, 100, 200, 500, 1000, 2000]
+  test:
+    build:
+      nlist: [1024]
+      ratio: [1]
+      niter: [20]
+    search:
+      nprobe: [1, 5]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
index d68e7973ab..154ae4b149 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_ivf_pq.yaml
@@ -39,3 +39,15 @@ groups:
       internalDistanceDtype: ["float"]
       smemLutDtype: ["float", "fp8", "half"]
       refine_ratio: [1]
+  test:
+    build:
+      nlist: [1024]
+      pq_dim: [64]
+      pq_bits: [8]
+      ratio: [10]
+      niter: [25]
+    search:
+      nprobe: [1]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml
index d730e33fb5..195755b321 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_cagra.yaml
@@ -11,3 +11,11 @@ groups:
     search:
       itopk: [32, 64, 128, 256, 512]
       search_width: [1, 2, 4, 8, 16, 32, 64]
+  test:
+    build:
+      graph_degree: [32]
+      intermediate_graph_degree: [32]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      itopk: [32]
+      search_width: [1, 2]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml
index 317e615e5b..68373b90f4 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_flat.yaml
@@ -7,3 +7,10 @@ groups:
       niter: [20, 25]
     search:
       nprobe: [1, 5, 10, 50, 100, 200, 500, 1000, 2000]
+  test:
+    build:
+      nlist: [1024]
+      ratio: [1]
+      niter: [20]
+    search:
+      nprobe: [1, 5]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml
index 7213968f4f..511018c37d 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_mg_ivf_pq.yaml
@@ -39,3 +39,15 @@ groups:
       internalDistanceDtype: ["float"]
       smemLutDtype: ["float", "fp8", "half"]
       refine_ratio: [1]
+  test:
+    build:
+      nlist: [1024]
+      pq_dim: [64]
+      pq_bits: [8]
+      ratio: [10]
+      niter: [25]
+    search:
+      nprobe: [1]
+      internalDistanceDtype: ["float"]
+      smemLutDtype: ["float", "fp8", "half"]
+      refine_ratio: [1]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
index aa7409dce8..6e11df8d70 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_flat.yaml
@@ -8,3 +8,11 @@ groups:
     search:
       nprobe: [1, 5, 10, 50, 100, 200]
       refine_ratio: [1]
+  test:
+    build:
+      nlist: [2048]
+      ratio: [10]
+      useFloat16: [False]
+    search:
+      nprobe: [1, 5]
+      refine_ratio: [1]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml
index a531ec8294..10b9440700 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_ivf_pq.yaml
@@ -16,3 +16,11 @@ groups:
       bitsPerCode: [8, 6, 5, 4]
     search:
       nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
+  test:
+    build:
+      nlist: [1024]
+      M: [48]
+      ratio: [10]
+      bitsPerCode: [8]
+    search:
+      nprobe: [1, 5]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
index 5788850968..3ec60c7b8e 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
@@ -11,3 +11,11 @@ groups:
     search:
       itopk: [32, 64, 128, 256, 512]
       search_width: [1, 2, 4, 8, 16, 32, 64]
+  test:
+    build:
+      graph_degree: [32]
+      intermediate_graph_degree: [32]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      itopk: [32]
+      search_width: [1, 2]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
index 20329e60d5..148121ec3f 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_flat.yaml
@@ -28,3 +28,10 @@ groups:
       use_cuvs: [True]
     search:
       nprobe: [10, 20, 30, 50, 100, 200, 500, 1000]
+  test:
+    build:
+      nlist: [1024]
+      ratio: [4]
+      use_cuvs: [False]
+    search:
+      nprobe: [1, 5]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
index d6cfe0569a..158246a030 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
@@ -74,3 +74,15 @@ groups:
     search:
       nprobe: [20, 30, 40, 50, 100, 200, 500, 1000]
       refine_ratio: [1, 2, 4]
+  test:
+    build:
+      nlist: [1024]
+      M: [96]
+      ratio: [4]
+      usePrecomputed: [True]
+      useFloat16: [True]
+      use_cuvs: [False]
+      bitsPerCode: [8]
+    search:
+      nprobe: [10]
+      refine_ratio: [1, 2]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml b/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
index 93d8cff2d1..cf2862289a 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/hnswlib.yaml
@@ -8,3 +8,9 @@ groups:
       efConstruction: [64, 128, 256, 512]
     search:
       ef: [10, 20, 40, 60, 80, 120, 200, 400, 600, 800]
+  test:
+    build:
+      M: [12]
+      efConstruction: [64]
+    search:
+      ef: [10, 20]
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/bigann-100M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/bigann-100M.yaml
deleted file mode 100644
index 69f75d1f71..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/bigann-100M.yaml
+++ /dev/null
@@ -1,295 +0,0 @@
-dataset:
-  base_file: bigann-1B/base.1B.u8bin
-  distance: euclidean
-  groundtruth_neighbors_file: bigann-100M/groundtruth.neighbors.ibin
-  name: bigann-100M
-  query_file: bigann-1B/query.public.10K.u8bin
-  subset_size: 100000000
-index:
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 5000
-    pq_dim: 64
-    ratio: 10
-  file: bigann-100M/raft_ivf_pq/dimpq64-cluster5K
-  name: raft_ivf_pq.dimpq64-cluster5K
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 20
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 1000
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 10000
-    pq_dim: 64
-    ratio: 10
-  file: bigann-100M/raft_ivf_pq/dimpq64-cluster5K
-  name: raft_ivf_pq.dimpq64-cluster10K
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 20
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 1000
-    smemLutDtype: half
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: bigann-100M/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: bigann-100M/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: bigann-100M/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: bigann-100M/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 100000
-    ratio: 5
-  file: bigann-100M/raft_ivf_flat/nlist100K
-  name: raft_ivf_flat.nlist100K
-  search_params:
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 20
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 30
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 40
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 50
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 100
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 200
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 500
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 1000
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: bigann-100M/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: bigann-100M/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-search_basic_param:
-  batch_size: 10000
-  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
index d64e232b7c..d86b92ea63 100644
--- a/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/datasets/datasets.yaml
@@ -105,6 +105,13 @@
   groundtruth_neighbors_file: sift-128-euclidean/groundtruth.neighbors.ibin
   distance: euclidean
 
+- name: test-data
+  dims: 32
+  base_file: test-data/ann_benchmarks_like.base.fbin
+  query_file: test-data/ann_benchmarks_like.query.fbin
+  groundtruth_neighbors_file: test-data/ann_benchmarks_like.groundtruth.neighbors.ibin
+  distance: euclidean
+
 - name: wiki_all_1M
   dims: 768
   base_file: wiki_all_1M/base.1M.fbin
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/deep-100M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/deep-100M.yaml
deleted file mode 100644
index 90b1f9721b..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/deep-100M.yaml
+++ /dev/null
@@ -1,896 +0,0 @@
-dataset:
-  base_file: deep-100M/base.1B.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: deep-100M/groundtruth.neighbors.ibin
-  name: deep-100M
-  query_file: deep-100M/query.public.10K.fbin
-  subset_size: 100000000
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: deep-100M/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: deep-100M/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: deep-100M/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: deep-100M/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 50000
-  file: deep-100M/faiss_gpu_ivf_flat/nlist50K
-  name: faiss_gpu_ivf_flat.nlist50K
-  search_params:
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 100000
-  file: deep-100M/faiss_gpu_ivf_flat/nlist100K
-  name: faiss_gpu_ivf_flat.nlist100K
-  search_params:
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 200000
-  file: deep-100M/faiss_gpu_ivf_flat/nlist200K
-  name: faiss_gpu_ivf_flat.nlist200K
-  search_params:
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 48
-    nlist: 16384
-  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist16K
-  name: faiss_gpu_ivf_pq.M48-nlist16K
-  search_params:
-  - nprobe: 10
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 48
-    nlist: 50000
-  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist50K
-  name: faiss_gpu_ivf_pq.M48-nlist50K
-  search_params:
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 48
-    nlist: 100000
-  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist100K
-  name: faiss_gpu_ivf_pq.M48-nlist100K
-  search_params:
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 48
-    nlist: 200000
-  file: deep-100M/faiss_gpu_ivf_pq/M48-nlist200K
-  name: faiss_gpu_ivf_pq.M48-nlist200K
-  search_params:
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 50000
-    ratio: 5
-  file: deep-100M/raft_ivf_flat/nlist50K
-  name: raft_ivf_flat.nlist50K
-  search_params:
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 20
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 30
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 40
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 50
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 100
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 200
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 500
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 1000
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 100000
-    ratio: 5
-  file: deep-100M/raft_ivf_flat/nlist100K
-  name: raft_ivf_flat.nlist100K
-  search_params:
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 20
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 30
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 40
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 50
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 100
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 200
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 500
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 1000
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 200000
-    ratio: 5
-  file: deep-100M/raft_ivf_flat/nlist200K
-  name: raft_ivf_flat.nlist200K
-  search_params:
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 20
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 30
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 40
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 50
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 100
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 200
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 500
-  - max_batch: 10000
-    max_k: 10
-    nprobe: 1000
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 50000
-    pq_bits: 5
-    pq_dim: 96
-    ratio: 10
-  file: deep-100M/raft_ivf_pq/d96b5n50K
-  name: raft_ivf_pq.d96b5n50K
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 20
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 30
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 40
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1000
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 2000
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 5000
-    refine_ratio: 2
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 20
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 30
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 40
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 50
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 100
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 200
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 1000
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 2000
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 5000
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 20
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 30
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 40
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1000
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 2000
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 5000
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 1000
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 2000
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 5000
-    refine_ratio: 2
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 1000
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 2000
-    refine_ratio: 2
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 5000
-    refine_ratio: 2
-    smemLutDtype: fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 50000
-    pq_bits: 5
-    pq_dim: 64
-    ratio: 10
-  file: deep-100M/raft_ivf_pq/d64b5n50K
-  name: raft_ivf_pq.d64b5n50K
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1000
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 2000
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 5000
-    refine_ratio: 4
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 1000
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 2000
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 5000
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1000
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 2000
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 5000
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 1000
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 2000
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 5000
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 1000
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 2000
-    refine_ratio: 4
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 5000
-    refine_ratio: 4
-    smemLutDtype: fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/deep-image-96-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/deep-image-96-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-    intermediate_graph_degree: 48
-  file: deep-100M/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - algo: single_cta
-    itopk: 32
-    max_iterations: 0
-    search_width: 1
-  - algo: single_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 1
-  - algo: single_cta
-    itopk: 64
-    max_iterations: 16
-    search_width: 4
-  - algo: single_cta
-    itopk: 64
-    max_iterations: 64
-    search_width: 1
-  - algo: single_cta
-    itopk: 96
-    max_iterations: 48
-    search_width: 2
-  - algo: single_cta
-    itopk: 128
-    max_iterations: 16
-    search_width: 8
-  - algo: single_cta
-    itopk: 128
-    max_iterations: 64
-    search_width: 2
-  - algo: single_cta
-    itopk: 192
-    max_iterations: 24
-    search_width: 8
-  - algo: single_cta
-    itopk: 192
-    max_iterations: 96
-    search_width: 2
-  - algo: single_cta
-    itopk: 256
-    max_iterations: 32
-    search_width: 8
-  - algo: single_cta
-    itopk: 384
-    max_iterations: 48
-    search_width: 8
-  - algo: single_cta
-    itopk: 512
-    max_iterations: 64
-    search_width: 8
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-    intermediate_graph_degree: 48
-  file: deep-100M/raft_cagra/dim32
-  name: raft_cagra.dim32.multi_cta
-  search_params:
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 0
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 1
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 16
-    search_width: 4
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 64
-    search_width: 1
-  - algo: multi_cta
-    itopk: 96
-    max_iterations: 48
-    search_width: 2
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 16
-    search_width: 8
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 64
-    search_width: 2
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 24
-    search_width: 8
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 96
-    search_width: 2
-  - algo: multi_cta
-    itopk: 256
-    max_iterations: 32
-    search_width: 8
-  - algo: multi_cta
-    itopk: 384
-    max_iterations: 48
-    search_width: 8
-  - algo: multi_cta
-    itopk: 512
-    max_iterations: 64
-    search_width: 8
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-    intermediate_graph_degree: 48
-  file: deep-100M/raft_cagra/dim32
-  name: raft_cagra.dim32.multi_kernel
-  search_params:
-  - algo: multi_kernel
-    itopk: 32
-    max_iterations: 0
-    search_width: 1
-  - algo: multi_kernel
-    itopk: 32
-    max_iterations: 32
-    search_width: 1
-  - algo: multi_kernel
-    itopk: 64
-    max_iterations: 16
-    search_width: 4
-  - algo: multi_kernel
-    itopk: 64
-    max_iterations: 64
-    search_width: 1
-  - algo: multi_kernel
-    itopk: 96
-    max_iterations: 48
-    search_width: 2
-  - algo: multi_kernel
-    itopk: 128
-    max_iterations: 16
-    search_width: 8
-  - algo: multi_kernel
-    itopk: 128
-    max_iterations: 64
-    search_width: 2
-  - algo: multi_kernel
-    itopk: 192
-    max_iterations: 24
-    search_width: 8
-  - algo: multi_kernel
-    itopk: 192
-    max_iterations: 96
-    search_width: 2
-  - algo: multi_kernel
-    itopk: 256
-    max_iterations: 32
-    search_width: 8
-  - algo: multi_kernel
-    itopk: 384
-    max_iterations: 48
-    search_width: 8
-  - algo: multi_kernel
-    itopk: 512
-    max_iterations: 64
-    search_width: 8
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: deep-100M/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-    max_iterations: 0
-    search_width: 1
-  - itopk: 32
-    max_iterations: 32
-    search_width: 1
-  - itopk: 64
-    max_iterations: 16
-    search_width: 4
-  - itopk: 64
-    max_iterations: 64
-    search_width: 1
-  - itopk: 96
-    max_iterations: 48
-    search_width: 2
-  - itopk: 128
-    max_iterations: 16
-    search_width: 8
-  - itopk: 128
-    max_iterations: 64
-    search_width: 2
-  - itopk: 192
-    max_iterations: 24
-    search_width: 8
-  - itopk: 192
-    max_iterations: 96
-    search_width: 2
-  - itopk: 256
-    max_iterations: 32
-    search_width: 8
-  - itopk: 384
-    max_iterations: 48
-    search_width: 8
-  - itopk: 512
-    max_iterations: 64
-    search_width: 8
-search_basic_param:
-  batch_size: 10000
-  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/deep-1B.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/deep-1B.yaml
deleted file mode 100644
index 061677e621..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/deep-1B.yaml
+++ /dev/null
@@ -1,26 +0,0 @@
-dataset:
-  base_file: deep-1B/base.1B.fbin
-  distance: inner_product
-  groundtruth_neighbors_file: deep-1B/groundtruth.neighbors.ibin
-  name: deep-1B
-  query_file: deep-1B/query.public.10K.fbin
-index:
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 48
-    nlist: 50000
-  file: deep-1B/faiss_gpu_ivf_pq/M48-nlist50K
-  name: faiss_gpu_ivf_pq.M48-nlist50K
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-search_basic_param:
-  batch_size: 10000
-  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/deep-image-96-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/deep-image-96-inner.yaml
deleted file mode 100644
index 2ec584eb56..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/deep-image-96-inner.yaml
+++ /dev/null
@@ -1,712 +0,0 @@
-dataset:
-  base_file: deep-image-96-inner/base.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: deep-image-96-inner/groundtruth.neighbors.ibin
-  name: deep-image-96-inner
-  query_file: deep-image-96-inner/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/deep-image-96-inner/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/deep-image-96-inner/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/deep-image-96-inner/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/deep-image-96-inner/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/deep-image-96-inner/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/deep-image-96-inner/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/deep-image-96-inner/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/deep-image-96-inner/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/deep-image-96-inner/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/deep-image-96-inner/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/deep-image-96-inner/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/deep-image-96-inner/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/deep-image-96-inner/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/deep-image-96-inner/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/deep-image-96-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/deep-image-96-inner/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/deep-image-96-inner/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/deep-image-96-inner/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/deep-image-96-inner/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/deep-image-96-inner/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/deep-image-96-inner/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/deep-image-96-inner/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/deep-image-96-inner/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/fashion-mnist-784-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/fashion-mnist-784-euclidean.yaml
deleted file mode 100644
index e92d3badcb..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/fashion-mnist-784-euclidean.yaml
+++ /dev/null
@@ -1,778 +0,0 @@
-dataset:
-  base_file: fashion-mnist-784-euclidean/base.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: fashion-mnist-784-euclidean/groundtruth.neighbors.ibin
-  name: fashion-mnist-784-euclidean
-  query_file: fashion-mnist-784-euclidean/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/fashion-mnist-784-euclidean/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/fashion-mnist-784-euclidean/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/fashion-mnist-784-euclidean/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/fashion-mnist-784-euclidean/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/fashion-mnist-784-euclidean/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/fashion-mnist-784-euclidean/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/fashion-mnist-784-euclidean/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/fashion-mnist-784-euclidean/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/fashion-mnist-784-euclidean/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/fashion-mnist-784-euclidean/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/fashion-mnist-784-euclidean/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/fashion-mnist-784-euclidean/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/fashion-mnist-784-euclidean/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/fashion-mnist-784-euclidean/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/fashion-mnist-784-euclidean/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/fashion-mnist-784-euclidean/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/gist-960-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/gist-960-euclidean.yaml
deleted file mode 100644
index d72dff74d2..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/gist-960-euclidean.yaml
+++ /dev/null
@@ -1,777 +0,0 @@
-dataset:
-  base_file: gist-960-euclidean/base.fbin
-  distance: euclidean
-  name: gist-960-euclidean
-  query_file: gist-960-euclidean/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/gist-960-euclidean/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/gist-960-euclidean/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/gist-960-euclidean/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/gist-960-euclidean/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/gist-960-euclidean/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/gist-960-euclidean/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/gist-960-euclidean/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/gist-960-euclidean/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/gist-960-euclidean/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/gist-960-euclidean/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/gist-960-euclidean/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/gist-960-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/gist-960-euclidean/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/gist-960-euclidean/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/gist-960-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/gist-960-euclidean/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/gist-960-euclidean/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/gist-960-euclidean/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/gist-960-euclidean/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/gist-960-euclidean/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/gist-960-euclidean/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/gist-960-euclidean/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/gist-960-euclidean/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-angular.yaml
deleted file mode 100644
index dab60ed4cf..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-angular.yaml
+++ /dev/null
@@ -1,777 +0,0 @@
-dataset:
-  base_file: glove-100-angular/base.fbin
-  distance: euclidean
-  name: glove-100-angular
-  query_file: glove-100-angular/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-angular/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-angular/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-angular/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-angular/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-angular/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-angular/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-angular/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-angular/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/glove-100-angular/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/glove-100-angular/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/glove-100-angular/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-angular/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/glove-100-angular/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/glove-100-angular/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-100-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/glove-100-angular/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-angular/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/glove-100-angular/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-angular/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/glove-100-angular/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-100-angular/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/glove-100-angular/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-100-angular/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-inner.yaml
deleted file mode 100644
index 972703735f..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/glove-100-inner.yaml
+++ /dev/null
@@ -1,777 +0,0 @@
-dataset:
-  base_file: glove-100-inner/base.fbin
-  distance: euclidean
-  name: glove-100-inner
-  query_file: glove-100-inner/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-inner/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-inner/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-inner/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-inner/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-inner/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-inner/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-100-inner/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-100-inner/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/glove-100-inner/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/glove-100-inner/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: glove-100-inner/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: glove-100-inner/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: glove-100-inner/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: glove-100-inner/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/glove-100-inner/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-inner/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/glove-100-inner/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/glove-100-inner/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/glove-100-inner/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: glove-100-inner/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-inner/faiss_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: glove-100-inner/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/glove-100-inner/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-100-inner/raft_gpu_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-100-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/glove-100-inner/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-100-inner/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/glove-100-inner/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-100-inner/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/glove-100-inner/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-100-inner/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/glove-100-inner/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-100-inner/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-angular.yaml
deleted file mode 100644
index d68d1700c8..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-angular.yaml
+++ /dev/null
@@ -1,777 +0,0 @@
-dataset:
-  base_file: glove-50-angular/base.fbin
-  distance: euclidean
-  name: glove-50-angular
-  query_file: glove-50-angular/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-angular/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-angular/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-angular/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-angular/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-angular/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-angular/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-angular/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-angular/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/glove-50-angular/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/glove-50-angular/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/glove-50-angular/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-angular/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/glove-50-angular/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/glove-50-angular/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-50-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/glove-50-angular/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-angular/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/glove-50-angular/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-angular/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/glove-50-angular/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-50-angular/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/glove-50-angular/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-50-angular/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-inner.yaml
deleted file mode 100644
index 379c797964..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/glove-50-inner.yaml
+++ /dev/null
@@ -1,777 +0,0 @@
-dataset:
-  base_file: glove-50-inner/base.fbin
-  distance: euclidean
-  name: glove-50-inner
-  query_file: glove-50-inner/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-inner/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-inner/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-inner/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-inner/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-inner/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-inner/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/glove-50-inner/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/glove-50-inner/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/glove-50-inner/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/glove-50-inner/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/glove-50-inner/faiss_ivf_flat/nlist1024
-  name: faiss_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/glove-50-inner/faiss_ivf_flat/nlist2048
-  name: faiss_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/glove-50-inner/faiss_ivf_flat/nlist4096
-  name: faiss_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/glove-50-inner/faiss_ivf_flat/nlist8192
-  name: faiss_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/glove-50-inner/faiss_ivf_flat/nlist16384
-  name: faiss_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-inner/faiss_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/glove-50-inner/faiss_ivf_pq/M64-nlist1024
-  name: faiss_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/glove-50-inner/faiss_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/glove-50-inner/faiss_ivf_sq/nlist1024-fp16
-  name: faiss_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/glove-50-inner/faiss_ivf_sq/nlist2048-fp16
-  name: faiss_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/glove-50-inner/faiss_ivf_sq/nlist4096-fp16
-  name: faiss_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/glove-50-inner/faiss_ivf_sq/nlist8192-fp16
-  name: faiss_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/glove-50-inner/faiss_ivf_sq/nlist16384-fp16
-  name: faiss_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/glove-50-inner/faiss_ivf_sq/nlist1024-int8
-  name: faiss_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/glove-50-inner/faiss_ivf_sq/nlist2048-int8
-  name: faiss_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/glove-50-inner/faiss_ivf_sq/nlist4096-int8
-  name: faiss_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/glove-50-inner/faiss_ivf_sq/nlist8192-int8
-  name: faiss_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/glove-50-inner/faiss_ivf_sq/nlist16384-int8
-  name: faiss_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-inner/faiss_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/glove-50-inner/faiss_flat/flat
-  name: faiss_flat
-  search_params:
-  - {}
-  search_result_file: result/glove-50-inner/faiss_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: half
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: fp8
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    nprobe: 1024
-    smemLutDtype: float
-  search_result_file: result/glove-50-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/glove-50-inner/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/glove-50-inner/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/glove-50-inner/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/glove-50-inner/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/glove-50-inner/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-50-inner/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/glove-50-inner/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/glove-50-inner/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/lastfm-65-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/lastfm-65-angular.yaml
deleted file mode 100644
index f28dc5850d..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/lastfm-65-angular.yaml
+++ /dev/null
@@ -1,777 +0,0 @@
-dataset:
-  base_file: lastfm-65-angular/base.fbin
-  distance: euclidean
-  name: lastfm-65-angular
-  query_file: lastfm-65-angular/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/lastfm-65-angular/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/lastfm-65-angular/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/lastfm-65-angular/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/lastfm-65-angular/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/lastfm-65-angular/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/lastfm-65-angular/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/lastfm-65-angular/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/lastfm-65-angular/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/lastfm-65-angular/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/lastfm-65-angular/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/lastfm-65-angular/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/lastfm-65-angular/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/lastfm-65-angular/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/lastfm-65-angular/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/lastfm-65-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/lastfm-65-angular/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/lastfm-65-angular/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/lastfm-65-angular/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/lastfm-65-angular/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/lastfm-65-angular/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/lastfm-65-angular/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/lastfm-65-angular/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/lastfm-65-angular/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/mnist-784-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/mnist-784-euclidean.yaml
deleted file mode 100644
index 1ffdd21244..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/mnist-784-euclidean.yaml
+++ /dev/null
@@ -1,778 +0,0 @@
-dataset:
-  base_file: mnist-784-euclidean/base.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: mnist-784-euclidean/groundtruth.neighbors.ibin
-  name: mnist-784-euclidean
-  query_file: mnist-784-euclidean/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/mnist-784-euclidean/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/mnist-784-euclidean/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/mnist-784-euclidean/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/mnist-784-euclidean/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/mnist-784-euclidean/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/mnist-784-euclidean/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/mnist-784-euclidean/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/mnist-784-euclidean/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/mnist-784-euclidean/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/mnist-784-euclidean/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/mnist-784-euclidean/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/mnist-784-euclidean/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/mnist-784-euclidean/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/mnist-784-euclidean/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/mnist-784-euclidean/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/mnist-784-euclidean/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/mnist-784-euclidean/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/mnist-784-euclidean/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/mnist-784-euclidean/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/mnist-784-euclidean/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/mnist-784-euclidean/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-angular.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-angular.yaml
deleted file mode 100644
index e9dcd83178..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-angular.yaml
+++ /dev/null
@@ -1,778 +0,0 @@
-dataset:
-  base_file: nytimes-256-angular/base.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: nytimes-256-angular/groundtruth.neighbors.ibin
-  name: nytimes-256-angular
-  query_file: nytimes-256-angular/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-angular/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-angular/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-angular/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-angular/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-angular/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-angular/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-angular/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-angular/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/nytimes-256-angular/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/nytimes-256-angular/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/nytimes-256-angular/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-angular/faiss_gpu_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/nytimes-256-angular/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-  search_result_file: result/nytimes-256-angular/faiss_gpu_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/nytimes-256-angular/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/nytimes-256-angular/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-angular/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/nytimes-256-angular/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-angular/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/nytimes-256-angular/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/nytimes-256-angular/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/nytimes-256-angular/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/nytimes-256-angular/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-inner.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-inner.yaml
deleted file mode 100644
index fa0094e11f..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/nytimes-256-inner.yaml
+++ /dev/null
@@ -1,778 +0,0 @@
-dataset:
-  base_file: nytimes-256-inner/base.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: nytimes-256-inner/groundtruth.neighbors.ibin
-  name: nytimes-256-inner
-  query_file: nytimes-256-inner/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-inner/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-inner/hnswlib/M12
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-inner/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-inner/hnswlib/M16
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-inner/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-inner/hnswlib/M24
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: index/nytimes-256-inner/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-  search_result_file: result/nytimes-256-inner/hnswlib/M36
-- algo: raft_bfknn
-  build_param: {}
-  file: index/nytimes-256-inner/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-  search_result_file: result/nytimes-256-inner/raft_bfknn/bfknn
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: index/nytimes-256-inner/faiss_ivf_flat/nlist1024
-  name: faiss_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist1024
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: index/nytimes-256-inner/faiss_ivf_flat/nlist2048
-  name: faiss_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist2048
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: index/nytimes-256-inner/faiss_ivf_flat/nlist4096
-  name: faiss_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist4096
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: index/nytimes-256-inner/faiss_ivf_flat/nlist8192
-  name: faiss_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist8192
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: index/nytimes-256-inner/faiss_ivf_flat/nlist16384
-  name: faiss_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_flat/nlist16384
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: index/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024
-  name: faiss_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: index/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_pq/M64-nlist1024
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist1024-fp16
-  name: faiss_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist1024-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist2048-fp16
-  name: faiss_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist2048-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist4096-fp16
-  name: faiss_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist4096-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist8192-fp16
-  name: faiss_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist8192-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist16384-fp16
-  name: faiss_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist16384-fp16
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist1024-int8
-  name: faiss_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist1024-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist2048-int8
-  name: faiss_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist2048-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist4096-int8
-  name: faiss_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist4096-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist8192-int8
-  name: faiss_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist8192-int8
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: index/nytimes-256-inner/faiss_ivf_sq/nlist16384-int8
-  name: faiss_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-inner/faiss_ivf_sq/nlist16384-int8
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: index/nytimes-256-inner/faiss_flat/flat
-  name: faiss_flat
-  search_params:
-  - {}
-  search_result_file: result/nytimes-256-inner/faiss_flat/flat
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024
-  name: raft_ivf_pq.dimpq128-cluster1024
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-  name: raft_ivf_pq.dimpq128-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 5
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-  name: raft_ivf_pq.dimpq128-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq128-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq64-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 64
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-  name: raft_ivf_pq.dimpq64-cluster1024-float-half
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: half
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: half
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq64-cluster1024-float-half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 32
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq32-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq32-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 16
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-  name: raft_ivf_pq.dimpq16-cluster1024-float-fp8
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: fp8
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq16-cluster1024-float-fp8
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 128
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-  name: raft_ivf_pq.dimpq128-cluster1024-half-float
-  search_params:
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: half
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq128-cluster1024-half-float
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1024
-    pq_dim: 512
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-  name: raft_ivf_pq.dimpq512-cluster1024-float-float
-  search_params:
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 10
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    k: 10
-    numProbes: 1024
-    smemLutDtype: float
-  search_result_file: result/nytimes-256-inner/raft_ivf_pq/dimpq512-cluster1024-float-float
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: index/nytimes-256-inner/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  search_result_file: result/nytimes-256-inner/raft_ivf_flat/nlist1024
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: index/nytimes-256-inner/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-  search_result_file: result/nytimes-256-inner/raft_ivf_flat/nlist16384
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: index/nytimes-256-inner/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/nytimes-256-inner/raft_cagra/dim32
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: index/nytimes-256-inner/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-  search_result_file: result/nytimes-256-inner/raft_cagra/dim64
-search_basic_param:
-  batch_size: 5000
-  k: 10
-  run_count: 3
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/sift-128-euclidean.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/sift-128-euclidean.yaml
deleted file mode 100644
index 2588d8a366..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/sift-128-euclidean.yaml
+++ /dev/null
@@ -1,562 +0,0 @@
-dataset:
-  base_file: sift-128-euclidean/base.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: sift-128-euclidean/groundtruth.neighbors.ibin
-  name: sift-128-euclidean
-  query_file: sift-128-euclidean/query.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 12
-    efConstruction: 500
-    numThreads: 32
-  file: sift-128-euclidean/hnswlib/M12
-  name: hnswlib.M12
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 500
-    numThreads: 32
-  file: sift-128-euclidean/hnswlib/M16
-  name: hnswlib.M16
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 24
-    efConstruction: 500
-    numThreads: 32
-  file: sift-128-euclidean/hnswlib/M24
-  name: hnswlib.M24
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: hnswlib
-  build_param:
-    M: 36
-    efConstruction: 500
-    numThreads: 32
-  file: sift-128-euclidean/hnswlib/M36
-  name: hnswlib.M36
-  search_params:
-  - ef: 10
-  - ef: 20
-  - ef: 40
-  - ef: 60
-  - ef: 80
-  - ef: 120
-  - ef: 200
-  - ef: 400
-  - ef: 600
-  - ef: 800
-- algo: raft_bfknn
-  build_param: {}
-  file: sift-128-euclidean/raft_bfknn/bfknn
-  name: raft_bfknn
-  search_params:
-  - probe: 1
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 1024
-  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist1024
-  name: faiss_gpu_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 2048
-  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist2048
-  name: faiss_gpu_ivf_flat.nlist2048
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 4096
-  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist4096
-  name: faiss_gpu_ivf_flat.nlist4096
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 8192
-  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist8192
-  name: faiss_gpu_ivf_flat.nlist8192
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_flat
-  build_param:
-    nlist: 16384
-  file: sift-128-euclidean/faiss_gpu_ivf_flat/nlist16384
-  name: faiss_gpu_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: true
-  file: sift-128-euclidean/faiss_gpu_ivf_pq/M64-nlist1024
-  name: faiss_gpu_ivf_pq.M64-nlist1024
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 1024
-    useFloat16: true
-    usePrecomputed: false
-  file: sift-128-euclidean/faiss_gpu_ivf_pq/M64-nlist1024.noprecomp
-  name: faiss_gpu_ivf_pq.M64-nlist1024.noprecomp
-  search_params:
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: fp16
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist1024-fp16
-  name: faiss_gpu_ivf_sq.nlist1024-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: fp16
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist2048-fp16
-  name: faiss_gpu_ivf_sq.nlist2048-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: fp16
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist4096-fp16
-  name: faiss_gpu_ivf_sq.nlist4096-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: fp16
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist8192-fp16
-  name: faiss_gpu_ivf_sq.nlist8192-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: fp16
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist16384-fp16
-  name: faiss_gpu_ivf_sq.nlist16384-fp16
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 1024
-    quantizer_type: int8
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist1024-int8
-  name: faiss_gpu_ivf_sq.nlist1024-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 2048
-    quantizer_type: int8
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist2048-int8
-  name: faiss_gpu_ivf_sq.nlist2048-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 4096
-    quantizer_type: int8
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist4096-int8
-  name: faiss_gpu_ivf_sq.nlist4096-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 8192
-    quantizer_type: int8
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist8192-int8
-  name: faiss_gpu_ivf_sq.nlist8192-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: faiss_gpu_ivf_sq
-  build_param:
-    nlist: 16384
-    quantizer_type: int8
-  file: sift-128-euclidean/faiss_gpu_ivf_sq/nlist16384-int8
-  name: faiss_gpu_ivf_sq.nlist16384-int8
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-- algo: faiss_gpu_flat
-  build_param: {}
-  file: sift-128-euclidean/faiss_gpu_flat/flat
-  name: faiss_gpu_flat
-  search_params:
-  - {}
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1000
-    pq_bits: 8
-    pq_dim: 64
-    ratio: 1
-  file: sift-128-euclidean/raft_ivf_pq/dimpq64-bitpq8-cluster1K
-  name: raft_ivf_pq.dimpq64-bitpq8-cluster1K
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 20
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 1000
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 25
-    nlist: 1000
-    pq_bits: 6
-    pq_dim: 128
-    ratio: 1
-  file: sift-128-euclidean/raft_ivf_pq/dimpq128-bitpq6-cluster1K
-  name: raft_ivf_pq.dimpq128-bitpq6-cluster1K
-  search_params:
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: float
-  - internalDistanceDtype: float
-    nprobe: 20
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 30
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 40
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 50
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 100
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 200
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 500
-    smemLutDtype: fp8
-  - internalDistanceDtype: float
-    nprobe: 1000
-    smemLutDtype: fp8
-  - internalDistanceDtype: half
-    nprobe: 20
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 1000
-    smemLutDtype: half
-- algo: raft_ivf_flat
-  build_param:
-    niter: 25
-    nlist: 1024
-    ratio: 1
-  file: sift-128-euclidean/raft_ivf_flat/nlist1024
-  name: raft_ivf_flat.nlist1024
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-- algo: raft_ivf_flat
-  build_param:
-    niter: 20
-    nlist: 16384
-    ratio: 2
-  file: sift-128-euclidean/raft_ivf_flat/nlist16384
-  name: raft_ivf_flat.nlist16384
-  search_params:
-  - nprobe: 1
-  - nprobe: 5
-  - nprobe: 10
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-  - nprobe: 1000
-  - nprobe: 2000
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-  file: sift-128-euclidean/raft_cagra/dim32
-  name: raft_cagra.dim32
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-- algo: raft_cagra
-  build_param:
-    graph_degree: 64
-  file: sift-128-euclidean/raft_cagra/dim64
-  name: raft_cagra.dim64
-  search_params:
-  - itopk: 32
-  - itopk: 64
-  - itopk: 128
-search_basic_param:
-  batch_size: 5000
-  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_10M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_10M.yaml
deleted file mode 100644
index 090247be9d..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_10M.yaml
+++ /dev/null
@@ -1,357 +0,0 @@
-dataset:
-  base_file: wiki_all_10M/base.88M.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: wiki_all_10M/groundtruth.88M.neighbors.ibin
-  name: wiki_all_10M
-  query_file: wiki_all_10M/queries.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 50
-    numThreads: 56
-  file: wiki_all_10M/hnswlib/M16.ef50
-  name: hnswlib.M16.ef50
-  search_params:
-  - ef: 10
-    numThreads: 56
-  - ef: 20
-    numThreads: 56
-  - ef: 40
-    numThreads: 56
-  - ef: 60
-    numThreads: 56
-  - ef: 80
-    numThreads: 56
-  - ef: 120
-    numThreads: 56
-  - ef: 200
-    numThreads: 56
-  - ef: 400
-    numThreads: 56
-  - ef: 600
-    numThreads: 56
-  - ef: 800
-    numThreads: 56
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 32
-    nlist: 16384
-    ratio: 2
-  file: wiki_all_10M/faiss_ivf_pq/M32-nlist16K_ratio2
-  name: faiss_ivf_pq.M32-nlist16K
-  search_params:
-  - nprobe: 10
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 16384
-    ratio: 2
-  file: wiki_all_10M/faiss_ivf_pq/M64-nlist16K_ratio2
-  name: faiss_ivf_pq.M64-nlist16K
-  search_params:
-  - nprobe: 10
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 128
-    ratio: 10
-  file: wiki_all_10M/raft_ivf_pq/d128-nlist16K
-  name: raft_ivf_pq.d128-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 1
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 64
-    ratio: 10
-  file: wiki_all_10M/raft_ivf_pq/d64-nlist16K
-  name: raft_ivf_pq.d64-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 4
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 32
-    ratio: 10
-  file: wiki_all_10M/raft_ivf_pq/d32-nlist16K
-  name: raft_ivf_pq.d32-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 32
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 32
-    ratio: 10
-  file: wiki_all_10M/raft_ivf_pq/d32-nlist16K
-  name: raft_ivf_pq.d32X-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 4
-    smemLutDtype: half
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-    intermediate_graph_degree: 48
-  file: wiki_all_10M/raft_cagra/dim32.ibin
-  name: raft_cagra.dim32.multi_cta
-  search_params:
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 0
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 36
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 40
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 44
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 48
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 16
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 24
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 26
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 2
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 16
-    search_width: 4
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 64
-    search_width: 1
-  - algo: multi_cta
-    itopk: 96
-    max_iterations: 48
-    search_width: 2
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 16
-    search_width: 8
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 64
-    search_width: 2
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 24
-    search_width: 8
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 96
-    search_width: 2
-  - algo: multi_cta
-    itopk: 256
-    max_iterations: 32
-    search_width: 8
-  - algo: multi_cta
-    itopk: 384
-    max_iterations: 48
-    search_width: 8
-  - algo: multi_cta
-    itopk: 512
-    max_iterations: 64
-    search_width: 8
-search_basic_param:
-  batch_size: 10000
-  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_1M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_1M.yaml
deleted file mode 100644
index bc5abaca2a..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_1M.yaml
+++ /dev/null
@@ -1,371 +0,0 @@
-dataset:
-  base_file: wiki_all_1M/base.1M.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: wiki_all_1M/groundtruth.1M.neighbors.ibin
-  name: wiki_all_1M
-  query_file: wiki_all_1M/queries.fbin
-  subset_size: 1000000
-index:
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 50
-    numThreads: 56
-  file: wiki_all_1M/hnswlib/M16.ef50
-  name: hnswlib.M16.ef50
-  search_params:
-  - ef: 10
-    numThreads: 56
-  - ef: 20
-    numThreads: 56
-  - ef: 40
-    numThreads: 56
-  - ef: 60
-    numThreads: 56
-  - ef: 80
-    numThreads: 56
-  - ef: 120
-    numThreads: 56
-  - ef: 200
-    numThreads: 56
-  - ef: 400
-    numThreads: 56
-  - ef: 600
-    numThreads: 56
-  - ef: 800
-    numThreads: 56
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 32
-    nlist: 16384
-    ratio: 2
-  file: wiki_all_1M/faiss_ivf_pq/M32-nlist16K_ratio2
-  name: faiss_ivf_pq.M32-nlist16K
-  search_params:
-  - nprobe: 10
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 16384
-    ratio: 2
-  file: wiki_all_1M/faiss_ivf_pq/M64-nlist16K_ratio2
-  name: faiss_ivf_pq.M64-nlist16K
-  search_params:
-  - nprobe: 10
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 128
-    ratio: 10
-  file: wiki_all_1M/raft_ivf_pq/d128-nlist16K
-  name: raft_ivf_pq.d128-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 1
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 64
-    ratio: 10
-  file: wiki_all_1M/raft_ivf_pq/d64-nlist16K
-  name: raft_ivf_pq.d64-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 4
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 32
-    ratio: 10
-  file: wiki_all_1M/raft_ivf_pq/d32-nlist16K
-  name: raft_ivf_pq.d32-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 32
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 32
-    ratio: 10
-  file: wiki_all_1M/raft_ivf_pq/d32-nlist16K
-  name: raft_ivf_pq.d32X-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 4
-    smemLutDtype: half
-- algo: raft_cagra
-  build_param:
-    graph_build_algo: NN_DESCENT
-    graph_degree: 32
-    intermediate_graph_degree: 48
-    ivf_pq_build_niter: 10
-    ivf_pq_build_nlist: 16384
-    ivf_pq_build_pq_bits: 8
-    ivf_pq_build_pq_dim: 32
-    ivf_pq_build_ratio: 10
-    ivf_pq_search_internalDistanceDtype: half
-    ivf_pq_search_nprobe: 30
-    ivf_pq_search_refine_ratio: 8
-    ivf_pq_search_smemLutDtype: half
-    nn_descent_intermediate_graph_degree: 72
-    nn_descent_max_iterations: 10
-    nn_descent_termination_threshold: 0.001
-  file: wiki_all_1M/raft_cagra/dim32.ibin
-  name: raft_cagra.dim32.multi_cta
-  search_params:
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 0
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 36
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 40
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 44
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 48
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 16
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 24
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 26
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 2
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 16
-    search_width: 4
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 64
-    search_width: 1
-  - algo: multi_cta
-    itopk: 96
-    max_iterations: 48
-    search_width: 2
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 16
-    search_width: 8
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 64
-    search_width: 2
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 24
-    search_width: 8
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 96
-    search_width: 2
-  - algo: multi_cta
-    itopk: 256
-    max_iterations: 32
-    search_width: 8
-  - algo: multi_cta
-    itopk: 384
-    max_iterations: 48
-    search_width: 8
-  - algo: multi_cta
-    itopk: 512
-    max_iterations: 64
-    search_width: 8
-search_basic_param:
-  batch_size: 10000
-  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_88M.yaml b/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_88M.yaml
deleted file mode 100644
index a06d6de54e..0000000000
--- a/python/cuvs_bench/cuvs_bench/config/datasets/wiki_all_88M.yaml
+++ /dev/null
@@ -1,357 +0,0 @@
-dataset:
-  base_file: wiki_all_88M/base.88M.fbin
-  distance: euclidean
-  groundtruth_neighbors_file: wiki_all_88M/groundtruth.88M.neighbors.ibin
-  name: wiki_all_88M
-  query_file: wiki_all_88M/queries.fbin
-index:
-- algo: hnswlib
-  build_param:
-    M: 16
-    efConstruction: 50
-    numThreads: 56
-  file: wiki_all_88M/hnswlib/M16.ef50
-  name: hnswlib.M16.ef50
-  search_params:
-  - ef: 10
-    numThreads: 56
-  - ef: 20
-    numThreads: 56
-  - ef: 40
-    numThreads: 56
-  - ef: 60
-    numThreads: 56
-  - ef: 80
-    numThreads: 56
-  - ef: 120
-    numThreads: 56
-  - ef: 200
-    numThreads: 56
-  - ef: 400
-    numThreads: 56
-  - ef: 600
-    numThreads: 56
-  - ef: 800
-    numThreads: 56
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 32
-    nlist: 16384
-    ratio: 2
-  file: wiki_all_88M/faiss_ivf_pq/M32-nlist16K_ratio2
-  name: faiss_ivf_pq.M32-nlist16K
-  search_params:
-  - nprobe: 10
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-- algo: faiss_gpu_ivf_pq
-  build_param:
-    M: 64
-    nlist: 16384
-    ratio: 2
-  file: wiki_all_88M/faiss_ivf_pq/M64-nlist16K_ratio2
-  name: faiss_ivf_pq.M64-nlist16K
-  search_params:
-  - nprobe: 10
-  - nprobe: 20
-  - nprobe: 30
-  - nprobe: 40
-  - nprobe: 50
-  - nprobe: 100
-  - nprobe: 200
-  - nprobe: 500
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 128
-    ratio: 10
-  file: wiki_all_88M/raft_ivf_pq/d128-nlist16K
-  name: raft_ivf_pq.d128-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 1
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 1
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 64
-    ratio: 10
-  file: wiki_all_88M/raft_ivf_pq/d64-nlist16K
-  name: raft_ivf_pq.d64-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 4
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 32
-    ratio: 10
-  file: wiki_all_88M/raft_ivf_pq/d32-nlist16K
-  name: raft_ivf_pq.d32-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 32
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 32
-    smemLutDtype: half
-- algo: raft_ivf_pq
-  build_param:
-    niter: 10
-    nlist: 16384
-    pq_bits: 8
-    pq_dim: 32
-    ratio: 10
-  file: wiki_all_88M/raft_ivf_pq/d32-nlist16K
-  name: raft_ivf_pq.d32X-nlist16K
-  search_params:
-  - internalDistanceDtype: half
-    nprobe: 20
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 16
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 8
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 30
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 40
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 50
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 100
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 200
-    refine_ratio: 4
-    smemLutDtype: half
-  - internalDistanceDtype: half
-    nprobe: 500
-    refine_ratio: 4
-    smemLutDtype: half
-- algo: raft_cagra
-  build_param:
-    graph_degree: 32
-    intermediate_graph_degree: 48
-  file: wiki_all_88M/raft_cagra/dim32.ibin
-  name: raft_cagra.dim32.multi_cta
-  search_params:
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 0
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 36
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 40
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 44
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 48
-    search_width: 1
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 16
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 24
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 26
-    search_width: 2
-  - algo: multi_cta
-    itopk: 32
-    max_iterations: 32
-    search_width: 2
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 16
-    search_width: 4
-  - algo: multi_cta
-    itopk: 64
-    max_iterations: 64
-    search_width: 1
-  - algo: multi_cta
-    itopk: 96
-    max_iterations: 48
-    search_width: 2
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 16
-    search_width: 8
-  - algo: multi_cta
-    itopk: 128
-    max_iterations: 64
-    search_width: 2
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 24
-    search_width: 8
-  - algo: multi_cta
-    itopk: 192
-    max_iterations: 96
-    search_width: 2
-  - algo: multi_cta
-    itopk: 256
-    max_iterations: 32
-    search_width: 8
-  - algo: multi_cta
-    itopk: 384
-    max_iterations: 48
-    search_width: 8
-  - algo: multi_cta
-    itopk: 512
-    max_iterations: 64
-    search_width: 8
-search_basic_param:
-  batch_size: 10000
-  k: 10
diff --git a/python/cuvs_bench/cuvs_bench/get_dataset/__main__.py b/python/cuvs_bench/cuvs_bench/get_dataset/__main__.py
index a6b154ef21..d0c8023f47 100644
--- a/python/cuvs_bench/cuvs_bench/get_dataset/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/get_dataset/__main__.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,11 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse
 import os
 import subprocess
-import sys
-from urllib.request import urlretrieve
+
+import click
+import h5py
+import numpy as np
+import requests
+from scipy.spatial.distance import cdist
+from sklearn.datasets import make_blobs
 
 
 def get_dataset_path(name, ann_bench_data_path):
@@ -29,7 +33,12 @@ def get_dataset_path(name, ann_bench_data_path):
 def download_dataset(url, path):
     if not os.path.exists(path):
         print(f"downloading {url} -> {path}...")
-        urlretrieve(url, path)
+        with requests.get(url, stream=True) as r:
+            r.raise_for_status()
+            with open(path, "wb") as f:
+                for chunk in r.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
 
 
 def convert_hdf5_to_fbin(path, normalize):
@@ -80,35 +89,140 @@ def download(name, normalize, ann_bench_data_path):
         raise
 
 
-def main():
-    call_path = os.getcwd()
-    if "RAPIDS_DATASET_ROOT_DIR" in os.environ:
-        default_dataset_path = os.getenv("RAPIDS_DATASET_ROOT_DIR")
-    else:
-        default_dataset_path = os.path.join(call_path, "datasets/")
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+def generate_ann_benchmark_like_data(
+    output_file="ann_benchmarks_like.hdf5",
+    n_train=1000,
+    n_test=100,
+    d=32,
+    centers=3,
+    k=100,
+    metric="euclidean",
+    dataset_path="test-data/",
+):
+    """
+    Generate a synthetic dataset in HDF5 format with a structure
+    similar to ann-benchmarks datasets. By default, ground truth
+    is computed for the top-100 nearest neighbors.
+    """
+
+    train_data, _ = make_blobs(
+        n_samples=n_train, n_features=d, centers=centers, random_state=42
     )
-    parser.add_argument(
-        "--dataset", help="dataset to download", default="glove-100-angular"
+
+    test_data, _ = make_blobs(
+        n_samples=n_test, n_features=d, centers=centers, random_state=84
     )
-    parser.add_argument(
-        "--dataset-path",
-        help="path to download dataset",
-        default=default_dataset_path,
+
+    test_data = test_data.astype(np.float32)
+    train_data = train_data.astype(np.float32)
+
+    dist_matrix = cdist(test_data, train_data, metric=metric)
+
+    actual_k = min(k, n_train)
+    neighbors = np.argsort(dist_matrix, axis=1)[:, :actual_k].astype(np.int32)
+    distances = np.take_along_axis(dist_matrix, neighbors, axis=1).astype(
+        np.float32
     )
-    parser.add_argument(
-        "--normalize",
-        help="normalize cosine distance to inner product",
-        action="store_true",
+
+    full_path = os.path.join(dataset_path, "test-data")
+    os.makedirs(full_path, exist_ok=True)
+    full_path = os.path.join(full_path, output_file)
+
+    with h5py.File(full_path, "w") as f:
+        # Datasets
+        f.create_dataset("train", data=train_data)
+        f.create_dataset("test", data=test_data)
+        f.create_dataset("neighbors", data=neighbors)
+        f.create_dataset("distances", data=distances)
+
+        f.attrs["distance"] = metric
+
+    convert_hdf5_to_fbin(full_path, normalize=True)
+
+    print(f"Created {full_path} with:")
+    print(f" - train shape = {train_data.shape}")
+    print(f" - test shape = {test_data.shape}")
+    print(f" - neighbors shape = {neighbors.shape}")
+    print(f" - distances shape = {distances.shape}")
+    print(f" - metric = {metric}")
+    print(f" - neighbors per test sample = {actual_k}")
+
+
+def get_default_dataset_path():
+    return os.getenv(
+        "RAPIDS_DATASET_ROOT_DIR", os.path.join(os.getcwd(), "datasets")
     )
 
-    if len(sys.argv) == 1:
-        parser.print_help()
-        sys.exit(1)
-    args = parser.parse_args()
 
-    download(args.dataset, args.normalize, args.dataset_path)
+@click.command()
+@click.option(
+    "--dataset",
+    default="glove-100-angular",
+    help="Dataset to download.",
+)
+@click.option(
+    "--test-data-n-train",
+    default=10000,
+    help="Number of training examples for the test data.",
+)
+@click.option(
+    "--test-data-n-test",
+    default=1000,
+    help="Number of test examples for the test data.",
+)
+@click.option(
+    "--test-data-dims",
+    default=32,
+    help="Dimensionality for the test data.",
+)
+@click.option(
+    "--test-data-k",
+    default=100,
+    help="K value for the test data.",
+)
+@click.option(
+    "--test-data-output-file",
+    default="ann_benchmarks_like.hdf5",
+    help="Output file name for the test data.",
+)
+@click.option(
+    "--dataset-path",
+    default=None,
+    help="Path to download the dataset. If not provided, defaults to "
+    "the value of RAPIDS_DATASET_ROOT_DIR or '<cwd>/datasets'.",
+)
+@click.option(
+    "--normalize",
+    is_flag=True,
+    help="Normalize cosine distance to inner product.",
+)
+def main(
+    dataset,
+    test_data_n_train,
+    test_data_n_test,
+    test_data_dims,
+    test_data_k,
+    test_data_output_file,
+    dataset_path,
+    normalize,
+):
+    # Compute default dataset_path if not provided.
+    if dataset_path is None:
+        dataset_path = get_default_dataset_path()
+
+    if dataset == "test-data":
+        generate_ann_benchmark_like_data(
+            output_file=test_data_output_file,
+            n_train=test_data_n_train,
+            n_test=test_data_n_test,
+            d=test_data_dims,
+            centers=3,
+            k=test_data_k,
+            metric="euclidean",
+            dataset_path=dataset_path,
+        )
+    else:
+        download(dataset, normalize, dataset_path)
 
 
 if __name__ == "__main__":
diff --git a/python/cuvs_bench/cuvs_bench/plot/__main__.py b/python/cuvs_bench/cuvs_bench/plot/__main__.py
index 7d5e3ae83f..02d598c9f3 100644
--- a/python/cuvs_bench/cuvs_bench/plot/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/plot/__main__.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -497,8 +497,9 @@ def load_all_results(
 )
 @click.option(
     "--mode",
+    "-m",
     type=click.Choice(["throughput", "latency"], case_sensitive=False),
-    default="throughput",
+    default="latency",
     help="Search mode whose Pareto frontier is used on the Y-axis.",
 )
 @click.option(
diff --git a/python/cuvs_bench/cuvs_bench/run/data_export.py b/python/cuvs_bench/cuvs_bench/run/data_export.py
index 3b03995d47..d42e872467 100644
--- a/python/cuvs_bench/cuvs_bench/run/data_export.py
+++ b/python/cuvs_bench/cuvs_bench/run/data_export.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -110,7 +110,8 @@ def clean_algo_name(algo_name):
         Cleaned algorithm name.
     """
 
-    return algo_name[0] if "base" in algo_name[1] else "_".join(algo_name)
+    name = algo_name[0] if "base" in algo_name[1] else "_".join(algo_name)
+    return name.removesuffix(".json")
 
 
 def write_csv(file, algo_name, df, extra_columns=None, skip_cols=None):
@@ -138,6 +139,7 @@ def write_csv(file, algo_name, df, extra_columns=None, skip_cols=None):
             "time": df["real_time"],
         }
     )
+
     # Add extra columns if provided
     if extra_columns:
         for col in extra_columns:
diff --git a/python/cuvs_bench/cuvs_bench/run/run.py b/python/cuvs_bench/cuvs_bench/run/run.py
index a16f01b94b..bec7766793 100644
--- a/python/cuvs_bench/cuvs_bench/run/run.py
+++ b/python/cuvs_bench/cuvs_bench/run/run.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -245,6 +245,7 @@ def prepare_executables(
         configurations.
     """
     executables_to_run = {}
+
     for algo, algo_conf in algos_conf.items():
         validate_algorithm(algos_yaml, algo, gpu_present)
         for group, group_conf in algo_conf["groups"].items():
@@ -345,8 +346,9 @@ def get_build_path(executable: str) -> Optional[str]:
 
     devcontainer_path = "/home/coder/cuvs/cpp/build/latest/bench/ann"
     if os.path.exists(devcontainer_path):
-        print(f"-- Detected devcontainer artifacts in {devcontainer_path}.")
-        return devcontainer_path
+        devc_executable = os.path.join(devcontainer_path, executable)
+        print(f"-- Detected devcontainer artifact {devc_executable}.")
+        return devc_executable
 
     build_path = os.getenv("CUVS_HOME")
     if build_path:
@@ -354,7 +356,7 @@ def get_build_path(executable: str) -> Optional[str]:
             build_path, "cpp", "build", "release", executable
         )
         if os.path.exists(build_path):
-            print(f"-- Using RAFT bench from repository in {build_path}.")
+            print(f"-- Using cuVS bench from repository in {build_path}.")
             return build_path
 
     conda_path = os.getenv("CONDA_PREFIX")
diff --git a/python/cuvs_bench/cuvs_bench/tests/test_cli.py b/python/cuvs_bench/cuvs_bench/tests/test_cli.py
new file mode 100644
index 0000000000..78685d7bd4
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/tests/test_cli.py
@@ -0,0 +1,422 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pathlib import Path
+
+import pandas as pd
+import pytest
+from click.testing import CliRunner
+from cuvs_bench.get_dataset.__main__ import main
+
+
+@pytest.fixture(scope="session")
+def temp_datasets_dir(tmp_path_factory):
+    return tmp_path_factory.mktemp("datasets")
+
+
+def test_get_dataset_creates_expected_files(temp_datasets_dir: Path):
+    runner = CliRunner()
+    dataset_path_arg = str(temp_datasets_dir)
+
+    # Invoke the CLI command as if calling:
+    # python -m cuvs_bench.get_dataset --dataset test-data \
+    # --dataset-path <temp_datasets_dir>
+    result = runner.invoke(
+        main, ["--dataset", "test-data", "--dataset-path", dataset_path_arg]
+    )
+
+    assert result.exit_code == 0, f"CLI call failed: {result.output}"
+
+    expected_files = [
+        "test-data/ann_benchmarks_like.groundtruth.distances.fbin",
+        "test-data/ann_benchmarks_like.base.fbin",
+        "test-data/ann_benchmarks_like.groundtruth.neighbors.ibin",
+        "test-data/ann_benchmarks_like.query.fbin",
+        "test-data/ann_benchmarks_like.hdf5",
+    ]
+
+    # Verify that each expected file exists in the datasets directory.
+    for filename in expected_files:
+        file_path = temp_datasets_dir / filename
+        assert (
+            file_path.exists()
+        ), f"Expected file {filename} was not generated."
+
+
+def test_run_command_creates_results(temp_datasets_dir: Path):
+    """
+    This test simulates running the command:
+
+        python -m cuvs_bench.run --dataset test-data --dataset-path datasets/ \
+            --algorithms faiss_gpu_ivf_flat,faiss_gpu_ivf_sq,cuvs_ivf_flat,\
+            cuvs_cagra,ggnn,cuvs_cagra_hnswlib, \
+            --batch-size 100 -k 10 --groups test -m latency --force
+
+    It then verifies that the set of expected result files
+         (both under result/build and result/search)
+         are created under datasets/test-data/ and are not empty.
+    """
+
+    dataset_path_arg = str(temp_datasets_dir)
+
+    from cuvs_bench.run.__main__ import main as run_main
+
+    runner = CliRunner()
+    run_args = [
+        "--dataset",
+        "test-data",
+        "--dataset-path",
+        dataset_path_arg,
+        "--algorithms",
+        "faiss_gpu_ivf_flat,faiss_gpu_ivf_sq,cuvs_ivf_flat,cuvs_cagra,ggnn,cuvs_cagra_hnswlib,",  # noqa: E501
+        "--batch-size",
+        "100",
+        "-k",
+        "10",
+        "--groups",
+        "test",
+        "-m",
+        "latency",
+        "--force",
+    ]
+    result = runner.invoke(run_main, run_args)
+    assert (
+        result.exit_code == 0
+    ), f"Run command failed with output:\n{result.output}"
+
+    common_build_header = [
+        "algo_name",
+        "index_name",
+        "time",
+        "threads",
+        "cpu_time",
+        "GPU",
+    ]
+
+    common_search_header = [
+        "algo_name",
+        "index_name",
+        "recall",
+        "throughput",
+        "latency",
+        "threads",
+        "cpu_time",
+        "GPU",
+    ]
+
+    # --- Verify that the expected result files exist and are not empty ---
+    expected_files = {
+        # Build files:
+        "test-data/result/build/cuvs_ivf_flat,test.csv": {
+            "header": common_build_header
+            + [
+                "niter",
+                "nlist",
+                "ratio",
+            ],
+            "rows": 1,
+        },
+        "test-data/result/build/cuvs_cagra_hnswlib,test.csv": {
+            "header": common_build_header
+            + [
+                "ef_construction",
+                "graph_degree",
+                "intermediate_graph_degree",
+                "label",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/build/faiss_gpu_ivf_flat,test.csv": {
+            "header": common_build_header
+            + [
+                "nlist",
+                "ratio",
+                "use_cuvs",
+            ],
+            "rows": 1,
+        },
+        "test-data/result/build/cuvs_cagra,test.csv": {
+            "header": common_build_header
+            + [
+                "graph_degree",
+                "intermediate_graph_degree",
+                "label",
+            ],
+            "rows": 1,
+        },
+        # Search files:
+        "test-data/result/search/cuvs_cagra_hnswlib,test,k10,bs100,raw.csv": {
+            "header": common_search_header
+            + [
+                "ef",
+                "end_to_end",
+                "k",
+                "n_queries",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "ef_construction",
+                "graph_degree",
+                "intermediate_graph_degree",
+                "label",
+            ],
+            "rows": 4,
+        },
+        "test-data/result/search/cuvs_cagra,test,k10,bs100,latency.csv": {
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "itopk",
+                "k",
+                "n_queries",
+                "search_width",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "graph_degree",
+                "intermediate_graph_degree",
+                "label",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/cuvs_cagra,test,k10,bs100,throughput.csv": {
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "itopk",
+                "k",
+                "n_queries",
+                "search_width",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "graph_degree",
+                "intermediate_graph_degree",
+                "label",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/cuvs_cagra,test,k10,bs100,raw.csv": {
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "itopk",
+                "k",
+                "n_queries",
+                "search_width",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "graph_degree",
+                "intermediate_graph_degree",
+                "label",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/cuvs_ivf_flat,test,k10,bs100,latency.csv": {
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "k",
+                "n_queries",
+                "nprobe",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "niter",
+                "nlist",
+                "ratio",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/cuvs_ivf_flat,test,k10,bs100,raw.csv": {
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "k",
+                "n_queries",
+                "nprobe",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "niter",
+                "nlist",
+                "ratio",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/cuvs_ivf_flat,test,k10,bs100,throughput.csv": {  # noqa: E501
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "k",
+                "n_queries",
+                "nprobe",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "niter",
+                "nlist",
+                "ratio",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/faiss_gpu_ivf_flat,test,k10,bs100,latency.csv": {  # noqa: E501
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "k",
+                "n_queries",
+                "nprobe",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "nlist",
+                "ratio",
+                "use_cuvs",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/faiss_gpu_ivf_flat,test,k10,bs100,raw.csv": {
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "k",
+                "n_queries",
+                "nprobe",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "nlist",
+                "ratio",
+                "use_cuvs",
+            ],
+            "rows": 2,
+        },
+        "test-data/result/search/faiss_gpu_ivf_flat,test,k10,bs100,throughput.csv": {  # noqa: E501
+            "header": common_search_header
+            + [
+                "end_to_end",
+                "k",
+                "n_queries",
+                "nprobe",
+                "total_queries",
+                "build time",
+                "build threads",
+                "build cpu_time",
+                "build GPU",
+                "nlist",
+                "ratio",
+                "use_cuvs",
+            ],
+            "rows": 2,
+        },
+    }
+
+    for rel_path, expectations in expected_files.items():
+        file_path = temp_datasets_dir / rel_path
+        assert file_path.exists(), f"Expected file {file_path} does not exist."
+        assert (
+            file_path.stat().st_size > 0
+        ), f"Expected file {file_path} is empty."
+
+        df = pd.read_csv(file_path)
+
+        actual_header = list(df.columns)
+        actual_rows = len(df)
+
+        # breakpoint()
+        assert (
+            actual_header == expectations["header"]
+        ), f"Wrong header produced in file f{rel_path}"
+        assert actual_rows == expectations["rows"]
+
+
+def test_plot_command_creates_png_files(temp_datasets_dir: Path):
+    """
+    This test simulates running the command:
+
+      python -m cuvs_bench.plot --dataset test-data --dataset-path datasets/ \
+          --algorithms faiss_gpu_ivf_flat,faiss_gpu_ivf_sq, \
+          cuvs_ivf_flat,cuvs_cagra,ggnn,cuvs_cagra_hnswlib \
+          --batch-size 100 -k 10 --groups test -m latency
+
+    and then verifies that the following files are produced in the
+    working directory:
+      - search-test-data-k10-batch_size100.png
+      - build-test-data-k10-batch_size100.png
+
+    It also checks that these files are not empty.
+    """
+
+    dataset_path_arg = str(temp_datasets_dir)
+
+    from cuvs_bench.plot.__main__ import main as plot_main
+
+    runner = CliRunner()
+    args = [
+        "--dataset",
+        "test-data",
+        "--dataset-path",
+        dataset_path_arg,
+        "--output-filepath",
+        dataset_path_arg,
+        "--algorithms",
+        "faiss_gpu_ivf_flat,faiss_gpu_ivf_sq,cuvs_ivf_flat,cuvs_cagra,ggnn,cuvs_cagra_hnswlib",  # noqa: E501
+        "--batch-size",
+        "100",
+        "-k",
+        "10",
+        "--groups",
+        "test",
+        "-m",
+        "latency",
+    ]
+    result = runner.invoke(plot_main, args)
+    assert (
+        result.exit_code == 0
+    ), f"Plot command failed with output:\n{result.output}"
+
+    # Expected output file names.
+    expected_files = [
+        "search-test-data-k10-batch_size100.png",
+        "build-test-data-k10-batch_size100.png",
+    ]
+
+    for filename in expected_files:
+        file_path = temp_datasets_dir / filename
+        assert file_path.exists(), f"Expected file {filename} does not exist."
+        assert (
+            file_path.stat().st_size > 0
+        ), f"Expected file {filename} is empty."
diff --git a/python/cuvs_bench/cuvs_bench/tests/test_run.py b/python/cuvs_bench/cuvs_bench/tests/test_run.py
index 7b7a481a07..c24dee1d26 100644
--- a/python/cuvs_bench/cuvs_bench/tests/test_run.py
+++ b/python/cuvs_bench/cuvs_bench/tests/test_run.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,10 +16,11 @@
 
 
 import itertools
+import os
 from unittest.mock import MagicMock, mock_open, patch
 
 import pytest
-from benchmark import (
+from cuvs_bench.run.run import (
     find_executable,
     gather_algorithm_configs,
     get_dataset_configuration,
@@ -60,15 +61,25 @@ def test_prepare_conf_file():
         "search_basic_param": {"k": 10, "batch_size": 128},
     }
     assert result == expected_result
-    result_no_subset = prepare_conf_file(dataset_conf, None, 10, 128)
-    assert result_no_subset["dataset"].get("subset_size") is None
 
 
 def test_gather_algorithm_configs(tmpdir):
-    scripts_path = tmpdir.mkdir("scripts")
-    algos_path = scripts_path.mkdir("algos")
-    algos_path.join("algo1.yaml").write("key: value")
-    algos_path.join("algo2.yaml").write("key: value")
+    scripts_base_path = tmpdir.mkdir("scripts_base")
+
+    scripts_path = os.path.join(scripts_base_path, "scripts")
+    os.mkdir(scripts_path)
+
+    algos_path = os.path.join(scripts_base_path, "config")
+    os.mkdir(algos_path)
+    algos_path = os.path.join(algos_path, "algos")
+    os.mkdir(algos_path)
+
+    with open(os.path.join(algos_path, "algo1.yaml"), "w") as f:
+        f.write("key: value")
+
+    with open(os.path.join(algos_path, "algo2.yaml"), "w") as f:
+        f.write("key: value")
+
     result = gather_algorithm_configs(str(scripts_path), None)
     assert len(result) == 2
 
@@ -80,7 +91,7 @@ def test_gather_algorithm_configs(tmpdir):
     custom_conf_file = custom_conf_dir.join("custom_algo_file.yaml")
     custom_conf_file.write("key: value")
     result = gather_algorithm_configs(str(scripts_path), str(custom_conf_file))
-    assert len(result) == 4
+    assert len(result) == 3
 
 
 def test_load_algorithms_conf():
@@ -91,23 +102,24 @@ def test_load_algorithms_conf():
       group1: {}
     """
     with patch("builtins.open", mock_open(read_data=yaml_content)):
-        result = load_algorithms_conf(algos_conf_fs, None, None)
+        result = load_algorithms_conf(algos_conf_fs, None, None, None)
         assert "algo1" in result
 
     with patch("builtins.open", mock_open(read_data=yaml_content)):
-        result = load_algorithms_conf(algos_conf_fs, ["algo1"], None)
+        result = load_algorithms_conf(algos_conf_fs, ["algo1"], None, None)
         assert "algo1" in result
-        result = load_algorithms_conf(algos_conf_fs, ["algo2"], None)
+        result = load_algorithms_conf(algos_conf_fs, ["algo2"], None, None)
         assert "algo1" not in result
 
 
 @patch(
-    "benchmark.find_executable",
+    "cuvs_bench.run.run.find_executable",
     return_value=("executable", "path", "filename"),
 )
-@patch("benchmark.validate_algorithm", return_value=True)
+@patch("cuvs_bench.run.run.validate_algorithm", return_value=True)
 @patch(
-    "benchmark.prepare_indexes", return_value=[{"index_key": "index_value"}]
+    "cuvs_bench.run.run.prepare_indexes",
+    return_value=[{"index_key": "index_value"}],
 )
 def test_prepare_executables(
     mock_prepare_indexes, mock_validate_algorithm, mock_find_executable
@@ -130,25 +142,102 @@ def test_prepare_executables(
         count,
         batch_size,
     )
-    assert "executable" in result
-    assert len(result["executable"]["index"]) == 1
+    assert ("executable", "path", "filename") in result.keys()
+    assert len(result[("executable", "path", "filename")]["index"]) == 1
 
 
-def test_prepare_indexes():
-    group_conf = {"build": {"param1": [1, 2]}, "search": {"param2": [3, 4]}}
+@patch("cuvs_bench.run.run.validate_constraints", return_value=True)
+@patch(
+    "cuvs_bench.run.run.validate_search_params",
+    return_value=[{"sparam": "dummy_value"}],
+)
+def test_prepare_indexes_valid(
+    mock_validate_search_params, mock_validate_constraints
+):
+    group_conf = {
+        "build": {
+            "param1": [1, 2],
+            "param2": [3, 4],
+        },
+        "search": {"sparam1": [True, False]},
+    }
+    algo = "algo1"
+    group = "base"
     conf_file = {"dataset": {"dims": 128}}
-    result = prepare_indexes(
+    algos_conf = {}
+    dataset_path = "/tmp/dataset"
+    dataset = "data1"
+    count = 10
+    batch_size = 32
+
+    indexes = prepare_indexes(
         group_conf,
-        "algo",
-        "group",
+        algo,
+        group,
         conf_file,
-        "dataset_path",
-        "dataset",
-        10,
-        128,
+        algos_conf,
+        dataset_path,
+        dataset,
+        count,
+        batch_size,
     )
-    assert len(result) == 2
-    assert "param1" in result[0]["build_param"]
+
+    # There are 2 build parameters with 2 values each, so we expect 4 indexes.
+    assert len(indexes) == 4
+
+    for index in indexes:
+        assert index["algo"] == algo
+
+        expected_filename = (
+            index["name"]
+            if len(index["name"]) < 128
+            else str(hash(index["name"]))
+        )
+        expected_file = os.path.join(
+            dataset_path, dataset, "index", expected_filename
+        )
+        assert index["file"] == expected_file
+        # Verify that our dummy search parameters were set.
+        assert index["search_params"] == [{"sparam": "dummy_value"}]
+
+
+@patch("cuvs_bench.run.run.validate_constraints", return_value=False)
+@patch(
+    "cuvs_bench.run.run.validate_search_params",
+    return_value=[{"sparam": "dummy_value"}],
+)
+def test_prepare_indexes_invalid(
+    mock_validate_search_params, mock_validate_constraints
+):
+    group_conf = {
+        "build": {
+            "param1": [1, 2],
+        },
+        "search": {},
+    }
+    algo = "algo1"
+    group = "base"
+    conf_file = {"dataset": {"dims": 128}}
+    algos_conf = {}
+    dataset_path = "/tmp/dataset"
+    dataset = "data1"
+    count = 10
+    batch_size = 32
+
+    indexes = prepare_indexes(
+        group_conf,
+        algo,
+        group,
+        conf_file,
+        algos_conf,
+        dataset_path,
+        dataset,
+        count,
+        batch_size,
+    )
+
+    # Since constraints fail, no indexes should be created.
+    assert indexes == []
 
 
 def test_validate_search_params():
@@ -159,8 +248,10 @@ def test_validate_search_params():
     result = validate_search_params(
         all_search_params,
         search_param_names,
+        {},
         "algo",
         group_conf,
+        {"algo": {"constraints": []}},
         conf_file,
         10,
         128,
@@ -175,7 +266,7 @@ def test_rmm_present():
         assert rmm_present() is False
 
 
-@patch("benchmark.get_build_path", return_value="build_path")
+@patch("cuvs_bench.run.run.get_build_path", return_value="build_path")
 def test_find_executable(mock_get_build_path):
     algos_conf = {"algo1": {"executable": "executable1"}}
     result = find_executable(algos_conf, "algo1", "group1", 10, 128)
@@ -200,28 +291,33 @@ def test_validate_algorithm():
     assert result is False
 
 
-@patch("benchmark.import_module")
+@patch("cuvs_bench.run.run.import_module")
 def test_validate_constraints(mock_import_module):
+    # Create a mock validator and have import_module return it.
     mock_validator = MagicMock()
     mock_import_module.return_value = mock_validator
+
+    # Test case 1: The constraint function returns True.
     mock_validator.constraint_func.return_value = True
     algos_conf = {
         "algo1": {"constraints": {"build": "module.constraint_func"}}
     }
     result = validate_constraints(
-        algos_conf, "algo1", "build", {"param1": "value1"}, 128, None, None
+        algos_conf, "algo1", "build", {"param1": "value1"}, {}, 128, None, None
     )
     assert result is True
 
+    # Test case 2: No constraints are specified; should return True.
     algos_conf = {"algo1": {"constraints": {}}}
     result = validate_constraints(
-        algos_conf, "algo1", "build", {"param1": "value1"}, 128, None, None
+        algos_conf, "algo1", "build", {"param1": "value1"}, {}, 128, None, None
     )
     assert result is True
 
+    # Test case 3: The constraint function returns False.
     mock_validator.constraint_func.return_value = False
     algos_conf["algo1"]["constraints"]["build"] = "module.constraint_func"
     result = validate_constraints(
-        algos_conf, "algo1", "build", {"param1": "value1"}, 128, None, None
+        algos_conf, "algo1", "build", {"param1": "value1"}, {}, 128, None, None
     )
     assert result is False
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 75e5406d4b..4205719633 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -23,6 +23,8 @@ dependencies = [
     "matplotlib",
     "pandas",
     "pyyaml",
+    "requests",
+    "scikit-learn",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",

From 8e6db9215813d3b4294b45a58baa02e2c2dcbbbc Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Fri, 7 Feb 2025 11:35:55 -0800
Subject: [PATCH 317/623] Use shared-workflows branch-25.04 (#669)

This completes the migration to NVKS runners now that all libraries have been tested and https://github.com/rapidsai/shared-workflows/pull/273 has been merged.

xref: https://github.com/rapidsai/build-infra/issues/184

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/669
---
 .github/workflows/build.yaml                  | 18 ++++++------
 .github/workflows/pr.yaml                     | 28 +++++++++----------
 .github/workflows/test.yaml                   |  8 +++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 4 files changed, 28 insertions(+), 28 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 479b2d66cb..837e448671 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -59,7 +59,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -82,7 +82,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -94,7 +94,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -105,7 +105,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -115,7 +115,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index cea4f0f03c..3c9576021d 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - wheel-tests-cuvs
       - devcontainer
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.04
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -45,7 +45,7 @@ jobs:
           repo: cuvs
   changed-files:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.04
     with:
       files_yaml: |
         test_cpp:
@@ -78,27 +78,27 @@ jobs:
           - '!thirdparty/LICENSES/**'
   checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.04
     with:
       enable_check_generated_files: false
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -106,20 +106,20 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -129,7 +129,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -139,7 +139,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -148,21 +148,21 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
       script: ci/test_wheel_cuvs.sh
   devcontainer:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.04
     with:
       arch: '["amd64"]'
       cuda: '["12.8"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index acfd766470..524078c30d 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -19,7 +19,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -29,7 +29,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -45,7 +45,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 7b5b4810fb..9764c62c15 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@nvks-runners
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.04
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}

From fe6c1c5f5d215a24b112a9ea1770e253b8012ff7 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Tue, 11 Feb 2025 03:43:06 -0800
Subject: [PATCH 318/623] Use new rapids-logger library (#644)

Contributes to https://github.com/rapidsai/build-planning/issues/104.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/644
---
 ci/build_cpp.sh                               |  3 +-
 ci/build_wheel.sh                             |  1 +
 .../cuvs-bench-cpu/conda_build_config.yaml    |  6 --
 conda/recipes/cuvs-bench-cpu/meta.yaml        |  2 -
 cpp/CMakeLists.txt                            | 12 ++--
 cpp/bench/ann/CMakeLists.txt                  | 20 +------
 cpp/cmake/thirdparty/get_raft.cmake           |  2 +-
 cpp/include/cuvs/cluster/kmeans.hpp           |  3 +-
 cpp/src/cluster/detail/kmeans_balanced.cuh    |  1 -
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |  1 -
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |  1 -
 cpp/tests/CMakeLists.txt                      |  4 --
 dependencies.yaml                             |  1 +
 docs/source/developer_guide.md                | 55 -------------------
 examples/cpp/CMakeLists.txt                   | 25 ++-------
 python/cuvs/CMakeLists.txt                    |  3 -
 python/cuvs/cuvs/common/CMakeLists.txt        |  4 --
 python/cuvs/cuvs/distance/CMakeLists.txt      |  4 --
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |  4 --
 .../cuvs/neighbors/brute_force/CMakeLists.txt |  4 --
 .../cuvs/cuvs/neighbors/cagra/CMakeLists.txt  |  4 --
 .../cuvs/neighbors/filters/CMakeLists.txt     |  4 --
 .../cuvs/cuvs/neighbors/hnsw/CMakeLists.txt   |  4 --
 .../cuvs/neighbors/ivf_flat/CMakeLists.txt    |  4 --
 .../cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt |  4 --
 .../cuvs/neighbors/nn_descent/CMakeLists.txt  |  4 --
 .../quantize/scalar/CMakeLists.txt            |  4 --
 python/libcuvs/libcuvs/load.py                | 17 +++---
 python/libcuvs/pyproject.toml                 |  1 +
 29 files changed, 28 insertions(+), 174 deletions(-)

diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index db4c496cc5..d0344061b1 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -17,7 +17,8 @@ rapids-logger "Begin cpp build"
 
 sccache --zero-stats
 
-RAPIDS_PACKAGE_VERSION=$(rapids-generate-version) rapids-conda-retry mambabuild conda/recipes/libcuvs
+RAPIDS_PACKAGE_VERSION=$(rapids-generate-version) rapids-conda-retry mambabuild \
+  conda/recipes/libcuvs
 
 sccache --show-adv-stats
 
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index f3b68c0b10..2c0040cfd7 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -25,6 +25,7 @@ EXCLUDE_ARGS=(
   --exclude "libcusolver.so.*"
   --exclude "libcusparse.so.*"
   --exclude "libnvJitLink.so.*"
+  --exclude "librapids_logger.so"
 )
 
 if [[ "${package_dir}" != "python/libcuvs" ]]; then
diff --git a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
index 5407d7c17f..74f45d911e 100644
--- a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
@@ -23,9 +23,3 @@ h5py_version:
 
 nlohmann_json_version:
   - ">=3.11.2"
-
-spdlog_version:
-  - ">=1.14.1,<1.15"
-
-fmt_version:
-  - ">=11.0.2,<12"
diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
index 016df56be1..30c5a6b52a 100644
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -45,13 +45,11 @@ requirements:
 
   host:
     - benchmark
-    - fmt {{ fmt_version }}
     - glog {{ glog_version }}
     - nlohmann_json {{ nlohmann_json_version }}
     - openblas
     - python
     - rapids-build-backend>=0.3.0,<0.4.0.dev0
-    - spdlog {{ spdlog_version }}
 
   run:
     - benchmark
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index fb33c1ab7f..e73478fb9f 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -495,16 +495,14 @@ if(BUILD_SHARED_LIBS)
                       "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
   target_link_libraries(
-    cuvs_objs
-    PUBLIC raft::raft rmm::rmm rmm::rmm_logger ${CUVS_CTK_MATH_DEPENDENCIES}
-           $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-    PRIVATE rmm::rmm_logger_impl raft::raft_logger_impl
+    cuvs_objs PUBLIC raft::raft rmm::rmm ${CUVS_CTK_MATH_DEPENDENCIES}
+                     $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
   )
 
-  add_library(cuvs SHARED $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
+  add_library(cuvs SHARED $<TARGET_OBJECTS:cuvs_objs>)
 
   if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
-    add_library(cuvs_static STATIC $<FILTER:$<TARGET_OBJECTS:cuvs_objs>,EXCLUDE,rmm.*logger>)
+    add_library(cuvs_static STATIC $<TARGET_OBJECTS:cuvs_objs>)
   endif()
 
   target_compile_options(
@@ -726,7 +724,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
     target_link_libraries(
       cuvs_c
       PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
-      PRIVATE raft::raft rmm::rmm_logger_impl raft::raft_logger_impl
+      PRIVATE raft::raft
     )
 
     # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 1ec9eeb9f3..fff2daa750 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -127,11 +127,9 @@ function(ConfigureAnnBench)
     PRIVATE ${ConfigureAnnBench_LINKS}
             nlohmann_json::nlohmann_json
             Threads::Threads
-            $<TARGET_NAME_IF_EXISTS:raft::raft_logger>
             $<$<BOOL:${GPU_BUILD}>:CUDA::cudart_static>
             $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
             $<TARGET_NAME_IF_EXISTS:conda_env>
-            $<TARGET_NAME_IF_EXISTS:cuvs_bench_logger>
   )
 
   set_target_properties(
@@ -177,13 +175,6 @@ function(ConfigureAnnBench)
   add_dependencies(CUVS_ANN_BENCH_ALL ${BENCH_NAME})
 endfunction()
 
-if(CUVS_FAISS_ENABLE_GPU OR CUVS_ANN_BENCH_SINGLE_EXE)
-  add_library(cuvs_bench_logger OBJECT)
-  target_link_libraries(
-    cuvs_bench_logger PRIVATE rmm::rmm_logger_impl $<TARGET_NAME_IF_EXISTS:raft::raft_logger>
-  )
-endif()
-
 # ##################################################################################################
 # * Configure benchmark targets -------------------------------------------------------------
 
@@ -316,15 +307,8 @@ if(CUVS_ANN_BENCH_SINGLE_EXE)
   target_include_directories(ANN_BENCH PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
 
   target_link_libraries(
-    ANN_BENCH
-    PRIVATE raft::raft
-            nlohmann_json::nlohmann_json
-            benchmark::benchmark
-            dl
-            fmt::fmt-header-only
-            spdlog::spdlog_header_only
-            $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3>
-            cuvs_bench_logger
+    ANN_BENCH PRIVATE raft::raft nlohmann_json::nlohmann_json benchmark::benchmark dl
+                      $<$<TARGET_EXISTS:CUDA::nvtx3>:CUDA::nvtx3>
   )
   set_target_properties(
     ANN_BENCH
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 3c352b6936..a71dc4cb43 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -39,7 +39,7 @@ function(find_and_configure_raft)
     # Invoke CPM find_package()
     #-----------------------------------------------------
     rapids_cpm_find(raft ${PKG_VERSION}
-            GLOBAL_TARGETS      raft::raft raft::raft_logger raft::raft_logger_impl
+            GLOBAL_TARGETS      raft::raft
             BUILD_EXPORT_SET    cuvs-exports
             INSTALL_EXPORT_SET  cuvs-exports
             COMPONENTS          ${RAFT_COMPONENTS}
diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index cb8d36b10b..64ac813ab1 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -20,6 +20,7 @@
 #include <raft/core/logger.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/random/rng_state.hpp>
+#include <rapids_logger/logger.hpp>
 
 namespace cuvs::cluster::kmeans {
 
@@ -86,7 +87,7 @@ struct params : base_params {
   /**
    * verbosity level.
    */
-  raft::level_enum verbosity = raft::level_enum::info;
+  rapids_logger::level_enum verbosity = rapids_logger::level_enum::info;
 
   /**
    * Seed to the random number generator.
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index ba4cabbded..89d8b05a31 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -25,7 +25,6 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/cudart_utils.hpp>
-#include <raft/core/logger-macros.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index f594343c76..2a4f1ab92c 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -27,7 +27,6 @@
 #include "../../cluster/kmeans_balanced.cuh"
 #include "../detail/ann_utils.cuh"
 #include <cuvs/distance/distance.hpp>
-#include <raft/core/logger-macros.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/core/mdarray.hpp>
 #include <raft/core/operators.hpp>
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index 2df6f4f0ef..b7ecdf4387 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -27,7 +27,6 @@
 #include <cuvs/distance/distance.hpp>   // is_min_close, DistanceType
 #include <cuvs/selection/select_k.hpp>  // cuvs::selection::select_k
 #include <raft/core/error.hpp>
-#include <raft/core/logger-macros.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>   // raft::resources
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index cea4e0ce60..2a352b875e 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -49,7 +49,6 @@ function(ConfigureTest)
     PRIVATE cuvs
             cuvs::cuvs
             raft::raft
-            test_rmm_logger
             GTest::gtest
             GTest::gtest_main
             Threads::Threads
@@ -88,9 +87,6 @@ function(ConfigureTest)
   )
 endfunction()
 
-add_library(test_rmm_logger OBJECT)
-target_link_libraries(test_rmm_logger PRIVATE rmm::rmm_logger_impl raft::raft_logger_impl)
-
 # ##################################################################################################
 # test sources ##################################################################################
 # ##################################################################################################
diff --git a/dependencies.yaml b/dependencies.yaml
index eabfd1ca6c..1a0c64e2d9 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -106,6 +106,7 @@ files:
     includes:
       - cuda_wheels
       - depends_on_libraft
+      - depends_on_librmm
   py_build_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 5ba0d77de7..a8da506b5a 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -227,61 +227,6 @@ Keep in mind that this only applies to files tracked by git that have been modif
 ## Error handling
 Call CUDA APIs via the provided helper macros `RAFT_CUDA_TRY`, `RAFT_CUBLAS_TRY` and `RAFT_CUSOLVER_TRY`. These macros take care of checking the return values of the used API calls and generate an exception when the command is not successful. If you need to avoid an exception, e.g. inside a destructor, use `RAFT_CUDA_TRY_NO_THROW`, `RAFT_CUBLAS_TRY_NO_THROW ` and `RAFT_CUSOLVER_TRY_NO_THROW`. These macros log the error but do not throw an exception.
 
-## Logging
-
-### Introduction
-Anything and everything about logging is defined inside [logger.hpp](https://github.com/rapidsai/raft/blob/branch-25.04/cpp/include/raft/core/logger.hpp). It uses [spdlog](https://github.com/gabime/spdlog) underneath, but this information is transparent to all.
-
-### Usage
-```cpp
-#include <raft/core/logger.hpp>
-
-// Inside your method or function, use any of these macros
-RAFT_LOG_TRACE("Hello %s!", "world");
-RAFT_LOG_DEBUG("Hello %s!", "world");
-RAFT_LOG_INFO("Hello %s!", "world");
-RAFT_LOG_WARN("Hello %s!", "world");
-RAFT_LOG_ERROR("Hello %s!", "world");
-RAFT_LOG_CRITICAL("Hello %s!", "world");
-```
-
-### Changing logging level
-There are 7 logging levels with each successive level becoming quieter:
-1. RAFT_LEVEL_TRACE
-2. RAFT_LEVEL_DEBUG
-3. RAFT_LEVEL_INFO
-4. RAFT_LEVEL_WARN
-5. RAFT_LEVEL_ERROR
-6. RAFT_LEVEL_CRITICAL
-7. RAFT_LEVEL_OFF
-   Pass one of these as per your needs into the `set_level()` method as follows:
-```cpp
-raft::logger::get().set_level(RAFT_LEVEL_WARN);
-// From now onwards, this will print only WARN and above kind of messages
-```
-
-### Changing logging pattern
-Pass the [format string](https://github.com/gabime/spdlog/wiki/3.-Custom-formatting) as follows in order use a different logging pattern than the default.
-```cpp
-raft::logger::get.set_pattern(YourFavoriteFormat);
-```
-One can also use the corresponding `get_pattern()` method to know the current format as well.
-
-### Temporarily changing the logging pattern
-Sometimes, we need to temporarily change the log pattern (eg: for reporting decision tree structure). This can be achieved in a RAII-like approach as follows:
-```cpp
-{
-  PatternSetter _(MyNewTempFormat);
-  // new log format is in effect from here onwards
-  doStuff();
-  // once the above temporary object goes out-of-scope, the old format will be restored
-}
-```
-
-### Tips
-* Do NOT end your logging messages with a newline! It is automatically added by spdlog.
-* The `RAFT_LOG_TRACE()` is by default not compiled due to the `RAFT_ACTIVE_LEVEL` macro setup, for performance reasons. If you need it to be enabled, change this macro accordingly during compilation time
-
 ## Common Design Considerations
 
 1. Use the `hpp` extension for files which can be compiled with `gcc` against the CUDA-runtime. Use the `cuh` extension for files which require `nvcc` to be compiled. `hpp` can also be used for functions marked `__host__ __device__` only if proper checks are in place to remove the `__device__` designation when not compiling with `nvcc`.
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 6bf8f34080..96414534cb 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -46,29 +46,14 @@ add_executable(VAMANA_EXAMPLE src/vamana_example.cu)
 
 # `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets are
 # installed in a conda environment, if one exists
-add_library(rmm_logger OBJECT)
-target_link_libraries(rmm_logger PRIVATE rmm::rmm_logger_impl)
-
-target_link_libraries(
-  BRUTE_FORCE_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
-)
-target_link_libraries(
-  CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
-)
+target_link_libraries(BRUTE_FORCE_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(CAGRA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(
   CAGRA_PERSISTENT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
-                                   rmm_logger
 )
 target_link_libraries(
   DYNAMIC_BATCHING_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> Threads::Threads
-                                   rmm_logger
-)
-target_link_libraries(
-  IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
-)
-target_link_libraries(
-  IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
-)
-target_link_libraries(
-  VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env> rmm_logger
 )
+target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 465a2ba8ad..93946cfdb0 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -56,9 +56,6 @@ target_include_directories(cuvs::cuvs INTERFACE "$<BUILD_INTERFACE:${DLPACK_INCL
 
 rapids_cython_init()
 
-add_library(cuvs_rmm_logger OBJECT)
-target_link_libraries(cuvs_rmm_logger PRIVATE rmm::rmm_logger_impl)
-
 add_subdirectory(cuvs/common)
 add_subdirectory(cuvs/distance)
 add_subdirectory(cuvs/neighbors)
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index b0e1cb335a..f03632c662 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX common_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/distance/CMakeLists.txt b/python/cuvs/cuvs/distance/CMakeLists.txt
index ded07395cf..a7a1ca1107 100644
--- a/python/cuvs/cuvs/distance/CMakeLists.txt
+++ b/python/cuvs/cuvs/distance/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX distance_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 3ac426f2b2..d5085b0551 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -30,7 +30,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_refine_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
index 3c646f4986..47f6878f3d 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/brute_force/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_brute_force_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
index 6cf0956a2d..1133351ecf 100644
--- a/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/cagra/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_cagra_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
index 43e0083635..b4a5035c04 100644
--- a/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/filters/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_prefilter_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
index c33313c3c1..23d1e22bdd 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/hnsw/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_hnsw_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
index eadb8934c9..f9c9b93e6b 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_ivf_flat_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
index df61793b89..8ecfd8b7da 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_pq_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt b/python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt
index 20f37bc238..b724568c35 100644
--- a/python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/nn_descent/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_nn_descent_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt b/python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt
index dc55edbf04..4ec258d195 100644
--- a/python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt
+++ b/python/cuvs/cuvs/preprocessing/quantize/scalar/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
                    preprocessing_quantize_scalar_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()
diff --git a/python/libcuvs/libcuvs/load.py b/python/libcuvs/libcuvs/load.py
index a9c6a93250..c034f85b15 100644
--- a/python/libcuvs/libcuvs/load.py
+++ b/python/libcuvs/libcuvs/load.py
@@ -46,19 +46,18 @@ def _load_wheel_installation(soname: str):
 def load_library():
     """Dynamically load libcuvs.so and its dependencies"""
     try:
-        # libraft must be loaded before libcuvs because libcuvs
-        # references its symbols
+        # These libraries  must be loaded before libcuvs because libcuvs
+        # references their symbols
         import libraft
+        import librmm
 
+        librmm.load_library()
         libraft.load_library()
     except ModuleNotFoundError:
-        # 'libcuvs' has a runtime dependency on 'libraft'. However,
-        # that dependency might be satisfied by the 'libraft' conda package
-        # (which does not have any Python modules), instead of the
-        # 'libraft' wheel.
-        #
-        # In that situation, assume that 'libraft.so' is in a place where
-        # the loader can find it.
+        # These runtime dependencies might be satisfied by conda packages
+        # (which do not have any Python modules) instead of wheels. In that
+        # situation, assume that the libraries are in a place where the loader
+        # can find them.
         pass
 
     prefer_system_installation = (
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 64dd54cfc3..986c95011f 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -31,6 +31,7 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "libraft==25.4.*,>=0.0.0a0",
+    "librmm==25.4.*,>=0.0.0a0",
     "nvidia-cublas",
     "nvidia-curand",
     "nvidia-cusolver",

From b2e5d9c88e057a80d04141da5a06a2d499afabeb Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 11 Feb 2025 11:29:47 -0800
Subject: [PATCH 319/623] update to latest rmm logging changes

---
 python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt b/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
index 5e2d3bde34..66b319f3bf 100644
--- a/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
+++ b/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
@@ -22,7 +22,3 @@ rapids_cython_create_modules(
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
                    preprocessing_quantize_scalar_
 )
-
-foreach(tgt IN LISTS RAPIDS_CYTHON_CREATED_TARGETS)
-  target_link_libraries(${tgt} PRIVATE cuvs_rmm_logger)
-endforeach()

From fb3934f2561a4567e6ec0710639a33314f12794c Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 11 Feb 2025 11:41:16 -0800
Subject: [PATCH 320/623] update version on GHA for the go build

---
 .github/workflows/build.yaml | 2 +-
 .github/workflows/pr.yaml    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 2b3f78230a..8c8551d55d 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -50,7 +50,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 72b805de90..b852f47f0a 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -157,7 +157,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.02
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"

From 0fb4d886e5954ab5593764aa8862c7bbe7e08db2 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Wed, 12 Feb 2025 00:17:02 -0500
Subject: [PATCH 321/623] Account for RAFT sparse types updates (#629)

From PR https://github.com/rapidsai/raft/pull/2541

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Gil Forsyth (https://github.com/gforsyth)
  - Bradley Dice (https://github.com/bdice)
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/629
---
 cpp/src/cluster/detail/connectivities.cuh     | 20 +++++++-------
 cpp/src/cluster/single_linkage.cuh            |  8 +++---
 cpp/src/neighbors/detail/knn_brute_force.cuh  |  4 ++-
 cpp/src/neighbors/detail/reachability.cuh     | 18 ++++++++-----
 cpp/src/neighbors/reachability.cu             |  4 +--
 cpp/src/sparse/cluster/detail/spectral.cuh    |  3 ++-
 .../spectral/modularity_maximization.hpp      | 25 ++++++++++-------
 .../cluster/detail/spectral/partition.hpp     | 27 +++++++++++--------
 .../cluster/detail/spectral/spectral_util.cuh | 21 ++++++++-------
 cpp/src/sparse/cluster/eigen_solvers.cuh      |  4 +--
 .../cluster/modularity_maximization.cuh       | 25 ++++++++++-------
 cpp/src/sparse/cluster/partition.cuh          | 27 +++++++++++--------
 .../sparse/neighbors/cross_component_nn.cuh   |  8 +++---
 .../neighbors/detail/cross_component_nn.cuh   | 19 ++++++-------
 cpp/tests/sparse/cluster/cluster_solvers.cu   |  5 ++--
 cpp/tests/sparse/cluster/eigen_solvers.cu     | 10 ++++---
 cpp/tests/sparse/cluster/spectral.cu          |  4 +--
 17 files changed, 133 insertions(+), 99 deletions(-)

diff --git a/cpp/src/cluster/detail/connectivities.cuh b/cpp/src/cluster/detail/connectivities.cuh
index e61c9166f0..5f62b2ecf8 100644
--- a/cpp/src/cluster/detail/connectivities.cuh
+++ b/cpp/src/cluster/detail/connectivities.cuh
@@ -43,8 +43,8 @@ template <Linkage dist_type, typename value_idx, typename value_t>
 struct distance_graph_impl {
   void run(raft::resources const& handle,
            const value_t* X,
-           size_t m,
-           size_t n,
+           value_idx m,
+           value_idx n,
            cuvs::distance::DistanceType metric,
            rmm::device_uvector<value_idx>& indptr,
            rmm::device_uvector<value_idx>& indices,
@@ -61,8 +61,8 @@ template <typename value_idx, typename value_t>
 struct distance_graph_impl<Linkage::KNN_GRAPH, value_idx, value_t> {
   void run(raft::resources const& handle,
            const value_t* X,
-           size_t m,
-           size_t n,
+           value_idx m,
+           value_idx n,
            cuvs::distance::DistanceType metric,
            rmm::device_uvector<value_idx>& indptr,
            rmm::device_uvector<value_idx>& indices,
@@ -131,8 +131,8 @@ RAFT_KERNEL fill_indices2(value_idx* indices, size_t m, size_t nnz)
 template <typename value_idx, typename value_t>
 void pairwise_distances(const raft::resources& handle,
                         const value_t* X,
-                        size_t m,
-                        size_t n,
+                        value_idx m,
+                        value_idx n,
                         cuvs::distance::DistanceType metric,
                         value_idx* indptr,
                         value_idx* indices,
@@ -183,8 +183,8 @@ template <typename value_idx, typename value_t>
 struct distance_graph_impl<Linkage::PAIRWISE, value_idx, value_t> {
   void run(const raft::resources& handle,
            const value_t* X,
-           size_t m,
-           size_t n,
+           value_idx m,
+           value_idx n,
            cuvs::distance::DistanceType metric,
            rmm::device_uvector<value_idx>& indptr,
            rmm::device_uvector<value_idx>& indices,
@@ -221,8 +221,8 @@ struct distance_graph_impl<Linkage::PAIRWISE, value_idx, value_t> {
 template <typename value_idx, typename value_t, Linkage dist_type>
 void get_distance_graph(raft::resources const& handle,
                         const value_t* X,
-                        size_t m,
-                        size_t n,
+                        value_idx m,
+                        value_idx n,
                         cuvs::distance::DistanceType metric,
                         rmm::device_uvector<value_idx>& indptr,
                         rmm::device_uvector<value_idx>& indices,
diff --git a/cpp/src/cluster/single_linkage.cuh b/cpp/src/cluster/single_linkage.cuh
index 364482d789..f389bdf00b 100644
--- a/cpp/src/cluster/single_linkage.cuh
+++ b/cpp/src/cluster/single_linkage.cuh
@@ -50,8 +50,8 @@ namespace cuvs::cluster::agglomerative {
 template <typename value_idx, typename value_t, Linkage dist_type = Linkage::KNN_GRAPH>
 void single_linkage(raft::resources const& handle,
                     const value_t* X,
-                    size_t m,
-                    size_t n,
+                    value_idx m,
+                    value_idx n,
                     cuvs::distance::DistanceType metric,
                     single_linkage_output<value_idx>* out,
                     int c,
@@ -94,8 +94,8 @@ void single_linkage(raft::resources const& handle,
 
   single_linkage<idx_t, value_t, dist_type>(handle,
                                             X.data_handle(),
-                                            static_cast<std::size_t>(X.extent(0)),
-                                            static_cast<std::size_t>(X.extent(1)),
+                                            X.extent(0),
+                                            X.extent(1),
                                             metric,
                                             &out_arrs,
                                             c.has_value() ? c.value() : DEFAULT_CONST_C,
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 5caf84cc79..989c4adce4 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -273,11 +273,13 @@ void tiled_brute_force_knn(const raft::resources& handle,
                          });
       }
 
+      std::optional<raft::device_matrix_view<const IndexType, int64_t, raft::row_major>>
+        null_optional = std::nullopt;
       cuvs::selection::select_k(
         handle,
         raft::make_device_matrix_view<const DistanceT, int64_t, raft::row_major>(
           temp_distances.data(), current_query_size, current_centroid_size),
-        std::nullopt,
+        null_optional,
         raft::make_device_matrix_view<DistanceT, int64_t, raft::row_major>(
           distances + i * k, current_query_size, current_k),
         raft::make_device_matrix_view<IndexType, int64_t, raft::row_major>(
diff --git a/cpp/src/neighbors/detail/reachability.cuh b/cpp/src/neighbors/detail/reachability.cuh
index 903c6f1da8..ead63ac866 100644
--- a/cpp/src/neighbors/detail/reachability.cuh
+++ b/cpp/src/neighbors/detail/reachability.cuh
@@ -186,17 +186,17 @@ void mutual_reachability_knn_l2(const raft::resources& handle,
       epilogue);
 }
 
-template <typename value_idx, typename value_t>
+template <typename value_idx, typename value_t, typename nnz_t>
 void mutual_reachability_graph(const raft::resources& handle,
                                const value_t* X,
-                               size_t m,
-                               size_t n,
+                               value_idx m,
+                               value_idx n,
                                cuvs::distance::DistanceType metric,
                                int min_samples,
                                value_t alpha,
                                value_idx* indptr,
                                value_t* core_dists,
-                               raft::sparse::COO<value_t, value_idx>& out)
+                               raft::sparse::COO<value_t, value_idx, nnz_t>& out)
 {
   RAFT_EXPECTS(metric == cuvs::distance::DistanceType::L2SqrtExpanded,
                "Currently only L2 expanded distance is supported");
@@ -228,8 +228,14 @@ void mutual_reachability_graph(const raft::resources& handle,
                     coo_rows.data(),
                     [min_samples] __device__(value_idx c) -> value_idx { return c / min_samples; });
 
-  raft::sparse::linalg::symmetrize(
-    handle, coo_rows.data(), inds.data(), dists.data(), m, m, min_samples * m, out);
+  raft::sparse::linalg::symmetrize(handle,
+                                   coo_rows.data(),
+                                   inds.data(),
+                                   dists.data(),
+                                   m,
+                                   m,
+                                   static_cast<nnz_t>(min_samples * m),
+                                   out);
 
   raft::sparse::convert::sorted_coo_to_csr(out.rows(), out.nnz, indptr, m + 1, stream);
 
diff --git a/cpp/src/neighbors/reachability.cu b/cpp/src/neighbors/reachability.cu
index 2e366106c3..c90009ab24 100644
--- a/cpp/src/neighbors/reachability.cu
+++ b/cpp/src/neighbors/reachability.cu
@@ -25,7 +25,7 @@ void mutual_reachability_graph(const raft::resources& handle,
                                int min_samples,
                                raft::device_vector_view<int> indptr,
                                raft::device_vector_view<float> core_dists,
-                               raft::sparse::COO<float, int>& out,
+                               raft::sparse::COO<float, int, size_t>& out,
                                cuvs::distance::DistanceType metric,
                                float alpha)
 {
@@ -34,7 +34,7 @@ void mutual_reachability_graph(const raft::resources& handle,
   RAFT_EXPECTS(indptr.extent(0) == static_cast<size_t>(X.extent(0) + 1),
                "indptr doesn't have expected size");
 
-  cuvs::neighbors::detail::reachability::mutual_reachability_graph<int, float>(
+  cuvs::neighbors::detail::reachability::mutual_reachability_graph<int, float, size_t>(
     handle,
     X.data_handle(),
     X.extent(0),
diff --git a/cpp/src/sparse/cluster/detail/spectral.cuh b/cpp/src/sparse/cluster/detail/spectral.cuh
index 3d44bd4a20..b5aa5139e5 100644
--- a/cpp/src/sparse/cluster/detail/spectral.cuh
+++ b/cpp/src/sparse/cluster/detail/spectral.cuh
@@ -56,12 +56,13 @@ void fit_embedding(raft::resources const& handle,
    */
   using index_type = int;
   using value_type = T;
+  using nnz_type   = int;
 
   index_type* ro = src_offsets.data();
   index_type* ci = dst_cols.data();
   value_type* vs = dst_vals.data();
 
-  raft::spectral::matrix::sparse_matrix_t<index_type, value_type> const r_csr_m{
+  raft::spectral::matrix::sparse_matrix_t<index_type, value_type, nnz_type> const r_csr_m{
     handle, ro, ci, vs, n, nnz};
 
   index_type neigvs       = n_components + 1;
diff --git a/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp b/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
index a42ad2dc11..3642dc4f42 100644
--- a/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
+++ b/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
@@ -66,10 +66,14 @@ namespace detail {
  *    performed.
  *  @return error flag.
  */
-template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+template <typename vertex_t,
+          typename weight_t,
+          typename nnz_t,
+          typename EigenSolver,
+          typename ClusterSolver>
 std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
   raft::resources const& handle,
-  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
   EigenSolver const& eigen_solver,
   ClusterSolver const& cluster_solver,
   vertex_t* __restrict__ clusters,
@@ -91,7 +95,7 @@ std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
   // Compute eigenvectors of Modularity Matrix
 
   // Initialize Modularity Matrix
-  raft::spectral::matrix::modularity_matrix_t<vertex_t, weight_t> B{handle, csr_m};
+  raft::spectral::matrix::modularity_matrix_t<vertex_t, weight_t, nnz_t> B{handle, csr_m};
 
   auto eigen_config = eigen_solver.get_config();
   auto nEigVecs     = eigen_config.n_eigVecs;
@@ -127,12 +131,13 @@ std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
  *  @param clusters (Input, device memory, n entries) Cluster assignments.
  *  @param modularity On exit, modularity
  */
-template <typename vertex_t, typename weight_t>
-void analyzeModularity(raft::resources const& handle,
-                       raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
-                       vertex_t nClusters,
-                       vertex_t const* __restrict__ clusters,
-                       weight_t& modularity)
+template <typename vertex_t, typename weight_t, typename nnz_t>
+void analyzeModularity(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
+  vertex_t nClusters,
+  vertex_t const* __restrict__ clusters,
+  weight_t& modularity)
 {
   RAFT_EXPECTS(clusters != nullptr, "Null clusters buffer.");
 
@@ -152,7 +157,7 @@ void analyzeModularity(raft::resources const& handle,
     raft::linalg::detail::cublassetpointermode(cublas_h, CUBLAS_POINTER_MODE_HOST, stream));
 
   // Initialize Modularity
-  raft::spectral::matrix::modularity_matrix_t<vertex_t, weight_t> B{handle, csr_m};
+  raft::spectral::matrix::modularity_matrix_t<vertex_t, weight_t, nnz_t> B{handle, csr_m};
 
   // Initialize output
   modularity = 0;
diff --git a/cpp/src/sparse/cluster/detail/spectral/partition.hpp b/cpp/src/sparse/cluster/detail/spectral/partition.hpp
index 77e83c17d3..d0ea793f53 100644
--- a/cpp/src/sparse/cluster/detail/spectral/partition.hpp
+++ b/cpp/src/sparse/cluster/detail/spectral/partition.hpp
@@ -66,10 +66,14 @@ namespace detail {
  *    performed.
  *  @return statistics: number of eigensolver iterations, .
  */
-template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+template <typename vertex_t,
+          typename weight_t,
+          typename nnz_t,
+          typename EigenSolver,
+          typename ClusterSolver>
 std::tuple<vertex_t, weight_t, vertex_t> partition(
   raft::resources const& handle,
-  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
   EigenSolver const& eigen_solver,
   ClusterSolver const& cluster_solver,
   vertex_t* __restrict__ clusters,
@@ -97,7 +101,7 @@ std::tuple<vertex_t, weight_t, vertex_t> partition(
 
   // Initialize Laplacian
   /// sparse_matrix_t<vertex_t, weight_t> A{handle, graph};
-  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t> L{handle, csr_m};
+  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t, nnz_t> L{handle, csr_m};
 
   auto eigen_config = eigen_solver.get_config();
   auto nEigVecs     = eigen_config.n_eigVecs;
@@ -135,13 +139,14 @@ std::tuple<vertex_t, weight_t, vertex_t> partition(
  *  @param cost On exit, partition cost function.
  *  @return error flag.
  */
-template <typename vertex_t, typename weight_t>
-void analyzePartition(raft::resources const& handle,
-                      raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
-                      vertex_t nClusters,
-                      const vertex_t* __restrict__ clusters,
-                      weight_t& edgeCut,
-                      weight_t& cost)
+template <typename vertex_t, typename weight_t, typename nnz_t>
+void analyzePartition(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
+  vertex_t nClusters,
+  const vertex_t* __restrict__ clusters,
+  weight_t& edgeCut,
+  weight_t& cost)
 {
   RAFT_EXPECTS(clusters != nullptr, "Null clusters buffer.");
 
@@ -163,7 +168,7 @@ void analyzePartition(raft::resources const& handle,
 
   // Initialize Laplacian
   /// sparse_matrix_t<vertex_t, weight_t> A{handle, graph};
-  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t> L{handle, csr_m};
+  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t, nnz_t> L{handle, csr_m};
 
   // Initialize output
   cost    = 0;
diff --git a/cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh b/cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh
index 1d2e58e2a5..725ea04d96 100644
--- a/cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh
+++ b/cpp/src/sparse/cluster/detail/spectral/spectral_util.cuh
@@ -136,16 +136,17 @@ struct equal_to_i_op {
 
 // Construct indicator vector for ith partition
 //
-template <typename vertex_t, typename edge_t, typename weight_t>
-bool construct_indicator(raft::resources const& handle,
-                         edge_t index,
-                         edge_t n,
-                         weight_t& clustersize,
-                         weight_t& partStats,
-                         vertex_t const* __restrict__ clusters,
-                         raft::spectral::matrix::vector_t<weight_t>& part_i,
-                         raft::spectral::matrix::vector_t<weight_t>& Bx,
-                         raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t> const& B)
+template <typename vertex_t, typename edge_t, typename weight_t, typename nnz_t>
+bool construct_indicator(
+  raft::resources const& handle,
+  edge_t index,
+  edge_t n,
+  weight_t& clustersize,
+  weight_t& partStats,
+  vertex_t const* __restrict__ clusters,
+  raft::spectral::matrix::vector_t<weight_t>& part_i,
+  raft::spectral::matrix::vector_t<weight_t>& Bx,
+  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t, nnz_t> const& B)
 {
   auto stream             = raft::resource::get_cuda_stream(handle);
   auto cublas_h           = raft::resource::get_cublas_handle(handle);
diff --git a/cpp/src/sparse/cluster/eigen_solvers.cuh b/cpp/src/sparse/cluster/eigen_solvers.cuh
index 1b2501d68b..6df1e86a8d 100644
--- a/cpp/src/sparse/cluster/eigen_solvers.cuh
+++ b/cpp/src/sparse/cluster/eigen_solvers.cuh
@@ -51,7 +51,7 @@ struct lanczos_solver_t {
 
   index_type_t solve_smallest_eigenvectors(
     raft::resources const& handle,
-    raft::spectral::matrix::sparse_matrix_t<index_type_t, value_type_t> const& A,
+    raft::spectral::matrix::sparse_matrix_t<index_type_t, value_type_t, size_type_t> const& A,
     value_type_t* __restrict__ eigVals,
     value_type_t* __restrict__ eigVecs) const
   {
@@ -74,7 +74,7 @@ struct lanczos_solver_t {
 
   index_type_t solve_largest_eigenvectors(
     raft::resources const& handle,
-    raft::spectral::matrix::sparse_matrix_t<index_type_t, value_type_t> const& A,
+    raft::spectral::matrix::sparse_matrix_t<index_type_t, value_type_t, size_type_t> const& A,
     value_type_t* __restrict__ eigVals,
     value_type_t* __restrict__ eigVecs) const
   {
diff --git a/cpp/src/sparse/cluster/modularity_maximization.cuh b/cpp/src/sparse/cluster/modularity_maximization.cuh
index ce60116d13..f1343b97a1 100644
--- a/cpp/src/sparse/cluster/modularity_maximization.cuh
+++ b/cpp/src/sparse/cluster/modularity_maximization.cuh
@@ -43,10 +43,14 @@ namespace spectral {
  *  @param eigVecs Output eigenvector array pointer on device
  *  @return statistics: number of eigensolver iterations, .
  */
-template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+template <typename vertex_t,
+          typename weight_t,
+          typename nnz_t,
+          typename EigenSolver,
+          typename ClusterSolver>
 std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
   raft::resources const& handle,
-  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
   EigenSolver const& eigen_solver,
   ClusterSolver const& cluster_solver,
   vertex_t* __restrict__ clusters,
@@ -54,7 +58,7 @@ std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
   weight_t* eigVecs)
 {
   return cuvs::spectral::detail::
-    modularity_maximization<vertex_t, weight_t, EigenSolver, ClusterSolver>(
+    modularity_maximization<vertex_t, weight_t, nnz_t, EigenSolver, ClusterSolver>(
       handle, csr_m, eigen_solver, cluster_solver, clusters, eigVals, eigVecs);
 }
 //===================================================
@@ -69,14 +73,15 @@ std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
  *  @param clusters (Input, device memory, n entries) Cluster assignments.
  *  @param modularity On exit, modularity
  */
-template <typename vertex_t, typename weight_t>
-void analyzeModularity(raft::resources const& handle,
-                       raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
-                       vertex_t nClusters,
-                       vertex_t const* __restrict__ clusters,
-                       weight_t& modularity)
+template <typename vertex_t, typename weight_t, typename nnz_t>
+void analyzeModularity(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
+  vertex_t nClusters,
+  vertex_t const* __restrict__ clusters,
+  weight_t& modularity)
 {
-  cuvs::spectral::detail::analyzeModularity<vertex_t, weight_t>(
+  cuvs::spectral::detail::analyzeModularity<vertex_t, weight_t, nnz_t>(
     handle, csr_m, nClusters, clusters, modularity);
 }
 
diff --git a/cpp/src/sparse/cluster/partition.cuh b/cpp/src/sparse/cluster/partition.cuh
index 111decadf3..8f28545868 100644
--- a/cpp/src/sparse/cluster/partition.cuh
+++ b/cpp/src/sparse/cluster/partition.cuh
@@ -45,17 +45,21 @@ namespace spectral {
  *  @param eigVecs Output eigenvector array pointer on device
  *  @return statistics: number of eigensolver iterations, .
  */
-template <typename vertex_t, typename weight_t, typename EigenSolver, typename ClusterSolver>
+template <typename vertex_t,
+          typename weight_t,
+          typename nnz_t,
+          typename EigenSolver,
+          typename ClusterSolver>
 std::tuple<vertex_t, weight_t, vertex_t> partition(
   raft::resources const& handle,
-  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
   EigenSolver const& eigen_solver,
   ClusterSolver const& cluster_solver,
   vertex_t* __restrict__ clusters,
   weight_t* eigVals,
   weight_t* eigVecs)
 {
-  return cuvs::spectral::detail::partition<vertex_t, weight_t, EigenSolver, ClusterSolver>(
+  return cuvs::spectral::detail::partition<vertex_t, weight_t, nnz_t, EigenSolver, ClusterSolver>(
     handle, csr_m, eigen_solver, cluster_solver, clusters, eigVals, eigVecs);
 }
 
@@ -77,15 +81,16 @@ std::tuple<vertex_t, weight_t, vertex_t> partition(
  *  @param edgeCut On exit, weight of edges cut by partition.
  *  @param cost On exit, partition cost function.
  */
-template <typename vertex_t, typename weight_t>
-void analyzePartition(raft::resources const& handle,
-                      raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t> const& csr_m,
-                      vertex_t nClusters,
-                      const vertex_t* __restrict__ clusters,
-                      weight_t& edgeCut,
-                      weight_t& cost)
+template <typename vertex_t, typename weight_t, typename nnz_t>
+void analyzePartition(
+  raft::resources const& handle,
+  raft::spectral::matrix::sparse_matrix_t<vertex_t, weight_t, nnz_t> const& csr_m,
+  vertex_t nClusters,
+  const vertex_t* __restrict__ clusters,
+  weight_t& edgeCut,
+  weight_t& cost)
 {
-  cuvs::spectral::detail::analyzePartition<vertex_t, weight_t>(
+  cuvs::spectral::detail::analyzePartition<vertex_t, weight_t, nnz_t>(
     handle, csr_m, nClusters, clusters, edgeCut, cost);
 }
 
diff --git a/cpp/src/sparse/neighbors/cross_component_nn.cuh b/cpp/src/sparse/neighbors/cross_component_nn.cuh
index 02ce240c4a..2da017ad1e 100644
--- a/cpp/src/sparse/neighbors/cross_component_nn.cuh
+++ b/cpp/src/sparse/neighbors/cross_component_nn.cuh
@@ -77,11 +77,11 @@ void cross_component_nn(
   raft::sparse::COO<value_t, value_idx>& out,
   const value_t* X,
   const value_idx* orig_colors,
-  size_t n_rows,
-  size_t n_cols,
+  value_idx n_rows,
+  value_idx n_cols,
   red_op reduction_op,
-  size_t row_batch_size               = 0,
-  size_t col_batch_size               = 0,
+  value_idx row_batch_size            = 0,
+  value_idx col_batch_size            = 0,
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded)
 {
   detail::cross_component_nn(handle,
diff --git a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
index 43bb6d8a81..fe5a5d2963 100644
--- a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
+++ b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
@@ -449,17 +449,17 @@ void min_components_by_color(raft::sparse::COO<value_t, value_idx>& coo,
  * is done
  * @param[in] metric distance metric
  */
-template <typename value_idx, typename value_t, typename red_op>
+template <typename value_idx, typename value_t, typename red_op, typename nnz_t = size_t>
 void cross_component_nn(
   raft::resources const& handle,
-  raft::sparse::COO<value_t, value_idx>& out,
+  raft::sparse::COO<value_t, value_idx, nnz_t>& out,
   const value_t* X,
   const value_idx* orig_colors,
-  size_t n_rows,
-  size_t n_cols,
+  value_idx n_rows,
+  value_idx n_cols,
   red_op reduction_op,
-  size_t row_batch_size,
-  size_t col_batch_size,
+  value_idx row_batch_size,
+  value_idx col_batch_size,
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded)
 {
   auto stream = raft::resource::get_cuda_stream(handle);
@@ -520,13 +520,14 @@ void cross_component_nn(
                          out_index.data());
 
   // compute final size
-  value_idx size = 0;
-  raft::update_host(&size, out_index.data() + (out_index.size() - 1), 1, stream);
+  value_idx size_int = 0;
+  raft::update_host(&size_int, out_index.data() + (out_index.size() - 1), 1, stream);
   raft::resource::sync_stream(handle, stream);
+  nnz_t size = static_cast<nnz_t>(size_int);
 
   size++;
 
-  raft::sparse::COO<value_t, value_idx> min_edges(stream);
+  raft::sparse::COO<value_t, value_idx, nnz_t> min_edges(stream);
   min_edges.allocate(size, n_rows, n_rows, true, stream);
 
   min_components_by_color(
diff --git a/cpp/tests/sparse/cluster/cluster_solvers.cu b/cpp/tests/sparse/cluster/cluster_solvers.cu
index c0b6c1a78c..77a0c0e333 100644
--- a/cpp/tests/sparse/cluster/cluster_solvers.cu
+++ b/cpp/tests/sparse/cluster/cluster_solvers.cu
@@ -64,6 +64,7 @@ TEST(Raft, ModularitySolvers)
   using namespace raft::spectral::matrix;
   using index_type = int;
   using value_type = double;
+  using nnz_type   = int;
 
   raft::resources h;
   ASSERT_EQ(0, raft::resource::get_device_id(h));
@@ -84,7 +85,7 @@ TEST(Raft, ModularitySolvers)
 
   eigen_solver_config_t<index_type, value_type> eig_cfg{
     neigvs, maxiter, restart_iter, tol, reorthog, seed};
-  lanczos_solver_t<index_type, value_type> eig_solver{eig_cfg};
+  lanczos_solver_t<index_type, value_type, nnz_type> eig_solver{eig_cfg};
 
   index_type k{5};
 
@@ -92,7 +93,7 @@ TEST(Raft, ModularitySolvers)
   kmeans_solver_t<index_type, value_type> cluster_solver{clust_cfg};
 
   auto stream = raft::resource::get_cuda_stream(h);
-  sparse_matrix_t<index_type, value_type> sm{h, nullptr, nullptr, nullptr, 0, 0};
+  sparse_matrix_t<index_type, value_type, nnz_type> sm{h, nullptr, nullptr, nullptr, 0, 0};
 
   EXPECT_ANY_THROW(cuvs::spectral::modularity_maximization(
     h, sm, eig_solver, cluster_solver, clusters, eigvals, eigvecs));
diff --git a/cpp/tests/sparse/cluster/eigen_solvers.cu b/cpp/tests/sparse/cluster/eigen_solvers.cu
index 8de0b49e7d..9e6626c3a7 100644
--- a/cpp/tests/sparse/cluster/eigen_solvers.cu
+++ b/cpp/tests/sparse/cluster/eigen_solvers.cu
@@ -36,6 +36,7 @@ TEST(Raft, EigenSolvers)
   using namespace raft::spectral::matrix;
   using index_type = int;
   using value_type = double;
+  using nnz_type   = int;
 
   raft::resources h;
   ASSERT_EQ(0, raft::resource::get_device_id(h));
@@ -43,10 +44,10 @@ TEST(Raft, EigenSolvers)
   index_type* ro{nullptr};
   index_type* ci{nullptr};
   value_type* vs{nullptr};
-  index_type nnz   = 0;
+  nnz_type nnz     = 0;
   index_type nrows = 0;
 
-  sparse_matrix_t<index_type, value_type> sm1{h, ro, ci, vs, nrows, nnz};
+  sparse_matrix_t<index_type, value_type, nnz_type> sm1{h, ro, ci, vs, nrows, nnz};
   ASSERT_EQ(nullptr, sm1.row_offsets_);
 
   index_type neigvs{10};
@@ -77,6 +78,7 @@ TEST(Raft, SpectralSolvers)
   using namespace raft::spectral::matrix;
   using index_type = int;
   using value_type = double;
+  using nnz_type   = int;
 
   raft::resources h;
   ASSERT_EQ(0, raft::resource::get_device_id(h)
@@ -99,14 +101,14 @@ TEST(Raft, SpectralSolvers)
 
   eigen_solver_config_t<index_type, value_type> eig_cfg{
     neigvs, maxiter, restart_iter, tol, reorthog, seed};
-  lanczos_solver_t<index_type, value_type> eig_solver{eig_cfg};
+  lanczos_solver_t<index_type, value_type, nnz_type> eig_solver{eig_cfg};
 
   index_type k{5};
 
   cluster_solver_config_t<index_type, value_type> clust_cfg{k, maxiter, tol, seed};
   kmeans_solver_t<index_type, value_type> cluster_solver{clust_cfg};
 
-  sparse_matrix_t<index_type, value_type> sm{h, nullptr, nullptr, nullptr, 0, 0};
+  sparse_matrix_t<index_type, value_type, nnz_type> sm{h, nullptr, nullptr, nullptr, 0, 0};
   EXPECT_ANY_THROW(
     spectral::partition(h, sm, eig_solver, cluster_solver, clusters, eigvals, eigvecs));
 
diff --git a/cpp/tests/sparse/cluster/spectral.cu b/cpp/tests/sparse/cluster/spectral.cu
index 98b963451f..7162f3b1c8 100644
--- a/cpp/tests/sparse/cluster/spectral.cu
+++ b/cpp/tests/sparse/cluster/spectral.cu
@@ -73,7 +73,7 @@ TEST(Raft, Spectral)
   raft::update_device(indices.data(), h_indices.data(), h_indices.size(), handle.get_stream());
   raft::update_device(values.data(), h_values.data(), h_values.size(), handle.get_stream());
 
-  raft::spectral::matrix::sparse_matrix_t<int32_t, float> const matrix{
+  raft::spectral::matrix::sparse_matrix_t<int32_t, float, int32_t> const matrix{
     handle,
     offsets.data(),
     indices.data(),
@@ -83,7 +83,7 @@ TEST(Raft, Spectral)
 
   cuvs::spectral::eigen_solver_config_t<int32_t, float> eig_cfg{
     n_eigenvectors, evs_max_it, restartIter_lanczos, evs_tol, reorthog, seed1};
-  cuvs::spectral::lanczos_solver_t<int32_t, float> eig_solver{eig_cfg};
+  cuvs::spectral::lanczos_solver_t<int32_t, float, int32_t> eig_solver{eig_cfg};
 
   cuvs::spectral::cluster_solver_config_t<int32_t, float> clust_cfg{
     n_clusters, kmean_max_it, kmean_tol, seed2};

From 211deb58a380aa385b439e1dae2c8ad15d018452 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Thu, 13 Feb 2025 09:30:55 -0500
Subject: [PATCH 322/623] Create Conda CI test env in one step (#684)

Issue: https://github.com/rapidsai/build-planning/issues/22

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/684
---
 ci/release/update-version.sh |  2 ++
 ci/test_cpp.sh               | 12 ++++--------
 ci/test_python.sh            | 20 +++++++-------------
 dependencies.yaml            | 20 ++++++++++++++++++++
 4 files changed, 33 insertions(+), 21 deletions(-)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 9bc916a871..058e0e46a6 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -44,7 +44,9 @@ echo "${NEXT_FULL_TAG}" > VERSION
 DEPENDENCIES=(
   dask-cuda
   cuvs
+  cuvs-bench
   libcuvs
+  libcuvs-tests
   libraft
   librmm
   pylibraft
diff --git a/ci/test_cpp.sh b/ci/test_cpp.sh
index 134dc44212..34098175c4 100755
--- a/ci/test_cpp.sh
+++ b/ci/test_cpp.sh
@@ -5,13 +5,15 @@ set -euo pipefail
 
 . /opt/conda/etc/profile.d/conda.sh
 
-RAPIDS_VERSION="$(rapids-version)"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
 
 rapids-logger "Generate C++ testing dependencies"
 rapids-dependency-file-generator \
   --output conda \
   --file-key test_cpp \
-  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch)" | tee env.yaml
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch)" \
+  --prepend-channel "${CPP_CHANNEL}" \
+  | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n test
 
@@ -20,17 +22,11 @@ set +u
 conda activate test
 set -u
 
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
 RAPIDS_TESTS_DIR=${RAPIDS_TESTS_DIR:-"${PWD}/test-results"}/
 mkdir -p "${RAPIDS_TESTS_DIR}"
 
 rapids-print-env
 
-rapids-mamba-retry install \
-  --channel "${CPP_CHANNEL}" \
-  "libcuvs=${RAPIDS_VERSION}" \
-  "libcuvs-tests=${RAPIDS_VERSION}"
-
 rapids-logger "Check GPU usage"
 nvidia-smi
 
diff --git a/ci/test_python.sh b/ci/test_python.sh
index 6ba36bc5a4..c040416b00 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -5,13 +5,18 @@ set -euo pipefail
 
 . /opt/conda/etc/profile.d/conda.sh
 
-RAPIDS_VERSION="$(rapids-version)"
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
 
 rapids-logger "Generate Python testing dependencies"
 rapids-dependency-file-generator \
   --output conda \
   --file-key test_python \
-  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" \
+  --prepend-channel "${CPP_CHANNEL}" \
+  --prepend-channel "${PYTHON_CHANNEL}" \
+  | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n test
 
@@ -20,23 +25,12 @@ set +u
 conda activate test
 set -u
 
-rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
-
 RAPIDS_TESTS_DIR=${RAPIDS_TESTS_DIR:-"${PWD}/test-results"}
 RAPIDS_COVERAGE_DIR=${RAPIDS_COVERAGE_DIR:-"${PWD}/coverage-results"}
 mkdir -p "${RAPIDS_TESTS_DIR}" "${RAPIDS_COVERAGE_DIR}"
 
 rapids-print-env
 
-rapids-mamba-retry install \
-  --channel "${CPP_CHANNEL}" \
-  --channel "${PYTHON_CHANNEL}" \
-  "libcuvs=${RAPIDS_VERSION}" \
-  "cuvs=${RAPIDS_VERSION}" \
-  "cuvs-bench=${RAPIDS_VERSION}"
-
 rapids-logger "Check GPU usage"
 nvidia-smi
 
diff --git a/dependencies.yaml b/dependencies.yaml
index 483e3fe762..474e629a67 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -49,6 +49,8 @@ files:
     includes:
       - cuda_version
       - test_libcuvs
+      - depends_on_libcuvs
+      - depends_on_libcuvs_tests
   test_python:
     output: none
     includes:
@@ -57,6 +59,9 @@ files:
       - py_version
       - test_python_common
       - test_py_cuvs
+      - depends_on_libcuvs
+      - depends_on_cuvs
+      - depends_on_cuvs_bench
   checks:
     output: none
     includes:
@@ -566,6 +571,16 @@ dependencies:
       - output_types: [requirements, pyproject]
         packages:
           - matplotlib
+  depends_on_cuvs:
+    common:
+      - output_types: conda
+        packages:
+          - cuvs==25.4.*,>=0.0.0a0
+  depends_on_cuvs_bench:
+    common:
+      - output_types: conda
+        packages:
+          - cuvs-bench==25.4.*,>=0.0.0a0
   depends_on_libcuvs:
     common:
       - output_types: conda
@@ -590,6 +605,11 @@ dependencies:
             packages:
               - libcuvs-cu11==25.4.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
+  depends_on_libcuvs_tests:
+    common:
+      - output_types: conda
+        packages:
+          - libcuvs-tests==25.4.*,>=0.0.0a0
   depends_on_libraft:
     common:
       - output_types: conda

From ab91f93b7c5182c45fc1133f081dc7f9b8691833 Mon Sep 17 00:00:00 2001
From: Jake Awe <jawe@nvidia.com>
Date: Thu, 13 Feb 2025 09:43:21 -0600
Subject: [PATCH 323/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 104 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ed9429d55d..b7ae6a8383 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,107 @@
+# cuvs 25.02.00 (13 Feb 2025)
+
+## 🚨 Breaking Changes
+
+- update pip devcontainers to UCX 1.18, small update-version.sh fixes ([#604](https://github.com/rapidsai/cuvs/pull/604)) [@jameslamb](https://github.com/jameslamb)
+- Improve the performance of CAGRA new vector addition with the default params ([#569](https://github.com/rapidsai/cuvs/pull/569)) [@enp1s0](https://github.com/enp1s0)
+- Update for raft logger changes ([#540](https://github.com/rapidsai/cuvs/pull/540)) [@vyasr](https://github.com/vyasr)
+
+## 🐛 Bug Fixes
+
+- Fix ann-bench dataset blob integer overflow leading to incorrect data copy beyond 4B elems ([#671](https://github.com/rapidsai/cuvs/pull/671)) [@achirkin](https://github.com/achirkin)
+- Fix ann-bench deadlocking on HNSW destruction due to task locks ([#667](https://github.com/rapidsai/cuvs/pull/667)) [@achirkin](https://github.com/achirkin)
+- cuvs-bench Fixes ([#654](https://github.com/rapidsai/cuvs/pull/654)) [@tarang-jain](https://github.com/tarang-jain)
+- Fix std::lock_guard use for gcc 14 support ([#639](https://github.com/rapidsai/cuvs/pull/639)) [@enp1s0](https://github.com/enp1s0)
+- Fix indexing bug when using parallelism to build CPU hierarchy in HNSW ([#620](https://github.com/rapidsai/cuvs/pull/620)) [@divyegala](https://github.com/divyegala)
+- add runtime dependency on libcuvs in cuvs wheels ([#615](https://github.com/rapidsai/cuvs/pull/615)) [@jameslamb](https://github.com/jameslamb)
+- Temporarily skip CUDA 11 wheel CI ([#599](https://github.com/rapidsai/cuvs/pull/599)) [@bdice](https://github.com/bdice)
+- [Fix] l2_exp random fail in half-float32 mixed precision on self-neighboring ([#596](https://github.com/rapidsai/cuvs/pull/596)) [@rhdong](https://github.com/rhdong)
+- Add CAGRA InnerProduct test and fix a bug ([#595](https://github.com/rapidsai/cuvs/pull/595)) [@enp1s0](https://github.com/enp1s0)
+- fix cuvs_bench.run --groups options ([#592](https://github.com/rapidsai/cuvs/pull/592)) [@jiangyinzuo](https://github.com/jiangyinzuo)
+- Fix cagra_hnsw serialization when dataset is not part of index ([#591](https://github.com/rapidsai/cuvs/pull/591)) [@tfeher](https://github.com/tfeher)
+- fix create_pointset for throughput mode ([#589](https://github.com/rapidsai/cuvs/pull/589)) [@jiangyinzuo](https://github.com/jiangyinzuo)
+- Fix the use of constexpr in the dynamic batching header ([#582](https://github.com/rapidsai/cuvs/pull/582)) [@achirkin](https://github.com/achirkin)
+- Reduce the recall threshold for IVF-PQ low-precision LUT inner product  tests ([#573](https://github.com/rapidsai/cuvs/pull/573)) [@achirkin](https://github.com/achirkin)
+- Small fixes to docs and pairwise distances ([#570](https://github.com/rapidsai/cuvs/pull/570)) [@cjnolet](https://github.com/cjnolet)
+- [BUG] Fix CAGRA graph optimization bug ([#565](https://github.com/rapidsai/cuvs/pull/565)) [@enp1s0](https://github.com/enp1s0)
+- Fix broken link to python doc ([#564](https://github.com/rapidsai/cuvs/pull/564)) [@lowener](https://github.com/lowener)
+- Fix cagra::extend error message ([#532](https://github.com/rapidsai/cuvs/pull/532)) [@enp1s0](https://github.com/enp1s0)
+- Fix Grace-specific issues in CAGRA ([#527](https://github.com/rapidsai/cuvs/pull/527)) [@achirkin](https://github.com/achirkin)
+
+## 📖 Documentation
+
+- add docs for nn_descent ([#668](https://github.com/rapidsai/cuvs/pull/668)) [@Intron7](https://github.com/Intron7)
+- Fixing small typo in cuvs bench docs ([#586](https://github.com/rapidsai/cuvs/pull/586)) [@cjnolet](https://github.com/cjnolet)
+- Fix typos in README ([#543](https://github.com/rapidsai/cuvs/pull/543)) [@nvanbenschoten](https://github.com/nvanbenschoten)
+- Use nvidia-sphinx-theme for docs ([#528](https://github.com/rapidsai/cuvs/pull/528)) [@benfred](https://github.com/benfred)
+
+## 🚀 New Features
+
+- Add deep-100M to datasets.yaml for cuvs-bench ([#670](https://github.com/rapidsai/cuvs/pull/670)) [@tarang-jain](https://github.com/tarang-jain)
+- Expose configuration singleton as a global context for ann-bench algos ([#647](https://github.com/rapidsai/cuvs/pull/647)) [@achirkin](https://github.com/achirkin)
+- ANN_BENCH enhanced dataset support ([#624](https://github.com/rapidsai/cuvs/pull/624)) [@achirkin](https://github.com/achirkin)
+- [Feat] Add Support for Index `merge` in CAGRA ([#618](https://github.com/rapidsai/cuvs/pull/618)) [@rhdong](https://github.com/rhdong)
+- HNSW GPU hierarchy ([#616](https://github.com/rapidsai/cuvs/pull/616)) [@divyegala](https://github.com/divyegala)
+- CAGRA binary Hamming distance support ([#610](https://github.com/rapidsai/cuvs/pull/610)) [@enp1s0](https://github.com/enp1s0)
+- Add cuda 12.8 support ([#605](https://github.com/rapidsai/cuvs/pull/605)) [@robertmaynard](https://github.com/robertmaynard)
+- Add support for refinement with `uint32_t` index type ([#563](https://github.com/rapidsai/cuvs/pull/563)) [@lowener](https://github.com/lowener)
+- [Feat] Support `bitset` filter for Brute Force ([#560](https://github.com/rapidsai/cuvs/pull/560)) [@rhdong](https://github.com/rhdong)
+- Remove upper bounds on cuda-python to allow 12.6.2 and 11.8.5 ([#508](https://github.com/rapidsai/cuvs/pull/508)) [@bdice](https://github.com/bdice)
+
+## 🛠️ Improvements
+
+- Add filtering to python for ivf_flat ([#664](https://github.com/rapidsai/cuvs/pull/664)) [@benfred](https://github.com/benfred)
+- Expose binary quantizer to C and Python ([#660](https://github.com/rapidsai/cuvs/pull/660)) [@benfred](https://github.com/benfred)
+- Add telemetry ([#652](https://github.com/rapidsai/cuvs/pull/652)) [@gforsyth](https://github.com/gforsyth)
+- Revert docs builds to CI latest tag. ([#643](https://github.com/rapidsai/cuvs/pull/643)) [@bdice](https://github.com/bdice)
+- Add float16 support in python for cagra/brute_force/ivf_pq and scalar quantizer ([#637](https://github.com/rapidsai/cuvs/pull/637)) [@benfred](https://github.com/benfred)
+- Expose NN-Descent to C and Python ([#635](https://github.com/rapidsai/cuvs/pull/635)) [@benfred](https://github.com/benfred)
+- Revert CUDA 12.8 shared workflow branch changes ([#630](https://github.com/rapidsai/cuvs/pull/630)) [@vyasr](https://github.com/vyasr)
+- cuvs-java: Rework the api to be Java 21 friendly ([#628](https://github.com/rapidsai/cuvs/pull/628)) [@ChrisHegarty](https://github.com/ChrisHegarty)
+- Build and test with CUDA 12.8.0 ([#621](https://github.com/rapidsai/cuvs/pull/621)) [@bdice](https://github.com/bdice)
+- Add Scalar Quantization to the c and python apis ([#617](https://github.com/rapidsai/cuvs/pull/617)) [@benfred](https://github.com/benfred)
+- Iteratively build graph index ([#612](https://github.com/rapidsai/cuvs/pull/612)) [@anaruse](https://github.com/anaruse)
+- update pip devcontainers to UCX 1.18, small update-version.sh fixes ([#604](https://github.com/rapidsai/cuvs/pull/604)) [@jameslamb](https://github.com/jameslamb)
+- Reduce CAGRA test runtime ([#602](https://github.com/rapidsai/cuvs/pull/602)) [@bdice](https://github.com/bdice)
+- Revert &quot;Temporarily skip CUDA 11 wheel CI&quot; ([#601](https://github.com/rapidsai/cuvs/pull/601)) [@bdice](https://github.com/bdice)
+- introduce libcuvs wheels ([#594](https://github.com/rapidsai/cuvs/pull/594)) [@jameslamb](https://github.com/jameslamb)
+- Normalize whitespace ([#593](https://github.com/rapidsai/cuvs/pull/593)) [@bdice](https://github.com/bdice)
+- Rename test to tests. ([#590](https://github.com/rapidsai/cuvs/pull/590)) [@bdice](https://github.com/bdice)
+- Use cuda.bindings layout. ([#588](https://github.com/rapidsai/cuvs/pull/588)) [@bdice](https://github.com/bdice)
+- run_cuvs_pytests.sh uses proper test dir ([#584](https://github.com/rapidsai/cuvs/pull/584)) [@robertmaynard](https://github.com/robertmaynard)
+- expose col-major bfknn to python ([#575](https://github.com/rapidsai/cuvs/pull/575)) [@benfred](https://github.com/benfred)
+- Run cuvs-bench pytests and end-to-end tests in CI ([#574](https://github.com/rapidsai/cuvs/pull/574)) [@dantegd](https://github.com/dantegd)
+- Expose col-major pairwise distances to python ([#572](https://github.com/rapidsai/cuvs/pull/572)) [@benfred](https://github.com/benfred)
+- Improve the performance of CAGRA new vector addition with the default params ([#569](https://github.com/rapidsai/cuvs/pull/569)) [@enp1s0](https://github.com/enp1s0)
+- Improve filtering documentation ([#568](https://github.com/rapidsai/cuvs/pull/568)) [@lowener](https://github.com/lowener)
+- Use GCC 13 in CUDA 12 conda builds. ([#567](https://github.com/rapidsai/cuvs/pull/567)) [@bdice](https://github.com/bdice)
+- Allow brute_force::build to work on host matrix dataset ([#562](https://github.com/rapidsai/cuvs/pull/562)) [@benfred](https://github.com/benfred)
+- FAISS with cuVS enabled in cuvs-bench ([#561](https://github.com/rapidsai/cuvs/pull/561)) [@tarang-jain](https://github.com/tarang-jain)
+- Vamana build improvement and added docs ([#558](https://github.com/rapidsai/cuvs/pull/558)) [@bkarsin](https://github.com/bkarsin)
+- Support raft&#39;s logger targets ([#557](https://github.com/rapidsai/cuvs/pull/557)) [@vyasr](https://github.com/vyasr)
+- Get Breathe from conda again ([#554](https://github.com/rapidsai/cuvs/pull/554)) [@vyasr](https://github.com/vyasr)
+- Check if nightlies have succeeded recently enough ([#548](https://github.com/rapidsai/cuvs/pull/548)) [@vyasr](https://github.com/vyasr)
+- Add support for float16 to the python pairwise distance api ([#547](https://github.com/rapidsai/cuvs/pull/547)) [@benfred](https://github.com/benfred)
+- Additional Distances for CAGRA C and Python API ([#546](https://github.com/rapidsai/cuvs/pull/546)) [@tarang-jain](https://github.com/tarang-jain)
+- remove setup.cfg files, other packaging cleanup ([#544](https://github.com/rapidsai/cuvs/pull/544)) [@jameslamb](https://github.com/jameslamb)
+- Fix CI for python cuvs_bench ([#541](https://github.com/rapidsai/cuvs/pull/541)) [@benfred](https://github.com/benfred)
+- Update for raft logger changes ([#540](https://github.com/rapidsai/cuvs/pull/540)) [@vyasr](https://github.com/vyasr)
+- Change brute_force api to match ivf*/cagra ([#536](https://github.com/rapidsai/cuvs/pull/536)) [@benfred](https://github.com/benfred)
+- Branch 25.02 merge 24.12 ([#526](https://github.com/rapidsai/cuvs/pull/526)) [@benfred](https://github.com/benfred)
+- Update cuda-python lower bounds to 12.6.2 / 11.8.5 ([#524](https://github.com/rapidsai/cuvs/pull/524)) [@bdice](https://github.com/bdice)
+- Automatic adjustment of itopk size according to filtering rate ([#509](https://github.com/rapidsai/cuvs/pull/509)) [@anaruse](https://github.com/anaruse)
+- prefer system install of UCX in devcontainers ([#501](https://github.com/rapidsai/cuvs/pull/501)) [@jameslamb](https://github.com/jameslamb)
+- Adapt to rmm logger changes ([#499](https://github.com/rapidsai/cuvs/pull/499)) [@vyasr](https://github.com/vyasr)
+- Require approval to run CI on draft PRs ([#498](https://github.com/rapidsai/cuvs/pull/498)) [@bdice](https://github.com/bdice)
+- Remove RAFT BUILD_ANN_BENCH option ([#497](https://github.com/rapidsai/cuvs/pull/497)) [@bdice](https://github.com/bdice)
+- Update example code fetching rapids-cmake to use CUVS instead of RAFT ([#493](https://github.com/rapidsai/cuvs/pull/493)) [@bdice](https://github.com/bdice)
+- Improve multi-CTA algorithm ([#492](https://github.com/rapidsai/cuvs/pull/492)) [@anaruse](https://github.com/anaruse)
+- Add filtering for CAGRA to C API ([#452](https://github.com/rapidsai/cuvs/pull/452)) [@ajit283](https://github.com/ajit283)
+- Initial cut for a cuVS Java API ([#450](https://github.com/rapidsai/cuvs/pull/450)) [@chatman](https://github.com/chatman)
+- Add breaking change workflow trigger ([#442](https://github.com/rapidsai/cuvs/pull/442)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Expose `extend()` in C API ([#276](https://github.com/rapidsai/cuvs/pull/276)) [@ajit283](https://github.com/ajit283)
+- Go API - [WIP] ([#212](https://github.com/rapidsai/cuvs/pull/212)) [@ajit283](https://github.com/ajit283)
+
 # cuvs 24.12.00 (11 Dec 2024)
 
 ## 🚨 Breaking Changes

From d99d2854552f8bed8bd0ba6b782ce49ce65ba49c Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 18 Feb 2025 13:47:54 -0500
Subject: [PATCH 324/623] Consolidate more Conda solves in CI (#701)

Issue: https://github.com/rapidsai/build-planning/issues/22

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/701
---
 ci/build_docs.sh  | 20 ++++++++------------
 ci/build_go.sh    | 17 ++++++-----------
 dependencies.yaml |  4 ++++
 3 files changed, 18 insertions(+), 23 deletions(-)

diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index 053e0200af..fe13aa275d 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -3,17 +3,23 @@
 
 set -euo pipefail
 
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
 
-RAPIDS_VERSION="$(rapids-version)"
 RAPIDS_VERSION_MAJOR_MINOR="$(rapids-version-major-minor)"
 export RAPIDS_VERSION_MAJOR_MINOR
 
 rapids-dependency-file-generator \
   --output conda \
   --file-key docs \
-  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" \
+  --prepend-channel "${CPP_CHANNEL}" \
+  --prepend-channel "${PYTHON_CHANNEL}" \
+  | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n docs
 
@@ -25,16 +31,6 @@ set -eu
 
 rapids-print-env
 
-rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
-
-rapids-mamba-retry install \
-  --channel "${CPP_CHANNEL}" \
-  --channel "${PYTHON_CHANNEL}" \
-  "libcuvs=${RAPIDS_VERSION}" \
-  "cuvs=${RAPIDS_VERSION}"
-
 RAPIDS_DOCS_DIR="$(mktemp -d)"
 export RAPIDS_DOCS_DIR
 
diff --git a/ci/build_go.sh b/ci/build_go.sh
index 9496f74139..2dd38a7757 100755
--- a/ci/build_go.sh
+++ b/ci/build_go.sh
@@ -3,15 +3,18 @@
 
 set -euo pipefail
 
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
 
-RAPIDS_VERSION="$(rapids-version)"
-
 rapids-dependency-file-generator \
   --output conda \
   --file-key go \
-  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" \
+  --prepend-channel "${CPP_CHANNEL}" \
+  | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n go
 
@@ -28,12 +31,4 @@ export CGO_LDFLAGS="-L${CONDA_PREFIX}/lib -lcudart -lcuvs -lcuvs_c"
 export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
 export CC=clang
 
-rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-
-rapids-mamba-retry install \
-  --channel "${CPP_CHANNEL}" \
-  "libcuvs=${RAPIDS_VERSION}" \
-  "libraft=${RAPIDS_VERSION}"
-
 bash ./build.sh go
diff --git a/dependencies.yaml b/dependencies.yaml
index 474e629a67..4b3ba55d0a 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -78,6 +78,8 @@ files:
       - py_version
       - rapids_build
       - rust
+      - depends_on_libcuvs
+      - depends_on_cuvs
   rust:
     output: none
     includes:
@@ -95,6 +97,8 @@ files:
       - cuda_version
       - rapids_build
       - go
+      - depends_on_libcuvs
+      - depends_on_libraft
   py_build_libcuvs:
     output: pyproject
     pyproject_dir: python/libcuvs

From 2f999ba875d724a80bc177c7df947544f5d01a8e Mon Sep 17 00:00:00 2001
From: Michael Schellenberger Costa <miscco@nvidia.com>
Date: Thu, 20 Feb 2025 16:56:33 +0100
Subject: [PATCH 325/623] Replace `cub::TransformInputIterator` with
 `thrust::transform_iterator` (#707)

The former is deprecated and will be removed in a future release

Authors:
  - Michael Schellenberger Costa (https://github.com/miscco)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/707
---
 cpp/src/cluster/detail/kmeans.cuh          | 6 +++---
 cpp/src/cluster/detail/kmeans_balanced.cuh | 5 +++--
 cpp/src/cluster/detail/kmeans_common.cuh   | 9 ++++-----
 cpp/src/cluster/detail/kmeans_mg.cuh       | 5 ++---
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh  | 5 +++--
 cpp/tests/cluster/kmeans.cu                | 6 +++---
 6 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
index e943b8afca..3bed639c34 100644
--- a/cpp/src/cluster/detail/kmeans.cuh
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -45,6 +45,7 @@
 
 #include <cuda.h>
 #include <thrust/fill.h>
+#include <thrust/iterator/transform_iterator.h>
 #include <thrust/transform.h>
 
 #include <algorithm>
@@ -450,9 +451,8 @@ void kmeans_fit_main(raft::resources const& handle,
     // raft::KeyValuePair and converting them to just return the Key to be used
     // in reduce_rows_by_key prims
     cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT> conversion_op;
-    cub::TransformInputIterator<IndexT,
-                                cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT>,
-                                raft::KeyValuePair<IndexT, DataT>*>
+    thrust::transform_iterator<cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT>,
+                               raft::KeyValuePair<IndexT, DataT>*>
       itr(minClusterAndDistance.data_handle(), conversion_op);
 
     update_centroids(handle,
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index 89d8b05a31..0c5ce644ac 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -50,6 +50,7 @@
 #include <rmm/resource_ref.hpp>
 
 #include <thrust/gather.h>
+#include <thrust/iterator/transform_iterator.h>
 #include <thrust/transform.h>
 
 #include <limits>
@@ -341,7 +342,7 @@ void calc_centers_and_sizes(const raft::resources& handle,
       dataset, dim, labels, nullptr, n_rows, dim, n_clusters, centers, stream, reset_counters);
   } else {
     // todo(lsugy): use iterator from KV output of fusedL2NN
-    cub::TransformInputIterator<MathT, MappingOpT, const T*> mapping_itr(dataset, mapping_op);
+    thrust::transform_iterator<MappingOpT, const T*> mapping_itr(dataset, mapping_op);
     raft::linalg::reduce_rows_by_key(
       mapping_itr, dim, labels, nullptr, n_rows, dim, n_clusters, centers, stream, reset_counters);
   }
@@ -966,7 +967,7 @@ auto build_fine_clusters(const raft::resources& handle,
                    "Number of fine clusters must be non-zero for a non-empty mesocluster");
     }
 
-    cub::TransformInputIterator<MathT, MappingOpT, const T*> mapping_itr(dataset_mptr, mapping_op);
+    thrust::transform_iterator<MappingOpT, const T*> mapping_itr(dataset_mptr, mapping_op);
     raft::matrix::gather(mapping_itr, dim, n_rows, mc_trainset_ids, k, mc_trainset, stream);
     if (params.metric == cuvs::distance::DistanceType::L2Expanded ||
         params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
diff --git a/cpp/src/cluster/detail/kmeans_common.cuh b/cpp/src/cluster/detail/kmeans_common.cuh
index 03db08bd10..45b425d3bd 100644
--- a/cpp/src/cluster/detail/kmeans_common.cuh
+++ b/cpp/src/cluster/detail/kmeans_common.cuh
@@ -45,6 +45,7 @@
 #include <cuda.h>
 #include <thrust/fill.h>
 #include <thrust/for_each.h>
+#include <thrust/iterator/transform_iterator.h>
 
 #include <algorithm>
 #include <cmath>
@@ -199,8 +200,7 @@ void computeClusterCost(raft::resources const& handle,
 {
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
-  cub::TransformInputIterator<OutputT, MainOpT, InputT*> itr(minClusterDistance.data_handle(),
-                                                             main_op);
+  thrust::transform_iterator<MainOpT, InputT*> itr(minClusterDistance.data_handle(), main_op);
 
   size_t temp_storage_bytes = 0;
   RAFT_CUDA_TRY(cub::DeviceReduce::Reduce(nullptr,
@@ -659,9 +659,8 @@ void countSamplesInCluster(raft::resources const& handle,
   // and converting them to just return the Key to be used in reduce_rows_by_key
   // prims
   cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT> conversion_op;
-  cub::TransformInputIterator<IndexT,
-                              cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT>,
-                              raft::KeyValuePair<IndexT, DataT>*>
+  thrust::transform_iterator<cuvs::cluster::kmeans::detail::KeyValueIndexOp<IndexT, DataT>,
+                             raft::KeyValuePair<IndexT, DataT>*>
     itr(minClusterAndDistance.data_handle(), conversion_op);
 
   // count # of samples in each cluster
diff --git a/cpp/src/cluster/detail/kmeans_mg.cuh b/cpp/src/cluster/detail/kmeans_mg.cuh
index b0f4355020..4747eb8791 100644
--- a/cpp/src/cluster/detail/kmeans_mg.cuh
+++ b/cpp/src/cluster/detail/kmeans_mg.cuh
@@ -41,6 +41,7 @@
 #include <cuda/functional>
 #include <thrust/execution_policy.h>
 #include <thrust/fill.h>
+#include <thrust/iterator/transform_iterator.h>
 #include <thrust/reduce.h>
 #include <thrust/scan.h>
 #include <thrust/transform.h>
@@ -615,9 +616,7 @@ void fit(const raft::resources& handle,
     // cub::KeyValuePair and converting them to just return the Key to be used
     // in reduce_rows_by_key prims
     KeyValueIndexOp<IndexT, DataT> conversion_op;
-    cub::TransformInputIterator<IndexT,
-                                KeyValueIndexOp<IndexT, DataT>,
-                                raft::KeyValuePair<IndexT, DataT>*>
+    thrust::transform_iterator<KeyValueIndexOp<IndexT, DataT>, raft::KeyValuePair<IndexT, DataT>*>
       itr(minClusterAndDistance.data_handle(), conversion_op);
 
     workspace.resize(n_samples, stream);
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 0e492da6c4..21a4b3185b 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -62,6 +62,7 @@
 
 #include <cuda_fp16.h>
 #include <thrust/extrema.h>
+#include <thrust/iterator/transform_iterator.h>
 #include <thrust/scan.h>
 
 #include <memory>
@@ -96,8 +97,8 @@ void select_residuals(raft::resources const& handle,
   rmm::device_uvector<float> tmp(size_t(n_rows) * size_t(dim), stream, device_memory);
   // Note: the number of rows of the input dataset isn't actually n_rows, but matrix::gather doesn't
   // need to know it, any strictly positive number would work.
-  cub::TransformInputIterator<float, utils::mapping<float>, const T*> mapping_itr(
-    dataset, utils::mapping<float>{});
+  thrust::transform_iterator<utils::mapping<float>, const T*, thrust::use_default, float>
+    mapping_itr(dataset, utils::mapping<float>{});
   raft::matrix::gather(mapping_itr, (IdxT)dim, n_rows, row_ids, n_rows, tmp.data(), stream);
 
   raft::matrix::linewise_op(handle,
diff --git a/cpp/tests/cluster/kmeans.cu b/cpp/tests/cluster/kmeans.cu
index 3caf00c41e..7b6cfda28b 100644
--- a/cpp/tests/cluster/kmeans.cu
+++ b/cpp/tests/cluster/kmeans.cu
@@ -28,6 +28,7 @@
 #include <rmm/device_uvector.hpp>
 
 #include <thrust/fill.h>
+#include <thrust/iterator/transform_iterator.h>
 
 #include <gtest/gtest.h>
 
@@ -201,9 +202,8 @@ class KmeansTest : public ::testing::TestWithParam<KmeansInputs<T>> {
   //      params.batch_centroids,
   //      workspace);
   //    raft::cluster::kmeans::KeyValueIndexOp<int, T> conversion_op;
-  //    cub::TransformInputIterator<int,
-  //                                raft::cluster::kmeans::KeyValueIndexOp<int, T>,
-  //                                raft::KeyValuePair<int, T>*>
+  //    thrust::transform_iterator<raft::cluster::kmeans::KeyValueIndexOp<int, T>,
+  //                               raft::KeyValuePair<int, T>*>
   //      itr(minClusterAndDistance.data_handle(), conversion_op);
   //
   //    auto sampleCountInCluster = raft::make_device_vector<T, int>(handle, params.n_clusters);

From 3c9e74534659a751bcc2de3d3f448511507d4f67 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Thu, 20 Feb 2025 14:47:09 -0500
Subject: [PATCH 326/623] Add `verify-codeowners` hook (#633)

Issue: https://github.com/rapidsai/pre-commit-hooks/issues/61

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/633
---
 .github/CODEOWNERS      | 19 +++++++++++--------
 .pre-commit-config.yaml |  4 +++-
 2 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 67d14835a9..81085a5cfa 100755
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -10,9 +10,12 @@ rust/              @rapidsai/cuvs-rust-codeowners
 #docs code owners
 docs/              @rapidsai/cuvs-docs-codeowners
 
+#cmake code owners
+CMakeLists.txt     @rapidsai/cuvs-cmake-codeowners
+**/cmake/          @rapidsai/cuvs-cmake-codeowners
+*.cmake            @rapidsai/cuvs-cmake-codeowners
+
 #build code owners
-**/CMakeLists.txt  @rapidsai/cuvs-build-codeowners
-**/cmake/          @rapidsai/cuvs-build-codeowners
 python/setup.py    @rapidsai/cuvs-build-codeowners
 build.sh           @rapidsai/cuvs-build-codeowners
 **/build.sh        @rapidsai/cuvs-build-codeowners
@@ -20,11 +23,11 @@ build.sh           @rapidsai/cuvs-build-codeowners
 #CI code owners
 /.github/                @rapidsai/ci-codeowners
 /ci/                     @rapidsai/ci-codeowners
-/.pre-commit-config.yaml @rapidsai/ci-codeowners
 
 #packaging code owners
-/.devcontainer/    @rapidsai/packaging-codeowners
-/conda/            @rapidsai/packaging-codeowners
-/dependencies.yaml @rapidsai/packaging-codeowners
-/build.sh          @rapidsai/packaging-codeowners
-pyproject.toml     @rapidsai/packaging-codeowners
+/.pre-commit-config.yaml @rapidsai/packaging-codeowners
+/.devcontainer/          @rapidsai/packaging-codeowners
+/conda/                  @rapidsai/packaging-codeowners
+dependencies.yaml        @rapidsai/packaging-codeowners
+/build.sh                @rapidsai/packaging-codeowners
+pyproject.toml           @rapidsai/packaging-codeowners
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index bbfe734019..fef4e75d91 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -105,7 +105,7 @@ repos:
         hooks:
               - id: check-json
       - repo: https://github.com/rapidsai/pre-commit-hooks
-        rev: v0.4.0
+        rev: v0.6.0
         hooks:
           - id: verify-copyright
             files: |
@@ -119,6 +119,8 @@ repos:
                   docs/source/sphinxext/github_link\.py|
                   cpp/cmake/modules/FindAVX\.cmake|
           - id: verify-alpha-spec
+          - id: verify-codeowners
+            args: [--fix, --project-prefix=cuvs]
       - repo: https://github.com/rapidsai/dependency-file-generator
         rev: v1.17.0
         hooks:

From 6b5b472ea479ecb11675283ddd43d28c4f821302 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 20 Feb 2025 21:14:35 +0100
Subject: [PATCH 327/623] Expand NVTX annotations in CAGRA build and HNSW
 (#711)

Add a few more NVTX annotations in CAGRA and HNSW routines to make it easier to analyze index build times.
Add the missing `NVTX_ENABLED` definition to `cuvs_objs` (without this, only CAGRA-search-related annotations are enabled as they are a part of a different component).

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/711
---
 cpp/CMakeLists.txt                            |  1 +
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 15 +++++++++++++++
 cpp/src/neighbors/hnsw.cpp                    | 18 ++++++++++--------
 3 files changed, 26 insertions(+), 8 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 8698548470..945a181579 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -636,6 +636,7 @@ SECTIONS
   if(CUVS_NVTX)
     # This enables NVTX within the project with no option to disable it downstream.
     target_link_libraries(cuvs PUBLIC CUDA::nvtx3)
+    target_compile_definitions(cuvs_objs PUBLIC NVTX_ENABLED)
     target_compile_definitions(cuvs PUBLIC NVTX_ENABLED)
 
     target_link_libraries(cuvs-cagra-search PUBLIC CUDA::nvtx3)
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 4adaa7a936..39037b745d 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -24,6 +24,7 @@
 #include <raft/core/resources.hpp>
 
 // TODO: This shouldn't be invoking anything from spatial/knn
+#include "../../../core/nvtx.hpp"
 #include "../ann_utils.cuh"
 
 #include <raft/util/bitonic_sort.cuh>
@@ -1086,6 +1087,8 @@ void optimize(
   const uint64_t graph_size          = new_graph.extent(0);
   auto input_graph_ptr               = knn_graph.data_handle();
   auto output_graph_ptr              = new_graph.data_handle();
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "cagra::graph::optimize(%zu, %zu, %u)", graph_size, input_graph_degree, output_graph_degree);
 
   // MST optimization
   auto mst_graph               = raft::make_host_matrix<IdxT, int64_t, raft::row_major>(0, 0);
@@ -1096,6 +1099,8 @@ void optimize(
     mst_graph_num_edges_ptr[i] = 0;
   }
   if (guarantee_connectivity) {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+      "cagra::graph::optimize/check_connectivity");
     mst_graph =
       raft::make_host_matrix<IdxT, int64_t, raft::row_major>(graph_size, output_graph_degree);
     RAFT_LOG_INFO("MST optimization is used to guarantee graph connectivity.");
@@ -1110,6 +1115,8 @@ void optimize(
   }
 
   {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+      "cagra::graph::optimize/prune");
     //
     // Prune kNN graph
     //
@@ -1270,6 +1277,8 @@ void optimize(
   auto rev_graph_count = raft::make_host_vector<uint32_t, int64_t>(graph_size);
 
   {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+      "cagra::graph::optimize/reverse");
     //
     // Make reverse graph
     //
@@ -1335,6 +1344,8 @@ void optimize(
   }
 
   {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+      "cagra::graph::optimize/combine");
     //
     // Create search graphs from MST and pruned and reverse graphs
     //
@@ -1435,6 +1446,8 @@ void optimize(
 
   // Check number of incoming edges
   {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+      "cagra::graph::optimize/check_edges");
     auto in_edge_count     = raft::make_host_vector<uint32_t, int64_t>(graph_size);
     auto in_edge_count_ptr = in_edge_count.data_handle();
 #pragma omp parallel for
@@ -1477,6 +1490,8 @@ void optimize(
 
   // Check duplication and out-of-range indices
   {
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+      "cagra::graph::optimize/check_duplicates");
     uint64_t num_dup = 0;
     uint64_t num_oor = 0;
 #pragma omp parallel for reduction(+ : num_dup) reduction(+ : num_oor)
diff --git a/cpp/src/neighbors/hnsw.cpp b/cpp/src/neighbors/hnsw.cpp
index f165176ece..bf274b0d98 100644
--- a/cpp/src/neighbors/hnsw.cpp
+++ b/cpp/src/neighbors/hnsw.cpp
@@ -15,20 +15,22 @@
  */
 
 #include "detail/hnsw.hpp"
+#include "../core/nvtx.hpp"
 #include <cstdint>
 #include <cuvs/neighbors/hnsw.hpp>
 #include <sys/types.h>
 
 namespace cuvs::neighbors::hnsw {
 
-#define CUVS_INST_HNSW_FROM_CAGRA(T)                                                  \
-  std::unique_ptr<index<T>> from_cagra(                                               \
-    raft::resources const& res,                                                       \
-    const index_params& params,                                                       \
-    const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,                    \
-    std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset) \
-  {                                                                                   \
-    return detail::from_cagra<T>(res, params, cagra_index, dataset);                  \
+#define CUVS_INST_HNSW_FROM_CAGRA(T)                                                           \
+  std::unique_ptr<index<T>> from_cagra(                                                        \
+    raft::resources const& res,                                                                \
+    const index_params& params,                                                                \
+    const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,                             \
+    std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)          \
+  {                                                                                            \
+    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("hnsw::from_cagra"); \
+    return detail::from_cagra<T>(res, params, cagra_index, dataset);                           \
   }
 
 CUVS_INST_HNSW_FROM_CAGRA(float);

From a1e0cc06d666be60663e15780fc66939606fd67d Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Fri, 21 Feb 2025 15:36:41 -0500
Subject: [PATCH 328/623] Require CMake 3.30.4 (#691)

Update CMake minimum required to 3.30.4 across all of RAPIDS

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/691
---
 conda/environments/all_cuda-118_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-118_arch-x86_64.yaml        | 2 +-
 conda/environments/all_cuda-128_arch-aarch64.yaml       | 2 +-
 conda/environments/all_cuda-128_arch-x86_64.yaml        | 2 +-
 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/bench_ann_cuda-128_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-128_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs-bench-cpu/conda_build_config.yaml    | 2 +-
 conda/recipes/cuvs-bench/conda_build_config.yaml        | 2 +-
 conda/recipes/cuvs/conda_build_config.yaml              | 2 +-
 conda/recipes/libcuvs/conda_build_config.yaml           | 2 +-
 cpp/CMakeLists.txt                                      | 2 +-
 dependencies.yaml                                       | 2 +-
 examples/c/CMakeLists.txt                               | 2 +-
 examples/cpp/CMakeLists.txt                             | 2 +-
 java/internal/CMakeLists.txt                            | 2 +-
 python/cuvs/CMakeLists.txt                              | 2 +-
 python/cuvs/pyproject.toml                              | 2 +-
 python/libcuvs/CMakeLists.txt                           | 2 +-
 python/libcuvs/pyproject.toml                           | 2 +-
 rust/cuvs-sys/CMakeLists.txt                            | 2 +-
 22 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 638c0ee367..5e99876a1a 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -11,7 +11,7 @@ dependencies:
 - c-compiler
 - clang-tools==16.0.6
 - clang==16.0.6
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
 - cuda-python>=11.8.5,<12.0a0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index bb50937b3e..9038901c2c 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -11,7 +11,7 @@ dependencies:
 - c-compiler
 - clang-tools==16.0.6
 - clang==16.0.6
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
 - cuda-python>=11.8.5,<12.0a0
diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
index 5c12fbe772..0cbf9abc13 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -11,7 +11,7 @@ dependencies:
 - c-compiler
 - clang-tools==16.0.6
 - clang==16.0.6
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
 - cuda-nvtx-dev
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
index b2835312c7..17bdcd8c52 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -11,7 +11,7 @@ dependencies:
 - c-compiler
 - clang-tools==16.0.6
 - clang==16.0.6
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
 - cuda-nvtx-dev
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 88aa212355..a471423d93 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang-tools==16.0.6
 - clang==16.0.6
 - click
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
 - cuda-python>=11.8.5,<12.0a0
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index a1d3a5d115..5b1309a665 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang-tools==16.0.6
 - clang==16.0.6
 - click
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
 - cuda-python>=11.8.5,<12.0a0
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index cba83da04d..ce32927d40 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang-tools==16.0.6
 - clang==16.0.6
 - click
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
 - cuda-nvtx-dev
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index d8f050f328..f9b2b8aa6c 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -12,7 +12,7 @@ dependencies:
 - clang-tools==16.0.6
 - clang==16.0.6
 - click
-- cmake>=3.26.4,!=3.30.0
+- cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
 - cuda-nvtx-dev
diff --git a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
index 74f45d911e..736028df69 100644
--- a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
@@ -13,7 +13,7 @@ c_stdlib_version:
   - "2.28"
 
 cmake_version:
-  - ">=3.26.4,!=3.30.0"
+  - ">=3.30.4"
 
 glog_version:
   - ">=0.6.0"
diff --git a/conda/recipes/cuvs-bench/conda_build_config.yaml b/conda/recipes/cuvs-bench/conda_build_config.yaml
index ccd7341d17..6154e90071 100644
--- a/conda/recipes/cuvs-bench/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench/conda_build_config.yaml
@@ -17,7 +17,7 @@ c_stdlib_version:
   - "2.28"
 
 cmake_version:
-  - ">=3.26.4,!=3.30.0"
+  - ">=3.30.4"
 
 nccl_version:
   - ">=2.19"
diff --git a/conda/recipes/cuvs/conda_build_config.yaml b/conda/recipes/cuvs/conda_build_config.yaml
index 83f5ebcb15..0a1108eb51 100644
--- a/conda/recipes/cuvs/conda_build_config.yaml
+++ b/conda/recipes/cuvs/conda_build_config.yaml
@@ -17,4 +17,4 @@ c_stdlib_version:
   - "2.28"
 
 cmake_version:
-  - ">=3.26.4,!=3.30.0"
+  - ">=3.30.4"
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index 72cc4415d4..3a54a5f346 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -17,7 +17,7 @@ c_stdlib_version:
   - "2.28"
 
 cmake_version:
-  - ">=3.26.4,!=3.30.0"
+  - ">=3.30.4"
 
 h5py_version:
   - ">=3.8.0"
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 945a181579..9d505f862e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -10,7 +10,7 @@
 # is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
-cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 include(../rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
diff --git a/dependencies.yaml b/dependencies.yaml
index 4b3ba55d0a..97e6099f5a 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -212,7 +212,7 @@ dependencies:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - &cmake_ver cmake>=3.26.4,!=3.30.0
+          - &cmake_ver cmake>=3.30.4
           - ninja
       - output_types: [conda]
         packages:
diff --git a/examples/c/CMakeLists.txt b/examples/c/CMakeLists.txt
index 2a7e70522d..52f3b34d19 100644
--- a/examples/c/CMakeLists.txt
+++ b/examples/c/CMakeLists.txt
@@ -11,7 +11,7 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 # ------------- configure rapids-cmake --------------#
 
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 96414534cb..311be31f7c 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -11,7 +11,7 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 # ------------- configure rapids-cmake --------------#
 
diff --git a/java/internal/CMakeLists.txt b/java/internal/CMakeLists.txt
index 3bd316e2d9..c764745b10 100644
--- a/java/internal/CMakeLists.txt
+++ b/java/internal/CMakeLists.txt
@@ -12,7 +12,7 @@
 # the License.
 # =============================================================================
 
-cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 include(rapids_config.cmake)
 include(rapids-cmake)
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 93946cfdb0..97eb53cc42 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -12,7 +12,7 @@
 # the License.
 # =============================================================================
 
-cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 include(../../rapids_config.cmake)
 
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index d6847b3818..f4752d5207 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -115,7 +115,7 @@ regex = "(?P<value>.*)"
 
 [tool.rapids-build-backend]
 requires = [
-    "cmake>=3.26.4,!=3.30.0",
+    "cmake>=3.30.4",
     "cuda-python",
     "cython>=3.0.0",
     "libcuvs==25.4.*,>=0.0.0a0",
diff --git a/python/libcuvs/CMakeLists.txt b/python/libcuvs/CMakeLists.txt
index 569652b711..a5a1c66b7b 100644
--- a/python/libcuvs/CMakeLists.txt
+++ b/python/libcuvs/CMakeLists.txt
@@ -12,7 +12,7 @@
 # the License.
 # =============================================================================
 
-cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 include(../../rapids_config.cmake)
 
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 33958c8235..24596e0ab7 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -92,7 +92,7 @@ regex = "(?P<value>.*)"
 [tool.rapids-build-backend]
 build-backend = "scikit_build_core.build"
 requires = [
-    "cmake>=3.26.4,!=3.30.0",
+    "cmake>=3.30.4",
     "libraft==25.4.*,>=0.0.0a0",
     "librmm==25.4.*,>=0.0.0a0",
     "ninja",
diff --git a/rust/cuvs-sys/CMakeLists.txt b/rust/cuvs-sys/CMakeLists.txt
index 759152d907..0fcb0348a3 100644
--- a/rust/cuvs-sys/CMakeLists.txt
+++ b/rust/cuvs-sys/CMakeLists.txt
@@ -12,7 +12,7 @@
 # the License.
 # =============================================================================
 
-cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 include(rapids_config.cmake)
 include(rapids-cmake)

From 1591029d9dcbb549c5fca58e498bcbbcecbe2af3 Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Mon, 24 Feb 2025 14:39:23 -0800
Subject: [PATCH 329/623] [Fix] Various fixes for 25.02.01 point release (#695)

- https://github.com/rapidsai/cuvs/issues/694
- https://github.com/rapidsai/cuvs/issues/626
- https://github.com/rapidsai/cuvs/issues/680
- https://github.com/rapidsai/cuvs/pull/706

Fixes https://github.com/rapidsai/cuvs/issues/694
Fixes https://github.com/rapidsai/cuvs/issues/626
Fixes https://github.com/rapidsai/cuvs/issues/680
Closes https://github.com/rapidsai/cuvs/pull/706

---------

Co-authored-by: Ishan Chattopadhyaya <ishan@apache.org>
Co-authored-by: Vivek Narang <vivek@searchscale.com>
Co-authored-by: Dante Gama Dessavre <danteg@nvidia.com>
---
 conda/recipes/cuvs-bench-cpu/meta.yaml        |   1 +
 conda/recipes/cuvs-bench/meta.yaml            |   1 +
 cpp/include/cuvs/neighbors/cagra.hpp          |   8 +-
 .../neighbors/detail/cagra/cagra_merge.cuh    |   9 +-
 cpp/src/neighbors/detail/nn_descent.cuh       |  24 ++-
 cpp/tests/neighbors/ann_cagra.cuh             |  24 +++
 .../java/com/nvidia/cuvs/BruteForceQuery.java |  43 +++--
 .../cuvs/internal/BruteForceIndexImpl.java    |  24 ++-
 .../com/nvidia/cuvs/internal/common/Util.java |  26 +++
 .../nvidia/cuvs/BruteForceAndSearchIT.java    |  62 +++---
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |  19 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 179 ++++++++++++------
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   2 +-
 .../java/com/nvidia/cuvs/CuVSTestCase.java    |  43 ++++-
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |   2 +-
 java/internal/src/cuvs_java.c                 |  37 ++--
 16 files changed, 358 insertions(+), 146 deletions(-)

diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
index ddc13d568f..f5ca3cba6d 100644
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -64,6 +64,7 @@ requirements:
     - pyyaml
     - python
     - requests
+    - sklearn>=1.5
 about:
   home: https://rapids.ai/
   license: Apache-2.0
diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
index 1b151a16bc..21050e1cf1 100644
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -101,6 +101,7 @@ requirements:
     - python
     - requests
     - rmm ={{ minor_version }}
+    - sklearn>=1.5
 about:
   home: https://rapids.ai/
   license: Apache-2.0
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 1d0acbe355..fc558e3f31 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -337,6 +337,8 @@ struct index : cuvs::neighbors::index {
   using search_params_type = cagra::search_params;
   using index_type         = IdxT;
   using value_type         = T;
+  using dataset_index_type = int64_t;
+
   static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
                 "IdxT must be able to represent all values of uint32_t");
 
@@ -510,14 +512,14 @@ struct index : cuvs::neighbors::index {
    */
   template <typename DatasetT>
   auto update_dataset(raft::resources const& res, DatasetT&& dataset)
-    -> std::enable_if_t<std::is_base_of_v<cuvs::neighbors::dataset<int64_t>, DatasetT>>
+    -> std::enable_if_t<std::is_base_of_v<cuvs::neighbors::dataset<dataset_index_type>, DatasetT>>
   {
     dataset_ = std::make_unique<DatasetT>(std::move(dataset));
   }
 
   template <typename DatasetT>
   auto update_dataset(raft::resources const& res, std::unique_ptr<DatasetT>&& dataset)
-    -> std::enable_if_t<std::is_base_of_v<neighbors::dataset<int64_t>, DatasetT>>
+    -> std::enable_if_t<std::is_base_of_v<neighbors::dataset<dataset_index_type>, DatasetT>>
   {
     dataset_ = std::move(dataset);
   }
@@ -561,7 +563,7 @@ struct index : cuvs::neighbors::index {
   cuvs::distance::DistanceType metric_;
   raft::device_matrix<IdxT, int64_t, raft::row_major> graph_;
   raft::device_matrix_view<const IdxT, int64_t, raft::row_major> graph_view_;
-  std::unique_ptr<neighbors::dataset<int64_t>> dataset_;
+  std::unique_ptr<neighbors::dataset<dataset_index_type>> dataset_;
 };
 /**
  * @}
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index bc29cb2060..d85866161d 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -43,14 +43,16 @@ index<T, IdxT> merge(raft::resources const& handle,
                      const cagra::merge_params& params,
                      std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices)
 {
+  using cagra_index_t = cuvs::neighbors::cagra::index<T, IdxT>;
+  using ds_idx_type   = typename cagra_index_t::dataset_index_type;
+
   std::size_t dim              = 0;
   std::size_t new_dataset_size = 0;
   int64_t stride               = -1;
 
-  for (auto index : indices) {
+  for (cagra_index_t* index : indices) {
     RAFT_EXPECTS(index != nullptr,
                  "Null pointer detected in 'indices'. Ensure all elements are valid before usage.");
-    using ds_idx_type = decltype(index->data().n_rows());
     if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index->data());
         strided_dset != nullptr) {
       if (dim == 0) {
@@ -74,8 +76,7 @@ index<T, IdxT> merge(raft::resources const& handle,
   IdxT offset = 0;
 
   auto merge_dataset = [&](T* dst) {
-    for (auto index : indices) {
-      using ds_idx_type  = decltype(index->data().n_rows());
+    for (cagra_index_t* index : indices) {
       auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index->data());
 
       RAFT_CUDA_TRY(cudaMemcpy2DAsync(dst + offset * dim,
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 310d4e7a67..dffc94f06c 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -1047,24 +1047,32 @@ void GnndGraph<Index_t>::init_random_graph()
   for (size_t seg_idx = 0; seg_idx < static_cast<size_t>(num_segments); seg_idx++) {
     // random sequence (range: 0~nrow)
     // segment_x stores neighbors which id % num_segments == x
-    std::vector<Index_t> rand_seq(nrow / num_segments);
+    std::vector<Index_t> rand_seq((nrow + num_segments - 1) / num_segments);
     std::iota(rand_seq.begin(), rand_seq.end(), 0);
     auto gen = std::default_random_engine{seg_idx};
     std::shuffle(rand_seq.begin(), rand_seq.end(), gen);
 
 #pragma omp parallel for
     for (size_t i = 0; i < nrow; i++) {
-      size_t base_idx      = i * node_degree + seg_idx * segment_size;
-      auto h_neighbor_list = h_graph + base_idx;
-      auto h_dist_list     = h_dists.data_handle() + base_idx;
+      size_t base_idx         = i * node_degree + seg_idx * segment_size;
+      auto h_neighbor_list    = h_graph + base_idx;
+      auto h_dist_list        = h_dists.data_handle() + base_idx;
+      size_t idx              = base_idx;
+      size_t self_in_this_seg = 0;
       for (size_t j = 0; j < static_cast<size_t>(segment_size); j++) {
-        size_t idx = base_idx + j;
         Index_t id = rand_seq[idx % rand_seq.size()] * num_segments + seg_idx;
         if ((size_t)id == i) {
-          id = rand_seq[(idx + segment_size) % rand_seq.size()] * num_segments + seg_idx;
+          idx++;
+          id               = rand_seq[idx % rand_seq.size()] * num_segments + seg_idx;
+          self_in_this_seg = 1;
         }
-        h_neighbor_list[j].id_with_flag() = id;
-        h_dist_list[j]                    = std::numeric_limits<DistData_t>::max();
+
+        h_neighbor_list[j].id_with_flag() =
+          j < (rand_seq.size() - self_in_this_seg) && size_t(id) < nrow
+            ? id
+            : std::numeric_limits<Index_t>::max();
+        h_dist_list[j] = std::numeric_limits<DistData_t>::max();
+        idx++;
       }
     }
   }
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 1e695f9a8f..e9408264e5 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -952,6 +952,12 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
         (ps.k * ps.dim * 8 / 5 /*(=magic number)*/ < ps.n_rows))
       GTEST_SKIP();
 
+    // Avoid splitting datasets with a size of 0
+    if (ps.n_rows <= 3) GTEST_SKIP();
+
+    // IVF_PQ requires the `n_rows >= n_lists`.
+    if (ps.n_rows < 8 && ps.build_algo == graph_build_algo::IVF_PQ) GTEST_SKIP();
+
     size_t queries_size = ps.n_queries * ps.k;
     std::vector<IdxT> indices_Cagra(queries_size);
     std::vector<IdxT> indices_naive(queries_size);
@@ -1161,6 +1167,24 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
+  // Corner cases for small datasets
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {2},
+    {3, 5, 31, 32, 64, 101},
+    {1, 10},
+    {2},  // k
+    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
+    {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
+    {0},  // query size
+    {0},
+    {256},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {true},
+    {0.995});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
   // Varying dim and build algo.
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
index 7febc3ba37..019e27dcd8 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
@@ -17,6 +17,7 @@
 package com.nvidia.cuvs;
 
 import java.util.Arrays;
+import java.util.BitSet;
 import java.util.List;
 
 /**
@@ -28,7 +29,8 @@ public class BruteForceQuery {
 
   private List<Integer> mapping;
   private float[][] queryVectors;
-  private long[] prefilter;
+  private BitSet[] prefilters;
+  private int numDocs = -1;
   private int topK;
 
   /**
@@ -40,12 +42,15 @@ public class BruteForceQuery {
    * @param topK         the top k results to return
    * @param prefilter    the prefilter data to use while searching the BRUTEFORCE
    *                     index
+   * @param numDocs      Maximum of bits in each prefilter, representing number of documents in this index.
+   *                     Used only when prefilter(s) is/are passed.
    */
-  public BruteForceQuery(float[][] queryVectors, List<Integer> mapping, int topK, long[] prefilter) {
+  public BruteForceQuery(float[][] queryVectors, List<Integer> mapping, int topK, BitSet[] prefilters, int numDocs) {
     this.queryVectors = queryVectors;
     this.mapping = mapping;
     this.topK = topK;
-    this.prefilter = prefilter;
+    this.prefilters = prefilters;
+    this.numDocs = numDocs;
   }
 
   /**
@@ -78,16 +83,25 @@ public int getTopK() {
   /**
    * Gets the prefilter long array
    *
-   * @return a long array
+   * @return an array of bitsets
    */
-  public long[] getPrefilter() {
-    return prefilter;
+  public BitSet[] getPrefilters() {
+    return prefilters;
+  }
+
+  /**
+   * Gets the number of documents supposed to be in this index, as used for prefilters
+   *
+   * @return number of documents as an integer
+   */
+  public int getNumDocs() {
+    return numDocs;
   }
 
   @Override
   public String toString() {
     return "BruteForceQuery [mapping=" + mapping + ", queryVectors=" + Arrays.toString(queryVectors) + ", prefilter="
-        + Arrays.toString(prefilter) + ", topK=" + topK + "]";
+        + Arrays.toString(prefilters) + ", topK=" + topK + "]";
   }
 
   /**
@@ -96,7 +110,8 @@ public String toString() {
   public static class Builder {
 
     private float[][] queryVectors;
-    private long[] prefilter;
+    private BitSet[] prefilters;
+    private int numDocs;
     private List<Integer> mapping;
     private int topK = 2;
 
@@ -134,13 +149,15 @@ public Builder withTopK(int topK) {
     }
 
     /**
-     * Sets the prefilter data for building the {@link BruteForceQuery}.
+     * Sets the prefilters data for building the {@link BruteForceQuery}.
      *
-     * @param prefilter a one-dimensional long array
+     * @param prefilters array of bitsets, as many as queries, each containing as
+     *        many bits as there are vectors in the index
      * @return an instance of this Builder
      */
-    public Builder withPrefilter(long[] prefilter) {
-      this.prefilter = prefilter;
+    public Builder withPrefilter(BitSet[] prefilters, int numDocs) {
+      this.prefilters = prefilters;
+      this.numDocs = numDocs;
       return this;
     }
 
@@ -150,7 +167,7 @@ public Builder withPrefilter(long[] prefilter) {
      * @return an instance of {@link BruteForceQuery}
      */
     public BruteForceQuery build() {
-      return new BruteForceQuery(queryVectors, mapping, topK, prefilter);
+      return new BruteForceQuery(queryVectors, mapping, topK, prefilters, numDocs);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 7ea2801f31..decb13133c 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -26,8 +26,12 @@
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.lang.invoke.MethodHandle;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
 import java.nio.file.Files;
 import java.nio.file.Path;
+import java.util.Arrays;
+import java.util.BitSet;
 import java.util.Objects;
 import java.util.UUID;
 
@@ -59,7 +63,7 @@ public class BruteForceIndexImpl implements BruteForceIndex{
       FunctionDescriptor.of(ADDRESS, ADDRESS, C_LONG, C_LONG, ADDRESS, ADDRESS, C_INT));
 
   private static final MethodHandle searchMethodHandle = downcallHandle("search_brute_force_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_LONG, C_LONG));
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_LONG));
 
   private static final MethodHandle destroyIndexMethodHandle = downcallHandle("destroy_brute_force_index",
       FunctionDescriptor.ofVoid(ADDRESS, ADDRESS));
@@ -169,16 +173,24 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
     long numQueries = cuvsQuery.getQueryVectors().length;
     long numBlocks = cuvsQuery.getTopK() * numQueries;
     int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
-    long prefilterDataLength = cuvsQuery.getPrefilter() != null ? cuvsQuery.getPrefilter().length : 0;
     long numRows = dataset != null ? dataset.length : 0;
 
     SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
     SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
     MemorySegment neighborsMemorySegment = resources.getArena().allocate(neighborsSequenceLayout);
     MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
-    MemorySegment prefilterDataMemorySegment = cuvsQuery.getPrefilter() != null
-            ? Util.buildMemorySegment(resources.getArena(), cuvsQuery.getPrefilter())
-            : MemorySegment.NULL;
+
+    // prepare the prefiltering data
+    long prefilterDataLength = 0;
+    MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
+    BitSet[] prefilters = cuvsQuery.getPrefilters();
+    if (prefilters != null && prefilters.length > 0) {
+      BitSet concatenatedFilters = Util.concatenate(prefilters, cuvsQuery.getNumDocs());
+      long filters[] = concatenatedFilters.toLongArray();
+      prefilterDataMemorySegment = Util.buildMemorySegment(resources.getArena(), filters);
+      prefilterDataLength = cuvsQuery.getNumDocs() * prefilters.length;
+    }
+
     MemorySegment querySeg = Util.buildMemorySegment(resources.getArena(), cuvsQuery.getQueryVectors());
     try (var localArena = Arena.ofConfined()) {
       MemorySegment returnValue = localArena.allocate(C_INT);
@@ -193,7 +205,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
         distancesMemorySegment,
         returnValue,
         prefilterDataMemorySegment,
-        prefilterDataLength, numRows
+        prefilterDataLength
       );
       checkError(returnValue.get(C_INT, 0L), "searchMethodHandle");
     }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 5bc695ae3b..bc8f3bbcc4 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -25,6 +25,8 @@
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.VarHandle;
 import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.BitSet;
 import java.util.List;
 
 import com.nvidia.cuvs.GPUInfo;
@@ -184,6 +186,14 @@ public static MemorySegment buildMemorySegment(Arena arena, long[] data) {
     return dataMemorySegment;
   }
 
+  public static MemorySegment buildMemorySegment(Arena arena, byte[] data) {
+    int cells = data.length;
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(cells, C_CHAR);
+    MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+    MemorySegment.copy(data, 0, dataMemorySegment, C_CHAR, 0, cells);
+    return dataMemorySegment;
+  }
+
   /**
    * A utility method for building a {@link MemorySegment} for a 2D float array.
    *
@@ -201,4 +211,20 @@ public static MemorySegment buildMemorySegment(Arena arena, float[][] data) {
     }
     return dataMemorySegment;
   }
+
+  public static BitSet concatenate(BitSet[] arr, int maxSizeOfEachBitSet) {
+    BitSet ret = new BitSet(maxSizeOfEachBitSet * arr.length);
+    for (int i = 0; i < arr.length; i++) {
+      BitSet b = arr[i];
+      if (b == null || b.length() == 0) {
+        ret.set(i * maxSizeOfEachBitSet, (i + 1) * maxSizeOfEachBitSet);
+      } else {
+        for (int j = 0; j < maxSizeOfEachBitSet; j++) {
+          ret.set(i * maxSizeOfEachBitSet + j, b.get(j));
+        }
+      }
+    }
+    return ret;
+  }
+
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index 3a1ea25dd4..888f4ed69d 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -16,12 +16,15 @@
 
 package com.nvidia.cuvs;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.invoke.MethodHandles;
 import java.util.Arrays;
+import java.util.BitSet;
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
@@ -31,9 +34,6 @@
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-import static org.junit.Assert.assertEquals;
-
 public class BruteForceAndSearchIT extends CuVSTestCase{
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
@@ -57,14 +57,14 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         { 0.03902049f, 0.9689629f },
         { 0.92514056f, 0.4463501f },
         { 0.6673192f, 0.10993068f }
-      };
+    };
     List<Integer> map = List.of(0, 1, 2, 3);
     float[][] queries = {
         { 0.48216683f, 0.0428398f },
         { 0.5084142f, 0.6545497f },
         { 0.51260436f, 0.2643005f },
         { 0.05198065f, 0.5789965f }
-      };
+    };
 
     // Expected search results
     List<Map<Integer, Float>> expectedResults = Arrays.asList(
@@ -72,19 +72,26 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         Map.of(0, 0.12472606f, 2, 0.21700788f, 1, 0.3191862f),
         Map.of(3, 0.047766685f, 2, 0.20332813f, 0, 0.48305476f),
         Map.of(1, 0.15224183f, 0, 0.5906347f, 3, 0.5986643f)
-      );
+        );
+
+    // pre-filtering
+    BitSet prefilter = new BitSet();
+    prefilter.set(0);
+    prefilter.set(1);
+    prefilter.clear(2);
+    prefilter.set(3);
+
+    final List<Map<Integer, Float>> expectedResultsWithFiltering = Arrays.asList(
+        Map.of(0, 0.83774555f, 1, 1.0540828f, 3, 0.038782537f),
+        Map.of(0, 0.12472606f, 1, 0.3191862f, 3, 0.32186073f),
+        Map.of(0, 0.48305476f, 1, 0.7208309f, 3, 0.047766685f),
+        Map.of(0, 0.5906347f, 1, 0.15224195f, 3, 0.5986643f)
+        );
 
     for (int j = 0; j < 10; j++) {
 
       try (CuVSResources resources = CuVSResources.create()) {
 
-        // Create a query object with the query vectors
-        BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
-            .withTopK(3)
-            .withQueryVectors(queries)
-            .withMapping(map)
-            .build();
-
         // Set index parameters
         BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
             .withNumWriterThreads(32)
@@ -108,18 +115,29 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             .build();
 
         // Perform the search
-        SearchResults resultsFromLoadedIndex = loadedIndex.search(cuvsQuery);
+        BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
+            .withTopK(3)
+            .withQueryVectors(queries)
+            .withMapping(map)
+            .build();
+        BruteForceQuery cuvsQueryWithFiltering = new BruteForceQuery.Builder()
+            .withTopK(3)
+            .withQueryVectors(queries)
+            .withPrefilter(new BitSet[] {prefilter, prefilter, prefilter, prefilter}, dataset.length)
+            .withMapping(map)
+            .build();
 
-        // Check results
-        log.info(resultsFromLoadedIndex.getResults().toString());
-        assertEquals(expectedResults, resultsFromLoadedIndex.getResults());
+        // search the loaded index
+        SearchResults results = loadedIndex.search(cuvsQuery);
+        checkResults(expectedResults, results.getResults());
 
-        // Perform the search
-        SearchResults results = index.search(cuvsQuery);
+        // search the first index
+        results = index.search(cuvsQuery);
+        checkResults(expectedResults, results.getResults());
 
-        // Check results
-        log.info(results.getResults().toString());
-        assertEquals(expectedResults, results.getResults());
+        // search with pre-filtering
+        results = index.search(cuvsQueryWithFiltering);
+        checkResults(expectedResultsWithFiltering, results.getResults());
 
         // Cleanup
         index.destroyIndex();
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 030b3b0224..1db86db438 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -17,6 +17,8 @@
 package com.nvidia.cuvs;
 
 import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.BitSet;
 import java.util.List;
 
 import org.junit.Before;
@@ -58,10 +60,22 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     int dimensions = random.nextInt(DIMENSIONS_LIMIT) + 1;
     int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
     int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
-
+    boolean usePrefilter = random.nextBoolean();
     if (datasetSize < topK)
       datasetSize = topK;
 
+    BitSet[] prefilters = null;
+    if (usePrefilter) {
+      prefilters = new BitSet[numQueries];
+      for (int i = 0; i < numQueries; i++) {
+        BitSet randomFilter = new BitSet(datasetSize);
+        for (int j = 0; j < datasetSize; j++) {
+          randomFilter.set(j, random.nextBoolean());
+        }
+        prefilters[i] = randomFilter;
+      }
+    }
+
     // Generate a random dataset
     float[][] dataset = generateData(random, datasetSize, dimensions);
 
@@ -90,7 +104,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
 
     // Generate expected results using brute force
-    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
+    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, prefilters, log);
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
@@ -98,6 +112,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
       BruteForceQuery query = new BruteForceQuery.Builder()
           .withTopK(topK)
           .withQueryVectors(queries)
+          .withPrefilter(prefilters, dataset.length)
           .build();
 
       BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 874c3e525c..75b21aed82 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -21,6 +21,7 @@
 
 import java.io.File;
 import java.io.FileInputStream;
+import java.io.FileNotFoundException;
 import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.invoke.MethodHandles;
@@ -28,15 +29,21 @@
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.TimeUnit;
 
 import org.junit.Before;
 import org.junit.Test;
+import org.junit.runner.RunWith;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
 
+@RunWith(RandomizedRunner.class)
 public class CagraBuildAndSearchIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
@@ -44,6 +51,8 @@ public class CagraBuildAndSearchIT extends CuVSTestCase {
   @Before
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+    initializeRandom();
+    log.info("Random context initialized for test.");
   }
 
   /**
@@ -60,14 +69,14 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         { 0.03902049f, 0.9689629f },
         { 0.92514056f, 0.4463501f },
         { 0.6673192f, 0.10993068f }
-      };
+    };
     List<Integer> map = List.of(0, 1, 2, 3);
     float[][] queries = {
         { 0.48216683f, 0.0428398f },
         { 0.5084142f, 0.6545497f },
         { 0.51260436f, 0.2643005f },
         { 0.05198065f, 0.5789965f }
-      };
+    };
 
     // Expected search results
     List<Map<Integer, Float>> expectedResults = Arrays.asList(
@@ -76,68 +85,114 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
         Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.5986642f));
 
-    for (int j = 0; j < 10; j++) {
-
-      try (CuVSResources resources = CuVSResources.create()) {
-
-        // Configure index parameters
-        CagraIndexParams indexParams = new CagraIndexParams.Builder()
-            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-            .withGraphDegree(1)
-            .withIntermediateGraphDegree(2)
-            .withNumWriterThreads(32)
-            .withMetric(CuvsDistanceType.L2Expanded)
-            .build();
-
-        // Create the index with the dataset
-        CagraIndex index = CagraIndex.newBuilder(resources)
-            .withDataset(dataset)
-            .withIndexParams(indexParams)
-            .build();
-
-        // Saving the index on to the disk.
-        String indexFileName = UUID.randomUUID().toString() + ".cag";
-        index.serialize(new FileOutputStream(indexFileName));
-
-        // Loading a CAGRA index from disk.
-        File indexFile = new File(indexFileName);
-        InputStream inputStream = new FileInputStream(indexFile);
-        CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
-            .from(inputStream)
-            .build();
-
-        // Configure search parameters
-        CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-            .build();
-
-        // Create a query object with the query vectors
-        CagraQuery cuvsQuery = new CagraQuery.Builder()
-            .withTopK(3)
-            .withSearchParams(searchParams)
-            .withQueryVectors(queries)
-            .withMapping(map)
-            .build();
-
-        // Perform the search
-        SearchResults results = index.search(cuvsQuery);
-
-        // Check results
-        log.info(results.getResults().toString());
-        assertEquals(expectedResults, results.getResults());
-
-        // Search from deserialized index
-        results = loadedIndex.search(cuvsQuery);
-
-        // Check results
-        log.info(results.getResults().toString());
-        assertEquals(expectedResults, results.getResults());
-
-        // Cleanup
-        if (indexFile.exists()) {
-          indexFile.delete();
+    int numTestsRuns = 10;
+
+    try (CuVSResources resources = CuVSResources.create()) {
+      // sometimes run this test using different threads?
+      boolean runTestInDifferentThreads = random.nextBoolean();
+      // if running in different threads, run concurrently or one after the other?
+      boolean runConcurrently = runTestInDifferentThreads ? random.nextBoolean(): false;
+
+      log.info("Running in different threads? " + runTestInDifferentThreads);
+      log.info("Running concurrently? " + runConcurrently);
+
+      ExecutorService parallelExecutor = runConcurrently ? Executors.newFixedThreadPool(numTestsRuns): null;
+
+      for (int j = 0; j < numTestsRuns; j++) {
+        Runnable testLogic = indexAndQueryOnce(dataset, map, queries, expectedResults, resources);
+        if (runTestInDifferentThreads) {
+          if (runConcurrently) {
+            parallelExecutor.submit(testLogic);
+          } else {
+            ExecutorService singleExecutor = Executors.newSingleThreadExecutor();
+            singleExecutor.submit(testLogic);
+            singleExecutor.shutdown();
+            singleExecutor.awaitTermination(2000, TimeUnit.SECONDS);
+          }
+        } else {
+          // run the test logic in the main thread
+          testLogic.run();
         }
-        index.destroyIndex();
       }
+      if (parallelExecutor != null) {
+        parallelExecutor.shutdown();
+        parallelExecutor.awaitTermination(2000, TimeUnit.SECONDS);
+      }
+
     }
   }
+
+  private Runnable indexAndQueryOnce(float[][] dataset, List<Integer> map, float[][] queries,
+      List<Map<Integer, Float>> expectedResults, CuVSResources resources) throws Throwable, FileNotFoundException {
+
+    Runnable thread = new Runnable() {
+
+      @Override
+      public void run() {
+        try {
+
+          // Configure index parameters
+          CagraIndexParams indexParams = new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(1)
+              .withIntermediateGraphDegree(2)
+              .withNumWriterThreads(32)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+          // Create the index with the dataset
+          CagraIndex index = CagraIndex.newBuilder(resources)
+              .withDataset(dataset)
+              .withIndexParams(indexParams)
+              .build();
+
+          // Saving the index on to the disk.
+          String indexFileName = UUID.randomUUID().toString() + ".cag";
+          index.serialize(new FileOutputStream(indexFileName));
+
+          // Loading a CAGRA index from disk.
+          File indexFile = new File(indexFileName);
+          InputStream inputStream = new FileInputStream(indexFile);
+          CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
+              .from(inputStream)
+              .build();
+
+          // Configure search parameters
+          CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+              .build();
+
+          // Create a query object with the query vectors
+          CagraQuery cuvsQuery = new CagraQuery.Builder()
+              .withTopK(3)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .withMapping(map)
+              .build();
+
+          // Perform the search
+          SearchResults results = index.search(cuvsQuery);
+
+          // Check results
+          log.info(results.getResults().toString());
+          assertEquals(expectedResults, results.getResults());
+
+          // Search from deserialized index
+          results = loadedIndex.search(cuvsQuery);
+
+          // Check results
+          log.info(results.getResults().toString());
+          assertEquals(expectedResults, results.getResults());
+
+          // Cleanup
+          if (indexFile.exists()) {
+            indexFile.delete();
+          }
+          index.destroyIndex();
+        } catch (Throwable ex) {
+          throw new RuntimeException("Exception during indexing/querying", ex);
+        }
+      }
+    };
+    return thread;
+  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 811866dc61..34665d3eb3 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -91,7 +91,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
 
     // Generate expected results using brute force
-    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
+    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, null, log);
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
index 8a7d84a199..5e40ff5798 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
@@ -21,6 +21,7 @@
 
 import java.lang.invoke.MethodHandles;
 import java.util.ArrayList;
+import java.util.BitSet;
 import java.util.List;
 import java.util.Map;
 import java.util.Random;
@@ -50,16 +51,21 @@ protected float[][] generateData(Random random, int rows, int cols) {
     return data;
   }
 
-  protected List<List<Integer>> generateExpectedResults(int topK, float[][] dataset, float[][] queries, Logger log) {
+  protected List<List<Integer>> generateExpectedResults(int topK, float[][] dataset, float[][] queries, BitSet[] prefilters, Logger log) {
     List<List<Integer>> neighborsResult = new ArrayList<>();
     int dimensions = dataset[0].length;
 
-    for (float[] query : queries) {
+    for (int q = 0; q < queries.length; q++) {
+      float[] query = queries[q];
       Map<Integer, Double> distances = new TreeMap<>();
       for (int j = 0; j < dataset.length; j++) {
         double distance = 0;
-        for (int k = 0; k < dimensions; k++) {
-          distance += (query[k] - dataset[j][k]) * (query[k] - dataset[j][k]);
+        if (prefilters != null && prefilters[q].get(j) == false) {
+          distance = Double.POSITIVE_INFINITY;
+        } else {
+          for (int k = 0; k < dimensions; k++) {
+            distance += (query[k] - dataset[j][k]) * (query[k] - dataset[j][k]);
+          }
         }
         distances.put(j, Math.sqrt(distance));
       }
@@ -85,7 +91,6 @@ protected void compareResults(SearchResults results, List<List<Integer>> expecte
     // actual vs. expected results
     for (int i = 0; i < results.getResults().size(); i++) {
       Map<Integer, Float> result = results.getResults().get(i);
-      assertEquals("TopK mismatch for query.", Math.min(topK, datasetSize), result.size());
 
       // Sort result by values (distances) and extract keys
       List<Integer> sortedResultKeys = result.entrySet().stream().sorted(Map.Entry.comparingByValue())
@@ -101,6 +106,34 @@ protected void compareResults(SearchResults results, List<List<Integer>> expecte
     }
   }
 
+  protected static void checkResults(List<Map<Integer, Float>> expected, List<Map<Integer, Float>> actual) {
+    List<Map<Integer, Float>> sortedExpected = new ArrayList<Map<Integer,Float>>();
+    List<Map<Integer, Float>> sortedActual   = new ArrayList<Map<Integer,Float>>();
+    for (Map<Integer, Float> map: expected) {
+      sortedExpected.add(new TreeMap<Integer, Float>(map) {
+        @Override
+        public boolean equals(Object o) {
+          Map<Integer, Float> map = (Map<Integer, Float>) o;
+          if (this.size() != map.size()) return false;
+          for (Integer key: map.keySet()) {
+            try {
+              if (Math.abs((float)map.get(key) - ((float)get(key))) < 0.0001f == false) {
+                return false;
+              }
+            } catch (Exception ex) {
+              return false;
+            }
+          }
+          return true;
+        }
+      });
+    }
+    for (Map<Integer, Float> map: actual) {
+      sortedActual.add(new TreeMap<Integer, Float>(map));
+    }
+    assertEquals(sortedExpected, sortedActual);
+  }
+
   protected static boolean isLinuxAmd64() {
     String name = System.getProperty("os.name");
     return (name.startsWith("Linux")) && System.getProperty("os.arch").equals("amd64");
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index 6d367efc43..07033391ba 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -97,7 +97,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
 
     // Generate expected results using brute force
-    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, log);
+    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, null, log);
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
diff --git a/java/internal/src/cuvs_java.c b/java/internal/src/cuvs_java.c
index febde8463b..025b0f6e98 100644
--- a/java/internal/src/cuvs_java.c
+++ b/java/internal/src/cuvs_java.c
@@ -96,7 +96,6 @@ cuvsCagraIndex_t build_cagra_index(float *dataset, long rows, long dimensions, c
   cuvsStreamGet(cuvs_resources, &stream);
 
   omp_set_num_threads(n_writer_threads);
-  cuvsRMMPoolMemoryResourceEnable(95, 95, false);
 
   int64_t dataset_shape[2] = {rows, dimensions};
   DLManagedTensor dataset_tensor = prepare_tensor(dataset, dataset_shape, kDLFloat, 32, 2, kDLCUDA);
@@ -226,7 +225,6 @@ cuvsBruteForceIndex_t build_brute_force_index(float *dataset, long rows, long di
   int *return_value, int n_writer_threads) {
 
   omp_set_num_threads(n_writer_threads);
-  cuvsRMMPoolMemoryResourceEnable(95, 95, false);
 
   cudaStream_t stream;
   cuvsStreamGet(cuvs_resources, &stream);
@@ -266,26 +264,22 @@ cuvsBruteForceIndex_t build_brute_force_index(float *dataset, long rows, long di
  * @param[in] n_rows number of rows in the dataset
  */
 void search_brute_force_index(cuvsBruteForceIndex_t index, float *queries, int topk, long n_queries, int dimensions,
-    cuvsResources_t cuvs_resources, int64_t *neighbors_h, float *distances_h, int *return_value, long *prefilter_data,
-    long prefilter_data_length, long n_rows) {
+    cuvsResources_t cuvs_resources, int64_t *neighbors_h, float *distances_h, int *return_value, uint32_t *prefilter_data,
+    long prefilter_data_length) {
 
   cudaStream_t stream;
   cuvsStreamGet(cuvs_resources, &stream);
 
   int64_t *neighbors;
   float *distances, *queries_d;
-  long *prefilter_data_d;
-
-  long prefilter_data_32_size = sizeof(uint32_t) * prefilter_data_length * 2;
-  uint32_t *prefilter_data_32 = (uint32_t *)malloc(prefilter_data_32_size);
+  uint32_t *prefilter_d = NULL;
+  int64_t prefilter_len = 0;
 
   cuvsRMMAlloc(cuvs_resources, (void**) &queries_d, sizeof(float) * n_queries * dimensions);
   cuvsRMMAlloc(cuvs_resources, (void**) &neighbors, sizeof(int64_t) * n_queries * topk);
   cuvsRMMAlloc(cuvs_resources, (void**) &distances, sizeof(float) * n_queries * topk);
-  cuvsRMMAlloc(cuvs_resources, (void**) &prefilter_data_d, prefilter_data_32_size);
 
   cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * dimensions, cudaMemcpyDefault);
-  cudaMemcpy(prefilter_data_d, prefilter_data_32, prefilter_data_32_size, cudaMemcpyDefault);
 
   int64_t queries_shape[2] = {n_queries, dimensions};
   DLManagedTensor queries_tensor = prepare_tensor(queries_d, queries_shape, kDLFloat, 32, 2, kDLCUDA);
@@ -296,20 +290,22 @@ void search_brute_force_index(cuvsBruteForceIndex_t index, float *queries, int t
   int64_t distances_shape[2] = {n_queries, topk};
   DLManagedTensor distances_tensor = prepare_tensor(distances, distances_shape, kDLFloat, 32, 2, kDLCUDA);
 
-  // unpack the incoming long into two 32bit ints
-  for (long i = 0; i < prefilter_data_length; i++) {
-    *(prefilter_data_32 + (2 * i)) = (int)(*(prefilter_data + i) >> 32);
-    *(prefilter_data_32 + ((2 * i) + 1)) = (int)*(prefilter_data + i);
-    //long l = (((long)*(prefilter_data_32 + (2 * i))) << 32) | (*(prefilter_data_32 + ((2 * i) + 1)) & 0xffffffffL);
-  }
-
   cuvsFilter prefilter;
+  DLManagedTensor prefilter_tensor;
   if (prefilter_data == NULL) {
     prefilter.type = NO_FILTER;
     prefilter.addr = (uintptr_t)NULL;
   } else {
-    int64_t prefilter_shape[1] = {(n_queries * n_rows + 31) / 32};
-    DLManagedTensor prefilter_tensor = prepare_tensor(prefilter_data_d, prefilter_shape, kDLUInt, 32, 1, kDLCUDA);
+    // Parse the filters data
+    int num_integers = (prefilter_data_length+63)/64 * 2;
+    int extraPaddingByteExists = prefilter_data_length % 64 > 32? 0: 1;
+    int64_t prefilter_shape[1] = {(prefilter_data_length + 31) / 32};
+
+	prefilter_len = prefilter_shape[0];
+    cuvsRMMAlloc(cuvs_resources, (void**) &prefilter_d, sizeof(uint32_t) * prefilter_len);
+	cudaMemcpy(prefilter_d, prefilter_data, sizeof(uint32_t) * prefilter_len, cudaMemcpyHostToDevice);
+
+    prefilter_tensor = prepare_tensor(prefilter_d, prefilter_shape, kDLUInt, 32, 1, kDLCUDA);
     prefilter.type = BITMAP;
     prefilter.addr = (uintptr_t)&prefilter_tensor;
   }
@@ -323,6 +319,9 @@ void search_brute_force_index(cuvsBruteForceIndex_t index, float *queries, int t
   cuvsRMMFree(cuvs_resources, neighbors, sizeof(int64_t) * n_queries * topk);
   cuvsRMMFree(cuvs_resources, distances, sizeof(float) * n_queries * topk);
   cuvsRMMFree(cuvs_resources, queries_d, sizeof(float) * n_queries * dimensions);
+  if(prefilter_d != NULL) {
+    cuvsRMMFree(cuvs_resources, prefilter_d, sizeof(uint32_t) * prefilter_len);
+  }
 }
 
 /**

From 28a15e6ccd44cc999ad5ef81e88c57671e386433 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 25 Feb 2025 17:56:30 -0600
Subject: [PATCH 330/623] Use conda-build instead of conda-mambabuild (#723)

This changes from `conda mambabuild` to `conda build`. Conda now uses the mamba solver so no performance regressions are expected.

This is a temporary change as we plan to migrate to `rattler-build` in the near future. However, this is needed sooner to drop `boa` and unblock Python 3.13 migrations.

xref: https://github.com/rapidsai/build-planning/issues/149

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/723
---
 ci/build_cpp.sh    | 2 +-
 ci/build_python.sh | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index d0344061b1..afd50a626d 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -17,7 +17,7 @@ rapids-logger "Begin cpp build"
 
 sccache --zero-stats
 
-RAPIDS_PACKAGE_VERSION=$(rapids-generate-version) rapids-conda-retry mambabuild \
+RAPIDS_PACKAGE_VERSION=$(rapids-generate-version) rapids-conda-retry build \
   conda/recipes/libcuvs
 
 sccache --show-adv-stats
diff --git a/ci/build_python.sh b/ci/build_python.sh
index d9ea64de91..6981c08fc3 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -25,7 +25,7 @@ sccache --zero-stats
 
 # TODO: Remove `--no-test` flags once importing on a CPU
 # node works correctly
-rapids-conda-retry mambabuild \
+rapids-conda-retry build \
   --no-test \
   --channel "${CPP_CHANNEL}" \
   conda/recipes/cuvs
@@ -34,7 +34,7 @@ sccache --show-adv-stats
 sccache --zero-stats
 
 # Build cuvs-bench for each cuda and python version
-rapids-conda-retry mambabuild \
+rapids-conda-retry build \
   --no-test \
   --channel "${CPP_CHANNEL}" \
   --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
@@ -47,7 +47,7 @@ sccache --zero-stats
 # version
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
-  rapids-conda-retry mambabuild \
+  rapids-conda-retry build \
   --no-test \
   --channel "${CPP_CHANNEL}" \
   --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \

From ce321a9bc8be6a29ebef2fcebe15778cba9ebae6 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Thu, 27 Feb 2025 22:19:54 +0530
Subject: [PATCH 331/623] Diskann Benchmarking Wrapper (#260)

Brings DiskANN into cuvs-bench
- [x] Build and search in-memory DiskANN index
- [x] Build and search SSD DiskANN index
- [x] Build a cuvs Vamana index on GPU and serialize it in DiskANN format. Search on CPU using in-memory DiskANN search API.

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Artem M. Chirkin (https://github.com/achirkin)
  - Gil Forsyth (https://github.com/gforsyth)
  - https://github.com/jakirkham

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Karsin (https://github.com/bkarsin)
  - Artem M. Chirkin (https://github.com/achirkin)
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/260
---
 .../bench_ann_cuda-118_arch-x86_64.yaml       |   3 +
 .../bench_ann_cuda-128_arch-x86_64.yaml       |   3 +
 conda/recipes/cuvs-bench-cpu/meta.yaml        |   4 +
 conda/recipes/cuvs-bench/meta.yaml            |   4 +
 cpp/bench/ann/CMakeLists.txt                  |  27 +-
 cpp/bench/ann/src/common/benchmark.hpp        |  12 +-
 cpp/bench/ann/src/cuvs/cuvs_vamana.cu         |  93 +++++
 cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h  | 125 +++++++
 .../ann/src/diskann/diskann_benchmark.cpp     | 139 +++++++
 cpp/bench/ann/src/diskann/diskann_wrapper.h   | 341 ++++++++++++++++++
 cpp/cmake/patches/diskann.diff                | 250 +++++++++++++
 cpp/cmake/patches/diskann_override.json       |  16 +
 cpp/cmake/patches/faiss_override.json         |   2 +-
 cpp/cmake/thirdparty/get_diskann.cmake        |  49 +++
 cpp/cmake/thirdparty/get_faiss.cmake          |  14 +-
 cpp/include/cuvs/neighbors/vamana.hpp         |  14 +-
 .../detail/vamana/vamana_serialize.cuh        |  45 ++-
 cpp/src/neighbors/vamana.cuh                  |   4 +-
 cpp/src/neighbors/vamana_serialize.cuh        |  16 +-
 dependencies.yaml                             |  12 +
 .../cuvs_bench/config/algorithms.yaml         |   9 +
 .../config/algos/constraints/__init__.py      |  23 +-
 .../cuvs_bench/config/algos/cuvs_vamana.yaml  |  10 +
 .../config/algos/diskann_memory.yaml          |  13 +
 .../cuvs_bench/config/algos/diskann_ssd.yaml  |  13 +
 25 files changed, 1210 insertions(+), 31 deletions(-)
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_vamana.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h
 create mode 100644 cpp/bench/ann/src/diskann/diskann_benchmark.cpp
 create mode 100644 cpp/bench/ann/src/diskann/diskann_wrapper.h
 create mode 100644 cpp/cmake/patches/diskann.diff
 create mode 100644 cpp/cmake/patches/diskann_override.json
 create mode 100644 cpp/cmake/thirdparty/get_diskann.cmake
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml

diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 5b1309a665..332bc02a7c 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -26,6 +26,8 @@ dependencies:
 - gcc_linux-64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
+- libaio
+- libboost-devel=1.87
 - libclang==16.0.6
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
@@ -38,6 +40,7 @@ dependencies:
 - libcuvs==25.4.*,>=0.0.0a0
 - librmm==25.4.*,>=0.0.0a0
 - matplotlib-base
+- mkl-devel=2023
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index f9b2b8aa6c..864a831bc6 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -27,6 +27,8 @@ dependencies:
 - gcc_linux-64=13.*
 - glog>=0.6.0
 - h5py>=3.8.0
+- libaio
+- libboost-devel=1.87
 - libclang==16.0.6
 - libcublas-dev
 - libcurand-dev
@@ -35,6 +37,7 @@ dependencies:
 - libcuvs==25.4.*,>=0.0.0a0
 - librmm==25.4.*,>=0.0.0a0
 - matplotlib-base
+- mkl-devel=2023
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
index 19e5be6b48..9dea3f6c4c 100644
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -46,6 +46,9 @@ requirements:
   host:
     - benchmark
     - glog {{ glog_version }}
+    - libaio  # [linux64]
+    - libboost-devel=1.87  # [linux64]
+    - mkl-devel=2023  # [linux64]
     - nlohmann_json {{ nlohmann_json_version }}
     - openblas
     - python
@@ -57,6 +60,7 @@ requirements:
     - glog {{ glog_version }}
     - h5py {{ h5py_version }}
     - matplotlib-base
+    - mkl =2023
     - numpy >=1.23,<3.0a0
     - pandas
     - pyyaml
diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
index 21050e1cf1..a83297098c 100644
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -70,7 +70,10 @@ requirements:
     - libcublas-dev
     {% endif %}
     - glog {{ glog_version }}
+    - libaio  # [linux64]
+    - libboost-devel=1.87  # [linux64]
     - libcuvs {{ version }}
+    - mkl-devel=2023  # [linux64]
     - nlohmann_json {{ nlohmann_json_version }}
     - openblas
     # rmm is needed to determine if package is gpu-enabled
@@ -94,6 +97,7 @@ requirements:
     - cuvs {{ version }}
     - h5py {{ h5py_version }}
     - matplotlib-base
+    - mkl =2023  # [linux64]
     - pandas
     - pyyaml
     # rmm is needed to determine if package is gpu-enabled
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index fff2daa750..a2bc5645fc 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -33,6 +33,12 @@ option(CUVS_ANN_BENCH_USE_CUVS_BRUTE_FORCE "Include cuVS brute force knn in benc
 option(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB "Include cuVS CAGRA with HNSW search in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_HNSWLIB "Include hnsw algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_GGNN "Include ggnn algorithm in benchmark" OFF)
+option(CUVS_ANN_BENCH_USE_DISKANN "Include DISKANN search in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_CUVS_VAMANA "Include cuVS Vamana with DiskANN search in benchmark" ON)
+if(CMAKE_SYSTEM_PROCESSOR MATCHES "(ARM|arm|aarch64)")
+  set(CUVS_ANN_BENCH_USE_DISKANN OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_VAMANA OFF)
+endif()
 option(CUVS_ANN_BENCH_USE_CUVS_MG "Include cuVS ann mg algorithm in benchmark" ${BUILD_MG_ALGOS})
 option(CUVS_ANN_BENCH_SINGLE_EXE
        "Make a single executable with benchmark as shared library modules" OFF
@@ -58,6 +64,7 @@ if(BUILD_CPU_ONLY)
   set(CUVS_ANN_BENCH_USE_GGNN OFF)
   set(CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_MG OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_VAMANA OFF)
 else()
   set(CUVS_FAISS_ENABLE_GPU ON)
 endif()
@@ -70,6 +77,7 @@ if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
    OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB
    OR CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE
    OR CUVS_ANN_BENCH_USE_CUVS_MG
+   OR CUVS_ANN_BENCH_USE_CUVS_VAMANA
 )
   set(CUVS_ANN_BENCH_USE_CUVS ON)
 endif()
@@ -87,6 +95,10 @@ if(CUVS_ANN_BENCH_USE_GGNN)
   include(cmake/thirdparty/get_ggnn)
 endif()
 
+if(CUVS_ANN_BENCH_USE_DISKANN OR CUVS_ANN_BENCH_USE_CUVS_VAMANA)
+  include(cmake/thirdparty/get_diskann)
+endif()
+
 if(CUVS_ANN_BENCH_USE_FAISS)
   include(cmake/thirdparty/get_faiss)
 endif()
@@ -299,6 +311,19 @@ if(CUVS_ANN_BENCH_USE_GGNN)
   )
 endif()
 
+if(CUVS_ANN_BENCH_USE_DISKANN)
+  ConfigureAnnBench(
+    NAME DISKANN_MEMORY PATH src/diskann/diskann_benchmark.cpp LINKS diskann::diskann aio
+  )
+  ConfigureAnnBench(
+    NAME DISKANN_SSD PATH src/diskann/diskann_benchmark.cpp LINKS diskann::diskann aio
+  )
+endif()
+
+if(CUVS_ANN_BENCH_USE_CUVS_VAMANA)
+  ConfigureAnnBench(NAME CUVS_VAMANA PATH src/cuvs/cuvs_vamana.cu LINKS cuvs diskann::diskann aio)
+endif()
+
 # ##################################################################################################
 # * Dynamically-loading ANN_BENCH executable -------------------------------------------------------
 if(CUVS_ANN_BENCH_SINGLE_EXE)
diff --git a/cpp/bench/ann/src/common/benchmark.hpp b/cpp/bench/ann/src/common/benchmark.hpp
index d2b3f89ff9..92f2205a39 100644
--- a/cpp/bench/ann/src/common/benchmark.hpp
+++ b/cpp/bench/ann/src/common/benchmark.hpp
@@ -118,7 +118,7 @@ inline auto parse_algo_property(algo_property prop, const nlohmann::json& conf)
 template <typename T>
 void bench_build(::benchmark::State& state,
                  std::shared_ptr<const dataset<T>> dataset,
-                 configuration::index index,
+                 const configuration::index& index,
                  bool force_overwrite,
                  bool no_lap_sync)
 {
@@ -185,7 +185,7 @@ void bench_build(::benchmark::State& state,
 
 template <typename T>
 void bench_search(::benchmark::State& state,
-                  configuration::index index,
+                  const configuration::index& index,
                   std::size_t search_param_ix,
                   std::shared_ptr<const dataset<T>> dataset,
                   bool no_lap_sync)
@@ -469,11 +469,11 @@ inline void printf_usage()
 
 template <typename T>
 void register_build(std::shared_ptr<const dataset<T>> dataset,
-                    std::vector<configuration::index> indices,
+                    std::vector<configuration::index>& indices,
                     bool force_overwrite,
                     bool no_lap_sync)
 {
-  for (auto index : indices) {
+  for (auto& index : indices) {
     auto suf      = static_cast<std::string>(index.build_param["override_suffix"]);
     auto file_suf = suf;
     index.build_param.erase("override_suffix");
@@ -489,12 +489,12 @@ void register_build(std::shared_ptr<const dataset<T>> dataset,
 
 template <typename T>
 void register_search(std::shared_ptr<const dataset<T>> dataset,
-                     std::vector<configuration::index> indices,
+                     std::vector<configuration::index>& indices,
                      Mode metric_objective,
                      const std::vector<int>& threads,
                      bool no_lap_sync)
 {
-  for (auto index : indices) {
+  for (auto& index : indices) {
     for (std::size_t i = 0; i < index.search_params.size(); i++) {
       auto suf = static_cast<std::string>(index.search_params[i]["override_suffix"]);
       index.search_params[i].erase("override_suffix");
diff --git a/cpp/bench/ann/src/cuvs/cuvs_vamana.cu b/cpp/bench/ann/src/cuvs/cuvs_vamana.cu
new file mode 100644
index 0000000000..2bc0ca9596
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_vamana.cu
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "cuvs_vamana_wrapper.h"
+
+#include <rmm/cuda_device.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+#include <rmm/resource_ref.hpp>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::cuvs_vamana<T, IdxT>::build_param& param)
+{
+  if (conf.contains("graph_degree")) { param.graph_degree = conf.at("graph_degree"); }
+  if (conf.contains("visited_size")) { param.visited_size = conf.at("visited_size"); }
+  if (conf.contains("alpha")) { param.alpha = conf.at("alpha"); }
+}
+
+template <typename T, typename IdxT>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::cuvs_vamana<T, IdxT>::search_param& param)
+{
+  if (conf.contains("L_search")) { param.L_search = conf.at("L_search"); }
+  if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  [[maybe_unused]] cuvs::bench::Metric metric = parse_metric(distance);
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float> or std::is_same_v<T, std::uint8_t>) {
+    if (algo_name == "cuvs_vamana") {
+      typename cuvs::bench::cuvs_vamana<T, uint32_t>::build_param param;
+      parse_build_param<T, uint32_t>(conf, param);
+      a = std::make_unique<cuvs::bench::cuvs_vamana<T, uint32_t>>(metric, dim, param);
+    }
+  }
+
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+  if (algo_name == "cuvs_vamana") {
+    auto param = std::make_unique<typename cuvs::bench::cuvs_vamana<T, uint32_t>::search_param>();
+    parse_search_param<T, uint32_t>(conf, *param);
+    return param;
+  }
+
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+}  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+/*
+[NOTE] Dear developer,
+
+Please don't modify the content of the `main` function; this will make the behavior of the benchmark
+executable differ depending on the cmake flags and will complicate the debugging. In particular,
+don't try to setup an RMM memory resource here; it will anyway be modified by the memory resource
+set on per-algorithm basis. For example, see `cuvs/cuvs_ann_bench_utils.h`.
+*/
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h
new file mode 100644
index 0000000000..410f5c2a02
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+#include "../diskann/diskann_wrapper.h"
+#include "cuvs_ann_bench_utils.h"
+#include <cuvs/neighbors/vamana.hpp>
+#include <utils.h>
+
+#include <memory>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/mdspan.hpp>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+class cuvs_vamana : public algo<T>, public algo_gpu {
+ public:
+  using build_param       = cuvs::neighbors::vamana::index_params;
+  using search_param_base = typename algo<T>::search_param;
+  using search_param      = typename diskann_memory<T>::search_param;
+
+  cuvs_vamana(Metric metric, int dim, const build_param& param);
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return handle_.get_sync_stream();
+  }
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kDevice;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<cuvs_vamana<T, IdxT>>(*this); }
+
+ private:
+  std::shared_ptr<cuvs::neighbors::vamana::index<T, IdxT>> vamana_index_;
+  std::shared_ptr<diskann_memory<T>> diskann_memory_search_;
+  configured_raft_resources handle_{};
+  build_param vamana_index_params_;
+};
+
+template <typename T, typename IdxT>
+cuvs_vamana<T, IdxT>::cuvs_vamana(Metric metric, int dim, const build_param& param)
+  : algo<T>(metric, dim)
+{
+  this->vamana_index_params_ = param;
+  diskann_memory_search_     = std::make_shared<cuvs::bench::diskann_memory<T>>(
+    metric, dim, typename diskann_memory<T>::build_param{param.graph_degree, param.visited_size});
+}
+
+template <typename T, typename IdxT>
+void cuvs_vamana<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  auto dataset_view_host = raft::make_mdspan<const T, int64_t, raft::row_major, true, false>(
+    dataset, raft::make_extents<int64_t>(nrow, this->dim_));
+  auto dataset_view_device = raft::make_mdspan<const T, int64_t, raft::row_major, true, false>(
+    dataset, raft::make_extents<int64_t>(nrow, this->dim_));
+  bool dataset_is_on_host = raft::get_device_for_address(dataset) == -1;
+
+  vamana_index_ = std::make_shared<cuvs::neighbors::vamana::index<T, uint32_t>>(std::move(
+    dataset_is_on_host
+      ? cuvs::neighbors::vamana::build(handle_, vamana_index_params_, dataset_view_host)
+      : cuvs::neighbors::vamana::build(handle_, vamana_index_params_, dataset_view_device)));
+}
+
+template <typename T, typename IdxT>
+void cuvs_vamana<T, IdxT>::set_search_param(const search_param_base& param,
+                                            const void* filter_bitset)
+{
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
+  diskann_memory_search_->set_search_param(param, nullptr);
+}
+
+template <typename T, typename IdxT>
+void cuvs_vamana<T, IdxT>::save(const std::string& file) const
+{
+  cuvs::neighbors::vamana::serialize(handle_, file, *vamana_index_);
+}
+
+template <typename T, typename IdxT>
+void cuvs_vamana<T, IdxT>::load(const std::string& file)
+{
+  diskann_memory_search_->load(file);
+}
+
+template <typename T, typename IdxT>
+void cuvs_vamana<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  diskann_memory_search_->search(queries, batch_size, k, neighbors, distances);
+}
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/diskann/diskann_benchmark.cpp b/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
new file mode 100644
index 0000000000..c410995334
--- /dev/null
+++ b/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "../common/conf.hpp"
+#include "diskann_wrapper.h"
+
+#define JSON_DIAGNOSTICS 1
+#include <nlohmann/json.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace cuvs::bench {
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::diskann_memory<T>::build_param& param)
+{
+  param.R = conf.at("R");
+  if (conf.contains("L_build")) { param.L_build = conf.at("L_build"); }
+  if (conf.contains("alpha")) { param.num_threads = conf.at("alpha"); }
+  if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::diskann_ssd<T>::build_param& param)
+{
+  param.R = conf.at("R");
+  if (conf.contains("L_build")) { param.L_build = conf.at("L_build"); }
+  if (conf.contains("alpha")) { param.num_threads = conf.at("alpha"); }
+  if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
+  if (conf.contains("QD")) { param.QD = conf.at("QD"); }
+  param.dataset_base_file = cuvs::bench::configuration::singleton().get_dataset_conf().base_file;
+  for (const auto& index : cuvs::bench::configuration::singleton().get_indices()) {
+    // The nlohmann::json operator== comparator compares each key and value by content. Reference:
+    // https://json.nlohmann.me/api/basic_json/operator_eq/
+    if (index.build_param == conf) {
+      param.index_file = index.file;
+      break;
+    }
+  }
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::diskann_memory<T>::search_param& param)
+{
+  param.L_search    = conf.at("L_search");
+  param.num_threads = conf.at("num_threads");
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::diskann_ssd<T>::search_param& param)
+{
+  param.L_search    = conf.at("L_search");
+  param.num_threads = conf.at("num_threads");
+  if (conf.contains("num_nodes_to_cache")) {
+    param.num_nodes_to_cache = conf.at("num_nodes_to_cache");
+  }
+  if (conf.contains("beam_width")) { param.beam_width = conf.at("beam_width"); }
+}
+
+template <typename T, template <typename> class Algo>
+std::unique_ptr<cuvs::bench::algo<T>> make_algo(cuvs::bench::Metric metric,
+                                                int dim,
+                                                const nlohmann::json& conf)
+{
+  typename Algo<T>::build_param param;
+  parse_build_param<T>(conf, param);
+  return std::make_unique<Algo<T>>(metric, dim, param);
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  cuvs::bench::Metric metric = parse_metric(distance);
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, uint8_t> ||
+                std::is_same_v<T, int8_t>) {
+    if (algo_name == "diskann_memory") {
+      a = make_algo<T, cuvs::bench::diskann_memory>(metric, dim, conf);
+    } else if (algo_name == "diskann_ssd") {
+      a = make_algo<T, cuvs::bench::diskann_ssd>(metric, dim, conf);
+    }
+  }
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+std::unique_ptr<typename cuvs::bench::algo<T>::search_param> create_search_param(
+  const std::string& algo_name, const nlohmann::json& conf)
+{
+  if (algo_name == "diskann_memory") {
+    auto param = std::make_unique<typename cuvs::bench::diskann_memory<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
+  } else if (algo_name == "diskann_ssd") {
+    auto param = std::make_unique<typename cuvs::bench::diskann_ssd<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
+  }
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+};  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/diskann/diskann_wrapper.h b/cpp/bench/ann/src/diskann/diskann_wrapper.h
new file mode 100644
index 0000000000..7f57b1a321
--- /dev/null
+++ b/cpp/bench/ann/src/diskann/diskann_wrapper.h
@@ -0,0 +1,341 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../common/ann_types.hpp"
+
+#include <limits>
+
+#include <disk_utils.h>
+#include <index.h>
+#include <linux_aligned_file_reader.h>
+#include <omp.h>
+#include <pq_flash_index.h>
+#include <utils.h>
+
+#include <chrono>
+#include <memory>
+#include <vector>
+
+namespace cuvs::bench {
+
+diskann::Metric parse_metric_to_diskann(cuvs::bench::Metric metric)
+{
+  if (metric == cuvs::bench::Metric::kInnerProduct) {
+    return diskann::Metric::INNER_PRODUCT;
+  } else if (metric == cuvs::bench::Metric::kEuclidean) {
+    return diskann::Metric::L2;
+  } else {
+    throw std::runtime_error("currently only inner product and L2 supported for benchmarking");
+  }
+}
+
+// TODO (tarangj): Remaining features are tracked at https://github.com/rapidsai/cuvs/issues/656
+template <typename T>
+class diskann_memory : public algo<T> {
+ public:
+  struct build_param {
+    uint32_t R;
+    uint32_t L_build;
+    uint32_t build_pq_bytes = 0;
+    float alpha             = 1.2;
+    int num_threads         = omp_get_num_procs();
+  };
+
+  using search_param_base = typename algo<T>::search_param;
+  struct search_param : public search_param_base {
+    uint32_t L_search;
+    uint32_t num_threads = omp_get_num_procs();
+    // Mode metric_objective;
+  };
+
+  diskann_memory(Metric metric, int dim, const build_param& param);
+
+  void build(const T* dataset, size_t nrow) override;
+
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* indices,
+              float* distances) const override;
+
+  void save(const std::string& index_file) const override;
+  void load(const std::string& index_file) override;
+  diskann_memory(const diskann_memory<T>& other) = default;
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<diskann_memory<T>>(*this); }
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHostMmap;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+ private:
+  std::shared_ptr<diskann::IndexWriteParameters> diskann_index_write_params_{nullptr};
+  uint32_t max_points_;
+  uint32_t build_pq_bytes_ = 0;
+  int num_threads_;
+  uint32_t L_search_;
+  Mode bench_mode_;
+  int num_search_threads_;
+  std::string index_path_prefix_;
+  std::shared_ptr<diskann::Index<T>> mem_index_{nullptr};
+  void initialize_index_(size_t max_points);
+};
+
+template <typename T>
+diskann_memory<T>::diskann_memory(Metric metric, int dim, const build_param& param)
+  : algo<T>(metric, dim)
+{
+  assert(this->dim_ > 0);
+  num_threads_                = param.num_threads;
+  diskann_index_write_params_ = std::make_shared<diskann::IndexWriteParameters>(
+    diskann::IndexWriteParametersBuilder(param.L_build, param.R)
+      .with_filter_list_size(0)
+      .with_alpha(param.alpha)
+      .with_saturate_graph(false)
+      .with_num_threads(param.num_threads)
+      .build());
+}
+
+template <typename T>
+void diskann_memory<T>::initialize_index_(size_t max_points)
+{
+  this->mem_index_ = std::make_shared<diskann::Index<T>>(parse_metric_to_diskann(this->metric_),
+                                                         this->dim_,
+                                                         max_points,
+                                                         diskann_index_write_params_,
+                                                         nullptr,
+                                                         0,
+                                                         false,
+                                                         false,
+                                                         false,
+                                                         build_pq_bytes_ > 0,
+                                                         build_pq_bytes_,
+                                                         false,
+                                                         false);
+}
+template <typename T>
+void diskann_memory<T>::build(const T* dataset, size_t nrow)
+{
+  initialize_index_(nrow);
+  mem_index_->build(dataset, nrow, std::vector<uint32_t>());
+}
+
+template <typename T>
+void diskann_memory<T>::set_search_param(const search_param_base& param, const void* filter_bitset)
+{
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
+  auto sp             = dynamic_cast<const search_param&>(param);
+  L_search_           = sp.L_search;
+  num_search_threads_ = sp.num_threads;
+
+  // only latency mode supported. Use the num_threads search param to run search with multiple
+  // threads
+  bench_mode_ = Mode::kLatency;
+
+  // Create a pool if multiple query threads have been set and the pool hasn't been created already
+  initialize_index_(0);
+  this->mem_index_->load(index_path_prefix_.c_str(), num_search_threads_, L_search_);
+}
+
+template <typename T>
+void diskann_memory<T>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* indices, float* distances) const
+{
+#pragma omp parallel for schedule(dynamic, 1)
+  for (int i = 0; i < batch_size; i++) {
+    mem_index_->search(queries + i * this->dim_,
+                       static_cast<size_t>(k),
+                       L_search_,
+                       reinterpret_cast<uint64_t*>(indices + i * k),
+                       distances + i * k);
+  }
+}
+
+template <typename T>
+void diskann_memory<T>::save(const std::string& index_file) const
+{
+  this->mem_index_->save(index_file.c_str());
+}
+
+template <typename T>
+void diskann_memory<T>::load(const std::string& index_file)
+{
+  // only save the index path prefix here
+  index_path_prefix_ = index_file;
+}
+
+template <typename T>
+class diskann_ssd : public algo<T> {
+ public:
+  struct build_param {
+    uint32_t R;
+    uint32_t L_build;
+    uint32_t build_pq_bytes       = 0;
+    float alpha                   = 1.2;
+    int num_threads               = omp_get_num_procs();
+    uint32_t QD                   = 192;
+    std::string dataset_base_file = "";
+    std::string index_file        = "";
+  };
+  using search_param_base = typename algo<T>::search_param;
+
+  struct search_param : public search_param_base {
+    uint32_t L_search;
+    uint32_t num_threads        = omp_get_num_procs() / 2;
+    uint32_t num_nodes_to_cache = 10000;
+    int beam_width              = 2;
+    // Mode metric_objective;
+  };
+
+  diskann_ssd(Metric metric, int dim, const build_param& param);
+
+  void build(const T* dataset, size_t nrow) override;
+
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+
+  void save(const std::string& index_file) const override;
+  void load(const std::string& index_file) override;
+  diskann_ssd(const diskann_ssd<T>& other) = default;
+  std::unique_ptr<algo<T>> copy() override { return std::make_unique<diskann_ssd<T>>(*this); }
+
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHost;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+ private:
+  std::string index_build_params_str;
+  int beam_width_;
+  uint32_t num_nodes_to_cache_;
+
+  // in-memory index params
+  uint32_t build_pq_bytes_ = 0;
+  uint32_t max_points_;
+  // for safe scratch space allocs, set the default to half the number of procs for loading the
+  // index. User must ensure that the number of search threads is less than or equal to this value
+  int num_search_threads_ = omp_get_num_procs() / 2;
+  // L_search is hardcoded to the maximum visited list size in the search params. This default is
+  // for loading the index
+  uint32_t L_search_ = 384;
+  Mode bench_mode_;
+  std::string base_file_;
+  std::string index_path_prefix_;
+  std::shared_ptr<AlignedFileReader> reader = nullptr;
+  std::shared_ptr<diskann::PQFlashIndex<T, uint32_t>> p_flash_index_;
+};
+
+template <typename T>
+diskann_ssd<T>::diskann_ssd(Metric metric, int dim, const build_param& param) : algo<T>(metric, dim)
+{
+  // Currently set the indexing RAM budget and the search RAM budget to max value to avoid sharding
+  uint32_t build_dram_budget  = std::numeric_limits<uint32_t>::max();
+  uint32_t search_dram_budget = std::numeric_limits<uint32_t>::max();
+  index_build_params_str =
+    std::string(std::to_string(param.R)) + " " + std::string(std::to_string(param.L_build)) + " " +
+    std::string(std::to_string(search_dram_budget)) + " " +
+    std::string(std::to_string(build_dram_budget)) + " " +
+    std::string(std::to_string(param.num_threads)) + " " + std::string(std::to_string(false)) +
+    " " + std::string(std::to_string(false)) + " " + std::string(std::to_string(0)) + " " +
+    std::string(std::to_string(param.QD));
+  base_file_         = param.dataset_base_file;
+  index_path_prefix_ = param.index_file;
+}
+
+template <typename T>
+void diskann_ssd<T>::build(const T* dataset, size_t nrow)
+{
+  diskann::build_disk_index<float>(base_file_.c_str(),
+                                   index_path_prefix_.c_str(),
+                                   index_build_params_str.c_str(),
+                                   parse_metric_to_diskann(this->metric_),
+                                   false,
+                                   std::string(""),
+                                   false,
+                                   std::string(""),
+                                   std::string(""),
+                                   0,
+                                   0);
+}
+
+template <typename T>
+void diskann_ssd<T>::set_search_param(const search_param_base& param, const void* filter_bitset)
+{
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
+  auto sp             = dynamic_cast<const search_param&>(param);
+  L_search_           = sp.L_search;
+  num_search_threads_ = sp.num_threads;
+  num_nodes_to_cache_ = sp.num_nodes_to_cache;
+  beam_width_         = sp.beam_width;
+
+  // only latency mode supported with thread pool
+  bench_mode_ = Mode::kLatency;
+}
+
+template <typename T>
+void diskann_ssd<T>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+#pragma omp parallel for schedule(dynamic, 1)
+  for (int64_t i = 0; i < (int64_t)batch_size; i++) {
+    p_flash_index_->cached_beam_search(queries + (i * this->dim_),
+                                       static_cast<size_t>(k),
+                                       L_search_,
+                                       reinterpret_cast<uint64_t*>(neighbors + i * k),
+                                       distances + i * k,
+                                       beam_width_,
+                                       false,
+                                       nullptr);
+  }
+}
+
+template <typename T>
+void diskann_ssd<T>::save(const std::string& index_file) const
+{
+  // Nothing to do here. Index already saved in build stage, but an empty file has to be created
+  // with the index filename.
+  std::ofstream of(index_file);
+  of.close();
+}
+
+template <typename T>
+void diskann_ssd<T>::load(const std::string& index_file)
+{
+  reader.reset(new LinuxAlignedFileReader());
+  p_flash_index_ =
+    std::make_shared<diskann::PQFlashIndex<T>>(reader, parse_metric_to_diskann(this->metric_));
+  int result = p_flash_index_->load(num_search_threads_, index_file.c_str());
+  std::vector<uint32_t> node_list;
+  p_flash_index_->cache_bfs_levels(num_nodes_to_cache_, node_list);
+  p_flash_index_->load_cache_list(node_list);
+  node_list.clear();
+  node_list.shrink_to_fit();
+}
+};  // namespace cuvs::bench
diff --git a/cpp/cmake/patches/diskann.diff b/cpp/cmake/patches/diskann.diff
new file mode 100644
index 0000000000..ee0c2cb165
--- /dev/null
+++ b/cpp/cmake/patches/diskann.diff
@@ -0,0 +1,250 @@
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 3d3d2b8..a007fa4 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -23,10 +23,6 @@ set(CMAKE_STANDARD 17)
+ set(CMAKE_CXX_STANDARD 17)
+ set(CMAKE_CXX_STANDARD_REQUIRED ON)
+
+-if(NOT MSVC)
+-	set(CMAKE_CXX_COMPILER g++)
+-endif()
+-
+ set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake;${CMAKE_MODULE_PATH}")
+
+ # Install nuget packages for dependencies.
+@@ -145,62 +141,14 @@ if (MSVC)
+         "${DISKANN_MKL_LIB_PATH}/mkl_intel_thread.lib")
+ else()
+     # expected path for manual intel mkl installs
+-    set(POSSIBLE_OMP_PATHS "/opt/intel/oneapi/compiler/latest/linux/compiler/lib/intel64_lin/libiomp5.so;/usr/lib/x86_64-linux-gnu/libiomp5.so;/opt/intel/lib/intel64_lin/libiomp5.so")
+-    foreach(POSSIBLE_OMP_PATH ${POSSIBLE_OMP_PATHS})
+-        if (EXISTS ${POSSIBLE_OMP_PATH})
+-            get_filename_component(OMP_PATH ${POSSIBLE_OMP_PATH} DIRECTORY)
+-        endif()
+-    endforeach()
+-
+-    if(NOT OMP_PATH)
+-        message(FATAL_ERROR "Could not find Intel OMP in standard locations; use -DOMP_PATH to specify the install location for your environment")
+-    endif()
+-    link_directories(${OMP_PATH})
+-
+-    set(POSSIBLE_MKL_LIB_PATHS "/opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_core.so;/usr/lib/x86_64-linux-gnu/libmkl_core.so;/opt/intel/mkl/lib/intel64/libmkl_core.so")
+-    foreach(POSSIBLE_MKL_LIB_PATH ${POSSIBLE_MKL_LIB_PATHS})
+-        if (EXISTS ${POSSIBLE_MKL_LIB_PATH})
+-            get_filename_component(MKL_PATH ${POSSIBLE_MKL_LIB_PATH} DIRECTORY)
+-        endif()
+-    endforeach()
+-
+-    set(POSSIBLE_MKL_INCLUDE_PATHS "/opt/intel/oneapi/mkl/latest/include;/usr/include/mkl;/opt/intel/mkl/include/;")
+-    foreach(POSSIBLE_MKL_INCLUDE_PATH ${POSSIBLE_MKL_INCLUDE_PATHS})
+-        if (EXISTS ${POSSIBLE_MKL_INCLUDE_PATH})
+-            set(MKL_INCLUDE_PATH ${POSSIBLE_MKL_INCLUDE_PATH})
+-        endif()
+-    endforeach()
+-    if(NOT MKL_PATH)
+-        message(FATAL_ERROR "Could not find Intel MKL in standard locations; use -DMKL_PATH to specify the install location for your environment")
+-    elseif(NOT MKL_INCLUDE_PATH)
+-        message(FATAL_ERROR "Could not find Intel MKL in standard locations; use -DMKL_INCLUDE_PATH to specify the install location for headers for your environment")
+-    endif()
+-    if (EXISTS ${MKL_PATH}/libmkl_def.so.2)
+-        set(MKL_DEF_SO ${MKL_PATH}/libmkl_def.so.2)
+-    elseif(EXISTS ${MKL_PATH}/libmkl_def.so)
+-        set(MKL_DEF_SO ${MKL_PATH}/libmkl_def.so)
+-    else()
+-        message(FATAL_ERROR "Despite finding MKL, libmkl_def.so was not found in expected locations.")
+-    endif()
+-    link_directories(${MKL_PATH})
+-    include_directories(${MKL_INCLUDE_PATH})
++    find_package(MKL CONFIG REQUIRED)
++    include_directories($<TARGET_PROPERTY:MKL::MKL,INTERFACE_INCLUDE_DIRECTORIES>)
++    link_libraries($<LINK_ONLY:MKL::MKL>)
+
+     # compile flags and link libraries
+     add_compile_options(-m64 -Wl,--no-as-needed)
+     if (NOT PYBIND)
+         link_libraries(mkl_intel_ilp64 mkl_intel_thread mkl_core iomp5 pthread m dl)
+-    else()
+-        # static linking for python so as to minimize customer dependency issues
+-        link_libraries(
+-                ${MKL_PATH}/libmkl_intel_ilp64.a
+-                ${MKL_PATH}/libmkl_intel_thread.a
+-                ${MKL_PATH}/libmkl_core.a
+-                ${MKL_DEF_SO}
+-                iomp5
+-                pthread
+-                m
+-                dl
+-        )
+     endif()
+ endif()
+
+@@ -286,7 +234,7 @@ if(MSVC)
+ 	set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY_RELEASE ${PROJECT_SOURCE_DIR}/x64/Release)
+ else()
+     set(ENV{TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD} 500000000000)
+-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx2 -mfma -msse2 -ftree-vectorize -fno-builtin-malloc -fno-builtin-calloc -fno-builtin-realloc -fno-builtin-free -fopenmp -fopenmp-simd -funroll-loops -Wfatal-errors -DUSE_AVX2")
++    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mavx2 -mfma -msse2 -ftree-vectorize -fno-builtin-malloc -fno-builtin-calloc -fno-builtin-realloc -fno-builtin-free -fopenmp -fopenmp-simd -funroll-loops -Wfatal-errors -DUSE_AVX2 -fno-finite-math-only -laio")
+     set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -g -DDEBUG")
+     if (NOT PYBIND)
+         set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -DNDEBUG -Ofast")
+@@ -300,10 +248,6 @@ else()
+ endif()
+
+ add_subdirectory(src)
+-if (NOT PYBIND)
+-    add_subdirectory(apps)
+-    add_subdirectory(apps/utils)
+-endif()
+
+ if (UNIT_TEST)
+     enable_testing()
+diff --git a/include/ann_exception.h b/include/ann_exception.h
+index 6b81373..a385198 100644
+--- a/include/ann_exception.h
++++ b/include/ann_exception.h
+@@ -2,6 +2,7 @@
+ // Licensed under the MIT license.
+
+ #pragma once
++#include <cstdint>
+ #include <string>
+ #include <stdexcept>
+ #include <system_error>
+diff --git a/include/distance.h b/include/distance.h
+index f3b1de2..d4da72e 100644
+--- a/include/distance.h
++++ b/include/distance.h
+@@ -77,6 +77,7 @@ class DistanceCosineInt8 : public Distance<int8_t>
+     DistanceCosineInt8() : Distance<int8_t>(diskann::Metric::COSINE)
+     {
+     }
++    using Distance<int8_t>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const int8_t *a, const int8_t *b, uint32_t length) const;
+ };
+
+@@ -86,6 +87,7 @@ class DistanceL2Int8 : public Distance<int8_t>
+     DistanceL2Int8() : Distance<int8_t>(diskann::Metric::L2)
+     {
+     }
++    using Distance<int8_t>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const int8_t *a, const int8_t *b, uint32_t size) const;
+ };
+
+@@ -96,6 +98,7 @@ class AVXDistanceL2Int8 : public Distance<int8_t>
+     AVXDistanceL2Int8() : Distance<int8_t>(diskann::Metric::L2)
+     {
+     }
++    using Distance<int8_t>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const int8_t *a, const int8_t *b, uint32_t length) const;
+ };
+
+@@ -105,6 +108,7 @@ class DistanceCosineFloat : public Distance<float>
+     DistanceCosineFloat() : Distance<float>(diskann::Metric::COSINE)
+     {
+     }
++    using Distance<float>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const;
+ };
+
+@@ -114,7 +118,7 @@ class DistanceL2Float : public Distance<float>
+     DistanceL2Float() : Distance<float>(diskann::Metric::L2)
+     {
+     }
+-
++    using Distance<float>::compare;
+ #ifdef _WINDOWS
+     DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t size) const;
+ #else
+@@ -128,6 +132,7 @@ class AVXDistanceL2Float : public Distance<float>
+     AVXDistanceL2Float() : Distance<float>(diskann::Metric::L2)
+     {
+     }
++    using Distance<float>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const;
+ };
+
+@@ -146,6 +151,7 @@ class SlowDistanceCosineUInt8 : public Distance<uint8_t>
+     SlowDistanceCosineUInt8() : Distance<uint8_t>(diskann::Metric::COSINE)
+     {
+     }
++    using Distance<uint8_t>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const uint8_t *a, const uint8_t *b, uint32_t length) const;
+ };
+
+@@ -155,6 +161,7 @@ class DistanceL2UInt8 : public Distance<uint8_t>
+     DistanceL2UInt8() : Distance<uint8_t>(diskann::Metric::L2)
+     {
+     }
++    using Distance<uint8_t>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const uint8_t *a, const uint8_t *b, uint32_t size) const;
+ };
+
+@@ -198,6 +205,7 @@ class AVXDistanceInnerProductFloat : public Distance<float>
+     AVXDistanceInnerProductFloat() : Distance<float>(diskann::Metric::INNER_PRODUCT)
+     {
+     }
++    using Distance<float>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const;
+ };
+
+@@ -213,6 +221,7 @@ class AVXNormalizedCosineDistanceFloat : public Distance<float>
+     AVXNormalizedCosineDistanceFloat() : Distance<float>(diskann::Metric::COSINE)
+     {
+     }
++    using Distance<float>::compare;
+     DISKANN_DLLEXPORT virtual float compare(const float *a, const float *b, uint32_t length) const
+     {
+         // Inner product returns negative values to indicate distance.
+diff --git a/include/utils.h b/include/utils.h
+index d3af5c3..417af31 100644
+--- a/include/utils.h
++++ b/include/utils.h
+@@ -29,6 +29,7 @@ typedef int FileHandle;
+ #include "types.h"
+ #include "tag_uint128.h"
+ #include <any>
++#include <xmmintrin.h>
+
+ #ifdef EXEC_ENV_OLS
+ #include "content_buf.h"
+diff --git a/src/index.cpp b/src/index.cpp
+index bf93344..9d8336c 100644
+--- a/src/index.cpp
++++ b/src/index.cpp
+@@ -17,9 +17,7 @@
+ #include "gperftools/malloc_extension.h"
+ #endif
+
+-#ifdef _WINDOWS
+ #include <xmmintrin.h>
+-#endif
+
+ #include "index.h"
+
+diff --git a/src/partition.cpp b/src/partition.cpp
+index 570d45c..fb54cbf 100644
+--- a/src/partition.cpp
++++ b/src/partition.cpp
+@@ -21,9 +21,7 @@
+ #include "parameters.h"
+ #include "memory_mapper.h"
+ #include "partition.h"
+-#ifdef _WINDOWS
+ #include <xmmintrin.h>
+-#endif
+
+ // block size for reading/ processing large files and matrices in blocks
+ #define BLOCK_SIZE 5000000
+diff --git a/src/pq_flash_index.cpp b/src/pq_flash_index.cpp
+index d9ad506..145a978 100644
+--- a/src/pq_flash_index.cpp
++++ b/src/pq_flash_index.cpp
+@@ -8,6 +8,7 @@
+ #include "pq_scratch.h"
+ #include "pq_flash_index.h"
+ #include "cosine_similarity.h"
++#include <xmmintrin.h>
+
+ #ifdef _WINDOWS
+ #include "windows_aligned_file_reader.h"
diff --git a/cpp/cmake/patches/diskann_override.json b/cpp/cmake/patches/diskann_override.json
new file mode 100644
index 0000000000..c83898548f
--- /dev/null
+++ b/cpp/cmake/patches/diskann_override.json
@@ -0,0 +1,16 @@
+{
+  "packages" : {
+    "diskann" : {
+      "version": "0.7.0",
+      "git_url": "https://github.com/microsoft/DiskANN.git",
+      "git_tag": "main",
+      "patches" : [
+        {
+          "file" : "${current_json_dir}/diskann.diff",
+          "issue" : "Correct compilation issues",
+          "fixed_in" : ""
+        }
+      ]
+    }
+  }
+}
diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
index c211f4455f..37cd51216b 100644
--- a/cpp/cmake/patches/faiss_override.json
+++ b/cpp/cmake/patches/faiss_override.json
@@ -3,7 +3,7 @@
       "faiss" : {
         "version": "1.10.0",
         "git_url": "https://github.com/facebookresearch/faiss.git",
-        "git_tag": "v1.10.0"
+        "git_tag": "main"
       }
     }
   }
diff --git a/cpp/cmake/thirdparty/get_diskann.cmake b/cpp/cmake/thirdparty/get_diskann.cmake
new file mode 100644
index 0000000000..685ee2ff68
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_diskann.cmake
@@ -0,0 +1,49 @@
+#=============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#=============================================================================
+
+function(find_and_configure_diskann)
+  set(oneValueArgs VERSION REPOSITORY PINNED_TAG)
+  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+                        "${multiValueArgs}" ${ARGN} )
+
+    include(${rapids-cmake-dir}/cpm/package_override.cmake)
+  set(patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/../patches")
+  rapids_cpm_package_override("${patch_dir}/diskann_override.json")
+
+  include("${rapids-cmake-dir}/cpm/detail/package_details.cmake")
+  rapids_cpm_package_details(diskann version repository tag shallow exclude)
+
+  include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
+  rapids_cpm_generate_patch_command(diskann ${version} patch_command)
+
+  rapids_cpm_find(diskann ${version}
+          GLOBAL_TARGETS diskann
+          CPM_ARGS
+          OPTIONS
+          "PYBIND OFF"
+          "UNIT_TEST OFF"
+          "RESTAPI OFF"
+          "PORTABLE OFF")
+
+  include("${rapids-cmake-dir}/cpm/detail/display_patch_status.cmake")
+  rapids_cpm_display_patch_status(diskann)
+
+  if(NOT TARGET diskann::diskann)
+      target_include_directories(diskann INTERFACE "$<BUILD_INTERFACE:${diskann_SOURCE_DIR}/include>")
+      add_library(diskann::diskann ALIAS diskann)
+  endif()
+endfunction()
+find_and_configure_diskann()
diff --git a/cpp/cmake/thirdparty/get_faiss.cmake b/cpp/cmake/thirdparty/get_faiss.cmake
index bc5b0ad1bc..89f4548612 100644
--- a/cpp/cmake/thirdparty/get_faiss.cmake
+++ b/cpp/cmake/thirdparty/get_faiss.cmake
@@ -47,7 +47,7 @@ function(find_and_configure_faiss)
   endif()
 
   rapids_cpm_find(faiss ${version}
-    GLOBAL_TARGETS faiss faiss_avx2 faiss_gpu faiss::faiss faiss::faiss_avx2
+    GLOBAL_TARGETS faiss faiss_avx2 faiss_gpu_objs faiss::faiss faiss::faiss_avx2
     CPM_ARGS
     GIT_REPOSITORY ${repository}
     GIT_TAG ${tag}
@@ -79,11 +79,11 @@ function(find_and_configure_faiss)
     # faiss will have the conda includes/link dirs
     target_link_libraries(faiss_avx2 PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>)
   endif()
-  if(TARGET faiss_gpu AND NOT TARGET faiss::faiss_gpu)
-    add_library(faiss::faiss_gpu ALIAS faiss_gpu)
+  if(TARGET faiss_gpu_objs AND NOT TARGET faiss::faiss_gpu_objs)
+    add_library(faiss::faiss_gpu_objs ALIAS faiss_gpu_objs)
     # We need to ensure that faiss has all the conda information. So we use this approach so that
     # faiss will have the conda includes/link dirs
-    target_link_libraries(faiss_gpu PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>)
+    target_link_libraries(faiss_gpu_objs PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>)
   endif()
 
   if(faiss_ADDED)
@@ -93,7 +93,7 @@ function(find_and_configure_faiss)
                   NAMESPACE faiss::)
   endif()
 
-  # Need to tell CMake to rescan the link group of faiss::faiss_gpu and faiss
+  # Need to tell CMake to rescan the link group of faiss::faiss_gpu_objs and faiss
   # so that we get proper link order when they are static
   #
   # We don't look at the existence of `faiss_avx2` as it will always exist
@@ -102,9 +102,9 @@ function(find_and_configure_faiss)
   # a dependency to it. Adding a dependency will cause it to compile,
   # and fail due to invalid compiler flags.
   if(PKG_ENABLE_GPU AND PKG_BUILD_STATIC_LIBS AND CXX_AVX2_FOUND)
-    set(CUVS_FAISS_TARGETS "$<LINK_GROUP:RESCAN,$<LINK_LIBRARY:WHOLE_ARCHIVE,faiss_gpu>,faiss::faiss_avx2>" PARENT_SCOPE)
+    set(CUVS_FAISS_TARGETS "$<LINK_GROUP:RESCAN,$<LINK_LIBRARY:WHOLE_ARCHIVE,faiss_gpu_objs>,faiss::faiss_avx2>" PARENT_SCOPE)
   elseif(PKG_ENABLE_GPU AND  PKG_BUILD_STATIC_LIBS)
-    set(CUVS_FAISS_TARGETS "$<LINK_GROUP:RESCAN,$<LINK_LIBRARY:WHOLE_ARCHIVE,faiss_gpu>,faiss::faiss>" PARENT_SCOPE)
+    set(CUVS_FAISS_TARGETS "$<LINK_GROUP:RESCAN,$<LINK_LIBRARY:WHOLE_ARCHIVE,faiss_gpu_objs>,faiss::faiss>" PARENT_SCOPE)
   elseif(CXX_AVX2_FOUND)
     set(CUVS_FAISS_TARGETS faiss::faiss_avx2 PARENT_SCOPE)
   else()
diff --git a/cpp/include/cuvs/neighbors/vamana.hpp b/cpp/include/cuvs/neighbors/vamana.hpp
index bc205a6f4f..007337df21 100644
--- a/cpp/include/cuvs/neighbors/vamana.hpp
+++ b/cpp/include/cuvs/neighbors/vamana.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -456,12 +456,14 @@ auto build(raft::resources const& res,
  * @param[in] handle the raft handle
  * @param[in] file_prefix prefix of path and name of index files
  * @param[in] index Vamana index
+ * @param[in] include_dataset whether or not to serialize the dataset
  *
  */
 
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
-               const cuvs::neighbors::vamana::index<float, uint32_t>& index);
+               const cuvs::neighbors::vamana::index<float, uint32_t>& index,
+               bool include_dataset = true);
 
 /**
  * Save the index to file.
@@ -483,11 +485,13 @@ void serialize(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] file_prefix prefix of path and name of index files
  * @param[in] index Vamana index
+ * @param[in] include_dataset whether or not to serialize the dataset
  *
  */
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
-               const cuvs::neighbors::vamana::index<int8_t, uint32_t>& index);
+               const cuvs::neighbors::vamana::index<int8_t, uint32_t>& index,
+               bool include_dataset = true);
 
 /**
  * Save the index to file.
@@ -509,11 +513,13 @@ void serialize(raft::resources const& handle,
  * @param[in] handle the raft handle
  * @param[in] file_prefix prefix of path and name of index files
  * @param[in] index Vamana index
+ * @param[in] include_dataset whether or not to serialize the dataset
  *
  */
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
-               const cuvs::neighbors::vamana::index<uint8_t, uint32_t>& index);
+               const cuvs::neighbors::vamana::index<uint8_t, uint32_t>& index,
+               bool include_dataset = true);
 
 /**
  * @}
diff --git a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
index 27a17205e6..34766b13ca 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -44,13 +44,15 @@ namespace cuvs::neighbors::vamana::detail {
  * @param[in] res the raft resource handle
  * @param[in] file_name the path and name of the DiskAN index file generated
  * @param[in] index_ VAMANA index
+ * @param[in] include_dataset whether to include the dataset in the serialized output
  *
  */
 
 template <typename T, typename IdxT>
 void serialize(raft::resources const& res,
                const std::string& file_name,
-               const index<T, IdxT>& index_)
+               const index<T, IdxT>& index_,
+               bool include_dataset)
 {
   // Write graph to first index file (format from MSFT DiskANN OSS)
   std::ofstream index_of(file_name, std::ios::out | std::ios::binary);
@@ -75,6 +77,7 @@ void serialize(raft::resources const& res,
              d_graph.data_handle(),
              d_graph.size(),
              raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
 
   size_t total_edges = 0;
   size_t num_sparse  = 0;
@@ -115,6 +118,44 @@ void serialize(raft::resources const& res,
 
   index_of.close();
   if (!index_of) { RAFT_FAIL("Error writing output %s", file_name.c_str()); }
+
+  if (include_dataset) {
+    // try allocating a buffer for the dataset on host
+    try {
+      const cuvs::neighbors::strided_dataset<T, int64_t>* strided_dataset =
+        dynamic_cast<cuvs::neighbors::strided_dataset<T, int64_t>*>(
+          const_cast<cuvs::neighbors::dataset<int64_t>*>(&index_.data()));
+      if (strided_dataset == nullptr) {
+        RAFT_LOG_DEBUG("dynamic_cast to strided_dataset failed");
+      } else {
+        auto h_dataset =
+          raft::make_host_matrix<T, int64_t>(strided_dataset->n_rows(), strided_dataset->dim());
+        raft::copy(h_dataset.data_handle(),
+                   strided_dataset->view().data_handle(),
+                   strided_dataset->n_rows() * strided_dataset->dim(),
+                   raft::resource::get_cuda_stream(res));
+        std::string dataset_base_file = file_name + ".data";
+        std::ofstream dataset_of(dataset_base_file, std::ios::out | std::ios::binary);
+        if (!dataset_of) { RAFT_FAIL("Cannot open file %s", dataset_base_file.c_str()); }
+        size_t dataset_file_offset = 0;
+        int size                   = static_cast<int>(index_.size());
+        int dim                    = static_cast<int>(index_.dim());
+        dataset_of.seekp(dataset_file_offset, dataset_of.beg);
+        dataset_of.write((char*)&size, sizeof(int));
+        dataset_of.write((char*)&dim, sizeof(int));
+        for (int i = 0; i < size; i++) {
+          dataset_of.write((char*)(h_dataset.data_handle() + i * h_dataset.extent(1)),
+                           dim * sizeof(T));
+        }
+        dataset_of.close();
+        if (!dataset_of) { RAFT_FAIL("Error writing output %s", dataset_base_file.c_str()); }
+      }
+    } catch (std::bad_alloc& e) {
+      RAFT_LOG_INFO("Failed to serialize dataset");
+    } catch (raft::logic_error& e) {
+      RAFT_LOG_INFO("Failed to serialize dataset");
+    }
+  }
 }
 
 }  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/vamana.cuh b/cpp/src/neighbors/vamana.cuh
index 964d7a9a09..5b66d0525c 100644
--- a/cpp/src/neighbors/vamana.cuh
+++ b/cpp/src/neighbors/vamana.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -93,7 +93,7 @@ void serialize(raft::resources const& res,
                const std::string& file_prefix,
                const index<T, IdxT>& index_)
 {
-  cuvs::neighbors::vamana::detail::build<T, IdxT>(res, file_prefix, index_);
+  cuvs::neighbors::vamana::detail::serialize<T, IdxT>(res, file_prefix, index_);
 }
 
 /** @} */  // end group vamana
diff --git a/cpp/src/neighbors/vamana_serialize.cuh b/cpp/src/neighbors/vamana_serialize.cuh
index b8cb580a82..44f69efc99 100644
--- a/cpp/src/neighbors/vamana_serialize.cuh
+++ b/cpp/src/neighbors/vamana_serialize.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,12 +25,14 @@ namespace cuvs::neighbors::vamana {
  * @{
  */
 
-#define CUVS_INST_VAMANA_SERIALIZE(DTYPE)                                                     \
-  void serialize(raft::resources const& handle,                                               \
-                 const std::string& file_prefix,                                              \
-                 const cuvs::neighbors::vamana::index<DTYPE, uint32_t>& index_)               \
-  {                                                                                           \
-    cuvs::neighbors::vamana::detail::serialize<DTYPE, uint32_t>(handle, file_prefix, index_); \
+#define CUVS_INST_VAMANA_SERIALIZE(DTYPE)                                       \
+  void serialize(raft::resources const& handle,                                 \
+                 const std::string& file_prefix,                                \
+                 const cuvs::neighbors::vamana::index<DTYPE, uint32_t>& index_, \
+                 bool include_dataset)                                          \
+  {                                                                             \
+    cuvs::neighbors::vamana::detail::serialize<DTYPE, uint32_t>(                \
+      handle, file_prefix, index_, include_dataset);                            \
   };
 
 /** @} */  // end group vamana
diff --git a/dependencies.yaml b/dependencies.yaml
index 97e6099f5a..b6c3f77ee2 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -559,6 +559,18 @@ dependencies:
           - h5py>=3.8.0
           - benchmark>=1.8.2
           - openblas
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              arch: x86_64
+            packages:
+              - mkl-devel=2023
+              - libaio
+              - libboost-devel=1.87
+          - matrix:
+              arch: aarch64
+            packages: null
   bench_python:
     common:
       - output_types: [conda, pyproject, requirements]
diff --git a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
index 609a8378e6..16d449117a 100644
--- a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
@@ -52,3 +52,12 @@ hnswlib:
 cuvs_cagra_hnswlib:
   executable: CUVS_CAGRA_HNSWLIB_ANN_BENCH
   requires_gpu: true
+diskann_memory:
+  executable: DISKANN_MEMORY_ANN_BENCH
+  requires_gpu: false
+diskann_ssd:
+  executable: DISKANN_SSD_ANN_BENCH
+  requires_gpu: false
+cuvs_vamana:
+  executable: CUVS_VAMANA_ANN_BENCH
+  requires_gpu: true
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
index 71a105bc85..0dfd3ddecc 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
+++ b/python/cuvs_bench/cuvs_bench/config/algos/constraints/__init__.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -97,3 +97,24 @@ def faiss_gpu_ivf_pq_search(params, build_params, k, batch_size):
 def hnswlib_search(params, build_params, k, batch_size):
     if "ef" in params:
         return params["ef"] >= k
+
+
+###############################################################################
+#                              DiskANN constraints                            #
+###############################################################################
+
+
+def diskann_memory_build(params, dim):
+    ret = True
+    if "R" in params and "L_build" in params:
+        ret = params["R"] <= params["L_build"]
+    return ret
+
+
+def diskann_ssd_build(params, dim):
+    ret = True
+    if "R" in params and "L_build" in params:
+        ret = params["R"] <= params["L_build"]
+    if "QD" in params:
+        ret = params["QD"] <= dim
+    return ret
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml
new file mode 100644
index 0000000000..5252f818c5
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml
@@ -0,0 +1,10 @@
+name: cuvs_vamana
+groups:
+  base:
+    build:
+      graph_degree: [32, 64]
+      visited_size: [128, 256]
+      alpha: [1.2]
+    search:
+      L_search: [10, 20, 30, 40, 50, 100, 200, 300]
+      num_threads: [32]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml b/python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml
new file mode 100644
index 0000000000..faf1224656
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml
@@ -0,0 +1,13 @@
+name: diskann_memory
+constraints:
+  build: cuvs_bench.config.algos.constraints.diskann_memory_build
+groups:
+  base:
+    build:
+      R: [64, 96]
+      L_build: [128, 256, 384]
+      alpha: [1.2]
+      num_threads: [32]
+    search:
+      num_threads: [32]
+      L_search: [10, 20, 30, 40, 50, 100, 200, 300]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml b/python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml
new file mode 100644
index 0000000000..40afc6fce8
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml
@@ -0,0 +1,13 @@
+name: diskann_ssd
+constraints:
+  build: cuvs_bench.config.algos.constraints.diskann_ssd_build
+groups:
+  base:
+    build:
+      R: [64, 96]
+      L_build: [128, 256, 384]
+      QD: [192]
+      num_threads: [32]
+    search:
+      L_search: [10, 20, 30, 40, 50, 100, 200, 300]
+      num_threads: [32]

From 22cf61ba8ce282e66b299715f1adbf373beb8858 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 28 Feb 2025 15:34:17 +0100
Subject: [PATCH 332/623] ANN_BENCH: Fix segfault in CAGRA wrapper when moving
 its graph (#733)

When the graph in CAGRA wrapper needs to be relocated to a different memory resource, the old one is destructed before the data is copied. This leads to a segfault during copy. The fix is to retain the old graph pointer until the data is copied.

This error is likely to happen when one runs CAGRA search benchmark sweeping over multiple values of `graph_memory_type`.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/733
---
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index f5f6097108..e611ea36bf 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -250,13 +250,17 @@ void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param,
     RAFT_LOG_DEBUG("moving graph to new memory space: %s", allocator_to_string(graph_mem_).c_str());
     // We create a new graph and copy to it from existing graph
     auto mr = get_mr(graph_mem_);
-    *graph_ = raft::make_device_mdarray<IdxT, int64_t>(
-      handle_, mr, raft::make_extents<int64_t>(index_->graph().extent(0), index_->graph_degree()));
 
-    raft::copy(graph_->data_handle(),
-               index_->graph().data_handle(),
-               index_->graph().size(),
+    // Create a new graph, then copy, and __only then__ replace the shared pointer.
+    auto old_graph =
+      index_->graph();  // view of graph_ if it exists, of an internal index member otherwise
+    auto new_graph = raft::make_device_mdarray<IdxT, int64_t>(handle_, mr, old_graph.extents());
+    raft::copy(new_graph.data_handle(),
+               old_graph.data_handle(),
+               old_graph.size(),
                raft::resource::get_cuda_stream(handle_));
+    raft::resource::sync_stream(handle_);
+    *graph_ = std::move(new_graph);
 
     // NB: update_graph() only stores a view in the index. We need to keep the graph object alive.
     index_->update_graph(handle_, make_const_mdspan(graph_->view()));

From fac323dae6ae798356675464f5c0195321afdc8e Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Fri, 28 Feb 2025 09:36:56 -0800
Subject: [PATCH 333/623] [Opt]  add required content to cuvs_java `pom.xml`
 (#731)

Fixes https://github.com/rapidsai/cuvs/issues/722

---------

Co-authored-by: Corey J. Nolet <cjnolet@gmail.com>
---
 java/cuvs-java/pom.xml | 106 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)

diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index c036c6c66e..f5b37097de 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -24,6 +24,37 @@
     <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
     <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.02.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     <name>cuvs-java</name>
+    <description>
+        This project provides Java bindings for cuVS, enabling approximate nearest neighbors search and clustering
+        on the GPU. As this is experimental, some APIs may change.
+    </description>
+    <url>https://rapids.ai</url>
+
+    <licenses>
+        <license>
+            <name>Apache License, Version 2.0</name>
+            <url>https://www.apache.org/licenses/LICENSE-2.0.txt</url>
+            <distribution>repo</distribution>
+            <comments>A business-friendly OSS license</comments>
+        </license>
+    </licenses>
+    <scm>
+        <connection>scm:git:https://github.com/rapidsai/cuvs.git</connection>
+        <developerConnection>scm:git:git@github.com:rapidsai/cuvs.git</developerConnection>
+        <tag>HEAD</tag>
+        <url>https://github.com/rapidsai/cuvs</url>
+    </scm>
+    <developers>
+        <developer>
+            <id>rapidsai</id>
+            <name>rapidsai</name>
+            <email>cuvs@rapids.ai</email>
+            <roles>
+                <role>Maintainer</role>
+                <role>Committer</role>
+            </roles>
+        </developer>
+    </developers>
     <packaging>jar</packaging>
 
     <properties>
@@ -31,8 +62,17 @@
         <maven.compiler.source>22</maven.compiler.source>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
+        <gpg.passphrase>${GPG_PASSPHRASE}</gpg.passphrase>
     </properties>
 
+    <distributionManagement>
+        <snapshotRepository>
+            <id>ossrh</id>
+            <url>https://oss.sonatype.org/content/repositories/snapshots</url>
+        </snapshotRepository>
+    </distributionManagement>
+
+
     <dependencies>
 
         <dependency>
@@ -193,6 +233,72 @@
                     <reportOutputDirectory>${project.build.directory}</reportOutputDirectory>
                 </configuration>
             </plugin>
+        <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-source-plugin</artifactId>
+            <version>2.2.1</version>
+            <executions>
+                <execution>
+                    <id>attach-sources</id>
+                    <goals>
+                        <goal>jar-no-fork</goal>
+                    </goals>
+                </execution>
+                <execution>
+                    <id>test-jars</id>
+                    <goals>
+                        <goal>test-jar</goal>
+                    </goals>
+                </execution>
+            </executions>
+            <configuration>
+                <excludeResources>true</excludeResources>
+            </configuration>
+        </plugin>
+        <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-javadoc-plugin</artifactId>
+            <version>2.9.1</version>
+            <executions>
+                <execution>
+                    <id>attach-javadocs</id>
+                    <goals>
+                        <goal>jar</goal>
+                    </goals>
+                    <configuration>
+                        <additionalparam>-Xdoclint:none</additionalparam>
+                    </configuration>
+                </execution>
+            </executions>
+        </plugin>
+        <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-gpg-plugin</artifactId>
+            <version>1.5</version>
+            <executions>
+                <execution>
+                    <id>sign-artifacts</id>
+                    <phase>verify</phase>
+                    <goals>
+                        <goal>sign</goal>
+                    </goals>
+                </execution>
+            </executions>
+        </plugin>
+        <plugin>
+            <groupId>org.sonatype.plugins</groupId>
+            <artifactId>nexus-staging-maven-plugin</artifactId>
+            <version>1.6.7</version>
+            <extensions>true</extensions>
+            <configuration>
+                <serverId>ossrh</serverId>
+                <nexusUrl>https://oss.sonatype.org/</nexusUrl>
+                <autoReleaseAfterClose>false</autoReleaseAfterClose>
+            </configuration>
+        </plugin>
         </plugins>
     </build>
+
+
+
 </project>

From af81a61eb5d06d4b1fd45b844435e1b59874ce87 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Mon, 3 Mar 2025 18:58:01 -0500
Subject: [PATCH 334/623] Consolidate Conda environment for Rust (#745)

Issue: https://github.com/rapidsai/build-planning/issues/22

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - https://github.com/jakirkham
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/745
---
 ci/build_rust.sh  | 18 ++++++------------
 dependencies.yaml |  2 ++
 2 files changed, 8 insertions(+), 12 deletions(-)

diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index 0d0832031d..b36262bda7 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -3,15 +3,18 @@
 
 set -euo pipefail
 
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
 
-RAPIDS_VERSION="$(rapids-version)"
-
 rapids-dependency-file-generator \
   --output conda \
   --file-key rust \
-  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" \
+  --prepend-channel "${CPP_CHANNEL}" \
+  | tee env.yaml
 
 rapids-mamba-retry env create --yes -f env.yaml -n rust
 
@@ -29,13 +32,4 @@ LIBCLANG_PATH=$(dirname "$(find /opt/conda -name libclang.so | head -n 1)")
 export LIBCLANG_PATH
 echo "LIBCLANG_PATH=$LIBCLANG_PATH"
 
-rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-
-# installing libcuvs/libraft will speed up the rust build substantially
-rapids-mamba-retry install \
-  --channel "${CPP_CHANNEL}" \
-  "libcuvs=${RAPIDS_VERSION}" \
-  "libraft=${RAPIDS_VERSION}"
-
 bash ./build.sh rust
diff --git a/dependencies.yaml b/dependencies.yaml
index b6c3f77ee2..99d2f6021b 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -89,6 +89,8 @@ files:
       - cuda_version
       - rapids_build
       - rust
+      - depends_on_libcuvs
+      - depends_on_libraft
   go:
     output: none
     includes:

From 89b03493b487910d2125fde6680590adde8e2a95 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 4 Mar 2025 09:58:26 -0600
Subject: [PATCH 335/623] rename 'sklearn' conda dependency to 'scikit-learn'
 (#743)

#695 introduced a conda dependency on `"sklearn"`. There is no `sklearn`... the package is called `scikit-learn`.

This fixes that.

## Notes for Reviewers

### How this fixes CI

On #738, @rhdong was facing the following problem. The `conda-cpp-build` jobs were producing packages with version `25.04.00a94`:

```text
BUILD START: ['libcuvs-25.04.00a94-cuda11_250303_g2ffe160_94.conda',
'libcuvs-static-25.04.00a94-cuda11_250303_g2ffe160_94.conda',
'libcuvs-examples-25.04.00a94-cuda11_250303_g2ffe160_94.conda',
'libcuvs-tests-25.04.00a94-cuda11_250303_g2ffe160_94.conda']
```

([logs link](https://github.com/rapidsai/cuvs/actions/runs/13636442011/job/38116419908?pr=738#step:9:489))

But at test time, they were getting `25.05.00a84`:

```text
...
libcuvs                   25.04.00a84     cuda11_250224_ga1e0cc0_84    rapidsai-nightly
```

([logs link](https://github.com/rapidsai/cuvs/actions/runs/13636442011/job/38125502858?pr=738#step:9:4583))

It looks like this `sklearn` dependency was the root cause, as explained in https://github.com/rapidsai/cuvs/pull/743#issuecomment-2695771538

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/743
---
 conda/recipes/cuvs-bench-cpu/meta.yaml | 2 +-
 conda/recipes/cuvs-bench/meta.yaml     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
index 9dea3f6c4c..116da1b98f 100644
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -66,7 +66,7 @@ requirements:
     - pyyaml
     - python
     - requests
-    - sklearn>=1.5
+    - scikit-learn>=1.5
 about:
   home: https://rapids.ai/
   license: Apache-2.0
diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
index a83297098c..5a6ff5abae 100644
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ b/conda/recipes/cuvs-bench/meta.yaml
@@ -105,7 +105,7 @@ requirements:
     - python
     - requests
     - rmm ={{ minor_version }}
-    - sklearn>=1.5
+    - scikit-learn>=1.5
 about:
   home: https://rapids.ai/
   license: Apache-2.0

From e8d5b9b62f4e6213d01fd071ab09e932c04c83da Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Thu, 6 Mar 2025 12:22:32 -0800
Subject: [PATCH 336/623] [Feat] Expose search with bitset C API for Brute
 Force (#717)

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/717
---
 cpp/src/neighbors/brute_force_c.cpp           |  18 ++-
 cpp/tests/neighbors/brute_force_c.cu          | 105 ++++++++++++++----
 cpp/tests/neighbors/run_brute_force_c.c       |  14 ++-
 .../neighbors/brute_force/brute_force.pyx     |  28 +++--
 python/cuvs/cuvs/tests/test_brute_force.py    |  44 ++++++--
 5 files changed, 158 insertions(+), 51 deletions(-)

diff --git a/cpp/src/neighbors/brute_force_c.cpp b/cpp/src/neighbors/brute_force_c.cpp
index 01779ac6c1..85a46c5b86 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/cpp/src/neighbors/brute_force_c.cpp
@@ -68,7 +68,6 @@ void _search(cuvsResources_t res,
   using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
   using distances_mdspan_type = raft::device_matrix_view<DistT, int64_t, raft::row_major>;
   using prefilter_mds_type    = raft::device_vector_view<uint32_t, int64_t>;
-  using prefilter_bmp_type    = cuvs::core::bitmap_view<uint32_t, int64_t>;
 
   auto queries_mds   = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
   auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
@@ -85,16 +84,25 @@ void _search(cuvsResources_t res,
                                          distances_mds,
                                          cuvs::neighbors::filtering::none_sample_filter{});
   } else if (prefilter.type == BITMAP) {
-    auto prefilter_ptr   = reinterpret_cast<DLManagedTensor*>(prefilter.addr);
-    auto prefilter_mds   = cuvs::core::from_dlpack<prefilter_mds_type>(prefilter_ptr);
-    const auto prefilter = cuvs::neighbors::filtering::bitmap_filter(
+    using prefilter_bmp_type = cuvs::core::bitmap_view<uint32_t, int64_t>;
+    auto prefilter_ptr       = reinterpret_cast<DLManagedTensor*>(prefilter.addr);
+    auto prefilter_mds       = cuvs::core::from_dlpack<prefilter_mds_type>(prefilter_ptr);
+    const auto prefilter     = cuvs::neighbors::filtering::bitmap_filter(
       prefilter_bmp_type((uint32_t*)prefilter_mds.data_handle(),
                          queries_mds.extent(0),
                          index_ptr->dataset().extent(0)));
     cuvs::neighbors::brute_force::search(
       *res_ptr, params, *index_ptr, queries_mds, neighbors_mds, distances_mds, prefilter);
+  } else if (prefilter.type == BITSET) {
+    using prefilter_bst_type = cuvs::core::bitset_view<uint32_t, int64_t>;
+    auto prefilter_ptr       = reinterpret_cast<DLManagedTensor*>(prefilter.addr);
+    auto prefilter_mds       = cuvs::core::from_dlpack<prefilter_mds_type>(prefilter_ptr);
+    const auto prefilter     = cuvs::neighbors::filtering::bitset_filter(
+      prefilter_bst_type((uint32_t*)prefilter_mds.data_handle(), index_ptr->dataset().extent(0)));
+    cuvs::neighbors::brute_force::search(
+      *res_ptr, params, *index_ptr, queries_mds, neighbors_mds, distances_mds, prefilter);
   } else {
-    RAFT_FAIL("Unsupported prefilter type: BITSET");
+    RAFT_FAIL("Unsupported prefilter type");
   }
 }
 
diff --git a/cpp/tests/neighbors/brute_force_c.cu b/cpp/tests/neighbors/brute_force_c.cu
index 8abc340715..d41efb24a2 100644
--- a/cpp/tests/neighbors/brute_force_c.cu
+++ b/cpp/tests/neighbors/brute_force_c.cu
@@ -39,6 +39,7 @@ extern "C" void run_brute_force(int64_t n_rows,
                                 float* index_data,
                                 float* query_data,
                                 uint32_t* prefilter_data,
+                                enum cuvsFilterType filter_type,
                                 float* distances_data,
                                 int64_t* neighbors_data,
                                 cuvsDistanceType metric);
@@ -80,6 +81,35 @@ index_t create_sparse_matrix(index_t m, index_t n, float sparsity, std::vector<b
   return nnz;
 }
 
+template <typename bitset_t = uint32_t>
+void repeat_cpu_bitset(std::vector<bitset_t>& input,
+                       size_t input_bits,
+                       size_t repeat,
+                       std::vector<bitset_t>& output)
+{
+  const size_t output_bits  = input_bits * repeat;
+  const size_t output_units = (output_bits + sizeof(bitset_t) * 8 - 1) / (sizeof(bitset_t) * 8);
+
+  std::memset(output.data(), 0, output_units * sizeof(bitset_t));
+
+  size_t output_bit_index = 0;
+
+  for (size_t r = 0; r < repeat; ++r) {
+    for (size_t i = 0; i < input_bits; ++i) {
+      size_t input_unit_index = i / (sizeof(bitset_t) * 8);
+      size_t input_bit_offset = i % (sizeof(bitset_t) * 8);
+      bool bit                = (input[input_unit_index] >> input_bit_offset) & 1;
+
+      size_t output_unit_index = output_bit_index / (sizeof(bitset_t) * 8);
+      size_t output_bit_offset = output_bit_index % (sizeof(bitset_t) * 8);
+
+      output[output_unit_index] |= (static_cast<bitset_t>(bit) << output_bit_offset);
+
+      ++output_bit_index;
+    }
+  }
+}
+
 template <typename index_t, typename bitmap_t = uint32_t>
 void cpu_convert_to_csr(std::vector<bitmap_t>& bitmap,
                         index_t rows,
@@ -205,10 +235,11 @@ void cpu_select_k(const std::vector<index_t>& indptr_h,
   }
 }
 
-template <typename value_t, typename index_t, typename bitmap_t = uint32_t>
+template <typename value_t, typename index_t, typename bits_t = uint32_t>
 void cpu_brute_force_with_filter(value_t* query_data,
                                  value_t* index_data,
-                                 std::vector<bitmap_t>& filter,
+                                 std::vector<bits_t>& filter,
+                                 enum cuvsFilterType filter_type,
                                  std::vector<index_t>& out_indices_h,
                                  std::vector<value_t>& out_values_h,
                                  size_t n_queries,
@@ -219,11 +250,21 @@ void cpu_brute_force_with_filter(value_t* query_data,
                                  bool select_min,
                                  cuvsDistanceType metric)
 {
-  std::vector<value_t> values_h(nnz);
-  std::vector<index_t> indices_h(nnz);
+  size_t actual_nnz = nnz;
+  size_t element    = raft::ceildiv(n_queries * n_dataset, size_t(sizeof(bits_t) * 8));
+
+  std::vector<bits_t> filter_repeat_h(element);
+  if (filter_type == BITSET) {
+    actual_nnz = nnz * n_queries;
+    repeat_cpu_bitset(filter, n_dataset, n_queries, filter_repeat_h);
+  } else {
+    filter_repeat_h = filter;
+  }
+  std::vector<value_t> values_h(actual_nnz);
+  std::vector<index_t> indices_h(actual_nnz);
   std::vector<index_t> indptr_h(n_queries + 1);
 
-  cpu_convert_to_csr(filter, (index_t)n_queries, (index_t)n_dataset, indices_h, indptr_h);
+  cpu_convert_to_csr(filter_repeat_h, (index_t)n_queries, (index_t)n_dataset, indices_h, indptr_h);
 
   cpu_sddmm(query_data,
             index_data,
@@ -302,10 +343,11 @@ void recall_eval(T* query_data,
                                                min_recall));
 };
 
-template <typename T, typename IdxT, typename bitmap_t = uint32_t>
+template <typename T, typename IdxT, typename bits_t = uint32_t>
 void recall_eval_with_filter(T* query_data,
                              T* index_data,
-                             std::vector<bitmap_t>& filter_h,
+                             std::vector<bits_t>& filter_h,
+                             enum cuvsFilterType filter_type,
                              IdxT* neighbors_d,
                              T* distances_d,
                              std::vector<T>& distances_ref_h,
@@ -337,6 +379,7 @@ void recall_eval_with_filter(T* query_data,
   cpu_brute_force_with_filter(queries_h.data(),
                               indices_h.data(),
                               filter_h,
+                              filter_type,
                               neighbors_ref_h,
                               distances_ref_h,
                               n_queries,
@@ -388,6 +431,7 @@ TEST(BruteForceC, BuildSearch)
                   index_data.data(),
                   query_data.data(),
                   filter_data,
+                  NO_FILTER,
                   distances_data.data(),
                   neighbors_data.data(),
                   metric);
@@ -404,20 +448,20 @@ TEST(BruteForceC, BuildSearch)
               metric);
 }
 
-TEST(BruteForceC, BuildSearchWithFilter)
+void run_test_with_filter(int64_t n_samples,
+                          int64_t n_queries,
+                          int64_t n_dim,
+                          uint32_t n_neighbors,
+                          enum cuvsFilterType filter_type)
 {
-  int64_t n_rows       = 8096;
-  int64_t n_queries    = 128;
-  int64_t n_dim        = 32;
-  uint32_t n_neighbors = 8;
-
   raft::resources handle;
   auto stream = raft::resource::get_cuda_stream(handle);
 
-  float sparsity   = 0.2;
-  int64_t n_filter = (n_queries * n_rows + 31) / 32;
+  float sparsity        = 0.2;
+  int64_t n_rows_filter = (filter_type == BITMAP ? n_queries : 1);
+  int64_t n_filter      = (n_rows_filter * n_samples + 31) / 32;
   std::vector<uint32_t> filter_h(n_filter);
-  int64_t nnz = create_sparse_matrix(n_queries, n_rows, sparsity, filter_h);
+  int64_t nnz = create_sparse_matrix(n_rows_filter, n_samples, sparsity, filter_h);
 
   cuvsDistanceType metric = L2Expanded;
   bool select_min         = cuvs::distance::is_min_close(metric);
@@ -427,7 +471,7 @@ TEST(BruteForceC, BuildSearchWithFilter)
     select_min ? std::numeric_limits<float>::infinity() : std::numeric_limits<float>::lowest());
   std::vector<int64_t> neighbors_ref_h(n_queries * n_neighbors, static_cast<int64_t>(0));
 
-  rmm::device_uvector<float> index_data(n_rows * n_dim, stream);
+  rmm::device_uvector<float> index_data(n_samples * n_dim, stream);
   rmm::device_uvector<float> query_data(n_queries * n_dim, stream);
   rmm::device_uvector<int64_t> neighbors_data(n_queries * n_neighbors, stream);
   rmm::device_uvector<float> distances_data(n_queries * n_neighbors, stream);
@@ -436,18 +480,19 @@ TEST(BruteForceC, BuildSearchWithFilter)
   raft::copy(neighbors_data.data(), neighbors_ref_h.data(), n_queries * n_neighbors, stream);
   raft::copy(distances_data.data(), distances_ref_h.data(), n_queries * n_neighbors, stream);
 
-  generate_random_data(index_data.data(), n_rows * n_dim);
+  generate_random_data(index_data.data(), n_samples * n_dim);
   generate_random_data(query_data.data(), n_queries * n_dim);
 
   raft::copy(filter_data.data(), filter_h.data(), n_filter, stream);
 
-  run_brute_force(n_rows,
+  run_brute_force(n_samples,
                   n_queries,
                   n_dim,
                   n_neighbors,
                   index_data.data(),
                   query_data.data(),
                   filter_data.data(),
+                  filter_type,
                   distances_data.data(),
                   neighbors_data.data(),
                   metric);
@@ -455,14 +500,34 @@ TEST(BruteForceC, BuildSearchWithFilter)
   recall_eval_with_filter(query_data.data(),
                           index_data.data(),
                           filter_h,
+                          filter_type,
                           neighbors_data.data(),
                           distances_data.data(),
                           distances_ref_h,
                           neighbors_ref_h,
                           n_queries,
-                          n_rows,
+                          n_samples,
                           n_dim,
                           n_neighbors,
                           nnz,
                           metric);
 }
+TEST(BruteForceC, BuildSearchWithBitmapFilter)
+{
+  int64_t n_rows       = 8096;
+  int64_t n_queries    = 128;
+  int64_t n_dim        = 32;
+  uint32_t n_neighbors = 8;
+
+  run_test_with_filter(n_rows, n_queries, n_dim, n_neighbors, BITMAP);
+}
+
+TEST(BruteForceC, BuildSearchWithBitsetFilter)
+{
+  int64_t n_rows       = 2000;
+  int64_t n_queries    = 100;
+  int64_t n_dim        = 128;
+  uint32_t n_neighbors = 100;
+
+  run_test_with_filter(n_rows, n_queries, n_dim, n_neighbors, BITSET);
+}
diff --git a/cpp/tests/neighbors/run_brute_force_c.c b/cpp/tests/neighbors/run_brute_force_c.c
index ceb1247314..ba71ea53a9 100644
--- a/cpp/tests/neighbors/run_brute_force_c.c
+++ b/cpp/tests/neighbors/run_brute_force_c.c
@@ -24,6 +24,7 @@ void run_brute_force(int64_t n_rows,
                      float* index_data,
                      float* query_data,
                      uint32_t* prefilter_data,
+                     enum cuvsFilterType prefilter_type,
                      float* distances_data,
                      int64_t* neighbors_data,
                      cuvsDistanceType metric)
@@ -90,7 +91,7 @@ void run_brute_force(int64_t n_rows,
   cuvsFilter prefilter;
 
   DLManagedTensor prefilter_tensor;
-  if (prefilter_data == NULL) {
+  if (prefilter_data == NULL || prefilter_type == NO_FILTER) {
     prefilter.type = NO_FILTER;
     prefilter.addr = (uintptr_t)NULL;
   } else {
@@ -100,11 +101,14 @@ void run_brute_force(int64_t n_rows,
     prefilter_tensor.dl_tensor.dtype.code         = kDLUInt;
     prefilter_tensor.dl_tensor.dtype.bits         = 32;
     prefilter_tensor.dl_tensor.dtype.lanes        = 1;
-    int64_t prefilter_shape[1]                    = {(n_queries * n_rows + 31) / 32};
-    prefilter_tensor.dl_tensor.shape              = prefilter_shape;
-    prefilter_tensor.dl_tensor.strides            = NULL;
 
-    prefilter.type = BITMAP;
+    int64_t prefilter_bits_num = (prefilter_type == BITMAP) ? n_queries * n_rows : n_rows;
+    int64_t prefilter_shape[1] = {(prefilter_bits_num + 31) / 32};
+
+    prefilter_tensor.dl_tensor.shape   = prefilter_shape;
+    prefilter_tensor.dl_tensor.strides = NULL;
+
+    prefilter.type = prefilter_type;
     prefilter.addr = (uintptr_t)&prefilter_tensor;
   }
 
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 2c80385728..08690d0932 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -152,12 +152,15 @@ def search(Index index,
     distances : Optional CUDA array interface compliant matrix shape
                 (n_queries, k) If supplied, the distances to the
                 neighbors will be written here in-place. (default None)
-    prefilter : Optional cuvs.neighbors.cuvsFilter can be used to filter
-                queries and neighbors based on a given bitmap. The filter
-                function should have a row-major layout and logical shape
-                [n_queries, n_samples], using the first n_samples bits to
-                indicate whether queries[0] should compute the distance with
-                index.
+    prefilter : Optional, cuvs.neighbors.cuvsFilter
+                An optional filter to exclude certain query-neighbor
+                pairs using a bitmap or bitset. The filter function should
+                have a row-major layout with logical shape
+                `(n_prefilter_rows, n_samples)`, where:
+                - `n_prefilter_rows == n_queries` when using a bitmap filter.
+                - `n_prefilter_rows == 1` when using a bitset prefilter.
+                Each bit in `n_samples` determines whether `queries[i]`
+                should be considered for distance computation with the index.
         (default None)
     {resources_docstring}
 
@@ -203,14 +206,21 @@ def search(Index index,
     >>> # Build filters
     >>> n_bitmap = np.ceil(n_samples * n_queries / 32).astype(int)
     >>> # Create your own bitmap as the filter by replacing the random one.
-    >>> bitmap = cp.random.randint(1, 1000, size=(n_bitmap,), dtype=cp.uint32)
-    >>> prefilter = filters.from_bitmap(bitmap)
+    >>> bitmap = cp.random.randint(1, 100, size=(n_bitmap,), dtype=cp.uint32)
+    >>> bitmap_prefilter = filters.from_bitmap(bitmap)
+    >>>
+    >>> # or Build bitset prefilter:
+    >>> # n_bitset = np.ceil(n_samples * 1 / 32).astype(int)
+    >>> # # Create your own bitset as the filter by replacing the random one.
+    >>> # bitset = cp.random.randint(1, 100, size=(n_bitset,), dtype=cp.uint32)
+    >>> # bitset_prefilter = filters.from_bitset(bitset)
+    >>>
     >>> k = 10
     >>> # Using a pooling allocator reduces overhead of temporary array
     >>> # creation during search. This is useful if multiple searches
     >>> # are performed with same query size.
     >>> distances, neighbors = brute_force.search(index, queries, k,
-    ...                                           prefilter=prefilter)
+    ...                                           prefilter=bitmap_prefilter)
     >>> neighbors = cp.asarray(neighbors)
     >>> distances = cp.asarray(distances)
     """
diff --git a/python/cuvs/cuvs/tests/test_brute_force.py b/python/cuvs/cuvs/tests/test_brute_force.py
index 0c39c601a8..d4094a07b6 100644
--- a/python/cuvs/cuvs/tests/test_brute_force.py
+++ b/python/cuvs/cuvs/tests/test_brute_force.py
@@ -128,27 +128,45 @@ def create_sparse_array(shape, sparsity):
 )
 @pytest.mark.parametrize("inplace", [True])
 @pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize("filter_type", ["bitset", "bitmap"])
 def test_prefiltered_brute_force_knn(
-    n_index_rows, n_query_rows, n_cols, k, sparsity, inplace, metric, dtype
+    n_index_rows,
+    n_query_rows,
+    n_cols,
+    k,
+    sparsity,
+    inplace,
+    metric,
+    dtype,
+    filter_type,
 ):
     index = np.random.random_sample((n_index_rows, n_cols)).astype(dtype)
     queries = np.random.random_sample((n_query_rows, n_cols)).astype(dtype)
-    bitmap = create_sparse_array(
-        (np.ceil(n_query_rows * n_index_rows / 32).astype(np.uint32)), sparsity
+    n_prefilter_rows = n_query_rows if filter_type == "bitmap" else 1
+    prefilter_bits = create_sparse_array(
+        (np.ceil(n_prefilter_rows * n_index_rows / 32).astype(np.uint32)),
+        sparsity,
     )
 
     is_min = metric != "inner_product"
-
+    initial_dist = np.inf if is_min else -np.inf
     indices = np.zeros((n_query_rows, k), dtype="int64")
-    distances = np.zeros((n_query_rows, k), dtype=dtype)
+    distances = np.full((n_query_rows, k), initial_dist, dtype=dtype)
 
     index_device = device_ndarray(index)
     queries_device = device_ndarray(queries)
     indices_device = device_ndarray(indices)
     distances_device = device_ndarray(distances)
-    bitmap_device = device_ndarray(bitmap)
-    prefilter = filters.from_bitmap(bitmap_device)
-    bitmap_device = None
+    bits_device = device_ndarray(prefilter_bits)
+    prefilter = None
+    if filter_type == "bitmap":
+        prefilter = filters.from_bitmap(bits_device)
+    elif filter_type == "bitset":
+        prefilter = filters.from_bitset(bits_device)
+    else:
+        assert False, "unsupported filter type!"
+
+    bits_device = None
     brute_force_index = brute_force.build(index_device, metric)
     ret_distances, ret_indices = brute_force.search(
         brute_force_index,
@@ -162,15 +180,17 @@ def test_prefiltered_brute_force_knn(
     pw_dists = cdist(queries, index, metric=metric)
 
     # convert bitmap to bool array.
-    bitmap_as_uint8 = bitmap.view(np.uint8)
-    bool_filter = np.unpackbits(bitmap_as_uint8)
+    bits_as_uint8 = prefilter_bits.view(np.uint8)
+    bool_filter = np.unpackbits(bits_as_uint8)
     bool_filter = bool_filter.reshape(-1, 4, 8)
     bool_filter = np.flip(bool_filter, axis=2)
-    bool_filter = bool_filter.reshape(-1)[: (n_query_rows * n_index_rows)]
+    bool_filter = bool_filter.reshape(-1)[: (n_prefilter_rows * n_index_rows)]
+    if filter_type == "bitset":
+        bool_filter = np.tile(bool_filter, n_query_rows)
     bool_filter = bool_filter.reshape(-1, n_index_rows)
     bool_filter = np.logical_not(bool_filter)
 
-    pw_dists[bool_filter] = np.inf if is_min else -np.inf
+    pw_dists[bool_filter] = initial_dist
 
     distances_device = ret_distances if not inplace else distances_device
 

From f14266c1651f6fb76e3a3a0e4c83724a21893371 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Thu, 6 Mar 2025 16:02:26 -0600
Subject: [PATCH 337/623] cuvs-bench-cpu: avoid 'mkl' dependency on aarch64
 (#750)

Fixes https://github.com/rapidsai/docker/issues/739

#260 introduced a runtime dependency on `mkl` for the `cuvs-bench-cpu` conda package. There are not aarch64 packages for `mkl` on conda-forge, so this makes `cuvs-bench-cpu` impossible to install on aarch64.

This fixes that, by applying the same "only add on x86_64" guard used for `mkl` everywhere else in this project, e.g. like this:

https://github.com/rapidsai/cuvs/blob/89b03493b487910d2125fde6680590adde8e2a95/conda/recipes/cuvs-bench-cpu/meta.yaml#L51

It also proposes adding import tests to the `cuvs-bench-cpu` conda recipe, so issues like this can be caught in CI in the future.

## Notes for Reviewers

I searched for references like this

```shell
git grep -i mkl
```

### How I tested this

Saw lines like this in `conda-python-build` logs:

```text
BUILD START: ['cuvs-bench-cpu-25.04.00a96-py312_250305_g94340bc_96.conda']
...
TEST START: /tmp/conda-bld-output/linux-aarch64/cuvs-bench-cpu-25.04.00a96-py312_250305_g94340bc_96.conda
...
import: 'cuvs_bench'
import: 'cuvs_bench.generate_groundtruth'
import: 'cuvs_bench.get_dataset'
import: 'cuvs_bench.plot'
import: 'cuvs_bench.run'
import: 'cuvs_bench.run.data_export'
import: 'cuvs_bench.split_groundtruth'
import: 'cuvs_bench'
import: 'cuvs_bench.generate_groundtruth'
import: 'cuvs_bench.get_dataset'
import: 'cuvs_bench.plot'
import: 'cuvs_bench.run'
import: 'cuvs_bench.run.data_export'
import: 'cuvs_bench.split_groundtruth'
...
TEST END: /tmp/conda-bld-output/linux-aarch64/cuvs-bench-cpu-25.04.00a96-py312_250305_g94340bc_96.conda
```

([build link](https://github.com/rapidsai/cuvs/actions/runs/13687659537/job/38276160494?pr=750#step:9:5961))

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Gil Forsyth (https://github.com/gforsyth)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/750
---
 ci/build_python.sh                     |  1 -
 conda/recipes/cuvs-bench-cpu/meta.yaml | 13 ++++++++++++-
 docs/source/cuvs_bench/index.rst       |  4 ++--
 3 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/ci/build_python.sh b/ci/build_python.sh
index 6981c08fc3..4b85059dd2 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -48,7 +48,6 @@ sccache --zero-stats
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
   rapids-conda-retry build \
-  --no-test \
   --channel "${CPP_CHANNEL}" \
   --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
   conda/recipes/cuvs-bench-cpu
diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
index 116da1b98f..d5502cb374 100644
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ b/conda/recipes/cuvs-bench-cpu/meta.yaml
@@ -60,13 +60,24 @@ requirements:
     - glog {{ glog_version }}
     - h5py {{ h5py_version }}
     - matplotlib-base
-    - mkl =2023
+    - mkl =2023  # [linux64]
     - numpy >=1.23,<3.0a0
     - pandas
     - pyyaml
     - python
     - requests
     - scikit-learn>=1.5
+
+test:
+  # test that the package is installable and these modules are importable
+  imports:
+    - cuvs_bench
+    - cuvs_bench.generate_groundtruth
+    - cuvs_bench.get_dataset
+    - cuvs_bench.plot
+    - cuvs_bench.run
+    - cuvs_bench.split_groundtruth
+
 about:
   home: https://rapids.ai/
   license: Apache-2.0
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index c15aa41c10..d3da585afd 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -120,7 +120,7 @@ The steps below demonstrate how to download, install, and run benchmarks on a su
     python -m cuvs_bench.run --dataset deep-image-96-inner --algorithms cuvs_cagra --batch-size 10 -k 10
 
     # (3) export data
-    python -m cuvs_bench.data_export --dataset deep-image-96-inner
+    python -m cuvs_bench.run --data-export --dataset deep-image-96-inner
 
     # (4) plot results
     python -m cuvs_bench.plot --dataset deep-image-96-inner
@@ -204,7 +204,7 @@ The steps below demonstrate how to download, install, and run benchmarks on a su
     python -m cuvs_bench.run --dataset deep-1B --algorithms cuvs_cagra --batch-size 10 -k 10
 
     # (3) export data
-    python -m cuvs_bench.data_export --dataset deep-1B
+    python -m cuvs_bench.run --data-export --dataset deep-1B
 
     # (4) plot results
     python -m cuvs_bench.plot --dataset deep-1B

From bd6d4a9934ad7f3f3cf2e2c6446abdbdf10ffba0 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Mon, 10 Mar 2025 17:30:24 -0700
Subject: [PATCH 338/623] Fix duplicate indices in batch NN Descent (#702)

### Purpose of this PR
Handling duplicate indices in batch NN Descent graph.

Resolves the following issues
- https://github.com/rapidsai/raft/issues/2450
- https://github.com/rapidsai/cuvs/issues/559
- https://github.com/rapidsai/cuvs/issues/753
### Notes
- Also fixed in RAFT [here](https://github.com/rapidsai/raft/pull/2586) for current use with cuML

Authors:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/702
---
 cpp/src/neighbors/detail/nn_descent.cuh                |  6 +++++-
 cpp/src/neighbors/detail/nn_descent_batch.cuh          |  9 +++++----
 cpp/tests/neighbors/ann_nn_descent.cuh                 |  7 ++-----
 .../neighbors/ann_nn_descent/test_float_uint32_t.cu    | 10 +++++-----
 4 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index dffc94f06c..66118f0ac1 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -244,6 +244,8 @@ class BloomFilter {
     }
   }
 
+  void set_nrow(size_t nrow) { nrow_ = nrow; }
+
   bool check(size_t list_id, Index_t key)
   {
     bool is_present       = true;
@@ -1273,7 +1275,9 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
   cudaStream_t stream = raft::resource::get_cuda_stream(res);
   nrow_               = nrow;
   graph_.nrow         = nrow;
-  graph_.h_graph      = (InternalID_t<Index_t>*)output_graph;
+  graph_.bloom_filter.set_nrow(nrow);
+  update_counter_ = 0;
+  graph_.h_graph  = (InternalID_t<Index_t>*)output_graph;
 
   cudaPointerAttributes data_ptr_attr;
   RAFT_CUDA_TRY(cudaPointerGetAttributes(&data_ptr_attr, data));
diff --git a/cpp/src/neighbors/detail/nn_descent_batch.cuh b/cpp/src/neighbors/detail/nn_descent_batch.cuh
index 842dbe7887..f8e10c6e17 100644
--- a/cpp/src/neighbors/detail/nn_descent_batch.cuh
+++ b/cpp/src/neighbors/detail/nn_descent_batch.cuh
@@ -170,10 +170,10 @@ void get_global_nearest_k(
 
       if (i == num_batches - 1) { batch_size_ = num_rows - batch_size * i; }
       thrust::copy(raft::resource::get_thrust_policy(res),
-                   nearest_clusters_idx.data_handle() + i * batch_size_ * k,
-                   nearest_clusters_idx.data_handle() + (i + 1) * batch_size_ * k,
+                   nearest_clusters_idx.data_handle() + i * batch_size * k,
+                   nearest_clusters_idx.data_handle() + (i * batch_size + batch_size_) * k,
                    nearest_clusters_idxt.data_handle());
-      raft::copy(global_nearest_cluster.data_handle() + i * batch_size_ * k,
+      raft::copy(global_nearest_cluster.data_handle() + i * batch_size * k,
                  nearest_clusters_idxt.data_handle(),
                  batch_size_ * k,
                  resource::get_cuda_stream(res));
@@ -650,7 +650,8 @@ void batch_build(raft::resources const& res,
                            .internal_node_degree  = extended_intermediate_degree,
                            .max_iterations        = params.max_iterations,
                            .termination_threshold = params.termination_threshold,
-                           .output_graph_degree   = graph_degree};
+                           .output_graph_degree   = graph_degree,
+                           .metric                = params.metric};
 
   auto global_indices_h   = raft::make_managed_matrix<IdxT, int64_t>(res, num_rows, graph_degree);
   auto global_distances_h = raft::make_managed_matrix<float, int64_t>(res, num_rows, graph_degree);
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index 09861a2194..6ffbf91e2f 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -241,7 +241,7 @@ class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchI
         index_params.metric                    = ps.metric;
         index_params.graph_degree              = ps.graph_degree;
         index_params.intermediate_graph_degree = 2 * ps.graph_degree;
-        index_params.max_iterations            = 10;
+        index_params.max_iterations            = 100;
         index_params.return_distances          = true;
         index_params.n_clusters                = ps.recall_cluster.second;
 
@@ -287,8 +287,7 @@ class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchI
                                   ps.graph_degree,
                                   0.01,
                                   min_recall,
-                                  true,
-                                  static_cast<size_t>(ps.graph_degree * 0.1)));
+                                  true));
     }
   }
 
@@ -328,8 +327,6 @@ const std::vector<AnnNNDescentInputs> inputs =
                                                      {false, true},
                                                      {0.90});
 
-// TODO : Investigate why this test is failing Reference issue https
-// :  // github.com/rapidsai/raft/issues/2450
 const std::vector<AnnNNDescentBatchInputs> inputsBatch =
   raft::util::itertools::product<AnnNNDescentBatchInputs>(
     {std::make_pair(0.9, 3lu), std::make_pair(0.9, 2lu)},  // min_recall, n_clusters
diff --git a/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
index 7a24f96a17..ab59b06a36 100644
--- a/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
@@ -23,12 +23,12 @@ namespace cuvs::neighbors::nn_descent {
 typedef AnnNNDescentTest<float, float, std::uint32_t> AnnNNDescentTestF_U32;
 TEST_P(AnnNNDescentTestF_U32, AnnNNDescent) { this->testNNDescent(); }
 
-// typedef AnnNNDescentBatchTest<float, float, std::uint32_t> AnnNNDescentBatchTestF_U32;
-// TEST_P(AnnNNDescentBatchTestF_U32, AnnNNDescentBatch) { this->testNNDescentBatch(); }
+typedef AnnNNDescentBatchTest<float, float, std::uint32_t> AnnNNDescentBatchTestF_U32;
+TEST_P(AnnNNDescentBatchTestF_U32, AnnNNDescentBatch) { this->testNNDescentBatch(); }
 
 INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestF_U32, ::testing::ValuesIn(inputs));
-// INSTANTIATE_TEST_CASE_P(AnnNNDescentBatchTest,
-//                         AnnNNDescentBatchTestF_U32,
-//                         ::testing::ValuesIn(inputsBatch));
+INSTANTIATE_TEST_CASE_P(AnnNNDescentBatchTest,
+                        AnnNNDescentBatchTestF_U32,
+                        ::testing::ValuesIn(inputsBatch));
 
 }  // namespace   cuvs::neighbors::nn_descent

From 2f9df39e52cc61444a30696c75edd1a91f85cdaa Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Thu, 13 Mar 2025 15:35:19 -0400
Subject: [PATCH 339/623] DOC v25.06 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  6 ++--
 .devcontainer/cuda11.8-pip/devcontainer.json  |  8 ++---
 .../cuda12.8-conda/devcontainer.json          |  6 ++--
 .devcontainer/cuda12.8-pip/devcontainer.json  |  8 ++---
 .github/workflows/build.yaml                  | 20 ++++++------
 .github/workflows/pr.yaml                     | 30 ++++++++---------
 .github/workflows/test.yaml                   |  8 ++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 README.md                                     |  6 ++--
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 +--
 .../all_cuda-118_arch-x86_64.yaml             |  4 +--
 .../all_cuda-128_arch-aarch64.yaml            |  4 +--
 .../all_cuda-128_arch-x86_64.yaml             |  4 +--
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  8 ++---
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  8 ++---
 .../bench_ann_cuda-128_arch-aarch64.yaml      |  8 ++---
 .../bench_ann_cuda-128_arch-x86_64.yaml       |  8 ++---
 dependencies.yaml                             | 32 +++++++++----------
 docs/source/developer_guide.md                |  4 +--
 examples/cmake/thirdparty/fetch_rapids.cmake  |  2 +-
 java/build.sh                                 |  2 +-
 java/cuvs-java/pom.xml                        |  4 +--
 java/examples/pom.xml                         |  4 +--
 python/cuvs/pyproject.toml                    | 10 +++---
 python/cuvs_bench/cuvs_bench/plot/__main__.py |  2 +-
 python/cuvs_bench/pyproject.toml              |  2 +-
 python/libcuvs/pyproject.toml                 |  8 ++---
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 30 files changed, 109 insertions(+), 109 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index d93877bfa8..6dea71a0e7 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda11.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index f83de4c220..0de380d05a 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda11.8-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda11.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda11.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.4": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.6": {
       "version": "11.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.8-conda/devcontainer.json b/.devcontainer/cuda12.8-conda/devcontainer.json
index 4b374a2184..dd078a3009 100644
--- a/.devcontainer/cuda12.8-conda/devcontainer.json
+++ b/.devcontainer/cuda12.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.04-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.06-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.8-pip/devcontainer.json b/.devcontainer/cuda12.8-pip/devcontainer.json
index 1548c63ac7..0729bb953c 100644
--- a/.devcontainer/cuda12.8-pip/devcontainer.json
+++ b/.devcontainer/cuda12.8-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.4": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.6": {
       "version": "12.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.4": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 8c8551d55d..667742a411 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -50,7 +50,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -63,7 +63,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -72,7 +72,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -83,7 +83,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -95,7 +95,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -107,7 +107,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -118,7 +118,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -128,7 +128,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index b852f47f0a..c347afd6c6 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -29,7 +29,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.06
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -58,7 +58,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.06
     with:
       files_yaml: |
         test_cpp:
@@ -95,28 +95,28 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.06
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -124,20 +124,20 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -147,7 +147,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -157,7 +157,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: "gpu-v100-latest-1"
@@ -167,7 +167,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -176,14 +176,14 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -191,7 +191,7 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.06
     with:
       arch: '["amd64"]'
       cuda: '["12.8"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 524078c30d..8e1dc631aa 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -19,7 +19,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -29,7 +29,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -45,7 +45,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 9764c62c15..475380cb68 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.04
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.06
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/README.md b/README.md
index abbfb26965..11001baa4b 100755
--- a/README.md
+++ b/README.md
@@ -109,7 +109,7 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.04
+conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.06
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
@@ -169,7 +169,7 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
-For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.04/examples/c)
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.06/examples/c)
 
 ### Rust API
 
@@ -232,7 +232,7 @@ fn cagra_example() -> Result<()> {
 }
 ```
 
-For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.04/examples/rust).
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.06/examples/rust).
 
 ## Contributing
 
diff --git a/VERSION b/VERSION
index b922658ff3..cc83d7ab43 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-25.04.00
+25.06.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 5e99876a1a..1e296b7e55 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==25.4.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pre-commit
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index 9038901c2c..a18cc592d2 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==25.4.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pre-commit
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
index 0cbf9abc13..7880a8b53a 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.4.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -40,7 +40,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
index 17bdcd8c52..0cc536c948 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.4.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -40,7 +40,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index a471423d93..46d574552c 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -19,7 +19,7 @@ dependencies:
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
-- cuvs==25.4.*,>=0.0.0a0
+- cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -35,8 +35,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.4.*,>=0.0.0a0
-- librmm==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - matplotlib-base
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pandas
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 332bc02a7c..4c2d2c7c81 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -19,7 +19,7 @@ dependencies:
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
-- cuvs==25.4.*,>=0.0.0a0
+- cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -37,8 +37,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.4.*,>=0.0.0a0
-- librmm==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - matplotlib-base
 - mkl-devel=2023
 - nccl>=2.19
@@ -47,7 +47,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pandas
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index ce32927d40..f2f27f4d4c 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -20,7 +20,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.8
 - cupy>=12.0.0
-- cuvs==25.4.*,>=0.0.0a0
+- cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,15 +32,15 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.4.*,>=0.0.0a0
-- librmm==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - matplotlib-base
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index 864a831bc6..e70edb3b77 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -20,7 +20,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.8
 - cupy>=12.0.0
-- cuvs==25.4.*,>=0.0.0a0
+- cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -34,8 +34,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.4.*,>=0.0.0a0
-- librmm==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- librmm==25.6.*,>=0.0.0a0
 - matplotlib-base
 - mkl-devel=2023
 - nccl>=2.19
@@ -43,7 +43,7 @@ dependencies:
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.4.*,>=0.0.0a0
+- pylibraft==25.6.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/dependencies.yaml b/dependencies.yaml
index 99d2f6021b..a132a87305 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -578,7 +578,7 @@ dependencies:
       - output_types: [conda, pyproject, requirements]
         packages:
           - click
-          - cuvs==25.4.*,>=0.0.0a0
+          - cuvs==25.6.*,>=0.0.0a0
           - pandas
           - pyyaml
           - requests
@@ -593,17 +593,17 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - cuvs==25.4.*,>=0.0.0a0
+          - cuvs==25.6.*,>=0.0.0a0
   depends_on_cuvs_bench:
     common:
       - output_types: conda
         packages:
-          - cuvs-bench==25.4.*,>=0.0.0a0
+          - cuvs-bench==25.6.*,>=0.0.0a0
   depends_on_libcuvs:
     common:
       - output_types: conda
         packages:
-          - &libcuvs_unsuffixed libcuvs==25.4.*,>=0.0.0a0
+          - &libcuvs_unsuffixed libcuvs==25.6.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -616,23 +616,23 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu12==25.4.*,>=0.0.0a0
+              - libcuvs-cu12==25.6.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu11==25.4.*,>=0.0.0a0
+              - libcuvs-cu11==25.6.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
   depends_on_libcuvs_tests:
     common:
       - output_types: conda
         packages:
-          - libcuvs-tests==25.4.*,>=0.0.0a0
+          - libcuvs-tests==25.6.*,>=0.0.0a0
   depends_on_libraft:
     common:
       - output_types: conda
         packages:
-          - &libraft_unsuffixed libraft==25.4.*,>=0.0.0a0
+          - &libraft_unsuffixed libraft==25.6.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -645,18 +645,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu12==25.4.*,>=0.0.0a0
+              - libraft-cu12==25.6.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu11==25.4.*,>=0.0.0a0
+              - libraft-cu11==25.6.*,>=0.0.0a0
           - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
       - output_types: conda
         packages:
-          - &librmm_unsuffixed librmm==25.4.*,>=0.0.0a0
+          - &librmm_unsuffixed librmm==25.6.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -669,18 +669,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu12==25.4.*,>=0.0.0a0
+              - librmm-cu12==25.6.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu11==25.4.*,>=0.0.0a0
+              - librmm-cu11==25.6.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
       - output_types: conda
         packages:
-          - &pylibraft_unsuffixed pylibraft==25.4.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==25.6.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -693,10 +693,10 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu12==25.4.*,>=0.0.0a0
+              - pylibraft-cu12==25.6.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu11==25.4.*,>=0.0.0a0
+              - pylibraft-cu11==25.6.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index a8da506b5a..9358770e93 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.04/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.06/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.04/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.06/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
index 07fe407653..54b89a6c42 100644
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ b/examples/cmake/thirdparty/fetch_rapids.cmake
@@ -12,7 +12,7 @@
 # the License.
 
 # Use this variable to update RAPIDS and cuVS versions
-set(RAPIDS_VERSION "25.04")
+set(RAPIDS_VERSION "25.06")
 
 if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
     file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
diff --git a/java/build.sh b/java/build.sh
index 5f969e29ce..34c7ae83e6 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -1,4 +1,4 @@
-VERSION="25.04.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
+VERSION="25.06.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
 SO_FILE_PATH="./internal"
 
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 63e48c5563..835fb6eaf2 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -22,7 +22,7 @@
     <groupId>com.nvidia.cuvs</groupId>
     <artifactId>cuvs-java</artifactId>
     <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.04.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     <name>cuvs-java</name>
     <description>
         This project provides Java bindings for cuVS, enabling approximate nearest neighbors search and clustering
@@ -174,7 +174,7 @@
                                     <groupId>com.nvidia.cuvs</groupId>
                                     <artifactId>cuvs-java-internal</artifactId>
                                     <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-                                    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.04.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+                                    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
                                     <type>so</type>
                                     <overWrite>false</overWrite>
                                     <outputDirectory>
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
index d2d03da0bb..c5798864ce 100644
--- a/java/examples/pom.xml
+++ b/java/examples/pom.xml
@@ -5,7 +5,7 @@
   <groupId>com.nvidia.cuvs.examples</groupId>
   <artifactId>cuvs-java-examples</artifactId>
   <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.04.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
   <name>cuvs-java-examples</name>
 
   <properties>
@@ -18,7 +18,7 @@
       <groupId>com.nvidia.cuvs</groupId>
       <artifactId>cuvs-java</artifactId>
       <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.04.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     </dependency>
     <dependency>
       <groupId>org.slf4j</groupId>
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index f4752d5207..519dca62d0 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -32,9 +32,9 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
-    "libcuvs==25.4.*,>=0.0.0a0",
+    "libcuvs==25.6.*,>=0.0.0a0",
     "numpy>=1.23,<3.0a0",
-    "pylibraft==25.4.*,>=0.0.0a0",
+    "pylibraft==25.6.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -118,9 +118,9 @@ requires = [
     "cmake>=3.30.4",
     "cuda-python",
     "cython>=3.0.0",
-    "libcuvs==25.4.*,>=0.0.0a0",
-    "libraft==25.4.*,>=0.0.0a0",
-    "librmm==25.4.*,>=0.0.0a0",
+    "libcuvs==25.6.*,>=0.0.0a0",
+    "libraft==25.6.*,>=0.0.0a0",
+    "librmm==25.6.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
diff --git a/python/cuvs_bench/cuvs_bench/plot/__main__.py b/python/cuvs_bench/cuvs_bench/plot/__main__.py
index 78ed74884f..7781d5eb9d 100644
--- a/python/cuvs_bench/cuvs_bench/plot/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/plot/__main__.py
@@ -17,7 +17,7 @@
 # 1: https://github.com/erikbern/ann-benchmarks/blob/main/plot.py
 # 2: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/utils.py  # noqa: E501
 # 3: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/metrics.py  # noqa: E501
-# License: https://github.com/rapidsai/cuvs/blob/branch-25.04/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
+# License: https://github.com/rapidsai/cuvs/blob/branch-25.06/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
 
 import itertools
 import os
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 6ae5a08fac..e527813e29 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -19,7 +19,7 @@ license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "click",
-    "cuvs==25.4.*,>=0.0.0a0",
+    "cuvs==25.6.*,>=0.0.0a0",
     "matplotlib",
     "pandas",
     "pyyaml",
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 24596e0ab7..c2d948b755 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -30,8 +30,8 @@ authors = [
 license = { text = "Apache 2.0" }
 requires-python = ">=3.10"
 dependencies = [
-    "libraft==25.4.*,>=0.0.0a0",
-    "librmm==25.4.*,>=0.0.0a0",
+    "libraft==25.6.*,>=0.0.0a0",
+    "librmm==25.6.*,>=0.0.0a0",
     "nvidia-cublas",
     "nvidia-curand",
     "nvidia-cusolver",
@@ -93,8 +93,8 @@ regex = "(?P<value>.*)"
 build-backend = "scikit_build_core.build"
 requires = [
     "cmake>=3.30.4",
-    "libraft==25.4.*,>=0.0.0a0",
-    "librmm==25.4.*,>=0.0.0a0",
+    "libraft==25.6.*,>=0.0.0a0",
+    "librmm==25.6.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 dependencies-file = "../../dependencies.yaml"
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index 9e857806cd..de35027bc6 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "25.4.0"
+version = "25.6.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index 187a53c09d..9b76c86bfa 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.4.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.6.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From a45e63fa61b841451e041587910f671d8356a5b2 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Mon, 17 Mar 2025 14:39:10 -0700
Subject: [PATCH 340/623] Relax max duplicates in batched NN Descent (#770)

As discussed, relaxing max duplicates for batch NN Descent for now to not block other PRs.
Related issue
- https://github.com/rapidsai/cuvs/issues/771

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/770
---
 cpp/tests/neighbors/ann_nn_descent.cuh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index 6ffbf91e2f..d27c005aa2 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -287,7 +287,8 @@ class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchI
                                   ps.graph_degree,
                                   0.01,
                                   min_recall,
-                                  true));
+                                  true,
+                                  static_cast<size_t>(ps.graph_degree)));
     }
   }
 

From 7f634bec82b42850db2fcf0025d41e79718d88ad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20F=C3=B6rster?=
 <97973773+mfoerste4@users.noreply.github.com>
Date: Mon, 17 Mar 2025 23:57:53 +0100
Subject: [PATCH 341/623] Reduce memory consumption of scalar quantizer (#736)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This is an optimization raised by #718 .

The quantile selection process currently relies on sampling. The memory requirements for the originally used
`raft::random::sample_without_replacement`
are roughly `48*input_len bytes` (`4*len*8byte + ~2*len*8byte for sort`).
This has now been replaced by `raft::matrix::sample_rows` which essentially has the same requirement but based on the sample size instead of the original input.

Authors:
  - Malte Förster (https://github.com/mfoerste4)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/736
---
 .../preprocessing/quantize/detail/scalar.cuh  | 58 +++++--------------
 1 file changed, 15 insertions(+), 43 deletions(-)

diff --git a/cpp/src/preprocessing/quantize/detail/scalar.cuh b/cpp/src/preprocessing/quantize/detail/scalar.cuh
index fc132eb7f8..da7c98fba2 100644
--- a/cpp/src/preprocessing/quantize/detail/scalar.cuh
+++ b/cpp/src/preprocessing/quantize/detail/scalar.cuh
@@ -19,8 +19,8 @@
 #include <cuvs/preprocessing/quantize/scalar.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/sample_rows.cuh>
 #include <raft/random/rng.cuh>
-#include <raft/random/sample_without_replacement.cuh>
 #include <thrust/execution_policy.h>
 #include <thrust/sort.h>
 #include <thrust/system/omp/execution_policy.h>
@@ -72,10 +72,11 @@ struct quantize_op {
   }
 };
 
-template <typename T>
-std::tuple<T, T> quantile_min_max(raft::resources const& res,
-                                  raft::device_matrix_view<const T, int64_t> dataset,
-                                  double quantile)
+template <typename T, typename IdxT = int64_t, typename accessor>
+std::tuple<T, T> quantile_min_max(
+  raft::resources const& res,
+  raft::mdspan<const T, raft::matrix_extent<IdxT>, raft::row_major, accessor> dataset,
+  double quantile)
 {
   // settings for quantile approximation
   constexpr size_t max_num_samples = 1000000;
@@ -85,14 +86,15 @@ std::tuple<T, T> quantile_min_max(raft::resources const& res,
 
   // select subsample
   raft::random::RngState rng(seed);
-  size_t n_elements  = dataset.extent(0) * dataset.extent(1);
-  size_t subset_size = std::min(max_num_samples, n_elements);
-  auto subset        = raft::make_device_vector<T>(res, subset_size);
-  auto dataset_view  = raft::make_device_vector_view<const T>(dataset.data_handle(), n_elements);
-  raft::random::sample_without_replacement(
-    res, rng, dataset_view, std::nullopt, subset.view(), std::nullopt);
-
-  // quantile / sort and pick for now
+  size_t n_rows        = dataset.extent(0);
+  size_t dim           = dataset.extent(1);
+  size_t n_sample_rows = std::min<size_t>(std::ceil(max_num_samples / dim), n_rows);
+
+  // select subsample rows (this returns device data for both device and host input)
+  auto subset = raft::matrix::sample_rows(res, rng, dataset, (IdxT)n_sample_rows);
+
+  // quantile / sort element-wise and pick for now
+  size_t subset_size = n_sample_rows * dim;
   thrust::sort(raft::resource::get_thrust_policy(res),
                subset.data_handle(),
                subset.data_handle() + subset_size);
@@ -105,39 +107,9 @@ std::tuple<T, T> quantile_min_max(raft::resources const& res,
   raft::update_host(&(minmax_h[0]), subset.data_handle() + pos_min, 1, stream);
   raft::update_host(&(minmax_h[1]), subset.data_handle() + pos_max, 1, stream);
   raft::resource::sync_stream(res);
-
   return {minmax_h[0], minmax_h[1]};
 }
 
-template <typename T>
-std::tuple<T, T> quantile_min_max(raft::resources const& res,
-                                  raft::host_matrix_view<const T, int64_t> dataset,
-                                  double quantile)
-{
-  // settings for quantile approximation
-  constexpr size_t max_num_samples = 1000000;
-  constexpr int seed               = 137;
-
-  // select subsample
-  std::mt19937 rng(seed);
-  size_t n_elements  = dataset.extent(0) * dataset.extent(1);
-  size_t subset_size = std::min(max_num_samples, n_elements);
-  std::vector<T> subset;
-  std::sample(dataset.data_handle(),
-              dataset.data_handle() + n_elements,
-              std::back_inserter(subset),
-              subset_size,
-              rng);
-
-  // quantile / sort and pick for now
-  thrust::sort(thrust::omp::par, subset.data(), subset.data() + subset_size, fp_lt<T>);
-  double half_quantile_pos = (0.5 + 0.5 * quantile) * subset_size;
-  int pos_max              = std::ceil(half_quantile_pos) - 1;
-  int pos_min              = subset_size - pos_max - 1;
-
-  return {subset[pos_min], subset[pos_max]};
-}
-
 template <typename T>
 cuvs::preprocessing::quantize::scalar::quantizer<T> train(
   raft::resources const& res,

From 2c3df758ee62a941f0e859ca29d294db66b12e96 Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Wed, 19 Mar 2025 13:41:51 -0400
Subject: [PATCH 342/623] feat(libcuvs): port libcuvs to rattler-build (#751)

This PR ports cuVS over to `rattler-build` for building conda packages.

Some changes to the build process:

* `cuvs-bench` was recompiling the entire library separately -- caching was hopefully helping with this but it was an unnecessary double-compile.  Instead, I've added the `bench-ann` to the pre-build stage in `libcuvs` and create a `libcuvs-bench` output there, that `cuvs-bench` then depends on to provide the necessary shared objects.

* Edited `build.sh` so that if `-n` is passed, the `cuvs-bench` Python package is not installed.

* `libcuvs-static` seems to be a leftover from a previous incarnation -- the existing `libcuvs-static` packages on `rapidsai-nightly` consist only of build metadata files and are otherwise empty.  I've removed the unused `--compile-static-lib` option from `build.sh`

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: https://github.com/rapidsai/cuvs/pull/751
---
 .gitignore                                    |   4 +-
 build.sh                                      |   7 +-
 ci/build_cpp.sh                               |  20 +-
 ci/build_python.sh                            |  52 ++-
 conda/recipes/cuvs-bench-cpu/build.sh         |   5 -
 conda/recipes/cuvs-bench-cpu/meta.yaml        |  84 ----
 conda/recipes/cuvs-bench-cpu/recipe.yaml      | 110 +++++
 conda/recipes/cuvs-bench/build.sh             |   5 -
 conda/recipes/cuvs-bench/meta.yaml            | 112 -----
 conda/recipes/cuvs-bench/recipe.yaml          |  84 ++++
 conda/recipes/cuvs/build.sh                   |   5 -
 conda/recipes/cuvs/meta.yaml                  |  81 ----
 conda/recipes/cuvs/recipe.yaml                |  97 ++++
 conda/recipes/libcuvs/build_libcuvs.sh        |   4 -
 .../recipes/libcuvs/build_libcuvs_examples.sh |   5 -
 conda/recipes/libcuvs/build_libcuvs_static.sh |   5 -
 conda/recipes/libcuvs/build_libcuvs_tests.sh  |   5 -
 conda/recipes/libcuvs/meta.yaml               | 303 -------------
 conda/recipes/libcuvs/recipe.yaml             | 424 ++++++++++++++++++
 examples/build.sh                             |   3 +
 python/cuvs/pyproject.toml                    |   2 +-
 python/cuvs_bench/pyproject.toml              |   2 +-
 python/libcuvs/pyproject.toml                 |   2 +-
 23 files changed, 780 insertions(+), 641 deletions(-)
 delete mode 100644 conda/recipes/cuvs-bench-cpu/build.sh
 delete mode 100644 conda/recipes/cuvs-bench-cpu/meta.yaml
 create mode 100644 conda/recipes/cuvs-bench-cpu/recipe.yaml
 delete mode 100644 conda/recipes/cuvs-bench/build.sh
 delete mode 100644 conda/recipes/cuvs-bench/meta.yaml
 create mode 100644 conda/recipes/cuvs-bench/recipe.yaml
 delete mode 100644 conda/recipes/cuvs/build.sh
 delete mode 100644 conda/recipes/cuvs/meta.yaml
 create mode 100644 conda/recipes/cuvs/recipe.yaml
 delete mode 100644 conda/recipes/libcuvs/build_libcuvs.sh
 delete mode 100644 conda/recipes/libcuvs/build_libcuvs_examples.sh
 delete mode 100644 conda/recipes/libcuvs/build_libcuvs_static.sh
 delete mode 100644 conda/recipes/libcuvs/build_libcuvs_tests.sh
 delete mode 100644 conda/recipes/libcuvs/meta.yaml
 create mode 100644 conda/recipes/libcuvs/recipe.yaml

diff --git a/.gitignore b/.gitignore
index a70ca197ff..3627558ff5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -81,7 +81,9 @@ ivf_flat_index
 ivf_pq_index
 
 # cuvs_bench
-datasets/
+# Leading slash to only ignore top-level `datasets/ directory -- otherwise
+# `python/cuvs_bench/cuvs_bench/config/datasets/` also gets ignored
+/datasets/
 /*.json
 
 # java
diff --git a/build.sh b/build.sh
index 4aaa5617c3..2cb25991ba 100755
--- a/build.sh
+++ b/build.sh
@@ -18,7 +18,7 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd $(dirname $0); pwd)
 
-VALIDARGS="clean libcuvs python rust go java docs tests bench-ann examples --uninstall  -v -g -n --compile-static-lib --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python rust go java docs tests bench-ann examples --uninstall  -v -g -n --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
@@ -38,7 +38,6 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    -g                          - build for debug
    -n                          - no install step
    --uninstall                 - uninstall files for specified targets which were built and installed prior
-   --compile-static-lib        - compile static library for all components
    --cpu-only                  - build CPU only components without CUDA. Currently only applies to bench-ann.
    --limit-tests               - semicolon-separated list of test executables to compile (e.g. NEIGHBORS_TEST;CLUSTER_TEST)
    --limit-bench-ann           - semicolon-separated list of ann benchmark executables to compute (e.g. HNSWLIB_ANN_BENCH;RAFT_IVF_PQ_ANN_BENCH)
@@ -332,7 +331,7 @@ fi
 
 ################################################################################
 # Configure for building all C++ targets
-if (( ${NUMARGS} == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg bench-prims || hasArg bench-ann; then
+if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg bench-prims || hasArg bench-ann; then
     COMPILE_LIBRARY=ON
     if (( ${BUILD_SHARED_LIBS} == "OFF" )); then
         CMAKE_TARGET="${CMAKE_TARGET};"
@@ -437,7 +436,7 @@ if (( ${NUMARGS} == 0 )) || hasArg python; then
 fi
 
 # Build and (optionally) install the cuvs-bench Python package
-if (( ${NUMARGS} == 0 )) || hasArg bench-ann; then
+if (( NUMARGS == 0 )) || (hasArg bench-ann && ! hasArg -n); then
     python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs_bench
 fi
 
diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index afd50a626d..7a6e5e56a7 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -17,9 +17,25 @@ rapids-logger "Begin cpp build"
 
 sccache --zero-stats
 
-RAPIDS_PACKAGE_VERSION=$(rapids-generate-version) rapids-conda-retry build \
-  conda/recipes/libcuvs
+
+RAPIDS_PACKAGE_VERSION=$(rapids-generate-version)
+export RAPIDS_PACKAGE_VERSION
+
+source rapids-rattler-channel-string
+
+# --no-build-id allows for caching with `sccache`
+# more info is available at
+# https://rattler.build/latest/tips_and_tricks/#using-sccache-or-ccache-with-rattler-build
+rattler-build build --recipe conda/recipes/libcuvs \
+                    --experimental \
+                    --no-build-id \
+                    --channel-priority disabled \
+                    --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
+                    "${RATTLER_CHANNELS[@]}"
 
 sccache --show-adv-stats
 
+# remove build_cache directory
+rm -rf "$RAPIDS_CONDA_BLD_OUTPUT_DIR"/build_cache
+
 rapids-upload-conda-to-s3 cpp
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 4b85059dd2..4576bd9880 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -23,22 +23,39 @@ echo "${version}" > VERSION
 
 sccache --zero-stats
 
-# TODO: Remove `--no-test` flags once importing on a CPU
-# node works correctly
-rapids-conda-retry build \
-  --no-test \
-  --channel "${CPP_CHANNEL}" \
-  conda/recipes/cuvs
+
+# populates `RATTLER_CHANNELS` array
+source rapids-rattler-channel-string
+
+rapids-logger "Prepending channel ${CPP_CHANNEL} to RATTLER_CHANNELS"
+
+RATTLER_CHANNELS=("--channel" "${CPP_CHANNEL}" "${RATTLER_CHANNELS[@]}")
+
+sccache --zero-stats
+
+rapids-logger "Building cuvs"
+
+# --no-build-id allows for caching with `sccache`
+# more info is available at
+# https://rattler.build/latest/tips_and_tricks/#using-sccache-or-ccache-with-rattler-build
+rattler-build build --recipe conda/recipes/cuvs \
+                    --experimental \
+                    --no-build-id \
+                    --channel-priority disabled \
+                    --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
+                    --test skip \
+                    "${RATTLER_CHANNELS[@]}"
 
 sccache --show-adv-stats
 sccache --zero-stats
 
-# Build cuvs-bench for each cuda and python version
-rapids-conda-retry build \
-  --no-test \
-  --channel "${CPP_CHANNEL}" \
-  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
-  conda/recipes/cuvs-bench
+rattler-build build --recipe conda/recipes/cuvs-bench \
+                    --experimental \
+                    --no-build-id \
+                    --channel-priority disabled \
+                    --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
+                    --test skip \
+                    "${RATTLER_CHANNELS[@]}"
 
 sccache --show-adv-stats
 sccache --zero-stats
@@ -47,11 +64,12 @@ sccache --zero-stats
 # version
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
-  rapids-conda-retry build \
-  --channel "${CPP_CHANNEL}" \
-  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
-  conda/recipes/cuvs-bench-cpu
-
+  rattler-build build --recipe conda/recipes/cuvs-bench-cpu \
+                      --experimental \
+                      --no-build-id \
+                      --channel-priority disabled \
+                      --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
+                      "${RATTLER_CHANNELS[@]}"
   sccache --show-adv-stats
 fi
 
diff --git a/conda/recipes/cuvs-bench-cpu/build.sh b/conda/recipes/cuvs-bench-cpu/build.sh
deleted file mode 100644
index 163872053e..0000000000
--- a/conda/recipes/cuvs-bench-cpu/build.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) 2024, NVIDIA CORPORATION.
-
-./build.sh bench-ann --cpu-only --no-nvtx --build-metrics=bench_ann_cpu --incl-cache-stats
-cmake --install cpp/build --component ann_bench
diff --git a/conda/recipes/cuvs-bench-cpu/meta.yaml b/conda/recipes/cuvs-bench-cpu/meta.yaml
deleted file mode 100644
index d5502cb374..0000000000
--- a/conda/recipes/cuvs-bench-cpu/meta.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
-
-# Usage:
-#   conda build .  -c rapidsai -c conda-forge -c nvidia
-{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
-{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
-{% set py_version = environ['CONDA_PY'] %}
-{% set date_string = environ['RAPIDS_DATE_STRING'] %}
-
-package:
-  name: cuvs-bench-cpu
-  version: {{ version }}
-  script: build.sh
-
-source:
-  path: ../../..
-
-build:
-  script_env:
-    - AWS_ACCESS_KEY_ID
-    - AWS_SECRET_ACCESS_KEY
-    - AWS_SESSION_TOKEN
-    - CMAKE_C_COMPILER_LAUNCHER
-    - CMAKE_CUDA_COMPILER_LAUNCHER
-    - CMAKE_CXX_COMPILER_LAUNCHER
-    - CMAKE_GENERATOR
-    - PARALLEL_LEVEL
-    - RAPIDS_ARTIFACTS_DIR
-    - SCCACHE_BUCKET
-    - SCCACHE_IDLE_TIMEOUT
-    - SCCACHE_REGION
-    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-cpu-aarch64 # [aarch64]
-    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-cpu-linux64 # [linux64]
-    - SCCACHE_S3_USE_SSL
-  number: {{ GIT_DESCRIBE_NUMBER }}
-  string: py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
-
-requirements:
-  build:
-    - {{ compiler('c') }}
-    - {{ compiler('cxx') }}
-    - cmake {{ cmake_version }}
-    - ninja
-    - {{ stdlib("c") }}
-
-  host:
-    - benchmark
-    - glog {{ glog_version }}
-    - libaio  # [linux64]
-    - libboost-devel=1.87  # [linux64]
-    - mkl-devel=2023  # [linux64]
-    - nlohmann_json {{ nlohmann_json_version }}
-    - openblas
-    - python
-    - rapids-build-backend>=0.3.0,<0.4.0.dev0
-
-  run:
-    - benchmark
-    - click
-    - glog {{ glog_version }}
-    - h5py {{ h5py_version }}
-    - matplotlib-base
-    - mkl =2023  # [linux64]
-    - numpy >=1.23,<3.0a0
-    - pandas
-    - pyyaml
-    - python
-    - requests
-    - scikit-learn>=1.5
-
-test:
-  # test that the package is installable and these modules are importable
-  imports:
-    - cuvs_bench
-    - cuvs_bench.generate_groundtruth
-    - cuvs_bench.get_dataset
-    - cuvs_bench.plot
-    - cuvs_bench.run
-    - cuvs_bench.split_groundtruth
-
-about:
-  home: https://rapids.ai/
-  license: Apache-2.0
-  summary: cuVS CPU benchmarks
diff --git a/conda/recipes/cuvs-bench-cpu/recipe.yaml b/conda/recipes/cuvs-bench-cpu/recipe.yaml
new file mode 100644
index 0000000000..3061f16eaf
--- /dev/null
+++ b/conda/recipes/cuvs-bench-cpu/recipe.yaml
@@ -0,0 +1,110 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+schema_version: 1
+
+context:
+  version: ${{ env.get("RAPIDS_PACKAGE_VERSION") }}
+  date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
+  py_version: ${{ env.get("RAPIDS_PY_VERSION") }}
+  py_buildstring: ${{ py_version | version_to_buildstring }}
+  head_rev: ${{ git.head_rev(".")[:8] }}
+  linux64: ${{ linux and x86_64 }}
+
+package:
+  name: cuvs-bench-cpu
+  version: ${{ version }}
+
+source:
+  path: ../../..
+
+build:
+  string: py${{ py_buildstring }}_${{ date_string }}_${{ head_rev }}
+  script:
+    content: |
+
+      # Remove `-fdebug-prefix-map` line from CFLAGS and CXXFLAGS so the
+      # incrementing version number in the compile line doesn't break the
+      # cache
+      set -x
+      export CFLAGS=$(echo $CFLAGS | sed -E 's@\-fdebug\-prefix\-map[^ ]*@@g')
+      export CXXFLAGS=$(echo $CXXFLAGS | sed -E 's@\-fdebug\-prefix\-map[^ ]*@@g')
+      set +x
+
+      ./build.sh bench-ann --cpu-only --no-nvtx --build-metrics=bench_ann_cpu --incl-cache-stats
+      cmake --install cpp/build --component ann_bench
+
+    secrets:
+      - AWS_ACCESS_KEY_ID
+      - AWS_SECRET_ACCESS_KEY
+      - AWS_SESSION_TOKEN
+    env:
+      CMAKE_C_COMPILER_LAUNCHER: ${{ env.get("CMAKE_C_COMPILER_LAUNCHER") }}
+      CMAKE_CXX_COMPILER_LAUNCHER: ${{ env.get("CMAKE_CXX_COMPILER_LAUNCHER") }}
+      CMAKE_GENERATOR: ${{ env.get("CMAKE_GENERATOR") }}
+      PARALLEL_LEVEL: ${{ env.get("PARALLEL_LEVEL") }}
+      RAPIDS_ARTIFACTS_DIR: ${{ env.get("RAPIDS_ARTIFACTS_DIR") }}
+      SCCACHE_BUCKET: ${{ env.get("SCCACHE_BUCKET") }}
+      SCCACHE_IDLE_TIMEOUT: ${{ env.get("SCCACHE_IDLE_TIMEOUT") }}
+      SCCACHE_REGION: ${{ env.get("SCCACHE_REGION") }}
+      SCCACHE_S3_USE_SSL: ${{ env.get("SCCACHE_S3_USE_SSL") }}
+      SCCACHE_S3_NO_CREDENTIALS: ${{ env.get("SCCACHE_S3_NO_CREDENTIALS") }}
+      SCCACHE_S3_KEY_PREFIX: cuvs-bench-cpu/${{ env.get("RAPIDS_CONDA_ARCH") }}
+
+requirements:
+  build:
+    - ${{ compiler("c") }}
+    - ${{ compiler("cxx") }}
+    - cmake ${{ cmake_version }}
+    - ninja
+    - ${{ stdlib("c") }}
+  host:
+    - benchmark
+    - glog ${{ glog_version }}
+    - nlohmann_json ${{ nlohmann_json_version }}
+    - openblas
+    - pip
+    - python =${{ py_version }}
+    - rapids-build-backend>=0.3.0,<0.4.0.dev0
+    - if: linux64
+      then:
+        - libaio
+        - libboost-devel=1.87
+        - mkl-devel=2023
+  run:
+    - benchmark
+    - click
+    - glog ${{ glog_version }}
+    - h5py ${{ h5py_version }}
+    - matplotlib-base
+    - numpy >=1.23,<3.0a0
+    - pandas
+    - pyyaml
+    - python
+    - requests
+    - scikit-learn>=1.5
+    - if: linux64
+      then:
+        - mkl =2023
+  ignore_run_exports:
+    by_name:
+      - glog
+      - libaio
+      - libboost
+      - libcublas
+      - mkl  # relaxes pin from host `mkl-devel`
+
+
+tests:
+  - python:
+      imports:
+        - cuvs_bench
+        - cuvs_bench.generate_groundtruth
+        - cuvs_bench.get_dataset
+        - cuvs_bench.plot
+        - cuvs_bench.run
+        - cuvs_bench.split_groundtruth
+      pip_check: false
+
+about:
+  homepage: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.urls.Homepage }}
+  license: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.license.text }}
+  summary: cuVS CPU benchmark
diff --git a/conda/recipes/cuvs-bench/build.sh b/conda/recipes/cuvs-bench/build.sh
deleted file mode 100644
index 05fb7bada3..0000000000
--- a/conda/recipes/cuvs-bench/build.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) 2024, NVIDIA CORPORATION.
-
-./build.sh bench-ann --allgpuarch --no-nvtx --build-metrics=bench_ann --incl-cache-stats
-cmake --install cpp/build --component ann_bench
diff --git a/conda/recipes/cuvs-bench/meta.yaml b/conda/recipes/cuvs-bench/meta.yaml
deleted file mode 100644
index 5a6ff5abae..0000000000
--- a/conda/recipes/cuvs-bench/meta.yaml
+++ /dev/null
@@ -1,112 +0,0 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
-
-# Usage:
-#   conda build . -c rapidsai -c conda-forge -c nvidia
-{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
-{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
-{% set py_version = environ['CONDA_PY'] %}
-{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
-{% set cuda_major = cuda_version.split('.')[0] %}
-{% set date_string = environ['RAPIDS_DATE_STRING'] %}
-
-package:
-  name: cuvs-bench
-  version: {{ version }}
-  script: build.sh
-
-source:
-  path: ../../..
-
-build:
-  script_env:
-    - AWS_ACCESS_KEY_ID
-    - AWS_SECRET_ACCESS_KEY
-    - AWS_SESSION_TOKEN
-    - CMAKE_C_COMPILER_LAUNCHER
-    - CMAKE_CUDA_COMPILER_LAUNCHER
-    - CMAKE_CXX_COMPILER_LAUNCHER
-    - CMAKE_GENERATOR
-    - PARALLEL_LEVEL
-    - RAPIDS_ARTIFACTS_DIR
-    - SCCACHE_BUCKET
-    - SCCACHE_IDLE_TIMEOUT
-    - SCCACHE_REGION
-    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-aarch64 # [aarch64]
-    - SCCACHE_S3_KEY_PREFIX=cuvs-bench-linux64 # [linux64]
-    - SCCACHE_S3_USE_SSL
-  number: {{ GIT_DESCRIBE_NUMBER }}
-  string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
-  ignore_run_exports_from:
-    - {{ compiler('cuda') }}
-    {% if cuda_major != "11" %}
-    - cuda-cudart-dev
-    - libcublas-dev
-    {% endif %}
-
-requirements:
-  build:
-    - {{ compiler('c') }}
-    - {{ compiler('cxx') }}
-    {% if cuda_major == "11" %}
-    - {{ compiler('cuda') }} ={{ cuda_version }}
-    {% else %}
-    - {{ compiler('cuda') }}
-    {% endif %}
-    - cuda-version ={{ cuda_version }}
-    - cmake {{ cmake_version }}
-    - ninja
-    - {{ stdlib("c") }}
-
-  host:
-    - benchmark
-    - cuda-version ={{ cuda_version }}
-    {% if cuda_major == "11" %}
-    - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
-    - libcublas {{ cuda11_libcublas_host_version }}
-    - libcublas-dev {{ cuda11_libcublas_host_version }}
-    {% else %}
-    - cuda-cudart-dev
-    - cuda-profiler-api
-    - libcublas-dev
-    {% endif %}
-    - glog {{ glog_version }}
-    - libaio  # [linux64]
-    - libboost-devel=1.87  # [linux64]
-    - libcuvs {{ version }}
-    - mkl-devel=2023  # [linux64]
-    - nlohmann_json {{ nlohmann_json_version }}
-    - openblas
-    # rmm is needed to determine if package is gpu-enabled
-    - python
-    - rapids-build-backend>=0.3.0,<0.4.0.dev0
-    - rmm ={{ minor_version }}
-    - setuptools>=64.0.0
-
-  run:
-    - benchmark
-    - click
-    - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
-    {% if cuda_major == "11" %}
-    - cudatoolkit
-    {% else %}
-    - cuda-cudart
-    - cupy>=12.0.0
-    - libcublas
-    {% endif %}
-    - glog {{ glog_version }}
-    - cuvs {{ version }}
-    - h5py {{ h5py_version }}
-    - matplotlib-base
-    - mkl =2023  # [linux64]
-    - pandas
-    - pyyaml
-    # rmm is needed to determine if package is gpu-enabled
-    - pylibraft ={{ minor_version }}
-    - python
-    - requests
-    - rmm ={{ minor_version }}
-    - scikit-learn>=1.5
-about:
-  home: https://rapids.ai/
-  license: Apache-2.0
-  summary: cuVS GPU and CPU benchmarks
diff --git a/conda/recipes/cuvs-bench/recipe.yaml b/conda/recipes/cuvs-bench/recipe.yaml
new file mode 100644
index 0000000000..5244915ae9
--- /dev/null
+++ b/conda/recipes/cuvs-bench/recipe.yaml
@@ -0,0 +1,84 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+schema_version: 1
+
+context:
+  version: ${{ env.get("RAPIDS_PACKAGE_VERSION") }}
+  minor_version: ${{ (version | split("."))[:2] | join(".") }}
+  cuda_version: ${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[:2] | join(".") }}
+  cuda_major: '${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[0] }}'
+  date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
+  py_version: ${{ env.get("RAPIDS_PY_VERSION") }}
+  py_buildstring: ${{ py_version | version_to_buildstring }}
+  head_rev: ${{ git.head_rev(".")[:8] }}
+  linux64: ${{ linux and x86_64 }}
+
+package:
+  name: cuvs-bench
+  version: ${{ version }}
+
+source:
+  path: ../../..
+
+build:
+  string: cuda${{ cuda_major }}_py${{ py_buildstring }}_${{ date_string }}_${{ head_rev }}
+  script:
+    content: |
+      python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ./python/cuvs_bench
+
+requirements:
+  host:
+    - cuda-version =${{ cuda_version }}
+    - libcuvs =${{ version }}
+    - libcuvs-bench-ann =${{ version }}
+    - python =${{ py_version }}
+    - pip
+    - rapids-build-backend >=0.3.0,<0.4.0.dev0
+    - rmm =${{ minor_version }}
+    - setuptools >=64.0.0
+  run:
+    - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
+    - benchmark
+    - click
+    - cuvs =${{ version }}
+    - glog ${{ glog_version }}
+    - h5py ${{ h5py_version }}
+    - libcuvs-bench-ann =${{ version }}
+    - matplotlib-base
+    - pandas
+    - pylibraft =${{ minor_version }}
+    - python
+    - pyyaml
+    - requests
+    # rmm is needed to determine if package is gpu-enabled
+    - rmm =${{ minor_version }}
+    - scikit-learn>=1.5
+    - if: linux64
+      then: mkl =2023
+    - if: cuda_major == "11"
+      then:
+        - cudatoolkit
+      else:
+        - cuda-cudart
+        - cupy >=12.0.0
+        - libcublas
+  ignore_run_exports:
+    by_name:
+      - cuda-version
+      - cuda-cudart
+      - glog
+      - libaio
+      - libboost
+      - libcublas
+      - mkl
+      - if: cuda_major == "11"
+        then: cudatoolkit
+
+tests:
+  - package_contents:
+      files:
+        - lib/python*/site-packages/cuvs_bench/config/datasets/datasets.yaml
+
+about:
+  homepage: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.urls.Homepage }}
+  license: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.license.text }}
+  summary: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.description }}
diff --git a/conda/recipes/cuvs/build.sh b/conda/recipes/cuvs/build.sh
deleted file mode 100644
index 767d066722..0000000000
--- a/conda/recipes/cuvs/build.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
-#!/usr/bin/env bash
-
-# This assumes the script is executed from the root of the repo directory
-./build.sh python --no-nvtx -v
diff --git a/conda/recipes/cuvs/meta.yaml b/conda/recipes/cuvs/meta.yaml
deleted file mode 100644
index 25fc204a85..0000000000
--- a/conda/recipes/cuvs/meta.yaml
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
-
-# Usage:
-#   conda build . -c rapidsai -c conda-forge -c nvidia
-{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
-{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
-{% set py_version = environ['CONDA_PY'] %}
-{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
-{% set cuda_major = cuda_version.split('.')[0] %}
-{% set date_string = environ['RAPIDS_DATE_STRING'] %}
-
-package:
-  name: cuvs
-  version: {{ version }}
-
-source:
-  path: ../../..
-
-build:
-  number: {{ GIT_DESCRIBE_NUMBER }}
-  string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
-  ignore_run_exports_from:
-    - {{ compiler('cuda') }}
-    {% if cuda_major != "11" %}
-    - cuda-cudart-dev
-    {% endif %}
-    - cuda-python
-
-requirements:
-  build:
-    - {{ compiler('c') }}
-    - {{ compiler('cxx') }}
-    {% if cuda_major == "11" %}
-    - {{ compiler('cuda') }} ={{ cuda_version }}
-    {% else %}
-    - {{ compiler('cuda') }}
-    {% endif %}
-    - cuda-version ={{ cuda_version }}
-    - cmake {{ cmake_version }}
-    - ninja
-    - {{ stdlib("c") }}
-  host:
-    {% if cuda_major == "11" %}
-    - cuda-python >=11.8.5,<12.0a0
-    - cudatoolkit
-    {% else %}
-    - cuda-python >=12.6.2,<13.0a0
-    - cuda-cudart-dev
-    {% endif %}
-    - cuda-version ={{ cuda_version }}
-    - cython >=3.0.0
-    - dlpack >=0.8
-    - pylibraft {{ minor_version }}
-    - libcuvs {{ version }}
-    - python x.x
-    - rapids-build-backend>=0.3.0,<0.4.0.dev0
-    - scikit-build-core >=0.10.0
-  run:
-    - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
-    {% if cuda_major == "11" %}
-    - cudatoolkit
-    - cuda-python >=11.8.5,<12.0a0
-    {% else %}
-    - cuda-cudart
-    - cuda-python >=12.6.2,<13.0a0
-    {% endif %}
-    - pylibraft {{ minor_version }}
-    - libcuvs {{ version }}
-    - python x.x
-    - numpy >=1.23,<3.0a0
-
-tests:
-  requirements:
-    - cuda-version ={{ cuda_version }}
-  imports:
-    - cuvs
-
-about:
-  home: https://rapids.ai/
-  license: Apache-2.0
-  summary: cuvs python library
diff --git a/conda/recipes/cuvs/recipe.yaml b/conda/recipes/cuvs/recipe.yaml
new file mode 100644
index 0000000000..36249d3746
--- /dev/null
+++ b/conda/recipes/cuvs/recipe.yaml
@@ -0,0 +1,97 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+schema_version: 1
+
+context:
+  version: ${{ env.get("RAPIDS_PACKAGE_VERSION") }}
+  minor_version: ${{ (version | split("."))[:2] | join(".") }}
+  cuda_version: ${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[:2] | join(".") }}
+  cuda_major: '${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[0] }}'
+  date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
+  py_version: ${{ env.get("RAPIDS_PY_VERSION") }}
+  py_buildstring: ${{ py_version | version_to_buildstring }}
+  head_rev: ${{ git.head_rev(".")[:8] }}
+
+package:
+  name: cuvs
+  version: ${{ version }}
+
+source:
+  path: ../../..
+
+build:
+  string: cuda${{ cuda_major }}_py${{ py_buildstring }}_${{ date_string }}_${{ head_rev }}
+  script:
+    content: |
+      ./build.sh python --no-nvtx -v
+    secrets:
+      - AWS_ACCESS_KEY_ID
+      - AWS_SECRET_ACCESS_KEY
+      - AWS_SESSION_TOKEN
+    env:
+      CMAKE_C_COMPILER_LAUNCHER: ${{ env.get("CMAKE_C_COMPILER_LAUNCHER") }}
+      CMAKE_CUDA_COMPILER_LAUNCHER: ${{ env.get("CMAKE_CUDA_COMPILER_LAUNCHER") }}
+      CMAKE_CXX_COMPILER_LAUNCHER: ${{ env.get("CMAKE_CXX_COMPILER_LAUNCHER") }}
+      CMAKE_GENERATOR: ${{ env.get("CMAKE_GENERATOR") }}
+      SCCACHE_BUCKET: ${{ env.get("SCCACHE_BUCKET") }}
+      SCCACHE_IDLE_TIMEOUT: ${{ env.get("SCCACHE_IDLE_TIMEOUT") }}
+      SCCACHE_REGION: ${{ env.get("SCCACHE_REGION") }}
+      SCCACHE_S3_USE_SSL: ${{ env.get("SCCACHE_S3_USE_SSL") }}
+      SCCACHE_S3_NO_CREDENTIALS: ${{ env.get("SCCACHE_S3_NO_CREDENTIALS") }}
+      SCCACHE_S3_KEY_PREFIX: cuvs/${{ env.get("RAPIDS_CONDA_ARCH") }}/cuda${{ cuda_major }}
+
+requirements:
+  build:
+    - cmake ${{ cmake_version }}
+    - ninja
+    - ${{ compiler("c") }}
+    - ${{ compiler("cxx") }}
+    - ${{ compiler("cuda") }}
+    - cuda-version =${{ cuda_version }}
+    - ${{ stdlib("c") }}
+  host:
+    - cuda-version =${{ cuda_version }}
+    - cython >=3.0.0
+    - dlpack >=0.8
+    - libcuvs =${{ version }}
+    - pip
+    - pylibraft =${{ minor_version }}
+    - python =${{ py_version }}
+    - rapids-build-backend >=0.3.0,<0.4.0.dev0
+    - scikit-build-core >=0.10.0
+    - if: cuda_major == "11"
+      then:
+        - cuda-python >=11.8.5,<12.0a0
+        - cudatoolkit
+      else:
+        - cuda-python >=12.6.2,<13.0a0
+        - cuda-cudart-dev
+  run:
+    - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
+    - libcuvs =${{ version }}
+    - pylibraft =${{ minor_version }}
+    - python
+    - numpy >=1.23,<3.0a0
+    - if: cuda_major == "11"
+      then:
+        - cuda-python >=11.8.5,<12.0a0
+        - cudatoolkit
+      else:
+        - cuda-python >=12.6.2,<13.0a0
+        - cuda-cudart
+  ignore_run_exports:
+    by_name:
+      - cuda-version
+      - cuda-cudart
+      - if: cuda_major == "11"
+        then: cudatoolkit
+
+tests:
+  - python:
+      imports:
+        - cuvs
+      pip_check: false
+
+about:
+  homepage: ${{ load_from_file("python/cuvs/pyproject.toml").project.urls.Homepage }}
+  license: ${{ load_from_file("python/cuvs/pyproject.toml").project.license.text }}
+  summary: ${{ load_from_file("python/cuvs/pyproject.toml").project.description }}
diff --git a/conda/recipes/libcuvs/build_libcuvs.sh b/conda/recipes/libcuvs/build_libcuvs.sh
deleted file mode 100644
index cbde787a36..0000000000
--- a/conda/recipes/libcuvs/build_libcuvs.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
-
-./build.sh libcuvs --allgpuarch --build-metrics=compile_lib --incl-cache-stats --no-nvtx
diff --git a/conda/recipes/libcuvs/build_libcuvs_examples.sh b/conda/recipes/libcuvs/build_libcuvs_examples.sh
deleted file mode 100644
index 6286a530e6..0000000000
--- a/conda/recipes/libcuvs/build_libcuvs_examples.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
-
-# Just building template so we verify it uses libraft.so and fail if it doesn't build
-./build.sh examples
diff --git a/conda/recipes/libcuvs/build_libcuvs_static.sh b/conda/recipes/libcuvs/build_libcuvs_static.sh
deleted file mode 100644
index a107a9d6d1..0000000000
--- a/conda/recipes/libcuvs/build_libcuvs_static.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
-
-./build.sh libcuvs --allgpuarch --compile-static-lib --build-metrics=compile_lib_static --incl-cache-stats --no-nvtx -n
-cmake --install cpp/build --component compiled-static
diff --git a/conda/recipes/libcuvs/build_libcuvs_tests.sh b/conda/recipes/libcuvs/build_libcuvs_tests.sh
deleted file mode 100644
index b077dbe609..0000000000
--- a/conda/recipes/libcuvs/build_libcuvs_tests.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
-
-./build.sh tests --allgpuarch --no-nvtx --build-metrics=tests --incl-cache-stats
-cmake --install cpp/build --component testing
diff --git a/conda/recipes/libcuvs/meta.yaml b/conda/recipes/libcuvs/meta.yaml
deleted file mode 100644
index fd466cd225..0000000000
--- a/conda/recipes/libcuvs/meta.yaml
+++ /dev/null
@@ -1,303 +0,0 @@
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
-
-# Usage:
-#   conda build . -c rapidsai -c conda-forge -c nvidia
-{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
-{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
-{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
-{% set cuda_major = cuda_version.split('.')[0] %}
-{% set date_string = environ['RAPIDS_DATE_STRING'] %}
-
-package:
-  name: libcuvs-split
-
-source:
-  path: ../../..
-
-outputs:
-  - name: libcuvs-static
-    version: {{ version }}
-    script: build_libcuvs_static.sh
-    build:
-      script_env: &script_env
-        - AWS_ACCESS_KEY_ID
-        - AWS_SECRET_ACCESS_KEY
-        - AWS_SESSION_TOKEN
-        - CMAKE_C_COMPILER_LAUNCHER
-        - CMAKE_CUDA_COMPILER_LAUNCHER
-        - CMAKE_CXX_COMPILER_LAUNCHER
-        - CMAKE_GENERATOR
-        - PARALLEL_LEVEL
-        - RAPIDS_ARTIFACTS_DIR
-        - SCCACHE_BUCKET
-        - SCCACHE_IDLE_TIMEOUT
-        - SCCACHE_REGION
-        - SCCACHE_S3_KEY_PREFIX=libcuvs-aarch64 # [aarch64]
-        - SCCACHE_S3_KEY_PREFIX=libcuvs-linux64 # [linux64]
-        - SCCACHE_S3_USE_SSL
-        - SCCACHE_S3_NO_CREDENTIALS
-      number: {{ GIT_DESCRIBE_NUMBER }}
-      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
-      ignore_run_exports_from:
-        - {{ compiler('cuda') }}
-        {% if cuda_major != "11" %}
-        - cuda-cudart-dev
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-    requirements:
-      build:
-        - {{ compiler('c') }}
-        - {{ compiler('cxx') }}
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda') }} ={{ cuda_version }}
-        {% else %}
-        - {{ compiler('cuda') }}
-        {% endif %}
-        - cuda-version ={{ cuda_version }}
-        - cmake {{ cmake_version }}
-        - ninja
-        - {{ stdlib("c") }}
-      host:
-        - librmm ={{ minor_version }}
-        - libraft-headers ={{ minor_version }}
-        - nccl {{ nccl_version }}
-        - cuda-version ={{ cuda_version }}
-        {% if cuda_major == "11" %}
-        - cuda-profiler-api {{ cuda11_cuda_profiler_api_host_version }}
-        - libcublas {{ cuda11_libcublas_host_version }}
-        - libcublas-dev {{ cuda11_libcublas_host_version }}
-        - libcurand {{ cuda11_libcurand_host_version }}
-        - libcurand-dev {{ cuda11_libcurand_host_version }}
-        - libcusolver {{ cuda11_libcusolver_host_version }}
-        - libcusolver-dev {{ cuda11_libcusolver_host_version }}
-        - libcusparse {{ cuda11_libcusparse_host_version }}
-        - libcusparse-dev {{ cuda11_libcusparse_host_version }}
-        {% else %}
-        - cuda-cudart-dev
-        - cuda-profiler-api
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-      run:
-        - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
-        {% if cuda_major != "11" %}
-        - cuda-cudart
-        - libcublas
-        - libcurand
-        - libcusolver
-        - libcusparse
-        {% endif %}
-    about:
-      home: https://rapids.ai/
-      license: Apache-2.0
-      summary: libcuvs static library
-  - name: libcuvs
-    version: {{ version }}
-    script: build_libcuvs.sh
-    build:
-      script_env: *script_env
-      number: {{ GIT_DESCRIBE_NUMBER }}
-      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
-      ignore_run_exports_from:
-        - {{ compiler('cuda') }}
-        {% if cuda_major != "11" %}
-        - cuda-cudart-dev
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-    requirements:
-      build:
-        - {{ compiler('c') }}
-        - {{ compiler('cxx') }}
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda') }} ={{ cuda_version }}
-        {% else %}
-        - {{ compiler('cuda') }}
-        {% endif %}
-        - cuda-version ={{ cuda_version }}
-        - cmake {{ cmake_version }}
-        - ninja
-        - {{ stdlib("c") }}
-      host:
-        - librmm ={{ minor_version }}
-        - libraft-headers ={{ minor_version }}
-        - nccl {{ nccl_version }}
-        - cuda-version ={{ cuda_version }}
-        {% if cuda_major == "11" %}
-        - cuda-profiler-api {{ cuda11_cuda_profiler_api_host_version }}
-        - libcublas {{ cuda11_libcublas_host_version }}
-        - libcublas-dev {{ cuda11_libcublas_host_version }}
-        - libcurand {{ cuda11_libcurand_host_version }}
-        - libcurand-dev {{ cuda11_libcurand_host_version }}
-        - libcusolver {{ cuda11_libcusolver_host_version }}
-        - libcusolver-dev {{ cuda11_libcusolver_host_version }}
-        - libcusparse {{ cuda11_libcusparse_host_version }}
-        - libcusparse-dev {{ cuda11_libcusparse_host_version }}
-        {% else %}
-        - cuda-cudart-dev
-        - cuda-profiler-api
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-      run:
-        - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
-        {% if cuda_major != "11" %}
-        - cuda-cudart
-        - libcublas
-        - libcurand
-        - libcusolver
-        - libcusparse
-        {% endif %}
-        - libraft-headers ={{ minor_version }}
-    about:
-      home: https://rapids.ai/
-      license: Apache-2.0
-      summary: libcuvs library
-  - name: libcuvs-tests
-    version: {{ version }}
-    script: build_libcuvs_tests.sh
-    build:
-      script_env: *script_env
-      number: {{ GIT_DESCRIBE_NUMBER }}
-      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
-      ignore_run_exports_from:
-        - {{ compiler('cuda') }}
-        {% if cuda_major != "11" %}
-        - cuda-cudart-dev
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-    requirements:
-      build:
-        - {{ compiler('c') }}
-        - {{ compiler('cxx') }}
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda') }} ={{ cuda_version }}
-        {% else %}
-        - {{ compiler('cuda') }}
-        {% endif %}
-        - cuda-version ={{ cuda_version }}
-        - cmake {{ cmake_version }}
-        - ninja
-        - {{ stdlib("c") }}
-      host:
-        - librmm ={{ minor_version }}
-        - libraft-headers ={{ minor_version }}
-        - nccl {{ nccl_version }}
-        - {{ pin_subpackage('libcuvs', exact=True) }}
-        - cuda-version ={{ cuda_version }}
-        - openblas # required by some CPU algos in benchmarks
-        {% if cuda_major == "11" %}
-        - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
-        - libcublas {{ cuda11_libcublas_host_version }}
-        - libcublas-dev {{ cuda11_libcublas_host_version }}
-        - libcurand {{ cuda11_libcurand_host_version }}
-        - libcurand-dev {{ cuda11_libcurand_host_version }}
-        - libcusolver {{ cuda11_libcusolver_host_version }}
-        - libcusolver-dev {{ cuda11_libcusolver_host_version }}
-        - libcusparse {{ cuda11_libcusparse_host_version }}
-        - libcusparse-dev {{ cuda11_libcusparse_host_version }}
-        {% else %}
-        - cuda-cudart-dev
-        - cuda-profiler-api
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-      run:
-        - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
-        {% if cuda_major == "11" %}
-        - cudatoolkit
-        {% else %}
-        - cuda-cudart
-        - libcublas
-        - libcurand
-        - libcusolver
-        - libcusparse
-        {% endif %}
-        - {{ pin_subpackage('libcuvs', exact=True) }}
-    about:
-      home: https://rapids.ai/
-      license: Apache-2.0
-      summary: libcuvs tests
-  - name: libcuvs-examples
-    version: {{ version }}
-    script: build_libcuvs_examples.sh
-    build:
-      script_env: *script_env
-      number: {{ GIT_DESCRIBE_NUMBER }}
-      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
-      ignore_run_exports_from:
-        - {{ compiler('cuda') }}
-        {% if cuda_major != "11" %}
-        - cuda-cudart-dev
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-    requirements:
-      build:
-        - {{ compiler('c') }}
-        - {{ compiler('cxx') }}
-        {% if cuda_major == "11" %}
-        - {{ compiler('cuda') }} ={{ cuda_version }}
-        {% else %}
-        - {{ compiler('cuda') }}
-        {% endif %}
-        - cuda-version ={{ cuda_version }}
-        - cmake {{ cmake_version }}
-        - ninja
-        - {{ stdlib("c") }}
-      host:
-        - librmm ={{ minor_version }}
-        - libraft-headers ={{ minor_version }}
-        - nccl {{ nccl_version }}
-        - {{ pin_subpackage('libcuvs', exact=True) }}
-        - cuda-version ={{ cuda_version }}
-        {% if cuda_major == "11" %}
-        - cuda-profiler-api {{ cuda11_cuda_profiler_api_run_version }}
-        - libcublas {{ cuda11_libcublas_host_version }}
-        - libcublas-dev {{ cuda11_libcublas_host_version }}
-        - libcurand {{ cuda11_libcurand_host_version }}
-        - libcurand-dev {{ cuda11_libcurand_host_version }}
-        - libcusolver {{ cuda11_libcusolver_host_version }}
-        - libcusolver-dev {{ cuda11_libcusolver_host_version }}
-        - libcusparse {{ cuda11_libcusparse_host_version }}
-        - libcusparse-dev {{ cuda11_libcusparse_host_version }}
-        {% else %}
-        - cuda-cudart-dev
-        - cuda-profiler-api
-        - libcublas-dev
-        - libcurand-dev
-        - libcusolver-dev
-        - libcusparse-dev
-        {% endif %}
-      run:
-        - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
-        {% if cuda_major == "11" %}
-        - cudatoolkit
-        {% else %}
-        - cuda-cudart
-        - libcublas
-        - libcurand
-        - libcusolver
-        - libcusparse
-        {% endif %}
-        - {{ pin_subpackage('libcuvs', exact=True) }}
-    about:
-      home: https://rapids.ai/
-      license: Apache-2.0
-      summary: libcuvs examples
diff --git a/conda/recipes/libcuvs/recipe.yaml b/conda/recipes/libcuvs/recipe.yaml
new file mode 100644
index 0000000000..0be4317a24
--- /dev/null
+++ b/conda/recipes/libcuvs/recipe.yaml
@@ -0,0 +1,424 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+schema_version: 1
+
+context:
+  version: ${{ env.get("RAPIDS_PACKAGE_VERSION") }}
+  minor_version: ${{ (version | split("."))[:2] | join(".") }}
+  cuda_version: ${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[:2] | join(".") }}
+  cuda_major: '${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[0] }}'
+  date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
+  head_rev: ${{ git.head_rev(".")[:8] }}
+  linux64: ${{ linux and x86_64 }}
+
+recipe:
+  name: libcuvs-split
+
+cache:
+  source:
+    path: ../../..
+
+  build:
+    script:
+      content: |
+
+        # Remove `-fdebug-prefix-map` line from CFLAGS and CXXFLAGS so the
+        # incrementing version number in the compile line doesn't break the
+        # cache
+        set -x
+        export CFLAGS=$(echo $CFLAGS | sed -E 's@\-fdebug\-prefix\-map[^ ]*@@g')
+        export CXXFLAGS=$(echo $CXXFLAGS | sed -E 's@\-fdebug\-prefix\-map[^ ]*@@g')
+        set +x
+
+        ./build.sh libcuvs bench-ann tests --allgpuarch --build-metrics=compile_lib --incl-cache-stats --no-nvtx -n
+
+      secrets:
+        - AWS_ACCESS_KEY_ID
+        - AWS_SECRET_ACCESS_KEY
+        - AWS_SESSION_TOKEN
+      env:
+        CMAKE_C_COMPILER_LAUNCHER: ${{ env.get("CMAKE_C_COMPILER_LAUNCHER") }}
+        CMAKE_CUDA_COMPILER_LAUNCHER: ${{ env.get("CMAKE_CUDA_COMPILER_LAUNCHER") }}
+        CMAKE_CXX_COMPILER_LAUNCHER: ${{ env.get("CMAKE_CXX_COMPILER_LAUNCHER") }}
+        CMAKE_GENERATOR: ${{ env.get("CMAKE_GENERATOR") }}
+        PARALLEL_LEVEL: ${{ env.get("PARALLEL_LEVEL") }}
+        RAPIDS_ARTIFACTS_DIR: ${{ env.get("RAPIDS_ARTIFACTS_DIR") }}
+        SCCACHE_BUCKET: ${{ env.get("SCCACHE_BUCKET") }}
+        SCCACHE_IDLE_TIMEOUT: ${{ env.get("SCCACHE_IDLE_TIMEOUT") }}
+        SCCACHE_REGION: ${{ env.get("SCCACHE_REGION") }}
+        SCCACHE_S3_USE_SSL: ${{ env.get("SCCACHE_S3_USE_SSL") }}
+        SCCACHE_S3_NO_CREDENTIALS: ${{ env.get("SCCACHE_S3_NO_CREDENTIALS") }}
+        SCCACHE_S3_KEY_PREFIX: libcuvs/${{ env.get("RAPIDS_CONDA_ARCH") }}/cuda${{ cuda_major }}
+  requirements:
+    build:
+      - ${{ compiler("c") }}
+      - ${{ compiler("cxx") }}
+      - ${{ compiler("cuda") }}
+      - cuda-version =${{ cuda_version }}
+      - cmake ${{ cmake_version }}
+      - ninja
+      - ${{ stdlib("c") }}
+    host:
+      - librmm =${{ minor_version }}
+      - libraft-headers =${{ minor_version }}
+      - nccl ${{ nccl_version }}
+      - cuda-version =${{ cuda_version }}
+      - if: cuda_major == "11"
+        then:
+          - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
+          - libcublas =${{ cuda11_libcublas_host_version }}
+          - libcublas-dev =${{ cuda11_libcublas_host_version }}
+          - libcurand =${{ cuda11_libcurand_host_version }}
+          - libcurand-dev =${{ cuda11_libcurand_host_version }}
+          - libcusolver =${{ cuda11_libcusolver_host_version }}
+          - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
+          - libcusparse =${{ cuda11_libcusparse_host_version }}
+          - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
+        else:
+          - cuda-cudart-dev
+          - cuda-profiler-api
+          - libcublas-dev
+          - libcurand-dev
+          - libcusolver-dev
+          - libcusparse-dev
+      # These are used for bench-ann
+      - openblas
+      - if: linux64
+        then:
+          - libaio
+          - libboost-devel =1.87
+          - mkl-devel =2023
+
+outputs:
+  - package:
+      name: libcuvs
+      version: ${{ version }}
+    build:
+      string: cuda${{ cuda_major }}_${{ date_string }}_${{ head_rev }}
+      dynamic_linking:
+        overlinking_behavior: "error"
+      prefix_detection:
+        ignore_binary_files: True
+      script:
+        content: |
+          cmake --install cpp/build --component cuvs
+          cmake --install cpp/build --component c_api
+          cmake --install cpp/build --component hnswlib
+    requirements:
+      build:
+        - cmake ${{ cmake_version }}
+        - ${{ stdlib("c") }}
+      host:
+        - librmm =${{ minor_version }}
+        - libraft-headers =${{ minor_version }}
+        - nccl ${{ nccl_version }}
+        - cuda-version =${{ cuda_version }}
+        - if: cuda_major == "11"
+          then:
+            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
+            - libcublas =${{ cuda11_libcublas_host_version }}
+            - libcublas-dev =${{ cuda11_libcublas_host_version }}
+            - libcurand =${{ cuda11_libcurand_host_version }}
+            - libcurand-dev =${{ cuda11_libcurand_host_version }}
+            - libcusolver =${{ cuda11_libcusolver_host_version }}
+            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
+            - libcusparse =${{ cuda11_libcusparse_host_version }}
+            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
+          else:
+            - cuda-cudart-dev
+            - cuda-profiler-api
+            - libcublas-dev
+            - libcurand-dev
+            - libcusolver-dev
+            - libcusparse-dev
+      run:
+        - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
+        - libraft-headers =${{ minor_version }}
+        - nccl
+        - if: cuda_major == "11"
+          then: cudatoolkit
+          else:
+            - cuda-cudart
+            - libcublas
+            - libcurand
+            - libcusolver
+            - libcusparse
+      ignore_run_exports:
+        by_name:
+          - cuda-cudart
+          - cuda-version
+          - libaio
+          - libboost
+          - libcublas
+          - libcurand
+          - libcusolver
+          - libcusparse
+          - librmm
+          - mkl
+          - nccl
+          - if: cuda_major == "11"
+            then: cudatoolkit
+    about:
+      homepage: ${{ load_from_file("python/libcuvs/pyproject.toml").project.urls.Homepage }}
+      license: ${{ load_from_file("python/libcuvs/pyproject.toml").project.license.text }}
+      summary: ${{ load_from_file("python/libcuvs/pyproject.toml").project.description }}
+
+  - package:
+      name: libcuvs-tests
+      version: ${{ version }}
+    build:
+      script: cmake --install cpp/build --component testing
+      string: cuda${{ cuda_major }}_${{ date_string }}_${{ head_rev }}
+      dynamic_linking:
+        overlinking_behavior: "error"
+      prefix_detection:
+        ignore_binary_files: True
+    requirements:
+      build:
+        - cmake ${{ cmake_version }}
+        - ${{ stdlib("c") }}
+      host:
+        - librmm =${{ minor_version }}
+        - libraft-headers =${{ minor_version }}
+        - nccl ${{ nccl_version }}
+        - ${{ pin_subpackage("libcuvs", exact=True) }}
+        - cuda-version =${{ cuda_version }}
+        - openblas  # required by some CPU algos in benchmarks
+        - if: cuda_major == "11"
+          then:
+            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
+            - libcublas =${{ cuda11_libcublas_host_version }}
+            - libcublas-dev =${{ cuda11_libcublas_host_version }}
+            - libcurand =${{ cuda11_libcurand_host_version }}
+            - libcurand-dev =${{ cuda11_libcurand_host_version }}
+            - libcusolver =${{ cuda11_libcusolver_host_version }}
+            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
+            - libcusparse =${{ cuda11_libcusparse_host_version }}
+            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
+          else:
+            - cuda-cudart-dev
+            - cuda-profiler-api
+            - libcublas-dev
+            - libcurand-dev
+            - libcusolver-dev
+            - libcusparse-dev
+      run:
+        - ${{ pin_subpackage("libcuvs", exact=True) }}
+        - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
+        - libraft-headers =${{ minor_version }}
+        - nccl
+        - if: cuda_major == "11"
+          then: cudatoolkit
+          else:
+            - cuda-cudart
+            - libcublas
+            - libcurand
+            - libcusolver
+            - libcusparse
+      ignore_run_exports:
+        by_name:
+          - cuda-cudart
+          - cuda-version
+          - libaio
+          - libboost
+          - libcublas
+          - libcurand
+          - libcusolver
+          - libcusparse
+          - librmm
+          - mkl
+          - nccl
+          - if: cuda_major == "11"
+            then: cudatoolkit
+    about:
+      homepage: ${{ load_from_file("python/libcuvs/pyproject.toml").project.urls.Homepage }}
+      license: ${{ load_from_file("python/libcuvs/pyproject.toml").project.license.text }}
+      summary: libcuvs test executables
+
+  - package:
+      name: libcuvs-examples
+      version: ${{ version }}
+    build:
+      string: cuda${{ cuda_major }}_${{ date_string }}_${{ head_rev }}
+      dynamic_linking:
+        overlinking_behavior: "error"
+      prefix_detection:
+        ignore_binary_files: True
+      script:
+        content: |
+          # Remove `-fdebug-prefix-map` line from CFLAGS and CXXFLAGS so the
+          # incrementing version number in the compile line doesn't break the
+          # cache
+          set -x
+          export CFLAGS=$(echo $CFLAGS | sed -E 's@\-fdebug\-prefix\-map[^ ]*@@g')
+          export CXXFLAGS=$(echo $CXXFLAGS | sed -E 's@\-fdebug\-prefix\-map[^ ]*@@g')
+          set +x
+
+          ./build.sh examples
+
+        secrets:
+          - AWS_ACCESS_KEY_ID
+          - AWS_SECRET_ACCESS_KEY
+          - AWS_SESSION_TOKEN
+        env:
+          CMAKE_C_COMPILER_LAUNCHER: ${{ env.get("CMAKE_C_COMPILER_LAUNCHER") }}
+          CMAKE_CUDA_COMPILER_LAUNCHER: ${{ env.get("CMAKE_CUDA_COMPILER_LAUNCHER") }}
+          CMAKE_CXX_COMPILER_LAUNCHER: ${{ env.get("CMAKE_CXX_COMPILER_LAUNCHER") }}
+          CMAKE_GENERATOR: ${{ env.get("CMAKE_GENERATOR") }}
+          PARALLEL_LEVEL: ${{ env.get("PARALLEL_LEVEL") }}
+          RAPIDS_ARTIFACTS_DIR: ${{ env.get("RAPIDS_ARTIFACTS_DIR") }}
+          SCCACHE_BUCKET: ${{ env.get("SCCACHE_BUCKET") }}
+          SCCACHE_IDLE_TIMEOUT: ${{ env.get("SCCACHE_IDLE_TIMEOUT") }}
+          SCCACHE_REGION: ${{ env.get("SCCACHE_REGION") }}
+          SCCACHE_S3_USE_SSL: ${{ env.get("SCCACHE_S3_USE_SSL") }}
+          SCCACHE_S3_NO_CREDENTIALS: ${{ env.get("SCCACHE_S3_NO_CREDENTIALS") }}
+          SCCACHE_S3_KEY_PREFIX: libcuvs/${{ env.get("RAPIDS_CONDA_ARCH") }}/cuda${{ cuda_major }}
+    requirements:
+      build:
+        - ${{ compiler("c") }}
+        - ${{ compiler("cxx") }}
+        - ${{ compiler("cuda") }}
+        - cuda-version =${{ cuda_version }}
+        - cmake ${{ cmake_version }}
+        - ninja
+        - ${{ stdlib("c") }}
+      host:
+        - ${{ pin_subpackage("libcuvs", exact=True) }}
+        - cuda-version =${{ cuda_version }}
+        - libraft-headers =${{ minor_version }}
+        - librmm =${{ minor_version }}
+        - nccl ${{ nccl_version }}
+        - if: cuda_major == "11"
+          then:
+            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
+            - libcublas =${{ cuda11_libcublas_host_version }}
+            - libcublas-dev =${{ cuda11_libcublas_host_version }}
+            - libcurand =${{ cuda11_libcurand_host_version }}
+            - libcurand-dev =${{ cuda11_libcurand_host_version }}
+            - libcusolver =${{ cuda11_libcusolver_host_version }}
+            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
+            - libcusparse =${{ cuda11_libcusparse_host_version }}
+            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
+          else:
+            - cuda-cudart-dev
+            - cuda-profiler-api
+            - libcublas-dev
+            - libcurand-dev
+            - libcusolver-dev
+            - libcusparse-dev
+      run:
+        - ${{ pin_subpackage("libcuvs", exact=True) }}
+        - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
+        - nccl
+        - if: cuda_major == "11"
+          then: cudatoolkit
+          else:
+            - cuda-cudart
+            - libcublas
+            - libcurand
+            - libcusolver
+            - libcusparse
+      ignore_run_exports:
+        by_name:
+          - cuda-cudart
+          - cuda-version
+          - libaio
+          - libboost
+          - libcublas
+          - libcurand
+          - libcusolver
+          - libcusparse
+          - librmm
+          - mkl
+          - nccl
+          - if: cuda_major == "11"
+            then: cudatoolkit
+    about:
+      homepage: ${{ load_from_file("python/libcuvs/pyproject.toml").project.urls.Homepage }}
+      license: ${{ load_from_file("python/libcuvs/pyproject.toml").project.license.text }}
+      summary: libcuvs example executables
+
+  - package:
+      name: libcuvs-bench-ann
+      version: ${{ version }}
+    build:
+      script:
+        content: |
+          cmake --install cpp/build --component ann_bench
+
+          if [ "$RAPIDS_CONDA_ARCH" != "aarch64" ]; then
+            # Need this so from the diskann compile, but there isn't a
+            # component associated with it. Probably a better way to do this.
+            cp cpp/build/_deps/diskann-build/src/libdiskann.so $PREFIX/lib/
+          fi
+      string: cuda${{ cuda_major }}_${{ date_string }}_${{ head_rev }}
+      dynamic_linking:
+        overlinking_behavior: "error"
+      prefix_detection:
+        ignore_binary_files: True
+    requirements:
+      build:
+        - cmake ${{ cmake_version }}
+        - ${{ stdlib("c") }}
+      host:
+        - librmm =${{ minor_version }}
+        - libraft-headers =${{ minor_version }}
+        - nccl ${{ nccl_version }}
+        - ${{ pin_subpackage("libcuvs", exact=True) }}
+        - cuda-version =${{ cuda_version }}
+        - openblas  # required by some CPU algos in benchmarks
+        - libgomp
+        - if: linux64
+          then:
+            - libaio
+            - libboost-devel =1.87
+            - mkl-devel =2023
+        - if: cuda_major == "11"
+          then:
+            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
+            - libcublas =${{ cuda11_libcublas_host_version }}
+            - libcublas-dev =${{ cuda11_libcublas_host_version }}
+            - libcurand =${{ cuda11_libcurand_host_version }}
+            - libcurand-dev =${{ cuda11_libcurand_host_version }}
+            - libcusolver =${{ cuda11_libcusolver_host_version }}
+            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
+            - libcusparse =${{ cuda11_libcusparse_host_version }}
+            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
+          else:
+            - cuda-cudart-dev
+            - cuda-profiler-api
+            - libcublas-dev
+            - libcurand-dev
+            - libcusolver-dev
+            - libcusparse-dev
+      run:
+        - ${{ pin_subpackage("libcuvs", exact=True) }}
+        - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
+        - libraft-headers =${{ minor_version }}
+        - nccl
+        - if: linux64
+          then: mkl =2023
+        - if: cuda_major == "11"
+          then: cudatoolkit
+          else:
+            - cuda-cudart
+            - libcublas
+            - libcurand
+            - libcusolver
+            - libcusparse
+      ignore_run_exports:
+        by_name:
+          - cuda-cudart
+          - cuda-version
+          - libcublas
+          - libcurand
+          - libcusolver
+          - libcusparse
+          - librmm
+          - mkl
+          - nccl
+          - if: cuda_major == "11"
+            then: cudatoolkit
+    about:
+      homepage: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.urls.Homepage }}
+      license: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.license.text }}
+      summary: libcuvs benchmark
diff --git a/examples/build.sh b/examples/build.sh
index 6aad8fe46c..a91d312953 100755
--- a/examples/build.sh
+++ b/examples/build.sh
@@ -7,6 +7,9 @@
 # Abort script on first error
 set -e
 
+function hasArg {
+    (( NUMARGS != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+}
 
 PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
 
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index f4752d5207..d2e3e9224b 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -28,7 +28,7 @@ readme = { file = "README.md", content-type = "text/markdown" }
 authors = [
     { name = "NVIDIA Corporation" },
 ]
-license = { text = "Apache 2.0" }
+license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 6ae5a08fac..9867502f13 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -15,7 +15,7 @@ description = "cuVS benchmarks"
 authors = [
     { name = "NVIDIA Corporation" },
 ]
-license = { text = "Apache 2.0" }
+license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "click",
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 24596e0ab7..96fc9cc3a6 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -27,7 +27,7 @@ readme = { file = "README.md", content-type = "text/markdown" }
 authors = [
     { name = "NVIDIA Corporation" },
 ]
-license = { text = "Apache 2.0" }
+license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "libraft==25.4.*,>=0.0.0a0",

From 813c946f8160bc529c83382ad3b8df1db2c0bfd7 Mon Sep 17 00:00:00 2001
From: Ajit Mistry <55892788+ajit283@users.noreply.github.com>
Date: Fri, 21 Mar 2025 22:34:54 -0700
Subject: [PATCH 343/623] Support passing a dataset to `FromCagra` (#767)

For out-of-core CAGRA index builds where the dataset does not fit on GPU memory, a host tensor can be passed using this.

Authors:
  - Ajit Mistry (https://github.com/ajit283)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/767
---
 cpp/include/cuvs/neighbors/hnsw.h |  6 +++++
 cpp/src/neighbors/detail/hnsw.hpp | 10 ++++++--
 cpp/src/neighbors/hnsw_c.cpp      | 38 +++++++++++++++++++++++++++----
 3 files changed, 47 insertions(+), 7 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
index 61426e9314..b5f129e96d 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -204,6 +204,12 @@ cuvsError_t cuvsHnswFromCagra(cuvsResources_t res,
                               cuvsCagraIndex_t cagra_index,
                               cuvsHnswIndex_t hnsw_index);
 
+cuvsError_t cuvsHnswFromCagraWithDataset(cuvsResources_t res,
+                                         cuvsHnswIndexParams_t params,
+                                         cuvsCagraIndex_t cagra_index,
+                                         cuvsHnswIndex_t hnsw_index,
+                                         DLManagedTensor* dataset_tensor);
+
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index 6ab8631d43..07e1d6b53f 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -129,8 +129,14 @@ std::enable_if_t<hierarchy == HnswHierarchy::NONE, std::unique_ptr<index<T>>> fr
   cuvs::neighbors::cagra::serialize_to_hnswlib(res, filepath, cagra_index, dataset);
 
   index<T>* hnsw_index = nullptr;
-  cuvs::neighbors::hnsw::deserialize(
-    res, params, filepath, cagra_index.dim(), cagra_index.metric(), &hnsw_index);
+  int dim;
+  if (dataset.has_value()) {
+    dim = dataset.value().extent(1);
+  } else {
+    dim = cagra_index.dim();
+  }
+
+  cuvs::neighbors::hnsw::deserialize(res, params, filepath, dim, cagra_index.metric(), &hnsw_index);
   std::filesystem::remove(filepath);
   return std::unique_ptr<index<T>>(hnsw_index);
 }
diff --git a/cpp/src/neighbors/hnsw_c.cpp b/cpp/src/neighbors/hnsw_c.cpp
index 628d87e007..b643187bd8 100644
--- a/cpp/src/neighbors/hnsw_c.cpp
+++ b/cpp/src/neighbors/hnsw_c.cpp
@@ -36,7 +36,8 @@ template <typename T>
 void _from_cagra(cuvsResources_t res,
                  cuvsHnswIndexParams_t params,
                  cuvsCagraIndex_t cagra_index,
-                 cuvsHnswIndex_t hnsw_index)
+                 cuvsHnswIndex_t hnsw_index,
+                 std::optional<DLManagedTensor*> dataset_tensor)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
   auto index   = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(cagra_index->addr);
@@ -44,7 +45,13 @@ void _from_cagra(cuvsResources_t res,
   cpp_params.hierarchy       = static_cast<cuvs::neighbors::hnsw::HnswHierarchy>(params->hierarchy);
   cpp_params.ef_construction = params->ef_construction;
   cpp_params.num_threads     = params->num_threads;
-  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset = std::nullopt;
+  std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset;
+  if (dataset_tensor.has_value()) {
+    using dataset_mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    dataset                   = cuvs::core::from_dlpack<dataset_mdspan_type>(*dataset_tensor);
+  } else {
+    dataset = std::nullopt;
+  }
 
   auto hnsw_index_unique_ptr =
     cuvs::neighbors::hnsw::from_cagra(*res_ptr, cpp_params, *index, dataset);
@@ -175,11 +182,32 @@ extern "C" cuvsError_t cuvsHnswFromCagra(cuvsResources_t res,
     auto index        = *cagra_index;
     hnsw_index->dtype = index.dtype;
     if (index.dtype.code == kDLFloat) {
-      _from_cagra<float>(res, params, cagra_index, hnsw_index);
+      _from_cagra<float>(res, params, cagra_index, hnsw_index, std::nullopt);
     } else if (index.dtype.code == kDLUInt) {
-      _from_cagra<uint8_t>(res, params, cagra_index, hnsw_index);
+      _from_cagra<uint8_t>(res, params, cagra_index, hnsw_index, std::nullopt);
     } else if (index.dtype.code == kDLInt) {
-      _from_cagra<int8_t>(res, params, cagra_index, hnsw_index);
+      _from_cagra<int8_t>(res, params, cagra_index, hnsw_index, std::nullopt);
+    } else {
+      RAFT_FAIL("Unsupported dtype: %d", index.dtype.code);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsHnswFromCagraWithDataset(cuvsResources_t res,
+                                                    cuvsHnswIndexParams_t params,
+                                                    cuvsCagraIndex_t cagra_index,
+                                                    cuvsHnswIndex_t hnsw_index,
+                                                    DLManagedTensor* dataset_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index        = *cagra_index;
+    hnsw_index->dtype = index.dtype;
+    if (index.dtype.code == kDLFloat) {
+      _from_cagra<float>(res, params, cagra_index, hnsw_index, dataset_tensor);
+    } else if (index.dtype.code == kDLUInt) {
+      _from_cagra<uint8_t>(res, params, cagra_index, hnsw_index, dataset_tensor);
+    } else if (index.dtype.code == kDLInt) {
+      _from_cagra<int8_t>(res, params, cagra_index, hnsw_index, dataset_tensor);
     } else {
       RAFT_FAIL("Unsupported dtype: %d", index.dtype.code);
     }

From cfebba1aa97444cb85c27f54afb2e76a05b1f9cd Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 25 Mar 2025 15:50:17 -0400
Subject: [PATCH 344/623] Use new build_patch_only argument (#780)

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/780
---
 cpp/cmake/thirdparty/get_cutlass.cmake | 4 ++--
 cpp/cmake/thirdparty/get_diskann.cmake | 4 ++--
 cpp/cmake/thirdparty/get_faiss.cmake   | 4 ++--
 cpp/cmake/thirdparty/get_ggnn.cmake    | 4 ++--
 cpp/cmake/thirdparty/get_hnswlib.cmake | 4 ++--
 5 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/cpp/cmake/thirdparty/get_cutlass.cmake b/cpp/cmake/thirdparty/get_cutlass.cmake
index 71bd2d26c9..69fec3a1e0 100644
--- a/cpp/cmake/thirdparty/get_cutlass.cmake
+++ b/cpp/cmake/thirdparty/get_cutlass.cmake
@@ -42,10 +42,10 @@ function(find_and_configure_cutlass)
   rapids_cpm_package_details(cutlass version repository tag shallow exclude)
 
   include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
-  rapids_cpm_generate_patch_command(cutlass ${version} patch_command)
+  rapids_cpm_generate_patch_command(cutlass ${version} patch_command build_patch_only)
 
   rapids_cpm_find(
-    NvidiaCutlass ${version}
+    NvidiaCutlass ${version} ${build_patch_only}
     GLOBAL_TARGETS nvidia::cutlass::cutlass
     CPM_ARGS
     GIT_REPOSITORY ${repository}
diff --git a/cpp/cmake/thirdparty/get_diskann.cmake b/cpp/cmake/thirdparty/get_diskann.cmake
index 685ee2ff68..6a37bc6639 100644
--- a/cpp/cmake/thirdparty/get_diskann.cmake
+++ b/cpp/cmake/thirdparty/get_diskann.cmake
@@ -27,9 +27,9 @@ function(find_and_configure_diskann)
   rapids_cpm_package_details(diskann version repository tag shallow exclude)
 
   include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
-  rapids_cpm_generate_patch_command(diskann ${version} patch_command)
+  rapids_cpm_generate_patch_command(diskann ${version} patch_command build_patch_only)
 
-  rapids_cpm_find(diskann ${version}
+  rapids_cpm_find(diskann ${version} ${build_patch_only}
           GLOBAL_TARGETS diskann
           CPM_ARGS
           OPTIONS
diff --git a/cpp/cmake/thirdparty/get_faiss.cmake b/cpp/cmake/thirdparty/get_faiss.cmake
index 89f4548612..26965273e2 100644
--- a/cpp/cmake/thirdparty/get_faiss.cmake
+++ b/cpp/cmake/thirdparty/get_faiss.cmake
@@ -31,7 +31,7 @@ function(find_and_configure_faiss)
   rapids_cpm_package_details(faiss version repository tag shallow exclude)
 
   include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
-  rapids_cpm_generate_patch_command(faiss ${version} patch_command)
+  rapids_cpm_generate_patch_command(faiss ${version} patch_command build_patch_only)
 
   set(BUILD_SHARED_LIBS ON)
   if (PKG_BUILD_STATIC_LIBS)
@@ -46,7 +46,7 @@ function(find_and_configure_faiss)
     set(CUVS_FAISS_OPT_LEVEL "avx2")
   endif()
 
-  rapids_cpm_find(faiss ${version}
+  rapids_cpm_find(faiss ${version} ${build_patch_only}
     GLOBAL_TARGETS faiss faiss_avx2 faiss_gpu_objs faiss::faiss faiss::faiss_avx2
     CPM_ARGS
     GIT_REPOSITORY ${repository}
diff --git a/cpp/cmake/thirdparty/get_ggnn.cmake b/cpp/cmake/thirdparty/get_ggnn.cmake
index 8ee5bfd1d3..5bd77215c8 100644
--- a/cpp/cmake/thirdparty/get_ggnn.cmake
+++ b/cpp/cmake/thirdparty/get_ggnn.cmake
@@ -24,10 +24,10 @@ function(find_and_configure_ggnn)
   rapids_cpm_package_details(ggnn version repository tag shallow exclude)
 
   include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
-  rapids_cpm_generate_patch_command(ggnn ${version} patch_command)
+  rapids_cpm_generate_patch_command(ggnn ${version} patch_command build_patch_only)
 
   rapids_cpm_find(
-    ggnn ${version}
+    ggnn ${version} ${build_patch_only}
     GLOBAL_TARGETS ggnn::ggnn
     CPM_ARGS
     GIT_REPOSITORY ${repository}
diff --git a/cpp/cmake/thirdparty/get_hnswlib.cmake b/cpp/cmake/thirdparty/get_hnswlib.cmake
index 5b4d89aa2a..a99e3b032f 100644
--- a/cpp/cmake/thirdparty/get_hnswlib.cmake
+++ b/cpp/cmake/thirdparty/get_hnswlib.cmake
@@ -26,10 +26,10 @@ function(find_and_configure_hnswlib)
   rapids_cpm_package_details(hnswlib version repository tag shallow exclude)
 
   include("${rapids-cmake-dir}/cpm/detail/generate_patch_command.cmake")
-  rapids_cpm_generate_patch_command(hnswlib ${version} patch_command)
+  rapids_cpm_generate_patch_command(hnswlib ${version} patch_command build_patch_only)
 
   rapids_cpm_find(
-    hnswlib ${version}
+    hnswlib ${version} ${build_patch_only}
     GLOBAL_TARGETS hnswlib hnswlib::hnswlib
     CPM_ARGS
     GIT_REPOSITORY ${repository}

From ff8682798536b5975f47ac86b3f3d2e2c801904b Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Wed, 26 Mar 2025 07:18:25 +0900
Subject: [PATCH 345/623] [BUG] Fix graph index sorting in CAGRA graph build by
 NN Descent (#763)

Graph neighbor sorting after the initial kNN graph construction by NN descent always uses the L2 norm, while NN descent also supports the inner product and cosine distance. This PR adds support for the inner product and cosine distance in graph neighbor sorting.

## TODO
- [x] Normalize vector when metric == Cosine

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/763
---
 cpp/src/neighbors/cagra.cuh                   |  6 +-
 .../neighbors/detail/cagra/cagra_build.cuh    |  3 +-
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 62 +++++++++++++++----
 3 files changed, 57 insertions(+), 14 deletions(-)

diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 305788f7c4..ad1324bbca 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -172,7 +172,7 @@ void build_knn_graph(
  *   // build KNN graph not using `cagra::build_knn_graph`
  *   // build(knn_graph, dataset, ...);
  *   // sort graph index
- *   sort_knn_graph(res, dataset.view(), knn_graph.view());
+ *   sort_knn_graph(res, build_params.metric, dataset.view(), knn_graph.view());
  *   // optimize graph
  *   cagra::optimize(res, dataset, knn_graph.view(), optimized_graph.view());
  *   // Construct an index from dataset and optimized knn_graph
@@ -184,6 +184,7 @@ void build_knn_graph(
  * @tparam IdxT type of the dataset vector indices
  *
  * @param[in] res raft resources
+ * @param[in] metric metric
  * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
  * @param[in,out] knn_graph a matrix view (host or device) of the input knn graph [n_rows,
  * knn_graph_degree]
@@ -197,6 +198,7 @@ template <
     raft::host_device_accessor<std::experimental::default_accessor<IdxT>, raft::memory_type::host>>
 void sort_knn_graph(
   raft::resources const& res,
+  cuvs::distance::DistanceType metric,
   raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, d_accessor> dataset,
   raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph)
 {
@@ -215,7 +217,7 @@ void sort_knn_graph(
     raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, d_accessor>(
       dataset.data_handle(), dataset.extent(0), dataset.extent(1));
 
-  cagra::detail::graph::sort_knn_graph(res, dataset_internal, knn_graph_internal);
+  cagra::detail::graph::sort_knn_graph(res, metric, dataset_internal, knn_graph_internal);
 }
 
 /**
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 4d559a6626..7c749cf6e9 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -368,7 +368,8 @@ void build_knn_graph(
       nn_descent_idx.graph().extent(0),
       nn_descent_idx.graph().extent(1));
 
-  cuvs::neighbors::cagra::detail::graph::sort_knn_graph(res, dataset, knn_graph_internal);
+  cuvs::neighbors::cagra::detail::graph::sort_knn_graph(
+    res, build_params.metric, dataset, knn_graph_internal);
 }
 
 template <
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 39037b745d..b55d69edb4 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -78,7 +78,8 @@ __global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size,
                           const uint32_t dataset_dim,
                           IdxT* const knn_graph,  // [graph_chunk_size, graph_degree]
                           const uint32_t graph_size,
-                          const uint32_t graph_degree)
+                          const uint32_t graph_degree,
+                          const cuvs::distance::DistanceType metric)
 {
   const IdxT srcNode = (blockDim.x * blockIdx.x + threadIdx.x) / raft::WarpSize;
   if (srcNode >= graph_size) { return; }
@@ -91,19 +92,46 @@ __global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size,
   // Compute distance from a src node to its neighbors
   for (int k = 0; k < graph_degree; k++) {
     const IdxT dstNode = knn_graph[k + static_cast<uint64_t>(graph_degree) * srcNode];
-    float dist         = 0.0;
-    for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
-      float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
-                     dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) -
-                   cuvs::spatial::knn::detail::utils::mapping<float>{}(
-                     dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
-      dist += diff * diff;
+    float dist         = 0;
+    float norm2_dst    = 0;
+    if (metric == cuvs::distance::DistanceType::InnerProduct ||
+        metric == cuvs::distance::DistanceType::CosineExpanded) {
+      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+        auto elem_b = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+          dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
+        dist -= cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                  dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) *
+                elem_b;
+
+        if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+          norm2_dst += elem_b * elem_b;
+        }
+      }
+    } else {
+      // L2Expanded
+      for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+        float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                       dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode]) -
+                     cuvs::spatial::knn::detail::utils::mapping<float>{}(
+                       dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
+        dist += diff * diff;
+      }
     }
     dist += __shfl_xor_sync(0xffffffff, dist, 1);
     dist += __shfl_xor_sync(0xffffffff, dist, 2);
     dist += __shfl_xor_sync(0xffffffff, dist, 4);
     dist += __shfl_xor_sync(0xffffffff, dist, 8);
     dist += __shfl_xor_sync(0xffffffff, dist, 16);
+
+    if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 1);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 2);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 4);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 8);
+      norm2_dst += __shfl_xor_sync(0xffffffff, norm2_dst, 16);
+      if (lane_id == (k % raft::WarpSize)) { dist /= sqrt(norm2_dst); }
+    }
+
     if (lane_id == (k % raft::WarpSize)) {
       my_keys[k / raft::WarpSize] = dist;
       my_vals[k / raft::WarpSize] = dstNode;
@@ -471,11 +499,17 @@ template <
     raft::host_device_accessor<std::experimental::default_accessor<IdxT>, raft::memory_type::host>>
 void sort_knn_graph(
   raft::resources const& res,
+  const cuvs::distance::DistanceType metric,
   raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, d_accessor> dataset,
   raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph)
 {
   RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
                "dataset size is expected to have the same number of graph index size");
+  RAFT_EXPECTS(
+    metric == cuvs::distance::DistanceType::InnerProduct ||
+      metric == cuvs::distance::DistanceType::CosineExpanded ||
+      metric == cuvs::distance::DistanceType::L2Expanded,
+    "Unsupported metric. Only InnerProduct, CosineExpanded, and L2Expanded are supported");
   const uint64_t dataset_size = dataset.extent(0);
   const uint64_t dataset_dim  = dataset.extent(1);
   const DataT* dataset_ptr    = dataset.data_handle();
@@ -507,8 +541,13 @@ void sort_knn_graph(
              graph_size * input_graph_degree,
              raft::resource::get_cuda_stream(res));
 
-  void (*kernel_sort)(
-    const DataT* const, const IdxT, const uint32_t, IdxT* const, const uint32_t, const uint32_t);
+  void (*kernel_sort)(const DataT* const,
+                      const IdxT,
+                      const uint32_t,
+                      IdxT* const,
+                      const uint32_t,
+                      const uint32_t,
+                      const cuvs::distance::DistanceType);
   if (input_graph_degree <= 32) {
     constexpr int numElementsPerThread = 1;
     kernel_sort                        = kern_sort<DataT, IdxT, numElementsPerThread>;
@@ -545,7 +584,8 @@ void sort_knn_graph(
     dataset_dim,
     d_input_graph.data_handle(),
     graph_size,
-    input_graph_degree);
+    input_graph_degree,
+    metric);
   raft::resource::sync_stream(res);
   RAFT_LOG_DEBUG(".");
   raft::copy(input_graph_ptr,

From daede3ea7f43a9849d6ee8dd3633344ff1c466b4 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Mon, 31 Mar 2025 12:22:33 -0700
Subject: [PATCH 346/623] Removing print statement (#786)

Removing a debugging print statement

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/786
---
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 21a4b3185b..4e2410405f 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1696,9 +1696,6 @@ auto build(raft::resources const& handle,
                   std::is_same_v<T, int8_t>,
                 "Unsupported data type");
 
-  std::cout << "using ivf_pq::index_params nrows " << (int)dataset.extent(0) << ", dim "
-            << (int)dataset.extent(1) << ", n_lists " << (int)params.n_lists << ", pq_dim "
-            << (int)params.pq_dim << std::endl;
   RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
   RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
   if (params.metric == distance::DistanceType::CosineExpanded) {

From 61a714b00915e833b4f62c36e2152a8eea48e064 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 1 Apr 2025 09:12:18 +0200
Subject: [PATCH 347/623] CAGRA search: int64_t indices in the API (#769)

Add `int64_t` as the type of returned neighbors in cagra::search while keeping the index type unchanged.

Down the implementation, the PR introduces the `OutputIndexT` template parameter and handles it differently for each implementation:
  - multi-kernel implementation already does the copy after search in a separate kernel, we change the index type during this copy.
  - multi-cta kernel runs as before, followed by top-k; conversion of the index type happens in a separate map kernel only if a simple type cast wouldn't suffice (i.e. data sizes do not match).
  - single-cta kernel must always run as a single kernel to maintain 'persistent' mode performance. I erase the index type in the kernel signature and pass the `sizeof(OutputIndexT)` as a tag in the pointer. Based on this tag, a proper output-writing function is dispatched in the epilogue of the single-cta kernel. By using this technique I avoid instantiating multiple kernels at the negligible runtime cost.

Note: we cannot simply instantiate cagra with index type `int64_t`, because this will make the graph twice larger and will reduce the search throughput.

To test the new search overloads, I turn `AnnCagraTest::testCagra` into a template and add just two instances to the `*.cu files` to avoid bloating the number of tests.

The new search overloads are used in the ann-bench wrappers, which slightly improves the reported QPS in some cases due to reducing the index conversion overhead.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/769
---
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |  74 ++++--------
 cpp/include/cuvs/neighbors/cagra.h            |   1 +
 cpp/include/cuvs/neighbors/cagra.hpp          | 106 +++++++++++++++++-
 cpp/src/neighbors/cagra.cuh                   |  29 ++---
 cpp/src/neighbors/cagra_c.cpp                 |  34 ++++--
 cpp/src/neighbors/cagra_search_float.cu       |  25 +++--
 cpp/src/neighbors/cagra_search_half.cu        |  25 +++--
 cpp/src/neighbors/cagra_search_int8.cu        |  26 +++--
 cpp/src/neighbors/cagra_search_uint8.cu       |  25 +++--
 .../neighbors/detail/cagra/cagra_search.cuh   |  40 ++++---
 cpp/src/neighbors/detail/cagra/factory.cuh    |  29 ++---
 .../detail/cagra/search_multi_cta.cuh         |  32 ++++--
 .../detail/cagra/search_multi_kernel.cuh      |  22 ++--
 .../neighbors/detail/cagra/search_plan.cuh    |   8 +-
 .../detail/cagra/search_single_cta.cuh        |  28 ++++-
 .../detail/cagra/search_single_cta_inst.cuh   |   2 +-
 .../cagra/search_single_cta_kernel-inl.cuh    |  77 +++++++++++--
 .../detail/cagra/search_single_cta_kernel.cuh |   2 +-
 cpp/src/neighbors/dynamic_batching.cu         |  13 +++
 cpp/tests/neighbors/ann_cagra.cuh             |  35 +++---
 .../ann_cagra/test_float_uint32_t.cu          |   3 +-
 .../neighbors/ann_cagra/test_half_uint32_t.cu |   3 +-
 22 files changed, 422 insertions(+), 217 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index e611ea36bf..e4f1d0833b 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -132,8 +132,7 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
                    int batch_size,
                    int k,
                    algo_base::index_type* neighbors,
-                   float* distances,
-                   IdxT* neighbors_idx_t) const;
+                   float* distances) const;
 
   [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
   {
@@ -181,7 +180,8 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
   std::shared_ptr<raft::device_matrix<T, int64_t, raft::row_major>> dataset_;
   std::shared_ptr<raft::device_matrix_view<const T, int64_t, raft::row_major>> input_dataset_v_;
 
-  std::shared_ptr<cuvs::neighbors::dynamic_batching::index<T, IdxT>> dynamic_batcher_;
+  std::shared_ptr<cuvs::neighbors::dynamic_batching::index<T, algo_base::index_type>>
+    dynamic_batcher_;
   cuvs::neighbors::dynamic_batching::search_params dynamic_batcher_sp_{};
   int64_t dynamic_batching_max_batch_size_;
   size_t dynamic_batching_n_queues_;
@@ -292,16 +292,18 @@ void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param,
   // dynamic batching
   if (sp.dynamic_batching) {
     if (!dynamic_batcher_ || needs_dynamic_batcher_update) {
-      dynamic_batcher_ = std::make_shared<cuvs::neighbors::dynamic_batching::index<T, IdxT>>(
-        handle_,
-        cuvs::neighbors::dynamic_batching::index_params{{},
-                                                        sp.dynamic_batching_k,
-                                                        sp.dynamic_batching_max_batch_size,
-                                                        sp.dynamic_batching_n_queues,
-                                                        sp.dynamic_batching_conservative_dispatch},
-        *index_,
-        search_params_,
-        filter_.get());
+      dynamic_batcher_ =
+        std::make_shared<cuvs::neighbors::dynamic_batching::index<T, algo_base::index_type>>(
+          handle_,
+          cuvs::neighbors::dynamic_batching::index_params{
+            {},
+            sp.dynamic_batching_k,
+            sp.dynamic_batching_max_batch_size,
+            sp.dynamic_batching_n_queues,
+            sp.dynamic_batching_conservative_dispatch},
+          *index_,
+          search_params_,
+          filter_.get());
     }
     dynamic_batcher_sp_.dispatch_timeout_ms = sp.dynamic_batching_dispatch_timeout_ms;
   } else {
@@ -359,24 +361,16 @@ std::unique_ptr<algo<T>> cuvs_cagra<T, IdxT>::copy()
 }
 
 template <typename T, typename IdxT>
-void cuvs_cagra<T, IdxT>::search_base(const T* queries,
-                                      int batch_size,
-                                      int k,
-                                      algo_base::index_type* neighbors,
-                                      float* distances,
-                                      IdxT* neighbors_idx_t) const
+void cuvs_cagra<T, IdxT>::search_base(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
 {
   static_assert(std::is_integral_v<algo_base::index_type>);
   static_assert(std::is_integral_v<IdxT>);
 
-  if constexpr (sizeof(IdxT) == sizeof(algo_base::index_type)) {
-    neighbors_idx_t = reinterpret_cast<IdxT*>(neighbors);
-  }
-
   auto queries_view =
     raft::make_device_matrix_view<const T, int64_t>(queries, batch_size, dimension_);
   auto neighbors_view =
-    raft::make_device_matrix_view<IdxT, int64_t>(neighbors_idx_t, batch_size, k);
+    raft::make_device_matrix_view<algo_base::index_type, int64_t>(neighbors, batch_size, k);
   auto distances_view = raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k);
 
   if (dynamic_batcher_) {
@@ -390,26 +384,6 @@ void cuvs_cagra<T, IdxT>::search_base(const T* queries,
     cuvs::neighbors::cagra::search(
       handle_, search_params_, *index_, queries_view, neighbors_view, distances_view, *filter_);
   }
-
-  if constexpr (sizeof(IdxT) != sizeof(algo_base::index_type)) {
-    if (raft::get_device_for_address(neighbors) < 0 &&
-        raft::get_device_for_address(neighbors_idx_t) < 0) {
-      // Both pointers on the host, let's use host-side mapping
-      if (uses_stream()) {
-        // Need to wait for GPU to finish filling source
-        raft::resource::sync_stream(handle_);
-      }
-      for (int i = 0; i < batch_size * k; i++) {
-        neighbors[i] = algo_base::index_type(neighbors_idx_t[i]);
-      }
-    } else {
-      raft::linalg::unaryOp(neighbors,
-                            neighbors_idx_t,
-                            batch_size * k,
-                            raft::cast_op<algo_base::index_type>(),
-                            raft::resource::get_cuda_stream(handle_));
-    }
-  }
 }
 
 template <typename T, typename IdxT>
@@ -418,7 +392,6 @@ void cuvs_cagra<T, IdxT>::search(
 {
   static_assert(std::is_integral_v<algo_base::index_type>);
   static_assert(std::is_integral_v<IdxT>);
-  constexpr bool kNeedsIoMapping = sizeof(IdxT) != sizeof(algo_base::index_type);
 
   auto k0                       = static_cast<size_t>(refine_ratio_ * k);
   const bool disable_refinement = k0 <= static_cast<size_t>(k);
@@ -434,9 +407,8 @@ void cuvs_cagra<T, IdxT>::search(
                                                dynamic_batching_max_batch_size_, batch_size) *
                                                dynamic_batching_n_queues_
                                            : 1;
-  auto tmp_buf_size = ((disable_refinement ? 0 : (sizeof(float) + sizeof(algo_base::index_type))) +
-                       (kNeedsIoMapping ? sizeof(IdxT) : 0)) *
-                      batch_size * k0;
+  auto tmp_buf_size =
+    ((disable_refinement ? 0 : (sizeof(float) + sizeof(algo_base::index_type)))) * batch_size * k0;
   auto& tmp_buf = get_tmp_buffer_from_global_pool(tmp_buf_size * max_dyn_grouping);
   thread_local static int64_t group_id = 0;
   auto* candidates_ptr                 = reinterpret_cast<algo_base::index_type*>(
@@ -444,13 +416,11 @@ void cuvs_cagra<T, IdxT>::search(
   group_id = (group_id + 1) % max_dyn_grouping;
   auto* candidate_dists_ptr =
     reinterpret_cast<float*>(candidates_ptr + (disable_refinement ? 0 : batch_size * k0));
-  auto* neighbors_idx_t =
-    reinterpret_cast<IdxT*>(candidate_dists_ptr + (disable_refinement ? 0 : batch_size * k0));
 
   if (disable_refinement) {
-    search_base(queries, batch_size, k, neighbors, distances, neighbors_idx_t);
+    search_base(queries, batch_size, k, neighbors, distances);
   } else {
-    search_base(queries, batch_size, k0, candidates_ptr, candidate_dists_ptr, neighbors_idx_t);
+    search_base(queries, batch_size, k0, candidates_ptr, candidate_dists_ptr);
 
     if (mem_type == MemoryType::kHostPinned && uses_stream()) {
       // If the algorithm uses a stream to synchronize (non-persistent kernel), but the data is in
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index f3dc0d5c2f..ce97ee4a3b 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -428,6 +428,7 @@ cuvsError_t cuvsCagraExtend(cuvsResources_t res,
  *          c. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
  *          d. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *        2. `neighbors`: `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 32`
+ *                     or `kDLDataType.code == kDLInt`  and `kDLDataType.bits = 64`
  *        3. `distances`: `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
  *
  * @code {.c}
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index fc558e3f31..f92835e282 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -328,7 +328,8 @@ static_assert(std::is_aggregate_v<search_params>);
  * The index stores the dataset and a kNN graph in device memory.
  *
  * @tparam T data element type
- * @tparam IdxT type of the vector indices (represent dataset.extent(0))
+ * @tparam IdxT the data type used to store the neighbor indices in the  search graph.
+ *              It must be large enough to represent values up to dataset.extent(0).
  *
  */
 template <typename T, typename IdxT>
@@ -1116,7 +1117,7 @@ void extend(
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
- * @param[in] idx cagra index
+ * @param[in] index cagra index
  * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
  * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
  * [n_queries, k]
@@ -1210,6 +1211,107 @@ void search(raft::resources const& res,
             const cuvs::neighbors::filtering::base_filter& sample_filter =
               cuvs::neighbors::filtering::none_sample_filter{});
 
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
+ */
+
+void search(raft::resources const& res,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<float, uint32_t>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
+ */
+void search(raft::resources const& res,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<half, uint32_t>& index,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
+ */
+void search(raft::resources const& res,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<int8_t, uint32_t>& index,
+            raft::device_matrix_view<const int8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [cagra::build](#cagra::build) documentation for a usage example.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] index cagra index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
+ */
+void search(raft::resources const& res,
+            cuvs::neighbors::cagra::search_params const& params,
+            const cuvs::neighbors::cagra::index<uint8_t, uint32_t>& index,
+            raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index ad1324bbca..3919626c80 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -283,9 +283,10 @@ index<T, IdxT> build(
  * @endcode
  *
  * @tparam T data element type
- * @tparam IdxT type of the indices
+ * @tparam IdxT type of the indices in the CAGRA graph
  * @tparam CagraSampleFilterT Device filter function, with the signature
  *         `(uint32_t query ix, uint32_t sample_ix) -> bool`
+ * @tparam OutputIdxT type of the returned indices
  *
  * @param[in] res raft resources
  * @param[in] params configure the search
@@ -297,12 +298,12 @@ index<T, IdxT> build(
  * k]
  * @param[in] sample_filter a device filter function that greenlights samples for a given query
  */
-template <typename T, typename IdxT, typename CagraSampleFilterT>
+template <typename T, typename IdxT, typename CagraSampleFilterT, typename OutputIdxT = IdxT>
 void search_with_filtering(raft::resources const& res,
                            const search_params& params,
                            const index<T, IdxT>& idx,
                            raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
-                           raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
+                           raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors,
                            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
                            CagraSampleFilterT sample_filter = CagraSampleFilterT())
 {
@@ -315,26 +316,16 @@ void search_with_filtering(raft::resources const& res,
   RAFT_EXPECTS(queries.extent(1) == idx.dim(),
                "Number of query dimensions should equal number of dimensions in the index.");
 
-  using internal_IdxT   = typename std::make_unsigned<IdxT>::type;
-  auto queries_internal = raft::make_device_matrix_view<const T, int64_t, raft::row_major>(
-    queries.data_handle(), queries.extent(0), queries.extent(1));
-  auto neighbors_internal = raft::make_device_matrix_view<internal_IdxT, int64_t, raft::row_major>(
-    reinterpret_cast<internal_IdxT*>(neighbors.data_handle()),
-    neighbors.extent(0),
-    neighbors.extent(1));
-  auto distances_internal = raft::make_device_matrix_view<float, int64_t, raft::row_major>(
-    distances.data_handle(), distances.extent(0), distances.extent(1));
-
-  return cagra::detail::search_main<T, internal_IdxT, CagraSampleFilterT, IdxT>(
-    res, params, idx, queries_internal, neighbors_internal, distances_internal, sample_filter);
+  return cagra::detail::search_main<T, OutputIdxT, CagraSampleFilterT, IdxT>(
+    res, params, idx, queries, neighbors, distances, sample_filter);
 }
 
-template <typename T, typename IdxT>
+template <typename T, typename IdxT, typename OutputIdxT = IdxT>
 void search(raft::resources const& res,
             const search_params& params,
             const index<T, IdxT>& idx,
             raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
-            raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             const cuvs::neighbors::filtering::base_filter& sample_filter_ref)
 {
@@ -344,7 +335,7 @@ void search(raft::resources const& res,
     search_params params_copy = params;
     if (params.filtering_rate < 0.0) { params_copy.filtering_rate = 0.0; }
     auto sample_filter_copy = sample_filter;
-    return search_with_filtering<T, IdxT, none_filter_type>(
+    return search_with_filtering<T, IdxT, none_filter_type, OutputIdxT>(
       res, params_copy, idx, queries, neighbors, distances, sample_filter_copy);
     return;
   } catch (const std::bad_cast&) {
@@ -364,7 +355,7 @@ void search(raft::resources const& res,
         std::min(std::max(filtering_rate, min_filtering_rate), max_filtering_rate);
     }
     auto sample_filter_copy = sample_filter;
-    return search_with_filtering<T, IdxT, decltype(sample_filter_copy)>(
+    return search_with_filtering<T, IdxT, decltype(sample_filter_copy), OutputIdxT>(
       res, params_copy, idx, queries, neighbors, distances, sample_filter_copy);
   } catch (const std::bad_cast&) {
     RAFT_FAIL("Unsupported sample filter type");
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 9b86072ef1..f39d44b82d 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -1,6 +1,5 @@
-
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -130,7 +129,7 @@ void _extend(cuvsResources_t res,
   }
 }
 
-template <typename T>
+template <typename T, typename IdxT>
 void _search(cuvsResources_t res,
              cuvsCagraSearchParams params,
              cuvsCagraIndex index,
@@ -158,7 +157,7 @@ void _search(cuvsResources_t res,
   search_params.rand_xor_mask         = params.rand_xor_mask;
 
   using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, raft::row_major>;
-  using neighbors_mdspan_type = raft::device_matrix_view<uint32_t, int64_t, raft::row_major>;
+  using neighbors_mdspan_type = raft::device_matrix_view<IdxT, int64_t, raft::row_major>;
   using distances_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
   auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
   auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
@@ -185,6 +184,28 @@ void _search(cuvsResources_t res,
   }
 }
 
+template <typename T>
+void _search(cuvsResources_t res,
+             cuvsCagraSearchParams params,
+             cuvsCagraIndex index,
+             DLManagedTensor* queries_tensor,
+             DLManagedTensor* neighbors_tensor,
+             DLManagedTensor* distances_tensor,
+             cuvsFilter filter)
+{
+  if (neighbors_tensor->dl_tensor.dtype.code == kDLUInt &&
+      neighbors_tensor->dl_tensor.dtype.bits == 32) {
+    _search<T, uint32_t>(
+      res, params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
+  } else if (neighbors_tensor->dl_tensor.dtype.code == kDLInt &&
+             neighbors_tensor->dl_tensor.dtype.bits == 64) {
+    _search<T, int64_t>(
+      res, params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
+  } else {
+    RAFT_FAIL("neighbors should be of type uint32_t or int64_t");
+  }
+}
+
 template <typename T>
 void _serialize(cuvsResources_t res,
                 const char* filename,
@@ -318,9 +339,8 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
     RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(distances),
                  "distances should have device compatible memory");
 
-    RAFT_EXPECTS(neighbors.dtype.code == kDLUInt && neighbors.dtype.bits == 32,
-                 "neighbors should be of type uint32_t");
-    RAFT_EXPECTS(distances.dtype.code == kDLFloat && neighbors.dtype.bits == 32,
+    // NB: the dtype of neighbors is checked later in _search function
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
                  "distances should be of type float32");
 
     auto index = *index_c_ptr;
diff --git a/cpp/src/neighbors/cagra_search_float.cu b/cpp/src/neighbors/cagra_search_float.cu
index 3aca84f74b..de6fb2a860 100644
--- a/cpp/src/neighbors/cagra_search_float.cu
+++ b/cpp/src/neighbors/cagra_search_float.cu
@@ -19,20 +19,21 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
-  void search(raft::resources const& handle,                                       \
-              cuvs::neighbors::cagra::search_params const& params,                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
-              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
-  {                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
-      handle, params, index, queries, neighbors, distances, sample_filter);        \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT, OutputIdxT)                                     \
+  void search(raft::resources const& handle,                                            \
+              cuvs::neighbors::cagra::search_params const& params,                      \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                      \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,      \
+              raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors, \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances,      \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)             \
+  {                                                                                     \
+    cuvs::neighbors::cagra::search<T, IdxT, OutputIdxT>(                                \
+      handle, params, index, queries, neighbors, distances, sample_filter);             \
   }
 
-CUVS_INST_CAGRA_SEARCH(float, uint32_t);
+CUVS_INST_CAGRA_SEARCH(float, uint32_t, uint32_t);
+CUVS_INST_CAGRA_SEARCH(float, uint32_t, int64_t);
 
 #undef CUVS_INST_CAGRA_SEARCH
 
diff --git a/cpp/src/neighbors/cagra_search_half.cu b/cpp/src/neighbors/cagra_search_half.cu
index 02be127318..4ce7f05322 100644
--- a/cpp/src/neighbors/cagra_search_half.cu
+++ b/cpp/src/neighbors/cagra_search_half.cu
@@ -19,20 +19,21 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
-  void search(raft::resources const& handle,                                       \
-              cuvs::neighbors::cagra::search_params const& params,                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
-              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
-  {                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
-      handle, params, index, queries, neighbors, distances, sample_filter);        \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT, OutputIdxT)                                     \
+  void search(raft::resources const& handle,                                            \
+              cuvs::neighbors::cagra::search_params const& params,                      \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                      \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,      \
+              raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors, \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances,      \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)             \
+  {                                                                                     \
+    cuvs::neighbors::cagra::search<T, IdxT, OutputIdxT>(                                \
+      handle, params, index, queries, neighbors, distances, sample_filter);             \
   }
 
-CUVS_INST_CAGRA_SEARCH(half, uint32_t);
+CUVS_INST_CAGRA_SEARCH(half, uint32_t, uint32_t);
+CUVS_INST_CAGRA_SEARCH(half, uint32_t, int64_t);
 
 #undef CUVS_INST_CAGRA_SEARCH
 
diff --git a/cpp/src/neighbors/cagra_search_int8.cu b/cpp/src/neighbors/cagra_search_int8.cu
index 3442ef55fb..3db286d027 100644
--- a/cpp/src/neighbors/cagra_search_int8.cu
+++ b/cpp/src/neighbors/cagra_search_int8.cu
@@ -16,22 +16,24 @@
 
 #include "cagra.cuh"
 #include <cuvs/neighbors/cagra.hpp>
+
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
-  void search(raft::resources const& handle,                                       \
-              cuvs::neighbors::cagra::search_params const& params,                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
-              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
-  {                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
-      handle, params, index, queries, neighbors, distances, sample_filter);        \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT, OutputIdxT)                                     \
+  void search(raft::resources const& handle,                                            \
+              cuvs::neighbors::cagra::search_params const& params,                      \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                      \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,      \
+              raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors, \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances,      \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)             \
+  {                                                                                     \
+    cuvs::neighbors::cagra::search<T, IdxT, OutputIdxT>(                                \
+      handle, params, index, queries, neighbors, distances, sample_filter);             \
   }
 
-CUVS_INST_CAGRA_SEARCH(int8_t, uint32_t);
+CUVS_INST_CAGRA_SEARCH(int8_t, uint32_t, uint32_t);
+CUVS_INST_CAGRA_SEARCH(int8_t, uint32_t, int64_t);
 
 #undef CUVS_INST_CAGRA_SEARCH
 
diff --git a/cpp/src/neighbors/cagra_search_uint8.cu b/cpp/src/neighbors/cagra_search_uint8.cu
index 08fe1861b1..f5436b4c80 100644
--- a/cpp/src/neighbors/cagra_search_uint8.cu
+++ b/cpp/src/neighbors/cagra_search_uint8.cu
@@ -19,20 +19,21 @@
 
 namespace cuvs::neighbors::cagra {
 
-#define CUVS_INST_CAGRA_SEARCH(T, IdxT)                                            \
-  void search(raft::resources const& handle,                                       \
-              cuvs::neighbors::cagra::search_params const& params,                 \
-              const cuvs::neighbors::cagra::index<T, IdxT>& index,                 \
-              raft::device_matrix_view<const T, int64_t, raft::row_major> queries, \
-              raft::device_matrix_view<IdxT, int64_t, raft::row_major> neighbors,  \
-              raft::device_matrix_view<float, int64_t, raft::row_major> distances, \
-              const cuvs::neighbors::filtering::base_filter& sample_filter)        \
-  {                                                                                \
-    cuvs::neighbors::cagra::search<T, IdxT>(                                       \
-      handle, params, index, queries, neighbors, distances, sample_filter);        \
+#define CUVS_INST_CAGRA_SEARCH(T, IdxT, OutputIdxT)                                     \
+  void search(raft::resources const& handle,                                            \
+              cuvs::neighbors::cagra::search_params const& params,                      \
+              const cuvs::neighbors::cagra::index<T, IdxT>& index,                      \
+              raft::device_matrix_view<const T, int64_t, raft::row_major> queries,      \
+              raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors, \
+              raft::device_matrix_view<float, int64_t, raft::row_major> distances,      \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)             \
+  {                                                                                     \
+    cuvs::neighbors::cagra::search<T, IdxT, OutputIdxT>(                                \
+      handle, params, index, queries, neighbors, distances, sample_filter);             \
   }
 
-CUVS_INST_CAGRA_SEARCH(uint8_t, uint32_t);
+CUVS_INST_CAGRA_SEARCH(uint8_t, uint32_t, uint32_t);
+CUVS_INST_CAGRA_SEARCH(uint8_t, uint32_t, int64_t);
 
 #undef CUVS_INST_CAGRA_SEARCH
 
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index b4f701819d..8ae5445961 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -43,13 +43,17 @@
 
 namespace cuvs::neighbors::cagra::detail {
 
-template <typename DataT, typename IndexT, typename DistanceT, typename CagraSampleFilterT>
+template <typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          typename CagraSampleFilterT,
+          typename OutputIdxT = IndexT>
 void search_main_core(raft::resources const& res,
                       search_params params,
                       const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                       raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
                       raft::device_matrix_view<const DataT, int64_t, raft::row_major> queries,
-                      raft::device_matrix_view<IndexT, int64_t, raft::row_major> neighbors,
+                      raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors,
                       raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
                       CagraSampleFilterT sample_filter = CagraSampleFilterT())
 {
@@ -73,8 +77,8 @@ void search_main_core(raft::resources const& res,
     queries.extent(1));
 
   using CagraSampleFilterT_s = typename CagraSampleFilterT_Selector<CagraSampleFilterT>::type;
-  std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT_s>> plan =
-    factory<DataT, IndexT, DistanceT, CagraSampleFilterT_s>::create(
+  std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT_s, OutputIdxT>>
+    plan = factory<DataT, IndexT, DistanceT, CagraSampleFilterT_s, OutputIdxT>::create(
       res, params, dataset_desc, queries.extent(1), graph.extent(0), graph.extent(1), topk);
 
   plan->check(topk);
@@ -85,7 +89,7 @@ void search_main_core(raft::resources const& res,
 
   for (unsigned qid = 0; qid < queries.extent(0); qid += max_queries) {
     const uint32_t n_queries = std::min<std::size_t>(max_queries, queries.extent(0) - qid);
-    auto _topk_indices_ptr   = reinterpret_cast<IndexT*>(neighbors.data_handle()) + (topk * qid);
+    auto _topk_indices_ptr   = neighbors.data_handle() + (topk * qid);
     auto _topk_distances_ptr = distances.data_handle() + (topk * qid);
     // todo(tfeher): one could keep distances optional and pass nullptr
     const auto* _query_ptr = queries.data_handle() + (query_dim * qid);
@@ -114,9 +118,8 @@ void search_main_core(raft::resources const& res,
  * See the [build](#build) documentation for a usage example.
  *
  * @tparam T data element type
- * @tparam IdxT type of database vector indices
- * @tparam internal_IdxT during search we map IdxT to internal_IdxT, this way we do not need
- * separate kernels for int/uint.
+ * @tparam IdxT type of the indices in the CAGRA graph
+ * @tparam OutputIdxT type of the returned indices
  *
  * @param[in] handle
  * @param[in] params configure the search
@@ -128,7 +131,7 @@ void search_main_core(raft::resources const& res,
  * k]
  */
 template <typename T,
-          typename InternalIdxT,
+          typename OutputIdxT,
           typename CagraSampleFilterT,
           typename IdxT      = uint32_t,
           typename DistanceT = float>
@@ -136,35 +139,30 @@ void search_main(raft::resources const& res,
                  search_params params,
                  const index<T, IdxT>& index,
                  raft::device_matrix_view<const T, int64_t, raft::row_major> queries,
-                 raft::device_matrix_view<InternalIdxT, int64_t, raft::row_major> neighbors,
+                 raft::device_matrix_view<OutputIdxT, int64_t, raft::row_major> neighbors,
                  raft::device_matrix_view<DistanceT, int64_t, raft::row_major> distances,
                  CagraSampleFilterT sample_filter = CagraSampleFilterT())
 {
-  auto stream         = raft::resource::get_cuda_stream(res);
-  const auto& graph   = index.graph();
-  auto graph_internal = raft::make_device_matrix_view<const InternalIdxT, int64_t, raft::row_major>(
-    reinterpret_cast<const InternalIdxT*>(graph.data_handle()), graph.extent(0), graph.extent(1));
-
   // n_rows has the same type as the dataset index (the array extents type)
   using ds_idx_type = decltype(index.data().n_rows());
   // Dispatch search parameters based on the dataset kind.
   if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index.data());
       strided_dset != nullptr) {
     // Search using a plain (strided) row-major dataset
-    auto desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
+    auto desc = dataset_descriptor_init_with_cache<T, IdxT, DistanceT>(
       res, params, *strided_dset, index.metric());
-    search_main_core<T, InternalIdxT, DistanceT, CagraSampleFilterT>(
-      res, params, desc, graph_internal, queries, neighbors, distances, sample_filter);
+    search_main_core<T, IdxT, DistanceT, CagraSampleFilterT, OutputIdxT>(
+      res, params, desc, index.graph(), queries, neighbors, distances, sample_filter);
   } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<float, ds_idx_type>*>(&index.data());
              vpq_dset != nullptr) {
     // Search using a compressed dataset
     RAFT_FAIL("FP32 VPQ dataset support is coming soon");
   } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<half, ds_idx_type>*>(&index.data());
              vpq_dset != nullptr) {
-    auto desc = dataset_descriptor_init_with_cache<T, InternalIdxT, DistanceT>(
+    auto desc = dataset_descriptor_init_with_cache<T, IdxT, DistanceT>(
       res, params, *vpq_dset, index.metric());
-    search_main_core<T, InternalIdxT, DistanceT, CagraSampleFilterT>(
-      res, params, desc, graph_internal, queries, neighbors, distances, sample_filter);
+    search_main_core<T, IdxT, DistanceT, CagraSampleFilterT, OutputIdxT>(
+      res, params, desc, index.graph(), queries, neighbors, distances, sample_filter);
   } else if (auto* empty_dset = dynamic_cast<const empty_dataset<ds_idx_type>*>(&index.data());
              empty_dset != nullptr) {
     // Forgot to add a dataset.
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index d2ae5c55b7..3d1ea1efc9 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -29,42 +29,45 @@ namespace cuvs::neighbors::cagra::detail {
 template <typename DataT,
           typename IndexT,
           typename DistanceT,
-          typename CagraSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
+          typename CagraSampleFilterT = cuvs::neighbors::filtering::none_sample_filter,
+          typename OutputIndexT       = IndexT>
 class factory {
  public:
   /**
    * Create a search structure for dataset with dim features.
    */
-  static std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT>> create(
-    raft::resources const& res,
-    search_params const& params,
-    const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
-    int64_t dim,
-    int64_t dataset_size,
-    int64_t graph_degree,
-    uint32_t topk)
+  static std::unique_ptr<
+    search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT, OutputIndexT>>
+  create(raft::resources const& res,
+         search_params const& params,
+         const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
+         int64_t dim,
+         int64_t dataset_size,
+         int64_t graph_degree,
+         uint32_t topk)
   {
     search_plan_impl_base plan(params, dim, dataset_size, graph_degree, topk);
     return dispatch_kernel(res, plan, dataset_desc);
   }
 
  private:
-  static std::unique_ptr<search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT>>
+  static std::unique_ptr<
+    search_plan_impl<DataT, IndexT, DistanceT, CagraSampleFilterT, OutputIndexT>>
   dispatch_kernel(raft::resources const& res,
                   search_plan_impl_base& plan,
                   const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc)
   {
     if (plan.algo == search_algo::SINGLE_CTA) {
       return std::make_unique<
-        single_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
+        single_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT, OutputIndexT>>(
         res, plan, dataset_desc, plan.dim, plan.dataset_size, plan.graph_degree, plan.topk);
     } else if (plan.algo == search_algo::MULTI_CTA) {
       return std::make_unique<
-        multi_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
+        multi_cta_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT, OutputIndexT>>(
         res, plan, dataset_desc, plan.dim, plan.dataset_size, plan.graph_degree, plan.topk);
     } else {
       return std::make_unique<
-        multi_kernel_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT>>(
+        multi_kernel_search::search<DataT, IndexT, DistanceT, CagraSampleFilterT, OutputIndexT>>(
         res, plan, dataset_desc, plan.dim, plan.dataset_size, plan.graph_degree, plan.topk);
     }
   }
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
index 19839f7b6e..edb30e3e4e 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_cta.cuh
@@ -51,9 +51,13 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace multi_cta_search {
 
-template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
-struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
-  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T>;
+template <typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          typename SAMPLE_FILTER_T,
+          typename OutputIndexT = IndexT>
+struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T, OutputIndexT> {
+  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T, OutputIndexT>;
   using DATA_T     = typename base_type::DATA_T;
   using INDEX_T    = typename base_type ::INDEX_T;
   using DISTANCE_T = typename base_type::DISTANCE_T;
@@ -92,6 +96,8 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
   using base_type::num_executed_iterations;
   using base_type::num_seeds;
 
+  constexpr static bool kNeedIndexCopy = sizeof(INDEX_T) != sizeof(OutputIndexT);
+
   uint32_t num_cta_per_query;
   lightweight_uvector<INDEX_T> intermediate_indices;
   lightweight_uvector<float> intermediate_distances;
@@ -178,8 +184,9 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
     // Allocate memory for intermediate buffer and workspace.
     //
     uint32_t num_intermediate_results = num_cta_per_query * itopk_size;
-    intermediate_indices.resize(num_intermediate_results * max_queries,
-                                raft::resource::get_cuda_stream(res));
+    intermediate_indices.resize(
+      num_intermediate_results * max_queries + (kNeedIndexCopy ? itopk_size * max_queries : 0),
+      raft::resource::get_cuda_stream(res));
     intermediate_distances.resize(num_intermediate_results * max_queries,
                                   raft::resource::get_cuda_stream(res));
 
@@ -205,7 +212,7 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
 
   void operator()(raft::resources const& res,
                   raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
-                  INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+                  OutputIndexT* const topk_indices_ptr,  // [num_queries, topk]
                   DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
                   const DATA_T* const queries_ptr,       // [num_queries, dataset_dim]
                   const uint32_t num_queries,
@@ -239,6 +246,9 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
 
     // Select the top-k results from the intermediate results
     const uint32_t num_intermediate_results = num_cta_per_query * itopk_size;
+    auto* output_indices_ptr =
+      kNeedIndexCopy ? intermediate_indices.data() + num_intermediate_results * max_queries
+                     : reinterpret_cast<INDEX_T*>(topk_indices_ptr);
     _cuann_find_topk(topk,
                      num_queries,
                      num_intermediate_results,
@@ -248,12 +258,20 @@ struct search : public search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_
                      num_intermediate_results,
                      topk_distances_ptr,
                      topk,
-                     topk_indices_ptr,
+                     output_indices_ptr,
                      topk,
                      topk_workspace.data(),
                      true,
                      NULL,
                      stream);
+    if constexpr (kNeedIndexCopy) {
+      raft::linalg::map(
+        res,
+        raft::make_device_matrix_view<OutputIndexT, int64_t>(topk_indices_ptr, num_queries, topk),
+        raft::cast_op<OutputIndexT>{},
+        raft::make_device_matrix_view<const IndexT, int64_t>(
+          output_indices_ptr, num_queries, topk));
+    }
   }
 };
 
diff --git a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
index cb3e819c96..e11857f6ae 100644
--- a/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_multi_kernel.cuh
@@ -506,10 +506,10 @@ void apply_filter(INDEX_T* const result_indices_ptr,
                                                                  sample_filter);
 }
 
-template <class T>
+template <class T, class S>
 RAFT_KERNEL batched_memcpy_kernel(T* const dst,  // [batch_size, ld_dst]
                                   const uint64_t ld_dst,
-                                  const T* const src,  // [batch_size, ld_src]
+                                  const S* const src,  // [batch_size, ld_src]
                                   const uint64_t ld_src,
                                   const uint64_t count,
                                   const uint64_t batch_size)
@@ -521,10 +521,10 @@ RAFT_KERNEL batched_memcpy_kernel(T* const dst,  // [batch_size, ld_dst]
   dst[i + (ld_dst * j)] = src[i + (ld_src * j)];
 }
 
-template <class T>
+template <class T, class S>
 void batched_memcpy(T* const dst,  // [batch_size, ld_dst]
                     const uint64_t ld_dst,
-                    const T* const src,  // [batch_size, ld_src]
+                    const S* const src,  // [batch_size, ld_src]
                     const uint64_t ld_src,
                     const uint64_t count,
                     const uint64_t batch_size,
@@ -534,7 +534,7 @@ void batched_memcpy(T* const dst,  // [batch_size, ld_dst]
   assert(ld_src >= count);
   constexpr uint32_t block_size = 256;
   const auto grid_size          = (batch_size * count + block_size - 1) / block_size;
-  batched_memcpy_kernel<T>
+  batched_memcpy_kernel<T, S>
     <<<grid_size, block_size, 0, cuda_stream>>>(dst, ld_dst, src, ld_src, count, batch_size);
 }
 
@@ -574,9 +574,13 @@ void set_value_batch(T* const dev_ptr,
 // |<---                 result_buffer_allocation_size                 --->|
 // |<---                       result_buffer_size  --->|                     // Double buffer (A)
 //                      |<---  result_buffer_size                      --->| // Double buffer (B)
-template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
-struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
-  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T>;
+template <typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          typename SAMPLE_FILTER_T,
+          typename OutputIndexT = IndexT>
+struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T, OutputIndexT> {
+  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T, OutputIndexT>;
   using DATA_T     = typename base_type::DATA_T;
   using INDEX_T    = typename base_type::INDEX_T;
   using DISTANCE_T = typename base_type::DISTANCE_T;
@@ -772,7 +776,7 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
 
   void operator()(raft::resources const& res,
                   raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
-                  INDEX_T* const topk_indices_ptr,       // [num_queries, topk]
+                  OutputIndexT* const topk_indices_ptr,  // [num_queries, topk]
                   DISTANCE_T* const topk_distances_ptr,  // [num_queries, topk]
                   const DATA_T* const queries_ptr,       // [num_queries, dataset_dim]
                   const uint32_t num_queries,
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index adbedd8409..ab2396cc21 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -133,7 +133,11 @@ struct search_plan_impl_base : public search_params {
   }
 };
 
-template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
+template <typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          typename SAMPLE_FILTER_T,
+          typename OutputIndexT = IndexT>
 struct search_plan_impl : public search_plan_impl_base {
   using DATA_T     = DataT;
   using INDEX_T    = IndexT;
@@ -181,7 +185,7 @@ struct search_plan_impl : public search_plan_impl_base {
 
   virtual void operator()(raft::resources const& res,
                           raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
-                          INDEX_T* const result_indices_ptr,       // [num_queries, topk]
+                          OutputIndexT* const result_indices_ptr,  // [num_queries, topk]
                           DISTANCE_T* const result_distances_ptr,  // [num_queries, topk]
                           const DATA_T* const queries_ptr,         // [num_queries, dataset_dim]
                           const std::uint32_t num_queries,
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
index 20070487bf..3989111229 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta.cuh
@@ -36,6 +36,7 @@
 
 #include <raft/util/cuda_rt_essentials.hpp>
 #include <raft/util/cudart_utils.hpp>  // RAFT_CUDA_TRY_NOT_THROW is used TODO(tfeher): consider moving this to cuda_rt_essentials.hpp
+#include <raft/util/pow2_utils.cuh>
 
 #include <algorithm>
 #include <cassert>
@@ -47,9 +48,13 @@
 namespace cuvs::neighbors::cagra::detail {
 namespace single_cta_search {
 
-template <typename DataT, typename IndexT, typename DistanceT, typename SAMPLE_FILTER_T>
-struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
-  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T>;
+template <typename DataT,
+          typename IndexT,
+          typename DistanceT,
+          typename SAMPLE_FILTER_T,
+          typename OutputIndexT = IndexT>
+struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T, OutputIndexT> {
+  using base_type  = search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T, OutputIndexT>;
   using DATA_T     = typename base_type::DATA_T;
   using INDEX_T    = typename base_type::INDEX_T;
   using DISTANCE_T = typename base_type::DISTANCE_T;
@@ -217,7 +222,7 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
 
   void operator()(raft::resources const& res,
                   raft::device_matrix_view<const INDEX_T, int64_t, raft::row_major> graph,
-                  INDEX_T* const result_indices_ptr,       // [num_queries, topk]
+                  OutputIndexT* const result_indices_ptr,  // [num_queries, topk]
                   DISTANCE_T* const result_distances_ptr,  // [num_queries, topk]
                   const DATA_T* const queries_ptr,         // [num_queries, dataset_dim]
                   const std::uint32_t num_queries,
@@ -226,10 +231,21 @@ struct search : search_plan_impl<DataT, IndexT, DistanceT, SAMPLE_FILTER_T> {
                   uint32_t topk,
                   SAMPLE_FILTER_T sample_filter)
   {
-    cudaStream_t stream = raft::resource::get_cuda_stream(res);
+    cudaStream_t stream                 = raft::resource::get_cuda_stream(res);
+    constexpr uintptr_t kOutputIndexTag = raft::Pow2<sizeof(OutputIndexT)>::Log2;
+    const auto result_indices_uintptr   = reinterpret_cast<uintptr_t>(result_indices_ptr);
+    static_assert(kOutputIndexTag <= 3, "OutputIndexT can't be more than 8 bytes");
+    if constexpr (kOutputIndexTag <= 1) {
+      // NB: there's no need for runtime check here for larger OutputIndexT naturally aligned
+      RAFT_EXPECTS((result_indices_uintptr & 0x3) == 0,
+                   "result_indices_ptr must be at least 4-byte aligned");
+    }
     select_and_run(dataset_desc,
                    graph,
-                   result_indices_ptr,
+                   // NB: tag the indices pointer with its element size.
+                   //     This allows us to avoid multiplying kernel instantiations
+                   //     and any costs for extra registers in the kernel signature.
+                   result_indices_uintptr | kOutputIndexTag,
                    result_distances_ptr,
                    queries_ptr,
                    num_queries,
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
index f734b05827..f6de1b4806 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_inst.cuh
@@ -25,7 +25,7 @@ namespace cuvs::neighbors::cagra::detail::single_cta_search {
   template void select_and_run<DataT, IndexT, DistanceT, SampleFilterT>(      \
     const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,    \
     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,   \
-    IndexT* topk_indices_ptr,                                                 \
+    uintptr_t topk_indices_ptr,                                               \
     DistanceT* topk_distances_ptr,                                            \
     const DataT* queries_ptr,                                                 \
     uint32_t num_queries,                                                     \
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
index 1e072f5406..21c88fd607 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel-inl.cuh
@@ -40,6 +40,7 @@
 
 #include <raft/util/cuda_rt_essentials.hpp>
 #include <raft/util/integer_utils.hpp>
+#include <raft/util/pow2_utils.cuh>
 
 #include <rmm/cuda_stream.hpp>
 #include <rmm/device_uvector.hpp>
@@ -509,14 +510,47 @@ RAFT_DEVICE_INLINE_FUNCTION void hashmap_restore(INDEX_T* const hashmap_ptr,
   }
 }
 
-// One query one thread block
+/**
+ * @brief Search operation for a single query using a single thread block.
+ * *
+ * @tparam MAX_ITOPK Maximum for the internal_topk argument.
+ * @tparam MAX_CANDIDATES
+ * @tparam TOPK_BY_BITONIC_SORT
+ * @tparam DATASET_DESCRIPTOR_T
+ * @tparam SAMPLE_FILTER_T
+ *
+ * @param result_indices_ptr
+ *    Tagged pointer to the result neighbors [num_queries, top_k]; the tag is the two lower bits to
+ *    identify the index element type (see the code below).
+ * @param result_distances_ptr Pointer to the result distances buffer [num_queries, top_k].
+ * @param top_k Number of top-k results to retrieve.
+ * @param dataset_desc Pointer to the dataset descriptor.
+ * @param queries_ptr Pointer to the queries [num_queries, dataset_dim].
+ * @param knn_graph Pointer to the k-nearest neighbors graph [dataset_size, graph_degree].
+ * @param graph_degree Degree of the graph.
+ * @param num_distilation Number of distillation steps.
+ * @param rand_xor_mask Random XOR mask for randomization.
+ * @param seed_ptr Pointer to the seed indices [num_queries, num_seeds].
+ * @param num_seeds Number of seeds.
+ * @param visited_hashmap_ptr
+ *    Pointer to the hashmap of visited nodes [num_queries, 1 << hash_bitlen].
+ * @param internal_topk Internal top-k size.
+ * @param search_width Width of the search.
+ * @param min_iteration Minimum number of iterations.
+ * @param max_iteration Maximum number of iterations.
+ * @param num_executed_iterations Pointer to the number of executed iterations [num_queries].
+ * @param hash_bitlen Bit length of the hash.
+ * @param small_hash_bitlen Bit length of the small hash.
+ * @param small_hash_reset_interval Interval for resetting the small hash.
+ * @param query_id sequential id of the query in the batch
+ */
 template <unsigned MAX_ITOPK,
           unsigned MAX_CANDIDATES,
           unsigned TOPK_BY_BITONIC_SORT,
           class DATASET_DESCRIPTOR_T,
           class SAMPLE_FILTER_T>
 __device__ void search_core(
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, top_k]
+  uintptr_t result_indices_ptr,                                           // [num_queries, top_k]
   typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, top_k]
   const std::uint32_t top_k,
   const DATASET_DESCRIPTOR_T* dataset_desc,
@@ -863,6 +897,28 @@ __device__ void search_core(
     __syncthreads();
   }
 
+  // NB: The indices pointer is tagged with its element size.
+  //     Here we select the correct conversion operator at runtime.
+  //     This allows us to avoid multiplying kernel instantiations
+  //     and any costs for extra registers in the kernel signature.
+  const uint32_t index_element_tag = result_indices_ptr & 0x3;
+  result_indices_ptr ^= index_element_tag;
+  auto write_indices =
+    index_element_tag == 3
+      ? [](uintptr_t ptr,
+           uint32_t i,
+           INDEX_T x) { reinterpret_cast<uint64_t*>(ptr)[i] = static_cast<uint64_t>(x); }
+    : index_element_tag == 2
+      ? [](uintptr_t ptr,
+           uint32_t i,
+           INDEX_T x) { reinterpret_cast<uint32_t*>(ptr)[i] = static_cast<uint32_t>(x); }
+    : index_element_tag == 1
+      ? [](uintptr_t ptr,
+           uint32_t i,
+           INDEX_T x) { reinterpret_cast<uint16_t*>(ptr)[i] = static_cast<uint16_t>(x); }
+      : [](uintptr_t ptr, uint32_t i, INDEX_T x) {
+          reinterpret_cast<uint8_t*>(ptr)[i] = static_cast<uint8_t>(x);
+        };
   for (std::uint32_t i = threadIdx.x; i < top_k; i += blockDim.x) {
     unsigned j  = i + (top_k * query_id);
     unsigned ii = i;
@@ -870,8 +926,9 @@ __device__ void search_core(
     if (result_distances_ptr != nullptr) { result_distances_ptr[j] = result_distances_buffer[ii]; }
     constexpr INDEX_T index_msb_1_mask = utils::gen_index_msb_1_mask<INDEX_T>::value;
 
-    result_indices_ptr[j] =
-      result_indices_buffer[ii] & ~index_msb_1_mask;  // clear most significant bit
+    write_indices(result_indices_ptr,
+                  j,
+                  result_indices_buffer[ii] & ~index_msb_1_mask);  // clear most significant bit
   }
   if (threadIdx.x == 0 && num_executed_iterations != nullptr) {
     num_executed_iterations[query_id] = iter + 1;
@@ -910,7 +967,7 @@ template <unsigned MAX_ITOPK,
           class DATASET_DESCRIPTOR_T,
           class SAMPLE_FILTER_T>
 RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel(
-  typename DATASET_DESCRIPTOR_T::INDEX_T* const result_indices_ptr,       // [num_queries, top_k]
+  uintptr_t result_indices_ptr,                                           // [num_queries, top_k]
   typename DATASET_DESCRIPTOR_T::DISTANCE_T* const result_distances_ptr,  // [num_queries, top_k]
   const std::uint32_t top_k,
   const DATASET_DESCRIPTOR_T* dataset_desc,
@@ -979,7 +1036,7 @@ struct alignas(kCacheLineBytes) job_desc_t {
   using data_type     = typename DATASET_DESCRIPTOR_T::DATA_T;
   // The algorithm input parameters
   struct value_t {
-    index_type* result_indices_ptr;       // [num_queries, top_k]
+    uintptr_t result_indices_ptr;         // [num_queries, top_k]
     distance_type* result_distances_ptr;  // [num_queries, top_k]
     const data_type* queries_ptr;         // [num_queries, dataset_dim]
     uint32_t top_k;
@@ -1082,7 +1139,7 @@ RAFT_KERNEL __launch_bounds__(1024, 1) search_kernel_p(
     if (worker_data.handle == kNoMoreWork) { break; }
 
     // reading phase
-    auto* result_indices_ptr   = job_descriptor.value.result_indices_ptr;
+    auto result_indices_ptr    = job_descriptor.value.result_indices_ptr;
     auto* result_distances_ptr = job_descriptor.value.result_distances_ptr;
     auto* queries_ptr          = job_descriptor.value.queries_ptr;
     auto top_k                 = job_descriptor.value.top_k;
@@ -1806,7 +1863,7 @@ struct alignas(kCacheLineBytes) persistent_runner_t : public persistent_runner_b
     auto* job_descriptors_ptr     = job_descriptors.data();
     for (uint32_t i = 0; i < kMaxJobsNum; i++) {
       auto& jd                = job_descriptors_ptr[i].input.value;
-      jd.result_indices_ptr   = nullptr;
+      jd.result_indices_ptr   = 0;
       jd.result_distances_ptr = nullptr;
       jd.queries_ptr          = nullptr;
       jd.top_k                = 0;
@@ -1880,7 +1937,7 @@ struct alignas(kCacheLineBytes) persistent_runner_t : public persistent_runner_b
     RAFT_LOG_INFO("Destroyed the persistent runner.");
   }
 
-  void launch(index_type* result_indices_ptr,       // [num_queries, top_k]
+  void launch(uintptr_t result_indices_ptr,         // [num_queries, top_k]
               distance_type* result_distances_ptr,  // [num_queries, top_k]
               const data_type* queries_ptr,         // [num_queries, dataset_dim]
               uint32_t num_queries,
@@ -2022,7 +2079,7 @@ auto get_runner(Args... args) -> std::shared_ptr<RunnerT>
 template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
 void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
-                    IndexT* topk_indices_ptr,       // [num_queries, topk]
+                    uintptr_t topk_indices_ptr,     // [num_queries, topk]
                     DistanceT* topk_distances_ptr,  // [num_queries, topk]
                     const DataT* queries_ptr,       // [num_queries, dataset_dim]
                     uint32_t num_queries,
diff --git a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
index 4d8b72b417..81aeb4c3f8 100644
--- a/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_single_cta_kernel.cuh
@@ -24,7 +24,7 @@ namespace cuvs::neighbors::cagra::detail::single_cta_search {
 template <typename DataT, typename IndexT, typename DistanceT, typename SampleFilterT>
 void select_and_run(const dataset_descriptor_host<DataT, IndexT, DistanceT>& dataset_desc,
                     raft::device_matrix_view<const IndexT, int64_t, raft::row_major> graph,
-                    IndexT* topk_indices_ptr,       // [num_queries, topk]
+                    uintptr_t topk_indices_ptr,     // [num_queries, topk]
                     DistanceT* topk_distances_ptr,  // [num_queries, topk]
                     const DataT* queries_ptr,       // [num_queries, dataset_dim]
                     uint32_t num_queries,
diff --git a/cpp/src/neighbors/dynamic_batching.cu b/cpp/src/neighbors/dynamic_batching.cu
index 84c8a2cf1d..e7ca9519b9 100644
--- a/cpp/src/neighbors/dynamic_batching.cu
+++ b/cpp/src/neighbors/dynamic_batching.cu
@@ -54,8 +54,10 @@ namespace cuvs::neighbors::dynamic_batching {
     return index.runner->search(res, params, queries, neighbors, distances);       \
   }
 
+// Brute-force search with 64-bit indices
 CUVS_INST_DYNAMIC_BATCHING_INDEX(float, int64_t, cuvs::neighbors::brute_force, index<float, float>);
 
+// CAGRA build and search with 32-bit indices
 CUVS_INST_DYNAMIC_BATCHING_INDEX(float, uint32_t, cuvs::neighbors::cagra, index<float, uint32_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(half, uint32_t, cuvs::neighbors::cagra, index<half, uint32_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t, uint32_t, cuvs::neighbors::cagra, index<int8_t, uint32_t>);
@@ -64,11 +66,22 @@ CUVS_INST_DYNAMIC_BATCHING_INDEX(uint8_t,
                                  cuvs::neighbors::cagra,
                                  index<uint8_t, uint32_t>);
 
+// CAGRA build with 32-bit indices, search with 64-bit indices
+CUVS_INST_DYNAMIC_BATCHING_INDEX(float, int64_t, cuvs::neighbors::cagra, index<float, uint32_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(half, int64_t, cuvs::neighbors::cagra, index<half, uint32_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t, int64_t, cuvs::neighbors::cagra, index<int8_t, uint32_t>);
+CUVS_INST_DYNAMIC_BATCHING_INDEX(uint8_t,
+                                 int64_t,
+                                 cuvs::neighbors::cagra,
+                                 index<uint8_t, uint32_t>);
+
+// IVF-PQ with 64-bit indices
 CUVS_INST_DYNAMIC_BATCHING_INDEX(float, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(half, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(uint8_t, int64_t, cuvs::neighbors::ivf_pq, index<int64_t>);
 
+// IVF-Flat with 64-bit indices
 CUVS_INST_DYNAMIC_BATCHING_INDEX(float, int64_t, cuvs::neighbors::ivf_flat, index<float, int64_t>);
 CUVS_INST_DYNAMIC_BATCHING_INDEX(int8_t,
                                  int64_t,
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index e9408264e5..0270a5979b 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -327,6 +327,7 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
   }
 
  protected:
+  template <typename SearchIdxT = IdxT>
   void testCagra()
   {
     // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
@@ -342,25 +343,25 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
       GTEST_SKIP();
 
     size_t queries_size = ps.n_queries * ps.k;
-    std::vector<IdxT> indices_Cagra(queries_size);
-    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<SearchIdxT> indices_Cagra(queries_size);
+    std::vector<SearchIdxT> indices_naive(queries_size);
     std::vector<DistanceT> distances_Cagra(queries_size);
     std::vector<DistanceT> distances_naive(queries_size);
 
     {
       rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
-      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
-
-      cuvs::neighbors::naive_knn<DistanceT, DataT, IdxT>(handle_,
-                                                         distances_naive_dev.data(),
-                                                         indices_naive_dev.data(),
-                                                         search_queries.data(),
-                                                         database.data(),
-                                                         ps.n_queries,
-                                                         ps.n_rows,
-                                                         ps.dim,
-                                                         ps.k,
-                                                         ps.metric);
+      rmm::device_uvector<SearchIdxT> indices_naive_dev(queries_size, stream_);
+
+      cuvs::neighbors::naive_knn<DistanceT, DataT, SearchIdxT>(handle_,
+                                                               distances_naive_dev.data(),
+                                                               indices_naive_dev.data(),
+                                                               search_queries.data(),
+                                                               database.data(),
+                                                               ps.n_queries,
+                                                               ps.n_rows,
+                                                               ps.dim,
+                                                               ps.k,
+                                                               ps.metric);
       raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
       raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
       raft::resource::sync_stream(handle_);
@@ -368,7 +369,7 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
 
     {
       rmm::device_uvector<DistanceT> distances_dev(queries_size, stream_);
-      rmm::device_uvector<IdxT> indices_dev(queries_size, stream_);
+      rmm::device_uvector<SearchIdxT> indices_dev(queries_size, stream_);
 
       {
         cagra::index_params index_params;
@@ -431,8 +432,8 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
 
         auto search_queries_view = raft::make_device_matrix_view<const DataT, int64_t>(
           search_queries.data(), ps.n_queries, ps.dim);
-        auto indices_out_view =
-          raft::make_device_matrix_view<IdxT, int64_t>(indices_dev.data(), ps.n_queries, ps.k);
+        auto indices_out_view = raft::make_device_matrix_view<SearchIdxT, int64_t>(
+          indices_dev.data(), ps.n_queries, ps.k);
         auto dists_out_view = raft::make_device_matrix_view<DistanceT, int64_t>(
           distances_dev.data(), ps.n_queries, ps.k);
 
diff --git a/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
index 16c0d3d2a0..aed372b84c 100644
--- a/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
@@ -21,7 +21,8 @@
 namespace cuvs::neighbors::cagra {
 
 typedef AnnCagraTest<float, float, std::uint32_t> AnnCagraTestF_U32;
-TEST_P(AnnCagraTestF_U32, AnnCagra) { this->testCagra(); }
+TEST_P(AnnCagraTestF_U32, AnnCagra_U32) { this->testCagra<uint32_t>(); }
+TEST_P(AnnCagraTestF_U32, AnnCagra_I64) { this->testCagra<int64_t>(); }
 
 typedef AnnCagraAddNodesTest<float, float, std::uint32_t> AnnCagraAddNodesTestF_U32;
 TEST_P(AnnCagraAddNodesTestF_U32, AnnCagraAddNodes) { this->testCagra(); }
diff --git a/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
index de682cee49..fdf0ca3bd9 100644
--- a/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
@@ -21,7 +21,8 @@
 namespace cuvs::neighbors::cagra {
 
 typedef AnnCagraTest<float, half, std::uint32_t> AnnCagraTestF16_U32;
-TEST_P(AnnCagraTestF16_U32, AnnCagra) { this->testCagra(); }
+TEST_P(AnnCagraTestF16_U32, AnnCagra_U32) { this->testCagra<uint32_t>(); }
+TEST_P(AnnCagraTestF16_U32, AnnCagra_I64) { this->testCagra<int64_t>(); }
 
 typedef AnnCagraIndexMergeTest<float, half, std::uint32_t> AnnCagraIndexMergeTestF16_U32;
 TEST_P(AnnCagraIndexMergeTestF16_U32, AnnCagraIndexMerge) { this->testCagra(); }

From d77c73a2f03adb6ec17e4e4b35a7836429033ffa Mon Sep 17 00:00:00 2001
From: Rohan Varma <164543155+nvrohanv@users.noreply.github.com>
Date: Tue, 1 Apr 2025 03:04:03 -0700
Subject: [PATCH 348/623] Fix resource import so generate_groundtruth works
 (#774)

`generate_groundtruth/__main__.py` was importing DeviceResources directly from pylibraft. However generate_groundtruth attempts to call cuvs brute_force build which expects to get `cuvs.common.Resources`. This mismatch was causing the groundtruth generation script to crash. This PR fixes this and imports `cuvs.common.Resources`

Authors:
  - Rohan Varma (https://github.com/nvrohanv)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/774
---
 python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
index 88ec55dfa5..ca0eefcb1c 100644
--- a/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/generate_groundtruth/__main__.py
@@ -84,9 +84,9 @@ def force_fallback_to_numpy():
 if rmm is not None:
     gpu_system = True
     try:
-        from pylibraft.common import DeviceResources
         from rmm.allocators.cupy import rmm_cupy_allocator
 
+        from cuvs.common import Resources
         from cuvs.neighbors.brute_force import build, search
     except ImportError:
         # RMM is available, cupy is available, but cuVS is not
@@ -188,7 +188,7 @@ def calc_truth(dataset, queries, k, metric="sqeuclidean"):
     queries = xp.asarray(queries, dtype=xp.float32)
 
     if gpu_system:
-        resources = DeviceResources()
+        resources = Resources()
 
     while i < n_samples:
         print("Step {0}/{1}:".format(i // n, n_samples // n))

From af3a4ace680fd8fb0fb20b765926411254cea604 Mon Sep 17 00:00:00 2001
From: Rohan Varma <164543155+nvrohanv@users.noreply.github.com>
Date: Wed, 2 Apr 2025 03:17:18 +0900
Subject: [PATCH 349/623] add function to build specific gpu-arches [REVIEW]
 (#766)

This PR adds in functions to the root level build.sh so that you can specify which GPU architectures you want to build for. Currently the only options were to build for the current system or for all systems. It also adds this option into `examples/build.sh` in addition to fixing bugs with the clean command.

A `--gpu-arch` option is added that is meant to be used to provide which architectures to build for e.g. `./build.sh bench-ann --gpu-arch="80-real;90-real"`

Authors:
  - Rohan Varma (https://github.com/nvrohanv)
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Ben Karsin (https://github.com/bkarsin)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Micka (https://github.com/lowener)
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/766
---
 build.sh          | 63 +++++++++++++++++++++++++++++++--------
 examples/build.sh | 75 ++++++++++++++++++++++++++++++++++-------------
 2 files changed, 106 insertions(+), 32 deletions(-)

diff --git a/build.sh b/build.sh
index 2cb25991ba..60f9a8291b 100755
--- a/build.sh
+++ b/build.sh
@@ -42,6 +42,13 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    --limit-tests               - semicolon-separated list of test executables to compile (e.g. NEIGHBORS_TEST;CLUSTER_TEST)
    --limit-bench-ann           - semicolon-separated list of ann benchmark executables to compute (e.g. HNSWLIB_ANN_BENCH;RAFT_IVF_PQ_ANN_BENCH)
    --allgpuarch                - build for all supported GPU architectures
+   --gpu-arch=\"<arch>\"        - build for specific GPU architectures (e.g. \"80-real;90-real\")
+                                Values from this flag are passed to CUDA_ARCHITECTURES in cmake.
+                                See https://cmake.org/cmake/help/latest/prop_tgt/CUDA_ARCHITECTURES.html
+                                for more details.
+                                To summarize, specifying \"80-virtual\" would generate PTX for compute capability 8.0
+                                whereas \"80-real\" will generate SASS.
+                                If you are unsure which to do, use -real suffix --gpu-arch=\"80-real;90-real\"
    --no-mg                     - disable multi-GPU support
    --no-nvtx                   - disable nvtx (profiling markers), but allow enabling it in downstream projects
    --no-shared-libs            - build without shared libraries
@@ -194,6 +201,37 @@ function buildMetrics {
     fi
 }
 
+function gpuArch {
+    # Check if both --gpu-arch and --allgpuarch are specified
+    if hasArg --allgpuarch && [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+        echo "Error: Cannot specify both --gpu-arch and --allgpuarch"
+        echo "Use either:"
+        echo "  --gpu-arch=\"80-real;90-real\"    (for specific architectures)"
+        echo "  --allgpuarch        (for all supported architectures)"
+        exit 1
+    fi
+
+    # Check for multiple gpu-arch options
+    if [[ $(echo $ARGS | { grep -Eo "\-\-gpu\-arch" || true; } | wc -l ) -gt 1 ]]; then
+        echo "Error: Multiple --gpu-arch options were provided. Please combine architectures into a single option."
+        echo "Instead of: --gpu-arch=80-real --gpu-arch=90-real"
+        echo "Use:       --gpu-arch=\"80-real;90-real\""
+        exit 1
+    fi
+
+    # Check for gpu-arch option
+    if [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+        GPU_ARCH_ARG=$(echo $ARGS | { grep -Eo "\-\-gpu\-arch=.+( |$)" || true; })
+        if [[ -n ${GPU_ARCH_ARG} ]]; then
+            # Remove the full argument from ARGS
+            ARGS=${ARGS//$GPU_ARCH_ARG/}
+            # Extract just the architecture value
+            CUVS_CMAKE_CUDA_ARCHITECTURES=$(echo $GPU_ARCH_ARG | sed -e 's/--gpu-arch=//' -e 's/ .*//')
+            echo "Building for specified GPU architectures: ${CUVS_CMAKE_CUDA_ARCHITECTURES}"
+        fi
+    fi
+}
+
 if hasArg -h || hasArg --help; then
     echo "${HELP}"
     exit 0
@@ -206,6 +244,7 @@ if (( ${NUMARGS} != 0 )); then
     limitTests
     limitAnnBench
     buildMetrics
+    gpuArch
     for a in ${ARGS}; do
         if ! (echo " ${VALIDARGS} " | grep -q " ${a} "); then
             echo "Invalid option: ${a}"
@@ -264,10 +303,6 @@ if hasArg -g; then
     BUILD_TYPE=Debug
 fi
 
-if hasArg --allgpuarch; then
-    BUILD_ALL_GPU_ARCH=1
-fi
-
 if hasArg --no-mg; then
     BUILD_MG_ALGOS=OFF
 fi
@@ -311,6 +346,7 @@ fi
 if hasArg --incl-cache-stats; then
     BUILD_REPORT_INCL_CACHE_STATS=ON
 fi
+
 if [[ ${CMAKE_TARGET} == "" ]]; then
     CMAKE_TARGET="all"
 fi
@@ -339,20 +375,23 @@ if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg
         CMAKE_TARGET="${CMAKE_TARGET};cuvs"
     fi
 
-    if (( ${BUILD_ALL_GPU_ARCH} == 0 )); then
-        CUVS_CMAKE_CUDA_ARCHITECTURES="NATIVE"
-        echo "Building for the architecture of the GPU in the system..."
-    else
-        CUVS_CMAKE_CUDA_ARCHITECTURES="RAPIDS"
-        echo "Building for *ALL* supported GPU architectures..."
-    fi
-
     # get the current count before the compile starts
     CACHE_TOOL=${CACHE_TOOL:-sccache}
     if [[ "$BUILD_REPORT_INCL_CACHE_STATS" == "ON" && -x "$(command -v ${CACHE_TOOL})" ]]; then
         "${CACHE_TOOL}" --zero-stats
     fi
 
+    # Set default GPU architecture if not already set by gpuArch function
+    if [[ -z "${CUVS_CMAKE_CUDA_ARCHITECTURES}" ]]; then
+        if hasArg --allgpuarch; then
+            CUVS_CMAKE_CUDA_ARCHITECTURES="RAPIDS"
+            echo "Building for *ALL* supported GPU architectures..."
+        else
+            CUVS_CMAKE_CUDA_ARCHITECTURES="NATIVE"
+            echo "Building for the architecture of the GPU in the system..."
+        fi
+    fi
+
     mkdir -p ${LIBCUVS_BUILD_DIR}
     cd ${LIBCUVS_BUILD_DIR}
     cmake -S ${REPODIR}/cpp -B ${LIBCUVS_BUILD_DIR} \
diff --git a/examples/build.sh b/examples/build.sh
index a91d312953..af506c0e8e 100755
--- a/examples/build.sh
+++ b/examples/build.sh
@@ -7,21 +7,69 @@
 # Abort script on first error
 set -e
 
+NUMARGS=$#
+ARGS=$*
+
 function hasArg {
-    (( NUMARGS != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+    (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
 }
 
-PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
+if hasArg clean; then
+  rm -rf c/build
+  rm -rf cpp/build
+  exit 0
+fi
+
+function gpuArch {
+
+    if hasArg --allgpuarch && [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+        echo "Error: Cannot specify both --gpu-arch and --allgpuarch"
+        echo "Use either:"
+        echo "  --gpu-arch=\"80-real;90-real\"    (for specific architectures)"
+        echo "  --allgpuarch        (for all supported architectures)"
+        exit 1
+    fi
+
+    if [[ $(echo $ARGS | { grep -Eo "\-\-gpu\-arch" || true; } | wc -l ) -gt 1 ]]; then
+        echo "Error: Multiple --gpu-arch options were provided. Please combine architectures into a single option."
+        echo "Instead of: --gpu-arch=80-real --gpu-arch=90-real"
+        echo "Use:       --gpu-arch=\"80-real;90-real\""
+        exit 1
+    fi
+
+    if [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+        GPU_ARCH_ARG=$(echo $ARGS | { grep -Eo "\-\-gpu\-arch=.+( |$)" || true; })
+        if [[ -n ${GPU_ARCH_ARG} ]]; then
+            # Extract just the architecture value
+            echo ${GPU_ARCH_ARG} | sed -e 's/--gpu-arch=//' -e 's/ .*//'
+            return
+        fi
+    fi
+
+    # Handle --allgpuarch
+    if hasArg --allgpuarch; then
+        echo "RAPIDS"
+        return
+    fi
+
+    # Default to NATIVE
+    echo "NATIVE"
+}
 
+# Set up build configuration
+PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
 BUILD_TYPE=Release
 BUILD_DIR=build/
-
 CUVS_REPO_REL=""
 EXTRA_CMAKE_ARGS=""
-BUILD_ALL_GPU_ARCH=0
-if hasArg --allgpuarch; then
-    BUILD_ALL_GPU_ARCH=1
-fi
+
+
+CUVS_CMAKE_CUDA_ARCHITECTURES=$(gpuArch)
+case ${CUVS_CMAKE_CUDA_ARCHITECTURES} in
+    "RAPIDS") echo "Building for *ALL* supported GPU architectures..." ;;
+    "NATIVE") echo "Building for the architecture of the GPU in the system..." ;;
+    *) echo "Building for specified GPU architectures: ${CUVS_CMAKE_CUDA_ARCHITECTURES}" ;;
+esac
 
 # Root of examples
 EXAMPLES_DIR=$(dirname "$(realpath "$0")")
@@ -34,19 +82,6 @@ else
   EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -Dcuvs_ROOT=${LIB_BUILD_DIR}"
 fi
 
-if [ "$1" == "clean" ]; then
-  rm -rf build
-  exit 0
-fi
-
-if (( ${BUILD_ALL_GPU_ARCH} == 0 )); then
-    CUVS_CMAKE_CUDA_ARCHITECTURES="NATIVE"
-    echo "Building for the architecture of the GPU in the system..."
-else
-    CUVS_CMAKE_CUDA_ARCHITECTURES="RAPIDS"
-    echo "Building for *ALL* supported GPU architectures..."
-fi
-
 ################################################################################
 # Add individual libcuvs examples build scripts down below
 

From 12b8fb3a994ed72d5f18395cbe15b3fbb97746d7 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 2 Apr 2025 12:39:11 -0700
Subject: [PATCH 350/623] `L2SqrtExpanded` metric support for NN Descent (#790)

Adding `L2SqrtExpanded` support for NN Descent.
This is for better usages with cuML UMAP (since UMAP's default metric is `L2SqrtExpanded`)

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/790
---
 cpp/src/neighbors/detail/nn_descent.cuh | 20 +++++++++++++++-----
 cpp/tests/neighbors/ann_nn_descent.cuh  |  1 +
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 66118f0ac1..d3ddca57fb 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -31,6 +31,7 @@
 #include <raft/core/pinned_mdarray.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/linalg/map.cuh>
 #include <raft/matrix/init.cuh>
 #include <raft/matrix/slice.cuh>
 #include <raft/util/arch.cuh>  // raft::util::arch::SM_*
@@ -476,6 +477,7 @@ RAFT_KERNEL preprocess_data_kernel(
   __syncthreads();
 
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
       metric == cuvs::distance::DistanceType::CosineExpanded) {
     int lane_id = threadIdx.x % raft::warp_size();
     for (int step = 0; step < raft::ceildiv(dim, raft::warp_size()); step++) {
@@ -502,7 +504,7 @@ RAFT_KERNEL preprocess_data_kernel(
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         output_data[list_id * dim + idx] =
           (float)input_data[(size_t)blockIdx.x * dim + idx] / sqrt(l2_norm);
-      } else {
+      } else {  // L2Expanded or L2SqrtExpanded
         output_data[list_id * dim + idx] = input_data[(size_t)blockIdx.x * dim + idx];
         if (idx == 0) { l2_norms[list_id] = l2_norm; }
       }
@@ -845,7 +847,7 @@ __launch_bounds__(BLOCK_SIZE, 4)
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
-      } else {
+      } else {  // L2Expanded or L2SqrtExpanded
         s_distances[i] = l2_norms[new_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
                          l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
                          2.0 * s_distances[i];
@@ -926,7 +928,7 @@ __launch_bounds__(BLOCK_SIZE, 4)
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
-      } else {
+      } else {  // L2Expanded or L2SqrtExpanded
         s_distances[i] = l2_norms[old_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
                          l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
                          2.0 * s_distances[i];
@@ -1214,7 +1216,8 @@ GNND<Data_t, Index_t>::GNND(raft::resources const& res, const BuildConfig& build
   raft::matrix::fill(res, graph_buffer_view, std::numeric_limits<Index_t>::max());
   raft::matrix::fill(res, d_locks_.view(), 0);
 
-  if (build_config.metric == cuvs::distance::DistanceType::L2Expanded) {
+  if (build_config.metric == cuvs::distance::DistanceType::L2Expanded ||
+      build_config.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
     l2_norms_ = raft::make_device_vector<DistData_t, size_t>(res, nrow_);
   }
 };
@@ -1411,6 +1414,11 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
                                            static_cast<int64_t>(build_config_.output_graph_degree)};
     raft::matrix::slice<DistData_t, int64_t, raft::row_major>(
       res, raft::make_const_mdspan(graph_d_dists.view()), output_dist_view, coords);
+
+    if (build_config_.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+      raft::linalg::map(
+        res, output_dist_view, raft::sqrt_op{}, raft::make_const_mdspan(output_dist_view));
+    }
     raft::resource::sync_stream(res);
   }
 
@@ -1453,10 +1461,12 @@ void build(raft::resources const& res,
                "The dataset size for GNND should be less than %d",
                std::numeric_limits<int>::max() - 1);
   auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                         params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
                          params.metric == cuvs::distance::DistanceType::CosineExpanded ||
                          params.metric == cuvs::distance::DistanceType::InnerProduct;
   RAFT_EXPECTS(allowed_metrics,
-               "The metric for NN Descent should be L2Expanded, CosineExpanded or InnerProduct");
+               "The metric for NN Descent should be L2Expanded, L2SqrtExpanded, CosineExpanded or "
+               "InnerProduct");
   RAFT_EXPECTS(
     idx.metric() == params.metric,
     "The metrics set in nn_descent::index_params and nn_descent::index are inconsistent");
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index d27c005aa2..f06e362b94 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -323,6 +323,7 @@ const std::vector<AnnNNDescentInputs> inputs =
                                                      {4, 16, 64, 256, 1024},  // dim
                                                      {32, 64},                // graph_degree
                                                      {cuvs::distance::DistanceType::L2Expanded,
+                                                      cuvs::distance::DistanceType::L2SqrtExpanded,
                                                       cuvs::distance::DistanceType::InnerProduct,
                                                       cuvs::distance::DistanceType::CosineExpanded},
                                                      {false, true},

From 18a3d903aa393b4e0b98366258a66b561ae828e5 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Wed, 2 Apr 2025 15:53:16 -0400
Subject: [PATCH 351/623] Expose IVF PQ params in CAGRA C API (#768)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Malte Förster (https://github.com/mfoerste4)
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/768
---
 cpp/include/cuvs/neighbors/cagra.h | 13 ++++++++
 cpp/src/neighbors/cagra_c.cpp      | 49 +++++++++++++++++++++++++-----
 2 files changed, 54 insertions(+), 8 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index ce97ee4a3b..e182610884 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -19,6 +19,7 @@
 #include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.h>
 #include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/ivf_pq.h>
 #include <dlpack/dlpack.h>
 #include <stdbool.h>
 #include <stdint.h>
@@ -86,6 +87,14 @@ struct cuvsCagraCompressionParams {
 
 typedef struct cuvsCagraCompressionParams* cuvsCagraCompressionParams_t;
 
+struct cuvsIvfPqParams {
+  cuvsIvfPqIndexParams_t ivf_pq_build_params;
+  cuvsIvfPqSearchParams_t ivf_pq_search_params;
+  float refinement_rate;
+};
+
+typedef struct cuvsIvfPqParams* cuvsIvfPqParams_t;
+
 /**
  * @brief Supplemental parameters to build CAGRA Index
  *
@@ -107,6 +116,10 @@ struct cuvsCagraIndexParams {
    * NOTE: this is experimental new API, consider it unsafe.
    */
   cuvsCagraCompressionParams_t compression;
+  /**
+   * Optional: specify ivf pq params when `build_algo = IVF_PQ`
+   */
+  cuvsIvfPqParams_t graph_build_params;
 };
 
 typedef struct cuvsCagraIndexParams* cuvsCagraIndexParams_t;
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index f39d44b82d..6a4f2dc291 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -50,8 +50,37 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
     case cuvsCagraGraphBuildAlgo::AUTO_SELECT: break;
     case cuvsCagraGraphBuildAlgo::IVF_PQ: {
       auto dataset_extent = raft::matrix_extent<int64_t>(dataset.shape[0], dataset.shape[1]);
-      index_params.graph_build_params =
-        cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(dataset_extent);
+      auto pq_params = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(dataset_extent);
+      auto ivf_pq_build_params  = params.graph_build_params->ivf_pq_build_params;
+      auto ivf_pq_search_params = params.graph_build_params->ivf_pq_search_params;
+      if (ivf_pq_build_params) {
+        pq_params.build_params.add_data_on_build = ivf_pq_build_params->add_data_on_build;
+        pq_params.build_params.n_lists           = ivf_pq_build_params->n_lists;
+        pq_params.build_params.kmeans_n_iters    = ivf_pq_build_params->kmeans_n_iters;
+        pq_params.build_params.kmeans_trainset_fraction =
+          ivf_pq_build_params->kmeans_trainset_fraction;
+        pq_params.build_params.pq_bits = ivf_pq_build_params->pq_bits;
+        pq_params.build_params.pq_dim  = ivf_pq_build_params->pq_dim;
+        pq_params.build_params.codebook_kind =
+          static_cast<cuvs::neighbors::ivf_pq::codebook_gen>(ivf_pq_build_params->codebook_kind);
+        pq_params.build_params.force_random_rotation = ivf_pq_build_params->force_random_rotation;
+        pq_params.build_params.conservative_memory_allocation =
+          ivf_pq_build_params->conservative_memory_allocation;
+        pq_params.build_params.max_train_points_per_pq_code =
+          ivf_pq_build_params->max_train_points_per_pq_code;
+      }
+      if (ivf_pq_search_params) {
+        pq_params.search_params.n_probes  = ivf_pq_search_params->n_probes;
+        pq_params.search_params.lut_dtype = ivf_pq_search_params->lut_dtype;
+        pq_params.search_params.internal_distance_dtype =
+          ivf_pq_search_params->internal_distance_dtype;
+        pq_params.search_params.preferred_shmem_carveout =
+          ivf_pq_search_params->preferred_shmem_carveout;
+      }
+      if (params.graph_build_params->refinement_rate > 1) {
+        pq_params.refinement_rate = params.graph_build_params->refinement_rate;
+      }
+      index_params.graph_build_params = pq_params;
       break;
     }
     case cuvsCagraGraphBuildAlgo::NN_DESCENT: {
@@ -369,17 +398,21 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
 extern "C" cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
-    *params = new cuvsCagraIndexParams{.metric                    = L2Expanded,
-                                       .intermediate_graph_degree = 128,
-                                       .graph_degree              = 64,
-                                       .build_algo                = IVF_PQ,
-                                       .nn_descent_niter          = 20};
+    *params                       = new cuvsCagraIndexParams{.metric                    = L2Expanded,
+                                                             .intermediate_graph_degree = 128,
+                                                             .graph_degree              = 64,
+                                                             .build_algo                = IVF_PQ,
+                                                             .nn_descent_niter          = 20};
+    (*params)->graph_build_params = new cuvsIvfPqParams{nullptr, nullptr, 1};
   });
 }
 
 extern "C" cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t params)
 {
-  return cuvs::core::translate_exceptions([=] { delete params; });
+  return cuvs::core::translate_exceptions([=] {
+    delete params->graph_build_params;
+    delete params;
+  });
 }
 
 extern "C" cuvsError_t cuvsCagraCompressionParamsCreate(cuvsCagraCompressionParams_t* params)

From a73c39cb6deb92d6e2793f9f3a3a70473b4cb361 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 2 Apr 2025 17:39:09 -0500
Subject: [PATCH 352/623] Use L4 instead of V100 (#797)

We migrated all our V100 jobs to use L4 GPUs earlier in #627, but some new jobs were added that still reference the old runners. This fixes that.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - AJ Schmidt (https://github.com/ajschmidt8)

URL: https://github.com/rapidsai/cuvs/pull/797
---
 .github/workflows/build.yaml | 2 +-
 .github/workflows/pr.yaml    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 8c8551d55d..e59b5a228a 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -57,7 +57,7 @@ jobs:
       arch: "amd64"
       date: ${{ inputs.date }}
       container_image: "rapidsai/ci-conda:latest"
-      node_type: "gpu-v100-latest-1"
+      node_type: "gpu-l4-latest-1"
       run_script: "ci/build_go.sh"
       sha: ${{ inputs.sha }}
   python-build:
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index b852f47f0a..157bf06ea7 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -160,7 +160,7 @@ jobs:
     uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.04
     with:
       build_type: pull-request
-      node_type: "gpu-v100-latest-1"
+      node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
       run_script: "ci/build_go.sh"

From 51ac562f265010eb46a3253d491c61f0263ad9b6 Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Thu, 3 Apr 2025 11:40:16 -0500
Subject: [PATCH 353/623] Merge pull request #779 from AyodeAwe/go-publishing

Go module - Usage docs
---
 ci/release/update-version.sh                  |   1 +
 .../go_cuda-118_arch-aarch64.yaml             |  37 +++++
 .../environments/go_cuda-118_arch-x86_64.yaml |  37 +++++
 .../go_cuda-128_arch-aarch64.yaml             |  33 +++++
 .../environments/go_cuda-128_arch-x86_64.yaml |  33 +++++
 dependencies.yaml                             |   5 +-
 examples/go/README.md                         |  47 +++++++
 examples/go/go.mod                            |   7 +
 examples/go/go.sum                            |   2 +
 examples/go/main.go                           | 133 ++++++++++++++++++
 10 files changed, 334 insertions(+), 1 deletion(-)
 create mode 100644 conda/environments/go_cuda-118_arch-aarch64.yaml
 create mode 100644 conda/environments/go_cuda-118_arch-x86_64.yaml
 create mode 100644 conda/environments/go_cuda-128_arch-aarch64.yaml
 create mode 100644 conda/environments/go_cuda-128_arch-x86_64.yaml
 create mode 100644 examples/go/README.md
 create mode 100644 examples/go/go.mod
 create mode 100644 examples/go/go.sum
 create mode 100644 examples/go/main.go

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 058e0e46a6..c928d7e6c1 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -81,6 +81,7 @@ sed_runner "/rapidsai\/raft/ s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_
 
 sed_runner "s|=[0-9][0-9].[0-9][0-9]|=${NEXT_SHORT_TAG}|g" README.md
 sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" README.md
+sed_runner "s|@v[0-9][0-9].[0-9][0-9].[0-9][0-9]|@v${NEXT_FULL_TAG}|g" examples/go/README.md
 
 # references to license files
 sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" python/cuvs_bench/cuvs_bench/plot/__main__.py
diff --git a/conda/environments/go_cuda-118_arch-aarch64.yaml b/conda/environments/go_cuda-118_arch-aarch64.yaml
new file mode 100644
index 0000000000..90dc030b72
--- /dev/null
+++ b/conda/environments/go_cuda-118_arch-aarch64.yaml
@@ -0,0 +1,37 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- c-compiler
+- clang-tools==16.0.6
+- clang==16.0.6
+- cmake>=3.30.4
+- cuda-nvtx=11.8
+- cuda-profiler-api=11.8.86
+- cuda-version=11.8
+- cudatoolkit
+- cxx-compiler
+- dlpack>=0.8,<1.0
+- gcc_linux-aarch64=11.*
+- go
+- libclang==16.0.6
+- libcublas-dev=11.11.3.6
+- libcublas=11.11.3.6
+- libcurand-dev=10.3.0.86
+- libcurand=10.3.0.86
+- libcusolver-dev=11.4.1.48
+- libcusolver=11.4.1.48
+- libcusparse-dev=11.7.5.86
+- libcusparse=11.7.5.86
+- libcuvs==25.4.*,>=0.0.0a0
+- libraft==25.4.*,>=0.0.0a0
+- nccl>=2.19
+- ninja
+- nvcc_linux-aarch64=11.8
+- sysroot_linux-aarch64==2.28
+name: go_cuda-118_arch-aarch64
diff --git a/conda/environments/go_cuda-118_arch-x86_64.yaml b/conda/environments/go_cuda-118_arch-x86_64.yaml
new file mode 100644
index 0000000000..c67dd78ec4
--- /dev/null
+++ b/conda/environments/go_cuda-118_arch-x86_64.yaml
@@ -0,0 +1,37 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- c-compiler
+- clang-tools==16.0.6
+- clang==16.0.6
+- cmake>=3.30.4
+- cuda-nvtx=11.8
+- cuda-profiler-api=11.8.86
+- cuda-version=11.8
+- cudatoolkit
+- cxx-compiler
+- dlpack>=0.8,<1.0
+- gcc_linux-64=11.*
+- go
+- libclang==16.0.6
+- libcublas-dev=11.11.3.6
+- libcublas=11.11.3.6
+- libcurand-dev=10.3.0.86
+- libcurand=10.3.0.86
+- libcusolver-dev=11.4.1.48
+- libcusolver=11.4.1.48
+- libcusparse-dev=11.7.5.86
+- libcusparse=11.7.5.86
+- libcuvs==25.4.*,>=0.0.0a0
+- libraft==25.4.*,>=0.0.0a0
+- nccl>=2.19
+- ninja
+- nvcc_linux-64=11.8
+- sysroot_linux-64==2.28
+name: go_cuda-118_arch-x86_64
diff --git a/conda/environments/go_cuda-128_arch-aarch64.yaml b/conda/environments/go_cuda-128_arch-aarch64.yaml
new file mode 100644
index 0000000000..b4d9a7e61f
--- /dev/null
+++ b/conda/environments/go_cuda-128_arch-aarch64.yaml
@@ -0,0 +1,33 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- c-compiler
+- clang-tools==16.0.6
+- clang==16.0.6
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=12.8
+- cxx-compiler
+- dlpack>=0.8,<1.0
+- gcc_linux-aarch64=13.*
+- go
+- libclang==16.0.6
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.4.*,>=0.0.0a0
+- libraft==25.4.*,>=0.0.0a0
+- nccl>=2.19
+- ninja
+- sysroot_linux-aarch64==2.28
+name: go_cuda-128_arch-aarch64
diff --git a/conda/environments/go_cuda-128_arch-x86_64.yaml b/conda/environments/go_cuda-128_arch-x86_64.yaml
new file mode 100644
index 0000000000..0e8ddc0abf
--- /dev/null
+++ b/conda/environments/go_cuda-128_arch-x86_64.yaml
@@ -0,0 +1,33 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- c-compiler
+- clang-tools==16.0.6
+- clang==16.0.6
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=12.8
+- cxx-compiler
+- dlpack>=0.8,<1.0
+- gcc_linux-64=13.*
+- go
+- libclang==16.0.6
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.4.*,>=0.0.0a0
+- libraft==25.4.*,>=0.0.0a0
+- nccl>=2.19
+- ninja
+- sysroot_linux-64==2.28
+name: go_cuda-128_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index 99d2f6021b..02f5237727 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -92,7 +92,10 @@ files:
       - depends_on_libcuvs
       - depends_on_libraft
   go:
-    output: none
+    output: conda
+    matrix:
+      cuda: ["11.8", "12.8"]
+      arch: [x86_64, aarch64]
     includes:
       - clang
       - cuda
diff --git a/examples/go/README.md b/examples/go/README.md
new file mode 100644
index 0000000000..28d11351b2
--- /dev/null
+++ b/examples/go/README.md
@@ -0,0 +1,47 @@
+# cuVS Go Bindings
+
+This package provides Go bindings for the cuVS (CUDA Vector Search) library.
+
+## Prerequisites
+
+The required dependencies can be installed with a simple command (which creates your build environment):
+
+```bash
+conda env create --name go -f conda/environments/go_cuda-128_arch-x86_64.yaml
+conda activate go
+```
+You may prefer to use `mamba`, as it provides significant speedup over `conda`.
+
+## Installation
+
+1. Set up the required environment variables:
+```bash
+export CGO_CFLAGS="-I${CONDA_PREFIX}/include"
+export CGO_LDFLAGS="-L${CONDA_PREFIX}/lib -lcudart -lcuvs -lcuvs_c"
+export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+export CC=clang
+```
+
+2. Install the Go module:
+```bash
+go get github.com/rapidsai/cuvs/go@v25.02.00 # 25.02.00 being your desired version, selected from https://github.com/rapidsai/cuvs/tags
+```
+Then you can build your project with the usual `go build`.
+
+Note: The installation will fail if the C libraries are not properly installed and the environment variables are not set correctly, as this module requires CGO compilation.
+
+## Example Usage
+
+```go
+package main
+
+import (
+    "github.com/rapidsai/cuvs/go"
+    "github.com/rapidsai/cuvs/go/cagra"
+)
+
+func main() {
+    // Example code showing how to use the library
+}
+```
+See [main.go](./main.go) for an example implementation.
diff --git a/examples/go/go.mod b/examples/go/go.mod
new file mode 100644
index 0000000000..7f034ddfdc
--- /dev/null
+++ b/examples/go/go.mod
@@ -0,0 +1,7 @@
+module cuvs-example
+
+go 1.22.4
+
+toolchain go1.23.6
+
+require github.com/rapidsai/cuvs/go v0.0.0-20250313193519-2f9df39e52cc
diff --git a/examples/go/go.sum b/examples/go/go.sum
new file mode 100644
index 0000000000..1f7a1b9c3d
--- /dev/null
+++ b/examples/go/go.sum
@@ -0,0 +1,2 @@
+github.com/rapidsai/cuvs/go v0.0.0-20250313193519-2f9df39e52cc h1:KaikmdTcld8icYGg5/5K1U69FTRpXlnYJZPBPFPrV5g=
+github.com/rapidsai/cuvs/go v0.0.0-20250313193519-2f9df39e52cc/go.mod h1:qQPopaJ6Z5DXM+HqtP8TzatknrfiCE7vBf/p1+lVFr8=
diff --git a/examples/go/main.go b/examples/go/main.go
new file mode 100644
index 0000000000..d3b45b0eda
--- /dev/null
+++ b/examples/go/main.go
@@ -0,0 +1,133 @@
+package main
+
+import (
+	"fmt"
+	"log"
+	"math/rand"
+	"time"
+
+	cuvs "github.com/rapidsai/cuvs/go"
+	"github.com/rapidsai/cuvs/go/cagra"
+)
+
+func main() {
+	// Initialize resources
+	resources, err := cuvs.NewResource(nil)
+	if err != nil {
+		log.Fatalf("Failed to create resources: %v", err)
+	}
+	defer resources.Close()
+
+	// Dataset
+	const (
+		nDatapoints = 65536
+		nFeatures   = 512
+		nQueries    = 4
+		k           = 10
+	)
+
+	// Create random dataset
+	rand.Seed(time.Now().UnixNano())
+	dataset := make([][]float32, nDatapoints)
+	for i := range dataset {
+		dataset[i] = make([]float32, nFeatures)
+		for j := range dataset[i] {
+			dataset[i][j] = rand.Float32()
+		}
+	}
+
+	// Create tensor from dataset
+	datasetTensor, err := cuvs.NewTensor(dataset)
+	if err != nil {
+		log.Fatalf("Failed to create dataset tensor: %v", err)
+	}
+	defer datasetTensor.Close()
+
+	// Move dataset to GPU
+	if _, err := datasetTensor.ToDevice(&resources); err != nil {
+		log.Fatalf("Failed to move dataset to GPU: %v", err)
+	}
+
+	// Create and configure CAGRA index
+	indexParams, err := cagra.CreateIndexParams()
+	if err != nil {
+		log.Fatalf("Failed to create index params: %v", err)
+	}
+	defer indexParams.Close()
+
+	index, err := cagra.CreateIndex()
+	if err != nil {
+		log.Fatalf("Failed to create index: %v", err)
+	}
+	defer index.Close()
+
+	// Build the index
+	fmt.Printf("Building index for %d vectors with %d dimensions...\n", nDatapoints, nFeatures)
+	if err := cagra.BuildIndex(resources, indexParams, &datasetTensor, index); err != nil {
+		log.Fatalf("Failed to build index: %v", err)
+	}
+
+	// Create query tensor (using first few vectors as queries)
+	queries, err := cuvs.NewTensor(dataset[:nQueries])
+	if err != nil {
+		log.Fatalf("Failed to create queries tensor: %v", err)
+	}
+	defer queries.Close()
+
+	// Move queries to GPU
+	if _, err := queries.ToDevice(&resources); err != nil {
+		log.Fatalf("Failed to move queries to GPU: %v", err)
+	}
+
+	// Create tensors for results
+	neighbors, err := cuvs.NewTensorOnDevice[uint32](&resources, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		log.Fatalf("Failed to create neighbors tensor: %v", err)
+	}
+	defer neighbors.Close()
+
+	distances, err := cuvs.NewTensorOnDevice[float32](&resources, []int64{int64(nQueries), int64(k)})
+	if err != nil {
+		log.Fatalf("Failed to create distances tensor: %v", err)
+	}
+	defer distances.Close()
+
+	// Create search parameters
+	searchParams, err := cagra.CreateSearchParams()
+	if err != nil {
+		log.Fatalf("Failed to create search params: %v", err)
+	}
+	defer searchParams.Close()
+
+	// Perform the search
+	fmt.Printf("Searching for %d nearest neighbors for %d queries...\n", k, nQueries)
+	if err := cagra.SearchIndex(resources, searchParams, index, &queries, &neighbors, &distances, nil); err != nil {
+		log.Fatalf("Failed to search index: %v", err)
+	}
+
+	// Get results
+	if _, err := neighbors.ToHost(&resources); err != nil {
+		log.Fatalf("Failed to move neighbors to host: %v", err)
+	}
+	if _, err := distances.ToHost(&resources); err != nil {
+		log.Fatalf("Failed to move distances to host: %v", err)
+	}
+	resources.Sync()
+
+	neighborsResult, err := neighbors.Slice()
+	if err != nil {
+		log.Fatalf("Failed to get neighbors result: %v", err)
+	}
+	distancesResult, err := distances.Slice()
+	if err != nil {
+		log.Fatalf("Failed to get distances result: %v", err)
+	}
+
+	// Print results
+	fmt.Println("\nSearch Results:")
+	for i := 0; i < nQueries; i++ {
+		fmt.Printf("\nQuery %d:\n", i)
+		fmt.Printf("Neighbors: %v\n", neighborsResult[i])
+		fmt.Printf("Distances: %v\n", distancesResult[i])
+	}
+}

From dfe612b42f7cf41494b9bde2e63c21778830acaf Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Thu, 3 Apr 2025 11:40:33 -0500
Subject: [PATCH 354/623] Rust publishing updates (#789)

This PR adds automation for publishing the `cuvs` and `cuvs-sys` rust
crates.

I updated the rust usage
[README](https://github.com/rapidsai/cuvs/pull/789/files#diff-bcb0eb230db7a19167a81000c18bfb176049613f522d077479aa702384af7097)
to include simple steps to import the `cuvs` module in any consuming
rust project. I've added conda environment files that would facilitate
the installation steps described in the
[README](https://github.com/rapidsai/cuvs/pull/789/files#diff-bcb0eb230db7a19167a81000c18bfb176049613f522d077479aa702384af7097).

---

**Testing the changes in this PR included the following steps**:

- I first replicated local builds of the `cuvs` rust module.
- Then I attempted a build of the rust example in the `examples`
directory which imports the current `cuvs` library as is, and
successfully ran the rust example project.
- I updated the rust bindings to use different package names and
published both as:
    - [jawe-cuvs-iii](https://crates.io/crates/jawe-cuvs-iii) (cuvs)
- [jave-cuvs-sys-iii](https://crates.io/crates/jawe-cuvs-sys-iii)
(cuvs-sys)
- I published by running the `publish-rust.yaml` workflow. First run can
be found
[here](https://github.com/rapidsai/cuvs/actions/runs/14158410647/job/39660631194),
and a second run
[here](https://github.com/rapidsai/cuvs/actions/runs/14205715742/job/39802866340).
- After publishing, I locally compiled the example rust module by
importing the published modules (`jawe-cuvs-iii` & `jawe-cuvs-sys-iii`)
and was able to successfully run the example rust project
- I also tested the build & imports on both ARM and AMD architectures.
<details>
<summary>Cargo run output </summary>

```
# cargo run
    Finished `dev` profile [unoptimized + debuginfo] target(s) in 0.05s
     Running `target/debug/cuvs-rust-example`
using ivf_pq::index_params nrows 65536, dim 512, n_lists 256, pq_dim 128
[ 18669][17:16:49:911962][info  ] optimizing graph
[ 18669][17:16:50:057608][info  ] Graph optimized, creating index
Indexed 65536x512 datapoints into cagra index
Neighbors [[0, 6880, 12927, 59705, 45003, 33779, 39003, 56796, 6491, 34338],
 [1, 28206, 47512, 44788, 14031, 49376, 43330, 3429, 41428, 49044],
 [2, 45154, 4777, 62753, 45378, 13104, 4082, 46221, 65234, 45638],
 [3, 41243, 59296, 4950, 55157, 32839, 42927, 10811, 37504, 57827]], shape=[4, 10], strides=[10, 1], layout=Cc (0x5), const ndim=2
Distances [[0.0, 68.72183, 69.83197, 70.23868, 71.08637, 71.15034, 71.60148, 71.71672, 71.82242, 71.90005],
 [0.0, 68.85643, 69.43584, 69.72554, 69.883835, 70.03081, 70.05081, 70.1477, 70.169556, 70.26346],
 [0.0, 69.09878, 69.42933, 70.06172, 70.10106, 70.16475, 70.19414, 70.64023, 70.64905, 70.71358],
 [0.0, 64.99479, 65.39395, 66.827286, 66.99862, 67.110855, 67.57162, 67.779526, 67.964035, 68.002716]], shape=[4, 10], strides=[10, 1], layout=Cc (0x5), const ndim=2
```
</details>
---
 .github/workflows/build.yaml                  |  4 ++
 .github/workflows/publish-rust.yaml           | 59 +++++++++++++++++++
 .../rust_cuda-128_arch-aarch64.yaml           | 33 +++++++++++
 .../rust_cuda-128_arch-x86_64.yaml            | 33 +++++++++++
 dependencies.yaml                             |  5 +-
 examples/rust/README.md                       | 26 ++++++++
 6 files changed, 159 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/publish-rust.yaml
 create mode 100644 conda/environments/rust_cuda-128_arch-aarch64.yaml
 create mode 100644 conda/environments/rust_cuda-128_arch-x86_64.yaml

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index e59b5a228a..4a99fd2d95 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -47,6 +47,10 @@ jobs:
       node_type: "gpu-l4-latest-1"
       run_script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
+  rust-publish:
+    needs: rust-build
+    secrets: inherit
+    uses: ./.github/workflows/publish-rust.yaml
   go-build:
     needs: cpp-build
     secrets: inherit
diff --git a/.github/workflows/publish-rust.yaml b/.github/workflows/publish-rust.yaml
new file mode 100644
index 0000000000..86fb14712e
--- /dev/null
+++ b/.github/workflows/publish-rust.yaml
@@ -0,0 +1,59 @@
+name: publish
+
+on:
+  workflow_call:
+
+defaults:
+  run:
+    shell: bash
+
+jobs:
+  rust-publish:
+    runs-on: ubuntu-latest
+    container:
+      image: rapidsai/ci-conda:latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Check if release build
+        id: check_release
+        run: |
+          if rapids-is-release-build; then
+            echo "is_release=true" >> $GITHUB_OUTPUT
+          else
+            echo "is_release=false" >> $GITHUB_OUTPUT
+          fi
+      - name: Setup and publish crates
+        if: steps.check_release.outputs.is_release == 'true'
+        run: |
+          . /opt/conda/etc/profile.d/conda.sh
+
+          rapids-dependency-file-generator \
+            --output conda \
+            --file-key rust \
+            --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" \
+            | tee env.yaml
+
+          rapids-mamba-retry env create --yes -f env.yaml -n rust
+
+          set +eu
+          conda activate rust
+          set -eu
+
+          LIBCLANG_PATH=$(dirname "$(find /opt/conda -name libclang.so | head -n 1)")
+          export LIBCLANG_PATH
+          echo "LIBCLANG_PATH=$LIBCLANG_PATH"
+
+          # Publish sys crate first
+          pushd ./rust/cuvs-sys
+          cargo publish --token ${CARGO_REGISTRY_TOKEN}
+          popd
+
+          # Wait for crates.io index update
+          sleep 30
+
+          # Publish main crate
+          pushd ./rust/cuvs
+          cargo publish --token ${CARGO_REGISTRY_TOKEN}
+          popd
+        env:
+          CARGO_REGISTRY_TOKEN: ${{ secrets.GPUTESTER_CRATES_TOKEN }}
diff --git a/conda/environments/rust_cuda-128_arch-aarch64.yaml b/conda/environments/rust_cuda-128_arch-aarch64.yaml
new file mode 100644
index 0000000000..c26ce15172
--- /dev/null
+++ b/conda/environments/rust_cuda-128_arch-aarch64.yaml
@@ -0,0 +1,33 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- c-compiler
+- clang-tools==16.0.6
+- clang==16.0.6
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=12.8
+- cxx-compiler
+- gcc_linux-aarch64=13.*
+- libclang==16.0.6
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.4.*,>=0.0.0a0
+- libraft==25.4.*,>=0.0.0a0
+- make
+- nccl>=2.19
+- ninja
+- rust
+- sysroot_linux-aarch64==2.28
+name: rust_cuda-128_arch-aarch64
diff --git a/conda/environments/rust_cuda-128_arch-x86_64.yaml b/conda/environments/rust_cuda-128_arch-x86_64.yaml
new file mode 100644
index 0000000000..af28e160b6
--- /dev/null
+++ b/conda/environments/rust_cuda-128_arch-x86_64.yaml
@@ -0,0 +1,33 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- conda-forge
+- nvidia
+dependencies:
+- c-compiler
+- clang-tools==16.0.6
+- clang==16.0.6
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=12.8
+- cxx-compiler
+- gcc_linux-64=13.*
+- libclang==16.0.6
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.4.*,>=0.0.0a0
+- libraft==25.4.*,>=0.0.0a0
+- make
+- nccl>=2.19
+- ninja
+- rust
+- sysroot_linux-64==2.28
+name: rust_cuda-128_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index 02f5237727..e315ba3646 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -81,7 +81,10 @@ files:
       - depends_on_libcuvs
       - depends_on_cuvs
   rust:
-    output: none
+    output: conda
+    matrix:
+      cuda: ["12.8"]
+      arch: [x86_64, aarch64]
     includes:
       # clang/libclang only needed for bindgen support
       - clang
diff --git a/examples/rust/README.md b/examples/rust/README.md
index 255ffcfb64..98fe526975 100644
--- a/examples/rust/README.md
+++ b/examples/rust/README.md
@@ -5,3 +5,29 @@ This template project provides a drop-in sample to for using cuVS in a rust proj
 First, please refer to our [installation docs](https://docs.rapids.ai/api/cuvs/stable/build.html#cuda-gpu-requirements) for the minimum requirements to use cuVS. Note that you will have to have the libcuvs.so and libcuvs_c.so binaries installed to compile this example project.
 
 Once the minimum requirements are satisfied, this example template application can be built using 'cargo build', and this directory can be copied directly in order to build a new application with cuVS.
+
+You may follow these steps to quickly get set up:
+
+```bash
+conda env create --name rust -f conda/environments/rust_cuda-128_arch-x86_64.yaml
+conda activate rust
+```
+You may prefer to use `mamba`, as it provides significant speedup over `conda`.
+
+1. Set up the required environment variables:
+```bash
+LIBCLANG_PATH=$(dirname "$(find /opt/conda -name libclang.so | head -n 1)")
+export LIBCLANG_PATH
+echo "LIBCLANG_PATH=$LIBCLANG_PATH"
+```
+
+2. Add the cuvs dependency in your project:
+```TOML
+# Cargo.toml
+[dependencies]
+cuvs = ">=24.6.0"
+#...rest of your dependencies
+```
+Then you can run your project with `cargo run`.
+
+See [main.rs](./src/main.rs) for an example implementation.

From 33664bd4321d1e7cb409ee1a12de9505c07692b4 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Tue, 8 Apr 2025 13:51:58 -0700
Subject: [PATCH 355/623] Update libcuvs libraft ver to 25.06 in conda env
 (#808)

Update libcuvs libraft ver to 25.06 in conda env

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/808
---
 conda/environments/go_cuda-118_arch-aarch64.yaml   | 4 ++--
 conda/environments/go_cuda-118_arch-x86_64.yaml    | 4 ++--
 conda/environments/go_cuda-128_arch-aarch64.yaml   | 4 ++--
 conda/environments/go_cuda-128_arch-x86_64.yaml    | 4 ++--
 conda/environments/rust_cuda-128_arch-aarch64.yaml | 4 ++--
 conda/environments/rust_cuda-128_arch-x86_64.yaml  | 4 ++--
 6 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/conda/environments/go_cuda-118_arch-aarch64.yaml b/conda/environments/go_cuda-118_arch-aarch64.yaml
index 90dc030b72..4a14dc43ef 100644
--- a/conda/environments/go_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-118_arch-aarch64.yaml
@@ -28,8 +28,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.4.*,>=0.0.0a0
-- libraft==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- libraft==25.6.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - nvcc_linux-aarch64=11.8
diff --git a/conda/environments/go_cuda-118_arch-x86_64.yaml b/conda/environments/go_cuda-118_arch-x86_64.yaml
index c67dd78ec4..fb889411d6 100644
--- a/conda/environments/go_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-118_arch-x86_64.yaml
@@ -28,8 +28,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.4.*,>=0.0.0a0
-- libraft==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- libraft==25.6.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - nvcc_linux-64=11.8
diff --git a/conda/environments/go_cuda-128_arch-aarch64.yaml b/conda/environments/go_cuda-128_arch-aarch64.yaml
index b4d9a7e61f..21bae9da1b 100644
--- a/conda/environments/go_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-128_arch-aarch64.yaml
@@ -25,8 +25,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.4.*,>=0.0.0a0
-- libraft==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- libraft==25.6.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
diff --git a/conda/environments/go_cuda-128_arch-x86_64.yaml b/conda/environments/go_cuda-128_arch-x86_64.yaml
index 0e8ddc0abf..b3dcf18b70 100644
--- a/conda/environments/go_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-128_arch-x86_64.yaml
@@ -25,8 +25,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.4.*,>=0.0.0a0
-- libraft==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- libraft==25.6.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
diff --git a/conda/environments/rust_cuda-128_arch-aarch64.yaml b/conda/environments/rust_cuda-128_arch-aarch64.yaml
index c26ce15172..a3844ddad5 100644
--- a/conda/environments/rust_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-128_arch-aarch64.yaml
@@ -23,8 +23,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.4.*,>=0.0.0a0
-- libraft==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- libraft==25.6.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/conda/environments/rust_cuda-128_arch-x86_64.yaml b/conda/environments/rust_cuda-128_arch-x86_64.yaml
index af28e160b6..d8775fe193 100644
--- a/conda/environments/rust_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-128_arch-x86_64.yaml
@@ -23,8 +23,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.4.*,>=0.0.0a0
-- libraft==25.4.*,>=0.0.0a0
+- libcuvs==25.6.*,>=0.0.0a0
+- libraft==25.6.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja

From c226179d52ec7a4c08892f773164354b382d89db Mon Sep 17 00:00:00 2001
From: Jake Awe <jawe@nvidia.com>
Date: Wed, 9 Apr 2025 09:11:38 -0700
Subject: [PATCH 356/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b7ae6a8383..af6bc8935b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,59 @@
+# cuvs 25.04.00 (9 Apr 2025)
+
+## 🚨 Breaking Changes
+
+- Use new rapids-logger library ([#644](https://github.com/rapidsai/cuvs/pull/644)) [@vyasr](https://github.com/vyasr)
+
+## 🐛 Bug Fixes
+
+- Use new build_patch_only argument ([#780](https://github.com/rapidsai/cuvs/pull/780)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Fix resource import so generate_groundtruth works ([#774](https://github.com/rapidsai/cuvs/pull/774)) [@nvrohanv](https://github.com/nvrohanv)
+- Relax max duplicates in batched NN Descent ([#770](https://github.com/rapidsai/cuvs/pull/770)) [@jinsolp](https://github.com/jinsolp)
+- [BUG] Fix graph index sorting in CAGRA graph build by NN Descent ([#763](https://github.com/rapidsai/cuvs/pull/763)) [@enp1s0](https://github.com/enp1s0)
+- cuvs-bench-cpu: avoid &#39;mkl&#39; dependency on aarch64 ([#750](https://github.com/rapidsai/cuvs/pull/750)) [@jameslamb](https://github.com/jameslamb)
+- ANN_BENCH: Fix segfault in CAGRA wrapper when moving its graph ([#733](https://github.com/rapidsai/cuvs/pull/733)) [@achirkin](https://github.com/achirkin)
+- Fix duplicate indices in batch NN Descent ([#702](https://github.com/rapidsai/cuvs/pull/702)) [@jinsolp](https://github.com/jinsolp)
+
+## 📖 Documentation
+
+- Go module - Usage docs ([#779](https://github.com/rapidsai/cuvs/pull/779)) [@AyodeAwe](https://github.com/AyodeAwe)
+
+## 🚀 New Features
+
+- `L2SqrtExpanded` metric support for NN Descent ([#790](https://github.com/rapidsai/cuvs/pull/790)) [@jinsolp](https://github.com/jinsolp)
+- CAGRA search: int64_t indices in the API ([#769](https://github.com/rapidsai/cuvs/pull/769)) [@achirkin](https://github.com/achirkin)
+- Expose IVF PQ params in CAGRA C API ([#768](https://github.com/rapidsai/cuvs/pull/768)) [@divyegala](https://github.com/divyegala)
+- [Feat] Expose search with bitset C API for Brute Force ([#717](https://github.com/rapidsai/cuvs/pull/717)) [@rhdong](https://github.com/rhdong)
+- Diskann Benchmarking Wrapper ([#260](https://github.com/rapidsai/cuvs/pull/260)) [@tarang-jain](https://github.com/tarang-jain)
+
+## 🛠️ Improvements
+
+- Use L4 instead of V100 ([#797](https://github.com/rapidsai/cuvs/pull/797)) [@bdice](https://github.com/bdice)
+- Rust publishing updates ([#789](https://github.com/rapidsai/cuvs/pull/789)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Removing print statement ([#786](https://github.com/rapidsai/cuvs/pull/786)) [@jinsolp](https://github.com/jinsolp)
+- Support passing a dataset to `FromCagra` ([#767](https://github.com/rapidsai/cuvs/pull/767)) [@ajit283](https://github.com/ajit283)
+- add function to build specific gpu-arches [REVIEW] ([#766](https://github.com/rapidsai/cuvs/pull/766)) [@nvrohanv](https://github.com/nvrohanv)
+- feat(libcuvs): port libcuvs to rattler-build ([#751](https://github.com/rapidsai/cuvs/pull/751)) [@gforsyth](https://github.com/gforsyth)
+- Consolidate Conda environment for Rust ([#745](https://github.com/rapidsai/cuvs/pull/745)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- rename &#39;sklearn&#39; conda dependency to &#39;scikit-learn&#39; ([#743](https://github.com/rapidsai/cuvs/pull/743)) [@jameslamb](https://github.com/jameslamb)
+- Reduce memory consumption of scalar quantizer ([#736](https://github.com/rapidsai/cuvs/pull/736)) [@mfoerste4](https://github.com/mfoerste4)
+- Use conda-build instead of conda-mambabuild ([#723](https://github.com/rapidsai/cuvs/pull/723)) [@bdice](https://github.com/bdice)
+- Expand NVTX annotations in CAGRA build and HNSW ([#711](https://github.com/rapidsai/cuvs/pull/711)) [@achirkin](https://github.com/achirkin)
+- Replace `cub::TransformInputIterator` with `thrust::transform_iterator` ([#707](https://github.com/rapidsai/cuvs/pull/707)) [@miscco](https://github.com/miscco)
+- Consolidate more Conda solves in CI ([#701](https://github.com/rapidsai/cuvs/pull/701)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Require CMake 3.30.4 ([#691](https://github.com/rapidsai/cuvs/pull/691)) [@robertmaynard](https://github.com/robertmaynard)
+- Create Conda CI test env in one step ([#684](https://github.com/rapidsai/cuvs/pull/684)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Use shared-workflows branch-25.04 ([#669](https://github.com/rapidsai/cuvs/pull/669)) [@bdice](https://github.com/bdice)
+- Add `shellcheck` to pre-commit and fix warnings ([#662](https://github.com/rapidsai/cuvs/pull/662)) [@gforsyth](https://github.com/gforsyth)
+- Add build_type input field for test.yaml ([#661](https://github.com/rapidsai/cuvs/pull/661)) [@gforsyth](https://github.com/gforsyth)
+- Use `rapids-pip-retry` in CI jobs that might need retries ([#648](https://github.com/rapidsai/cuvs/pull/648)) [@gforsyth](https://github.com/gforsyth)
+- Use new rapids-logger library ([#644](https://github.com/rapidsai/cuvs/pull/644)) [@vyasr](https://github.com/vyasr)
+- disallow fallback to Make in Python builds ([#636](https://github.com/rapidsai/cuvs/pull/636)) [@jameslamb](https://github.com/jameslamb)
+- Add `verify-codeowners` hook ([#633](https://github.com/rapidsai/cuvs/pull/633)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Forward-merge branch-25.02 into branch-25.04 ([#632](https://github.com/rapidsai/cuvs/pull/632)) [@bdice](https://github.com/bdice)
+- Account for RAFT sparse types updates ([#629](https://github.com/rapidsai/cuvs/pull/629)) [@divyegala](https://github.com/divyegala)
+- Migrate to NVKS for amd64 CI runners ([#627](https://github.com/rapidsai/cuvs/pull/627)) [@bdice](https://github.com/bdice)
+
 # cuvs 25.02.00 (13 Feb 2025)
 
 ## 🚨 Breaking Changes

From 2aee75517f6fb31881d6c445b69dcf3b29efaa0a Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 10 Apr 2025 17:46:18 -0500
Subject: [PATCH 357/623] Vendor RAPIDS.cmake (#816)

This works around a problem where GitHub is rejecting requests to the `githubusercontent.com` CDN. We vendor the contents of `RAPIDS.cmake` from `rapidsai/rapids-cmake` to avoid a network call.

xref: https://github.com/rapidsai/rapids-cmake/pull/809
xref: https://github.com/rapidsai/build-infra/issues/206

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/816
---
 cmake/RAPIDS.cmake                            | 90 +++++++++++++++++++
 .../rapids_config.cmake                       | 14 +--
 cpp/CMakeLists.txt                            |  2 +-
 java/internal/CMakeLists.txt                  |  2 +-
 python/cuvs/CMakeLists.txt                    |  2 +-
 python/libcuvs/CMakeLists.txt                 |  2 +-
 rust/cuvs-sys/CMakeLists.txt                  |  2 +-
 7 files changed, 99 insertions(+), 15 deletions(-)
 create mode 100644 cmake/RAPIDS.cmake
 rename rapids_config.cmake => cmake/rapids_config.cmake (71%)

diff --git a/cmake/RAPIDS.cmake b/cmake/RAPIDS.cmake
new file mode 100644
index 0000000000..d112951d3c
--- /dev/null
+++ b/cmake/RAPIDS.cmake
@@ -0,0 +1,90 @@
+# =============================================================================
+# Copyright (c) 2021-2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+#
+# This is the preferred entry point for projects using rapids-cmake
+#
+# Enforce the minimum required CMake version for all users
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
+
+# Allow users to control which version is used
+if(NOT rapids-cmake-version OR NOT rapids-cmake-version MATCHES [[^([0-9][0-9])\.([0-9][0-9])$]])
+  message(
+    FATAL_ERROR "The CMake variable rapids-cmake-version must be defined in the format MAJOR.MINOR."
+  )
+endif()
+
+# Allow users to control which GitHub repo is fetched
+if(NOT rapids-cmake-repo)
+  # Define a default repo if the user doesn't set one
+  set(rapids-cmake-repo rapidsai/rapids-cmake)
+endif()
+
+# Allow users to control which branch is fetched
+if(NOT rapids-cmake-branch)
+  # Define a default branch if the user doesn't set one
+  set(rapids-cmake-branch "branch-${rapids-cmake-version}")
+endif()
+
+# Allow users to control the exact URL passed to FetchContent
+if(NOT rapids-cmake-url)
+  # Construct a default URL if the user doesn't set one
+  set(rapids-cmake-url "https://github.com/${rapids-cmake-repo}/")
+
+  # In order of specificity
+  if(rapids-cmake-fetch-via-git)
+    if(rapids-cmake-sha)
+      # An exact git SHA takes precedence over anything
+      set(rapids-cmake-value-to-clone "${rapids-cmake-sha}")
+    elseif(rapids-cmake-tag)
+      # Followed by a git tag name
+      set(rapids-cmake-value-to-clone "${rapids-cmake-tag}")
+    else()
+      # Or if neither of the above two were defined, use a branch
+      set(rapids-cmake-value-to-clone "${rapids-cmake-branch}")
+    endif()
+  else()
+    if(rapids-cmake-sha)
+      # An exact git SHA takes precedence over anything
+      set(rapids-cmake-value-to-clone "archive/${rapids-cmake-sha}.zip")
+    elseif(rapids-cmake-tag)
+      # Followed by a git tag name
+      set(rapids-cmake-value-to-clone "archive/refs/tags/${rapids-cmake-tag}.zip")
+    else()
+      # Or if neither of the above two were defined, use a branch
+      set(rapids-cmake-value-to-clone "archive/refs/heads/${rapids-cmake-branch}.zip")
+    endif()
+  endif()
+endif()
+
+include(FetchContent)
+if(rapids-cmake-fetch-via-git)
+  FetchContent_Declare(
+    rapids-cmake
+    GIT_REPOSITORY "${rapids-cmake-url}"
+    GIT_TAG "${rapids-cmake-value-to-clone}"
+  )
+else()
+  string(APPEND rapids-cmake-url "${rapids-cmake-value-to-clone}")
+  FetchContent_Declare(rapids-cmake URL "${rapids-cmake-url}")
+endif()
+FetchContent_GetProperties(rapids-cmake)
+if(rapids-cmake_POPULATED)
+  # Something else has already populated rapids-cmake, only thing we need to do is setup the
+  # CMAKE_MODULE_PATH
+  if(NOT "${rapids-cmake-dir}" IN_LIST CMAKE_MODULE_PATH)
+    list(APPEND CMAKE_MODULE_PATH "${rapids-cmake-dir}")
+  endif()
+else()
+  FetchContent_MakeAvailable(rapids-cmake)
+endif()
diff --git a/rapids_config.cmake b/cmake/rapids_config.cmake
similarity index 71%
rename from rapids_config.cmake
rename to cmake/rapids_config.cmake
index 4ceefb9916..abe468dce8 100644
--- a/rapids_config.cmake
+++ b/cmake/rapids_config.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2018-2024, NVIDIA CORPORATION.
+# Copyright (c) 2018-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -11,7 +11,7 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 # =============================================================================
-file(READ "${CMAKE_CURRENT_LIST_DIR}/VERSION" _rapids_version)
+file(READ "${CMAKE_CURRENT_LIST_DIR}/../VERSION" _rapids_version)
 if(_rapids_version MATCHES [[^([0-9][0-9])\.([0-9][0-9])\.([0-9][0-9])]])
   set(RAPIDS_VERSION_MAJOR "${CMAKE_MATCH_1}")
   set(RAPIDS_VERSION_MINOR "${CMAKE_MATCH_2}")
@@ -26,11 +26,5 @@ else()
   )
 endif()
 
-if(NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")
-  file(
-    DOWNLOAD
-    "https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION_MAJOR_MINOR}/RAPIDS.cmake"
-    "${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake"
-  )
-endif()
-include("${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS-${RAPIDS_VERSION_MAJOR_MINOR}.cmake")
+set(rapids-cmake-version "${RAPIDS_VERSION_MAJOR_MINOR}")
+include("${CMAKE_CURRENT_LIST_DIR}/RAPIDS.cmake")
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 9d505f862e..e459ca33ad 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -11,7 +11,7 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
-include(../rapids_config.cmake)
+include(../cmake/rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-export)
diff --git a/java/internal/CMakeLists.txt b/java/internal/CMakeLists.txt
index c764745b10..7d8408e1ab 100644
--- a/java/internal/CMakeLists.txt
+++ b/java/internal/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
-include(rapids_config.cmake)
+include(../../cmake/rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-export)
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 97eb53cc42..345d26f9b0 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
-include(../../rapids_config.cmake)
+include(../../cmake/rapids_config.cmake)
 
 # We always need CUDA for cuvs because the cuvs dependency brings in a header-only cuco dependency
 # that enables CUDA unconditionally.
diff --git a/python/libcuvs/CMakeLists.txt b/python/libcuvs/CMakeLists.txt
index a5a1c66b7b..2c677eff74 100644
--- a/python/libcuvs/CMakeLists.txt
+++ b/python/libcuvs/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
-include(../../rapids_config.cmake)
+include(../../cmake/rapids_config.cmake)
 
 include(rapids-cuda)
 rapids_cuda_init_architectures(libcuvs-python)
diff --git a/rust/cuvs-sys/CMakeLists.txt b/rust/cuvs-sys/CMakeLists.txt
index 0fcb0348a3..a96cef28b0 100644
--- a/rust/cuvs-sys/CMakeLists.txt
+++ b/rust/cuvs-sys/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
-include(rapids_config.cmake)
+include(../../cmake/rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-export)

From 52f9fca8a352cb650f6013c88d718e49c2bd0456 Mon Sep 17 00:00:00 2001
From: Jaya Venkatesh <jjayabaskar@nvidia.com>
Date: Fri, 11 Apr 2025 14:55:45 -0700
Subject: [PATCH 358/623] Moving wheel builds to specified location and
 uploading build artifacts to Github (#777)

This work is towards moving build artifacts from `downloads.rapids.ai` to Github Artifact Store (see https://github.com/rapidsai/ops/issues/2982)

As part of these changes, all wheel builds need to be in a specified temporary directory indicated by the environment variable `RAPIDS_WHEEL_BLD_OUTPUT_DIR` set on the `ci-wheel` Docker image used for building wheels. This lets us upload all wheel artifacts seamlessly to Github Artifacts.

Authors:
  - Jaya Venkatesh (https://github.com/VenkateshJaya)

Approvers:
  - AJ Schmidt (https://github.com/ajschmidt8)

URL: https://github.com/rapidsai/cuvs/pull/777
---
 .github/workflows/build.yaml | 4 ++++
 .github/workflows/pr.yaml    | 4 ++++
 ci/build_wheel.sh            | 6 +++---
 ci/build_wheel_cuvs.sh       | 2 +-
 ci/build_wheel_libcuvs.sh    | 2 +-
 5 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index b1d2e61f2f..6221054c2c 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -108,6 +108,8 @@ jobs:
       script: ci/build_wheel_libcuvs.sh
       # build for every combination of arch and CUDA version, but only for the latest Python
       matrix_filter: group_by([.ARCH, (.CUDA_VER|split(".")|map(tonumber)|.[0])]) | map(max_by(.PY_VER|split(".")|map(tonumber)))
+      package-type: cpp
+      wheel-name: libcuvs
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
@@ -129,6 +131,8 @@ jobs:
       sha: ${{ inputs.sha }}
       date: ${{ inputs.date }}
       script: ci/build_wheel_cuvs.sh
+      package-type: python
+      wheel-name: cuvs
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 2ab3771746..66499f2516 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -173,6 +173,8 @@ jobs:
       script: ci/build_wheel_libcuvs.sh
       # build for every combination of arch and CUDA version, but only for the latest Python
       matrix_filter: group_by([.ARCH, (.CUDA_VER|split(".")|map(tonumber)|.[0])]) | map(max_by(.PY_VER|split(".")|map(tonumber)))
+      package-type: cpp
+      wheel-name: libcuvs
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
@@ -180,6 +182,8 @@ jobs:
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
+      package-type: python
+      wheel-name: cuvs
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 2c0040cfd7..ee9edae627 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -48,7 +48,7 @@ rapids-pip-retry wheel \
 
 sccache --show-adv-stats
 
-mkdir -p final_dist
-python -m auditwheel repair -w final_dist "${EXCLUDE_ARGS[@]}" dist/*
+# repair wheels and write to the location that artifact-uploading code expects to find them
+python -m auditwheel repair -w "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}" "${EXCLUDE_ARGS[@]}" dist/*
 
-RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 "${package_type}" final_dist
+RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 "${package_type}" "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}"
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index 8557d65d11..f6337fc27e 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -17,4 +17,4 @@ echo "libcuvs-${RAPIDS_PY_CUDA_SUFFIX} @ file://$(echo /tmp/libcuvs_dist/libcuvs
 export PIP_CONSTRAINT="/tmp/constraints.txt"
 
 ci/build_wheel.sh cuvs ${package_dir} python
-ci/validate_wheel.sh ${package_dir} final_dist
+ci/validate_wheel.sh ${package_dir} "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}"
diff --git a/ci/build_wheel_libcuvs.sh b/ci/build_wheel_libcuvs.sh
index 0d45a11b13..129eff8444 100755
--- a/ci/build_wheel_libcuvs.sh
+++ b/ci/build_wheel_libcuvs.sh
@@ -27,4 +27,4 @@ rapids-pip-retry install \
 export PIP_NO_BUILD_ISOLATION=0
 
 ci/build_wheel.sh libcuvs ${package_dir} cpp
-ci/validate_wheel.sh ${package_dir} final_dist libcuvs
+ci/validate_wheel.sh ${package_dir} "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}"

From 35f21e66f0d888f5be4bd5036733cfeef743380a Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 16 Apr 2025 01:27:35 -0500
Subject: [PATCH 359/623] Use vendored RAPIDS.cmake in example code. (#824)

Fixes example CMake code to use the vendored RAPIDS.cmake introduced in #816.

xref: https://github.com/rapidsai/build-infra/issues/206

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/824
---
 cpp/cmake/thirdparty/get_cuvs.cmake          |  2 +-
 examples/c/CMakeLists.txt                    |  2 +-
 examples/cmake/thirdparty/fetch_rapids.cmake | 21 --------------------
 examples/cmake/thirdparty/get_cuvs.cmake     |  6 +++---
 examples/cpp/CMakeLists.txt                  |  2 +-
 5 files changed, 6 insertions(+), 27 deletions(-)
 delete mode 100644 examples/cmake/thirdparty/fetch_rapids.cmake

diff --git a/cpp/cmake/thirdparty/get_cuvs.cmake b/cpp/cmake/thirdparty/get_cuvs.cmake
index c21cccbcc0..b37249745b 100644
--- a/cpp/cmake/thirdparty/get_cuvs.cmake
+++ b/cpp/cmake/thirdparty/get_cuvs.cmake
@@ -11,7 +11,7 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-# Use RAPIDS_VERSION_MAJOR_MINOR from rapids_config.cmake
+# Use RAPIDS_VERSION_MAJOR_MINOR from cmake/rapids_config.cmake
 set(CUVS_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
 set(CUVS_FORK "rapidsai")
 set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
diff --git a/examples/c/CMakeLists.txt b/examples/c/CMakeLists.txt
index 52f3b34d19..a897207d0e 100644
--- a/examples/c/CMakeLists.txt
+++ b/examples/c/CMakeLists.txt
@@ -15,7 +15,7 @@ cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 # ------------- configure rapids-cmake --------------#
 
-include(../cmake/thirdparty/fetch_rapids.cmake)
+include(../../cmake/rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-cuda)
diff --git a/examples/cmake/thirdparty/fetch_rapids.cmake b/examples/cmake/thirdparty/fetch_rapids.cmake
deleted file mode 100644
index 54b89a6c42..0000000000
--- a/examples/cmake/thirdparty/fetch_rapids.cmake
+++ /dev/null
@@ -1,21 +0,0 @@
-# =============================================================================
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
-# in compliance with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software distributed under the License
-# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
-# or implied. See the License for the specific language governing permissions and limitations under
-# the License.
-
-# Use this variable to update RAPIDS and cuVS versions
-set(RAPIDS_VERSION "25.06")
-
-if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
-    file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake
-            ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
-endif()
-include(${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake)
diff --git a/examples/cmake/thirdparty/get_cuvs.cmake b/examples/cmake/thirdparty/get_cuvs.cmake
index a2a4e88420..49f0ab7017 100644
--- a/examples/cmake/thirdparty/get_cuvs.cmake
+++ b/examples/cmake/thirdparty/get_cuvs.cmake
@@ -11,10 +11,10 @@
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 
-# Use RAPIDS_VERSION from cmake/thirdparty/fetch_rapids.cmake
-set(CUVS_VERSION "${RAPIDS_VERSION}")
+# Use RAPIDS_VERSION_MAJOR_MINOR from cmake/rapids_config.cmake
+set(CUVS_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
 set(CUVS_FORK "rapidsai")
-set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION}")
+set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
 
 function(find_and_configure_cuvs)
     set(oneValueArgs VERSION FORK PINNED_TAG ENABLE_NVTX BUILD_CUVS_C_LIBRARY)
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 311be31f7c..2d6f1d3665 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -15,7 +15,7 @@ cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 # ------------- configure rapids-cmake --------------#
 
-include(../cmake/thirdparty/fetch_rapids.cmake)
+include(../../cmake/rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-cuda)

From de748d8e16064f67ab38f764c328f88a52064d48 Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Wed, 16 Apr 2025 09:57:34 -0400
Subject: [PATCH 360/623] refactor(rattler): enable strict channel priority for
 builds (#823)

This PR enables strict channel priority for building conda packages with `rattler-build`.

xref rapidsai/build-planning#84

Authors:
  - Gil Forsyth (https://github.com/gforsyth)
  - https://github.com/jakirkham

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/823
---
 ci/build_cpp.sh    |  6 ++----
 ci/build_python.sh | 18 ++++--------------
 2 files changed, 6 insertions(+), 18 deletions(-)

diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index 7a6e5e56a7..ca9bac0b6c 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -21,16 +21,14 @@ sccache --zero-stats
 RAPIDS_PACKAGE_VERSION=$(rapids-generate-version)
 export RAPIDS_PACKAGE_VERSION
 
+# populates `RATTLER_CHANNELS` array and `RATTLER_ARGS` array
 source rapids-rattler-channel-string
 
 # --no-build-id allows for caching with `sccache`
 # more info is available at
 # https://rattler.build/latest/tips_and_tricks/#using-sccache-or-ccache-with-rattler-build
 rattler-build build --recipe conda/recipes/libcuvs \
-                    --experimental \
-                    --no-build-id \
-                    --channel-priority disabled \
-                    --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
+                    "${RATTLER_ARGS[@]}" \
                     "${RATTLER_CHANNELS[@]}"
 
 sccache --show-adv-stats
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 4576bd9880..64d1c16f27 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -24,7 +24,7 @@ echo "${version}" > VERSION
 sccache --zero-stats
 
 
-# populates `RATTLER_CHANNELS` array
+# populates `RATTLER_CHANNELS` array and `RATTLER_ARGS` array
 source rapids-rattler-channel-string
 
 rapids-logger "Prepending channel ${CPP_CHANNEL} to RATTLER_CHANNELS"
@@ -39,22 +39,15 @@ rapids-logger "Building cuvs"
 # more info is available at
 # https://rattler.build/latest/tips_and_tricks/#using-sccache-or-ccache-with-rattler-build
 rattler-build build --recipe conda/recipes/cuvs \
-                    --experimental \
-                    --no-build-id \
-                    --channel-priority disabled \
-                    --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
-                    --test skip \
+                    "${RATTLER_ARGS[@]}" \
                     "${RATTLER_CHANNELS[@]}"
 
 sccache --show-adv-stats
 sccache --zero-stats
 
 rattler-build build --recipe conda/recipes/cuvs-bench \
-                    --experimental \
-                    --no-build-id \
-                    --channel-priority disabled \
-                    --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
                     --test skip \
+                    "${RATTLER_ARGS[@]}" \
                     "${RATTLER_CHANNELS[@]}"
 
 sccache --show-adv-stats
@@ -65,10 +58,7 @@ sccache --zero-stats
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
   rattler-build build --recipe conda/recipes/cuvs-bench-cpu \
-                      --experimental \
-                      --no-build-id \
-                      --channel-priority disabled \
-                      --output-dir "$RAPIDS_CONDA_BLD_OUTPUT_DIR" \
+                      "${RATTLER_ARGS[@]}" \
                       "${RATTLER_CHANNELS[@]}"
   sccache --show-adv-stats
 fi

From 72d92a34d6f6bd80df385b718e8a853a069a1d3b Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Wed, 16 Apr 2025 13:15:20 -0400
Subject: [PATCH 361/623] Remove `@rapidsai/cuvs-build-codeowners` (#783)

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - https://github.com/jakirkham
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/783
---
 .github/CODEOWNERS | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 81085a5cfa..7ed1d46c0c 100755
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -15,11 +15,6 @@ CMakeLists.txt     @rapidsai/cuvs-cmake-codeowners
 **/cmake/          @rapidsai/cuvs-cmake-codeowners
 *.cmake            @rapidsai/cuvs-cmake-codeowners
 
-#build code owners
-python/setup.py    @rapidsai/cuvs-build-codeowners
-build.sh           @rapidsai/cuvs-build-codeowners
-**/build.sh        @rapidsai/cuvs-build-codeowners
-
 #CI code owners
 /.github/                @rapidsai/ci-codeowners
 /ci/                     @rapidsai/ci-codeowners
@@ -31,3 +26,6 @@ build.sh           @rapidsai/cuvs-build-codeowners
 dependencies.yaml        @rapidsai/packaging-codeowners
 /build.sh                @rapidsai/packaging-codeowners
 pyproject.toml           @rapidsai/packaging-codeowners
+python/setup.py          @rapidsai/packaging-codeowners
+build.sh                 @rapidsai/packaging-codeowners
+**/build.sh              @rapidsai/packaging-codeowners

From d0f500558e7c6012dbcd4b88d926781f006d6345 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 16 Apr 2025 12:02:25 -0700
Subject: [PATCH 362/623] Moving NN Descent class and struct declarations to
 `nn_descent_gnnd.hpp` (#803)

Moves declaration of the `GNND` class and a few other classes and structs to `nn_descent_gnnd.hpp` file.
Also adds a helper function for getting the build configurations based on a few checks.

## Why this is needed
Actual work for NN Descent build is done in the `GNND::build()` function, which is not exposed as a public API.
This complicates usage of NN Descent, especially when we want to create the GNND separately, and call gnnd.build() directly.

## Use Cases
Specifically, this is needed for the batched all-neighbors knn graph building implementation.
GNND allocates all necessary device and host matrices at initialization.
The NN Descent's `build` exposed as the public API makes a new GNND, then calls gnnd.build().
So originally it's like this
```
public api build() {
     gnnd = GNND{};   // allocates a bunch of device/host memory
     gnnd.build(data);    // where all the work is done
}
```
However, now that we have to build iteratively in the batching algorithm, calling the public build API means we make GNND for every new cluster, thereby allocating (and deallocating) a large chunk of memory for every cluster. This takes a lot of time.

In such cases, it is better to make a GNND once, and reuse it over multiple clusters to save time. For example;
```
gnnd = GNND{}    // this needs to be made outside the for loop
for cluster in clusters:
    cluster_data = get_cluster_data(data)
    gnnd.build(cluster_data)
   ```
This PR will allow including `nn_descent_gnnd.hpp` instead of the entire `nn_descent.cuh` file.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/803
---
 cpp/src/neighbors/detail/nn_descent.cuh       | 275 +--------------
 cpp/src/neighbors/detail/nn_descent_batch.cuh |   8 +-
 cpp/src/neighbors/detail/nn_descent_gnnd.hpp  | 318 ++++++++++++++++++
 cpp/src/neighbors/nn_descent_float.cu         |   3 +-
 4 files changed, 330 insertions(+), 274 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/nn_descent_gnnd.hpp

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index d3ddca57fb..4205674476 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -18,6 +18,7 @@
 
 #include "ann_utils.cuh"
 #include "cagra/device_common.hpp"
+#include "nn_descent_gnnd.hpp"
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
@@ -54,38 +55,6 @@
 
 namespace cuvs::neighbors::nn_descent::detail {
 
-using DistData_t = float;
-constexpr int DEGREE_ON_DEVICE{32};
-constexpr int SEGMENT_SIZE{32};
-constexpr int counter_interval{100};
-template <typename Index_t>
-struct InternalID_t;
-
-// InternalID_t uses 1 bit for marking (new or old).
-template <>
-class InternalID_t<int> {
- private:
-  using Index_t = int;
-  Index_t id_{std::numeric_limits<Index_t>::max()};
-
- public:
-  __host__ __device__ bool is_new() const { return id_ >= 0; }
-  __host__ __device__ Index_t& id_with_flag() { return id_; }
-  __host__ __device__ Index_t id() const
-  {
-    if (is_new()) return id_;
-    return -id_ - 1;
-  }
-  __host__ __device__ void mark_old()
-  {
-    if (id_ >= 0) id_ = -id_ - 1;
-  }
-  __host__ __device__ bool operator==(const InternalID_t<int>& other) const
-  {
-    return id() == other.id();
-  }
-};
-
 template <typename Index_t>
 struct ResultItem;
 
@@ -209,194 +178,6 @@ __device__ __forceinline__ void warp_bitonic_sort(T* element_ptr, const int lane
   return;
 }
 
-struct BuildConfig {
-  size_t max_dataset_size;
-  size_t dataset_dim;
-  size_t node_degree{64};
-  size_t internal_node_degree{0};
-  // If internal_node_degree == 0, the value of node_degree will be assigned to it
-  size_t max_iterations{50};
-  float termination_threshold{0.0001};
-  size_t output_graph_degree{32};
-  cuvs::distance::DistanceType metric{cuvs::distance::DistanceType::L2Expanded};
-};
-
-template <typename Index_t>
-class BloomFilter {
- public:
-  BloomFilter(size_t nrow, size_t num_sets_per_list, size_t num_hashs)
-    : nrow_(nrow),
-      num_sets_per_list_(num_sets_per_list),
-      num_hashs_(num_hashs),
-      bitsets_(nrow * num_bits_per_set_ * num_sets_per_list)
-  {
-  }
-
-  void add(size_t list_id, Index_t key)
-  {
-    if (is_cleared) { is_cleared = false; }
-    uint32_t hash         = hash_0(key);
-    size_t global_set_idx = list_id * num_bits_per_set_ * num_sets_per_list_ +
-                            key % num_sets_per_list_ * num_bits_per_set_;
-    bitsets_[global_set_idx + hash % num_bits_per_set_] = 1;
-    for (size_t i = 1; i < num_hashs_; i++) {
-      hash                                                = hash + hash_1(key);
-      bitsets_[global_set_idx + hash % num_bits_per_set_] = 1;
-    }
-  }
-
-  void set_nrow(size_t nrow) { nrow_ = nrow; }
-
-  bool check(size_t list_id, Index_t key)
-  {
-    bool is_present       = true;
-    uint32_t hash         = hash_0(key);
-    size_t global_set_idx = list_id * num_bits_per_set_ * num_sets_per_list_ +
-                            key % num_sets_per_list_ * num_bits_per_set_;
-    is_present &= bitsets_[global_set_idx + hash % num_bits_per_set_];
-
-    if (!is_present) return false;
-    for (size_t i = 1; i < num_hashs_; i++) {
-      hash = hash + hash_1(key);
-      is_present &= bitsets_[global_set_idx + hash % num_bits_per_set_];
-      if (!is_present) return false;
-    }
-    return true;
-  }
-
-  void clear()
-  {
-    if (is_cleared) return;
-#pragma omp parallel for
-    for (size_t i = 0; i < nrow_ * num_bits_per_set_ * num_sets_per_list_; i++) {
-      bitsets_[i] = 0;
-    }
-    is_cleared = true;
-  }
-
- private:
-  uint32_t hash_0(uint32_t value)
-  {
-    value *= 1103515245;
-    value += 12345;
-    value ^= value << 13;
-    value ^= value >> 17;
-    value ^= value << 5;
-    return value;
-  }
-
-  uint32_t hash_1(uint32_t value)
-  {
-    value *= 1664525;
-    value += 1013904223;
-    value ^= value << 13;
-    value ^= value >> 17;
-    value ^= value << 5;
-    return value;
-  }
-
-  static constexpr int num_bits_per_set_ = 512;
-  bool is_cleared{true};
-  std::vector<bool> bitsets_;
-  size_t nrow_;
-  size_t num_sets_per_list_;
-  size_t num_hashs_;
-};
-
-template <typename Index_t>
-struct GnndGraph {
-  raft::resources const& res;
-  static constexpr int segment_size = 32;
-  InternalID_t<Index_t>* h_graph;
-
-  size_t nrow;
-  size_t node_degree;
-  int num_samples;
-  int num_segments;
-
-  raft::host_matrix<DistData_t, size_t, raft::row_major> h_dists;
-
-  raft::pinned_matrix<Index_t, size_t> h_graph_new;
-  raft::pinned_vector<int2, size_t> h_list_sizes_new;
-
-  raft::pinned_matrix<Index_t, size_t> h_graph_old;
-  raft::pinned_vector<int2, size_t> h_list_sizes_old;
-  BloomFilter<Index_t> bloom_filter;
-
-  GnndGraph(const GnndGraph&)            = delete;
-  GnndGraph& operator=(const GnndGraph&) = delete;
-  GnndGraph(raft::resources const& res,
-            const size_t nrow,
-            const size_t node_degree,
-            const size_t internal_node_degree,
-            const size_t num_samples);
-  void init_random_graph();
-  // TODO: Create a generic bloom filter utility https://github.com/rapidsai/raft/issues/1827
-  // Use Bloom filter to sample "new" neighbors for local joining
-  void sample_graph_new(InternalID_t<Index_t>* new_neighbors, const size_t width);
-  void sample_graph(bool sample_new);
-  void update_graph(const InternalID_t<Index_t>* new_neighbors,
-                    const DistData_t* new_dists,
-                    const size_t width,
-                    std::atomic<int64_t>& update_counter);
-  void sort_lists();
-  void clear();
-  ~GnndGraph();
-};
-
-template <typename Data_t = float, typename Index_t = int>
-class GNND {
- public:
-  GNND(raft::resources const& res, const BuildConfig& build_config);
-  GNND(const GNND&)            = delete;
-  GNND& operator=(const GNND&) = delete;
-
-  void build(Data_t* data,
-             const Index_t nrow,
-             Index_t* output_graph,
-             bool return_distances,
-             DistData_t* output_distances);
-  ~GNND()    = default;
-  using ID_t = InternalID_t<Index_t>;
-  void reset(raft::resources const& res);
-
- private:
-  void add_reverse_edges(Index_t* graph_ptr,
-                         Index_t* h_rev_graph_ptr,
-                         Index_t* d_rev_graph_ptr,
-                         int2* list_sizes,
-                         cudaStream_t stream = 0);
-  void local_join(cudaStream_t stream = 0);
-
-  raft::resources const& res;
-
-  BuildConfig build_config_;
-  GnndGraph<Index_t> graph_;
-  std::atomic<int64_t> update_counter_;
-
-  size_t nrow_;
-  size_t ndim_;
-
-  raft::device_matrix<__half, size_t, raft::row_major> d_data_;
-  raft::device_vector<DistData_t, size_t> l2_norms_;
-
-  raft::device_matrix<ID_t, size_t, raft::row_major> graph_buffer_;
-  raft::device_matrix<DistData_t, size_t, raft::row_major> dists_buffer_;
-
-  raft::pinned_matrix<ID_t, size_t> graph_host_buffer_;
-  raft::pinned_matrix<DistData_t, size_t> dists_host_buffer_;
-
-  raft::device_vector<int, size_t> d_locks_;
-
-  raft::pinned_matrix<Index_t, size_t> h_rev_graph_new_;
-  raft::pinned_matrix<Index_t, size_t> h_graph_old_;
-  raft::pinned_matrix<Index_t, size_t> h_rev_graph_old_;
-  // int2.x is the number of forward edges, int2.y is the number of reverse edges
-
-  raft::device_vector<int2, size_t> d_list_sizes_new_;
-  raft::device_vector<int2, size_t> d_list_sizes_old_;
-};
-
 constexpr int TILE_ROW_WIDTH = 64;
 constexpr int TILE_COL_WIDTH = 128;
 
@@ -1457,56 +1238,12 @@ void build(raft::resources const& res,
            raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
            index<IdxT>& idx)
 {
-  RAFT_EXPECTS(dataset.extent(0) < std::numeric_limits<int>::max() - 1,
-               "The dataset size for GNND should be less than %d",
-               std::numeric_limits<int>::max() - 1);
-  auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
-                         params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
-                         params.metric == cuvs::distance::DistanceType::CosineExpanded ||
-                         params.metric == cuvs::distance::DistanceType::InnerProduct;
-  RAFT_EXPECTS(allowed_metrics,
-               "The metric for NN Descent should be L2Expanded, L2SqrtExpanded, CosineExpanded or "
-               "InnerProduct");
-  RAFT_EXPECTS(
-    idx.metric() == params.metric,
-    "The metrics set in nn_descent::index_params and nn_descent::index are inconsistent");
-  size_t intermediate_degree = params.intermediate_graph_degree;
-  size_t graph_degree        = params.graph_degree;
-
-  if (intermediate_degree >= static_cast<size_t>(dataset.extent(0))) {
-    RAFT_LOG_WARN(
-      "Intermediate graph degree cannot be larger than dataset size, reducing it to %lu",
-      dataset.extent(0));
-    intermediate_degree = dataset.extent(0) - 1;
-  }
-  if (intermediate_degree < graph_degree) {
-    RAFT_LOG_WARN(
-      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
-      "graph_degree.",
-      graph_degree,
-      intermediate_degree);
-    graph_degree = intermediate_degree;
-  }
+  size_t extended_graph_degree, graph_degree;
+  auto build_config =
+    get_build_config(res, params, dataset, idx.metric(), extended_graph_degree, graph_degree);
 
-  // The elements in each knn-list are partitioned into different buckets, and we need more buckets
-  // to mitigate bucket collisions. `intermediate_degree` is OK to larger than
-  // extended_graph_degree.
-  size_t extended_graph_degree =
-    align32::roundUp(static_cast<size_t>(graph_degree * (graph_degree <= 32 ? 1.0 : 1.3)));
-  size_t extended_intermediate_degree = align32::roundUp(
-    static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
-
-  auto int_graph = raft::make_host_matrix<int, int64_t, raft::row_major>(
-    dataset.extent(0), static_cast<int64_t>(extended_graph_degree));
-
-  BuildConfig build_config{.max_dataset_size      = static_cast<size_t>(dataset.extent(0)),
-                           .dataset_dim           = static_cast<size_t>(dataset.extent(1)),
-                           .node_degree           = extended_graph_degree,
-                           .internal_node_degree  = extended_intermediate_degree,
-                           .max_iterations        = params.max_iterations,
-                           .termination_threshold = params.termination_threshold,
-                           .output_graph_degree   = params.graph_degree,
-                           .metric                = params.metric};
+  auto int_graph =
+    raft::make_host_matrix<int, int64_t, raft::row_major>(dataset.extent(0), extended_graph_degree);
 
   GNND<const T, int> nnd(res, build_config);
 
diff --git a/cpp/src/neighbors/detail/nn_descent_batch.cuh b/cpp/src/neighbors/detail/nn_descent_batch.cuh
index f8e10c6e17..c0726409a2 100644
--- a/cpp/src/neighbors/detail/nn_descent_batch.cuh
+++ b/cpp/src/neighbors/detail/nn_descent_batch.cuh
@@ -19,7 +19,7 @@
 #include <sys/types.h>
 #undef RAFT_EXPLICIT_INSTANTIATE_ONLY
 
-#include "nn_descent.cuh"
+#include "nn_descent_gnnd.hpp"
 #include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 
@@ -637,9 +637,9 @@ void batch_build(raft::resources const& res,
   }
 
   size_t extended_graph_degree =
-    align32::roundUp(static_cast<size_t>(graph_degree * (graph_degree <= 32 ? 1.0 : 1.3)));
-  size_t extended_intermediate_degree = align32::roundUp(
-    static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
+    roundUp32(static_cast<size_t>(graph_degree * (graph_degree <= 32 ? 1.0 : 1.3)));
+  size_t extended_intermediate_degree =
+    roundUp32(static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
 
   auto int_graph = raft::make_host_matrix<int, int64_t, row_major>(
     max_cluster_size, static_cast<int64_t>(extended_graph_degree));
diff --git a/cpp/src/neighbors/detail/nn_descent_gnnd.hpp b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
new file mode 100644
index 0000000000..372024fdf9
--- /dev/null
+++ b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
@@ -0,0 +1,318 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+#include <raft/core/detail/macros.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/pinned_mdarray.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#include <limits>
+
+namespace cuvs::neighbors::nn_descent::detail {
+
+using DistData_t = float;
+constexpr int DEGREE_ON_DEVICE{32};
+constexpr int SEGMENT_SIZE{32};
+constexpr int counter_interval{100};
+template <typename Index_t>
+struct InternalID_t;
+
+inline size_t roundUp32(size_t num) { return (num + 31) / 32 * 32; }
+
+// InternalID_t uses 1 bit for marking (new or old).
+template <>
+struct InternalID_t<int> {
+ private:
+  using Index_t = int;
+  Index_t id_{std::numeric_limits<Index_t>::max()};
+
+ public:
+  inline _RAFT_HOST_DEVICE bool is_new() const { return id_ >= 0; }
+  inline _RAFT_HOST_DEVICE Index_t& id_with_flag() { return id_; }
+  inline _RAFT_HOST_DEVICE Index_t id() const
+  {
+    if (is_new()) return id_;
+    return -id_ - 1;
+  }
+  inline _RAFT_HOST_DEVICE void mark_old()
+  {
+    if (id_ >= 0) id_ = -id_ - 1;
+  }
+  inline _RAFT_HOST_DEVICE bool operator==(const InternalID_t<int>& other) const
+  {
+    return id() == other.id();
+  }
+};
+
+struct BuildConfig {
+  size_t max_dataset_size;
+  size_t dataset_dim;
+  size_t node_degree{64};
+  size_t internal_node_degree{0};
+  // If internal_node_degree == 0, the value of node_degree will be assigned to it
+  size_t max_iterations{50};
+  float termination_threshold{0.0001};
+  size_t output_graph_degree{32};
+  cuvs::distance::DistanceType metric{cuvs::distance::DistanceType::L2Expanded};
+};
+
+template <typename Index_t>
+class BloomFilter {
+ public:
+  BloomFilter(size_t nrow, size_t num_sets_per_list, size_t num_hashs)
+    : nrow_(nrow),
+      num_sets_per_list_(num_sets_per_list),
+      num_hashs_(num_hashs),
+      bitsets_(nrow * num_bits_per_set_ * num_sets_per_list)
+  {
+  }
+
+  void add(size_t list_id, Index_t key)
+  {
+    if (is_cleared) { is_cleared = false; }
+    uint32_t hash         = hash_0(key);
+    size_t global_set_idx = list_id * num_bits_per_set_ * num_sets_per_list_ +
+                            key % num_sets_per_list_ * num_bits_per_set_;
+    bitsets_[global_set_idx + hash % num_bits_per_set_] = 1;
+    for (size_t i = 1; i < num_hashs_; i++) {
+      hash                                                = hash + hash_1(key);
+      bitsets_[global_set_idx + hash % num_bits_per_set_] = 1;
+    }
+  }
+
+  void set_nrow(size_t nrow) { nrow_ = nrow; }
+
+  bool check(size_t list_id, Index_t key)
+  {
+    bool is_present       = true;
+    uint32_t hash         = hash_0(key);
+    size_t global_set_idx = list_id * num_bits_per_set_ * num_sets_per_list_ +
+                            key % num_sets_per_list_ * num_bits_per_set_;
+    is_present &= bitsets_[global_set_idx + hash % num_bits_per_set_];
+
+    if (!is_present) return false;
+    for (size_t i = 1; i < num_hashs_; i++) {
+      hash = hash + hash_1(key);
+      is_present &= bitsets_[global_set_idx + hash % num_bits_per_set_];
+      if (!is_present) return false;
+    }
+    return true;
+  }
+
+  void clear()
+  {
+    if (is_cleared) return;
+#pragma omp parallel for
+    for (size_t i = 0; i < nrow_ * num_bits_per_set_ * num_sets_per_list_; i++) {
+      bitsets_[i] = 0;
+    }
+    is_cleared = true;
+  }
+
+ private:
+  uint32_t hash_0(uint32_t value)
+  {
+    value *= 1103515245;
+    value += 12345;
+    value ^= value << 13;
+    value ^= value >> 17;
+    value ^= value << 5;
+    return value;
+  }
+
+  uint32_t hash_1(uint32_t value)
+  {
+    value *= 1664525;
+    value += 1013904223;
+    value ^= value << 13;
+    value ^= value >> 17;
+    value ^= value << 5;
+    return value;
+  }
+
+  static constexpr int num_bits_per_set_ = 512;
+  bool is_cleared{true};
+  std::vector<bool> bitsets_;
+  size_t nrow_;
+  size_t num_sets_per_list_;
+  size_t num_hashs_;
+};
+
+template <typename Index_t>
+struct GnndGraph {
+  raft::resources const& res;
+  static constexpr int segment_size = 32;
+  InternalID_t<Index_t>* h_graph;
+
+  size_t nrow;
+  size_t node_degree;
+  int num_samples;
+  int num_segments;
+
+  raft::host_matrix<DistData_t, size_t, raft::row_major> h_dists;
+
+  raft::pinned_matrix<Index_t, size_t> h_graph_new;
+  raft::pinned_vector<int2, size_t> h_list_sizes_new;
+
+  raft::pinned_matrix<Index_t, size_t> h_graph_old;
+  raft::pinned_vector<int2, size_t> h_list_sizes_old;
+  BloomFilter<Index_t> bloom_filter;
+
+  GnndGraph(const GnndGraph&)            = delete;
+  GnndGraph& operator=(const GnndGraph&) = delete;
+  GnndGraph(raft::resources const& res,
+            const size_t nrow,
+            const size_t node_degree,
+            const size_t internal_node_degree,
+            const size_t num_samples);
+  void init_random_graph();
+  // TODO: Create a generic bloom filter utility https://github.com/rapidsai/raft/issues/1827
+  // Use Bloom filter to sample "new" neighbors for local joining
+  void sample_graph_new(InternalID_t<Index_t>* new_neighbors, const size_t width);
+  void sample_graph(bool sample_new);
+  void update_graph(const InternalID_t<Index_t>* new_neighbors,
+                    const DistData_t* new_dists,
+                    const size_t width,
+                    std::atomic<int64_t>& update_counter);
+  void sort_lists();
+  void clear();
+  ~GnndGraph();
+};
+
+template <typename Data_t = float, typename Index_t = int>
+class GNND {
+ public:
+  GNND(raft::resources const& res, const BuildConfig& build_config);
+  GNND(const GNND&)            = delete;
+  GNND& operator=(const GNND&) = delete;
+
+  void build(Data_t* data,
+             const Index_t nrow,
+             Index_t* output_graph,
+             bool return_distances,
+             DistData_t* output_distances);
+  ~GNND()    = default;
+  using ID_t = InternalID_t<Index_t>;
+  void reset(raft::resources const& res);
+
+ private:
+  void add_reverse_edges(Index_t* graph_ptr,
+                         Index_t* h_rev_graph_ptr,
+                         Index_t* d_rev_graph_ptr,
+                         int2* list_sizes,
+                         cudaStream_t stream = 0);
+  void local_join(cudaStream_t stream = 0);
+
+  raft::resources const& res;
+
+  BuildConfig build_config_;
+  GnndGraph<Index_t> graph_;
+  std::atomic<int64_t> update_counter_;
+
+  size_t nrow_;
+  size_t ndim_;
+
+  raft::device_matrix<__half, size_t, raft::row_major> d_data_;
+  raft::device_vector<DistData_t, size_t> l2_norms_;
+
+  raft::device_matrix<ID_t, size_t, raft::row_major> graph_buffer_;
+  raft::device_matrix<DistData_t, size_t, raft::row_major> dists_buffer_;
+
+  raft::pinned_matrix<ID_t, size_t> graph_host_buffer_;
+  raft::pinned_matrix<DistData_t, size_t> dists_host_buffer_;
+
+  raft::device_vector<int, size_t> d_locks_;
+
+  raft::pinned_matrix<Index_t, size_t> h_rev_graph_new_;
+  raft::pinned_matrix<Index_t, size_t> h_graph_old_;
+  raft::pinned_matrix<Index_t, size_t> h_rev_graph_old_;
+  // int2.x is the number of forward edges, int2.y is the number of reverse edges
+
+  raft::device_vector<int2, size_t> d_list_sizes_new_;
+  raft::device_vector<int2, size_t> d_list_sizes_old_;
+};
+
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+BuildConfig get_build_config(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  const cuvs::distance::DistanceType metric,
+  size_t& extended_graph_degree,
+  size_t& graph_degree)
+{
+  RAFT_EXPECTS(dataset.extent(0) < std::numeric_limits<int>::max() - 1,
+               "The dataset size for GNND should be less than %d",
+               std::numeric_limits<int>::max() - 1);
+  auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                         params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                         params.metric == cuvs::distance::DistanceType::CosineExpanded ||
+                         params.metric == cuvs::distance::DistanceType::InnerProduct;
+  RAFT_EXPECTS(allowed_metrics,
+               "The metric for NN Descent should be L2Expanded, L2SqrtExpanded, CosineExpanded or "
+               "InnerProduct");
+  RAFT_EXPECTS(
+    metric == params.metric,
+    "The metrics set in nn_descent::index_params and nn_descent::index are inconsistent");
+  size_t intermediate_degree = params.intermediate_graph_degree;
+  graph_degree               = params.graph_degree;
+
+  if (intermediate_degree >= static_cast<size_t>(dataset.extent(0))) {
+    RAFT_LOG_WARN(
+      "Intermediate graph degree cannot be larger than dataset size, reducing it to %lu",
+      dataset.extent(0));
+    intermediate_degree = dataset.extent(0) - 1;
+  }
+  if (intermediate_degree < graph_degree) {
+    RAFT_LOG_WARN(
+      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
+      "graph_degree.",
+      graph_degree,
+      intermediate_degree);
+    graph_degree = intermediate_degree;
+  }
+
+  // The elements in each knn-list are partitioned into different buckets, and we need more buckets
+  // to mitigate bucket collisions. `intermediate_degree` is OK to larger than
+  // extended_graph_degree.
+  extended_graph_degree =
+    roundUp32(static_cast<size_t>(graph_degree * (graph_degree <= 32 ? 1.0 : 1.3)));
+  size_t extended_intermediate_degree =
+    roundUp32(static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
+
+  BuildConfig build_config{.max_dataset_size      = static_cast<size_t>(dataset.extent(0)),
+                           .dataset_dim           = static_cast<size_t>(dataset.extent(1)),
+                           .node_degree           = extended_graph_degree,
+                           .internal_node_degree  = extended_intermediate_degree,
+                           .max_iterations        = params.max_iterations,
+                           .termination_threshold = params.termination_threshold,
+                           .output_graph_degree   = params.graph_degree,
+                           .metric                = params.metric};
+  return build_config;
+}
+
+}  // namespace cuvs::neighbors::nn_descent::detail
diff --git a/cpp/src/neighbors/nn_descent_float.cu b/cpp/src/neighbors/nn_descent_float.cu
index 9af1c5c697..3942775570 100644
--- a/cpp/src/neighbors/nn_descent_float.cu
+++ b/cpp/src/neighbors/nn_descent_float.cu
@@ -53,7 +53,8 @@ namespace cuvs::neighbors::nn_descent {
       cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset, idx);              \
       return idx;                                                                             \
     }                                                                                         \
-  };
+  };                                                                                          \
+  template class detail::GNND<const T, int>;
 
 CUVS_INST_NN_DESCENT_BUILD(float, uint32_t);
 

From 4e0fb09c84c3cd7f8fbd9981bfd78e7013610d10 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Wed, 16 Apr 2025 15:07:00 -0400
Subject: [PATCH 363/623] Update cuvs to properly create a NCCL::NCCL target
 (#720)

Previously cuvs was relying on the fact that nccl would be found when passed as `-lnccl` and be found on one of the implicit link directories.

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/720
---
 cpp/CMakeLists.txt       | 11 +++++++++--
 cpp/tests/CMakeLists.txt |  3 ++-
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index e459ca33ad..97376fa88f 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -579,7 +579,14 @@ if(BUILD_SHARED_LIBS)
     )
 
     if(BUILD_MG_ALGOS)
-      set(CUVS_COMMS_DEPENDENCY nccl)
+      rapids_find_generate_module(
+        NCCL
+        HEADER_NAMES nccl.h
+        LIBRARY_NAMES nccl
+      )
+      find_package(NCCL REQUIRED)
+      target_link_libraries(cuvs_objs PRIVATE NCCL::NCCL)
+      target_link_libraries(cuvs PRIVATE NCCL::NCCL)
     endif()
 
     # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
@@ -587,7 +594,7 @@ if(BUILD_SHARED_LIBS)
       cuvs
       PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
       PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-              cuvs-cagra-search ${CUVS_COMMS_DEPENDENCY}
+              cuvs-cagra-search
     )
 
     if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index 2a352b875e..ee2ddac197 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -206,7 +206,8 @@ if(BUILD_TESTS)
 
   if(BUILD_MG_ALGOS)
     ConfigureTest(
-      NAME NEIGHBORS_MG_TEST PATH neighbors/mg/test_float.cu GPUS 1 PERCENT 100 ADDITIONAL_DEP nccl
+      NAME NEIGHBORS_MG_TEST PATH neighbors/mg/test_float.cu GPUS 1 PERCENT 100 ADDITIONAL_DEP
+      NCCL::NCCL
     )
   endif()
 

From 1d8f9d3748677b260b676b6a0954d2054cb0597e Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 22 Apr 2025 11:22:16 -0700
Subject: [PATCH 364/623] Add NN-Descent return_distances functionality to
 python/C (#833)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/833
---
 cpp/include/cuvs/core/detail/interop.hpp      | 26 ++++++
 cpp/include/cuvs/core/interop.hpp             | 15 +++-
 cpp/include/cuvs/neighbors/nn_descent.h       | 22 ++++-
 cpp/src/neighbors/nn_descent_c.cpp            | 81 ++++++++++++++++---
 .../cuvs/neighbors/nn_descent/nn_descent.pxd  |  7 +-
 .../cuvs/neighbors/nn_descent/nn_descent.pyx  | 48 +++++++++--
 python/cuvs/cuvs/tests/test_nn_descent.py     | 20 ++++-
 7 files changed, 196 insertions(+), 23 deletions(-)

diff --git a/cpp/include/cuvs/core/detail/interop.hpp b/cpp/include/cuvs/core/detail/interop.hpp
index 19e4a922cf..205d493962 100644
--- a/cpp/include/cuvs/core/detail/interop.hpp
+++ b/cpp/include/cuvs/core/detail/interop.hpp
@@ -141,4 +141,30 @@ inline bool is_c_contiguous(DLManagedTensor* managed_tensor)
   return true;
 }
 
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-function"
+static void free_dlmanaged_tensor_shape(DLManagedTensor* tensor)
+{
+  delete[] tensor->dl_tensor.shape;
+}
+#pragma GCC diagnostic pop
+
+template <typename MdspanType, typename = raft::is_mdspan_t<MdspanType>>
+static void to_dlpack(MdspanType src, DLManagedTensor* dst)
+{
+  auto tensor = &dst->dl_tensor;
+
+  tensor->dtype  = data_type_to_DLDataType<typename MdspanType::value_type>();
+  tensor->device = accessor_type_to_DLDevice<typename MdspanType::accessor_type>();
+  tensor->ndim   = MdspanType::extents_type::rank();
+  tensor->data   = src.data_handle();
+
+  tensor->shape = new int64_t[tensor->ndim];
+  dst->deleter  = free_dlmanaged_tensor_shape;
+
+  for (int64_t i = 0; i < tensor->ndim; ++i) {
+    tensor->shape[i] = src.extent(i);
+  }
+}
+
 }  // namespace cuvs::core::detail
diff --git a/cpp/include/cuvs/core/interop.hpp b/cpp/include/cuvs/core/interop.hpp
index 096885f2f7..e7bf4af81d 100644
--- a/cpp/include/cuvs/core/interop.hpp
+++ b/cpp/include/cuvs/core/interop.hpp
@@ -68,7 +68,7 @@ inline bool is_c_contiguous(DLManagedTensor* tensor) { return detail::is_c_conti
 inline bool is_f_contiguous(DLManagedTensor* tensor) { return detail::is_f_contiguous(tensor); }
 
 /**
- * @brief Convert a DLManagedTensor to an mdspan
+ * @brief Convert a DLManagedTensor to a mdspan
  * NOTE: This function only supports compact row-major and col-major layouts.
  *
  * @code {.cpp}
@@ -93,6 +93,19 @@ inline MdspanType from_dlpack(DLManagedTensor* managed_tensor)
   return detail::from_dlpack<MdspanType>(managed_tensor);
 }
 
+/**
+ * @brief Convert a mdspan to a DLManagedTensor
+ *
+ * Converts a mdspan to a DLManagedTensor object. This lets us pass non-owning
+ * views from C++ to C code without copying.  Note that returned DLManagedTensor
+ * is a non-owning view, and doesn't ensure that the underlying memory stays valid.
+ */
+template <typename MdspanType, typename = raft::is_mdspan_t<MdspanType>>
+void to_dlpack(MdspanType src, DLManagedTensor* dst)
+{
+  return detail::to_dlpack(src, dst);
+}
+
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/nn_descent.h b/cpp/include/cuvs/neighbors/nn_descent.h
index 81c162598f..7dd87f2248 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.h
+++ b/cpp/include/cuvs/neighbors/nn_descent.h
@@ -171,11 +171,29 @@ cuvsError_t cuvsNNDescentBuild(cuvsResources_t res,
 /**
  * @brief Get the KNN graph from a built NN-Descent index
  *
+ * @param[in] res cuvsResources_t opaque C handle
  * @param[in] index cuvsNNDescentIndex_t Built NN-Descent index
- * @param[inout] graph Optional preallocated graph on host memory to store output
+ * @param[out] graph Preallocated graph on host memory to store output
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsNNDescentIndexGetGraph(cuvsResources_t res,
+                                       cuvsNNDescentIndex_t index,
+                                       DLManagedTensor* graph);
+
+/**
+ * @brief Get the distances from a build NN_Descent index
+ *
+ * This requires that the `return_distances` parameter was set when building the
+ * graph
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index cuvsNNDescentIndex_t Built NN-Descent index
+ * @param[out] distances Preallocated memory to store the output distances tensor
  * @return cuvsError_t
  */
-cuvsError_t cuvsNNDescentIndexGetGraph(cuvsNNDescentIndex_t index, DLManagedTensor* graph);
+cuvsError_t cuvsNNDescentIndexGetDistances(cuvsResources_t res,
+                                           cuvsNNDescentIndex_t index,
+                                           DLManagedTensor* distances);
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/src/neighbors/nn_descent_c.cpp b/cpp/src/neighbors/nn_descent_c.cpp
index 727b332cb5..a3cc760ef5 100644
--- a/cpp/src/neighbors/nn_descent_c.cpp
+++ b/cpp/src/neighbors/nn_descent_c.cpp
@@ -71,6 +71,57 @@ void* _build(cuvsResources_t res,
     RAFT_FAIL("dataset must be accessible on host or device memory");
   }
 }
+
+template <typename output_mdspan_type>
+void _get_graph(cuvsResources_t res, cuvsNNDescentIndex_t index, DLManagedTensor* graph)
+{
+  auto dtype = index->dtype;
+  if ((dtype.code == kDLUInt) && (dtype.bits == 32)) {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::nn_descent::index<uint32_t>*>(index->addr);
+    auto dst       = cuvs::core::from_dlpack<output_mdspan_type>(graph);
+    auto src       = index_ptr->graph();
+    auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+
+    RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output graph has incorrect number of rows");
+    RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output graph has incorrect number of cols");
+
+    cudaMemcpyAsync(dst.data_handle(),
+                    src.data_handle(),
+                    dst.extent(0) * dst.extent(1) * sizeof(uint32_t),
+                    cudaMemcpyDefault,
+                    raft::resource::get_cuda_stream(*res_ptr));
+  } else {
+    RAFT_FAIL("Unsupported nn-descent index dtype: %d and bits: %d", dtype.code, dtype.bits);
+  }
+}
+
+template <typename output_mdspan_type>
+void _get_distances(cuvsResources_t res, cuvsNNDescentIndex_t index, DLManagedTensor* distances)
+{
+  auto dtype = index->dtype;
+  if ((dtype.code == kDLUInt) && (dtype.bits == 32)) {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::nn_descent::index<uint32_t>*>(index->addr);
+    auto src       = index_ptr->distances();
+    if (!src.has_value()) {
+      RAFT_FAIL("nn-descent index doesn't contain distances - set return_distances when building");
+    }
+
+    auto res_ptr = reinterpret_cast<raft::resources*>(res);
+    auto dst     = cuvs::core::from_dlpack<output_mdspan_type>(distances);
+
+    RAFT_EXPECTS(src->extent(0) == dst.extent(0), "Output distances has incorrect number of rows");
+    RAFT_EXPECTS(src->extent(1) == dst.extent(1), "Output distances has incorrect number of cols");
+
+    cudaMemcpyAsync(dst.data_handle(),
+                    src->data_handle(),
+                    dst.extent(0) * dst.extent(1) * sizeof(float),
+                    cudaMemcpyDefault,
+                    raft::resource::get_cuda_stream(*res_ptr));
+
+  } else {
+    RAFT_FAIL("Unsupported nn-descent index dtype: %d and bits: %d", dtype.code, dtype.bits);
+  }
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsNNDescentIndexCreate(cuvsNNDescentIndex_t* index)
@@ -146,22 +197,32 @@ extern "C" cuvsError_t cuvsNNDescentIndexParamsDestroy(cuvsNNDescentIndexParams_
   return cuvs::core::translate_exceptions([=] { delete params; });
 }
 
-extern "C" cuvsError_t cuvsNNDescentIndexGetGraph(cuvsNNDescentIndex_t index,
+extern "C" cuvsError_t cuvsNNDescentIndexGetGraph(cuvsResources_t res,
+                                                  cuvsNNDescentIndex_t index,
                                                   DLManagedTensor* graph)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto dtype = index->dtype;
-    if ((dtype.code == kDLUInt) && (dtype.bits == 32)) {
-      auto index_ptr = reinterpret_cast<cuvs::neighbors::nn_descent::index<uint32_t>*>(index->addr);
+    if (cuvs::core::is_dlpack_device_compatible(graph->dl_tensor)) {
+      using output_mdspan_type = raft::device_matrix_view<uint32_t, int64_t, raft::row_major>;
+      _get_graph<output_mdspan_type>(res, index, graph);
+    } else {
       using output_mdspan_type = raft::host_matrix_view<uint32_t, int64_t, raft::row_major>;
-      auto dst                 = cuvs::core::from_dlpack<output_mdspan_type>(graph);
-      auto src                 = index_ptr->graph();
+      _get_graph<output_mdspan_type>(res, index, graph);
+    }
+  });
+}
 
-      RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output graph has incorrect number of rows");
-      RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output graph has incorrect number of cols");
-      std::copy(src.data_handle(), src.data_handle() + dst.size(), dst.data_handle());
+extern "C" cuvsError_t cuvsNNDescentIndexGetDistances(cuvsResources_t res,
+                                                      cuvsNNDescentIndex_t index,
+                                                      DLManagedTensor* distances)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (cuvs::core::is_dlpack_device_compatible(distances->dl_tensor)) {
+      using output_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
+      _get_distances<output_mdspan_type>(res, index, distances);
     } else {
-      RAFT_FAIL("Unsupported nn-descent index dtype: %d and bits: %d", dtype.code, dtype.bits);
+      using output_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
+      _get_distances<output_mdspan_type>(res, index, distances);
     }
   });
 }
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
index 6d0edb1f30..6753d9f016 100644
--- a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
@@ -53,9 +53,14 @@ cdef extern from "cuvs/neighbors/nn_descent.h" nogil:
 
     cuvsError_t cuvsNNDescentIndexDestroy(cuvsNNDescentIndex_t index)
 
-    cuvsError_t cuvsNNDescentIndexGetGraph(cuvsNNDescentIndex_t index,
+    cuvsError_t cuvsNNDescentIndexGetGraph(cuvsResources_t res,
+                                           cuvsNNDescentIndex_t index,
                                            DLManagedTensor * output)
 
+    cuvsError_t cuvsNNDescentIndexGetDistances(cuvsResources_t res,
+                                               cuvsNNDescentIndex_t index,
+                                               DLManagedTensor * output)
+
     cuvsError_t cuvsNNDescentBuild(cuvsResources_t res,
                                    cuvsNNDescentIndexParams* params,
                                    DLManagedTensor* dataset,
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
index 9dd14c27e7..a6277b911d 100644
--- a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
@@ -88,7 +88,8 @@ cdef class IndexParams:
                  intermediate_graph_degree=None,
                  max_iterations=None,
                  termination_threshold=None,
-                 n_clusters=None
+                 n_clusters=None,
+                 return_distances=None
                  ):
         if metric is not None:
             self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
@@ -102,11 +103,8 @@ cdef class IndexParams:
             self.params.termination_threshold = termination_threshold
         if n_clusters is not None:
             self.params.n_clusters = n_clusters
-
-        # setting this parameter to true will cause an exception in the c++
-        # api (`Using return_distances set to true requires distance view to
-        # be allocated.`) - so instead force to be false here
-        self.params.return_distances = False
+        if return_distances is not None:
+            self.params.return_distances = return_distances
 
     @property
     def metric(self):
@@ -163,13 +161,39 @@ cdef class Index:
 
     @property
     def graph(self):
+        return self._get_graph()
+
+    @property
+    def distances(self):
+        return self._get_distances()
+
+    @auto_sync_resources
+    def _get_graph(self, resources=None):
         if not self.trained:
             raise ValueError("Index needs to be built before getting graph")
 
+        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
         output = np.empty((self.num_rows, self.graph_degree), dtype='uint32')
         ai = wrap_array(output)
         cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
-        check_cuvs(cuvsNNDescentIndexGetGraph(self.index, output_dlpack))
+        check_cuvs(cuvsNNDescentIndexGetGraph(res, self.index, output_dlpack))
+        return output
+
+    @auto_sync_resources
+    def _get_distances(self, resources=None):
+        if not self.trained:
+            raise ValueError("Index needs to be built before getting"
+                             " distances")
+
+        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+        output = np.empty((self.num_rows, self.graph_degree), dtype='float32')
+        ai = wrap_array(output)
+        cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
+        check_cuvs(cuvsNNDescentIndexGetDistances(res,
+                                                  self.index,
+                                                  output_dlpack))
         return output
 
     def __repr__(self):
@@ -221,6 +245,16 @@ def build(IndexParams index_params, dataset, graph=None, resources=None):
 
     cdef cydlpack.DLManagedTensor* graph_dlpack = NULL
     if graph is not None:
+        if params.return_distances:
+            # When using a pre-existing graph - having return_distances set to
+            # true will cause an exception in the C++ api
+            # (`Using return_distances set to true requires distance view to
+            # be allocated.`). Raise a more informative error here instead of
+            # the C++ exception
+            raise ValueError("Can't use return_distances with an existing"
+                             " graph. Either set params.return_distances to"
+                             " False, or set graph to None")
+
         graph_ai = wrap_array(graph)
         graph_dlpack = cydlpack.dlpack_c(graph_ai)
 
diff --git a/python/cuvs/cuvs/tests/test_nn_descent.py b/python/cuvs/cuvs/tests/test_nn_descent.py
index e2cc5555f4..45289ef120 100644
--- a/python/cuvs/cuvs/tests/test_nn_descent.py
+++ b/python/cuvs/cuvs/tests/test_nn_descent.py
@@ -26,7 +26,15 @@
 @pytest.mark.parametrize("device_memory", [True, False])
 @pytest.mark.parametrize("dtype", [np.float32])
 @pytest.mark.parametrize("inplace", [True, False])
-def test_nn_descent(n_rows, n_cols, device_memory, dtype, inplace):
+@pytest.mark.parametrize("return_distances", [True, False])
+def test_nn_descent(
+    n_rows, n_cols, device_memory, dtype, inplace, return_distances
+):
+    # because of a limitation in the c++ api, we can't both return the
+    # distances and have an inplace graph
+    if inplace and return_distances:
+        pytest.skip("Can't return distances with an inplace graph")
+
     metric = "sqeuclidean"
     graph_degree = 64
 
@@ -34,7 +42,11 @@ def test_nn_descent(n_rows, n_cols, device_memory, dtype, inplace):
     input1_device = device_ndarray(input1)
     graph = np.zeros((n_rows, graph_degree), dtype="uint32")
 
-    params = nn_descent.IndexParams(metric=metric, graph_degree=graph_degree)
+    params = nn_descent.IndexParams(
+        metric=metric,
+        graph_degree=graph_degree,
+        return_distances=return_distances,
+    )
     index = nn_descent.build(
         params,
         input1_device if device_memory else input1,
@@ -50,4 +62,8 @@ def test_nn_descent(n_rows, n_cols, device_memory, dtype, inplace):
     )
     bfknn_graph = bfknn_graph.copy_to_host()
 
+    if return_distances:
+        distances = index.distances
+        assert distances.shape == graph.shape
+
     assert calc_recall(graph, bfknn_graph) > 0.9

From 23ba17bd7aee2049d4035ed0667e7a61b99a1bf3 Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Tue, 22 Apr 2025 16:16:41 -0400
Subject: [PATCH 365/623] Use NCCL wheels from PyPI for CUDA 12 builds (#827)

Reference PR in RAFT https://github.com/rapidsai/raft/pull/2629.

The size of `libcuvs-cu12` wheel goes down from about 1.2G to 854M.

Authors:
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/827
---
 ci/build_wheel.sh             |  8 ++++++++
 ci/test_wheel_cuvs.sh         |  6 ++++++
 dependencies.yaml             | 22 +++++++++++++++++++++-
 python/libcuvs/CMakeLists.txt |  7 +++++++
 4 files changed, 42 insertions(+), 1 deletion(-)

diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index ee9edae627..250be5d9cc 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -35,6 +35,14 @@ if [[ "${package_dir}" != "python/libcuvs" ]]; then
     )
 fi
 
+RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
+if [[ ${RAPIDS_CUDA_MAJOR} != "11" ]]; then
+    EXCLUDE_ARGS+=(
+      --exclude "libnccl.so.*"
+    )
+    export SKBUILD_CMAKE_ARGS="-DUSE_NCCL_RUNTIME_WHEEL=ON"
+fi
+
 rapids-logger "Building '${package_name}' wheel"
 
 sccache --zero-stats
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 382a42b2f9..99d36c24e8 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -3,6 +3,12 @@
 
 set -euo pipefail
 
+# Delete system libnccl.so to ensure the wheel is used
+RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
+if [[ ${RAPIDS_CUDA_MAJOR} != "11" ]]; then
+  rm -rf /usr/lib64/libnccl*
+fi
+
 mkdir -p ./dist
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 cpp ./local-libcuvs-dep
diff --git a/dependencies.yaml b/dependencies.yaml
index 80a5c4036f..baf4987cce 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -17,6 +17,7 @@ files:
       - depends_on_cupy
       - depends_on_librmm
       - depends_on_pylibraft
+      - depends_on_nccl
       - docs
       - rapids_build
       - run_py_cuvs
@@ -42,6 +43,7 @@ files:
       - depends_on_pylibraft
       - depends_on_libcuvs
       - depends_on_librmm
+      - depends_on_nccl
       - rapids_build
       - rapids_build_setuptools
   test_cpp:
@@ -94,6 +96,7 @@ files:
       - rust
       - depends_on_libcuvs
       - depends_on_libraft
+      - depends_on_nccl
   go:
     output: conda
     matrix:
@@ -107,6 +110,7 @@ files:
       - go
       - depends_on_libcuvs
       - depends_on_libraft
+      - depends_on_nccl
   py_build_libcuvs:
     output: pyproject
     pyproject_dir: python/libcuvs
@@ -123,6 +127,7 @@ files:
     includes:
       - depends_on_libraft
       - depends_on_librmm
+      - depends_on_nccl
       - rapids_build
   py_run_libcuvs:
     output: pyproject
@@ -133,6 +138,7 @@ files:
       - cuda_wheels
       - depends_on_libraft
       - depends_on_librmm
+      - depends_on_nccl
   py_build_cuvs:
     output: pyproject
     pyproject_dir: python/cuvs
@@ -226,7 +232,6 @@ dependencies:
         packages:
           - c-compiler
           - cxx-compiler
-          - nccl>=2.19
     specific:
       - output_types: conda
         matrices:
@@ -706,3 +711,18 @@ dependencies:
             packages:
               - pylibraft-cu11==25.6.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
+  depends_on_nccl:
+    common:
+      - output_types: conda
+        packages:
+          - &nccl_unsuffixed nccl>=2.19
+    specific:
+      - output_types: [pyproject, requirements]
+        matrices:
+          - matrix:
+              cuda: "12.*"
+              cuda_suffixed: "true"
+            packages:
+              - nvidia-nccl-cu12>=2.19
+          - matrix:
+            packages:
diff --git a/python/libcuvs/CMakeLists.txt b/python/libcuvs/CMakeLists.txt
index 2c677eff74..da43cb48a8 100644
--- a/python/libcuvs/CMakeLists.txt
+++ b/python/libcuvs/CMakeLists.txt
@@ -25,6 +25,8 @@ project(
   LANGUAGES CXX CUDA
 )
 
+option(USE_NCCL_RUNTIME_WHEEL "Use the NCCL wheel at runtime instead of the system library" OFF)
+
 # Check if cuVS is already available. If so, it is the user's responsibility to ensure that the
 # CMake package is also available at build time of the Python cuvs package.
 find_package(cuvs "${RAPIDS_VERSION}")
@@ -57,6 +59,11 @@ set(rpaths
     "$ORIGIN/../../nvidia/cusparse/lib"
     "$ORIGIN/../../nvidia/nvjitlink/lib"
 )
+
+if(USE_NCCL_RUNTIME_WHEEL)
+  list(APPEND rpaths "$ORIGIN/../../nvidia/nccl/lib")
+endif()
+
 set_property(
   TARGET cuvs
   PROPERTY INSTALL_RPATH ${rpaths}

From d2adc6fcd56d5d4111bb82dfbae07a8f18ba6f1e Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Wed, 23 Apr 2025 23:21:34 +0200
Subject: [PATCH 366/623] Moving MG functions into unified API +
 `raft::device_resources_snmg` as device resource type for MG functions (#454)

- Moving MG functions into unified API
- Using `raft::device_resources_snmg` as device resource type for MG functions
- Removing instantiation of `raft::comms::build_comms_nccl_only` solving https://github.com/rapidsai/raft/issues/2465

`raft::device_resources_snmg` is introduced in this PR : https://github.com/rapidsai/raft/pull/2549

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/454
---
 cpp/CMakeLists.txt                            |    2 +-
 cpp/bench/ann/src/cuvs/cuvs_benchmark.cu      |   16 +-
 .../ann/src/cuvs/cuvs_mg_cagra_wrapper.h      |   42 +-
 .../ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h   |   41 +-
 .../ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h     |   39 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |  452 ++++++
 cpp/include/cuvs/neighbors/common.hpp         |   96 +-
 cpp/include/cuvs/neighbors/ivf_flat.hpp       |  353 +++++
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  443 ++++++
 cpp/include/cuvs/neighbors/mg.hpp             | 1367 -----------------
 cpp/src/neighbors/iface/generate_iface.py     |   54 +-
 cpp/src/neighbors/iface/iface.hpp             |   22 +-
 .../iface/iface_cagra_float_uint32_t.cu       |   18 +-
 .../iface/iface_cagra_half_uint32_t.cu        |   18 +-
 .../iface/iface_cagra_int8_t_uint32_t.cu      |   18 +-
 .../iface/iface_cagra_uint8_t_uint32_t.cu     |   18 +-
 .../iface/iface_flat_float_int64_t.cu         |   18 +-
 .../iface/iface_flat_int8_t_int64_t.cu        |   18 +-
 .../iface/iface_flat_uint8_t_int64_t.cu       |   18 +-
 .../neighbors/iface/iface_pq_float_int64_t.cu |   18 +-
 .../neighbors/iface/iface_pq_half_int64_t.cu  |   18 +-
 .../iface/iface_pq_int8_t_int64_t.cu          |   18 +-
 .../iface/iface_pq_uint8_t_int64_t.cu         |   18 +-
 cpp/src/neighbors/mg/generate_mg.py           |  356 ++---
 .../neighbors/mg/mg_cagra_float_uint32_t.cu   |  122 +-
 .../neighbors/mg/mg_cagra_half_uint32_t.cu    |  122 +-
 .../neighbors/mg/mg_cagra_int8_t_uint32_t.cu  |  122 +-
 .../neighbors/mg/mg_cagra_uint8_t_uint32_t.cu |  122 +-
 cpp/src/neighbors/mg/mg_flat_float_int64_t.cu |  138 +-
 .../neighbors/mg/mg_flat_int8_t_int64_t.cu    |  138 +-
 .../neighbors/mg/mg_flat_uint8_t_int64_t.cu   |  138 +-
 cpp/src/neighbors/mg/mg_pq_float_int64_t.cu   |  138 +-
 cpp/src/neighbors/mg/mg_pq_half_int64_t.cu    |  138 +-
 cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu  |  138 +-
 cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu |  138 +-
 cpp/src/neighbors/mg/nccl_comm.cpp            |    8 -
 cpp/src/neighbors/mg/omp_checks.cpp           |    4 +-
 cpp/src/neighbors/mg/{mg.cuh => snmg.cuh}     |  284 ++--
 cpp/tests/neighbors/mg.cuh                    |  243 +--
 docs/source/cpp_api/neighbors_mg.rst          |    6 +-
 40 files changed, 2689 insertions(+), 2791 deletions(-)
 delete mode 100644 cpp/include/cuvs/neighbors/mg.hpp
 delete mode 100644 cpp/src/neighbors/mg/nccl_comm.cpp
 rename cpp/src/neighbors/mg/{mg.cuh => snmg.cuh} (72%)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 97376fa88f..60cab78b25 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -306,7 +306,6 @@ if(BUILD_SHARED_LIBS)
         src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
         src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
         src/neighbors/mg/omp_checks.cpp
-        src/neighbors/mg/nccl_comm.cpp
     )
   endif()
 
@@ -609,6 +608,7 @@ if(BUILD_SHARED_LIBS)
   if(BUILD_MG_ALGOS)
     target_compile_definitions(cuvs PUBLIC CUVS_BUILD_MG_ALGOS)
     target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_MG_ALGOS)
+    target_compile_definitions(cuvs-cagra-search PUBLIC CUVS_BUILD_MG_ALGOS)
   endif()
 
   if(BUILD_CAGRA_HNSWLIB)
diff --git a/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
index 8930972365..821b80cc72 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_benchmark.cu
@@ -30,38 +30,38 @@
 namespace cuvs::bench {
 
 #ifdef CUVS_ANN_BENCH_USE_CUVS_MG
-void add_distribution_mode(cuvs::neighbors::mg::distribution_mode* dist_mode,
+void add_distribution_mode(cuvs::neighbors::distribution_mode* dist_mode,
                            const nlohmann::json& conf)
 {
   if (conf.contains("distribution_mode")) {
     std::string distribution_mode = conf.at("distribution_mode");
     if (distribution_mode == "replicated") {
-      *dist_mode = cuvs::neighbors::mg::distribution_mode::REPLICATED;
+      *dist_mode = cuvs::neighbors::distribution_mode::REPLICATED;
     } else if (distribution_mode == "sharded") {
-      *dist_mode = cuvs::neighbors::mg::distribution_mode::SHARDED;
+      *dist_mode = cuvs::neighbors::distribution_mode::SHARDED;
     } else {
       throw std::runtime_error("invalid value for distribution_mode");
     }
   } else {
     // default
-    *dist_mode = cuvs::neighbors::mg::distribution_mode::SHARDED;
+    *dist_mode = cuvs::neighbors::distribution_mode::SHARDED;
   }
 };
 
-void add_merge_mode(cuvs::neighbors::mg::sharded_merge_mode* merge_mode, const nlohmann::json& conf)
+void add_merge_mode(cuvs::neighbors::sharded_merge_mode* merge_mode, const nlohmann::json& conf)
 {
   if (conf.contains("merge_mode")) {
     std::string sharded_merge_mode = conf.at("merge_mode");
     if (sharded_merge_mode == "tree_merge") {
-      *merge_mode = cuvs::neighbors::mg::sharded_merge_mode::TREE_MERGE;
+      *merge_mode = cuvs::neighbors::sharded_merge_mode::TREE_MERGE;
     } else if (sharded_merge_mode == "merge_on_root_rank") {
-      *merge_mode = cuvs::neighbors::mg::sharded_merge_mode::MERGE_ON_ROOT_RANK;
+      *merge_mode = cuvs::neighbors::sharded_merge_mode::MERGE_ON_ROOT_RANK;
     } else {
       throw std::runtime_error("invalid value for merge_mode");
     }
   } else {
     // default
-    *merge_mode = cuvs::neighbors::mg::sharded_merge_mode::TREE_MERGE;
+    *merge_mode = cuvs::neighbors::sharded_merge_mode::TREE_MERGE;
   }
 };
 #endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
index cb2bbd9b5a..a655fdf674 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
@@ -17,8 +17,8 @@
 
 #include "cuvs_ann_bench_utils.h"
 #include "cuvs_cagra_wrapper.h"
-#include <cuvs/neighbors/mg.hpp>
-#include <raft/core/resource/nccl_clique.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+#include <raft/core/device_resources_snmg.hpp>
 
 namespace cuvs::bench {
 using namespace cuvs::neighbors;
@@ -33,21 +33,20 @@ class cuvs_mg_cagra : public algo<T>, public algo_gpu {
   using algo<T>::dim_;
 
   struct build_param : public cuvs::bench::cuvs_cagra<T, IdxT>::build_param {
-    cuvs::neighbors::mg::distribution_mode mode;
+    cuvs::neighbors::distribution_mode mode;
   };
 
   struct search_param : public cuvs::bench::cuvs_cagra<T, IdxT>::search_param {
-    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+    cuvs::neighbors::sharded_merge_mode merge_mode;
   };
 
   cuvs_mg_cagra(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
-    : algo<T>(metric, dim), index_params_(param)
+    : algo<T>(metric, dim), index_params_(param), clique_()
   {
     index_params_.cagra_params.metric         = parse_metric_type(metric);
     index_params_.ivf_pq_build_params->metric = parse_metric_type(metric);
 
-    // init nccl clique outside as to not affect benchmark
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle_);
+    clique_.set_memory_pool(80);
   }
 
   void build(const T* dataset, size_t nrow) final;
@@ -69,7 +68,7 @@ class cuvs_mg_cagra : public algo<T>, public algo_gpu {
 
   [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
   {
-    auto stream = raft::resource::get_cuda_stream(handle_);
+    auto stream = raft::resource::get_cuda_stream(clique_);
     return stream;
   }
 
@@ -87,11 +86,11 @@ class cuvs_mg_cagra : public algo<T>, public algo_gpu {
   std::unique_ptr<algo<T>> copy() override;
 
  private:
-  raft::device_resources handle_;
+  raft::device_resources_snmg clique_;
   float refine_ratio_;
   build_param index_params_;
-  cuvs::neighbors::mg::search_params<cagra::search_params> search_params_;
-  std::shared_ptr<cuvs::neighbors::mg::index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>
+  cuvs::neighbors::mg_search_params<cagra::search_params> search_params_;
+  std::shared_ptr<cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>
     index_;
 };
 
@@ -100,14 +99,14 @@ void cuvs_mg_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
 {
   auto dataset_extents = raft::make_extents<IdxT>(nrow, dim_);
   index_params_.prepare_build_params(dataset_extents);
-  cuvs::neighbors::mg::index_params<cagra::index_params> build_params = index_params_.cagra_params;
-  build_params.mode                                                   = index_params_.mode;
+  cuvs::neighbors::mg_index_params<cagra::index_params> build_params = index_params_.cagra_params;
+  build_params.mode                                                  = index_params_.mode;
 
   auto dataset_view =
     raft::make_host_matrix_view<const T, int64_t, raft::row_major>(dataset, nrow, dim_);
-  auto idx = cuvs::neighbors::mg::build(handle_, build_params, dataset_view);
+  auto idx = cuvs::neighbors::cagra::build(clique_, build_params, dataset_view);
   index_ =
-    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>(
+    std::make_shared<cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>(
       std::move(idx));
 }
 
@@ -118,8 +117,7 @@ void cuvs_mg_cagra<T, IdxT>::set_search_param(const search_param_base& param,
                                               const void* filter_bitset)
 {
   if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
-  auto sp = dynamic_cast<const search_param&>(param);
-  // search_params_ = static_cast<mg::search_params<cagra::search_params>>(sp.p);
+  auto sp                                  = dynamic_cast<const search_param&>(param);
   cagra::search_params* search_params_ptr_ = static_cast<cagra::search_params*>(&search_params_);
   *search_params_ptr_                      = sp.p;
   search_params_.merge_mode                = sp.merge_mode;
@@ -134,15 +132,15 @@ void cuvs_mg_cagra<T, IdxT>::set_search_dataset(const T* dataset, size_t nrow)
 template <typename T, typename IdxT>
 void cuvs_mg_cagra<T, IdxT>::save(const std::string& file) const
 {
-  cuvs::neighbors::mg::serialize(handle_, *index_, file);
+  cuvs::neighbors::cagra::serialize(clique_, *index_, file);
 }
 
 template <typename T, typename IdxT>
 void cuvs_mg_cagra<T, IdxT>::load(const std::string& file)
 {
   index_ =
-    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>(
-      std::move(cuvs::neighbors::mg::deserialize_cagra<T, IdxT>(handle_, file)));
+    std::make_shared<cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, IdxT>, T, IdxT>>(
+      std::move(cuvs::neighbors::cagra::deserialize<T, IdxT>(clique_, file)));
 }
 
 template <typename T, typename IdxT>
@@ -165,8 +163,8 @@ void cuvs_mg_cagra<T, IdxT>::search_base(
   auto distances_view =
     raft::make_host_matrix_view<float, int64_t, raft::row_major>(distances, batch_size, k);
 
-  cuvs::neighbors::mg::search(
-    handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
+  cuvs::neighbors::cagra::search(
+    clique_, *index_, search_params_, queries_view, neighbors_view, distances_view);
 }
 
 template <typename T, typename IdxT>
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
index b9f8b4b322..ca05ecdf0a 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
@@ -18,8 +18,8 @@
 
 #include "cuvs_ann_bench_utils.h"
 #include "cuvs_ivf_flat_wrapper.h"
-#include <cuvs/neighbors/mg.hpp>
-#include <raft/core/resource/nccl_clique.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft/core/device_resources_snmg.hpp>
 
 namespace cuvs::bench {
 using namespace cuvs::neighbors;
@@ -30,18 +30,18 @@ class cuvs_mg_ivf_flat : public algo<T>, public algo_gpu {
   using search_param_base = typename algo<T>::search_param;
   using algo<T>::dim_;
 
-  using build_param = cuvs::neighbors::mg::index_params<ivf_flat::index_params>;
+  using build_param = cuvs::neighbors::mg_index_params<ivf_flat::index_params>;
 
   struct search_param : public cuvs::bench::cuvs_ivf_flat<T, IdxT>::search_param {
-    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+    cuvs::neighbors::sharded_merge_mode merge_mode;
   };
 
   cuvs_mg_ivf_flat(Metric metric, int dim, const build_param& param)
-    : algo<T>(metric, dim), index_params_(param)
+    : algo<T>(metric, dim), index_params_(param), clique_()
   {
     index_params_.metric = parse_metric_type(metric);
-    // init nccl clique outside as to not affect benchmark
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle_);
+
+    clique_.set_memory_pool(80);
   }
 
   void build(const T* dataset, size_t nrow) final;
@@ -62,7 +62,7 @@ class cuvs_mg_ivf_flat : public algo<T>, public algo_gpu {
 
   [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
   {
-    auto stream = raft::resource::get_cuda_stream(handle_);
+    auto stream = raft::resource::get_cuda_stream(clique_);
     return stream;
   }
 
@@ -73,10 +73,10 @@ class cuvs_mg_ivf_flat : public algo<T>, public algo_gpu {
   std::unique_ptr<algo<T>> copy() override;
 
  private:
-  raft::device_resources handle_;
+  raft::device_resources_snmg clique_;
   build_param index_params_;
-  cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params_;
-  std::shared_ptr<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>
+  cuvs::neighbors::mg_search_params<ivf_flat::search_params> search_params_;
+  std::shared_ptr<cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>
     index_;
 };
 
@@ -85,9 +85,10 @@ void cuvs_mg_ivf_flat<T, IdxT>::build(const T* dataset, size_t nrow)
 {
   auto dataset_view =
     raft::make_host_matrix_view<const T, int64_t, raft::row_major>(dataset, IdxT(nrow), IdxT(dim_));
-  auto idx = cuvs::neighbors::mg::build(handle_, index_params_, dataset_view);
-  index_   = std::make_shared<
-    cuvs::neighbors::mg::index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>(std::move(idx));
+  auto idx = cuvs::neighbors::ivf_flat::build(clique_, index_params_, dataset_view);
+  index_ =
+    std::make_shared<cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>(
+      std::move(idx));
 }
 
 template <typename T, typename IdxT>
@@ -107,15 +108,15 @@ void cuvs_mg_ivf_flat<T, IdxT>::set_search_param(const search_param_base& param,
 template <typename T, typename IdxT>
 void cuvs_mg_ivf_flat<T, IdxT>::save(const std::string& file) const
 {
-  cuvs::neighbors::mg::serialize(handle_, *index_, file);
+  cuvs::neighbors::ivf_flat::serialize(clique_, *index_, file);
 }
 
 template <typename T, typename IdxT>
 void cuvs_mg_ivf_flat<T, IdxT>::load(const std::string& file)
 {
-  index_ = std::make_shared<
-    cuvs::neighbors::mg::index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>(
-    std::move(cuvs::neighbors::mg::deserialize_flat<T, IdxT>(handle_, file)));
+  index_ =
+    std::make_shared<cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, IdxT>, T, IdxT>>(
+      std::move(cuvs::neighbors::ivf_flat::deserialize<T, IdxT>(clique_, file)));
 }
 
 template <typename T, typename IdxT>
@@ -135,8 +136,8 @@ void cuvs_mg_ivf_flat<T, IdxT>::search(
   auto distances_view = raft::make_host_matrix_view<float, int64_t, raft::row_major>(
     distances, IdxT(batch_size), IdxT(k));
 
-  cuvs::neighbors::mg::search(
-    handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
+  cuvs::neighbors::ivf_flat::search(
+    clique_, *index_, search_params_, queries_view, neighbors_view, distances_view);
 }
 
 }  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
index 26781c522b..c37bbdf18b 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
@@ -18,8 +18,8 @@
 
 #include "cuvs_ann_bench_utils.h"
 #include "cuvs_ivf_pq_wrapper.h"
-#include <cuvs/neighbors/mg.hpp>
-#include <raft/core/resource/nccl_clique.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_resources_snmg.hpp>
 
 namespace cuvs::bench {
 using namespace cuvs::neighbors;
@@ -30,18 +30,18 @@ class cuvs_mg_ivf_pq : public algo<T>, public algo_gpu {
   using search_param_base = typename algo<T>::search_param;
   using algo<T>::dim_;
 
-  using build_param = cuvs::neighbors::mg::index_params<ivf_pq::index_params>;
+  using build_param = cuvs::neighbors::mg_index_params<ivf_pq::index_params>;
 
   struct search_param : public cuvs::bench::cuvs_ivf_pq<T, IdxT>::search_param {
-    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+    cuvs::neighbors::sharded_merge_mode merge_mode;
   };
 
   cuvs_mg_ivf_pq(Metric metric, int dim, const build_param& param)
-    : algo<T>(metric, dim), index_params_(param)
+    : algo<T>(metric, dim), index_params_(param), clique_()
   {
     index_params_.metric = parse_metric_type(metric);
-    // init nccl clique outside as to not affect benchmark
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle_);
+
+    clique_.set_memory_pool(80);
   }
 
   void build(const T* dataset, size_t nrow) final;
@@ -62,7 +62,7 @@ class cuvs_mg_ivf_pq : public algo<T>, public algo_gpu {
 
   [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
   {
-    auto stream = raft::resource::get_cuda_stream(handle_);
+    auto stream = raft::resource::get_cuda_stream(clique_);
     return stream;
   }
 
@@ -73,10 +73,10 @@ class cuvs_mg_ivf_pq : public algo<T>, public algo_gpu {
   std::unique_ptr<algo<T>> copy() override;
 
  private:
-  raft::device_resources handle_;
+  raft::device_resources_snmg clique_;
   build_param index_params_;
-  cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params_;
-  std::shared_ptr<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>> index_;
+  cuvs::neighbors::mg_search_params<ivf_pq::search_params> search_params_;
+  std::shared_ptr<cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>> index_;
 };
 
 template <typename T, typename IdxT>
@@ -84,9 +84,9 @@ void cuvs_mg_ivf_pq<T, IdxT>::build(const T* dataset, size_t nrow)
 {
   auto dataset_view =
     raft::make_host_matrix_view<const T, int64_t, raft::row_major>(dataset, IdxT(nrow), IdxT(dim_));
-  auto idx = cuvs::neighbors::mg::build(handle_, index_params_, dataset_view);
+  auto idx = cuvs::neighbors::ivf_pq::build(clique_, index_params_, dataset_view);
   index_ =
-    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>>(
+    std::make_shared<cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>>(
       std::move(idx));
 }
 
@@ -95,8 +95,7 @@ void cuvs_mg_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param,
                                                const void* filter_bitset)
 {
   if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
-  auto sp = dynamic_cast<const search_param&>(param);
-  // search_params_ = static_cast<mg::search_params<ivf_pq::search_params>>(sp.pq_param);
+  auto sp                                   = dynamic_cast<const search_param&>(param);
   ivf_pq::search_params* search_params_ptr_ = static_cast<ivf_pq::search_params*>(&search_params_);
   *search_params_ptr_                       = sp.pq_param;
   search_params_.merge_mode                 = sp.merge_mode;
@@ -106,15 +105,15 @@ void cuvs_mg_ivf_pq<T, IdxT>::set_search_param(const search_param_base& param,
 template <typename T, typename IdxT>
 void cuvs_mg_ivf_pq<T, IdxT>::save(const std::string& file) const
 {
-  cuvs::neighbors::mg::serialize(handle_, *index_, file);
+  cuvs::neighbors::ivf_pq::serialize(clique_, *index_, file);
 }
 
 template <typename T, typename IdxT>
 void cuvs_mg_ivf_pq<T, IdxT>::load(const std::string& file)
 {
   index_ =
-    std::make_shared<cuvs::neighbors::mg::index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>>(
-      std::move(cuvs::neighbors::mg::deserialize_pq<T, IdxT>(handle_, file)));
+    std::make_shared<cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<IdxT>, T, IdxT>>(
+      std::move(cuvs::neighbors::ivf_pq::deserialize<T, IdxT>(clique_, file)));
 }
 
 template <typename T, typename IdxT>
@@ -134,8 +133,8 @@ void cuvs_mg_ivf_pq<T, IdxT>::search(
   auto distances_view = raft::make_host_matrix_view<float, int64_t, raft::row_major>(
     distances, IdxT(batch_size), IdxT(k));
 
-  cuvs::neighbors::mg::search(
-    handle_, *index_, search_params_, queries_view, neighbors_view, distances_view);
+  cuvs::neighbors::ivf_pq::search(
+    clique_, *index_, search_params_, queries_view, neighbors_view, distances_view);
 }
 
 }  // namespace cuvs::bench
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index f92835e282..07aa21e62c 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2091,4 +2091,456 @@ auto merge(raft::resources const& res,
  * @}
  */
 
+/// \defgroup mg_cpp_index_build ANN MG index build
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<cagra::index<float, uint32_t>, float, uint32_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const half, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<cagra::index<half, uint32_t>, half, uint32_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed CAGRA MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<cagra::index_params>& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>;
+
+/// \defgroup mg_cpp_index_extend ANN MG index extend
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::cagra::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<cagra::index<float, uint32_t>, float, uint32_t>& index,
+            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::cagra::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<cagra::index<half, uint32_t>, half, uint32_t>& index,
+            raft::host_matrix_view<const half, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::cagra::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::cagra::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
+
+/// \defgroup mg_cpp_index_search ANN MG index search
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<cagra::index<float, uint32_t>, float, uint32_t>& index,
+            const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const float, int64_t, row_major> queries,
+            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<cagra::index<half, uint32_t>, half, uint32_t>& index,
+            const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const half, int64_t, row_major> queries,
+            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
+  const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+  raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
+  raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
+  const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+  raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
+  raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \defgroup mg_cpp_serialize ANN MG index serialization
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::cagra::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<cagra::index<float, uint32_t>, float, uint32_t>& index,
+  const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::cagra::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::resources& clique,
+               const cuvs::neighbors::mg_index<cagra::index<half, uint32_t>, half, uint32_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::cagra::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
+  const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::cagra::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
+  const std::string& filename);
+
+/// \defgroup mg_cpp_deserialize ANN MG index deserialization
+
+/// \ingroup mg_cpp_deserialize
+/**
+ * @brief Deserializes a CAGRA multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::cagra::serialize(clique, index, filename);
+ * auto new_index = cuvs::neighbors::cagra::deserialize<float, uint32_t>(clique, filename);
+ *
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] filename path to the file to be deserialized
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize(const raft::resources& clique, const std::string& filename)
+  -> cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>;
+
+/// \defgroup mg_cpp_distribute ANN MG local index distribution
+
+/// \ingroup mg_cpp_distribute
+/**
+ * @brief Replicates a locally built and serialized CAGRA index to all GPUs to form a distributed
+ * multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::cagra::index_params index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * const std::string filename = "local_index.cuvs";
+ * cuvs::neighbors::cagra::serialize(clique, filename, index);
+ * auto new_index = cuvs::neighbors::cagra::distribute<float, uint32_t>(clique, filename);
+ *
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] filename path to the file to be deserialized : a local index
+ *
+ */
+template <typename T, typename IdxT>
+auto distribute(const raft::resources& clique, const std::string& filename)
+  -> cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>;
+
 }  // namespace cuvs::neighbors::cagra
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 038b6b1da5..4c31987989 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -740,9 +740,7 @@ enable_if_valid_list_t<ListT> deserialize_list(const raft::resources& handle,
                                                const typename ListT::spec_type& store_spec,
                                                const typename ListT::spec_type& device_spec);
 }  // namespace ivf
-}  // namespace cuvs::neighbors
 
-namespace cuvs::neighbors {
 using namespace raft;
 
 template <typename AnnIndexType, typename T, typename IdxT>
@@ -756,21 +754,21 @@ struct iface {
 };
 
 template <typename AnnIndexType, typename T, typename IdxT, typename Accessor>
-void build(const raft::device_resources& handle,
+void build(const raft::resources& handle,
            cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
            const cuvs::neighbors::index_params* index_params,
            raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> index_dataset);
 
 template <typename AnnIndexType, typename T, typename IdxT, typename Accessor1, typename Accessor2>
 void extend(
-  const raft::device_resources& handle,
+  const raft::resources& handle,
   cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
   raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor1> new_vectors,
   std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, Accessor2>>
     new_indices);
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void search(const raft::device_resources& handle,
+void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::device_matrix_view<const T, int64_t, row_major> h_queries,
@@ -778,18 +776,98 @@ void search(const raft::device_resources& handle,
             raft::device_matrix_view<float, int64_t, row_major> d_distances);
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void serialize(const raft::device_resources& handle,
+void serialize(const raft::resources& handle,
                const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                std::ostream& os);
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void deserialize(const raft::device_resources& handle,
+void deserialize(const raft::resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  std::istream& is);
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void deserialize(const raft::device_resources& handle,
+void deserialize(const raft::resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  const std::string& filename);
 
-};  // namespace cuvs::neighbors
+/// \defgroup mg_cpp_index_params ANN MG index build parameters
+
+/** Distribution mode */
+/// \ingroup mg_cpp_index_params
+enum distribution_mode {
+  /** Index is replicated on each device, favors throughput */
+  REPLICATED,
+  /** Index is split on several devices, favors scaling */
+  SHARDED
+};
+
+/// \defgroup mg_cpp_search_params ANN MG search parameters
+
+/** Search mode when using a replicated index */
+/// \ingroup mg_cpp_search_params
+enum replicated_search_mode {
+  /** Search queries are splited to maintain equal load on GPUs */
+  LOAD_BALANCER,
+  /** Each search query is processed by a single GPU in a round-robin fashion */
+  ROUND_ROBIN
+};
+
+/** Merge mode when using a sharded index */
+/// \ingroup mg_cpp_search_params
+enum sharded_merge_mode {
+  /** Search batches are merged on the root rank */
+  MERGE_ON_ROOT_RANK,
+  /** Search batches are merged in a tree reduction fashion */
+  TREE_MERGE
+};
+
+/** Build parameters */
+/// \ingroup mg_cpp_index_params
+template <typename Upstream>
+struct mg_index_params : public Upstream {
+  mg_index_params() : mode(SHARDED) {}
+
+  mg_index_params(const Upstream& sp) : Upstream(sp), mode(SHARDED) {}
+
+  /** Distribution mode */
+  cuvs::neighbors::distribution_mode mode = SHARDED;
+};
+
+/** Search parameters */
+/// \ingroup mg_cpp_search_params
+template <typename Upstream>
+struct mg_search_params : public Upstream {
+  mg_search_params() : search_mode(LOAD_BALANCER), merge_mode(TREE_MERGE) {}
+
+  mg_search_params(const Upstream& sp)
+    : Upstream(sp), search_mode(LOAD_BALANCER), merge_mode(TREE_MERGE)
+  {
+  }
+
+  /** Replicated search mode */
+  cuvs::neighbors::replicated_search_mode search_mode = LOAD_BALANCER;
+  /** Sharded merge mode */
+  cuvs::neighbors::sharded_merge_mode merge_mode = TREE_MERGE;
+  /** Number of rows per batch */
+  int64_t n_rows_per_batch = 1 << 20;
+};
+
+template <typename AnnIndexType, typename T, typename IdxT>
+struct mg_index {
+  mg_index(const raft::resources& clique, distribution_mode mode);
+  mg_index(const raft::resources& clique, const std::string& filename);
+
+  mg_index(const mg_index&)                    = delete;
+  mg_index(mg_index&&)                         = default;
+  auto operator=(const mg_index&) -> mg_index& = delete;
+  auto operator=(mg_index&&) -> mg_index&      = default;
+
+  distribution_mode mode_;
+  int num_ranks_;
+  std::vector<iface<AnnIndexType, T, IdxT>> ann_interfaces_;
+
+  // for load balancing mechanism
+  std::shared_ptr<std::atomic<int64_t>> round_robin_counter_;
+};
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index e017946d9c..bba6436dbb 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -1598,6 +1598,359 @@ void deserialize(raft::resources const& handle,
  * @}
  */
 
+/// \defgroup mg_cpp_index_build ANN MG index build
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-Flat MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_flat::index_params>& index_params,
+           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_flat::index<float, int64_t>, float, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-Flat MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_flat::index_params>& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-Flat MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_flat::index_params>& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>;
+
+/// \defgroup mg_cpp_index_extend ANN MG index extend
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_flat::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
+            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_flat::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_flat::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \defgroup mg_cpp_index_search ANN MG index search
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_flat::search_params> search_params;
+ * cuvs::neighbors::ivf_flat::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
+            const cuvs::neighbors::mg_search_params<ivf_flat::search_params>& search_params,
+            raft::host_matrix_view<const float, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_flat::search_params> search_params;
+ * cuvs::neighbors::ivf_flat::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
+  const cuvs::neighbors::mg_search_params<ivf_flat::search_params>& search_params,
+  raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
+  raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_flat::search_params> search_params;
+ * cuvs::neighbors::ivf_flat::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
+  const cuvs::neighbors::mg_search_params<ivf_flat::search_params>& search_params,
+  raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
+  raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \defgroup mg_cpp_serialize ANN MG index serialization
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_flat::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
+  const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_flat::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
+  const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_flat::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
+  const std::string& filename);
+
+/// \ingroup mg_cpp_deserialize
+/**
+ * @brief Deserializes an IVF-Flat multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_flat::serialize(clique, index, filename);
+ * auto new_index = cuvs::neighbors::ivf_flat::deserialize<float, int64_t>(clique, filename);
+ *
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] filename path to the file to be deserialized
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize(const raft::resources& clique, const std::string& filename)
+  -> cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>;
+
+/// \defgroup mg_cpp_distribute ANN MG local index distribution
+
+/// \ingroup mg_cpp_distribute
+/**
+ * @brief Replicates a locally built and serialized IVF-Flat index to all GPUs to form a distributed
+ * multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::ivf_flat::index_params index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * const std::string filename = "local_index.cuvs";
+ * cuvs::neighbors::ivf_flat::serialize(clique, filename, index);
+ * auto new_index = cuvs::neighbors::ivf_flat::distribute<float, int64_t>(clique, filename);
+ *
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] filename path to the file to be deserialized : a local index
+ *
+ */
+template <typename T, typename IdxT>
+auto distribute(const raft::resources& clique, const std::string& filename)
+  -> cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>;
+
 namespace helpers {
 
 /**
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index d85753b7f2..73f9a831bc 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -1728,6 +1728,449 @@ void deserialize(raft::resources const& handle,
  * @}
  */
 
+/// \defgroup mg_cpp_index_build ANN MG index build
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, float, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const half, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, half, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, int8_t, int64_t>;
+
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-PQ MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_pq::index_params>& index_params,
+           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, uint8_t, int64_t>;
+
+/// \defgroup mg_cpp_index_extend ANN MG index extend
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_pq::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, float, int64_t>& index,
+            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_pq::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, half, int64_t>& index,
+            raft::host_matrix_view<const half, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_pq::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_pq::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
+/// \defgroup mg_cpp_index_search ANN MG index search
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::ivf_pq::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, float, int64_t>& index,
+            const cuvs::neighbors::mg_search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const float, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::ivf_pq::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, half, int64_t>& index,
+            const cuvs::neighbors::mg_search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const half, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::ivf_pq::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
+            const cuvs::neighbors::mg_search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_pq::search_params> search_params;
+ * cuvs::neighbors::ivf_pq::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
+            const cuvs::neighbors::mg_search_params<ivf_pq::search_params>& search_params,
+            raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \defgroup mg_cpp_serialize ANN MG index serialization
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_pq::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::resources& clique,
+               const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, float, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_pq::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::resources& clique,
+               const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, half, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_pq::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::resources& clique,
+               const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_pq::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(const raft::resources& clique,
+               const cuvs::neighbors::mg_index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
+               const std::string& filename);
+
+/// \ingroup mg_cpp_deserialize
+/**
+ * @brief Deserializes an IVF-PQ multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_pq::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_pq::serialize(clique, index, filename);
+ * auto new_index = cuvs::neighbors::ivf_pq::deserialize<float, int64_t>(clique, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] filename path to the file to be deserialized
+ *
+ */
+template <typename T, typename IdxT>
+auto deserialize(const raft::resources& clique, const std::string& filename)
+  -> cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>;
+
+/// \defgroup mg_cpp_distribute ANN MG local index distribution
+
+/// \ingroup mg_cpp_distribute
+/**
+ * @brief Replicates a locally built and serialized IVF-PQ index to all GPUs to form a distributed
+ * multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::ivf_pq::index_params index_params;
+ * auto index = cuvs::neighbors::ivf_pq::build(clique, index_params, index_dataset);
+ * const std::string filename = "local_index.cuvs";
+ * cuvs::neighbors::ivf_pq::serialize(clique, filename, index);
+ * auto new_index = cuvs::neighbors::ivf_pq::distribute<float, int64_t>(clique, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] filename path to the file to be deserialized : a local index
+ *
+ */
+template <typename T, typename IdxT>
+auto distribute(const raft::resources& clique, const std::string& filename)
+  -> cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>;
+
 namespace helpers {
 /**
  * @defgroup ivf_pq_cpp_helpers IVF-PQ helper methods
diff --git a/cpp/include/cuvs/neighbors/mg.hpp b/cpp/include/cuvs/neighbors/mg.hpp
deleted file mode 100644
index 4657fa8fb0..0000000000
--- a/cpp/include/cuvs/neighbors/mg.hpp
+++ /dev/null
@@ -1,1367 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#ifdef CUVS_BUILD_MG_ALGOS
-
-#include <atomic>
-#include <memory>
-
-#include <raft/core/device_resources.hpp>
-#include <rmm/mr/device/per_device_resource.hpp>
-
-#include <cuvs/neighbors/cagra.hpp>
-#include <cuvs/neighbors/common.hpp>
-#include <cuvs/neighbors/ivf_flat.hpp>
-#include <cuvs/neighbors/ivf_pq.hpp>
-
-#define DEFAULT_SEARCH_BATCH_SIZE 1 << 20
-
-/// \defgroup mg_cpp_index_params ANN MG index build parameters
-
-namespace cuvs::neighbors::mg {
-/** Distribution mode */
-/// \ingroup mg_cpp_index_params
-enum distribution_mode {
-  /** Index is replicated on each device, favors throughput */
-  REPLICATED,
-  /** Index is split on several devices, favors scaling */
-  SHARDED
-};
-
-/// \defgroup mg_cpp_search_params ANN MG search parameters
-
-/** Search mode when using a replicated index */
-/// \ingroup mg_cpp_search_params
-enum replicated_search_mode {
-  /** Search queries are splited to maintain equal load on GPUs */
-  LOAD_BALANCER,
-  /** Each search query is processed by a single GPU in a round-robin fashion */
-  ROUND_ROBIN
-};
-
-/** Merge mode when using a sharded index */
-/// \ingroup mg_cpp_search_params
-enum sharded_merge_mode {
-  /** Search batches are merged on the root rank */
-  MERGE_ON_ROOT_RANK,
-  /** Search batches are merged in a tree reduction fashion */
-  TREE_MERGE
-};
-
-/** Build parameters */
-/// \ingroup mg_cpp_index_params
-template <typename Upstream>
-struct index_params : public Upstream {
-  index_params() : mode(SHARDED) {}
-
-  index_params(const Upstream& sp) : Upstream(sp), mode(SHARDED) {}
-
-  /** Distribution mode */
-  cuvs::neighbors::mg::distribution_mode mode = SHARDED;
-};
-
-/** Search parameters */
-/// \ingroup mg_cpp_search_params
-template <typename Upstream>
-struct search_params : public Upstream {
-  search_params() : search_mode(LOAD_BALANCER), merge_mode(TREE_MERGE) {}
-
-  search_params(const Upstream& sp)
-    : Upstream(sp), search_mode(LOAD_BALANCER), merge_mode(TREE_MERGE)
-  {
-  }
-
-  /** Replicated search mode */
-  cuvs::neighbors::mg::replicated_search_mode search_mode = LOAD_BALANCER;
-  /** Sharded merge mode */
-  cuvs::neighbors::mg::sharded_merge_mode merge_mode = TREE_MERGE;
-};
-
-}  // namespace cuvs::neighbors::mg
-
-namespace cuvs::neighbors::mg {
-
-using namespace raft;
-
-template <typename AnnIndexType, typename T, typename IdxT>
-struct index {
-  index(distribution_mode mode, int num_ranks_);
-  index(const raft::device_resources& handle, const std::string& filename);
-
-  index(const index&)                    = delete;
-  index(index&&)                         = default;
-  auto operator=(const index&) -> index& = delete;
-  auto operator=(index&&) -> index&      = default;
-
-  distribution_mode mode_;
-  int num_ranks_;
-  std::vector<iface<AnnIndexType, T, IdxT>> ann_interfaces_;
-
-  // for load balancing mechanism
-  std::shared_ptr<std::atomic<int64_t>> round_robin_counter_;
-};
-
-/// \defgroup mg_cpp_index_build ANN MG index build
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed IVF-Flat MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<ivf_flat::index_params>& index_params,
-           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
-  -> index<ivf_flat::index<float, int64_t>, float, int64_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed IVF-Flat MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<ivf_flat::index_params>& index_params,
-           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
-  -> index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed IVF-Flat MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<ivf_flat::index_params>& index_params,
-           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
-  -> index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed IVF-PQ MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<ivf_pq::index_params>& index_params,
-           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
-  -> index<ivf_pq::index<int64_t>, float, int64_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed IVF-PQ MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<ivf_pq::index_params>& index_params,
-           raft::host_matrix_view<const half, int64_t, row_major> index_dataset)
-  -> index<ivf_pq::index<int64_t>, half, int64_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed IVF-PQ MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<ivf_pq::index_params>& index_params,
-           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
-  -> index<ivf_pq::index<int64_t>, int8_t, int64_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed IVF-PQ MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<ivf_pq::index_params>& index_params,
-           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
-  -> index<ivf_pq::index<int64_t>, uint8_t, int64_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed CAGRA MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<cagra::index_params>& index_params,
-           raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
-  -> index<cagra::index<float, uint32_t>, float, uint32_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed CAGRA MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<cagra::index_params>& index_params,
-           raft::host_matrix_view<const half, int64_t, row_major> index_dataset)
-  -> index<cagra::index<half, uint32_t>, half, uint32_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed CAGRA MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<cagra::index_params>& index_params,
-           raft::host_matrix_view<const int8_t, int64_t, row_major> index_dataset)
-  -> index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>;
-
-/// \ingroup mg_cpp_index_build
-/**
- * @brief Builds a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index_params configure the index building
- * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
- *
- * @return the constructed CAGRA MG index
- */
-auto build(const raft::device_resources& handle,
-           const mg::index_params<cagra::index_params>& index_params,
-           raft::host_matrix_view<const uint8_t, int64_t, row_major> index_dataset)
-  -> index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>;
-
-/// \defgroup mg_cpp_index_extend ANN MG index extend
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
-            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
-            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
-            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<ivf_pq::index<int64_t>, float, int64_t>& index,
-            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<ivf_pq::index<int64_t>, half, int64_t>& index,
-            raft::host_matrix_view<const half, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
-            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
-            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<cagra::index<float, uint32_t>, float, uint32_t>& index,
-            raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<cagra::index<half, uint32_t>, half, uint32_t>& index,
-            raft::host_matrix_view<const half, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
-            raft::host_matrix_view<const int8_t, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
-
-/// \ingroup mg_cpp_index_extend
-/**
- * @brief Extends a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::extend(handle, index, new_vectors, std::nullopt);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
- * @param[in] new_indices optional vector on host [n_rows],
- * `std::nullopt` means default continuous range `[0...n_rows)`
- *
- */
-void extend(const raft::device_resources& handle,
-            index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
-            raft::host_matrix_view<const uint8_t, int64_t, row_major> new_vectors,
-            std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices);
-
-/// \defgroup mg_cpp_index_search ANN MG index search
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
-            const mg::search_params<ivf_flat::search_params>& search_params,
-            raft::host_matrix_view<const float, int64_t, row_major> queries,
-            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
-            const mg::search_params<ivf_flat::search_params>& search_params,
-            raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
-            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<ivf_flat::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
-            const mg::search_params<ivf_flat::search_params>& search_params,
-            raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
-            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<ivf_pq::index<int64_t>, float, int64_t>& index,
-            const mg::search_params<ivf_pq::search_params>& search_params,
-            raft::host_matrix_view<const float, int64_t, row_major> queries,
-            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<ivf_pq::index<int64_t>, half, int64_t>& index,
-            const mg::search_params<ivf_pq::search_params>& search_params,
-            raft::host_matrix_view<const half, int64_t, row_major> queries,
-            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
-            const mg::search_params<ivf_pq::search_params>& search_params,
-            raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
-            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<ivf_pq::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
-            const mg::search_params<ivf_pq::search_params>& search_params,
-            raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
-            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<cagra::index<float, uint32_t>, float, uint32_t>& index,
-            const mg::search_params<cagra::search_params>& search_params,
-            raft::host_matrix_view<const float, int64_t, row_major> queries,
-            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<cagra::index<half, uint32_t>, half, uint32_t>& index,
-            const mg::search_params<cagra::search_params>& search_params,
-            raft::host_matrix_view<const half, int64_t, row_major> queries,
-            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
-            const mg::search_params<cagra::search_params>& search_params,
-            raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
-            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \ingroup mg_cpp_index_search
-/**
- * @brief Searches a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * cuvs::neighbors::mg::search_params<cagra::search_params> search_params;
- * cuvs::neighbors::mg::search(handle, index, search_params, queries, neighbors,
- * distances);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] search_params configure the index search
- * @param[in] queries a row-major matrix on host [n_rows, dim]
- * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
- * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
- * @param[in] n_rows_per_batch (optional) search batch size
- *
- */
-void search(const raft::device_resources& handle,
-            const index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
-            const mg::search_params<cagra::search_params>& search_params,
-            raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
-            raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch = DEFAULT_SEARCH_BATCH_SIZE);
-
-/// \defgroup mg_cpp_serialize ANN MG index serialization
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<ivf_pq::index<int64_t>, float, int64_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<ivf_pq::index<int64_t>, half, int64_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<ivf_pq::index<int64_t>, int8_t, int64_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<ivf_pq::index<int64_t>, uint8_t, int64_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<cagra::index<float, uint32_t>, float, uint32_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<cagra::index<half, uint32_t>, half, uint32_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
-               const std::string& filename);
-
-/// \ingroup mg_cpp_serialize
-/**
- * @brief Serializes a multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] index the pre-built index
- * @param[in] filename path to the file to be serialized
- *
- */
-void serialize(const raft::device_resources& handle,
-               const index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
-               const std::string& filename);
-
-/// \defgroup mg_cpp_deserialize ANN MG index deserialization
-
-/// \ingroup mg_cpp_deserialize
-/**
- * @brief Deserializes an IVF-Flat multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_flat::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * auto new_index = cuvs::neighbors::mg::deserialize_flat<float, int64_t>(handle, filename);
- *
- * @endcode
- *
- * @param[in] handle
- * @param[in] filename path to the file to be deserialized
- *
- */
-template <typename T, typename IdxT>
-auto deserialize_flat(const raft::device_resources& handle, const std::string& filename)
-  -> index<ivf_flat::index<T, IdxT>, T, IdxT>;
-
-/// \ingroup mg_cpp_deserialize
-/**
- * @brief Deserializes an IVF-PQ multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<ivf_pq::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * auto new_index = cuvs::neighbors::mg::deserialize_pq<float, int64_t>(handle, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] filename path to the file to be deserialized
- *
- */
-template <typename T, typename IdxT>
-auto deserialize_pq(const raft::device_resources& handle, const std::string& filename)
-  -> index<ivf_pq::index<IdxT>, T, IdxT>;
-
-/// \ingroup mg_cpp_deserialize
-/**
- * @brief Deserializes a CAGRA multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::mg::index_params<cagra::index_params> index_params;
- * auto index = cuvs::neighbors::mg::build(handle, index_params, index_dataset);
- * const std::string filename = "mg_index.cuvs";
- * cuvs::neighbors::mg::serialize(handle, index, filename);
- * auto new_index = cuvs::neighbors::mg::deserialize_cagra<float, uint32_t>(handle, filename);
- *
- * @endcode
- *
- * @param[in] handle
- * @param[in] filename path to the file to be deserialized
- *
- */
-template <typename T, typename IdxT>
-auto deserialize_cagra(const raft::device_resources& handle, const std::string& filename)
-  -> index<cagra::index<T, IdxT>, T, IdxT>;
-
-/// \defgroup mg_cpp_distribute ANN MG local index distribution
-
-/// \ingroup mg_cpp_distribute
-/**
- * @brief Replicates a locally built and serialized IVF-Flat index to all GPUs to form a distributed
- * multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::ivf_flat::index_params index_params;
- * auto index = cuvs::neighbors::ivf_flat::build(handle, index_params, index_dataset);
- * const std::string filename = "local_index.cuvs";
- * cuvs::neighbors::ivf_flat::serialize(handle, filename, index);
- * auto new_index = cuvs::neighbors::mg::distribute_flat<float, int64_t>(handle, filename);
- *
- * @endcode
- *
- * @param[in] handle
- * @param[in] filename path to the file to be deserialized : a local index
- *
- */
-template <typename T, typename IdxT>
-auto distribute_flat(const raft::device_resources& handle, const std::string& filename)
-  -> index<ivf_flat::index<T, IdxT>, T, IdxT>;
-
-/// \ingroup mg_cpp_distribute
-/**
- * @brief Replicates a locally built and serialized IVF-PQ index to all GPUs to form a distributed
- * multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::ivf_pq::index_params index_params;
- * auto index = cuvs::neighbors::ivf_pq::build(handle, index_params, index_dataset);
- * const std::string filename = "local_index.cuvs";
- * cuvs::neighbors::ivf_pq::serialize(handle, filename, index);
- * auto new_index = cuvs::neighbors::mg::distribute_pq<float, int64_t>(handle, filename);
- * @endcode
- *
- * @param[in] handle
- * @param[in] filename path to the file to be deserialized : a local index
- *
- */
-template <typename T, typename IdxT>
-auto distribute_pq(const raft::device_resources& handle, const std::string& filename)
-  -> index<ivf_pq::index<IdxT>, T, IdxT>;
-
-/// \ingroup mg_cpp_distribute
-/**
- * @brief Replicates a locally built and serialized CAGRA index to all GPUs to form a distributed
- * multi-GPU index
- *
- * Usage example:
- * @code{.cpp}
- * raft::handle_t handle;
- * cuvs::neighbors::cagra::index_params index_params;
- * auto index = cuvs::neighbors::cagra::build(handle, index_params, index_dataset);
- * const std::string filename = "local_index.cuvs";
- * cuvs::neighbors::cagra::serialize(handle, filename, index);
- * auto new_index = cuvs::neighbors::mg::distribute_cagra<float, uint32_t>(handle, filename);
- *
- * @endcode
- *
- * @param[in] handle
- * @param[in] filename path to the file to be deserialized : a local index
- *
- */
-template <typename T, typename IdxT>
-auto distribute_cagra(const raft::device_resources& handle, const std::string& filename)
-  -> index<cagra::index<T, IdxT>, T, IdxT>;
-
-}  // namespace cuvs::neighbors::mg
-
-#else
-
-static_assert(false,
-              "FORBIDEN_MG_ALGORITHM_IMPORT\n\n"
-              "Please recompile the cuVS library with MG algorithms BUILD_MG_ALGOS=ON.\n");
-
-#endif
diff --git a/cpp/src/neighbors/iface/generate_iface.py b/cpp/src/neighbors/iface/generate_iface.py
index 794219bbf3..698bee88d8 100644
--- a/cpp/src/neighbors/iface/generate_iface.py
+++ b/cpp/src/neighbors/iface/generate_iface.py
@@ -58,49 +58,49 @@
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::device>;       \\
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::host>;         \\
                                                                                                                                 \\
-  template void build(const raft::device_resources& handle,                                                                     \\
+  template void build(const raft::resources& handle,                                                                            \\
              cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                              \\
              const cuvs::neighbors::index_params* index_params,                                                                 \\
              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                                     \\
                                                                                                                                 \\
-  template void build(const raft::device_resources& handle,                                                                     \\
+  template void build(const raft::resources& handle,                                                                            \\
              cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                              \\
              const cuvs::neighbors::index_params* index_params,                                                                 \\
              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                                     \\
                                                                                                                                 \\
-  template void extend(const raft::device_resources& handle,                                                                    \\
+  template void extend(const raft::resources& handle,                                                                           \\
               cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                             \\
               raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                                       \\
               std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> new_indices);       \\
                                                                                                                                 \\
-  template void extend(const raft::device_resources& handle,                                                                    \\
+  template void extend(const raft::resources& handle,                                                                           \\
               cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                             \\
               raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                                       \\
               std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> new_indices);       \\
                                                                                                                                 \\
-  template void search(const raft::device_resources& handle,                                                                    \\
+  template void search(const raft::resources& handle,                                                                           \\
               const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                       \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
               raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,                                                     \\
               raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
                                                                                                                                 \\
-  template void search(const raft::device_resources& handle,                                                                    \\
+  template void search(const raft::resources& handle,                                                                           \\
               const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                       \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
               raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,                                                   \\
               raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
                                                                                                                                 \\
-  template void serialize(const raft::device_resources& handle,                                                                 \\
+  template void serialize(const raft::resources& handle,                                                                        \\
                  const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                    \\
                  std::ostream& os);                                                                                             \\
                                                                                                                                 \\
-  template void deserialize(const raft::device_resources& handle,                                                               \\
+  template void deserialize(const raft::resources& handle,                                                                      \\
                    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                        \\
                    std::istream& is);                                                                                           \\
                                                                                                                                 \\
-  template void deserialize(const raft::device_resources& handle,                                                               \\
+  template void deserialize(const raft::resources& handle,                                                                      \\
                    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                                        \\
                    const std::string& filename);
 """
@@ -112,49 +112,49 @@
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::device>;       \\
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::host>;         \\
                                                                                                                                 \\
-  template void build(const raft::device_resources& handle,                                                                     \\
+  template void build(const raft::resources& handle,                                                                            \\
              cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                   \\
              const cuvs::neighbors::index_params* index_params,                                                                 \\
              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                                     \\
                                                                                                                                 \\
-  template void build(const raft::device_resources& handle,                                                                     \\
+  template void build(const raft::resources& handle,                                                                            \\
              cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                   \\
              const cuvs::neighbors::index_params* index_params,                                                                 \\
              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                                     \\
                                                                                                                                 \\
-  template void extend(const raft::device_resources& handle,                                                                    \\
+  template void extend(const raft::resources& handle,                                                                           \\
               cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                  \\
               raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                                       \\
               std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> new_indices);       \\
                                                                                                                                 \\
-  template void extend(const raft::device_resources& handle,                                                                    \\
+  template void extend(const raft::resources& handle,                                                                           \\
               cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                                  \\
               raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                                       \\
               std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> new_indices);       \\
                                                                                                                                 \\
-  template void search(const raft::device_resources& handle,                                                                    \\
+  template void search(const raft::resources& handle,                                                                           \\
               const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                            \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
               raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,                                                     \\
               raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
                                                                                                                                 \\
-  template void search(const raft::device_resources& handle,                                                                    \\
+  template void search(const raft::resources& handle,                                                                           \\
               const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                            \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
               raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,                                                   \\
               raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
                                                                                                                                 \\
-  template void serialize(const raft::device_resources& handle,                                                                 \\
+  template void serialize(const raft::resources& handle,                                                                        \\
                  const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                         \\
                  std::ostream& os);                                                                                             \\
                                                                                                                                 \\
-  template void deserialize(const raft::device_resources& handle,                                                               \\
+  template void deserialize(const raft::resources& handle,                                                                      \\
                    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                             \\
                    std::istream& is);                                                                                           \\
                                                                                                                                 \\
-  template void deserialize(const raft::device_resources& handle,                                                               \\
+  template void deserialize(const raft::resources& handle,                                                                      \\
                    cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                                             \\
                    const std::string& filename);
 """
@@ -166,49 +166,49 @@
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::device>;       \\
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>, raft::memory_type::host>;         \\
                                                                                                                                 \\
-  template void build(const raft::device_resources& handle,                                                                     \\
+  template void build(const raft::resources& handle,                                                                            \\
              cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                 \\
              const cuvs::neighbors::index_params* index_params,                                                                 \\
              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                                     \\
                                                                                                                                 \\
-  template void build(const raft::device_resources& handle,                                                                     \\
+  template void build(const raft::resources& handle,                                                                            \\
              cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                 \\
              const cuvs::neighbors::index_params* index_params,                                                                 \\
              raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                                     \\
                                                                                                                                 \\
-  template void extend(const raft::device_resources& handle,                                                                    \\
+  template void extend(const raft::resources& handle,                                                                           \\
               cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                \\
               raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                                       \\
               std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> new_indices);       \\
                                                                                                                                 \\
-  template void extend(const raft::device_resources& handle,                                                                    \\
+  template void extend(const raft::resources& handle,                                                                           \\
               cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                                \\
               raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                                       \\
               std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> new_indices);       \\
                                                                                                                                 \\
-  template void search(const raft::device_resources& handle,                                                                    \\
+  template void search(const raft::resources& handle,                                                                           \\
               const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
               raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,                                                     \\
               raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
                                                                                                                                 \\
-    template void search(const raft::device_resources& handle,                                                                  \\
+    template void search(const raft::resources& handle,                                                                         \\
               const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
               raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,                                                   \\
               raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
                                                                                                                                 \\
-  template void serialize(const raft::device_resources& handle,                                                                 \\
+  template void serialize(const raft::resources& handle,                                                                        \\
                  const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                       \\
                  std::ostream& os);                                                                                             \\
                                                                                                                                 \\
-  template void deserialize(const raft::device_resources& handle,                                                               \\
+  template void deserialize(const raft::resources& handle,                                                                      \\
                    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                           \\
                    std::istream& is);                                                                                           \\
                                                                                                                                 \\
-  template void deserialize(const raft::device_resources& handle,                                                               \\
+  template void deserialize(const raft::resources& handle,                                                                      \\
                    cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                           \\
                    const std::string& filename);
 """
diff --git a/cpp/src/neighbors/iface/iface.hpp b/cpp/src/neighbors/iface/iface.hpp
index 59d3e12d0e..4f7a6f5cc9 100644
--- a/cpp/src/neighbors/iface/iface.hpp
+++ b/cpp/src/neighbors/iface/iface.hpp
@@ -31,7 +31,7 @@ namespace cuvs::neighbors {
 using namespace raft;
 
 template <typename AnnIndexType, typename T, typename IdxT, typename Accessor>
-void build(const raft::device_resources& handle,
+void build(const raft::resources& handle,
            cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
            const cuvs::neighbors::index_params* index_params,
            raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> index_dataset)
@@ -56,7 +56,7 @@ void build(const raft::device_resources& handle,
 
 template <typename AnnIndexType, typename T, typename IdxT, typename Accessor1, typename Accessor2>
 void extend(
-  const raft::device_resources& handle,
+  const raft::resources& handle,
   cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
   raft::mdspan<const T, matrix_extent<int64_t>, row_major, Accessor1> new_vectors,
   std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, Accessor2>>
@@ -79,7 +79,7 @@ void extend(
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void search(const raft::device_resources& handle,
+void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::device_matrix_view<const T, int64_t, row_major> queries,
@@ -115,7 +115,7 @@ void search(const raft::device_resources& handle,
 
 // for MG ANN only
 template <typename AnnIndexType, typename T, typename IdxT>
-void search(const raft::device_resources& handle,
+void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::host_matrix_view<const T, int64_t, row_major> h_queries,
@@ -137,7 +137,7 @@ void search(const raft::device_resources& handle,
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void serialize(const raft::device_resources& handle,
+void serialize(const raft::resources& handle,
                const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                std::ostream& os)
 {
@@ -150,10 +150,12 @@ void serialize(const raft::device_resources& handle,
   } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
     cagra::serialize(handle, os, interface.index_.value(), true);
   }
+
+  resource::sync_stream(handle);
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void deserialize(const raft::device_resources& handle,
+void deserialize(const raft::resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  std::istream& is)
 {
@@ -162,20 +164,23 @@ void deserialize(const raft::device_resources& handle,
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     ivf_flat::index<T, IdxT> idx(handle);
     ivf_flat::deserialize(handle, is, &idx);
+    resource::sync_stream(handle);
     interface.index_.emplace(std::move(idx));
   } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
     ivf_pq::index<IdxT> idx(handle);
     ivf_pq::deserialize(handle, is, &idx);
+    resource::sync_stream(handle);
     interface.index_.emplace(std::move(idx));
   } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
     cagra::index<T, IdxT> idx(handle);
     cagra::deserialize(handle, is, &idx);
+    resource::sync_stream(handle);
     interface.index_.emplace(std::move(idx));
   }
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void deserialize(const raft::device_resources& handle,
+void deserialize(const raft::resources& handle,
                  cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
                  const std::string& filename)
 {
@@ -187,14 +192,17 @@ void deserialize(const raft::device_resources& handle,
   if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
     ivf_flat::index<T, IdxT> idx(handle);
     ivf_flat::deserialize(handle, is, &idx);
+    resource::sync_stream(handle);
     interface.index_.emplace(std::move(idx));
   } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
     ivf_pq::index<IdxT> idx(handle);
     ivf_pq::deserialize(handle, is, &idx);
+    resource::sync_stream(handle);
     interface.index_.emplace(std::move(idx));
   } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
     cagra::index<T, IdxT> idx(handle);
     cagra::deserialize(handle, is, &idx);
+    resource::sync_stream(handle);
     interface.index_.emplace(std::move(idx));
   }
 
diff --git a/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
index b5e329dd82..b7ad428ad8 100644
--- a/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                             \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
       new_indices);                                                                                \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
       new_indices);                                                                                \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
-  template void serialize(const raft::device_resources& handle,                                    \
+  template void serialize(const raft::resources& handle,                                           \
                           const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
                           std::ostream& os);                                                       \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             std::istream& is);                                                     \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             const std::string& filename);
 CUVS_INST_MG_CAGRA(float, uint32_t);
diff --git a/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
index 23fcffc59a..86e0633bb1 100644
--- a/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                             \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
       new_indices);                                                                                \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
       new_indices);                                                                                \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
-  template void serialize(const raft::device_resources& handle,                                    \
+  template void serialize(const raft::resources& handle,                                           \
                           const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
                           std::ostream& os);                                                       \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             std::istream& is);                                                     \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             const std::string& filename);
 CUVS_INST_MG_CAGRA(half, uint32_t);
diff --git a/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
index 30377ab666..64f174184b 100644
--- a/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                             \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
       new_indices);                                                                                \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
       new_indices);                                                                                \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
-  template void serialize(const raft::device_resources& handle,                                    \
+  template void serialize(const raft::resources& handle,                                           \
                           const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
                           std::ostream& os);                                                       \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             std::istream& is);                                                     \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             const std::string& filename);
 CUVS_INST_MG_CAGRA(int8_t, uint32_t);
diff --git a/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
index 59a1640e89..9f6db32df8 100644
--- a/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                             \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
       new_indices);                                                                                \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
       new_indices);                                                                                \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
-  template void serialize(const raft::device_resources& handle,                                    \
+  template void serialize(const raft::resources& handle,                                           \
                           const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface, \
                           std::ostream& os);                                                       \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             std::istream& is);                                                     \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,     \
                             const std::string& filename);
 CUVS_INST_MG_CAGRA(uint8_t, uint32_t);
diff --git a/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
index a0a4553753..0afffe0ad3 100644
--- a/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
@@ -38,39 +38,39 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                             \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
       new_indices);                                                                                \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
       new_indices);                                                                                \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
@@ -78,15 +78,15 @@ namespace cuvs::neighbors {
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
   template void serialize(                                                                         \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                    \
     std::ostream& os);                                                                             \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
                             std::istream& is);                                                     \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
                             const std::string& filename);
 CUVS_INST_MG_FLAT(float, int64_t);
diff --git a/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
index 9fdd6464ff..5afd77053e 100644
--- a/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
@@ -38,39 +38,39 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                             \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
       new_indices);                                                                                \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
       new_indices);                                                                                \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
@@ -78,15 +78,15 @@ namespace cuvs::neighbors {
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
   template void serialize(                                                                         \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                    \
     std::ostream& os);                                                                             \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
                             std::istream& is);                                                     \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
                             const std::string& filename);
 CUVS_INST_MG_FLAT(int8_t, int64_t);
diff --git a/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
index daee59c4a8..4f2f85700c 100644
--- a/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
@@ -38,39 +38,39 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                             \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
                                                                                                    \
   template void build(                                                                             \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     const cuvs::neighbors::index_params* index_params,                                             \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
       new_indices);                                                                                \
                                                                                                    \
   template void extend(                                                                            \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
       new_indices);                                                                                \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
-  template void search(const raft::device_resources& handle,                                       \
+  template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
@@ -78,15 +78,15 @@ namespace cuvs::neighbors {
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
   template void serialize(                                                                         \
-    const raft::device_resources& handle,                                                          \
+    const raft::resources& handle,                                                                 \
     const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                    \
     std::ostream& os);                                                                             \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
                             std::istream& is);                                                     \
                                                                                                    \
-  template void deserialize(const raft::device_resources& handle,                                  \
+  template void deserialize(const raft::resources& handle,                                         \
                             cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
                             const std::string& filename);
 CUVS_INST_MG_FLAT(uint8_t, int64_t);
diff --git a/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
index 7282d6bd07..90759d5f1a 100644
--- a/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                            \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
       new_indices);                                                                               \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
       new_indices);                                                                               \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
                        raft::device_matrix_view<float, int64_t, row_major> distances);            \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
                                                                                                   \
-  template void serialize(const raft::device_resources& handle,                                   \
+  template void serialize(const raft::resources& handle,                                          \
                           const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
                           std::ostream& os);                                                      \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             std::istream& is);                                                    \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             const std::string& filename);
 CUVS_INST_MG_PQ(float, int64_t);
diff --git a/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
index 4d67f9aed1..c92d6fd651 100644
--- a/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                            \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
       new_indices);                                                                               \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
       new_indices);                                                                               \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
                        raft::device_matrix_view<float, int64_t, row_major> distances);            \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
                                                                                                   \
-  template void serialize(const raft::device_resources& handle,                                   \
+  template void serialize(const raft::resources& handle,                                          \
                           const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
                           std::ostream& os);                                                      \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             std::istream& is);                                                    \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             const std::string& filename);
 CUVS_INST_MG_PQ(half, int64_t);
diff --git a/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
index 46537b3f9f..59269e9da1 100644
--- a/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                            \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
       new_indices);                                                                               \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
       new_indices);                                                                               \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
                        raft::device_matrix_view<float, int64_t, row_major> distances);            \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
                                                                                                   \
-  template void serialize(const raft::device_resources& handle,                                   \
+  template void serialize(const raft::resources& handle,                                          \
                           const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
                           std::ostream& os);                                                      \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             std::istream& is);                                                    \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             const std::string& filename);
 CUVS_INST_MG_PQ(int8_t, int64_t);
diff --git a/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
index 591ac881a8..c407e64cac 100644
--- a/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
@@ -38,54 +38,54 @@ namespace cuvs::neighbors {
                                              raft::memory_type::host>;                            \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                \
                                                                                                   \
   template void build(                                                                            \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     const cuvs::neighbors::index_params* index_params,                                            \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>> \
       new_indices);                                                                               \
                                                                                                   \
   template void extend(                                                                           \
-    const raft::device_resources& handle,                                                         \
+    const raft::resources& handle,                                                                \
     cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,                              \
     raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                   \
     std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>> \
       new_indices);                                                                               \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,              \
                        raft::device_matrix_view<float, int64_t, row_major> distances);            \
                                                                                                   \
-  template void search(const raft::device_resources& handle,                                      \
+  template void search(const raft::resources& handle,                                             \
                        const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,     \
                        const cuvs::neighbors::search_params* search_params,                       \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,             \
                        raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);          \
                                                                                                   \
-  template void serialize(const raft::device_resources& handle,                                   \
+  template void serialize(const raft::resources& handle,                                          \
                           const cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,  \
                           std::ostream& os);                                                      \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             std::istream& is);                                                    \
                                                                                                   \
-  template void deserialize(const raft::device_resources& handle,                                 \
+  template void deserialize(const raft::resources& handle,                                        \
                             cuvs::neighbors::iface<ivf_pq::index<IdxT>, T, IdxT>& interface,      \
                             const std::string& filename);
 CUVS_INST_MG_PQ(uint8_t, int64_t);
diff --git a/cpp/src/neighbors/mg/generate_mg.py b/cpp/src/neighbors/mg/generate_mg.py
index af5e605456..49d4609fcd 100644
--- a/cpp/src/neighbors/mg/generate_mg.py
+++ b/cpp/src/neighbors/mg/generate_mg.py
@@ -40,190 +40,194 @@
 """
 
 include_macro = """
-#include "mg.cuh"
-"""
-
-namespace_macro = """
-namespace cuvs::neighbors::mg {
-"""
-
-footer = """
-}  // namespace cuvs::neighbors::mg
+#include "snmg.cuh"
 """
 
 flat_macro = """
-#define CUVS_INST_MG_FLAT(T, IdxT)                                                                                          \\
-  index<ivf_flat::index<T, IdxT>, T, IdxT> build(const raft::device_resources& handle,                                      \\
-                                                 const mg::index_params<ivf_flat::index_params>& index_params,              \\
-                                                 raft::host_matrix_view<const T, int64_t, row_major> index_dataset)         \\
-  {                                                                                                                         \\
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                       \\
-    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);                                   \\
-    cuvs::neighbors::mg::detail::build(handle, index,                                                                       \\
-                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),                    \\
-                                       index_dataset);                                                                      \\
-    return index;                                                                                                           \\
-  }                                                                                                                         \\
-                                                                                                                            \\
-  void extend(const raft::device_resources& handle,                                                                         \\
-              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                                                              \\
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                                              \\
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)                                       \\
-  {                                                                                                                         \\
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                                           \\
-  }                                                                                                                         \\
-                                                                                                                            \\
-  void search(const raft::device_resources& handle,                                                                         \\
-              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                                                        \\
-              const mg::search_params<ivf_flat::search_params>& search_params,                                              \\
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                                                  \\
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                                   \\
-              raft::host_matrix_view<float, int64_t, row_major> distances,                                                  \\
-              int64_t n_rows_per_batch)                                                                                     \\
-  {                                                                                                                         \\
-    cuvs::neighbors::mg::detail::search(handle, index,                                                                      \\
-                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \\
-                                        queries, neighbors, distances, n_rows_per_batch);                                   \\
-  }                                                                                                                         \\
-                                                                                                                            \\
-  void serialize(const raft::device_resources& handle,                                                                      \\
-                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                                                     \\
-                 const std::string& filename)                                                                               \\
-  {                                                                                                                         \\
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                                                        \\
-  }                                                                                                                         \\
-                                                                                                                            \\
-  template<>                                                                                                                \\
-  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(const raft::device_resources& handle,                  \\
-                                                                     const std::string& filename)                           \\
-  {                                                                                                                         \\
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                                                  \\
-    return idx;                                                                                                             \\
-  }                                                                                                                         \\
-                                                                                                                            \\
-  template<>                                                                                                                \\
-  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(const raft::device_resources& handle,                   \\
-                                                                    const std::string& filename)                            \\
-  {                                                                                                                         \\
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                       \\
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                                     \\
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                                         \\
-    return idx;                                                                                                             \\
-  }
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                                                   \\
+namespace cuvs::neighbors::ivf_flat {                                                                                \\
+  using namespace cuvs::neighbors;                                                                                   \\
+                                                                                                                     \\
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> build(                                                \\
+    const raft::resources& res,                                                                                      \\
+    const mg_index_params<ivf_flat::index_params>& index_params,                                                     \\
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                                               \\
+  {                                                                                                                  \\
+    cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> index(res, index_params.mode);                      \\
+    cuvs::neighbors::snmg::detail::build(res, index,                                                                 \\
+                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),             \\
+                                       index_dataset);                                                               \\
+    return index;                                                                                                    \\
+  }                                                                                                                  \\
+                                                                                                                     \\
+  void extend(const raft::resources& res,                                                                            \\
+              cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                                   \\
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                                       \\
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)                                \\
+  {                                                                                                                  \\
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);                                     \\
+  }                                                                                                                  \\
+                                                                                                                     \\
+  void search(const raft::resources& res,                                                                            \\
+              const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                             \\
+              const mg_search_params<ivf_flat::search_params>& search_params,                                        \\
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                                           \\
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                            \\
+              raft::host_matrix_view<float, int64_t, row_major> distances)                                           \\
+  {                                                                                                                  \\
+    cuvs::neighbors::snmg::detail::search(res, index,                                                                \\
+                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),          \\
+                                        queries, neighbors, distances);                                              \\
+  }                                                                                                                  \\
+                                                                                                                     \\
+  void serialize(const raft::resources& res,                                                                         \\
+                 const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                          \\
+                 const std::string& filename)                                                                        \\
+  {                                                                                                                  \\
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                                                  \\
+  }                                                                                                                  \\
+                                                                                                                     \\
+  template<>                                                                                                         \\
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(                                 \\
+    const raft::resources& res,                                                                                      \\
+    const std::string& filename)                                                                                     \\
+  {                                                                                                                  \\
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, filename);                          \\
+    return idx;                                                                                                      \\
+  }                                                                                                                  \\
+                                                                                                                     \\
+  template<>                                                                                                         \\
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(                                  \\
+    const raft::resources& res,                                                                                      \\
+    const std::string& filename)                                                                                     \\
+  {                                                                                                                  \\
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, REPLICATED);                        \\
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);                                   \\
+    return idx;                                                                                                      \\
+  }                                                                                                                  \\
+}  // namespace cuvs::neighbors::ivf_flat
 """
 
 pq_macro = """
-#define CUVS_INST_MG_PQ(T, IdxT)                                                                                          \\
-  index<ivf_pq::index<IdxT>, T, IdxT> build(const raft::device_resources& handle,                                         \\
-                                            const mg::index_params<ivf_pq::index_params>& index_params,                   \\
-                                            raft::host_matrix_view<const T, int64_t, row_major> index_dataset)            \\
-  {                                                                                                                       \\
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
-    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);                                      \\
-    cuvs::neighbors::mg::detail::build(handle, index,                                                                     \\
-                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),                  \\
-                                       index_dataset);                                                                    \\
-    return index;                                                                                                         \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  void extend(const raft::device_resources& handle,                                                                       \\
-              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                                                 \\
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                                            \\
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)                                     \\
-  {                                                                                                                       \\
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                                         \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  void search(const raft::device_resources& handle,                                                                       \\
-              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                                           \\
-              const mg::search_params<ivf_pq::search_params>& search_params,                                              \\
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                                                \\
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                                 \\
-              raft::host_matrix_view<float, int64_t, row_major> distances,                                                \\
-              int64_t n_rows_per_batch)                                                                                   \\
-  {                                                                                                                       \\
-    cuvs::neighbors::mg::detail::search(handle, index,                                                                    \\
-                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),               \\
-                                        queries, neighbors, distances, n_rows_per_batch);                                 \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  void serialize(const raft::device_resources& handle,                                                                    \\
-                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                                        \\
-                 const std::string& filename)                                                                             \\
-  {                                                                                                                       \\
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                                                      \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  template<>                                                                                                              \\
-  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(const raft::device_resources& handle,                       \\
-                                                              const std::string& filename)                                \\
-  {                                                                                                                       \\
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                                                     \\
-    return idx;                                                                                                           \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  template<>                                                                                                              \\
-  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle,                        \\
-                                                             const std::string& filename)                                 \\
-  {                                                                                                                       \\
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                                        \\
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                                       \\
-    return idx;                                                                                                           \\
-  }
+#define CUVS_INST_MG_PQ(T, IdxT)                                                                                \\
+namespace cuvs::neighbors::ivf_pq {                                                                             \\
+  using namespace cuvs::neighbors;                                                                              \\
+                                                                                                                \\
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> build(                                                \\
+    const raft::resources& res,                                                                                 \\
+    const mg_index_params<ivf_pq::index_params>& index_params,                                                  \\
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                                          \\
+  {                                                                                                             \\
+    cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> index(res, index_params.mode);                      \\
+    cuvs::neighbors::snmg::detail::build(res, index,                                                            \\
+                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),        \\
+                                       index_dataset);                                                          \\
+    return index;                                                                                               \\
+  }                                                                                                             \\
+                                                                                                                \\
+  void extend(const raft::resources& res,                                                                       \\
+              cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,                                   \\
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                                  \\
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)                           \\
+  {                                                                                                             \\
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);                                \\
+  }                                                                                                             \\
+                                                                                                                \\
+  void search(const raft::resources& res,                                                                       \\
+              const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,                             \\
+              const mg_search_params<ivf_pq::search_params>& search_params,                                     \\
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                                      \\
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                       \\
+              raft::host_matrix_view<float, int64_t, row_major> distances)                                      \\
+  {                                                                                                             \\
+    cuvs::neighbors::snmg::detail::search(res, index,                                                           \\
+                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),     \\
+                                        queries, neighbors, distances);                                         \\
+  }                                                                                                             \\
+                                                                                                                \\
+  void serialize(const raft::resources& res,                                                                    \\
+                 const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,                          \\
+                 const std::string& filename)                                                                   \\
+  {                                                                                                             \\
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                                             \\
+  }                                                                                                             \\
+                                                                                                                \\
+  template<>                                                                                                    \\
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> deserialize<T, IdxT>(                                 \\
+    const raft::resources& res,                                                                                 \\
+    const std::string& filename)                                                                                \\
+  {                                                                                                             \\
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, filename);                          \\
+    return idx;                                                                                                 \\
+  }                                                                                                             \\
+                                                                                                                \\
+  template<>                                                                                                    \\
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> distribute<T, IdxT>(                                  \\
+    const raft::resources& res,                                                                                 \\
+    const std::string& filename)                                                                                \\
+  {                                                                                                             \\
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, REPLICATED);                        \\
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);                              \\
+    return idx;                                                                                                 \\
+  }                                                                                                             \\
+}  // namespace cuvs::neighbors::ivf_pq
 """
 
 cagra_macro = """
-#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                                       \\
-  index<cagra::index<T, IdxT>, T, IdxT> build(const raft::device_resources& handle,                                       \\
-                                              const mg::index_params<cagra::index_params>& index_params,                  \\
-                                              raft::host_matrix_view<const T, int64_t, row_major> index_dataset)          \\
-  {                                                                                                                       \\
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
-    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);                                    \\
-    cuvs::neighbors::mg::detail::build(handle, index,                                                                     \\
-                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),                  \\
-                                       index_dataset);                                                                    \\
-    return index;                                                                                                         \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  void search(const raft::device_resources& handle,                                                                       \\
-              const index<cagra::index<T, IdxT>, T, IdxT>& index,                                                         \\
-              const mg::search_params<cagra::search_params>& search_params,                                               \\
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                                                \\
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                                 \\
-              raft::host_matrix_view<float, int64_t, row_major> distances,                                                \\
-              int64_t n_rows_per_batch)                                                                                   \\
-  {                                                                                                                       \\
-    cuvs::neighbors::mg::detail::search(handle, index,                                                                    \\
-                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),               \\
-                                        queries, neighbors, distances, n_rows_per_batch);                                 \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  void serialize(const raft::device_resources& handle,                                                                    \\
-                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                                                      \\
-                 const std::string& filename)                                                                             \\
-  {                                                                                                                       \\
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                                                      \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  template<>                                                                                                              \\
-  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(const raft::device_resources& handle,                  \\
-                                                                   const std::string& filename)                           \\
-  {                                                                                                                       \\
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                                                   \\
-    return idx;                                                                                                           \\
-  }                                                                                                                       \\
-                                                                                                                          \\
-  template<>                                                                                                              \\
-  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(const raft::device_resources& handle,                   \\
-                                                                  const std::string& filename)                            \\
-  {                                                                                                                       \\
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);                                     \\
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                                      \\
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                                       \\
-    return idx;                                                                                                           \\
-  }
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                                               \\
+namespace cuvs::neighbors::cagra {                                                                                \\
+  using namespace cuvs::neighbors;                                                                                \\
+                                                                                                                  \\
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> build(                                                \\
+    const raft::resources& res,                                                                                   \\
+    const mg_index_params<cagra::index_params>& index_params,                                                     \\
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                                            \\
+  {                                                                                                               \\
+    cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> index(res, index_params.mode);                      \\
+    cuvs::neighbors::snmg::detail::build(res, index,                                                              \\
+                                       static_cast<const cuvs::neighbors::index_params*>(&index_params),          \\
+                                       index_dataset);                                                            \\
+    return index;                                                                                                 \\
+  }                                                                                                               \\
+                                                                                                                  \\
+  void search(const raft::resources& res,                                                                         \\
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,                             \\
+              const mg_search_params<cagra::search_params>& search_params,                                        \\
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                                        \\
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                         \\
+              raft::host_matrix_view<float, int64_t, row_major> distances)                                        \\
+  {                                                                                                               \\
+    cuvs::neighbors::snmg::detail::search(res, index,                                                             \\
+                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),       \\
+                                        queries, neighbors, distances);                                           \\
+  }                                                                                                               \\
+                                                                                                                  \\
+  void serialize(const raft::resources& res,                                                                      \\
+                 const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,                          \\
+                 const std::string& filename)                                                                     \\
+  {                                                                                                               \\
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                                               \\
+  }                                                                                                               \\
+                                                                                                                  \\
+  template<>                                                                                                      \\
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(                                 \\
+    const raft::resources& res,                                                                                   \\
+    const std::string& filename)                                                                                  \\
+  {                                                                                                               \\
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, filename);                          \\
+    return idx;                                                                                                   \\
+  }                                                                                                               \\
+                                                                                                                  \\
+  template<>                                                                                                      \\
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(                                  \\
+    const raft::resources& res,                                                                                   \\
+    const std::string& filename)                                                                                  \\
+  {                                                                                                               \\
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, REPLICATED);                        \\
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);                                \\
+    return idx;                                                                                                   \\
+  }                                                                                                               \\
+}  // namespace cuvs::neighbors::cagra
 """
 
 flat_macros = dict (
@@ -277,10 +281,8 @@
           with open(path, "w") as f:
               f.write(header)
               f.write(macro['include'])
-              f.write(namespace_macro)
               f.write(macro["definition"])
               f.write(f"{macro['name']}({T}, {IdxT});\n\n")
               f.write(f"#undef {macro['name']}\n")
-              f.write(footer)
 
           print(f"src/neighbors/mg/{path}")
diff --git a/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
index b11610fb48..3260df7050 100644
--- a/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
@@ -23,70 +23,66 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
-  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
-    const raft::device_resources& handle,                                              \
-    const mg::index_params<cagra::index_params>& index_params,                         \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
-    cuvs::neighbors::mg::detail::build(                                                \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
-      index_dataset);                                                                  \
-    return index;                                                                      \
-  }                                                                                    \
-                                                                                       \
-  void search(const raft::device_resources& handle,                                    \
-              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
-              const mg::search_params<cagra::search_params>& search_params,            \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
-              raft::host_matrix_view<float, int64_t, row_major> distances,             \
-              int64_t n_rows_per_batch)                                                \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::search(                                               \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
-      queries,                                                                         \
-      neighbors,                                                                       \
-      distances,                                                                       \
-      n_rows_per_batch);                                                               \
-  }                                                                                    \
-                                                                                       \
-  void serialize(const raft::device_resources& handle,                                 \
-                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
-                 const std::string& filename)                                          \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
-    return idx;                                                                        \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
-    return idx;                                                                        \
-  }
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                          \
+  namespace cuvs::neighbors::cagra {                                                         \
+  using namespace cuvs::neighbors;                                                           \
+                                                                                             \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                              \
+    const mg_index_params<cagra::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                       \
+  {                                                                                          \
+    cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                    \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                      \
+      index_dataset);                                                                        \
+    return index;                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void serialize(const raft::resources& res,                                                 \
+                 const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                          \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                              \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);           \
+    return idx;                                                                              \
+  }                                                                                          \
+  }  // namespace cuvs::neighbors::cagra
 CUVS_INST_MG_CAGRA(float, uint32_t);
 
 #undef CUVS_INST_MG_CAGRA
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
index 8f76c69a34..da8cbb1e49 100644
--- a/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
@@ -23,70 +23,66 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
-  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
-    const raft::device_resources& handle,                                              \
-    const mg::index_params<cagra::index_params>& index_params,                         \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
-    cuvs::neighbors::mg::detail::build(                                                \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
-      index_dataset);                                                                  \
-    return index;                                                                      \
-  }                                                                                    \
-                                                                                       \
-  void search(const raft::device_resources& handle,                                    \
-              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
-              const mg::search_params<cagra::search_params>& search_params,            \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
-              raft::host_matrix_view<float, int64_t, row_major> distances,             \
-              int64_t n_rows_per_batch)                                                \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::search(                                               \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
-      queries,                                                                         \
-      neighbors,                                                                       \
-      distances,                                                                       \
-      n_rows_per_batch);                                                               \
-  }                                                                                    \
-                                                                                       \
-  void serialize(const raft::device_resources& handle,                                 \
-                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
-                 const std::string& filename)                                          \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
-    return idx;                                                                        \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
-    return idx;                                                                        \
-  }
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                          \
+  namespace cuvs::neighbors::cagra {                                                         \
+  using namespace cuvs::neighbors;                                                           \
+                                                                                             \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                              \
+    const mg_index_params<cagra::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                       \
+  {                                                                                          \
+    cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                    \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                      \
+      index_dataset);                                                                        \
+    return index;                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void serialize(const raft::resources& res,                                                 \
+                 const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                          \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                              \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);           \
+    return idx;                                                                              \
+  }                                                                                          \
+  }  // namespace cuvs::neighbors::cagra
 CUVS_INST_MG_CAGRA(half, uint32_t);
 
 #undef CUVS_INST_MG_CAGRA
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
index 67b88d7429..5aae599e22 100644
--- a/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
@@ -23,70 +23,66 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
-  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
-    const raft::device_resources& handle,                                              \
-    const mg::index_params<cagra::index_params>& index_params,                         \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
-    cuvs::neighbors::mg::detail::build(                                                \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
-      index_dataset);                                                                  \
-    return index;                                                                      \
-  }                                                                                    \
-                                                                                       \
-  void search(const raft::device_resources& handle,                                    \
-              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
-              const mg::search_params<cagra::search_params>& search_params,            \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
-              raft::host_matrix_view<float, int64_t, row_major> distances,             \
-              int64_t n_rows_per_batch)                                                \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::search(                                               \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
-      queries,                                                                         \
-      neighbors,                                                                       \
-      distances,                                                                       \
-      n_rows_per_batch);                                                               \
-  }                                                                                    \
-                                                                                       \
-  void serialize(const raft::device_resources& handle,                                 \
-                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
-                 const std::string& filename)                                          \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
-    return idx;                                                                        \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
-    return idx;                                                                        \
-  }
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                          \
+  namespace cuvs::neighbors::cagra {                                                         \
+  using namespace cuvs::neighbors;                                                           \
+                                                                                             \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                              \
+    const mg_index_params<cagra::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                       \
+  {                                                                                          \
+    cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                    \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                      \
+      index_dataset);                                                                        \
+    return index;                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void serialize(const raft::resources& res,                                                 \
+                 const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                          \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                              \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);           \
+    return idx;                                                                              \
+  }                                                                                          \
+  }  // namespace cuvs::neighbors::cagra
 CUVS_INST_MG_CAGRA(int8_t, uint32_t);
 
 #undef CUVS_INST_MG_CAGRA
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
index f721749233..8384a6a972 100644
--- a/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
@@ -23,70 +23,66 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_CAGRA(T, IdxT)                                                    \
-  index<cagra::index<T, IdxT>, T, IdxT> build(                                         \
-    const raft::device_resources& handle,                                              \
-    const mg::index_params<cagra::index_params>& index_params,                         \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    index<cagra::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
-    cuvs::neighbors::mg::detail::build(                                                \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                \
-      index_dataset);                                                                  \
-    return index;                                                                      \
-  }                                                                                    \
-                                                                                       \
-  void search(const raft::device_resources& handle,                                    \
-              const index<cagra::index<T, IdxT>, T, IdxT>& index,                      \
-              const mg::search_params<cagra::search_params>& search_params,            \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,             \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,              \
-              raft::host_matrix_view<float, int64_t, row_major> distances,             \
-              int64_t n_rows_per_batch)                                                \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::search(                                               \
-      handle,                                                                          \
-      index,                                                                           \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),              \
-      queries,                                                                         \
-      neighbors,                                                                       \
-      distances,                                                                       \
-      n_rows_per_batch);                                                               \
-  }                                                                                    \
-                                                                                       \
-  void serialize(const raft::device_resources& handle,                                 \
-                 const index<cagra::index<T, IdxT>, T, IdxT>& index,                   \
-                 const std::string& filename)                                          \
-  {                                                                                    \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                   \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> deserialize_cagra<T, IdxT>(                    \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(handle, filename);                \
-    return idx;                                                                        \
-  }                                                                                    \
-                                                                                       \
-  template <>                                                                          \
-  index<cagra::index<T, IdxT>, T, IdxT> distribute_cagra<T, IdxT>(                     \
-    const raft::device_resources& handle, const std::string& filename)                 \
-  {                                                                                    \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);  \
-    auto idx = index<cagra::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);    \
-    return idx;                                                                        \
-  }
+#define CUVS_INST_MG_CAGRA(T, IdxT)                                                          \
+  namespace cuvs::neighbors::cagra {                                                         \
+  using namespace cuvs::neighbors;                                                           \
+                                                                                             \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                              \
+    const mg_index_params<cagra::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                       \
+  {                                                                                          \
+    cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                    \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                      \
+      index_dataset);                                                                        \
+    return index;                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void serialize(const raft::resources& res,                                                 \
+                 const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                          \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                              \
+  }                                                                                          \
+                                                                                             \
+  template <>                                                                                \
+  cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                 \
+  {                                                                                          \
+    auto idx = cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);           \
+    return idx;                                                                              \
+  }                                                                                          \
+  }  // namespace cuvs::neighbors::cagra
 CUVS_INST_MG_CAGRA(uint8_t, uint32_t);
 
 #undef CUVS_INST_MG_CAGRA
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_flat_float_int64_t.cu b/cpp/src/neighbors/mg/mg_flat_float_int64_t.cu
index 4495e2527c..1948ab8c8a 100644
--- a/cpp/src/neighbors/mg/mg_flat_float_int64_t.cu
+++ b/cpp/src/neighbors/mg/mg_flat_float_int64_t.cu
@@ -23,78 +23,74 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_FLAT(T, IdxT)                                                        \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> build(                                         \
-    const raft::device_resources& handle,                                                 \
-    const mg::index_params<ivf_flat::index_params>& index_params,                         \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                    \
-  {                                                                                       \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
-    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
-    cuvs::neighbors::mg::detail::build(                                                   \
-      handle,                                                                             \
-      index,                                                                              \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                   \
-      index_dataset);                                                                     \
-    return index;                                                                         \
-  }                                                                                       \
-                                                                                          \
-  void extend(const raft::device_resources& handle,                                       \
-              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                            \
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,            \
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)     \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);         \
-  }                                                                                       \
-                                                                                          \
-  void search(const raft::device_resources& handle,                                       \
-              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                      \
-              const mg::search_params<ivf_flat::search_params>& search_params,            \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                 \
-              raft::host_matrix_view<float, int64_t, row_major> distances,                \
-              int64_t n_rows_per_batch)                                                   \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::search(                                                  \
-      handle,                                                                             \
-      index,                                                                              \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \
-      queries,                                                                            \
-      neighbors,                                                                          \
-      distances,                                                                          \
-      n_rows_per_batch);                                                                  \
-  }                                                                                       \
-                                                                                          \
-  void serialize(const raft::device_resources& handle,                                    \
-                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                   \
-                 const std::string& filename)                                             \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                      \
-  }                                                                                       \
-                                                                                          \
-  template <>                                                                             \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(                     \
-    const raft::device_resources& handle, const std::string& filename)                    \
-  {                                                                                       \
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                \
-    return idx;                                                                           \
-  }                                                                                       \
-                                                                                          \
-  template <>                                                                             \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(                      \
-    const raft::device_resources& handle, const std::string& filename)                    \
-  {                                                                                       \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);       \
-    return idx;                                                                           \
-  }
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                              \
+  namespace cuvs::neighbors::ivf_flat {                                                         \
+  using namespace cuvs::neighbors;                                                              \
+                                                                                                \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                                 \
+    const mg_index_params<ivf_flat::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                          \
+  {                                                                                             \
+    cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                       \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                         \
+      index_dataset);                                                                           \
+    return index;                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void extend(const raft::resources& res,                                                       \
+              cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                  \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)           \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);                \
+  }                                                                                             \
+                                                                                                \
+  void search(const raft::resources& res,                                                       \
+              const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_flat::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                      \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                       \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                      \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::search(                                                      \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                       \
+      queries,                                                                                  \
+      neighbors,                                                                                \
+      distances);                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void serialize(const raft::resources& res,                                                    \
+                 const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                   \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                             \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                                 \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);              \
+    return idx;                                                                                 \
+  }                                                                                             \
+  }  // namespace cuvs::neighbors::ivf_flat
 CUVS_INST_MG_FLAT(float, int64_t);
 
 #undef CUVS_INST_MG_FLAT
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu
index 5494414a6a..1e0c9fddda 100644
--- a/cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/mg/mg_flat_int8_t_int64_t.cu
@@ -23,78 +23,74 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_FLAT(T, IdxT)                                                        \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> build(                                         \
-    const raft::device_resources& handle,                                                 \
-    const mg::index_params<ivf_flat::index_params>& index_params,                         \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                    \
-  {                                                                                       \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
-    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
-    cuvs::neighbors::mg::detail::build(                                                   \
-      handle,                                                                             \
-      index,                                                                              \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                   \
-      index_dataset);                                                                     \
-    return index;                                                                         \
-  }                                                                                       \
-                                                                                          \
-  void extend(const raft::device_resources& handle,                                       \
-              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                            \
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,            \
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)     \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);         \
-  }                                                                                       \
-                                                                                          \
-  void search(const raft::device_resources& handle,                                       \
-              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                      \
-              const mg::search_params<ivf_flat::search_params>& search_params,            \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                 \
-              raft::host_matrix_view<float, int64_t, row_major> distances,                \
-              int64_t n_rows_per_batch)                                                   \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::search(                                                  \
-      handle,                                                                             \
-      index,                                                                              \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \
-      queries,                                                                            \
-      neighbors,                                                                          \
-      distances,                                                                          \
-      n_rows_per_batch);                                                                  \
-  }                                                                                       \
-                                                                                          \
-  void serialize(const raft::device_resources& handle,                                    \
-                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                   \
-                 const std::string& filename)                                             \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                      \
-  }                                                                                       \
-                                                                                          \
-  template <>                                                                             \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(                     \
-    const raft::device_resources& handle, const std::string& filename)                    \
-  {                                                                                       \
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                \
-    return idx;                                                                           \
-  }                                                                                       \
-                                                                                          \
-  template <>                                                                             \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(                      \
-    const raft::device_resources& handle, const std::string& filename)                    \
-  {                                                                                       \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);       \
-    return idx;                                                                           \
-  }
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                              \
+  namespace cuvs::neighbors::ivf_flat {                                                         \
+  using namespace cuvs::neighbors;                                                              \
+                                                                                                \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                                 \
+    const mg_index_params<ivf_flat::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                          \
+  {                                                                                             \
+    cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                       \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                         \
+      index_dataset);                                                                           \
+    return index;                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void extend(const raft::resources& res,                                                       \
+              cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                  \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)           \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);                \
+  }                                                                                             \
+                                                                                                \
+  void search(const raft::resources& res,                                                       \
+              const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_flat::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                      \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                       \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                      \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::search(                                                      \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                       \
+      queries,                                                                                  \
+      neighbors,                                                                                \
+      distances);                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void serialize(const raft::resources& res,                                                    \
+                 const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                   \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                             \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                                 \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);              \
+    return idx;                                                                                 \
+  }                                                                                             \
+  }  // namespace cuvs::neighbors::ivf_flat
 CUVS_INST_MG_FLAT(int8_t, int64_t);
 
 #undef CUVS_INST_MG_FLAT
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
index 35df2146bf..cc3945a05b 100644
--- a/cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
@@ -23,78 +23,74 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_FLAT(T, IdxT)                                                        \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> build(                                         \
-    const raft::device_resources& handle,                                                 \
-    const mg::index_params<ivf_flat::index_params>& index_params,                         \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                    \
-  {                                                                                       \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
-    index<ivf_flat::index<T, IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_); \
-    cuvs::neighbors::mg::detail::build(                                                   \
-      handle,                                                                             \
-      index,                                                                              \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                   \
-      index_dataset);                                                                     \
-    return index;                                                                         \
-  }                                                                                       \
-                                                                                          \
-  void extend(const raft::device_resources& handle,                                       \
-              index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                            \
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,            \
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)     \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);         \
-  }                                                                                       \
-                                                                                          \
-  void search(const raft::device_resources& handle,                                       \
-              const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                      \
-              const mg::search_params<ivf_flat::search_params>& search_params,            \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                 \
-              raft::host_matrix_view<float, int64_t, row_major> distances,                \
-              int64_t n_rows_per_batch)                                                   \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::search(                                                  \
-      handle,                                                                             \
-      index,                                                                              \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),                 \
-      queries,                                                                            \
-      neighbors,                                                                          \
-      distances,                                                                          \
-      n_rows_per_batch);                                                                  \
-  }                                                                                       \
-                                                                                          \
-  void serialize(const raft::device_resources& handle,                                    \
-                 const index<ivf_flat::index<T, IdxT>, T, IdxT>& index,                   \
-                 const std::string& filename)                                             \
-  {                                                                                       \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                      \
-  }                                                                                       \
-                                                                                          \
-  template <>                                                                             \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize_flat<T, IdxT>(                     \
-    const raft::device_resources& handle, const std::string& filename)                    \
-  {                                                                                       \
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(handle, filename);                \
-    return idx;                                                                           \
-  }                                                                                       \
-                                                                                          \
-  template <>                                                                             \
-  index<ivf_flat::index<T, IdxT>, T, IdxT> distribute_flat<T, IdxT>(                      \
-    const raft::device_resources& handle, const std::string& filename)                    \
-  {                                                                                       \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);     \
-    auto idx = index<ivf_flat::index<T, IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);   \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);       \
-    return idx;                                                                           \
-  }
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                              \
+  namespace cuvs::neighbors::ivf_flat {                                                         \
+  using namespace cuvs::neighbors;                                                              \
+                                                                                                \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                                 \
+    const mg_index_params<ivf_flat::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                          \
+  {                                                                                             \
+    cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                       \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                         \
+      index_dataset);                                                                           \
+    return index;                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void extend(const raft::resources& res,                                                       \
+              cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                  \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)           \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);                \
+  }                                                                                             \
+                                                                                                \
+  void search(const raft::resources& res,                                                       \
+              const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_flat::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                      \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                       \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                      \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::search(                                                      \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                       \
+      queries,                                                                                  \
+      neighbors,                                                                                \
+      distances);                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void serialize(const raft::resources& res,                                                    \
+                 const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                   \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                             \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                                 \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);              \
+    return idx;                                                                                 \
+  }                                                                                             \
+  }  // namespace cuvs::neighbors::ivf_flat
 CUVS_INST_MG_FLAT(uint8_t, int64_t);
 
 #undef CUVS_INST_MG_FLAT
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_float_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_float_int64_t.cu
index c671740e61..d25cd3656d 100644
--- a/cpp/src/neighbors/mg/mg_pq_float_int64_t.cu
+++ b/cpp/src/neighbors/mg/mg_pq_float_int64_t.cu
@@ -23,78 +23,74 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
-  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
-    const raft::device_resources& handle,                                                          \
-    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
-    cuvs::neighbors::mg::detail::build(                                                            \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
-      index_dataset);                                                                              \
-    return index;                                                                                  \
-  }                                                                                                \
-                                                                                                   \
-  void extend(const raft::device_resources& handle,                                                \
-              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
-  }                                                                                                \
-                                                                                                   \
-  void search(const raft::device_resources& handle,                                                \
-              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
-              const mg::search_params<ivf_pq::search_params>& search_params,                       \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
-              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
-              int64_t n_rows_per_batch)                                                            \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::search(                                                           \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
-      queries,                                                                                     \
-      neighbors,                                                                                   \
-      distances,                                                                                   \
-      n_rows_per_batch);                                                                           \
-  }                                                                                                \
-                                                                                                   \
-  void serialize(const raft::device_resources& handle,                                             \
-                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
-                 const std::string& filename)                                                      \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
-    const raft::device_resources& handle, const std::string& filename)                             \
-  {                                                                                                \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
-    return idx;                                                                                    \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
-                                                             const std::string& filename)          \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
-    return idx;                                                                                    \
-  }
+#define CUVS_INST_MG_PQ(T, IdxT)                                                           \
+  namespace cuvs::neighbors::ivf_pq {                                                      \
+  using namespace cuvs::neighbors;                                                         \
+                                                                                           \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                            \
+    const mg_index_params<ivf_pq::index_params>& index_params,                             \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                     \
+  {                                                                                        \
+    cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                  \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                    \
+      index_dataset);                                                                      \
+    return index;                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void extend(const raft::resources& res,                                                  \
+              cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,             \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)      \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);           \
+  }                                                                                        \
+                                                                                           \
+  void search(const raft::resources& res,                                                  \
+              const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_pq::search_params>& search_params,                \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                 \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                  \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                 \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::search(                                                 \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                  \
+      queries,                                                                             \
+      neighbors,                                                                           \
+      distances);                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void serialize(const raft::resources& res,                                               \
+                 const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                              \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                        \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                            \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);         \
+    return idx;                                                                            \
+  }                                                                                        \
+  }  // namespace cuvs::neighbors::ivf_pq
 CUVS_INST_MG_PQ(float, int64_t);
 
 #undef CUVS_INST_MG_PQ
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_half_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_half_int64_t.cu
index b167239c68..f9808df864 100644
--- a/cpp/src/neighbors/mg/mg_pq_half_int64_t.cu
+++ b/cpp/src/neighbors/mg/mg_pq_half_int64_t.cu
@@ -23,78 +23,74 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
-  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
-    const raft::device_resources& handle,                                                          \
-    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
-    cuvs::neighbors::mg::detail::build(                                                            \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
-      index_dataset);                                                                              \
-    return index;                                                                                  \
-  }                                                                                                \
-                                                                                                   \
-  void extend(const raft::device_resources& handle,                                                \
-              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
-  }                                                                                                \
-                                                                                                   \
-  void search(const raft::device_resources& handle,                                                \
-              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
-              const mg::search_params<ivf_pq::search_params>& search_params,                       \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
-              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
-              int64_t n_rows_per_batch)                                                            \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::search(                                                           \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
-      queries,                                                                                     \
-      neighbors,                                                                                   \
-      distances,                                                                                   \
-      n_rows_per_batch);                                                                           \
-  }                                                                                                \
-                                                                                                   \
-  void serialize(const raft::device_resources& handle,                                             \
-                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
-                 const std::string& filename)                                                      \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
-    const raft::device_resources& handle, const std::string& filename)                             \
-  {                                                                                                \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
-    return idx;                                                                                    \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
-                                                             const std::string& filename)          \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
-    return idx;                                                                                    \
-  }
+#define CUVS_INST_MG_PQ(T, IdxT)                                                           \
+  namespace cuvs::neighbors::ivf_pq {                                                      \
+  using namespace cuvs::neighbors;                                                         \
+                                                                                           \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                            \
+    const mg_index_params<ivf_pq::index_params>& index_params,                             \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                     \
+  {                                                                                        \
+    cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                  \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                    \
+      index_dataset);                                                                      \
+    return index;                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void extend(const raft::resources& res,                                                  \
+              cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,             \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)      \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);           \
+  }                                                                                        \
+                                                                                           \
+  void search(const raft::resources& res,                                                  \
+              const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_pq::search_params>& search_params,                \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                 \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                  \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                 \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::search(                                                 \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                  \
+      queries,                                                                             \
+      neighbors,                                                                           \
+      distances);                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void serialize(const raft::resources& res,                                               \
+                 const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                              \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                        \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                            \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);         \
+    return idx;                                                                            \
+  }                                                                                        \
+  }  // namespace cuvs::neighbors::ivf_pq
 CUVS_INST_MG_PQ(half, int64_t);
 
 #undef CUVS_INST_MG_PQ
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu
index 127baf8fd5..f2205626d8 100644
--- a/cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/mg/mg_pq_int8_t_int64_t.cu
@@ -23,78 +23,74 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
-  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
-    const raft::device_resources& handle,                                                          \
-    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
-    cuvs::neighbors::mg::detail::build(                                                            \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
-      index_dataset);                                                                              \
-    return index;                                                                                  \
-  }                                                                                                \
-                                                                                                   \
-  void extend(const raft::device_resources& handle,                                                \
-              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
-  }                                                                                                \
-                                                                                                   \
-  void search(const raft::device_resources& handle,                                                \
-              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
-              const mg::search_params<ivf_pq::search_params>& search_params,                       \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
-              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
-              int64_t n_rows_per_batch)                                                            \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::search(                                                           \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
-      queries,                                                                                     \
-      neighbors,                                                                                   \
-      distances,                                                                                   \
-      n_rows_per_batch);                                                                           \
-  }                                                                                                \
-                                                                                                   \
-  void serialize(const raft::device_resources& handle,                                             \
-                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
-                 const std::string& filename)                                                      \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
-    const raft::device_resources& handle, const std::string& filename)                             \
-  {                                                                                                \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
-    return idx;                                                                                    \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
-                                                             const std::string& filename)          \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
-    return idx;                                                                                    \
-  }
+#define CUVS_INST_MG_PQ(T, IdxT)                                                           \
+  namespace cuvs::neighbors::ivf_pq {                                                      \
+  using namespace cuvs::neighbors;                                                         \
+                                                                                           \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                            \
+    const mg_index_params<ivf_pq::index_params>& index_params,                             \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                     \
+  {                                                                                        \
+    cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                  \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                    \
+      index_dataset);                                                                      \
+    return index;                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void extend(const raft::resources& res,                                                  \
+              cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,             \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)      \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);           \
+  }                                                                                        \
+                                                                                           \
+  void search(const raft::resources& res,                                                  \
+              const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_pq::search_params>& search_params,                \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                 \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                  \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                 \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::search(                                                 \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                  \
+      queries,                                                                             \
+      neighbors,                                                                           \
+      distances);                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void serialize(const raft::resources& res,                                               \
+                 const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                              \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                        \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                            \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);         \
+    return idx;                                                                            \
+  }                                                                                        \
+  }  // namespace cuvs::neighbors::ivf_pq
 CUVS_INST_MG_PQ(int8_t, int64_t);
 
 #undef CUVS_INST_MG_PQ
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu b/cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
index 869e009a5b..1ec306a34a 100644
--- a/cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/mg/mg_pq_uint8_t_int64_t.cu
@@ -23,78 +23,74 @@
  *
  */
 
-#include "mg.cuh"
+#include "snmg.cuh"
 
-namespace cuvs::neighbors::mg {
-
-#define CUVS_INST_MG_PQ(T, IdxT)                                                                   \
-  index<ivf_pq::index<IdxT>, T, IdxT> build(                                                       \
-    const raft::device_resources& handle,                                                          \
-    const mg::index_params<ivf_pq::index_params>& index_params,                                    \
-    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                             \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    index<ivf_pq::index<IdxT>, T, IdxT> index(index_params.mode, clique.num_ranks_);               \
-    cuvs::neighbors::mg::detail::build(                                                            \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::index_params*>(&index_params),                            \
-      index_dataset);                                                                              \
-    return index;                                                                                  \
-  }                                                                                                \
-                                                                                                   \
-  void extend(const raft::device_resources& handle,                                                \
-              index<ivf_pq::index<IdxT>, T, IdxT>& index,                                          \
-              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                     \
-              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)              \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::extend(handle, index, new_vectors, new_indices);                  \
-  }                                                                                                \
-                                                                                                   \
-  void search(const raft::device_resources& handle,                                                \
-              const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                    \
-              const mg::search_params<ivf_pq::search_params>& search_params,                       \
-              raft::host_matrix_view<const T, int64_t, row_major> queries,                         \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                          \
-              raft::host_matrix_view<float, int64_t, row_major> distances,                         \
-              int64_t n_rows_per_batch)                                                            \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::search(                                                           \
-      handle,                                                                                      \
-      index,                                                                                       \
-      static_cast<const cuvs::neighbors::search_params*>(&search_params),                          \
-      queries,                                                                                     \
-      neighbors,                                                                                   \
-      distances,                                                                                   \
-      n_rows_per_batch);                                                                           \
-  }                                                                                                \
-                                                                                                   \
-  void serialize(const raft::device_resources& handle,                                             \
-                 const index<ivf_pq::index<IdxT>, T, IdxT>& index,                                 \
-                 const std::string& filename)                                                      \
-  {                                                                                                \
-    cuvs::neighbors::mg::detail::serialize(handle, index, filename);                               \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> deserialize_pq<T, IdxT>(                                     \
-    const raft::device_resources& handle, const std::string& filename)                             \
-  {                                                                                                \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(handle, filename);                              \
-    return idx;                                                                                    \
-  }                                                                                                \
-                                                                                                   \
-  template <>                                                                                      \
-  index<ivf_pq::index<IdxT>, T, IdxT> distribute_pq<T, IdxT>(const raft::device_resources& handle, \
-                                                             const std::string& filename)          \
-  {                                                                                                \
-    const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);              \
-    auto idx = index<ivf_pq::index<IdxT>, T, IdxT>(REPLICATED, clique.num_ranks_);                 \
-    cuvs::neighbors::mg::detail::deserialize_and_distribute(handle, idx, filename);                \
-    return idx;                                                                                    \
-  }
+#define CUVS_INST_MG_PQ(T, IdxT)                                                           \
+  namespace cuvs::neighbors::ivf_pq {                                                      \
+  using namespace cuvs::neighbors;                                                         \
+                                                                                           \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                            \
+    const mg_index_params<ivf_pq::index_params>& index_params,                             \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                     \
+  {                                                                                        \
+    cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                  \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                    \
+      index_dataset);                                                                      \
+    return index;                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void extend(const raft::resources& res,                                                  \
+              cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,             \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)      \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);           \
+  }                                                                                        \
+                                                                                           \
+  void search(const raft::resources& res,                                                  \
+              const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_pq::search_params>& search_params,                \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                 \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                  \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                 \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::search(                                                 \
+      res,                                                                                 \
+      index,                                                                               \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                  \
+      queries,                                                                             \
+      neighbors,                                                                           \
+      distances);                                                                          \
+  }                                                                                        \
+                                                                                           \
+  void serialize(const raft::resources& res,                                               \
+                 const cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                              \
+  {                                                                                        \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                        \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                            \
+  }                                                                                        \
+                                                                                           \
+  template <>                                                                              \
+  cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                               \
+  {                                                                                        \
+    auto idx = cuvs::neighbors::mg_index<ivf_pq::index<IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);         \
+    return idx;                                                                            \
+  }                                                                                        \
+  }  // namespace cuvs::neighbors::ivf_pq
 CUVS_INST_MG_PQ(uint8_t, int64_t);
 
 #undef CUVS_INST_MG_PQ
-
-}  // namespace cuvs::neighbors::mg
diff --git a/cpp/src/neighbors/mg/nccl_comm.cpp b/cpp/src/neighbors/mg/nccl_comm.cpp
deleted file mode 100644
index c4556957ae..0000000000
--- a/cpp/src/neighbors/mg/nccl_comm.cpp
+++ /dev/null
@@ -1,8 +0,0 @@
-#include <nccl.h>
-#include <raft/core/resources.hpp>
-
-namespace raft::comms {
-void build_comms_nccl_only(raft::resources* handle, ncclComm_t nccl_comm, int num_ranks, int rank)
-{
-}
-}  // namespace raft::comms
diff --git a/cpp/src/neighbors/mg/omp_checks.cpp b/cpp/src/neighbors/mg/omp_checks.cpp
index c8cc274141..91a23c8f32 100644
--- a/cpp/src/neighbors/mg/omp_checks.cpp
+++ b/cpp/src/neighbors/mg/omp_checks.cpp
@@ -17,7 +17,7 @@
 #include <omp.h>
 #include <raft/core/logger.hpp>
 
-namespace cuvs::neighbors::mg {
+namespace cuvs::neighbors::snmg {
 
 void check_omp_threads(const int requirements)
 {
@@ -30,4 +30,4 @@ void check_omp_threads(const int requirements)
       requirements);
 }
 
-}  // namespace cuvs::neighbors::mg
+}  // namespace cuvs::neighbors::snmg
diff --git a/cpp/src/neighbors/mg/mg.cuh b/cpp/src/neighbors/mg/snmg.cuh
similarity index 72%
rename from cpp/src/neighbors/mg/mg.cuh
rename to cpp/src/neighbors/mg/snmg.cuh
index e9cdc30f6b..3d9e795adc 100644
--- a/cpp/src/neighbors/mg/mg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -22,8 +22,10 @@
 #include <raft/linalg/add.cuh>
 #include <raft/util/cuda_dev_essentials.cuh>
 
+#include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.hpp>
-#include <cuvs/neighbors/mg.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
 
 #include <fstream>
 
@@ -31,7 +33,7 @@ namespace cuvs::neighbors {
 using namespace raft;
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void search(const raft::device_resources& handle,
+void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::host_matrix_view<const T, int64_t, row_major> h_queries,
@@ -39,55 +41,49 @@ void search(const raft::device_resources& handle,
             raft::device_matrix_view<float, int64_t, row_major> d_distances);
 }  // namespace cuvs::neighbors
 
-namespace cuvs::neighbors::mg {
+namespace cuvs::neighbors::snmg {
 void check_omp_threads(const int requirements);
-}  // namespace cuvs::neighbors::mg
+}  // namespace cuvs::neighbors::snmg
 
-namespace cuvs::neighbors::mg::detail {
+namespace cuvs::neighbors::snmg::detail {
 using namespace cuvs::neighbors;
 using namespace raft;
 
 // local index deserialization and distribution
 template <typename AnnIndexType, typename T, typename IdxT>
-void deserialize_and_distribute(const raft::device_resources& handle,
-                                index<AnnIndexType, T, IdxT>& index,
+void deserialize_and_distribute(const raft::resources& clique,
+                                mg_index<AnnIndexType, T, IdxT>& index,
                                 const std::string& filename)
 {
-  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
   for (int rank = 0; rank < index.num_ranks_; rank++) {
-    int dev_id                            = clique.device_ids_[rank];
-    const raft::device_resources& dev_res = clique.device_resources_[rank];
-    RAFT_CUDA_TRY(cudaSetDevice(dev_id));
-    auto& ann_if = index.ann_interfaces_.emplace_back();
+    const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+    auto& ann_if                   = index.ann_interfaces_.emplace_back();
     cuvs::neighbors::deserialize(dev_res, ann_if, filename);
   }
 }
 
 // MG index deserialization
 template <typename AnnIndexType, typename T, typename IdxT>
-void deserialize(const raft::device_resources& handle,
-                 index<AnnIndexType, T, IdxT>& index,
+void deserialize(const raft::resources& clique,
+                 mg_index<AnnIndexType, T, IdxT>& index,
                  const std::string& filename)
 {
   std::ifstream is(filename, std::ios::in | std::ios::binary);
   if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
 
-  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
+  const auto& handle = raft::resource::set_current_device_to_root_rank(clique);
+  index.mode_        = (cuvs::neighbors::distribution_mode)deserialize_scalar<int>(handle, is);
+  index.num_ranks_   = deserialize_scalar<int>(handle, is);
 
-  index.mode_      = (cuvs::neighbors::mg::distribution_mode)deserialize_scalar<int>(handle, is);
-  index.num_ranks_ = deserialize_scalar<int>(handle, is);
-
-  if (index.num_ranks_ != clique.num_ranks_) {
+  if (index.num_ranks_ != raft::resource::get_nccl_num_ranks(clique)) {
     RAFT_FAIL("Serialized index has %d ranks whereas NCCL clique has %d ranks",
               index.num_ranks_,
-              clique.num_ranks_);
+              raft::resource::get_nccl_num_ranks(clique));
   }
 
   for (int rank = 0; rank < index.num_ranks_; rank++) {
-    int dev_id                            = clique.device_ids_[rank];
-    const raft::device_resources& dev_res = clique.device_resources_[rank];
-    RAFT_CUDA_TRY(cudaSetDevice(dev_id));
-    auto& ann_if = index.ann_interfaces_.emplace_back();
+    const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+    auto& ann_if                   = index.ann_interfaces_.emplace_back();
     cuvs::neighbors::deserialize(dev_res, ann_if, is);
   }
 
@@ -95,24 +91,20 @@ void deserialize(const raft::device_resources& handle,
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void build(const raft::device_resources& handle,
-           index<AnnIndexType, T, IdxT>& index,
+void build(const raft::resources& clique,
+           mg_index<AnnIndexType, T, IdxT>& index,
            const cuvs::neighbors::index_params* index_params,
            raft::host_matrix_view<const T, int64_t, row_major> index_dataset)
 {
-  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
-
   if (index.mode_ == REPLICATED) {
     int64_t n_rows = index_dataset.extent(0);
-    RAFT_LOG_INFO("REPLICATED BUILD: %d*%drows", index.num_ranks_, n_rows);
+    RAFT_LOG_DEBUG("REPLICATED BUILD: %d*%drows", index.num_ranks_, n_rows);
 
     index.ann_interfaces_.resize(index.num_ranks_);
 #pragma omp parallel for
     for (int rank = 0; rank < index.num_ranks_; rank++) {
-      int dev_id                            = clique.device_ids_[rank];
-      const raft::device_resources& dev_res = clique.device_resources_[rank];
-      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
-      auto& ann_if = index.ann_interfaces_[rank];
+      const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+      auto& ann_if                   = index.ann_interfaces_[rank];
       cuvs::neighbors::build(dev_res, ann_if, index_params, index_dataset);
       resource::sync_stream(dev_res);
     }
@@ -121,14 +113,12 @@ void build(const raft::device_resources& handle,
     int64_t n_cols           = index_dataset.extent(1);
     int64_t n_rows_per_shard = raft::ceildiv(n_rows, (int64_t)index.num_ranks_);
 
-    RAFT_LOG_INFO("SHARDED BUILD: %d*%drows", index.num_ranks_, n_rows_per_shard);
+    RAFT_LOG_DEBUG("SHARDED BUILD: %d*%drows", index.num_ranks_, n_rows_per_shard);
 
     index.ann_interfaces_.resize(index.num_ranks_);
 #pragma omp parallel for
     for (int rank = 0; rank < index.num_ranks_; rank++) {
-      int dev_id                            = clique.device_ids_[rank];
-      const raft::device_resources& dev_res = clique.device_resources_[rank];
-      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      const raft::resources& dev_res  = raft::resource::set_current_device_to_rank(clique, rank);
       int64_t offset                  = rank * n_rows_per_shard;
       int64_t n_rows_of_current_shard = std::min(n_rows_per_shard, n_rows - offset);
       const T* partition_ptr          = index_dataset.data_handle() + (offset * n_cols);
@@ -142,23 +132,19 @@ void build(const raft::device_resources& handle,
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void extend(const raft::device_resources& handle,
-            index<AnnIndexType, T, IdxT>& index,
+void extend(const raft::resources& clique,
+            mg_index<AnnIndexType, T, IdxT>& index,
             raft::host_matrix_view<const T, int64_t, row_major> new_vectors,
             std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)
 {
-  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
-
   int64_t n_rows = new_vectors.extent(0);
   if (index.mode_ == REPLICATED) {
-    RAFT_LOG_INFO("REPLICATED EXTEND: %d*%drows", index.num_ranks_, n_rows);
+    RAFT_LOG_DEBUG("REPLICATED EXTEND: %d*%drows", index.num_ranks_, n_rows);
 
 #pragma omp parallel for
     for (int rank = 0; rank < index.num_ranks_; rank++) {
-      int dev_id                            = clique.device_ids_[rank];
-      const raft::device_resources& dev_res = clique.device_resources_[rank];
-      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
-      auto& ann_if = index.ann_interfaces_[rank];
+      const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+      auto& ann_if                   = index.ann_interfaces_[rank];
       cuvs::neighbors::extend(dev_res, ann_if, new_vectors, new_indices);
       resource::sync_stream(dev_res);
     }
@@ -166,13 +152,11 @@ void extend(const raft::device_resources& handle,
     int64_t n_cols           = new_vectors.extent(1);
     int64_t n_rows_per_shard = raft::ceildiv(n_rows, (int64_t)index.num_ranks_);
 
-    RAFT_LOG_INFO("SHARDED EXTEND: %d*%drows", index.num_ranks_, n_rows_per_shard);
+    RAFT_LOG_DEBUG("SHARDED EXTEND: %d*%drows", index.num_ranks_, n_rows_per_shard);
 
 #pragma omp parallel for
     for (int rank = 0; rank < index.num_ranks_; rank++) {
-      int dev_id                            = clique.device_ids_[rank];
-      const raft::device_resources& dev_res = clique.device_resources_[rank];
-      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      const raft::resources& dev_res  = raft::resource::set_current_device_to_rank(clique, rank);
       int64_t offset                  = rank * n_rows_per_shard;
       int64_t n_rows_of_current_shard = std::min(n_rows_per_shard, n_rows - offset);
       const T* new_vectors_ptr        = new_vectors.data_handle() + (offset * n_cols);
@@ -193,8 +177,8 @@ void extend(const raft::device_resources& handle,
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
-                                      const index<AnnIndexType, T, IdxT>& index,
+void sharded_search_with_direct_merge(const raft::resources& clique,
+                                      const mg_index<AnnIndexType, T, IdxT>& index,
                                       const cuvs::neighbors::search_params* search_params,
                                       raft::host_matrix_view<const T, int64_t, row_major> queries,
                                       raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
@@ -205,7 +189,7 @@ void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
                                       int64_t n_neighbors,
                                       int64_t n_batches)
 {
-  const auto& root_handle = clique.set_current_device_to_root_rank();
+  const auto& root_handle = raft::resource::set_current_device_to_root_rank(clique);
   auto in_neighbors       = raft::make_device_matrix<IdxT, int64_t, row_major>(
     root_handle, index.num_ranks_ * n_rows_per_batch, n_neighbors);
   auto in_distances = raft::make_device_matrix<float, int64_t, row_major>(
@@ -228,13 +212,11 @@ void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
     check_omp_threads(requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
 #pragma omp parallel for num_threads(index.num_ranks_)
     for (int rank = 0; rank < index.num_ranks_; rank++) {
-      int dev_id                            = clique.device_ids_[rank];
-      const raft::device_resources& dev_res = clique.device_resources_[rank];
-      auto& ann_if                          = index.ann_interfaces_[rank];
-      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+      auto& ann_if                   = index.ann_interfaces_[rank];
 
-      if (rank == clique.root_rank_) {  // root rank
-        uint64_t batch_offset = clique.root_rank_ * part_size;
+      if (rank == raft::resource::get_nccl_clique_root_rank(clique)) {  // root rank
+        uint64_t batch_offset = raft::resource::get_nccl_clique_root_rank(clique) * part_size;
         auto d_neighbors      = raft::make_device_matrix_view<IdxT, int64_t, row_major>(
           in_neighbors.data_handle() + batch_offset, n_rows_of_current_batch, n_neighbors);
         auto d_distances = raft::make_device_matrix_view<float, int64_t, row_major>(
@@ -245,21 +227,21 @@ void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
         // wait for other ranks
         ncclGroupStart();
         for (int from_rank = 0; from_rank < index.num_ranks_; from_rank++) {
-          if (from_rank == clique.root_rank_) continue;
+          if (from_rank == raft::resource::get_nccl_clique_root_rank(clique)) continue;
 
           batch_offset = from_rank * part_size;
           ncclRecv(in_neighbors.data_handle() + batch_offset,
                    part_size * sizeof(IdxT),
                    ncclUint8,
                    from_rank,
-                   clique.nccl_comms_[rank],
-                   resource::get_cuda_stream(dev_res));
+                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_cuda_stream(dev_res));
           ncclRecv(in_distances.data_handle() + batch_offset,
                    part_size * sizeof(float),
                    ncclUint8,
                    from_rank,
-                   clique.nccl_comms_[rank],
-                   resource::get_cuda_stream(dev_res));
+                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_cuda_stream(dev_res));
         }
         ncclGroupEnd();
         resource::sync_stream(dev_res);
@@ -276,21 +258,21 @@ void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
         ncclSend(d_neighbors.data_handle(),
                  part_size * sizeof(IdxT),
                  ncclUint8,
-                 clique.root_rank_,
-                 clique.nccl_comms_[rank],
-                 resource::get_cuda_stream(dev_res));
+                 raft::resource::get_nccl_clique_root_rank(clique),
+                 raft::resource::get_nccl_comm(dev_res),
+                 raft::resource::get_cuda_stream(dev_res));
         ncclSend(d_distances.data_handle(),
                  part_size * sizeof(float),
                  ncclUint8,
-                 clique.root_rank_,
-                 clique.nccl_comms_[rank],
-                 resource::get_cuda_stream(dev_res));
+                 raft::resource::get_nccl_clique_root_rank(clique),
+                 raft::resource::get_nccl_comm(dev_res),
+                 raft::resource::get_cuda_stream(dev_res));
         ncclGroupEnd();
         resource::sync_stream(dev_res);
       }
     }
 
-    const auto& root_handle_   = clique.set_current_device_to_root_rank();
+    const auto& root_handle_   = raft::resource::set_current_device_to_root_rank(clique);
     auto h_trans               = std::vector<IdxT>(index.num_ranks_);
     int64_t translation_offset = 0;
     for (int rank = 0; rank < index.num_ranks_; rank++) {
@@ -301,7 +283,7 @@ void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
     raft::copy(d_trans.data_handle(),
                h_trans.data(),
                index.num_ranks_,
-               resource::get_cuda_stream(root_handle_));
+               raft::resource::get_cuda_stream(root_handle_));
 
     cuvs::neighbors::detail::knn_merge_parts(in_distances.data_handle(),
                                              in_neighbors.data_handle(),
@@ -310,25 +292,25 @@ void sharded_search_with_direct_merge(const raft::comms::nccl_clique& clique,
                                              n_rows_of_current_batch,
                                              index.num_ranks_,
                                              n_neighbors,
-                                             resource::get_cuda_stream(root_handle_),
+                                             raft::resource::get_cuda_stream(root_handle_),
                                              d_trans.data_handle());
 
     raft::copy(neighbors.data_handle() + output_offset,
                out_neighbors.data_handle(),
                part_size,
-               resource::get_cuda_stream(root_handle_));
+               raft::resource::get_cuda_stream(root_handle_));
     raft::copy(distances.data_handle() + output_offset,
                out_distances.data_handle(),
                part_size,
-               resource::get_cuda_stream(root_handle_));
+               raft::resource::get_cuda_stream(root_handle_));
 
     resource::sync_stream(root_handle_);
   }
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
-                                    const index<AnnIndexType, T, IdxT>& index,
+void sharded_search_with_tree_merge(const raft::resources& clique,
+                                    const mg_index<AnnIndexType, T, IdxT>& index,
                                     const cuvs::neighbors::search_params* search_params,
                                     raft::host_matrix_view<const T, int64_t, row_major> queries,
                                     raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
@@ -351,10 +333,8 @@ void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
     check_omp_threads(requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
 #pragma omp parallel for num_threads(index.num_ranks_)
     for (int rank = 0; rank < index.num_ranks_; rank++) {
-      int dev_id                            = clique.device_ids_[rank];
-      const raft::device_resources& dev_res = clique.device_resources_[rank];
-      auto& ann_if                          = index.ann_interfaces_[rank];
-      RAFT_CUDA_TRY(cudaSetDevice(dev_id));
+      const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+      auto& ann_if                   = index.ann_interfaces_[rank];
 
       int64_t part_size = n_rows_of_current_batch * n_neighbors;
 
@@ -377,11 +357,11 @@ void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
                               neighbors_view.data_handle(),
                               (IdxT)translation_offset,
                               part_size,
-                              resource::get_cuda_stream(dev_res));
+                              raft::resource::get_cuda_stream(dev_res));
 
       auto d_trans = raft::make_device_vector<IdxT, IdxT>(dev_res, 2);
       cudaMemsetAsync(
-        d_trans.data_handle(), 0, 2 * sizeof(IdxT), resource::get_cuda_stream(dev_res));
+        d_trans.data_handle(), 0, 2 * sizeof(IdxT), raft::resource::get_cuda_stream(dev_res));
 
       int64_t remaining = index.num_ranks_;
       int64_t radix     = 2;
@@ -399,14 +379,14 @@ void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
                      part_size * sizeof(IdxT),
                      ncclUint8,
                      other_id,
-                     clique.nccl_comms_[rank],
-                     resource::get_cuda_stream(dev_res));
+                     raft::resource::get_nccl_comm(dev_res),
+                     raft::resource::get_cuda_stream(dev_res));
             ncclRecv(tmp_distances.data_handle() + part_size,
                      part_size * sizeof(float),
                      ncclUint8,
                      other_id,
-                     clique.nccl_comms_[rank],
-                     resource::get_cuda_stream(dev_res));
+                     raft::resource::get_nccl_comm(dev_res),
+                     raft::resource::get_cuda_stream(dev_res));
             received_something = true;
           }
         } else if (rank % radix == offset)  // This is one of the senders
@@ -416,14 +396,14 @@ void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
                    part_size * sizeof(IdxT),
                    ncclUint8,
                    other_id,
-                   clique.nccl_comms_[rank],
-                   resource::get_cuda_stream(dev_res));
+                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_cuda_stream(dev_res));
           ncclSend(tmp_distances.data_handle(),
                    part_size * sizeof(float),
                    ncclUint8,
                    other_id,
-                   clique.nccl_comms_[rank],
-                   resource::get_cuda_stream(dev_res));
+                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_cuda_stream(dev_res));
         }
         ncclGroupEnd();
 
@@ -439,7 +419,7 @@ void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
                                                    n_rows_of_current_batch,
                                                    2,
                                                    n_neighbors,
-                                                   resource::get_cuda_stream(dev_res),
+                                                   raft::resource::get_cuda_stream(dev_res),
                                                    d_trans.data_handle());
 
           // If done, copy the final result
@@ -447,11 +427,11 @@ void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
             raft::copy(neighbors.data_handle() + output_offset,
                        tmp_neighbors.data_handle(),
                        part_size,
-                       resource::get_cuda_stream(dev_res));
+                       raft::resource::get_cuda_stream(dev_res));
             raft::copy(distances.data_handle() + output_offset,
                        tmp_distances.data_handle(),
                        part_size,
-                       resource::get_cuda_stream(dev_res));
+                       raft::resource::get_cuda_stream(dev_res));
 
             resource::sync_stream(dev_res);
           }
@@ -462,8 +442,8 @@ void sharded_search_with_tree_merge(const raft::comms::nccl_clique& clique,
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void run_search_batch(const raft::comms::nccl_clique& clique,
-                      const index<AnnIndexType, T, IdxT>& index,
+void run_search_batch(const raft::resources& clique,
+                      const mg_index<AnnIndexType, T, IdxT>& index,
                       int rank,
                       const cuvs::neighbors::search_params* search_params,
                       raft::host_matrix_view<const T, int64_t, row_major>& queries,
@@ -475,10 +455,8 @@ void run_search_batch(const raft::comms::nccl_clique& clique,
                       int64_t n_cols,
                       int64_t n_neighbors)
 {
-  int dev_id = clique.device_ids_[rank];
-  RAFT_CUDA_TRY(cudaSetDevice(dev_id));
-  const raft::device_resources& dev_res = clique.device_resources_[rank];
-  auto& ann_if                          = index.ann_interfaces_[rank];
+  const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+  auto& ann_if                   = index.ann_interfaces_[rank];
 
   auto query_partition = raft::make_host_matrix_view<const T, int64_t, row_major>(
     queries.data_handle() + query_offset, n_rows_of_current_batch, n_cols);
@@ -493,46 +471,46 @@ void run_search_batch(const raft::comms::nccl_clique& clique,
   raft::copy(neighbors.data_handle() + output_offset,
              d_neighbors.data_handle(),
              n_rows_of_current_batch * n_neighbors,
-             resource::get_cuda_stream(dev_res));
+             raft::resource::get_cuda_stream(dev_res));
   raft::copy(distances.data_handle() + output_offset,
              d_distances.data_handle(),
              n_rows_of_current_batch * n_neighbors,
-             resource::get_cuda_stream(dev_res));
+             raft::resource::get_cuda_stream(dev_res));
 
   resource::sync_stream(dev_res);
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void search(const raft::device_resources& handle,
-            const index<AnnIndexType, T, IdxT>& index,
+void search(const raft::resources& clique,
+            const mg_index<AnnIndexType, T, IdxT>& index,
             const cuvs::neighbors::search_params* search_params,
             raft::host_matrix_view<const T, int64_t, row_major> queries,
             raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
-            raft::host_matrix_view<float, int64_t, row_major> distances,
-            int64_t n_rows_per_batch)
+            raft::host_matrix_view<float, int64_t, row_major> distances)
 {
-  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
-
   int64_t n_rows      = queries.extent(0);
   int64_t n_cols      = queries.extent(1);
   int64_t n_neighbors = neighbors.extent(1);
 
+  int64_t n_rows_per_batch = -1;
   if (index.mode_ == REPLICATED) {
-    cuvs::neighbors::mg::replicated_search_mode search_mode;
+    cuvs::neighbors::replicated_search_mode search_mode;
     if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
-      const cuvs::neighbors::mg::search_params<ivf_flat::search_params>* mg_search_params =
-        static_cast<const cuvs::neighbors::mg::search_params<ivf_flat::search_params>*>(
+      const cuvs::neighbors::mg_search_params<ivf_flat::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg_search_params<ivf_flat::search_params>*>(
           search_params);
-      search_mode = mg_search_params->search_mode;
+      search_mode      = mg_search_params->search_mode;
+      n_rows_per_batch = mg_search_params->n_rows_per_batch;
     } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
-      const cuvs::neighbors::mg::search_params<ivf_pq::search_params>* mg_search_params =
-        static_cast<const cuvs::neighbors::mg::search_params<ivf_pq::search_params>*>(
-          search_params);
-      search_mode = mg_search_params->search_mode;
+      const cuvs::neighbors::mg_search_params<ivf_pq::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg_search_params<ivf_pq::search_params>*>(search_params);
+      search_mode      = mg_search_params->search_mode;
+      n_rows_per_batch = mg_search_params->n_rows_per_batch;
     } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
-      const cuvs::neighbors::mg::search_params<cagra::search_params>* mg_search_params =
-        static_cast<const cuvs::neighbors::mg::search_params<cagra::search_params>*>(search_params);
-      search_mode = mg_search_params->search_mode;
+      const cuvs::neighbors::mg_search_params<cagra::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg_search_params<cagra::search_params>*>(search_params);
+      search_mode      = mg_search_params->search_mode;
+      n_rows_per_batch = mg_search_params->n_rows_per_batch;
     }
 
     if (search_mode == LOAD_BALANCER) {
@@ -542,7 +520,7 @@ void search(const raft::device_resources& handle,
       int64_t n_batches = raft::ceildiv(n_rows, (int64_t)n_rows_per_batch);
       if (n_batches <= 1) n_rows_per_batch = n_rows;
 
-      RAFT_LOG_INFO(
+      RAFT_LOG_DEBUG(
         "REPLICATED SEARCH IN LOAD BALANCER MODE: %d*%drows", n_batches, n_rows_per_batch);
 
 #pragma omp parallel for
@@ -567,7 +545,7 @@ void search(const raft::device_resources& handle,
                          n_neighbors);
       }
     } else if (search_mode == ROUND_ROBIN) {
-      RAFT_LOG_INFO("REPLICATED SEARCH IN ROUND ROBIN MODE: %d*%drows", 1, n_rows);
+      RAFT_LOG_DEBUG("REPLICATED SEARCH IN ROUND ROBIN MODE: %d*%drows", 1, n_rows);
 
       ASSERT(n_rows <= n_rows_per_batch,
              "In round-robin mode, n_rows must lower or equal to n_rows_per_batch");
@@ -590,30 +568,32 @@ void search(const raft::device_resources& handle,
                        n_neighbors);
     }
   } else if (index.mode_ == SHARDED) {
-    cuvs::neighbors::mg::sharded_merge_mode merge_mode;
+    cuvs::neighbors::sharded_merge_mode merge_mode;
     if constexpr (std::is_same<AnnIndexType, ivf_flat::index<T, IdxT>>::value) {
-      const cuvs::neighbors::mg::search_params<ivf_flat::search_params>* mg_search_params =
-        static_cast<const cuvs::neighbors::mg::search_params<ivf_flat::search_params>*>(
+      const cuvs::neighbors::mg_search_params<ivf_flat::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg_search_params<ivf_flat::search_params>*>(
           search_params);
-      merge_mode = mg_search_params->merge_mode;
+      merge_mode       = mg_search_params->merge_mode;
+      n_rows_per_batch = mg_search_params->n_rows_per_batch;
     } else if constexpr (std::is_same<AnnIndexType, ivf_pq::index<IdxT>>::value) {
-      const cuvs::neighbors::mg::search_params<ivf_pq::search_params>* mg_search_params =
-        static_cast<const cuvs::neighbors::mg::search_params<ivf_pq::search_params>*>(
-          search_params);
-      merge_mode = mg_search_params->merge_mode;
+      const cuvs::neighbors::mg_search_params<ivf_pq::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg_search_params<ivf_pq::search_params>*>(search_params);
+      merge_mode       = mg_search_params->merge_mode;
+      n_rows_per_batch = mg_search_params->n_rows_per_batch;
     } else if constexpr (std::is_same<AnnIndexType, cagra::index<T, IdxT>>::value) {
-      const cuvs::neighbors::mg::search_params<cagra::search_params>* mg_search_params =
-        static_cast<const cuvs::neighbors::mg::search_params<cagra::search_params>*>(search_params);
-      merge_mode = mg_search_params->merge_mode;
+      const cuvs::neighbors::mg_search_params<cagra::search_params>* mg_search_params =
+        static_cast<const cuvs::neighbors::mg_search_params<cagra::search_params>*>(search_params);
+      merge_mode       = mg_search_params->merge_mode;
+      n_rows_per_batch = mg_search_params->n_rows_per_batch;
     }
 
     int64_t n_batches = raft::ceildiv(n_rows, (int64_t)n_rows_per_batch);
     if (n_batches <= 1) n_rows_per_batch = n_rows;
 
     if (merge_mode == MERGE_ON_ROOT_RANK) {
-      RAFT_LOG_INFO("SHARDED SEARCH WITH MERGE_ON_ROOT_RANK MERGE MODE: %d*%drows",
-                    n_batches,
-                    n_rows_per_batch);
+      RAFT_LOG_DEBUG("SHARDED SEARCH WITH MERGE_ON_ROOT_RANK MERGE MODE: %d*%drows",
+                     n_batches,
+                     n_rows_per_batch);
       sharded_search_with_direct_merge(clique,
                                        index,
                                        search_params,
@@ -626,7 +606,7 @@ void search(const raft::device_resources& handle,
                                        n_neighbors,
                                        n_batches);
     } else if (merge_mode == TREE_MERGE) {
-      RAFT_LOG_INFO(
+      RAFT_LOG_DEBUG(
         "SHARDED SEARCH WITH TREE_MERGE MERGE MODE %d*%drows", n_batches, n_rows_per_batch);
       sharded_search_with_tree_merge(clique,
                                      index,
@@ -644,23 +624,20 @@ void search(const raft::device_resources& handle,
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-void serialize(const raft::device_resources& handle,
-               const index<AnnIndexType, T, IdxT>& index,
+void serialize(const raft::resources& clique,
+               const mg_index<AnnIndexType, T, IdxT>& index,
                const std::string& filename)
 {
   std::ofstream of(filename, std::ios::out | std::ios::binary);
   if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
 
-  const raft::comms::nccl_clique& clique = raft::resource::get_nccl_clique(handle);
-
+  const auto& handle = raft::resource::set_current_device_to_root_rank(clique);
   serialize_scalar(handle, of, (int)index.mode_);
   serialize_scalar(handle, of, index.num_ranks_);
 
   for (int rank = 0; rank < index.num_ranks_; rank++) {
-    int dev_id                            = clique.device_ids_[rank];
-    const raft::device_resources& dev_res = clique.device_resources_[rank];
-    RAFT_CUDA_TRY(cudaSetDevice(dev_id));
-    auto& ann_if = index.ann_interfaces_[rank];
+    const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
+    auto& ann_if                   = index.ann_interfaces_[rank];
     cuvs::neighbors::serialize(dev_res, ann_if, of);
   }
 
@@ -668,25 +645,24 @@ void serialize(const raft::device_resources& handle,
   if (!of) { RAFT_FAIL("Error writing output %s", filename.c_str()); }
 }
 
-}  // namespace cuvs::neighbors::mg::detail
+}  // namespace cuvs::neighbors::snmg::detail
 
-namespace cuvs::neighbors::mg {
+namespace cuvs::neighbors {
 using namespace cuvs::neighbors;
 using namespace raft;
 
 template <typename AnnIndexType, typename T, typename IdxT>
-index<AnnIndexType, T, IdxT>::index(distribution_mode mode, int num_ranks_)
-  : mode_(mode),
-    num_ranks_(num_ranks_),
-    round_robin_counter_(std::make_shared<std::atomic<int64_t>>(0))
+mg_index<AnnIndexType, T, IdxT>::mg_index(const raft::resources& clique, distribution_mode mode)
+  : mode_(mode), round_robin_counter_(std::make_shared<std::atomic<int64_t>>(0))
 {
+  num_ranks_ = raft::resource::get_nccl_num_ranks(clique);
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>
-index<AnnIndexType, T, IdxT>::index(const raft::device_resources& handle,
-                                    const std::string& filename)
+mg_index<AnnIndexType, T, IdxT>::mg_index(const raft::resources& clique,
+                                          const std::string& filename)
   : round_robin_counter_(std::make_shared<std::atomic<int64_t>>(0))
 {
-  cuvs::neighbors::mg::detail::deserialize(handle, *this, filename);
+  cuvs::neighbors::snmg::detail::deserialize(clique, *this, filename);
 }
-}  // namespace cuvs::neighbors::mg
+}  // namespace cuvs::neighbors
diff --git a/cpp/tests/neighbors/mg.cuh b/cpp/tests/neighbors/mg.cuh
index be30ca6153..c2133393b6 100644
--- a/cpp/tests/neighbors/mg.cuh
+++ b/cpp/tests/neighbors/mg.cuh
@@ -19,8 +19,10 @@
 #include "ann_utils.cuh"
 #include "naive_knn.cuh"
 
-#include <cuvs/neighbors/mg.hpp>
-#include <raft/core/resource/nccl_clique.hpp>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_resources_snmg.hpp>
 
 namespace cuvs::neighbors::mg {
 
@@ -46,14 +48,14 @@ template <typename T, typename DataT>
 class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
  public:
   AnnMGTest()
-    : stream_(resource::get_cuda_stream(handle_)),
-      clique_(raft::resource::get_nccl_clique(handle_)),
+    : clique_(),
       ps(::testing::TestWithParam<AnnMGInputs>::GetParam()),
-      d_index_dataset(0, stream_),
-      d_queries(0, stream_),
+      d_index_dataset(0, resource::get_cuda_stream(clique_)),
+      d_queries(0, resource::get_cuda_stream(clique_)),
       h_index_dataset(0),
       h_queries(0)
   {
+    clique_.set_memory_pool(80);
   }
 
   void testAnnMG()
@@ -67,9 +69,10 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
     std::vector<uint32_t> neighbors_snmg_ann_32bits(queries_size);
 
     {
-      rmm::device_uvector<T> distances_ref_dev(queries_size, stream_);
-      rmm::device_uvector<int64_t> neighbors_ref_dev(queries_size, stream_);
-      cuvs::neighbors::naive_knn<T, DataT, int64_t>(handle_,
+      rmm::device_uvector<T> distances_ref_dev(queries_size, resource::get_cuda_stream(clique_));
+      rmm::device_uvector<int64_t> neighbors_ref_dev(queries_size,
+                                                     resource::get_cuda_stream(clique_));
+      cuvs::neighbors::naive_knn<T, DataT, int64_t>(clique_,
                                                     distances_ref_dev.data(),
                                                     neighbors_ref_dev.data(),
                                                     d_queries.data(),
@@ -79,9 +82,15 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
                                                     ps.dim,
                                                     ps.k,
                                                     ps.metric);
-      update_host(distances_ref.data(), distances_ref_dev.data(), queries_size, stream_);
-      update_host(neighbors_ref.data(), neighbors_ref_dev.data(), queries_size, stream_);
-      resource::sync_stream(handle_);
+      update_host(distances_ref.data(),
+                  distances_ref_dev.data(),
+                  queries_size,
+                  resource::get_cuda_stream(clique_));
+      update_host(neighbors_ref.data(),
+                  neighbors_ref_dev.data(),
+                  queries_size,
+                  resource::get_cuda_stream(clique_));
+      resource::sync_stream(clique_);
     }
 
     int64_t n_rows_per_search_batch = 3000;  // [3000, 3000, 1000] == 7000 rows
@@ -95,7 +104,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       else
         d_mode = distribution_mode::SHARDED;
 
-      mg::index_params<ivf_flat::index_params> index_params;
+      mg_index_params<ivf_flat::index_params> index_params;
       index_params.n_lists                  = ps.nlist;
       index_params.metric                   = ps.metric;
       index_params.adaptive_centers         = ps.adaptive_centers;
@@ -104,7 +113,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       index_params.metric_arg               = 0;
       index_params.mode                     = d_mode;
 
-      mg::search_params<ivf_flat::search_params> search_params;
+      mg_search_params<ivf_flat::search_params> search_params;
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = LOAD_BALANCER;
 
@@ -118,20 +127,22 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       {
-        auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
-        cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
-        cuvs::neighbors::mg::serialize(handle_, index, "mg_ivf_flat_index");
+        auto index = cuvs::neighbors::ivf_flat::build(clique_, index_params, index_dataset);
+        cuvs::neighbors::ivf_flat::extend(clique_, index, index_dataset, std::nullopt);
+        cuvs::neighbors::ivf_flat::serialize(clique_, index, "mg_ivf_flat_index");
       }
       auto new_index =
-        cuvs::neighbors::mg::deserialize_flat<DataT, int64_t>(handle_, "mg_ivf_flat_index");
+        cuvs::neighbors::ivf_flat::deserialize<DataT, int64_t>(clique_, "mg_ivf_flat_index");
 
       if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
         search_params.merge_mode = MERGE_ON_ROOT_RANK;
       else
         search_params.merge_mode = TREE_MERGE;
-      cuvs::neighbors::mg::search(
-        handle_, new_index, search_params, queries, neighbors, distances, n_rows_per_search_batch);
-      resource::sync_stream(handle_);
+
+      search_params.n_rows_per_batch = n_rows_per_search_batch;
+      cuvs::neighbors::ivf_flat::search(
+        clique_, new_index, search_params, queries, neighbors, distances);
+      resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
       ASSERT_TRUE(eval_neighbours(neighbors_ref,
@@ -155,7 +166,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       else
         d_mode = distribution_mode::SHARDED;
 
-      mg::index_params<ivf_pq::index_params> index_params;
+      mg_index_params<ivf_pq::index_params> index_params;
       index_params.n_lists                  = ps.nlist;
       index_params.metric                   = ps.metric;
       index_params.add_data_on_build        = false;
@@ -163,7 +174,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       index_params.metric_arg               = 0;
       index_params.mode                     = d_mode;
 
-      mg::search_params<ivf_pq::search_params> search_params;
+      mg_search_params<ivf_pq::search_params> search_params;
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = LOAD_BALANCER;
 
@@ -177,20 +188,22 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       {
-        auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
-        cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
-        cuvs::neighbors::mg::serialize(handle_, index, "mg_ivf_pq_index");
+        auto index = cuvs::neighbors::ivf_pq::build(clique_, index_params, index_dataset);
+        cuvs::neighbors::ivf_pq::extend(clique_, index, index_dataset, std::nullopt);
+        cuvs::neighbors::ivf_pq::serialize(clique_, index, "mg_ivf_pq_index");
       }
       auto new_index =
-        cuvs::neighbors::mg::deserialize_pq<DataT, int64_t>(handle_, "mg_ivf_pq_index");
+        cuvs::neighbors::ivf_pq::deserialize<DataT, int64_t>(clique_, "mg_ivf_pq_index");
 
       if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
         search_params.merge_mode = MERGE_ON_ROOT_RANK;
       else
         search_params.merge_mode = TREE_MERGE;
-      cuvs::neighbors::mg::search(
-        handle_, new_index, search_params, queries, neighbors, distances, n_rows_per_search_batch);
-      resource::sync_stream(handle_);
+
+      search_params.n_rows_per_batch = n_rows_per_search_batch;
+      cuvs::neighbors::ivf_pq::search(
+        clique_, new_index, search_params, queries, neighbors, distances);
+      resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
       ASSERT_TRUE(eval_neighbours(neighbors_ref,
@@ -214,12 +227,12 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       else
         d_mode = distribution_mode::SHARDED;
 
-      mg::index_params<cagra::index_params> index_params;
+      mg_index_params<cagra::index_params> index_params;
       index_params.graph_build_params = cagra::graph_build_params::ivf_pq_params(
         raft::matrix_extent<int64_t>(ps.num_db_vecs, ps.dim));
       index_params.mode = d_mode;
 
-      mg::search_params<cagra::search_params> search_params;
+      mg_search_params<cagra::search_params> search_params;
 
       auto index_dataset = raft::make_host_matrix_view<const DataT, uint32_t, row_major>(
         h_index_dataset.data(), ps.num_db_vecs, ps.dim);
@@ -231,19 +244,21 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       {
-        auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
-        cuvs::neighbors::mg::serialize(handle_, index, "mg_cagra_index");
+        auto index = cuvs::neighbors::cagra::build(clique_, index_params, index_dataset);
+        cuvs::neighbors::cagra::serialize(clique_, index, "mg_cagra_index");
       }
       auto new_index =
-        cuvs::neighbors::mg::deserialize_cagra<DataT, uint32_t>(handle_, "mg_cagra_index");
+        cuvs::neighbors::cagra::deserialize<DataT, uint32_t>(clique_, "mg_cagra_index");
 
       if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
         search_params.merge_mode = MERGE_ON_ROOT_RANK;
       else
         search_params.merge_mode = TREE_MERGE;
-      cuvs::neighbors::mg::search(
-        handle_, new_index, search_params, queries, neighbors, distances, n_rows_per_search_batch);
-      resource::sync_stream(handle_);
+
+      search_params.n_rows_per_batch = n_rows_per_search_batch;
+      cuvs::neighbors::cagra::search(
+        clique_, new_index, search_params, queries, neighbors, distances);
+      resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
       ASSERT_TRUE(eval_neighbours(neighbors_ref_32bits,
@@ -267,15 +282,15 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       index_params.kmeans_trainset_fraction = 1.0;
       index_params.metric_arg               = 0;
 
-      mg::search_params<ivf_flat::search_params> search_params;
+      mg_search_params<ivf_flat::search_params> search_params;
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = LOAD_BALANCER;
 
       {
         auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
           d_index_dataset.data(), ps.num_db_vecs, ps.dim);
-        auto index = cuvs::neighbors::ivf_flat::build(handle_, index_params, index_dataset);
-        ivf_flat::serialize(handle_, "local_ivf_flat_index", index);
+        auto index = cuvs::neighbors::ivf_flat::build(clique_, index_params, index_dataset);
+        ivf_flat::serialize(clique_, "local_ivf_flat_index", index);
       }
 
       auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
@@ -286,17 +301,14 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       auto distributed_index =
-        cuvs::neighbors::mg::distribute_flat<DataT, int64_t>(handle_, "local_ivf_flat_index");
+        cuvs::neighbors::ivf_flat::distribute<DataT, int64_t>(clique_, "local_ivf_flat_index");
       search_params.merge_mode = TREE_MERGE;
-      cuvs::neighbors::mg::search(handle_,
-                                  distributed_index,
-                                  search_params,
-                                  queries,
-                                  neighbors,
-                                  distances,
-                                  n_rows_per_search_batch);
 
-      resource::sync_stream(handle_);
+      search_params.n_rows_per_batch = n_rows_per_search_batch;
+      cuvs::neighbors::ivf_flat::search(
+        clique_, distributed_index, search_params, queries, neighbors, distances);
+
+      resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
       ASSERT_TRUE(eval_neighbours(neighbors_ref,
@@ -319,15 +331,15 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       index_params.kmeans_trainset_fraction = 1.0;
       index_params.metric_arg               = 0;
 
-      mg::search_params<ivf_pq::search_params> search_params;
+      mg_search_params<ivf_pq::search_params> search_params;
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = LOAD_BALANCER;
 
       {
         auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
           d_index_dataset.data(), ps.num_db_vecs, ps.dim);
-        auto index = cuvs::neighbors::ivf_pq::build(handle_, index_params, index_dataset);
-        ivf_pq::serialize(handle_, "local_ivf_pq_index", index);
+        auto index = cuvs::neighbors::ivf_pq::build(clique_, index_params, index_dataset);
+        ivf_pq::serialize(clique_, "local_ivf_pq_index", index);
       }
 
       auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
@@ -338,17 +350,14 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       auto distributed_index =
-        cuvs::neighbors::mg::distribute_pq<DataT, int64_t>(handle_, "local_ivf_pq_index");
+        cuvs::neighbors::ivf_pq::distribute<DataT, int64_t>(clique_, "local_ivf_pq_index");
       search_params.merge_mode = TREE_MERGE;
-      cuvs::neighbors::mg::search(handle_,
-                                  distributed_index,
-                                  search_params,
-                                  queries,
-                                  neighbors,
-                                  distances,
-                                  n_rows_per_search_batch);
 
-      resource::sync_stream(handle_);
+      search_params.n_rows_per_batch = n_rows_per_search_batch;
+      cuvs::neighbors::ivf_pq::search(
+        clique_, distributed_index, search_params, queries, neighbors, distances);
+
+      resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
       ASSERT_TRUE(eval_neighbours(neighbors_ref,
@@ -368,13 +377,13 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       index_params.graph_build_params = cagra::graph_build_params::ivf_pq_params(
         raft::matrix_extent<int64_t>(ps.num_db_vecs, ps.dim));
 
-      mg::search_params<cagra::search_params> search_params;
+      mg_search_params<cagra::search_params> search_params;
 
       {
         auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
           d_index_dataset.data(), ps.num_db_vecs, ps.dim);
-        auto index = cuvs::neighbors::cagra::build(handle_, index_params, index_dataset);
-        cuvs::neighbors::cagra::serialize(handle_, "local_cagra_index", index);
+        auto index = cuvs::neighbors::cagra::build(clique_, index_params, index_dataset);
+        cuvs::neighbors::cagra::serialize(clique_, "local_cagra_index", index);
       }
 
       auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
@@ -385,18 +394,15 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       auto distributed_index =
-        cuvs::neighbors::mg::distribute_cagra<DataT, uint32_t>(handle_, "local_cagra_index");
+        cuvs::neighbors::cagra::distribute<DataT, uint32_t>(clique_, "local_cagra_index");
 
       search_params.merge_mode = TREE_MERGE;
-      cuvs::neighbors::mg::search(handle_,
-                                  distributed_index,
-                                  search_params,
-                                  queries,
-                                  neighbors,
-                                  distances,
-                                  n_rows_per_search_batch);
 
-      resource::sync_stream(handle_);
+      search_params.n_rows_per_batch = n_rows_per_search_batch;
+      cuvs::neighbors::cagra::search(
+        clique_, distributed_index, search_params, queries, neighbors, distances);
+
+      resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
       ASSERT_TRUE(eval_neighbours(neighbors_ref_32bits,
@@ -414,7 +420,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
     if (ps.algo == algo_t::IVF_FLAT && ps.d_mode == d_mode_t::ROUND_ROBIN) {
       ASSERT_TRUE(ps.num_queries <= 4);
 
-      mg::index_params<ivf_flat::index_params> index_params;
+      mg_index_params<ivf_flat::index_params> index_params;
       index_params.n_lists                  = ps.nlist;
       index_params.metric                   = ps.metric;
       index_params.adaptive_centers         = ps.adaptive_centers;
@@ -423,7 +429,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       index_params.metric_arg               = 0;
       index_params.mode                     = REPLICATED;
 
-      mg::search_params<ivf_flat::search_params> search_params;
+      mg_search_params<ivf_flat::search_params> search_params;
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = ROUND_ROBIN;
 
@@ -432,8 +438,8 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       auto small_batch_query = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
         h_queries.data(), ps.num_queries, ps.dim);
 
-      auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
-      cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
+      auto index = cuvs::neighbors::ivf_flat::build(clique_, index_params, index_dataset);
+      cuvs::neighbors::ivf_flat::extend(clique_, index, index_dataset, std::nullopt);
 
       int n_parallel_searches = 16;
       std::vector<char> searches_correctness(n_parallel_searches);
@@ -448,13 +454,14 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
           load_balancer_neighbors_snmg_ann.data() + offset, ps.num_queries, ps.k);
         auto small_batch_distances = raft::make_host_matrix_view<float, int64_t, row_major>(
           load_balancer_distances_snmg_ann.data() + offset, ps.num_queries, ps.k);
-        cuvs::neighbors::mg::search(handle_,
-                                    index,
-                                    search_params,
-                                    small_batch_query,
-                                    small_batch_neighbors,
-                                    small_batch_distances,
-                                    n_rows_per_search_batch);
+
+        search_params.n_rows_per_batch = n_rows_per_search_batch;
+        cuvs::neighbors::ivf_flat::search(clique_,
+                                          index,
+                                          search_params,
+                                          small_batch_query,
+                                          small_batch_neighbors,
+                                          small_batch_distances);
 
         std::vector<int64_t> small_batch_neighbors_vec(
           small_batch_neighbors.data_handle(),
@@ -479,7 +486,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
     if (ps.algo == algo_t::IVF_PQ && ps.d_mode == d_mode_t::ROUND_ROBIN) {
       ASSERT_TRUE(ps.num_queries <= 4);
 
-      mg::index_params<ivf_pq::index_params> index_params;
+      mg_index_params<ivf_pq::index_params> index_params;
       index_params.n_lists                  = ps.nlist;
       index_params.metric                   = ps.metric;
       index_params.add_data_on_build        = false;
@@ -487,7 +494,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       index_params.metric_arg               = 0;
       index_params.mode                     = REPLICATED;
 
-      mg::search_params<ivf_pq::search_params> search_params;
+      mg_search_params<ivf_pq::search_params> search_params;
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = ROUND_ROBIN;
 
@@ -496,8 +503,8 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       auto small_batch_query = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
         h_queries.data(), ps.num_queries, ps.dim);
 
-      auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
-      cuvs::neighbors::mg::extend(handle_, index, index_dataset, std::nullopt);
+      auto index = cuvs::neighbors::ivf_pq::build(clique_, index_params, index_dataset);
+      cuvs::neighbors::ivf_pq::extend(clique_, index, index_dataset, std::nullopt);
 
       int n_parallel_searches = 16;
       std::vector<char> searches_correctness(n_parallel_searches);
@@ -512,13 +519,14 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
           load_balancer_neighbors_snmg_ann.data() + offset, ps.num_queries, ps.k);
         auto small_batch_distances = raft::make_host_matrix_view<float, int64_t, row_major>(
           load_balancer_distances_snmg_ann.data() + offset, ps.num_queries, ps.k);
-        cuvs::neighbors::mg::search(handle_,
-                                    index,
-                                    search_params,
-                                    small_batch_query,
-                                    small_batch_neighbors,
-                                    small_batch_distances,
-                                    n_rows_per_search_batch);
+
+        search_params.n_rows_per_batch = n_rows_per_search_batch;
+        cuvs::neighbors::ivf_pq::search(clique_,
+                                        index,
+                                        search_params,
+                                        small_batch_query,
+                                        small_batch_neighbors,
+                                        small_batch_distances);
 
         std::vector<int64_t> small_batch_neighbors_vec(
           small_batch_neighbors.data_handle(),
@@ -543,12 +551,12 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
     if (ps.algo == algo_t::CAGRA && ps.d_mode == d_mode_t::ROUND_ROBIN) {
       ASSERT_TRUE(ps.num_queries <= 4);
 
-      mg::index_params<cagra::index_params> index_params;
+      mg_index_params<cagra::index_params> index_params;
       index_params.graph_build_params = cagra::graph_build_params::ivf_pq_params(
         raft::matrix_extent<int64_t>(ps.num_db_vecs, ps.dim));
       index_params.mode = REPLICATED;
 
-      mg::search_params<cagra::search_params> search_params;
+      mg_search_params<cagra::search_params> search_params;
       search_params.search_mode = ROUND_ROBIN;
 
       auto index_dataset = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
@@ -556,7 +564,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       auto small_batch_query = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
         h_queries.data(), ps.num_queries, ps.dim);
 
-      auto index = cuvs::neighbors::mg::build(handle_, index_params, index_dataset);
+      auto index = cuvs::neighbors::cagra::build(clique_, index_params, index_dataset);
 
       int n_parallel_searches = 16;
       std::vector<char> searches_correctness(n_parallel_searches);
@@ -571,13 +579,14 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
           load_balancer_neighbors_snmg_ann.data() + offset, ps.num_queries, ps.k);
         auto small_batch_distances = raft::make_host_matrix_view<float, int64_t, row_major>(
           load_balancer_distances_snmg_ann.data() + offset, ps.num_queries, ps.k);
-        cuvs::neighbors::mg::search(handle_,
-                                    index,
-                                    search_params,
-                                    small_batch_query,
-                                    small_batch_neighbors,
-                                    small_batch_distances,
-                                    n_rows_per_search_batch);
+
+        search_params.n_rows_per_batch = n_rows_per_search_batch;
+        cuvs::neighbors::cagra::search(clique_,
+                                       index,
+                                       search_params,
+                                       small_batch_query,
+                                       small_batch_neighbors,
+                                       small_batch_distances);
 
         std::vector<uint32_t> small_batch_neighbors_vec(
           small_batch_neighbors.data_handle(),
@@ -602,37 +611,35 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
 
   void SetUp() override
   {
-    d_index_dataset.resize(ps.num_db_vecs * ps.dim, stream_);
-    d_queries.resize(ps.num_queries * ps.dim, stream_);
+    d_index_dataset.resize(ps.num_db_vecs * ps.dim, resource::get_cuda_stream(clique_));
+    d_queries.resize(ps.num_queries * ps.dim, resource::get_cuda_stream(clique_));
     h_index_dataset.resize(ps.num_db_vecs * ps.dim);
     h_queries.resize(ps.num_queries * ps.dim);
 
     raft::random::RngState r(1234ULL);
     if constexpr (std::is_same<DataT, float>{}) {
       raft::random::uniform(
-        handle_, r, d_index_dataset.data(), d_index_dataset.size(), DataT(0.1), DataT(2.0));
-      raft::random::uniform(handle_, r, d_queries.data(), d_queries.size(), DataT(0.1), DataT(2.0));
+        clique_, r, d_index_dataset.data(), d_index_dataset.size(), DataT(0.1), DataT(2.0));
+      raft::random::uniform(clique_, r, d_queries.data(), d_queries.size(), DataT(0.1), DataT(2.0));
     } else {
       raft::random::uniformInt(
-        handle_, r, d_index_dataset.data(), d_index_dataset.size(), DataT(1), DataT(20));
-      raft::random::uniformInt(handle_, r, d_queries.data(), d_queries.size(), DataT(1), DataT(20));
+        clique_, r, d_index_dataset.data(), d_index_dataset.size(), DataT(1), DataT(20));
+      raft::random::uniformInt(clique_, r, d_queries.data(), d_queries.size(), DataT(1), DataT(20));
     }
 
     raft::copy(h_index_dataset.data(),
                d_index_dataset.data(),
                d_index_dataset.size(),
-               resource::get_cuda_stream(handle_));
+               resource::get_cuda_stream(clique_));
     raft::copy(
-      h_queries.data(), d_queries.data(), d_queries.size(), resource::get_cuda_stream(handle_));
-    resource::sync_stream(handle_);
+      h_queries.data(), d_queries.data(), d_queries.size(), resource::get_cuda_stream(clique_));
+    resource::sync_stream(clique_);
   }
 
   void TearDown() override {}
 
  private:
-  raft::device_resources handle_;
-  rmm::cuda_stream_view stream_;
-  raft::comms::nccl_clique clique_;
+  raft::device_resources_snmg clique_;
   AnnMGInputs ps;
   std::vector<DataT> h_index_dataset;
   std::vector<DataT> h_queries;
diff --git a/docs/source/cpp_api/neighbors_mg.rst b/docs/source/cpp_api/neighbors_mg.rst
index b68defec9f..6192c4f9d6 100644
--- a/docs/source/cpp_api/neighbors_mg.rst
+++ b/docs/source/cpp_api/neighbors_mg.rst
@@ -7,9 +7,9 @@ The SNMG (single-node multi-GPUs) ANN API provides a set of functions to deploy
    :language: c++
    :class: highlight
 
-``#include <cuvs/neighbors/mg.hpp>``
+``#include <cuvs/neighbors/common.hpp>``
 
-namespace *cuvs::neighbors::mg*
+namespace *cuvs::neighbors*
 
 Index build parameters
 ----------------------
@@ -20,7 +20,7 @@ Index build parameters
     :content-only:
 
 Search parameters
-----------------------
+-----------------
 
 .. doxygengroup:: mg_cpp_search_params
     :project: cuvs

From b8012181eec391e77ae2e02dd13ac83d02f73525 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 24 Apr 2025 07:46:50 +0200
Subject: [PATCH 367/623] IVF-PQ tests: fix segfault when accessing empty lists
 (#838)

Check if the `index.lists()[label]` is not an empty shared pointer before accessing its content in tests.

If the list size is zero, the list shared pointer can be `.reset()` during deserialization/resizing. Hence IVF-Flat and IVF-PQ can sometimes have empty shared pointers in their `.lists()` members. In case of IVF-PQ, we've had a piece of tests where this wasn't checked before accessing. As a result, we have been observing occasional CI failures with segfaults - when the IVF-PQ index was very unbalanced, i.e. had empty lists.

So far, the affected test is the only place I could identify where the access to the list pointer is unprotected (either via explicit check or by the invariant list_size > 0).

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/838
---
 cpp/tests/neighbors/ann_ivf_pq.cuh | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/cpp/tests/neighbors/ann_ivf_pq.cuh b/cpp/tests/neighbors/ann_ivf_pq.cuh
index 6c0fdc6088..659f350368 100644
--- a/cpp/tests/neighbors/ann_ivf_pq.cuh
+++ b/cpp/tests/neighbors/ann_ivf_pq.cuh
@@ -283,9 +283,11 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     // the original data cannot be reconstructed since the dataset was normalized
     if (index.metric() == cuvs::distance::DistanceType::CosineExpanded) { return; }
     auto& rec_list = index.lists()[label];
-    auto dim       = index.dim();
-    n_take         = std::min<uint32_t>(n_take, rec_list->size.load());
-    n_skip         = std::min<uint32_t>(n_skip, rec_list->size.load() - n_take);
+    // If the data is unbalanced the list might be empty, which is actually nullptr
+    if (!rec_list) { return; }
+    auto dim = index.dim();
+    n_take   = std::min<uint32_t>(n_take, rec_list->size.load());
+    n_skip   = std::min<uint32_t>(n_skip, rec_list->size.load() - n_take);
 
     if (n_take == 0) { return; }
 

From c3a77ec8383a67b473d4387789096ceb98b83108 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 24 Apr 2025 14:10:37 +0200
Subject: [PATCH 368/623] Use random tmp names for index files in tests (#837)

Replace all hard-coded index file names in tests with a randomly-generated unique paths in the system temporary folder and delete the files after use.

This is a slightly opinionated convenience PR to give the following benefits:
 - Temporary index files do not clutter the project folder
 - One can run multiple test instances in parallel without the danger of data corruption (e.g. for stress-testing)
 - The tests do not fail if the current folder is read-only

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/837
---
 cpp/tests/neighbors/ann_brute_force.cuh |  5 +++--
 cpp/tests/neighbors/ann_cagra.cuh       |  5 +++--
 cpp/tests/neighbors/ann_ivf_flat.cuh    |  6 ++---
 cpp/tests/neighbors/ann_ivf_pq.cuh      |  6 ++---
 cpp/tests/neighbors/ann_utils.cuh       | 26 +++++++++++++++++++++
 cpp/tests/neighbors/ann_vamana.cuh      |  3 ++-
 cpp/tests/neighbors/mg.cuh              | 30 +++++++++++++++----------
 7 files changed, 58 insertions(+), 23 deletions(-)

diff --git a/cpp/tests/neighbors/ann_brute_force.cuh b/cpp/tests/neighbors/ann_brute_force.cuh
index 03d6e820cd..dffe2dc25a 100644
--- a/cpp/tests/neighbors/ann_brute_force.cuh
+++ b/cpp/tests/neighbors/ann_brute_force.cuh
@@ -115,9 +115,10 @@ class AnnBruteForceTest : public ::testing::TestWithParam<AnnBruteForceInputs<Id
                                                       stream_,
                                                       true));
 
-      brute_force::serialize(handle_, std::string{"brute_force_index"}, idx, true);
+      tmp_index_file index_file;
+      brute_force::serialize(handle_, index_file.filename, idx, true);
       auto index_loaded = brute_force::index<DataT, T>(handle_);
-      brute_force::deserialize(handle_, std::string{"brute_force_index"}, &index_loaded);
+      brute_force::deserialize(handle_, index_file.filename, &index_loaded);
 
       brute_force::search(handle_,
                           index_loaded,
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 0270a5979b..4f09de90c9 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -408,6 +408,7 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
         auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
           (const DataT*)database.data(), ps.n_rows, ps.dim);
 
+        tmp_index_file index_file;
         {
           std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
           cagra::index<DataT, IdxT> index(handle_, index_params.metric);
@@ -422,11 +423,11 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
             index = cagra::build(handle_, index_params, database_view);
           };
 
-          cagra::serialize(handle_, "cagra_index", index, ps.include_serialized_dataset);
+          cagra::serialize(handle_, index_file.filename, index, ps.include_serialized_dataset);
         }
 
         cagra::index<DataT, IdxT> index(handle_);
-        cagra::deserialize(handle_, "cagra_index", &index);
+        cagra::deserialize(handle_, index_file.filename, &index);
 
         if (!ps.include_serialized_dataset) { index.update_dataset(handle_, database_view); }
 
diff --git a/cpp/tests/neighbors/ann_ivf_flat.cuh b/cpp/tests/neighbors/ann_ivf_flat.cuh
index 23d84ca98f..cb30261641 100644
--- a/cpp/tests/neighbors/ann_ivf_flat.cuh
+++ b/cpp/tests/neighbors/ann_ivf_flat.cuh
@@ -197,10 +197,10 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
           indices_ivfflat_dev.data(), ps.num_queries, ps.k);
         auto dists_out_view = raft::make_device_matrix_view<T, IdxT>(
           distances_ivfflat_dev.data(), ps.num_queries, ps.k);
-        const std::string filename = "ivf_flat_index";
-        cuvs::neighbors::ivf_flat::serialize(handle_, filename, index_2);
+        tmp_index_file index_file;
+        cuvs::neighbors::ivf_flat::serialize(handle_, index_file.filename, index_2);
         cuvs::neighbors::ivf_flat::index<DataT, IdxT> index_loaded(handle_);
-        cuvs::neighbors::ivf_flat::deserialize(handle_, filename, &index_loaded);
+        cuvs::neighbors::ivf_flat::deserialize(handle_, index_file.filename, &index_loaded);
         ASSERT_EQ(index_2.size(), index_loaded.size());
 
         cuvs::neighbors::ivf_flat::search(handle_,
diff --git a/cpp/tests/neighbors/ann_ivf_pq.cuh b/cpp/tests/neighbors/ann_ivf_pq.cuh
index 659f350368..0ebe604f80 100644
--- a/cpp/tests/neighbors/ann_ivf_pq.cuh
+++ b/cpp/tests/neighbors/ann_ivf_pq.cuh
@@ -267,10 +267,10 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
 
   auto build_serialize()
   {
-    std::string filename = "ivf_pq_index";
-    cuvs::neighbors::ivf_pq::serialize(handle_, filename, build_only());
+    tmp_index_file index_file;
+    cuvs::neighbors::ivf_pq::serialize(handle_, index_file.filename, build_only());
     cuvs::neighbors::ivf_pq::index<IdxT> index(handle_);
-    cuvs::neighbors::ivf_pq::deserialize(handle_, filename, &index);
+    cuvs::neighbors::ivf_pq::deserialize(handle_, index_file.filename, &index);
     return index;
   }
 
diff --git a/cpp/tests/neighbors/ann_utils.cuh b/cpp/tests/neighbors/ann_utils.cuh
index ded8cb5af8..0c01c48c9c 100644
--- a/cpp/tests/neighbors/ann_utils.cuh
+++ b/cpp/tests/neighbors/ann_utils.cuh
@@ -32,6 +32,9 @@
 #include "naive_knn.cuh"
 
 #include "../test_utils.cuh"
+#include <atomic>
+#include <cstdio>
+#include <filesystem>
 #include <gtest/gtest.h>
 #include <iostream>
 #include <limits>
@@ -346,4 +349,27 @@ auto eval_distances(raft::resources const& handle,
   }
   return testing::AssertionSuccess();
 }
+
+/**
+ * A helper class to create a temporary file for a cuVS index object in the system's temp directory.
+ * The file will be automatically deleted when the object is destroyed.
+ */
+struct tmp_index_file {
+  // Ideally, we should use std::tmpfile() or another system-provided API to create a temporary
+  // file. However, our API requires a file name, so we cannot use the file descriptors. There's no
+  // recommended way to generate a robust unique temp filenames, so we use a combination of a
+  // counter, process id, and random number.
+  std::string filename = (std::filesystem::temp_directory_path() /
+                          ("cuvs_" + std::to_string(getpid()) + "_" + std::to_string(counter++) +
+                           "_" + std::to_string(std::rand())))
+                           .string();
+  ~tmp_index_file()
+  {
+    if (std::filesystem::exists(filename)) { std::filesystem::remove(filename); }
+  }
+
+ private:
+  static inline std::atomic<uint64_t> counter = 0;
+};
+
 }  // namespace cuvs::neighbors
diff --git a/cpp/tests/neighbors/ann_vamana.cuh b/cpp/tests/neighbors/ann_vamana.cuh
index 9fe0324d78..0bb6d3340e 100644
--- a/cpp/tests/neighbors/ann_vamana.cuh
+++ b/cpp/tests/neighbors/ann_vamana.cuh
@@ -155,7 +155,8 @@ class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
 
     CheckGraph<DataT, IdxT>(&index, ps, stream_);
 
-    vamana::serialize(handle_, "vamana_index", index);
+    tmp_index_file index_file;
+    vamana::serialize(handle_, index_file.filename, index);
 
     // Test recall by searching with CAGRA search
     if (ps.graph_degree < 256) {  // CAGRA search result buffer cannot support larger graph degree
diff --git a/cpp/tests/neighbors/mg.cuh b/cpp/tests/neighbors/mg.cuh
index c2133393b6..7909ce9155 100644
--- a/cpp/tests/neighbors/mg.cuh
+++ b/cpp/tests/neighbors/mg.cuh
@@ -126,13 +126,14 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
+      tmp_index_file index_file;
       {
         auto index = cuvs::neighbors::ivf_flat::build(clique_, index_params, index_dataset);
         cuvs::neighbors::ivf_flat::extend(clique_, index, index_dataset, std::nullopt);
-        cuvs::neighbors::ivf_flat::serialize(clique_, index, "mg_ivf_flat_index");
+        cuvs::neighbors::ivf_flat::serialize(clique_, index, index_file.filename);
       }
       auto new_index =
-        cuvs::neighbors::ivf_flat::deserialize<DataT, int64_t>(clique_, "mg_ivf_flat_index");
+        cuvs::neighbors::ivf_flat::deserialize<DataT, int64_t>(clique_, index_file.filename);
 
       if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
         search_params.merge_mode = MERGE_ON_ROOT_RANK;
@@ -187,13 +188,14 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
+      tmp_index_file index_file;
       {
         auto index = cuvs::neighbors::ivf_pq::build(clique_, index_params, index_dataset);
         cuvs::neighbors::ivf_pq::extend(clique_, index, index_dataset, std::nullopt);
-        cuvs::neighbors::ivf_pq::serialize(clique_, index, "mg_ivf_pq_index");
+        cuvs::neighbors::ivf_pq::serialize(clique_, index, index_file.filename);
       }
       auto new_index =
-        cuvs::neighbors::ivf_pq::deserialize<DataT, int64_t>(clique_, "mg_ivf_pq_index");
+        cuvs::neighbors::ivf_pq::deserialize<DataT, int64_t>(clique_, index_file.filename);
 
       if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
         search_params.merge_mode = MERGE_ON_ROOT_RANK;
@@ -243,12 +245,13 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       auto distances = raft::make_host_matrix_view<float, uint32_t, row_major>(
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
+      tmp_index_file index_file;
       {
         auto index = cuvs::neighbors::cagra::build(clique_, index_params, index_dataset);
-        cuvs::neighbors::cagra::serialize(clique_, index, "mg_cagra_index");
+        cuvs::neighbors::cagra::serialize(clique_, index, index_file.filename);
       }
       auto new_index =
-        cuvs::neighbors::cagra::deserialize<DataT, uint32_t>(clique_, "mg_cagra_index");
+        cuvs::neighbors::cagra::deserialize<DataT, uint32_t>(clique_, index_file.filename);
 
       if (ps.m_mode == m_mode_t::MERGE_ON_ROOT_RANK)
         search_params.merge_mode = MERGE_ON_ROOT_RANK;
@@ -286,11 +289,12 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = LOAD_BALANCER;
 
+      tmp_index_file index_file;
       {
         auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
           d_index_dataset.data(), ps.num_db_vecs, ps.dim);
         auto index = cuvs::neighbors::ivf_flat::build(clique_, index_params, index_dataset);
-        ivf_flat::serialize(clique_, "local_ivf_flat_index", index);
+        ivf_flat::serialize(clique_, index_file.filename, index);
       }
 
       auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
@@ -301,7 +305,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       auto distributed_index =
-        cuvs::neighbors::ivf_flat::distribute<DataT, int64_t>(clique_, "local_ivf_flat_index");
+        cuvs::neighbors::ivf_flat::distribute<DataT, int64_t>(clique_, index_file.filename);
       search_params.merge_mode = TREE_MERGE;
 
       search_params.n_rows_per_batch = n_rows_per_search_batch;
@@ -335,11 +339,12 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       search_params.n_probes    = ps.nprobe;
       search_params.search_mode = LOAD_BALANCER;
 
+      tmp_index_file index_file;
       {
         auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
           d_index_dataset.data(), ps.num_db_vecs, ps.dim);
         auto index = cuvs::neighbors::ivf_pq::build(clique_, index_params, index_dataset);
-        ivf_pq::serialize(clique_, "local_ivf_pq_index", index);
+        ivf_pq::serialize(clique_, index_file.filename, index);
       }
 
       auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
@@ -350,7 +355,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       auto distributed_index =
-        cuvs::neighbors::ivf_pq::distribute<DataT, int64_t>(clique_, "local_ivf_pq_index");
+        cuvs::neighbors::ivf_pq::distribute<DataT, int64_t>(clique_, index_file.filename);
       search_params.merge_mode = TREE_MERGE;
 
       search_params.n_rows_per_batch = n_rows_per_search_batch;
@@ -379,11 +384,12 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
 
       mg_search_params<cagra::search_params> search_params;
 
+      tmp_index_file index_file;
       {
         auto index_dataset = raft::make_device_matrix_view<const DataT, int64_t>(
           d_index_dataset.data(), ps.num_db_vecs, ps.dim);
         auto index = cuvs::neighbors::cagra::build(clique_, index_params, index_dataset);
-        cuvs::neighbors::cagra::serialize(clique_, "local_cagra_index", index);
+        cuvs::neighbors::cagra::serialize(clique_, index_file.filename, index);
       }
 
       auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
@@ -394,7 +400,7 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       auto distributed_index =
-        cuvs::neighbors::cagra::distribute<DataT, uint32_t>(clique_, "local_cagra_index");
+        cuvs::neighbors::cagra::distribute<DataT, uint32_t>(clique_, index_file.filename);
 
       search_params.merge_mode = TREE_MERGE;
 

From 341cc3f707ba7e11805f3243acc1568edc1dca0a Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 24 Apr 2025 11:59:12 -0700
Subject: [PATCH 369/623] Specify matplotlib version (#839)

For security scanning we've been asked to specify a version for matplotlib, so require at least matplotlib 3.9 since according to https://github.com/matplotlib/matplotlib/security its the oldest still supported release of matplotlib

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Gil Forsyth (https://github.com/gforsyth)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cuvs/pull/839
---
 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml  | 2 +-
 conda/environments/bench_ann_cuda-128_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-128_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs-bench-cpu/recipe.yaml                | 2 +-
 conda/recipes/cuvs-bench/recipe.yaml                    | 2 +-
 dependencies.yaml                                       | 4 ++--
 python/cuvs_bench/pyproject.toml                        | 2 +-
 8 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 46d574552c..7594a38e5b 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -37,7 +37,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - libcuvs==25.6.*,>=0.0.0a0
 - librmm==25.6.*,>=0.0.0a0
-- matplotlib-base
+- matplotlib-base>=3.9
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 4c2d2c7c81..75699d828d 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -39,7 +39,7 @@ dependencies:
 - libcusparse=11.7.5.86
 - libcuvs==25.6.*,>=0.0.0a0
 - librmm==25.6.*,>=0.0.0a0
-- matplotlib-base
+- matplotlib-base>=3.9
 - mkl-devel=2023
 - nccl>=2.19
 - ninja
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index f2f27f4d4c..3ff76841d7 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -34,7 +34,7 @@ dependencies:
 - libcusparse-dev
 - libcuvs==25.6.*,>=0.0.0a0
 - librmm==25.6.*,>=0.0.0a0
-- matplotlib-base
+- matplotlib-base>=3.9
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index e70edb3b77..36ad1941f4 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -36,7 +36,7 @@ dependencies:
 - libcusparse-dev
 - libcuvs==25.6.*,>=0.0.0a0
 - librmm==25.6.*,>=0.0.0a0
-- matplotlib-base
+- matplotlib-base>=3.9
 - mkl-devel=2023
 - nccl>=2.19
 - ninja
diff --git a/conda/recipes/cuvs-bench-cpu/recipe.yaml b/conda/recipes/cuvs-bench-cpu/recipe.yaml
index 3061f16eaf..e0b3debcba 100644
--- a/conda/recipes/cuvs-bench-cpu/recipe.yaml
+++ b/conda/recipes/cuvs-bench-cpu/recipe.yaml
@@ -74,7 +74,7 @@ requirements:
     - click
     - glog ${{ glog_version }}
     - h5py ${{ h5py_version }}
-    - matplotlib-base
+    - matplotlib-base>=3.9
     - numpy >=1.23,<3.0a0
     - pandas
     - pyyaml
diff --git a/conda/recipes/cuvs-bench/recipe.yaml b/conda/recipes/cuvs-bench/recipe.yaml
index 5244915ae9..3632a982b3 100644
--- a/conda/recipes/cuvs-bench/recipe.yaml
+++ b/conda/recipes/cuvs-bench/recipe.yaml
@@ -43,7 +43,7 @@ requirements:
     - glog ${{ glog_version }}
     - h5py ${{ h5py_version }}
     - libcuvs-bench-ann =${{ version }}
-    - matplotlib-base
+    - matplotlib-base>=3.9
     - pandas
     - pylibraft =${{ minor_version }}
     - python
diff --git a/dependencies.yaml b/dependencies.yaml
index baf4987cce..362c78799c 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -596,10 +596,10 @@ dependencies:
           - scikit-learn
       - output_types: [conda]
         packages:
-          - matplotlib-base
+          - matplotlib-base>=3.9
       - output_types: [requirements, pyproject]
         packages:
-          - matplotlib
+          - matplotlib>=3.9
   depends_on_cuvs:
     common:
       - output_types: conda
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index af9cd487aa..a1f170cceb 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -20,7 +20,7 @@ requires-python = ">=3.10"
 dependencies = [
     "click",
     "cuvs==25.6.*,>=0.0.0a0",
-    "matplotlib",
+    "matplotlib>=3.9",
     "pandas",
     "pyyaml",
     "requests",

From 461b006f9b0fd7a7f47e18b0fe9a14f3dbdd2bfb Mon Sep 17 00:00:00 2001
From: Jaya Venkatesh <jjayabaskar@nvidia.com>
Date: Thu, 24 Apr 2025 14:37:29 -0700
Subject: [PATCH 370/623] Download build artifacts from Github for CI (#834)

This work is towards moving build artifacts from `downloads.rapids.ai` to Github Artifact Store (see https://github.com/rapidsai/ops/issues/2982)

Updates conda and wheel artifact download source from S3 to GitHub across CI scripts, wherever applicable.

Uses dynamic temporary paths for wheel downloads returned by `rapids-download-wheels-from-github` instead of using fixed directories, to streamline wheel downloads in the same way as conda downloads.

Also updates CI workflows to follow `package-name` convention between wheel build and wheel publish jobs.

Authors:
  - Jaya Venkatesh (https://github.com/VenkateshJaya)
  - AJ Schmidt (https://github.com/ajschmidt8)

Approvers:
  - Jake Awe (https://github.com/AyodeAwe)
  - AJ Schmidt (https://github.com/ajschmidt8)

URL: https://github.com/rapidsai/cuvs/pull/834
---
 .github/workflows/build.yaml | 4 ++--
 .github/workflows/pr.yaml    | 4 ++--
 ci/build_docs.sh             | 4 ++--
 ci/build_go.sh               | 2 +-
 ci/build_python.sh           | 2 +-
 ci/build_rust.sh             | 2 +-
 ci/build_wheel_cuvs.sh       | 4 ++--
 ci/test_cpp.sh               | 2 +-
 ci/test_python.sh            | 4 ++--
 ci/test_wheel_cuvs.sh        | 8 ++++----
 10 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 6221054c2c..fd13413fe3 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -108,8 +108,8 @@ jobs:
       script: ci/build_wheel_libcuvs.sh
       # build for every combination of arch and CUDA version, but only for the latest Python
       matrix_filter: group_by([.ARCH, (.CUDA_VER|split(".")|map(tonumber)|.[0])]) | map(max_by(.PY_VER|split(".")|map(tonumber)))
+      package-name: libcuvs
       package-type: cpp
-      wheel-name: libcuvs
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
@@ -131,8 +131,8 @@ jobs:
       sha: ${{ inputs.sha }}
       date: ${{ inputs.date }}
       script: ci/build_wheel_cuvs.sh
+      package-name: cuvs
       package-type: python
-      wheel-name: cuvs
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 66499f2516..d774f7a558 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -173,8 +173,8 @@ jobs:
       script: ci/build_wheel_libcuvs.sh
       # build for every combination of arch and CUDA version, but only for the latest Python
       matrix_filter: group_by([.ARCH, (.CUDA_VER|split(".")|map(tonumber)|.[0])]) | map(max_by(.PY_VER|split(".")|map(tonumber)))
+      package-name: libcuvs
       package-type: cpp
-      wheel-name: libcuvs
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
@@ -182,8 +182,8 @@ jobs:
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
+      package-name: cuvs
       package-type: python
-      wheel-name: cuvs
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index fe13aa275d..c53e6a0e9d 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -4,8 +4,8 @@
 set -euo pipefail
 
 rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+CPP_CHANNEL=$(rapids-download-conda-from-github cpp)
+PYTHON_CHANNEL=$(rapids-download-conda-from-github python)
 
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
diff --git a/ci/build_go.sh b/ci/build_go.sh
index 2dd38a7757..39a654c2b4 100755
--- a/ci/build_go.sh
+++ b/ci/build_go.sh
@@ -4,7 +4,7 @@
 set -euo pipefail
 
 rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+CPP_CHANNEL=$(rapids-download-conda-from-github cpp)
 
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 64d1c16f27..0e152ea652 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -15,7 +15,7 @@ rapids-print-env
 
 rapids-logger "Begin py build"
 
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+CPP_CHANNEL=$(rapids-download-conda-from-github cpp)
 
 version=$(rapids-generate-version)
 export RAPIDS_PACKAGE_VERSION=${version}
diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index b36262bda7..5cf1df8ea5 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -4,7 +4,7 @@
 set -euo pipefail
 
 rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+CPP_CHANNEL=$(rapids-download-conda-from-github cpp)
 
 rapids-logger "Create test conda environment"
 . /opt/conda/etc/profile.d/conda.sh
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index f6337fc27e..3b400f9edf 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -12,8 +12,8 @@ RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 #
 # Using env variable PIP_CONSTRAINT is necessary to ensure the constraints
 # are used when creating the isolated build environment.
-RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 cpp /tmp/libcuvs_dist
-echo "libcuvs-${RAPIDS_PY_CUDA_SUFFIX} @ file://$(echo /tmp/libcuvs_dist/libcuvs_*.whl)" > /tmp/constraints.txt
+LIBCUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github cpp)
+echo "libcuvs-${RAPIDS_PY_CUDA_SUFFIX} @ file://$(echo "${LIBCUVS_WHEELHOUSE}"/libcuvs_*.whl)" > /tmp/constraints.txt
 export PIP_CONSTRAINT="/tmp/constraints.txt"
 
 ci/build_wheel.sh cuvs ${package_dir} python
diff --git a/ci/test_cpp.sh b/ci/test_cpp.sh
index 34098175c4..758f39ba3b 100755
--- a/ci/test_cpp.sh
+++ b/ci/test_cpp.sh
@@ -5,7 +5,7 @@ set -euo pipefail
 
 . /opt/conda/etc/profile.d/conda.sh
 
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+CPP_CHANNEL=$(rapids-download-conda-from-github cpp)
 
 rapids-logger "Generate C++ testing dependencies"
 rapids-dependency-file-generator \
diff --git a/ci/test_python.sh b/ci/test_python.sh
index c040416b00..beefebf6c4 100755
--- a/ci/test_python.sh
+++ b/ci/test_python.sh
@@ -6,8 +6,8 @@ set -euo pipefail
 . /opt/conda/etc/profile.d/conda.sh
 
 rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
-PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+CPP_CHANNEL=$(rapids-download-conda-from-github cpp)
+PYTHON_CHANNEL=$(rapids-download-conda-from-github python)
 
 rapids-logger "Generate Python testing dependencies"
 rapids-dependency-file-generator \
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 99d36c24e8..8e758ab7d9 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -11,12 +11,12 @@ fi
 
 mkdir -p ./dist
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
-RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 cpp ./local-libcuvs-dep
-RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 python ./dist
+LIBCUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github cpp)
+CUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github python)
 
 # echo to expand wildcard before adding `[extra]` requires for pip
 rapids-pip-retry install \
-    ./local-libcuvs-dep/libcuvs*.whl \
-    "$(echo ./dist/cuvs*.whl)[test]"
+    "${LIBCUVS_WHEELHOUSE}"/libcuvs*.whl \
+    "$(echo "${CUVS_WHEELHOUSE}"/cuvs*.whl)[test]"
 
 python -m pytest ./python/cuvs/cuvs/tests

From 5b1825c4b19d854889078748d137cab7a03fdfb3 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Fri, 25 Apr 2025 00:18:19 +0200
Subject: [PATCH 371/623] Fix test_brute_force conversion (#821)

This method of conversion created some failures on DLFW tests

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/821
---
 python/cuvs/cuvs/tests/test_brute_force.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/cuvs/cuvs/tests/test_brute_force.py b/python/cuvs/cuvs/tests/test_brute_force.py
index d4094a07b6..3e0cad3e94 100644
--- a/python/cuvs/cuvs/tests/test_brute_force.py
+++ b/python/cuvs/cuvs/tests/test_brute_force.py
@@ -144,7 +144,7 @@ def test_prefiltered_brute_force_knn(
     queries = np.random.random_sample((n_query_rows, n_cols)).astype(dtype)
     n_prefilter_rows = n_query_rows if filter_type == "bitmap" else 1
     prefilter_bits = create_sparse_array(
-        (np.ceil(n_prefilter_rows * n_index_rows / 32).astype(np.uint32)),
+        int(np.ceil(n_prefilter_rows * n_index_rows / 32)),
         sparsity,
     )
 

From 24da9aa222706b8540b3bfe5bb0403b1ae95ea92 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 25 Apr 2025 13:32:23 +0200
Subject: [PATCH 372/623] Ann-bench: fix unsafe lazy blobs (#828)

The ann-bench dataset uses lazy-loading blobs to move data between storage and host and device memory.
The data may be moved between memory spaces at the moment some properties/pointers are requested.

In the search throughput mode, this sometimes causes a problem: two concurrent benchmark threads access the same property and concurrently modify the state of the blobs, which leads to various segfaults.

The fix is to guard the critical sections with a mutex lock. There shouldn't be any impact on benchmark QPS results.
Only one method, `dataset->dim()` is accessed within the benchmark loop. To avoid locking the mutex in this method, this PR changes the way `dim()` is evaluated; it's cached in `dim_` variable while still maintaining the behavior of loading it either from the query set or the base set depending on what is available/accessed first.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/828
---
 cpp/bench/ann/src/common/dataset.hpp | 74 +++++++++++++++++++---------
 1 file changed, 52 insertions(+), 22 deletions(-)

diff --git a/cpp/bench/ann/src/common/dataset.hpp b/cpp/bench/ann/src/common/dataset.hpp
index ba801d1651..45cfb5aec4 100644
--- a/cpp/bench/ann/src/common/dataset.hpp
+++ b/cpp/bench/ann/src/common/dataset.hpp
@@ -18,8 +18,10 @@
 #include "ann_types.hpp"
 #include "blob.hpp"
 
+#include <atomic>
 #include <cstdint>
 #include <cstdio>
+#include <mutex>
 #include <optional>
 #include <random>
 #include <string>
@@ -56,8 +58,19 @@ struct dataset {
   std::optional<blob<IdxT>> ground_truth_set_;
   std::optional<blob<bitset_carrier_type>> filter_bitset_;
 
-  mutable bool base_set_accessed_  = false;
-  mutable bool query_set_accessed_ = false;
+  // Protects the lazy mutations of the blobs accessed by multiple threads
+  mutable std::mutex mutex_;
+  // The dim can be read either from the training set or from the query set.
+  // This cache variable is filled from either of the two sets loaded first.
+  mutable std::atomic<int> dim_ = -1;
+
+  // Cache the dim value from the passed blob.
+  inline void cache_dim(const blob<DataT>& blob) const
+  {
+    if (dim_.load(std::memory_order_relaxed) == -1) {
+      dim_.store(static_cast<int>(blob.n_cols()), std::memory_order_relaxed);
+    }
+  }
 
  public:
   dataset(std::string name,
@@ -98,71 +111,87 @@ struct dataset {
   [[nodiscard]] auto distance() const -> std::string { return distance_; }
   [[nodiscard]] auto dim() const -> int
   {
-    // If any of base/query set are already accessed, use those
-    if (base_set_accessed_) { return static_cast<int>(base_set_.n_cols()); }
-    if (query_set_accessed_) { return static_cast<int>(query_set_.n_cols()); }
+    auto d = dim_.load(std::memory_order_relaxed);
+    if (d > -1) { return d; }
+    std::lock_guard<std::mutex> lock(mutex_);
     // Otherwise, try reading both (one of the two sets may be missing)
     try {
-      query_set_accessed_ = true;
-      return static_cast<int>(query_set_.n_cols());
+      d = static_cast<int>(query_set_.n_cols());
     } catch (const std::runtime_error& e) {
       // Any exception raised above will re-raise next time we try to access the query set.
-      query_set_accessed_ = false;
       query_set_.reset_lazy_state();
+      // If the query set is not accessible, use the base set.
+      // Don't catch the exception here, because we have nothing else to do anyway.
+      d = static_cast<int>(base_set_.n_cols());
     }
-    base_set_accessed_ = true;
-    return static_cast<int>(base_set_.n_cols());
+    dim_.store(d, std::memory_order_relaxed);
+    return d;
   }
   [[nodiscard]] auto max_k() const -> uint32_t
   {
+    std::lock_guard<std::mutex> lock(mutex_);
     if (ground_truth_set_.has_value()) { return ground_truth_set_->n_cols(); }
     return 0;
   }
   [[nodiscard]] auto base_set_size() const -> size_t
   {
-    base_set_accessed_ = true;
-    return base_set_.n_rows();
+    std::lock_guard<std::mutex> lock(mutex_);
+    auto r = base_set_.n_rows();
+    cache_dim(base_set_);
+    return r;
   }
   [[nodiscard]] auto query_set_size() const -> size_t
   {
-    query_set_accessed_ = true;
-    return query_set_.n_rows();
+    std::lock_guard<std::mutex> lock(mutex_);
+    auto r = query_set_.n_rows();
+    cache_dim(query_set_);
+    return r;
   }
 
   [[nodiscard]] auto gt_set() const -> const IdxT*
   {
+    std::lock_guard<std::mutex> lock(mutex_);
     if (ground_truth_set_.has_value()) { return ground_truth_set_->data(); }
     return nullptr;
   }
 
   [[nodiscard]] auto query_set() const -> const DataT*
   {
-    query_set_accessed_ = true;
-    return query_set_.data();
+    std::lock_guard<std::mutex> lock(mutex_);
+    auto* r = query_set_.data();
+    cache_dim(query_set_);
+    return r;
   }
   [[nodiscard]] auto query_set(MemoryType memory_type,
                                HugePages request_hugepages_2mb = HugePages::kDisable) const
     -> const DataT*
   {
-    query_set_accessed_ = true;
-    return query_set_.data(memory_type, request_hugepages_2mb);
+    std::lock_guard<std::mutex> lock(mutex_);
+    auto* r = query_set_.data(memory_type, request_hugepages_2mb);
+    cache_dim(query_set_);
+    return r;
   }
 
   [[nodiscard]] auto base_set() const -> const DataT*
   {
-    base_set_accessed_ = true;
-    return base_set_.data();
+    std::lock_guard<std::mutex> lock(mutex_);
+    auto* r = base_set_.data();
+    cache_dim(base_set_);
+    return r;
   }
   [[nodiscard]] auto base_set(MemoryType memory_type,
                               HugePages request_hugepages_2mb = HugePages::kDisable) const
     -> const DataT*
   {
-    base_set_accessed_ = true;
-    return base_set_.data(memory_type, request_hugepages_2mb);
+    std::lock_guard<std::mutex> lock(mutex_);
+    auto* r = base_set_.data(memory_type, request_hugepages_2mb);
+    cache_dim(base_set_);
+    return r;
   }
 
   [[nodiscard]] auto filter_bitset() const -> const bitset_carrier_type*
   {
+    std::lock_guard<std::mutex> lock(mutex_);
     if (filter_bitset_.has_value()) { return filter_bitset_->data(); }
     return nullptr;
   }
@@ -171,6 +200,7 @@ struct dataset {
                                    HugePages request_hugepages_2mb = HugePages::kDisable) const
     -> const bitset_carrier_type*
   {
+    std::lock_guard<std::mutex> lock(mutex_);
     if (filter_bitset_.has_value()) {
       return filter_bitset_->data(memory_type, request_hugepages_2mb);
     }

From fd845a9ed48fefee98a3805e51092482a540e031 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 25 Apr 2025 16:29:06 +0200
Subject: [PATCH 373/623] IVF-PQ: low-precision coarse search (#715)

Enable low-precision (half / int8) element type for use in the cuBLAS GEMM performed during coarse search (select clusters to probe). This makes cuBLAS use tensor cores and thus speeds up the coarse search.

Also propagate `kMaxQueries` compile time constant to a runtime search parameter: this allows to improve GPU utilization in extremely large batch size case, such as using IVF-PQ for constructing a nearest-neighbor graph for the whole dataset.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/715
---
 cpp/CMakeLists.txt                            |   2 +-
 .../src/cuvs/cuvs_ann_bench_param_parser.h    |  19 ++
 cpp/include/cuvs/neighbors/ivf_pq.h           |  15 +
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  39 +++
 cpp/src/neighbors/detail/ann_utils.cuh        |  16 +
 .../neighbors/detail/cagra/cagra_build.cpp    |   4 +-
 .../neighbors/detail/cagra/cagra_build.cuh    |  24 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |   4 +
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    | 303 +++++++++++++++---
 cpp/src/neighbors/ivf_pq_c.cpp                |   4 +
 .../{ivf_pq_index.cpp => ivf_pq_index.cu}     | 111 +++++++
 cpp/tests/neighbors/ann_ivf_pq.cuh            |  48 +++
 12 files changed, 531 insertions(+), 58 deletions(-)
 rename cpp/src/neighbors/{ivf_pq_index.cpp => ivf_pq_index.cu} (68%)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 60cab78b25..e919be49d2 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -425,7 +425,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
-    src/neighbors/ivf_pq_index.cpp
+    src/neighbors/ivf_pq_index.cu
     src/neighbors/ivf_pq/ivf_pq_build_common.cu
     src/neighbors/ivf_pq/ivf_pq_serialize.cu
     src/neighbors/ivf_pq/ivf_pq_deserialize.cu
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 7617bfa66f..48a0f39ab7 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -154,6 +154,25 @@ void parse_search_param(const nlohmann::json& conf,
     // set half as default
     param.pq_param.lut_dtype = CUDA_R_16F;
   }
+
+  if (conf.contains("coarse_search_dtype")) {
+    std::string type = conf.at("coarse_search_dtype");
+    if (type == "float") {
+      param.pq_param.coarse_search_dtype = CUDA_R_32F;
+    } else if (type == "half") {
+      param.pq_param.coarse_search_dtype = CUDA_R_16F;
+    } else if (type == "int8") {
+      param.pq_param.coarse_search_dtype = CUDA_R_8I;
+    } else {
+      throw std::runtime_error("coarse_search_dtype: '" + type +
+                               "', should be either 'float', 'half' or 'int8'");
+    }
+  }
+
+  if (conf.contains("max_internal_batch_size")) {
+    param.pq_param.max_internal_batch_size = conf.at("max_internal_batch_size");
+  }
+
   if (conf.contains("refine_ratio")) {
     param.refine_ratio = conf.at("refine_ratio");
     if (param.refine_ratio < 1.0f) { throw std::runtime_error("refine_ratio should be >= 1.0"); }
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index 08fe600ae8..66f4a86fa6 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -178,6 +178,21 @@ struct cuvsIvfPqSearchParams {
    * performance slightly.
    */
   cudaDataType_t internal_distance_dtype;
+  /**
+   * The data type to use as the GEMM element type when searching the clusters to probe.
+   *
+   * Possible values: [CUDA_R_8I, CUDA_R_16F, CUDA_R_32F].
+   *
+   * - Legacy default: CUDA_R_32F (float)
+   * - Recommended for performance: CUDA_R_16F (half)
+   * - Experimental/low-precision: CUDA_R_8I (int8_t)
+   *    (WARNING: int8_t variant degrades recall unless data is normalized and low-dimensional)
+   */
+  cudaDataType_t coarse_search_dtype;
+  /**
+   * Set the internal batch size to improve GPU utilization at the cost of larger memory footprint.
+   */
+  uint32_t max_internal_batch_size;
   /**
    * Preferred fraction of SM's unified memory / L1 cache to be used as shared memory.
    *
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 73f9a831bc..5efe3775f9 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -27,6 +27,11 @@
 #include <raft/core/resources.hpp>
 #include <raft/util/integer_utils.hpp>
 
+#include <optional>
+#include <tuple>
+#include <variant>
+#include <vector>
+
 namespace cuvs::neighbors::ivf_pq {
 
 /**
@@ -181,6 +186,22 @@ struct search_params : cuvs::neighbors::search_params {
    * performance if tweaked incorrectly.
    */
   double preferred_shmem_carveout = 1.0;
+  /**
+   * [Experimental] The data type to use as the GEMM element type when searching the clusters to
+   * probe.
+   *
+   * Possible values: [CUDA_R_8I, CUDA_R_16F, CUDA_R_32F].
+   *
+   * - Legacy default: CUDA_R_32F (float)
+   * - Recommended for performance: CUDA_R_16F (half)
+   * - Experimental/low-precision: CUDA_R_8I (int8_t)
+   *    (WARNING: int8_t variant degrades recall unless data is normalized and low-dimensional)
+   */
+  cudaDataType_t coarse_search_dtype = CUDA_R_32F;
+  /**
+   * Set the internal batch size to improve GPU utilization at the cost of larger memory footprint.
+   */
+  uint32_t max_internal_batch_size = 4096;
 };
 /**
  * @}
@@ -427,6 +448,11 @@ struct index : cuvs::neighbors::index {
   raft::device_matrix_view<float, uint32_t, raft::row_major> rotation_matrix() noexcept;
   raft::device_matrix_view<const float, uint32_t, raft::row_major> rotation_matrix() const noexcept;
 
+  raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> rotation_matrix_int8(
+    const raft::resources& res) const;
+  raft::device_matrix_view<const half, uint32_t, raft::row_major> rotation_matrix_half(
+    const raft::resources& res) const;
+
   /**
    * Accumulated list sizes, sorted in descending order [n_lists + 1].
    * The last value contains the total length of the index.
@@ -447,6 +473,11 @@ struct index : cuvs::neighbors::index {
   raft::device_matrix_view<float, uint32_t, raft::row_major> centers() noexcept;
   raft::device_matrix_view<const float, uint32_t, raft::row_major> centers() const noexcept;
 
+  raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> centers_int8(
+    const raft::resources& res) const;
+  raft::device_matrix_view<const half, uint32_t, raft::row_major> centers_half(
+    const raft::resources& res) const;
+
   /** Cluster centers corresponding to the lists in the rotated space [n_lists, rot_dim] */
   raft::device_matrix_view<float, uint32_t, raft::row_major> centers_rot() noexcept;
   raft::device_matrix_view<const float, uint32_t, raft::row_major> centers_rot() const noexcept;
@@ -485,6 +516,14 @@ struct index : cuvs::neighbors::index {
   raft::device_matrix<float, uint32_t, raft::row_major> centers_rot_;
   raft::device_matrix<float, uint32_t, raft::row_major> rotation_matrix_;
 
+  // Lazy-initialized low-precision variants of index members - for low-precision coarse search.
+  // These are never serialized and not touched during build/extend.
+  mutable std::optional<raft::device_matrix<int8_t, uint32_t, raft::row_major>> centers_int8_;
+  mutable std::optional<raft::device_matrix<half, uint32_t, raft::row_major>> centers_half_;
+  mutable std::optional<raft::device_matrix<int8_t, uint32_t, raft::row_major>>
+    rotation_matrix_int8_;
+  mutable std::optional<raft::device_matrix<half, uint32_t, raft::row_major>> rotation_matrix_half_;
+
   // Computed members for accelerating search.
   raft::device_vector<uint8_t*, uint32_t, raft::row_major> data_ptrs_;
   raft::device_vector<IdxT*, uint32_t, raft::row_major> inds_ptrs_;
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index 149eea3f1e..4c62d5bac0 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -195,6 +195,22 @@ struct mapping {
   /** @} */
 };
 
+template <>
+template <>
+HDI constexpr auto mapping<int8_t>::operator()(const uint8_t& x) const -> int8_t
+{
+  // Avoid overflows when converting uint8_t -> int_8
+  return static_cast<int8_t>(x >> 1);
+}
+
+template <>
+template <>
+HDI constexpr auto mapping<int8_t>::operator()(const float& x) const -> int8_t
+{
+  // Carefully clamp floats if out-of-bounds.
+  return static_cast<int8_t>(std::clamp<float>(x * 128.0f, -128.0f, 127.0f));
+}
+
 /**
  * @brief Sets the first num bytes of the block of memory pointed by ptr to the specified value.
  *
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cpp b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
index 490dc0f304..0fdfd1bcf8 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cpp
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
@@ -29,7 +29,9 @@ ivf_pq_params::ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
   search_params.n_probes                = std::max<uint32_t>(10, build_params.n_lists * 0.01);
   search_params.lut_dtype               = CUDA_R_16F;
   search_params.internal_distance_dtype = CUDA_R_16F;
+  search_params.coarse_search_dtype     = CUDA_R_16F;
+  search_params.max_internal_batch_size = 128 * 1024;
 
-  refinement_rate = 2;
+  refinement_rate = 1;
 }
 }  // namespace cuvs::neighbors::cagra::graph_build_params
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 7c749cf6e9..861c025619 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -164,13 +164,13 @@ void build_knn_graph(
   const auto num_queries = dataset.extent(0);
 
   // Use the same maximum batch size as the ivf_pq::search to avoid allocating more than needed.
-  constexpr uint32_t kMaxQueries = 4096;
+  const uint32_t max_queries = pq.search_params.max_internal_batch_size;
 
   // Heuristic: the build_knn_graph code should use only a fraction of the workspace memory; the
   // rest should be used by the ivf_pq::search. Here we say that the workspace size should be a good
   // multiple of what is required for the I/O batching below.
   constexpr size_t kMinWorkspaceRatio = 5;
-  auto desired_workspace_size         = kMaxQueries * kMinWorkspaceRatio *
+  auto desired_workspace_size         = max_queries * kMinWorkspaceRatio *
                                 (sizeof(DataT) * dataset.extent(1)  // queries (dataset batch)
                                  + sizeof(float) * gpu_top_k        // distances
                                  + sizeof(int64_t) * gpu_top_k      // neighbors
@@ -189,21 +189,21 @@ void build_knn_graph(
     node_degree,
     top_k,
     gpu_top_k,
-    kMaxQueries,
+    max_queries,
     pq.search_params.n_probes);
 
   auto distances = raft::make_device_mdarray<float>(
-    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, gpu_top_k));
+    res, workspace_mr, raft::make_extents<int64_t>(max_queries, gpu_top_k));
   auto neighbors = raft::make_device_mdarray<int64_t>(
-    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, gpu_top_k));
+    res, workspace_mr, raft::make_extents<int64_t>(max_queries, gpu_top_k));
   auto refined_distances = raft::make_device_mdarray<float>(
-    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, top_k));
+    res, workspace_mr, raft::make_extents<int64_t>(max_queries, top_k));
   auto refined_neighbors = raft::make_device_mdarray<int64_t>(
-    res, workspace_mr, raft::make_extents<int64_t>(kMaxQueries, top_k));
-  auto neighbors_host = raft::make_host_matrix<int64_t, int64_t>(kMaxQueries, gpu_top_k);
-  auto queries_host   = raft::make_host_matrix<DataT, int64_t>(kMaxQueries, dataset.extent(1));
-  auto refined_neighbors_host = raft::make_host_matrix<int64_t, int64_t>(kMaxQueries, top_k);
-  auto refined_distances_host = raft::make_host_matrix<float, int64_t>(kMaxQueries, top_k);
+    res, workspace_mr, raft::make_extents<int64_t>(max_queries, top_k));
+  auto neighbors_host = raft::make_host_matrix<int64_t, int64_t>(max_queries, gpu_top_k);
+  auto queries_host   = raft::make_host_matrix<DataT, int64_t>(max_queries, dataset.extent(1));
+  auto refined_neighbors_host = raft::make_host_matrix<int64_t, int64_t>(max_queries, top_k);
+  auto refined_distances_host = raft::make_host_matrix<float, int64_t>(max_queries, top_k);
 
   // TODO(tfeher): batched search with multiple GPUs
   std::size_t num_self_included = 0;
@@ -214,7 +214,7 @@ void build_knn_graph(
     dataset.data_handle(),
     dataset.extent(0),
     dataset.extent(1),
-    static_cast<int64_t>(kMaxQueries),
+    static_cast<int64_t>(max_queries),
     raft::resource::get_cuda_stream(res),
     workspace_mr);
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 4e2410405f..2ee0335f6e 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -239,6 +239,10 @@ void set_centers(raft::resources const& handle, index<IdxT>* index, const float*
   auto stream         = raft::resource::get_cuda_stream(handle);
   auto* device_memory = raft::resource::get_workspace_resource(handle);
 
+  // Make sure to have trailing zeroes between dim and dim_ext;
+  // We rely on this to enable padded tensor gemm kernels during coarse search.
+  cuvs::spatial::knn::detail::utils::memzero(
+    index->centers().data_handle(), index->centers().size(), stream);
   // combine cluster_centers and their norms
   RAFT_CUDA_TRY(cudaMemcpy2DAsync(index->centers().data_handle(),
                                   sizeof(float) * index->dim_ext(),
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index 05bb993533..feba4e7ae7 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -42,6 +42,7 @@
 #include <raft/linalg/normalize.cuh>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/matrix/detail/select_warpsort.cuh>
+#include <raft/matrix/select_k.cuh>
 #include <raft/util/cache.hpp>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/device_atomics.cuh>
@@ -131,19 +132,17 @@ void select_clusters(raft::resources const& handle,
     handle, float_queries_view, [queries, dim, dim_ext, norm_factor] __device__(uint32_t ix) {
       uint32_t col = ix % dim_ext;
       uint32_t row = ix / dim_ext;
-      return col < dim ? utils::mapping<float>{}(queries[col + dim * row]) : norm_factor;
+      if (col < dim) { return utils::mapping<float>{}(queries[col + dim * row]); }
+      return col == dim ? norm_factor : 0.0f;
     });
 
   float alpha;
   float beta;
-  uint32_t gemm_k = dim;
   switch (metric) {
     case cuvs::distance::DistanceType::L2SqrtExpanded:
     case cuvs::distance::DistanceType::L2Expanded: {
-      alpha  = -2.0;
-      beta   = 0.0;
-      gemm_k = dim + 1;
-      RAFT_EXPECTS(gemm_k <= dim_ext, "unexpected gemm_k or dim_ext");
+      alpha = -2.0;
+      beta  = 0.0;
     } break;
     case cuvs::distance::DistanceType::CosineExpanded:
     case cuvs::distance::DistanceType::InnerProduct: {
@@ -158,7 +157,7 @@ void select_clusters(raft::resources const& handle,
                      false,
                      n_lists,
                      n_queries,
-                     gemm_k,
+                     dim_ext,
                      &alpha,
                      cluster_centers,
                      dim_ext,
@@ -180,6 +179,178 @@ void select_clusters(raft::resources const& handle,
     true);
 }
 
+template <typename T>
+void select_clusters(raft::resources const& handle,
+                     uint32_t* clusters_to_probe,  // [n_queries, n_probes]
+                     int8_t* float_queries,        // [n_queries, dim_ext]
+                     uint32_t n_queries,
+                     uint32_t n_probes,
+                     uint32_t n_lists,
+                     uint32_t dim,
+                     uint32_t dim_ext,
+                     cuvs::distance::DistanceType metric,
+                     const T* queries,               // [n_queries, dim]
+                     const int8_t* cluster_centers,  // [n_lists, dim_ext]
+                     rmm::mr::device_memory_resource* mr)
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::search::select_clusters(n_probes = %u, n_queries = %u, n_lists = %u, dim = %u)",
+    n_probes,
+    n_queries,
+    n_lists,
+    dim);
+  auto stream = raft::resource::get_cuda_stream(handle);
+  int8_t norm_factor;
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded: norm_factor = -128; break;
+    case cuvs::distance::DistanceType::CosineExpanded:
+    case cuvs::distance::DistanceType::InnerProduct: norm_factor = 0; break;
+    default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
+  }
+  auto float_queries_view =
+    raft::make_device_vector_view<int8_t, uint32_t>(float_queries, dim_ext * n_queries);
+  raft::linalg::map_offset(
+    handle, float_queries_view, [queries, dim, dim_ext, norm_factor] __device__(uint32_t ix) {
+      uint32_t col = ix % dim_ext;
+      uint32_t row = ix / dim_ext;
+      if (col < dim) { return utils::mapping<int8_t>{}(queries[col + dim * row]); }
+      auto m = dim_ext - dim;
+      // see 'NOTE: maximizing the range and the precision of int8_t GEMM' in ivf_pq_index.cu
+      if (m == 1 || col > dim) { return norm_factor; }  // times `y` (higher bits)
+      return static_cast<int8_t>(1 - m);                // times `z` (lower bits)
+    });
+
+  using dist_type = int32_t;
+  dist_type alpha;
+  dist_type beta;
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded: {
+      alpha = -2;
+      beta  = 0;
+    } break;
+    case cuvs::distance::DistanceType::CosineExpanded:
+    case cuvs::distance::DistanceType::InnerProduct: {
+      alpha = -1;
+      beta  = 0;
+    } break;
+    default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
+  }
+  rmm::device_uvector<dist_type> qc_distances(n_queries * n_lists, stream, mr);
+  raft::linalg::gemm(handle,
+                     true,
+                     false,
+                     n_lists,
+                     n_queries,
+                     dim_ext,
+                     &alpha,
+                     cluster_centers,
+                     dim_ext,
+                     float_queries,
+                     dim_ext,
+                     &beta,
+                     qc_distances.data(),
+                     n_lists,
+                     stream);
+
+  // Select neighbor clusters for each query.
+  rmm::device_uvector<dist_type> cluster_dists(n_queries * n_probes, stream, mr);
+  // cuvs::selection::select_k lacks uint32_t-as-a-value support at the moment
+  raft::matrix::select_k<dist_type, uint32_t>(
+    handle,
+    raft::make_device_matrix_view<const dist_type, int64_t>(
+      qc_distances.data(), n_queries, n_lists),
+    std::nullopt,
+    raft::make_device_matrix_view<dist_type, int64_t>(cluster_dists.data(), n_queries, n_probes),
+    raft::make_device_matrix_view<uint32_t, int64_t>(clusters_to_probe, n_queries, n_probes),
+    true);
+}
+
+template <typename T>
+void select_clusters(raft::resources const& handle,
+                     uint32_t* clusters_to_probe,  // [n_queries, n_probes]
+                     half* float_queries,          // [n_queries, dim_ext]
+                     uint32_t n_queries,
+                     uint32_t n_probes,
+                     uint32_t n_lists,
+                     uint32_t dim,
+                     uint32_t dim_ext,
+                     cuvs::distance::DistanceType metric,
+                     const T* queries,             // [n_queries, dim]
+                     const half* cluster_centers,  // [n_lists, dim_ext]
+                     rmm::mr::device_memory_resource* mr)
+{
+  raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
+    "ivf_pq::search::select_clusters(n_probes = %u, n_queries = %u, n_lists = %u, dim = %u)",
+    n_probes,
+    n_queries,
+    n_lists,
+    dim);
+  auto stream = raft::resource::get_cuda_stream(handle);
+  half norm_factor;
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded: norm_factor = 1.0 / -2.0; break;
+    case cuvs::distance::DistanceType::CosineExpanded:
+    case cuvs::distance::DistanceType::InnerProduct: norm_factor = 0; break;
+    default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
+  }
+  auto float_queries_view =
+    raft::make_device_vector_view<half, uint32_t>(float_queries, dim_ext * n_queries);
+  raft::linalg::map_offset(
+    handle, float_queries_view, [queries, dim, dim_ext, norm_factor] __device__(uint32_t ix) {
+      uint32_t col = ix % dim_ext;
+      uint32_t row = ix / dim_ext;
+      if (col < dim) { return utils::mapping<half>{}(queries[col + dim * row]); }
+      return col == dim ? norm_factor : half(0);
+    });
+
+  using dist_type = half;
+  dist_type alpha;
+  dist_type beta;
+  switch (metric) {
+    case cuvs::distance::DistanceType::L2SqrtExpanded:
+    case cuvs::distance::DistanceType::L2Expanded: {
+      alpha = -2.0;
+      beta  = 0.0;
+    } break;
+    case cuvs::distance::DistanceType::CosineExpanded:
+    case cuvs::distance::DistanceType::InnerProduct: {
+      alpha = -1.0;
+      beta  = 0.0;
+    } break;
+    default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
+  }
+  rmm::device_uvector<dist_type> qc_distances(n_queries * n_lists, stream, mr);
+  raft::linalg::gemm(handle,
+                     true,
+                     false,
+                     n_lists,
+                     n_queries,
+                     dim_ext,
+                     &alpha,
+                     cluster_centers,
+                     dim_ext,
+                     float_queries,
+                     dim_ext,
+                     &beta,
+                     qc_distances.data(),
+                     n_lists,
+                     stream);
+
+  // Select neighbor clusters for each query.
+  rmm::device_uvector<dist_type> cluster_dists(n_queries * n_probes, stream, mr);
+  cuvs::selection::select_k(
+    handle,
+    raft::make_device_matrix_view<const dist_type, int64_t>(
+      qc_distances.data(), n_queries, n_lists),
+    std::nullopt,
+    raft::make_device_matrix_view<dist_type, int64_t>(cluster_dists.data(), n_queries, n_probes),
+    raft::make_device_matrix_view<uint32_t, int64_t>(clusters_to_probe, n_queries, n_probes),
+    true);
+}
+
 /**
  * An approximation to the number of times each cluster appears in a batched sample.
  *
@@ -607,8 +778,23 @@ inline auto get_max_batch_size(raft::resources const& res,
   return max_batch_size;
 }
 
-/** Maximum number of queries ivf_pq::search can process in one batch. */
-constexpr uint32_t kMaxQueries = 4096;
+template <typename T, typename IdxT>
+inline auto get_rotation_matrix(const raft::resources& res, const index<IdxT>& index)
+  -> raft::device_matrix_view<const T, uint32_t, raft::row_major>
+{
+  if constexpr (std::is_same_v<T, float>) { return index.rotation_matrix(); }
+  if constexpr (std::is_same_v<T, half>) { return index.rotation_matrix_half(res); }
+  if constexpr (std::is_same_v<T, int8_t>) { return index.rotation_matrix_int8(res); }
+}
+
+template <typename T, typename IdxT>
+inline auto get_centers(const raft::resources& res, const index<IdxT>& index)
+  -> raft::device_matrix_view<const T, uint32_t, raft::row_major>
+{
+  if constexpr (std::is_same_v<T, float>) { return index.centers(); }
+  if constexpr (std::is_same_v<T, half>) { return index.centers_half(res); }
+  if constexpr (std::is_same_v<T, int8_t>) { return index.centers_int8(res); }
+}
 
 /** See raft::spatial::knn::ivf_pq::search docs */
 template <typename T,
@@ -663,8 +849,11 @@ inline void search(raft::resources const& handle,
 
   auto stream = raft::resource::get_cuda_stream(handle);
 
-  auto dim      = index.dim();
-  auto dim_ext  = index.dim_ext();
+  auto dim = index.dim();
+  // int8_t coarse search uses more padding than others.
+  auto dim_ext  = params.coarse_search_dtype == CUDA_R_8I
+                    ? get_centers<int8_t, IdxT>(handle, index).extent(1)
+                    : index.dim_ext();
   auto n_probes = std::min<uint32_t>(params.n_probes, index.n_lists());
 
   uint32_t max_samples = 0;
@@ -678,10 +867,24 @@ inline void search(raft::resources const& handle,
   auto mr = raft::resource::get_workspace_resource(handle);
 
   // Maximum number of query vectors to search at the same time.
-  const auto max_queries = std::min<uint32_t>(std::max<uint32_t>(n_queries, 1), kMaxQueries);
-  auto max_batch_size    = get_max_batch_size(handle, k, n_probes, max_queries, max_samples);
-
-  rmm::device_uvector<float> float_queries(max_queries * dim_ext, stream, mr);
+  const auto max_queries =
+    std::min<uint32_t>(std::max<uint32_t>(n_queries, 1), params.max_internal_batch_size);
+  auto max_batch_size = get_max_batch_size(handle, k, n_probes, max_queries, max_samples);
+
+  using some_query_t = std::
+    variant<rmm::device_uvector<float>, rmm::device_uvector<half>, rmm::device_uvector<int8_t>>;
+  some_query_t gemm_queries(
+    params.coarse_search_dtype == CUDA_R_32F
+      ? std::move(some_query_t{
+          std::in_place_type_t<rmm::device_uvector<float>>{}, max_queries * dim_ext, stream, mr})
+    : params.coarse_search_dtype == CUDA_R_16F
+      ? std::move(some_query_t{
+          std::in_place_type_t<rmm::device_uvector<half>>{}, max_queries * dim_ext, stream, mr})
+    : params.coarse_search_dtype == CUDA_R_8I
+      ? std::move(some_query_t{
+          std::in_place_type_t<rmm::device_uvector<int8_t>>{}, max_queries * dim_ext, stream, mr})
+      : throw raft::logic_error("Unsupported coarse_search_dtype (only CUDA_R_32F, "
+                                "CUDA_R_16F, and CUDA_R_8I are supported)"));
   rmm::device_uvector<float> rot_queries(max_queries * index.rot_dim(), stream, mr);
   rmm::device_uvector<uint32_t> clusters_to_probe(max_queries * n_probes, stream, mr);
 
@@ -694,37 +897,49 @@ inline void search(raft::resources const& handle,
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> batch_scope(
       "ivf_pq::search-batch(queries: %u - %u)", offset_q, offset_q + queries_batch);
 
-    select_clusters(handle,
-                    clusters_to_probe.data(),
-                    float_queries.data(),
-                    queries_batch,
-                    n_probes,
-                    index.n_lists(),
-                    dim,
-                    dim_ext,
-                    index.metric(),
-                    queries + static_cast<size_t>(dim) * offset_q,
-                    index.centers().data_handle(),
-                    mr);
+    std::visit(
+      [&](auto&& gemm_qs) {
+        using gemm_type  = std::remove_reference_t<decltype(gemm_qs)>;
+        using value_type = std::remove_cv_t<typename gemm_type::value_type>;
+        return select_clusters(handle,
+                               clusters_to_probe.data(),
+                               gemm_qs.data(),
+                               queries_batch,
+                               n_probes,
+                               index.n_lists(),
+                               dim,
+                               dim_ext,
+                               index.metric(),
+                               queries + static_cast<size_t>(dim) * offset_q,
+                               get_centers<value_type, IdxT>(handle, index).data_handle(),
+                               mr);
+      },
+      gemm_queries);
 
     // Rotate queries
-    float alpha = 1.0;
-    float beta  = 0.0;
-    raft::linalg::gemm(handle,
-                       true,
-                       false,
-                       index.rot_dim(),
-                       queries_batch,
-                       dim,
-                       &alpha,
-                       index.rotation_matrix().data_handle(),
-                       dim,
-                       float_queries.data(),
-                       dim_ext,
-                       &beta,
-                       rot_queries.data(),
-                       index.rot_dim(),
-                       stream);
+    std::visit(
+      [&](auto&& gemm_qs) {
+        using gemm_type  = std::remove_reference_t<decltype(gemm_qs)>;
+        using value_type = std::remove_cv_t<typename gemm_type::value_type>;
+        float alpha      = std::is_same_v<value_type, int8_t> ? 1.0 / 128.0 / 128.0 : 1.0;
+        float beta       = 0.0;
+        raft::linalg::gemm(handle,
+                           true,
+                           false,
+                           index.rot_dim(),
+                           queries_batch,
+                           dim,
+                           &alpha,
+                           get_rotation_matrix<value_type, IdxT>(handle, index).data_handle(),
+                           dim,
+                           gemm_qs.data(),
+                           dim_ext,
+                           &beta,
+                           rot_queries.data(),
+                           index.rot_dim(),
+                           stream);
+      },
+      gemm_queries);
     if (index.metric() == distance::DistanceType::CosineExpanded) {
       auto rot_queries_view = raft::make_device_matrix_view<float, uint32_t>(
         rot_queries.data(), max_queries, index.rot_dim());
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 00ede91c09..6548155ef1 100755
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -79,6 +79,8 @@ void _search(cuvsResources_t res,
   search_params.lut_dtype                = params.lut_dtype;
   search_params.internal_distance_dtype  = params.internal_distance_dtype;
   search_params.preferred_shmem_carveout = params.preferred_shmem_carveout;
+  search_params.coarse_search_dtype      = params.coarse_search_dtype;
+  search_params.max_internal_batch_size  = params.max_internal_batch_size;
 
   using queries_mdspan_type   = raft::device_matrix_view<const T, IdxT, raft::row_major>;
   using neighbors_mdspan_type = raft::device_matrix_view<IdxT, IdxT, raft::row_major>;
@@ -246,6 +248,8 @@ extern "C" cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t* para
     *params = new cuvsIvfPqSearchParams{.n_probes                 = 20,
                                         .lut_dtype                = CUDA_R_32F,
                                         .internal_distance_dtype  = CUDA_R_32F,
+                                        .coarse_search_dtype      = CUDA_R_32F,
+                                        .max_internal_batch_size  = 4096,
                                         .preferred_shmem_carveout = 1.0};
   });
 }
diff --git a/cpp/src/neighbors/ivf_pq_index.cpp b/cpp/src/neighbors/ivf_pq_index.cu
similarity index 68%
rename from cpp/src/neighbors/ivf_pq_index.cpp
rename to cpp/src/neighbors/ivf_pq_index.cu
index 8f4e5b3316..1bd6b6291d 100644
--- a/cpp/src/neighbors/ivf_pq_index.cpp
+++ b/cpp/src/neighbors/ivf_pq_index.cu
@@ -16,6 +16,14 @@
 
 #include <cuvs/neighbors/ivf_pq.hpp>
 
+#include "detail/ann_utils.cuh"
+
+#include <raft/core/operators.hpp>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/reduce.cuh>
+
+#include <raft/util/cudart_utils.hpp>
+
 namespace cuvs::neighbors::ivf_pq {
 index_params index_params::from_dataset(raft::matrix_extent<int64_t> dataset,
                                         cuvs::distance::DistanceType metric)
@@ -339,6 +347,109 @@ uint32_t index<IdxT>::calculate_pq_dim(uint32_t dim)
   return r;
 }
 
+template <typename IdxT>
+raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> index<IdxT>::rotation_matrix_int8(
+  const raft::resources& res) const
+{
+  if (!rotation_matrix_int8_.has_value()) {
+    rotation_matrix_int8_.emplace(
+      raft::make_device_mdarray<int8_t, uint32_t>(res, rotation_matrix().extents()));
+    raft::linalg::map(res,
+                      rotation_matrix_int8_->view(),
+                      cuvs::spatial::knn::detail::utils::mapping<int8_t>{},
+                      rotation_matrix());
+  }
+  return rotation_matrix_int8_->view();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> index<IdxT>::centers_int8(
+  const raft::resources& res) const
+{
+  if (!centers_int8_.has_value()) {
+    uint32_t n_lists      = this->n_lists();
+    uint32_t dim          = this->dim();
+    uint32_t dim_ext      = this->dim_ext();
+    uint32_t dim_ext_int8 = raft::round_up_safe(dim + 2, 16u);
+    centers_int8_.emplace(raft::make_device_matrix<int8_t, uint32_t>(res, n_lists, dim_ext_int8));
+    auto* inputs = centers().data_handle();
+    /* NOTE: maximizing the range and the precision of int8_t GEMM
+
+    int8_t has a very limited range [-128, 127], which is problematic when storing both vectors and
+    their squared norms in one place.
+
+    We map all dimensions by multiplying by 128. But that means we need to multiply the squared norm
+    component by `128^2`, which we cannot afford, since it most likely overflows.
+    So, a naive mapping would be:
+    ```
+      [c_1 * 128, c_2, * 128, ...., c_(dim-1) * 128,  n2 * 128 * 128, 0 ... 0]
+      • [q_1 * 128, q_2 * 128, ..., q_(dim-1)*128, -0.5, 0, ... 0]
+    ```
+
+    Which is at first can be improved by moving one 128 to the query side:
+    ```
+      [c_1 * 128, c_2, * 128, ...., c_(dim-1) * 128,  n2 * 128, 0 ... 0]
+      • [q_1 * 128, q_2 * 128, ..., q_(dim-1)*128, -64, 0, ... 0]
+    ```
+
+    Yet this still only works for vectors with L2 norms not bigger than one and has a rather awful
+    granularity of 64. To improve both the range and the precision, we count the number of available
+    slots `m > 2` and decompose the squared norm, such that:
+    ```
+      0.5 * 128 * n2 = 64 * n2 = 128 * z + (m - 1) * y
+    ```
+    where `y` maximizes the available range while `z` encodes the rounding error.
+    Then we get following dot product during the coarse search:
+    ```
+      [c_1 * 128, c_2, * 128, ...., c_(dim-1) * 128,  z, y, ... y]
+      • [q_1 * 128, q_2 * 128, ..., q_(dim-1)*128, 1 - m,  -128, ... -128]
+    ```
+    `m` is maximum 16, so we get the coefficient much lower than the naive 64 on the query side; and
+    it is limited by the range we can cover (the squared norm must be within `m * 2` before
+    normalization).
+    */
+    raft::linalg::map_offset(
+      res, centers_int8_->view(), [dim, dim_ext, dim_ext_int8, inputs] __device__(uint32_t ix) {
+        uint32_t col = ix % dim_ext_int8;
+        uint32_t row = ix / dim_ext_int8;
+        if (col < dim) {
+          return static_cast<int8_t>(
+            std::clamp(inputs[col + row * dim_ext] * 128.0f, -128.0f, 127.f));
+        }
+        auto x = inputs[row * dim_ext + dim];
+        auto c = 64.0f / static_cast<float>(dim_ext_int8 - dim - 1);
+        auto y = std::clamp(x * c, -128.0f, 127.f);
+        auto z = std::clamp((y - std::round(y)) * 128.0f, -128.0f, 127.f);
+        if (col > dim) { return static_cast<int8_t>(std::round(y)); }
+        return static_cast<int8_t>(z);
+      });
+  }
+  return centers_int8_->view();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<const half, uint32_t, raft::row_major> index<IdxT>::rotation_matrix_half(
+  const raft::resources& res) const
+{
+  if (!rotation_matrix_half_.has_value()) {
+    rotation_matrix_half_.emplace(
+      raft::make_device_mdarray<half, uint32_t>(res, rotation_matrix().extents()));
+    raft::linalg::map(res, rotation_matrix_half_->view(), raft::cast_op<half>{}, rotation_matrix());
+  }
+  return rotation_matrix_half_->view();
+}
+
+template <typename IdxT>
+raft::device_matrix_view<const half, uint32_t, raft::row_major> index<IdxT>::centers_half(
+  const raft::resources& res) const
+{
+  if (!centers_half_.has_value()) {
+    centers_half_.emplace(raft::make_device_mdarray<half, uint32_t>(res, centers().extents()));
+    raft::linalg::map(res, centers_half_->view(), raft::cast_op<half>{}, centers());
+  }
+  return centers_half_->view();
+}
+
 template struct index<int64_t>;
 
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/tests/neighbors/ann_ivf_pq.cuh b/cpp/tests/neighbors/ann_ivf_pq.cuh
index 0ebe604f80..97f252bff9 100644
--- a/cpp/tests/neighbors/ann_ivf_pq.cuh
+++ b/cpp/tests/neighbors/ann_ivf_pq.cuh
@@ -99,6 +99,9 @@ inline auto operator<<(std::ostream& os, const ivf_pq_inputs& p) -> std::ostream
   PRINT_DIFF_V(.search_params.lut_dtype, cuvs::neighbors::print_dtype{p.search_params.lut_dtype});
   PRINT_DIFF_V(.search_params.internal_distance_dtype,
                cuvs::neighbors::print_dtype{p.search_params.internal_distance_dtype});
+  PRINT_DIFF_V(.search_params.coarse_search_dtype,
+               cuvs::neighbors::print_dtype{p.search_params.coarse_search_dtype});
+  PRINT_DIFF(.search_params.max_internal_batch_size);
   os << "}";
   return os;
 }
@@ -849,6 +852,17 @@ inline auto enum_variety() -> test_cases_t
     x.search_params.lut_dtype = CUDA_R_8U;
     x.min_recall              = 0.84;
   });
+  ADD_CASE({
+    x.search_params.coarse_search_dtype = CUDA_R_16F;
+    x.min_recall                        = 0.86;
+  });
+  ADD_CASE({
+    x.search_params.coarse_search_dtype = CUDA_R_8I;
+    // 8-bit coarse search is experimental and there's no go guarantee of any recall
+    // if the data is not normalized. Especially for L2, because we store vector norms alongside the
+    // cluster centers.
+    x.min_recall = 0.1;
+  });
 
   ADD_CASE({
     x.search_params.internal_distance_dtype = CUDA_R_32F;
@@ -859,6 +873,12 @@ inline auto enum_variety() -> test_cases_t
     x.search_params.lut_dtype               = CUDA_R_16F;
     x.min_recall                            = 0.86;
   });
+  ADD_CASE({
+    x.search_params.internal_distance_dtype = CUDA_R_16F;
+    x.search_params.lut_dtype               = CUDA_R_16F;
+    x.search_params.coarse_search_dtype     = CUDA_R_16F;
+    x.min_recall                            = 0.86;
+  });
 
   return xs;
 }
@@ -994,6 +1014,34 @@ inline auto special_cases() -> test_cases_t
     x.search_params.n_probes     = 50;
   });
 
+  // Test large max_internal_batch_size
+  ADD_CASE({
+    x.num_db_vecs                           = 500000;
+    x.dim                                   = 100;
+    x.num_queries                           = 128 * 1024 * 1024;
+    x.k                                     = 10;
+    x.index_params.codebook_kind            = ivf_pq::codebook_gen::PER_SUBSPACE;
+    x.index_params.pq_dim                   = 10;
+    x.index_params.pq_bits                  = 8;
+    x.index_params.n_lists                  = 1024;
+    x.search_params.n_probes                = 50;
+    x.search_params.max_internal_batch_size = 64 * 1024 * 1024;
+  });
+
+  // Test small max_internal_batch_size
+  ADD_CASE({
+    x.num_db_vecs                           = 500000;
+    x.dim                                   = 100;
+    x.num_queries                           = 128 * 1024 * 1024;
+    x.k                                     = 10;
+    x.index_params.codebook_kind            = ivf_pq::codebook_gen::PER_SUBSPACE;
+    x.index_params.pq_dim                   = 10;
+    x.index_params.pq_bits                  = 8;
+    x.index_params.n_lists                  = 1024;
+    x.search_params.n_probes                = 50;
+    x.search_params.max_internal_batch_size = 1024 * 1024;
+  });
+
   ADD_CASE({
     x.num_db_vecs                = 10000;
     x.dim                        = 16;

From 0778a959ad482ea7da80198c36a375c658efee6c Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 25 Apr 2025 17:00:16 +0200
Subject: [PATCH 374/623] Optimize hnsw::from_cagra<GPU> (#826)

Reduce the CAGRA-for-HNSW build times by:
 - avoiding unnecessary copies of the data between cagra::build and hnsw::from_cagra in the benchmarks
 - avoiding unnecessary temporary data buffers in hnsw::from_cagra<GPU>
 - reducing random reads via forcing 1-1 mapping between the internal indices and external labels during HNSW import

As a side-effect, this PR also fixes the bug where hnsw::from_cagra segfaults in benchmarks if the dataset is passed in device memory (and incorrectly wrapped in a host_matrix_view).

In addition, this PR adds a bit more verbose NVTX reporting of different stages during the CAGRA/HNSW index build.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/826
---
 .../ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h |  49 ++--
 .../neighbors/detail/cagra/cagra_build.cuh    |   7 +
 cpp/src/neighbors/detail/hnsw.hpp             | 218 ++++++++++++------
 cpp/src/neighbors/hnsw.cpp                    |  20 +-
 .../cuvs_bench/cuvs_bench/tests/test_cli.py   |  15 +-
 5 files changed, 210 insertions(+), 99 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
index af72dd02bb..ed5075fc1d 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
@@ -39,9 +39,7 @@ class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
   };
 
   cuvs_cagra_hnswlib(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
-    : algo<T>(metric, dim),
-      build_param_{param},
-      cagra_build_{metric, dim, param.cagra_build_param, concurrent_searches}
+    : algo<T>(metric, dim), build_param_{param}
   {
   }
 
@@ -57,7 +55,13 @@ class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
 
   [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
   {
-    return cagra_build_.get_sync_stream();
+    return handle_.get_sync_stream();
+  }
+
+  [[nodiscard]] auto uses_stream() const noexcept -> bool override
+  {
+    // there's no need to synchronize with the GPU neither on build nor on search
+    return false;
   }
 
   // to enable dataset access from GPU memory
@@ -77,28 +81,37 @@ class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
   }
 
  private:
-  raft::resources handle_{};
+  configured_raft_resources handle_{};
   build_param build_param_;
   search_param search_param_;
-  cuvs_cagra<T, IdxT> cagra_build_;
   std::shared_ptr<cuvs::neighbors::hnsw::index<T>> hnsw_index_;
 };
 
 template <typename T, typename IdxT>
 void cuvs_cagra_hnswlib<T, IdxT>::build(const T* dataset, size_t nrow)
 {
-  cagra_build_.build(dataset, nrow);
-  auto* cagra_index      = cagra_build_.get_index();
-  auto host_dataset_view = raft::make_host_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
-  auto opt_dataset_view =
-    std::optional<raft::host_matrix_view<const T, int64_t>>(std::move(host_dataset_view));
-  const auto start_clock = std::chrono::system_clock::now();
-  hnsw_index_            = cuvs::neighbors::hnsw::from_cagra(
-    handle_, build_param_.hnsw_index_params, *cagra_index, opt_dataset_view);
-  int time =
-    std::chrono::duration_cast<std::chrono::seconds>(std::chrono::system_clock::now() - start_clock)
-      .count();
-  RAFT_LOG_DEBUG("Graph saved to HNSW format in %d:%d min", time / 60, time % 60);
+  // when the data set is on host, we can pass it directly to HNSW
+  bool dataset_is_on_host = raft::get_device_for_address(dataset) == -1;
+
+  // re-use the CAGRA wrapper to parse build params
+  auto bps                                 = build_param_.cagra_build_param;
+  bps.cagra_params.attach_dataset_on_build = !dataset_is_on_host;
+  cuvs_cagra<T, IdxT> cagra_wrapper{this->metric_, this->dim_, bps};
+
+  // build the CAGRA index
+  cagra_wrapper.build(dataset, nrow);
+  auto& cagra_index = *cagra_wrapper.get_index();
+
+  // pass the dataset directly to HNSW if it's on the host
+  std::optional<raft::host_matrix_view<const T, int64_t>> opt_dataset_view = std::nullopt;
+  if (dataset_is_on_host) {
+    opt_dataset_view.emplace(
+      raft::make_host_matrix_view<const T, int64_t>(dataset, nrow, this->dim_));
+  }
+
+  // convert the index to HNSW format
+  hnsw_index_ = cuvs::neighbors::hnsw::from_cagra(
+    handle_, build_param_.hnsw_index_params, cagra_index, opt_dataset_view);
 }
 
 template <typename T, typename IdxT>
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 861c025619..447f4b188a 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -574,6 +574,13 @@ index<T, IdxT> build(
 {
   size_t intermediate_degree = params.intermediate_graph_degree;
   size_t graph_degree        = params.graph_degree;
+  common::nvtx::range<common::nvtx::domain::cuvs> function_scope(
+    "cagra::build<%s>(%zu, %zu)",
+    Accessor::is_managed_type::value ? "managed"
+    : Accessor::is_host_type::value  ? "host"
+                                     : "device",
+    intermediate_degree,
+    graph_degree);
   if (intermediate_degree >= static_cast<size_t>(dataset.extent(0))) {
     RAFT_LOG_WARN(
       "Intermediate graph degree cannot be larger than dataset size, reducing it to %lu",
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index 07e1d6b53f..de9554587a 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,14 +16,20 @@
 
 #pragma once
 
+#include "../../core/nvtx.hpp"
+
 #include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/hnsw.hpp>
-#include <filesystem>
+
+#include <raft/core/logger.hpp>
+#include <raft/core/pinned_mdarray.hpp>
+
 #include <hnswlib/hnswalg.h>
 #include <hnswlib/hnswlib.h>
+
+#include <filesystem>
 #include <memory>
 #include <omp.h>
-#include <raft/core/logger.hpp>
 #include <random>
 #include <thread>
 
@@ -121,6 +127,7 @@ std::enable_if_t<hierarchy == HnswHierarchy::NONE, std::unique_ptr<index<T>>> fr
   const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
   std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
+  common::nvtx::range<common::nvtx::domain::cuvs> fun_scope("hnsw::from_cagra<NONE>");
   std::random_device dev;
   std::mt19937 rng(dev());
   std::uniform_int_distribution<std::mt19937::result_type> dist(0);
@@ -148,6 +155,7 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
   const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
   std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
+  common::nvtx::range<common::nvtx::domain::cuvs> fun_scope("hnsw::from_cagra<CPU>");
   auto host_dataset = raft::make_host_matrix<T, int64_t>(0, 0);
   raft::host_matrix_view<const T, int64_t, raft::row_major> host_dataset_view(
     host_dataset.data_handle(), host_dataset.extent(0), host_dataset.extent(1));
@@ -226,7 +234,7 @@ template <typename T, typename DistT>
 int initialize_point_in_hnsw(hnswlib::HierarchicalNSW<DistT>* appr_algo,
                              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
                              int64_t real_index,
-                             int curlevel)
+                             int32_t curlevel)
 {
   auto cur_c                        = appr_algo->cur_element_count++;
   appr_algo->element_levels_[cur_c] = curlevel;
@@ -271,56 +279,127 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
   const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,
   std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
-  auto host_dataset = raft::make_host_matrix<T, int64_t>(0, 0);
-  raft::host_matrix_view<const T, int64_t, raft::row_major> host_dataset_view(
-    host_dataset.data_handle(), host_dataset.extent(0), host_dataset.extent(1));
+  common::nvtx::range<common::nvtx::domain::cuvs> fun_scope("hnsw::from_cagra<GPU>");
+  auto stream      = raft::resource::get_cuda_stream(res);
+  auto num_threads = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+
+  /* Note: NNSW data layout
+
+  offsetLevel0_      = seems to always be 0, and it would break things otherwise
+  size_links_level0_ = maxM0_ * sizeof(tableint) + sizeof(linklistsizeint);
+  offsetData_        = size_links_level0_;
+  label_offset_      = size_links_level0_ + data_size_;
+
+  get_linklist0(i)       = data_level0_memory_ + i * size_data_per_element_ + offsetLevel0_
+  getDataByInternalId(i) = data_level0_memory_ + i * size_data_per_element_ + offsetData_
+  getExternalLabeLp(i)   = data_level0_memory_ + i * size_data_per_element_ + label_offset_
+
+  Hence the layout:
+      2M x uint32_t  +   1 x uint32_t        dim x T    1 x size_t
+     [linked list + linked list sizes]        [data]     [label]
+  */
+
+  const T* source_dataset = nullptr;
+  int64_t n_rows, dim, source_stride;
+  bool device_copy;
   if (dataset.has_value()) {
-    host_dataset_view = dataset.value();
+    n_rows         = dataset->extent(0);
+    dim            = dataset->extent(1);
+    device_copy    = false;
+    source_dataset = dataset->data_handle();
+    source_stride  = dim;
+  } else if (auto cagra_dataset = cagra_index.dataset(); cagra_dataset.data_handle() != nullptr) {
+    n_rows         = cagra_dataset.extent(0);
+    dim            = cagra_dataset.extent(1);
+    device_copy    = true;
+    source_dataset = cagra_dataset.data_handle();
+    source_stride  = cagra_dataset.stride(0);
   } else {
-    // move dataset to host, remove padding
-    auto cagra_dataset = cagra_index.dataset();
-    host_dataset =
-      raft::make_host_matrix<T, int64_t>(cagra_dataset.extent(0), cagra_dataset.extent(1));
-    RAFT_CUDA_TRY(cudaMemcpy2DAsync(host_dataset.data_handle(),
-                                    sizeof(T) * host_dataset.extent(1),
-                                    cagra_dataset.data_handle(),
-                                    sizeof(T) * cagra_dataset.stride(0),
-                                    sizeof(T) * host_dataset.extent(1),
-                                    cagra_dataset.extent(0),
-                                    cudaMemcpyDefault,
-                                    raft::resource::get_cuda_stream(res)));
-    raft::resource::sync_stream(res);
-    host_dataset_view = host_dataset.view();
+    RAFT_FAIL("hnsw::from_cagra<GPU>: No dataset provided");
   }
 
-  // initialize hnsw index
-  auto hnsw_index =
-    std::make_unique<index_impl<T>>(host_dataset_view.extent(1), cagra_index.metric(), hierarchy);
-  auto appr_algo = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
-    hnsw_index->get_space(),
-    host_dataset_view.extent(0),
-    cagra_index.graph().extent(1) / 2,
-    params.ef_construction);
-
-  // assign a level to each point and initialize the points in hnsw
-  std::vector<size_t> levels(host_dataset_view.extent(0));
-  std::vector<uint32_t> hnsw_internal_ids(host_dataset_view.extent(0));
+  // initialize HNSW index
+  auto hnsw_index = std::make_unique<index_impl<T>>(dim, cagra_index.metric(), hierarchy);
+  auto appr_algo  = std::make_unique<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
+    hnsw_index->get_space(), n_rows, cagra_index.graph().extent(1) / 2, params.ef_construction);
+  appr_algo->cur_element_count = n_rows;
 
-  auto num_threads = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+  // Initialize linked lists
+  auto& levels = appr_algo->element_levels_;
+  {
+    common::nvtx::range<common::nvtx::domain::cuvs> block_scope(
+      "parallel::initialize_data<%s>(%d threads)", device_copy ? "device" : "host", num_threads);
+    /* Note: batching
+
+    If the dataset is on the device, we want to copy it to HNSW in parallel to the rest of the
+    initialization loop. Ideally, we could use cudaMemcpy2DAsync to do this, but this call is very
+    likely to sync with the CPU, because normally the allocated host memory is paged. To avoid this,
+    we use double-buffering and copy data in small batches via pinned memory. Hence, the cuda
+    device-to-host copy is completely overlapped with the host loop.
+
+    The batching is completely disabled if the source dataset is on the host.
+    */
+    const int64_t max_batch_size =
+      device_copy ? raft::div_rounding_up_safe<int64_t>(64 * 1024 * 1024, source_stride * sizeof(T))
+                  : n_rows;
+    T* bufs[2]                                                  = {nullptr, nullptr};
+    std::optional<raft::pinned_matrix<T, int64_t>> bufs_storage = std::nullopt;
+    if (device_copy) {
+      bufs_storage.emplace(
+        std::move(raft::make_pinned_matrix<T, int64_t>(res, max_batch_size * 2, source_stride)));
+      bufs[0] = bufs_storage->data_handle();
+      bufs[1] = bufs[0] + max_batch_size * source_stride;
+    }
+    auto n_batches = raft::div_rounding_up_safe<int64_t>(n_rows, max_batch_size);
+    for (int64_t batch_i = -1; batch_i < n_batches; batch_i++) {
+      if (device_copy) {
+        if (batch_i >= 0) {
+          // Sync previous batch load
+          raft::resource::sync_stream(res);
+        }
+        auto next_batch_i = batch_i + 1;
+        if (next_batch_i < n_batches) {
+          auto offset     = next_batch_i * max_batch_size;
+          auto batch_size = std::min(max_batch_size, n_rows - offset);
+          raft::copy(bufs[next_batch_i % 2],
+                     source_dataset + offset * source_stride,
+                     batch_size * source_stride,
+                     stream);
+        }
+      }
+      if (batch_i < 0) { continue; }
+      const auto i0 = batch_i * max_batch_size;
+      const auto i1 = std::min(i0 + max_batch_size, n_rows);
 #pragma omp parallel for num_threads(num_threads)
-  for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
-    levels[i] = appr_algo->getRandomLevel(appr_algo->mult_) + 1;
-    hnsw_internal_ids[i] =
-      initialize_point_in_hnsw(appr_algo.get(), host_dataset_view, i, levels[i] - 1);
+      for (int64_t i = i0; i < i1; i++) {
+        // clear the storage (TODO: it's not clear if this is necessary)
+        memset(appr_algo->get_linklist0(i), 0, appr_algo->size_links_level0_);
+        // copy the data section
+        auto* source_ptr = device_copy ? bufs[batch_i % 2] + (i - i0) * source_stride
+                                       : source_dataset + i * source_stride;
+        memcpy(appr_algo->getDataByInternalId(i), source_ptr, appr_algo->data_size_);
+        // As we build the index from scratch, we assign labels 1-1 in data order
+        *appr_algo->getExternalLabeLp(i) = static_cast<hnswlib::labeltype>(i);
+        int32_t curlevel                 = appr_algo->getRandomLevel(appr_algo->mult_);
+        levels[i]                        = curlevel;
+        if (curlevel) {
+          appr_algo->linkLists_[i] =
+            (char*)malloc(appr_algo->size_links_per_element_ * curlevel + 1);
+          if (appr_algo->linkLists_[i] == nullptr)
+            throw std::runtime_error("Not enough memory: addPoint failed to allocate linklist");
+          memset(appr_algo->linkLists_[i], 0, appr_algo->size_links_per_element_ * curlevel + 1);
+        }
+      }
+    }
   }
 
   // sort the points by levels
   // build histogram
   std::vector<size_t> hist;
-  std::vector<size_t> order(host_dataset_view.extent(0));
-  for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
-    auto pt_level = levels[i] - 1;
-    while (pt_level >= hist.size())
+  std::vector<size_t> order(n_rows);
+  for (int64_t i = 0; i < n_rows; i++) {
+    auto pt_level = levels[i];
+    while (pt_level >= static_cast<int32_t>(hist.size()))
       hist.push_back(0);
     hist[pt_level]++;
   }
@@ -332,17 +411,18 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
   }
 
   // bucket sort
-  for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
-    auto pt_level              = levels[i] - 1;
+  for (int64_t i = 0; i < n_rows; i++) {
+    auto pt_level              = levels[i];
     order[offsets[pt_level]++] = i;
   }
 
   // set last point of the highest level as the entry point
-  appr_algo->enterpoint_node_ = hnsw_internal_ids[order.back()];
+  appr_algo->enterpoint_node_ = order.back();
   appr_algo->maxlevel_        = hist.size() - 1;
 
   // iterate over the points in the descending order of their levels
   for (size_t pt_level = hist.size() - 1; pt_level >= 1; pt_level--) {
+    common::nvtx::range<common::nvtx::domain::cuvs> level_scope("level %zu", pt_level);
     auto start_idx     = offsets[pt_level - 1];
     auto end_idx       = offsets[hist.size() - 1];
     auto num_pts       = end_idx - start_idx;
@@ -353,14 +433,14 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
     }
 
     // gather points from dataset to form query set on host
-    auto host_query_set = raft::make_host_matrix<T, int64_t>(num_pts, host_dataset_view.extent(1));
+    auto host_query_set = raft::make_host_matrix<T, int64_t>(num_pts, dim);
     // TODO: Use `raft::matrix::gather` when available as a public API
     // Issue: https://github.com/rapidsai/raft/issues/2572
 #pragma omp parallel for num_threads(num_threads)
     for (auto i = start_idx; i < end_idx; i++) {
       auto pt_id = order[i];
-      std::copy(&host_dataset_view(pt_id, 0),
-                &host_dataset_view(pt_id + 1, 0),
+      std::copy(appr_algo->getDataByInternalId(pt_id),
+                appr_algo->getDataByInternalId(pt_id) + dim,
                 &host_query_set(i - start_idx, 0));
     }
 
@@ -371,19 +451,20 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
                         host_neighbors.view(),
                         cagra_index.metric());
 
-    // add points to the HNSW index upper layers
+    {
+      common::nvtx::range<common::nvtx::domain::cuvs> copy_scope(
+        "get_linklist(%zu, %zu)", start_idx, end_idx);
+      // add points to the HNSW index upper layers
 #pragma omp parallel for num_threads(num_threads)
-    for (auto i = start_idx; i < end_idx; i++) {
-      auto pt_id       = order[i];
-      auto internal_id = hnsw_internal_ids[pt_id];
-      auto ll_cur      = appr_algo->get_linklist(internal_id, pt_level);
-      appr_algo->setListCount(ll_cur, host_neighbors.extent(1));
-      auto* data     = (uint32_t*)(ll_cur + 1);
-      auto neighbors = &host_neighbors(i - start_idx, 0);
-      for (auto j = 0; j < host_neighbors.extent(1); j++) {
-        auto neighbor_id          = order[neighbors[j] + start_idx];
-        auto neighbor_internal_id = hnsw_internal_ids[neighbor_id];
-        data[j]                   = neighbor_internal_id;
+      for (auto i = start_idx; i < end_idx; i++) {
+        auto pt_id  = order[i];
+        auto ll_cur = appr_algo->get_linklist(pt_id, pt_level);
+        appr_algo->setListCount(ll_cur, host_neighbors.extent(1));
+        auto* data     = (uint32_t*)(ll_cur + 1);
+        auto neighbors = &host_neighbors(i - start_idx, 0);
+        for (auto j = 0; j < host_neighbors.extent(1); j++) {
+          data[j] = order[neighbors[j] + start_idx];
+        }
       }
     }
   }
@@ -403,14 +484,17 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
     host_graph_view = host_graph.view();
   }
 
+  {
+    common::nvtx::range<common::nvtx::domain::cuvs> copy_scope("get_linklist0");
 // copy cagra graph to hnswlib base layer
 #pragma omp parallel for num_threads(num_threads)
-  for (size_t i = 0; i < static_cast<size_t>(host_graph_view.extent(0)); ++i) {
-    auto ll_i = appr_algo->get_linklist0(hnsw_internal_ids[i]);
-    appr_algo->setListCount(ll_i, host_graph_view.extent(1));
-    auto* data = (uint32_t*)(ll_i + 1);
-    for (size_t j = 0; j < static_cast<size_t>(host_graph_view.extent(1)); ++j) {
-      data[j] = hnsw_internal_ids[host_graph_view(i, j)];
+    for (size_t i = 0; i < static_cast<size_t>(host_graph_view.extent(0)); ++i) {
+      auto ll_i = appr_algo->get_linklist0(i);
+      appr_algo->setListCount(ll_i, host_graph_view.extent(1));
+      auto* data = (uint32_t*)(ll_i + 1);
+      for (size_t j = 0; j < static_cast<size_t>(host_graph_view.extent(1)); ++j) {
+        data[j] = host_graph_view(i, j);
+      }
     }
   }
 
diff --git a/cpp/src/neighbors/hnsw.cpp b/cpp/src/neighbors/hnsw.cpp
index bf274b0d98..2b6bff7b34 100644
--- a/cpp/src/neighbors/hnsw.cpp
+++ b/cpp/src/neighbors/hnsw.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,22 +15,20 @@
  */
 
 #include "detail/hnsw.hpp"
-#include "../core/nvtx.hpp"
 #include <cstdint>
 #include <cuvs/neighbors/hnsw.hpp>
 #include <sys/types.h>
 
 namespace cuvs::neighbors::hnsw {
 
-#define CUVS_INST_HNSW_FROM_CAGRA(T)                                                           \
-  std::unique_ptr<index<T>> from_cagra(                                                        \
-    raft::resources const& res,                                                                \
-    const index_params& params,                                                                \
-    const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,                             \
-    std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)          \
-  {                                                                                            \
-    raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope("hnsw::from_cagra"); \
-    return detail::from_cagra<T>(res, params, cagra_index, dataset);                           \
+#define CUVS_INST_HNSW_FROM_CAGRA(T)                                                  \
+  std::unique_ptr<index<T>> from_cagra(                                               \
+    raft::resources const& res,                                                       \
+    const index_params& params,                                                       \
+    const cuvs::neighbors::cagra::index<T, uint32_t>& cagra_index,                    \
+    std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset) \
+  {                                                                                   \
+    return detail::from_cagra<T>(res, params, cagra_index, dataset);                  \
   }
 
 CUVS_INST_HNSW_FROM_CAGRA(float);
diff --git a/python/cuvs_bench/cuvs_bench/tests/test_cli.py b/python/cuvs_bench/cuvs_bench/tests/test_cli.py
index 78685d7bd4..7993660da0 100644
--- a/python/cuvs_bench/cuvs_bench/tests/test_cli.py
+++ b/python/cuvs_bench/cuvs_bench/tests/test_cli.py
@@ -103,7 +103,6 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "time",
         "threads",
         "cpu_time",
-        "GPU",
     ]
 
     common_search_header = [
@@ -114,7 +113,6 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "latency",
         "threads",
         "cpu_time",
-        "GPU",
     ]
 
     # --- Verify that the expected result files exist and are not empty ---
@@ -123,6 +121,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/build/cuvs_ivf_flat,test.csv": {
             "header": common_build_header
             + [
+                "GPU",
                 "niter",
                 "nlist",
                 "ratio",
@@ -142,6 +141,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/build/faiss_gpu_ivf_flat,test.csv": {
             "header": common_build_header
             + [
+                "GPU",
                 "nlist",
                 "ratio",
                 "use_cuvs",
@@ -151,6 +151,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/build/cuvs_cagra,test.csv": {
             "header": common_build_header
             + [
+                "GPU",
                 "graph_degree",
                 "intermediate_graph_degree",
                 "label",
@@ -170,7 +171,6 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
                 "build threads",
                 "build cpu_time",
                 "build GPU",
-                "ef_construction",
                 "graph_degree",
                 "intermediate_graph_degree",
                 "label",
@@ -180,6 +180,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/cuvs_cagra,test,k10,bs100,latency.csv": {
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "itopk",
                 "k",
@@ -199,6 +200,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/cuvs_cagra,test,k10,bs100,throughput.csv": {
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "itopk",
                 "k",
@@ -218,6 +220,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/cuvs_cagra,test,k10,bs100,raw.csv": {
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "itopk",
                 "k",
@@ -237,6 +240,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/cuvs_ivf_flat,test,k10,bs100,latency.csv": {
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "k",
                 "n_queries",
@@ -255,6 +259,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/cuvs_ivf_flat,test,k10,bs100,raw.csv": {
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "k",
                 "n_queries",
@@ -273,6 +278,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/cuvs_ivf_flat,test,k10,bs100,throughput.csv": {  # noqa: E501
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "k",
                 "n_queries",
@@ -291,6 +297,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/faiss_gpu_ivf_flat,test,k10,bs100,latency.csv": {  # noqa: E501
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "k",
                 "n_queries",
@@ -309,6 +316,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/faiss_gpu_ivf_flat,test,k10,bs100,raw.csv": {
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "k",
                 "n_queries",
@@ -327,6 +335,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
         "test-data/result/search/faiss_gpu_ivf_flat,test,k10,bs100,throughput.csv": {  # noqa: E501
             "header": common_search_header
             + [
+                "GPU",
                 "end_to_end",
                 "k",
                 "n_queries",

From bd1c31c9ab8cec5d3f7acb57c3c7208dc5cb1e12 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Wed, 30 Apr 2025 20:34:54 +0200
Subject: [PATCH 375/623] Add support of half dtype in IVF-FLAT (#730)

All the other indexes support half except IVF-FLAT at the moment

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/730
---
 cpp/CMakeLists.txt                            |   3 +
 cpp/include/cuvs/neighbors/ivf_flat.hpp       | 565 +++++++++++++++++-
 .../neighbors/ivf_flat/generate_ivf_flat.py   |   1 +
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh |   3 +-
 .../ivf_flat_build_extend_half_int64_t.cu     | 103 ++++
 .../neighbors/ivf_flat/ivf_flat_helpers.cu    |  82 +--
 .../neighbors/ivf_flat/ivf_flat_search.cuh    |   6 +-
 .../ivf_flat/ivf_flat_search_half_int64_t.cu  |  48 ++
 .../ivf_flat_serialize_half_int64_t.cu        |  35 ++
 cpp/src/neighbors/ivf_flat_c.cpp              |  20 +-
 cpp/tests/CMakeLists.txt                      |   1 +
 cpp/tests/neighbors/ann_ivf_flat.cuh          |  15 +-
 .../ann_ivf_flat/test_half_int64_t.cu         |  33 +
 cpp/tests/test_utils.cuh                      |   8 +-
 cpp/tests/test_utils.h                        |   9 +
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |  19 +-
 python/cuvs/cuvs/tests/test_ivf_flat.py       |   2 +-
 17 files changed, 888 insertions(+), 65 deletions(-)
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_search_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_serialize_half_int64_t.cu
 create mode 100644 cpp/tests/neighbors/ann_ivf_flat/test_half_int64_t.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index e919be49d2..715739b1bc 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -416,13 +416,16 @@ if(BUILD_SHARED_LIBS)
     $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
     src/neighbors/ivf_flat_index.cpp
     src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_helpers.cu
     src/neighbors/ivf_flat/ivf_flat_search_float_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_search_half_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_serialize_half_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_uint8_t_int64_t.cu
     src/neighbors/ivf_pq_index.cu
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index bba6436dbb..c206fefde1 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -364,6 +364,65 @@ void build(raft::resources const& handle,
            raft::device_matrix_view<const float, int64_t, raft::row_major> dataset,
            cuvs::neighbors::ivf_flat::index<float, int64_t>& idx);
 
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<half, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::device_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::device_matrix_view<const half, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<half, int64_t>& idx);
+
 /**
  * @brief Build the index from the dataset for efficient search.
  *
@@ -555,6 +614,79 @@ void build(raft::resources const& handle,
            raft::host_matrix_view<const float, int64_t, raft::row_major> dataset,
            cuvs::neighbors::ivf_flat::index<float, int64_t>& idx);
 
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = ivf_flat::build(handle, dataset, index_params);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed ivf-flat index
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_flat::index<half, int64_t>;
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
+ * Note, if index_params.add_data_on_build is set to true, the user can set a
+ * stream pool in the input raft::resource with at least one stream to enable kernel and copy
+ * overlapping.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping. This is only applicable if index_params.add_data_on_build is set to true
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
+ *   // create and fill the index from a [N, D] dataset
+ *   ivf_flat::index<decltype(dataset::value_type), decltype(dataset::index_type)> index;
+ *   ivf_flat::build(handle, dataset, index_params, index);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] index_params configure the index building
+ * @param[in] dataset raft::host_matrix_view to a row-major matrix [n_rows, dim]
+ * @param[out] idx reference to ivf_flat::index
+ *
+ */
+void build(raft::resources const& handle,
+           const cuvs::neighbors::ivf_flat::index_params& index_params,
+           raft::host_matrix_view<const half, int64_t, raft::row_major> dataset,
+           cuvs::neighbors::ivf_flat::index<half, int64_t>& idx);
+
 /**
  * @brief Build the index from the dataset for efficient search.
  *
@@ -773,6 +905,70 @@ void extend(raft::resources const& handle,
             std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
             cuvs::neighbors::ivf_flat::index<float, int64_t>* idx);
 
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<half, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<half, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // fill the index with the data
+ *   std::optional<raft::device_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::device_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::device_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::device_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<half, int64_t>* idx);
+
 /**
  * @brief Build a new index containing the data of the original plus new extra vectors.
  *
@@ -978,6 +1174,82 @@ void extend(raft::resources const& handle,
             std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
             cuvs::neighbors::ivf_flat::index<float, int64_t>* idx);
 
+/**
+ * @brief Build a new index containing the data of the original plus new extra vectors.
+ *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
+ * Implementation note:
+ *    The new data is clustered according to existing kmeans clusters, then the cluster
+ *    centers are adjusted to match the newly labeled data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   auto index = ivf_flat::extend(handle, new_vectors, no_op, index_empty);
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[in] idx original index
+ *
+ * @return the constructed extended ivf-flat index
+ */
+auto extend(raft::resources const& handle,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            const cuvs::neighbors::ivf_flat::index<half, int64_t>& idx)
+  -> cuvs::neighbors::ivf_flat::index<half, int64_t>;
+
+/**
+ * @brief Extend the index in-place with the new data.
+ *
+ * Note, the user can set a stream pool in the input raft::resource with
+ * at least one stream to enable kernel and copy overlapping.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   ivf_flat::index_params index_params;
+ *   index_params.add_data_on_build = false;      // don't populate index on build
+ *   index_params.kmeans_trainset_fraction = 1.0; // use whole dataset for kmeans training
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = ivf_flat::build(handle, index_params, dataset);
+ *   // optional: create a stream pool with at least one stream to enable kernel and copy
+ *   // overlapping
+ *   raft::resource::set_cuda_stream_pool(handle, std::make_shared<rmm::cuda_stream_pool>(1));
+ *   // fill the index with the data
+ *   std::optional<raft::host_vector_view<const IdxT, IdxT>> no_op = std::nullopt;
+ *   ivf_flat::extend(handle, dataset, no_opt, &index_empty);
+ * @endcode
+ *
+ *
+ * @param[in] handle
+ * @param[in] new_vectors raft::host_matrix_view to a row-major matrix [n_rows, index.dim()]
+ * @param[in] new_indices optional raft::host_vector_view to a vector of indices [n_rows].
+ *    If the original index is empty (`orig_index.size() == 0`), you can pass `std::nullopt`
+ *    here to imply a continuous range `[0...n_rows)`.
+ * @param[inout] idx pointer to index, to be overwritten in-place
+ */
+void extend(raft::resources const& handle,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices,
+            cuvs::neighbors::ivf_flat::index<half, int64_t>* idx);
+
 /**
  * @brief Build a new index containing the data of the original plus new extra vectors.
  *
@@ -1172,13 +1444,53 @@ void extend(raft::resources const& handle,
  */
 void search(raft::resources const& handle,
             const cuvs::neighbors::ivf_flat::search_params& params,
-            const cuvs::neighbors::ivf_flat::index<float, int64_t>& index,
-            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            const cuvs::neighbors::ivf_flat::index<float, int64_t>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
+/**
+ * @brief Search ANN using the constructed index.
+ *
+ * See the [ivf_flat::build](#ivf_flat::build) documentation for a usage example.
+ *
+ * Note, this function requires a temporary buffer to store intermediate results between cuda kernel
+ * calls, which may lead to undesirable allocations and slowdown. To alleviate the problem, you can
+ * pass a pool memory resource or a large enough pre-allocated memory resource to reduce or
+ * eliminate entirely allocations happening within `search`:
+ * @code{.cpp}
+ *   ...
+ *   // use default search parameters
+ *   ivf_flat::search_params search_params;
+ *   // Use the same allocator across multiple searches to reduce the number of
+ *   // cuda memory allocations
+ *   ivf_flat::search(handle, search_params, index, queries1, out_inds1, out_dists1);
+ *   ivf_flat::search(handle, search_params, index, queries2, out_inds2, out_dists2);
+ *   ivf_flat::search(handle, search_params, index, queries3, out_inds3, out_dists3);
+ *   ...
+ * @endcode
+ *
+ * @param[in] handle
+ * @param[in] params configure the search
+ * @param[in] index ivf-flat constructed index
+ * @param[in] queries raft::device_matrix_view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors raft::device_matrix_view to the indices of the neighbors in the source
+ * dataset [n_queries, k]
+ * @param[out] distances raft::device_matrix_view to the distances to the selected neighbors
+ * [n_queries, k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
+ */
+void search(raft::resources const& handle,
+            const cuvs::neighbors::ivf_flat::search_params& params,
+            const cuvs::neighbors::ivf_flat::index<half, int64_t>& index,
+            raft::device_matrix_view<const half, int64_t, raft::row_major> queries,
             raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             const cuvs::neighbors::filtering::base_filter& sample_filter =
               cuvs::neighbors::filtering::none_sample_filter{});
-
 /**
  * @brief Search ANN using the constructed index.
  *
@@ -1378,6 +1690,114 @@ void deserialize(raft::resources const& handle,
                  std::istream& is,
                  cuvs::neighbors::ivf_flat::index<float, int64_t>* index);
 
+/**
+ * Save the index to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::neighbors::ivf_flat::serialize(handle, filename, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize(raft::resources const& handle,
+               const std::string& filename,
+               const cuvs::neighbors::ivf_flat::index<half, int64_t>& index);
+
+/**
+ * Load index from file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * using T    = half; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
+ * cuvs::neighbors::ivf_flat::deserialize(handle, filename, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the name of the file that stores the index
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 const std::string& filename,
+                 cuvs::neighbors::ivf_flat::index<half, int64_t>* index);
+
+/**
+ * Write the index to an output stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = ivf_flat::build(...);`
+ * cuvs::neighbors::ivf_flat::serialize(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index IVF-Flat index
+ *
+ */
+void serialize(raft::resources const& handle,
+               std::ostream& os,
+               const cuvs::neighbors::ivf_flat::index<half, int64_t>& index);
+
+/**
+ * Load index from input stream
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/ivf_flat.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an input stream
+ * std::istream is(std::cin.rdbuf());
+ * using T    = half; // data element type
+ * using IdxT = int64_t; // type of the index
+ * // create an empty index with `ivf_flat::index<T, IdxT> index(handle, index_params, dim);`
+ * cuvs::neighbors::ivf_flat::deserialize(handle, is, &index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] is input stream
+ * @param[in] index IVF-Flat index
+ *
+ */
+void deserialize(raft::resources const& handle,
+                 std::istream& is,
+                 cuvs::neighbors::ivf_flat::index<half, int64_t>* index);
+
 /**
  * Save the index to file.
  *
@@ -1990,6 +2410,36 @@ void pack(raft::resources const& res,
                               typename list_spec<uint32_t, float, int64_t>::list_extents,
                               raft::row_major> list_data);
 
+/**
+ * Write flat codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_vec).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data  = index.lists()[label]->data.view();
+ *   // allocate the buffer for the input codes
+ *   auto codes = raft::make_device_matrix<half>(res, n_vec, index.dim());
+ *   ... prepare n_vecs to pack into the list in codes ...
+ *   // write codes into the list starting from the 42nd position
+ *   ivf_flat::helpers::codepacker::pack(
+ *       res, make_const_mdspan(codes.view()), index.veclen(), 42, list_data);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] codes flat codes [n_vec, dim]
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ * @param[inout] list_data block to write into
+ */
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const half, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<half,
+                              typename list_spec<uint32_t, half, int64_t>::list_extents,
+                              raft::row_major> list_data);
+
 /**
  * Write flat codes into an existing list by the given offset.
  *
@@ -2083,6 +2533,38 @@ void unpack(raft::resources const& res,
             uint32_t offset,
             raft::device_matrix_view<float, uint32_t, raft::row_major> codes);
 
+/**
+ * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
+ * starting at given `offset`.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   auto list_data = index.lists()[label]->data.view();
+ *   // allocate the buffer for the output
+ *   uint32_t n_take = 4;
+ *   auto codes = raft::make_device_matrix<half>(res, n_take, index.dim());
+ *   uint32_t offset = 0;
+ *   // unpack n_take elements from the list
+ *   ivf_fat::helpers::codepacker::unpack(res, list_data, index.veclen(), offset, codes.view());
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] list_data block to read from
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset
+ *   How many records in the list to skip.
+ * @param[inout] codes
+ *   the destination buffer [n_take, index.dim()].
+ *   The length `n_take` defines how many records to unpack,
+ *   it must be <= the list size.
+ */
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const half,
+                                typename list_spec<uint32_t, half, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<half, uint32_t, raft::row_major> codes);
 /**
  * @brief Unpack `n_take` consecutive records of a single list (cluster) in the compressed index
  * starting at given `offset`.
@@ -2163,6 +2645,20 @@ void unpack(raft::resources const& res,
  */
 void pack_1(const float* flat_code, float* block, uint32_t dim, uint32_t veclen, uint32_t offset);
 
+/**
+ * Write one flat code into a block by the given offset. The offset indicates the id of the record
+ * in the list. This function interleaves the code and is intended to later copy the interleaved
+ * codes over to the IVF list on device. NB: no memory allocation happens here; the block must fit
+ * the record (offset + 1).
+ *
+ * @param[in] flat_code input flat code
+ * @param[out] block block of memory to write interleaved codes to
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset how many records to skip before writing the data into the list
+ */
+void pack_1(const half* flat_code, half* block, uint32_t dim, uint32_t veclen, uint32_t offset);
+
 /**
  * Write one flat code into a block by the given offset. The offset indicates the id of the record
  * in the list. This function interleaves the code and is intended to later copy the interleaved
@@ -2205,6 +2701,19 @@ void pack_1(
  */
 void unpack_1(const float* block, float* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset);
 
+/**
+ * Unpack 1 record of a single list (cluster) in the index to fetch the flat code. The offset
+ * indicates the id of the record. This function fetches one flat code from an interleaved code.
+ *
+ * @param[in] block interleaved block. The block can be thought of as the whole inverted list in
+ * interleaved format.
+ * @param[out] flat_code output flat code
+ * @param[in] dim dimension of the flat code
+ * @param[in] veclen size of interleaved data chunks
+ * @param[in] offset fetch the flat code by the given offset
+ */
+void unpack_1(const half* block, half* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset);
+
 /**
  * Unpack 1 record of a single list (cluster) in the index to fetch the flat code. The offset
  * indicates the id of the record. This function fetches one flat code from an interleaved code.
@@ -2257,6 +2766,28 @@ void unpack_1(
  */
 void reset_index(const raft::resources& res, index<float, int64_t>* index);
 
+/**
+ * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
+ * externally modifying the index without going through the build stage. The data and indices of the
+ * IVF lists will be lost.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_flat::index<half, int64_t> index(res, index_params, D);
+ *   // reset the index's state and list sizes
+ *   ivf_flat::helpers::reset_index(res, &index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-Flat index
+ */
+void reset_index(const raft::resources& res, index<half, int64_t>* index);
+
 /**
  * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
  * externally modifying the index without going through the build stage. The data and indices of the
@@ -2329,6 +2860,34 @@ void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index);
  */
 void recompute_internal_state(const raft::resources& res, index<float, int64_t>* index);
 
+/**
+ * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
+ * modified externally.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   raft::resources res;
+ *   // use default index parameters
+ *   ivf_flat::index_params index_params;
+ *   // initialize an empty index
+ *   ivf_flat::index<uint8_t, int64_t> index(res, index_params, D);
+ *   ivf_flat::helpers::reset_index(res, &index);
+ *   // resize the first IVF list to hold 5 records
+ *   auto spec = list_spec<uint32_t, uint8_t, int64_t>{
+ *     index->dim(), index->conservative_memory_allocation()};
+ *   uint32_t new_size = 5;
+ *   ivf::resize_list(res, list, spec, new_size, 0);
+ *   raft::update_device(index.list_sizes(), &new_size, 1, stream);
+ *   // recompute the internal state of the index
+ *   ivf_flat::helpers::recompute_internal_state(res, index);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[inout] index pointer to IVF-PQ index
+ */
+void recompute_internal_state(const raft::resources& res, index<half, int64_t>* index);
+
 /**
  * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
  * modified externally.
diff --git a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
index 39baeeafae..8b5412280f 100644
--- a/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
+++ b/cpp/src/neighbors/ivf_flat/generate_ivf_flat.py
@@ -61,6 +61,7 @@
 
 types = dict(
     float_int64_t=("float", "int64_t"),
+    half_int64_t=("half", "int64_t"),
     int8_t_int64_t=("int8_t", "int64_t"),
     uint8_t_int64_t=("uint8_t", "int64_t"),
 )
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index 2a4f1ab92c..a1bec125b1 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -406,7 +406,8 @@ inline auto build(raft::resources const& handle,
   auto stream = raft::resource::get_cuda_stream(handle);
   cuvs::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "ivf_flat::build(%zu, %u)", size_t(n_rows), dim);
-  static_assert(std::is_same_v<T, float> || std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>,
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, uint8_t> ||
+                  std::is_same_v<T, int8_t>,
                 "unsupported data type");
   RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
   RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu
new file mode 100644
index 0000000000..8f4b253d7b
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_build.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_BUILD_EXTEND(T, IdxT)                                                 \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset,                   \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
+      std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
+  }                                                                                              \
+                                                                                                 \
+  void build(raft::resources const& handle,                                                      \
+             const cuvs::neighbors::ivf_flat::index_params& params,                              \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset,                     \
+             cuvs::neighbors::ivf_flat::index<T, IdxT>& idx)                                     \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset, idx);                      \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
+              std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }                                                                                              \
+  auto extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
+    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+  {                                                                                              \
+    return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
+      cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
+  }                                                                                              \
+                                                                                                 \
+  void extend(raft::resources const& handle,                                                     \
+              raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
+              std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
+              cuvs::neighbors::ivf_flat::index<T, IdxT>* idx)                                    \
+  {                                                                                              \
+    cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, idx);            \
+  }
+CUVS_INST_IVF_FLAT_BUILD_EXTEND(half, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_BUILD_EXTEND
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
index 7a34e92627..6b63ed506b 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_helpers.cu
@@ -35,6 +35,17 @@ void pack(raft::resources const& res,
   detail::pack<float, int64_t>(res, codes, veclen, offset, list_data);
 }
 
+void pack(raft::resources const& res,
+          raft::device_matrix_view<const half, uint32_t, raft::row_major> codes,
+          uint32_t veclen,
+          uint32_t offset,
+          raft::device_mdspan<half,
+                              typename list_spec<uint32_t, half, int64_t>::list_extents,
+                              raft::row_major> list_data)
+{
+  detail::pack<half, int64_t>(res, codes, veclen, offset, list_data);
+}
+
 void pack(raft::resources const& res,
           raft::device_matrix_view<const int8_t, uint32_t, raft::row_major> codes,
           uint32_t veclen,
@@ -68,6 +79,17 @@ void unpack(raft::resources const& res,
   detail::unpack<float, int64_t>(res, list_data, veclen, offset, codes);
 }
 
+void unpack(raft::resources const& res,
+            raft::device_mdspan<const half,
+                                typename list_spec<uint32_t, half, int64_t>::list_extents,
+                                raft::row_major> list_data,
+            uint32_t veclen,
+            uint32_t offset,
+            raft::device_matrix_view<half, uint32_t, raft::row_major> codes)
+{
+  detail::unpack<half, int64_t>(res, list_data, veclen, offset, codes);
+}
+
 void unpack(raft::resources const& res,
             raft::device_mdspan<const int8_t,
                                 typename list_spec<uint32_t, int8_t, int64_t>::list_extents,
@@ -95,6 +117,11 @@ void pack_1(const float* flat_code, float* block, uint32_t dim, uint32_t veclen,
   detail::pack_1<float>(flat_code, block, dim, veclen, offset);
 }
 
+void pack_1(const half* flat_code, half* block, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::pack_1<half>(flat_code, block, dim, veclen, offset);
+}
+
 void pack_1(const int8_t* flat_code, int8_t* block, uint32_t dim, uint32_t veclen, uint32_t offset)
 {
   detail::pack_1<int8_t>(flat_code, block, dim, veclen, offset);
@@ -111,6 +138,11 @@ void unpack_1(const float* block, float* flat_code, uint32_t dim, uint32_t vecle
   detail::unpack_1<float>(block, flat_code, dim, veclen, offset);
 }
 
+void unpack_1(const half* block, half* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
+{
+  detail::unpack_1<half>(block, flat_code, dim, veclen, offset);
+}
+
 void unpack_1(
   const int8_t* block, int8_t* flat_code, uint32_t dim, uint32_t veclen, uint32_t offset)
 {
@@ -149,51 +181,16 @@ void reset_index(const raft::resources& res, index<float, int64_t>* index)
   detail::reset_index<float, int64_t>(res, index);
 }
 
-/**
- * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
- * externally modifying the index without going through the build stage. The data and indices of the
- * IVF lists will be lost.
- *
- * Usage example:
- * @code{.cpp}
- *   raft::resources res;
- *   using namespace cuvs::neighbors;
- *   // use default index parameters
- *   ivf_flat::index_params index_params;
- *   // initialize an empty index
- *   ivf_flat::index<int8_t, int64_t> index(res, index_params, D);
- *   // reset the index's state and list sizes
- *   ivf_flat::helpers::reset_index(res, &index);
- * @endcode
- *
- * @param[in] res raft resource
- * @param[inout] index pointer to IVF-Flat index
- */
+void reset_index(const raft::resources& res, index<half, int64_t>* index)
+{
+  detail::reset_index<half, int64_t>(res, index);
+}
+
 void reset_index(const raft::resources& res, index<int8_t, int64_t>* index)
 {
   detail::reset_index<int8_t, int64_t>(res, index);
 }
 
-/**
- * @brief Public helper API to reset the data and indices ptrs, and the list sizes. Useful for
- * externally modifying the index without going through the build stage. The data and indices of the
- * IVF lists will be lost.
- *
- * Usage example:
- * @code{.cpp}
- *   raft::resources res;
- *   using namespace cuvs::neighbors;
- *   // use default index parameters
- *   ivf_flat::index_params index_params;
- *   // initialize an empty index
- *   ivf_flat::index<uint8_t, int64_t> index(res, index_params, D);
- *   // reset the index's state and list sizes
- *   ivf_flat::helpers::reset_index(res, &index);
- * @endcode
- *
- * @param[in] res raft resource
- * @param[inout] index pointer to IVF-Flat index
- */
 void reset_index(const raft::resources& res, index<uint8_t, int64_t>* index)
 {
   detail::reset_index<uint8_t, int64_t>(res, index);
@@ -204,6 +201,11 @@ void recompute_internal_state(const raft::resources& res, index<float, int64_t>*
   ivf::detail::recompute_internal_state(res, *index);
 }
 
+void recompute_internal_state(const raft::resources& res, index<half, int64_t>* index)
+{
+  ivf::detail::recompute_internal_state(res, *index);
+}
+
 void recompute_internal_state(const raft::resources& res, index<int8_t, int64_t>* index)
 {
   ivf::detail::recompute_internal_state(res, *index);
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index b7ecdf4387..c51236d1aa 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -84,10 +84,10 @@ void search_impl(raft::resources const& handle,
     distance_buffer_dev.data(), n_queries, index.n_lists());
 
   size_t float_query_size;
-  if constexpr (std::is_integral_v<T>) {
-    float_query_size = n_queries * index.dim();
-  } else {
+  if constexpr (std::is_same_v<T, float>) {
     float_query_size = 0;
+  } else {
+    float_query_size = n_queries * index.dim();
   }
   rmm::device_uvector<float> converted_queries_dev(float_query_size, stream, search_mr);
   float* converted_queries_ptr = converted_queries_dev.data();
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search_half_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_search_half_int64_t.cu
new file mode 100644
index 0000000000..79bc26e054
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search_half_int64_t.cu
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_search.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+#define CUVS_INST_IVF_FLAT_SEARCH(T, IdxT)                                      \
+  void search(raft::resources const& handle,                                    \
+              const cuvs::neighbors::ivf_flat::search_params& params,           \
+              const cuvs::neighbors::ivf_flat::index<T, IdxT>& index,           \
+              raft::device_matrix_view<const T, IdxT, raft::row_major> queries, \
+              raft::device_matrix_view<IdxT, IdxT, raft::row_major> neighbors,  \
+              raft::device_matrix_view<float, IdxT, raft::row_major> distances, \
+              const cuvs::neighbors::filtering::base_filter& sample_filter)     \
+  {                                                                             \
+    cuvs::neighbors::ivf_flat::detail::search(                                  \
+      handle, params, index, queries, neighbors, distances, sample_filter);     \
+  }
+CUVS_INST_IVF_FLAT_SEARCH(half, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SEARCH
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_half_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_half_int64_t.cu
new file mode 100644
index 0000000000..dd75667eaf
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_serialize_half_int64_t.cu
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_ivf_flat.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_ivf_flat.py
+ *
+ */
+
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+#include "ivf_flat_serialize.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+CUVS_INST_IVF_FLAT_SERIALIZE(half, int64_t);
+
+#undef CUVS_INST_IVF_FLAT_SERIALIZE
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index b38f1808dd..4f930521a6 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -151,10 +151,14 @@ extern "C" cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index_c_ptr)
   return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
-    if (index.dtype.code == kDLFloat) {
+    if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
       auto index_ptr =
         reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index.addr);
       delete index_ptr;
+    } else if (index.dtype.code == kDLFloat && index.dtype.bits == 16) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<half, int64_t>*>(index.addr);
+      delete index_ptr;
     } else if (index.dtype.code == kDLInt) {
       auto index_ptr =
         reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index.addr);
@@ -180,6 +184,9 @@ extern "C" cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<float, int64_t>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_build<half, int64_t>(res, *params, dataset_tensor));
     } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
       index->addr =
         reinterpret_cast<uintptr_t>(_build<int8_t, int64_t>(res, *params, dataset_tensor));
@@ -226,6 +233,9 @@ extern "C" cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
       _search<float, int64_t>(
         res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      _search<half, int64_t>(
+        res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
     } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
       _search<int8_t, int64_t>(
         res, *params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
@@ -286,6 +296,10 @@ extern "C" cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float, int64_t>(res, filename));
       index->dtype.code = kDLFloat;
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<half, int64_t>(res, filename));
+      index->dtype.code = kDLFloat;
+      index->dtype.bits = 16;
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<int8_t, int64_t>(res, filename));
       index->dtype.code = kDLInt;
@@ -306,6 +320,8 @@ extern "C" cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       _serialize<float, int64_t>(res, filename, *index);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _serialize<half, int64_t>(res, filename, *index);
     } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
       _serialize<int8_t, int64_t>(res, filename, *index);
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
@@ -324,6 +340,8 @@ extern "C" cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       _extend<float, int64_t>(res, new_vectors, new_indices, *index);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _extend<half, int64_t>(res, new_vectors, new_indices, *index);
     } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
       _extend<int8_t, int64_t>(res, new_vectors, new_indices, *index);
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index ee2ddac197..d33f0cbcf5 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -107,6 +107,7 @@ if(BUILD_TESTS)
     NEIGHBORS_ANN_IVF_FLAT_TEST
     PATH
     neighbors/ann_ivf_flat/test_float_int64_t.cu
+    neighbors/ann_ivf_flat/test_half_int64_t.cu
     neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
     neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
     GPUS
diff --git a/cpp/tests/neighbors/ann_ivf_flat.cuh b/cpp/tests/neighbors/ann_ivf_flat.cuh
index cb30261641..11a1fba9cb 100644
--- a/cpp/tests/neighbors/ann_ivf_flat.cuh
+++ b/cpp/tests/neighbors/ann_ivf_flat.cuh
@@ -58,9 +58,10 @@ template <typename IdxT>
 ::std::ostream& operator<<(::std::ostream& os, const AnnIvfFlatInputs<IdxT>& p)
 {
   os << "{ " << p.num_queries << ", " << p.num_db_vecs << ", " << p.dim << ", " << p.k << ", "
-     << p.nprobe << ", " << p.nlist << ", " << static_cast<int>(p.metric) << ", "
-     << p.adaptive_centers << "," << p.host_dataset << "," << p.kernel_copy_overlapping << '}'
-     << std::endl;
+     << p.nprobe << ", " << p.nlist << ", "
+     << cuvs::neighbors::print_metric{static_cast<cuvs::distance::DistanceType>((int)p.metric)}
+     << ", " << p.adaptive_centers << "," << p.host_dataset << "," << p.kernel_copy_overlapping
+     << '}' << std::endl;
   return os;
 }
 
@@ -255,13 +256,14 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
                                         stream_));
         }
       }
+      float eps = std::is_same_v<DataT, half> ? 0.005 : 0.001;
       ASSERT_TRUE(eval_neighbours(indices_naive,
                                   indices_ivfflat,
                                   distances_naive,
                                   distances_ivfflat,
                                   ps.num_queries,
                                   ps.k,
-                                  0.001,
+                                  eps,
                                   min_recall));
     }
   }
@@ -487,13 +489,14 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
           indices_ivfflat.data(), indices_ivfflat_dev.data_handle(), queries_size, stream_);
         raft::resource::sync_stream(handle_);
       }
+      float eps = std::is_same_v<DataT, half> ? 0.005 : 0.001;
       ASSERT_TRUE(eval_neighbours(indices_naive,
                                   indices_ivfflat,
                                   distances_naive,
                                   distances_ivfflat,
                                   ps.num_queries,
                                   ps.k,
-                                  0.001,
+                                  eps,
                                   min_recall));
     }
   }
@@ -504,7 +507,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
     search_queries.resize(ps.num_queries * ps.dim, stream_);
 
     raft::random::RngState r(1234ULL);
-    if constexpr (std::is_same<DataT, float>{}) {
+    if constexpr (std::is_same_v<DataT, float> || std::is_same_v<DataT, half>) {
       raft::random::uniform(
         handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(0.1), DataT(2.0));
       raft::random::uniform(
diff --git a/cpp/tests/neighbors/ann_ivf_flat/test_half_int64_t.cu b/cpp/tests/neighbors/ann_ivf_flat/test_half_int64_t.cu
new file mode 100644
index 0000000000..6362ec3e15
--- /dev/null
+++ b/cpp/tests/neighbors/ann_ivf_flat/test_half_int64_t.cu
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../ann_ivf_flat.cuh"
+
+namespace cuvs::neighbors::ivf_flat {
+
+typedef AnnIVFFlatTest<float, half, int64_t> AnnIVFFlatTestF_half;
+TEST_P(AnnIVFFlatTestF_half, AnnIVFFlat)
+{
+  this->testIVFFlat();
+  this->testPacker();
+  this->testFilter();
+}
+
+INSTANTIATE_TEST_CASE_P(AnnIVFFlatTest, AnnIVFFlatTestF_half, ::testing::ValuesIn(inputs));
+
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/tests/test_utils.cuh b/cpp/tests/test_utils.cuh
index 022202ffb2..79ae1e7181 100644
--- a/cpp/tests/test_utils.cuh
+++ b/cpp/tests/test_utils.cuh
@@ -61,7 +61,13 @@ testing::AssertionResult devArrMatch(
     auto exp = exp_h.get()[i];
     auto act = act_h.get()[i];
     if (!eq_compare(exp, act)) {
-      return testing::AssertionFailure() << "actual=" << act << " != expected=" << exp << " @" << i;
+      if constexpr (std::is_same_v<T, half>) {
+        return testing::AssertionFailure()
+               << "actual=" << float{act} << " != expected=" << float{exp} << " @" << i;
+      } else {
+        return testing::AssertionFailure()
+               << "actual=" << act << " != expected=" << exp << " @" << i;
+      }
     }
   }
   return testing::AssertionSuccess();
diff --git a/cpp/tests/test_utils.h b/cpp/tests/test_utils.h
index 2d9115a809..ee4e553fa3 100644
--- a/cpp/tests/test_utils.h
+++ b/cpp/tests/test_utils.h
@@ -30,6 +30,15 @@ struct Compare {
   bool operator()(const T& a, const T& b) const { return a == b; }
 };
 
+#if CUDART_VERSION < 12040
+// Workaround to support half precision on older CUDA versions. See:
+// https://docs.nvidia.com/cuda/archive/12.8.0/cuda-toolkit-release-notes/#cuda-math-release-12-4
+template <>
+struct Compare<half> {
+  bool operator()(const half& a, const half& b) const { return float{a} == float{b}; }
+};
+#endif
+
 template <typename Key, typename Value>
 struct Compare<raft::KeyValuePair<Key, Value>> {
   bool operator()(const raft::KeyValuePair<Key, Value>& a,
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 437499c1ea..658a32c838 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -191,7 +191,7 @@ def build(IndexParams index_params, dataset, resources=None):
     ----------
     index_params : :py:class:`cuvs.neighbors.ivf_flat.IndexParams`
     dataset : CUDA array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float, int8, uint8]
+        Supported dtype [float32, float16, int8, uint8]
     {resources_docstring}
 
     Returns
@@ -219,8 +219,8 @@ def build(IndexParams index_params, dataset, resources=None):
     """
 
     dataset_ai = wrap_array(dataset)
-    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('byte'),
-                                    np.dtype('ubyte')])
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('float16'),
+                                    np.dtype('byte'), np.dtype('ubyte')])
 
     cdef Index idx = Index()
     cdef cydlpack.DLManagedTensor* dataset_dlpack = \
@@ -286,7 +286,7 @@ def search(SearchParams search_params,
     index : py:class:`cuvs.neighbors.ivf_flat.Index`
         Trained IvfFlat index.
     queries : CUDA array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float, int8, uint8]
+        Supported dtype [float32, float16, int8, uint8]
     k : int
         The number of neighbors.
     neighbors : Optional CUDA array interface compliant matrix shape
@@ -324,8 +324,8 @@ def search(SearchParams search_params,
         raise ValueError("Index needs to be built before calling search.")
 
     queries_cai = wrap_array(queries)
-    _check_input_array(queries_cai, [np.dtype('float32'), np.dtype('byte'),
-                                     np.dtype('ubyte')])
+    _check_input_array(queries_cai, [np.dtype('float32'), np.dtype('float16'),
+                                     np.dtype('byte'), np.dtype('ubyte')])
 
     cdef uint32_t n_queries = queries_cai.shape[0]
 
@@ -454,7 +454,7 @@ def extend(Index index, new_vectors, new_indices, resources=None):
     index : ivf_flat.Index
         Trained ivf_flat object.
     new_vectors : array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float, int8, uint8]
+        Supported dtype [float32, float16, int8, uint8]
     new_indices : array interface compliant vector shape (n_samples)
         Supported dtype [int64]
     {resources_docstring}
@@ -488,8 +488,9 @@ def extend(Index index, new_vectors, new_indices, resources=None):
     """
 
     new_vectors_ai = wrap_array(new_vectors)
-    _check_input_array(new_vectors_ai, [np.dtype('float32'), np.dtype('byte'),
-                                        np.dtype('ubyte')])
+    _check_input_array(new_vectors_ai,
+                       [np.dtype('float32'), np.dtype('float16'),
+                        np.dtype('byte'), np.dtype('ubyte')])
 
     new_indices_ai = wrap_array(new_indices)
     _check_input_array(new_indices_ai, [np.dtype('int64')])
diff --git a/python/cuvs/cuvs/tests/test_ivf_flat.py b/python/cuvs/cuvs/tests/test_ivf_flat.py
index 6b89041fa8..e0cd7b08b1 100644
--- a/python/cuvs/cuvs/tests/test_ivf_flat.py
+++ b/python/cuvs/cuvs/tests/test_ivf_flat.py
@@ -122,7 +122,7 @@ def test_ivf_flat(inplace, dtype, metric):
     )
 
 
-@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
 def test_extend(dtype):
     run_ivf_flat_build_search_test(
         n_rows=10000,

From f2d70ae648e47e660352f97443d2c087e680e23b Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 30 Apr 2025 11:36:08 -0700
Subject: [PATCH 376/623] Expose kmeans to python (#729)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/729
---
 cpp/CMakeLists.txt                            |   2 +
 cpp/include/cuvs/cluster/kmeans.h             | 227 ++++++++++
 cpp/include/cuvs/cluster/kmeans.hpp           | 130 ++++--
 cpp/include/cuvs/neighbors/cagra.h            |  24 ++
 cpp/src/cluster/kmeans.cuh                    |  54 +++
 .../cluster/kmeans_balanced_predict_float.cu  |   2 +-
 cpp/src/cluster/kmeans_c.cpp                  | 279 ++++++++++++
 cpp/src/cluster/kmeans_cluster_cost.cu        |  37 ++
 cpp/src/neighbors/cagra_c.cpp                 |  26 +-
 python/cuvs/CMakeLists.txt                    |   1 +
 python/cuvs/cuvs/cluster/CMakeLists.txt       |  15 +
 python/cuvs/cuvs/cluster/__init__.pxd         |   0
 python/cuvs/cuvs/cluster/__init__.py          |  18 +
 .../cuvs/cuvs/cluster/kmeans/CMakeLists.txt   |  23 +
 python/cuvs/cuvs/cluster/kmeans/__init__.pxd  |   0
 python/cuvs/cuvs/cluster/kmeans/__init__.py   |  18 +
 python/cuvs/cuvs/cluster/kmeans/kmeans.pxd    |  72 ++++
 python/cuvs/cuvs/cluster/kmeans/kmeans.pyx    | 405 ++++++++++++++++++
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |   3 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  18 +-
 python/cuvs/cuvs/tests/test_doctests.py       |   2 +
 python/cuvs/cuvs/tests/test_kmeans.py         |  82 ++++
 22 files changed, 1401 insertions(+), 37 deletions(-)
 create mode 100644 cpp/include/cuvs/cluster/kmeans.h
 create mode 100644 cpp/src/cluster/kmeans_c.cpp
 create mode 100644 cpp/src/cluster/kmeans_cluster_cost.cu
 create mode 100644 python/cuvs/cuvs/cluster/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/cluster/__init__.pxd
 create mode 100644 python/cuvs/cuvs/cluster/__init__.py
 create mode 100644 python/cuvs/cuvs/cluster/kmeans/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/cluster/kmeans/__init__.pxd
 create mode 100644 python/cuvs/cuvs/cluster/kmeans/__init__.py
 create mode 100644 python/cuvs/cuvs/cluster/kmeans/kmeans.pxd
 create mode 100644 python/cuvs/cuvs/cluster/kmeans/kmeans.pyx
 create mode 100644 python/cuvs/cuvs/tests/test_kmeans.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 715739b1bc..336e396824 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -312,6 +312,7 @@ if(BUILD_SHARED_LIBS)
   add_library(
     cuvs_objs OBJECT
     src/cluster/kmeans_balanced_fit_float.cu
+    src/cluster/kmeans_cluster_cost.cu
     src/cluster/kmeans_fit_mg_float.cu
     src/cluster/kmeans_fit_mg_double.cu
     src/cluster/kmeans_fit_double.cu
@@ -699,6 +700,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
     add_library(
       cuvs_c SHARED
       src/core/c_api.cpp
+      src/cluster/kmeans_c.cpp
       src/neighbors/brute_force_c.cpp
       src/neighbors/ivf_flat_c.cpp
       src/neighbors/ivf_pq_c.cpp
diff --git a/cpp/include/cuvs/cluster/kmeans.h b/cpp/include/cuvs/cluster/kmeans.h
new file mode 100644
index 0000000000..99849b9a96
--- /dev/null
+++ b/cpp/include/cuvs/cluster/kmeans.h
@@ -0,0 +1,227 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup kmeans_c_params k-means hyperparameters
+ * @{
+ */
+
+enum cuvsKMeansInitMethod {
+  /**
+   * Sample the centroids using the kmeans++ strategy
+   */
+  KMeansPlusPlus,
+
+  /**
+   * Sample the centroids uniformly at random
+   */
+  Random,
+
+  /**
+   * User provides the array of initial centroids
+   */
+  Array
+};
+
+/**
+ * @brief Hyper-parameters for the kmeans algorithm
+ */
+struct cuvsKMeansParams {
+  cuvsDistanceType metric;
+
+  /**
+   * The number of clusters to form as well as the number of centroids to generate (default:8).
+   */
+  int n_clusters;
+
+  /**
+   * Method for initialization, defaults to k-means++:
+   *  - cuvsKMeansInitMethod::KMeansPlusPlus (k-means++): Use scalable k-means++ algorithm
+   * to select the initial cluster centers.
+   *  - cuvsKMeansInitMethod::Random (random): Choose 'n_clusters' observations (rows) at
+   * random from the input data for the initial centroids.
+   *  - cuvsKMeansInitMethod::Array (ndarray): Use 'centroids' as initial cluster centers.
+   */
+  cuvsKMeansInitMethod init;
+
+  /**
+   * Maximum number of iterations of the k-means algorithm for a single run.
+   */
+  int max_iter;
+
+  /**
+   * Relative tolerance with regards to inertia to declare convergence.
+   */
+  double tol;
+
+  /**
+   * Number of instance k-means algorithm will be run with different seeds.
+   */
+  int n_init;
+
+  /**
+   * Oversampling factor for use in the k-means|| algorithm
+   */
+  double oversampling_factor;
+
+  /**
+   * batch_samples and batch_centroids are used to tile 1NN computation which is
+   * useful to optimize/control the memory footprint
+   * Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0
+   * then don't tile the centroids
+   */
+  int batch_samples;
+
+  /**
+   * if 0 then batch_centroids = n_clusters
+   */
+  int batch_centroids;
+
+  bool inertia_check;
+
+  /**
+   * Whether to use hierarchical (balanced) kmeans or not
+   */
+  bool hierarchical;
+
+  /**
+   * For hierarchical k-means , defines the number of training iterations
+   */
+  int hierarchical_n_iters;
+};
+
+typedef struct cuvsKMeansParams* cuvsKMeansParams_t;
+
+/**
+ * @brief Allocate KMeans params, and populate with default values
+ *
+ * @param[in] params cuvsKMeansParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsKMeansParamsCreate(cuvsKMeansParams_t* params);
+
+/**
+ * @brief De-allocate KMeans params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsKMeansParamsDestroy(cuvsKMeansParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup kmeans_c k-means clustering APIs
+ * @{
+ */
+
+/**
+ * @brief Find clusters with k-means algorithm.
+ *
+ *   Initial centroids are chosen with k-means++ algorithm. Empty
+ *   clusters are reinitialized by choosing new centroids with
+ *   k-means++ algorithm.
+ *
+ * @param[in]     res           opaque C handle
+ * @param[in]     params        Parameters for KMeans model.
+ * @param[in]     X             Training instances to cluster. The data must
+ *                              be in row-major format.
+ *                              [dim = n_samples x n_features]
+ * @param[in]     sample_weight Optional weights for each observation in X.
+ *                              [len = n_samples]
+ * @param[inout]  centroids     [in] When init is InitMethod::Array, use
+ *                              centroids as the initial cluster centers.
+ *                              [out] The generated centroids from the
+ *                              kmeans algorithm are stored at the address
+ *                              pointed by 'centroids'.
+ *                              [dim = n_clusters x n_features]
+ * @param[out]    inertia       Sum of squared distances of samples to their
+ *                              closest cluster center.
+ * @param[out]    n_iter        Number of iterations run.
+ */
+cuvsError_t cuvsKMeansFit(cuvsResources_t res,
+                          cuvsKMeansParams_t params,
+                          DLManagedTensor* X,
+                          DLManagedTensor* sample_weight,
+                          DLManagedTensor* centroids,
+                          double* inertia,
+                          int* n_iter);
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @param[in]     res              opaque C handle
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     sample_weight    Optional weights for each observation in X.
+ *                                 [len = n_samples]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[in]     normalize_weight True if the weights should be normalized
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ * @param[out]    inertia          Sum of squared distances of samples to
+ *                                 their closest cluster center.
+ */
+cuvsError_t cuvsKMeansPredict(cuvsResources_t res,
+                              cuvsKMeansParams_t params,
+                              DLManagedTensor* X,
+                              DLManagedTensor* sample_weight,
+                              DLManagedTensor* centroids,
+                              DLManagedTensor* labels,
+                              bool normalize_weight,
+                              double* inertia);
+
+/**
+ * @brief Compute cluster cost
+ *
+ * @param[in]  res            opaque C handle
+ * @param[in]  X              Training instances to cluster. The data must
+ *                            be in row-major format.
+ *                            [dim = n_samples x n_features]
+ * @param[in]  centroids      Cluster centroids. The data must be in
+ *                            row-major format.
+ *                            [dim = n_clusters x n_features]
+ * @param[out] cost           Resulting cluster cost
+ *
+ */
+cuvsError_t cuvsKMeansClusterCost(cuvsResources_t res,
+                                  DLManagedTensor* X,
+                                  DLManagedTensor* centroids,
+                                  double* cost);
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index 64ac813ab1..ad08cd1394 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -104,10 +104,12 @@ struct params : base_params {
    */
   double oversampling_factor = 2.0;
 
-  // batch_samples and batch_centroids are used to tile 1NN computation which is
-  // useful to optimize/control the memory footprint
-  // Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0
-  // then don't tile the centroids
+  /**
+   * batch_samples and batch_centroids are used to tile 1NN computation which is
+   * useful to optimize/control the memory footprint
+   * Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0
+   * then don't tile the centroids
+   */
   int batch_samples = 1 << 15;
 
   /**
@@ -406,7 +408,8 @@ void fit(raft::resources const& handle,
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
  *   int n_features = 15;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   int n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
@@ -439,8 +442,8 @@ void fit(const raft::resources& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   int n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<int8_t, int>(handle, n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
@@ -492,7 +495,7 @@ void fit(const raft::resources& handle,
  *                   centroids.view(),
  *                   false,
  *                   labels.view(),
- *                   raft::make_scalar_view(&ineratia));
+ *                   raft::make_scalar_view(&inertia));
  * @endcode
  *
  * @param[in]     handle           The raft handle.
@@ -550,7 +553,7 @@ void predict(raft::resources const& handle,
  *                   centroids.view(),
  *                   false,
  *                   labels.view(),
- *                   raft::make_scalar_view(&ineratia));
+ *                   raft::make_scalar_view(&inertia));
  * @endcode
  *
  * @param[in]     handle           The raft handle.
@@ -608,7 +611,7 @@ void predict(raft::resources const& handle,
  *                   centroids.view(),
  *                   false,
  *                   labels.view(),
- *                   raft::make_scalar_view(&ineratia));
+ *                   raft::make_scalar_view(&inertia));
  * @endcode
  *
  * @param[in]     handle           The raft handle.
@@ -666,7 +669,7 @@ void predict(raft::resources const& handle,
  *                   centroids.view(),
  *                   false,
  *                   labels.view(),
- *                   raft::make_scalar_view(&ineratia));
+ *                   raft::make_scalar_view(&inertia));
  * @endcode
  *
  * @param[in]     handle           The raft handle.
@@ -703,28 +706,22 @@ void predict(raft::resources const& handle,
  *   using namespace  cuvs::cluster;
  *   ...
  *   raft::resources handle;
- *   cuvs::cluster::kmeans::params params;
- *   int n_features = 15, inertia, n_iter;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   cuvs::cluster::kmeans::balanced_params params;
+ *   int n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
  *               X,
- *               std::nullopt,
- *               centroids.view(),
- *               raft::make_scalar_view(&inertia),
- *               raft::make_scalar_view(&n_iter));
+ *               centroids.view());
  *   ...
  *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
  *
  *   kmeans::predict(handle,
  *                   params,
  *                   X,
- *                   std::nullopt,
  *                   centroids.view(),
- *                   false,
- *                   labels.view(),
- *                   raft::make_scalar_view(&ineratia));
+ *                   labels.view());
  * @endcode
  *
  * @param[in]     handle           The raft handle.
@@ -744,6 +741,50 @@ void predict(const raft::resources& handle,
              raft::device_matrix_view<const float, int> centroids,
              raft::device_vector_view<uint32_t, int> labels);
 
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::balanced_params params;
+ *   int n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               centroids.view());
+ *   ...
+ *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   centroids.view(),
+ *                   labels.view());
+ * @endcode
+ *
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ */
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const float, int> X,
+             raft::device_matrix_view<const float, int> centroids,
+             raft::device_vector_view<int, int> labels);
+
 /**
  * @brief Compute k-means clustering and predicts cluster index for each sample
  * in the input.
@@ -975,8 +1016,8 @@ void fit_predict(raft::resources const& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   int n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
  *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
  *
  *   kmeans::fit_predict(handle,
@@ -1019,8 +1060,8 @@ void fit_predict(const raft::resources& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);
+ *   int n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
  *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
  *
  *   kmeans::fit_predict(handle,
@@ -1089,6 +1130,43 @@ void transform(raft::resources const& handle,
                raft::device_matrix_view<const double, int> X,
                raft::device_matrix_view<const double, int> centroids,
                raft::device_matrix_view<double, int> X_new);
+
+/**
+ * @brief Compute cluster cost
+ *
+ * @param[in]  handle         The raft handle
+ * @param[in]  X              Training instances to cluster. The data must
+ *                            be in row-major format.
+ *                            [dim = n_samples x n_features]
+ * @param[in]  centroids      Cluster centroids. The data must be in
+ *                            row-major format.
+ *                            [dim = n_clusters x n_features]
+ * @param[out] cost           Resulting cluster cost
+ *
+ */
+void cluster_cost(const raft::resources& handle,
+                  raft::device_matrix_view<const float, int> X,
+                  raft::device_matrix_view<const float, int> centroids,
+                  raft::host_scalar_view<float> cost);
+
+/**
+ * @brief Compute cluster cost
+ *
+ * @param[in]  handle         The raft handle
+ * @param[in]  X              Training instances to cluster. The data must
+ *                            be in row-major format.
+ *                            [dim = n_samples x n_features]
+ * @param[in]  centroids      Cluster centroids. The data must be in
+ *                            row-major format.
+ *                            [dim = n_clusters x n_features]
+ * @param[out] cost           Resulting cluster cost
+ *
+ */
+void cluster_cost(const raft::resources& handle,
+                  raft::device_matrix_view<const double, int> X,
+                  raft::device_matrix_view<const double, int> centroids,
+                  raft::host_scalar_view<double> cost);
+
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index e182610884..664cc7e9c3 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -269,6 +269,30 @@ struct cuvsCagraSearchParams {
   uint32_t num_random_samplings;
   /** Bit mask used for initial random seed node selection. */
   uint64_t rand_xor_mask;
+
+  /** Whether to use the persistent version of the kernel (only SINGLE_CTA is supported a.t.m.) */
+  bool persistent;
+  /** Persistent kernel: time in seconds before the kernel stops if no requests received. */
+  float persistent_lifetime;
+  /**
+   * Set the fraction of maximum grid size used by persistent kernel.
+   * Value 1.0 means the kernel grid size is maximum possible for the selected device.
+   * The value must be greater than 0.0 and not greater than 1.0.
+   *
+   * One may need to run other kernels alongside this persistent kernel. This parameter can
+   * be used to reduce the grid size of the persistent kernel to leave a few SMs idle.
+   * Note: running any other work on GPU alongside with the persistent kernel makes the setup
+   * fragile.
+   *   - Running another kernel in another thread usually works, but no progress guaranteed
+   *   - Any CUDA allocations block the context (this issue may be obscured by using pools)
+   *   - Memory copies to not-pinned host memory may block the context
+   *
+   * Even when we know there are no other kernels working at the same time, setting
+   * kDeviceUsage to 1.0 surprisingly sometimes hurts performance. Proceed with care.
+   * If you suspect this is an issue, you can reduce this number to ~0.9 without a significant
+   * impact on the throughput.
+   */
+  float persistent_device_usage;
 };
 
 typedef struct cuvsCagraSearchParams* cuvsCagraSearchParams_t;
diff --git a/cpp/src/cluster/kmeans.cuh b/cpp/src/cluster/kmeans.cuh
index 5e6d756cca..13036795ab 100644
--- a/cpp/src/cluster/kmeans.cuh
+++ b/cpp/src/cluster/kmeans.cuh
@@ -465,6 +465,60 @@ void min_cluster_distance(raft::resources const& handle,
                                                                           workspace);
 }
 
+template <typename DataT, typename IndexT>
+void cluster_cost(raft::resources const& handle,
+                  raft::device_matrix_view<const DataT, IndexT> X,
+                  raft::device_matrix_view<const DataT, IndexT> centroids,
+                  raft::host_scalar_view<DataT> cost)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  auto n_clusters = centroids.extent(0);
+  auto n_samples  = X.extent(0);
+  auto n_features = X.extent(1);
+
+  rmm::device_uvector<char> workspace(n_samples * sizeof(IndexT), stream);
+
+  auto x_norms = raft::make_device_vector<DataT>(handle, n_samples);
+
+  raft::linalg::rowNorm(x_norms.data_handle(),
+                        X.data_handle(),
+                        n_features,
+                        n_samples,
+                        raft::linalg::L2Norm,
+                        true,
+                        stream);
+
+  auto min_cluster_distance = raft::make_device_vector<DataT>(handle, n_samples);
+  rmm::device_uvector<DataT> l2_norm_or_distance_buffer(0, stream);
+
+  auto metric = cuvs::distance::DistanceType::L2Expanded;
+
+  cuvs::cluster::kmeans::min_cluster_distance<DataT, IndexT>(
+    handle,
+    X,
+    raft::make_device_matrix_view<DataT, IndexT>(
+      const_cast<DataT*>(centroids.data_handle()), n_clusters, n_features),
+    min_cluster_distance.view(),
+    x_norms.view(),
+    l2_norm_or_distance_buffer,
+    metric,
+    n_samples,
+    n_clusters,
+    workspace);
+
+  rmm::device_scalar<DataT> device_cost(0, stream);
+
+  cuvs::cluster::kmeans::cluster_cost(handle,
+                                      min_cluster_distance.view(),
+                                      workspace,
+                                      raft::make_device_scalar_view<DataT>(device_cost.data()),
+                                      raft::add_op{});
+  raft::update_host(cost.data_handle(), device_cost.data(), 1, stream);
+
+  raft::resource::sync_stream(handle);
+}
+
 /**
  * @brief Calculates a <key, value> pair for every sample in input 'X' where key is an
  * index of one of the 'centroids' (index of the nearest centroid) and 'value'
diff --git a/cpp/src/cluster/kmeans_balanced_predict_float.cu b/cpp/src/cluster/kmeans_balanced_predict_float.cu
index e8cd1ffeb0..e951c20748 100644
--- a/cpp/src/cluster/kmeans_balanced_predict_float.cu
+++ b/cpp/src/cluster/kmeans_balanced_predict_float.cu
@@ -26,7 +26,7 @@ void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
              raft::device_matrix_view<const float, int> X,
              raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<uint32_t, int> labels)
+             raft::device_vector_view<int, int> labels)
 {
   cuvs::cluster::kmeans_balanced::predict(
     handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
diff --git a/cpp/src/cluster/kmeans_c.cpp b/cpp/src/cluster/kmeans_c.cpp
new file mode 100644
index 0000000000..135fcf3245
--- /dev/null
+++ b/cpp/src/cluster/kmeans_c.cpp
@@ -0,0 +1,279 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <cuvs/cluster/kmeans.h>
+#include <cuvs/cluster/kmeans.hpp>
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+
+namespace {
+
+cuvs::cluster::kmeans::params convert_params(const cuvsKMeansParams& params)
+{
+  auto kmeans_params       = cuvs::cluster::kmeans::params();
+  kmeans_params.metric     = params.metric;
+  kmeans_params.init       = static_cast<cuvs::cluster::kmeans::params::InitMethod>(params.init);
+  kmeans_params.n_clusters = params.n_clusters;
+  kmeans_params.max_iter   = params.max_iter;
+  kmeans_params.tol        = params.tol;
+  kmeans_params.oversampling_factor = params.oversampling_factor;
+  kmeans_params.batch_samples       = params.batch_samples;
+  kmeans_params.batch_centroids     = params.batch_centroids;
+  kmeans_params.inertia_check       = params.inertia_check;
+  return kmeans_params;
+}
+
+cuvs::cluster::kmeans::balanced_params convert_balanced_params(const cuvsKMeansParams& params)
+{
+  auto kmeans_params    = cuvs::cluster::kmeans::balanced_params();
+  kmeans_params.metric  = params.metric;
+  kmeans_params.n_iters = params.hierarchical_n_iters;
+  return kmeans_params;
+}
+
+template <typename T, typename IdxT = int32_t>
+void _fit(cuvsResources_t res,
+          const cuvsKMeansParams& params,
+          DLManagedTensor* X_tensor,
+          DLManagedTensor* sample_weight_tensor,
+          DLManagedTensor* centroids_tensor,
+          double* inertia,
+          int* n_iter)
+{
+  auto X       = X_tensor->dl_tensor;
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  if (cuvs::core::is_dlpack_device_compatible(X)) {
+    using const_mdspan_type = raft::device_matrix_view<T const, IdxT, raft::row_major>;
+    using mdspan_type       = raft::device_matrix_view<T, IdxT, raft::row_major>;
+
+    if (params.hierarchical) {
+      if (sample_weight_tensor != NULL) {
+        RAFT_FAIL("sample_weight cannot be used with hierarchical kmeans");
+      }
+
+      if constexpr (std::is_same_v<T, double>) {
+        RAFT_FAIL("float64 is an unsupported dtype for hierarchical kmeans");
+      } else {
+        auto kmeans_params = convert_balanced_params(params);
+        cuvs::cluster::kmeans::fit(*res_ptr,
+                                   kmeans_params,
+                                   cuvs::core::from_dlpack<const_mdspan_type>(X_tensor),
+                                   cuvs::core::from_dlpack<mdspan_type>(centroids_tensor));
+
+        *inertia = 0;
+        *n_iter  = params.hierarchical_n_iters;
+      }
+    } else {
+      T inertia_temp;
+      IdxT n_iter_temp;
+
+      std::optional<raft::device_vector_view<T const, IdxT>> sample_weight;
+      if (sample_weight_tensor != NULL) {
+        sample_weight =
+          cuvs::core::from_dlpack<raft::device_vector_view<T const, IdxT>>(sample_weight_tensor);
+      }
+
+      auto kmeans_params = convert_params(params);
+      cuvs::cluster::kmeans::fit(*res_ptr,
+                                 kmeans_params,
+                                 cuvs::core::from_dlpack<const_mdspan_type>(X_tensor),
+                                 sample_weight,
+                                 cuvs::core::from_dlpack<mdspan_type>(centroids_tensor),
+                                 raft::make_host_scalar_view<T, IdxT>(&inertia_temp),
+                                 raft::make_host_scalar_view<IdxT, IdxT>(&n_iter_temp));
+      *inertia = inertia_temp;
+      *n_iter  = n_iter_temp;
+    }
+  } else {
+    RAFT_FAIL("X dataset must be accessible on device memory");
+  }
+}
+
+template <typename T, typename IdxT = int32_t, typename LabelsT = int32_t>
+void _predict(cuvsResources_t res,
+              const cuvsKMeansParams& params,
+              DLManagedTensor* X_tensor,
+              DLManagedTensor* sample_weight_tensor,
+              DLManagedTensor* centroids_tensor,
+              DLManagedTensor* labels_tensor,
+              bool normalize_weight,
+              double* inertia)
+{
+  auto X       = X_tensor->dl_tensor;
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  if (cuvs::core::is_dlpack_device_compatible(X)) {
+    using labels_mdspan_type = raft::device_vector_view<LabelsT, IdxT, raft::row_major>;
+    using const_mdspan_type  = raft::device_matrix_view<T const, IdxT, raft::row_major>;
+
+    if (params.hierarchical) {
+      if (sample_weight_tensor != NULL) {
+        RAFT_FAIL("sample_weight cannot be used with hierarchical kmeans");
+      }
+
+      if constexpr (std::is_same_v<T, double>) {
+        RAFT_FAIL("float64 is an unsupported dtype for hierarchical kmeans");
+      } else {
+        auto kmeans_params = convert_balanced_params(params);
+        cuvs::cluster::kmeans::predict(*res_ptr,
+                                       kmeans_params,
+                                       cuvs::core::from_dlpack<const_mdspan_type>(X_tensor),
+                                       cuvs::core::from_dlpack<const_mdspan_type>(centroids_tensor),
+                                       cuvs::core::from_dlpack<labels_mdspan_type>(labels_tensor));
+        *inertia = 0;
+      }
+    } else {
+      auto kmeans_params = convert_params(params);
+      T inertia_temp;
+      std::optional<raft::device_vector_view<T const, IdxT>> sample_weight;
+      if (sample_weight_tensor != NULL) {
+        sample_weight =
+          cuvs::core::from_dlpack<raft::device_vector_view<T const, IdxT>>(sample_weight_tensor);
+      }
+      cuvs::cluster::kmeans::predict(*res_ptr,
+                                     kmeans_params,
+                                     cuvs::core::from_dlpack<const_mdspan_type>(X_tensor),
+                                     sample_weight,
+                                     cuvs::core::from_dlpack<const_mdspan_type>(centroids_tensor),
+                                     cuvs::core::from_dlpack<labels_mdspan_type>(labels_tensor),
+                                     normalize_weight,
+                                     raft::make_host_scalar_view<T, IdxT>(&inertia_temp));
+      *inertia = inertia_temp;
+    }
+  } else {
+    RAFT_FAIL("X dataset must be accessible on device memory");
+  }
+}
+
+template <typename T, typename IdxT = int32_t>
+void _cluster_cost(cuvsResources_t res,
+                   DLManagedTensor* X_tensor,
+                   DLManagedTensor* centroids_tensor,
+                   double* cost)
+{
+  auto X       = X_tensor->dl_tensor;
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  T cost_temp;
+
+  if (cuvs::core::is_dlpack_device_compatible(X)) {
+    using mdspan_type = raft::device_matrix_view<const T, IdxT, raft::row_major>;
+
+    cuvs::cluster::kmeans::cluster_cost(*res_ptr,
+                                        cuvs::core::from_dlpack<mdspan_type>(X_tensor),
+                                        cuvs::core::from_dlpack<mdspan_type>(centroids_tensor),
+                                        raft::make_host_scalar_view<T, IdxT>(&cost_temp));
+  } else {
+    RAFT_FAIL("X dataset must be accessible on device memory");
+  }
+
+  *cost = cost_temp;
+}
+}  // namespace
+
+extern "C" cuvsError_t cuvsKMeansParamsCreate(cuvsKMeansParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    cuvs::cluster::kmeans::params cpp_params;
+    cuvs::cluster::kmeans::balanced_params cpp_balanced_params;
+    *params =
+      new cuvsKMeansParams{.metric     = cpp_params.metric,
+                           .n_clusters = cpp_params.n_clusters,
+                           .init       = static_cast<cuvsKMeansInitMethod>(cpp_params.init),
+                           .max_iter   = cpp_params.max_iter,
+                           .tol        = cpp_params.tol,
+                           .oversampling_factor  = cpp_params.oversampling_factor,
+                           .batch_samples        = cpp_params.batch_samples,
+                           .inertia_check        = cpp_params.inertia_check,
+                           .hierarchical         = false,
+                           .hierarchical_n_iters = static_cast<int>(cpp_balanced_params.n_iters)};
+  });
+}
+
+extern "C" cuvsError_t cuvsKMeansParamsDestroy(cuvsKMeansParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsKMeansFit(cuvsResources_t res,
+                                     cuvsKMeansParams_t params,
+                                     DLManagedTensor* X,
+                                     DLManagedTensor* sample_weight,
+                                     DLManagedTensor* centroids,
+                                     double* inertia,
+                                     int* n_iter)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = X->dl_tensor;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      _fit<float>(res, *params, X, sample_weight, centroids, inertia, n_iter);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
+      _fit<double>(res, *params, X, sample_weight, centroids, inertia, n_iter);
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsKMeansPredict(cuvsResources_t res,
+                                         cuvsKMeansParams_t params,
+                                         DLManagedTensor* X,
+                                         DLManagedTensor* sample_weight,
+                                         DLManagedTensor* centroids,
+                                         DLManagedTensor* labels,
+                                         bool normalize_weight,
+                                         double* inertia)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = X->dl_tensor;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      _predict<float>(res, *params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
+      _predict<double>(
+        res, *params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsKMeansClusterCost(cuvsResources_t res,
+                                             DLManagedTensor* X,
+                                             DLManagedTensor* centroids,
+                                             double* cost)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = X->dl_tensor;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      _cluster_cost<float>(res, X, centroids, cost);
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
+      _cluster_cost<double>(res, X, centroids, cost);
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
diff --git a/cpp/src/cluster/kmeans_cluster_cost.cu b/cpp/src/cluster/kmeans_cluster_cost.cu
new file mode 100644
index 0000000000..d55569255b
--- /dev/null
+++ b/cpp/src/cluster/kmeans_cluster_cost.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kmeans.cuh"
+#include <cuvs/cluster/kmeans.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::cluster::kmeans {
+void cluster_cost(const raft::resources& handle,
+                  raft::device_matrix_view<const float, int> X,
+                  raft::device_matrix_view<const float, int> centroids,
+                  raft::host_scalar_view<float> cost)
+{
+  cuvs::cluster::kmeans::cluster_cost<float, int>(handle, X, centroids, cost);
+}
+
+void cluster_cost(const raft::resources& handle,
+                  raft::device_matrix_view<const double, int> X,
+                  raft::device_matrix_view<const double, int> centroids,
+                  raft::host_scalar_view<double> cost)
+{
+  cuvs::cluster::kmeans::cluster_cost<double, int>(handle, X, centroids, cost);
+}
+}  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 6a4f2dc291..bfe8aaca2c 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -180,10 +180,13 @@ void _search(cuvsResources_t res,
   search_params.min_iterations    = params.min_iterations;
   search_params.thread_block_size = params.thread_block_size;
   search_params.hashmap_mode = static_cast<cuvs::neighbors::cagra::hash_mode>(params.hashmap_mode);
-  search_params.hashmap_min_bitlen    = params.hashmap_min_bitlen;
-  search_params.hashmap_max_fill_rate = params.hashmap_max_fill_rate;
-  search_params.num_random_samplings  = params.num_random_samplings;
-  search_params.rand_xor_mask         = params.rand_xor_mask;
+  search_params.hashmap_min_bitlen      = params.hashmap_min_bitlen;
+  search_params.hashmap_max_fill_rate   = params.hashmap_max_fill_rate;
+  search_params.num_random_samplings    = params.num_random_samplings;
+  search_params.rand_xor_mask           = params.rand_xor_mask;
+  search_params.persistent              = params.persistent;
+  search_params.persistent_lifetime     = params.persistent_lifetime;
+  search_params.persistent_device_usage = params.persistent_device_usage;
 
   using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, raft::row_major>;
   using neighbors_mdspan_type = raft::device_matrix_view<IdxT, int64_t, raft::row_major>;
@@ -448,11 +451,16 @@ extern "C" cuvsError_t cuvsCagraExtendParamsDestroy(cuvsCagraExtendParams_t para
 extern "C" cuvsError_t cuvsCagraSearchParamsCreate(cuvsCagraSearchParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
-    *params = new cuvsCagraSearchParams{.itopk_size            = 64,
-                                        .search_width          = 1,
-                                        .hashmap_max_fill_rate = 0.5,
-                                        .num_random_samplings  = 1,
-                                        .rand_xor_mask         = 0x128394};
+    *params = new cuvsCagraSearchParams{
+      .itopk_size              = 64,
+      .search_width            = 1,
+      .hashmap_max_fill_rate   = 0.5,
+      .num_random_samplings    = 1,
+      .rand_xor_mask           = 0x128394,
+      .persistent              = false,
+      .persistent_lifetime     = 2,
+      .persistent_device_usage = 1.0,
+    };
   });
 }
 
diff --git a/python/cuvs/CMakeLists.txt b/python/cuvs/CMakeLists.txt
index 345d26f9b0..9c2f8f80de 100644
--- a/python/cuvs/CMakeLists.txt
+++ b/python/cuvs/CMakeLists.txt
@@ -57,6 +57,7 @@ target_include_directories(cuvs::cuvs INTERFACE "$<BUILD_INTERFACE:${DLPACK_INCL
 rapids_cython_init()
 
 add_subdirectory(cuvs/common)
+add_subdirectory(cuvs/cluster)
 add_subdirectory(cuvs/distance)
 add_subdirectory(cuvs/neighbors)
 add_subdirectory(cuvs/preprocessing)
diff --git a/python/cuvs/cuvs/cluster/CMakeLists.txt b/python/cuvs/cuvs/cluster/CMakeLists.txt
new file mode 100644
index 0000000000..8f9b7e3b7e
--- /dev/null
+++ b/python/cuvs/cuvs/cluster/CMakeLists.txt
@@ -0,0 +1,15 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+add_subdirectory(kmeans)
diff --git a/python/cuvs/cuvs/cluster/__init__.pxd b/python/cuvs/cuvs/cluster/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/cluster/__init__.py b/python/cuvs/cuvs/cluster/__init__.py
new file mode 100644
index 0000000000..2d4c4c0d3a
--- /dev/null
+++ b/python/cuvs/cuvs/cluster/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from cuvs.cluster import kmeans
+
+__all__ = ["kmeans"]
diff --git a/python/cuvs/cuvs/cluster/kmeans/CMakeLists.txt b/python/cuvs/cuvs/cluster/kmeans/CMakeLists.txt
new file mode 100644
index 0000000000..ae2a91dbc8
--- /dev/null
+++ b/python/cuvs/cuvs/cluster/kmeans/CMakeLists.txt
@@ -0,0 +1,23 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources kmeans.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX cluster_kmeans_
+)
diff --git a/python/cuvs/cuvs/cluster/kmeans/__init__.pxd b/python/cuvs/cuvs/cluster/kmeans/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/cluster/kmeans/__init__.py b/python/cuvs/cuvs/cluster/kmeans/__init__.py
new file mode 100644
index 0000000000..baab195958
--- /dev/null
+++ b/python/cuvs/cuvs/cluster/kmeans/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .kmeans import KMeansParams, cluster_cost, fit, predict
+
+__all__ = ["KMeansParams", "cluster_cost", "fit", "predict"]
diff --git a/python/cuvs/cuvs/cluster/kmeans/kmeans.pxd b/python/cuvs/cuvs/cluster/kmeans/kmeans.pxd
new file mode 100644
index 0000000000..157fd5cf8b
--- /dev/null
+++ b/python/cuvs/cuvs/cluster/kmeans/kmeans.pxd
@@ -0,0 +1,72 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uintptr_t
+from libcpp cimport bool
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "cuvs/cluster/kmeans.h" nogil:
+    ctypedef enum cuvsKMeansInitMethod:
+        KMeansPlusPlus
+        Random
+        Array
+
+    ctypedef struct cuvsKMeansParams:
+        cuvsDistanceType metric,
+        int n_clusters,
+        cuvsKMeansInitMethod init,
+        int max_iter,
+        double tol,
+        int n_init,
+        double oversampling_factor,
+        int batch_samples,
+        int batch_centroids,
+        bool inertia_check,
+        bool hierarchical,
+        int hierarchical_n_iters
+
+    ctypedef cuvsKMeansParams* cuvsKMeansParams_t
+
+    cuvsError_t cuvsKMeansParamsCreate(cuvsKMeansParams_t* index)
+
+    cuvsError_t cuvsKMeansParamsDestroy(cuvsKMeansParams_t index)
+
+    cuvsError_t cuvsKMeansFit(cuvsResources_t res,
+                              cuvsKMeansParams_t params,
+                              DLManagedTensor* X,
+                              DLManagedTensor* sample_weight,
+                              DLManagedTensor * centroids,
+                              double * inertia,
+                              int * n_iter) except +
+
+    cuvsError_t cuvsKMeansPredict(cuvsResources_t res,
+                                  cuvsKMeansParams_t params,
+                                  DLManagedTensor* X,
+                                  DLManagedTensor* sample_weight,
+                                  DLManagedTensor * centroids,
+                                  DLManagedTensor * labels,
+                                  bool normalize_weight,
+                                  double * inertia)
+
+    cuvsError_t cuvsKMeansClusterCost(cuvsResources_t res,
+                                      DLManagedTensor* X,
+                                      DLManagedTensor* centroids,
+                                      double* cost)
diff --git a/python/cuvs/cuvs/cluster/kmeans/kmeans.pyx b/python/cuvs/cuvs/cluster/kmeans/kmeans.pyx
new file mode 100644
index 0000000000..aa74b33921
--- /dev/null
+++ b/python/cuvs/cuvs/cluster/kmeans/kmeans.pyx
@@ -0,0 +1,405 @@
+#
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from collections import namedtuple
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libcpp cimport bool, cast
+from libcpp.string cimport string
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
+from cuvs.neighbors.common import _check_input_array
+
+from libc.stdint cimport (
+    int8_t,
+    int64_t,
+    uint8_t,
+    uint32_t,
+    uint64_t,
+    uintptr_t,
+)
+
+from cuvs.common.exceptions import check_cuvs
+
+INIT_METHOD_TYPES = {
+    "KMeansPlusPlus" : cuvsKMeansInitMethod.KMeansPlusPlus,
+    "Random" : cuvsKMeansInitMethod.Random,
+    "Array" : cuvsKMeansInitMethod.Array}
+
+INIT_METHOD_NAMES = {v: k for k, v in INIT_METHOD_TYPES.items()}
+
+cdef class KMeansParams:
+    """
+    Hyper-parameters for the kmeans algorithm
+
+    Parameters
+    ----------
+    metric : str
+        String denoting the metric type.
+    n_clusters : int
+        The number of clusters to form as well as the number of centroids
+        to generate
+    init_method : str
+        Method for initializing clusters. One of:
+        "KMeansPlusPlus" : Use scalable k-means++ algorithm to select initial
+        cluster centers
+        "Random" : Choose 'n_clusters' observations at random from the input
+        data
+        "Array" : Use centroids as initial cluster centers
+    max_iter : int
+        Maximum number of iterations of the k-means algorithm for a single run
+    tol : float
+        Relative tolerance with regards to inertia to declare convergence.
+    n_init : int
+        Number of instance k-means algorithm will be run with different seeds
+    oversampling_factor : double
+        Oversampling factor for use in the k-means|| algorithm
+    hierarchical : bool
+        Whether to use hierarchical (balanced) kmeans or not
+    hierarchical_n_iters : int
+        For hierarchical k-means , defines the number of training iterations
+    """
+
+    cdef cuvsKMeansParams* params
+
+    def __cinit__(self):
+        cuvsKMeansParamsCreate(&self.params)
+
+    def __dealloc__(self):
+        check_cuvs(cuvsKMeansParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 metric=None,
+                 n_clusters=None,
+                 init_method=None,
+                 max_iter=None,
+                 tol=None,
+                 n_init=None,
+                 oversampling_factor=None,
+                 hierarchical=None,
+                 hierarchical_n_iters=None):
+        if metric is not None:
+            self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+        if n_clusters is not None:
+            self.params.n_clusters = n_clusters
+        if init_method is not None:
+            c_method = INIT_METHOD_TYPES[init_method]
+            self.params.init = <cuvsKMeansInitMethod>c_method
+        if max_iter is not None:
+            self.params.max_iter = max_iter
+        if tol is not None:
+            self.params.tol = tol
+        if n_init is not None:
+            self.params.n_init = n_init
+        if oversampling_factor is not None:
+            self.params.oversampling_factor = oversampling_factor
+        if hierarchical is not None:
+            self.params.hierarchical = hierarchical
+        if hierarchical_n_iters is not None:
+            if not self.params.hierarchical:
+                raise ValueError("Setting hierarchical_n_iters requires"
+                                 " `hierarchical` to be also set to True")
+            self.params.hierarchical_n_iters = hierarchical_n_iters
+
+    @property
+    def metric(self):
+        return DISTANCE_NAMES[self.params.metric]
+
+    @property
+    def n_clusters(self):
+        return self.params.n_clusters
+
+    @property
+    def init_method(self):
+        return INIT_METHOD_NAMES[self.params.init]
+
+    @property
+    def max_iter(self):
+        return self.params.max_iter
+
+    @property
+    def tol(self):
+        return self.params.tol
+
+    @property
+    def n_init(self):
+        return self.params.n_init
+
+    @property
+    def oversampling_factor(self):
+        return self.params.oversampling_factor
+
+    @property
+    def hierarchical(self):
+        return self.params.hierarchical
+
+    @property
+    def hierarchical_n_iters(self):
+        return self.params.hierarchical_n_iters
+
+
+FitOutput = namedtuple("FitOutput", "centroids inertia n_iter")
+
+
+@auto_sync_resources
+@auto_convert_output
+def fit(
+    KMeansParams params, X, centroids=None, sample_weights=None, resources=None
+):
+    """
+    Find clusters with the k-means algorithm
+
+    Parameters
+    ----------
+
+    params : KMeansParams
+        Parameters to use to fit KMeans model
+    X : Input CUDA array interface compliant matrix shape (m, k)
+    centroids : Optional writable CUDA array interface compliant matrix
+                shape (n_clusters, k)
+    sample_weights : Optional input CUDA array interface compliant matrix shape
+                     (n_clusters, 1) default: None
+    {resources_docstring}
+
+    Returns
+    -------
+    centroids : raft.device_ndarray
+        The computed centroids for each cluster
+    inertia : float
+       Sum of squared distances of samples to their closest cluster center
+    n_iter : int
+        The number of iterations used to fit the model
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>>
+    >>> from cuvs.cluster.kmeans import fit, KMeansParams
+    >>>
+    >>> n_samples = 5000
+    >>> n_features = 50
+    >>> n_clusters = 3
+    >>>
+    >>> X = cp.random.random_sample((n_samples, n_features),
+    ...                             dtype=cp.float32)
+
+    >>> params = KMeansParams(n_clusters=n_clusters)
+    >>> centroids, inertia, n_iter = fit(params, X)
+    """
+
+    x_ai = wrap_array(X)
+    _check_input_array(x_ai, [np.dtype('float32'), np.dtype('float64')])
+
+    cdef cydlpack.DLManagedTensor* x_dlpack = cydlpack.dlpack_c(x_ai)
+    cdef cydlpack.DLManagedTensor* sample_weight_dlpack = NULL
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef double inertia = 0
+    cdef int n_iter = 0
+
+    if centroids is None:
+        centroids = device_ndarray.empty((params.n_clusters, x_ai.shape[1]),
+                                         dtype=x_ai.dtype)
+
+    centroids_ai = wrap_array(centroids)
+    cdef cydlpack.DLManagedTensor * centroids_dlpack = \
+        cydlpack.dlpack_c(centroids_ai)
+
+    if sample_weights is not None:
+        sample_weight_dlpack = cydlpack.dlpack_c(wrap_array(sample_weights))
+
+    with cuda_interruptible():
+        check_cuvs(cuvsKMeansFit(
+            res,
+            params.params,
+            x_dlpack,
+            sample_weight_dlpack,
+            centroids_dlpack,
+            &inertia,
+            &n_iter))
+
+    return FitOutput(centroids, inertia, n_iter)
+
+
+PredictOutput = namedtuple("PredictOutput", "labels inertia")
+
+
+@auto_sync_resources
+@auto_convert_output
+def predict(
+    KMeansParams params, X, centroids, sample_weights=None, labels=None,
+    normalize_weight=True, resources=None
+):
+    """
+    Predict clusters with the k-means algorithm
+
+    Parameters
+    ----------
+
+    params : KMeansParams
+        Parameters to used in fitting KMeans model
+    X : Input CUDA array interface compliant matrix shape (m, k)
+    centroids : CUDA array interface compliant matrix, calculated by fit
+                shape (n_clusters, k)
+    sample_weights : Optional input CUDA array interface compliant matrix shape
+                     (n_clusters, 1) default: None
+    labels : Optional preallocated CUDA array interface matrix shape (m, 1)
+        to hold the output
+    normalize_weight: bool
+        True if the weights should be normalized
+    {resources_docstring}
+
+    Returns
+    -------
+    labels : raft.device_ndarray
+        The label for each datapoint in X
+    inertia : float
+       Sum of squared distances of samples to their closest cluster center
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>>
+    >>> from cuvs.cluster.kmeans import fit, predict, KMeansParams
+    >>>
+    >>> n_samples = 5000
+    >>> n_features = 50
+    >>> n_clusters = 3
+    >>>
+    >>> X = cp.random.random_sample((n_samples, n_features),
+    ...                             dtype=cp.float32)
+
+    >>> params = KMeansParams(n_clusters=n_clusters)
+    >>> centroids, inertia, n_iter = fit(params, X)
+    >>>
+    >>> labels, inertia = predict(params, X, centroids)
+    """
+
+    x_ai = wrap_array(X)
+    _check_input_array(x_ai, [np.dtype('float32'), np.dtype('float64')])
+    cdef cydlpack.DLManagedTensor* x_dlpack = cydlpack.dlpack_c(x_ai)
+
+    cdef cydlpack.DLManagedTensor* sample_weight_dlpack = NULL
+    if sample_weights is not None:
+        sample_weight_dlpack = cydlpack.dlpack_c(wrap_array(sample_weights))
+
+    if labels is None:
+        labels = device_ndarray.empty((x_ai.shape[0]), dtype='int32')
+
+    labels_ai = wrap_array(labels)
+    _check_input_array(labels_ai, [np.dtype('int32')])
+    cdef cydlpack.DLManagedTensor * labels_dlpack = \
+        cydlpack.dlpack_c(labels_ai)
+
+    centroids_ai = wrap_array(centroids)
+    _check_input_array(centroids_ai, [np.dtype('float32'),
+                                      np.dtype('float64')])
+    cdef cydlpack.DLManagedTensor * centroids_dlpack = \
+        cydlpack.dlpack_c(centroids_ai)
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    cdef double inertia = 0
+
+    with cuda_interruptible():
+        check_cuvs(cuvsKMeansPredict(
+            res,
+            params.params,
+            x_dlpack,
+            sample_weight_dlpack,
+            centroids_dlpack,
+            labels_dlpack,
+            normalize_weight,
+            &inertia))
+
+    return PredictOutput(labels, inertia)
+
+
+@auto_sync_resources
+@auto_convert_output
+def cluster_cost(X, centroids, resources=None):
+    """
+    Compute cluster cost given an input matrix and existing centroids
+
+    Parameters
+    ----------
+    X : Input CUDA array interface compliant matrix shape (m, k)
+    centroids : Input CUDA array interface compliant matrix shape
+                    (n_clusters, k)
+    {resources_docstring}
+
+    Returns
+    -------
+    inertia : float
+        The cluster cost between the input matrix and existing centroids
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>>
+    >>> from cuvs.cluster.kmeans import cluster_cost
+    >>>
+    >>> n_samples = 5000
+    >>> n_features = 50
+    >>> n_clusters = 3
+    >>>
+    >>> X = cp.random.random_sample((n_samples, n_features),
+    ...                             dtype=cp.float32)
+
+    >>> centroids = cp.random.random_sample((n_clusters, n_features),
+    ...                                      dtype=cp.float32)
+
+    >>> inertia = cluster_cost(X, centroids)
+    """
+
+    x_ai = wrap_array(X)
+    _check_input_array(x_ai, [np.dtype('float32'), np.dtype('float64')])
+    cdef cydlpack.DLManagedTensor* x_dlpack = cydlpack.dlpack_c(x_ai)
+
+    centroids_ai = wrap_array(centroids)
+    _check_input_array(centroids_ai, [np.dtype('float32'),
+                                      np.dtype('float64')])
+    cdef cydlpack.DLManagedTensor* centroids_dlpack = \
+        cydlpack.dlpack_c(centroids_ai)
+
+    cdef double inertia = 0
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsKMeansClusterCost(
+            res,
+            x_dlpack,
+            centroids_dlpack,
+            &inertia))
+
+    return inertia
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index 41d74dbc7c..f6435a5b2c 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -84,6 +84,9 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
         float hashmap_max_fill_rate
         uint32_t num_random_samplings
         uint64_t rand_xor_mask
+        bool persistent
+        float persistent_lifetime
+        float persistent_device_usage
 
     ctypedef struct cuvsCagraIndex:
         uintptr_t addr
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 56a7c061bb..c31df724f5 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -375,6 +375,13 @@ cdef class SearchParams:
         more.
     rand_xor_mask: int, default = 0x128394
         Bit mask used for initial random seed node selection.
+    persistent: bool, default = false
+         Whether to use the persistent version of the kernel
+    persistent_lifetime: float
+         Persistent kernel: time in seconds before the kernel stops if no
+         requests are received.
+    persistent_device_usage : float
+        Sets the fraction of maximum grid size used by persistent kernel.
     """
 
     cdef cuvsCagraSearchParams params
@@ -392,7 +399,11 @@ cdef class SearchParams:
                  hashmap_min_bitlen=0,
                  hashmap_max_fill_rate=0.5,
                  num_random_samplings=1,
-                 rand_xor_mask=0x128394):
+                 rand_xor_mask=0x128394,
+                 persistent=False,
+                 persistent_lifetime=None,
+                 persistent_device_usage=None
+                 ):
         self.params.max_queries = max_queries
         self.params.itopk_size = itopk_size
         self.params.max_iterations = max_iterations
@@ -424,6 +435,11 @@ cdef class SearchParams:
         self.params.hashmap_max_fill_rate = hashmap_max_fill_rate
         self.params.num_random_samplings = num_random_samplings
         self.params.rand_xor_mask = rand_xor_mask
+        self.params.persistent = persistent
+        if persistent_lifetime is not None:
+            self.params.persistent_lifetime = persistent_lifetime
+        if persistent_device_usage is not None:
+            self.params.persistent_device_usage = persistent_device_usage
 
     def __repr__(self):
         attr_str = [attr + "=" + str(getattr(self, attr))
diff --git a/python/cuvs/cuvs/tests/test_doctests.py b/python/cuvs/cuvs/tests/test_doctests.py
index a35ef4eca7..6b9ad26c7a 100644
--- a/python/cuvs/cuvs/tests/test_doctests.py
+++ b/python/cuvs/cuvs/tests/test_doctests.py
@@ -21,6 +21,7 @@
 
 import pytest
 
+import cuvs.cluster
 import cuvs.distance
 import cuvs.neighbors
 import cuvs.preprocessing.quantize
@@ -96,6 +97,7 @@ def _find_doctests_in_obj(obj, finder=None, criteria=None):
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.brute_force))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.neighbors.ivf_flat))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.common))
+DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.cluster))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.distance))
 DOC_STRINGS.extend(_find_doctests_in_obj(cuvs.preprocessing.quantize))
 
diff --git a/python/cuvs/cuvs/tests/test_kmeans.py b/python/cuvs/cuvs/tests/test_kmeans.py
new file mode 100644
index 0000000000..bb8f368a88
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_kmeans.py
@@ -0,0 +1,82 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+
+from cuvs.cluster.kmeans import KMeansParams, cluster_cost, fit, predict
+from cuvs.distance import pairwise_distance
+
+
+@pytest.mark.parametrize("n_rows", [100])
+@pytest.mark.parametrize("n_cols", [5, 25])
+@pytest.mark.parametrize("n_clusters", [5, 15])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+@pytest.mark.parametrize("hierarchical", [True, False])
+def test_kmeans_fit(n_rows, n_cols, n_clusters, dtype, hierarchical):
+    if hierarchical and dtype == np.float64:
+        pytest.skip("hierarchical kmeans doesn't support float64")
+
+    # generate some random input points / centroids
+    X_host = np.random.random_sample((n_rows, n_cols)).astype(dtype)
+    centroids = device_ndarray(X_host[:n_clusters])
+    X = device_ndarray(X_host)
+
+    # compute the inertia, before fitting centroids
+    original_inertia = cluster_cost(X, centroids)
+
+    params = KMeansParams(n_clusters=n_clusters, hierarchical=hierarchical)
+
+    # fit the centroids, make sure inertia has gone down
+    centroids, inertia, n_iter = fit(params, X, centroids)
+    assert n_iter >= 1
+
+    # balanced kmeans doesn't return inertia
+    if not hierarchical:
+        assert inertia < original_inertia
+        assert np.allclose(cluster_cost(X, centroids), inertia, rtol=1e-6)
+
+    # make sure the prediction for each centroid is the centroid itself
+    labels, inertia = predict(params, centroids, centroids)
+    assert np.all(labels.copy_to_host() == np.arange(labels.shape[0]))
+
+
+@pytest.mark.parametrize("n_rows", [100])
+@pytest.mark.parametrize("n_cols", [5, 25])
+@pytest.mark.parametrize("n_clusters", [4, 15])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+def test_cluster_cost(n_rows, n_cols, n_clusters, dtype):
+    X = np.random.random_sample((n_rows, n_cols)).astype(dtype)
+    X_device = device_ndarray(X)
+
+    centroids = X[:n_clusters]
+    centroids_device = device_ndarray(centroids)
+
+    inertia = cluster_cost(X_device, centroids_device)
+
+    # compute the nearest centroid to each sample
+    distances = pairwise_distance(
+        X_device, centroids_device, metric="sqeuclidean"
+    ).copy_to_host()
+    cluster_ids = np.argmin(distances, axis=1)
+
+    cluster_distances = np.take_along_axis(
+        distances, cluster_ids[:, None], axis=1
+    )
+
+    # need reduced tolerance for float32
+    tol = 1e-3 if dtype == np.float32 else 1e-6
+    assert np.allclose(inertia, sum(cluster_distances), rtol=tol, atol=tol)

From 17ce0a9f48b345ced4a0def86b147ea85f6cfd54 Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Wed, 30 Apr 2025 15:12:12 -0400
Subject: [PATCH 377/623] DOC v25.08 Updates [skip ci]

---
 .../cuda11.8-conda/devcontainer.json          |  6 ++--
 .devcontainer/cuda11.8-pip/devcontainer.json  |  8 ++---
 .../cuda12.8-conda/devcontainer.json          |  6 ++--
 .devcontainer/cuda12.8-pip/devcontainer.json  |  8 ++---
 .github/workflows/build.yaml                  | 20 ++++++------
 .github/workflows/pr.yaml                     | 30 ++++++++---------
 .github/workflows/test.yaml                   |  8 ++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 README.md                                     |  6 ++--
 VERSION                                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  4 +--
 .../all_cuda-118_arch-x86_64.yaml             |  4 +--
 .../all_cuda-128_arch-aarch64.yaml            |  4 +--
 .../all_cuda-128_arch-x86_64.yaml             |  4 +--
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  8 ++---
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  8 ++---
 .../bench_ann_cuda-128_arch-aarch64.yaml      |  8 ++---
 .../bench_ann_cuda-128_arch-x86_64.yaml       |  8 ++---
 .../go_cuda-118_arch-aarch64.yaml             |  4 +--
 .../environments/go_cuda-118_arch-x86_64.yaml |  4 +--
 .../go_cuda-128_arch-aarch64.yaml             |  4 +--
 .../environments/go_cuda-128_arch-x86_64.yaml |  4 +--
 .../rust_cuda-128_arch-aarch64.yaml           |  4 +--
 .../rust_cuda-128_arch-x86_64.yaml            |  4 +--
 dependencies.yaml                             | 32 +++++++++----------
 docs/source/developer_guide.md                |  4 +--
 examples/go/README.md                         |  2 +-
 java/build.sh                                 |  2 +-
 java/cuvs-java/pom.xml                        |  4 +--
 java/examples/pom.xml                         |  4 +--
 python/cuvs/pyproject.toml                    | 10 +++---
 python/cuvs_bench/cuvs_bench/plot/__main__.py |  2 +-
 python/cuvs_bench/pyproject.toml              |  2 +-
 python/libcuvs/pyproject.toml                 |  8 ++---
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 36 files changed, 121 insertions(+), 121 deletions(-)

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
index 6dea71a0e7..73574ed688 100644
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ b/.devcontainer/cuda11.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda11.8-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda11.8-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda11.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda11.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
index 0de380d05a..634291a467 100644
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ b/.devcontainer/cuda11.8-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "11.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda11.8-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda11.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda11.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda11.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.6": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.8": {
       "version": "11.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda12.8-conda/devcontainer.json b/.devcontainer/cuda12.8-conda/devcontainer.json
index dd078a3009..f70a011292 100644
--- a/.devcontainer/cuda12.8-conda/devcontainer.json
+++ b/.devcontainer/cuda12.8-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.06-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.08-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.8-pip/devcontainer.json b/.devcontainer/cuda12.8-pip/devcontainer.json
index 0729bb953c..52de35b8cd 100644
--- a/.devcontainer/cuda12.8-pip/devcontainer.json
+++ b/.devcontainer/cuda12.8-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.6": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.8": {
       "version": "12.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.6": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index fd13413fe3..28dbacdd47 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -54,7 +54,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -67,7 +67,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -76,7 +76,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -87,7 +87,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -99,7 +99,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -113,7 +113,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -124,7 +124,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -136,7 +136,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index d774f7a558..1317e8800d 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -29,7 +29,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.08
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -58,7 +58,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.08
     with:
       files_yaml: |
         test_cpp:
@@ -95,28 +95,28 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.08
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.08
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.08
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.08
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -124,20 +124,20 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.08
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.08
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -147,7 +147,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -157,7 +157,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -167,7 +167,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -178,7 +178,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -187,7 +187,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.08
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -195,7 +195,7 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.08
     with:
       arch: '["amd64"]'
       cuda: '["12.8"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 8e1dc631aa..773d8b7e20 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -19,7 +19,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -29,7 +29,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -45,7 +45,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 475380cb68..593fcb1086 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.08
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/README.md b/README.md
index 11001baa4b..cbb0cde10f 100755
--- a/README.md
+++ b/README.md
@@ -109,7 +109,7 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.06
+conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.08
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
@@ -169,7 +169,7 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
-For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.06/examples/c)
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.08/examples/c)
 
 ### Rust API
 
@@ -232,7 +232,7 @@ fn cagra_example() -> Result<()> {
 }
 ```
 
-For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.06/examples/rust).
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.08/examples/rust).
 
 ## Contributing
 
diff --git a/VERSION b/VERSION
index cc83d7ab43..3af4bda020 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-25.06.00
+25.08.00
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 1e296b7e55..beaa38c66a 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==25.6.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pre-commit
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index a18cc592d2..68814a1dc4 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -35,7 +35,7 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- librmm==25.6.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pre-commit
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
index 7880a8b53a..d0bfb07aec 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.6.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -40,7 +40,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
index 0cc536c948..aceef8546d 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -32,7 +32,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.6.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -40,7 +40,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pytest-cov
 - pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 7594a38e5b..0fa891d0c7 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -19,7 +19,7 @@ dependencies:
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
-- cuvs==25.6.*,>=0.0.0a0
+- cuvs==25.8.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -35,8 +35,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.6.*,>=0.0.0a0
-- librmm==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - nccl>=2.19
 - ninja
@@ -44,7 +44,7 @@ dependencies:
 - nvcc_linux-aarch64=11.8
 - openblas
 - pandas
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 75699d828d..a710083237 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -19,7 +19,7 @@ dependencies:
 - cuda-version=11.8
 - cudatoolkit
 - cupy>=12.0.0
-- cuvs==25.6.*,>=0.0.0a0
+- cuvs==25.8.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -37,8 +37,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.6.*,>=0.0.0a0
-- librmm==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - mkl-devel=2023
 - nccl>=2.19
@@ -47,7 +47,7 @@ dependencies:
 - nvcc_linux-64=11.8
 - openblas
 - pandas
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index 3ff76841d7..3de02e0525 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -20,7 +20,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.8
 - cupy>=12.0.0
-- cuvs==25.6.*,>=0.0.0a0
+- cuvs==25.8.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,15 +32,15 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.6.*,>=0.0.0a0
-- librmm==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index 36ad1941f4..09dc89b359 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -20,7 +20,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.8
 - cupy>=12.0.0
-- cuvs==25.6.*,>=0.0.0a0
+- cuvs==25.8.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -34,8 +34,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.6.*,>=0.0.0a0
-- librmm==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- librmm==25.8.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - mkl-devel=2023
 - nccl>=2.19
@@ -43,7 +43,7 @@ dependencies:
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.6.*,>=0.0.0a0
+- pylibraft==25.8.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/go_cuda-118_arch-aarch64.yaml b/conda/environments/go_cuda-118_arch-aarch64.yaml
index 4a14dc43ef..e79504c24a 100644
--- a/conda/environments/go_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-118_arch-aarch64.yaml
@@ -28,8 +28,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.6.*,>=0.0.0a0
-- libraft==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- libraft==25.8.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - nvcc_linux-aarch64=11.8
diff --git a/conda/environments/go_cuda-118_arch-x86_64.yaml b/conda/environments/go_cuda-118_arch-x86_64.yaml
index fb889411d6..ba0dbfd4e0 100644
--- a/conda/environments/go_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-118_arch-x86_64.yaml
@@ -28,8 +28,8 @@ dependencies:
 - libcusolver=11.4.1.48
 - libcusparse-dev=11.7.5.86
 - libcusparse=11.7.5.86
-- libcuvs==25.6.*,>=0.0.0a0
-- libraft==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- libraft==25.8.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - nvcc_linux-64=11.8
diff --git a/conda/environments/go_cuda-128_arch-aarch64.yaml b/conda/environments/go_cuda-128_arch-aarch64.yaml
index 21bae9da1b..a408a53220 100644
--- a/conda/environments/go_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-128_arch-aarch64.yaml
@@ -25,8 +25,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.6.*,>=0.0.0a0
-- libraft==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- libraft==25.8.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
diff --git a/conda/environments/go_cuda-128_arch-x86_64.yaml b/conda/environments/go_cuda-128_arch-x86_64.yaml
index b3dcf18b70..6bf8c3c282 100644
--- a/conda/environments/go_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-128_arch-x86_64.yaml
@@ -25,8 +25,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.6.*,>=0.0.0a0
-- libraft==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- libraft==25.8.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
diff --git a/conda/environments/rust_cuda-128_arch-aarch64.yaml b/conda/environments/rust_cuda-128_arch-aarch64.yaml
index a3844ddad5..a9da34c8a6 100644
--- a/conda/environments/rust_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-128_arch-aarch64.yaml
@@ -23,8 +23,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.6.*,>=0.0.0a0
-- libraft==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- libraft==25.8.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/conda/environments/rust_cuda-128_arch-x86_64.yaml b/conda/environments/rust_cuda-128_arch-x86_64.yaml
index d8775fe193..214ce10930 100644
--- a/conda/environments/rust_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-128_arch-x86_64.yaml
@@ -23,8 +23,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.6.*,>=0.0.0a0
-- libraft==25.6.*,>=0.0.0a0
+- libcuvs==25.8.*,>=0.0.0a0
+- libraft==25.8.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/dependencies.yaml b/dependencies.yaml
index 362c78799c..67435c6cac 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -589,7 +589,7 @@ dependencies:
       - output_types: [conda, pyproject, requirements]
         packages:
           - click
-          - cuvs==25.6.*,>=0.0.0a0
+          - cuvs==25.8.*,>=0.0.0a0
           - pandas
           - pyyaml
           - requests
@@ -604,17 +604,17 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - cuvs==25.6.*,>=0.0.0a0
+          - cuvs==25.8.*,>=0.0.0a0
   depends_on_cuvs_bench:
     common:
       - output_types: conda
         packages:
-          - cuvs-bench==25.6.*,>=0.0.0a0
+          - cuvs-bench==25.8.*,>=0.0.0a0
   depends_on_libcuvs:
     common:
       - output_types: conda
         packages:
-          - &libcuvs_unsuffixed libcuvs==25.6.*,>=0.0.0a0
+          - &libcuvs_unsuffixed libcuvs==25.8.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -627,23 +627,23 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu12==25.6.*,>=0.0.0a0
+              - libcuvs-cu12==25.8.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu11==25.6.*,>=0.0.0a0
+              - libcuvs-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
   depends_on_libcuvs_tests:
     common:
       - output_types: conda
         packages:
-          - libcuvs-tests==25.6.*,>=0.0.0a0
+          - libcuvs-tests==25.8.*,>=0.0.0a0
   depends_on_libraft:
     common:
       - output_types: conda
         packages:
-          - &libraft_unsuffixed libraft==25.6.*,>=0.0.0a0
+          - &libraft_unsuffixed libraft==25.8.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -656,18 +656,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu12==25.6.*,>=0.0.0a0
+              - libraft-cu12==25.8.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu11==25.6.*,>=0.0.0a0
+              - libraft-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
       - output_types: conda
         packages:
-          - &librmm_unsuffixed librmm==25.6.*,>=0.0.0a0
+          - &librmm_unsuffixed librmm==25.8.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -680,18 +680,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu12==25.6.*,>=0.0.0a0
+              - librmm-cu12==25.8.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu11==25.6.*,>=0.0.0a0
+              - librmm-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
       - output_types: conda
         packages:
-          - &pylibraft_unsuffixed pylibraft==25.6.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==25.8.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -704,12 +704,12 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu12==25.6.*,>=0.0.0a0
+              - pylibraft-cu12==25.8.*,>=0.0.0a0
           - matrix:
               cuda: "11.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu11==25.6.*,>=0.0.0a0
+              - pylibraft-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
   depends_on_nccl:
     common:
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 9358770e93..8e8cc28017 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.06/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.08/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.06/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.08/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
diff --git a/examples/go/README.md b/examples/go/README.md
index 28d11351b2..ebeb012308 100644
--- a/examples/go/README.md
+++ b/examples/go/README.md
@@ -24,7 +24,7 @@ export CC=clang
 
 2. Install the Go module:
 ```bash
-go get github.com/rapidsai/cuvs/go@v25.02.00 # 25.02.00 being your desired version, selected from https://github.com/rapidsai/cuvs/tags
+go get github.com/rapidsai/cuvs/go@v25.08.00 # 25.02.00 being your desired version, selected from https://github.com/rapidsai/cuvs/tags
 ```
 Then you can build your project with the usual `go build`.
 
diff --git a/java/build.sh b/java/build.sh
index 34c7ae83e6..01eb829da1 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -1,4 +1,4 @@
-VERSION="25.06.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
+VERSION="25.08.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
 SO_FILE_PATH="./internal"
 
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 835fb6eaf2..3b5a68c33d 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -22,7 +22,7 @@
     <groupId>com.nvidia.cuvs</groupId>
     <artifactId>cuvs-java</artifactId>
     <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     <name>cuvs-java</name>
     <description>
         This project provides Java bindings for cuVS, enabling approximate nearest neighbors search and clustering
@@ -174,7 +174,7 @@
                                     <groupId>com.nvidia.cuvs</groupId>
                                     <artifactId>cuvs-java-internal</artifactId>
                                     <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-                                    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+                                    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
                                     <type>so</type>
                                     <overWrite>false</overWrite>
                                     <outputDirectory>
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
index c5798864ce..73ca7ea75d 100644
--- a/java/examples/pom.xml
+++ b/java/examples/pom.xml
@@ -5,7 +5,7 @@
   <groupId>com.nvidia.cuvs.examples</groupId>
   <artifactId>cuvs-java-examples</artifactId>
   <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
   <name>cuvs-java-examples</name>
 
   <properties>
@@ -18,7 +18,7 @@
       <groupId>com.nvidia.cuvs</groupId>
       <artifactId>cuvs-java</artifactId>
       <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     </dependency>
     <dependency>
       <groupId>org.slf4j</groupId>
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 826c5b78d6..ba677b7a03 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -32,9 +32,9 @@ license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
-    "libcuvs==25.6.*,>=0.0.0a0",
+    "libcuvs==25.8.*,>=0.0.0a0",
     "numpy>=1.23,<3.0a0",
-    "pylibraft==25.6.*,>=0.0.0a0",
+    "pylibraft==25.8.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -118,9 +118,9 @@ requires = [
     "cmake>=3.30.4",
     "cuda-python",
     "cython>=3.0.0",
-    "libcuvs==25.6.*,>=0.0.0a0",
-    "libraft==25.6.*,>=0.0.0a0",
-    "librmm==25.6.*,>=0.0.0a0",
+    "libcuvs==25.8.*,>=0.0.0a0",
+    "libraft==25.8.*,>=0.0.0a0",
+    "librmm==25.8.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
diff --git a/python/cuvs_bench/cuvs_bench/plot/__main__.py b/python/cuvs_bench/cuvs_bench/plot/__main__.py
index 7781d5eb9d..e0dbc27837 100644
--- a/python/cuvs_bench/cuvs_bench/plot/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/plot/__main__.py
@@ -17,7 +17,7 @@
 # 1: https://github.com/erikbern/ann-benchmarks/blob/main/plot.py
 # 2: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/utils.py  # noqa: E501
 # 3: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/metrics.py  # noqa: E501
-# License: https://github.com/rapidsai/cuvs/blob/branch-25.06/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
+# License: https://github.com/rapidsai/cuvs/blob/branch-25.08/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
 
 import itertools
 import os
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index a1f170cceb..1664f14ac1 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -19,7 +19,7 @@ license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "click",
-    "cuvs==25.6.*,>=0.0.0a0",
+    "cuvs==25.8.*,>=0.0.0a0",
     "matplotlib>=3.9",
     "pandas",
     "pyyaml",
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 88861f5d69..ed56841d26 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -30,8 +30,8 @@ authors = [
 license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
-    "libraft==25.6.*,>=0.0.0a0",
-    "librmm==25.6.*,>=0.0.0a0",
+    "libraft==25.8.*,>=0.0.0a0",
+    "librmm==25.8.*,>=0.0.0a0",
     "nvidia-cublas",
     "nvidia-curand",
     "nvidia-cusolver",
@@ -93,8 +93,8 @@ regex = "(?P<value>.*)"
 build-backend = "scikit_build_core.build"
 requires = [
     "cmake>=3.30.4",
-    "libraft==25.6.*,>=0.0.0a0",
-    "librmm==25.6.*,>=0.0.0a0",
+    "libraft==25.8.*,>=0.0.0a0",
+    "librmm==25.8.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 dependencies-file = "../../dependencies.yaml"
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index de35027bc6..98c2c94018 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "25.6.0"
+version = "25.8.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index 9b76c86bfa..d2774e9140 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.6.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.8.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From 3131a95e7cbccacd673305582048401df51742b6 Mon Sep 17 00:00:00 2001
From: Vivek Narang <123010842+narangvivek10@users.noreply.github.com>
Date: Thu, 1 May 2025 05:34:03 +0530
Subject: [PATCH 378/623] [CuVS-Java] Automate panama bindings generation,
 Include IVF_PQ parameters in CAGRA index parameters and other changes (#831)

The PR includes code changes for the following:

- Automation of Panama bindings generation using jextract.
- Adding the ability to configure IVF_PQ index and search parameters via the cuvs-java API (to adapt with the following [underlying changes](https://github.com/rapidsai/cuvs/commit/18a3d903aa393b4e0b98366258a66b561ae828e5)).
- Updating HNSW example to show the above.
- Updating the readme files.
- Simplifying logging in examples.
- Bumping up the maven-javadoc-plugin version.
- Updating and consolidating gitignore file.
- Removing unused imports etc.

Please note that the existing Panama classes are being deleted because they were manually created and managed. With the new cleaner approach, this will not be needed anymore. Now these binding classes will be generated at build time and so no need to be in the codebase.

Authors:
  - Vivek Narang (https://github.com/narangvivek10)
  - Ishan Chattopadhyaya (https://github.com/chatman)

Approvers:
  - rhdong (https://github.com/rhdong)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/831
---
 java/.gitignore                               |    7 +
 java/README.md                                |   31 +-
 java/build.sh                                 |   21 +-
 java/cuvs-java/.gitignore                     |    1 -
 java/cuvs-java/pom.xml                        |    5 +-
 .../java/com/nvidia/cuvs/BruteForceIndex.java |    5 +-
 .../java/com/nvidia/cuvs/BruteForceQuery.java |    2 +-
 .../main/java/com/nvidia/cuvs/CagraIndex.java |    1 -
 .../com/nvidia/cuvs/CagraIndexParams.java     |  127 +-
 .../com/nvidia/cuvs/CuVSIvfPqIndexParams.java |  513 +++
 .../java/com/nvidia/cuvs/CuVSIvfPqParams.java |  126 +
 .../nvidia/cuvs/CuVSIvfPqSearchParams.java    |  231 ++
 .../cuvs/internal/BruteForceIndexImpl.java    |   22 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |  112 +-
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |   34 +-
 .../com/nvidia/cuvs/internal/common/Util.java |   47 +-
 .../cuvs/internal/panama/BruteForceH.java     | 1912 ----------
 .../nvidia/cuvs/internal/panama/CagraH.java   | 2298 ------------
 .../internal/panama/CuVSBruteForceIndex.java  |  183 -
 .../panama/CuVSCagraCompressionParams.java    |  352 --
 .../cuvs/internal/panama/CuVSCagraIndex.java  |  183 -
 .../internal/panama/CuVSCagraIndexParams.java |  354 --
 .../panama/CuVSCagraSearchParams.java         |  644 ----
 .../cuvs/internal/panama/CuVSFilter.java      |  188 -
 .../internal/panama/CuVSHnswExtendParams.java |  142 -
 .../cuvs/internal/panama/CuVSHnswIndex.java   |  183 -
 .../internal/panama/CuVSHnswIndexParams.java  |  224 --
 .../internal/panama/CuVSHnswSearchParams.java |  183 -
 .../cuvs/internal/panama/DLDataType.java      |  233 --
 .../nvidia/cuvs/internal/panama/DLDevice.java |  186 -
 .../cuvs/internal/panama/DLManagedTensor.java |  288 --
 .../panama/DLManagedTensorVersioned.java      |  381 --
 .../cuvs/internal/panama/DLPackVersion.java   |  186 -
 .../nvidia/cuvs/internal/panama/DLTensor.java |  418 ---
 .../cuvs/internal/panama/DistanceH.java       |  274 --
 .../nvidia/cuvs/internal/panama/DlpackH.java  | 1898 ----------
 .../nvidia/cuvs/internal/panama/Fsidt.java    |  175 -
 .../nvidia/cuvs/internal/panama/GpuInfo.java  |  342 --
 .../nvidia/cuvs/internal/panama/GpuInfoH.java |   86 -
 .../nvidia/cuvs/internal/panama/HnswH.java    | 2350 ------------
 .../nvidia/cuvs/internal/panama/IvfFlatH.java | 2845 ---------------
 .../nvidia/cuvs/internal/panama/IvfPqH.java   | 3182 -----------------
 .../cuvs/internal/panama/MaxAlignT.java       |  142 -
 java/examples/.gitignore                      |    1 -
 java/examples/README.md                       |   29 +-
 java/examples/pom.xml                         |   11 -
 .../cuvs/examples/BruteForceExample.java      |    9 +-
 .../nvidia/cuvs/examples/CagraExample.java    |    9 +-
 .../com/nvidia/cuvs/examples/HnswExample.java |   27 +-
 java/examples/src/main/resources/.gitkeep     |    0
 java/examples/src/main/resources/log4j2.xml   |   14 -
 java/internal/src/cuvs_java.c                 |    6 +
 java/panama-bindings/generate-bindings.sh     |   46 +
 java/panama-bindings/gpuinfo.h                |   26 +
 java/panama-bindings/headers.h                |   23 +
 55 files changed, 1313 insertions(+), 20005 deletions(-)
 create mode 100644 java/.gitignore
 delete mode 100644 java/cuvs-java/.gitignore
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/BruteForceH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CagraH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSBruteForceIndex.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraCompressionParams.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndex.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndexParams.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraSearchParams.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSFilter.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswExtendParams.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndex.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndexParams.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswSearchParams.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDataType.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDevice.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensor.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensorVersioned.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLPackVersion.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLTensor.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DistanceH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DlpackH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/Fsidt.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfo.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfoH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/HnswH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfFlatH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfPqH.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/MaxAlignT.java
 delete mode 100644 java/examples/.gitignore
 create mode 100644 java/examples/src/main/resources/.gitkeep
 delete mode 100644 java/examples/src/main/resources/log4j2.xml
 create mode 100755 java/panama-bindings/generate-bindings.sh
 create mode 100644 java/panama-bindings/gpuinfo.h
 create mode 100644 java/panama-bindings/headers.h

diff --git a/java/.gitignore b/java/.gitignore
new file mode 100644
index 0000000000..815e71455b
--- /dev/null
+++ b/java/.gitignore
@@ -0,0 +1,7 @@
+# cuvs-java
+/cuvs-java/target/
+/cuvs-java/bin/
+/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/
+/cuvs-java/*.cag
+# examples
+/examples/target/
diff --git a/java/README.md b/java/README.md
index e5676146a2..c032fa36fe 100644
--- a/java/README.md
+++ b/java/README.md
@@ -1,14 +1,27 @@
-Prerequisites
--------------
+# CuVS Java API
 
-* JDK 22
-* Maven 3.9.6 or later
 
-To build this API, please do `./build.sh java` in the top level directory. Since this API is dependent on `libcuvs` it must be noted that `libcuvs` gets built automatically before building this API.
+CuVS Java API provides a Java based simple, efficient, and a robust vector search API.
 
-Alternatively, please build libcuvs (`./build.sh libcuvs` from top level directory) before building the Java API with `./build.sh` from this directory.
+> [!CAUTION]
+> CuVS 25.06 contains an experimental version and updates to this API are expected in the coming release.
 
-Building
---------
+## Prerequisites
 
-`./build.sh` will generate the `libcuvs_java.so` file in the `internal/` directory, and then build the final jar file for the cuVS Java API in the `cuvs-java/` directory.
+- [CuVS libraries](https://docs.rapids.ai/api/cuvs/stable/build/#build-from-source)
+- [maven 3.9.6 or above](https://maven.apache.org/download.cgi)
+- [JDK 22](https://openjdk.org/projects/jdk/22/)
+- [jextract for JDK 22](https://jdk.java.net/jextract/) (If not already installed, the build script downloads it)
+
+
+## Building
+
+The libcuvs C and C++ libraries are needed for this API. If libcuvs libraries have not been built and installed, use `./build.sh libcuvs java` in the top level directory to build this API.
+
+Alternatively, if libcuvs libraries are already built and you just want to build this API, please
+do `./build.sh java` in the top level directory or just do `./build.sh` in this directory.
+
+
+## Examples
+
+A few starter examples of CAGRA, HNSW, and Bruteforce index are provided in the `examples` directory.
diff --git a/java/build.sh b/java/build.sh
index 34c7ae83e6..84e44c71f9 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -1,14 +1,23 @@
+#!/bin/bash
+
+set -e -u -o pipefail
+
 VERSION="25.06.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
-SO_FILE_PATH="./internal"
+SO_FILE_PATH="./internal/build"
 
-if [ -z "$CMAKE_PREFIX_PATH" ]; then
-    export CMAKE_PREFIX_PATH=`pwd`/../cpp/build
+if [ -z "${CMAKE_PREFIX_PATH:=}" ]; then
+  export CMAKE_PREFIX_PATH="$(pwd)/../cpp/build"
 fi
 
-cd internal && cmake . && cmake --build . \
-  && cd .. \
-  && mvn install:install-file -DgroupId=$GROUP_ID -DartifactId=cuvs-java-internal -Dversion=$VERSION -Dpackaging=so -Dfile=$SO_FILE_PATH/libcuvs_java.so \
+cmake -B ./internal/build -S ./internal
+cmake --build ./internal/build
+
+# Generate Panama FFM API bindings and update (if any of them changed)
+./panama-bindings/generate-bindings.sh
+
+# Build the java layer
+mvn install:install-file -DgroupId=$GROUP_ID -DartifactId=cuvs-java-internal -Dversion=$VERSION -Dpackaging=so -Dfile=$SO_FILE_PATH/libcuvs_java.so \
   && cd cuvs-java \
   && mvn verify \
   && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar
diff --git a/java/cuvs-java/.gitignore b/java/cuvs-java/.gitignore
deleted file mode 100644
index b83d22266a..0000000000
--- a/java/cuvs-java/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-/target/
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 835fb6eaf2..7e492167c3 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -258,7 +258,7 @@
         <plugin>
             <groupId>org.apache.maven.plugins</groupId>
             <artifactId>maven-javadoc-plugin</artifactId>
-            <version>2.9.1</version>
+            <version>3.11.2</version>
             <executions>
                 <execution>
                     <id>attach-javadocs</id>
@@ -298,7 +298,4 @@
         </plugin>
         </plugins>
     </build>
-
-
-
 </project>
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
index e09c0228fb..511d4279f9 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
@@ -16,13 +16,12 @@
 
 package com.nvidia.cuvs;
 
-import com.nvidia.cuvs.spi.CuVSProvider;
-
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.nio.file.Path;
 import java.util.Objects;
-import java.util.UUID;
+
+import com.nvidia.cuvs.spi.CuVSProvider;
 
 /**
  *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
index 019e27dcd8..70fa662210 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
@@ -40,7 +40,7 @@ public class BruteForceQuery {
    * @param queryVectors 2D float query vector array
    * @param mapping      an instance of ID mapping
    * @param topK         the top k results to return
-   * @param prefilter    the prefilter data to use while searching the BRUTEFORCE
+   * @param prefilters   the prefilters data to use while searching the BRUTEFORCE
    *                     index
    * @param numDocs      Maximum of bits in each prefilter, representing number of documents in this index.
    *                     Used only when prefilter(s) is/are passed.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index f92d97edd5..d0d7b5ceb8 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -20,7 +20,6 @@
 import java.io.OutputStream;
 import java.nio.file.Path;
 import java.util.Objects;
-import java.util.UUID;
 
 import com.nvidia.cuvs.spi.CuVSProvider;
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
index 11b4f4b90a..6959938cea 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
@@ -29,6 +29,7 @@ public class CagraIndexParams {
   private final int graphDegree;
   private final int nnDescentNiter;
   private final int numWriterThreads;
+  private final CuVSIvfPqParams cuVSIvfPqParams;
 
   /**
    * Enum that denotes which ANN algorithm is used to build CAGRA graph.
@@ -158,15 +159,106 @@ private CuvsDistanceType(int value) {
 
   }
 
-  private CagraIndexParams(int intermediateGraphDegree, int graphDegree,
-      CagraGraphBuildAlgo CuvsCagraGraphBuildAlgo, int nnDescentNiter, int writerThreads,
-      CuvsDistanceType cuvsDistanceType) {
+  /**
+   * Enum that denotes codebook gen options.
+   */
+  public enum CodebookGen {
+
+    PER_SUBSPACE(0),
+
+    PER_CLUSTER(1);
+
+    /**
+     * The value for the enum choice.
+     */
+    public final int value;
+
+    private CodebookGen(int value) {
+      this.value = value;
+    }
+  }
+
+  /**
+   * Enum that denotes cuda datatypes.
+   */
+  public enum CudaDataType {
+
+    CUDA_R_16F(2),
+
+    CUDA_C_16F(6),
+
+    CUDA_R_16BF(14),
+
+    CUDA_C_16BF(15),
+
+    CUDA_R_32F(0),
+
+    CUDA_C_32F(4),
+
+    CUDA_R_64F(1),
+
+    CUDA_C_64F(5),
+
+    CUDA_R_4I(16),
+
+    CUDA_C_4I(17),
+
+    CUDA_R_4U(18),
+
+    CUDA_C_4U(19),
+
+    CUDA_R_8I(3),
+
+    CUDA_C_8I(7),
+
+    CUDA_R_8U(8),
+
+    CUDA_C_8U(9),
+
+    CUDA_R_16I(20),
+
+    CUDA_C_16I(21),
+
+    CUDA_R_16U(22),
+
+    CUDA_C_16U(23),
+
+    CUDA_R_32I(10),
+
+    CUDA_C_32I(11),
+
+    CUDA_R_32U(12),
+
+    CUDA_C_32U(13),
+
+    CUDA_R_64I(24),
+
+    CUDA_C_64I(25),
+
+    CUDA_R_64U(26),
+
+    CUDA_C_64U(27),
+
+    CUDA_R_8F_E4M3(28),
+
+    CUDA_R_8F_E5M2(29);
+
+    public final int value;
+
+    private CudaDataType(int value) {
+      this.value = value;
+    }
+  }
+
+  private CagraIndexParams(int intermediateGraphDegree, int graphDegree, CagraGraphBuildAlgo CuvsCagraGraphBuildAlgo,
+      int nnDescentNiter, int writerThreads, CuvsDistanceType cuvsDistanceType, CuVSIvfPqParams cuVSIvfPqParams) {
     this.intermediateGraphDegree = intermediateGraphDegree;
     this.graphDegree = graphDegree;
     this.cuvsCagraGraphBuildAlgo = CuvsCagraGraphBuildAlgo;
     this.nnDescentNiter = nnDescentNiter;
     this.numWriterThreads = writerThreads;
     this.cuvsDistanceType = cuvsDistanceType;
+    this.cuVSIvfPqParams = cuVSIvfPqParams;
   }
 
   /**
@@ -216,11 +308,19 @@ public int getNumWriterThreads() {
     return numWriterThreads;
   }
 
+  /**
+   * Gets the IVF_PQ parameters.
+   */
+  public CuVSIvfPqParams getCuVSIvfPqParams() {
+    return cuVSIvfPqParams;
+  }
+
   @Override
   public String toString() {
     return "CagraIndexParams [cuvsCagraGraphBuildAlgo=" + cuvsCagraGraphBuildAlgo + ", cuvsDistanceType="
         + cuvsDistanceType + ", intermediateGraphDegree=" + intermediateGraphDegree + ", graphDegree=" + graphDegree
-        + ", nnDescentNiter=" + nnDescentNiter + ", numWriterThreads=" + numWriterThreads + "]";
+        + ", nnDescentNiter=" + nnDescentNiter + ", numWriterThreads=" + numWriterThreads + ", cuVSIvfPqParams="
+        + cuVSIvfPqParams + "]";
   }
 
   /**
@@ -234,8 +334,10 @@ public static class Builder {
     private int graphDegree = 64;
     private int nnDescentNumIterations = 20;
     private int numWriterThreads = 2;
+    private CuVSIvfPqParams cuVSIvfPqParams = new CuVSIvfPqParams.Builder().build();
 
-    public Builder() { }
+    public Builder() {
+    }
 
     /**
      * Sets the degree of input graph for pruning.
@@ -305,14 +407,25 @@ public Builder withNumWriterThreads(int numWriterThreads) {
       return this;
     }
 
+    /**
+     * Sets the IVF_PQ index parameters.
+     *
+     * @param cuVSIvfPqParams the IVF_PQ index parameters
+     * @return an instance of Builder
+     */
+    public Builder withCuVSIvfPqParams(CuVSIvfPqParams cuVSIvfPqParams) {
+      this.cuVSIvfPqParams = cuVSIvfPqParams;
+      return this;
+    }
+
     /**
      * Builds an instance of {@link CagraIndexParams}.
      *
      * @return an instance of {@link CagraIndexParams}
      */
     public CagraIndexParams build() {
-      return new CagraIndexParams(intermediateGraphDegree, graphDegree, cuvsCagraGraphBuildAlgo,
-          nnDescentNumIterations, numWriterThreads, cuvsDistanceType);
+      return new CagraIndexParams(intermediateGraphDegree, graphDegree, cuvsCagraGraphBuildAlgo, nnDescentNumIterations,
+          numWriterThreads, cuvsDistanceType, cuVSIvfPqParams);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java
new file mode 100644
index 0000000000..1ce1d96568
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java
@@ -0,0 +1,513 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.CagraIndexParams.CodebookGen;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+
+public class CuVSIvfPqIndexParams {
+
+  /** Distance type. */
+  private final CuvsDistanceType metric;
+
+  /** How PQ codebooks are created. */
+  private final CodebookGen codebookKind;
+
+  /** The argument used by some distance metrics. */
+  private final float metricArg;
+
+  /** The fraction of data to use during iterative kmeans building. */
+  private final double kmeansTrainsetFraction;
+
+  /**
+   * The number of inverted lists (clusters)
+   *
+   * Hint: the number of vectors per cluster (`n_rows/n_lists`) should be
+   * approximately 1,000 to 10,000.
+   */
+  private final int nLists;
+
+  /** The number of iterations searching for kmeans centers (index building). */
+  private final int kmeansNIters;
+
+  /**
+   * The bit length of the vector element after compression by PQ.
+   *
+   * Possible values: [4, 5, 6, 7, 8].
+   *
+   * Hint: the smaller the 'pq_bits', the smaller the index size and the better
+   * the search performance, but the lower the recall.
+   */
+  private final int pqBits;
+
+  /**
+   * The dimensionality of the vector after compression by PQ. When zero, an
+   * optimal value is selected using a heuristic.
+   *
+   * NB: `pq_dim * pq_bits` must be a multiple of 8.
+   *
+   * Hint: a smaller 'pq_dim' results in a smaller index size and better search
+   * performance, but lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any
+   * number, but multiple of 8 are desirable for good performance. If 'pq_bits' is
+   * not 8, 'pq_dim' should be a multiple of 8. For good performance, it is
+   * desirable that 'pq_dim' is a multiple of 32. Ideally, 'pq_dim' should be also
+   * a divisor of the dataset dim.
+   */
+  private final int pqDim;
+
+  /**
+   * Whether to add the dataset content to the index, i.e.:
+   *
+   * - `true` means the index is filled with the dataset vectors and ready to
+   * search after calling `build`. - `false` means `build` only trains the
+   * underlying model (e.g. quantizer or clustering), but the index is left empty;
+   * you'd need to call `extend` on the index afterwards to populate it.
+   */
+  private final boolean addDataOnBuild;
+
+  /**
+   * Apply a random rotation matrix on the input data and queries even if `dim %
+   * pq_dim == 0`.
+   *
+   * Note: if `dim` is not multiple of `pq_dim`, a random rotation is always
+   * applied to the input data and queries to transform the working space from
+   * `dim` to `rot_dim`, which may be slightly larger than the original space and
+   * and is a multiple of `pq_dim` (`rot_dim % pq_dim == 0`). However, this
+   * transform is not necessary when `dim` is multiple of `pq_dim` (`dim ==
+   * rot_dim`, hence no need in adding "extra" data columns / features).
+   *
+   * By default, if `dim == rot_dim`, the rotation transform is initialized with
+   * the identity matrix. When `force_random_rotation == true`, a random
+   * orthogonal transform matrix is generated regardless of the values of `dim`
+   * and `pq_dim`.
+   */
+  private final boolean forceRandomRotation;
+
+  /**
+   * By default, the algorithm allocates more space than necessary for individual
+   * clusters (`list_data`). This allows to amortize the cost of memory allocation
+   * and reduce the number of data copies during repeated calls to `extend`
+   * (extending the database).
+   *
+   * The alternative is the conservative allocation behavior; when enabled, the
+   * algorithm always allocates the minimum amount of memory required to store the
+   * given number of records. Set this flag to `true` if you prefer to use as
+   * little GPU memory for the database as possible.
+   */
+  private final boolean conservativeMemoryAllocation;
+
+  /**
+   * The max number of data points to use per PQ code during PQ codebook training.
+   * Using more data points per PQ code may increase the quality of PQ codebook
+   * but may also increase the build time. The parameter is applied to both PQ
+   * codebook generation methods, i.e., PER_SUBSPACE and PER_CLUSTER. In both
+   * cases, we will use `pq_book_size * max_train_points_per_pq_code` training
+   * points to train each codebook.
+   */
+  private final int maxTrainPointsPerPqCode;
+
+  private CuVSIvfPqIndexParams(CuvsDistanceType metric, CodebookGen codebookKind, float metricArg,
+      double kmeansTrainsetFraction, int nLists, int kmeansNIters, int pqBits, int pqDim, boolean addDataOnBuild,
+      boolean forceRandomRotation, boolean conservativeMemoryAllocation, int maxTrainPointsPerPqCode) {
+    super();
+    this.metric = metric;
+    this.codebookKind = codebookKind;
+    this.metricArg = metricArg;
+    this.kmeansTrainsetFraction = kmeansTrainsetFraction;
+    this.nLists = nLists;
+    this.kmeansNIters = kmeansNIters;
+    this.pqBits = pqBits;
+    this.pqDim = pqDim;
+    this.addDataOnBuild = addDataOnBuild;
+    this.forceRandomRotation = forceRandomRotation;
+    this.conservativeMemoryAllocation = conservativeMemoryAllocation;
+    this.maxTrainPointsPerPqCode = maxTrainPointsPerPqCode;
+  }
+
+  /**
+   * Gets the distance type.
+   *
+   * @return the distance type
+   */
+  public CuvsDistanceType getMetric() {
+    return metric;
+  }
+
+  /**
+   * Gets how PQ codebooks are created
+   *
+   * @return how PQ codebooks are created
+   */
+  public CodebookGen getCodebookKind() {
+    return codebookKind;
+  }
+
+  /**
+   * Gets the argument used by some distance metrics
+   *
+   * @return the argument used by some distance metrics
+   */
+  public float getMetricArg() {
+    return metricArg;
+  }
+
+  /**
+   * Gets the fraction of data to use during iterative kmeans building
+   *
+   * @return the fraction of data to use during iterative kmeans building
+   */
+  public double getKmeansTrainsetFraction() {
+    return kmeansTrainsetFraction;
+  }
+
+  /**
+   * Gets the number of inverted lists (clusters)
+   *
+   * @return the number of inverted lists (clusters)
+   */
+  public int getnLists() {
+    return nLists;
+  }
+
+  /**
+   * Gets the number of iterations searching for kmeans centers
+   *
+   * @return the number of iterations searching for kmeans centers
+   */
+  public int getKmeansNIters() {
+    return kmeansNIters;
+  }
+
+  /**
+   * Gets the bit length of the vector element after compression by PQ
+   *
+   * @return the bit length of the vector element after compression by PQ
+   */
+  public int getPqBits() {
+    return pqBits;
+  }
+
+  /**
+   * Gets the dimensionality of the vector after compression by PQ
+   *
+   * @return the dimensionality of the vector after compression by PQ
+   */
+  public int getPqDim() {
+    return pqDim;
+  }
+
+  /**
+   * Gets whether the dataset content is added to the index
+   *
+   * @return whether the dataset content is added to the index
+   */
+  public boolean isAddDataOnBuild() {
+    return addDataOnBuild;
+  }
+
+  /**
+   * Gets the random rotation matrix on the input data and queries
+   *
+   * @return the random rotation matrix on the input data and queries
+   */
+  public boolean isForceRandomRotation() {
+    return forceRandomRotation;
+  }
+
+  /**
+   * Gets if conservative allocation behavior is set
+   *
+   * @return if conservative allocation behavior is set
+   */
+  public boolean isConservativeMemoryAllocation() {
+    return conservativeMemoryAllocation;
+  }
+
+  /**
+   * Gets whether max number of data points to use per PQ code during PQ codebook
+   * training is set
+   *
+   * @return whether max number of data points to use per PQ code during PQ
+   *         codebook training is set
+   */
+  public int getMaxTrainPointsPerPqCode() {
+    return maxTrainPointsPerPqCode;
+  }
+
+  @Override
+  public String toString() {
+    return "CuVSIvfPqIndexParams [metric=" + metric + ", codebookKind=" + codebookKind + ", metricArg=" + metricArg
+        + ", kmeansTrainsetFraction=" + kmeansTrainsetFraction + ", nLists=" + nLists + ", kmeansNIters=" + kmeansNIters
+        + ", pqBits=" + pqBits + ", pqDim=" + pqDim + ", addDataOnBuild=" + addDataOnBuild + ", forceRandomRotation="
+        + forceRandomRotation + ", conservativeMemoryAllocation=" + conservativeMemoryAllocation
+        + ", maxTrainPointsPerPqCode=" + maxTrainPointsPerPqCode + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of {@link CuVSIvfPqIndexParams}.
+   */
+  public static class Builder {
+
+    /** Distance type. */
+    private CuvsDistanceType metric = CuvsDistanceType.L2Expanded;
+
+    /** How PQ codebooks are created. */
+    private CodebookGen codebookKind = CodebookGen.PER_SUBSPACE;
+
+    /** The argument used by some distance metrics. */
+    private float metricArg = 2.0f;
+
+    /** The fraction of data to use during iterative kmeans building. */
+    private double kmeansTrainsetFraction = 0.5;
+
+    /**
+     * The number of inverted lists (clusters)
+     *
+     * Hint: the number of vectors per cluster (`n_rows/n_lists`) should be
+     * approximately 1,000 to 10,000.
+     */
+    private int nLists = 1024;
+
+    /** The number of iterations searching for kmeans centers (index building). */
+    private int kmeansNIters = 20;
+
+    /**
+     * The bit length of the vector element after compression by PQ.
+     *
+     * Possible values: [4, 5, 6, 7, 8].
+     *
+     * Hint: the smaller the 'pq_bits', the smaller the index size and the better
+     * the search performance, but the lower the recall.
+     */
+    private int pqBits = 8;
+
+    /**
+     * The dimensionality of the vector after compression by PQ. When zero, an
+     * optimal value is selected using a heuristic.
+     *
+     * NB: `pq_dim * pq_bits` must be a multiple of 8.
+     *
+     * Hint: a smaller 'pq_dim' results in a smaller index size and better search
+     * performance, but lower recall. If 'pq_bits' is 8, 'pq_dim' can be set to any
+     * number, but multiple of 8 are desirable for good performance. If 'pq_bits' is
+     * not 8, 'pq_dim' should be a multiple of 8. For good performance, it is
+     * desirable that 'pq_dim' is a multiple of 32. Ideally, 'pq_dim' should be also
+     * a divisor of the dataset dim.
+     */
+    private int pqDim = 0;
+
+    /**
+     * Whether to add the dataset content to the index, i.e.:
+     *
+     * - `true` means the index is filled with the dataset vectors and ready to
+     * search after calling `build`. - `false` means `build` only trains the
+     * underlying model (e.g. quantizer or clustering), but the index is left empty;
+     * you'd need to call `extend` on the index afterwards to populate it.
+     */
+    private boolean addDataOnBuild = true;
+
+    /**
+     * Apply a random rotation matrix on the input data and queries even if `dim %
+     * pq_dim == 0`.
+     *
+     * Note: if `dim` is not multiple of `pq_dim`, a random rotation is always
+     * applied to the input data and queries to transform the working space from
+     * `dim` to `rot_dim`, which may be slightly larger than the original space and
+     * and is a multiple of `pq_dim` (`rot_dim % pq_dim == 0`). However, this
+     * transform is not necessary when `dim` is multiple of `pq_dim` (`dim ==
+     * rot_dim`, hence no need in adding "extra" data columns / features).
+     *
+     * By default, if `dim == rot_dim`, the rotation transform is initialized with
+     * the identity matrix. When `force_random_rotation == true`, a random
+     * orthogonal transform matrix is generated regardless of the values of `dim`
+     * and `pq_dim`.
+     */
+    private boolean forceRandomRotation = false;
+
+    /**
+     * By default, the algorithm allocates more space than necessary for individual
+     * clusters (`list_data`). This allows to amortize the cost of memory allocation
+     * and reduce the number of data copies during repeated calls to `extend`
+     * (extending the database).
+     *
+     * The alternative is the conservative allocation behavior; when enabled, the
+     * algorithm always allocates the minimum amount of memory required to store the
+     * given number of records. Set this flag to `true` if you prefer to use as
+     * little GPU memory for the database as possible.
+     */
+    private boolean conservativeMemoryAllocation = false;
+
+    /**
+     * The max number of data points to use per PQ code during PQ codebook training.
+     * Using more data points per PQ code may increase the quality of PQ codebook
+     * but may also increase the build time. The parameter is applied to both PQ
+     * codebook generation methods, i.e., PER_SUBSPACE and PER_CLUSTER. In both
+     * cases, we will use `pq_book_size * max_train_points_per_pq_code` training
+     * points to train each codebook.
+     */
+    private int maxTrainPointsPerPqCode = 256;
+
+    public Builder() {
+    }
+
+    /**
+     * Sets the distance type.
+     *
+     * @param metric distance type
+     * @return an instance of Builder
+     */
+    public Builder withMetric(CuvsDistanceType metric) {
+      this.metric = metric;
+      return this;
+    }
+
+    /**
+     * Sets the argument used by some distance metrics.
+     *
+     * @param metricArg argument used by some distance metrics
+     * @return an instance of Builder
+     */
+    public Builder withMetricArg(float metricArg) {
+      this.metricArg = metricArg;
+      return this;
+    }
+
+    /**
+     * Sets whether to add the dataset content to the index.
+     *
+     * @param addDataOnBuild whether to add the dataset content to the index
+     * @return an instance of Builder
+     */
+    public Builder withAddDataOnBuild(boolean addDataOnBuild) {
+      this.addDataOnBuild = addDataOnBuild;
+      return this;
+    }
+
+    /**
+     * Sets the number of inverted lists (clusters)
+     *
+     * @param nLists number of inverted lists (clusters)
+     * @return an instance of Builder
+     */
+    public Builder withNLists(int nLists) {
+      this.nLists = nLists;
+      return this;
+    }
+
+    /**
+     * Sets the number of iterations searching for kmeans centers
+     *
+     * @param kmeansNIters number of iterations searching for kmeans centers
+     * @return an instance of Builder
+     */
+    public Builder withKmeansNIters(int kmeansNIters) {
+      this.kmeansNIters = kmeansNIters;
+      return this;
+    }
+
+    /**
+     * Sets the fraction of data to use during iterative kmeans building.
+     *
+     * @param kmeansTrainsetFraction fraction of data to use during iterative kmeans
+     *                               building
+     * @return an instance of Builder
+     */
+    public Builder withKmeansTrainsetFraction(double kmeansTrainsetFraction) {
+      this.kmeansTrainsetFraction = kmeansTrainsetFraction;
+      return this;
+    }
+
+    /**
+     * Sets the bit length of the vector element after compression by PQ.
+     *
+     * @param pqBits bit length of the vector element after compression by PQ
+     * @return an instance of Builder
+     */
+    public Builder withPqBits(int pqBits) {
+      this.pqBits = pqBits;
+      return this;
+    }
+
+    /**
+     * Sets the dimensionality of the vector after compression by PQ.
+     *
+     * @param pqDim dimensionality of the vector after compression by PQ
+     * @return an instance of Builder
+     */
+    public Builder withPqDim(int pqDim) {
+      this.pqDim = pqDim;
+      return this;
+    }
+
+    /**
+     * Sets how PQ codebooks are created.
+     *
+     * @param codebookKind how PQ codebooks are created
+     * @return an instance of Builder
+     */
+    public Builder withCodebookKind(CodebookGen codebookKind) {
+      this.codebookKind = codebookKind;
+      return this;
+    }
+
+    /**
+     * Sets the random rotation matrix on the input data and queries.
+     *
+     * @param forceRandomRotation random rotation matrix on the input data and
+     *                            queries
+     * @return an instance of Builder
+     */
+    public Builder withForceRandomRotation(boolean forceRandomRotation) {
+      this.forceRandomRotation = forceRandomRotation;
+      return this;
+    }
+
+    /**
+     * Sets the conservative allocation behavior
+     *
+     * @param conservativeMemoryAllocation conservative allocation behavior
+     * @return an instance of Builder
+     */
+    public Builder withConservativeMemoryAllocation(boolean conservativeMemoryAllocation) {
+      this.conservativeMemoryAllocation = conservativeMemoryAllocation;
+      return this;
+    }
+
+    /**
+     * Sets the max number of data points to use per PQ code during PQ codebook
+     * training
+     *
+     * @param maxTrainPointsPerPqCode max number of data points to use per PQ code
+     *                                during PQ codebook training
+     * @return an instance of Builder
+     */
+    public Builder withMaxTrainPointsPerPqCode(int maxTrainPointsPerPqCode) {
+      this.maxTrainPointsPerPqCode = maxTrainPointsPerPqCode;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link CuVSIvfPqIndexParams}.
+     *
+     * @return an instance of {@link CuVSIvfPqIndexParams}
+     */
+    public CuVSIvfPqIndexParams build() {
+      return new CuVSIvfPqIndexParams(metric, codebookKind, metricArg, kmeansTrainsetFraction, nLists, kmeansNIters,
+          pqBits, pqDim, addDataOnBuild, forceRandomRotation, conservativeMemoryAllocation, maxTrainPointsPerPqCode);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java
new file mode 100644
index 0000000000..1b6dfca149
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+public class CuVSIvfPqParams {
+
+  /** CuVS IVF_PQ index parameters */
+  private final CuVSIvfPqIndexParams indexParams;
+
+  /** CuVS IVF_PQ search parameters */
+  private final CuVSIvfPqSearchParams searchParams;
+
+  /** refinement rate */
+  private final float refinementRate;
+
+  private CuVSIvfPqParams(CuVSIvfPqIndexParams indexParams, CuVSIvfPqSearchParams searchParams, float refinementRate) {
+    super();
+    this.indexParams = indexParams;
+    this.searchParams = searchParams;
+    this.refinementRate = refinementRate;
+  }
+
+  /**
+   *
+   * @return
+   */
+  public CuVSIvfPqIndexParams getIndexParams() {
+    return indexParams;
+  }
+
+  /**
+   *
+   * @return
+   */
+  public CuVSIvfPqSearchParams getSearchParams() {
+    return searchParams;
+  }
+
+  /**
+   *
+   * @return
+   */
+  public float getRefinementRate() {
+    return refinementRate;
+  }
+
+  @Override
+  public String toString() {
+    return "CuVSIvfPqParams [indexParams=" + indexParams + ", searchParams=" + searchParams + ", refinementRate="
+        + refinementRate + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of {@link CuVSIvfPqParams}.
+   */
+  public static class Builder {
+
+    /** CuVS IVF_PQ index parameters */
+    private CuVSIvfPqIndexParams cuVSIvfPqIndexParams = new CuVSIvfPqIndexParams.Builder().build();
+
+    /** CuVS IVF_PQ search parameters */
+    private CuVSIvfPqSearchParams cuVSIvfPqSearchParams = new CuVSIvfPqSearchParams.Builder().build();
+
+    /** refinement rate */
+    private float refinementRate = 2.0f;
+
+    public Builder() {
+    }
+
+    /**
+     * Sets the CuVS IVF_PQ index parameters.
+     *
+     * @param cuVSIvfPqIndexParams the CuVS IVF_PQ index parameters
+     * @return an instance of Builder
+     */
+    public Builder withCuVSIvfPqIndexParams(CuVSIvfPqIndexParams cuVSIvfPqIndexParams) {
+      this.cuVSIvfPqIndexParams = cuVSIvfPqIndexParams;
+      return this;
+    }
+
+    /**
+     * Sets the CuVS IVF_PQ search parameters.
+     *
+     * @param cuVSIvfPqSearchParams the CuVS IVF_PQ search parameters
+     * @return an instance of Builder
+     */
+    public Builder withCuVSIvfPqSearchParams(CuVSIvfPqSearchParams cuVSIvfPqSearchParams) {
+      this.cuVSIvfPqSearchParams = cuVSIvfPqSearchParams;
+      return this;
+    }
+
+    /**
+     * Sets the refinement rate, default 2.0.
+     *
+     * @param refinementRate the refinement rate
+     * @return an instance of Builder
+     */
+    public Builder withRefinementRate(float refinementRate) {
+      this.refinementRate = refinementRate;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link CuVSIvfPqParams}.
+     *
+     * @return an instance of {@link CuVSIvfPqParams}
+     */
+    public CuVSIvfPqParams build() {
+      return new CuVSIvfPqParams(cuVSIvfPqIndexParams, cuVSIvfPqSearchParams, refinementRate);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java
new file mode 100644
index 0000000000..2dab8dcb3f
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java
@@ -0,0 +1,231 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.CagraIndexParams.CudaDataType;
+
+public class CuVSIvfPqSearchParams {
+
+  /** The number of clusters to search. */
+  private final int nProbes;
+
+  /**
+   * Data type of look up table to be created dynamically at search time.
+   *
+   * Possible values: [CUDA_R_32F, CUDA_R_16F, CUDA_R_8U]
+   *
+   * The use of low-precision types reduces the amount of shared memory required
+   * at search time, so fast shared memory kernels can be used even for datasets
+   * with large dimansionality. Note that the recall is slightly degraded when
+   * low-precision type is selected.
+   */
+  private final CudaDataType lutDtype;
+
+  /**
+   * Storage data type for distance/similarity computed at search time.
+   *
+   * Possible values: [CUDA_R_16F, CUDA_R_32F]
+   *
+   * If the performance limiter at search time is device memory access, selecting
+   * FP16 will improve performance slightly.
+   */
+  private final CudaDataType internalDistanceDtype;
+
+  /**
+   * Preferred fraction of SM's unified memory / L1 cache to be used as shared
+   * memory.
+   *
+   * Possible values: [0.0 - 1.0] as a fraction of the
+   * `sharedMemPerMultiprocessor`.
+   *
+   * One wants to increase the carveout to make sure a good GPU occupancy for the
+   * main search kernel, but not to keep it too high to leave some memory to be
+   * used as L1 cache. Note, this value is interpreted only as a hint. Moreover, a
+   * GPU usually allows only a fixed set of cache configurations, so the provided
+   * value is rounded up to the nearest configuration. Refer to the NVIDIA tuning
+   * guide for the target GPU architecture.
+   *
+   * Note, this is a low-level tuning parameter that can have drastic negative
+   * effects on the search performance if tweaked incorrectly.
+   */
+  private final double preferredShmemCarveout;
+
+  private CuVSIvfPqSearchParams(int nProbes, CudaDataType lutDtype, CudaDataType internalDistanceDtype,
+      double preferredShmemCarveout) {
+    super();
+    this.nProbes = nProbes;
+    this.lutDtype = lutDtype;
+    this.internalDistanceDtype = internalDistanceDtype;
+    this.preferredShmemCarveout = preferredShmemCarveout;
+  }
+
+  /**
+   * Gets the number of clusters to search
+   *
+   * @return the number of clusters to search
+   */
+  public int getnProbes() {
+    return nProbes;
+  }
+
+  /**
+   * Gets the data type of look up table to be created dynamically at search time
+   *
+   * @return the data type of look up table to be created dynamically at search
+   *         time
+   */
+  public CudaDataType getLutDtype() {
+    return lutDtype;
+  }
+
+  /**
+   * Gets the storage data type for distance/similarity computed at search time
+   *
+   * @return the storage data type for distance/similarity computed at search time
+   */
+  public CudaDataType getInternalDistanceDtype() {
+    return internalDistanceDtype;
+  }
+
+  /**
+   * Gets the preferred fraction of SM's unified memory / L1 cache to be used as
+   * shared memory
+   *
+   * @return the preferred fraction of SM's unified memory / L1 cache to be used
+   *         as shared memory
+   */
+  public double getPreferredShmemCarveout() {
+    return preferredShmemCarveout;
+  }
+
+  @Override
+  public String toString() {
+    return "CuVSIvfPqSearchParams [nProbes=" + nProbes + ", lutDtype=" + lutDtype + ", internalDistanceDtype="
+        + internalDistanceDtype + ", preferredShmemCarveout=" + preferredShmemCarveout + "]";
+  }
+
+  /**
+   * Builder configures and creates an instance of {@link CuVSIvfPqSearchParams}.
+   */
+  public static class Builder {
+
+    /** The number of clusters to search. */
+    private int nProbes = 20;
+
+    /**
+     * Data type of look up table to be created dynamically at search time.
+     *
+     * Possible values: [CUDA_R_32F, CUDA_R_16F, CUDA_R_8U]
+     *
+     * The use of low-precision types reduces the amount of shared memory required
+     * at search time, so fast shared memory kernels can be used even for datasets
+     * with large dimansionality. Note that the recall is slightly degraded when
+     * low-precision type is selected.
+     */
+    private CudaDataType lutDtype = CudaDataType.CUDA_R_32F;
+
+    /**
+     * Storage data type for distance/similarity computed at search time.
+     *
+     * Possible values: [CUDA_R_16F, CUDA_R_32F]
+     *
+     * If the performance limiter at search time is device memory access, selecting
+     * FP16 will improve performance slightly.
+     */
+    private CudaDataType internalDistanceDtype = CudaDataType.CUDA_R_32F;
+
+    /**
+     * Preferred fraction of SM's unified memory / L1 cache to be used as shared
+     * memory.
+     *
+     * Possible values: [0.0 - 1.0] as a fraction of the
+     * `sharedMemPerMultiprocessor`.
+     *
+     * One wants to increase the carveout to make sure a good GPU occupancy for the
+     * main search kernel, but not to keep it too high to leave some memory to be
+     * used as L1 cache. Note, this value is interpreted only as a hint. Moreover, a
+     * GPU usually allows only a fixed set of cache configurations, so the provided
+     * value is rounded up to the nearest configuration. Refer to the NVIDIA tuning
+     * guide for the target GPU architecture.
+     *
+     * Note, this is a low-level tuning parameter that can have drastic negative
+     * effects on the search performance if tweaked incorrectly.
+     */
+    private double preferredShmemCarveout = 1.0;
+
+    public Builder() {
+    }
+
+    /**
+     * Sets the number of clusters to search.
+     *
+     * @param nProbes the number of clusters to search
+     * @return an instance of Builder
+     */
+    public Builder withNProbes(int nProbes) {
+      this.nProbes = nProbes;
+      return this;
+    }
+
+    /**
+     * Sets the the data type of look up table to be created dynamically at search
+     * time.
+     *
+     * @param lutDtype the data type of look up table to be created dynamically at
+     *                 search time
+     * @return an instance of Builder
+     */
+    public Builder withLutDtype(CudaDataType lutDtype) {
+      this.lutDtype = lutDtype;
+      return this;
+    }
+
+    /**
+     * Sets the storage data type for distance/similarity computed at search time.
+     *
+     * @param internalDistanceDtype storage data type for distance/similarity
+     *                              computed at search time
+     * @return an instance of Builder
+     */
+    public Builder withInternalDistanceDtype(CudaDataType internalDistanceDtype) {
+      this.internalDistanceDtype = internalDistanceDtype;
+      return this;
+    }
+
+    /**
+     * Sets the preferred fraction of SM's unified memory / L1 cache to be used as
+     * shared memory.
+     *
+     * @param preferredShmemCarveout preferred fraction of SM's unified memory / L1
+     *                               cache to be used as shared memory
+     * @return an instance of Builder
+     */
+    public Builder withPreferredShmemCarveout(double preferredShmemCarveout) {
+      this.preferredShmemCarveout = preferredShmemCarveout;
+      return this;
+    }
+
+    /**
+     * Builds an instance of {@link CuVSIvfPqSearchParams}.
+     *
+     * @return an instance of {@link CuVSIvfPqSearchParams}
+     */
+    public CuVSIvfPqSearchParams build() {
+      return new CuVSIvfPqSearchParams(nProbes, lutDtype, internalDistanceDtype, preferredShmemCarveout);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index decb13133c..a96f633ffd 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -16,6 +16,13 @@
 
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.common.Util.checkError;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
@@ -26,11 +33,8 @@
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.lang.invoke.MethodHandle;
-import java.nio.ByteBuffer;
-import java.nio.ByteOrder;
 import java.nio.file.Files;
 import java.nio.file.Path;
-import java.util.Arrays;
 import java.util.BitSet;
 import java.util.Objects;
 import java.util.UUID;
@@ -41,14 +45,7 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
-import com.nvidia.cuvs.internal.panama.CuVSBruteForceIndex;
-
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static com.nvidia.cuvs.internal.common.Util.checkError;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import com.nvidia.cuvs.internal.panama.cuvsBruteForceIndex;
 
 /**
  *
@@ -173,7 +170,6 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
     long numQueries = cuvsQuery.getQueryVectors().length;
     long numBlocks = cuvsQuery.getTopK() * numQueries;
     int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
-    long numRows = dataset != null ? dataset.length : 0;
 
     SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
     SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
@@ -366,7 +362,7 @@ protected static class IndexReference {
      * Constructs CagraIndexReference and allocate the MemorySegment.
      */
     protected IndexReference(CuVSResourcesImpl resources) {
-      memorySegment = CuVSBruteForceIndex.allocate(resources.getArena());
+      memorySegment = cuvsBruteForceIndex.allocate(resources.getArena());
     }
 
     /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 0577276d34..7788354022 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -16,6 +16,13 @@
 
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.common.Util.checkError;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
@@ -34,22 +41,19 @@
 import com.nvidia.cuvs.CagraCompressionParams;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraQuery;
 import com.nvidia.cuvs.CagraSearchParams;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
-import com.nvidia.cuvs.internal.panama.CuVSCagraCompressionParams;
-import com.nvidia.cuvs.internal.panama.CuVSCagraIndex;
-import com.nvidia.cuvs.internal.panama.CuVSCagraIndexParams;
-import com.nvidia.cuvs.internal.panama.CuVSCagraSearchParams;
-
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static com.nvidia.cuvs.internal.common.Util.checkError;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
+import com.nvidia.cuvs.internal.panama.cuvsCagraIndex;
+import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
+import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
+import com.nvidia.cuvs.internal.panama.cuvsIvfPqIndexParams;
+import com.nvidia.cuvs.internal.panama.cuvsIvfPqParams;
+import com.nvidia.cuvs.internal.panama.cuvsIvfPqSearchParams;
 
 /**
  * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
@@ -374,13 +378,13 @@ public CuVSResourcesImpl getCuVSResources() {
    * Allocates the configured compression parameters in the MemorySegment.
    */
   private MemorySegment segmentFromCompressionParams(CagraCompressionParams params) {
-    MemorySegment seg = CuVSCagraCompressionParams.allocate(resources.getArena());
-    CuVSCagraCompressionParams.pq_bits(seg, params.getPqBits());
-    CuVSCagraCompressionParams.pq_dim(seg, params.getPqDim());
-    CuVSCagraCompressionParams.vq_n_centers(seg, params.getVqNCenters());
-    CuVSCagraCompressionParams.kmeans_n_iters(seg, params.getKmeansNIters());
-    CuVSCagraCompressionParams.vq_kmeans_trainset_fraction(seg, params.getVqKmeansTrainsetFraction());
-    CuVSCagraCompressionParams.pq_kmeans_trainset_fraction(seg, params.getPqKmeansTrainsetFraction());
+    MemorySegment seg = cuvsCagraCompressionParams.allocate(resources.getArena());
+    cuvsCagraCompressionParams.pq_bits(seg, params.getPqBits());
+    cuvsCagraCompressionParams.pq_dim(seg, params.getPqDim());
+    cuvsCagraCompressionParams.vq_n_centers(seg, params.getVqNCenters());
+    cuvsCagraCompressionParams.kmeans_n_iters(seg, params.getKmeansNIters());
+    cuvsCagraCompressionParams.vq_kmeans_trainset_fraction(seg, params.getVqKmeansTrainsetFraction());
+    cuvsCagraCompressionParams.pq_kmeans_trainset_fraction(seg, params.getPqKmeansTrainsetFraction());
     return seg;
   }
 
@@ -388,12 +392,44 @@ private MemorySegment segmentFromCompressionParams(CagraCompressionParams params
    * Allocates the configured index parameters in the MemorySegment.
    */
   private MemorySegment segmentFromIndexParams(CagraIndexParams params) {
-    MemorySegment seg = CuVSCagraIndexParams.allocate(resources.getArena());
-    CuVSCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
-    CuVSCagraIndexParams.graph_degree(seg, params.getGraphDegree());
-    CuVSCagraIndexParams.build_algo(seg, params.getCagraGraphBuildAlgo().value);
-    CuVSCagraIndexParams.nn_descent_niter(seg, params.getNNDescentNumIterations());
-    CuVSCagraIndexParams.metric(seg, params.getCuvsDistanceType().value);
+    MemorySegment seg = cuvsCagraIndexParams.allocate(resources.getArena());
+    cuvsCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
+    cuvsCagraIndexParams.graph_degree(seg, params.getGraphDegree());
+    cuvsCagraIndexParams.build_algo(seg, params.getCagraGraphBuildAlgo().value);
+    cuvsCagraIndexParams.nn_descent_niter(seg, params.getNNDescentNumIterations());
+    cuvsCagraIndexParams.metric(seg, params.getCuvsDistanceType().value);
+
+
+    if (params.getCagraGraphBuildAlgo().equals(CagraGraphBuildAlgo.IVF_PQ)) {
+
+      MemorySegment ivfpqIndexParamsMemorySegment = cuvsIvfPqIndexParams.allocate(resources.getArena());
+      cuvsIvfPqIndexParams.metric(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getMetric().value);
+      cuvsIvfPqIndexParams.metric_arg(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getMetricArg());
+      cuvsIvfPqIndexParams.add_data_on_build(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().isAddDataOnBuild());
+      cuvsIvfPqIndexParams.n_lists(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getnLists());
+      cuvsIvfPqIndexParams.kmeans_n_iters(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getKmeansNIters());
+      cuvsIvfPqIndexParams.kmeans_trainset_fraction(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getKmeansTrainsetFraction());
+      cuvsIvfPqIndexParams.pq_bits(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getPqBits());
+      cuvsIvfPqIndexParams.pq_dim(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getPqDim());
+      cuvsIvfPqIndexParams.codebook_kind(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getCodebookKind().value);
+      cuvsIvfPqIndexParams.force_random_rotation(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().isForceRandomRotation());
+      cuvsIvfPqIndexParams.conservative_memory_allocation(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().isConservativeMemoryAllocation());
+      cuvsIvfPqIndexParams.max_train_points_per_pq_code(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getMaxTrainPointsPerPqCode());
+
+      MemorySegment ivfpqSearchParamsMemorySegment = cuvsIvfPqSearchParams.allocate(resources.getArena());
+      cuvsIvfPqSearchParams.n_probes(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getnProbes());
+      cuvsIvfPqSearchParams.lut_dtype(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getLutDtype().value);
+      cuvsIvfPqSearchParams.internal_distance_dtype(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getInternalDistanceDtype().value);
+      cuvsIvfPqSearchParams.preferred_shmem_carveout(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getPreferredShmemCarveout());
+
+      MemorySegment cuvsIvfPqParamsMemorySegment = cuvsIvfPqParams.allocate(resources.getArena());
+      cuvsIvfPqParams.ivf_pq_build_params(cuvsIvfPqParamsMemorySegment, ivfpqIndexParamsMemorySegment);
+      cuvsIvfPqParams.ivf_pq_search_params(cuvsIvfPqParamsMemorySegment, ivfpqSearchParamsMemorySegment);
+      cuvsIvfPqParams.refinement_rate(cuvsIvfPqParamsMemorySegment, params.getCuVSIvfPqParams().getRefinementRate());
+
+      cuvsCagraIndexParams.graph_build_params(seg, cuvsIvfPqParamsMemorySegment);
+    }
+
     return seg;
   }
 
@@ -401,23 +437,23 @@ private MemorySegment segmentFromIndexParams(CagraIndexParams params) {
    * Allocates the configured search parameters in the MemorySegment.
    */
   private MemorySegment segmentFromSearchParams(CagraSearchParams params) {
-    MemorySegment seg = CuVSCagraSearchParams.allocate(resources.getArena());
-    CuVSCagraSearchParams.max_queries(seg, params.getMaxQueries());
-    CuVSCagraSearchParams.itopk_size(seg, params.getITopKSize());
-    CuVSCagraSearchParams.max_iterations(seg, params.getMaxIterations());
+    MemorySegment seg = cuvsCagraSearchParams.allocate(resources.getArena());
+    cuvsCagraSearchParams.max_queries(seg, params.getMaxQueries());
+    cuvsCagraSearchParams.itopk_size(seg, params.getITopKSize());
+    cuvsCagraSearchParams.max_iterations(seg, params.getMaxIterations());
     if (params.getCagraSearchAlgo() != null) {
-      CuVSCagraSearchParams.algo(seg, params.getCagraSearchAlgo().value);
+      cuvsCagraSearchParams.algo(seg, params.getCagraSearchAlgo().value);
     }
-    CuVSCagraSearchParams.team_size(seg, params.getTeamSize());
-    CuVSCagraSearchParams.search_width(seg, params.getSearchWidth());
-    CuVSCagraSearchParams.min_iterations(seg, params.getMinIterations());
-    CuVSCagraSearchParams.thread_block_size(seg, params.getThreadBlockSize());
+    cuvsCagraSearchParams.team_size(seg, params.getTeamSize());
+    cuvsCagraSearchParams.search_width(seg, params.getSearchWidth());
+    cuvsCagraSearchParams.min_iterations(seg, params.getMinIterations());
+    cuvsCagraSearchParams.thread_block_size(seg, params.getThreadBlockSize());
     if (params.getHashMapMode() != null) {
-      CuVSCagraSearchParams.hashmap_mode(seg, params.getHashMapMode().value);
+      cuvsCagraSearchParams.hashmap_mode(seg, params.getHashMapMode().value);
     }
-    CuVSCagraSearchParams.hashmap_max_fill_rate(seg, params.getHashMapMaxFillRate());
-    CuVSCagraSearchParams.num_random_samplings(seg, params.getNumRandomSamplings());
-    CuVSCagraSearchParams.rand_xor_mask(seg, params.getRandXORMask());
+    cuvsCagraSearchParams.hashmap_max_fill_rate(seg, params.getHashMapMaxFillRate());
+    cuvsCagraSearchParams.num_random_samplings(seg, params.getNumRandomSamplings());
+    cuvsCagraSearchParams.rand_xor_mask(seg, params.getRandXORMask());
     return seg;
   }
 
@@ -489,7 +525,7 @@ protected static class IndexReference {
      * Constructs CagraIndexReference and allocate the MemorySegment.
      */
     protected IndexReference(CuVSResourcesImpl resources) {
-      memorySegment = CuVSCagraIndex.allocate(resources.getArena());
+      memorySegment = cuvsCagraIndex.allocate(resources.getArena());
     }
 
     /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index bb9553e255..97dab9f27a 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -16,6 +16,13 @@
 
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.common.Util.checkError;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
 import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.foreign.Arena;
@@ -36,16 +43,9 @@
 import com.nvidia.cuvs.HnswSearchParams;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
-import com.nvidia.cuvs.internal.panama.CuVSHnswIndex;
-import com.nvidia.cuvs.internal.panama.CuVSHnswIndexParams;
-import com.nvidia.cuvs.internal.panama.CuVSHnswSearchParams;
-
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static com.nvidia.cuvs.internal.common.Util.checkError;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import com.nvidia.cuvs.internal.panama.cuvsHnswIndex;
+import com.nvidia.cuvs.internal.panama.cuvsHnswIndexParams;
+import com.nvidia.cuvs.internal.panama.cuvsHnswSearchParams;
 
 /**
  * {@link HnswIndex} encapsulates a HNSW index, along with methods to interact
@@ -187,9 +187,9 @@ private IndexReference deserialize(InputStream inputStream, int bufferLength) th
    * Allocates the configured search parameters in the MemorySegment.
    */
   private MemorySegment segmentFromIndexParams(HnswIndexParams params) {
-    MemorySegment seg = CuVSHnswIndexParams.allocate(resources.getArena());
-    CuVSHnswIndexParams.ef_construction(seg, params.getEfConstruction());
-    CuVSHnswIndexParams.num_threads(seg, params.getNumThreads());
+    MemorySegment seg = cuvsHnswIndexParams.allocate(resources.getArena());
+    cuvsHnswIndexParams.ef_construction(seg, params.getEfConstruction());
+    cuvsHnswIndexParams.num_threads(seg, params.getNumThreads());
     return seg;
   }
 
@@ -197,9 +197,9 @@ private MemorySegment segmentFromIndexParams(HnswIndexParams params) {
    * Allocates the configured search parameters in the MemorySegment.
    */
   private MemorySegment segmentFromSearchParams(HnswSearchParams params) {
-    MemorySegment seg = CuVSHnswSearchParams.allocate(resources.getArena());
-    CuVSHnswSearchParams.ef(seg, params.ef());
-    CuVSHnswSearchParams.num_threads(seg, params.numThreads());
+    MemorySegment seg = cuvsHnswSearchParams.allocate(resources.getArena());
+    cuvsHnswSearchParams.ef(seg, params.ef());
+    cuvsHnswSearchParams.num_threads(seg, params.numThreads());
     return seg;
   }
 
@@ -277,7 +277,7 @@ protected static class IndexReference {
      * Constructs CagraIndexReference and allocate the MemorySegment.
      */
     protected IndexReference(CuVSResourcesImpl resources) {
-      memorySegment = CuVSHnswIndex.allocate(resources.getArena());
+      memorySegment = cuvsHnswIndex.allocate(resources.getArena());
     }
 
     /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index bc8f3bbcc4..ddf1ea4d7e 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -16,6 +16,13 @@
 
 package com.nvidia.cuvs.internal.common;
 
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static java.lang.foreign.ValueLayout.ADDRESS;
+
 import java.lang.foreign.Arena;
 import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.MemoryLayout;
@@ -25,36 +32,29 @@
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.VarHandle;
 import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.BitSet;
 import java.util.List;
 
 import com.nvidia.cuvs.GPUInfo;
-import com.nvidia.cuvs.internal.panama.GpuInfo;
-
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import com.nvidia.cuvs.internal.panama.gpuInfo;
 
 public class Util {
 
   public static final int CUVS_SUCCESS = 1;
 
   private static final MethodHandle getGpuInfoMethodHandle = downcallHandle("get_gpu_info",
-          FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS));
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS));
 
   private static final MethodHandle getLastErrorTextMethodHandle = downcallHandle("cuvsGetLastErrorText",
-          FunctionDescriptor.of(ADDRESS));
+      FunctionDescriptor.of(ADDRESS));
 
-  private Util() {}
+  private Util() {
+  }
 
   /**
    * Checks the result value of a native method handle call.
    *
-   * @param value the return value
+   * @param value  the return value
    * @param caller the native method handle that was called
    */
   public static void checkError(int value, String caller) {
@@ -123,31 +123,31 @@ public static List<GPUInfo> availableGPUs() throws Throwable {
        * before the function is invoked as cudaGetDeviceCount is inside the
        * get_gpu_info function.
        */
-      MemorySegment GpuInfoArrayMemorySegment = GpuInfo.allocateArray(1024, localArena);
+      MemorySegment GpuInfoArrayMemorySegment = gpuInfo.allocateArray(1024, localArena);
       getGpuInfoMethodHandle.invokeExact(returnValueMemorySegment, numGpuMemorySegment, GpuInfoArrayMemorySegment);
       int numGPUs = numGpuMemorySegment.get(ValueLayout.JAVA_INT, 0);
-      MemoryLayout ml = MemoryLayout.sequenceLayout(numGPUs, GpuInfo.layout());
+      MemoryLayout ml = MemoryLayout.sequenceLayout(numGPUs, gpuInfo.layout());
       for (int i = 0; i < numGPUs; i++) {
         VarHandle gpuIdVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("gpu_id"));
         VarHandle freeMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-                PathElement.groupElement("free_memory"));
+            PathElement.groupElement("free_memory"));
         VarHandle totalMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-                PathElement.groupElement("total_memory"));
+            PathElement.groupElement("total_memory"));
         VarHandle ComputeCapabilityVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-                PathElement.groupElement("compute_capability"));
+            PathElement.groupElement("compute_capability"));
         StringBuilder gpuName = new StringBuilder();
         char b = 1;
         int p = 0;
         while (b != 0x00) {
           VarHandle gpuNameVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("name"),
-                  PathElement.sequenceElement(p++));
+              PathElement.sequenceElement(p++));
           b = (char) (byte) gpuNameVarHandle.get(GpuInfoArrayMemorySegment, 0L);
           gpuName.append(b);
         }
         results.add(new GPUInfo((int) gpuIdVarHandle.get(GpuInfoArrayMemorySegment, 0L), gpuName.toString().trim(),
-                (long) freeMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
-                (long) totalMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
-                (float) ComputeCapabilityVarHandle.get(GpuInfoArrayMemorySegment, 0L)));
+            (long) freeMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
+            (long) totalMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
+            (float) ComputeCapabilityVarHandle.get(GpuInfoArrayMemorySegment, 0L)));
       }
       return results;
     }
@@ -206,8 +206,7 @@ public static MemorySegment buildMemorySegment(Arena arena, float[][] data) {
     MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(rows * cols, C_FLOAT);
     MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
     for (int r = 0; r < rows; r++) {
-      MemorySegment.copy(data[r], 0, dataMemorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()),
-          (int) cols);
+      MemorySegment.copy(data[r], 0, dataMemorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()), (int) cols);
     }
     return dataMemorySegment;
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/BruteForceH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/BruteForceH.java
deleted file mode 100644
index 0e34eb2cff..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/BruteForceH.java
+++ /dev/null
@@ -1,1912 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.ValueLayout.JAVA_BYTE;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.PaddingLayout;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.StructLayout;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.foreign.ValueLayout.OfByte;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.foreign.ValueLayout.OfShort;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-public class BruteForceH {
-
-  BruteForceH() {
-    // Should not be called directly
-  }
-
-  static final Arena LIBRARY_ARENA = Arena.ofAuto();
-  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-  static void traceDowncall(String name, Object... args) {
-    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
-    System.out.printf("%s(%s)\n", name, traceArgs);
-  }
-
-  static MemorySegment findOrThrow(String symbol) {
-    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-  }
-
-  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-    try {
-      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-    } catch (ReflectiveOperationException ex) {
-      throw new AssertionError(ex);
-    }
-  }
-
-  static MemoryLayout align(MemoryLayout layout, long align) {
-    return switch (layout) {
-    case PaddingLayout p -> p;
-    case ValueLayout v -> v.withByteAlignment(align);
-    case GroupLayout g -> {
-      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
-          : MemoryLayout.unionLayout(alignedMembers);
-    }
-    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-    };
-  }
-
-  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
-
-  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-  private static final int DLPACK_VERSION = (int) 80L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_VERSION 80
-   * }
-   */
-  public static int DLPACK_VERSION() {
-    return DLPACK_VERSION;
-  }
-
-  private static final int DLPACK_ABI_VERSION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_ABI_VERSION 1
-   * }
-   */
-  public static int DLPACK_ABI_VERSION() {
-    return DLPACK_ABI_VERSION;
-  }
-
-  private static final int _STDINT_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDINT_H 1
-   * }
-   */
-  public static int _STDINT_H() {
-    return _STDINT_H;
-  }
-
-  private static final int _FEATURES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _FEATURES_H 1
-   * }
-   */
-  public static int _FEATURES_H() {
-    return _FEATURES_H;
-  }
-
-  private static final int _DEFAULT_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
-   * }
-   */
-  public static int _DEFAULT_SOURCE() {
-    return _DEFAULT_SOURCE;
-  }
-
-  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
-   * }
-   */
-  public static int __GLIBC_USE_ISOC2X() {
-    return __GLIBC_USE_ISOC2X;
-  }
-
-  private static final int __USE_ISOC11 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC11 1
-   * }
-   */
-  public static int __USE_ISOC11() {
-    return __USE_ISOC11;
-  }
-
-  private static final int __USE_ISOC99 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC99 1
-   * }
-   */
-  public static int __USE_ISOC99() {
-    return __USE_ISOC99;
-  }
-
-  private static final int __USE_ISOC95 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC95 1
-   * }
-   */
-  public static int __USE_ISOC95() {
-    return __USE_ISOC95;
-  }
-
-  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
-   * }
-   */
-  public static int __USE_POSIX_IMPLICITLY() {
-    return __USE_POSIX_IMPLICITLY;
-  }
-
-  private static final int _POSIX_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_SOURCE 1
-   * }
-   */
-  public static int _POSIX_SOURCE() {
-    return _POSIX_SOURCE;
-  }
-
-  private static final int __USE_POSIX = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX 1
-   * }
-   */
-  public static int __USE_POSIX() {
-    return __USE_POSIX;
-  }
-
-  private static final int __USE_POSIX2 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX2 1
-   * }
-   */
-  public static int __USE_POSIX2() {
-    return __USE_POSIX2;
-  }
-
-  private static final int __USE_POSIX199309 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199309 1
-   * }
-   */
-  public static int __USE_POSIX199309() {
-    return __USE_POSIX199309;
-  }
-
-  private static final int __USE_POSIX199506 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199506 1
-   * }
-   */
-  public static int __USE_POSIX199506() {
-    return __USE_POSIX199506;
-  }
-
-  private static final int __USE_XOPEN2K = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K 1
-   * }
-   */
-  public static int __USE_XOPEN2K() {
-    return __USE_XOPEN2K;
-  }
-
-  private static final int __USE_XOPEN2K8 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
-   * }
-   */
-  public static int __USE_XOPEN2K8() {
-    return __USE_XOPEN2K8;
-  }
-
-  private static final int _ATFILE_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
-   * }
-   */
-  public static int _ATFILE_SOURCE() {
-    return _ATFILE_SOURCE;
-  }
-
-  private static final int __WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE 64
-   * }
-   */
-  public static int __WORDSIZE() {
-    return __WORDSIZE;
-  }
-
-  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
-   * }
-   */
-  public static int __WORDSIZE_TIME64_COMPAT32() {
-    return __WORDSIZE_TIME64_COMPAT32;
-  }
-
-  private static final int __SYSCALL_WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
-   * }
-   */
-  public static int __SYSCALL_WORDSIZE() {
-    return __SYSCALL_WORDSIZE;
-  }
-
-  private static final int __USE_MISC = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_MISC 1
-   * }
-   */
-  public static int __USE_MISC() {
-    return __USE_MISC;
-  }
-
-  private static final int __USE_ATFILE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ATFILE 1
-   * }
-   */
-  public static int __USE_ATFILE() {
-    return __USE_ATFILE;
-  }
-
-  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
-   * }
-   */
-  public static int __USE_FORTIFY_LEVEL() {
-    return __USE_FORTIFY_LEVEL;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_GETS() {
-    return __GLIBC_USE_DEPRECATED_GETS;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_SCANF() {
-    return __GLIBC_USE_DEPRECATED_SCANF;
-  }
-
-  private static final int _STDC_PREDEF_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
-   * }
-   */
-  public static int _STDC_PREDEF_H() {
-    return _STDC_PREDEF_H;
-  }
-
-  private static final int __STDC_IEC_559__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
-   * }
-   */
-  public static int __STDC_IEC_559__() {
-    return __STDC_IEC_559__;
-  }
-
-  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
-   * }
-   */
-  public static int __STDC_IEC_559_COMPLEX__() {
-    return __STDC_IEC_559_COMPLEX__;
-  }
-
-  private static final int __GNU_LIBRARY__ = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
-   * }
-   */
-  public static int __GNU_LIBRARY__() {
-    return __GNU_LIBRARY__;
-  }
-
-  private static final int __GLIBC__ = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC__ 2
-   * }
-   */
-  public static int __GLIBC__() {
-    return __GLIBC__;
-  }
-
-  private static final int __GLIBC_MINOR__ = (int) 35L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
-   * }
-   */
-  public static int __GLIBC_MINOR__() {
-    return __GLIBC_MINOR__;
-  }
-
-  private static final int _SYS_CDEFS_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
-   * }
-   */
-  public static int _SYS_CDEFS_H() {
-    return _SYS_CDEFS_H;
-  }
-
-  private static final int __glibc_c99_flexarr_available = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
-   * }
-   */
-  public static int __glibc_c99_flexarr_available() {
-    return __glibc_c99_flexarr_available;
-  }
-
-  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
-   * }
-   */
-  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
-    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
-  }
-
-  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
-   * }
-   */
-  public static int __HAVE_GENERIC_SELECTION() {
-    return __HAVE_GENERIC_SELECTION;
-  }
-
-  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
-   * }
-   */
-  public static int __GLIBC_USE_LIB_EXT2() {
-    return __GLIBC_USE_LIB_EXT2;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_EXT() {
-    return __GLIBC_USE_IEC_60559_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
-    return __GLIBC_USE_IEC_60559_TYPES_EXT;
-  }
-
-  private static final int _BITS_TYPES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPES_H 1
-   * }
-   */
-  public static int _BITS_TYPES_H() {
-    return _BITS_TYPES_H;
-  }
-
-  private static final int _BITS_TYPESIZES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
-   * }
-   */
-  public static int _BITS_TYPESIZES_H() {
-    return _BITS_TYPESIZES_H;
-  }
-
-  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
-   * }
-   */
-  public static int __OFF_T_MATCHES_OFF64_T() {
-    return __OFF_T_MATCHES_OFF64_T;
-  }
-
-  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
-   * }
-   */
-  public static int __INO_T_MATCHES_INO64_T() {
-    return __INO_T_MATCHES_INO64_T;
-  }
-
-  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
-   * }
-   */
-  public static int __RLIM_T_MATCHES_RLIM64_T() {
-    return __RLIM_T_MATCHES_RLIM64_T;
-  }
-
-  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
-   * }
-   */
-  public static int __STATFS_MATCHES_STATFS64() {
-    return __STATFS_MATCHES_STATFS64;
-  }
-
-  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
-   * }
-   */
-  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
-    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
-  }
-
-  private static final int __FD_SETSIZE = (int) 1024L;
-
-  /**
-   * {@snippet lang = c : * #define __FD_SETSIZE 1024
-   * }
-   */
-  public static int __FD_SETSIZE() {
-    return __FD_SETSIZE;
-  }
-
-  private static final int _BITS_TIME64_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TIME64_H 1
-   * }
-   */
-  public static int _BITS_TIME64_H() {
-    return _BITS_TIME64_H;
-  }
-
-  private static final int _BITS_WCHAR_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
-   * }
-   */
-  public static int _BITS_WCHAR_H() {
-    return _BITS_WCHAR_H;
-  }
-
-  private static final int _BITS_STDINT_INTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_INTN_H() {
-    return _BITS_STDINT_INTN_H;
-  }
-
-  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_UINTN_H() {
-    return _BITS_STDINT_UINTN_H;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef unsigned char __u_char
-   * }
-   */
-  public static final OfByte __u_char = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __u_short
-   * }
-   */
-  public static final OfShort __u_short = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __u_int
-   * }
-   */
-  public static final OfInt __u_int = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_long
-   * }
-   */
-  public static final OfLong __u_long = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char __int8_t
-   * }
-   */
-  public static final OfByte __int8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned char __uint8_t
-   * }
-   */
-  public static final OfByte __uint8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef short __int16_t
-   * }
-   */
-  public static final OfShort __int16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __uint16_t
-   * }
-   */
-  public static final OfShort __uint16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef int __int32_t
-   * }
-   */
-  public static final OfInt __int32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uint32_t
-   * }
-   */
-  public static final OfInt __uint32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __int64_t
-   * }
-   */
-  public static final OfLong __int64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uint64_t
-   * }
-   */
-  public static final OfLong __uint64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int8_t __int_least8_t
-   * }
-   */
-  public static final OfByte __int_least8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
-   * }
-   */
-  public static final OfByte __uint_least8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t __int_least16_t
-   * }
-   */
-  public static final OfShort __int_least16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
-   * }
-   */
-  public static final OfShort __uint_least16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t __int_least32_t
-   * }
-   */
-  public static final OfInt __int_least32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
-   * }
-   */
-  public static final OfInt __uint_least32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t __int_least64_t
-   * }
-   */
-  public static final OfLong __int_least64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
-   * }
-   */
-  public static final OfLong __uint_least64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __quad_t
-   * }
-   */
-  public static final OfLong __quad_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_quad_t
-   * }
-   */
-  public static final OfLong __u_quad_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __intmax_t
-   * }
-   */
-  public static final OfLong __intmax_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uintmax_t
-   * }
-   */
-  public static final OfLong __uintmax_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __dev_t
-   * }
-   */
-  public static final OfLong __dev_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uid_t
-   * }
-   */
-  public static final OfInt __uid_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __gid_t
-   * }
-   */
-  public static final OfInt __gid_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino_t
-   * }
-   */
-  public static final OfLong __ino_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino64_t
-   * }
-   */
-  public static final OfLong __ino64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __mode_t
-   * }
-   */
-  public static final OfInt __mode_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __nlink_t
-   * }
-   */
-  public static final OfLong __nlink_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off_t
-   * }
-   */
-  public static final OfLong __off_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off64_t
-   * }
-   */
-  public static final OfLong __off64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __pid_t
-   * }
-   */
-  public static final OfInt __pid_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __clock_t
-   * }
-   */
-  public static final OfLong __clock_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim_t
-   * }
-   */
-  public static final OfLong __rlim_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim64_t
-   * }
-   */
-  public static final OfLong __rlim64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __id_t
-   * }
-   */
-  public static final OfInt __id_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __time_t
-   * }
-   */
-  public static final OfLong __time_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __useconds_t
-   * }
-   */
-  public static final OfInt __useconds_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds_t
-   * }
-   */
-  public static final OfLong __suseconds_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds64_t
-   * }
-   */
-  public static final OfLong __suseconds64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __daddr_t
-   * }
-   */
-  public static final OfInt __daddr_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __key_t
-   * }
-   */
-  public static final OfInt __key_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __clockid_t
-   * }
-   */
-  public static final OfInt __clockid_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef void *__timer_t
-   * }
-   */
-  public static final AddressLayout __timer_t = BruteForceH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __blksize_t
-   * }
-   */
-  public static final OfLong __blksize_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt_t
-   * }
-   */
-  public static final OfLong __blkcnt_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt64_t
-   * }
-   */
-  public static final OfLong __blkcnt64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
-   * }
-   */
-  public static final OfLong __fsblkcnt_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
-   * }
-   */
-  public static final OfLong __fsblkcnt64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
-   * }
-   */
-  public static final OfLong __fsfilcnt_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
-   * }
-   */
-  public static final OfLong __fsfilcnt64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __fsword_t
-   * }
-   */
-  public static final OfLong __fsword_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __ssize_t
-   * }
-   */
-  public static final OfLong __ssize_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __syscall_slong_t
-   * }
-   */
-  public static final OfLong __syscall_slong_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
-   * }
-   */
-  public static final OfLong __syscall_ulong_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __off64_t __loff_t
-   * }
-   */
-  public static final OfLong __loff_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef char *__caddr_t
-   * }
-   */
-  public static final AddressLayout __caddr_t = BruteForceH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __intptr_t
-   * }
-   */
-  public static final OfLong __intptr_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __socklen_t
-   * }
-   */
-  public static final OfInt __socklen_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __sig_atomic_t
-   * }
-   */
-  public static final OfInt __sig_atomic_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int8_t int8_t
-   * }
-   */
-  public static final OfByte int8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t int16_t
-   * }
-   */
-  public static final OfShort int16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t int32_t
-   * }
-   */
-  public static final OfInt int32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t int64_t
-   * }
-   */
-  public static final OfLong int64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t uint8_t
-   * }
-   */
-  public static final OfByte uint8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t uint16_t
-   * }
-   */
-  public static final OfShort uint16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t uint32_t
-   * }
-   */
-  public static final OfInt uint32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t uint64_t
-   * }
-   */
-  public static final OfLong uint64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
-   * }
-   */
-  public static final OfByte int_least8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
-   * }
-   */
-  public static final OfShort int_least16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
-   * }
-   */
-  public static final OfInt int_least32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
-   * }
-   */
-  public static final OfLong int_least64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
-   * }
-   */
-  public static final OfByte uint_least8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
-   * }
-   */
-  public static final OfShort uint_least16_t = BruteForceH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
-   * }
-   */
-  public static final OfInt uint_least32_t = BruteForceH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
-   * }
-   */
-  public static final OfLong uint_least64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char int_fast8_t
-   * }
-   */
-  public static final OfByte int_fast8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef long int_fast16_t
-   * }
-   */
-  public static final OfLong int_fast16_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast32_t
-   * }
-   */
-  public static final OfLong int_fast32_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast64_t
-   * }
-   */
-  public static final OfLong int_fast64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
-   * }
-   */
-  public static final OfByte uint_fast8_t = BruteForceH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
-   * }
-   */
-  public static final OfLong uint_fast16_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
-   * }
-   */
-  public static final OfLong uint_fast32_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
-   * }
-   */
-  public static final OfLong uint_fast64_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long intptr_t
-   * }
-   */
-  public static final OfLong intptr_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uintptr_t
-   * }
-   */
-  public static final OfLong uintptr_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __intmax_t intmax_t
-   * }
-   */
-  public static final OfLong intmax_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
-   * }
-   */
-  public static final OfLong uintmax_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long ptrdiff_t
-   * }
-   */
-  public static final OfLong ptrdiff_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long size_t
-   * }
-   */
-  public static final OfLong size_t = BruteForceH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int wchar_t
-   * }
-   */
-  public static final OfInt wchar_t = BruteForceH.C_INT;
-  private static final int kDLCPU = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
-   * }
-   */
-  public static int kDLCPU() {
-    return kDLCPU;
-  }
-
-  private static final int kDLCUDA = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
-   * }
-   */
-  public static int kDLCUDA() {
-    return kDLCUDA;
-  }
-
-  private static final int kDLCUDAHost = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
-   * }
-   */
-  public static int kDLCUDAHost() {
-    return kDLCUDAHost;
-  }
-
-  private static final int kDLOpenCL = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
-   * }
-   */
-  public static int kDLOpenCL() {
-    return kDLOpenCL;
-  }
-
-  private static final int kDLVulkan = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
-   * }
-   */
-  public static int kDLVulkan() {
-    return kDLVulkan;
-  }
-
-  private static final int kDLMetal = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
-   * }
-   */
-  public static int kDLMetal() {
-    return kDLMetal;
-  }
-
-  private static final int kDLVPI = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
-   * }
-   */
-  public static int kDLVPI() {
-    return kDLVPI;
-  }
-
-  private static final int kDLROCM = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
-   * }
-   */
-  public static int kDLROCM() {
-    return kDLROCM;
-  }
-
-  private static final int kDLROCMHost = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
-   * }
-   */
-  public static int kDLROCMHost() {
-    return kDLROCMHost;
-  }
-
-  private static final int kDLExtDev = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
-   * }
-   */
-  public static int kDLExtDev() {
-    return kDLExtDev;
-  }
-
-  private static final int kDLCUDAManaged = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
-   * }
-   */
-  public static int kDLCUDAManaged() {
-    return kDLCUDAManaged;
-  }
-
-  private static final int kDLOneAPI = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
-   * }
-   */
-  public static int kDLOneAPI() {
-    return kDLOneAPI;
-  }
-
-  private static final int kDLWebGPU = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
-   * }
-   */
-  public static int kDLWebGPU() {
-    return kDLWebGPU;
-  }
-
-  private static final int kDLHexagon = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
-   * }
-   */
-  public static int kDLHexagon() {
-    return kDLHexagon;
-  }
-
-  private static final int kDLInt = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
-   * }
-   */
-  public static int kDLInt() {
-    return kDLInt;
-  }
-
-  private static final int kDLUInt = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
-   * }
-   */
-  public static int kDLUInt() {
-    return kDLUInt;
-  }
-
-  private static final int kDLFloat = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
-   * }
-   */
-  public static int kDLFloat() {
-    return kDLFloat;
-  }
-
-  private static final int kDLOpaqueHandle = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
-   * }
-   */
-  public static int kDLOpaqueHandle() {
-    return kDLOpaqueHandle;
-  }
-
-  private static final int kDLBfloat = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
-   * }
-   */
-  public static int kDLBfloat() {
-    return kDLBfloat;
-  }
-
-  private static final int kDLComplex = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
-   * }
-   */
-  public static int kDLComplex() {
-    return kDLComplex;
-  }
-
-  private static final int kDLBool = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
-   * }
-   */
-  public static int kDLBool() {
-    return kDLBool;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef cuvsBruteForceIndex *cuvsBruteForceIndex_t
-   * }
-   */
-  public static final AddressLayout cuvsBruteForceIndex_t = BruteForceH.C_POINTER;
-  private static final long _POSIX_C_SOURCE = 200809L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
-   * }
-   */
-  public static long _POSIX_C_SOURCE() {
-    return _POSIX_C_SOURCE;
-  }
-
-  private static final int __TIMESIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __TIMESIZE 64
-   * }
-   */
-  public static int __TIMESIZE() {
-    return __TIMESIZE;
-  }
-
-  private static final long __STDC_IEC_60559_BFP__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_BFP__() {
-    return __STDC_IEC_60559_BFP__;
-  }
-
-  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_COMPLEX__() {
-    return __STDC_IEC_60559_COMPLEX__;
-  }
-
-  private static final long __STDC_ISO_10646__ = 201706L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
-   * }
-   */
-  public static long __STDC_ISO_10646__() {
-    return __STDC_ISO_10646__;
-  }
-
-  private static final int __WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
-   * }
-   */
-  public static int __WCHAR_MAX() {
-    return __WCHAR_MAX;
-  }
-
-  private static final int __WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
-   * }
-   */
-  public static int __WCHAR_MIN() {
-    return __WCHAR_MIN;
-  }
-
-  private static final int INT8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MIN -128
-   * }
-   */
-  public static int INT8_MIN() {
-    return INT8_MIN;
-  }
-
-  private static final int INT16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MIN -32768
-   * }
-   */
-  public static int INT16_MIN() {
-    return INT16_MIN;
-  }
-
-  private static final int INT32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MIN -2147483648
-   * }
-   */
-  public static int INT32_MIN() {
-    return INT32_MIN;
-  }
-
-  private static final long INT64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT64_MIN() {
-    return INT64_MIN;
-  }
-
-  private static final int INT8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MAX 127
-   * }
-   */
-  public static int INT8_MAX() {
-    return INT8_MAX;
-  }
-
-  private static final int INT16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MAX 32767
-   * }
-   */
-  public static int INT16_MAX() {
-    return INT16_MAX;
-  }
-
-  private static final int INT32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MAX 2147483647
-   * }
-   */
-  public static int INT32_MAX() {
-    return INT32_MAX;
-  }
-
-  private static final long INT64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT64_MAX() {
-    return INT64_MAX;
-  }
-
-  private static final int UINT8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT8_MAX 255
-   * }
-   */
-  public static int UINT8_MAX() {
-    return UINT8_MAX;
-  }
-
-  private static final int UINT16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT16_MAX 65535
-   * }
-   */
-  public static int UINT16_MAX() {
-    return UINT16_MAX;
-  }
-
-  private static final int UINT32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT32_MAX 4294967295
-   * }
-   */
-  public static int UINT32_MAX() {
-    return UINT32_MAX;
-  }
-
-  private static final long UINT64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT64_MAX -1
-   * }
-   */
-  public static long UINT64_MAX() {
-    return UINT64_MAX;
-  }
-
-  private static final int INT_LEAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
-   * }
-   */
-  public static int INT_LEAST8_MIN() {
-    return INT_LEAST8_MIN;
-  }
-
-  private static final int INT_LEAST16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
-   * }
-   */
-  public static int INT_LEAST16_MIN() {
-    return INT_LEAST16_MIN;
-  }
-
-  private static final int INT_LEAST32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
-   * }
-   */
-  public static int INT_LEAST32_MIN() {
-    return INT_LEAST32_MIN;
-  }
-
-  private static final long INT_LEAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_LEAST64_MIN() {
-    return INT_LEAST64_MIN;
-  }
-
-  private static final int INT_LEAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
-   * }
-   */
-  public static int INT_LEAST8_MAX() {
-    return INT_LEAST8_MAX;
-  }
-
-  private static final int INT_LEAST16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
-   * }
-   */
-  public static int INT_LEAST16_MAX() {
-    return INT_LEAST16_MAX;
-  }
-
-  private static final int INT_LEAST32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
-   * }
-   */
-  public static int INT_LEAST32_MAX() {
-    return INT_LEAST32_MAX;
-  }
-
-  private static final long INT_LEAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_LEAST64_MAX() {
-    return INT_LEAST64_MAX;
-  }
-
-  private static final int UINT_LEAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
-   * }
-   */
-  public static int UINT_LEAST8_MAX() {
-    return UINT_LEAST8_MAX;
-  }
-
-  private static final int UINT_LEAST16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
-   * }
-   */
-  public static int UINT_LEAST16_MAX() {
-    return UINT_LEAST16_MAX;
-  }
-
-  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
-   * }
-   */
-  public static int UINT_LEAST32_MAX() {
-    return UINT_LEAST32_MAX;
-  }
-
-  private static final long UINT_LEAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
-   * }
-   */
-  public static long UINT_LEAST64_MAX() {
-    return UINT_LEAST64_MAX;
-  }
-
-  private static final int INT_FAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MIN -128
-   * }
-   */
-  public static int INT_FAST8_MIN() {
-    return INT_FAST8_MIN;
-  }
-
-  private static final long INT_FAST16_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST16_MIN() {
-    return INT_FAST16_MIN;
-  }
-
-  private static final long INT_FAST32_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST32_MIN() {
-    return INT_FAST32_MIN;
-  }
-
-  private static final long INT_FAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST64_MIN() {
-    return INT_FAST64_MIN;
-  }
-
-  private static final int INT_FAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MAX 127
-   * }
-   */
-  public static int INT_FAST8_MAX() {
-    return INT_FAST8_MAX;
-  }
-
-  private static final long INT_FAST16_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST16_MAX() {
-    return INT_FAST16_MAX;
-  }
-
-  private static final long INT_FAST32_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST32_MAX() {
-    return INT_FAST32_MAX;
-  }
-
-  private static final long INT_FAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST64_MAX() {
-    return INT_FAST64_MAX;
-  }
-
-  private static final int UINT_FAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
-   * }
-   */
-  public static int UINT_FAST8_MAX() {
-    return UINT_FAST8_MAX;
-  }
-
-  private static final long UINT_FAST16_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
-   * }
-   */
-  public static long UINT_FAST16_MAX() {
-    return UINT_FAST16_MAX;
-  }
-
-  private static final long UINT_FAST32_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
-   * }
-   */
-  public static long UINT_FAST32_MAX() {
-    return UINT_FAST32_MAX;
-  }
-
-  private static final long UINT_FAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
-   * }
-   */
-  public static long UINT_FAST64_MAX() {
-    return UINT_FAST64_MAX;
-  }
-
-  private static final long INTPTR_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
-   * }
-   */
-  public static long INTPTR_MIN() {
-    return INTPTR_MIN;
-  }
-
-  private static final long INTPTR_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
-   * }
-   */
-  public static long INTPTR_MAX() {
-    return INTPTR_MAX;
-  }
-
-  private static final long UINTPTR_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTPTR_MAX -1
-   * }
-   */
-  public static long UINTPTR_MAX() {
-    return UINTPTR_MAX;
-  }
-
-  private static final long INTMAX_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
-   * }
-   */
-  public static long INTMAX_MIN() {
-    return INTMAX_MIN;
-  }
-
-  private static final long INTMAX_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
-   * }
-   */
-  public static long INTMAX_MAX() {
-    return INTMAX_MAX;
-  }
-
-  private static final long UINTMAX_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTMAX_MAX -1
-   * }
-   */
-  public static long UINTMAX_MAX() {
-    return UINTMAX_MAX;
-  }
-
-  private static final long PTRDIFF_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
-   * }
-   */
-  public static long PTRDIFF_MIN() {
-    return PTRDIFF_MIN;
-  }
-
-  private static final long PTRDIFF_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
-   * }
-   */
-  public static long PTRDIFF_MAX() {
-    return PTRDIFF_MAX;
-  }
-
-  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
-   * }
-   */
-  public static int SIG_ATOMIC_MIN() {
-    return SIG_ATOMIC_MIN;
-  }
-
-  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
-   * }
-   */
-  public static int SIG_ATOMIC_MAX() {
-    return SIG_ATOMIC_MAX;
-  }
-
-  private static final long SIZE_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define SIZE_MAX -1
-   * }
-   */
-  public static long SIZE_MAX() {
-    return SIZE_MAX;
-  }
-
-  private static final int WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
-   * }
-   */
-  public static int WCHAR_MIN() {
-    return WCHAR_MIN;
-  }
-
-  private static final int WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
-   * }
-   */
-  public static int WCHAR_MAX() {
-    return WCHAR_MAX;
-  }
-
-  private static final int WINT_MIN = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MIN 0
-   * }
-   */
-  public static int WINT_MIN() {
-    return WINT_MIN;
-  }
-
-  private static final int WINT_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MAX 4294967295
-   * }
-   */
-  public static int WINT_MAX() {
-    return WINT_MAX;
-  }
-
-  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
-
-  /**
-   * {@snippet lang = c : * #define NULL (void*) 0
-   * }
-   */
-  public static MemorySegment NULL() {
-    return NULL;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CagraH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CagraH.java
deleted file mode 100644
index 82ecbdc34a..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CagraH.java
+++ /dev/null
@@ -1,2298 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.ValueLayout.JAVA_BYTE;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.PaddingLayout;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.StructLayout;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.foreign.ValueLayout.OfByte;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.foreign.ValueLayout.OfShort;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-public class CagraH {
-
-  CagraH() {
-    // Should not be called directly
-  }
-
-  static final Arena LIBRARY_ARENA = Arena.ofAuto();
-  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-  static void traceDowncall(String name, Object... args) {
-    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
-    System.out.printf("%s(%s)\n", name, traceArgs);
-  }
-
-  static MemorySegment findOrThrow(String symbol) {
-    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-  }
-
-  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-    try {
-      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-    } catch (ReflectiveOperationException ex) {
-      throw new AssertionError(ex);
-    }
-  }
-
-  static MemoryLayout align(MemoryLayout layout, long align) {
-    return switch (layout) {
-    case PaddingLayout p -> p;
-    case ValueLayout v -> v.withByteAlignment(align);
-    case GroupLayout g -> {
-      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
-          : MemoryLayout.unionLayout(alignedMembers);
-    }
-    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-    };
-  }
-
-  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
-
-  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-  private static final int DLPACK_VERSION = (int) 80L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_VERSION 80
-   * }
-   */
-  public static int DLPACK_VERSION() {
-    return DLPACK_VERSION;
-  }
-
-  private static final int DLPACK_ABI_VERSION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_ABI_VERSION 1
-   * }
-   */
-  public static int DLPACK_ABI_VERSION() {
-    return DLPACK_ABI_VERSION;
-  }
-
-  private static final int _STDINT_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDINT_H 1
-   * }
-   */
-  public static int _STDINT_H() {
-    return _STDINT_H;
-  }
-
-  private static final int _FEATURES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _FEATURES_H 1
-   * }
-   */
-  public static int _FEATURES_H() {
-    return _FEATURES_H;
-  }
-
-  private static final int _DEFAULT_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
-   * }
-   */
-  public static int _DEFAULT_SOURCE() {
-    return _DEFAULT_SOURCE;
-  }
-
-  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
-   * }
-   */
-  public static int __GLIBC_USE_ISOC2X() {
-    return __GLIBC_USE_ISOC2X;
-  }
-
-  private static final int __USE_ISOC11 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC11 1
-   * }
-   */
-  public static int __USE_ISOC11() {
-    return __USE_ISOC11;
-  }
-
-  private static final int __USE_ISOC99 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC99 1
-   * }
-   */
-  public static int __USE_ISOC99() {
-    return __USE_ISOC99;
-  }
-
-  private static final int __USE_ISOC95 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC95 1
-   * }
-   */
-  public static int __USE_ISOC95() {
-    return __USE_ISOC95;
-  }
-
-  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
-   * }
-   */
-  public static int __USE_POSIX_IMPLICITLY() {
-    return __USE_POSIX_IMPLICITLY;
-  }
-
-  private static final int _POSIX_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_SOURCE 1
-   * }
-   */
-  public static int _POSIX_SOURCE() {
-    return _POSIX_SOURCE;
-  }
-
-  private static final int __USE_POSIX = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX 1
-   * }
-   */
-  public static int __USE_POSIX() {
-    return __USE_POSIX;
-  }
-
-  private static final int __USE_POSIX2 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX2 1
-   * }
-   */
-  public static int __USE_POSIX2() {
-    return __USE_POSIX2;
-  }
-
-  private static final int __USE_POSIX199309 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199309 1
-   * }
-   */
-  public static int __USE_POSIX199309() {
-    return __USE_POSIX199309;
-  }
-
-  private static final int __USE_POSIX199506 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199506 1
-   * }
-   */
-  public static int __USE_POSIX199506() {
-    return __USE_POSIX199506;
-  }
-
-  private static final int __USE_XOPEN2K = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K 1
-   * }
-   */
-  public static int __USE_XOPEN2K() {
-    return __USE_XOPEN2K;
-  }
-
-  private static final int __USE_XOPEN2K8 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
-   * }
-   */
-  public static int __USE_XOPEN2K8() {
-    return __USE_XOPEN2K8;
-  }
-
-  private static final int _ATFILE_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
-   * }
-   */
-  public static int _ATFILE_SOURCE() {
-    return _ATFILE_SOURCE;
-  }
-
-  private static final int __WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE 64
-   * }
-   */
-  public static int __WORDSIZE() {
-    return __WORDSIZE;
-  }
-
-  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
-   * }
-   */
-  public static int __WORDSIZE_TIME64_COMPAT32() {
-    return __WORDSIZE_TIME64_COMPAT32;
-  }
-
-  private static final int __SYSCALL_WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
-   * }
-   */
-  public static int __SYSCALL_WORDSIZE() {
-    return __SYSCALL_WORDSIZE;
-  }
-
-  private static final int __USE_MISC = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_MISC 1
-   * }
-   */
-  public static int __USE_MISC() {
-    return __USE_MISC;
-  }
-
-  private static final int __USE_ATFILE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ATFILE 1
-   * }
-   */
-  public static int __USE_ATFILE() {
-    return __USE_ATFILE;
-  }
-
-  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
-   * }
-   */
-  public static int __USE_FORTIFY_LEVEL() {
-    return __USE_FORTIFY_LEVEL;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_GETS() {
-    return __GLIBC_USE_DEPRECATED_GETS;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_SCANF() {
-    return __GLIBC_USE_DEPRECATED_SCANF;
-  }
-
-  private static final int _STDC_PREDEF_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
-   * }
-   */
-  public static int _STDC_PREDEF_H() {
-    return _STDC_PREDEF_H;
-  }
-
-  private static final int __STDC_IEC_559__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
-   * }
-   */
-  public static int __STDC_IEC_559__() {
-    return __STDC_IEC_559__;
-  }
-
-  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
-   * }
-   */
-  public static int __STDC_IEC_559_COMPLEX__() {
-    return __STDC_IEC_559_COMPLEX__;
-  }
-
-  private static final int __GNU_LIBRARY__ = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
-   * }
-   */
-  public static int __GNU_LIBRARY__() {
-    return __GNU_LIBRARY__;
-  }
-
-  private static final int __GLIBC__ = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC__ 2
-   * }
-   */
-  public static int __GLIBC__() {
-    return __GLIBC__;
-  }
-
-  private static final int __GLIBC_MINOR__ = (int) 35L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
-   * }
-   */
-  public static int __GLIBC_MINOR__() {
-    return __GLIBC_MINOR__;
-  }
-
-  private static final int _SYS_CDEFS_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
-   * }
-   */
-  public static int _SYS_CDEFS_H() {
-    return _SYS_CDEFS_H;
-  }
-
-  private static final int __glibc_c99_flexarr_available = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
-   * }
-   */
-  public static int __glibc_c99_flexarr_available() {
-    return __glibc_c99_flexarr_available;
-  }
-
-  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
-   * }
-   */
-  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
-    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
-  }
-
-  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
-   * }
-   */
-  public static int __HAVE_GENERIC_SELECTION() {
-    return __HAVE_GENERIC_SELECTION;
-  }
-
-  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
-   * }
-   */
-  public static int __GLIBC_USE_LIB_EXT2() {
-    return __GLIBC_USE_LIB_EXT2;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_EXT() {
-    return __GLIBC_USE_IEC_60559_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
-    return __GLIBC_USE_IEC_60559_TYPES_EXT;
-  }
-
-  private static final int _BITS_TYPES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPES_H 1
-   * }
-   */
-  public static int _BITS_TYPES_H() {
-    return _BITS_TYPES_H;
-  }
-
-  private static final int _BITS_TYPESIZES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
-   * }
-   */
-  public static int _BITS_TYPESIZES_H() {
-    return _BITS_TYPESIZES_H;
-  }
-
-  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
-   * }
-   */
-  public static int __OFF_T_MATCHES_OFF64_T() {
-    return __OFF_T_MATCHES_OFF64_T;
-  }
-
-  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
-   * }
-   */
-  public static int __INO_T_MATCHES_INO64_T() {
-    return __INO_T_MATCHES_INO64_T;
-  }
-
-  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
-   * }
-   */
-  public static int __RLIM_T_MATCHES_RLIM64_T() {
-    return __RLIM_T_MATCHES_RLIM64_T;
-  }
-
-  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
-   * }
-   */
-  public static int __STATFS_MATCHES_STATFS64() {
-    return __STATFS_MATCHES_STATFS64;
-  }
-
-  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
-   * }
-   */
-  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
-    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
-  }
-
-  private static final int __FD_SETSIZE = (int) 1024L;
-
-  /**
-   * {@snippet lang = c : * #define __FD_SETSIZE 1024
-   * }
-   */
-  public static int __FD_SETSIZE() {
-    return __FD_SETSIZE;
-  }
-
-  private static final int _BITS_TIME64_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TIME64_H 1
-   * }
-   */
-  public static int _BITS_TIME64_H() {
-    return _BITS_TIME64_H;
-  }
-
-  private static final int _BITS_WCHAR_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
-   * }
-   */
-  public static int _BITS_WCHAR_H() {
-    return _BITS_WCHAR_H;
-  }
-
-  private static final int _BITS_STDINT_INTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_INTN_H() {
-    return _BITS_STDINT_INTN_H;
-  }
-
-  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_UINTN_H() {
-    return _BITS_STDINT_UINTN_H;
-  }
-
-  private static final int true_ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define true 1
-   * }
-   */
-  public static int true_() {
-    return true_;
-  }
-
-  private static final int false_ = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define false 0
-   * }
-   */
-  public static int false_() {
-    return false_;
-  }
-
-  private static final int __bool_true_false_are_defined = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
-   * }
-   */
-  public static int __bool_true_false_are_defined() {
-    return __bool_true_false_are_defined;
-  }
-
-  private static final int L2Expanded = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
-   * }
-   */
-  public static int L2Expanded() {
-    return L2Expanded;
-  }
-
-  private static final int L2SqrtExpanded = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
-   * }
-   */
-  public static int L2SqrtExpanded() {
-    return L2SqrtExpanded;
-  }
-
-  private static final int CosineExpanded = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
-   * }
-   */
-  public static int CosineExpanded() {
-    return CosineExpanded;
-  }
-
-  private static final int L1 = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L1 = 3
-   * }
-   */
-  public static int L1() {
-    return L1;
-  }
-
-  private static final int L2Unexpanded = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
-   * }
-   */
-  public static int L2Unexpanded() {
-    return L2Unexpanded;
-  }
-
-  private static final int L2SqrtUnexpanded = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
-   * }
-   */
-  public static int L2SqrtUnexpanded() {
-    return L2SqrtUnexpanded;
-  }
-
-  private static final int InnerProduct = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
-   * }
-   */
-  public static int InnerProduct() {
-    return InnerProduct;
-  }
-
-  private static final int Linf = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Linf = 7
-   * }
-   */
-  public static int Linf() {
-    return Linf;
-  }
-
-  private static final int Canberra = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
-   * }
-   */
-  public static int Canberra() {
-    return Canberra;
-  }
-
-  private static final int LpUnexpanded = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
-   * }
-   */
-  public static int LpUnexpanded() {
-    return LpUnexpanded;
-  }
-
-  private static final int CorrelationExpanded = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
-   * }
-   */
-  public static int CorrelationExpanded() {
-    return CorrelationExpanded;
-  }
-
-  private static final int JaccardExpanded = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
-   * }
-   */
-  public static int JaccardExpanded() {
-    return JaccardExpanded;
-  }
-
-  private static final int HellingerExpanded = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
-   * }
-   */
-  public static int HellingerExpanded() {
-    return HellingerExpanded;
-  }
-
-  private static final int Haversine = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
-   * }
-   */
-  public static int Haversine() {
-    return Haversine;
-  }
-
-  private static final int BrayCurtis = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
-   * }
-   */
-  public static int BrayCurtis() {
-    return BrayCurtis;
-  }
-
-  private static final int JensenShannon = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
-   * }
-   */
-  public static int JensenShannon() {
-    return JensenShannon;
-  }
-
-  private static final int HammingUnexpanded = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
-   * }
-   */
-  public static int HammingUnexpanded() {
-    return HammingUnexpanded;
-  }
-
-  private static final int KLDivergence = (int) 17L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
-   * }
-   */
-  public static int KLDivergence() {
-    return KLDivergence;
-  }
-
-  private static final int RusselRaoExpanded = (int) 18L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
-   * }
-   */
-  public static int RusselRaoExpanded() {
-    return RusselRaoExpanded;
-  }
-
-  private static final int DiceExpanded = (int) 19L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
-   * }
-   */
-  public static int DiceExpanded() {
-    return DiceExpanded;
-  }
-
-  private static final int Precomputed = (int) 100L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
-   * }
-   */
-  public static int Precomputed() {
-    return Precomputed;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef unsigned char __u_char
-   * }
-   */
-  public static final OfByte __u_char = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __u_short
-   * }
-   */
-  public static final OfShort __u_short = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __u_int
-   * }
-   */
-  public static final OfInt __u_int = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_long
-   * }
-   */
-  public static final OfLong __u_long = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char __int8_t
-   * }
-   */
-  public static final OfByte __int8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned char __uint8_t
-   * }
-   */
-  public static final OfByte __uint8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef short __int16_t
-   * }
-   */
-  public static final OfShort __int16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __uint16_t
-   * }
-   */
-  public static final OfShort __uint16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef int __int32_t
-   * }
-   */
-  public static final OfInt __int32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uint32_t
-   * }
-   */
-  public static final OfInt __uint32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __int64_t
-   * }
-   */
-  public static final OfLong __int64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uint64_t
-   * }
-   */
-  public static final OfLong __uint64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int8_t __int_least8_t
-   * }
-   */
-  public static final OfByte __int_least8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
-   * }
-   */
-  public static final OfByte __uint_least8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t __int_least16_t
-   * }
-   */
-  public static final OfShort __int_least16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
-   * }
-   */
-  public static final OfShort __uint_least16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t __int_least32_t
-   * }
-   */
-  public static final OfInt __int_least32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
-   * }
-   */
-  public static final OfInt __uint_least32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t __int_least64_t
-   * }
-   */
-  public static final OfLong __int_least64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
-   * }
-   */
-  public static final OfLong __uint_least64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __quad_t
-   * }
-   */
-  public static final OfLong __quad_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_quad_t
-   * }
-   */
-  public static final OfLong __u_quad_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __intmax_t
-   * }
-   */
-  public static final OfLong __intmax_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uintmax_t
-   * }
-   */
-  public static final OfLong __uintmax_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __dev_t
-   * }
-   */
-  public static final OfLong __dev_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uid_t
-   * }
-   */
-  public static final OfInt __uid_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __gid_t
-   * }
-   */
-  public static final OfInt __gid_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino_t
-   * }
-   */
-  public static final OfLong __ino_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino64_t
-   * }
-   */
-  public static final OfLong __ino64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __mode_t
-   * }
-   */
-  public static final OfInt __mode_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __nlink_t
-   * }
-   */
-  public static final OfLong __nlink_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off_t
-   * }
-   */
-  public static final OfLong __off_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off64_t
-   * }
-   */
-  public static final OfLong __off64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __pid_t
-   * }
-   */
-  public static final OfInt __pid_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __clock_t
-   * }
-   */
-  public static final OfLong __clock_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim_t
-   * }
-   */
-  public static final OfLong __rlim_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim64_t
-   * }
-   */
-  public static final OfLong __rlim64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __id_t
-   * }
-   */
-  public static final OfInt __id_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __time_t
-   * }
-   */
-  public static final OfLong __time_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __useconds_t
-   * }
-   */
-  public static final OfInt __useconds_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds_t
-   * }
-   */
-  public static final OfLong __suseconds_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds64_t
-   * }
-   */
-  public static final OfLong __suseconds64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __daddr_t
-   * }
-   */
-  public static final OfInt __daddr_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __key_t
-   * }
-   */
-  public static final OfInt __key_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __clockid_t
-   * }
-   */
-  public static final OfInt __clockid_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef void *__timer_t
-   * }
-   */
-  public static final AddressLayout __timer_t = CagraH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __blksize_t
-   * }
-   */
-  public static final OfLong __blksize_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt_t
-   * }
-   */
-  public static final OfLong __blkcnt_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt64_t
-   * }
-   */
-  public static final OfLong __blkcnt64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
-   * }
-   */
-  public static final OfLong __fsblkcnt_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
-   * }
-   */
-  public static final OfLong __fsblkcnt64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
-   * }
-   */
-  public static final OfLong __fsfilcnt_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
-   * }
-   */
-  public static final OfLong __fsfilcnt64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __fsword_t
-   * }
-   */
-  public static final OfLong __fsword_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __ssize_t
-   * }
-   */
-  public static final OfLong __ssize_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __syscall_slong_t
-   * }
-   */
-  public static final OfLong __syscall_slong_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
-   * }
-   */
-  public static final OfLong __syscall_ulong_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __off64_t __loff_t
-   * }
-   */
-  public static final OfLong __loff_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef char *__caddr_t
-   * }
-   */
-  public static final AddressLayout __caddr_t = CagraH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __intptr_t
-   * }
-   */
-  public static final OfLong __intptr_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __socklen_t
-   * }
-   */
-  public static final OfInt __socklen_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __sig_atomic_t
-   * }
-   */
-  public static final OfInt __sig_atomic_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int8_t int8_t
-   * }
-   */
-  public static final OfByte int8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t int16_t
-   * }
-   */
-  public static final OfShort int16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t int32_t
-   * }
-   */
-  public static final OfInt int32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t int64_t
-   * }
-   */
-  public static final OfLong int64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t uint8_t
-   * }
-   */
-  public static final OfByte uint8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t uint16_t
-   * }
-   */
-  public static final OfShort uint16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t uint32_t
-   * }
-   */
-  public static final OfInt uint32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t uint64_t
-   * }
-   */
-  public static final OfLong uint64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
-   * }
-   */
-  public static final OfByte int_least8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
-   * }
-   */
-  public static final OfShort int_least16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
-   * }
-   */
-  public static final OfInt int_least32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
-   * }
-   */
-  public static final OfLong int_least64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
-   * }
-   */
-  public static final OfByte uint_least8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
-   * }
-   */
-  public static final OfShort uint_least16_t = CagraH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
-   * }
-   */
-  public static final OfInt uint_least32_t = CagraH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
-   * }
-   */
-  public static final OfLong uint_least64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char int_fast8_t
-   * }
-   */
-  public static final OfByte int_fast8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef long int_fast16_t
-   * }
-   */
-  public static final OfLong int_fast16_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast32_t
-   * }
-   */
-  public static final OfLong int_fast32_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast64_t
-   * }
-   */
-  public static final OfLong int_fast64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
-   * }
-   */
-  public static final OfByte uint_fast8_t = CagraH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
-   * }
-   */
-  public static final OfLong uint_fast16_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
-   * }
-   */
-  public static final OfLong uint_fast32_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
-   * }
-   */
-  public static final OfLong uint_fast64_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long intptr_t
-   * }
-   */
-  public static final OfLong intptr_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uintptr_t
-   * }
-   */
-  public static final OfLong uintptr_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __intmax_t intmax_t
-   * }
-   */
-  public static final OfLong intmax_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
-   * }
-   */
-  public static final OfLong uintmax_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long ptrdiff_t
-   * }
-   */
-  public static final OfLong ptrdiff_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long size_t
-   * }
-   */
-  public static final OfLong size_t = CagraH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int wchar_t
-   * }
-   */
-  public static final OfInt wchar_t = CagraH.C_INT;
-  private static final int kDLCPU = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
-   * }
-   */
-  public static int kDLCPU() {
-    return kDLCPU;
-  }
-
-  private static final int kDLCUDA = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
-   * }
-   */
-  public static int kDLCUDA() {
-    return kDLCUDA;
-  }
-
-  private static final int kDLCUDAHost = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
-   * }
-   */
-  public static int kDLCUDAHost() {
-    return kDLCUDAHost;
-  }
-
-  private static final int kDLOpenCL = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
-   * }
-   */
-  public static int kDLOpenCL() {
-    return kDLOpenCL;
-  }
-
-  private static final int kDLVulkan = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
-   * }
-   */
-  public static int kDLVulkan() {
-    return kDLVulkan;
-  }
-
-  private static final int kDLMetal = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
-   * }
-   */
-  public static int kDLMetal() {
-    return kDLMetal;
-  }
-
-  private static final int kDLVPI = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
-   * }
-   */
-  public static int kDLVPI() {
-    return kDLVPI;
-  }
-
-  private static final int kDLROCM = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
-   * }
-   */
-  public static int kDLROCM() {
-    return kDLROCM;
-  }
-
-  private static final int kDLROCMHost = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
-   * }
-   */
-  public static int kDLROCMHost() {
-    return kDLROCMHost;
-  }
-
-  private static final int kDLExtDev = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
-   * }
-   */
-  public static int kDLExtDev() {
-    return kDLExtDev;
-  }
-
-  private static final int kDLCUDAManaged = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
-   * }
-   */
-  public static int kDLCUDAManaged() {
-    return kDLCUDAManaged;
-  }
-
-  private static final int kDLOneAPI = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
-   * }
-   */
-  public static int kDLOneAPI() {
-    return kDLOneAPI;
-  }
-
-  private static final int kDLWebGPU = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
-   * }
-   */
-  public static int kDLWebGPU() {
-    return kDLWebGPU;
-  }
-
-  private static final int kDLHexagon = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
-   * }
-   */
-  public static int kDLHexagon() {
-    return kDLHexagon;
-  }
-
-  private static final int kDLInt = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
-   * }
-   */
-  public static int kDLInt() {
-    return kDLInt;
-  }
-
-  private static final int kDLUInt = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
-   * }
-   */
-  public static int kDLUInt() {
-    return kDLUInt;
-  }
-
-  private static final int kDLFloat = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
-   * }
-   */
-  public static int kDLFloat() {
-    return kDLFloat;
-  }
-
-  private static final int kDLOpaqueHandle = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
-   * }
-   */
-  public static int kDLOpaqueHandle() {
-    return kDLOpaqueHandle;
-  }
-
-  private static final int kDLBfloat = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
-   * }
-   */
-  public static int kDLBfloat() {
-    return kDLBfloat;
-  }
-
-  private static final int kDLComplex = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
-   * }
-   */
-  public static int kDLComplex() {
-    return kDLComplex;
-  }
-
-  private static final int kDLBool = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
-   * }
-   */
-  public static int kDLBool() {
-    return kDLBool;
-  }
-
-  private static final int AUTO_SELECT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.AUTO_SELECT = 0
-   * }
-   */
-  public static int AUTO_SELECT() {
-    return AUTO_SELECT;
-  }
-
-  private static final int IVF_PQ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.IVF_PQ = 1
-   * }
-   */
-  public static int IVF_PQ() {
-    return IVF_PQ;
-  }
-
-  private static final int NN_DESCENT = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.NN_DESCENT = 2
-   * }
-   */
-  public static int NN_DESCENT() {
-    return NN_DESCENT;
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsCagraCompressionParams {
-   *     uint32_t pq_bits;
-   *     uint32_t pq_dim;
-   *     uint32_t vq_n_centers;
-   *     uint32_t kmeans_n_iters;
-   *     double vq_kmeans_trainset_fraction;
-   *     double pq_kmeans_trainset_fraction;
-   * } *cuvsCagraCompressionParams_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraCompressionParams_t = CagraH.C_POINTER;
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsCagraIndexParams {
-   *     cuvsDistanceType metric;
-   *     long intermediate_graph_degree;
-   *     long graph_degree;
-   *     enum cuvsCagraGraphBuildAlgo build_algo;
-   *     long nn_descent_niter;
-   *     cuvsCagraCompressionParams_t compression;
-   * } *cuvsCagraIndexParams_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraIndexParams_t = CagraH.C_POINTER;
-  private static final int SINGLE_CTA = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.SINGLE_CTA = 0
-   * }
-   */
-  public static int SINGLE_CTA() {
-    return SINGLE_CTA;
-  }
-
-  private static final int MULTI_CTA = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_CTA = 1
-   * }
-   */
-  public static int MULTI_CTA() {
-    return MULTI_CTA;
-  }
-
-  private static final int MULTI_KERNEL = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_KERNEL = 2
-   * }
-   */
-  public static int MULTI_KERNEL() {
-    return MULTI_KERNEL;
-  }
-
-  private static final int AUTO = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.AUTO = 3
-   * }
-   */
-  public static int AUTO() {
-    return AUTO;
-  }
-
-  private static final int HASH = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraHashMode.HASH = 0
-   * }
-   */
-  public static int HASH() {
-    return HASH;
-  }
-
-  private static final int SMALL = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraHashMode.SMALL = 1
-   * }
-   */
-  public static int SMALL() {
-    return SMALL;
-  }
-
-  private static final int AUTO_HASH = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraHashMode.AUTO_HASH = 2
-   * }
-   */
-  public static int AUTO_HASH() {
-    return AUTO_HASH;
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsCagraSearchParams {
-   *     long max_queries;
-   *     long itopk_size;
-   *     long max_iterations;
-   *     enum cuvsCagraSearchAlgo algo;
-   *     long team_size;
-   *     long search_width;
-   *     long min_iterations;
-   *     long thread_block_size;
-   *     enum cuvsCagraHashMode hashmap_mode;
-   *     long hashmap_min_bitlen;
-   *     float hashmap_max_fill_rate;
-   *     uint32_t num_random_samplings;
-   *     uint64_t rand_xor_mask;
-   * } *cuvsCagraSearchParams_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraSearchParams_t = CagraH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef cuvsCagraIndex *cuvsCagraIndex_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraIndex_t = CagraH.C_POINTER;
-  private static final long _POSIX_C_SOURCE = 200809L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
-   * }
-   */
-  public static long _POSIX_C_SOURCE() {
-    return _POSIX_C_SOURCE;
-  }
-
-  private static final int __TIMESIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __TIMESIZE 64
-   * }
-   */
-  public static int __TIMESIZE() {
-    return __TIMESIZE;
-  }
-
-  private static final long __STDC_IEC_60559_BFP__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_BFP__() {
-    return __STDC_IEC_60559_BFP__;
-  }
-
-  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_COMPLEX__() {
-    return __STDC_IEC_60559_COMPLEX__;
-  }
-
-  private static final long __STDC_ISO_10646__ = 201706L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
-   * }
-   */
-  public static long __STDC_ISO_10646__() {
-    return __STDC_ISO_10646__;
-  }
-
-  private static final int __WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
-   * }
-   */
-  public static int __WCHAR_MAX() {
-    return __WCHAR_MAX;
-  }
-
-  private static final int __WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
-   * }
-   */
-  public static int __WCHAR_MIN() {
-    return __WCHAR_MIN;
-  }
-
-  private static final int INT8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MIN -128
-   * }
-   */
-  public static int INT8_MIN() {
-    return INT8_MIN;
-  }
-
-  private static final int INT16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MIN -32768
-   * }
-   */
-  public static int INT16_MIN() {
-    return INT16_MIN;
-  }
-
-  private static final int INT32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MIN -2147483648
-   * }
-   */
-  public static int INT32_MIN() {
-    return INT32_MIN;
-  }
-
-  private static final long INT64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT64_MIN() {
-    return INT64_MIN;
-  }
-
-  private static final int INT8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MAX 127
-   * }
-   */
-  public static int INT8_MAX() {
-    return INT8_MAX;
-  }
-
-  private static final int INT16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MAX 32767
-   * }
-   */
-  public static int INT16_MAX() {
-    return INT16_MAX;
-  }
-
-  private static final int INT32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MAX 2147483647
-   * }
-   */
-  public static int INT32_MAX() {
-    return INT32_MAX;
-  }
-
-  private static final long INT64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT64_MAX() {
-    return INT64_MAX;
-  }
-
-  private static final int UINT8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT8_MAX 255
-   * }
-   */
-  public static int UINT8_MAX() {
-    return UINT8_MAX;
-  }
-
-  private static final int UINT16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT16_MAX 65535
-   * }
-   */
-  public static int UINT16_MAX() {
-    return UINT16_MAX;
-  }
-
-  private static final int UINT32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT32_MAX 4294967295
-   * }
-   */
-  public static int UINT32_MAX() {
-    return UINT32_MAX;
-  }
-
-  private static final long UINT64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT64_MAX -1
-   * }
-   */
-  public static long UINT64_MAX() {
-    return UINT64_MAX;
-  }
-
-  private static final int INT_LEAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
-   * }
-   */
-  public static int INT_LEAST8_MIN() {
-    return INT_LEAST8_MIN;
-  }
-
-  private static final int INT_LEAST16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
-   * }
-   */
-  public static int INT_LEAST16_MIN() {
-    return INT_LEAST16_MIN;
-  }
-
-  private static final int INT_LEAST32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
-   * }
-   */
-  public static int INT_LEAST32_MIN() {
-    return INT_LEAST32_MIN;
-  }
-
-  private static final long INT_LEAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_LEAST64_MIN() {
-    return INT_LEAST64_MIN;
-  }
-
-  private static final int INT_LEAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
-   * }
-   */
-  public static int INT_LEAST8_MAX() {
-    return INT_LEAST8_MAX;
-  }
-
-  private static final int INT_LEAST16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
-   * }
-   */
-  public static int INT_LEAST16_MAX() {
-    return INT_LEAST16_MAX;
-  }
-
-  private static final int INT_LEAST32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
-   * }
-   */
-  public static int INT_LEAST32_MAX() {
-    return INT_LEAST32_MAX;
-  }
-
-  private static final long INT_LEAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_LEAST64_MAX() {
-    return INT_LEAST64_MAX;
-  }
-
-  private static final int UINT_LEAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
-   * }
-   */
-  public static int UINT_LEAST8_MAX() {
-    return UINT_LEAST8_MAX;
-  }
-
-  private static final int UINT_LEAST16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
-   * }
-   */
-  public static int UINT_LEAST16_MAX() {
-    return UINT_LEAST16_MAX;
-  }
-
-  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
-   * }
-   */
-  public static int UINT_LEAST32_MAX() {
-    return UINT_LEAST32_MAX;
-  }
-
-  private static final long UINT_LEAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
-   * }
-   */
-  public static long UINT_LEAST64_MAX() {
-    return UINT_LEAST64_MAX;
-  }
-
-  private static final int INT_FAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MIN -128
-   * }
-   */
-  public static int INT_FAST8_MIN() {
-    return INT_FAST8_MIN;
-  }
-
-  private static final long INT_FAST16_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST16_MIN() {
-    return INT_FAST16_MIN;
-  }
-
-  private static final long INT_FAST32_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST32_MIN() {
-    return INT_FAST32_MIN;
-  }
-
-  private static final long INT_FAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST64_MIN() {
-    return INT_FAST64_MIN;
-  }
-
-  private static final int INT_FAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MAX 127
-   * }
-   */
-  public static int INT_FAST8_MAX() {
-    return INT_FAST8_MAX;
-  }
-
-  private static final long INT_FAST16_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST16_MAX() {
-    return INT_FAST16_MAX;
-  }
-
-  private static final long INT_FAST32_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST32_MAX() {
-    return INT_FAST32_MAX;
-  }
-
-  private static final long INT_FAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST64_MAX() {
-    return INT_FAST64_MAX;
-  }
-
-  private static final int UINT_FAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
-   * }
-   */
-  public static int UINT_FAST8_MAX() {
-    return UINT_FAST8_MAX;
-  }
-
-  private static final long UINT_FAST16_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
-   * }
-   */
-  public static long UINT_FAST16_MAX() {
-    return UINT_FAST16_MAX;
-  }
-
-  private static final long UINT_FAST32_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
-   * }
-   */
-  public static long UINT_FAST32_MAX() {
-    return UINT_FAST32_MAX;
-  }
-
-  private static final long UINT_FAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
-   * }
-   */
-  public static long UINT_FAST64_MAX() {
-    return UINT_FAST64_MAX;
-  }
-
-  private static final long INTPTR_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
-   * }
-   */
-  public static long INTPTR_MIN() {
-    return INTPTR_MIN;
-  }
-
-  private static final long INTPTR_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
-   * }
-   */
-  public static long INTPTR_MAX() {
-    return INTPTR_MAX;
-  }
-
-  private static final long UINTPTR_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTPTR_MAX -1
-   * }
-   */
-  public static long UINTPTR_MAX() {
-    return UINTPTR_MAX;
-  }
-
-  private static final long INTMAX_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
-   * }
-   */
-  public static long INTMAX_MIN() {
-    return INTMAX_MIN;
-  }
-
-  private static final long INTMAX_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
-   * }
-   */
-  public static long INTMAX_MAX() {
-    return INTMAX_MAX;
-  }
-
-  private static final long UINTMAX_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTMAX_MAX -1
-   * }
-   */
-  public static long UINTMAX_MAX() {
-    return UINTMAX_MAX;
-  }
-
-  private static final long PTRDIFF_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
-   * }
-   */
-  public static long PTRDIFF_MIN() {
-    return PTRDIFF_MIN;
-  }
-
-  private static final long PTRDIFF_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
-   * }
-   */
-  public static long PTRDIFF_MAX() {
-    return PTRDIFF_MAX;
-  }
-
-  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
-   * }
-   */
-  public static int SIG_ATOMIC_MIN() {
-    return SIG_ATOMIC_MIN;
-  }
-
-  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
-   * }
-   */
-  public static int SIG_ATOMIC_MAX() {
-    return SIG_ATOMIC_MAX;
-  }
-
-  private static final long SIZE_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define SIZE_MAX -1
-   * }
-   */
-  public static long SIZE_MAX() {
-    return SIZE_MAX;
-  }
-
-  private static final int WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
-   * }
-   */
-  public static int WCHAR_MIN() {
-    return WCHAR_MIN;
-  }
-
-  private static final int WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
-   * }
-   */
-  public static int WCHAR_MAX() {
-    return WCHAR_MAX;
-  }
-
-  private static final int WINT_MIN = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MIN 0
-   * }
-   */
-  public static int WINT_MIN() {
-    return WINT_MIN;
-  }
-
-  private static final int WINT_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MAX 4294967295
-   * }
-   */
-  public static int WINT_MAX() {
-    return WINT_MAX;
-  }
-
-  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
-
-  /**
-   * {@snippet lang = c : * #define NULL (void*) 0
-   * }
-   */
-  public static MemorySegment NULL() {
-    return NULL;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSBruteForceIndex.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSBruteForceIndex.java
deleted file mode 100644
index 0a1ac29e22..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSBruteForceIndex.java
+++ /dev/null
@@ -1,183 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct {
- *     uintptr_t addr;
- *     DLDataType dtype;
- * }
- * }
- */
-public class CuVSBruteForceIndex {
-
-  CuVSBruteForceIndex() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(BruteForceH.C_LONG.withName("addr"),
-      DLDataType.layout().withName("dtype"), MemoryLayout.paddingLayout(4)).withName("$anon$22:9");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfLong addr$LAYOUT = (OfLong) $LAYOUT.select(groupElement("addr"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static final OfLong addr$layout() {
-    return addr$LAYOUT;
-  }
-
-  private static final long addr$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static final long addr$offset() {
-    return addr$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static long addr(MemorySegment struct) {
-    return struct.get(addr$LAYOUT, addr$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static void addr(MemorySegment struct, long fieldValue) {
-    struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
-  }
-
-  private static final GroupLayout dtype$LAYOUT = (GroupLayout) $LAYOUT.select(groupElement("dtype"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static final GroupLayout dtype$layout() {
-    return dtype$LAYOUT;
-  }
-
-  private static final long dtype$OFFSET = 8;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static final long dtype$offset() {
-    return dtype$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static MemorySegment dtype(MemorySegment struct) {
-    return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
-    MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraCompressionParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraCompressionParams.java
deleted file mode 100644
index 923287fe40..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraCompressionParams.java
+++ /dev/null
@@ -1,352 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfDouble;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct cuvsCagraCompressionParams {
- *     uint32_t pq_bits;
- *     uint32_t pq_dim;
- *     uint32_t vq_n_centers;
- *     uint32_t kmeans_n_iters;
- *     double vq_kmeans_trainset_fraction;
- *     double pq_kmeans_trainset_fraction;
- * }
- * }
- */
-public class CuVSCagraCompressionParams {
-
-  CuVSCagraCompressionParams() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(CagraH.C_INT.withName("pq_bits"),
-      CagraH.C_INT.withName("pq_dim"), CagraH.C_INT.withName("vq_n_centers"),
-      CagraH.C_INT.withName("kmeans_n_iters"), CagraH.C_DOUBLE.withName("vq_kmeans_trainset_fraction"),
-      CagraH.C_DOUBLE.withName("pq_kmeans_trainset_fraction")).withName("cuvsCagraCompressionParams");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfInt pq_bits$LAYOUT = (OfInt) $LAYOUT.select(groupElement("pq_bits"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uint32_t pq_bits
-   * }
-   */
-  public static final OfInt pq_bits$layout() {
-    return pq_bits$LAYOUT;
-  }
-
-  private static final long pq_bits$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uint32_t pq_bits
-   * }
-   */
-  public static final long pq_bits$offset() {
-    return pq_bits$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uint32_t pq_bits
-   * }
-   */
-  public static int pq_bits(MemorySegment struct) {
-    return struct.get(pq_bits$LAYOUT, pq_bits$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uint32_t pq_bits
-   * }
-   */
-  public static void pq_bits(MemorySegment struct, int fieldValue) {
-    struct.set(pq_bits$LAYOUT, pq_bits$OFFSET, fieldValue);
-  }
-
-  private static final OfInt pq_dim$LAYOUT = (OfInt) $LAYOUT.select(groupElement("pq_dim"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uint32_t pq_dim
-   * }
-   */
-  public static final OfInt pq_dim$layout() {
-    return pq_dim$LAYOUT;
-  }
-
-  private static final long pq_dim$OFFSET = 4;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uint32_t pq_dim
-   * }
-   */
-  public static final long pq_dim$offset() {
-    return pq_dim$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uint32_t pq_dim
-   * }
-   */
-  public static int pq_dim(MemorySegment struct) {
-    return struct.get(pq_dim$LAYOUT, pq_dim$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uint32_t pq_dim
-   * }
-   */
-  public static void pq_dim(MemorySegment struct, int fieldValue) {
-    struct.set(pq_dim$LAYOUT, pq_dim$OFFSET, fieldValue);
-  }
-
-  private static final OfInt vq_n_centers$LAYOUT = (OfInt) $LAYOUT.select(groupElement("vq_n_centers"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uint32_t vq_n_centers
-   * }
-   */
-  public static final OfInt vq_n_centers$layout() {
-    return vq_n_centers$LAYOUT;
-  }
-
-  private static final long vq_n_centers$OFFSET = 8;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uint32_t vq_n_centers
-   * }
-   */
-  public static final long vq_n_centers$offset() {
-    return vq_n_centers$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uint32_t vq_n_centers
-   * }
-   */
-  public static int vq_n_centers(MemorySegment struct) {
-    return struct.get(vq_n_centers$LAYOUT, vq_n_centers$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uint32_t vq_n_centers
-   * }
-   */
-  public static void vq_n_centers(MemorySegment struct, int fieldValue) {
-    struct.set(vq_n_centers$LAYOUT, vq_n_centers$OFFSET, fieldValue);
-  }
-
-  private static final OfInt kmeans_n_iters$LAYOUT = (OfInt) $LAYOUT.select(groupElement("kmeans_n_iters"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uint32_t kmeans_n_iters
-   * }
-   */
-  public static final OfInt kmeans_n_iters$layout() {
-    return kmeans_n_iters$LAYOUT;
-  }
-
-  private static final long kmeans_n_iters$OFFSET = 12;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uint32_t kmeans_n_iters
-   * }
-   */
-  public static final long kmeans_n_iters$offset() {
-    return kmeans_n_iters$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uint32_t kmeans_n_iters
-   * }
-   */
-  public static int kmeans_n_iters(MemorySegment struct) {
-    return struct.get(kmeans_n_iters$LAYOUT, kmeans_n_iters$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uint32_t kmeans_n_iters
-   * }
-   */
-  public static void kmeans_n_iters(MemorySegment struct, int fieldValue) {
-    struct.set(kmeans_n_iters$LAYOUT, kmeans_n_iters$OFFSET, fieldValue);
-  }
-
-  private static final OfDouble vq_kmeans_trainset_fraction$LAYOUT = (OfDouble) $LAYOUT
-      .select(groupElement("vq_kmeans_trainset_fraction"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
-   * }
-   */
-  public static final OfDouble vq_kmeans_trainset_fraction$layout() {
-    return vq_kmeans_trainset_fraction$LAYOUT;
-  }
-
-  private static final long vq_kmeans_trainset_fraction$OFFSET = 16;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
-   * }
-   */
-  public static final long vq_kmeans_trainset_fraction$offset() {
-    return vq_kmeans_trainset_fraction$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
-   * }
-   */
-  public static double vq_kmeans_trainset_fraction(MemorySegment struct) {
-    return struct.get(vq_kmeans_trainset_fraction$LAYOUT, vq_kmeans_trainset_fraction$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * double vq_kmeans_trainset_fraction
-   * }
-   */
-  public static void vq_kmeans_trainset_fraction(MemorySegment struct, double fieldValue) {
-    struct.set(vq_kmeans_trainset_fraction$LAYOUT, vq_kmeans_trainset_fraction$OFFSET, fieldValue);
-  }
-
-  private static final OfDouble pq_kmeans_trainset_fraction$LAYOUT = (OfDouble) $LAYOUT
-      .select(groupElement("pq_kmeans_trainset_fraction"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
-   * }
-   */
-  public static final OfDouble pq_kmeans_trainset_fraction$layout() {
-    return pq_kmeans_trainset_fraction$LAYOUT;
-  }
-
-  private static final long pq_kmeans_trainset_fraction$OFFSET = 24;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
-   * }
-   */
-  public static final long pq_kmeans_trainset_fraction$offset() {
-    return pq_kmeans_trainset_fraction$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
-   * }
-   */
-  public static double pq_kmeans_trainset_fraction(MemorySegment struct) {
-    return struct.get(pq_kmeans_trainset_fraction$LAYOUT, pq_kmeans_trainset_fraction$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * double pq_kmeans_trainset_fraction
-   * }
-   */
-  public static void pq_kmeans_trainset_fraction(MemorySegment struct, double fieldValue) {
-    struct.set(pq_kmeans_trainset_fraction$LAYOUT, pq_kmeans_trainset_fraction$OFFSET, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndex.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndex.java
deleted file mode 100644
index 7ea435ba0f..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndex.java
+++ /dev/null
@@ -1,183 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct {
- *     uintptr_t addr;
- *     DLDataType dtype;
- * }
- * }
- */
-public class CuVSCagraIndex {
-
-  CuVSCagraIndex() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(CagraH.C_LONG.withName("addr"),
-      DLDataType.layout().withName("dtype"), MemoryLayout.paddingLayout(4)).withName("$anon$175:9");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfLong addr$LAYOUT = (OfLong) $LAYOUT.select(groupElement("addr"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static final OfLong addr$layout() {
-    return addr$LAYOUT;
-  }
-
-  private static final long addr$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static final long addr$offset() {
-    return addr$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static long addr(MemorySegment struct) {
-    return struct.get(addr$LAYOUT, addr$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static void addr(MemorySegment struct, long fieldValue) {
-    struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
-  }
-
-  private static final GroupLayout dtype$LAYOUT = (GroupLayout) $LAYOUT.select(groupElement("dtype"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static final GroupLayout dtype$layout() {
-    return dtype$LAYOUT;
-  }
-
-  private static final long dtype$OFFSET = 8;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static final long dtype$offset() {
-    return dtype$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static MemorySegment dtype(MemorySegment struct) {
-    return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
-    MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndexParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndexParams.java
deleted file mode 100644
index ffe88effaf..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraIndexParams.java
+++ /dev/null
@@ -1,354 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct cuvsCagraIndexParams {
- *     cuvsDistanceType metric;
- *     long intermediate_graph_degree;
- *     long graph_degree;
- *     enum cuvsCagraGraphBuildAlgo build_algo;
- *     long nn_descent_niter;
- *     cuvsCagraCompressionParams_t compression;
- * }
- * }
- */
-public class CuVSCagraIndexParams {
-
-  CuVSCagraIndexParams() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout
-      .structLayout(CagraH.C_INT.withName("metric"), MemoryLayout.paddingLayout(4),
-          CagraH.C_LONG.withName("intermediate_graph_degree"), CagraH.C_LONG.withName("graph_degree"),
-          CagraH.C_INT.withName("build_algo"), MemoryLayout.paddingLayout(4),
-          CagraH.C_LONG.withName("nn_descent_niter"), CagraH.C_POINTER.withName("compression"))
-      .withName("cuvsCagraIndexParams");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfInt metric$LAYOUT = (OfInt) $LAYOUT.select(groupElement("metric"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * cuvsDistanceType metric
-   * }
-   */
-  public static final OfInt metric$layout() {
-    return metric$LAYOUT;
-  }
-
-  private static final long metric$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * cuvsDistanceType metric
-   * }
-   */
-  public static final long metric$offset() {
-    return metric$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * cuvsDistanceType metric
-   * }
-   */
-  public static int metric(MemorySegment struct) {
-    return struct.get(metric$LAYOUT, metric$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * cuvsDistanceType metric
-   * }
-   */
-  public static void metric(MemorySegment struct, int fieldValue) {
-    struct.set(metric$LAYOUT, metric$OFFSET, fieldValue);
-  }
-
-  private static final OfLong intermediate_graph_degree$LAYOUT = (OfLong) $LAYOUT
-      .select(groupElement("intermediate_graph_degree"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long intermediate_graph_degree
-   * }
-   */
-  public static final OfLong intermediate_graph_degree$layout() {
-    return intermediate_graph_degree$LAYOUT;
-  }
-
-  private static final long intermediate_graph_degree$OFFSET = 8;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long intermediate_graph_degree
-   * }
-   */
-  public static final long intermediate_graph_degree$offset() {
-    return intermediate_graph_degree$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long intermediate_graph_degree
-   * }
-   */
-  public static long intermediate_graph_degree(MemorySegment struct) {
-    return struct.get(intermediate_graph_degree$LAYOUT, intermediate_graph_degree$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long intermediate_graph_degree
-   * }
-   */
-  public static void intermediate_graph_degree(MemorySegment struct, long fieldValue) {
-    struct.set(intermediate_graph_degree$LAYOUT, intermediate_graph_degree$OFFSET, fieldValue);
-  }
-
-  private static final OfLong graph_degree$LAYOUT = (OfLong) $LAYOUT.select(groupElement("graph_degree"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long graph_degree
-   * }
-   */
-  public static final OfLong graph_degree$layout() {
-    return graph_degree$LAYOUT;
-  }
-
-  private static final long graph_degree$OFFSET = 16;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long graph_degree
-   * }
-   */
-  public static final long graph_degree$offset() {
-    return graph_degree$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long graph_degree
-   * }
-   */
-  public static long graph_degree(MemorySegment struct) {
-    return struct.get(graph_degree$LAYOUT, graph_degree$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long graph_degree
-   * }
-   */
-  public static void graph_degree(MemorySegment struct, long fieldValue) {
-    struct.set(graph_degree$LAYOUT, graph_degree$OFFSET, fieldValue);
-  }
-
-  private static final OfInt build_algo$LAYOUT = (OfInt) $LAYOUT.select(groupElement("build_algo"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
-   * }
-   */
-  public static final OfInt build_algo$layout() {
-    return build_algo$LAYOUT;
-  }
-
-  private static final long build_algo$OFFSET = 24;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
-   * }
-   */
-  public static final long build_algo$offset() {
-    return build_algo$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
-   * }
-   */
-  public static int build_algo(MemorySegment struct) {
-    return struct.get(build_algo$LAYOUT, build_algo$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo build_algo
-   * }
-   */
-  public static void build_algo(MemorySegment struct, int fieldValue) {
-    struct.set(build_algo$LAYOUT, build_algo$OFFSET, fieldValue);
-  }
-
-  private static final OfLong nn_descent_niter$LAYOUT = (OfLong) $LAYOUT.select(groupElement("nn_descent_niter"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long nn_descent_niter
-   * }
-   */
-  public static final OfLong nn_descent_niter$layout() {
-    return nn_descent_niter$LAYOUT;
-  }
-
-  private static final long nn_descent_niter$OFFSET = 32;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long nn_descent_niter
-   * }
-   */
-  public static final long nn_descent_niter$offset() {
-    return nn_descent_niter$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long nn_descent_niter
-   * }
-   */
-  public static long nn_descent_niter(MemorySegment struct) {
-    return struct.get(nn_descent_niter$LAYOUT, nn_descent_niter$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long nn_descent_niter
-   * }
-   */
-  public static void nn_descent_niter(MemorySegment struct, long fieldValue) {
-    struct.set(nn_descent_niter$LAYOUT, nn_descent_niter$OFFSET, fieldValue);
-  }
-
-  private static final AddressLayout compression$LAYOUT = (AddressLayout) $LAYOUT.select(groupElement("compression"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
-   * }
-   */
-  public static final AddressLayout compression$layout() {
-    return compression$LAYOUT;
-  }
-
-  private static final long compression$OFFSET = 40;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
-   * }
-   */
-  public static final long compression$offset() {
-    return compression$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
-   * }
-   */
-  public static MemorySegment compression(MemorySegment struct) {
-    return struct.get(compression$LAYOUT, compression$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * cuvsCagraCompressionParams_t compression
-   * }
-   */
-  public static void compression(MemorySegment struct, MemorySegment fieldValue) {
-    struct.set(compression$LAYOUT, compression$OFFSET, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraSearchParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraSearchParams.java
deleted file mode 100644
index 2a1efbfd7f..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSCagraSearchParams.java
+++ /dev/null
@@ -1,644 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfFloat;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct cuvsCagraSearchParams {
- *     long max_queries;
- *     long itopk_size;
- *     long max_iterations;
- *     enum cuvsCagraSearchAlgo algo;
- *     long team_size;
- *     long search_width;
- *     long min_iterations;
- *     long thread_block_size;
- *     enum cuvsCagraHashMode hashmap_mode;
- *     long hashmap_min_bitlen;
- *     float hashmap_max_fill_rate;
- *     uint32_t num_random_samplings;
- *     uint64_t rand_xor_mask;
- * }
- * }
- */
-public class CuVSCagraSearchParams {
-
-  CuVSCagraSearchParams() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout
-      .structLayout(CagraH.C_LONG.withName("max_queries"), CagraH.C_LONG.withName("itopk_size"),
-          CagraH.C_LONG.withName("max_iterations"), CagraH.C_INT.withName("algo"), MemoryLayout.paddingLayout(4),
-          CagraH.C_LONG.withName("team_size"), CagraH.C_LONG.withName("search_width"),
-          CagraH.C_LONG.withName("min_iterations"), CagraH.C_LONG.withName("thread_block_size"),
-          CagraH.C_INT.withName("hashmap_mode"), MemoryLayout.paddingLayout(4),
-          CagraH.C_LONG.withName("hashmap_min_bitlen"), CagraH.C_FLOAT.withName("hashmap_max_fill_rate"),
-          CagraH.C_INT.withName("num_random_samplings"), CagraH.C_LONG.withName("rand_xor_mask"))
-      .withName("cuvsCagraSearchParams");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfLong max_queries$LAYOUT = (OfLong) $LAYOUT.select(groupElement("max_queries"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long max_queries
-   * }
-   */
-  public static final OfLong max_queries$layout() {
-    return max_queries$LAYOUT;
-  }
-
-  private static final long max_queries$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long max_queries
-   * }
-   */
-  public static final long max_queries$offset() {
-    return max_queries$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long max_queries
-   * }
-   */
-  public static long max_queries(MemorySegment struct) {
-    return struct.get(max_queries$LAYOUT, max_queries$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long max_queries
-   * }
-   */
-  public static void max_queries(MemorySegment struct, long fieldValue) {
-    struct.set(max_queries$LAYOUT, max_queries$OFFSET, fieldValue);
-  }
-
-  private static final OfLong itopk_size$LAYOUT = (OfLong) $LAYOUT.select(groupElement("itopk_size"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long itopk_size
-   * }
-   */
-  public static final OfLong itopk_size$layout() {
-    return itopk_size$LAYOUT;
-  }
-
-  private static final long itopk_size$OFFSET = 8;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long itopk_size
-   * }
-   */
-  public static final long itopk_size$offset() {
-    return itopk_size$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long itopk_size
-   * }
-   */
-  public static long itopk_size(MemorySegment struct) {
-    return struct.get(itopk_size$LAYOUT, itopk_size$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long itopk_size
-   * }
-   */
-  public static void itopk_size(MemorySegment struct, long fieldValue) {
-    struct.set(itopk_size$LAYOUT, itopk_size$OFFSET, fieldValue);
-  }
-
-  private static final OfLong max_iterations$LAYOUT = (OfLong) $LAYOUT.select(groupElement("max_iterations"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long max_iterations
-   * }
-   */
-  public static final OfLong max_iterations$layout() {
-    return max_iterations$LAYOUT;
-  }
-
-  private static final long max_iterations$OFFSET = 16;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long max_iterations
-   * }
-   */
-  public static final long max_iterations$offset() {
-    return max_iterations$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long max_iterations
-   * }
-   */
-  public static long max_iterations(MemorySegment struct) {
-    return struct.get(max_iterations$LAYOUT, max_iterations$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long max_iterations
-   * }
-   */
-  public static void max_iterations(MemorySegment struct, long fieldValue) {
-    struct.set(max_iterations$LAYOUT, max_iterations$OFFSET, fieldValue);
-  }
-
-  private static final OfInt algo$LAYOUT = (OfInt) $LAYOUT.select(groupElement("algo"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
-   * }
-   */
-  public static final OfInt algo$layout() {
-    return algo$LAYOUT;
-  }
-
-  private static final long algo$OFFSET = 24;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
-   * }
-   */
-  public static final long algo$offset() {
-    return algo$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
-   * }
-   */
-  public static int algo(MemorySegment struct) {
-    return struct.get(algo$LAYOUT, algo$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo algo
-   * }
-   */
-  public static void algo(MemorySegment struct, int fieldValue) {
-    struct.set(algo$LAYOUT, algo$OFFSET, fieldValue);
-  }
-
-  private static final OfLong team_size$LAYOUT = (OfLong) $LAYOUT.select(groupElement("team_size"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long team_size
-   * }
-   */
-  public static final OfLong team_size$layout() {
-    return team_size$LAYOUT;
-  }
-
-  private static final long team_size$OFFSET = 32;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long team_size
-   * }
-   */
-  public static final long team_size$offset() {
-    return team_size$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long team_size
-   * }
-   */
-  public static long team_size(MemorySegment struct) {
-    return struct.get(team_size$LAYOUT, team_size$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long team_size
-   * }
-   */
-  public static void team_size(MemorySegment struct, long fieldValue) {
-    struct.set(team_size$LAYOUT, team_size$OFFSET, fieldValue);
-  }
-
-  private static final OfLong search_width$LAYOUT = (OfLong) $LAYOUT.select(groupElement("search_width"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long search_width
-   * }
-   */
-  public static final OfLong search_width$layout() {
-    return search_width$LAYOUT;
-  }
-
-  private static final long search_width$OFFSET = 40;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long search_width
-   * }
-   */
-  public static final long search_width$offset() {
-    return search_width$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long search_width
-   * }
-   */
-  public static long search_width(MemorySegment struct) {
-    return struct.get(search_width$LAYOUT, search_width$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long search_width
-   * }
-   */
-  public static void search_width(MemorySegment struct, long fieldValue) {
-    struct.set(search_width$LAYOUT, search_width$OFFSET, fieldValue);
-  }
-
-  private static final OfLong min_iterations$LAYOUT = (OfLong) $LAYOUT.select(groupElement("min_iterations"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long min_iterations
-   * }
-   */
-  public static final OfLong min_iterations$layout() {
-    return min_iterations$LAYOUT;
-  }
-
-  private static final long min_iterations$OFFSET = 48;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long min_iterations
-   * }
-   */
-  public static final long min_iterations$offset() {
-    return min_iterations$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long min_iterations
-   * }
-   */
-  public static long min_iterations(MemorySegment struct) {
-    return struct.get(min_iterations$LAYOUT, min_iterations$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long min_iterations
-   * }
-   */
-  public static void min_iterations(MemorySegment struct, long fieldValue) {
-    struct.set(min_iterations$LAYOUT, min_iterations$OFFSET, fieldValue);
-  }
-
-  private static final OfLong thread_block_size$LAYOUT = (OfLong) $LAYOUT.select(groupElement("thread_block_size"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long thread_block_size
-   * }
-   */
-  public static final OfLong thread_block_size$layout() {
-    return thread_block_size$LAYOUT;
-  }
-
-  private static final long thread_block_size$OFFSET = 56;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long thread_block_size
-   * }
-   */
-  public static final long thread_block_size$offset() {
-    return thread_block_size$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long thread_block_size
-   * }
-   */
-  public static long thread_block_size(MemorySegment struct) {
-    return struct.get(thread_block_size$LAYOUT, thread_block_size$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long thread_block_size
-   * }
-   */
-  public static void thread_block_size(MemorySegment struct, long fieldValue) {
-    struct.set(thread_block_size$LAYOUT, thread_block_size$OFFSET, fieldValue);
-  }
-
-  private static final OfInt hashmap_mode$LAYOUT = (OfInt) $LAYOUT.select(groupElement("hashmap_mode"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
-   * }
-   */
-  public static final OfInt hashmap_mode$layout() {
-    return hashmap_mode$LAYOUT;
-  }
-
-  private static final long hashmap_mode$OFFSET = 64;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
-   * }
-   */
-  public static final long hashmap_mode$offset() {
-    return hashmap_mode$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
-   * }
-   */
-  public static int hashmap_mode(MemorySegment struct) {
-    return struct.get(hashmap_mode$LAYOUT, hashmap_mode$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * enum cuvsCagraHashMode hashmap_mode
-   * }
-   */
-  public static void hashmap_mode(MemorySegment struct, int fieldValue) {
-    struct.set(hashmap_mode$LAYOUT, hashmap_mode$OFFSET, fieldValue);
-  }
-
-  private static final OfLong hashmap_min_bitlen$LAYOUT = (OfLong) $LAYOUT.select(groupElement("hashmap_min_bitlen"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long hashmap_min_bitlen
-   * }
-   */
-  public static final OfLong hashmap_min_bitlen$layout() {
-    return hashmap_min_bitlen$LAYOUT;
-  }
-
-  private static final long hashmap_min_bitlen$OFFSET = 72;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long hashmap_min_bitlen
-   * }
-   */
-  public static final long hashmap_min_bitlen$offset() {
-    return hashmap_min_bitlen$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long hashmap_min_bitlen
-   * }
-   */
-  public static long hashmap_min_bitlen(MemorySegment struct) {
-    return struct.get(hashmap_min_bitlen$LAYOUT, hashmap_min_bitlen$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long hashmap_min_bitlen
-   * }
-   */
-  public static void hashmap_min_bitlen(MemorySegment struct, long fieldValue) {
-    struct.set(hashmap_min_bitlen$LAYOUT, hashmap_min_bitlen$OFFSET, fieldValue);
-  }
-
-  private static final OfFloat hashmap_max_fill_rate$LAYOUT = (OfFloat) $LAYOUT
-      .select(groupElement("hashmap_max_fill_rate"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * float hashmap_max_fill_rate
-   * }
-   */
-  public static final OfFloat hashmap_max_fill_rate$layout() {
-    return hashmap_max_fill_rate$LAYOUT;
-  }
-
-  private static final long hashmap_max_fill_rate$OFFSET = 80;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * float hashmap_max_fill_rate
-   * }
-   */
-  public static final long hashmap_max_fill_rate$offset() {
-    return hashmap_max_fill_rate$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * float hashmap_max_fill_rate
-   * }
-   */
-  public static float hashmap_max_fill_rate(MemorySegment struct) {
-    return struct.get(hashmap_max_fill_rate$LAYOUT, hashmap_max_fill_rate$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * float hashmap_max_fill_rate
-   * }
-   */
-  public static void hashmap_max_fill_rate(MemorySegment struct, float fieldValue) {
-    struct.set(hashmap_max_fill_rate$LAYOUT, hashmap_max_fill_rate$OFFSET, fieldValue);
-  }
-
-  private static final OfInt num_random_samplings$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_random_samplings"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uint32_t num_random_samplings
-   * }
-   */
-  public static final OfInt num_random_samplings$layout() {
-    return num_random_samplings$LAYOUT;
-  }
-
-  private static final long num_random_samplings$OFFSET = 84;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uint32_t num_random_samplings
-   * }
-   */
-  public static final long num_random_samplings$offset() {
-    return num_random_samplings$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uint32_t num_random_samplings
-   * }
-   */
-  public static int num_random_samplings(MemorySegment struct) {
-    return struct.get(num_random_samplings$LAYOUT, num_random_samplings$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uint32_t num_random_samplings
-   * }
-   */
-  public static void num_random_samplings(MemorySegment struct, int fieldValue) {
-    struct.set(num_random_samplings$LAYOUT, num_random_samplings$OFFSET, fieldValue);
-  }
-
-  private static final OfLong rand_xor_mask$LAYOUT = (OfLong) $LAYOUT.select(groupElement("rand_xor_mask"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uint64_t rand_xor_mask
-   * }
-   */
-  public static final OfLong rand_xor_mask$layout() {
-    return rand_xor_mask$LAYOUT;
-  }
-
-  private static final long rand_xor_mask$OFFSET = 88;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uint64_t rand_xor_mask
-   * }
-   */
-  public static final long rand_xor_mask$offset() {
-    return rand_xor_mask$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uint64_t rand_xor_mask
-   * }
-   */
-  public static long rand_xor_mask(MemorySegment struct) {
-    return struct.get(rand_xor_mask$LAYOUT, rand_xor_mask$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uint64_t rand_xor_mask
-   * }
-   */
-  public static void rand_xor_mask(MemorySegment struct, long fieldValue) {
-    struct.set(rand_xor_mask$LAYOUT, rand_xor_mask$OFFSET, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSFilter.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSFilter.java
deleted file mode 100644
index dceaca994b..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSFilter.java
+++ /dev/null
@@ -1,188 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct {
- *     uintptr_t addr;
- *     enum cuvsFilterType type;
- * }
- * }
- */
-public class CuVSFilter {
-
-    CuVSFilter() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        BruteForceH.C_LONG.withName("addr"),
-        BruteForceH.C_INT.withName("type"),
-        MemoryLayout.paddingLayout(4)
-    ).withName("$anon$50:9");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final OfLong addr$LAYOUT = (OfLong)$LAYOUT.select(groupElement("addr"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uintptr_t addr
-     * }
-     */
-    public static final OfLong addr$layout() {
-        return addr$LAYOUT;
-    }
-
-    private static final long addr$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uintptr_t addr
-     * }
-     */
-    public static final long addr$offset() {
-        return addr$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uintptr_t addr
-     * }
-     */
-    public static long addr(MemorySegment struct) {
-        return struct.get(addr$LAYOUT, addr$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uintptr_t addr
-     * }
-     */
-    public static void addr(MemorySegment struct, long fieldValue) {
-        struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
-    }
-
-    private static final OfInt type$LAYOUT = (OfInt)$LAYOUT.select(groupElement("type"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * enum cuvsFilterType type
-     * }
-     */
-    public static final OfInt type$layout() {
-        return type$LAYOUT;
-    }
-
-    private static final long type$OFFSET = 8;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * enum cuvsFilterType type
-     * }
-     */
-    public static final long type$offset() {
-        return type$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * enum cuvsFilterType type
-     * }
-     */
-    public static int type(MemorySegment struct) {
-        return struct.get(type$LAYOUT, type$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * enum cuvsFilterType type
-     * }
-     */
-    public static void type(MemorySegment struct, int fieldValue) {
-        struct.set(type$LAYOUT, type$OFFSET, fieldValue);
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswExtendParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswExtendParams.java
deleted file mode 100644
index 83358e2338..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswExtendParams.java
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct cuvsHnswExtendParams {
- *     int num_threads;
- * }
- * }
- */
-public class CuVSHnswExtendParams {
-
-  CuVSHnswExtendParams() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(HnswH.C_INT.withName("num_threads"))
-      .withName("cuvsHnswExtendParams");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfInt num_threads$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_threads"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static final OfInt num_threads$layout() {
-    return num_threads$LAYOUT;
-  }
-
-  private static final long num_threads$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static final long num_threads$offset() {
-    return num_threads$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static int num_threads(MemorySegment struct) {
-    return struct.get(num_threads$LAYOUT, num_threads$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static void num_threads(MemorySegment struct, int fieldValue) {
-    struct.set(num_threads$LAYOUT, num_threads$OFFSET, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndex.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndex.java
deleted file mode 100644
index aa12e4400b..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndex.java
+++ /dev/null
@@ -1,183 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct {
- *     uintptr_t addr;
- *     DLDataType dtype;
- * }
- * }
- */
-public class CuVSHnswIndex {
-
-  CuVSHnswIndex() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(HnswH.C_LONG.withName("addr"),
-      DLDataType.layout().withName("dtype"), MemoryLayout.paddingLayout(4)).withName("$anon$66:9");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfLong addr$LAYOUT = (OfLong) $LAYOUT.select(groupElement("addr"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static final OfLong addr$layout() {
-    return addr$LAYOUT;
-  }
-
-  private static final long addr$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static final long addr$offset() {
-    return addr$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static long addr(MemorySegment struct) {
-    return struct.get(addr$LAYOUT, addr$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * uintptr_t addr
-   * }
-   */
-  public static void addr(MemorySegment struct, long fieldValue) {
-    struct.set(addr$LAYOUT, addr$OFFSET, fieldValue);
-  }
-
-  private static final GroupLayout dtype$LAYOUT = (GroupLayout) $LAYOUT.select(groupElement("dtype"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static final GroupLayout dtype$layout() {
-    return dtype$LAYOUT;
-  }
-
-  private static final long dtype$OFFSET = 8;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static final long dtype$offset() {
-    return dtype$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static MemorySegment dtype(MemorySegment struct) {
-    return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * DLDataType dtype
-   * }
-   */
-  public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
-    MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndexParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndexParams.java
deleted file mode 100644
index d6b06872f6..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswIndexParams.java
+++ /dev/null
@@ -1,224 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct cuvsHnswIndexParams {
- *     cuvsHnswHierarchy hierarchy;
- *     int ef_construction;
- *     int num_threads;
- * }
- * }
- */
-public class CuVSHnswIndexParams {
-
-  CuVSHnswIndexParams() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(HnswH.C_INT.withName("hierarchy"),
-      HnswH.C_INT.withName("ef_construction"), HnswH.C_INT.withName("num_threads")).withName("cuvsHnswIndexParams");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfInt hierarchy$LAYOUT = (OfInt) $LAYOUT.select(groupElement("hierarchy"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
-   * }
-   */
-  public static final OfInt hierarchy$layout() {
-    return hierarchy$LAYOUT;
-  }
-
-  private static final long hierarchy$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
-   * }
-   */
-  public static final long hierarchy$offset() {
-    return hierarchy$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
-   * }
-   */
-  public static int hierarchy(MemorySegment struct) {
-    return struct.get(hierarchy$LAYOUT, hierarchy$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * cuvsHnswHierarchy hierarchy
-   * }
-   */
-  public static void hierarchy(MemorySegment struct, int fieldValue) {
-    struct.set(hierarchy$LAYOUT, hierarchy$OFFSET, fieldValue);
-  }
-
-  private static final OfInt ef_construction$LAYOUT = (OfInt) $LAYOUT.select(groupElement("ef_construction"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * int ef_construction
-   * }
-   */
-  public static final OfInt ef_construction$layout() {
-    return ef_construction$LAYOUT;
-  }
-
-  private static final long ef_construction$OFFSET = 4;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * int ef_construction
-   * }
-   */
-  public static final long ef_construction$offset() {
-    return ef_construction$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * int ef_construction
-   * }
-   */
-  public static int ef_construction(MemorySegment struct) {
-    return struct.get(ef_construction$LAYOUT, ef_construction$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * int ef_construction
-   * }
-   */
-  public static void ef_construction(MemorySegment struct, int fieldValue) {
-    struct.set(ef_construction$LAYOUT, ef_construction$OFFSET, fieldValue);
-  }
-
-  private static final OfInt num_threads$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_threads"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static final OfInt num_threads$layout() {
-    return num_threads$LAYOUT;
-  }
-
-  private static final long num_threads$OFFSET = 8;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static final long num_threads$offset() {
-    return num_threads$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static int num_threads(MemorySegment struct) {
-    return struct.get(num_threads$LAYOUT, num_threads$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * int num_threads
-   * }
-   */
-  public static void num_threads(MemorySegment struct, int fieldValue) {
-    struct.set(num_threads$LAYOUT, num_threads$OFFSET, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswSearchParams.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswSearchParams.java
deleted file mode 100644
index ff8329a57c..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/CuVSHnswSearchParams.java
+++ /dev/null
@@ -1,183 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct cuvsHnswSearchParams {
- *     int32_t ef;
- *     int32_t num_threads;
- * }
- * }
- */
-public class CuVSHnswSearchParams {
-
-  CuVSHnswSearchParams() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout
-      .structLayout(HnswH.C_INT.withName("ef"), HnswH.C_INT.withName("num_threads")).withName("cuvsHnswSearchParams");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfInt ef$LAYOUT = (OfInt) $LAYOUT.select(groupElement("ef"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * int32_t ef
-   * }
-   */
-  public static final OfInt ef$layout() {
-    return ef$LAYOUT;
-  }
-
-  private static final long ef$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * int32_t ef
-   * }
-   */
-  public static final long ef$offset() {
-    return ef$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * int32_t ef
-   * }
-   */
-  public static int ef(MemorySegment struct) {
-    return struct.get(ef$LAYOUT, ef$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * int32_t ef
-   * }
-   */
-  public static void ef(MemorySegment struct, int fieldValue) {
-    struct.set(ef$LAYOUT, ef$OFFSET, fieldValue);
-  }
-
-  private static final OfInt num_threads$LAYOUT = (OfInt) $LAYOUT.select(groupElement("num_threads"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * int32_t num_threads
-   * }
-   */
-  public static final OfInt num_threads$layout() {
-    return num_threads$LAYOUT;
-  }
-
-  private static final long num_threads$OFFSET = 4;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * int32_t num_threads
-   * }
-   */
-  public static final long num_threads$offset() {
-    return num_threads$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * int32_t num_threads
-   * }
-   */
-  public static int num_threads(MemorySegment struct) {
-    return struct.get(num_threads$LAYOUT, num_threads$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * int32_t num_threads
-   * }
-   */
-  public static void num_threads(MemorySegment struct, int fieldValue) {
-    struct.set(num_threads$LAYOUT, num_threads$OFFSET, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDataType.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDataType.java
deleted file mode 100644
index aaa2f4d2dd..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDataType.java
+++ /dev/null
@@ -1,233 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfByte;
-import java.lang.foreign.ValueLayout.OfShort;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct {
- *     uint8_t code;
- *     uint8_t bits;
- *     uint16_t lanes;
- * }
- * }
- */
-public class DLDataType {
-
-    DLDataType() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        DlpackH.C_CHAR.withName("code"),
-        DlpackH.C_CHAR.withName("bits"),
-        DlpackH.C_SHORT.withName("lanes")
-    ).withName("$anon$174:9");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final OfByte code$LAYOUT = (OfByte)$LAYOUT.select(groupElement("code"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uint8_t code
-     * }
-     */
-    public static final OfByte code$layout() {
-        return code$LAYOUT;
-    }
-
-    private static final long code$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uint8_t code
-     * }
-     */
-    public static final long code$offset() {
-        return code$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uint8_t code
-     * }
-     */
-    public static byte code(MemorySegment struct) {
-        return struct.get(code$LAYOUT, code$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uint8_t code
-     * }
-     */
-    public static void code(MemorySegment struct, byte fieldValue) {
-        struct.set(code$LAYOUT, code$OFFSET, fieldValue);
-    }
-
-    private static final OfByte bits$LAYOUT = (OfByte)$LAYOUT.select(groupElement("bits"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uint8_t bits
-     * }
-     */
-    public static final OfByte bits$layout() {
-        return bits$LAYOUT;
-    }
-
-    private static final long bits$OFFSET = 1;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uint8_t bits
-     * }
-     */
-    public static final long bits$offset() {
-        return bits$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uint8_t bits
-     * }
-     */
-    public static byte bits(MemorySegment struct) {
-        return struct.get(bits$LAYOUT, bits$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uint8_t bits
-     * }
-     */
-    public static void bits(MemorySegment struct, byte fieldValue) {
-        struct.set(bits$LAYOUT, bits$OFFSET, fieldValue);
-    }
-
-    private static final OfShort lanes$LAYOUT = (OfShort)$LAYOUT.select(groupElement("lanes"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uint16_t lanes
-     * }
-     */
-    public static final OfShort lanes$layout() {
-        return lanes$LAYOUT;
-    }
-
-    private static final long lanes$OFFSET = 2;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uint16_t lanes
-     * }
-     */
-    public static final long lanes$offset() {
-        return lanes$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uint16_t lanes
-     * }
-     */
-    public static short lanes(MemorySegment struct) {
-        return struct.get(lanes$LAYOUT, lanes$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uint16_t lanes
-     * }
-     */
-    public static void lanes(MemorySegment struct, short fieldValue) {
-        struct.set(lanes$LAYOUT, lanes$OFFSET, fieldValue);
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDevice.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDevice.java
deleted file mode 100644
index ceadb6a698..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLDevice.java
+++ /dev/null
@@ -1,186 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct {
- *     DLDeviceType device_type;
- *     int32_t device_id;
- * }
- * }
- */
-public class DLDevice {
-
-    DLDevice() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        DlpackH.C_INT.withName("device_type"),
-        DlpackH.C_INT.withName("device_id")
-    ).withName("$anon$126:9");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final OfInt device_type$LAYOUT = (OfInt)$LAYOUT.select(groupElement("device_type"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * DLDeviceType device_type
-     * }
-     */
-    public static final OfInt device_type$layout() {
-        return device_type$LAYOUT;
-    }
-
-    private static final long device_type$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * DLDeviceType device_type
-     * }
-     */
-    public static final long device_type$offset() {
-        return device_type$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * DLDeviceType device_type
-     * }
-     */
-    public static int device_type(MemorySegment struct) {
-        return struct.get(device_type$LAYOUT, device_type$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * DLDeviceType device_type
-     * }
-     */
-    public static void device_type(MemorySegment struct, int fieldValue) {
-        struct.set(device_type$LAYOUT, device_type$OFFSET, fieldValue);
-    }
-
-    private static final OfInt device_id$LAYOUT = (OfInt)$LAYOUT.select(groupElement("device_id"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * int32_t device_id
-     * }
-     */
-    public static final OfInt device_id$layout() {
-        return device_id$LAYOUT;
-    }
-
-    private static final long device_id$OFFSET = 4;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * int32_t device_id
-     * }
-     */
-    public static final long device_id$offset() {
-        return device_id$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * int32_t device_id
-     * }
-     */
-    public static int device_id(MemorySegment struct) {
-        return struct.get(device_id$LAYOUT, device_id$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * int32_t device_id
-     * }
-     */
-    public static void device_id(MemorySegment struct, int fieldValue) {
-        struct.set(device_id$LAYOUT, device_id$OFFSET, fieldValue);
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensor.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensor.java
deleted file mode 100644
index c28dd71c37..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensor.java
+++ /dev/null
@@ -1,288 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.invoke.MethodHandle;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct DLManagedTensor {
- *     DLTensor dl_tensor;
- *     void *manager_ctx;
- *     void (*deleter)(struct DLManagedTensor *);
- * }
- * }
- */
-public class DLManagedTensor {
-
-    DLManagedTensor() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        DLTensor.layout().withName("dl_tensor"),
-        DlpackH.C_POINTER.withName("manager_ctx"),
-        DlpackH.C_POINTER.withName("deleter")
-    ).withName("DLManagedTensor");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final GroupLayout dl_tensor$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("dl_tensor"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static final GroupLayout dl_tensor$layout() {
-        return dl_tensor$LAYOUT;
-    }
-
-    private static final long dl_tensor$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static final long dl_tensor$offset() {
-        return dl_tensor$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static MemorySegment dl_tensor(MemorySegment struct) {
-        return struct.asSlice(dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static void dl_tensor(MemorySegment struct, MemorySegment fieldValue) {
-        MemorySegment.copy(fieldValue, 0L, struct, dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
-    }
-
-    private static final AddressLayout manager_ctx$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("manager_ctx"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static final AddressLayout manager_ctx$layout() {
-        return manager_ctx$LAYOUT;
-    }
-
-    private static final long manager_ctx$OFFSET = 48;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static final long manager_ctx$offset() {
-        return manager_ctx$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static MemorySegment manager_ctx(MemorySegment struct) {
-        return struct.get(manager_ctx$LAYOUT, manager_ctx$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static void manager_ctx(MemorySegment struct, MemorySegment fieldValue) {
-        struct.set(manager_ctx$LAYOUT, manager_ctx$OFFSET, fieldValue);
-    }
-
-    /**
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensor *)
-     * }
-     */
-    public static class deleter {
-
-        deleter() {
-            // Should not be called directly
-        }
-
-        /**
-         * The function pointer signature, expressed as a functional interface
-         */
-        public interface Function {
-            void apply(MemorySegment _x0);
-        }
-
-        private static final FunctionDescriptor $DESC = FunctionDescriptor.ofVoid(
-            DlpackH.C_POINTER
-        );
-
-        /**
-         * The descriptor of this function pointer
-         */
-        public static FunctionDescriptor descriptor() {
-            return $DESC;
-        }
-
-        private static final MethodHandle UP$MH = DlpackH.upcallHandle(deleter.Function.class, "apply", $DESC);
-
-        /**
-         * Allocates a new upcall stub, whose implementation is defined by {@code fi}.
-         * The lifetime of the returned segment is managed by {@code arena}
-         */
-        public static MemorySegment allocate(deleter.Function fi, Arena arena) {
-            return Linker.nativeLinker().upcallStub(UP$MH.bindTo(fi), $DESC, arena);
-        }
-
-        private static final MethodHandle DOWN$MH = Linker.nativeLinker().downcallHandle($DESC);
-
-        /**
-         * Invoke the upcall stub {@code funcPtr}, with given parameters
-         */
-        public static void invoke(MemorySegment funcPtr,MemorySegment _x0) {
-            try {
-                 DOWN$MH.invokeExact(funcPtr, _x0);
-            } catch (Throwable ex$) {
-                throw new AssertionError("should not reach here", ex$);
-            }
-        }
-    }
-
-    private static final AddressLayout deleter$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("deleter"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensor *)
-     * }
-     */
-    public static final AddressLayout deleter$layout() {
-        return deleter$LAYOUT;
-    }
-
-    private static final long deleter$OFFSET = 56;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensor *)
-     * }
-     */
-    public static final long deleter$offset() {
-        return deleter$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensor *)
-     * }
-     */
-    public static MemorySegment deleter(MemorySegment struct) {
-        return struct.get(deleter$LAYOUT, deleter$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensor *)
-     * }
-     */
-    public static void deleter(MemorySegment struct, MemorySegment fieldValue) {
-        struct.set(deleter$LAYOUT, deleter$OFFSET, fieldValue);
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensorVersioned.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensorVersioned.java
deleted file mode 100644
index f631140e23..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLManagedTensorVersioned.java
+++ /dev/null
@@ -1,381 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.invoke.MethodHandle;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct DLManagedTensorVersioned {
- *     DLPackVersion version;
- *     void *manager_ctx;
- *     void (*deleter)(struct DLManagedTensorVersioned *);
- *     uint64_t flags;
- *     DLTensor dl_tensor;
- * }
- * }
- */
-public class DLManagedTensorVersioned {
-
-    DLManagedTensorVersioned() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        DLPackVersion.layout().withName("version"),
-        DlpackH.C_POINTER.withName("manager_ctx"),
-        DlpackH.C_POINTER.withName("deleter"),
-        DlpackH.C_LONG.withName("flags"),
-        DLTensor.layout().withName("dl_tensor")
-    ).withName("DLManagedTensorVersioned");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final GroupLayout version$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("version"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * DLPackVersion version
-     * }
-     */
-    public static final GroupLayout version$layout() {
-        return version$LAYOUT;
-    }
-
-    private static final long version$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * DLPackVersion version
-     * }
-     */
-    public static final long version$offset() {
-        return version$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * DLPackVersion version
-     * }
-     */
-    public static MemorySegment version(MemorySegment struct) {
-        return struct.asSlice(version$OFFSET, version$LAYOUT.byteSize());
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * DLPackVersion version
-     * }
-     */
-    public static void version(MemorySegment struct, MemorySegment fieldValue) {
-        MemorySegment.copy(fieldValue, 0L, struct, version$OFFSET, version$LAYOUT.byteSize());
-    }
-
-    private static final AddressLayout manager_ctx$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("manager_ctx"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static final AddressLayout manager_ctx$layout() {
-        return manager_ctx$LAYOUT;
-    }
-
-    private static final long manager_ctx$OFFSET = 8;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static final long manager_ctx$offset() {
-        return manager_ctx$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static MemorySegment manager_ctx(MemorySegment struct) {
-        return struct.get(manager_ctx$LAYOUT, manager_ctx$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * void *manager_ctx
-     * }
-     */
-    public static void manager_ctx(MemorySegment struct, MemorySegment fieldValue) {
-        struct.set(manager_ctx$LAYOUT, manager_ctx$OFFSET, fieldValue);
-    }
-
-    /**
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensorVersioned *)
-     * }
-     */
-    public static class deleter {
-
-        deleter() {
-            // Should not be called directly
-        }
-
-        /**
-         * The function pointer signature, expressed as a functional interface
-         */
-        public interface Function {
-            void apply(MemorySegment _x0);
-        }
-
-        private static final FunctionDescriptor $DESC = FunctionDescriptor.ofVoid(
-            DlpackH.C_POINTER
-        );
-
-        /**
-         * The descriptor of this function pointer
-         */
-        public static FunctionDescriptor descriptor() {
-            return $DESC;
-        }
-
-        private static final MethodHandle UP$MH = DlpackH.upcallHandle(deleter.Function.class, "apply", $DESC);
-
-        /**
-         * Allocates a new upcall stub, whose implementation is defined by {@code fi}.
-         * The lifetime of the returned segment is managed by {@code arena}
-         */
-        public static MemorySegment allocate(deleter.Function fi, Arena arena) {
-            return Linker.nativeLinker().upcallStub(UP$MH.bindTo(fi), $DESC, arena);
-        }
-
-        private static final MethodHandle DOWN$MH = Linker.nativeLinker().downcallHandle($DESC);
-
-        /**
-         * Invoke the upcall stub {@code funcPtr}, with given parameters
-         */
-        public static void invoke(MemorySegment funcPtr,MemorySegment _x0) {
-            try {
-                 DOWN$MH.invokeExact(funcPtr, _x0);
-            } catch (Throwable ex$) {
-                throw new AssertionError("should not reach here", ex$);
-            }
-        }
-    }
-
-    private static final AddressLayout deleter$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("deleter"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensorVersioned *)
-     * }
-     */
-    public static final AddressLayout deleter$layout() {
-        return deleter$LAYOUT;
-    }
-
-    private static final long deleter$OFFSET = 16;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensorVersioned *)
-     * }
-     */
-    public static final long deleter$offset() {
-        return deleter$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensorVersioned *)
-     * }
-     */
-    public static MemorySegment deleter(MemorySegment struct) {
-        return struct.get(deleter$LAYOUT, deleter$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * void (*deleter)(struct DLManagedTensorVersioned *)
-     * }
-     */
-    public static void deleter(MemorySegment struct, MemorySegment fieldValue) {
-        struct.set(deleter$LAYOUT, deleter$OFFSET, fieldValue);
-    }
-
-    private static final OfLong flags$LAYOUT = (OfLong)$LAYOUT.select(groupElement("flags"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uint64_t flags
-     * }
-     */
-    public static final OfLong flags$layout() {
-        return flags$LAYOUT;
-    }
-
-    private static final long flags$OFFSET = 24;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uint64_t flags
-     * }
-     */
-    public static final long flags$offset() {
-        return flags$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uint64_t flags
-     * }
-     */
-    public static long flags(MemorySegment struct) {
-        return struct.get(flags$LAYOUT, flags$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uint64_t flags
-     * }
-     */
-    public static void flags(MemorySegment struct, long fieldValue) {
-        struct.set(flags$LAYOUT, flags$OFFSET, fieldValue);
-    }
-
-    private static final GroupLayout dl_tensor$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("dl_tensor"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static final GroupLayout dl_tensor$layout() {
-        return dl_tensor$LAYOUT;
-    }
-
-    private static final long dl_tensor$OFFSET = 32;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static final long dl_tensor$offset() {
-        return dl_tensor$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static MemorySegment dl_tensor(MemorySegment struct) {
-        return struct.asSlice(dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * DLTensor dl_tensor
-     * }
-     */
-    public static void dl_tensor(MemorySegment struct, MemorySegment fieldValue) {
-        MemorySegment.copy(fieldValue, 0L, struct, dl_tensor$OFFSET, dl_tensor$LAYOUT.byteSize());
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLPackVersion.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLPackVersion.java
deleted file mode 100644
index c65fdd8871..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLPackVersion.java
+++ /dev/null
@@ -1,186 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct {
- *     uint32_t major;
- *     uint32_t minor;
- * }
- * }
- */
-public class DLPackVersion {
-
-    DLPackVersion() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        DlpackH.C_INT.withName("major"),
-        DlpackH.C_INT.withName("minor")
-    ).withName("$anon$61:9");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final OfInt major$LAYOUT = (OfInt)$LAYOUT.select(groupElement("major"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uint32_t major
-     * }
-     */
-    public static final OfInt major$layout() {
-        return major$LAYOUT;
-    }
-
-    private static final long major$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uint32_t major
-     * }
-     */
-    public static final long major$offset() {
-        return major$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uint32_t major
-     * }
-     */
-    public static int major(MemorySegment struct) {
-        return struct.get(major$LAYOUT, major$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uint32_t major
-     * }
-     */
-    public static void major(MemorySegment struct, int fieldValue) {
-        struct.set(major$LAYOUT, major$OFFSET, fieldValue);
-    }
-
-    private static final OfInt minor$LAYOUT = (OfInt)$LAYOUT.select(groupElement("minor"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uint32_t minor
-     * }
-     */
-    public static final OfInt minor$layout() {
-        return minor$LAYOUT;
-    }
-
-    private static final long minor$OFFSET = 4;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uint32_t minor
-     * }
-     */
-    public static final long minor$offset() {
-        return minor$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uint32_t minor
-     * }
-     */
-    public static int minor(MemorySegment struct) {
-        return struct.get(minor$LAYOUT, minor$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uint32_t minor
-     * }
-     */
-    public static void minor(MemorySegment struct, int fieldValue) {
-        struct.set(minor$LAYOUT, minor$OFFSET, fieldValue);
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLTensor.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLTensor.java
deleted file mode 100644
index 21d928905e..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DLTensor.java
+++ /dev/null
@@ -1,418 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct {
- *     void *data;
- *     DLDevice device;
- *     int32_t ndim;
- *     DLDataType dtype;
- *     int64_t *shape;
- *     int64_t *strides;
- *     uint64_t byte_offset;
- * }
- * }
- */
-public class DLTensor {
-
-    DLTensor() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        DlpackH.C_POINTER.withName("data"),
-        DLDevice.layout().withName("device"),
-        DlpackH.C_INT.withName("ndim"),
-        DLDataType.layout().withName("dtype"),
-        DlpackH.C_POINTER.withName("shape"),
-        DlpackH.C_POINTER.withName("strides"),
-        DlpackH.C_LONG.withName("byte_offset")
-    ).withName("$anon$192:9");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final AddressLayout data$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("data"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * void *data
-     * }
-     */
-    public static final AddressLayout data$layout() {
-        return data$LAYOUT;
-    }
-
-    private static final long data$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * void *data
-     * }
-     */
-    public static final long data$offset() {
-        return data$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * void *data
-     * }
-     */
-    public static MemorySegment data(MemorySegment struct) {
-        return struct.get(data$LAYOUT, data$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * void *data
-     * }
-     */
-    public static void data(MemorySegment struct, MemorySegment fieldValue) {
-        struct.set(data$LAYOUT, data$OFFSET, fieldValue);
-    }
-
-    private static final GroupLayout device$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("device"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * DLDevice device
-     * }
-     */
-    public static final GroupLayout device$layout() {
-        return device$LAYOUT;
-    }
-
-    private static final long device$OFFSET = 8;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * DLDevice device
-     * }
-     */
-    public static final long device$offset() {
-        return device$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * DLDevice device
-     * }
-     */
-    public static MemorySegment device(MemorySegment struct) {
-        return struct.asSlice(device$OFFSET, device$LAYOUT.byteSize());
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * DLDevice device
-     * }
-     */
-    public static void device(MemorySegment struct, MemorySegment fieldValue) {
-        MemorySegment.copy(fieldValue, 0L, struct, device$OFFSET, device$LAYOUT.byteSize());
-    }
-
-    private static final OfInt ndim$LAYOUT = (OfInt)$LAYOUT.select(groupElement("ndim"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * int32_t ndim
-     * }
-     */
-    public static final OfInt ndim$layout() {
-        return ndim$LAYOUT;
-    }
-
-    private static final long ndim$OFFSET = 16;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * int32_t ndim
-     * }
-     */
-    public static final long ndim$offset() {
-        return ndim$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * int32_t ndim
-     * }
-     */
-    public static int ndim(MemorySegment struct) {
-        return struct.get(ndim$LAYOUT, ndim$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * int32_t ndim
-     * }
-     */
-    public static void ndim(MemorySegment struct, int fieldValue) {
-        struct.set(ndim$LAYOUT, ndim$OFFSET, fieldValue);
-    }
-
-    private static final GroupLayout dtype$LAYOUT = (GroupLayout)$LAYOUT.select(groupElement("dtype"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * DLDataType dtype
-     * }
-     */
-    public static final GroupLayout dtype$layout() {
-        return dtype$LAYOUT;
-    }
-
-    private static final long dtype$OFFSET = 20;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * DLDataType dtype
-     * }
-     */
-    public static final long dtype$offset() {
-        return dtype$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * DLDataType dtype
-     * }
-     */
-    public static MemorySegment dtype(MemorySegment struct) {
-        return struct.asSlice(dtype$OFFSET, dtype$LAYOUT.byteSize());
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * DLDataType dtype
-     * }
-     */
-    public static void dtype(MemorySegment struct, MemorySegment fieldValue) {
-        MemorySegment.copy(fieldValue, 0L, struct, dtype$OFFSET, dtype$LAYOUT.byteSize());
-    }
-
-    private static final AddressLayout shape$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("shape"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * int64_t *shape
-     * }
-     */
-    public static final AddressLayout shape$layout() {
-        return shape$LAYOUT;
-    }
-
-    private static final long shape$OFFSET = 24;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * int64_t *shape
-     * }
-     */
-    public static final long shape$offset() {
-        return shape$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * int64_t *shape
-     * }
-     */
-    public static MemorySegment shape(MemorySegment struct) {
-        return struct.get(shape$LAYOUT, shape$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * int64_t *shape
-     * }
-     */
-    public static void shape(MemorySegment struct, MemorySegment fieldValue) {
-        struct.set(shape$LAYOUT, shape$OFFSET, fieldValue);
-    }
-
-    private static final AddressLayout strides$LAYOUT = (AddressLayout)$LAYOUT.select(groupElement("strides"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * int64_t *strides
-     * }
-     */
-    public static final AddressLayout strides$layout() {
-        return strides$LAYOUT;
-    }
-
-    private static final long strides$OFFSET = 32;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * int64_t *strides
-     * }
-     */
-    public static final long strides$offset() {
-        return strides$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * int64_t *strides
-     * }
-     */
-    public static MemorySegment strides(MemorySegment struct) {
-        return struct.get(strides$LAYOUT, strides$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * int64_t *strides
-     * }
-     */
-    public static void strides(MemorySegment struct, MemorySegment fieldValue) {
-        struct.set(strides$LAYOUT, strides$OFFSET, fieldValue);
-    }
-
-    private static final OfLong byte_offset$LAYOUT = (OfLong)$LAYOUT.select(groupElement("byte_offset"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * uint64_t byte_offset
-     * }
-     */
-    public static final OfLong byte_offset$layout() {
-        return byte_offset$LAYOUT;
-    }
-
-    private static final long byte_offset$OFFSET = 40;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * uint64_t byte_offset
-     * }
-     */
-    public static final long byte_offset$offset() {
-        return byte_offset$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * uint64_t byte_offset
-     * }
-     */
-    public static long byte_offset(MemorySegment struct) {
-        return struct.get(byte_offset$LAYOUT, byte_offset$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * uint64_t byte_offset
-     * }
-     */
-    public static void byte_offset(MemorySegment struct, long fieldValue) {
-        struct.set(byte_offset$LAYOUT, byte_offset$OFFSET, fieldValue);
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DistanceH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DistanceH.java
deleted file mode 100644
index b98f95ae23..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DistanceH.java
+++ /dev/null
@@ -1,274 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import java.lang.invoke.*;
-import java.lang.foreign.*;
-import java.nio.ByteOrder;
-import java.util.*;
-import java.util.function.*;
-import java.util.stream.*;
-
-import static java.lang.foreign.ValueLayout.*;
-import static java.lang.foreign.MemoryLayout.PathElement.*;
-
-public class DistanceH {
-
-    DistanceH() {
-        // Should not be called directly
-    }
-
-    static final Arena LIBRARY_ARENA = Arena.ofAuto();
-    static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-    static void traceDowncall(String name, Object... args) {
-         String traceArgs = Arrays.stream(args)
-                       .map(Object::toString)
-                       .collect(Collectors.joining(", "));
-         System.out.printf("%s(%s)\n", name, traceArgs);
-    }
-
-    static MemorySegment findOrThrow(String symbol) {
-        return SYMBOL_LOOKUP.find(symbol)
-            .orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-    }
-
-    static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-        try {
-            return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-        } catch (ReflectiveOperationException ex) {
-            throw new AssertionError(ex);
-        }
-    }
-
-    static MemoryLayout align(MemoryLayout layout, long align) {
-        return switch (layout) {
-            case PaddingLayout p -> p;
-            case ValueLayout v -> v.withByteAlignment(align);
-            case GroupLayout g -> {
-                MemoryLayout[] alignedMembers = g.memberLayouts().stream()
-                        .map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-                yield g instanceof StructLayout ?
-                        MemoryLayout.structLayout(alignedMembers) : MemoryLayout.unionLayout(alignedMembers);
-            }
-            case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-        };
-    }
-
-    static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup()
-            .or(Linker.nativeLinker().defaultLookup());
-
-    public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-    public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-    public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-    public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-    public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-    public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-    public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-    public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-            .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-    public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-    private static final int L2Expanded = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.L2Expanded = 0
-     * }
-     */
-    public static int L2Expanded() {
-        return L2Expanded;
-    }
-    private static final int L2SqrtExpanded = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.L2SqrtExpanded = 1
-     * }
-     */
-    public static int L2SqrtExpanded() {
-        return L2SqrtExpanded;
-    }
-    private static final int CosineExpanded = (int)2L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.CosineExpanded = 2
-     * }
-     */
-    public static int CosineExpanded() {
-        return CosineExpanded;
-    }
-    private static final int L1 = (int)3L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.L1 = 3
-     * }
-     */
-    public static int L1() {
-        return L1;
-    }
-    private static final int L2Unexpanded = (int)4L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.L2Unexpanded = 4
-     * }
-     */
-    public static int L2Unexpanded() {
-        return L2Unexpanded;
-    }
-    private static final int L2SqrtUnexpanded = (int)5L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.L2SqrtUnexpanded = 5
-     * }
-     */
-    public static int L2SqrtUnexpanded() {
-        return L2SqrtUnexpanded;
-    }
-    private static final int InnerProduct = (int)6L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.InnerProduct = 6
-     * }
-     */
-    public static int InnerProduct() {
-        return InnerProduct;
-    }
-    private static final int Linf = (int)7L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.Linf = 7
-     * }
-     */
-    public static int Linf() {
-        return Linf;
-    }
-    private static final int Canberra = (int)8L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.Canberra = 8
-     * }
-     */
-    public static int Canberra() {
-        return Canberra;
-    }
-    private static final int LpUnexpanded = (int)9L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.LpUnexpanded = 9
-     * }
-     */
-    public static int LpUnexpanded() {
-        return LpUnexpanded;
-    }
-    private static final int CorrelationExpanded = (int)10L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.CorrelationExpanded = 10
-     * }
-     */
-    public static int CorrelationExpanded() {
-        return CorrelationExpanded;
-    }
-    private static final int JaccardExpanded = (int)11L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.JaccardExpanded = 11
-     * }
-     */
-    public static int JaccardExpanded() {
-        return JaccardExpanded;
-    }
-    private static final int HellingerExpanded = (int)12L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.HellingerExpanded = 12
-     * }
-     */
-    public static int HellingerExpanded() {
-        return HellingerExpanded;
-    }
-    private static final int Haversine = (int)13L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.Haversine = 13
-     * }
-     */
-    public static int Haversine() {
-        return Haversine;
-    }
-    private static final int BrayCurtis = (int)14L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.BrayCurtis = 14
-     * }
-     */
-    public static int BrayCurtis() {
-        return BrayCurtis;
-    }
-    private static final int JensenShannon = (int)15L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.JensenShannon = 15
-     * }
-     */
-    public static int JensenShannon() {
-        return JensenShannon;
-    }
-    private static final int HammingUnexpanded = (int)16L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.HammingUnexpanded = 16
-     * }
-     */
-    public static int HammingUnexpanded() {
-        return HammingUnexpanded;
-    }
-    private static final int KLDivergence = (int)17L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.KLDivergence = 17
-     * }
-     */
-    public static int KLDivergence() {
-        return KLDivergence;
-    }
-    private static final int RusselRaoExpanded = (int)18L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.RusselRaoExpanded = 18
-     * }
-     */
-    public static int RusselRaoExpanded() {
-        return RusselRaoExpanded;
-    }
-    private static final int DiceExpanded = (int)19L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.DiceExpanded = 19
-     * }
-     */
-    public static int DiceExpanded() {
-        return DiceExpanded;
-    }
-    private static final int Precomputed = (int)100L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.Precomputed = 100
-     * }
-     */
-    public static int Precomputed() {
-        return Precomputed;
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DlpackH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DlpackH.java
deleted file mode 100644
index 6ef78336f7..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/DlpackH.java
+++ /dev/null
@@ -1,1898 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.ValueLayout.JAVA_BYTE;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.PaddingLayout;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.StructLayout;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.foreign.ValueLayout.OfByte;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.foreign.ValueLayout.OfShort;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-public class DlpackH {
-
-    DlpackH() {
-        // Should not be called directly
-    }
-
-    static final Arena LIBRARY_ARENA = Arena.ofAuto();
-    static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-    static void traceDowncall(String name, Object... args) {
-         String traceArgs = Arrays.stream(args)
-                       .map(Object::toString)
-                       .collect(Collectors.joining(", "));
-         System.out.printf("%s(%s)\n", name, traceArgs);
-    }
-
-    static MemorySegment findOrThrow(String symbol) {
-        return SYMBOL_LOOKUP.find(symbol)
-            .orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-    }
-
-    static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-        try {
-            return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-        } catch (ReflectiveOperationException ex) {
-            throw new AssertionError(ex);
-        }
-    }
-
-    static MemoryLayout align(MemoryLayout layout, long align) {
-        return switch (layout) {
-            case PaddingLayout p -> p;
-            case ValueLayout v -> v.withByteAlignment(align);
-            case GroupLayout g -> {
-                MemoryLayout[] alignedMembers = g.memberLayouts().stream()
-                        .map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-                yield g instanceof StructLayout ?
-                        MemoryLayout.structLayout(alignedMembers) : MemoryLayout.unionLayout(alignedMembers);
-            }
-            case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-        };
-    }
-
-    static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup()
-            .or(Linker.nativeLinker().defaultLookup());
-
-    public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-    public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-    public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-    public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-    public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-    public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-    public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-    public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-            .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-    public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-    private static final int DLPACK_MAJOR_VERSION = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define DLPACK_MAJOR_VERSION 1
-     * }
-     */
-    public static int DLPACK_MAJOR_VERSION() {
-        return DLPACK_MAJOR_VERSION;
-    }
-    private static final int DLPACK_MINOR_VERSION = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define DLPACK_MINOR_VERSION 0
-     * }
-     */
-    public static int DLPACK_MINOR_VERSION() {
-        return DLPACK_MINOR_VERSION;
-    }
-    private static final int _STDINT_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _STDINT_H 1
-     * }
-     */
-    public static int _STDINT_H() {
-        return _STDINT_H;
-    }
-    private static final int _FEATURES_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _FEATURES_H 1
-     * }
-     */
-    public static int _FEATURES_H() {
-        return _FEATURES_H;
-    }
-    private static final int _DEFAULT_SOURCE = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _DEFAULT_SOURCE 1
-     * }
-     */
-    public static int _DEFAULT_SOURCE() {
-        return _DEFAULT_SOURCE;
-    }
-    private static final int __GLIBC_USE_ISOC2X = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_ISOC2X 0
-     * }
-     */
-    public static int __GLIBC_USE_ISOC2X() {
-        return __GLIBC_USE_ISOC2X;
-    }
-    private static final int __USE_ISOC11 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_ISOC11 1
-     * }
-     */
-    public static int __USE_ISOC11() {
-        return __USE_ISOC11;
-    }
-    private static final int __USE_ISOC99 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_ISOC99 1
-     * }
-     */
-    public static int __USE_ISOC99() {
-        return __USE_ISOC99;
-    }
-    private static final int __USE_ISOC95 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_ISOC95 1
-     * }
-     */
-    public static int __USE_ISOC95() {
-        return __USE_ISOC95;
-    }
-    private static final int __USE_POSIX_IMPLICITLY = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_POSIX_IMPLICITLY 1
-     * }
-     */
-    public static int __USE_POSIX_IMPLICITLY() {
-        return __USE_POSIX_IMPLICITLY;
-    }
-    private static final int _POSIX_SOURCE = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _POSIX_SOURCE 1
-     * }
-     */
-    public static int _POSIX_SOURCE() {
-        return _POSIX_SOURCE;
-    }
-    private static final int __USE_POSIX = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_POSIX 1
-     * }
-     */
-    public static int __USE_POSIX() {
-        return __USE_POSIX;
-    }
-    private static final int __USE_POSIX2 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_POSIX2 1
-     * }
-     */
-    public static int __USE_POSIX2() {
-        return __USE_POSIX2;
-    }
-    private static final int __USE_POSIX199309 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_POSIX199309 1
-     * }
-     */
-    public static int __USE_POSIX199309() {
-        return __USE_POSIX199309;
-    }
-    private static final int __USE_POSIX199506 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_POSIX199506 1
-     * }
-     */
-    public static int __USE_POSIX199506() {
-        return __USE_POSIX199506;
-    }
-    private static final int __USE_XOPEN2K = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_XOPEN2K 1
-     * }
-     */
-    public static int __USE_XOPEN2K() {
-        return __USE_XOPEN2K;
-    }
-    private static final int __USE_XOPEN2K8 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_XOPEN2K8 1
-     * }
-     */
-    public static int __USE_XOPEN2K8() {
-        return __USE_XOPEN2K8;
-    }
-    private static final int _ATFILE_SOURCE = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _ATFILE_SOURCE 1
-     * }
-     */
-    public static int _ATFILE_SOURCE() {
-        return _ATFILE_SOURCE;
-    }
-    private static final int __WORDSIZE = (int)64L;
-    /**
-     * {@snippet lang=c :
-     * #define __WORDSIZE 64
-     * }
-     */
-    public static int __WORDSIZE() {
-        return __WORDSIZE;
-    }
-    private static final int __WORDSIZE_TIME64_COMPAT32 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __WORDSIZE_TIME64_COMPAT32 1
-     * }
-     */
-    public static int __WORDSIZE_TIME64_COMPAT32() {
-        return __WORDSIZE_TIME64_COMPAT32;
-    }
-    private static final int __SYSCALL_WORDSIZE = (int)64L;
-    /**
-     * {@snippet lang=c :
-     * #define __SYSCALL_WORDSIZE 64
-     * }
-     */
-    public static int __SYSCALL_WORDSIZE() {
-        return __SYSCALL_WORDSIZE;
-    }
-    private static final int __USE_MISC = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_MISC 1
-     * }
-     */
-    public static int __USE_MISC() {
-        return __USE_MISC;
-    }
-    private static final int __USE_ATFILE = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_ATFILE 1
-     * }
-     */
-    public static int __USE_ATFILE() {
-        return __USE_ATFILE;
-    }
-    private static final int __USE_FORTIFY_LEVEL = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __USE_FORTIFY_LEVEL 0
-     * }
-     */
-    public static int __USE_FORTIFY_LEVEL() {
-        return __USE_FORTIFY_LEVEL;
-    }
-    private static final int __GLIBC_USE_DEPRECATED_GETS = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_DEPRECATED_GETS 0
-     * }
-     */
-    public static int __GLIBC_USE_DEPRECATED_GETS() {
-        return __GLIBC_USE_DEPRECATED_GETS;
-    }
-    private static final int __GLIBC_USE_DEPRECATED_SCANF = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_DEPRECATED_SCANF 0
-     * }
-     */
-    public static int __GLIBC_USE_DEPRECATED_SCANF() {
-        return __GLIBC_USE_DEPRECATED_SCANF;
-    }
-    private static final int _STDC_PREDEF_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _STDC_PREDEF_H 1
-     * }
-     */
-    public static int _STDC_PREDEF_H() {
-        return _STDC_PREDEF_H;
-    }
-    private static final int __STDC_IEC_559__ = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __STDC_IEC_559__ 1
-     * }
-     */
-    public static int __STDC_IEC_559__() {
-        return __STDC_IEC_559__;
-    }
-    private static final int __STDC_IEC_559_COMPLEX__ = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __STDC_IEC_559_COMPLEX__ 1
-     * }
-     */
-    public static int __STDC_IEC_559_COMPLEX__() {
-        return __STDC_IEC_559_COMPLEX__;
-    }
-    private static final int __GNU_LIBRARY__ = (int)6L;
-    /**
-     * {@snippet lang=c :
-     * #define __GNU_LIBRARY__ 6
-     * }
-     */
-    public static int __GNU_LIBRARY__() {
-        return __GNU_LIBRARY__;
-    }
-    private static final int __GLIBC__ = (int)2L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC__ 2
-     * }
-     */
-    public static int __GLIBC__() {
-        return __GLIBC__;
-    }
-    private static final int __GLIBC_MINOR__ = (int)35L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_MINOR__ 35
-     * }
-     */
-    public static int __GLIBC_MINOR__() {
-        return __GLIBC_MINOR__;
-    }
-    private static final int _SYS_CDEFS_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _SYS_CDEFS_H 1
-     * }
-     */
-    public static int _SYS_CDEFS_H() {
-        return _SYS_CDEFS_H;
-    }
-    private static final int __glibc_c99_flexarr_available = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __glibc_c99_flexarr_available 1
-     * }
-     */
-    public static int __glibc_c99_flexarr_available() {
-        return __glibc_c99_flexarr_available;
-    }
-    private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
-     * }
-     */
-    public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
-        return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
-    }
-    private static final int __HAVE_GENERIC_SELECTION = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __HAVE_GENERIC_SELECTION 1
-     * }
-     */
-    public static int __HAVE_GENERIC_SELECTION() {
-        return __HAVE_GENERIC_SELECTION;
-    }
-    private static final int __GLIBC_USE_LIB_EXT2 = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_LIB_EXT2 0
-     * }
-     */
-    public static int __GLIBC_USE_LIB_EXT2() {
-        return __GLIBC_USE_LIB_EXT2;
-    }
-    private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
-     * }
-     */
-    public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
-        return __GLIBC_USE_IEC_60559_BFP_EXT;
-    }
-    private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
-     * }
-     */
-    public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
-        return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
-    }
-    private static final int __GLIBC_USE_IEC_60559_EXT = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_IEC_60559_EXT 0
-     * }
-     */
-    public static int __GLIBC_USE_IEC_60559_EXT() {
-        return __GLIBC_USE_IEC_60559_EXT;
-    }
-    private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
-     * }
-     */
-    public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
-        return __GLIBC_USE_IEC_60559_FUNCS_EXT;
-    }
-    private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
-     * }
-     */
-    public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
-        return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
-    }
-    private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
-     * }
-     */
-    public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
-        return __GLIBC_USE_IEC_60559_TYPES_EXT;
-    }
-    private static final int _BITS_TYPES_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _BITS_TYPES_H 1
-     * }
-     */
-    public static int _BITS_TYPES_H() {
-        return _BITS_TYPES_H;
-    }
-    private static final int _BITS_TYPESIZES_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _BITS_TYPESIZES_H 1
-     * }
-     */
-    public static int _BITS_TYPESIZES_H() {
-        return _BITS_TYPESIZES_H;
-    }
-    private static final int __OFF_T_MATCHES_OFF64_T = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __OFF_T_MATCHES_OFF64_T 1
-     * }
-     */
-    public static int __OFF_T_MATCHES_OFF64_T() {
-        return __OFF_T_MATCHES_OFF64_T;
-    }
-    private static final int __INO_T_MATCHES_INO64_T = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __INO_T_MATCHES_INO64_T 1
-     * }
-     */
-    public static int __INO_T_MATCHES_INO64_T() {
-        return __INO_T_MATCHES_INO64_T;
-    }
-    private static final int __RLIM_T_MATCHES_RLIM64_T = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __RLIM_T_MATCHES_RLIM64_T 1
-     * }
-     */
-    public static int __RLIM_T_MATCHES_RLIM64_T() {
-        return __RLIM_T_MATCHES_RLIM64_T;
-    }
-    private static final int __STATFS_MATCHES_STATFS64 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __STATFS_MATCHES_STATFS64 1
-     * }
-     */
-    public static int __STATFS_MATCHES_STATFS64() {
-        return __STATFS_MATCHES_STATFS64;
-    }
-    private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
-     * }
-     */
-    public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
-        return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
-    }
-    private static final int __FD_SETSIZE = (int)1024L;
-    /**
-     * {@snippet lang=c :
-     * #define __FD_SETSIZE 1024
-     * }
-     */
-    public static int __FD_SETSIZE() {
-        return __FD_SETSIZE;
-    }
-    private static final int _BITS_TIME64_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _BITS_TIME64_H 1
-     * }
-     */
-    public static int _BITS_TIME64_H() {
-        return _BITS_TIME64_H;
-    }
-    private static final int _BITS_WCHAR_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _BITS_WCHAR_H 1
-     * }
-     */
-    public static int _BITS_WCHAR_H() {
-        return _BITS_WCHAR_H;
-    }
-    private static final int _BITS_STDINT_INTN_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _BITS_STDINT_INTN_H 1
-     * }
-     */
-    public static int _BITS_STDINT_INTN_H() {
-        return _BITS_STDINT_INTN_H;
-    }
-    private static final int _BITS_STDINT_UINTN_H = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * #define _BITS_STDINT_UINTN_H 1
-     * }
-     */
-    public static int _BITS_STDINT_UINTN_H() {
-        return _BITS_STDINT_UINTN_H;
-    }
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned char __u_char
-     * }
-     */
-    public static final OfByte __u_char = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned short __u_short
-     * }
-     */
-    public static final OfShort __u_short = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __u_int
-     * }
-     */
-    public static final OfInt __u_int = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __u_long
-     * }
-     */
-    public static final OfLong __u_long = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef signed char __int8_t
-     * }
-     */
-    public static final OfByte __int8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned char __uint8_t
-     * }
-     */
-    public static final OfByte __uint8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef short __int16_t
-     * }
-     */
-    public static final OfShort __int16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned short __uint16_t
-     * }
-     */
-    public static final OfShort __uint16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef int __int32_t
-     * }
-     */
-    public static final OfInt __int32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __uint32_t
-     * }
-     */
-    public static final OfInt __uint32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef long __int64_t
-     * }
-     */
-    public static final OfLong __int64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __uint64_t
-     * }
-     */
-    public static final OfLong __uint64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __int8_t __int_least8_t
-     * }
-     */
-    public static final OfByte __int_least8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint8_t __uint_least8_t
-     * }
-     */
-    public static final OfByte __uint_least8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef __int16_t __int_least16_t
-     * }
-     */
-    public static final OfShort __int_least16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint16_t __uint_least16_t
-     * }
-     */
-    public static final OfShort __uint_least16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef __int32_t __int_least32_t
-     * }
-     */
-    public static final OfInt __int_least32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint32_t __uint_least32_t
-     * }
-     */
-    public static final OfInt __uint_least32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef __int64_t __int_least64_t
-     * }
-     */
-    public static final OfLong __int_least64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint64_t __uint_least64_t
-     * }
-     */
-    public static final OfLong __uint_least64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __quad_t
-     * }
-     */
-    public static final OfLong __quad_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __u_quad_t
-     * }
-     */
-    public static final OfLong __u_quad_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __intmax_t
-     * }
-     */
-    public static final OfLong __intmax_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __uintmax_t
-     * }
-     */
-    public static final OfLong __uintmax_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __dev_t
-     * }
-     */
-    public static final OfLong __dev_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __uid_t
-     * }
-     */
-    public static final OfInt __uid_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __gid_t
-     * }
-     */
-    public static final OfInt __gid_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __ino_t
-     * }
-     */
-    public static final OfLong __ino_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __ino64_t
-     * }
-     */
-    public static final OfLong __ino64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __mode_t
-     * }
-     */
-    public static final OfInt __mode_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __nlink_t
-     * }
-     */
-    public static final OfLong __nlink_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __off_t
-     * }
-     */
-    public static final OfLong __off_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __off64_t
-     * }
-     */
-    public static final OfLong __off64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef int __pid_t
-     * }
-     */
-    public static final OfInt __pid_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef long __clock_t
-     * }
-     */
-    public static final OfLong __clock_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __rlim_t
-     * }
-     */
-    public static final OfLong __rlim_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __rlim64_t
-     * }
-     */
-    public static final OfLong __rlim64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __id_t
-     * }
-     */
-    public static final OfInt __id_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef long __time_t
-     * }
-     */
-    public static final OfLong __time_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __useconds_t
-     * }
-     */
-    public static final OfInt __useconds_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef long __suseconds_t
-     * }
-     */
-    public static final OfLong __suseconds_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __suseconds64_t
-     * }
-     */
-    public static final OfLong __suseconds64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef int __daddr_t
-     * }
-     */
-    public static final OfInt __daddr_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef int __key_t
-     * }
-     */
-    public static final OfInt __key_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef int __clockid_t
-     * }
-     */
-    public static final OfInt __clockid_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef void *__timer_t
-     * }
-     */
-    public static final AddressLayout __timer_t = DlpackH.C_POINTER;
-    /**
-     * {@snippet lang=c :
-     * typedef long __blksize_t
-     * }
-     */
-    public static final OfLong __blksize_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __blkcnt_t
-     * }
-     */
-    public static final OfLong __blkcnt_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __blkcnt64_t
-     * }
-     */
-    public static final OfLong __blkcnt64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __fsblkcnt_t
-     * }
-     */
-    public static final OfLong __fsblkcnt_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __fsblkcnt64_t
-     * }
-     */
-    public static final OfLong __fsblkcnt64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __fsfilcnt_t
-     * }
-     */
-    public static final OfLong __fsfilcnt_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __fsfilcnt64_t
-     * }
-     */
-    public static final OfLong __fsfilcnt64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __fsword_t
-     * }
-     */
-    public static final OfLong __fsword_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __ssize_t
-     * }
-     */
-    public static final OfLong __ssize_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long __syscall_slong_t
-     * }
-     */
-    public static final OfLong __syscall_slong_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long __syscall_ulong_t
-     * }
-     */
-    public static final OfLong __syscall_ulong_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __off64_t __loff_t
-     * }
-     */
-    public static final OfLong __loff_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef char *__caddr_t
-     * }
-     */
-    public static final AddressLayout __caddr_t = DlpackH.C_POINTER;
-    /**
-     * {@snippet lang=c :
-     * typedef long __intptr_t
-     * }
-     */
-    public static final OfLong __intptr_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned int __socklen_t
-     * }
-     */
-    public static final OfInt __socklen_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef int __sig_atomic_t
-     * }
-     */
-    public static final OfInt __sig_atomic_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef __int8_t int8_t
-     * }
-     */
-    public static final OfByte int8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef __int16_t int16_t
-     * }
-     */
-    public static final OfShort int16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef __int32_t int32_t
-     * }
-     */
-    public static final OfInt int32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef __int64_t int64_t
-     * }
-     */
-    public static final OfLong int64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint8_t uint8_t
-     * }
-     */
-    public static final OfByte uint8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint16_t uint16_t
-     * }
-     */
-    public static final OfShort uint16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint32_t uint32_t
-     * }
-     */
-    public static final OfInt uint32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint64_t uint64_t
-     * }
-     */
-    public static final OfLong uint64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __int_least8_t int_least8_t
-     * }
-     */
-    public static final OfByte int_least8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef __int_least16_t int_least16_t
-     * }
-     */
-    public static final OfShort int_least16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef __int_least32_t int_least32_t
-     * }
-     */
-    public static final OfInt int_least32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef __int_least64_t int_least64_t
-     * }
-     */
-    public static final OfLong int_least64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint_least8_t uint_least8_t
-     * }
-     */
-    public static final OfByte uint_least8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint_least16_t uint_least16_t
-     * }
-     */
-    public static final OfShort uint_least16_t = DlpackH.C_SHORT;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint_least32_t uint_least32_t
-     * }
-     */
-    public static final OfInt uint_least32_t = DlpackH.C_INT;
-    /**
-     * {@snippet lang=c :
-     * typedef __uint_least64_t uint_least64_t
-     * }
-     */
-    public static final OfLong uint_least64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef signed char int_fast8_t
-     * }
-     */
-    public static final OfByte int_fast8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef long int_fast16_t
-     * }
-     */
-    public static final OfLong int_fast16_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long int_fast32_t
-     * }
-     */
-    public static final OfLong int_fast32_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long int_fast64_t
-     * }
-     */
-    public static final OfLong int_fast64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned char uint_fast8_t
-     * }
-     */
-    public static final OfByte uint_fast8_t = DlpackH.C_CHAR;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long uint_fast16_t
-     * }
-     */
-    public static final OfLong uint_fast16_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long uint_fast32_t
-     * }
-     */
-    public static final OfLong uint_fast32_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long uint_fast64_t
-     * }
-     */
-    public static final OfLong uint_fast64_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long intptr_t
-     * }
-     */
-    public static final OfLong intptr_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long uintptr_t
-     * }
-     */
-    public static final OfLong uintptr_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __intmax_t intmax_t
-     * }
-     */
-    public static final OfLong intmax_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef __uintmax_t uintmax_t
-     * }
-     */
-    public static final OfLong uintmax_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef long ptrdiff_t
-     * }
-     */
-    public static final OfLong ptrdiff_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef unsigned long size_t
-     * }
-     */
-    public static final OfLong size_t = DlpackH.C_LONG;
-    /**
-     * {@snippet lang=c :
-     * typedef int wchar_t
-     * }
-     */
-    public static final OfInt wchar_t = DlpackH.C_INT;
-    private static final int kDLCPU = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLCPU = 1
-     * }
-     */
-    public static int kDLCPU() {
-        return kDLCPU;
-    }
-    private static final int kDLCUDA = (int)2L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLCUDA = 2
-     * }
-     */
-    public static int kDLCUDA() {
-        return kDLCUDA;
-    }
-    private static final int kDLCUDAHost = (int)3L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLCUDAHost = 3
-     * }
-     */
-    public static int kDLCUDAHost() {
-        return kDLCUDAHost;
-    }
-    private static final int kDLOpenCL = (int)4L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLOpenCL = 4
-     * }
-     */
-    public static int kDLOpenCL() {
-        return kDLOpenCL;
-    }
-    private static final int kDLVulkan = (int)7L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLVulkan = 7
-     * }
-     */
-    public static int kDLVulkan() {
-        return kDLVulkan;
-    }
-    private static final int kDLMetal = (int)8L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLMetal = 8
-     * }
-     */
-    public static int kDLMetal() {
-        return kDLMetal;
-    }
-    private static final int kDLVPI = (int)9L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLVPI = 9
-     * }
-     */
-    public static int kDLVPI() {
-        return kDLVPI;
-    }
-    private static final int kDLROCM = (int)10L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLROCM = 10
-     * }
-     */
-    public static int kDLROCM() {
-        return kDLROCM;
-    }
-    private static final int kDLROCMHost = (int)11L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLROCMHost = 11
-     * }
-     */
-    public static int kDLROCMHost() {
-        return kDLROCMHost;
-    }
-    private static final int kDLExtDev = (int)12L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLExtDev = 12
-     * }
-     */
-    public static int kDLExtDev() {
-        return kDLExtDev;
-    }
-    private static final int kDLCUDAManaged = (int)13L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLCUDAManaged = 13
-     * }
-     */
-    public static int kDLCUDAManaged() {
-        return kDLCUDAManaged;
-    }
-    private static final int kDLOneAPI = (int)14L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLOneAPI = 14
-     * }
-     */
-    public static int kDLOneAPI() {
-        return kDLOneAPI;
-    }
-    private static final int kDLWebGPU = (int)15L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLWebGPU = 15
-     * }
-     */
-    public static int kDLWebGPU() {
-        return kDLWebGPU;
-    }
-    private static final int kDLHexagon = (int)16L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLHexagon = 16
-     * }
-     */
-    public static int kDLHexagon() {
-        return kDLHexagon;
-    }
-    private static final int kDLMAIA = (int)17L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLMAIA = 17
-     * }
-     */
-    public static int kDLMAIA() {
-        return kDLMAIA;
-    }
-    private static final int kDLInt = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLInt = 0
-     * }
-     */
-    public static int kDLInt() {
-        return kDLInt;
-    }
-    private static final int kDLUInt = (int)1L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLUInt = 1
-     * }
-     */
-    public static int kDLUInt() {
-        return kDLUInt;
-    }
-    private static final int kDLFloat = (int)2L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLFloat = 2
-     * }
-     */
-    public static int kDLFloat() {
-        return kDLFloat;
-    }
-    private static final int kDLOpaqueHandle = (int)3L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLOpaqueHandle = 3
-     * }
-     */
-    public static int kDLOpaqueHandle() {
-        return kDLOpaqueHandle;
-    }
-    private static final int kDLBfloat = (int)4L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLBfloat = 4
-     * }
-     */
-    public static int kDLBfloat() {
-        return kDLBfloat;
-    }
-    private static final int kDLComplex = (int)5L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLComplex = 5
-     * }
-     */
-    public static int kDLComplex() {
-        return kDLComplex;
-    }
-    private static final int kDLBool = (int)6L;
-    /**
-     * {@snippet lang=c :
-     * enum <anonymous>.kDLBool = 6
-     * }
-     */
-    public static int kDLBool() {
-        return kDLBool;
-    }
-    private static final long _POSIX_C_SOURCE = 200809L;
-    /**
-     * {@snippet lang=c :
-     * #define _POSIX_C_SOURCE 200809
-     * }
-     */
-    public static long _POSIX_C_SOURCE() {
-        return _POSIX_C_SOURCE;
-    }
-    private static final int __TIMESIZE = (int)64L;
-    /**
-     * {@snippet lang=c :
-     * #define __TIMESIZE 64
-     * }
-     */
-    public static int __TIMESIZE() {
-        return __TIMESIZE;
-    }
-    private static final long __STDC_IEC_60559_BFP__ = 201404L;
-    /**
-     * {@snippet lang=c :
-     * #define __STDC_IEC_60559_BFP__ 201404
-     * }
-     */
-    public static long __STDC_IEC_60559_BFP__() {
-        return __STDC_IEC_60559_BFP__;
-    }
-    private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
-    /**
-     * {@snippet lang=c :
-     * #define __STDC_IEC_60559_COMPLEX__ 201404
-     * }
-     */
-    public static long __STDC_IEC_60559_COMPLEX__() {
-        return __STDC_IEC_60559_COMPLEX__;
-    }
-    private static final long __STDC_ISO_10646__ = 201706L;
-    /**
-     * {@snippet lang=c :
-     * #define __STDC_ISO_10646__ 201706
-     * }
-     */
-    public static long __STDC_ISO_10646__() {
-        return __STDC_ISO_10646__;
-    }
-    private static final int __WCHAR_MAX = (int)2147483647L;
-    /**
-     * {@snippet lang=c :
-     * #define __WCHAR_MAX 2147483647
-     * }
-     */
-    public static int __WCHAR_MAX() {
-        return __WCHAR_MAX;
-    }
-    private static final int __WCHAR_MIN = (int)-2147483648L;
-    /**
-     * {@snippet lang=c :
-     * #define __WCHAR_MIN -2147483648
-     * }
-     */
-    public static int __WCHAR_MIN() {
-        return __WCHAR_MIN;
-    }
-    private static final int INT8_MIN = (int)-128L;
-    /**
-     * {@snippet lang=c :
-     * #define INT8_MIN -128
-     * }
-     */
-    public static int INT8_MIN() {
-        return INT8_MIN;
-    }
-    private static final int INT16_MIN = (int)-32768L;
-    /**
-     * {@snippet lang=c :
-     * #define INT16_MIN -32768
-     * }
-     */
-    public static int INT16_MIN() {
-        return INT16_MIN;
-    }
-    private static final int INT32_MIN = (int)-2147483648L;
-    /**
-     * {@snippet lang=c :
-     * #define INT32_MIN -2147483648
-     * }
-     */
-    public static int INT32_MIN() {
-        return INT32_MIN;
-    }
-    private static final long INT64_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define INT64_MIN -9223372036854775808
-     * }
-     */
-    public static long INT64_MIN() {
-        return INT64_MIN;
-    }
-    private static final int INT8_MAX = (int)127L;
-    /**
-     * {@snippet lang=c :
-     * #define INT8_MAX 127
-     * }
-     */
-    public static int INT8_MAX() {
-        return INT8_MAX;
-    }
-    private static final int INT16_MAX = (int)32767L;
-    /**
-     * {@snippet lang=c :
-     * #define INT16_MAX 32767
-     * }
-     */
-    public static int INT16_MAX() {
-        return INT16_MAX;
-    }
-    private static final int INT32_MAX = (int)2147483647L;
-    /**
-     * {@snippet lang=c :
-     * #define INT32_MAX 2147483647
-     * }
-     */
-    public static int INT32_MAX() {
-        return INT32_MAX;
-    }
-    private static final long INT64_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define INT64_MAX 9223372036854775807
-     * }
-     */
-    public static long INT64_MAX() {
-        return INT64_MAX;
-    }
-    private static final int UINT8_MAX = (int)255L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT8_MAX 255
-     * }
-     */
-    public static int UINT8_MAX() {
-        return UINT8_MAX;
-    }
-    private static final int UINT16_MAX = (int)65535L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT16_MAX 65535
-     * }
-     */
-    public static int UINT16_MAX() {
-        return UINT16_MAX;
-    }
-    private static final int UINT32_MAX = (int)4294967295L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT32_MAX 4294967295
-     * }
-     */
-    public static int UINT32_MAX() {
-        return UINT32_MAX;
-    }
-    private static final long UINT64_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT64_MAX -1
-     * }
-     */
-    public static long UINT64_MAX() {
-        return UINT64_MAX;
-    }
-    private static final int INT_LEAST8_MIN = (int)-128L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST8_MIN -128
-     * }
-     */
-    public static int INT_LEAST8_MIN() {
-        return INT_LEAST8_MIN;
-    }
-    private static final int INT_LEAST16_MIN = (int)-32768L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST16_MIN -32768
-     * }
-     */
-    public static int INT_LEAST16_MIN() {
-        return INT_LEAST16_MIN;
-    }
-    private static final int INT_LEAST32_MIN = (int)-2147483648L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST32_MIN -2147483648
-     * }
-     */
-    public static int INT_LEAST32_MIN() {
-        return INT_LEAST32_MIN;
-    }
-    private static final long INT_LEAST64_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST64_MIN -9223372036854775808
-     * }
-     */
-    public static long INT_LEAST64_MIN() {
-        return INT_LEAST64_MIN;
-    }
-    private static final int INT_LEAST8_MAX = (int)127L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST8_MAX 127
-     * }
-     */
-    public static int INT_LEAST8_MAX() {
-        return INT_LEAST8_MAX;
-    }
-    private static final int INT_LEAST16_MAX = (int)32767L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST16_MAX 32767
-     * }
-     */
-    public static int INT_LEAST16_MAX() {
-        return INT_LEAST16_MAX;
-    }
-    private static final int INT_LEAST32_MAX = (int)2147483647L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST32_MAX 2147483647
-     * }
-     */
-    public static int INT_LEAST32_MAX() {
-        return INT_LEAST32_MAX;
-    }
-    private static final long INT_LEAST64_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_LEAST64_MAX 9223372036854775807
-     * }
-     */
-    public static long INT_LEAST64_MAX() {
-        return INT_LEAST64_MAX;
-    }
-    private static final int UINT_LEAST8_MAX = (int)255L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_LEAST8_MAX 255
-     * }
-     */
-    public static int UINT_LEAST8_MAX() {
-        return UINT_LEAST8_MAX;
-    }
-    private static final int UINT_LEAST16_MAX = (int)65535L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_LEAST16_MAX 65535
-     * }
-     */
-    public static int UINT_LEAST16_MAX() {
-        return UINT_LEAST16_MAX;
-    }
-    private static final int UINT_LEAST32_MAX = (int)4294967295L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_LEAST32_MAX 4294967295
-     * }
-     */
-    public static int UINT_LEAST32_MAX() {
-        return UINT_LEAST32_MAX;
-    }
-    private static final long UINT_LEAST64_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_LEAST64_MAX -1
-     * }
-     */
-    public static long UINT_LEAST64_MAX() {
-        return UINT_LEAST64_MAX;
-    }
-    private static final int INT_FAST8_MIN = (int)-128L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST8_MIN -128
-     * }
-     */
-    public static int INT_FAST8_MIN() {
-        return INT_FAST8_MIN;
-    }
-    private static final long INT_FAST16_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST16_MIN -9223372036854775808
-     * }
-     */
-    public static long INT_FAST16_MIN() {
-        return INT_FAST16_MIN;
-    }
-    private static final long INT_FAST32_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST32_MIN -9223372036854775808
-     * }
-     */
-    public static long INT_FAST32_MIN() {
-        return INT_FAST32_MIN;
-    }
-    private static final long INT_FAST64_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST64_MIN -9223372036854775808
-     * }
-     */
-    public static long INT_FAST64_MIN() {
-        return INT_FAST64_MIN;
-    }
-    private static final int INT_FAST8_MAX = (int)127L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST8_MAX 127
-     * }
-     */
-    public static int INT_FAST8_MAX() {
-        return INT_FAST8_MAX;
-    }
-    private static final long INT_FAST16_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST16_MAX 9223372036854775807
-     * }
-     */
-    public static long INT_FAST16_MAX() {
-        return INT_FAST16_MAX;
-    }
-    private static final long INT_FAST32_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST32_MAX 9223372036854775807
-     * }
-     */
-    public static long INT_FAST32_MAX() {
-        return INT_FAST32_MAX;
-    }
-    private static final long INT_FAST64_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define INT_FAST64_MAX 9223372036854775807
-     * }
-     */
-    public static long INT_FAST64_MAX() {
-        return INT_FAST64_MAX;
-    }
-    private static final int UINT_FAST8_MAX = (int)255L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_FAST8_MAX 255
-     * }
-     */
-    public static int UINT_FAST8_MAX() {
-        return UINT_FAST8_MAX;
-    }
-    private static final long UINT_FAST16_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_FAST16_MAX -1
-     * }
-     */
-    public static long UINT_FAST16_MAX() {
-        return UINT_FAST16_MAX;
-    }
-    private static final long UINT_FAST32_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_FAST32_MAX -1
-     * }
-     */
-    public static long UINT_FAST32_MAX() {
-        return UINT_FAST32_MAX;
-    }
-    private static final long UINT_FAST64_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define UINT_FAST64_MAX -1
-     * }
-     */
-    public static long UINT_FAST64_MAX() {
-        return UINT_FAST64_MAX;
-    }
-    private static final long INTPTR_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define INTPTR_MIN -9223372036854775808
-     * }
-     */
-    public static long INTPTR_MIN() {
-        return INTPTR_MIN;
-    }
-    private static final long INTPTR_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define INTPTR_MAX 9223372036854775807
-     * }
-     */
-    public static long INTPTR_MAX() {
-        return INTPTR_MAX;
-    }
-    private static final long UINTPTR_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define UINTPTR_MAX -1
-     * }
-     */
-    public static long UINTPTR_MAX() {
-        return UINTPTR_MAX;
-    }
-    private static final long INTMAX_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define INTMAX_MIN -9223372036854775808
-     * }
-     */
-    public static long INTMAX_MIN() {
-        return INTMAX_MIN;
-    }
-    private static final long INTMAX_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define INTMAX_MAX 9223372036854775807
-     * }
-     */
-    public static long INTMAX_MAX() {
-        return INTMAX_MAX;
-    }
-    private static final long UINTMAX_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define UINTMAX_MAX -1
-     * }
-     */
-    public static long UINTMAX_MAX() {
-        return UINTMAX_MAX;
-    }
-    private static final long PTRDIFF_MIN = -9223372036854775808L;
-    /**
-     * {@snippet lang=c :
-     * #define PTRDIFF_MIN -9223372036854775808
-     * }
-     */
-    public static long PTRDIFF_MIN() {
-        return PTRDIFF_MIN;
-    }
-    private static final long PTRDIFF_MAX = 9223372036854775807L;
-    /**
-     * {@snippet lang=c :
-     * #define PTRDIFF_MAX 9223372036854775807
-     * }
-     */
-    public static long PTRDIFF_MAX() {
-        return PTRDIFF_MAX;
-    }
-    private static final int SIG_ATOMIC_MIN = (int)-2147483648L;
-    /**
-     * {@snippet lang=c :
-     * #define SIG_ATOMIC_MIN -2147483648
-     * }
-     */
-    public static int SIG_ATOMIC_MIN() {
-        return SIG_ATOMIC_MIN;
-    }
-    private static final int SIG_ATOMIC_MAX = (int)2147483647L;
-    /**
-     * {@snippet lang=c :
-     * #define SIG_ATOMIC_MAX 2147483647
-     * }
-     */
-    public static int SIG_ATOMIC_MAX() {
-        return SIG_ATOMIC_MAX;
-    }
-    private static final long SIZE_MAX = -1L;
-    /**
-     * {@snippet lang=c :
-     * #define SIZE_MAX -1
-     * }
-     */
-    public static long SIZE_MAX() {
-        return SIZE_MAX;
-    }
-    private static final int WCHAR_MIN = (int)-2147483648L;
-    /**
-     * {@snippet lang=c :
-     * #define WCHAR_MIN -2147483648
-     * }
-     */
-    public static int WCHAR_MIN() {
-        return WCHAR_MIN;
-    }
-    private static final int WCHAR_MAX = (int)2147483647L;
-    /**
-     * {@snippet lang=c :
-     * #define WCHAR_MAX 2147483647
-     * }
-     */
-    public static int WCHAR_MAX() {
-        return WCHAR_MAX;
-    }
-    private static final int WINT_MIN = (int)0L;
-    /**
-     * {@snippet lang=c :
-     * #define WINT_MIN 0
-     * }
-     */
-    public static int WINT_MIN() {
-        return WINT_MIN;
-    }
-    private static final int WINT_MAX = (int)4294967295L;
-    /**
-     * {@snippet lang=c :
-     * #define WINT_MAX 4294967295
-     * }
-     */
-    public static int WINT_MAX() {
-        return WINT_MAX;
-    }
-    private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
-    /**
-     * {@snippet lang=c :
-     * #define NULL (void*) 0
-     * }
-     */
-    public static MemorySegment NULL() {
-        return NULL;
-    }
-    private static final long DLPACK_FLAG_BITMASK_READ_ONLY = 1L;
-    /**
-     * {@snippet lang=c :
-     * #define DLPACK_FLAG_BITMASK_READ_ONLY 1
-     * }
-     */
-    public static long DLPACK_FLAG_BITMASK_READ_ONLY() {
-        return DLPACK_FLAG_BITMASK_READ_ONLY;
-    }
-    private static final long DLPACK_FLAG_BITMASK_IS_COPIED = 2L;
-    /**
-     * {@snippet lang=c :
-     * #define DLPACK_FLAG_BITMASK_IS_COPIED 2
-     * }
-     */
-    public static long DLPACK_FLAG_BITMASK_IS_COPIED() {
-        return DLPACK_FLAG_BITMASK_IS_COPIED;
-    }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/Fsidt.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/Fsidt.java
deleted file mode 100644
index fb5f82ed18..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/Fsidt.java
+++ /dev/null
@@ -1,175 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-import static java.lang.foreign.MemoryLayout.PathElement.sequenceElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.SequenceLayout;
-import java.lang.invoke.VarHandle;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct {
- *     int __val[2];
- * }
- * }
- */
-public class Fsidt {
-
-  Fsidt() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout
-      .structLayout(MemoryLayout.sequenceLayout(2, CagraH.C_INT).withName("__val")).withName("$anon$155:12");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final SequenceLayout __val$LAYOUT = (SequenceLayout) $LAYOUT.select(groupElement("__val"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * int __val[2]
-   * }
-   */
-  public static final SequenceLayout __val$layout() {
-    return __val$LAYOUT;
-  }
-
-  private static final long __val$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * int __val[2]
-   * }
-   */
-  public static final long __val$offset() {
-    return __val$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * int __val[2]
-   * }
-   */
-  public static MemorySegment __val(MemorySegment struct) {
-    return struct.asSlice(__val$OFFSET, __val$LAYOUT.byteSize());
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * int __val[2]
-   * }
-   */
-  public static void __val(MemorySegment struct, MemorySegment fieldValue) {
-    MemorySegment.copy(fieldValue, 0L, struct, __val$OFFSET, __val$LAYOUT.byteSize());
-  }
-
-  private static long[] __val$DIMS = { 2 };
-
-  /**
-   * Dimensions for array field:
-   * {@snippet lang = c : * int __val[2]
-   * }
-   */
-  public static long[] __val$dimensions() {
-    return __val$DIMS;
-  }
-
-  private static final VarHandle __val$ELEM_HANDLE = __val$LAYOUT.varHandle(sequenceElement());
-
-  /**
-   * Indexed getter for field:
-   * {@snippet lang = c : * int __val[2]
-   * }
-   */
-  public static int __val(MemorySegment struct, long index0) {
-    return (int) __val$ELEM_HANDLE.get(struct, 0L, index0);
-  }
-
-  /**
-   * Indexed setter for field:
-   * {@snippet lang = c : * int __val[2]
-   * }
-   */
-  public static void __val(MemorySegment struct, long index0, int fieldValue) {
-    __val$ELEM_HANDLE.set(struct, 0L, index0, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfo.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfo.java
deleted file mode 100644
index eb8aba2235..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfo.java
+++ /dev/null
@@ -1,342 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-import static java.lang.foreign.MemoryLayout.PathElement.sequenceElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.ValueLayout.OfFloat;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.invoke.VarHandle;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang = c :
- * struct gpuInfo {
- *     int gpu_id;
- *     char name[256];
- *     long free_memory;
- *     long total_memory;
- *     float compute_capability;
- * }
- * }
- */
-public class GpuInfo {
-  GpuInfo() {
-    // Should not be called directly
-  }
-
-  private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(GpuInfoH.C_INT.withName("gpu_id"),
-      MemoryLayout.sequenceLayout(256, GpuInfoH.C_CHAR).withName("name"), MemoryLayout.paddingLayout(4),
-      GpuInfoH.C_LONG.withName("free_memory"), GpuInfoH.C_LONG.withName("total_memory"),
-      GpuInfoH.C_FLOAT.withName("compute_capability"), MemoryLayout.paddingLayout(4)).withName("gpuInfo");
-
-  /**
-   * The layout of this struct
-   */
-  public static final GroupLayout layout() {
-    return $LAYOUT;
-  }
-
-  private static final OfInt gpu_id$LAYOUT = (OfInt) $LAYOUT.select(groupElement("gpu_id"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * int gpu_id
-   * }
-   */
-  public static final OfInt gpu_id$layout() {
-    return gpu_id$LAYOUT;
-  }
-
-  private static final long gpu_id$OFFSET = 0;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * int gpu_id
-   * }
-   */
-  public static final long gpu_id$offset() {
-    return gpu_id$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * int gpu_id
-   * }
-   */
-  public static int gpu_id(MemorySegment struct) {
-    return struct.get(gpu_id$LAYOUT, gpu_id$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * int gpu_id
-   * }
-   */
-  public static void gpu_id(MemorySegment struct, int fieldValue) {
-    struct.set(gpu_id$LAYOUT, gpu_id$OFFSET, fieldValue);
-  }
-
-  private static final SequenceLayout name$LAYOUT = (SequenceLayout) $LAYOUT.select(groupElement("name"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * char name[256]
-   * }
-   */
-  public static final SequenceLayout name$layout() {
-    return name$LAYOUT;
-  }
-
-  private static final long name$OFFSET = 4;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * char name[256]
-   * }
-   */
-  public static final long name$offset() {
-    return name$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * char name[256]
-   * }
-   */
-  public static MemorySegment name(MemorySegment struct) {
-    return struct.asSlice(name$OFFSET, name$LAYOUT.byteSize());
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * char name[256]
-   * }
-   */
-  public static void name(MemorySegment struct, MemorySegment fieldValue) {
-    MemorySegment.copy(fieldValue, 0L, struct, name$OFFSET, name$LAYOUT.byteSize());
-  }
-
-  private static long[] name$DIMS = { 256 };
-
-  /**
-   * Dimensions for array field:
-   * {@snippet lang = c : * char name[256]
-   * }
-   */
-  public static long[] name$dimensions() {
-    return name$DIMS;
-  }
-
-  private static final VarHandle name$ELEM_HANDLE = name$LAYOUT.varHandle(sequenceElement());
-
-  /**
-   * Indexed getter for field:
-   * {@snippet lang = c : * char name[256]
-   * }
-   */
-  public static byte name(MemorySegment struct, long index0) {
-    return (byte) name$ELEM_HANDLE.get(struct, 0L, index0);
-  }
-
-  /**
-   * Indexed setter for field:
-   * {@snippet lang = c : * char name[256]
-   * }
-   */
-  public static void name(MemorySegment struct, long index0, byte fieldValue) {
-    name$ELEM_HANDLE.set(struct, 0L, index0, fieldValue);
-  }
-
-  private static final OfLong free_memory$LAYOUT = (OfLong) $LAYOUT.select(groupElement("free_memory"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long free_memory
-   * }
-   */
-  public static final OfLong free_memory$layout() {
-    return free_memory$LAYOUT;
-  }
-
-  private static final long free_memory$OFFSET = 264;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long free_memory
-   * }
-   */
-  public static final long free_memory$offset() {
-    return free_memory$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long free_memory
-   * }
-   */
-  public static long free_memory(MemorySegment struct) {
-    return struct.get(free_memory$LAYOUT, free_memory$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long free_memory
-   * }
-   */
-  public static void free_memory(MemorySegment struct, long fieldValue) {
-    struct.set(free_memory$LAYOUT, free_memory$OFFSET, fieldValue);
-  }
-
-  private static final OfLong total_memory$LAYOUT = (OfLong) $LAYOUT.select(groupElement("total_memory"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * long total_memory
-   * }
-   */
-  public static final OfLong total_memory$layout() {
-    return total_memory$LAYOUT;
-  }
-
-  private static final long total_memory$OFFSET = 272;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * long total_memory
-   * }
-   */
-  public static final long total_memory$offset() {
-    return total_memory$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * long total_memory
-   * }
-   */
-  public static long total_memory(MemorySegment struct) {
-    return struct.get(total_memory$LAYOUT, total_memory$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * long total_memory
-   * }
-   */
-  public static void total_memory(MemorySegment struct, long fieldValue) {
-    struct.set(total_memory$LAYOUT, total_memory$OFFSET, fieldValue);
-  }
-
-  private static final OfFloat compute_capability$LAYOUT = (OfFloat) $LAYOUT.select(groupElement("compute_capability"));
-
-  /**
-   * Layout for field:
-   * {@snippet lang = c : * float compute_capability
-   * }
-   */
-  public static final OfFloat compute_capability$layout() {
-    return compute_capability$LAYOUT;
-  }
-
-  private static final long compute_capability$OFFSET = 280;
-
-  /**
-   * Offset for field:
-   * {@snippet lang = c : * float compute_capability
-   * }
-   */
-  public static final long compute_capability$offset() {
-    return compute_capability$OFFSET;
-  }
-
-  /**
-   * Getter for field:
-   * {@snippet lang = c : * float compute_capability
-   * }
-   */
-  public static float compute_capability(MemorySegment struct) {
-    return struct.get(compute_capability$LAYOUT, compute_capability$OFFSET);
-  }
-
-  /**
-   * Setter for field:
-   * {@snippet lang = c : * float compute_capability
-   * }
-   */
-  public static void compute_capability(MemorySegment struct, float fieldValue) {
-    struct.set(compute_capability$LAYOUT, compute_capability$OFFSET, fieldValue);
-  }
-
-  /**
-   * Obtains a slice of {@code arrayParam} which selects the array element at
-   * {@code index}. The returned segment has address
-   * {@code arrayParam.address() + index * layout().byteSize()}
-   */
-  public static MemorySegment asSlice(MemorySegment array, long index) {
-    return array.asSlice(layout().byteSize() * index);
-  }
-
-  /**
-   * The size (in bytes) of this struct
-   */
-  public static long sizeof() {
-    return layout().byteSize();
-  }
-
-  /**
-   * Allocate a segment of size {@code layout().byteSize()} using
-   * {@code allocator}
-   */
-  public static MemorySegment allocate(SegmentAllocator allocator) {
-    return allocator.allocate(layout());
-  }
-
-  /**
-   * Allocate an array of size {@code elementCount} using {@code allocator}. The
-   * returned segment has size {@code elementCount * layout().byteSize()}.
-   */
-  public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-    return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-    return reinterpret(addr, 1, arena, cleanup);
-  }
-
-  /**
-   * Reinterprets {@code addr} using target {@code arena} and
-   * {@code cleanupAction} (if any). The returned segment has size
-   * {@code elementCount * layout().byteSize()}
-   */
-  public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena,
-      Consumer<MemorySegment> cleanup) {
-    return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfoH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfoH.java
deleted file mode 100644
index 0457d50be0..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/GpuInfoH.java
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.ValueLayout.JAVA_BYTE;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.PaddingLayout;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.StructLayout;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-public class GpuInfoH {
-  GpuInfoH() {
-    // Should not be called directly
-  }
-
-  static final Arena LIBRARY_ARENA = Arena.ofAuto();
-  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-  static void traceDowncall(String name, Object... args) {
-    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
-    System.out.printf("%s(%s)\n", name, traceArgs);
-  }
-
-  static MemorySegment findOrThrow(String symbol) {
-    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-  }
-
-  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-    try {
-      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-    } catch (ReflectiveOperationException ex) {
-      throw new AssertionError(ex);
-    }
-  }
-
-  static MemoryLayout align(MemoryLayout layout, long align) {
-    return switch (layout) {
-    case PaddingLayout p -> p;
-    case ValueLayout v -> v.withByteAlignment(align);
-    case GroupLayout g -> {
-      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
-          : MemoryLayout.unionLayout(alignedMembers);
-    }
-    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-    };
-  }
-
-  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
-  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/HnswH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/HnswH.java
deleted file mode 100644
index b6945030b4..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/HnswH.java
+++ /dev/null
@@ -1,2350 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.ValueLayout.JAVA_BYTE;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.PaddingLayout;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.StructLayout;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.foreign.ValueLayout.OfByte;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.foreign.ValueLayout.OfShort;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-public class HnswH {
-
-  HnswH() {
-    // Should not be called directly
-  }
-
-  static final Arena LIBRARY_ARENA = Arena.ofAuto();
-  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-  static void traceDowncall(String name, Object... args) {
-    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
-    System.out.printf("%s(%s)\n", name, traceArgs);
-  }
-
-  static MemorySegment findOrThrow(String symbol) {
-    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-  }
-
-  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-    try {
-      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-    } catch (ReflectiveOperationException ex) {
-      throw new AssertionError(ex);
-    }
-  }
-
-  static MemoryLayout align(MemoryLayout layout, long align) {
-    return switch (layout) {
-    case PaddingLayout p -> p;
-    case ValueLayout v -> v.withByteAlignment(align);
-    case GroupLayout g -> {
-      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
-          : MemoryLayout.unionLayout(alignedMembers);
-    }
-    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-    };
-  }
-
-  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
-
-  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-  private static final int DLPACK_VERSION = (int) 80L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_VERSION 80
-   * }
-   */
-  public static int DLPACK_VERSION() {
-    return DLPACK_VERSION;
-  }
-
-  private static final int DLPACK_ABI_VERSION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_ABI_VERSION 1
-   * }
-   */
-  public static int DLPACK_ABI_VERSION() {
-    return DLPACK_ABI_VERSION;
-  }
-
-  private static final int _STDINT_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDINT_H 1
-   * }
-   */
-  public static int _STDINT_H() {
-    return _STDINT_H;
-  }
-
-  private static final int _FEATURES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _FEATURES_H 1
-   * }
-   */
-  public static int _FEATURES_H() {
-    return _FEATURES_H;
-  }
-
-  private static final int _DEFAULT_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
-   * }
-   */
-  public static int _DEFAULT_SOURCE() {
-    return _DEFAULT_SOURCE;
-  }
-
-  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
-   * }
-   */
-  public static int __GLIBC_USE_ISOC2X() {
-    return __GLIBC_USE_ISOC2X;
-  }
-
-  private static final int __USE_ISOC11 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC11 1
-   * }
-   */
-  public static int __USE_ISOC11() {
-    return __USE_ISOC11;
-  }
-
-  private static final int __USE_ISOC99 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC99 1
-   * }
-   */
-  public static int __USE_ISOC99() {
-    return __USE_ISOC99;
-  }
-
-  private static final int __USE_ISOC95 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC95 1
-   * }
-   */
-  public static int __USE_ISOC95() {
-    return __USE_ISOC95;
-  }
-
-  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
-   * }
-   */
-  public static int __USE_POSIX_IMPLICITLY() {
-    return __USE_POSIX_IMPLICITLY;
-  }
-
-  private static final int _POSIX_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_SOURCE 1
-   * }
-   */
-  public static int _POSIX_SOURCE() {
-    return _POSIX_SOURCE;
-  }
-
-  private static final int __USE_POSIX = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX 1
-   * }
-   */
-  public static int __USE_POSIX() {
-    return __USE_POSIX;
-  }
-
-  private static final int __USE_POSIX2 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX2 1
-   * }
-   */
-  public static int __USE_POSIX2() {
-    return __USE_POSIX2;
-  }
-
-  private static final int __USE_POSIX199309 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199309 1
-   * }
-   */
-  public static int __USE_POSIX199309() {
-    return __USE_POSIX199309;
-  }
-
-  private static final int __USE_POSIX199506 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199506 1
-   * }
-   */
-  public static int __USE_POSIX199506() {
-    return __USE_POSIX199506;
-  }
-
-  private static final int __USE_XOPEN2K = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K 1
-   * }
-   */
-  public static int __USE_XOPEN2K() {
-    return __USE_XOPEN2K;
-  }
-
-  private static final int __USE_XOPEN2K8 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
-   * }
-   */
-  public static int __USE_XOPEN2K8() {
-    return __USE_XOPEN2K8;
-  }
-
-  private static final int _ATFILE_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
-   * }
-   */
-  public static int _ATFILE_SOURCE() {
-    return _ATFILE_SOURCE;
-  }
-
-  private static final int __WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE 64
-   * }
-   */
-  public static int __WORDSIZE() {
-    return __WORDSIZE;
-  }
-
-  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
-   * }
-   */
-  public static int __WORDSIZE_TIME64_COMPAT32() {
-    return __WORDSIZE_TIME64_COMPAT32;
-  }
-
-  private static final int __SYSCALL_WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
-   * }
-   */
-  public static int __SYSCALL_WORDSIZE() {
-    return __SYSCALL_WORDSIZE;
-  }
-
-  private static final int __USE_MISC = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_MISC 1
-   * }
-   */
-  public static int __USE_MISC() {
-    return __USE_MISC;
-  }
-
-  private static final int __USE_ATFILE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ATFILE 1
-   * }
-   */
-  public static int __USE_ATFILE() {
-    return __USE_ATFILE;
-  }
-
-  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
-   * }
-   */
-  public static int __USE_FORTIFY_LEVEL() {
-    return __USE_FORTIFY_LEVEL;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_GETS() {
-    return __GLIBC_USE_DEPRECATED_GETS;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_SCANF() {
-    return __GLIBC_USE_DEPRECATED_SCANF;
-  }
-
-  private static final int _STDC_PREDEF_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
-   * }
-   */
-  public static int _STDC_PREDEF_H() {
-    return _STDC_PREDEF_H;
-  }
-
-  private static final int __STDC_IEC_559__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
-   * }
-   */
-  public static int __STDC_IEC_559__() {
-    return __STDC_IEC_559__;
-  }
-
-  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
-   * }
-   */
-  public static int __STDC_IEC_559_COMPLEX__() {
-    return __STDC_IEC_559_COMPLEX__;
-  }
-
-  private static final int __GNU_LIBRARY__ = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
-   * }
-   */
-  public static int __GNU_LIBRARY__() {
-    return __GNU_LIBRARY__;
-  }
-
-  private static final int __GLIBC__ = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC__ 2
-   * }
-   */
-  public static int __GLIBC__() {
-    return __GLIBC__;
-  }
-
-  private static final int __GLIBC_MINOR__ = (int) 35L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
-   * }
-   */
-  public static int __GLIBC_MINOR__() {
-    return __GLIBC_MINOR__;
-  }
-
-  private static final int _SYS_CDEFS_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
-   * }
-   */
-  public static int _SYS_CDEFS_H() {
-    return _SYS_CDEFS_H;
-  }
-
-  private static final int __glibc_c99_flexarr_available = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
-   * }
-   */
-  public static int __glibc_c99_flexarr_available() {
-    return __glibc_c99_flexarr_available;
-  }
-
-  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
-   * }
-   */
-  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
-    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
-  }
-
-  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
-   * }
-   */
-  public static int __HAVE_GENERIC_SELECTION() {
-    return __HAVE_GENERIC_SELECTION;
-  }
-
-  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
-   * }
-   */
-  public static int __GLIBC_USE_LIB_EXT2() {
-    return __GLIBC_USE_LIB_EXT2;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_EXT() {
-    return __GLIBC_USE_IEC_60559_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
-    return __GLIBC_USE_IEC_60559_TYPES_EXT;
-  }
-
-  private static final int _BITS_TYPES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPES_H 1
-   * }
-   */
-  public static int _BITS_TYPES_H() {
-    return _BITS_TYPES_H;
-  }
-
-  private static final int _BITS_TYPESIZES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
-   * }
-   */
-  public static int _BITS_TYPESIZES_H() {
-    return _BITS_TYPESIZES_H;
-  }
-
-  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
-   * }
-   */
-  public static int __OFF_T_MATCHES_OFF64_T() {
-    return __OFF_T_MATCHES_OFF64_T;
-  }
-
-  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
-   * }
-   */
-  public static int __INO_T_MATCHES_INO64_T() {
-    return __INO_T_MATCHES_INO64_T;
-  }
-
-  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
-   * }
-   */
-  public static int __RLIM_T_MATCHES_RLIM64_T() {
-    return __RLIM_T_MATCHES_RLIM64_T;
-  }
-
-  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
-   * }
-   */
-  public static int __STATFS_MATCHES_STATFS64() {
-    return __STATFS_MATCHES_STATFS64;
-  }
-
-  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
-   * }
-   */
-  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
-    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
-  }
-
-  private static final int __FD_SETSIZE = (int) 1024L;
-
-  /**
-   * {@snippet lang = c : * #define __FD_SETSIZE 1024
-   * }
-   */
-  public static int __FD_SETSIZE() {
-    return __FD_SETSIZE;
-  }
-
-  private static final int _BITS_TIME64_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TIME64_H 1
-   * }
-   */
-  public static int _BITS_TIME64_H() {
-    return _BITS_TIME64_H;
-  }
-
-  private static final int _BITS_WCHAR_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
-   * }
-   */
-  public static int _BITS_WCHAR_H() {
-    return _BITS_WCHAR_H;
-  }
-
-  private static final int _BITS_STDINT_INTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_INTN_H() {
-    return _BITS_STDINT_INTN_H;
-  }
-
-  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_UINTN_H() {
-    return _BITS_STDINT_UINTN_H;
-  }
-
-  private static final int true_ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define true 1
-   * }
-   */
-  public static int true_() {
-    return true_;
-  }
-
-  private static final int false_ = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define false 0
-   * }
-   */
-  public static int false_() {
-    return false_;
-  }
-
-  private static final int __bool_true_false_are_defined = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
-   * }
-   */
-  public static int __bool_true_false_are_defined() {
-    return __bool_true_false_are_defined;
-  }
-
-  private static final int L2Expanded = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
-   * }
-   */
-  public static int L2Expanded() {
-    return L2Expanded;
-  }
-
-  private static final int L2SqrtExpanded = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
-   * }
-   */
-  public static int L2SqrtExpanded() {
-    return L2SqrtExpanded;
-  }
-
-  private static final int CosineExpanded = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
-   * }
-   */
-  public static int CosineExpanded() {
-    return CosineExpanded;
-  }
-
-  private static final int L1 = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L1 = 3
-   * }
-   */
-  public static int L1() {
-    return L1;
-  }
-
-  private static final int L2Unexpanded = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
-   * }
-   */
-  public static int L2Unexpanded() {
-    return L2Unexpanded;
-  }
-
-  private static final int L2SqrtUnexpanded = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
-   * }
-   */
-  public static int L2SqrtUnexpanded() {
-    return L2SqrtUnexpanded;
-  }
-
-  private static final int InnerProduct = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
-   * }
-   */
-  public static int InnerProduct() {
-    return InnerProduct;
-  }
-
-  private static final int Linf = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Linf = 7
-   * }
-   */
-  public static int Linf() {
-    return Linf;
-  }
-
-  private static final int Canberra = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
-   * }
-   */
-  public static int Canberra() {
-    return Canberra;
-  }
-
-  private static final int LpUnexpanded = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
-   * }
-   */
-  public static int LpUnexpanded() {
-    return LpUnexpanded;
-  }
-
-  private static final int CorrelationExpanded = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
-   * }
-   */
-  public static int CorrelationExpanded() {
-    return CorrelationExpanded;
-  }
-
-  private static final int JaccardExpanded = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
-   * }
-   */
-  public static int JaccardExpanded() {
-    return JaccardExpanded;
-  }
-
-  private static final int HellingerExpanded = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
-   * }
-   */
-  public static int HellingerExpanded() {
-    return HellingerExpanded;
-  }
-
-  private static final int Haversine = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
-   * }
-   */
-  public static int Haversine() {
-    return Haversine;
-  }
-
-  private static final int BrayCurtis = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
-   * }
-   */
-  public static int BrayCurtis() {
-    return BrayCurtis;
-  }
-
-  private static final int JensenShannon = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
-   * }
-   */
-  public static int JensenShannon() {
-    return JensenShannon;
-  }
-
-  private static final int HammingUnexpanded = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
-   * }
-   */
-  public static int HammingUnexpanded() {
-    return HammingUnexpanded;
-  }
-
-  private static final int KLDivergence = (int) 17L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
-   * }
-   */
-  public static int KLDivergence() {
-    return KLDivergence;
-  }
-
-  private static final int RusselRaoExpanded = (int) 18L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
-   * }
-   */
-  public static int RusselRaoExpanded() {
-    return RusselRaoExpanded;
-  }
-
-  private static final int DiceExpanded = (int) 19L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
-   * }
-   */
-  public static int DiceExpanded() {
-    return DiceExpanded;
-  }
-
-  private static final int Precomputed = (int) 100L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
-   * }
-   */
-  public static int Precomputed() {
-    return Precomputed;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef unsigned char __u_char
-   * }
-   */
-  public static final OfByte __u_char = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __u_short
-   * }
-   */
-  public static final OfShort __u_short = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __u_int
-   * }
-   */
-  public static final OfInt __u_int = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_long
-   * }
-   */
-  public static final OfLong __u_long = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char __int8_t
-   * }
-   */
-  public static final OfByte __int8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned char __uint8_t
-   * }
-   */
-  public static final OfByte __uint8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef short __int16_t
-   * }
-   */
-  public static final OfShort __int16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __uint16_t
-   * }
-   */
-  public static final OfShort __uint16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef int __int32_t
-   * }
-   */
-  public static final OfInt __int32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uint32_t
-   * }
-   */
-  public static final OfInt __uint32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __int64_t
-   * }
-   */
-  public static final OfLong __int64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uint64_t
-   * }
-   */
-  public static final OfLong __uint64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int8_t __int_least8_t
-   * }
-   */
-  public static final OfByte __int_least8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
-   * }
-   */
-  public static final OfByte __uint_least8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t __int_least16_t
-   * }
-   */
-  public static final OfShort __int_least16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
-   * }
-   */
-  public static final OfShort __uint_least16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t __int_least32_t
-   * }
-   */
-  public static final OfInt __int_least32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
-   * }
-   */
-  public static final OfInt __uint_least32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t __int_least64_t
-   * }
-   */
-  public static final OfLong __int_least64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
-   * }
-   */
-  public static final OfLong __uint_least64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __quad_t
-   * }
-   */
-  public static final OfLong __quad_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_quad_t
-   * }
-   */
-  public static final OfLong __u_quad_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __intmax_t
-   * }
-   */
-  public static final OfLong __intmax_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uintmax_t
-   * }
-   */
-  public static final OfLong __uintmax_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __dev_t
-   * }
-   */
-  public static final OfLong __dev_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uid_t
-   * }
-   */
-  public static final OfInt __uid_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __gid_t
-   * }
-   */
-  public static final OfInt __gid_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino_t
-   * }
-   */
-  public static final OfLong __ino_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino64_t
-   * }
-   */
-  public static final OfLong __ino64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __mode_t
-   * }
-   */
-  public static final OfInt __mode_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __nlink_t
-   * }
-   */
-  public static final OfLong __nlink_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off_t
-   * }
-   */
-  public static final OfLong __off_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off64_t
-   * }
-   */
-  public static final OfLong __off64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __pid_t
-   * }
-   */
-  public static final OfInt __pid_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __clock_t
-   * }
-   */
-  public static final OfLong __clock_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim_t
-   * }
-   */
-  public static final OfLong __rlim_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim64_t
-   * }
-   */
-  public static final OfLong __rlim64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __id_t
-   * }
-   */
-  public static final OfInt __id_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __time_t
-   * }
-   */
-  public static final OfLong __time_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __useconds_t
-   * }
-   */
-  public static final OfInt __useconds_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds_t
-   * }
-   */
-  public static final OfLong __suseconds_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds64_t
-   * }
-   */
-  public static final OfLong __suseconds64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __daddr_t
-   * }
-   */
-  public static final OfInt __daddr_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __key_t
-   * }
-   */
-  public static final OfInt __key_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __clockid_t
-   * }
-   */
-  public static final OfInt __clockid_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef void *__timer_t
-   * }
-   */
-  public static final AddressLayout __timer_t = HnswH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __blksize_t
-   * }
-   */
-  public static final OfLong __blksize_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt_t
-   * }
-   */
-  public static final OfLong __blkcnt_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt64_t
-   * }
-   */
-  public static final OfLong __blkcnt64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
-   * }
-   */
-  public static final OfLong __fsblkcnt_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
-   * }
-   */
-  public static final OfLong __fsblkcnt64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
-   * }
-   */
-  public static final OfLong __fsfilcnt_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
-   * }
-   */
-  public static final OfLong __fsfilcnt64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __fsword_t
-   * }
-   */
-  public static final OfLong __fsword_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __ssize_t
-   * }
-   */
-  public static final OfLong __ssize_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __syscall_slong_t
-   * }
-   */
-  public static final OfLong __syscall_slong_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
-   * }
-   */
-  public static final OfLong __syscall_ulong_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __off64_t __loff_t
-   * }
-   */
-  public static final OfLong __loff_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef char *__caddr_t
-   * }
-   */
-  public static final AddressLayout __caddr_t = HnswH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __intptr_t
-   * }
-   */
-  public static final OfLong __intptr_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __socklen_t
-   * }
-   */
-  public static final OfInt __socklen_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __sig_atomic_t
-   * }
-   */
-  public static final OfInt __sig_atomic_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int8_t int8_t
-   * }
-   */
-  public static final OfByte int8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t int16_t
-   * }
-   */
-  public static final OfShort int16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t int32_t
-   * }
-   */
-  public static final OfInt int32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t int64_t
-   * }
-   */
-  public static final OfLong int64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t uint8_t
-   * }
-   */
-  public static final OfByte uint8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t uint16_t
-   * }
-   */
-  public static final OfShort uint16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t uint32_t
-   * }
-   */
-  public static final OfInt uint32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t uint64_t
-   * }
-   */
-  public static final OfLong uint64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
-   * }
-   */
-  public static final OfByte int_least8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
-   * }
-   */
-  public static final OfShort int_least16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
-   * }
-   */
-  public static final OfInt int_least32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
-   * }
-   */
-  public static final OfLong int_least64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
-   * }
-   */
-  public static final OfByte uint_least8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
-   * }
-   */
-  public static final OfShort uint_least16_t = HnswH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
-   * }
-   */
-  public static final OfInt uint_least32_t = HnswH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
-   * }
-   */
-  public static final OfLong uint_least64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char int_fast8_t
-   * }
-   */
-  public static final OfByte int_fast8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef long int_fast16_t
-   * }
-   */
-  public static final OfLong int_fast16_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast32_t
-   * }
-   */
-  public static final OfLong int_fast32_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast64_t
-   * }
-   */
-  public static final OfLong int_fast64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
-   * }
-   */
-  public static final OfByte uint_fast8_t = HnswH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
-   * }
-   */
-  public static final OfLong uint_fast16_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
-   * }
-   */
-  public static final OfLong uint_fast32_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
-   * }
-   */
-  public static final OfLong uint_fast64_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long intptr_t
-   * }
-   */
-  public static final OfLong intptr_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uintptr_t
-   * }
-   */
-  public static final OfLong uintptr_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __intmax_t intmax_t
-   * }
-   */
-  public static final OfLong intmax_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
-   * }
-   */
-  public static final OfLong uintmax_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long ptrdiff_t
-   * }
-   */
-  public static final OfLong ptrdiff_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long size_t
-   * }
-   */
-  public static final OfLong size_t = HnswH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int wchar_t
-   * }
-   */
-  public static final OfInt wchar_t = HnswH.C_INT;
-  private static final int kDLCPU = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
-   * }
-   */
-  public static int kDLCPU() {
-    return kDLCPU;
-  }
-
-  private static final int kDLCUDA = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
-   * }
-   */
-  public static int kDLCUDA() {
-    return kDLCUDA;
-  }
-
-  private static final int kDLCUDAHost = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
-   * }
-   */
-  public static int kDLCUDAHost() {
-    return kDLCUDAHost;
-  }
-
-  private static final int kDLOpenCL = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
-   * }
-   */
-  public static int kDLOpenCL() {
-    return kDLOpenCL;
-  }
-
-  private static final int kDLVulkan = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
-   * }
-   */
-  public static int kDLVulkan() {
-    return kDLVulkan;
-  }
-
-  private static final int kDLMetal = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
-   * }
-   */
-  public static int kDLMetal() {
-    return kDLMetal;
-  }
-
-  private static final int kDLVPI = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
-   * }
-   */
-  public static int kDLVPI() {
-    return kDLVPI;
-  }
-
-  private static final int kDLROCM = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
-   * }
-   */
-  public static int kDLROCM() {
-    return kDLROCM;
-  }
-
-  private static final int kDLROCMHost = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
-   * }
-   */
-  public static int kDLROCMHost() {
-    return kDLROCMHost;
-  }
-
-  private static final int kDLExtDev = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
-   * }
-   */
-  public static int kDLExtDev() {
-    return kDLExtDev;
-  }
-
-  private static final int kDLCUDAManaged = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
-   * }
-   */
-  public static int kDLCUDAManaged() {
-    return kDLCUDAManaged;
-  }
-
-  private static final int kDLOneAPI = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
-   * }
-   */
-  public static int kDLOneAPI() {
-    return kDLOneAPI;
-  }
-
-  private static final int kDLWebGPU = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
-   * }
-   */
-  public static int kDLWebGPU() {
-    return kDLWebGPU;
-  }
-
-  private static final int kDLHexagon = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
-   * }
-   */
-  public static int kDLHexagon() {
-    return kDLHexagon;
-  }
-
-  private static final int kDLInt = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
-   * }
-   */
-  public static int kDLInt() {
-    return kDLInt;
-  }
-
-  private static final int kDLUInt = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
-   * }
-   */
-  public static int kDLUInt() {
-    return kDLUInt;
-  }
-
-  private static final int kDLFloat = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
-   * }
-   */
-  public static int kDLFloat() {
-    return kDLFloat;
-  }
-
-  private static final int kDLOpaqueHandle = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
-   * }
-   */
-  public static int kDLOpaqueHandle() {
-    return kDLOpaqueHandle;
-  }
-
-  private static final int kDLBfloat = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
-   * }
-   */
-  public static int kDLBfloat() {
-    return kDLBfloat;
-  }
-
-  private static final int kDLComplex = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
-   * }
-   */
-  public static int kDLComplex() {
-    return kDLComplex;
-  }
-
-  private static final int kDLBool = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
-   * }
-   */
-  public static int kDLBool() {
-    return kDLBool;
-  }
-
-  private static final int AUTO_SELECT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.AUTO_SELECT = 0
-   * }
-   */
-  public static int AUTO_SELECT() {
-    return AUTO_SELECT;
-  }
-
-  private static final int IVF_PQ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.IVF_PQ = 1
-   * }
-   */
-  public static int IVF_PQ() {
-    return IVF_PQ;
-  }
-
-  private static final int NN_DESCENT = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraGraphBuildAlgo.NN_DESCENT = 2
-   * }
-   */
-  public static int NN_DESCENT() {
-    return NN_DESCENT;
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsCagraCompressionParams {
-   *     uint32_t pq_bits;
-   *     uint32_t pq_dim;
-   *     uint32_t vq_n_centers;
-   *     uint32_t kmeans_n_iters;
-   *     double vq_kmeans_trainset_fraction;
-   *     double pq_kmeans_trainset_fraction;
-   * } *cuvsCagraCompressionParams_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraCompressionParams_t = HnswH.C_POINTER;
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsCagraIndexParams {
-   *     cuvsDistanceType metric;
-   *     long intermediate_graph_degree;
-   *     long graph_degree;
-   *     enum cuvsCagraGraphBuildAlgo build_algo;
-   *     long nn_descent_niter;
-   *     cuvsCagraCompressionParams_t compression;
-   * } *cuvsCagraIndexParams_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraIndexParams_t = HnswH.C_POINTER;
-  private static final int SINGLE_CTA = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.SINGLE_CTA = 0
-   * }
-   */
-  public static int SINGLE_CTA() {
-    return SINGLE_CTA;
-  }
-
-  private static final int MULTI_CTA = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_CTA = 1
-   * }
-   */
-  public static int MULTI_CTA() {
-    return MULTI_CTA;
-  }
-
-  private static final int MULTI_KERNEL = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.MULTI_KERNEL = 2
-   * }
-   */
-  public static int MULTI_KERNEL() {
-    return MULTI_KERNEL;
-  }
-
-  private static final int AUTO = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraSearchAlgo.AUTO = 3
-   * }
-   */
-  public static int AUTO() {
-    return AUTO;
-  }
-
-  private static final int HASH = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraHashMode.HASH = 0
-   * }
-   */
-  public static int HASH() {
-    return HASH;
-  }
-
-  private static final int SMALL = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraHashMode.SMALL = 1
-   * }
-   */
-  public static int SMALL() {
-    return SMALL;
-  }
-
-  private static final int AUTO_HASH = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsCagraHashMode.AUTO_HASH = 2
-   * }
-   */
-  public static int AUTO_HASH() {
-    return AUTO_HASH;
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsCagraSearchParams {
-   *     long max_queries;
-   *     long itopk_size;
-   *     long max_iterations;
-   *     enum cuvsCagraSearchAlgo algo;
-   *     long team_size;
-   *     long search_width;
-   *     long min_iterations;
-   *     long thread_block_size;
-   *     enum cuvsCagraHashMode hashmap_mode;
-   *     long hashmap_min_bitlen;
-   *     float hashmap_max_fill_rate;
-   *     uint32_t num_random_samplings;
-   *     uint64_t rand_xor_mask;
-   * } *cuvsCagraSearchParams_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraSearchParams_t = HnswH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef cuvsCagraIndex *cuvsCagraIndex_t
-   * }
-   */
-  public static final AddressLayout cuvsCagraIndex_t = HnswH.C_POINTER;
-  private static final int NONE = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsHnswHierarchy.NONE = 0
-   * }
-   */
-  public static int NONE() {
-    return NONE;
-  }
-
-  private static final int CPU = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cuvsHnswHierarchy.CPU = 1
-   * }
-   */
-  public static int CPU() {
-    return CPU;
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsHnswIndexParams {
-   *     cuvsHnswHierarchy hierarchy;
-   *     int ef_construction;
-   *     int num_threads;
-   * } *cuvsHnswIndexParams_t
-   * }
-   */
-  public static final AddressLayout cuvsHnswIndexParams_t = HnswH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef cuvsHnswIndex *cuvsHnswIndex_t
-   * }
-   */
-  public static final AddressLayout cuvsHnswIndex_t = HnswH.C_POINTER;
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsHnswExtendParams {
-   *     int num_threads;
-   * } *cuvsHnswExtendParams_t
-   * }
-   */
-  public static final AddressLayout cuvsHnswExtendParams_t = HnswH.C_POINTER;
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsHnswSearchParams {
-   *     int32_t ef;
-   *     int32_t num_threads;
-   * } *cuvsHnswSearchParams_t
-   * }
-   */
-  public static final AddressLayout cuvsHnswSearchParams_t = HnswH.C_POINTER;
-  private static final long _POSIX_C_SOURCE = 200809L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
-   * }
-   */
-  public static long _POSIX_C_SOURCE() {
-    return _POSIX_C_SOURCE;
-  }
-
-  private static final int __TIMESIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __TIMESIZE 64
-   * }
-   */
-  public static int __TIMESIZE() {
-    return __TIMESIZE;
-  }
-
-  private static final long __STDC_IEC_60559_BFP__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_BFP__() {
-    return __STDC_IEC_60559_BFP__;
-  }
-
-  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_COMPLEX__() {
-    return __STDC_IEC_60559_COMPLEX__;
-  }
-
-  private static final long __STDC_ISO_10646__ = 201706L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
-   * }
-   */
-  public static long __STDC_ISO_10646__() {
-    return __STDC_ISO_10646__;
-  }
-
-  private static final int __WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
-   * }
-   */
-  public static int __WCHAR_MAX() {
-    return __WCHAR_MAX;
-  }
-
-  private static final int __WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
-   * }
-   */
-  public static int __WCHAR_MIN() {
-    return __WCHAR_MIN;
-  }
-
-  private static final int INT8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MIN -128
-   * }
-   */
-  public static int INT8_MIN() {
-    return INT8_MIN;
-  }
-
-  private static final int INT16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MIN -32768
-   * }
-   */
-  public static int INT16_MIN() {
-    return INT16_MIN;
-  }
-
-  private static final int INT32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MIN -2147483648
-   * }
-   */
-  public static int INT32_MIN() {
-    return INT32_MIN;
-  }
-
-  private static final long INT64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT64_MIN() {
-    return INT64_MIN;
-  }
-
-  private static final int INT8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MAX 127
-   * }
-   */
-  public static int INT8_MAX() {
-    return INT8_MAX;
-  }
-
-  private static final int INT16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MAX 32767
-   * }
-   */
-  public static int INT16_MAX() {
-    return INT16_MAX;
-  }
-
-  private static final int INT32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MAX 2147483647
-   * }
-   */
-  public static int INT32_MAX() {
-    return INT32_MAX;
-  }
-
-  private static final long INT64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT64_MAX() {
-    return INT64_MAX;
-  }
-
-  private static final int UINT8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT8_MAX 255
-   * }
-   */
-  public static int UINT8_MAX() {
-    return UINT8_MAX;
-  }
-
-  private static final int UINT16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT16_MAX 65535
-   * }
-   */
-  public static int UINT16_MAX() {
-    return UINT16_MAX;
-  }
-
-  private static final int UINT32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT32_MAX 4294967295
-   * }
-   */
-  public static int UINT32_MAX() {
-    return UINT32_MAX;
-  }
-
-  private static final long UINT64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT64_MAX -1
-   * }
-   */
-  public static long UINT64_MAX() {
-    return UINT64_MAX;
-  }
-
-  private static final int INT_LEAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
-   * }
-   */
-  public static int INT_LEAST8_MIN() {
-    return INT_LEAST8_MIN;
-  }
-
-  private static final int INT_LEAST16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
-   * }
-   */
-  public static int INT_LEAST16_MIN() {
-    return INT_LEAST16_MIN;
-  }
-
-  private static final int INT_LEAST32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
-   * }
-   */
-  public static int INT_LEAST32_MIN() {
-    return INT_LEAST32_MIN;
-  }
-
-  private static final long INT_LEAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_LEAST64_MIN() {
-    return INT_LEAST64_MIN;
-  }
-
-  private static final int INT_LEAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
-   * }
-   */
-  public static int INT_LEAST8_MAX() {
-    return INT_LEAST8_MAX;
-  }
-
-  private static final int INT_LEAST16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
-   * }
-   */
-  public static int INT_LEAST16_MAX() {
-    return INT_LEAST16_MAX;
-  }
-
-  private static final int INT_LEAST32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
-   * }
-   */
-  public static int INT_LEAST32_MAX() {
-    return INT_LEAST32_MAX;
-  }
-
-  private static final long INT_LEAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_LEAST64_MAX() {
-    return INT_LEAST64_MAX;
-  }
-
-  private static final int UINT_LEAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
-   * }
-   */
-  public static int UINT_LEAST8_MAX() {
-    return UINT_LEAST8_MAX;
-  }
-
-  private static final int UINT_LEAST16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
-   * }
-   */
-  public static int UINT_LEAST16_MAX() {
-    return UINT_LEAST16_MAX;
-  }
-
-  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
-   * }
-   */
-  public static int UINT_LEAST32_MAX() {
-    return UINT_LEAST32_MAX;
-  }
-
-  private static final long UINT_LEAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
-   * }
-   */
-  public static long UINT_LEAST64_MAX() {
-    return UINT_LEAST64_MAX;
-  }
-
-  private static final int INT_FAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MIN -128
-   * }
-   */
-  public static int INT_FAST8_MIN() {
-    return INT_FAST8_MIN;
-  }
-
-  private static final long INT_FAST16_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST16_MIN() {
-    return INT_FAST16_MIN;
-  }
-
-  private static final long INT_FAST32_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST32_MIN() {
-    return INT_FAST32_MIN;
-  }
-
-  private static final long INT_FAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST64_MIN() {
-    return INT_FAST64_MIN;
-  }
-
-  private static final int INT_FAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MAX 127
-   * }
-   */
-  public static int INT_FAST8_MAX() {
-    return INT_FAST8_MAX;
-  }
-
-  private static final long INT_FAST16_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST16_MAX() {
-    return INT_FAST16_MAX;
-  }
-
-  private static final long INT_FAST32_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST32_MAX() {
-    return INT_FAST32_MAX;
-  }
-
-  private static final long INT_FAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST64_MAX() {
-    return INT_FAST64_MAX;
-  }
-
-  private static final int UINT_FAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
-   * }
-   */
-  public static int UINT_FAST8_MAX() {
-    return UINT_FAST8_MAX;
-  }
-
-  private static final long UINT_FAST16_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
-   * }
-   */
-  public static long UINT_FAST16_MAX() {
-    return UINT_FAST16_MAX;
-  }
-
-  private static final long UINT_FAST32_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
-   * }
-   */
-  public static long UINT_FAST32_MAX() {
-    return UINT_FAST32_MAX;
-  }
-
-  private static final long UINT_FAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
-   * }
-   */
-  public static long UINT_FAST64_MAX() {
-    return UINT_FAST64_MAX;
-  }
-
-  private static final long INTPTR_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
-   * }
-   */
-  public static long INTPTR_MIN() {
-    return INTPTR_MIN;
-  }
-
-  private static final long INTPTR_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
-   * }
-   */
-  public static long INTPTR_MAX() {
-    return INTPTR_MAX;
-  }
-
-  private static final long UINTPTR_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTPTR_MAX -1
-   * }
-   */
-  public static long UINTPTR_MAX() {
-    return UINTPTR_MAX;
-  }
-
-  private static final long INTMAX_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
-   * }
-   */
-  public static long INTMAX_MIN() {
-    return INTMAX_MIN;
-  }
-
-  private static final long INTMAX_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
-   * }
-   */
-  public static long INTMAX_MAX() {
-    return INTMAX_MAX;
-  }
-
-  private static final long UINTMAX_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTMAX_MAX -1
-   * }
-   */
-  public static long UINTMAX_MAX() {
-    return UINTMAX_MAX;
-  }
-
-  private static final long PTRDIFF_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
-   * }
-   */
-  public static long PTRDIFF_MIN() {
-    return PTRDIFF_MIN;
-  }
-
-  private static final long PTRDIFF_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
-   * }
-   */
-  public static long PTRDIFF_MAX() {
-    return PTRDIFF_MAX;
-  }
-
-  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
-   * }
-   */
-  public static int SIG_ATOMIC_MIN() {
-    return SIG_ATOMIC_MIN;
-  }
-
-  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
-   * }
-   */
-  public static int SIG_ATOMIC_MAX() {
-    return SIG_ATOMIC_MAX;
-  }
-
-  private static final long SIZE_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define SIZE_MAX -1
-   * }
-   */
-  public static long SIZE_MAX() {
-    return SIZE_MAX;
-  }
-
-  private static final int WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
-   * }
-   */
-  public static int WCHAR_MIN() {
-    return WCHAR_MIN;
-  }
-
-  private static final int WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
-   * }
-   */
-  public static int WCHAR_MAX() {
-    return WCHAR_MAX;
-  }
-
-  private static final int WINT_MIN = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MIN 0
-   * }
-   */
-  public static int WINT_MIN() {
-    return WINT_MIN;
-  }
-
-  private static final int WINT_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MAX 4294967295
-   * }
-   */
-  public static int WINT_MAX() {
-    return WINT_MAX;
-  }
-
-  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
-
-  /**
-   * {@snippet lang = c : * #define NULL (void*) 0
-   * }
-   */
-  public static MemorySegment NULL() {
-    return NULL;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfFlatH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfFlatH.java
deleted file mode 100644
index 32cdeb8132..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfFlatH.java
+++ /dev/null
@@ -1,2845 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.ValueLayout.JAVA_BYTE;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.PaddingLayout;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.StructLayout;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.foreign.ValueLayout.OfByte;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.foreign.ValueLayout.OfShort;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-public class IvfFlatH {
-
-  IvfFlatH() {
-    // Should not be called directly
-  }
-
-  static final Arena LIBRARY_ARENA = Arena.ofAuto();
-  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-  static void traceDowncall(String name, Object... args) {
-    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
-    System.out.printf("%s(%s)\n", name, traceArgs);
-  }
-
-  static MemorySegment findOrThrow(String symbol) {
-    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-  }
-
-  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-    try {
-      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-    } catch (ReflectiveOperationException ex) {
-      throw new AssertionError(ex);
-    }
-  }
-
-  static MemoryLayout align(MemoryLayout layout, long align) {
-    return switch (layout) {
-    case PaddingLayout p -> p;
-    case ValueLayout v -> v.withByteAlignment(align);
-    case GroupLayout g -> {
-      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
-          : MemoryLayout.unionLayout(alignedMembers);
-    }
-    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-    };
-  }
-
-  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
-
-  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-  private static final int _STDINT_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDINT_H 1
-   * }
-   */
-  public static int _STDINT_H() {
-    return _STDINT_H;
-  }
-
-  private static final int _FEATURES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _FEATURES_H 1
-   * }
-   */
-  public static int _FEATURES_H() {
-    return _FEATURES_H;
-  }
-
-  private static final int _DEFAULT_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
-   * }
-   */
-  public static int _DEFAULT_SOURCE() {
-    return _DEFAULT_SOURCE;
-  }
-
-  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
-   * }
-   */
-  public static int __GLIBC_USE_ISOC2X() {
-    return __GLIBC_USE_ISOC2X;
-  }
-
-  private static final int __USE_ISOC11 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC11 1
-   * }
-   */
-  public static int __USE_ISOC11() {
-    return __USE_ISOC11;
-  }
-
-  private static final int __USE_ISOC99 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC99 1
-   * }
-   */
-  public static int __USE_ISOC99() {
-    return __USE_ISOC99;
-  }
-
-  private static final int __USE_ISOC95 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC95 1
-   * }
-   */
-  public static int __USE_ISOC95() {
-    return __USE_ISOC95;
-  }
-
-  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
-   * }
-   */
-  public static int __USE_POSIX_IMPLICITLY() {
-    return __USE_POSIX_IMPLICITLY;
-  }
-
-  private static final int _POSIX_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_SOURCE 1
-   * }
-   */
-  public static int _POSIX_SOURCE() {
-    return _POSIX_SOURCE;
-  }
-
-  private static final int __USE_POSIX = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX 1
-   * }
-   */
-  public static int __USE_POSIX() {
-    return __USE_POSIX;
-  }
-
-  private static final int __USE_POSIX2 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX2 1
-   * }
-   */
-  public static int __USE_POSIX2() {
-    return __USE_POSIX2;
-  }
-
-  private static final int __USE_POSIX199309 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199309 1
-   * }
-   */
-  public static int __USE_POSIX199309() {
-    return __USE_POSIX199309;
-  }
-
-  private static final int __USE_POSIX199506 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199506 1
-   * }
-   */
-  public static int __USE_POSIX199506() {
-    return __USE_POSIX199506;
-  }
-
-  private static final int __USE_XOPEN2K = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K 1
-   * }
-   */
-  public static int __USE_XOPEN2K() {
-    return __USE_XOPEN2K;
-  }
-
-  private static final int __USE_XOPEN2K8 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
-   * }
-   */
-  public static int __USE_XOPEN2K8() {
-    return __USE_XOPEN2K8;
-  }
-
-  private static final int _ATFILE_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
-   * }
-   */
-  public static int _ATFILE_SOURCE() {
-    return _ATFILE_SOURCE;
-  }
-
-  private static final int __WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE 64
-   * }
-   */
-  public static int __WORDSIZE() {
-    return __WORDSIZE;
-  }
-
-  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
-   * }
-   */
-  public static int __WORDSIZE_TIME64_COMPAT32() {
-    return __WORDSIZE_TIME64_COMPAT32;
-  }
-
-  private static final int __SYSCALL_WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
-   * }
-   */
-  public static int __SYSCALL_WORDSIZE() {
-    return __SYSCALL_WORDSIZE;
-  }
-
-  private static final int __USE_MISC = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_MISC 1
-   * }
-   */
-  public static int __USE_MISC() {
-    return __USE_MISC;
-  }
-
-  private static final int __USE_ATFILE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ATFILE 1
-   * }
-   */
-  public static int __USE_ATFILE() {
-    return __USE_ATFILE;
-  }
-
-  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
-   * }
-   */
-  public static int __USE_FORTIFY_LEVEL() {
-    return __USE_FORTIFY_LEVEL;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_GETS() {
-    return __GLIBC_USE_DEPRECATED_GETS;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_SCANF() {
-    return __GLIBC_USE_DEPRECATED_SCANF;
-  }
-
-  private static final int _STDC_PREDEF_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
-   * }
-   */
-  public static int _STDC_PREDEF_H() {
-    return _STDC_PREDEF_H;
-  }
-
-  private static final int __STDC_IEC_559__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
-   * }
-   */
-  public static int __STDC_IEC_559__() {
-    return __STDC_IEC_559__;
-  }
-
-  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
-   * }
-   */
-  public static int __STDC_IEC_559_COMPLEX__() {
-    return __STDC_IEC_559_COMPLEX__;
-  }
-
-  private static final int __GNU_LIBRARY__ = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
-   * }
-   */
-  public static int __GNU_LIBRARY__() {
-    return __GNU_LIBRARY__;
-  }
-
-  private static final int __GLIBC__ = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC__ 2
-   * }
-   */
-  public static int __GLIBC__() {
-    return __GLIBC__;
-  }
-
-  private static final int __GLIBC_MINOR__ = (int) 35L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
-   * }
-   */
-  public static int __GLIBC_MINOR__() {
-    return __GLIBC_MINOR__;
-  }
-
-  private static final int _SYS_CDEFS_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
-   * }
-   */
-  public static int _SYS_CDEFS_H() {
-    return _SYS_CDEFS_H;
-  }
-
-  private static final int __glibc_c99_flexarr_available = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
-   * }
-   */
-  public static int __glibc_c99_flexarr_available() {
-    return __glibc_c99_flexarr_available;
-  }
-
-  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
-   * }
-   */
-  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
-    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
-  }
-
-  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
-   * }
-   */
-  public static int __HAVE_GENERIC_SELECTION() {
-    return __HAVE_GENERIC_SELECTION;
-  }
-
-  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
-   * }
-   */
-  public static int __GLIBC_USE_LIB_EXT2() {
-    return __GLIBC_USE_LIB_EXT2;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_EXT() {
-    return __GLIBC_USE_IEC_60559_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
-    return __GLIBC_USE_IEC_60559_TYPES_EXT;
-  }
-
-  private static final int _BITS_TYPES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPES_H 1
-   * }
-   */
-  public static int _BITS_TYPES_H() {
-    return _BITS_TYPES_H;
-  }
-
-  private static final int _BITS_TYPESIZES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
-   * }
-   */
-  public static int _BITS_TYPESIZES_H() {
-    return _BITS_TYPESIZES_H;
-  }
-
-  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
-   * }
-   */
-  public static int __OFF_T_MATCHES_OFF64_T() {
-    return __OFF_T_MATCHES_OFF64_T;
-  }
-
-  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
-   * }
-   */
-  public static int __INO_T_MATCHES_INO64_T() {
-    return __INO_T_MATCHES_INO64_T;
-  }
-
-  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
-   * }
-   */
-  public static int __RLIM_T_MATCHES_RLIM64_T() {
-    return __RLIM_T_MATCHES_RLIM64_T;
-  }
-
-  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
-   * }
-   */
-  public static int __STATFS_MATCHES_STATFS64() {
-    return __STATFS_MATCHES_STATFS64;
-  }
-
-  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
-   * }
-   */
-  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
-    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
-  }
-
-  private static final int __FD_SETSIZE = (int) 1024L;
-
-  /**
-   * {@snippet lang = c : * #define __FD_SETSIZE 1024
-   * }
-   */
-  public static int __FD_SETSIZE() {
-    return __FD_SETSIZE;
-  }
-
-  private static final int _BITS_TIME64_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TIME64_H 1
-   * }
-   */
-  public static int _BITS_TIME64_H() {
-    return _BITS_TIME64_H;
-  }
-
-  private static final int _BITS_WCHAR_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
-   * }
-   */
-  public static int _BITS_WCHAR_H() {
-    return _BITS_WCHAR_H;
-  }
-
-  private static final int _BITS_STDINT_INTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_INTN_H() {
-    return _BITS_STDINT_INTN_H;
-  }
-
-  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_UINTN_H() {
-    return _BITS_STDINT_UINTN_H;
-  }
-
-  private static final int DLPACK_MAJOR_VERSION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_MAJOR_VERSION 1
-   * }
-   */
-  public static int DLPACK_MAJOR_VERSION() {
-    return DLPACK_MAJOR_VERSION;
-  }
-
-  private static final int DLPACK_MINOR_VERSION = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_MINOR_VERSION 0
-   * }
-   */
-  public static int DLPACK_MINOR_VERSION() {
-    return DLPACK_MINOR_VERSION;
-  }
-
-  private static final int true_ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define true 1
-   * }
-   */
-  public static int true_() {
-    return true_;
-  }
-
-  private static final int false_ = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define false 0
-   * }
-   */
-  public static int false_() {
-    return false_;
-  }
-
-  private static final int __bool_true_false_are_defined = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
-   * }
-   */
-  public static int __bool_true_false_are_defined() {
-    return __bool_true_false_are_defined;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef unsigned char __u_char
-   * }
-   */
-  public static final OfByte __u_char = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __u_short
-   * }
-   */
-  public static final OfShort __u_short = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __u_int
-   * }
-   */
-  public static final OfInt __u_int = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_long
-   * }
-   */
-  public static final OfLong __u_long = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char __int8_t
-   * }
-   */
-  public static final OfByte __int8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned char __uint8_t
-   * }
-   */
-  public static final OfByte __uint8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef short __int16_t
-   * }
-   */
-  public static final OfShort __int16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __uint16_t
-   * }
-   */
-  public static final OfShort __uint16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef int __int32_t
-   * }
-   */
-  public static final OfInt __int32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uint32_t
-   * }
-   */
-  public static final OfInt __uint32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __int64_t
-   * }
-   */
-  public static final OfLong __int64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uint64_t
-   * }
-   */
-  public static final OfLong __uint64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int8_t __int_least8_t
-   * }
-   */
-  public static final OfByte __int_least8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
-   * }
-   */
-  public static final OfByte __uint_least8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t __int_least16_t
-   * }
-   */
-  public static final OfShort __int_least16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
-   * }
-   */
-  public static final OfShort __uint_least16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t __int_least32_t
-   * }
-   */
-  public static final OfInt __int_least32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
-   * }
-   */
-  public static final OfInt __uint_least32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t __int_least64_t
-   * }
-   */
-  public static final OfLong __int_least64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
-   * }
-   */
-  public static final OfLong __uint_least64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __quad_t
-   * }
-   */
-  public static final OfLong __quad_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_quad_t
-   * }
-   */
-  public static final OfLong __u_quad_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __intmax_t
-   * }
-   */
-  public static final OfLong __intmax_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uintmax_t
-   * }
-   */
-  public static final OfLong __uintmax_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __dev_t
-   * }
-   */
-  public static final OfLong __dev_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uid_t
-   * }
-   */
-  public static final OfInt __uid_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __gid_t
-   * }
-   */
-  public static final OfInt __gid_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino_t
-   * }
-   */
-  public static final OfLong __ino_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino64_t
-   * }
-   */
-  public static final OfLong __ino64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __mode_t
-   * }
-   */
-  public static final OfInt __mode_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __nlink_t
-   * }
-   */
-  public static final OfLong __nlink_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off_t
-   * }
-   */
-  public static final OfLong __off_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off64_t
-   * }
-   */
-  public static final OfLong __off64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __pid_t
-   * }
-   */
-  public static final OfInt __pid_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __clock_t
-   * }
-   */
-  public static final OfLong __clock_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim_t
-   * }
-   */
-  public static final OfLong __rlim_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim64_t
-   * }
-   */
-  public static final OfLong __rlim64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __id_t
-   * }
-   */
-  public static final OfInt __id_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __time_t
-   * }
-   */
-  public static final OfLong __time_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __useconds_t
-   * }
-   */
-  public static final OfInt __useconds_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds_t
-   * }
-   */
-  public static final OfLong __suseconds_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds64_t
-   * }
-   */
-  public static final OfLong __suseconds64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __daddr_t
-   * }
-   */
-  public static final OfInt __daddr_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __key_t
-   * }
-   */
-  public static final OfInt __key_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __clockid_t
-   * }
-   */
-  public static final OfInt __clockid_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef void *__timer_t
-   * }
-   */
-  public static final AddressLayout __timer_t = IvfFlatH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __blksize_t
-   * }
-   */
-  public static final OfLong __blksize_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt_t
-   * }
-   */
-  public static final OfLong __blkcnt_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt64_t
-   * }
-   */
-  public static final OfLong __blkcnt64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
-   * }
-   */
-  public static final OfLong __fsblkcnt_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
-   * }
-   */
-  public static final OfLong __fsblkcnt64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
-   * }
-   */
-  public static final OfLong __fsfilcnt_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
-   * }
-   */
-  public static final OfLong __fsfilcnt64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __fsword_t
-   * }
-   */
-  public static final OfLong __fsword_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __ssize_t
-   * }
-   */
-  public static final OfLong __ssize_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __syscall_slong_t
-   * }
-   */
-  public static final OfLong __syscall_slong_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
-   * }
-   */
-  public static final OfLong __syscall_ulong_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __off64_t __loff_t
-   * }
-   */
-  public static final OfLong __loff_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef char *__caddr_t
-   * }
-   */
-  public static final AddressLayout __caddr_t = IvfFlatH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __intptr_t
-   * }
-   */
-  public static final OfLong __intptr_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __socklen_t
-   * }
-   */
-  public static final OfInt __socklen_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __sig_atomic_t
-   * }
-   */
-  public static final OfInt __sig_atomic_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int8_t int8_t
-   * }
-   */
-  public static final OfByte int8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t int16_t
-   * }
-   */
-  public static final OfShort int16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t int32_t
-   * }
-   */
-  public static final OfInt int32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t int64_t
-   * }
-   */
-  public static final OfLong int64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t uint8_t
-   * }
-   */
-  public static final OfByte uint8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t uint16_t
-   * }
-   */
-  public static final OfShort uint16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t uint32_t
-   * }
-   */
-  public static final OfInt uint32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t uint64_t
-   * }
-   */
-  public static final OfLong uint64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
-   * }
-   */
-  public static final OfByte int_least8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
-   * }
-   */
-  public static final OfShort int_least16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
-   * }
-   */
-  public static final OfInt int_least32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
-   * }
-   */
-  public static final OfLong int_least64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
-   * }
-   */
-  public static final OfByte uint_least8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
-   * }
-   */
-  public static final OfShort uint_least16_t = IvfFlatH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
-   * }
-   */
-  public static final OfInt uint_least32_t = IvfFlatH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
-   * }
-   */
-  public static final OfLong uint_least64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char int_fast8_t
-   * }
-   */
-  public static final OfByte int_fast8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef long int_fast16_t
-   * }
-   */
-  public static final OfLong int_fast16_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast32_t
-   * }
-   */
-  public static final OfLong int_fast32_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast64_t
-   * }
-   */
-  public static final OfLong int_fast64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
-   * }
-   */
-  public static final OfByte uint_fast8_t = IvfFlatH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
-   * }
-   */
-  public static final OfLong uint_fast16_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
-   * }
-   */
-  public static final OfLong uint_fast32_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
-   * }
-   */
-  public static final OfLong uint_fast64_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long intptr_t
-   * }
-   */
-  public static final OfLong intptr_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uintptr_t
-   * }
-   */
-  public static final OfLong uintptr_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __intmax_t intmax_t
-   * }
-   */
-  public static final OfLong intmax_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
-   * }
-   */
-  public static final OfLong uintmax_t = IvfFlatH.C_LONG;
-  private static final int CUVS_ERROR = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CUVS_ERROR = 0
-   * }
-   */
-  public static int CUVS_ERROR() {
-    return CUVS_ERROR;
-  }
-
-  private static final int CUVS_SUCCESS = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CUVS_SUCCESS = 1
-   * }
-   */
-  public static int CUVS_SUCCESS() {
-    return CUVS_SUCCESS;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef uintptr_t cuvsResources_t
-   * }
-   */
-  public static final OfLong cuvsResources_t = IvfFlatH.C_LONG;
-  private static final int L2Expanded = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
-   * }
-   */
-  public static int L2Expanded() {
-    return L2Expanded;
-  }
-
-  private static final int L2SqrtExpanded = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
-   * }
-   */
-  public static int L2SqrtExpanded() {
-    return L2SqrtExpanded;
-  }
-
-  private static final int CosineExpanded = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
-   * }
-   */
-  public static int CosineExpanded() {
-    return CosineExpanded;
-  }
-
-  private static final int L1 = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L1 = 3
-   * }
-   */
-  public static int L1() {
-    return L1;
-  }
-
-  private static final int L2Unexpanded = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
-   * }
-   */
-  public static int L2Unexpanded() {
-    return L2Unexpanded;
-  }
-
-  private static final int L2SqrtUnexpanded = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
-   * }
-   */
-  public static int L2SqrtUnexpanded() {
-    return L2SqrtUnexpanded;
-  }
-
-  private static final int InnerProduct = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
-   * }
-   */
-  public static int InnerProduct() {
-    return InnerProduct;
-  }
-
-  private static final int Linf = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Linf = 7
-   * }
-   */
-  public static int Linf() {
-    return Linf;
-  }
-
-  private static final int Canberra = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
-   * }
-   */
-  public static int Canberra() {
-    return Canberra;
-  }
-
-  private static final int LpUnexpanded = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
-   * }
-   */
-  public static int LpUnexpanded() {
-    return LpUnexpanded;
-  }
-
-  private static final int CorrelationExpanded = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
-   * }
-   */
-  public static int CorrelationExpanded() {
-    return CorrelationExpanded;
-  }
-
-  private static final int JaccardExpanded = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
-   * }
-   */
-  public static int JaccardExpanded() {
-    return JaccardExpanded;
-  }
-
-  private static final int HellingerExpanded = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
-   * }
-   */
-  public static int HellingerExpanded() {
-    return HellingerExpanded;
-  }
-
-  private static final int Haversine = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
-   * }
-   */
-  public static int Haversine() {
-    return Haversine;
-  }
-
-  private static final int BrayCurtis = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
-   * }
-   */
-  public static int BrayCurtis() {
-    return BrayCurtis;
-  }
-
-  private static final int JensenShannon = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
-   * }
-   */
-  public static int JensenShannon() {
-    return JensenShannon;
-  }
-
-  private static final int HammingUnexpanded = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
-   * }
-   */
-  public static int HammingUnexpanded() {
-    return HammingUnexpanded;
-  }
-
-  private static final int KLDivergence = (int) 17L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
-   * }
-   */
-  public static int KLDivergence() {
-    return KLDivergence;
-  }
-
-  private static final int RusselRaoExpanded = (int) 18L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
-   * }
-   */
-  public static int RusselRaoExpanded() {
-    return RusselRaoExpanded;
-  }
-
-  private static final int DiceExpanded = (int) 19L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
-   * }
-   */
-  public static int DiceExpanded() {
-    return DiceExpanded;
-  }
-
-  private static final int Precomputed = (int) 100L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
-   * }
-   */
-  public static int Precomputed() {
-    return Precomputed;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef long ptrdiff_t
-   * }
-   */
-  public static final OfLong ptrdiff_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long size_t
-   * }
-   */
-  public static final OfLong size_t = IvfFlatH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int wchar_t
-   * }
-   */
-  public static final OfInt wchar_t = IvfFlatH.C_INT;
-  private static final int kDLCPU = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
-   * }
-   */
-  public static int kDLCPU() {
-    return kDLCPU;
-  }
-
-  private static final int kDLCUDA = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
-   * }
-   */
-  public static int kDLCUDA() {
-    return kDLCUDA;
-  }
-
-  private static final int kDLCUDAHost = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
-   * }
-   */
-  public static int kDLCUDAHost() {
-    return kDLCUDAHost;
-  }
-
-  private static final int kDLOpenCL = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
-   * }
-   */
-  public static int kDLOpenCL() {
-    return kDLOpenCL;
-  }
-
-  private static final int kDLVulkan = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
-   * }
-   */
-  public static int kDLVulkan() {
-    return kDLVulkan;
-  }
-
-  private static final int kDLMetal = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
-   * }
-   */
-  public static int kDLMetal() {
-    return kDLMetal;
-  }
-
-  private static final int kDLVPI = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
-   * }
-   */
-  public static int kDLVPI() {
-    return kDLVPI;
-  }
-
-  private static final int kDLROCM = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
-   * }
-   */
-  public static int kDLROCM() {
-    return kDLROCM;
-  }
-
-  private static final int kDLROCMHost = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
-   * }
-   */
-  public static int kDLROCMHost() {
-    return kDLROCMHost;
-  }
-
-  private static final int kDLExtDev = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
-   * }
-   */
-  public static int kDLExtDev() {
-    return kDLExtDev;
-  }
-
-  private static final int kDLCUDAManaged = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
-   * }
-   */
-  public static int kDLCUDAManaged() {
-    return kDLCUDAManaged;
-  }
-
-  private static final int kDLOneAPI = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
-   * }
-   */
-  public static int kDLOneAPI() {
-    return kDLOneAPI;
-  }
-
-  private static final int kDLWebGPU = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
-   * }
-   */
-  public static int kDLWebGPU() {
-    return kDLWebGPU;
-  }
-
-  private static final int kDLHexagon = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
-   * }
-   */
-  public static int kDLHexagon() {
-    return kDLHexagon;
-  }
-
-  private static final int kDLMAIA = (int) 17L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLMAIA = 17
-   * }
-   */
-  public static int kDLMAIA() {
-    return kDLMAIA;
-  }
-
-  private static final int kDLInt = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
-   * }
-   */
-  public static int kDLInt() {
-    return kDLInt;
-  }
-
-  private static final int kDLUInt = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
-   * }
-   */
-  public static int kDLUInt() {
-    return kDLUInt;
-  }
-
-  private static final int kDLFloat = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
-   * }
-   */
-  public static int kDLFloat() {
-    return kDLFloat;
-  }
-
-  private static final int kDLOpaqueHandle = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
-   * }
-   */
-  public static int kDLOpaqueHandle() {
-    return kDLOpaqueHandle;
-  }
-
-  private static final int kDLBfloat = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
-   * }
-   */
-  public static int kDLBfloat() {
-    return kDLBfloat;
-  }
-
-  private static final int kDLComplex = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
-   * }
-   */
-  public static int kDLComplex() {
-    return kDLComplex;
-  }
-
-  private static final int kDLBool = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
-   * }
-   */
-  public static int kDLBool() {
-    return kDLBool;
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsIvfFlatIndexParams {
-   *     cuvsDistanceType metric;
-   *     float metric_arg;
-   *     _Bool add_data_on_build;
-   *     uint32_t n_lists;
-   *     uint32_t kmeans_n_iters;
-   *     double kmeans_trainset_fraction;
-   *     _Bool adaptive_centers;
-   *     _Bool conservative_memory_allocation;
-   * } *cuvsIvfFlatIndexParams_t
-   * }
-   */
-  public static final AddressLayout cuvsIvfFlatIndexParams_t = IvfFlatH.C_POINTER;
-
-  private static class cuvsIvfFlatIndexParamsCreate {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexParamsCreate");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatIndexParamsCreate$descriptor() {
-    return cuvsIvfFlatIndexParamsCreate.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatIndexParamsCreate$handle() {
-    return cuvsIvfFlatIndexParamsCreate.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatIndexParamsCreate$address() {
-    return cuvsIvfFlatIndexParamsCreate.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsCreate(cuvsIvfFlatIndexParams_t *index_params)
-   * }
-   */
-  public static int cuvsIvfFlatIndexParamsCreate(MemorySegment index_params) {
-    var mh$ = cuvsIvfFlatIndexParamsCreate.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatIndexParamsCreate", index_params);
-      }
-      return (int) mh$.invokeExact(index_params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatIndexParamsDestroy {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexParamsDestroy");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatIndexParamsDestroy$descriptor() {
-    return cuvsIvfFlatIndexParamsDestroy.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatIndexParamsDestroy$handle() {
-    return cuvsIvfFlatIndexParamsDestroy.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatIndexParamsDestroy$address() {
-    return cuvsIvfFlatIndexParamsDestroy.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexParamsDestroy(cuvsIvfFlatIndexParams_t index_params)
-   * }
-   */
-  public static int cuvsIvfFlatIndexParamsDestroy(MemorySegment index_params) {
-    var mh$ = cuvsIvfFlatIndexParamsDestroy.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatIndexParamsDestroy", index_params);
-      }
-      return (int) mh$.invokeExact(index_params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsIvfFlatSearchParams {
-   *     uint32_t n_probes;
-   * } *cuvsIvfFlatSearchParams_t
-   * }
-   */
-  public static final AddressLayout cuvsIvfFlatSearchParams_t = IvfFlatH.C_POINTER;
-
-  private static class cuvsIvfFlatSearchParamsCreate {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSearchParamsCreate");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatSearchParamsCreate$descriptor() {
-    return cuvsIvfFlatSearchParamsCreate.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatSearchParamsCreate$handle() {
-    return cuvsIvfFlatSearchParamsCreate.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatSearchParamsCreate$address() {
-    return cuvsIvfFlatSearchParamsCreate.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsCreate(cuvsIvfFlatSearchParams_t *params)
-   * }
-   */
-  public static int cuvsIvfFlatSearchParamsCreate(MemorySegment params) {
-    var mh$ = cuvsIvfFlatSearchParamsCreate.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatSearchParamsCreate", params);
-      }
-      return (int) mh$.invokeExact(params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatSearchParamsDestroy {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSearchParamsDestroy");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatSearchParamsDestroy$descriptor() {
-    return cuvsIvfFlatSearchParamsDestroy.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatSearchParamsDestroy$handle() {
-    return cuvsIvfFlatSearchParamsDestroy.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatSearchParamsDestroy$address() {
-    return cuvsIvfFlatSearchParamsDestroy.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearchParamsDestroy(cuvsIvfFlatSearchParams_t params)
-   * }
-   */
-  public static int cuvsIvfFlatSearchParamsDestroy(MemorySegment params) {
-    var mh$ = cuvsIvfFlatSearchParamsDestroy.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatSearchParamsDestroy", params);
-      }
-      return (int) mh$.invokeExact(params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  /**
-   * {@snippet lang = c : * typedef cuvsIvfFlatIndex *cuvsIvfFlatIndex_t
-   * }
-   */
-  public static final AddressLayout cuvsIvfFlatIndex_t = IvfFlatH.C_POINTER;
-
-  private static class cuvsIvfFlatIndexCreate {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexCreate");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatIndexCreate$descriptor() {
-    return cuvsIvfFlatIndexCreate.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatIndexCreate$handle() {
-    return cuvsIvfFlatIndexCreate.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatIndexCreate$address() {
-    return cuvsIvfFlatIndexCreate.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t *index)
-   * }
-   */
-  public static int cuvsIvfFlatIndexCreate(MemorySegment index) {
-    var mh$ = cuvsIvfFlatIndexCreate.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatIndexCreate", index);
-      }
-      return (int) mh$.invokeExact(index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatIndexDestroy {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatIndexDestroy");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatIndexDestroy$descriptor() {
-    return cuvsIvfFlatIndexDestroy.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatIndexDestroy$handle() {
-    return cuvsIvfFlatIndexDestroy.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatIndexDestroy$address() {
-    return cuvsIvfFlatIndexDestroy.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static int cuvsIvfFlatIndexDestroy(MemorySegment index) {
-    var mh$ = cuvsIvfFlatIndexDestroy.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatIndexDestroy", index);
-      }
-      return (int) mh$.invokeExact(index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatBuild {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
-        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatBuild");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatBuild$descriptor() {
-    return cuvsIvfFlatBuild.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatBuild$handle() {
-    return cuvsIvfFlatBuild.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatBuild$address() {
-    return cuvsIvfFlatBuild.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res, cuvsIvfFlatIndexParams_t index_params, DLManagedTensor *dataset, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static int cuvsIvfFlatBuild(long res, MemorySegment index_params, MemorySegment dataset, MemorySegment index) {
-    var mh$ = cuvsIvfFlatBuild.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatBuild", res, index_params, dataset, index);
-      }
-      return (int) mh$.invokeExact(res, index_params, dataset, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatSearch {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
-        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSearch");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatSearch$descriptor() {
-    return cuvsIvfFlatSearch.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatSearch$handle() {
-    return cuvsIvfFlatSearch.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatSearch$address() {
-    return cuvsIvfFlatSearch.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res, cuvsIvfFlatSearchParams_t search_params, cuvsIvfFlatIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static int cuvsIvfFlatSearch(long res, MemorySegment search_params, MemorySegment index, MemorySegment queries,
-      MemorySegment neighbors, MemorySegment distances) {
-    var mh$ = cuvsIvfFlatSearch.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatSearch", res, search_params, index, queries, neighbors, distances);
-      }
-      return (int) mh$.invokeExact(res, search_params, index, queries, neighbors, distances);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatSerialize {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
-        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatSerialize");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatSerialize$descriptor() {
-    return cuvsIvfFlatSerialize.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatSerialize$handle() {
-    return cuvsIvfFlatSerialize.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatSerialize$address() {
-    return cuvsIvfFlatSerialize.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatSerialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static int cuvsIvfFlatSerialize(long res, MemorySegment filename, MemorySegment index) {
-    var mh$ = cuvsIvfFlatSerialize.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatSerialize", res, filename, index);
-      }
-      return (int) mh$.invokeExact(res, filename, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatDeserialize {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
-        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatDeserialize");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatDeserialize$descriptor() {
-    return cuvsIvfFlatDeserialize.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatDeserialize$handle() {
-    return cuvsIvfFlatDeserialize.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatDeserialize$address() {
-    return cuvsIvfFlatDeserialize.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res, const char *filename, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static int cuvsIvfFlatDeserialize(long res, MemorySegment filename, MemorySegment index) {
-    var mh$ = cuvsIvfFlatDeserialize.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatDeserialize", res, filename, index);
-      }
-      return (int) mh$.invokeExact(res, filename, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfFlatExtend {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfFlatH.C_INT, IvfFlatH.C_LONG,
-        IvfFlatH.C_POINTER, IvfFlatH.C_POINTER, IvfFlatH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfFlatH.findOrThrow("cuvsIvfFlatExtend");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfFlatExtend$descriptor() {
-    return cuvsIvfFlatExtend.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfFlatExtend$handle() {
-    return cuvsIvfFlatExtend.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfFlatExtend$address() {
-    return cuvsIvfFlatExtend.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfFlatIndex_t index)
-   * }
-   */
-  public static int cuvsIvfFlatExtend(long res, MemorySegment new_vectors, MemorySegment new_indices,
-      MemorySegment index) {
-    var mh$ = cuvsIvfFlatExtend.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfFlatExtend", res, new_vectors, new_indices, index);
-      }
-      return (int) mh$.invokeExact(res, new_vectors, new_indices, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static final long _POSIX_C_SOURCE = 200809L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
-   * }
-   */
-  public static long _POSIX_C_SOURCE() {
-    return _POSIX_C_SOURCE;
-  }
-
-  private static final int __TIMESIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __TIMESIZE 64
-   * }
-   */
-  public static int __TIMESIZE() {
-    return __TIMESIZE;
-  }
-
-  private static final long __STDC_IEC_60559_BFP__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_BFP__() {
-    return __STDC_IEC_60559_BFP__;
-  }
-
-  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_COMPLEX__() {
-    return __STDC_IEC_60559_COMPLEX__;
-  }
-
-  private static final long __STDC_ISO_10646__ = 201706L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
-   * }
-   */
-  public static long __STDC_ISO_10646__() {
-    return __STDC_ISO_10646__;
-  }
-
-  private static final int __WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
-   * }
-   */
-  public static int __WCHAR_MAX() {
-    return __WCHAR_MAX;
-  }
-
-  private static final int __WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
-   * }
-   */
-  public static int __WCHAR_MIN() {
-    return __WCHAR_MIN;
-  }
-
-  private static final int INT8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MIN -128
-   * }
-   */
-  public static int INT8_MIN() {
-    return INT8_MIN;
-  }
-
-  private static final int INT16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MIN -32768
-   * }
-   */
-  public static int INT16_MIN() {
-    return INT16_MIN;
-  }
-
-  private static final int INT32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MIN -2147483648
-   * }
-   */
-  public static int INT32_MIN() {
-    return INT32_MIN;
-  }
-
-  private static final long INT64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT64_MIN() {
-    return INT64_MIN;
-  }
-
-  private static final int INT8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MAX 127
-   * }
-   */
-  public static int INT8_MAX() {
-    return INT8_MAX;
-  }
-
-  private static final int INT16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MAX 32767
-   * }
-   */
-  public static int INT16_MAX() {
-    return INT16_MAX;
-  }
-
-  private static final int INT32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MAX 2147483647
-   * }
-   */
-  public static int INT32_MAX() {
-    return INT32_MAX;
-  }
-
-  private static final long INT64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT64_MAX() {
-    return INT64_MAX;
-  }
-
-  private static final int UINT8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT8_MAX 255
-   * }
-   */
-  public static int UINT8_MAX() {
-    return UINT8_MAX;
-  }
-
-  private static final int UINT16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT16_MAX 65535
-   * }
-   */
-  public static int UINT16_MAX() {
-    return UINT16_MAX;
-  }
-
-  private static final int UINT32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT32_MAX 4294967295
-   * }
-   */
-  public static int UINT32_MAX() {
-    return UINT32_MAX;
-  }
-
-  private static final long UINT64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT64_MAX -1
-   * }
-   */
-  public static long UINT64_MAX() {
-    return UINT64_MAX;
-  }
-
-  private static final int INT_LEAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
-   * }
-   */
-  public static int INT_LEAST8_MIN() {
-    return INT_LEAST8_MIN;
-  }
-
-  private static final int INT_LEAST16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
-   * }
-   */
-  public static int INT_LEAST16_MIN() {
-    return INT_LEAST16_MIN;
-  }
-
-  private static final int INT_LEAST32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
-   * }
-   */
-  public static int INT_LEAST32_MIN() {
-    return INT_LEAST32_MIN;
-  }
-
-  private static final long INT_LEAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_LEAST64_MIN() {
-    return INT_LEAST64_MIN;
-  }
-
-  private static final int INT_LEAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
-   * }
-   */
-  public static int INT_LEAST8_MAX() {
-    return INT_LEAST8_MAX;
-  }
-
-  private static final int INT_LEAST16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
-   * }
-   */
-  public static int INT_LEAST16_MAX() {
-    return INT_LEAST16_MAX;
-  }
-
-  private static final int INT_LEAST32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
-   * }
-   */
-  public static int INT_LEAST32_MAX() {
-    return INT_LEAST32_MAX;
-  }
-
-  private static final long INT_LEAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_LEAST64_MAX() {
-    return INT_LEAST64_MAX;
-  }
-
-  private static final int UINT_LEAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
-   * }
-   */
-  public static int UINT_LEAST8_MAX() {
-    return UINT_LEAST8_MAX;
-  }
-
-  private static final int UINT_LEAST16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
-   * }
-   */
-  public static int UINT_LEAST16_MAX() {
-    return UINT_LEAST16_MAX;
-  }
-
-  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
-   * }
-   */
-  public static int UINT_LEAST32_MAX() {
-    return UINT_LEAST32_MAX;
-  }
-
-  private static final long UINT_LEAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
-   * }
-   */
-  public static long UINT_LEAST64_MAX() {
-    return UINT_LEAST64_MAX;
-  }
-
-  private static final int INT_FAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MIN -128
-   * }
-   */
-  public static int INT_FAST8_MIN() {
-    return INT_FAST8_MIN;
-  }
-
-  private static final long INT_FAST16_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST16_MIN() {
-    return INT_FAST16_MIN;
-  }
-
-  private static final long INT_FAST32_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST32_MIN() {
-    return INT_FAST32_MIN;
-  }
-
-  private static final long INT_FAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST64_MIN() {
-    return INT_FAST64_MIN;
-  }
-
-  private static final int INT_FAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MAX 127
-   * }
-   */
-  public static int INT_FAST8_MAX() {
-    return INT_FAST8_MAX;
-  }
-
-  private static final long INT_FAST16_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST16_MAX() {
-    return INT_FAST16_MAX;
-  }
-
-  private static final long INT_FAST32_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST32_MAX() {
-    return INT_FAST32_MAX;
-  }
-
-  private static final long INT_FAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST64_MAX() {
-    return INT_FAST64_MAX;
-  }
-
-  private static final int UINT_FAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
-   * }
-   */
-  public static int UINT_FAST8_MAX() {
-    return UINT_FAST8_MAX;
-  }
-
-  private static final long UINT_FAST16_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
-   * }
-   */
-  public static long UINT_FAST16_MAX() {
-    return UINT_FAST16_MAX;
-  }
-
-  private static final long UINT_FAST32_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
-   * }
-   */
-  public static long UINT_FAST32_MAX() {
-    return UINT_FAST32_MAX;
-  }
-
-  private static final long UINT_FAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
-   * }
-   */
-  public static long UINT_FAST64_MAX() {
-    return UINT_FAST64_MAX;
-  }
-
-  private static final long INTPTR_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
-   * }
-   */
-  public static long INTPTR_MIN() {
-    return INTPTR_MIN;
-  }
-
-  private static final long INTPTR_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
-   * }
-   */
-  public static long INTPTR_MAX() {
-    return INTPTR_MAX;
-  }
-
-  private static final long UINTPTR_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTPTR_MAX -1
-   * }
-   */
-  public static long UINTPTR_MAX() {
-    return UINTPTR_MAX;
-  }
-
-  private static final long INTMAX_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
-   * }
-   */
-  public static long INTMAX_MIN() {
-    return INTMAX_MIN;
-  }
-
-  private static final long INTMAX_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
-   * }
-   */
-  public static long INTMAX_MAX() {
-    return INTMAX_MAX;
-  }
-
-  private static final long UINTMAX_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTMAX_MAX -1
-   * }
-   */
-  public static long UINTMAX_MAX() {
-    return UINTMAX_MAX;
-  }
-
-  private static final long PTRDIFF_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
-   * }
-   */
-  public static long PTRDIFF_MIN() {
-    return PTRDIFF_MIN;
-  }
-
-  private static final long PTRDIFF_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
-   * }
-   */
-  public static long PTRDIFF_MAX() {
-    return PTRDIFF_MAX;
-  }
-
-  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
-   * }
-   */
-  public static int SIG_ATOMIC_MIN() {
-    return SIG_ATOMIC_MIN;
-  }
-
-  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
-   * }
-   */
-  public static int SIG_ATOMIC_MAX() {
-    return SIG_ATOMIC_MAX;
-  }
-
-  private static final long SIZE_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define SIZE_MAX -1
-   * }
-   */
-  public static long SIZE_MAX() {
-    return SIZE_MAX;
-  }
-
-  private static final int WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
-   * }
-   */
-  public static int WCHAR_MIN() {
-    return WCHAR_MIN;
-  }
-
-  private static final int WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
-   * }
-   */
-  public static int WCHAR_MAX() {
-    return WCHAR_MAX;
-  }
-
-  private static final int WINT_MIN = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MIN 0
-   * }
-   */
-  public static int WINT_MIN() {
-    return WINT_MIN;
-  }
-
-  private static final int WINT_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MAX 4294967295
-   * }
-   */
-  public static int WINT_MAX() {
-    return WINT_MAX;
-  }
-
-  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
-
-  /**
-   * {@snippet lang = c : * #define NULL (void*) 0
-   * }
-   */
-  public static MemorySegment NULL() {
-    return NULL;
-  }
-
-  private static final long DLPACK_FLAG_BITMASK_READ_ONLY = 1L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_READ_ONLY 1
-   * }
-   */
-  public static long DLPACK_FLAG_BITMASK_READ_ONLY() {
-    return DLPACK_FLAG_BITMASK_READ_ONLY;
-  }
-
-  private static final long DLPACK_FLAG_BITMASK_IS_COPIED = 2L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_IS_COPIED 2
-   * }
-   */
-  public static long DLPACK_FLAG_BITMASK_IS_COPIED() {
-    return DLPACK_FLAG_BITMASK_IS_COPIED;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfPqH.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfPqH.java
deleted file mode 100644
index 66d0ba5acb..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/IvfPqH.java
+++ /dev/null
@@ -1,3182 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.ValueLayout.JAVA_BYTE;
-
-import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.Linker;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.PaddingLayout;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.StructLayout;
-import java.lang.foreign.SymbolLookup;
-import java.lang.foreign.ValueLayout;
-import java.lang.foreign.ValueLayout.OfByte;
-import java.lang.foreign.ValueLayout.OfInt;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.lang.foreign.ValueLayout.OfShort;
-import java.lang.invoke.MethodHandle;
-import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
-import java.util.stream.Collectors;
-
-public class IvfPqH {
-
-  IvfPqH() {
-    // Should not be called directly
-  }
-
-  static final Arena LIBRARY_ARENA = Arena.ofAuto();
-  static final boolean TRACE_DOWNCALLS = Boolean.getBoolean("jextract.trace.downcalls");
-
-  static void traceDowncall(String name, Object... args) {
-    String traceArgs = Arrays.stream(args).map(Object::toString).collect(Collectors.joining(", "));
-    System.out.printf("%s(%s)\n", name, traceArgs);
-  }
-
-  static MemorySegment findOrThrow(String symbol) {
-    return SYMBOL_LOOKUP.find(symbol).orElseThrow(() -> new UnsatisfiedLinkError("unresolved symbol: " + symbol));
-  }
-
-  static MethodHandle upcallHandle(Class<?> fi, String name, FunctionDescriptor fdesc) {
-    try {
-      return MethodHandles.lookup().findVirtual(fi, name, fdesc.toMethodType());
-    } catch (ReflectiveOperationException ex) {
-      throw new AssertionError(ex);
-    }
-  }
-
-  static MemoryLayout align(MemoryLayout layout, long align) {
-    return switch (layout) {
-    case PaddingLayout p -> p;
-    case ValueLayout v -> v.withByteAlignment(align);
-    case GroupLayout g -> {
-      MemoryLayout[] alignedMembers = g.memberLayouts().stream().map(m -> align(m, align)).toArray(MemoryLayout[]::new);
-      yield g instanceof StructLayout ? MemoryLayout.structLayout(alignedMembers)
-          : MemoryLayout.unionLayout(alignedMembers);
-    }
-    case SequenceLayout s -> MemoryLayout.sequenceLayout(s.elementCount(), align(s.elementLayout(), align));
-    };
-  }
-
-  static final SymbolLookup SYMBOL_LOOKUP = SymbolLookup.loaderLookup().or(Linker.nativeLinker().defaultLookup());
-
-  public static final ValueLayout.OfBoolean C_BOOL = ValueLayout.JAVA_BOOLEAN;
-  public static final ValueLayout.OfByte C_CHAR = ValueLayout.JAVA_BYTE;
-  public static final ValueLayout.OfShort C_SHORT = ValueLayout.JAVA_SHORT;
-  public static final ValueLayout.OfInt C_INT = ValueLayout.JAVA_INT;
-  public static final ValueLayout.OfLong C_LONG_LONG = ValueLayout.JAVA_LONG;
-  public static final ValueLayout.OfFloat C_FLOAT = ValueLayout.JAVA_FLOAT;
-  public static final ValueLayout.OfDouble C_DOUBLE = ValueLayout.JAVA_DOUBLE;
-  public static final AddressLayout C_POINTER = ValueLayout.ADDRESS
-      .withTargetLayout(MemoryLayout.sequenceLayout(java.lang.Long.MAX_VALUE, JAVA_BYTE));
-  public static final ValueLayout.OfLong C_LONG = ValueLayout.JAVA_LONG;
-  private static final int _STDINT_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDINT_H 1
-   * }
-   */
-  public static int _STDINT_H() {
-    return _STDINT_H;
-  }
-
-  private static final int _FEATURES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _FEATURES_H 1
-   * }
-   */
-  public static int _FEATURES_H() {
-    return _FEATURES_H;
-  }
-
-  private static final int _DEFAULT_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _DEFAULT_SOURCE 1
-   * }
-   */
-  public static int _DEFAULT_SOURCE() {
-    return _DEFAULT_SOURCE;
-  }
-
-  private static final int __GLIBC_USE_ISOC2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_ISOC2X 0
-   * }
-   */
-  public static int __GLIBC_USE_ISOC2X() {
-    return __GLIBC_USE_ISOC2X;
-  }
-
-  private static final int __USE_ISOC11 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC11 1
-   * }
-   */
-  public static int __USE_ISOC11() {
-    return __USE_ISOC11;
-  }
-
-  private static final int __USE_ISOC99 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC99 1
-   * }
-   */
-  public static int __USE_ISOC99() {
-    return __USE_ISOC99;
-  }
-
-  private static final int __USE_ISOC95 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ISOC95 1
-   * }
-   */
-  public static int __USE_ISOC95() {
-    return __USE_ISOC95;
-  }
-
-  private static final int __USE_POSIX_IMPLICITLY = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX_IMPLICITLY 1
-   * }
-   */
-  public static int __USE_POSIX_IMPLICITLY() {
-    return __USE_POSIX_IMPLICITLY;
-  }
-
-  private static final int _POSIX_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_SOURCE 1
-   * }
-   */
-  public static int _POSIX_SOURCE() {
-    return _POSIX_SOURCE;
-  }
-
-  private static final int __USE_POSIX = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX 1
-   * }
-   */
-  public static int __USE_POSIX() {
-    return __USE_POSIX;
-  }
-
-  private static final int __USE_POSIX2 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX2 1
-   * }
-   */
-  public static int __USE_POSIX2() {
-    return __USE_POSIX2;
-  }
-
-  private static final int __USE_POSIX199309 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199309 1
-   * }
-   */
-  public static int __USE_POSIX199309() {
-    return __USE_POSIX199309;
-  }
-
-  private static final int __USE_POSIX199506 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_POSIX199506 1
-   * }
-   */
-  public static int __USE_POSIX199506() {
-    return __USE_POSIX199506;
-  }
-
-  private static final int __USE_XOPEN2K = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K 1
-   * }
-   */
-  public static int __USE_XOPEN2K() {
-    return __USE_XOPEN2K;
-  }
-
-  private static final int __USE_XOPEN2K8 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_XOPEN2K8 1
-   * }
-   */
-  public static int __USE_XOPEN2K8() {
-    return __USE_XOPEN2K8;
-  }
-
-  private static final int _ATFILE_SOURCE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _ATFILE_SOURCE 1
-   * }
-   */
-  public static int _ATFILE_SOURCE() {
-    return _ATFILE_SOURCE;
-  }
-
-  private static final int __WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE 64
-   * }
-   */
-  public static int __WORDSIZE() {
-    return __WORDSIZE;
-  }
-
-  private static final int __WORDSIZE_TIME64_COMPAT32 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __WORDSIZE_TIME64_COMPAT32 1
-   * }
-   */
-  public static int __WORDSIZE_TIME64_COMPAT32() {
-    return __WORDSIZE_TIME64_COMPAT32;
-  }
-
-  private static final int __SYSCALL_WORDSIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __SYSCALL_WORDSIZE 64
-   * }
-   */
-  public static int __SYSCALL_WORDSIZE() {
-    return __SYSCALL_WORDSIZE;
-  }
-
-  private static final int __USE_MISC = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_MISC 1
-   * }
-   */
-  public static int __USE_MISC() {
-    return __USE_MISC;
-  }
-
-  private static final int __USE_ATFILE = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_ATFILE 1
-   * }
-   */
-  public static int __USE_ATFILE() {
-    return __USE_ATFILE;
-  }
-
-  private static final int __USE_FORTIFY_LEVEL = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __USE_FORTIFY_LEVEL 0
-   * }
-   */
-  public static int __USE_FORTIFY_LEVEL() {
-    return __USE_FORTIFY_LEVEL;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_GETS = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_GETS 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_GETS() {
-    return __GLIBC_USE_DEPRECATED_GETS;
-  }
-
-  private static final int __GLIBC_USE_DEPRECATED_SCANF = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_DEPRECATED_SCANF 0
-   * }
-   */
-  public static int __GLIBC_USE_DEPRECATED_SCANF() {
-    return __GLIBC_USE_DEPRECATED_SCANF;
-  }
-
-  private static final int _STDC_PREDEF_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _STDC_PREDEF_H 1
-   * }
-   */
-  public static int _STDC_PREDEF_H() {
-    return _STDC_PREDEF_H;
-  }
-
-  private static final int __STDC_IEC_559__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559__ 1
-   * }
-   */
-  public static int __STDC_IEC_559__() {
-    return __STDC_IEC_559__;
-  }
-
-  private static final int __STDC_IEC_559_COMPLEX__ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_559_COMPLEX__ 1
-   * }
-   */
-  public static int __STDC_IEC_559_COMPLEX__() {
-    return __STDC_IEC_559_COMPLEX__;
-  }
-
-  private static final int __GNU_LIBRARY__ = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * #define __GNU_LIBRARY__ 6
-   * }
-   */
-  public static int __GNU_LIBRARY__() {
-    return __GNU_LIBRARY__;
-  }
-
-  private static final int __GLIBC__ = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC__ 2
-   * }
-   */
-  public static int __GLIBC__() {
-    return __GLIBC__;
-  }
-
-  private static final int __GLIBC_MINOR__ = (int) 35L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_MINOR__ 35
-   * }
-   */
-  public static int __GLIBC_MINOR__() {
-    return __GLIBC_MINOR__;
-  }
-
-  private static final int _SYS_CDEFS_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _SYS_CDEFS_H 1
-   * }
-   */
-  public static int _SYS_CDEFS_H() {
-    return _SYS_CDEFS_H;
-  }
-
-  private static final int __glibc_c99_flexarr_available = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __glibc_c99_flexarr_available 1
-   * }
-   */
-  public static int __glibc_c99_flexarr_available() {
-    return __glibc_c99_flexarr_available;
-  }
-
-  private static final int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI 0
-   * }
-   */
-  public static int __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI() {
-    return __LDOUBLE_REDIRECTS_TO_FLOAT128_ABI;
-  }
-
-  private static final int __HAVE_GENERIC_SELECTION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __HAVE_GENERIC_SELECTION 1
-   * }
-   */
-  public static int __HAVE_GENERIC_SELECTION() {
-    return __HAVE_GENERIC_SELECTION;
-  }
-
-  private static final int __GLIBC_USE_LIB_EXT2 = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_LIB_EXT2 0
-   * }
-   */
-  public static int __GLIBC_USE_LIB_EXT2() {
-    return __GLIBC_USE_LIB_EXT2;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_BFP_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_BFP_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_BFP_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_BFP_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_EXT() {
-    return __GLIBC_USE_IEC_60559_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X() {
-    return __GLIBC_USE_IEC_60559_FUNCS_EXT_C2X;
-  }
-
-  private static final int __GLIBC_USE_IEC_60559_TYPES_EXT = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define __GLIBC_USE_IEC_60559_TYPES_EXT 0
-   * }
-   */
-  public static int __GLIBC_USE_IEC_60559_TYPES_EXT() {
-    return __GLIBC_USE_IEC_60559_TYPES_EXT;
-  }
-
-  private static final int _BITS_TYPES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPES_H 1
-   * }
-   */
-  public static int _BITS_TYPES_H() {
-    return _BITS_TYPES_H;
-  }
-
-  private static final int _BITS_TYPESIZES_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TYPESIZES_H 1
-   * }
-   */
-  public static int _BITS_TYPESIZES_H() {
-    return _BITS_TYPESIZES_H;
-  }
-
-  private static final int __OFF_T_MATCHES_OFF64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __OFF_T_MATCHES_OFF64_T 1
-   * }
-   */
-  public static int __OFF_T_MATCHES_OFF64_T() {
-    return __OFF_T_MATCHES_OFF64_T;
-  }
-
-  private static final int __INO_T_MATCHES_INO64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __INO_T_MATCHES_INO64_T 1
-   * }
-   */
-  public static int __INO_T_MATCHES_INO64_T() {
-    return __INO_T_MATCHES_INO64_T;
-  }
-
-  private static final int __RLIM_T_MATCHES_RLIM64_T = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __RLIM_T_MATCHES_RLIM64_T 1
-   * }
-   */
-  public static int __RLIM_T_MATCHES_RLIM64_T() {
-    return __RLIM_T_MATCHES_RLIM64_T;
-  }
-
-  private static final int __STATFS_MATCHES_STATFS64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __STATFS_MATCHES_STATFS64 1
-   * }
-   */
-  public static int __STATFS_MATCHES_STATFS64() {
-    return __STATFS_MATCHES_STATFS64;
-  }
-
-  private static final int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64 1
-   * }
-   */
-  public static int __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64() {
-    return __KERNEL_OLD_TIMEVAL_MATCHES_TIMEVAL64;
-  }
-
-  private static final int __FD_SETSIZE = (int) 1024L;
-
-  /**
-   * {@snippet lang = c : * #define __FD_SETSIZE 1024
-   * }
-   */
-  public static int __FD_SETSIZE() {
-    return __FD_SETSIZE;
-  }
-
-  private static final int _BITS_TIME64_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_TIME64_H 1
-   * }
-   */
-  public static int _BITS_TIME64_H() {
-    return _BITS_TIME64_H;
-  }
-
-  private static final int _BITS_WCHAR_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_WCHAR_H 1
-   * }
-   */
-  public static int _BITS_WCHAR_H() {
-    return _BITS_WCHAR_H;
-  }
-
-  private static final int _BITS_STDINT_INTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_INTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_INTN_H() {
-    return _BITS_STDINT_INTN_H;
-  }
-
-  private static final int _BITS_STDINT_UINTN_H = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define _BITS_STDINT_UINTN_H 1
-   * }
-   */
-  public static int _BITS_STDINT_UINTN_H() {
-    return _BITS_STDINT_UINTN_H;
-  }
-
-  private static final int DLPACK_MAJOR_VERSION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_MAJOR_VERSION 1
-   * }
-   */
-  public static int DLPACK_MAJOR_VERSION() {
-    return DLPACK_MAJOR_VERSION;
-  }
-
-  private static final int DLPACK_MINOR_VERSION = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_MINOR_VERSION 0
-   * }
-   */
-  public static int DLPACK_MINOR_VERSION() {
-    return DLPACK_MINOR_VERSION;
-  }
-
-  private static final int true_ = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define true 1
-   * }
-   */
-  public static int true_() {
-    return true_;
-  }
-
-  private static final int false_ = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define false 0
-   * }
-   */
-  public static int false_() {
-    return false_;
-  }
-
-  private static final int __bool_true_false_are_defined = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * #define __bool_true_false_are_defined 1
-   * }
-   */
-  public static int __bool_true_false_are_defined() {
-    return __bool_true_false_are_defined;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef unsigned char __u_char
-   * }
-   */
-  public static final OfByte __u_char = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __u_short
-   * }
-   */
-  public static final OfShort __u_short = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __u_int
-   * }
-   */
-  public static final OfInt __u_int = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_long
-   * }
-   */
-  public static final OfLong __u_long = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char __int8_t
-   * }
-   */
-  public static final OfByte __int8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned char __uint8_t
-   * }
-   */
-  public static final OfByte __uint8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef short __int16_t
-   * }
-   */
-  public static final OfShort __int16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef unsigned short __uint16_t
-   * }
-   */
-  public static final OfShort __uint16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef int __int32_t
-   * }
-   */
-  public static final OfInt __int32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uint32_t
-   * }
-   */
-  public static final OfInt __uint32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __int64_t
-   * }
-   */
-  public static final OfLong __int64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uint64_t
-   * }
-   */
-  public static final OfLong __uint64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int8_t __int_least8_t
-   * }
-   */
-  public static final OfByte __int_least8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t __uint_least8_t
-   * }
-   */
-  public static final OfByte __uint_least8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t __int_least16_t
-   * }
-   */
-  public static final OfShort __int_least16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t __uint_least16_t
-   * }
-   */
-  public static final OfShort __uint_least16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t __int_least32_t
-   * }
-   */
-  public static final OfInt __int_least32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t __uint_least32_t
-   * }
-   */
-  public static final OfInt __uint_least32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t __int_least64_t
-   * }
-   */
-  public static final OfLong __int_least64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t __uint_least64_t
-   * }
-   */
-  public static final OfLong __uint_least64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __quad_t
-   * }
-   */
-  public static final OfLong __quad_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __u_quad_t
-   * }
-   */
-  public static final OfLong __u_quad_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __intmax_t
-   * }
-   */
-  public static final OfLong __intmax_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __uintmax_t
-   * }
-   */
-  public static final OfLong __uintmax_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __dev_t
-   * }
-   */
-  public static final OfLong __dev_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __uid_t
-   * }
-   */
-  public static final OfInt __uid_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __gid_t
-   * }
-   */
-  public static final OfInt __gid_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino_t
-   * }
-   */
-  public static final OfLong __ino_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __ino64_t
-   * }
-   */
-  public static final OfLong __ino64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __mode_t
-   * }
-   */
-  public static final OfInt __mode_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __nlink_t
-   * }
-   */
-  public static final OfLong __nlink_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off_t
-   * }
-   */
-  public static final OfLong __off_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __off64_t
-   * }
-   */
-  public static final OfLong __off64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __pid_t
-   * }
-   */
-  public static final OfInt __pid_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __clock_t
-   * }
-   */
-  public static final OfLong __clock_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim_t
-   * }
-   */
-  public static final OfLong __rlim_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __rlim64_t
-   * }
-   */
-  public static final OfLong __rlim64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __id_t
-   * }
-   */
-  public static final OfInt __id_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __time_t
-   * }
-   */
-  public static final OfLong __time_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __useconds_t
-   * }
-   */
-  public static final OfInt __useconds_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds_t
-   * }
-   */
-  public static final OfLong __suseconds_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __suseconds64_t
-   * }
-   */
-  public static final OfLong __suseconds64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int __daddr_t
-   * }
-   */
-  public static final OfInt __daddr_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __key_t
-   * }
-   */
-  public static final OfInt __key_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __clockid_t
-   * }
-   */
-  public static final OfInt __clockid_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef void *__timer_t
-   * }
-   */
-  public static final AddressLayout __timer_t = IvfPqH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __blksize_t
-   * }
-   */
-  public static final OfLong __blksize_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt_t
-   * }
-   */
-  public static final OfLong __blkcnt_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __blkcnt64_t
-   * }
-   */
-  public static final OfLong __blkcnt64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt_t
-   * }
-   */
-  public static final OfLong __fsblkcnt_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsblkcnt64_t
-   * }
-   */
-  public static final OfLong __fsblkcnt64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt_t
-   * }
-   */
-  public static final OfLong __fsfilcnt_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __fsfilcnt64_t
-   * }
-   */
-  public static final OfLong __fsfilcnt64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __fsword_t
-   * }
-   */
-  public static final OfLong __fsword_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __ssize_t
-   * }
-   */
-  public static final OfLong __ssize_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long __syscall_slong_t
-   * }
-   */
-  public static final OfLong __syscall_slong_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long __syscall_ulong_t
-   * }
-   */
-  public static final OfLong __syscall_ulong_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __off64_t __loff_t
-   * }
-   */
-  public static final OfLong __loff_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef char *__caddr_t
-   * }
-   */
-  public static final AddressLayout __caddr_t = IvfPqH.C_POINTER;
-  /**
-   * {@snippet lang = c : * typedef long __intptr_t
-   * }
-   */
-  public static final OfLong __intptr_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned int __socklen_t
-   * }
-   */
-  public static final OfInt __socklen_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef int __sig_atomic_t
-   * }
-   */
-  public static final OfInt __sig_atomic_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int8_t int8_t
-   * }
-   */
-  public static final OfByte int8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int16_t int16_t
-   * }
-   */
-  public static final OfShort int16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int32_t int32_t
-   * }
-   */
-  public static final OfInt int32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int64_t int64_t
-   * }
-   */
-  public static final OfLong int64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint8_t uint8_t
-   * }
-   */
-  public static final OfByte uint8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint16_t uint16_t
-   * }
-   */
-  public static final OfShort uint16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint32_t uint32_t
-   * }
-   */
-  public static final OfInt uint32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint64_t uint64_t
-   * }
-   */
-  public static final OfLong uint64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __int_least8_t int_least8_t
-   * }
-   */
-  public static final OfByte int_least8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __int_least16_t int_least16_t
-   * }
-   */
-  public static final OfShort int_least16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __int_least32_t int_least32_t
-   * }
-   */
-  public static final OfInt int_least32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __int_least64_t int_least64_t
-   * }
-   */
-  public static final OfLong int_least64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uint_least8_t uint_least8_t
-   * }
-   */
-  public static final OfByte uint_least8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef __uint_least16_t uint_least16_t
-   * }
-   */
-  public static final OfShort uint_least16_t = IvfPqH.C_SHORT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least32_t uint_least32_t
-   * }
-   */
-  public static final OfInt uint_least32_t = IvfPqH.C_INT;
-  /**
-   * {@snippet lang = c : * typedef __uint_least64_t uint_least64_t
-   * }
-   */
-  public static final OfLong uint_least64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef signed char int_fast8_t
-   * }
-   */
-  public static final OfByte int_fast8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef long int_fast16_t
-   * }
-   */
-  public static final OfLong int_fast16_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast32_t
-   * }
-   */
-  public static final OfLong int_fast32_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long int_fast64_t
-   * }
-   */
-  public static final OfLong int_fast64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned char uint_fast8_t
-   * }
-   */
-  public static final OfByte uint_fast8_t = IvfPqH.C_CHAR;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast16_t
-   * }
-   */
-  public static final OfLong uint_fast16_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast32_t
-   * }
-   */
-  public static final OfLong uint_fast32_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uint_fast64_t
-   * }
-   */
-  public static final OfLong uint_fast64_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef long intptr_t
-   * }
-   */
-  public static final OfLong intptr_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long uintptr_t
-   * }
-   */
-  public static final OfLong uintptr_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __intmax_t intmax_t
-   * }
-   */
-  public static final OfLong intmax_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef __uintmax_t uintmax_t
-   * }
-   */
-  public static final OfLong uintmax_t = IvfPqH.C_LONG;
-  private static final int CUVS_ERROR = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CUVS_ERROR = 0
-   * }
-   */
-  public static int CUVS_ERROR() {
-    return CUVS_ERROR;
-  }
-
-  private static final int CUVS_SUCCESS = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CUVS_SUCCESS = 1
-   * }
-   */
-  public static int CUVS_SUCCESS() {
-    return CUVS_SUCCESS;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef uintptr_t cuvsResources_t
-   * }
-   */
-  public static final OfLong cuvsResources_t = IvfPqH.C_LONG;
-  private static final int L2Expanded = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Expanded = 0
-   * }
-   */
-  public static int L2Expanded() {
-    return L2Expanded;
-  }
-
-  private static final int L2SqrtExpanded = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtExpanded = 1
-   * }
-   */
-  public static int L2SqrtExpanded() {
-    return L2SqrtExpanded;
-  }
-
-  private static final int CosineExpanded = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CosineExpanded = 2
-   * }
-   */
-  public static int CosineExpanded() {
-    return CosineExpanded;
-  }
-
-  private static final int L1 = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L1 = 3
-   * }
-   */
-  public static int L1() {
-    return L1;
-  }
-
-  private static final int L2Unexpanded = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2Unexpanded = 4
-   * }
-   */
-  public static int L2Unexpanded() {
-    return L2Unexpanded;
-  }
-
-  private static final int L2SqrtUnexpanded = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.L2SqrtUnexpanded = 5
-   * }
-   */
-  public static int L2SqrtUnexpanded() {
-    return L2SqrtUnexpanded;
-  }
-
-  private static final int InnerProduct = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.InnerProduct = 6
-   * }
-   */
-  public static int InnerProduct() {
-    return InnerProduct;
-  }
-
-  private static final int Linf = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Linf = 7
-   * }
-   */
-  public static int Linf() {
-    return Linf;
-  }
-
-  private static final int Canberra = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Canberra = 8
-   * }
-   */
-  public static int Canberra() {
-    return Canberra;
-  }
-
-  private static final int LpUnexpanded = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.LpUnexpanded = 9
-   * }
-   */
-  public static int LpUnexpanded() {
-    return LpUnexpanded;
-  }
-
-  private static final int CorrelationExpanded = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.CorrelationExpanded = 10
-   * }
-   */
-  public static int CorrelationExpanded() {
-    return CorrelationExpanded;
-  }
-
-  private static final int JaccardExpanded = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JaccardExpanded = 11
-   * }
-   */
-  public static int JaccardExpanded() {
-    return JaccardExpanded;
-  }
-
-  private static final int HellingerExpanded = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HellingerExpanded = 12
-   * }
-   */
-  public static int HellingerExpanded() {
-    return HellingerExpanded;
-  }
-
-  private static final int Haversine = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Haversine = 13
-   * }
-   */
-  public static int Haversine() {
-    return Haversine;
-  }
-
-  private static final int BrayCurtis = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.BrayCurtis = 14
-   * }
-   */
-  public static int BrayCurtis() {
-    return BrayCurtis;
-  }
-
-  private static final int JensenShannon = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.JensenShannon = 15
-   * }
-   */
-  public static int JensenShannon() {
-    return JensenShannon;
-  }
-
-  private static final int HammingUnexpanded = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.HammingUnexpanded = 16
-   * }
-   */
-  public static int HammingUnexpanded() {
-    return HammingUnexpanded;
-  }
-
-  private static final int KLDivergence = (int) 17L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.KLDivergence = 17
-   * }
-   */
-  public static int KLDivergence() {
-    return KLDivergence;
-  }
-
-  private static final int RusselRaoExpanded = (int) 18L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.RusselRaoExpanded = 18
-   * }
-   */
-  public static int RusselRaoExpanded() {
-    return RusselRaoExpanded;
-  }
-
-  private static final int DiceExpanded = (int) 19L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.DiceExpanded = 19
-   * }
-   */
-  public static int DiceExpanded() {
-    return DiceExpanded;
-  }
-
-  private static final int Precomputed = (int) 100L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.Precomputed = 100
-   * }
-   */
-  public static int Precomputed() {
-    return Precomputed;
-  }
-
-  /**
-   * {@snippet lang = c : * typedef long ptrdiff_t
-   * }
-   */
-  public static final OfLong ptrdiff_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef unsigned long size_t
-   * }
-   */
-  public static final OfLong size_t = IvfPqH.C_LONG;
-  /**
-   * {@snippet lang = c : * typedef int wchar_t
-   * }
-   */
-  public static final OfInt wchar_t = IvfPqH.C_INT;
-  private static final int kDLCPU = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCPU = 1
-   * }
-   */
-  public static int kDLCPU() {
-    return kDLCPU;
-  }
-
-  private static final int kDLCUDA = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDA = 2
-   * }
-   */
-  public static int kDLCUDA() {
-    return kDLCUDA;
-  }
-
-  private static final int kDLCUDAHost = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAHost = 3
-   * }
-   */
-  public static int kDLCUDAHost() {
-    return kDLCUDAHost;
-  }
-
-  private static final int kDLOpenCL = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpenCL = 4
-   * }
-   */
-  public static int kDLOpenCL() {
-    return kDLOpenCL;
-  }
-
-  private static final int kDLVulkan = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVulkan = 7
-   * }
-   */
-  public static int kDLVulkan() {
-    return kDLVulkan;
-  }
-
-  private static final int kDLMetal = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLMetal = 8
-   * }
-   */
-  public static int kDLMetal() {
-    return kDLMetal;
-  }
-
-  private static final int kDLVPI = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLVPI = 9
-   * }
-   */
-  public static int kDLVPI() {
-    return kDLVPI;
-  }
-
-  private static final int kDLROCM = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCM = 10
-   * }
-   */
-  public static int kDLROCM() {
-    return kDLROCM;
-  }
-
-  private static final int kDLROCMHost = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLROCMHost = 11
-   * }
-   */
-  public static int kDLROCMHost() {
-    return kDLROCMHost;
-  }
-
-  private static final int kDLExtDev = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLExtDev = 12
-   * }
-   */
-  public static int kDLExtDev() {
-    return kDLExtDev;
-  }
-
-  private static final int kDLCUDAManaged = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLCUDAManaged = 13
-   * }
-   */
-  public static int kDLCUDAManaged() {
-    return kDLCUDAManaged;
-  }
-
-  private static final int kDLOneAPI = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOneAPI = 14
-   * }
-   */
-  public static int kDLOneAPI() {
-    return kDLOneAPI;
-  }
-
-  private static final int kDLWebGPU = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLWebGPU = 15
-   * }
-   */
-  public static int kDLWebGPU() {
-    return kDLWebGPU;
-  }
-
-  private static final int kDLHexagon = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLHexagon = 16
-   * }
-   */
-  public static int kDLHexagon() {
-    return kDLHexagon;
-  }
-
-  private static final int kDLMAIA = (int) 17L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLMAIA = 17
-   * }
-   */
-  public static int kDLMAIA() {
-    return kDLMAIA;
-  }
-
-  private static final int kDLInt = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLInt = 0
-   * }
-   */
-  public static int kDLInt() {
-    return kDLInt;
-  }
-
-  private static final int kDLUInt = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLUInt = 1
-   * }
-   */
-  public static int kDLUInt() {
-    return kDLUInt;
-  }
-
-  private static final int kDLFloat = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLFloat = 2
-   * }
-   */
-  public static int kDLFloat() {
-    return kDLFloat;
-  }
-
-  private static final int kDLOpaqueHandle = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLOpaqueHandle = 3
-   * }
-   */
-  public static int kDLOpaqueHandle() {
-    return kDLOpaqueHandle;
-  }
-
-  private static final int kDLBfloat = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBfloat = 4
-   * }
-   */
-  public static int kDLBfloat() {
-    return kDLBfloat;
-  }
-
-  private static final int kDLComplex = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLComplex = 5
-   * }
-   */
-  public static int kDLComplex() {
-    return kDLComplex;
-  }
-
-  private static final int kDLBool = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum <anonymous>.kDLBool = 6
-   * }
-   */
-  public static int kDLBool() {
-    return kDLBool;
-  }
-
-  private static final int CUDA_R_16F = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16F = 2
-   * }
-   */
-  public static int CUDA_R_16F() {
-    return CUDA_R_16F;
-  }
-
-  private static final int CUDA_C_16F = (int) 6L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16F = 6
-   * }
-   */
-  public static int CUDA_C_16F() {
-    return CUDA_C_16F;
-  }
-
-  private static final int CUDA_R_16BF = (int) 14L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16BF = 14
-   * }
-   */
-  public static int CUDA_R_16BF() {
-    return CUDA_R_16BF;
-  }
-
-  private static final int CUDA_C_16BF = (int) 15L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16BF = 15
-   * }
-   */
-  public static int CUDA_C_16BF() {
-    return CUDA_C_16BF;
-  }
-
-  private static final int CUDA_R_32F = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_32F = 0
-   * }
-   */
-  public static int CUDA_R_32F() {
-    return CUDA_R_32F;
-  }
-
-  private static final int CUDA_C_32F = (int) 4L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_32F = 4
-   * }
-   */
-  public static int CUDA_C_32F() {
-    return CUDA_C_32F;
-  }
-
-  private static final int CUDA_R_64F = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_64F = 1
-   * }
-   */
-  public static int CUDA_R_64F() {
-    return CUDA_R_64F;
-  }
-
-  private static final int CUDA_C_64F = (int) 5L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_64F = 5
-   * }
-   */
-  public static int CUDA_C_64F() {
-    return CUDA_C_64F;
-  }
-
-  private static final int CUDA_R_4I = (int) 16L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_4I = 16
-   * }
-   */
-  public static int CUDA_R_4I() {
-    return CUDA_R_4I;
-  }
-
-  private static final int CUDA_C_4I = (int) 17L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_4I = 17
-   * }
-   */
-  public static int CUDA_C_4I() {
-    return CUDA_C_4I;
-  }
-
-  private static final int CUDA_R_4U = (int) 18L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_4U = 18
-   * }
-   */
-  public static int CUDA_R_4U() {
-    return CUDA_R_4U;
-  }
-
-  private static final int CUDA_C_4U = (int) 19L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_4U = 19
-   * }
-   */
-  public static int CUDA_C_4U() {
-    return CUDA_C_4U;
-  }
-
-  private static final int CUDA_R_8I = (int) 3L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_8I = 3
-   * }
-   */
-  public static int CUDA_R_8I() {
-    return CUDA_R_8I;
-  }
-
-  private static final int CUDA_C_8I = (int) 7L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_8I = 7
-   * }
-   */
-  public static int CUDA_C_8I() {
-    return CUDA_C_8I;
-  }
-
-  private static final int CUDA_R_8U = (int) 8L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_8U = 8
-   * }
-   */
-  public static int CUDA_R_8U() {
-    return CUDA_R_8U;
-  }
-
-  private static final int CUDA_C_8U = (int) 9L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_8U = 9
-   * }
-   */
-  public static int CUDA_C_8U() {
-    return CUDA_C_8U;
-  }
-
-  private static final int CUDA_R_16I = (int) 20L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16I = 20
-   * }
-   */
-  public static int CUDA_R_16I() {
-    return CUDA_R_16I;
-  }
-
-  private static final int CUDA_C_16I = (int) 21L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16I = 21
-   * }
-   */
-  public static int CUDA_C_16I() {
-    return CUDA_C_16I;
-  }
-
-  private static final int CUDA_R_16U = (int) 22L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_16U = 22
-   * }
-   */
-  public static int CUDA_R_16U() {
-    return CUDA_R_16U;
-  }
-
-  private static final int CUDA_C_16U = (int) 23L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_16U = 23
-   * }
-   */
-  public static int CUDA_C_16U() {
-    return CUDA_C_16U;
-  }
-
-  private static final int CUDA_R_32I = (int) 10L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_32I = 10
-   * }
-   */
-  public static int CUDA_R_32I() {
-    return CUDA_R_32I;
-  }
-
-  private static final int CUDA_C_32I = (int) 11L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_32I = 11
-   * }
-   */
-  public static int CUDA_C_32I() {
-    return CUDA_C_32I;
-  }
-
-  private static final int CUDA_R_32U = (int) 12L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_32U = 12
-   * }
-   */
-  public static int CUDA_R_32U() {
-    return CUDA_R_32U;
-  }
-
-  private static final int CUDA_C_32U = (int) 13L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_32U = 13
-   * }
-   */
-  public static int CUDA_C_32U() {
-    return CUDA_C_32U;
-  }
-
-  private static final int CUDA_R_64I = (int) 24L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_64I = 24
-   * }
-   */
-  public static int CUDA_R_64I() {
-    return CUDA_R_64I;
-  }
-
-  private static final int CUDA_C_64I = (int) 25L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_64I = 25
-   * }
-   */
-  public static int CUDA_C_64I() {
-    return CUDA_C_64I;
-  }
-
-  private static final int CUDA_R_64U = (int) 26L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_R_64U = 26
-   * }
-   */
-  public static int CUDA_R_64U() {
-    return CUDA_R_64U;
-  }
-
-  private static final int CUDA_C_64U = (int) 27L;
-
-  /**
-   * {@snippet lang = c : * enum cudaDataType_t.CUDA_C_64U = 27
-   * }
-   */
-  public static int CUDA_C_64U() {
-    return CUDA_C_64U;
-  }
-
-  private static final int MAJOR_VERSION = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum libraryPropertyType_t.MAJOR_VERSION = 0
-   * }
-   */
-  public static int MAJOR_VERSION() {
-    return MAJOR_VERSION;
-  }
-
-  private static final int MINOR_VERSION = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum libraryPropertyType_t.MINOR_VERSION = 1
-   * }
-   */
-  public static int MINOR_VERSION() {
-    return MINOR_VERSION;
-  }
-
-  private static final int PATCH_LEVEL = (int) 2L;
-
-  /**
-   * {@snippet lang = c : * enum libraryPropertyType_t.PATCH_LEVEL = 2
-   * }
-   */
-  public static int PATCH_LEVEL() {
-    return PATCH_LEVEL;
-  }
-
-  private static final int PER_SUBSPACE = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * enum codebook_gen.PER_SUBSPACE = 0
-   * }
-   */
-  public static int PER_SUBSPACE() {
-    return PER_SUBSPACE;
-  }
-
-  private static final int PER_CLUSTER = (int) 1L;
-
-  /**
-   * {@snippet lang = c : * enum codebook_gen.PER_CLUSTER = 1
-   * }
-   */
-  public static int PER_CLUSTER() {
-    return PER_CLUSTER;
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsIvfPqIndexParams {
-   *     cuvsDistanceType metric;
-   *     float metric_arg;
-   *     _Bool add_data_on_build;
-   *     uint32_t n_lists;
-   *     uint32_t kmeans_n_iters;
-   *     double kmeans_trainset_fraction;
-   *     uint32_t pq_bits;
-   *     uint32_t pq_dim;
-   *     enum codebook_gen codebook_kind;
-   *     _Bool force_random_rotation;
-   *     _Bool conservative_memory_allocation;
-   *     uint32_t max_train_points_per_pq_code;
-   * } *cuvsIvfPqIndexParams_t
-   * }
-   */
-  public static final AddressLayout cuvsIvfPqIndexParams_t = IvfPqH.C_POINTER;
-
-  private static class cuvsIvfPqIndexParamsCreate {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexParamsCreate");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqIndexParamsCreate$descriptor() {
-    return cuvsIvfPqIndexParamsCreate.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqIndexParamsCreate$handle() {
-    return cuvsIvfPqIndexParamsCreate.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqIndexParamsCreate$address() {
-    return cuvsIvfPqIndexParamsCreate.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsCreate(cuvsIvfPqIndexParams_t *index_params)
-   * }
-   */
-  public static int cuvsIvfPqIndexParamsCreate(MemorySegment index_params) {
-    var mh$ = cuvsIvfPqIndexParamsCreate.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqIndexParamsCreate", index_params);
-      }
-      return (int) mh$.invokeExact(index_params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqIndexParamsDestroy {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexParamsDestroy");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqIndexParamsDestroy$descriptor() {
-    return cuvsIvfPqIndexParamsDestroy.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqIndexParamsDestroy$handle() {
-    return cuvsIvfPqIndexParamsDestroy.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqIndexParamsDestroy$address() {
-    return cuvsIvfPqIndexParamsDestroy.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexParamsDestroy(cuvsIvfPqIndexParams_t index_params)
-   * }
-   */
-  public static int cuvsIvfPqIndexParamsDestroy(MemorySegment index_params) {
-    var mh$ = cuvsIvfPqIndexParamsDestroy.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqIndexParamsDestroy", index_params);
-      }
-      return (int) mh$.invokeExact(index_params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  /**
-   * {@snippet lang = c :
-   * typedef struct cuvsIvfPqSearchParams {
-   *     uint32_t n_probes;
-   *     cudaDataType_t lut_dtype;
-   *     cudaDataType_t internal_distance_dtype;
-   *     double preferred_shmem_carveout;
-   * } *cuvsIvfPqSearchParams_t
-   * }
-   */
-  public static final AddressLayout cuvsIvfPqSearchParams_t = IvfPqH.C_POINTER;
-
-  private static class cuvsIvfPqSearchParamsCreate {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSearchParamsCreate");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqSearchParamsCreate$descriptor() {
-    return cuvsIvfPqSearchParamsCreate.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqSearchParamsCreate$handle() {
-    return cuvsIvfPqSearchParamsCreate.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqSearchParamsCreate$address() {
-    return cuvsIvfPqSearchParamsCreate.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsCreate(cuvsIvfPqSearchParams_t *params)
-   * }
-   */
-  public static int cuvsIvfPqSearchParamsCreate(MemorySegment params) {
-    var mh$ = cuvsIvfPqSearchParamsCreate.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqSearchParamsCreate", params);
-      }
-      return (int) mh$.invokeExact(params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqSearchParamsDestroy {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSearchParamsDestroy");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqSearchParamsDestroy$descriptor() {
-    return cuvsIvfPqSearchParamsDestroy.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqSearchParamsDestroy$handle() {
-    return cuvsIvfPqSearchParamsDestroy.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqSearchParamsDestroy$address() {
-    return cuvsIvfPqSearchParamsDestroy.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearchParamsDestroy(cuvsIvfPqSearchParams_t params)
-   * }
-   */
-  public static int cuvsIvfPqSearchParamsDestroy(MemorySegment params) {
-    var mh$ = cuvsIvfPqSearchParamsDestroy.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqSearchParamsDestroy", params);
-      }
-      return (int) mh$.invokeExact(params);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  /**
-   * {@snippet lang = c : * typedef cuvsIvfPq *cuvsIvfPqIndex_t
-   * }
-   */
-  public static final AddressLayout cuvsIvfPqIndex_t = IvfPqH.C_POINTER;
-
-  private static class cuvsIvfPqIndexCreate {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexCreate");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqIndexCreate$descriptor() {
-    return cuvsIvfPqIndexCreate.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqIndexCreate$handle() {
-    return cuvsIvfPqIndexCreate.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqIndexCreate$address() {
-    return cuvsIvfPqIndexCreate.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t *index)
-   * }
-   */
-  public static int cuvsIvfPqIndexCreate(MemorySegment index) {
-    var mh$ = cuvsIvfPqIndexCreate.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqIndexCreate", index);
-      }
-      return (int) mh$.invokeExact(index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqIndexDestroy {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqIndexDestroy");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqIndexDestroy$descriptor() {
-    return cuvsIvfPqIndexDestroy.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqIndexDestroy$handle() {
-    return cuvsIvfPqIndexDestroy.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqIndexDestroy$address() {
-    return cuvsIvfPqIndexDestroy.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static int cuvsIvfPqIndexDestroy(MemorySegment index) {
-    var mh$ = cuvsIvfPqIndexDestroy.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqIndexDestroy", index);
-      }
-      return (int) mh$.invokeExact(index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqBuild {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
-        IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqBuild");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqBuild$descriptor() {
-    return cuvsIvfPqBuild.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqBuild$handle() {
-    return cuvsIvfPqBuild.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqBuild$address() {
-    return cuvsIvfPqBuild.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqBuild(cuvsResources_t res, cuvsIvfPqIndexParams_t params, DLManagedTensor *dataset, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static int cuvsIvfPqBuild(long res, MemorySegment params, MemorySegment dataset, MemorySegment index) {
-    var mh$ = cuvsIvfPqBuild.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqBuild", res, params, dataset, index);
-      }
-      return (int) mh$.invokeExact(res, params, dataset, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqSearch {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
-        IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSearch");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqSearch$descriptor() {
-    return cuvsIvfPqSearch.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqSearch$handle() {
-    return cuvsIvfPqSearch.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqSearch$address() {
-    return cuvsIvfPqSearch.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSearch(cuvsResources_t res, cuvsIvfPqSearchParams_t search_params, cuvsIvfPqIndex_t index, DLManagedTensor *queries, DLManagedTensor *neighbors, DLManagedTensor *distances)
-   * }
-   */
-  public static int cuvsIvfPqSearch(long res, MemorySegment search_params, MemorySegment index, MemorySegment queries,
-      MemorySegment neighbors, MemorySegment distances) {
-    var mh$ = cuvsIvfPqSearch.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqSearch", res, search_params, index, queries, neighbors, distances);
-      }
-      return (int) mh$.invokeExact(res, search_params, index, queries, neighbors, distances);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqSerialize {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
-        IvfPqH.C_POINTER, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqSerialize");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqSerialize$descriptor() {
-    return cuvsIvfPqSerialize.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqSerialize$handle() {
-    return cuvsIvfPqSerialize.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqSerialize$address() {
-    return cuvsIvfPqSerialize.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static int cuvsIvfPqSerialize(long res, MemorySegment filename, MemorySegment index) {
-    var mh$ = cuvsIvfPqSerialize.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqSerialize", res, filename, index);
-      }
-      return (int) mh$.invokeExact(res, filename, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqDeserialize {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
-        IvfPqH.C_POINTER, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqDeserialize");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqDeserialize$descriptor() {
-    return cuvsIvfPqDeserialize.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqDeserialize$handle() {
-    return cuvsIvfPqDeserialize.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqDeserialize$address() {
-    return cuvsIvfPqDeserialize.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res, const char *filename, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static int cuvsIvfPqDeserialize(long res, MemorySegment filename, MemorySegment index) {
-    var mh$ = cuvsIvfPqDeserialize.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqDeserialize", res, filename, index);
-      }
-      return (int) mh$.invokeExact(res, filename, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static class cuvsIvfPqExtend {
-    public static final FunctionDescriptor DESC = FunctionDescriptor.of(IvfPqH.C_INT, IvfPqH.C_LONG,
-        IvfPqH.C_POINTER, IvfPqH.C_POINTER, IvfPqH.C_POINTER);
-
-    public static final MemorySegment ADDR = IvfPqH.findOrThrow("cuvsIvfPqExtend");
-
-    public static final MethodHandle HANDLE = Linker.nativeLinker().downcallHandle(ADDR, DESC);
-  }
-
-  /**
-   * Function descriptor for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static FunctionDescriptor cuvsIvfPqExtend$descriptor() {
-    return cuvsIvfPqExtend.DESC;
-  }
-
-  /**
-   * Downcall method handle for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MethodHandle cuvsIvfPqExtend$handle() {
-    return cuvsIvfPqExtend.HANDLE;
-  }
-
-  /**
-   * Address for:
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static MemorySegment cuvsIvfPqExtend$address() {
-    return cuvsIvfPqExtend.ADDR;
-  }
-
-  /**
-   * {@snippet lang = c
-   * : * cuvsError_t cuvsIvfPqExtend(cuvsResources_t res, DLManagedTensor *new_vectors, DLManagedTensor *new_indices, cuvsIvfPqIndex_t index)
-   * }
-   */
-  public static int cuvsIvfPqExtend(long res, MemorySegment new_vectors, MemorySegment new_indices,
-      MemorySegment index) {
-    var mh$ = cuvsIvfPqExtend.HANDLE;
-    try {
-      if (TRACE_DOWNCALLS) {
-        traceDowncall("cuvsIvfPqExtend", res, new_vectors, new_indices, index);
-      }
-      return (int) mh$.invokeExact(res, new_vectors, new_indices, index);
-    } catch (Throwable ex$) {
-      throw new AssertionError("should not reach here", ex$);
-    }
-  }
-
-  private static final long _POSIX_C_SOURCE = 200809L;
-
-  /**
-   * {@snippet lang = c : * #define _POSIX_C_SOURCE 200809
-   * }
-   */
-  public static long _POSIX_C_SOURCE() {
-    return _POSIX_C_SOURCE;
-  }
-
-  private static final int __TIMESIZE = (int) 64L;
-
-  /**
-   * {@snippet lang = c : * #define __TIMESIZE 64
-   * }
-   */
-  public static int __TIMESIZE() {
-    return __TIMESIZE;
-  }
-
-  private static final long __STDC_IEC_60559_BFP__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_BFP__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_BFP__() {
-    return __STDC_IEC_60559_BFP__;
-  }
-
-  private static final long __STDC_IEC_60559_COMPLEX__ = 201404L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_IEC_60559_COMPLEX__ 201404
-   * }
-   */
-  public static long __STDC_IEC_60559_COMPLEX__() {
-    return __STDC_IEC_60559_COMPLEX__;
-  }
-
-  private static final long __STDC_ISO_10646__ = 201706L;
-
-  /**
-   * {@snippet lang = c : * #define __STDC_ISO_10646__ 201706
-   * }
-   */
-  public static long __STDC_ISO_10646__() {
-    return __STDC_ISO_10646__;
-  }
-
-  private static final int __WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MAX 2147483647
-   * }
-   */
-  public static int __WCHAR_MAX() {
-    return __WCHAR_MAX;
-  }
-
-  private static final int __WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define __WCHAR_MIN -2147483648
-   * }
-   */
-  public static int __WCHAR_MIN() {
-    return __WCHAR_MIN;
-  }
-
-  private static final int INT8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MIN -128
-   * }
-   */
-  public static int INT8_MIN() {
-    return INT8_MIN;
-  }
-
-  private static final int INT16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MIN -32768
-   * }
-   */
-  public static int INT16_MIN() {
-    return INT16_MIN;
-  }
-
-  private static final int INT32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MIN -2147483648
-   * }
-   */
-  public static int INT32_MIN() {
-    return INT32_MIN;
-  }
-
-  private static final long INT64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT64_MIN() {
-    return INT64_MIN;
-  }
-
-  private static final int INT8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT8_MAX 127
-   * }
-   */
-  public static int INT8_MAX() {
-    return INT8_MAX;
-  }
-
-  private static final int INT16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT16_MAX 32767
-   * }
-   */
-  public static int INT16_MAX() {
-    return INT16_MAX;
-  }
-
-  private static final int INT32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT32_MAX 2147483647
-   * }
-   */
-  public static int INT32_MAX() {
-    return INT32_MAX;
-  }
-
-  private static final long INT64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT64_MAX() {
-    return INT64_MAX;
-  }
-
-  private static final int UINT8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT8_MAX 255
-   * }
-   */
-  public static int UINT8_MAX() {
-    return UINT8_MAX;
-  }
-
-  private static final int UINT16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT16_MAX 65535
-   * }
-   */
-  public static int UINT16_MAX() {
-    return UINT16_MAX;
-  }
-
-  private static final int UINT32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT32_MAX 4294967295
-   * }
-   */
-  public static int UINT32_MAX() {
-    return UINT32_MAX;
-  }
-
-  private static final long UINT64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT64_MAX -1
-   * }
-   */
-  public static long UINT64_MAX() {
-    return UINT64_MAX;
-  }
-
-  private static final int INT_LEAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MIN -128
-   * }
-   */
-  public static int INT_LEAST8_MIN() {
-    return INT_LEAST8_MIN;
-  }
-
-  private static final int INT_LEAST16_MIN = (int) -32768L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MIN -32768
-   * }
-   */
-  public static int INT_LEAST16_MIN() {
-    return INT_LEAST16_MIN;
-  }
-
-  private static final int INT_LEAST32_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MIN -2147483648
-   * }
-   */
-  public static int INT_LEAST32_MIN() {
-    return INT_LEAST32_MIN;
-  }
-
-  private static final long INT_LEAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_LEAST64_MIN() {
-    return INT_LEAST64_MIN;
-  }
-
-  private static final int INT_LEAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST8_MAX 127
-   * }
-   */
-  public static int INT_LEAST8_MAX() {
-    return INT_LEAST8_MAX;
-  }
-
-  private static final int INT_LEAST16_MAX = (int) 32767L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST16_MAX 32767
-   * }
-   */
-  public static int INT_LEAST16_MAX() {
-    return INT_LEAST16_MAX;
-  }
-
-  private static final int INT_LEAST32_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST32_MAX 2147483647
-   * }
-   */
-  public static int INT_LEAST32_MAX() {
-    return INT_LEAST32_MAX;
-  }
-
-  private static final long INT_LEAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_LEAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_LEAST64_MAX() {
-    return INT_LEAST64_MAX;
-  }
-
-  private static final int UINT_LEAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST8_MAX 255
-   * }
-   */
-  public static int UINT_LEAST8_MAX() {
-    return UINT_LEAST8_MAX;
-  }
-
-  private static final int UINT_LEAST16_MAX = (int) 65535L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST16_MAX 65535
-   * }
-   */
-  public static int UINT_LEAST16_MAX() {
-    return UINT_LEAST16_MAX;
-  }
-
-  private static final int UINT_LEAST32_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST32_MAX 4294967295
-   * }
-   */
-  public static int UINT_LEAST32_MAX() {
-    return UINT_LEAST32_MAX;
-  }
-
-  private static final long UINT_LEAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_LEAST64_MAX -1
-   * }
-   */
-  public static long UINT_LEAST64_MAX() {
-    return UINT_LEAST64_MAX;
-  }
-
-  private static final int INT_FAST8_MIN = (int) -128L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MIN -128
-   * }
-   */
-  public static int INT_FAST8_MIN() {
-    return INT_FAST8_MIN;
-  }
-
-  private static final long INT_FAST16_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST16_MIN() {
-    return INT_FAST16_MIN;
-  }
-
-  private static final long INT_FAST32_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST32_MIN() {
-    return INT_FAST32_MIN;
-  }
-
-  private static final long INT_FAST64_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MIN -9223372036854775808
-   * }
-   */
-  public static long INT_FAST64_MIN() {
-    return INT_FAST64_MIN;
-  }
-
-  private static final int INT_FAST8_MAX = (int) 127L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST8_MAX 127
-   * }
-   */
-  public static int INT_FAST8_MAX() {
-    return INT_FAST8_MAX;
-  }
-
-  private static final long INT_FAST16_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST16_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST16_MAX() {
-    return INT_FAST16_MAX;
-  }
-
-  private static final long INT_FAST32_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST32_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST32_MAX() {
-    return INT_FAST32_MAX;
-  }
-
-  private static final long INT_FAST64_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INT_FAST64_MAX 9223372036854775807
-   * }
-   */
-  public static long INT_FAST64_MAX() {
-    return INT_FAST64_MAX;
-  }
-
-  private static final int UINT_FAST8_MAX = (int) 255L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST8_MAX 255
-   * }
-   */
-  public static int UINT_FAST8_MAX() {
-    return UINT_FAST8_MAX;
-  }
-
-  private static final long UINT_FAST16_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST16_MAX -1
-   * }
-   */
-  public static long UINT_FAST16_MAX() {
-    return UINT_FAST16_MAX;
-  }
-
-  private static final long UINT_FAST32_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST32_MAX -1
-   * }
-   */
-  public static long UINT_FAST32_MAX() {
-    return UINT_FAST32_MAX;
-  }
-
-  private static final long UINT_FAST64_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINT_FAST64_MAX -1
-   * }
-   */
-  public static long UINT_FAST64_MAX() {
-    return UINT_FAST64_MAX;
-  }
-
-  private static final long INTPTR_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MIN -9223372036854775808
-   * }
-   */
-  public static long INTPTR_MIN() {
-    return INTPTR_MIN;
-  }
-
-  private static final long INTPTR_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTPTR_MAX 9223372036854775807
-   * }
-   */
-  public static long INTPTR_MAX() {
-    return INTPTR_MAX;
-  }
-
-  private static final long UINTPTR_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTPTR_MAX -1
-   * }
-   */
-  public static long UINTPTR_MAX() {
-    return UINTPTR_MAX;
-  }
-
-  private static final long INTMAX_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MIN -9223372036854775808
-   * }
-   */
-  public static long INTMAX_MIN() {
-    return INTMAX_MIN;
-  }
-
-  private static final long INTMAX_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define INTMAX_MAX 9223372036854775807
-   * }
-   */
-  public static long INTMAX_MAX() {
-    return INTMAX_MAX;
-  }
-
-  private static final long UINTMAX_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define UINTMAX_MAX -1
-   * }
-   */
-  public static long UINTMAX_MAX() {
-    return UINTMAX_MAX;
-  }
-
-  private static final long PTRDIFF_MIN = -9223372036854775808L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MIN -9223372036854775808
-   * }
-   */
-  public static long PTRDIFF_MIN() {
-    return PTRDIFF_MIN;
-  }
-
-  private static final long PTRDIFF_MAX = 9223372036854775807L;
-
-  /**
-   * {@snippet lang = c : * #define PTRDIFF_MAX 9223372036854775807
-   * }
-   */
-  public static long PTRDIFF_MAX() {
-    return PTRDIFF_MAX;
-  }
-
-  private static final int SIG_ATOMIC_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MIN -2147483648
-   * }
-   */
-  public static int SIG_ATOMIC_MIN() {
-    return SIG_ATOMIC_MIN;
-  }
-
-  private static final int SIG_ATOMIC_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define SIG_ATOMIC_MAX 2147483647
-   * }
-   */
-  public static int SIG_ATOMIC_MAX() {
-    return SIG_ATOMIC_MAX;
-  }
-
-  private static final long SIZE_MAX = -1L;
-
-  /**
-   * {@snippet lang = c : * #define SIZE_MAX -1
-   * }
-   */
-  public static long SIZE_MAX() {
-    return SIZE_MAX;
-  }
-
-  private static final int WCHAR_MIN = (int) -2147483648L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MIN -2147483648
-   * }
-   */
-  public static int WCHAR_MIN() {
-    return WCHAR_MIN;
-  }
-
-  private static final int WCHAR_MAX = (int) 2147483647L;
-
-  /**
-   * {@snippet lang = c : * #define WCHAR_MAX 2147483647
-   * }
-   */
-  public static int WCHAR_MAX() {
-    return WCHAR_MAX;
-  }
-
-  private static final int WINT_MIN = (int) 0L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MIN 0
-   * }
-   */
-  public static int WINT_MIN() {
-    return WINT_MIN;
-  }
-
-  private static final int WINT_MAX = (int) 4294967295L;
-
-  /**
-   * {@snippet lang = c : * #define WINT_MAX 4294967295
-   * }
-   */
-  public static int WINT_MAX() {
-    return WINT_MAX;
-  }
-
-  private static final MemorySegment NULL = MemorySegment.ofAddress(0L);
-
-  /**
-   * {@snippet lang = c : * #define NULL (void*) 0
-   * }
-   */
-  public static MemorySegment NULL() {
-    return NULL;
-  }
-
-  private static final long DLPACK_FLAG_BITMASK_READ_ONLY = 1L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_READ_ONLY 1
-   * }
-   */
-  public static long DLPACK_FLAG_BITMASK_READ_ONLY() {
-    return DLPACK_FLAG_BITMASK_READ_ONLY;
-  }
-
-  private static final long DLPACK_FLAG_BITMASK_IS_COPIED = 2L;
-
-  /**
-   * {@snippet lang = c : * #define DLPACK_FLAG_BITMASK_IS_COPIED 2
-   * }
-   */
-  public static long DLPACK_FLAG_BITMASK_IS_COPIED() {
-    return DLPACK_FLAG_BITMASK_IS_COPIED;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/MaxAlignT.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/MaxAlignT.java
deleted file mode 100644
index d07c524dbe..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/MaxAlignT.java
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.panama;
-
-import static java.lang.foreign.MemoryLayout.PathElement.groupElement;
-
-import java.lang.foreign.Arena;
-import java.lang.foreign.GroupLayout;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SegmentAllocator;
-import java.lang.foreign.ValueLayout.OfLong;
-import java.util.function.Consumer;
-
-/**
- * {@snippet lang=c :
- * struct {
- *     long long __clang_max_align_nonce1;
- *     long double __clang_max_align_nonce2;
- * }
- * }
- */
-public class MaxAlignT {
-
-    MaxAlignT() {
-        // Should not be called directly
-    }
-
-    private static final GroupLayout $LAYOUT = MemoryLayout.structLayout(
-        DlpackH.C_LONG_LONG.withName("__clang_max_align_nonce1"),
-        MemoryLayout.paddingLayout(24)
-    ).withName("$anon$19:9");
-
-    /**
-     * The layout of this struct
-     */
-    public static final GroupLayout layout() {
-        return $LAYOUT;
-    }
-
-    private static final OfLong __clang_max_align_nonce1$LAYOUT = (OfLong)$LAYOUT.select(groupElement("__clang_max_align_nonce1"));
-
-    /**
-     * Layout for field:
-     * {@snippet lang=c :
-     * long long __clang_max_align_nonce1
-     * }
-     */
-    public static final OfLong __clang_max_align_nonce1$layout() {
-        return __clang_max_align_nonce1$LAYOUT;
-    }
-
-    private static final long __clang_max_align_nonce1$OFFSET = 0;
-
-    /**
-     * Offset for field:
-     * {@snippet lang=c :
-     * long long __clang_max_align_nonce1
-     * }
-     */
-    public static final long __clang_max_align_nonce1$offset() {
-        return __clang_max_align_nonce1$OFFSET;
-    }
-
-    /**
-     * Getter for field:
-     * {@snippet lang=c :
-     * long long __clang_max_align_nonce1
-     * }
-     */
-    public static long __clang_max_align_nonce1(MemorySegment struct) {
-        return struct.get(__clang_max_align_nonce1$LAYOUT, __clang_max_align_nonce1$OFFSET);
-    }
-
-    /**
-     * Setter for field:
-     * {@snippet lang=c :
-     * long long __clang_max_align_nonce1
-     * }
-     */
-    public static void __clang_max_align_nonce1(MemorySegment struct, long fieldValue) {
-        struct.set(__clang_max_align_nonce1$LAYOUT, __clang_max_align_nonce1$OFFSET, fieldValue);
-    }
-
-    /**
-     * Obtains a slice of {@code arrayParam} which selects the array element at {@code index}.
-     * The returned segment has address {@code arrayParam.address() + index * layout().byteSize()}
-     */
-    public static MemorySegment asSlice(MemorySegment array, long index) {
-        return array.asSlice(layout().byteSize() * index);
-    }
-
-    /**
-     * The size (in bytes) of this struct
-     */
-    public static long sizeof() { return layout().byteSize(); }
-
-    /**
-     * Allocate a segment of size {@code layout().byteSize()} using {@code allocator}
-     */
-    public static MemorySegment allocate(SegmentAllocator allocator) {
-        return allocator.allocate(layout());
-    }
-
-    /**
-     * Allocate an array of size {@code elementCount} using {@code allocator}.
-     * The returned segment has size {@code elementCount * layout().byteSize()}.
-     */
-    public static MemorySegment allocateArray(long elementCount, SegmentAllocator allocator) {
-        return allocator.allocate(MemoryLayout.sequenceLayout(elementCount, layout()));
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, Arena arena, Consumer<MemorySegment> cleanup) {
-        return reinterpret(addr, 1, arena, cleanup);
-    }
-
-    /**
-     * Reinterprets {@code addr} using target {@code arena} and {@code cleanupAction} (if any).
-     * The returned segment has size {@code elementCount * layout().byteSize()}
-     */
-    public static MemorySegment reinterpret(MemorySegment addr, long elementCount, Arena arena, Consumer<MemorySegment> cleanup) {
-        return addr.reinterpret(layout().byteSize() * elementCount, arena, cleanup);
-    }
-}
diff --git a/java/examples/.gitignore b/java/examples/.gitignore
deleted file mode 100644
index b83d22266a..0000000000
--- a/java/examples/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-/target/
diff --git a/java/examples/README.md b/java/examples/README.md
index d05d7b9117..b8e03608e2 100644
--- a/java/examples/README.md
+++ b/java/examples/README.md
@@ -1,8 +1,27 @@
-Building and Running
---------------------
+# CuVS Java API Examples
 
-Make sure to have JDK 22 and Maven 3.9.6+.
+This maven project contains examples for CAGRA, HNSW, and Bruteforce algorithms.
 
-    mvn clean compile assembly:single
+## Prerequisites
+- [CuVS libraries](https://docs.rapids.ai/api/cuvs/stable/build/#build-from-source)
+- Build the CuVS-Java API
 
-    java --enable-native-access=ALL-UNNAMED -jar ./target/cagra-sample-1.0-SNAPSHOT-jar-with-dependencies.jar
+## Run Examples
+
+### CAGRA Example
+In the current directory do:
+```
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.06.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.06.0/cuvs-java-25.06.0.jar com.nvidia.cuvs.examples.CagraExample
+```
+
+### HNSW Example
+In the current directory do:
+```
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.06.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.06.0/cuvs-java-25.06.0.jar com.nvidia.cuvs.examples.HnswExample
+```
+
+### Bruteforce Example
+In the current directory do:
+```
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.06.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.06.0/cuvs-java-25.06.0.jar com.nvidia.cuvs.examples.BruteForceExample
+```
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
index c5798864ce..9a86a99b39 100644
--- a/java/examples/pom.xml
+++ b/java/examples/pom.xml
@@ -20,17 +20,6 @@
       <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
       <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.06.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     </dependency>
-    <dependency>
-      <groupId>org.slf4j</groupId>
-      <artifactId>slf4j-api</artifactId>
-      <version>2.0.13</version>
-    </dependency>
-    <dependency>
-      <groupId>org.slf4j</groupId>
-      <artifactId>slf4j-simple</artifactId>
-      <version>2.0.13</version>
-      <scope>runtime</scope>
-   </dependency>
   </dependencies>
 
   <build>
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
index c859129418..72924779ed 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
@@ -4,21 +4,18 @@
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
-import java.lang.invoke.MethodHandles;
 import java.util.UUID;
-
-import com.nvidia.cuvs.SearchResults;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
+import java.util.logging.Logger;
 
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.BruteForceIndexParams;
 import com.nvidia.cuvs.BruteForceQuery;
 import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.SearchResults;
 
 public class BruteForceExample {
 
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+  private static final Logger log = Logger.getLogger(BruteForceExample.class.getName());
 
   public static void main(String[] args) throws Throwable {
 
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
index 9dabcc6bca..76767fc4c0 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
@@ -4,12 +4,8 @@
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
-import java.lang.invoke.MethodHandles;
 import java.util.UUID;
-
-import com.nvidia.cuvs.SearchResults;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
+import java.util.logging.Logger;
 
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
@@ -18,10 +14,11 @@
 import com.nvidia.cuvs.CagraQuery;
 import com.nvidia.cuvs.CagraSearchParams;
 import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.SearchResults;
 
 public class CagraExample {
 
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+  private static final Logger log = Logger.getLogger(CagraExample.class.getName());
 
   public static void main(String[] args) throws Throwable {
 
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
index 7fbbccf643..0268ff62ee 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
@@ -4,26 +4,26 @@
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
-import java.lang.invoke.MethodHandles;
 import java.util.UUID;
-
-import com.nvidia.cuvs.SearchResults;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
+import java.util.logging.Logger;
 
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import com.nvidia.cuvs.CuVSIvfPqIndexParams;
+import com.nvidia.cuvs.CuVSIvfPqParams;
+import com.nvidia.cuvs.CuVSIvfPqSearchParams;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.HnswIndex;
 import com.nvidia.cuvs.HnswIndexParams;
 import com.nvidia.cuvs.HnswQuery;
 import com.nvidia.cuvs.HnswSearchParams;
+import com.nvidia.cuvs.SearchResults;
 
 public class HnswExample {
 
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+  private static final Logger log = Logger.getLogger(HnswExample.class.getName());
 
   public static void main(String[] args) throws Throwable {
 
@@ -44,6 +44,20 @@ public static void main(String[] args) throws Throwable {
 
     try (CuVSResources resources = CuVSResources.create()) {
 
+      // Configure IVF_PQ index parameters (optional - default values set when not defined)
+      CuVSIvfPqIndexParams cuVSIvfPqIndexParams = new CuVSIvfPqIndexParams.Builder()
+          .build();
+
+      // Configure IVF_PQ search parameters (optional - default values set when not defined)
+      CuVSIvfPqSearchParams cuVSIvfPqSearchParams = new CuVSIvfPqSearchParams.Builder()
+          .build();
+
+      // Configure IVF_PQ search parameters (used when build algo is IVF_PQ, optional otherwise)
+      CuVSIvfPqParams cuVSIvfPqParams = new CuVSIvfPqParams.Builder()
+          .withCuVSIvfPqIndexParams(cuVSIvfPqIndexParams)
+          .withCuVSIvfPqSearchParams(cuVSIvfPqSearchParams)
+          .build();
+
       // Configure index parameters
       CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
@@ -51,6 +65,7 @@ public static void main(String[] args) throws Throwable {
           .withIntermediateGraphDegree(128)
           .withNumWriterThreads(32)
           .withMetric(CuvsDistanceType.L2Expanded)
+          .withCuVSIvfPqParams(cuVSIvfPqParams)
           .build();
 
       // Create the index with the dataset
diff --git a/java/examples/src/main/resources/.gitkeep b/java/examples/src/main/resources/.gitkeep
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/java/examples/src/main/resources/log4j2.xml b/java/examples/src/main/resources/log4j2.xml
deleted file mode 100644
index bf0eb598c1..0000000000
--- a/java/examples/src/main/resources/log4j2.xml
+++ /dev/null
@@ -1,14 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Configuration status="INFO">
-    <Appenders>
-        <Console name="Console" target="SYSTEM_OUT">
-            <PatternLayout pattern="%d{HH:mm:ss.SSS} [%t] %-5level %logger{36} - %msg%n"/>
-        </Console>
-    </Appenders>
-
-    <Loggers>
-        <Root level="debug">
-            <AppenderRef ref="Console"/>
-        </Root>
-    </Loggers>
-</Configuration>
diff --git a/java/internal/src/cuvs_java.c b/java/internal/src/cuvs_java.c
index 025b0f6e98..0b9c2f794e 100644
--- a/java/internal/src/cuvs_java.c
+++ b/java/internal/src/cuvs_java.c
@@ -103,6 +103,12 @@ cuvsCagraIndex_t build_cagra_index(float *dataset, long rows, long dimensions, c
   cuvsCagraIndex_t index;
   cuvsCagraIndexCreate(&index);
 
+  if (index_params->build_algo == 1) { // when build algo is IVF_PQ
+    uint32_t n_lists = index_params->graph_build_params->ivf_pq_build_params->n_lists;
+    // As rows cannot be less than n_lists value so trim down.
+    index_params->graph_build_params->ivf_pq_build_params->n_lists = rows < n_lists ? rows : n_lists;
+  }
+
   index_params->compression = compression_params;
   cuvsStreamSync(cuvs_resources);
   *return_value = cuvsCagraBuild(cuvs_resources, index_params, &dataset_tensor, index);
diff --git a/java/panama-bindings/generate-bindings.sh b/java/panama-bindings/generate-bindings.sh
new file mode 100755
index 0000000000..3984d4a16c
--- /dev/null
+++ b/java/panama-bindings/generate-bindings.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+
+set -e -u -o pipefail
+
+echo "Starting Panama FFM API bindings generation ..."
+REPODIR=$(cd $(dirname $0); cd ../../ ; pwd)
+CURDIR=$(cd $(dirname $0); pwd)
+CUDA_HOME=$(which nvcc | cut -d/ -f-4)
+TARGET_PACKAGE="com.nvidia.cuvs.internal.panama"
+
+# Try to verify that a include directory exists inside CUDA_HOME
+if [ ! -d "$CUDA_HOME/include" ];
+then
+  echo "$CUDA_HOME/include does not exist."
+  if [ -d "/usr/local/cuda/include" ];
+  then
+    echo "Setting CUDA_HOME to /usr/local/cuda"
+    CUDA_HOME=/usr/local/cuda
+  else
+    echo "Couldn't find a suitable CUDA include directory."
+    exit 1
+  fi
+fi
+
+if [[ `command -v jextract` == "" ]];
+then
+  JEXTRACT_FILENAME="openjdk-22-jextract+6-47_linux-x64_bin.tar.gz"
+  JEXTRACT_DOWNLOAD_URL="https://download.java.net/java/early_access/jextract/22/6/${JEXTRACT_FILENAME}"
+  echo "jextract doesn't exist. Downloading it from $JEXTRACT_DOWNLOAD_URL.";
+  wget -c $JEXTRACT_DOWNLOAD_URL
+  tar -xvf ./"${JEXTRACT_FILENAME}"
+  export PATH="$(pwd)/jextract-22/bin/jextract:${PATH}"
+  echo "jextract downloaded to $(pwd)/jextract-22"
+fi
+
+# Use Jextract utility to generate panama bindings
+jextract \
+ --include-dir ${REPODIR}/java/internal/build/_deps/dlpack-src/include/ \
+ --include-dir ${CUDA_HOME}/targets/x86_64-linux/include \
+ --include-dir ${REPODIR}/cpp/include \
+ --output "${REPODIR}/java/cuvs-java/src/main/java22/" \
+ --target-package ${TARGET_PACKAGE} \
+ --header-class-name PanamaFFMAPI \
+ ${CURDIR}/headers.h
+
+echo "Panama FFM API bindings generation done"
diff --git a/java/panama-bindings/gpuinfo.h b/java/panama-bindings/gpuinfo.h
new file mode 100644
index 0000000000..dfd7e7444b
--- /dev/null
+++ b/java/panama-bindings/gpuinfo.h
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @brief struct for containing gpu information
+ */
+typedef struct gpuInfo {
+    int gpu_id;
+    char name[256];
+    long free_memory;
+    long total_memory;
+    float compute_capability;
+} gpuInfo;
diff --git a/java/panama-bindings/headers.h b/java/panama-bindings/headers.h
new file mode 100644
index 0000000000..2b035f23d3
--- /dev/null
+++ b/java/panama-bindings/headers.h
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Please add the required header files below for which panama FFM API bindings are needed
+
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/brute_force.h>
+#include <cuvs/neighbors/hnsw.h>
+#include <cuvs/neighbors/ivf_pq.h>
+#include "gpuinfo.h"

From 7bc467a6438d2588321f72b0ac48b21d090b04ab Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 2 May 2025 13:38:11 -0500
Subject: [PATCH 379/623] run shellcheck on all files, other small pre-commit
 updates (#865)

Contributes to https://github.com/rapidsai/build-planning/issues/135

Follow-up to #662

While reviewing #805 and #831, I found myself suggesting things manually that I know `shellcheck` would have caught automatically. To prevent that for reviewers in the future, this proposes running `shellcheck` on **all** shell scripts in the repo, not just those in the `ci/` directory.

Other changes:

* updates `rapids-dependency-file-generator` to its latest version (1.18.1)
* consolidates duplicate entries for https://github.com/pre-commit/pre-commit-hooks in `.pre-commit-config.yaml`

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/865
---
 .pre-commit-config.yaml                   |  8 ++------
 build.sh                                  | 11 +++++------
 cpp/scripts/run-cmake-format.sh           |  6 +++---
 examples/build.sh                         |  1 -
 java/build.sh                             |  3 ++-
 java/panama-bindings/generate-bindings.sh |  7 ++++---
 6 files changed, 16 insertions(+), 20 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index fef4e75d91..5ba3c70237 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,6 +4,7 @@ repos:
       - repo: https://github.com/pre-commit/pre-commit-hooks
         rev: v5.0.0
         hooks:
+              - id: check-json
               - id: trailing-whitespace
               - id: end-of-file-fixer
       - repo: https://github.com/PyCQA/isort
@@ -100,10 +101,6 @@ repos:
                   (?x)
                     ^CHANGELOG[.]md$|
                     ^cpp/cmake/patches/cutlass/build-export[.]patch$
-      - repo: https://github.com/pre-commit/pre-commit-hooks
-        rev: v4.5.0
-        hooks:
-              - id: check-json
       - repo: https://github.com/rapidsai/pre-commit-hooks
         rev: v0.6.0
         hooks:
@@ -122,7 +119,7 @@ repos:
           - id: verify-codeowners
             args: [--fix, --project-prefix=cuvs]
       - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.17.0
+        rev: v1.18.1
         hooks:
             - id: rapids-dependency-file-generator
               args: ["--clean"]
@@ -131,7 +128,6 @@ repos:
         hooks:
           - id: shellcheck
             args: ["--severity=warning"]
-            files: ^ci/
 
 default_language_version:
       python: python3
diff --git a/build.sh b/build.sh
index 60f9a8291b..90ad3a13da 100755
--- a/build.sh
+++ b/build.sh
@@ -16,7 +16,7 @@ ARGS=$*
 
 # NOTE: ensure all dir changes are relative to the location of this
 # scripts, and that this script resides in the repo dir!
-REPODIR=$(cd $(dirname $0); pwd)
+REPODIR=$(cd "$(dirname "$0")"; pwd)
 
 VALIDARGS="clean libcuvs python rust go java docs tests bench-ann examples --uninstall  -v -g -n --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
@@ -75,7 +75,6 @@ BUILD_DIRS="${LIBCUVS_BUILD_DIR} ${PYTHON_BUILD_DIR} ${RUST_BUILD_DIR} ${JAVA_BU
 # Set defaults for vars modified by flags to this script
 CMAKE_LOG_LEVEL=""
 VERBOSE_FLAG=""
-BUILD_ALL_GPU_ARCH=0
 BUILD_TESTS=ON
 BUILD_MG_ALGOS=ON
 BUILD_TYPE=Release
@@ -92,7 +91,6 @@ CACHE_ARGS=""
 NVTX=ON
 LOG_COMPILE_TIME=OFF
 CLEAN=0
-UNINSTALL=0
 DISABLE_DEPRECATION_WARNINGS=ON
 CMAKE_TARGET=""
 EXTRA_CMAKE_ARGS=""
@@ -255,7 +253,6 @@ fi
 
 # This should run before build/install
 if hasArg --uninstall; then
-    UNINSTALL=1
 
     if hasArg cuvs || hasArg libcuvs || (( ${NUMARGS} == 1 )); then
 
@@ -502,8 +499,10 @@ if (( ${NUMARGS} == 0 )) || hasArg java; then
     ./build.sh
 fi
 
-export RAPIDS_VERSION="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2.\3/' "${REPODIR}/VERSION")"
-export RAPIDS_VERSION_MAJOR_MINOR="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2/' "${REPODIR}/VERSION")"
+RAPIDS_VERSION="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2.\3/' "${REPODIR}/VERSION")"
+export RAPIDS_VERSION
+RAPIDS_VERSION_MAJOR_MINOR="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2/' "${REPODIR}/VERSION")"
+export RAPIDS_VERSION_MAJOR_MINOR
 
 if hasArg docs; then
     set -x
diff --git a/cpp/scripts/run-cmake-format.sh b/cpp/scripts/run-cmake-format.sh
index 7e30258fcb..5438a6a932 100755
--- a/cpp/scripts/run-cmake-format.sh
+++ b/cpp/scripts/run-cmake-format.sh
@@ -48,7 +48,7 @@ DEFAULT_FORMAT_FILE_LOCATIONS=(
 )
 
 if [ -z ${RAPIDS_CMAKE_FORMAT_FILE:+PLACEHOLDER} ]; then
-    for file_path in ${DEFAULT_FORMAT_FILE_LOCATIONS[@]}; do
+    for file_path in "${DEFAULT_FORMAT_FILE_LOCATIONS[@]}"; do
         if [ -f ${file_path} ]; then
             RAPIDS_CMAKE_FORMAT_FILE=${file_path}
             break
@@ -68,12 +68,12 @@ else
 fi
 
 if [[ $1 == "cmake-format" ]]; then
-  cmake-format -i --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- ${@:2}
+  cmake-format -i --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- "${@:2}"
 elif [[ $1 == "cmake-lint" ]]; then
   # Since the pre-commit hook is verbose, we have to be careful to only
   # present cmake-lint's output (which is quite verbose) if we actually
   # observe a failure.
-  OUTPUT=$(cmake-lint --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- ${@:2})
+  OUTPUT=$(cmake-lint --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- "${@:2}")
   status=$?
 
   if ! [ ${status} -eq 0 ]; then
diff --git a/examples/build.sh b/examples/build.sh
index af506c0e8e..434cdcd5a3 100755
--- a/examples/build.sh
+++ b/examples/build.sh
@@ -59,7 +59,6 @@ function gpuArch {
 # Set up build configuration
 PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
 BUILD_TYPE=Release
-BUILD_DIR=build/
 CUVS_REPO_REL=""
 EXTRA_CMAKE_ARGS=""
 
diff --git a/java/build.sh b/java/build.sh
index 84e44c71f9..48dac82dff 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -7,7 +7,8 @@ GROUP_ID="com.nvidia.cuvs"
 SO_FILE_PATH="./internal/build"
 
 if [ -z "${CMAKE_PREFIX_PATH:=}" ]; then
-  export CMAKE_PREFIX_PATH="$(pwd)/../cpp/build"
+  CMAKE_PREFIX_PATH="$(pwd)/../cpp/build"
+  export CMAKE_PREFIX_PATH
 fi
 
 cmake -B ./internal/build -S ./internal
diff --git a/java/panama-bindings/generate-bindings.sh b/java/panama-bindings/generate-bindings.sh
index 3984d4a16c..5ef016383f 100755
--- a/java/panama-bindings/generate-bindings.sh
+++ b/java/panama-bindings/generate-bindings.sh
@@ -3,8 +3,8 @@
 set -e -u -o pipefail
 
 echo "Starting Panama FFM API bindings generation ..."
-REPODIR=$(cd $(dirname $0); cd ../../ ; pwd)
-CURDIR=$(cd $(dirname $0); pwd)
+REPODIR=$(cd "$(dirname $0)"; cd ../../ ; pwd)
+CURDIR=$(cd "$(dirname $0)"; pwd)
 CUDA_HOME=$(which nvcc | cut -d/ -f-4)
 TARGET_PACKAGE="com.nvidia.cuvs.internal.panama"
 
@@ -29,7 +29,8 @@ then
   echo "jextract doesn't exist. Downloading it from $JEXTRACT_DOWNLOAD_URL.";
   wget -c $JEXTRACT_DOWNLOAD_URL
   tar -xvf ./"${JEXTRACT_FILENAME}"
-  export PATH="$(pwd)/jextract-22/bin/jextract:${PATH}"
+  PATH="$(pwd)/jextract-22/bin/jextract:${PATH}"
+  export PATH
   echo "jextract downloaded to $(pwd)/jextract-22"
 fi
 

From 7affbc034e36cb66288391302ba6f910b54cd517 Mon Sep 17 00:00:00 2001
From: Anna Verner <anvernum@gmail.com>
Date: Mon, 5 May 2025 13:05:24 +0400
Subject: [PATCH 380/623] Optimize euclidean distance in host refine phase
 (#689)

### Issue

Original code (below) generated serial assembly and used strictly-ordered `fadda` instruction on ARM with gcc & clang. That resulted in suboptimal performance.
```c++
for (size_t k = 0; k < dim; k++) {
  distance += DC::template eval<DistanceT>(query[k], row[k]);
}
```

### Proposed solution

This PR provides euclidean distance optimized with partial vector sum (below), that helps vectorization but loses strcictly-ordered compliance.
```c++
template <typename DC, typename DistanceT, typename DataT>
DistanceT euclidean_distance_squared_generic(DataT const* a, DataT const* b, size_t n) {
  size_t constexpr max_vreg_len = 512 / (8 * sizeof(DistanceT));

  // max_vreg_len is a power of two
  size_t n_rounded = n & (0xFFFFFFFF ^ (max_vreg_len - 1));
  DistanceT distance[max_vreg_len] = {0};

  for (size_t i = 0; i < n_rounded; i += max_vreg_len) {
    for (size_t j = 0; j < max_vreg_len; ++j) {
      distance[j] += DC::template eval<DistanceT>(a[i + j], b[i + j]);
    }
  }

  for (size_t i = n_rounded; i < n; ++i) {
    distance[i] += DC::template eval<DistanceT>(a[i], b[i]);
  }

  for (size_t i = 1; i < max_vreg_len; ++i) {
    distance[0] += distance[i];
  }

  return distance[0];
}
```
In addition, it has an implementation with NEON intrinsics which provides further speedup on certain test cases (can be removed if arch-specific code is undesired).

### Results
![image](https://github.com/user-attachments/assets/24131d06-fd75-4490-b758-20a21f376ea9)

Authors:
  - Anna Verner (https://github.com/anstellaire)
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/689
---
 cpp/src/neighbors/refine/refine_host.hpp | 314 ++++++++++++++++++++++-
 1 file changed, 311 insertions(+), 3 deletions(-)

diff --git a/cpp/src/neighbors/refine/refine_host.hpp b/cpp/src/neighbors/refine/refine_host.hpp
index 105023d590..ca95b89d9e 100644
--- a/cpp/src/neighbors/refine/refine_host.hpp
+++ b/cpp/src/neighbors/refine/refine_host.hpp
@@ -25,10 +25,320 @@
 
 #include <algorithm>
 
+#if defined(__arm__) || defined(__aarch64__)
+#include <arm_neon.h>
+#endif
+
 namespace cuvs::neighbors {
 
 namespace detail {
 
+// -----------------------------------------------------------------------------
+//  Generic implementation
+// -----------------------------------------------------------------------------
+
+template <typename DC, typename DistanceT, typename DataT>
+DistanceT euclidean_distance_squared_generic(DataT const* a, DataT const* b, size_t n)
+{
+  size_t constexpr max_vreg_len = 512 / (8 * sizeof(DistanceT));
+
+  // max_vreg_len is a power of two
+  size_t n_rounded                 = n - (n % max_vreg_len);
+  DistanceT distance[max_vreg_len] = {0};
+
+  for (size_t i = 0; i < n_rounded; i += max_vreg_len) {
+    for (size_t j = 0; j < max_vreg_len; ++j) {
+      distance[j] += DC::template eval<DistanceT>(a[i + j], b[i + j]);
+    }
+  }
+
+  for (size_t i = n_rounded; i < n; ++i) {
+    distance[i - n_rounded] += DC::template eval<DistanceT>(a[i], b[i]);
+  }
+
+  for (size_t i = 1; i < max_vreg_len; ++i) {
+    distance[0] += distance[i];
+  }
+
+  return distance[0];
+}
+
+// -----------------------------------------------------------------------------
+//  NEON implementation
+// -----------------------------------------------------------------------------
+
+struct distance_comp_l2;
+struct distance_comp_inner;
+
+// fallback
+template <typename DC, typename DistanceT, typename DataT>
+DistanceT euclidean_distance_squared(DataT const* a, DataT const* b, size_t n)
+{
+  return euclidean_distance_squared_generic<DC, DistanceT, DataT>(a, b, n);
+}
+
+#if defined(__arm__) || defined(__aarch64__)
+
+template <>
+inline float euclidean_distance_squared<distance_comp_l2, float, float>(float const* a,
+                                                                        float const* b,
+                                                                        size_t n)
+{
+  size_t n_rounded = n - (n % 4);
+
+  float32x4_t vreg_dsum = vdupq_n_f32(0.f);
+  for (size_t i = 0; i < n_rounded; i += 4) {
+    float32x4_t vreg_a = vld1q_f32(&a[i]);
+    float32x4_t vreg_b = vld1q_f32(&b[i]);
+    float32x4_t vreg_d = vsubq_f32(vreg_a, vreg_b);
+    vreg_dsum          = vfmaq_f32(vreg_dsum, vreg_d, vreg_d);
+  }
+
+  float dsum = vaddvq_f32(vreg_dsum);
+  for (size_t i = n_rounded; i < n; ++i) {
+    float d = a[i] - b[i];
+    dsum += d * d;
+  }
+
+  return dsum;
+}
+
+template <>
+inline float euclidean_distance_squared<distance_comp_l2, float, ::std::int8_t>(
+  ::std::int8_t const* a, ::std::int8_t const* b, size_t n)
+{
+  size_t n_rounded = n - (n % 16);
+  float dsum       = 0.f;
+
+  if (n_rounded > 0) {
+    float32x4_t vreg_dsum_fp32_0 = vdupq_n_f32(0.f);
+    float32x4_t vreg_dsum_fp32_1 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_2 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_3 = vreg_dsum_fp32_0;
+
+    for (size_t i = 0; i < n_rounded; i += 16) {
+      int8x16_t vreg_a       = vld1q_s8(&a[i]);
+      int16x8_t vreg_a_s16_0 = vmovl_s8(vget_low_s8(vreg_a));
+      int16x8_t vreg_a_s16_1 = vmovl_s8(vget_high_s8(vreg_a));
+
+      int8x16_t vreg_b       = vld1q_s8(&b[i]);
+      int16x8_t vreg_b_s16_0 = vmovl_s8(vget_low_s8(vreg_b));
+      int16x8_t vreg_b_s16_1 = vmovl_s8(vget_high_s8(vreg_b));
+
+      int16x8_t vreg_d_s16_0 = vsubq_s16(vreg_a_s16_0, vreg_b_s16_0);
+      int16x8_t vreg_d_s16_1 = vsubq_s16(vreg_a_s16_1, vreg_b_s16_1);
+
+      float32x4_t vreg_d_fp32_0 = vcvtq_f32_s32(vmovl_s16(vget_low_s16(vreg_d_s16_0)));
+      float32x4_t vreg_d_fp32_1 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vreg_d_s16_0)));
+      float32x4_t vreg_d_fp32_2 = vcvtq_f32_s32(vmovl_s16(vget_low_s16(vreg_d_s16_1)));
+      float32x4_t vreg_d_fp32_3 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vreg_d_s16_1)));
+
+      vreg_dsum_fp32_0 = vfmaq_f32(vreg_dsum_fp32_0, vreg_d_fp32_0, vreg_d_fp32_0);
+      vreg_dsum_fp32_1 = vfmaq_f32(vreg_dsum_fp32_1, vreg_d_fp32_1, vreg_d_fp32_1);
+      vreg_dsum_fp32_2 = vfmaq_f32(vreg_dsum_fp32_2, vreg_d_fp32_2, vreg_d_fp32_2);
+      vreg_dsum_fp32_3 = vfmaq_f32(vreg_dsum_fp32_3, vreg_d_fp32_3, vreg_d_fp32_3);
+    }
+
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_1);
+    vreg_dsum_fp32_2 = vaddq_f32(vreg_dsum_fp32_2, vreg_dsum_fp32_3);
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_2);
+
+    dsum = vaddvq_f32(vreg_dsum_fp32_0);  // faddp
+  }
+
+  for (size_t i = n_rounded; i < n; ++i) {
+    float d = a[i] - b[i];
+    dsum += d * d;  // [nvc++] faddp, [clang] fadda, [gcc] vecsum+fadda
+  }
+
+  return dsum;
+}
+
+template <>
+inline float euclidean_distance_squared<distance_comp_l2, float, ::std::uint8_t>(
+  ::std::uint8_t const* a, ::std::uint8_t const* b, size_t n)
+{
+  size_t n_rounded = n - (n % 16);
+  float dsum       = 0.f;
+
+  if (n_rounded > 0) {
+    float32x4_t vreg_dsum_fp32_0 = vdupq_n_f32(0.f);
+    float32x4_t vreg_dsum_fp32_1 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_2 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_3 = vreg_dsum_fp32_0;
+
+    for (size_t i = 0; i < n_rounded; i += 16) {
+      uint8x16_t vreg_a         = vld1q_u8(&a[i]);
+      uint16x8_t vreg_a_u16_0   = vmovl_u8(vget_low_u8(vreg_a));
+      uint16x8_t vreg_a_u16_1   = vmovl_u8(vget_high_u8(vreg_a));
+      float32x4_t vreg_a_fp32_0 = vcvtq_f32_u32(vmovl_u16(vget_low_u16(vreg_a_u16_0)));
+      float32x4_t vreg_a_fp32_1 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vreg_a_u16_0)));
+      float32x4_t vreg_a_fp32_2 = vcvtq_f32_u32(vmovl_u16(vget_low_u16(vreg_a_u16_1)));
+      float32x4_t vreg_a_fp32_3 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vreg_a_u16_1)));
+
+      uint8x16_t vreg_b         = vld1q_u8(&b[i]);
+      uint16x8_t vreg_b_u16_0   = vmovl_u8(vget_low_u8(vreg_b));
+      uint16x8_t vreg_b_u16_1   = vmovl_u8(vget_high_u8(vreg_b));
+      float32x4_t vreg_b_fp32_0 = vcvtq_f32_u32(vmovl_u16(vget_low_u16(vreg_b_u16_0)));
+      float32x4_t vreg_b_fp32_1 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vreg_b_u16_0)));
+      float32x4_t vreg_b_fp32_2 = vcvtq_f32_u32(vmovl_u16(vget_low_u16(vreg_b_u16_1)));
+      float32x4_t vreg_b_fp32_3 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vreg_b_u16_1)));
+
+      float32x4_t vreg_d_fp32_0 = vsubq_f32(vreg_a_fp32_0, vreg_b_fp32_0);
+      float32x4_t vreg_d_fp32_1 = vsubq_f32(vreg_a_fp32_1, vreg_b_fp32_1);
+      float32x4_t vreg_d_fp32_2 = vsubq_f32(vreg_a_fp32_2, vreg_b_fp32_2);
+      float32x4_t vreg_d_fp32_3 = vsubq_f32(vreg_a_fp32_3, vreg_b_fp32_3);
+
+      vreg_dsum_fp32_0 = vfmaq_f32(vreg_dsum_fp32_0, vreg_d_fp32_0, vreg_d_fp32_0);
+      vreg_dsum_fp32_1 = vfmaq_f32(vreg_dsum_fp32_1, vreg_d_fp32_1, vreg_d_fp32_1);
+      vreg_dsum_fp32_2 = vfmaq_f32(vreg_dsum_fp32_2, vreg_d_fp32_2, vreg_d_fp32_2);
+      vreg_dsum_fp32_3 = vfmaq_f32(vreg_dsum_fp32_3, vreg_d_fp32_3, vreg_d_fp32_3);
+    }
+
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_1);
+    vreg_dsum_fp32_2 = vaddq_f32(vreg_dsum_fp32_2, vreg_dsum_fp32_3);
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_2);
+
+    dsum = vaddvq_f32(vreg_dsum_fp32_0);  // faddp
+  }
+
+  for (size_t i = n_rounded; i < n; ++i) {
+    float d = a[i] - b[i];
+    dsum += d * d;  // [nvc++] faddp, [clang] fadda, [gcc] vecsum+fadda
+  }
+
+  return dsum;
+}
+
+template <>
+inline float euclidean_distance_squared<distance_comp_inner, float, float>(float const* a,
+                                                                           float const* b,
+                                                                           size_t n)
+{
+  size_t n_rounded = n - (n % 4);
+
+  float32x4_t vreg_dsum = vdupq_n_f32(0.f);
+  for (size_t i = 0; i < n_rounded; i += 4) {
+    float32x4_t vreg_a = vld1q_f32(&a[i]);
+    float32x4_t vreg_b = vld1q_f32(&b[i]);
+    vreg_a             = vnegq_f32(vreg_a);
+    vreg_dsum          = vfmaq_f32(vreg_dsum, vreg_a, vreg_b);
+  }
+
+  float dsum = vaddvq_f32(vreg_dsum);
+  for (size_t i = n_rounded; i < n; ++i) {
+    dsum += -a[i] * b[i];
+  }
+
+  return dsum;
+}
+
+template <>
+inline float euclidean_distance_squared<distance_comp_inner, float, ::std::int8_t>(
+  ::std::int8_t const* a, ::std::int8_t const* b, size_t n)
+{
+  size_t n_rounded = n - (n % 16);
+  float dsum       = 0.f;
+
+  if (n_rounded > 0) {
+    float32x4_t vreg_dsum_fp32_0 = vdupq_n_f32(0.f);
+    float32x4_t vreg_dsum_fp32_1 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_2 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_3 = vreg_dsum_fp32_0;
+
+    for (size_t i = 0; i < n_rounded; i += 16) {
+      int8x16_t vreg_a       = vld1q_s8(&a[i]);
+      int16x8_t vreg_a_s16_0 = vmovl_s8(vget_low_s8(vreg_a));
+      int16x8_t vreg_a_s16_1 = vmovl_s8(vget_high_s8(vreg_a));
+
+      int8x16_t vreg_b       = vld1q_s8(&b[i]);
+      int16x8_t vreg_b_s16_0 = vmovl_s8(vget_low_s8(vreg_b));
+      int16x8_t vreg_b_s16_1 = vmovl_s8(vget_high_s8(vreg_b));
+
+      vreg_a_s16_0 = vmulq_s16(vreg_a_s16_0, vreg_b_s16_0);
+      vreg_a_s16_1 = vmulq_s16(vreg_a_s16_1, vreg_b_s16_1);
+
+      float32x4_t vreg_res_fp32_0 = vcvtq_f32_s32(vmovl_s16(vget_low_s16(vreg_a_s16_0)));
+      float32x4_t vreg_res_fp32_1 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vreg_a_s16_0)));
+      float32x4_t vreg_res_fp32_2 = vcvtq_f32_s32(vmovl_s16(vget_low_s16(vreg_a_s16_1)));
+      float32x4_t vreg_res_fp32_3 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vreg_a_s16_1)));
+
+      vreg_dsum_fp32_0 = vsubq_f32(vreg_dsum_fp32_0, vreg_res_fp32_0);
+      vreg_dsum_fp32_1 = vsubq_f32(vreg_dsum_fp32_1, vreg_res_fp32_1);
+      vreg_dsum_fp32_2 = vsubq_f32(vreg_dsum_fp32_2, vreg_res_fp32_2);
+      vreg_dsum_fp32_3 = vsubq_f32(vreg_dsum_fp32_3, vreg_res_fp32_3);
+    }
+
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_1);
+    vreg_dsum_fp32_2 = vaddq_f32(vreg_dsum_fp32_2, vreg_dsum_fp32_3);
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_2);
+
+    dsum = vaddvq_f32(vreg_dsum_fp32_0);  // faddp
+  }
+
+  for (size_t i = n_rounded; i < n; ++i) {
+    dsum += -a[i] * b[i];
+  }
+
+  return dsum;
+}
+
+template <>
+inline float euclidean_distance_squared<distance_comp_inner, float, ::std::uint8_t>(
+  ::std::uint8_t const* a, ::std::uint8_t const* b, size_t n)
+{
+  size_t n_rounded = n - (n % 16);
+  float dsum       = 0.f;
+
+  if (n_rounded > 0) {
+    float32x4_t vreg_dsum_fp32_0 = vdupq_n_f32(0.f);
+    float32x4_t vreg_dsum_fp32_1 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_2 = vreg_dsum_fp32_0;
+    float32x4_t vreg_dsum_fp32_3 = vreg_dsum_fp32_0;
+
+    for (size_t i = 0; i < n_rounded; i += 16) {
+      uint8x16_t vreg_a       = vld1q_u8(&a[i]);
+      uint16x8_t vreg_a_u16_0 = vmovl_u8(vget_low_u8(vreg_a));
+      uint16x8_t vreg_a_u16_1 = vmovl_u8(vget_high_u8(vreg_a));
+
+      uint8x16_t vreg_b       = vld1q_u8(&b[i]);
+      uint16x8_t vreg_b_u16_0 = vmovl_u8(vget_low_u8(vreg_b));
+      uint16x8_t vreg_b_u16_1 = vmovl_u8(vget_high_u8(vreg_b));
+
+      vreg_a_u16_0 = vmulq_u16(vreg_a_u16_0, vreg_b_u16_0);
+      vreg_a_u16_1 = vmulq_u16(vreg_a_u16_1, vreg_b_u16_1);
+
+      float32x4_t vreg_res_fp32_0 = vcvtq_f32_u32(vmovl_u16(vget_low_u16(vreg_a_u16_0)));
+      float32x4_t vreg_res_fp32_1 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vreg_a_u16_0)));
+      float32x4_t vreg_res_fp32_2 = vcvtq_f32_u32(vmovl_u16(vget_low_u16(vreg_a_u16_1)));
+      float32x4_t vreg_res_fp32_3 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vreg_a_u16_1)));
+
+      vreg_dsum_fp32_0 = vsubq_f32(vreg_dsum_fp32_0, vreg_res_fp32_0);
+      vreg_dsum_fp32_1 = vsubq_f32(vreg_dsum_fp32_1, vreg_res_fp32_1);
+      vreg_dsum_fp32_2 = vsubq_f32(vreg_dsum_fp32_2, vreg_res_fp32_2);
+      vreg_dsum_fp32_3 = vsubq_f32(vreg_dsum_fp32_3, vreg_res_fp32_3);
+    }
+
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_1);
+    vreg_dsum_fp32_2 = vaddq_f32(vreg_dsum_fp32_2, vreg_dsum_fp32_3);
+    vreg_dsum_fp32_0 = vaddq_f32(vreg_dsum_fp32_0, vreg_dsum_fp32_2);
+
+    dsum = vaddvq_f32(vreg_dsum_fp32_0);  // faddp
+  }
+
+  for (size_t i = n_rounded; i < n; ++i) {
+    dsum += -a[i] * b[i];
+  }
+
+  return dsum;
+}
+
+#endif  // defined(__arm__) || defined(__aarch64__)
+
+// -----------------------------------------------------------------------------
+//  Refine kernel
+// -----------------------------------------------------------------------------
+
 template <typename DC, typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
 [[gnu::optimize(3), gnu::optimize("tree-vectorize")]] void refine_host_impl(
   raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,
@@ -117,9 +427,7 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
             distance = std::numeric_limits<DistanceT>::max();
           } else {
             const DataT* row = dataset.data_handle() + dim * id;
-            for (size_t k = 0; k < dim; k++) {
-              distance += DC::template eval<DistanceT>(query[k], row[k]);
-            }
+            distance         = euclidean_distance_squared<DC, DistanceT, DataT>(query, row, dim);
           }
           refined_pairs[tid][j] = std::make_tuple(distance, id);
         }

From 436673a69fcfefa31c0b5bab844f86b752884b58 Mon Sep 17 00:00:00 2001
From: Yinzuo Jiang <jiangyinzuo@foxmail.com>
Date: Tue, 6 May 2025 00:42:05 +0800
Subject: [PATCH 381/623] [cuvs_bench] distinguish search label from build
 label in data_export.py (#818)

When `configs/algos/*.yaml` has string configurations, they will be converted into "label" column in exported CSV files.

But the exported CSV files only have "label" column for build configuration. search configuration is missing.

Before this patch, the search label is overriden by build label in exported CSV files.

After this patch, CSV files have "label" column as build label and "search_label" column as search label.

Authors:
  - Yinzuo Jiang (https://github.com/jiangyinzuo)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/818
---
 python/cuvs_bench/cuvs_bench/run/data_export.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/python/cuvs_bench/cuvs_bench/run/data_export.py b/python/cuvs_bench/cuvs_bench/run/data_export.py
index d42e872467..9954d234ed 100644
--- a/python/cuvs_bench/cuvs_bench/run/data_export.py
+++ b/python/cuvs_bench/cuvs_bench/run/data_export.py
@@ -207,7 +207,10 @@ def convert_json_to_csv_search(dataset, dataset_path):
             # Append build data
             for name in df:
                 if name not in skip_search_cols:
-                    write[name] = df[name]
+                    # distinguish search label from build label
+                    write["search_label" if name == "label" else name] = df[
+                        name
+                    ]
             if os.path.exists(build_file):
                 build_df = pd.read_csv(build_file)
                 write_ncols = len(write.columns)

From 0f6a9a8c8b7318a6f4af5697f564d858437b5191 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Mon, 5 May 2025 11:04:04 -0700
Subject: [PATCH 382/623] [DOCS] Fix Integration Docs (Faiss) (#782)

Closes https://github.com/rapidsai/cuvs/issues/649

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/782
---
 docs/source/integrations/faiss.rst | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/docs/source/integrations/faiss.rst b/docs/source/integrations/faiss.rst
index bf8be8225f..1fc88d921c 100644
--- a/docs/source/integrations/faiss.rst
+++ b/docs/source/integrations/faiss.rst
@@ -1,14 +1,6 @@
-FAISS
+Faiss
 -----
 
-FAISS v1.8 provides a special conda package that enables a RAFT backend for the Flat, IVF-Flat and IVF-PQ indexes on the GPU. Like the classical FAISS GPU indexes, the RAFT backend also enables interoperability between FAISS CPU indexes, allowing an index to be trained on GPU, searched on CPU, and vice versa.
+Faiss v1.10.0 and beyond provides a special conda package that enables a cuVS backend for the Flat, IVF-Flat, IVF-PQ and CAGRA indexes on the GPU. Like the classical Faiss GPU indexes, the cuVS backend also enables interoperability between Faiss CPU indexes, allowing an index to be trained on GPU, searched on CPU, and vice versa.
 
-The RAFT backend can be enabled by building FAISS from source with the `FAISS_USE_RAFT` cmake flag enabled and setting the `use_raft` configuration option for the RAFT-enabled GPU indexes.
-
-A pre-compiled conda package can also be installed using the following command:
-
-.. code-block:: bash
-
-    conda install -c conda-forge -c pytorch -c rapidsai -c nvidia -c "nvidia/label/cuda-11.8.0" faiss-gpu-raft
-
-The next release of FAISS will feature cuVS support as we continue to migrate the vector search algorithms from RAFT to cuVS.
+The cuVS backend can be enabled by setting the appropriate cmake flag while building Faiss from source. A pre-compiled conda package can also be installed. Refer to `Faiss installation guidelines <https://github.com/facebookresearch/faiss/blob/main/INSTALL.md>`_ for more information.

From 4a4d86363fd700f487593e65dd9e9ceb321a9c12 Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Mon, 5 May 2025 16:43:37 -0400
Subject: [PATCH 383/623] chore: lower wheel size threshold (#872)

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Jake Awe (https://github.com/AyodeAwe)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/872
---
 ci/validate_wheel.sh          | 20 ++++++++++++++++++++
 python/libcuvs/pyproject.toml |  2 +-
 2 files changed, 21 insertions(+), 1 deletion(-)

diff --git a/ci/validate_wheel.sh b/ci/validate_wheel.sh
index 5c2c5f2cf6..618c137736 100755
--- a/ci/validate_wheel.sh
+++ b/ci/validate_wheel.sh
@@ -7,12 +7,32 @@ package_dir=$1
 wheel_dir_relative_path=$2
 
 
+RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
+
+# some packages are much larger on CUDA 11 than on CUDA 12
+if [[ "${package_dir}" == "python/libcuvs" ]]; then
+    if [[ "${RAPIDS_CUDA_MAJOR}" == "11" ]]; then
+        PYDISTCHECK_ARGS=(
+            --max-allowed-size-compressed '1.0G'
+        )
+    else
+        PYDISTCHECK_ARGS=(
+            --max-allowed-size-compressed '900M'
+        )
+    fi
+else
+    PYDISTCHECK_ARGS=()
+fi
+
+
+
 cd "${package_dir}"
 
 rapids-logger "validate packages with 'pydistcheck'"
 
 pydistcheck \
     --inspect \
+    "${PYDISTCHECK_ARGS[@]}" \
     "$(echo "${wheel_dir_relative_path}"/*.whl)"
 
 rapids-logger "validate packages with 'twine'"
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 88861f5d69..14ab69b049 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -106,4 +106,4 @@ select = [
 ]
 
 # detect when package size grows significantly
-max_allowed_size_compressed = '1.2G'
+max_allowed_size_compressed = '1.0G'

From f644b41b4aeff1cb3ec2169d42f62cb196486f0f Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Wed, 7 May 2025 09:18:46 -0400
Subject: [PATCH 384/623] Add support for Python 3.13 (#874)

Contributes to https://github.com/rapidsai/build-planning/issues/120

This PR adds support for Python 3.13.

## Notes for Reviewers

This is part of ongoing work to add Python 3.13 support across RAPIDS.
It temporarily introduces a build/test matrix including Python 3.13, from https://github.com/rapidsai/shared-workflows/pull/268.

A follow-up PR will revert back to pointing at the `branch-25.06` branch of `shared-workflows` once all
RAPIDS repos have added Python 3.13 support.

### This will fail until all dependencies have been updated to Python 3.13

CI here is expected to fail until all of this project's upstream dependencies support Python 3.13.

This can be merged whenever all CI jobs are passing.

Authors:
  - Gil Forsyth (https://github.com/gforsyth)
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/874
---
 .github/workflows/build.yaml                  | 20 ++++++-------
 .github/workflows/pr.yaml                     | 30 +++++++++----------
 .github/workflows/test.yaml                   |  8 ++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 conda/recipes/cuvs-bench-cpu/recipe.yaml      |  1 +
 dependencies.yaml                             |  6 +++-
 python/cuvs/pyproject.toml                    |  1 +
 python/cuvs_bench/pyproject.toml              |  1 +
 8 files changed, 38 insertions(+), 31 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index fd13413fe3..5a8a30c917 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -54,7 +54,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -67,7 +67,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -76,7 +76,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -87,7 +87,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -99,7 +99,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -113,7 +113,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -124,7 +124,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -136,7 +136,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index d774f7a558..895d2d8462 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -29,7 +29,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@python-3.13
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -58,7 +58,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@python-3.13
     with:
       files_yaml: |
         test_cpp:
@@ -95,28 +95,28 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@python-3.13
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.13
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.13
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.13
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -124,20 +124,20 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.13
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.13
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -147,7 +147,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -157,7 +157,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -167,7 +167,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -178,7 +178,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -187,7 +187,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.13
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -195,7 +195,7 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@python-3.13
     with:
       arch: '["amd64"]'
       cuda: '["12.8"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 8e1dc631aa..51875b88a7 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -19,7 +19,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -29,7 +29,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -45,7 +45,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.13
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 475380cb68..e9399716e2 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@python-3.13
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/conda/recipes/cuvs-bench-cpu/recipe.yaml b/conda/recipes/cuvs-bench-cpu/recipe.yaml
index e0b3debcba..0ae9859460 100644
--- a/conda/recipes/cuvs-bench-cpu/recipe.yaml
+++ b/conda/recipes/cuvs-bench-cpu/recipe.yaml
@@ -64,6 +64,7 @@ requirements:
     - pip
     - python =${{ py_version }}
     - rapids-build-backend>=0.3.0,<0.4.0.dev0
+    - setuptools >=64.0.0
     - if: linux64
       then:
         - libaio
diff --git a/dependencies.yaml b/dependencies.yaml
index 362c78799c..bc4bef9907 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -531,8 +531,12 @@ dependencies:
             packages:
               - python=3.12
           - matrix:
+              py: "3.13"
             packages:
-              - python>=3.10,<3.13
+              - python=3.13
+          - matrix:
+            packages:
+              - python>=3.10,<3.14
   run_py_cuvs:
     common:
       - output_types: [conda, requirements, pyproject]
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 826c5b78d6..cef15ad56d 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -42,6 +42,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 
 [project.optional-dependencies]
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index a1f170cceb..6bac4b7122 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -35,6 +35,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 
 [project.urls]

From 4299c98a1b208feeec0ece62d5046dae80eddf4d Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Wed, 7 May 2025 16:56:49 +0200
Subject: [PATCH 385/623] Add support for half in CAGRA+HNSW (#813)

This PR add support for half dtype for HNSW in C++, C and python, as well as some tests with it. I had to modify a bit the HNSW patch in order to enable computation on half data types.
I also added the support of inner-product distance for int8/uint8 data type.

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/813
---
 cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu  |   4 +-
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |   6 +-
 .../ann/src/hnswlib/hnswlib_benchmark.cpp     |   8 +-
 cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h   |  41 ++--
 cpp/cmake/patches/hnswlib.diff                | 208 +++++++++++++++++-
 cpp/include/cuvs/neighbors/cagra.h            |   5 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |  91 +++++++-
 cpp/include/cuvs/neighbors/hnsw.hpp           | 195 +++++++++++++++-
 cpp/src/neighbors/cagra_c.cpp                 |  14 +-
 cpp/src/neighbors/detail/hnsw.hpp             |  21 +-
 cpp/src/neighbors/hnsw.cpp                    |   4 +
 cpp/src/neighbors/hnsw_c.cpp                  |  28 ++-
 cpp/tests/neighbors/hnsw.cu                   |  51 +++--
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx      |  11 +-
 python/cuvs/cuvs/tests/test_hnsw.py           |  39 ++--
 15 files changed, 619 insertions(+), 107 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
index ed6c7b20fe..0c14658357 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
@@ -66,7 +66,8 @@ auto create_algo(const std::string& algo_name,
   [[maybe_unused]] cuvs::bench::Metric metric = parse_metric(distance);
   std::unique_ptr<cuvs::bench::algo<T>> a;
 
-  if constexpr (std::is_same_v<T, float> or std::is_same_v<T, std::uint8_t>) {
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, int8_t> ||
+                std::is_same_v<T, uint8_t>) {
     if (algo_name == "raft_cagra_hnswlib" || algo_name == "cuvs_cagra_hnswlib") {
       typename cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>::build_param bparam;
       ::parse_build_param<T, uint32_t>(conf, bparam.cagra_build_param);
@@ -97,6 +98,7 @@ auto create_search_param(const std::string& algo_name, const nlohmann::json& con
 }  // namespace cuvs::bench
 
 REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(half);
 REGISTER_ALGO_INSTANCE(std::int8_t);
 REGISTER_ALGO_INSTANCE(std::uint8_t);
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index e4f1d0833b..a57c568f8d 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -340,11 +340,7 @@ void cuvs_cagra<T, IdxT>::save(const std::string& file) const
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::save_to_hnswlib(const std::string& file) const
 {
-  if constexpr (!std::is_same_v<T, half>) {
-    cuvs::neighbors::cagra::serialize_to_hnswlib(handle_, file, *index_);
-  } else {
-    RAFT_FAIL("Cannot save fp16 index to hnswlib format");
-  }
+  cuvs::neighbors::cagra::serialize_to_hnswlib(handle_, file, *index_);
 }
 
 template <typename T, typename IdxT>
diff --git a/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp b/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
index 6e219d2a77..90dc4bf5cc 100644
--- a/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
+++ b/cpp/bench/ann/src/hnswlib/hnswlib_benchmark.cpp
@@ -62,11 +62,8 @@ auto create_algo(const std::string& algo_name,
   cuvs::bench::Metric metric = parse_metric(distance);
   std::unique_ptr<cuvs::bench::algo<T>> a;
 
-  if constexpr (std::is_same_v<T, float>) {
-    if (algo_name == "hnswlib") { a = make_algo<T, cuvs::bench::hnsw_lib>(metric, dim, conf); }
-  }
-
-  if constexpr (std::is_same_v<T, uint8_t>) {
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, int8_t> ||
+                std::is_same_v<T, uint8_t>) {
     if (algo_name == "hnswlib") { a = make_algo<T, cuvs::bench::hnsw_lib>(metric, dim, conf); }
   }
 
@@ -90,6 +87,7 @@ auto create_search_param(const std::string& algo_name, const nlohmann::json& con
 };  // namespace cuvs::bench
 
 REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(half);
 REGISTER_ALGO_INSTANCE(std::int8_t);
 REGISTER_ALGO_INSTANCE(std::uint8_t);
 
diff --git a/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h b/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
index d6870ae1c3..0170e3351c 100644
--- a/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
+++ b/cpp/bench/ann/src/hnswlib/hnswlib_wrapper.h
@@ -49,6 +49,11 @@ struct hnsw_dist_t<float> {
   using type = float;
 };
 
+template <>
+struct hnsw_dist_t<half> {
+  using type = float;
+};
+
 template <>
 struct hnsw_dist_t<uint8_t> {
   using type = int;
@@ -122,12 +127,9 @@ template <typename T>
 hnsw_lib<T>::hnsw_lib(Metric metric, int dim, const build_param& param) : algo<T>(metric, dim)
 {
   assert(dim_ > 0);
-  static_assert(std::is_same_v<T, float> || std::is_same_v<T, uint8_t>);
-  if constexpr (std::is_same_v<T, uint8_t>) {
-    if (metric_ != Metric::kEuclidean) {
-      throw std::runtime_error("hnswlib<uint8_t> only supports Euclidean distance");
-    }
-  }
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, int8_t> ||
+                  std::is_same_v<T, uint8_t>,
+                "Only float, half, uint8, and int8 are supported");
 
   ef_construction_ = param.ef_construction;
   m_               = param.m;
@@ -137,14 +139,17 @@ hnsw_lib<T>::hnsw_lib(Metric metric, int dim, const build_param& param) : algo<T
 template <typename T>
 void hnsw_lib<T>::build(const T* dataset, size_t nrow)
 {
-  if constexpr (std::is_same_v<T, float>) {
-    if (metric_ == Metric::kInnerProduct) {
-      space_ = std::make_shared<hnswlib::InnerProductSpace>(dim_);
+  static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, int8_t> ||
+                  std::is_same_v<T, uint8_t>,
+                "Only float, half, uint8, and int8 are supported");
+  if (metric_ == Metric::kInnerProduct) {
+    space_ = std::make_shared<hnswlib::InnerProductSpace<T, typename hnsw_dist_t<T>::type>>(dim_);
+  } else {
+    if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half>) {
+      space_ = std::make_shared<hnswlib::L2Space<T, typename hnsw_dist_t<T>::type>>(dim_);
     } else {
-      space_ = std::make_shared<hnswlib::L2Space>(dim_);
+      space_ = std::make_shared<hnswlib::L2SpaceI<T>>(dim_);
     }
-  } else if constexpr (std::is_same_v<T, uint8_t>) {
-    space_ = std::make_shared<hnswlib::L2SpaceI<T>>(dim_);
   }
 
   appr_alg_ = std::make_shared<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
@@ -209,14 +214,14 @@ void hnsw_lib<T>::save(const std::string& path_to_index) const
 template <typename T>
 void hnsw_lib<T>::load(const std::string& path_to_index)
 {
-  if constexpr (std::is_same_v<T, float>) {
-    if (metric_ == Metric::kInnerProduct) {
-      space_ = std::make_shared<hnswlib::InnerProductSpace>(dim_);
+  if (metric_ == Metric::kInnerProduct) {
+    space_ = std::make_shared<hnswlib::InnerProductSpace<T, typename hnsw_dist_t<T>::type>>(dim_);
+  } else {
+    if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half>) {
+      space_ = std::make_shared<hnswlib::L2Space<T, typename hnsw_dist_t<T>::type>>(dim_);
     } else {
-      space_ = std::make_shared<hnswlib::L2Space>(dim_);
+      space_ = std::make_shared<hnswlib::L2SpaceI<T>>(dim_);
     }
-  } else if constexpr (std::is_same_v<T, uint8_t>) {
-    space_ = std::make_shared<hnswlib::L2SpaceI<T>>(dim_);
   }
 
   appr_alg_ = std::make_shared<hnswlib::HierarchicalNSW<typename hnsw_dist_t<T>::type>>(
diff --git a/cpp/cmake/patches/hnswlib.diff b/cpp/cmake/patches/hnswlib.diff
index 96be2b2380..52ed5ab377 100644
--- a/cpp/cmake/patches/hnswlib.diff
+++ b/cpp/cmake/patches/hnswlib.diff
@@ -102,11 +102,209 @@ index bef0017..0ee7931 100644
                      }
                  }
              }
+diff --git a/hnswlib/space_ip.h b/hnswlib/space_ip.h
+index 2b1c359..e311f9d 100644
+--- a/hnswlib/space_ip.h
++++ b/hnswlib/space_ip.h
+@@ -3,19 +3,22 @@
+
+ namespace hnswlib {
+
+-static float
++template <typename DataType, typename DistanceType>
++static DistanceType
+ InnerProduct(const void *pVect1, const void *pVect2, const void *qty_ptr) {
+     size_t qty = *((size_t *) qty_ptr);
+-    float res = 0;
++    DistanceType res = 0;
+     for (unsigned i = 0; i < qty; i++) {
+-        res += ((float *) pVect1)[i] * ((float *) pVect2)[i];
++        const DistanceType t = ((DataType *) pVect1)[i] * ((DataType *) pVect2)[i];
++        res += t;
+     }
+     return res;
+ }
+
+-static float
++template <typename DataType, typename DistanceType>
++static DistanceType
+ InnerProductDistance(const void *pVect1, const void *pVect2, const void *qty_ptr) {
+-    return 1.0f - InnerProduct(pVect1, pVect2, qty_ptr);
++    return DistanceType{1} - InnerProduct<DataType, DistanceType>(pVect1, pVect2, qty_ptr);
+ }
+
+ #if defined(USE_AVX)
+@@ -294,7 +297,7 @@ InnerProductDistanceSIMD16ExtResiduals(const void *pVect1v, const void *pVect2v,
+     float *pVect2 = (float *) pVect2v + qty16;
+
+     size_t qty_left = qty - qty16;
+-    float res_tail = InnerProduct(pVect1, pVect2, &qty_left);
++    float res_tail = InnerProduct<float, float>(pVect1, pVect2, &qty_left);
+     return 1.0f - (res + res_tail);
+ }
+
+@@ -308,20 +311,21 @@ InnerProductDistanceSIMD4ExtResiduals(const void *pVect1v, const void *pVect2v,
+
+     float *pVect1 = (float *) pVect1v + qty4;
+     float *pVect2 = (float *) pVect2v + qty4;
+-    float res_tail = InnerProduct(pVect1, pVect2, &qty_left);
++    float res_tail = InnerProduct<float, float>(pVect1, pVect2, &qty_left);
+
+     return 1.0f - (res + res_tail);
+ }
+ #endif
+
+-class InnerProductSpace : public SpaceInterface<float> {
+-    DISTFUNC<float> fstdistfunc_;
++template <typename DataType, typename DistanceType>
++class InnerProductSpace : public SpaceInterface<DistanceType> {
++    DISTFUNC<DistanceType> fstdistfunc_;
+     size_t data_size_;
+     size_t dim_;
+
+  public:
+     InnerProductSpace(size_t dim) {
+-        fstdistfunc_ = InnerProductDistance;
++        fstdistfunc_ = InnerProductDistance<DataType, DistanceType>;
+ #if defined(USE_AVX) || defined(USE_SSE) || defined(USE_AVX512)
+     #if defined(USE_AVX512)
+         if (AVX512Capable()) {
+@@ -344,24 +348,26 @@ class InnerProductSpace : public SpaceInterface<float> {
+         }
+     #endif
+
+-        if (dim % 16 == 0)
+-            fstdistfunc_ = InnerProductDistanceSIMD16Ext;
+-        else if (dim % 4 == 0)
+-            fstdistfunc_ = InnerProductDistanceSIMD4Ext;
+-        else if (dim > 16)
+-            fstdistfunc_ = InnerProductDistanceSIMD16ExtResiduals;
+-        else if (dim > 4)
+-            fstdistfunc_ = InnerProductDistanceSIMD4ExtResiduals;
++        if constexpr (std::is_same_v<DataType, float>) {
++            if (dim % 16 == 0)
++                fstdistfunc_ = InnerProductDistanceSIMD16Ext;
++            else if (dim % 4 == 0)
++                fstdistfunc_ = InnerProductDistanceSIMD4Ext;
++            else if (dim > 16)
++                fstdistfunc_ = InnerProductDistanceSIMD16ExtResiduals;
++            else if (dim > 4)
++                fstdistfunc_ = InnerProductDistanceSIMD4ExtResiduals;
++        }
+ #endif
+         dim_ = dim;
+-        data_size_ = dim * sizeof(float);
++        data_size_ = dim * sizeof(DataType);
+     }
+
+     size_t get_data_size() {
+         return data_size_;
+     }
+
+-    DISTFUNC<float> get_dist_func() {
++    DISTFUNC<DistanceType> get_dist_func() {
+         return fstdistfunc_;
+     }
+
 diff --git a/hnswlib/space_l2.h b/hnswlib/space_l2.h
-index 834d19f..0c0af26 100644
+index 834d19f..c57c87a 100644
 --- a/hnswlib/space_l2.h
 +++ b/hnswlib/space_l2.h
-@@ -252,12 +252,13 @@ class L2Space : public SpaceInterface<float> {
+@@ -3,15 +3,16 @@
+
+ namespace hnswlib {
+
+-static float
++template <typename DataType, typename DistanceType>
++static DistanceType
+ L2Sqr(const void *pVect1v, const void *pVect2v, const void *qty_ptr) {
+-    float *pVect1 = (float *) pVect1v;
+-    float *pVect2 = (float *) pVect2v;
++    DataType *pVect1 = (DataType *) pVect1v;
++    DataType *pVect2 = (DataType *) pVect2v;
+     size_t qty = *((size_t *) qty_ptr);
+
+-    float res = 0;
++    DistanceType res = 0;
+     for (size_t i = 0; i < qty; i++) {
+-        float t = *pVect1 - *pVect2;
++        DistanceType t = *pVect1 - *pVect2;
+         pVect1++;
+         pVect2++;
+         res += t * t;
+@@ -155,7 +156,7 @@ L2SqrSIMD16ExtResiduals(const void *pVect1v, const void *pVect2v, const void *qt
+     float *pVect2 = (float *) pVect2v + qty16;
+
+     size_t qty_left = qty - qty16;
+-    float res_tail = L2Sqr(pVect1, pVect2, &qty_left);
++    float res_tail = L2Sqr<float, float>(pVect1, pVect2, &qty_left);
+     return (res + res_tail);
+ }
+ #endif
+@@ -199,20 +200,21 @@ L2SqrSIMD4ExtResiduals(const void *pVect1v, const void *pVect2v, const void *qty
+
+     float *pVect1 = (float *) pVect1v + qty4;
+     float *pVect2 = (float *) pVect2v + qty4;
+-    float res_tail = L2Sqr(pVect1, pVect2, &qty_left);
++    float res_tail = L2Sqr<float, float>(pVect1, pVect2, &qty_left);
+
+     return (res + res_tail);
+ }
+ #endif
+
+-class L2Space : public SpaceInterface<float> {
+-    DISTFUNC<float> fstdistfunc_;
++template <typename DataType, typename DistanceType=float>
++class L2Space : public SpaceInterface<DistanceType> {
++    DISTFUNC<DistanceType> fstdistfunc_;
+     size_t data_size_;
+     size_t dim_;
+
+  public:
+     L2Space(size_t dim) {
+-        fstdistfunc_ = L2Sqr;
++        fstdistfunc_ = L2Sqr<DataType, DistanceType>;
+ #if defined(USE_SSE) || defined(USE_AVX) || defined(USE_AVX512)
+     #if defined(USE_AVX512)
+         if (AVX512Capable())
+@@ -224,24 +226,26 @@ class L2Space : public SpaceInterface<float> {
+             L2SqrSIMD16Ext = L2SqrSIMD16ExtAVX;
+     #endif
+
+-        if (dim % 16 == 0)
+-            fstdistfunc_ = L2SqrSIMD16Ext;
+-        else if (dim % 4 == 0)
+-            fstdistfunc_ = L2SqrSIMD4Ext;
+-        else if (dim > 16)
+-            fstdistfunc_ = L2SqrSIMD16ExtResiduals;
+-        else if (dim > 4)
+-            fstdistfunc_ = L2SqrSIMD4ExtResiduals;
++        if constexpr (std::is_same_v<DataType, float>) {
++            if (dim % 16 == 0)
++                fstdistfunc_ = L2SqrSIMD16Ext;
++            else if (dim % 4 == 0)
++                fstdistfunc_ = L2SqrSIMD4Ext;
++            else if (dim > 16)
++                fstdistfunc_ = L2SqrSIMD16ExtResiduals;
++            else if (dim > 4)
++                fstdistfunc_ = L2SqrSIMD4ExtResiduals;
++        }
+ #endif
+         dim_ = dim;
+-        data_size_ = dim * sizeof(float);
++        data_size_ = dim * sizeof(DataType);
+     }
+
+     size_t get_data_size() {
+         return data_size_;
+     }
+
+-    DISTFUNC<float> get_dist_func() {
++    DISTFUNC<DistanceType> get_dist_func() {
+         return fstdistfunc_;
+     }
+
+@@ -252,12 +256,13 @@ class L2Space : public SpaceInterface<float> {
      ~L2Space() {}
  };
 
@@ -122,7 +320,7 @@ index 834d19f..0c0af26 100644
 
      qty = qty >> 2;
      for (size_t i = 0; i < qty; i++) {
-@@ -277,11 +278,12 @@ L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const voi
+@@ -277,11 +282,12 @@ L2SqrI4x(const void *__restrict pVect1, const void *__restrict pVect2, const voi
      return (res);
  }
 
@@ -137,7 +335,7 @@ index 834d19f..0c0af26 100644
 
      for (size_t i = 0; i < qty; i++) {
          res += ((*a) - (*b)) * ((*a) - (*b));
-@@ -291,6 +293,7 @@ static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2,
+@@ -291,6 +297,7 @@ static int L2SqrI(const void* __restrict pVect1, const void* __restrict pVect2,
      return (res);
  }
 
@@ -145,7 +343,7 @@ index 834d19f..0c0af26 100644
  class L2SpaceI : public SpaceInterface<int> {
      DISTFUNC<int> fstdistfunc_;
      size_t data_size_;
-@@ -299,9 +302,9 @@ class L2SpaceI : public SpaceInterface<int> {
+@@ -299,9 +306,9 @@ class L2SpaceI : public SpaceInterface<int> {
   public:
      L2SpaceI(size_t dim) {
          if (dim % 4 == 0) {
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 664cc7e9c3..f43435e971 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -429,8 +429,9 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
  *        or `kDLCPU`. Also, acceptable underlying types are:
  *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
- *        2. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
- *        3. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
+ *        2. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 16`
+ *        3. `kDLDataType.code == kDLInt` and `kDLDataType.bits = 8`
+ *        4. `kDLDataType.code == kDLUInt` and `kDLDataType.bits = 8`
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] params cuvsCagraExtendParams_t used to extend CAGRA index
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 07aa21e62c..e70cf6c2ec 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1751,13 +1751,13 @@ void deserialize(raft::resources const& handle,
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
  *
  * raft::resources handle;
  *
  * // create an output stream
  * std::ostream os(std::cout.rdbuf());
- * // create an index with `auto index = raft::cagra::build(...);`
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
  * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, os, index);
  * @endcode
  *
@@ -1784,13 +1784,13 @@ void serialize_to_hnswlib(
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
  *
  * raft::resources handle;
  *
  * // create a string with a filepath
  * std::string filename("/path/to/index");
- * // create an index with `auto index = raft::cagra::build(...);`
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
  * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, filename, index);
  * @endcode
  *
@@ -1818,13 +1818,80 @@ void serialize_to_hnswlib(
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
  *
  * raft::resources handle;
  *
  * // create an output stream
  * std::ostream os(std::cout.rdbuf());
- * // create an index with `auto index = raft::cagra::build(...);`
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, os, index);
+ * @endcode
+ *
+ * @param[in] handle the raft handle
+ * @param[in] os output stream
+ * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
+ *
+ */
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  std::ostream& os,
+  const cuvs::neighbors::cagra::index<half, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const half, int64_t, raft::row_major>> dataset =
+    std::nullopt);
+
+/**
+ * Save a CAGRA build index in hnswlib base-layer-only serialized format
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create a string with a filepath
+ * std::string filename("/path/to/index");
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
+ * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, filename, index);
+ * @endcode
+ *
+ *
+ * @param[in] handle the raft handle
+ * @param[in] filename the file name for saving the index
+ * @param[in] index CAGRA index
+ * @param[in] dataset [optional] host array that stores the dataset, required if the index
+ *            does not contain the dataset.
+ *
+ */
+void serialize_to_hnswlib(
+  raft::resources const& handle,
+  const std::string& filename,
+  const cuvs::neighbors::cagra::index<half, uint32_t>& index,
+  std::optional<raft::host_matrix_view<const half, int64_t, raft::row_major>> dataset =
+    std::nullopt);
+
+/**
+ * Write the CAGRA built index as a base layer HNSW index to an output stream
+ * NOTE: The saved index can only be read by the hnswlib wrapper in cuVS,
+ *       as the serialization format is not compatible with the original hnswlib.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // create an output stream
+ * std::ostream os(std::cout.rdbuf());
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
  * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, os, index);
  * @endcode
  *
@@ -1851,13 +1918,13 @@ void serialize_to_hnswlib(
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
  *
  * raft::resources handle;
  *
  * // create a string with a filepath
  * std::string filename("/path/to/index");
- * // create an index with `auto index = raft::cagra::build(...);`
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
  * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, filename, index);
  * @endcode
  *
@@ -1885,13 +1952,13 @@ void serialize_to_hnswlib(
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
  *
  * raft::resources handle;
  *
  * // create an output stream
  * std::ostream os(std::cout.rdbuf());
- * // create an index with `auto index = raft::cagra::build(...);`
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
  * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, os, index);
  * @endcode
  *
@@ -1918,13 +1985,13 @@ void serialize_to_hnswlib(
  *
  * @code{.cpp}
  * #include <raft/core/resources.hpp>
- * #include <cuvs/neighbors/cagra_serialize.hpp>
+ * #include <cuvs/neighbors/cagra.hpp>
  *
  * raft::resources handle;
  *
  * // create a string with a filepath
  * std::string filename("/path/to/index");
- * // create an index with `auto index = raft::cagra::build(...);`
+ * // create an index with `auto index = cuvs::neighbors::cagra::build(...);`
  * cuvs::neighbors::cagra::serialize_to_hnswlib(handle, filename, index);
  * @endcode
  *
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 88cbf88b0c..4605dfc4cf 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -167,6 +167,42 @@ std::unique_ptr<index<float>> from_cagra(
   std::optional<raft::host_matrix_view<const float, int64_t, raft::row_major>> dataset =
     std::nullopt);
 
+/**
+ * @brief Construct an hnswlib index from a CAGRA index
+ * NOTE: When `hnsw::index_params.hierarchy` is:
+ *       1. `NONE`: This method uses the filesystem to write the CAGRA index in
+ * `/tmp/<random_number>.bin` before reading it as an hnswlib index, then deleting the temporary
+ * file. The returned index is immutable and can only be searched by the hnswlib wrapper in cuVS, as
+ * the format is not compatible with the original hnswlib.
+ *       2. `CPU`: The returned index is mutable and can be extended with additional vectors. The
+ * serialized index is also compatible with the original hnswlib library.
+ *
+ * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
+ * @param[in] cagra_index cagra index
+ * @param[in] dataset optional dataset to avoid extra memory copy when hierarchy is `CPU`
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace raft::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ * @endcode
+ */
+std::unique_ptr<index<half>> from_cagra(
+  raft::resources const& res,
+  const index_params& params,
+  const cuvs::neighbors::cagra::index<half, uint32_t>& cagra_index,
+  std::optional<raft::host_matrix_view<const half, int64_t, raft::row_major>> dataset =
+    std::nullopt);
+
 /**
  * @brief Construct an hnswlib index from a CAGRA index
  * NOTE: When `hnsw::index_params.hierarchy` is:
@@ -303,7 +339,40 @@ void extend(raft::resources const& res,
  *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  *
  *   // Extend the HNSW index with additional vectors
- *   auto additional_dataset = raft::make_host_matrix<float>(res, add_size, index->dim());
+ *   auto additional_dataset = raft::make_host_matrix<half>(res, add_size, index->dim());
+ *   hnsw::extend_params extend_params;
+ *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ */
+void extend(raft::resources const& res,
+            const extend_params& params,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> additional_dataset,
+            index<half>& idx);
+
+/**
+ * @brief Add new vectors to an HNSW index
+ * NOTE: The HNSW index can only be extended when the `hnsw::index_params.hierarchy` is `CPU`
+ *       when converting from a CAGRA index.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the extend
+ * @param[in] additional_dataset a host matrix view to a row-major matrix [n_rows, index->dim()]
+ * @param[inout] idx HNSW index to extend
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   hnsw_params.hierarchy = hnsw::HnswHierarchy::CPU;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *
+ *   // Extend the HNSW index with additional vectors
+ *   auto additional_dataset = raft::make_host_matrix<uint8_t>(res, add_size, index->dim());
  *   hnsw::extend_params extend_params;
  *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
  */
@@ -336,7 +405,7 @@ void extend(raft::resources const& res,
  *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
  *
  *   // Extend the HNSW index with additional vectors
- *   auto additional_dataset = raft::make_host_matrix<float>(res, add_size, index->dim());
+ *   auto additional_dataset = raft::make_host_matrix<int8_t>(res, add_size, index->dim());
  *   hnsw::extend_params extend_params;
  *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
  */
@@ -411,6 +480,50 @@ void search(raft::resources const& res,
             raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances);
 
+/**
+ * @brief Search HNSW index constructed from a CAGRA index
+ * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS when the hierarchy is
+ * `NONE`, as the format is not compatible with the original hnswlib.
+ *
+ * @param[in] res raft resources
+ * @param[in] params configure the search
+ * @param[in] idx cagra index
+ * @param[in] queries a host matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a host matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a host matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *
+ *   // Search K nearest neighbors as an hnswlib index
+ *   // using host threads for concurrency
+ *   hnsw::search_params search_params;
+ *   search_params.ef = 50 // ef >= K;
+ *   search_params.num_threads = 10;
+ *   auto neighbors = raft::make_host_matrix<uint64_t>(res, n_queries, k);
+ *   auto distances = raft::make_host_matrix<float>(res, n_queries, k);
+ *   hnsw::search(res, search_params, *index.get(), queries, neighbors, distances);
+ * @endcode
+ */
+void search(raft::resources const& res,
+            const search_params& params,
+            const index<half>& idx,
+            raft::host_matrix_view<const half, int64_t, raft::row_major> queries,
+            raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, raft::row_major> distances);
+
 /**
  * @brief Search HNSWindex constructed from a CAGRA index
  * NOTE: The HNSW index can only be searched by the hnswlib wrapper in cuVS when the hierarchy is
@@ -507,7 +620,7 @@ void search(raft::resources const& res,
  */
 
 /**
- * @brief Serialize a CAGRA index to a file as an hnswlib index
+ * @brief Serialize the HNSW index to file
  * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
  * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
  * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
@@ -536,7 +649,36 @@ void search(raft::resources const& res,
 void serialize(raft::resources const& res, const std::string& filename, const index<float>& idx);
 
 /**
- * @brief Serialize a CAGRA index to a file as an hnswlib index
+ * @brief Serialize the HNSW index to file
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
+ *
+ * @param[in] res raft resources
+ * @param[in] filename path to the file to save the serialized CAGRA index
+ * @param[in] idx cagra index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *   hnsw::index_params hnsw_params;
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *   // Save the index
+ *   hnsw::serialize(res, "index.bin", index);
+ * @endcode
+ */
+void serialize(raft::resources const& res, const std::string& filename, const index<half>& idx);
+
+/**
+ * @brief Serialize the HNSW index to file
  * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
  * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
  * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
@@ -565,7 +707,7 @@ void serialize(raft::resources const& res, const std::string& filename, const in
 void serialize(raft::resources const& res, const std::string& filename, const index<uint8_t>& idx);
 
 /**
- * @brief Serialize a CAGRA index to a file as an hnswlib index
+ * @brief Serialize the HNSW index to file
  * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
  * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
  * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
@@ -636,6 +778,49 @@ void deserialize(raft::resources const& res,
                  cuvs::distance::DistanceType metric,
                  index<float>** index);
 
+/**
+ * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
+ * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
+ * hnswlib wrapper in cuVS, as the serialization format is not compatible with the original hnswlib.
+ * However, when hierarchy is `CPU`, the saved hnswlib index is compatible with the original hnswlib
+ * library.
+ *
+ * @param[in] res raft resources
+ * @param[in] params hnsw index parameters
+ * @param[in] filename path to the file containing the serialized CAGRA index
+ * @param[in] dim dimensions of the training dataset
+ * @param[in] metric distance metric to search. Supported metrics ("L2Expanded", "InnerProduct")
+ * @param[out] index hnsw index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   // Build a CAGRA index
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   cagra::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = cagra::build(res, index_params, dataset);
+ *
+ *   // Load CAGRA index as an HNSW index
+ *  hnsw::index_params hnsw_params;
+ *  auto hnsw_index = hnsw::from_cagra(res, hnsw_params, index);
+ *  // save HNSW index to a file
+ *  hnsw::serialize(res, "index.bin", hnsw_index);
+ *  // De-serialize the HNSW index
+ *  index<half>* hnsw_index = nullptr;
+ *  hnsw::deserialize(res, hnsw_params, "index.bin", index->dim(), index->metric(), &hnsw_index);
+ *
+ *   // Delete index after use
+ *   delete hnsw_index;
+ * @endcode
+ */
+void deserialize(raft::resources const& res,
+                 const index_params& params,
+                 const std::string& filename,
+                 int dim,
+                 cuvs::distance::DistanceType metric,
+                 index<half>** index);
+
 /**
  * @brief De-serialize a CAGRA index saved to a file as an hnswlib index
  * NOTE: When hierarchy is `NONE`, the saved hnswlib index is immutable and can only be read by the
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index bfe8aaca2c..1356737000 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -278,15 +278,18 @@ extern "C" cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index_c_ptr)
   return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
-    if (index.dtype.code == kDLFloat) {
+    if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
       auto index_ptr =
         reinterpret_cast<cuvs::neighbors::cagra::index<float, uint32_t>*>(index.addr);
       delete index_ptr;
-    } else if (index.dtype.code == kDLInt) {
+    } else if (index.dtype.code == kDLFloat && index.dtype.bits == 16) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<half, uint32_t>*>(index.addr);
+      delete index_ptr;
+    } else if (index.dtype.code == kDLInt && index.dtype.bits == 8) {
       auto index_ptr =
         reinterpret_cast<cuvs::neighbors::cagra::index<int8_t, uint32_t>*>(index.addr);
       delete index_ptr;
-    } else if (index.dtype.code == kDLUInt) {
+    } else if (index.dtype.code == kDLUInt && index.dtype.bits == 8) {
       auto index_ptr =
         reinterpret_cast<cuvs::neighbors::cagra::index<uint8_t, uint32_t>*>(index.addr);
       delete index_ptr;
@@ -485,6 +488,9 @@ extern "C" cuvsError_t cuvsCagraDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename));
       index->dtype.code = kDLFloat;
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->addr       = reinterpret_cast<uintptr_t>(_deserialize<half>(res, filename));
+      index->dtype.code = kDLFloat;
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<int8_t>(res, filename));
       index->dtype.code = kDLInt;
@@ -524,6 +530,8 @@ extern "C" cuvsError_t cuvsCagraSerializeToHnswlib(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       _serialize_to_hnswlib<float>(res, filename, index);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _serialize_to_hnswlib<half>(res, filename, index);
     } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
       _serialize_to_hnswlib<int8_t>(res, filename, index);
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index de9554587a..403655cc28 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -50,6 +50,11 @@ struct hnsw_dist_t<float> {
   using type = float;
 };
 
+template <>
+struct hnsw_dist_t<half> {
+  using type = float;
+};
+
 template <>
 struct hnsw_dist_t<uint8_t> {
   using type = int;
@@ -74,14 +79,12 @@ struct index_impl : index<T> {
   index_impl(int dim, cuvs::distance::DistanceType metric, HnswHierarchy hierarchy)
     : index<T>{dim, metric, hierarchy}
   {
-    if constexpr (std::is_same_v<T, float>) {
-      if (metric == cuvs::distance::DistanceType::L2Expanded) {
-        space_ = std::make_unique<hnswlib::L2Space>(dim);
-      } else if (metric == cuvs::distance::DistanceType::InnerProduct) {
-        space_ = std::make_unique<hnswlib::InnerProductSpace>(dim);
-      }
-    } else if constexpr (std::is_same_v<T, std::int8_t> or std::is_same_v<T, std::uint8_t>) {
-      if (metric == cuvs::distance::DistanceType::L2Expanded) {
+    if (metric == cuvs::distance::DistanceType::InnerProduct) {
+      space_ = std::make_unique<hnswlib::InnerProductSpace<T, typename hnsw_dist_t<T>::type>>(dim);
+    } else if (metric == cuvs::distance::DistanceType::L2Expanded) {
+      if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half>) {
+        space_ = std::make_unique<hnswlib::L2Space<T, typename hnsw_dist_t<T>::type>>(dim);
+      } else if constexpr (std::is_same_v<T, std::int8_t> or std::is_same_v<T, std::uint8_t>) {
         space_ = std::make_unique<hnswlib::L2SpaceI<T>>(dim);
       }
     }
@@ -441,7 +444,7 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
       auto pt_id = order[i];
       std::copy(appr_algo->getDataByInternalId(pt_id),
                 appr_algo->getDataByInternalId(pt_id) + dim,
-                &host_query_set(i - start_idx, 0));
+                reinterpret_cast<char*>(&host_query_set(i - start_idx, 0)));
     }
 
     // find neighbors of the query set
diff --git a/cpp/src/neighbors/hnsw.cpp b/cpp/src/neighbors/hnsw.cpp
index 2b6bff7b34..b12e3217e4 100644
--- a/cpp/src/neighbors/hnsw.cpp
+++ b/cpp/src/neighbors/hnsw.cpp
@@ -32,6 +32,7 @@ namespace cuvs::neighbors::hnsw {
   }
 
 CUVS_INST_HNSW_FROM_CAGRA(float);
+CUVS_INST_HNSW_FROM_CAGRA(half);
 CUVS_INST_HNSW_FROM_CAGRA(uint8_t);
 CUVS_INST_HNSW_FROM_CAGRA(int8_t);
 
@@ -47,6 +48,7 @@ CUVS_INST_HNSW_FROM_CAGRA(int8_t);
   }
 
 CUVS_INST_HNSW_EXTEND(float);
+CUVS_INST_HNSW_EXTEND(half);
 CUVS_INST_HNSW_EXTEND(uint8_t);
 CUVS_INST_HNSW_EXTEND(int8_t);
 
@@ -64,6 +66,7 @@ CUVS_INST_HNSW_EXTEND(int8_t);
   }
 
 CUVS_INST_HNSW_SEARCH(float);
+CUVS_INST_HNSW_SEARCH(half);
 CUVS_INST_HNSW_SEARCH(uint8_t);
 CUVS_INST_HNSW_SEARCH(int8_t);
 
@@ -85,6 +88,7 @@ CUVS_INST_HNSW_SEARCH(int8_t);
   }
 
 CUVS_INST_HNSW_SERIALIZE(float);
+CUVS_INST_HNSW_SERIALIZE(half);
 CUVS_INST_HNSW_SERIALIZE(uint8_t);
 CUVS_INST_HNSW_SERIALIZE(int8_t);
 
diff --git a/cpp/src/neighbors/hnsw_c.cpp b/cpp/src/neighbors/hnsw_c.cpp
index b643187bd8..3241676770 100644
--- a/cpp/src/neighbors/hnsw_c.cpp
+++ b/cpp/src/neighbors/hnsw_c.cpp
@@ -148,9 +148,12 @@ extern "C" cuvsError_t cuvsHnswIndexDestroy(cuvsHnswIndex_t index_c_ptr)
   return cuvs::core::translate_exceptions([=] {
     auto index = *index_c_ptr;
 
-    if (index.dtype.code == kDLFloat) {
+    if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
       auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<float>*>(index.addr);
       delete index_ptr;
+    } else if (index.dtype.code == kDLFloat && index.dtype.bits == 16) {
+      auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<half>*>(index.addr);
+      delete index_ptr;
     } else if (index.dtype.code == kDLInt) {
       auto index_ptr = reinterpret_cast<cuvs::neighbors::hnsw::index<int8_t>*>(index.addr);
       delete index_ptr;
@@ -181,8 +184,10 @@ extern "C" cuvsError_t cuvsHnswFromCagra(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     auto index        = *cagra_index;
     hnsw_index->dtype = index.dtype;
-    if (index.dtype.code == kDLFloat) {
+    if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
       _from_cagra<float>(res, params, cagra_index, hnsw_index, std::nullopt);
+    } else if (index.dtype.code == kDLFloat && index.dtype.bits == 16) {
+      _from_cagra<half>(res, params, cagra_index, hnsw_index, std::nullopt);
     } else if (index.dtype.code == kDLUInt) {
       _from_cagra<uint8_t>(res, params, cagra_index, hnsw_index, std::nullopt);
     } else if (index.dtype.code == kDLInt) {
@@ -202,8 +207,10 @@ extern "C" cuvsError_t cuvsHnswFromCagraWithDataset(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     auto index        = *cagra_index;
     hnsw_index->dtype = index.dtype;
-    if (index.dtype.code == kDLFloat) {
+    if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
       _from_cagra<float>(res, params, cagra_index, hnsw_index, dataset_tensor);
+    } else if (index.dtype.code == kDLFloat && index.dtype.bits == 16) {
+      _from_cagra<half>(res, params, cagra_index, hnsw_index, dataset_tensor);
     } else if (index.dtype.code == kDLUInt) {
       _from_cagra<uint8_t>(res, params, cagra_index, hnsw_index, dataset_tensor);
     } else if (index.dtype.code == kDLInt) {
@@ -220,8 +227,10 @@ extern "C" cuvsError_t cuvsHnswExtend(cuvsResources_t res,
                                       cuvsHnswIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
-    if (index->dtype.code == kDLFloat) {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       _extend<float>(res, params, additional_dataset, *index);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _extend<half>(res, params, additional_dataset, *index);
     } else if (index->dtype.code == kDLUInt) {
       _extend<uint8_t>(res, params, additional_dataset, *index);
     } else if (index->dtype.code == kDLInt) {
@@ -270,8 +279,10 @@ extern "C" cuvsError_t cuvsHnswSearch(cuvsResources_t res,
     auto index = *index_c_ptr;
     RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
 
-    if (index.dtype.code == kDLFloat) {
+    if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
       _search<float>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (index.dtype.code == kDLFloat && index.dtype.bits == 16) {
+      _search<half>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
     } else if (index.dtype.code == kDLUInt) {
       _search<uint8_t>(res, *params, index, queries_tensor, neighbors_tensor, distances_tensor);
     } else if (index.dtype.code == kDLInt) {
@@ -287,8 +298,10 @@ extern "C" cuvsError_t cuvsHnswSerialize(cuvsResources_t res,
                                          cuvsHnswIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
-    if (index->dtype.code == kDLFloat) {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       _serialize<float>(res, filename, *index);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _serialize<half>(res, filename, *index);
     } else if (index->dtype.code == kDLInt) {
       _serialize<int8_t>(res, filename, *index);
     } else if (index->dtype.code == kDLUInt) {
@@ -310,6 +323,9 @@ extern "C" cuvsError_t cuvsHnswDeserialize(cuvsResources_t res,
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
       index->addr =
         reinterpret_cast<uintptr_t>(_deserialize<float>(res, params, filename, dim, metric));
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_deserialize<half>(res, params, filename, dim, metric));
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
       index->addr =
         reinterpret_cast<uintptr_t>(_deserialize<uint8_t>(res, params, filename, dim, metric));
diff --git a/cpp/tests/neighbors/hnsw.cu b/cpp/tests/neighbors/hnsw.cu
index 20ee83a114..eebae8f19b 100644
--- a/cpp/tests/neighbors/hnsw.cu
+++ b/cpp/tests/neighbors/hnsw.cu
@@ -48,7 +48,9 @@ struct AnnHNSWInputs {
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnHNSWInputs& p)
 {
   os << "dataset shape=" << p.n_rows << "x" << p.dim << ", graph_degree=" << p.graph_degree
-     << ", metric=" << static_cast<int>(p.metric) << ", ef=" << (p.ef) << std::endl;
+     << ", metric="
+     << cuvs::neighbors::print_metric{static_cast<cuvs::distance::DistanceType>((int)p.metric)}
+     << ", ef=" << (p.ef) << std::endl;
   return os;
 }
 
@@ -67,9 +69,9 @@ class AnnHNSWTest : public ::testing::TestWithParam<AnnHNSWInputs> {
   void testHNSW()
   {
     std::vector<IdxT> indices_HNSW(ps.n_queries * ps.k);
-    std::vector<DataT> distances_HNSW(ps.n_queries * ps.k);
+    std::vector<DistanceT> distances_HNSW(ps.n_queries * ps.k);
     std::vector<IdxT> indices_naive(ps.n_queries * ps.k);
-    std::vector<DataT> distances_naive(ps.n_queries * ps.k);
+    std::vector<DistanceT> distances_naive(ps.n_queries * ps.k);
 
     std::vector<DataT> queries_h(ps.n_queries * ps.dim);
     raft::update_host(queries_h.data(), queries.data(), ps.n_queries * ps.dim, stream_);
@@ -140,16 +142,16 @@ class AnnHNSWTest : public ::testing::TestWithParam<AnnHNSWInputs> {
     database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
     queries.resize(((size_t)ps.n_queries) * ps.dim, stream_);
     raft::random::RngState r(1234ULL);
-    if constexpr (std::is_same<DataT, float>{}) {
-      raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
-      raft::random::normal(
+    if constexpr (std::is_same_v<DataT, float> || std::is_same_v<DataT, half>) {
+      raft::random::uniform(
+        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
+      raft::random::uniform(
         handle_, r, queries.data(), ps.n_queries * ps.dim, DataT(0.1), DataT(2.0));
-
     } else {
       raft::random::uniformInt(
-        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(20));
+        handle_, r, database.data(), ps.n_rows * ps.dim, DataT(1), DataT(100));
       raft::random::uniformInt(
-        handle_, r, queries.data(), ps.n_queries * ps.dim, DataT(1), DataT(20));
+        handle_, r, queries.data(), ps.n_queries * ps.dim, DataT(1), DataT(100));
     }
     raft::resource::sync_stream(handle_);
   }
@@ -170,14 +172,14 @@ class AnnHNSWTest : public ::testing::TestWithParam<AnnHNSWInputs> {
 };
 
 const std::vector<AnnHNSWInputs> inputs = raft::util::itertools::product<AnnHNSWInputs>(
-  {1000, 2000},                                // n_rows
-  {5, 10, 25, 50, 100, 250, 500, 1000},        // dim
-  {32, 64},                                    // graph_degree
-  {cuvs::distance::DistanceType::L2Expanded},  // metric
-  {50},                                        // k
-  {500},                                       // n_queries
-  {200},                                       // ef
-  {0.98}                                       // min_recall
+  {1000, 2000},                          // n_rows
+  {5, 10, 25, 50, 100, 250, 500, 1000},  // dim
+  {32, 64},                              // graph_degree
+  {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},  // metric
+  {50},                                                                                    // k
+  {500},  // n_queries
+  {250},  // ef
+  {0.98}  // min_recall
 );
 
 typedef AnnHNSWTest<float, float, uint64_t> AnnHNSW_F;
@@ -185,4 +187,19 @@ TEST_P(AnnHNSW_F, AnnHNSW) { this->testHNSW(); }
 
 INSTANTIATE_TEST_CASE_P(AnnHNSWTest, AnnHNSW_F, ::testing::ValuesIn(inputs));
 
+typedef AnnHNSWTest<float, half, uint64_t> AnnHNSW_H;
+TEST_P(AnnHNSW_H, AnnHNSW) { this->testHNSW(); }
+
+INSTANTIATE_TEST_CASE_P(AnnHNSWTest, AnnHNSW_H, ::testing::ValuesIn(inputs));
+
+typedef AnnHNSWTest<float, int8_t, uint64_t> AnnHNSW_I8;
+TEST_P(AnnHNSW_I8, AnnHNSW) { this->testHNSW(); }
+
+INSTANTIATE_TEST_CASE_P(AnnHNSWTest, AnnHNSW_I8, ::testing::ValuesIn(inputs));
+
+typedef AnnHNSWTest<float, uint8_t, uint64_t> AnnHNSW_U8;
+TEST_P(AnnHNSW_U8, AnnHNSW) { this->testHNSW(); }
+
+INSTANTIATE_TEST_CASE_P(AnnHNSWTest, AnnHNSW_U8, ::testing::ValuesIn(inputs));
+
 }  // namespace cuvs::neighbors::hnsw
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
index 789a3d93bf..35bf2c8d95 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
@@ -265,6 +265,10 @@ def load(IndexParams index_params, filename, dim, dtype, metric="sqeuclidean",
         dl_dtype.code = cydlpack.kDLFloat
         dl_dtype.bits = 32
         dl_dtype.lanes = 1
+    elif dtype == np.float16:
+        dl_dtype.code = cydlpack.kDLFloat
+        dl_dtype.bits = 16
+        dl_dtype.lanes = 1
     elif dtype == np.ubyte:
         dl_dtype.code = cydlpack.kDLUInt
         dl_dtype.bits = 8
@@ -274,7 +278,8 @@ def load(IndexParams index_params, filename, dim, dtype, metric="sqeuclidean",
         dl_dtype.bits = 8
         dl_dtype.lanes = 1
     else:
-        raise ValueError("Only float32 is supported for dtype")
+        raise ValueError("Only float32, float16, uint8, and int8 are supported"
+                         " for dtype")
 
     idx.index.dtype = dl_dtype
     cdef cuvsDistanceType distance_type = DISTANCE_TYPES[metric]
@@ -361,7 +366,7 @@ def extend(ExtendParams extend_params, Index index, data, resources=None):
     index : Index
         Trained HNSW index.
     data : Host array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float32, int8, uint8]
+        Supported dtype [float32, float16, int8, uint8]
     {resources_docstring}
 
     Examples
@@ -384,6 +389,7 @@ def extend(ExtendParams extend_params, Index index, data, resources=None):
 
     data_ai = wrap_array(data)
     _check_input_array(data_ai, [np.dtype('float32'),
+                                 np.dtype('float16'),
                                  np.dtype('uint8'),
                                  np.dtype('int8')])
 
@@ -500,6 +506,7 @@ def search(SearchParams search_params,
     # in RAFT to make this a single call
     queries_ai = wrap_array(queries)
     _check_input_array(queries_ai, [np.dtype('float32'),
+                                    np.dtype('float16'),
                                     np.dtype('uint8'),
                                     np.dtype('int8')])
 
diff --git a/python/cuvs/cuvs/tests/test_hnsw.py b/python/cuvs/cuvs/tests/test_hnsw.py
index c24cdf8a15..31b78f6cfd 100644
--- a/python/cuvs/cuvs/tests/test_hnsw.py
+++ b/python/cuvs/cuvs/tests/test_hnsw.py
@@ -34,15 +34,18 @@ def run_hnsw_build_search_test(
     graph_degree=64,
     hierarchy="none",
     search_params={},
+    expected_recall=0.9,
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
+    queries = generate_data((n_queries, n_cols), dtype)
     if metric == "inner_product":
         dataset = normalize(dataset, norm="l2", axis=1)
+        queries = normalize(queries, norm="l2", axis=1)
         if dtype in [np.int8, np.uint8]:
-            pytest.skip(
-                "inner_product metric is not supported for int8/uint8 data"
-            )
-
+            # Quantize the normalized data to the int8/uint8 range
+            dtype_max = np.iinfo(dtype).max
+            dataset = (dataset * dtype_max).astype(dtype)
+            queries = (queries * dtype_max).astype(dtype)
     build_params = cagra.IndexParams(
         metric=metric,
         intermediate_graph_degree=intermediate_graph_degree,
@@ -57,8 +60,6 @@ def run_hnsw_build_search_test(
     hnsw_params = hnsw.IndexParams(hierarchy=hierarchy)
     hnsw_index = hnsw.from_cagra(hnsw_params, index)
 
-    queries = generate_data((n_queries, n_cols), dtype)
-
     search_params = hnsw.SearchParams(**search_params)
 
     out_dist, out_idx = hnsw.search(search_params, hnsw_index, queries, k)
@@ -76,19 +77,20 @@ def run_hnsw_build_search_test(
     skl_dist, skl_idx = nn_skl.kneighbors(queries, return_distance=True)
 
     recall = calc_recall(out_idx, skl_idx)
-    assert recall > 0.95
+    assert recall >= expected_recall
 
 
-@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
 @pytest.mark.parametrize("k", [10, 20])
-@pytest.mark.parametrize("ef", [30, 40])
+@pytest.mark.parametrize("ef", [50, 150])
 @pytest.mark.parametrize("num_threads", [2, 4])
 @pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
 @pytest.mark.parametrize("hierarchy", ["none", "cpu", "gpu"])
 def test_hnsw(dtype, k, ef, num_threads, metric, build_algo, hierarchy):
-    # Note that inner_product tests use normalized input which we cannot
-    # represent in int8, therefore we test only sqeuclidean metric here.
+    expected_recall = (
+        0.9 if metric == "inner_product" and dtype == np.uint8 else 0.95
+    )
     run_hnsw_build_search_test(
         dtype=dtype,
         k=k,
@@ -96,6 +98,7 @@ def test_hnsw(dtype, k, ef, num_threads, metric, build_algo, hierarchy):
         build_algo=build_algo,
         hierarchy=hierarchy,
         search_params={"ef": ef, "num_threads": num_threads},
+        expected_recall=expected_recall,
     )
 
 
@@ -115,13 +118,17 @@ def run_hnsw_extend_test(
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     add_dataset = generate_data((add_rows, n_cols), dtype)
+    queries = generate_data((n_queries, n_cols), dtype)
     if metric == "inner_product":
         dataset = normalize(dataset, norm="l2", axis=1)
         add_dataset = normalize(add_dataset, norm="l2", axis=1)
+        queries = normalize(queries, norm="l2", axis=1)
         if dtype in [np.int8, np.uint8]:
-            pytest.skip(
-                "inner_product metric is not supported for int8/uint8 data"
-            )
+            # Quantize the normalized data to the int8/uint8 range
+            dtype_max = np.iinfo(dtype).max
+            dataset = (dataset * dtype_max).astype(dtype)
+            add_dataset = (add_dataset * dtype_max).astype(dtype)
+            queries = (queries * dtype_max).astype(dtype)
         if build_algo == "nn_descent":
             pytest.skip("inner_product metric is not supported for nn_descent")
 
@@ -140,8 +147,6 @@ def run_hnsw_extend_test(
     hnsw_index = hnsw.from_cagra(hnsw_params, index)
     hnsw.extend(hnsw.ExtendParams(), hnsw_index, add_dataset)
 
-    queries = generate_data((n_queries, n_cols), dtype)
-
     search_params = hnsw.SearchParams(**search_params)
 
     out_dist, out_idx = hnsw.search(search_params, hnsw_index, queries, k)
@@ -162,7 +167,7 @@ def run_hnsw_extend_test(
     assert recall > 0.95
 
 
-@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
 @pytest.mark.parametrize("k", [10, 20])
 @pytest.mark.parametrize("ef", [30, 40])
 @pytest.mark.parametrize("num_threads", [2, 4])

From a732451cfd8b0de284a7ff58cb6e179eebb41ca4 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 7 May 2025 10:42:09 -0500
Subject: [PATCH 386/623] Exclude librmm.so from auditwheel (#878)

librmm will ship a shared library component in 25.06 (xref: https://github.com/rapidsai/rmm/issues/1779). This PR updates `auditwheel` calls to exclude `librmm.so`.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/878
---
 ci/build_wheel.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 250be5d9cc..2ef5a7b77f 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -26,6 +26,7 @@ EXCLUDE_ARGS=(
   --exclude "libcusparse.so.*"
   --exclude "libnvJitLink.so.*"
   --exclude "librapids_logger.so"
+  --exclude "librmm.so"
 )
 
 if [[ "${package_dir}" != "python/libcuvs" ]]; then

From d8be9842a34c7a12329e9f56146bd458ecdb3e22 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 7 May 2025 18:16:15 +0200
Subject: [PATCH 387/623] ANN_BENCH: Expose parallel_mode parameter of FAISS
 CPU IVF implementation (#861)

parallel_mode determines how queries are parallelized with OpenMP in FAISS CPU IVF implementation:

-  0 Split over queries.
-  1 Parallelize over inverted lists.
-  2 Parallelize over both queries and inverted lists.
-  3 Split over queries with finer granularity.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/861
---
 cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp |  1 +
 cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h     | 16 +++++++++++++++-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
index 354a9b2910..e03abd7255 100644
--- a/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
@@ -75,6 +75,7 @@ void parse_search_param(const nlohmann::json& conf,
   param.nprobe = conf.at("nprobe");
   if (conf.contains("refine_ratio")) { param.refine_ratio = conf.at("refine_ratio"); }
   if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
+  if (conf.contains("parallel_mode")) { param.parallel_mode = conf.at("parallel_mode"); }
 }
 
 template <typename T, template <typename> class Algo>
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
index 4cb630ed23..bda579be31 100644
--- a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
@@ -57,6 +57,16 @@ class faiss_cpu : public algo<T> {
     int nprobe;
     float refine_ratio = 1.0;
     int num_threads    = omp_get_num_procs();
+    /**
+     * parallel_mode determines how queries are parallelized with OpenMP.
+     *
+     * Options:
+     * 0: Split over queries.
+     * 1: Parallelize over inverted lists.
+     * 2: Parallelize over both queries and inverted lists.
+     * 3: Split over queries with finer granularity.
+     */
+    int parallel_mode = 0;
   };
 
   struct build_param {
@@ -159,7 +169,11 @@ void faiss_cpu<T>::set_search_param(const search_param_base& param, const void*
   auto sp    = dynamic_cast<const search_param&>(param);
   int nprobe = sp.nprobe;
   assert(nprobe <= nlist_);
-  dynamic_cast<faiss::IndexIVF*>(index_.get())->nprobe = nprobe;
+  auto index_ivf = dynamic_cast<faiss::IndexIVF*>(index_.get());
+  if (index_ivf != nullptr) {
+    index_ivf->nprobe        = nprobe;
+    index_ivf->parallel_mode = sp.parallel_mode;
+  }
 
   if (sp.refine_ratio > 1.0) { this->index_refine_.get()->k_factor = sp.refine_ratio; }
 

From 05494cbd2313edc41025c632e66b28d7d66e208d Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 7 May 2025 13:12:28 -0700
Subject: [PATCH 388/623] [FIX] Revert negated InnerProduct distance for NN
 Descent (#859)

Closing https://github.com/rapidsai/cuvs/issues/849

Many parts of NN Descent is written on top of the assumption that smaller distance is closer.
Therefore, using the current trick (using -(dot product) values) to make it consistent with other metrics to be sorted in ascending order, then negating the value at the end should be the neatest solution.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/859
---
 cpp/src/neighbors/detail/nn_descent.cuh | 7 +++++++
 cpp/tests/neighbors/ann_nn_descent.cuh  | 7 -------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 4205674476..9beea05646 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -1196,9 +1196,16 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
     raft::matrix::slice<DistData_t, int64_t, raft::row_major>(
       res, raft::make_const_mdspan(graph_d_dists.view()), output_dist_view, coords);
 
+    // distance post-processing
     if (build_config_.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
       raft::linalg::map(
         res, output_dist_view, raft::sqrt_op{}, raft::make_const_mdspan(output_dist_view));
+    } else if (!cuvs::distance::is_min_close(build_config_.metric)) {
+      // revert negated innerproduct
+      raft::linalg::map(res,
+                        output_dist_view,
+                        raft::mul_const_op<DistData_t>(-1),
+                        raft::make_const_mdspan(output_dist_view));
     }
     raft::resource::sync_stream(res);
   }
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index f06e362b94..3f5c0397f3 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -152,13 +152,6 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
         raft::resource::sync_stream(handle_);
       }
 
-      if (ps.metric == cuvs::distance::DistanceType::InnerProduct) {
-        std::transform(
-          distances_naive.begin(), distances_naive.end(), distances_naive.begin(), [](auto x) {
-            return -x;
-          });
-      }
-
       double min_recall = ps.min_recall;
       EXPECT_TRUE(eval_neighbours(indices_naive,
                                   indices_NNDescent,

From 6625e33a23a252fe77d55aea1e78dc15090f15a6 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 8 May 2025 06:50:26 -0700
Subject: [PATCH 389/623] Accept host inputs in python for ivf-pq build and
 extend (#880)

Closes #858

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/880
---
 cpp/src/neighbors/ivf_pq_c.cpp               | 41 ++++++++++++++------
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx |  7 +++-
 python/cuvs/cuvs/tests/test_ivf_pq.py        | 31 ++++++++++-----
 3 files changed, 56 insertions(+), 23 deletions(-)
 mode change 100755 => 100644 cpp/src/neighbors/ivf_pq_c.cpp

diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
old mode 100755
new mode 100644
index 6548155ef1..33a927cf6f
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -55,10 +55,15 @@ void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor*
 
   auto index = new cuvs::neighbors::ivf_pq::index<IdxT>(*res_ptr, build_params, dim);
 
-  using mdspan_type = raft::device_matrix_view<const T, IdxT, raft::row_major>;
-  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
-
-  cuvs::neighbors::ivf_pq::build(*res_ptr, build_params, mds, index);
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type = raft::device_matrix_view<const T, IdxT, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    cuvs::neighbors::ivf_pq::build(*res_ptr, build_params, mds, index);
+  } else {
+    using mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    cuvs::neighbors::ivf_pq::build(*res_ptr, build_params, mds, index);
+  }
 
   return index;
 }
@@ -116,15 +121,27 @@ void _extend(cuvsResources_t res,
              DLManagedTensor* new_indices,
              cuvsIvfPqIndex index)
 {
-  auto res_ptr              = reinterpret_cast<raft::resources*>(res);
-  auto index_ptr            = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
-  using vectors_mdspan_type = raft::device_matrix_view<const T, IdxT, raft::row_major>;
-  using indices_mdspan_type = raft::device_vector_view<IdxT, IdxT>;
-
-  auto vectors_mds = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors);
-  auto indices_mds = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices);
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
 
-  cuvs::neighbors::ivf_pq::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
+  bool on_device = cuvs::core::is_dlpack_device_compatible(new_vectors->dl_tensor);
+  if (on_device != cuvs::core::is_dlpack_device_compatible(new_indices->dl_tensor)) {
+    RAFT_FAIL("extend inputs must both either be on device memory or host memory");
+  }
+
+  if (on_device) {
+    using vectors_mdspan_type = raft::device_matrix_view<const T, IdxT, raft::row_major>;
+    using indices_mdspan_type = raft::device_vector_view<IdxT, IdxT>;
+    auto vectors_mds          = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors);
+    auto indices_mds          = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices);
+    cuvs::neighbors::ivf_pq::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
+  } else {
+    using vectors_mdspan_type = raft::host_matrix_view<const T, IdxT, raft::row_major>;
+    using indices_mdspan_type = raft::host_vector_view<IdxT, IdxT>;
+    auto vectors_mds          = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors);
+    auto indices_mds          = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices);
+    cuvs::neighbors::ivf_pq::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
+  }
 }
 }  // namespace
 
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 36f56983a9..d815ea365d 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -244,12 +244,15 @@ def build(IndexParams index_params, dataset, resources=None):
     """
     Build the IvfPq index from the dataset for efficient search.
 
+    The input dataset array can be either CUDA array interface compliant matrix
+    or an array interface compliant matrix in host memory.
+
     Parameters
     ----------
     index_params : :py:class:`cuvs.neighbors.ivf_pq.IndexParams`
         Parameters on how to build the index
-    dataset : CUDA array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float, int8, uint8]
+    dataset : Array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32, float16, int8, uint8]
     {resources_docstring}
 
     Returns
diff --git a/python/cuvs/cuvs/tests/test_ivf_pq.py b/python/cuvs/cuvs/tests/test_ivf_pq.py
index 9e0c5572a2..70e775b194 100644
--- a/python/cuvs/cuvs/tests/test_ivf_pq.py
+++ b/python/cuvs/cuvs/tests/test_ivf_pq.py
@@ -43,6 +43,7 @@ def run_ivf_pq_build_search_test(
     kmeans_n_iters=20,
     compare=True,
     inplace=True,
+    array_type="device",
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     if metric == "inner_product":
@@ -61,19 +62,27 @@ def run_ivf_pq_build_search_test(
         add_data_on_build=add_data_on_build,
     )
 
-    index = ivf_pq.build(build_params, dataset_device)
+    if array_type == "device":
+        index = ivf_pq.build(build_params, dataset_device)
+    else:
+        index = ivf_pq.build(build_params, dataset)
+
     if not add_data_on_build:
         dataset_1 = dataset[: n_rows // 2, :]
         dataset_2 = dataset[n_rows // 2 :, :]
         indices_1 = np.arange(n_rows // 2, dtype=np.int64)
         indices_2 = np.arange(n_rows // 2, n_rows, dtype=np.int64)
 
-        dataset_1_device = device_ndarray(dataset_1)
-        dataset_2_device = device_ndarray(dataset_2)
-        indices_1_device = device_ndarray(indices_1)
-        indices_2_device = device_ndarray(indices_2)
-        index = ivf_pq.extend(index, dataset_1_device, indices_1_device)
-        index = ivf_pq.extend(index, dataset_2_device, indices_2_device)
+        if array_type == "device":
+            dataset_1_device = device_ndarray(dataset_1)
+            dataset_2_device = device_ndarray(dataset_2)
+            indices_1_device = device_ndarray(indices_1)
+            indices_2_device = device_ndarray(indices_2)
+            index = ivf_pq.extend(index, dataset_1_device, indices_1_device)
+            index = ivf_pq.extend(index, dataset_2_device, indices_2_device)
+        else:
+            index = ivf_pq.extend(index, dataset_1, indices_1)
+            index = ivf_pq.extend(index, dataset_2, indices_2)
 
     queries = generate_data((n_queries, n_cols), dtype)
     out_idx = np.zeros((n_queries, k), dtype=np.int64)
@@ -181,7 +190,8 @@ def test_ivf_pq_search_params(params):
 
 
 @pytest.mark.parametrize("dtype", [np.float32])
-def test_extend(dtype):
+@pytest.mark.parametrize("array_type", ["host", "device"])
+def test_extend(dtype, array_type):
     run_ivf_pq_build_search_test(
         n_rows=10000,
         n_cols=10,
@@ -191,13 +201,16 @@ def test_extend(dtype):
         metric="sqeuclidean",
         dtype=dtype,
         add_data_on_build=False,
+        array_type=array_type,
     )
 
 
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
-def test_ivf_pq_dtype(inplace, dtype):
+@pytest.mark.parametrize("array_type", ["host", "device"])
+def test_ivf_pq_dtype(inplace, dtype, array_type):
     run_ivf_pq_build_search_test(
         dtype=dtype,
         inplace=inplace,
+        array_type=array_type,
     )

From d14552a4e5130ad38bd1303df04eb0e8ec79f593 Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Thu, 8 May 2025 07:33:46 -0700
Subject: [PATCH 390/623] [CI] Enable Java test in CI workflow (#805)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR adds changes for Java CI.

Some scripts modified here also appear in [PR #831](https://github.com/rapidsai/cuvs/pull/831). Once 831 is merged, I’ll rebase and make sure everything stays consistent.

Authors:
  - rhdong (https://github.com/rhdong)
  - Vivek Narang (https://github.com/narangvivek10)
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - James Lamb (https://github.com/jameslamb)
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/805
---
 .github/workflows/build.yaml                  | 13 +++++
 .github/workflows/pr.yaml                     | 25 ++++++++++
 .github/workflows/test.yaml                   | 12 +++++
 build.sh                                      |  9 +++-
 ci/build_java.sh                              | 47 +++++++++++++++++++
 ci/release/update-version.sh                  |  4 ++
 ci/test_java.sh                               | 20 ++++++++
 dependencies.yaml                             | 18 +++++++
 java/build.sh                                 | 14 +++++-
 java/cuvs-java/pom.xml                        | 27 +----------
 .../com/nvidia/cuvs/HnswBuildAndSearchIT.java |  2 +-
 java/panama-bindings/generate-bindings.sh     | 25 ++++------
 12 files changed, 172 insertions(+), 44 deletions(-)
 create mode 100755 ci/build_java.sh
 create mode 100755 ci/test_java.sh

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 5a8a30c917..6620dc0d0b 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -64,6 +64,19 @@ jobs:
       node_type: "gpu-l4-latest-1"
       run_script: "ci/build_go.sh"
       sha: ${{ inputs.sha }}
+  java-build:
+    needs: cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      arch: "amd64"
+      date: ${{ inputs.date }}
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/build_java.sh"
+      file_to_upload: "java/cuvs-java/target/"
+      sha: ${{ inputs.sha }}
   python-build:
     needs: [cpp-build]
     secrets: inherit
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 895d2d8462..0af6489a67 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -20,6 +20,7 @@ jobs:
       - conda-cpp-checks
       - conda-python-build
       - conda-python-tests
+      - conda-java-tests
       - docs-build
       - rust-build
       - go-build
@@ -73,6 +74,18 @@ jobs:
           - '!rust/**'
           - '!go/**'
           - '!thirdparty/LICENSES/**'
+        test_java:
+          - '**'
+          - '!.devcontainer/**'
+          - '!.pre-commit-config.yaml'
+          - '!README.md'
+          - '!docs/**'
+          - '!img/**'
+          - '!notebooks/**'
+          - '!python/**'
+          - '!rust/**'
+          - '!go/**'
+          - '!thirdparty/LICENSES/**'
         test_notebooks:
           - '**'
           - '!.devcontainer/**'
@@ -134,6 +147,18 @@ jobs:
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
+  conda-java-tests:
+    needs: [conda-cpp-build, changed-files]
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_java
+    with:
+      build_type: pull-request
+      node_type: "gpu-l4-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/test_java.sh"
+      file_to_upload: "java/cuvs-java/target/"
   docs-build:
     needs: conda-python-build
     secrets: inherit
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 51875b88a7..ee22794806 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -43,6 +43,18 @@ jobs:
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
+  conda-java-tests:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    with:
+      build_type: ${{ inputs.build_type }}
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      node_type: "gpu-l4-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.13
diff --git a/build.sh b/build.sh
index 90ad3a13da..308e80e9da 100755
--- a/build.sh
+++ b/build.sh
@@ -18,7 +18,7 @@ ARGS=$*
 # scripts, and that this script resides in the repo dir!
 REPODIR=$(cd "$(dirname "$0")"; pwd)
 
-VALIDARGS="clean libcuvs python rust go java docs tests bench-ann examples --uninstall  -v -g -n --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h"
+VALIDARGS="clean libcuvs python rust go java docs tests bench-ann examples --uninstall  -v -g -n --allgpuarch --no-mg --no-cpu --cpu-only --no-shared-libs --no-nvtx --show_depr_warn --incl-cache-stats --time -h --run-java-tests"
 HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<tool>] [--limit-tests=<targets>] [--limit-bench-ann=<targets>] [--build-metrics=<filename>]
  where <target> is:
    clean            - remove all existing build artifacts and configuration (start over)
@@ -53,6 +53,7 @@ HELP="$0 [<target> ...] [<flag> ...] [--cmake-args=\"<args>\"] [--cache-tool=<to
    --no-nvtx                   - disable nvtx (profiling markers), but allow enabling it in downstream projects
    --no-shared-libs            - build without shared libraries
    --show_depr_warn            - show cmake deprecation warnings
+   --run-java-test             - run Java tests after building
    --build-metrics             - filename for generating build metrics report for libcuvs
    --incl-cache-stats          - include cache statistics in build metrics report
    --cmake-args=\\\"<args>\\\" - pass arbitrary list of CMake configuration options (escape all quotes in argument)
@@ -496,7 +497,11 @@ if (( ${NUMARGS} == 0 )) || hasArg java; then
         echo "Please add 'libcuvs' to this script's arguments (ex. './build.sh libcuvs java') if libcuvs libraries are not already built"
     fi
     cd ${REPODIR}/java
-    ./build.sh
+    if hasArg --run-java-tests; then
+        ./build.sh --run-java-tests
+    else
+        ./build.sh
+    fi
 fi
 
 RAPIDS_VERSION="$(sed -E -e 's/^([0-9]{2})\.([0-9]{2})\.([0-9]{2}).*$/\1.\2.\3/' "${REPODIR}/VERSION")"
diff --git a/ci/build_java.sh b/ci/build_java.sh
new file mode 100755
index 0000000000..4831215e94
--- /dev/null
+++ b/ci/build_java.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+# TODO: Remove this argument-handling when build and test workflows are separated,
+#       and test_java.sh no longer calls build_java.sh
+#       ref: https://github.com/rapidsai/cuvs/issues/868
+EXTRA_BUILD_ARGS=()
+if [[ "${1:-}" == "--run-java-tests" ]]; then
+  EXTRA_BUILD_ARGS+=("--run-java-tests")
+fi
+
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+
+rapids-logger "Generate Java testing dependencies"
+
+ENV_YAML_DIR="$(mktemp -d)"
+
+rapids-dependency-file-generator \
+  --output conda \
+  --file-key java \
+  --prepend-channel "${CPP_CHANNEL}" \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch)" | tee "${ENV_YAML_DIR}/env.yaml"
+
+rapids-mamba-retry env create --yes -f "${ENV_YAML_DIR}/env.yaml" -n java
+
+# Temporarily allow unbound variables for conda activation.
+set +u
+conda activate java
+set -u
+
+rapids-print-env
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+
+rapids-logger "Run Java build"
+
+bash ./build.sh java "${EXTRA_BUILD_ARGS[@]}"
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index c928d7e6c1..3cfaf2fabf 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -106,3 +106,7 @@ sed_runner "s/VERSION=\".*\"/VERSION=\"${NEXT_FULL_JAVA_TAG}\"/g" java/build.sh
 for FILE in java/*/pom.xml; do
   sed_runner "/<!--CUVS_JAVA#VERSION_UPDATE_MARKER_START-->.*<!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->/s//<!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>${NEXT_FULL_JAVA_TAG}<\/version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->/g" "${FILE}"
 done
+
+sed_runner "s| CuVS [[:digit:]]\{2\}\.[[:digit:]]\{2\} | CuVS ${NEXT_SHORT_TAG} |g" java/README.md
+sed_runner "s|-[[:digit:]]\{2\}\.[[:digit:]]\{2\}\.[[:digit:]]\{1,2\}\.jar|-${NEXT_FULL_JAVA_TAG}\.jar|g" java/examples/README.md
+sed_runner "s|/[[:digit:]]\{2\}\.[[:digit:]]\{2\}\.[[:digit:]]\{1,2\}/|/${NEXT_FULL_JAVA_TAG}/|g" java/examples/README.md
diff --git a/ci/test_java.sh b/ci/test_java.sh
new file mode 100755
index 0000000000..7c7d77860a
--- /dev/null
+++ b/ci/test_java.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+
+rapids-logger "Check GPU usage"
+nvidia-smi
+
+rapids-logger "Run Java build and tests"
+
+# TODO: switch to installing pre-built artifacts instead of rebuilding in test jobs
+#       ref: https://github.com/rapidsai/cuvs/issues/868
+ci/build_java.sh --run-java-tests
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/dependencies.yaml b/dependencies.yaml
index bc4bef9907..20b50c32e2 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -111,6 +111,16 @@ files:
       - depends_on_libcuvs
       - depends_on_libraft
       - depends_on_nccl
+  java:
+    output: none
+    includes:
+      - cuda
+      - cuda_version
+      - depends_on_libcuvs
+      - depends_on_libraft
+      - depends_on_nccl
+      - java
+      - rapids_build
   py_build_libcuvs:
     output: pyproject
     pyproject_dir: python/libcuvs
@@ -567,6 +577,14 @@ dependencies:
       - output_types: [conda, requirements, pyproject]
         packages:
           - scikit-learn
+  java:
+    common:
+      - output_types: conda
+        packages:
+          - *cmake_ver
+          - libboost-devel
+          - maven
+          - openjdk=22.*
   bench:
     common:
       - output_types: [conda, pyproject, requirements]
diff --git a/java/build.sh b/java/build.sh
index 48dac82dff..cb35c12db1 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -2,6 +2,9 @@
 
 set -e -u -o pipefail
 
+ARGS="$*"
+NUMARGS=$#
+
 VERSION="25.06.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
 SO_FILE_PATH="./internal/build"
@@ -17,8 +20,17 @@ cmake --build ./internal/build
 # Generate Panama FFM API bindings and update (if any of them changed)
 ./panama-bindings/generate-bindings.sh
 
+function hasArg {
+    (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+}
+
+MAVEN_VERIFY_ARGS=()
+if ! hasArg --run-java-tests; then
+  MAVEN_VERIFY_ARGS=("-DskipTests")
+fi
+
 # Build the java layer
 mvn install:install-file -DgroupId=$GROUP_ID -DartifactId=cuvs-java-internal -Dversion=$VERSION -Dpackaging=so -Dfile=$SO_FILE_PATH/libcuvs_java.so \
   && cd cuvs-java \
-  && mvn verify \
+  && mvn verify "${MAVEN_VERIFY_ARGS[@]}" \
   && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 7e492167c3..3f684d11ef 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -62,7 +62,6 @@
         <maven.compiler.source>22</maven.compiler.source>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
-        <gpg.passphrase>${GPG_PASSPHRASE}</gpg.passphrase>
     </properties>
 
     <distributionManagement>
@@ -224,15 +223,6 @@
                     </archive>
                 </configuration>
             </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-javadoc-plugin</artifactId>
-                <version>3.6.2</version>
-                <configuration>
-                    <excludePackageNames>com.nvidia.cuvs.examples,com.nvidia.cuvs.panama</excludePackageNames>
-                    <reportOutputDirectory>${project.build.directory}</reportOutputDirectory>
-                </configuration>
-            </plugin>
         <plugin>
             <groupId>org.apache.maven.plugins</groupId>
             <artifactId>maven-source-plugin</artifactId>
@@ -266,25 +256,12 @@
                         <goal>jar</goal>
                     </goals>
                     <configuration>
-                        <additionalparam>-Xdoclint:none</additionalparam>
+                        <excludePackageNames>com.nvidia.cuvs.examples,com.nvidia.cuvs.panama</excludePackageNames>
+                        <doclint>all,-missing</doclint>
                     </configuration>
                 </execution>
             </executions>
         </plugin>
-        <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-gpg-plugin</artifactId>
-            <version>1.5</version>
-            <executions>
-                <execution>
-                    <id>sign-artifacts</id>
-                    <phase>verify</phase>
-                    <goals>
-                        <goal>sign</goal>
-                    </goals>
-                </execution>
-            </executions>
-        </plugin>
         <plugin>
             <groupId>org.sonatype.plugins</groupId>
             <artifactId>nexus-staging-maven-plugin</artifactId>
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index eb4933cf91..e2fad43c84 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -83,7 +83,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
 
         // Configure index parameters
         CagraIndexParams indexParams = new CagraIndexParams.Builder()
-            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
+            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
             .withGraphDegree(64)
             .withIntermediateGraphDegree(128)
             .withNumWriterThreads(32)
diff --git a/java/panama-bindings/generate-bindings.sh b/java/panama-bindings/generate-bindings.sh
index 5ef016383f..f37097e656 100755
--- a/java/panama-bindings/generate-bindings.sh
+++ b/java/panama-bindings/generate-bindings.sh
@@ -5,21 +5,16 @@ set -e -u -o pipefail
 echo "Starting Panama FFM API bindings generation ..."
 REPODIR=$(cd "$(dirname $0)"; cd ../../ ; pwd)
 CURDIR=$(cd "$(dirname $0)"; pwd)
-CUDA_HOME=$(which nvcc | cut -d/ -f-4)
 TARGET_PACKAGE="com.nvidia.cuvs.internal.panama"
 
-# Try to verify that a include directory exists inside CUDA_HOME
-if [ ! -d "$CUDA_HOME/include" ];
-then
-  echo "$CUDA_HOME/include does not exist."
-  if [ -d "/usr/local/cuda/include" ];
-  then
-    echo "Setting CUDA_HOME to /usr/local/cuda"
-    CUDA_HOME=/usr/local/cuda
-  else
-    echo "Couldn't find a suitable CUDA include directory."
-    exit 1
-  fi
+TARGET_DIR="targets/x86_64-linux/include"
+if [ -n "${CONDA_PREFIX:-}" ] && [ -d "${CONDA_PREFIX}/${TARGET_DIR}" ]; then
+  CUDA_INCLUDE_DIR="${CONDA_PREFIX}/${TARGET_DIR}"
+elif [ -d "/usr/local/cuda/${TARGET_DIR}" ]; then
+  CUDA_INCLUDE_DIR="/usr/local/cuda/${TARGET_DIR}"
+else
+  echo "Couldn't find a suitable CUDA include directory."
+  exit 1
 fi
 
 if [[ `command -v jextract` == "" ]];
@@ -29,7 +24,7 @@ then
   echo "jextract doesn't exist. Downloading it from $JEXTRACT_DOWNLOAD_URL.";
   wget -c $JEXTRACT_DOWNLOAD_URL
   tar -xvf ./"${JEXTRACT_FILENAME}"
-  PATH="$(pwd)/jextract-22/bin/jextract:${PATH}"
+  PATH="$(pwd)/jextract-22/bin/:${PATH}"
   export PATH
   echo "jextract downloaded to $(pwd)/jextract-22"
 fi
@@ -37,7 +32,7 @@ fi
 # Use Jextract utility to generate panama bindings
 jextract \
  --include-dir ${REPODIR}/java/internal/build/_deps/dlpack-src/include/ \
- --include-dir ${CUDA_HOME}/targets/x86_64-linux/include \
+ --include-dir ${CUDA_INCLUDE_DIR} \
  --include-dir ${REPODIR}/cpp/include \
  --output "${REPODIR}/java/cuvs-java/src/main/java22/" \
  --target-package ${TARGET_PACKAGE} \

From 19a17599d60bd1251f43c0e48565811c6a498c51 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Thu, 8 May 2025 18:45:36 +0200
Subject: [PATCH 391/623] Fix IVF PQ build metric for CAGRA (#862)

Related to #841.
The IVF-PQ build metric is not properly initialized in the case of InnerProduct, so I am adding here correct initialization on the C layer, as well as some checks on the C++ side so that the CAGRA metric match the knn metric.

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Tarang Jain (https://github.com/tarang-jain)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/862
---
 cpp/src/neighbors/cagra_c.cpp                 | 10 ++++----
 .../neighbors/detail/cagra/cagra_build.cuh    | 24 +++++++++++++++++++
 cpp/src/neighbors/refine/refine_device.cuh    |  6 ++---
 cpp/tests/neighbors/ann_cagra.cuh             | 12 +++++-----
 4 files changed, 38 insertions(+), 14 deletions(-)

diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 1356737000..a9fdd01d1f 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -42,7 +42,7 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
 
   auto index_params   = cuvs::neighbors::cagra::index_params();
-  index_params.metric = static_cast<cuvs::distance::DistanceType>((int)params.metric),
+  index_params.metric = static_cast<cuvs::distance::DistanceType>((int)params.metric);
   index_params.intermediate_graph_degree = params.intermediate_graph_degree;
   index_params.graph_degree              = params.graph_degree;
 
@@ -50,7 +50,8 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
     case cuvsCagraGraphBuildAlgo::AUTO_SELECT: break;
     case cuvsCagraGraphBuildAlgo::IVF_PQ: {
       auto dataset_extent = raft::matrix_extent<int64_t>(dataset.shape[0], dataset.shape[1]);
-      auto pq_params = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(dataset_extent);
+      auto pq_params      = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(
+        dataset_extent, index_params.metric);
       auto ivf_pq_build_params  = params.graph_build_params->ivf_pq_build_params;
       auto ivf_pq_search_params = params.graph_build_params->ivf_pq_search_params;
       if (ivf_pq_build_params) {
@@ -84,9 +85,8 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
       break;
     }
     case cuvsCagraGraphBuildAlgo::NN_DESCENT: {
-      cuvs::neighbors::cagra::graph_build_params::nn_descent_params nn_descent_params{};
-      nn_descent_params =
-        cuvs::neighbors::nn_descent::index_params(index_params.intermediate_graph_degree);
+      auto nn_descent_params = cuvs::neighbors::cagra::graph_build_params::nn_descent_params{
+        index_params.intermediate_graph_degree, index_params.metric};
       nn_descent_params.max_iterations = params.nn_descent_niter;
       index_params.graph_build_params  = nn_descent_params;
       break;
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 447f4b188a..5829443f1e 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -344,6 +344,14 @@ void build_knn_graph(
   }
 
   if (!first) RAFT_LOG_DEBUG("# Finished building kNN graph");
+  if (static_cast<double>(num_self_included) / dataset.extent(0) * 100. < 5) {
+    RAFT_LOG_WARN(
+      "Self-included ratio is low: %2.2f %%. This can lead to poor recall. "
+      "Consider using a different configuration for the IVF-PQ index, "
+      "increasing the refinement rate, or using higher-precision data type for "
+      "LUT/Internal Distance.",
+      static_cast<double>(num_self_included) / dataset.extent(0) * 100.);
+  }
 }
 
 template <typename DataT, typename IdxT, typename accessor>
@@ -629,11 +637,27 @@ index<T, IdxT> build(
     if (std::holds_alternative<cagra::graph_build_params::ivf_pq_params>(knn_build_params)) {
       auto ivf_pq_params =
         std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(knn_build_params);
+      if (ivf_pq_params.build_params.metric != params.metric) {
+        RAFT_LOG_WARN(
+          "Metric (%lu) for IVF-PQ needs to match cagra metric (%lu), "
+          "aligning IVF-PQ metric.",
+          ivf_pq_params.build_params.metric,
+          params.metric);
+        ivf_pq_params.build_params.metric = params.metric;
+      }
       build_knn_graph(res, dataset, knn_graph->view(), ivf_pq_params);
     } else {
       auto nn_descent_params =
         std::get<cagra::graph_build_params::nn_descent_params>(knn_build_params);
 
+      if (nn_descent_params.metric != params.metric) {
+        RAFT_LOG_WARN(
+          "Metric (%lu) for nn-descent needs to match cagra metric (%lu), "
+          "aligning nn-descent metric.",
+          nn_descent_params.metric,
+          params.metric);
+        nn_descent_params.metric = params.metric;
+      }
       if (nn_descent_params.graph_degree != intermediate_degree) {
         RAFT_LOG_WARN(
           "Graph degree (%lu) for nn-descent needs to match cagra intermediate graph degree (%lu), "
diff --git a/cpp/src/neighbors/refine/refine_device.cuh b/cpp/src/neighbors/refine/refine_device.cuh
index 606ba58e39..52ae8c409e 100644
--- a/cpp/src/neighbors/refine/refine_device.cuh
+++ b/cpp/src/neighbors/refine/refine_device.cuh
@@ -82,7 +82,7 @@ void refine_device(
                    fake_coarse_idx.data() + n_queries);
 
   cuvs::neighbors::ivf_flat::index<data_t, idx_t> refinement_index(
-    handle, cuvs::distance::DistanceType(metric), n_queries, false, true, dim);
+    handle, metric, n_queries, false, true, dim);
 
   cuvs::neighbors::ivf_flat::detail::fill_refinement_index<data_t, idx_t>(
     handle,
@@ -121,12 +121,12 @@ void refine_device(
            fake_coarse_idx.data(),
            static_cast<uint32_t>(n_queries),
            0,
-           cuvs::distance::DistanceType(refinement_index.metric()),
+           metric,
            1,
            k,
            0,
            chunk_index.data(),
-           cuvs::distance::is_min_close(cuvs::distance::DistanceType(metric)),
+           cuvs::distance::is_min_close(metric),
            cuvs::neighbors::filtering::none_sample_filter(),
            neighbors_uint32,
            distances.data_handle(),
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 4f09de90c9..27812e57c9 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -570,8 +570,8 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
 
         switch (ps.build_algo) {
           case graph_build_algo::IVF_PQ:
-            index_params.graph_build_params =
-              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            index_params.graph_build_params = graph_build_params::ivf_pq_params(
+              raft::matrix_extent<int64_t>(ps.n_rows, ps.dim), index_params.metric);
             if (ps.ivf_pq_search_refine_ratio) {
               std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
                 index_params.graph_build_params)
@@ -786,8 +786,8 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
 
         switch (ps.build_algo) {
           case graph_build_algo::IVF_PQ:
-            index_params.graph_build_params =
-              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            index_params.graph_build_params = graph_build_params::ivf_pq_params(
+              raft::matrix_extent<int64_t>(ps.n_rows, ps.dim), index_params.metric);
             if (ps.ivf_pq_search_refine_ratio) {
               std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
                 index_params.graph_build_params)
@@ -996,8 +996,8 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
 
         switch (ps.build_algo) {
           case graph_build_algo::IVF_PQ:
-            index_params.graph_build_params =
-              graph_build_params::ivf_pq_params(raft::matrix_extent<int64_t>(ps.n_rows, ps.dim));
+            index_params.graph_build_params = graph_build_params::ivf_pq_params(
+              raft::matrix_extent<int64_t>(ps.n_rows, ps.dim), index_params.metric);
             if (ps.ivf_pq_search_refine_ratio) {
               std::get<cuvs::neighbors::cagra::graph_build_params::ivf_pq_params>(
                 index_params.graph_build_params)

From c80fc1db9d2b60f46cb8613e5c51144dfe6f01f8 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 8 May 2025 09:50:26 -0700
Subject: [PATCH 392/623] Expose ivf-pq centers to python/c (#881)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/881
---
 cpp/include/cuvs/neighbors/ivf_pq.h          | 18 ++++++++
 cpp/src/neighbors/ivf_pq_c.cpp               | 45 ++++++++++++++++++++
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd | 16 +++++--
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx | 29 +++++++++++++
 python/cuvs/cuvs/tests/test_ivf_pq.py        |  3 ++
 5 files changed, 107 insertions(+), 4 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index 66f4a86fa6..b00418ecb8 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -260,6 +260,24 @@ cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index);
  * @param[in] index cuvsIvfPqIndex_t to de-allocate
  */
 cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index);
+
+/** Get the number of clusters/inverted lists */
+uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index);
+
+/** Get the dimensionality of the cluster centers */
+uint32_t cuvsIvfPqIndexGetDimExt(cuvsIvfPqIndex_t index);
+
+/**
+ * @brief Get the cluster centers corresponding to the lists in the original space
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index cuvsIvfPqIndex_t Built NN-Descent index
+ * @param[out] centers Preallocated array on host memory to store output, [n_lists, dim_ext]
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
+                                     cuvsIvfPqIndex_t index,
+                                     DLManagedTensor* centers);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 33a927cf6f..0a3db6724a 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -143,6 +143,24 @@ void _extend(cuvsResources_t res,
     cuvs::neighbors::ivf_pq::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
   }
 }
+
+template <typename output_mdspan_type, typename IdxT>
+void _get_centers(cuvsResources_t res, cuvsIvfPqIndex index, DLManagedTensor* centers)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
+  auto dst       = cuvs::core::from_dlpack<output_mdspan_type>(centers);
+  auto src       = index_ptr->centers();
+
+  RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output centers has incorrect number of rows");
+  RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output centers has incorrect number of cols");
+
+  cudaMemcpyAsync(dst.data_handle(),
+                  src.data_handle(),
+                  dst.extent(0) * dst.extent(1) * sizeof(float),
+                  cudaMemcpyDefault,
+                  raft::resource::get_cuda_stream(*res_ptr));
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index)
@@ -312,3 +330,30 @@ extern "C" cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
     }
   });
 }
+
+extern "C" uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index)
+{
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
+  return index_ptr->n_lists();
+}
+
+extern "C" uint32_t cuvsIvfPqIndexGetDimExt(cuvsIvfPqIndex_t index)
+{
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
+  return index_ptr->dim_ext();
+}
+
+extern "C" cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
+                                                cuvsIvfPqIndex_t index,
+                                                DLManagedTensor* centers)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (cuvs::core::is_dlpack_device_compatible(centers->dl_tensor)) {
+      using output_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
+      _get_centers<output_mdspan_type, int64_t>(res, *index, centers);
+    } else {
+      using output_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
+      _get_centers<output_mdspan_type, int64_t>(res, *index, centers);
+    }
+  });
+}
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index e732cd7d25..1ed2091644 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -81,25 +81,33 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
 
     cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
 
+    uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index)
+
+    uint32_t cuvsIvfPqIndexGetDimExt(cuvsIvfPqIndex_t index)
+
+    cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
+                                         cuvsIvfPqIndex_t index,
+                                         DLManagedTensor * centers)
+
     cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
                                cuvsIvfPqIndexParams* params,
                                DLManagedTensor* dataset,
-                               cuvsIvfPqIndex_t index) except +
+                               cuvsIvfPqIndex_t index)
 
     cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
                                 cuvsIvfPqSearchParams* params,
                                 cuvsIvfPqIndex_t index,
                                 DLManagedTensor* queries,
                                 DLManagedTensor* neighbors,
-                                DLManagedTensor* distances) except +
+                                DLManagedTensor* distances)
 
     cuvsError_t cuvsIvfPqSerialize(cuvsResources_t res,
                                    const char * filename,
-                                   cuvsIvfPqIndex_t index) except +
+                                   cuvsIvfPqIndex_t index)
 
     cuvsError_t cuvsIvfPqDeserialize(cuvsResources_t res,
                                      const char * filename,
-                                     cuvsIvfPqIndex_t index) except +
+                                     cuvsIvfPqIndex_t index)
 
     cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
                                 DLManagedTensor* new_vectors,
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index d815ea365d..374279b31b 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -238,6 +238,35 @@ cdef class Index:
     def __repr__(self):
         return "Index(type=IvfPq)"
 
+    @property
+    def n_lists(self):
+        """ The number of inverted lists (clusters) """
+        return cuvsIvfPqIndexGetNLists(self.index)
+
+    @property
+    def dim_ext(self):
+        """ dimensionality of the cluster centers """
+        return cuvsIvfPqIndexGetDimExt(self.index)
+
+    @property
+    def centers(self):
+        """ Get the cluster centers corresponding to the lists in the
+        original space """
+        return self._get_centers()
+
+    @auto_sync_resources
+    def _get_centers(self, resources=None):
+        if not self.trained:
+            raise ValueError("Index needs to be built before getting centers")
+
+        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+        output = np.empty((self.n_lists, self.dim_ext), dtype='float32')
+        ai = wrap_array(output)
+        cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
+        check_cuvs(cuvsIvfPqIndexGetCenters(res, self.index, output_dlpack))
+        return output
+
 
 @auto_sync_resources
 def build(IndexParams index_params, dataset, resources=None):
diff --git a/python/cuvs/cuvs/tests/test_ivf_pq.py b/python/cuvs/cuvs/tests/test_ivf_pq.py
index 70e775b194..b6365c5485 100644
--- a/python/cuvs/cuvs/tests/test_ivf_pq.py
+++ b/python/cuvs/cuvs/tests/test_ivf_pq.py
@@ -110,6 +110,9 @@ def run_ivf_pq_build_search_test(
     if not inplace:
         out_dist_device, out_idx_device = ret_output
 
+    centers = index.centers
+    assert centers.shape[0] == n_lists
+
     if not compare:
         return
 

From 3c303a0e637d0484d06116667bfed66a64ecd4a7 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Fri, 9 May 2025 16:17:18 -0400
Subject: [PATCH 393/623] Moving random ball cover (#218)

This PR brings in RBC implementation from RAFT while also reducing the number of templates that are instantiated by moving the following templates to runtime parameters:
1. `dims`
2. booleans
3. distance functor

## Notes for Reviewers

### Benefits of these changes

Allows for a reduction in cuML binary sizes, once cuML switches from RAFT's implementation to this one.

See:

https://github.com/rapidsai/cuml/issues/6626#issuecomment-2864936395

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/218
---
 ci/validate_wheel.sh                          |    4 +-
 cpp/CMakeLists.txt                            |    4 +
 cpp/include/cuvs/neighbors/ball_cover.hpp     |  352 ++++
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |    2 +-
 cpp/src/neighbors/ball_cover.cu               |   71 +
 cpp/src/neighbors/ball_cover.cuh              |  450 +++++
 cpp/src/neighbors/ball_cover/ball_cover.cuh   |  577 +++++++
 cpp/src/neighbors/ball_cover/common.cuh       |   66 +
 .../ball_cover/registers_00_generate.py       |  150 ++
 .../registers_eps_pass_euclidean.cu           |   57 +
 .../detail/ball_cover/registers_pass_one.cu   |   45 +
 .../detail/ball_cover/registers_pass_two.cu   |   45 +
 cpp/src/neighbors/ball_cover/registers.cuh    | 1496 +++++++++++++++++
 cpp/src/neighbors/ball_cover/registers.hpp    |   76 +
 .../neighbors/ball_cover/registers_types.cuh  |   95 ++
 cpp/tests/CMakeLists.txt                      |    2 +
 cpp/tests/neighbors/ball_cover.cu             |  403 +++++
 cpp/tests/neighbors/spatial_data.h            |   38 +
 18 files changed, 3930 insertions(+), 3 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/ball_cover.hpp
 create mode 100644 cpp/src/neighbors/ball_cover.cu
 create mode 100644 cpp/src/neighbors/ball_cover.cuh
 create mode 100644 cpp/src/neighbors/ball_cover/ball_cover.cuh
 create mode 100644 cpp/src/neighbors/ball_cover/common.cuh
 create mode 100644 cpp/src/neighbors/ball_cover/detail/ball_cover/registers_00_generate.py
 create mode 100644 cpp/src/neighbors/ball_cover/detail/ball_cover/registers_eps_pass_euclidean.cu
 create mode 100644 cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_one.cu
 create mode 100644 cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_two.cu
 create mode 100644 cpp/src/neighbors/ball_cover/registers.cuh
 create mode 100644 cpp/src/neighbors/ball_cover/registers.hpp
 create mode 100644 cpp/src/neighbors/ball_cover/registers_types.cuh
 create mode 100644 cpp/tests/neighbors/ball_cover.cu
 create mode 100644 cpp/tests/neighbors/spatial_data.h

diff --git a/ci/validate_wheel.sh b/ci/validate_wheel.sh
index 618c137736..5c0d21baef 100755
--- a/ci/validate_wheel.sh
+++ b/ci/validate_wheel.sh
@@ -13,11 +13,11 @@ RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ "${package_dir}" == "python/libcuvs" ]]; then
     if [[ "${RAPIDS_CUDA_MAJOR}" == "11" ]]; then
         PYDISTCHECK_ARGS=(
-            --max-allowed-size-compressed '1.0G'
+            --max-allowed-size-compressed '1.1G'
         )
     else
         PYDISTCHECK_ARGS=(
-            --max-allowed-size-compressed '900M'
+            --max-allowed-size-compressed '950M'
         )
     fi
 else
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 336e396824..1e565b3386 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -382,6 +382,10 @@ if(BUILD_SHARED_LIBS)
     src/distance/pairwise_distance.cu
     src/distance/sparse_distance.cu
     src/embed/spectral.cu
+    src/neighbors/ball_cover.cu
+    src/neighbors/ball_cover/detail/ball_cover/registers_eps_pass_euclidean.cu
+    src/neighbors/ball_cover/detail/ball_cover/registers_pass_one.cu
+    src/neighbors/ball_cover/detail/ball_cover/registers_pass_two.cu
     src/neighbors/brute_force.cu
     src/neighbors/brute_force_serialize.cu
     src/neighbors/cagra_build_float.cu
diff --git a/cpp/include/cuvs/neighbors/ball_cover.hpp b/cpp/include/cuvs/neighbors/ball_cover.hpp
new file mode 100644
index 0000000000..6f4e2f45bb
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/ball_cover.hpp
@@ -0,0 +1,352 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::ball_cover {
+
+/**
+ * @ingroup random_ball_cover
+ * @{
+ */
+
+/**
+ * Stores raw index data points, sampled landmarks, the 1-nns of index points
+ * to their closest landmarks, and the ball radii of each landmark. This
+ * class is intended to be constructed once and reused across subsequent
+ * queries.
+ * @tparam int64_t
+ * @tparam float
+ * @tparam int
+ */
+template <typename idx_t, typename value_t>
+struct index : cuvs::neighbors::index {
+ public:
+  explicit index(raft::resources const& handle_,
+                 raft::device_matrix_view<const float, int64_t, raft::row_major> X_,
+                 cuvs::distance::DistanceType metric_)
+    : handle(handle_),
+      X(X_),
+      m(X_.extent(0)),
+      n(X_.extent(1)),
+      metric(metric_),
+      /**
+       * the sqrt() here makes the sqrt(m)^2 a linear-time lower bound
+       *
+       * Total memory footprint of index: (2 * sqrt(m)) + (n * sqrt(m)) + (2 * m)
+       */
+      n_landmarks(raft::sqrt(X_.extent(0))),
+      R_indptr(raft::make_device_vector<idx_t, int64_t>(handle, raft::sqrt(X_.extent(0)) + 1)),
+      R_1nn_cols(raft::make_device_vector<idx_t, int64_t>(handle, X_.extent(0))),
+      R_1nn_dists(raft::make_device_vector<float, int64_t>(handle, X_.extent(0))),
+      R_closest_landmark_dists(raft::make_device_vector<float, int64_t>(handle, X_.extent(0))),
+      R(raft::make_device_matrix<float, int64_t>(handle, raft::sqrt(X_.extent(0)), X_.extent(1))),
+      X_reordered(raft::make_device_matrix<float, int64_t>(handle, X_.extent(0), X_.extent(1))),
+      R_radius(raft::make_device_vector<float, int64_t>(handle, raft::sqrt(X_.extent(0)))),
+      index_trained(false)
+  {
+  }
+
+  auto get_R_indptr() const -> raft::device_vector_view<const idx_t, int64_t>
+  {
+    return R_indptr.view();
+  }
+  auto get_R_1nn_cols() const -> raft::device_vector_view<const idx_t, int64_t>
+  {
+    return R_1nn_cols.view();
+  }
+  auto get_R_1nn_dists() const -> raft::device_vector_view<const float, int64_t>
+  {
+    return R_1nn_dists.view();
+  }
+  auto get_R_radius() const -> raft::device_vector_view<const float, int64_t>
+  {
+    return R_radius.view();
+  }
+  auto get_R() const -> raft::device_matrix_view<const float, int64_t, raft::row_major>
+  {
+    return R.view();
+  }
+  auto get_R_closest_landmark_dists() const -> raft::device_vector_view<const float, int64_t>
+  {
+    return R_closest_landmark_dists.view();
+  }
+  auto get_X_reordered() const -> raft::device_matrix_view<const float, int64_t, raft::row_major>
+  {
+    return X_reordered.view();
+  }
+
+  raft::device_vector_view<idx_t, int64_t> get_R_indptr() { return R_indptr.view(); }
+  raft::device_vector_view<idx_t, int64_t> get_R_1nn_cols() { return R_1nn_cols.view(); }
+  raft::device_vector_view<float, int64_t> get_R_1nn_dists() { return R_1nn_dists.view(); }
+  raft::device_vector_view<float, int64_t> get_R_radius() { return R_radius.view(); }
+  raft::device_matrix_view<float, int64_t, raft::row_major> get_R() { return R.view(); }
+  raft::device_vector_view<float, int64_t> get_R_closest_landmark_dists()
+  {
+    return R_closest_landmark_dists.view();
+  }
+  raft::device_matrix_view<float, int64_t, raft::row_major> get_X_reordered()
+  {
+    return X_reordered.view();
+  }
+  raft::device_matrix_view<const float, int64_t, raft::row_major> get_X() const { return X; }
+
+  cuvs::distance::DistanceType get_metric() const { return metric; }
+
+  int get_n_landmarks() const { return n_landmarks; }
+  bool is_index_trained() const { return index_trained; };
+
+  // This should only be set by internal functions
+  void set_index_trained() { index_trained = true; }
+
+  raft::resources const& handle;
+
+  int64_t m;
+  int64_t n;
+  int64_t n_landmarks;
+
+  raft::device_matrix_view<const float, idx_t, raft::row_major> X;
+
+  cuvs::distance::DistanceType metric;
+
+ private:
+  // CSR storing the neighborhoods for each data point
+  raft::device_vector<idx_t, int64_t> R_indptr;
+  raft::device_vector<idx_t, int64_t> R_1nn_cols;
+  raft::device_vector<float, int64_t> R_1nn_dists;
+  raft::device_vector<float, int64_t> R_closest_landmark_dists;
+
+  raft::device_vector<float, int64_t> R_radius;
+
+  raft::device_matrix<float, int64_t, raft::row_major> R;
+  raft::device_matrix<float, int64_t, raft::row_major> X_reordered;
+
+ protected:
+  bool index_trained;
+};
+
+/** @} */
+
+/**
+ * @defgroup random_ball_cover Random Ball Cover algorithm
+ * @{
+ */
+
+/**
+ * Builds and populates a previously unbuilt cuvs::neighbors::ball_cover::index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *
+ *  #include <raft/core/resources.hpp>
+ *  #include <cuvs/neighbors/ball_cover.hpp>
+ *  #include <cuvs/distance/distance.hpp>
+ *  using namespace cuvs::neighbors;
+ *
+ *  raft::resources handle;
+ *  ...
+ *  auto metric = cuvs::distance::DistanceType::L2Expanded;
+ *  ball_cover::index index(handle, X, metric);
+ *  ball_cover::build_index(handle, index);
+ * @endcode
+ *
+ * @param[in] handle library resource management handle
+ * @param[inout] index an empty (and not previous built) instance of
+ * cuvs::neighbors::ball_cover::index
+ */
+void build(raft::resources const& handle, index<int64_t, float>& index);
+
+/** @} */  // end group random_ball_cover
+
+/**
+ * @ingroup random_ball_cover
+ * @{
+ */
+
+/**
+ * Performs a faster exact knn in metric spaces using the triangle
+ * inequality with a number of landmark points to reduce the
+ * number of distance computations from O(n^2) to O(sqrt(n)). This
+ * performs an all neighbors knn, which can reuse memory when
+ * the index and query are the same array. This function will
+ * build the index and assumes rbc_build_index() has not already
+ * been called.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *
+ *  #include <raft/core/resources.hpp>
+ *  #include <cuvs/neighbors/ball_cover.hpp>
+ *  #include <cuvs/distance/distance.hpp>
+ *  using namespace cuvs::neighbors;
+ *
+ *  raft::resources handle;
+ *  ...
+ *  auto metric = cuvs::distance::DistanceType::L2Expanded;
+ *
+ *  // Construct a ball cover index
+ *  ball_cover::index index(handle, X, metric);
+ *
+ *  // Perform all neighbors knn query
+ *  ball_cover::all_knn_query(handle, index, inds, dists, k);
+ * @endcode
+ *
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has not yet been built
+ * @param[out] inds output knn indices
+ * @param[out] dists output knn distances
+ * @param[in] perform_post_filtering if this is false, only the closest k landmarks
+ *                               are considered (which will return approximate
+ *                               results).
+ * @param[in] weight a weight for overlap between the closest landmark and
+ *               the radius of other landmarks when pruning distances.
+ *               Setting this value below 1 can effectively turn off
+ *               computing distances against many other balls, enabling
+ *               approximate nearest neighbors. Recall can be adjusted
+ *               based on how many relevant balls are ignored. Note that
+ *               many datasets can still have great recall even by only
+ *               looking in the closest landmark.
+ */
+void all_knn_query(raft::resources const& handle,
+                   index<int64_t, float>& index,
+                   raft::device_matrix_view<int64_t, int64_t, raft::row_major> inds,
+                   raft::device_matrix_view<float, int64_t, raft::row_major> dists,
+                   bool perform_post_filtering = true,
+                   float weight                = 1.0);
+
+/** @} */
+
+/**
+ * @brief Computes epsilon neighborhood for the L2 distance metric using rbc
+ *
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has been built
+ * @param[out] adj    adjacency matrix [row-major] [on device] [dim = m x n]
+ * @param[out] vd     vertex degree array [on device] [len = m + 1]
+ *                    `vd + m` stores the total number of edges in the adjacency
+ *                    matrix. Pass a nullptr if you don't need this info.
+ * @param[in]  query  first matrix [row-major] [on device] [dim = m x k]
+ * @param[in]  eps    defines epsilon neighborhood radius
+ */
+void eps_nn(raft::resources const& handle,
+            const index<int64_t, float>& index,
+            raft::device_matrix_view<bool, int64_t, raft::row_major> adj,
+            raft::device_vector_view<int64_t, int64_t> vd,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> query,
+            float eps);
+/**
+ * @brief Computes epsilon neighborhood for the L2 distance metric using rbc
+ *
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has been built
+ * @param[out] adj_ia    adjacency matrix CSR row offsets
+ * @param[out] adj_ja    adjacency matrix CSR column indices, needs to be nullptr
+ *                       in first pass with max_k nullopt
+ * @param[out] vd     vertex degree array [on device] [len = m + 1]
+ *                    `vd + m` stores the total number of edges in the adjacency
+ *                    matrix. Pass a nullptr if you don't need this info.
+ * @param[in]  query  first matrix [row-major] [on device] [dim = m x k]
+ * @param[in]  eps    defines epsilon neighborhood radius
+ * @param[inout] max_k if nullopt (default), the user needs to make 2 subsequent calls:
+ *                     The first call computes row offsets in adj_ia, where adj_ia[m]
+ *                     contains the minimum required size for adj_ja.
+ *                     The second call fills in adj_ja based on adj_ia.
+ *                     If max_k != nullopt the algorithm only fills up neighbors up to a
+ *                     maximum number of max_k for each row in a single pass. Note
+ *                     that it is not guarantueed to return the nearest neighbors.
+ *                     Upon return max_k is overwritten with the actual max_k found during
+ *                     computation.
+ */
+void eps_nn(raft::resources const& handle,
+            const index<int64_t, float>& index,
+            raft::device_vector_view<int64_t, int64_t> adj_ia,
+            raft::device_vector_view<int64_t, int64_t> adj_ja,
+            raft::device_vector_view<int64_t, int64_t> vd,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> query,
+            float eps,
+            std::optional<raft::host_scalar_view<int64_t, int64_t>> max_k = std::nullopt);
+
+/**
+ * @ingroup random_ball_cover
+ * @{
+ */
+
+/**
+ * Performs a faster exact knn in metric spaces using the triangle
+ * inequality with a number of landmark points to reduce the
+ * number of distance computations from O(n^2) to O(sqrt(n)). This
+ * function does not build the index and assumes rbc_build_index() has
+ * already been called. Use this function when the index and
+ * query arrays are different, otherwise use rbc_all_knn_query().
+ *
+ * Usage example:
+ * @code{.cpp}
+ *
+ *  #include <raft/core/resources.hpp>
+ *  #include <cuvs/neighbors/ball_cover.hpp>
+ *  #include <cuvs/distance/distance.hpp>
+ *  using namespace cuvs::neighbors;
+ *
+ *  raft::resources handle;
+ *  ...
+ *  auto metric = cuvs::distance::DistanceType::L2Expanded;
+ *
+ *  // Build a ball cover index
+ *  ball_cover::index index(handle, X, metric);
+ *  ball_cover::build_index(handle, index);
+ *
+ *  // Perform all neighbors knn query
+ *  ball_cover::knn_query(handle, index, inds, dists, k);
+ * @endcode
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has not yet been built
+ * @param[in] query device matrix containing query data points
+ * @param[out] inds output knn indices
+ * @param[out] dists output knn distances
+ * @param[in] perform_post_filtering if this is false, only the closest k landmarks
+ *                               are considered (which will return approximate
+ *                               results).
+ * @param[in] weight a weight for overlap between the closest landmark and
+ *               the radius of other landmarks when pruning distances.
+ *               Setting this value below 1 can effectively turn off
+ *               computing distances against many other balls, enabling
+ *               approximate nearest neighbors. Recall can be adjusted
+ *               based on how many relevant balls are ignored. Note that
+ *               many datasets can still have great recall even by only
+ *               looking in the closest landmark.
+ */
+void knn_query(raft::resources const& handle,
+               const index<int64_t, float>& index,
+               raft::device_matrix_view<const float, int64_t, raft::row_major> query,
+               raft::device_matrix_view<int64_t, int64_t, raft::row_major> inds,
+               raft::device_matrix_view<float, int64_t, raft::row_major> dists,
+               bool perform_post_filtering = true,
+               float weight                = 1.0);
+
+/** @} */
+
+}  // namespace cuvs::neighbors::ball_cover
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 5efe3775f9..7c2a3e9f85 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -132,7 +132,7 @@ struct index_params : cuvs::neighbors::index_params {
    *   // create index_params for a [N. D] dataset and have InnerProduct as the distance metric
    *   auto dataset = raft::make_device_matrix<float, int64_t>(res, N, D);
    *   ivf_pq::index_params index_params =
-   *     ivf_pq::index_params::from_dataset(dataset.extents(), raft::distance::InnerProduct);
+   *     ivf_pq::index_params::from_dataset(dataset.extents(), cuvs::distance::InnerProduct);
    *   // modify/update index_params as needed
    *   index_params.add_data_on_build = true;
    * @endcode
diff --git a/cpp/src/neighbors/ball_cover.cu b/cpp/src/neighbors/ball_cover.cu
new file mode 100644
index 0000000000..348803e790
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ball_cover.cuh"
+#include <cuvs/neighbors/ball_cover.hpp>
+
+namespace cuvs::neighbors::ball_cover {
+
+void build(raft::resources const& handle, cuvs::neighbors::ball_cover::index<int64_t, float>& index)
+{
+  detail::build_index<int64_t, float>(handle, index);
+}
+
+void all_knn_query(raft::resources const& handle,
+                   cuvs::neighbors::ball_cover::index<int64_t, float>& index,
+                   raft::device_matrix_view<int64_t, int64_t, raft::row_major> inds,
+                   raft::device_matrix_view<float, int64_t, raft::row_major> dists,
+                   bool perform_post_filtering,
+                   float weight)
+{
+  detail::all_knn_query<int64_t, float>(handle, index, inds, dists, perform_post_filtering, weight);
+}
+
+void eps_nn(raft::resources const& handle,
+            const cuvs::neighbors::ball_cover::index<int64_t, float>& index,
+            raft::device_matrix_view<bool, int64_t, raft::row_major> adj,
+            raft::device_vector_view<int64_t, int64_t> vd,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> query,
+            float eps)
+{
+  detail::eps_nn<int64_t, float>(handle, index, adj, vd, query, eps);
+}
+
+void eps_nn(raft::resources const& handle,
+            const cuvs::neighbors::ball_cover::index<int64_t, float>& index,
+            raft::device_vector_view<int64_t, int64_t> adj_ia,
+            raft::device_vector_view<int64_t, int64_t> adj_ja,
+            raft::device_vector_view<int64_t, int64_t> vd,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> query,
+            float eps,
+            std::optional<raft::host_scalar_view<int64_t, int64_t>> max_k)
+{
+  detail::eps_nn<int64_t, float>(handle, index, adj_ia, adj_ja, vd, query, eps, max_k);
+}
+
+void knn_query(raft::resources const& handle,
+               const cuvs::neighbors::ball_cover::index<int64_t, float>& index,
+               raft::device_matrix_view<const float, int64_t, raft::row_major> query,
+               raft::device_matrix_view<int64_t, int64_t, raft::row_major> inds,
+               raft::device_matrix_view<float, int64_t, raft::row_major> dists,
+               bool perform_post_filtering,
+               float weight)
+{
+  detail::knn_query<int64_t, float>(
+    handle, index, query, inds, dists, perform_post_filtering, weight);
+}
+
+}  // namespace cuvs::neighbors::ball_cover
diff --git a/cpp/src/neighbors/ball_cover.cuh b/cpp/src/neighbors/ball_cover.cuh
new file mode 100644
index 0000000000..1024f6a5ff
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover.cuh
@@ -0,0 +1,450 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "ball_cover/ball_cover.cuh"
+#include "ball_cover/registers_types.cuh"
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/ball_cover.hpp>
+
+#include <thrust/transform.h>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::ball_cover::detail {
+
+/**
+ * @defgroup random_ball_cover Random Ball Cover algorithm
+ * @{
+ */
+
+/**
+ * Builds and populates a previously unbuilt cuvs::neighbors::ball_cover::index
+ *
+ * Usage example:
+ * @code{.cpp}
+ *
+ *  #include <raft/core/resources.hpp>
+ *  #include <raft/neighbors/ball_cover.cuh>
+ *  #include <raft/distance/distance_types.hpp>
+ *  using namespace raft::neighbors;
+ *
+ *  raft::resources handle;
+ *  ...
+ *  auto metric = cuvs::distance::DistanceType::L2Expanded;
+ *  cuvs::neighbors::ball_cover::index index(handle, X, metric);
+ *
+ *  ball_cover::build_index(handle, index);
+ * @endcode
+ *
+ * @tparam idx_t knn index type
+ * @tparam value_t knn value type
+ * @tparam int_t integral type for knn params
+ * @tparam matrix_idx_t matrix indexing type
+ * @param[in] handle library resource management handle
+ * @param[inout] index an empty (and not previous built) instance of
+ * cuvs::neighbors::ball_cover::index
+ */
+template <typename idx_t, typename value_t>
+void build_index(raft::resources const& handle,
+                 cuvs::neighbors::ball_cover::index<idx_t, value_t>& index)
+{
+  RAFT_EXPECTS(index.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                 index.metric == cuvs::distance::DistanceType::L2SqrtUnexpanded ||
+                 index.metric == cuvs::distance::DistanceType::Haversine,
+               "Metric not supported");
+  cuvs::neighbors::ball_cover::detail::rbc_build_index(handle, index);
+
+  index.set_index_trained();
+}
+
+/** @} */  // end group random_ball_cover
+
+/**
+ * Performs a faster exact knn in metric spaces using the triangle
+ * inequality with a number of landmark points to reduce the
+ * number of distance computations from O(n^2) to O(sqrt(n)). This
+ * performs an all neighbors knn, which can reuse memory when
+ * the index and query are the same array. This function will
+ * build the index and assumes rbc_build_index() has not already
+ * been called.
+ * @tparam idx_t knn index type
+ * @tparam value_t knn distance type
+ * @tparam int_t type for integers, such as number of rows/cols
+ * @param[in] handle raft handle for resource management
+ * @param[inout] index ball cover index which has not yet been built
+ * @param[in] k number of nearest neighbors to find
+ * @param[in] perform_post_filtering if this is false, only the closest k landmarks
+ *                               are considered (which will return approximate
+ *                               results).
+ * @param[out] inds output knn indices
+ * @param[out] dists output knn distances
+ * @param[in] weight a weight for overlap between the closest landmark and
+ *               the radius of other landmarks when pruning distances.
+ *               Setting this value below 1 can effectively turn off
+ *               computing distances against many other balls, enabling
+ *               approximate nearest neighbors. Recall can be adjusted
+ *               based on how many relevant balls are ignored. Note that
+ *               many datasets can still have great recall even by only
+ *               looking in the closest landmark.
+ */
+template <typename idx_t, typename value_t>
+void all_knn_query(raft::resources const& handle,
+                   cuvs::neighbors::ball_cover::index<idx_t, value_t>& index,
+                   int64_t k,
+                   idx_t* inds,
+                   value_t* dists,
+                   bool perform_post_filtering = true,
+                   float weight                = 1.0)
+{
+  ASSERT(index.n <= 3, "only 2d and 3d vectors are supported in current implementation");
+  RAFT_EXPECTS(index.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                 index.metric == cuvs::distance::DistanceType::L2SqrtUnexpanded ||
+                 index.metric == cuvs::distance::DistanceType::Haversine,
+               "Metric not supported");
+  cuvs::neighbors::ball_cover::detail::rbc_all_knn_query(
+    handle, index, k, inds, dists, perform_post_filtering, weight);
+
+  index.set_index_trained();
+}
+
+/**
+ * @ingroup random_ball_cover
+ * @{
+ */
+
+/**
+ * Performs a faster exact knn in metric spaces using the triangle
+ * inequality with a number of landmark points to reduce the
+ * number of distance computations from O(n^2) to O(sqrt(n)). This
+ * performs an all neighbors knn, which can reuse memory when
+ * the index and query are the same array. This function will
+ * build the index and assumes rbc_build_index() has not already
+ * been called.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *
+ *  #include <raft/core/resources.hpp>
+ *  #include <cuvs/neighbors/ball_cover.hpp>
+ *  #include <cuvs/distance/distance.hpp>
+ *  using namespace raft::neighbors;
+ *
+ *  raft::resources handle;
+ *  ...
+ *  auto metric = cuvs::distance::DistanceType::L2Expanded;
+ *
+ *  // Construct a ball cover index
+ *  cuvs::neighbors::ball_cover::index index(handle, X, metric);
+ *
+ *  // Perform all neighbors knn query
+ *  ball_cover::all_knn_query(handle, index, inds, dists, k);
+ * @endcode
+ *
+ * @tparam idx_t knn index type
+ * @tparam value_t knn distance type
+ * @tparam int_t type for integers, such as number of rows/cols
+ * @tparam matrix_idx_t matrix indexing type
+ *
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has not yet been built
+ * @param[out] inds output knn indices
+ * @param[out] dists output knn distances
+ * @param[in] perform_post_filtering if this is false, only the closest k landmarks
+ *                               are considered (which will return approximate
+ *                               results).
+ * @param[in] weight a weight for overlap between the closest landmark and
+ *               the radius of other landmarks when pruning distances.
+ *               Setting this value below 1 can effectively turn off
+ *               computing distances against many other balls, enabling
+ *               approximate nearest neighbors. Recall can be adjusted
+ *               based on how many relevant balls are ignored. Note that
+ *               many datasets can still have great recall even by only
+ *               looking in the closest landmark.
+ */
+template <typename idx_t, typename value_t>
+void all_knn_query(raft::resources const& handle,
+                   cuvs::neighbors::ball_cover::index<idx_t, value_t>& index,
+                   raft::device_matrix_view<idx_t, int64_t, raft::row_major> inds,
+                   raft::device_matrix_view<value_t, int64_t, raft::row_major> dists,
+                   bool perform_post_filtering = true,
+                   float weight                = 1.0)
+{
+  RAFT_EXPECTS(index.n <= 3, "only 2d and 3d vectors are supported in current implementation");
+  RAFT_EXPECTS(inds.extent(1) <= index.m,
+               "k must be less than or equal to the number of data points in the index");
+  RAFT_EXPECTS(inds.extent(1) == dists.extent(1),
+               "Number of columns in output indices and distances matrices must be equal");
+
+  RAFT_EXPECTS(inds.extent(0) == dists.extent(0) && dists.extent(0) == index.get_X().extent(0),
+               "Number of rows in output indices and distances matrices must equal number of rows "
+               "in index matrix.");
+
+  all_knn_query(handle,
+                index,
+                inds.extent(1),
+                inds.data_handle(),
+                dists.data_handle(),
+                perform_post_filtering,
+                weight);
+}
+
+/** @} */
+
+/**
+ * Performs a faster exact knn in metric spaces using the triangle
+ * inequality with a number of landmark points to reduce the
+ * number of distance computations from O(n^2) to O(sqrt(n)). This
+ * function does not build the index and assumes rbc_build_index() has
+ * already been called. Use this function when the index and
+ * query arrays are different, otherwise use rbc_all_knn_query().
+ * @tparam idx_t index type
+ * @tparam value_t distances type
+ * @tparam int_t integer type for size info
+ * @param[in] handle raft handle for resource management
+ * @param[inout] index ball cover index which has not yet been built
+ * @param[in] k number of nearest neighbors to find
+ * @param[in] query the
+ * @param[in] perform_post_filtering if this is false, only the closest k landmarks
+ *                               are considered (which will return approximate
+ *                               results).
+ * @param[out] inds output knn indices
+ * @param[out] dists output knn distances
+ * @param[in] weight a weight for overlap between the closest landmark and
+ *               the radius of other landmarks when pruning distances.
+ *               Setting this value below 1 can effectively turn off
+ *               computing distances against many other balls, enabling
+ *               approximate nearest neighbors. Recall can be adjusted
+ *               based on how many relevant balls are ignored. Note that
+ *               many datasets can still have great recall even by only
+ *               looking in the closest landmark.
+ * @param[in] n_query_pts number of query points
+ */
+template <typename idx_t, typename value_t>
+void knn_query(raft::resources const& handle,
+               const cuvs::neighbors::ball_cover::index<idx_t, value_t>& index,
+               int64_t k,
+               const value_t* query,
+               int64_t n_query_pts,
+               idx_t* inds,
+               value_t* dists,
+               bool perform_post_filtering = true,
+               float weight                = 1.0)
+{
+  ASSERT(index.n <= 3, "only 2d and 3d vectors are supported in current implementation");
+  RAFT_EXPECTS(index.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                 index.metric == cuvs::distance::DistanceType::L2SqrtUnexpanded ||
+                 index.metric == cuvs::distance::DistanceType::Haversine,
+               "Metric not supported");
+  cuvs::neighbors::ball_cover::detail::rbc_knn_query(
+    handle, index, k, query, n_query_pts, inds, dists, perform_post_filtering, weight);
+}
+
+/**
+ * @brief Computes epsilon neighborhood for the L2 distance metric using rbc
+ *
+ * @tparam value_t   IO and math type
+ * @tparam idx_t    Index type
+ *
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has been built
+ * @param[out] adj    adjacency matrix [row-major] [on device] [dim = m x n]
+ * @param[out] vd     vertex degree array [on device] [len = m + 1]
+ *                    `vd + m` stores the total number of edges in the adjacency
+ *                    matrix. Pass a nullptr if you don't need this info.
+ * @param[in]  query  first matrix [row-major] [on device] [dim = m x k]
+ * @param[in]  eps    defines epsilon neighborhood radius
+ */
+template <typename idx_t, typename value_t>
+void eps_nn(raft::resources const& handle,
+            const cuvs::neighbors::ball_cover::index<idx_t, value_t>& index,
+            raft::device_matrix_view<bool, int64_t, raft::row_major> adj,
+            raft::device_vector_view<idx_t, int64_t> vd,
+            raft::device_matrix_view<const value_t, int64_t, raft::row_major> query,
+            value_t eps)
+{
+  ASSERT(index.n == query.extent(1), "vector dimension needs to be the same for index and queries");
+  ASSERT(index.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+           index.metric == cuvs::distance::DistanceType::L2SqrtUnexpanded,
+         "Metric not supported");
+  ASSERT(index.is_index_trained(), "index must be previously trained");
+
+  // run query
+  cuvs::neighbors::ball_cover::detail::rbc_eps_nn_query(
+    handle,
+    index,
+    eps,
+    query.data_handle(),
+    query.extent(0),
+    adj.data_handle(),
+    vd.data_handle(),
+    cuvs::neighbors::ball_cover::detail::EuclideanSqFunc<value_t, int64_t>());
+}
+
+/**
+ * @brief Computes epsilon neighborhood for the L2 distance metric using rbc
+ *
+ * @tparam value_t   IO and math type
+ * @tparam idx_t    Index type
+ *
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has been built
+ * @param[out] adj_ia    adjacency matrix CSR row offsets
+ * @param[out] adj_ja    adjacency matrix CSR column indices, needs to be nullptr
+ *                       in first pass with max_k nullopt
+ * @param[out] vd     vertex degree array [on device] [len = m + 1]
+ *                    `vd + m` stores the total number of edges in the adjacency
+ *                    matrix. Pass a nullptr if you don't need this info.
+ * @param[in]  query  first matrix [row-major] [on device] [dim = m x k]
+ * @param[in]  eps    defines epsilon neighborhood radius
+ * @param[inout] max_k if nullopt (default), the user needs to make 2 subsequent calls:
+ *                     The first call computes row offsets in adj_ia, where adj_ia[m]
+ *                     contains the minimum required size for adj_ja.
+ *                     The second call fills in adj_ja based on adj_ia.
+ *                     If max_k != nullopt the algorithm only fills up neighbors up to a
+ *                     maximum number of max_k for each row in a single pass. Note
+ *                     that it is not guarantueed to return the nearest neighbors.
+ *                     Upon return max_k is overwritten with the actual max_k found during
+ *                     computation.
+ */
+template <typename idx_t, typename value_t>
+void eps_nn(raft::resources const& handle,
+            const cuvs::neighbors::ball_cover::index<idx_t, value_t>& index,
+            raft::device_vector_view<idx_t, int64_t> adj_ia,
+            raft::device_vector_view<idx_t, int64_t> adj_ja,
+            raft::device_vector_view<idx_t, int64_t> vd,
+            raft::device_matrix_view<const value_t, int64_t, raft::row_major> query,
+            value_t eps,
+            std::optional<raft::host_scalar_view<int64_t, int64_t>> max_k = std::nullopt)
+{
+  ASSERT(index.n == query.extent(1), "vector dimension needs to be the same for index and queries");
+  ASSERT(index.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+           index.metric == cuvs::distance::DistanceType::L2SqrtUnexpanded,
+         "Metric not supported");
+  ASSERT(index.is_index_trained(), "index must be previously trained");
+
+  int64_t* max_k_ptr = nullptr;
+  if (max_k.has_value()) { max_k_ptr = max_k.value().data_handle(); }
+
+  // run query
+  cuvs::neighbors::ball_cover::detail::rbc_eps_nn_query(
+    handle,
+    index,
+    eps,
+    max_k_ptr,
+    query.data_handle(),
+    query.extent(0),
+    adj_ia.data_handle(),
+    adj_ja.data_handle(),
+    vd.data_handle(),
+    cuvs::neighbors::ball_cover::detail::EuclideanSqFunc<value_t, int64_t>());
+}
+
+/**
+ * @ingroup random_ball_cover
+ * @{
+ */
+
+/**
+ * Performs a faster exact knn in metric spaces using the triangle
+ * inequality with a number of landmark points to reduce the
+ * number of distance computations from O(n^2) to O(sqrt(n)). This
+ * function does not build the index and assumes rbc_build_index() has
+ * already been called. Use this function when the index and
+ * query arrays are different, otherwise use rbc_all_knn_query().
+ *
+ * Usage example:
+ * @code{.cpp}
+ *
+ *  #include <raft/core/resources.hpp>
+ *  #include <raft/neighbors/ball_cover.cuh>
+ *  #include <raft/distance/distance_types.hpp>
+ *  using namespace raft::neighbors;
+ *
+ *  raft::resources handle;
+ *  ...
+ *  auto metric = cuvs::distance::DistanceType::L2Expanded;
+ *
+ *  // Build a ball cover index
+ *  cuvs::neighbors::ball_cover::index index(handle, X, metric);
+ *  ball_cover::build_index(handle, index);
+ *
+ *  // Perform all neighbors knn query
+ *  ball_cover::knn_query(handle, index, inds, dists, k);
+ * @endcode
+
+ *
+ * @tparam idx_t index type
+ * @tparam value_t distances type
+ * @tparam int_t integer type for size info
+ * @tparam matrix_idx_t
+ * @param[in] handle raft handle for resource management
+ * @param[in] index ball cover index which has not yet been built
+ * @param[in] query device matrix containing query data points
+ * @param[out] inds output knn indices
+ * @param[out] dists output knn distances
+ * @param[in] perform_post_filtering if this is false, only the closest k landmarks
+ *                               are considered (which will return approximate
+ *                               results).
+ * @param[in] weight a weight for overlap between the closest landmark and
+ *               the radius of other landmarks when pruning distances.
+ *               Setting this value below 1 can effectively turn off
+ *               computing distances against many other balls, enabling
+ *               approximate nearest neighbors. Recall can be adjusted
+ *               based on how many relevant balls are ignored. Note that
+ *               many datasets can still have great recall even by only
+ *               looking in the closest landmark.
+ */
+template <typename idx_t, typename value_t>
+void knn_query(raft::resources const& handle,
+               const cuvs::neighbors::ball_cover::index<idx_t, value_t>& index,
+               raft::device_matrix_view<const value_t, int64_t, raft::row_major> query,
+               raft::device_matrix_view<idx_t, int64_t, raft::row_major> inds,
+               raft::device_matrix_view<value_t, int64_t, raft::row_major> dists,
+               bool perform_post_filtering = true,
+               float weight                = 1.0)
+{
+  RAFT_EXPECTS(inds.extent(1) <= index.m,
+               "k must be less than or equal to the number of data points in the index");
+  RAFT_EXPECTS(inds.extent(1) == dists.extent(1),
+               "Number of columns in output indices and distances matrices must be equal");
+
+  RAFT_EXPECTS(inds.extent(0) == dists.extent(0) && dists.extent(0) == query.extent(0),
+               "Number of rows in output indices and distances matrices must equal number of rows "
+               "in search matrix.");
+
+  RAFT_EXPECTS(query.extent(1) == index.get_X().extent(1),
+               "Number of columns in query and index matrices must match.");
+
+  knn_query(handle,
+            index,
+            inds.extent(1),
+            query.data_handle(),
+            query.extent(0),
+            inds.data_handle(),
+            dists.data_handle(),
+            perform_post_filtering,
+            weight);
+}
+
+/** @} */
+
+// TODO: implement functions for:
+//  4. rbc_eps_neigh() - given a populated index, perform query against different query array
+//  5. rbc_all_eps_neigh() - populate a cuvs::neighbors::ball_cover::index and query against
+//  training data
+
+}  // namespace cuvs::neighbors::ball_cover::detail
diff --git a/cpp/src/neighbors/ball_cover/ball_cover.cuh b/cpp/src/neighbors/ball_cover/ball_cover.cuh
new file mode 100644
index 0000000000..b0c44c3526
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/ball_cover.cuh
@@ -0,0 +1,577 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "common.cuh"
+#include "registers.hpp"
+#include <cuvs/neighbors/ball_cover.hpp>
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/matrix/copy.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+#include <thrust/tuple.h>
+
+#include <limits.h>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::ball_cover::detail {
+
+/**
+ * Given a set of points in row-major order which are to be
+ * used as a set of index points, uniformly samples a subset
+ * of points to be used as landmarks.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param handle
+ * @param index
+ */
+template <typename value_idx, typename value_t>
+void sample_landmarks(raft::resources const& handle,
+                      cuvs::neighbors::ball_cover::index<value_idx, value_t>& index)
+{
+  rmm::device_uvector<value_idx> R_1nn_cols2(index.n_landmarks,
+                                             raft::resource::get_cuda_stream(handle));
+  rmm::device_uvector<value_t> R_1nn_ones(index.m, raft::resource::get_cuda_stream(handle));
+  rmm::device_uvector<value_idx> R_indices(index.n_landmarks,
+                                           raft::resource::get_cuda_stream(handle));
+
+  thrust::sequence(raft::resource::get_thrust_policy(handle),
+                   index.get_R_1nn_cols().data_handle(),
+                   index.get_R_1nn_cols().data_handle() + index.m,
+                   (value_idx)0);
+
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_1nn_ones.data(),
+               R_1nn_ones.data() + R_1nn_ones.size(),
+               1.0);
+
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_indices.data(),
+               R_indices.data() + R_indices.size(),
+               0.0);
+
+  /**
+   * 1. Randomly sample sqrt(n) points from X
+   */
+  raft::random::RngState rng_state(12345);
+  raft::random::sampleWithoutReplacement(handle,
+                                         rng_state,
+                                         R_indices.data(),
+                                         R_1nn_cols2.data(),
+                                         index.get_R_1nn_cols().data_handle(),
+                                         R_1nn_ones.data(),
+                                         (value_idx)index.n_landmarks,
+                                         (value_idx)index.m);
+
+  auto x = index.get_X();
+  auto r = index.get_R();
+
+  raft::matrix::copy_rows<value_t, value_idx>(
+    handle,
+    raft::make_device_matrix_view<const value_t, value_idx>(
+      x.data_handle(), x.extent(0), x.extent(1)),
+    raft::make_device_matrix_view<value_t, value_idx>(r.data_handle(), r.extent(0), r.extent(1)),
+    raft::make_device_vector_view(R_1nn_cols2.data(), index.n_landmarks));
+}
+
+/**
+ * Constructs a 1-nn index mapping each landmark to their closest points.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param handle
+ * @param R_knn_inds_ptr
+ * @param R_knn_dists_ptr
+ * @param k
+ * @param index
+ */
+template <typename value_idx, typename value_t>
+void construct_landmark_1nn(raft::resources const& handle,
+                            const value_idx* R_knn_inds_ptr,
+                            const value_t* R_knn_dists_ptr,
+                            int64_t k,
+                            cuvs::neighbors::ball_cover::index<value_idx, value_t>& index)
+{
+  rmm::device_uvector<value_idx> R_1nn_inds(index.m, raft::resource::get_cuda_stream(handle));
+
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_1nn_inds.data(),
+               R_1nn_inds.data() + index.m,
+               std::numeric_limits<value_idx>::max());
+
+  value_idx* R_1nn_inds_ptr = R_1nn_inds.data();
+  value_t* R_1nn_dists_ptr  = index.get_R_1nn_dists().data_handle();
+
+  auto idxs = thrust::make_counting_iterator<value_idx>(0);
+  thrust::for_each(
+    raft::resource::get_thrust_policy(handle), idxs, idxs + index.m, [=] __device__(value_idx i) {
+      R_1nn_inds_ptr[i]  = R_knn_inds_ptr[i * k];
+      R_1nn_dists_ptr[i] = R_knn_dists_ptr[i * k];
+    });
+
+  auto keys = thrust::make_zip_iterator(
+    thrust::make_tuple(R_1nn_inds.data(), index.get_R_1nn_dists().data_handle()));
+
+  // group neighborhoods for each reference landmark and sort each group by distance
+  thrust::sort_by_key(raft::resource::get_thrust_policy(handle),
+                      keys,
+                      keys + index.m,
+                      index.get_R_1nn_cols().data_handle(),
+                      NNComp());
+
+  // convert to CSR for fast lookup
+  raft::sparse::convert::sorted_coo_to_csr(R_1nn_inds.data(),
+                                           index.m,
+                                           index.get_R_indptr().data_handle(),
+                                           index.n_landmarks + 1,
+                                           raft::resource::get_cuda_stream(handle));
+
+  // reorder X to allow aligned access
+  raft::matrix::copy_rows<value_t, value_idx>(
+    handle, index.get_X(), index.get_X_reordered(), index.get_R_1nn_cols());
+}
+
+/**
+ * Computes the k closest landmarks to a set of query points.
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam value_int
+ * @param handle
+ * @param index
+ * @param query_pts
+ * @param n_query_pts
+ * @param k
+ * @param R_knn_inds
+ * @param R_knn_dists
+ */
+template <typename value_idx, typename value_t>
+void k_closest_landmarks(raft::resources const& handle,
+                         const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                         const value_t* query_pts,
+                         int64_t n_query_pts,
+                         int64_t k,
+                         value_idx* R_knn_inds,
+                         value_t* R_knn_dists)
+{
+  raft::device_matrix_view<const value_t, int64_t> inputs = index.get_R();
+  auto bf_index_params   = cuvs::neighbors::brute_force::index_params();
+  bf_index_params.metric = index.get_metric();
+  auto bfknn             = cuvs::neighbors::brute_force::build(handle, bf_index_params, inputs);
+
+  auto bf_search_params = cuvs::neighbors::brute_force::search_params();
+  cuvs::neighbors::brute_force::search(
+    handle,
+    bf_search_params,
+    bfknn,
+    raft::make_device_matrix_view<const value_t, int64_t>(query_pts, n_query_pts, inputs.extent(1)),
+    raft::make_device_matrix_view<value_idx, int64_t>(R_knn_inds, n_query_pts, k),
+    raft::make_device_matrix_view<value_t, int64_t>(R_knn_dists, n_query_pts, k));
+}
+
+/**
+ * Uses the sorted data points in the 1-nn landmark index to compute
+ * an array of radii for each landmark.
+ * @tparam value_idx
+ * @tparam value_t
+ * @param handle
+ * @param index
+ */
+template <typename value_idx, typename value_t>
+void compute_landmark_radii(raft::resources const& handle,
+                            cuvs::neighbors::ball_cover::index<value_idx, value_t>& index)
+{
+  auto entries = thrust::make_counting_iterator<value_idx>(0);
+
+  const value_idx* R_indptr_ptr  = index.get_R_indptr().data_handle();
+  const value_t* R_1nn_dists_ptr = index.get_R_1nn_dists().data_handle();
+  value_t* R_radius_ptr          = index.get_R_radius().data_handle();
+  thrust::for_each(raft::resource::get_thrust_policy(handle),
+                   entries,
+                   entries + index.n_landmarks,
+                   [=] __device__(value_idx input) {
+                     value_idx last_row_idx = R_indptr_ptr[input + 1] - 1;
+                     R_radius_ptr[input]    = R_1nn_dists_ptr[last_row_idx];
+                   });
+}
+
+/**
+ * 4. Perform k-select over original KNN, using L_r to filter distances
+ *
+ * a. Map 1 row to each warp/block
+ * b. Add closest k R points to heap
+ * c. Iterate through batches of R, having each thread in the warp load a set
+ * of distances y from R (only if d(q, r) < 3 * distance to closest r) and
+ * marking the distance to be computed between x, y only
+ * if knn[k].distance >= d(x_i, R_k) + d(R_k, y)
+ */
+template <typename value_idx, typename value_t>
+void perform_rbc_query(raft::resources const& handle,
+                       const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                       const value_t* query,
+                       int64_t n_query_pts,
+                       int64_t k,
+                       const value_idx* R_knn_inds,
+                       const value_t* R_knn_dists,
+                       value_idx* inds,
+                       value_t* dists,
+                       float weight                = 1.0,
+                       bool perform_post_filtering = true)
+{
+  // initialize output inds and dists
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               inds,
+               inds + (k * n_query_pts),
+               std::numeric_limits<value_idx>::max());
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               dists,
+               dists + (k * n_query_pts),
+               std::numeric_limits<value_t>::max());
+
+  // Compute nearest k for each neighborhood in each closest R
+  rbc_low_dim_pass_one<value_idx, value_t>(
+    handle, index, query, n_query_pts, k, R_knn_inds, R_knn_dists, inds, dists, weight, index.n);
+
+  if (perform_post_filtering) {
+    rbc_low_dim_pass_two<value_idx, value_t>(
+      handle, index, query, n_query_pts, k, R_knn_inds, R_knn_dists, inds, dists, weight, index.n);
+  }
+}
+
+/**
+ * Perform eps-select
+ *
+ */
+template <typename value_idx, typename value_t, typename dist_func>
+void perform_rbc_eps_nn_query(raft::resources const& handle,
+                              const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                              const value_t* query,
+                              int64_t n_query_pts,
+                              value_t eps,
+                              const value_t* landmarks,
+                              dist_func dfunc,
+                              bool* adj,
+                              value_idx* vd)
+{
+  // initialize output
+  RAFT_CUDA_TRY(cudaMemsetAsync(
+    adj, 0, index.m * n_query_pts * sizeof(bool), raft::resource::get_cuda_stream(handle)));
+
+  raft::resource::sync_stream(handle);
+
+  rbc_eps_pass<value_idx, value_t>(
+    handle, index, query, n_query_pts, eps, landmarks, dfunc, adj, vd);
+
+  raft::resource::sync_stream(handle);
+}
+
+template <typename value_idx, typename value_t, typename dist_func>
+void perform_rbc_eps_nn_query(raft::resources const& handle,
+                              const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                              const value_t* query,
+                              int64_t n_query_pts,
+                              value_t eps,
+                              int64_t* max_k,
+                              const value_t* landmarks,
+                              dist_func dfunc,
+                              value_idx* adj_ia,
+                              value_idx* adj_ja,
+                              value_idx* vd)
+{
+  rbc_eps_pass<value_idx, value_t>(
+    handle, index, query, n_query_pts, eps, max_k, landmarks, dfunc, adj_ia, adj_ja, vd);
+
+  raft::resource::sync_stream(handle);
+}
+
+/**
+ * Similar to a ball tree, the random ball cover algorithm
+ * uses the triangle inequality to prune distance computations
+ * in any metric space with a guarantee of sqrt(n) * c^{3/2}
+ * where `c` is an expansion constant based on the distance
+ * metric.
+ *
+ * This function variant performs an all nearest neighbors
+ * query which is useful for algorithms that need to perform
+ * A * A.T.
+ */
+template <typename value_idx = std::int64_t, typename value_t>
+void rbc_build_index(raft::resources const& handle,
+                     cuvs::neighbors::ball_cover::index<value_idx, value_t>& index)
+{
+  ASSERT(!index.is_index_trained(), "index cannot be previously trained");
+
+  rmm::device_uvector<value_idx> R_knn_inds(index.m, raft::resource::get_cuda_stream(handle));
+
+  // Initialize the uvectors
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_knn_inds.begin(),
+               R_knn_inds.end(),
+               std::numeric_limits<value_idx>::max());
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               index.get_R_closest_landmark_dists().data_handle(),
+               index.get_R_closest_landmark_dists().data_handle() + index.m,
+               std::numeric_limits<value_t>::max());
+
+  /**
+   * 1. Randomly sample sqrt(n) points from X
+   */
+  sample_landmarks<value_idx, value_t>(handle, index);
+
+  /**
+   * 2. Perform knn = bfknn(X, R, k)
+   */
+  int64_t k = 1;
+  k_closest_landmarks(handle,
+                      index,
+                      index.get_X().data_handle(),
+                      index.m,
+                      k,
+                      R_knn_inds.data(),
+                      index.get_R_closest_landmark_dists().data_handle());
+
+  /**
+   * 3. Create L_r = knn[:,0].T (CSR)
+   *
+   * Slice closest neighboring R
+   * Secondary sort by (R_knn_inds, R_knn_dists)
+   */
+  construct_landmark_1nn(
+    handle, R_knn_inds.data(), index.get_R_closest_landmark_dists().data_handle(), k, index);
+
+  /**
+   * Compute radius of each R for filtering: p(q, r) <= p(q, q_r) + radius(r)
+   * (need to take the
+   */
+  compute_landmark_radii(handle, index);
+}
+
+/**
+ * Performs an all neighbors knn query (e.g. index == query)
+ */
+template <typename value_idx = std::int64_t, typename value_t>
+void rbc_all_knn_query(raft::resources const& handle,
+                       cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                       int64_t k,
+                       value_idx* inds,
+                       value_t* dists,
+                       // approximate nn options
+                       bool perform_post_filtering = true,
+                       float weight                = 1.0)
+{
+  ASSERT(index.n <= 3, "only 2d and 3d vectors are supported in current implementation");
+  ASSERT(index.n_landmarks >= k, "number of landmark samples must be >= k");
+  ASSERT(!index.is_index_trained(), "index cannot be previously trained");
+
+  rmm::device_uvector<value_idx> R_knn_inds(k * index.m, raft::resource::get_cuda_stream(handle));
+  rmm::device_uvector<value_t> R_knn_dists(k * index.m, raft::resource::get_cuda_stream(handle));
+
+  // Initialize the uvectors
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_knn_inds.begin(),
+               R_knn_inds.end(),
+               std::numeric_limits<value_idx>::max());
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_knn_dists.begin(),
+               R_knn_dists.end(),
+               std::numeric_limits<value_t>::max());
+
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               inds,
+               inds + (k * index.m),
+               std::numeric_limits<value_idx>::max());
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               dists,
+               dists + (k * index.m),
+               std::numeric_limits<value_t>::max());
+
+  sample_landmarks<value_idx, value_t>(handle, index);
+
+  k_closest_landmarks(
+    handle, index, index.get_X().data_handle(), index.m, k, R_knn_inds.data(), R_knn_dists.data());
+
+  construct_landmark_1nn(handle, R_knn_inds.data(), R_knn_dists.data(), k, index);
+
+  compute_landmark_radii(handle, index);
+
+  perform_rbc_query(handle,
+                    index,
+                    index.get_X().data_handle(),
+                    index.m,
+                    k,
+                    R_knn_inds.data(),
+                    R_knn_dists.data(),
+                    inds,
+                    dists,
+                    weight,
+                    perform_post_filtering);
+}
+
+/**
+ * Performs a knn query against an index. This assumes the index has
+ * already been built.
+ */
+template <typename value_idx = std::int64_t, typename value_t>
+void rbc_knn_query(raft::resources const& handle,
+                   const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                   int64_t k,
+                   const value_t* query,
+                   int64_t n_query_pts,
+                   value_idx* inds,
+                   value_t* dists,
+                   // approximate nn options
+                   bool perform_post_filtering = true,
+                   float weight                = 1.0)
+{
+  ASSERT(index.n <= 3, "only 2d and 3d vectors are supported in current implementation");
+  ASSERT(index.n_landmarks >= k, "number of landmark samples must be >= k");
+  ASSERT(index.is_index_trained(), "index must be previously trained");
+
+  rmm::device_uvector<value_idx> R_knn_inds(k * n_query_pts,
+                                            raft::resource::get_cuda_stream(handle));
+  rmm::device_uvector<value_t> R_knn_dists(k * n_query_pts,
+                                           raft::resource::get_cuda_stream(handle));
+
+  // Initialize the uvectors
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_knn_inds.begin(),
+               R_knn_inds.end(),
+               std::numeric_limits<value_idx>::max());
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               R_knn_dists.begin(),
+               R_knn_dists.end(),
+               std::numeric_limits<value_t>::max());
+
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               inds,
+               inds + (k * n_query_pts),
+               std::numeric_limits<value_idx>::max());
+  thrust::fill(raft::resource::get_thrust_policy(handle),
+               dists,
+               dists + (k * n_query_pts),
+               std::numeric_limits<value_t>::max());
+
+  k_closest_landmarks(handle, index, query, n_query_pts, k, R_knn_inds.data(), R_knn_dists.data());
+
+  perform_rbc_query(handle,
+                    index,
+                    query,
+                    n_query_pts,
+                    k,
+                    R_knn_inds.data(),
+                    R_knn_dists.data(),
+                    inds,
+                    dists,
+                    weight,
+                    perform_post_filtering);
+}
+
+template <typename value_idx, typename value_t>
+void compute_landmark_dists(raft::resources const& handle,
+                            const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                            const value_t* query_pts,
+                            int64_t n_query_pts,
+                            value_t* R_dists)
+{
+  // compute distances for all queries against all landmarks
+  // index.get_R() -- landmark points in row order (index.n_landmarks x index.k)
+  // query_pts     -- query points in row order (n_query_pts x index.k)
+  RAFT_EXPECTS(std::max<size_t>(index.n_landmarks, n_query_pts) * index.n <
+                 static_cast<size_t>(std::numeric_limits<int>::max()),
+               "Too large input for pairwise_distance with `int` index.");
+  RAFT_EXPECTS(n_query_pts * static_cast<size_t>(index.n_landmarks) <
+                 static_cast<size_t>(std::numeric_limits<int>::max()),
+               "Too large input for pairwise_distance with `int` index.");
+  cuvs::distance::pairwise_distance(handle,
+                                    query_pts,
+                                    index.get_R().data_handle(),
+                                    R_dists,
+                                    n_query_pts,
+                                    index.n_landmarks,
+                                    index.n,
+                                    index.get_metric());
+}
+
+/**
+ * Performs a knn query against an index. This assumes the index has
+ * already been built.
+ * Modified version that takes an eps as threshold and outputs to a dense adj matrix (row-major)
+ * we are assuming that there are sufficiently many landmarks
+ */
+template <typename value_idx = std::int64_t, typename value_t, typename distance_func>
+void rbc_eps_nn_query(raft::resources const& handle,
+                      const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                      const value_t eps,
+                      const value_t* query,
+                      int64_t n_query_pts,
+                      bool* adj,
+                      value_idx* vd,
+                      distance_func dfunc)
+{
+  ASSERT(index.is_index_trained(), "index must be previously trained");
+
+  // query all points and write to adj
+  perform_rbc_eps_nn_query(
+    handle, index, query, n_query_pts, eps, index.get_R().data_handle(), dfunc, adj, vd);
+}
+
+template <typename value_idx = std::int64_t, typename value_t, typename distance_func>
+void rbc_eps_nn_query(raft::resources const& handle,
+                      const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                      const value_t eps,
+                      int64_t* max_k,
+                      const value_t* query,
+                      int64_t n_query_pts,
+                      value_idx* adj_ia,
+                      value_idx* adj_ja,
+                      value_idx* vd,
+                      distance_func dfunc)
+{
+  ASSERT(index.is_index_trained(), "index must be previously trained");
+
+  // query all points and write to adj
+  perform_rbc_eps_nn_query(handle,
+                           index,
+                           query,
+                           n_query_pts,
+                           eps,
+                           max_k,
+                           index.get_R().data_handle(),
+                           dfunc,
+                           adj_ia,
+                           adj_ja,
+                           vd);
+}
+
+};  // namespace cuvs::neighbors::ball_cover::detail
diff --git a/cpp/src/neighbors/ball_cover/common.cuh b/cpp/src/neighbors/ball_cover/common.cuh
new file mode 100644
index 0000000000..4e125c8c9a
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/common.cuh
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <thrust/functional.h>
+#include <thrust/tuple.h>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::ball_cover::detail {
+
+struct NNComp {
+  template <typename one, typename two>
+  __host__ __device__ bool operator()(const one& t1, const two& t2)
+  {
+    // sort first by each sample's reference landmark,
+    if (thrust::get<0>(t1) < thrust::get<0>(t2)) return true;
+    if (thrust::get<0>(t1) > thrust::get<0>(t2)) return false;
+
+    // then by closest neighbor,
+    return thrust::get<1>(t1) < thrust::get<1>(t2);
+  }
+};
+
+/**
+ * Zeros the bit at location h in a one-hot encoded 32-bit int array
+ */
+__device__ inline void _zero_bit(std::uint32_t* arr, std::uint32_t h)
+{
+  int bit = h % 32;
+  int idx = h / 32;
+
+  std::uint32_t assumed;
+  std::uint32_t old = arr[idx];
+  do {
+    assumed = old;
+    old     = atomicCAS(arr + idx, assumed, assumed & ~(1 << bit));
+  } while (assumed != old);
+}
+
+/**
+ * Returns whether or not bit at location h is nonzero in a one-hot
+ * encoded 32-bit in array.
+ */
+__device__ inline bool _get_val(std::uint32_t* arr, std::uint32_t h)
+{
+  int bit = h % 32;
+  int idx = h / 32;
+  return (arr[idx] & (1 << bit)) > 0;
+}
+
+};  // namespace cuvs::neighbors::ball_cover::detail
diff --git a/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_00_generate.py b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_00_generate.py
new file mode 100644
index 0000000000..e6442c6e9e
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_00_generate.py
@@ -0,0 +1,150 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+header = """/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by registers_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python registers_00_generate.py
+ *
+ */
+
+#include <cstdint> // int64_t
+#include <cuvs/neighbors/ball_cover.hpp>
+#include "../../registers.cuh"
+
+"""
+
+
+macro_pass_one = """
+#define instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_one(                            \\
+  Mvalue_idx, Mvalue_t)                          \\
+  template void                                                                              \\
+  cuvs::neighbors::ball_cover::detail::rbc_low_dim_pass_one<Mvalue_idx, Mvalue_t>( \\
+    raft::resources const& handle,                                                           \\
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,              \\
+    const Mvalue_t* query,                                                                   \\
+    const int64_t n_query_rows,                                                           \\
+    const int64_t k,                                                                            \\
+    const Mvalue_idx* R_knn_inds,                                                            \\
+    const Mvalue_t* R_knn_dists,                                                             \\
+    Mvalue_idx* inds,                                                                        \\
+    Mvalue_t* dists,                                                                         \\
+    float weight,                                                                            \\
+    int dims);
+
+"""
+
+macro_pass_two = """
+#define instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_two(                            \\
+  Mvalue_idx, Mvalue_t)                          \\
+  template void                                                                              \\
+  cuvs::neighbors::ball_cover::detail::rbc_low_dim_pass_two<Mvalue_idx, Mvalue_t>( \\
+    raft::resources const& handle,                                                           \\
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,              \\
+    const Mvalue_t* query,                                                                   \\
+    const int64_t n_query_rows,                                                           \\
+    const int64_t k,                                                                            \\
+    const Mvalue_idx* R_knn_inds,                                                            \\
+    const Mvalue_t* R_knn_dists,                                                             \\
+    Mvalue_idx* inds,                                                                        \\
+    Mvalue_t* dists,                                                                         \\
+    float weight,                                                                            \\
+    int dims);
+
+"""
+
+macro_pass_eps = """
+#define instantiate_cuvs_neighbors_detail_rbc_eps_pass(                                  \\
+  Mvalue_idx, Mvalue_t, Mdist_func)                               \\
+  template void                                                                            \\
+  cuvs::neighbors::ball_cover::detail::rbc_eps_pass<Mvalue_idx, Mvalue_t>( \\
+    raft::resources const& handle,                                                         \\
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,            \\
+    const Mvalue_t* query,                                                                 \\
+    const int64_t n_query_rows,                                                         \\
+    Mvalue_t eps,                                                                          \\
+    const Mvalue_t* R_dists,                                                               \\
+    Mdist_func<Mvalue_t, int64_t>& dfunc,                                               \\
+    bool* adj,                                                                             \\
+    Mvalue_idx* vd);                                                                       \\
+                                                                                           \\
+  template void                                                                            \\
+  cuvs::neighbors::ball_cover::detail::rbc_eps_pass<Mvalue_idx, Mvalue_t>( \\
+    raft::resources const& handle,                                                         \\
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,            \\
+    const Mvalue_t* query,                                                                 \\
+    const int64_t n_query_rows,                                                         \\
+    Mvalue_t eps,                                                                          \\
+    int64_t* max_k,                                                                     \\
+    const Mvalue_t* R_dists,                                                               \\
+    Mdist_func<Mvalue_t, int64_t>& dfunc,                                               \\
+    Mvalue_idx* adj_ia,                                                                    \\
+    Mvalue_idx* adj_ja,                                                                    \\
+    Mvalue_idx* vd)
+
+"""
+
+euclideanSq="cuvs::neighbors::ball_cover::detail::EuclideanSqFunc"
+
+types = dict(
+    int64_float=("std::int64_t", "float"),
+)
+
+path = f"registers_pass_one.cu"
+with open(path, "w") as f:
+    f.write(header)
+    f.write(macro_pass_one)
+    for type_path, (int_t, data_t) in types.items():
+        f.write(f"instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_one(\n")
+        f.write(f"  {int_t}, {data_t});\n")
+    f.write("#undef instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_one\n")
+print(f"src/neighbors/ball_cover/detail/ball_cover/{path}")
+
+path = f"registers_pass_two.cu"
+with open(path, "w") as f:
+    f.write(header)
+    f.write(macro_pass_two)
+    for type_path, (int_t, data_t) in types.items():
+        f.write(f"instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_two(\n")
+        f.write(f"  {int_t}, {data_t});\n")
+    f.write("#undef instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_two\n")
+print(f"src/neighbors/ball_cover/detail/ball_cover/{path}")
+
+path="registers_eps_pass_euclidean.cu"
+with open(path, "w") as f:
+    f.write(header)
+    f.write(macro_pass_eps)
+    for type_path, (int_t, data_t) in types.items():
+        f.write(f"instantiate_cuvs_neighbors_detail_rbc_eps_pass(\n")
+        f.write(f"  {int_t}, {data_t}, {euclideanSq});\n")
+    f.write("#undef instantiate_cuvs_neighbors_detail_rbc_eps_pass\n")
+    print(f"src/neighbors/ball_cover/detail/ball_cover/{path}")
diff --git a/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_eps_pass_euclidean.cu b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_eps_pass_euclidean.cu
new file mode 100644
index 0000000000..368c14025d
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_eps_pass_euclidean.cu
@@ -0,0 +1,57 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by registers_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python registers_00_generate.py
+ *
+ */
+
+#include "../../registers.cuh"
+#include <cstdint>  // int64_t
+#include <cuvs/neighbors/ball_cover.hpp>
+
+#define instantiate_cuvs_neighbors_detail_rbc_eps_pass(Mvalue_idx, Mvalue_t, Mdist_func) \
+  template void cuvs::neighbors::ball_cover::detail::rbc_eps_pass<Mvalue_idx, Mvalue_t>( \
+    raft::resources const& handle,                                                       \
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,               \
+    const Mvalue_t* query,                                                               \
+    const int64_t n_query_rows,                                                          \
+    Mvalue_t eps,                                                                        \
+    const Mvalue_t* R_dists,                                                             \
+    Mdist_func<Mvalue_t, int64_t>& dfunc,                                                \
+    bool* adj,                                                                           \
+    Mvalue_idx* vd);                                                                     \
+                                                                                         \
+  template void cuvs::neighbors::ball_cover::detail::rbc_eps_pass<Mvalue_idx, Mvalue_t>( \
+    raft::resources const& handle,                                                       \
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,               \
+    const Mvalue_t* query,                                                               \
+    const int64_t n_query_rows,                                                          \
+    Mvalue_t eps,                                                                        \
+    int64_t* max_k,                                                                      \
+    const Mvalue_t* R_dists,                                                             \
+    Mdist_func<Mvalue_t, int64_t>& dfunc,                                                \
+    Mvalue_idx* adj_ia,                                                                  \
+    Mvalue_idx* adj_ja,                                                                  \
+    Mvalue_idx* vd)
+
+instantiate_cuvs_neighbors_detail_rbc_eps_pass(
+  std::int64_t, float, cuvs::neighbors::ball_cover::detail::EuclideanSqFunc);
+#undef instantiate_cuvs_neighbors_detail_rbc_eps_pass
diff --git a/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_one.cu b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_one.cu
new file mode 100644
index 0000000000..8e29cc2232
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_one.cu
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by registers_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python registers_00_generate.py
+ *
+ */
+
+#include "../../registers.cuh"
+#include <cstdint>  // int64_t
+#include <cuvs/neighbors/ball_cover.hpp>
+
+#define instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_one(Mvalue_idx, Mvalue_t)             \
+  template void cuvs::neighbors::ball_cover::detail::rbc_low_dim_pass_one<Mvalue_idx, Mvalue_t>( \
+    raft::resources const& handle,                                                               \
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,                       \
+    const Mvalue_t* query,                                                                       \
+    const int64_t n_query_rows,                                                                  \
+    const int64_t k,                                                                             \
+    const Mvalue_idx* R_knn_inds,                                                                \
+    const Mvalue_t* R_knn_dists,                                                                 \
+    Mvalue_idx* inds,                                                                            \
+    Mvalue_t* dists,                                                                             \
+    float weight,                                                                                \
+    int dims);
+
+instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_one(std::int64_t, float);
+#undef instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_one
diff --git a/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_two.cu b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_two.cu
new file mode 100644
index 0000000000..20808af5f7
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/detail/ball_cover/registers_pass_two.cu
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by registers_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python registers_00_generate.py
+ *
+ */
+
+#include "../../registers.cuh"
+#include <cstdint>  // int64_t
+#include <cuvs/neighbors/ball_cover.hpp>
+
+#define instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_two(Mvalue_idx, Mvalue_t)             \
+  template void cuvs::neighbors::ball_cover::detail::rbc_low_dim_pass_two<Mvalue_idx, Mvalue_t>( \
+    raft::resources const& handle,                                                               \
+    const cuvs::neighbors::ball_cover::index<Mvalue_idx, Mvalue_t>& index,                       \
+    const Mvalue_t* query,                                                                       \
+    const int64_t n_query_rows,                                                                  \
+    const int64_t k,                                                                             \
+    const Mvalue_idx* R_knn_inds,                                                                \
+    const Mvalue_t* R_knn_dists,                                                                 \
+    Mvalue_idx* inds,                                                                            \
+    Mvalue_t* dists,                                                                             \
+    float weight,                                                                                \
+    int dims);
+
+instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_two(std::int64_t, float);
+#undef instantiate_cuvs_neighbors_detail_rbc_low_dim_pass_two
diff --git a/cpp/src/neighbors/ball_cover/registers.cuh b/cpp/src/neighbors/ball_cover/registers.cuh
new file mode 100644
index 0000000000..9931275f33
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/registers.cuh
@@ -0,0 +1,1496 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../detail/haversine_distance.cuh"
+#include "common.cuh"
+#include "registers.hpp"
+#include "registers_types.cuh"  // DistFunc
+#include <cuvs/neighbors/ball_cover.hpp>
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/neighbors/detail/faiss_select/key_value_block_select.cuh>
+#include <raft/util/cuda_utils.cuh>
+
+#include <thrust/count.h>
+#include <thrust/fill.h>
+#include <thrust/scan.h>
+
+#include <limits.h>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::ball_cover::detail {
+
+constexpr int MAX_COL_Q = 3;
+
+/**
+ * To find exact neighbors, we perform a post-processing stage
+ * that filters out those points which might have neighbors outside
+ * of their k closest landmarks. This is usually a very small portion
+ * of the total points.
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam value_int
+ * @tparam tpb
+ * @param X
+ * @param n_cols
+ * @param R_knn_inds
+ * @param R_knn_dists
+ * @param R_radius
+ * @param landmarks
+ * @param n_landmarks
+ * @param bitset_size
+ * @param k
+ * @param output
+ * @param weight
+ */
+template <typename value_idx, typename value_t, typename value_int = std::uint32_t, int tpb = 32>
+RAFT_KERNEL perform_post_filter_registers(const value_t* X,
+                                          int64_t n_cols,
+                                          const value_idx* R_knn_inds,
+                                          const value_t* R_knn_dists,
+                                          const value_t* R_radius,
+                                          const value_t* landmarks,
+                                          int n_landmarks,
+                                          value_int bitset_size,
+                                          int64_t k,
+                                          cuvs::distance::DistanceType metric,
+                                          std::uint32_t* output,
+                                          float weight = 1.0)
+{
+  // allocate array of size n_landmarks / 32 ints
+  extern __shared__ std::uint32_t shared_mem[];
+
+  // Start with all bits on
+  for (value_int i = threadIdx.x; i < bitset_size; i += tpb) {
+    shared_mem[i] = 0xffffffff;
+  }
+
+  __syncthreads();
+
+  // TODO: Would it be faster to use L1 for this?
+  value_t local_x_ptr[MAX_COL_Q];
+  for (value_int j = 0; j < n_cols; ++j) {
+    local_x_ptr[j] = X[n_cols * blockIdx.x + j];
+  }
+
+  value_t closest_R_dist = R_knn_dists[blockIdx.x * k + (k - 1)];
+
+  // zero out bits for closest k landmarks
+  for (value_int j = threadIdx.x; j < k; j += tpb) {
+    _zero_bit(shared_mem, (std::uint32_t)R_knn_inds[blockIdx.x * k + j]);
+  }
+
+  __syncthreads();
+
+  // Discard any landmarks where p(q, r) > p(q, r_q) + radius(r)
+  // That is, the distance between the current point and the current
+  // landmark is > the distance between the current point and
+  // its closest landmark + the radius of the current landmark.
+  for (value_int l = threadIdx.x; l < n_landmarks; l += tpb) {
+    // compute p(q, r)
+    value_t dist =
+      compute_distance_by_metric(local_x_ptr, landmarks + (n_cols * l), n_cols, metric);
+    if (dist > weight * (closest_R_dist + R_radius[l]) || dist > 3 * closest_R_dist) {
+      _zero_bit(shared_mem, l);
+    }
+  }
+
+  __syncthreads();
+
+  /**
+   * Output bitset
+   */
+  for (value_int l = threadIdx.x; l < bitset_size; l += tpb) {
+    output[blockIdx.x * bitset_size + l] = shared_mem[l];
+  }
+}
+
+/**
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam value_int
+ * @tparam bitset_type
+ * @tparam warp_q number of registers to use per warp
+ * @tparam thread_q number of registers to use within each thread
+ * @tparam tpb number of threads per block
+ * @param X
+ * @param n_cols
+ * @param bitset
+ * @param bitset_size
+ * @param R_knn_dists
+ * @param R_indptr
+ * @param R_1nn_inds
+ * @param R_1nn_dists
+ * @param knn_inds
+ * @param knn_dists
+ * @param n_landmarks
+ * @param k
+ */
+template <typename value_idx,
+          typename value_t,
+          typename value_int   = std::uint32_t,
+          typename bitset_type = std::uint32_t,
+          int warp_q           = 32,
+          int thread_q         = 2,
+          int tpb              = 128>
+RAFT_KERNEL compute_final_dists_registers(const value_t* X_reordered,
+                                          const value_t* X,
+                                          const int64_t n_cols,
+                                          bitset_type* bitset,
+                                          value_int bitset_size,
+                                          const value_t* R_closest_landmark_dists,
+                                          const value_idx* R_indptr,
+                                          const value_idx* R_1nn_inds,
+                                          const value_t* R_1nn_dists,
+                                          value_idx* knn_inds,
+                                          value_t* knn_dists,
+                                          int64_t n_landmarks,
+                                          int64_t k,
+                                          cuvs::distance::DistanceType metric)
+{
+  static constexpr int kNumWarps = tpb / raft::WarpSize;
+
+  __shared__ value_t shared_memK[kNumWarps * warp_q];
+  __shared__ raft::KeyValuePair<value_t, value_idx> shared_memV[kNumWarps * warp_q];
+
+  const value_t* x_ptr = X + (n_cols * blockIdx.x);
+  value_t local_x_ptr[MAX_COL_Q];
+  for (value_int j = 0; j < n_cols; ++j) {
+    local_x_ptr[j] = x_ptr[j];
+  }
+
+  using namespace raft::neighbors::detail::faiss_select;
+  KeyValueBlockSelect<value_t, value_idx, false, Comparator<value_t>, warp_q, thread_q, tpb> heap(
+    std::numeric_limits<value_t>::max(),
+    std::numeric_limits<value_t>::max(),
+    -1,
+    shared_memK,
+    shared_memV,
+    k);
+
+  const value_int n_k = raft::Pow2<raft::WarpSize>::roundDown(k);
+  value_int i         = threadIdx.x;
+  for (; i < n_k; i += tpb) {
+    value_idx ind = knn_inds[blockIdx.x * k + i];
+    heap.add(knn_dists[blockIdx.x * k + i], R_closest_landmark_dists[ind], ind);
+  }
+
+  if (i < k) {
+    value_idx ind = knn_inds[blockIdx.x * k + i];
+    heap.addThreadQ(knn_dists[blockIdx.x * k + i], R_closest_landmark_dists[ind], ind);
+  }
+
+  heap.checkThreadQ();
+
+  for (value_int cur_R_ind = 0; cur_R_ind < n_landmarks; ++cur_R_ind) {
+    // if cur R overlaps cur point's closest R, it could be a
+    // candidate
+    if (_get_val(bitset + (blockIdx.x * bitset_size), cur_R_ind)) {
+      value_idx R_start_offset = R_indptr[cur_R_ind];
+      value_idx R_stop_offset  = R_indptr[cur_R_ind + 1];
+      value_idx R_size         = R_stop_offset - R_start_offset;
+
+      // Loop through R's neighborhood in parallel
+
+      // Round R_size to the nearest warp threads so they can
+      // all be computing in parallel.
+
+      const value_int limit = raft::Pow2<raft::WarpSize>::roundDown(R_size);
+
+      i = threadIdx.x;
+      for (; i < limit; i += tpb) {
+        value_idx cur_candidate_ind = R_1nn_inds[R_start_offset + i];
+        value_t cur_candidate_dist  = R_1nn_dists[R_start_offset + i];
+
+        value_t z = heap.warpKTopRDist == 0.00 ? 0.0
+                                               : (abs(heap.warpKTop - heap.warpKTopRDist) *
+                                                    abs(heap.warpKTopRDist - cur_candidate_dist) -
+                                                  heap.warpKTop * cur_candidate_dist) /
+                                                   heap.warpKTopRDist;
+        z         = isnan(z) || isinf(z) ? 0.0 : z;
+
+        // If lower bound on distance could possibly be in
+        // the closest k neighbors, compute it and add to k-select
+        value_t dist = std::numeric_limits<value_t>::max();
+        if (z <= heap.warpKTop) {
+          const value_t* y_ptr = X_reordered + (n_cols * (R_start_offset + i));
+          value_t local_y_ptr[MAX_COL_Q];
+          for (value_int j = 0; j < n_cols; ++j) {
+            local_y_ptr[j] = y_ptr[j];
+          }
+
+          dist = compute_distance_by_metric(local_x_ptr, local_y_ptr, n_cols, metric);
+        }
+
+        heap.add(dist, cur_candidate_dist, cur_candidate_ind);
+      }
+
+      // second round guarantees to be only a single warp.
+      if (i < R_size) {
+        value_idx cur_candidate_ind = R_1nn_inds[R_start_offset + i];
+        value_t cur_candidate_dist  = R_1nn_dists[R_start_offset + i];
+
+        value_t z = heap.warpKTopRDist == 0.00 ? 0.0
+                                               : (abs(heap.warpKTop - heap.warpKTopRDist) *
+                                                    abs(heap.warpKTopRDist - cur_candidate_dist) -
+                                                  heap.warpKTop * cur_candidate_dist) /
+                                                   heap.warpKTopRDist;
+
+        z = isnan(z) || isinf(z) ? 0.0 : z;
+
+        // If lower bound on distance could possibly be in
+        // the closest k neighbors, compute it and add to k-select
+        value_t dist = std::numeric_limits<value_t>::max();
+        if (z <= heap.warpKTop) {
+          const value_t* y_ptr = X_reordered + (n_cols * (R_start_offset + i));
+          value_t local_y_ptr[MAX_COL_Q];
+          for (value_int j = 0; j < n_cols; ++j) {
+            local_y_ptr[j] = y_ptr[j];
+          }
+          dist = compute_distance_by_metric(local_x_ptr, local_y_ptr, n_cols, metric);
+        }
+        heap.addThreadQ(dist, cur_candidate_dist, cur_candidate_ind);
+      }
+      heap.checkThreadQ();
+    }
+  }
+
+  heap.reduce();
+
+  for (value_int i = threadIdx.x; i < k; i += tpb) {
+    knn_dists[blockIdx.x * k + i] = shared_memK[i];
+    knn_inds[blockIdx.x * k + i]  = shared_memV[i].value;
+  }
+}
+
+/**
+ * Random ball cover kernel for n_dims == 2
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam warp_q
+ * @tparam thread_q
+ * @tparam tpb
+ * @tparam value_idx
+ * @tparam value_t
+ * @param R_knn_inds
+ * @param R_knn_dists
+ * @param m
+ * @param k
+ * @param R_indptr
+ * @param R_1nn_cols
+ * @param R_1nn_dists
+ */
+template <typename value_idx = std::int64_t,
+          typename value_t,
+          int warp_q   = 32,
+          int thread_q = 2,
+          int tpb      = 128>
+RAFT_KERNEL block_rbc_kernel_registers(const value_t* X_reordered,
+                                       const value_t* X,
+                                       int64_t n_cols,  // n_cols should be 2 or 3 dims
+                                       const value_idx* R_knn_inds,
+                                       const value_t* R_knn_dists,
+                                       int64_t m,
+                                       int64_t k,
+                                       const value_idx* R_indptr,
+                                       const value_idx* R_1nn_cols,
+                                       const value_t* R_1nn_dists,
+                                       value_idx* out_inds,
+                                       value_t* out_dists,
+                                       const value_t* R_radius,
+                                       cuvs::distance::DistanceType metric,
+                                       float weight = 1.0)
+{
+  static constexpr int64_t kNumWarps = tpb / raft::WarpSize;
+
+  __shared__ value_t shared_memK[kNumWarps * warp_q];
+  __shared__ raft::KeyValuePair<value_t, value_idx> shared_memV[kNumWarps * warp_q];
+
+  // TODO: Separate kernels for different widths:
+  // 1. Very small (between 3 and 32) just use registers for columns of "blockIdx.x"
+  // 2. Can fit comfortably in shared memory (32 to a few thousand?)
+  // 3. Load each time individually.
+  const value_t* x_ptr = X + (n_cols * blockIdx.x);
+
+  // Use registers only for 2d or 3d
+  value_t local_x_ptr[MAX_COL_Q];
+  for (int64_t i = 0; i < n_cols; ++i) {
+    local_x_ptr[i] = x_ptr[i];
+  }
+
+  // Each warp works on 1 R
+  using namespace raft::neighbors::detail::faiss_select;
+  KeyValueBlockSelect<value_t, value_idx, false, Comparator<value_t>, warp_q, thread_q, tpb> heap(
+    std::numeric_limits<value_t>::max(),
+    std::numeric_limits<value_t>::max(),
+    -1,
+    shared_memK,
+    shared_memV,
+    k);
+
+  value_t min_R_dist       = R_knn_dists[blockIdx.x * k + (k - 1)];
+  int64_t n_dists_computed = 0;
+
+  /**
+   * First add distances for k closest neighbors of R
+   * to the heap
+   */
+  // Start iterating through elements of each set from closest R elements,
+  // determining if the distance could even potentially be in the heap.
+  for (int64_t cur_k = 0; cur_k < k; ++cur_k) {
+    // index and distance to current blockIdx.x's closest landmark
+    value_t cur_R_dist  = R_knn_dists[blockIdx.x * k + cur_k];
+    value_idx cur_R_ind = R_knn_inds[blockIdx.x * k + cur_k];
+
+    // Equation (2) in Cayton's paper- prune out R's which are > 3 * p(q, r_q)
+    if (cur_R_dist > weight * (min_R_dist + R_radius[cur_R_ind])) continue;
+    if (cur_R_dist > 3 * min_R_dist) return;
+
+    // The whole warp should iterate through the elements in the current R
+    value_idx R_start_offset = R_indptr[cur_R_ind];
+    value_idx R_stop_offset  = R_indptr[cur_R_ind + 1];
+
+    value_idx R_size = R_stop_offset - R_start_offset;
+
+    int64_t limit = raft::Pow2<raft::WarpSize>::roundDown(R_size);
+    int64_t i     = threadIdx.x;
+    for (; i < limit; i += tpb) {
+      // Index and distance of current candidate's nearest landmark
+      value_idx cur_candidate_ind = R_1nn_cols[R_start_offset + i];
+      value_t cur_candidate_dist  = R_1nn_dists[R_start_offset + i];
+
+      // Take 2 landmarks l_1 and l_2 where l_1 is the furthest point in the heap
+      // and l_2 is the current landmark R. s is the current data point and
+      // t is the new candidate data point. We know that:
+      // d(s, t) cannot possibly be any smaller than | d(s, l_1) - d(l_1, l_2) | * | d(l_1, l_2)
+      // - d(l_2, t) | - d(s, l_1) * d(l_2, t)
+
+      // Therefore, if d(s, t) >= d(s, l_1) from the computation above, we know that the
+      // distance to the candidate point cannot possibly be in the nearest neighbors. However,
+      // if d(s, t) < d(s, l_1) then we should compute the distance because it's possible it
+      // could be smaller.
+      //
+      value_t z = heap.warpKTopRDist == 0.00 ? 0.0
+                                             : (abs(heap.warpKTop - heap.warpKTopRDist) *
+                                                  abs(heap.warpKTopRDist - cur_candidate_dist) -
+                                                heap.warpKTop * cur_candidate_dist) /
+                                                 heap.warpKTopRDist;
+
+      z            = isnan(z) || isinf(z) ? 0.0 : z;
+      value_t dist = std::numeric_limits<value_t>::max();
+
+      if (z <= heap.warpKTop) {
+        const value_t* y_ptr = X_reordered + (n_cols * (R_start_offset + i));
+        value_t local_y_ptr[MAX_COL_Q];
+        for (int64_t j = 0; j < n_cols; ++j) {
+          local_y_ptr[j] = y_ptr[j];
+        }
+        dist = compute_distance_by_metric(local_x_ptr, local_y_ptr, n_cols, metric);
+        ++n_dists_computed;
+      }
+
+      heap.add(dist, cur_candidate_dist, cur_candidate_ind);
+    }
+
+    if (i < R_size) {
+      value_idx cur_candidate_ind = R_1nn_cols[R_start_offset + i];
+      value_t cur_candidate_dist  = R_1nn_dists[R_start_offset + i];
+      value_t z                   = heap.warpKTopRDist == 0.0 ? 0.0
+                                                              : (abs(heap.warpKTop - heap.warpKTopRDist) *
+                                                 abs(heap.warpKTopRDist - cur_candidate_dist) -
+                                               heap.warpKTop * cur_candidate_dist) /
+                                                heap.warpKTopRDist;
+
+      z            = isnan(z) || isinf(z) ? 0.0 : z;
+      value_t dist = std::numeric_limits<value_t>::max();
+
+      if (z <= heap.warpKTop) {
+        const value_t* y_ptr = X_reordered + (n_cols * (R_start_offset + i));
+        value_t local_y_ptr[MAX_COL_Q];
+        for (int64_t j = 0; j < n_cols; ++j) {
+          local_y_ptr[j] = y_ptr[j];
+        }
+        dist = compute_distance_by_metric(local_x_ptr, local_y_ptr, n_cols, metric);
+        ++n_dists_computed;
+      }
+
+      heap.addThreadQ(dist, cur_candidate_dist, cur_candidate_ind);
+    }
+
+    heap.checkThreadQ();
+  }
+
+  heap.reduce();
+
+  for (int i = threadIdx.x; i < k; i += tpb) {
+    out_dists[blockIdx.x * k + i] = shared_memK[i];
+    out_inds[blockIdx.x * k + i]  = shared_memV[i].value;
+  }
+}
+
+template <typename value_t>
+__device__ value_t squared(const value_t& a)
+{
+  return a * a;
+}
+
+template <typename value_idx = std::int64_t,
+          typename value_t,
+          int tpb = 128,
+          typename distance_func>
+RAFT_KERNEL block_rbc_kernel_eps_dense(const value_t* X_reordered,
+                                       const value_t* X,
+                                       const int64_t n_queries,
+                                       const int64_t n_cols,
+                                       const value_t* R,
+                                       const int64_t m,
+                                       const value_t eps,
+                                       const int64_t n_landmarks,
+                                       const value_idx* R_indptr,
+                                       const value_idx* R_1nn_cols,
+                                       const value_t* R_1nn_dists,
+                                       const value_t* R_radius,
+                                       distance_func dfunc,
+                                       bool* adj,
+                                       value_idx* vd)
+{
+  constexpr int num_warps = tpb / raft::WarpSize;
+
+  // process 1 query per warp
+  const uint32_t lid = raft::laneId();
+
+  // this should help the compiler to prevent branches
+  const int query_id = raft::shfl(blockIdx.x * num_warps + (threadIdx.x / raft::WarpSize), 0);
+
+  // this is an early out for a full warp
+  if (query_id >= n_queries) return;
+
+  value_idx column_count = 0;
+
+  const value_t* x_ptr = X + (n_cols * query_id);
+  adj += query_id * m;
+
+  // we omit the sqrt() in the inner distance compute
+  const value_t eps2 = eps * eps;
+
+#pragma nounroll
+  for (uint32_t cur_k0 = 0; cur_k0 < n_landmarks; cur_k0 += raft::WarpSize) {
+    // Pre-compute landmark_dist & triangularization checks for 32 iterations
+    const uint32_t lane_k        = cur_k0 + lid;
+    const value_t lane_R_dist_sq = lane_k < n_landmarks ? dfunc(x_ptr, R + lane_k * n_cols, n_cols)
+                                                        : std::numeric_limits<value_idx>::max();
+    const int lane_check         = lane_k < n_landmarks
+                                     ? static_cast<int>(lane_R_dist_sq <= squared(eps + R_radius[lane_k]))
+                                     : 0;
+
+    int lane_mask = raft::ballot(lane_check);
+    if (lane_mask == 0) continue;
+
+    // reverse to use __clz instead of __ffs
+    lane_mask = __brev(lane_mask);
+    do {
+      // look for next k_offset
+      const uint32_t k_offset = __clz(lane_mask);
+
+      const uint32_t cur_k = cur_k0 + k_offset;
+
+      // The whole warp should iterate through the elements in the current R
+      const value_idx R_start_offset = R_indptr[cur_k];
+
+      // update lane_mask for next iteration - erase bits up to k_offset
+      lane_mask &= (0x7fffffff >> k_offset);
+
+      const uint32_t R_size = R_indptr[cur_k + 1] - R_start_offset;
+
+      // we have precomputed the query<->landmark distance
+      const value_t cur_R_dist = raft::sqrt(raft::shfl(lane_R_dist_sq, k_offset));
+
+      const uint32_t limit = raft::Pow2<raft::WarpSize>::roundDown(R_size);
+      uint32_t i           = limit + lid;
+
+      // R_1nn_dists are sorted ascendingly for each landmark
+      // Iterating backwards, after pruning the first point w.r.t. triangle
+      // inequality all subsequent points can be pruned as well
+      const value_t* y_ptr = X_reordered + (n_cols * (R_start_offset + i));
+      {
+        const value_t min_warp_dist =
+          limit < R_size ? R_1nn_dists[R_start_offset + limit] : cur_R_dist;
+        const value_t dist =
+          (i < R_size) ? dfunc(x_ptr, y_ptr, n_cols) : std::numeric_limits<value_idx>::max();
+        const bool in_range = (dist <= eps2);
+        if (in_range) {
+          auto index = R_1nn_cols[R_start_offset + i];
+          column_count++;
+          adj[index] = true;
+        }
+        // abort in case subsequent points cannot possibly be in reach
+        i *= (cur_R_dist - min_warp_dist <= eps);
+      }
+
+      uint32_t i0 = raft::shfl(i, 0);
+
+      while (i0 >= raft::WarpSize) {
+        y_ptr -= raft::WarpSize * n_cols;
+        i0 -= raft::WarpSize;
+        const value_t min_warp_dist = R_1nn_dists[R_start_offset + i0];
+        const value_t dist          = dfunc(x_ptr, y_ptr, n_cols);
+        const bool in_range         = (dist <= eps2);
+        if (in_range) {
+          auto index = R_1nn_cols[R_start_offset + i0 + lid];
+          column_count++;
+          adj[index] = true;
+        }
+        // abort in case subsequent points cannot possibly be in reach
+        i0 *= (cur_R_dist - min_warp_dist <= eps);
+      }
+    } while (lane_mask);
+  }
+
+  if (vd != nullptr) {
+    value_idx row_sum = raft::warpReduce(column_count);
+    if (lid == 0) vd[query_id] = row_sum;
+  }
+}
+
+template <typename value_idx = std::int64_t,
+          typename value_t,
+          int tpb = 128,
+          typename distance_func>
+RAFT_KERNEL block_rbc_kernel_eps_csr_pass(const value_t* X_reordered,
+                                          const value_t* X,
+                                          const int64_t n_queries,
+                                          const int64_t n_cols,
+                                          const value_t* R,
+                                          const int64_t m,
+                                          const value_t eps,
+                                          const int64_t n_landmarks,
+                                          const value_idx* R_indptr,
+                                          const value_idx* R_1nn_cols,
+                                          const value_t* R_1nn_dists,
+                                          const value_t* R_radius,
+                                          distance_func dfunc,
+                                          value_idx* adj_ia,
+                                          value_idx* adj_ja,
+                                          bool write_pass)
+{
+  constexpr int num_warps = tpb / raft::WarpSize;
+
+  // process 1 query per warp
+  const uint32_t lid      = raft::laneId();
+  const uint32_t lid_mask = (1 << lid) - 1;
+
+  // this should help the compiler to prevent branches
+  const int query_id = raft::shfl(blockIdx.x * num_warps + (threadIdx.x / raft::WarpSize), 0);
+
+  // this is an early out for a full warp
+  if (query_id >= n_queries) return;
+
+  uint32_t column_index_offset = 0;
+
+  if (write_pass) {
+    value_idx offset = adj_ia[query_id];
+    // we have no neighbors to fill for this query
+    if (offset == adj_ia[query_id + 1]) return;
+    adj_ja += offset;
+  }
+
+  const value_t* x_ptr = X + (n_cols * query_id);
+
+  // we omit the sqrt() in the inner distance compute
+  const value_t eps2 = eps * eps;
+
+#pragma nounroll
+  for (uint32_t cur_k0 = 0; cur_k0 < n_landmarks; cur_k0 += raft::WarpSize) {
+    // Pre-compute landmark_dist & triangularization checks for 32 iterations
+    const uint32_t lane_k        = cur_k0 + lid;
+    const value_t lane_R_dist_sq = lane_k < n_landmarks ? dfunc(x_ptr, R + lane_k * n_cols, n_cols)
+                                                        : std::numeric_limits<value_idx>::max();
+    const int lane_check         = lane_k < n_landmarks
+                                     ? static_cast<int>(lane_R_dist_sq <= squared(eps + R_radius[lane_k]))
+                                     : 0;
+
+    int lane_mask = raft::ballot(lane_check);
+    if (lane_mask == 0) continue;
+
+    // reverse to use __clz instead of __ffs
+    lane_mask = __brev(lane_mask);
+    do {
+      // look for next k_offset
+      const uint32_t k_offset = __clz(lane_mask);
+
+      const uint32_t cur_k = cur_k0 + k_offset;
+
+      // The whole warp should iterate through the elements in the current R
+      const value_idx R_start_offset = R_indptr[cur_k];
+
+      // update lane_mask for next iteration - erase bits up to k_offset
+      lane_mask &= (0x7fffffff >> k_offset);
+
+      const uint32_t R_size = R_indptr[cur_k + 1] - R_start_offset;
+
+      // we have precomputed the query<->landmark distance
+      const value_t cur_R_dist = raft::sqrt(raft::shfl(lane_R_dist_sq, k_offset));
+
+      const uint32_t limit = raft::Pow2<raft::WarpSize>::roundDown(R_size);
+      uint32_t i           = limit + lid;
+
+      // R_1nn_dists are sorted ascendingly for each landmark
+      // Iterating backwards, after pruning the first point w.r.t. triangle
+      // inequality all subsequent points can be pruned as well
+      const value_t* y_ptr = X_reordered + (n_cols * (R_start_offset + i));
+      {
+        const value_t min_warp_dist =
+          limit < R_size ? R_1nn_dists[R_start_offset + limit] : cur_R_dist;
+        const value_t dist =
+          (i < R_size) ? dfunc(x_ptr, y_ptr, n_cols) : std::numeric_limits<value_idx>::max();
+        const bool in_range = (dist <= eps2);
+        if (write_pass) {
+          const int mask = raft::ballot(in_range);
+          if (in_range) {
+            const uint32_t index   = R_1nn_cols[R_start_offset + i];
+            const uint32_t row_pos = __popc(mask & lid_mask);
+            adj_ja[row_pos]        = index;
+          }
+          adj_ja += __popc(mask);
+        } else {
+          column_index_offset += (in_range);
+        }
+        // abort in case subsequent points cannot possibly be in reach
+        i *= (cur_R_dist - min_warp_dist <= eps);
+      }
+
+      uint32_t i0 = raft::shfl(i, 0);
+
+      while (i0 >= raft::WarpSize) {
+        y_ptr -= raft::WarpSize * n_cols;
+        i0 -= raft::WarpSize;
+        const value_t min_warp_dist = R_1nn_dists[R_start_offset + i0];
+        const value_t dist          = dfunc(x_ptr, y_ptr, n_cols);
+        const bool in_range         = (dist <= eps2);
+        if (write_pass) {
+          const int mask = raft::ballot(in_range);
+          if (in_range) {
+            const uint32_t index   = R_1nn_cols[R_start_offset + i0 + lid];
+            const uint32_t row_pos = __popc(mask & lid_mask);
+            adj_ja[row_pos]        = index;
+          }
+          adj_ja += __popc(mask);
+        } else {
+          column_index_offset += (in_range);
+        }
+        // abort in case subsequent points cannot possibly be in reach
+        i0 *= (cur_R_dist - min_warp_dist <= eps);
+      }
+    } while (lane_mask);
+  }
+
+  if (!write_pass) {
+    value_idx row_sum = raft::warpReduce(column_index_offset);
+    if (lid == 0) adj_ia[query_id] = row_sum;
+  }
+}
+
+template <typename value_idx = std::int64_t,
+          typename value_t,
+          int tpb = 128,
+          typename distance_func>
+RAFT_KERNEL __launch_bounds__(tpb)
+  block_rbc_kernel_eps_csr_pass_xd(const value_t* __restrict__ X_reordered,
+                                   const value_t* __restrict__ X,
+                                   const int64_t n_queries,
+                                   const int64_t n_cols,
+                                   const value_t* __restrict__ R,
+                                   const int64_t m,
+                                   const value_t eps,
+                                   const int64_t n_landmarks,
+                                   const value_idx* __restrict__ R_indptr,
+                                   const value_idx* __restrict__ R_1nn_cols,
+                                   const value_t* __restrict__ R_1nn_dists,
+                                   const value_t* __restrict__ R_radius,
+                                   distance_func dfunc,
+                                   value_idx* __restrict__ adj_ia,
+                                   value_idx* __restrict__ adj_ja,
+                                   bool write_pass,
+                                   int dim)
+{
+  constexpr int num_warps = tpb / raft::WarpSize;
+
+  // process 1 query per warp
+  const uint32_t lid      = raft::laneId();
+  const uint32_t lid_mask = (1 << lid) - 1;
+
+  // this should help the compiler to prevent branches
+  const int query_id = raft::shfl(blockIdx.x * num_warps + (threadIdx.x / raft::WarpSize), 0);
+
+  // this is an early out for a full warp
+  if (query_id >= n_queries) return;
+
+  uint32_t column_index_offset = 0;
+
+  if (write_pass) {
+    value_idx offset = adj_ia[query_id];
+    // we have no neighbors to fill for this query
+    if (offset == adj_ia[query_id + 1]) return;
+    adj_ja += offset;
+  }
+
+  const value_t* x_ptr = X + (dim * query_id);
+  value_t local_x_ptr[MAX_COL_Q];
+#pragma unroll
+  for (uint32_t i = 0; i < dim; ++i) {
+    local_x_ptr[i] = x_ptr[i];
+  }
+
+  // we omit the sqrt() in the inner distance compute
+  const value_t eps2 = eps * eps;
+
+#pragma nounroll
+  for (uint32_t cur_k0 = 0; cur_k0 < n_landmarks; cur_k0 += raft::WarpSize) {
+    // Pre-compute landmark_dist & triangularization checks for 32 iterations
+    const uint32_t lane_k        = cur_k0 + lid;
+    const value_t lane_R_dist_sq = lane_k < n_landmarks ? dfunc(local_x_ptr, R + lane_k * dim, dim)
+                                                        : std::numeric_limits<value_idx>::max();
+    const int lane_check         = lane_k < n_landmarks
+                                     ? static_cast<int>(lane_R_dist_sq <= squared(eps + R_radius[lane_k]))
+                                     : 0;
+
+    int lane_mask = raft::ballot(lane_check);
+    if (lane_mask == 0) continue;
+
+    // reverse to use __clz instead of __ffs
+    lane_mask = __brev(lane_mask);
+    do {
+      // look for next k_offset
+      const uint32_t k_offset = __clz(lane_mask);
+
+      const uint32_t cur_k = cur_k0 + k_offset;
+
+      // The whole warp should iterate through the elements in the current R
+      const value_idx R_start_offset = R_indptr[cur_k];
+
+      // update lane_mask for next iteration - erase bits up to k_offset
+      lane_mask &= (0x7fffffff >> k_offset);
+
+      const uint32_t R_size = R_indptr[cur_k + 1] - R_start_offset;
+
+      // we have precomputed the query<->landmark distance
+      const value_t cur_R_dist = raft::sqrt(raft::shfl(lane_R_dist_sq, k_offset));
+
+      const uint32_t limit = raft::Pow2<raft::WarpSize>::roundDown(R_size);
+      uint32_t i           = limit + lid;
+
+      // R_1nn_dists are sorted ascendingly for each landmark
+      // Iterating backwards, after pruning the first point w.r.t. triangle
+      // inequality all subsequent points can be pruned as well
+      const value_t* y_ptr = X_reordered + (dim * (R_start_offset + i));
+      {
+        const value_t min_warp_dist =
+          limit < R_size ? R_1nn_dists[R_start_offset + limit] : cur_R_dist;
+        const value_t dist =
+          (i < R_size) ? dfunc(local_x_ptr, y_ptr, dim) : std::numeric_limits<value_idx>::max();
+        const bool in_range = (dist <= eps2);
+        if (write_pass) {
+          const int mask = raft::ballot(in_range);
+          if (in_range) {
+            const uint32_t index   = R_1nn_cols[R_start_offset + i];
+            const uint32_t row_pos = __popc(mask & lid_mask);
+            adj_ja[row_pos]        = index;
+          }
+          adj_ja += __popc(mask);
+        } else {
+          column_index_offset += (in_range);
+        }
+        // abort in case subsequent points cannot possibly be in reach
+        i *= (cur_R_dist - min_warp_dist <= eps);
+      }
+
+      uint32_t i0 = raft::shfl(i, 0);
+
+      while (i0 >= raft::WarpSize) {
+        y_ptr -= raft::WarpSize * dim;
+        i0 -= raft::WarpSize;
+        const value_t min_warp_dist = R_1nn_dists[R_start_offset + i0];
+        const value_t dist          = dfunc(local_x_ptr, y_ptr, dim);
+        const bool in_range         = (dist <= eps2);
+        if (write_pass) {
+          const int mask = raft::ballot(in_range);
+          if (in_range) {
+            const uint32_t index   = R_1nn_cols[R_start_offset + i0 + lid];
+            const uint32_t row_pos = __popc(mask & lid_mask);
+            adj_ja[row_pos]        = index;
+          }
+          adj_ja += __popc(mask);
+        } else {
+          column_index_offset += (in_range);
+        }
+        // abort in case subsequent points cannot possibly be in reach
+        i0 *= (cur_R_dist - min_warp_dist <= eps);
+      }
+    } while (lane_mask);
+  }
+
+  if (!write_pass) {
+    value_idx row_sum = raft::warpReduce(column_index_offset);
+    if (lid == 0) adj_ia[query_id] = row_sum;
+  }
+}
+
+template <typename value_idx = std::int64_t,
+          typename value_t,
+          int tpb = 128,
+          typename distance_func>
+RAFT_KERNEL block_rbc_kernel_eps_max_k(const value_t* X_reordered,
+                                       const value_t* X,
+                                       const int64_t n_queries,
+                                       const int64_t n_cols,
+                                       const value_t* R,
+                                       const int64_t m,
+                                       const value_t eps,
+                                       const int64_t n_landmarks,
+                                       const value_idx* R_indptr,
+                                       const value_idx* R_1nn_cols,
+                                       const value_t* R_1nn_dists,
+                                       const value_t* R_radius,
+                                       distance_func dfunc,
+                                       value_idx* vd,
+                                       int64_t max_k,
+                                       value_idx* tmp)
+{
+  constexpr int num_warps = tpb / raft::WarpSize;
+
+  // process 1 query per warp
+  const uint32_t lid      = raft::laneId();
+  const uint32_t lid_mask = (1 << lid) - 1;
+
+  // this should help the compiler to prevent branches
+  const int query_id = raft::shfl(blockIdx.x * num_warps + (threadIdx.x / raft::WarpSize), 0);
+
+  // this is an early out for a full warp
+  if (query_id >= n_queries) return;
+
+  value_idx column_count = 0;
+
+  const value_t* x_ptr = X + (n_cols * query_id);
+  tmp += query_id * max_k;
+
+  // we omit the sqrt() in the inner distance compute
+  const value_t eps2 = eps * eps;
+
+#pragma nounroll
+  for (uint32_t cur_k0 = 0; cur_k0 < n_landmarks; cur_k0 += raft::WarpSize) {
+    // Pre-compute landmark_dist & triangularization checks for 32 iterations
+    const uint32_t lane_k        = cur_k0 + lid;
+    const value_t lane_R_dist_sq = lane_k < n_landmarks ? dfunc(x_ptr, R + lane_k * n_cols, n_cols)
+                                                        : std::numeric_limits<value_idx>::max();
+    const int lane_check         = lane_k < n_landmarks
+                                     ? static_cast<int>(lane_R_dist_sq <= squared(eps + R_radius[lane_k]))
+                                     : 0;
+
+    int lane_mask = raft::ballot(lane_check);
+    if (lane_mask == 0) continue;
+
+    // reverse to use __clz instead of __ffs
+    lane_mask = __brev(lane_mask);
+    do {
+      // look for next k_offset
+      const uint32_t k_offset = __clz(lane_mask);
+
+      const uint32_t cur_k = cur_k0 + k_offset;
+
+      // The whole warp should iterate through the elements in the current R
+      const value_idx R_start_offset = R_indptr[cur_k];
+
+      // update lane_mask for next iteration - erase bits up to k_offset
+      lane_mask &= (0x7fffffff >> k_offset);
+
+      const uint32_t R_size = R_indptr[cur_k + 1] - R_start_offset;
+
+      // we have precomputed the query<->landmark distance
+      const value_t cur_R_dist = raft::sqrt(raft::shfl(lane_R_dist_sq, k_offset));
+
+      const uint32_t limit = raft::Pow2<raft::WarpSize>::roundDown(R_size);
+      uint32_t i           = limit + lid;
+
+      // R_1nn_dists are sorted ascendingly for each landmark
+      // Iterating backwards, after pruning the first point w.r.t. triangle
+      // inequality all subsequent points can be pruned as well
+      const value_t* y_ptr = X_reordered + (n_cols * (R_start_offset + i));
+      {
+        const value_t min_warp_dist =
+          limit < R_size ? R_1nn_dists[R_start_offset + limit] : cur_R_dist;
+        const value_t dist =
+          (i < R_size) ? dfunc(x_ptr, y_ptr, n_cols) : std::numeric_limits<value_idx>::max();
+        const bool in_range = (dist <= eps2);
+        const int mask      = raft::ballot(in_range);
+        if (in_range) {
+          auto row_pos = column_count + __popc(mask & lid_mask);
+          // we still continue to look for more hits to return valid vd
+          if (row_pos < max_k) {
+            auto index   = R_1nn_cols[R_start_offset + i];
+            tmp[row_pos] = index;
+          }
+        }
+        column_count += __popc(mask);
+        // abort in case subsequent points cannot possibly be in reach
+        i *= (cur_R_dist - min_warp_dist <= eps);
+      }
+
+      uint32_t i0 = raft::shfl(i, 0);
+
+      while (i0 >= raft::WarpSize) {
+        y_ptr -= raft::WarpSize * n_cols;
+        i0 -= raft::WarpSize;
+        const value_t min_warp_dist = R_1nn_dists[R_start_offset + i0];
+        const value_t dist          = dfunc(x_ptr, y_ptr, n_cols);
+        const bool in_range         = (dist <= eps2);
+        const int mask              = raft::ballot(in_range);
+        if (in_range) {
+          auto row_pos = column_count + __popc(mask & lid_mask);
+          // we still continue to look for more hits to return valid vd
+          if (row_pos < max_k) {
+            auto index   = R_1nn_cols[R_start_offset + i0 + lid];
+            tmp[row_pos] = index;
+          }
+        }
+        column_count += __popc(mask);
+        // abort in case subsequent points cannot possibly be in reach
+        i0 *= (cur_R_dist - min_warp_dist <= eps);
+      }
+    } while (lane_mask);
+  }
+
+  if (lid == 0) vd[query_id] = column_count;
+}
+
+template <typename value_idx = std::int64_t, int tpb = 128>
+RAFT_KERNEL block_rbc_kernel_eps_max_k_copy(const int64_t max_k,
+                                            const value_idx* adj_ia,
+                                            const value_idx* tmp,
+                                            value_idx* adj_ja)
+{
+  int64_t offset = blockIdx.x * max_k;
+
+  int64_t row_idx       = blockIdx.x;
+  int64_t col_start_idx = adj_ia[row_idx];
+  int64_t num_cols      = adj_ia[row_idx + 1] - col_start_idx;
+
+  int64_t limit = raft::Pow2<raft::WarpSize>::roundDown(num_cols);
+  int64_t i     = threadIdx.x;
+  for (; i < limit; i += tpb) {
+    adj_ja[col_start_idx + i] = tmp[offset + i];
+  }
+  if (i < num_cols) { adj_ja[col_start_idx + i] = tmp[offset + i]; }
+}
+
+template <typename value_idx, typename value_t>
+void rbc_low_dim_pass_one(raft::resources const& handle,
+                          const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                          const value_t* query,
+                          const int64_t n_query_rows,
+                          int64_t k,
+                          const value_idx* R_knn_inds,
+                          const value_t* R_knn_dists,
+                          value_idx* inds,
+                          value_t* dists,
+                          float weight,
+                          int dims)
+{
+  if (k <= 32)
+    block_rbc_kernel_registers<value_idx, value_t, 32, 2, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        R_knn_inds,
+        R_knn_dists,
+        index.m,
+        k,
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.get_R_radius().data_handle(),
+        index.metric,
+        weight);
+
+  else if (k <= 64)
+    block_rbc_kernel_registers<value_idx, value_t, 64, 3, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        R_knn_inds,
+        R_knn_dists,
+        index.m,
+        k,
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.get_R_radius().data_handle(),
+        index.metric,
+        weight);
+  else if (k <= 128)
+    block_rbc_kernel_registers<value_idx, value_t, 128, 3, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        R_knn_inds,
+        R_knn_dists,
+        index.m,
+        k,
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.get_R_radius().data_handle(),
+        index.metric,
+        weight);
+
+  else if (k <= 256)
+    block_rbc_kernel_registers<value_idx, value_t, 256, 4, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        R_knn_inds,
+        R_knn_dists,
+        index.m,
+        k,
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.get_R_radius().data_handle(),
+        index.metric,
+        weight);
+
+  else if (k <= 512)
+    block_rbc_kernel_registers<value_idx, value_t, 512, 8, 64>
+      <<<n_query_rows, 64, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        R_knn_inds,
+        R_knn_dists,
+        index.m,
+        k,
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.get_R_radius().data_handle(),
+        index.metric,
+        weight);
+
+  else if (k <= 1024)
+    block_rbc_kernel_registers<value_idx, value_t, 1024, 8, 64>
+      <<<n_query_rows, 64, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        R_knn_inds,
+        R_knn_dists,
+        index.m,
+        k,
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.get_R_radius().data_handle(),
+        index.metric,
+        weight);
+}
+
+template <typename value_idx, typename value_t>
+void rbc_low_dim_pass_two(raft::resources const& handle,
+                          const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                          const value_t* query,
+                          const int64_t n_query_rows,
+                          int64_t k,
+                          const value_idx* R_knn_inds,
+                          const value_t* R_knn_dists,
+                          value_idx* inds,
+                          value_t* dists,
+                          float weight,
+                          int dims)
+{
+  const uint32_t bitset_size = ceil(index.n_landmarks / 32.0);
+
+  rmm::device_uvector<std::uint32_t> bitset(bitset_size * n_query_rows,
+                                            raft::resource::get_cuda_stream(handle));
+  thrust::fill(
+    raft::resource::get_thrust_policy(handle), bitset.data(), bitset.data() + bitset.size(), 0);
+
+  perform_post_filter_registers<value_idx, value_t, std::uint32_t, 128>
+    <<<n_query_rows,
+       128,
+       bitset_size * sizeof(std::uint32_t),
+       raft::resource::get_cuda_stream(handle)>>>(query,
+                                                  index.n,
+                                                  R_knn_inds,
+                                                  R_knn_dists,
+                                                  index.get_R_radius().data_handle(),
+                                                  index.get_R().data_handle(),
+                                                  index.n_landmarks,
+                                                  bitset_size,
+                                                  k,
+                                                  index.metric,
+                                                  bitset.data(),
+                                                  weight);
+
+  if (k <= 32)
+    compute_final_dists_registers<value_idx, value_t, std::uint32_t, std::uint32_t, 32, 2, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        bitset.data(),
+        bitset_size,
+        index.get_R_closest_landmark_dists().data_handle(),
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.n_landmarks,
+        k,
+        index.metric);
+  else if (k <= 64)
+    compute_final_dists_registers<value_idx, value_t, std::uint32_t, std::uint32_t, 64, 3, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        bitset.data(),
+        bitset_size,
+        index.get_R_closest_landmark_dists().data_handle(),
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.n_landmarks,
+        k,
+        index.metric);
+  else if (k <= 128)
+    compute_final_dists_registers<value_idx, value_t, std::uint32_t, std::uint32_t, 128, 3, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        bitset.data(),
+        bitset_size,
+        index.get_R_closest_landmark_dists().data_handle(),
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.n_landmarks,
+        k,
+        index.metric);
+  else if (k <= 256)
+    compute_final_dists_registers<value_idx, value_t, std::uint32_t, std::uint32_t, 256, 4, 128>
+      <<<n_query_rows, 128, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        bitset.data(),
+        bitset_size,
+        index.get_R_closest_landmark_dists().data_handle(),
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.n_landmarks,
+        k,
+        index.metric);
+  else if (k <= 512)
+    compute_final_dists_registers<value_idx, value_t, std::uint32_t, std::uint32_t, 512, 8, 64>
+      <<<n_query_rows, 64, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        bitset.data(),
+        bitset_size,
+        index.get_R_closest_landmark_dists().data_handle(),
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.n_landmarks,
+        k,
+        index.metric);
+  else if (k <= 1024)
+    compute_final_dists_registers<value_idx, value_t, std::uint32_t, std::uint32_t, 1024, 8, 64>
+      <<<n_query_rows, 64, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        index.n,
+        bitset.data(),
+        bitset_size,
+        index.get_R_closest_landmark_dists().data_handle(),
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        inds,
+        dists,
+        index.n_landmarks,
+        k,
+        index.metric);
+}
+
+template <typename value_idx, typename value_t, typename dist_func>
+void rbc_eps_pass(raft::resources const& handle,
+                  const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                  const value_t* query,
+                  const int64_t n_query_rows,
+                  value_t eps,
+                  const value_t* R,
+                  dist_func& dfunc,
+                  bool* adj,
+                  value_idx* vd)
+{
+  block_rbc_kernel_eps_dense<value_idx, value_t, 64>
+    <<<n_query_rows, 64, 0, raft::resource::get_cuda_stream(handle)>>>(
+      index.get_X_reordered().data_handle(),
+      query,
+      n_query_rows,
+      index.n,
+      R,
+      index.m,
+      eps,
+      index.n_landmarks,
+      index.get_R_indptr().data_handle(),
+      index.get_R_1nn_cols().data_handle(),
+      index.get_R_1nn_dists().data_handle(),
+      index.get_R_radius().data_handle(),
+      dfunc,
+      adj,
+      vd);
+
+  if (vd != nullptr) {
+    value_idx sum =
+      thrust::reduce(raft::resource::get_thrust_policy(handle), vd, vd + n_query_rows);
+    // copy sum to last element
+    RAFT_CUDA_TRY(cudaMemcpyAsync(vd + n_query_rows,
+                                  &sum,
+                                  sizeof(value_idx),
+                                  cudaMemcpyHostToDevice,
+                                  raft::resource::get_cuda_stream(handle)));
+  }
+
+  raft::resource::sync_stream(handle);
+}
+
+template <typename value_idx, typename value_t, typename dist_func>
+void rbc_eps_pass(raft::resources const& handle,
+                  const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                  const value_t* query,
+                  const int64_t n_query_rows,
+                  value_t eps,
+                  int64_t* max_k,
+                  const value_t* R,
+                  dist_func& dfunc,
+                  value_idx* adj_ia,
+                  value_idx* adj_ja,
+                  value_idx* vd)
+{
+  // if max_k == nullptr we are either pass 1 or pass 2
+  if (max_k == nullptr) {
+    if (adj_ja == nullptr) {
+      // pass 1 -> only compute adj_ia / vd
+      value_idx* vd_ptr = (vd != nullptr) ? vd : adj_ia;
+      if (index.n == 2 || index.n == 3) {
+        block_rbc_kernel_eps_csr_pass_xd<value_idx, value_t, 64>
+          <<<raft::ceildiv<int64_t>(n_query_rows, 2),
+             64,
+             0,
+             raft::resource::get_cuda_stream(handle)>>>(index.get_X_reordered().data_handle(),
+                                                        query,
+                                                        n_query_rows,
+                                                        index.n,
+                                                        R,
+                                                        index.m,
+                                                        eps,
+                                                        index.n_landmarks,
+                                                        index.get_R_indptr().data_handle(),
+                                                        index.get_R_1nn_cols().data_handle(),
+                                                        index.get_R_1nn_dists().data_handle(),
+                                                        index.get_R_radius().data_handle(),
+                                                        dfunc,
+                                                        vd_ptr,
+                                                        nullptr,
+                                                        false,
+                                                        index.n);
+      } else {
+        block_rbc_kernel_eps_csr_pass<value_idx, value_t, 64>
+          <<<raft::ceildiv<int64_t>(n_query_rows, 2),
+             64,
+             0,
+             raft::resource::get_cuda_stream(handle)>>>(index.get_X_reordered().data_handle(),
+                                                        query,
+                                                        n_query_rows,
+                                                        index.n,
+                                                        R,
+                                                        index.m,
+                                                        eps,
+                                                        index.n_landmarks,
+                                                        index.get_R_indptr().data_handle(),
+                                                        index.get_R_1nn_cols().data_handle(),
+                                                        index.get_R_1nn_dists().data_handle(),
+                                                        index.get_R_radius().data_handle(),
+                                                        dfunc,
+                                                        vd_ptr,
+                                                        nullptr,
+                                                        false);
+      }
+
+      thrust::exclusive_scan(raft::resource::get_thrust_policy(handle),
+                             vd_ptr,
+                             vd_ptr + n_query_rows + 1,
+                             adj_ia,
+                             (value_idx)0);
+
+    } else {
+      // pass 2 -> fill in adj_ja
+      if (index.n == 2 || index.n == 3) {
+        block_rbc_kernel_eps_csr_pass_xd<value_idx, value_t, 64>
+          <<<raft::ceildiv<int64_t>(n_query_rows, 2),
+             64,
+             0,
+             raft::resource::get_cuda_stream(handle)>>>(index.get_X_reordered().data_handle(),
+                                                        query,
+                                                        n_query_rows,
+                                                        index.n,
+                                                        R,
+                                                        index.m,
+                                                        eps,
+                                                        index.n_landmarks,
+                                                        index.get_R_indptr().data_handle(),
+                                                        index.get_R_1nn_cols().data_handle(),
+                                                        index.get_R_1nn_dists().data_handle(),
+                                                        index.get_R_radius().data_handle(),
+                                                        dfunc,
+                                                        adj_ia,
+                                                        adj_ja,
+                                                        true,
+                                                        index.n);
+      } else {
+        block_rbc_kernel_eps_csr_pass<value_idx, value_t, 64>
+          <<<raft::ceildiv<int64_t>(n_query_rows, 2), 64, 0, resource::get_cuda_stream(handle)>>>(
+            index.get_X_reordered().data_handle(),
+            query,
+            n_query_rows,
+            index.n,
+            R,
+            index.m,
+            eps,
+            index.n_landmarks,
+            index.get_R_indptr().data_handle(),
+            index.get_R_1nn_cols().data_handle(),
+            index.get_R_1nn_dists().data_handle(),
+            index.get_R_radius().data_handle(),
+            dfunc,
+            adj_ia,
+            adj_ja,
+            true);
+      }
+    }
+  } else {
+    int64_t max_k_in  = *max_k;
+    value_idx* vd_ptr = (vd != nullptr) ? vd : adj_ia;
+
+    rmm::device_uvector<value_idx> tmp(n_query_rows * max_k_in,
+                                       raft::resource::get_cuda_stream(handle));
+
+    block_rbc_kernel_eps_max_k<value_idx, value_t, 64>
+      <<<raft::ceildiv<int64_t>(n_query_rows, 2), 64, 0, raft::resource::get_cuda_stream(handle)>>>(
+        index.get_X_reordered().data_handle(),
+        query,
+        n_query_rows,
+        index.n,
+        R,
+        index.m,
+        eps,
+        index.n_landmarks,
+        index.get_R_indptr().data_handle(),
+        index.get_R_1nn_cols().data_handle(),
+        index.get_R_1nn_dists().data_handle(),
+        index.get_R_radius().data_handle(),
+        dfunc,
+        vd_ptr,
+        max_k_in,
+        tmp.data());
+
+    int64_t actual_max = thrust::reduce(raft::resource::get_thrust_policy(handle),
+                                        vd_ptr,
+                                        vd_ptr + n_query_rows,
+                                        (value_idx)0,
+                                        thrust::maximum<value_idx>());
+
+    if (actual_max > max_k_in) {
+      // ceil vd to max_k
+      thrust::transform(raft::resource::get_thrust_policy(handle),
+                        vd_ptr,
+                        vd_ptr + n_query_rows,
+                        vd_ptr,
+                        [max_k_in] __device__(value_idx vd_count) {
+                          return vd_count > max_k_in ? max_k_in : vd_count;
+                        });
+    }
+
+    thrust::exclusive_scan(raft::resource::get_thrust_policy(handle),
+                           vd_ptr,
+                           vd_ptr + n_query_rows + 1,
+                           adj_ia,
+                           (value_idx)0);
+
+    block_rbc_kernel_eps_max_k_copy<value_idx, 32>
+      <<<n_query_rows, 32, 0, raft::resource::get_cuda_stream(handle)>>>(
+        max_k_in, adj_ia, tmp.data(), adj_ja);
+
+    // return 'new' max-k
+    *max_k = actual_max;
+  }
+
+  if (vd != nullptr && (max_k != nullptr || adj_ja == nullptr)) {
+    // copy sum to last element
+    RAFT_CUDA_TRY(cudaMemcpyAsync(vd + n_query_rows,
+                                  adj_ia + n_query_rows,
+                                  sizeof(value_idx),
+                                  cudaMemcpyDeviceToDevice,
+                                  raft::resource::get_cuda_stream(handle)));
+  }
+
+  raft::resource::sync_stream(handle);
+}
+
+};  // namespace cuvs::neighbors::ball_cover::detail
diff --git a/cpp/src/neighbors/ball_cover/registers.hpp b/cpp/src/neighbors/ball_cover/registers.hpp
new file mode 100644
index 0000000000..17acaadfee
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/registers.hpp
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "registers_types.cuh"
+#include <cuvs/neighbors/ball_cover.hpp>
+
+#include <raft/core/resources.hpp>
+
+namespace cuvs::neighbors::ball_cover::detail {
+
+template <typename value_idx, typename value_t>
+void rbc_low_dim_pass_one(raft::resources const& handle,
+                          const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                          const value_t* query,
+                          const int64_t n_query_rows,
+                          const int64_t k,
+                          const value_idx* R_knn_inds,
+                          const value_t* R_knn_dists,
+                          value_idx* inds,
+                          value_t* dists,
+                          float weight,
+                          int dims);
+
+template <typename value_idx, typename value_t>
+void rbc_low_dim_pass_two(raft::resources const& handle,
+                          const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                          const value_t* query,
+                          const int64_t n_query_rows,
+                          const int64_t k,
+                          const value_idx* R_knn_inds,
+                          const value_t* R_knn_dists,
+                          value_idx* inds,
+                          value_t* dists,
+                          float weight,
+                          int dims);
+
+template <typename value_idx, typename value_t, typename dist_func>
+void rbc_eps_pass(raft::resources const& handle,
+                  const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                  const value_t* query,
+                  const int64_t n_query_rows,
+                  value_t eps,
+                  const value_t* R,
+                  dist_func& dfunc,
+                  bool* adj,
+                  value_idx* vd);
+
+template <typename value_idx, typename value_t, typename dist_func>
+void rbc_eps_pass(raft::resources const& handle,
+                  const cuvs::neighbors::ball_cover::index<value_idx, value_t>& index,
+                  const value_t* query,
+                  const int64_t n_query_rows,
+                  value_t eps,
+                  int64_t* max_k,
+                  const value_t* R,
+                  dist_func& dfunc,
+                  value_idx* adj_ia,
+                  value_idx* adj_ja,
+                  value_idx* vd);
+
+}  // namespace cuvs::neighbors::ball_cover::detail
diff --git a/cpp/src/neighbors/ball_cover/registers_types.cuh b/cpp/src/neighbors/ball_cover/registers_types.cuh
new file mode 100644
index 0000000000..cfb522044b
--- /dev/null
+++ b/cpp/src/neighbors/ball_cover/registers_types.cuh
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../detail/haversine_distance.cuh"  // compute_haversine
+#include <cuvs/distance/distance.hpp>
+
+#include <cstdint>  // uint32_t
+
+namespace cuvs::neighbors::ball_cover::detail {
+
+template <typename value_t, typename value_int>
+struct DistFunc {
+  virtual __device__ __host__ __forceinline__ value_t operator()(const value_t* a,
+                                                                 const value_t* b,
+                                                                 const value_int n_dims)
+  {
+    return -1;
+  };
+};
+
+template <typename value_t, typename value_int>
+struct HaversineFunc : public DistFunc<value_t, value_int> {
+  __device__ __host__ __forceinline__ value_t operator()(const value_t* a,
+                                                         const value_t* b,
+                                                         const value_int n_dims) override
+  {
+    return cuvs::neighbors::detail::compute_haversine<value_t, value_t>(a[0], b[0], a[1], b[1]);
+  }
+};
+
+template <typename value_t, typename value_int>
+struct EuclideanFunc : public DistFunc<value_t, value_int> {
+  __device__ __host__ __forceinline__ value_t operator()(const value_t* a,
+                                                         const value_t* b,
+                                                         const value_int n_dims) override
+  {
+    value_t sum_sq = 0;
+    for (value_int i = 0; i < n_dims; ++i) {
+      value_t diff = a[i] - b[i];
+      sum_sq += diff * diff;
+    }
+
+    return raft::sqrt(sum_sq);
+  }
+};
+
+template <typename value_t, typename value_int>
+struct EuclideanSqFunc : public DistFunc<value_t, value_int> {
+  __device__ __host__ __forceinline__ value_t operator()(const value_t* a,
+                                                         const value_t* b,
+                                                         const value_int n_dims) override
+  {
+    value_t sum_sq = 0;
+    for (value_int i = 0; i < n_dims; ++i) {
+      value_t diff = a[i] - b[i];
+      sum_sq += diff * diff;
+    }
+    return sum_sq;
+  }
+};
+
+// Direct distance function for use in kernels that need metric information
+template <typename value_t, typename value_int>
+__device__ __host__ __forceinline__ value_t compute_distance_by_metric(
+  const value_t* a, const value_t* b, const value_int n_dims, cuvs::distance::DistanceType metric)
+{
+  if (metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+      metric == cuvs::distance::DistanceType::L2SqrtUnexpanded) {
+    // Euclidean distance implementation
+    return EuclideanFunc<value_t, value_int>{}(a, b, n_dims);
+  } else if (metric == cuvs::distance::DistanceType::Haversine) {
+    // Haversine distance implementation
+    return HaversineFunc<value_t, value_int>{}(a, b, n_dims);
+  }
+
+  // Default case
+  return -1;
+}
+
+};  // namespace cuvs::neighbors::ball_cover::detail
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index d33f0cbcf5..ae14bf6e55 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -185,6 +185,8 @@ if(BUILD_TESTS)
     100
   )
 
+  ConfigureTest(NAME NEIGHBORS_BALL_COVER_TEST PATH neighbors/ball_cover.cu GPUS 1 PERCENT 100)
+
   ConfigureTest(
     NAME
     NEIGHBORS_DYNAMIC_BATCHING_TEST
diff --git a/cpp/tests/neighbors/ball_cover.cu b/cpp/tests/neighbors/ball_cover.cu
new file mode 100644
index 0000000000..429c939d71
--- /dev/null
+++ b/cpp/tests/neighbors/ball_cover.cu
@@ -0,0 +1,403 @@
+/*
+ * Copyright (c) 2021-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "spatial_data.h"
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/ball_cover.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/fill.h>
+#include <thrust/transform.h>
+
+#include <gtest/gtest.h>
+
+#include <cstdint>
+#include <iostream>
+#include <vector>
+
+namespace cuvs::neighbors::ball_cover {
+using namespace std;
+
+template <typename value_idx, typename value_t>
+RAFT_KERNEL count_discrepancies_kernel(value_idx* actual_idx,
+                                       value_idx* expected_idx,
+                                       value_t* actual,
+                                       value_t* expected,
+                                       uint32_t m,
+                                       uint32_t n,
+                                       uint32_t* out,
+                                       float thres = 1e-3)
+{
+  uint32_t row = blockDim.x * blockIdx.x + threadIdx.x;
+
+  int n_diffs = 0;
+  if (row < m) {
+    for (uint32_t i = 0; i < n; i++) {
+      value_t d    = actual[row * n + i] - expected[row * n + i];
+      bool matches = (fabsf(d) <= thres) || (actual_idx[row * n + i] == expected_idx[row * n + i] &&
+                                             actual_idx[row * n + i] == row);
+
+      if (!matches) {
+        printf(
+          "row=%ud, n=%ud, actual_dist=%f, actual_ind=%ld, expected_dist=%f, expected_ind=%ld\n",
+          row,
+          i,
+          actual[row * n + i],
+          actual_idx[row * n + i],
+          expected[row * n + i],
+          expected_idx[row * n + i]);
+      }
+      n_diffs += !matches;
+      out[row] = n_diffs;
+    }
+  }
+}
+
+struct is_nonzero {
+  __host__ __device__ bool operator()(uint32_t i) { return i > 0; }
+};
+
+template <typename value_idx, typename value_t>
+uint32_t count_discrepancies(value_idx* actual_idx,
+                             value_idx* expected_idx,
+                             value_t* actual,
+                             value_t* expected,
+                             uint32_t m,
+                             uint32_t n,
+                             uint32_t* out,
+                             cudaStream_t stream)
+{
+  uint32_t tpb = 256;
+  count_discrepancies_kernel<<<raft::ceildiv(m, tpb), tpb, 0, stream>>>(
+    actual_idx, expected_idx, actual, expected, m, n, out);
+
+  // Copy results to host
+  std::vector<uint32_t> h_out(m);
+  raft::copy(h_out.data(), out, m, stream);
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+
+  // Count non-zero elements on the host
+  uint32_t count = 0;
+  for (uint32_t i = 0; i < m; i++) {
+    if (h_out[i] > 0) count++;
+  }
+
+  return count;
+}
+
+template <typename value_t>
+void compute_bfknn(const raft::resources& handle,
+                   const value_t* X1,
+                   const value_t* X2,
+                   uint32_t n_rows,
+                   uint32_t n_query_rows,
+                   uint32_t d,
+                   uint32_t k,
+                   const cuvs::distance::DistanceType metric,
+                   value_t* dists,
+                   int64_t* inds)
+{
+  raft::device_matrix_view<const value_t, int64_t> input_vec =
+    raft::make_device_matrix_view(X1, n_rows, d);
+  auto bf_index_params   = cuvs::neighbors::brute_force::index_params();
+  bf_index_params.metric = metric;
+  auto bfindex           = cuvs::neighbors::brute_force::build(handle, bf_index_params, input_vec);
+
+  auto bf_search_params = cuvs::neighbors::brute_force::search_params();
+  cuvs::neighbors::brute_force::search(
+    handle,
+    bf_search_params,
+    bfindex,
+    raft::make_device_matrix_view<const value_t, int64_t>(X2, n_query_rows, d),
+    raft::make_device_matrix_view<int64_t, int64_t>(inds, n_query_rows, k),
+    raft::make_device_matrix_view<value_t, int64_t>(dists, n_query_rows, k));
+}
+
+struct ToRadians {
+  __device__ __host__ float operator()(float a) { return a * (CUDART_PI_F / 180.0); }
+};
+
+template <typename value_int = std::int64_t>
+struct BallCoverInputs {
+  value_int k;
+  value_int n_rows;
+  value_int n_cols;
+  float weight;
+  value_int n_query;
+  cuvs::distance::DistanceType metric;
+};
+
+template <typename value_idx, typename value_t, typename value_int = std::int64_t>
+class BallCoverKNNQueryTest : public ::testing::TestWithParam<BallCoverInputs<value_int>> {
+ protected:
+  void basicTest()
+  {
+    params = ::testing::TestWithParam<BallCoverInputs<value_int>>::GetParam();
+    raft::resources handle;
+
+    uint32_t k         = params.k;
+    uint32_t n_centers = 25;
+    float weight       = params.weight;
+    auto metric        = params.metric;
+
+    rmm::device_uvector<value_t> X(params.n_rows * params.n_cols,
+                                   raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_idx> Y(params.n_rows, raft::resource::get_cuda_stream(handle));
+
+    // Make sure the train and query sets are completely disjoint
+    rmm::device_uvector<value_t> X2(params.n_query * params.n_cols,
+                                    raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_idx> Y2(params.n_query, raft::resource::get_cuda_stream(handle));
+
+    raft::random::make_blobs<value_t, value_idx>(X.data(),
+                                                 Y.data(),
+                                                 params.n_rows,
+                                                 params.n_cols,
+                                                 n_centers,
+                                                 raft::resource::get_cuda_stream(handle));
+
+    raft::random::make_blobs<value_t, value_idx>(X2.data(),
+                                                 Y2.data(),
+                                                 params.n_query,
+                                                 params.n_cols,
+                                                 n_centers,
+                                                 raft::resource::get_cuda_stream(handle));
+
+    rmm::device_uvector<value_idx> d_ref_I(params.n_query * k,
+                                           raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_t> d_ref_D(params.n_query * k,
+                                         raft::resource::get_cuda_stream(handle));
+
+    if (metric == cuvs::distance::DistanceType::Haversine) {
+      thrust::transform(raft::resource::get_thrust_policy(handle),
+                        X.data(),
+                        X.data() + X.size(),
+                        X.data(),
+                        ToRadians());
+      thrust::transform(raft::resource::get_thrust_policy(handle),
+                        X2.data(),
+                        X2.data() + X2.size(),
+                        X2.data(),
+                        ToRadians());
+    }
+
+    compute_bfknn(handle,
+                  X.data(),
+                  X2.data(),
+                  params.n_rows,
+                  params.n_query,
+                  params.n_cols,
+                  k,
+                  metric,
+                  d_ref_D.data(),
+                  d_ref_I.data());
+
+    raft::resource::sync_stream(handle);
+
+    // Allocate predicted arrays
+    rmm::device_uvector<value_idx> d_pred_I(params.n_query * k,
+                                            raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_t> d_pred_D(params.n_query * k,
+                                          raft::resource::get_cuda_stream(handle));
+
+    auto X_view =
+      raft::make_device_matrix_view<value_t, value_int>(X.data(), params.n_rows, params.n_cols);
+    auto X2_view = raft::make_device_matrix_view<const value_t, value_int>(
+      (const value_t*)X2.data(), params.n_query, params.n_cols);
+
+    auto d_pred_I_view =
+      raft::make_device_matrix_view<value_idx, value_int>(d_pred_I.data(), params.n_query, k);
+    auto d_pred_D_view =
+      raft::make_device_matrix_view<value_t, value_int>(d_pred_D.data(), params.n_query, k);
+
+    cuvs::neighbors::ball_cover::index<value_idx, value_t> index(handle, X_view, metric);
+    cuvs::neighbors::ball_cover::build(handle, index);
+    cuvs::neighbors::ball_cover::knn_query(
+      handle, index, X2_view, d_pred_I_view, d_pred_D_view, true);
+
+    raft::resource::sync_stream(handle);
+    // What we really want are for the distances to match exactly. The
+    // indices may or may not match exactly, depending upon the ordering which
+    // can be nondeterministic.
+
+    rmm::device_uvector<uint32_t> discrepancies(params.n_query,
+                                                raft::resource::get_cuda_stream(handle));
+    thrust::fill(raft::resource::get_thrust_policy(handle),
+                 discrepancies.data(),
+                 discrepancies.data() + discrepancies.size(),
+                 0);
+    //
+    int res = count_discrepancies(d_ref_I.data(),
+                                  d_pred_I.data(),
+                                  d_ref_D.data(),
+                                  d_pred_D.data(),
+                                  params.n_query,
+                                  k,
+                                  discrepancies.data(),
+                                  raft::resource::get_cuda_stream(handle));
+
+    ASSERT_TRUE(res == 0);
+  }
+
+  void SetUp() override {}
+
+  void TearDown() override {}
+
+ protected:
+  uint32_t d = 2;
+  BallCoverInputs<value_int> params;
+};
+
+template <typename value_idx, typename value_t, typename value_int = std::int64_t>
+class BallCoverAllKNNTest : public ::testing::TestWithParam<BallCoverInputs<value_int>> {
+ protected:
+  void basicTest()
+  {
+    params = ::testing::TestWithParam<BallCoverInputs<value_int>>::GetParam();
+    raft::resources handle;
+
+    uint32_t k         = params.k;
+    uint32_t n_centers = 25;
+    float weight       = params.weight;
+    auto metric        = params.metric;
+
+    rmm::device_uvector<value_t> X(params.n_rows * params.n_cols,
+                                   raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_int> Y(params.n_rows, raft::resource::get_cuda_stream(handle));
+
+    raft::random::make_blobs<value_t, value_idx>(X.data(),
+                                                 Y.data(),
+                                                 params.n_rows,
+                                                 params.n_cols,
+                                                 n_centers,
+                                                 raft::resource::get_cuda_stream(handle));
+
+    rmm::device_uvector<value_idx> d_ref_I(params.n_rows * k,
+                                           raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_t> d_ref_D(params.n_rows * k,
+                                         raft::resource::get_cuda_stream(handle));
+
+    auto X_view = raft::make_device_matrix_view<const value_t, value_int>(
+      (const value_t*)X.data(), params.n_rows, params.n_cols);
+
+    if (metric == cuvs::distance::DistanceType::Haversine) {
+      thrust::transform(raft::resource::get_thrust_policy(handle),
+                        X.data(),
+                        X.data() + X.size(),
+                        X.data(),
+                        ToRadians());
+    }
+
+    compute_bfknn(handle,
+                  X.data(),
+                  X.data(),
+                  params.n_rows,
+                  params.n_rows,
+                  params.n_cols,
+                  k,
+                  metric,
+                  d_ref_D.data(),
+                  d_ref_I.data());
+
+    raft::resource::sync_stream(handle);
+
+    // Allocate predicted arrays
+    rmm::device_uvector<value_idx> d_pred_I(params.n_rows * k,
+                                            raft::resource::get_cuda_stream(handle));
+    rmm::device_uvector<value_t> d_pred_D(params.n_rows * k,
+                                          raft::resource::get_cuda_stream(handle));
+
+    auto d_pred_I_view =
+      raft::make_device_matrix_view<value_idx, value_int>(d_pred_I.data(), params.n_rows, k);
+    auto d_pred_D_view =
+      raft::make_device_matrix_view<value_t, value_int>(d_pred_D.data(), params.n_rows, k);
+
+    cuvs::neighbors::ball_cover::index<value_idx, value_t> index(handle, X_view, metric);
+
+    cuvs::neighbors::ball_cover::all_knn_query(handle, index, d_pred_I_view, d_pred_D_view, true);
+
+    raft::resource::sync_stream(handle);
+    // What we really want are for the distances to match exactly. The
+    // indices may or may not match exactly, depending upon the ordering which
+    // can be nondeterministic.
+
+    rmm::device_uvector<uint32_t> discrepancies(params.n_rows,
+                                                raft::resource::get_cuda_stream(handle));
+    thrust::fill(raft::resource::get_thrust_policy(handle),
+                 discrepancies.data(),
+                 discrepancies.data() + discrepancies.size(),
+                 0);
+    //
+    uint32_t res = count_discrepancies(d_ref_I.data(),
+                                       d_pred_I.data(),
+                                       d_ref_D.data(),
+                                       d_pred_D.data(),
+                                       params.n_rows,
+                                       k,
+                                       discrepancies.data(),
+                                       raft::resource::get_cuda_stream(handle));
+
+    // TODO: There seem to be discrepancies here only when
+    // the entire test suite is executed.
+    // Ref: https://github.com/rapidsai/raft/issues/
+    // 1-5 mismatches in 8000 samples is 0.0125% - 0.0625%
+    ASSERT_TRUE(res <= 5);
+  }
+
+  void SetUp() override {}
+
+  void TearDown() override {}
+
+ protected:
+  BallCoverInputs<value_int> params;
+};
+
+typedef BallCoverAllKNNTest<int64_t, float> BallCoverAllKNNTestF;
+typedef BallCoverKNNQueryTest<int64_t, float> BallCoverKNNQueryTestF;
+
+const std::vector<BallCoverInputs<std::int64_t>> ballcover_inputs = {
+  {11, 5000, 2, 1.0, 10000, cuvs::distance::DistanceType::Haversine},
+  {25, 10000, 2, 1.0, 5000, cuvs::distance::DistanceType::Haversine},
+  {2, 10000, 2, 1.0, 5000, cuvs::distance::DistanceType::L2SqrtUnexpanded},
+  {2, 5000, 2, 1.0, 10000, cuvs::distance::DistanceType::Haversine},
+  {11, 10000, 2, 1.0, 5000, cuvs::distance::DistanceType::L2SqrtUnexpanded},
+  {25, 5000, 2, 1.0, 10000, cuvs::distance::DistanceType::L2SqrtUnexpanded},
+  {5, 8000, 3, 1.0, 10000, cuvs::distance::DistanceType::L2SqrtUnexpanded},
+  {11, 6000, 3, 1.0, 10000, cuvs::distance::DistanceType::L2SqrtUnexpanded},
+  {25, 10000, 3, 1.0, 5000, cuvs::distance::DistanceType::L2SqrtUnexpanded}};
+
+INSTANTIATE_TEST_CASE_P(BallCoverAllKNNTest,
+                        BallCoverAllKNNTestF,
+                        ::testing::ValuesIn(ballcover_inputs));
+INSTANTIATE_TEST_CASE_P(BallCoverKNNQueryTest,
+                        BallCoverKNNQueryTestF,
+                        ::testing::ValuesIn(ballcover_inputs));
+
+TEST_P(BallCoverAllKNNTestF, Fit) { basicTest(); }
+TEST_P(BallCoverKNNQueryTestF, Fit) { basicTest(); }
+
+}  // namespace cuvs::neighbors::ball_cover
diff --git a/cpp/tests/neighbors/spatial_data.h b/cpp/tests/neighbors/spatial_data.h
new file mode 100644
index 0000000000..34d5f4dfb9
--- /dev/null
+++ b/cpp/tests/neighbors/spatial_data.h
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <vector>
+
+namespace cuvs {
+namespace spatial {
+
+// Latitude and longitude coordinates of 51 US states / territories
+std::vector<float> spatial_data = {
+  63.588753, -154.493062, 32.318231, -86.902298,  35.20105,  -91.831833,  34.048928, -111.093731,
+  36.778261, -119.417932, 39.550051, -105.782067, 41.603221, -73.087749,  38.905985, -77.033418,
+  38.910832, -75.52767,   27.664827, -81.515754,  32.157435, -82.907123,  19.898682, -155.665857,
+  41.878003, -93.097702,  44.068202, -114.742041, 40.633125, -89.398528,  40.551217, -85.602364,
+  39.011902, -98.484246,  37.839333, -84.270018,  31.244823, -92.145024,  42.407211, -71.382437,
+  39.045755, -76.641271,  45.253783, -69.445469,  44.314844, -85.602364,  46.729553, -94.6859,
+  37.964253, -91.831833,  32.354668, -89.398528,  46.879682, -110.362566, 35.759573, -79.0193,
+  47.551493, -101.002012, 41.492537, -99.901813,  43.193852, -71.572395,  40.058324, -74.405661,
+  34.97273,  -105.032363, 38.80261,  -116.419389, 43.299428, -74.217933,  40.417287, -82.907123,
+  35.007752, -97.092877,  43.804133, -120.554201, 41.203322, -77.194525,  18.220833, -66.590149,
+  41.580095, -71.477429,  33.836081, -81.163725,  43.969515, -99.901813,  35.517491, -86.580447,
+  31.968599, -99.901813,  39.32098,  -111.093731, 37.431573, -78.656894,  44.558803, -72.577841,
+  47.751074, -120.740139, 43.78444,  -88.787868,  38.597626, -80.454903,  43.075968, -107.290284};
+};  // namespace spatial
+};  // namespace cuvs

From 470d86b2f684d5c7fbcc7b21df6ac725d77cf718 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Mon, 12 May 2025 09:12:43 -0500
Subject: [PATCH 394/623] update.version.sh: remove broken reference, skip most
 CI on PRs that only modify update-version.sh (#875)

While reviewing #805, I found an issue with this project's `update-version.sh`... it refers to a script `examples/cmake/thirdparty/fetch_rapids.cmake` which no long exists (removed in #824).

This proposes the following:

* removing that reference from `update-version.sh`
* skipping most CI on PRs that only modify `update-version.sh`

## Notes for Reviewers

`ci/release/update-version.sh` is standardized (same filepath, same usage) across almost all RAPIDS repos. I cannot think of a situation where a PR that only changes that file would need to have any CI re-run (other than linting, e.g. for `shellcheck`).

If folks agree, I'll roll out a change like that more broadly across RAPIDS.

Authors:
  - James Lamb (https://github.com/jameslamb)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/875
---
 .github/workflows/pr.yaml    | 3 +++
 ci/release/update-version.sh | 2 --
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 0af6489a67..935682a513 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -67,6 +67,7 @@ jobs:
           - '!.devcontainer/**'
           - '!.pre-commit-config.yaml'
           - '!README.md'
+          - '!ci/release/update-version.sh'
           - '!docs/**'
           - '!img/**'
           - '!notebooks/**'
@@ -90,6 +91,7 @@ jobs:
           - '**'
           - '!.devcontainer/**'
           - '!.pre-commit-config.yaml'
+          - '!ci/release/update-version.sh'
           - '!README.md'
           - '!rust/**'
           - '!go/**'
@@ -98,6 +100,7 @@ jobs:
           - '**'
           - '!.devcontainer/**'
           - '!.pre-commit-config.yaml'
+          - '!ci/release/update-version.sh'
           - '!README.md'
           - '!docs/**'
           - '!img/**'
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 3cfaf2fabf..ac6f0e3d35 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -36,8 +36,6 @@ function sed_runner() {
     sed -i.bak ''"$1"'' "$2" && rm -f "${2}".bak
 }
 
-sed_runner "s/set(RAPIDS_VERSION .*)/set(RAPIDS_VERSION \"${NEXT_SHORT_TAG}\")/g" examples/cmake/thirdparty/fetch_rapids.cmake
-
 # Centralized version file update
 echo "${NEXT_FULL_TAG}" > VERSION
 

From f7573fe7866e2152e9430c073b1458eeaad1ea76 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 13 May 2025 12:14:52 -0500
Subject: [PATCH 395/623] Quote head_rev in conda recipes (#892)

This quotes `head_rev` to ensure that commits with leading zeros in the git SHA include those zeros in the output package name.

xref: https://github.com/rapidsai/build-planning/issues/176

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/892
---
 conda/recipes/cuvs-bench-cpu/recipe.yaml | 2 +-
 conda/recipes/cuvs-bench/recipe.yaml     | 2 +-
 conda/recipes/cuvs/recipe.yaml           | 2 +-
 conda/recipes/libcuvs/recipe.yaml        | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/conda/recipes/cuvs-bench-cpu/recipe.yaml b/conda/recipes/cuvs-bench-cpu/recipe.yaml
index 0ae9859460..fe9d26618e 100644
--- a/conda/recipes/cuvs-bench-cpu/recipe.yaml
+++ b/conda/recipes/cuvs-bench-cpu/recipe.yaml
@@ -6,7 +6,7 @@ context:
   date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
   py_version: ${{ env.get("RAPIDS_PY_VERSION") }}
   py_buildstring: ${{ py_version | version_to_buildstring }}
-  head_rev: ${{ git.head_rev(".")[:8] }}
+  head_rev: '${{ git.head_rev(".")[:8] }}'
   linux64: ${{ linux and x86_64 }}
 
 package:
diff --git a/conda/recipes/cuvs-bench/recipe.yaml b/conda/recipes/cuvs-bench/recipe.yaml
index 3632a982b3..70fd1e0115 100644
--- a/conda/recipes/cuvs-bench/recipe.yaml
+++ b/conda/recipes/cuvs-bench/recipe.yaml
@@ -9,7 +9,7 @@ context:
   date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
   py_version: ${{ env.get("RAPIDS_PY_VERSION") }}
   py_buildstring: ${{ py_version | version_to_buildstring }}
-  head_rev: ${{ git.head_rev(".")[:8] }}
+  head_rev: '${{ git.head_rev(".")[:8] }}'
   linux64: ${{ linux and x86_64 }}
 
 package:
diff --git a/conda/recipes/cuvs/recipe.yaml b/conda/recipes/cuvs/recipe.yaml
index 36249d3746..bbff5fba7e 100644
--- a/conda/recipes/cuvs/recipe.yaml
+++ b/conda/recipes/cuvs/recipe.yaml
@@ -9,7 +9,7 @@ context:
   date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
   py_version: ${{ env.get("RAPIDS_PY_VERSION") }}
   py_buildstring: ${{ py_version | version_to_buildstring }}
-  head_rev: ${{ git.head_rev(".")[:8] }}
+  head_rev: '${{ git.head_rev(".")[:8] }}'
 
 package:
   name: cuvs
diff --git a/conda/recipes/libcuvs/recipe.yaml b/conda/recipes/libcuvs/recipe.yaml
index 0be4317a24..4f847b3e1f 100644
--- a/conda/recipes/libcuvs/recipe.yaml
+++ b/conda/recipes/libcuvs/recipe.yaml
@@ -7,7 +7,7 @@ context:
   cuda_version: ${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[:2] | join(".") }}
   cuda_major: '${{ (env.get("RAPIDS_CUDA_VERSION") | split("."))[0] }}'
   date_string: '${{ env.get("RAPIDS_DATE_STRING") }}'
-  head_rev: ${{ git.head_rev(".")[:8] }}
+  head_rev: '${{ git.head_rev(".")[:8] }}'
   linux64: ${{ linux and x86_64 }}
 
 recipe:

From 73e4fbcb981a67048a7f63b39416a87cc878c1ba Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 13 May 2025 16:16:25 -0500
Subject: [PATCH 396/623] get Java artifacts from GitHub Actions artifact store
 (#893)

Follow-up to #834

Contributes to https://github.com/rapidsai/build-infra/issues/237

Uses GitHub Actions artifact store, instead of `downloads.rapids.ai`, to download `libcuvs` conda artifacts in CI.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/893
---
 ci/build_java.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ci/build_java.sh b/ci/build_java.sh
index 4831215e94..175cc9a559 100755
--- a/ci/build_java.sh
+++ b/ci/build_java.sh
@@ -14,7 +14,7 @@ fi
 . /opt/conda/etc/profile.d/conda.sh
 
 rapids-logger "Downloading artifacts from previous jobs"
-CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+CPP_CHANNEL=$(rapids-download-conda-from-github cpp)
 
 rapids-logger "Generate Java testing dependencies"
 

From 8f3e6969bdc7d3c4f15b1d9fe76f5b3746bf20e6 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Wed, 14 May 2025 17:00:35 -0400
Subject: [PATCH 397/623] CUDA 12.9 use updated compression flags (#887)

Set more fatbin compression flags to maintain existing binary sizes

Authors:
  - Robert Maynard (https://github.com/robertmaynard)
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/887
---
 .../devcontainer.json                         |  8 ++---
 .../devcontainer.json                         | 12 +++----
 .github/workflows/build.yaml                  | 20 ++++++------
 .github/workflows/pr.yaml                     | 32 +++++++++----------
 .github/workflows/test.yaml                   |  8 ++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 ...64.yaml => all_cuda-129_arch-aarch64.yaml} |  4 +--
 ..._64.yaml => all_cuda-129_arch-x86_64.yaml} |  4 +--
 ...l => bench_ann_cuda-129_arch-aarch64.yaml} |  4 +--
 ...ml => bench_ann_cuda-129_arch-x86_64.yaml} |  4 +--
 ...h64.yaml => go_cuda-129_arch-aarch64.yaml} |  4 +--
 ...6_64.yaml => go_cuda-129_arch-x86_64.yaml} |  4 +--
 ...4.yaml => rust_cuda-129_arch-aarch64.yaml} |  4 +--
 ...64.yaml => rust_cuda-129_arch-x86_64.yaml} |  4 +--
 cpp/cmake/modules/ConfigureCUDA.cmake         |  6 ++++
 dependencies.yaml                             | 12 ++++---
 docs/source/build.rst                         |  6 ++--
 examples/go/README.md                         |  2 +-
 examples/rust/README.md                       |  2 +-
 19 files changed, 76 insertions(+), 66 deletions(-)
 rename .devcontainer/{cuda12.8-conda => cuda12.9-conda}/devcontainer.json (91%)
 rename .devcontainer/{cuda12.8-pip => cuda12.9-pip}/devcontainer.json (88%)
 rename conda/environments/{all_cuda-128_arch-aarch64.yaml => all_cuda-129_arch-aarch64.yaml} (95%)
 rename conda/environments/{all_cuda-128_arch-x86_64.yaml => all_cuda-129_arch-x86_64.yaml} (95%)
 rename conda/environments/{bench_ann_cuda-128_arch-aarch64.yaml => bench_ann_cuda-129_arch-aarch64.yaml} (94%)
 rename conda/environments/{bench_ann_cuda-128_arch-x86_64.yaml => bench_ann_cuda-129_arch-x86_64.yaml} (94%)
 rename conda/environments/{go_cuda-128_arch-aarch64.yaml => go_cuda-129_arch-aarch64.yaml} (92%)
 rename conda/environments/{go_cuda-128_arch-x86_64.yaml => go_cuda-129_arch-x86_64.yaml} (92%)
 rename conda/environments/{rust_cuda-128_arch-aarch64.yaml => rust_cuda-129_arch-aarch64.yaml} (92%)
 rename conda/environments/{rust_cuda-128_arch-x86_64.yaml => rust_cuda-129_arch-x86_64.yaml} (92%)

diff --git a/.devcontainer/cuda12.8-conda/devcontainer.json b/.devcontainer/cuda12.9-conda/devcontainer.json
similarity index 91%
rename from .devcontainer/cuda12.8-conda/devcontainer.json
rename to .devcontainer/cuda12.9-conda/devcontainer.json
index dd078a3009..94afe6cc7c 100644
--- a/.devcontainer/cuda12.8-conda/devcontainer.json
+++ b/.devcontainer/cuda12.9-conda/devcontainer.json
@@ -3,7 +3,7 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.8",
+      "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "conda",
       "BASE": "rapidsai/devcontainers:25.06-cpp-mambaforge-ubuntu22.04"
     }
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.9-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
@@ -20,7 +20,7 @@
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.8-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.9-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -29,7 +29,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.8-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.9-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.devcontainer/cuda12.8-pip/devcontainer.json b/.devcontainer/cuda12.9-pip/devcontainer.json
similarity index 88%
rename from .devcontainer/cuda12.8-pip/devcontainer.json
rename to .devcontainer/cuda12.9-pip/devcontainer.json
index 0729bb953c..af440522a4 100644
--- a/.devcontainer/cuda12.8-pip/devcontainer.json
+++ b/.devcontainer/cuda12.9-pip/devcontainer.json
@@ -3,20 +3,20 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.8",
+      "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda12.9-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.9-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/cuda:25.6": {
-      "version": "12.8",
+      "version": "12.9",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
@@ -29,7 +29,7 @@
     "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.9-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -37,7 +37,7 @@
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.9-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 6620dc0d0b..b386354005 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -54,7 +54,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -80,7 +80,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -89,7 +89,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -100,7 +100,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -112,7 +112,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -126,7 +126,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -137,7 +137,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -149,7 +149,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 935682a513..ad7e19e94b 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -30,7 +30,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda-12.9.0
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -59,7 +59,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@cuda-12.9.0
     with:
       files_yaml: |
         test_cpp:
@@ -111,28 +111,28 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda-12.9.0
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.9.0
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.9.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.9.0
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -140,13 +140,13 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.9.0
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.9.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -165,7 +165,7 @@ jobs:
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -175,7 +175,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -185,7 +185,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -195,7 +195,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -206,7 +206,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -215,7 +215,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.9.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -223,10 +223,10 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-12.9.0
     with:
       arch: '["amd64"]'
-      cuda: '["12.8"]'
+      cuda: '["12.9"]'
       build_command: |
         sccache -z;
         build-all --verbose;
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index ee22794806..9e0692e68f 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -19,7 +19,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -29,7 +29,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -57,7 +57,7 @@ jobs:
       run_script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.9.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index e9399716e2..83deb407bf 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@python-3.13
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@cuda-12.9.0
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
similarity index 95%
rename from conda/environments/all_cuda-128_arch-aarch64.yaml
rename to conda/environments/all_cuda-129_arch-aarch64.yaml
index 7880a8b53a..fdd99e97d2 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -54,4 +54,4 @@ dependencies:
 - sysroot_linux-aarch64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-128_arch-aarch64
+name: all_cuda-129_arch-aarch64
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
similarity index 95%
rename from conda/environments/all_cuda-128_arch-x86_64.yaml
rename to conda/environments/all_cuda-129_arch-x86_64.yaml
index 0cc536c948..92d276a42f 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -54,4 +54,4 @@ dependencies:
 - sysroot_linux-64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-128_arch-x86_64
+name: all_cuda-129_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
rename to conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index 3ff76841d7..59fd76afa7 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
@@ -48,4 +48,4 @@ dependencies:
 - setuptools
 - sysroot_linux-aarch64==2.28
 - wheel
-name: bench_ann_cuda-128_arch-aarch64
+name: bench_ann_cuda-129_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
rename to conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index 36ad1941f4..46938afc96 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
@@ -51,4 +51,4 @@ dependencies:
 - setuptools
 - sysroot_linux-64==2.28
 - wheel
-name: bench_ann_cuda-128_arch-x86_64
+name: bench_ann_cuda-129_arch-x86_64
diff --git a/conda/environments/go_cuda-128_arch-aarch64.yaml b/conda/environments/go_cuda-129_arch-aarch64.yaml
similarity index 92%
rename from conda/environments/go_cuda-128_arch-aarch64.yaml
rename to conda/environments/go_cuda-129_arch-aarch64.yaml
index 21bae9da1b..23bb006719 100644
--- a/conda/environments/go_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-129_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.8
+- cuda-version=12.9
 - cxx-compiler
 - dlpack>=0.8,<1.0
 - gcc_linux-aarch64=13.*
@@ -30,4 +30,4 @@ dependencies:
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
-name: go_cuda-128_arch-aarch64
+name: go_cuda-129_arch-aarch64
diff --git a/conda/environments/go_cuda-128_arch-x86_64.yaml b/conda/environments/go_cuda-129_arch-x86_64.yaml
similarity index 92%
rename from conda/environments/go_cuda-128_arch-x86_64.yaml
rename to conda/environments/go_cuda-129_arch-x86_64.yaml
index b3dcf18b70..fd47e8d78c 100644
--- a/conda/environments/go_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-129_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.8
+- cuda-version=12.9
 - cxx-compiler
 - dlpack>=0.8,<1.0
 - gcc_linux-64=13.*
@@ -30,4 +30,4 @@ dependencies:
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
-name: go_cuda-128_arch-x86_64
+name: go_cuda-129_arch-x86_64
diff --git a/conda/environments/rust_cuda-128_arch-aarch64.yaml b/conda/environments/rust_cuda-129_arch-aarch64.yaml
similarity index 92%
rename from conda/environments/rust_cuda-128_arch-aarch64.yaml
rename to conda/environments/rust_cuda-129_arch-aarch64.yaml
index a3844ddad5..61811c6bd7 100644
--- a/conda/environments/rust_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-129_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.8
+- cuda-version=12.9
 - cxx-compiler
 - gcc_linux-aarch64=13.*
 - libclang==16.0.6
@@ -30,4 +30,4 @@ dependencies:
 - ninja
 - rust
 - sysroot_linux-aarch64==2.28
-name: rust_cuda-128_arch-aarch64
+name: rust_cuda-129_arch-aarch64
diff --git a/conda/environments/rust_cuda-128_arch-x86_64.yaml b/conda/environments/rust_cuda-129_arch-x86_64.yaml
similarity index 92%
rename from conda/environments/rust_cuda-128_arch-x86_64.yaml
rename to conda/environments/rust_cuda-129_arch-x86_64.yaml
index d8775fe193..34988efbcb 100644
--- a/conda/environments/rust_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-129_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.8
+- cuda-version=12.9
 - cxx-compiler
 - gcc_linux-64=13.*
 - libclang==16.0.6
@@ -30,4 +30,4 @@ dependencies:
 - ninja
 - rust
 - sysroot_linux-64==2.28
-name: rust_cuda-128_arch-x86_64
+name: rust_cuda-129_arch-x86_64
diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index e99a530497..0b6ebbaad2 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -49,6 +49,12 @@ list(APPEND CUVS_CXX_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
 list(APPEND CUVS_CUDA_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
 # make sure we produce smallest binary size
 list(APPEND CUVS_CUDA_FLAGS -Xfatbin=-compress-all)
+if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA"
+   AND (CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9 AND CMAKE_CUDA_COMPILER_VERSION
+                                                                   VERSION_LESS 13.0)
+)
+  list(APPEND CUVS_CUDA_FLAGS -Xfatbin=--compress-level=3)
+endif()
 
 # Option to enable line info in CUDA device compilation to allow introspection when profiling /
 # memchecking
diff --git a/dependencies.yaml b/dependencies.yaml
index 20b50c32e2..dd35a2d0dd 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,7 +3,7 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["11.8", "12.8"]
+      cuda: ["11.8", "12.9"]
       arch: [x86_64, aarch64]
     includes:
       - build
@@ -29,7 +29,7 @@ files:
   bench_ann:
     output: conda
     matrix:
-      cuda: ["11.8", "12.8"]
+      cuda: ["11.8", "12.9"]
       arch: [x86_64, aarch64]
     includes:
       - bench
@@ -85,7 +85,7 @@ files:
   rust:
     output: conda
     matrix:
-      cuda: ["12.8"]
+      cuda: ["12.9"]
       arch: [x86_64, aarch64]
     includes:
       # clang/libclang only needed for bindgen support
@@ -100,7 +100,7 @@ files:
   go:
     output: conda
     matrix:
-      cuda: ["11.8", "12.8"]
+      cuda: ["11.8", "12.9"]
       arch: [x86_64, aarch64]
     includes:
       - clang
@@ -365,6 +365,10 @@ dependencies:
               cuda: "12.8"
             packages:
               - cuda-version=12.8
+          - matrix:
+              cuda: "12.9"
+            packages:
+              - cuda-version=12.9
   cuda:
     specific:
       - output_types: conda
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 5d1b8e354d..8e3608990f 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -43,14 +43,14 @@ C/C++ Package
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.8
+   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.9
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.8
+   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.9
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
@@ -92,7 +92,7 @@ Conda environment scripts are provided for installing the necessary dependencies
 
 .. code-block:: bash
 
-    conda env create --name cuvs -f conda/environments/all_cuda-128_arch-x86_64.yaml
+    conda env create --name cuvs -f conda/environments/all_cuda-129_arch-x86_64.yaml
     conda activate cuvs
 
 The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
diff --git a/examples/go/README.md b/examples/go/README.md
index 28d11351b2..615b8e75e4 100644
--- a/examples/go/README.md
+++ b/examples/go/README.md
@@ -7,7 +7,7 @@ This package provides Go bindings for the cuVS (CUDA Vector Search) library.
 The required dependencies can be installed with a simple command (which creates your build environment):
 
 ```bash
-conda env create --name go -f conda/environments/go_cuda-128_arch-x86_64.yaml
+conda env create --name go -f conda/environments/go_cuda-129_arch-x86_64.yaml
 conda activate go
 ```
 You may prefer to use `mamba`, as it provides significant speedup over `conda`.
diff --git a/examples/rust/README.md b/examples/rust/README.md
index 98fe526975..36cbab8cdd 100644
--- a/examples/rust/README.md
+++ b/examples/rust/README.md
@@ -9,7 +9,7 @@ Once the minimum requirements are satisfied, this example template application c
 You may follow these steps to quickly get set up:
 
 ```bash
-conda env create --name rust -f conda/environments/rust_cuda-128_arch-x86_64.yaml
+conda env create --name rust -f conda/environments/rust_cuda-129_arch-x86_64.yaml
 conda activate rust
 ```
 You may prefer to use `mamba`, as it provides significant speedup over `conda`.

From 079165dbb9740ae6af65c443199fd8c15721b7b6 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 15 May 2025 00:42:39 -0500
Subject: [PATCH 398/623] Update to clang 20 (#898)

Contributes to https://github.com/rapidsai/build-planning/issues/177

This PR updates the clang version to 20.

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/898
---
 .pre-commit-config.yaml                       |  2 +-
 .../all_cuda-118_arch-aarch64.yaml            |  6 +-
 .../all_cuda-118_arch-x86_64.yaml             |  6 +-
 .../all_cuda-129_arch-aarch64.yaml            |  6 +-
 .../all_cuda-129_arch-x86_64.yaml             |  6 +-
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  6 +-
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  6 +-
 .../bench_ann_cuda-129_arch-aarch64.yaml      |  6 +-
 .../bench_ann_cuda-129_arch-x86_64.yaml       |  6 +-
 .../go_cuda-118_arch-aarch64.yaml             |  6 +-
 .../environments/go_cuda-118_arch-x86_64.yaml |  6 +-
 .../go_cuda-129_arch-aarch64.yaml             |  6 +-
 .../environments/go_cuda-129_arch-x86_64.yaml |  6 +-
 .../rust_cuda-129_arch-aarch64.yaml           |  6 +-
 .../rust_cuda-129_arch-x86_64.yaml            |  6 +-
 cpp/bench/ann/src/common/ann_types.hpp        |  6 +-
 cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h |  8 +-
 cpp/include/cuvs/distance/grammian.hpp        | 16 ++--
 cpp/include/cuvs/neighbors/common.hpp         |  3 +-
 cpp/scripts/run-clang-tidy.py                 |  2 +-
 .../fused_distance_nn/persistent_gemm.h       |  4 +-
 .../distance/detail/masked_distance_base.cuh  |  2 +-
 cpp/src/distance/detail/masked_nn.cuh         | 14 ++--
 cpp/src/neighbors/brute_force.cu              | 12 ++-
 cpp/src/neighbors/cagra_build_float.cu        |  4 +-
 cpp/src/neighbors/cagra_build_int8.cu         |  4 +-
 cpp/src/neighbors/cagra_build_uint8.cu        |  4 +-
 cpp/src/neighbors/cagra_merge_float.cu        |  2 +-
 cpp/src/neighbors/cagra_merge_half.cu         |  2 +-
 cpp/src/neighbors/cagra_merge_int8.cu         |  2 +-
 cpp/src/neighbors/cagra_merge_uint8.cu        |  2 +-
 .../neighbors/detail/cagra/search_plan.cuh    |  2 +-
 cpp/src/neighbors/detail/knn_brute_force.cuh  |  4 +-
 cpp/src/neighbors/detail/nn_descent.cuh       |  4 +-
 .../detail/vamana/priority_queue.cuh          |  3 +-
 .../detail/vamana/vamana_structs.cuh          |  3 +-
 .../iface/iface_cagra_float_uint32_t.cu       |  4 +-
 .../iface/iface_cagra_half_uint32_t.cu        |  4 +-
 .../iface/iface_cagra_int8_t_uint32_t.cu      |  4 +-
 .../iface/iface_cagra_uint8_t_uint32_t.cu     |  4 +-
 .../iface/iface_flat_float_int64_t.cu         |  4 +-
 .../iface/iface_flat_int8_t_int64_t.cu        |  4 +-
 .../iface/iface_flat_uint8_t_int64_t.cu       |  4 +-
 .../neighbors/iface/iface_pq_float_int64_t.cu |  4 +-
 .../neighbors/iface/iface_pq_half_int64_t.cu  |  4 +-
 .../iface/iface_pq_int8_t_int64_t.cu          |  4 +-
 .../iface/iface_pq_uint8_t_int64_t.cu         |  4 +-
 cpp/src/neighbors/ivf_common.cuh              |  2 +-
 .../ivf_flat_build_extend_float_int64_t.cu    |  8 +-
 .../ivf_flat_build_extend_half_int64_t.cu     |  8 +-
 .../ivf_flat_build_extend_int8_t_int64_t.cu   |  8 +-
 .../ivf_flat_build_extend_uint8_t_int64_t.cu  |  8 +-
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    | 18 ++---
 cpp/src/neighbors/ivf_list.cuh                |  8 +-
 .../detail/ivf_pq_build_extend_inst.cuh       |  8 +-
 .../ivf_pq_compute_similarity_float_float.cu  | 79 +++++++++----------
 ...compute_similarity_float_float_bitset64.cu | 79 +++++++++----------
 ...f_pq_compute_similarity_float_fp8_false.cu | 79 +++++++++----------
 ...ute_similarity_float_fp8_false_bitset64.cu | 79 +++++++++----------
 ...vf_pq_compute_similarity_float_fp8_true.cu | 79 +++++++++----------
 ...pute_similarity_float_fp8_true_bitset64.cu | 79 +++++++++----------
 .../ivf_pq_compute_similarity_float_half.cu   | 79 +++++++++----------
 ..._compute_similarity_float_half_bitset64.cu | 79 +++++++++----------
 ...vf_pq_compute_similarity_half_fp8_false.cu | 79 +++++++++----------
 ...pute_similarity_half_fp8_false_bitset64.cu | 79 +++++++++----------
 ...ivf_pq_compute_similarity_half_fp8_true.cu | 79 +++++++++----------
 ...mpute_similarity_half_fp8_true_bitset64.cu | 79 +++++++++----------
 .../ivf_pq_compute_similarity_half_half.cu    | 79 +++++++++----------
 ...q_compute_similarity_half_half_bitset64.cu | 79 +++++++++----------
 .../ivf_pq/ivf_pq_compute_similarity.cuh      | 79 +++++++++----------
 cpp/src/neighbors/nn_descent_float.cu         |  4 +-
 cpp/src/neighbors/nn_descent_half.cu          |  4 +-
 cpp/src/neighbors/nn_descent_int8.cu          |  4 +-
 cpp/src/neighbors/nn_descent_uint8.cu         |  4 +-
 cpp/src/neighbors/vamana_build_float.cu       |  4 +-
 cpp/src/neighbors/vamana_build_int8.cu        |  4 +-
 cpp/src/neighbors/vamana_build_uint8.cu       |  4 +-
 cpp/src/preprocessing/quantize/scalar.cu      |  6 +-
 .../neighbors/detail/cross_component_nn.cuh   |  2 +-
 cpp/tests/distance/distance_base.cuh          |  2 +-
 .../neighbors/brute_force_prefiltered.cu      |  2 +-
 .../sparse/neighbors/cross_component_nn.cu    |  2 +-
 dependencies.yaml                             |  6 +-
 83 files changed, 757 insertions(+), 777 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5ba3c70237..1af49aab62 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -48,7 +48,7 @@ repos:
                 additional_dependencies: [toml]
                 args: ["--config=pyproject.toml"]
       - repo: https://github.com/pre-commit/mirrors-clang-format
-        rev: v16.0.6
+        rev: v20.1.4
         hooks:
               - id: clang-format
                 types_or: [c, c++, cuda]
diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
index 1e296b7e55..75be7ae0b2 100644
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-118_arch-aarch64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
@@ -26,7 +26,7 @@ dependencies:
 - go
 - graphviz
 - ipython
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
index a18cc592d2..ebaab2b524 100644
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
@@ -26,7 +26,7 @@ dependencies:
 - go
 - graphviz
 - ipython
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
index fdd99e97d2..db1307bbab 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
@@ -27,7 +27,7 @@ dependencies:
 - go
 - graphviz
 - ipython
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
index 92d276a42f..6ac0ce5c4f 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - breathe>=4.35.0
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
@@ -27,7 +27,7 @@ dependencies:
 - go
 - graphviz
 - ipython
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
index 7594a38e5b..379be62760 100644
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - click
 - cmake>=3.30.4
 - cuda-nvtx=11.8
@@ -26,7 +26,7 @@ dependencies:
 - gcc_linux-aarch64=11.*
 - glog>=0.6.0
 - h5py>=3.8.0
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
index 75699d828d..2fd2287ab4 100644
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - click
 - cmake>=3.30.4
 - cuda-nvtx=11.8
@@ -28,7 +28,7 @@ dependencies:
 - h5py>=3.8.0
 - libaio
 - libboost-devel=1.87
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index 59fd76afa7..cc69a6df1d 100644
--- a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - click
 - cmake>=3.30.4
 - cuda-cudart-dev
@@ -27,7 +27,7 @@ dependencies:
 - gcc_linux-aarch64=13.*
 - glog>=0.6.0
 - h5py>=3.8.0
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index 46938afc96..2aa8c4d188 100644
--- a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -9,8 +9,8 @@ channels:
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - click
 - cmake>=3.30.4
 - cuda-cudart-dev
@@ -29,7 +29,7 @@ dependencies:
 - h5py>=3.8.0
 - libaio
 - libboost-devel=1.87
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/go_cuda-118_arch-aarch64.yaml b/conda/environments/go_cuda-118_arch-aarch64.yaml
index 4a14dc43ef..f664093aad 100644
--- a/conda/environments/go_cuda-118_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-118_arch-aarch64.yaml
@@ -8,8 +8,8 @@ channels:
 - nvidia
 dependencies:
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
@@ -19,7 +19,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - gcc_linux-aarch64=11.*
 - go
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/go_cuda-118_arch-x86_64.yaml b/conda/environments/go_cuda-118_arch-x86_64.yaml
index fb889411d6..3aeb6a39ba 100644
--- a/conda/environments/go_cuda-118_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-118_arch-x86_64.yaml
@@ -8,8 +8,8 @@ channels:
 - nvidia
 dependencies:
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-nvtx=11.8
 - cuda-profiler-api=11.8.86
@@ -19,7 +19,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - gcc_linux-64=11.*
 - go
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev=11.11.3.6
 - libcublas=11.11.3.6
 - libcurand-dev=10.3.0.86
diff --git a/conda/environments/go_cuda-129_arch-aarch64.yaml b/conda/environments/go_cuda-129_arch-aarch64.yaml
index 23bb006719..97d9e4832d 100644
--- a/conda/environments/go_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-129_arch-aarch64.yaml
@@ -8,8 +8,8 @@ channels:
 - nvidia
 dependencies:
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
@@ -20,7 +20,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - gcc_linux-aarch64=13.*
 - go
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/go_cuda-129_arch-x86_64.yaml b/conda/environments/go_cuda-129_arch-x86_64.yaml
index fd47e8d78c..4589f254d6 100644
--- a/conda/environments/go_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-129_arch-x86_64.yaml
@@ -8,8 +8,8 @@ channels:
 - nvidia
 dependencies:
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
@@ -20,7 +20,7 @@ dependencies:
 - dlpack>=0.8,<1.0
 - gcc_linux-64=13.*
 - go
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/rust_cuda-129_arch-aarch64.yaml b/conda/environments/rust_cuda-129_arch-aarch64.yaml
index 61811c6bd7..d97c34d062 100644
--- a/conda/environments/rust_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-129_arch-aarch64.yaml
@@ -8,8 +8,8 @@ channels:
 - nvidia
 dependencies:
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
@@ -18,7 +18,7 @@ dependencies:
 - cuda-version=12.9
 - cxx-compiler
 - gcc_linux-aarch64=13.*
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/conda/environments/rust_cuda-129_arch-x86_64.yaml b/conda/environments/rust_cuda-129_arch-x86_64.yaml
index 34988efbcb..82b71d872d 100644
--- a/conda/environments/rust_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-129_arch-x86_64.yaml
@@ -8,8 +8,8 @@ channels:
 - nvidia
 dependencies:
 - c-compiler
-- clang-tools==16.0.6
-- clang==16.0.6
+- clang-tools==20.1.4
+- clang==20.1.4
 - cmake>=3.30.4
 - cuda-cudart-dev
 - cuda-nvcc
@@ -18,7 +18,7 @@ dependencies:
 - cuda-version=12.9
 - cxx-compiler
 - gcc_linux-64=13.*
-- libclang==16.0.6
+- libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
 - libcusolver-dev
diff --git a/cpp/bench/ann/src/common/ann_types.hpp b/cpp/bench/ann/src/common/ann_types.hpp
index eed18272cd..464932f5a1 100644
--- a/cpp/bench/ann/src/common/ann_types.hpp
+++ b/cpp/bench/ann/src/common/ann_types.hpp
@@ -168,7 +168,7 @@ class algo : public algo_base {
   // and set_search_dataset() should save the passed-in pointer somewhere.
   // The client code should call set_search_dataset() before searching,
   // and should not release dataset before searching is finished.
-  virtual void set_search_dataset(const T* /*dataset*/, size_t /*nrow*/){};
+  virtual void set_search_dataset(const T* /*dataset*/, size_t /*nrow*/) {};
 
   /**
    * Make a shallow copy of the algo wrapper that shares the resources and ensures thread-safe
@@ -181,6 +181,6 @@ class algo : public algo_base {
 #define REGISTER_ALGO_INSTANCE(DataT)                                                              \
   template auto cuvs::bench::create_algo<DataT>(                                                   \
     const std::string&, const std::string&, int, const nlohmann::json&)                            \
-    ->std::unique_ptr<cuvs::bench::algo<DataT>>;                                                   \
+    -> std::unique_ptr<cuvs::bench::algo<DataT>>;                                                  \
   template auto cuvs::bench::create_search_param<DataT>(const std::string&, const nlohmann::json&) \
-    ->std::unique_ptr<typename cuvs::bench::algo<DataT>::search_param>;
+    -> std::unique_ptr<typename cuvs::bench::algo<DataT>::search_param>;
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
index 441a1eafac..37d1190e66 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
@@ -81,9 +81,11 @@ class shared_raft_resources {
   using large_mr_type = rmm::mr::managed_memory_resource;
 
   shared_raft_resources()
-  try : orig_resource_{rmm::mr::get_current_device_resource()},
-    pool_resource_(orig_resource_, 1024 * 1024 * 1024ull),
-    resource_(&pool_resource_, rmm_oom_callback, nullptr), large_mr_() {
+  try
+    : orig_resource_{rmm::mr::get_current_device_resource()},
+      pool_resource_(orig_resource_, 1024 * 1024 * 1024ull),
+      resource_(&pool_resource_, rmm_oom_callback, nullptr),
+      large_mr_() {
     rmm::mr::set_current_device_resource(&resource_);
   } catch (const std::exception& e) {
     auto cuda_status = cudaGetLastError();
diff --git a/cpp/include/cuvs/distance/grammian.hpp b/cpp/include/cuvs/distance/grammian.hpp
index 0c904d493c..4d0a90e6cd 100644
--- a/cpp/include/cuvs/distance/grammian.hpp
+++ b/cpp/include/cuvs/distance/grammian.hpp
@@ -51,11 +51,11 @@ class GramMatrixBase {
   bool legacy_interface;
 
  public:
-  GramMatrixBase() : legacy_interface(false){};
+  GramMatrixBase() : legacy_interface(false) {};
   [[deprecated]] GramMatrixBase(cublasHandle_t cublas_handle)
-    : cublas_handle(cublas_handle), legacy_interface(true){};
+    : cublas_handle(cublas_handle), legacy_interface(true) {};
 
-  virtual ~GramMatrixBase(){};
+  virtual ~GramMatrixBase() {};
 
   /** Convenience function to evaluate the Gram matrix for two vector sets.
    *  Vector sets are provided in Matrix format
@@ -331,10 +331,10 @@ class PolynomialKernel : public GramMatrixBase<math_t> {
    * @param offset
    */
   PolynomialKernel(exp_t exponent, math_t gain, math_t offset)
-    : GramMatrixBase<math_t>(), exponent(exponent), gain(gain), offset(offset){};
+    : GramMatrixBase<math_t>(), exponent(exponent), gain(gain), offset(offset) {};
 
   [[deprecated]] PolynomialKernel(exp_t exponent, math_t gain, math_t offset, cublasHandle_t handle)
-    : GramMatrixBase<math_t>(handle), exponent(exponent), gain(gain), offset(offset){};
+    : GramMatrixBase<math_t>(handle), exponent(exponent), gain(gain), offset(offset) {};
 
   /** Evaluate kernel matrix using polynomial kernel.
    *
@@ -447,7 +447,7 @@ class TanhKernel : public GramMatrixBase<math_t> {
   TanhKernel(math_t gain, math_t offset) : GramMatrixBase<math_t>(), gain(gain), offset(offset) {}
 
   [[deprecated]] TanhKernel(math_t gain, math_t offset, cublasHandle_t handle)
-    : GramMatrixBase<math_t>(handle), gain(gain), offset(offset){};
+    : GramMatrixBase<math_t>(handle), gain(gain), offset(offset) {};
 
   /** Evaluate kernel matrix using tanh kernel.
    *
@@ -562,10 +562,10 @@ class RBFKernel : public GramMatrixBase<math_t> {
    * @tparam math_t floating point type
    * @param gain
    */
-  RBFKernel(math_t gain) : GramMatrixBase<math_t>(), gain(gain){};
+  RBFKernel(math_t gain) : GramMatrixBase<math_t>(), gain(gain) {};
 
   [[deprecated]] RBFKernel(math_t gain, cublasHandle_t handle)
-    : GramMatrixBase<math_t>(handle), gain(gain){};
+    : GramMatrixBase<math_t>(handle), gain(gain) {};
 
   void matrixRowNormL2(raft::resources const& handle,
                        dense_input_matrix_view_t<math_t> matrix,
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 4c31987989..cb56e9f8c2 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -691,8 +691,7 @@ template <typename ListT, class T = void>
 struct enable_if_valid_list {};
 
 template <class T,
-          template <typename, typename...>
-          typename SpecT,
+          template <typename, typename...> typename SpecT,
           typename SizeT,
           typename... SpecExtraArgs>
 struct enable_if_valid_list<list<SpecT, SizeT, SpecExtraArgs...>, T> {
diff --git a/cpp/scripts/run-clang-tidy.py b/cpp/scripts/run-clang-tidy.py
index cad08ca551..ebfaa74515 100644
--- a/cpp/scripts/run-clang-tidy.py
+++ b/cpp/scripts/run-clang-tidy.py
@@ -28,7 +28,7 @@
 import subprocess
 
 
-EXPECTED_VERSIONS = ("16.0.6",)
+EXPECTED_VERSIONS = ("20.1.4",)
 VERSION_REGEX = re.compile(r"clang version ([0-9.]+)")
 CMAKE_COMPILER_REGEX = re.compile(
     r"^\s*CMAKE_CXX_COMPILER:FILEPATH=(.+)\s*$", re.MULTILINE)
diff --git a/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h b/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
index c506da4ff2..61d65d85dc 100644
--- a/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
+++ b/cpp/src/distance/detail/fused_distance_nn/persistent_gemm.h
@@ -147,8 +147,8 @@ struct FusedDistanceNNPersistent {
   struct temp_problem_visitor {
     int problem_count;
 
-    CUTLASS_HOST_DEVICE temp_problem_visitor() : problem_count(0){};
-    CUTLASS_HOST_DEVICE temp_problem_visitor(int problem_count_) : problem_count(problem_count_){};
+    CUTLASS_HOST_DEVICE temp_problem_visitor() : problem_count(0) {};
+    CUTLASS_HOST_DEVICE temp_problem_visitor(int problem_count_) : problem_count(problem_count_) {};
   };
 
   /// Argument structure
diff --git a/cpp/src/distance/detail/masked_distance_base.cuh b/cpp/src/distance/detail/masked_distance_base.cuh
index ec7270baa2..34a1382e2d 100644
--- a/cpp/src/distance/detail/masked_distance_base.cuh
+++ b/cpp/src/distance/detail/masked_distance_base.cuh
@@ -215,7 +215,7 @@ struct MaskedDistances : public BaseClass {
             }
           }
         }  // tile_idx_n
-      }    // idx_g
+      }  // idx_g
       rowEpilog_op(tile_idx_m);
     }  // tile_idx_m
   }
diff --git a/cpp/src/distance/detail/masked_nn.cuh b/cpp/src/distance/detail/masked_nn.cuh
index e10d2b7c69..1ef10a504a 100644
--- a/cpp/src/distance/detail/masked_nn.cuh
+++ b/cpp/src/distance/detail/masked_nn.cuh
@@ -291,13 +291,13 @@ void masked_l2_nn_impl(raft::resources const& handle,
   auto fin_op      = raft::identity_op{};
 
   auto kernel               = masked_l2_nn_kernel<DataT,
-                                    OutT,
-                                    IdxT,
-                                    P,
-                                    ReduceOpT,
-                                    KVPReduceOpT,
-                                    decltype(core_lambda),
-                                    decltype(fin_op)>;
+                                                  OutT,
+                                                  IdxT,
+                                                  P,
+                                                  ReduceOpT,
+                                                  KVPReduceOpT,
+                                                  decltype(core_lambda),
+                                                  decltype(fin_op)>;
   constexpr size_t smemSize = P::SmemSize + ((P::Mblk + P::Nblk) * sizeof(DataT));
   dim3 block(P::Nthreads);
   dim3 grid = launchConfigGenerator<P>(m, n, smemSize, kernel);
diff --git a/cpp/src/neighbors/brute_force.cu b/cpp/src/neighbors/brute_force.cu
index d54a758791..332940b0b1 100644
--- a/cpp/src/neighbors/brute_force.cu
+++ b/cpp/src/neighbors/brute_force.cu
@@ -164,37 +164,35 @@ void index<T, DistT>::update_dataset(
   auto build(raft::resources const& res,                                                        \
              const cuvs::neighbors::brute_force::index_params& index_params,                    \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)               \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+    -> cuvs::neighbors::brute_force::index<T, DistT>                                            \
   {                                                                                             \
     return detail::build<T, DistT>(res, dataset, index_params.metric, index_params.metric_arg); \
   }                                                                                             \
   auto build(raft::resources const& res,                                                        \
              const cuvs::neighbors::brute_force::index_params& index_params,                    \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)                 \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+    -> cuvs::neighbors::brute_force::index<T, DistT>                                            \
   {                                                                                             \
     return detail::build<T, DistT>(res, dataset, index_params.metric, index_params.metric_arg); \
   }                                                                                             \
   auto build(raft::resources const& res,                                                        \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,               \
              cuvs::distance::DistanceType metric,                                               \
-             DistT metric_arg)                                                                  \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+             DistT metric_arg) -> cuvs::neighbors::brute_force::index<T, DistT>                 \
   {                                                                                             \
     return detail::build<T, DistT>(res, dataset, metric, metric_arg);                           \
   }                                                                                             \
   auto build(raft::resources const& res,                                                        \
              const cuvs::neighbors::brute_force::index_params& index_params,                    \
              raft::device_matrix_view<const T, int64_t, raft::col_major> dataset)               \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+    -> cuvs::neighbors::brute_force::index<T, DistT>                                            \
   {                                                                                             \
     return detail::build<T, DistT>(res, dataset, index_params.metric, index_params.metric_arg); \
   }                                                                                             \
   auto build(raft::resources const& res,                                                        \
              raft::device_matrix_view<const T, int64_t, raft::col_major> dataset,               \
              cuvs::distance::DistanceType metric,                                               \
-             DistT metric_arg)                                                                  \
-    ->cuvs::neighbors::brute_force::index<T, DistT>                                             \
+             DistT metric_arg) -> cuvs::neighbors::brute_force::index<T, DistT>                 \
   {                                                                                             \
     return detail::build<T, DistT>(res, dataset, metric, metric_arg);                           \
   }                                                                                             \
diff --git a/cpp/src/neighbors/cagra_build_float.cu b/cpp/src/neighbors/cagra_build_float.cu
index b990d1b329..3f51b0d2aa 100644
--- a/cpp/src/neighbors/cagra_build_float.cu
+++ b/cpp/src/neighbors/cagra_build_float.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::cagra::index_params& params,                  \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                                     \
   {                                                                               \
     return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }                                                                               \
@@ -31,7 +31,7 @@ namespace cuvs::neighbors::cagra {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::cagra::index_params& params,                  \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                                     \
   {                                                                               \
     return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }
diff --git a/cpp/src/neighbors/cagra_build_int8.cu b/cpp/src/neighbors/cagra_build_int8.cu
index 624ebd060a..046ee31312 100644
--- a/cpp/src/neighbors/cagra_build_int8.cu
+++ b/cpp/src/neighbors/cagra_build_int8.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::cagra::index_params& params,                  \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                                     \
   {                                                                               \
     return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }                                                                               \
@@ -31,7 +31,7 @@ namespace cuvs::neighbors::cagra {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::cagra::index_params& params,                  \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                                     \
   {                                                                               \
     return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }
diff --git a/cpp/src/neighbors/cagra_build_uint8.cu b/cpp/src/neighbors/cagra_build_uint8.cu
index 8fd806c64e..7a8c710e6e 100644
--- a/cpp/src/neighbors/cagra_build_uint8.cu
+++ b/cpp/src/neighbors/cagra_build_uint8.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::cagra::index_params& params,                  \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                                     \
   {                                                                               \
     return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }                                                                               \
@@ -31,7 +31,7 @@ namespace cuvs::neighbors::cagra {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::cagra::index_params& params,                  \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                      \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                                     \
   {                                                                               \
     return cuvs::neighbors::cagra::build<T, IdxT>(handle, params, dataset);       \
   }
diff --git a/cpp/src/neighbors/cagra_merge_float.cu b/cpp/src/neighbors/cagra_merge_float.cu
index 951c0c5fe9..01526d7b3c 100644
--- a/cpp/src/neighbors/cagra_merge_float.cu
+++ b/cpp/src/neighbors/cagra_merge_float.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra {
   auto merge(raft::resources const& handle,                                 \
              const cuvs::neighbors::cagra::merge_params& params,            \
              std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                               \
   {                                                                         \
     return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
   }
diff --git a/cpp/src/neighbors/cagra_merge_half.cu b/cpp/src/neighbors/cagra_merge_half.cu
index 704a00f747..90bdafb255 100644
--- a/cpp/src/neighbors/cagra_merge_half.cu
+++ b/cpp/src/neighbors/cagra_merge_half.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra {
   auto merge(raft::resources const& handle,                                 \
              const cuvs::neighbors::cagra::merge_params& params,            \
              std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                               \
   {                                                                         \
     return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
   }
diff --git a/cpp/src/neighbors/cagra_merge_int8.cu b/cpp/src/neighbors/cagra_merge_int8.cu
index a7e9035626..854261336e 100644
--- a/cpp/src/neighbors/cagra_merge_int8.cu
+++ b/cpp/src/neighbors/cagra_merge_int8.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra {
   auto merge(raft::resources const& handle,                                 \
              const cuvs::neighbors::cagra::merge_params& params,            \
              std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                               \
   {                                                                         \
     return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
   }
diff --git a/cpp/src/neighbors/cagra_merge_uint8.cu b/cpp/src/neighbors/cagra_merge_uint8.cu
index a4fc7149c1..d536029cc1 100644
--- a/cpp/src/neighbors/cagra_merge_uint8.cu
+++ b/cpp/src/neighbors/cagra_merge_uint8.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::cagra {
   auto merge(raft::resources const& handle,                                 \
              const cuvs::neighbors::cagra::merge_params& params,            \
              std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices) \
-    ->cuvs::neighbors::cagra::index<T, IdxT>                                \
+    -> cuvs::neighbors::cagra::index<T, IdxT>                               \
   {                                                                         \
     return cuvs::neighbors::cagra::merge<T, IdxT>(handle, params, indices); \
   }
diff --git a/cpp/src/neighbors/detail/cagra/search_plan.cuh b/cpp/src/neighbors/detail/cagra/search_plan.cuh
index ab2396cc21..c15006257b 100644
--- a/cpp/src/neighbors/detail/cagra/search_plan.cuh
+++ b/cpp/src/neighbors/detail/cagra/search_plan.cuh
@@ -192,7 +192,7 @@ struct search_plan_impl : public search_plan_impl_base {
                           const INDEX_T* dev_seed_ptr,                   // [num_queries, num_seeds]
                           std::uint32_t* const num_executed_iterations,  // [num_queries]
                           uint32_t topk,
-                          SAMPLE_FILTER_T sample_filter){};
+                          SAMPLE_FILTER_T sample_filter) {};
 
   void adjust_search_params()
   {
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 989c4adce4..72b3532dfc 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -265,7 +265,7 @@ void tiled_brute_force_knn(const raft::resources& handle,
                            IndexType col      = j + (idx % current_centroid_size);
                            IndexType g_idx    = row * n_cols + col;
                            IndexType item_idx = (g_idx) >> 5;
-                           uint32_t bit_idx   = (g_idx)&31;
+                           uint32_t bit_idx   = (g_idx) & 31;
                            uint32_t filter    = filter_bits[item_idx];
                            if ((filter & (uint32_t(1) << bit_idx)) == 0) {
                              distances_ptr[idx] = masked_distance;
@@ -800,7 +800,7 @@ cuvs::neighbors::brute_force::index<T, DistT> build(
     auto dataset_storage = std::optional<device_matrix<T, int64_t, LayoutT>>{};
     auto dataset_view    = [&res, &dataset_storage, dataset]() {
       if constexpr (std::is_same_v<decltype(dataset),
-                                   raft::device_matrix_view<const T, int64_t, row_major>>) {
+                                      raft::device_matrix_view<const T, int64_t, row_major>>) {
         return dataset;
       } else {
         dataset_storage =
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 9beea05646..03002569d8 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -67,9 +67,9 @@ class ResultItem<int> {
 
  public:
   __host__ __device__ ResultItem()
-    : id_(std::numeric_limits<Index_t>::max()), dist_(std::numeric_limits<DistData_t>::max()){};
+    : id_(std::numeric_limits<Index_t>::max()), dist_(std::numeric_limits<DistData_t>::max()) {};
   __host__ __device__ ResultItem(const Index_t id_with_flag, const DistData_t dist)
-    : id_(id_with_flag), dist_(dist){};
+    : id_(id_with_flag), dist_(dist) {};
   __host__ __device__ bool is_new() const { return id_ >= 0; }
   __host__ __device__ Index_t& id_with_flag() { return id_; }
   __host__ __device__ Index_t id() const
diff --git a/cpp/src/neighbors/detail/vamana/priority_queue.cuh b/cpp/src/neighbors/detail/vamana/priority_queue.cuh
index 6dc1dc94a1..883dc8d440 100644
--- a/cpp/src/neighbors/detail/vamana/priority_queue.cuh
+++ b/cpp/src/neighbors/detail/vamana/priority_queue.cuh
@@ -202,8 +202,7 @@ class PriorityQueue {
  * Used for other operations like checking for duplicates, etc.
  ****************************************************************************************/
 template <typename SUMTYPE>
-class __align__(16) Node
-{
+class __align__(16) Node {
  public:
   SUMTYPE distance;
   int nodeid;
diff --git a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
index 22678c1962..0cf29a5b42 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
@@ -47,8 +47,7 @@ static const int DEGREE_SIZES[4] = {32, 64, 128, 256};
 
 // Object used to store id,distance combination graph construction operations
 template <typename IdxT, typename accT>
-struct __align__(16) DistPair
-{
+struct __align__(16) DistPair {
   accT dist;
   IdxT idx;
 
diff --git a/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
index b7ad428ad8..b256dac5a3 100644
--- a/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::device>;                           \
+                                             raft::memory_type::device>;                           \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::host>;                             \
+                                             raft::memory_type::host>;                             \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
                                              raft::memory_type::device>;                           \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
diff --git a/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
index 86e0633bb1..a650cf95c3 100644
--- a/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::device>;                           \
+                                             raft::memory_type::device>;                           \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::host>;                             \
+                                             raft::memory_type::host>;                             \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
                                              raft::memory_type::device>;                           \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
diff --git a/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
index 64f174184b..746ffeb961 100644
--- a/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::device>;                           \
+                                             raft::memory_type::device>;                           \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::host>;                             \
+                                             raft::memory_type::host>;                             \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
                                              raft::memory_type::device>;                           \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
diff --git a/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
index 9f6db32df8..948e0993b3 100644
--- a/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_CAGRA(T, IdxT)                                                                \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::device>;                           \
+                                             raft::memory_type::device>;                           \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::host>;                             \
+                                             raft::memory_type::host>;                             \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
                                              raft::memory_type::device>;                           \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
diff --git a/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
index 0afffe0ad3..9d7e40db15 100644
--- a/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_flat_float_int64_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_FLAT(T, IdxT)                                                                 \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::device>;                           \
+                                             raft::memory_type::device>;                           \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::host>;                             \
+                                             raft::memory_type::host>;                             \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
                                              raft::memory_type::device>;                           \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
diff --git a/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
index 5afd77053e..8daa6b435d 100644
--- a/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_flat_int8_t_int64_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_FLAT(T, IdxT)                                                                 \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::device>;                           \
+                                             raft::memory_type::device>;                           \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::host>;                             \
+                                             raft::memory_type::host>;                             \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
                                              raft::memory_type::device>;                           \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
diff --git a/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
index 4f2f85700c..0fdaef195d 100644
--- a/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_FLAT(T, IdxT)                                                                 \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::device>;                           \
+                                             raft::memory_type::device>;                           \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
-                                          raft::memory_type::host>;                             \
+                                             raft::memory_type::host>;                             \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
                                              raft::memory_type::device>;                           \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
diff --git a/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
index 90759d5f1a..c36d92d859 100644
--- a/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_float_int64_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::device>;                          \
+                                             raft::memory_type::device>;                          \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::host>;                            \
+                                             raft::memory_type::host>;                            \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
                                              raft::memory_type::device>;                          \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
diff --git a/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
index c92d6fd651..3f8ec9ac42 100644
--- a/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_half_int64_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::device>;                          \
+                                             raft::memory_type::device>;                          \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::host>;                            \
+                                             raft::memory_type::host>;                            \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
                                              raft::memory_type::device>;                          \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
diff --git a/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
index 59269e9da1..e58132b5b0 100644
--- a/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_int8_t_int64_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::device>;                          \
+                                             raft::memory_type::device>;                          \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::host>;                            \
+                                             raft::memory_type::host>;                            \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
                                              raft::memory_type::device>;                          \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
diff --git a/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu b/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
index c407e64cac..b6ce641b54 100644
--- a/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
@@ -29,9 +29,9 @@ namespace cuvs::neighbors {
 
 #define CUVS_INST_MG_PQ(T, IdxT)                                                                  \
   using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::device>;                          \
+                                             raft::memory_type::device>;                          \
   using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,        \
-                                          raft::memory_type::host>;                            \
+                                             raft::memory_type::host>;                            \
   using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
                                              raft::memory_type::device>;                          \
   using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,     \
diff --git a/cpp/src/neighbors/ivf_common.cuh b/cpp/src/neighbors/ivf_common.cuh
index 0c6f7c5ba9..2169f72998 100644
--- a/cpp/src/neighbors/ivf_common.cuh
+++ b/cpp/src/neighbors/ivf_common.cuh
@@ -37,7 +37,7 @@ struct dummy_block_sort_t {
   using queue_t = raft::matrix::detail::select::warpsort::
     warp_sort_distributed<raft::WarpSize, Ascending, T, IdxT>;
   template <typename... Args>
-  __device__ dummy_block_sort_t(int k, Args...){};
+  __device__ dummy_block_sort_t(int k, Args...) {};
 };
 
 /**
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
index 52026172c7..485612ab7e 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
@@ -33,7 +33,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -49,7 +49,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -66,7 +66,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
               std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
@@ -83,7 +83,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
               std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu
index 8f4b253d7b..71916587e6 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_half_int64_t.cu
@@ -33,7 +33,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -49,7 +49,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -66,7 +66,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
               std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
@@ -83,7 +83,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
               std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
index ef326f2415..f36d3c3bb2 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_int8_t_int64_t.cu
@@ -33,7 +33,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -49,7 +49,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -66,7 +66,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
               std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
@@ -83,7 +83,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
               std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
index 8a1f3e42fa..899c1049f3 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build_extend_uint8_t_int64_t.cu
@@ -33,7 +33,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)                   \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -49,7 +49,7 @@ namespace cuvs::neighbors::ivf_flat {
   auto build(raft::resources const& handle,                                                      \
              const cuvs::neighbors::ivf_flat::index_params& params,                              \
              raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)                     \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(                                            \
       std::move(cuvs::neighbors::ivf_flat::detail::build(handle, params, dataset)));             \
@@ -66,7 +66,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::device_matrix_view<const T, IdxT, raft::row_major> new_vectors,              \
               std::optional<raft::device_vector_view<const IdxT, IdxT>> new_indices,             \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
@@ -83,7 +83,7 @@ namespace cuvs::neighbors::ivf_flat {
               raft::host_matrix_view<const T, IdxT, raft::row_major> new_vectors,                \
               std::optional<raft::host_vector_view<const IdxT, IdxT>> new_indices,               \
               const cuvs::neighbors::ivf_flat::index<T, IdxT>& orig_index)                       \
-    ->cuvs::neighbors::ivf_flat::index<T, IdxT>                                                  \
+    -> cuvs::neighbors::ivf_flat::index<T, IdxT>                                                 \
   {                                                                                              \
     return cuvs::neighbors::ivf_flat::index<T, IdxT>(std::move(                                  \
       cuvs::neighbors::ivf_flat::detail::extend(handle, new_vectors, new_indices, orig_index))); \
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index 79b4f1a188..9bf4ae6784 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -1023,15 +1023,15 @@ void launch_kernel(Lambda lambda,
   RAFT_EXPECTS(Veclen == index.veclen(),
                "Configured Veclen does not match the index interleaving pattern.");
   constexpr auto kKernel   = interleaved_scan_kernel<Capacity,
-                                                   Veclen,
-                                                   Ascending,
-                                                   ComputeNorm,
-                                                   T,
-                                                   AccT,
-                                                   IdxT,
-                                                   IvfSampleFilterT,
-                                                   Lambda,
-                                                   PostLambda>;
+                                                     Veclen,
+                                                     Ascending,
+                                                     ComputeNorm,
+                                                     T,
+                                                     AccT,
+                                                     IdxT,
+                                                     IvfSampleFilterT,
+                                                     Lambda,
+                                                     PostLambda>;
   const int max_query_smem = 16384;
   int query_smem_elems     = std::min<int>(max_query_smem / sizeof(T),
                                        raft::Pow2<Veclen * raft::WarpSize>::roundUp(index.dim()));
diff --git a/cpp/src/neighbors/ivf_list.cuh b/cpp/src/neighbors/ivf_list.cuh
index 9d8aef5033..2cfe02a903 100644
--- a/cpp/src/neighbors/ivf_list.cuh
+++ b/cpp/src/neighbors/ivf_list.cuh
@@ -101,10 +101,10 @@ void resize_list(raft::resources const& res,
   if (old_used_size > 0) {
     auto copied_data_extents = spec.make_list_extents(old_used_size);
     auto copied_view         = raft::make_mdspan<typename ListT::value_type,
-                                         typename ListT::size_type,
-                                         raft::row_major,
-                                         false,
-                                         true>(new_list->data.data_handle(), copied_data_extents);
+                                                 typename ListT::size_type,
+                                                 raft::row_major,
+                                                 false,
+                                                 true>(new_list->data.data_handle(), copied_data_extents);
     raft::copy(copied_view.data_handle(),
                orig_list->data.data_handle(),
                copied_view.size(),
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh
index 4b963b0b37..6060d2b7a1 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_build_extend_inst.cuh
@@ -29,7 +29,7 @@ namespace cuvs::neighbors::ivf_pq {
   auto build(raft::resources const& handle,                                                       \
              const cuvs::neighbors::ivf_pq::index_params& params,                                 \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)                 \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+    -> cuvs::neighbors::ivf_pq::index<IdxT>                                                       \
   {                                                                                               \
     return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
   }                                                                                               \
@@ -45,7 +45,7 @@ namespace cuvs::neighbors::ivf_pq {
   auto build(raft::resources const& handle,                                                       \
              const cuvs::neighbors::ivf_pq::index_params& params,                                 \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)                   \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+    -> cuvs::neighbors::ivf_pq::index<IdxT>                                                       \
   {                                                                                               \
     return cuvs::neighbors::ivf_pq::detail::build(handle, params, dataset);                       \
   }                                                                                               \
@@ -62,7 +62,7 @@ namespace cuvs::neighbors::ivf_pq {
     raft::device_matrix_view<const T, int64_t, raft::row_major> new_vectors,                      \
     std::optional<raft::device_vector_view<const IdxT, int64_t, raft::row_major>> new_indices,    \
     const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                                       \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+    -> cuvs::neighbors::ivf_pq::index<IdxT>                                                       \
   {                                                                                               \
     return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
   }                                                                                               \
@@ -77,7 +77,7 @@ namespace cuvs::neighbors::ivf_pq {
               raft::host_matrix_view<const T, int64_t, raft::row_major> new_vectors,              \
               std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices,             \
               const cuvs::neighbors::ivf_pq::index<IdxT>& orig_index)                             \
-    ->cuvs::neighbors::ivf_pq::index<IdxT>                                                        \
+    -> cuvs::neighbors::ivf_pq::index<IdxT>                                                       \
   {                                                                                               \
     return cuvs::neighbors::ivf_pq::detail::extend(handle, new_vectors, new_indices, orig_index); \
   }                                                                                               \
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
index bc73ff5a36..33e7067a70 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
index 6e9e0bcf77..8c28e2c5b9 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
index 2aa0bacf4c..4cb3424cb5 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
index 4a57add148..e3f330dccb 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
index d4e3fdf5cc..75f8f2cf8a 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
index 1ad240b5bb..3d0c610dd2 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
index 02e118158d..9a288a1c7c 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
index fcc883c92f..07af7815e2 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
index cde961c72c..8886b01bef 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
index 5b34cb3536..a921545f80 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
index f1efe79f99..05241135b5 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
index dee55e22b7..2ebd3d5270 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
index bb56fd08d5..95a6f0bedd 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
index 4340956157..a0be0b392f 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
@@ -24,46 +24,45 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  template auto                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  template void                                                                             \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  template auto                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  template void                                                                                \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
index 37612402c4..235f0aacb3 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
@@ -130,46 +130,45 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
 
 }  // namespace cuvs::neighbors::ivf_pq::detail
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                 \
-  OutT, LutT, IvfSampleFilterT)                                                             \
-  extern template auto                                                                      \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>( \
-    const cudaDeviceProp& dev_props,                                                        \
-    bool manage_local_topk,                                                                 \
-    int locality_hint,                                                                      \
-    double preferred_shmem_carveout,                                                        \
-    uint32_t pq_bits,                                                                       \
-    uint32_t pq_dim,                                                                        \
-    uint32_t precomp_data_count,                                                            \
-    uint32_t n_queries,                                                                     \
-    uint32_t n_probes,                                                                      \
-    uint32_t topk)                                                                          \
-    ->cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>;              \
-                                                                                            \
-  extern template void                                                                      \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(    \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,              \
-    rmm::cuda_stream_view stream,                                                           \
-    uint32_t dim,                                                                           \
-    uint32_t n_probes,                                                                      \
-    uint32_t pq_dim,                                                                        \
-    uint32_t n_queries,                                                                     \
-    uint32_t queries_offset,                                                                \
-    cuvs::distance::DistanceType metric,                                                    \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                    \
-    uint32_t topk,                                                                          \
-    uint32_t max_samples,                                                                   \
-    const float* cluster_centers,                                                           \
-    const float* pq_centers,                                                                \
-    const uint8_t* const* pq_dataset,                                                       \
-    const uint32_t* cluster_labels,                                                         \
-    const uint32_t* _chunk_indices,                                                         \
-    const float* queries,                                                                   \
-    const uint32_t* index_list,                                                             \
-    float* query_kths,                                                                      \
-    IvfSampleFilterT sample_filter,                                                         \
-    LutT* lut_scores,                                                                       \
-    OutT* _out_scores,                                                                      \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
+  OutT, LutT, IvfSampleFilterT)                                                                \
+  extern template auto                                                                         \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+    const cudaDeviceProp& dev_props,                                                           \
+    bool manage_local_topk,                                                                    \
+    int locality_hint,                                                                         \
+    double preferred_shmem_carveout,                                                           \
+    uint32_t pq_bits,                                                                          \
+    uint32_t pq_dim,                                                                           \
+    uint32_t precomp_data_count,                                                               \
+    uint32_t n_queries,                                                                        \
+    uint32_t n_probes,                                                                         \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+                                                                                               \
+  extern template void                                                                         \
+  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+    rmm::cuda_stream_view stream,                                                              \
+    uint32_t dim,                                                                              \
+    uint32_t n_probes,                                                                         \
+    uint32_t pq_dim,                                                                           \
+    uint32_t n_queries,                                                                        \
+    uint32_t queries_offset,                                                                   \
+    cuvs::distance::DistanceType metric,                                                       \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
+    uint32_t topk,                                                                             \
+    uint32_t max_samples,                                                                      \
+    const float* cluster_centers,                                                              \
+    const float* pq_centers,                                                                   \
+    const uint8_t* const* pq_dataset,                                                          \
+    const uint32_t* cluster_labels,                                                            \
+    const uint32_t* _chunk_indices,                                                            \
+    const float* queries,                                                                      \
+    const uint32_t* index_list,                                                                \
+    float* query_kths,                                                                         \
+    IvfSampleFilterT sample_filter,                                                            \
+    LutT* lut_scores,                                                                          \
+    OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
diff --git a/cpp/src/neighbors/nn_descent_float.cu b/cpp/src/neighbors/nn_descent_float.cu
index 3942775570..b1e58b2330 100644
--- a/cpp/src/neighbors/nn_descent_float.cu
+++ b/cpp/src/neighbors/nn_descent_float.cu
@@ -24,7 +24,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
@@ -41,7 +41,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
diff --git a/cpp/src/neighbors/nn_descent_half.cu b/cpp/src/neighbors/nn_descent_half.cu
index 0b6ba74b18..06d3a8c612 100644
--- a/cpp/src/neighbors/nn_descent_half.cu
+++ b/cpp/src/neighbors/nn_descent_half.cu
@@ -24,7 +24,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
@@ -42,7 +42,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
diff --git a/cpp/src/neighbors/nn_descent_int8.cu b/cpp/src/neighbors/nn_descent_int8.cu
index a43e68d4ed..be20b00a10 100644
--- a/cpp/src/neighbors/nn_descent_int8.cu
+++ b/cpp/src/neighbors/nn_descent_int8.cu
@@ -24,7 +24,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
@@ -42,7 +42,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
diff --git a/cpp/src/neighbors/nn_descent_uint8.cu b/cpp/src/neighbors/nn_descent_uint8.cu
index 6b565c8342..9c9ffedaf1 100644
--- a/cpp/src/neighbors/nn_descent_uint8.cu
+++ b/cpp/src/neighbors/nn_descent_uint8.cu
@@ -24,7 +24,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,             \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
@@ -42,7 +42,7 @@ namespace cuvs::neighbors::nn_descent {
              const cuvs::neighbors::nn_descent::index_params& params,                         \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,               \
              std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph) \
-    ->cuvs::neighbors::nn_descent::index<IdxT>                                                \
+    -> cuvs::neighbors::nn_descent::index<IdxT>                                               \
   {                                                                                           \
     if (!graph.has_value()) {                                                                 \
       return cuvs::neighbors::nn_descent::build<T, IdxT>(handle, params, dataset);            \
diff --git a/cpp/src/neighbors/vamana_build_float.cu b/cpp/src/neighbors/vamana_build_float.cu
index 0e09d63994..37b917acb2 100644
--- a/cpp/src/neighbors/vamana_build_float.cu
+++ b/cpp/src/neighbors/vamana_build_float.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::vamana {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::vamana::index_params& params,                 \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+    -> cuvs::neighbors::vamana::index<T, IdxT>                                    \
   {                                                                               \
     return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }                                                                               \
@@ -31,7 +31,7 @@ namespace cuvs::neighbors::vamana {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::vamana::index_params& params,                 \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+    -> cuvs::neighbors::vamana::index<T, IdxT>                                    \
   {                                                                               \
     return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }
diff --git a/cpp/src/neighbors/vamana_build_int8.cu b/cpp/src/neighbors/vamana_build_int8.cu
index f70b9ea276..d06954ab17 100644
--- a/cpp/src/neighbors/vamana_build_int8.cu
+++ b/cpp/src/neighbors/vamana_build_int8.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::vamana {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::vamana::index_params& params,                 \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+    -> cuvs::neighbors::vamana::index<T, IdxT>                                    \
   {                                                                               \
     return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }                                                                               \
@@ -31,7 +31,7 @@ namespace cuvs::neighbors::vamana {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::vamana::index_params& params,                 \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+    -> cuvs::neighbors::vamana::index<T, IdxT>                                    \
   {                                                                               \
     return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }
diff --git a/cpp/src/neighbors/vamana_build_uint8.cu b/cpp/src/neighbors/vamana_build_uint8.cu
index 8daf0c065c..e614c69685 100644
--- a/cpp/src/neighbors/vamana_build_uint8.cu
+++ b/cpp/src/neighbors/vamana_build_uint8.cu
@@ -23,7 +23,7 @@ namespace cuvs::neighbors::vamana {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::vamana::index_params& params,                 \
              raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) \
-    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+    -> cuvs::neighbors::vamana::index<T, IdxT>                                    \
   {                                                                               \
     return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }                                                                               \
@@ -31,7 +31,7 @@ namespace cuvs::neighbors::vamana {
   auto build(raft::resources const& handle,                                       \
              const cuvs::neighbors::vamana::index_params& params,                 \
              raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)   \
-    ->cuvs::neighbors::vamana::index<T, IdxT>                                     \
+    -> cuvs::neighbors::vamana::index<T, IdxT>                                    \
   {                                                                               \
     return cuvs::neighbors::vamana::build<T, IdxT>(handle, params, dataset);      \
   }
diff --git a/cpp/src/preprocessing/quantize/scalar.cu b/cpp/src/preprocessing/quantize/scalar.cu
index 68cba4cc8d..b4d8737b7f 100644
--- a/cpp/src/preprocessing/quantize/scalar.cu
+++ b/cpp/src/preprocessing/quantize/scalar.cu
@@ -23,15 +23,13 @@ namespace cuvs::preprocessing::quantize::scalar {
 #define CUVS_INST_QUANTIZATION(T, QuantI)                                         \
   auto train(raft::resources const& res,                                          \
              const params params,                                                 \
-             raft::device_matrix_view<const T, int64_t> dataset)                  \
-    ->quantizer<T>                                                                \
+             raft::device_matrix_view<const T, int64_t> dataset) -> quantizer<T>  \
   {                                                                               \
     return detail::train(res, params, dataset);                                   \
   }                                                                               \
   auto train(raft::resources const& res,                                          \
              const params params,                                                 \
-             raft::host_matrix_view<const T, int64_t> dataset)                    \
-    ->quantizer<T>                                                                \
+             raft::host_matrix_view<const T, int64_t> dataset) -> quantizer<T>    \
   {                                                                               \
     return detail::train(res, params, dataset);                                   \
   }                                                                               \
diff --git a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
index fe5a5d2963..c5cf52a449 100644
--- a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
+++ b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
@@ -67,7 +67,7 @@ struct FixConnectivitiesRedOp {
   // default constructor for cutlass
   DI FixConnectivitiesRedOp() : m(0) {}
 
-  FixConnectivitiesRedOp(value_idx m_) : m(m_){};
+  FixConnectivitiesRedOp(value_idx m_) : m(m_) {};
 
   typedef typename raft::KeyValuePair<value_idx, value_t> KVP;
   DI void operator()(value_idx rit, KVP* out, const KVP& other) const
diff --git a/cpp/tests/distance/distance_base.cuh b/cpp/tests/distance/distance_base.cuh
index 8a431f49a1..e9a7bd286a 100644
--- a/cpp/tests/distance/distance_base.cuh
+++ b/cpp/tests/distance/distance_base.cuh
@@ -707,7 +707,7 @@ class BigMatrixDistanceTest : public ::testing::Test {
  public:
   BigMatrixDistanceTest()
     : x(m * k, raft::resource::get_cuda_stream(handle)),
-      dist(std::size_t(m) * m, raft::resource::get_cuda_stream(handle)){};
+      dist(std::size_t(m) * m, raft::resource::get_cuda_stream(handle)) {};
   void SetUp() override
   {
     auto testInfo = testing::UnitTest::GetInstance()->current_test_info();
diff --git a/cpp/tests/neighbors/brute_force_prefiltered.cu b/cpp/tests/neighbors/brute_force_prefiltered.cu
index bf7dce7eec..7f2c1f195b 100644
--- a/cpp/tests/neighbors/brute_force_prefiltered.cu
+++ b/cpp/tests/neighbors/brute_force_prefiltered.cu
@@ -127,7 +127,7 @@ RAFT_KERNEL set_bitmap_kernel(
     index_t col      = dst[idx];
     index_t g_idx    = row * n_cols + col;
     index_t item_idx = (g_idx) >> 5;
-    uint32_t bit_idx = (g_idx)&31;
+    uint32_t bit_idx = (g_idx) & 31;
     atomicOr(bitmap + item_idx, (uint32_t(1) << bit_idx));
   }
 }
diff --git a/cpp/tests/sparse/neighbors/cross_component_nn.cu b/cpp/tests/sparse/neighbors/cross_component_nn.cu
index d931fedc46..374e273d68 100644
--- a/cpp/tests/sparse/neighbors/cross_component_nn.cu
+++ b/cpp/tests/sparse/neighbors/cross_component_nn.cu
@@ -392,7 +392,7 @@ struct MutualReachabilityFixConnectivitiesRedOp {
   DI MutualReachabilityFixConnectivitiesRedOp() : m(0) {}
 
   MutualReachabilityFixConnectivitiesRedOp(value_t* core_dists_, value_idx m_)
-    : core_dists(core_dists_), m(m_){};
+    : core_dists(core_dists_), m(m_) {};
 
   typedef typename raft::KeyValuePair<value_idx, value_t> KVP;
   DI void operator()(value_idx rit, KVP* out, const KVP& other) const
diff --git a/dependencies.yaml b/dependencies.yaml
index dd35a2d0dd..df8f0f504f 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -326,9 +326,9 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - clang==16.0.6
-          - clang-tools==16.0.6
-          - libclang==16.0.6
+          - clang==20.1.4
+          - clang-tools==20.1.4
+          - libclang==20.1.4
   cuda_version:
     specific:
       - output_types: conda

From 2c4e34c36e152ab03a9fe1c29502e8ccde927b6b Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 14 May 2025 22:47:40 -0700
Subject: [PATCH 399/623] Change snmg index to use updated multi gpu resource
 API (#869)

These changes are dependent on [this breaking PR](https://github.com/rapidsai/raft/pull/2647) in raft.
This PR itself doesn't introduce any new features, but makes changes to the existing code to work with the breaking changes in the PR in raft.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/869
---
 cpp/src/neighbors/mg/snmg.cuh | 35 ++++++++++++++++++-----------------
 1 file changed, 18 insertions(+), 17 deletions(-)

diff --git a/cpp/src/neighbors/mg/snmg.cuh b/cpp/src/neighbors/mg/snmg.cuh
index 3d9e795adc..119c94fe14 100644
--- a/cpp/src/neighbors/mg/snmg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -17,7 +17,8 @@
 #pragma once
 
 #include "../detail/knn_merge_parts.cuh"
-#include <raft/core/resource/nccl_clique.hpp>
+#include <raft/core/resource/multi_gpu.hpp>
+#include <raft/core/resource/nccl_comm.hpp>
 #include <raft/core/serialize.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/util/cuda_dev_essentials.cuh>
@@ -75,10 +76,10 @@ void deserialize(const raft::resources& clique,
   index.mode_        = (cuvs::neighbors::distribution_mode)deserialize_scalar<int>(handle, is);
   index.num_ranks_   = deserialize_scalar<int>(handle, is);
 
-  if (index.num_ranks_ != raft::resource::get_nccl_num_ranks(clique)) {
+  if (index.num_ranks_ != raft::resource::get_num_ranks(clique)) {
     RAFT_FAIL("Serialized index has %d ranks whereas NCCL clique has %d ranks",
               index.num_ranks_,
-              raft::resource::get_nccl_num_ranks(clique));
+              raft::resource::get_num_ranks(clique));
   }
 
   for (int rank = 0; rank < index.num_ranks_; rank++) {
@@ -215,8 +216,8 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
       const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
       auto& ann_if                   = index.ann_interfaces_[rank];
 
-      if (rank == raft::resource::get_nccl_clique_root_rank(clique)) {  // root rank
-        uint64_t batch_offset = raft::resource::get_nccl_clique_root_rank(clique) * part_size;
+      if (rank == raft::resource::get_root_rank(clique)) {  // root rank
+        uint64_t batch_offset = raft::resource::get_root_rank(clique) * part_size;
         auto d_neighbors      = raft::make_device_matrix_view<IdxT, int64_t, row_major>(
           in_neighbors.data_handle() + batch_offset, n_rows_of_current_batch, n_neighbors);
         auto d_distances = raft::make_device_matrix_view<float, int64_t, row_major>(
@@ -227,20 +228,20 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
         // wait for other ranks
         ncclGroupStart();
         for (int from_rank = 0; from_rank < index.num_ranks_; from_rank++) {
-          if (from_rank == raft::resource::get_nccl_clique_root_rank(clique)) continue;
+          if (from_rank == raft::resource::get_root_rank(clique)) continue;
 
           batch_offset = from_rank * part_size;
           ncclRecv(in_neighbors.data_handle() + batch_offset,
                    part_size * sizeof(IdxT),
                    ncclUint8,
                    from_rank,
-                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_nccl_comm_for_rank(clique, rank),
                    raft::resource::get_cuda_stream(dev_res));
           ncclRecv(in_distances.data_handle() + batch_offset,
                    part_size * sizeof(float),
                    ncclUint8,
                    from_rank,
-                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_nccl_comm_for_rank(clique, rank),
                    raft::resource::get_cuda_stream(dev_res));
         }
         ncclGroupEnd();
@@ -258,14 +259,14 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
         ncclSend(d_neighbors.data_handle(),
                  part_size * sizeof(IdxT),
                  ncclUint8,
-                 raft::resource::get_nccl_clique_root_rank(clique),
-                 raft::resource::get_nccl_comm(dev_res),
+                 raft::resource::get_root_rank(clique),
+                 raft::resource::get_nccl_comm_for_rank(clique, rank),
                  raft::resource::get_cuda_stream(dev_res));
         ncclSend(d_distances.data_handle(),
                  part_size * sizeof(float),
                  ncclUint8,
-                 raft::resource::get_nccl_clique_root_rank(clique),
-                 raft::resource::get_nccl_comm(dev_res),
+                 raft::resource::get_root_rank(clique),
+                 raft::resource::get_nccl_comm_for_rank(clique, rank),
                  raft::resource::get_cuda_stream(dev_res));
         ncclGroupEnd();
         resource::sync_stream(dev_res);
@@ -379,13 +380,13 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
                      part_size * sizeof(IdxT),
                      ncclUint8,
                      other_id,
-                     raft::resource::get_nccl_comm(dev_res),
+                     raft::resource::get_nccl_comm_for_rank(clique, rank),
                      raft::resource::get_cuda_stream(dev_res));
             ncclRecv(tmp_distances.data_handle() + part_size,
                      part_size * sizeof(float),
                      ncclUint8,
                      other_id,
-                     raft::resource::get_nccl_comm(dev_res),
+                     raft::resource::get_nccl_comm_for_rank(clique, rank),
                      raft::resource::get_cuda_stream(dev_res));
             received_something = true;
           }
@@ -396,13 +397,13 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
                    part_size * sizeof(IdxT),
                    ncclUint8,
                    other_id,
-                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_nccl_comm_for_rank(clique, rank),
                    raft::resource::get_cuda_stream(dev_res));
           ncclSend(tmp_distances.data_handle(),
                    part_size * sizeof(float),
                    ncclUint8,
                    other_id,
-                   raft::resource::get_nccl_comm(dev_res),
+                   raft::resource::get_nccl_comm_for_rank(clique, rank),
                    raft::resource::get_cuda_stream(dev_res));
         }
         ncclGroupEnd();
@@ -655,7 +656,7 @@ template <typename AnnIndexType, typename T, typename IdxT>
 mg_index<AnnIndexType, T, IdxT>::mg_index(const raft::resources& clique, distribution_mode mode)
   : mode_(mode), round_robin_counter_(std::make_shared<std::atomic<int64_t>>(0))
 {
-  num_ranks_ = raft::resource::get_nccl_num_ranks(clique);
+  num_ranks_ = raft::resource::get_num_ranks(clique);
 }
 
 template <typename AnnIndexType, typename T, typename IdxT>

From 6ffd24274628fc97d890933992415b4b69f20f61 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 15 May 2025 13:49:47 -0500
Subject: [PATCH 400/623] Finish CUDA 12.9 migration and use branch-25.06
 workflows (#901)

This PR reverts shared-workflows branches to `branch-25.06` and ensures builds use CUDA 12.8.

CUDA 12.9 will be used in the test matrix but not for builds in RAPIDS 25.06. See https://github.com/rapidsai/build-planning/issues/173#issuecomment-2882179838 for more information.

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Gil Forsyth (https://github.com/gforsyth)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/901
---
 .../devcontainer.json                         |  8 ++---
 .../devcontainer.json                         | 12 +++----
 .github/workflows/build.yaml                  | 20 ++++++------
 .github/workflows/pr.yaml                     | 32 +++++++++----------
 .github/workflows/test.yaml                   |  8 ++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 ...64.yaml => all_cuda-128_arch-aarch64.yaml} |  4 +--
 ..._64.yaml => all_cuda-128_arch-x86_64.yaml} |  4 +--
 ...l => bench_ann_cuda-128_arch-aarch64.yaml} |  4 +--
 ...ml => bench_ann_cuda-128_arch-x86_64.yaml} |  4 +--
 ...h64.yaml => go_cuda-128_arch-aarch64.yaml} |  4 +--
 ...6_64.yaml => go_cuda-128_arch-x86_64.yaml} |  4 +--
 dependencies.yaml                             |  6 ++--
 docs/source/build.rst                         |  6 ++--
 examples/go/README.md                         |  2 +-
 examples/rust/README.md                       |  2 +-
 16 files changed, 61 insertions(+), 61 deletions(-)
 rename .devcontainer/{cuda12.9-conda => cuda12.8-conda}/devcontainer.json (91%)
 rename .devcontainer/{cuda12.9-pip => cuda12.8-pip}/devcontainer.json (88%)
 rename conda/environments/{all_cuda-129_arch-aarch64.yaml => all_cuda-128_arch-aarch64.yaml} (95%)
 rename conda/environments/{all_cuda-129_arch-x86_64.yaml => all_cuda-128_arch-x86_64.yaml} (95%)
 rename conda/environments/{bench_ann_cuda-129_arch-aarch64.yaml => bench_ann_cuda-128_arch-aarch64.yaml} (94%)
 rename conda/environments/{bench_ann_cuda-129_arch-x86_64.yaml => bench_ann_cuda-128_arch-x86_64.yaml} (94%)
 rename conda/environments/{go_cuda-129_arch-aarch64.yaml => go_cuda-128_arch-aarch64.yaml} (92%)
 rename conda/environments/{go_cuda-129_arch-x86_64.yaml => go_cuda-128_arch-x86_64.yaml} (92%)

diff --git a/.devcontainer/cuda12.9-conda/devcontainer.json b/.devcontainer/cuda12.8-conda/devcontainer.json
similarity index 91%
rename from .devcontainer/cuda12.9-conda/devcontainer.json
rename to .devcontainer/cuda12.8-conda/devcontainer.json
index 94afe6cc7c..dd078a3009 100644
--- a/.devcontainer/cuda12.9-conda/devcontainer.json
+++ b/.devcontainer/cuda12.8-conda/devcontainer.json
@@ -3,7 +3,7 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.9",
+      "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
       "BASE": "rapidsai/devcontainers:25.06-cpp-mambaforge-ubuntu22.04"
     }
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.9-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
@@ -20,7 +20,7 @@
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.9-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.8-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -29,7 +29,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.9-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.8-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.devcontainer/cuda12.9-pip/devcontainer.json b/.devcontainer/cuda12.8-pip/devcontainer.json
similarity index 88%
rename from .devcontainer/cuda12.9-pip/devcontainer.json
rename to .devcontainer/cuda12.8-pip/devcontainer.json
index af440522a4..0729bb953c 100644
--- a/.devcontainer/cuda12.9-pip/devcontainer.json
+++ b/.devcontainer/cuda12.8-pip/devcontainer.json
@@ -3,20 +3,20 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.9",
+      "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda12.9-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.06-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.9-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.06-cuda12.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/cuda:25.6": {
-      "version": "12.9",
+      "version": "12.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
@@ -29,7 +29,7 @@
     "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.9-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -37,7 +37,7 @@
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.9-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index b386354005..9efeb93c77 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -28,7 +28,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -54,7 +54,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -80,7 +80,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -89,7 +89,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -100,7 +100,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -112,7 +112,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -126,7 +126,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -137,7 +137,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -149,7 +149,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index ad7e19e94b..01e30bffc2 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -30,7 +30,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.06
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -59,7 +59,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.06
     with:
       files_yaml: |
         test_cpp:
@@ -111,28 +111,28 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.06
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: cpu16
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
     with:
       build_type: pull-request
       enable_check_symbols: true
@@ -140,13 +140,13 @@ jobs:
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
     with:
       build_type: pull-request
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -165,7 +165,7 @@ jobs:
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -175,7 +175,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -185,7 +185,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -195,7 +195,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -206,7 +206,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.06
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -215,7 +215,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -223,10 +223,10 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.06
     with:
       arch: '["amd64"]'
-      cuda: '["12.9"]'
+      cuda: '["12.8"]'
       build_command: |
         sccache -z;
         build-all --verbose;
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 9e0692e68f..5c2192daa1 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -19,7 +19,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -29,7 +29,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -37,7 +37,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -57,7 +57,7 @@ jobs:
       run_script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 83deb407bf..475380cb68 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@cuda-12.9.0
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.06
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
similarity index 95%
rename from conda/environments/all_cuda-129_arch-aarch64.yaml
rename to conda/environments/all_cuda-128_arch-aarch64.yaml
index db1307bbab..972e35346f 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.9
+- cuda-version=12.8
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -54,4 +54,4 @@ dependencies:
 - sysroot_linux-aarch64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-129_arch-aarch64
+name: all_cuda-128_arch-aarch64
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
similarity index 95%
rename from conda/environments/all_cuda-129_arch-x86_64.yaml
rename to conda/environments/all_cuda-128_arch-x86_64.yaml
index 6ac0ce5c4f..3cf5c241a6 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.9
+- cuda-version=12.8
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -54,4 +54,4 @@ dependencies:
 - sysroot_linux-64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-129_arch-x86_64
+name: all_cuda-128_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
rename to conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index cc69a6df1d..c785f0bc06 100644
--- a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.9
+- cuda-version=12.8
 - cupy>=12.0.0
 - cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
@@ -48,4 +48,4 @@ dependencies:
 - setuptools
 - sysroot_linux-aarch64==2.28
 - wheel
-name: bench_ann_cuda-129_arch-aarch64
+name: bench_ann_cuda-128_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
rename to conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index 2aa8c4d188..616ec0a33c 100644
--- a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.9
+- cuda-version=12.8
 - cupy>=12.0.0
 - cuvs==25.6.*,>=0.0.0a0
 - cxx-compiler
@@ -51,4 +51,4 @@ dependencies:
 - setuptools
 - sysroot_linux-64==2.28
 - wheel
-name: bench_ann_cuda-129_arch-x86_64
+name: bench_ann_cuda-128_arch-x86_64
diff --git a/conda/environments/go_cuda-129_arch-aarch64.yaml b/conda/environments/go_cuda-128_arch-aarch64.yaml
similarity index 92%
rename from conda/environments/go_cuda-129_arch-aarch64.yaml
rename to conda/environments/go_cuda-128_arch-aarch64.yaml
index 97d9e4832d..b205da3595 100644
--- a/conda/environments/go_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-128_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.9
+- cuda-version=12.8
 - cxx-compiler
 - dlpack>=0.8,<1.0
 - gcc_linux-aarch64=13.*
@@ -30,4 +30,4 @@ dependencies:
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
-name: go_cuda-129_arch-aarch64
+name: go_cuda-128_arch-aarch64
diff --git a/conda/environments/go_cuda-129_arch-x86_64.yaml b/conda/environments/go_cuda-128_arch-x86_64.yaml
similarity index 92%
rename from conda/environments/go_cuda-129_arch-x86_64.yaml
rename to conda/environments/go_cuda-128_arch-x86_64.yaml
index 4589f254d6..2c39acd4e0 100644
--- a/conda/environments/go_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-128_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.9
+- cuda-version=12.8
 - cxx-compiler
 - dlpack>=0.8,<1.0
 - gcc_linux-64=13.*
@@ -30,4 +30,4 @@ dependencies:
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
-name: go_cuda-129_arch-x86_64
+name: go_cuda-128_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index df8f0f504f..b5bfcc5ffe 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,7 +3,7 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["11.8", "12.9"]
+      cuda: ["11.8", "12.8"]
       arch: [x86_64, aarch64]
     includes:
       - build
@@ -29,7 +29,7 @@ files:
   bench_ann:
     output: conda
     matrix:
-      cuda: ["11.8", "12.9"]
+      cuda: ["11.8", "12.8"]
       arch: [x86_64, aarch64]
     includes:
       - bench
@@ -100,7 +100,7 @@ files:
   go:
     output: conda
     matrix:
-      cuda: ["11.8", "12.9"]
+      cuda: ["11.8", "12.8"]
       arch: [x86_64, aarch64]
     includes:
       - clang
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 8e3608990f..5d1b8e354d 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -43,14 +43,14 @@ C/C++ Package
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.9
+   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.8
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.9
+   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.8
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
@@ -92,7 +92,7 @@ Conda environment scripts are provided for installing the necessary dependencies
 
 .. code-block:: bash
 
-    conda env create --name cuvs -f conda/environments/all_cuda-129_arch-x86_64.yaml
+    conda env create --name cuvs -f conda/environments/all_cuda-128_arch-x86_64.yaml
     conda activate cuvs
 
 The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
diff --git a/examples/go/README.md b/examples/go/README.md
index 615b8e75e4..28d11351b2 100644
--- a/examples/go/README.md
+++ b/examples/go/README.md
@@ -7,7 +7,7 @@ This package provides Go bindings for the cuVS (CUDA Vector Search) library.
 The required dependencies can be installed with a simple command (which creates your build environment):
 
 ```bash
-conda env create --name go -f conda/environments/go_cuda-129_arch-x86_64.yaml
+conda env create --name go -f conda/environments/go_cuda-128_arch-x86_64.yaml
 conda activate go
 ```
 You may prefer to use `mamba`, as it provides significant speedup over `conda`.
diff --git a/examples/rust/README.md b/examples/rust/README.md
index 36cbab8cdd..98fe526975 100644
--- a/examples/rust/README.md
+++ b/examples/rust/README.md
@@ -9,7 +9,7 @@ Once the minimum requirements are satisfied, this example template application c
 You may follow these steps to quickly get set up:
 
 ```bash
-conda env create --name rust -f conda/environments/rust_cuda-129_arch-x86_64.yaml
+conda env create --name rust -f conda/environments/rust_cuda-128_arch-x86_64.yaml
 conda activate rust
 ```
 You may prefer to use `mamba`, as it provides significant speedup over `conda`.

From 69ec5002b29fe917102715df071d842c0130a740 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Thu, 15 May 2025 13:30:20 -0700
Subject: [PATCH 401/623] [FEA] New algos and updates/corrections to Faiss
 cuvs-bench (#677)

Reproducible cuvs-bench benchmarks for cuVS algorithms supported through Faiss.
- [x] Faiss HNSW Flat CPU wrapper
- [x] Faiss CAGRA GPU build + HNSW CPU search wrapper
- [x] Update param sweep for Faiss IVF indexes
- [x] Custom IVF build and search params for Faiss CAGRA wrapper

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/677
---
 cpp/bench/ann/CMakeLists.txt                  |  17 +++
 .../ann/src/faiss/faiss_cpu_benchmark.cpp     |  31 +++++
 cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h   |  77 ++++++++++--
 .../ann/src/faiss/faiss_gpu_benchmark.cu      | 106 +++++++++++++++-
 cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h   | 118 ++++++++++++++++--
 .../cuvs_bench/config/algorithms.yaml         |   6 +
 .../config/algos/faiss_cpu_hnsw_flat.yaml     |  10 ++
 .../config/algos/faiss_gpu_cagra.yaml         |  13 +-
 .../config/algos/faiss_gpu_cagra_hnsw.yaml    |  12 ++
 .../config/algos/faiss_gpu_ivf_pq.yaml        |  12 +-
 10 files changed, 366 insertions(+), 36 deletions(-)
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_hnsw_flat.yaml
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra_hnsw.yaml

diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index a2bc5645fc..7fc49c9e80 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -21,11 +21,15 @@ option(CUVS_ANN_BENCH_USE_FAISS_GPU_FLAT "Include faiss' brute-force knn algorit
 option(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT "Include faiss' ivf flat algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_PQ "Include faiss' ivf pq algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_FAISS_GPU_CAGRA "Include faiss' cagra algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_FAISS_GPU_CAGRA_HNSW
+       "Include faiss' cagra algorithm for build and hnsw for search in benchmark" ON
+)
 option(CUVS_ANN_BENCH_USE_FAISS_CPU_FLAT "Include faiss' cpu brute-force algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_FLAT "Include faiss' cpu ivf flat algorithm in benchmark"
        ON
 )
 option(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ "Include faiss' cpu ivf pq algorithm in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_FAISS_CPU_HNSW_FLAT "Include faiss' hnsw algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_CUVS_IVF_FLAT "Include cuVS ivf flat algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ "Include cuVS ivf pq algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_CUVS_CAGRA "Include cuVS CAGRA in benchmark" ON)
@@ -276,6 +280,12 @@ if(CUVS_ANN_BENCH_USE_FAISS_CPU_IVF_PQ)
   )
 endif()
 
+if(CUVS_ANN_BENCH_USE_FAISS_CPU_HNSW_FLAT)
+  ConfigureAnnBench(
+    NAME FAISS_CPU_HNSW_FLAT PATH src/faiss/faiss_cpu_benchmark.cpp LINKS ${CUVS_FAISS_TARGETS}
+  )
+endif()
+
 if(CUVS_ANN_BENCH_USE_FAISS_GPU_IVF_FLAT AND CUVS_FAISS_ENABLE_GPU)
   ConfigureAnnBench(
     NAME FAISS_GPU_IVF_FLAT PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
@@ -304,6 +314,13 @@ if(CUVS_ANN_BENCH_USE_FAISS_GPU_CAGRA AND CUVS_FAISS_ENABLE_GPU)
   )
 endif()
 
+if(CUVS_ANN_BENCH_USE_FAISS_GPU_CAGRA_HNSW AND CUVS_FAISS_ENABLE_GPU)
+  ConfigureAnnBench(
+    NAME FAISS_GPU_CAGRA_HNSW PATH src/faiss/faiss_gpu_benchmark.cu LINKS ${CUVS_FAISS_TARGETS}
+    raft::raft
+  )
+endif()
+
 if(CUVS_ANN_BENCH_USE_GGNN)
   include(cmake/thirdparty/get_glog)
   ConfigureAnnBench(
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
index e03abd7255..c34d840132 100644
--- a/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
@@ -68,6 +68,18 @@ void parse_build_param(const nlohmann::json& conf,
   param.quantizer_type = conf.at("quantizer_type");
 }
 
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_cpu_hnsw_flat<T>::build_param& param)
+{
+  param.M = conf.at("M");
+  if (conf.contains("efConstruction")) {
+    param.efConstruction = conf.at("efConstruction");
+  } else {
+    param.efConstruction = 40;
+  }
+}
+
 template <typename T>
 void parse_search_param(const nlohmann::json& conf,
                         typename cuvs::bench::faiss_cpu<T>::search_param& param)
@@ -78,6 +90,19 @@ void parse_search_param(const nlohmann::json& conf,
   if (conf.contains("parallel_mode")) { param.parallel_mode = conf.at("parallel_mode"); }
 }
 
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::faiss_cpu_hnsw_flat<T>::search_param& param)
+{
+  faiss::SearchParametersHNSW p;
+  if (conf.contains("efSearch")) { p.efSearch = conf.at("efSearch"); }
+  if (conf.contains("check_relative_distance")) {
+    p.check_relative_distance = conf.at("check_relative_distance");
+  }
+  if (conf.contains("bounded_queue")) { p.bounded_queue = conf.at("bounded_queue"); }
+  param.p = p;
+}
+
 template <typename T, template <typename> class Algo>
 auto make_algo(cuvs::bench::Metric metric, int dim, const nlohmann::json& conf)
   -> std::unique_ptr<cuvs::bench::algo<T>>
@@ -105,6 +130,8 @@ auto create_algo(const std::string& algo_name,
       a = make_algo<T, cuvs::bench::faiss_cpu_ivfsq>(metric, dim, conf);
     } else if (algo_name == "faiss_cpu_flat") {
       a = std::make_unique<cuvs::bench::faiss_cpu_flat<T>>(metric, dim);
+    } else if (algo_name == "faiss_cpu_hnsw_flat") {
+      a = make_algo<T, cuvs::bench::faiss_cpu_hnsw_flat>(metric, dim, conf);
     }
   }
 
@@ -127,6 +154,10 @@ auto create_search_param(const std::string& algo_name, const nlohmann::json& con
   } else if (algo_name == "faiss_cpu_flat") {
     auto param = std::make_unique<typename cuvs::bench::faiss_cpu<T>::search_param>();
     return param;
+  } else if (algo_name == "faiss_cpu_hnsw_flat") {
+    auto param = std::make_unique<typename cuvs::bench::faiss_cpu_hnsw_flat<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
   }
   // else
   throw std::runtime_error("invalid algo: '" + algo_name + "'");
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
index bda579be31..649a24c8d5 100644
--- a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
@@ -20,6 +20,7 @@
 #include "../common/util.hpp"
 
 #include <faiss/IndexFlat.h>
+#include <faiss/IndexHNSW.h>
 #include <faiss/IndexIVFFlat.h>
 #include <faiss/IndexIVFPQ.h>
 #include <faiss/IndexRefine.h>
@@ -98,11 +99,11 @@ class faiss_cpu : public algo<T> {
 
   // TODO(snanditale): if the number of results is less than k, the remaining elements of
   // 'neighbors' will be filled with (size_t)-1
-  void search(const T* queries,
-              int batch_size,
-              int k,
-              algo_base::index_type* neighbors,
-              float* distances) const final;
+  virtual void search(const T* queries,
+                      int batch_size,
+                      int k,
+                      algo_base::index_type* neighbors,
+                      float* distances) const;
 
   [[nodiscard]] auto get_preference() const -> algo_property override
   {
@@ -156,6 +157,7 @@ void faiss_cpu<T>::build(const T* dataset, size_t nrow)
     index_ivf->cp.max_points_per_centroid = max_ppc;
     index_ivf->cp.min_points_per_centroid = min_ppc;
   }
+  faiss::IndexHNSWFlat* hnsw_index = dynamic_cast<faiss::IndexHNSWFlat*>(index_.get());
   index_->train(nrow, dataset);  // faiss::IndexFlat::train() will do nothing
   assert(index_->is_trained);
   index_->add(nrow, dataset);
@@ -190,12 +192,7 @@ void faiss_cpu<T>::search(
   static_assert(sizeof(size_t) == sizeof(faiss::idx_t),
                 "sizes of size_t and faiss::idx_t are different");
 
-  thread_pool_->submit(
-    [&](int i) {
-      // Use thread pool for batch size = 1. FAISS multi-threads internally for batch size > 1.
-      index_->search(batch_size, queries, k, distances, reinterpret_cast<faiss::idx_t*>(neighbors));
-    },
-    1);
+  index_->search(batch_size, queries, k, distances, reinterpret_cast<faiss::idx_t*>(neighbors));
 }
 
 template <typename T>
@@ -341,4 +338,62 @@ class faiss_cpu_flat : public faiss_cpu<T> {
   }
 };
 
+template <typename T>
+class faiss_cpu_hnsw_flat : public faiss_cpu<T> {
+ public:
+  struct build_param : public faiss_cpu<T>::build_param {
+    int M;
+    int efConstruction;
+  };
+  struct search_param : public faiss_cpu<T>::search_param {
+    faiss::SearchParametersHNSW p;
+  };
+  faiss_cpu_hnsw_flat(Metric metric, int dim, const build_param& param)
+    : faiss_cpu<T>(metric, dim, param)
+  {
+    this->index_ = std::make_shared<faiss::IndexHNSWFlat>(dim, param.M, this->metric_type_);
+    faiss::IndexHNSWFlat* hnsw_index = static_cast<faiss::IndexHNSWFlat*>(this->index_.get());
+    hnsw_index->hnsw.efConstruction  = param.efConstruction;
+  }
+
+  void set_search_param(const typename algo<T>::search_param& param,
+                        const void* filter_bitset) override
+  {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
+    auto sp              = static_cast<const typename faiss_cpu_hnsw_flat<T>::search_param&>(param);
+    this->search_params_ = std::make_shared<faiss::SearchParametersHNSW>(sp.p);
+  };
+
+  void save(const std::string& file) const override
+  {
+    this->template save_<faiss::IndexHNSWFlat>(file);
+  }
+  void load(const std::string& file) override { this->template load_<faiss::IndexHNSWFlat>(file); }
+
+  std::unique_ptr<algo<T>> copy()
+  {
+    return std::make_unique<faiss_cpu_hnsw_flat<T>>(*this);  // use copy constructor
+  }
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override
+  {
+    static_assert(sizeof(size_t) == sizeof(faiss::idx_t),
+                  "sizes of size_t and faiss::idx_t are different");
+
+    this->index_->search(batch_size,
+                         queries,
+                         k,
+                         distances,
+                         reinterpret_cast<faiss::idx_t*>(neighbors),
+                         search_params_.get());
+  }
+
+ private:
+  std::shared_ptr<faiss::SearchParameters> search_params_;
+};
+
 }  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
index 8483e52d81..f9ad76dc59 100644
--- a/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
@@ -27,6 +27,22 @@
 #include <type_traits>
 #include <utility>
 
+namespace {
+nlohmann::json collect_conf_with_prefix(const nlohmann::json& conf,
+                                        const std::string& prefix,
+                                        bool remove_prefix = true)
+{
+  nlohmann::json out;
+  for (auto& i : conf.items()) {
+    if (i.key().compare(0, prefix.size(), prefix) == 0) {
+      auto new_key = remove_prefix ? i.key().substr(prefix.size()) : i.key();
+      out[new_key] = i.value();
+    }
+  }
+  return out;
+}
+}  // namespace
+
 namespace cuvs::bench {
 
 template <typename T>
@@ -100,6 +116,82 @@ void parse_build_param(const nlohmann::json& conf,
     param.intermediate_graph_degree = 128;
   }
   if (conf.contains("cagra_build_algo")) { param.cagra_build_algo = conf.at("cagra_build_algo"); }
+  if (conf.contains("nn_descent_niter")) {
+    param.nn_descent_niter = conf.at("nn_descent_niter");
+  } else {
+    param.nn_descent_niter = 20;
+  }
+  nlohmann::json ivf_pq_build_conf = collect_conf_with_prefix(conf, "b_");
+  if (!ivf_pq_build_conf.empty()) {
+    faiss::gpu::IVFPQBuildCagraConfig ivf_pq_build_p;
+
+    if (ivf_pq_build_conf.contains("nlist")) {
+      ivf_pq_build_p.n_lists = ivf_pq_build_conf.at("nlist");
+    }
+    if (ivf_pq_build_conf.contains("niter")) {
+      ivf_pq_build_p.kmeans_n_iters = ivf_pq_build_conf.at("niter");
+    }
+    if (ivf_pq_build_conf.contains("ratio")) {
+      ivf_pq_build_p.kmeans_trainset_fraction = 1.0 / (double)conf.at("ratio");
+    }
+    if (ivf_pq_build_conf.contains("pq_bits")) {
+      ivf_pq_build_p.pq_bits = ivf_pq_build_conf.at("pq_bits");
+    }
+    if (ivf_pq_build_conf.contains("pq_dim")) {
+      ivf_pq_build_p.pq_dim = ivf_pq_build_conf.at("pq_dim");
+    }
+    param.ivf_pq_build_params = std::make_shared<faiss::gpu::IVFPQBuildCagraConfig>(ivf_pq_build_p);
+  }
+  nlohmann::json ivf_pq_search_conf = collect_conf_with_prefix(conf, "s_");
+  if (!ivf_pq_search_conf.empty()) {
+    faiss::gpu::IVFPQSearchCagraConfig ivf_pq_search_p;
+    if (ivf_pq_search_conf.contains("nprobe")) {
+      ivf_pq_search_p.n_probes = ivf_pq_search_conf.at("nprobe");
+    }
+    if (ivf_pq_search_conf.contains("internalDistanceDtype")) {
+      std::string type = ivf_pq_search_conf.at("internalDistanceDtype");
+      if (type == "float") {
+        ivf_pq_search_p.internal_distance_dtype = CUDA_R_32F;
+      } else if (type == "half") {
+        ivf_pq_search_p.internal_distance_dtype = CUDA_R_16F;
+      } else {
+        throw std::runtime_error("internalDistanceDtype: '" + type +
+                                 "', should be either 'float' or 'half'");
+      }
+    } else {
+      // set half as default type
+      ivf_pq_search_p.internal_distance_dtype = CUDA_R_16F;
+    }
+
+    if (ivf_pq_search_conf.contains("smemLutDtype")) {
+      std::string type = ivf_pq_search_conf.at("smemLutDtype");
+      if (type == "float") {
+        ivf_pq_search_p.lut_dtype = CUDA_R_32F;
+      } else if (type == "half") {
+        ivf_pq_search_p.lut_dtype = CUDA_R_16F;
+      } else if (type == "fp8") {
+        ivf_pq_search_p.lut_dtype = CUDA_R_8U;
+      } else {
+        throw std::runtime_error("smemLutDtype: '" + type +
+                                 "', should be either 'float', 'half' or 'fp8'");
+      }
+    } else {
+      // set half as default
+      ivf_pq_search_p.lut_dtype = CUDA_R_16F;
+    }
+    param.ivf_pq_search_params =
+      std::make_shared<faiss::gpu::IVFPQSearchCagraConfig>(ivf_pq_search_p);
+  }
+}
+
+template <typename T>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::faiss_gpu_cagra_hnsw<T>::build_param& param)
+{
+  typename cuvs::bench::faiss_gpu_cagra<T>::build_param p;
+  parse_build_param<T>(conf, p);
+  param.p = p;
+  if (conf.contains("base_level_only")) { param.base_level_only = conf.at("base_level_only"); }
 }
 
 template <typename T>
@@ -131,7 +223,13 @@ void parse_search_param(const nlohmann::json& conf,
       THROW("Invalid value for algo: %s", tmp.c_str());
     }
   }
-  if (conf.contains("refine_ratio")) { param.refine_ratio = conf.at("refine_ratio"); }
+}
+
+template <typename T>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::faiss_gpu_cagra_hnsw<T>::search_param& param)
+{
+  if (conf.contains("efSearch")) { param.p.efSearch = conf.at("efSearch"); }
 }
 
 template <typename T, template <typename> class Algo>
@@ -163,6 +261,8 @@ auto create_algo(const std::string& algo_name,
       a = std::make_unique<cuvs::bench::faiss_gpu_flat<T>>(metric, dim);
     } else if (algo_name == "faiss_gpu_cagra") {
       a = make_algo<T, cuvs::bench::faiss_gpu_cagra>(metric, dim, conf);
+    } else if (algo_name == "faiss_gpu_cagra_hnsw") {
+      a = make_algo<T, cuvs::bench::faiss_gpu_cagra_hnsw>(metric, dim, conf);
     }
   }
 
@@ -187,6 +287,10 @@ auto create_search_param(const std::string& algo_name, const nlohmann::json& con
     auto param = std::make_unique<typename cuvs::bench::faiss_gpu_cagra<T>::search_param>();
     parse_search_param<T>(conf, *param);
     return param;
+  } else if (algo_name == "faiss_gpu_cagra_hnsw") {
+    auto param = std::make_unique<typename cuvs::bench::faiss_gpu_cagra_hnsw<T>::search_param>();
+    parse_search_param<T>(conf, *param);
+    return param;
   }
   // else
   throw std::runtime_error("invalid algo: '" + algo_name + "'");
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
index ed80917e85..54f685fe79 100644
--- a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
@@ -107,9 +107,12 @@ class faiss_gpu : public algo<T>, public algo_gpu {
   {
     static_assert(std::is_same_v<T, float>, "faiss support only float type");
     cudaGetDevice(&device_);
+    // Disable Faiss' generic temporary memory reservation. All such allocations happen through the
+    // pool memory resource.
+    gpu_resource_->noTempMemory();
   }
 
-  void build(const T* dataset, size_t nrow) final;
+  virtual void build(const T* dataset, size_t nrow);
 
   virtual void set_search_param(const search_param_base& param, const void* filter_bitset) {}
 
@@ -117,11 +120,11 @@ class faiss_gpu : public algo<T>, public algo_gpu {
 
   // TODO(snanditale): if the number of results is less than k, the remaining elements of
   // 'neighbors' will be filled with (size_t)-1
-  void search(const T* queries,
-              int batch_size,
-              int k,
-              algo_base::index_type* neighbors,
-              float* distances) const final;
+  virtual void search(const T* queries,
+                      int batch_size,
+                      int k,
+                      algo_base::index_type* neighbors,
+                      float* distances) const;
 
   [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
   {
@@ -168,7 +171,7 @@ class faiss_gpu : public algo<T>, public algo_gpu {
   std::shared_ptr<faiss::IndexRefineFlat> index_refine_{nullptr};
   faiss::MetricType metric_type_;
   int nlist_;
-  int device_;
+  int device_ = 0;
   double training_sample_fraction_;
   std::shared_ptr<faiss::SearchParameters> search_params_;
   std::shared_ptr<faiss::IndexRefineSearchParameters> refine_search_params_{nullptr};
@@ -289,8 +292,10 @@ void faiss_gpu<T>::save_(const std::string& file) const
 {
   omp_single_thread_scope omp_single_thread;
 
-  auto cpu_index = std::make_unique<CpuIndex>();
-  dynamic_cast<GpuIndex*>(index_.get())->copyTo(cpu_index.get());
+  auto cpu_index  = std::make_unique<CpuIndex>();
+  auto hnsw_index = dynamic_cast<faiss::IndexHNSWCagra*>(cpu_index.get());
+  if (hnsw_index) { hnsw_index->base_level_only = true; }
+  static_cast<GpuIndex*>(index_.get())->copyTo(cpu_index.get());
   faiss::write_index(cpu_index.get(), file.c_str());
 }
 
@@ -353,7 +358,7 @@ class faiss_gpu_ivf_flat : public faiss_gpu<T> {
   std::unique_ptr<algo<T>> copy() override
   {
     return std::make_unique<faiss_gpu_ivf_flat<T>>(*this);
-  };
+  }
 };
 
 template <typename T>
@@ -528,6 +533,10 @@ class faiss_gpu_cagra : public faiss_gpu<T> {
     std::string cagra_build_algo;
     /// Number of Iterations to run if building with NN_DESCENT
     size_t nn_descent_niter;
+
+    std::shared_ptr<faiss::gpu::IVFPQBuildCagraConfig> ivf_pq_build_params = nullptr;
+
+    std::shared_ptr<faiss::gpu::IVFPQSearchCagraConfig> ivf_pq_search_params = nullptr;
   };
   using typename faiss_gpu<T>::search_param_base;
   struct search_param : public faiss_gpu<T>::search_param {
@@ -541,8 +550,14 @@ class faiss_gpu_cagra : public faiss_gpu<T> {
     config.graph_degree              = param.graph_degree;
     config.intermediate_graph_degree = param.intermediate_graph_degree;
     config.device                    = this->device_;
+    config.store_dataset             = false;
     if (param.cagra_build_algo == "IVF_PQ") {
-      config.build_algo = faiss::gpu::graph_build_algo::IVF_PQ;
+      config.build_algo           = faiss::gpu::graph_build_algo::IVF_PQ;
+      this->ivf_pq_build_params_  = param.ivf_pq_build_params;
+      config.ivf_pq_params        = this->ivf_pq_build_params_;
+      this->ivf_pq_search_params_ = param.ivf_pq_search_params;
+      config.ivf_pq_search_params = this->ivf_pq_search_params_;
+      config.refine_rate          = 1.0;
     } else {
       config.build_algo = faiss::gpu::graph_build_algo::NN_DESCENT;
     }
@@ -561,13 +576,92 @@ class faiss_gpu_cagra : public faiss_gpu<T> {
 
   void save(const std::string& file) const override
   {
-    this->template save_<faiss::gpu::GpuIndexCagra, faiss::IndexHNSWCagra>(file);
+    omp_single_thread_scope omp_single_thread;
+
+    auto cpu_hnsw_index = std::make_unique<faiss::IndexHNSWCagra>();
+    // Only add the base HNSW layer to serialize the CAGRA index.
+    cpu_hnsw_index->base_level_only = true;
+    static_cast<faiss::gpu::GpuIndexCagra*>(this->index_.get())->copyTo(cpu_hnsw_index.get());
+    faiss::write_index(cpu_hnsw_index.get(), file.c_str());
   }
   void load(const std::string& file) override
   {
     this->template load_<faiss::gpu::GpuIndexCagra, faiss::IndexHNSWCagra>(file);
   }
   std::unique_ptr<algo<T>> copy() override { return std::make_unique<faiss_gpu_cagra<T>>(*this); };
+
+  std::shared_ptr<faiss::gpu::GpuIndex> faiss_index() { return this->index_; }
+
+ private:
+  std::shared_ptr<faiss::gpu::IVFPQBuildCagraConfig> ivf_pq_build_params_;
+  std::shared_ptr<faiss::gpu::IVFPQSearchCagraConfig> ivf_pq_search_params_;
 };
 
+template <typename T>
+class faiss_gpu_cagra_hnsw : public faiss_gpu<T> {
+ public:
+  struct build_param : public faiss_gpu<T>::build_param {
+    typename faiss_gpu_cagra<T>::build_param p;
+    bool base_level_only = true;
+  };
+  using typename faiss_gpu<T>::search_param_base;
+  struct search_param : public faiss_gpu<T>::search_param {
+    faiss::SearchParametersHNSW p;
+  };
+
+  faiss_gpu_cagra_hnsw(Metric metric, int dim, const build_param& param)
+    : faiss_gpu<T>(metric, dim, param)
+  {
+    this->build_index_  = std::make_shared<faiss_gpu_cagra<T>>(metric, dim, param.p);
+    this->search_index_ = std::make_shared<faiss::IndexHNSWCagra>(
+      dim, int(param.p.graph_degree / 2), this->metric_type_);
+    this->search_index_->base_level_only = param.base_level_only;
+  }
+
+  void build(const T* dataset, size_t nrow) override
+  {
+    this->build_index_->build(dataset, nrow);
+    static_cast<faiss::gpu::GpuIndexCagra*>((build_index_->faiss_index()).get())
+      ->copyTo(search_index_.get());
+  }
+
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override
+  {
+    if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
+    auto sp = static_cast<const typename faiss_gpu_cagra_hnsw<T>::search_param&>(param);
+    this->search_params_ = std::make_shared<faiss::SearchParametersHNSW>(sp.p);
+  }
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override
+  {
+    search_index_->search(batch_size,
+                          queries,
+                          k,
+                          distances,
+                          reinterpret_cast<faiss::idx_t*>(neighbors),
+                          this->search_params_.get());
+  }
+
+  void save(const std::string& file) const override
+  {
+    faiss::write_index(search_index_.get(), file.c_str());
+  }
+  void load(const std::string& file) override
+  {
+    omp_single_thread_scope omp_single_thread;
+    this->search_index_.reset(static_cast<faiss::IndexHNSWCagra*>(faiss::read_index(file.c_str())));
+  }
+  std::unique_ptr<algo<T>> copy() override
+  {
+    return std::make_unique<faiss_gpu_cagra_hnsw<T>>(*this);
+  };
+
+ private:
+  std::shared_ptr<faiss_gpu_cagra<T>> build_index_;
+  std::shared_ptr<faiss::IndexHNSWCagra> search_index_;
+};
 }  // namespace cuvs::bench
diff --git a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
index 16d449117a..191c640c00 100644
--- a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
@@ -13,6 +13,9 @@ faiss_gpu_ivf_sq:
 faiss_gpu_cagra:
   executable: FAISS_GPU_CAGRA_ANN_BENCH
   requires_gpu: true
+faiss_gpu_cagra_hnsw:
+  executable: FAISS_GPU_CAGRA_HNSW_ANN_BENCH
+  requires_gpu: true
 faiss_cpu_flat:
   executable: FAISS_CPU_FLAT_ANN_BENCH
   requires_gpu: false
@@ -22,6 +25,9 @@ faiss_cpu_ivf_flat:
 faiss_cpu_ivf_pq:
   executable: FAISS_CPU_IVF_PQ_ANN_BENCH
   requires_gpu: false
+faiss_cpu_hnsw_flat:
+  executable: FAISS_CPU_HNSW_FLAT_ANN_BENCH
+  requires_gpu: false
 cuvs_ivf_flat:
   executable: CUVS_IVF_FLAT_ANN_BENCH
   requires_gpu: true
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_hnsw_flat.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_hnsw_flat.yaml
new file mode 100644
index 0000000000..403cd29e91
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_cpu_hnsw_flat.yaml
@@ -0,0 +1,10 @@
+name: faiss_cpu_hnsw_flat
+groups:
+  base:
+    build:
+      M: [16, 32]
+      efConstruction: [32, 64, 128, 256, 512]
+    search:
+      bounded_queue: [False, True]
+      check_relative_distance: [False, True]
+      efSearch: [8, 16, 32, 64, 128, 256, 512, 768, 1024]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
index 3ec60c7b8e..47a92e3d20 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra.yaml
@@ -5,17 +5,18 @@ constraints:
 groups:
   base:
     build:
-      graph_degree: [32, 64, 96, 128]
-      intermediate_graph_degree: [32, 64, 96, 128]
-      graph_build_algo: ["NN_DESCENT"]
+      graph_degree: [32]
+      intermediate_graph_degree: [64]
+      cagra_build_algo: ["NN_DESCENT"]
     search:
-      itopk: [32, 64, 128, 256, 512]
-      search_width: [1, 2, 4, 8, 16, 32, 64]
+      itopk: [32, 64, 128, 256, 512, 768, 1024]
+      search_width: [1 ,2 ,4 ,8, 16, 32]
+      max_iterations: [16, 20, 24, 28, 32, 64, 128]
   test:
     build:
       graph_degree: [32]
       intermediate_graph_degree: [32]
-      graph_build_algo: ["NN_DESCENT"]
+      cagra_build_algo: ["IVF_PQ", "NN_DESCENT"]
     search:
       itopk: [32]
       search_width: [1, 2]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra_hnsw.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra_hnsw.yaml
new file mode 100644
index 0000000000..097a20aa32
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_cagra_hnsw.yaml
@@ -0,0 +1,12 @@
+name: faiss_gpu_cagra_hnsw
+constraints:
+  build: cuvs_bench.config.algos.constraints.cuvs_cagra_build
+groups:
+  base:
+    build:
+      graph_degree: [32, 64]
+      intermediate_graph_degree: [64, 128]
+      cagra_build_algo: ["NN_DESCENT"]
+      base_level_only: [False, True]
+    search:
+      efSearch: [16, 32, 64, 128, 256, 512, 768, 1024]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
index 158246a030..a533ff6d8c 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/faiss_gpu_ivf_pq.yaml
@@ -53,9 +53,9 @@ groups:
       refine_ratio: [1, 2, 4]
   100M:
     build:
-      nlist: [100000]
-      M: [96, 64]
-      ratio: [4]
+      nlist: [8192, 16384, 32768]
+      M: [96, 48]
+      ratio: [10]
       usePrecomputed: [False, True]
       useFloat16: [True]
       use_cuvs: [False]
@@ -65,9 +65,9 @@ groups:
       refine_ratio: [1, 2, 4]
   100Mcuvs:
     build:
-      nlist: [100000]
-      M: [96, 64]
-      ratio: [4]
+      nlist: [8192, 16384, 50000, 100000]
+      M: [96, 48]
+      ratio: [10]
       useFloat16: [False, True]
       use_cuvs: [True]
       bitsPerCode: [8, 6, 5, 4]

From 2db85161fb59ce196c1cd03634622d41d4d60650 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 15 May 2025 22:30:54 +0200
Subject: [PATCH 402/623] ANN_BENCH: fix the reported core count (#896)

Take into account physical package id when computing the physical core count.
Before the fix, cores with the same ids in different sockets were considered as one in the counting of unique cores.

Fixes https://github.com/rapidsai/cuvs/issues/556

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/896
---
 cpp/bench/ann/src/common/util.hpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
index dbde74ccc6..e3d55cb83d 100644
--- a/cpp/bench/ann/src/common/util.hpp
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -388,7 +388,7 @@ inline auto host_info()
   uint64_t cpu_freq_max    = 0;
   int host_processors_used = 0;
   int host_cores_used      = 0;
-  std::set<int> host_cores_selected{};
+  std::set<std::pair<int, int>> host_cores_selected{};  // pairs of (socket_id, core_id)
   for (int cpu_id = 0; cpu_id < host_processors_configured; cpu_id++) {
     if (CPU_ISSET_S(cpu_id, affinity_mask_buf.size(), affinity_mask) == 0) { continue; }
     host_processors_used++;
@@ -396,12 +396,14 @@ inline auto host_info()
     if (!std::filesystem::exists(cpu_fpath)) { continue; }
 
     int this_cpu_core          = 0;
+    int this_cpu_package       = 0;
     uint64_t this_cpu_freq_min = 0;
     uint64_t this_cpu_freq_max = 0;
     std::ifstream(cpu_fpath + "/topology/core_id") >> this_cpu_core;
+    std::ifstream(cpu_fpath + "/topology/physical_package_id") >> this_cpu_package;
     std::ifstream(cpu_fpath + "/cpufreq/scaling_min_freq") >> this_cpu_freq_min;
     std::ifstream(cpu_fpath + "/cpufreq/scaling_max_freq") >> this_cpu_freq_max;
-    host_cores_selected.insert(this_cpu_core);
+    host_cores_selected.insert(std::make_pair(this_cpu_package, this_cpu_core));
     cpu_freq_min = cpu_freq_min == 0
                      ? (this_cpu_freq_min * 1000ull)
                      : std::min<uint64_t>(this_cpu_freq_min * 1000ull, cpu_freq_min);

From 6bba2ef71560c9a12fbaddcb083f5d80ca90933d Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Fri, 16 May 2025 10:07:46 -0700
Subject: [PATCH 403/623] [Feat] Expose C API for CAGRA `merge` (#860)

- Expose C API for CAGRA `merge`

Authors:
  - rhdong (https://github.com/rhdong)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/860
---
 cpp/include/cuvs/neighbors/cagra.h   |  85 +++++++++++
 cpp/include/cuvs/neighbors/cagra.hpp |  21 +--
 cpp/include/cuvs/neighbors/common.h  |  19 ++-
 cpp/src/neighbors/cagra_c.cpp        | 219 ++++++++++++++++++++-------
 cpp/tests/neighbors/ann_cagra_c.cu   | 124 +++++++++++++++
 5 files changed, 400 insertions(+), 68 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index f43435e971..5959124870 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -358,6 +358,33 @@ cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index);
  */
 cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim);
 
+/**
+ * @}
+ */
+
+/**
+ * @defgroup cagra_c_merge_params C API for CUDA ANN Graph-based nearest neighbor search
+ * @{
+ */
+
+/**
+ * @brief Supplemental parameters to merge CAGRA index
+ *
+ */
+
+struct cuvsCagraMergeParams {
+  cuvsCagraIndexParams_t output_index_params;
+  cuvsMergeStrategy strategy;
+};
+
+typedef struct cuvsCagraMergeParams* cuvsCagraMergeParams_t;
+
+/** Allocate CAGRA merge params with default values */
+cuvsError_t cuvsCagraMergeParamsCreate(cuvsCagraMergeParams_t* params);
+
+/** De-allocate CAGRA merge params */
+cuvsError_t cuvsCagraMergeParamsDestroy(cuvsCagraMergeParams_t params);
+
 /**
  * @}
  */
@@ -585,6 +612,64 @@ cuvsError_t cuvsCagraSerializeToHnswlib(cuvsResources_t res,
  * @param[out] index CAGRA index loaded disk
  */
 cuvsError_t cuvsCagraDeserialize(cuvsResources_t res, const char* filename, cuvsCagraIndex_t index);
+
+/**
+ * @brief Merge multiple CAGRA indices into a single CAGRA index.
+ *
+ * All input indices must have been built with the same data type (`index.dtype`) and
+ * have the same dimensionality (`index.dims`). The merged index uses the output
+ * parameters specified in `cuvsCagraMergeParams`.
+ *
+ * Input indices must have:
+ *  - `index.dtype.code` and `index.dtype.bits` matching across all indices.
+ *  - Supported data types for indices:
+ *      a. `kDLFloat` with `bits = 32`
+ *      b. `kDLFloat` with `bits = 16`
+ *      c. `kDLInt` with `bits = 8`
+ *      d. `kDLUInt` with `bits = 8`
+ *
+ * The resulting output index will have the same data type as the input indices.
+ *
+ * Example:
+ * @code{.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/cagra.h>
+ *
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * cuvsCagraIndex_t index1, index2, merged_index;
+ * cuvsCagraIndexCreate(&index1);
+ * cuvsCagraIndexCreate(&index2);
+ * cuvsCagraIndexCreate(&merged_index);
+ *
+ * // Assume index1 and index2 have been built using cuvsCagraBuild
+ *
+ * cuvsCagraMergeParams_t merge_params;
+ * cuvsError_t params_create_status = cuvsCagraMergeParamsCreate(&merge_params);
+ *
+ * cuvsError_t merge_status = cuvsCagraMerge(res, merge_params, (cuvsCagraIndex_t[]){index1,
+ * index2}, 2, merged_index);
+ *
+ * // Use merged_index for search operations
+ *
+ * cuvsError_t params_destroy_status = cuvsCagraMergeParamsDestroy(merge_params);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsCagraMergeParams_t parameters controlling merge behavior
+ * @param[in] indices Array of input cuvsCagraIndex_t handles to merge
+ * @param[in] num_indices Number of input indices
+ * @param[out] output_index Output handle that will store the merged index.
+ *                          Must be initialized using `cuvsCagraIndexCreate` before use.
+ */
+cuvsError_t cuvsCagraMerge(cuvsResources_t res,
+                           cuvsCagraMergeParams_t params,
+                           cuvsCagraIndex_t* indices,
+                           size_t num_indices,
+                           cuvsCagraIndex_t output_index);
+
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index e70cf6c2ec..036dbba9c8 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -18,6 +18,7 @@
 
 #include "common.hpp"
 #include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/common.h>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
@@ -120,6 +121,7 @@ struct index_params : cuvs::neighbors::index_params {
    * Whether to use MST optimization to guarantee graph connectivity.
    */
   bool guarantee_connectivity = false;
+
   /**
    * Whether to add the dataset content to the index, i.e.:
    *
@@ -276,20 +278,9 @@ struct extend_params {
 /**
  * @brief Determines the strategy for merging CAGRA graphs.
  *
- * @note Currently, only the PHYSICAL strategy is supported.
+ * @note Currently, only the MERGE_STRATEGY_PHYSICAL strategy is supported.
  */
-enum MergeStrategy {
-  /**
-   * @brief Physical merge: Builds a new CAGRA graph from the union of dataset points
-   * in existing CAGRA graphs.
-   *
-   * This is expensive to build but does not impact search latency or quality.
-   * Preferred for many smaller CAGRA graphs.
-   *
-   * @note Currently, this is the only supported strategy.
-   */
-  PHYSICAL
-};
+using MergeStrategy = cuvsMergeStrategy;
 
 /**
  * @brief Parameters for merging CAGRA indexes.
@@ -306,8 +297,8 @@ struct merge_params {
   /// Parameters for creating the output index.
   cagra::index_params output_index_params;
 
-  /// Strategy for merging. Defaults to `MergeStrategy::PHYSICAL`.
-  MergeStrategy strategy = MergeStrategy::PHYSICAL;
+  /// Strategy for merging. Defaults to `MergeStrategy::MERGE_STRATEGY_PHYSICAL`.
+  MergeStrategy strategy = MergeStrategy::MERGE_STRATEGY_PHYSICAL;
 };
 
 /**
diff --git a/cpp/include/cuvs/neighbors/common.h b/cpp/include/cuvs/neighbors/common.h
index d7ca878b99..6bcc4db6b0 100644
--- a/cpp/include/cuvs/neighbors/common.h
+++ b/cpp/include/cuvs/neighbors/common.h
@@ -16,9 +16,7 @@
 
 #pragma once
 
-#include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.h>
-#include <dlpack/dlpack.h>
 #include <stdint.h>
 
 #ifdef __cplusplus
@@ -56,6 +54,23 @@ typedef struct {
  * @}
  */
 
+/**
+ * @defgroup index_merge Index Merge
+ * @brief Common definitions related to index merging.
+ * @{
+ */
+
+/**
+ * @brief Strategy for merging indices.
+ */
+typedef enum {
+  MERGE_STRATEGY_PHYSICAL = 0,  ///< Merge indices physically
+  MERGE_STRATEGY_LOGICAL  = 1   ///< Merge indices logically
+} cuvsMergeStrategy;
+
+/**
+ * @}
+ */
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index a9fdd01d1f..e921216bf9 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -33,70 +33,84 @@
 
 namespace {
 
-template <typename T>
-void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor* dataset_tensor)
-{
-  auto dataset = dataset_tensor->dl_tensor;
+static void _set_graph_build_params(
+  std::variant<std::monostate,
+               cuvs::neighbors::cagra::graph_build_params::ivf_pq_params,
+               cuvs::neighbors::cagra::graph_build_params::nn_descent_params,
+               cuvs::neighbors::cagra::graph_build_params::iterative_search_params>& out_params,
+  cuvsCagraIndexParams& params,
+  cuvsCagraGraphBuildAlgo algo,
+  int64_t n_rows,
+  int64_t dim)
 
-  auto res_ptr = reinterpret_cast<raft::resources*>(res);
-  auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
-
-  auto index_params   = cuvs::neighbors::cagra::index_params();
-  index_params.metric = static_cast<cuvs::distance::DistanceType>((int)params.metric);
-  index_params.intermediate_graph_degree = params.intermediate_graph_degree;
-  index_params.graph_degree              = params.graph_degree;
-
-  switch (params.build_algo) {
+{
+  auto metric = static_cast<cuvs::distance::DistanceType>((int)params.metric);
+  switch (algo) {
     case cuvsCagraGraphBuildAlgo::AUTO_SELECT: break;
     case cuvsCagraGraphBuildAlgo::IVF_PQ: {
-      auto dataset_extent = raft::matrix_extent<int64_t>(dataset.shape[0], dataset.shape[1]);
-      auto pq_params      = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(
-        dataset_extent, index_params.metric);
-      auto ivf_pq_build_params  = params.graph_build_params->ivf_pq_build_params;
-      auto ivf_pq_search_params = params.graph_build_params->ivf_pq_search_params;
-      if (ivf_pq_build_params) {
-        pq_params.build_params.add_data_on_build = ivf_pq_build_params->add_data_on_build;
-        pq_params.build_params.n_lists           = ivf_pq_build_params->n_lists;
-        pq_params.build_params.kmeans_n_iters    = ivf_pq_build_params->kmeans_n_iters;
-        pq_params.build_params.kmeans_trainset_fraction =
-          ivf_pq_build_params->kmeans_trainset_fraction;
-        pq_params.build_params.pq_bits = ivf_pq_build_params->pq_bits;
-        pq_params.build_params.pq_dim  = ivf_pq_build_params->pq_dim;
-        pq_params.build_params.codebook_kind =
-          static_cast<cuvs::neighbors::ivf_pq::codebook_gen>(ivf_pq_build_params->codebook_kind);
-        pq_params.build_params.force_random_rotation = ivf_pq_build_params->force_random_rotation;
-        pq_params.build_params.conservative_memory_allocation =
-          ivf_pq_build_params->conservative_memory_allocation;
-        pq_params.build_params.max_train_points_per_pq_code =
-          ivf_pq_build_params->max_train_points_per_pq_code;
+      auto pq_params = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(
+        raft::matrix_extent<int64_t>(n_rows, dim), metric);
+      if (params.graph_build_params) {
+        auto ivf_params = static_cast<cuvsIvfPqParams*>(params.graph_build_params);
+        if (ivf_params->ivf_pq_build_params) {
+          auto bp                                         = ivf_params->ivf_pq_build_params;
+          pq_params.build_params.add_data_on_build        = bp->add_data_on_build;
+          pq_params.build_params.n_lists                  = bp->n_lists;
+          pq_params.build_params.kmeans_n_iters           = bp->kmeans_n_iters;
+          pq_params.build_params.kmeans_trainset_fraction = bp->kmeans_trainset_fraction;
+          pq_params.build_params.pq_bits                  = bp->pq_bits;
+          pq_params.build_params.pq_dim                   = bp->pq_dim;
+          pq_params.build_params.codebook_kind =
+            static_cast<cuvs::neighbors::ivf_pq::codebook_gen>(bp->codebook_kind);
+          pq_params.build_params.force_random_rotation = bp->force_random_rotation;
+          pq_params.build_params.conservative_memory_allocation =
+            bp->conservative_memory_allocation;
+          pq_params.build_params.max_train_points_per_pq_code = bp->max_train_points_per_pq_code;
+        }
+        if (ivf_params->ivf_pq_search_params) {
+          auto sp                                          = ivf_params->ivf_pq_search_params;
+          pq_params.search_params.n_probes                 = sp->n_probes;
+          pq_params.search_params.lut_dtype                = sp->lut_dtype;
+          pq_params.search_params.internal_distance_dtype  = sp->internal_distance_dtype;
+          pq_params.search_params.preferred_shmem_carveout = sp->preferred_shmem_carveout;
+        }
+        if (ivf_params->refinement_rate > 1.0f) {
+          pq_params.refinement_rate = ivf_params->refinement_rate;
+        }
       }
-      if (ivf_pq_search_params) {
-        pq_params.search_params.n_probes  = ivf_pq_search_params->n_probes;
-        pq_params.search_params.lut_dtype = ivf_pq_search_params->lut_dtype;
-        pq_params.search_params.internal_distance_dtype =
-          ivf_pq_search_params->internal_distance_dtype;
-        pq_params.search_params.preferred_shmem_carveout =
-          ivf_pq_search_params->preferred_shmem_carveout;
-      }
-      if (params.graph_build_params->refinement_rate > 1) {
-        pq_params.refinement_rate = params.graph_build_params->refinement_rate;
-      }
-      index_params.graph_build_params = pq_params;
+      out_params = pq_params;
       break;
     }
     case cuvsCagraGraphBuildAlgo::NN_DESCENT: {
-      auto nn_descent_params = cuvs::neighbors::cagra::graph_build_params::nn_descent_params{
-        index_params.intermediate_graph_degree, index_params.metric};
-      nn_descent_params.max_iterations = params.nn_descent_niter;
-      index_params.graph_build_params  = nn_descent_params;
+      auto nn_params =
+        cuvs::neighbors::nn_descent::index_params(params.intermediate_graph_degree, metric);
+      nn_params.max_iterations = params.nn_descent_niter;
+      out_params               = nn_params;
       break;
     }
     case cuvsCagraGraphBuildAlgo::ITERATIVE_CAGRA_SEARCH: {
       cuvs::neighbors::cagra::graph_build_params::iterative_search_params p;
-      index_params.graph_build_params = p;
+      out_params = p;
       break;
     }
-  };
+  }
+}
+
+template <typename T>
+void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor* dataset_tensor)
+{
+  auto dataset = dataset_tensor->dl_tensor;
+
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
+
+  auto index_params   = cuvs::neighbors::cagra::index_params();
+  index_params.metric = static_cast<cuvs::distance::DistanceType>((int)params.metric),
+  index_params.intermediate_graph_degree = params.intermediate_graph_degree;
+  index_params.graph_degree              = params.graph_degree;
+
+  _set_graph_build_params(
+    index_params.graph_build_params, params, params.build_algo, dataset.shape[0], dataset.shape[1]);
 
   if (auto* cparams = params.compression; cparams != nullptr) {
     auto compression_params                        = cuvs::neighbors::vpq_params();
@@ -266,6 +280,54 @@ void* _deserialize(cuvsResources_t res, const char* filename)
   return index;
 }
 
+template <typename T>
+void* _merge(cuvsResources_t res,
+             cuvsCagraMergeParams params,
+             cuvsCagraIndex_t* indices,
+             size_t num_indices)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  cuvs::neighbors::cagra::merge_params merge_params_cpp;
+  auto& out_idx_params = *params.output_index_params;
+
+  merge_params_cpp.output_index_params.metric =
+    static_cast<cuvs::distance::DistanceType>((int)out_idx_params.metric);
+  merge_params_cpp.output_index_params.intermediate_graph_degree =
+    out_idx_params.intermediate_graph_degree;
+  merge_params_cpp.output_index_params.graph_degree = out_idx_params.graph_degree;
+
+  int64_t total_size = 0;
+  int64_t dim        = 0;
+  if (out_idx_params.build_algo == cuvsCagraGraphBuildAlgo::IVF_PQ) {
+    auto first_idx_ptr =
+      reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(indices[0]->addr);
+    dim = first_idx_ptr->dim();
+    for (size_t i = 0; i < num_indices; ++i) {
+      auto idx_ptr =
+        reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(indices[i]->addr);
+      total_size += idx_ptr->size();
+    }
+  }
+
+  _set_graph_build_params(merge_params_cpp.output_index_params.graph_build_params,
+                          out_idx_params,
+                          out_idx_params.build_algo,
+                          total_size,
+                          dim);
+
+  std::vector<cuvs::neighbors::cagra::index<T, uint32_t>*> index_ptrs;
+  index_ptrs.reserve(num_indices);
+  for (size_t i = 0; i < num_indices; ++i) {
+    auto idx_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(indices[i]->addr);
+    index_ptrs.push_back(idx_ptr);
+  }
+
+  auto merged_index = new cuvs::neighbors::cagra::index<T, uint32_t>(
+    cuvs::neighbors::cagra::merge(*res_ptr, merge_params_cpp, index_ptrs));
+
+  return merged_index;
+}
+
 }  // namespace
 
 extern "C" cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index)
@@ -401,6 +463,43 @@ extern "C" cuvsError_t cuvsCagraSearch(cuvsResources_t res,
   });
 }
 
+extern "C" cuvsError_t cuvsCagraMerge(cuvsResources_t res,
+                                      cuvsCagraMergeParams_t params,
+                                      cuvsCagraIndex_t* indices,
+                                      size_t num_indices,
+                                      cuvsCagraIndex_t output_index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Basic checks on inputs
+    RAFT_EXPECTS(indices != nullptr && num_indices > 0, "indices array cannot be null or empty");
+    // Use first index dtype as reference
+    auto dtype = (*indices[0]).dtype;
+    for (size_t i = 1; i < num_indices; ++i) {
+      RAFT_EXPECTS((*indices[i]).dtype.code == dtype.code && (*indices[i]).dtype.bits == dtype.bits,
+                   "All input indices must have the same data type");
+      RAFT_EXPECTS((*indices[i]).addr != 0, "All input indices must be built (non-empty)");
+    }
+    RAFT_EXPECTS(output_index != nullptr, "Output index pointer must not be null");
+    output_index->dtype = dtype;  // output index type matches inputs
+    // Dispatch based on data type
+    if (dtype.code == kDLFloat && dtype.bits == 32) {
+      output_index->addr =
+        reinterpret_cast<uintptr_t>(_merge<float>(res, *params, indices, num_indices));
+    } else if (dtype.code == kDLFloat && dtype.bits == 16) {
+      output_index->addr =
+        reinterpret_cast<uintptr_t>(_merge<half>(res, *params, indices, num_indices));
+    } else if (dtype.code == kDLInt && dtype.bits == 8) {
+      output_index->addr =
+        reinterpret_cast<uintptr_t>(_merge<int8_t>(res, *params, indices, num_indices));
+    } else if (dtype.code == kDLUInt && dtype.bits == 8) {
+      output_index->addr =
+        reinterpret_cast<uintptr_t>(_merge<uint8_t>(res, *params, indices, num_indices));
+    } else {
+      RAFT_FAIL("Unsupported index data type: code=%d, bits=%d", dtype.code, dtype.bits);
+    }
+  });
+}
+
 extern "C" cuvsError_t cuvsCagraIndexParamsCreate(cuvsCagraIndexParams_t* params)
 {
   return cuvs::core::translate_exceptions([=] {
@@ -472,6 +571,24 @@ extern "C" cuvsError_t cuvsCagraSearchParamsDestroy(cuvsCagraSearchParams_t para
   return cuvs::core::translate_exceptions([=] { delete params; });
 }
 
+extern "C" cuvsError_t cuvsCagraMergeParamsCreate(cuvsCagraMergeParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    cuvsCagraIndexParams_t idx_params;
+    cuvsCagraIndexParamsCreate(&idx_params);
+    *params = new cuvsCagraMergeParams{.output_index_params = idx_params,
+                                       .strategy            = MERGE_STRATEGY_PHYSICAL};
+  });
+}
+
+extern "C" cuvsError_t cuvsCagraMergeParamsDestroy(cuvsCagraMergeParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    cuvsCagraIndexParamsDestroy(params->output_index_params);
+    delete params;
+  });
+}
+
 extern "C" cuvsError_t cuvsCagraDeserialize(cuvsResources_t res,
                                             const char* filename,
                                             cuvsCagraIndex_t index)
diff --git a/cpp/tests/neighbors/ann_cagra_c.cu b/cpp/tests/neighbors/ann_cagra_c.cu
index 9e0890c34d..ae80cc8986 100644
--- a/cpp/tests/neighbors/ann_cagra_c.cu
+++ b/cpp/tests/neighbors/ann_cagra_c.cu
@@ -476,3 +476,127 @@ TEST(CagraC, BuildSearchFiltered)
   cuvsCagraIndexDestroy(index);
   cuvsResourcesDestroy(res);
 }
+
+TEST(CagraC, BuildMergeSearch)
+{
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+  cudaStream_t stream;
+  cuvsStreamGet(res, &stream);
+
+  float dataset[7][2] = {{0.74021935f, 0.92099380f},
+                         {0.03902049f, 0.96896291f},
+                         {0.92514056f, 0.44635010f},
+                         {0.12345678f, 0.87654321f},
+                         {0.50112233f, 0.33221100f},
+                         {0.66731918f, 0.10993068f},
+                         {0.77777777f, 0.88888888f}};
+
+  float* main_data_ptr       = &dataset[0][0];
+  float* additional_data_ptr = &dataset[4][0];
+  float* query_data_ptr      = &dataset[6][0];
+
+  rmm::device_uvector<float> main_d(8, stream);
+  rmm::device_uvector<float> additional_d(6, stream);
+  rmm::device_uvector<float> queries_d(2, stream);
+  raft::copy(main_d.data(), main_data_ptr, 8, stream);
+  raft::copy(additional_d.data(), additional_data_ptr, 6, stream);
+  raft::copy(queries_d.data(), query_data_ptr, 2, stream);
+
+  DLManagedTensor main_dataset_tensor;
+  int64_t main_shape[2]                            = {4, 2};
+  main_dataset_tensor.dl_tensor.data               = main_d.data();
+  main_dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  main_dataset_tensor.dl_tensor.device.device_id   = 0;
+  main_dataset_tensor.dl_tensor.ndim               = 2;
+  main_dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  main_dataset_tensor.dl_tensor.dtype.bits         = 32;
+  main_dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  main_dataset_tensor.dl_tensor.shape              = main_shape;
+  main_dataset_tensor.dl_tensor.strides            = nullptr;
+
+  DLManagedTensor additional_dataset_tensor = main_dataset_tensor;
+  int64_t additional_shape[2]               = {3, 2};
+  additional_dataset_tensor.dl_tensor.data  = additional_d.data();
+  additional_dataset_tensor.dl_tensor.shape = additional_shape;
+
+  DLManagedTensor query_tensor = main_dataset_tensor;
+  int64_t query_shape[2]       = {1, 2};
+  query_tensor.dl_tensor.data  = queries_d.data();
+  query_tensor.dl_tensor.shape = query_shape;
+
+  cuvsCagraIndexParams_t build_params;
+  cuvsCagraIndexParamsCreate(&build_params);
+  cuvsCagraIndex_t index_main, index_add;
+  cuvsCagraIndexCreate(&index_main);
+  cuvsCagraIndexCreate(&index_add);
+  ASSERT_EQ(cuvsCagraBuild(res, build_params, &main_dataset_tensor, index_main), CUVS_SUCCESS);
+  ASSERT_EQ(cuvsCagraBuild(res, build_params, &additional_dataset_tensor, index_add), CUVS_SUCCESS);
+
+  cuvsCagraMergeParams_t merge_params;
+  cuvsCagraMergeParamsCreate(&merge_params);
+  cuvsCagraIndex_t index_merged;
+  cuvsCagraIndexCreate(&index_merged);
+
+  cuvsCagraIndex_t index_array[2] = {index_main, index_add};
+  ASSERT_EQ(cuvsCagraMerge(res, merge_params, index_array, 2, index_merged), CUVS_SUCCESS);
+
+  int merged_dim = -1;
+  ASSERT_EQ(cuvsCagraIndexGetDims(index_merged, &merged_dim), CUVS_SUCCESS);
+  EXPECT_EQ(merged_dim, 2);
+
+  DLManagedTensor neighbors_tensor, distances_tensor;
+  rmm::device_uvector<int64_t> neighbors_d(1, stream);
+  rmm::device_uvector<float> distances_d(1, stream);
+  int64_t neighbors_shape[2]             = {1, 1};
+  int64_t distances_shape[2]             = {1, 1};
+  neighbors_tensor.dl_tensor.data        = neighbors_d.data();
+  neighbors_tensor.dl_tensor.device      = main_dataset_tensor.dl_tensor.device;
+  neighbors_tensor.dl_tensor.ndim        = 2;
+  neighbors_tensor.dl_tensor.dtype.code  = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits  = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes = 1;
+  neighbors_tensor.dl_tensor.shape       = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides     = nullptr;
+  distances_tensor.dl_tensor.data        = distances_d.data();
+  distances_tensor.dl_tensor.device      = main_dataset_tensor.dl_tensor.device;
+  distances_tensor.dl_tensor.ndim        = 2;
+  distances_tensor.dl_tensor.dtype.code  = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits  = 32;
+  distances_tensor.dl_tensor.dtype.lanes = 1;
+  distances_tensor.dl_tensor.shape       = distances_shape;
+  distances_tensor.dl_tensor.strides     = nullptr;
+
+  cuvsCagraSearchParams_t search_params;
+  cuvsCagraSearchParamsCreate(&search_params);
+  (*search_params).itopk_size = 1;
+
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = 0;
+  ASSERT_EQ(cuvsCagraSearch(res,
+                            search_params,
+                            index_merged,
+                            &query_tensor,
+                            &neighbors_tensor,
+                            &distances_tensor,
+                            filter),
+            CUVS_SUCCESS);
+
+  int64_t neighbor_host = -1;
+  float distance_host   = 1.0f;
+  raft::copy(&neighbor_host, neighbors_d.data(), 1, stream);
+  raft::copy(&distance_host, distances_d.data(), 1, stream);
+  cudaStreamSynchronize(stream);
+
+  EXPECT_EQ(neighbor_host, 6);
+  EXPECT_NEAR(distance_host, 0.0f, 1e-6);
+
+  cuvsCagraSearchParamsDestroy(search_params);
+  cuvsCagraMergeParamsDestroy(merge_params);
+  cuvsCagraIndexParamsDestroy(build_params);
+  cuvsCagraIndexDestroy(index_merged);
+  cuvsCagraIndexDestroy(index_add);
+  cuvsCagraIndexDestroy(index_main);
+  cuvsResourcesDestroy(res);
+}

From 0a84eb0006bb99560e87b69c9c208481232fc111 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 20 May 2025 11:15:53 +0200
Subject: [PATCH 404/623] ANN_BENCH: Avoid repeated calls to
 raft::get_device_for_address in CAGRA search (#908)

Cache the information about the IO buffer location between the benchmark search iterations.
This helps to avoid one extra CUDA context call per benchmark iteration, which otherwise adds a little bit of overhead under many-threads-throughput mode.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/908
---
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index a57c568f8d..e4c441639c 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -392,8 +392,18 @@ void cuvs_cagra<T, IdxT>::search(
   auto k0                       = static_cast<size_t>(refine_ratio_ * k);
   const bool disable_refinement = k0 <= static_cast<size_t>(k);
   const raft::resources& res    = handle_;
-  auto mem_type =
-    raft::get_device_for_address(neighbors) >= 0 ? MemoryType::kDevice : MemoryType::kHostPinned;
+  // NOTE: caching mem_type to reduce mutex locks
+  // raft::get_device_for_address call cuda API to get the pointer properties,
+  // this means it locks the context mutex for a very small amount of time.
+  // In the event of thread contention (such as thousands threads), this time can actually increase.
+  // Hence we try to bypass this check for repeated search calls.
+  thread_local MemoryType mem_type                   = MemoryType::kDevice;
+  thread_local algo_base::index_type* prev_neighbors = nullptr;
+  if (prev_neighbors != neighbors) {
+    prev_neighbors = neighbors;
+    mem_type =
+      raft::get_device_for_address(neighbors) >= 0 ? MemoryType::kDevice : MemoryType::kHostPinned;
+  }
 
   // If dynamic batching is used and there's no sync between benchmark laps, multiple sequential
   // requests can group together. The data is copied asynchronously, and if the same intermediate

From 14d137c522aa417296356b24f52e28f11047f1e3 Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 20 May 2025 11:39:24 +0200
Subject: [PATCH 405/623] CUVS_ANN_BENCH_SINGLE_EXE: find the mg library (#890)

Let the benchmark in CUVS_ANN_BENCH_SINGLE_EXE mode find the library for multi-gpu implementations.

Originally, all library names coincided with the generated target (library) names, which was used by the loader to find a proper implementation for an algorithm in the benchmark config.
A group of cuVS mg algorithms broke this pattern by using a single target for all cuVS mg algorithms. This PR adds this exception for the library discovery logic.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/890
---
 cpp/bench/ann/src/common/benchmark.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/cpp/bench/ann/src/common/benchmark.cpp b/cpp/bench/ann/src/common/benchmark.cpp
index 7ec8558a4a..6d0c16a451 100644
--- a/cpp/bench/ann/src/common/benchmark.cpp
+++ b/cpp/bench/ann/src/common/benchmark.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -49,11 +49,15 @@ struct lib_handle {
 auto load_lib(const std::string& algo) -> void*
 {
   static std::unordered_map<std::string, lib_handle> libs{};
-  auto found = libs.find(algo);
+  auto lib_short_name = algo;
+  // cuvs_mg library is responsible for all mg algorithms
+  if (algo.rfind("cuvs_mg_", 0) == 0) { lib_short_name = "cuvs_mg"; }
 
+  auto found = libs.find(lib_short_name);
   if (found != libs.end()) { return found->second.handle; }
-  auto lib_name = "lib" + algo + "_ann_bench.so";
-  return libs.emplace(algo, lib_name).first->second.handle;
+
+  auto lib_name = "lib" + lib_short_name + "_ann_bench.so";
+  return libs.emplace(lib_short_name, lib_name).first->second.handle;
 }
 
 /*

From 382f9cdb5f963efb24f785adb39238a918bc98ac Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Thu, 22 May 2025 13:12:35 -0400
Subject: [PATCH 406/623] use 'rapids-init-pip' in wheel CI, other CI changes
 (#917)

Proposes a batch of small, mostly-unrelated changes to building and packaging.

These are all part of RAPIDS-wide efforts to make it easier to reproduce CI locally and to use artifacts from one project's CI in another project's CI.

Contributes to https://github.com/rapidsai/shared-workflows/issues/356

* explicitly provides an input for `script` to workflows using it, instead of relying on a default value coming from the workflow file

Contributes to https://github.com/rapidsai/shared-workflows/issues/337

* switches from input `run_script` to `script` in uses of the `custom-job` workflow

Contributes to https://github.com/rapidsai/build-planning/issues/179

* adds a call to `rapids-init-pip` near the beginning of all CI scripts that use `pip`
* removes redefinitions of `PIP_CONSTRAINT` in scripts, in favor of using the one set up by `rapids-pip-init`

Contributes to https://github.com/rapidsai/gha-tools/issues/145

* removes unnecessary uses of `rapids-configure-conda-channels` in builds scripts using `rattler-build`

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/917
---
 .github/workflows/build.yaml | 10 ++++++----
 .github/workflows/pr.yaml    | 12 ++++++++----
 .github/workflows/test.yaml  |  4 +++-
 ci/build_cpp.sh              |  2 --
 ci/build_python.sh           |  2 --
 ci/build_wheel.sh            |  1 +
 ci/build_wheel_cuvs.sh       |  7 ++++---
 ci/build_wheel_libcuvs.sh    |  2 ++
 ci/test_wheel_cuvs.sh        |  3 ++-
 9 files changed, 26 insertions(+), 17 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 9efeb93c77..d683f8ff4d 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -33,6 +33,7 @@ jobs:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
+      script: ci/build_cpp.sh
       sha: ${{ inputs.sha }}
   rust-build:
     needs: cpp-build
@@ -45,7 +46,7 @@ jobs:
       date: ${{ inputs.date }}
       container_image: "rapidsai/ci-conda:latest"
       node_type: "gpu-l4-latest-1"
-      run_script: "ci/build_rust.sh"
+      script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
   rust-publish:
     needs: rust-build
@@ -62,7 +63,7 @@ jobs:
       date: ${{ inputs.date }}
       container_image: "rapidsai/ci-conda:latest"
       node_type: "gpu-l4-latest-1"
-      run_script: "ci/build_go.sh"
+      script: "ci/build_go.sh"
       sha: ${{ inputs.sha }}
   java-build:
     needs: cpp-build
@@ -74,7 +75,7 @@ jobs:
       arch: "amd64"
       date: ${{ inputs.date }}
       container_image: "rapidsai/ci-conda:latest"
-      run_script: "ci/build_java.sh"
+      script: "ci/build_java.sh"
       file_to_upload: "java/cuvs-java/target/"
       sha: ${{ inputs.sha }}
   python-build:
@@ -85,6 +86,7 @@ jobs:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
+      script: ci/build_python.sh
       sha: ${{ inputs.sha }}
   upload-conda:
     needs: [cpp-build, python-build]
@@ -108,7 +110,7 @@ jobs:
       container_image: "rapidsai/ci-conda:latest"
       date: ${{ inputs.date }}
       node_type: "gpu-l4-latest-1"
-      run_script: "ci/build_docs.sh"
+      script: "ci/build_docs.sh"
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 01e30bffc2..464f62101f 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -122,6 +122,7 @@ jobs:
     with:
       build_type: pull-request
       node_type: cpu16
+      script: ci/build_cpp.sh
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
@@ -129,6 +130,7 @@ jobs:
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
+      script: ci/test_cpp.sh
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
@@ -143,6 +145,7 @@ jobs:
     uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.06
     with:
       build_type: pull-request
+      script: ci/build_python.sh
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
@@ -150,6 +153,7 @@ jobs:
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
+      script: ci/test_python.sh
   conda-java-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
@@ -160,7 +164,7 @@ jobs:
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
-      run_script: "ci/test_java.sh"
+      script: "ci/test_java.sh"
       file_to_upload: "java/cuvs-java/target/"
   docs-build:
     needs: conda-python-build
@@ -171,7 +175,7 @@ jobs:
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
-      run_script: "ci/build_docs.sh"
+      script: "ci/build_docs.sh"
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
@@ -181,7 +185,7 @@ jobs:
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
-      run_script: "ci/build_rust.sh"
+      script: "ci/build_rust.sh"
   go-build:
     needs: conda-cpp-build
     secrets: inherit
@@ -191,7 +195,7 @@ jobs:
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
-      run_script: "ci/build_go.sh"
+      script: "ci/build_go.sh"
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 5c2192daa1..cecd58c204 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -34,6 +34,7 @@ jobs:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
+      script: ci/test_cpp.sh
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
@@ -42,6 +43,7 @@ jobs:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
+      script: ci/test_python.sh
       sha: ${{ inputs.sha }}
   conda-java-tests:
     secrets: inherit
@@ -54,7 +56,7 @@ jobs:
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
       container_image: "rapidsai/ci-conda:latest"
-      run_script: "ci/test_java.sh"
+      script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.06
diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index ca9bac0b6c..9df8c1d11d 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -3,8 +3,6 @@
 
 set -euo pipefail
 
-rapids-configure-conda-channels
-
 source rapids-configure-sccache
 
 source rapids-date-string
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 0e152ea652..efb4c1233c 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -3,8 +3,6 @@
 
 set -euo pipefail
 
-rapids-configure-conda-channels
-
 source rapids-configure-sccache
 
 source rapids-date-string
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 2ef5a7b77f..cd31d05fdd 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -10,6 +10,7 @@ underscore_package_name=$(echo "${package_name}" | tr "-" "_")
 
 source rapids-configure-sccache
 source rapids-date-string
+source rapids-init-pip
 
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index 3b400f9edf..e6d97d0abb 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -3,6 +3,8 @@
 
 set -euo pipefail
 
+source rapids-init-pip
+
 package_dir="python/cuvs"
 
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
@@ -11,10 +13,9 @@ RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 # then ensures 'cuvs' wheel builds always use the 'libcuvs' just built in the same CI run.
 #
 # Using env variable PIP_CONSTRAINT is necessary to ensure the constraints
-# are used when creating the isolated build environment.
+# Using env variable PIP_CONSTRAINT (initialized by 'rapids-init-pip') is necessary to ensure the constraints
 LIBCUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github cpp)
-echo "libcuvs-${RAPIDS_PY_CUDA_SUFFIX} @ file://$(echo "${LIBCUVS_WHEELHOUSE}"/libcuvs_*.whl)" > /tmp/constraints.txt
-export PIP_CONSTRAINT="/tmp/constraints.txt"
+echo "libcuvs-${RAPIDS_PY_CUDA_SUFFIX} @ file://$(echo "${LIBCUVS_WHEELHOUSE}"/libcuvs_*.whl)" >> "${PIP_CONSTRAINT}"
 
 ci/build_wheel.sh cuvs ${package_dir} python
 ci/validate_wheel.sh ${package_dir} "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}"
diff --git a/ci/build_wheel_libcuvs.sh b/ci/build_wheel_libcuvs.sh
index 129eff8444..6d80282ff5 100755
--- a/ci/build_wheel_libcuvs.sh
+++ b/ci/build_wheel_libcuvs.sh
@@ -3,6 +3,8 @@
 
 set -euo pipefail
 
+source rapids-init-pip
+
 package_name="libcuvs"
 package_dir="python/libcuvs"
 
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 8e758ab7d9..510c35db97 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -3,13 +3,14 @@
 
 set -euo pipefail
 
+source rapids-init-pip
+
 # Delete system libnccl.so to ensure the wheel is used
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ ${RAPIDS_CUDA_MAJOR} != "11" ]]; then
   rm -rf /usr/lib64/libnccl*
 fi
 
-mkdir -p ./dist
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 LIBCUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github cpp)
 CUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="cuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github python)

From 84b5ec460faf6446c60bf4cebfcf3095078724fb Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Fri, 23 May 2025 17:21:54 +0200
Subject: [PATCH 407/623] batch_load_iterator shall copy data located in host
 memory (#926)

This PR changes the `batch_load_iterator` helper to copy the data explicitly if it is host accessible.

Previously, if the data was both host and device accessible, then there was no explicit copy done by `batch_load_iterator`, since we could access the data directly on the GPU.

In practice, the datasets handled by `batch_load_iterator` are expected to be large, and in that case relying on automatic page migration to access the data is not as efficient as the explicit copy facilitated by `batch_load_iterator`. This PR enables the explicit copy op by `batch_load_iterator` in this case.

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/926
---
 cpp/src/neighbors/detail/ann_utils.cuh | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index 4c62d5bac0..fa0e2a9217 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -467,10 +467,16 @@ struct batch_load_iterator {
       cudaPointerAttributes attr;
       RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, source_));
       dev_ptr_ = reinterpret_cast<T*>(attr.devicePointer);
-      if (dev_ptr_ == nullptr) {
-        buf_0_.resize(row_width_ * batch_size_, stream);
-        dev_ptr_    = buf_0_.data();
+
+      if (dev_ptr_ == nullptr) { needs_copy_ = true; }
+      if (attr.type != cudaMemoryTypeDevice) {
+        // Although data might be accessible on device through HMM or ATS,
+        // it is preferred to copy the dataset explicitly when it is not device data.
         needs_copy_ = true;
+      }
+      if (needs_copy_) {
+        buf_0_.resize(row_width_ * batch_size_, stream);
+        dev_ptr_ = buf_0_.data();
         if (prefetch_) {
           buf_1_.resize(row_width_ * batch_size_, stream);
           prefetch_dev_ptr_ = buf_1_.data();

From 2dcba18121ea03cf99a3e06cfd4fbdc95cdb9663 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Fri, 23 May 2025 17:41:01 -0700
Subject: [PATCH 408/623] [FEA] Use Native Brute Force for Sparse Pairwise KNN
 (#927)

Bring in `knn_graph` sparse APIs from raft and replace calls to it with cuVS functions.
Closes https://github.com/rapidsai/cuvs/issues/883

Binary size reduction:
branch-25.06 libcuvs.so size = 1403.169 MB
This PR: libcuvs.so size = 1375.059 MB

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jinsol Park (https://github.com/jinsolp)

URL: https://github.com/rapidsai/cuvs/pull/927
---
 cpp/src/cluster/detail/connectivities.cuh     |   7 +-
 cpp/src/neighbors/detail/knn_graph.cuh        | 108 ++++++++++++++++++
 .../neighbors/detail/vamana/vamana_build.cuh  |   4 +-
 .../sparse/neighbors/cross_component_nn.cu    |  12 +-
 examples/cpp/src/vamana_example.cu            |   1 -
 5 files changed, 118 insertions(+), 14 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/knn_graph.cuh

diff --git a/cpp/src/cluster/detail/connectivities.cuh b/cpp/src/cluster/detail/connectivities.cuh
index 5f62b2ecf8..39be707bb7 100644
--- a/cpp/src/cluster/detail/connectivities.cuh
+++ b/cpp/src/cluster/detail/connectivities.cuh
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include "../../neighbors/detail/knn_graph.cuh"
 #include "./kmeans_common.cuh"
 #include <cuvs/cluster/agglomerative.hpp>
 #include <cuvs/distance/distance.hpp>
@@ -25,7 +26,6 @@
 #include <raft/linalg/unary_op.cuh>
 #include <raft/sparse/convert/csr.cuh>
 #include <raft/sparse/coo.hpp>
-#include <raft/sparse/neighbors/knn_graph.cuh>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
 
@@ -75,8 +75,9 @@ struct distance_graph_impl<Linkage::KNN_GRAPH, value_idx, value_t> {
     // Need to symmetrize knn into undirected graph
     raft::sparse::COO<value_t, value_idx> knn_graph_coo(stream);
 
-    raft::sparse::neighbors::knn_graph(
-      handle, X, m, n, static_cast<raft::distance::DistanceType>(metric), knn_graph_coo, c);
+    auto X_view = raft::make_device_matrix_view<const value_t, value_idx, raft::row_major>(X, m, n);
+    cuvs::neighbors::detail::knn_graph<value_idx, value_t, size_t>(
+      handle, X_view, metric, knn_graph_coo, c);
 
     indices.resize(knn_graph_coo.nnz, stream);
     data.resize(knn_graph_coo.nnz, stream);
diff --git a/cpp/src/neighbors/detail/knn_graph.cuh b/cpp/src/neighbors/detail/knn_graph.cuh
new file mode 100644
index 0000000000..6d19cd705d
--- /dev/null
+++ b/cpp/src/neighbors/detail/knn_graph.cuh
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "knn_brute_force.cuh"
+#include <cuvs/distance/distance.hpp>
+
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/map.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/sparse/linalg/symmetrize.cuh>
+#include <raft/util/cuda_dev_essentials.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <algorithm>
+
+namespace cuvs::neighbors::detail {
+
+template <typename value_idx>
+value_idx build_k(value_idx n_samples, int c)
+{
+  // from "kNN-MST-Agglomerative: A fast & scalable graph-based data clustering
+  // approach on GPU"
+  return std::min(n_samples, std::max((value_idx)2, (value_idx)floor(raft::log2(n_samples)) + c));
+}
+/**
+ * Constructs a (symmetrized) knn graph edge list from
+ * dense input vectors.
+ *
+ * Note: The resulting KNN graph is not guaranteed to be connected.
+ *
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam nnz_t
+ * @param[in] res raft res
+ * @param[in] X dense matrix of input data samples and observations (size m * n)
+ * @param[in] metric distance metric to use when constructing neighborhoods
+ * @param[out] out output edge list
+ * @param[in] c a constant used when constructing linkage from knn graph. Allows the indirect
+ control of k. The algorithm will set `k = log(n) + c`
+ */
+template <typename value_idx = int, typename value_t = float, typename nnz_t = size_t>
+void knn_graph(raft::resources const& res,
+               raft::device_matrix_view<const value_t, value_idx> X,
+               cuvs::distance::DistanceType metric,
+               raft::sparse::COO<value_t, value_idx, nnz_t>& out,
+               int c = 15)
+{
+  size_t m = X.extent(0);
+  size_t n = X.extent(1);
+  size_t k = build_k(m, c);
+
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  nnz_t nnz = m * k;
+
+  rmm::device_uvector<value_idx> rows(nnz, stream);
+  rmm::device_uvector<value_idx> indices(nnz, stream);
+  rmm::device_uvector<value_t> data(nnz, stream);
+
+  auto rows_view = raft::make_device_vector_view<value_idx, nnz_t>(rows.data(), nnz);
+
+  raft::linalg::map_offset(res, rows_view, [k] __device__(nnz_t i) { return value_idx(i / k); });
+
+  std::vector<value_t*> inputs;
+  inputs.push_back(const_cast<value_t*>(X.data_handle()));
+
+  std::vector<size_t> sizes;
+  sizes.push_back(m);
+
+  brute_force_knn_impl<size_t, value_idx, value_t, value_t>(res,
+                                                            inputs,
+                                                            sizes,
+                                                            n,
+                                                            const_cast<value_t*>(X.data_handle()),
+                                                            m,
+                                                            indices.data(),
+                                                            data.data(),
+                                                            k,
+                                                            true,
+                                                            true,
+                                                            nullptr,
+                                                            metric);
+
+  raft::sparse::linalg::symmetrize(res,
+                                   rows.data(),
+                                   indices.data(),
+                                   data.data(),
+                                   static_cast<value_idx>(m),
+                                   static_cast<value_idx>(k),
+                                   nnz,
+                                   out);
+}
+};  // namespace cuvs::neighbors::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_build.cuh b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
index 184b024f86..6701d839a0 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_build.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include "../../../sparse/neighbors/cross_component_nn.cuh"
 #include "greedy_search.cuh"
 #include "robust_prune.cuh"
 #include "vamana_structs.cuh"
@@ -34,7 +35,6 @@
 #include <raft/matrix/copy.cuh>
 #include <raft/matrix/init.cuh>
 #include <raft/random/make_blobs.cuh>
-#include <raft/sparse/neighbors/cross_component_nn.cuh>
 
 #include <thrust/device_vector.h>
 #include <thrust/sequence.h>
@@ -284,7 +284,7 @@ void batched_insert_vamana(
 
       // Get number of unique node destinations
       IdxT unique_dests =
-        raft::sparse::neighbors::get_n_components(edge_dest.data_handle(), total_edges, stream);
+        cuvs::sparse::neighbors::get_n_components(edge_dest.data_handle(), total_edges, stream);
 
       // Find which node IDs have reverse edges and their indices in the reverse edge list
       thrust::device_vector<IdxT> edge_dest_vec(edge_dest.data_handle(),
diff --git a/cpp/tests/sparse/neighbors/cross_component_nn.cu b/cpp/tests/sparse/neighbors/cross_component_nn.cu
index 374e273d68..7810b0f9cf 100644
--- a/cpp/tests/sparse/neighbors/cross_component_nn.cu
+++ b/cpp/tests/sparse/neighbors/cross_component_nn.cu
@@ -35,7 +35,6 @@
 #include <raft/sparse/coo.hpp>
 #include <raft/sparse/linalg/symmetrize.cuh>
 #include <raft/sparse/mst/mst.cuh>
-#include <raft/sparse/neighbors/knn_graph.cuh>
 #include <raft/util/cuda_utils.cuh>
 #include <raft/util/cudart_utils.hpp>
 
@@ -88,13 +87,10 @@ class ConnectComponentsTest
      */
     raft::sparse::COO<value_t, value_idx> knn_graph_coo(stream);
 
-    raft::sparse::neighbors::knn_graph(handle,
-                                       data.data(),
-                                       params.n_row,
-                                       params.n_col,
-                                       raft::distance::DistanceType::L2SqrtExpanded,
-                                       knn_graph_coo,
-                                       params.c);
+    auto data_view = raft::make_device_matrix_view<value_t, value_idx, raft::row_major>(
+      data.data(), params.n_row, params.n_col);
+    cuvs::neighbors::detail::knn_graph<value_idx, value_t, size_t>(
+      handle, data_view, cuvs::distance::DistanceType::L2SqrtExpanded, knn_graph_coo, params.c);
 
     raft::sparse::convert::sorted_coo_to_csr(
       knn_graph_coo.rows(), knn_graph_coo.nnz, indptr.data(), params.n_row + 1, stream);
diff --git a/examples/cpp/src/vamana_example.cu b/examples/cpp/src/vamana_example.cu
index 9e5201d31c..dbda6f032f 100644
--- a/examples/cpp/src/vamana_example.cu
+++ b/examples/cpp/src/vamana_example.cu
@@ -19,7 +19,6 @@
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_resources.hpp>
 #include <raft/random/make_blobs.cuh>
-#include <raft/sparse/neighbors/cross_component_nn.cuh>
 
 #include <cuvs/neighbors/vamana.hpp>
 

From dbe45c28ced896908a08cf9b894f9a99ab92edd6 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Tue, 27 May 2025 11:17:23 -0400
Subject: [PATCH 409/623] Wrapper for all-neighbors knn graph building (#785)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR introduces a wrapper for building all-neighbors knn graphs.
The multi-GPU batched all-neighbors knn graph building algorithm is part of this PR, which enables all-neighbors graph building for datasets that do not fit on device memory.

Batching can be done by using `n_clusters > 1`. Multi-GPU feature is supported by using the `device_resources_snmg` as the resource.

Related Issue
- https://github.com/rapidsai/cuvs/issues/727


**[Supported ANN algorithms]**

1. IVFPQ: higher recall, takes longer to run
2. NN Descent: lower recall in general (and has unacceptable low recall issues for some data distributions), but faster to run. Also uses less memory (since it uses half precision)
3. Plan to add brute force in the future as a separate PR (only usable when `n_clusters=1` because using batching means we cannot guarantee 100% accuracy)

**[Supported data types]**
Currently only supports fp32 data types, but plan to explore and add fp16 data types in the future.

**[Number of nearest clusters and total number of clusters]**
- `n_nearest_clusters`: number of clusters each data is assigned to
- `n_clusters`: total number of clusters

Some important notes about these two parameters;
1. the ratio of `n_nearest_clusters` / `n_clusters` determines device memory usage.
    - Approximately (`n_nearest_clusters` / `n_clusters` ) * `num_rows_in_entire_data` number of rows will be put on device memory at once.
    - E.g. between (`n_nearest_clusters` / `n_clusters` ) = 2/10 and 2/20, the latter will use less device memory.
2. larger `n_nearest_clusters` results in better accuracy of the final all-neighbors knn graph.
    - E.g. With the similar device memory usages, (`n_nearest_clusters` / `n_clusters`) = 4/20 will have better accuracy than 2/10 at the cost of performance.
4. When `n_clusters`=1, does not perform batching.

---
## How to Use
Can be used like the following
```
// in test.cpp file
all_neighbors::all_neighbors_params params;
params.n_clusters         = 10; // total number of clusters
params.n_nearest_clusters = 2; // number of clusters each data is assigned to

// Option 1: For usage with IVFPQ
params.graph_build_params = all_neighbors::graph_build_params::ivf_pq_params{};
// Option 2: For usage with NN Descent
params.graph_build_params = all_neighbors::graph_build_params::nn_descent_params{};

// if want to use multi-gpu
auto res = raft::resource::device_resources_snmg{};

all_neighbors::build(handle_, params, host_data_view, indices_view, distances_view);
```

---
## Numbers
All experiments run on L40 GPUs
**[Large dataset to check performance and memory usage]**
Below is a result of running on a 269GB dataset (wiki all).
- IVFPQ takes longer (because it goes through build - search -refine steps), and uses more memory. The reason for using more memory is because NN Descent puts data on device in half precision floating points, regardless of the given data type. Plan to test the impact of using fp16 IVFPQ on recall in the future.
- Gains are better for IVFPQ because it does more work on the GPU compared to NN Descent.
- With longer end to end runtime and more memory usage (unless it uses fp16), IVFPQ is still worth using because of recall issues with NN Descent.
<img width="1075" alt="Screenshot 2025-03-25 at 1 57 53 PM" src="https://github.com/user-attachments/assets/b05f82e1-3e25-4909-8d0a-740bc42599ac" />

**[Smaller dataset to verify recall]**
It is impossible to generate ground truth all-neighbors graph for a large dataset, so below is the result on a 3.8GB dataset (Deep images) to check how `n_nearest_clusters` and `n_clusters` affects recall.
- Recalls consistent regardless of how many GPUs are used
- Note the recall differences between (`n_nearest_clusters` / `n_clusters` ) = 2/20 and 4/40. These two configurations use similar amount of device memory, but the latter has much better recall
- Note that NN Descent recalls are generally lower than IVFPQ recalls (NN Descent results would have been much worse for other data distributions at this data size)
- At this data size, adding more GPUs doesn't always help
<img width="1077" alt="Screenshot 2025-03-25 at 2 09 01 PM" src="https://github.com/user-attachments/assets/bda31a8f-76d5-42f3-b948-c68d70b17cff" />

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/785
---
 cpp/CMakeLists.txt                            |   1 +
 cpp/include/cuvs/neighbors/all_neighbors.hpp  | 152 ++++++
 .../neighbors/all_neighbors/all_neighbors.cu  |  44 ++
 .../neighbors/all_neighbors/all_neighbors.cuh | 113 ++++
 .../all_neighbors/all_neighbors_batched.cuh   | 466 +++++++++++++++++
 .../all_neighbors/all_neighbors_builder.cuh   | 488 ++++++++++++++++++
 .../all_neighbors/all_neighbors_merge.cuh     | 268 ++++++++++
 cpp/src/neighbors/detail/nn_descent.cuh       |   9 +-
 cpp/src/neighbors/detail/nn_descent_gnnd.hpp  |  33 +-
 cpp/tests/CMakeLists.txt                      |   5 +
 cpp/tests/neighbors/all_neighbors.cuh         | 237 +++++++++
 .../neighbors/all_neighbors/test_float.cu     |  31 ++
 12 files changed, 1827 insertions(+), 20 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/all_neighbors.hpp
 create mode 100644 cpp/src/neighbors/all_neighbors/all_neighbors.cu
 create mode 100644 cpp/src/neighbors/all_neighbors/all_neighbors.cuh
 create mode 100644 cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
 create mode 100644 cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
 create mode 100644 cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh
 create mode 100644 cpp/tests/neighbors/all_neighbors.cuh
 create mode 100644 cpp/tests/neighbors/all_neighbors/test_float.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 1e565b3386..a4a60d6a7f 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -382,6 +382,7 @@ if(BUILD_SHARED_LIBS)
     src/distance/pairwise_distance.cu
     src/distance/sparse_distance.cu
     src/embed/spectral.cu
+    src/neighbors/all_neighbors/all_neighbors.cu
     src/neighbors/ball_cover.cu
     src/neighbors/ball_cover/detail/ball_cover/registers_eps_pass_euclidean.cu
     src/neighbors/ball_cover/detail/ball_cover/registers_pass_one.cu
diff --git a/cpp/include/cuvs/neighbors/all_neighbors.hpp b/cpp/include/cuvs/neighbors/all_neighbors.hpp
new file mode 100644
index 0000000000..2f1fb0949d
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/all_neighbors.hpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+
+#include <variant>
+
+namespace cuvs::neighbors::all_neighbors {
+
+/**
+ * @brief Parameters used to build an all-neighbors knn graph (find nearest neighbors for all the
+ * training vectors)
+ */
+namespace graph_build_params {
+
+/** Specialized parameters utilizing IVF-PQ to build knn graph */
+struct ivf_pq_params {
+  cuvs::neighbors::ivf_pq::index_params build_params;
+  cuvs::neighbors::ivf_pq::search_params search_params;
+  float refinement_rate = 2.0;
+};
+
+using nn_descent_params = cuvs::neighbors::nn_descent::index_params;
+}  // namespace graph_build_params
+
+using GraphBuildParams =
+  std::variant<graph_build_params::ivf_pq_params, graph_build_params::nn_descent_params>;
+
+/**
+ * @brief Parameters used to build an all-neighbors graph  (find nearest neighbors for all the
+ * training vectors)
+ *
+ * graph_build_params: graph building parameters for the given graph building algorithm. defaults
+ * to ivfpq.
+ * n_nearest_clusters: number of nearest clusters each data point will be assigned to in
+ * the batching algorithm
+ * n_clusters: number of total clusters (aka batches) to split the data into. If set to 1, algorithm
+ * creates an all-neighbors graph without batching
+ * metric: metric type
+ *
+ */
+struct all_neighbors_params {
+  /** Parameters for knn graph building algorithm
+   *
+   * Set ivf_pq_params, or nn_descent_params to select the graph build
+   * algorithm and control their parameters.
+   *
+   * @code{.cpp}
+   * all_neighbors::index_params params;
+   * // 1. Choose IVF-PQ algorithm
+   * params.graph_build_params = all_neighbors::graph_build_params::ivf_pq_params{};
+   *
+   * // 2. Choose NN Descent algorithm for kNN graph construction
+   * params.graph_build_params = all_neighbors::graph_build_params::nn_descent_params{};
+   *
+   * @endcode
+   */
+  GraphBuildParams graph_build_params;
+
+  /**
+   * Usage of n_nearest_clusters and n_clusters
+   *
+   * The ratio of n_nearest_clusters / n_clusters determines device memory usage.
+   * Approximately (n_nearest_clusters / n_clusters) * num_rows_in_entire_data number of rows will
+   * be put on device memory at once.
+   * E.g. between (n_nearest_clusters / n_clusters) = 2/10 and 2/20, the latter will use less device
+   * memory.
+   *
+   * Larger n_nearest_clusters results in better accuracy of the final all-neighbors knn
+   * graph. E.g. With the similar device memory usages, (n_nearest_clusters / n_clusters) = 4/20
+   * will have better accuracy than 2/10 at the cost of performance.
+   */
+  size_t n_nearest_clusters           = 2;
+  size_t n_clusters                   = 1;  // defaults to not batching
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
+};
+
+/**
+ * @brief Builds an approximate all-neighbors knn graph  (find nearest neighbors for all the
+ * training vectors)
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   all_neighbors::all_neighbors_params params;
+ *  auto indices = raft::make_device_matrix<int64_t, int64_t>(handle, n_row, k);
+ *  auto distances = raft::make_device_matrix<float, int64_t>(handle, n_row, k);
+ *   all_neighbors::build(res, params, dataset, indices.view(), distances.view());
+ * @endcode
+ *
+ * @param[in] handle raft::resources is an object mangaging resources
+ * @param[in] params an instance of all_neighbors::all_neighbors_params that are parameters
+ *               to build all-neighbors knn graph
+ * @param[in] dataset raft::host_matrix_view input dataset expected to be located
+ *                in host memory
+ * @param[out] indices nearest neighbor indices of shape [n_row x k]
+ * @param[out] distances nearest neighbor distances [n_row x k]
+ */
+void build(
+  const raft::resources& handle,
+  const all_neighbors_params& params,
+  raft::host_matrix_view<const float, int64_t, row_major> dataset,
+  raft::device_matrix_view<int64_t, int64_t, row_major> indices,
+  std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances = std::nullopt);
+
+/**
+ * @brief Builds an approximate all-neighbors knn graph (find nearest neighbors for all the training
+ * vectors) params.n_clusters should be 1 for data on device. To use a larger params.n_clusters for
+ * efficient device memory usage, put data on host RAM.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   all_neighbors::all_neighbors_params params;
+ *  auto indices = raft::make_device_matrix<int64_t, int64_t>(handle, n_row, k);
+ *  auto distances = raft::make_device_matrix<float, int64_t>(handle, n_row, k);
+ *   all_neighbors::build(res, params, dataset, indices.view(), distances.view());
+ * @endcode
+ *
+ * @param[in] handle raft::resources is an object mangaging resources
+ * @param[in] params an instance of all_neighbors::all_neighbors_params that are parameters
+ *               to build all-neighbors knn graph
+ * @param[in] dataset raft::device_matrix_view input dataset expected to be located
+ *                in device memory
+ * @param[out] indices nearest neighbor indices of shape [n_row x k]
+ * @param[out] distances nearest neighbor distances [n_row x k]
+ */
+void build(
+  const raft::resources& handle,
+  const all_neighbors_params& params,
+  raft::device_matrix_view<const float, int64_t, row_major> dataset,
+  raft::device_matrix_view<int64_t, int64_t, row_major> indices,
+  std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances = std::nullopt);
+}  // namespace cuvs::neighbors::all_neighbors
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors.cu b/cpp/src/neighbors/all_neighbors/all_neighbors.cu
new file mode 100644
index 0000000000..8ba57df398
--- /dev/null
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors.cu
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "all_neighbors.cuh"
+
+namespace cuvs::neighbors::all_neighbors {
+
+#define CUVS_INST_ALL_NEIGHBORS(T, IdxT)                                                       \
+  void build(const raft::resources& handle,                                                    \
+             const all_neighbors_params& params,                                               \
+             raft::host_matrix_view<const T, IdxT, row_major> dataset,                         \
+             raft::device_matrix_view<IdxT, IdxT, row_major> indices,                          \
+             std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances)            \
+  {                                                                                            \
+    return all_neighbors::detail::build<T, IdxT>(handle, params, dataset, indices, distances); \
+  }                                                                                            \
+                                                                                               \
+  void build(const raft::resources& handle,                                                    \
+             const all_neighbors_params& params,                                               \
+             raft::device_matrix_view<const T, IdxT, row_major> dataset,                       \
+             raft::device_matrix_view<IdxT, IdxT, row_major> indices,                          \
+             std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances)            \
+  {                                                                                            \
+    return all_neighbors::detail::build<T, IdxT>(handle, params, dataset, indices, distances); \
+  }
+
+CUVS_INST_ALL_NEIGHBORS(float, int64_t);
+
+#undef CUVS_INST_ALL_NEIGHBORS
+
+}  // namespace cuvs::neighbors::all_neighbors
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
new file mode 100644
index 0000000000..1caab57468
--- /dev/null
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "all_neighbors_batched.cuh"
+#include <cuvs/neighbors/all_neighbors.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+namespace cuvs::neighbors::all_neighbors::detail {
+using namespace cuvs::neighbors;
+
+void check_metric(const all_neighbors_params& params)
+{
+  if (std::holds_alternative<graph_build_params::nn_descent_params>(params.graph_build_params)) {
+    auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                           params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                           params.metric == cuvs::distance::DistanceType::CosineExpanded ||
+                           params.metric == cuvs::distance::DistanceType::InnerProduct;
+    RAFT_EXPECTS(allowed_metrics,
+                 "Distance metric for all-neighbors build with NN Descent should be L2Expanded, "
+                 "L2SqrtExpanded, CosineExpanded, or InnerProduct");
+  } else if (std::holds_alternative<graph_build_params::ivf_pq_params>(params.graph_build_params)) {
+    RAFT_EXPECTS(params.metric == cuvs::distance::DistanceType::L2Expanded,
+                 "Distance metric for all-neighbors build with IVFPQ should be L2Expanded");
+  } else {
+    RAFT_FAIL("Invalid all-neighbors build algo");
+  }
+}
+
+// Single build (i.e. no batching) supports both host and device datasets
+template <typename T, typename IdxT, typename Accessor>
+void single_build(
+  const raft::resources& handle,
+  const all_neighbors_params& params,
+  mdspan<const T, matrix_extent<IdxT>, row_major, Accessor> dataset,
+  raft::device_matrix_view<IdxT, IdxT, row_major> indices,
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+{
+  size_t num_rows = static_cast<size_t>(dataset.extent(0));
+  size_t num_cols = static_cast<size_t>(dataset.extent(1));
+
+  auto knn_builder = get_knn_builder<T, IdxT>(
+    handle, params, num_rows, num_rows, indices.extent(1), indices, distances);
+
+  knn_builder->prepare_build(dataset);
+  knn_builder->build_knn(dataset);
+}
+
+template <typename T, typename IdxT>
+void build(const raft::resources& handle,
+           const all_neighbors_params& params,
+           raft::host_matrix_view<const T, IdxT, row_major> dataset,
+           raft::device_matrix_view<IdxT, IdxT, row_major> indices,
+           std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+{
+  check_metric(params);
+
+  RAFT_EXPECTS(dataset.extent(0) == indices.extent(0),
+               "number of rows in dataset should be the same as number of rows in indices matrix");
+
+  if (distances.has_value()) {
+    RAFT_EXPECTS(indices.extent(0) == distances.value().extent(0) &&
+                   indices.extent(1) == distances.value().extent(1),
+                 "indices matrix and distances matrix has to be the same shape.");
+  }
+
+  if (params.n_clusters == 1) {
+    single_build(handle, params, dataset, indices, distances);
+  } else {
+    batch_build(handle, params, dataset, indices, distances);
+  }
+}
+
+template <typename T, typename IdxT>
+void build(const raft::resources& handle,
+           const all_neighbors_params& params,
+           raft::device_matrix_view<const T, IdxT, row_major> dataset,
+           raft::device_matrix_view<IdxT, IdxT, row_major> indices,
+           std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+{
+  check_metric(params);
+
+  RAFT_EXPECTS(dataset.extent(0) == indices.extent(0),
+               "number of rows in dataset should be the same as number of rows in indices matrix");
+
+  if (distances.has_value()) {
+    RAFT_EXPECTS(indices.extent(0) == distances.value().extent(0) &&
+                   indices.extent(1) == distances.value().extent(1),
+                 "indices matrix and distances matrix has to be the same shape.");
+  }
+
+  if (params.n_clusters > 1) {
+    RAFT_FAIL(
+      "Batched all-neighbors build is not supported with data on device. Put data on host for "
+      "batch build.");
+  } else {
+    single_build(handle, params, dataset, indices, distances);
+  }
+}
+}  // namespace cuvs::neighbors::all_neighbors::detail
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
new file mode 100644
index 0000000000..60c3751b1c
--- /dev/null
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
@@ -0,0 +1,466 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "all_neighbors_builder.cuh"
+#include <cuvs/cluster/kmeans.hpp>
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/refine.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/managed_mdarray.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/multi_gpu.hpp>
+#include <raft/matrix/sample_rows.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <variant>
+
+namespace cuvs::neighbors::all_neighbors::detail {
+using namespace cuvs::neighbors;
+
+/**
+ * Run balanced kmeans on a subsample of the dataset to get centroids.
+ * Arguments:
+ * - [in] res: raft resource
+ * - [in] metric
+ * - [in] dataset [num_rows x num_cols]: entire dataset on host
+ * - [out] centroids [n_clusters x num_cols] : centroid vectors
+ */
+template <typename T, typename IdxT>
+void get_centroids_on_data_subsample(raft::resources const& res,
+                                     cuvs::distance::DistanceType metric,
+                                     raft::host_matrix_view<const T, IdxT, row_major> dataset,
+                                     raft::device_matrix_view<T, IdxT> centroids)
+{
+  size_t num_rows       = static_cast<size_t>(dataset.extent(0));
+  size_t num_cols       = static_cast<size_t>(dataset.extent(1));
+  size_t n_clusters     = centroids.extent(0);
+  size_t num_subsamples = std::min(static_cast<size_t>(num_rows / n_clusters), 50000lu);
+
+  if (num_subsamples <= 1000) {
+    // heuristically running kmeans for something this small doesn't work well
+    num_subsamples = std::min(num_rows, 5000lu);
+  }
+  auto dataset_subsample_d = raft::make_device_matrix<T, IdxT>(res, num_subsamples, num_cols);
+
+  raft::matrix::sample_rows<T, IdxT>(
+    res, raft::random::RngState{0}, dataset, dataset_subsample_d.view());
+  cuvs::cluster::kmeans::balanced_params kmeans_params;
+  kmeans_params.metric = metric;
+
+  cuvs::cluster::kmeans::fit(
+    res, kmeans_params, raft::make_const_mdspan(dataset_subsample_d.view()), centroids);
+}
+
+template <typename T, typename IdxT>
+void single_gpu_assign_clusters(
+  raft::resources const& res,
+  size_t n_nearest_clusters,
+  size_t n_clusters,
+  size_t n_rows_per_batch,
+  size_t base_row_offset,
+  raft::host_matrix_view<const T, IdxT, row_major> dataset,
+  raft::device_matrix_view<T, IdxT, raft::row_major> centroids,
+  cuvs::distance::DistanceType metric,
+  raft::host_matrix_view<IdxT, IdxT, raft::row_major> global_nearest_cluster)
+{
+  size_t num_rows = static_cast<size_t>(dataset.extent(0));
+  size_t num_cols = static_cast<size_t>(dataset.extent(1));
+
+  // number of batches for this GPU to process
+  size_t num_batches = n_clusters;
+
+  auto dataset_batch_d =
+    raft::make_device_matrix<T, IdxT, raft::row_major>(res, n_rows_per_batch, num_cols);
+
+  auto nearest_clusters_idx_d = raft::make_device_matrix<IdxT, int64_t, raft::row_major>(
+    res, n_rows_per_batch, n_nearest_clusters);
+  auto nearest_clusters_dist_d = raft::make_device_matrix<T, int64_t, raft::row_major>(
+    res, n_rows_per_batch, n_nearest_clusters);
+
+  std::optional<raft::device_vector_view<const T, int64_t>> norms_view;
+  cuvs::neighbors::brute_force::index<T> brute_force_index(res, centroids, norms_view, metric);
+
+  for (size_t i = 0; i < num_batches; i++) {
+    size_t row_offset              = n_rows_per_batch * i + base_row_offset;
+    size_t n_rows_of_current_batch = std::min(n_rows_per_batch, num_rows - row_offset);
+    raft::copy(dataset_batch_d.data_handle(),
+               dataset.data_handle() + row_offset * num_cols,
+               n_rows_of_current_batch * num_cols,
+               resource::get_cuda_stream(res));
+
+    // n_clusters is usually not large, so okay to do this brute-force
+    cuvs::neighbors::brute_force::search(res,
+                                         brute_force_index,
+                                         raft::make_const_mdspan(dataset_batch_d.view()),
+                                         nearest_clusters_idx_d.view(),
+                                         nearest_clusters_dist_d.view());
+    raft::copy(global_nearest_cluster.data_handle() + row_offset * n_nearest_clusters,
+               nearest_clusters_idx_d.data_handle(),
+               n_rows_of_current_batch * n_nearest_clusters,
+               resource::get_cuda_stream(res));
+  }
+}
+
+/**
+ * Assign each data point to top n_nearest_clusters number of clusters. Loads the data in batches
+ * onto device for efficiency. Arguments:
+ * - [in] res: raft resource
+ * - [in] params: params for graph building
+ * - [in] dataset [num_rows x num_cols]: entire dataset located on host memory
+ * - [in] centroids [n_clusters x num_cols] : centroid vectors
+ * - [out] global_nearest_cluster [num_rows X n_nearest_clusters] : top n_nearest_clusters closest
+ * clusters for each data point
+ */
+template <typename T, typename IdxT>
+void assign_clusters(raft::resources const& res,
+                     const all_neighbors_params& params,
+                     raft::host_matrix_view<const T, IdxT, row_major> dataset,
+                     raft::device_matrix_view<T, IdxT, raft::row_major> centroids,
+                     raft::host_matrix_view<IdxT, IdxT, raft::row_major> global_nearest_cluster)
+{
+  size_t num_rows = static_cast<size_t>(dataset.extent(0));
+  size_t num_cols = static_cast<size_t>(dataset.extent(1));
+
+  auto centroids_h = raft::make_host_matrix<T, IdxT>(params.n_clusters, num_cols);
+  raft::copy(centroids_h.data_handle(),
+             centroids.data_handle(),
+             params.n_clusters * num_cols,
+             raft::resource::get_cuda_stream(res));
+
+  size_t n_rows_per_cluster = (num_rows + params.n_clusters - 1) / params.n_clusters;
+
+  if (raft::resource::is_multi_gpu(res)) {
+    int num_ranks = raft::resource::get_num_ranks(res);
+    // multi-gpu assign clusters
+    size_t clusters_per_rank = params.n_clusters / num_ranks;
+    size_t rem               = params.n_clusters - clusters_per_rank * num_ranks;
+
+#pragma omp parallel for num_threads(num_ranks)
+    for (int rank = 0; rank < num_ranks; rank++) {
+      auto dev_res = raft::resource::set_current_device_to_rank(res, rank);
+
+      auto centroids_matrix = raft::make_device_matrix<T, IdxT>(res, params.n_clusters, num_cols);
+      raft::copy(centroids_matrix.data_handle(),
+                 centroids_h.data_handle(),
+                 params.n_clusters * num_cols,
+                 raft::resource::get_cuda_stream(dev_res));
+
+      size_t base_cluster_idx = rank * clusters_per_rank + std::min((size_t)rank, rem);
+
+      size_t n_clusters_for_this_rank =
+        (size_t)rank < rem ? clusters_per_rank + 1 : clusters_per_rank;
+      size_t base_row_offset_for_this_rank = n_rows_per_cluster * base_cluster_idx;
+
+      single_gpu_assign_clusters(dev_res,
+                                 params.n_nearest_clusters,
+                                 n_clusters_for_this_rank,
+                                 n_rows_per_cluster,
+                                 base_row_offset_for_this_rank,
+                                 dataset,
+                                 centroids_matrix.view(),
+                                 params.metric,
+                                 global_nearest_cluster);
+    }
+  } else {
+    single_gpu_assign_clusters(res,
+                               params.n_nearest_clusters,
+                               params.n_clusters,
+                               n_rows_per_cluster,
+                               0,
+                               dataset,
+                               centroids,
+                               params.metric,
+                               global_nearest_cluster);
+  }
+}
+
+/**
+ * Getting data indices that belong to cluster
+ * Arguments:
+ * - [in] res: raft resource
+ * - [in] global_nearest_cluster [num_rows X n_nearest_clusters] : top n_nearest_clusters closest
+ * clusters for each data point
+ * - [out] inverted_indices [num_rows x n_nearest_clusters sized vector] : vector for data indices
+ * for each cluster
+ * - [out] cluster_sizes [n_cluster] : cluster size for each cluster
+ * - [out] cluster_offsets [n_cluster] : offset in inverted_indices for each cluster
+ */
+template <typename IdxT = int64_t>
+void get_inverted_indices(raft::resources const& res,
+                          raft::host_matrix_view<IdxT, IdxT> global_nearest_cluster,
+                          raft::host_vector_view<IdxT, IdxT> inverted_indices,
+                          raft::host_vector_view<IdxT, IdxT> cluster_sizes,
+                          raft::host_vector_view<IdxT, IdxT> cluster_offsets)
+{
+  // build sparse inverted indices and get number of data points for each cluster
+  size_t num_rows           = global_nearest_cluster.extent(0);
+  size_t n_nearest_clusters = global_nearest_cluster.extent(1);
+  size_t n_clusters         = cluster_sizes.extent(0);
+
+  auto local_offsets = raft::make_host_vector<IdxT>(n_clusters);
+
+  std::fill(cluster_sizes.data_handle(), cluster_sizes.data_handle() + n_clusters, 0);
+  std::fill(local_offsets.data_handle(), local_offsets.data_handle() + n_clusters, 0);
+
+  for (size_t i = 0; i < num_rows; i++) {
+    for (size_t j = 0; j < n_nearest_clusters; j++) {
+      IdxT cluster_id = global_nearest_cluster(i, j);
+      cluster_sizes(cluster_id) += 1;
+    }
+  }
+
+  cluster_offsets(0) = 0;
+  for (size_t i = 1; i < n_clusters; i++) {
+    cluster_offsets(i) = cluster_offsets(i - 1) + cluster_sizes(i - 1);
+  }
+  for (size_t i = 0; i < num_rows; i++) {
+    for (size_t j = 0; j < n_nearest_clusters; j++) {
+      IdxT cluster_id = global_nearest_cluster(i, j);
+      inverted_indices(cluster_offsets(cluster_id) + local_offsets(cluster_id)) = i;
+      local_offsets(cluster_id) += 1;
+    }
+  }
+}
+
+template <typename IdxT = int64_t>
+void get_min_max_cluster_size(size_t k,
+                              size_t& max_cluster_size,
+                              size_t& min_cluster_size,
+                              raft::host_vector_view<IdxT, IdxT> cluster_sizes)
+{
+  max_cluster_size = 0;
+  min_cluster_size = std::numeric_limits<size_t>::max();
+
+  size_t n_clusters = cluster_sizes.extent(0);
+  max_cluster_size  = static_cast<size_t>(
+    *std::max_element(cluster_sizes.data_handle(), cluster_sizes.data_handle() + n_clusters));
+  min_cluster_size = static_cast<size_t>(*std::min_element(
+    cluster_sizes.data_handle(), cluster_sizes.data_handle() + n_clusters, [k](size_t a, size_t b) {
+      return (a > k && (b <= k || a < b));  // Compare only elements larger than k
+    }));
+}
+
+template <typename T, typename IdxT>
+void single_gpu_batch_build(const raft::resources& handle,
+                            raft::host_matrix_view<const T, IdxT, row_major> dataset,
+                            detail::all_neighbors_builder<T, IdxT>& knn_builder,
+                            size_t n_clusters,
+                            raft::managed_matrix_view<IdxT, IdxT> global_neighbors,
+                            raft::managed_matrix_view<T, IdxT> global_distances,
+                            raft::host_vector_view<IdxT, IdxT, row_major> cluster_sizes,
+                            raft::host_vector_view<IdxT, IdxT, row_major> cluster_offsets,
+                            raft::host_vector_view<IdxT, IdxT, row_major> inverted_indices)
+{
+  size_t num_rows = dataset.extent(0);
+  size_t num_cols = dataset.extent(1);
+
+  auto cluster_data =
+    raft::make_host_matrix<T, IdxT, row_major>(knn_builder.max_cluster_size, num_cols);
+
+  knn_builder.prepare_build(dataset);
+
+  for (size_t cluster_id = 0; cluster_id < n_clusters; cluster_id++) {
+    size_t num_data_in_cluster = cluster_sizes(cluster_id);
+    size_t offset              = cluster_offsets(cluster_id);
+    if (num_data_in_cluster < knn_builder.k) {
+      // for the unlikely event where clustering was done lopsidedly and this cluster has less than
+      // k data
+      continue;
+    }
+
+    // gathering dataset from host to host memory
+    // cluster_data is on host for now because NN Descent allocates a new device matrix for data of
+    // half fp regardless of whether data is on host or device
+#pragma omp parallel for
+    for (size_t i = 0; i < num_data_in_cluster; i++) {
+      for (size_t j = 0; j < num_cols; j++) {
+        size_t global_row  = inverted_indices(offset + i);
+        cluster_data(i, j) = dataset(global_row, j);
+      }
+    }
+
+    auto cluster_data_view = raft::make_host_matrix_view<const T, IdxT>(
+      cluster_data.data_handle(), num_data_in_cluster, num_cols);
+    auto inverted_indices_view = raft::make_host_vector_view<IdxT, IdxT>(
+      inverted_indices.data_handle() + offset, num_data_in_cluster);
+
+    knn_builder.build_knn(
+      cluster_data_view, inverted_indices_view, global_neighbors, global_distances);
+  }
+}
+
+template <typename T, typename IdxT>
+void multi_gpu_batch_build(const raft::resources& handle,
+                           const all_neighbors_params& params,
+                           raft::host_matrix_view<const T, IdxT, row_major> dataset,
+                           raft::managed_matrix_view<IdxT, IdxT> global_neighbors,
+                           raft::managed_matrix_view<T, IdxT> global_distances,
+                           raft::host_vector_view<IdxT, IdxT, row_major> cluster_sizes,
+                           raft::host_vector_view<IdxT, IdxT, row_major> cluster_offsets,
+                           raft::host_vector_view<IdxT, IdxT, row_major> inverted_indices)
+{
+  size_t num_rows = dataset.extent(0);
+  size_t num_cols = dataset.extent(1);
+  size_t k        = global_neighbors.extent(1);
+
+  int num_ranks = raft::resource::get_num_ranks(handle);
+
+  size_t clusters_per_rank = params.n_clusters / num_ranks;
+  size_t rem               = params.n_clusters - clusters_per_rank * num_ranks;
+
+#pragma omp parallel for num_threads(num_ranks)
+  for (int rank = 0; rank < num_ranks; rank++) {
+    auto dev_res = raft::resource::set_current_device_to_rank(handle, rank);
+
+    // This part is to distribute clusters across ranks as equally as possible
+    // E.g. if we had 5 clusters and 3 ranks, instead of splitting into 1, 1, 3
+    // we split into 2, 2, 1
+    size_t num_data_for_this_rank = 0;
+    size_t base_cluster_idx       = rank * clusters_per_rank + std::min((size_t)rank, rem);
+    size_t num_clusters_for_this_rank =
+      (size_t)rank < rem ? clusters_per_rank + 1 : clusters_per_rank;
+    for (size_t p = 0; p < num_clusters_for_this_rank; p++) {
+      num_data_for_this_rank += cluster_sizes(base_cluster_idx + p);
+    }
+
+    size_t rank_offset = cluster_offsets(base_cluster_idx);
+
+    // remap offsets for each rank
+    for (size_t p = 0; p < num_clusters_for_this_rank; p++) {
+      cluster_offsets(base_cluster_idx + p) -= rank_offset;
+    }
+
+    auto cluster_sizes_for_this_rank = raft::make_host_vector_view<IdxT, IdxT>(
+      cluster_sizes.data_handle() + base_cluster_idx, num_clusters_for_this_rank);
+    auto cluster_offsets_for_this_rank = raft::make_host_vector_view<IdxT, IdxT>(
+      cluster_offsets.data_handle() + base_cluster_idx, num_clusters_for_this_rank);
+    auto inverted_indices_for_this_rank = raft::make_host_vector_view<IdxT, IdxT>(
+      inverted_indices.data_handle() + rank_offset, num_data_for_this_rank);
+
+    size_t max_cluster_size, min_cluster_size;
+    get_min_max_cluster_size(k, max_cluster_size, min_cluster_size, cluster_sizes_for_this_rank);
+
+    std::unique_ptr<all_neighbors_builder<T, IdxT>> knn_builder =
+      get_knn_builder<T, IdxT>(dev_res, params, min_cluster_size, max_cluster_size, k);
+
+    single_gpu_batch_build(dev_res,
+                           dataset,
+                           *knn_builder,
+                           num_clusters_for_this_rank,
+                           global_neighbors,
+                           global_distances,
+                           cluster_sizes_for_this_rank,
+                           cluster_offsets_for_this_rank,
+                           inverted_indices_for_this_rank);
+  }
+}
+
+template <typename T, typename IdxT>
+void batch_build(
+  const raft::resources& handle,
+  const all_neighbors_params& params,
+  raft::host_matrix_view<const T, IdxT, row_major> dataset,
+  raft::device_matrix_view<IdxT, IdxT, row_major> indices,
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+{
+  if (raft::resource::is_multi_gpu(handle)) {
+    // For efficient CPU-computation of omp parallel for regions per GPU
+    omp_set_nested(1);
+  }
+
+  size_t num_rows = static_cast<size_t>(dataset.extent(0));
+  size_t num_cols = static_cast<size_t>(dataset.extent(1));
+  size_t k        = indices.extent(1);
+
+  RAFT_EXPECTS(params.n_clusters > params.n_nearest_clusters,
+               "n_nearest_clusters should be smaller than n_clusters. We recommend starting from "
+               "n_nearest_clusters=2 and gradually increase it for better knn graph recall.");
+
+  auto centroids = raft::make_device_matrix<T, IdxT>(handle, params.n_clusters, num_cols);
+  get_centroids_on_data_subsample<T, IdxT>(handle, params.metric, dataset, centroids.view());
+
+  auto global_nearest_cluster =
+    raft::make_host_matrix<IdxT, IdxT>(num_rows, params.n_nearest_clusters);
+  assign_clusters<T, IdxT>(
+    handle, params, dataset, centroids.view(), global_nearest_cluster.view());
+
+  auto inverted_indices =
+    raft::make_host_vector<IdxT, IdxT, raft::row_major>(num_rows * params.n_nearest_clusters);
+  auto cluster_sizes   = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
+  auto cluster_offsets = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
+  get_inverted_indices(handle,
+                       global_nearest_cluster.view(),
+                       inverted_indices.view(),
+                       cluster_sizes.view(),
+                       cluster_offsets.view());
+
+  auto global_neighbors = raft::make_managed_matrix<IdxT, IdxT>(handle, num_rows, k);
+  auto global_distances = raft::make_managed_matrix<T, IdxT>(handle, num_rows, k);
+
+  bool select_min = cuvs::distance::is_min_close(params.metric);
+  IdxT global_neighbors_fill_value =
+    select_min ? std::numeric_limits<IdxT>::max() : std::numeric_limits<IdxT>::min();
+  T global_distances_fill_value =
+    select_min ? std::numeric_limits<T>::max() : std::numeric_limits<T>::min();
+  std::fill(global_neighbors.data_handle(),
+            global_neighbors.data_handle() + num_rows * k,
+            global_neighbors_fill_value);
+  std::fill(global_distances.data_handle(),
+            global_distances.data_handle() + num_rows * k,
+            global_distances_fill_value);
+
+  if (raft::resource::is_multi_gpu(handle)) {
+    multi_gpu_batch_build(handle,
+                          params,
+                          dataset,
+                          global_neighbors.view(),
+                          global_distances.view(),
+                          cluster_sizes.view(),
+                          cluster_offsets.view(),
+                          inverted_indices.view());
+  } else {
+    size_t max_cluster_size, min_cluster_size;
+    get_min_max_cluster_size(k, max_cluster_size, min_cluster_size, cluster_sizes.view());
+    std::unique_ptr<all_neighbors_builder<T, IdxT>> knn_builder =
+      get_knn_builder<T, IdxT>(handle, params, min_cluster_size, max_cluster_size, k);
+    single_gpu_batch_build(handle,
+                           dataset,
+                           *knn_builder,
+                           params.n_clusters,
+                           global_neighbors.view(),
+                           global_distances.view(),
+                           cluster_sizes.view(),
+                           cluster_offsets.view(),
+                           inverted_indices.view());
+  }
+
+  raft::copy(indices.data_handle(),
+             global_neighbors.data_handle(),
+             num_rows * k,
+             raft::resource::get_cuda_stream(handle));
+  if (distances.has_value()) {
+    raft::copy(distances.value().data_handle(),
+               global_distances.data_handle(),
+               num_rows * k,
+               raft::resource::get_cuda_stream(handle));
+  }
+}
+
+}  // namespace cuvs::neighbors::all_neighbors::detail
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
new file mode 100644
index 0000000000..61b414feca
--- /dev/null
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
@@ -0,0 +1,488 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../detail/nn_descent_gnnd.hpp"
+#include "all_neighbors_merge.cuh"
+#include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+#include <cuvs/neighbors/refine.hpp>
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/managed_mdspan.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+namespace cuvs::neighbors::all_neighbors::detail {
+using namespace cuvs::neighbors;
+
+template <typename T, typename IdxT = int64_t>
+struct all_neighbors_builder {
+  all_neighbors_builder(
+    raft::resources const& res,
+    size_t n_clusters,
+    size_t min_cluster_size,
+    size_t max_cluster_size,
+    size_t k,
+    std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
+    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+    : res{res},
+      n_clusters{n_clusters},
+      min_cluster_size{min_cluster_size},
+      max_cluster_size{max_cluster_size},
+      k{k},
+      indices_{indices},
+      distances_{distances}
+  {
+    RAFT_EXPECTS(this->n_clusters > 1 || indices.has_value(),
+                 "indices should be preallocated to create knn builder for n_clusters == 1 (no "
+                 "batching mode)");
+    if (n_clusters > 1) {  // allocating additional space needed for batching
+      inverted_indices_d.emplace(raft::make_device_vector<IdxT, IdxT>(res, max_cluster_size));
+      batch_neighbors_h.emplace(raft::make_host_matrix<IdxT, IdxT>(max_cluster_size, k));
+      batch_neighbors_d.emplace(raft::make_device_matrix<IdxT, IdxT>(res, max_cluster_size, k));
+      batch_distances_d.emplace(raft::make_device_matrix<T, IdxT>(res, max_cluster_size, k));
+    }
+  }
+
+  /**
+   * Some memory-heavy allocations that can be used over multiple clusters should be allocated here
+   * Arguments:
+   * - [in] dataset: host_matrix_view or device_matrix_view of the the ENTIRE dataset
+   */
+  virtual void prepare_build(raft::host_matrix_view<const T, IdxT, row_major> dataset) {}
+  virtual void prepare_build(raft::device_matrix_view<const T, IdxT, row_major> dataset) {}
+
+  /**
+   * Running the ann algorithm on the given cluster, and merging it into the global result
+   * Arguments:
+   * - [in] dataset: host_matrix_view or device_matrix_view of the cluster dataset
+   * - [in] inverted_indices (optional): global data indices for the data points in the current
+   * cluster of size [num_data_in_cluster]. Only needed when using the batching algorithm.
+   * - [out] global_neighbors (optional): raft::managed_matrix_view type of [total_num_rows, k] for
+   * final all-neighbors graph indices. Only needed when using the batching algorithm.
+   * - [out] global_distances (optional): raft::managed_matrix_view type of [total_num_rows, k] for
+   * final all-neighbors graph distances. Only needed when using the batching algorithm.
+   */
+  virtual void build_knn(
+    raft::host_matrix_view<const T, IdxT, row_major> dataset,
+    std::optional<raft::host_vector_view<IdxT, IdxT>> inverted_indices    = std::nullopt,
+    std::optional<raft::managed_matrix_view<IdxT, IdxT>> global_neighbors = std::nullopt,
+    std::optional<raft::managed_matrix_view<T, IdxT>> global_distances    = std::nullopt)
+  {
+  }
+
+  virtual void build_knn(raft::device_matrix_view<const T, IdxT, row_major> dataset) {}
+
+  virtual ~all_neighbors_builder() = default;
+
+  raft::resources const& res;
+  size_t n_clusters, min_cluster_size, max_cluster_size, k;
+
+  // these are optional types since we do not know the size at time of all_neighbors_builder
+  // construction
+  std::optional<raft::device_vector<IdxT, IdxT>> inverted_indices_d;
+  std::optional<raft::host_matrix<IdxT, IdxT>> batch_neighbors_h;
+  std::optional<raft::device_matrix<IdxT, IdxT>> batch_neighbors_d;
+  std::optional<raft::device_matrix<T, IdxT>> batch_distances_d;
+
+  // optional indices and distances, only used when n_clusters=1
+  // when n_clusters > 1 (i.e. doing batching), we write to the global_neighbors and
+  // global_distances managed memory arrays.
+  std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices_;
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances_;
+};
+
+template <typename T, typename IdxT = int64_t>
+struct all_neighbors_builder_ivfpq : public all_neighbors_builder<T, IdxT> {
+  all_neighbors_builder_ivfpq(
+    raft::resources const& res,
+    size_t n_clusters,
+    size_t min_cluster_size,
+    size_t max_cluster_size,
+    size_t k,
+    all_neighbors::graph_build_params::ivf_pq_params& params,
+    std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
+    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+    : all_neighbors_builder<T, IdxT>(
+        res, n_clusters, min_cluster_size, max_cluster_size, k, indices, distances),
+      all_ivf_pq_params{params}
+  {
+  }
+
+  void prepare_build_common(size_t num_cols)
+  {
+    candidate_k = std::min<IdxT>(
+      std::max(static_cast<size_t>(this->k * all_ivf_pq_params.refinement_rate), this->k),
+      this->min_cluster_size);
+
+    candidate_distances_d.emplace(
+      raft::make_device_matrix<T, IdxT, row_major>(this->res, this->max_cluster_size, candidate_k));
+    candidate_neighbors_d.emplace(raft::make_device_matrix<IdxT, IdxT, row_major>(
+      this->res, this->max_cluster_size, candidate_k));
+    candidate_neighbors_h.emplace(
+      raft::make_host_matrix<IdxT, IdxT, row_major>(this->max_cluster_size, candidate_k));
+
+    refined_neighbors_h.emplace(
+      raft::make_host_matrix<IdxT, IdxT, row_major>(this->max_cluster_size, this->k));
+    refined_distances_h.emplace(
+      raft::make_host_matrix<T, IdxT, row_major>(this->max_cluster_size, this->k));
+  }
+
+  void prepare_build(raft::host_matrix_view<const T, IdxT, row_major> dataset) override
+  {
+    // if dataset is on host, then allocate space for device data because ivfpq requires data to be
+    // on device
+    size_t num_cols = dataset.extent(1);
+    data_d.emplace(
+      raft::make_device_matrix<T, IdxT, row_major>(this->res, this->max_cluster_size, num_cols));
+    prepare_build_common(num_cols);
+  }
+
+  void prepare_build(raft::device_matrix_view<const T, IdxT, row_major> dataset) override
+  {
+    prepare_build_common(dataset.extent(1));
+  }
+
+  // Actual build logic using ivfpq.
+  // need device and host views of the dataset because ivfpq build and search uses the device view,
+  // and refine uses the host view
+  void build_knn_common(
+    raft::device_matrix_view<const T, IdxT, row_major> dataset_d,
+    raft::host_matrix_view<const T, IdxT, row_major> dataset_h,
+    std::optional<raft::host_vector_view<IdxT, IdxT>> inverted_indices    = std::nullopt,
+    std::optional<raft::managed_matrix_view<IdxT, IdxT>> global_neighbors = std::nullopt,
+    std::optional<raft::managed_matrix_view<T, IdxT>> global_distances    = std::nullopt)
+  {
+    RAFT_EXPECTS(
+      this->n_clusters <= 1 || (inverted_indices.has_value() && global_neighbors.has_value() &&
+                                global_distances.has_value()),
+      "need valid inverted_indices, global_neighbors, and global_distances for "
+      "build_knn if doing batching.");
+
+    size_t num_data_in_cluster = dataset_d.extent(0);
+    size_t num_cols            = dataset_d.extent(1);
+
+    auto index_ivfpq = ivf_pq::build(this->res, all_ivf_pq_params.build_params, dataset_d);
+
+    auto candidate_distances_view = raft::make_device_matrix_view<T, IdxT>(
+      candidate_distances_d.value().data_handle(), num_data_in_cluster, candidate_k);
+    auto candidate_neighbors_view = raft::make_device_matrix_view<IdxT, IdxT>(
+      candidate_neighbors_d.value().data_handle(), num_data_in_cluster, candidate_k);
+    cuvs::neighbors::ivf_pq::search(this->res,
+                                    all_ivf_pq_params.search_params,
+                                    index_ivfpq,
+                                    dataset_d,
+                                    candidate_neighbors_view,
+                                    candidate_distances_view);
+
+    // copy candidate neighbors to host
+    raft::copy(candidate_neighbors_h.value().data_handle(),
+               candidate_neighbors_view.data_handle(),
+               num_data_in_cluster * candidate_k,
+               raft::resource::get_cuda_stream(this->res));
+    auto candidate_neighbors_h_view = raft::make_host_matrix_view<IdxT, IdxT>(
+      candidate_neighbors_h.value().data_handle(), num_data_in_cluster, candidate_k);
+    auto refined_distances_h_view = raft::make_host_matrix_view<T, IdxT>(
+      refined_distances_h.value().data_handle(), num_data_in_cluster, this->k);
+    auto refined_neighbors_h_view = raft::make_host_matrix_view<IdxT, IdxT>(
+      refined_neighbors_h.value().data_handle(), num_data_in_cluster, this->k);
+
+    refine(this->res,
+           dataset_h,
+           dataset_h,
+           raft::make_const_mdspan(candidate_neighbors_h_view),
+           refined_neighbors_h_view,
+           refined_distances_h_view,
+           all_ivf_pq_params.build_params.metric);
+
+    if (this->n_clusters > 1) {  // do batching
+      raft::copy(this->batch_distances_d.value().data_handle(),
+                 refined_distances_h_view.data_handle(),
+                 num_data_in_cluster * this->k,
+                 raft::resource::get_cuda_stream(this->res));
+
+      remap_and_merge_subgraphs<T, IdxT, IdxT>(
+        this->res,
+        this->inverted_indices_d.value().view(),
+        inverted_indices.value(),
+        refined_neighbors_h.value().view(),
+        this->batch_neighbors_h.value().view(),
+        this->batch_neighbors_d.value().view(),
+        this->batch_distances_d.value().view(),
+        global_neighbors.value(),
+        global_distances.value(),
+        num_data_in_cluster,
+        this->k,
+        cuvs::distance::is_min_close(all_ivf_pq_params.build_params.metric));
+    } else {
+      size_t num_rows = num_data_in_cluster;
+      // copy resulting indices and distances to device output
+      raft::copy(this->indices_.value().data_handle(),
+                 refined_neighbors_h_view.data_handle(),
+                 num_rows * this->k,
+                 raft::resource::get_cuda_stream(this->res));
+      if (this->distances_.has_value()) {
+        raft::copy(this->distances_.value().data_handle(),
+                   refined_distances_h_view.data_handle(),
+                   num_rows * this->k,
+                   raft::resource::get_cuda_stream(this->res));
+      }
+    }
+  }
+
+  void build_knn(
+    raft::host_matrix_view<const T, IdxT, row_major> dataset,
+    std::optional<raft::host_vector_view<IdxT, IdxT>> inverted_indices    = std::nullopt,
+    std::optional<raft::managed_matrix_view<IdxT, IdxT>> global_neighbors = std::nullopt,
+    std::optional<raft::managed_matrix_view<T, IdxT>> global_distances    = std::nullopt) override
+  {
+    // we need data on device for ivfpq build and search.
+    raft::copy(data_d.value().data_handle(),
+               dataset.data_handle(),
+               dataset.size(),
+               raft::resource::get_cuda_stream(this->res));
+
+    build_knn_common(raft::make_device_matrix_view<const T, IdxT, row_major>(
+                       data_d.value().data_handle(), dataset.extent(0), dataset.extent(1)),
+                     dataset,
+                     inverted_indices,
+                     global_neighbors,
+                     global_distances);
+  }
+
+  void build_knn(raft::device_matrix_view<const T, IdxT, row_major> dataset) override
+  {
+    RAFT_EXPECTS(this->n_clusters <= 1,
+                 "building all-neighbors knn graph with dataset on device is not supported with "
+                 "batching (n_clusters > 1)");
+
+    // we allocate host memory here and not in the prepare_build function because this function is
+    // not called for batching
+    auto dataset_h = raft::make_host_matrix<T, IdxT>(dataset.extent(0), dataset.extent(1));
+
+    // we need data on host for refining
+    raft::copy(dataset_h.data_handle(),
+               dataset.data_handle(),
+               dataset.size(),
+               raft::resource::get_cuda_stream(this->res));
+
+    build_knn_common(dataset,
+                     raft::make_host_matrix_view<const T, IdxT, row_major>(
+                       dataset_h.data_handle(), dataset.extent(0), dataset.extent(1)));
+  }
+
+  all_neighbors::graph_build_params::ivf_pq_params all_ivf_pq_params;
+  size_t candidate_k;
+
+  std::optional<raft::device_matrix<T, IdxT>> data_d;
+
+  std::optional<raft::device_matrix<T, IdxT>> candidate_distances_d;
+  std::optional<raft::device_matrix<IdxT, IdxT>> candidate_neighbors_d;
+  std::optional<raft::host_matrix<IdxT, IdxT>> candidate_neighbors_h;
+
+  std::optional<raft::host_matrix<IdxT, IdxT>> refined_neighbors_h;
+  std::optional<raft::host_matrix<T, IdxT>> refined_distances_h;
+};
+
+template <typename T, typename IdxT = int64_t>
+struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT> {
+  all_neighbors_builder_nn_descent(
+    raft::resources const& res,
+    size_t n_clusters,
+    size_t min_cluster_size,
+    size_t max_cluster_size,
+    size_t k,
+    all_neighbors::graph_build_params::nn_descent_params& params,
+    std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
+    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+    : all_neighbors_builder<T, IdxT>(
+        res, n_clusters, min_cluster_size, max_cluster_size, k, indices, distances),
+      nnd_params{params}
+  {
+  }
+
+  template <typename Accessor>
+  void prepare_build_common(mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset)
+  {
+    if (nnd_params.graph_degree < this->k) {
+      RAFT_LOG_WARN(
+        "NN Descent's graph degree (%lu) has to be larger than or equal to k. Setting graph_degree "
+        "to k (%lu).",
+        nnd_params.graph_degree,
+        this->k);
+      nnd_params.graph_degree = this->k;
+    }
+
+    size_t extended_graph_degree, graph_degree;
+
+    auto build_config                = nn_descent::detail::get_build_config(this->res,
+                                                             nnd_params,
+                                                             this->max_cluster_size,
+                                                             static_cast<size_t>(dataset.extent(1)),
+                                                             nnd_params.metric,
+                                                             extended_graph_degree,
+                                                             graph_degree);
+    build_config.output_graph_degree = this->k;
+    nnd_builder.emplace(this->res, build_config);
+    int_graph.emplace(raft::make_host_matrix<int, IdxT, row_major>(
+      this->max_cluster_size, static_cast<IdxT>(extended_graph_degree)));
+  }
+
+  void prepare_build(raft::device_matrix_view<const T, IdxT, row_major> dataset) override
+  {
+    prepare_build_common(dataset);
+  }
+
+  void prepare_build(raft::host_matrix_view<const T, IdxT, row_major> dataset) override
+  {
+    prepare_build_common(dataset);
+  }
+
+  template <typename Accessor>
+  void build_knn_common(
+    mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset,
+    std::optional<raft::host_vector_view<IdxT, IdxT>> inverted_indices    = std::nullopt,
+    std::optional<raft::managed_matrix_view<IdxT, IdxT>> global_neighbors = std::nullopt,
+    std::optional<raft::managed_matrix_view<T, IdxT>> global_distances    = std::nullopt)
+  {
+    RAFT_EXPECTS(
+      this->n_clusters <= 1 || (inverted_indices.has_value() && global_neighbors.has_value() &&
+                                global_distances.has_value()),
+      "need valid inverted_indices, global_neighbors, and global_distances for "
+      "build_knn if doing batching.");
+
+    if (this->n_clusters > 1) {
+      bool return_distances      = true;
+      size_t num_data_in_cluster = dataset.extent(0);
+      nnd_builder.value().build(dataset.data_handle(),
+                                static_cast<int>(num_data_in_cluster),
+                                int_graph.value().data_handle(),
+                                return_distances,
+                                this->batch_distances_d.value().data_handle());
+
+      remap_and_merge_subgraphs<T, IdxT, int>(this->res,
+                                              this->inverted_indices_d.value().view(),
+                                              inverted_indices.value(),
+                                              int_graph.value().view(),
+                                              this->batch_neighbors_h.value().view(),
+                                              this->batch_neighbors_d.value().view(),
+                                              this->batch_distances_d.value().view(),
+                                              global_neighbors.value(),
+                                              global_distances.value(),
+                                              num_data_in_cluster,
+                                              this->k,
+                                              cuvs::distance::is_min_close(nnd_params.metric));
+    } else {
+      size_t num_rows = dataset.extent(0);
+
+      nnd_builder.value().build(
+        dataset.data_handle(),
+        static_cast<int>(num_rows),
+        int_graph.value().data_handle(),
+        this->distances_.has_value(),
+        this->distances_.value_or(raft::make_device_matrix<T, IdxT>(this->res, 0, 0).view())
+          .data_handle());
+
+      auto tmp_indices = raft::make_host_matrix<IdxT, IdxT>(int_graph.value().extent(0), this->k);
+
+      // host slice
+#pragma omp parallel for
+      for (size_t i = 0; i < num_rows; i++) {
+        for (size_t j = 0; j < this->k; j++) {
+          tmp_indices(i, j) = static_cast<IdxT>(int_graph.value()(i, j));
+        }
+      }
+
+      // copy to final device output
+      raft::copy(this->indices_.value().data_handle(),
+                 tmp_indices.data_handle(),
+                 tmp_indices.extent(0) * this->k,
+                 raft::resource::get_cuda_stream(this->res));
+    }
+  }
+
+  void build_knn(
+    raft::host_matrix_view<const T, IdxT> dataset,
+    std::optional<raft::host_vector_view<IdxT, IdxT>> inverted_indices    = std::nullopt,
+    std::optional<raft::managed_matrix_view<IdxT, IdxT>> global_neighbors = std::nullopt,
+    std::optional<raft::managed_matrix_view<T, IdxT>> global_distances    = std::nullopt) override
+  {
+    build_knn_common(dataset, inverted_indices, global_neighbors, global_distances);
+  }
+
+  void build_knn(raft::device_matrix_view<const T, IdxT> dataset) override
+  {
+    RAFT_EXPECTS(this->n_clusters <= 1,
+                 "building all-neighbors knn graph with dataset on device is not supported with "
+                 "batching (n_clusters > 1)");
+    build_knn_common(dataset);
+  }
+
+  nn_descent::index_params nnd_params;
+  nn_descent::detail::BuildConfig build_config;
+
+  std::optional<nn_descent::detail::GNND<const T, int>> nnd_builder;
+  std::optional<raft::host_matrix<int, IdxT>> int_graph;
+};
+
+template <typename T, typename IdxT>
+std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
+  const raft::resources& handle,
+  const all_neighbors_params& params,
+  size_t min_cluster_size,
+  size_t max_cluster_size,
+  size_t k,
+  std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+{
+  if (std::holds_alternative<graph_build_params::nn_descent_params>(params.graph_build_params)) {
+    auto nn_descent_params =
+      std::get<graph_build_params::nn_descent_params>(params.graph_build_params);
+    if (nn_descent_params.metric != params.metric) {
+      RAFT_LOG_WARN("Setting nnd_params metric to metric given for batching algorithm");
+      nn_descent_params.metric = params.metric;
+    }
+    return std::make_unique<all_neighbors_builder_nn_descent<T, IdxT>>(handle,
+                                                                       params.n_clusters,
+                                                                       min_cluster_size,
+                                                                       max_cluster_size,
+                                                                       k,
+                                                                       nn_descent_params,
+                                                                       indices,
+                                                                       distances);
+  } else if (std::holds_alternative<graph_build_params::ivf_pq_params>(params.graph_build_params)) {
+    auto ivf_pq_params = std::get<graph_build_params::ivf_pq_params>(params.graph_build_params);
+    if (ivf_pq_params.build_params.metric != params.metric) {
+      RAFT_LOG_WARN("Setting ivfpq_params metric to metric given for batching algorithm");
+      ivf_pq_params.build_params.metric = params.metric;
+    }
+    return std::make_unique<all_neighbors_builder_ivfpq<T, IdxT>>(handle,
+                                                                  params.n_clusters,
+                                                                  min_cluster_size,
+                                                                  max_cluster_size,
+                                                                  k,
+                                                                  ivf_pq_params,
+                                                                  indices,
+                                                                  distances);
+  } else {
+    RAFT_FAIL("Batch KNN build algos only supporting NN Descent and IVFPQ");
+  }
+}
+
+}  // namespace cuvs::neighbors::all_neighbors::detail
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh
new file mode 100644
index 0000000000..1ce8576f5a
--- /dev/null
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh
@@ -0,0 +1,268 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <optional>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/kvp.hpp>
+#include <raft/core/managed_mdspan.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+namespace cuvs::neighbors::all_neighbors::detail {
+using namespace cuvs::neighbors;
+
+template <typename IdxT, int BLOCK_SIZE, int ITEMS_PER_THREAD>
+RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
+                                   size_t graph_degree,
+                                   size_t num_cluster_in_batch,
+                                   float* global_distances,
+                                   float* batch_distances,
+                                   IdxT* global_indices,
+                                   IdxT* batch_indices,
+                                   bool select_min)
+{
+  size_t batch_row = blockIdx.x;
+  typedef cub::BlockMergeSort<raft::KeyValuePair<float, IdxT>, BLOCK_SIZE, ITEMS_PER_THREAD>
+    BlockMergeSortType;
+  __shared__ typename cub::BlockMergeSort<raft::KeyValuePair<float, IdxT>,
+                                          BLOCK_SIZE,
+                                          ITEMS_PER_THREAD>::TempStorage tmpSmem;
+
+  extern __shared__ char sharedMem[];
+  float* blockKeys  = reinterpret_cast<float*>(sharedMem);
+  IdxT* blockValues = reinterpret_cast<IdxT*>(&sharedMem[graph_degree * 2 * sizeof(float)]);
+  int16_t* uniqueMask =
+    reinterpret_cast<int16_t*>(&sharedMem[graph_degree * 2 * (sizeof(float) + sizeof(IdxT))]);
+
+  if (batch_row < num_cluster_in_batch) {
+    // load batch or global depending on threadIdx
+    size_t global_row = cluster_data_indices[batch_row];
+
+    raft::KeyValuePair<float, IdxT> threadKeyValuePair[ITEMS_PER_THREAD];
+
+    size_t halfway   = BLOCK_SIZE / 2;
+    size_t do_global = threadIdx.x < halfway;
+
+    float* distances;
+    IdxT* indices;
+
+    if (do_global) {
+      distances = global_distances;
+      indices   = global_indices;
+    } else {
+      distances = batch_distances;
+      indices   = batch_indices;
+    }
+
+    size_t idxBase = (threadIdx.x * do_global + (threadIdx.x - halfway) * (1lu - do_global)) *
+                     static_cast<size_t>(ITEMS_PER_THREAD);
+    size_t arrIdxBase = (global_row * do_global + batch_row * (1lu - do_global)) * graph_degree;
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId < graph_degree) {
+        threadKeyValuePair[i].key   = distances[arrIdxBase + colId];
+        threadKeyValuePair[i].value = indices[arrIdxBase + colId];
+      } else {
+        if (select_min) {
+          threadKeyValuePair[i].key   = std::numeric_limits<float>::max();
+          threadKeyValuePair[i].value = std::numeric_limits<IdxT>::max();
+        } else {
+          threadKeyValuePair[i].key   = std::numeric_limits<float>::min();
+          threadKeyValuePair[i].value = std::numeric_limits<IdxT>::min();
+        }
+      }
+    }
+
+    __syncthreads();
+
+    if (select_min) {
+      BlockMergeSortType(tmpSmem).Sort(threadKeyValuePair, raft::less_op{});
+    } else {
+      BlockMergeSortType(tmpSmem).Sort(threadKeyValuePair, raft::greater_op{});
+    }
+
+    __syncthreads();
+
+    // load sorted result into shared memory to get unique values
+    idxBase = threadIdx.x * ITEMS_PER_THREAD;
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId < 2 * graph_degree) {
+        blockKeys[colId]   = threadKeyValuePair[i].key;
+        blockValues[colId] = threadKeyValuePair[i].value;
+      }
+    }
+
+    __syncthreads();
+
+    // get unique mask
+    if (threadIdx.x == 0) { uniqueMask[0] = 1; }
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId > 0 && colId < 2 * graph_degree) {
+        uniqueMask[colId] = static_cast<int16_t>(blockValues[colId] != blockValues[colId - 1]);
+      }
+    }
+
+    __syncthreads();
+
+    // prefix sum
+    if (threadIdx.x == 0) {
+      for (int i = 1; i < 2 * graph_degree; i++) {
+        uniqueMask[i] += uniqueMask[i - 1];
+      }
+    }
+
+    __syncthreads();
+    // load unique values to global memory
+    if (threadIdx.x == 0) {
+      global_distances[global_row * graph_degree] = blockKeys[0];
+      global_indices[global_row * graph_degree]   = blockValues[0];
+    }
+
+    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
+      size_t colId = idxBase + i;
+      if (colId > 0 && colId < 2 * graph_degree) {
+        bool is_unique       = uniqueMask[colId] != uniqueMask[colId - 1];
+        int16_t global_colId = uniqueMask[colId] - 1;
+        if (is_unique && static_cast<size_t>(global_colId) < graph_degree) {
+          global_distances[global_row * graph_degree + global_colId] = blockKeys[colId];
+          global_indices[global_row * graph_degree + global_colId]   = blockValues[colId];
+        }
+      }
+    }
+  }
+}
+
+template <typename T, typename IdxT = int64_t>
+void merge_subgraphs(raft::resources const& res,
+                     size_t k,
+                     size_t num_data_in_cluster,
+                     IdxT* inverted_indices_d,
+                     T* global_distances,
+                     T* batch_distances_d,
+                     IdxT* global_neighbors,
+                     IdxT* batch_neighbors_d,
+                     bool select_min)
+{
+  size_t num_elems     = k * 2;
+  size_t sharedMemSize = num_elems * (sizeof(float) + sizeof(IdxT) + sizeof(int16_t));
+
+#pragma omp critical  // for omp-using multi-gpu purposes
+  {
+    if (num_elems <= 128) {
+      merge_subgraphs_kernel<IdxT, 32, 4>
+        <<<num_data_in_cluster, 32, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+          inverted_indices_d,
+          k,
+          num_data_in_cluster,
+          global_distances,
+          batch_distances_d,
+          global_neighbors,
+          batch_neighbors_d,
+          select_min);
+    } else if (num_elems <= 512) {
+      merge_subgraphs_kernel<IdxT, 128, 4>
+        <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+          inverted_indices_d,
+          k,
+          num_data_in_cluster,
+          global_distances,
+          batch_distances_d,
+          global_neighbors,
+          batch_neighbors_d,
+          select_min);
+    } else if (num_elems <= 1024) {
+      merge_subgraphs_kernel<IdxT, 128, 8>
+        <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+          inverted_indices_d,
+          k,
+          num_data_in_cluster,
+          global_distances,
+          batch_distances_d,
+          global_neighbors,
+          batch_neighbors_d,
+          select_min);
+    } else if (num_elems <= 2048) {
+      merge_subgraphs_kernel<IdxT, 256, 8>
+        <<<num_data_in_cluster, 256, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
+          inverted_indices_d,
+          k,
+          num_data_in_cluster,
+          global_distances,
+          batch_distances_d,
+          global_neighbors,
+          batch_neighbors_d,
+          select_min);
+    } else {
+      // this is as far as we can get due to the shared mem usage of cub::BlockMergeSort
+      RAFT_FAIL("The degree of knn is too large (%lu). It must be smaller than 1024", k);
+    }
+    raft::resource::sync_stream(res);
+  }
+}
+
+template <typename T, typename IdxT = int64_t, typename BeforeRemapT = int64_t>
+void remap_and_merge_subgraphs(raft::resources const& res,
+                               raft::device_vector_view<IdxT, IdxT> inverted_indices_d,
+                               raft::host_vector_view<IdxT, IdxT> inverted_indices,
+                               raft::host_matrix_view<BeforeRemapT, IdxT> indices_for_remap_h,
+                               raft::host_matrix_view<IdxT, IdxT> batch_neighbors_h,
+                               raft::device_matrix_view<IdxT, IdxT> batch_neighbors_d,
+                               raft::device_matrix_view<T, IdxT> batch_distances_d,
+                               raft::managed_matrix_view<IdxT, IdxT> global_neighbors,
+                               raft::managed_matrix_view<T, IdxT> global_distances,
+                               size_t num_data_in_cluster,
+                               size_t k,
+                               bool select_min)
+{
+  // remap indices
+#pragma omp parallel for
+  for (size_t i = 0; i < num_data_in_cluster; i++) {
+    for (size_t j = 0; j < k; j++) {
+      size_t local_idx        = indices_for_remap_h(i, j);
+      batch_neighbors_h(i, j) = inverted_indices(local_idx);
+    }
+  }
+
+  raft::copy(inverted_indices_d.data_handle(),
+             inverted_indices.data_handle(),
+             num_data_in_cluster,
+             raft::resource::get_cuda_stream(res));
+
+  raft::copy(batch_neighbors_d.data_handle(),
+             batch_neighbors_h.data_handle(),
+             num_data_in_cluster * k,
+             raft::resource::get_cuda_stream(res));
+
+  merge_subgraphs(res,
+                  k,
+                  num_data_in_cluster,
+                  inverted_indices_d.data_handle(),
+                  global_distances.data_handle(),
+                  batch_distances_d.data_handle(),
+                  global_neighbors.data_handle(),
+                  batch_neighbors_d.data_handle(),
+                  select_min);
+}
+
+}  // namespace cuvs::neighbors::all_neighbors::detail
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 03002569d8..7cec3bae61 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -1246,8 +1246,13 @@ void build(raft::resources const& res,
            index<IdxT>& idx)
 {
   size_t extended_graph_degree, graph_degree;
-  auto build_config =
-    get_build_config(res, params, dataset, idx.metric(), extended_graph_degree, graph_degree);
+  auto build_config = get_build_config(res,
+                                       params,
+                                       static_cast<size_t>(dataset.extent(0)),
+                                       static_cast<size_t>(dataset.extent(1)),
+                                       idx.metric(),
+                                       extended_graph_degree,
+                                       graph_degree);
 
   auto int_graph =
     raft::make_host_matrix<int, int64_t, raft::row_major>(dataset.extent(0), extended_graph_degree);
diff --git a/cpp/src/neighbors/detail/nn_descent_gnnd.hpp b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
index 372024fdf9..4dc1b3e8a5 100644
--- a/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
+++ b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
@@ -253,19 +253,15 @@ class GNND {
   raft::device_vector<int2, size_t> d_list_sizes_old_;
 };
 
-template <typename T,
-          typename IdxT     = uint32_t,
-          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
-                                                         raft::memory_type::host>>
-BuildConfig get_build_config(
-  raft::resources const& res,
-  const index_params& params,
-  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
-  const cuvs::distance::DistanceType metric,
-  size_t& extended_graph_degree,
-  size_t& graph_degree)
+inline BuildConfig get_build_config(raft::resources const& res,
+                                    const index_params& params,
+                                    size_t num_rows,
+                                    size_t num_cols,
+                                    const cuvs::distance::DistanceType metric,
+                                    size_t& extended_graph_degree,
+                                    size_t& graph_degree)
 {
-  RAFT_EXPECTS(dataset.extent(0) < std::numeric_limits<int>::max() - 1,
+  RAFT_EXPECTS(num_rows < std::numeric_limits<int>::max() - 1,
                "The dataset size for GNND should be less than %d",
                std::numeric_limits<int>::max() - 1);
   auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
@@ -281,11 +277,12 @@ BuildConfig get_build_config(
   size_t intermediate_degree = params.intermediate_graph_degree;
   graph_degree               = params.graph_degree;
 
-  if (intermediate_degree >= static_cast<size_t>(dataset.extent(0))) {
+  if (intermediate_degree >= num_rows) {
     RAFT_LOG_WARN(
-      "Intermediate graph degree cannot be larger than dataset size, reducing it to %lu",
-      dataset.extent(0));
-    intermediate_degree = dataset.extent(0) - 1;
+      "Intermediate graph degree cannot be larger than number of rows in dataset, reducing it to "
+      "%lu",
+      num_rows - 1);
+    intermediate_degree = num_rows - 1;
   }
   if (intermediate_degree < graph_degree) {
     RAFT_LOG_WARN(
@@ -304,8 +301,8 @@ BuildConfig get_build_config(
   size_t extended_intermediate_degree =
     roundUp32(static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
 
-  BuildConfig build_config{.max_dataset_size      = static_cast<size_t>(dataset.extent(0)),
-                           .dataset_dim           = static_cast<size_t>(dataset.extent(1)),
+  BuildConfig build_config{.max_dataset_size      = num_rows,
+                           .dataset_dim           = num_cols,
                            .node_degree           = extended_graph_degree,
                            .internal_node_degree  = extended_intermediate_degree,
                            .max_iterations        = params.max_iterations,
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index ae14bf6e55..06b06c6c0f 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -187,6 +187,11 @@ if(BUILD_TESTS)
 
   ConfigureTest(NAME NEIGHBORS_BALL_COVER_TEST PATH neighbors/ball_cover.cu GPUS 1 PERCENT 100)
 
+  ConfigureTest(
+    NAME NEIGHBORS_ALL_NEIGHBORS_TEST PATH neighbors/all_neighbors/test_float.cu GPUS 1 PERCENT 100
+    ADDITIONAL_DEP nccl
+  )
+
   ConfigureTest(
     NAME
     NEIGHBORS_DYNAMIC_BATCHING_TEST
diff --git a/cpp/tests/neighbors/all_neighbors.cuh b/cpp/tests/neighbors/all_neighbors.cuh
new file mode 100644
index 0000000000..6d045f796c
--- /dev/null
+++ b/cpp/tests/neighbors/all_neighbors.cuh
@@ -0,0 +1,237 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+#include "naive_knn.cuh"
+#include <cstddef>
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+#include <gtest/gtest.h>
+#include <iostream>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources_snmg.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/itertools.hpp>
+#include <rapids_logger/logger.hpp>
+#include <rmm/device_uvector.hpp>
+#include <tuple>
+#include <vector>
+
+namespace cuvs::neighbors::all_neighbors {
+
+enum knn_build_algo { NN_DESCENT, IVF_PQ };
+
+struct AllNeighborsInputs {
+  std::tuple<knn_build_algo, cuvs::distance::DistanceType, double> build_algo_metric_recall;
+  std::tuple<size_t, size_t> cluster_nearestcluster;
+  int n_rows;
+  int dim;
+  int k;
+  bool data_on_host;
+};
+
+inline ::std::ostream& operator<<(::std::ostream& os, const AllNeighborsInputs& p)
+{
+  os << "dataset shape=" << p.n_rows << "x" << p.dim << ", k=" << p.k
+     << ", metric=" << static_cast<int>(std::get<1>(p.build_algo_metric_recall))
+     << ", clusters=" << std::get<0>(p.cluster_nearestcluster)
+     << ", num nearest clusters=" << std::get<1>(p.cluster_nearestcluster) << std::endl;
+  return os;
+}
+
+template <typename DistanceT, typename DataT, typename IdxT = int64_t>
+void get_graphs(raft::resources& handle,
+                rmm::device_uvector<DataT>& database,
+                std::vector<IdxT>& indices_bf,
+                std::vector<DistanceT>& distances_bf,
+                std::vector<IdxT>& indices_allNN,
+                std::vector<DistanceT>& distances_allNN,
+                AllNeighborsInputs& ps,
+                size_t queries_size)
+{
+  all_neighbors_params params;
+  params.n_clusters         = std::get<0>(ps.cluster_nearestcluster);
+  params.n_nearest_clusters = std::get<1>(ps.cluster_nearestcluster);
+  params.metric             = std::get<1>(ps.build_algo_metric_recall);
+
+  auto build_algo = std::get<0>(ps.build_algo_metric_recall);
+
+  if (build_algo == NN_DESCENT) {
+    auto nn_descent_params                      = graph_build_params::nn_descent_params{};
+    nn_descent_params.max_iterations            = 100;
+    nn_descent_params.graph_degree              = ps.k;
+    nn_descent_params.intermediate_graph_degree = ps.k * 2;
+    nn_descent_params.metric                    = params.metric;
+    params.graph_build_params                   = nn_descent_params;
+  } else if (build_algo == IVF_PQ) {
+    auto ivfq_build_params                = graph_build_params::ivf_pq_params{};
+    ivfq_build_params.build_params.metric = params.metric;
+    // heuristically good ivfpq n_lists
+    ivfq_build_params.build_params.n_lists = std::max(
+      5u,
+      static_cast<uint32_t>(ps.n_rows * params.n_nearest_clusters / (5000 * params.n_clusters)));
+    params.graph_build_params = ivfq_build_params;
+  }
+
+  auto cuda_stream = raft::resource::get_cuda_stream(handle);
+  {
+    rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, cuda_stream);
+    rmm::device_uvector<IdxT> indices_naive_dev(queries_size, cuda_stream);
+    naive_knn<DistanceT, DataT, IdxT>(handle,
+                                      distances_naive_dev.data(),
+                                      indices_naive_dev.data(),
+                                      database.data(),
+                                      database.data(),
+                                      ps.n_rows,
+                                      ps.n_rows,
+                                      ps.dim,
+                                      ps.k,
+                                      std::get<1>(ps.build_algo_metric_recall));
+    raft::update_host(indices_bf.data(), indices_naive_dev.data(), queries_size, cuda_stream);
+    raft::update_host(distances_bf.data(), distances_naive_dev.data(), queries_size, cuda_stream);
+
+    raft::resource::sync_stream(handle);
+  }
+
+  {
+    rmm::device_uvector<DistanceT> distances_allNN_dev(queries_size, cuda_stream);
+    rmm::device_uvector<IdxT> indices_allNN_dev(queries_size, cuda_stream);
+
+    if (ps.data_on_host) {
+      auto database_h = raft::make_host_matrix<DataT, IdxT>(ps.n_rows, ps.dim);
+      raft::copy(database_h.data_handle(), database.data(), ps.n_rows * ps.dim, cuda_stream);
+
+      all_neighbors::build(
+        handle,
+        params,
+        raft::make_const_mdspan(database_h.view()),
+        raft::make_device_matrix_view<IdxT>(indices_allNN_dev.data(), ps.n_rows, ps.k),
+        raft::make_device_matrix_view<DistanceT>(distances_allNN_dev.data(), ps.n_rows, ps.k));
+
+    } else {
+      all_neighbors::build(
+        handle,
+        params,
+        raft::make_device_matrix_view<const DataT, IdxT>(database.data(), ps.n_rows, ps.dim),
+        raft::make_device_matrix_view<IdxT>(indices_allNN_dev.data(), ps.n_rows, ps.k),
+        raft::make_device_matrix_view<DistanceT>(distances_allNN_dev.data(), ps.n_rows, ps.k));
+    }
+
+    raft::copy(indices_allNN.data(), indices_allNN_dev.data(), queries_size, cuda_stream);
+    raft::copy(distances_allNN.data(), distances_allNN_dev.data(), queries_size, cuda_stream);
+    raft::resource::sync_stream(handle);
+  }
+}
+
+template <typename DistanceT, typename DataT, typename IdxT = int64_t>
+class AllNeighborsTest : public ::testing::TestWithParam<AllNeighborsInputs> {
+ public:
+  AllNeighborsTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AllNeighborsInputs>::GetParam()),
+      database(0, stream_)
+  {
+  }
+
+ protected:
+  void run()
+  {
+    size_t queries_size = static_cast<size_t>(ps.n_rows) * static_cast<size_t>(ps.k);
+
+    std::vector<IdxT> indices_allNN(queries_size);
+    std::vector<DistanceT> distances_allNN(queries_size);
+    std::vector<DistanceT> distances_bf(queries_size);
+    std::vector<IdxT> indices_bf(queries_size);
+
+    get_graphs(handle_,
+               database,
+               indices_bf,
+               distances_bf,
+               indices_allNN,
+               distances_allNN,
+               ps,
+               queries_size);
+
+    double min_recall = std::get<2>(ps.build_algo_metric_recall);
+    EXPECT_TRUE(eval_recall(indices_bf, indices_allNN, ps.n_rows, ps.k, 0.01, min_recall, true));
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    auto database_view =
+      raft::make_device_matrix_view<float, IdxT>(database.data(), ps.n_rows, ps.dim);
+    auto labels = raft::make_device_vector<IdxT, IdxT>(handle_, ps.n_rows);
+    raft::random::make_blobs(handle_, database_view, labels.view());
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+  }
+
+ private:
+  raft::device_resources_snmg handle_;
+  rmm::cuda_stream_view stream_;
+  AllNeighborsInputs ps;
+  rmm::device_uvector<DataT> database;
+};
+
+const std::vector<AllNeighborsInputs> inputsSingle =
+  raft::util::itertools::product<AllNeighborsInputs>(
+    {std::make_tuple(IVF_PQ, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::CosineExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::InnerProduct, 0.8)},
+    {std::make_tuple(1lu, 2lu)},  // min_recall, n_clusters, num_nearest_cluster
+    {5000, 7151},                 // n_rows
+    {64, 137},                    // dim
+    {16, 23},                     // graph_degree
+    {false, true}                 // data on host
+  );
+
+const std::vector<AllNeighborsInputs> inputsBatch =
+  raft::util::itertools::product<AllNeighborsInputs>(
+    {std::make_tuple(IVF_PQ, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::CosineExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::InnerProduct, 0.9)},
+    {
+      std::make_tuple(4lu, 2lu),
+      std::make_tuple(7lu, 2lu),
+      std::make_tuple(10lu, 2lu),
+    },             // min_recall, n_clusters, num_nearest_cluster
+    {5000, 7151},  // n_rows
+    {64, 137},     // dim
+    {16, 23},      // graph_degree
+    {true}         // data on host
+  );
+
+}  // namespace cuvs::neighbors::all_neighbors
diff --git a/cpp/tests/neighbors/all_neighbors/test_float.cu b/cpp/tests/neighbors/all_neighbors/test_float.cu
new file mode 100644
index 0000000000..3fa41efd5c
--- /dev/null
+++ b/cpp/tests/neighbors/all_neighbors/test_float.cu
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <gtest/gtest.h>
+
+#include "../all_neighbors.cuh"
+
+namespace cuvs::neighbors::all_neighbors {
+
+typedef AllNeighborsTest<float, float> AllNeighborsTestF;
+TEST_P(AllNeighborsTestF, AllNeighbors) { this->run(); }
+
+INSTANTIATE_TEST_CASE_P(AllNeighborsSingleTest,
+                        AllNeighborsTestF,
+                        ::testing::ValuesIn(inputsSingle));
+
+INSTANTIATE_TEST_CASE_P(AllNeighborsBatchTest, AllNeighborsTestF, ::testing::ValuesIn(inputsBatch));
+}  // namespace cuvs::neighbors::all_neighbors

From 079e0305bf8e0a65c7eb0fdce24a2c97df5d90ba Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 27 May 2025 19:27:08 +0200
Subject: [PATCH 410/623] IVF-PQ tests: fix segfault when accessing empty lists
 (#933)

Check if the `index.lists()[label]` is not an empty shared pointer before accessing its content in tests.

This is a follow-up on https://github.com/rapidsai/cuvs/pull/838, in which I fixed this problem in one place, but not in two others in the same file.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/933
---
 cpp/tests/neighbors/ann_ivf_pq.cuh | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/cpp/tests/neighbors/ann_ivf_pq.cuh b/cpp/tests/neighbors/ann_ivf_pq.cuh
index 97f252bff9..484d83e296 100644
--- a/cpp/tests/neighbors/ann_ivf_pq.cuh
+++ b/cpp/tests/neighbors/ann_ivf_pq.cuh
@@ -316,7 +316,9 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
     // NB: this is not reference, the list is retained; the index will have to create a new list on
     // `erase_list` op.
     auto old_list = index->lists()[label];
-    auto n_rows   = old_list->size.load();
+    // If the data is unbalanced the list might be empty, which is actually nullptr
+    if (!old_list) { return; }
+    auto n_rows = old_list->size.load();
     if (n_rows == 0) { return; }
     if (index->metric() == cuvs::distance::DistanceType::CosineExpanded) { return; }
 
@@ -350,7 +352,9 @@ class ivf_pq_test : public ::testing::TestWithParam<ivf_pq_inputs> {
   void check_packing(index<IdxT>* index, uint32_t label)
   {
     auto old_list = index->lists()[label];
-    auto n_rows   = old_list->size.load();
+    // If the data is unbalanced the list might be empty, which is actually nullptr
+    if (!old_list) { return; }
+    auto n_rows = old_list->size.load();
 
     if (n_rows == 0) { return; }
 

From c62666e415de8c8148c79ec27785af5566451f68 Mon Sep 17 00:00:00 2001
From: Akira Naruse <anaruse@nvidia.com>
Date: Wed, 28 May 2025 04:26:04 +0900
Subject: [PATCH 411/623] Reduce device memory usage for CAGRA's graph
 optimization process (2-hop detour counting) (#822)

CAGRA takes the initial knn graph as input and optimizes it to create a search graph. Several types of processing are performed in the graph optimization, the most memory-intensive of which is the counting of 2-hop detours.

Currently, the counting of 2-hop detours is performed on the GPU to speed up processing, and this requires that the entire initial knn graph be placed in device memory. In general, the size of the initial knn graph is 2x the size of the search graph. In other words, in the current implementation, roughly half the device memory size is the upper limit of the search graph that can be created. As it is, creating search graphs for huge datasets requires a GPU with a large amount of device memory, which is not practical.

To address this issue, this PR adds a CPU implementation of 2-hop detour counting and uses this CPU implementation to count 2-hop detours when device memory is insufficient.

The CPU implementation supports thread parallelism and is optimized to reduce conditional branches and is sufficiently fast. Of course, it is slower than the GPU implementation, but it can count 2-hop detours in about 3 to 4 times the time of the GPU implementation. Since the time for counting 2-hop detours on GPU is approximately 10% of the total indexing time, the overall time will increase by 20-30% when using the CPU implementation, but this is well within the practical range.

Authors:
  - Akira Naruse (https://github.com/anaruse)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/822
---
 .../neighbors/detail/cagra/cagra_build.cuh    |  64 ++++-
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 268 ++++++++++++------
 2 files changed, 245 insertions(+), 87 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 5829443f1e..6a8e916774 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -44,6 +44,8 @@
 #include <cstdio>
 #include <vector>
 
+#include <sys/mman.h>
+
 namespace cuvs::neighbors::cagra::detail {
 
 template <typename IdxT>
@@ -410,6 +412,52 @@ void optimize(
     res, knn_graph_internal, new_graph_internal, guarantee_connectivity);
 }
 
+// RAII wrapper for allocating memory with Transparent HugePage
+struct mmap_owner {
+  // Allocate a new memory (not backed by a file)
+  mmap_owner(size_t size) : size_{size}
+  {
+    int flags = MAP_ANONYMOUS | MAP_PRIVATE;
+    ptr_      = mmap(nullptr, size, PROT_READ | PROT_WRITE, flags, -1, 0);
+    if (ptr_ == MAP_FAILED) {
+      ptr_ = nullptr;
+      throw std::runtime_error("cuvs::mmap_owner error");
+    }
+    if (madvise(ptr_, size, MADV_HUGEPAGE) != 0) {
+      munmap(ptr_, size);
+      ptr_ = nullptr;
+      throw std::runtime_error("cuvs::mmap_owner error");
+    }
+  }
+
+  ~mmap_owner() noexcept
+  {
+    if (ptr_ != nullptr) { munmap(ptr_, size_); }
+  }
+
+  // No copies for owning struct
+  mmap_owner(const mmap_owner& res)                      = delete;
+  auto operator=(const mmap_owner& other) -> mmap_owner& = delete;
+  // Moving is fine
+  mmap_owner(mmap_owner&& other)
+    : ptr_{std::exchange(other.ptr_, nullptr)}, size_{std::exchange(other.size_, 0)}
+  {
+  }
+  auto operator=(mmap_owner&& other) -> mmap_owner&
+  {
+    std::swap(this->ptr_, other.ptr_);
+    std::swap(this->size_, other.size_);
+    return *this;
+  }
+
+  [[nodiscard]] auto data() const -> void* { return ptr_; }
+  [[nodiscard]] auto size() const -> size_t { return size_; }
+
+ private:
+  void* ptr_;
+  size_t size_;
+};
+
 template <typename T,
           typename IdxT     = uint32_t,
           typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
@@ -493,6 +541,14 @@ auto iterative_build_graph(
     }
   }
 
+  // Allocate memory for neighbors list using Transparent HugePage
+  constexpr size_t thp_size = 2 * 1024 * 1024;
+  size_t byte_size          = sizeof(IdxT) * final_graph_size * topk;
+  if (byte_size % thp_size) { byte_size += thp_size - (byte_size % thp_size); }
+  mmap_owner neighbors_list(byte_size);
+  IdxT* neighbors_ptr = (IdxT*)neighbors_list.data();
+  memset(neighbors_ptr, 0, byte_size);
+
   auto curr_graph_size = initial_graph_size;
   while (true) {
     RAFT_LOG_DEBUG("# graph_size = %lu (%.3lf)",
@@ -524,7 +580,9 @@ auto iterative_build_graph(
 
     auto dev_query_view = raft::make_device_matrix_view<const T, int64_t>(
       dev_dataset.data_handle(), (int64_t)curr_query_size, dev_dataset.extent(1));
-    auto neighbors = raft::make_host_matrix<IdxT, int64_t>(curr_query_size, curr_topk);
+
+    auto neighbors_view =
+      raft::make_host_matrix_view<IdxT, int64_t>(neighbors_ptr, curr_query_size, curr_topk);
 
     // Search.
     // Since there are many queries, divide them into batches and search them.
@@ -551,7 +609,7 @@ auto iterative_build_graph(
                                      batch_dev_distances_view);
 
       auto batch_neighbors_view = raft::make_host_matrix_view<IdxT, int64_t>(
-        neighbors.data_handle() + batch.offset() * curr_topk, batch.size(), curr_topk);
+        neighbors_view.data_handle() + batch.offset() * curr_topk, batch.size(), curr_topk);
       raft::copy(batch_neighbors_view.data_handle(),
                  batch_dev_neighbors_view.data_handle(),
                  batch_neighbors_view.size(),
@@ -564,7 +622,7 @@ auto iterative_build_graph(
     cagra_graph     = raft::make_host_matrix<IdxT, int64_t>(0, 0);  // delete existing grahp
     cagra_graph     = raft::make_host_matrix<IdxT, int64_t>(curr_graph_size, curr_graph_degree);
     optimize<IdxT>(
-      res, neighbors.view(), cagra_graph.view(), flag_last ? params.guarantee_connectivity : 0);
+      res, neighbors_view, cagra_graph.view(), flag_last ? params.guarantee_connectivity : 0);
     if (flag_last) { break; }
   }
 
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index b55d69edb4..5dbee191c5 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -1104,6 +1104,58 @@ void mst_optimization(raft::resources const& res,
   RAFT_LOG_DEBUG("# MST optimization time: %.1lf sec", time_mst_opt_end - time_mst_opt_start);
 }
 
+template <typename IdxT = uint32_t>
+void count_2hop_detours(raft::host_matrix_view<IdxT, int64_t, raft::row_major> knn_graph,
+                        raft::host_matrix_view<uint8_t, int64_t, raft::row_major> detour_count)
+{
+  RAFT_EXPECTS(knn_graph.extent(0) == detour_count.extent(0),
+               "knn_graph and detour_count are expected to have the same number of rows");
+  RAFT_EXPECTS(knn_graph.extent(1) == detour_count.extent(1),
+               "knn_graph and detour_count are expected to have the same number of cols");
+  const uint64_t graph_size   = knn_graph.extent(0);
+  const uint64_t graph_degree = knn_graph.extent(1);
+
+#pragma omp parallel for
+  for (IdxT iA = 0; iA < graph_size; iA++) {
+    // Create a list of nodes, iB_candidates, that can be reached in 2-hops from node A.
+    auto iB_candidates =
+      raft::make_host_vector<IdxT, int64_t>((graph_degree - 1) * (graph_degree - 1));
+    for (uint64_t kAC = 0; kAC < graph_degree - 1; kAC++) {
+      IdxT iC = knn_graph(iA, kAC);
+      for (uint64_t kCB = 0; kCB < graph_degree - 1; kCB++) {
+        IdxT iB_candidate;
+        if (iC == iA || iC >= graph_size) {
+          iB_candidate = graph_size;
+        } else {
+          iB_candidate = knn_graph(iC, kCB);
+          if (iB_candidate == iA || iB_candidate == iC) { iB_candidate = graph_size; }
+        }
+        uint64_t idx;
+        if (kAC < kCB) {
+          idx = (kCB * kCB) + kAC;
+        } else {
+          idx = (kAC * (kAC + 1)) + kCB;
+        }
+        iB_candidates(idx) = iB_candidate;
+      }
+    }
+    // Count how many 2-hop detours are on each edge of node A.
+    for (uint64_t kAB = 0; kAB < graph_degree; kAB++) {
+      constexpr uint32_t max_count = 255;
+      uint32_t count               = 0;
+      IdxT iB                      = knn_graph(iA, kAB);
+      if (iB == iA) {
+        count = max_count;
+      } else {
+        for (uint64_t idx = 0; idx < kAB * kAB; idx++) {
+          if (iB_candidates(idx) == iB) { count += 1; }
+        }
+      }
+      detour_count(iA, kAB) = std::min(count, max_count);
+    }
+  }
+}
+
 template <
   typename IdxT = uint32_t,
   typename g_accessor =
@@ -1112,7 +1164,8 @@ void optimize(
   raft::resources const& res,
   raft::mdspan<IdxT, raft::matrix_extent<int64_t>, raft::row_major, g_accessor> knn_graph,
   raft::host_matrix_view<IdxT, int64_t, raft::row_major> new_graph,
-  const bool guarantee_connectivity = true)
+  const bool guarantee_connectivity = true,
+  const bool use_gpu                = true)
 {
   RAFT_LOG_DEBUG(
     "# Pruning kNN graph (size=%lu, degree=%lu)\n", knn_graph.extent(0), knn_graph.extent(1));
@@ -1122,13 +1175,14 @@ void optimize(
                "Each input array is expected to have the same number of rows");
   RAFT_EXPECTS(new_graph.extent(1) <= knn_graph.extent(1),
                "output graph cannot have more columns than input graph");
-  const uint64_t input_graph_degree  = knn_graph.extent(1);
+  // const uint64_t input_graph_degree  = knn_graph.extent(1);
+  const uint64_t knn_graph_degree    = knn_graph.extent(1);
   const uint64_t output_graph_degree = new_graph.extent(1);
   const uint64_t graph_size          = new_graph.extent(0);
-  auto input_graph_ptr               = knn_graph.data_handle();
-  auto output_graph_ptr              = new_graph.data_handle();
+  // auto input_graph_ptr               = knn_graph.data_handle();
+  auto output_graph_ptr = new_graph.data_handle();
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
-    "cagra::graph::optimize(%zu, %zu, %u)", graph_size, input_graph_degree, output_graph_degree);
+    "cagra::graph::optimize(%zu, %zu, %u)", graph_size, knn_graph_degree, output_graph_degree);
 
   // MST optimization
   auto mst_graph               = raft::make_host_matrix<IdxT, int64_t, raft::row_major>(0, 0);
@@ -1144,7 +1198,6 @@ void optimize(
     mst_graph =
       raft::make_host_matrix<IdxT, int64_t, raft::row_major>(graph_size, output_graph_degree);
     RAFT_LOG_INFO("MST optimization is used to guarantee graph connectivity.");
-    constexpr bool use_gpu = true;
     mst_optimization(res, knn_graph, mst_graph.view(), mst_graph_num_edges.view(), use_gpu);
 
     for (uint64_t i = 0; i < graph_size; i++) {
@@ -1157,26 +1210,7 @@ void optimize(
   {
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
       "cagra::graph::optimize/prune");
-    //
-    // Prune kNN graph
-    //
-    auto d_detour_count = raft::make_device_mdarray<uint8_t>(
-      res, large_tmp_mr, raft::make_extents<int64_t>(graph_size, input_graph_degree));
-
-    RAFT_CUDA_TRY(cudaMemsetAsync(d_detour_count.data_handle(),
-                                  0xff,
-                                  graph_size * input_graph_degree * sizeof(uint8_t),
-                                  raft::resource::get_cuda_stream(res)));
-
-    auto d_num_no_detour_edges = raft::make_device_mdarray<uint32_t>(
-      res, large_tmp_mr, raft::make_extents<int64_t>(graph_size));
-    RAFT_CUDA_TRY(cudaMemsetAsync(d_num_no_detour_edges.data_handle(),
-                                  0x00,
-                                  graph_size * sizeof(uint32_t),
-                                  raft::resource::get_cuda_stream(res)));
-
-    auto dev_stats  = raft::make_device_vector<uint64_t>(res, 2);
-    auto host_stats = raft::make_host_vector<uint64_t>(2);
+    const double time_prune_start = cur_time();
 
     //
     // Prune unimportant edges.
@@ -1191,57 +1225,130 @@ void optimize(
     // specified number of edges are picked up for each node, starting with the
     // edge with the lowest number of 2-hop detours.
     //
-    const double time_prune_start = cur_time();
-    RAFT_LOG_DEBUG("# Pruning kNN Graph on GPUs\r");
-
-    // Copy input_graph_ptr over to device if necessary
-    device_matrix_view_from_host d_input_graph(
-      res,
-      raft::make_host_matrix_view<IdxT, int64_t>(input_graph_ptr, graph_size, input_graph_degree));
-
-    constexpr int MAX_DEGREE = 1024;
-    if (input_graph_degree > MAX_DEGREE) {
-      RAFT_FAIL(
-        "The degree of input knn graph is too large (%zu). "
-        "It must be equal to or smaller than %d.",
-        input_graph_degree,
-        1024);
+    auto detour_count = raft::make_host_matrix<uint8_t, int64_t>(graph_size, knn_graph_degree);
+
+    //
+    // If the available device memory is insufficient, do not use the GPU to count
+    // the number of 2-hop detours, but use the CPU.
+    //
+    bool _use_gpu = use_gpu;
+    if (_use_gpu) {
+      try {
+        auto d_detour_count =
+          raft::make_device_matrix<uint8_t, int64_t>(res, graph_size, knn_graph_degree);
+        auto d_num_no_detour_edges = raft::make_device_vector<uint32_t, int64_t>(res, graph_size);
+        auto d_input_graph =
+          raft::make_device_matrix<IdxT, int64_t>(res, graph_size, knn_graph_degree);
+      } catch (std::bad_alloc& e) {
+        RAFT_LOG_DEBUG("Insufficient memory for 2-hop node counting on GPU");
+        _use_gpu = false;
+      } catch (raft::logic_error& e) {
+        RAFT_LOG_DEBUG("Insufficient memory for 2-hop node counting on GPU (logic error)");
+        _use_gpu = false;
+      }
     }
-    const uint32_t batch_size =
-      std::min(static_cast<uint32_t>(graph_size), static_cast<uint32_t>(256 * 1024));
-    const uint32_t num_batch = (graph_size + batch_size - 1) / batch_size;
-    const dim3 threads_prune(32, 1, 1);
-    const dim3 blocks_prune(batch_size, 1, 1);
-
-    RAFT_CUDA_TRY(cudaMemsetAsync(
-      dev_stats.data_handle(), 0, sizeof(uint64_t) * 2, raft::resource::get_cuda_stream(res)));
-
-    for (uint32_t i_batch = 0; i_batch < num_batch; i_batch++) {
-      kern_prune<MAX_DEGREE, IdxT>
-        <<<blocks_prune, threads_prune, 0, raft::resource::get_cuda_stream(res)>>>(
-          d_input_graph.data_handle(),
-          graph_size,
-          input_graph_degree,
-          output_graph_degree,
-          batch_size,
-          i_batch,
-          d_detour_count.data_handle(),
-          d_num_no_detour_edges.data_handle(),
-          dev_stats.data_handle());
+    if (_use_gpu) {
+      // Count 2-hop detours on GPU
+      raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+        "cagra::graph::optimize/prune/2-hop-counting-by-GPU");
+      const double time_2hop_count_start = cur_time();
+
+      uint64_t num_keep __attribute__((unused)) = 0;
+      uint64_t num_full __attribute__((unused)) = 0;
+      auto d_detour_count                       = raft::make_device_mdarray<uint8_t>(
+        res, large_tmp_mr, raft::make_extents<int64_t>(graph_size, knn_graph_degree));
+
+      RAFT_CUDA_TRY(cudaMemsetAsync(d_detour_count.data_handle(),
+                                    0xff,
+                                    graph_size * knn_graph_degree * sizeof(uint8_t),
+                                    raft::resource::get_cuda_stream(res)));
+
+      auto d_num_no_detour_edges = raft::make_device_mdarray<uint32_t>(
+        res, large_tmp_mr, raft::make_extents<int64_t>(graph_size));
+      RAFT_CUDA_TRY(cudaMemsetAsync(d_num_no_detour_edges.data_handle(),
+                                    0x00,
+                                    graph_size * sizeof(uint32_t),
+                                    raft::resource::get_cuda_stream(res)));
+
+      auto dev_stats  = raft::make_device_vector<uint64_t>(res, 2);
+      auto host_stats = raft::make_host_vector<uint64_t>(2);
+
+      RAFT_LOG_DEBUG("# Pruning kNN Graph on GPUs\r");
+
+      // Copy knn_graph over to device if necessary
+      device_matrix_view_from_host d_input_graph(
+        res,
+        raft::make_host_matrix_view<IdxT, int64_t>(
+          knn_graph.data_handle(), graph_size, knn_graph_degree));
+
+      constexpr int MAX_DEGREE = 1024;
+      if (knn_graph_degree > MAX_DEGREE) {
+        RAFT_FAIL(
+          "The degree of input knn graph is too large (%zu). "
+          "It must be equal to or smaller than %d.",
+          knn_graph_degree,
+          MAX_DEGREE);
+      }
+      const uint32_t batch_size =
+        std::min(static_cast<uint32_t>(graph_size), static_cast<uint32_t>(256 * 1024));
+      const uint32_t num_batch = (graph_size + batch_size - 1) / batch_size;
+      const dim3 threads_prune(32, 1, 1);
+      const dim3 blocks_prune(batch_size, 1, 1);
+
+      RAFT_CUDA_TRY(cudaMemsetAsync(
+        dev_stats.data_handle(), 0, sizeof(uint64_t) * 2, raft::resource::get_cuda_stream(res)));
+
+      for (uint32_t i_batch = 0; i_batch < num_batch; i_batch++) {
+        kern_prune<MAX_DEGREE, IdxT>
+          <<<blocks_prune, threads_prune, 0, raft::resource::get_cuda_stream(res)>>>(
+            d_input_graph.data_handle(),
+            graph_size,
+            knn_graph_degree,
+            output_graph_degree,
+            batch_size,
+            i_batch,
+            d_detour_count.data_handle(),
+            d_num_no_detour_edges.data_handle(),
+            dev_stats.data_handle());
+        raft::resource::sync_stream(res);
+        RAFT_LOG_DEBUG(
+          "# Pruning kNN Graph on GPUs (%.1lf %%)\r",
+          (double)std::min<IdxT>((i_batch + 1) * batch_size, graph_size) / graph_size * 100);
+      }
       raft::resource::sync_stream(res);
+      RAFT_LOG_DEBUG("\n");
+
+      raft::copy(detour_count.data_handle(),
+                 d_detour_count.data_handle(),
+                 detour_count.size(),
+                 raft::resource::get_cuda_stream(res));
+
+      raft::copy(
+        host_stats.data_handle(), dev_stats.data_handle(), 2, raft::resource::get_cuda_stream(res));
+      num_keep = host_stats.data_handle()[0];
+      num_full = host_stats.data_handle()[1];
+
+      const double time_2hop_count_end = cur_time();
       RAFT_LOG_DEBUG(
-        "# Pruning kNN Graph on GPUs (%.1lf %%)\r",
-        (double)std::min<IdxT>((i_batch + 1) * batch_size, graph_size) / graph_size * 100);
-    }
-    raft::resource::sync_stream(res);
-    RAFT_LOG_DEBUG("\n");
+        "# Time for 2-hop detour counting on GPU: %.1lf sec, "
+        "avg_no_detour_edges_per_node: %.2lf/%u, "
+        "nodes_with_no_detour_at_all_edges: %.1lf%%",
+        time_2hop_count_end - time_2hop_count_start,
+        (double)num_keep / graph_size,
+        output_graph_degree,
+        (double)num_full / graph_size * 100);
+    } else {
+      // Count 2-hop detours on CPU
+      raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> block_scope(
+        "cagra::graph::optimize/prune/2-hop-counting-by-CPU");
+      const double time_2hop_count_start = cur_time();
 
-    host_matrix_view_from_device<uint8_t, int64_t> detour_count(res, d_detour_count.view());
+      count_2hop_detours(knn_graph, detour_count.view());
 
-    raft::copy(
-      host_stats.data_handle(), dev_stats.data_handle(), 2, raft::resource::get_cuda_stream(res));
-    const auto num_keep = host_stats.data_handle()[0];
-    const auto num_full = host_stats.data_handle()[1];
+      const double time_2hop_count_end = cur_time();
+      RAFT_LOG_DEBUG("# Time for 2-hop detour counting on CPU: %.1lf sec",
+                     time_2hop_count_end - time_2hop_count_start);
+    }
 
     // Create pruned kNN graph
     bool invalid_neighbor_list = false;
@@ -1251,10 +1358,10 @@ void optimize(
       // count of the neighbors while increasing the target detourable count from zero.
       uint64_t pk         = 0;
       uint32_t num_detour = 0;
-      for (uint32_t l = 0; l < input_graph_degree && pk < output_graph_degree; l++) {
+      for (uint32_t l = 0; l < knn_graph_degree && pk < output_graph_degree; l++) {
         uint32_t next_num_detour = std::numeric_limits<uint32_t>::max();
-        for (uint64_t k = 0; k < input_graph_degree; k++) {
-          const auto num_detour_k = detour_count.data_handle()[k + (input_graph_degree * i)];
+        for (uint64_t k = 0; k < knn_graph_degree; k++) {
+          const auto num_detour_k = detour_count(i, k);
           // Find the detourable count to check in the next iteration
           if (num_detour_k > num_detour) {
             next_num_detour = std::min(static_cast<uint32_t>(num_detour_k), next_num_detour);
@@ -1264,7 +1371,7 @@ void optimize(
           if (num_detour_k != num_detour) { continue; }
 
           // Check duplication and append
-          const auto candidate_node = input_graph_ptr[k + (input_graph_degree * i)];
+          const auto candidate_node = knn_graph(i, k);
           bool dup                  = false;
           for (uint32_t dk = 0; dk < pk; dk++) {
             if (candidate_node == output_graph_ptr[i * output_graph_degree + dk]) {
@@ -1303,14 +1410,7 @@ void optimize(
       "overflows occur during the norm computation between the dataset vectors.");
 
     const double time_prune_end = cur_time();
-    RAFT_LOG_DEBUG(
-      "# Pruning time: %.1lf sec, "
-      "avg_no_detour_edges_per_node: %.2lf/%u, "
-      "nodes_with_no_detour_at_all_edges: %.1lf%%\n",
-      time_prune_end - time_prune_start,
-      (double)num_keep / graph_size,
-      output_graph_degree,
-      (double)num_full / graph_size * 100);
+    RAFT_LOG_DEBUG("# Pruning time: %.1lf sec", time_prune_end - time_prune_start);
   }
 
   auto rev_graph       = raft::make_host_matrix<IdxT, int64_t>(graph_size, output_graph_degree);

From d8733c6dc40708e99fadbe23820d854a0ad35546 Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Wed, 28 May 2025 04:43:33 +0900
Subject: [PATCH 412/623] Update score calculation for CAGRA-Q instance
 selection (#938)

The current CAGRA-Q instance selection criterion is the same as the one for the standard CAGRA, which is not always optimal for CAGRA-Q. This PR updates the criterion for CAGRA-Q to improve the throughput when `team_size=AUTO`.


The size (Byte) of each vector is smaller in a dataset compressed with CAGRA-Q compared to an uncompressed one. Because of this, we may be able to improve throughput by using a smaller `team_size`. This PR updates the scoring method for selecting a CAGRA-Q instance to take that into account. Based on my performance tests for SIFT, GloVe, GIST, NYTimes, and OpenAI 5M, the updated scoring method avoided selecting the worst `team_size` values, unlike the current method.

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/938
---
 cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
index 4f7d24f176..2985a4d38c 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
@@ -80,7 +80,11 @@ struct vpq_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT> {
     if (dataset.pq_bits() != PqBits) { return -1.0; }
     if (dataset.pq_len() != PqLen) { return -1.0; }
     // Otherwise, favor the closest dataset dimensionality.
-    return 1.0 / (0.1 + std::abs(double(dataset.dim()) - double(DatasetBlockDim)));
+    constexpr std::uint32_t preferred_load_elmes_per_thread =
+      16; /*magic number that is good based on experiments.*/
+    return 1.0 / (0.1 + std::abs(double(dataset.dim()) - double(DatasetBlockDim))) * TeamSize +
+           1.0 / (0.1 + std::abs(double(dataset.dim()) / TeamSize / PqLen -
+                                 preferred_load_elmes_per_thread));
   }
 
  private:

From 2972a829fe6a2b8235bbf3557f9fe0762139715a Mon Sep 17 00:00:00 2001
From: Eli Boyarski <eli@boyar.ski>
Date: Tue, 27 May 2025 23:09:48 +0300
Subject: [PATCH 413/623] cagra.rst: fitered -> filtered (#866)

Fixed a typo in cagra.rst: fitered -> *filtered

Authors:
  - Eli Boyarski (https://github.com/eli-b)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/866
---
 docs/source/indexes/cagra.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/indexes/cagra.rst b/docs/source/indexes/cagra.rst
index 651ffc7dd9..e62274787a 100644
--- a/docs/source/indexes/cagra.rst
+++ b/docs/source/indexes/cagra.rst
@@ -26,7 +26,7 @@ Filtering considerations
 
 CAGRA supports filtered search and has improved multi-CTA algorithm in branch-25.02 to provide reasonable recall and performance for filtering rate as high as 90% or more.
 
-To obtain an appropriate recall in fitered search, it is necessary to set search parameters according to the filtering rate, but since it is difficult for users to to this, CAGRA automatically adjusts `itopk_size` internally according to the filtering rate on a heuristic basis. If you want to disable this automatic adjustment, set `filtering_rate`, one of the search parameters, to `0.0`, and `itopk_size` will not be adjusted automatically.
+To obtain an appropriate recall in filtered search, it is necessary to set search parameters according to the filtering rate, but since it is difficult for users to to this, CAGRA automatically adjusts `itopk_size` internally according to the filtering rate on a heuristic basis. If you want to disable this automatic adjustment, set `filtering_rate`, one of the search parameters, to `0.0`, and `itopk_size` will not be adjusted automatically.
 
 Configuration parameters
 ------------------------

From 5642af36b027210a119ba99cfa29bf1af12960d3 Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Wed, 28 May 2025 06:42:58 +0900
Subject: [PATCH 414/623] Fix kmeans::predict argument order (#915)

This PR updates the order of arguments in `kmeans::predict` to match the order in the documentation. The order in the documentation is considered appropriate because it lists the output arguments last, unlike the order of the actual function arguments.

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/915
---
 cpp/include/cuvs/cluster/kmeans.hpp      | 72 ++++++++++++++++++++++--
 cpp/src/cluster/kmeans.cuh               |  2 +-
 cpp/src/cluster/kmeans_predict_double.cu |  8 +--
 cpp/src/cluster/kmeans_predict_float.cu  |  8 +--
 4 files changed, 77 insertions(+), 13 deletions(-)

diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index ad08cd1394..990f69d188 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -519,10 +519,26 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<int, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int, int> labels,
              raft::host_scalar_view<float> inertia);
 
+// This overload is retained for backward compatibility.
+[[deprecated(
+  "The argument order of kmeans::predict has been corrected. Please use the new function "
+  "instead.")]]
+inline void predict(raft::resources const& handle,
+                    const kmeans::params& params,
+                    raft::device_matrix_view<const float, int> X,
+                    std::optional<raft::device_vector_view<const float, int>> sample_weight,
+                    raft::device_matrix_view<const float, int> centroids,
+                    raft::device_vector_view<int, int> labels,
+                    bool normalize_weight,
+                    raft::host_scalar_view<float> inertia)
+{
+  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+}
+
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -577,10 +593,26 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<int64_t, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int64_t, int> labels,
              raft::host_scalar_view<float> inertia);
 
+// This overload is retained for backward compatibility.
+[[deprecated(
+  "The argument order of kmeans::predict has been corrected. Please use the new function "
+  "instead.")]]
+inline void predict(raft::resources const& handle,
+                    const kmeans::params& params,
+                    raft::device_matrix_view<const float, int> X,
+                    std::optional<raft::device_vector_view<const float, int>> sample_weight,
+                    raft::device_matrix_view<const float, int> centroids,
+                    raft::device_vector_view<int64_t, int> labels,
+                    bool normalize_weight,
+                    raft::host_scalar_view<float> inertia)
+{
+  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+}
+
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -635,10 +667,26 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             raft::device_vector_view<int, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int, int> labels,
              raft::host_scalar_view<double> inertia);
 
+// This overload is retained for backward compatibility.
+[[deprecated(
+  "The argument order of kmeans::predict has been corrected. Please use the new function "
+  "instead.")]]
+inline void predict(raft::resources const& handle,
+                    const kmeans::params& params,
+                    raft::device_matrix_view<const double, int> X,
+                    std::optional<raft::device_vector_view<const double, int>> sample_weight,
+                    raft::device_matrix_view<const double, int> centroids,
+                    raft::device_vector_view<int, int> labels,
+                    bool normalize_weight,
+                    raft::host_scalar_view<double> inertia)
+{
+  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+}
+
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -693,10 +741,26 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             raft::device_vector_view<int64_t, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int64_t, int> labels,
              raft::host_scalar_view<double> inertia);
 
+// This overload is retained for backward compatibility.
+[[deprecated(
+  "The argument order of kmeans::predict has been corrected. Please use the new function "
+  "instead.")]]
+inline void predict(raft::resources const& handle,
+                    const kmeans::params& params,
+                    raft::device_matrix_view<const double, int> X,
+                    std::optional<raft::device_vector_view<const double, int>> sample_weight,
+                    raft::device_matrix_view<const double, int> centroids,
+                    raft::device_vector_view<int64_t, int> labels,
+                    bool normalize_weight,
+                    raft::host_scalar_view<double> inertia)
+{
+  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+}
+
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
diff --git a/cpp/src/cluster/kmeans.cuh b/cpp/src/cluster/kmeans.cuh
index 13036795ab..f6a61279b0 100644
--- a/cpp/src/cluster/kmeans.cuh
+++ b/cpp/src/cluster/kmeans.cuh
@@ -163,8 +163,8 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const DataT, IndexT> X,
              std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
              raft::device_matrix_view<const DataT, IndexT> centroids,
-             raft::device_vector_view<IndexT, IndexT> labels,
              bool normalize_weight,
+             raft::device_vector_view<IndexT, IndexT> labels,
              raft::host_scalar_view<DataT> inertia)
 {
   cuvs::cluster::kmeans::detail::kmeans_predict<DataT, IndexT>(
diff --git a/cpp/src/cluster/kmeans_predict_double.cu b/cpp/src/cluster/kmeans_predict_double.cu
index 1fcc393acb..1992e018a8 100644
--- a/cpp/src/cluster/kmeans_predict_double.cu
+++ b/cpp/src/cluster/kmeans_predict_double.cu
@@ -24,13 +24,13 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             raft::device_vector_view<int, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int, int> labels,
              raft::host_scalar_view<double> inertia)
 
 {
   cuvs::cluster::kmeans::predict<double, int>(
-    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
 }
 
 void predict(raft::resources const& handle,
@@ -38,12 +38,12 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             raft::device_vector_view<int64_t, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int64_t, int> labels,
              raft::host_scalar_view<double> inertia)
 
 {
   cuvs::cluster::kmeans::predict<double, int64_t>(
-    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
 }
 }  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_predict_float.cu b/cpp/src/cluster/kmeans_predict_float.cu
index b5f9f9e510..52951b46fc 100644
--- a/cpp/src/cluster/kmeans_predict_float.cu
+++ b/cpp/src/cluster/kmeans_predict_float.cu
@@ -24,25 +24,25 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<int, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int, int> labels,
              raft::host_scalar_view<float> inertia)
 
 {
   cuvs::cluster::kmeans::predict<float, int>(
-    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
 }
 void predict(raft::resources const& handle,
              const kmeans::params& params,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<int64_t, int> labels,
              bool normalize_weight,
+             raft::device_vector_view<int64_t, int> labels,
              raft::host_scalar_view<float> inertia)
 
 {
   cuvs::cluster::kmeans::predict<float, int64_t>(
-    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
+    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
 }
 }  // namespace cuvs::cluster::kmeans

From 13138c62e51576e4a503034d80f43cd18c69bf5b Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 27 May 2025 17:35:20 -0500
Subject: [PATCH 415/623] stop uploading packages to downloads.rapids.ai (#940)

Contributes to https://github.com/rapidsai/build-planning/issues/181

* removes all uploads of conda packages and wheels to `downloads.rapids.ai`

## Notes for Reviewers

### How I identified changes

Looked for uses of the relevant `gha-tools` tools, as well as documentation about `downloads.rapids.ai`, being on the NVIDIA VPN, using S3, etc. like this:

```shell
git grep -i -E 's3|upload|downloads\.rapids|vpn'
```

### How I tested this

See "How I tested this" on https://github.com/rapidsai/shared-workflows/pull/364

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Gil Forsyth (https://github.com/gforsyth)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/940
---
 ci/build_cpp.sh           | 2 --
 ci/build_python.sh        | 2 --
 ci/build_wheel.sh         | 6 ------
 ci/build_wheel_cuvs.sh    | 2 +-
 ci/build_wheel_libcuvs.sh | 2 +-
 5 files changed, 2 insertions(+), 12 deletions(-)

diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index 9df8c1d11d..e55ba04fdb 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -33,5 +33,3 @@ sccache --show-adv-stats
 
 # remove build_cache directory
 rm -rf "$RAPIDS_CONDA_BLD_OUTPUT_DIR"/build_cache
-
-rapids-upload-conda-to-s3 cpp
diff --git a/ci/build_python.sh b/ci/build_python.sh
index efb4c1233c..78fc6f2bda 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -60,5 +60,3 @@ if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
                       "${RATTLER_CHANNELS[@]}"
   sccache --show-adv-stats
 fi
-
-rapids-upload-conda-to-s3 python
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index cd31d05fdd..1d02bc0dd7 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -5,15 +5,11 @@ set -euo pipefail
 
 package_name=$1
 package_dir=$2
-package_type=$3
-underscore_package_name=$(echo "${package_name}" | tr "-" "_")
 
 source rapids-configure-sccache
 source rapids-date-string
 source rapids-init-pip
 
-RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
-
 rapids-generate-version > ./VERSION
 
 cd "${package_dir}"
@@ -60,5 +56,3 @@ sccache --show-adv-stats
 
 # repair wheels and write to the location that artifact-uploading code expects to find them
 python -m auditwheel repair -w "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}" "${EXCLUDE_ARGS[@]}" dist/*
-
-RAPIDS_PY_WHEEL_NAME="${underscore_package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 "${package_type}" "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}"
diff --git a/ci/build_wheel_cuvs.sh b/ci/build_wheel_cuvs.sh
index e6d97d0abb..352a2238e8 100755
--- a/ci/build_wheel_cuvs.sh
+++ b/ci/build_wheel_cuvs.sh
@@ -17,5 +17,5 @@ RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 LIBCUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github cpp)
 echo "libcuvs-${RAPIDS_PY_CUDA_SUFFIX} @ file://$(echo "${LIBCUVS_WHEELHOUSE}"/libcuvs_*.whl)" >> "${PIP_CONSTRAINT}"
 
-ci/build_wheel.sh cuvs ${package_dir} python
+ci/build_wheel.sh cuvs ${package_dir}
 ci/validate_wheel.sh ${package_dir} "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}"
diff --git a/ci/build_wheel_libcuvs.sh b/ci/build_wheel_libcuvs.sh
index 6d80282ff5..8bd4467f93 100755
--- a/ci/build_wheel_libcuvs.sh
+++ b/ci/build_wheel_libcuvs.sh
@@ -28,5 +28,5 @@ rapids-pip-retry install \
 # 0 really means "add --no-build-isolation" (ref: https://github.com/pypa/pip/issues/5735)
 export PIP_NO_BUILD_ISOLATION=0
 
-ci/build_wheel.sh libcuvs ${package_dir} cpp
+ci/build_wheel.sh libcuvs ${package_dir}
 ci/validate_wheel.sh ${package_dir} "${RAPIDS_WHEEL_BLD_OUTPUT_DIR}"

From b0f45bb1aafb62146c79d17f2443032c32a8466c Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 27 May 2025 16:01:46 -0700
Subject: [PATCH 416/623] Expose ivf-flat centers to python/c (#888)

Similar to #881 - also expose centers for ivf-flat as well as ivf-pq

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/888
---
 cpp/include/cuvs/neighbors/ivf_flat.h         | 19 ++++
 cpp/include/cuvs/neighbors/ivf_pq.h           |  9 +-
 cpp/include/cuvs/neighbors/ivf_pq.hpp         |  4 +
 cpp/src/neighbors/ivf_flat_c.cpp              | 95 +++++++++++++++++++
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     | 22 +++++
 .../neighbors/ivf_pq/ivf_pq_build_common.cu   | 23 ++---
 cpp/src/neighbors/ivf_pq_c.cpp                | 14 +--
 cpp/src/neighbors/nn_descent_c.cpp            | 10 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |  8 ++
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx | 29 ++++++
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd  |  2 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  |  6 +-
 python/cuvs/cuvs/tests/test_ivf_flat.py       |  4 +
 13 files changed, 206 insertions(+), 39 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index c46a9b2cdb..e274af93b9 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -164,6 +164,25 @@ cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index);
  * @param[in] index cuvsIvfFlatIndex_t to de-allocate
  */
 cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index);
+
+/** Get the number of clusters/inverted lists */
+uint32_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index);
+
+/** Get the dimensionality of the data */
+uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index);
+
+/**
+ * @brief Get the cluster centers corresponding to the lists [n_lists, dim]
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index cuvsIvfFlatIndex_t Built Ivf-Flat Index
+ * @param[out] centers Preallocated array on host or device memory to store output, [n_lists, dim]
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsResources_t res,
+                                       cuvsIvfFlatIndex_t index,
+                                       DLManagedTensor* centers);
+
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index b00418ecb8..40b9e9b839 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -264,15 +264,16 @@ cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index);
 /** Get the number of clusters/inverted lists */
 uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index);
 
-/** Get the dimensionality of the cluster centers */
-uint32_t cuvsIvfPqIndexGetDimExt(cuvsIvfPqIndex_t index);
+/** Get the dimensionality */
+uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index);
 
 /**
  * @brief Get the cluster centers corresponding to the lists in the original space
  *
  * @param[in] res cuvsResources_t opaque C handle
- * @param[in] index cuvsIvfPqIndex_t Built NN-Descent index
- * @param[out] centers Preallocated array on host memory to store output, [n_lists, dim_ext]
+ * @param[in] index cuvsIvfPqIndex_t Built Ivf-Pq index
+ * @param[out] centers Preallocated array on host or device memory to store output,
+ * dimensions [n_lists, dim]
  * @return cuvsError_t
  */
 cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 7c2a3e9f85..2db1f33eb2 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -2861,6 +2861,10 @@ void extract_centers(raft::resources const& res,
                      const index<int64_t>& index,
                      raft::device_matrix_view<float, uint32_t, raft::row_major> cluster_centers);
 
+/** @copydoc extract_centers */
+void extract_centers(raft::resources const& res,
+                     const index<int64_t>& index,
+                     raft::host_matrix_view<float, uint32_t, raft::row_major> cluster_centers);
 /**
  * @brief Helper exposing the re-computation of list sizes and related arrays if IVF lists have been
  * modified externally.
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index 4f930521a6..df02ea17ca 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -22,6 +22,7 @@
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/core/serialize.hpp>
+#include <raft/util/cudart_utils.hpp>
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/core/exceptions.hpp>
@@ -139,6 +140,35 @@ void _extend(cuvsResources_t res,
 
   cuvs::neighbors::ivf_flat::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
 }
+
+template <typename output_mdspan_type, typename T, typename IdxT>
+void _get_centers(cuvsResources_t res, cuvsIvfFlatIndex index, DLManagedTensor* centers)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
+  auto dst       = cuvs::core::from_dlpack<output_mdspan_type>(centers);
+  auto src       = index_ptr->centers();
+
+  RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output centers has incorrect number of rows");
+  RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output centers has incorrect number of cols");
+
+  raft::copy(dst.data_handle(),
+             src.data_handle(),
+             dst.extent(0) * dst.extent(1),
+             raft::resource::get_cuda_stream(*res_ptr));
+}
+
+template <typename T, typename IdxT>
+void get_centers(cuvsResources_t res, cuvsIvfFlatIndex index, DLManagedTensor* centers)
+{
+  if (cuvs::core::is_dlpack_device_compatible(centers->dl_tensor)) {
+    using output_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
+    _get_centers<output_mdspan_type, T, IdxT>(res, index, centers);
+  } else {
+    using output_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
+    _get_centers<output_mdspan_type, T, IdxT>(res, index, centers);
+  }
+}
 }  // namespace
 
 extern "C" cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index)
@@ -351,3 +381,68 @@ extern "C" cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res,
     }
   });
 }
+
+extern "C" uint32_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index)
+{
+  if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index->addr);
+    return index_ptr->n_lists();
+  } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<half, int64_t>*>(index->addr);
+    return index_ptr->n_lists();
+  } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index->addr);
+    return index_ptr->n_lists();
+  } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>*>(index->addr);
+    return index_ptr->n_lists();
+  } else {
+    return 0;
+  }
+}
+
+extern "C" uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index)
+{
+  if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index->addr);
+    return index_ptr->dim();
+  } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<half, int64_t>*>(index->addr);
+    return index_ptr->dim();
+  } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index->addr);
+    return index_ptr->dim();
+  } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+    auto index_ptr =
+      reinterpret_cast<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>*>(index->addr);
+    return index_ptr->dim();
+  } else {
+    return 0;
+  }
+}
+
+extern "C" cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsResources_t res,
+                                                  cuvsIvfFlatIndex_t index,
+                                                  DLManagedTensor* centers)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      get_centers<float, int64_t>(res, *index, centers);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      get_centers<half, int64_t>(res, *index, centers);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      get_centers<int8_t, int64_t>(res, *index, centers);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      get_centers<uint8_t, int64_t>(res, *index, centers);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 2ee0335f6e..aaeb2cdd5e 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1904,4 +1904,26 @@ void extend(
                   new_indices.has_value() ? new_indices.value().data_handle() : nullptr,
                   n_rows);
 }
+
+template <typename output_mdspan_type>
+inline void extract_centers(raft::resources const& res,
+                            const cuvs::neighbors::ivf_pq::index<int64_t>& index,
+                            output_mdspan_type cluster_centers)
+{
+  RAFT_EXPECTS(cluster_centers.extent(0) == index.n_lists(),
+               "Number of rows in the output buffer for cluster centers must be equal to the "
+               "number of IVF lists");
+  RAFT_EXPECTS(
+    cluster_centers.extent(1) == index.dim(),
+    "Number of columns in the output buffer for cluster centers and index dim are different");
+  auto stream = raft::resource::get_cuda_stream(res);
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(cluster_centers.data_handle(),
+                                  sizeof(float) * index.dim(),
+                                  index.centers().data_handle(),
+                                  sizeof(float) * index.dim_ext(),
+                                  sizeof(float) * index.dim(),
+                                  index.n_lists(),
+                                  cudaMemcpyDefault,
+                                  stream));
+}
 }  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
index b34611c1d6..dc54964275 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build_common.cu
@@ -294,21 +294,14 @@ void extract_centers(raft::resources const& res,
                      const cuvs::neighbors::ivf_pq::index<int64_t>& index,
                      raft::device_matrix_view<float, uint32_t, raft::row_major> cluster_centers)
 {
-  RAFT_EXPECTS(cluster_centers.extent(0) == index.n_lists(),
-               "Number of rows in the output buffer for cluster centers must be equal to the "
-               "number of IVF lists");
-  RAFT_EXPECTS(
-    cluster_centers.extent(1) == index.dim(),
-    "Number of columns in the output buffer for cluster centers and index dim are different");
-  auto stream = raft::resource::get_cuda_stream(res);
-  RAFT_CUDA_TRY(cudaMemcpy2DAsync(cluster_centers.data_handle(),
-                                  sizeof(float) * index.dim(),
-                                  index.centers().data_handle(),
-                                  sizeof(float) * index.dim_ext(),
-                                  sizeof(float) * index.dim(),
-                                  index.n_lists(),
-                                  cudaMemcpyDefault,
-                                  stream));
+  detail::extract_centers(res, index, cluster_centers);
+}
+
+void extract_centers(raft::resources const& res,
+                     const cuvs::neighbors::ivf_pq::index<int64_t>& index,
+                     raft::host_matrix_view<float, uint32_t, raft::row_major> cluster_centers)
+{
+  detail::extract_centers(res, index, cluster_centers);
 }
 
 void recompute_internal_state(const raft::resources& res, index<int64_t>* index)
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 0a3db6724a..2412bcbcc0 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -150,16 +150,8 @@ void _get_centers(cuvsResources_t res, cuvsIvfPqIndex index, DLManagedTensor* ce
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
   auto dst       = cuvs::core::from_dlpack<output_mdspan_type>(centers);
-  auto src       = index_ptr->centers();
 
-  RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output centers has incorrect number of rows");
-  RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output centers has incorrect number of cols");
-
-  cudaMemcpyAsync(dst.data_handle(),
-                  src.data_handle(),
-                  dst.extent(0) * dst.extent(1) * sizeof(float),
-                  cudaMemcpyDefault,
-                  raft::resource::get_cuda_stream(*res_ptr));
+  cuvs::neighbors::ivf_pq::helpers::extract_centers(*res_ptr, *index_ptr, dst);
 }
 }  // namespace
 
@@ -337,10 +329,10 @@ extern "C" uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index)
   return index_ptr->n_lists();
 }
 
-extern "C" uint32_t cuvsIvfPqIndexGetDimExt(cuvsIvfPqIndex_t index)
+extern "C" uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index)
 {
   auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
-  return index_ptr->dim_ext();
+  return index_ptr->dim();
 }
 
 extern "C" cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
diff --git a/cpp/src/neighbors/nn_descent_c.cpp b/cpp/src/neighbors/nn_descent_c.cpp
index a3cc760ef5..ddebb11e73 100644
--- a/cpp/src/neighbors/nn_descent_c.cpp
+++ b/cpp/src/neighbors/nn_descent_c.cpp
@@ -23,6 +23,7 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/core/serialize.hpp>
+#include <raft/util/cudart_utils.hpp>
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/core/exceptions.hpp>
@@ -85,11 +86,10 @@ void _get_graph(cuvsResources_t res, cuvsNNDescentIndex_t index, DLManagedTensor
     RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output graph has incorrect number of rows");
     RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output graph has incorrect number of cols");
 
-    cudaMemcpyAsync(dst.data_handle(),
-                    src.data_handle(),
-                    dst.extent(0) * dst.extent(1) * sizeof(uint32_t),
-                    cudaMemcpyDefault,
-                    raft::resource::get_cuda_stream(*res_ptr));
+    raft::copy(dst.data_handle(),
+               src.data_handle(),
+               dst.extent(0) * dst.extent(1),
+               raft::resource::get_cuda_stream(*res_ptr));
   } else {
     RAFT_FAIL("Unsupported nn-descent index dtype: %d and bits: %d", dtype.code, dtype.bits);
   }
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
index f2bd6a9b18..b97faa1ad9 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -62,6 +62,14 @@ cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
 
     cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
 
+    uint32_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index)
+
+    uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index)
+
+    cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsResources_t res,
+                                           cuvsIvfFlatIndex_t index,
+                                           DLManagedTensor * centers)
+
     cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
                                  cuvsIvfFlatIndexParams* params,
                                  DLManagedTensor* dataset,
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 658a32c838..4db2854365 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -181,6 +181,35 @@ cdef class Index:
     def __repr__(self):
         return "Index(type=IvfFlat)"
 
+    @property
+    def n_lists(self):
+        """ The number of inverted lists (clusters) """
+        return cuvsIvfFlatIndexGetNLists(self.index)
+
+    @property
+    def dim(self):
+        """ dimensionality of the cluster centers """
+        return cuvsIvfFlatIndexGetDim(self.index)
+
+    @property
+    def centers(self):
+        """ Get the cluster centers corresponding to the lists in the
+        original space """
+        return self._get_centers()
+
+    @auto_sync_resources
+    def _get_centers(self, resources=None):
+        if not self.trained:
+            raise ValueError("Index needs to be built before getting centers")
+
+        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+        output = np.empty((self.n_lists, self.dim), dtype='float32')
+        ai = wrap_array(output)
+        cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
+        check_cuvs(cuvsIvfFlatIndexGetCenters(res, self.index, output_dlpack))
+        return output
+
 
 @auto_sync_resources
 def build(IndexParams index_params, dataset, resources=None):
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index 1ed2091644..635bb970d5 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -83,7 +83,7 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
 
     uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index)
 
-    uint32_t cuvsIvfPqIndexGetDimExt(cuvsIvfPqIndex_t index)
+    uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index)
 
     cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
                                          cuvsIvfPqIndex_t index,
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 374279b31b..678ca2705c 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -244,9 +244,9 @@ cdef class Index:
         return cuvsIvfPqIndexGetNLists(self.index)
 
     @property
-    def dim_ext(self):
+    def dim(self):
         """ dimensionality of the cluster centers """
-        return cuvsIvfPqIndexGetDimExt(self.index)
+        return cuvsIvfPqIndexGetDim(self.index)
 
     @property
     def centers(self):
@@ -261,7 +261,7 @@ cdef class Index:
 
         cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
 
-        output = np.empty((self.n_lists, self.dim_ext), dtype='float32')
+        output = np.empty((self.n_lists, self.dim), dtype='float32')
         ai = wrap_array(output)
         cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
         check_cuvs(cuvsIvfPqIndexGetCenters(res, self.index, output_dlpack))
diff --git a/python/cuvs/cuvs/tests/test_ivf_flat.py b/python/cuvs/cuvs/tests/test_ivf_flat.py
index e0cd7b08b1..ea51f3a02c 100644
--- a/python/cuvs/cuvs/tests/test_ivf_flat.py
+++ b/python/cuvs/cuvs/tests/test_ivf_flat.py
@@ -108,6 +108,10 @@ def run_ivf_flat_build_search_test(
     recall = calc_recall(out_idx, skl_idx)
     assert recall > 0.7
 
+    centers = index.centers
+    assert centers.shape[0] == build_params.n_lists
+    assert centers.shape[1] == n_cols
+
 
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("dtype", [np.float32])

From 3f95d4ab16ccfe96301f77925edcf23d954ffe05 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 28 May 2025 13:28:38 -0400
Subject: [PATCH 417/623] All-neighbors API docs (#944)

docs for all-neighbors API

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/944
---
 cpp/include/cuvs/neighbors/all_neighbors.hpp  | 79 +++++++++++++------
 .../all_neighbors/all_neighbors_batched.cuh   | 47 ++++++-----
 cpp/tests/neighbors/all_neighbors.cuh         | 15 ++--
 docs/source/cpp_api/neighbors.rst             |  1 +
 .../cpp_api/neighbors_all_neighbors.rst       | 29 +++++++
 5 files changed, 113 insertions(+), 58 deletions(-)
 create mode 100644 docs/source/cpp_api/neighbors_all_neighbors.rst

diff --git a/cpp/include/cuvs/neighbors/all_neighbors.hpp b/cpp/include/cuvs/neighbors/all_neighbors.hpp
index 2f1fb0949d..683f0348c0 100644
--- a/cpp/include/cuvs/neighbors/all_neighbors.hpp
+++ b/cpp/include/cuvs/neighbors/all_neighbors.hpp
@@ -22,6 +22,10 @@
 #include <variant>
 
 namespace cuvs::neighbors::all_neighbors {
+/**
+ * @defgroup all_neighbors_cpp_params The all-neighbors algorithm parameters.
+ * @{
+ */
 
 /**
  * @brief Parameters used to build an all-neighbors knn graph (find nearest neighbors for all the
@@ -43,20 +47,21 @@ using GraphBuildParams =
   std::variant<graph_build_params::ivf_pq_params, graph_build_params::nn_descent_params>;
 
 /**
- * @brief Parameters used to build an all-neighbors graph  (find nearest neighbors for all the
- * training vectors)
- *
- * graph_build_params: graph building parameters for the given graph building algorithm. defaults
- * to ivfpq.
- * n_nearest_clusters: number of nearest clusters each data point will be assigned to in
- * the batching algorithm
- * n_clusters: number of total clusters (aka batches) to split the data into. If set to 1, algorithm
- * creates an all-neighbors graph without batching
- * metric: metric type
+ * @brief Parameters used to build an all-neighbors graph (find nearest neighbors for all the
+ * training vectors).
+ * For scalability, the all-neighbors graph construction algorithm partitions a set of training
+ * vectors into overlapping clusters, computes a local knn graph on each cluster, and merges the
+ * local graphs into a single global graph.
+ * Device memory usage and accuracy can be configured by changing the `overlap_factor` and
+ * `n_clusters`.
+ * The algorithm used to build each local graph is also configurable.
  *
  */
 struct all_neighbors_params {
   /** Parameters for knn graph building algorithm
+   * Approximate nearest neighbors methods are used to build the knn graph. Currently supported
+   * options are 'IVF-PQ' and 'NN Descent'. IVF-PQ is more accurate, but slower compared to NN
+   * Descent.
    *
    * Set ivf_pq_params, or nn_descent_params to select the graph build
    * algorithm and control their parameters.
@@ -74,35 +79,55 @@ struct all_neighbors_params {
   GraphBuildParams graph_build_params;
 
   /**
-   * Usage of n_nearest_clusters and n_clusters
+   * Number of nearest clusters each data point will be assigned to in the batching algorithm.
+   * Start with `overlap_factor = 2` and gradually increase (2->3->4 ...) for better accuracy at the
+   * cost of device memory usage.
+   */
+  size_t overlap_factor = 2;
+
+  /**
+   * Number of total clusters (aka batches) to split the data into. If set to 1, algorithm creates
+   * an all-neighbors graph without batching.
+   * Start with `n_clusters = 4` and increase (4 → 8 → 16...) for less device memory usage at the
+   * cost of accuracy. This is independent from `overlap_factor` as long as `overlap_factor` <
+   * `n_clusters`.
    *
-   * The ratio of n_nearest_clusters / n_clusters determines device memory usage.
-   * Approximately (n_nearest_clusters / n_clusters) * num_rows_in_entire_data number of rows will
+   * The ratio of `overlap_factor / n_clusters` determines device memory usage.
+   * Approximately `(overlap_factor / n_clusters) * num_rows_in_entire_data` number of rows will
    * be put on device memory at once.
-   * E.g. between (n_nearest_clusters / n_clusters) = 2/10 and 2/20, the latter will use less device
+   * E.g. between `(overlap_factor / n_clusters)` = 2/10 and 2/20, the latter will use less device
    * memory.
    *
-   * Larger n_nearest_clusters results in better accuracy of the final all-neighbors knn
-   * graph. E.g. With the similar device memory usages, (n_nearest_clusters / n_clusters) = 4/20
+   * Larger `overlap_factor` results in better accuracy of the final all-neighbors knn
+   * graph. E.g. While using similar device memory, `(overlap_factor / n_clusters)` = 4/20
    * will have better accuracy than 2/10 at the cost of performance.
+   *
    */
-  size_t n_nearest_clusters           = 2;
-  size_t n_clusters                   = 1;  // defaults to not batching
+  size_t n_clusters = 1;  // defaults to not batching
+
+  /** Metric used. */
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
 };
 
+/** @} */
+
+/**
+ * @defgroup all_neighbors_cpp_build The all-neighbors knn graph build
+ * @{
+ */
+
 /**
  * @brief Builds an approximate all-neighbors knn graph  (find nearest neighbors for all the
  * training vectors)
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors;
- *   // use default index parameters
- *   all_neighbors::all_neighbors_params params;
+ *  using namespace cuvs::neighbors;
+ *  // use default index parameters
+ *  all_neighbors::all_neighbors_params params;
  *  auto indices = raft::make_device_matrix<int64_t, int64_t>(handle, n_row, k);
  *  auto distances = raft::make_device_matrix<float, int64_t>(handle, n_row, k);
- *   all_neighbors::build(res, params, dataset, indices.view(), distances.view());
+ *  all_neighbors::build(res, params, dataset, indices.view(), distances.view());
  * @endcode
  *
  * @param[in] handle raft::resources is an object mangaging resources
@@ -127,12 +152,12 @@ void build(
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors;
- *   // use default index parameters
- *   all_neighbors::all_neighbors_params params;
+ *  using namespace cuvs::neighbors;
+ *  // use default index parameters
+ *  all_neighbors::all_neighbors_params params;
  *  auto indices = raft::make_device_matrix<int64_t, int64_t>(handle, n_row, k);
  *  auto distances = raft::make_device_matrix<float, int64_t>(handle, n_row, k);
- *   all_neighbors::build(res, params, dataset, indices.view(), distances.view());
+ *  all_neighbors::build(res, params, dataset, indices.view(), distances.view());
  * @endcode
  *
  * @param[in] handle raft::resources is an object mangaging resources
@@ -149,4 +174,6 @@ void build(
   raft::device_matrix_view<const float, int64_t, row_major> dataset,
   raft::device_matrix_view<int64_t, int64_t, row_major> indices,
   std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances = std::nullopt);
+
+/** @} */
 }  // namespace cuvs::neighbors::all_neighbors
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
index 60c3751b1c..6254b448dd 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
@@ -71,7 +71,7 @@ void get_centroids_on_data_subsample(raft::resources const& res,
 template <typename T, typename IdxT>
 void single_gpu_assign_clusters(
   raft::resources const& res,
-  size_t n_nearest_clusters,
+  size_t overlap_factor,
   size_t n_clusters,
   size_t n_rows_per_batch,
   size_t base_row_offset,
@@ -89,10 +89,10 @@ void single_gpu_assign_clusters(
   auto dataset_batch_d =
     raft::make_device_matrix<T, IdxT, raft::row_major>(res, n_rows_per_batch, num_cols);
 
-  auto nearest_clusters_idx_d = raft::make_device_matrix<IdxT, int64_t, raft::row_major>(
-    res, n_rows_per_batch, n_nearest_clusters);
-  auto nearest_clusters_dist_d = raft::make_device_matrix<T, int64_t, raft::row_major>(
-    res, n_rows_per_batch, n_nearest_clusters);
+  auto nearest_clusters_idx_d =
+    raft::make_device_matrix<IdxT, int64_t, raft::row_major>(res, n_rows_per_batch, overlap_factor);
+  auto nearest_clusters_dist_d =
+    raft::make_device_matrix<T, int64_t, raft::row_major>(res, n_rows_per_batch, overlap_factor);
 
   std::optional<raft::device_vector_view<const T, int64_t>> norms_view;
   cuvs::neighbors::brute_force::index<T> brute_force_index(res, centroids, norms_view, metric);
@@ -111,21 +111,21 @@ void single_gpu_assign_clusters(
                                          raft::make_const_mdspan(dataset_batch_d.view()),
                                          nearest_clusters_idx_d.view(),
                                          nearest_clusters_dist_d.view());
-    raft::copy(global_nearest_cluster.data_handle() + row_offset * n_nearest_clusters,
+    raft::copy(global_nearest_cluster.data_handle() + row_offset * overlap_factor,
                nearest_clusters_idx_d.data_handle(),
-               n_rows_of_current_batch * n_nearest_clusters,
+               n_rows_of_current_batch * overlap_factor,
                resource::get_cuda_stream(res));
   }
 }
 
 /**
- * Assign each data point to top n_nearest_clusters number of clusters. Loads the data in batches
+ * Assign each data point to top overlap_factor number of clusters. Loads the data in batches
  * onto device for efficiency. Arguments:
  * - [in] res: raft resource
  * - [in] params: params for graph building
  * - [in] dataset [num_rows x num_cols]: entire dataset located on host memory
  * - [in] centroids [n_clusters x num_cols] : centroid vectors
- * - [out] global_nearest_cluster [num_rows X n_nearest_clusters] : top n_nearest_clusters closest
+ * - [out] global_nearest_cluster [num_rows X overlap_factor] : top overlap_factor closest
  * clusters for each data point
  */
 template <typename T, typename IdxT>
@@ -169,7 +169,7 @@ void assign_clusters(raft::resources const& res,
       size_t base_row_offset_for_this_rank = n_rows_per_cluster * base_cluster_idx;
 
       single_gpu_assign_clusters(dev_res,
-                                 params.n_nearest_clusters,
+                                 params.overlap_factor,
                                  n_clusters_for_this_rank,
                                  n_rows_per_cluster,
                                  base_row_offset_for_this_rank,
@@ -180,7 +180,7 @@ void assign_clusters(raft::resources const& res,
     }
   } else {
     single_gpu_assign_clusters(res,
-                               params.n_nearest_clusters,
+                               params.overlap_factor,
                                params.n_clusters,
                                n_rows_per_cluster,
                                0,
@@ -195,9 +195,9 @@ void assign_clusters(raft::resources const& res,
  * Getting data indices that belong to cluster
  * Arguments:
  * - [in] res: raft resource
- * - [in] global_nearest_cluster [num_rows X n_nearest_clusters] : top n_nearest_clusters closest
+ * - [in] global_nearest_cluster [num_rows X overlap_factor] : top overlap_factor closest
  * clusters for each data point
- * - [out] inverted_indices [num_rows x n_nearest_clusters sized vector] : vector for data indices
+ * - [out] inverted_indices [num_rows x overlap_factor sized vector] : vector for data indices
  * for each cluster
  * - [out] cluster_sizes [n_cluster] : cluster size for each cluster
  * - [out] cluster_offsets [n_cluster] : offset in inverted_indices for each cluster
@@ -210,9 +210,9 @@ void get_inverted_indices(raft::resources const& res,
                           raft::host_vector_view<IdxT, IdxT> cluster_offsets)
 {
   // build sparse inverted indices and get number of data points for each cluster
-  size_t num_rows           = global_nearest_cluster.extent(0);
-  size_t n_nearest_clusters = global_nearest_cluster.extent(1);
-  size_t n_clusters         = cluster_sizes.extent(0);
+  size_t num_rows       = global_nearest_cluster.extent(0);
+  size_t overlap_factor = global_nearest_cluster.extent(1);
+  size_t n_clusters     = cluster_sizes.extent(0);
 
   auto local_offsets = raft::make_host_vector<IdxT>(n_clusters);
 
@@ -220,7 +220,7 @@ void get_inverted_indices(raft::resources const& res,
   std::fill(local_offsets.data_handle(), local_offsets.data_handle() + n_clusters, 0);
 
   for (size_t i = 0; i < num_rows; i++) {
-    for (size_t j = 0; j < n_nearest_clusters; j++) {
+    for (size_t j = 0; j < overlap_factor; j++) {
       IdxT cluster_id = global_nearest_cluster(i, j);
       cluster_sizes(cluster_id) += 1;
     }
@@ -231,7 +231,7 @@ void get_inverted_indices(raft::resources const& res,
     cluster_offsets(i) = cluster_offsets(i - 1) + cluster_sizes(i - 1);
   }
   for (size_t i = 0; i < num_rows; i++) {
-    for (size_t j = 0; j < n_nearest_clusters; j++) {
+    for (size_t j = 0; j < overlap_factor; j++) {
       IdxT cluster_id = global_nearest_cluster(i, j);
       inverted_indices(cluster_offsets(cluster_id) + local_offsets(cluster_id)) = i;
       local_offsets(cluster_id) += 1;
@@ -389,20 +389,19 @@ void batch_build(
   size_t num_cols = static_cast<size_t>(dataset.extent(1));
   size_t k        = indices.extent(1);
 
-  RAFT_EXPECTS(params.n_clusters > params.n_nearest_clusters,
-               "n_nearest_clusters should be smaller than n_clusters. We recommend starting from "
-               "n_nearest_clusters=2 and gradually increase it for better knn graph recall.");
+  RAFT_EXPECTS(params.n_clusters > params.overlap_factor,
+               "overlap_factor should be smaller than n_clusters. We recommend starting from "
+               "overlap_factor=2 and gradually increase it for better knn graph recall.");
 
   auto centroids = raft::make_device_matrix<T, IdxT>(handle, params.n_clusters, num_cols);
   get_centroids_on_data_subsample<T, IdxT>(handle, params.metric, dataset, centroids.view());
 
-  auto global_nearest_cluster =
-    raft::make_host_matrix<IdxT, IdxT>(num_rows, params.n_nearest_clusters);
+  auto global_nearest_cluster = raft::make_host_matrix<IdxT, IdxT>(num_rows, params.overlap_factor);
   assign_clusters<T, IdxT>(
     handle, params, dataset, centroids.view(), global_nearest_cluster.view());
 
   auto inverted_indices =
-    raft::make_host_vector<IdxT, IdxT, raft::row_major>(num_rows * params.n_nearest_clusters);
+    raft::make_host_vector<IdxT, IdxT, raft::row_major>(num_rows * params.overlap_factor);
   auto cluster_sizes   = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
   auto cluster_offsets = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
   get_inverted_indices(handle,
diff --git a/cpp/tests/neighbors/all_neighbors.cuh b/cpp/tests/neighbors/all_neighbors.cuh
index 6d045f796c..3c606629ce 100644
--- a/cpp/tests/neighbors/all_neighbors.cuh
+++ b/cpp/tests/neighbors/all_neighbors.cuh
@@ -58,7 +58,7 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AllNeighborsInputs&
   os << "dataset shape=" << p.n_rows << "x" << p.dim << ", k=" << p.k
      << ", metric=" << static_cast<int>(std::get<1>(p.build_algo_metric_recall))
      << ", clusters=" << std::get<0>(p.cluster_nearestcluster)
-     << ", num nearest clusters=" << std::get<1>(p.cluster_nearestcluster) << std::endl;
+     << ", overlap_factor=" << std::get<1>(p.cluster_nearestcluster) << std::endl;
   return os;
 }
 
@@ -73,9 +73,9 @@ void get_graphs(raft::resources& handle,
                 size_t queries_size)
 {
   all_neighbors_params params;
-  params.n_clusters         = std::get<0>(ps.cluster_nearestcluster);
-  params.n_nearest_clusters = std::get<1>(ps.cluster_nearestcluster);
-  params.metric             = std::get<1>(ps.build_algo_metric_recall);
+  params.n_clusters     = std::get<0>(ps.cluster_nearestcluster);
+  params.overlap_factor = std::get<1>(ps.cluster_nearestcluster);
+  params.metric         = std::get<1>(ps.build_algo_metric_recall);
 
   auto build_algo = std::get<0>(ps.build_algo_metric_recall);
 
@@ -91,8 +91,7 @@ void get_graphs(raft::resources& handle,
     ivfq_build_params.build_params.metric = params.metric;
     // heuristically good ivfpq n_lists
     ivfq_build_params.build_params.n_lists = std::max(
-      5u,
-      static_cast<uint32_t>(ps.n_rows * params.n_nearest_clusters / (5000 * params.n_clusters)));
+      5u, static_cast<uint32_t>(ps.n_rows * params.overlap_factor / (5000 * params.n_clusters)));
     params.graph_build_params = ivfq_build_params;
   }
 
@@ -209,7 +208,7 @@ const std::vector<AllNeighborsInputs> inputsSingle =
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::CosineExpanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::InnerProduct, 0.8)},
-    {std::make_tuple(1lu, 2lu)},  // min_recall, n_clusters, num_nearest_cluster
+    {std::make_tuple(1lu, 2lu)},  // min_recall, n_clusters, overlap_factor
     {5000, 7151},                 // n_rows
     {64, 137},                    // dim
     {16, 23},                     // graph_degree
@@ -227,7 +226,7 @@ const std::vector<AllNeighborsInputs> inputsBatch =
       std::make_tuple(4lu, 2lu),
       std::make_tuple(7lu, 2lu),
       std::make_tuple(10lu, 2lu),
-    },             // min_recall, n_clusters, num_nearest_cluster
+    },             // min_recall, n_clusters, overlap_factor
     {5000, 7151},  // n_rows
     {64, 137},     // dim
     {16, 23},      // graph_degree
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index ff1566e6ca..0032e17e2b 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -9,6 +9,7 @@ Nearest Neighbors
    :maxdepth: 2
    :caption: Contents:
 
+   neighbors_all_neighbors.rst
    neighbors_bruteforce.rst
    neighbors_cagra.rst
    neighbors_dynamic_batching.rst
diff --git a/docs/source/cpp_api/neighbors_all_neighbors.rst b/docs/source/cpp_api/neighbors_all_neighbors.rst
new file mode 100644
index 0000000000..50ba35bca2
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_all_neighbors.rst
@@ -0,0 +1,29 @@
+All-neighbors
+==========
+
+All-neighbors allows building an approximate all-neighbors knn graph. Given a full dataset, it finds nearest neighbors for all the training vectors in the dataset.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/all_neighbors.hpp>``
+
+namespace *cuvs::neighbors::all_neighbors*
+
+All neighbors knn graph build parameters
+----------------------
+
+.. doxygengroup:: all_neighbors_cpp_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+
+Build
+-----------
+
+.. doxygengroup:: all_neighbors_cpp_build
+    :project: cuvs
+    :members:
+    :content-only:

From e00fabea513e5b778bde9dc0369901a1cef7a93b Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Wed, 28 May 2025 14:12:59 -0700
Subject: [PATCH 418/623] [Feat] Add support of logical merge in Cagra (#713)

- https://github.com/rapidsai/cuvs/issues/712

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/713
---
 cpp/CMakeLists.txt                            |   3 +
 .../src/cuvs/cuvs_ann_bench_param_parser.h    |  14 ++
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   | 187 ++++++++++++++--
 cpp/include/cuvs/neighbors/cagra.hpp          |  18 +-
 .../cuvs/neighbors/cagra_index_wrapper.hpp    | 142 +++++++++++++
 cpp/include/cuvs/neighbors/common.hpp         |  22 +-
 .../cuvs/neighbors/composite/index.hpp        | 103 +++++++++
 .../cuvs/neighbors/composite/merge.hpp        |  49 +++++
 cpp/include/cuvs/neighbors/index_base.hpp     | 103 +++++++++
 cpp/include/cuvs/neighbors/index_wrappers.hpp | 199 ++++++++++++++++++
 cpp/src/neighbors/cagra.cuh                   |   1 -
 cpp/src/neighbors/cagra_index_wrapper.cu      | 110 ++++++++++
 cpp/src/neighbors/composite/index.cu          | 130 ++++++++++++
 cpp/src/neighbors/composite/merge.cpp         |  83 ++++++++
 .../neighbors/detail/cagra/cagra_merge.cuh    |  17 +-
 .../neighbors/detail/cagra/cagra_search.cuh   |   1 +
 cpp/tests/neighbors/ann_cagra.cuh             | 157 +++++++++-----
 .../ann_cagra/test_float_uint32_t.cu          |   3 +-
 .../neighbors/ann_cagra/test_half_uint32_t.cu |   3 +-
 19 files changed, 1252 insertions(+), 93 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/cagra_index_wrapper.hpp
 create mode 100644 cpp/include/cuvs/neighbors/composite/index.hpp
 create mode 100644 cpp/include/cuvs/neighbors/composite/merge.hpp
 create mode 100644 cpp/include/cuvs/neighbors/index_base.hpp
 create mode 100644 cpp/include/cuvs/neighbors/index_wrappers.hpp
 create mode 100644 cpp/src/neighbors/cagra_index_wrapper.cu
 create mode 100644 cpp/src/neighbors/composite/index.cu
 create mode 100644 cpp/src/neighbors/composite/merge.cpp

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index a4a60d6a7f..52858db577 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -419,6 +419,9 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/detail/cagra/cagra_build.cpp
     src/neighbors/detail/cagra/topk_for_cagra/topk.cu
     src/neighbors/dynamic_batching.cu
+    src/neighbors/cagra_index_wrapper.cu
+    src/neighbors/composite/index.cu
+    src/neighbors/composite/merge.cpp
     $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
     src/neighbors/ivf_flat_index.cpp
     src/neighbors/ivf_flat/ivf_flat_build_extend_float_int64_t.cu
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 48a0f39ab7..7ad8ef91b5 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -277,6 +277,20 @@ void parse_build_param(const nlohmann::json& conf,
     parse_build_param(comp_search_conf, vpq_pams);
     param.cagra_params.compression.emplace(vpq_pams);
   }
+
+  if (conf.contains("num_dataset_splits")) {
+    param.num_dataset_splits = conf.at("num_dataset_splits");
+  }
+  if (conf.contains("merge_type")) {
+    std::string mt = conf.at("merge_type");
+    if (mt == "PHYSICAL") {
+      param.merge_type = cuvs::bench::CagraMergeType::kPhysical;
+    } else if (mt == "LOGICAL") {
+      param.merge_type = cuvs::bench::CagraMergeType::kLogical;
+    } else {
+      throw std::runtime_error("invalid value for merge_type");
+    }
+  }
 }
 
 cuvs::bench::AllocatorType parse_allocator(std::string mem_type)
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index e4c441639c..ed21179b5e 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -24,6 +24,7 @@
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/composite/merge.hpp>
 #include <cuvs/neighbors/dynamic_batching.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
@@ -44,14 +45,17 @@
 #include <iostream>
 #include <memory>
 #include <optional>
+#include <raft/util/integer_utils.hpp>
 #include <stdexcept>
 #include <string>
 #include <type_traits>
+#include <vector>
 
 namespace cuvs::bench {
 
 enum class AllocatorType { kHostPinned, kHostHugePage, kDevice };
 enum class CagraBuildAlgo { kAuto, kIvfPq, kNnDescent };
+enum class CagraMergeType { kPhysical, kLogical };
 
 template <typename T, typename IdxT>
 class cuvs_cagra : public algo<T>, public algo_gpu {
@@ -80,6 +84,8 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
     std::optional<float> ivf_pq_refine_rate                                    = std::nullopt;
     std::optional<cuvs::neighbors::ivf_pq::index_params> ivf_pq_build_params   = std::nullopt;
     std::optional<cuvs::neighbors::ivf_pq::search_params> ivf_pq_search_params = std::nullopt;
+    size_t num_dataset_splits                                                  = 1;
+    CagraMergeType merge_type = CagraMergeType::kPhysical;
 
     void prepare_build_params(const raft::extent_2d<IdxT>& dataset_extents)
     {
@@ -188,6 +194,7 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
   bool dynamic_batching_conservative_dispatch_;
 
   std::shared_ptr<cuvs::neighbors::filtering::base_filter> filter_;
+  std::vector<std::shared_ptr<cuvs::neighbors::cagra::index<T, IdxT>>> sub_indices_;
 
   inline rmm::device_async_resource_ref get_mr(AllocatorType mem_type)
   {
@@ -211,10 +218,57 @@ void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
   auto dataset_view_device =
     raft::make_mdspan<const T, IdxT, raft::row_major, false, true>(dataset, dataset_extents);
   bool dataset_is_on_host = raft::get_device_for_address(dataset) == -1;
+  if (index_params_.num_dataset_splits <= 1) {
+    index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(std::move(
+      dataset_is_on_host ? cuvs::neighbors::cagra::build(handle_, params, dataset_view_host)
+                         : cuvs::neighbors::cagra::build(handle_, params, dataset_view_device)));
+  } else {
+    IdxT rows_per_split =
+      raft::ceildiv<IdxT>(nrow, static_cast<IdxT>(index_params_.num_dataset_splits));
+    for (size_t i = 0; i < index_params_.num_dataset_splits; ++i) {
+      IdxT start = static_cast<IdxT>(i * rows_per_split);
+      if (start >= nrow) break;
+      IdxT rows        = std::min(rows_per_split, static_cast<IdxT>(nrow) - start);
+      const T* sub_ptr = dataset + static_cast<size_t>(start) * dimension_;
+      auto sub_host =
+        raft::make_host_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+      auto sub_dev =
+        raft::make_device_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+
+      auto sub_index =
+        cuvs::neighbors::cagra::index<T, IdxT>(handle_, index_params_.cagra_params.metric);
+      if (index_params_.merge_type == CagraMergeType::kPhysical) {
+        if (dataset_is_on_host) {
+          sub_index.update_dataset(handle_, sub_host);
+        } else {
+          sub_index.update_dataset(handle_, sub_dev);
+        }
+      }
+      if (index_params_.merge_type == CagraMergeType::kLogical) {
+        if (dataset_is_on_host) {
+          sub_index = cuvs::neighbors::cagra::build(handle_, params, sub_host);
+        } else {
+          sub_index = cuvs::neighbors::cagra::build(handle_, params, sub_dev);
+        }
+      }
+      auto sub_index_shared =
+        std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(std::move(sub_index));
+      sub_indices_.push_back(std::move(sub_index_shared));
+    }
+    if (index_params_.merge_type == CagraMergeType::kPhysical) {
+      cuvs::neighbors::cagra::merge_params merge_params{index_params_.cagra_params};
+      merge_params.merge_strategy = cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL;
+
+      std::vector<cuvs::neighbors::cagra::index<T, IdxT>*> indices;
+      indices.reserve(sub_indices_.size());
+      for (auto& ptr : sub_indices_) {
+        indices.push_back(ptr.get());
+      }
 
-  index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(std::move(
-    dataset_is_on_host ? cuvs::neighbors::cagra::build(handle_, params, dataset_view_host)
-                       : cuvs::neighbors::cagra::build(handle_, params, dataset_view_device)));
+      index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(
+        std::move(cuvs::neighbors::cagra::merge(handle_, merge_params, indices)));
+    }
+  }
 }
 
 inline auto allocator_to_string(AllocatorType mem_type) -> std::string
@@ -233,7 +287,7 @@ template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param,
                                            const void* filter_bitset)
 {
-  filter_ = make_cuvs_filter(filter_bitset, index_->size());
+  if (index_) { filter_ = make_cuvs_filter(filter_bitset, index_->size()); }
   auto sp = dynamic_cast<const search_param&>(param);
   bool needs_dynamic_batcher_update =
     (dynamic_batching_max_batch_size_ != sp.dynamic_batching_max_batch_size) ||
@@ -314,27 +368,65 @@ void cuvs_cagra<T, IdxT>::set_search_param(const search_param_base& param,
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::set_search_dataset(const T* dataset, size_t nrow)
 {
-  using ds_idx_type = decltype(index_->data().n_rows());
-  bool is_vpq =
-    dynamic_cast<const cuvs::neighbors::vpq_dataset<half, ds_idx_type>*>(&index_->data()) ||
-    dynamic_cast<const cuvs::neighbors::vpq_dataset<float, ds_idx_type>*>(&index_->data());
-  // It can happen that we are re-using a previous algo object which already has
-  // the dataset set. Check if we need update.
-  if (static_cast<size_t>(input_dataset_v_->extent(0)) != nrow ||
-      input_dataset_v_->data_handle() != dataset) {
-    *input_dataset_v_ = raft::make_device_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
-    need_dataset_update_ = !is_vpq;  // ignore update if this is a VPQ dataset.
+  if (index_params_.num_dataset_splits > 1 &&
+      index_params_.merge_type == CagraMergeType::kLogical) {
+    bool dataset_is_on_host = raft::get_device_for_address(dataset) == -1;
+    IdxT rows_per_split =
+      raft::ceildiv<IdxT>(nrow, static_cast<IdxT>(index_params_.num_dataset_splits));
+    for (size_t i = 0; i < sub_indices_.size(); ++i) {
+      IdxT start = static_cast<IdxT>(i * rows_per_split);
+      if (start >= nrow) break;
+      IdxT rows        = std::min(rows_per_split, static_cast<IdxT>(nrow) - start);
+      const T* sub_ptr = dataset + static_cast<size_t>(start) * dimension_;
+      auto sub_host =
+        raft::make_host_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+      auto sub_dev =
+        raft::make_device_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+      auto sub_index = sub_indices_[i].get();
+      if (index_params_.merge_type == CagraMergeType::kLogical) {
+        if (dataset_is_on_host) {
+          sub_index->update_dataset(handle_, sub_host);
+        } else {
+          sub_index->update_dataset(handle_, sub_dev);
+        }
+      }
+    }
+    need_dataset_update_ = false;
+  } else {
+    using ds_idx_type = decltype(index_->data().n_rows());
+    bool is_vpq =
+      dynamic_cast<const cuvs::neighbors::vpq_dataset<half, ds_idx_type>*>(&index_->data()) ||
+      dynamic_cast<const cuvs::neighbors::vpq_dataset<float, ds_idx_type>*>(&index_->data());
+    // It can happen that we are re-using a previous algo object which already has
+    // the dataset set. Check if we need update.
+    if (static_cast<size_t>(input_dataset_v_->extent(0)) != nrow ||
+        input_dataset_v_->data_handle() != dataset) {
+      *input_dataset_v_ =
+        raft::make_device_matrix_view<const T, int64_t>(dataset, nrow, this->dim_);
+      need_dataset_update_ = !is_vpq;  // ignore update if this is a VPQ dataset.
+    }
   }
 }
 
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::save(const std::string& file) const
 {
-  using ds_idx_type = decltype(index_->data().n_rows());
-  bool is_vpq =
-    dynamic_cast<const cuvs::neighbors::vpq_dataset<half, ds_idx_type>*>(&index_->data()) ||
-    dynamic_cast<const cuvs::neighbors::vpq_dataset<float, ds_idx_type>*>(&index_->data());
-  cuvs::neighbors::cagra::serialize(handle_, file, *index_, is_vpq);
+  if (index_params_.num_dataset_splits > 1 &&
+      index_params_.merge_type == CagraMergeType::kLogical) {
+    for (size_t i = 0; i < sub_indices_.size(); ++i) {
+      std::string subfile = file + (i == 0 ? "" : ".subidx." + std::to_string(i));
+      cuvs::neighbors::cagra::serialize(handle_, subfile, *sub_indices_[i], false);
+    }
+    std::ofstream f(file + ".submeta", std::ios::out);
+    f << sub_indices_.size();
+    f.close();
+  } else {
+    using ds_idx_type = decltype(index_->data().n_rows());
+    bool is_vpq =
+      dynamic_cast<const cuvs::neighbors::vpq_dataset<half, ds_idx_type>*>(&index_->data()) ||
+      dynamic_cast<const cuvs::neighbors::vpq_dataset<float, ds_idx_type>*>(&index_->data());
+    cuvs::neighbors::cagra::serialize(handle_, file, *index_, is_vpq);
+  }
 }
 
 template <typename T, typename IdxT>
@@ -346,8 +438,24 @@ void cuvs_cagra<T, IdxT>::save_to_hnswlib(const std::string& file) const
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::load(const std::string& file)
 {
-  index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(handle_);
-  cuvs::neighbors::cagra::deserialize(handle_, file, index_.get());
+  std::ifstream meta(file + ".submeta", std::ios::in);
+  if (index_params_.num_dataset_splits > 1 &&
+      index_params_.merge_type == CagraMergeType::kLogical && meta.good()) {
+    // Load multiple sub-indices for logical merge
+    size_t count;
+    meta >> count;
+    meta.close();
+    sub_indices_.clear();
+    for (size_t i = 0; i < count; ++i) {
+      std::string subfile = file + (i == 0 ? "" : ".subidx." + std::to_string(i));
+      auto sub_index      = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(handle_);
+      cuvs::neighbors::cagra::deserialize(handle_, subfile, sub_index.get());
+      sub_indices_.push_back(std::move(sub_index));
+    }
+  } else {
+    index_ = std::make_shared<cuvs::neighbors::cagra::index<T, IdxT>>(handle_);
+    cuvs::neighbors::cagra::deserialize(handle_, file, index_.get());
+  }
 }
 
 template <typename T, typename IdxT>
@@ -377,8 +485,41 @@ void cuvs_cagra<T, IdxT>::search_base(
                                               neighbors_view,
                                               distances_view);
   } else {
-    cuvs::neighbors::cagra::search(
-      handle_, search_params_, *index_, queries_view, neighbors_view, distances_view, *filter_);
+    if (index_params_.num_dataset_splits <= 1 ||
+        index_params_.merge_type == CagraMergeType::kPhysical) {
+      cuvs::neighbors::cagra::search(
+        handle_, search_params_, *index_, queries_view, neighbors_view, distances_view, *filter_);
+    } else {
+      if (index_params_.merge_type == CagraMergeType::kLogical) {
+        cuvs::neighbors::cagra::merge_params merge_params{index_params_.cagra_params};
+        merge_params.merge_strategy = cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL;
+
+        // Create wrapped indices for composite merge
+        std::vector<std::shared_ptr<cuvs::neighbors::IndexWrapper<T, IdxT, algo_base::index_type>>>
+          wrapped_indices;
+        wrapped_indices.reserve(sub_indices_.size());
+        for (auto& ptr : sub_indices_) {
+          auto index_wrapper =
+            cuvs::neighbors::cagra::make_index_wrapper<T, IdxT, algo_base::index_type>(ptr.get());
+          wrapped_indices.push_back(index_wrapper);
+        }
+
+        raft::resources composite_handle(handle_);
+        size_t n_streams = wrapped_indices.size();
+        raft::resource::set_cuda_stream_pool(composite_handle,
+                                             std::make_shared<rmm::cuda_stream_pool>(n_streams));
+
+        auto merged_index =
+          cuvs::neighbors::composite::merge(composite_handle, merge_params, wrapped_indices);
+        cuvs::neighbors::filtering::none_sample_filter empty_filter;
+        merged_index->search(composite_handle,
+                             search_params_,
+                             queries_view,
+                             neighbors_view,
+                             distances_view,
+                             empty_filter);
+      }
+    }
   }
 }
 
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 036dbba9c8..488c769312 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -18,7 +18,6 @@
 
 #include "common.hpp"
 #include <cuvs/distance/distance.hpp>
-#include <cuvs/neighbors/common.h>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
@@ -275,17 +274,10 @@ struct extend_params {
  * @{
  */
 
-/**
- * @brief Determines the strategy for merging CAGRA graphs.
- *
- * @note Currently, only the MERGE_STRATEGY_PHYSICAL strategy is supported.
- */
-using MergeStrategy = cuvsMergeStrategy;
-
 /**
  * @brief Parameters for merging CAGRA indexes.
  */
-struct merge_params {
+struct merge_params : cuvs::neighbors::merge_params {
   merge_params() = default;
 
   /**
@@ -298,7 +290,11 @@ struct merge_params {
   cagra::index_params output_index_params;
 
   /// Strategy for merging. Defaults to `MergeStrategy::MERGE_STRATEGY_PHYSICAL`.
-  MergeStrategy strategy = MergeStrategy::MERGE_STRATEGY_PHYSICAL;
+  cuvs::neighbors::MergeStrategy merge_strategy =
+    cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL;
+
+  /// Implementation of the polymorphic strategy() method
+  cuvs::neighbors::MergeStrategy strategy() const { return merge_strategy; }
 };
 
 /**
@@ -2602,3 +2598,5 @@ auto distribute(const raft::resources& clique, const std::string& filename)
   -> cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>;
 
 }  // namespace cuvs::neighbors::cagra
+
+#include <cuvs/neighbors/cagra_index_wrapper.hpp>
diff --git a/cpp/include/cuvs/neighbors/cagra_index_wrapper.hpp b/cpp/include/cuvs/neighbors/cagra_index_wrapper.hpp
new file mode 100644
index 0000000000..c7223d12cf
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/cagra_index_wrapper.hpp
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/index_wrappers.hpp>
+#include <memory>
+
+// Forward declarations to avoid circular dependencies
+namespace cuvs::neighbors::cagra {
+template <typename T, typename IdxT>
+struct index;
+struct merge_params;
+}  // namespace cuvs::neighbors::cagra
+
+namespace cuvs::neighbors::cagra {
+
+/**
+ * @brief Wrapper for CAGRA index implementing IndexWrapper.
+ *
+ * This class wraps a CAGRA index and provides compatibility with the IndexBase interface.
+ * It serves as a bridge to help the CAGRA index implementation transition from its
+ * original design to the new object-oriented polymorphic design based on IndexBase.
+ *
+ * The wrapper enables:
+ * - CAGRA index to work seamlessly with the new polymorphic IndexBase interface
+ * - Gradual migration from the original CAGRA API to the unified index architecture
+ * - Compatibility with composite index patterns and other polymorphic usage scenarios
+ * - Preservation of existing CAGRA functionality while adopting the new design patterns
+ *
+ * This allows existing CAGRA users to benefit from the new architecture without
+ * requiring immediate changes to their existing code, while new users can adopt
+ * the unified interface from the start.
+ */
+template <typename T, typename IdxT, typename OutputIdxT = IdxT>
+class IndexWrapper : public cuvs::neighbors::IndexWrapper<T, IdxT, OutputIdxT> {
+ public:
+  using base_type         = cuvs::neighbors::IndexWrapper<T, IdxT, OutputIdxT>;
+  using value_type        = typename base_type::value_type;
+  using index_type        = typename base_type::index_type;
+  using out_index_type    = typename base_type::out_index_type;
+  using matrix_index_type = typename base_type::matrix_index_type;
+
+  explicit IndexWrapper(cuvs::neighbors::cagra::index<T, IdxT>* idx);
+
+  void search(
+    const raft::resources& handle,
+    const cuvs::neighbors::search_params& params,
+    raft::device_matrix_view<const value_type, matrix_index_type, raft::row_major> queries,
+    raft::device_matrix_view<out_index_type, matrix_index_type, raft::row_major> neighbors,
+    raft::device_matrix_view<float, matrix_index_type, raft::row_major> distances,
+    const cuvs::neighbors::filtering::base_filter& filter =
+      cuvs::neighbors::filtering::none_sample_filter{}) const override;
+
+  index_type size() const noexcept override;
+
+  cuvs::distance::DistanceType metric() const noexcept override;
+
+  /**
+   * @brief Merge this CAGRA index with other CAGRA indices.
+   *
+   * This method provides merge capability for CAGRA indices. It supports both
+   * physical merge (calling native CAGRA merge) and logical merge (creating
+   * CompositeIndex with wrapped indices).
+   *
+   * @param[in] handle RAFT resources for executing operations
+   * @param[in] params Merge parameters containing strategy and CAGRA-specific settings
+   * @param[in] other_indices Vector of other indices to merge with this one
+   * @return Shared pointer to merged index
+   */
+  std::shared_ptr<cuvs::neighbors::IndexBase<value_type, index_type, out_index_type>> merge(
+    const raft::resources& handle,
+    const cuvs::neighbors::merge_params& params,
+    const std::vector<
+      std::shared_ptr<cuvs::neighbors::IndexBase<value_type, index_type, out_index_type>>>&
+      other_indices) const override;
+
+ protected:
+  const cuvs::neighbors::search_params& convert_search_params(
+    const cuvs::neighbors::search_params& params) const override
+  {
+    // For CAGRA, we expect the params to be cagra::search_params
+    // This is handled in the search method via static_cast
+    return params;
+  }
+
+ private:
+  cuvs::neighbors::cagra::index<T, IdxT>* index_;
+};
+
+/**
+ * @brief Factory function for creating a wrapped CAGRA index.
+ *
+ * This function creates a shared pointer to an IndexWrapper that wraps a CAGRA index,
+ * enabling it to work with the polymorphic IndexBase interface and composite operations.
+ *
+ * @tparam T Data type
+ * @tparam IdxT Index type
+ * @tparam OutputIdxT Output index type
+ * @param index Pointer to the CAGRA index
+ * @return Shared pointer to the wrapped index
+ *
+ * @par Example usage:
+ * @code{.cpp}
+ * // Create multiple CAGRA indices
+ * auto cagra_index1 = cuvs::neighbors::cagra::build(res, params, dataset1);
+ * auto cagra_index2 = cuvs::neighbors::cagra::build(res, params, dataset2);
+ *
+ * // Wrap them for polymorphic usage
+ * auto wrapped_index1 = cuvs::neighbors::cagra::make_index_wrapper(&cagra_index1);
+ * auto wrapped_index2 = cuvs::neighbors::cagra::make_index_wrapper(&cagra_index2);
+ *
+ * // Merge indices using the composite merge function
+ * std::vector<std::shared_ptr<cuvs::neighbors::IndexWrapper<float, uint32_t>>> indices;
+ * indices.push_back(wrapped_index1);
+ * indices.push_back(wrapped_index2);
+ *
+ * cuvs::neighbors::cagra::merge_params merge_params;
+ * auto merged_index = cuvs::neighbors::composite::merge(res, merge_params, indices);
+ * @endcode
+ */
+template <typename T, typename IdxT, typename OutputIdxT = IdxT>
+inline auto make_index_wrapper(cuvs::neighbors::cagra::index<T, IdxT>* index)
+  -> std::shared_ptr<cuvs::neighbors::IndexWrapper<T, IdxT, OutputIdxT>>
+{
+  return std::make_shared<IndexWrapper<T, IdxT, OutputIdxT>>(index);
+}
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index cb56e9f8c2..47155ed47e 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -96,6 +96,26 @@ struct index_params {
 
 struct search_params {};
 
+/**
+ * @brief Strategy for merging indices.
+ *
+ * This enum is declared separately to avoid namespace pollution when including common.hpp.
+ * It provides a generic merge strategy that can be used across different index types.
+ */
+enum class MergeStrategy {
+  /** Merge indices physically by combining their data structures */
+  MERGE_STRATEGY_PHYSICAL = 0,
+  /** Merge indices logically by creating a composite wrapper */
+  MERGE_STRATEGY_LOGICAL = 1
+};
+
+/** Base merge parameters with polymorphic interface. */
+struct merge_params {
+  virtual ~merge_params() = default;
+
+  virtual MergeStrategy strategy() const = 0;
+};
+
 /** @} */  // end group neighbors_index
 
 /** Two-dimensional dataset; maybe owning, maybe compressed, maybe strided. */
diff --git a/cpp/include/cuvs/neighbors/composite/index.hpp b/cpp/include/cuvs/neighbors/composite/index.hpp
new file mode 100644
index 0000000000..df256e43d0
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/composite/index.hpp
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/index_base.hpp>
+#include <raft/core/device_mdspan.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cuvs::neighbors::composite {
+
+/**
+ * @brief Composite index made of other IndexBase implementations.
+ */
+template <typename T, typename IdxT, typename OutputIdxT = IdxT>
+class CompositeIndex : public IndexBase<T, IdxT, OutputIdxT> {
+ public:
+  using value_type        = typename IndexBase<T, IdxT, OutputIdxT>::value_type;
+  using index_type        = typename IndexBase<T, IdxT, OutputIdxT>::index_type;
+  using out_index_type    = typename IndexBase<T, IdxT, OutputIdxT>::out_index_type;
+  using matrix_index_type = typename IndexBase<T, IdxT, OutputIdxT>::matrix_index_type;
+
+  using index_ptr = std::shared_ptr<IndexBase<value_type, index_type, out_index_type>>;
+
+  explicit CompositeIndex(std::vector<index_ptr> children) : children_(std::move(children)) {}
+
+  /**
+   * @brief Search the composite index for the k nearest neighbors.
+   *
+   * When the composite index contains multiple sub-indices, the user can set a
+   * stream pool in the input raft::resource to enable parallel search across
+   * sub-indices for improved performance.
+   *
+   * Usage example:
+   * @code{.cpp}
+   *   using namespace cuvs::neighbors;
+   *   // create a composite index with multiple sub-indices
+   *   std::vector<CompositeIndex<T, IdxT>::index_ptr> sub_indices;
+   *   // ... populate sub_indices ...
+   *   auto composite_index = CompositeIndex<T, IdxT>(std::move(sub_indices));
+   *
+   *   // optional: create a stream pool to enable parallel search across sub-indices
+   *   // recommended stream count: min(number_of_sub_indices, 8)
+   *   size_t n_streams = std::min(sub_indices.size(), size_t(8));
+   *   raft::resource::set_cuda_stream_pool(handle,
+   *                                        std::make_shared<rmm::cuda_stream_pool>(n_streams));
+   *
+   *   // perform search with parallel sub-index execution
+   *   composite_index.search(handle, search_params, queries, neighbors, distances);
+   * @endcode
+   *
+   * @param[in] handle raft resource handle
+   * @param[in] params search parameters
+   * @param[in] queries device matrix view of query vectors [n_queries, dim]
+   * @param[out] neighbors device matrix view for neighbor indices [n_queries, k]
+   * @param[out] distances device matrix view for distances [n_queries, k]
+   * @param[in] filter optional filter for search results
+   */
+  void search(
+    const raft::resources& handle,
+    const cuvs::neighbors::search_params& params,
+    raft::device_matrix_view<const value_type, matrix_index_type, raft::row_major> queries,
+    raft::device_matrix_view<out_index_type, matrix_index_type, raft::row_major> neighbors,
+    raft::device_matrix_view<float, matrix_index_type, raft::row_major> distances,
+    const cuvs::neighbors::filtering::base_filter& filter =
+      cuvs::neighbors::filtering::none_sample_filter{}) const override;
+
+  index_type size() const noexcept override
+  {
+    index_type total = 0;
+    for (const auto& c : children_) {
+      total += c->size();
+    }
+    return total;
+  }
+
+  cuvs::distance::DistanceType metric() const noexcept override
+  {
+    return children_.empty() ? cuvs::distance::DistanceType::L2Expanded
+                             : children_.front()->metric();
+  }
+
+ private:
+  std::vector<index_ptr> children_;
+};
+
+}  // namespace cuvs::neighbors::composite
diff --git a/cpp/include/cuvs/neighbors/composite/merge.hpp b/cpp/include/cuvs/neighbors/composite/merge.hpp
new file mode 100644
index 0000000000..6635b7722e
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/composite/merge.hpp
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../index_base.hpp"
+#include "../index_wrappers.hpp"
+#include <cuvs/neighbors/cagra.hpp>
+#include <raft/core/resources.hpp>
+
+#include <vector>
+
+namespace cuvs::neighbors::composite {
+
+/**
+ * @brief Merge multiple indices into a single composite index.
+ *
+ * This function provides polymorphic merge capability for different index types.
+ * It delegates to the first index's merge method, which handles the actual merging
+ * logic based on the index type and merge parameters.
+ *
+ * @tparam T Data element type
+ * @tparam IdxT Index type for vector indices
+ * @tparam OutputIdxT Output index type
+ * @param[in] handle RAFT resources for executing operations
+ * @param[in] params Merge parameters containing strategy and algorithm-specific settings
+ * @param[in] indices Vector of IndexWrapper pointers to merge
+ * @return Shared pointer to merged composite index
+ */
+template <typename T, typename IdxT, typename OutputIdxT = IdxT>
+std::shared_ptr<IndexBase<T, IdxT, OutputIdxT>> merge(
+  const raft::resources& handle,
+  const cuvs::neighbors::merge_params& params,
+  std::vector<std::shared_ptr<cuvs::neighbors::IndexWrapper<T, IdxT, OutputIdxT>>>& indices);
+
+}  // namespace cuvs::neighbors::composite
diff --git a/cpp/include/cuvs/neighbors/index_base.hpp b/cpp/include/cuvs/neighbors/index_base.hpp
new file mode 100644
index 0000000000..f7cc65c0f0
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/index_base.hpp
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/common.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cuvs::neighbors {
+
+/**
+ * @brief Common interface for ANN index implementations.
+ *
+ * Provides a unified abstraction for different approximate nearest neighbor (ANN) index types,
+ * enabling polymorphic access to core operations such as build, search, size, and metric.
+ *
+ * This interface allows:
+ * - Polymorphic usage of index implementations across the library
+ * - Easy integration of new index types via a consistent API
+ * - Support for composite or hierarchical index structures
+ * - Seamless compatibility between existing and future components
+ *
+ * @tparam T Data type of vectors (e.g., float, int8_t)
+ * @tparam IdxT Index type for dataset rows
+ * @tparam OutputIdxT Output index type (defaults to IdxT)
+ */
+template <typename T, typename IdxT, typename OutputIdxT = IdxT>
+struct IndexBase {
+  using value_type        = T;
+  using index_type        = IdxT;
+  using out_index_type    = OutputIdxT;
+  using matrix_index_type = int64_t;
+
+  // Don't allow copying the index for performance reasons
+  IndexBase()                                    = default;
+  IndexBase(const IndexBase&)                    = delete;
+  IndexBase(IndexBase&&)                         = default;
+  auto operator=(const IndexBase&) -> IndexBase& = delete;
+  auto operator=(IndexBase&&) -> IndexBase&      = default;
+  virtual ~IndexBase()                           = default;
+
+  /* Future implementation:
+  virtual void build(
+    const raft::resources& handle,
+    const cuvs::neighbors::build_params& params,
+    raft::device_matrix_view<const value_type, matrix_index_type, raft::row_major> dataset) = 0;
+  */
+
+  /**
+   * @brief Perform approximate nearest neighbor search.
+   *
+   * Searches the index for the k-nearest neighbors of each query point.
+   * The number of neighbors to find is determined by the neighbors matrix extent.
+   *
+   * @param[in] handle CUDA resources for executing operations
+   * @param[in] params Search parameters specific to the index implementation
+   * @param[in] queries Matrix of query vectors to search for [n_queries, dim]
+   * @param[out] neighbors Matrix to store neighbor indices [n_queries, k]
+   * @param[out] distances Matrix to store distances to neighbors [n_queries, k]
+   * @param[in] filter Optional filter to exclude certain vectors from search results
+   */
+  virtual void search(
+    const raft::resources& handle,
+    const cuvs::neighbors::search_params& params,
+    raft::device_matrix_view<const value_type, matrix_index_type, raft::row_major> queries,
+    raft::device_matrix_view<out_index_type, matrix_index_type, raft::row_major> neighbors,
+    raft::device_matrix_view<float, matrix_index_type, raft::row_major> distances,
+    const cuvs::neighbors::filtering::base_filter& filter =
+      cuvs::neighbors::filtering::none_sample_filter{}) const = 0;
+
+  /**
+   * @brief Get the number of vectors in the index.
+   *
+   * @return Number of indexed vectors
+   */
+  virtual index_type size() const noexcept = 0;
+
+  /**
+   * @brief Get the distance metric used by the index.
+   *
+   * @return Distance metric type (e.g., L2, InnerProduct)
+   */
+  virtual cuvs::distance::DistanceType metric() const noexcept = 0;
+};
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/include/cuvs/neighbors/index_wrappers.hpp b/cpp/include/cuvs/neighbors/index_wrappers.hpp
new file mode 100644
index 0000000000..fd13f5f1a5
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/index_wrappers.hpp
@@ -0,0 +1,199 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/index_base.hpp>
+#include <memory>
+
+namespace cuvs::neighbors {
+
+/**
+ * @brief Intermediate wrapper layer for handling transition between new and old index designs.
+ *
+ * This class provides a common interface for wrapping various index implementations
+ * and handles compatibility issues between different algorithm implementations.
+ * It serves as an adapter layer that:
+ * - Provides default implementations for common functionality
+ * - Handles parameter conversion between different index types
+ * - Manages post-processing of search results
+ * - Facilitates gradual migration from old to new index designs
+ *
+ * The primary purpose of this wrapper is to help existing index implementations
+ * transition from their original designs to the new object-oriented polymorphic
+ * design based on IndexBase. This allows:
+ * - Legacy index implementations to work with the new polymorphic interface
+ * - Gradual refactoring of existing code without breaking changes
+ * - Unified access patterns across different index types
+ * - Smooth migration path for users adopting the new architecture
+ *
+ * By using this wrapper, existing index types (like CAGRA, IVF-PQ, IVF-Flat, HNSW)
+ * can be adapted to work with the IndexBase interface without requiring immediate
+ * complete rewrites of their internal implementations.
+ *
+ * @tparam T Data element type (e.g., float, int8, uint8)
+ * @tparam IdxT Index type for vector indices
+ * @tparam OutputIdxT Output index type, defaults to IdxT
+ */
+template <typename T, typename IdxT, typename OutputIdxT = IdxT>
+class IndexWrapper : public IndexBase<T, IdxT, OutputIdxT> {
+ public:
+  /** Type definitions inherited from base class */
+  using value_type        = typename IndexBase<T, IdxT, OutputIdxT>::value_type;
+  using index_type        = typename IndexBase<T, IdxT, OutputIdxT>::index_type;
+  using out_index_type    = typename IndexBase<T, IdxT, OutputIdxT>::out_index_type;
+  using matrix_index_type = typename IndexBase<T, IdxT, OutputIdxT>::matrix_index_type;
+
+  /** Virtual destructor to enable proper cleanup of derived classes */
+  virtual ~IndexWrapper() = default;
+
+  /**
+   * @brief Merge this index with other indices (optional functionality).
+   *
+   * This interface provides polymorphic merge capability for index types that support merging.
+   * The merge strategy and parameters are determined by the specific merge_params implementation.
+   * Not all index types need to support merging, so this has a default implementation that
+   * throws an error.
+   *
+   * @param[in] handle RAFT resources for executing operations
+   * @param[in] params Merge parameters containing strategy and algorithm-specific settings
+   * @param[in] other_indices Vector of other indices to merge with this one
+   * @return Shared pointer to merged index
+   */
+  virtual std::shared_ptr<IndexBase<value_type, index_type, out_index_type>> merge(
+    const raft::resources& handle,
+    const cuvs::neighbors::merge_params& params,
+    const std::vector<std::shared_ptr<IndexBase<value_type, index_type, out_index_type>>>&
+      other_indices) const
+  {
+    // Default implementation: not supported
+    RAFT_FAIL("Merge operation not supported for this index type");
+  }
+
+ protected:
+  /**
+   * @brief Helper method for derived classes to handle parameter conversion.
+   *
+   * Derived classes can override this to provide custom parameter handling
+   * for their specific index types. The default implementation returns
+   * the parameters unchanged.
+   *
+   * @param[in] params Search parameters to convert
+   * @return Converted search parameters
+   */
+  virtual const search_params& convert_search_params(const search_params& params) const
+  {
+    return params;
+  }
+};
+
+/**
+ * @brief Migrating Existing Algorithms to New Polymorphic Index Architecture
+ *
+ * To migrate an existing index algorithm (e.g., IVF-PQ, IVF-Flat, HNSW) to the new polymorphic
+ * IndexBase architecture, follow these steps:
+ *
+ * 1. **Create algorithm-specific wrapper header**:
+ *    - Create `cpp/include/cuvs/neighbors/<algorithm>_index_wrapper.hpp`
+ *    - Define `<algorithm>::IndexWrapper` class inheriting from `cuvs::neighbors::IndexWrapper`
+ *    - Place it in the `cuvs::neighbors::<algorithm>` namespace
+ *    - Provide a `make_index_wrapper()` factory function
+ *
+ * 2. **Create algorithm-specific wrapper implementation**:
+ *    - Create `cpp/src/neighbors/<algorithm>_index_wrapper.cu`
+ *    - Implement the wrapper methods (search, size, metric, merge if supported)
+ *    - Bridge existing algorithm API to new IndexBase interface
+ *    - Add explicit template instantiations for supported data types
+ *
+ * 3. **Include wrapper in main algorithm header**:
+ *    - Add `#include <cuvs/neighbors/<algorithm>_index_wrapper.hpp>` at the end of
+ *      `cpp/include/cuvs/neighbors/<algorithm>.hpp`
+ *
+ * 4. **Update composite merge support** (if merge is supported):
+ *    - Update `cpp/src/neighbors/composite/merge.cpp` to handle the algorithm
+ *
+ * Example structure for algorithm "ivf_pq":
+ * ```
+ * cpp/include/cuvs/neighbors/ivf_pq_index_wrapper.hpp:
+ *   namespace cuvs::neighbors::ivf_pq {
+ *     template<typename T, typename IdxT, typename OutputIdxT = IdxT>
+ *     class IndexWrapper : public cuvs::neighbors::IndexWrapper<T, IdxT, OutputIdxT> {
+ *       // Bridge existing ivf_pq::index to new interface
+ *     };
+ *
+ *     template<typename T, typename IdxT, typename OutputIdxT = IdxT>
+ *     auto make_index_wrapper(ivf_pq::index<T, IdxT>* index)
+ *       -> std::shared_ptr<cuvs::neighbors::IndexBase<T, IdxT, OutputIdxT>>;
+ *   }
+ *
+ * cpp/src/neighbors/ivf_pq_index_wrapper.cu:
+ *   // Implementation bridging old API to new interface + explicit instantiations
+ *
+ * cpp/include/cuvs/neighbors/ivf_pq.hpp:
+ *   // ... existing ivf_pq algorithm code ...
+ *   }  // namespace cuvs::neighbors::ivf_pq
+ *
+ *   #include <cuvs/neighbors/ivf_pq_index_wrapper.hpp>
+ * ```
+ *
+ * Usage example (following CAGRA pattern for same-algorithm composite):
+ * ```cpp
+ * // 1. Build multiple CAGRA indices on different data partitions
+ * auto dataset1 = raft::make_device_matrix<float, int64_t>(res, size1, dim);
+ * auto dataset2 = raft::make_device_matrix<float, int64_t>(res, size2, dim);
+ * auto dataset3 = raft::make_device_matrix<float, int64_t>(res, size3, dim);
+ *
+ * auto cagra_index1 = cuvs::neighbors::cagra::build(res, params, dataset1.view());
+ * auto cagra_index2 = cuvs::neighbors::cagra::build(res, params, dataset2.view());
+ * auto cagra_index3 = cuvs::neighbors::cagra::build(res, params, dataset3.view());
+ *
+ * // 2. Wrap each index for polymorphic usage
+ * auto wrapped_index1 = cuvs::neighbors::cagra::make_index_wrapper(&cagra_index1);
+ * auto wrapped_index2 = cuvs::neighbors::cagra::make_index_wrapper(&cagra_index2);
+ * auto wrapped_index3 = cuvs::neighbors::cagra::make_index_wrapper(&cagra_index3);
+ *
+ * // 3. Merge indices using the composite merge function
+ * std::vector<std::shared_ptr<cuvs::neighbors::IndexWrapper<float, uint32_t>>> indices;
+ * indices.push_back(wrapped_index1);
+ * indices.push_back(wrapped_index2);
+ * indices.push_back(wrapped_index3);
+ *
+ * cuvs::neighbors::cagra::merge_params merge_params;
+ * auto merged_index = cuvs::neighbors::composite::merge(res, merge_params, indices);
+ *
+ * // 4. Search using the merged index
+ * auto queries = raft::make_device_matrix<float, int64_t>(res, n_queries, dim);
+ * auto neighbors = raft::make_device_matrix<uint32_t, int64_t>(res, n_queries, k);
+ * auto distances = raft::make_device_matrix<float, int64_t>(res, n_queries, k);
+ *
+ * cuvs::neighbors::cagra::search_params search_params;
+ * merged_index->search(res, search_params, queries.view(), neighbors.view(), distances.view());
+ *
+ * // The merge function automatically:
+ * // - Merges all 3 CAGRA indices based on the merge strategy
+ * // - Returns a unified index that can search across all partitions
+ * // - Handles global addressing and result merging internally
+ * ```
+ *
+ * Migration benefits:
+ * - Enables distributed/partitioned indexing with same design
+ * - Maintains full backward compatibility with existing APIs
+ * - Allows gradual transition to new architecture
+ * - Enables composite index functionality for data partitioning
+ * - Provides unified search interface across multiple index instances
+ */
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 3919626c80..2e528cec7c 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -337,7 +337,6 @@ void search(raft::resources const& res,
     auto sample_filter_copy = sample_filter;
     return search_with_filtering<T, IdxT, none_filter_type, OutputIdxT>(
       res, params_copy, idx, queries, neighbors, distances, sample_filter_copy);
-    return;
   } catch (const std::bad_cast&) {
   }
 
diff --git a/cpp/src/neighbors/cagra_index_wrapper.cu b/cpp/src/neighbors/cagra_index_wrapper.cu
new file mode 100644
index 0000000000..445ed77cec
--- /dev/null
+++ b/cpp/src/neighbors/cagra_index_wrapper.cu
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda_fp16.h>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/cagra_index_wrapper.hpp>
+#include <cuvs/neighbors/composite/index.hpp>
+
+namespace cuvs::neighbors::cagra {
+
+template <typename T, typename IdxT, typename OutputIdxT>
+IndexWrapper<T, IdxT, OutputIdxT>::IndexWrapper(cuvs::neighbors::cagra::index<T, IdxT>* idx)
+  : index_(idx)
+{
+}
+
+template <typename T, typename IdxT, typename OutputIdxT>
+void IndexWrapper<T, IdxT, OutputIdxT>::search(
+  const raft::resources& handle,
+  const cuvs::neighbors::search_params& params,
+  raft::device_matrix_view<const value_type, matrix_index_type, raft::row_major> queries,
+  raft::device_matrix_view<out_index_type, matrix_index_type, raft::row_major> neighbors,
+  raft::device_matrix_view<float, matrix_index_type, raft::row_major> distances,
+  const cuvs::neighbors::filtering::base_filter& filter) const
+{
+  auto const& cagra_params = static_cast<const cuvs::neighbors::cagra::search_params&>(params);
+  cuvs::neighbors::cagra::search(
+    handle, cagra_params, *index_, queries, neighbors, distances, filter);
+}
+
+template <typename T, typename IdxT, typename OutputIdxT>
+typename IndexWrapper<T, IdxT, OutputIdxT>::index_type IndexWrapper<T, IdxT, OutputIdxT>::size()
+  const noexcept
+{
+  return index_->size();
+}
+
+template <typename T, typename IdxT, typename OutputIdxT>
+cuvs::distance::DistanceType IndexWrapper<T, IdxT, OutputIdxT>::metric() const noexcept
+{
+  return index_->metric();
+}
+
+template <typename T, typename IdxT, typename OutputIdxT>
+std::shared_ptr<
+  cuvs::neighbors::IndexBase<typename IndexWrapper<T, IdxT, OutputIdxT>::value_type,
+                             typename IndexWrapper<T, IdxT, OutputIdxT>::index_type,
+                             typename IndexWrapper<T, IdxT, OutputIdxT>::out_index_type>>
+IndexWrapper<T, IdxT, OutputIdxT>::merge(
+  const raft::resources& handle,
+  const cuvs::neighbors::merge_params& params,
+  const std::vector<
+    std::shared_ptr<cuvs::neighbors::IndexBase<value_type, index_type, out_index_type>>>&
+    other_indices) const
+{
+  const auto* cagra_params = dynamic_cast<const cuvs::neighbors::cagra::merge_params*>(&params);
+  if (!cagra_params) { RAFT_FAIL("CAGRA IndexWrapper::merge requires cagra::merge_params"); }
+
+  std::vector<cuvs::neighbors::cagra::index<T, IdxT>*> cagra_indices;
+  cagra_indices.push_back(index_);
+
+  for (const auto& other : other_indices) {
+    const auto* other_wrapper = dynamic_cast<const IndexWrapper<T, IdxT, OutputIdxT>*>(other.get());
+    if (!other_wrapper) {
+      RAFT_FAIL("CAGRA IndexWrapper::merge can only merge with other CAGRA indices");
+    }
+    cagra_indices.push_back(other_wrapper->index_);
+  }
+
+  if (cagra_params->strategy() == cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL) {
+    std::vector<std::shared_ptr<cuvs::neighbors::IndexBase<T, IdxT, OutputIdxT>>> wrappers;
+    wrappers.reserve(cagra_indices.size());
+    for (auto* idx : cagra_indices) {
+      wrappers.push_back(std::make_shared<IndexWrapper<T, IdxT, OutputIdxT>>(idx));
+    }
+    return std::make_shared<cuvs::neighbors::composite::CompositeIndex<T, IdxT, OutputIdxT>>(
+      std::move(wrappers));
+  } else if (cagra_params->strategy() == cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL) {
+    auto merged_index = cuvs::neighbors::cagra::merge(handle, *cagra_params, cagra_indices);
+    auto* idx         = new decltype(merged_index)(std::move(merged_index));
+    return std::make_shared<IndexWrapper<T, IdxT, OutputIdxT>>(idx);
+  }
+
+  RAFT_FAIL("Invalid merge strategy");
+}
+
+template class IndexWrapper<float, uint32_t, uint32_t>;
+template class IndexWrapper<half, uint32_t, uint32_t>;
+template class IndexWrapper<int8_t, uint32_t, uint32_t>;
+template class IndexWrapper<uint8_t, uint32_t, uint32_t>;
+
+template class IndexWrapper<float, uint32_t, int64_t>;
+template class IndexWrapper<half, uint32_t, int64_t>;
+template class IndexWrapper<int8_t, uint32_t, int64_t>;
+template class IndexWrapper<uint8_t, uint32_t, int64_t>;
+
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/composite/index.cu b/cpp/src/neighbors/composite/index.cu
new file mode 100644
index 0000000000..314ec681f7
--- /dev/null
+++ b/cpp/src/neighbors/composite/index.cu
@@ -0,0 +1,130 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/composite/index.hpp>
+#include <cuvs/selection/select_k.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/cuda_stream_pool.hpp>
+#include <raft/linalg/add.cuh>
+#include <rmm/device_uvector.hpp>
+
+namespace cuvs::neighbors::composite {
+
+template <typename T, typename IdxT, typename OutputIdxT>
+void CompositeIndex<T, IdxT, OutputIdxT>::search(
+  const raft::resources& handle,
+  const cuvs::neighbors::search_params& params,
+  raft::device_matrix_view<const value_type, matrix_index_type, raft::row_major> queries,
+  raft::device_matrix_view<out_index_type, matrix_index_type, raft::row_major> neighbors,
+  raft::device_matrix_view<float, matrix_index_type, raft::row_major> distances,
+  const cuvs::neighbors::filtering::base_filter& filter) const
+{
+  if (children_.empty()) {
+    RAFT_FAIL("The composite index is empty!");
+    return;
+  }
+
+  if (children_.size() == 1) {
+    children_.front()->search(handle, params, queries, neighbors, distances, filter);
+    return;
+  }
+
+  size_t num_queries = queries.extent(0);
+  size_t K           = neighbors.extent(1);
+  size_t num_indices = children_.size();
+  size_t buffer_size = num_queries * K * num_indices;
+
+  auto main_stream = raft::resource::get_cuda_stream(handle);
+  auto tmp_res     = raft::resource::get_workspace_resource(handle);
+
+  rmm::device_uvector<out_index_type> neighbors_buffer(buffer_size, main_stream, tmp_res);
+  rmm::device_uvector<float> distances_buffer(buffer_size, main_stream, tmp_res);
+
+  std::vector<rmm::device_uvector<out_index_type>> temp_neighbors;
+  std::vector<rmm::device_uvector<float>> temp_distances;
+
+  for (size_t i = 0; i < num_indices; i++) {
+    temp_neighbors.emplace_back(num_queries * K, main_stream, tmp_res);
+    temp_distances.emplace_back(num_queries * K, main_stream, tmp_res);
+  }
+
+  raft::resource::wait_stream_pool_on_stream(handle);
+
+  out_index_type offset = 0;
+  out_index_type stride = K * num_indices;
+
+  for (size_t i = 0; i < num_indices; i++) {
+    const auto& sub_index = children_[i];
+
+    auto stream = raft::resource::get_next_usable_stream(handle, i);
+
+    raft::resources stream_pool_handle(handle);
+    raft::resource::set_cuda_stream(stream_pool_handle, stream);
+
+    auto temp_neighbors_view =
+      raft::make_device_matrix_view<out_index_type, matrix_index_type, raft::row_major>(
+        temp_neighbors[i].data(), num_queries, K);
+    auto temp_distances_view =
+      raft::make_device_matrix_view<float, matrix_index_type, raft::row_major>(
+        temp_distances[i].data(), num_queries, K);
+
+    sub_index->search(
+      stream_pool_handle, params, queries, temp_neighbors_view, temp_distances_view, filter);
+
+    if (offset != 0) {
+      raft::linalg::addScalar(temp_neighbors[i].data(),
+                              temp_neighbors[i].data(),
+                              offset,
+                              temp_neighbors[i].size(),
+                              stream);
+    }
+
+    raft::copy_matrix(
+      neighbors_buffer.data() + i * K, stride, temp_neighbors[i].data(), K, K, num_queries, stream);
+    raft::copy_matrix(
+      distances_buffer.data() + i * K, stride, temp_distances[i].data(), K, K, num_queries, stream);
+
+    offset += sub_index->size();
+  }
+  raft::resource::sync_stream_pool(handle);
+
+  auto distances_view = raft::make_device_matrix_view<const float, matrix_index_type>(
+    distances_buffer.data(), num_queries, K * num_indices);
+  auto neighbors_view = raft::make_device_matrix_view<const out_index_type, matrix_index_type>(
+    neighbors_buffer.data(), num_queries, K * num_indices);
+
+  cuvs::selection::select_k(handle,
+                            distances_view,
+                            neighbors_view,
+                            distances,
+                            neighbors,
+                            cuvs::distance::is_min_close(metric()),
+                            true,  // stable_sort
+                            cuvs::selection::SelectAlgo::kAuto);
+}
+
+// Explicit instantiations
+template class CompositeIndex<float, uint32_t, uint32_t>;
+template class CompositeIndex<float, uint32_t, int64_t>;
+template class CompositeIndex<half, uint32_t, uint32_t>;
+template class CompositeIndex<half, uint32_t, int64_t>;
+template class CompositeIndex<int8_t, uint32_t, uint32_t>;
+template class CompositeIndex<int8_t, uint32_t, int64_t>;
+template class CompositeIndex<uint8_t, uint32_t, uint32_t>;
+template class CompositeIndex<uint8_t, uint32_t, int64_t>;
+
+}  // namespace cuvs::neighbors::composite
diff --git a/cpp/src/neighbors/composite/merge.cpp b/cpp/src/neighbors/composite/merge.cpp
new file mode 100644
index 0000000000..5a4351c548
--- /dev/null
+++ b/cpp/src/neighbors/composite/merge.cpp
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cstddef>
+#include <cstdint>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/composite/merge.hpp>
+#include <cuvs/neighbors/index_base.hpp>
+#include <cuvs/neighbors/index_wrappers.hpp>
+#include <memory>
+#include <raft/core/error.hpp>
+#include <raft/core/resources.hpp>
+#include <vector>
+
+namespace cuvs::neighbors::composite {
+
+/**
+ * @brief Merge multiple indices into a single composite index.
+ *
+ * This function provides polymorphic merge capability for different index types.
+ * It delegates to the first index's merge method, which handles the actual merging
+ * logic based on the index type and merge parameters.
+ *
+ * @tparam T Data element type
+ * @tparam IdxT Index type for vector indices
+ * @tparam OutputIdxT Output index type
+ * @param[in] handle RAFT resources for executing operations
+ * @param[in] params Merge parameters containing strategy and algorithm-specific settings
+ * @param[in] indices Vector of IndexWrapper pointers to merge
+ * @return Shared pointer to merged composite index
+ */
+template <typename T, typename IdxT, typename OutputIdxT>
+std::shared_ptr<cuvs::neighbors::IndexBase<T, IdxT, OutputIdxT>> merge(
+  const raft::resources& handle,
+  const cuvs::neighbors::merge_params& params,
+  std::vector<std::shared_ptr<cuvs::neighbors::IndexWrapper<T, IdxT, OutputIdxT>>>& indices)
+{
+  if (indices.empty()) { RAFT_FAIL("Cannot merge empty indices vector"); }
+
+  if (indices.size() == 1) { return indices[0]; }
+
+  auto first_index = indices[0];
+  std::vector<std::shared_ptr<cuvs::neighbors::IndexBase<T, IdxT, OutputIdxT>>> other_indices;
+  for (std::size_t i = 1; i < indices.size(); ++i) {
+    other_indices.push_back(indices[i]);
+  }
+
+  // Delegate to the first index's merge method
+  return first_index->merge(handle, params, other_indices);
+}
+
+#define INSTANTIATE(T, IdxT, OutputIdxT)                                    \
+  template std::shared_ptr<cuvs::neighbors::IndexBase<T, IdxT, OutputIdxT>> \
+  merge<T, IdxT, OutputIdxT>(                                               \
+    const raft::resources&,                                                 \
+    const cuvs::neighbors::merge_params&,                                   \
+    std::vector<std::shared_ptr<cuvs::neighbors::IndexWrapper<T, IdxT, OutputIdxT>>>&);
+
+INSTANTIATE(float, uint32_t, uint32_t);
+INSTANTIATE(half, uint32_t, uint32_t);
+INSTANTIATE(int8_t, uint32_t, uint32_t);
+INSTANTIATE(uint8_t, uint32_t, uint32_t);
+
+INSTANTIATE(float, uint32_t, int64_t);
+INSTANTIATE(half, uint32_t, int64_t);
+INSTANTIATE(int8_t, uint32_t, int64_t);
+INSTANTIATE(uint8_t, uint32_t, int64_t);
+
+#undef INSTANTIATE
+
+}  // namespace cuvs::neighbors::composite
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index d85866161d..192f7f1632 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -25,6 +25,7 @@
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/cudart_utils.hpp>
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
@@ -78,15 +79,13 @@ index<T, IdxT> merge(raft::resources const& handle,
   auto merge_dataset = [&](T* dst) {
     for (cagra_index_t* index : indices) {
       auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index->data());
-
-      RAFT_CUDA_TRY(cudaMemcpy2DAsync(dst + offset * dim,
-                                      sizeof(T) * dim,
-                                      strided_dset->view().data_handle(),
-                                      sizeof(T) * stride,
-                                      sizeof(T) * dim,
-                                      strided_dset->n_rows(),
-                                      cudaMemcpyDefault,
-                                      raft::resource::get_cuda_stream(handle)));
+      raft::copy_matrix(dst + offset * dim,
+                        dim,
+                        strided_dset->view().data_handle(),
+                        static_cast<size_t>(stride),
+                        dim,
+                        static_cast<size_t>(strided_dset->n_rows()),
+                        raft::resource::get_cuda_stream(handle));
 
       offset += IdxT(index->data().n_rows());
     }
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index 8ae5445961..79daf2ad6d 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -26,6 +26,7 @@
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/util/cudart_utils.hpp>
 
 #include <cuvs/distance/distance.hpp>
 
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 27812e57c9..4ddc3bd6a7 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -23,6 +23,8 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/composite/merge.hpp>
+#include <cuvs/neighbors/index_wrappers.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/device_resources.hpp>
 #include <raft/core/host_mdarray.hpp>
@@ -282,6 +284,8 @@ struct AnnCagraInputs {
   std::optional<vpq_params> compression           = std::nullopt;
 
   std::optional<bool> non_owning_memory_buffer_flag = std::nullopt;
+  cuvs::neighbors::MergeStrategy merge_strategy =
+    cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL;
 };
 
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
@@ -298,11 +302,13 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
 
   std::vector<std::string> algo       = {"single-cta", "multi_cta", "multi_kernel", "auto"};
   std::vector<std::string> build_algo = {"IVF_PQ", "NN_DESCENT", "ITERATIVE_CAGRA_SEARCH", "AUTO"};
+  std::vector<std::string> merge_strategy = {"PHYSICAL", "LOGICAL"};
   os << "{n_queries=" << p.n_queries << ", dataset shape=" << p.n_rows << "x" << p.dim
      << ", k=" << p.k << ", " << algo.at((int)p.algo) << ", max_queries=" << p.max_queries
      << ", itopk_size=" << p.itopk_size << ", search_width=" << p.search_width
      << ", metric=" << metric_str(p.metric) << ", " << (p.host_dataset ? "host" : "device")
-     << ", build_algo=" << build_algo.at((int)p.build_algo);
+     << ", build_algo=" << build_algo.at((int)p.build_algo)
+     << ", merge_logic=" << merge_strategy.at((int)p.merge_strategy);
   if ((int)p.build_algo == 0 && p.ivf_pq_search_refine_ratio) {
     os << "(refine_rate=" << *p.ivf_pq_search_refine_ratio << ')';
   }
@@ -936,6 +942,7 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
   }
 
  protected:
+  template <typename SearchIdxT = IdxT>
   void testCagra()
   {
     // TODO (tarang-jain): remove when NN Descent index building support InnerProduct. Reference
@@ -961,25 +968,25 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
     if (ps.n_rows < 8 && ps.build_algo == graph_build_algo::IVF_PQ) GTEST_SKIP();
 
     size_t queries_size = ps.n_queries * ps.k;
-    std::vector<IdxT> indices_Cagra(queries_size);
-    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<SearchIdxT> indices_Cagra(queries_size);
+    std::vector<SearchIdxT> indices_naive(queries_size);
     std::vector<DistanceT> distances_Cagra(queries_size);
     std::vector<DistanceT> distances_naive(queries_size);
 
     {
       rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
-      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+      rmm::device_uvector<SearchIdxT> indices_naive_dev(queries_size, stream_);
 
-      cuvs::neighbors::naive_knn<DistanceT, DataT, IdxT>(handle_,
-                                                         distances_naive_dev.data(),
-                                                         indices_naive_dev.data(),
-                                                         search_queries.data(),
-                                                         database.data(),
-                                                         ps.n_queries,
-                                                         ps.n_rows,
-                                                         ps.dim,
-                                                         ps.k,
-                                                         ps.metric);
+      cuvs::neighbors::naive_knn<DistanceT, DataT, SearchIdxT>(handle_,
+                                                               distances_naive_dev.data(),
+                                                               indices_naive_dev.data(),
+                                                               search_queries.data(),
+                                                               database.data(),
+                                                               ps.n_queries,
+                                                               ps.n_rows,
+                                                               ps.dim,
+                                                               ps.k,
+                                                               ps.metric);
       raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
       raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
       raft::resource::sync_stream(handle_);
@@ -987,7 +994,7 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
 
     {
       rmm::device_uvector<DistanceT> distances_dev(queries_size, stream_);
-      rmm::device_uvector<IdxT> indices_dev(queries_size, stream_);
+      rmm::device_uvector<SearchIdxT> indices_dev(queries_size, stream_);
 
       {
         cagra::index_params index_params;
@@ -1057,14 +1064,22 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
           index0 = cagra::build(handle_, index_params, database0_view);
           index1 = cagra::build(handle_, index_params, database1_view);
         };
-        std::vector<cagra::index<DataT, IdxT>*> indices{&index0, &index1};
+
+        // Convert traditional CAGRA indices to wrappers for polymorphic usage
+        std::vector<std::shared_ptr<cuvs::neighbors::IndexWrapper<DataT, IdxT, SearchIdxT>>>
+          wrapped_indices;
+        wrapped_indices.push_back(
+          std::make_shared<cuvs::neighbors::cagra::IndexWrapper<DataT, IdxT, SearchIdxT>>(&index0));
+        wrapped_indices.push_back(
+          std::make_shared<cuvs::neighbors::cagra::IndexWrapper<DataT, IdxT, SearchIdxT>>(&index1));
+
         cagra::merge_params merge_params{index_params};
-        auto index = cagra::merge(handle_, merge_params, indices);
+        merge_params.merge_strategy = ps.merge_strategy;
 
         auto search_queries_view = raft::make_device_matrix_view<const DataT, int64_t>(
           search_queries.data(), ps.n_queries, ps.dim);
-        auto indices_out_view =
-          raft::make_device_matrix_view<IdxT, int64_t>(indices_dev.data(), ps.n_queries, ps.k);
+        auto indices_out_view = raft::make_device_matrix_view<SearchIdxT, int64_t>(
+          indices_dev.data(), ps.n_queries, ps.k);
         auto dists_out_view = raft::make_device_matrix_view<DistanceT, int64_t>(
           distances_dev.data(), ps.n_queries, ps.k);
 
@@ -1074,8 +1089,11 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
         search_params.team_size   = ps.team_size;
         search_params.itopk_size  = ps.itopk_size;
 
-        cagra::search(
-          handle_, search_params, index, search_queries_view, indices_out_view, dists_out_view);
+        auto index = cuvs::neighbors::composite::merge<DataT, IdxT, SearchIdxT>(
+          handle_, merge_params, wrapped_indices);
+        index->search(
+          handle_, search_params, search_queries_view, indices_out_view, dists_out_view);
+
         raft::update_host(distances_Cagra.data(), distances_dev.data(), queries_size, stream_);
         raft::update_host(indices_Cagra.data(), indices_dev.data(), queries_size, stream_);
         raft::resource::sync_stream(handle_);
@@ -1136,7 +1154,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
   std::vector<AnnCagraInputs> inputs = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
-    {1, 8, 17},
+    {1, 8, 16},
     {16},  // k
     {graph_build_algo::NN_DESCENT,
      graph_build_algo::ITERATIVE_CAGRA_SEARCH},  // build algo. ITERATIVE_CAGRA_SEARCH is needed to
@@ -1149,7 +1167,12 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {true},
-    {0.995});
+    {0.995},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
 
   // Fixed dim, and changing neighbors and query size (output matrix size)
   auto inputs2 = raft::util::itertools::product<AnnCagraInputs>(
@@ -1172,7 +1195,7 @@ inline std::vector<AnnCagraInputs> generate_inputs()
   // Corner cases for small datasets
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {2},
-    {3, 5, 31, 32, 64, 101},
+    {3, 6, 31, 32, 64, 101},
     {1, 10},
     {2},  // k
     {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},
@@ -1184,7 +1207,12 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded},
     {false},
     {true},
-    {0.995});
+    {0.995},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Varying dim and build algo.
@@ -1206,7 +1234,12 @@ inline std::vector<AnnCagraInputs> generate_inputs()
      cuvs::distance::DistanceType::BitwiseHamming},
     {false},
     {true},
-    {0.995});
+    {0.995},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Varying team_size, graph_build_algo
@@ -1226,7 +1259,12 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {false},
-    {0.995});
+    {0.995},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Varying graph_build_algo, itopk_size
@@ -1246,7 +1284,12 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false},
     {true},
-    {0.995});
+    {0.995},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Varying n_rows, host_dataset
@@ -1264,7 +1307,12 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
     {false, true},
     {false},
-    {0.985});
+    {0.985},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // A few PQ configurations.
@@ -1283,8 +1331,14 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded},
     {false},
     {true},
-    {0.6});                      // don't demand high recall without refinement
-  for (uint32_t pq_len : {2}) {  // for now, only pq_len = 2 is supported, more options coming soon
+    {0.6},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});  // don't demand high recall
+                                                                // without refinement
+  for (uint32_t pq_len : {2}) {  // for now, only pq_len = 2 is supported, more options coming  soon
     for (uint32_t vq_n_centers : {100, 1000}) {
       for (auto input : inputs2) {
         vpq_params ps{};
@@ -1313,25 +1367,34 @@ inline std::vector<AnnCagraInputs> generate_inputs()
     {false, true},
     {false},
     {0.99},
-    {1.0f, 2.0f, 3.0f});
+    {1.0f, 2.0f, 3.0f},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Varying dim, adding non_owning_memory_buffer_flag
-  inputs2 =
-    raft::util::itertools::product<AnnCagraInputs>({100},
-                                                   {1000},
-                                                   {1, 5, 8, 64, 137, 256, 619, 1024},  // dim
-                                                   {10},
-                                                   {graph_build_algo::IVF_PQ},
-                                                   {search_algo::AUTO},
-                                                   {10},
-                                                   {0},  // team_size
-                                                   {64},
-                                                   {1},
-                                                   {cuvs::distance::DistanceType::L2Expanded},
-                                                   {false},
-                                                   {false},
-                                                   {0.995});
+  inputs2 = raft::util::itertools::product<AnnCagraInputs>(
+    {100},
+    {1000},
+    {1, 5, 8, 64, 137, 256, 619, 1024},  // dim
+    {10},
+    {graph_build_algo::IVF_PQ},
+    {search_algo::AUTO},
+    {10},
+    {0},  // team_size
+    {64},
+    {1},
+    {cuvs::distance::DistanceType::L2Expanded},
+    {false},
+    {false},
+    {0.995},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   for (auto input : inputs2) {
     input.non_owning_memory_buffer_flag = true;
     inputs.push_back(input);
diff --git a/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
index aed372b84c..501550c3b0 100644
--- a/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_float_uint32_t.cu
@@ -31,7 +31,8 @@ typedef AnnCagraFilterTest<float, float, std::uint32_t> AnnCagraFilterTestF_U32;
 TEST_P(AnnCagraFilterTestF_U32, AnnCagra) { this->testCagra(); }
 
 typedef AnnCagraIndexMergeTest<float, float, std::uint32_t> AnnCagraIndexMergeTestF_U32;
-TEST_P(AnnCagraIndexMergeTestF_U32, AnnCagraIndexMerge) { this->testCagra(); }
+TEST_P(AnnCagraIndexMergeTestF_U32, AnnCagraIndexMerge_U32) { this->testCagra<uint32_t>(); }
+TEST_P(AnnCagraIndexMergeTestF_U32, AnnCagraIndexMerge_I64) { this->testCagra<int64_t>(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraAddNodesTest,
diff --git a/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
index fdf0ca3bd9..8e44cca410 100644
--- a/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_cagra/test_half_uint32_t.cu
@@ -25,7 +25,8 @@ TEST_P(AnnCagraTestF16_U32, AnnCagra_U32) { this->testCagra<uint32_t>(); }
 TEST_P(AnnCagraTestF16_U32, AnnCagra_I64) { this->testCagra<int64_t>(); }
 
 typedef AnnCagraIndexMergeTest<float, half, std::uint32_t> AnnCagraIndexMergeTestF16_U32;
-TEST_P(AnnCagraIndexMergeTestF16_U32, AnnCagraIndexMerge) { this->testCagra(); }
+TEST_P(AnnCagraIndexMergeTestF16_U32, AnnCagraIndexMerge_U32) { this->testCagra<uint32_t>(); }
+TEST_P(AnnCagraIndexMergeTestF16_U32, AnnCagraIndexMerge_I64) { this->testCagra<int64_t>(); }
 
 INSTANTIATE_TEST_CASE_P(AnnCagraTest, AnnCagraTestF16_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnCagraIndexMergeTest,

From 043c06f7456895bcf0e64f89d07a718f9e60603e Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Wed, 28 May 2025 14:24:05 -0700
Subject: [PATCH 419/623] Merge pull request #951 from divyegala/revert-pr-915

Revert "Fix kmeans::predict argument order (#915)"
---
 cpp/include/cuvs/cluster/kmeans.hpp      | 72 ++----------------------
 cpp/src/cluster/kmeans.cuh               |  2 +-
 cpp/src/cluster/kmeans_predict_double.cu |  8 +--
 cpp/src/cluster/kmeans_predict_float.cu  |  8 +--
 4 files changed, 13 insertions(+), 77 deletions(-)

diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index 990f69d188..ad08cd1394 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -519,26 +519,10 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<float> inertia);
 
-// This overload is retained for backward compatibility.
-[[deprecated(
-  "The argument order of kmeans::predict has been corrected. Please use the new function "
-  "instead.")]]
-inline void predict(raft::resources const& handle,
-                    const kmeans::params& params,
-                    raft::device_matrix_view<const float, int> X,
-                    std::optional<raft::device_vector_view<const float, int>> sample_weight,
-                    raft::device_matrix_view<const float, int> centroids,
-                    raft::device_vector_view<int, int> labels,
-                    bool normalize_weight,
-                    raft::host_scalar_view<float> inertia)
-{
-  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
-}
-
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -593,26 +577,10 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<float> inertia);
 
-// This overload is retained for backward compatibility.
-[[deprecated(
-  "The argument order of kmeans::predict has been corrected. Please use the new function "
-  "instead.")]]
-inline void predict(raft::resources const& handle,
-                    const kmeans::params& params,
-                    raft::device_matrix_view<const float, int> X,
-                    std::optional<raft::device_vector_view<const float, int>> sample_weight,
-                    raft::device_matrix_view<const float, int> centroids,
-                    raft::device_vector_view<int64_t, int> labels,
-                    bool normalize_weight,
-                    raft::host_scalar_view<float> inertia)
-{
-  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
-}
-
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -667,26 +635,10 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<double> inertia);
 
-// This overload is retained for backward compatibility.
-[[deprecated(
-  "The argument order of kmeans::predict has been corrected. Please use the new function "
-  "instead.")]]
-inline void predict(raft::resources const& handle,
-                    const kmeans::params& params,
-                    raft::device_matrix_view<const double, int> X,
-                    std::optional<raft::device_vector_view<const double, int>> sample_weight,
-                    raft::device_matrix_view<const double, int> centroids,
-                    raft::device_vector_view<int, int> labels,
-                    bool normalize_weight,
-                    raft::host_scalar_view<double> inertia)
-{
-  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
-}
-
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -741,26 +693,10 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<double> inertia);
 
-// This overload is retained for backward compatibility.
-[[deprecated(
-  "The argument order of kmeans::predict has been corrected. Please use the new function "
-  "instead.")]]
-inline void predict(raft::resources const& handle,
-                    const kmeans::params& params,
-                    raft::device_matrix_view<const double, int> X,
-                    std::optional<raft::device_vector_view<const double, int>> sample_weight,
-                    raft::device_matrix_view<const double, int> centroids,
-                    raft::device_vector_view<int64_t, int> labels,
-                    bool normalize_weight,
-                    raft::host_scalar_view<double> inertia)
-{
-  predict(handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
-}
-
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
diff --git a/cpp/src/cluster/kmeans.cuh b/cpp/src/cluster/kmeans.cuh
index f6a61279b0..13036795ab 100644
--- a/cpp/src/cluster/kmeans.cuh
+++ b/cpp/src/cluster/kmeans.cuh
@@ -163,8 +163,8 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const DataT, IndexT> X,
              std::optional<raft::device_vector_view<const DataT, IndexT>> sample_weight,
              raft::device_matrix_view<const DataT, IndexT> centroids,
-             bool normalize_weight,
              raft::device_vector_view<IndexT, IndexT> labels,
+             bool normalize_weight,
              raft::host_scalar_view<DataT> inertia)
 {
   cuvs::cluster::kmeans::detail::kmeans_predict<DataT, IndexT>(
diff --git a/cpp/src/cluster/kmeans_predict_double.cu b/cpp/src/cluster/kmeans_predict_double.cu
index 1992e018a8..1fcc393acb 100644
--- a/cpp/src/cluster/kmeans_predict_double.cu
+++ b/cpp/src/cluster/kmeans_predict_double.cu
@@ -24,13 +24,13 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<double> inertia)
 
 {
   cuvs::cluster::kmeans::predict<double, int>(
-    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
 }
 
 void predict(raft::resources const& handle,
@@ -38,12 +38,12 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const double, int> X,
              std::optional<raft::device_vector_view<const double, int>> sample_weight,
              raft::device_matrix_view<const double, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<double> inertia)
 
 {
   cuvs::cluster::kmeans::predict<double, int64_t>(
-    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
 }
 }  // namespace cuvs::cluster::kmeans
diff --git a/cpp/src/cluster/kmeans_predict_float.cu b/cpp/src/cluster/kmeans_predict_float.cu
index 52951b46fc..b5f9f9e510 100644
--- a/cpp/src/cluster/kmeans_predict_float.cu
+++ b/cpp/src/cluster/kmeans_predict_float.cu
@@ -24,25 +24,25 @@ void predict(raft::resources const& handle,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<float> inertia)
 
 {
   cuvs::cluster::kmeans::predict<float, int>(
-    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
 }
 void predict(raft::resources const& handle,
              const kmeans::params& params,
              raft::device_matrix_view<const float, int> X,
              std::optional<raft::device_vector_view<const float, int>> sample_weight,
              raft::device_matrix_view<const float, int> centroids,
-             bool normalize_weight,
              raft::device_vector_view<int64_t, int> labels,
+             bool normalize_weight,
              raft::host_scalar_view<float> inertia)
 
 {
   cuvs::cluster::kmeans::predict<float, int64_t>(
-    handle, params, X, sample_weight, centroids, normalize_weight, labels, inertia);
+    handle, params, X, sample_weight, centroids, labels, normalize_weight, inertia);
 }
 }  // namespace cuvs::cluster::kmeans

From 5460c620e64586f2c9ff204f82695f62e19b5980 Mon Sep 17 00:00:00 2001
From: Yinzuo Jiang <jiangyinzuo@foxmail.com>
Date: Thu, 29 May 2025 08:20:47 +0800
Subject: [PATCH 420/623] Remove unused raft cagra header in add_nodes.cuh
 (#741)

Vector search headers in raft will be removed in the future, so we should not include the raft cagra header.

Authors:
  - Yinzuo Jiang (https://github.com/jiangyinzuo)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/741
---
 cpp/src/neighbors/detail/cagra/add_nodes.cuh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
index 913094e2af..8023d8f9ef 100644
--- a/cpp/src/neighbors/detail/cagra/add_nodes.cuh
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -19,7 +19,6 @@
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
-#include <raft/neighbors/cagra_types.hpp>
 #include <raft/spatial/knn/detail/ann_utils.cuh>
 #include <raft/stats/histogram.cuh>
 

From e8dbb887cf3a9646ed20dcf4b749528ecd3e15e6 Mon Sep 17 00:00:00 2001
From: Ishan Chattopadhyaya <ichattopadhyaya@gmail.com>
Date: Thu, 29 May 2025 06:08:52 +0530
Subject: [PATCH 421/623] [Java] New off-heap Dataset support for CAGRA and
 Bruteforce (#902)

As reported in #698, current `withDataset(float[][] arr)` requires the entire dataset to be copied in heap first, before writing out the MemorySegment for it.

Introducing a new `Dataset` (interface and impl) support with a `addVector(float[] vector)` support for adding the vectors into the MemorySegment one by one, without needing to load them all at once.

Authors:
  - Ishan Chattopadhyaya (https://github.com/chatman)
  - Vivek Narang (https://github.com/narangvivek10)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/902
---
 .../java/com/nvidia/cuvs/BruteForceIndex.java | 12 +++-
 .../main/java/com/nvidia/cuvs/CagraIndex.java | 13 +++-
 .../main/java/com/nvidia/cuvs/Dataset.java    | 62 +++++++++++++++++
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  4 ++
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |  6 ++
 .../cuvs/internal/BruteForceIndexImpl.java    | 38 ++++++++---
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 34 +++++++---
 .../com/nvidia/cuvs/internal/DatasetImpl.java | 68 +++++++++++++++++++
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  7 ++
 .../nvidia/cuvs/BruteForceRandomizedIT.java   | 43 ++++++++----
 .../com/nvidia/cuvs/CagraRandomizedIT.java    | 37 +++++++---
 .../com/nvidia/cuvs/HnswRandomizedIT.java     | 34 +++++++---
 .../cuvs/examples/BruteForceExample.java      |  4 +-
 .../nvidia/cuvs/examples/CagraExample.java    |  6 +-
 .../com/nvidia/cuvs/examples/HnswExample.java |  7 +-
 15 files changed, 306 insertions(+), 69 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
index 511d4279f9..70b079c9de 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
@@ -101,13 +101,21 @@ interface Builder {
      */
     Builder from(InputStream inputStream);
 
+    /**
+     * Sets the dataset vectors for building the {@link BruteForceIndex}.
+     *
+     * @param vectors a two-dimensional float array
+     * @return an instance of this Builder
+     */
+    Builder withDataset(float[][] vectors);
+
     /**
      * Sets the dataset for building the {@link BruteForceIndex}.
      *
-     * @param dataset a two-dimensional float array
+     * @param dataset a {@link Dataset} object containing the vectors
      * @return an instance of this Builder
      */
-    Builder withDataset(float[][] dataset);
+    Builder withDataset(Dataset dataset);
 
     /**
      * Builds and returns an instance of {@link BruteForceIndex}.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index d0d7b5ceb8..e8cd6c23f5 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -21,6 +21,7 @@
 import java.nio.file.Path;
 import java.util.Objects;
 
+import com.nvidia.cuvs.BruteForceIndex.Builder;
 import com.nvidia.cuvs.spi.CuVSProvider;
 
 /**
@@ -183,13 +184,21 @@ interface Builder {
          */
         Builder from(InputStream inputStream);
 
+        /**
+         * Sets the dataset vectors for building the {@link CagraIndex}.
+         *
+         * @param vectors a two-dimensional float array
+         * @return an instance of this Builder
+         */
+        Builder withDataset(float[][] vectors);
+
         /**
          * Sets the dataset for building the {@link CagraIndex}.
          *
-         * @param dataset a two-dimensional float array
+         * @param dataset a {@link Dataset} object containing the vectors
          * @return an instance of this Builder
          */
-        Builder withDataset(float[][] dataset);
+        Builder withDataset(Dataset dataset);
 
         /**
          * Registers an instance of configured {@link CagraIndexParams} with this
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
new file mode 100644
index 0000000000..6fbbfee098
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.spi.CuVSProvider;
+
+/**
+ * This represents a wrapper for a dataset to be used for index construction.
+ * The purpose is to allow a caller to place the vectors into native memory
+ * directly, instead of requiring the caller to load all the vectors into the heap
+ * (e.g. with a float[][]).
+ *
+ * @since 25.06
+ */
+public interface Dataset extends AutoCloseable {
+
+  /**
+   * Add a single vector to the dataset.
+   *
+   * @param vector A float array of as many elements as the dimensions
+   */
+  public void addVector(float[] vector);
+
+  /**
+   * Create a new instance of a dataset
+   *
+   * @param size       Number of vectors in the dataset
+   * @param dimensions Size of each vector in the dataset
+   * @return new instance of {@link Dataset}
+   */
+  static Dataset create(int size, int dimensions) {
+    return CuVSProvider.provider().newDataset(size, dimensions);
+  }
+
+  /**
+   * Gets the size of the dataset
+   *
+   * @return Size of the dataset
+   */
+  public int size();
+
+  /**
+   * Gets the dimensions of the vectors in this dataset
+   *
+   * @return Dimensions of the vectors in the dataset
+   */
+  public int dimensions();
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 04bce4cc72..27d0518632 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -19,6 +19,7 @@
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
 
 import java.nio.file.Path;
@@ -50,6 +51,9 @@ default Path nativeLibraryPath() {
   CuVSResources newCuVSResources(Path tempDirectory)
       throws Throwable;
 
+  /** Create a {@link Dataset} instance **/
+  Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException;
+
   /** Creates a new BruteForceIndex Builder. */
   BruteForceIndex.Builder newBruteForceIndexBuilder(CuVSResources cuVSResources)
       throws UnsupportedOperationException;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 7dc8207509..185eccf9f3 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -19,6 +19,7 @@
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
 
 import java.nio.file.Path;
@@ -47,4 +48,9 @@ public CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources) {
   public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
     throw new UnsupportedOperationException();
   }
+
+  @Override
+  public Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException {
+    throw new UnsupportedOperationException();
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index a96f633ffd..7ea9a56279 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -43,6 +43,7 @@
 import com.nvidia.cuvs.BruteForceIndexParams;
 import com.nvidia.cuvs.BruteForceQuery;
 import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsBruteForceIndex;
@@ -71,7 +72,8 @@ public class BruteForceIndexImpl implements BruteForceIndex{
   private static final MethodHandle deserializeMethodHandle = downcallHandle("deserialize_brute_force_index",
       FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
 
-  private final float[][] dataset;
+  private final float[][] vectors;
+  private final Dataset dataset;
   private final CuVSResourcesImpl resources;
   private final IndexReference bruteForceIndexReference;
   private final BruteForceIndexParams bruteForceIndexParams;
@@ -86,8 +88,10 @@ public class BruteForceIndexImpl implements BruteForceIndex{
    * @param bruteForceIndexParams an instance of {@link BruteForceIndexParams}
    *                              holding the index parameters
    */
-  private BruteForceIndexImpl(float[][] dataset, CuVSResourcesImpl resources, BruteForceIndexParams bruteForceIndexParams)
+  private BruteForceIndexImpl(float[][] vectors, Dataset dataset, CuVSResourcesImpl resources,
+      BruteForceIndexParams bruteForceIndexParams)
       throws Throwable {
+    this.vectors = vectors;
     this.dataset = dataset;
     this.resources = resources;
     this.bruteForceIndexParams = bruteForceIndexParams;
@@ -102,6 +106,7 @@ private BruteForceIndexImpl(float[][] dataset, CuVSResourcesImpl resources, Brut
    */
   private BruteForceIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
     this.bruteForceIndexParams = null;
+    this.vectors = null;
     this.dataset = null;
     this.resources = resources;
     this.bruteForceIndexReference = deserialize(inputStream);
@@ -127,6 +132,7 @@ public void destroyIndex() throws Throwable {
     } finally {
       destroyed = true;
     }
+    if (dataset != null) dataset.close();
   }
 
   /**
@@ -137,10 +143,11 @@ public void destroyIndex() throws Throwable {
    *         index
    */
   private IndexReference build() throws Throwable {
-    long rows = dataset.length;
-    long cols = rows > 0 ? dataset[0].length : 0;
+    long rows = dataset != null? dataset.size(): vectors.length;
+    long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
 
-    MemorySegment dataSeg = Util.buildMemorySegment(resources.getArena(), dataset);
+    MemorySegment dataSeg = dataset != null? ((DatasetImpl) dataset).seg:
+      Util.buildMemorySegment(resources.getArena(), vectors);
     try (var localArena = Arena.ofConfined()) {
       MemorySegment returnValue = localArena.allocate(C_INT);
       MemorySegment indexSeg = (MemorySegment) indexMethodHandle.invokeExact(
@@ -284,7 +291,8 @@ public static BruteForceIndex.Builder newBuilder(CuVSResources cuvsResources) {
    */
   public static class Builder implements BruteForceIndex.Builder {
 
-    private float[][] dataset;
+    private float[][] vectors;
+    private Dataset dataset;
     private final CuVSResourcesImpl cuvsResources;
     private BruteForceIndexParams bruteForceIndexParams;
     private InputStream inputStream;
@@ -327,11 +335,23 @@ public Builder from(InputStream inputStream) {
     /**
      * Sets the dataset for building the {@link BruteForceIndex}.
      *
-     * @param dataset a two-dimensional float array
+     * @param vectors a two-dimensional float array
      * @return an instance of this Builder
      */
     @Override
-    public Builder withDataset(float[][] dataset) {
+    public Builder withDataset(float[][] vectors) {
+      this.vectors = vectors;
+      return this;
+    }
+
+    /**
+     * Sets the dataset for building the {@link BruteForceIndex}.
+     *
+     * @param dataset a {@link Dataset} object containing the vectors
+     * @return an instance of this Builder
+     */
+    @Override
+    public Builder withDataset(Dataset dataset) {
       this.dataset = dataset;
       return this;
     }
@@ -346,7 +366,7 @@ public BruteForceIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new BruteForceIndexImpl(inputStream, cuvsResources);
       } else {
-        return new BruteForceIndexImpl(dataset, cuvsResources, bruteForceIndexParams);
+        return new BruteForceIndexImpl(vectors, dataset, cuvsResources, bruteForceIndexParams);
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 7788354022..ae13c8df9c 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -45,6 +45,7 @@
 import com.nvidia.cuvs.CagraQuery;
 import com.nvidia.cuvs.CagraSearchParams;
 import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
@@ -87,7 +88,8 @@ public class CagraIndexImpl implements CagraIndex {
   private static final MethodHandle serializeCAGRAIndexToHNSWMethodHandle = downcallHandle("serialize_cagra_index_to_hnsw",
       FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
 
-  private final float[][] dataset;
+  private final float[][] vectors;
+  private final Dataset dataset;
   private final CuVSResourcesImpl resources;
   private final CagraIndexParams cagraIndexParameters;
   private final CagraCompressionParams cagraCompressionParams;
@@ -104,10 +106,11 @@ public class CagraIndexImpl implements CagraIndex {
    * @param dataset                the dataset for indexing
    * @param resources              an instance of {@link CuVSResources}
    */
-  private CagraIndexImpl(CagraIndexParams indexParameters, CagraCompressionParams cagraCompressionParams, float[][] dataset,
-      CuVSResourcesImpl resources) throws Throwable {
+  private CagraIndexImpl(CagraIndexParams indexParameters, CagraCompressionParams cagraCompressionParams, float[][] vectors,
+      Dataset dataset, CuVSResourcesImpl resources) throws Throwable {
     this.cagraIndexParameters = indexParameters;
     this.cagraCompressionParams = cagraCompressionParams;
+    this.vectors = vectors;
     this.dataset = dataset;
     this.resources = resources;
     this.cagraIndexReference = build();
@@ -122,6 +125,7 @@ private CagraIndexImpl(CagraIndexParams indexParameters, CagraCompressionParams
   private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
     this.cagraIndexParameters = null;
     this.cagraCompressionParams = null;
+    this.vectors = null;
     this.dataset = null;
     this.resources = resources;
     this.cagraIndexReference = deserialize(inputStream);
@@ -146,6 +150,7 @@ public void destroyIndex() throws Throwable {
     } finally {
       destroyed = true;
     }
+    if (dataset != null) dataset.close();
   }
 
   /**
@@ -156,8 +161,8 @@ public void destroyIndex() throws Throwable {
    *         index
    */
   private IndexReference build() throws Throwable {
-    long rows = dataset.length;
-    long cols = rows > 0 ? dataset[0].length : 0;
+    long rows = dataset != null? dataset.size(): vectors.length;
+    long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
 
     MemorySegment indexParamsMemorySegment = cagraIndexParameters != null
         ? segmentFromIndexParams(cagraIndexParameters)
@@ -169,7 +174,8 @@ private IndexReference build() throws Throwable {
         ? segmentFromCompressionParams(cagraCompressionParams)
         : MemorySegment.NULL;
 
-    MemorySegment dataSeg = Util.buildMemorySegment(resources.getArena(), dataset);
+    MemorySegment dataSeg = dataset != null? ((DatasetImpl) dataset).seg:
+    	Util.buildMemorySegment(resources.getArena(), vectors);
 
     try (var localArena = Arena.ofConfined()) {
       MemorySegment returnValue = localArena.allocate(C_INT);
@@ -470,7 +476,8 @@ public static CagraIndex.Builder newBuilder(CuVSResources cuvsResources) {
    */
   public static class Builder implements CagraIndex.Builder{
 
-    private float[][] dataset;
+    private float[][] vectors;
+    private Dataset dataset;
     private CagraIndexParams cagraIndexParams;
     private CagraCompressionParams cagraCompressionParams;
     private CuVSResourcesImpl cuvsResources;
@@ -487,7 +494,13 @@ public Builder from(InputStream inputStream) {
     }
 
     @Override
-    public Builder withDataset(float[][] dataset) {
+    public Builder withDataset(float[][] vectors) {
+      this.vectors = vectors;
+      return this;
+    }
+
+    @Override
+    public Builder withDataset(Dataset dataset) {
       this.dataset = dataset;
       return this;
     }
@@ -509,7 +522,10 @@ public CagraIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new CagraIndexImpl(inputStream, cuvsResources);
       } else {
-        return new CagraIndexImpl(cagraIndexParams, cagraCompressionParams, dataset, cuvsResources);
+    	if (vectors != null && dataset != null) {
+    		throw new IllegalArgumentException("Please specify only one type of dataset (a float[] or a Dataset instance)");
+    	}
+        return new CagraIndexImpl(cagraIndexParams, cagraCompressionParams, vectors, dataset, cuvsResources);
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
new file mode 100644
index 0000000000..13ac77e27e
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+
+import com.nvidia.cuvs.Dataset;
+
+public class DatasetImpl implements Dataset {
+  private final Arena arena;
+  protected final MemorySegment seg;
+  private final int size;
+  private final int dimensions;
+  private int current = 0;
+
+  public DatasetImpl(int size, int dimensions) {
+    this.size = size;
+    this.dimensions = dimensions;
+
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(size * dimensions, C_FLOAT);
+
+    this.arena = Arena.ofShared();
+    seg = arena.allocate(dataMemoryLayout);
+  }
+
+  @Override
+  public void addVector(float[] vector) {
+    if (current >= size)
+      throw new ArrayIndexOutOfBoundsException();
+    MemorySegment.copy(vector, 0, seg, C_FLOAT, ((current++) * dimensions * C_FLOAT.byteSize()), (int) dimensions);
+  }
+
+  @Override
+  public void close() {
+    if (!arena.scope().isAlive()) {
+      arena.close();
+    }
+  }
+
+  @Override
+  public int size() {
+    return size;
+  }
+
+  @Override
+  public int dimensions() {
+    return dimensions;
+  }
+
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 32f0225de0..b46278ac91 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -19,10 +19,12 @@
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
 import com.nvidia.cuvs.internal.BruteForceIndexImpl;
 import com.nvidia.cuvs.internal.CagraIndexImpl;
 import com.nvidia.cuvs.internal.CuVSResourcesImpl;
+import com.nvidia.cuvs.internal.DatasetImpl;
 import com.nvidia.cuvs.internal.HnswIndexImpl;
 
 import java.nio.file.Files;
@@ -57,4 +59,9 @@ public CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources) {
   public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
     return HnswIndexImpl.newBuilder(Objects.requireNonNull(cuVSResources));
   }
+
+  @Override
+  public Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException {
+      return new DatasetImpl(size, dimensions);
+  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 1db86db438..c4b52984b3 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -16,8 +16,9 @@
 
 package com.nvidia.cuvs;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+
 import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
 import java.util.BitSet;
 import java.util.List;
 
@@ -29,8 +30,6 @@
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 
-import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-
 @RunWith(RandomizedRunner.class)
 public class BruteForceRandomizedIT extends CuVSTestCase {
 
@@ -45,12 +44,15 @@ public void setup() {
 
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
-    for (int i = 0; i < 10; i++) {
-      tmpResultsTopKWithRandomValues();
+	boolean useNativeMemoryDatasets[] = {true, false};
+	for (int i = 0; i < 10; i++) {
+      for (boolean use: useNativeMemoryDatasets) {
+        tmpResultsTopKWithRandomValues(use);
+      }
     }
   }
 
-  private void tmpResultsTopKWithRandomValues() throws Throwable {
+  private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) throws Throwable {
     int DATASET_SIZE_LIMIT = 10_000;
     int DIMENSIONS_LIMIT = 2048;
     int NUM_QUERIES_LIMIT = 10;
@@ -77,7 +79,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     }
 
     // Generate a random dataset
-    float[][] dataset = generateData(random, datasetSize, dimensions);
+    float[][] vectors = generateData(random, datasetSize, dimensions);
 
     // Generate random query vectors
     float[][] queries = generateData(random, numQueries, dimensions);
@@ -85,11 +87,12 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     log.info("Dataset size: {}x{}", datasetSize, dimensions);
     log.info("Query size: {}x{}", numQueries, dimensions);
     log.info("TopK: {}", topK);
+    log.info("Use native memory dataset? " + useNativeMemoryDataset);
 
     // Debugging: Log dataset and queries
     if (log.isDebugEnabled()) {
       log.debug("Dataset:");
-      for (float[] row : dataset) {
+      for (float[] row : vectors) {
         log.debug(java.util.Arrays.toString(row));
       }
       log.debug("Queries:");
@@ -98,13 +101,13 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
       }
     }
     // Sanity checks
-    assert dataset.length > 0 : "Dataset is empty.";
+    assert vectors.length > 0 : "Dataset is empty.";
     assert queries.length > 0 : "Queries are empty.";
     assert dimensions > 0 : "Invalid dimensions.";
     assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
 
     // Generate expected results using brute force
-    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, prefilters, log);
+    List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, prefilters, log);
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
@@ -112,17 +115,27 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
       BruteForceQuery query = new BruteForceQuery.Builder()
           .withTopK(topK)
           .withQueryVectors(queries)
-          .withPrefilter(prefilters, dataset.length)
+          .withPrefilter(prefilters, vectors.length)
           .build();
 
       BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
           .withNumWriterThreads(32)
           .build();
 
-      BruteForceIndex index = BruteForceIndex.newBuilder(resources)
-          .withDataset(dataset)
-          .withIndexParams(indexParams)
-          .build();
+      BruteForceIndex index;
+      if (useNativeMemoryDataset) {
+        Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
+        for (float[] v: vectors) dataset.addVector(v);
+        index = BruteForceIndex.newBuilder(resources)
+                .withDataset(dataset)
+                .withIndexParams(indexParams)
+                .build();
+      } else {
+        index = BruteForceIndex.newBuilder(resources)
+                .withDataset(vectors)
+                .withIndexParams(indexParams)
+                .build();
+      }
 
       log.info("Index built successfully. Executing search...");
       SearchResults results = index.search(query);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 34665d3eb3..cf065da5b0 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -44,12 +44,15 @@ public void setup() {
 
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
-    for (int i = 0; i < 10; i++) {
-      tmpResultsTopKWithRandomValues();
+    boolean useNativeMemoryDatasets[] = {true, false};
+	for (int i = 0; i < 10; i++) {
+	  for (boolean use: useNativeMemoryDatasets) {
+		  tmpResultsTopKWithRandomValues(use);
+	  }
     }
   }
 
-  private void tmpResultsTopKWithRandomValues() throws Throwable {
+  private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) throws Throwable {
     int DATASET_SIZE_LIMIT = 10_000;
     int DIMENSIONS_LIMIT = 2048;
     int NUM_QUERIES_LIMIT = 10;
@@ -64,7 +67,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
       datasetSize = topK;
 
     // Generate a random dataset
-    float[][] dataset = generateData(random, datasetSize, dimensions);
+    float[][] vectors = generateData(random, datasetSize, dimensions);
 
     // Generate random query vectors
     float[][] queries = generateData(random, numQueries, dimensions);
@@ -72,11 +75,12 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     log.info("Dataset size: {}x{}", datasetSize, dimensions);
     log.info("Query size: {}x{}", numQueries, dimensions);
     log.info("TopK: {}", topK);
+    log.info("Use native memory dataset? " + useNativeMemoryDataset);
 
     // Debugging: Log dataset and queries
     if (log.isDebugEnabled()) {
       log.debug("Dataset:");
-      for (float[] row : dataset) {
+      for (float[] row : vectors) {
         log.debug(java.util.Arrays.toString(row));
       }
       log.debug("Queries:");
@@ -85,23 +89,34 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
       }
     }
     // Sanity checks
-    assert dataset.length > 0 : "Dataset is empty.";
+    assert vectors.length > 0 : "Dataset is empty.";
     assert queries.length > 0 : "Queries are empty.";
     assert dimensions > 0 : "Invalid dimensions.";
     assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
 
     // Generate expected results using brute force
-    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, null, log);
+    List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, null, log);
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
       CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
           .build();
-      CagraIndex index = CagraIndex.newBuilder(resources)
-          .withDataset(dataset)
-          .withIndexParams(indexParams)
-          .build();
+
+      CagraIndex index;
+      if (useNativeMemoryDataset) {
+          Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
+          for (float[] v: vectors) dataset.addVector(v);
+          index = CagraIndex.newBuilder(resources)
+              .withDataset(dataset)
+              .withIndexParams(indexParams)
+              .build();
+      } else {
+        index = CagraIndex.newBuilder(resources)
+                .withDataset(vectors)
+                .withIndexParams(indexParams)
+                .build();
+      }
       log.info("Index built successfully.");
 
       try {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index 07033391ba..b6062aecbf 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -50,12 +50,15 @@ public void setup() {
 
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
+	boolean useNativeMemoryDatasets[] = {true, false};
     for (int i = 0; i < 10; i++) {
-      tmpResultsTopKWithRandomValues();
+      for (boolean use: useNativeMemoryDatasets) {
+        tmpResultsTopKWithRandomValues(use);
+      }
     }
   }
 
-  private void tmpResultsTopKWithRandomValues() throws Throwable {
+  private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) throws Throwable {
     int DATASET_SIZE_LIMIT = 10_000;
     int DIMENSIONS_LIMIT = 2048;
     int NUM_QUERIES_LIMIT = 10;
@@ -70,7 +73,7 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
       datasetSize = topK;
 
     // Generate a random dataset
-    float[][] dataset = generateData(random, datasetSize, dimensions);
+    float[][] vectors = generateData(random, datasetSize, dimensions);
 
     // Generate random query vectors
     float[][] queries = generateData(random, numQueries, dimensions);
@@ -78,11 +81,12 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
     log.info("Dataset size: {}x{}", datasetSize, dimensions);
     log.info("Query size: {}x{}", numQueries, dimensions);
     log.info("TopK: {}", topK);
+    log.info("Use native memory dataset? " + useNativeMemoryDataset);
 
     // Debugging: Log dataset and queries
     if (log.isDebugEnabled()) {
       log.debug("Dataset:");
-      for (float[] row : dataset) {
+      for (float[] row : vectors) {
         log.debug(java.util.Arrays.toString(row));
       }
       log.debug("Queries:");
@@ -91,13 +95,13 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
       }
     }
     // Sanity checks
-    assert dataset.length > 0 : "Dataset is empty.";
+    assert vectors.length > 0 : "Dataset is empty.";
     assert queries.length > 0 : "Queries are empty.";
     assert dimensions > 0 : "Invalid dimensions.";
     assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
 
     // Generate expected results using brute force
-    List<List<Integer>> expected = generateExpectedResults(topK, dataset, queries, null, log);
+    List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, null, log);
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
@@ -112,10 +116,20 @@ private void tmpResultsTopKWithRandomValues() throws Throwable {
           .build();
 
       // Create the index with the dataset
-      CagraIndex index = CagraIndex.newBuilder(resources)
-          .withDataset(dataset)
-          .withIndexParams(indexParams)
-          .build();
+      CagraIndex index;
+      if (useNativeMemoryDataset) {
+        Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
+        for (float[] v: vectors) dataset.addVector(v);
+        index = CagraIndex.newBuilder(resources)
+            .withDataset(dataset)
+            .withIndexParams(indexParams)
+            .build();
+      } else {
+        index = CagraIndex.newBuilder(resources)
+             .withDataset(vectors)
+             .withIndexParams(indexParams)
+             .build();
+      }
 
       // Saving the HNSW index on to the disk.
       String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
index 72924779ed..4324317fb1 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
@@ -20,7 +20,7 @@ public class BruteForceExample {
   public static void main(String[] args) throws Throwable {
 
     // Sample data and query
-    float[][] dataset = {
+    float[][] vectors = {
         { 0.74021935f, 0.9209938f },
         { 0.03902049f, 0.9689629f },
         { 0.92514056f, 0.4463501f },
@@ -48,7 +48,7 @@ public static void main(String[] args) throws Throwable {
 
       // Create the index with the dataset
       BruteForceIndex index = BruteForceIndex.newBuilder(resources)
-          .withDataset(dataset)
+          .withDataset(vectors)
           .withIndexParams(indexParams)
           .build();
 
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
index 76767fc4c0..78c23f2bda 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
@@ -23,7 +23,7 @@ public class CagraExample {
   public static void main(String[] args) throws Throwable {
 
     // Sample data and query
-    float[][] dataset = {
+    float[][] vectors = {
         { 0.74021935f, 0.9209938f },
         { 0.03902049f, 0.9689629f },
         { 0.92514056f, 0.4463501f },
@@ -42,14 +42,12 @@ public static void main(String[] args) throws Throwable {
       // Configure index parameters
       CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-          .withGraphDegree(1)
-          .withIntermediateGraphDegree(2)
           .withMetric(CuvsDistanceType.L2Expanded)
           .build();
 
       // Create the index with the dataset
       CagraIndex index = CagraIndex.newBuilder(resources)
-          .withDataset(dataset)
+          .withDataset(vectors)
           .withIndexParams(indexParams)
           .build();
 
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
index 0268ff62ee..7b43f5fd01 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
@@ -28,7 +28,7 @@ public class HnswExample {
   public static void main(String[] args) throws Throwable {
 
     // Sample data and query
-    float[][] dataset = {
+    float[][] vectors = {
         { 0.74021935f, 0.9209938f },
         { 0.03902049f, 0.9689629f },
         { 0.92514056f, 0.4463501f },
@@ -61,16 +61,13 @@ public static void main(String[] args) throws Throwable {
       // Configure index parameters
       CagraIndexParams indexParams = new CagraIndexParams.Builder()
           .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
-          .withGraphDegree(64)
-          .withIntermediateGraphDegree(128)
-          .withNumWriterThreads(32)
           .withMetric(CuvsDistanceType.L2Expanded)
           .withCuVSIvfPqParams(cuVSIvfPqParams)
           .build();
 
       // Create the index with the dataset
       CagraIndex index = CagraIndex.newBuilder(resources)
-          .withDataset(dataset)
+          .withDataset(vectors)
           .withIndexParams(indexParams)
           .build();
 

From c7a1b577426f9a6387b8871e1e5f96bcbbd10986 Mon Sep 17 00:00:00 2001
From: punAhuja <167976912+punAhuja@users.noreply.github.com>
Date: Thu, 29 May 2025 17:11:46 +0530
Subject: [PATCH 422/623] [Java] Exposing  merge API for multiple CAGRA indices
 (#891)

Exposing the C merge API in the Java layer.

This is work in progress.

Pull request for merge API in C is not merged yet:
https://github.com/rapidsai/cuvs/pull/860

Authors:
  - https://github.com/punAhuja
  - Vivek Narang (https://github.com/narangvivek10)

Approvers:
  - Ishan Chattopadhyaya (https://github.com/chatman)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/891
---
 .../main/java/com/nvidia/cuvs/CagraIndex.java |  34 +++
 .../com/nvidia/cuvs/CagraMergeParams.java     | 105 ++++++++++
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  23 ++
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |   5 +
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 103 ++++++++-
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  17 ++
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 197 ++++++++++++++++++
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |  10 +-
 .../java/com/nvidia/cuvs/CuVSTestCase.java    |   2 +-
 java/internal/src/cuvs_java.c                 |  29 +++
 10 files changed, 516 insertions(+), 9 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index e8cd6c23f5..ef98841064 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -170,6 +170,40 @@ static Builder newBuilder(CuVSResources cuvsResources) {
         return CuVSProvider.provider().newCagraIndexBuilder(cuvsResources);
     }
 
+    /**
+     * Merges multiple CAGRA indexes into a single index using default merge parameters.
+     *
+     * @param indexes Array of CAGRA indexes to merge
+     * @return A new merged CAGRA index
+     * @throws Throwable if an error occurs during the merge operation
+     */
+    static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
+        return merge(indexes, null);
+    }
+
+    /**
+     * Merges multiple CAGRA indexes into a single index with the specified merge parameters.
+     *
+     * @param indexes Array of CAGRA indexes to merge
+     * @param mergeParams Parameters to control the merge operation, or null to use defaults
+     * @return A new merged CAGRA index
+     * @throws Throwable if an error occurs during the merge operation
+     */
+    static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
+        if (indexes == null || indexes.length == 0) {
+            throw new IllegalArgumentException("At least one index must be provided for merging");
+        }
+
+        CuVSResources resources = indexes[0].getCuVSResources();
+        for (int i = 1; i < indexes.length; i++) {
+            if (!resources.equals(indexes[i].getCuVSResources())) {
+                throw new IllegalArgumentException("All indexes must use the same CuVSResources instance");
+            }
+        }
+
+        return CuVSProvider.provider().mergeCagraIndexes(indexes, mergeParams);
+    }
+
     /**
      * Builder helps configure and create an instance of {@link CagraIndex}.
      */
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java
new file mode 100644
index 0000000000..d1998dcdf1
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+public class CagraMergeParams {
+
+    private final CagraIndexParams outputIndexParams;
+    private final MergeStrategy strategy;
+
+    /**
+     * Constructs a CagraMergeParams with the given output index parameters and merge strategy.
+     *
+     * @param outputIndexParams Index parameters for the output index
+     * @param strategy Merge strategy to use
+     */
+    private CagraMergeParams(CagraIndexParams outputIndexParams, MergeStrategy strategy) {
+        this.outputIndexParams = outputIndexParams;
+        this.strategy = strategy;
+    }
+
+    /**
+     * Gets the index parameters for the output index.
+     *
+     * @return Index parameters to use for the output index
+     */
+    public CagraIndexParams getOutputIndexParams() {
+        return outputIndexParams;
+    }
+
+    /**
+     * Gets the merge strategy to use.
+     *
+     * @return The merge strategy
+     */
+    public MergeStrategy getStrategy() {
+        return strategy;
+    }
+
+    /**
+     * Strategy to use when merging CAGRA indexes.
+     */
+    public enum MergeStrategy {
+        PHYSICAL(0),
+
+        LOGICAL(1);
+
+        public final int value;
+
+        MergeStrategy(int value) {
+            this.value = value;
+        }
+    }
+
+    /**
+     * Builder class for {@link CagraMergeParams}.
+     */
+    public static class Builder {
+        private CagraIndexParams outputIndexParams = new CagraIndexParams.Builder().build();
+        private MergeStrategy strategy = MergeStrategy.PHYSICAL; // Default to PHYSICAL
+
+        /**
+         * Sets the index parameters for the output index.
+         *
+         * @param outputIndexParams Index parameters to use for the output index
+         * @return This builder
+         */
+        public Builder withOutputIndexParams(CagraIndexParams outputIndexParams) {
+            this.outputIndexParams = outputIndexParams;
+            return this;
+        }
+
+        /**
+         * Sets the merge strategy.
+         *
+         * @param strategy The merge strategy to use
+         * @return This builder
+         */
+        public Builder withStrategy(MergeStrategy strategy) {
+            this.strategy = strategy;
+            return this;
+        }
+
+        /**
+         * Builds the {@link CagraMergeParams} object.
+         *
+         * @return The built parameters
+         */
+        public CagraMergeParams build() {
+            return new CagraMergeParams(outputIndexParams, strategy);
+        }
+    }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 27d0518632..a0d0f9a55c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -21,6 +21,7 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.CagraMergeParams;
 
 import java.nio.file.Path;
 
@@ -66,6 +67,28 @@ CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources)
   HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources)
       throws UnsupportedOperationException;
 
+  /**
+   * Merges multiple CAGRA indexes into a single index.
+   *
+   * @param indexes Array of CAGRA indexes to merge
+   * @return A new merged CAGRA index
+   * @throws Throwable if an error occurs during the merge operation
+   */
+  CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable;
+
+  /**
+   * Merges multiple CAGRA indexes into a single index with the specified merge parameters.
+   *
+   * @param indexes Array of CAGRA indexes to merge
+   * @param mergeParams Parameters to control the merge operation, or null to use defaults
+   * @return A new merged CAGRA index
+   * @throws Throwable if an error occurs during the merge operation
+   */
+  default CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
+    // Default implementation falls back to the method without parameters
+    return mergeCagraIndexes(indexes);
+  }
+
   /** Retrieves the system-wide provider. */
   static CuVSProvider provider() {
     return CuVSServiceProvider.Holder.INSTANCE;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 185eccf9f3..5fe372dce0 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -49,6 +49,11 @@ public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
     throw new UnsupportedOperationException();
   }
 
+  @Override
+  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
+    throw new UnsupportedOperationException();
+  }
+
   @Override
   public Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException {
     throw new UnsupportedOperationException();
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index ae13c8df9c..f3f3fbfbe2 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -42,6 +42,7 @@
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraMergeParams;
 import com.nvidia.cuvs.CagraQuery;
 import com.nvidia.cuvs.CagraSearchParams;
 import com.nvidia.cuvs.CuVSResources;
@@ -51,6 +52,7 @@
 import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndex;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
+import com.nvidia.cuvs.internal.panama.cuvsCagraMergeParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqParams;
@@ -88,6 +90,10 @@ public class CagraIndexImpl implements CagraIndex {
   private static final MethodHandle serializeCAGRAIndexToHNSWMethodHandle = downcallHandle("serialize_cagra_index_to_hnsw",
       FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
 
+
+  private static final MethodHandle mergeMethodHandle = downcallHandle("merge_cagra_indexes",
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, C_INT, ADDRESS, ADDRESS));
+
   private final float[][] vectors;
   private final Dataset dataset;
   private final CuVSResourcesImpl resources;
@@ -131,6 +137,23 @@ private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) thr
     this.cagraIndexReference = deserialize(inputStream);
   }
 
+  /**
+   * Constructor for creating an index from an existing index reference.
+   * Used primarily for the merge operation.
+   *
+   * @param indexReference The reference to the existing index
+   * @param resources The resources instance
+   */
+  private CagraIndexImpl(IndexReference indexReference, CuVSResourcesImpl resources) {
+    this.vectors = null;
+    this.cagraIndexParameters = null;
+    this.cagraCompressionParams = null;
+    this.dataset = null;
+    this.resources = resources;
+    this.cagraIndexReference = indexReference;
+    this.destroyed = false;
+  }
+
   private void checkNotDestroyed() {
     if (destroyed) {
       throw new IllegalStateException("destroyed");
@@ -165,7 +188,7 @@ private IndexReference build() throws Throwable {
     long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
 
     MemorySegment indexParamsMemorySegment = cagraIndexParameters != null
-        ? segmentFromIndexParams(cagraIndexParameters)
+        ? segmentFromIndexParams(resources, cagraIndexParameters)
         : MemorySegment.NULL;
 
     int numWriterThreads = cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
@@ -397,7 +420,7 @@ private MemorySegment segmentFromCompressionParams(CagraCompressionParams params
   /**
    * Allocates the configured index parameters in the MemorySegment.
    */
-  private MemorySegment segmentFromIndexParams(CagraIndexParams params) {
+  private static MemorySegment segmentFromIndexParams(CuVSResourcesImpl resources, CagraIndexParams params) {
     MemorySegment seg = cuvsCagraIndexParams.allocate(resources.getArena());
     cuvsCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
     cuvsCagraIndexParams.graph_degree(seg, params.getGraphDegree());
@@ -471,6 +494,80 @@ public static CagraIndex.Builder newBuilder(CuVSResources cuvsResources) {
     return new CagraIndexImpl.Builder((CuVSResourcesImpl)cuvsResources);
   }
 
+  /**
+   * Merges multiple CAGRA indexes into a single index.
+   *
+   * @param indexes Array of CAGRA indexes to merge
+   * @return A new merged CAGRA index
+   * @throws Throwable if an error occurs during the merge operation
+   */
+  public static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
+    return merge(indexes, null);
+  }
+
+  /**
+   * Merges multiple CAGRA indexes into a single index with specified merge parameters.
+   *
+   * @param indexes Array of CAGRA indexes to merge
+   * @param mergeParams Parameters to control the merge operation, or null to use defaults
+   * @return A new merged CAGRA index
+   * @throws Throwable if an error occurs during the merge operation
+   */
+  public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
+    CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
+    IndexReference mergedIndexReference = new IndexReference(resources);
+
+    try (var arena = Arena.ofConfined()) {
+      MemorySegment indexesSegment = arena.allocate(indexes.length * ADDRESS.byteSize());
+      for (int i = 0; i < indexes.length; i++) {
+        CagraIndexImpl indexImpl = (CagraIndexImpl) indexes[i];
+        indexesSegment.setAtIndex(ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
+      }
+
+      MemorySegment returnValue = arena.allocate(C_INT);
+
+      MemorySegment mergeParamsSegment = MemorySegment.NULL;
+      if (mergeParams != null) {
+        mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
+      }
+
+      mergeMethodHandle.invokeExact(
+        resources.getMemorySegment(),
+        indexesSegment,
+        mergedIndexReference.getMemorySegment(),
+        indexes.length,
+        returnValue,
+        mergeParamsSegment
+      );
+
+      checkError(returnValue.get(C_INT, 0L), "mergeMethodHandle");
+    }
+
+    return new CagraIndexImpl(mergedIndexReference, resources);
+  }
+
+  /**
+   * Creates a memory segment for merge parameters.
+   *
+   * @param mergeParams The merge parameters
+   * @param resources The CuVS resources
+   * @return A memory segment with the merge parameters
+   */
+  private static MemorySegment createMergeParamsSegment(CagraMergeParams mergeParams, CuVSResourcesImpl resources) {
+    MemorySegment seg = cuvsCagraMergeParams.allocate(resources.getArena());
+
+    if (mergeParams.getOutputIndexParams() != null) {
+      MemorySegment outputIndexParamsSeg = segmentFromIndexParams(resources, mergeParams.getOutputIndexParams());
+      cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
+    } else {
+      cuvsCagraMergeParams.output_index_params(seg, MemorySegment.NULL);
+    }
+
+    cuvsCagraMergeParams.strategy(seg, mergeParams.getStrategy().value);
+
+    return seg;
+  }
+
   /**
    * Builder helps configure and create an instance of {@link CagraIndex}.
    */
@@ -533,7 +630,7 @@ public CagraIndexImpl build() throws Throwable {
   /**
    * Holds the memory reference to a CAGRA index.
    */
-  protected static class IndexReference {
+  public static class IndexReference {
 
     private final MemorySegment memorySegment;
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index b46278ac91..e7a2a73e27 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -21,6 +21,7 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.CagraMergeParams;
 import com.nvidia.cuvs.internal.BruteForceIndexImpl;
 import com.nvidia.cuvs.internal.CagraIndexImpl;
 import com.nvidia.cuvs.internal.CuVSResourcesImpl;
@@ -60,6 +61,22 @@ public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
     return HnswIndexImpl.newBuilder(Objects.requireNonNull(cuVSResources));
   }
 
+  @Override
+  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
+    if (indexes == null || indexes.length == 0) {
+      throw new IllegalArgumentException("At least one index must be provided for merging");
+    }
+    return CagraIndexImpl.merge(indexes);
+  }
+
+  @Override
+  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
+    if (indexes == null || indexes.length == 0) {
+      throw new IllegalArgumentException("At least one index must be provided for merging");
+    }
+    return CagraIndexImpl.merge(indexes, mergeParams);
+  }
+
   @Override
   public Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException {
       return new DatasetImpl(size, dimensions);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 75b21aed82..371d7a3876 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -18,6 +18,7 @@
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
 
 import java.io.File;
 import java.io.FileInputStream;
@@ -42,6 +43,7 @@
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import com.nvidia.cuvs.CagraMergeParams.MergeStrategy;
 
 @RunWith(RandomizedRunner.class)
 public class CagraBuildAndSearchIT extends CuVSTestCase {
@@ -195,4 +197,199 @@ public void run() {
     };
     return thread;
   }
+
+  @Test
+    public void testMergingIndexes() throws Throwable {
+        float[][] vector1 = {
+            {0.0f, 0.0f},
+            {1.0f, 1.0f}
+        };
+
+        float[][] vector2 = {
+            {10.0f, 10.0f},
+            {11.0f, 11.0f}
+        };
+
+        float[][] queries = {
+            {1.0f, 1.0f},    // Should be closest to vector1[1] -> index 1
+            {10.5f, 10.5f},  // Should be closest to vector2[0] -> index 2
+            {0.0f, 0.0f}     // Should be closest to vector1[0] -> index 0
+        };
+
+        // Expected search results for each query (nearest neighbor and its distance)
+        List<Map<Integer, Float>> expectedResults = Arrays.asList(
+            Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
+            Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
+            Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
+        );
+
+        try (CuVSResources resources = CuVSResources.create()) {
+            CagraIndexParams indexParams = new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(1)
+                .withIntermediateGraphDegree(2)
+                .withNumWriterThreads(4)
+                .withMetric(CuvsDistanceType.L2Expanded)
+                .build();
+
+            log.info("Building first index...");
+            CagraIndex index1 = CagraIndex.newBuilder(resources)
+                .withDataset(vector1)
+                .withIndexParams(indexParams)
+                .build();
+
+            log.info("Building second index...");
+            CagraIndex index2 = CagraIndex.newBuilder(resources)
+                .withDataset(vector2)
+                .withIndexParams(indexParams)
+                .build();
+
+            log.info("Merging indexes...");
+            CagraIndex mergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2});
+            log.info("Merge completed successfully");
+
+            CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+                .build();
+
+            List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
+            CagraQuery query = new CagraQuery.Builder()
+                .withTopK(3)
+                .withSearchParams(searchParams)
+                .withQueryVectors(queries)
+                .withMapping(mergedMap)
+                .build();
+
+                log.info("Searching merged index...");
+                SearchResults results = mergedIndex.search(query);
+                log.info("Search results: " + results.getResults().toString());
+
+                assertEquals(expectedResults, results.getResults());
+
+                // --- Serialization/deserialization check ---
+                String indexFileName = UUID.randomUUID().toString() + ".cag";
+                mergedIndex.serialize(new FileOutputStream(indexFileName));
+
+                File indexFile = new File(indexFileName);
+                InputStream inputStream = new FileInputStream(indexFile);
+                CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources)
+                    .from(inputStream)
+                    .build();
+
+                SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
+                assertEquals(expectedResults, resultsFromLoaded.getResults());
+
+                if (indexFile.exists()) {
+                    indexFile.delete();
+                }
+                index1.destroyIndex();
+                index2.destroyIndex();
+                mergedIndex.destroyIndex();
+                loadedMergedIndex.destroyIndex();
+
+        }
+    }
+
+    // Commented out test for Logical merge strategy as it is not yet implemented in C yet
+    @Test
+    public void testMergeStrategies() throws Throwable {
+        float[][] vector1 = {
+            {0.0f, 0.0f},
+            {1.0f, 1.0f}
+        };
+
+        float[][] vector2 = {
+            {10.0f, 10.0f},
+            {11.0f, 11.0f}
+        };
+
+        float[][] queries = {
+            {1.0f, 1.0f},
+            {10.5f, 10.5f},
+            {0.0f, 0.0f}
+        };
+
+        List<Map<Integer, Float>> expectedResults = Arrays.asList(
+            Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
+            Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
+            Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
+        );
+
+        try (CuVSResources resources = CuVSResources.create()) {
+            CagraIndexParams indexParams = new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(1)
+                .withIntermediateGraphDegree(2)
+                .withNumWriterThreads(4)
+                .withMetric(CuvsDistanceType.L2Expanded)
+                .build();
+
+            log.info("Building first index...");
+            CagraIndex index1 = CagraIndex.newBuilder(resources)
+                .withDataset(vector1)
+                .withIndexParams(indexParams)
+                .build();
+
+            log.info("Building second index...");
+            CagraIndex index2 = CagraIndex.newBuilder(resources)
+                .withDataset(vector2)
+                .withIndexParams(indexParams)
+                .build();
+
+            CagraIndexParams outputIndexParams = new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(2)
+                .withIntermediateGraphDegree(4)
+                .withNumWriterThreads(4)
+                .withMetric(CuvsDistanceType.L2Expanded)
+                .build();
+
+            CagraMergeParams physicalMergeParams = new CagraMergeParams.Builder()
+                .withOutputIndexParams(outputIndexParams)
+                .withStrategy(MergeStrategy.PHYSICAL)
+                .build();
+
+            log.info("Merging indexes with PHYSICAL strategy...");
+            CagraIndex physicalMergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2}, physicalMergeParams);
+            log.info("Physical merge completed successfully");
+
+            CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+                .build();
+
+            List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
+            CagraQuery query = new CagraQuery.Builder()
+                .withTopK(3)
+                .withSearchParams(searchParams)
+                .withQueryVectors(queries)
+                .withMapping(mergedMap)
+                .build();
+
+            log.info("Searching physically merged index...");
+            SearchResults physicalResults = physicalMergedIndex.search(query);
+            assertNotNull("Physical merge search results should not be null", physicalResults);
+            assertEquals("Physical merge search results should match expected", expectedResults, physicalResults.getResults());
+
+            // --- Serialization/deserialization check for both merged indexes ---
+            String physicalIndexFileName = UUID.randomUUID().toString() + ".cag";
+            physicalMergedIndex.serialize(new FileOutputStream(physicalIndexFileName));
+
+            File physicalIndexFile = new File(physicalIndexFileName);
+            InputStream physicalInputStream = new FileInputStream(physicalIndexFile);
+            CagraIndex loadedPhysicalIndex = CagraIndex.newBuilder(resources)
+                .from(physicalInputStream)
+                .build();
+
+            SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
+            assertEquals("Loaded physical index search results should match expected",
+                expectedResults, resultsFromLoadedPhysical.getResults());
+
+            if (physicalIndexFile.exists()) {
+                physicalIndexFile.delete();
+            }
+            index1.destroyIndex();
+            index2.destroyIndex();
+            physicalMergedIndex.destroyIndex();
+            loadedPhysicalIndex.destroyIndex();
+        }
+    }
+
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index cf065da5b0..397222b787 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -45,10 +45,10 @@ public void setup() {
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
     boolean useNativeMemoryDatasets[] = {true, false};
-	for (int i = 0; i < 10; i++) {
-	  for (boolean use: useNativeMemoryDatasets) {
-		  tmpResultsTopKWithRandomValues(use);
-	  }
+	  for (int i = 0; i < 100; i++) {
+	    for (boolean use: useNativeMemoryDatasets) {
+		     tmpResultsTopKWithRandomValues(use);
+	    }
     }
   }
 
@@ -58,7 +58,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     int NUM_QUERIES_LIMIT = 10;
     int TOP_K_LIMIT = 64; // nocommit This fails beyond 64
 
-    int datasetSize = random.nextInt(DATASET_SIZE_LIMIT) + 1;
+    int datasetSize = random.nextInt(DATASET_SIZE_LIMIT) + 2; // datasetSize of 1 fails/crashed due to a bug, hence adding 2 here.
     int dimensions = random.nextInt(DIMENSIONS_LIMIT) + 1;
     int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
     int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
index 5e40ff5798..5f6c4c735b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
@@ -73,7 +73,7 @@ protected List<List<Integer>> generateExpectedResults(int topK, float[][] datase
       // Sort by distance and select the topK nearest neighbors
       List<Integer> neighbors = distances.entrySet().stream().sorted(Map.Entry.comparingByValue())
           .map(Map.Entry::getKey).toList();
-      neighborsResult.add(neighbors.subList(0, Math.min(topK * 2, dataset.length)));
+      neighborsResult.add(neighbors.subList(0, Math.min(topK * 2 + 10, dataset.length)));
     }
 
     log.info("Expected results generated successfully.");
diff --git a/java/internal/src/cuvs_java.c b/java/internal/src/cuvs_java.c
index 0b9c2f794e..34abfa1b2d 100644
--- a/java/internal/src/cuvs_java.c
+++ b/java/internal/src/cuvs_java.c
@@ -206,6 +206,35 @@ void search_cagra_index(cuvsCagraIndex_t index, float *queries, int topk, long n
   cuvsRMMFree(cuvs_resources, queries_d, sizeof(float) * n_queries * dimensions);
 }
 
+/**
+ * @brief A function to merge multiple CAGRA indexes into a single index
+ *
+ * @param[in] cuvs_resources Reference to the underlying opaque C handle
+ * @param[in] index_array Array of input CAGRA index handles to be merged
+ * @param[out] merged_index Output handle for the merged CAGRA index
+ * @param[in] num_indices Number of indexes to merge
+ * @param[out] return_value Return value for cuvsCagraMerge function call
+ * @param[in] merge_params Reference to merge parameters, or NULL for defaults
+ */
+void merge_cagra_indexes(cuvsResources_t cuvs_resources,
+                       cuvsCagraIndex_t* index_array,
+                       cuvsCagraIndex_t merged_index,
+                       int num_indices,
+                       int* return_value,
+                       cuvsCagraMergeParams_t merge_params) {
+  // default merge_params when merge_params is null
+  cuvsCagraMergeParams_t local_merge_params = merge_params;
+  if (local_merge_params == NULL) {
+    cuvsCagraMergeParamsCreate(&local_merge_params);
+  }
+
+  *return_value = cuvsCagraMerge(cuvs_resources, local_merge_params, index_array, num_indices, merged_index);
+
+  if (merge_params == NULL && local_merge_params != NULL) {
+    cuvsCagraMergeParamsDestroy(local_merge_params);
+  }
+}
+
 /**
  * @brief De-allocate BRUTEFORCE index
  *

From eb2292db06b6d402dda54498a4e8aac39fb46cdc Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Thu, 29 May 2025 13:48:01 +0200
Subject: [PATCH 423/623] Add IVF-PQ support in CAGRA index params in Python
 (#918)

- Updated CAGRA Index Params to include support for IVF-PQ parameters.
- Added `coarse_search_dtype` in IVFPQ params
- Added tests for IVF-PQ integration in `test_cagra.py`.

Closes #468
Related PR: #809

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/918
---
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd   | 20 ++++++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx   | 34 ++++++++-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd |  3 +
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx | 34 ++++++++-
 python/cuvs/cuvs/tests/test_cagra.py         | 75 +++++++++++++++++++-
 5 files changed, 162 insertions(+), 4 deletions(-)

diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index f6435a5b2c..b8347d8ff6 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -30,7 +30,20 @@ from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
 from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
 from cuvs.distance_type cimport cuvsDistanceType
 from cuvs.neighbors.filters.filters cimport cuvsFilter
+from cuvs.neighbors.ivf_pq.ivf_pq cimport (
+    cuvsIvfPqIndexParams_t,
+    cuvsIvfPqSearchParams_t,
+)
+
 
+cdef extern from "library_types.h":
+    ctypedef enum cudaDataType_t:
+        CUDA_R_32F "CUDA_R_32F"  # float
+        CUDA_R_16F "CUDA_R_16F"  # half
+
+        # uint8 - used to refer to IVF-PQ's fp8 storage type
+        CUDA_R_8U "CUDA_R_8U"
+        CUDA_R_8I "CUDA_R_8I"
 
 cdef extern from "cuvs/neighbors/cagra.h" nogil:
 
@@ -49,6 +62,12 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
 
     ctypedef cuvsCagraCompressionParams* cuvsCagraCompressionParams_t
 
+    ctypedef struct cuvsIvfPqParams:
+        cuvsIvfPqIndexParams_t ivf_pq_build_params
+        cuvsIvfPqSearchParams_t ivf_pq_search_params
+        float refinement_rate
+    ctypedef cuvsIvfPqParams* cuvsIvfPqParams_t
+
     ctypedef struct cuvsCagraIndexParams:
         cuvsDistanceType metric
         size_t intermediate_graph_degree
@@ -56,6 +75,7 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
         cuvsCagraGraphBuildAlgo build_algo
         size_t nn_descent_niter
         cuvsCagraCompressionParams_t compression
+        cuvsIvfPqParams_t graph_build_params
 
     ctypedef cuvsCagraIndexParams* cuvsCagraIndexParams_t
 
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index c31df724f5..49cd29942e 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -48,6 +48,7 @@ from libc.stdint cimport (
 )
 
 from cuvs.common.exceptions import check_cuvs
+from cuvs.neighbors import ivf_pq
 from cuvs.neighbors.filters import no_filter
 
 
@@ -156,6 +157,13 @@ cdef class IndexParams:
     compression: CompressionParams, optional
         If compression is desired should be a CompressionParams object. If None
         compression will be disabled.
+    ivf_pq_build_params: cuvs.neighbors.ivf_pq.IndexParams, optional
+        Parameters for IVF-PQ algorithm. If provided, it will be used for
+        building the graph.
+    ivf_pq_search_params: cuvs.neighbors.ivf_pq.SearchParams, optional
+        Parameters for IVF-PQ search. If provided, it will be used for
+        searching the graph.
+    refinement_rate: float, default = 1.0
     """
 
     cdef cuvsCagraIndexParams* params
@@ -163,10 +171,14 @@ cdef class IndexParams:
 
     # hold on to a reference to the compression, to keep from being GC'ed
     cdef public object compression
+    cdef public object ivf_pq_build_params
+    cdef public object ivf_pq_search_params
 
     def __cinit__(self):
         check_cuvs(cuvsCagraIndexParamsCreate(&self.params))
         self.compression = None
+        self.ivf_pq_build_params = None
+        self.ivf_pq_search_params = None
 
     def __dealloc__(self):
         check_cuvs(cuvsCagraIndexParamsDestroy(self.params))
@@ -177,7 +189,10 @@ cdef class IndexParams:
                  graph_degree=64,
                  build_algo="ivf_pq",
                  nn_descent_niter=20,
-                 compression=None):
+                 compression=None,
+                 ivf_pq_build_params: ivf_pq.IndexParams = None,
+                 ivf_pq_search_params: ivf_pq.SearchParams = None,
+                 refinement_rate: float = 1.0):
 
         self._metric = metric
         self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
@@ -198,6 +213,19 @@ cdef class IndexParams:
             self.compression = compression
             self.params.compression = \
                 <cuvsCagraCompressionParams_t><size_t>compression.get_handle()
+        if ivf_pq_build_params is not None:
+            if ivf_pq_build_params.metric != self.metric:
+                raise ValueError("Metric mismatch with IVF-PQ build params")
+            self.ivf_pq_build_params = ivf_pq_build_params
+            self.params.graph_build_params.ivf_pq_build_params = \
+                <cuvsIvfPqIndexParams_t><size_t> \
+                ivf_pq_build_params.get_handle()
+        if ivf_pq_search_params is not None:
+            self.ivf_pq_search_params = ivf_pq_search_params
+            self.params.graph_build_params.ivf_pq_search_params = \
+                <cuvsIvfPqSearchParams_t><size_t> \
+                ivf_pq_search_params.get_handle()
+        self.params.graph_build_params.refinement_rate = refinement_rate
 
     @property
     def metric(self):
@@ -219,6 +247,10 @@ cdef class IndexParams:
     def nn_descent_niter(self):
         return self.params.nn_descent_niter
 
+    @property
+    def refinement_rate(self):
+        return self.params.graph_build_params.refinement_rate
+
 
 cdef class Index:
     def __cinit__(self):
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index 635bb970d5..06932ac601 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -30,6 +30,7 @@ cdef extern from "library_types.h":
 
         # uint8 - used to refer to IVF-PQ's fp8 storage type
         CUDA_R_8U "CUDA_R_8U"
+        CUDA_R_8I "CUDA_R_8I"
 
 
 cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
@@ -59,6 +60,8 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
         cudaDataType_t lut_dtype
         cudaDataType_t internal_distance_dtype
         double preferred_shmem_carveout
+        cudaDataType_t coarse_search_dtype
+        uint32_t max_internal_batch_size
 
     ctypedef cuvsIvfPqSearchParams* cuvsIvfPqSearchParams_t
 
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 678ca2705c..4e4c96db8d 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -215,6 +215,9 @@ cdef class IndexParams:
     def max_train_points_per_pq_code(self):
         return self.params.max_train_points_per_pq_code
 
+    def get_handle(self):
+        return <size_t>self.params
+
 cdef class Index:
     """
     IvfPq index object. This object stores the trained IvfPq index state
@@ -339,7 +342,8 @@ cdef _map_dtype_np_to_cuda(dtype, supported_dtypes=None):
         raise TypeError("Type %s is not supported" % str(dtype))
     return {np.float32: cudaDataType_t.CUDA_R_32F,
             np.float16: cudaDataType_t.CUDA_R_16F,
-            np.uint8: cudaDataType_t.CUDA_R_8U}[dtype]
+            np.uint8: cudaDataType_t.CUDA_R_8U,
+            np.int8: cudaDataType_t.CUDA_R_8I}[dtype]
 
 
 cdef class SearchParams:
@@ -360,6 +364,16 @@ cdef class SearchParams:
     internal_distance_dtype: default = np.float32
         Storage data type for distance/similarity computation.
         Possible values [np.float32, np.float16]
+    coarse_search_dtype: default = np.float32
+        [Experimental] The data type to use as the GEMM element type when
+        searching the clusters to probe.
+        Possible values: [np.float32, np.float16, np.int8].
+        - Legacy default: np.float32
+        - Recommended for performance: np.float16 (half)
+        - Experimental/low-precision: np.int8
+    max_internal_batch_size: default = 4096
+        Set the internal batch size to improve GPU utilization at the cost
+        of larger memory footprint.
     """
 
     cdef cuvsIvfPqSearchParams* params
@@ -371,11 +385,16 @@ cdef class SearchParams:
         check_cuvs(cuvsIvfPqSearchParamsDestroy(self.params))
 
     def __init__(self, *, n_probes=20, lut_dtype=np.float32,
-                 internal_distance_dtype=np.float32):
+                 internal_distance_dtype=np.float32,
+                 coarse_search_dtype=np.float32,
+                 max_internal_batch_size=4096):
         self.params.n_probes = n_probes
         self.params.lut_dtype = _map_dtype_np_to_cuda(lut_dtype)
         self.params.internal_distance_dtype = \
             _map_dtype_np_to_cuda(internal_distance_dtype)
+        self.params.coarse_search_dtype = \
+            _map_dtype_np_to_cuda(coarse_search_dtype)
+        self.params.max_internal_batch_size = max_internal_batch_size
 
     @property
     def n_probes(self):
@@ -393,6 +412,17 @@ cdef class SearchParams:
     def internal_distance_dtype(self):
         return self.params.internal_distance_dtype
 
+    @property
+    def coarse_search_dtype(self):
+        return self.params.coarse_search_dtype
+
+    @property
+    def max_internal_batch_size(self):
+        return self.params.max_internal_batch_size
+
+    def get_handle(self):
+        return <size_t>self.params
+
 
 @auto_sync_resources
 @auto_convert_output
diff --git a/python/cuvs/cuvs/tests/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
index f3de488dab..1e577e343d 100644
--- a/python/cuvs/cuvs/tests/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -19,7 +19,7 @@
 from sklearn.neighbors import NearestNeighbors
 from sklearn.preprocessing import normalize
 
-from cuvs.neighbors import cagra
+from cuvs.neighbors import cagra, ivf_pq
 from cuvs.tests.ann_utils import (
     calc_recall,
     generate_data,
@@ -196,3 +196,76 @@ def test_cagra_vpq_compression():
     run_cagra_build_search_test(
         n_cols=dim, compression=cagra.CompressionParams(pq_dim=dim / pq_len)
     )
+
+
+@pytest.mark.parametrize("internal_dtype", [np.float32, np.float16, np.uint8])
+def test_cagra_ivf_pq(
+    internal_dtype,
+    n_rows=1000,
+    n_cols=30,
+    n_queries=20,
+    k=5,
+    dtype=np.float16,
+    metric="inner_product",
+    intermediate_graph_degree=32,
+    graph_degree=16,
+    build_algo="ivf_pq",
+):
+    dataset = generate_data((n_rows, n_cols), dtype)
+    dataset = normalize(dataset, norm="l2", axis=1)
+    dataset_device = device_ndarray(dataset)
+
+    ivf_pq_params_build = ivf_pq.IndexParams(metric=metric, n_lists=10)
+    ivf_pq_params_search = ivf_pq.SearchParams(
+        n_probes=5,
+        lut_dtype=internal_dtype,
+        coarse_search_dtype=np.int8,
+    )
+    build_params = cagra.IndexParams(
+        metric=metric,
+        intermediate_graph_degree=intermediate_graph_degree,
+        graph_degree=graph_degree,
+        build_algo=build_algo,
+        ivf_pq_build_params=ivf_pq_params_build,
+        ivf_pq_search_params=ivf_pq_params_search,
+        refinement_rate=1.2,
+    )
+    cudadtype_to_np = {np.float32: 0, np.float16: 2, np.int8: 3, np.uint8: 8}
+
+    assert (
+        build_params.ivf_pq_search_params.lut_dtype
+        == cudadtype_to_np[internal_dtype]
+    )
+    assert (
+        build_params.ivf_pq_search_params.coarse_search_dtype
+        == cudadtype_to_np[np.int8]
+    )
+    assert np.isclose(build_params.refinement_rate, 1.2)
+    index = cagra.build(build_params, dataset_device)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    queries_device = device_ndarray(queries)
+    out_idx = np.zeros((n_queries, k), dtype=np.uint32)
+    out_dist = np.zeros((n_queries, k), dtype=np.float32)
+    out_idx_device = device_ndarray(out_idx)
+    out_dist_device = device_ndarray(out_dist)
+
+    cagra.search(
+        cagra.SearchParams(),
+        index,
+        queries_device,
+        k,
+        neighbors=out_idx_device,
+        distances=out_dist_device,
+    )
+    out_idx = out_idx_device.copy_to_host()
+
+    skl_idx = (
+        NearestNeighbors(n_neighbors=k, algorithm="brute", metric="cosine")
+        .fit(dataset)
+        .kneighbors(queries, return_distance=False)
+    )
+
+    recall = calc_recall(out_idx, skl_idx)
+
+    assert recall > 0.9

From 3937a7c155bbfea2125d3876da9e6b51f1a40001 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 29 May 2025 09:12:20 -0700
Subject: [PATCH 424/623] Add tiered_index support (#879)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/879
---
 cpp/CMakeLists.txt                            |   3 +
 cpp/include/cuvs/neighbors/brute_force.hpp    |   4 +-
 .../cuvs/neighbors/knn_merge_parts.hpp        |  54 +++
 cpp/include/cuvs/neighbors/tiered_index.h     | 254 ++++++++++++
 cpp/include/cuvs/neighbors/tiered_index.hpp   | 200 +++++++++
 cpp/src/neighbors/cagra_c.cpp                 |  84 ++--
 cpp/src/neighbors/cagra_c.hpp                 |  29 ++
 cpp/src/neighbors/detail/knn_brute_force.cuh  |  10 +-
 cpp/src/neighbors/detail/sparse_knn.cuh       |  19 +-
 cpp/src/neighbors/detail/tiered_index.cuh     | 383 +++++++++++++++++
 cpp/src/neighbors/ivf_flat_c.cpp              |  35 +-
 cpp/src/neighbors/ivf_flat_c.hpp              |  25 ++
 cpp/src/neighbors/ivf_pq_c.cpp                |  55 ++-
 cpp/src/neighbors/ivf_pq_c.hpp                |  27 ++
 cpp/src/neighbors/knn_merge_parts.cu          |  74 ++++
 cpp/src/neighbors/mg/snmg.cuh                 |  32 +-
 cpp/src/neighbors/tiered_index.cu             | 194 +++++++++
 cpp/src/neighbors/tiered_index_c.cpp          | 307 ++++++++++++++
 cpp/tests/CMakeLists.txt                      |   2 +
 cpp/tests/neighbors/tiered_index.cu           | 209 ++++++++++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |  16 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  22 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |  12 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  |  13 +-
 .../neighbors/tiered_index/CMakeLists.txt     |  24 ++
 .../cuvs/neighbors/tiered_index/__init__.pxd  |   0
 .../cuvs/neighbors/tiered_index/__init__.py   |  24 ++
 .../neighbors/tiered_index/tiered_index.pxd   |  78 ++++
 .../neighbors/tiered_index/tiered_index.pyx   | 389 ++++++++++++++++++
 30 files changed, 2457 insertions(+), 122 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/knn_merge_parts.hpp
 create mode 100644 cpp/include/cuvs/neighbors/tiered_index.h
 create mode 100644 cpp/include/cuvs/neighbors/tiered_index.hpp
 create mode 100644 cpp/src/neighbors/cagra_c.hpp
 create mode 100644 cpp/src/neighbors/detail/tiered_index.cuh
 create mode 100644 cpp/src/neighbors/ivf_flat_c.hpp
 create mode 100644 cpp/src/neighbors/ivf_pq_c.hpp
 create mode 100644 cpp/src/neighbors/knn_merge_parts.cu
 create mode 100644 cpp/src/neighbors/tiered_index.cu
 create mode 100644 cpp/src/neighbors/tiered_index_c.cpp
 create mode 100644 cpp/tests/neighbors/tiered_index.cu
 create mode 100644 python/cuvs/cuvs/neighbors/tiered_index/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/tiered_index/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/tiered_index/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pyx

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 52858db577..5ccb6765ab 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -463,6 +463,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
     src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
     src/neighbors/ivf_pq/detail/ivf_pq_search_uint8_t_int64_t.cu
+    src/neighbors/knn_merge_parts.cu
     src/neighbors/nn_descent.cu
     src/neighbors/nn_descent_float.cu
     src/neighbors/nn_descent_half.cu
@@ -479,6 +480,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/refine/detail/refine_host_int8_t_float.cpp
     src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
     src/neighbors/sample_filter.cu
+    src/neighbors/tiered_index.cu
     src/neighbors/sparse_brute_force.cu
     src/neighbors/vamana_build_float.cu
     src/neighbors/vamana_build_uint8.cu
@@ -716,6 +718,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
       src/neighbors/nn_descent_c.cpp
       src/neighbors/refine/refine_c.cpp
+      src/neighbors/tiered_index_c.cpp
       src/preprocessing/quantize/binary_c.cpp
       src/preprocessing/quantize/scalar_c.cpp
       src/distance/pairwise_distance_c.cpp
diff --git a/cpp/include/cuvs/neighbors/brute_force.hpp b/cpp/include/cuvs/neighbors/brute_force.hpp
index 72a5cac121..dade5d8c0e 100644
--- a/cpp/include/cuvs/neighbors/brute_force.hpp
+++ b/cpp/include/cuvs/neighbors/brute_force.hpp
@@ -276,7 +276,7 @@ auto build(raft::resources const& handle,
  *
  * @param[in] handle
  * @param[in] index_params parameters such as the distance metric to use
- * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ * @param[in] dataset a device pointer to a col-major matrix [n_rows, dim]
  *
  * @return the constructed brute force index
  */
@@ -302,7 +302,7 @@ auto build(raft::resources const& handle,
  *
  * @param[in] handle
  * @param[in] index_params parameters such as the distance metric to use
- * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ * @param[in] dataset a device pointer to a col-major matrix [n_rows, dim]
  *
  * @return the constructed brute force index
  */
diff --git a/cpp/include/cuvs/neighbors/knn_merge_parts.hpp b/cpp/include/cuvs/neighbors/knn_merge_parts.hpp
new file mode 100644
index 0000000000..2116cbbdcc
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/knn_merge_parts.hpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdint>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/device_resources.hpp>
+
+namespace cuvs::neighbors {
+/**
+ * @brief Merge knn distances and index matrix, which have been partitioned
+ * by row, into a single matrix with only the k-nearest neighbors.
+ *
+ * @param res raft resources
+ * @param inK partitioned knn distance matrix
+ * @param inV partitioned knn index matrix
+ * @param outK merged knn distance matrix
+ * @param outV merged knn index matrix
+ * @param translations mapping of index offsets for each partition
+ */
+void knn_merge_parts(raft::resources const& res,
+                     raft::device_matrix_view<const float, int64_t> inK,
+                     raft::device_matrix_view<const int64_t, int64_t> inV,
+                     raft::device_matrix_view<float, int64_t> outK,
+                     raft::device_matrix_view<int64_t, int64_t> outV,
+                     raft::device_vector_view<int64_t> translations);
+void knn_merge_parts(raft::resources const& res,
+                     raft::device_matrix_view<const float, int64_t> inK,
+                     raft::device_matrix_view<const uint32_t, int64_t> inV,
+                     raft::device_matrix_view<float, int64_t> outK,
+                     raft::device_matrix_view<uint32_t, int64_t> outV,
+                     raft::device_vector_view<uint32_t> translations);
+void knn_merge_parts(raft::resources const& res,
+                     raft::device_matrix_view<const float, int64_t> inK,
+                     raft::device_matrix_view<const int32_t, int64_t> inV,
+                     raft::device_matrix_view<float, int64_t> outK,
+                     raft::device_matrix_view<int32_t, int64_t> outV,
+                     raft::device_vector_view<int32_t> translations);
+}  // namespace cuvs::neighbors
diff --git a/cpp/include/cuvs/neighbors/tiered_index.h b/cpp/include/cuvs/neighbors/tiered_index.h
new file mode 100644
index 0000000000..a5bf9b3b9c
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/tiered_index.h
@@ -0,0 +1,254 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/ivf_flat.h>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Enum to hold which ANN algorithm is being used in the tiered index
+ */
+enum cuvsTieredIndexANNAlgo {
+  CUVS_TIERED_INDEX_ALGO_CAGRA,
+  CUVS_TIERED_INDEX_ALGO_IVF_FLAT,
+  CUVS_TIERED_INDEX_ALGO_IVF_PQ
+};
+
+/**
+ * @defgroup tiered_index_c_index Tiered Index
+ * @{
+ */
+/**
+ * @brief Struct to hold address of cuvs::neighbors::tiered_index::index and its active trained
+ * dtype
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+  cuvsTieredIndexANNAlgo algo;
+} cuvsTieredIndex;
+
+typedef cuvsTieredIndex* cuvsTieredIndex_t;
+
+/**
+ * @brief Allocate Tiered Index
+ *
+ * @param[in] index cuvsTieredIndex_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsTieredIndexCreate(cuvsTieredIndex_t* index);
+
+/**
+ * @brief De-allocate Tiered index
+ *
+ * @param[in] index cuvsTieredIndex_t to de-allocate
+ */
+cuvsError_t cuvsTieredIndexDestroy(cuvsTieredIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup tiered_c_index_params Tiered Index build parameters
+ * @{
+ */
+/**
+ * @brief Supplemental parameters to build a TieredIndex
+ */
+struct cuvsTieredIndexParams {
+  /** Distance type. */
+  cuvsDistanceType metric;
+
+  /** The type of ANN algorithm we are using */
+  cuvsTieredIndexANNAlgo algo;
+
+  /** The minimum number of rows necessary in the index to create an
+ ann index */
+  int64_t min_ann_rows;
+
+  /** Whether or not to create a new ann index on extend, if the number
+  of rows in the incremental (bfknn) portion is above min_ann_rows */
+  bool create_ann_index_on_extend;
+
+  /** Optional parameters for building a cagra index */
+  cuvsCagraIndexParams_t cagra_params;
+
+  /** Optional parameters for building a ivf_flat index */
+  cuvsIvfFlatIndexParams_t ivf_flat_params;
+
+  /** Optional parameters for building a ivf-pq index */
+  cuvsIvfPqIndexParams_t ivf_pq_params;
+};
+
+typedef struct cuvsTieredIndexParams* cuvsTieredIndexParams_t;
+
+/**
+ * @brief Allocate Tiered Index Params and populate with default values
+ *
+ * @param[in] index_params cuvsTieredIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsTieredIndexParamsCreate(cuvsTieredIndexParams_t* index_params);
+
+/**
+ * @brief De-allocate Tiered Index params
+ *
+ * @param[in] index_params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsTieredIndexParamsDestroy(cuvsTieredIndexParams_t index_params);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup tieredindex_c_index_build Tiered index build
+ * @{
+ */
+/**
+ * @brief Build a TieredIndex index with a `DLManagedTensor` which has underlying
+ *        `DLDeviceType` equal to `kDLCUDA`, `kDLCUDAHost`, `kDLCUDAManaged`,
+ *        or `kDLCPU`. Also, acceptable underlying types are:
+ *        1. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 32`
+ *        2. `kDLDataType.code == kDLFloat` and `kDLDataType.bits = 16`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/tiered_index.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ *
+ * // Create TieredIndex index
+ * cuvsTieredIndex_t index;
+ * cuvsError_t index_create_status = cuvsTieredIndexCreate(&index);
+ *
+ * // Create default index params
+ * cuvsTieredIndexParams_t index_params;
+ * cuvsError_t params_create_status = cuvsTieredIndexParamsCreate(&index_params);
+ *
+ * // Build the TieredIndex Index
+ * cuvsError_t build_status = cuvsTieredIndexBuild(res, index_params, &dataset_tensor, index);
+ *
+ * // de-allocate `index` and `res`
+ * cuvsError_t index_destroy_status = cuvsTieredIndexDestroy(index);
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] dataset DLManagedTensor* training dataset
+ * @param[in] index_params Index parameters to use when building the index
+ * @param[out] index cuvsTieredIndex_t Newly built TieredIndex index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsTieredIndexBuild(cuvsResources_t res,
+                                 cuvsTieredIndexParams_t index_params,
+                                 DLManagedTensor* dataset,
+                                 cuvsTieredIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup tieredindex_c_index_search Tiered index search
+ * @{
+ */
+/**
+ * @brief Search a TieredIndex index with a `DLManagedTensor`
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/tiered_index.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Assume a populated `DLManagedTensor` type here
+ * DLManagedTensor dataset;
+ * DLManagedTensor queries;
+ * DLManagedTensor neighbors;
+ * DLManagedTensor bitmap;
+ *
+ * cuvsFilter prefilter{(uintptr_t)&bitmap, BITMAP};
+ *
+ * // Search the `index` built using `cuvsTieredIndexBuild`
+ * cuvsError_t search_status = cuvsTieredIndexSearch(res, index, &queries, &neighbors, &distances,
+ * prefilter);
+ *
+ * // de-allocate `res`
+ * cuvsError_t res_destroy_status = cuvsResourcesDestroy(res);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] search_params params used to the ANN index, should be one of
+ * cuvsCagraSearchParams_t, cuvsIvfFlatSearchParams_t, cuvsIvfPqSearchParams_t
+ * depending on the type of the tiered index used
+ * @param[in] index cuvsTieredIndex which has been returned by `cuvsTieredIndexBuild`
+ * @param[in] queries DLManagedTensor* queries dataset to search
+ * @param[out] neighbors DLManagedTensor* output `k` neighbors for queries
+ * @param[out] distances DLManagedTensor* output `k` distances for queries
+ * @param[in] prefilter cuvsFilter input prefilter that can be used
+              to filter queries and neighbors based on the given bitmap.
+ */
+cuvsError_t cuvsTieredIndexSearch(cuvsResources_t res,
+                                  void* search_params,
+                                  cuvsTieredIndex_t index,
+                                  DLManagedTensor* queries,
+                                  DLManagedTensor* neighbors,
+                                  DLManagedTensor* distances,
+                                  cuvsFilter prefilter);
+
+/**
+ * @}
+ */
+/**
+ * @defgroup tiered_c_index_extend Tiered index extend
+ * @{
+ */
+/**
+ * @brief Extend the index with the new data.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] new_vectors DLManagedTensor* the new vectors to add to the index
+ * @param[inout] index Tiered index to be extended
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsTieredIndexExtend(cuvsResources_t res,
+                                  DLManagedTensor* new_vectors,
+                                  cuvsTieredIndex_t index);
+/**
+ * @}
+ */
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/tiered_index.hpp b/cpp/include/cuvs/neighbors/tiered_index.hpp
new file mode 100644
index 0000000000..1037955309
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/tiered_index.hpp
@@ -0,0 +1,200 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <mutex>
+#include <shared_mutex>
+
+namespace cuvs::neighbors::ivf_pq {
+// The default ivf-pq index doesn't have a 'value_type', since it
+// can accept multiple different types at search time.
+// However, the tiered index code needs a value_type (for the bfknn tier),
+// defined in the ann index - so this class adds this for compatibility
+template <typename T, typename IdxT>
+struct typed_index : index<IdxT> {
+  using value_type = T;
+};
+}  // namespace cuvs::neighbors::ivf_pq
+
+namespace cuvs::neighbors::tiered_index {
+
+// forward reference to tiered_index implementation.
+namespace detail {
+template <typename UpstreamT>
+struct index_state;
+}  // namespace detail
+
+/**
+ * @brief Tiered Index class
+ */
+template <typename UpstreamT>
+struct index : cuvs::neighbors::index {
+  std::shared_ptr<detail::index_state<UpstreamT>> state;
+  std::mutex write_mutex;
+  mutable std::shared_mutex ann_mutex;
+
+  explicit index(std::shared_ptr<detail::index_state<UpstreamT>> state) : state(state) {}
+  explicit index(const index<UpstreamT>& other) : state(other.state) {}
+
+  /** Total length of the index. */
+  int64_t size() const noexcept;
+
+  /** Dimensionality of the data. */
+  int64_t dim() const noexcept;
+};
+
+template <typename upstream_index_params_type>
+struct index_params : upstream_index_params_type {
+  /** The minimum number of rows necessary in the index to create an
+  ann index */
+  int64_t min_ann_rows = 100000;
+
+  /** Whether or not to create a new ann index on extend, if the number
+  of rows in the incremental (bfknn) portion is above min_ann_rows */
+  bool create_ann_index_on_extend = false;
+};
+
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   tiered_index::index_params<cagra::index_params> build_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = tiered_index::build(handle, build_params, dataset);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] index_params configure the index building
+ * @param[in] dataset a device matrix view to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed tiered index
+ */
+auto build(raft::resources const& res,
+           const index_params<cagra::index_params>& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> tiered_index::index<cagra::index<float, uint32_t>>;
+
+/** @copydoc build */
+auto build(raft::resources const& res,
+           const index_params<ivf_flat::index_params>& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> tiered_index::index<ivf_flat::index<float, int64_t>>;
+
+/** @copydoc build */
+auto build(raft::resources const& res,
+           const index_params<ivf_pq::index_params>& index_params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> tiered_index::index<ivf_pq::typed_index<float, int64_t>>;
+
+/**
+ * @brief Extend the index with the new data.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   tiered_index::index_params<cagra::index_params> index_params;
+ *   // train the index from a [N, D] dataset
+ *   auto index_empty = tiered_index::build(res, index_params, dataset);
+ *
+ *   // add new data to the index
+ *   tiered_index::extend(res, new_vectors, &index_empty);
+ * @endcode
+ *
+ * @param[in] res
+ * @param[in] new_vectors a device matrix view to a row-major matrix [n_rows, idx.dim()]
+ * @param[inout] idx
+ */
+void extend(raft::resources const& res,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            tiered_index::index<cagra::index<float, uint32_t>>* idx);
+
+/** @copydoc extend */
+void extend(raft::resources const& res,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            tiered_index::index<ivf_flat::index<float, int64_t>>* idx);
+
+/** @copydoc extend */
+void extend(raft::resources const& res,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            tiered_index::index<ivf_pq::typed_index<float, int64_t>>* idx);
+/**
+ * @brief Compact the index
+ *
+ * This function takes any data that has been added incrementally, and ensures that it
+ * been added to the ANN index.
+ *
+ * @param[in] res
+ * @param[inout] idx
+ */
+void compact(raft::resources const& res, tiered_index::index<cagra::index<float, uint32_t>>* idx);
+
+/** @copydoc compact */
+void compact(raft::resources const& res, tiered_index::index<ivf_flat::index<float, int64_t>>* idx);
+
+/** @copydoc compact */
+void compact(raft::resources const& res,
+             tiered_index::index<ivf_pq::typed_index<float, int64_t>>* idx);
+
+/**
+ * @brief Search the tiered_index
+ *
+ * @param[in] res
+ * @param[in] search_params configure the search
+ * @param[in] index tiered-index constructed index
+ * @param[in] queries a device matrix view to a row-major matrix [n_queries, index->dim()]
+ * @param[out] neighbors a device matrix view to the indices of the neighbors in the source dataset
+ * [n_queries, k]
+ * @param[out] distances a device matrix view to the distances to the selected neighbors [n_queries,
+ * k]
+ * @param[in] sample_filter an optional device filter function object that greenlights samples
+ * for a given query. (none_sample_filter for no filtering)
+ */
+void search(raft::resources const& res,
+            const cagra::search_params& search_params,
+            const tiered_index::index<cagra::index<float, uint32_t>>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            const ivf_flat::search_params& search_params,
+            const tiered_index::index<ivf_flat::index<float, int64_t>>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+
+/** @copydoc search */
+void search(raft::resources const& res,
+            const ivf_pq::search_params& search_params,
+            const tiered_index::index<ivf_pq::typed_index<float, int64_t>>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter =
+              cuvs::neighbors::filtering::none_sample_filter{});
+}  // namespace cuvs::neighbors::tiered_index
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index e921216bf9..656724826e 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -29,6 +29,7 @@
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.h>
 
+#include "cagra_c.hpp"
 #include <fstream>
 
 namespace {
@@ -104,24 +105,8 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
   auto index   = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr);
 
-  auto index_params   = cuvs::neighbors::cagra::index_params();
-  index_params.metric = static_cast<cuvs::distance::DistanceType>((int)params.metric),
-  index_params.intermediate_graph_degree = params.intermediate_graph_degree;
-  index_params.graph_degree              = params.graph_degree;
-
-  _set_graph_build_params(
-    index_params.graph_build_params, params, params.build_algo, dataset.shape[0], dataset.shape[1]);
-
-  if (auto* cparams = params.compression; cparams != nullptr) {
-    auto compression_params                        = cuvs::neighbors::vpq_params();
-    compression_params.pq_bits                     = cparams->pq_bits;
-    compression_params.pq_dim                      = cparams->pq_dim;
-    compression_params.vq_n_centers                = cparams->vq_n_centers;
-    compression_params.kmeans_n_iters              = cparams->kmeans_n_iters;
-    compression_params.vq_kmeans_trainset_fraction = cparams->vq_kmeans_trainset_fraction;
-    compression_params.pq_kmeans_trainset_fraction = cparams->pq_kmeans_trainset_fraction;
-    index_params.compression.emplace(compression_params);
-  }
+  auto index_params = cuvs::neighbors::cagra::index_params();
+  convert_c_index_params(params, dataset.shape[0], dataset.shape[1], &index_params);
 
   if (cuvs::core::is_dlpack_device_compatible(dataset)) {
     using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
@@ -184,23 +169,8 @@ void _search(cuvsResources_t res,
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index.addr);
 
-  auto search_params              = cuvs::neighbors::cagra::search_params();
-  search_params.max_queries       = params.max_queries;
-  search_params.itopk_size        = params.itopk_size;
-  search_params.max_iterations    = params.max_iterations;
-  search_params.algo              = static_cast<cuvs::neighbors::cagra::search_algo>(params.algo);
-  search_params.team_size         = params.team_size;
-  search_params.search_width      = params.search_width;
-  search_params.min_iterations    = params.min_iterations;
-  search_params.thread_block_size = params.thread_block_size;
-  search_params.hashmap_mode = static_cast<cuvs::neighbors::cagra::hash_mode>(params.hashmap_mode);
-  search_params.hashmap_min_bitlen      = params.hashmap_min_bitlen;
-  search_params.hashmap_max_fill_rate   = params.hashmap_max_fill_rate;
-  search_params.num_random_samplings    = params.num_random_samplings;
-  search_params.rand_xor_mask           = params.rand_xor_mask;
-  search_params.persistent              = params.persistent;
-  search_params.persistent_lifetime     = params.persistent_lifetime;
-  search_params.persistent_device_usage = params.persistent_device_usage;
+  auto search_params = cuvs::neighbors::cagra::search_params();
+  convert_c_search_params(params, &search_params);
 
   using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, raft::row_major>;
   using neighbors_mdspan_type = raft::device_matrix_view<IdxT, int64_t, raft::row_major>;
@@ -327,9 +297,51 @@ void* _merge(cuvsResources_t res,
 
   return merged_index;
 }
-
 }  // namespace
 
+namespace cuvs::neighbors::cagra {
+void convert_c_index_params(cuvsCagraIndexParams params,
+                            int64_t n_rows,
+                            int64_t dim,
+                            cuvs::neighbors::cagra::index_params* out)
+{
+  out->metric                    = static_cast<cuvs::distance::DistanceType>((int)params.metric);
+  out->intermediate_graph_degree = params.intermediate_graph_degree;
+  out->graph_degree              = params.graph_degree;
+  _set_graph_build_params(out->graph_build_params, params, params.build_algo, n_rows, dim);
+
+  if (auto* cparams = params.compression; cparams != nullptr) {
+    auto compression_params                        = cuvs::neighbors::vpq_params();
+    compression_params.pq_bits                     = cparams->pq_bits;
+    compression_params.pq_dim                      = cparams->pq_dim;
+    compression_params.vq_n_centers                = cparams->vq_n_centers;
+    compression_params.kmeans_n_iters              = cparams->kmeans_n_iters;
+    compression_params.vq_kmeans_trainset_fraction = cparams->vq_kmeans_trainset_fraction;
+    compression_params.pq_kmeans_trainset_fraction = cparams->pq_kmeans_trainset_fraction;
+    out->compression.emplace(compression_params);
+  }
+}
+void convert_c_search_params(cuvsCagraSearchParams params,
+                             cuvs::neighbors::cagra::search_params* out)
+{
+  out->max_queries           = params.max_queries;
+  out->itopk_size            = params.itopk_size;
+  out->max_iterations        = params.max_iterations;
+  out->algo                  = static_cast<cuvs::neighbors::cagra::search_algo>(params.algo);
+  out->team_size             = params.team_size;
+  out->search_width          = params.search_width;
+  out->min_iterations        = params.min_iterations;
+  out->thread_block_size     = params.thread_block_size;
+  out->hashmap_mode          = static_cast<cuvs::neighbors::cagra::hash_mode>(params.hashmap_mode);
+  out->hashmap_min_bitlen    = params.hashmap_min_bitlen;
+  out->hashmap_max_fill_rate = params.hashmap_max_fill_rate;
+  out->num_random_samplings  = params.num_random_samplings;
+  out->rand_xor_mask         = params.rand_xor_mask;
+  out->persistent            = params.persistent;
+  out->persistent_lifetime   = params.persistent_lifetime;
+  out->persistent_device_usage = params.persistent_device_usage;
+}
+}  // namespace cuvs::neighbors::cagra
 extern "C" cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index)
 {
   return cuvs::core::translate_exceptions([=] { *index = new cuvsCagraIndex{}; });
diff --git a/cpp/src/neighbors/cagra_c.hpp b/cpp/src/neighbors/cagra_c.hpp
new file mode 100644
index 0000000000..b61bdcdedc
--- /dev/null
+++ b/cpp/src/neighbors/cagra_c.hpp
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/cagra.hpp>
+
+namespace cuvs::neighbors::cagra {
+/// Converts a cuvsCagraIndexParams struct (c) to a cagra::index_params (C++) struct
+void convert_c_index_params(cuvsCagraIndexParams params,
+                            int64_t n_rows,
+                            int64_t dim,
+                            cuvs::neighbors::cagra::index_params* out);
+
+/// Converts C search params to C++
+void convert_c_search_params(cuvsCagraSearchParams params,
+                             cuvs::neighbors::cagra::search_params* out);
+}  // namespace cuvs::neighbors::cagra
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 72b3532dfc..9202fe1c89 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -18,13 +18,13 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/neighbors/knn_merge_parts.hpp>
 #include <cuvs/selection/select_k.hpp>
 
 #include "../../distance/detail/distance_ops/l2_exp.cuh"
 #include "./faiss_distance_utils.h"
 #include "./fused_l2_knn.cuh"
 #include "./haversine_distance.cuh"
-#include "./knn_merge_parts.cuh"
 #include "./knn_utils.cuh"
 
 #include <raft/core/bitmap.cuh>
@@ -537,7 +537,13 @@ void brute_force_knn_impl(
   if (input.size() > 1 || translations != nullptr) {
     // This is necessary for proper index translations. If there are
     // no translations or partitions to combine, it can be skipped.
-    knn_merge_parts(out_D, out_I, res_D, res_I, n, input.size(), k, userStream, trans.data());
+    knn_merge_parts(
+      handle,
+      raft::make_device_matrix_view<const DistType, int64_t>(out_D, n, input.size() * k),
+      raft::make_device_matrix_view<const IdxType, int64_t>(out_I, n, input.size() * k),
+      raft::make_device_matrix_view<DistType, int64_t>(res_D, n, k),
+      raft::make_device_matrix_view<IdxType, int64_t>(res_I, n, k),
+      raft::make_device_vector_view<IdxType>(trans.data(), input.size()));
   }
 };
 
diff --git a/cpp/src/neighbors/detail/sparse_knn.cuh b/cpp/src/neighbors/detail/sparse_knn.cuh
index 9c8e971b90..4fef49d189 100644
--- a/cpp/src/neighbors/detail/sparse_knn.cuh
+++ b/cpp/src/neighbors/detail/sparse_knn.cuh
@@ -16,12 +16,12 @@
 
 #pragma once
 #include "../../distance/sparse_distance.cuh"
-#include "knn_merge_parts.cuh"
 #include <cuvs/distance/distance.hpp>
 
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/linalg/unary_op.cuh>
 
+#include <cuvs/neighbors/knn_merge_parts.hpp>
 #include <cuvs/selection/select_k.hpp>
 
 #include <raft/sparse/coo.hpp>
@@ -339,15 +339,14 @@ class sparse_knn_t {
       trans.data(), id_ranges.data(), id_ranges.size(), raft::resource::get_cuda_stream(handle));
 
     // combine merge buffers only if there's more than 1 partition to combine
-    cuvs::neighbors::detail::knn_merge_parts(merge_buffer_dists,
-                                             merge_buffer_indices,
-                                             out_dists,
-                                             out_indices,
-                                             query_batcher.batch_rows(),
-                                             2,
-                                             k,
-                                             raft::resource::get_cuda_stream(handle),
-                                             trans.data());
+    auto rows = query_batcher.batch_rows();
+    knn_merge_parts(
+      handle,
+      raft::make_device_matrix_view<const value_t, int64_t>(merge_buffer_dists, rows, 2 * k),
+      raft::make_device_matrix_view<const value_idx, int64_t>(merge_buffer_indices, rows, 2 * k),
+      raft::make_device_matrix_view<value_t, int64_t>(out_dists, rows, k),
+      raft::make_device_matrix_view<value_idx, int64_t>(out_indices, rows, k),
+      raft::make_device_vector_view<value_idx>(trans.data(), id_ranges.size()));
   }
 
   void perform_k_selection(csr_batcher_t<value_idx, value_t> idx_batcher,
diff --git a/cpp/src/neighbors/detail/tiered_index.cuh b/cpp/src/neighbors/detail/tiered_index.cuh
new file mode 100644
index 0000000000..2251448aab
--- /dev/null
+++ b/cpp/src/neighbors/detail/tiered_index.cuh
@@ -0,0 +1,383 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <memory>
+
+#include <raft/core/copy.cuh>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/norm.cuh>
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/knn_merge_parts.hpp>
+#include <cuvs/neighbors/tiered_index.hpp>
+
+namespace cuvs::neighbors::tiered_index::detail {
+/**
+  Storage for brute force based incremental indices
+
+  This lets you incrementally add vectors to the storage , without having to reallocate
+  and copy on every insert.
+  */
+template <typename T>
+struct brute_force_storage {
+  rmm::device_uvector<T> dataset;
+  std::optional<rmm::device_uvector<T>> norms;
+
+  size_t num_rows_used;
+  size_t num_rows_allocated;
+  size_t dim;
+  bool include_norms;
+
+  brute_force_storage(const raft::resources& res,
+                      size_t initial_rows,
+                      size_t dim,
+                      bool include_norms)
+    : dataset(initial_rows * dim * sizeof(T), raft::resource::get_cuda_stream(res)),
+      num_rows_used(0),
+      num_rows_allocated(initial_rows),
+      dim(dim),
+      include_norms(include_norms)
+  {
+    if (include_norms) {
+      norms =
+        rmm::device_uvector<T>(initial_rows * sizeof(T), raft::resource::get_cuda_stream(res));
+    }
+  }
+
+  size_t num_rows_available() const { return num_rows_allocated - num_rows_used; }
+
+  void append_vectors(const raft::resources& res,
+                      raft::device_matrix_view<const T, int64_t, raft::row_major> new_vectors,
+                      cuvs::distance::DistanceType metric)
+  {
+    RAFT_EXPECTS(num_rows_available() >= static_cast<size_t>(new_vectors.extent(0)),
+                 "Insufficient storage to append new vectors");
+    RAFT_EXPECTS(dim == static_cast<size_t>(new_vectors.extent(1)),
+                 "Dimension mismatch on appending new vectors");
+
+    // append the vectors to the end of the allocated storage
+    auto dst_ptr  = dataset.data() + num_rows_used * dim;
+    auto dst_view = raft::make_device_matrix_view<T, int64_t, raft::row_major>(
+      dst_ptr, new_vectors.extent(0), new_vectors.extent(1));
+    raft::copy(res, dst_view, new_vectors);
+
+    if (include_norms) {
+      auto norms_view =
+        raft::make_device_vector_view<T>(norms->data() + num_rows_used, new_vectors.extent(0));
+      if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+        raft::linalg::norm(res,
+                           new_vectors,
+                           norms_view,
+                           raft::linalg::NormType::L2Norm,
+                           raft::linalg::Apply::ALONG_ROWS,
+                           raft::sqrt_op{});
+      } else {
+        raft::linalg::norm(res,
+                           new_vectors,
+                           norms_view,
+                           raft::linalg::NormType::L2Norm,
+                           raft::linalg::Apply::ALONG_ROWS);
+      }
+    }
+    num_rows_used += new_vectors.extent(0);
+  }
+};
+
+template <typename UpstreamT>
+using upstream_build_function_type = UpstreamT(
+  raft::resources const&,
+  typename UpstreamT::index_params_type const&,
+  raft::device_matrix_view<const typename UpstreamT::value_type, int64_t, raft::row_major>);
+template <typename UpstreamT>
+using upstream_search_function_type =
+  void(raft::resources const&,
+       typename UpstreamT::search_params_type const&,
+       UpstreamT const&,
+       raft::device_matrix_view<const typename UpstreamT::value_type, int64_t, raft::row_major>,
+       raft::device_matrix_view<int64_t, int64_t, raft::row_major>,
+       raft::device_matrix_view<typename UpstreamT::value_type, int64_t, raft::row_major>,
+       const cuvs::neighbors::filtering::base_filter&);
+
+/**
+  Holds a view of the current tiered index state
+*/
+template <typename UpstreamT>
+struct index_state {
+  using value_type = typename UpstreamT::value_type;
+
+  index_state(const index_state<UpstreamT>& other)
+    : storage(other.storage),
+      ann_index(other.ann_index),
+      build_params(other.build_params),
+      build_fn(other.build_fn)
+  {
+  }
+
+  index_state(raft::resources const& res,
+              const index_params<typename UpstreamT::index_params_type>& index_params,
+              upstream_build_function_type<UpstreamT> build_fn,
+              raft::device_matrix_view<const value_type, int64_t, raft::row_major> dataset)
+    : build_params(index_params), build_fn(build_fn)
+  {
+    // allocate new storage for growing the index, keeping only a small buffer over the initial
+    // dataset size for
+    auto initial_size = dataset.extent(0) + dataset.extent(0) / 16;
+
+    // Create an ANN index if we have sufficient rows in initial dataset
+    if (dataset.extent(0) > index_params.min_ann_rows) {
+      ann_index = std::make_shared<UpstreamT>(std::move(build_fn(res, index_params, dataset)));
+    }
+
+    // allocate bfknn storage for growing the index incrementally
+    auto dim       = dataset.extent(1);
+    auto metric    = build_params.metric;
+    bool use_norms = (metric == cuvs::distance::DistanceType::L2Expanded ||
+                      metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                      metric == cuvs::distance::DistanceType::CosineExpanded);
+    storage        = std::make_shared<brute_force_storage<typename UpstreamT::value_type>>(
+      res, initial_size, dim, use_norms);
+    storage->append_vectors(res, dataset, metric);
+  }
+
+  size_t dim() const { return storage->dim; }
+  size_t size() const { return storage->num_rows_used; }
+
+  // Number of rows inside the ann index
+  size_t ann_rows() const { return ann_index ? ann_index->size() : 0; }
+
+  // Number of rows for bkfnn
+  size_t bfknn_rows() const { return storage->num_rows_used - ann_rows(); }
+
+  void search(raft::resources const& res,
+              const typename UpstreamT::search_params_type& search_params,
+              upstream_search_function_type<UpstreamT> search_fn,
+              raft::device_matrix_view<const value_type, int64_t, raft::row_major> queries,
+              raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+              raft::device_matrix_view<value_type, int64_t, raft::row_major> distances,
+              const cuvs::neighbors::filtering::base_filter& sample_filter)
+  {
+    // if we only have ANN vectors, search those and return immendiately
+    if (bfknn_rows() == 0) {
+      search_fn(res, search_params, *ann_index, queries, neighbors, distances, sample_filter);
+      return;
+    }
+
+    // Create a BFKNN index - since we already have precalculated norms this is basically a no-op
+    std::optional<raft::device_vector_view<const value_type, int64_t>> norms_view;
+    if (storage->include_norms) {
+      norms_view = raft::make_device_vector_view<const value_type, int64_t>(
+        storage->norms->data() + ann_rows(), bfknn_rows());
+    }
+
+    auto bfknn_dataset_view = raft::make_device_matrix_view<const value_type, int64_t>(
+      storage->dataset.data() + ann_rows() * storage->dim, bfknn_rows(), storage->dim);
+
+    brute_force::index<value_type> bfknn_index(
+      res, bfknn_dataset_view, norms_view, build_params.metric);
+
+    // if we don't have an ANN index, just return the bfknn results right away
+    if (!ann_index) {
+      brute_force::search(res,
+                          brute_force::search_params(),
+                          bfknn_index,
+                          queries,
+                          neighbors,
+                          distances,
+                          sample_filter);
+      return;
+    }
+
+    // query the ann/bfknn results into temporary memory. Since the knn_merge_parts
+    // api expects inputs to be in a contiguous layout - create a single temporary
+    // and then slice from it for the ann/bfknn search
+    auto n_queries      = distances.extent(0);
+    auto k              = distances.extent(1);
+    auto temp_distances = raft::make_device_matrix<value_type, int64_t>(res, 2 * n_queries, k);
+    auto temp_neighbors = raft::make_device_matrix<int64_t, int64_t>(res, 2 * n_queries, k);
+
+    // search the ann index
+    search_fn(
+      res,
+      search_params,
+      *ann_index,
+      queries,
+      raft::make_device_matrix_view<int64_t, int64_t>(temp_neighbors.data_handle(), n_queries, k),
+      raft::make_device_matrix_view<value_type, int64_t>(
+        temp_distances.data_handle(), n_queries, k),
+      sample_filter);
+
+    // search the bfknn index
+    auto offset          = n_queries * k;
+    auto bfknn_neighbors = raft::make_device_matrix_view<int64_t, int64_t>(
+      temp_neighbors.data_handle() + offset, n_queries, k);
+    auto bfknn_distances = raft::make_device_matrix_view<value_type, int64_t>(
+      temp_distances.data_handle() + offset, n_queries, k);
+    brute_force::search(res,
+                        brute_force::search_params(),
+                        bfknn_index,
+                        queries,
+                        bfknn_neighbors,
+                        bfknn_distances,
+                        sample_filter);
+
+    if (!distance::is_min_close(build_params.metric)) {
+      // knn_merge_parts doesn't currently support InnerProduct distances etc
+      // instead negate here and then undo after
+      raft::linalg::map(res,
+                        temp_distances.view(),
+                        raft::mul_const_op<value_type>(-1),
+                        raft::make_const_mdspan(temp_distances.view()));
+    }
+
+    // merge results from ann_index/bfknn together, translating the bfknn ids
+    auto stream                  = raft::resource::get_cuda_stream(res);
+    int64_t host_translations[2] = {0, static_cast<int64_t>(ann_rows())};
+    auto device_translations     = raft::make_device_vector<int64_t>(res, 2);
+    raft::copy(device_translations.data_handle(), host_translations, 2, stream);
+
+    knn_merge_parts(res,
+                    temp_distances.view(),
+                    temp_neighbors.view(),
+                    distances,
+                    neighbors,
+                    device_translations.view());
+
+    if (!distance::is_min_close(build_params.metric)) {
+      raft::linalg::map(
+        res, distances, raft::mul_const_op<value_type>(-1), raft::make_const_mdspan(distances));
+    }
+  }
+
+  // Stores the raw vectors that we use to do bfknn on
+  std::shared_ptr<brute_force_storage<value_type>> storage;
+
+  // ANN index data
+  std::shared_ptr<UpstreamT> ann_index;
+
+  // stores a copy of the build params - used during compact
+  index_params<typename UpstreamT::index_params_type> build_params;
+
+  // stores a pointer to the build
+  std::function<upstream_build_function_type<UpstreamT>> build_fn;
+};
+
+/**
+ * @brief Build the tiered index from the dataset for efficient search.
+ *
+ * @param[in] res
+ * @param[in] index_params upstream index parameters such as the distance metric to use
+ * @param[in] dataset a device pointer to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed tiered_index
+ */
+template <typename UpstreamT>
+auto build(
+  raft::resources const& res,
+  const index_params<typename UpstreamT::index_params_type>& index_params,
+  upstream_build_function_type<UpstreamT> build_fn,
+  raft::device_matrix_view<const typename UpstreamT::value_type, int64_t, raft::row_major> dataset)
+  -> std::shared_ptr<index_state<UpstreamT>>
+{
+  auto ret = new index_state<UpstreamT>(res, index_params, build_fn, dataset);
+  return std::shared_ptr<index_state<UpstreamT>>(ret);
+}
+
+template <typename UpstreamT>
+auto extend(raft::resources const& res,
+            const index_state<UpstreamT>& current,
+            raft::device_matrix_view<const typename UpstreamT::value_type, int64_t, raft::row_major>
+              new_vectors) -> std::shared_ptr<index_state<UpstreamT>>
+{
+  using value_type = typename UpstreamT::value_type;
+
+  auto next_state = std::make_shared<index_state<UpstreamT>>(current);
+  auto storage    = next_state->storage;
+
+  RAFT_EXPECTS(static_cast<size_t>(new_vectors.extent(1)) == storage->dim,
+               "Dimension of new vectors must match existing data");
+
+  size_t new_rows    = new_vectors.extent(0);
+  auto include_norms = storage->include_norms;
+  auto dim           = storage->dim;
+
+  // check to see if we have enough storage to append the new_rows without allocating
+  if (storage->num_rows_available() < new_rows) {
+    // allocate new storage, growing exponentially by doubling the allocated space
+    // (or potentially larger as needed)
+    size_t min_needed  = new_vectors.extent(0) + storage->num_rows_used;
+    size_t to_allocate = std::max(min_needed, 2 * storage->num_rows_allocated);
+
+    auto new_storage =
+      std::make_shared<brute_force_storage<value_type>>(res, to_allocate, dim, include_norms);
+
+    // copy over dataset to new storage
+    raft::copy(res,
+               raft::make_device_matrix_view<value_type, int64_t, raft::row_major>(
+                 new_storage->dataset.data(), storage->num_rows_used, dim),
+               raft::make_device_matrix_view<const value_type, int64_t, raft::row_major>(
+                 storage->dataset.data(), storage->num_rows_used, dim));
+
+    // copy over precalculated norms
+    if (include_norms) {
+      raft::copy(res,
+                 raft::make_device_vector_view<value_type, int64_t, raft::row_major>(
+                   new_storage->norms->data(), storage->num_rows_used),
+                 raft::make_device_vector_view<const value_type, int64_t, raft::row_major>(
+                   storage->norms->data(), storage->num_rows_used));
+    }
+
+    new_storage->num_rows_used = storage->num_rows_used;
+    next_state->storage = storage = new_storage;
+  }
+  storage->append_vectors(res, new_vectors, next_state->build_params.metric);
+
+  // Create a new ann index if we are over the threshold
+  if (next_state->build_params.create_ann_index_on_extend) {
+    if (next_state->bfknn_rows() > static_cast<size_t>(next_state->build_params.min_ann_rows)) {
+      next_state = compact(res, *next_state);
+    }
+  }
+  return next_state;
+}
+
+template <typename UpstreamT>
+auto compact(raft::resources const& res, const index_state<UpstreamT>& current)
+  -> std::shared_ptr<index_state<UpstreamT>>
+{
+  auto next_state = std::make_shared<index_state<UpstreamT>>(current);
+
+  // if we don't have any new data to compact, then we're done
+  if (next_state->storage->num_rows_used == next_state->ann_rows()) { return next_state; }
+
+  // Create the new ann index based off all available data
+  using value_type = typename UpstreamT::value_type;
+  auto storage     = next_state->storage;
+  auto dataset     = raft::make_device_matrix_view<const value_type, int64_t>(
+    storage->dataset.data(), storage->num_rows_used, storage->dim);
+
+  next_state->ann_index = std::make_shared<UpstreamT>(
+    std::move(next_state->build_fn(res, next_state->build_params, dataset)));
+  return next_state;
+}
+}  // namespace cuvs::neighbors::tiered_index::detail
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index df02ea17ca..2e660f6377 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -32,6 +32,26 @@
 
 #include <fstream>
 
+namespace cuvs::neighbors::ivf_flat {
+void convert_c_index_params(cuvsIvfFlatIndexParams params,
+                            cuvs::neighbors::ivf_flat::index_params* out)
+{
+  out->metric                   = static_cast<cuvs::distance::DistanceType>((int)params.metric),
+  out->metric_arg               = params.metric_arg;
+  out->add_data_on_build        = params.add_data_on_build;
+  out->n_lists                  = params.n_lists;
+  out->kmeans_n_iters           = params.kmeans_n_iters;
+  out->kmeans_trainset_fraction = params.kmeans_trainset_fraction;
+  out->adaptive_centers         = params.adaptive_centers;
+  out->conservative_memory_allocation = params.conservative_memory_allocation;
+}
+void convert_c_search_params(cuvsIvfFlatSearchParams params,
+                             cuvs::neighbors::ivf_flat::search_params* out)
+{
+  out->n_probes = params.n_probes;
+}
+}  // namespace cuvs::neighbors::ivf_flat
+
 namespace {
 
 template <typename T, typename IdxT>
@@ -39,15 +59,8 @@ void* _build(cuvsResources_t res, cuvsIvfFlatIndexParams params, DLManagedTensor
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
 
-  auto build_params              = cuvs::neighbors::ivf_flat::index_params();
-  build_params.metric            = static_cast<cuvs::distance::DistanceType>((int)params.metric),
-  build_params.metric_arg        = params.metric_arg;
-  build_params.add_data_on_build = params.add_data_on_build;
-  build_params.n_lists           = params.n_lists;
-  build_params.kmeans_n_iters    = params.kmeans_n_iters;
-  build_params.kmeans_trainset_fraction       = params.kmeans_trainset_fraction;
-  build_params.adaptive_centers               = params.adaptive_centers;
-  build_params.conservative_memory_allocation = params.conservative_memory_allocation;
+  auto build_params = cuvs::neighbors::ivf_flat::index_params();
+  cuvs::neighbors::ivf_flat::convert_c_index_params(params, &build_params);
 
   auto dataset = dataset_tensor->dl_tensor;
   auto dim     = dataset.shape[1];
@@ -74,8 +87,8 @@ void _search(cuvsResources_t res,
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
 
-  auto search_params     = cuvs::neighbors::ivf_flat::search_params();
-  search_params.n_probes = params.n_probes;
+  auto search_params = cuvs::neighbors::ivf_flat::search_params();
+  convert_c_search_params(params, &search_params);
 
   using queries_mdspan_type   = raft::device_matrix_view<T const, IdxT, raft::row_major>;
   using neighbors_mdspan_type = raft::device_matrix_view<IdxT, IdxT, raft::row_major>;
diff --git a/cpp/src/neighbors/ivf_flat_c.hpp b/cpp/src/neighbors/ivf_flat_c.hpp
new file mode 100644
index 0000000000..1dbff013f7
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat_c.hpp
@@ -0,0 +1,25 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cuvs/neighbors/ivf_flat.h>
+#include <cuvs/neighbors/ivf_flat.hpp>
+
+namespace cuvs::neighbors::ivf_flat {
+/// Converts a cuvsIvfFlatIndexParams struct (c) to a ivf_flat::index_params (C++) struct
+void convert_c_index_params(cuvsIvfFlatIndexParams params,
+                            cuvs::neighbors::ivf_flat::index_params* out);
+void convert_c_search_params(cuvsIvfFlatSearchParams params,
+                             cuvs::neighbors::ivf_flat::search_params* out);
+}  // namespace cuvs::neighbors::ivf_flat
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 2412bcbcc0..40871f7ef8 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -28,6 +28,36 @@
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
+namespace cuvs::neighbors::ivf_pq {
+void convert_c_index_params(cuvsIvfPqIndexParams params, cuvs::neighbors::ivf_pq::index_params* out)
+{
+  out->metric                   = static_cast<cuvs::distance::DistanceType>((int)params.metric),
+  out->metric_arg               = params.metric_arg;
+  out->add_data_on_build        = params.add_data_on_build;
+  out->n_lists                  = params.n_lists;
+  out->kmeans_n_iters           = params.kmeans_n_iters;
+  out->kmeans_trainset_fraction = params.kmeans_trainset_fraction;
+  out->pq_bits                  = params.pq_bits;
+  out->pq_dim                   = params.pq_dim;
+  out->codebook_kind =
+    static_cast<cuvs::neighbors::ivf_pq::codebook_gen>((int)params.codebook_kind);
+  out->force_random_rotation          = params.force_random_rotation;
+  out->conservative_memory_allocation = params.conservative_memory_allocation;
+  out->max_train_points_per_pq_code   = params.max_train_points_per_pq_code;
+}
+void convert_c_search_params(cuvsIvfPqSearchParams params,
+                             cuvs::neighbors::ivf_pq::search_params* out)
+{
+  out->n_probes                 = params.n_probes;
+  out->lut_dtype                = params.lut_dtype;
+  out->internal_distance_dtype  = params.internal_distance_dtype;
+  out->preferred_shmem_carveout = params.preferred_shmem_carveout;
+  out->coarse_search_dtype      = params.coarse_search_dtype;
+  out->max_internal_batch_size  = params.max_internal_batch_size;
+}
+
+}  // namespace cuvs::neighbors::ivf_pq
+
 namespace {
 
 template <typename T, typename IdxT>
@@ -35,20 +65,8 @@ void* _build(cuvsResources_t res, cuvsIvfPqIndexParams params, DLManagedTensor*
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
 
-  auto build_params              = cuvs::neighbors::ivf_pq::index_params();
-  build_params.metric            = static_cast<cuvs::distance::DistanceType>((int)params.metric),
-  build_params.metric_arg        = params.metric_arg;
-  build_params.add_data_on_build = params.add_data_on_build;
-  build_params.n_lists           = params.n_lists;
-  build_params.kmeans_n_iters    = params.kmeans_n_iters;
-  build_params.kmeans_trainset_fraction = params.kmeans_trainset_fraction;
-  build_params.pq_bits                  = params.pq_bits;
-  build_params.pq_dim                   = params.pq_dim;
-  build_params.codebook_kind =
-    static_cast<cuvs::neighbors::ivf_pq::codebook_gen>((int)params.codebook_kind);
-  build_params.force_random_rotation          = params.force_random_rotation;
-  build_params.conservative_memory_allocation = params.conservative_memory_allocation;
-  build_params.max_train_points_per_pq_code   = params.max_train_points_per_pq_code;
+  auto build_params = cuvs::neighbors::ivf_pq::index_params();
+  convert_c_index_params(params, &build_params);
 
   auto dataset = dataset_tensor->dl_tensor;
   auto dim     = dataset.shape[1];
@@ -79,13 +97,8 @@ void _search(cuvsResources_t res,
   auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
 
-  auto search_params                     = cuvs::neighbors::ivf_pq::search_params();
-  search_params.n_probes                 = params.n_probes;
-  search_params.lut_dtype                = params.lut_dtype;
-  search_params.internal_distance_dtype  = params.internal_distance_dtype;
-  search_params.preferred_shmem_carveout = params.preferred_shmem_carveout;
-  search_params.coarse_search_dtype      = params.coarse_search_dtype;
-  search_params.max_internal_batch_size  = params.max_internal_batch_size;
+  auto search_params = cuvs::neighbors::ivf_pq::search_params();
+  cuvs::neighbors::ivf_pq::convert_c_search_params(params, &search_params);
 
   using queries_mdspan_type   = raft::device_matrix_view<const T, IdxT, raft::row_major>;
   using neighbors_mdspan_type = raft::device_matrix_view<IdxT, IdxT, raft::row_major>;
diff --git a/cpp/src/neighbors/ivf_pq_c.hpp b/cpp/src/neighbors/ivf_pq_c.hpp
new file mode 100644
index 0000000000..e3591d65be
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq_c.hpp
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+/// Converts a cuvsIvfPqIndexParams struct (c) to a ivf_pq::index_params (C++) struct
+void convert_c_index_params(cuvsIvfPqIndexParams params,
+                            cuvs::neighbors::ivf_pq::index_params* out);
+
+/// Converts search params from C struct to C++ struct
+void convert_c_search_params(cuvsIvfPqSearchParams params,
+                             cuvs::neighbors::ivf_pq::search_params* out);
+}  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/src/neighbors/knn_merge_parts.cu b/cpp/src/neighbors/knn_merge_parts.cu
new file mode 100644
index 0000000000..f0178abc19
--- /dev/null
+++ b/cpp/src/neighbors/knn_merge_parts.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/knn_merge_parts.cuh"
+
+#include <cuvs/neighbors/knn_merge_parts.hpp>
+
+namespace cuvs::neighbors {
+namespace {
+template <typename T, typename IdxT>
+void _knn_merge_parts(raft::resources const& res,
+                      raft::device_matrix_view<const T, int64_t> inK,
+                      raft::device_matrix_view<const IdxT, int64_t> inV,
+                      raft::device_matrix_view<T, int64_t> outK,
+                      raft::device_matrix_view<IdxT, int64_t> outV,
+                      raft::device_vector_view<IdxT> translations)
+{
+  auto parts = translations.extent(0);
+  auto rows  = outK.extent(0);
+  auto k     = outK.extent(1);
+
+  detail::knn_merge_parts(inK.data_handle(),
+                          inV.data_handle(),
+                          outK.data_handle(),
+                          outV.data_handle(),
+                          rows,
+                          parts,
+                          k,
+                          raft::resource::get_cuda_stream(res),
+                          translations.data_handle());
+}
+}  // namespace
+
+void knn_merge_parts(raft::resources const& res,
+                     raft::device_matrix_view<const float, int64_t> inK,
+                     raft::device_matrix_view<const int64_t, int64_t> inV,
+                     raft::device_matrix_view<float, int64_t> outK,
+                     raft::device_matrix_view<int64_t, int64_t> outV,
+                     raft::device_vector_view<int64_t> translations)
+{
+  _knn_merge_parts(res, inK, inV, outK, outV, translations);
+}
+void knn_merge_parts(raft::resources const& res,
+                     raft::device_matrix_view<const float, int64_t> inK,
+                     raft::device_matrix_view<const uint32_t, int64_t> inV,
+                     raft::device_matrix_view<float, int64_t> outK,
+                     raft::device_matrix_view<uint32_t, int64_t> outV,
+                     raft::device_vector_view<uint32_t> translations)
+{
+  _knn_merge_parts(res, inK, inV, outK, outV, translations);
+}
+void knn_merge_parts(raft::resources const& res,
+                     raft::device_matrix_view<const float, int64_t> inK,
+                     raft::device_matrix_view<const int32_t, int64_t> inV,
+                     raft::device_matrix_view<float, int64_t> outK,
+                     raft::device_matrix_view<int32_t, int64_t> outV,
+                     raft::device_vector_view<int32_t> translations)
+{
+  _knn_merge_parts(res, inK, inV, outK, outV, translations);
+}
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/mg/snmg.cuh b/cpp/src/neighbors/mg/snmg.cuh
index 119c94fe14..c6280699e7 100644
--- a/cpp/src/neighbors/mg/snmg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -16,7 +16,6 @@
 
 #pragma once
 
-#include "../detail/knn_merge_parts.cuh"
 #include <raft/core/resource/multi_gpu.hpp>
 #include <raft/core/resource/nccl_comm.hpp>
 #include <raft/core/serialize.hpp>
@@ -27,6 +26,7 @@
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/knn_merge_parts.hpp>
 
 #include <fstream>
 
@@ -286,15 +286,12 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
                index.num_ranks_,
                raft::resource::get_cuda_stream(root_handle_));
 
-    cuvs::neighbors::detail::knn_merge_parts(in_distances.data_handle(),
-                                             in_neighbors.data_handle(),
-                                             out_distances.data_handle(),
-                                             out_neighbors.data_handle(),
-                                             n_rows_of_current_batch,
-                                             index.num_ranks_,
-                                             n_neighbors,
-                                             raft::resource::get_cuda_stream(root_handle_),
-                                             d_trans.data_handle());
+    knn_merge_parts(root_handle_,
+                    in_distances.view(),
+                    in_neighbors.view(),
+                    out_distances.view(),
+                    out_neighbors.view(),
+                    d_trans.view());
 
     raft::copy(neighbors.data_handle() + output_offset,
                out_neighbors.data_handle(),
@@ -413,15 +410,12 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
 
         if (received_something) {
           // merge inplace
-          cuvs::neighbors::detail::knn_merge_parts(tmp_distances.data_handle(),
-                                                   tmp_neighbors.data_handle(),
-                                                   tmp_distances.data_handle(),
-                                                   tmp_neighbors.data_handle(),
-                                                   n_rows_of_current_batch,
-                                                   2,
-                                                   n_neighbors,
-                                                   raft::resource::get_cuda_stream(dev_res),
-                                                   d_trans.data_handle());
+          knn_merge_parts(dev_res,
+                          tmp_distances.view(),
+                          tmp_neighbors.view(),
+                          tmp_distances.view(),
+                          tmp_neighbors.view(),
+                          d_trans.view());
 
           // If done, copy the final result
           if (remaining <= 1) {
diff --git a/cpp/src/neighbors/tiered_index.cu b/cpp/src/neighbors/tiered_index.cu
new file mode 100644
index 0000000000..1a3a410419
--- /dev/null
+++ b/cpp/src/neighbors/tiered_index.cu
@@ -0,0 +1,194 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/tiered_index.cuh"
+
+#include <cuvs/neighbors/tiered_index.hpp>
+
+namespace cuvs::neighbors::ivf_pq {
+auto typed_build(raft::resources const& res,
+                 const cuvs::neighbors::ivf_pq::index_params& index_params,
+                 raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::ivf_pq::typed_index<float, int64_t>
+{
+  return static_cast<typed_index<float, int64_t>&&>(ivf_pq::build(res, index_params, dataset));
+}
+
+void typed_search(raft::resources const& res,
+                  const ivf_pq::search_params& search_params,
+                  const ivf_pq::typed_index<float, int64_t>& index,
+                  raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+                  raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+                  raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+                  const cuvs::neighbors::filtering::base_filter& sample_filter)
+{
+  ivf_pq::search(res, search_params, index, queries, neighbors, distances, sample_filter);
+}
+}  // namespace cuvs::neighbors::ivf_pq
+
+namespace cuvs::neighbors::tiered_index {
+auto build(raft::resources const& res,
+           const index_params<cagra::index_params>& params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> tiered_index::index<cagra::index<float, uint32_t>>
+{
+  auto state = detail::build<cagra::index<float, uint32_t>>(res, params, cagra::build, dataset);
+  return cuvs::neighbors::tiered_index::index<cagra::index<float, uint32_t>>(state);
+}
+
+auto build(raft::resources const& res,
+           const index_params<ivf_flat::index_params>& params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> tiered_index::index<ivf_flat::index<float, int64_t>>
+{
+  auto state =
+    detail::build<ivf_flat::index<float, int64_t>>(res, params, ivf_flat::build, dataset);
+  return cuvs::neighbors::tiered_index::index<ivf_flat::index<float, int64_t>>(state);
+}
+
+auto build(raft::resources const& res,
+           const index_params<ivf_pq::index_params>& params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> tiered_index::index<ivf_pq::typed_index<float, int64_t>>
+{
+  auto state =
+    detail::build<ivf_pq::typed_index<float, int64_t>>(res, params, ivf_pq::typed_build, dataset);
+  return cuvs::neighbors::tiered_index::index<ivf_pq::typed_index<float, int64_t>>(state);
+}
+
+void extend(raft::resources const& res,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            tiered_index::index<cagra::index<float, uint32_t>>* idx)
+{
+  std::scoped_lock lock(idx->write_mutex);
+  auto next_state = detail::extend(res, *idx->state, new_vectors);
+
+  auto storage = next_state->storage;
+  if (storage->num_rows_allocated != idx->state->storage->num_rows_allocated) {
+    // CAGRA could be holding on to a non-owning view of the previous dataset in the ann_index,
+    // which is problematic since the underlying ownership of the dataset could be freed here
+    // call cagra::index::update_dataset on it to update the ann_index to point to the
+    // new dataset
+    if (next_state->ann_index) {
+      auto dataset = raft::make_device_matrix_view<const float, int64_t>(
+        storage->dataset.data(), next_state->ann_rows(), storage->dim);
+
+      // Block 'search' calls during the update_dataset call to ensure that this
+      // doesn't cause issues in a multithreaded environment
+      std::unique_lock<std::shared_mutex> lock(idx->ann_mutex);
+      next_state->ann_index->update_dataset(res, dataset);
+    }
+  }
+
+  idx->state = next_state;
+}
+
+void extend(raft::resources const& res,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            tiered_index::index<ivf_flat::index<float, int64_t>>* idx)
+{
+  std::scoped_lock lock(idx->write_mutex);
+  auto next_state = detail::extend(res, *idx->state, new_vectors);
+  idx->state      = next_state;
+}
+
+void extend(raft::resources const& res,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> new_vectors,
+            tiered_index::index<ivf_pq::typed_index<float, int64_t>>* idx)
+{
+  std::scoped_lock lock(idx->write_mutex);
+  auto next_state = detail::extend(res, *idx->state, new_vectors);
+  idx->state      = next_state;
+}
+
+void compact(raft::resources const& res, tiered_index::index<cagra::index<float, uint32_t>>* idx)
+{
+  std::scoped_lock lock(idx->write_mutex);
+  auto next_state = detail::compact(res, *idx->state);
+  idx->state      = next_state;
+}
+
+void compact(raft::resources const& res, tiered_index::index<ivf_flat::index<float, int64_t>>* idx)
+{
+  std::scoped_lock lock(idx->write_mutex);
+  auto next_state = detail::compact(res, *idx->state);
+  idx->state      = next_state;
+}
+
+void compact(raft::resources const& res,
+             tiered_index::index<ivf_pq::typed_index<float, int64_t>>* idx)
+{
+  std::scoped_lock lock(idx->write_mutex);
+  auto next_state = detail::compact(res, *idx->state);
+  idx->state      = next_state;
+}
+
+void search(raft::resources const& res,
+            const cagra::search_params& search_params,
+            const tiered_index::index<cagra::index<float, uint32_t>>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter)
+{
+  // use a read-write lock to handle calls to update_dataset for cagra
+  // This allows multiple readers concurrently, but only one writer
+  std::shared_lock<std::shared_mutex> lock(index.ann_mutex);
+  index.state->search(
+    res, search_params, cagra::search, queries, neighbors, distances, sample_filter);
+}
+
+void search(raft::resources const& res,
+            const ivf_flat::search_params& search_params,
+            const tiered_index::index<ivf_flat::index<float, int64_t>>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter)
+{
+  index.state->search(
+    res, search_params, ivf_flat::search, queries, neighbors, distances, sample_filter);
+}
+
+void search(raft::resources const& res,
+            const ivf_pq::search_params& search_params,
+            const tiered_index::index<ivf_pq::typed_index<float, int64_t>>& index,
+            raft::device_matrix_view<const float, int64_t, raft::row_major> queries,
+            raft::device_matrix_view<int64_t, int64_t, raft::row_major> neighbors,
+            raft::device_matrix_view<float, int64_t, raft::row_major> distances,
+            const cuvs::neighbors::filtering::base_filter& sample_filter)
+{
+  index.state->search(
+    res, search_params, ivf_pq::typed_search, queries, neighbors, distances, sample_filter);
+}
+
+template <typename UpstreamT>
+int64_t index<UpstreamT>::size() const noexcept
+{
+  return state->size();
+}
+
+template <typename UpstreamT>
+int64_t index<UpstreamT>::dim() const noexcept
+{
+  return state->dim();
+}
+
+template struct index<cagra::index<float, uint32_t>>;
+template struct index<ivf_flat::index<float, int64_t>>;
+template struct index<ivf_pq::typed_index<float, int64_t>>;
+
+}  // namespace cuvs::neighbors::tiered_index
diff --git a/cpp/src/neighbors/tiered_index_c.cpp b/cpp/src/neighbors/tiered_index_c.cpp
new file mode 100644
index 0000000000..196b199e10
--- /dev/null
+++ b/cpp/src/neighbors/tiered_index_c.cpp
@@ -0,0 +1,307 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/tiered_index.h>
+#include <cuvs/neighbors/tiered_index.hpp>
+
+#include <fstream>
+
+#include "cagra_c.hpp"
+#include "ivf_flat_c.hpp"
+#include "ivf_pq_c.hpp"
+
+namespace {
+using namespace cuvs::neighbors;
+
+template <typename T>
+void* _build(cuvsResources_t res, cuvsTieredIndexParams params, DLManagedTensor* dataset_tensor)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  using mdspan_type = raft::device_matrix_view<const T, int64_t, raft::row_major>;
+  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+
+  switch (params.algo) {
+    case CUVS_TIERED_INDEX_ALGO_CAGRA: {
+      auto build_params = tiered_index::index_params<cagra::index_params>();
+      if (params.cagra_params != NULL) {
+        auto dataset = dataset_tensor->dl_tensor;
+        cagra::convert_c_index_params(
+          *params.cagra_params, dataset.shape[0], dataset.shape[1], &build_params);
+      }
+      build_params.min_ann_rows               = params.min_ann_rows;
+      build_params.create_ann_index_on_extend = params.create_ann_index_on_extend;
+      build_params.metric                     = params.metric;
+      return new tiered_index::index<cagra::index<T, uint32_t>>(
+        tiered_index::build(*res_ptr, build_params, mds));
+    }
+    case CUVS_TIERED_INDEX_ALGO_IVF_FLAT: {
+      auto build_params = tiered_index::index_params<ivf_flat::index_params>();
+      if (params.ivf_flat_params != NULL) {
+        ivf_flat::convert_c_index_params(*params.ivf_flat_params, &build_params);
+      }
+      build_params.metric                     = params.metric;
+      build_params.min_ann_rows               = params.min_ann_rows;
+      build_params.create_ann_index_on_extend = params.create_ann_index_on_extend;
+      return new tiered_index::index<ivf_flat::index<T, int64_t>>(
+        tiered_index::build(*res_ptr, build_params, mds));
+    }
+    case CUVS_TIERED_INDEX_ALGO_IVF_PQ: {
+      auto build_params   = tiered_index::index_params<ivf_pq::index_params>();
+      build_params.metric = params.metric;
+      if (params.ivf_pq_params != NULL) {
+        ivf_pq::convert_c_index_params(*params.ivf_pq_params, &build_params);
+      }
+      build_params.metric                     = params.metric;
+      build_params.min_ann_rows               = params.min_ann_rows;
+      build_params.create_ann_index_on_extend = params.create_ann_index_on_extend;
+      return new tiered_index::index<ivf_pq::typed_index<T, int64_t>>(
+        tiered_index::build(*res_ptr, build_params, mds));
+    }
+    default: RAFT_FAIL("unsupported tiered index algorithm");
+  }
+}
+
+template <typename UpstreamT>
+void _search(cuvsResources_t res,
+             void* params,
+             cuvsTieredIndex index,
+             DLManagedTensor* queries_tensor,
+             DLManagedTensor* neighbors_tensor,
+             DLManagedTensor* distances_tensor,
+             cuvsFilter filter)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<tiered_index::index<UpstreamT>*>(index.addr);
+
+  auto search_params = typename UpstreamT::search_params_type();
+  if (params != NULL) {
+    if constexpr (std::is_same_v<typename UpstreamT::search_params_type, cagra::search_params>) {
+      auto c_params = reinterpret_cast<cuvsCagraSearchParams*>(params);
+      cagra::convert_c_search_params(*c_params, &search_params);
+    }
+    if constexpr (std::is_same_v<typename UpstreamT::search_params_type, ivf_flat::search_params>) {
+      auto c_params = reinterpret_cast<cuvsIvfFlatSearchParams*>(params);
+      ivf_flat::convert_c_search_params(*c_params, &search_params);
+    }
+    if constexpr (std::is_same_v<typename UpstreamT::search_params_type, ivf_pq::search_params>) {
+      auto c_params = reinterpret_cast<cuvsIvfPqSearchParams*>(params);
+      ivf_pq::convert_c_search_params(*c_params, &search_params);
+    }
+  }
+
+  using T = typename UpstreamT::value_type;
+
+  using queries_mdspan_type   = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+  using neighbors_mdspan_type = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::device_matrix_view<T, int64_t, raft::row_major>;
+  auto queries_mds            = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds          = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds          = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  if (filter.type == NO_FILTER) {
+    tiered_index::search(
+      *res_ptr, search_params, *index_ptr, queries_mds, neighbors_mds, distances_mds);
+  } else if (filter.type == BITSET) {
+    using filter_mdspan_type    = raft::device_vector_view<std::uint32_t, int64_t, raft::row_major>;
+    auto removed_indices_tensor = reinterpret_cast<DLManagedTensor*>(filter.addr);
+    auto removed_indices = cuvs::core::from_dlpack<filter_mdspan_type>(removed_indices_tensor);
+    cuvs::core::bitset_view<std::uint32_t, int64_t> removed_indices_bitset(removed_indices,
+                                                                           index_ptr->size());
+    auto bitset_filter_obj = cuvs::neighbors::filtering::bitset_filter(removed_indices_bitset);
+
+    tiered_index::search(*res_ptr,
+                         search_params,
+                         *index_ptr,
+                         queries_mds,
+                         neighbors_mds,
+                         distances_mds,
+                         bitset_filter_obj);
+  } else {
+    RAFT_FAIL("Unsupported filter type: BITMAP");
+  }
+}
+
+template <typename UpstreamT>
+void _extend(cuvsResources_t res, DLManagedTensor* new_vectors, cuvsTieredIndex index)
+{
+  auto res_ptr              = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr            = reinterpret_cast<tiered_index::index<UpstreamT>*>(index.addr);
+  using T                   = typename UpstreamT::value_type;
+  using vectors_mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+
+  auto vectors_mds = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors);
+
+  tiered_index::extend(*res_ptr, vectors_mds, index_ptr);
+}
+
+}  // namespace
+
+extern "C" cuvsError_t cuvsTieredIndexCreate(cuvsTieredIndex_t* index)
+{
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsTieredIndex{}; });
+}
+
+extern "C" cuvsError_t cuvsTieredIndexDestroy(cuvsTieredIndex_t index_c_ptr)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index = *index_c_ptr;
+    if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
+      switch (index.algo) {
+        case CUVS_TIERED_INDEX_ALGO_CAGRA: {
+          auto index_ptr =
+            reinterpret_cast<tiered_index::index<cagra::index<float, uint32_t>>*>(index.addr);
+          delete index_ptr;
+          break;
+        }
+        case CUVS_TIERED_INDEX_ALGO_IVF_FLAT: {
+          auto index_ptr =
+            reinterpret_cast<tiered_index::index<ivf_flat::index<float, int64_t>>*>(index.addr);
+          delete index_ptr;
+          break;
+        }
+        case CUVS_TIERED_INDEX_ALGO_IVF_PQ: {
+          auto index_ptr =
+            reinterpret_cast<tiered_index::index<ivf_pq::typed_index<float, int64_t>>*>(index.addr);
+          delete index_ptr;
+          break;
+        }
+        default: RAFT_FAIL("unsupported tiered index algorithm");
+      }
+    }
+    delete index_c_ptr;
+  });
+}
+
+extern "C" cuvsError_t cuvsTieredIndexBuild(cuvsResources_t res,
+                                            cuvsTieredIndexParams_t params,
+                                            DLManagedTensor* dataset_tensor,
+                                            cuvsTieredIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = dataset_tensor->dl_tensor;
+    index->dtype = dataset.dtype;
+    index->algo  = params->algo;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr = reinterpret_cast<uintptr_t>(_build<float>(res, *params, dataset_tensor));
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsTieredIndexSearch(cuvsResources_t res,
+                                             void* search_params,
+                                             cuvsTieredIndex_t index_c_ptr,
+                                             DLManagedTensor* queries_tensor,
+                                             DLManagedTensor* neighbors_tensor,
+                                             DLManagedTensor* distances_tensor,
+                                             cuvsFilter filter)
+
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(queries),
+                 "queries should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(neighbors),
+                 "neighbors should have device compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(distances),
+                 "distances should have device compatible memory");
+
+    RAFT_EXPECTS(neighbors.dtype.code == kDLInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    auto index = *index_c_ptr;
+    RAFT_EXPECTS(queries.dtype.code == index.dtype.code, "type mismatch between index and queries");
+
+    switch (index.algo) {
+      case CUVS_TIERED_INDEX_ALGO_CAGRA: {
+        _search<cagra::index<float, uint32_t>>(
+          res, search_params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
+        break;
+      }
+      case CUVS_TIERED_INDEX_ALGO_IVF_FLAT: {
+        _search<ivf_flat::index<float, int64_t>>(
+          res, search_params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
+        break;
+      }
+      case CUVS_TIERED_INDEX_ALGO_IVF_PQ: {
+        _search<ivf_pq::typed_index<float, int64_t>>(
+          res, search_params, index, queries_tensor, neighbors_tensor, distances_tensor, filter);
+        break;
+      }
+      default: RAFT_FAIL("unsupported tiered index algorithm");
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsTieredIndexParamsCreate(cuvsTieredIndexParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    cuvs::neighbors::tiered_index::index_params<cagra::index_params> cpp_params;
+    *params = new cuvsTieredIndexParams{
+      .metric                     = cpp_params.metric,
+      .algo                       = CUVS_TIERED_INDEX_ALGO_CAGRA,
+      .min_ann_rows               = cpp_params.min_ann_rows,
+      .create_ann_index_on_extend = cpp_params.create_ann_index_on_extend};
+  });
+}
+
+extern "C" cuvsError_t cuvsTieredIndexParamsDestroy(cuvsTieredIndexParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsTieredIndexExtend(cuvsResources_t res,
+                                             DLManagedTensor* new_vectors,
+                                             cuvsTieredIndex_t index_c_ptr)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index = *index_c_ptr;
+    switch (index.algo) {
+      case CUVS_TIERED_INDEX_ALGO_CAGRA: {
+        _extend<cagra::index<float, uint32_t>>(res, new_vectors, index);
+        break;
+      }
+      case CUVS_TIERED_INDEX_ALGO_IVF_FLAT: {
+        _extend<ivf_flat::index<float, int64_t>>(res, new_vectors, index);
+        break;
+      }
+      case CUVS_TIERED_INDEX_ALGO_IVF_PQ: {
+        _extend<ivf_pq::typed_index<float, int64_t>>(res, new_vectors, index);
+        break;
+      }
+      default: RAFT_FAIL("unsupported tiered index algorithm");
+    }
+  });
+}
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index 06b06c6c0f..b49b5acd7d 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -97,6 +97,8 @@ if(BUILD_TESTS)
     neighbors/sparse_brute_force.cu neighbors/refine.cu GPUS 1 PERCENT 100
   )
 
+  ConfigureTest(NAME NEIGHBORS_TIERED_INDEX_TEST PATH neighbors/tiered_index.cu GPUS 1 PERCENT 100)
+
   ConfigureTest(
     NAME CLUSTER_TEST PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu
     cluster/linkage.cu GPUS 1 PERCENT 100
diff --git a/cpp/tests/neighbors/tiered_index.cu b/cpp/tests/neighbors/tiered_index.cu
new file mode 100644
index 0000000000..4aab6ba6ff
--- /dev/null
+++ b/cpp/tests/neighbors/tiered_index.cu
@@ -0,0 +1,209 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+
+#include <cstdint>
+#include <cuvs/neighbors/tiered_index.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/util/itertools.hpp>
+
+#include "naive_knn.cuh"
+
+#include <gtest/gtest.h>
+
+#include <cstddef>
+#include <iostream>
+#include <string>
+#include <vector>
+
+namespace cuvs::neighbors::tiered_index {
+
+struct AnnTieredIndexInputs {
+  int n_rows;
+  int dim;
+  cuvs::distance::DistanceType metric;
+  int k;
+  int n_queries;
+  double min_recall;
+};
+
+inline ::std::ostream& operator<<(::std::ostream& os, const AnnTieredIndexInputs& p)
+{
+  os << "dataset shape=" << p.n_rows << "x" << p.dim << ", metric=" << print_metric{p.metric}
+     << std::endl;
+  return os;
+}
+
+template <typename UpstreamT>
+class ANNTieredIndexTest : public ::testing::TestWithParam<AnnTieredIndexInputs> {
+ public:
+  using value_type = typename UpstreamT::value_type;
+  ANNTieredIndexTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnTieredIndexInputs>::GetParam()),
+      database(0, stream_),
+      queries(0, stream_)
+  {
+  }
+
+ protected:
+  void testTieredIndex()
+  {
+    // Calculate the naive results
+    std::vector<int64_t> indices_naive(ps.n_queries * ps.k);
+    std::vector<value_type> distances_naive(ps.n_queries * ps.k);
+
+    std::vector<value_type> queries_h(ps.n_queries * ps.dim);
+    raft::update_host(queries_h.data(), queries.data(), ps.n_queries * ps.dim, stream_);
+
+    {
+      rmm::device_uvector<value_type> distances_naive_dev(ps.n_queries * ps.k, stream_);
+      rmm::device_uvector<int64_t> indices_naive_dev(ps.n_queries * ps.k, stream_);
+      naive_knn<value_type, value_type, int64_t>(handle_,
+                                                 distances_naive_dev.data(),
+                                                 indices_naive_dev.data(),
+                                                 queries.data(),
+                                                 database.data(),
+                                                 ps.n_queries,
+                                                 ps.n_rows,
+                                                 ps.dim,
+                                                 ps.k,
+                                                 ps.metric);
+      raft::update_host(
+        indices_naive.data(), indices_naive_dev.data(), ps.n_queries * ps.k, stream_);
+      raft::update_host(
+        distances_naive.data(), distances_naive_dev.data(), ps.n_queries * ps.k, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    // Calculate tiered index
+    std::vector<int64_t> indices_tiered(ps.n_queries * ps.k);
+    std::vector<value_type> distances_tiered(ps.n_queries * ps.k);
+    {
+      index_params<typename UpstreamT::index_params_type> build_params;
+      build_params.metric                     = ps.metric;
+      build_params.min_ann_rows               = 2000;
+      build_params.create_ann_index_on_extend = true;
+
+      // include 50% of the rows in the initial build - and then incrementally add
+      // the rest row by row
+      auto initial_rows  = ps.n_rows / 2;
+      auto database_view = raft::make_device_matrix_view<const value_type, int64_t>(
+        (const value_type*)database.data(), initial_rows, ps.dim);
+
+      auto index = cuvs::neighbors::tiered_index::build(handle_, build_params, database_view);
+      for (int i = initial_rows; i < ps.n_rows; ++i) {
+        cuvs::neighbors::tiered_index::extend(
+          handle_,
+          raft::make_device_matrix_view<const value_type, int64_t>(
+            (const value_type*)database.data() + i * ps.dim, 1, ps.dim),
+          &index);
+      }
+      raft::resource::sync_stream(handle_);
+
+      auto queries_view = raft::make_device_matrix_view<const value_type, int64_t>(
+        (const value_type*)queries.data(), ps.n_queries, ps.dim);
+      rmm::device_uvector<value_type> distances_tiered_dev(ps.n_queries * ps.k, stream_);
+      rmm::device_uvector<int64_t> indices_tiered_dev(ps.n_queries * ps.k, stream_);
+
+      typename UpstreamT::search_params_type search_params;
+
+      if constexpr (std::is_same_v<ivf_flat::search_params,
+                                   typename UpstreamT::search_params_type>) {
+        search_params.n_probes = 128;
+      }
+      if constexpr (std::is_same_v<ivf_pq::search_params, typename UpstreamT::search_params_type>) {
+        search_params.n_probes = 128;
+      }
+
+      auto distances_view = raft::make_device_matrix_view<value_type, int64_t>(
+        (value_type*)distances_tiered_dev.data(), ps.n_queries, ps.k);
+      auto indices_view = raft::make_device_matrix_view<int64_t, int64_t>(
+        (int64_t*)indices_tiered_dev.data(), ps.n_queries, ps.k);
+
+      cuvs::neighbors::tiered_index::search(
+        handle_, search_params, index, queries_view, indices_view, distances_view);
+
+      raft::update_host(
+        distances_tiered.data(), distances_tiered_dev.data(), ps.n_queries * ps.k, stream_);
+      raft::update_host(
+        indices_tiered.data(), indices_tiered_dev.data(), ps.n_queries * ps.k, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    double min_recall = ps.min_recall;
+    EXPECT_TRUE(eval_neighbours(indices_naive,
+                                indices_tiered,
+                                distances_naive,
+                                distances_tiered,
+                                ps.n_queries,
+                                ps.k,
+                                0.006,
+                                min_recall));
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    queries.resize(((size_t)ps.n_queries) * ps.dim, stream_);
+    raft::random::RngState r(42ULL);
+    raft::random::normal(
+      handle_, r, database.data(), ps.n_rows * ps.dim, value_type(0.1), value_type(2.0));
+    raft::random::normal(
+      handle_, r, queries.data(), ps.n_queries * ps.dim, value_type(0.1), value_type(2.0));
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+    queries.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnTieredIndexInputs ps;
+  rmm::device_uvector<value_type> database;
+  rmm::device_uvector<value_type> queries;
+};
+
+const std::vector<AnnTieredIndexInputs> inputs =
+  raft::util::itertools::product<AnnTieredIndexInputs>(
+    {2000, 4000},   // n_rows
+    {16, 29, 256},  // dim
+    {cuvs::distance::DistanceType::L2Expanded,
+     cuvs::distance::DistanceType::InnerProduct},  // metric
+    {10},                                          // k
+    {10},                                          // n_queries
+    {0.85}                                         // min_recall
+  );
+typedef ANNTieredIndexTest<cagra::index<float, uint32_t>> CAGRA_F;
+TEST_P(CAGRA_F, AnnTieredIndex) { this->testTieredIndex(); }
+INSTANTIATE_TEST_CASE_P(ANNTieredIndexTest, CAGRA_F, ::testing::ValuesIn(inputs));
+
+typedef ANNTieredIndexTest<ivf_flat::index<float, int64_t>> IvfFlat_F;
+TEST_P(IvfFlat_F, AnnTieredIndex) { this->testTieredIndex(); }
+INSTANTIATE_TEST_CASE_P(ANNTieredIndexTest, IvfFlat_F, ::testing::ValuesIn(inputs));
+
+typedef ANNTieredIndexTest<ivf_pq::typed_index<float, int64_t>> IvfPq_F;
+TEST_P(IvfPq_F, AnnTieredIndex) { this->testTieredIndex(); }
+INSTANTIATE_TEST_CASE_P(ANNTieredIndexTest, IvfPq_F, ::testing::ValuesIn(inputs));
+}  // namespace cuvs::neighbors::tiered_index
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index d5085b0551..1b08a32544 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -19,6 +19,7 @@ add_subdirectory(ivf_flat)
 add_subdirectory(ivf_pq)
 add_subdirectory(filters)
 add_subdirectory(nn_descent)
+add_subdirectory(tiered_index)
 
 # Set the list of Cython files to build
 set(cython_sources refine.pyx)
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index b8347d8ff6..9503897f39 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -108,6 +108,8 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
         float persistent_lifetime
         float persistent_device_usage
 
+    ctypedef cuvsCagraSearchParams* cuvsCagraSearchParams_t
+
     ctypedef struct cuvsCagraIndex:
         uintptr_t addr
         DLDataType dtype
@@ -124,6 +126,10 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
 
     cuvsError_t cuvsCagraIndexParamsDestroy(cuvsCagraIndexParams_t index)
 
+    cuvsError_t cuvsCagraSearchParamsCreate(cuvsCagraSearchParams_t* params)
+
+    cuvsError_t cuvsCagraSearchParamsDestroy(cuvsCagraSearchParams_t index)
+
     cuvsError_t cuvsCagraIndexCreate(cuvsCagraIndex_t* index)
 
     cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index)
@@ -133,7 +139,7 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
     cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                                cuvsCagraIndexParams* params,
                                DLManagedTensor* dataset,
-                               cuvsCagraIndex_t index) except +
+                               cuvsCagraIndex_t index)
 
     cuvsError_t cuvsCagraSearch(cuvsResources_t res,
                                 cuvsCagraSearchParams* params,
@@ -141,20 +147,20 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
                                 DLManagedTensor* queries,
                                 DLManagedTensor* neighbors,
                                 DLManagedTensor* distances,
-                                cuvsFilter filter) except +
+                                cuvsFilter filter)
 
     cuvsError_t cuvsCagraSerialize(cuvsResources_t res,
                                    const char * filename,
                                    cuvsCagraIndex_t index,
-                                   bool include_dataset) except +
+                                   bool include_dataset)
 
     cuvsError_t cuvsCagraSerializeToHnswlib(cuvsResources_t res,
                                             const char * filename,
-                                            cuvsCagraIndex_t index) except +
+                                            cuvsCagraIndex_t index)
 
     cuvsError_t cuvsCagraDeserialize(cuvsResources_t res,
                                      const char * filename,
-                                     cuvsCagraIndex_t index) except +
+                                     cuvsCagraIndex_t index)
 
 cdef class Index:
     """
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 49cd29942e..f395efe684 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -34,7 +34,7 @@ from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
-from cuvs.distance import DISTANCE_TYPES
+from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
 
 from libc.stdint cimport (
@@ -167,7 +167,6 @@ cdef class IndexParams:
     """
 
     cdef cuvsCagraIndexParams* params
-    cdef object _metric
 
     # hold on to a reference to the compression, to keep from being GC'ed
     cdef public object compression
@@ -194,7 +193,6 @@ cdef class IndexParams:
                  ivf_pq_search_params: ivf_pq.SearchParams = None,
                  refinement_rate: float = 1.0):
 
-        self._metric = metric
         self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
         self.params.intermediate_graph_degree = intermediate_graph_degree
         self.params.graph_degree = graph_degree
@@ -227,9 +225,12 @@ cdef class IndexParams:
                 ivf_pq_search_params.get_handle()
         self.params.graph_build_params.refinement_rate = refinement_rate
 
+    def get_handle(self):
+        return <size_t> self.params
+
     @property
     def metric(self):
-        return self._metric
+        return DISTANCE_NAMES[self.params.metric]
 
     @property
     def intermediate_graph_degree(self):
@@ -416,7 +417,13 @@ cdef class SearchParams:
         Sets the fraction of maximum grid size used by persistent kernel.
     """
 
-    cdef cuvsCagraSearchParams params
+    cdef cuvsCagraSearchParams * params
+
+    def __cinit__(self):
+        check_cuvs(cuvsCagraSearchParamsCreate(&self.params))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsCagraSearchParamsDestroy(self.params))
 
     def __init__(self, *,
                  max_queries=0,
@@ -483,6 +490,9 @@ cdef class SearchParams:
                         "num_random_samplings", "rand_xor_mask"]]
         return "SearchParams(type=CAGRA, " + (", ".join(attr_str)) + ")"
 
+    def get_handle(self):
+        return <size_t> self.params
+
     @property
     def max_queries(self):
         return self.params.max_queries
@@ -626,7 +636,7 @@ def search(SearchParams search_params,
     if filter is None:
         filter = no_filter()
 
-    cdef cuvsCagraSearchParams* params = &search_params.params
+    cdef cuvsCagraSearchParams* params = search_params.params
     cdef cydlpack.DLManagedTensor* queries_dlpack = \
         cydlpack.dlpack_c(queries_cai)
     cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 4db2854365..1ad8167cdc 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -32,7 +32,7 @@ from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
-from cuvs.distance import DISTANCE_TYPES
+from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
 from cuvs.neighbors.filters import no_filter
 
@@ -97,7 +97,6 @@ cdef class IndexParams:
     """
 
     cdef cuvsIvfFlatIndexParams* params
-    cdef object _metric
 
     def __cinit__(self):
         cuvsIvfFlatIndexParamsCreate(&self.params)
@@ -114,7 +113,6 @@ cdef class IndexParams:
                  adaptive_centers=False,
                  add_data_on_build=True,
                  conservative_memory_allocation=False):
-        self._metric = metric
         self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
         self.params.metric_arg = metric_arg
         self.params.add_data_on_build = add_data_on_build
@@ -125,9 +123,12 @@ cdef class IndexParams:
         self.params.conservative_memory_allocation = \
             conservative_memory_allocation
 
+    def get_handle(self):
+        return <size_t> self.params
+
     @property
     def metric(self):
-        return self._metric
+        return DISTANCE_NAMES[self.params.metric]
 
     @property
     def metric_arg(self):
@@ -291,6 +292,9 @@ cdef class SearchParams:
     def __init__(self, *, n_probes=20):
         self.params.n_probes = n_probes
 
+    def get_handle(self):
+        return <size_t> self.params
+
     @property
     def n_probes(self):
         return self.params.n_probes
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 4e4c96db8d..a2208b5148 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -32,7 +32,7 @@ from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
-from cuvs.distance import DISTANCE_TYPES
+from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
 
 from libc.stdint cimport (
@@ -143,7 +143,6 @@ cdef class IndexParams:
                  conservative_memory_allocation=False,
                  max_train_points_per_pq_code=256):
         self.params.n_lists = n_lists
-        self._metric = metric
         self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
         self.params.metric_arg = metric_arg
         self.params.kmeans_n_iters = kmeans_n_iters
@@ -163,9 +162,12 @@ cdef class IndexParams:
         self.params.max_train_points_per_pq_code = \
             max_train_points_per_pq_code
 
+    def get_handle(self):
+        return <size_t> self.params
+
     @property
     def metric(self):
-        return self._metric
+        return DISTANCE_NAMES[self.params.metric]
 
     @property
     def metric_arg(self):
@@ -396,9 +398,8 @@ cdef class SearchParams:
             _map_dtype_np_to_cuda(coarse_search_dtype)
         self.params.max_internal_batch_size = max_internal_batch_size
 
-    @property
-    def n_probes(self):
-        return self.params.n_probes
+    def get_handle(self):
+        return <size_t> self.params
 
     @property
     def n_probes(self):
diff --git a/python/cuvs/cuvs/neighbors/tiered_index/CMakeLists.txt b/python/cuvs/cuvs/neighbors/tiered_index/CMakeLists.txt
new file mode 100644
index 0000000000..b4cec8b13a
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/tiered_index/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources tiered_index.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_tiered_index_
+)
diff --git a/python/cuvs/cuvs/neighbors/tiered_index/__init__.pxd b/python/cuvs/cuvs/neighbors/tiered_index/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/tiered_index/__init__.py b/python/cuvs/cuvs/neighbors/tiered_index/__init__.py
new file mode 100644
index 0000000000..d6440431f3
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/tiered_index/__init__.py
@@ -0,0 +1,24 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .tiered_index import Index, IndexParams, build, extend, search
+
+__all__ = [
+    "Index",
+    "IndexParams",
+    "build",
+    "extend",
+    "search",
+]
diff --git a/python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pxd b/python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pxd
new file mode 100644
index 0000000000..9c0e35c609
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pxd
@@ -0,0 +1,78 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport int64_t, uint32_t, uintptr_t
+from libcpp cimport bool
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+from cuvs.neighbors.cagra.cagra cimport cuvsCagraIndexParams_t
+from cuvs.neighbors.filters.filters cimport cuvsFilter
+from cuvs.neighbors.ivf_flat.ivf_flat cimport cuvsIvfFlatIndexParams_t
+from cuvs.neighbors.ivf_pq.ivf_pq cimport cuvsIvfPqIndexParams_t
+
+
+cdef extern from "cuvs/neighbors/tiered_index.h" nogil:
+    ctypedef enum cuvsTieredIndexANNAlgo:
+        CUVS_TIERED_INDEX_ALGO_CAGRA
+        CUVS_TIERED_INDEX_ALGO_IVF_FLAT
+        CUVS_TIERED_INDEX_ALGO_IVF_PQ
+
+    ctypedef struct cuvsTieredIndexParams:
+        cuvsDistanceType metric
+        cuvsTieredIndexANNAlgo algo
+        int64_t min_ann_rows
+        bool create_ann_index_on_extend
+
+        cuvsCagraIndexParams_t cagra_params
+        cuvsIvfFlatIndexParams_t ivf_flat_params
+        cuvsIvfPqIndexParams_t ivf_pq_params
+
+    ctypedef cuvsTieredIndexParams* cuvsTieredIndexParams_t
+
+    ctypedef struct cuvsTieredIndex:
+        uintptr_t addr
+        DLDataType dtype
+        cuvsTieredIndexANNAlgo algo
+
+    ctypedef cuvsTieredIndex* cuvsTieredIndex_t
+
+    cuvsError_t cuvsTieredIndexParamsCreate(cuvsTieredIndexParams_t* params)
+
+    cuvsError_t cuvsTieredIndexParamsDestroy(cuvsTieredIndexParams_t index)
+
+    cuvsError_t cuvsTieredIndexCreate(cuvsTieredIndex_t* index)
+
+    cuvsError_t cuvsTieredIndexDestroy(cuvsTieredIndex_t index)
+
+    cuvsError_t cuvsTieredIndexBuild(cuvsResources_t res,
+                                     cuvsTieredIndexParams* params,
+                                     DLManagedTensor* dataset,
+                                     cuvsTieredIndex_t index)
+
+    cuvsError_t cuvsTieredIndexSearch(cuvsResources_t res,
+                                      void * params,
+                                      cuvsTieredIndex_t index,
+                                      DLManagedTensor* queries,
+                                      DLManagedTensor* neighbors,
+                                      DLManagedTensor* distances,
+                                      cuvsFilter filter) except +
+
+    cuvsError_t cuvsTieredIndexExtend(cuvsResources_t res,
+                                      DLManagedTensor* new_vectors,
+                                      cuvsTieredIndex_t index)
diff --git a/python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pyx b/python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pyx
new file mode 100644
index 0000000000..53766f99c8
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/tiered_index/tiered_index.pyx
@@ -0,0 +1,389 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libcpp cimport bool, cast
+from libcpp.string cimport string
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
+from cuvs.neighbors import cagra, ivf_flat, ivf_pq
+from cuvs.neighbors.common import _check_input_array
+from cuvs.neighbors.filters import no_filter
+
+from libc.stdint cimport (
+    int8_t,
+    int64_t,
+    uint8_t,
+    uint32_t,
+    uint64_t,
+    uintptr_t,
+)
+
+from cuvs.common.exceptions import check_cuvs
+
+ALGO_TYPES = {
+    "cagra": CUVS_TIERED_INDEX_ALGO_CAGRA,
+    "ivf_flat": CUVS_TIERED_INDEX_ALGO_IVF_FLAT,
+    "ivf_pq": CUVS_TIERED_INDEX_ALGO_IVF_PQ
+}
+ALGO_NAMES = {v: k for k, v in ALGO_TYPES.items()}
+
+
+cdef class IndexParams:
+    """
+    Parameters to build index for Tiered Index nearest neighbor search
+
+    Parameters
+    ----------
+    metric : str, default = "sqeuclidean"
+        String denoting the metric type.
+        Valid values for metric: ["sqeuclidean", "inner_product",
+        "euclidean", "cosine"], where
+            - sqeuclidean is the euclidean distance without the square root
+              operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2,
+            - euclidean is the euclidean distance
+            - inner product distance is defined as
+              distance(a, b) = \\sum_i a_i * b_i.
+            - cosine distance is defined as
+              distance(a, b) = 1 - \\sum_i a_i * b_i / ( ||a||_2 * ||b||_2).
+    algo : str, default = "cagra"
+        The algorithm to use for the ANN portion of the tiered index
+    upstream_params : object, optional
+        The IndexParams for the upstream ANN object to use (ie the Cagra
+        IndexParams for cagra etc)
+    min_ann_rows : int
+        The minimum number of rows necessary to create an ann index
+    create_ann_index_on_extend : bool
+        Whether or not to create a new ann index on extend, if the number
+        of rows in the incremental (bfknn) portion is above min_ann_rows
+    """
+
+    cdef cuvsTieredIndexParams* params
+    cdef object _upstream_params
+
+    def __cinit__(self):
+        cuvsTieredIndexParamsCreate(&self.params)
+
+    def __dealloc__(self):
+        check_cuvs(cuvsTieredIndexParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 metric="sqeuclidean",
+                 algo="cagra",
+                 upstream_params=None,
+                 min_ann_rows=None,
+                 create_ann_index_on_extend=None,):
+        self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+        self.params.algo = <cuvsTieredIndexANNAlgo>ALGO_TYPES[algo]
+        if min_ann_rows is not None:
+            self.params.min_ann_rows = min_ann_rows
+        if create_ann_index_on_extend is not None:
+            self.params.create_ann_index_on_extend = create_ann_index_on_extend
+
+        self._upstream_params = upstream_params
+        if upstream_params is not None:
+            if algo == "cagra":
+                if not isinstance(upstream_params, cagra.IndexParams):
+                    raise TypeError("Expected cagra.IndexParams, got "
+                                    f"{upstream_params.__class__} ")
+                h = upstream_params.get_handle()
+                self.params.cagra_params = <cuvsCagraIndexParams_t><size_t> h
+            elif algo == "ivf_flat":
+                if not isinstance(upstream_params, ivf_flat.IndexParams):
+                    raise TypeError("Expected ivf_flat.IndexParams, got "
+                                    f"{upstream_params.__class__} ")
+                h = upstream_params.get_handle()
+                self.params.ivf_flat_params = \
+                    <cuvsIvfFlatIndexParams_t><size_t> h
+            elif algo == "ivf_pq":
+                if not isinstance(upstream_params, ivf_pq.IndexParams):
+                    raise TypeError("Expected ivf_pq.IndexParams, got "
+                                    f"{upstream_params.__class__} ")
+                h = upstream_params.get_handle()
+                self.params.ivf_pq_params = <cuvsIvfPqIndexParams_t><size_t> h
+            else:
+                raise ValueError(f"Unknown algorithm '{algo}")
+
+    @property
+    def metric(self):
+        return DISTANCE_NAMES[self.params.metric]
+
+    @property
+    def algo(self):
+        return ALGO_NAMES[self.params.algo]
+
+    @property
+    def min_ann_rows(self):
+        return self.params.min_ann_rows
+
+    @property
+    def create_ann_index_on_extend(self):
+        return self.params.create_ann_index_on_extend
+
+    @property
+    def upstream_params(self):
+        return self._upstream_params
+
+cdef class Index:
+    """
+    Tiered Index object.
+    """
+
+    cdef cuvsTieredIndex_t index
+    cdef bool trained
+
+    def __cinit__(self):
+        self.trained = False
+        check_cuvs(cuvsTieredIndexCreate(&self.index))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsTieredIndexDestroy(self.index))
+
+    @property
+    def trained(self):
+        return self.trained
+
+
+@auto_sync_resources
+def build(IndexParams index_params, dataset, resources=None):
+    """
+    Build the Tiered index from the dataset for efficient search.
+
+    Parameters
+    ----------
+    index_params : :py:class:`cuvs.neighbors.tiered_index.IndexParams`
+    dataset : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.tiered_index.Index`
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra, tiered_index
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> build_params = tiered_index.IndexParams(metric="sqeuclidean",
+    ...                                         algo="cagra")
+    >>> index = tiered_index.build(build_params, dataset)
+    >>> distances, neighbors = tiered_index.search(cagra.SearchParams(),
+    ...                                            index, dataset, k)
+    >>> distances = cp.asarray(distances)
+    >>> neighbors = cp.asarray(neighbors)
+    """
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32')])
+
+    cdef Index idx = Index()
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cuvsTieredIndexParams* params = index_params.params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsTieredIndexBuild(
+            res,
+            params,
+            dataset_dlpack,
+            idx.index
+        ))
+        idx.trained = True
+
+    return idx
+
+
+@auto_sync_resources
+@auto_convert_output
+def search(search_params,
+           Index index,
+           queries,
+           k,
+           neighbors=None,
+           distances=None,
+           resources=None,
+           filter=None):
+    """
+    Find the k nearest neighbors for each query.
+
+    Parameters
+    ----------
+    search_params : SearchParams for the upstream ANN index
+    index : py:class:`cuvs.neighbors.tiered_index.Index`
+        Trained Tiered index.
+    queries : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32]
+    k : int
+        The number of neighbors.
+    neighbors : Optional CUDA array interface compliant matrix shape
+                (n_queries, k), dtype int64_t. If supplied, neighbor
+                indices will be written here in-place. (default None)
+    distances : Optional CUDA array interface compliant matrix shape
+                (n_queries, k) If supplied, the distances to the
+                neighbors will be written here in-place. (default None)
+    filter:     Optional cuvs.neighbors.cuvsFilter can be used to filter
+                neighbors based on a given bitset. (default None)
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import cagra, tiered_index
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build the index
+    >>> index = tiered_index.build(tiered_index.IndexParams(algo="cagra"),
+    ...                            dataset)
+    >>>
+    >>> # Search using the built index
+    >>> queries = cp.random.random_sample((n_queries, n_features),
+    ...                                   dtype=cp.float32)
+    >>> k = 10
+    >>> search_params = cagra.SearchParams()
+    >>>
+    >>> distances, neighbors = tiered_index.search(search_params, index,
+    ...                                            queries, k)
+    """
+    if not index.trained:
+        raise ValueError("Index needs to be built before calling search.")
+
+    queries_cai = wrap_array(queries)
+    _check_input_array(queries_cai, [np.dtype('float32')])
+
+    cdef uint32_t n_queries = queries_cai.shape[0]
+
+    if neighbors is None:
+        neighbors = device_ndarray.empty((n_queries, k), dtype='int64')
+
+    neighbors_cai = wrap_array(neighbors)
+    _check_input_array(neighbors_cai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    if distances is None:
+        distances = device_ndarray.empty((n_queries, k), dtype='float32')
+
+    distances_cai = wrap_array(distances)
+    _check_input_array(distances_cai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    if filter is None:
+        filter = no_filter()
+
+    cdef void* params = <void*><size_t>search_params.get_handle()
+
+    cdef cuvsError_t search_status
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_cai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_cai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_cai)
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsTieredIndexSearch(
+            res,
+            params,
+            index.index,
+            queries_dlpack,
+            neighbors_dlpack,
+            distances_dlpack,
+            filter.prefilter
+        ))
+
+    return (distances, neighbors)
+
+
+@auto_sync_resources
+def extend(Index index, new_vectors, resources=None):
+    """
+    Extend an existing index with new vectors.
+
+    The input array can be either CUDA array interface compliant matrix or
+    array interface compliant matrix in host memory.
+
+
+    Parameters
+    ----------
+    index : tiered_index.Index
+        Trained tiered_index object.
+    new_vectors : array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.tiered_index.Index`
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import tiered_index
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> index = tiered_index.build(tiered_index.IndexParams(), dataset)
+    >>> n_rows = 100
+    >>> more_data = cp.random.random_sample((n_rows, n_features),
+    ...                                     dtype=cp.float32)
+    >>> index = tiered_index.extend(index, more_data)
+    """
+
+    new_vectors_ai = wrap_array(new_vectors)
+    _check_input_array(new_vectors_ai, [np.dtype('float32')])
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* new_vectors_dlpack = \
+        cydlpack.dlpack_c(new_vectors_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsTieredIndexExtend(
+            res,
+            new_vectors_dlpack,
+            index.index
+        ))
+
+    return index

From 0a35104e5e41995126696069804af09bfdae4fdf Mon Sep 17 00:00:00 2001
From: punAhuja <167976912+punAhuja@users.noreply.github.com>
Date: Thu, 29 May 2025 22:44:04 +0530
Subject: [PATCH 425/623] [Java] Adding support for prefiltering in CAGRA
 (#870)

Added prefiltering logic for CAGRA search in the java layer.

Added tests to test prefiltering in CAGRA search

Authors:
  - https://github.com/punAhuja
  - Vivek Narang (https://github.com/narangvivek10)
  - Ishan Chattopadhyaya (https://github.com/chatman)

Approvers:
  - Ishan Chattopadhyaya (https://github.com/chatman)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/870
---
 .../java/com/nvidia/cuvs/BruteForceQuery.java |  2 +-
 .../main/java/com/nvidia/cuvs/CagraQuery.java | 47 +++++++++++-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 16 +++-
 .../cuvs/internal/CagraSearchResults.java     |  4 +-
 .../nvidia/cuvs/BruteForceAndSearchIT.java    |  2 +-
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |  2 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 76 +++++++++++++++++++
 .../com/nvidia/cuvs/CagraRandomizedIT.java    | 32 ++++++--
 java/internal/src/cuvs_java.c                 | 63 ++++++++++++---
 9 files changed, 220 insertions(+), 24 deletions(-)

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
index 70fa662210..947e9e7fdc 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
@@ -155,7 +155,7 @@ public Builder withTopK(int topK) {
      *        many bits as there are vectors in the index
      * @return an instance of this Builder
      */
-    public Builder withPrefilter(BitSet[] prefilters, int numDocs) {
+    public Builder withPrefilters(BitSet[] prefilters, int numDocs) {
       this.prefilters = prefilters;
       this.numDocs = numDocs;
       return this;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
index de2fc3f417..9af2d87e42 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -18,6 +18,7 @@
 
 import java.util.Arrays;
 import java.util.List;
+import java.util.BitSet;
 
 /**
  * CagraQuery holds the CagraSearchParams and the query vectors to be used while
@@ -31,6 +32,8 @@ public class CagraQuery {
   private List<Integer> mapping;
   private float[][] queryVectors;
   private int topK;
+  private BitSet prefilter;
+  private int numDocs;
 
   /**
    * Constructs an instance of {@link CagraQuery} using cagraSearchParameters,
@@ -41,13 +44,17 @@ public class CagraQuery {
    * @param queryVectors          2D float query vector array
    * @param mapping               an instance of ID mapping
    * @param topK                  the top k results to return
+   * @param prefilter             A single BitSet to use as filter while searching the CAGRA index
+   * @param numDocs               Total number of dataset vectors; used to align the prefilter correctly
    */
-  public CagraQuery(CagraSearchParams cagraSearchParameters, float[][] queryVectors, List<Integer> mapping, int topK) {
+  public CagraQuery(CagraSearchParams cagraSearchParameters, float[][] queryVectors, List<Integer> mapping, int topK, BitSet prefilter, int numDocs) {
     super();
     this.cagraSearchParameters = cagraSearchParameters;
     this.queryVectors = queryVectors;
     this.mapping = mapping;
     this.topK = topK;
+    this.prefilter = prefilter;
+    this.numDocs = numDocs;
   }
 
   /**
@@ -86,6 +93,24 @@ public int getTopK() {
     return topK;
   }
 
+  /**
+   * Gets the prefilter BitSet.
+   *
+   * @return a BitSet object representing the prefilter
+   */
+  public BitSet getPrefilter() {
+    return prefilter;
+  }
+
+  /**
+   * Gets the number of documents in this index, as used for prefilter
+   *
+   * @return number of documents as an integer
+   */
+  public int getNumDocs() {
+    return numDocs;
+  }
+
   @Override
   public String toString() {
     return "CuVSQuery [cagraSearchParameters=" + cagraSearchParameters + ", queryVectors="
@@ -101,6 +126,8 @@ public static class Builder {
     private float[][] queryVectors;
     private List<Integer> mapping;
     private int topK = 2;
+    private BitSet prefilter;
+    private int numDocs;
 
     /**
      * Default constructor.
@@ -153,13 +180,29 @@ public Builder withTopK(int topK) {
       return this;
     }
 
+    /**
+     * Sets a global prefilter for all queries in this {@link CagraQuery}.
+     * The {@code prefilter} array must contain exactly one {@link BitSet},
+     * which is applied to all queries. A bit value of {@code 1} includes the
+     * corresponding dataset vector; {@code 0} excludes it.
+     *
+     * @param prefilter an array with the global filter BitSet
+     * @param numDocs total number of vectors in the dataset (for alignment)
+     * @return this {@link Builder} instance
+     */
+    public Builder withPrefilter(BitSet prefilter, int numDocs) {
+      this.prefilter = prefilter;
+      this.numDocs = numDocs;
+      return this;
+  }
+
     /**
      * Builds an instance of CuVSQuery.
      *
      * @return an instance of CuVSQuery
      */
     public CagraQuery build() {
-      return new CagraQuery(cagraSearchParams, queryVectors, mapping, topK);
+      return new CagraQuery(cagraSearchParams, queryVectors, mapping, topK, prefilter, numDocs);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index f3f3fbfbe2..2663818316 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -57,6 +57,7 @@
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqSearchParams;
+import java.util.BitSet;
 
 /**
  * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
@@ -76,7 +77,7 @@ public class CagraIndexImpl implements CagraIndex {
       FunctionDescriptor.of(ADDRESS, ADDRESS, C_LONG, C_LONG, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_INT));
 
   private static final MethodHandle searchMethodHandle = downcallHandle("search_cagra_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_LONG));
 
   private static final MethodHandle serializeMethodHandle = downcallHandle("serialize_cagra_index",
       FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
@@ -239,6 +240,15 @@ public SearchResults search(CagraQuery query) throws Throwable {
     MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
     MemorySegment floatsSeg = Util.buildMemorySegment(resources.getArena(), query.getQueryVectors());
 
+    long prefilterDataLength = 0;
+    MemorySegment prefilterData = MemorySegment.NULL;
+    if (query.getPrefilter() != null) {
+      long[] longArray = query.getPrefilter().toLongArray();
+      prefilterData = Util.buildMemorySegment(resources.getArena(), longArray);
+      prefilterDataLength = query.getNumDocs();
+    }
+
+
     try (var localArena = Arena.ofConfined()) {
       MemorySegment returnValue = localArena.allocate(C_INT);
       searchMethodHandle.invokeExact(
@@ -251,7 +261,9 @@ public SearchResults search(CagraQuery query) throws Throwable {
         neighborsMemorySegment,
         distancesMemorySegment,
         returnValue,
-        segmentFromSearchParams(query.getCagraSearchParameters())
+        segmentFromSearchParams(query.getCagraSearchParameters()),
+        prefilterData,
+        prefilterDataLength
       );
       checkError(returnValue.get(C_INT, 0L), "searchMethodHandle");
     }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
index 4902013073..3e8a906228 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
@@ -49,7 +49,9 @@ protected void readResultMemorySegments() {
     for (long i = 0; i < topK * numberOfQueries; i++) {
       int id = (int) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
       float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
-      intermediateResultMap.put(mapping != null ? mapping.get(id) : id, dst);
+      if (id != Integer.MAX_VALUE) {
+        intermediateResultMap.put(mapping != null ? mapping.get(id) : id, dst);
+      }
       count += 1;
       if (count == topK) {
         results.add(intermediateResultMap);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index 888f4ed69d..45ada8b50b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -123,7 +123,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         BruteForceQuery cuvsQueryWithFiltering = new BruteForceQuery.Builder()
             .withTopK(3)
             .withQueryVectors(queries)
-            .withPrefilter(new BitSet[] {prefilter, prefilter, prefilter, prefilter}, dataset.length)
+            .withPrefilters(new BitSet[] {prefilter, prefilter, prefilter, prefilter}, dataset.length)
             .withMapping(map)
             .build();
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index c4b52984b3..481289fe02 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -115,7 +115,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       BruteForceQuery query = new BruteForceQuery.Builder()
           .withTopK(topK)
           .withQueryVectors(queries)
-          .withPrefilter(prefilters, vectors.length)
+          .withPrefilters(prefilters, vectors.length)
           .build();
 
       BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 371d7a3876..007243fc55 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -33,6 +33,7 @@
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 import java.util.concurrent.TimeUnit;
+import java.util.BitSet;
 
 import org.junit.Before;
 import org.junit.Test;
@@ -124,6 +125,81 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     }
   }
 
+  /**
+   * A test that checks the pre-filtering feature.
+   *
+   * @throws Throwable
+   */
+  @Test
+  public void testPrefilteringReducesResults() throws Throwable {
+
+    // Sample data and query
+    float[][] dataset = {
+        { 0.74021935f, 0.9209938f },
+        { 0.03902049f, 0.9689629f },
+        { 0.92514056f, 0.4463501f },
+        { 0.6673192f, 0.10993068f }
+       };
+    float[][] queries = {
+        { 0.48216683f, 0.0428398f }
+       };
+
+    // Expected search results
+    List<Map<Integer, Float>> expectedResults = Arrays.asList(
+        Map.of(3, 0.038782578f, 2, 0.3590463f));
+
+    // Expected filtered search results
+    List<Map<Integer, Float>> expectedFilteredResults = Arrays.asList(
+        Map.of(2, 0.3590463f, 0, 0.83774555f));
+
+    CagraIndexParams indexParams = new CagraIndexParams.Builder()
+        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+        .withGraphDegree(2)
+        .withIntermediateGraphDegree(4)
+        .withNumWriterThreads(2)
+        .withMetric(CuvsDistanceType.L2Expanded)
+        .build();
+
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndex index = CagraIndex.newBuilder(resources)
+          .withDataset(dataset)
+          .withIndexParams(indexParams)
+          .build();
+
+      // No prefilter (all points allowed)
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+          .build();
+      CagraQuery fullQuery = new CagraQuery.Builder()
+          .withTopK(2)
+          .withSearchParams(searchParams)
+          .withQueryVectors(queries)
+          .build();
+
+      SearchResults fullSearchResults = index.search(fullQuery);
+      List<Map<Integer, Float>> fullResults = fullSearchResults.getResults();
+      log.info("Full results: {}", fullResults);
+
+      // Apply prefilter: only allow ids 0 and 2 (bitset: 1100)
+      BitSet prefilter = new BitSet(4);
+      prefilter.set(0);
+      prefilter.set(2);
+
+      CagraQuery filteredQuery = new CagraQuery.Builder()
+          .withTopK(2)
+          .withSearchParams(searchParams)
+          .withQueryVectors(queries)
+          .withPrefilter(prefilter, 4)
+          .build();
+
+      SearchResults filteredSearchResults = index.search(filteredQuery);
+      List<Map<Integer, Float>> filteredResults = filteredSearchResults.getResults();
+      log.info("Filtered results: {}", filteredResults);
+
+      assertEquals(expectedResults, fullResults);
+      assertEquals(expectedFilteredResults, filteredResults);
+    }
+  }
+
   private Runnable indexAndQueryOnce(float[][] dataset, List<Integer> map, float[][] queries,
       List<Map<Integer, Float>> expectedResults, CuVSResources resources) throws Throwable, FileNotFoundException {
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 397222b787..693521760b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -18,7 +18,7 @@
 
 import java.lang.invoke.MethodHandles;
 import java.util.List;
-
+import java.util.BitSet;
 import org.junit.Before;
 import org.junit.Test;
 import org.junit.runner.RunWith;
@@ -62,10 +62,27 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     int dimensions = random.nextInt(DIMENSIONS_LIMIT) + 1;
     int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
     int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
+    boolean usePrefilter = random.nextBoolean();
 
     if (datasetSize < topK)
       datasetSize = topK;
 
+    BitSet sharedPrefilter = null;
+    if (usePrefilter) {
+      sharedPrefilter = new BitSet(datasetSize);
+      for (int j = 0; j < datasetSize; j++) {
+        sharedPrefilter.set(j, random.nextBoolean());
+      }
+    }
+
+    BitSet[] prefilters = null;
+    if (sharedPrefilter != null) {
+      prefilters = new BitSet[numQueries];
+      for (int i = 0; i < numQueries; i++) {
+        prefilters[i] = sharedPrefilter;
+      }
+    }
+
     // Generate a random dataset
     float[][] vectors = generateData(random, datasetSize, dimensions);
 
@@ -95,7 +112,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     assert topK > 0 && topK <= datasetSize : "Invalid topK value.";
 
     // Generate expected results using brute force
-    List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, null, log);
+    List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, prefilters, log);
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
@@ -121,12 +138,17 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
       try {
         // Execute search and retrieve results
-        CagraQuery query = new CagraQuery.Builder()
+        CagraQuery.Builder queryBuilder = new CagraQuery.Builder()
                 .withQueryVectors(queries)
                 .withTopK(topK)
                 .withSearchParams(new CagraSearchParams.Builder(resources)
-                        .build())
-                .build();
+                        .build());
+
+        if (sharedPrefilter != null) {
+          queryBuilder.withPrefilter(sharedPrefilter, datasetSize);
+        }
+
+        CagraQuery query = queryBuilder.build();
         log.info("Query built successfully. Executing search...");
         SearchResults results = index.search(query);
 
diff --git a/java/internal/src/cuvs_java.c b/java/internal/src/cuvs_java.c
index 34abfa1b2d..890867a71e 100644
--- a/java/internal/src/cuvs_java.c
+++ b/java/internal/src/cuvs_java.c
@@ -165,18 +165,30 @@ void deserialize_cagra_index(cuvsResources_t cuvs_resources, cuvsCagraIndex_t in
  * @param[out] distances_h reference to the distance results on the host memory
  * @param[out] return_value return value for cuvsCagraSearch function call
  * @param[in] search_params reference to cuvsCagraSearchParams_t holding the search parameters
+ * @param[in] prefilter_data host pointer to bit-packed filter bitmap (uint32_t array)
+ * @param[in] prefilter_data_length total number of bits in the filter (should be equal to num_docs * num_filters)
  */
-void search_cagra_index(cuvsCagraIndex_t index, float *queries, int topk, long n_queries, int dimensions,
-    cuvsResources_t cuvs_resources, int *neighbors_h, float *distances_h, int *return_value, cuvsCagraSearchParams_t search_params) {
-
+void search_cagra_index(cuvsCagraIndex_t index,
+                        float *queries,
+                        int topk,
+                        long n_queries,
+                        int dimensions,
+                        cuvsResources_t cuvs_resources,
+                        int *neighbors_h,
+                        float *distances_h,
+                        int *return_value,
+                        cuvsCagraSearchParams_t search_params,
+                        uint32_t *prefilter_data,
+                        long prefilter_data_length) {
   cudaStream_t stream;
   cuvsStreamGet(cuvs_resources, &stream);
 
   uint32_t *neighbors;
   float *distances, *queries_d;
-  cuvsRMMAlloc(cuvs_resources, (void**) &queries_d, sizeof(float) * n_queries * dimensions);
-  cuvsRMMAlloc(cuvs_resources, (void**) &neighbors, sizeof(uint32_t) * n_queries * topk);
-  cuvsRMMAlloc(cuvs_resources, (void**) &distances, sizeof(float) * n_queries * topk);
+
+  cuvsRMMAlloc(cuvs_resources, (void **) &queries_d, sizeof(float) * n_queries * dimensions);
+  cuvsRMMAlloc(cuvs_resources, (void **) &neighbors, sizeof(uint32_t) * n_queries * topk);
+  cuvsRMMAlloc(cuvs_resources, (void **) &distances, sizeof(float) * n_queries * topk);
 
   cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * dimensions, cudaMemcpyDefault);
 
@@ -191,12 +203,35 @@ void search_cagra_index(cuvsCagraIndex_t index, float *queries, int topk, long n
 
   cuvsStreamSync(cuvs_resources);
 
-  cuvsFilter filter; // TODO: Implement Cagra Pre-Filtering, but leave it as no-op for now
-  filter.type = NO_FILTER;
-  filter.addr = (uintptr_t)NULL;
+  cuvsFilter filter;
+  uint32_t *prefilter_d = NULL;
+  int64_t prefilter_len = 0;
+  DLManagedTensor *prefilter_tensor_ptr = NULL;
+
+  if (prefilter_data == NULL || prefilter_data_length == 0) {
+    filter.type = NO_FILTER;
+    filter.addr = (uintptr_t) NULL;
+  } else {
+    int64_t prefilter_shape[1] = {(prefilter_data_length + 31) / 32};
+    prefilter_len = prefilter_shape[0];
+
+    cuvsRMMAlloc(cuvs_resources, (void **) &prefilter_d, sizeof(uint32_t) * prefilter_len);
+    cudaMemcpy(prefilter_d, prefilter_data, sizeof(uint32_t) * prefilter_len, cudaMemcpyHostToDevice);
+
+    prefilter_tensor_ptr = (DLManagedTensor *) malloc(sizeof(DLManagedTensor));
+    *prefilter_tensor_ptr = prepare_tensor(prefilter_d, prefilter_shape, kDLUInt, 32, 1, kDLCUDA);
+
+    filter.type = BITSET;
+    filter.addr = (uintptr_t) prefilter_tensor_ptr;
+  }
 
-  *return_value = cuvsCagraSearch(cuvs_resources, search_params, index, &queries_tensor, &neighbors_tensor,
-                  &distances_tensor, filter);
+  *return_value = cuvsCagraSearch(cuvs_resources,
+                                  search_params,
+                                  index,
+                                  &queries_tensor,
+                                  &neighbors_tensor,
+                                  &distances_tensor,
+                                  filter);
 
   cudaMemcpy(neighbors_h, neighbors, sizeof(uint32_t) * n_queries * topk, cudaMemcpyDefault);
   cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
@@ -204,6 +239,12 @@ void search_cagra_index(cuvsCagraIndex_t index, float *queries, int topk, long n
   cuvsRMMFree(cuvs_resources, distances, sizeof(float) * n_queries * topk);
   cuvsRMMFree(cuvs_resources, neighbors, sizeof(uint32_t) * n_queries * topk);
   cuvsRMMFree(cuvs_resources, queries_d, sizeof(float) * n_queries * dimensions);
+  if (prefilter_d != NULL) {
+    cuvsRMMFree(cuvs_resources, prefilter_d, sizeof(uint32_t) * prefilter_len);
+  }
+  if (prefilter_tensor_ptr != NULL) {
+    free(prefilter_tensor_ptr);
+  }
 }
 
 /**

From 4462d9257bba157fc2561ec00e677720bed66dda Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Fri, 30 May 2025 11:58:12 -0500
Subject: [PATCH 426/623] Remove CUDA 11 devcontainers and update CI scripts
 (#960)

This PR removes CUDA 11 devcontainers and updates CI scripts.

xref: https://github.com/rapidsai/build-planning/issues/184

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: https://github.com/rapidsai/cuvs/pull/960
---
 .../cuda11.8-conda/devcontainer.json          | 42 ----------------
 .devcontainer/cuda11.8-pip/devcontainer.json  | 50 -------------------
 .github/workflows/pr.yaml                     |  5 +-
 ci/build_python.sh                            |  4 +-
 ci/build_wheel.sh                             | 12 ++---
 ci/test_wheel_cuvs.sh                         |  5 +-
 ci/validate_wheel.sh                          | 20 --------
 python/libcuvs/pyproject.toml                 |  2 +-
 8 files changed, 9 insertions(+), 131 deletions(-)
 delete mode 100644 .devcontainer/cuda11.8-conda/devcontainer.json
 delete mode 100644 .devcontainer/cuda11.8-pip/devcontainer.json

diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json
deleted file mode 100644
index 73574ed688..0000000000
--- a/.devcontainer/cuda11.8-conda/devcontainer.json
+++ /dev/null
@@ -1,42 +0,0 @@
-{
-  "build": {
-    "context": "${localWorkspaceFolder}/.devcontainer",
-    "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
-    "args": {
-      "CUDA": "11.8",
-      "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda11.8-mambaforge-ubuntu22.04"
-    }
-  },
-  "runArgs": [
-    "--rm",
-    "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda11.8-conda"
-  ],
-  "hostRequirements": {"gpu": "optional"},
-  "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
-  },
-  "overrideFeatureInstallOrder": [
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
-  ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda11.8-envs}"],
-  "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
-  "workspaceFolder": "/home/coder",
-  "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
-  "mounts": [
-    "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda11.8-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
-  ],
-  "customizations": {
-    "vscode": {
-      "extensions": [
-        "ms-python.flake8",
-        "nvidia.nsight-vscode-edition"
-      ]
-    }
-  }
-}
diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json
deleted file mode 100644
index 634291a467..0000000000
--- a/.devcontainer/cuda11.8-pip/devcontainer.json
+++ /dev/null
@@ -1,50 +0,0 @@
-{
-  "build": {
-    "context": "${localWorkspaceFolder}/.devcontainer",
-    "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
-    "args": {
-      "CUDA": "11.8",
-      "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda11.8-ucx1.18.0-openmpi-ubuntu22.04"
-    }
-  },
-  "runArgs": [
-    "--rm",
-    "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda11.8-pip"
-  ],
-  "hostRequirements": {"gpu": "optional"},
-  "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.8": {
-      "version": "11.8",
-      "installcuBLAS": true,
-      "installcuSOLVER": true,
-      "installcuRAND": true,
-      "installcuSPARSE": true
-    },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
-  },
-  "overrideFeatureInstallOrder": [
-    "ghcr.io/rapidsai/devcontainers/features/ucx",
-    "ghcr.io/rapidsai/devcontainers/features/cuda",
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
-  ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda11.8-venvs}"],
-  "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
-  "workspaceFolder": "/home/coder",
-  "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
-  "mounts": [
-    "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda11.8-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
-  ],
-  "customizations": {
-    "vscode": {
-      "extensions": [
-        "ms-python.flake8",
-        "nvidia.nsight-vscode-edition"
-      ]
-    }
-  }
-}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index a05bba4662..4fca4cc7ba 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -45,10 +45,7 @@ jobs:
         uses: rapidsai/shared-actions/telemetry-dispatch-stash-base-env-vars@main
   check-nightly-ci:
     needs: telemetry-setup
-    # Switch to ubuntu-latest once it defaults to a version of Ubuntu that
-    # provides at least Python 3.11 (see
-    # https://docs.python.org/3/library/datetime.html#datetime.date.fromisoformat)
-    runs-on: ubuntu-24.04
+    runs-on: ubuntu-latest
     env:
       RAPIDS_GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 78fc6f2bda..8ee818341c 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -51,8 +51,8 @@ rattler-build build --recipe conda/recipes/cuvs-bench \
 sccache --show-adv-stats
 sccache --zero-stats
 
-# Build cuvs-bench-cpu only in CUDA 12 jobs since it only depends on python
-# version
+# Build cuvs-bench-cpu only in one CUDA major version since it only depends on
+# python version
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
 if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
   rattler-build build --recipe conda/recipes/cuvs-bench-cpu \
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
index 1d02bc0dd7..48bea18916 100755
--- a/ci/build_wheel.sh
+++ b/ci/build_wheel.sh
@@ -15,13 +15,14 @@ rapids-generate-version > ./VERSION
 cd "${package_dir}"
 
 EXCLUDE_ARGS=(
-  --exclude "libraft.so"
   --exclude "libcublas.so.*"
   --exclude "libcublasLt.so.*"
   --exclude "libcurand.so.*"
   --exclude "libcusolver.so.*"
   --exclude "libcusparse.so.*"
+  --exclude "libnccl.so.*"
   --exclude "libnvJitLink.so.*"
+  --exclude "libraft.so"
   --exclude "librapids_logger.so"
   --exclude "librmm.so"
 )
@@ -33,13 +34,8 @@ if [[ "${package_dir}" != "python/libcuvs" ]]; then
     )
 fi
 
-RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
-if [[ ${RAPIDS_CUDA_MAJOR} != "11" ]]; then
-    EXCLUDE_ARGS+=(
-      --exclude "libnccl.so.*"
-    )
-    export SKBUILD_CMAKE_ARGS="-DUSE_NCCL_RUNTIME_WHEEL=ON"
-fi
+SKBUILD_CMAKE_ARGS="-DUSE_NCCL_RUNTIME_WHEEL=ON"
+export SKBUILD_CMAKE_ARGS
 
 rapids-logger "Building '${package_name}' wheel"
 
diff --git a/ci/test_wheel_cuvs.sh b/ci/test_wheel_cuvs.sh
index 510c35db97..8ce684c11b 100755
--- a/ci/test_wheel_cuvs.sh
+++ b/ci/test_wheel_cuvs.sh
@@ -6,10 +6,7 @@ set -euo pipefail
 source rapids-init-pip
 
 # Delete system libnccl.so to ensure the wheel is used
-RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
-if [[ ${RAPIDS_CUDA_MAJOR} != "11" ]]; then
-  rm -rf /usr/lib64/libnccl*
-fi
+rm -rf /usr/lib64/libnccl*
 
 RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen "${RAPIDS_CUDA_VERSION}")"
 LIBCUVS_WHEELHOUSE=$(RAPIDS_PY_WHEEL_NAME="libcuvs_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-github cpp)
diff --git a/ci/validate_wheel.sh b/ci/validate_wheel.sh
index 5c0d21baef..550ac8fb4c 100755
--- a/ci/validate_wheel.sh
+++ b/ci/validate_wheel.sh
@@ -6,26 +6,6 @@ set -euo pipefail
 package_dir=$1
 wheel_dir_relative_path=$2
 
-
-RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
-
-# some packages are much larger on CUDA 11 than on CUDA 12
-if [[ "${package_dir}" == "python/libcuvs" ]]; then
-    if [[ "${RAPIDS_CUDA_MAJOR}" == "11" ]]; then
-        PYDISTCHECK_ARGS=(
-            --max-allowed-size-compressed '1.1G'
-        )
-    else
-        PYDISTCHECK_ARGS=(
-            --max-allowed-size-compressed '950M'
-        )
-    fi
-else
-    PYDISTCHECK_ARGS=()
-fi
-
-
-
 cd "${package_dir}"
 
 rapids-logger "validate packages with 'pydistcheck'"
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index a2c36e7e0c..9d357e6928 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -106,4 +106,4 @@ select = [
 ]
 
 # detect when package size grows significantly
-max_allowed_size_compressed = '1.0G'
+max_allowed_size_compressed = '950M'

From 5098d310d2257d43d7fb840de04d8e63c3f03ba5 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Fri, 30 May 2025 17:00:37 -0400
Subject: [PATCH 427/623] Remove CUDA 11 from dependencies.yaml (#962)

Issue: https://github.com/rapidsai/build-planning/issues/184

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/962
---
 .../all_cuda-118_arch-aarch64.yaml            |  61 -------
 .../all_cuda-118_arch-x86_64.yaml             |  61 -------
 .../bench_ann_cuda-118_arch-aarch64.yaml      |  55 -------
 .../bench_ann_cuda-118_arch-x86_64.yaml       |  58 -------
 .../go_cuda-118_arch-aarch64.yaml             |  37 -----
 .../environments/go_cuda-118_arch-x86_64.yaml |  37 -----
 dependencies.yaml                             | 149 +-----------------
 python/cuvs/pyproject.toml                    |   2 +-
 8 files changed, 5 insertions(+), 455 deletions(-)
 delete mode 100644 conda/environments/all_cuda-118_arch-aarch64.yaml
 delete mode 100644 conda/environments/all_cuda-118_arch-x86_64.yaml
 delete mode 100644 conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
 delete mode 100644 conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
 delete mode 100644 conda/environments/go_cuda-118_arch-aarch64.yaml
 delete mode 100644 conda/environments/go_cuda-118_arch-x86_64.yaml

diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml
deleted file mode 100644
index 754238e484..0000000000
--- a/conda/environments/all_cuda-118_arch-aarch64.yaml
+++ /dev/null
@@ -1,61 +0,0 @@
-# This file is generated by `rapids-dependency-file-generator`.
-# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-channels:
-- rapidsai
-- rapidsai-nightly
-- dask/label/dev
-- conda-forge
-- nvidia
-dependencies:
-- breathe>=4.35.0
-- c-compiler
-- clang-tools==20.1.4
-- clang==20.1.4
-- cmake>=3.30.4
-- cuda-nvtx=11.8
-- cuda-profiler-api=11.8.86
-- cuda-python>=11.8.5,<12.0a0
-- cuda-version=11.8
-- cudatoolkit
-- cupy>=12.0.0
-- cxx-compiler
-- cython>=3.0.0
-- dlpack>=0.8,<1.0
-- doxygen>=1.8.20
-- gcc_linux-aarch64=11.*
-- go
-- graphviz
-- ipython
-- libclang==20.1.4
-- libcublas-dev=11.11.3.6
-- libcublas=11.11.3.6
-- libcurand-dev=10.3.0.86
-- libcurand=10.3.0.86
-- libcusolver-dev=11.4.1.48
-- libcusolver=11.4.1.48
-- libcusparse-dev=11.7.5.86
-- libcusparse=11.7.5.86
-- librmm==25.8.*,>=0.0.0a0
-- make
-- nccl>=2.19
-- ninja
-- numpy>=1.23,<3.0a0
-- numpydoc
-- nvcc_linux-aarch64=11.8
-- openblas
-- pre-commit
-- pylibraft==25.8.*,>=0.0.0a0
-- pytest-cov
-- pytest==7.*
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
-- recommonmark
-- rust
-- scikit-build-core>=0.10.0
-- scikit-learn
-- sphinx-copybutton
-- sphinx-markdown-tables
-- sphinx>=8.0.0
-- sysroot_linux-aarch64==2.28
-- pip:
-  - nvidia-sphinx-theme
-name: all_cuda-118_arch-aarch64
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
deleted file mode 100644
index a4eb320701..0000000000
--- a/conda/environments/all_cuda-118_arch-x86_64.yaml
+++ /dev/null
@@ -1,61 +0,0 @@
-# This file is generated by `rapids-dependency-file-generator`.
-# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-channels:
-- rapidsai
-- rapidsai-nightly
-- dask/label/dev
-- conda-forge
-- nvidia
-dependencies:
-- breathe>=4.35.0
-- c-compiler
-- clang-tools==20.1.4
-- clang==20.1.4
-- cmake>=3.30.4
-- cuda-nvtx=11.8
-- cuda-profiler-api=11.8.86
-- cuda-python>=11.8.5,<12.0a0
-- cuda-version=11.8
-- cudatoolkit
-- cupy>=12.0.0
-- cxx-compiler
-- cython>=3.0.0
-- dlpack>=0.8,<1.0
-- doxygen>=1.8.20
-- gcc_linux-64=11.*
-- go
-- graphviz
-- ipython
-- libclang==20.1.4
-- libcublas-dev=11.11.3.6
-- libcublas=11.11.3.6
-- libcurand-dev=10.3.0.86
-- libcurand=10.3.0.86
-- libcusolver-dev=11.4.1.48
-- libcusolver=11.4.1.48
-- libcusparse-dev=11.7.5.86
-- libcusparse=11.7.5.86
-- librmm==25.8.*,>=0.0.0a0
-- make
-- nccl>=2.19
-- ninja
-- numpy>=1.23,<3.0a0
-- numpydoc
-- nvcc_linux-64=11.8
-- openblas
-- pre-commit
-- pylibraft==25.8.*,>=0.0.0a0
-- pytest-cov
-- pytest==7.*
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
-- recommonmark
-- rust
-- scikit-build-core>=0.10.0
-- scikit-learn
-- sphinx-copybutton
-- sphinx-markdown-tables
-- sphinx>=8.0.0
-- sysroot_linux-64==2.28
-- pip:
-  - nvidia-sphinx-theme
-name: all_cuda-118_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
deleted file mode 100644
index 4c3aa33dfc..0000000000
--- a/conda/environments/bench_ann_cuda-118_arch-aarch64.yaml
+++ /dev/null
@@ -1,55 +0,0 @@
-# This file is generated by `rapids-dependency-file-generator`.
-# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-channels:
-- rapidsai
-- rapidsai-nightly
-- dask/label/dev
-- conda-forge
-- nvidia
-dependencies:
-- benchmark>=1.8.2
-- c-compiler
-- clang-tools==20.1.4
-- clang==20.1.4
-- click
-- cmake>=3.30.4
-- cuda-nvtx=11.8
-- cuda-profiler-api=11.8.86
-- cuda-python>=11.8.5,<12.0a0
-- cuda-version=11.8
-- cudatoolkit
-- cupy>=12.0.0
-- cuvs==25.8.*,>=0.0.0a0
-- cxx-compiler
-- cython>=3.0.0
-- dlpack>=0.8,<1.0
-- gcc_linux-aarch64=11.*
-- glog>=0.6.0
-- h5py>=3.8.0
-- libclang==20.1.4
-- libcublas-dev=11.11.3.6
-- libcublas=11.11.3.6
-- libcurand-dev=10.3.0.86
-- libcurand=10.3.0.86
-- libcusolver-dev=11.4.1.48
-- libcusolver=11.4.1.48
-- libcusparse-dev=11.7.5.86
-- libcusparse=11.7.5.86
-- libcuvs==25.8.*,>=0.0.0a0
-- librmm==25.8.*,>=0.0.0a0
-- matplotlib-base>=3.9
-- nccl>=2.19
-- ninja
-- nlohmann_json>=3.11.2
-- nvcc_linux-aarch64=11.8
-- openblas
-- pandas
-- pylibraft==25.8.*,>=0.0.0a0
-- pyyaml
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
-- requests
-- scikit-learn
-- setuptools
-- sysroot_linux-aarch64==2.28
-- wheel
-name: bench_ann_cuda-118_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
deleted file mode 100644
index 49e6443eb9..0000000000
--- a/conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
+++ /dev/null
@@ -1,58 +0,0 @@
-# This file is generated by `rapids-dependency-file-generator`.
-# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-channels:
-- rapidsai
-- rapidsai-nightly
-- dask/label/dev
-- conda-forge
-- nvidia
-dependencies:
-- benchmark>=1.8.2
-- c-compiler
-- clang-tools==20.1.4
-- clang==20.1.4
-- click
-- cmake>=3.30.4
-- cuda-nvtx=11.8
-- cuda-profiler-api=11.8.86
-- cuda-python>=11.8.5,<12.0a0
-- cuda-version=11.8
-- cudatoolkit
-- cupy>=12.0.0
-- cuvs==25.8.*,>=0.0.0a0
-- cxx-compiler
-- cython>=3.0.0
-- dlpack>=0.8,<1.0
-- gcc_linux-64=11.*
-- glog>=0.6.0
-- h5py>=3.8.0
-- libaio
-- libboost-devel=1.87
-- libclang==20.1.4
-- libcublas-dev=11.11.3.6
-- libcublas=11.11.3.6
-- libcurand-dev=10.3.0.86
-- libcurand=10.3.0.86
-- libcusolver-dev=11.4.1.48
-- libcusolver=11.4.1.48
-- libcusparse-dev=11.7.5.86
-- libcusparse=11.7.5.86
-- libcuvs==25.8.*,>=0.0.0a0
-- librmm==25.8.*,>=0.0.0a0
-- matplotlib-base>=3.9
-- mkl-devel=2023
-- nccl>=2.19
-- ninja
-- nlohmann_json>=3.11.2
-- nvcc_linux-64=11.8
-- openblas
-- pandas
-- pylibraft==25.8.*,>=0.0.0a0
-- pyyaml
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
-- requests
-- scikit-learn
-- setuptools
-- sysroot_linux-64==2.28
-- wheel
-name: bench_ann_cuda-118_arch-x86_64
diff --git a/conda/environments/go_cuda-118_arch-aarch64.yaml b/conda/environments/go_cuda-118_arch-aarch64.yaml
deleted file mode 100644
index 86a0d5b1a2..0000000000
--- a/conda/environments/go_cuda-118_arch-aarch64.yaml
+++ /dev/null
@@ -1,37 +0,0 @@
-# This file is generated by `rapids-dependency-file-generator`.
-# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-channels:
-- rapidsai
-- rapidsai-nightly
-- dask/label/dev
-- conda-forge
-- nvidia
-dependencies:
-- c-compiler
-- clang-tools==20.1.4
-- clang==20.1.4
-- cmake>=3.30.4
-- cuda-nvtx=11.8
-- cuda-profiler-api=11.8.86
-- cuda-version=11.8
-- cudatoolkit
-- cxx-compiler
-- dlpack>=0.8,<1.0
-- gcc_linux-aarch64=11.*
-- go
-- libclang==20.1.4
-- libcublas-dev=11.11.3.6
-- libcublas=11.11.3.6
-- libcurand-dev=10.3.0.86
-- libcurand=10.3.0.86
-- libcusolver-dev=11.4.1.48
-- libcusolver=11.4.1.48
-- libcusparse-dev=11.7.5.86
-- libcusparse=11.7.5.86
-- libcuvs==25.8.*,>=0.0.0a0
-- libraft==25.8.*,>=0.0.0a0
-- nccl>=2.19
-- ninja
-- nvcc_linux-aarch64=11.8
-- sysroot_linux-aarch64==2.28
-name: go_cuda-118_arch-aarch64
diff --git a/conda/environments/go_cuda-118_arch-x86_64.yaml b/conda/environments/go_cuda-118_arch-x86_64.yaml
deleted file mode 100644
index 16130813b5..0000000000
--- a/conda/environments/go_cuda-118_arch-x86_64.yaml
+++ /dev/null
@@ -1,37 +0,0 @@
-# This file is generated by `rapids-dependency-file-generator`.
-# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
-channels:
-- rapidsai
-- rapidsai-nightly
-- dask/label/dev
-- conda-forge
-- nvidia
-dependencies:
-- c-compiler
-- clang-tools==20.1.4
-- clang==20.1.4
-- cmake>=3.30.4
-- cuda-nvtx=11.8
-- cuda-profiler-api=11.8.86
-- cuda-version=11.8
-- cudatoolkit
-- cxx-compiler
-- dlpack>=0.8,<1.0
-- gcc_linux-64=11.*
-- go
-- libclang==20.1.4
-- libcublas-dev=11.11.3.6
-- libcublas=11.11.3.6
-- libcurand-dev=10.3.0.86
-- libcurand=10.3.0.86
-- libcusolver-dev=11.4.1.48
-- libcusolver=11.4.1.48
-- libcusparse-dev=11.7.5.86
-- libcusparse=11.7.5.86
-- libcuvs==25.8.*,>=0.0.0a0
-- libraft==25.8.*,>=0.0.0a0
-- nccl>=2.19
-- ninja
-- nvcc_linux-64=11.8
-- sysroot_linux-64==2.28
-name: go_cuda-118_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index 55d7bf149b..c7a2daa9e7 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,7 +3,7 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["11.8", "12.8"]
+      cuda: ["12.8"]
       arch: [x86_64, aarch64]
     includes:
       - build
@@ -29,7 +29,7 @@ files:
   bench_ann:
     output: conda
     matrix:
-      cuda: ["11.8", "12.8"]
+      cuda: ["12.8"]
       arch: [x86_64, aarch64]
     includes:
       - bench
@@ -100,7 +100,7 @@ files:
   go:
     output: conda
     matrix:
-      cuda: ["11.8", "12.8"]
+      cuda: ["12.8"]
       arch: [x86_64, aarch64]
     includes:
       - clang
@@ -245,18 +245,6 @@ dependencies:
     specific:
       - output_types: conda
         matrices:
-          - matrix:
-              arch: x86_64
-              cuda: "11.*"
-            packages:
-              - gcc_linux-64=11.*
-              - sysroot_linux-64==2.28
-          - matrix:
-              arch: aarch64
-              cuda: "11.*"
-            packages:
-              - gcc_linux-aarch64=11.*
-              - sysroot_linux-aarch64==2.28
           - matrix:
               arch: x86_64
               cuda: "12.*"
@@ -273,22 +261,6 @@ dependencies:
         matrices:
           - matrix: {cuda: "12.*"}
             packages: [cuda-nvcc]
-          - matrix: {cuda: "11.8", arch: x86_64}
-            packages: [nvcc_linux-64=11.8]
-          - matrix: {cuda: "11.8", arch: aarch64}
-            packages: [nvcc_linux-aarch64=11.8]
-          - matrix: {cuda: "11.5", arch: x86_64}
-            packages: [nvcc_linux-64=11.5]
-          - matrix: {cuda: "11.5", arch: aarch64}
-            packages: [nvcc_linux-aarch64=11.5]
-          - matrix: {cuda: "11.4", arch: x86_64}
-            packages: [nvcc_linux-64=11.4]
-          - matrix: {cuda: "11.4", arch: aarch64}
-            packages: [nvcc_linux-aarch64=11.4]
-          - matrix: {cuda: "11.2", arch: x86_64}
-            packages: [nvcc_linux-64=11.2]
-          - matrix: {cuda: "11.2", arch: aarch64}
-            packages: [nvcc_linux-aarch64=11.2]
 
   rapids_build_setuptools:
     common:
@@ -310,10 +282,6 @@ dependencies:
               cuda: "12.*"
             packages:
               - &cuda_python12 cuda-python>=12.6.2,<13.0a0
-          - matrix:
-              cuda: "11.*"
-            packages:
-              - &cuda_python11 cuda-python>=11.8.5,<12.0a0
           - matrix:
             packages:
               - &cuda_python cuda-python
@@ -333,22 +301,6 @@ dependencies:
     specific:
       - output_types: conda
         matrices:
-          - matrix:
-              cuda: "11.2"
-            packages:
-              - cuda-version=11.2
-          - matrix:
-              cuda: "11.4"
-            packages:
-              - cuda-version=11.4
-          - matrix:
-              cuda: "11.5"
-            packages:
-              - cuda-version=11.5
-          - matrix:
-              cuda: "11.8"
-            packages:
-              - cuda-version=11.8
           - matrix:
               cuda: "12.0"
             packages:
@@ -383,64 +335,6 @@ dependencies:
               - libcurand-dev
               - libcusolver-dev
               - libcusparse-dev
-          - matrix:
-              cuda: "11.8"
-            packages:
-              - cudatoolkit
-              - cuda-nvtx=11.8
-              - cuda-profiler-api=11.8.86
-              - libcublas-dev=11.11.3.6
-              - libcublas=11.11.3.6
-              - libcurand-dev=10.3.0.86
-              - libcurand=10.3.0.86
-              - libcusolver-dev=11.4.1.48
-              - libcusolver=11.4.1.48
-              - libcusparse-dev=11.7.5.86
-              - libcusparse=11.7.5.86
-          - matrix:
-              cuda: "11.5"
-            packages:
-              - cudatoolkit
-              - cuda-nvtx=11.5
-              - cuda-profiler-api>=11.4.240,<=11.8.86 # use any `11.x` version since pkg is missing several CUDA/arch packages
-              - libcublas-dev>=11.7.3.1,<=11.7.4.6
-              - libcublas>=11.7.3.1,<=11.7.4.6
-              - libcurand-dev>=10.2.6.48,<=10.2.7.107
-              - libcurand>=10.2.6.48,<=10.2.7.107
-              - libcusolver-dev>=11.2.1.48,<=11.3.2.107
-              - libcusolver>=11.2.1.48,<=11.3.2.107
-              - libcusparse-dev>=11.7.0.31,<=11.7.0.107
-              - libcusparse>=11.7.0.31,<=11.7.0.107
-          - matrix:
-              cuda: "11.4"
-            packages:
-              - cudatoolkit
-              - &cudanvtx114 cuda-nvtx=11.4
-              - cuda-profiler-api>=11.4.240,<=11.8.86 # use any `11.x` version since pkg is missing several CUDA/arch packages
-              - &libcublas_dev114 libcublas-dev>=11.5.2.43,<=11.6.5.2
-              - &libcublas114 libcublas>=11.5.2.43,<=11.6.5.2
-              - &libcurand_dev114 libcurand-dev>=10.2.5.43,<=10.2.5.120
-              - &libcurand114 libcurand>=10.2.5.43,<=10.2.5.120
-              - &libcusolver_dev114 libcusolver-dev>=11.2.0.43,<=11.2.0.120
-              - &libcusolver114 libcusolver>=11.2.0.43,<=11.2.0.120
-              - &libcusparse_dev114 libcusparse-dev>=11.6.0.43,<=11.6.0.120
-              - &libcusparse114 libcusparse>=11.6.0.43,<=11.6.0.120
-          - matrix:
-              cuda: "11.2"
-            packages:
-              - cudatoolkit
-              - *cudanvtx114
-              - cuda-profiler-api>=11.4.240,<=11.8.86 # use any `11.x` version since pkg is missing several CUDA/arch packages
-              # The NVIDIA channel doesn't publish pkgs older than 11.4 for these libs,
-              # so 11.2 uses 11.4 packages (the oldest available).
-              - *libcublas_dev114
-              - *libcublas114
-              - *libcurand_dev114
-              - *libcurand114
-              - *libcusolver_dev114
-              - *libcusolver114
-              - *libcusparse_dev114
-              - *libcusparse114
   cuda_wheels:
     specific:
       - output_types: [requirements, pyproject]
@@ -453,14 +347,6 @@ dependencies:
               - nvidia-curand-cu12
               - nvidia-cusolver-cu12
               - nvidia-cusparse-cu12
-          - matrix:
-              cuda: "11.*"
-              use_cuda_wheels: "true"
-            packages:
-              - nvidia-cublas-cu11
-              - nvidia-curand-cu11
-              - nvidia-cusolver-cu11
-              - nvidia-cusparse-cu11
           - matrix:
               use_cuda_wheels: "false"
             packages:
@@ -484,10 +370,7 @@ dependencies:
           - matrix: {cuda: "12.*"}
             packages:
               - cupy-cuda12x>=12.0.0
-          - matrix: {cuda: "11.*"}
-            packages:
-              - cupy-cuda11x>=12.0.0
-          - {matrix: null, packages: [cupy-cuda11x>=12.0.0]}
+          - {matrix: null, packages: [cupy-cuda12x>=12.0.0]}
 
   test_libcuvs:
     common:
@@ -563,10 +446,6 @@ dependencies:
               cuda: "12.*"
             packages:
               - *cuda_python12
-          - matrix:
-              cuda: "11.*"
-            packages:
-              - *cuda_python11
           - matrix:
             packages:
               - *cuda_python
@@ -654,11 +533,6 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - libcuvs-cu12==25.8.*,>=0.0.0a0
-          - matrix:
-              cuda: "11.*"
-              cuda_suffixed: "true"
-            packages:
-              - libcuvs-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
   depends_on_libcuvs_tests:
     common:
@@ -683,11 +557,6 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - libraft-cu12==25.8.*,>=0.0.0a0
-          - matrix:
-              cuda: "11.*"
-              cuda_suffixed: "true"
-            packages:
-              - libraft-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
@@ -707,11 +576,6 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - librmm-cu12==25.8.*,>=0.0.0a0
-          - matrix:
-              cuda: "11.*"
-              cuda_suffixed: "true"
-            packages:
-              - librmm-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
@@ -731,11 +595,6 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - pylibraft-cu12==25.8.*,>=0.0.0a0
-          - matrix:
-              cuda: "11.*"
-              cuda_suffixed: "true"
-            packages:
-              - pylibraft-cu11==25.8.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
   depends_on_nccl:
     common:
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 8a1ce1edb0..7a3a4097a2 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -47,7 +47,7 @@ classifiers = [
 
 [project.optional-dependencies]
 test = [
-    "cupy-cuda11x>=12.0.0",
+    "cupy-cuda12x>=12.0.0",
     "pytest-cov",
     "pytest==7.*",
     "scikit-learn",

From db10359a9332f82890110cc589a2ce7458e9df7d Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Fri, 30 May 2025 18:05:53 -0400
Subject: [PATCH 428/623] refactor(rattler): remove cuda11 options and general
 cleanup (#961)

xref rapidsai/build-planning#184

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/961
---
 .../cuvs-bench-cpu/conda_build_config.yaml    |   6 +-
 .../cuvs-bench/conda_build_config.yaml        |  65 ------
 conda/recipes/cuvs-bench/recipe.yaml          |  12 +-
 conda/recipes/cuvs/conda_build_config.yaml    |   9 +-
 conda/recipes/cuvs/recipe.yaml                |  20 +-
 conda/recipes/libcuvs/conda_build_config.yaml |  47 +----
 conda/recipes/libcuvs/recipe.yaml             | 188 +++++-------------
 7 files changed, 69 insertions(+), 278 deletions(-)

diff --git a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
index 736028df69..8bad5c58e9 100644
--- a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
@@ -1,10 +1,8 @@
 c_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 13
 
 cxx_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 13
 
 c_stdlib:
   - sysroot
diff --git a/conda/recipes/cuvs-bench/conda_build_config.yaml b/conda/recipes/cuvs-bench/conda_build_config.yaml
index 6154e90071..fb8f5e5407 100644
--- a/conda/recipes/cuvs-bench/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench/conda_build_config.yaml
@@ -1,70 +1,5 @@
-c_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-
-cxx_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-
-cuda_compiler:
-  - cuda-nvcc  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - nvcc  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-
-c_stdlib:
-  - sysroot
-
-c_stdlib_version:
-  - "2.28"
-
-cmake_version:
-  - ">=3.30.4"
-
-nccl_version:
-  - ">=2.19"
-
 glog_version:
   - ">=0.6.0"
 
 h5py_version:
   - ">=3.8.0"
-
-nlohmann_json_version:
-  - ">=3.11.2"
-
-# The CTK libraries below are missing from the conda-forge::cudatoolkit package
-# for CUDA 11. The "*_host_*" version specifiers correspond to `11.8` packages
-# and the "*_run_*" version specifiers correspond to `11.x` packages.
-
-cuda11_libcublas_host_version:
-  - "=11.11.3.6"
-
-cuda11_libcublas_run_version:
-  - ">=11.5.2.43,<12.0.0"
-
-cuda11_libcurand_host_version:
-  - "=10.3.0.86"
-
-cuda11_libcurand_run_version:
-  - ">=10.2.5.43,<10.3.1"
-
-cuda11_libcusolver_host_version:
-  - "=11.4.1.48"
-
-cuda11_libcusolver_run_version:
-  - ">=11.2.0.43,<11.4.2"
-
-cuda11_libcusparse_host_version:
-  - "=11.7.5.86"
-
-cuda11_libcusparse_run_version:
-  - ">=11.6.0.43,<12.0.0"
-
-# `cuda-profiler-api` only has `11.8.0` and `12.0.0` packages for all
-# architectures. The "*_host_*" version specifiers correspond to `11.8` packages and the
-# "*_run_*" version specifiers correspond to `11.x` packages.
-
-cuda11_cuda_profiler_api_host_version:
-  - "=11.8.86"
-
-cuda11_cuda_profiler_api_run_version:
-  - ">=11.4.240,<12"
diff --git a/conda/recipes/cuvs-bench/recipe.yaml b/conda/recipes/cuvs-bench/recipe.yaml
index 70fd1e0115..b5e001b62d 100644
--- a/conda/recipes/cuvs-bench/recipe.yaml
+++ b/conda/recipes/cuvs-bench/recipe.yaml
@@ -39,9 +39,12 @@ requirements:
     - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
     - benchmark
     - click
+    - cuda-cudart
+    - cupy >=12.0.0
     - cuvs =${{ version }}
     - glog ${{ glog_version }}
     - h5py ${{ h5py_version }}
+    - libcublas
     - libcuvs-bench-ann =${{ version }}
     - matplotlib-base>=3.9
     - pandas
@@ -54,13 +57,6 @@ requirements:
     - scikit-learn>=1.5
     - if: linux64
       then: mkl =2023
-    - if: cuda_major == "11"
-      then:
-        - cudatoolkit
-      else:
-        - cuda-cudart
-        - cupy >=12.0.0
-        - libcublas
   ignore_run_exports:
     by_name:
       - cuda-version
@@ -70,8 +66,6 @@ requirements:
       - libboost
       - libcublas
       - mkl
-      - if: cuda_major == "11"
-        then: cudatoolkit
 
 tests:
   - package_contents:
diff --git a/conda/recipes/cuvs/conda_build_config.yaml b/conda/recipes/cuvs/conda_build_config.yaml
index 0a1108eb51..caf5489e1d 100644
--- a/conda/recipes/cuvs/conda_build_config.yaml
+++ b/conda/recipes/cuvs/conda_build_config.yaml
@@ -1,14 +1,11 @@
 c_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 13
 
 cxx_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 13
 
 cuda_compiler:
-  - cuda-nvcc  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - nvcc  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - cuda-nvcc
 
 c_stdlib:
   - sysroot
diff --git a/conda/recipes/cuvs/recipe.yaml b/conda/recipes/cuvs/recipe.yaml
index bbff5fba7e..0c3610771a 100644
--- a/conda/recipes/cuvs/recipe.yaml
+++ b/conda/recipes/cuvs/recipe.yaml
@@ -58,32 +58,20 @@ requirements:
     - python =${{ py_version }}
     - rapids-build-backend >=0.3.0,<0.4.0.dev0
     - scikit-build-core >=0.10.0
-    - if: cuda_major == "11"
-      then:
-        - cuda-python >=11.8.5,<12.0a0
-        - cudatoolkit
-      else:
-        - cuda-python >=12.6.2,<13.0a0
-        - cuda-cudart-dev
+    - cuda-python >=12.6.2,<13.0a0
+    - cuda-cudart-dev
   run:
     - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
     - libcuvs =${{ version }}
     - pylibraft =${{ minor_version }}
     - python
     - numpy >=1.23,<3.0a0
-    - if: cuda_major == "11"
-      then:
-        - cuda-python >=11.8.5,<12.0a0
-        - cudatoolkit
-      else:
-        - cuda-python >=12.6.2,<13.0a0
-        - cuda-cudart
+    - cuda-python >=12.6.2,<13.0a0
+    - cuda-cudart
   ignore_run_exports:
     by_name:
       - cuda-version
       - cuda-cudart
-      - if: cuda_major == "11"
-        then: cudatoolkit
 
 tests:
   - python:
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index 3a54a5f346..65b9e3e8da 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -1,14 +1,11 @@
 c_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 13
 
 cxx_compiler_version:
-  - 13  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - 11  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - 13
 
 cuda_compiler:
-  - cuda-nvcc  # [not os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
-  - nvcc  # [os.environ.get("RAPIDS_CUDA_VERSION", "").startswith("11")]
+  - cuda-nvcc
 
 c_stdlib:
   - sysroot
@@ -24,41 +21,3 @@ h5py_version:
 
 nccl_version:
   - ">=2.19"
-
-# The CTK libraries below are missing from the conda-forge::cudatoolkit package
-# for CUDA 11. The "*_host_*" version specifiers correspond to `11.8` packages
-# and the "*_run_*" version specifiers correspond to `11.x` packages.
-
-cuda11_libcublas_host_version:
-  - "=11.11.3.6"
-
-cuda11_libcublas_run_version:
-  - ">=11.5.2.43,<12.0.0"
-
-cuda11_libcurand_host_version:
-  - "=10.3.0.86"
-
-cuda11_libcurand_run_version:
-  - ">=10.2.5.43,<10.3.1"
-
-cuda11_libcusolver_host_version:
-  - "=11.4.1.48"
-
-cuda11_libcusolver_run_version:
-  - ">=11.2.0.43,<11.4.2"
-
-cuda11_libcusparse_host_version:
-  - "=11.7.5.86"
-
-cuda11_libcusparse_run_version:
-  - ">=11.6.0.43,<12.0.0"
-
-# `cuda-profiler-api` only has `11.8.0` and `12.0.0` packages for all
-# architectures. The "*_host_*" version specifiers correspond to `11.8` packages and the
-# "*_run_*" version specifiers correspond to `11.x` packages.
-
-cuda11_cuda_profiler_api_host_version:
-  - "=11.8.86"
-
-cuda11_cuda_profiler_api_run_version:
-  - ">=11.4.240,<12"
diff --git a/conda/recipes/libcuvs/recipe.yaml b/conda/recipes/libcuvs/recipe.yaml
index 4f847b3e1f..cd5f47050e 100644
--- a/conda/recipes/libcuvs/recipe.yaml
+++ b/conda/recipes/libcuvs/recipe.yaml
@@ -62,24 +62,12 @@ cache:
       - libraft-headers =${{ minor_version }}
       - nccl ${{ nccl_version }}
       - cuda-version =${{ cuda_version }}
-      - if: cuda_major == "11"
-        then:
-          - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
-          - libcublas =${{ cuda11_libcublas_host_version }}
-          - libcublas-dev =${{ cuda11_libcublas_host_version }}
-          - libcurand =${{ cuda11_libcurand_host_version }}
-          - libcurand-dev =${{ cuda11_libcurand_host_version }}
-          - libcusolver =${{ cuda11_libcusolver_host_version }}
-          - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
-          - libcusparse =${{ cuda11_libcusparse_host_version }}
-          - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
-        else:
-          - cuda-cudart-dev
-          - cuda-profiler-api
-          - libcublas-dev
-          - libcurand-dev
-          - libcusolver-dev
-          - libcusparse-dev
+      - cuda-cudart-dev
+      - cuda-profiler-api
+      - libcublas-dev
+      - libcurand-dev
+      - libcusolver-dev
+      - libcusparse-dev
       # These are used for bench-ann
       - openblas
       - if: linux64
@@ -112,36 +100,21 @@ outputs:
         - libraft-headers =${{ minor_version }}
         - nccl ${{ nccl_version }}
         - cuda-version =${{ cuda_version }}
-        - if: cuda_major == "11"
-          then:
-            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
-            - libcublas =${{ cuda11_libcublas_host_version }}
-            - libcublas-dev =${{ cuda11_libcublas_host_version }}
-            - libcurand =${{ cuda11_libcurand_host_version }}
-            - libcurand-dev =${{ cuda11_libcurand_host_version }}
-            - libcusolver =${{ cuda11_libcusolver_host_version }}
-            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
-            - libcusparse =${{ cuda11_libcusparse_host_version }}
-            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
-          else:
-            - cuda-cudart-dev
-            - cuda-profiler-api
-            - libcublas-dev
-            - libcurand-dev
-            - libcusolver-dev
-            - libcusparse-dev
+        - cuda-cudart-dev
+        - cuda-profiler-api
+        - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
       run:
         - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
         - libraft-headers =${{ minor_version }}
         - nccl
-        - if: cuda_major == "11"
-          then: cudatoolkit
-          else:
-            - cuda-cudart
-            - libcublas
-            - libcurand
-            - libcusolver
-            - libcusparse
+        - cuda-cudart
+        - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
       ignore_run_exports:
         by_name:
           - cuda-cudart
@@ -155,8 +128,6 @@ outputs:
           - librmm
           - mkl
           - nccl
-          - if: cuda_major == "11"
-            then: cudatoolkit
     about:
       homepage: ${{ load_from_file("python/libcuvs/pyproject.toml").project.urls.Homepage }}
       license: ${{ load_from_file("python/libcuvs/pyproject.toml").project.license.text }}
@@ -183,37 +154,22 @@ outputs:
         - ${{ pin_subpackage("libcuvs", exact=True) }}
         - cuda-version =${{ cuda_version }}
         - openblas  # required by some CPU algos in benchmarks
-        - if: cuda_major == "11"
-          then:
-            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
-            - libcublas =${{ cuda11_libcublas_host_version }}
-            - libcublas-dev =${{ cuda11_libcublas_host_version }}
-            - libcurand =${{ cuda11_libcurand_host_version }}
-            - libcurand-dev =${{ cuda11_libcurand_host_version }}
-            - libcusolver =${{ cuda11_libcusolver_host_version }}
-            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
-            - libcusparse =${{ cuda11_libcusparse_host_version }}
-            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
-          else:
-            - cuda-cudart-dev
-            - cuda-profiler-api
-            - libcublas-dev
-            - libcurand-dev
-            - libcusolver-dev
-            - libcusparse-dev
+        - cuda-cudart-dev
+        - cuda-profiler-api
+        - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
       run:
         - ${{ pin_subpackage("libcuvs", exact=True) }}
         - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
         - libraft-headers =${{ minor_version }}
         - nccl
-        - if: cuda_major == "11"
-          then: cudatoolkit
-          else:
-            - cuda-cudart
-            - libcublas
-            - libcurand
-            - libcusolver
-            - libcusparse
+        - cuda-cudart
+        - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
       ignore_run_exports:
         by_name:
           - cuda-cudart
@@ -227,8 +183,6 @@ outputs:
           - librmm
           - mkl
           - nccl
-          - if: cuda_major == "11"
-            then: cudatoolkit
     about:
       homepage: ${{ load_from_file("python/libcuvs/pyproject.toml").project.urls.Homepage }}
       license: ${{ load_from_file("python/libcuvs/pyproject.toml").project.license.text }}
@@ -287,36 +241,21 @@ outputs:
         - libraft-headers =${{ minor_version }}
         - librmm =${{ minor_version }}
         - nccl ${{ nccl_version }}
-        - if: cuda_major == "11"
-          then:
-            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
-            - libcublas =${{ cuda11_libcublas_host_version }}
-            - libcublas-dev =${{ cuda11_libcublas_host_version }}
-            - libcurand =${{ cuda11_libcurand_host_version }}
-            - libcurand-dev =${{ cuda11_libcurand_host_version }}
-            - libcusolver =${{ cuda11_libcusolver_host_version }}
-            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
-            - libcusparse =${{ cuda11_libcusparse_host_version }}
-            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
-          else:
-            - cuda-cudart-dev
-            - cuda-profiler-api
-            - libcublas-dev
-            - libcurand-dev
-            - libcusolver-dev
-            - libcusparse-dev
+        - cuda-cudart-dev
+        - cuda-profiler-api
+        - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
       run:
         - ${{ pin_subpackage("libcuvs", exact=True) }}
         - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
         - nccl
-        - if: cuda_major == "11"
-          then: cudatoolkit
-          else:
-            - cuda-cudart
-            - libcublas
-            - libcurand
-            - libcusolver
-            - libcusparse
+        - cuda-cudart
+        - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
       ignore_run_exports:
         by_name:
           - cuda-cudart
@@ -330,8 +269,6 @@ outputs:
           - librmm
           - mkl
           - nccl
-          - if: cuda_major == "11"
-            then: cudatoolkit
     about:
       homepage: ${{ load_from_file("python/libcuvs/pyproject.toml").project.urls.Homepage }}
       license: ${{ load_from_file("python/libcuvs/pyproject.toml").project.license.text }}
@@ -360,51 +297,36 @@ outputs:
         - cmake ${{ cmake_version }}
         - ${{ stdlib("c") }}
       host:
-        - librmm =${{ minor_version }}
-        - libraft-headers =${{ minor_version }}
-        - nccl ${{ nccl_version }}
         - ${{ pin_subpackage("libcuvs", exact=True) }}
+        - cuda-cudart-dev
+        - cuda-profiler-api
         - cuda-version =${{ cuda_version }}
-        - openblas  # required by some CPU algos in benchmarks
+        - libcublas-dev
+        - libcurand-dev
+        - libcusolver-dev
+        - libcusparse-dev
         - libgomp
+        - libraft-headers =${{ minor_version }}
+        - librmm =${{ minor_version }}
+        - nccl ${{ nccl_version }}
+        - openblas  # required by some CPU algos in benchmarks
         - if: linux64
           then:
             - libaio
             - libboost-devel =1.87
             - mkl-devel =2023
-        - if: cuda_major == "11"
-          then:
-            - cuda-profiler-api =${{ cuda11_cuda_profiler_api_host_version }}
-            - libcublas =${{ cuda11_libcublas_host_version }}
-            - libcublas-dev =${{ cuda11_libcublas_host_version }}
-            - libcurand =${{ cuda11_libcurand_host_version }}
-            - libcurand-dev =${{ cuda11_libcurand_host_version }}
-            - libcusolver =${{ cuda11_libcusolver_host_version }}
-            - libcusolver-dev =${{ cuda11_libcusolver_host_version }}
-            - libcusparse =${{ cuda11_libcusparse_host_version }}
-            - libcusparse-dev =${{ cuda11_libcusparse_host_version }}
-          else:
-            - cuda-cudart-dev
-            - cuda-profiler-api
-            - libcublas-dev
-            - libcurand-dev
-            - libcusolver-dev
-            - libcusparse-dev
       run:
         - ${{ pin_subpackage("libcuvs", exact=True) }}
         - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
+        - cuda-cudart
+        - libcublas
+        - libcurand
+        - libcusolver
+        - libcusparse
         - libraft-headers =${{ minor_version }}
         - nccl
         - if: linux64
           then: mkl =2023
-        - if: cuda_major == "11"
-          then: cudatoolkit
-          else:
-            - cuda-cudart
-            - libcublas
-            - libcurand
-            - libcusolver
-            - libcusparse
       ignore_run_exports:
         by_name:
           - cuda-cudart
@@ -416,8 +338,6 @@ outputs:
           - librmm
           - mkl
           - nccl
-          - if: cuda_major == "11"
-            then: cudatoolkit
     about:
       homepage: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.urls.Homepage }}
       license: ${{ load_from_file("python/cuvs_bench/pyproject.toml").project.license.text }}

From 6ed94165bbf85c7aee0586fc16ca866ec978dd60 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Tue, 3 Jun 2025 15:11:54 +0200
Subject: [PATCH 429/623] Fix for recent NCCL resource update (#968)

The `device_resources_snmg` utility was previously responsible for
[initializing NCCL communicators during
construction](https://github.com/jinsolp/raft/blob/fbdec327eca8b434fda4eb36df1b115a01dca4d3/cpp/include/raft/core/device_resources_snmg.hpp#L80).
However, with the recent decoupling of multi-GPU resource management
from NCCL usage, the communicators are now initialized lazily. As a
result, SNMG ANN may trigger NCCL initialization during the search phase
inside an OpenMP thread which can lead to the NCCL clique being invalid
across threads.

This PR ensures that the NCCL clique is initialized ahead of time,
before entering any OpenMP thread, thereby avoiding thread-local
initialization issues and ensuring global validity of the communicator
group.
---
 cpp/src/neighbors/mg/snmg.cuh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/cpp/src/neighbors/mg/snmg.cuh b/cpp/src/neighbors/mg/snmg.cuh
index c6280699e7..f74c931423 100644
--- a/cpp/src/neighbors/mg/snmg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -483,6 +483,10 @@ void search(const raft::resources& clique,
             raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
             raft::host_matrix_view<float, int64_t, row_major> distances)
 {
+  // Making sure that the NCCL comms are instantiated at this stage.
+  // This prevents it being done inside of an OpenMP thread.
+  raft::resource::get_nccl_comms(clique);
+
   int64_t n_rows      = queries.extent(0);
   int64_t n_cols      = queries.extent(1);
   int64_t n_neighbors = neighbors.extent(1);

From 3db4d2719423cba7265155826d58ec3c46bb532d Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Wed, 4 Jun 2025 08:20:59 -0700
Subject: [PATCH 430/623] Fix failing CAGRA merge google tests for 25.06 (#974)

This PR fixes failing CAGRA merge google tests that are causing CI to
fail for all PRs and surfaced after code freeze for 25.06.

---------

Co-authored-by: Artem M. Chirkin <9253178+achirkin@users.noreply.github.com>
Co-authored-by: achirkin <achirkin@users.noreply.github.com>
Co-authored-by: Bradley Dice <bdice@bradleydice.com>
Co-authored-by: jakirkham <jakirkham@gmail.com>
---
 cpp/src/neighbors/detail/nn_descent.cuh |  9 +++++----
 cpp/tests/neighbors/ann_cagra.cuh       | 23 ++++++++---------------
 2 files changed, 13 insertions(+), 19 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 7cec3bae61..941af82a0f 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -491,11 +491,12 @@ __device__ __forceinline__ void remove_duplicates(
 template <typename Index_t, typename ID_t = InternalID_t<Index_t>>
 RAFT_KERNEL
 #ifdef __CUDA_ARCH__
-#if (__CUDA_ARCH__) == 750 || ((__CUDA_ARCH__) >= 860 && (__CUDA_ARCH__) <= 890) || \
-  (__CUDA_ARCH__) == 1200
-__launch_bounds__(BLOCK_SIZE)
-#else
+// Use minBlocksPerMultiprocessor = 4 on specific arches
+#if (__CUDA_ARCH__) == 700 || (__CUDA_ARCH__) == 800 || (__CUDA_ARCH__) == 900 || \
+  (__CUDA_ARCH__) == 1000
 __launch_bounds__(BLOCK_SIZE, 4)
+#else
+__launch_bounds__(BLOCK_SIZE)
 #endif
 #endif
   local_join_kernel(const Index_t* graph_new,
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 4ddc3bd6a7..c0f80f1e82 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -1035,29 +1035,22 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
         auto database1_view = raft::make_device_matrix_view<const DataT, int64_t>(
           (const DataT*)database.data() + database0_view.size(), database1_size, ps.dim);
 
-        cagra::index<DataT, IdxT> index0(handle_);
-        cagra::index<DataT, IdxT> index1(handle_);
+        cagra::index<DataT, IdxT> index0(handle_, index_params.metric);
+        cagra::index<DataT, IdxT> index1(handle_, index_params.metric);
+        std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
         if (ps.host_dataset) {
+          database_host = raft::make_host_matrix<DataT, int64_t>(handle_, ps.n_rows, ps.dim);
+          raft::copy(database_host->data_handle(), database.data(), database.size(), stream_);
           {
-            std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
-            database_host = raft::make_host_matrix<DataT, int64_t>(database0_size, ps.dim);
-            raft::copy(database_host->data_handle(),
-                       database0_view.data_handle(),
-                       database0_view.size(),
-                       stream_);
             auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
               (const DataT*)database_host->data_handle(), database0_size, ps.dim);
             index0 = cagra::build(handle_, index_params, database_host_view);
           }
           {
-            std::optional<raft::host_matrix<DataT, int64_t>> database_host{std::nullopt};
-            database_host = raft::make_host_matrix<DataT, int64_t>(database1_size, ps.dim);
-            raft::copy(database_host->data_handle(),
-                       database1_view.data_handle(),
-                       database1_view.size(),
-                       stream_);
             auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
-              (const DataT*)database_host->data_handle(), database1_size, ps.dim);
+              (const DataT*)database_host->data_handle() + database0_size * ps.dim,
+              database1_size,
+              ps.dim);
             index1 = cagra::build(handle_, index_params, database_host_view);
           }
         } else {

From b18e19948de5efa472045226184f9b88fe212c92 Mon Sep 17 00:00:00 2001
From: Vivek Narang <123010842+narangvivek10@users.noreply.github.com>
Date: Wed, 4 Jun 2025 22:13:27 -0400
Subject: [PATCH 431/623] [Java] Replace the C wrapper layer with the Panama
 API-based Java implementation (#916)

The code changes in this pull request intends to replace the C wrapper layer with the Panama API-based Java implementation.

Authors:
  - Vivek Narang (https://github.com/narangvivek10)
  - Ishan Chattopadhyaya (https://github.com/chatman)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/916
---
 java/README.md                                |  12 +-
 java/build.sh                                 |  13 +-
 java/cuvs-java/pom.xml                        |  29 -
 .../nvidia/cuvs/CagraCompressionParams.java   |  11 +-
 .../main/java/com/nvidia/cuvs/CagraIndex.java |   9 -
 .../com/nvidia/cuvs/CagraIndexParams.java     |  43 +-
 .../cuvs/internal/BruteForceIndexImpl.java    | 323 ++++++----
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 559 +++++++++++-------
 .../cuvs/internal/CuVSResourcesImpl.java      |  82 +--
 .../nvidia/cuvs/internal/HnswIndexImpl.java   | 152 ++---
 .../cuvs/internal/common/LinkerHelper.java    |  21 +-
 .../cuvs/internal/common/LoaderUtils.java     | 100 ----
 .../com/nvidia/cuvs/internal/common/Util.java | 147 +++--
 java/internal/CMakeLists.txt                  |   7 -
 java/internal/src/cuvs_java.c                 | 539 -----------------
 java/panama-bindings/generate-bindings.sh     |   2 +-
 java/panama-bindings/gpuinfo.h                |  26 -
 java/panama-bindings/headers.h                |  14 +-
 18 files changed, 810 insertions(+), 1279 deletions(-)
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java
 delete mode 100644 java/internal/src/cuvs_java.c
 delete mode 100644 java/panama-bindings/gpuinfo.h

diff --git a/java/README.md b/java/README.md
index c032fa36fe..4fb0005f6b 100644
--- a/java/README.md
+++ b/java/README.md
@@ -1,10 +1,10 @@
-# CuVS Java API
+# cuVS Java API
 
 
-CuVS Java API provides a Java based simple, efficient, and a robust vector search API.
+cuVS Java API provides a Java based simple, efficient, and a robust vector search API.
 
 > [!CAUTION]
-> CuVS 25.06 contains an experimental version and updates to this API are expected in the coming release.
+> This contains an experimental APIs and may change in upcoming releases.
 
 ## Prerequisites
 
@@ -21,7 +21,11 @@ The libcuvs C and C++ libraries are needed for this API. If libcuvs libraries ha
 Alternatively, if libcuvs libraries are already built and you just want to build this API, please
 do `./build.sh java` in the top level directory or just do `./build.sh` in this directory.
 
+## Testing
+
+Run `./build.sh --run-java-tests` from this directory.
+
 
 ## Examples
 
-A few starter examples of CAGRA, HNSW, and Bruteforce index are provided in the `examples` directory.
+A few starter examples of CAGRA, HNSW, and Bruteforce indexing and searching are provided in the `examples` directory.
diff --git a/java/build.sh b/java/build.sh
index 3cee156dbc..ac74ff671e 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -5,9 +5,9 @@ set -e -u -o pipefail
 ARGS="$*"
 NUMARGS=$#
 
+CURDIR=$(cd "$(dirname $0)"; pwd)
 VERSION="25.08.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
-SO_FILE_PATH="./internal/build"
 
 if [ -z "${CMAKE_PREFIX_PATH:=}" ]; then
   CMAKE_PREFIX_PATH="$(pwd)/../cpp/build"
@@ -30,7 +30,12 @@ if ! hasArg --run-java-tests; then
 fi
 
 # Build the java layer
-mvn install:install-file -DgroupId=$GROUP_ID -DartifactId=cuvs-java-internal -Dversion=$VERSION -Dpackaging=so -Dfile=$SO_FILE_PATH/libcuvs_java.so \
-  && cd cuvs-java \
-  && mvn verify "${MAVEN_VERIFY_ARGS[@]}" \
+if [ -z ${LD_LIBRARY_PATH+x} ]
+then export LD_LIBRARY_PATH=${CURDIR}/../cpp/build
+else export LD_LIBRARY_PATH=${CURDIR}/../cpp/build:${LD_LIBRARY_PATH}
+fi
+
+export LD_LIBRARY_PATH=${CURDIR}/../cpp/build:${LD_LIBRARY_PATH}
+cd cuvs-java
+mvn verify "${MAVEN_VERIFY_ARGS[@]}" \
   && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 1f2370ae49..15e0193ad9 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -156,35 +156,6 @@
                     </execution>
                 </executions>
             </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-dependency-plugin</artifactId>
-                <version>2.10</version>
-                <executions>
-                    <execution>
-                        <id>copy</id>
-                        <phase>compile</phase>
-                        <goals>
-                            <goal>copy</goal>
-                        </goals>
-                        <configuration>
-                            <artifactItems>
-                                <artifactItem>
-                                    <groupId>com.nvidia.cuvs</groupId>
-                                    <artifactId>cuvs-java-internal</artifactId>
-                                    <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-                                    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
-                                    <type>so</type>
-                                    <overWrite>false</overWrite>
-                                    <outputDirectory>
-                                        ${project.build.directory}/classes/META-INF/native/linux_x64</outputDirectory>
-                                    <destFileName>libcuvs_java.so</destFileName>
-                                </artifactItem>
-                            </artifactItems>
-                        </configuration>
-                    </execution>
-                </executions>
-            </plugin>
             <plugin>
                 <groupId>org.apache.maven.plugins</groupId>
                 <artifactId>maven-assembly-plugin</artifactId>
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
index 83c9c9acb6..430d9d8b47 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
@@ -23,7 +23,6 @@
  */
 public class CagraCompressionParams {
 
-  private CuVSResources resources;
   private final int pqBits;
   private final int pqDim;
   private final int vqNCenters;
@@ -35,7 +34,6 @@ public class CagraCompressionParams {
    * Constructs an instance of CagraCompressionParams with passed search
    * parameters.
    *
-   * @param resources                the resources instance to use
    * @param pqBits                   the bit length of the vector element after
    *                                 compression by PQ
    * @param pqDim                    the dimensionality of the vector after
@@ -49,9 +47,8 @@ public class CagraCompressionParams {
    * @param pqKmeansTrainsetFraction the fraction of data to use during iterative
    *                                 kmeans building (PQ phase)
    */
-  private CagraCompressionParams(CuVSResources resources, int pqBits, int pqDim, int vqNCenters, int kmeansNIters,
+  private CagraCompressionParams(int pqBits, int pqDim, int vqNCenters, int kmeansNIters,
       double vqKmeansTrainsetFraction, double pqKmeansTrainsetFraction) {
-    this.resources = resources;
     this.pqBits = pqBits;
     this.pqDim = pqDim;
     this.vqNCenters = vqNCenters;
@@ -132,7 +129,6 @@ public String toString() {
    */
   public static class Builder {
 
-    private CuVSResources resources;
     private int pqBits = 8;
     private int pqDim = 0;
     private int vqNCenters = 0;
@@ -140,8 +136,7 @@ public static class Builder {
     private double vqKmeansTrainsetFraction = 0;
     private double pqKmeansTrainsetFraction = 0;
 
-    public Builder(CuVSResources resources) {
-      this.resources = resources;
+    public Builder() {
     }
 
     /**
@@ -230,7 +225,7 @@ public Builder withPqKmeansTrainsetFraction(double pqKmeansTrainsetFraction) {
      * @return an instance of {@link CagraCompressionParams}
      */
     public CagraCompressionParams build() {
-      return new CagraCompressionParams(resources, pqBits, pqDim, vqNCenters, kmeansNIters, vqKmeansTrainsetFraction,
+      return new CagraCompressionParams(pqBits, pqDim, vqNCenters, kmeansNIters, vqKmeansTrainsetFraction,
           pqKmeansTrainsetFraction);
     }
   }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index ef98841064..a9a94df545 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -243,15 +243,6 @@ interface Builder {
          */
         Builder withIndexParams(CagraIndexParams cagraIndexParameters);
 
-        /**
-         * Registers an instance of configured {@link CagraCompressionParams} with this
-         * Builder.
-         *
-         * @param cagraCompressionParams An instance of CagraCompressionParams.
-         * @return An instance of this Builder.
-         */
-        public Builder withCompressionParams(CagraCompressionParams cagraCompressionParams);
-
         /**
          * Builds and returns an instance of CagraIndex.
          *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
index 6959938cea..1888b2f59e 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
@@ -30,6 +30,7 @@ public class CagraIndexParams {
   private final int nnDescentNiter;
   private final int numWriterThreads;
   private final CuVSIvfPqParams cuVSIvfPqParams;
+  private final CagraCompressionParams cagraCompressionParams;
 
   /**
    * Enum that denotes which ANN algorithm is used to build CAGRA graph.
@@ -251,7 +252,8 @@ private CudaDataType(int value) {
   }
 
   private CagraIndexParams(int intermediateGraphDegree, int graphDegree, CagraGraphBuildAlgo CuvsCagraGraphBuildAlgo,
-      int nnDescentNiter, int writerThreads, CuvsDistanceType cuvsDistanceType, CuVSIvfPqParams cuVSIvfPqParams) {
+      int nnDescentNiter, int writerThreads, CuvsDistanceType cuvsDistanceType, CuVSIvfPqParams cuVSIvfPqParams,
+      CagraCompressionParams cagraCompressionParams) {
     this.intermediateGraphDegree = intermediateGraphDegree;
     this.graphDegree = graphDegree;
     this.cuvsCagraGraphBuildAlgo = CuvsCagraGraphBuildAlgo;
@@ -259,6 +261,7 @@ private CagraIndexParams(int intermediateGraphDegree, int graphDegree, CagraGrap
     this.numWriterThreads = writerThreads;
     this.cuvsDistanceType = cuvsDistanceType;
     this.cuVSIvfPqParams = cuVSIvfPqParams;
+    this.cagraCompressionParams = cagraCompressionParams;
   }
 
   /**
@@ -315,12 +318,33 @@ public CuVSIvfPqParams getCuVSIvfPqParams() {
     return cuVSIvfPqParams;
   }
 
+  /**
+   * Gets the CAGRA build algorithm.
+   */
+  public CagraGraphBuildAlgo getCuvsCagraGraphBuildAlgo() {
+    return cuvsCagraGraphBuildAlgo;
+  }
+
+  /**
+   * Gets the number of Iterations to run.
+   */
+  public int getNnDescentNiter() {
+    return nnDescentNiter;
+  }
+
+  /**
+   * Gets the CAGRA compression parameters.
+   */
+  public CagraCompressionParams getCagraCompressionParams() {
+    return cagraCompressionParams;
+  }
+
   @Override
   public String toString() {
     return "CagraIndexParams [cuvsCagraGraphBuildAlgo=" + cuvsCagraGraphBuildAlgo + ", cuvsDistanceType="
         + cuvsDistanceType + ", intermediateGraphDegree=" + intermediateGraphDegree + ", graphDegree=" + graphDegree
         + ", nnDescentNiter=" + nnDescentNiter + ", numWriterThreads=" + numWriterThreads + ", cuVSIvfPqParams="
-        + cuVSIvfPqParams + "]";
+        + cuVSIvfPqParams + ", cagraCompressionParams=" + cagraCompressionParams + "]";
   }
 
   /**
@@ -335,6 +359,7 @@ public static class Builder {
     private int nnDescentNumIterations = 20;
     private int numWriterThreads = 2;
     private CuVSIvfPqParams cuVSIvfPqParams = new CuVSIvfPqParams.Builder().build();
+    private CagraCompressionParams cagraCompressionParams;
 
     public Builder() {
     }
@@ -418,6 +443,18 @@ public Builder withCuVSIvfPqParams(CuVSIvfPqParams cuVSIvfPqParams) {
       return this;
     }
 
+    /**
+     * Registers an instance of configured {@link CagraCompressionParams} with this
+     * Builder.
+     *
+     * @param cagraCompressionParams An instance of CagraCompressionParams.
+     * @return An instance of this Builder.
+     */
+    public Builder withCompressionParams(CagraCompressionParams cagraCompressionParams) {
+      this.cagraCompressionParams = cagraCompressionParams;
+      return this;
+    }
+
     /**
      * Builds an instance of {@link CagraIndexParams}.
      *
@@ -425,7 +462,7 @@ public Builder withCuVSIvfPqParams(CuVSIvfPqParams cuVSIvfPqParams) {
      */
     public CagraIndexParams build() {
       return new CagraIndexParams(intermediateGraphDegree, graphDegree, cuvsCagraGraphBuildAlgo, nnDescentNumIterations,
-          numWriterThreads, cuvsDistanceType, cuVSIvfPqParams);
+          numWriterThreads, cuvsDistanceType, cuVSIvfPqParams, cagraCompressionParams);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 7ea9a56279..a1645beb01 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -17,22 +17,40 @@
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static com.nvidia.cuvs.internal.common.Util.checkError;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
+import static com.nvidia.cuvs.internal.common.Util.concatenate;
+import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceBuild;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceDeserialize;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndexCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndexDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndex_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSearch;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSerialize;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
+import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
-import java.lang.invoke.MethodHandle;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.BitSet;
@@ -47,6 +65,7 @@
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsBruteForceIndex;
+import com.nvidia.cuvs.internal.panama.cuvsFilter;
 
 /**
  *
@@ -55,22 +74,7 @@
  *
  * @since 25.02
  */
-public class BruteForceIndexImpl implements BruteForceIndex{
-
-  private static final MethodHandle indexMethodHandle = downcallHandle("build_brute_force_index",
-      FunctionDescriptor.of(ADDRESS, ADDRESS, C_LONG, C_LONG, ADDRESS, ADDRESS, C_INT));
-
-  private static final MethodHandle searchMethodHandle = downcallHandle("search_brute_force_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_LONG));
-
-  private static final MethodHandle destroyIndexMethodHandle = downcallHandle("destroy_brute_force_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS));
-
-  private static final MethodHandle serializeMethodHandle = downcallHandle("serialize_brute_force_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
-
-  private static final MethodHandle deserializeMethodHandle = downcallHandle("deserialize_brute_force_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
+public class BruteForceIndexImpl implements BruteForceIndex {
 
   private final float[][] vectors;
   private final Dataset dataset;
@@ -125,10 +129,9 @@ private void checkNotDestroyed() {
   @Override
   public void destroyIndex() throws Throwable {
     checkNotDestroyed();
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      destroyIndexMethodHandle.invokeExact(bruteForceIndexReference.getMemorySegment(), returnValue);
-      checkError(returnValue.get(C_INT, 0L), "destroyIndexMethodHandle");
+    try {
+      int returnValue = cuvsBruteForceIndexDestroy(bruteForceIndexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsBruteForceIndexDestroy");
     } finally {
       destroyed = true;
     }
@@ -143,23 +146,53 @@ public void destroyIndex() throws Throwable {
    *         index
    */
   private IndexReference build() throws Throwable {
-    long rows = dataset != null? dataset.size(): vectors.length;
-    long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
-
-    MemorySegment dataSeg = dataset != null? ((DatasetImpl) dataset).seg:
-      Util.buildMemorySegment(resources.getArena(), vectors);
     try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      MemorySegment indexSeg = (MemorySegment) indexMethodHandle.invokeExact(
-        dataSeg,
-        rows,
-        cols,
-        resources.getMemorySegment(),
-        returnValue,
-        bruteForceIndexParams.getNumWriterThreads()
-      );
-      checkError(returnValue.get(C_INT, 0L), "indexMethodHandle");
-      return new IndexReference(indexSeg);
+      long rows = dataset != null? dataset.size(): vectors.length;
+      long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
+
+      Arena arena = resources.getArena();
+      MemorySegment datasetMemSegment = dataset != null? ((DatasetImpl) dataset).seg:
+          Util.buildMemorySegment(resources.getArena(), vectors);
+
+      long cuvsResources = resources.getMemorySegment().get(cuvsResources_t, 0);
+      MemorySegment stream = arena.allocate(cudaStream_t);
+      var returnValue = cuvsStreamGet(cuvsResources, stream);
+      checkCuVSError(returnValue, "cuvsStreamGet");
+
+      omp_set_num_threads(bruteForceIndexParams.getNumWriterThreads());
+
+      MemorySegment datasetMemorySegment = arena.allocate(C_POINTER);
+
+      long datasetBytes = C_FLOAT_BYTE_SIZE * rows * cols;
+      returnValue = cuvsRMMAlloc(cuvsResources, datasetMemorySegment, datasetBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+      // IMPORTANT: this should only come AFTER cuvsRMMAlloc call
+      MemorySegment datasetMemorySegmentP = datasetMemorySegment.get(C_POINTER, 0);
+
+      returnValue = cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, 4);
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      long datasetShape[] = { rows, cols };
+      MemorySegment datasetTensor = prepareTensor(arena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
+
+      MemorySegment index = arena.allocate(cuvsBruteForceIndex_t);
+
+      returnValue = cuvsBruteForceIndexCreate(index);
+      checkCuVSError(returnValue, "cuvsBruteForceIndexCreate");
+
+      returnValue = cuvsStreamSync(cuvsResources);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      returnValue = cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, index);
+      checkCuVSError(returnValue, "cuvsBruteForceBuild");
+
+      returnValue = cuvsStreamSync(cuvsResources);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      omp_set_num_threads(1);
+
+      return new IndexReference(index);
     }
   }
 
@@ -173,75 +206,145 @@ private IndexReference build() throws Throwable {
    */
   @Override
   public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
-    checkNotDestroyed();
-    long numQueries = cuvsQuery.getQueryVectors().length;
-    long numBlocks = cuvsQuery.getTopK() * numQueries;
-    int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
-
-    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
-    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
-    MemorySegment neighborsMemorySegment = resources.getArena().allocate(neighborsSequenceLayout);
-    MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
-
-    // prepare the prefiltering data
-    long prefilterDataLength = 0;
-    MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
-    BitSet[] prefilters = cuvsQuery.getPrefilters();
-    if (prefilters != null && prefilters.length > 0) {
-      BitSet concatenatedFilters = Util.concatenate(prefilters, cuvsQuery.getNumDocs());
-      long filters[] = concatenatedFilters.toLongArray();
-      prefilterDataMemorySegment = Util.buildMemorySegment(resources.getArena(), filters);
-      prefilterDataLength = cuvsQuery.getNumDocs() * prefilters.length;
-    }
-
-    MemorySegment querySeg = Util.buildMemorySegment(resources.getArena(), cuvsQuery.getQueryVectors());
     try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      searchMethodHandle.invokeExact(
-        bruteForceIndexReference.getMemorySegment(),
-        querySeg,
-        cuvsQuery.getTopK(),
-        numQueries,
-        vectorDimension,
-        resources.getMemorySegment(),
-        neighborsMemorySegment,
-        distancesMemorySegment,
-        returnValue,
-        prefilterDataMemorySegment,
-        prefilterDataLength
-      );
-      checkError(returnValue.get(C_INT, 0L), "searchMethodHandle");
+      checkNotDestroyed();
+      long numQueries = cuvsQuery.getQueryVectors().length;
+      long numBlocks = cuvsQuery.getTopK() * numQueries;
+      int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
+      Arena arena = resources.getArena();
+
+      SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
+      SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+      MemorySegment neighborsMemorySegment = arena.allocate(neighborsSequenceLayout);
+      MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
+
+      // prepare the prefiltering data
+      long prefilterDataLength = 0;
+      MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
+      BitSet[] prefilters = cuvsQuery.getPrefilters();
+      if (prefilters != null && prefilters.length > 0) {
+        BitSet concatenatedFilters = concatenate(prefilters, cuvsQuery.getNumDocs());
+        long filters[] = concatenatedFilters.toLongArray();
+        prefilterDataMemorySegment = buildMemorySegment(arena, filters);
+        prefilterDataLength = cuvsQuery.getNumDocs() * prefilters.length;
+      }
+
+      MemorySegment querySeg = buildMemorySegment(arena, cuvsQuery.getQueryVectors());
+
+      int topk = cuvsQuery.getTopK();
+      long cuvsResources = resources.getMemorySegment().get(cuvsResources_t, 0);
+      MemorySegment stream = arena.allocate(cudaStream_t);
+      var returnValue = cuvsStreamGet(cuvsResources, stream);
+      checkCuVSError(returnValue, "cuvsStreamGet");
+
+      MemorySegment queriesD = arena.allocate(C_POINTER);
+      MemorySegment neighborsD = arena.allocate(C_POINTER);
+      MemorySegment distancesD = arena.allocate(C_POINTER);
+      MemorySegment prefilterD = arena.allocate(C_POINTER);
+      MemorySegment prefilterDP = MemorySegment.NULL;
+      long prefilterLen = 0;
+
+      long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+      long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
+      long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
+      long prefilterBytes = 0; // size assigned later
+
+      returnValue = cuvsRMMAlloc(cuvsResources, queriesD, queriesBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+      returnValue = cuvsRMMAlloc(cuvsResources, neighborsD, neighborsBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+      returnValue = cuvsRMMAlloc(cuvsResources, distancesD, distanceBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+      // IMPORTANT: these three should only come AFTER cuvsRMMAlloc calls
+      MemorySegment queriesDP = queriesD.get(C_POINTER, 0);
+      MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
+      MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
+
+      returnValue = cudaMemcpy(queriesDP, querySeg, queriesBytes, 4);
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      long queriesShape[] = { numQueries, vectorDimension };
+      MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+      long neighborsShape[] = { numQueries, topk };
+      MemorySegment neighborsTensor = prepareTensor(arena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
+      long distancesShape[] = { numQueries, topk };
+      MemorySegment distancesTensor = prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+
+      MemorySegment prefilter = cuvsFilter.allocate(arena);
+      MemorySegment prefilterTensor;
+
+      if (prefilterDataMemorySegment == MemorySegment.NULL) {
+        cuvsFilter.type(prefilter, 0); // NO_FILTER
+        cuvsFilter.addr(prefilter, 0);
+      } else {
+        long prefilterShape[] = { (prefilterDataLength + 31) / 32 };
+        prefilterLen = prefilterShape[0];
+        prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
+
+        returnValue = cuvsRMMAlloc(cuvsResources, prefilterD, prefilterBytes);
+        checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+        prefilterDP = prefilterD.get(C_POINTER, 0);
+
+        returnValue = cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, 1);
+        checkCudaError(returnValue, "cudaMemcpy");
+
+        prefilterTensor = prepareTensor(arena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+
+        cuvsFilter.type(prefilter, 2);
+        cuvsFilter.addr(prefilter, prefilterTensor.address());
+      }
+
+      returnValue = cuvsStreamSync(cuvsResources);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      returnValue = cuvsBruteForceSearch(cuvsResources, bruteForceIndexReference.getMemorySegment(), queriesTensor,
+          neighborsTensor, distancesTensor, prefilter);
+      checkCuVSError(returnValue, "cuvsBruteForceSearch");
+
+      returnValue = cuvsStreamSync(cuvsResources);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      returnValue = cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, 4);
+      checkCudaError(returnValue, "cudaMemcpy");
+      returnValue = cudaMemcpy(distancesMemorySegment, distancesDP, distanceBytes, 4);
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      returnValue = cuvsRMMFree(cuvsResources, neighborsDP, neighborsBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+      returnValue = cuvsRMMFree(cuvsResources, distancesDP, distanceBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+      returnValue = cuvsRMMFree(cuvsResources, queriesDP, queriesBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+      returnValue = cuvsRMMFree(cuvsResources, prefilterDP, prefilterBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+
+      return new BruteForceSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+          distancesMemorySegment, cuvsQuery.getTopK(), cuvsQuery.getMapping(), numQueries);
     }
-    return new BruteForceSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-            distancesMemorySegment, cuvsQuery.getTopK(), cuvsQuery.getMapping(), numQueries);
   }
 
   @Override
   public void serialize(OutputStream outputStream) throws Throwable {
-    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
-    serialize(outputStream, p);
+    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
+    serialize(outputStream, path);
   }
 
   @Override
   public void serialize(OutputStream outputStream, Path tempFile) throws Throwable {
     checkNotDestroyed();
     tempFile = tempFile.toAbsolutePath();
-    MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tempFile.toString());
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      serializeMethodHandle.invokeExact(
-        resources.getMemorySegment(),
-        bruteForceIndexReference.getMemorySegment(),
-        returnValue,
-        pathSeg
-      );
-      checkError(returnValue.get(C_INT, 0L), "serializeMethodHandle");
-
-      try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
-        fileInputStream.transferTo(outputStream);
-      } finally {
-        Files.deleteIfExists(tempFile);
-      }
+
+    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+    int returnValue = cuvsBruteForceSerialize(cuvsRes, resources.getArena().allocateFrom(tempFile.toString()),
+        bruteForceIndexReference.getMemorySegment());
+    checkCuVSError(returnValue, "cuvsBruteForceSerialize");
+
+    try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
+      fileInputStream.transferTo(outputStream);
+    } finally {
+      Files.deleteIfExists(tempFile);
     }
   }
 
@@ -258,20 +361,14 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
     tmpIndexFile = tmpIndexFile.toAbsolutePath();
     IndexReference indexReference = new IndexReference(resources);
 
-    try (var in = inputStream;
-         FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+    try (var in = inputStream; FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
       in.transferTo(fileOutputStream);
-      MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tmpIndexFile.toString());
-      try (var localArena = Arena.ofConfined()) {
-        MemorySegment returnValue = localArena.allocate(C_INT);
-        deserializeMethodHandle.invokeExact(
-          resources.getMemorySegment(),
-          indexReference.getMemorySegment(),
-          returnValue,
-          pathSeg
-        );
-        checkError(returnValue.get(C_INT, 0L), "deserializeMethodHandle");
-      }
+
+      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      int returnValue = cuvsBruteForceDeserialize(cuvsRes, resources.getArena().allocateFrom(tmpIndexFile.toString()),
+          indexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsBruteForceDeserialize");
+
     } finally {
       Files.deleteIfExists(tmpIndexFile);
     }
@@ -283,7 +380,7 @@ public static BruteForceIndex.Builder newBuilder(CuVSResources cuvsResources) {
     if (!(cuvsResources instanceof CuVSResourcesImpl)) {
       throw new IllegalArgumentException("Unsupported " + cuvsResources);
     }
-    return new Builder((CuVSResourcesImpl)cuvsResources);
+    return new Builder((CuVSResourcesImpl) cuvsResources);
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 2663818316..d19106a2c8 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -16,12 +16,37 @@
 
 package com.nvidia.cuvs.internal;
 
+import static java.lang.foreign.ValueLayout.ADDRESS;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static com.nvidia.cuvs.internal.common.Util.checkError;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
+import static com.nvidia.cuvs.internal.common.Util.concatenate;
+import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraBuild;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraDeserialize;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexGetDims;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndex_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSearch;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraMerge;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraMergeParams_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerialize;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerializeToHnswlib;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
+import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
@@ -32,6 +57,7 @@
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout;
 import java.lang.invoke.MethodHandle;
 import java.nio.file.Files;
 import java.nio.file.Path;
@@ -45,6 +71,8 @@
 import com.nvidia.cuvs.CagraMergeParams;
 import com.nvidia.cuvs.CagraQuery;
 import com.nvidia.cuvs.CagraSearchParams;
+import com.nvidia.cuvs.CuVSIvfPqIndexParams;
+import com.nvidia.cuvs.CuVSIvfPqSearchParams;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
@@ -54,6 +82,7 @@
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraMergeParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
+import com.nvidia.cuvs.internal.panama.cuvsFilter;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqSearchParams;
@@ -72,51 +101,24 @@
  * @since 25.02
  */
 public class CagraIndexImpl implements CagraIndex {
-
-  private static final MethodHandle indexMethodHandle = downcallHandle("build_cagra_index",
-      FunctionDescriptor.of(ADDRESS, ADDRESS, C_LONG, C_LONG, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_INT));
-
-  private static final MethodHandle searchMethodHandle = downcallHandle("search_cagra_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, C_INT, C_LONG, C_INT, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_LONG));
-
-  private static final MethodHandle serializeMethodHandle = downcallHandle("serialize_cagra_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
-
-  private static final MethodHandle deserializeMethodHandle = downcallHandle("deserialize_cagra_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
-
-  private static final MethodHandle destroyIndexMethodHandle = downcallHandle("destroy_cagra_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS));
-
-  private static final MethodHandle serializeCAGRAIndexToHNSWMethodHandle = downcallHandle("serialize_cagra_index_to_hnsw",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS));
-
-
-  private static final MethodHandle mergeMethodHandle = downcallHandle("merge_cagra_indexes",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, C_INT, ADDRESS, ADDRESS));
-
   private final float[][] vectors;
   private final Dataset dataset;
   private final CuVSResourcesImpl resources;
   private final CagraIndexParams cagraIndexParameters;
-  private final CagraCompressionParams cagraCompressionParams;
   private final IndexReference cagraIndexReference;
   private boolean destroyed;
 
   /**
    * Constructor for building the index using specified dataset
    *
-   * @param indexParameters        an instance of {@link CagraIndexParams} holding
-   *                               the index parameters
-   * @param cagraCompressionParams an instance of {@link CagraCompressionParams}
-   *                               holding the compression parameters
-   * @param dataset                the dataset for indexing
-   * @param resources              an instance of {@link CuVSResources}
+   * @param indexParameters an instance of {@link CagraIndexParams} holding the
+   *                        index parameters
+   * @param dataset         the dataset for indexing
+   * @param resources       an instance of {@link CuVSResources}
    */
-  private CagraIndexImpl(CagraIndexParams indexParameters, CagraCompressionParams cagraCompressionParams, float[][] vectors,
+  private CagraIndexImpl(CagraIndexParams indexParameters, float[][] vectors,
       Dataset dataset, CuVSResourcesImpl resources) throws Throwable {
     this.cagraIndexParameters = indexParameters;
-    this.cagraCompressionParams = cagraCompressionParams;
     this.vectors = vectors;
     this.dataset = dataset;
     this.resources = resources;
@@ -131,7 +133,6 @@ private CagraIndexImpl(CagraIndexParams indexParameters, CagraCompressionParams
    */
   private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
     this.cagraIndexParameters = null;
-    this.cagraCompressionParams = null;
     this.vectors = null;
     this.dataset = null;
     this.resources = resources;
@@ -148,7 +149,6 @@ private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) thr
   private CagraIndexImpl(IndexReference indexReference, CuVSResourcesImpl resources) {
     this.vectors = null;
     this.cagraIndexParameters = null;
-    this.cagraCompressionParams = null;
     this.dataset = null;
     this.resources = resources;
     this.cagraIndexReference = indexReference;
@@ -168,9 +168,8 @@ private void checkNotDestroyed() {
   public void destroyIndex() throws Throwable {
     checkNotDestroyed();
     try (var arena = Arena.ofConfined()) {
-      MemorySegment returnValue = arena.allocate(C_INT);
-      destroyIndexMethodHandle.invokeExact(cagraIndexReference.getMemorySegment(), returnValue);
-      checkError(returnValue.get(C_INT, 0L), "destroyIndexMethodHandle");
+      int returnValue = cuvsCagraIndexDestroy(cagraIndexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsCagraIndexDestroy");
     } finally {
       destroyed = true;
     }
@@ -185,36 +184,53 @@ public void destroyIndex() throws Throwable {
    *         index
    */
   private IndexReference build() throws Throwable {
-    long rows = dataset != null? dataset.size(): vectors.length;
-    long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
+    try (var localArena = Arena.ofConfined()) {
+        long rows = dataset != null? dataset.size(): vectors.length;
+        long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
+
+      MemorySegment indexParamsMemorySegment = cagraIndexParameters != null
+          ? segmentFromIndexParams(resources, cagraIndexParameters)
+          : MemorySegment.NULL;
+
+      int numWriterThreads = cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
+      omp_set_num_threads(numWriterThreads);
+
+      MemorySegment dataSeg = dataset != null? ((DatasetImpl) dataset).seg:
+      	Util.buildMemorySegment(resources.getArena(), vectors);
+
+      Arena arena = resources.getArena();
+      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      MemorySegment stream = arena.allocate(cudaStream_t);
+      var returnValue = cuvsStreamGet(cuvsRes, stream);
+      checkCuVSError(returnValue, "cuvsStreamGet");
+
+      long datasetShape[] = { rows, cols };
+      MemorySegment datasetTensor = prepareTensor(arena, dataSeg, datasetShape, 2, 32, 2, 2, 1);
+
+      MemorySegment index = arena.allocate(cuvsCagraIndex_t);
+      returnValue = cuvsCagraIndexCreate(index);
+      checkCuVSError(returnValue, "cuvsCagraIndexCreate");
+
+      if (cuvsCagraIndexParams.build_algo(indexParamsMemorySegment) == 1) { // when build algo is IVF_PQ
+        MemorySegment cuvsIvfPqIndexParamsMS = cuvsIvfPqParams
+            .ivf_pq_build_params(cuvsCagraIndexParams.graph_build_params(indexParamsMemorySegment));
+        int n_lists = cuvsIvfPqIndexParams.n_lists(cuvsIvfPqIndexParamsMS);
+        // As rows cannot be less than n_lists value so trim down.
+        cuvsIvfPqIndexParams.n_lists(cuvsIvfPqIndexParamsMS, (int) (rows < n_lists ? rows : n_lists));
+      }
 
-    MemorySegment indexParamsMemorySegment = cagraIndexParameters != null
-        ? segmentFromIndexParams(resources, cagraIndexParameters)
-        : MemorySegment.NULL;
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
 
-    int numWriterThreads = cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
+      returnValue = cuvsCagraBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, index);
+      checkCuVSError(returnValue, "cuvsCagraBuild");
 
-    MemorySegment compressionParamsMemorySegment = cagraCompressionParams != null
-        ? segmentFromCompressionParams(cagraCompressionParams)
-        : MemorySegment.NULL;
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
 
-    MemorySegment dataSeg = dataset != null? ((DatasetImpl) dataset).seg:
-    	Util.buildMemorySegment(resources.getArena(), vectors);
+      omp_set_num_threads(1);
 
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      var indexSeg = (MemorySegment) indexMethodHandle.invokeExact(
-        dataSeg,
-        rows,
-        cols,
-        resources.getMemorySegment(),
-        returnValue,
-        indexParamsMemorySegment,
-        compressionParamsMemorySegment,
-        numWriterThreads
-      );
-      checkError(returnValue.get(C_INT, 0L), "indexMethodHandle");
-      return new IndexReference(indexSeg);
+      return new IndexReference(index);
     }
   }
 
@@ -228,75 +244,153 @@ private IndexReference build() throws Throwable {
    */
   @Override
   public SearchResults search(CagraQuery query) throws Throwable {
-    checkNotDestroyed();
-    int topK = query.getMapping() != null ? Math.min(query.getMapping().size(), query.getTopK()) : query.getTopK();
-    long numQueries = query.getQueryVectors().length;
-    long numBlocks = topK * numQueries;
-    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
-
-    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_INT);
-    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
-    MemorySegment neighborsMemorySegment = resources.getArena().allocate(neighborsSequenceLayout);
-    MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
-    MemorySegment floatsSeg = Util.buildMemorySegment(resources.getArena(), query.getQueryVectors());
-
-    long prefilterDataLength = 0;
-    MemorySegment prefilterData = MemorySegment.NULL;
-    if (query.getPrefilter() != null) {
-      long[] longArray = query.getPrefilter().toLongArray();
-      prefilterData = Util.buildMemorySegment(resources.getArena(), longArray);
-      prefilterDataLength = query.getNumDocs();
-    }
+    try (var localArena = Arena.ofConfined()) {
+      checkNotDestroyed();
+      int topK = query.getMapping() != null ? Math.min(query.getMapping().size(), query.getTopK()) : query.getTopK();
+      long numQueries = query.getQueryVectors().length;
+      long numBlocks = topK * numQueries;
+      int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
+      Arena arena = resources.getArena();
+
+      SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_INT);
+      SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+      MemorySegment neighborsMemorySegment = arena.allocate(neighborsSequenceLayout);
+      MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
+      MemorySegment floatsSeg = buildMemorySegment(arena, query.getQueryVectors());
+
+      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      MemorySegment stream = arena.allocate(cudaStream_t);
+      int returnValue = cuvsStreamGet(cuvsRes, stream);
+      checkCuVSError(returnValue, "cuvsStreamGet");
+
+      MemorySegment queriesD = arena.allocate(C_POINTER);
+      MemorySegment neighborsD = arena.allocate(C_POINTER);
+      MemorySegment distancesD = arena.allocate(C_POINTER);
+
+      long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+      long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
+      long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+      long prefilterBytes = 0; // size assigned later
+
+      returnValue = cuvsRMMAlloc(cuvsRes, queriesD, queriesBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+      returnValue = cuvsRMMAlloc(cuvsRes, neighborsD, neighborsBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+      returnValue = cuvsRMMAlloc(cuvsRes, distancesD, distancesBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+      // IMPORTANT: these three should only come AFTER cuvsRMMAlloc calls
+      MemorySegment queriesDP = queriesD.get(C_POINTER, 0);
+      MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
+      MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
+      MemorySegment prefilterD = arena.allocate(C_POINTER);
+      MemorySegment prefilterDP = MemorySegment.NULL;
+      long prefilterLen = 0;
+
+      returnValue = cudaMemcpy(queriesDP, floatsSeg, queriesBytes, 4);
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      long queriesShape[] = { numQueries, vectorDimension };
+      MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+      long neighborsShape[] = { numQueries, topK };
+      MemorySegment neighborsTensor = prepareTensor(arena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
+      long distancesShape[] = { numQueries, topK };
+      MemorySegment distancesTensor = prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+
+      // prepare the prefiltering data
+      long prefilterDataLength = 0;
+      MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
+      BitSet[] prefilters;
+      if (query.getPrefilter() != null) {
+        prefilters = new BitSet[] {query.getPrefilter()};
+        BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
+        long filters[] = concatenatedFilters.toLongArray();
+        prefilterDataMemorySegment = buildMemorySegment(arena, filters);
+        prefilterDataLength = query.getNumDocs() * prefilters.length;
+      }
 
+      MemorySegment prefilter = cuvsFilter.allocate(arena);
+      MemorySegment prefilterTensor;
 
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      searchMethodHandle.invokeExact(
-        cagraIndexReference.getMemorySegment(),
-        floatsSeg,
-        topK,
-        numQueries,
-        vectorDimension,
-        resources.getMemorySegment(),
-        neighborsMemorySegment,
-        distancesMemorySegment,
-        returnValue,
-        segmentFromSearchParams(query.getCagraSearchParameters()),
-        prefilterData,
-        prefilterDataLength
-      );
-      checkError(returnValue.get(C_INT, 0L), "searchMethodHandle");
+      if (prefilterDataMemorySegment == MemorySegment.NULL) {
+        cuvsFilter.type(prefilter, 0); // NO_FILTER
+        cuvsFilter.addr(prefilter, 0);
+      } else {
+        long prefilterShape[] = { (prefilterDataLength + 31) / 32 };
+        prefilterLen = prefilterShape[0];
+        prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
+
+        returnValue = cuvsRMMAlloc(cuvsRes, prefilterD, prefilterBytes);
+        checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+        prefilterDP = prefilterD.get(C_POINTER, 0);
+
+        returnValue = cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, 1);
+        checkCudaError(returnValue, "cudaMemcpy");
+
+        prefilterTensor = prepareTensor(arena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+
+        cuvsFilter.type(prefilter, 1);
+        cuvsFilter.addr(prefilter, prefilterTensor.address());
+      }
+
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      returnValue = cuvsCagraSearch(cuvsRes, segmentFromSearchParams(query.getCagraSearchParameters()),
+          cagraIndexReference.getMemorySegment(), queriesTensor, neighborsTensor, distancesTensor, prefilter);
+      checkCuVSError(returnValue, "cuvsCagraSearch");
+
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      returnValue = cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, 4);
+      checkCudaError(returnValue, "cudaMemcpy");
+      returnValue = cudaMemcpy(distancesMemorySegment, distancesDP, distancesBytes, 4);
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+      returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+      returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+
+      returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+
+
+      return new CagraSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+          distancesMemorySegment, topK, query.getMapping(), numQueries);
     }
-    return new CagraSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-        distancesMemorySegment, topK, query.getMapping(), numQueries);
   }
 
   @Override
   public void serialize(OutputStream outputStream) throws Throwable {
-    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
-    serialize(outputStream, p, 1024);
+    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    serialize(outputStream, path, 1024);
   }
 
   @Override
   public void serialize(OutputStream outputStream, int bufferLength) throws Throwable {
-    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
-    serialize(outputStream, p, bufferLength);
+    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    serialize(outputStream, path, bufferLength);
   }
 
   @Override
   public void serialize(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable {
     checkNotDestroyed();
     tempFile = tempFile.toAbsolutePath();
-    MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tempFile.toString());
     try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      serializeMethodHandle.invokeExact(
-        resources.getMemorySegment(),
-        cagraIndexReference.getMemorySegment(),
-        returnValue,
-        pathSeg
-      );
-      checkError(returnValue.get(C_INT, 0L), "serializeMethodHandle");
+
+      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      var returnValue = cuvsCagraSerialize(cuvsRes, resources.getArena().allocateFrom(tempFile.toString()),
+          cagraIndexReference.getMemorySegment(), true);
+      checkCuVSError(returnValue, "cuvsCagraSerialize");
 
       try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
         byte[] chunk = new byte[bufferLength];
@@ -312,40 +406,34 @@ public void serialize(OutputStream outputStream, Path tempFile, int bufferLength
 
   @Override
   public void serializeToHNSW(OutputStream outputStream) throws Throwable {
-    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
-    serializeToHNSW(outputStream, p, 1024);
+    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+    serializeToHNSW(outputStream, path, 1024);
   }
 
   @Override
   public void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable {
-    Path p = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
-    serializeToHNSW(outputStream, p, bufferLength);
+    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+    serializeToHNSW(outputStream, path, bufferLength);
   }
 
   @Override
   public void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable {
     checkNotDestroyed();
     tempFile = tempFile.toAbsolutePath();
-    MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tempFile.toString());
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      serializeCAGRAIndexToHNSWMethodHandle.invokeExact(
-        resources.getMemorySegment(),
-        pathSeg,
-        cagraIndexReference.getMemorySegment(),
-        returnValue
-      );
-      checkError(returnValue.get(C_INT, 0L), "serializeCAGRAIndexToHNSWMethodHandle");
+    MemorySegment pathSeg = buildMemorySegment(resources.getArena(), tempFile.toString());
 
-      try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
-        byte[] chunk = new byte[bufferLength];
-        int chunkLength;
-        while ((chunkLength = fileInputStream.read(chunk)) != -1) {
-          outputStream.write(chunk, 0, chunkLength);
-        }
-      } finally {
-        Files.deleteIfExists(tempFile);
+    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+    int returnValue = cuvsCagraSerializeToHnswlib(cuvsRes, pathSeg, cagraIndexReference.getMemorySegment());
+    checkCuVSError(returnValue, "cuvsCagraSerializeToHnswlib");
+
+    try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
+      byte[] chunk = new byte[bufferLength];
+      int chunkLength;
+      while ((chunkLength = fileInputStream.read(chunk)) != -1) {
+        outputStream.write(chunk, 0, chunkLength);
       }
+    } finally {
+      Files.deleteIfExists(tempFile);
     }
   }
 
@@ -374,20 +462,14 @@ private IndexReference deserialize(InputStream inputStream, int bufferLength) th
     tmpIndexFile = tmpIndexFile.toAbsolutePath();
     IndexReference indexReference = new IndexReference(resources);
 
-    try (var in = inputStream;
-         FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+    try (var in = inputStream; FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
       in.transferTo(fileOutputStream);
-      MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tmpIndexFile.toString());
-      try (var localArena = Arena.ofConfined()) {
-        MemorySegment returnValue = localArena.allocate(C_INT);
-        deserializeMethodHandle.invokeExact(
-          resources.getMemorySegment(),
-          indexReference.getMemorySegment(),
-          returnValue,
-          pathSeg
-        );
-        checkError(returnValue.get(C_INT, 0L), "deserializeMethodHandle");
-      }
+
+      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      var returnValue = cuvsCagraDeserialize(cuvsRes, resources.getArena().allocateFrom(tmpIndexFile.toString()),
+          indexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsCagraDeserialize");
+
     } finally {
       Files.deleteIfExists(tmpIndexFile);
     }
@@ -414,56 +496,66 @@ public CuVSResourcesImpl getCuVSResources() {
     return resources;
   }
 
-
-  /**
-   * Allocates the configured compression parameters in the MemorySegment.
-   */
-  private MemorySegment segmentFromCompressionParams(CagraCompressionParams params) {
-    MemorySegment seg = cuvsCagraCompressionParams.allocate(resources.getArena());
-    cuvsCagraCompressionParams.pq_bits(seg, params.getPqBits());
-    cuvsCagraCompressionParams.pq_dim(seg, params.getPqDim());
-    cuvsCagraCompressionParams.vq_n_centers(seg, params.getVqNCenters());
-    cuvsCagraCompressionParams.kmeans_n_iters(seg, params.getKmeansNIters());
-    cuvsCagraCompressionParams.vq_kmeans_trainset_fraction(seg, params.getVqKmeansTrainsetFraction());
-    cuvsCagraCompressionParams.pq_kmeans_trainset_fraction(seg, params.getPqKmeansTrainsetFraction());
-    return seg;
-  }
-
   /**
    * Allocates the configured index parameters in the MemorySegment.
    */
   private static MemorySegment segmentFromIndexParams(CuVSResourcesImpl resources, CagraIndexParams params) {
-    MemorySegment seg = cuvsCagraIndexParams.allocate(resources.getArena());
+    Arena arena = resources.getArena();
+    MemorySegment seg = cuvsCagraIndexParams.allocate(arena);
     cuvsCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
     cuvsCagraIndexParams.graph_degree(seg, params.getGraphDegree());
     cuvsCagraIndexParams.build_algo(seg, params.getCagraGraphBuildAlgo().value);
     cuvsCagraIndexParams.nn_descent_niter(seg, params.getNNDescentNumIterations());
     cuvsCagraIndexParams.metric(seg, params.getCuvsDistanceType().value);
 
+    CagraCompressionParams cagraCompressionParams = params.getCagraCompressionParams();
+    if (cagraCompressionParams != null) {
+    MemorySegment cuvsCagraCompressionParamsMemorySegment = cuvsCagraCompressionParams.allocate(arena);
+    cuvsCagraCompressionParams.pq_bits(cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getPqBits());
+    cuvsCagraCompressionParams.pq_dim(cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getPqDim());
+    cuvsCagraCompressionParams.vq_n_centers(cuvsCagraCompressionParamsMemorySegment,
+        cagraCompressionParams.getVqNCenters());
+    cuvsCagraCompressionParams.kmeans_n_iters(cuvsCagraCompressionParamsMemorySegment,
+        cagraCompressionParams.getKmeansNIters());
+    cuvsCagraCompressionParams.vq_kmeans_trainset_fraction(cuvsCagraCompressionParamsMemorySegment,
+        cagraCompressionParams.getVqKmeansTrainsetFraction());
+    cuvsCagraCompressionParams.pq_kmeans_trainset_fraction(cuvsCagraCompressionParamsMemorySegment,
+        cagraCompressionParams.getPqKmeansTrainsetFraction());
+    cuvsCagraIndexParams.compression(seg, cuvsCagraCompressionParamsMemorySegment);
+    }
 
     if (params.getCagraGraphBuildAlgo().equals(CagraGraphBuildAlgo.IVF_PQ)) {
 
-      MemorySegment ivfpqIndexParamsMemorySegment = cuvsIvfPqIndexParams.allocate(resources.getArena());
-      cuvsIvfPqIndexParams.metric(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getMetric().value);
-      cuvsIvfPqIndexParams.metric_arg(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getMetricArg());
-      cuvsIvfPqIndexParams.add_data_on_build(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().isAddDataOnBuild());
-      cuvsIvfPqIndexParams.n_lists(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getnLists());
-      cuvsIvfPqIndexParams.kmeans_n_iters(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getKmeansNIters());
-      cuvsIvfPqIndexParams.kmeans_trainset_fraction(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getKmeansTrainsetFraction());
-      cuvsIvfPqIndexParams.pq_bits(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getPqBits());
-      cuvsIvfPqIndexParams.pq_dim(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getPqDim());
-      cuvsIvfPqIndexParams.codebook_kind(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getCodebookKind().value);
-      cuvsIvfPqIndexParams.force_random_rotation(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().isForceRandomRotation());
-      cuvsIvfPqIndexParams.conservative_memory_allocation(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().isConservativeMemoryAllocation());
-      cuvsIvfPqIndexParams.max_train_points_per_pq_code(ivfpqIndexParamsMemorySegment, params.getCuVSIvfPqParams().getIndexParams().getMaxTrainPointsPerPqCode());
-
-      MemorySegment ivfpqSearchParamsMemorySegment = cuvsIvfPqSearchParams.allocate(resources.getArena());
-      cuvsIvfPqSearchParams.n_probes(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getnProbes());
-      cuvsIvfPqSearchParams.lut_dtype(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getLutDtype().value);
-      cuvsIvfPqSearchParams.internal_distance_dtype(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getInternalDistanceDtype().value);
-      cuvsIvfPqSearchParams.preferred_shmem_carveout(ivfpqSearchParamsMemorySegment, params.getCuVSIvfPqParams().getSearchParams().getPreferredShmemCarveout());
-
-      MemorySegment cuvsIvfPqParamsMemorySegment = cuvsIvfPqParams.allocate(resources.getArena());
+      MemorySegment ivfpqIndexParamsMemorySegment = cuvsIvfPqIndexParams.allocate(arena);
+      CuVSIvfPqIndexParams cuVSIvfPqIndexParams = params.getCuVSIvfPqParams().getIndexParams();
+
+      cuvsIvfPqIndexParams.metric(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMetric().value);
+      cuvsIvfPqIndexParams.metric_arg(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMetricArg());
+      cuvsIvfPqIndexParams.add_data_on_build(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.isAddDataOnBuild());
+      cuvsIvfPqIndexParams.n_lists(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getnLists());
+      cuvsIvfPqIndexParams.kmeans_n_iters(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getKmeansNIters());
+      cuvsIvfPqIndexParams.kmeans_trainset_fraction(ivfpqIndexParamsMemorySegment,
+          cuVSIvfPqIndexParams.getKmeansTrainsetFraction());
+      cuvsIvfPqIndexParams.pq_bits(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getPqBits());
+      cuvsIvfPqIndexParams.pq_dim(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getPqDim());
+      cuvsIvfPqIndexParams.codebook_kind(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getCodebookKind().value);
+      cuvsIvfPqIndexParams.force_random_rotation(ivfpqIndexParamsMemorySegment,
+          cuVSIvfPqIndexParams.isForceRandomRotation());
+      cuvsIvfPqIndexParams.conservative_memory_allocation(ivfpqIndexParamsMemorySegment,
+          cuVSIvfPqIndexParams.isConservativeMemoryAllocation());
+      cuvsIvfPqIndexParams.max_train_points_per_pq_code(ivfpqIndexParamsMemorySegment,
+          cuVSIvfPqIndexParams.getMaxTrainPointsPerPqCode());
+
+      MemorySegment ivfpqSearchParamsMemorySegment = cuvsIvfPqSearchParams.allocate(arena);
+      CuVSIvfPqSearchParams cuVSIvfPqSearchParams = params.getCuVSIvfPqParams().getSearchParams();
+      cuvsIvfPqSearchParams.n_probes(ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getnProbes());
+      cuvsIvfPqSearchParams.lut_dtype(ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getLutDtype().value);
+      cuvsIvfPqSearchParams.internal_distance_dtype(ivfpqSearchParamsMemorySegment,
+          cuVSIvfPqSearchParams.getInternalDistanceDtype().value);
+      cuvsIvfPqSearchParams.preferred_shmem_carveout(ivfpqSearchParamsMemorySegment,
+          cuVSIvfPqSearchParams.getPreferredShmemCarveout());
+
+      MemorySegment cuvsIvfPqParamsMemorySegment = cuvsIvfPqParams.allocate(arena);
       cuvsIvfPqParams.ivf_pq_build_params(cuvsIvfPqParamsMemorySegment, ivfpqIndexParamsMemorySegment);
       cuvsIvfPqParams.ivf_pq_search_params(cuvsIvfPqParamsMemorySegment, ivfpqSearchParamsMemorySegment);
       cuvsIvfPqParams.refinement_rate(cuvsIvfPqParamsMemorySegment, params.getCuVSIvfPqParams().getRefinementRate());
@@ -503,7 +595,21 @@ public static CagraIndex.Builder newBuilder(CuVSResources cuvsResources) {
     if (!(cuvsResources instanceof CuVSResourcesImpl)) {
       throw new IllegalArgumentException("Unsupported " + cuvsResources);
     }
-    return new CagraIndexImpl.Builder((CuVSResourcesImpl)cuvsResources);
+    return new CagraIndexImpl.Builder((CuVSResourcesImpl) cuvsResources);
+  }
+
+  /**
+   * Gets the CAGRA index dimensions
+   *
+   * @return the CAGRA index dimensions
+   */
+  public int getIndexDimensions() {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment dimensionsMemorySegment = localArena.allocate(C_INT);
+      var returnValue = cuvsCagraIndexGetDims(cagraIndexReference.getMemorySegment(), dimensionsMemorySegment);
+      checkCuVSError(returnValue, "cuvsCagraIndexGetDims");
+      return dimensionsMemorySegment.get(C_INT, 0);
+    }
   }
 
   /**
@@ -526,36 +632,35 @@ public static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
    * @throws Throwable if an error occurs during the merge operation
    */
   public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
-    CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
-    IndexReference mergedIndexReference = new IndexReference(resources);
+	    CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
+	    IndexReference mergedIndexReference = new IndexReference(resources);
+	    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
 
-    try (var arena = Arena.ofConfined()) {
-      MemorySegment indexesSegment = arena.allocate(indexes.length * ADDRESS.byteSize());
-      for (int i = 0; i < indexes.length; i++) {
-        CagraIndexImpl indexImpl = (CagraIndexImpl) indexes[i];
-        indexesSegment.setAtIndex(ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
-      }
+	    try (var arena = Arena.ofConfined()) {
+	      MemorySegment indexesSegment = arena.allocate(indexes.length * ValueLayout.ADDRESS.byteSize());
 
-      MemorySegment returnValue = arena.allocate(C_INT);
+	      for (int i = 0; i < indexes.length; i++) {
+	        CagraIndexImpl indexImpl = (CagraIndexImpl) indexes[i];
+	        indexesSegment.setAtIndex(ValueLayout.ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
+	      }
 
-      MemorySegment mergeParamsSegment = MemorySegment.NULL;
-      if (mergeParams != null) {
-        mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
-      }
+	      MemorySegment mergeParamsSegment = arena.allocate(cuvsCagraMergeParams_t);
+	      int returnValue;
 
-      mergeMethodHandle.invokeExact(
-        resources.getMemorySegment(),
-        indexesSegment,
-        mergedIndexReference.getMemorySegment(),
-        indexes.length,
-        returnValue,
-        mergeParamsSegment
-      );
+	      mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
 
-      checkError(returnValue.get(C_INT, 0L), "mergeMethodHandle");
-    }
+	      returnValue = cuvsCagraMerge(
+	          cuvsRes,
+	          mergeParamsSegment,
+	          indexesSegment,
+	          indexes.length,
+	          mergedIndexReference.getMemorySegment());
+
+	      checkCuVSError(returnValue, "cuvsCagraMerge");
 
-    return new CagraIndexImpl(mergedIndexReference, resources);
+	    }
+
+	    return new CagraIndexImpl(mergedIndexReference, resources);
   }
 
   /**
@@ -568,27 +673,31 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
   private static MemorySegment createMergeParamsSegment(CagraMergeParams mergeParams, CuVSResourcesImpl resources) {
     MemorySegment seg = cuvsCagraMergeParams.allocate(resources.getArena());
 
-    if (mergeParams.getOutputIndexParams() != null) {
-      MemorySegment outputIndexParamsSeg = segmentFromIndexParams(resources, mergeParams.getOutputIndexParams());
-      cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
+    if (mergeParams != null) {
+      if (mergeParams.getOutputIndexParams() != null) {
+        MemorySegment outputIndexParamsSeg = segmentFromIndexParams(resources, mergeParams.getOutputIndexParams());
+        cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
+      } else {
+        cuvsCagraMergeParams.output_index_params(seg, MemorySegment.NULL);
+      }
+
+      cuvsCagraMergeParams.strategy(seg, mergeParams.getStrategy().value);
     } else {
-      cuvsCagraMergeParams.output_index_params(seg, MemorySegment.NULL);
+      MemorySegment outputIndexParamsSeg = segmentFromIndexParams(resources, new CagraIndexParams.Builder().build());
+      cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
     }
 
-    cuvsCagraMergeParams.strategy(seg, mergeParams.getStrategy().value);
-
     return seg;
   }
 
   /**
    * Builder helps configure and create an instance of {@link CagraIndex}.
    */
-  public static class Builder implements CagraIndex.Builder{
+  public static class Builder implements CagraIndex.Builder {
 
     private float[][] vectors;
     private Dataset dataset;
     private CagraIndexParams cagraIndexParams;
-    private CagraCompressionParams cagraCompressionParams;
     private CuVSResourcesImpl cuvsResources;
     private InputStream inputStream;
 
@@ -620,12 +729,6 @@ public Builder withIndexParams(CagraIndexParams cagraIndexParameters) {
       return this;
     }
 
-    @Override
-    public Builder withCompressionParams(CagraCompressionParams cagraCompressionParams) {
-      this.cagraCompressionParams = cagraCompressionParams;
-      return this;
-    }
-
     @Override
     public CagraIndexImpl build() throws Throwable {
       if (inputStream != null) {
@@ -634,7 +737,7 @@ public CagraIndexImpl build() throws Throwable {
     	if (vectors != null && dataset != null) {
     		throw new IllegalArgumentException("Please specify only one type of dataset (a float[] or a Dataset instance)");
     	}
-        return new CagraIndexImpl(cagraIndexParams, cagraCompressionParams, vectors, dataset, cuvsResources);
+        return new CagraIndexImpl(cagraIndexParams, vectors, dataset, cuvsResources);
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
index a44d7ede41..41351d2ece 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -16,18 +16,16 @@
 
 package com.nvidia.cuvs.internal;
 
-import com.nvidia.cuvs.CuVSResources;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResourcesCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResourcesDestroy;
 
 import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.MemorySegment;
-import java.lang.invoke.MethodHandle;
 import java.nio.file.Path;
 
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static com.nvidia.cuvs.internal.common.Util.checkError;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import com.nvidia.cuvs.CuVSResources;
 
 /**
  * Used for allocating resources for cuVS
@@ -36,14 +34,6 @@
  */
 public class CuVSResourcesImpl implements CuVSResources {
 
-  static final MethodHandle createResourcesMethodHandle = downcallHandle(
-      "create_resources", FunctionDescriptor.of(ADDRESS, ADDRESS)
-  );
-
-  private static final MethodHandle destroyResourcesMethodHandle = downcallHandle(
-       "destroy_resources", FunctionDescriptor.ofVoid(ADDRESS, ADDRESS)
-  );
-
   private final Path tempDirectory;
   private final Arena arena;
   private final MemorySegment resourcesMemorySegment;
@@ -56,26 +46,18 @@ public class CuVSResourcesImpl implements CuVSResources {
    */
   public CuVSResourcesImpl(Path tempDirectory) throws Throwable {
     this.tempDirectory = tempDirectory;
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      resourcesMemorySegment = (MemorySegment) createResourcesMethodHandle.invokeExact(returnValue);
-      checkError(returnValue.get(C_INT, 0L), "createResourcesMethodHandle");
-    }
     arena = Arena.ofShared();
+    resourcesMemorySegment = arena.allocate(cuvsResources_t);
+    int returnValue = cuvsResourcesCreate(resourcesMemorySegment);
+    checkCuVSError(returnValue, "cuvsResourcesCreate");
   }
 
   @Override
   public void close() {
     checkNotDestroyed();
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      destroyResourcesMethodHandle.invokeExact(resourcesMemorySegment, returnValue);
-      checkError(returnValue.get(C_INT, 0L), "destroyResourcesMethodHandle");
-    } catch (Throwable e) {
-      e.printStackTrace();
-    } finally {
-      destroyed = true;
-    }
+    int returnValue = cuvsResourcesDestroy(resourcesMemorySegment.get(cuvsResources_t, 0));
+    checkCuVSError(returnValue, "cuvsResourcesDestroy");
+    destroyed = true;
     if (!arena.scope().isAlive()) {
       arena.close();
     }
@@ -109,46 +91,4 @@ protected Arena getArena() {
     checkNotDestroyed();
     return arena;
   }
-
-  /**
-   * Container for GPU information
-   */
-  public class GPUInfo {
-
-    private final int gpuId;
-    private final long freeMemory;
-    private final long totalMemory;
-    private final float computeCapability;
-
-    public GPUInfo(int gpuId, long freeMemory, long totalMemory, float computeCapability) {
-      super();
-      this.gpuId = gpuId;
-      this.freeMemory = freeMemory;
-      this.totalMemory = totalMemory;
-      this.computeCapability = computeCapability;
-    }
-
-    public int getGpuId() {
-      return gpuId;
-    }
-
-    public long getFreeMemory() {
-      return freeMemory;
-    }
-
-    public long getTotalMemory() {
-      return totalMemory;
-    }
-
-    public float getComputeCapability() {
-      return computeCapability;
-    }
-
-    @Override
-    public String toString() {
-      return "GPUInfo [gpuId=" + gpuId + ", freeMemory=" + freeMemory + ", totalMemory=" + totalMemory
-          + ", computeCapability=" + computeCapability + "]";
-    }
-
-  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index 97dab9f27a..f77fa82502 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -17,20 +17,23 @@
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
-import static com.nvidia.cuvs.internal.common.Util.checkError;
-import static java.lang.foreign.ValueLayout.ADDRESS;
+import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswDeserialize;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndexCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndexDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswSearch;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 
 import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
-import java.lang.invoke.MethodHandle;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.Objects;
@@ -42,7 +45,7 @@
 import com.nvidia.cuvs.HnswQuery;
 import com.nvidia.cuvs.HnswSearchParams;
 import com.nvidia.cuvs.SearchResults;
-import com.nvidia.cuvs.internal.common.Util;
+import com.nvidia.cuvs.internal.panama.DLDataType;
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndex;
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsHnswSearchParams;
@@ -55,15 +58,6 @@
  */
 public class HnswIndexImpl implements HnswIndex {
 
-  private static final MethodHandle deserializeHnswIndexMethodHandle = downcallHandle("deserialize_hnsw_index",
-      FunctionDescriptor.of(ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_INT));
-
-  private static final MethodHandle searchHnswIndexMethodHandle = downcallHandle("search_hnsw_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, ADDRESS, C_INT, C_INT, C_LONG));
-
-  private static final MethodHandle destroyHnswIndexMethodHandle = downcallHandle("destroy_hnsw_index",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS));
-
   private final CuVSResourcesImpl resources;
   private final HnswIndexParams hnswIndexParams;
   private final IndexReference hnswIndexReference;
@@ -86,11 +80,8 @@ private HnswIndexImpl(InputStream inputStream, CuVSResourcesImpl resources, Hnsw
    */
   @Override
   public void destroyIndex() throws Throwable {
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      destroyHnswIndexMethodHandle.invokeExact(hnswIndexReference.getMemorySegment(), returnValue);
-      checkError(returnValue.get(C_INT, 0L), "destroyHnswIndexMethodHandle");
-    }
+    int returnValue = cuvsHnswIndexDestroy(hnswIndexReference.getMemorySegment());
+    checkCuVSError(returnValue, "cuvsHnswIndexDestroy");
   }
 
   /**
@@ -103,34 +94,42 @@ public void destroyIndex() throws Throwable {
    */
   @Override
   public SearchResults search(HnswQuery query) throws Throwable {
-    long numQueries = query.getQueryVectors().length;
-    long numBlocks = query.getTopK() * numQueries;
-    int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
-
-    SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
-    SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
-    MemorySegment neighborsMemorySegment = resources.getArena().allocate(neighborsSequenceLayout);
-    MemorySegment distancesMemorySegment = resources.getArena().allocate(distancesSequenceLayout);
-    MemorySegment querySeg = Util.buildMemorySegment(resources.getArena(), query.getQueryVectors());
-
     try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValue = localArena.allocate(C_INT);
-      searchHnswIndexMethodHandle.invokeExact(
-        resources.getMemorySegment(),
-        hnswIndexReference.getMemorySegment(),
-        segmentFromSearchParams(query.getHnswSearchParams()),
-        returnValue,
-        neighborsMemorySegment,
-        distancesMemorySegment,
-        querySeg,
-        query.getTopK(),
-        vectorDimension,
-        numQueries
-      );
-      checkError(returnValue.get(C_INT, 0L), "searchHnswIndexMethodHandle");
+      int topK = query.getTopK();
+      float[][] queryVectors = query.getQueryVectors();
+      int numQueries = queryVectors.length;
+      long numBlocks = topK * numQueries;
+      int vectorDimension = numQueries > 0 ? queryVectors[0].length : 0;
+      Arena arena = resources.getArena();
+
+      SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
+      SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+      MemorySegment neighborsMemorySegment = arena.allocate(neighborsSequenceLayout);
+      MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
+      MemorySegment querySeg = buildMemorySegment(arena, queryVectors);
+
+      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+
+      long queriesShape[] = { numQueries, vectorDimension };
+      MemorySegment queriesTensor = prepareTensor(arena, querySeg, queriesShape, 2, 32, 2, 1, 1);
+      long neighborsShape[] = { numQueries, topK };
+      MemorySegment neighborsTensor = prepareTensor(arena, neighborsMemorySegment, neighborsShape, 1, 64, 2, 1, 1);
+      long distancesShape[] = { numQueries, topK };
+      MemorySegment distancesTensor = prepareTensor(arena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
+
+      int returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      returnValue = cuvsHnswSearch(cuvsRes, segmentFromSearchParams(query.getHnswSearchParams()),
+          hnswIndexReference.getMemorySegment(), queriesTensor, neighborsTensor, distancesTensor);
+      checkCuVSError(returnValue, "cuvsHnswSearch");
+
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      return new HnswSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
+          distancesMemorySegment, topK, query.getMapping(), numQueries);
     }
-    return new HnswSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-        distancesMemorySegment, query.getTopK(), query.getMapping(), numQueries);
   }
 
   /**
@@ -154,32 +153,41 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
    * @return an instance of {@link IndexReference}.
    */
   private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
-    Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
-    tmpIndexFile = tmpIndexFile.toAbsolutePath();
-
-    try (var in = inputStream;
-         FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
-      byte[] chunk = new byte[bufferLength];
-      int chunkLength;
-      while ((chunkLength = in.read(chunk)) != -1) {
-        fileOutputStream.write(chunk, 0, chunkLength);
-      }
+    try (var localArena = Arena.ofConfined()) {
+      Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+      tmpIndexFile = tmpIndexFile.toAbsolutePath();
+
+      try (var in = inputStream; FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+        byte[] chunk = new byte[bufferLength];
+        int chunkLength;
+        while ((chunkLength = in.read(chunk)) != -1) {
+          fileOutputStream.write(chunk, 0, chunkLength);
+        }
+
+        Arena arena = resources.getArena();
+        MemorySegment pathSeg = buildMemorySegment(arena, tmpIndexFile.toString());
+
+        long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+        MemorySegment hnswIndex = cuvsHnswIndex.allocate(arena);
+        int returnValue = cuvsHnswIndexCreate(hnswIndex);
+        checkCuVSError(returnValue, "cuvsHnswIndexCreate");
+
+        MemorySegment dtype = DLDataType.allocate(arena);
+        DLDataType.bits(dtype, (byte) 32);
+        DLDataType.code(dtype, (byte) 2); // kDLFloat
+        DLDataType.lanes(dtype, (byte) 1);
+
+        cuvsHnswIndex.dtype(hnswIndex, dtype);
+
+        returnValue = cuvsHnswDeserialize(cuvsRes, segmentFromIndexParams(hnswIndexParams), pathSeg,
+            hnswIndexParams.getVectorDimension(), 0, hnswIndex);
+        checkCuVSError(returnValue, "cuvsHnswDeserialize");
+
+        return new IndexReference(hnswIndex);
 
-      MemorySegment pathSeg = Util.buildMemorySegment(resources.getArena(), tmpIndexFile.toString());
-      try (var localArena = Arena.ofConfined()) {
-        MemorySegment returnValue = localArena.allocate(C_INT);
-        MemorySegment deserSeg = (MemorySegment) deserializeHnswIndexMethodHandle.invokeExact(
-          resources.getMemorySegment(),
-          pathSeg,
-          segmentFromIndexParams(hnswIndexParams),
-          returnValue,
-          hnswIndexParams.getVectorDimension()
-        );
-        checkError(returnValue.get(C_INT, 0L), "deserializeHnswIndexMethodHandle");
-        return new IndexReference(deserSeg);
+      } finally {
+        Files.deleteIfExists(tmpIndexFile);
       }
-    } finally {
-      Files.deleteIfExists(tmpIndexFile);
     }
   }
 
@@ -208,7 +216,7 @@ public static HnswIndex.Builder newBuilder(CuVSResources cuvsResources) {
     if (!(cuvsResources instanceof CuVSResourcesImpl)) {
       throw new IllegalArgumentException("Unsupported " + cuvsResources);
     }
-    return new HnswIndexImpl.Builder((CuVSResourcesImpl)cuvsResources);
+    return new HnswIndexImpl.Builder((CuVSResourcesImpl) cuvsResources);
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
index 176f4f90da..dfe74beef1 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
@@ -32,7 +32,6 @@
 public class LinkerHelper {
 
   private static final Linker LINKER = Linker.nativeLinker();
-  private static final SymbolLookup SYMBOL_LOOKUP;
 
   public static final ValueLayout.OfByte C_CHAR = (ValueLayout.OfByte) LINKER.canonicalLayouts().get("char");
 
@@ -43,22 +42,14 @@ public class LinkerHelper {
   public static final ValueLayout.OfFloat C_FLOAT = (ValueLayout.OfFloat) LINKER.canonicalLayouts().get("float");
 
   public static final AddressLayout C_POINTER = ((AddressLayout) LINKER.canonicalLayouts().get("void*"))
-          .withTargetLayout(MemoryLayout.sequenceLayout(Long.MAX_VALUE, C_CHAR));
+      .withTargetLayout(MemoryLayout.sequenceLayout(Long.MAX_VALUE, C_CHAR));
 
-  static {
-    var nativeLibrary = LoaderUtils.loadNativeLibrary();
-    // we use a global arena here, since the symbols obtained
-    // from the returned lookup are for the lifetime of the jvm
-    SYMBOL_LOOKUP = SymbolLookup.libraryLookup(nativeLibrary.toAbsolutePath(), Arena.global());
-  }
+  public static final long C_INT_BYTE_SIZE = LinkerHelper.C_INT.byteSize();
 
-  static MemorySegment functionAddress(String function) {
-    return SYMBOL_LOOKUP.find(function).orElseThrow(() -> new LinkageError("Native function " + function + " could not be found"));
-  }
+  public static final long C_FLOAT_BYTE_SIZE = LinkerHelper.C_FLOAT.byteSize();
 
-  public static MethodHandle downcallHandle(String function, FunctionDescriptor functionDescriptor, Linker.Option... options) {
-    return LINKER.downcallHandle(functionAddress(function), functionDescriptor, options);
-  }
+  public static final long C_LONG_BYTE_SIZE = LinkerHelper.C_LONG.byteSize();
 
-  private LinkerHelper() {}
+  private LinkerHelper() {
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java
deleted file mode 100644
index f97696984a..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LoaderUtils.java
+++ /dev/null
@@ -1,100 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.nvidia.cuvs.internal.common;
-
-import com.nvidia.cuvs.LibraryException;
-import com.nvidia.cuvs.spi.CuVSProvider;
-
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.OutputStream;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.util.Objects;
-
-class LoaderUtils {
-
-  private LoaderUtils() {}
-
-  /**
-   * Load the CuVS .so file from environment variable CUVS_JAVA_SO_PATH. If not
-   * found there, try to load it from the classpath to a temporary file.
-   */
-  static Path loadNativeLibrary() throws LibraryException {
-    String libraryPathFromEnvironment = System.getenv("CUVS_JAVA_SO_PATH");
-    if (libraryPathFromEnvironment != null) {
-      Path file = Path.of(libraryPathFromEnvironment).toAbsolutePath();
-      if (Files.notExists(file)) {
-        throw new LibraryException(
-          "Environment variable CUVS_JAVA_SO_PATH points to non-existent file: " + libraryPathFromEnvironment);
-      }
-      if (Files.isDirectory(file)) {
-        throw new LibraryException(
-          "Environment variable CUVS_JAVA_SO_PATH points to a directory: " + libraryPathFromEnvironment);
-      }
-      return file;
-    }
-    return loadLibraryFromJar("/META-INF/native/linux_x64/libcuvs_java.so");
-  }
-
-  static Path loadLibraryFromJar(String path) throws LibraryException {
-    if (!path.startsWith("/")) {
-      throw new IllegalArgumentException("The path has to be absolute (start with '/').");
-    }
-    // Obtain filename from path
-    String filename = path.substring(path.lastIndexOf("/") + 1);
-
-    // Split filename to prefix and suffix (extension)
-    String[] parts = filename.split("\\.", 2);
-    String prefix = parts[0];
-    String suffix = (parts.length > 1) ? "." + parts[parts.length - 1] : null;
-
-    // Prepare temporary file
-    try {
-      Path temp = Files.createTempFile(nativeLibraryPath(), prefix, suffix);
-      temp.toFile().deleteOnExit();
-      InputStream libraryStream = Util.class.getModule().getResourceAsStream(path);
-      if (libraryStream == null) {
-        throw new LibraryException("CuVS Library Not Found in ClassPath");
-      }
-      streamCopy(libraryStream, new FileOutputStream(temp.toFile()));
-      return temp;
-    } catch (IOException ioe) {
-      throw new LibraryException(ioe);
-    }
-  }
-
-  static void streamCopy(InputStream is, OutputStream os) throws IOException {
-    Objects.requireNonNull(is);
-    try (var in = is;
-         var out = os) {
-      in.transferTo(out);
-    }
-  }
-
-  static Path nativeLibraryPath() {
-    Path p = CuVSProvider.provider().nativeLibraryPath().toAbsolutePath();
-    if (Files.notExists(p)) {
-      throw new LibraryException("non-existent path: " + p);
-    }
-    if (!Files.isDirectory(p)) {
-      throw new LibraryException("not a directory: " + p);
-    }
-    return p;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index ddf1ea4d7e..36512225e3 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -20,7 +20,11 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.downcallHandle;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceCount;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceProperties_v2;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemGetInfo;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaSetDevice;
+import static com.nvidia.cuvs.internal.panama.headers_h.size_t;
 import static java.lang.foreign.ValueLayout.ADDRESS;
 
 import java.lang.foreign.Arena;
@@ -28,7 +32,6 @@
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemoryLayout.PathElement;
 import java.lang.foreign.MemorySegment;
-import java.lang.foreign.ValueLayout;
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.VarHandle;
 import java.util.ArrayList;
@@ -36,44 +39,55 @@
 import java.util.List;
 
 import com.nvidia.cuvs.GPUInfo;
-import com.nvidia.cuvs.internal.panama.gpuInfo;
+import com.nvidia.cuvs.internal.panama.DLDataType;
+import com.nvidia.cuvs.internal.panama.DLDevice;
+import com.nvidia.cuvs.internal.panama.DLManagedTensor;
+import com.nvidia.cuvs.internal.panama.DLTensor;
+import com.nvidia.cuvs.internal.panama.cudaDeviceProp;
+import com.nvidia.cuvs.internal.panama.headers_h;
 
 public class Util {
 
   public static final int CUVS_SUCCESS = 1;
-
-  private static final MethodHandle getGpuInfoMethodHandle = downcallHandle("get_gpu_info",
-      FunctionDescriptor.ofVoid(ADDRESS, ADDRESS, ADDRESS));
-
-  private static final MethodHandle getLastErrorTextMethodHandle = downcallHandle("cuvsGetLastErrorText",
-      FunctionDescriptor.of(ADDRESS));
+  public static final int CUDA_SUCCESS = 0;
 
   private Util() {
   }
 
   /**
-   * Checks the result value of a native method handle call.
+   * Checks the result value of a (CuVS) native method handle call.
    *
    * @param value  the return value
    * @param caller the native method handle that was called
    */
-  public static void checkError(int value, String caller) {
+  public static void checkCuVSError(int value, String caller) {
     if (value != CUVS_SUCCESS) {
       String errorMsg = getLastErrorText();
       throw new RuntimeException(caller + " returned " + value + "[" + errorMsg + "]");
     }
   }
 
+  /**
+   * Checks the result value of a (CUDA) native method handle call.
+   *
+   * @param value  the return value
+   * @param caller the native method handle that was called
+   */
+  public static void checkCudaError(int value, String caller) {
+    if (value != CUDA_SUCCESS) {
+      throw new RuntimeException(caller + " returned " + value);
+    }
+  }
+
   static final long MAX_ERROR_TEXT = 1_000_000L;
 
   static String getLastErrorText() {
     try {
-      MemorySegment seg = (MemorySegment) getLastErrorTextMethodHandle.invokeExact();
+      var seg = headers_h.cuvsGetLastErrorText.makeInvoker().apply();
       if (seg.equals(MemorySegment.NULL)) {
         return "no last error text";
       }
-      return seg.reinterpret(MAX_ERROR_TEXT).getString(0L);
-    } catch (Throwable t) {
+      return seg.reinterpret(MAX_ERROR_TEXT).getString(0);    } catch (Throwable t) {
       throw new RuntimeException(t);
     }
   }
@@ -113,43 +127,39 @@ public static List<GPUInfo> compatibleGPUs(double minComputeCapability, int minD
    * @return a list of {@link GPUInfo} objects with GPU details
    */
   public static List<GPUInfo> availableGPUs() throws Throwable {
-    List<GPUInfo> results = new ArrayList<>();
     try (var localArena = Arena.ofConfined()) {
-      MemorySegment returnValueMemorySegment = localArena.allocate(C_INT);
-      MemorySegment numGpuMemorySegment = localArena.allocate(C_INT);
-
-      /*
-       * Setting a value of 1024 because we cannot predict how much memory to allocate
-       * before the function is invoked as cudaGetDeviceCount is inside the
-       * get_gpu_info function.
-       */
-      MemorySegment GpuInfoArrayMemorySegment = gpuInfo.allocateArray(1024, localArena);
-      getGpuInfoMethodHandle.invokeExact(returnValueMemorySegment, numGpuMemorySegment, GpuInfoArrayMemorySegment);
-      int numGPUs = numGpuMemorySegment.get(ValueLayout.JAVA_INT, 0);
-      MemoryLayout ml = MemoryLayout.sequenceLayout(numGPUs, gpuInfo.layout());
-      for (int i = 0; i < numGPUs; i++) {
-        VarHandle gpuIdVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("gpu_id"));
-        VarHandle freeMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-            PathElement.groupElement("free_memory"));
-        VarHandle totalMemoryVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-            PathElement.groupElement("total_memory"));
-        VarHandle ComputeCapabilityVarHandle = ml.varHandle(PathElement.sequenceElement(i),
-            PathElement.groupElement("compute_capability"));
-        StringBuilder gpuName = new StringBuilder();
-        char b = 1;
-        int p = 0;
-        while (b != 0x00) {
-          VarHandle gpuNameVarHandle = ml.varHandle(PathElement.sequenceElement(i), PathElement.groupElement("name"),
-              PathElement.sequenceElement(p++));
-          b = (char) (byte) gpuNameVarHandle.get(GpuInfoArrayMemorySegment, 0L);
-          gpuName.append(b);
-        }
-        results.add(new GPUInfo((int) gpuIdVarHandle.get(GpuInfoArrayMemorySegment, 0L), gpuName.toString().trim(),
-            (long) freeMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
-            (long) totalMemoryVarHandle.get(GpuInfoArrayMemorySegment, 0L),
-            (float) ComputeCapabilityVarHandle.get(GpuInfoArrayMemorySegment, 0L)));
+
+      MemorySegment numGpus = localArena.allocate(C_INT);
+      int returnValue = cudaGetDeviceCount(numGpus);
+      checkCudaError(returnValue, "cudaGetDeviceCount");
+
+      int numGpuCount = numGpus.get(C_INT, 0);
+      List<GPUInfo> gpuInfoArr = new ArrayList<GPUInfo>();
+
+      MemorySegment free = localArena.allocate(size_t);
+      MemorySegment total = localArena.allocate(size_t);
+      MemorySegment deviceProp = cudaDeviceProp.allocate(localArena);
+
+      for (int i = 0; i < numGpuCount; i++) {
+
+        returnValue = cudaSetDevice(i);
+        checkCudaError(returnValue, "cudaSetDevice");
+
+        returnValue = cudaGetDeviceProperties_v2(deviceProp, i);
+        checkCudaError(returnValue, "cudaGetDeviceProperties_v2");
+
+        returnValue = cudaMemGetInfo(free, total);
+        checkCudaError(returnValue, "cudaMemGetInfo");
+
+        float computeCapability = Float
+            .parseFloat(cudaDeviceProp.major(deviceProp) + "." + cudaDeviceProp.minor(deviceProp));
+
+        GPUInfo gpuInfo = new GPUInfo(i, cudaDeviceProp.name(deviceProp).getString(0), free.get(C_LONG, 0),
+            total.get(C_LONG, 0), computeCapability);
+
+        gpuInfoArr.add(gpuInfo);
       }
-      return results;
+      return gpuInfoArr;
     }
   }
 
@@ -226,4 +236,43 @@ public static BitSet concatenate(BitSet[] arr, int maxSizeOfEachBitSet) {
     return ret;
   }
 
+  /**
+   * @brief Helper function for creating DLManagedTensor instance
+   *
+   * @param[in] data the data pointer points to the allocated data
+   * @param[in] shape the shape of the tensor
+   * @param[in] code the type code of base types
+   * @param[in] bits the shape of the tensor
+   * @param[in] ndim the number of dimensions
+   * @return DLManagedTensor
+   */
+  public static MemorySegment prepareTensor(Arena arena, MemorySegment data, long[] shape, int code, int bits, int ndim,
+      int deviceType, int lanes) {
+
+    MemorySegment tensor = DLManagedTensor.allocate(arena);
+    MemorySegment dlTensor = DLTensor.allocate(arena);
+
+    DLTensor.data(dlTensor, data);
+
+    MemorySegment dlDevice = DLDevice.allocate(arena);
+    DLDevice.device_type(dlDevice, deviceType);
+    DLTensor.device(dlTensor, dlDevice);
+
+    DLTensor.ndim(dlTensor, ndim);
+
+    MemorySegment dtype = DLDataType.allocate(arena);
+    DLDataType.code(dtype, (byte) code);
+    DLDataType.bits(dtype, (byte) bits);
+    DLDataType.lanes(dtype, (short) lanes);
+    DLTensor.dtype(dlTensor, dtype);
+
+    DLTensor.shape(dlTensor, Util.buildMemorySegment(arena, shape));
+
+    DLTensor.strides(dlTensor, MemorySegment.NULL);
+
+    DLManagedTensor.dl_tensor(tensor, dlTensor);
+
+    return tensor;
+  }
+
 }
diff --git a/java/internal/CMakeLists.txt b/java/internal/CMakeLists.txt
index 7d8408e1ab..20ad54ab89 100644
--- a/java/internal/CMakeLists.txt
+++ b/java/internal/CMakeLists.txt
@@ -53,10 +53,3 @@ if(NOT cuvs_FOUND)
 endif()
 
 include(get_dlpack.cmake)
-
-ADD_LIBRARY(cuvs_java SHARED src/cuvs_java.c)
-target_include_directories(cuvs_java PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
-target_link_libraries(
-  cuvs_java PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env> OpenMP::OpenMP_CXX
-                    Threads::Threads
-)
diff --git a/java/internal/src/cuvs_java.c b/java/internal/src/cuvs_java.c
deleted file mode 100644
index 890867a71e..0000000000
--- a/java/internal/src/cuvs_java.c
+++ /dev/null
@@ -1,539 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cuvs/core/c_api.h>
-#include <cuvs/neighbors/cagra.h>
-#include <cuvs/neighbors/brute_force.h>
-#include <cuvs/neighbors/hnsw.h>
-#include <dlpack/dlpack.h>
-#include <cuda_runtime.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <omp.h>
-#include <string.h>
-
-#define try bool __HadError=false;
-#define catch(x) ExitJmp:if(__HadError)
-#define throw(x) {__HadError=true;goto ExitJmp;}
-
-/**
- * @brief Create an Initialized opaque C handle
- *
- * @param[out] return_value return value for cuvsResourcesCreate function call
- * @return cuvsResources_t
- */
-cuvsResources_t create_resources(int *return_value) {
-  cuvsResources_t cuvs_resources;
-  *return_value = cuvsResourcesCreate(&cuvs_resources);
-  return cuvs_resources;
-}
-
-/**
- * @brief Destroy and de-allocate opaque C handle
- *
- * @param[in] cuvs_resources an opaque C handle
- * @param[out] return_value return value for cuvsResourcesDestroy function call
- */
-void destroy_resources(cuvsResources_t cuvs_resources, int *return_value) {
-  *return_value = cuvsResourcesDestroy(cuvs_resources);
-}
-
-/**
- * @brief Helper function for creating DLManagedTensor instance
- *
- * @param[in] data the data pointer points to the allocated data
- * @param[in] shape the shape of the tensor
- * @param[in] code the type code of base types
- * @param[in] bits the shape of the tensor
- * @param[in] ndim the number of dimensions
- * @return DLManagedTensor
- */
-DLManagedTensor prepare_tensor(void *data, int64_t shape[], DLDataTypeCode code, int bits, int ndim, DLDeviceType device_type) {
-  DLManagedTensor tensor;
-
-  tensor.dl_tensor.data = data;
-  tensor.dl_tensor.device.device_type = device_type; //kDLCUDA;
-  tensor.dl_tensor.ndim = ndim;
-  tensor.dl_tensor.dtype.code = code;
-  tensor.dl_tensor.dtype.bits = bits;
-  tensor.dl_tensor.dtype.lanes = 1;
-  tensor.dl_tensor.shape = shape;
-  tensor.dl_tensor.strides = NULL;
-
-  return tensor;
-}
-
-/**
- * @brief Function for building CAGRA index
- *
- * @param[in] dataset index dataset
- * @param[in] rows number of dataset rows
- * @param[in] dimensions vector dimension of the dataset
- * @param[in] cuvs_resources reference of the underlying opaque C handle
- * @param[out] return_value return value for cuvsCagraBuild function call
- * @param[in] index_params a reference to the index parameters
- * @param[in] compression_params a reference to the compression parameters
- * @param[in] n_writer_threads number of omp threads to use
- * @return cuvsCagraIndex_t
- */
-cuvsCagraIndex_t build_cagra_index(float *dataset, long rows, long dimensions, cuvsResources_t cuvs_resources, int *return_value,
-    cuvsCagraIndexParams_t index_params, cuvsCagraCompressionParams_t compression_params, int n_writer_threads) {
-
-  cudaStream_t stream;
-  cuvsStreamGet(cuvs_resources, &stream);
-
-  omp_set_num_threads(n_writer_threads);
-
-  int64_t dataset_shape[2] = {rows, dimensions};
-  DLManagedTensor dataset_tensor = prepare_tensor(dataset, dataset_shape, kDLFloat, 32, 2, kDLCUDA);
-
-  cuvsCagraIndex_t index;
-  cuvsCagraIndexCreate(&index);
-
-  if (index_params->build_algo == 1) { // when build algo is IVF_PQ
-    uint32_t n_lists = index_params->graph_build_params->ivf_pq_build_params->n_lists;
-    // As rows cannot be less than n_lists value so trim down.
-    index_params->graph_build_params->ivf_pq_build_params->n_lists = rows < n_lists ? rows : n_lists;
-  }
-
-  index_params->compression = compression_params;
-  cuvsStreamSync(cuvs_resources);
-  *return_value = cuvsCagraBuild(cuvs_resources, index_params, &dataset_tensor, index);
-
-  omp_set_num_threads(1);
-
-  return index;
-}
-
-/**
- * @brief A function to de-allocate CAGRA index
- *
- * @param[in] index cuvsCagraIndex_t to de-allocate
- * @param[out] return_value return value for cuvsCagraIndexDestroy function call
- */
-void destroy_cagra_index(cuvsCagraIndex_t index, int *return_value) {
-  *return_value = cuvsCagraIndexDestroy(index);
-}
-
-/**
- * @brief A function to serialize a CAGRA index
- *
- * @param[in] cuvs_resources reference of the underlying opaque C handle
- * @param[in] index cuvsCagraIndex_t reference
- * @param[out] return_value return value for cuvsCagraSerialize function call
- * @param[in] filename the filename of the index file
- */
-void serialize_cagra_index(cuvsResources_t cuvs_resources, cuvsCagraIndex_t index, int *return_value, char* filename) {
-  *return_value = cuvsCagraSerialize(cuvs_resources, filename, index, true);
-}
-
-/**
- * @brief A function to de-serialize a CAGRA index
- *
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[in] index cuvsCagraIndex_t reference
- * @param[out] return_value return value for cuvsCagraDeserialize function call
- * @param[in] filename the filename of the index file
- */
-void deserialize_cagra_index(cuvsResources_t cuvs_resources, cuvsCagraIndex_t index, int *return_value, char* filename) {
-  *return_value = cuvsCagraDeserialize(cuvs_resources, filename, index);
-}
-
-/**
- * @brief A function to search a CAGRA index and return results
- *
- * @param[in] index reference to a CAGRA index to search on
- * @param[in] queries query vectors
- * @param[in] topk topK results to return
- * @param[in] n_queries number of queries
- * @param[in] dimensions vector dimension
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[out] neighbors_h reference to the neighbor results on the host memory
- * @param[out] distances_h reference to the distance results on the host memory
- * @param[out] return_value return value for cuvsCagraSearch function call
- * @param[in] search_params reference to cuvsCagraSearchParams_t holding the search parameters
- * @param[in] prefilter_data host pointer to bit-packed filter bitmap (uint32_t array)
- * @param[in] prefilter_data_length total number of bits in the filter (should be equal to num_docs * num_filters)
- */
-void search_cagra_index(cuvsCagraIndex_t index,
-                        float *queries,
-                        int topk,
-                        long n_queries,
-                        int dimensions,
-                        cuvsResources_t cuvs_resources,
-                        int *neighbors_h,
-                        float *distances_h,
-                        int *return_value,
-                        cuvsCagraSearchParams_t search_params,
-                        uint32_t *prefilter_data,
-                        long prefilter_data_length) {
-  cudaStream_t stream;
-  cuvsStreamGet(cuvs_resources, &stream);
-
-  uint32_t *neighbors;
-  float *distances, *queries_d;
-
-  cuvsRMMAlloc(cuvs_resources, (void **) &queries_d, sizeof(float) * n_queries * dimensions);
-  cuvsRMMAlloc(cuvs_resources, (void **) &neighbors, sizeof(uint32_t) * n_queries * topk);
-  cuvsRMMAlloc(cuvs_resources, (void **) &distances, sizeof(float) * n_queries * topk);
-
-  cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * dimensions, cudaMemcpyDefault);
-
-  int64_t queries_shape[2] = {n_queries, dimensions};
-  DLManagedTensor queries_tensor = prepare_tensor(queries_d, queries_shape, kDLFloat, 32, 2, kDLCUDA);
-
-  int64_t neighbors_shape[2] = {n_queries, topk};
-  DLManagedTensor neighbors_tensor = prepare_tensor(neighbors, neighbors_shape, kDLUInt, 32, 2, kDLCUDA);
-
-  int64_t distances_shape[2] = {n_queries, topk};
-  DLManagedTensor distances_tensor = prepare_tensor(distances, distances_shape, kDLFloat, 32, 2, kDLCUDA);
-
-  cuvsStreamSync(cuvs_resources);
-
-  cuvsFilter filter;
-  uint32_t *prefilter_d = NULL;
-  int64_t prefilter_len = 0;
-  DLManagedTensor *prefilter_tensor_ptr = NULL;
-
-  if (prefilter_data == NULL || prefilter_data_length == 0) {
-    filter.type = NO_FILTER;
-    filter.addr = (uintptr_t) NULL;
-  } else {
-    int64_t prefilter_shape[1] = {(prefilter_data_length + 31) / 32};
-    prefilter_len = prefilter_shape[0];
-
-    cuvsRMMAlloc(cuvs_resources, (void **) &prefilter_d, sizeof(uint32_t) * prefilter_len);
-    cudaMemcpy(prefilter_d, prefilter_data, sizeof(uint32_t) * prefilter_len, cudaMemcpyHostToDevice);
-
-    prefilter_tensor_ptr = (DLManagedTensor *) malloc(sizeof(DLManagedTensor));
-    *prefilter_tensor_ptr = prepare_tensor(prefilter_d, prefilter_shape, kDLUInt, 32, 1, kDLCUDA);
-
-    filter.type = BITSET;
-    filter.addr = (uintptr_t) prefilter_tensor_ptr;
-  }
-
-  *return_value = cuvsCagraSearch(cuvs_resources,
-                                  search_params,
-                                  index,
-                                  &queries_tensor,
-                                  &neighbors_tensor,
-                                  &distances_tensor,
-                                  filter);
-
-  cudaMemcpy(neighbors_h, neighbors, sizeof(uint32_t) * n_queries * topk, cudaMemcpyDefault);
-  cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
-
-  cuvsRMMFree(cuvs_resources, distances, sizeof(float) * n_queries * topk);
-  cuvsRMMFree(cuvs_resources, neighbors, sizeof(uint32_t) * n_queries * topk);
-  cuvsRMMFree(cuvs_resources, queries_d, sizeof(float) * n_queries * dimensions);
-  if (prefilter_d != NULL) {
-    cuvsRMMFree(cuvs_resources, prefilter_d, sizeof(uint32_t) * prefilter_len);
-  }
-  if (prefilter_tensor_ptr != NULL) {
-    free(prefilter_tensor_ptr);
-  }
-}
-
-/**
- * @brief A function to merge multiple CAGRA indexes into a single index
- *
- * @param[in] cuvs_resources Reference to the underlying opaque C handle
- * @param[in] index_array Array of input CAGRA index handles to be merged
- * @param[out] merged_index Output handle for the merged CAGRA index
- * @param[in] num_indices Number of indexes to merge
- * @param[out] return_value Return value for cuvsCagraMerge function call
- * @param[in] merge_params Reference to merge parameters, or NULL for defaults
- */
-void merge_cagra_indexes(cuvsResources_t cuvs_resources,
-                       cuvsCagraIndex_t* index_array,
-                       cuvsCagraIndex_t merged_index,
-                       int num_indices,
-                       int* return_value,
-                       cuvsCagraMergeParams_t merge_params) {
-  // default merge_params when merge_params is null
-  cuvsCagraMergeParams_t local_merge_params = merge_params;
-  if (local_merge_params == NULL) {
-    cuvsCagraMergeParamsCreate(&local_merge_params);
-  }
-
-  *return_value = cuvsCagraMerge(cuvs_resources, local_merge_params, index_array, num_indices, merged_index);
-
-  if (merge_params == NULL && local_merge_params != NULL) {
-    cuvsCagraMergeParamsDestroy(local_merge_params);
-  }
-}
-
-/**
- * @brief De-allocate BRUTEFORCE index
- *
- * @param[in] index reference to BRUTEFORCE index
- * @param[out] return_value return value for cuvsBruteForceIndexDestroy function call
- */
-void destroy_brute_force_index(cuvsBruteForceIndex_t index, int *return_value) {
-  *return_value = cuvsBruteForceIndexDestroy(index);
-}
-
-/**
- * @brief A function to build BRUTEFORCE index
- *
- * @param[in] dataset the dataset to be indexed
- * @param[in] rows the number of rows in the dataset
- * @param[in] dimensions the vector dimension
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[out] return_value return value for cuvsBruteForceBuild function call
- * @param[in] n_writer_threads number of threads to use while indexing
- * @return cuvsBruteForceIndex_t
- */
-cuvsBruteForceIndex_t build_brute_force_index(float *dataset, long rows, long dimensions, cuvsResources_t cuvs_resources,
-  int *return_value, int n_writer_threads) {
-
-  omp_set_num_threads(n_writer_threads);
-
-  cudaStream_t stream;
-  cuvsStreamGet(cuvs_resources, &stream);
-
-  float *dataset_d;
-  cuvsRMMAlloc(cuvs_resources, (void**) &dataset_d, sizeof(float) * rows * dimensions);
-  cudaMemcpy(dataset_d, dataset, sizeof(float) * rows * dimensions, cudaMemcpyDefault);
-
-  int64_t dataset_shape[2] = {rows, dimensions};
-  DLManagedTensor dataset_tensor = prepare_tensor(dataset_d, dataset_shape, kDLFloat, 32, 2, kDLCUDA);
-
-  cuvsBruteForceIndex_t index;
-  cuvsError_t index_create_status = cuvsBruteForceIndexCreate(&index);
-
-  cuvsStreamSync(cuvs_resources);
-  *return_value = cuvsBruteForceBuild(cuvs_resources, &dataset_tensor, L2Expanded, 0.0f, index);
-
-  omp_set_num_threads(1);
-
-  return index;
-}
-
-/**
- * @brief A function to search the BRUTEFORCE index
- *
- * @param[in] index reference to a BRUTEFORCE index to search on
- * @param[in] queries reference to query vectors
- * @param[in] topk the top k results to return
- * @param[in] n_queries number of queries
- * @param[in] dimensions vector dimension
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[out] neighbors_h reference to the neighbor results on the host memory
- * @param[out] distances_h reference to the distance results on the host memory
- * @param[out] return_value return value for cuvsBruteForceSearch function call
- * @param[in] prefilter_data cuvsFilter input prefilter that can be used to filter queries and neighbors based on the given bitmap
- * @param[in] prefilter_data_length prefilter length input
- * @param[in] n_rows number of rows in the dataset
- */
-void search_brute_force_index(cuvsBruteForceIndex_t index, float *queries, int topk, long n_queries, int dimensions,
-    cuvsResources_t cuvs_resources, int64_t *neighbors_h, float *distances_h, int *return_value, uint32_t *prefilter_data,
-    long prefilter_data_length) {
-
-  cudaStream_t stream;
-  cuvsStreamGet(cuvs_resources, &stream);
-
-  int64_t *neighbors;
-  float *distances, *queries_d;
-  uint32_t *prefilter_d = NULL;
-  int64_t prefilter_len = 0;
-
-  cuvsRMMAlloc(cuvs_resources, (void**) &queries_d, sizeof(float) * n_queries * dimensions);
-  cuvsRMMAlloc(cuvs_resources, (void**) &neighbors, sizeof(int64_t) * n_queries * topk);
-  cuvsRMMAlloc(cuvs_resources, (void**) &distances, sizeof(float) * n_queries * topk);
-
-  cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * dimensions, cudaMemcpyDefault);
-
-  int64_t queries_shape[2] = {n_queries, dimensions};
-  DLManagedTensor queries_tensor = prepare_tensor(queries_d, queries_shape, kDLFloat, 32, 2, kDLCUDA);
-
-  int64_t neighbors_shape[2] = {n_queries, topk};
-  DLManagedTensor neighbors_tensor = prepare_tensor(neighbors, neighbors_shape, kDLInt, 64, 2, kDLCUDA);
-
-  int64_t distances_shape[2] = {n_queries, topk};
-  DLManagedTensor distances_tensor = prepare_tensor(distances, distances_shape, kDLFloat, 32, 2, kDLCUDA);
-
-  cuvsFilter prefilter;
-  DLManagedTensor prefilter_tensor;
-  if (prefilter_data == NULL) {
-    prefilter.type = NO_FILTER;
-    prefilter.addr = (uintptr_t)NULL;
-  } else {
-    // Parse the filters data
-    int num_integers = (prefilter_data_length+63)/64 * 2;
-    int extraPaddingByteExists = prefilter_data_length % 64 > 32? 0: 1;
-    int64_t prefilter_shape[1] = {(prefilter_data_length + 31) / 32};
-
-	prefilter_len = prefilter_shape[0];
-    cuvsRMMAlloc(cuvs_resources, (void**) &prefilter_d, sizeof(uint32_t) * prefilter_len);
-	cudaMemcpy(prefilter_d, prefilter_data, sizeof(uint32_t) * prefilter_len, cudaMemcpyHostToDevice);
-
-    prefilter_tensor = prepare_tensor(prefilter_d, prefilter_shape, kDLUInt, 32, 1, kDLCUDA);
-    prefilter.type = BITMAP;
-    prefilter.addr = (uintptr_t)&prefilter_tensor;
-  }
-
-  cuvsStreamSync(cuvs_resources);
-  *return_value = cuvsBruteForceSearch(cuvs_resources, index, &queries_tensor, &neighbors_tensor, &distances_tensor, prefilter);
-
-  cudaMemcpy(neighbors_h, neighbors, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
-  cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
-
-  cuvsRMMFree(cuvs_resources, neighbors, sizeof(int64_t) * n_queries * topk);
-  cuvsRMMFree(cuvs_resources, distances, sizeof(float) * n_queries * topk);
-  cuvsRMMFree(cuvs_resources, queries_d, sizeof(float) * n_queries * dimensions);
-  if(prefilter_d != NULL) {
-    cuvsRMMFree(cuvs_resources, prefilter_d, sizeof(uint32_t) * prefilter_len);
-  }
-}
-
-/**
- * @brief A function to serialize a BRUTEFORCE index
- *
- * @param[in] cuvs_resources reference of the underlying opaque C handle
- * @param[in] index cuvsBruteForceIndex_t reference
- * @param[out] return_value return value for cuvsBruteForceSerialize function call
- * @param[in] filename the filename of the index file
- */
-void serialize_brute_force_index(cuvsResources_t cuvs_resources, cuvsBruteForceIndex_t index, int *return_value, char* filename) {
-  *return_value = cuvsBruteForceSerialize(cuvs_resources, filename, index);
-}
-
-/**
- * @brief A function to de-serialize a BRUTEFORCE index
- *
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[in] index cuvsBruteForceIndex_t reference
- * @param[out] return_value return value for cuvsBruteForceDeserialize function call
- * @param[in] filename the filename of the index file
- */
-void deserialize_brute_force_index(cuvsResources_t cuvs_resources, cuvsBruteForceIndex_t index, int *return_value, char* filename) {
-  *return_value = cuvsBruteForceDeserialize(cuvs_resources, filename, index);
-}
-
-/**
- * @brief A function to create and serialize an HNSW index from CAGRA index
- *
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[in] file_path the path to the file of the created HNSW index
- * @param[in] index cuvsCagraIndex_t reference to the existing CAGRA index
- * @param[out] return_value return value for cuvsCagraSerializeToHnswlib function call
- */
-void serialize_cagra_index_to_hnsw(cuvsResources_t cuvs_resources, char *file_path, cuvsCagraIndex_t index, int *return_value) {
-  *return_value = cuvsCagraSerializeToHnswlib(cuvs_resources, file_path, index);
-}
-
-/**
- * @brief A function to deserialize the persisted HNSW index
- *
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[in] file_path the path to the persisted HNSW index file
- * @param[in] hnsw_params reference to the HNSW index params
- * @param[out] return_value return value for cuvsHnswDeserialize function call
- * @param[in] vector_dimension the dimension of the vectors in the HNSW index
- * @returns cuvsHnswIndex_t reference to the created HNSW index
- */
-cuvsHnswIndex_t deserialize_hnsw_index(cuvsResources_t cuvs_resources, char *file_path,
-  cuvsHnswIndexParams_t hnsw_params, int *return_value, int vector_dimension) {
-  cuvsHnswIndex_t hnsw_index;
-  cuvsError_t rv = cuvsHnswIndexCreate(&hnsw_index);
-  hnsw_index->dtype.bits = 32;
-  hnsw_index->dtype.code = kDLFloat;
-  hnsw_index->dtype.lanes = 1;
-  *return_value = cuvsHnswDeserialize(cuvs_resources, hnsw_params, file_path, vector_dimension, L2Expanded, hnsw_index);
-  return hnsw_index;
-}
-
-/**
- * @brief A Function to search in the HNSW index
- *
- * @param[in] cuvs_resources reference to the underlying opaque C handle
- * @param[in] hnsw_index the HNSW index reference
- * @param[in] search_params reference to the HNSW search parameters
- * @param[out] return_value return value for cuvsHnswSearch function call
- * @param[out] neighbors_h result container on host holding the neighbor ids
- * @param[out] distances_h result container on host holding the distances
- * @param[in] queries reference to the queries
- * @param[in] topk the top k results to return
- * @param[in] query_dimension the dimension of the query vectors
- * @param[in] n_queries the number of queries passed to the function
- */
-void search_hnsw_index(cuvsResources_t cuvs_resources, cuvsHnswIndex_t hnsw_index, cuvsHnswSearchParams_t search_params,
-  int *return_value, uint64_t *neighbors_h, float *distances_h, float *queries, int topk, int query_dimension, int n_queries) {
-
-  int64_t queries_shape[2] = {n_queries, query_dimension};
-  DLManagedTensor queries_tensor = prepare_tensor(queries, queries_shape, kDLFloat, 32, 2, kDLCPU);
-
-  int64_t neighbors_shape[2] = {n_queries, topk};
-  DLManagedTensor neighbors_tensor = prepare_tensor(neighbors_h, neighbors_shape, kDLUInt, 64, 2, kDLCPU);
-
-  int64_t distances_shape[2] = {n_queries, topk};
-  DLManagedTensor distances_tensor = prepare_tensor(distances_h, distances_shape, kDLFloat, 32, 2, kDLCPU);
-
-  *return_value = cuvsHnswSearch(
-    cuvs_resources, search_params, hnsw_index, &queries_tensor, &neighbors_tensor, &distances_tensor);
-}
-
-/**
- * @brief A function to destroy the HNSW index
- *
- * @param[in] hnsw_index the HNSW index reference
- * @param[out] return_value return value for cuvsHnswIndexDestroy function call
- */
-void destroy_hnsw_index(cuvsHnswIndex_t hnsw_index, int *return_value) {
-  *return_value = cuvsHnswIndexDestroy(hnsw_index);
-}
-
-/**
- * @brief struct for containing gpu information
- */
-typedef struct gpuInfo {
-  int gpu_id;
-  char name[256];
-  long free_memory;
-  long total_memory;
-  float compute_capability;
-} gpuInfo;
-
-/**
- * @brief A function to get GPU details
- *
- * @param[out] return_value return value for cudaMemGetInfo function call
- * @param[out] num_gpus the number of devices found
- * @param[out] gpu_info_arr reference to the array of gpuInfo objects
- */
-void get_gpu_info(int *return_value, int *num_gpus, gpuInfo *gpu_info_arr) {
-  cudaGetDeviceCount(num_gpus);
-  // Limiting the num_gpus to 1024. For more details please see comments in Util.availableGPUs()
-  *num_gpus = (*num_gpus > 1024) ? 1024 : *num_gpus;
-  struct gpuInfo gpuInfos[*num_gpus];
-  size_t free, total;
-  // https://docs.nvidia.com/cuda/cuda-runtime-api/structcudaDeviceProp.html#structcudaDeviceProp
-  struct cudaDeviceProp deviceProp;
-  for (int i = 0; i < *num_gpus; i++) {
-    cudaSetDevice(i);
-    cudaGetDeviceProperties(&deviceProp, i);
-    char buffer[10];
-    sprintf(buffer, "%d.%d", deviceProp.major, deviceProp.minor);
-    *return_value = cudaMemGetInfo(&free, &total);
-    gpuInfos[i].gpu_id = i;
-    strcpy(gpuInfos[i].name, deviceProp.name);
-    gpuInfos[i].free_memory = free;
-    gpuInfos[i].total_memory = total;
-    gpuInfos[i].compute_capability = atof(buffer);
-    *(gpu_info_arr + i) = gpuInfos[i];
-  }
-}
diff --git a/java/panama-bindings/generate-bindings.sh b/java/panama-bindings/generate-bindings.sh
index f37097e656..adcf49ce2b 100755
--- a/java/panama-bindings/generate-bindings.sh
+++ b/java/panama-bindings/generate-bindings.sh
@@ -36,7 +36,7 @@ jextract \
  --include-dir ${REPODIR}/cpp/include \
  --output "${REPODIR}/java/cuvs-java/src/main/java22/" \
  --target-package ${TARGET_PACKAGE} \
- --header-class-name PanamaFFMAPI \
+ --library cuvs_c \
  ${CURDIR}/headers.h
 
 echo "Panama FFM API bindings generation done"
diff --git a/java/panama-bindings/gpuinfo.h b/java/panama-bindings/gpuinfo.h
deleted file mode 100644
index dfd7e7444b..0000000000
--- a/java/panama-bindings/gpuinfo.h
+++ /dev/null
@@ -1,26 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/**
- * @brief struct for containing gpu information
- */
-typedef struct gpuInfo {
-    int gpu_id;
-    char name[256];
-    long free_memory;
-    long total_memory;
-    float compute_capability;
-} gpuInfo;
diff --git a/java/panama-bindings/headers.h b/java/panama-bindings/headers.h
index 2b035f23d3..08a381af14 100644
--- a/java/panama-bindings/headers.h
+++ b/java/panama-bindings/headers.h
@@ -16,8 +16,20 @@
 
 // Please add the required header files below for which panama FFM API bindings are needed
 
+#include <cuvs/core/c_api.h>
 #include <cuvs/neighbors/cagra.h>
 #include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/hnsw.h>
 #include <cuvs/neighbors/ivf_pq.h>
-#include "gpuinfo.h"
+#include <cuvs/neighbors/common.h>
+#include <cuda_runtime.h>
+
+/**
+ * @brief function signature for setting omp threads
+ */
+void omp_set_num_threads(int n_writer_threads);
+
+/**
+ * @brief function signature for getting omp threads
+ */
+int omp_get_num_threads(void);

From 192a917f05a1fdab1feebe57b478120eb6b90750 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Thu, 5 Jun 2025 16:31:12 +0200
Subject: [PATCH 432/623] Adding int64 search for MG CAGRA (#975)

Answers https://github.com/rapidsai/cuvs/issues/894 and https://github.com/rapidsai/cuvs/issues/895 by exposing new MG CAGRA search functions with `int64_t` neighbors output type.
The PR also updates the tests and benchs to use this new search function.
Additionally, the tree merge feature used to perform the KNN merge operation in-place which was an issue, this PR fixes this.

Authors:
  - Victor Lafargue (https://github.com/viclafargue)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/975
---
 .../ann/src/cuvs/cuvs_mg_cagra_wrapper.h      |   2 +-
 .../ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h   |   2 +-
 .../ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h     |   2 +-
 cpp/include/cuvs/neighbors/cagra.hpp          | 118 +++++++++++++++++
 cpp/include/cuvs/neighbors/common.hpp         |   4 +-
 cpp/src/neighbors/iface/generate_iface.py     |  18 ++-
 cpp/src/neighbors/iface/iface.hpp             |   8 +-
 .../iface/iface_cagra_float_uint32_t.cu       |  18 ++-
 .../iface/iface_cagra_half_uint32_t.cu        |  18 ++-
 .../iface/iface_cagra_int8_t_uint32_t.cu      |  18 ++-
 .../iface/iface_cagra_uint8_t_uint32_t.cu     |  18 ++-
 cpp/src/neighbors/mg/generate_mg.py           |  14 +-
 .../neighbors/mg/mg_cagra_float_uint32_t.cu   |  18 ++-
 .../neighbors/mg/mg_cagra_half_uint32_t.cu    |  18 ++-
 .../neighbors/mg/mg_cagra_int8_t_uint32_t.cu  |  18 ++-
 .../neighbors/mg/mg_cagra_uint8_t_uint32_t.cu |  18 ++-
 cpp/src/neighbors/mg/snmg.cuh                 | 121 ++++++++++--------
 cpp/tests/neighbors/mg.cuh                    |  53 ++++----
 18 files changed, 386 insertions(+), 100 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
index a655fdf674..f086ae7b89 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
@@ -159,7 +159,7 @@ void cuvs_mg_cagra<T, IdxT>::search_base(
   auto queries_view =
     raft::make_host_matrix_view<const T, int64_t, raft::row_major>(queries, batch_size, dim_);
   auto neighbors_view =
-    raft::make_host_matrix_view<IdxT, int64_t, raft::row_major>((IdxT*)neighbors, batch_size, k);
+    raft::make_host_matrix_view<int64_t, int64_t, raft::row_major>(neighbors, batch_size, k);
   auto distances_view =
     raft::make_host_matrix_view<float, int64_t, raft::row_major>(distances, batch_size, k);
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
index ca05ecdf0a..d42e3279bc 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_flat_wrapper.h
@@ -132,7 +132,7 @@ void cuvs_mg_ivf_flat<T, IdxT>::search(
   auto queries_view = raft::make_host_matrix_view<const T, int64_t, raft::row_major>(
     queries, IdxT(batch_size), IdxT(dim_));
   auto neighbors_view = raft::make_host_matrix_view<IdxT, int64_t, raft::row_major>(
-    (IdxT*)neighbors, IdxT(batch_size), IdxT(k));
+    neighbors, IdxT(batch_size), IdxT(k));
   auto distances_view = raft::make_host_matrix_view<float, int64_t, raft::row_major>(
     distances, IdxT(batch_size), IdxT(k));
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
index c37bbdf18b..f3ce1549c9 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_ivf_pq_wrapper.h
@@ -129,7 +129,7 @@ void cuvs_mg_ivf_pq<T, IdxT>::search(
   auto queries_view = raft::make_host_matrix_view<const T, int64_t, raft::row_major>(
     queries, IdxT(batch_size), IdxT(dim_));
   auto neighbors_view = raft::make_host_matrix_view<IdxT, int64_t, raft::row_major>(
-    (IdxT*)neighbors, IdxT(batch_size), IdxT(k));
+    neighbors, IdxT(batch_size), IdxT(k));
   auto distances_view = raft::make_host_matrix_view<float, int64_t, raft::row_major>(
     distances, IdxT(batch_size), IdxT(k));
 
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 488c769312..165faff5e1 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2335,6 +2335,124 @@ void extend(const raft::resources& clique,
 
 /// \defgroup mg_cpp_index_search ANN MG index search
 
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<cagra::index<float, uint32_t>, float, uint32_t>& index,
+            const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const float, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<cagra::index<half, uint32_t>, half, uint32_t>& index,
+            const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+            raft::host_matrix_view<const half, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<cagra::index<int8_t, uint32_t>, int8_t, uint32_t>& index,
+  const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+  raft::host_matrix_view<const int8_t, int64_t, row_major> queries,
+  raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances);
+
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<cagra::index_params> index_params;
+ * auto index = cuvs::neighbors::cagra::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<cagra::search_params> search_params;
+ * cuvs::neighbors::cagra::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>& index,
+  const cuvs::neighbors::mg_search_params<cagra::search_params>& search_params,
+  raft::host_matrix_view<const uint8_t, int64_t, row_major> queries,
+  raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances);
+
 /// \ingroup mg_cpp_index_search
 /**
  * @brief Searches a multi-GPU index
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 47155ed47e..8b2edaa424 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -786,12 +786,12 @@ void extend(
   std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, Accessor2>>
     new_indices);
 
-template <typename AnnIndexType, typename T, typename IdxT>
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
 void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::device_matrix_view<const T, int64_t, row_major> h_queries,
-            raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,
+            raft::device_matrix_view<searchIdxT, int64_t, row_major> d_neighbors,
             raft::device_matrix_view<float, int64_t, row_major> d_distances);
 
 template <typename AnnIndexType, typename T, typename IdxT>
diff --git a/cpp/src/neighbors/iface/generate_iface.py b/cpp/src/neighbors/iface/generate_iface.py
index 698bee88d8..d5fa6f56ca 100644
--- a/cpp/src/neighbors/iface/generate_iface.py
+++ b/cpp/src/neighbors/iface/generate_iface.py
@@ -190,14 +190,28 @@
               const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
-              raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,                                                     \\
+              raft::device_matrix_view<int64_t, int64_t, row_major> neighbors,                                                  \\
               raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
                                                                                                                                 \\
     template void search(const raft::resources& handle,                                                                         \\
               const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
               const cuvs::neighbors::search_params* search_params,                                                              \\
               raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
-              raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,                                                   \\
+              raft::device_matrix_view<int64_t, int64_t, row_major> d_neighbors,                                                \\
+              raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
+                                                                                                                                \\
+    template void search(const raft::resources& handle,                                                                         \\
+              const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::device_matrix_view<const T, int64_t, row_major> queries,                                                    \\
+              raft::device_matrix_view<uint32_t, int64_t, row_major> neighbors,                                                 \\
+              raft::device_matrix_view<float, int64_t, row_major> distances);                                                   \\
+                                                                                                                                \\
+    template void search(const raft::resources& handle,                                                                         \\
+              const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,                                          \\
+              const cuvs::neighbors::search_params* search_params,                                                              \\
+              raft::host_matrix_view<const T, int64_t, row_major> h_queries,                                                    \\
+              raft::device_matrix_view<uint32_t, int64_t, row_major> d_neighbors,                                               \\
               raft::device_matrix_view<float, int64_t, row_major> d_distances);                                                 \\
                                                                                                                                 \\
   template void serialize(const raft::resources& handle,                                                                        \\
diff --git a/cpp/src/neighbors/iface/iface.hpp b/cpp/src/neighbors/iface/iface.hpp
index 4f7a6f5cc9..4a7d3f9289 100644
--- a/cpp/src/neighbors/iface/iface.hpp
+++ b/cpp/src/neighbors/iface/iface.hpp
@@ -78,12 +78,12 @@ void extend(
   resource::sync_stream(handle);
 }
 
-template <typename AnnIndexType, typename T, typename IdxT>
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
 void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::device_matrix_view<const T, int64_t, row_major> queries,
-            raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,
+            raft::device_matrix_view<searchIdxT, int64_t, row_major> neighbors,
             raft::device_matrix_view<float, int64_t, row_major> distances)
 {
   // std::lock_guard(*interface.mutex_);
@@ -114,12 +114,12 @@ void search(const raft::resources& handle,
 }
 
 // for MG ANN only
-template <typename AnnIndexType, typename T, typename IdxT>
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
 void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::host_matrix_view<const T, int64_t, row_major> h_queries,
-            raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,
+            raft::device_matrix_view<searchIdxT, int64_t, row_major> d_neighbors,
             raft::device_matrix_view<float, int64_t, row_major> d_distances)
 {
   // std::lock_guard(*interface.mutex_);
diff --git a/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
index b256dac5a3..dae6508558 100644
--- a/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_float_uint32_t.cu
@@ -67,14 +67,28 @@ namespace cuvs::neighbors {
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
   template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> d_neighbors,          \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> neighbors,           \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> d_neighbors,         \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
   template void serialize(const raft::resources& handle,                                           \
diff --git a/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
index a650cf95c3..7219dd3ad9 100644
--- a/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_half_uint32_t.cu
@@ -67,14 +67,28 @@ namespace cuvs::neighbors {
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
   template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> d_neighbors,          \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> neighbors,           \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> d_neighbors,         \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
   template void serialize(const raft::resources& handle,                                           \
diff --git a/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
index 746ffeb961..227b03eb11 100644
--- a/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
@@ -67,14 +67,28 @@ namespace cuvs::neighbors {
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
   template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> d_neighbors,          \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> neighbors,           \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> d_neighbors,         \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
   template void serialize(const raft::resources& handle,                                           \
diff --git a/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
index 948e0993b3..b55d96c419 100644
--- a/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
+++ b/cpp/src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
@@ -67,14 +67,28 @@ namespace cuvs::neighbors {
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::device_matrix_view<const T, int64_t, row_major> queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> neighbors,            \
                        raft::device_matrix_view<float, int64_t, row_major> distances);             \
                                                                                                    \
   template void search(const raft::resources& handle,                                              \
                        const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
                        const cuvs::neighbors::search_params* search_params,                        \
                        raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
-                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<int64_t, int64_t, row_major> d_neighbors,          \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> neighbors,           \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<cagra::index<T, IdxT>, T, IdxT>& interface,    \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<uint32_t, int64_t, row_major> d_neighbors,         \
                        raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
                                                                                                    \
   template void serialize(const raft::resources& handle,                                           \
diff --git a/cpp/src/neighbors/mg/generate_mg.py b/cpp/src/neighbors/mg/generate_mg.py
index 49d4609fcd..f6ee47213e 100644
--- a/cpp/src/neighbors/mg/generate_mg.py
+++ b/cpp/src/neighbors/mg/generate_mg.py
@@ -194,7 +194,19 @@
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,                             \\
               const mg_search_params<cagra::search_params>& search_params,                                        \\
               raft::host_matrix_view<const T, int64_t, row_major> queries,                                        \\
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                                         \\
+              raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,                                      \\
+              raft::host_matrix_view<float, int64_t, row_major> distances)                                        \\
+  {                                                                                                               \\
+    cuvs::neighbors::snmg::detail::search(res, index,                                                             \\
+                                        static_cast<const cuvs::neighbors::search_params*>(&search_params),       \\
+                                        queries, neighbors, distances);                                           \\
+  }                                                                                                               \\
+                                                                                                                  \\
+    void search(const raft::resources& res,                                                                       \\
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,                             \\
+              const mg_search_params<cagra::search_params>& search_params,                                        \\
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                                        \\
+              raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,                                     \\
               raft::host_matrix_view<float, int64_t, row_major> distances)                                        \\
   {                                                                                                               \\
     cuvs::neighbors::snmg::detail::search(res, index,                                                             \\
diff --git a/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
index 3260df7050..19d6ccc4f9 100644
--- a/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
@@ -47,7 +47,23 @@
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
               raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,                 \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,                \
               raft::host_matrix_view<float, int64_t, row_major> distances)                   \
   {                                                                                          \
     cuvs::neighbors::snmg::detail::search(                                                   \
diff --git a/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
index da8cbb1e49..8b6b6a5c8c 100644
--- a/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
@@ -47,7 +47,23 @@
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
               raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,                 \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,                \
               raft::host_matrix_view<float, int64_t, row_major> distances)                   \
   {                                                                                          \
     cuvs::neighbors::snmg::detail::search(                                                   \
diff --git a/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
index 5aae599e22..4050a013a2 100644
--- a/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
@@ -47,7 +47,23 @@
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
               raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,                 \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,                \
               raft::host_matrix_view<float, int64_t, row_major> distances)                   \
   {                                                                                          \
     cuvs::neighbors::snmg::detail::search(                                                   \
diff --git a/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
index 8384a6a972..76d6a44aa7 100644
--- a/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
@@ -47,7 +47,23 @@
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
               raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
-              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                    \
+              raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,                 \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                   \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::search(                                                   \
+      res,                                                                                   \
+      index,                                                                                 \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                    \
+      queries,                                                                               \
+      neighbors,                                                                             \
+      distances);                                                                            \
+  }                                                                                          \
+                                                                                             \
+  void search(const raft::resources& res,                                                    \
+              const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<cagra::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                   \
+              raft::host_matrix_view<uint32_t, int64_t, row_major> neighbors,                \
               raft::host_matrix_view<float, int64_t, row_major> distances)                   \
   {                                                                                          \
     cuvs::neighbors::snmg::detail::search(                                                   \
diff --git a/cpp/src/neighbors/mg/snmg.cuh b/cpp/src/neighbors/mg/snmg.cuh
index f74c931423..16aec08b37 100644
--- a/cpp/src/neighbors/mg/snmg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -33,12 +33,12 @@
 namespace cuvs::neighbors {
 using namespace raft;
 
-template <typename AnnIndexType, typename T, typename IdxT>
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
 void search(const raft::resources& handle,
             const cuvs::neighbors::iface<AnnIndexType, T, IdxT>& interface,
             const cuvs::neighbors::search_params* search_params,
             raft::host_matrix_view<const T, int64_t, row_major> h_queries,
-            raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,
+            raft::device_matrix_view<searchIdxT, int64_t, row_major> d_neighbors,
             raft::device_matrix_view<float, int64_t, row_major> d_distances);
 }  // namespace cuvs::neighbors
 
@@ -177,26 +177,27 @@ void extend(const raft::resources& clique,
   }
 }
 
-template <typename AnnIndexType, typename T, typename IdxT>
-void sharded_search_with_direct_merge(const raft::resources& clique,
-                                      const mg_index<AnnIndexType, T, IdxT>& index,
-                                      const cuvs::neighbors::search_params* search_params,
-                                      raft::host_matrix_view<const T, int64_t, row_major> queries,
-                                      raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
-                                      raft::host_matrix_view<float, int64_t, row_major> distances,
-                                      int64_t n_rows_per_batch,
-                                      int64_t n_rows,
-                                      int64_t n_cols,
-                                      int64_t n_neighbors,
-                                      int64_t n_batches)
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
+void sharded_search_with_direct_merge(
+  const raft::resources& clique,
+  const mg_index<AnnIndexType, T, IdxT>& index,
+  const cuvs::neighbors::search_params* search_params,
+  raft::host_matrix_view<const T, int64_t, row_major> queries,
+  raft::host_matrix_view<searchIdxT, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances,
+  int64_t n_rows_per_batch,
+  int64_t n_rows,
+  int64_t n_cols,
+  int64_t n_neighbors,
+  int64_t n_batches)
 {
   const auto& root_handle = raft::resource::set_current_device_to_root_rank(clique);
-  auto in_neighbors       = raft::make_device_matrix<IdxT, int64_t, row_major>(
+  auto in_neighbors       = raft::make_device_matrix<searchIdxT, int64_t, row_major>(
     root_handle, index.num_ranks_ * n_rows_per_batch, n_neighbors);
   auto in_distances = raft::make_device_matrix<float, int64_t, row_major>(
     root_handle, index.num_ranks_ * n_rows_per_batch, n_neighbors);
-  auto out_neighbors =
-    raft::make_device_matrix<IdxT, int64_t, row_major>(root_handle, n_rows_per_batch, n_neighbors);
+  auto out_neighbors = raft::make_device_matrix<searchIdxT, int64_t, row_major>(
+    root_handle, n_rows_per_batch, n_neighbors);
   auto out_distances =
     raft::make_device_matrix<float, int64_t, row_major>(root_handle, n_rows_per_batch, n_neighbors);
 
@@ -218,7 +219,7 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
 
       if (rank == raft::resource::get_root_rank(clique)) {  // root rank
         uint64_t batch_offset = raft::resource::get_root_rank(clique) * part_size;
-        auto d_neighbors      = raft::make_device_matrix_view<IdxT, int64_t, row_major>(
+        auto d_neighbors      = raft::make_device_matrix_view<searchIdxT, int64_t, row_major>(
           in_neighbors.data_handle() + batch_offset, n_rows_of_current_batch, n_neighbors);
         auto d_distances = raft::make_device_matrix_view<float, int64_t, row_major>(
           in_distances.data_handle() + batch_offset, n_rows_of_current_batch, n_neighbors);
@@ -232,7 +233,7 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
 
           batch_offset = from_rank * part_size;
           ncclRecv(in_neighbors.data_handle() + batch_offset,
-                   part_size * sizeof(IdxT),
+                   part_size * sizeof(searchIdxT),
                    ncclUint8,
                    from_rank,
                    raft::resource::get_nccl_comm_for_rank(clique, rank),
@@ -247,7 +248,7 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
         ncclGroupEnd();
         resource::sync_stream(dev_res);
       } else {  // non-root ranks
-        auto d_neighbors = raft::make_device_matrix<IdxT, int64_t, row_major>(
+        auto d_neighbors = raft::make_device_matrix<searchIdxT, int64_t, row_major>(
           dev_res, n_rows_of_current_batch, n_neighbors);
         auto d_distances = raft::make_device_matrix<float, int64_t, row_major>(
           dev_res, n_rows_of_current_batch, n_neighbors);
@@ -257,7 +258,7 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
         // send results to root rank
         ncclGroupStart();
         ncclSend(d_neighbors.data_handle(),
-                 part_size * sizeof(IdxT),
+                 part_size * sizeof(searchIdxT),
                  ncclUint8,
                  raft::resource::get_root_rank(clique),
                  raft::resource::get_nccl_comm_for_rank(clique, rank),
@@ -273,14 +274,15 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
       }
     }
 
-    const auto& root_handle_   = raft::resource::set_current_device_to_root_rank(clique);
-    auto h_trans               = std::vector<IdxT>(index.num_ranks_);
-    int64_t translation_offset = 0;
+    const auto& root_handle_      = raft::resource::set_current_device_to_root_rank(clique);
+    auto h_trans                  = std::vector<searchIdxT>(index.num_ranks_);
+    searchIdxT translation_offset = 0;
     for (int rank = 0; rank < index.num_ranks_; rank++) {
       h_trans[rank] = translation_offset;
       translation_offset += index.ann_interfaces_[rank].size();
     }
-    auto d_trans = raft::make_device_vector<IdxT, IdxT>(root_handle_, index.num_ranks_);
+    auto d_trans = raft::make_device_vector<searchIdxT>(root_handle_, index.num_ranks_);
+
     raft::copy(d_trans.data_handle(),
                h_trans.data(),
                index.num_ranks_,
@@ -306,18 +308,19 @@ void sharded_search_with_direct_merge(const raft::resources& clique,
   }
 }
 
-template <typename AnnIndexType, typename T, typename IdxT>
-void sharded_search_with_tree_merge(const raft::resources& clique,
-                                    const mg_index<AnnIndexType, T, IdxT>& index,
-                                    const cuvs::neighbors::search_params* search_params,
-                                    raft::host_matrix_view<const T, int64_t, row_major> queries,
-                                    raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
-                                    raft::host_matrix_view<float, int64_t, row_major> distances,
-                                    int64_t n_rows_per_batch,
-                                    int64_t n_rows,
-                                    int64_t n_cols,
-                                    int64_t n_neighbors,
-                                    int64_t n_batches)
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
+void sharded_search_with_tree_merge(
+  const raft::resources& clique,
+  const mg_index<AnnIndexType, T, IdxT>& index,
+  const cuvs::neighbors::search_params* search_params,
+  raft::host_matrix_view<const T, int64_t, row_major> queries,
+  raft::host_matrix_view<searchIdxT, int64_t, row_major> neighbors,
+  raft::host_matrix_view<float, int64_t, row_major> distances,
+  int64_t n_rows_per_batch,
+  int64_t n_rows,
+  int64_t n_cols,
+  int64_t n_neighbors,
+  int64_t n_batches)
 {
   for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
     int64_t offset                  = batch_idx * n_rows_per_batch;
@@ -336,30 +339,30 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
 
       int64_t part_size = n_rows_of_current_batch * n_neighbors;
 
-      auto tmp_neighbors = raft::make_device_matrix<IdxT, int64_t, row_major>(
+      auto tmp_neighbors = raft::make_device_matrix<searchIdxT, int64_t, row_major>(
         dev_res, 2 * n_rows_of_current_batch, n_neighbors);
       auto tmp_distances = raft::make_device_matrix<float, int64_t, row_major>(
         dev_res, 2 * n_rows_of_current_batch, n_neighbors);
-      auto neighbors_view = raft::make_device_matrix_view<IdxT, int64_t, row_major>(
+      auto neighbors_view = raft::make_device_matrix_view<searchIdxT, int64_t, row_major>(
         tmp_neighbors.data_handle(), n_rows_of_current_batch, n_neighbors);
       auto distances_view = raft::make_device_matrix_view<float, int64_t, row_major>(
         tmp_distances.data_handle(), n_rows_of_current_batch, n_neighbors);
       cuvs::neighbors::search(
         dev_res, ann_if, search_params, query_partition, neighbors_view, distances_view);
 
-      int64_t translation_offset = 0;
+      searchIdxT translation_offset = 0;
       for (int r = 0; r < rank; r++) {
         translation_offset += index.ann_interfaces_[r].size();
       }
       raft::linalg::addScalar(neighbors_view.data_handle(),
                               neighbors_view.data_handle(),
-                              (IdxT)translation_offset,
+                              translation_offset,
                               part_size,
                               raft::resource::get_cuda_stream(dev_res));
 
-      auto d_trans = raft::make_device_vector<IdxT, IdxT>(dev_res, 2);
+      auto d_trans = raft::make_device_vector<searchIdxT>(dev_res, 2);
       cudaMemsetAsync(
-        d_trans.data_handle(), 0, 2 * sizeof(IdxT), raft::resource::get_cuda_stream(dev_res));
+        d_trans.data_handle(), 0, 2 * sizeof(searchIdxT), raft::resource::get_cuda_stream(dev_res));
 
       int64_t remaining = index.num_ranks_;
       int64_t radix     = 2;
@@ -374,7 +377,7 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
           if (other_id < index.num_ranks_)  // Make sure someone's sending anything
           {
             ncclRecv(tmp_neighbors.data_handle() + part_size,
-                     part_size * sizeof(IdxT),
+                     part_size * sizeof(searchIdxT),
                      ncclUint8,
                      other_id,
                      raft::resource::get_nccl_comm_for_rank(clique, rank),
@@ -391,7 +394,7 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
         {
           int other_id = rank - offset;
           ncclSend(tmp_neighbors.data_handle(),
-                   part_size * sizeof(IdxT),
+                   part_size * sizeof(searchIdxT),
                    ncclUint8,
                    other_id,
                    raft::resource::get_nccl_comm_for_rank(clique, rank),
@@ -409,13 +412,24 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
         radix *= 2;
 
         if (received_something) {
-          // merge inplace
+          auto neighbors_merge_res = raft::make_device_matrix<searchIdxT, int64_t, row_major>(
+            dev_res, n_rows_of_current_batch, n_neighbors);
+          auto distances_merge_res = raft::make_device_matrix<float, int64_t, row_major>(
+            dev_res, n_rows_of_current_batch, n_neighbors);
           knn_merge_parts(dev_res,
                           tmp_distances.view(),
                           tmp_neighbors.view(),
-                          tmp_distances.view(),
-                          tmp_neighbors.view(),
+                          distances_merge_res.view(),
+                          neighbors_merge_res.view(),
                           d_trans.view());
+          raft::copy(tmp_neighbors.data_handle(),
+                     neighbors_merge_res.data_handle(),
+                     part_size,
+                     raft::resource::get_cuda_stream(dev_res));
+          raft::copy(tmp_distances.data_handle(),
+                     distances_merge_res.data_handle(),
+                     part_size,
+                     raft::resource::get_cuda_stream(dev_res));
 
           // If done, copy the final result
           if (remaining <= 1) {
@@ -427,7 +441,6 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
                        tmp_distances.data_handle(),
                        part_size,
                        raft::resource::get_cuda_stream(dev_res));
-
             resource::sync_stream(dev_res);
           }
         }
@@ -436,13 +449,13 @@ void sharded_search_with_tree_merge(const raft::resources& clique,
   }
 }
 
-template <typename AnnIndexType, typename T, typename IdxT>
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
 void run_search_batch(const raft::resources& clique,
                       const mg_index<AnnIndexType, T, IdxT>& index,
                       int rank,
                       const cuvs::neighbors::search_params* search_params,
                       raft::host_matrix_view<const T, int64_t, row_major>& queries,
-                      raft::host_matrix_view<IdxT, int64_t, row_major>& neighbors,
+                      raft::host_matrix_view<searchIdxT, int64_t, row_major>& neighbors,
                       raft::host_matrix_view<float, int64_t, row_major>& distances,
                       int64_t query_offset,
                       int64_t output_offset,
@@ -455,7 +468,7 @@ void run_search_batch(const raft::resources& clique,
 
   auto query_partition = raft::make_host_matrix_view<const T, int64_t, row_major>(
     queries.data_handle() + query_offset, n_rows_of_current_batch, n_cols);
-  auto d_neighbors = raft::make_device_matrix<IdxT, int64_t, row_major>(
+  auto d_neighbors = raft::make_device_matrix<searchIdxT, int64_t, row_major>(
     dev_res, n_rows_of_current_batch, n_neighbors);
   auto d_distances = raft::make_device_matrix<float, int64_t, row_major>(
     dev_res, n_rows_of_current_batch, n_neighbors);
@@ -475,12 +488,12 @@ void run_search_batch(const raft::resources& clique,
   resource::sync_stream(dev_res);
 }
 
-template <typename AnnIndexType, typename T, typename IdxT>
+template <typename AnnIndexType, typename T, typename IdxT, typename searchIdxT>
 void search(const raft::resources& clique,
             const mg_index<AnnIndexType, T, IdxT>& index,
             const cuvs::neighbors::search_params* search_params,
             raft::host_matrix_view<const T, int64_t, row_major> queries,
-            raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,
+            raft::host_matrix_view<searchIdxT, int64_t, row_major> neighbors,
             raft::host_matrix_view<float, int64_t, row_major> distances)
 {
   // Making sure that the NCCL comms are instantiated at this stage.
diff --git a/cpp/tests/neighbors/mg.cuh b/cpp/tests/neighbors/mg.cuh
index 7909ce9155..046968ed2e 100644
--- a/cpp/tests/neighbors/mg.cuh
+++ b/cpp/tests/neighbors/mg.cuh
@@ -23,6 +23,7 @@
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <raft/core/device_resources_snmg.hpp>
+#include <raft/core/resource/nccl_comm.hpp>
 
 namespace cuvs::neighbors::mg {
 
@@ -65,8 +66,6 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
     std::vector<T> distances_ref(queries_size);
     std::vector<int64_t> neighbors_snmg_ann(queries_size);
     std::vector<T> distances_snmg_ann(queries_size);
-    std::vector<uint32_t> neighbors_ref_32bits(queries_size);
-    std::vector<uint32_t> neighbors_snmg_ann_32bits(queries_size);
 
     {
       rmm::device_uvector<T> distances_ref_dev(queries_size, resource::get_cuda_stream(clique_));
@@ -236,13 +235,13 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
 
       mg_search_params<cagra::search_params> search_params;
 
-      auto index_dataset = raft::make_host_matrix_view<const DataT, uint32_t, row_major>(
+      auto index_dataset = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
         h_index_dataset.data(), ps.num_db_vecs, ps.dim);
-      auto queries = raft::make_host_matrix_view<const DataT, uint32_t, row_major>(
+      auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
         h_queries.data(), ps.num_queries, ps.dim);
-      auto neighbors = raft::make_host_matrix_view<uint32_t, uint32_t, row_major>(
-        neighbors_snmg_ann_32bits.data(), ps.num_queries, ps.k);
-      auto distances = raft::make_host_matrix_view<float, uint32_t, row_major>(
+      auto neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+        neighbors_snmg_ann.data(), ps.num_queries, ps.k);
+      auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
       tmp_index_file index_file;
@@ -264,15 +263,15 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
-      ASSERT_TRUE(eval_neighbours(neighbors_ref_32bits,
-                                  neighbors_snmg_ann_32bits,
+      ASSERT_TRUE(eval_neighbours(neighbors_ref,
+                                  neighbors_snmg_ann,
                                   distances_ref,
                                   distances_snmg_ann,
                                   ps.num_queries,
                                   ps.k,
                                   0.001,
                                   min_recall));
-      std::fill(neighbors_snmg_ann_32bits.begin(), neighbors_snmg_ann_32bits.end(), 0);
+      std::fill(neighbors_snmg_ann.begin(), neighbors_snmg_ann.end(), 0);
       std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
     }
 
@@ -394,8 +393,8 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
 
       auto queries = raft::make_host_matrix_view<const DataT, int64_t, row_major>(
         h_queries.data(), ps.num_queries, ps.dim);
-      auto neighbors = raft::make_host_matrix_view<uint32_t, int64_t, row_major>(
-        neighbors_snmg_ann_32bits.data(), ps.num_queries, ps.k);
+      auto neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
+        neighbors_snmg_ann.data(), ps.num_queries, ps.k);
       auto distances = raft::make_host_matrix_view<float, int64_t, row_major>(
         distances_snmg_ann.data(), ps.num_queries, ps.k);
 
@@ -411,15 +410,15 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
       resource::sync_stream(clique_);
 
       double min_recall = static_cast<double>(ps.nprobe) / static_cast<double>(ps.nlist);
-      ASSERT_TRUE(eval_neighbours(neighbors_ref_32bits,
-                                  neighbors_snmg_ann_32bits,
+      ASSERT_TRUE(eval_neighbours(neighbors_ref,
+                                  neighbors_snmg_ann,
                                   distances_ref,
                                   distances_snmg_ann,
                                   ps.num_queries,
                                   ps.k,
                                   0.001,
                                   min_recall));
-      std::fill(neighbors_snmg_ann_32bits.begin(), neighbors_snmg_ann_32bits.end(), 0);
+      std::fill(neighbors_snmg_ann.begin(), neighbors_snmg_ann.end(), 0);
       std::fill(distances_snmg_ann.begin(), distances_snmg_ann.end(), 0);
     }
 
@@ -453,6 +452,10 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
                                                             ps.k);
       std::vector<float> load_balancer_distances_snmg_ann(n_parallel_searches * ps.num_queries *
                                                           ps.k);
+
+      // making sure that the NCCL comms are initialized before OMP section
+      raft::resource::get_nccl_comms(clique_);
+
 #pragma omp parallel for
       for (uint64_t search_idx = 0; search_idx < searches_correctness.size(); search_idx++) {
         uint64_t offset            = search_idx * ps.num_queries * ps.k;
@@ -518,6 +521,10 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
                                                             ps.k);
       std::vector<float> load_balancer_distances_snmg_ann(n_parallel_searches * ps.num_queries *
                                                           ps.k);
+
+      // making sure that the NCCL comms are initialized before OMP section
+      raft::resource::get_nccl_comms(clique_);
+
 #pragma omp parallel for
       for (uint64_t search_idx = 0; search_idx < searches_correctness.size(); search_idx++) {
         uint64_t offset            = search_idx * ps.num_queries * ps.k;
@@ -574,14 +581,17 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
 
       int n_parallel_searches = 16;
       std::vector<char> searches_correctness(n_parallel_searches);
-      std::vector<uint32_t> load_balancer_neighbors_snmg_ann(n_parallel_searches * ps.num_queries *
-                                                             ps.k);
+      std::vector<int64_t> load_balancer_neighbors_snmg_ann(n_parallel_searches * ps.num_queries *
+                                                            ps.k);
       std::vector<float> load_balancer_distances_snmg_ann(n_parallel_searches * ps.num_queries *
                                                           ps.k);
+
+      // making sure that the NCCL comms are initialized before OMP section
+      raft::resource::get_nccl_comms(clique_);
 #pragma omp parallel for
       for (uint64_t search_idx = 0; search_idx < searches_correctness.size(); search_idx++) {
         uint64_t offset            = search_idx * ps.num_queries * ps.k;
-        auto small_batch_neighbors = raft::make_host_matrix_view<uint32_t, int64_t, row_major>(
+        auto small_batch_neighbors = raft::make_host_matrix_view<int64_t, int64_t, row_major>(
           load_balancer_neighbors_snmg_ann.data() + offset, ps.num_queries, ps.k);
         auto small_batch_distances = raft::make_host_matrix_view<float, int64_t, row_major>(
           load_balancer_distances_snmg_ann.data() + offset, ps.num_queries, ps.k);
@@ -594,13 +604,13 @@ class AnnMGTest : public ::testing::TestWithParam<AnnMGInputs> {
                                        small_batch_neighbors,
                                        small_batch_distances);
 
-        std::vector<uint32_t> small_batch_neighbors_vec(
+        std::vector<int64_t> small_batch_neighbors_vec(
           small_batch_neighbors.data_handle(),
           small_batch_neighbors.data_handle() + small_batch_neighbors.size());
         std::vector<float> small_batch_distances_vec(
           small_batch_distances.data_handle(),
           small_batch_distances.data_handle() + small_batch_distances.size());
-        searches_correctness[search_idx] = eval_neighbours(neighbors_ref_32bits,
+        searches_correctness[search_idx] = eval_neighbours(neighbors_ref,
                                                            small_batch_neighbors_vec,
                                                            distances_ref,
                                                            small_batch_distances_vec,
@@ -725,7 +735,6 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-
   {7000,
    10000,
    8,
@@ -759,7 +768,7 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-  */
+   */
 
   {7000,
    10000,

From e03e25bfcb0ef4d09d8c18bf81bbadaf807987ec Mon Sep 17 00:00:00 2001
From: Ray Douglass <ray@raydouglass.com>
Date: Thu, 5 Jun 2025 11:13:10 -0400
Subject: [PATCH 433/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 88 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 88 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index af6bc8935b..04d849764c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,91 @@
+# cuvs 25.06.00 (5 Jun 2025)
+
+## 🚨 Breaking Changes
+
+- [Java] Adding support for prefiltering in CAGRA ([#870](https://github.com/rapidsai/cuvs/pull/870)) [@punAhuja](https://github.com/punAhuja)
+
+## 🐛 Bug Fixes
+
+- Fix failing CAGRA merge google tests for 25.06 ([#974](https://github.com/rapidsai/cuvs/pull/974)) [@rhdong](https://github.com/rhdong)
+- Fix for recent NCCL resource update ([#968](https://github.com/rapidsai/cuvs/pull/968)) [@viclafargue](https://github.com/viclafargue)
+- Revert &quot;Fix kmeans::predict argument order ([#915)&quot; (#951](https://github.com/rapidsai/cuvs/pull/915)&quot; (#951)) [@divyegala](https://github.com/divyegala)
+- IVF-PQ tests: fix segfault when accessing empty lists ([#933](https://github.com/rapidsai/cuvs/pull/933)) [@achirkin](https://github.com/achirkin)
+- batch_load_iterator shall copy data located in host memory ([#926](https://github.com/rapidsai/cuvs/pull/926)) [@tfeher](https://github.com/tfeher)
+- Fix kmeans::predict argument order ([#915](https://github.com/rapidsai/cuvs/pull/915)) [@enp1s0](https://github.com/enp1s0)
+- ANN_BENCH: fix the reported core count ([#896](https://github.com/rapidsai/cuvs/pull/896)) [@achirkin](https://github.com/achirkin)
+- CUVS_ANN_BENCH_SINGLE_EXE: find the mg library ([#890](https://github.com/rapidsai/cuvs/pull/890)) [@achirkin](https://github.com/achirkin)
+- [FIX] Revert negated InnerProduct distance for NN Descent ([#859](https://github.com/rapidsai/cuvs/pull/859)) [@jinsolp](https://github.com/jinsolp)
+- IVF-PQ tests: fix segfault when accessing empty lists ([#838](https://github.com/rapidsai/cuvs/pull/838)) [@achirkin](https://github.com/achirkin)
+- [CuVS-Java] Automate panama bindings generation, Include IVF_PQ parameters in CAGRA index parameters and other changes ([#831](https://github.com/rapidsai/cuvs/pull/831)) [@narangvivek10](https://github.com/narangvivek10)
+- Ann-bench: fix unsafe lazy blobs ([#828](https://github.com/rapidsai/cuvs/pull/828)) [@achirkin](https://github.com/achirkin)
+- Fix test_brute_force conversion ([#821](https://github.com/rapidsai/cuvs/pull/821)) [@lowener](https://github.com/lowener)
+- [FEA] New algos and updates/corrections to Faiss cuvs-bench ([#677](https://github.com/rapidsai/cuvs/pull/677)) [@tarang-jain](https://github.com/tarang-jain)
+
+## 📖 Documentation
+
+- All-neighbors API docs ([#944](https://github.com/rapidsai/cuvs/pull/944)) [@jinsolp](https://github.com/jinsolp)
+- cagra.rst: fitered -&gt; filtered ([#866](https://github.com/rapidsai/cuvs/pull/866)) [@eli-b](https://github.com/eli-b)
+- [DOCS] Fix Integration Docs (Faiss) ([#782](https://github.com/rapidsai/cuvs/pull/782)) [@tarang-jain](https://github.com/tarang-jain)
+
+## 🚀 New Features
+
+- Add IVF-PQ support in CAGRA index params in Python ([#918](https://github.com/rapidsai/cuvs/pull/918)) [@lowener](https://github.com/lowener)
+- [Feat] Expose C API for CAGRA `merge` ([#860](https://github.com/rapidsai/cuvs/pull/860)) [@rhdong](https://github.com/rhdong)
+- Use NCCL wheels from PyPI for CUDA 12 builds ([#827](https://github.com/rapidsai/cuvs/pull/827)) [@divyegala](https://github.com/divyegala)
+- Add support for half in CAGRA+HNSW ([#813](https://github.com/rapidsai/cuvs/pull/813)) [@lowener](https://github.com/lowener)
+- [CI] Enable Java test in CI workflow ([#805](https://github.com/rapidsai/cuvs/pull/805)) [@rhdong](https://github.com/rhdong)
+- Wrapper for all-neighbors knn graph building ([#785](https://github.com/rapidsai/cuvs/pull/785)) [@jinsolp](https://github.com/jinsolp)
+- Add support of half dtype in IVF-FLAT ([#730](https://github.com/rapidsai/cuvs/pull/730)) [@lowener](https://github.com/lowener)
+- IVF-PQ: low-precision coarse search ([#715](https://github.com/rapidsai/cuvs/pull/715)) [@achirkin](https://github.com/achirkin)
+- [Feat] Add support of logical merge in Cagra ([#713](https://github.com/rapidsai/cuvs/pull/713)) [@rhdong](https://github.com/rhdong)
+
+## 🛠️ Improvements
+
+- Update score calculation for CAGRA-Q instance selection ([#938](https://github.com/rapidsai/cuvs/pull/938)) [@enp1s0](https://github.com/enp1s0)
+- [FEA] Use Native Brute Force for Sparse Pairwise KNN ([#927](https://github.com/rapidsai/cuvs/pull/927)) [@tarang-jain](https://github.com/tarang-jain)
+- use &#39;rapids-init-pip&#39; in wheel CI, other CI changes ([#917](https://github.com/rapidsai/cuvs/pull/917)) [@jameslamb](https://github.com/jameslamb)
+- ANN_BENCH: Avoid repeated calls to raft::get_device_for_address in CAGRA search ([#908](https://github.com/rapidsai/cuvs/pull/908)) [@achirkin](https://github.com/achirkin)
+- [Java] New off-heap Dataset support for CAGRA and Bruteforce ([#902](https://github.com/rapidsai/cuvs/pull/902)) [@chatman](https://github.com/chatman)
+- Finish CUDA 12.9 migration and use branch-25.06 workflows ([#901](https://github.com/rapidsai/cuvs/pull/901)) [@bdice](https://github.com/bdice)
+- Update to clang 20 ([#898](https://github.com/rapidsai/cuvs/pull/898)) [@bdice](https://github.com/bdice)
+- get Java artifacts from GitHub Actions artifact store ([#893](https://github.com/rapidsai/cuvs/pull/893)) [@jameslamb](https://github.com/jameslamb)
+- Quote head_rev in conda recipes ([#892](https://github.com/rapidsai/cuvs/pull/892)) [@bdice](https://github.com/bdice)
+- [Java] Exposing  merge API for multiple CAGRA indices ([#891](https://github.com/rapidsai/cuvs/pull/891)) [@punAhuja](https://github.com/punAhuja)
+- Expose ivf-flat centers to python/c ([#888](https://github.com/rapidsai/cuvs/pull/888)) [@benfred](https://github.com/benfred)
+- CUDA 12.9 use updated compression flags ([#887](https://github.com/rapidsai/cuvs/pull/887)) [@robertmaynard](https://github.com/robertmaynard)
+- Expose ivf-pq centers to python/c ([#881](https://github.com/rapidsai/cuvs/pull/881)) [@benfred](https://github.com/benfred)
+- Accept host inputs in python for ivf-pq build and extend ([#880](https://github.com/rapidsai/cuvs/pull/880)) [@benfred](https://github.com/benfred)
+- Add tiered_index support ([#879](https://github.com/rapidsai/cuvs/pull/879)) [@benfred](https://github.com/benfred)
+- Exclude librmm.so from auditwheel ([#878](https://github.com/rapidsai/cuvs/pull/878)) [@bdice](https://github.com/bdice)
+- update.version.sh: remove broken reference, skip most CI on PRs that only modify update-version.sh ([#875](https://github.com/rapidsai/cuvs/pull/875)) [@jameslamb](https://github.com/jameslamb)
+- Add support for Python 3.13 ([#874](https://github.com/rapidsai/cuvs/pull/874)) [@gforsyth](https://github.com/gforsyth)
+- chore: lower wheel size threshold ([#872](https://github.com/rapidsai/cuvs/pull/872)) [@gforsyth](https://github.com/gforsyth)
+- [Java] Adding support for prefiltering in CAGRA ([#870](https://github.com/rapidsai/cuvs/pull/870)) [@punAhuja](https://github.com/punAhuja)
+- Change snmg index to use updated multi gpu resource API ([#869](https://github.com/rapidsai/cuvs/pull/869)) [@jinsolp](https://github.com/jinsolp)
+- run shellcheck on all files, other small pre-commit updates ([#865](https://github.com/rapidsai/cuvs/pull/865)) [@jameslamb](https://github.com/jameslamb)
+- Fix IVF PQ build metric for CAGRA ([#862](https://github.com/rapidsai/cuvs/pull/862)) [@lowener](https://github.com/lowener)
+- ANN_BENCH: Expose parallel_mode parameter of FAISS CPU IVF implementation ([#861](https://github.com/rapidsai/cuvs/pull/861)) [@achirkin](https://github.com/achirkin)
+- Specify matplotlib version ([#839](https://github.com/rapidsai/cuvs/pull/839)) [@benfred](https://github.com/benfred)
+- Use random tmp names for index files in tests ([#837](https://github.com/rapidsai/cuvs/pull/837)) [@achirkin](https://github.com/achirkin)
+- Download build artifacts from Github for CI ([#834](https://github.com/rapidsai/cuvs/pull/834)) [@VenkateshJaya](https://github.com/VenkateshJaya)
+- Add NN-Descent return_distances functionality to python/C ([#833](https://github.com/rapidsai/cuvs/pull/833)) [@benfred](https://github.com/benfred)
+- Optimize hnsw::from_cagra&lt;GPU&gt; ([#826](https://github.com/rapidsai/cuvs/pull/826)) [@achirkin](https://github.com/achirkin)
+- Use vendored RAPIDS.cmake in example code. ([#824](https://github.com/rapidsai/cuvs/pull/824)) [@bdice](https://github.com/bdice)
+- refactor(rattler): enable strict channel priority for builds ([#823](https://github.com/rapidsai/cuvs/pull/823)) [@gforsyth](https://github.com/gforsyth)
+- Reduce device memory usage for CAGRA&#39;s graph optimization process (2-hop detour counting) ([#822](https://github.com/rapidsai/cuvs/pull/822)) [@anaruse](https://github.com/anaruse)
+- [cuvs_bench] distinguish search label from build label in data_export.py ([#818](https://github.com/rapidsai/cuvs/pull/818)) [@jiangyinzuo](https://github.com/jiangyinzuo)
+- Vendor RAPIDS.cmake ([#816](https://github.com/rapidsai/cuvs/pull/816)) [@bdice](https://github.com/bdice)
+- Update libcuvs libraft ver to 25.06 in conda env ([#808](https://github.com/rapidsai/cuvs/pull/808)) [@jinsolp](https://github.com/jinsolp)
+- Moving NN Descent class and struct declarations to `nn_descent_gnnd.hpp` ([#803](https://github.com/rapidsai/cuvs/pull/803)) [@jinsolp](https://github.com/jinsolp)
+- Remove `[@rapidsai/cuvs-build-codeowners` ([#783](https://github.com/rapidsai/cuvs/pull/783)) @KyleFromNVIDIA](https://github.com/rapidsai/cuvs-build-codeowners` ([#783](https://github.com/rapidsai/cuvs/pull/783)) @KyleFromNVIDIA)
+- Moving wheel builds to specified location and uploading build artifacts to Github ([#777](https://github.com/rapidsai/cuvs/pull/777)) [@VenkateshJaya](https://github.com/VenkateshJaya)
+- Remove unused raft cagra header in add_nodes.cuh ([#741](https://github.com/rapidsai/cuvs/pull/741)) [@jiangyinzuo](https://github.com/jiangyinzuo)
+- Expose kmeans to python ([#729](https://github.com/rapidsai/cuvs/pull/729)) [@benfred](https://github.com/benfred)
+- Update cuvs to properly create a NCCL::NCCL target ([#720](https://github.com/rapidsai/cuvs/pull/720)) [@robertmaynard](https://github.com/robertmaynard)
+- Optimize euclidean distance in host refine phase ([#689](https://github.com/rapidsai/cuvs/pull/689)) [@anstellaire](https://github.com/anstellaire)
+- Moving MG functions into unified API + `raft::device_resources_snmg` as device resource type for MG functions ([#454](https://github.com/rapidsai/cuvs/pull/454)) [@viclafargue](https://github.com/viclafargue)
+- Moving random ball cover ([#218](https://github.com/rapidsai/cuvs/pull/218)) [@cjnolet](https://github.com/cjnolet)
+
 # cuvs 25.04.00 (9 Apr 2025)
 
 ## 🚨 Breaking Changes

From 325fdf0e1b09ec9f9df2bfcde985bc54247a3d2a Mon Sep 17 00:00:00 2001
From: MithunR <mythrocks@gmail.com>
Date: Thu, 5 Jun 2025 14:59:01 -0700
Subject: [PATCH 434/623] [java] Copy `pom.xml` to `target/` on build (#981)

Fixes #978.

This commit modifies the `java/build.sh` script to also copy the `pom.xml` to the `target/` directory.  This will allow all artifacts for publication to Maven (i.e. all the jars and the pom) to be fetched from a single location.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/981
---
 java/build.sh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/java/build.sh b/java/build.sh
index ac74ff671e..fb5796774a 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -38,4 +38,5 @@ fi
 export LD_LIBRARY_PATH=${CURDIR}/../cpp/build:${LD_LIBRARY_PATH}
 cd cuvs-java
 mvn verify "${MAVEN_VERIFY_ARGS[@]}" \
-  && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar
+  && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar \
+  && cp pom.xml ./target/

From 18f99a848591d9a7b315361df77633ba1d977ed0 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 6 Jun 2025 09:33:32 -0700
Subject: [PATCH 435/623] Fix rust publishing step (#984)

In https://github.com/rapidsai/cuvs/pull/816 we started to vendor
rapids_config.cmake - but this broke the symlinks that rust/java use to
build.

Fix the symlinks to point to the new location, and add a precommit hook
to check that symlinks are valid to prevent this from happening in the
future
---
 .pre-commit-config.yaml           | 1 +
 java/internal/rapids_config.cmake | 2 +-
 rust/cuvs-sys/rapids_config.cmake | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1af49aab62..f3960d82de 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,6 +7,7 @@ repos:
               - id: check-json
               - id: trailing-whitespace
               - id: end-of-file-fixer
+              - id: check-symlinks
       - repo: https://github.com/PyCQA/isort
         rev: 5.12.0
         hooks:
diff --git a/java/internal/rapids_config.cmake b/java/internal/rapids_config.cmake
index 398eea52da..7612a2dbe1 120000
--- a/java/internal/rapids_config.cmake
+++ b/java/internal/rapids_config.cmake
@@ -1 +1 @@
-../../rapids_config.cmake
\ No newline at end of file
+../../cmake/rapids_config.cmake
\ No newline at end of file
diff --git a/rust/cuvs-sys/rapids_config.cmake b/rust/cuvs-sys/rapids_config.cmake
index 398eea52da..7612a2dbe1 120000
--- a/rust/cuvs-sys/rapids_config.cmake
+++ b/rust/cuvs-sys/rapids_config.cmake
@@ -1 +1 @@
-../../rapids_config.cmake
\ No newline at end of file
+../../cmake/rapids_config.cmake
\ No newline at end of file

From 2a23de9a4042cae09749698c3b90a4b81eb2087d Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 6 Jun 2025 12:09:43 -0700
Subject: [PATCH 436/623] Merge pull request #990 from
 benfred/rust_publishing_fix2

Update rust/cuvs-sys
---
 ci/build_rust.sh                  | 5 +++++
 rust/cuvs-sys/CMakeLists.txt      | 2 +-
 rust/cuvs-sys/cmake               | 1 +
 rust/cuvs-sys/rapids_config.cmake | 1 -
 4 files changed, 7 insertions(+), 2 deletions(-)
 create mode 120000 rust/cuvs-sys/cmake
 delete mode 120000 rust/cuvs-sys/rapids_config.cmake

diff --git a/ci/build_rust.sh b/ci/build_rust.sh
index 5cf1df8ea5..4ab8f6ddcf 100755
--- a/ci/build_rust.sh
+++ b/ci/build_rust.sh
@@ -33,3 +33,8 @@ export LIBCLANG_PATH
 echo "LIBCLANG_PATH=$LIBCLANG_PATH"
 
 bash ./build.sh rust
+
+# Also test out that we can publish cuvs-sys via a dry-run
+pushd ./rust/cuvs-sys
+cargo publish --dry-run
+popd
diff --git a/rust/cuvs-sys/CMakeLists.txt b/rust/cuvs-sys/CMakeLists.txt
index a96cef28b0..db3c0f8d67 100644
--- a/rust/cuvs-sys/CMakeLists.txt
+++ b/rust/cuvs-sys/CMakeLists.txt
@@ -14,7 +14,7 @@
 
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
-include(../../cmake/rapids_config.cmake)
+include(cmake/rapids_config.cmake)
 include(rapids-cmake)
 include(rapids-cpm)
 include(rapids-export)
diff --git a/rust/cuvs-sys/cmake b/rust/cuvs-sys/cmake
new file mode 120000
index 0000000000..745c85c00a
--- /dev/null
+++ b/rust/cuvs-sys/cmake
@@ -0,0 +1 @@
+../../cmake
\ No newline at end of file
diff --git a/rust/cuvs-sys/rapids_config.cmake b/rust/cuvs-sys/rapids_config.cmake
deleted file mode 120000
index 7612a2dbe1..0000000000
--- a/rust/cuvs-sys/rapids_config.cmake
+++ /dev/null
@@ -1 +0,0 @@
-../../cmake/rapids_config.cmake
\ No newline at end of file

From 71a408a7f46c0c178051916d19869c6abc4bf017 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 6 Jun 2025 12:39:31 -0700
Subject: [PATCH 437/623] [java] Utility function for `cudaMemcpy` (#983)

This commit does a minor rephrasing of calls to `cudaMemCpy`.

Prior to this change:
  1. Calls to `cudaMemcpy` were using hardcoded / undocumented magic numbers for the direction of the copy. These have been replaced an enum that mirrors what is used in the CUDA Runtime call.
  2. `cudaMemcpy` would return an error value that needed to be checked / converted into a RuntimeError.  This has been moved into the utility function, to reduce the noise at the call site.

This change also includes some minor cleanup of unused imports in the modified files.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/983
---
 .../cuvs/internal/BruteForceIndexImpl.java    | 19 +++-----
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 20 +++------
 .../com/nvidia/cuvs/internal/common/Util.java | 45 +++++++++++++++++--
 3 files changed, 55 insertions(+), 29 deletions(-)

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index a1645beb01..141f5b0ab4 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -24,8 +24,9 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
-import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
+import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceBuild;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceDeserialize;
@@ -40,7 +41,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 
 import java.io.FileInputStream;
@@ -170,8 +170,7 @@ private IndexReference build() throws Throwable {
       // IMPORTANT: this should only come AFTER cuvsRMMAlloc call
       MemorySegment datasetMemorySegmentP = datasetMemorySegment.get(C_POINTER, 0);
 
-      returnValue = cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, 4);
-      checkCudaError(returnValue, "cudaMemcpy");
+      cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
 
       long datasetShape[] = { rows, cols };
       MemorySegment datasetTensor = prepareTensor(arena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
@@ -261,8 +260,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
       MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
 
-      returnValue = cudaMemcpy(queriesDP, querySeg, queriesBytes, 4);
-      checkCudaError(returnValue, "cudaMemcpy");
+      cudaMemcpy(queriesDP, querySeg, queriesBytes, INFER_DIRECTION);
 
       long queriesShape[] = { numQueries, vectorDimension };
       MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
@@ -287,8 +285,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
 
         prefilterDP = prefilterD.get(C_POINTER, 0);
 
-        returnValue = cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, 1);
-        checkCudaError(returnValue, "cudaMemcpy");
+        cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
         prefilterTensor = prepareTensor(arena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
 
@@ -306,10 +303,8 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       returnValue = cuvsStreamSync(cuvsResources);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, 4);
-      checkCudaError(returnValue, "cudaMemcpy");
-      returnValue = cudaMemcpy(distancesMemorySegment, distancesDP, distanceBytes, 4);
-      checkCudaError(returnValue, "cudaMemcpy");
+      cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
+      cudaMemcpy(distancesMemorySegment, distancesDP, distanceBytes, INFER_DIRECTION);
 
       returnValue = cuvsRMMFree(cuvsResources, neighborsDP, neighborsBytes);
       checkCuVSError(returnValue, "cuvsRMMFree");
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index d19106a2c8..33424e65c4 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -16,17 +16,16 @@
 
 package com.nvidia.cuvs.internal;
 
-import static java.lang.foreign.ValueLayout.ADDRESS;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
-import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
+import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraBuild;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraDeserialize;
@@ -45,7 +44,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 
 import java.io.FileInputStream;
@@ -53,12 +51,10 @@
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.Objects;
@@ -287,8 +283,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
       MemorySegment prefilterDP = MemorySegment.NULL;
       long prefilterLen = 0;
 
-      returnValue = cudaMemcpy(queriesDP, floatsSeg, queriesBytes, 4);
-      checkCudaError(returnValue, "cudaMemcpy");
+      cudaMemcpy(queriesDP, floatsSeg, queriesBytes, INFER_DIRECTION);
 
       long queriesShape[] = { numQueries, vectorDimension };
       MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
@@ -329,8 +324,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
 
         prefilterDP = prefilterD.get(C_POINTER, 0);
 
-        returnValue = cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, 1);
-        checkCudaError(returnValue, "cudaMemcpy");
+        cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
         prefilterTensor = prepareTensor(arena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
 
@@ -348,10 +342,8 @@ public SearchResults search(CagraQuery query) throws Throwable {
       returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, 4);
-      checkCudaError(returnValue, "cudaMemcpy");
-      returnValue = cudaMemcpy(distancesMemorySegment, distancesDP, distancesBytes, 4);
-      checkCudaError(returnValue, "cudaMemcpy");
+      cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
+      cudaMemcpy(distancesMemorySegment, distancesDP, distancesBytes, INFER_DIRECTION);
 
       returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
       checkCuVSError(returnValue, "cuvsRMMFree");
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 36512225e3..513c66e257 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -25,14 +25,11 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemGetInfo;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaSetDevice;
 import static com.nvidia.cuvs.internal.panama.headers_h.size_t;
-import static java.lang.foreign.ValueLayout.ADDRESS;
 
 import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemoryLayout.PathElement;
 import java.lang.foreign.MemorySegment;
-import java.lang.invoke.MethodHandle;
 import java.lang.invoke.VarHandle;
 import java.util.ArrayList;
 import java.util.BitSet;
@@ -79,6 +76,48 @@ public static void checkCudaError(int value, String caller) {
     }
   }
 
+  /**
+   * Java analog to CUDA's cudaMemcpyKind, used for cudaMemcpy() calls.
+   * @see <a href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html">CUDA Runtime API</a>
+   */
+  public enum CudaMemcpyKind {
+    HOST_TO_HOST(0),
+    HOST_TO_DEVICE(1),
+    DEVICE_TO_HOST(2),
+    DEVICE_TO_DEVICE(3),
+    INFER_DIRECTION(4);
+
+    CudaMemcpyKind(int k) {
+      this.kind = k;
+    }
+
+    public final int kind;
+  }
+
+  /**
+   * Helper to invoke cudaMemcpy CUDA runtime function to copy data between host/device memory.
+   * @param dest Destination address for data copy
+   * @param src Source address for data copy
+   * @param numBytes Number of bytes to be copied
+   * @param kind "Direction" of data copy (Host->Device, Device->Host, etc.)
+   * @throws RuntimeException on failure of copy
+   */
+  public static void cudaMemcpy(MemorySegment dest, MemorySegment src, long numBytes, CudaMemcpyKind kind) {
+    int returnValue = com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy(dest, src, numBytes, kind.kind);
+    checkCudaError(returnValue, "cudaMemcpy");
+  }
+
+  /**
+   * Helper to invoke cudaMemcpy CUDA runtime function to copy data between host/device memory.
+   * @param dest Destination address for data copy
+   * @param src Source address for data copy
+   * @param numBytes Number of bytes to be copied
+   * @throws RuntimeException on failure of copy
+   */
+  public static void cudaMemcpy(MemorySegment dest, MemorySegment src, long numBytes) {
+    Util.cudaMemcpy(dest, src, numBytes, CudaMemcpyKind.INFER_DIRECTION);
+  }
+
   static final long MAX_ERROR_TEXT = 1_000_000L;
 
   static String getLastErrorText() {

From 58735dbb718f7f8628cdef71fa54ed236fc69dfb Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 6 Jun 2025 18:51:17 -0700
Subject: [PATCH 438/623] Add C-API method to fetch cuVS version from Java.
 (#935)

Fixes #930.

This commit adds a C API function to fetch the cuVS version.

Authors:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - Corey J. Nolet (https://github.com/cjnolet)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/935
---
 ci/release/update-version.sh  |  5 +++++
 cpp/include/cuvs/core/c_api.h | 15 ++++++++++++++-
 cpp/src/core/c_api.cpp        |  8 ++++++++
 cpp/tests/core/c_api.c        |  8 ++++++++
 4 files changed, 35 insertions(+), 1 deletion(-)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index ac6f0e3d35..ce739d23a4 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -108,3 +108,8 @@ done
 sed_runner "s| CuVS [[:digit:]]\{2\}\.[[:digit:]]\{2\} | CuVS ${NEXT_SHORT_TAG} |g" java/README.md
 sed_runner "s|-[[:digit:]]\{2\}\.[[:digit:]]\{2\}\.[[:digit:]]\{1,2\}\.jar|-${NEXT_FULL_JAVA_TAG}\.jar|g" java/examples/README.md
 sed_runner "s|/[[:digit:]]\{2\}\.[[:digit:]]\{2\}\.[[:digit:]]\{1,2\}/|/${NEXT_FULL_JAVA_TAG}/|g" java/examples/README.md
+
+# Update C API version.
+sed_runner "s|CUVS_VERSION_MAJOR = [0-9][0-9]|CUVS_VERSION_MAJOR = ${NEXT_MAJOR}|g" cpp/include/cuvs/core/c_api.h
+sed_runner "s|CUVS_VERSION_MINOR = [0-9][0-9]|CUVS_VERSION_MINOR = ${NEXT_MINOR}|g" cpp/include/cuvs/core/c_api.h
+sed_runner "s|CUVS_VERSION_PATCH = [0-9][0-9]|CUVS_VERSION_PATCH = ${NEXT_PATCH}|g" cpp/include/cuvs/core/c_api.h
diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index 400d162ad7..cce266d448 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -20,6 +20,10 @@
 #include <stdbool.h>
 #include <stdint.h>
 
+static const uint16_t CUVS_VERSION_MAJOR = 25;
+static const uint16_t CUVS_VERSION_MINOR = 06;
+static const uint16_t CUVS_VERSION_PATCH = 00;
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -29,7 +33,7 @@ extern "C" {
  * @{
  */
 /**
- * @brief An enum denoting return values for function calls
+ * @brief An enum denoting error statuses for function calls
  *
  */
 typedef enum { CUVS_ERROR, CUVS_SUCCESS } cuvsError_t;
@@ -167,6 +171,15 @@ cuvsError_t cuvsRMMHostAlloc(void** ptr, size_t bytes);
  */
 cuvsError_t cuvsRMMHostFree(void* ptr, size_t bytes);
 
+/**
+ * @brief Get the version of the cuVS library
+ * @param[out] major Major version
+ * @param[out] minor Minor version
+ * @param[out] patch Patch version
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVersionGet(uint16_t* major, uint16_t* minor, uint16_t* patch);
+
 /** @} */
 
 #ifdef __cplusplus
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 4333bff0cd..530628a007 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -154,3 +154,11 @@ extern "C" const char* cuvsGetLastErrorText()
 }
 
 extern "C" void cuvsSetLastErrorText(const char* error) { last_error_text = error ? error : ""; }
+
+extern "C" cuvsError_t cuvsVersionGet(uint16_t* major, uint16_t* minor, uint16_t* patch)
+{
+  *major = CUVS_VERSION_MAJOR;
+  *minor = CUVS_VERSION_MINOR;
+  *patch = CUVS_VERSION_PATCH;
+  return CUVS_SUCCESS;
+}
diff --git a/cpp/tests/core/c_api.c b/cpp/tests/core/c_api.c
index a51824d2b9..0635066d31 100644
--- a/cpp/tests/core/c_api.c
+++ b/cpp/tests/core/c_api.c
@@ -86,5 +86,13 @@ int main()
   error = cuvsResourcesDestroy(res);
   if (error == CUVS_ERROR) { exit(EXIT_FAILURE); }
 
+  // Check version
+  uint16_t major, minor, patch;
+  error = cuvsVersionGet(&major, &minor, &patch);
+  if (error == CUVS_ERROR || major != CUVS_VERSION_MAJOR || minor != CUVS_VERSION_MINOR ||
+      patch != CUVS_VERSION_PATCH) {
+    exit(EXIT_FAILURE);
+  }
+
   return 0;
 }

From 6d071162169b67d0c5bd528bf33e0daa35505d2a Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Mon, 9 Jun 2025 19:22:42 -0400
Subject: [PATCH 439/623] Fix for NN Descent negative or NaN distances (#994)

Closes https://github.com/rapidsai/cuvs/issues/991

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/994
---
 cpp/src/neighbors/detail/nn_descent.cuh | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 941af82a0f..c010a7cfed 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -633,6 +633,9 @@ __launch_bounds__(BLOCK_SIZE)
         s_distances[i] = l2_norms[new_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
                          l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
                          2.0 * s_distances[i];
+        // for fp32 vs fp16 precision differences resulting in negative distances when distance
+        // should be 0 related issue: https://github.com/rapidsai/cuvs/issues/991
+        s_distances[i] = s_distances[i] < 0.0f ? 0.0f : s_distances[i];
       }
     } else {
       s_distances[i] = std::numeric_limits<float>::max();
@@ -714,6 +717,9 @@ __launch_bounds__(BLOCK_SIZE)
         s_distances[i] = l2_norms[old_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
                          l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
                          2.0 * s_distances[i];
+        // for fp32 vs fp16 precision differences resulting in negative distances when distance
+        // should be 0 related issue: https://github.com/rapidsai/cuvs/issues/991
+        s_distances[i] = s_distances[i] < 0.0f ? 0.0f : s_distances[i];
       }
     } else {
       s_distances[i] = std::numeric_limits<float>::max();

From 6dfdc7b9e54a8bbd8fdbfc54ee7cc3f2ef5534e8 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Wed, 11 Jun 2025 15:20:07 -0400
Subject: [PATCH 440/623] Update nightly CI check to allow 30 days of failing
 nightly build.  (#1008)

cuVS 25.08 CI is blocked because the 25.06 nightlies are failing.
@viclafargue is going to fix this when he's back in the office, but in
the meantime we'd like to unblock CI by extending the time before the
nightly checks fail. We will revert this change after nightly CI is
fixed.
---
 .github/workflows/pr.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 4fca4cc7ba..ede28c1438 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -53,6 +53,7 @@ jobs:
         uses: rapidsai/shared-actions/check_nightly_success/dispatch@main
         with:
           repo: cuvs
+          max_days_without_success: 30
   changed-files:
     needs: telemetry-setup
     secrets: inherit

From 8e76699a86ead404d02b58b48beaeb095bc45f4e Mon Sep 17 00:00:00 2001
From: Divye Gala <divyegala@gmail.com>
Date: Wed, 11 Jun 2025 21:11:18 -0700
Subject: [PATCH 441/623] Instantiate only specific RAFT reduction kernels
 (#925)

Depends on https://github.com/rapidsai/raft/pull/2679 with reference issue https://github.com/rapidsai/raft/issues/2681.

This PR reduces the wheel size from 936.9 MB to 917.1 MB.

Authors:
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Robert Maynard (https://github.com/robertmaynard)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Mike Sarahan (https://github.com/msarahan)
  - https://github.com/jakirkham

URL: https://github.com/rapidsai/cuvs/pull/925
---
 build.sh                                      |  2 +-
 cpp/cmake/patches/faiss.diff                  | 19 ++++
 cpp/cmake/patches/faiss_override.json         |  9 +-
 cpp/src/cluster/detail/kmeans.cuh             | 50 +++-------
 cpp/src/cluster/detail/kmeans_balanced.cuh    | 22 ++---
 cpp/src/cluster/detail/kmeans_common.cuh      | 24 ++---
 cpp/src/cluster/detail/kmeans_mg.cuh          | 18 +---
 cpp/src/cluster/kmeans.cuh                    |  9 +-
 cpp/src/distance/detail/distance.cuh          | 98 ++++++++-----------
 .../detail/kernels/kernel_matrices.cu         | 20 ++--
 cpp/src/neighbors/detail/fused_l2_knn.cuh     | 18 +++-
 cpp/src/neighbors/detail/knn_brute_force.cuh  | 72 +++++---------
 cpp/src/neighbors/detail/tiered_index.cuh     | 16 +--
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh | 49 ++++------
 .../neighbors/ivf_flat/ivf_flat_search.cuh    | 26 +++--
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     | 22 ++---
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |  6 +-
 .../spectral/modularity_maximization.hpp      |  4 +-
 .../neighbors/detail/cross_component_nn.cuh   |  4 +-
 .../stats/detail/batched/silhouette_score.cuh |  4 +-
 cpp/src/stats/detail/silhouette_score.cuh     |  4 +-
 cpp/tests/distance/masked_nn.cu               | 14 +--
 cpp/tests/neighbors/ann_cagra.cuh             | 28 +++---
 cpp/tests/neighbors/ann_ivf_flat.cuh          |  4 +-
 .../preprocessing/scalar_quantization.cu      | 15 ++-
 25 files changed, 237 insertions(+), 320 deletions(-)
 create mode 100644 cpp/cmake/patches/faiss.diff

diff --git a/build.sh b/build.sh
index 308e80e9da..6af2907a4e 100755
--- a/build.sh
+++ b/build.sh
@@ -450,7 +450,7 @@ if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg
       MSG="${MSG}<br/>parallel setting: $PARALLEL_LEVEL"
       MSG="${MSG}<br/>parallel build time: $compile_total seconds"
       if [[ -f "${LIBCUVS_BUILD_DIR}/libcuvs.so" ]]; then
-          LIBCUVS_FS=$(ls -lh ${LIBCUVS_BUILD_DIR}/libcuvs.so | awk '{print $5}')
+          LIBCUVS_FS=$(stat -c %s ${LIBCUVS_BUILD_DIR}/libcuvs.so | awk '{printf "%.2f MB", $1/1024/1024}')
           MSG="${MSG}<br/>libcuvs.so size: $LIBCUVS_FS"
       fi
       BMR_DIR=${RAPIDS_ARTIFACTS_DIR:-"${LIBCUVS_BUILD_DIR}"}
diff --git a/cpp/cmake/patches/faiss.diff b/cpp/cmake/patches/faiss.diff
new file mode 100644
index 0000000000..bf92f89d77
--- /dev/null
+++ b/cpp/cmake/patches/faiss.diff
@@ -0,0 +1,19 @@
+diff --git a/faiss/gpu/impl/CuvsIVFFlat.cu b/faiss/gpu/impl/CuvsIVFFlat.cu
+index 2cccee860..c4cb13f48 100644
+--- a/faiss/gpu/impl/CuvsIVFFlat.cu
++++ b/faiss/gpu/impl/CuvsIVFFlat.cu
+@@ -427,13 +427,11 @@ void CuvsIVFFlat::copyInvertedListsFrom(const InvertedLists* ivf) {
+     // Precompute the centers vector norms for L2Expanded distance
+     if (this->metric_ == faiss::METRIC_L2) {
+         cuvs_index->allocate_center_norms(raft_handle);
+-        raft::linalg::rowNorm(
++        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+                 cuvs_index->center_norms().value().data_handle(),
+                 cuvs_index->centers().data_handle(),
+                 cuvs_index->dim(),
+                 (uint32_t)nlist,
+-                raft::linalg::L2Norm,
+-                true,
+                 raft_handle.get_stream());
+     }
+ }
diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
index 37cd51216b..07d757f314 100644
--- a/cpp/cmake/patches/faiss_override.json
+++ b/cpp/cmake/patches/faiss_override.json
@@ -3,7 +3,14 @@
       "faiss" : {
         "version": "1.10.0",
         "git_url": "https://github.com/facebookresearch/faiss.git",
-        "git_tag": "main"
+        "git_tag": "main",
+        "patches" : [
+          {
+            "file" : "${current_json_dir}/faiss.diff",
+            "issue" : "Apply RAFT breaking changes",
+            "fixed_in" : ""
+          }
+        ]
       }
     }
   }
diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
index 3bed639c34..5ed432a7f2 100644
--- a/cpp/src/cluster/detail/kmeans.cuh
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -143,13 +143,8 @@ void kmeansPlusPlus(raft::resources const& handle,
 
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
-    raft::linalg::rowNorm(L2NormX.data_handle(),
-                          X.data_handle(),
-                          X.extent(1),
-                          X.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      L2NormX.data_handle(), X.data_handle(), X.extent(1), X.extent(0), stream);
   }
 
   raft::random::RngState rng(params.rng_state.seed, params.rng_state.type);
@@ -216,14 +211,12 @@ void kmeansPlusPlus(raft::resources const& handle,
 
     // Calculate costPerCandidate[n_trials] where costPerCandidate[i] is the cluster cost when using
     // centroid candidate-i
-    raft::linalg::reduce(costPerCandidate.data_handle(),
-                         minDistBuf.data_handle(),
-                         minDistBuf.extent(1),
-                         minDistBuf.extent(0),
-                         static_cast<DataT>(0),
-                         true,
-                         true,
-                         stream);
+    raft::linalg::reduce<true, true>(costPerCandidate.data_handle(),
+                                     minDistBuf.data_handle(),
+                                     minDistBuf.extent(1),
+                                     minDistBuf.extent(0),
+                                     static_cast<DataT>(0),
+                                     stream);
 
     // Greedy Choice - Choose the candidate that has minimum cluster cost
     // ArgMin operation below identifies the index of minimum cost in costPerCandidate
@@ -406,13 +399,8 @@ void kmeans_fit_main(raft::resources const& handle,
 
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
-    raft::linalg::rowNorm(L2NormX.data_handle(),
-                          X.data_handle(),
-                          X.extent(1),
-                          X.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      L2NormX.data_handle(), X.data_handle(), X.extent(1), X.extent(0), stream);
   }
 
   RAFT_LOG_DEBUG(
@@ -634,13 +622,8 @@ void initScalableKMeansPlusPlus(raft::resources const& handle,
   auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
-    raft::linalg::rowNorm(L2NormX.data_handle(),
-                          X.data_handle(),
-                          X.extent(1),
-                          X.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      L2NormX.data_handle(), X.data_handle(), X.extent(1), X.extent(0), stream);
   }
 
   auto minClusterDistanceVec = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
@@ -1049,13 +1032,8 @@ void kmeans_predict(raft::resources const& handle,
   auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
-    raft::linalg::rowNorm(L2NormX.data_handle(),
-                          X.data_handle(),
-                          X.extent(1),
-                          X.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      L2NormX.data_handle(), X.data_handle(), X.extent(1), X.extent(0), stream);
   }
 
   // computes minClusterAndDistance[0:n_samples) where  minClusterAndDistance[i]
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index 0c5ce644ac..c93eb45c3c 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -113,8 +113,8 @@ inline std::enable_if_t<std::is_floating_point_v<MathT>> predict_core(
 
       auto centroidsNorm =
         raft::make_device_mdarray<MathT, IdxT>(handle, mr, raft::make_extents<IdxT>(n_clusters));
-      raft::linalg::rowNorm<MathT, IdxT>(
-        centroidsNorm.data_handle(), centers, dim, n_clusters, raft::linalg::L2Norm, true, stream);
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true, MathT, IdxT>(
+        centroidsNorm.data_handle(), centers, dim, n_clusters, stream);
 
       cuvs::distance::fusedDistanceNNMinReduce<MathT, raft::KeyValuePair<IdxT, MathT>, IdxT>(
         minClusterAndDistance.data_handle(),
@@ -156,14 +156,8 @@ inline std::enable_if_t<std::is_floating_point_v<MathT>> predict_core(
 
       auto centroidsNorm =
         raft::make_device_mdarray<MathT, IdxT>(handle, mr, raft::make_extents<IdxT>(n_clusters));
-      raft::linalg::rowNorm<MathT, IdxT>(centroidsNorm.data_handle(),
-                                         centers,
-                                         dim,
-                                         n_clusters,
-                                         raft::linalg::L2Norm,
-                                         true,
-                                         stream,
-                                         raft::sqrt_op{});
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true, MathT, IdxT>(
+        centroidsNorm.data_handle(), centers, dim, n_clusters, stream, raft::sqrt_op{});
 
       cuvs::distance::fusedDistanceNNMinReduce<MathT, raft::KeyValuePair<IdxT, MathT>, IdxT>(
         minClusterAndDistance.data_handle(),
@@ -395,8 +389,8 @@ void compute_norm(const raft::resources& handle,
     dataset_ptr = static_cast<const MathT*>(mapped_dataset.data());
   }
 
-  raft::linalg::rowNorm<MathT, IdxT>(
-    dataset_norm, dataset_ptr, dim, n_rows, raft::linalg::L2Norm, true, stream, norm_fin_op);
+  raft::linalg::rowNorm<raft::linalg::L2Norm, true, MathT, IdxT>(
+    dataset_norm, dataset_ptr, dim, n_rows, stream, norm_fin_op);
 }
 
 /**
@@ -732,8 +726,8 @@ void balancing_em_iters(const raft::resources& handle,
           cluster_centers, n_clusters, dim);
         auto clusters_out_view = raft::make_device_matrix_view<MathT, IdxT, raft::row_major>(
           cluster_centers, n_clusters, dim);
-        raft::linalg::row_normalize(
-          handle, clusters_in_view, clusters_out_view, raft::linalg::L2Norm);
+        raft::linalg::row_normalize<raft::linalg::L2Norm>(
+          handle, clusters_in_view, clusters_out_view);
         break;
       }
       default: break;
diff --git a/cpp/src/cluster/detail/kmeans_common.cuh b/cpp/src/cluster/detail/kmeans_common.cuh
index 45b425d3bd..3caa5e8556 100644
--- a/cpp/src/cluster/detail/kmeans_common.cuh
+++ b/cpp/src/cluster/detail/kmeans_common.cuh
@@ -382,13 +382,11 @@ void minClusterAndDistanceCompute(
 
   if (is_fused) {
     L2NormBuf_OR_DistBuf.resize(n_clusters, stream);
-    raft::linalg::rowNorm(L2NormBuf_OR_DistBuf.data(),
-                          centroids.data_handle(),
-                          centroids.extent(1),
-                          centroids.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(L2NormBuf_OR_DistBuf.data(),
+                                                      centroids.data_handle(),
+                                                      centroids.extent(1),
+                                                      centroids.extent(0),
+                                                      stream);
   } else {
     // TODO: Unless pool allocator is used, passing in a workspace for this
     // isn't really increasing performance because this needs to do a re-allocation
@@ -518,13 +516,11 @@ void minClusterDistanceCompute(raft::resources const& handle,
 
   if (is_fused) {
     L2NormBuf_OR_DistBuf.resize(n_clusters, stream);
-    raft::linalg::rowNorm(L2NormBuf_OR_DistBuf.data(),
-                          centroids.data_handle(),
-                          centroids.extent(1),
-                          centroids.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(L2NormBuf_OR_DistBuf.data(),
+                                                      centroids.data_handle(),
+                                                      centroids.extent(1),
+                                                      centroids.extent(0),
+                                                      stream);
   } else {
     L2NormBuf_OR_DistBuf.resize(dataBatchSize * centroidsBatchSize, stream);
   }
diff --git a/cpp/src/cluster/detail/kmeans_mg.cuh b/cpp/src/cluster/detail/kmeans_mg.cuh
index 4747eb8791..c0168ad0f7 100644
--- a/cpp/src/cluster/detail/kmeans_mg.cuh
+++ b/cpp/src/cluster/detail/kmeans_mg.cuh
@@ -228,13 +228,8 @@ void initKMeansPlusPlus(const raft::resources& handle,
   auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
-    raft::linalg::rowNorm(L2NormX.data_handle(),
-                          X.data_handle(),
-                          X.extent(1),
-                          X.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      L2NormX.data_handle(), X.data_handle(), X.extent(1), X.extent(0), stream);
   }
 
   auto minClusterDistance = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
@@ -578,13 +573,8 @@ void fit(const raft::resources& handle,
   auto L2NormX = raft::make_device_vector<DataT, IndexT>(handle, n_samples);
   if (metric == cuvs::distance::DistanceType::L2Expanded ||
       metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
-    raft::linalg::rowNorm(L2NormX.data_handle(),
-                          X.data_handle(),
-                          X.extent(1),
-                          X.extent(0),
-                          raft::linalg::L2Norm,
-                          true,
-                          stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      L2NormX.data_handle(), X.data_handle(), X.extent(1), X.extent(0), stream);
   }
 
   DataT priorClusteringCost = 0;
diff --git a/cpp/src/cluster/kmeans.cuh b/cpp/src/cluster/kmeans.cuh
index 13036795ab..ae9703081a 100644
--- a/cpp/src/cluster/kmeans.cuh
+++ b/cpp/src/cluster/kmeans.cuh
@@ -481,13 +481,8 @@ void cluster_cost(raft::resources const& handle,
 
   auto x_norms = raft::make_device_vector<DataT>(handle, n_samples);
 
-  raft::linalg::rowNorm(x_norms.data_handle(),
-                        X.data_handle(),
-                        n_features,
-                        n_samples,
-                        raft::linalg::L2Norm,
-                        true,
-                        stream);
+  raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+    x_norms.data_handle(), X.data_handle(), n_features, n_samples, stream);
 
   auto min_cluster_distance = raft::make_device_vector<DataT>(handle, n_samples);
   rmm::device_uvector<DataT> l2_norm_or_distance_buffer(0, stream);
diff --git a/cpp/src/distance/detail/distance.cuh b/cpp/src/distance/detail/distance.cuh
index d6fd046469..034598be4e 100644
--- a/cpp/src/distance/detail/distance.cuh
+++ b/cpp/src/distance/detail/distance.cuh
@@ -141,50 +141,34 @@ void distance_impl(raft::resources const& handle,
   // perhaps the use of stridedSummationKernel could be causing this,
   // need to investigate and fix.
   if (x == y && is_row_major) {
-    raft::linalg::reduce(x_norm,
-                         x,
-                         k,
-                         std::max(m, n),
-                         (AccT)0,
-                         is_row_major,
-                         true,
-                         stream,
-                         false,
-                         raft::identity_op(),
-                         raft::add_op());
+    raft::linalg::reduce<true, true>(
+      x_norm, x, k, std::max(m, n), (AccT)0, stream, false, raft::identity_op(), raft::add_op());
     sq_x_norm += std::max(m, n);
     sq_y_norm = sq_x_norm;
-    raft::linalg::rowNorm(
-      sq_x_norm, x, k, std::max(m, n), raft::linalg::L2Norm, is_row_major, stream);
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(sq_x_norm, x, k, std::max(m, n), stream);
   } else {
     y_norm += m;
-    raft::linalg::reduce(x_norm,
-                         x,
-                         k,
-                         m,
-                         (AccT)0,
-                         is_row_major,
-                         true,
-                         stream,
-                         false,
-                         raft::identity_op(),
-                         raft::add_op());
-    raft::linalg::reduce(y_norm,
-                         y,
-                         k,
-                         n,
-                         (AccT)0,
-                         is_row_major,
-                         true,
-                         stream,
-                         false,
-                         raft::identity_op(),
-                         raft::add_op());
+    if (is_row_major) {
+      raft::linalg::reduce<true, true>(
+        x_norm, x, k, m, (AccT)0, stream, false, raft::identity_op(), raft::add_op());
+      raft::linalg::reduce<true, true>(
+        y_norm, y, k, n, (AccT)0, stream, false, raft::identity_op(), raft::add_op());
+    } else {
+      raft::linalg::reduce<false, true>(
+        x_norm, x, k, m, (AccT)0, stream, false, raft::identity_op(), raft::add_op());
+      raft::linalg::reduce<false, true>(
+        y_norm, y, k, n, (AccT)0, stream, false, raft::identity_op(), raft::add_op());
+    }
 
     sq_x_norm += (m + n);
     sq_y_norm = sq_x_norm + m;
-    raft::linalg::rowNorm(sq_x_norm, x, k, m, raft::linalg::L2Norm, is_row_major, stream);
-    raft::linalg::rowNorm(sq_y_norm, y, k, n, raft::linalg::L2Norm, is_row_major, stream);
+    if (is_row_major) {
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(sq_x_norm, x, k, m, stream);
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(sq_y_norm, y, k, n, stream);
+    } else {
+      raft::linalg::rowNorm<raft::linalg::L2Norm, false>(sq_x_norm, x, k, m, stream);
+      raft::linalg::rowNorm<raft::linalg::L2Norm, false>(sq_y_norm, y, k, n, stream);
+    }
   }
 
   using OpT = ops::correlation_distance_op<DataT, AccT, IdxT>;
@@ -224,14 +208,17 @@ void distance_impl(raft::resources const& handle,
   // perhaps the use of stridedSummationKernel could be causing this,
   // need to investigate and fix.
   if (x == y && is_row_major) {
-    raft::linalg::rowNorm(
-      x_norm, x, k, std::max(m, n), raft::linalg::L2Norm, is_row_major, stream, raft::sqrt_op{});
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      x_norm, x, k, std::max(m, n), stream, raft::sqrt_op{});
   } else {
     y_norm += m;
-    raft::linalg::rowNorm(
-      x_norm, x, k, m, raft::linalg::L2Norm, is_row_major, stream, raft::sqrt_op{});
-    raft::linalg::rowNorm(
-      y_norm, y, k, n, raft::linalg::L2Norm, is_row_major, stream, raft::sqrt_op{});
+    if (is_row_major) {
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(x_norm, x, k, m, stream, raft::sqrt_op{});
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(y_norm, y, k, n, stream, raft::sqrt_op{});
+    } else {
+      raft::linalg::rowNorm<raft::linalg::L2Norm, false>(x_norm, x, k, m, stream, raft::sqrt_op{});
+      raft::linalg::rowNorm<raft::linalg::L2Norm, false>(y_norm, y, k, n, stream, raft::sqrt_op{});
+    }
   }
 
   ops::cosine_distance_op<DataT, AccT, IdxT> distance_op{};
@@ -482,20 +469,21 @@ void distance_impl_l2_expanded(  // NOTE: different name
   // perhaps the use of stridedSummationKernel could be causing this,
   // need to investigate and fix.
   if ((x == y) && is_row_major) {
-    raft::linalg::rowNorm(x_norm,
-                          x,
-                          k,
-                          std::max(m, n),
-                          raft::linalg::L2Norm,
-                          is_row_major,
-                          stream,
-                          raft::identity_op{});
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+      x_norm, x, k, std::max(m, n), stream, raft::identity_op{});
   } else {
     y_norm += m;
-    raft::linalg::rowNorm(
-      x_norm, x, k, m, raft::linalg::L2Norm, is_row_major, stream, raft::identity_op{});
-    raft::linalg::rowNorm(
-      y_norm, y, k, n, raft::linalg::L2Norm, is_row_major, stream, raft::identity_op{});
+    if (is_row_major) {
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+        x_norm, x, k, m, stream, raft::identity_op{});
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+        y_norm, y, k, n, stream, raft::identity_op{});
+    } else {
+      raft::linalg::rowNorm<raft::linalg::L2Norm, false>(
+        x_norm, x, k, m, stream, raft::identity_op{});
+      raft::linalg::rowNorm<raft::linalg::L2Norm, false>(
+        y_norm, y, k, n, stream, raft::identity_op{});
+    }
   }
 
   ops::l2_exp_distance_op<DataT, AccT, IdxT> distance_op{perform_sqrt};
diff --git a/cpp/src/distance/detail/kernels/kernel_matrices.cu b/cpp/src/distance/detail/kernels/kernel_matrices.cu
index 526ca106ff..571635560e 100644
--- a/cpp/src/distance/detail/kernels/kernel_matrices.cu
+++ b/cpp/src/distance/detail/kernels/kernel_matrices.cu
@@ -477,13 +477,19 @@ void RBFKernel<math_t>::matrixRowNormL2(raft::resources const& handle,
   int minor         = is_row_major ? matrix.extent(1) : matrix.extent(0);
   int ld            = is_row_major ? matrix.stride(0) : matrix.stride(1);
   ASSERT(ld == minor, "RBF Kernel lazy rowNorm compute does not support ld parameter");
-  raft::linalg::rowNorm(target,
-                        matrix.data_handle(),
-                        matrix.extent(1),
-                        matrix.extent(0),
-                        raft::linalg::NormType::L2Norm,
-                        is_row_major,
-                        raft::resource::get_cuda_stream(handle));
+  if (is_row_major) {
+    raft::linalg::rowNorm<raft::linalg::L2Norm, true>(target,
+                                                      matrix.data_handle(),
+                                                      matrix.extent(1),
+                                                      matrix.extent(0),
+                                                      raft::resource::get_cuda_stream(handle));
+  } else {
+    raft::linalg::rowNorm<raft::linalg::L2Norm, false>(target,
+                                                       matrix.data_handle(),
+                                                       matrix.extent(1),
+                                                       matrix.extent(0),
+                                                       raft::resource::get_cuda_stream(handle));
+  }
 }
 
 template <typename math_t>
diff --git a/cpp/src/neighbors/detail/fused_l2_knn.cuh b/cpp/src/neighbors/detail/fused_l2_knn.cuh
index be904673ea..5a3d07a468 100644
--- a/cpp/src/neighbors/detail/fused_l2_knn.cuh
+++ b/cpp/src/neighbors/detail/fused_l2_knn.cuh
@@ -792,8 +792,13 @@ void fusedL2ExpKnnImpl(const DataT* x,
     if (!xn) {
       AccT* xn_ = (AccT*)workspace;
       workspace = xn_ + m;
-      raft::linalg::rowNorm(
-        xn_, x, k, m, raft::linalg::L2Norm, isRowMajor, stream, raft::identity_op{});
+      if (isRowMajor) {
+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+          xn_, x, k, m, stream, raft::identity_op{});
+      } else {
+        raft::linalg::rowNorm<raft::linalg::L2Norm, false>(
+          xn_, x, k, m, stream, raft::identity_op{});
+      }
       xn = xn_;
     }
     if (!yn) {
@@ -801,8 +806,13 @@ void fusedL2ExpKnnImpl(const DataT* x,
         yn = xn;
       } else {
         AccT* yn_ = (AccT*)(workspace);
-        raft::linalg::rowNorm(
-          yn_, y, k, n, raft::linalg::L2Norm, isRowMajor, stream, raft::identity_op{});
+        if (isRowMajor) {
+          raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+            yn_, y, k, n, stream, raft::identity_op{});
+        } else {
+          raft::linalg::rowNorm<raft::linalg::L2Norm, false>(
+            yn_, y, k, n, stream, raft::identity_op{});
+        }
         yn = yn_;
       }
     }
diff --git a/cpp/src/neighbors/detail/knn_brute_force.cuh b/cpp/src/neighbors/detail/knn_brute_force.cuh
index 9202fe1c89..7da7a0916d 100644
--- a/cpp/src/neighbors/detail/knn_brute_force.cuh
+++ b/cpp/src/neighbors/detail/knn_brute_force.cuh
@@ -122,33 +122,20 @@ void tiled_brute_force_knn(const raft::resources& handle,
     // cosine needs the l2norm, where as l2 distances needs the squared norm
     if (metric == cuvs::distance::DistanceType::CosineExpanded) {
       if (!precomputed_search_norms) {
-        raft::linalg::rowNorm(search_norms.data(),
-                              search,
-                              d,
-                              m,
-                              raft::linalg::NormType::L2Norm,
-                              true,
-                              stream,
-                              raft::sqrt_op{});
+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+          search_norms.data(), search, d, m, stream, raft::sqrt_op{});
       }
       if (!precomputed_index_norms) {
-        raft::linalg::rowNorm(index_norms.data(),
-                              index,
-                              d,
-                              n,
-                              raft::linalg::NormType::L2Norm,
-                              true,
-                              stream,
-                              raft::sqrt_op{});
+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+          index_norms.data(), index, d, n, stream, raft::sqrt_op{});
       }
     } else {
       if (!precomputed_search_norms) {
-        raft::linalg::rowNorm(
-          search_norms.data(), search, d, m, raft::linalg::NormType::L2Norm, true, stream);
+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+          search_norms.data(), search, d, m, stream);
       }
       if (!precomputed_index_norms) {
-        raft::linalg::rowNorm(
-          index_norms.data(), index, d, n, raft::linalg::NormType::L2Norm, true, stream);
+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(index_norms.data(), index, d, n, stream);
       }
     }
     pairwise_metric = cuvs::distance::DistanceType::InnerProduct;
@@ -706,26 +693,24 @@ void brute_force_search_filtered(
       if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         if (!query_norms) {
           query_norms_ = raft::make_device_vector<DistanceT, IdxT>(res, n_queries);
-          raft::linalg::rowNorm((DistanceT*)(query_norms_->data_handle()),
-                                queries.data_handle(),
-                                dim,
-                                n_queries,
-                                raft::linalg::L2Norm,
-                                true,
-                                stream,
-                                raft::sqrt_op{});
+          raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+            (DistanceT*)(query_norms_->data_handle()),
+            queries.data_handle(),
+            dim,
+            n_queries,
+            stream,
+            raft::sqrt_op{});
         }
       } else {
         if (!query_norms) {
           query_norms_ = raft::make_device_vector<DistanceT, IdxT>(res, n_queries);
-          raft::linalg::rowNorm((DistanceT*)(query_norms_->data_handle()),
-                                queries.data_handle(),
-                                dim,
-                                n_queries,
-                                raft::linalg::L2Norm,
-                                true,
-                                stream,
-                                raft::identity_op{});
+          raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+            (DistanceT*)(query_norms_->data_handle()),
+            queries.data_handle(),
+            dim,
+            n_queries,
+            stream,
+            raft::identity_op{});
         }
       }
       cuvs::neighbors::detail::epilogue_on_csr(
@@ -819,18 +804,11 @@ cuvs::neighbors::brute_force::index<T, DistT> build(
     norms = raft::make_device_vector<DistT, int64_t>(res, dataset.extent(0));
     // cosine needs the l2norm, where as l2 distances needs the squared norm
     if (metric == cuvs::distance::DistanceType::CosineExpanded) {
-      raft::linalg::norm(res,
-                         dataset_view,
-                         norms->view(),
-                         raft::linalg::NormType::L2Norm,
-                         raft::linalg::Apply::ALONG_ROWS,
-                         raft::sqrt_op{});
+      raft::linalg::norm<raft::linalg::L2Norm, raft::Apply::ALONG_ROWS>(
+        res, dataset_view, norms->view(), raft::sqrt_op{});
     } else {
-      raft::linalg::norm(res,
-                         dataset_view,
-                         norms->view(),
-                         raft::linalg::NormType::L2Norm,
-                         raft::linalg::Apply::ALONG_ROWS);
+      raft::linalg::norm<raft::linalg::L2Norm, raft::Apply::ALONG_ROWS>(
+        res, dataset_view, norms->view());
     }
   }
 
diff --git a/cpp/src/neighbors/detail/tiered_index.cuh b/cpp/src/neighbors/detail/tiered_index.cuh
index 2251448aab..ef7fd7d015 100644
--- a/cpp/src/neighbors/detail/tiered_index.cuh
+++ b/cpp/src/neighbors/detail/tiered_index.cuh
@@ -22,6 +22,7 @@
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/core/types.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/linalg/map.cuh>
 #include <raft/linalg/norm.cuh>
@@ -86,18 +87,11 @@ struct brute_force_storage {
       auto norms_view =
         raft::make_device_vector_view<T>(norms->data() + num_rows_used, new_vectors.extent(0));
       if (metric == cuvs::distance::DistanceType::CosineExpanded) {
-        raft::linalg::norm(res,
-                           new_vectors,
-                           norms_view,
-                           raft::linalg::NormType::L2Norm,
-                           raft::linalg::Apply::ALONG_ROWS,
-                           raft::sqrt_op{});
+        raft::linalg::norm<raft::linalg::NormType::L2Norm, raft::Apply::ALONG_ROWS>(
+          res, new_vectors, norms_view, raft::sqrt_op{});
       } else {
-        raft::linalg::norm(res,
-                           new_vectors,
-                           norms_view,
-                           raft::linalg::NormType::L2Norm,
-                           raft::linalg::Apply::ALONG_ROWS);
+        raft::linalg::norm<raft::linalg::NormType::L2Norm, raft::Apply::ALONG_ROWS>(
+          res, new_vectors, norms_view);
       }
     }
     num_rows_used += new_vectors.extent(0);
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index a1bec125b1..00fc9d4779 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -340,43 +340,32 @@ void extend(raft::resources const& handle,
     index->allocate_center_norms(handle);
     if (index->center_norms().has_value()) {
       if (index->metric() == cuvs::distance::DistanceType::CosineExpanded) {
-        raft::linalg::rowNorm(index->center_norms()->data_handle(),
-                              index->centers().data_handle(),
-                              dim,
-                              n_lists,
-                              raft::linalg::L2Norm,
-                              true,
-                              stream,
-                              raft::sqrt_op{});
+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(index->center_norms()->data_handle(),
+                                                          index->centers().data_handle(),
+                                                          dim,
+                                                          n_lists,
+                                                          stream,
+                                                          raft::sqrt_op{});
       } else {
-        raft::linalg::rowNorm(index->center_norms()->data_handle(),
-                              index->centers().data_handle(),
-                              dim,
-                              n_lists,
-                              raft::linalg::L2Norm,
-                              true,
-                              stream);
+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(index->center_norms()->data_handle(),
+                                                          index->centers().data_handle(),
+                                                          dim,
+                                                          n_lists,
+                                                          stream);
       }
       RAFT_LOG_TRACE_VEC(index->center_norms()->data_handle(), std::min<uint32_t>(dim, 20));
     }
   } else if (index->center_norms().has_value() && index->adaptive_centers()) {
     if (index->metric() == cuvs::distance::DistanceType::CosineExpanded) {
-      raft::linalg::rowNorm(index->center_norms()->data_handle(),
-                            index->centers().data_handle(),
-                            dim,
-                            n_lists,
-                            raft::linalg::L2Norm,
-                            true,
-                            stream,
-                            raft::sqrt_op{});
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(index->center_norms()->data_handle(),
+                                                        index->centers().data_handle(),
+                                                        dim,
+                                                        n_lists,
+                                                        stream,
+                                                        raft::sqrt_op{});
     } else {
-      raft::linalg::rowNorm(index->center_norms()->data_handle(),
-                            index->centers().data_handle(),
-                            dim,
-                            n_lists,
-                            raft::linalg::L2Norm,
-                            true,
-                            stream);
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+        index->center_norms()->data_handle(), index->centers().data_handle(), dim, n_lists, stream);
     }
     RAFT_LOG_TRACE_VEC(index->center_norms()->data_handle(), std::min<uint32_t>(dim, 20));
   }
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index c51236d1aa..d36043ad45 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -108,13 +108,11 @@ void search_impl(raft::resources const& handle,
     case cuvs::distance::DistanceType::L2SqrtExpanded: {
       alpha = -2.0f;
       beta  = 1.0f;
-      raft::linalg::rowNorm(query_norm_dev.data(),
-                            converted_queries_ptr,
-                            static_cast<IdxT>(index.dim()),
-                            static_cast<IdxT>(n_queries),
-                            raft::linalg::L2Norm,
-                            true,
-                            stream);
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(query_norm_dev.data(),
+                                                        converted_queries_ptr,
+                                                        static_cast<IdxT>(index.dim()),
+                                                        static_cast<IdxT>(n_queries),
+                                                        stream);
       utils::outer_add(query_norm_dev.data(),
                        (IdxT)n_queries,
                        index.center_norms()->data_handle(),
@@ -126,14 +124,12 @@ void search_impl(raft::resources const& handle,
       break;
     }
     case cuvs::distance::DistanceType::CosineExpanded: {
-      raft::linalg::rowNorm(query_norm_dev.data(),
-                            converted_queries_ptr,
-                            static_cast<IdxT>(index.dim()),
-                            static_cast<IdxT>(n_queries),
-                            raft::linalg::L2Norm,
-                            true,
-                            stream,
-                            raft::sqrt_op{});
+      raft::linalg::rowNorm<raft::linalg::L2Norm, true>(query_norm_dev.data(),
+                                                        converted_queries_ptr,
+                                                        static_cast<IdxT>(index.dim()),
+                                                        static_cast<IdxT>(n_queries),
+                                                        stream,
+                                                        raft::sqrt_op{});
       alpha = -1.0f;
       beta  = 0.0f;
       break;
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index aaeb2cdd5e..d4f1b6e686 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -254,13 +254,8 @@ void set_centers(raft::resources const& handle, index<IdxT>* index, const float*
                                   stream));
 
   rmm::device_uvector<float> center_norms(index->n_lists(), stream, device_memory);
-  raft::linalg::rowNorm(center_norms.data(),
-                        cluster_centers,
-                        index->dim(),
-                        index->n_lists(),
-                        raft::linalg::L2Norm,
-                        true,
-                        stream);
+  raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+    center_norms.data(), cluster_centers, index->dim(), index->n_lists(), stream);
   RAFT_CUDA_TRY(cudaMemcpy2DAsync(index->centers().data_handle() + index->dim(),
                                   sizeof(float) * index->dim_ext(),
                                   center_norms.data(),
@@ -1649,10 +1644,8 @@ void extend(raft::resources const& handle,
     if (index->metric() == CosineExpanded) {
       auto vec_batch_view = raft::make_device_matrix_view<T, internal_extents_t>(
         const_cast<T*>(vec_batch.data()), vec_batch.size(), index->dim());
-      raft::linalg::row_normalize(handle,
-                                  raft::make_const_mdspan(vec_batch_view),
-                                  vec_batch_view,
-                                  raft::linalg::NormType::L2Norm);
+      raft::linalg::row_normalize<raft::linalg::L2Norm>(
+        handle, raft::make_const_mdspan(vec_batch_view), vec_batch_view);
     }
     process_and_fill_codes(handle,
                            *index,
@@ -1789,8 +1782,8 @@ auto build(raft::resources const& handle,
     kmeans_params.metric  = static_cast<cuvs::distance::DistanceType>((int)index.metric());
 
     if (index.metric() == distance::DistanceType::CosineExpanded) {
-      raft::linalg::row_normalize(
-        handle, trainset_const_view, trainset.view(), raft::linalg::NormType::L2Norm);
+      raft::linalg::row_normalize<raft::linalg::L2Norm>(
+        handle, trainset_const_view, trainset.view());
     }
     cuvs::cluster::kmeans_balanced::fit(
       handle, kmeans_params, trainset_const_view, centers_view, utils::mapping<float>{});
@@ -1800,8 +1793,7 @@ auto build(raft::resources const& handle,
     auto centers_const_view = raft::make_device_matrix_view<const float, internal_extents_t>(
       cluster_centers, index.n_lists(), index.dim());
     if (index.metric() == distance::DistanceType::CosineExpanded) {
-      raft::linalg::row_normalize(
-        handle, centers_const_view, centers_view, raft::linalg::NormType::L2Norm);
+      raft::linalg::row_normalize<raft::linalg::L2Norm>(handle, centers_const_view, centers_view);
     }
     auto labels_view =
       raft::make_device_vector_view<uint32_t, internal_extents_t>(labels.data(), n_rows_train);
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index feba4e7ae7..3d86ff7a37 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -943,10 +943,8 @@ inline void search(raft::resources const& handle,
     if (index.metric() == distance::DistanceType::CosineExpanded) {
       auto rot_queries_view = raft::make_device_matrix_view<float, uint32_t>(
         rot_queries.data(), max_queries, index.rot_dim());
-      raft::linalg::row_normalize(handle,
-                                  raft::make_const_mdspan(rot_queries_view),
-                                  rot_queries_view,
-                                  raft::linalg::NormType::L2Norm);
+      raft::linalg::row_normalize<raft::linalg::L2Norm>(
+        handle, raft::make_const_mdspan(rot_queries_view), rot_queries_view);
     }
     for (uint32_t offset_b = 0; offset_b < queries_batch; offset_b += max_batch_size) {
       uint32_t batch_size = min(max_batch_size, queries_batch - offset_b);
diff --git a/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp b/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
index 3642dc4f42..d002ea8d24 100644
--- a/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
+++ b/cpp/src/sparse/cluster/detail/spectral/modularity_maximization.hpp
@@ -109,8 +109,8 @@ std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
   // notice that at this point the matrix has already been transposed, so we are scaling
   // columns
   auto dataset_view = raft::make_device_matrix_view(eigVecs, nEigVecs, n);
-  raft::linalg::row_normalize(
-    handle, raft::make_const_mdspan(dataset_view), dataset_view, raft::linalg::L2Norm);
+  raft::linalg::row_normalize<raft::linalg::L2Norm>(
+    handle, raft::make_const_mdspan(dataset_view), dataset_view);
 
   // Find partition clustering
   auto pair_cluster = cluster_solver.solve(handle, n, nEigVecs, eigVecs, clusters);
diff --git a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
index c5cf52a449..0bc85752a7 100644
--- a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
+++ b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
@@ -249,8 +249,8 @@ void perform_1nn(raft::resources const& handle,
     colors_group_idxs.data_handle() + 1, n_components);
 
   auto x_norm = raft::make_device_vector<value_t, value_idx>(handle, (value_idx)n_rows);
-  raft::linalg::rowNorm(
-    x_norm.data_handle(), X, n_cols, n_rows, raft::linalg::L2Norm, true, stream);
+  raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
+    x_norm.data_handle(), X, n_cols, n_rows, stream);
 
   auto adj     = raft::make_device_matrix<bool, value_idx>(handle, row_batch_size, n_components);
   using OutT   = raft::KeyValuePair<value_idx, value_t>;
diff --git a/cpp/src/stats/detail/batched/silhouette_score.cuh b/cpp/src/stats/detail/batched/silhouette_score.cuh
index aae14ea6cc..45f1c191be 100644
--- a/cpp/src/stats/detail/batched/silhouette_score.cuh
+++ b/cpp/src/stats/detail/batched/silhouette_score.cuh
@@ -259,14 +259,12 @@ value_t silhouette_score(
 
   // calculating row-wise minimum in b
   // this prim only supports int indices for now
-  raft::linalg::reduce<value_t, value_t, value_idx, raft::identity_op, raft::min_op>(
+  raft::linalg::reduce<true, true, value_t, value_t, value_idx, raft::identity_op, raft::min_op>(
     b_ptr,
     b_ptr,
     n_labels,
     n_rows,
     std::numeric_limits<value_t>::max(),
-    true,
-    true,
     stream,
     false,
     raft::identity_op(),
diff --git a/cpp/src/stats/detail/silhouette_score.cuh b/cpp/src/stats/detail/silhouette_score.cuh
index 6c876ce6e6..88cb051f15 100644
--- a/cpp/src/stats/detail/silhouette_score.cuh
+++ b/cpp/src/stats/detail/silhouette_score.cuh
@@ -286,14 +286,12 @@ DataT silhouette_score(
                                stream);
 
   // calculating row-wise minimum
-  raft::linalg::reduce<DataT, DataT, int, raft::identity_op, raft::min_op>(
+  raft::linalg::reduce<true, true, DataT, DataT, int, raft::identity_op, raft::min_op>(
     d_bArray.data(),
     averageDistanceBetweenSampleAndCluster.data(),
     nLabels,
     nRows,
     std::numeric_limits<DataT>::max(),
-    true,
-    true,
     stream,
     false,
     raft::identity_op{},
diff --git a/cpp/tests/distance/masked_nn.cu b/cpp/tests/distance/masked_nn.cu
index a1c7846693..63fcee1e31 100644
--- a/cpp/tests/distance/masked_nn.cu
+++ b/cpp/tests/distance/masked_nn.cu
@@ -251,16 +251,10 @@ auto run_masked_nn(const raft::handle_t& handle, Inputs<DataT> inp, const Params
   auto x_norm = raft::make_device_vector<DataT, int>(handle, p.m);
   auto y_norm = raft::make_device_vector<DataT, int>(handle, p.n);
 
-  raft::linalg::norm(handle,
-                     std::as_const(inp.x).view(),
-                     x_norm.view(),
-                     raft::linalg::L2Norm,
-                     raft::linalg::Apply::ALONG_ROWS);
-  raft::linalg::norm(handle,
-                     std::as_const(inp.y).view(),
-                     y_norm.view(),
-                     raft::linalg::L2Norm,
-                     raft::linalg::Apply::ALONG_ROWS);
+  raft::linalg::norm<raft::linalg::L2Norm, raft::Apply::ALONG_ROWS>(
+    handle, std::as_const(inp.x).view(), x_norm.view());
+  raft::linalg::norm<raft::linalg::L2Norm, raft::Apply::ALONG_ROWS>(
+    handle, std::as_const(inp.y).view(), y_norm.view());
 
   // Create parameters for masked_l2_nn
   using IdxT       = int;
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index c0f80f1e82..aac2c067d5 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -203,8 +203,8 @@ void InitDataset(const raft::resources& handle,
 
     if (metric == InnerProduct) {
       auto dataset_view = raft::make_device_matrix_view(datatset_ptr, size, dim);
-      raft::linalg::row_normalize(
-        handle, raft::make_const_mdspan(dataset_view), dataset_view, raft::linalg::L2Norm);
+      raft::linalg::row_normalize<raft::linalg::L2Norm>(
+        handle, raft::make_const_mdspan(dataset_view), dataset_view);
     }
   } else if constexpr (std::is_same_v<DataT, std::uint8_t> || std::is_same_v<DataT, std::int8_t>) {
     if constexpr (std::is_same_v<DataT, std::int8_t>) {
@@ -223,24 +223,22 @@ void InitDataset(const raft::resources& handle,
       const auto normalized_norm =
         (std::is_same_v<DataT, std::uint8_t> ? 40 : 20) * std::sqrt(static_cast<ComputeT>(dim));
 
-      raft::linalg::reduce(dev_row_norm.data_handle(),
-                           datatset_ptr,
-                           dim,
-                           size,
-                           0.f,
-                           true,
-                           true,
-                           raft::resource::get_cuda_stream(handle),
-                           false,
-                           raft::sq_op(),
-                           raft::add_op(),
-                           raft::sqrt_op());
+      raft::linalg::reduce<true, true>(dev_row_norm.data_handle(),
+                                       datatset_ptr,
+                                       dim,
+                                       size,
+                                       0.f,
+                                       raft::resource::get_cuda_stream(handle),
+                                       false,
+                                       raft::sq_op(),
+                                       raft::add_op(),
+                                       raft::sqrt_op());
       raft::linalg::matrix_vector_op(
         handle,
         raft::make_const_mdspan(dataset_view),
         raft::make_const_mdspan(dev_row_norm.view()),
         dataset_view,
-        raft::linalg::Apply::ALONG_COLUMNS,
+        raft::Apply::ALONG_COLUMNS,
         [normalized_norm] __device__(DataT elm, ComputeT norm) {
           const ComputeT v           = elm / norm * normalized_norm;
           const ComputeT max_v_range = std::numeric_limits<DataT>::max();
diff --git a/cpp/tests/neighbors/ann_ivf_flat.cuh b/cpp/tests/neighbors/ann_ivf_flat.cuh
index 11a1fba9cb..104ea91956 100644
--- a/cpp/tests/neighbors/ann_ivf_flat.cuh
+++ b/cpp/tests/neighbors/ann_ivf_flat.cuh
@@ -239,8 +239,8 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
                                                                     cluster_data.data(),
                                                                     (IdxT)ps.dim,
                                                                     stream_);
-            raft::stats::mean<float, uint32_t>(
-              centroid.data(), cluster_data.data(), ps.dim, list_sizes[l], false, true, stream_);
+            raft::stats::mean<true, float, uint32_t>(
+              centroid.data(), cluster_data.data(), ps.dim, list_sizes[l], false, stream_);
             ASSERT_TRUE(cuvs::devArrMatch(index_2.centers().data_handle() + ps.dim * l,
                                           centroid.data(),
                                           ps.dim,
diff --git a/cpp/tests/preprocessing/scalar_quantization.cu b/cpp/tests/preprocessing/scalar_quantization.cu
index 2fdfe7555b..bd1991d6f3 100644
--- a/cpp/tests/preprocessing/scalar_quantization.cu
+++ b/cpp/tests/preprocessing/scalar_quantization.cu
@@ -78,14 +78,13 @@ class QuantizationTest : public ::testing::TestWithParam<QuantizationInputs<T>>
     RAFT_CUDA_TRY(cudaMemsetAsync(mu.data(), 0, sizeof(double), stream));
 
     rmm::device_uvector<double> error_stddev(1, stream);
-    raft::stats::stddev(error_stddev.data(),
-                        relative_error.data(),
-                        mu.data(),
-                        1,
-                        elements_to_consider,
-                        false,
-                        true,
-                        stream);
+    raft::stats::stddev<true>(error_stddev.data(),
+                              relative_error.data(),
+                              mu.data(),
+                              1,
+                              elements_to_consider,
+                              false,
+                              stream);
 
     double error_stddev_h;
     raft::update_host(&error_stddev_h, error_stddev.data(), 1, stream);

From 4f1b035e4a543b1d3d1c7e4f72a0c85803179e2a Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 12 Jun 2025 07:25:46 -0700
Subject: [PATCH 442/623] Fix verify-copyright precommit hook (#1017)

The verify-copyright precommit hook was excluding all files, because of an incorrect regex in the exclude leading to match all files. Fix

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - MithunR (https://github.com/mythrocks)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/1017
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f3960d82de..f2a9f578b8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -115,7 +115,7 @@ repos:
             exclude: |
               (?x)
                   docs/source/sphinxext/github_link\.py|
-                  cpp/cmake/modules/FindAVX\.cmake|
+                  cpp/cmake/modules/FindAVX\.cmake
           - id: verify-alpha-spec
           - id: verify-codeowners
             args: [--fix, --project-prefix=cuvs]

From 9c4bb734a59ecafa8f9430c824d670f7758d00d2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 12 Jun 2025 16:27:59 +0200
Subject: [PATCH 443/623] [REVIEW][Java] Refactor CagraBuildAndSearchIT to
 explicitly express different execution modes (#1006)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR refactors `CagraBuildAndSearchIT`, introducing separate test for different execution modes (same thread, different thread, concurrent).
This way different modes are tested every time, without the need to rely on randomization to hit a particular test path.
The execution is handled by utility functions (e.g. `runConcurrently`); right now these are private to the test suite, but if we think they could be useful across other tests I can move them to a utility class.

It also fixes a issue where we did not wait and/or report failures in the concurrent case (now exceptions are propagated back to the calling JUnit thread).

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1006
---
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 714 +++++++++---------
 1 file changed, 371 insertions(+), 343 deletions(-)

diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 007243fc55..2690ab4302 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -19,21 +19,25 @@
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.fail;
 
 import java.io.File;
 import java.io.FileInputStream;
-import java.io.FileNotFoundException;
 import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.invoke.MethodHandles;
 import java.util.Arrays;
+import java.util.BitSet;
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
+import java.util.concurrent.CompletableFuture;
+import java.util.concurrent.ExecutionException;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 import java.util.concurrent.TimeUnit;
-import java.util.BitSet;
+import java.util.concurrent.TimeoutException;
+import java.util.function.Supplier;
 
 import org.junit.Before;
 import org.junit.Test;
@@ -58,70 +62,107 @@ public void setup() {
     log.info("Random context initialized for test.");
   }
 
+  private static void runConcurrently(int nThreads, Supplier<Runnable> runnableSupplier)
+    throws ExecutionException, InterruptedException, TimeoutException {
+    try (ExecutorService parallelExecutor = Executors.newFixedThreadPool(nThreads)) {
+      var futures = new CompletableFuture[nThreads];
+      for (int j = 0; j < nThreads; j++) {
+        futures[j] = CompletableFuture.runAsync(runnableSupplier.get(), parallelExecutor);
+      }
+
+      CompletableFuture.allOf(futures)
+        .exceptionally(t -> {
+          fail("Exception while executing runnable: " + t);
+          return null;
+        })
+        .get(2000, TimeUnit.SECONDS);
+    }
+  }
+
+  private static void runInAnotherThread(Runnable runnable) throws ExecutionException, InterruptedException, TimeoutException {
+    try (ExecutorService singleExecutor = Executors.newSingleThreadExecutor()) {
+      singleExecutor.submit(runnable).get(2000, TimeUnit.SECONDS);
+    }
+  }
+
+  private static List<Map<Integer, Float>> getExpectedResults() {
+    return Arrays.asList(
+      Map.of(3, 0.038782578f, 2, 0.3590463f, 0, 0.83774555f),
+      Map.of(0, 0.12472608f, 2, 0.21700792f, 1, 0.31918612f),
+      Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
+      Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.5986642f));
+  }
+
+  private static float[][] createSampleQueries() {
+    return new float[][]{
+      {0.48216683f, 0.0428398f},
+      {0.5084142f, 0.6545497f},
+      {0.51260436f, 0.2643005f},
+      {0.05198065f, 0.5789965f}
+    };
+  }
+
+  private static float[][] createSampleData() {
+    return new float[][]{
+      {0.74021935f, 0.9209938f},
+      {0.03902049f, 0.9689629f},
+      {0.92514056f, 0.4463501f},
+      {0.6673192f, 0.10993068f}
+    };
+  }
+
   /**
    * A basic test that checks the whole flow - from indexing to search.
-   *
-   * @throws Throwable
    */
   @Test
   public void testIndexingAndSearchingFlow() throws Throwable {
+    float[][] dataset = createSampleData();
+    float[][] queries = createSampleQueries();
+    List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
-    // Sample data and query
-    float[][] dataset = {
-        { 0.74021935f, 0.9209938f },
-        { 0.03902049f, 0.9689629f },
-        { 0.92514056f, 0.4463501f },
-        { 0.6673192f, 0.10993068f }
-    };
     List<Integer> map = List.of(0, 1, 2, 3);
-    float[][] queries = {
-        { 0.48216683f, 0.0428398f },
-        { 0.5084142f, 0.6545497f },
-        { 0.51260436f, 0.2643005f },
-        { 0.05198065f, 0.5789965f }
-    };
-
-    // Expected search results
-    List<Map<Integer, Float>> expectedResults = Arrays.asList(
-        Map.of(3, 0.038782578f, 2, 0.3590463f, 0, 0.83774555f),
-        Map.of(0, 0.12472608f, 2, 0.21700792f, 1, 0.31918612f),
-        Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
-        Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.5986642f));
-
-    int numTestsRuns = 10;
 
+    int numTestsRuns = 5;
     try (CuVSResources resources = CuVSResources.create()) {
-      // sometimes run this test using different threads?
-      boolean runTestInDifferentThreads = random.nextBoolean();
-      // if running in different threads, run concurrently or one after the other?
-      boolean runConcurrently = runTestInDifferentThreads ? random.nextBoolean(): false;
+      for (int j = 0; j < numTestsRuns; j++) {
+        indexAndQueryOnce(dataset, map, queries, expectedResults, resources);
+      }
+    }
+  }
 
-      log.info("Running in different threads? " + runTestInDifferentThreads);
-      log.info("Running concurrently? " + runConcurrently);
+  /**
+   * A basic test that checks the whole flow - from indexing to search.
+   */
+  @Test
+  public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
+    float[][] dataset = createSampleData();
+    float[][] queries = createSampleQueries();
+    List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
-      ExecutorService parallelExecutor = runConcurrently ? Executors.newFixedThreadPool(numTestsRuns): null;
+    List<Integer> map = List.of(0, 1, 2, 3);
 
+    int numTestsRuns = 5;
+    try (CuVSResources resources = CuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
-        Runnable testLogic = indexAndQueryOnce(dataset, map, queries, expectedResults, resources);
-        if (runTestInDifferentThreads) {
-          if (runConcurrently) {
-            parallelExecutor.submit(testLogic);
-          } else {
-            ExecutorService singleExecutor = Executors.newSingleThreadExecutor();
-            singleExecutor.submit(testLogic);
-            singleExecutor.shutdown();
-            singleExecutor.awaitTermination(2000, TimeUnit.SECONDS);
-          }
-        } else {
-          // run the test logic in the main thread
-          testLogic.run();
-        }
-      }
-      if (parallelExecutor != null) {
-        parallelExecutor.shutdown();
-        parallelExecutor.awaitTermination(2000, TimeUnit.SECONDS);
+        runInAnotherThread(() -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
       }
+    }
+  }
+
+  /**
+   * A basic test that checks the whole flow - from indexing to search.
+   */
+  @Test
+  public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
+    float[][] dataset = createSampleData();
+    float[][] queries = createSampleQueries();
+    List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
+    List<Integer> map = List.of(0, 1, 2, 3);
+
+    int numTestsRuns = 10;
+    try (CuVSResources resources = CuVSResources.create()) {
+      runConcurrently(numTestsRuns, () -> () -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
     }
   }
 
@@ -134,46 +175,41 @@ public void testIndexingAndSearchingFlow() throws Throwable {
   public void testPrefilteringReducesResults() throws Throwable {
 
     // Sample data and query
-    float[][] dataset = {
-        { 0.74021935f, 0.9209938f },
-        { 0.03902049f, 0.9689629f },
-        { 0.92514056f, 0.4463501f },
-        { 0.6673192f, 0.10993068f }
-       };
+    float[][] dataset = createSampleData();
     float[][] queries = {
-        { 0.48216683f, 0.0428398f }
-       };
+      {0.48216683f, 0.0428398f}
+    };
 
     // Expected search results
-    List<Map<Integer, Float>> expectedResults = Arrays.asList(
-        Map.of(3, 0.038782578f, 2, 0.3590463f));
+    List<Map<Integer, Float>> expectedResults = List.of(
+      Map.of(3, 0.038782578f, 2, 0.3590463f));
 
     // Expected filtered search results
-    List<Map<Integer, Float>> expectedFilteredResults = Arrays.asList(
-        Map.of(2, 0.3590463f, 0, 0.83774555f));
+    List<Map<Integer, Float>> expectedFilteredResults = List.of(
+      Map.of(2, 0.3590463f, 0, 0.83774555f));
 
     CagraIndexParams indexParams = new CagraIndexParams.Builder()
-        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-        .withGraphDegree(2)
-        .withIntermediateGraphDegree(4)
-        .withNumWriterThreads(2)
-        .withMetric(CuvsDistanceType.L2Expanded)
-        .build();
+      .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+      .withGraphDegree(2)
+      .withIntermediateGraphDegree(4)
+      .withNumWriterThreads(2)
+      .withMetric(CuvsDistanceType.L2Expanded)
+      .build();
 
     try (CuVSResources resources = CuVSResources.create()) {
       CagraIndex index = CagraIndex.newBuilder(resources)
-          .withDataset(dataset)
-          .withIndexParams(indexParams)
-          .build();
+        .withDataset(dataset)
+        .withIndexParams(indexParams)
+        .build();
 
       // No prefilter (all points allowed)
       CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-          .build();
+        .build();
       CagraQuery fullQuery = new CagraQuery.Builder()
-          .withTopK(2)
-          .withSearchParams(searchParams)
-          .withQueryVectors(queries)
-          .build();
+        .withTopK(2)
+        .withSearchParams(searchParams)
+        .withQueryVectors(queries)
+        .build();
 
       SearchResults fullSearchResults = index.search(fullQuery);
       List<Map<Integer, Float>> fullResults = fullSearchResults.getResults();
@@ -185,11 +221,11 @@ public void testPrefilteringReducesResults() throws Throwable {
       prefilter.set(2);
 
       CagraQuery filteredQuery = new CagraQuery.Builder()
-          .withTopK(2)
-          .withSearchParams(searchParams)
-          .withQueryVectors(queries)
-          .withPrefilter(prefilter, 4)
-          .build();
+        .withTopK(2)
+        .withSearchParams(searchParams)
+        .withQueryVectors(queries)
+        .withPrefilter(prefilter, 4)
+        .build();
 
       SearchResults filteredSearchResults = index.search(filteredQuery);
       List<Map<Integer, Float>> filteredResults = filteredSearchResults.getResults();
@@ -200,272 +236,264 @@ public void testPrefilteringReducesResults() throws Throwable {
     }
   }
 
-  private Runnable indexAndQueryOnce(float[][] dataset, List<Integer> map, float[][] queries,
-      List<Map<Integer, Float>> expectedResults, CuVSResources resources) throws Throwable, FileNotFoundException {
-
-    Runnable thread = new Runnable() {
-
-      @Override
-      public void run() {
-        try {
-
-          // Configure index parameters
-          CagraIndexParams indexParams = new CagraIndexParams.Builder()
-              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-              .withGraphDegree(1)
-              .withIntermediateGraphDegree(2)
-              .withNumWriterThreads(32)
-              .withMetric(CuvsDistanceType.L2Expanded)
-              .build();
-
-          // Create the index with the dataset
-          CagraIndex index = CagraIndex.newBuilder(resources)
-              .withDataset(dataset)
-              .withIndexParams(indexParams)
-              .build();
-
-          // Saving the index on to the disk.
-          String indexFileName = UUID.randomUUID().toString() + ".cag";
-          index.serialize(new FileOutputStream(indexFileName));
-
-          // Loading a CAGRA index from disk.
-          File indexFile = new File(indexFileName);
-          InputStream inputStream = new FileInputStream(indexFile);
-          CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
-              .from(inputStream)
-              .build();
-
-          // Configure search parameters
-          CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-              .build();
-
-          // Create a query object with the query vectors
-          CagraQuery cuvsQuery = new CagraQuery.Builder()
-              .withTopK(3)
-              .withSearchParams(searchParams)
-              .withQueryVectors(queries)
-              .withMapping(map)
-              .build();
-
-          // Perform the search
-          SearchResults results = index.search(cuvsQuery);
-
-          // Check results
-          log.info(results.getResults().toString());
-          assertEquals(expectedResults, results.getResults());
-
-          // Search from deserialized index
-          results = loadedIndex.search(cuvsQuery);
-
-          // Check results
-          log.info(results.getResults().toString());
-          assertEquals(expectedResults, results.getResults());
-
-          // Cleanup
-          if (indexFile.exists()) {
-            indexFile.delete();
-          }
-          index.destroyIndex();
-        } catch (Throwable ex) {
-          throw new RuntimeException("Exception during indexing/querying", ex);
-        }
+  private void indexAndQueryOnce(float[][] dataset, List<Integer> map, float[][] queries,
+                                 List<Map<Integer, Float>> expectedResults, CuVSResources resources) {
+
+    try {
+
+      // Configure index parameters
+      CagraIndexParams indexParams = new CagraIndexParams.Builder()
+        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+        .withGraphDegree(1)
+        .withIntermediateGraphDegree(2)
+        .withNumWriterThreads(32)
+        .withMetric(CuvsDistanceType.L2Expanded)
+        .build();
+
+      // Create the index with the dataset
+      CagraIndex index = CagraIndex.newBuilder(resources)
+        .withDataset(dataset)
+        .withIndexParams(indexParams)
+        .build();
+
+      // Saving the index on to the disk.
+      String indexFileName = UUID.randomUUID() + ".cag";
+      index.serialize(new FileOutputStream(indexFileName));
+
+      // Loading a CAGRA index from disk.
+      File indexFile = new File(indexFileName);
+      InputStream inputStream = new FileInputStream(indexFile);
+      CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
+        .from(inputStream)
+        .build();
+
+      // Configure search parameters
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+        .build();
+
+      // Create a query object with the query vectors
+      CagraQuery cuvsQuery = new CagraQuery.Builder()
+        .withTopK(3)
+        .withSearchParams(searchParams)
+        .withQueryVectors(queries)
+        .withMapping(map)
+        .build();
+
+      // Perform the search
+      SearchResults results = index.search(cuvsQuery);
+
+      // Check results
+      log.info(results.getResults().toString());
+      assertEquals(expectedResults, results.getResults());
+
+      // Search from deserialized index
+      results = loadedIndex.search(cuvsQuery);
+
+      // Check results
+      log.info(results.getResults().toString());
+      assertEquals(expectedResults, results.getResults());
+
+      // Cleanup
+      if (indexFile.exists()) {
+        indexFile.delete();
       }
-    };
-    return thread;
+      index.destroyIndex();
+    } catch (Throwable ex) {
+      throw new RuntimeException("Exception during indexing/querying", ex);
+    }
   }
 
   @Test
-    public void testMergingIndexes() throws Throwable {
-        float[][] vector1 = {
-            {0.0f, 0.0f},
-            {1.0f, 1.0f}
-        };
-
-        float[][] vector2 = {
-            {10.0f, 10.0f},
-            {11.0f, 11.0f}
-        };
-
-        float[][] queries = {
-            {1.0f, 1.0f},    // Should be closest to vector1[1] -> index 1
-            {10.5f, 10.5f},  // Should be closest to vector2[0] -> index 2
-            {0.0f, 0.0f}     // Should be closest to vector1[0] -> index 0
-        };
-
-        // Expected search results for each query (nearest neighbor and its distance)
-        List<Map<Integer, Float>> expectedResults = Arrays.asList(
-            Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
-            Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
-            Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
-        );
-
-        try (CuVSResources resources = CuVSResources.create()) {
-            CagraIndexParams indexParams = new CagraIndexParams.Builder()
-                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-                .withGraphDegree(1)
-                .withIntermediateGraphDegree(2)
-                .withNumWriterThreads(4)
-                .withMetric(CuvsDistanceType.L2Expanded)
-                .build();
-
-            log.info("Building first index...");
-            CagraIndex index1 = CagraIndex.newBuilder(resources)
-                .withDataset(vector1)
-                .withIndexParams(indexParams)
-                .build();
-
-            log.info("Building second index...");
-            CagraIndex index2 = CagraIndex.newBuilder(resources)
-                .withDataset(vector2)
-                .withIndexParams(indexParams)
-                .build();
-
-            log.info("Merging indexes...");
-            CagraIndex mergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2});
-            log.info("Merge completed successfully");
-
-            CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-                .build();
-
-            List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
-            CagraQuery query = new CagraQuery.Builder()
-                .withTopK(3)
-                .withSearchParams(searchParams)
-                .withQueryVectors(queries)
-                .withMapping(mergedMap)
-                .build();
-
-                log.info("Searching merged index...");
-                SearchResults results = mergedIndex.search(query);
-                log.info("Search results: " + results.getResults().toString());
-
-                assertEquals(expectedResults, results.getResults());
-
-                // --- Serialization/deserialization check ---
-                String indexFileName = UUID.randomUUID().toString() + ".cag";
-                mergedIndex.serialize(new FileOutputStream(indexFileName));
-
-                File indexFile = new File(indexFileName);
-                InputStream inputStream = new FileInputStream(indexFile);
-                CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources)
-                    .from(inputStream)
-                    .build();
-
-                SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
-                assertEquals(expectedResults, resultsFromLoaded.getResults());
-
-                if (indexFile.exists()) {
-                    indexFile.delete();
-                }
-                index1.destroyIndex();
-                index2.destroyIndex();
-                mergedIndex.destroyIndex();
-                loadedMergedIndex.destroyIndex();
-
-        }
-    }
+  public void testMergingIndexes() throws Throwable {
+    float[][] vector1 = {
+      {0.0f, 0.0f},
+      {1.0f, 1.0f}
+    };
+
+    float[][] vector2 = {
+      {10.0f, 10.0f},
+      {11.0f, 11.0f}
+    };
+
+    float[][] queries = {
+      {1.0f, 1.0f},    // Should be closest to vector1[1] -> index 1
+      {10.5f, 10.5f},  // Should be closest to vector2[0] -> index 2
+      {0.0f, 0.0f}     // Should be closest to vector1[0] -> index 0
+    };
+
+    // Expected search results for each query (nearest neighbor and its distance)
+    List<Map<Integer, Float>> expectedResults = Arrays.asList(
+      Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
+      Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
+      Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
+    );
+
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndexParams indexParams = new CagraIndexParams.Builder()
+        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+        .withGraphDegree(1)
+        .withIntermediateGraphDegree(2)
+        .withNumWriterThreads(4)
+        .withMetric(CuvsDistanceType.L2Expanded)
+        .build();
+
+      log.info("Building first index...");
+      CagraIndex index1 = CagraIndex.newBuilder(resources)
+        .withDataset(vector1)
+        .withIndexParams(indexParams)
+        .build();
+
+      log.info("Building second index...");
+      CagraIndex index2 = CagraIndex.newBuilder(resources)
+        .withDataset(vector2)
+        .withIndexParams(indexParams)
+        .build();
+
+      log.info("Merging indexes...");
+      CagraIndex mergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2});
+      log.info("Merge completed successfully");
+
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+        .build();
+
+      List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
+      CagraQuery query = new CagraQuery.Builder()
+        .withTopK(3)
+        .withSearchParams(searchParams)
+        .withQueryVectors(queries)
+        .withMapping(mergedMap)
+        .build();
+
+      log.info("Searching merged index...");
+      SearchResults results = mergedIndex.search(query);
+      log.info("Search results: " + results.getResults().toString());
+
+      assertEquals(expectedResults, results.getResults());
+
+      // --- Serialization/deserialization check ---
+      String indexFileName = UUID.randomUUID() + ".cag";
+      mergedIndex.serialize(new FileOutputStream(indexFileName));
+
+      File indexFile = new File(indexFileName);
+      InputStream inputStream = new FileInputStream(indexFile);
+      CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources)
+        .from(inputStream)
+        .build();
+
+      SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
+      assertEquals(expectedResults, resultsFromLoaded.getResults());
+
+      if (indexFile.exists()) {
+        indexFile.delete();
+      }
+      index1.destroyIndex();
+      index2.destroyIndex();
+      mergedIndex.destroyIndex();
+      loadedMergedIndex.destroyIndex();
 
-    // Commented out test for Logical merge strategy as it is not yet implemented in C yet
-    @Test
-    public void testMergeStrategies() throws Throwable {
-        float[][] vector1 = {
-            {0.0f, 0.0f},
-            {1.0f, 1.0f}
-        };
-
-        float[][] vector2 = {
-            {10.0f, 10.0f},
-            {11.0f, 11.0f}
-        };
-
-        float[][] queries = {
-            {1.0f, 1.0f},
-            {10.5f, 10.5f},
-            {0.0f, 0.0f}
-        };
-
-        List<Map<Integer, Float>> expectedResults = Arrays.asList(
-            Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
-            Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
-            Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
-        );
-
-        try (CuVSResources resources = CuVSResources.create()) {
-            CagraIndexParams indexParams = new CagraIndexParams.Builder()
-                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-                .withGraphDegree(1)
-                .withIntermediateGraphDegree(2)
-                .withNumWriterThreads(4)
-                .withMetric(CuvsDistanceType.L2Expanded)
-                .build();
-
-            log.info("Building first index...");
-            CagraIndex index1 = CagraIndex.newBuilder(resources)
-                .withDataset(vector1)
-                .withIndexParams(indexParams)
-                .build();
-
-            log.info("Building second index...");
-            CagraIndex index2 = CagraIndex.newBuilder(resources)
-                .withDataset(vector2)
-                .withIndexParams(indexParams)
-                .build();
-
-            CagraIndexParams outputIndexParams = new CagraIndexParams.Builder()
-                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-                .withGraphDegree(2)
-                .withIntermediateGraphDegree(4)
-                .withNumWriterThreads(4)
-                .withMetric(CuvsDistanceType.L2Expanded)
-                .build();
-
-            CagraMergeParams physicalMergeParams = new CagraMergeParams.Builder()
-                .withOutputIndexParams(outputIndexParams)
-                .withStrategy(MergeStrategy.PHYSICAL)
-                .build();
-
-            log.info("Merging indexes with PHYSICAL strategy...");
-            CagraIndex physicalMergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2}, physicalMergeParams);
-            log.info("Physical merge completed successfully");
-
-            CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-                .build();
-
-            List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
-            CagraQuery query = new CagraQuery.Builder()
-                .withTopK(3)
-                .withSearchParams(searchParams)
-                .withQueryVectors(queries)
-                .withMapping(mergedMap)
-                .build();
-
-            log.info("Searching physically merged index...");
-            SearchResults physicalResults = physicalMergedIndex.search(query);
-            assertNotNull("Physical merge search results should not be null", physicalResults);
-            assertEquals("Physical merge search results should match expected", expectedResults, physicalResults.getResults());
-
-            // --- Serialization/deserialization check for both merged indexes ---
-            String physicalIndexFileName = UUID.randomUUID().toString() + ".cag";
-            physicalMergedIndex.serialize(new FileOutputStream(physicalIndexFileName));
-
-            File physicalIndexFile = new File(physicalIndexFileName);
-            InputStream physicalInputStream = new FileInputStream(physicalIndexFile);
-            CagraIndex loadedPhysicalIndex = CagraIndex.newBuilder(resources)
-                .from(physicalInputStream)
-                .build();
-
-            SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
-            assertEquals("Loaded physical index search results should match expected",
-                expectedResults, resultsFromLoadedPhysical.getResults());
-
-            if (physicalIndexFile.exists()) {
-                physicalIndexFile.delete();
-            }
-            index1.destroyIndex();
-            index2.destroyIndex();
-            physicalMergedIndex.destroyIndex();
-            loadedPhysicalIndex.destroyIndex();
-        }
     }
+  }
 
+  // Commented out test for Logical merge strategy as it is not yet implemented in C yet
+  @Test
+  public void testMergeStrategies() throws Throwable {
+    float[][] vector1 = {
+      {0.0f, 0.0f},
+      {1.0f, 1.0f}
+    };
+
+    float[][] vector2 = {
+      {10.0f, 10.0f},
+      {11.0f, 11.0f}
+    };
+
+    float[][] queries = {
+      {1.0f, 1.0f},
+      {10.5f, 10.5f},
+      {0.0f, 0.0f}
+    };
+
+    List<Map<Integer, Float>> expectedResults = Arrays.asList(
+      Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
+      Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
+      Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
+    );
+
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndexParams indexParams = new CagraIndexParams.Builder()
+        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+        .withGraphDegree(1)
+        .withIntermediateGraphDegree(2)
+        .withNumWriterThreads(4)
+        .withMetric(CuvsDistanceType.L2Expanded)
+        .build();
+
+      log.info("Building first index...");
+      CagraIndex index1 = CagraIndex.newBuilder(resources)
+        .withDataset(vector1)
+        .withIndexParams(indexParams)
+        .build();
+
+      log.info("Building second index...");
+      CagraIndex index2 = CagraIndex.newBuilder(resources)
+        .withDataset(vector2)
+        .withIndexParams(indexParams)
+        .build();
+
+      CagraIndexParams outputIndexParams = new CagraIndexParams.Builder()
+        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+        .withGraphDegree(2)
+        .withIntermediateGraphDegree(4)
+        .withNumWriterThreads(4)
+        .withMetric(CuvsDistanceType.L2Expanded)
+        .build();
+
+      CagraMergeParams physicalMergeParams = new CagraMergeParams.Builder()
+        .withOutputIndexParams(outputIndexParams)
+        .withStrategy(MergeStrategy.PHYSICAL)
+        .build();
+
+      log.info("Merging indexes with PHYSICAL strategy...");
+      CagraIndex physicalMergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2}, physicalMergeParams);
+      log.info("Physical merge completed successfully");
+
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
+        .build();
+
+      List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
+      CagraQuery query = new CagraQuery.Builder()
+        .withTopK(3)
+        .withSearchParams(searchParams)
+        .withQueryVectors(queries)
+        .withMapping(mergedMap)
+        .build();
+
+      log.info("Searching physically merged index...");
+      SearchResults physicalResults = physicalMergedIndex.search(query);
+      assertNotNull("Physical merge search results should not be null", physicalResults);
+      assertEquals("Physical merge search results should match expected", expectedResults, physicalResults.getResults());
+
+      // --- Serialization/deserialization check for both merged indexes ---
+      String physicalIndexFileName = UUID.randomUUID().toString() + ".cag";
+      physicalMergedIndex.serialize(new FileOutputStream(physicalIndexFileName));
+
+      File physicalIndexFile = new File(physicalIndexFileName);
+      InputStream physicalInputStream = new FileInputStream(physicalIndexFile);
+      CagraIndex loadedPhysicalIndex = CagraIndex.newBuilder(resources)
+        .from(physicalInputStream)
+        .build();
+
+      SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
+      assertEquals("Loaded physical index search results should match expected",
+        expectedResults, resultsFromLoadedPhysical.getResults());
+
+      if (physicalIndexFile.exists()) {
+        physicalIndexFile.delete();
+      }
+      index1.destroyIndex();
+      index2.destroyIndex();
+      physicalMergedIndex.destroyIndex();
+      loadedPhysicalIndex.destroyIndex();
+    }
+  }
 }

From 969d6334990774373d88a62921a31139dbe56775 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 12 Jun 2025 17:34:09 +0200
Subject: [PATCH 444/623] [REVIEW][Java] Add jextract artifacts to gitignore
 (#1005)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The build process for Java uses `jextract` to generate panama bindings. Jextract is downloaded from the JDK website.
This PR excludes the downloaded Jextract archive and the Jextract files from the repository by adding them to `.gitignore`

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1005
---
 java/.gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/java/.gitignore b/java/.gitignore
index 815e71455b..2d3b14dd31 100644
--- a/java/.gitignore
+++ b/java/.gitignore
@@ -3,5 +3,7 @@
 /cuvs-java/bin/
 /cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/
 /cuvs-java/*.cag
+jextract-22/
+openjdk-22-jextract*
 # examples
 /examples/target/

From d0d9c15cbb26f439408af4e182f7a2e2fdb5c553 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 12 Jun 2025 21:47:13 +0200
Subject: [PATCH 445/623] [REVIEW][Java] Add instructions on how to run a
 single test (#1004)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR adds some lines to the Java specific readme, explaining how to run a single test/a single test suite (useful in case of debugging or error reproduction).

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1004
---
 java/README.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/java/README.md b/java/README.md
index 4fb0005f6b..b568a1ffa0 100644
--- a/java/README.md
+++ b/java/README.md
@@ -25,6 +25,18 @@ do `./build.sh java` in the top level directory or just do `./build.sh` in this
 
 Run `./build.sh --run-java-tests` from this directory.
 
+To run a single test:
+```sh
+cd cuvs-java/
+mvn verify -Dintegration-test=com.nvidia.cuvs.CagraBuildAndSearchIT
+```
+Be sure to set (manually, if needed) your `LD_LIBRARY_PATH` to include the directory with the appropriate (matching)
+version of `libcuvs.so`.
+Also, ensure that your panama bindings are up-to-date. They can be re-generated by running:
+```sh
+./panama-bindings/generate-bindings.sh
+```
+
 
 ## Examples
 

From 32be51a4fba427782edbdf05cf0daea453ee3eca Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 13 Jun 2025 09:54:21 -0700
Subject: [PATCH 446/623] Fix C compile error in `tiered_index.h` (#996)

This commit rephrases the definition of the `cuvsTieredIndexANNAlgo` enum in `tiered_index.h` to use a `typedef`.

With the old definition `enum cuvsTieredIndexANNAlgo{}`, compiling a C translation unit yields errors where `cuvsTieredIndexANNAlgo` is used without `enum`. (For instance at line#55 and line#88 of `tiered_index.h`.)
```
tiered_index.h:53:3: error: must use 'enum' tag to refer to type 'cuvsTieredIndexANNAlgo'
tiered_index.h:88:3: error: must use 'enum' tag to refer to type 'cuvsTieredIndexANNAlgo'
```

The typedef sorts this error out.

Authors:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/996
---
 cpp/include/cuvs/neighbors/tiered_index.h |  6 ++--
 cpp/tests/neighbors/c_api.c               | 41 +++++++++++++++++++++--
 2 files changed, 42 insertions(+), 5 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/tiered_index.h b/cpp/include/cuvs/neighbors/tiered_index.h
index a5bf9b3b9c..c72f929d95 100644
--- a/cpp/include/cuvs/neighbors/tiered_index.h
+++ b/cpp/include/cuvs/neighbors/tiered_index.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -32,11 +32,11 @@ extern "C" {
 /**
  * @brief Enum to hold which ANN algorithm is being used in the tiered index
  */
-enum cuvsTieredIndexANNAlgo {
+typedef enum {
   CUVS_TIERED_INDEX_ALGO_CAGRA,
   CUVS_TIERED_INDEX_ALGO_IVF_FLAT,
   CUVS_TIERED_INDEX_ALGO_IVF_PQ
-};
+} cuvsTieredIndexANNAlgo;
 
 /**
  * @defgroup tiered_index_c_index Tiered Index
diff --git a/cpp/tests/neighbors/c_api.c b/cpp/tests/neighbors/c_api.c
index 0c476e95bd..cc71847eaf 100644
--- a/cpp/tests/neighbors/c_api.c
+++ b/cpp/tests/neighbors/c_api.c
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,16 +16,53 @@
 
 #include <cuvs/core/c_api.h>
 #include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/tiered_index.h>
+
+#include <dlpack/dlpack.h>
+
+#include <assert.h>
 #include <stdio.h>
 #include <stdlib.h>
 
-int main()
+void test_compile_cagra()
 {
   // simple smoke test to make sure that we can compile the cagra.h API
   // using a c compiler. This isn't aiming to be a full test, just checking
   // that the exposed C-API is valid C code and doesn't contain C++ features
+  assert(!"test_compile_cagra is not meant to be run");
+
   cuvsCagraIndex_t index;
   cuvsCagraIndexCreate(&index);
   cuvsCagraIndexDestroy(index);
+}
+
+void test_compile_tiered_index()
+{
+  // Smoke test to ensure that the tiered_index.h API compiles correctly
+  // using a c compiler. Not a full test.
+  assert(!"test_compile_tiered_index is not meant to be run");
+
+  cuvsTieredIndex_t tiered_index;
+  cuvsTieredIndexCreate(&tiered_index);
+  cuvsTieredIndexDestroy(tiered_index);
+
+  cuvsTieredIndexParams_t index_params;
+  cuvsResources_t resources;
+  cuvsFilter prefilter;
+  DLManagedTensor dataset, neighbors, distances;
+  cuvsTieredIndexParamsCreate(&index_params);
+  cuvsTieredIndexParamsDestroy(index_params);
+  cuvsTieredIndexBuild(resources, index_params, &dataset, tiered_index);
+  cuvsTieredIndexSearch(resources, NULL, tiered_index, &dataset, &neighbors, &distances, prefilter);
+  cuvsTieredIndexExtend(resources, &dataset, tiered_index);
+}
+
+int main()
+{
+  // These are smoke tests that check that the C-APIs compile with a C compiler.
+  // These are not meant to be run.
+  test_compile_cagra();
+  test_compile_tiered_index();
+
   return 0;
 }

From a0804deebe093f5f1e737529ce1dad690bcfa61c Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Mon, 16 Jun 2025 17:12:22 -0400
Subject: [PATCH 447/623] [BUG] cuvs-bench data_export fixes (#863)

- [x] Identify the correct dataframe columns for CPU-only algorithms
- [x] Handle `num_threads` argument in build and search params (present in HDBSCAN and DiskANN benchmarks)
        * Do this in a better way by renaming params
Closes #796
Closes #1001

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/863
---
 python/cuvs_bench/cuvs_bench/run/data_export.py | 10 +++++++---
 python/cuvs_bench/cuvs_bench/tests/test_cli.py  |  2 +-
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/python/cuvs_bench/cuvs_bench/run/data_export.py b/python/cuvs_bench/cuvs_bench/run/data_export.py
index 9954d234ed..d576f1a761 100644
--- a/python/cuvs_bench/cuvs_bench/run/data_export.py
+++ b/python/cuvs_bench/cuvs_bench/run/data_export.py
@@ -217,12 +217,16 @@ def convert_json_to_csv_search(dataset, dataset_path):
                 write["build time"] = None
                 write["build threads"] = None
                 write["build cpu_time"] = None
-                write["build GPU"] = None
 
-                for col_idx in range(6, len(build_df.columns)):
+                start_idx = 5
+                if "GPU" in build_df.columns:
+                    start_idx = 6
+                    write["build GPU"] = None
+                for col_idx in range(start_idx, len(build_df.columns)):
                     col_name = build_df.columns[col_idx]
                     write[col_name] = None
-
+                    if col_name == "num_threads":
+                        write["build_num_threads"] = None
                 for s_index, search_row in write.iterrows():
                     for b_index, build_row in build_df.iterrows():
                         if search_row["index_name"] == build_row["index_name"]:
diff --git a/python/cuvs_bench/cuvs_bench/tests/test_cli.py b/python/cuvs_bench/cuvs_bench/tests/test_cli.py
index 7993660da0..48f65d8492 100644
--- a/python/cuvs_bench/cuvs_bench/tests/test_cli.py
+++ b/python/cuvs_bench/cuvs_bench/tests/test_cli.py
@@ -170,7 +170,7 @@ def test_run_command_creates_results(temp_datasets_dir: Path):
                 "build time",
                 "build threads",
                 "build cpu_time",
-                "build GPU",
+                "ef_construction",
                 "graph_degree",
                 "intermediate_graph_degree",
                 "label",

From 73cffe91b846cd76d60810a000310a0a3c3784ec Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Tue, 17 Jun 2025 18:59:24 -0400
Subject: [PATCH 448/623] Pin Faiss Version to Last Stable Release (#1029)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1029
---
 cpp/cmake/patches/faiss_override.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
index 07d757f314..0252cf3103 100644
--- a/cpp/cmake/patches/faiss_override.json
+++ b/cpp/cmake/patches/faiss_override.json
@@ -1,9 +1,9 @@
 {
     "packages" : {
       "faiss" : {
-        "version": "1.10.0",
+        "version": "1.11.0",
         "git_url": "https://github.com/facebookresearch/faiss.git",
-        "git_tag": "main",
+        "git_tag": "v1.11.0",
         "patches" : [
           {
             "file" : "${current_json_dir}/faiss.diff",

From dffbb496e18ee09a8490fd936d0e61500c65213b Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Wed, 18 Jun 2025 09:29:48 -0700
Subject: [PATCH 449/623] Remove pytest pin (#998)

Contributes to https://github.com/rapidsai/build-planning/issues/105.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/998
---
 conda/environments/all_cuda-128_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-128_arch-x86_64.yaml  | 2 +-
 dependencies.yaml                                 | 2 +-
 python/cuvs/pyproject.toml                        | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
index c1adb7062f..ef4f1f65d9 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -41,8 +41,8 @@ dependencies:
 - openblas
 - pre-commit
 - pylibraft==25.8.*,>=0.0.0a0
+- pytest
 - pytest-cov
-- pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
 - rust
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
index ec6a42d8b4..85ddc7a2d7 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -41,8 +41,8 @@ dependencies:
 - openblas
 - pre-commit
 - pylibraft==25.8.*,>=0.0.0a0
+- pytest
 - pytest-cov
-- pytest==7.*
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - recommonmark
 - rust
diff --git a/dependencies.yaml b/dependencies.yaml
index c7a2daa9e7..836c420623 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -453,7 +453,7 @@ dependencies:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - pytest==7.*
+          - pytest
           - pytest-cov
   test_py_cuvs:
     common:
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 7a3a4097a2..7a00b824df 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -48,8 +48,8 @@ classifiers = [
 [project.optional-dependencies]
 test = [
     "cupy-cuda12x>=12.0.0",
+    "pytest",
     "pytest-cov",
-    "pytest==7.*",
     "scikit-learn",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 

From 712e888653a5aacb5a394469bf0966cbf4d8f684 Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Tue, 24 Jun 2025 12:33:20 -0500
Subject: [PATCH 450/623] Make rust publish run after conda upload (#1047)

The `cuvs-sys` crate publishing depends on its version-equivalent
libcuvs conda artifact. This PR updates the job to run only after conda
uploads are complete.
---
 .github/workflows/build.yaml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index d997dde0cd..5c2f7fbd52 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -48,10 +48,6 @@ jobs:
       node_type: "gpu-l4-latest-1"
       script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
-  rust-publish:
-    needs: rust-build
-    secrets: inherit
-    uses: ./.github/workflows/publish-rust.yaml
   go-build:
     needs: cpp-build
     secrets: inherit
@@ -98,6 +94,10 @@ jobs:
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
       skip_upload_pkgs: libcuvs-template
+  rust-publish:
+    needs: [rust-build, upload-conda]
+    secrets: inherit
+    uses: ./.github/workflows/publish-rust.yaml
   docs-build:
     if: github.ref_type == 'branch'
     needs: python-build

From cad14c30c71835b1028ee6f08d23d37b4a3396e0 Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Wed, 25 Jun 2025 10:25:30 -0400
Subject: [PATCH 451/623] refactor(cuda11): remove cuda11-only conda channels,
 cleanup docs (#1046)

xref rapidsai/build-planning#184

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1046
---
 README.md                                            | 11 +++--------
 conda/environments/all_cuda-128_arch-aarch64.yaml    |  2 --
 conda/environments/all_cuda-128_arch-x86_64.yaml     |  2 --
 .../bench_ann_cuda-128_arch-aarch64.yaml             |  2 --
 .../environments/bench_ann_cuda-128_arch-x86_64.yaml |  2 --
 conda/environments/go_cuda-128_arch-aarch64.yaml     |  2 --
 conda/environments/go_cuda-128_arch-x86_64.yaml      |  2 --
 conda/environments/rust_cuda-129_arch-aarch64.yaml   |  2 --
 conda/environments/rust_cuda-129_arch-x86_64.yaml    |  2 --
 dependencies.yaml                                    |  2 --
 docs/source/build.rst                                | 12 +++---------
 docs/source/cuvs_bench/index.rst                     | 10 +++++-----
 notebooks/cuvs_hpo_example.ipynb                     |  2 +-
 python/cuvs/.coveragerc                              |  4 +++-
 python/cuvs_bench/.coveragerc                        |  2 ++
 15 files changed, 17 insertions(+), 42 deletions(-)

diff --git a/README.md b/README.md
index cbb0cde10f..bef1a8af6e 100755
--- a/README.md
+++ b/README.md
@@ -90,17 +90,12 @@ cuVS comes with pre-built packages that can be installed through [conda](https:/
 It is recommended to use [mamba](https://conda.github.io/conda-libmamba-solver/user-guide/) to install the desired packages. The following command will install the Python package. You can substitute `cuvs` for any of the packages in the table above:
 
 ```bash
-conda install -c conda-forge -c nvidia -c rapidsai cuvs
+conda install -c rapidsai -c conda-forge cuvs
 ```
 
 The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.
 
-For CUDA 11 packages:
-```bash
-pip install cuvs-cu11 --extra-index-url=https://pypi.nvidia.com
-````
-
-And CUDA 12 packages:
+For CUDA 12 packages:
 ```bash
 pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 ```
@@ -109,7 +104,7 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.08
+conda install -c rapidsai-nightly -c conda-forge cuvs=25.08
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml
index ef4f1f65d9..9fea5bfb9f 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-128_arch-aarch64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - breathe>=4.35.0
 - c-compiler
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml
index 85ddc7a2d7..221f0720cf 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-128_arch-x86_64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - breathe>=4.35.0
 - c-compiler
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
index c25d2b3aec..558994a982 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
index 27926c20be..a71da9e5b7 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - benchmark>=1.8.2
 - c-compiler
diff --git a/conda/environments/go_cuda-128_arch-aarch64.yaml b/conda/environments/go_cuda-128_arch-aarch64.yaml
index aabee554a7..65fffec19c 100644
--- a/conda/environments/go_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-128_arch-aarch64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - c-compiler
 - clang-tools==20.1.4
diff --git a/conda/environments/go_cuda-128_arch-x86_64.yaml b/conda/environments/go_cuda-128_arch-x86_64.yaml
index 4d02971fe1..baad4d43f9 100644
--- a/conda/environments/go_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-128_arch-x86_64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - c-compiler
 - clang-tools==20.1.4
diff --git a/conda/environments/rust_cuda-129_arch-aarch64.yaml b/conda/environments/rust_cuda-129_arch-aarch64.yaml
index f636fe0a4f..f25258a1af 100644
--- a/conda/environments/rust_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-129_arch-aarch64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - c-compiler
 - clang-tools==20.1.4
diff --git a/conda/environments/rust_cuda-129_arch-x86_64.yaml b/conda/environments/rust_cuda-129_arch-x86_64.yaml
index d75824ae4b..c6f36470a1 100644
--- a/conda/environments/rust_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-129_arch-x86_64.yaml
@@ -3,9 +3,7 @@
 channels:
 - rapidsai
 - rapidsai-nightly
-- dask/label/dev
 - conda-forge
-- nvidia
 dependencies:
 - c-compiler
 - clang-tools==20.1.4
diff --git a/dependencies.yaml b/dependencies.yaml
index 836c420623..ac5883dc94 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -212,9 +212,7 @@ files:
 channels:
   - rapidsai
   - rapidsai-nightly
-  - dask/label/dev
   - conda-forge
-  - nvidia
 dependencies:
   build:
     common:
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 5d1b8e354d..90725f8684 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -43,27 +43,21 @@ C/C++ Package
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.8
+   conda install -c rapidsai -c conda-forge libcuvs cuda-version=12.8
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.8
+   conda install -c rapidsai -c conda-forge cuvs cuda-version=12.8
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
 
 The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.
 
-For CUDA 11 packages:
-
-.. code-block:: bash
-
-    pip install cuvs-cu11 --extra-index-url=https://pypi.nvidia.com
-
-And CUDA 12 packages:
+For CUDA 12 packages:
 
 .. code-block:: bash
 
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index d3da585afd..d257c62ff1 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -65,12 +65,12 @@ Conda
    conda activate cuvs_benchmarks
 
    # to install GPU package:
-   conda install -c rapidsai -c conda-forge -c nvidia cuvs-bench=<rapids_version> cuda-version=11.8*
+   conda install -c rapidsai -c conda-forge cuvs-bench=<rapids_version> cuda-version=12.9*
 
    # to install CPU package for usage in CPU-only systems:
    conda install -c rapidsai -c conda-forge  cuvs-bench-cpu
 
-The channel `rapidsai` can easily be substituted `rapidsai-nightly` if nightly benchmarks are desired. The CPU package currently allows to run the HNSW benchmarks.
+The channel `rapidsai` can easily be substituted with `rapidsai-nightly` if nightly benchmarks are desired. The CPU package currently allows to run the HNSW benchmarks.
 
 Please see the :doc:`build instructions <build>` to build the benchmarks from source.
 
@@ -236,7 +236,7 @@ For GPU-enabled systems, the `DATA_FOLDER` variable should be a local folder whe
     export DATA_FOLDER=path/to/store/datasets/and/results
     docker run --gpus all --rm -it -u $(id -u)                      \
         -v $DATA_FOLDER:/data/benchmarks                            \
-        rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10              \
+        rapidsai/cuvs-bench:25.06-cuda12.8-py3.13              \
         "--dataset deep-image-96-angular"                           \
         "--normalize"                                               \
         "--algorithms cuvs_cagra,cuvs_ivf_pq --batch-size 10 -k 10" \
@@ -249,7 +249,7 @@ Usage of the above command is as follows:
  * - Argument
    - Description
 
- * - `rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10`
+ * - `rapidsai/cuvs-bench:25.06-cuda12.8-py3.13`
    - Image to use. Can be either `cuvs-bench` or `cuvs-bench-datasets`
 
  * - `"--dataset deep-image-96-angular"`
@@ -296,7 +296,7 @@ All of the `cuvs-bench` images contain the Conda packages, so they can be used d
         --entrypoint /bin/bash                          \
         --workdir /data/benchmarks                      \
         -v $DATA_FOLDER:/data/benchmarks                \
-        rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10
+        rapidsai/cuvs-bench:25.06-cuda12.8-py3.13
 
 This will drop you into a command line in the container, with the `cuvs-bench` python package ready to use, as described in the [Running the benchmarks](#running-the-benchmarks) section above:
 
diff --git a/notebooks/cuvs_hpo_example.ipynb b/notebooks/cuvs_hpo_example.ipynb
index d8b11a82c2..964110cb76 100644
--- a/notebooks/cuvs_hpo_example.ipynb
+++ b/notebooks/cuvs_hpo_example.ipynb
@@ -21,7 +21,7 @@
    "outputs": [],
    "source": [
     "#Install Required Packages\n",
-    "%mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs optuna -y\n",
+    "%mamba install -c rapidsai-nightly -c conda-forge cuvs optuna -y\n",
     "%pip install cupy"
    ]
   },
diff --git a/python/cuvs/.coveragerc b/python/cuvs/.coveragerc
index 3269e10b8a..8a693cbc79 100644
--- a/python/cuvs/.coveragerc
+++ b/python/cuvs/.coveragerc
@@ -1,3 +1,5 @@
 # Configuration file for Python coverage tests
 [run]
-source = pylibraft
+source = cuvs
+disable_warnings = module-not-measured
+                   no-data-collected
diff --git a/python/cuvs_bench/.coveragerc b/python/cuvs_bench/.coveragerc
index 2ee96a94d9..13d36ff6b3 100644
--- a/python/cuvs_bench/.coveragerc
+++ b/python/cuvs_bench/.coveragerc
@@ -1,3 +1,5 @@
 # Configuration file for Python coverage tests
 [run]
 source = cuvs_bench
+disable_warnings = module-not-measured
+                   no-data-collected

From c4c03e40367838dd5f8fda58051770142d8e6b5f Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Wed, 25 Jun 2025 18:00:27 -0400
Subject: [PATCH 452/623] refactor(shellcheck): fix all remaining
 warnings/errors (#1019)

Finishes up the shellcheck work tracked in rapidsai/build-planning#135

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - James Lamb (https://github.com/jameslamb)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1019
---
 .github/CODEOWNERS                        |   8 +-
 .pre-commit-config.yaml                   |   1 -
 .shellcheckrc                             |   2 +
 build.sh                                  | 163 +++++++++++-----------
 ci/checks/black_lists.sh                  |   4 +-
 cpp/scripts/run-cmake-format.sh           |  10 +-
 examples/build.sh                         |  32 ++---
 java/build.sh                             |   4 +-
 java/panama-bindings/generate-bindings.sh |  16 ++-
 9 files changed, 125 insertions(+), 115 deletions(-)
 create mode 100644 .shellcheckrc

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 7ed1d46c0c..6d9cfd4530 100755
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -11,13 +11,15 @@ rust/              @rapidsai/cuvs-rust-codeowners
 docs/              @rapidsai/cuvs-docs-codeowners
 
 #cmake code owners
-CMakeLists.txt     @rapidsai/cuvs-cmake-codeowners
-**/cmake/          @rapidsai/cuvs-cmake-codeowners
-*.cmake            @rapidsai/cuvs-cmake-codeowners
+CMakeLists.txt                  @rapidsai/cuvs-cmake-codeowners
+**/cmake/                       @rapidsai/cuvs-cmake-codeowners
+*.cmake                         @rapidsai/cuvs-cmake-codeowners
+cpp/scripts/run-cmake-format.sh @rapidsai/cuvs-cmake-codeowners
 
 #CI code owners
 /.github/                @rapidsai/ci-codeowners
 /ci/                     @rapidsai/ci-codeowners
+/.shellcheckrc           @rapidsai/ci-codeowners
 
 #packaging code owners
 /.pre-commit-config.yaml @rapidsai/packaging-codeowners
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f2a9f578b8..a45573a791 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -128,7 +128,6 @@ repos:
         rev: v0.10.0.1
         hooks:
           - id: shellcheck
-            args: ["--severity=warning"]
 
 default_language_version:
       python: python3
diff --git a/.shellcheckrc b/.shellcheckrc
new file mode 100644
index 0000000000..b57b9d1962
--- /dev/null
+++ b/.shellcheckrc
@@ -0,0 +1,2 @@
+# Disable file checks (otherwise every use of `gha-tools` will get flagged)
+disable=SC1091
diff --git a/build.sh b/build.sh
index 6af2907a4e..2caec74ca7 100755
--- a/build.sh
+++ b/build.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 
 # cuvs build scripts
 
@@ -88,75 +88,78 @@ BUILD_SHARED_LIBS=ON
 TEST_TARGETS=""
 ANN_BENCH_TARGETS=""
 
-CACHE_ARGS=""
+CACHE_ARGS=()
 NVTX=ON
 LOG_COMPILE_TIME=OFF
 CLEAN=0
 DISABLE_DEPRECATION_WARNINGS=ON
-CMAKE_TARGET=""
+CMAKE_TARGET=()
 EXTRA_CMAKE_ARGS=""
 
 # Set defaults for vars that may not have been defined externally
 INSTALL_PREFIX=${INSTALL_PREFIX:=${PREFIX:=${CONDA_PREFIX:=$LIBCUVS_BUILD_DIR/install}}}
-PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
+PARALLEL_LEVEL=${PARALLEL_LEVEL:=$(nproc)}
 BUILD_ABI=${BUILD_ABI:=ON}
 
 # Default to Ninja if generator is not specified
 export CMAKE_GENERATOR="${CMAKE_GENERATOR:=Ninja}"
 
 function hasArg {
-    (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+    (( NUMARGS != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
 }
 
 function cmakeArgs {
     # Check for multiple cmake args options
-    if [[ $(echo $ARGS | { grep -Eo "\-\-cmake\-args" || true; } | wc -l ) -gt 1 ]]; then
+    if [[ $(echo "$ARGS" | { grep -Eo "\-\-cmake\-args" || true; } | wc -l ) -gt 1 ]]; then
         echo "Multiple --cmake-args options were provided, please provide only one: ${ARGS}"
         exit 1
     fi
 
     # Check for cmake args option
-    if [[ -n $(echo $ARGS | { grep -E "\-\-cmake\-args" || true; } ) ]]; then
+    if [[ -n $(echo "$ARGS" | { grep -E "\-\-cmake\-args" || true; } ) ]]; then
         # There are possible weird edge cases that may cause this regex filter to output nothing and fail silently
         # the true pipe will catch any weird edge cases that may happen and will cause the program to fall back
         # on the invalid option error
-        EXTRA_CMAKE_ARGS=$(echo $ARGS | { grep -Eo "\-\-cmake\-args=\".+\"" || true; })
+        EXTRA_CMAKE_ARGS=$(echo "$ARGS" | { grep -Eo "\-\-cmake\-args=\".+\"" || true; })
         if [[ -n ${EXTRA_CMAKE_ARGS} ]]; then
             # Remove the full  EXTRA_CMAKE_ARGS argument from list of args so that it passes validArgs function
             ARGS=${ARGS//$EXTRA_CMAKE_ARGS/}
             # Filter the full argument down to just the extra string that will be added to cmake call
-            EXTRA_CMAKE_ARGS=$(echo $EXTRA_CMAKE_ARGS | grep -Eo "\".+\"" | sed -e 's/^"//' -e 's/"$//')
+            EXTRA_CMAKE_ARGS=$(echo "$EXTRA_CMAKE_ARGS" | grep -Eo "\".+\"" | sed -e 's/^"//' -e 's/"$//')
         fi
     fi
+    read -ra EXTRA_CMAKE_ARGS <<< "$EXTRA_CMAKE_ARGS"
 }
 
 function cacheTool {
     # Check for multiple cache options
-    if [[ $(echo $ARGS | { grep -Eo "\-\-cache\-tool" || true; } | wc -l ) -gt 1 ]]; then
+    if [[ $(echo "$ARGS" | { grep -Eo "\-\-cache\-tool" || true; } | wc -l ) -gt 1 ]]; then
         echo "Multiple --cache-tool options were provided, please provide only one: ${ARGS}"
         exit 1
     fi
     # Check for cache tool option
-    if [[ -n $(echo $ARGS | { grep -E "\-\-cache\-tool" || true; } ) ]]; then
+    if [[ -n $(echo "$ARGS" | { grep -E "\-\-cache\-tool" || true; } ) ]]; then
         # There are possible weird edge cases that may cause this regex filter to output nothing and fail silently
         # the true pipe will catch any weird edge cases that may happen and will cause the program to fall back
         # on the invalid option error
-        CACHE_TOOL=$(echo $ARGS | sed -e 's/.*--cache-tool=//' -e 's/ .*//')
+        CACHE_TOOL=$(echo "$ARGS" | sed -e 's/.*--cache-tool=//' -e 's/ .*//')
         if [[ -n ${CACHE_TOOL} ]]; then
             # Remove the full CACHE_TOOL argument from list of args so that it passes validArgs function
             ARGS=${ARGS//--cache-tool=$CACHE_TOOL/}
-            CACHE_ARGS="-DCMAKE_CUDA_COMPILER_LAUNCHER=${CACHE_TOOL} -DCMAKE_C_COMPILER_LAUNCHER=${CACHE_TOOL} -DCMAKE_CXX_COMPILER_LAUNCHER=${CACHE_TOOL}"
+            CACHE_ARGS=("-DCMAKE_CUDA_COMPILER_LAUNCHER=${CACHE_TOOL}"
+                        "-DCMAKE_C_COMPILER_LAUNCHER=${CACHE_TOOL}"
+                        "-DCMAKE_CXX_COMPILER_LAUNCHER=${CACHE_TOOL}")
         fi
     fi
 }
 
 function limitTests {
     # Check for option to limit the set of test binaries to build
-    if [[ -n $(echo $ARGS | { grep -E "\-\-limit\-tests" || true; } ) ]]; then
+    if [[ -n $(echo "$ARGS" | { grep -E "\-\-limit\-tests" || true; } ) ]]; then
         # There are possible weird edge cases that may cause this regex filter to output nothing and fail silently
         # the true pipe will catch any weird edge cases that may happen and will cause the program to fall back
         # on the invalid option error
-        LIMIT_TEST_TARGETS=$(echo $ARGS | sed -e 's/.*--limit-tests=//' -e 's/ .*//')
+        LIMIT_TEST_TARGETS=$(echo "$ARGS" | sed -e 's/.*--limit-tests=//' -e 's/ .*//')
         if [[ -n ${LIMIT_TEST_TARGETS} ]]; then
             # Remove the full LIMIT_TEST_TARGETS argument from list of args so that it passes validArgs function
             ARGS=${ARGS//--limit-tests=$LIMIT_TEST_TARGETS/}
@@ -168,11 +171,11 @@ function limitTests {
 
 function limitAnnBench {
     # Check for option to limit the set of test binaries to build
-    if [[ -n $(echo $ARGS | { grep -E "\-\-limit\-bench-ann" || true; } ) ]]; then
+    if [[ -n $(echo "$ARGS" | { grep -E "\-\-limit\-bench-ann" || true; } ) ]]; then
         # There are possible weird edge cases that may cause this regex filter to output nothing and fail silently
         # the true pipe will catch any weird edge cases that may happen and will cause the program to fall back
         # on the invalid option error
-        LIMIT_ANN_BENCH_TARGETS=$(echo $ARGS | sed -e 's/.*--limit-bench-ann=//' -e 's/ .*//')
+        LIMIT_ANN_BENCH_TARGETS=$(echo "$ARGS" | sed -e 's/.*--limit-bench-ann=//' -e 's/ .*//')
         if [[ -n ${LIMIT_ANN_BENCH_TARGETS} ]]; then
             # Remove the full LIMIT_TEST_TARGETS argument from list of args so that it passes validArgs function
             ARGS=${ARGS//--limit-bench-ann=$LIMIT_ANN_BENCH_TARGETS/}
@@ -183,16 +186,16 @@ function limitAnnBench {
 
 function buildMetrics {
     # Check for multiple build-metrics options
-    if [[ $(echo $ARGS | { grep -Eo "\-\-build\-metrics" || true; } | wc -l ) -gt 1 ]]; then
+    if [[ $(echo "$ARGS" | { grep -Eo "\-\-build\-metrics" || true; } | wc -l ) -gt 1 ]]; then
         echo "Multiple --build-metrics options were provided, please provide only one: ${ARGS}"
         exit 1
     fi
     # Check for build-metrics option
-    if [[ -n $(echo $ARGS | { grep -E "\-\-build\-metrics" || true; } ) ]]; then
+    if [[ -n $(echo "$ARGS" | { grep -E "\-\-build\-metrics" || true; } ) ]]; then
         # There are possible weird edge cases that may cause this regex filter to output nothing and fail silently
         # the true pipe will catch any weird edge cases that may happen and will cause the program to fall back
         # on the invalid option error
-        BUILD_REPORT_METRICS=$(echo $ARGS | sed -e 's/.*--build-metrics=//' -e 's/ .*//')
+        BUILD_REPORT_METRICS=$(echo "$ARGS" | sed -e 's/.*--build-metrics=//' -e 's/ .*//')
         if [[ -n ${BUILD_REPORT_METRICS} ]]; then
             # Remove the full BUILD_REPORT_METRICS argument from list of args so that it passes validArgs function
             ARGS=${ARGS//--build-metrics=$BUILD_REPORT_METRICS/}
@@ -202,7 +205,7 @@ function buildMetrics {
 
 function gpuArch {
     # Check if both --gpu-arch and --allgpuarch are specified
-    if hasArg --allgpuarch && [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+    if hasArg --allgpuarch && [[ -n $(echo "$ARGS" | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
         echo "Error: Cannot specify both --gpu-arch and --allgpuarch"
         echo "Use either:"
         echo "  --gpu-arch=\"80-real;90-real\"    (for specific architectures)"
@@ -211,7 +214,7 @@ function gpuArch {
     fi
 
     # Check for multiple gpu-arch options
-    if [[ $(echo $ARGS | { grep -Eo "\-\-gpu\-arch" || true; } | wc -l ) -gt 1 ]]; then
+    if [[ $(echo "$ARGS" | { grep -Eo "\-\-gpu\-arch" || true; } | wc -l ) -gt 1 ]]; then
         echo "Error: Multiple --gpu-arch options were provided. Please combine architectures into a single option."
         echo "Instead of: --gpu-arch=80-real --gpu-arch=90-real"
         echo "Use:       --gpu-arch=\"80-real;90-real\""
@@ -219,13 +222,13 @@ function gpuArch {
     fi
 
     # Check for gpu-arch option
-    if [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
-        GPU_ARCH_ARG=$(echo $ARGS | { grep -Eo "\-\-gpu\-arch=.+( |$)" || true; })
+    if [[ -n $(echo "$ARGS" | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+        GPU_ARCH_ARG=$(echo "$ARGS" | { grep -Eo "\-\-gpu\-arch=.+( |$)" || true; })
         if [[ -n ${GPU_ARCH_ARG} ]]; then
             # Remove the full argument from ARGS
             ARGS=${ARGS//$GPU_ARCH_ARG/}
             # Extract just the architecture value
-            CUVS_CMAKE_CUDA_ARCHITECTURES=$(echo $GPU_ARCH_ARG | sed -e 's/--gpu-arch=//' -e 's/ .*//')
+            CUVS_CMAKE_CUDA_ARCHITECTURES=$(echo "$GPU_ARCH_ARG" | sed -e 's/--gpu-arch=//' -e 's/ .*//')
             echo "Building for specified GPU architectures: ${CUVS_CMAKE_CUDA_ARCHITECTURES}"
         fi
     fi
@@ -237,7 +240,7 @@ if hasArg -h || hasArg --help; then
 fi
 
 # Check for valid usage
-if (( ${NUMARGS} != 0 )); then
+if (( NUMARGS != 0 )); then
     cmakeArgs
     cacheTool
     limitTests
@@ -255,18 +258,20 @@ fi
 # This should run before build/install
 if hasArg --uninstall; then
 
-    if hasArg cuvs || hasArg libcuvs || (( ${NUMARGS} == 1 )); then
+    if hasArg cuvs || hasArg libcuvs || (( NUMARGS == 1 )); then
 
       echo "Removing libcuvs files..."
-      if [ -e ${LIBCUVS_BUILD_DIR}/install_manifest.txt ]; then
-          xargs rm -fv < ${LIBCUVS_BUILD_DIR}/install_manifest.txt > /dev/null 2>&1
+      if [ -e "${LIBCUVS_BUILD_DIR}"/install_manifest.txt ]; then
+          xargs rm -fv < "${LIBCUVS_BUILD_DIR}"/install_manifest.txt > /dev/null 2>&1
       fi
     fi
 
-    if hasArg cuvs || (( ${NUMARGS} == 1 )); then
+    if hasArg cuvs || (( NUMARGS == 1 )); then
       echo "Uninstalling cuvs package..."
-      if [ -e ${PYLIBCUVS_BUILD_DIR}/install_manifest.txt ]; then
-          xargs rm -fv < ${PYLIBCUVS_BUILD_DIR}/install_manifest.txt > /dev/null 2>&1
+      # PYLIBCUVS_BUILD_DIR isn't defined in `build.sh` but maybe defined by user
+      # shellcheck disable=SC2153
+      if [ -e "${PYLIBCUVS_BUILD_DIR}"/install_manifest.txt ]; then
+          xargs rm -fv < "${PYLIBCUVS_BUILD_DIR}"/install_manifest.txt > /dev/null 2>&1
       fi
 
       # Try to uninstall via pip if it is installed
@@ -305,18 +310,18 @@ if hasArg --no-mg; then
     BUILD_MG_ALGOS=OFF
 fi
 
-if hasArg tests || (( ${NUMARGS} == 0 )); then
+if hasArg tests || (( NUMARGS == 0 )); then
     BUILD_TESTS=ON
-    CMAKE_TARGET="${CMAKE_TARGET};${TEST_TARGETS}"
+    CMAKE_TARGET+=("${TEST_TARGETS}")
 fi
 
-if hasArg bench-ann || (( ${NUMARGS} == 0 )); then
+if hasArg bench-ann || (( NUMARGS == 0 )); then
     BUILD_CUVS_BENCH=ON
     if ! hasArg tests; then
         BUILD_TESTS=OFF
     fi
     COMPILE_LIBRARY=OFF
-    CMAKE_TARGET="${CMAKE_TARGET};${ANN_BENCH_TARGETS}"
+    CMAKE_TARGET+=("${ANN_BENCH_TARGETS}")
     if hasArg --cpu-only; then
         BUILD_CPU_ONLY=ON
         BUILD_SHARED_LIBS=OFF
@@ -345,20 +350,20 @@ if hasArg --incl-cache-stats; then
     BUILD_REPORT_INCL_CACHE_STATS=ON
 fi
 
-if [[ ${CMAKE_TARGET} == "" ]]; then
-    CMAKE_TARGET="all"
+if [[ ${#CMAKE_TARGET} -eq 0 ]]; then
+    CMAKE_TARGET=("all")
 fi
 
 # If clean given, run it prior to any other steps
-if (( ${CLEAN} == 1 )); then
+if (( CLEAN == 1 )); then
     # If the dirs to clean are mounted dirs in a container, the
     # contents should be removed but the mounted dirs will remain.
     # The find removes all contents but leaves the dirs, the rmdir
     # attempts to remove the dirs but can fail safely.
     for bd in ${BUILD_DIRS}; do
-      if [ -d ${bd} ]; then
-          find ${bd} -mindepth 1 -delete
-          rmdir ${bd} || true
+      if [ -d "${bd}" ]; then
+          find "${bd}" -mindepth 1 -delete
+          rmdir "${bd}" || true
       fi
     done
 fi
@@ -367,15 +372,13 @@ fi
 # Configure for building all C++ targets
 if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg bench-prims || hasArg bench-ann; then
     COMPILE_LIBRARY=ON
-    if (( ${BUILD_SHARED_LIBS} == "OFF" )); then
-        CMAKE_TARGET="${CMAKE_TARGET};"
-    else
-        CMAKE_TARGET="${CMAKE_TARGET};cuvs"
+    if [[ "${BUILD_SHARED_LIBS}" != "OFF" ]]; then
+        CMAKE_TARGET+=("cuvs")
     fi
 
     # get the current count before the compile starts
     CACHE_TOOL=${CACHE_TOOL:-sccache}
-    if [[ "$BUILD_REPORT_INCL_CACHE_STATS" == "ON" && -x "$(command -v ${CACHE_TOOL})" ]]; then
+    if [[ "$BUILD_REPORT_INCL_CACHE_STATS" == "ON" && -x "$(command -v "${CACHE_TOOL}")" ]]; then
         "${CACHE_TOOL}" --zero-stats
     fi
 
@@ -390,11 +393,11 @@ if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg
         fi
     fi
 
-    mkdir -p ${LIBCUVS_BUILD_DIR}
-    cd ${LIBCUVS_BUILD_DIR}
-    cmake -S ${REPODIR}/cpp -B ${LIBCUVS_BUILD_DIR} \
-          -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \
-          -DCMAKE_CUDA_ARCHITECTURES=${CUVS_CMAKE_CUDA_ARCHITECTURES} \
+    mkdir -p "${LIBCUVS_BUILD_DIR}"
+    cd "${LIBCUVS_BUILD_DIR}"
+    cmake -S "${REPODIR}"/cpp -B "${LIBCUVS_BUILD_DIR}" \
+          -DCMAKE_INSTALL_PREFIX="${INSTALL_PREFIX}" \
+          -DCMAKE_CUDA_ARCHITECTURES="${CUVS_CMAKE_CUDA_ARCHITECTURES}" \
           -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
           -DBUILD_C_LIBRARY=${COMPILE_LIBRARY} \
           -DCUVS_NVTX=${NVTX} \
@@ -402,21 +405,21 @@ if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg
           -DDISABLE_DEPRECATION_WARNINGS=${DISABLE_DEPRECATION_WARNINGS} \
           -DBUILD_TESTS=${BUILD_TESTS} \
           -DBUILD_C_TESTS=${BUILD_TESTS} \
-          -DBUILD_CUVS_BENCH=${BUILD_CUVS_BENCH} \
-          -DBUILD_CPU_ONLY=${BUILD_CPU_ONLY} \
+          -DBUILD_CUVS_BENCH="${BUILD_CUVS_BENCH}" \
+          -DBUILD_CPU_ONLY="${BUILD_CPU_ONLY}" \
           -DBUILD_MG_ALGOS=${BUILD_MG_ALGOS} \
           -DCMAKE_MESSAGE_LOG_LEVEL=${CMAKE_LOG_LEVEL} \
           -DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS} \
-          ${CACHE_ARGS} \
-          ${EXTRA_CMAKE_ARGS}
+          "${CACHE_ARGS[@]}" \
+          "${EXTRA_CMAKE_ARGS[@]}"
 
   compile_start=$(date +%s)
-  if [[ ${CMAKE_TARGET} != "" ]]; then
-      echo "-- Compiling targets: ${CMAKE_TARGET}, verbose=${VERBOSE_FLAG}"
+  if [[ ${#CMAKE_TARGET} -ne 0 ]]; then
+      echo "-- Compiling targets: " "${CMAKE_TARGET[@]}" ", verbose=${VERBOSE_FLAG}"
       if [[ ${INSTALL_TARGET} != "" ]]; then
-        cmake --build  "${LIBCUVS_BUILD_DIR}" ${VERBOSE_FLAG} -j${PARALLEL_LEVEL} --target ${CMAKE_TARGET} ${INSTALL_TARGET}
+        cmake --build  "${LIBCUVS_BUILD_DIR}" ${VERBOSE_FLAG} -j"${PARALLEL_LEVEL}" --target "${CMAKE_TARGET[@]// /;}" ${INSTALL_TARGET}
       else
-        cmake --build  "${LIBCUVS_BUILD_DIR}" ${VERBOSE_FLAG} -j${PARALLEL_LEVEL} --target ${CMAKE_TARGET}
+        cmake --build  "${LIBCUVS_BUILD_DIR}" ${VERBOSE_FLAG} -j"${PARALLEL_LEVEL}" --target "${CMAKE_TARGET[@]// /;}"
       fi
   fi
   compile_end=$(date +%s)
@@ -439,7 +442,7 @@ if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg
               MSG="${MSG}<br/>cache hit rate ${HIT_RATE} %"
           elif [[ ${CACHE_TOOL} == "ccache" && -x "$(command -v ccache)" ]]; then
               CACHE_STATS_LINE=$(ccache -s | grep "Hits: \+ [0-9]\+ / [0-9]\+" | tail -n1)
-              if [[ ! -z "$CACHE_STATS_LINE" ]]; then
+              if [[ -n "$CACHE_STATS_LINE" ]]; then
                   CACHE_HITS=$(echo "$CACHE_STATS_LINE" - | awk '{ print $2 }')
                   COMPILE_REQUESTS=$(echo "$CACHE_STATS_LINE" - | awk '{ print $4 }')
                   HIT_RATE=$(COMPILE_REQUESTS="${COMPILE_REQUESTS}" CACHE_HITS="${CACHE_HITS}" python3 -c "import os; print(f'{int(os.getenv(\"CACHE_HITS\")) / int(os.getenv(\"COMPILE_REQUESTS\")):.2f}' if int(os.getenv(\"COMPILE_REQUESTS\")) else 'nan')")
@@ -450,53 +453,53 @@ if (( NUMARGS == 0 )) || hasArg libcuvs || hasArg docs || hasArg tests || hasArg
       MSG="${MSG}<br/>parallel setting: $PARALLEL_LEVEL"
       MSG="${MSG}<br/>parallel build time: $compile_total seconds"
       if [[ -f "${LIBCUVS_BUILD_DIR}/libcuvs.so" ]]; then
-          LIBCUVS_FS=$(stat -c %s ${LIBCUVS_BUILD_DIR}/libcuvs.so | awk '{printf "%.2f MB", $1/1024/1024}')
+          LIBCUVS_FS=$(find "${LIBCUVS_BUILD_DIR}" -name libcuvs.so -printf '%s'| awk '{printf "%.2f MB", $1/1024/1024}')
           MSG="${MSG}<br/>libcuvs.so size: $LIBCUVS_FS"
       fi
       BMR_DIR=${RAPIDS_ARTIFACTS_DIR:-"${LIBCUVS_BUILD_DIR}"}
       echo "The HTML report can be found at [${BMR_DIR}/${BUILD_REPORT_METRICS}.html]. In CI, this report"
       echo "will also be uploaded to the appropriate subdirectory of https://downloads.rapids.ai/ci/cuvs/, and"
       echo "the entire URL can be found in \"conda-cpp-build\" runs under the task \"Upload additional artifacts\""
-      mkdir -p ${BMR_DIR}
+      mkdir -p "${BMR_DIR}"
       MSG_OUTFILE="$(mktemp)"
       echo "$MSG" > "${MSG_OUTFILE}"
-      PATH=".:$PATH" python rapids-build-metrics-reporter.py ${LIBCUVS_BUILD_DIR}/.ninja_log --fmt html --msg "${MSG_OUTFILE}" > ${BMR_DIR}/${BUILD_REPORT_METRICS}.html
-      cp ${LIBCUVS_BUILD_DIR}/.ninja_log ${BMR_DIR}/ninja.log
+      PATH=".:$PATH" python rapids-build-metrics-reporter.py "${LIBCUVS_BUILD_DIR}"/.ninja_log --fmt html --msg "${MSG_OUTFILE}" > "${BMR_DIR}"/"${BUILD_REPORT_METRICS}".html
+      cp "${LIBCUVS_BUILD_DIR}"/.ninja_log "${BMR_DIR}"/ninja.log
   fi
 fi
 
 # Build and (optionally) install the cuvs Python package
-if (( ${NUMARGS} == 0 )) || hasArg python; then
-    SKBUILD_CMAKE_ARGS="${EXTRA_CMAKE_ARGS}" \
+if (( NUMARGS == 0 )) || hasArg python; then
+    SKBUILD_CMAKE_ARGS="${EXTRA_CMAKE_ARGS[*]}" \
         SKBUILD_BUILD_OPTIONS="-j${PARALLEL_LEVEL}" \
-        python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs
+        python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true "${REPODIR}"/python/cuvs
 fi
 
 # Build and (optionally) install the cuvs-bench Python package
 if (( NUMARGS == 0 )) || (hasArg bench-ann && ! hasArg -n); then
-    python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true ${REPODIR}/python/cuvs_bench
+    python -m pip install --no-build-isolation --no-deps --config-settings rapidsai.disable-cuda=true "${REPODIR}"/python/cuvs_bench
 fi
 
 # Build the cuvs Rust bindings
-if (( ${NUMARGS} == 0 )) || hasArg rust; then
-    cd ${REPODIR}/rust
+if (( NUMARGS == 0 )) || hasArg rust; then
+    cd "${REPODIR}"/rust
     cargo build --examples --lib
     cargo test
 fi
 
 # Build the cuvs Go bindings
-if (( ${NUMARGS} == 0 )) || hasArg go; then
-    cd ${REPODIR}/go
+if (( NUMARGS == 0 )) || hasArg go; then
+    cd "${REPODIR}"/go
     go build ./...
     go test ./...
 fi
 
 # Build the cuvs Java bindings
-if (( ${NUMARGS} == 0 )) || hasArg java; then
+if (( NUMARGS == 0 )) || hasArg java; then
     if ! hasArg libcuvs; then
         echo "Please add 'libcuvs' to this script's arguments (ex. './build.sh libcuvs java') if libcuvs libraries are not already built"
     fi
-    cd ${REPODIR}/java
+    cd "${REPODIR}"/java
     if hasArg --run-java-tests; then
         ./build.sh --run-java-tests
     else
@@ -511,20 +514,20 @@ export RAPIDS_VERSION_MAJOR_MINOR
 
 if hasArg docs; then
     set -x
-    cd ${DOXYGEN_BUILD_DIR}
+    cd "${DOXYGEN_BUILD_DIR}"
     doxygen Doxyfile
-    cd ${SPHINX_BUILD_DIR}
+    cd "${SPHINX_BUILD_DIR}"
     sphinx-build -b html source _html
-    cd ${REPODIR}/rust
+    cd "${REPODIR}"/rust
     cargo doc -p cuvs --no-deps
-    rsync -av ${RUST_BUILD_DIR}/doc/ ${SPHINX_BUILD_DIR}/_html/_static/rust
+    rsync -av "${RUST_BUILD_DIR}"/doc/ "${SPHINX_BUILD_DIR}"/_html/_static/rust
 fi
 
 ################################################################################
 # Initiate build for c++ examples (if needed)
 
 if hasArg examples; then
-    pushd ${REPODIR}/examples
+    pushd "${REPODIR}"/examples
     ./build.sh
     popd
 fi
diff --git a/ci/checks/black_lists.sh b/ci/checks/black_lists.sh
index ed9f3745b2..e255ca7479 100755
--- a/ci/checks/black_lists.sh
+++ b/ci/checks/black_lists.sh
@@ -1,12 +1,12 @@
 #!/bin/bash
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 ##########################################
 # RAFT black listed function call Tester #
 ##########################################
 
 # PR_TARGET_BRANCH is set by the CI environment
 
-git checkout --quiet $PR_TARGET_BRANCH
+git checkout --quiet "$PR_TARGET_BRANCH"
 
 # Switch back to tip of PR branch
 git checkout --quiet current-pr-branch
diff --git a/cpp/scripts/run-cmake-format.sh b/cpp/scripts/run-cmake-format.sh
index 5438a6a932..8d4d806199 100755
--- a/cpp/scripts/run-cmake-format.sh
+++ b/cpp/scripts/run-cmake-format.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 # This script is a wrapper for cmakelang that may be used with pre-commit. The
 # wrapping is necessary because RAPIDS libraries split configuration for
@@ -38,7 +38,7 @@ if ! [ ${status} -eq 0 ]; then
         echo "This script must be run inside the raft repository, or the RAFT_ROOT environment variable must be set."
     else
         echo "Script failed with unknown error attempting to determine project root:"
-        echo ${RAFT_BUILD_DIR}
+        echo "${RAFT_BUILD_DIR}"
     fi
     exit 1
 fi
@@ -49,7 +49,7 @@ DEFAULT_FORMAT_FILE_LOCATIONS=(
 
 if [ -z ${RAPIDS_CMAKE_FORMAT_FILE:+PLACEHOLDER} ]; then
     for file_path in "${DEFAULT_FORMAT_FILE_LOCATIONS[@]}"; do
-        if [ -f ${file_path} ]; then
+        if [ -f "${file_path}" ]; then
             RAPIDS_CMAKE_FORMAT_FILE=${file_path}
             break
         fi
@@ -68,12 +68,12 @@ else
 fi
 
 if [[ $1 == "cmake-format" ]]; then
-  cmake-format -i --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- "${@:2}"
+  cmake-format -i --config-files cpp/cmake/config.json "${RAPIDS_CMAKE_FORMAT_FILE}" -- "${@:2}"
 elif [[ $1 == "cmake-lint" ]]; then
   # Since the pre-commit hook is verbose, we have to be careful to only
   # present cmake-lint's output (which is quite verbose) if we actually
   # observe a failure.
-  OUTPUT=$(cmake-lint --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- "${@:2}")
+  OUTPUT=$(cmake-lint --config-files cpp/cmake/config.json "${RAPIDS_CMAKE_FORMAT_FILE}" -- "${@:2}")
   status=$?
 
   if ! [ ${status} -eq 0 ]; then
diff --git a/examples/build.sh b/examples/build.sh
index 434cdcd5a3..3f304cfae4 100755
--- a/examples/build.sh
+++ b/examples/build.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 
 # cuvs empty project template build script
 
@@ -11,7 +11,7 @@ NUMARGS=$#
 ARGS=$*
 
 function hasArg {
-    (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+    (( NUMARGS != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
 }
 
 if hasArg clean; then
@@ -22,7 +22,7 @@ fi
 
 function gpuArch {
 
-    if hasArg --allgpuarch && [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+    if hasArg --allgpuarch && [[ -n $(echo "$ARGS" | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
         echo "Error: Cannot specify both --gpu-arch and --allgpuarch"
         echo "Use either:"
         echo "  --gpu-arch=\"80-real;90-real\"    (for specific architectures)"
@@ -30,18 +30,18 @@ function gpuArch {
         exit 1
     fi
 
-    if [[ $(echo $ARGS | { grep -Eo "\-\-gpu\-arch" || true; } | wc -l ) -gt 1 ]]; then
+    if [[ $(echo "$ARGS" | { grep -Eo "\-\-gpu\-arch" || true; } | wc -l ) -gt 1 ]]; then
         echo "Error: Multiple --gpu-arch options were provided. Please combine architectures into a single option."
         echo "Instead of: --gpu-arch=80-real --gpu-arch=90-real"
         echo "Use:       --gpu-arch=\"80-real;90-real\""
         exit 1
     fi
 
-    if [[ -n $(echo $ARGS | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
-        GPU_ARCH_ARG=$(echo $ARGS | { grep -Eo "\-\-gpu\-arch=.+( |$)" || true; })
+    if [[ -n $(echo "$ARGS" | { grep -E "\-\-gpu\-arch" || true; } ) ]]; then
+        GPU_ARCH_ARG=$(echo "$ARGS" | { grep -Eo "\-\-gpu\-arch=.+( |$)" || true; })
         if [[ -n ${GPU_ARCH_ARG} ]]; then
             # Extract just the architecture value
-            echo ${GPU_ARCH_ARG} | sed -e 's/--gpu-arch=//' -e 's/ .*//'
+            echo "${GPU_ARCH_ARG}" | sed -e 's/--gpu-arch=//' -e 's/ .*//'
             return
         fi
     fi
@@ -57,10 +57,10 @@ function gpuArch {
 }
 
 # Set up build configuration
-PARALLEL_LEVEL=${PARALLEL_LEVEL:=`nproc`}
+PARALLEL_LEVEL=${PARALLEL_LEVEL:=$(nproc)}
 BUILD_TYPE=Release
 CUVS_REPO_REL=""
-EXTRA_CMAKE_ARGS=""
+EXTRA_CMAKE_ARGS=()
 
 
 CUVS_CMAKE_CUDA_ARCHITECTURES=$(gpuArch)
@@ -74,11 +74,11 @@ esac
 EXAMPLES_DIR=$(dirname "$(realpath "$0")")
 
 if [[ ${CUVS_REPO_REL} != "" ]]; then
-  CUVS_REPO_PATH="`readlink -f \"${CUVS_REPO_REL}\"`"
-  EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DCPM_cuvs_SOURCE=${CUVS_REPO_PATH}"
+  CUVS_REPO_PATH=$(readlink -f "${CUVS_REPO_REL}")
+  EXTRA_CMAKE_ARGS+=("-DCPM_cuvs_SOURCE=${CUVS_REPO_PATH}")
 else
   LIB_BUILD_DIR=${LIB_BUILD_DIR:-$(readlink -f "${EXAMPLES_DIR}/../cpp/build")}
-  EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -Dcuvs_ROOT=${LIB_BUILD_DIR}"
+  EXTRA_CMAKE_ARGS+=("-Dcuvs_ROOT=${LIB_BUILD_DIR}")
 fi
 
 ################################################################################
@@ -90,14 +90,14 @@ build_example() {
   build_dir="${example_dir}/build"
 
   # Configure
-  cmake -S ${example_dir} -B ${build_dir} \
+  cmake -S "${example_dir}" -B "${build_dir}" \
   -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
   -DCUVS_NVTX=OFF \
-  -DCMAKE_CUDA_ARCHITECTURES=${CUVS_CMAKE_CUDA_ARCHITECTURES} \
+  -DCMAKE_CUDA_ARCHITECTURES="${CUVS_CMAKE_CUDA_ARCHITECTURES}" \
   -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-  ${EXTRA_CMAKE_ARGS}
+  "${EXTRA_CMAKE_ARGS[@]}"
   # Build
-  cmake --build ${build_dir} -j${PARALLEL_LEVEL}
+  cmake --build "${build_dir}" -j"${PARALLEL_LEVEL}"
 }
 
 build_example c
diff --git a/java/build.sh b/java/build.sh
index fb5796774a..a0577d48e4 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -1,11 +1,13 @@
 #!/bin/bash
 
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
 set -e -u -o pipefail
 
 ARGS="$*"
 NUMARGS=$#
 
-CURDIR=$(cd "$(dirname $0)"; pwd)
+CURDIR=$(cd "$(dirname "$0")"; pwd)
 VERSION="25.08.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
 
diff --git a/java/panama-bindings/generate-bindings.sh b/java/panama-bindings/generate-bindings.sh
index adcf49ce2b..b7e8ccb067 100755
--- a/java/panama-bindings/generate-bindings.sh
+++ b/java/panama-bindings/generate-bindings.sh
@@ -1,10 +1,12 @@
 #!/bin/bash
 
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
 set -e -u -o pipefail
 
 echo "Starting Panama FFM API bindings generation ..."
-REPODIR=$(cd "$(dirname $0)"; cd ../../ ; pwd)
-CURDIR=$(cd "$(dirname $0)"; pwd)
+REPODIR=$(cd "$(dirname "$0")"; cd ../../ ; pwd)
+CURDIR=$(cd "$(dirname "$0")"; pwd)
 TARGET_PACKAGE="com.nvidia.cuvs.internal.panama"
 
 TARGET_DIR="targets/x86_64-linux/include"
@@ -17,7 +19,7 @@ else
   exit 1
 fi
 
-if [[ `command -v jextract` == "" ]];
+if [[ $(command -v jextract) == "" ]];
 then
   JEXTRACT_FILENAME="openjdk-22-jextract+6-47_linux-x64_bin.tar.gz"
   JEXTRACT_DOWNLOAD_URL="https://download.java.net/java/early_access/jextract/22/6/${JEXTRACT_FILENAME}"
@@ -31,12 +33,12 @@ fi
 
 # Use Jextract utility to generate panama bindings
 jextract \
- --include-dir ${REPODIR}/java/internal/build/_deps/dlpack-src/include/ \
- --include-dir ${CUDA_INCLUDE_DIR} \
- --include-dir ${REPODIR}/cpp/include \
+ --include-dir "${REPODIR}"/java/internal/build/_deps/dlpack-src/include/ \
+ --include-dir "${CUDA_INCLUDE_DIR}" \
+ --include-dir "${REPODIR}"/cpp/include \
  --output "${REPODIR}/java/cuvs-java/src/main/java22/" \
  --target-package ${TARGET_PACKAGE} \
  --library cuvs_c \
- ${CURDIR}/headers.h
+ "${CURDIR}"/headers.h
 
 echo "Panama FFM API bindings generation done"

From 4619592f25e5b967878c8dcb57daeb2cd5b82203 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Thu, 26 Jun 2025 14:33:14 -0400
Subject: [PATCH 453/623] Improve memory efficiency for returning NN Descent
 distances (#1053)

NN descent has its distances calculated and stored in a host matrix, while the return is expected to be on device.

For the slicing operation, right now we allocate a device matrix of size `nrow x node_degree`. This PR improves memory usage by doing the slicing operation on host, and copying the results into the pre-allocated device memory output.

This operation is done once at the end of NN Descent, so moving the slicing device computation to be computed on host should not bring noticeable perf issues.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1053
---
 cpp/src/neighbors/detail/nn_descent.cuh | 26 ++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index c010a7cfed..5262b64a97 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -1186,23 +1186,23 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
   static_assert(sizeof(decltype(*(graph_.h_dists.data_handle()))) >= sizeof(Index_t));
 
   if (return_distances) {
-    auto graph_d_dists = raft::make_device_matrix<DistData_t, int64_t, raft::row_major>(
-      res, nrow_, build_config_.node_degree);
-    raft::copy(graph_d_dists.data_handle(),
-               graph_.h_dists.data_handle(),
-               nrow_ * build_config_.node_degree,
+    auto graph_h_dists = raft::make_host_matrix<DistData_t, int64_t, raft::row_major>(
+      nrow_, build_config_.output_graph_degree);
+
+// slice on host
+#pragma omp parallel for
+    for (size_t i = 0; i < (size_t)nrow_; i++) {
+      for (size_t j = 0; j < build_config_.output_graph_degree; j++) {
+        graph_h_dists(i, j) = graph_.h_dists(i, j);
+      }
+    }
+    raft::copy(output_distances,
+               graph_h_dists.data_handle(),
+               nrow_ * build_config_.output_graph_degree,
                raft::resource::get_cuda_stream(res));
 
     auto output_dist_view = raft::make_device_matrix_view<DistData_t, int64_t, raft::row_major>(
       output_distances, nrow_, build_config_.output_graph_degree);
-
-    raft::matrix::slice_coordinates coords{static_cast<int64_t>(0),
-                                           static_cast<int64_t>(0),
-                                           static_cast<int64_t>(nrow_),
-                                           static_cast<int64_t>(build_config_.output_graph_degree)};
-    raft::matrix::slice<DistData_t, int64_t, raft::row_major>(
-      res, raft::make_const_mdspan(graph_d_dists.view()), output_dist_view, coords);
-
     // distance post-processing
     if (build_config_.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
       raft::linalg::map(

From 5a59c8add0e41d3616c5f0651ad42b5d2639be02 Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Thu, 26 Jun 2025 12:32:54 -0700
Subject: [PATCH 454/623] Remove nvidia and dask channels (#1050)

Now that we have dropped support for CUDA 11 we no longer require the nvidia channel.
With the changes in https://github.com/rapidsai/rapids-dask-dependency/pull/85, RAPIDS now only uses released versions of dask, so we no longer need the dask channel either.
Contributes to https://github.com/rapidsai/build-planning/issues/184

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/1050
---
 .pre-commit-config.yaml | 2 +-
 dependencies.yaml       | 6 +-----
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a45573a791..22db1342d1 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -120,7 +120,7 @@ repos:
           - id: verify-codeowners
             args: [--fix, --project-prefix=cuvs]
       - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.18.1
+        rev: v1.19.0
         hooks:
             - id: rapids-dependency-file-generator
               args: ["--clean"]
diff --git a/dependencies.yaml b/dependencies.yaml
index ac5883dc94..0fa0813ab3 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -259,7 +259,6 @@ dependencies:
         matrices:
           - matrix: {cuda: "12.*"}
             packages: [cuda-nvcc]
-
   rapids_build_setuptools:
     common:
       - output_types: [conda, requirements, pyproject]
@@ -267,7 +266,6 @@ dependencies:
           - &rapids_build_backend rapids-build-backend>=0.3.0,<0.4.0.dev0
           - setuptools
           - wheel
-
   build_py_cuvs:
     common:
       - output_types: [conda]
@@ -356,7 +354,6 @@ dependencies:
               - nvidia-curand
               - nvidia-cusolver
               - nvidia-cusparse
-
   depends_on_cupy:
     common:
       - output_types: conda
@@ -369,7 +366,6 @@ dependencies:
             packages:
               - cupy-cuda12x>=12.0.0
           - {matrix: null, packages: [cupy-cuda12x>=12.0.0]}
-
   test_libcuvs:
     common:
       - output_types: [conda]
@@ -390,7 +386,7 @@ dependencies:
           - sphinx-copybutton
           - sphinx-markdown-tables
           - pip:
-            - nvidia-sphinx-theme
+              - nvidia-sphinx-theme
   rust:
     common:
       - output_types: [conda]

From 987d002ff1bf197de15c3114adcf4f32d6677115 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Thu, 26 Jun 2025 20:24:09 -0400
Subject: [PATCH 455/623] Enable `build_sorted_mst` with data on host memory
 (#997)

Connecting knn graph requires the entire data to be on device memory to use `cuvs::sparse::neighbors::cross_component_nn`.

This PR enables connecting knn graph with data on host by randomly choosing edges between components. Thus, it ends up selecting `n_components-1` number of edges and adds that to the msf to make an mst.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/997
---
 cpp/src/cluster/detail/mst.cuh   | 145 ++++++++++++++++++++++++++-
 cpp/tests/CMakeLists.txt         |   4 +-
 cpp/tests/cluster/connect_knn.cu | 162 +++++++++++++++++++++++++++++++
 3 files changed, 306 insertions(+), 5 deletions(-)
 create mode 100644 cpp/tests/cluster/connect_knn.cu

diff --git a/cpp/src/cluster/detail/mst.cuh b/cpp/src/cluster/detail/mst.cuh
index 2929262d21..a6b5f3c71e 100644
--- a/cpp/src/cluster/detail/mst.cuh
+++ b/cpp/src/cluster/detail/mst.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2021-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,7 +17,11 @@
 #pragma once
 
 #include "../../sparse/neighbors/cross_component_nn.cuh"
+#include <cuvs/distance/distance.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/label/classlabels.cuh>
+#include <raft/matrix/detail/gather.cuh>
+#include <raft/matrix/diagonal.cuh>
 #include <raft/sparse/op/sort.cuh>
 #include <raft/sparse/solver/mst.cuh>
 #include <raft/util/cuda_utils.cuh>
@@ -59,7 +63,7 @@ void merge_msts(raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>&
  * @tparam value_idx index type
  * @tparam value_t floating-point value type
  * @param[in] handle raft handle
- * @param[in] X original dense data from which knn grpah was constructed
+ * @param[in] X original dense data on device memory from which knn graph was constructed
  * @param[inout] msf edge list containing the mst result
  * @param[in] m number of rows in X
  * @param[in] n number of columns in X
@@ -117,6 +121,132 @@ void connect_knn_graph(
   merge_msts<value_idx, value_t>(msf, new_mst, stream);
 }
 
+/**
+ * Connect an unconnected knn graph (one in which mst returns an msf). The
+ * device buffers underlying the Graph_COO object are modified in-place.
+ * @tparam value_idx index type
+ * @tparam value_t floating-point value type
+ * @param[in] handle raft handle
+ * @param[in] X original dense data on host memory from which knn graph was constructed
+ * @param[inout] msf edge list containing the mst result
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[in] n_components number of components in color
+ * @param[inout] color the color labels array returned from the mst invocation
+ * @return updated MST edge list
+ */
+template <typename value_idx, typename value_t>
+void connect_knn_graph(
+  raft::resources const& handle,
+  const value_t* X,
+  raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>& msf,
+  size_t m,
+  size_t n,
+  int n_components,
+  value_idx* color,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  rmm::device_uvector<value_idx> d_color_remapped(m, stream);
+  raft::label::make_monotonic(d_color_remapped.data(), color, m, stream, true);
+
+  std::vector<value_idx> h_color(m);
+  raft::copy(h_color.data(), d_color_remapped.data(), m, stream);
+  raft::resource::sync_stream(handle, stream);
+
+  // make key (color) : value (vector of ids that have that color)
+  std::unordered_map<value_idx, std::vector<value_idx>> component_map;
+  for (value_idx i = 0; i < static_cast<value_idx>(m); ++i) {
+    component_map[h_color[i]].push_back(i);
+  }
+
+  std::vector<std::tuple<value_idx, value_idx, value_t>> selected_edges;
+
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::uniform_int_distribution<> dis;
+
+  std::vector<value_idx> host_u_indices;
+  std::vector<value_idx> host_v_indices;
+
+  // connect i-1 component and i component
+  for (int i = 1; i < n_components; ++i) {
+    value_idx color_a = i - 1;
+    value_idx color_b = i;
+
+    const auto& nodes_a = component_map[color_a];
+    const auto& nodes_b = component_map[color_b];
+
+    // Randomly pick a data index from each component
+    dis.param(std::uniform_int_distribution<>::param_type(0, nodes_a.size() - 1));
+    value_idx u = nodes_a[dis(gen)];
+
+    dis.param(std::uniform_int_distribution<>::param_type(0, nodes_b.size() - 1));
+    value_idx v = nodes_b[dis(gen)];
+
+    host_u_indices.push_back(u);
+    host_v_indices.push_back(v);
+  }
+
+  auto device_u_indices = raft::make_device_vector<value_idx, int64_t>(handle, n_components - 1);
+  auto device_v_indices = raft::make_device_vector<value_idx, int64_t>(handle, n_components - 1);
+
+  raft::copy(device_u_indices.data_handle(), host_u_indices.data(), n_components - 1, stream);
+  raft::copy(device_v_indices.data_handle(), host_v_indices.data(), n_components - 1, stream);
+
+  auto X_view = raft::make_host_matrix_view<const value_t, int64_t>(X, m, n);
+  auto data_u = raft::make_device_matrix<value_t, int64_t>(handle, n_components - 1, n);
+  auto data_v = raft::make_device_matrix<value_t, int64_t>(handle, n_components - 1, n);
+
+  raft::matrix::detail::gather(
+    handle, X_view, raft::make_const_mdspan(device_u_indices.view()), data_u.view());
+  raft::matrix::detail::gather(
+    handle, X_view, raft::make_const_mdspan(device_v_indices.view()), data_v.view());
+
+  auto pairwise_dist =
+    raft::make_device_matrix<value_t, int64_t>(handle, n_components - 1, n_components - 1);
+  cuvs::distance::pairwise_distance(handle,
+                                    raft::make_const_mdspan(data_u.view()),
+                                    raft::make_const_mdspan(data_v.view()),
+                                    pairwise_dist.view(),
+                                    metric);
+
+  auto pairwise_dist_vec = raft::make_device_vector<value_t, int64_t>(handle, n_components - 1);
+  raft::matrix::get_diagonal(
+    handle, raft::make_const_mdspan(pairwise_dist.view()), pairwise_dist_vec.view());
+
+  size_t new_nnz = n_components - 1;
+
+  // sort in order of rows to run sorted_coo_to_csr
+  auto rows_begin = thrust::device_pointer_cast(device_u_indices.data_handle());
+  auto cols_begin = thrust::device_pointer_cast(device_v_indices.data_handle());
+  auto dist_begin = thrust::device_pointer_cast(pairwise_dist_vec.data_handle());
+
+  auto zipped_begin = thrust::make_zip_iterator(thrust::make_tuple(cols_begin, dist_begin));
+  thrust::sort_by_key(rows_begin, rows_begin + new_nnz, zipped_begin);
+
+  rmm::device_uvector<value_idx> indptr2(m + 1, stream);
+  raft::sparse::convert::sorted_coo_to_csr(
+    device_u_indices.data_handle(), new_nnz, indptr2.data(), m + 1, stream);
+
+  // On the second call, we hand the MST the original colors
+  // and the new set of edges and let it restart the optimization process
+  auto new_mst = raft::sparse::solver::mst<value_idx, value_idx, value_t, double>(
+    handle,
+    indptr2.data(),
+    device_v_indices.data_handle(),
+    pairwise_dist_vec.data_handle(),
+    m,
+    new_nnz,
+    color,
+    stream,
+    false,
+    false);
+
+  merge_msts<value_idx, value_t>(msf, new_mst, stream);
+}
+
 /**
  * Constructs an MST and sorts the resulting edges in ascending
  * order by their weight.
@@ -130,6 +260,7 @@ void connect_knn_graph(
  * @tparam value_idx
  * @tparam value_t
  * @param[in] handle raft handle
+ * @param[in] X dataset residing on host or device memory
  * @param[in] indptr CSR indptr of connectivities graph
  * @param[in] indices CSR indices array of connectivities graph
  * @param[in] pw_dists CSR weights array of connectivities graph
@@ -168,8 +299,16 @@ void build_sorted_mst(
   int iters        = 1;
   int n_components = cuvs::sparse::neighbors::get_n_components(color, m, stream);
 
+  cudaPointerAttributes attr;
+  RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, X));
+  bool data_on_device = attr.type == cudaMemoryTypeDevice;
+
   while (n_components > 1 && iters < max_iter) {
-    connect_knn_graph<value_idx, value_t>(handle, X, mst_coo, m, n, color, reduction_op);
+    if (data_on_device) {
+      connect_knn_graph<value_idx, value_t>(handle, X, mst_coo, m, n, color, reduction_op);
+    } else {
+      connect_knn_graph<value_idx, value_t>(handle, X, mst_coo, m, n, n_components, color, metric);
+    }
 
     iters++;
 
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index b49b5acd7d..f2ca71347b 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2021-2024, NVIDIA CORPORATION.
+# Copyright (c) 2021-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -101,7 +101,7 @@ if(BUILD_TESTS)
 
   ConfigureTest(
     NAME CLUSTER_TEST PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu
-    cluster/linkage.cu GPUS 1 PERCENT 100
+    cluster/linkage.cu cluster/connect_knn.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
diff --git a/cpp/tests/cluster/connect_knn.cu b/cpp/tests/cluster/connect_knn.cu
new file mode 100644
index 0000000000..70e999406d
--- /dev/null
+++ b/cpp/tests/cluster/connect_knn.cu
@@ -0,0 +1,162 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/sparse/linalg/symmetrize.cuh>
+#include <raft/util/cuda_utils.cuh>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/itertools.hpp>
+
+#include "../../src/cluster/detail/mst.cuh"
+#include "../neighbors/naive_knn.cuh"
+#include <rmm/device_uvector.hpp>
+
+#include <gtest/gtest.h>
+#include <stdio.h>
+#include <test_utils.h>
+#include <vector>
+
+namespace cuvs {
+
+struct ConnectKNNInputs {
+  int n_rows;
+  int dim;
+  int n_clusters;
+  int k;
+  cuvs::distance::DistanceType metric;
+};
+
+template <typename T>
+class ConnectKNNTest : public ::testing::TestWithParam<ConnectKNNInputs> {
+ public:
+  ConnectKNNTest()
+    : stream(handle.get_stream()),
+      ps(::testing::TestWithParam<ConnectKNNInputs>::GetParam()),
+      database(0, stream)
+  {
+  }
+
+  void basicTest()
+  {
+    int queries_size = ps.n_rows * ps.k;
+    rmm::device_uvector<T> dists(queries_size, stream);
+    rmm::device_uvector<int64_t> inds(queries_size, stream);
+    cuvs::neighbors::naive_knn<T, T, int64_t>(handle,
+                                              dists.data(),
+                                              inds.data(),
+                                              database.data(),
+                                              database.data(),
+                                              ps.n_rows,
+                                              ps.n_rows,
+                                              ps.dim,
+                                              ps.k,
+                                              ps.metric);
+
+    rmm::device_uvector<int64_t> coo_rows(queries_size, stream);
+
+    raft::sparse::COO<T, int64_t> knn_coo(stream, queries_size * 2);
+    rmm::device_uvector<int64_t> indptr(ps.n_rows + 1, stream);
+
+    // changing inds and dists to sparse format
+    int64_t k                  = ps.k;
+    auto coo_rows_counting_itr = thrust::make_counting_iterator<int64_t>(0);
+    thrust::transform(raft::resource::get_thrust_policy(handle),
+                      coo_rows_counting_itr,
+                      coo_rows_counting_itr + (ps.n_rows * ps.k),
+                      coo_rows.data(),
+                      [k] __device__(int64_t c) -> int64_t { return c / k; });
+
+    raft::sparse::linalg::symmetrize(handle,
+                                     coo_rows.data(),
+                                     inds.data(),
+                                     dists.data(),
+                                     (int64_t)ps.n_rows,
+                                     (int64_t)ps.n_rows,
+                                     static_cast<size_t>(queries_size),
+                                     knn_coo);
+
+    raft::sparse::convert::sorted_coo_to_csr(
+      knn_coo.rows(), knn_coo.nnz, indptr.data(), ps.n_rows + 1, stream);
+
+    // run mst solver
+    rmm::device_uvector<int64_t> color(ps.n_rows, stream);
+    auto mst_coo = raft::sparse::solver::mst<int64_t, int64_t, T, double>(handle,
+                                                                          indptr.data(),
+                                                                          knn_coo.cols(),
+                                                                          knn_coo.vals(),
+                                                                          (int64_t)ps.n_rows,
+                                                                          knn_coo.nnz,
+                                                                          color.data(),
+                                                                          stream,
+                                                                          false,
+                                                                          true);
+
+    // connect knn graph on host and checking final n_components
+    // because the dataset is a blobs dataset, original n_components results in n_clusters of the
+    // dataset
+    int n_components = cuvs::sparse::neighbors::get_n_components(color.data(), ps.n_rows, stream);
+
+    auto database_h = raft::make_host_matrix<T, int64_t>(ps.n_rows, ps.dim);
+    raft::copy(database_h.data_handle(), database.data(), ps.n_rows * ps.dim, stream);
+
+    cuvs::cluster::agglomerative::detail::connect_knn_graph<int64_t, T>(handle,
+                                                                        database_h.data_handle(),
+                                                                        mst_coo,
+                                                                        ps.n_rows,
+                                                                        ps.dim,
+                                                                        n_components,
+                                                                        color.data(),
+                                                                        ps.metric);
+    n_components = cuvs::sparse::neighbors::get_n_components(color.data(), ps.n_rows, stream);
+
+    ASSERT_TRUE(n_components == 1);
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream);
+    auto database_view =
+      raft::make_device_matrix_view<T, int64_t>(database.data(), ps.n_rows, ps.dim);
+    auto labels = raft::make_device_vector<int64_t, int64_t>(handle, ps.n_rows);
+    raft::random::make_blobs(handle, database_view, labels.view(), (int64_t)ps.n_clusters);
+    raft::resource::sync_stream(handle);
+  }
+
+ protected:
+  raft::handle_t handle;
+  cudaStream_t stream;
+  ConnectKNNInputs ps;
+  rmm::device_uvector<T> database;
+};
+
+const std::vector<ConnectKNNInputs> inputs = raft::util::itertools::product<ConnectKNNInputs>(
+  {5000, 7151},                                   // n_rows
+  {64, 137},                                      // dim
+  {5, 10},                                        // n_clusters of make_blobs data
+  {16},                                           // k
+  {cuvs::distance::DistanceType::L2SqrtExpanded}  // metric
+);
+
+typedef ConnectKNNTest<float> ConnectKNNTestF;
+TEST_P(ConnectKNNTestF, ConnectKNN) { this->basicTest(); }
+
+INSTANTIATE_TEST_CASE_P(ConnectKNNTests, ConnectKNNTestF, ::testing::ValuesIn(inputs));
+
+}  // end namespace cuvs

From 97c5a49a45717011bc66c5bd4e67f59aeab99fdb Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Fri, 27 Jun 2025 09:29:33 -0400
Subject: [PATCH 456/623] Common knn graph build params (#949)

Reusing graph build params for cagra and all neighbors.
Related issue: https://github.com/rapidsai/cuvs/issues/931

Authors:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/949
---
 cpp/CMakeLists.txt                            |  3 +-
 cpp/include/cuvs/neighbors/all_neighbors.hpp  | 17 +---
 cpp/include/cuvs/neighbors/cagra.hpp          | 43 +---------
 .../cuvs/neighbors/graph_build_types.hpp      | 78 +++++++++++++++++++
 .../all_neighbors/all_neighbors_builder.cuh   |  6 +-
 .../neighbors/detail/cagra/cagra_build.cpp    | 37 ---------
 cpp/tests/neighbors/all_neighbors.cuh         |  9 ++-
 7 files changed, 92 insertions(+), 101 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/graph_build_types.hpp
 delete mode 100644 cpp/src/neighbors/detail/cagra/cagra_build.cpp

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 5ccb6765ab..6fb154088e 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -416,7 +416,6 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/iface/iface_pq_half_int64_t.cu
     src/neighbors/iface/iface_pq_int8_t_int64_t.cu
     src/neighbors/iface/iface_pq_uint8_t_int64_t.cu
-    src/neighbors/detail/cagra/cagra_build.cpp
     src/neighbors/detail/cagra/topk_for_cagra/topk.cu
     src/neighbors/dynamic_batching.cu
     src/neighbors/cagra_index_wrapper.cu
diff --git a/cpp/include/cuvs/neighbors/all_neighbors.hpp b/cpp/include/cuvs/neighbors/all_neighbors.hpp
index 683f0348c0..452aa12311 100644
--- a/cpp/include/cuvs/neighbors/all_neighbors.hpp
+++ b/cpp/include/cuvs/neighbors/all_neighbors.hpp
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include <cuvs/neighbors/graph_build_types.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 
@@ -27,22 +28,6 @@ namespace cuvs::neighbors::all_neighbors {
  * @{
  */
 
-/**
- * @brief Parameters used to build an all-neighbors knn graph (find nearest neighbors for all the
- * training vectors)
- */
-namespace graph_build_params {
-
-/** Specialized parameters utilizing IVF-PQ to build knn graph */
-struct ivf_pq_params {
-  cuvs::neighbors::ivf_pq::index_params build_params;
-  cuvs::neighbors::ivf_pq::search_params search_params;
-  float refinement_rate = 2.0;
-};
-
-using nn_descent_params = cuvs::neighbors::nn_descent::index_params;
-}  // namespace graph_build_params
-
 using GraphBuildParams =
   std::variant<graph_build_params::ivf_pq_params, graph_build_params::nn_descent_params>;
 
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 165faff5e1..48ee128271 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,6 +19,7 @@
 #include "common.hpp"
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/graph_build_types.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 #include <raft/core/device_mdspan.hpp>
@@ -35,49 +36,13 @@
 #include <variant>
 
 namespace cuvs::neighbors::cagra {
+// For re-exporting into cagra namespace
+namespace graph_build_params = cuvs::neighbors::graph_build_params;
 /**
  * @defgroup cagra_cpp_index_params CAGRA index build parameters
  * @{
  */
 
-/**
- * @brief ANN parameters used by CAGRA to build knn graph
- *
- */
-namespace graph_build_params {
-
-/** Specialized parameters utilizing IVF-PQ to build knn graph */
-struct ivf_pq_params {
-  cuvs::neighbors::ivf_pq::index_params build_params;
-  cuvs::neighbors::ivf_pq::search_params search_params;
-  float refinement_rate;
-
-  ivf_pq_params() = default;
-  /**
-   * Set default parameters based on shape of the input dataset.
-   * Usage example:
-   * @code{.cpp}
-   *   using namespace cuvs::neighbors;
-   *   raft::resources res;
-   *   // create index_params for a [N. D] dataset
-   *   auto dataset = raft::make_device_matrix<float, int64_t>(res, N, D);
-   *   auto pq_params =
-   *     cagra::graph_build_params::ivf_pq_params(dataset.extents());
-   *   // modify/update index_params as needed
-   *   pq_params.kmeans_trainset_fraction = 0.1;
-   * @endcode
-   */
-  ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
-                cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded);
-};
-
-using nn_descent_params = cuvs::neighbors::nn_descent::index_params;
-
-// **** Experimental ****
-using iterative_search_params = cuvs::neighbors::search_params;
-
-}  // namespace graph_build_params
-
 struct index_params : cuvs::neighbors::index_params {
   /** Degree of input graph for pruning. */
   size_t intermediate_graph_degree = 128;
diff --git a/cpp/include/cuvs/neighbors/graph_build_types.hpp b/cpp/include/cuvs/neighbors/graph_build_types.hpp
new file mode 100644
index 0000000000..d63b13386c
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/graph_build_types.hpp
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/nn_descent.hpp>
+
+namespace cuvs::neighbors {
+
+/**
+ * @defgroup neighbors_build_algo Graph build algorithm types
+ * @{
+ */
+
+enum GRAPH_BUILD_ALGO { BRUTE_FORCE = 0, IVF_PQ = 1, NN_DESCENT = 1 };
+
+namespace graph_build_params {
+
+/** Specialized parameters utilizing IVF-PQ to build knn graph */
+struct ivf_pq_params {
+  cuvs::neighbors::ivf_pq::index_params build_params;
+  cuvs::neighbors::ivf_pq::search_params search_params;
+  float refinement_rate = 1.0;
+
+  ivf_pq_params() = default;
+
+  /**
+   * Set default parameters based on shape of the input dataset.
+   * Usage example:
+   * @code{.cpp}
+   *   using namespace cuvs::neighbors;
+   *   raft::resources res;
+   *   // create index_params for a [N. D] dataset
+   *   auto dataset = raft::make_device_matrix<float, int64_t>(res, N, D);
+   *   auto pq_params =
+   *     graph_build_params::ivf_pq_params(dataset.extents());
+   *   // modify/update index_params as needed
+   *   pq_params.kmeans_trainset_fraction = 0.1;
+   * @endcode
+   */
+  ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
+                cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
+  {
+    build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset_extents, metric);
+
+    search_params                         = cuvs::neighbors::ivf_pq::search_params{};
+    search_params.n_probes                = std::max<uint32_t>(10, build_params.n_lists * 0.01);
+    search_params.lut_dtype               = CUDA_R_16F;
+    search_params.internal_distance_dtype = CUDA_R_16F;
+    search_params.coarse_search_dtype     = CUDA_R_16F;
+    search_params.max_internal_batch_size = 128 * 1024;
+
+    refinement_rate = 1;
+  }
+};
+
+using nn_descent_params = cuvs::neighbors::nn_descent::index_params;
+
+// **** Experimental ****
+using iterative_search_params = cuvs::neighbors::search_params;
+}  // namespace graph_build_params
+
+/** @} */  // end group neighbors_build_algo
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
index 61b414feca..e17fb4a0aa 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
@@ -116,7 +116,7 @@ struct all_neighbors_builder_ivfpq : public all_neighbors_builder<T, IdxT> {
     size_t min_cluster_size,
     size_t max_cluster_size,
     size_t k,
-    all_neighbors::graph_build_params::ivf_pq_params& params,
+    graph_build_params::ivf_pq_params& params,
     std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
     std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
     : all_neighbors_builder<T, IdxT>(
@@ -287,7 +287,7 @@ struct all_neighbors_builder_ivfpq : public all_neighbors_builder<T, IdxT> {
                        dataset_h.data_handle(), dataset.extent(0), dataset.extent(1)));
   }
 
-  all_neighbors::graph_build_params::ivf_pq_params all_ivf_pq_params;
+  graph_build_params::ivf_pq_params all_ivf_pq_params;
   size_t candidate_k;
 
   std::optional<raft::device_matrix<T, IdxT>> data_d;
@@ -308,7 +308,7 @@ struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT>
     size_t min_cluster_size,
     size_t max_cluster_size,
     size_t k,
-    all_neighbors::graph_build_params::nn_descent_params& params,
+    graph_build_params::nn_descent_params& params,
     std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
     std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
     : all_neighbors_builder<T, IdxT>(
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cpp b/cpp/src/neighbors/detail/cagra/cagra_build.cpp
deleted file mode 100644
index 0fdfd1bcf8..0000000000
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cpp
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <cuvs/distance/distance.hpp>
-#include <cuvs/neighbors/cagra.hpp>
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <raft/core/mdspan.hpp>
-
-namespace cuvs::neighbors::cagra::graph_build_params {
-ivf_pq_params::ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
-                             cuvs::distance::DistanceType metric)
-{
-  build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset_extents, metric);
-
-  search_params                         = cuvs::neighbors::ivf_pq::search_params{};
-  search_params.n_probes                = std::max<uint32_t>(10, build_params.n_lists * 0.01);
-  search_params.lut_dtype               = CUDA_R_16F;
-  search_params.internal_distance_dtype = CUDA_R_16F;
-  search_params.coarse_search_dtype     = CUDA_R_16F;
-  search_params.max_internal_batch_size = 128 * 1024;
-
-  refinement_rate = 1;
-}
-}  // namespace cuvs::neighbors::cagra::graph_build_params
diff --git a/cpp/tests/neighbors/all_neighbors.cuh b/cpp/tests/neighbors/all_neighbors.cuh
index 3c606629ce..65be8ddcb4 100644
--- a/cpp/tests/neighbors/all_neighbors.cuh
+++ b/cpp/tests/neighbors/all_neighbors.cuh
@@ -87,12 +87,13 @@ void get_graphs(raft::resources& handle,
     nn_descent_params.metric                    = params.metric;
     params.graph_build_params                   = nn_descent_params;
   } else if (build_algo == IVF_PQ) {
-    auto ivfq_build_params                = graph_build_params::ivf_pq_params{};
-    ivfq_build_params.build_params.metric = params.metric;
+    auto ivfpq_build_params                = graph_build_params::ivf_pq_params{};
+    ivfpq_build_params.build_params.metric = params.metric;
+    ivfpq_build_params.refinement_rate     = 2.0;
     // heuristically good ivfpq n_lists
-    ivfq_build_params.build_params.n_lists = std::max(
+    ivfpq_build_params.build_params.n_lists = std::max(
       5u, static_cast<uint32_t>(ps.n_rows * params.overlap_factor / (5000 * params.n_clusters)));
-    params.graph_build_params = ivfq_build_params;
+    params.graph_build_params = ivfpq_build_params;
   }
 
   auto cuda_stream = raft::resource::get_cuda_stream(handle);

From 88bf26a8189b03e11547e61ab90a5f7451922dc9 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 27 Jun 2025 12:03:47 -0700
Subject: [PATCH 457/623] Reuse `<version_config.h>` instead of separate
 version constants in `c_api.h` (#1003)

This commit started off trying to fix an error in how version strings are replaced in the C API header. (Bumping the version to 25.08 causes the minor version to be written as "08" instead of "8", producing an invalid octal number.)

The octal problem was already solved as part of RAPIDS CMake, which is used in cuVS to generate a `version_configi.h` with the corrected version values.  This commit removes the old version constants, and switches to using what's provided by infrastructure.

Authors:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/1003
---
 .github/workflows/build.yaml  | 2 +-
 .github/workflows/pr.yaml     | 2 +-
 .github/workflows/test.yaml   | 2 +-
 ci/release/update-version.sh  | 7 +------
 cpp/CMakeLists.txt            | 5 +++--
 cpp/include/cuvs/core/c_api.h | 6 +-----
 cpp/src/core/c_api.cpp        | 9 ++++++---
 cpp/tests/core/c_api.c        | 4 +++-
 java/examples/README.md       | 6 +++---
 9 files changed, 20 insertions(+), 23 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 5c2f7fbd52..7730e5b64d 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -64,7 +64,7 @@ jobs:
   java-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index ede28c1438..b57169d3c2 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -155,7 +155,7 @@ jobs:
   conda-java-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_java
     with:
       build_type: pull-request
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 6af617cb4b..fa7a1ae3a7 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -47,7 +47,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-java-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.06
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index ce739d23a4..16cd845eed 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 ########################
 # CUVS Version Updater #
 ########################
@@ -108,8 +108,3 @@ done
 sed_runner "s| CuVS [[:digit:]]\{2\}\.[[:digit:]]\{2\} | CuVS ${NEXT_SHORT_TAG} |g" java/README.md
 sed_runner "s|-[[:digit:]]\{2\}\.[[:digit:]]\{2\}\.[[:digit:]]\{1,2\}\.jar|-${NEXT_FULL_JAVA_TAG}\.jar|g" java/examples/README.md
 sed_runner "s|/[[:digit:]]\{2\}\.[[:digit:]]\{2\}\.[[:digit:]]\{1,2\}/|/${NEXT_FULL_JAVA_TAG}/|g" java/examples/README.md
-
-# Update C API version.
-sed_runner "s|CUVS_VERSION_MAJOR = [0-9][0-9]|CUVS_VERSION_MAJOR = ${NEXT_MAJOR}|g" cpp/include/cuvs/core/c_api.h
-sed_runner "s|CUVS_VERSION_MINOR = [0-9][0-9]|CUVS_VERSION_MINOR = ${NEXT_MINOR}|g" cpp/include/cuvs/core/c_api.h
-sed_runner "s|CUVS_VERSION_PATCH = [0-9][0-9]|CUVS_VERSION_PATCH = ${NEXT_PATCH}|g" cpp/include/cuvs/core/c_api.h
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 6fb154088e..b5d6f51aa9 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -37,7 +37,7 @@ project(
 set(CMAKE_INSTALL_MESSAGE LAZY)
 
 # Write the version header
-rapids_cmake_write_version_file(include/cuvs/version_config.hpp)
+rapids_cmake_write_version_file(include/cuvs/version_config.h)
 
 # ##################################################################################################
 # * build type ---------------------------------------------------------------
@@ -746,6 +746,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
     target_include_directories(
       cuvs_c
       PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+             "$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>"
       INTERFACE "$<INSTALL_INTERFACE:include>"
     )
 
@@ -793,7 +794,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
   endif()
 
   install(
-    FILES ${CMAKE_CURRENT_BINARY_DIR}/include/cuvs/version_config.hpp
+    FILES ${CMAKE_CURRENT_BINARY_DIR}/include/cuvs/version_config.h
     COMPONENT cuvs
     DESTINATION include/cuvs
   )
diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index cce266d448..f1c94e1f5f 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -20,10 +20,6 @@
 #include <stdbool.h>
 #include <stdint.h>
 
-static const uint16_t CUVS_VERSION_MAJOR = 25;
-static const uint16_t CUVS_VERSION_MINOR = 06;
-static const uint16_t CUVS_VERSION_PATCH = 00;
-
 #ifdef __cplusplus
 extern "C" {
 #endif
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 530628a007..c820ebecbe 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,10 +14,10 @@
  * limitations under the License.
  */
 
-#include <cstdint>
 #include <cuvs/core/c_api.h>
 #include <cuvs/core/exceptions.hpp>
-#include <memory>
+#include <cuvs/version_config.h>
+
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
 #include <rmm/cuda_stream_view.hpp>
@@ -27,6 +27,9 @@
 #include <rmm/mr/device/per_device_resource.hpp>
 #include <rmm/mr/device/pool_memory_resource.hpp>
 #include <rmm/mr/host/pinned_memory_resource.hpp>
+
+#include <cstdint>
+#include <memory>
 #include <thread>
 
 extern "C" cuvsError_t cuvsResourcesCreate(cuvsResources_t* res)
diff --git a/cpp/tests/core/c_api.c b/cpp/tests/core/c_api.c
index 0635066d31..73379e2e5b 100644
--- a/cpp/tests/core/c_api.c
+++ b/cpp/tests/core/c_api.c
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,6 +15,8 @@
  */
 
 #include <cuvs/core/c_api.h>
+#include <cuvs/version_config.h>
+
 #include <stdio.h>
 #include <stdlib.h>
 
diff --git a/java/examples/README.md b/java/examples/README.md
index b8e03608e2..8477a3859f 100644
--- a/java/examples/README.md
+++ b/java/examples/README.md
@@ -11,17 +11,17 @@ This maven project contains examples for CAGRA, HNSW, and Bruteforce algorithms.
 ### CAGRA Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.06.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.06.0/cuvs-java-25.06.0.jar com.nvidia.cuvs.examples.CagraExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.08.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.08.0/cuvs-java-25.08.0.jar com.nvidia.cuvs.examples.CagraExample
 ```
 
 ### HNSW Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.06.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.06.0/cuvs-java-25.06.0.jar com.nvidia.cuvs.examples.HnswExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.08.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.08.0/cuvs-java-25.08.0.jar com.nvidia.cuvs.examples.HnswExample
 ```
 
 ### Bruteforce Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.06.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.06.0/cuvs-java-25.06.0.jar com.nvidia.cuvs.examples.BruteForceExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.08.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.08.0/cuvs-java-25.08.0.jar com.nvidia.cuvs.examples.BruteForceExample
 ```

From 4c3228183b80092cb95d526a8f11f5c28bcbe1d5 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Fri, 27 Jun 2025 17:47:56 -0400
Subject: [PATCH 458/623] Exporting changed `graph_build_params` namespace into
 `all_neighbors` (#1060)

Fix to make https://github.com/rapidsai/cuvs/pull/949 a non-breaking change.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1060
---
 cpp/include/cuvs/neighbors/all_neighbors.hpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/cpp/include/cuvs/neighbors/all_neighbors.hpp b/cpp/include/cuvs/neighbors/all_neighbors.hpp
index 452aa12311..3b7793be10 100644
--- a/cpp/include/cuvs/neighbors/all_neighbors.hpp
+++ b/cpp/include/cuvs/neighbors/all_neighbors.hpp
@@ -23,6 +23,8 @@
 #include <variant>
 
 namespace cuvs::neighbors::all_neighbors {
+// For re-exporting into all_neighbors namespace
+namespace graph_build_params = cuvs::neighbors::graph_build_params;
 /**
  * @defgroup all_neighbors_cpp_params The all-neighbors algorithm parameters.
  * @{

From d8e043bae4bbe38863c93a75ec7dcde29687cfb0 Mon Sep 17 00:00:00 2001
From: jakirkham <jakirkham@gmail.com>
Date: Mon, 30 Jun 2025 11:03:07 -0700
Subject: [PATCH 459/623] Use CUDA 12.9 in Conda, Devcontainers, Spark, GHA,
 etc. (#1063)

Use CUDA 12.9 throughout different build and test environments.

Authors:
  - https://github.com/jakirkham

Approvers:
  - Gil Forsyth (https://github.com/gforsyth)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1063
---
 .../devcontainer.json                                |  8 ++++----
 .../{cuda12.8-pip => cuda12.9-pip}/devcontainer.json | 12 ++++++------
 .github/workflows/pr.yaml                            |  2 +-
 ...h-aarch64.yaml => all_cuda-129_arch-aarch64.yaml} |  4 ++--
 ...rch-x86_64.yaml => all_cuda-129_arch-x86_64.yaml} |  4 ++--
 ...h64.yaml => bench_ann_cuda-129_arch-aarch64.yaml} |  4 ++--
 ...6_64.yaml => bench_ann_cuda-129_arch-x86_64.yaml} |  4 ++--
 ...ch-aarch64.yaml => go_cuda-129_arch-aarch64.yaml} |  4 ++--
 ...arch-x86_64.yaml => go_cuda-129_arch-x86_64.yaml} |  4 ++--
 dependencies.yaml                                    |  6 +++---
 docs/source/build.rst                                |  4 ++--
 docs/source/cuvs_bench/index.rst                     |  6 +++---
 12 files changed, 31 insertions(+), 31 deletions(-)
 rename .devcontainer/{cuda12.8-conda => cuda12.9-conda}/devcontainer.json (91%)
 rename .devcontainer/{cuda12.8-pip => cuda12.9-pip}/devcontainer.json (88%)
 rename conda/environments/{all_cuda-128_arch-aarch64.yaml => all_cuda-129_arch-aarch64.yaml} (95%)
 rename conda/environments/{all_cuda-128_arch-x86_64.yaml => all_cuda-129_arch-x86_64.yaml} (95%)
 rename conda/environments/{bench_ann_cuda-128_arch-aarch64.yaml => bench_ann_cuda-129_arch-aarch64.yaml} (94%)
 rename conda/environments/{bench_ann_cuda-128_arch-x86_64.yaml => bench_ann_cuda-129_arch-x86_64.yaml} (94%)
 rename conda/environments/{go_cuda-128_arch-aarch64.yaml => go_cuda-129_arch-aarch64.yaml} (92%)
 rename conda/environments/{go_cuda-128_arch-x86_64.yaml => go_cuda-129_arch-x86_64.yaml} (92%)

diff --git a/.devcontainer/cuda12.8-conda/devcontainer.json b/.devcontainer/cuda12.9-conda/devcontainer.json
similarity index 91%
rename from .devcontainer/cuda12.8-conda/devcontainer.json
rename to .devcontainer/cuda12.9-conda/devcontainer.json
index f70a011292..ce601856f1 100644
--- a/.devcontainer/cuda12.8-conda/devcontainer.json
+++ b/.devcontainer/cuda12.9-conda/devcontainer.json
@@ -3,7 +3,7 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.8",
+      "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "conda",
       "BASE": "rapidsai/devcontainers:25.08-cpp-mambaforge-ubuntu22.04"
     }
@@ -11,7 +11,7 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.8-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.9-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
@@ -20,7 +20,7 @@
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.8-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.9-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -29,7 +29,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.8-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.9-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.devcontainer/cuda12.8-pip/devcontainer.json b/.devcontainer/cuda12.9-pip/devcontainer.json
similarity index 88%
rename from .devcontainer/cuda12.8-pip/devcontainer.json
rename to .devcontainer/cuda12.9-pip/devcontainer.json
index 52de35b8cd..be81ad6c64 100644
--- a/.devcontainer/cuda12.8-pip/devcontainer.json
+++ b/.devcontainer/cuda12.9-pip/devcontainer.json
@@ -3,20 +3,20 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.8",
+      "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda12.9-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.8-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.9-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/cuda:25.8": {
-      "version": "12.8",
+      "version": "12.9",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
@@ -29,7 +29,7 @@
     "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.9-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
@@ -37,7 +37,7 @@
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.9-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index b57169d3c2..34fc4ed0b3 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -228,7 +228,7 @@ jobs:
     uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.08
     with:
       arch: '["amd64"]'
-      cuda: '["12.8"]'
+      cuda: '["12.9"]'
       build_command: |
         sccache -z;
         build-all --verbose;
diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
similarity index 95%
rename from conda/environments/all_cuda-128_arch-aarch64.yaml
rename to conda/environments/all_cuda-129_arch-aarch64.yaml
index 9fea5bfb9f..b4bde2bd48 100644
--- a/conda/environments/all_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -52,4 +52,4 @@ dependencies:
 - sysroot_linux-aarch64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-128_arch-aarch64
+name: all_cuda-129_arch-aarch64
diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
similarity index 95%
rename from conda/environments/all_cuda-128_arch-x86_64.yaml
rename to conda/environments/all_cuda-129_arch-x86_64.yaml
index 221f0720cf..249369bd41 100644
--- a/conda/environments/all_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
@@ -52,4 +52,4 @@ dependencies:
 - sysroot_linux-64==2.28
 - pip:
   - nvidia-sphinx-theme
-name: all_cuda-128_arch-x86_64
+name: all_cuda-129_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
rename to conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index 558994a982..d3c0fae8c9 100644
--- a/conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -16,7 +16,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cuvs==25.8.*,>=0.0.0a0
 - cxx-compiler
@@ -46,4 +46,4 @@ dependencies:
 - setuptools
 - sysroot_linux-aarch64==2.28
 - wheel
-name: bench_ann_cuda-128_arch-aarch64
+name: bench_ann_cuda-129_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
similarity index 94%
rename from conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
rename to conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index a71da9e5b7..4686f6b2bb 100644
--- a/conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -16,7 +16,7 @@ dependencies:
 - cuda-nvtx-dev
 - cuda-profiler-api
 - cuda-python>=12.6.2,<13.0a0
-- cuda-version=12.8
+- cuda-version=12.9
 - cupy>=12.0.0
 - cuvs==25.8.*,>=0.0.0a0
 - cxx-compiler
@@ -49,4 +49,4 @@ dependencies:
 - setuptools
 - sysroot_linux-64==2.28
 - wheel
-name: bench_ann_cuda-128_arch-x86_64
+name: bench_ann_cuda-129_arch-x86_64
diff --git a/conda/environments/go_cuda-128_arch-aarch64.yaml b/conda/environments/go_cuda-129_arch-aarch64.yaml
similarity index 92%
rename from conda/environments/go_cuda-128_arch-aarch64.yaml
rename to conda/environments/go_cuda-129_arch-aarch64.yaml
index 65fffec19c..e72a117827 100644
--- a/conda/environments/go_cuda-128_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-129_arch-aarch64.yaml
@@ -13,7 +13,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.8
+- cuda-version=12.9
 - cxx-compiler
 - dlpack>=0.8,<1.0
 - gcc_linux-aarch64=13.*
@@ -28,4 +28,4 @@ dependencies:
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
-name: go_cuda-128_arch-aarch64
+name: go_cuda-129_arch-aarch64
diff --git a/conda/environments/go_cuda-128_arch-x86_64.yaml b/conda/environments/go_cuda-129_arch-x86_64.yaml
similarity index 92%
rename from conda/environments/go_cuda-128_arch-x86_64.yaml
rename to conda/environments/go_cuda-129_arch-x86_64.yaml
index baad4d43f9..cdd23b95a3 100644
--- a/conda/environments/go_cuda-128_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-129_arch-x86_64.yaml
@@ -13,7 +13,7 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-version=12.8
+- cuda-version=12.9
 - cxx-compiler
 - dlpack>=0.8,<1.0
 - gcc_linux-64=13.*
@@ -28,4 +28,4 @@ dependencies:
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
-name: go_cuda-128_arch-x86_64
+name: go_cuda-129_arch-x86_64
diff --git a/dependencies.yaml b/dependencies.yaml
index 0fa0813ab3..236209de2c 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,7 +3,7 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["12.8"]
+      cuda: ["12.9"]
       arch: [x86_64, aarch64]
     includes:
       - build
@@ -29,7 +29,7 @@ files:
   bench_ann:
     output: conda
     matrix:
-      cuda: ["12.8"]
+      cuda: ["12.9"]
       arch: [x86_64, aarch64]
     includes:
       - bench
@@ -100,7 +100,7 @@ files:
   go:
     output: conda
     matrix:
-      cuda: ["12.8"]
+      cuda: ["12.9"]
       arch: [x86_64, aarch64]
     includes:
       - clang
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 90725f8684..b5a7eead27 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -43,14 +43,14 @@ C/C++ Package
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge libcuvs cuda-version=12.8
+   conda install -c rapidsai -c conda-forge libcuvs cuda-version=12.9
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-   conda install -c rapidsai -c conda-forge cuvs cuda-version=12.8
+   conda install -c rapidsai -c conda-forge cuvs cuda-version=12.9
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index d257c62ff1..1c231c6e56 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -236,7 +236,7 @@ For GPU-enabled systems, the `DATA_FOLDER` variable should be a local folder whe
     export DATA_FOLDER=path/to/store/datasets/and/results
     docker run --gpus all --rm -it -u $(id -u)                      \
         -v $DATA_FOLDER:/data/benchmarks                            \
-        rapidsai/cuvs-bench:25.06-cuda12.8-py3.13              \
+        rapidsai/cuvs-bench:25.08-cuda12.9-py3.13              \
         "--dataset deep-image-96-angular"                           \
         "--normalize"                                               \
         "--algorithms cuvs_cagra,cuvs_ivf_pq --batch-size 10 -k 10" \
@@ -249,7 +249,7 @@ Usage of the above command is as follows:
  * - Argument
    - Description
 
- * - `rapidsai/cuvs-bench:25.06-cuda12.8-py3.13`
+ * - `rapidsai/cuvs-bench:25.08-cuda12.9-py3.13`
    - Image to use. Can be either `cuvs-bench` or `cuvs-bench-datasets`
 
  * - `"--dataset deep-image-96-angular"`
@@ -296,7 +296,7 @@ All of the `cuvs-bench` images contain the Conda packages, so they can be used d
         --entrypoint /bin/bash                          \
         --workdir /data/benchmarks                      \
         -v $DATA_FOLDER:/data/benchmarks                \
-        rapidsai/cuvs-bench:25.06-cuda12.8-py3.13
+        rapidsai/cuvs-bench:25.08-cuda12.9-py3.13
 
 This will drop you into a command line in the container, with the `cuvs-bench` python package ready to use, as described in the [Running the benchmarks](#running-the-benchmarks) section above:
 

From 62e5d0dc319ac1e077d24137523a9c20ce76b7e6 Mon Sep 17 00:00:00 2001
From: Vivek Narang <123010842+narangvivek10@users.noreply.github.com>
Date: Mon, 30 Jun 2025 16:24:17 -0400
Subject: [PATCH 460/623] [Java] Enforce Java code format standard (#1049)

Configuration changes to enforce Java code format standard using [google-java-format](https://github.com/google/google-java-format) via [spotless's](https://github.com/diffplug/spotless) [maven plugin](https://github.com/diffplug/spotless/tree/main/plugin-maven#google-java-format) to comply with the [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html). Spotless is a popular choice and is also used in [Apache Lucene](https://github.com/apache/lucene/blob/branch_10_1/gradle/validation/spotless.gradle).

With this change, the Java code format is checked and fixes for identified style violations are automatically applied (including missing license headers) when invoking `mvn verify` and precisely during its `validate` phase.

@chatman FYI

Fixes #1042

Authors:
  - Vivek Narang (https://github.com/narangvivek10)

Approvers:
  - Ishan Chattopadhyaya (https://github.com/chatman)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1049
---
 java/cuvs-java/pom.xml                        | 138 ++++---
 .../java/com/nvidia/cuvs/BruteForceIndex.java |   5 +-
 .../nvidia/cuvs/BruteForceIndexParams.java    |   1 -
 .../java/com/nvidia/cuvs/BruteForceQuery.java |  15 +-
 .../nvidia/cuvs/CagraCompressionParams.java   |  36 +-
 .../main/java/com/nvidia/cuvs/CagraIndex.java | 382 +++++++++---------
 .../com/nvidia/cuvs/CagraIndexParams.java     |  49 ++-
 .../com/nvidia/cuvs/CagraMergeParams.java     | 140 +++----
 .../main/java/com/nvidia/cuvs/CagraQuery.java |  27 +-
 .../com/nvidia/cuvs/CagraSearchParams.java    |  65 ++-
 .../com/nvidia/cuvs/CuVSIvfPqIndexParams.java |  65 ++-
 .../java/com/nvidia/cuvs/CuVSIvfPqParams.java |  19 +-
 .../nvidia/cuvs/CuVSIvfPqSearchParams.java    |  23 +-
 .../java/com/nvidia/cuvs/CuVSResources.java   |   3 -
 .../main/java/com/nvidia/cuvs/Dataset.java    |   1 -
 .../main/java/com/nvidia/cuvs/GPUInfo.java    |   4 +-
 .../main/java/com/nvidia/cuvs/HnswIndex.java  |  94 +++--
 .../java/com/nvidia/cuvs/HnswIndexParams.java |  18 +-
 .../main/java/com/nvidia/cuvs/HnswQuery.java  |  12 +-
 .../com/nvidia/cuvs/HnswSearchParams.java     |  19 +-
 .../com/nvidia/cuvs/LibraryException.java     |   1 -
 .../java/com/nvidia/cuvs/SearchResults.java   |  13 +-
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  10 +-
 .../nvidia/cuvs/spi/CuVSServiceProvider.java  |   7 +-
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |   2 -
 .../cuvs/internal/BruteForceIndexImpl.java    | 104 +++--
 .../internal/BruteForceSearchResults.java     |  21 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 313 ++++++++------
 .../cuvs/internal/CagraSearchResults.java     |  23 +-
 .../cuvs/internal/CuVSResourcesImpl.java      |   6 +-
 .../com/nvidia/cuvs/internal/DatasetImpl.java |  11 +-
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |  73 ++--
 .../cuvs/internal/HnswSearchResults.java      |  21 +-
 .../cuvs/internal/common/LinkerHelper.java    |  26 +-
 .../internal/common/SearchResultsImpl.java    |  25 +-
 .../com/nvidia/cuvs/internal/common/Util.java |  63 +--
 .../com/nvidia/cuvs/spi/JDKProvider.java      |   9 +-
 .../nvidia/cuvs/BruteForceAndSearchIT.java    |  88 ++--
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |  38 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 340 ++++++++--------
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |  56 +--
 .../java/com/nvidia/cuvs/CuVSTestCase.java    |  80 ++--
 .../com/nvidia/cuvs/HnswBuildAndSearchIT.java |  97 +++--
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |  86 ++--
 .../nvidia/cuvs/internal/common/UtilIT.java   |  28 +-
 java/examples/pom.xml                         |  38 ++
 .../cuvs/examples/BruteForceExample.java      |  68 ++--
 .../nvidia/cuvs/examples/CagraExample.java    |  84 ++--
 .../com/nvidia/cuvs/examples/HnswExample.java | 111 ++---
 java/license-header.txt                       |  15 +
 50 files changed, 1729 insertions(+), 1244 deletions(-)
 create mode 100644 java/license-header.txt

diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 15e0193ad9..adfed375d2 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -102,6 +102,12 @@
             <scope>test</scope>
         </dependency>
 
+        <dependency>
+            <groupId>com.diffplug.spotless</groupId>
+            <artifactId>spotless-maven-plugin</artifactId>
+            <version>2.44.5</version>
+        </dependency>
+
     </dependencies>
 
     <build>
@@ -194,56 +200,88 @@
                     </archive>
                 </configuration>
             </plugin>
-        <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-source-plugin</artifactId>
-            <version>2.2.1</version>
-            <executions>
-                <execution>
-                    <id>attach-sources</id>
-                    <goals>
-                        <goal>jar-no-fork</goal>
-                    </goals>
-                </execution>
-                <execution>
-                    <id>test-jars</id>
-                    <goals>
-                        <goal>test-jar</goal>
-                    </goals>
-                </execution>
-            </executions>
-            <configuration>
-                <excludeResources>true</excludeResources>
-            </configuration>
-        </plugin>
-        <plugin>
-            <groupId>org.apache.maven.plugins</groupId>
-            <artifactId>maven-javadoc-plugin</artifactId>
-            <version>3.11.2</version>
-            <executions>
-                <execution>
-                    <id>attach-javadocs</id>
-                    <goals>
-                        <goal>jar</goal>
-                    </goals>
-                    <configuration>
-                        <excludePackageNames>com.nvidia.cuvs.examples,com.nvidia.cuvs.panama</excludePackageNames>
-                        <doclint>all,-missing</doclint>
-                    </configuration>
-                </execution>
-            </executions>
-        </plugin>
-        <plugin>
-            <groupId>org.sonatype.plugins</groupId>
-            <artifactId>nexus-staging-maven-plugin</artifactId>
-            <version>1.6.7</version>
-            <extensions>true</extensions>
-            <configuration>
-                <serverId>ossrh</serverId>
-                <nexusUrl>https://oss.sonatype.org/</nexusUrl>
-                <autoReleaseAfterClose>false</autoReleaseAfterClose>
-            </configuration>
-        </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-source-plugin</artifactId>
+                <version>2.2.1</version>
+                <executions>
+                    <execution>
+                        <id>attach-sources</id>
+                        <goals>
+                            <goal>jar-no-fork</goal>
+                        </goals>
+                    </execution>
+                    <execution>
+                        <id>test-jars</id>
+                        <goals>
+                            <goal>test-jar</goal>
+                        </goals>
+                    </execution>
+                </executions>
+                <configuration>
+                    <excludeResources>true</excludeResources>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-javadoc-plugin</artifactId>
+                <version>3.11.2</version>
+                <executions>
+                    <execution>
+                        <id>attach-javadocs</id>
+                        <goals>
+                            <goal>jar</goal>
+                        </goals>
+                        <configuration>
+                            <excludePackageNames>com.nvidia.cuvs.examples,com.nvidia.cuvs.panama</excludePackageNames>
+                            <doclint>all,-missing</doclint>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.sonatype.plugins</groupId>
+                <artifactId>nexus-staging-maven-plugin</artifactId>
+                <version>1.6.7</version>
+                <extensions>true</extensions>
+                <configuration>
+                    <serverId>ossrh</serverId>
+                    <nexusUrl>https://oss.sonatype.org/</nexusUrl>
+                    <autoReleaseAfterClose>false</autoReleaseAfterClose>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>com.diffplug.spotless</groupId>
+                <artifactId>spotless-maven-plugin</artifactId>
+                <version>2.44.5</version>
+                <executions>
+                    <execution>
+                        <goals>
+                            <goal>apply</goal>
+                        </goals>
+                        <phase>validate</phase>
+                    </execution>
+                </executions>
+                <configuration>
+                    <java>
+                        <includes>
+                            <include>src/**/*.java</include>
+                        </includes>
+                        <excludes>
+                            <exclude>src/**/panama/*.java</exclude>
+                        </excludes>
+                        <googleJavaFormat>
+                            <version>1.27.0</version>
+                            <style>GOOGLE</style>
+                            <reflowLongStrings>true</reflowLongStrings>
+                            <formatJavadoc>false</formatJavadoc>
+                        </googleJavaFormat>
+                        <licenseHeader>
+                            <file>${project.basedir}/../license-header.txt</file>
+                        </licenseHeader>
+                    </java>
+                </configuration>
+            </plugin>
         </plugins>
     </build>
 </project>
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
index 70b079c9de..b21109dc9e 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
@@ -13,16 +13,14 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
+import com.nvidia.cuvs.spi.CuVSProvider;
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.nio.file.Path;
 import java.util.Objects;
 
-import com.nvidia.cuvs.spi.CuVSProvider;
-
 /**
  *
  * {@link BruteForceIndex} encapsulates a BRUTEFORCE index, along with methods
@@ -56,6 +54,7 @@ public interface BruteForceIndex {
    *                     bytes into
    */
   void serialize(OutputStream outputStream) throws Throwable;
+
   /**
    * A method to persist a BRUTEFORCE index using an instance of
    * {@link OutputStream} and path to the intermediate temporary file.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java
index 832edf51a1..7f94e5871a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndexParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 /**
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
index 947e9e7fdc..084984b040 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import java.util.Arrays;
@@ -45,7 +44,8 @@ public class BruteForceQuery {
    * @param numDocs      Maximum of bits in each prefilter, representing number of documents in this index.
    *                     Used only when prefilter(s) is/are passed.
    */
-  public BruteForceQuery(float[][] queryVectors, List<Integer> mapping, int topK, BitSet[] prefilters, int numDocs) {
+  public BruteForceQuery(
+      float[][] queryVectors, List<Integer> mapping, int topK, BitSet[] prefilters, int numDocs) {
     this.queryVectors = queryVectors;
     this.mapping = mapping;
     this.topK = topK;
@@ -100,8 +100,15 @@ public int getNumDocs() {
 
   @Override
   public String toString() {
-    return "BruteForceQuery [mapping=" + mapping + ", queryVectors=" + Arrays.toString(queryVectors) + ", prefilter="
-        + Arrays.toString(prefilters) + ", topK=" + topK + "]";
+    return "BruteForceQuery [mapping="
+        + mapping
+        + ", queryVectors="
+        + Arrays.toString(queryVectors)
+        + ", prefilter="
+        + Arrays.toString(prefilters)
+        + ", topK="
+        + topK
+        + "]";
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
index 430d9d8b47..6e47de3935 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraCompressionParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 /**
@@ -47,8 +46,13 @@ public class CagraCompressionParams {
    * @param pqKmeansTrainsetFraction the fraction of data to use during iterative
    *                                 kmeans building (PQ phase)
    */
-  private CagraCompressionParams(int pqBits, int pqDim, int vqNCenters, int kmeansNIters,
-      double vqKmeansTrainsetFraction, double pqKmeansTrainsetFraction) {
+  private CagraCompressionParams(
+      int pqBits,
+      int pqDim,
+      int vqNCenters,
+      int kmeansNIters,
+      double vqKmeansTrainsetFraction,
+      double pqKmeansTrainsetFraction) {
     this.pqBits = pqBits;
     this.pqDim = pqDim;
     this.vqNCenters = vqNCenters;
@@ -119,9 +123,19 @@ public double getPqKmeansTrainsetFraction() {
 
   @Override
   public String toString() {
-    return "CagraCompressionParams [pqBits=" + pqBits + ", pqDim=" + pqDim + ", vqNCenters=" + vqNCenters
-        + ", kmeansNIters=" + kmeansNIters + ", vqKmeansTrainsetFraction=" + vqKmeansTrainsetFraction
-        + ", pqKmeansTrainsetFraction=" + pqKmeansTrainsetFraction + "]";
+    return "CagraCompressionParams [pqBits="
+        + pqBits
+        + ", pqDim="
+        + pqDim
+        + ", vqNCenters="
+        + vqNCenters
+        + ", kmeansNIters="
+        + kmeansNIters
+        + ", vqKmeansTrainsetFraction="
+        + vqKmeansTrainsetFraction
+        + ", pqKmeansTrainsetFraction="
+        + pqKmeansTrainsetFraction
+        + "]";
   }
 
   /**
@@ -136,8 +150,7 @@ public static class Builder {
     private double vqKmeansTrainsetFraction = 0;
     private double pqKmeansTrainsetFraction = 0;
 
-    public Builder() {
-    }
+    public Builder() {}
 
     /**
      * Sets the bit length of the vector element after compression by PQ.
@@ -225,7 +238,12 @@ public Builder withPqKmeansTrainsetFraction(double pqKmeansTrainsetFraction) {
      * @return an instance of {@link CagraCompressionParams}
      */
     public CagraCompressionParams build() {
-      return new CagraCompressionParams(pqBits, pqDim, vqNCenters, kmeansNIters, vqKmeansTrainsetFraction,
+      return new CagraCompressionParams(
+          pqBits,
+          pqDim,
+          vqNCenters,
+          kmeansNIters,
+          vqKmeansTrainsetFraction,
           pqKmeansTrainsetFraction);
     }
   }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index a9a94df545..e878f77109 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -13,17 +13,15 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
+import com.nvidia.cuvs.BruteForceIndex.Builder;
+import com.nvidia.cuvs.spi.CuVSProvider;
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.nio.file.Path;
 import java.util.Objects;
 
-import com.nvidia.cuvs.BruteForceIndex.Builder;
-import com.nvidia.cuvs.spi.CuVSProvider;
-
 /**
  * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
  * with it.
@@ -38,216 +36,216 @@
  */
 public interface CagraIndex {
 
-    /**
-     * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
-     */
-    void destroyIndex() throws Throwable;
-
-    /**
-     * Invokes the native search_cagra_index via the Panama API for searching a
-     * CAGRA index.
-     *
-     * @param query an instance of {@link CagraQuery} holding the query vectors and
-     *              other parameters
-     * @return an instance of {@link SearchResults} containing the results
-     */
-    SearchResults search(CagraQuery query) throws Throwable;
-
-    /**
-     * A method to persist a CAGRA index using an instance of {@link OutputStream}
-     * for writing index bytes.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes into
-     */
-    void serialize(OutputStream outputStream) throws Throwable;
-
-    /**
-     * A method to persist a CAGRA index using an instance of {@link OutputStream}
-     * for writing index bytes.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes into
-     * @param bufferLength the length of buffer to use for writing bytes. Default
-     *                     value is 1024
-     */
-    void serialize(OutputStream outputStream, int bufferLength) throws Throwable;
-
-    /**
-     * A method to persist a CAGRA index using an instance of {@link OutputStream}
-     * for writing index bytes.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes into
-     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
-     *                     temporarily
-     */
-    default void serialize(OutputStream outputStream, Path tempFile) throws Throwable {
-        serialize(outputStream, tempFile, 1024);
+  /**
+   * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
+   */
+  void destroyIndex() throws Throwable;
+
+  /**
+   * Invokes the native search_cagra_index via the Panama API for searching a
+   * CAGRA index.
+   *
+   * @param query an instance of {@link CagraQuery} holding the query vectors and
+   *              other parameters
+   * @return an instance of {@link SearchResults} containing the results
+   */
+  SearchResults search(CagraQuery query) throws Throwable;
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * for writing index bytes.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes into
+   */
+  void serialize(OutputStream outputStream) throws Throwable;
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * for writing index bytes.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes into
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  void serialize(OutputStream outputStream, int bufferLength) throws Throwable;
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * for writing index bytes.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes into
+   * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+   *                     temporarily
+   */
+  default void serialize(OutputStream outputStream, Path tempFile) throws Throwable {
+    serialize(outputStream, tempFile, 1024);
+  }
+
+  /**
+   * A method to persist a CAGRA index using an instance of {@link OutputStream}
+   * and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+   *                     temporarily
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  void serialize(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable;
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   */
+  void serializeToHNSW(OutputStream outputStream) throws Throwable;
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable;
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+   *                     temporarily
+   */
+  default void serializeToHNSW(OutputStream outputStream, Path tempFile) throws Throwable {
+    serializeToHNSW(outputStream, tempFile, 1024);
+  }
+
+  /**
+   * A method to create and persist HNSW index from CAGRA index using an instance
+   * of {@link OutputStream} and path to the intermediate temporary file.
+   *
+   * @param outputStream an instance of {@link OutputStream} to write the index
+   *                     bytes to
+   * @param tempFile     an intermediate {@link Path} where CAGRA index is written
+   *                     temporarily
+   * @param bufferLength the length of buffer to use for writing bytes. Default
+   *                     value is 1024
+   */
+  void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable;
+
+  /**
+   * Gets an instance of {@link CagraIndexParams}
+   *
+   * @return an instance of {@link CagraIndexParams}
+   */
+  CagraIndexParams getCagraIndexParameters();
+
+  /**
+   * Gets an instance of {@link CuVSResources}
+   *
+   * @return an instance of {@link CuVSResources}
+   */
+  CuVSResources getCuVSResources();
+
+  /**
+   * Creates a new Builder with an instance of {@link CuVSResources}.
+   *
+   * @param cuvsResources an instance of {@link CuVSResources}
+   * @throws UnsupportedOperationException if the provider does not cuvs
+   */
+  static Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    return CuVSProvider.provider().newCagraIndexBuilder(cuvsResources);
+  }
+
+  /**
+   * Merges multiple CAGRA indexes into a single index using default merge parameters.
+   *
+   * @param indexes Array of CAGRA indexes to merge
+   * @return A new merged CAGRA index
+   * @throws Throwable if an error occurs during the merge operation
+   */
+  static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
+    return merge(indexes, null);
+  }
+
+  /**
+   * Merges multiple CAGRA indexes into a single index with the specified merge parameters.
+   *
+   * @param indexes Array of CAGRA indexes to merge
+   * @param mergeParams Parameters to control the merge operation, or null to use defaults
+   * @return A new merged CAGRA index
+   * @throws Throwable if an error occurs during the merge operation
+   */
+  static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
+    if (indexes == null || indexes.length == 0) {
+      throw new IllegalArgumentException("At least one index must be provided for merging");
     }
 
-    /**
-     * A method to persist a CAGRA index using an instance of {@link OutputStream}
-     * and path to the intermediate temporary file.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes to
-     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
-     *                     temporarily
-     * @param bufferLength the length of buffer to use for writing bytes. Default
-     *                     value is 1024
-     */
-    void serialize(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable;
-
-    /**
-     * A method to create and persist HNSW index from CAGRA index using an instance
-     * of {@link OutputStream} and path to the intermediate temporary file.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes to
-     */
-    void serializeToHNSW(OutputStream outputStream) throws Throwable;
-
-    /**
-     * A method to create and persist HNSW index from CAGRA index using an instance
-     * of {@link OutputStream} and path to the intermediate temporary file.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes to
-     * @param bufferLength the length of buffer to use for writing bytes. Default
-     *                     value is 1024
-     */
-    void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable;
-
-    /**
-     * A method to create and persist HNSW index from CAGRA index using an instance
-     * of {@link OutputStream} and path to the intermediate temporary file.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes to
-     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
-     *                     temporarily
-     */
-    default void serializeToHNSW(OutputStream outputStream, Path tempFile) throws Throwable {
-        serializeToHNSW(outputStream, tempFile, 1024);
+    CuVSResources resources = indexes[0].getCuVSResources();
+    for (int i = 1; i < indexes.length; i++) {
+      if (!resources.equals(indexes[i].getCuVSResources())) {
+        throw new IllegalArgumentException("All indexes must use the same CuVSResources instance");
+      }
     }
 
-    /**
-     * A method to create and persist HNSW index from CAGRA index using an instance
-     * of {@link OutputStream} and path to the intermediate temporary file.
-     *
-     * @param outputStream an instance of {@link OutputStream} to write the index
-     *                     bytes to
-     * @param tempFile     an intermediate {@link Path} where CAGRA index is written
-     *                     temporarily
-     * @param bufferLength the length of buffer to use for writing bytes. Default
-     *                     value is 1024
-     */
-    void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable;
+    return CuVSProvider.provider().mergeCagraIndexes(indexes, mergeParams);
+  }
 
-    /**
-     * Gets an instance of {@link CagraIndexParams}
-     *
-     * @return an instance of {@link CagraIndexParams}
-     */
-    CagraIndexParams getCagraIndexParameters();
+  /**
+   * Builder helps configure and create an instance of {@link CagraIndex}.
+   */
+  interface Builder {
 
     /**
-     * Gets an instance of {@link CuVSResources}
+     * Sets an instance of InputStream typically used when index deserialization is
+     * needed.
      *
-     * @return an instance of {@link CuVSResources}
+     * @param inputStream an instance of {@link InputStream}
+     * @return an instance of this Builder
      */
-    CuVSResources getCuVSResources();
+    Builder from(InputStream inputStream);
 
     /**
-     * Creates a new Builder with an instance of {@link CuVSResources}.
+     * Sets the dataset vectors for building the {@link CagraIndex}.
      *
-     * @param cuvsResources an instance of {@link CuVSResources}
-     * @throws UnsupportedOperationException if the provider does not cuvs
+     * @param vectors a two-dimensional float array
+     * @return an instance of this Builder
      */
-    static Builder newBuilder(CuVSResources cuvsResources) {
-        Objects.requireNonNull(cuvsResources);
-        return CuVSProvider.provider().newCagraIndexBuilder(cuvsResources);
-    }
+    Builder withDataset(float[][] vectors);
 
     /**
-     * Merges multiple CAGRA indexes into a single index using default merge parameters.
+     * Sets the dataset for building the {@link CagraIndex}.
      *
-     * @param indexes Array of CAGRA indexes to merge
-     * @return A new merged CAGRA index
-     * @throws Throwable if an error occurs during the merge operation
+     * @param dataset a {@link Dataset} object containing the vectors
+     * @return an instance of this Builder
      */
-    static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
-        return merge(indexes, null);
-    }
+    Builder withDataset(Dataset dataset);
 
     /**
-     * Merges multiple CAGRA indexes into a single index with the specified merge parameters.
+     * Registers an instance of configured {@link CagraIndexParams} with this
+     * Builder.
      *
-     * @param indexes Array of CAGRA indexes to merge
-     * @param mergeParams Parameters to control the merge operation, or null to use defaults
-     * @return A new merged CAGRA index
-     * @throws Throwable if an error occurs during the merge operation
+     * @param cagraIndexParameters An instance of CagraIndexParams.
+     * @return An instance of this Builder.
      */
-    static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
-        if (indexes == null || indexes.length == 0) {
-            throw new IllegalArgumentException("At least one index must be provided for merging");
-        }
-
-        CuVSResources resources = indexes[0].getCuVSResources();
-        for (int i = 1; i < indexes.length; i++) {
-            if (!resources.equals(indexes[i].getCuVSResources())) {
-                throw new IllegalArgumentException("All indexes must use the same CuVSResources instance");
-            }
-        }
-
-        return CuVSProvider.provider().mergeCagraIndexes(indexes, mergeParams);
-    }
+    Builder withIndexParams(CagraIndexParams cagraIndexParameters);
 
     /**
-     * Builder helps configure and create an instance of {@link CagraIndex}.
+     * Builds and returns an instance of CagraIndex.
+     *
+     * @return an instance of CagraIndex
      */
-    interface Builder {
-
-        /**
-         * Sets an instance of InputStream typically used when index deserialization is
-         * needed.
-         *
-         * @param inputStream an instance of {@link InputStream}
-         * @return an instance of this Builder
-         */
-        Builder from(InputStream inputStream);
-
-        /**
-         * Sets the dataset vectors for building the {@link CagraIndex}.
-         *
-         * @param vectors a two-dimensional float array
-         * @return an instance of this Builder
-         */
-        Builder withDataset(float[][] vectors);
-
-        /**
-         * Sets the dataset for building the {@link CagraIndex}.
-         *
-         * @param dataset a {@link Dataset} object containing the vectors
-         * @return an instance of this Builder
-         */
-        Builder withDataset(Dataset dataset);
-
-        /**
-         * Registers an instance of configured {@link CagraIndexParams} with this
-         * Builder.
-         *
-         * @param cagraIndexParameters An instance of CagraIndexParams.
-         * @return An instance of this Builder.
-         */
-        Builder withIndexParams(CagraIndexParams cagraIndexParameters);
-
-        /**
-         * Builds and returns an instance of CagraIndex.
-         *
-         * @return an instance of CagraIndex
-         */
-         CagraIndex build() throws Throwable;
-    }
+    CagraIndex build() throws Throwable;
+  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
index 1888b2f59e..411b7af3eb 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndexParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 /**
@@ -157,14 +156,12 @@ public enum CuvsDistanceType {
     private CuvsDistanceType(int value) {
       this.value = value;
     }
-
   }
 
   /**
    * Enum that denotes codebook gen options.
    */
   public enum CodebookGen {
-
     PER_SUBSPACE(0),
 
     PER_CLUSTER(1);
@@ -183,7 +180,6 @@ private CodebookGen(int value) {
    * Enum that denotes cuda datatypes.
    */
   public enum CudaDataType {
-
     CUDA_R_16F(2),
 
     CUDA_C_16F(6),
@@ -251,8 +247,14 @@ private CudaDataType(int value) {
     }
   }
 
-  private CagraIndexParams(int intermediateGraphDegree, int graphDegree, CagraGraphBuildAlgo CuvsCagraGraphBuildAlgo,
-      int nnDescentNiter, int writerThreads, CuvsDistanceType cuvsDistanceType, CuVSIvfPqParams cuVSIvfPqParams,
+  private CagraIndexParams(
+      int intermediateGraphDegree,
+      int graphDegree,
+      CagraGraphBuildAlgo CuvsCagraGraphBuildAlgo,
+      int nnDescentNiter,
+      int writerThreads,
+      CuvsDistanceType cuvsDistanceType,
+      CuVSIvfPqParams cuVSIvfPqParams,
       CagraCompressionParams cagraCompressionParams) {
     this.intermediateGraphDegree = intermediateGraphDegree;
     this.graphDegree = graphDegree;
@@ -341,10 +343,23 @@ public CagraCompressionParams getCagraCompressionParams() {
 
   @Override
   public String toString() {
-    return "CagraIndexParams [cuvsCagraGraphBuildAlgo=" + cuvsCagraGraphBuildAlgo + ", cuvsDistanceType="
-        + cuvsDistanceType + ", intermediateGraphDegree=" + intermediateGraphDegree + ", graphDegree=" + graphDegree
-        + ", nnDescentNiter=" + nnDescentNiter + ", numWriterThreads=" + numWriterThreads + ", cuVSIvfPqParams="
-        + cuVSIvfPqParams + ", cagraCompressionParams=" + cagraCompressionParams + "]";
+    return "CagraIndexParams [cuvsCagraGraphBuildAlgo="
+        + cuvsCagraGraphBuildAlgo
+        + ", cuvsDistanceType="
+        + cuvsDistanceType
+        + ", intermediateGraphDegree="
+        + intermediateGraphDegree
+        + ", graphDegree="
+        + graphDegree
+        + ", nnDescentNiter="
+        + nnDescentNiter
+        + ", numWriterThreads="
+        + numWriterThreads
+        + ", cuVSIvfPqParams="
+        + cuVSIvfPqParams
+        + ", cagraCompressionParams="
+        + cagraCompressionParams
+        + "]";
   }
 
   /**
@@ -361,8 +376,7 @@ public static class Builder {
     private CuVSIvfPqParams cuVSIvfPqParams = new CuVSIvfPqParams.Builder().build();
     private CagraCompressionParams cagraCompressionParams;
 
-    public Builder() {
-    }
+    public Builder() {}
 
     /**
      * Sets the degree of input graph for pruning.
@@ -461,8 +475,15 @@ public Builder withCompressionParams(CagraCompressionParams cagraCompressionPara
      * @return an instance of {@link CagraIndexParams}
      */
     public CagraIndexParams build() {
-      return new CagraIndexParams(intermediateGraphDegree, graphDegree, cuvsCagraGraphBuildAlgo, nnDescentNumIterations,
-          numWriterThreads, cuvsDistanceType, cuVSIvfPqParams, cagraCompressionParams);
+      return new CagraIndexParams(
+          intermediateGraphDegree,
+          graphDegree,
+          cuvsCagraGraphBuildAlgo,
+          nnDescentNumIterations,
+          numWriterThreads,
+          cuvsDistanceType,
+          cuVSIvfPqParams,
+          cagraCompressionParams);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java
index d1998dcdf1..026154e76b 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraMergeParams.java
@@ -13,93 +13,93 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
+
 public class CagraMergeParams {
 
-    private final CagraIndexParams outputIndexParams;
-    private final MergeStrategy strategy;
+  private final CagraIndexParams outputIndexParams;
+  private final MergeStrategy strategy;
 
-    /**
-     * Constructs a CagraMergeParams with the given output index parameters and merge strategy.
-     *
-     * @param outputIndexParams Index parameters for the output index
-     * @param strategy Merge strategy to use
-     */
-    private CagraMergeParams(CagraIndexParams outputIndexParams, MergeStrategy strategy) {
-        this.outputIndexParams = outputIndexParams;
-        this.strategy = strategy;
-    }
+  /**
+   * Constructs a CagraMergeParams with the given output index parameters and merge strategy.
+   *
+   * @param outputIndexParams Index parameters for the output index
+   * @param strategy Merge strategy to use
+   */
+  private CagraMergeParams(CagraIndexParams outputIndexParams, MergeStrategy strategy) {
+    this.outputIndexParams = outputIndexParams;
+    this.strategy = strategy;
+  }
 
-    /**
-     * Gets the index parameters for the output index.
-     *
-     * @return Index parameters to use for the output index
-     */
-    public CagraIndexParams getOutputIndexParams() {
-        return outputIndexParams;
+  /**
+   * Gets the index parameters for the output index.
+   *
+   * @return Index parameters to use for the output index
+   */
+  public CagraIndexParams getOutputIndexParams() {
+    return outputIndexParams;
+  }
+
+  /**
+   * Gets the merge strategy to use.
+   *
+   * @return The merge strategy
+   */
+  public MergeStrategy getStrategy() {
+    return strategy;
+  }
+
+  /**
+   * Strategy to use when merging CAGRA indexes.
+   */
+  public enum MergeStrategy {
+    PHYSICAL(0),
+
+    LOGICAL(1);
+
+    public final int value;
+
+    MergeStrategy(int value) {
+      this.value = value;
     }
+  }
+
+  /**
+   * Builder class for {@link CagraMergeParams}.
+   */
+  public static class Builder {
+    private CagraIndexParams outputIndexParams = new CagraIndexParams.Builder().build();
+    private MergeStrategy strategy = MergeStrategy.PHYSICAL; // Default to PHYSICAL
 
     /**
-     * Gets the merge strategy to use.
+     * Sets the index parameters for the output index.
      *
-     * @return The merge strategy
+     * @param outputIndexParams Index parameters to use for the output index
+     * @return This builder
      */
-    public MergeStrategy getStrategy() {
-        return strategy;
+    public Builder withOutputIndexParams(CagraIndexParams outputIndexParams) {
+      this.outputIndexParams = outputIndexParams;
+      return this;
     }
 
     /**
-     * Strategy to use when merging CAGRA indexes.
+     * Sets the merge strategy.
+     *
+     * @param strategy The merge strategy to use
+     * @return This builder
      */
-    public enum MergeStrategy {
-        PHYSICAL(0),
-
-        LOGICAL(1);
-
-        public final int value;
-
-        MergeStrategy(int value) {
-            this.value = value;
-        }
+    public Builder withStrategy(MergeStrategy strategy) {
+      this.strategy = strategy;
+      return this;
     }
 
     /**
-     * Builder class for {@link CagraMergeParams}.
+     * Builds the {@link CagraMergeParams} object.
+     *
+     * @return The built parameters
      */
-    public static class Builder {
-        private CagraIndexParams outputIndexParams = new CagraIndexParams.Builder().build();
-        private MergeStrategy strategy = MergeStrategy.PHYSICAL; // Default to PHYSICAL
-
-        /**
-         * Sets the index parameters for the output index.
-         *
-         * @param outputIndexParams Index parameters to use for the output index
-         * @return This builder
-         */
-        public Builder withOutputIndexParams(CagraIndexParams outputIndexParams) {
-            this.outputIndexParams = outputIndexParams;
-            return this;
-        }
-
-        /**
-         * Sets the merge strategy.
-         *
-         * @param strategy The merge strategy to use
-         * @return This builder
-         */
-        public Builder withStrategy(MergeStrategy strategy) {
-            this.strategy = strategy;
-            return this;
-        }
-
-        /**
-         * Builds the {@link CagraMergeParams} object.
-         *
-         * @return The built parameters
-         */
-        public CagraMergeParams build() {
-            return new CagraMergeParams(outputIndexParams, strategy);
-        }
+    public CagraMergeParams build() {
+      return new CagraMergeParams(outputIndexParams, strategy);
     }
+  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
index 9af2d87e42..7667fe4355 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -13,12 +13,11 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import java.util.Arrays;
-import java.util.List;
 import java.util.BitSet;
+import java.util.List;
 
 /**
  * CagraQuery holds the CagraSearchParams and the query vectors to be used while
@@ -47,7 +46,13 @@ public class CagraQuery {
    * @param prefilter             A single BitSet to use as filter while searching the CAGRA index
    * @param numDocs               Total number of dataset vectors; used to align the prefilter correctly
    */
-  public CagraQuery(CagraSearchParams cagraSearchParameters, float[][] queryVectors, List<Integer> mapping, int topK, BitSet prefilter, int numDocs) {
+  public CagraQuery(
+      CagraSearchParams cagraSearchParameters,
+      float[][] queryVectors,
+      List<Integer> mapping,
+      int topK,
+      BitSet prefilter,
+      int numDocs) {
     super();
     this.cagraSearchParameters = cagraSearchParameters;
     this.queryVectors = queryVectors;
@@ -113,8 +118,15 @@ public int getNumDocs() {
 
   @Override
   public String toString() {
-    return "CuVSQuery [cagraSearchParameters=" + cagraSearchParameters + ", queryVectors="
-        + Arrays.toString(queryVectors) + ", mapping=" + mapping + ", topK=" + topK + "]";
+    return "CuVSQuery [cagraSearchParameters="
+        + cagraSearchParameters
+        + ", queryVectors="
+        + Arrays.toString(queryVectors)
+        + ", mapping="
+        + mapping
+        + ", topK="
+        + topK
+        + "]";
   }
 
   /**
@@ -132,8 +144,7 @@ public static class Builder {
     /**
      * Default constructor.
      */
-    public Builder() {
-    }
+    public Builder() {}
 
     /**
      * Sets the instance of configured CagraSearchParams to be passed for search.
@@ -194,7 +205,7 @@ public Builder withPrefilter(BitSet prefilter, int numDocs) {
       this.prefilter = prefilter;
       this.numDocs = numDocs;
       return this;
-  }
+    }
 
     /**
      * Builds an instance of CuVSQuery.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
index 74ce37c249..83211d8cf8 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 /**
@@ -120,9 +119,20 @@ private HashMapMode(int value) {
    * @param randXORMask        the bit mask used for initial random seed node
    *                           selection
    */
-  private CagraSearchParams(CuVSResources resources, int maxQueries, int iTopKSize, int maxIterations,
-      SearchAlgo searchAlgo, int teamSize, int searchWidth, int minIterations, int threadBlockSize,
-      HashMapMode hashmapMode, int hashmapMinBitlen, float hashmapMaxFillRate, int numRandomSamplings,
+  private CagraSearchParams(
+      CuVSResources resources,
+      int maxQueries,
+      int iTopKSize,
+      int maxIterations,
+      SearchAlgo searchAlgo,
+      int teamSize,
+      int searchWidth,
+      int minIterations,
+      int threadBlockSize,
+      HashMapMode hashmapMode,
+      int hashmapMinBitlen,
+      float hashmapMaxFillRate,
+      int numRandomSamplings,
       long randXORMask) {
     this.maxQueries = maxQueries;
     this.iTopKSize = iTopKSize;
@@ -259,11 +269,32 @@ public HashMapMode getHashMapMode() {
 
   @Override
   public String toString() {
-    return "CagraSearchParams [maxQueries=" + maxQueries + ", itopkSize=" + iTopKSize
-        + ", maxIterations=" + maxIterations + ", cuvsCagraSearchAlgo=" + searchAlgo + ", teamSize=" + teamSize
-        + ", searchWidth=" + searchWidth + ", minIterations=" + minIterations + ", threadBlockSize=" + threadBlockSize
-        + ", hashMapMode=" + hashMapMode + ", hashMapMinBitlen=" + hashmapMinBitlen + ", hashMapMaxFillRate="
-        + hashMapMaxFillRate + ", numRandomSamplings=" + numRandomSamplings + ", randXORMask=" + randXORMask
+    return "CagraSearchParams [maxQueries="
+        + maxQueries
+        + ", itopkSize="
+        + iTopKSize
+        + ", maxIterations="
+        + maxIterations
+        + ", cuvsCagraSearchAlgo="
+        + searchAlgo
+        + ", teamSize="
+        + teamSize
+        + ", searchWidth="
+        + searchWidth
+        + ", minIterations="
+        + minIterations
+        + ", threadBlockSize="
+        + threadBlockSize
+        + ", hashMapMode="
+        + hashMapMode
+        + ", hashMapMinBitlen="
+        + hashmapMinBitlen
+        + ", hashMapMaxFillRate="
+        + hashMapMaxFillRate
+        + ", numRandomSamplings="
+        + numRandomSamplings
+        + ", randXORMask="
+        + randXORMask
         + "]";
   }
 
@@ -454,8 +485,20 @@ public Builder withRandXorMask(long randXORMask) {
      * @return an instance of CagraSearchParams
      */
     public CagraSearchParams build() {
-      return new CagraSearchParams(resources, maxQueries, iTopKSize, maxIterations, searchAlgo, teamSize, searchWidth,
-          minIterations, threadBlockSize, hashMapMode, hashMapMinBitlen, hashMapMaxFillRate, numRandomSamplings,
+      return new CagraSearchParams(
+          resources,
+          maxQueries,
+          iTopKSize,
+          maxIterations,
+          searchAlgo,
+          teamSize,
+          searchWidth,
+          minIterations,
+          threadBlockSize,
+          hashMapMode,
+          hashMapMinBitlen,
+          hashMapMaxFillRate,
+          numRandomSamplings,
           randXORMask);
     }
   }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java
index 1ce1d96568..755b30825d 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqIndexParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import com.nvidia.cuvs.CagraIndexParams.CodebookGen;
@@ -120,9 +119,19 @@ public class CuVSIvfPqIndexParams {
    */
   private final int maxTrainPointsPerPqCode;
 
-  private CuVSIvfPqIndexParams(CuvsDistanceType metric, CodebookGen codebookKind, float metricArg,
-      double kmeansTrainsetFraction, int nLists, int kmeansNIters, int pqBits, int pqDim, boolean addDataOnBuild,
-      boolean forceRandomRotation, boolean conservativeMemoryAllocation, int maxTrainPointsPerPqCode) {
+  private CuVSIvfPqIndexParams(
+      CuvsDistanceType metric,
+      CodebookGen codebookKind,
+      float metricArg,
+      double kmeansTrainsetFraction,
+      int nLists,
+      int kmeansNIters,
+      int pqBits,
+      int pqDim,
+      boolean addDataOnBuild,
+      boolean forceRandomRotation,
+      boolean conservativeMemoryAllocation,
+      int maxTrainPointsPerPqCode) {
     super();
     this.metric = metric;
     this.codebookKind = codebookKind;
@@ -250,11 +259,31 @@ public int getMaxTrainPointsPerPqCode() {
 
   @Override
   public String toString() {
-    return "CuVSIvfPqIndexParams [metric=" + metric + ", codebookKind=" + codebookKind + ", metricArg=" + metricArg
-        + ", kmeansTrainsetFraction=" + kmeansTrainsetFraction + ", nLists=" + nLists + ", kmeansNIters=" + kmeansNIters
-        + ", pqBits=" + pqBits + ", pqDim=" + pqDim + ", addDataOnBuild=" + addDataOnBuild + ", forceRandomRotation="
-        + forceRandomRotation + ", conservativeMemoryAllocation=" + conservativeMemoryAllocation
-        + ", maxTrainPointsPerPqCode=" + maxTrainPointsPerPqCode + "]";
+    return "CuVSIvfPqIndexParams [metric="
+        + metric
+        + ", codebookKind="
+        + codebookKind
+        + ", metricArg="
+        + metricArg
+        + ", kmeansTrainsetFraction="
+        + kmeansTrainsetFraction
+        + ", nLists="
+        + nLists
+        + ", kmeansNIters="
+        + kmeansNIters
+        + ", pqBits="
+        + pqBits
+        + ", pqDim="
+        + pqDim
+        + ", addDataOnBuild="
+        + addDataOnBuild
+        + ", forceRandomRotation="
+        + forceRandomRotation
+        + ", conservativeMemoryAllocation="
+        + conservativeMemoryAllocation
+        + ", maxTrainPointsPerPqCode="
+        + maxTrainPointsPerPqCode
+        + "]";
   }
 
   /**
@@ -361,8 +390,7 @@ public static class Builder {
      */
     private int maxTrainPointsPerPqCode = 256;
 
-    public Builder() {
-    }
+    public Builder() {}
 
     /**
      * Sets the distance type.
@@ -506,8 +534,19 @@ public Builder withMaxTrainPointsPerPqCode(int maxTrainPointsPerPqCode) {
      * @return an instance of {@link CuVSIvfPqIndexParams}
      */
     public CuVSIvfPqIndexParams build() {
-      return new CuVSIvfPqIndexParams(metric, codebookKind, metricArg, kmeansTrainsetFraction, nLists, kmeansNIters,
-          pqBits, pqDim, addDataOnBuild, forceRandomRotation, conservativeMemoryAllocation, maxTrainPointsPerPqCode);
+      return new CuVSIvfPqIndexParams(
+          metric,
+          codebookKind,
+          metricArg,
+          kmeansTrainsetFraction,
+          nLists,
+          kmeansNIters,
+          pqBits,
+          pqDim,
+          addDataOnBuild,
+          forceRandomRotation,
+          conservativeMemoryAllocation,
+          maxTrainPointsPerPqCode);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java
index 1b6dfca149..0b6fb82082 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 public class CuVSIvfPqParams {
@@ -27,7 +26,8 @@ public class CuVSIvfPqParams {
   /** refinement rate */
   private final float refinementRate;
 
-  private CuVSIvfPqParams(CuVSIvfPqIndexParams indexParams, CuVSIvfPqSearchParams searchParams, float refinementRate) {
+  private CuVSIvfPqParams(
+      CuVSIvfPqIndexParams indexParams, CuVSIvfPqSearchParams searchParams, float refinementRate) {
     super();
     this.indexParams = indexParams;
     this.searchParams = searchParams;
@@ -60,8 +60,13 @@ public float getRefinementRate() {
 
   @Override
   public String toString() {
-    return "CuVSIvfPqParams [indexParams=" + indexParams + ", searchParams=" + searchParams + ", refinementRate="
-        + refinementRate + "]";
+    return "CuVSIvfPqParams [indexParams="
+        + indexParams
+        + ", searchParams="
+        + searchParams
+        + ", refinementRate="
+        + refinementRate
+        + "]";
   }
 
   /**
@@ -73,13 +78,13 @@ public static class Builder {
     private CuVSIvfPqIndexParams cuVSIvfPqIndexParams = new CuVSIvfPqIndexParams.Builder().build();
 
     /** CuVS IVF_PQ search parameters */
-    private CuVSIvfPqSearchParams cuVSIvfPqSearchParams = new CuVSIvfPqSearchParams.Builder().build();
+    private CuVSIvfPqSearchParams cuVSIvfPqSearchParams =
+        new CuVSIvfPqSearchParams.Builder().build();
 
     /** refinement rate */
     private float refinementRate = 2.0f;
 
-    public Builder() {
-    }
+    public Builder() {}
 
     /**
      * Sets the CuVS IVF_PQ index parameters.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java
index 2dab8dcb3f..3ce7a57226 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSIvfPqSearchParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import com.nvidia.cuvs.CagraIndexParams.CudaDataType;
@@ -64,7 +63,10 @@ public class CuVSIvfPqSearchParams {
    */
   private final double preferredShmemCarveout;
 
-  private CuVSIvfPqSearchParams(int nProbes, CudaDataType lutDtype, CudaDataType internalDistanceDtype,
+  private CuVSIvfPqSearchParams(
+      int nProbes,
+      CudaDataType lutDtype,
+      CudaDataType internalDistanceDtype,
       double preferredShmemCarveout) {
     super();
     this.nProbes = nProbes;
@@ -114,8 +116,15 @@ public double getPreferredShmemCarveout() {
 
   @Override
   public String toString() {
-    return "CuVSIvfPqSearchParams [nProbes=" + nProbes + ", lutDtype=" + lutDtype + ", internalDistanceDtype="
-        + internalDistanceDtype + ", preferredShmemCarveout=" + preferredShmemCarveout + "]";
+    return "CuVSIvfPqSearchParams [nProbes="
+        + nProbes
+        + ", lutDtype="
+        + lutDtype
+        + ", internalDistanceDtype="
+        + internalDistanceDtype
+        + ", preferredShmemCarveout="
+        + preferredShmemCarveout
+        + "]";
   }
 
   /**
@@ -167,8 +176,7 @@ public static class Builder {
      */
     private double preferredShmemCarveout = 1.0;
 
-    public Builder() {
-    }
+    public Builder() {}
 
     /**
      * Sets the number of clusters to search.
@@ -225,7 +233,8 @@ public Builder withPreferredShmemCarveout(double preferredShmemCarveout) {
      * @return an instance of {@link CuVSIvfPqSearchParams}
      */
     public CuVSIvfPqSearchParams build() {
-      return new CuVSIvfPqSearchParams(nProbes, lutDtype, internalDistanceDtype, preferredShmemCarveout);
+      return new CuVSIvfPqSearchParams(
+          nProbes, lutDtype, internalDistanceDtype, preferredShmemCarveout);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
index 371ff01cc4..6a84f737b1 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
@@ -13,11 +13,9 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import com.nvidia.cuvs.spi.CuVSProvider;
-
 import java.nio.file.Path;
 
 /**
@@ -33,7 +31,6 @@ public interface CuVSResources extends AutoCloseable {
   @Override
   void close();
 
-
   /**
    * The temporary directory to use for intermediate operations.
    * Defaults to {@systemProperty java.io.tmpdir}.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
index 6fbbfee098..7bc7dbd797 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import com.nvidia.cuvs.spi.CuVSProvider;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
index c9c3bd9f8f..c0ccaf99c3 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 /**
@@ -25,4 +24,5 @@
  * @param totalMemory       returned total memory in bytes
  * @param computeCapability the compute capability of the device
  */
-public record GPUInfo(int gpuId, String name, long freeMemory, long totalMemory, float computeCapability) { }
+public record GPUInfo(
+    int gpuId, String name, long freeMemory, long totalMemory, float computeCapability) {}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
index ebd7776a83..746dc05fd1 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
@@ -13,14 +13,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
+import com.nvidia.cuvs.spi.CuVSProvider;
 import java.io.InputStream;
 import java.util.Objects;
 
-import com.nvidia.cuvs.spi.CuVSProvider;
-
 /**
  * {@link HnswIndex} encapsulates a HNSW index, along with methods to interact
  * with it.
@@ -29,60 +27,60 @@
  */
 public interface HnswIndex {
 
-    /**
-     * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
-     */
-    void destroyIndex() throws Throwable;
+  /**
+   * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
+   */
+  void destroyIndex() throws Throwable;
+
+  /**
+   * Invokes the native search_hnsw_index via the Panama API for searching a HNSW
+   * index.
+   *
+   * @param query an instance of {@link HnswQuery} holding the query vectors and
+   *              other parameters
+   * @return an instance of {@link SearchResults} containing the results
+   */
+  SearchResults search(HnswQuery query) throws Throwable;
+
+  /**
+   * Creates a new Builder with an instance of {@link CuVSResources}.
+   *
+   * @param cuvsResources an instance of {@link CuVSResources}
+   * @throws UnsupportedOperationException if the provider does not cuvs
+   */
+  static HnswIndex.Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    return CuVSProvider.provider().newHnswIndexBuilder(cuvsResources);
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link HnswIndex}.
+   */
+  interface Builder {
 
     /**
-     * Invokes the native search_hnsw_index via the Panama API for searching a HNSW
-     * index.
+     * Sets an instance of InputStream typically used when index deserialization is
+     * needed.
      *
-     * @param query an instance of {@link HnswQuery} holding the query vectors and
-     *              other parameters
-     * @return an instance of {@link SearchResults} containing the results
+     * @param inputStream an instance of {@link InputStream}
+     * @return an instance of this Builder
      */
-    SearchResults search(HnswQuery query) throws Throwable;
+    Builder from(InputStream inputStream);
 
     /**
-     * Creates a new Builder with an instance of {@link CuVSResources}.
+     * Registers an instance of configured {@link HnswIndexParams} with this
+     * Builder.
      *
-     * @param cuvsResources an instance of {@link CuVSResources}
-     * @throws UnsupportedOperationException if the provider does not cuvs
+     * @param hnswIndexParameters An instance of HnswIndexParams.
+     * @return An instance of this Builder.
      */
-    static HnswIndex.Builder newBuilder(CuVSResources cuvsResources) {
-        Objects.requireNonNull(cuvsResources);
-        return CuVSProvider.provider().newHnswIndexBuilder(cuvsResources);
-    }
+    Builder withIndexParams(HnswIndexParams hnswIndexParameters);
 
     /**
-     * Builder helps configure and create an instance of {@link HnswIndex}.
+     * Builds and returns an instance of CagraIndex.
+     *
+     * @return an instance of CagraIndex
      */
-    interface Builder {
-
-        /**
-         * Sets an instance of InputStream typically used when index deserialization is
-         * needed.
-         *
-         * @param inputStream an instance of {@link InputStream}
-         * @return an instance of this Builder
-         */
-        Builder from(InputStream inputStream);
-
-        /**
-         * Registers an instance of configured {@link HnswIndexParams} with this
-         * Builder.
-         *
-         * @param hnswIndexParameters An instance of HnswIndexParams.
-         * @return An instance of this Builder.
-         */
-        Builder withIndexParams(HnswIndexParams hnswIndexParameters);
-
-        /**
-         * Builds and returns an instance of CagraIndex.
-         *
-         * @return an instance of CagraIndex
-         */
-        HnswIndex build() throws Throwable;
-    }
+    HnswIndex build() throws Throwable;
+  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
index 3960d5dae6..1dbbeb15ac 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndexParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 /**
@@ -56,8 +55,8 @@ private CuvsHnswHierarchy(int value) {
   private int numThreads = 2;
   private int vectorDimension;
 
-  private HnswIndexParams(CuvsHnswHierarchy hierarchy, int efConstruction, int numThreads,
-      int vectorDimension) {
+  private HnswIndexParams(
+      CuvsHnswHierarchy hierarchy, int efConstruction, int numThreads, int vectorDimension) {
     this.hierarchy = hierarchy;
     this.efConstruction = efConstruction;
     this.numThreads = numThreads;
@@ -98,8 +97,15 @@ public int getVectorDimension() {
 
   @Override
   public String toString() {
-    return "HnswIndexParams [hierarchy=" + hierarchy + ", efConstruction=" + efConstruction + ", numThreads="
-        + numThreads + ", vectorDimension=" + vectorDimension + "]";
+    return "HnswIndexParams [hierarchy="
+        + hierarchy
+        + ", efConstruction="
+        + efConstruction
+        + ", numThreads="
+        + numThreads
+        + ", vectorDimension="
+        + vectorDimension
+        + "]";
   }
 
   /**
@@ -115,7 +121,7 @@ public static class Builder {
     /**
      * Constructs this Builder with an instance of Arena.
      */
-    public Builder() { }
+    public Builder() {}
 
     /**
      * Sets the hierarchy for HNSW index when converting from CAGRA index.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
index 2575bbde40..2c3fac6658 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import java.util.Arrays;
@@ -41,7 +40,8 @@ public class HnswQuery {
    * @param mapping          an instance of ID mapping
    * @param topK             the top k results to return
    */
-  private HnswQuery(HnswSearchParams hnswSearchParams, float[][] queryVectors, List<Integer> mapping, int topK) {
+  private HnswQuery(
+      HnswSearchParams hnswSearchParams, float[][] queryVectors, List<Integer> mapping, int topK) {
     this.hnswSearchParams = hnswSearchParams;
     this.queryVectors = queryVectors;
     this.mapping = mapping;
@@ -86,7 +86,13 @@ public int getTopK() {
 
   @Override
   public String toString() {
-    return "HnswQuery [mapping=" + mapping + ", queryVectors=" + Arrays.toString(queryVectors) + ", topK=" + topK + "]";
+    return "HnswQuery [mapping="
+        + mapping
+        + ", queryVectors="
+        + Arrays.toString(queryVectors)
+        + ", topK="
+        + topK
+        + "]";
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
index 26b91a5a43..3e2f0496bf 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswSearchParams.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 /**
@@ -24,16 +23,16 @@
  * @param numThreads the number of threads
  * @since 25.02
  */
-public record HnswSearchParams (int ef, int numThreads) {
+public record HnswSearchParams(int ef, int numThreads) {
 
-    public HnswSearchParams {
-      if (ef < 0) {
-        throw new IllegalArgumentException();
-      }
-      if (numThreads < 0) {
-        throw new IllegalArgumentException();
-      }
+  public HnswSearchParams {
+    if (ef < 0) {
+      throw new IllegalArgumentException();
+    }
+    if (numThreads < 0) {
+      throw new IllegalArgumentException();
     }
+  }
 
   /**
    * Builder configures and creates an instance of HnswSearchParams.
@@ -49,7 +48,7 @@ public static class Builder {
     /**
      * Constructs this Builder with an instance of Arena.
      */
-    public Builder() { }
+    public Builder() {}
 
     /**
      * Sets the ef value
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
index ace825a95c..239899e93e 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/LibraryException.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 public class LibraryException extends RuntimeException {
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
index c01ea04a3c..b575500de9 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import java.util.List;
@@ -21,10 +20,10 @@
 
 public interface SearchResults {
 
-    /**
-     * Gets a list results as a map of neighbor IDs to distances.
-     *
-     * @return a list of results for each query as a map of neighbor IDs to distance
-     */
-    List<Map<Integer, Float>> getResults();
+  /**
+   * Gets a list results as a map of neighbor IDs to distances.
+   *
+   * @return a list of results for each query as a map of neighbor IDs to distance
+   */
+  List<Map<Integer, Float>> getResults();
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index a0d0f9a55c..12c0a99902 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -13,16 +13,14 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.spi;
 
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CagraMergeParams;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
-import com.nvidia.cuvs.CagraMergeParams;
-
 import java.nio.file.Path;
 
 /**
@@ -49,8 +47,7 @@ default Path nativeLibraryPath() {
   }
 
   /** Creates a new CuVSResources. */
-  CuVSResources newCuVSResources(Path tempDirectory)
-      throws Throwable;
+  CuVSResources newCuVSResources(Path tempDirectory) throws Throwable;
 
   /** Create a {@link Dataset} instance **/
   Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException;
@@ -84,7 +81,8 @@ HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources)
    * @return A new merged CAGRA index
    * @throws Throwable if an error occurs during the merge operation
    */
-  default CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
+  default CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams)
+      throws Throwable {
     // Default implementation falls back to the method without parameters
     return mergeCagraIndexes(indexes);
   }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
index e811263665..c8c886dc13 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.spi;
 
 import java.lang.invoke.MethodHandles;
@@ -38,9 +37,9 @@ static class Holder {
     private static CuVSProvider loadProvider() {
       var builtinProvider = builtinProvider();
       return ServiceLoader.load(CuVSServiceProvider.class)
-              .findFirst()
-              .map(p -> p.get(builtinProvider))
-              .orElse(builtinProvider);
+          .findFirst()
+          .map(p -> p.get(builtinProvider))
+          .orElse(builtinProvider);
     }
 
     static CuVSProvider builtinProvider() {
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 5fe372dce0..b9b5997905 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.spi;
 
 import com.nvidia.cuvs.BruteForceIndex;
@@ -21,7 +20,6 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
-
 import java.nio.file.Path;
 
 /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 141f5b0ab4..362b946e3a 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
@@ -22,12 +21,13 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
 import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
-import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceBuild;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceDeserialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndexCreate;
@@ -41,8 +41,16 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 
+import com.nvidia.cuvs.BruteForceIndex;
+import com.nvidia.cuvs.BruteForceIndexParams;
+import com.nvidia.cuvs.BruteForceQuery;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.Dataset;
+import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.internal.common.Util;
+import com.nvidia.cuvs.internal.panama.cuvsBruteForceIndex;
+import com.nvidia.cuvs.internal.panama.cuvsFilter;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
@@ -57,16 +65,6 @@
 import java.util.Objects;
 import java.util.UUID;
 
-import com.nvidia.cuvs.BruteForceIndex;
-import com.nvidia.cuvs.BruteForceIndexParams;
-import com.nvidia.cuvs.BruteForceQuery;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.Dataset;
-import com.nvidia.cuvs.SearchResults;
-import com.nvidia.cuvs.internal.common.Util;
-import com.nvidia.cuvs.internal.panama.cuvsBruteForceIndex;
-import com.nvidia.cuvs.internal.panama.cuvsFilter;
-
 /**
  *
  * {@link BruteForceIndex} encapsulates a BRUTEFORCE index, along with methods
@@ -92,7 +90,10 @@ public class BruteForceIndexImpl implements BruteForceIndex {
    * @param bruteForceIndexParams an instance of {@link BruteForceIndexParams}
    *                              holding the index parameters
    */
-  private BruteForceIndexImpl(float[][] vectors, Dataset dataset, CuVSResourcesImpl resources,
+  private BruteForceIndexImpl(
+      float[][] vectors,
+      Dataset dataset,
+      CuVSResourcesImpl resources,
       BruteForceIndexParams bruteForceIndexParams)
       throws Throwable {
     this.vectors = vectors;
@@ -108,7 +109,8 @@ private BruteForceIndexImpl(float[][] vectors, Dataset dataset, CuVSResourcesImp
    * @param inputStream an instance of stream to read the index bytes from
    * @param resources   an instance of {@link CuVSResourcesImpl}
    */
-  private BruteForceIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
+  private BruteForceIndexImpl(InputStream inputStream, CuVSResourcesImpl resources)
+      throws Throwable {
     this.bruteForceIndexParams = null;
     this.vectors = null;
     this.dataset = null;
@@ -147,12 +149,14 @@ public void destroyIndex() throws Throwable {
    */
   private IndexReference build() throws Throwable {
     try (var localArena = Arena.ofConfined()) {
-      long rows = dataset != null? dataset.size(): vectors.length;
-      long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
+      long rows = dataset != null ? dataset.size() : vectors.length;
+      long cols = dataset != null ? dataset.dimensions() : (rows > 0 ? vectors[0].length : 0);
 
       Arena arena = resources.getArena();
-      MemorySegment datasetMemSegment = dataset != null? ((DatasetImpl) dataset).seg:
-          Util.buildMemorySegment(resources.getArena(), vectors);
+      MemorySegment datasetMemSegment =
+          dataset != null
+              ? ((DatasetImpl) dataset).seg
+              : Util.buildMemorySegment(resources.getArena(), vectors);
 
       long cuvsResources = resources.getMemorySegment().get(cuvsResources_t, 0);
       MemorySegment stream = arena.allocate(cudaStream_t);
@@ -172,8 +176,9 @@ private IndexReference build() throws Throwable {
 
       cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
 
-      long datasetShape[] = { rows, cols };
-      MemorySegment datasetTensor = prepareTensor(arena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
+      long datasetShape[] = {rows, cols};
+      MemorySegment datasetTensor =
+          prepareTensor(arena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
 
       MemorySegment index = arena.allocate(cuvsBruteForceIndex_t);
 
@@ -262,12 +267,14 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
 
       cudaMemcpy(queriesDP, querySeg, queriesBytes, INFER_DIRECTION);
 
-      long queriesShape[] = { numQueries, vectorDimension };
+      long queriesShape[] = {numQueries, vectorDimension};
       MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
-      long neighborsShape[] = { numQueries, topk };
-      MemorySegment neighborsTensor = prepareTensor(arena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
-      long distancesShape[] = { numQueries, topk };
-      MemorySegment distancesTensor = prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+      long neighborsShape[] = {numQueries, topk};
+      MemorySegment neighborsTensor =
+          prepareTensor(arena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
+      long distancesShape[] = {numQueries, topk};
+      MemorySegment distancesTensor =
+          prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
 
       MemorySegment prefilter = cuvsFilter.allocate(arena);
       MemorySegment prefilterTensor;
@@ -276,7 +283,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
         cuvsFilter.type(prefilter, 0); // NO_FILTER
         cuvsFilter.addr(prefilter, 0);
       } else {
-        long prefilterShape[] = { (prefilterDataLength + 31) / 32 };
+        long prefilterShape[] = {(prefilterDataLength + 31) / 32};
         prefilterLen = prefilterShape[0];
         prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
@@ -296,8 +303,14 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       returnValue = cuvsStreamSync(cuvsResources);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsBruteForceSearch(cuvsResources, bruteForceIndexReference.getMemorySegment(), queriesTensor,
-          neighborsTensor, distancesTensor, prefilter);
+      returnValue =
+          cuvsBruteForceSearch(
+              cuvsResources,
+              bruteForceIndexReference.getMemorySegment(),
+              queriesTensor,
+              neighborsTensor,
+              distancesTensor,
+              prefilter);
       checkCuVSError(returnValue, "cuvsBruteForceSearch");
 
       returnValue = cuvsStreamSync(cuvsResources);
@@ -315,14 +328,21 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       returnValue = cuvsRMMFree(cuvsResources, prefilterDP, prefilterBytes);
       checkCuVSError(returnValue, "cuvsRMMFree");
 
-      return new BruteForceSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-          distancesMemorySegment, cuvsQuery.getTopK(), cuvsQuery.getMapping(), numQueries);
+      return new BruteForceSearchResults(
+          neighborsSequenceLayout,
+          distancesSequenceLayout,
+          neighborsMemorySegment,
+          distancesMemorySegment,
+          cuvsQuery.getTopK(),
+          cuvsQuery.getMapping(),
+          numQueries);
     }
   }
 
   @Override
   public void serialize(OutputStream outputStream) throws Throwable {
-    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
+    Path path =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
     serialize(outputStream, path);
   }
 
@@ -332,8 +352,11 @@ public void serialize(OutputStream outputStream, Path tempFile) throws Throwable
     tempFile = tempFile.toAbsolutePath();
 
     long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-    int returnValue = cuvsBruteForceSerialize(cuvsRes, resources.getArena().allocateFrom(tempFile.toString()),
-        bruteForceIndexReference.getMemorySegment());
+    int returnValue =
+        cuvsBruteForceSerialize(
+            cuvsRes,
+            resources.getArena().allocateFrom(tempFile.toString()),
+            bruteForceIndexReference.getMemorySegment());
     checkCuVSError(returnValue, "cuvsBruteForceSerialize");
 
     try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
@@ -352,16 +375,21 @@ public void serialize(OutputStream outputStream, Path tempFile) throws Throwable
    */
   private IndexReference deserialize(InputStream inputStream) throws Throwable {
     checkNotDestroyed();
-    Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
+    Path tmpIndexFile =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
     tmpIndexFile = tmpIndexFile.toAbsolutePath();
     IndexReference indexReference = new IndexReference(resources);
 
-    try (var in = inputStream; FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+    try (var in = inputStream;
+        FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
       in.transferTo(fileOutputStream);
 
       long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-      int returnValue = cuvsBruteForceDeserialize(cuvsRes, resources.getArena().allocateFrom(tmpIndexFile.toString()),
-          indexReference.getMemorySegment());
+      int returnValue =
+          cuvsBruteForceDeserialize(
+              cuvsRes,
+              resources.getArena().allocateFrom(tmpIndexFile.toString()),
+              indexReference.getMemorySegment());
       checkCuVSError(returnValue, "cuvsBruteForceDeserialize");
 
     } finally {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
index 1b872708d9..ce21cbd926 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
@@ -13,11 +13,9 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
 import com.nvidia.cuvs.internal.common.SearchResultsImpl;
-
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.util.LinkedHashMap;
@@ -31,11 +29,22 @@
  */
 public class BruteForceSearchResults extends SearchResultsImpl {
 
-  protected BruteForceSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
-      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+  protected BruteForceSearchResults(
+      SequenceLayout neighboursSequenceLayout,
+      SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment,
+      MemorySegment distancesMemorySegment,
+      int topK,
+      List<Integer> mapping,
       long numberOfQueries) {
-    super(neighboursSequenceLayout, distancesSequenceLayout, neighboursMemorySegment, distancesMemorySegment, topK,
-        mapping, numberOfQueries);
+    super(
+        neighboursSequenceLayout,
+        distancesSequenceLayout,
+        neighboursMemorySegment,
+        distancesMemorySegment,
+        topK,
+        mapping,
+        numberOfQueries);
     readResultMemorySegments();
   }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 33424e65c4..43bb92ff5c 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
@@ -21,21 +20,22 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
 import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
-import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraBuild;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraDeserialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexCreate;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexDestroy;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexGetDims;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndex_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSearch;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraMerge;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraMergeParams_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSearch;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerializeToHnswlib;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
@@ -44,21 +44,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
-
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.InputStream;
-import java.io.OutputStream;
-import java.lang.foreign.Arena;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.ValueLayout;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.util.Objects;
-import java.util.UUID;
 
 import com.nvidia.cuvs.CagraCompressionParams;
 import com.nvidia.cuvs.CagraIndex;
@@ -82,7 +67,20 @@
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqSearchParams;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout;
+import java.nio.file.Files;
+import java.nio.file.Path;
 import java.util.BitSet;
+import java.util.Objects;
+import java.util.UUID;
 
 /**
  * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
@@ -112,8 +110,12 @@ public class CagraIndexImpl implements CagraIndex {
    * @param dataset         the dataset for indexing
    * @param resources       an instance of {@link CuVSResources}
    */
-  private CagraIndexImpl(CagraIndexParams indexParameters, float[][] vectors,
-      Dataset dataset, CuVSResourcesImpl resources) throws Throwable {
+  private CagraIndexImpl(
+      CagraIndexParams indexParameters,
+      float[][] vectors,
+      Dataset dataset,
+      CuVSResourcesImpl resources)
+      throws Throwable {
     this.cagraIndexParameters = indexParameters;
     this.vectors = vectors;
     this.dataset = dataset;
@@ -181,18 +183,22 @@ public void destroyIndex() throws Throwable {
    */
   private IndexReference build() throws Throwable {
     try (var localArena = Arena.ofConfined()) {
-        long rows = dataset != null? dataset.size(): vectors.length;
-        long cols = dataset != null? dataset.dimensions(): (rows > 0 ? vectors[0].length : 0);
+      long rows = dataset != null ? dataset.size() : vectors.length;
+      long cols = dataset != null ? dataset.dimensions() : (rows > 0 ? vectors[0].length : 0);
 
-      MemorySegment indexParamsMemorySegment = cagraIndexParameters != null
-          ? segmentFromIndexParams(resources, cagraIndexParameters)
-          : MemorySegment.NULL;
+      MemorySegment indexParamsMemorySegment =
+          cagraIndexParameters != null
+              ? segmentFromIndexParams(resources, cagraIndexParameters)
+              : MemorySegment.NULL;
 
-      int numWriterThreads = cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
+      int numWriterThreads =
+          cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
       omp_set_num_threads(numWriterThreads);
 
-      MemorySegment dataSeg = dataset != null? ((DatasetImpl) dataset).seg:
-      	Util.buildMemorySegment(resources.getArena(), vectors);
+      MemorySegment dataSeg =
+          dataset != null
+              ? ((DatasetImpl) dataset).seg
+              : Util.buildMemorySegment(resources.getArena(), vectors);
 
       Arena arena = resources.getArena();
       long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
@@ -200,19 +206,22 @@ private IndexReference build() throws Throwable {
       var returnValue = cuvsStreamGet(cuvsRes, stream);
       checkCuVSError(returnValue, "cuvsStreamGet");
 
-      long datasetShape[] = { rows, cols };
+      long datasetShape[] = {rows, cols};
       MemorySegment datasetTensor = prepareTensor(arena, dataSeg, datasetShape, 2, 32, 2, 2, 1);
 
       MemorySegment index = arena.allocate(cuvsCagraIndex_t);
       returnValue = cuvsCagraIndexCreate(index);
       checkCuVSError(returnValue, "cuvsCagraIndexCreate");
 
-      if (cuvsCagraIndexParams.build_algo(indexParamsMemorySegment) == 1) { // when build algo is IVF_PQ
-        MemorySegment cuvsIvfPqIndexParamsMS = cuvsIvfPqParams
-            .ivf_pq_build_params(cuvsCagraIndexParams.graph_build_params(indexParamsMemorySegment));
+      if (cuvsCagraIndexParams.build_algo(indexParamsMemorySegment)
+          == 1) { // when build algo is IVF_PQ
+        MemorySegment cuvsIvfPqIndexParamsMS =
+            cuvsIvfPqParams.ivf_pq_build_params(
+                cuvsCagraIndexParams.graph_build_params(indexParamsMemorySegment));
         int n_lists = cuvsIvfPqIndexParams.n_lists(cuvsIvfPqIndexParamsMS);
         // As rows cannot be less than n_lists value so trim down.
-        cuvsIvfPqIndexParams.n_lists(cuvsIvfPqIndexParamsMS, (int) (rows < n_lists ? rows : n_lists));
+        cuvsIvfPqIndexParams.n_lists(
+            cuvsIvfPqIndexParamsMS, (int) (rows < n_lists ? rows : n_lists));
       }
 
       returnValue = cuvsStreamSync(cuvsRes);
@@ -242,7 +251,10 @@ private IndexReference build() throws Throwable {
   public SearchResults search(CagraQuery query) throws Throwable {
     try (var localArena = Arena.ofConfined()) {
       checkNotDestroyed();
-      int topK = query.getMapping() != null ? Math.min(query.getMapping().size(), query.getTopK()) : query.getTopK();
+      int topK =
+          query.getMapping() != null
+              ? Math.min(query.getMapping().size(), query.getTopK())
+              : query.getTopK();
       long numQueries = query.getQueryVectors().length;
       long numBlocks = topK * numQueries;
       int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
@@ -285,17 +297,18 @@ public SearchResults search(CagraQuery query) throws Throwable {
 
       cudaMemcpy(queriesDP, floatsSeg, queriesBytes, INFER_DIRECTION);
 
-      long queriesShape[] = { numQueries, vectorDimension };
+      long queriesShape[] = {numQueries, vectorDimension};
       MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
-      long neighborsShape[] = { numQueries, topK };
-      MemorySegment neighborsTensor = prepareTensor(arena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
-      long distancesShape[] = { numQueries, topK };
-      MemorySegment distancesTensor = prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+      long neighborsShape[] = {numQueries, topK};
+      MemorySegment neighborsTensor =
+          prepareTensor(arena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
+      long distancesShape[] = {numQueries, topK};
+      MemorySegment distancesTensor =
+          prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
 
       returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-
       // prepare the prefiltering data
       long prefilterDataLength = 0;
       MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
@@ -315,7 +328,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
         cuvsFilter.type(prefilter, 0); // NO_FILTER
         cuvsFilter.addr(prefilter, 0);
       } else {
-        long prefilterShape[] = { (prefilterDataLength + 31) / 32 };
+        long prefilterShape[] = {(prefilterDataLength + 31) / 32};
         prefilterLen = prefilterShape[0];
         prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
@@ -335,8 +348,15 @@ public SearchResults search(CagraQuery query) throws Throwable {
       returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsCagraSearch(cuvsRes, segmentFromSearchParams(query.getCagraSearchParameters()),
-          cagraIndexReference.getMemorySegment(), queriesTensor, neighborsTensor, distancesTensor, prefilter);
+      returnValue =
+          cuvsCagraSearch(
+              cuvsRes,
+              segmentFromSearchParams(query.getCagraSearchParameters()),
+              cagraIndexReference.getMemorySegment(),
+              queriesTensor,
+              neighborsTensor,
+              distancesTensor,
+              prefilter);
       checkCuVSError(returnValue, "cuvsCagraSearch");
 
       returnValue = cuvsStreamSync(cuvsRes);
@@ -355,33 +375,45 @@ public SearchResults search(CagraQuery query) throws Throwable {
       returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
       checkCuVSError(returnValue, "cuvsRMMFree");
 
-
-      return new CagraSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-          distancesMemorySegment, topK, query.getMapping(), numQueries);
+      return new CagraSearchResults(
+          neighborsSequenceLayout,
+          distancesSequenceLayout,
+          neighborsMemorySegment,
+          distancesMemorySegment,
+          topK,
+          query.getMapping(),
+          numQueries);
     }
   }
 
   @Override
   public void serialize(OutputStream outputStream) throws Throwable {
-    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    Path path =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
     serialize(outputStream, path, 1024);
   }
 
   @Override
   public void serialize(OutputStream outputStream, int bufferLength) throws Throwable {
-    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    Path path =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
     serialize(outputStream, path, bufferLength);
   }
 
   @Override
-  public void serialize(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable {
+  public void serialize(OutputStream outputStream, Path tempFile, int bufferLength)
+      throws Throwable {
     checkNotDestroyed();
     tempFile = tempFile.toAbsolutePath();
     try (var localArena = Arena.ofConfined()) {
 
       long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-      var returnValue = cuvsCagraSerialize(cuvsRes, resources.getArena().allocateFrom(tempFile.toString()),
-          cagraIndexReference.getMemorySegment(), true);
+      var returnValue =
+          cuvsCagraSerialize(
+              cuvsRes,
+              resources.getArena().allocateFrom(tempFile.toString()),
+              cagraIndexReference.getMemorySegment(),
+              true);
       checkCuVSError(returnValue, "cuvsCagraSerialize");
 
       try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
@@ -398,24 +430,28 @@ public void serialize(OutputStream outputStream, Path tempFile, int bufferLength
 
   @Override
   public void serializeToHNSW(OutputStream outputStream) throws Throwable {
-    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+    Path path =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
     serializeToHNSW(outputStream, path, 1024);
   }
 
   @Override
   public void serializeToHNSW(OutputStream outputStream, int bufferLength) throws Throwable {
-    Path path = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+    Path path =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
     serializeToHNSW(outputStream, path, bufferLength);
   }
 
   @Override
-  public void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable {
+  public void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength)
+      throws Throwable {
     checkNotDestroyed();
     tempFile = tempFile.toAbsolutePath();
     MemorySegment pathSeg = buildMemorySegment(resources.getArena(), tempFile.toString());
 
     long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-    int returnValue = cuvsCagraSerializeToHnswlib(cuvsRes, pathSeg, cagraIndexReference.getMemorySegment());
+    int returnValue =
+        cuvsCagraSerializeToHnswlib(cuvsRes, pathSeg, cagraIndexReference.getMemorySegment());
     checkCuVSError(returnValue, "cuvsCagraSerializeToHnswlib");
 
     try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
@@ -450,16 +486,21 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
    * @return an instance of {@link IndexReference}.
    */
   private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
-    Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
+    Path tmpIndexFile =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
     tmpIndexFile = tmpIndexFile.toAbsolutePath();
     IndexReference indexReference = new IndexReference(resources);
 
-    try (var in = inputStream; FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+    try (var in = inputStream;
+        FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
       in.transferTo(fileOutputStream);
 
       long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-      var returnValue = cuvsCagraDeserialize(cuvsRes, resources.getArena().allocateFrom(tmpIndexFile.toString()),
-          indexReference.getMemorySegment());
+      var returnValue =
+          cuvsCagraDeserialize(
+              cuvsRes,
+              resources.getArena().allocateFrom(tmpIndexFile.toString()),
+              indexReference.getMemorySegment());
       checkCuVSError(returnValue, "cuvsCagraDeserialize");
 
     } finally {
@@ -491,7 +532,8 @@ public CuVSResourcesImpl getCuVSResources() {
   /**
    * Allocates the configured index parameters in the MemorySegment.
    */
-  private static MemorySegment segmentFromIndexParams(CuVSResourcesImpl resources, CagraIndexParams params) {
+  private static MemorySegment segmentFromIndexParams(
+      CuVSResourcesImpl resources, CagraIndexParams params) {
     Arena arena = resources.getArena();
     MemorySegment seg = cuvsCagraIndexParams.allocate(arena);
     cuvsCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
@@ -502,18 +544,23 @@ private static MemorySegment segmentFromIndexParams(CuVSResourcesImpl resources,
 
     CagraCompressionParams cagraCompressionParams = params.getCagraCompressionParams();
     if (cagraCompressionParams != null) {
-    MemorySegment cuvsCagraCompressionParamsMemorySegment = cuvsCagraCompressionParams.allocate(arena);
-    cuvsCagraCompressionParams.pq_bits(cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getPqBits());
-    cuvsCagraCompressionParams.pq_dim(cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getPqDim());
-    cuvsCagraCompressionParams.vq_n_centers(cuvsCagraCompressionParamsMemorySegment,
-        cagraCompressionParams.getVqNCenters());
-    cuvsCagraCompressionParams.kmeans_n_iters(cuvsCagraCompressionParamsMemorySegment,
-        cagraCompressionParams.getKmeansNIters());
-    cuvsCagraCompressionParams.vq_kmeans_trainset_fraction(cuvsCagraCompressionParamsMemorySegment,
-        cagraCompressionParams.getVqKmeansTrainsetFraction());
-    cuvsCagraCompressionParams.pq_kmeans_trainset_fraction(cuvsCagraCompressionParamsMemorySegment,
-        cagraCompressionParams.getPqKmeansTrainsetFraction());
-    cuvsCagraIndexParams.compression(seg, cuvsCagraCompressionParamsMemorySegment);
+      MemorySegment cuvsCagraCompressionParamsMemorySegment =
+          cuvsCagraCompressionParams.allocate(arena);
+      cuvsCagraCompressionParams.pq_bits(
+          cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getPqBits());
+      cuvsCagraCompressionParams.pq_dim(
+          cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getPqDim());
+      cuvsCagraCompressionParams.vq_n_centers(
+          cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getVqNCenters());
+      cuvsCagraCompressionParams.kmeans_n_iters(
+          cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getKmeansNIters());
+      cuvsCagraCompressionParams.vq_kmeans_trainset_fraction(
+          cuvsCagraCompressionParamsMemorySegment,
+          cagraCompressionParams.getVqKmeansTrainsetFraction());
+      cuvsCagraCompressionParams.pq_kmeans_trainset_fraction(
+          cuvsCagraCompressionParamsMemorySegment,
+          cagraCompressionParams.getPqKmeansTrainsetFraction());
+      cuvsCagraIndexParams.compression(seg, cuvsCagraCompressionParamsMemorySegment);
     }
 
     if (params.getCagraGraphBuildAlgo().equals(CagraGraphBuildAlgo.IVF_PQ)) {
@@ -521,36 +568,46 @@ private static MemorySegment segmentFromIndexParams(CuVSResourcesImpl resources,
       MemorySegment ivfpqIndexParamsMemorySegment = cuvsIvfPqIndexParams.allocate(arena);
       CuVSIvfPqIndexParams cuVSIvfPqIndexParams = params.getCuVSIvfPqParams().getIndexParams();
 
-      cuvsIvfPqIndexParams.metric(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMetric().value);
-      cuvsIvfPqIndexParams.metric_arg(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMetricArg());
-      cuvsIvfPqIndexParams.add_data_on_build(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.isAddDataOnBuild());
+      cuvsIvfPqIndexParams.metric(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMetric().value);
+      cuvsIvfPqIndexParams.metric_arg(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMetricArg());
+      cuvsIvfPqIndexParams.add_data_on_build(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.isAddDataOnBuild());
       cuvsIvfPqIndexParams.n_lists(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getnLists());
-      cuvsIvfPqIndexParams.kmeans_n_iters(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getKmeansNIters());
-      cuvsIvfPqIndexParams.kmeans_trainset_fraction(ivfpqIndexParamsMemorySegment,
-          cuVSIvfPqIndexParams.getKmeansTrainsetFraction());
+      cuvsIvfPqIndexParams.kmeans_n_iters(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getKmeansNIters());
+      cuvsIvfPqIndexParams.kmeans_trainset_fraction(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getKmeansTrainsetFraction());
       cuvsIvfPqIndexParams.pq_bits(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getPqBits());
       cuvsIvfPqIndexParams.pq_dim(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getPqDim());
-      cuvsIvfPqIndexParams.codebook_kind(ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getCodebookKind().value);
-      cuvsIvfPqIndexParams.force_random_rotation(ivfpqIndexParamsMemorySegment,
-          cuVSIvfPqIndexParams.isForceRandomRotation());
-      cuvsIvfPqIndexParams.conservative_memory_allocation(ivfpqIndexParamsMemorySegment,
-          cuVSIvfPqIndexParams.isConservativeMemoryAllocation());
-      cuvsIvfPqIndexParams.max_train_points_per_pq_code(ivfpqIndexParamsMemorySegment,
-          cuVSIvfPqIndexParams.getMaxTrainPointsPerPqCode());
+      cuvsIvfPqIndexParams.codebook_kind(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getCodebookKind().value);
+      cuvsIvfPqIndexParams.force_random_rotation(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.isForceRandomRotation());
+      cuvsIvfPqIndexParams.conservative_memory_allocation(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.isConservativeMemoryAllocation());
+      cuvsIvfPqIndexParams.max_train_points_per_pq_code(
+          ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMaxTrainPointsPerPqCode());
 
       MemorySegment ivfpqSearchParamsMemorySegment = cuvsIvfPqSearchParams.allocate(arena);
       CuVSIvfPqSearchParams cuVSIvfPqSearchParams = params.getCuVSIvfPqParams().getSearchParams();
-      cuvsIvfPqSearchParams.n_probes(ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getnProbes());
-      cuvsIvfPqSearchParams.lut_dtype(ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getLutDtype().value);
-      cuvsIvfPqSearchParams.internal_distance_dtype(ivfpqSearchParamsMemorySegment,
-          cuVSIvfPqSearchParams.getInternalDistanceDtype().value);
-      cuvsIvfPqSearchParams.preferred_shmem_carveout(ivfpqSearchParamsMemorySegment,
-          cuVSIvfPqSearchParams.getPreferredShmemCarveout());
+      cuvsIvfPqSearchParams.n_probes(
+          ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getnProbes());
+      cuvsIvfPqSearchParams.lut_dtype(
+          ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getLutDtype().value);
+      cuvsIvfPqSearchParams.internal_distance_dtype(
+          ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getInternalDistanceDtype().value);
+      cuvsIvfPqSearchParams.preferred_shmem_carveout(
+          ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getPreferredShmemCarveout());
 
       MemorySegment cuvsIvfPqParamsMemorySegment = cuvsIvfPqParams.allocate(arena);
-      cuvsIvfPqParams.ivf_pq_build_params(cuvsIvfPqParamsMemorySegment, ivfpqIndexParamsMemorySegment);
-      cuvsIvfPqParams.ivf_pq_search_params(cuvsIvfPqParamsMemorySegment, ivfpqSearchParamsMemorySegment);
-      cuvsIvfPqParams.refinement_rate(cuvsIvfPqParamsMemorySegment, params.getCuVSIvfPqParams().getRefinementRate());
+      cuvsIvfPqParams.ivf_pq_build_params(
+          cuvsIvfPqParamsMemorySegment, ivfpqIndexParamsMemorySegment);
+      cuvsIvfPqParams.ivf_pq_search_params(
+          cuvsIvfPqParamsMemorySegment, ivfpqSearchParamsMemorySegment);
+      cuvsIvfPqParams.refinement_rate(
+          cuvsIvfPqParamsMemorySegment, params.getCuVSIvfPqParams().getRefinementRate());
 
       cuvsCagraIndexParams.graph_build_params(seg, cuvsIvfPqParamsMemorySegment);
     }
@@ -598,7 +655,8 @@ public static CagraIndex.Builder newBuilder(CuVSResources cuvsResources) {
   public int getIndexDimensions() {
     try (var localArena = Arena.ofConfined()) {
       MemorySegment dimensionsMemorySegment = localArena.allocate(C_INT);
-      var returnValue = cuvsCagraIndexGetDims(cagraIndexReference.getMemorySegment(), dimensionsMemorySegment);
+      var returnValue =
+          cuvsCagraIndexGetDims(cagraIndexReference.getMemorySegment(), dimensionsMemorySegment);
       checkCuVSError(returnValue, "cuvsCagraIndexGetDims");
       return dimensionsMemorySegment.get(C_INT, 0);
     }
@@ -623,36 +681,39 @@ public static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
    * @return A new merged CAGRA index
    * @throws Throwable if an error occurs during the merge operation
    */
-  public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
-	    CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
-	    IndexReference mergedIndexReference = new IndexReference(resources);
-	    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-
-	    try (var arena = Arena.ofConfined()) {
-	      MemorySegment indexesSegment = arena.allocate(indexes.length * ValueLayout.ADDRESS.byteSize());
+  public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams)
+      throws Throwable {
+    CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
+    IndexReference mergedIndexReference = new IndexReference(resources);
+    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
 
-	      for (int i = 0; i < indexes.length; i++) {
-	        CagraIndexImpl indexImpl = (CagraIndexImpl) indexes[i];
-	        indexesSegment.setAtIndex(ValueLayout.ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
-	      }
+    try (var arena = Arena.ofConfined()) {
+      MemorySegment indexesSegment =
+          arena.allocate(indexes.length * ValueLayout.ADDRESS.byteSize());
 
-	      MemorySegment mergeParamsSegment = arena.allocate(cuvsCagraMergeParams_t);
-	      int returnValue;
+      for (int i = 0; i < indexes.length; i++) {
+        CagraIndexImpl indexImpl = (CagraIndexImpl) indexes[i];
+        indexesSegment.setAtIndex(
+            ValueLayout.ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
+      }
 
-	      mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
+      MemorySegment mergeParamsSegment = arena.allocate(cuvsCagraMergeParams_t);
+      int returnValue;
 
-	      returnValue = cuvsCagraMerge(
-	          cuvsRes,
-	          mergeParamsSegment,
-	          indexesSegment,
-	          indexes.length,
-	          mergedIndexReference.getMemorySegment());
+      mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
 
-	      checkCuVSError(returnValue, "cuvsCagraMerge");
+      returnValue =
+          cuvsCagraMerge(
+              cuvsRes,
+              mergeParamsSegment,
+              indexesSegment,
+              indexes.length,
+              mergedIndexReference.getMemorySegment());
 
-	    }
+      checkCuVSError(returnValue, "cuvsCagraMerge");
+    }
 
-	    return new CagraIndexImpl(mergedIndexReference, resources);
+    return new CagraIndexImpl(mergedIndexReference, resources);
   }
 
   /**
@@ -662,12 +723,14 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
    * @param resources The CuVS resources
    * @return A memory segment with the merge parameters
    */
-  private static MemorySegment createMergeParamsSegment(CagraMergeParams mergeParams, CuVSResourcesImpl resources) {
+  private static MemorySegment createMergeParamsSegment(
+      CagraMergeParams mergeParams, CuVSResourcesImpl resources) {
     MemorySegment seg = cuvsCagraMergeParams.allocate(resources.getArena());
 
     if (mergeParams != null) {
       if (mergeParams.getOutputIndexParams() != null) {
-        MemorySegment outputIndexParamsSeg = segmentFromIndexParams(resources, mergeParams.getOutputIndexParams());
+        MemorySegment outputIndexParamsSeg =
+            segmentFromIndexParams(resources, mergeParams.getOutputIndexParams());
         cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
       } else {
         cuvsCagraMergeParams.output_index_params(seg, MemorySegment.NULL);
@@ -675,7 +738,8 @@ private static MemorySegment createMergeParamsSegment(CagraMergeParams mergePara
 
       cuvsCagraMergeParams.strategy(seg, mergeParams.getStrategy().value);
     } else {
-      MemorySegment outputIndexParamsSeg = segmentFromIndexParams(resources, new CagraIndexParams.Builder().build());
+      MemorySegment outputIndexParamsSeg =
+          segmentFromIndexParams(resources, new CagraIndexParams.Builder().build());
       cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
     }
 
@@ -726,9 +790,10 @@ public CagraIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new CagraIndexImpl(inputStream, cuvsResources);
       } else {
-    	if (vectors != null && dataset != null) {
-    		throw new IllegalArgumentException("Please specify only one type of dataset (a float[] or a Dataset instance)");
-    	}
+        if (vectors != null && dataset != null) {
+          throw new IllegalArgumentException(
+              "Please specify only one type of dataset (a float[] or a Dataset instance)");
+        }
         return new CagraIndexImpl(cagraIndexParams, vectors, dataset, cuvsResources);
       }
     }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
index 3e8a906228..6b3d76da84 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
@@ -13,17 +13,15 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
+import com.nvidia.cuvs.internal.common.SearchResultsImpl;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
 
-import com.nvidia.cuvs.internal.common.SearchResultsImpl;
-
 /**
  * SearchResult encapsulates the logic for reading and holding search results.
  *
@@ -31,11 +29,22 @@
  */
 public class CagraSearchResults extends SearchResultsImpl {
 
-  protected CagraSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
-      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+  protected CagraSearchResults(
+      SequenceLayout neighboursSequenceLayout,
+      SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment,
+      MemorySegment distancesMemorySegment,
+      int topK,
+      List<Integer> mapping,
       long numberOfQueries) {
-    super(neighboursSequenceLayout, distancesSequenceLayout, neighboursMemorySegment, distancesMemorySegment, topK,
-        mapping, numberOfQueries);
+    super(
+        neighboursSequenceLayout,
+        distancesSequenceLayout,
+        neighboursMemorySegment,
+        distancesMemorySegment,
+        topK,
+        mapping,
+        numberOfQueries);
     readResultMemorySegments();
   }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
index 41351d2ece..0270d31795 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -13,20 +13,18 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResourcesCreate;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResourcesDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
 
+import com.nvidia.cuvs.CuVSResources;
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemorySegment;
 import java.nio.file.Path;
 
-import com.nvidia.cuvs.CuVSResources;
-
 /**
  * Used for allocating resources for cuVS
  *
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
index 13ac77e27e..5e26dcd73a 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
@@ -13,17 +13,15 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 
+import com.nvidia.cuvs.Dataset;
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 
-import com.nvidia.cuvs.Dataset;
-
 public class DatasetImpl implements Dataset {
   private final Arena arena;
   protected final MemorySegment seg;
@@ -43,9 +41,9 @@ public DatasetImpl(int size, int dimensions) {
 
   @Override
   public void addVector(float[] vector) {
-    if (current >= size)
-      throw new ArrayIndexOutOfBoundsException();
-    MemorySegment.copy(vector, 0, seg, C_FLOAT, ((current++) * dimensions * C_FLOAT.byteSize()), (int) dimensions);
+    if (current >= size) throw new ArrayIndexOutOfBoundsException();
+    MemorySegment.copy(
+        vector, 0, seg, C_FLOAT, ((current++) * dimensions * C_FLOAT.byteSize()), (int) dimensions);
   }
 
   @Override
@@ -64,5 +62,4 @@ public int size() {
   public int dimensions() {
     return dimensions;
   }
-
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index f77fa82502..a068e6eee8 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
@@ -28,17 +27,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 
-import java.io.FileOutputStream;
-import java.io.InputStream;
-import java.lang.foreign.Arena;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SequenceLayout;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.util.Objects;
-import java.util.UUID;
-
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.HnswIndex;
 import com.nvidia.cuvs.HnswIndexParams;
@@ -49,6 +37,16 @@
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndex;
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsHnswSearchParams;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Objects;
+import java.util.UUID;
 
 /**
  * {@link HnswIndex} encapsulates a HNSW index, along with methods to interact
@@ -68,7 +66,8 @@ public class HnswIndexImpl implements HnswIndex {
    * @param inputStream an instance of stream to read the index bytes from
    * @param resources   an instance of {@link CuVSResourcesImpl}
    */
-  private HnswIndexImpl(InputStream inputStream, CuVSResourcesImpl resources, HnswIndexParams hnswIndexParams)
+  private HnswIndexImpl(
+      InputStream inputStream, CuVSResourcesImpl resources, HnswIndexParams hnswIndexParams)
       throws Throwable {
     this.hnswIndexParams = hnswIndexParams;
     this.resources = resources;
@@ -110,25 +109,39 @@ public SearchResults search(HnswQuery query) throws Throwable {
 
       long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
 
-      long queriesShape[] = { numQueries, vectorDimension };
+      long queriesShape[] = {numQueries, vectorDimension};
       MemorySegment queriesTensor = prepareTensor(arena, querySeg, queriesShape, 2, 32, 2, 1, 1);
-      long neighborsShape[] = { numQueries, topK };
-      MemorySegment neighborsTensor = prepareTensor(arena, neighborsMemorySegment, neighborsShape, 1, 64, 2, 1, 1);
-      long distancesShape[] = { numQueries, topK };
-      MemorySegment distancesTensor = prepareTensor(arena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
+      long neighborsShape[] = {numQueries, topK};
+      MemorySegment neighborsTensor =
+          prepareTensor(arena, neighborsMemorySegment, neighborsShape, 1, 64, 2, 1, 1);
+      long distancesShape[] = {numQueries, topK};
+      MemorySegment distancesTensor =
+          prepareTensor(arena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
 
       int returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsHnswSearch(cuvsRes, segmentFromSearchParams(query.getHnswSearchParams()),
-          hnswIndexReference.getMemorySegment(), queriesTensor, neighborsTensor, distancesTensor);
+      returnValue =
+          cuvsHnswSearch(
+              cuvsRes,
+              segmentFromSearchParams(query.getHnswSearchParams()),
+              hnswIndexReference.getMemorySegment(),
+              queriesTensor,
+              neighborsTensor,
+              distancesTensor);
       checkCuVSError(returnValue, "cuvsHnswSearch");
 
       returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      return new HnswSearchResults(neighborsSequenceLayout, distancesSequenceLayout, neighborsMemorySegment,
-          distancesMemorySegment, topK, query.getMapping(), numQueries);
+      return new HnswSearchResults(
+          neighborsSequenceLayout,
+          distancesSequenceLayout,
+          neighborsMemorySegment,
+          distancesMemorySegment,
+          topK,
+          query.getMapping(),
+          numQueries);
     }
   }
 
@@ -154,10 +167,12 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
    */
   private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
     try (var localArena = Arena.ofConfined()) {
-      Path tmpIndexFile = Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
+      Path tmpIndexFile =
+          Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
       tmpIndexFile = tmpIndexFile.toAbsolutePath();
 
-      try (var in = inputStream; FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
+      try (var in = inputStream;
+          FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
         byte[] chunk = new byte[bufferLength];
         int chunkLength;
         while ((chunkLength = in.read(chunk)) != -1) {
@@ -179,8 +194,14 @@ private IndexReference deserialize(InputStream inputStream, int bufferLength) th
 
         cuvsHnswIndex.dtype(hnswIndex, dtype);
 
-        returnValue = cuvsHnswDeserialize(cuvsRes, segmentFromIndexParams(hnswIndexParams), pathSeg,
-            hnswIndexParams.getVectorDimension(), 0, hnswIndex);
+        returnValue =
+            cuvsHnswDeserialize(
+                cuvsRes,
+                segmentFromIndexParams(hnswIndexParams),
+                pathSeg,
+                hnswIndexParams.getVectorDimension(),
+                0,
+                hnswIndex);
         checkCuVSError(returnValue, "cuvsHnswDeserialize");
 
         return new IndexReference(hnswIndex);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
index d2920c7177..2e22d27886 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
@@ -13,11 +13,9 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal;
 
 import com.nvidia.cuvs.internal.common.SearchResultsImpl;
-
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.util.LinkedHashMap;
@@ -31,11 +29,22 @@
  */
 public class HnswSearchResults extends SearchResultsImpl {
 
-  protected HnswSearchResults(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
-      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+  protected HnswSearchResults(
+      SequenceLayout neighboursSequenceLayout,
+      SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment,
+      MemorySegment distancesMemorySegment,
+      int topK,
+      List<Integer> mapping,
       long numberOfQueries) {
-    super(neighboursSequenceLayout, distancesSequenceLayout, neighboursMemorySegment, distancesMemorySegment, topK,
-        mapping, numberOfQueries);
+    super(
+        neighboursSequenceLayout,
+        distancesSequenceLayout,
+        neighboursMemorySegment,
+        distancesMemorySegment,
+        topK,
+        mapping,
+        numberOfQueries);
     readResultMemorySegments();
   }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
index dfe74beef1..e82679300d 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/LinkerHelper.java
@@ -13,18 +13,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal.common;
 
 import java.lang.foreign.AddressLayout;
-import java.lang.foreign.Arena;
-import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.Linker;
 import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SymbolLookup;
 import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandle;
 
 /**
  * Utility methods for calling into the native linker.
@@ -33,16 +27,21 @@ public class LinkerHelper {
 
   private static final Linker LINKER = Linker.nativeLinker();
 
-  public static final ValueLayout.OfByte C_CHAR = (ValueLayout.OfByte) LINKER.canonicalLayouts().get("char");
+  public static final ValueLayout.OfByte C_CHAR =
+      (ValueLayout.OfByte) LINKER.canonicalLayouts().get("char");
 
-  public static final ValueLayout.OfInt C_INT = (ValueLayout.OfInt) LINKER.canonicalLayouts().get("int");
+  public static final ValueLayout.OfInt C_INT =
+      (ValueLayout.OfInt) LINKER.canonicalLayouts().get("int");
 
-  public static final ValueLayout.OfLong C_LONG = (ValueLayout.OfLong) LINKER.canonicalLayouts().get("long");
+  public static final ValueLayout.OfLong C_LONG =
+      (ValueLayout.OfLong) LINKER.canonicalLayouts().get("long");
 
-  public static final ValueLayout.OfFloat C_FLOAT = (ValueLayout.OfFloat) LINKER.canonicalLayouts().get("float");
+  public static final ValueLayout.OfFloat C_FLOAT =
+      (ValueLayout.OfFloat) LINKER.canonicalLayouts().get("float");
 
-  public static final AddressLayout C_POINTER = ((AddressLayout) LINKER.canonicalLayouts().get("void*"))
-      .withTargetLayout(MemoryLayout.sequenceLayout(Long.MAX_VALUE, C_CHAR));
+  public static final AddressLayout C_POINTER =
+      ((AddressLayout) LINKER.canonicalLayouts().get("void*"))
+          .withTargetLayout(MemoryLayout.sequenceLayout(Long.MAX_VALUE, C_CHAR));
 
   public static final long C_INT_BYTE_SIZE = LinkerHelper.C_INT.byteSize();
 
@@ -50,6 +49,5 @@ public class LinkerHelper {
 
   public static final long C_LONG_BYTE_SIZE = LinkerHelper.C_LONG.byteSize();
 
-  private LinkerHelper() {
-  }
+  private LinkerHelper() {}
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
index dfd82c004f..06ff865f4f 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
@@ -1,7 +1,21 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 package com.nvidia.cuvs.internal.common;
 
 import com.nvidia.cuvs.SearchResults;
-
 import java.lang.foreign.MemoryLayout.PathElement;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
@@ -22,8 +36,13 @@ public abstract class SearchResultsImpl implements SearchResults {
   protected final VarHandle neighboursVarHandle;
   protected final VarHandle distancesVarHandle;
 
-  protected SearchResultsImpl(SequenceLayout neighboursSequenceLayout, SequenceLayout distancesSequenceLayout,
-      MemorySegment neighboursMemorySegment, MemorySegment distancesMemorySegment, int topK, List<Integer> mapping,
+  protected SearchResultsImpl(
+      SequenceLayout neighboursSequenceLayout,
+      SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment,
+      MemorySegment distancesMemorySegment,
+      int topK,
+      List<Integer> mapping,
       long numberOfQueries) {
     this.topK = topK;
     this.numberOfQueries = numberOfQueries;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 513c66e257..af882564c0 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.internal.common;
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
@@ -26,6 +25,13 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaSetDevice;
 import static com.nvidia.cuvs.internal.panama.headers_h.size_t;
 
+import com.nvidia.cuvs.GPUInfo;
+import com.nvidia.cuvs.internal.panama.DLDataType;
+import com.nvidia.cuvs.internal.panama.DLDevice;
+import com.nvidia.cuvs.internal.panama.DLManagedTensor;
+import com.nvidia.cuvs.internal.panama.DLTensor;
+import com.nvidia.cuvs.internal.panama.cudaDeviceProp;
+import com.nvidia.cuvs.internal.panama.headers_h;
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemoryLayout.PathElement;
@@ -35,21 +41,12 @@
 import java.util.BitSet;
 import java.util.List;
 
-import com.nvidia.cuvs.GPUInfo;
-import com.nvidia.cuvs.internal.panama.DLDataType;
-import com.nvidia.cuvs.internal.panama.DLDevice;
-import com.nvidia.cuvs.internal.panama.DLManagedTensor;
-import com.nvidia.cuvs.internal.panama.DLTensor;
-import com.nvidia.cuvs.internal.panama.cudaDeviceProp;
-import com.nvidia.cuvs.internal.panama.headers_h;
-
 public class Util {
 
   public static final int CUVS_SUCCESS = 1;
   public static final int CUDA_SUCCESS = 0;
 
-  private Util() {
-  }
+  private Util() {}
 
   /**
    * Checks the result value of a (CuVS) native method handle call.
@@ -102,8 +99,10 @@ public enum CudaMemcpyKind {
    * @param kind "Direction" of data copy (Host->Device, Device->Host, etc.)
    * @throws RuntimeException on failure of copy
    */
-  public static void cudaMemcpy(MemorySegment dest, MemorySegment src, long numBytes, CudaMemcpyKind kind) {
-    int returnValue = com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy(dest, src, numBytes, kind.kind);
+  public static void cudaMemcpy(
+      MemorySegment dest, MemorySegment src, long numBytes, CudaMemcpyKind kind) {
+    int returnValue =
+        com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy(dest, src, numBytes, kind.kind);
     checkCudaError(returnValue, "cudaMemcpy");
   }
 
@@ -126,7 +125,8 @@ static String getLastErrorText() {
       if (seg.equals(MemorySegment.NULL)) {
         return "no last error text";
       }
-      return seg.reinterpret(MAX_ERROR_TEXT).getString(0);    } catch (Throwable t) {
+      return seg.reinterpret(MAX_ERROR_TEXT).getString(0);
+    } catch (Throwable t) {
       throw new RuntimeException(t);
     }
   }
@@ -149,11 +149,13 @@ public static List<GPUInfo> compatibleGPUs() throws Throwable {
    * @param minDeviceMemoryMB    the minimum total available memory in MB
    * @return a list of compatible GPUs. See {@link GPUInfo}
    */
-  public static List<GPUInfo> compatibleGPUs(double minComputeCapability, int minDeviceMemoryMB) throws Throwable {
+  public static List<GPUInfo> compatibleGPUs(double minComputeCapability, int minDeviceMemoryMB)
+      throws Throwable {
     List<GPUInfo> compatibleGPUs = new ArrayList<GPUInfo>();
     double minDeviceMemoryB = Math.pow(2, 20) * minDeviceMemoryMB;
     for (GPUInfo gpuInfo : availableGPUs()) {
-      if (gpuInfo.computeCapability() >= minComputeCapability && gpuInfo.totalMemory() >= minDeviceMemoryB) {
+      if (gpuInfo.computeCapability() >= minComputeCapability
+          && gpuInfo.totalMemory() >= minDeviceMemoryB) {
         compatibleGPUs.add(gpuInfo);
       }
     }
@@ -190,11 +192,17 @@ public static List<GPUInfo> availableGPUs() throws Throwable {
         returnValue = cudaMemGetInfo(free, total);
         checkCudaError(returnValue, "cudaMemGetInfo");
 
-        float computeCapability = Float
-            .parseFloat(cudaDeviceProp.major(deviceProp) + "." + cudaDeviceProp.minor(deviceProp));
+        float computeCapability =
+            Float.parseFloat(
+                cudaDeviceProp.major(deviceProp) + "." + cudaDeviceProp.minor(deviceProp));
 
-        GPUInfo gpuInfo = new GPUInfo(i, cudaDeviceProp.name(deviceProp).getString(0), free.get(C_LONG, 0),
-            total.get(C_LONG, 0), computeCapability);
+        GPUInfo gpuInfo =
+            new GPUInfo(
+                i,
+                cudaDeviceProp.name(deviceProp).getString(0),
+                free.get(C_LONG, 0),
+                total.get(C_LONG, 0),
+                computeCapability);
 
         gpuInfoArr.add(gpuInfo);
       }
@@ -255,7 +263,8 @@ public static MemorySegment buildMemorySegment(Arena arena, float[][] data) {
     MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(rows * cols, C_FLOAT);
     MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
     for (int r = 0; r < rows; r++) {
-      MemorySegment.copy(data[r], 0, dataMemorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()), (int) cols);
+      MemorySegment.copy(
+          data[r], 0, dataMemorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()), (int) cols);
     }
     return dataMemorySegment;
   }
@@ -285,8 +294,15 @@ public static BitSet concatenate(BitSet[] arr, int maxSizeOfEachBitSet) {
    * @param[in] ndim the number of dimensions
    * @return DLManagedTensor
    */
-  public static MemorySegment prepareTensor(Arena arena, MemorySegment data, long[] shape, int code, int bits, int ndim,
-      int deviceType, int lanes) {
+  public static MemorySegment prepareTensor(
+      Arena arena,
+      MemorySegment data,
+      long[] shape,
+      int code,
+      int bits,
+      int ndim,
+      int deviceType,
+      int lanes) {
 
     MemorySegment tensor = DLManagedTensor.allocate(arena);
     MemorySegment dlTensor = DLTensor.allocate(arena);
@@ -313,5 +329,4 @@ public static MemorySegment prepareTensor(Arena arena, MemorySegment data, long[
 
     return tensor;
   }
-
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index e7a2a73e27..d5a348e1fc 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -13,21 +13,19 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs.spi;
 
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CagraMergeParams;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
-import com.nvidia.cuvs.CagraMergeParams;
 import com.nvidia.cuvs.internal.BruteForceIndexImpl;
 import com.nvidia.cuvs.internal.CagraIndexImpl;
 import com.nvidia.cuvs.internal.CuVSResourcesImpl;
 import com.nvidia.cuvs.internal.DatasetImpl;
 import com.nvidia.cuvs.internal.HnswIndexImpl;
-
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.Objects;
@@ -70,7 +68,8 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
   }
 
   @Override
-  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams) throws Throwable {
+  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams)
+      throws Throwable {
     if (indexes == null || indexes.length == 0) {
       throw new IllegalArgumentException("At least one index must be provided for merging");
     }
@@ -79,6 +78,6 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merge
 
   @Override
   public Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException {
-      return new DatasetImpl(size, dimensions);
+    return new DatasetImpl(size, dimensions);
   }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index 45ada8b50b..c7873e39fe 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
@@ -28,13 +27,12 @@
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
-
 import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-public class BruteForceAndSearchIT extends CuVSTestCase{
+public class BruteForceAndSearchIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
@@ -53,26 +51,26 @@ public void testIndexingAndSearchingFlow() throws Throwable {
 
     // Sample data and query
     float[][] dataset = {
-        { 0.74021935f, 0.9209938f },
-        { 0.03902049f, 0.9689629f },
-        { 0.92514056f, 0.4463501f },
-        { 0.6673192f, 0.10993068f }
+      {0.74021935f, 0.9209938f},
+      {0.03902049f, 0.9689629f},
+      {0.92514056f, 0.4463501f},
+      {0.6673192f, 0.10993068f}
     };
     List<Integer> map = List.of(0, 1, 2, 3);
     float[][] queries = {
-        { 0.48216683f, 0.0428398f },
-        { 0.5084142f, 0.6545497f },
-        { 0.51260436f, 0.2643005f },
-        { 0.05198065f, 0.5789965f }
+      {0.48216683f, 0.0428398f},
+      {0.5084142f, 0.6545497f},
+      {0.51260436f, 0.2643005f},
+      {0.05198065f, 0.5789965f}
     };
 
     // Expected search results
-    List<Map<Integer, Float>> expectedResults = Arrays.asList(
-        Map.of(3, 0.038782537f, 2, 0.35904616f, 0, 0.83774555f),
-        Map.of(0, 0.12472606f, 2, 0.21700788f, 1, 0.3191862f),
-        Map.of(3, 0.047766685f, 2, 0.20332813f, 0, 0.48305476f),
-        Map.of(1, 0.15224183f, 0, 0.5906347f, 3, 0.5986643f)
-        );
+    List<Map<Integer, Float>> expectedResults =
+        Arrays.asList(
+            Map.of(3, 0.038782537f, 2, 0.35904616f, 0, 0.83774555f),
+            Map.of(0, 0.12472606f, 2, 0.21700788f, 1, 0.3191862f),
+            Map.of(3, 0.047766685f, 2, 0.20332813f, 0, 0.48305476f),
+            Map.of(1, 0.15224183f, 0, 0.5906347f, 3, 0.5986643f));
 
     // pre-filtering
     BitSet prefilter = new BitSet();
@@ -81,27 +79,27 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     prefilter.clear(2);
     prefilter.set(3);
 
-    final List<Map<Integer, Float>> expectedResultsWithFiltering = Arrays.asList(
-        Map.of(0, 0.83774555f, 1, 1.0540828f, 3, 0.038782537f),
-        Map.of(0, 0.12472606f, 1, 0.3191862f, 3, 0.32186073f),
-        Map.of(0, 0.48305476f, 1, 0.7208309f, 3, 0.047766685f),
-        Map.of(0, 0.5906347f, 1, 0.15224195f, 3, 0.5986643f)
-        );
+    final List<Map<Integer, Float>> expectedResultsWithFiltering =
+        Arrays.asList(
+            Map.of(0, 0.83774555f, 1, 1.0540828f, 3, 0.038782537f),
+            Map.of(0, 0.12472606f, 1, 0.3191862f, 3, 0.32186073f),
+            Map.of(0, 0.48305476f, 1, 0.7208309f, 3, 0.047766685f),
+            Map.of(0, 0.5906347f, 1, 0.15224195f, 3, 0.5986643f));
 
     for (int j = 0; j < 10; j++) {
 
       try (CuVSResources resources = CuVSResources.create()) {
 
         // Set index parameters
-        BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
-            .withNumWriterThreads(32)
-            .build();
+        BruteForceIndexParams indexParams =
+            new BruteForceIndexParams.Builder().withNumWriterThreads(32).build();
 
         // Create the index with the dataset
-        BruteForceIndex index = BruteForceIndex.newBuilder(resources)
-            .withDataset(dataset)
-            .withIndexParams(indexParams)
-            .build();
+        BruteForceIndex index =
+            BruteForceIndex.newBuilder(resources)
+                .withDataset(dataset)
+                .withIndexParams(indexParams)
+                .build();
 
         // Saving the index on to the disk.
         String indexFileName = UUID.randomUUID().toString() + ".bf";
@@ -110,22 +108,24 @@ public void testIndexingAndSearchingFlow() throws Throwable {
         // Loading a BRUTEFORCE index from disk.
         File indexFile = new File(indexFileName);
         InputStream inputStream = new FileInputStream(indexFile);
-        BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources)
-            .from(inputStream)
-            .build();
+        BruteForceIndex loadedIndex =
+            BruteForceIndex.newBuilder(resources).from(inputStream).build();
 
         // Perform the search
-        BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
-            .withTopK(3)
-            .withQueryVectors(queries)
-            .withMapping(map)
-            .build();
-        BruteForceQuery cuvsQueryWithFiltering = new BruteForceQuery.Builder()
-            .withTopK(3)
-            .withQueryVectors(queries)
-            .withPrefilters(new BitSet[] {prefilter, prefilter, prefilter, prefilter}, dataset.length)
-            .withMapping(map)
-            .build();
+        BruteForceQuery cuvsQuery =
+            new BruteForceQuery.Builder()
+                .withTopK(3)
+                .withQueryVectors(queries)
+                .withMapping(map)
+                .build();
+        BruteForceQuery cuvsQueryWithFiltering =
+            new BruteForceQuery.Builder()
+                .withTopK(3)
+                .withQueryVectors(queries)
+                .withPrefilters(
+                    new BitSet[] {prefilter, prefilter, prefilter, prefilter}, dataset.length)
+                .withMapping(map)
+                .build();
 
         // search the loaded index
         SearchResults results = loadedIndex.search(cuvsQuery);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 481289fe02..6f6b24f316 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -13,23 +13,20 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import java.lang.invoke.MethodHandles;
 import java.util.BitSet;
 import java.util.List;
-
 import org.junit.Before;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import com.carrotsearch.randomizedtesting.RandomizedRunner;
-
 @RunWith(RandomizedRunner.class)
 public class BruteForceRandomizedIT extends CuVSTestCase {
 
@@ -44,9 +41,9 @@ public void setup() {
 
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
-	boolean useNativeMemoryDatasets[] = {true, false};
-	for (int i = 0; i < 10; i++) {
-      for (boolean use: useNativeMemoryDatasets) {
+    boolean useNativeMemoryDatasets[] = {true, false};
+    for (int i = 0; i < 10; i++) {
+      for (boolean use : useNativeMemoryDatasets) {
         tmpResultsTopKWithRandomValues(use);
       }
     }
@@ -63,8 +60,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
     int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
     boolean usePrefilter = random.nextBoolean();
-    if (datasetSize < topK)
-      datasetSize = topK;
+    if (datasetSize < topK) datasetSize = topK;
 
     BitSet[] prefilters = null;
     if (usePrefilter) {
@@ -112,26 +108,28 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
 
-      BruteForceQuery query = new BruteForceQuery.Builder()
-          .withTopK(topK)
-          .withQueryVectors(queries)
-          .withPrefilters(prefilters, vectors.length)
-          .build();
+      BruteForceQuery query =
+          new BruteForceQuery.Builder()
+              .withTopK(topK)
+              .withQueryVectors(queries)
+              .withPrefilters(prefilters, vectors.length)
+              .build();
 
-      BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
-          .withNumWriterThreads(32)
-          .build();
+      BruteForceIndexParams indexParams =
+          new BruteForceIndexParams.Builder().withNumWriterThreads(32).build();
 
       BruteForceIndex index;
       if (useNativeMemoryDataset) {
         Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
-        for (float[] v: vectors) dataset.addVector(v);
-        index = BruteForceIndex.newBuilder(resources)
+        for (float[] v : vectors) dataset.addVector(v);
+        index =
+            BruteForceIndex.newBuilder(resources)
                 .withDataset(dataset)
                 .withIndexParams(indexParams)
                 .build();
       } else {
-        index = BruteForceIndex.newBuilder(resources)
+        index =
+            BruteForceIndex.newBuilder(resources)
                 .withDataset(vectors)
                 .withIndexParams(indexParams)
                 .build();
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 2690ab4302..54e3815ef3 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
@@ -21,6 +20,10 @@
 import static org.junit.Assert.assertNotNull;
 import static org.junit.Assert.fail;
 
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import com.nvidia.cuvs.CagraMergeParams.MergeStrategy;
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
@@ -38,18 +41,12 @@
 import java.util.concurrent.TimeUnit;
 import java.util.concurrent.TimeoutException;
 import java.util.function.Supplier;
-
 import org.junit.Before;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import com.carrotsearch.randomizedtesting.RandomizedRunner;
-import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
-import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
-import com.nvidia.cuvs.CagraMergeParams.MergeStrategy;
-
 @RunWith(RandomizedRunner.class)
 public class CagraBuildAndSearchIT extends CuVSTestCase {
 
@@ -63,7 +60,7 @@ public void setup() {
   }
 
   private static void runConcurrently(int nThreads, Supplier<Runnable> runnableSupplier)
-    throws ExecutionException, InterruptedException, TimeoutException {
+      throws ExecutionException, InterruptedException, TimeoutException {
     try (ExecutorService parallelExecutor = Executors.newFixedThreadPool(nThreads)) {
       var futures = new CompletableFuture[nThreads];
       for (int j = 0; j < nThreads; j++) {
@@ -71,15 +68,17 @@ private static void runConcurrently(int nThreads, Supplier<Runnable> runnableSup
       }
 
       CompletableFuture.allOf(futures)
-        .exceptionally(t -> {
-          fail("Exception while executing runnable: " + t);
-          return null;
-        })
-        .get(2000, TimeUnit.SECONDS);
+          .exceptionally(
+              t -> {
+                fail("Exception while executing runnable: " + t);
+                return null;
+              })
+          .get(2000, TimeUnit.SECONDS);
     }
   }
 
-  private static void runInAnotherThread(Runnable runnable) throws ExecutionException, InterruptedException, TimeoutException {
+  private static void runInAnotherThread(Runnable runnable)
+      throws ExecutionException, InterruptedException, TimeoutException {
     try (ExecutorService singleExecutor = Executors.newSingleThreadExecutor()) {
       singleExecutor.submit(runnable).get(2000, TimeUnit.SECONDS);
     }
@@ -87,14 +86,14 @@ private static void runInAnotherThread(Runnable runnable) throws ExecutionExcept
 
   private static List<Map<Integer, Float>> getExpectedResults() {
     return Arrays.asList(
-      Map.of(3, 0.038782578f, 2, 0.3590463f, 0, 0.83774555f),
-      Map.of(0, 0.12472608f, 2, 0.21700792f, 1, 0.31918612f),
-      Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
-      Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.5986642f));
+        Map.of(3, 0.038782578f, 2, 0.3590463f, 0, 0.83774555f),
+        Map.of(0, 0.12472608f, 2, 0.21700792f, 1, 0.31918612f),
+        Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
+        Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.5986642f));
   }
 
   private static float[][] createSampleQueries() {
-    return new float[][]{
+    return new float[][] {
       {0.48216683f, 0.0428398f},
       {0.5084142f, 0.6545497f},
       {0.51260436f, 0.2643005f},
@@ -103,7 +102,7 @@ private static float[][] createSampleQueries() {
   }
 
   private static float[][] createSampleData() {
-    return new float[][]{
+    return new float[][] {
       {0.74021935f, 0.9209938f},
       {0.03902049f, 0.9689629f},
       {0.92514056f, 0.4463501f},
@@ -144,7 +143,8 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
     int numTestsRuns = 5;
     try (CuVSResources resources = CuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
-        runInAnotherThread(() -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
+        runInAnotherThread(
+            () -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
       }
     }
   }
@@ -162,7 +162,9 @@ public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
 
     int numTestsRuns = 10;
     try (CuVSResources resources = CuVSResources.create()) {
-      runConcurrently(numTestsRuns, () -> () -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
+      runConcurrently(
+          numTestsRuns,
+          () -> () -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
     }
   }
 
@@ -176,40 +178,39 @@ public void testPrefilteringReducesResults() throws Throwable {
 
     // Sample data and query
     float[][] dataset = createSampleData();
-    float[][] queries = {
-      {0.48216683f, 0.0428398f}
-    };
+    float[][] queries = {{0.48216683f, 0.0428398f}};
 
     // Expected search results
-    List<Map<Integer, Float>> expectedResults = List.of(
-      Map.of(3, 0.038782578f, 2, 0.3590463f));
+    List<Map<Integer, Float>> expectedResults = List.of(Map.of(3, 0.038782578f, 2, 0.3590463f));
 
     // Expected filtered search results
-    List<Map<Integer, Float>> expectedFilteredResults = List.of(
-      Map.of(2, 0.3590463f, 0, 0.83774555f));
-
-    CagraIndexParams indexParams = new CagraIndexParams.Builder()
-      .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-      .withGraphDegree(2)
-      .withIntermediateGraphDegree(4)
-      .withNumWriterThreads(2)
-      .withMetric(CuvsDistanceType.L2Expanded)
-      .build();
+    List<Map<Integer, Float>> expectedFilteredResults =
+        List.of(Map.of(2, 0.3590463f, 0, 0.83774555f));
+
+    CagraIndexParams indexParams =
+        new CagraIndexParams.Builder()
+            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+            .withGraphDegree(2)
+            .withIntermediateGraphDegree(4)
+            .withNumWriterThreads(2)
+            .withMetric(CuvsDistanceType.L2Expanded)
+            .build();
 
     try (CuVSResources resources = CuVSResources.create()) {
-      CagraIndex index = CagraIndex.newBuilder(resources)
-        .withDataset(dataset)
-        .withIndexParams(indexParams)
-        .build();
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(dataset)
+              .withIndexParams(indexParams)
+              .build();
 
       // No prefilter (all points allowed)
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-        .build();
-      CagraQuery fullQuery = new CagraQuery.Builder()
-        .withTopK(2)
-        .withSearchParams(searchParams)
-        .withQueryVectors(queries)
-        .build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+      CagraQuery fullQuery =
+          new CagraQuery.Builder()
+              .withTopK(2)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .build();
 
       SearchResults fullSearchResults = index.search(fullQuery);
       List<Map<Integer, Float>> fullResults = fullSearchResults.getResults();
@@ -220,12 +221,13 @@ public void testPrefilteringReducesResults() throws Throwable {
       prefilter.set(0);
       prefilter.set(2);
 
-      CagraQuery filteredQuery = new CagraQuery.Builder()
-        .withTopK(2)
-        .withSearchParams(searchParams)
-        .withQueryVectors(queries)
-        .withPrefilter(prefilter, 4)
-        .build();
+      CagraQuery filteredQuery =
+          new CagraQuery.Builder()
+              .withTopK(2)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .withPrefilter(prefilter, 4)
+              .build();
 
       SearchResults filteredSearchResults = index.search(filteredQuery);
       List<Map<Integer, Float>> filteredResults = filteredSearchResults.getResults();
@@ -236,25 +238,31 @@ public void testPrefilteringReducesResults() throws Throwable {
     }
   }
 
-  private void indexAndQueryOnce(float[][] dataset, List<Integer> map, float[][] queries,
-                                 List<Map<Integer, Float>> expectedResults, CuVSResources resources) {
+  private void indexAndQueryOnce(
+      float[][] dataset,
+      List<Integer> map,
+      float[][] queries,
+      List<Map<Integer, Float>> expectedResults,
+      CuVSResources resources) {
 
     try {
 
       // Configure index parameters
-      CagraIndexParams indexParams = new CagraIndexParams.Builder()
-        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-        .withGraphDegree(1)
-        .withIntermediateGraphDegree(2)
-        .withNumWriterThreads(32)
-        .withMetric(CuvsDistanceType.L2Expanded)
-        .build();
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(1)
+              .withIntermediateGraphDegree(2)
+              .withNumWriterThreads(32)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
 
       // Create the index with the dataset
-      CagraIndex index = CagraIndex.newBuilder(resources)
-        .withDataset(dataset)
-        .withIndexParams(indexParams)
-        .build();
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(dataset)
+              .withIndexParams(indexParams)
+              .build();
 
       // Saving the index on to the disk.
       String indexFileName = UUID.randomUUID() + ".cag";
@@ -263,21 +271,19 @@ private void indexAndQueryOnce(float[][] dataset, List<Integer> map, float[][] q
       // Loading a CAGRA index from disk.
       File indexFile = new File(indexFileName);
       InputStream inputStream = new FileInputStream(indexFile);
-      CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
-        .from(inputStream)
-        .build();
+      CagraIndex loadedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
 
       // Configure search parameters
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-        .build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
 
       // Create a query object with the query vectors
-      CagraQuery cuvsQuery = new CagraQuery.Builder()
-        .withTopK(3)
-        .withSearchParams(searchParams)
-        .withQueryVectors(queries)
-        .withMapping(map)
-        .build();
+      CagraQuery cuvsQuery =
+          new CagraQuery.Builder()
+              .withTopK(3)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .withMapping(map)
+              .build();
 
       // Perform the search
       SearchResults results = index.search(cuvsQuery);
@@ -316,53 +322,56 @@ public void testMergingIndexes() throws Throwable {
     };
 
     float[][] queries = {
-      {1.0f, 1.0f},    // Should be closest to vector1[1] -> index 1
-      {10.5f, 10.5f},  // Should be closest to vector2[0] -> index 2
-      {0.0f, 0.0f}     // Should be closest to vector1[0] -> index 0
+      {1.0f, 1.0f}, // Should be closest to vector1[1] -> index 1
+      {10.5f, 10.5f}, // Should be closest to vector2[0] -> index 2
+      {0.0f, 0.0f} // Should be closest to vector1[0] -> index 0
     };
 
     // Expected search results for each query (nearest neighbor and its distance)
-    List<Map<Integer, Float>> expectedResults = Arrays.asList(
-      Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
-      Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
-      Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
-    );
+    List<Map<Integer, Float>> expectedResults =
+        Arrays.asList(
+            Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
+            Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
+            Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f));
 
     try (CuVSResources resources = CuVSResources.create()) {
-      CagraIndexParams indexParams = new CagraIndexParams.Builder()
-        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-        .withGraphDegree(1)
-        .withIntermediateGraphDegree(2)
-        .withNumWriterThreads(4)
-        .withMetric(CuvsDistanceType.L2Expanded)
-        .build();
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(1)
+              .withIntermediateGraphDegree(2)
+              .withNumWriterThreads(4)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
 
       log.info("Building first index...");
-      CagraIndex index1 = CagraIndex.newBuilder(resources)
-        .withDataset(vector1)
-        .withIndexParams(indexParams)
-        .build();
+      CagraIndex index1 =
+          CagraIndex.newBuilder(resources)
+              .withDataset(vector1)
+              .withIndexParams(indexParams)
+              .build();
 
       log.info("Building second index...");
-      CagraIndex index2 = CagraIndex.newBuilder(resources)
-        .withDataset(vector2)
-        .withIndexParams(indexParams)
-        .build();
+      CagraIndex index2 =
+          CagraIndex.newBuilder(resources)
+              .withDataset(vector2)
+              .withIndexParams(indexParams)
+              .build();
 
       log.info("Merging indexes...");
-      CagraIndex mergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2});
+      CagraIndex mergedIndex = CagraIndex.merge(new CagraIndex[] {index1, index2});
       log.info("Merge completed successfully");
 
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-        .build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
 
       List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
-      CagraQuery query = new CagraQuery.Builder()
-        .withTopK(3)
-        .withSearchParams(searchParams)
-        .withQueryVectors(queries)
-        .withMapping(mergedMap)
-        .build();
+      CagraQuery query =
+          new CagraQuery.Builder()
+              .withTopK(3)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .withMapping(mergedMap)
+              .build();
 
       log.info("Searching merged index...");
       SearchResults results = mergedIndex.search(query);
@@ -376,9 +385,7 @@ public void testMergingIndexes() throws Throwable {
 
       File indexFile = new File(indexFileName);
       InputStream inputStream = new FileInputStream(indexFile);
-      CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources)
-        .from(inputStream)
-        .build();
+      CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
 
       SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
       assertEquals(expectedResults, resultsFromLoaded.getResults());
@@ -390,7 +397,6 @@ public void testMergingIndexes() throws Throwable {
       index2.destroyIndex();
       mergedIndex.destroyIndex();
       loadedMergedIndex.destroyIndex();
-
     }
   }
 
@@ -413,65 +419,74 @@ public void testMergeStrategies() throws Throwable {
       {0.0f, 0.0f}
     };
 
-    List<Map<Integer, Float>> expectedResults = Arrays.asList(
-      Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
-      Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
-      Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f)
-    );
+    List<Map<Integer, Float>> expectedResults =
+        Arrays.asList(
+            Map.of(1, 0.0f, 0, 2.0f, 2, 162.0f),
+            Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
+            Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f));
 
     try (CuVSResources resources = CuVSResources.create()) {
-      CagraIndexParams indexParams = new CagraIndexParams.Builder()
-        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-        .withGraphDegree(1)
-        .withIntermediateGraphDegree(2)
-        .withNumWriterThreads(4)
-        .withMetric(CuvsDistanceType.L2Expanded)
-        .build();
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(1)
+              .withIntermediateGraphDegree(2)
+              .withNumWriterThreads(4)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
 
       log.info("Building first index...");
-      CagraIndex index1 = CagraIndex.newBuilder(resources)
-        .withDataset(vector1)
-        .withIndexParams(indexParams)
-        .build();
+      CagraIndex index1 =
+          CagraIndex.newBuilder(resources)
+              .withDataset(vector1)
+              .withIndexParams(indexParams)
+              .build();
 
       log.info("Building second index...");
-      CagraIndex index2 = CagraIndex.newBuilder(resources)
-        .withDataset(vector2)
-        .withIndexParams(indexParams)
-        .build();
-
-      CagraIndexParams outputIndexParams = new CagraIndexParams.Builder()
-        .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-        .withGraphDegree(2)
-        .withIntermediateGraphDegree(4)
-        .withNumWriterThreads(4)
-        .withMetric(CuvsDistanceType.L2Expanded)
-        .build();
-
-      CagraMergeParams physicalMergeParams = new CagraMergeParams.Builder()
-        .withOutputIndexParams(outputIndexParams)
-        .withStrategy(MergeStrategy.PHYSICAL)
-        .build();
+      CagraIndex index2 =
+          CagraIndex.newBuilder(resources)
+              .withDataset(vector2)
+              .withIndexParams(indexParams)
+              .build();
+
+      CagraIndexParams outputIndexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(2)
+              .withIntermediateGraphDegree(4)
+              .withNumWriterThreads(4)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+      CagraMergeParams physicalMergeParams =
+          new CagraMergeParams.Builder()
+              .withOutputIndexParams(outputIndexParams)
+              .withStrategy(MergeStrategy.PHYSICAL)
+              .build();
 
       log.info("Merging indexes with PHYSICAL strategy...");
-      CagraIndex physicalMergedIndex = CagraIndex.merge(new CagraIndex[]{index1, index2}, physicalMergeParams);
+      CagraIndex physicalMergedIndex =
+          CagraIndex.merge(new CagraIndex[] {index1, index2}, physicalMergeParams);
       log.info("Physical merge completed successfully");
 
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-        .build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
 
       List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
-      CagraQuery query = new CagraQuery.Builder()
-        .withTopK(3)
-        .withSearchParams(searchParams)
-        .withQueryVectors(queries)
-        .withMapping(mergedMap)
-        .build();
+      CagraQuery query =
+          new CagraQuery.Builder()
+              .withTopK(3)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .withMapping(mergedMap)
+              .build();
 
       log.info("Searching physically merged index...");
       SearchResults physicalResults = physicalMergedIndex.search(query);
       assertNotNull("Physical merge search results should not be null", physicalResults);
-      assertEquals("Physical merge search results should match expected", expectedResults, physicalResults.getResults());
+      assertEquals(
+          "Physical merge search results should match expected",
+          expectedResults,
+          physicalResults.getResults());
 
       // --- Serialization/deserialization check for both merged indexes ---
       String physicalIndexFileName = UUID.randomUUID().toString() + ".cag";
@@ -479,13 +494,14 @@ public void testMergeStrategies() throws Throwable {
 
       File physicalIndexFile = new File(physicalIndexFileName);
       InputStream physicalInputStream = new FileInputStream(physicalIndexFile);
-      CagraIndex loadedPhysicalIndex = CagraIndex.newBuilder(resources)
-        .from(physicalInputStream)
-        .build();
+      CagraIndex loadedPhysicalIndex =
+          CagraIndex.newBuilder(resources).from(physicalInputStream).build();
 
       SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
-      assertEquals("Loaded physical index search results should match expected",
-        expectedResults, resultsFromLoadedPhysical.getResults());
+      assertEquals(
+          "Loaded physical index search results should match expected",
+          expectedResults,
+          resultsFromLoadedPhysical.getResults());
 
       if (physicalIndexFile.exists()) {
         physicalIndexFile.delete();
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 693521760b..e29163ba51 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -13,23 +13,21 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import java.lang.invoke.MethodHandles;
-import java.util.List;
 import java.util.BitSet;
+import java.util.List;
 import org.junit.Before;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import com.carrotsearch.randomizedtesting.RandomizedRunner;
-import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
-
-import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-
 @RunWith(RandomizedRunner.class)
 public class CagraRandomizedIT extends CuVSTestCase {
 
@@ -45,10 +43,10 @@ public void setup() {
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
     boolean useNativeMemoryDatasets[] = {true, false};
-	  for (int i = 0; i < 100; i++) {
-	    for (boolean use: useNativeMemoryDatasets) {
-		     tmpResultsTopKWithRandomValues(use);
-	    }
+    for (int i = 0; i < 100; i++) {
+      for (boolean use : useNativeMemoryDatasets) {
+        tmpResultsTopKWithRandomValues(use);
+      }
     }
   }
 
@@ -58,14 +56,15 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     int NUM_QUERIES_LIMIT = 10;
     int TOP_K_LIMIT = 64; // nocommit This fails beyond 64
 
-    int datasetSize = random.nextInt(DATASET_SIZE_LIMIT) + 2; // datasetSize of 1 fails/crashed due to a bug, hence adding 2 here.
+    int datasetSize =
+        random.nextInt(DATASET_SIZE_LIMIT)
+            + 2; // datasetSize of 1 fails/crashed due to a bug, hence adding 2 here.
     int dimensions = random.nextInt(DIMENSIONS_LIMIT) + 1;
     int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
     int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
     boolean usePrefilter = random.nextBoolean();
 
-    if (datasetSize < topK)
-      datasetSize = topK;
+    if (datasetSize < topK) datasetSize = topK;
 
     BitSet sharedPrefilter = null;
     if (usePrefilter) {
@@ -116,20 +115,23 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
     // Create CuVS index and query
     try (CuVSResources resources = CuVSResources.create()) {
-      CagraIndexParams indexParams = new CagraIndexParams.Builder()
-          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-          .build();
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .build();
 
       CagraIndex index;
       if (useNativeMemoryDataset) {
-          Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
-          for (float[] v: vectors) dataset.addVector(v);
-          index = CagraIndex.newBuilder(resources)
-              .withDataset(dataset)
-              .withIndexParams(indexParams)
-              .build();
+        Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
+        for (float[] v : vectors) dataset.addVector(v);
+        index =
+            CagraIndex.newBuilder(resources)
+                .withDataset(dataset)
+                .withIndexParams(indexParams)
+                .build();
       } else {
-        index = CagraIndex.newBuilder(resources)
+        index =
+            CagraIndex.newBuilder(resources)
                 .withDataset(vectors)
                 .withIndexParams(indexParams)
                 .build();
@@ -138,11 +140,11 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
       try {
         // Execute search and retrieve results
-        CagraQuery.Builder queryBuilder = new CagraQuery.Builder()
+        CagraQuery.Builder queryBuilder =
+            new CagraQuery.Builder()
                 .withQueryVectors(queries)
                 .withTopK(topK)
-                .withSearchParams(new CagraSearchParams.Builder(resources)
-                        .build());
+                .withSearchParams(new CagraSearchParams.Builder(resources).build());
 
         if (sharedPrefilter != null) {
           queryBuilder.withPrefilter(sharedPrefilter, datasetSize);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
index 5f6c4c735b..17fe7d5206 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
@@ -13,12 +13,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
+import com.carrotsearch.randomizedtesting.RandomizedContext;
 import java.lang.invoke.MethodHandles;
 import java.util.ArrayList;
 import java.util.BitSet;
@@ -26,12 +26,9 @@
 import java.util.Map;
 import java.util.Random;
 import java.util.TreeMap;
-
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import com.carrotsearch.randomizedtesting.RandomizedContext;
-
 public abstract class CuVSTestCase {
   protected Random random;
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
@@ -51,7 +48,8 @@ protected float[][] generateData(Random random, int rows, int cols) {
     return data;
   }
 
-  protected List<List<Integer>> generateExpectedResults(int topK, float[][] dataset, float[][] queries, BitSet[] prefilters, Logger log) {
+  protected List<List<Integer>> generateExpectedResults(
+      int topK, float[][] dataset, float[][] queries, BitSet[] prefilters, Logger log) {
     List<List<Integer>> neighborsResult = new ArrayList<>();
     int dimensions = dataset[0].length;
 
@@ -71,8 +69,11 @@ protected List<List<Integer>> generateExpectedResults(int topK, float[][] datase
       }
 
       // Sort by distance and select the topK nearest neighbors
-      List<Integer> neighbors = distances.entrySet().stream().sorted(Map.Entry.comparingByValue())
-          .map(Map.Entry::getKey).toList();
+      List<Integer> neighbors =
+          distances.entrySet().stream()
+              .sorted(Map.Entry.comparingByValue())
+              .map(Map.Entry::getKey)
+              .toList();
       neighborsResult.add(neighbors.subList(0, Math.min(topK * 2 + 10, dataset.length)));
     }
 
@@ -80,12 +81,20 @@ protected List<List<Integer>> generateExpectedResults(int topK, float[][] datase
     return neighborsResult;
   }
 
-  protected void compareResults(SearchResults results, List<List<Integer>> expected, int topK, int datasetSize,
+  protected void compareResults(
+      SearchResults results,
+      List<List<Integer>> expected,
+      int topK,
+      int datasetSize,
       int numQueries) {
 
     for (int i = 0; i < numQueries; i++) {
       log.info("Results returned for query " + i + ": " + results.getResults().get(i).keySet());
-      log.info("Expected results for query " + i + ": " + expected.get(i).subList(0, Math.min(topK, datasetSize)));
+      log.info(
+          "Expected results for query "
+              + i
+              + ": "
+              + expected.get(i).subList(0, Math.min(topK, datasetSize)));
     }
 
     // actual vs. expected results
@@ -93,42 +102,47 @@ protected void compareResults(SearchResults results, List<List<Integer>> expecte
       Map<Integer, Float> result = results.getResults().get(i);
 
       // Sort result by values (distances) and extract keys
-      List<Integer> sortedResultKeys = result.entrySet().stream().sorted(Map.Entry.comparingByValue())
-          .map(Map.Entry::getKey) // Extract sorted keys
-          .toList();
+      List<Integer> sortedResultKeys =
+          result.entrySet().stream()
+              .sorted(Map.Entry.comparingByValue())
+              .map(Map.Entry::getKey) // Extract sorted keys
+              .toList();
 
       // just make sure that the first 5 results are in the expected list (which
       // comprises of 2*topK results)
       for (int j = 0; j < Math.min(5, sortedResultKeys.size()); j++) {
-        assertTrue("Not found in expected list: " + sortedResultKeys.get(j),
+        assertTrue(
+            "Not found in expected list: " + sortedResultKeys.get(j),
             expected.get(i).contains(sortedResultKeys.get(j)));
       }
     }
   }
 
-  protected static void checkResults(List<Map<Integer, Float>> expected, List<Map<Integer, Float>> actual) {
-    List<Map<Integer, Float>> sortedExpected = new ArrayList<Map<Integer,Float>>();
-    List<Map<Integer, Float>> sortedActual   = new ArrayList<Map<Integer,Float>>();
-    for (Map<Integer, Float> map: expected) {
-      sortedExpected.add(new TreeMap<Integer, Float>(map) {
-        @Override
-        public boolean equals(Object o) {
-          Map<Integer, Float> map = (Map<Integer, Float>) o;
-          if (this.size() != map.size()) return false;
-          for (Integer key: map.keySet()) {
-            try {
-              if (Math.abs((float)map.get(key) - ((float)get(key))) < 0.0001f == false) {
-                return false;
+  protected static void checkResults(
+      List<Map<Integer, Float>> expected, List<Map<Integer, Float>> actual) {
+    List<Map<Integer, Float>> sortedExpected = new ArrayList<Map<Integer, Float>>();
+    List<Map<Integer, Float>> sortedActual = new ArrayList<Map<Integer, Float>>();
+    for (Map<Integer, Float> map : expected) {
+      sortedExpected.add(
+          new TreeMap<Integer, Float>(map) {
+            @Override
+            public boolean equals(Object o) {
+              Map<Integer, Float> map = (Map<Integer, Float>) o;
+              if (this.size() != map.size()) return false;
+              for (Integer key : map.keySet()) {
+                try {
+                  if (Math.abs((float) map.get(key) - ((float) get(key))) < 0.0001f == false) {
+                    return false;
+                  }
+                } catch (Exception ex) {
+                  return false;
+                }
               }
-            } catch (Exception ex) {
-              return false;
+              return true;
             }
-          }
-          return true;
-        }
-      });
+          });
     }
-    for (Map<Integer, Float> map: actual) {
+    for (Map<Integer, Float> map : actual) {
       sortedActual.add(new TreeMap<Integer, Float>(map));
     }
     assertEquals(sortedExpected, sortedActual);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index e2fad43c84..6c51ac85e0 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -13,12 +13,13 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 import static org.junit.Assert.assertEquals;
 
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
@@ -28,15 +29,11 @@
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
-
 import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
-import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
-
 public class HnswBuildAndSearchIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
@@ -56,70 +53,72 @@ public void testIndexingAndSearchingFlow() throws Throwable {
 
     // Sample data and query
     float[][] dataset = {
-        { 0.74021935f, 0.9209938f },
-        { 0.03902049f, 0.9689629f },
-        { 0.92514056f, 0.4463501f },
-        { 0.6673192f, 0.10993068f }
-      };
+      {0.74021935f, 0.9209938f},
+      {0.03902049f, 0.9689629f},
+      {0.92514056f, 0.4463501f},
+      {0.6673192f, 0.10993068f}
+    };
     List<Integer> map = List.of(0, 1, 2, 3);
     float[][] queries = {
-        { 0.48216683f, 0.0428398f },
-        { 0.5084142f, 0.6545497f },
-        { 0.51260436f, 0.2643005f },
-        { 0.05198065f, 0.5789965f }
-      };
+      {0.48216683f, 0.0428398f},
+      {0.5084142f, 0.6545497f},
+      {0.51260436f, 0.2643005f},
+      {0.05198065f, 0.5789965f}
+    };
 
     // Expected search results
-    List<Map<Integer, Float>> expectedResults = Arrays.asList(
-        Map.of(3, 0.038782578f, 2, 0.35904628f, 0, 0.8377455f),
-        Map.of(0, 0.12472608f, 2, 0.21700794f, 1, 0.31918612f),
-        Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
-        Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.59866416f)
-      );
+    List<Map<Integer, Float>> expectedResults =
+        Arrays.asList(
+            Map.of(3, 0.038782578f, 2, 0.35904628f, 0, 0.8377455f),
+            Map.of(0, 0.12472608f, 2, 0.21700794f, 1, 0.31918612f),
+            Map.of(3, 0.047766715f, 2, 0.20332818f, 0, 0.48305473f),
+            Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.59866416f));
 
     for (int j = 0; j < 10; j++) {
 
       try (CuVSResources resources = CuVSResources.create()) {
 
         // Configure index parameters
-        CagraIndexParams indexParams = new CagraIndexParams.Builder()
-            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-            .withGraphDegree(64)
-            .withIntermediateGraphDegree(128)
-            .withNumWriterThreads(32)
-            .withMetric(CuvsDistanceType.L2Expanded)
-            .build();
+        CagraIndexParams indexParams =
+            new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(64)
+                .withIntermediateGraphDegree(128)
+                .withNumWriterThreads(32)
+                .withMetric(CuvsDistanceType.L2Expanded)
+                .build();
 
         // Create the index with the dataset
-        CagraIndex index = CagraIndex.newBuilder(resources)
-            .withDataset(dataset)
-            .withIndexParams(indexParams)
-            .build();
+        CagraIndex index =
+            CagraIndex.newBuilder(resources)
+                .withDataset(dataset)
+                .withIndexParams(indexParams)
+                .build();
 
         // Saving the HNSW index on to the disk.
         String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
         index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
 
-        HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder()
-            .withVectorDimension(2)
-            .build();
+        HnswIndexParams hnswIndexParams =
+            new HnswIndexParams.Builder().withVectorDimension(2).build();
         InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
         File hnswIndexFile = new File(hnswIndexFileName);
 
-        HnswIndex hnswIndex = HnswIndex.newBuilder(resources)
-            .from(inputStreamHNSW)
-            .withIndexParams(hnswIndexParams)
-            .build();
-
-        HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder()
-            .build();
-
-        HnswQuery hnswQuery = new HnswQuery.Builder()
-            .withMapping(map)
-            .withQueryVectors(queries)
-            .withSearchParams(hnswSearchParams)
-            .withTopK(3)
-            .build();
+        HnswIndex hnswIndex =
+            HnswIndex.newBuilder(resources)
+                .from(inputStreamHNSW)
+                .withIndexParams(hnswIndexParams)
+                .build();
+
+        HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder().build();
+
+        HnswQuery hnswQuery =
+            new HnswQuery.Builder()
+                .withMapping(map)
+                .withQueryVectors(queries)
+                .withSearchParams(hnswSearchParams)
+                .withTopK(3)
+                .build();
 
         SearchResults results = hnswIndex.search(hnswQuery);
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index b6062aecbf..ffdf29fe10 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -13,9 +13,13 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package com.nvidia.cuvs;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
@@ -23,19 +27,12 @@
 import java.lang.invoke.MethodHandles;
 import java.util.List;
 import java.util.UUID;
-
 import org.junit.Before;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import com.carrotsearch.randomizedtesting.RandomizedRunner;
-import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
-import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
-
-import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-
 @RunWith(RandomizedRunner.class)
 public class HnswRandomizedIT extends CuVSTestCase {
 
@@ -50,9 +47,9 @@ public void setup() {
 
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
-	boolean useNativeMemoryDatasets[] = {true, false};
+    boolean useNativeMemoryDatasets[] = {true, false};
     for (int i = 0; i < 10; i++) {
-      for (boolean use: useNativeMemoryDatasets) {
+      for (boolean use : useNativeMemoryDatasets) {
         tmpResultsTopKWithRandomValues(use);
       }
     }
@@ -69,8 +66,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     int numQueries = random.nextInt(NUM_QUERIES_LIMIT) + 1;
     int topK = Math.min(random.nextInt(TOP_K_LIMIT) + 1, datasetSize);
 
-    if (datasetSize < topK)
-      datasetSize = topK;
+    if (datasetSize < topK) datasetSize = topK;
 
     // Generate a random dataset
     float[][] vectors = generateData(random, datasetSize, dimensions);
@@ -107,54 +103,56 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     try (CuVSResources resources = CuVSResources.create()) {
 
       // Configure index parameters
-      CagraIndexParams indexParams = new CagraIndexParams.Builder()
-          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-          .withGraphDegree(64)
-          .withIntermediateGraphDegree(128)
-          .withNumWriterThreads(32)
-          .withMetric(CuvsDistanceType.L2Expanded)
-          .build();
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(64)
+              .withIntermediateGraphDegree(128)
+              .withNumWriterThreads(32)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
 
       // Create the index with the dataset
       CagraIndex index;
       if (useNativeMemoryDataset) {
         Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
-        for (float[] v: vectors) dataset.addVector(v);
-        index = CagraIndex.newBuilder(resources)
-            .withDataset(dataset)
-            .withIndexParams(indexParams)
-            .build();
+        for (float[] v : vectors) dataset.addVector(v);
+        index =
+            CagraIndex.newBuilder(resources)
+                .withDataset(dataset)
+                .withIndexParams(indexParams)
+                .build();
       } else {
-        index = CagraIndex.newBuilder(resources)
-             .withDataset(vectors)
-             .withIndexParams(indexParams)
-             .build();
+        index =
+            CagraIndex.newBuilder(resources)
+                .withDataset(vectors)
+                .withIndexParams(indexParams)
+                .build();
       }
 
       // Saving the HNSW index on to the disk.
       String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
-      index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));   // fails here
+      index.serializeToHNSW(new FileOutputStream(hnswIndexFileName)); // fails here
 
-      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder()
-          .withVectorDimension(dimensions)
-          .build();
+      HnswIndexParams hnswIndexParams =
+          new HnswIndexParams.Builder().withVectorDimension(dimensions).build();
       InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
       File hnswIndexFile = new File(hnswIndexFileName);
 
-      HnswIndex hnswIndex = HnswIndex.newBuilder(resources)
-          .from(inputStreamHNSW)
-          .withIndexParams(hnswIndexParams)
-          .build();
+      HnswIndex hnswIndex =
+          HnswIndex.newBuilder(resources)
+              .from(inputStreamHNSW)
+              .withIndexParams(hnswIndexParams)
+              .build();
 
-      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder()
-          .withNumThreads(32)
-          .build();
+      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder().withNumThreads(32).build();
 
-      HnswQuery hnswQuery = new HnswQuery.Builder()
-          .withQueryVectors(queries)
-          .withSearchParams(hnswSearchParams)
-          .withTopK(topK)
-          .build();
+      HnswQuery hnswQuery =
+          new HnswQuery.Builder()
+              .withQueryVectors(queries)
+              .withSearchParams(hnswSearchParams)
+              .withTopK(topK)
+              .build();
 
       log.info("Index built successfully. Executing search...");
       SearchResults results = hnswIndex.search(hnswQuery);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java
index a86d1154c9..de33200779 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/internal/common/UtilIT.java
@@ -1,18 +1,32 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 package com.nvidia.cuvs.internal.common;
 
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static org.hamcrest.CoreMatchers.equalTo;
+import static org.hamcrest.MatcherAssert.assertThat;
+
 import com.nvidia.cuvs.CuVSTestCase;
+import java.lang.invoke.MethodHandles;
+import java.lang.invoke.MethodType;
 import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import java.lang.invoke.MethodHandles;
-import java.lang.invoke.MethodType;
-
-import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-import static org.hamcrest.CoreMatchers.equalTo;
-import static org.hamcrest.MatcherAssert.assertThat;
-
 public class UtilIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(UtilIT.class);
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
index af03a375b9..4c18fd5a28 100644
--- a/java/examples/pom.xml
+++ b/java/examples/pom.xml
@@ -20,9 +20,47 @@
       <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
       <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     </dependency>
+
+    <dependency>
+        <groupId>com.diffplug.spotless</groupId>
+        <artifactId>spotless-maven-plugin</artifactId>
+        <version>2.44.5</version>
+    </dependency>
+
   </dependencies>
 
   <build>
+    <plugins>
+        <plugin>
+            <groupId>com.diffplug.spotless</groupId>
+            <artifactId>spotless-maven-plugin</artifactId>
+            <version>2.44.5</version>
+            <executions>
+                <execution>
+                    <goals>
+                        <goal>apply</goal>
+                    </goals>
+                    <phase>validate</phase>
+                </execution>
+            </executions>
+            <configuration>
+                <java>
+                    <includes>
+                        <include>src/**/*.java</include>
+                    </includes>
+                    <googleJavaFormat>
+                        <version>1.27.0</version>
+                        <style>GOOGLE</style>
+                        <reflowLongStrings>true</reflowLongStrings>
+                        <formatJavadoc>false</formatJavadoc>
+                    </googleJavaFormat>
+                    <licenseHeader>
+                        <file>${project.basedir}/../license-header.txt</file>
+                    </licenseHeader>
+                </java>
+            </configuration>
+        </plugin>
+    </plugins>
     <pluginManagement><!-- lock down plugins versions to avoid using Maven defaults (may be moved to parent pom) -->
       <plugins>
         <!-- clean lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#clean_Lifecycle -->
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
index 4324317fb1..deac4cfd10 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/BruteForceExample.java
@@ -1,5 +1,25 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 package com.nvidia.cuvs.examples;
 
+import com.nvidia.cuvs.BruteForceIndex;
+import com.nvidia.cuvs.BruteForceIndexParams;
+import com.nvidia.cuvs.BruteForceQuery;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.SearchResults;
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
@@ -7,12 +27,6 @@
 import java.util.UUID;
 import java.util.logging.Logger;
 
-import com.nvidia.cuvs.BruteForceIndex;
-import com.nvidia.cuvs.BruteForceIndexParams;
-import com.nvidia.cuvs.BruteForceQuery;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.SearchResults;
-
 public class BruteForceExample {
 
   private static final Logger log = Logger.getLogger(BruteForceExample.class.getName());
@@ -21,36 +35,34 @@ public static void main(String[] args) throws Throwable {
 
     // Sample data and query
     float[][] vectors = {
-        { 0.74021935f, 0.9209938f },
-        { 0.03902049f, 0.9689629f },
-        { 0.92514056f, 0.4463501f },
-        { 0.6673192f, 0.10993068f }
-       };
+      {0.74021935f, 0.9209938f},
+      {0.03902049f, 0.9689629f},
+      {0.92514056f, 0.4463501f},
+      {0.6673192f, 0.10993068f}
+    };
 
     float[][] queries = {
-        { 0.48216683f, 0.0428398f },
-        { 0.5084142f, 0.6545497f },
-        { 0.51260436f, 0.2643005f },
-        { 0.05198065f, 0.5789965f }
-       };
+      {0.48216683f, 0.0428398f},
+      {0.5084142f, 0.6545497f},
+      {0.51260436f, 0.2643005f},
+      {0.05198065f, 0.5789965f}
+    };
 
     try (CuVSResources resources = CuVSResources.create()) {
 
       // Create a query object with the query vectors
-      BruteForceQuery cuvsQuery = new BruteForceQuery.Builder()
-          .withTopK(3)
-          .withQueryVectors(queries)
-          .build();
+      BruteForceQuery cuvsQuery =
+          new BruteForceQuery.Builder().withTopK(3).withQueryVectors(queries).build();
 
       // Set index parameters
-      BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder()
-          .build();
+      BruteForceIndexParams indexParams = new BruteForceIndexParams.Builder().build();
 
       // Create the index with the dataset
-      BruteForceIndex index = BruteForceIndex.newBuilder(resources)
-          .withDataset(vectors)
-          .withIndexParams(indexParams)
-          .build();
+      BruteForceIndex index =
+          BruteForceIndex.newBuilder(resources)
+              .withDataset(vectors)
+              .withIndexParams(indexParams)
+              .build();
 
       // Saving the index on to the disk.
       String indexFileName = UUID.randomUUID().toString() + ".bf";
@@ -59,9 +71,7 @@ public static void main(String[] args) throws Throwable {
       // Loading a BRUTEFORCE index from disk.
       File indexFile = new File(indexFileName);
       InputStream inputStream = new FileInputStream(indexFile);
-      BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources)
-          .from(inputStream)
-          .build();
+      BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources).from(inputStream).build();
 
       // Perform the search
       SearchResults resultsFromLoadedIndex = loadedIndex.search(cuvsQuery);
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
index 78c23f2bda..fc04cf544f 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/CagraExample.java
@@ -1,12 +1,20 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 package com.nvidia.cuvs.examples;
 
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.InputStream;
-import java.util.UUID;
-import java.util.logging.Logger;
-
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
@@ -15,6 +23,12 @@
 import com.nvidia.cuvs.CagraSearchParams;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.SearchResults;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.util.UUID;
+import java.util.logging.Logger;
 
 public class CagraExample {
 
@@ -24,32 +38,34 @@ public static void main(String[] args) throws Throwable {
 
     // Sample data and query
     float[][] vectors = {
-        { 0.74021935f, 0.9209938f },
-        { 0.03902049f, 0.9689629f },
-        { 0.92514056f, 0.4463501f },
-        { 0.6673192f, 0.10993068f }
-       };
+      {0.74021935f, 0.9209938f},
+      {0.03902049f, 0.9689629f},
+      {0.92514056f, 0.4463501f},
+      {0.6673192f, 0.10993068f}
+    };
 
     float[][] queries = {
-        { 0.48216683f, 0.0428398f },
-        { 0.5084142f, 0.6545497f },
-        { 0.51260436f, 0.2643005f },
-        { 0.05198065f, 0.5789965f }
-       };
+      {0.48216683f, 0.0428398f},
+      {0.5084142f, 0.6545497f},
+      {0.51260436f, 0.2643005f},
+      {0.05198065f, 0.5789965f}
+    };
 
     try (CuVSResources resources = CuVSResources.create()) {
 
       // Configure index parameters
-      CagraIndexParams indexParams = new CagraIndexParams.Builder()
-          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-          .withMetric(CuvsDistanceType.L2Expanded)
-          .build();
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
 
       // Create the index with the dataset
-      CagraIndex index = CagraIndex.newBuilder(resources)
-          .withDataset(vectors)
-          .withIndexParams(indexParams)
-          .build();
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(vectors)
+              .withIndexParams(indexParams)
+              .build();
 
       // Saving the index on to the disk.
       String indexFileName = UUID.randomUUID().toString() + ".cag";
@@ -58,20 +74,18 @@ public static void main(String[] args) throws Throwable {
       // Loading a CAGRA index from disk.
       File indexFile = new File(indexFileName);
       InputStream inputStream = new FileInputStream(indexFile);
-      CagraIndex loadedIndex = CagraIndex.newBuilder(resources)
-          .from(inputStream)
-          .build();
+      CagraIndex loadedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
 
       // Configure search parameters
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources)
-          .build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
 
       // Create a query object with the query vectors
-      CagraQuery cuvsQuery = new CagraQuery.Builder()
-          .withTopK(3)
-          .withSearchParams(searchParams)
-          .withQueryVectors(queries)
-          .build();
+      CagraQuery cuvsQuery =
+          new CagraQuery.Builder()
+              .withTopK(3)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .build();
 
       // Perform the search
       SearchResults results = index.search(cuvsQuery);
diff --git a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
index 7b43f5fd01..657354adfa 100644
--- a/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
+++ b/java/examples/src/main/java/com/nvidia/cuvs/examples/HnswExample.java
@@ -1,12 +1,20 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 package com.nvidia.cuvs.examples;
 
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.InputStream;
-import java.util.UUID;
-import java.util.logging.Logger;
-
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
@@ -20,6 +28,12 @@
 import com.nvidia.cuvs.HnswQuery;
 import com.nvidia.cuvs.HnswSearchParams;
 import com.nvidia.cuvs.SearchResults;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.InputStream;
+import java.util.UUID;
+import java.util.logging.Logger;
 
 public class HnswExample {
 
@@ -29,71 +43,72 @@ public static void main(String[] args) throws Throwable {
 
     // Sample data and query
     float[][] vectors = {
-        { 0.74021935f, 0.9209938f },
-        { 0.03902049f, 0.9689629f },
-        { 0.92514056f, 0.4463501f },
-        { 0.6673192f, 0.10993068f }
-       };
+      {0.74021935f, 0.9209938f},
+      {0.03902049f, 0.9689629f},
+      {0.92514056f, 0.4463501f},
+      {0.6673192f, 0.10993068f}
+    };
 
     float[][] queries = {
-        { 0.48216683f, 0.0428398f },
-        { 0.5084142f, 0.6545497f },
-        { 0.51260436f, 0.2643005f },
-        { 0.05198065f, 0.5789965f }
-       };
+      {0.48216683f, 0.0428398f},
+      {0.5084142f, 0.6545497f},
+      {0.51260436f, 0.2643005f},
+      {0.05198065f, 0.5789965f}
+    };
 
     try (CuVSResources resources = CuVSResources.create()) {
 
       // Configure IVF_PQ index parameters (optional - default values set when not defined)
-      CuVSIvfPqIndexParams cuVSIvfPqIndexParams = new CuVSIvfPqIndexParams.Builder()
-          .build();
+      CuVSIvfPqIndexParams cuVSIvfPqIndexParams = new CuVSIvfPqIndexParams.Builder().build();
 
       // Configure IVF_PQ search parameters (optional - default values set when not defined)
-      CuVSIvfPqSearchParams cuVSIvfPqSearchParams = new CuVSIvfPqSearchParams.Builder()
-          .build();
+      CuVSIvfPqSearchParams cuVSIvfPqSearchParams = new CuVSIvfPqSearchParams.Builder().build();
 
       // Configure IVF_PQ search parameters (used when build algo is IVF_PQ, optional otherwise)
-      CuVSIvfPqParams cuVSIvfPqParams = new CuVSIvfPqParams.Builder()
-          .withCuVSIvfPqIndexParams(cuVSIvfPqIndexParams)
-          .withCuVSIvfPqSearchParams(cuVSIvfPqSearchParams)
-          .build();
+      CuVSIvfPqParams cuVSIvfPqParams =
+          new CuVSIvfPqParams.Builder()
+              .withCuVSIvfPqIndexParams(cuVSIvfPqIndexParams)
+              .withCuVSIvfPqSearchParams(cuVSIvfPqSearchParams)
+              .build();
 
       // Configure index parameters
-      CagraIndexParams indexParams = new CagraIndexParams.Builder()
-          .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
-          .withMetric(CuvsDistanceType.L2Expanded)
-          .withCuVSIvfPqParams(cuVSIvfPqParams)
-          .build();
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.IVF_PQ)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .withCuVSIvfPqParams(cuVSIvfPqParams)
+              .build();
 
       // Create the index with the dataset
-      CagraIndex index = CagraIndex.newBuilder(resources)
-          .withDataset(vectors)
-          .withIndexParams(indexParams)
-          .build();
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(vectors)
+              .withIndexParams(indexParams)
+              .build();
 
       // Saving the HNSW index on to the disk.
       String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
       index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
 
-      HnswIndexParams hnswIndexParams = new HnswIndexParams.Builder()
-          .withVectorDimension(2)
-          .build();
+      HnswIndexParams hnswIndexParams =
+          new HnswIndexParams.Builder().withVectorDimension(2).build();
       InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
       File hnswIndexFile = new File(hnswIndexFileName);
 
-      HnswIndex hnswIndex = HnswIndex.newBuilder(resources)
-          .from(inputStreamHNSW)
-          .withIndexParams(hnswIndexParams)
-          .build();
+      HnswIndex hnswIndex =
+          HnswIndex.newBuilder(resources)
+              .from(inputStreamHNSW)
+              .withIndexParams(hnswIndexParams)
+              .build();
 
-      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder()
-          .build();
+      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder().build();
 
-      HnswQuery hnswQuery = new HnswQuery.Builder()
-          .withQueryVectors(queries)
-          .withSearchParams(hnswSearchParams)
-          .withTopK(3)
-          .build();
+      HnswQuery hnswQuery =
+          new HnswQuery.Builder()
+              .withQueryVectors(queries)
+              .withSearchParams(hnswSearchParams)
+              .withTopK(3)
+              .build();
 
       SearchResults results = hnswIndex.search(hnswQuery);
 
diff --git a/java/license-header.txt b/java/license-header.txt
new file mode 100644
index 0000000000..2001ad3d3a
--- /dev/null
+++ b/java/license-header.txt
@@ -0,0 +1,15 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */

From fb969cc6a0faed7964847adddbb5db9279e7b946 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Mon, 30 Jun 2025 15:26:21 -0700
Subject: [PATCH 461/623] [FEA] Build Single Linkage API (#820)

- Expose functions for building the dendrogram on the mutual reachability graph

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Jinsol Park (https://github.com/jinsolp)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/820
---
 cpp/include/cuvs/cluster/agglomerative.hpp    |  56 +++-
 cpp/src/cluster/detail/single_linkage.cuh     | 245 ++++++++++++++----
 cpp/src/cluster/single_linkage_float.cu       |  57 +++-
 .../sparse/neighbors/cross_component_nn.cuh   |   6 +-
 .../neighbors/detail/cross_component_nn.cuh   |  89 ++++++-
 .../sparse/neighbors/cross_component_nn.cu    |  93 +------
 6 files changed, 402 insertions(+), 144 deletions(-)

diff --git a/cpp/include/cuvs/cluster/agglomerative.hpp b/cpp/include/cuvs/cluster/agglomerative.hpp
index 8f7e8675aa..eb3099e0d2 100644
--- a/cpp/include/cuvs/cluster/agglomerative.hpp
+++ b/cpp/include/cuvs/cluster/agglomerative.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2021-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -18,7 +18,9 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <optional>
+#include <variant>
 
+#include <raft/core/device_coo_matrix.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
 
@@ -119,6 +121,58 @@ void single_linkage(
   cuvs::cluster::agglomerative::Linkage linkage = cuvs::cluster::agglomerative::Linkage::KNN_GRAPH,
   std::optional<int> c                          = std::make_optional<int>(DEFAULT_CONST_C));
 
+namespace helpers {
+
+namespace linkage_graph_params {
+/** Specialized parameters to build the KNN graph with regular distances */
+struct distance_params {
+  /** a constant used when constructing linkage from knn graph. Allows the indirect control of k.
+   * The algorithm will set `k = log(n) + c` */
+  int c = DEFAULT_CONST_C;
+
+  /** strategy for constructing the linkage. PAIRWISE uses more memory but can be faster for smaller
+   * datasets. KNN_GRAPH allows the memory usage to be controlled (using parameter c) */
+  cuvs::cluster::agglomerative::Linkage dist_type =
+    cuvs::cluster::agglomerative::Linkage::KNN_GRAPH;
+};
+
+/** Specialized parameters to build the Mutual Reachability graph */
+struct mutual_reachability_params {
+  /** this neighborhood will be selected for core distances. */
+  int min_samples;
+
+  /** weight applied when internal distance is chosen for mutual reachability (value of 1.0 disables
+   * the weighting) */
+  float alpha = 1.0;
+};
+}  // namespace linkage_graph_params
+/**
+ * Given a dataset, builds the KNN graph, connects graph components and builds a linkage
+ * (dendrogram). Returns the Minimum Spanning Tree edges sorted by weight and the dendrogram.
+ * @param[in] handle raft handle for resource reuse
+ * @param[in] X data points (size n_rows * d)
+ * @param[in] linkage_graph_params linkage params or mutual reachability params for building the KNN
+ * graph
+ * @param[in] metric distance metric to use
+ * @param[out] out_mst output MST sorted by edge weights (size n_rows - 1)
+ * @param[out] dendrogram output dendrogram (size [n_rows - 1] * 2)
+ * @param[out] out_distances distances for output
+ * @param[out] out_sizes cluster sizes of output
+ * @param[out] core_dists (optional) core distances (size m). Must be supplied in the Mutual
+ * Reachability space
+ */
+void build_linkage(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int, raft::row_major> X,
+  std::variant<linkage_graph_params::distance_params,
+               linkage_graph_params::mutual_reachability_params> linkage_graph_params,
+  cuvs::distance::DistanceType metric,
+  raft::device_coo_matrix_view<float, int, int, size_t> out_mst,
+  raft::device_matrix_view<int, int> dendrogram,
+  raft::device_vector_view<float, int> out_distances,
+  raft::device_vector_view<int, int> out_sizes,
+  std::optional<raft::device_vector_view<float, int>> core_dists);
+}  // namespace helpers
 /**
  * @}
  */
diff --git a/cpp/src/cluster/detail/single_linkage.cuh b/cpp/src/cluster/detail/single_linkage.cuh
index d1660f9ff3..d64a69f0cc 100644
--- a/cpp/src/cluster/detail/single_linkage.cuh
+++ b/cpp/src/cluster/detail/single_linkage.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2021-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,51 +16,138 @@
 
 #pragma once
 
+#include "../../neighbors/detail/reachability.cuh"
 #include "agglomerative.cuh"
 #include "connectivities.cuh"
 #include "mst.cuh"
 #include <cuvs/cluster/agglomerative.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/sparse/coo.hpp>
 #include <raft/util/cudart_utils.hpp>
 
 #include <rmm/device_uvector.hpp>
 
 namespace cuvs::cluster::agglomerative::detail {
 
+/**
+ * Constructs a linkage by computing the minimum spanning tree and dendrogram in the Mutual
+ * Reachability space. Returns mst edges sorted by weight and the dendrogram.
+ * @tparam value_t
+ * @tparam value_idx
+ * @tparam nnz_t
+ * @param[in] handle raft handle for resource reuse
+ * @param[in] X data points (size m * n)
+ * @param[in] metric distance metric to use
+ * @param[in] min_samples this neighborhood will be selected for core distances
+ * @param[in] alpha weight applied when internal distance is chosen for mutual reachability (value
+ * of 1.0 disables the weighting)
+ * @param[out] core_dists core distances (size m)
+ * @param[out] out_mst output MST sorted by edge weights (size m - 1)
+ * @param[out] out_dendrogram output dendrogram
+ * @param[out] out_distances distances of output
+ * @param[out] out_sizes cluster sizes of output
+ */
+template <typename value_t = float, typename value_idx = int, typename nnz_t = size_t>
+void build_mr_linkage(raft::resources const& handle,
+                      raft::device_matrix_view<const value_t, value_idx, raft::row_major> X,
+                      value_idx min_samples,
+                      float alpha,
+                      cuvs::distance::DistanceType metric,
+                      raft::device_vector_view<value_t, value_idx> core_dists,
+                      raft::device_coo_matrix_view<value_t, value_idx, value_idx, nnz_t> out_mst,
+                      raft::device_matrix_view<value_idx, value_idx> out_dendrogram,
+                      raft::device_vector_view<value_t, value_idx> out_distances,
+                      raft::device_vector_view<value_idx, value_idx> out_sizes)
+{
+  size_t m                        = X.extent(0);
+  size_t n                        = X.extent(1);
+  auto mutual_reachability_indptr = raft::make_device_vector<value_idx, value_idx>(handle, m + 1);
+  raft::sparse::COO<value_t, value_idx, nnz_t> mutual_reachability_coo(
+    raft::resource::get_cuda_stream(handle), min_samples * m * 2);
+
+  // Replace this with mutual reachability graph cronstruction from within all_neighbors wrapper.
+  // Reference: https://github.com/rapidsai/cuvs/issues/982
+  cuvs::neighbors::detail::reachability::mutual_reachability_graph<value_idx, value_t, nnz_t>(
+    handle,
+    X.data_handle(),
+    m,
+    n,
+    metric,
+    min_samples,
+    alpha,
+    mutual_reachability_indptr.data_handle(),
+    core_dists.data_handle(),
+    mutual_reachability_coo);
+
+  // auto color = raft::make_device_vector<value_idx, value_idx>(handle, static_cast<value_idx>(m));
+  rmm::device_uvector<value_idx> color(m, raft::resource::get_cuda_stream(handle));
+  cuvs::sparse::neighbors::MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t>
+    reduction_op(core_dists.data_handle(), m);
+
+  size_t nnz = m * min_samples;
+
+  detail::build_sorted_mst<value_idx, value_t>(handle,
+                                               X.data_handle(),
+                                               mutual_reachability_indptr.data_handle(),
+                                               mutual_reachability_coo.cols(),
+                                               mutual_reachability_coo.vals(),
+                                               m,
+                                               n,
+                                               out_mst.structure_view().get_rows().data(),
+                                               out_mst.structure_view().get_cols().data(),
+                                               out_mst.get_elements().data(),
+                                               color.data(),
+                                               mutual_reachability_coo.nnz,
+                                               reduction_op,
+                                               metric,
+                                               10);
+
+  /**
+   * Perform hierarchical labeling
+   */
+  size_t n_edges = m - 1;
+
+  detail::build_dendrogram_host<value_idx, value_t>(handle,
+                                                    out_mst.structure_view().get_rows().data(),
+                                                    out_mst.structure_view().get_cols().data(),
+                                                    out_mst.get_elements().data(),
+                                                    n_edges,
+                                                    out_dendrogram.data_handle(),
+                                                    out_distances.data_handle(),
+                                                    out_sizes.data_handle());
+}
+
 static const size_t EMPTY = 0;
 
 /**
- * Single-linkage clustering, capable of constructing a KNN graph to
- * scale the algorithm beyond the n^2 memory consumption of implementations
- * that use the fully-connected graph of pairwise distances by connecting
- * a knn graph when k is not large enough to connect it.
-
- * @tparam value_idx
+ * Constructs a linkage by computing the minimum spanning tree and dendrogram in the Mutual
+ * Reachability space. Returns mst edges sorted by weight and the dendrogram.
  * @tparam value_t
+ * @tparam value_idx
+ * @tparam nnz_t
  * @tparam dist_type method to use for constructing connectivities graph
- * @param[in] handle raft handle
- * @param[in] X dense input matrix in row-major layout
- * @param[in] m number of rows in X
- * @param[in] n number of columns in X
- * @param[in] metric distance metrix to use when constructing connectivities graph
- * @param[out] out struct containing output dendrogram and cluster assignments
- * @param[in] c a constant used when constructing connectivities from knn graph. Allows the indirect
- control
- *            of k. The algorithm will set `k = log(n) + c`
- * @param[in] n_clusters number of clusters to assign data samples
+ * @param[in] handle raft handle for resource reuse
+ * @param[in] X data points (size m * n)
+ * @param[in] c a constant used when constructing linkage from knn graph. Allows the indirect
+ * control of k. The algorithm will set `k = log(n) + c`
+ * @param[in] metric distance metric to use
+ * @param[out] out_mst output MST sorted by edge weights (size m - 1)
+ * @param[out] out_dendrogram output dendrogram
+ * @param[out] out_distances distances of output
+ * @param[out] out_sizes cluster sizes of output
  */
-template <typename value_idx, typename value_t, Linkage dist_type>
-void single_linkage(raft::resources const& handle,
-                    const value_t* X,
-                    size_t m,
-                    size_t n,
-                    cuvs::distance::DistanceType metric,
-                    single_linkage_output<value_idx>* out,
-                    int c,
-                    size_t n_clusters)
+template <typename value_t, typename value_idx, typename nnz_t, Linkage dist_type>
+void build_dist_linkage(raft::resources const& handle,
+                        raft::device_matrix_view<const value_t, value_idx, raft::row_major> X,
+                        int c,
+                        cuvs::distance::DistanceType metric,
+                        raft::device_coo_matrix_view<value_t, value_idx, value_idx, nnz_t> out_mst,
+                        raft::device_matrix_view<value_idx, value_idx> out_dendrogram,
+                        raft::device_vector_view<value_t, value_idx> out_distances,
+                        raft::device_vector_view<value_idx, value_idx> out_sizes)
 {
-  ASSERT(n_clusters <= m, "n_clusters must be less than or equal to the number of data points");
-
+  size_t m    = X.extent(0);
+  size_t n    = X.extent(1);
   auto stream = raft::resource::get_cuda_stream(handle);
 
   rmm::device_uvector<value_idx> indptr(EMPTY, stream);
@@ -70,51 +157,109 @@ void single_linkage(raft::resources const& handle,
   /**
    * 1. Construct distance graph
    */
-  detail::get_distance_graph<value_idx, value_t, dist_type>(
-    handle, X, m, n, metric, indptr, indices, pw_dists, c);
-
-  rmm::device_uvector<value_idx> mst_rows(m - 1, stream);
-  rmm::device_uvector<value_idx> mst_cols(m - 1, stream);
-  rmm::device_uvector<value_t> mst_data(m - 1, stream);
+  detail::get_distance_graph<value_idx, value_t, dist_type>(handle,
+                                                            X.data_handle(),
+                                                            static_cast<value_idx>(m),
+                                                            static_cast<value_idx>(n),
+                                                            metric,
+                                                            indptr,
+                                                            indices,
+                                                            pw_dists,
+                                                            c);
 
   /**
    * 2. Construct MST, sorted by weights
    */
   rmm::device_uvector<value_idx> color(m, stream);
   cuvs::sparse::neighbors::FixConnectivitiesRedOp<value_idx, value_t> op(m);
+
+  size_t n_edges = m - 1;
+
   detail::build_sorted_mst<value_idx, value_t>(handle,
-                                               X,
+                                               X.data_handle(),
                                                indptr.data(),
                                                indices.data(),
                                                pw_dists.data(),
                                                m,
                                                n,
-                                               mst_rows.data(),
-                                               mst_cols.data(),
-                                               mst_data.data(),
+                                               out_mst.structure_view().get_rows().data(),
+                                               out_mst.structure_view().get_cols().data(),
+                                               out_mst.get_elements().data(),
                                                color.data(),
                                                indices.size(),
                                                op,
                                                metric);
-
   pw_dists.release();
 
   /**
    * Perform hierarchical labeling
    */
-  size_t n_edges = mst_rows.size();
-
-  rmm::device_uvector<value_t> out_delta(n_edges, stream);
-  rmm::device_uvector<value_idx> out_size(n_edges, stream);
-  // Create dendrogram
   detail::build_dendrogram_host<value_idx, value_t>(handle,
-                                                    mst_rows.data(),
-                                                    mst_cols.data(),
-                                                    mst_data.data(),
+                                                    out_mst.structure_view().get_rows().data(),
+                                                    out_mst.structure_view().get_cols().data(),
+                                                    out_mst.get_elements().data(),
                                                     n_edges,
-                                                    out->children,
-                                                    out_delta.data(),
-                                                    out_size.data());
+                                                    out_dendrogram.data_handle(),
+                                                    out_distances.data_handle(),
+                                                    out_sizes.data_handle());
+}
+
+/**
+ * Single-linkage clustering, capable of constructing a KNN graph to
+ * scale the algorithm beyond the n^2 memory consumption of implementations
+ * that use the fully-connected graph of pairwise distances by connecting
+ * a knn graph when k is not large enough to connect it.
+
+ * @tparam value_idx
+ * @tparam value_t
+ * @tparam dist_type method to use for constructing connectivities graph
+ * @param[in] handle raft handle
+ * @param[in] X dense input matrix in row-major layout
+ * @param[in] m number of rows in X
+ * @param[in] n number of columns in X
+ * @param[in] metric distance metrix to use when constructing connectivities graph
+ * @param[out] out struct containing output dendrogram and cluster assignments
+ * @param[in] c a constant used when constructing connectivities from knn graph. Allows the indirect
+ control
+ *            of k. The algorithm will set `k = log(n) + c`
+ * @param[in] n_clusters number of clusters to assign data samples
+ */
+template <typename value_idx, typename value_t, Linkage dist_type>
+void single_linkage(raft::resources const& handle,
+                    const value_t* X,
+                    size_t m,
+                    size_t n,
+                    cuvs::distance::DistanceType metric,
+                    single_linkage_output<value_idx>* out,
+                    int c,
+                    size_t n_clusters)
+{
+  ASSERT(n_clusters <= m, "n_clusters must be less than or equal to the number of data points");
+
+  value_idx n_edges = m - 1;
+  auto mst_rows     = raft::make_device_vector<value_idx, value_idx>(handle, n_edges);
+  auto mst_cols     = raft::make_device_vector<value_idx, value_idx>(handle, n_edges);
+  auto mst_weights  = raft::make_device_vector<value_t, value_idx>(handle, n_edges);
+  auto structure_view =
+    raft::make_device_coordinate_structure_view<value_idx, value_idx, value_idx>(
+      mst_rows.data_handle(), mst_cols.data_handle(), m, m, n_edges);
+  auto mst_view = raft::make_device_coo_matrix_view<value_t, value_idx, value_idx, value_idx>(
+    mst_weights.data_handle(), structure_view);
+
+  auto out_delta = raft::make_device_vector<value_t, value_idx>(handle, n_edges);
+  auto out_sizes = raft::make_device_vector<value_idx, value_idx>(handle, n_edges);
+
+  build_dist_linkage<value_t, value_idx, value_idx, dist_type>(
+    handle,
+    raft::make_device_matrix_view<const value_t, value_idx, raft::row_major>(
+      X, static_cast<value_idx>(m), static_cast<value_idx>(n)),
+    c,
+    metric,
+    mst_view,
+    raft::make_device_matrix_view<value_idx, value_idx, raft::row_major>(out->children, n_edges, 2),
+    out_delta.view(),
+    out_sizes.view());
+
   detail::extract_flattened_clusters(handle, out->labels, out->children, n_clusters, m);
 
   out->m                      = m;
diff --git a/cpp/src/cluster/single_linkage_float.cu b/cpp/src/cluster/single_linkage_float.cu
index 1614c7eeef..4a33b969e3 100644
--- a/cpp/src/cluster/single_linkage_float.cu
+++ b/cpp/src/cluster/single_linkage_float.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -39,4 +39,59 @@ void single_linkage(raft::resources const& handle,
       handle, X, dendrogram, labels, metric, n_clusters, c);
   }
 }
+
+namespace helpers {
+void build_linkage(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int, raft::row_major> X,
+  std::variant<linkage_graph_params::distance_params,
+               linkage_graph_params::mutual_reachability_params> linkage_graph_params,
+  cuvs::distance::DistanceType metric,
+  raft::device_coo_matrix_view<float, int, int, size_t> out_mst,
+  raft::device_matrix_view<int, int> out_dendrogram,
+  raft::device_vector_view<float, int> out_distances,
+  raft::device_vector_view<int, int> out_sizes,
+  std::optional<raft::device_vector_view<float, int>> core_dists)
+{
+  /**
+   * Construct MST sorted by weights
+   */
+  if (std::holds_alternative<
+        cuvs::cluster::agglomerative::helpers::linkage_graph_params::mutual_reachability_params>(
+        linkage_graph_params)) {
+    RAFT_EXPECTS(core_dists.has_value(),
+                 "core distances must be pre-allocated to build the linkage with mutual "
+                 "reachability distances");
+    auto core_dists_mdspan = core_dists.value();
+    RAFT_EXPECTS(core_dists_mdspan.extent(0) == X.extent(0),
+                 "core_dists doesn't have expected size");
+    auto mr_params = std::get<
+      cuvs::cluster::agglomerative::helpers::linkage_graph_params::mutual_reachability_params>(
+      linkage_graph_params);
+    detail::build_mr_linkage<float, int>(handle,
+                                         X,
+                                         mr_params.min_samples,
+                                         mr_params.alpha,
+                                         metric,
+                                         core_dists_mdspan,
+                                         out_mst,
+                                         out_dendrogram,
+                                         out_distances,
+                                         out_sizes);
+  } else {
+    auto dist_params =
+      std::get<cuvs::cluster::agglomerative::helpers::linkage_graph_params::distance_params>(
+        linkage_graph_params);
+    if (dist_params.dist_type == cuvs::cluster::agglomerative::Linkage::KNN_GRAPH) {
+      detail::
+        build_dist_linkage<float, int, size_t, cuvs::cluster::agglomerative::Linkage::KNN_GRAPH>(
+          handle, X, dist_params.c, metric, out_mst, out_dendrogram, out_distances, out_sizes);
+    } else {
+      detail::
+        build_dist_linkage<float, int, size_t, cuvs::cluster::agglomerative::Linkage::PAIRWISE>(
+          handle, X, dist_params.c, metric, out_mst, out_dendrogram, out_distances, out_sizes);
+    }
+  }
+}
+}  // namespace helpers
 }  // namespace cuvs::cluster::agglomerative
diff --git a/cpp/src/sparse/neighbors/cross_component_nn.cuh b/cpp/src/sparse/neighbors/cross_component_nn.cuh
index 2da017ad1e..34bbebdb72 100644
--- a/cpp/src/sparse/neighbors/cross_component_nn.cuh
+++ b/cpp/src/sparse/neighbors/cross_component_nn.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2018-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -26,6 +26,10 @@ namespace cuvs::sparse::neighbors {
 template <typename value_idx, typename value_t>
 using FixConnectivitiesRedOp = detail::FixConnectivitiesRedOp<value_idx, value_t>;
 
+template <typename value_idx, typename value_t>
+using MutualReachabilityFixConnectivitiesRedOp =
+  detail::MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t>;
+
 /**
  * Gets the number of unique components from array of
  * colors or labels. This does not assume the components are
diff --git a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
index 0bc85752a7..8bd4f2cded 100644
--- a/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
+++ b/cpp/src/sparse/neighbors/detail/cross_component_nn.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2018-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -108,6 +108,93 @@ struct FixConnectivitiesRedOp {
   void scatter(const raft::resources& handle, value_idx* map) {}
 };
 
+template <typename value_idx, typename value_t>
+struct MutualReachabilityFixConnectivitiesRedOp {
+  value_t* core_dists;
+  value_idx m;
+
+  DI MutualReachabilityFixConnectivitiesRedOp() : m(0), core_dists(nullptr) {}
+
+  MutualReachabilityFixConnectivitiesRedOp(value_t* core_dists, value_idx m)
+    : core_dists(core_dists), m(m) {};
+
+  typedef typename raft::KeyValuePair<value_idx, value_t> KVP;
+  DI void operator()(value_idx rit, KVP* out, const KVP& other) const
+  {
+    if (rit < m && other.value < std::numeric_limits<value_t>::max()) {
+      value_t core_dist_rit   = core_dists[rit];
+      value_t core_dist_other = max(core_dist_rit, max(core_dists[other.key], other.value));
+
+      value_t core_dist_out;
+      if (out->key > -1) {
+        core_dist_out = max(core_dist_rit, max(core_dists[out->key], out->value));
+      } else {
+        core_dist_out = out->value;
+      }
+
+      bool smaller = core_dist_other < core_dist_out;
+      out->key     = smaller ? other.key : out->key;
+      out->value   = smaller ? core_dist_other : core_dist_out;
+    }
+  }
+
+  DI KVP operator()(value_idx rit, const KVP& a, const KVP& b) const
+  {
+    if (rit < m && a.key > -1) {
+      value_t core_dist_rit = core_dists[rit];
+      value_t core_dist_a   = max(core_dist_rit, max(core_dists[a.key], a.value));
+
+      value_t core_dist_b;
+      if (b.key > -1) {
+        core_dist_b = max(core_dist_rit, max(core_dists[b.key], b.value));
+      } else {
+        core_dist_b = b.value;
+      }
+
+      return core_dist_a < core_dist_b ? KVP(a.key, core_dist_a) : KVP(b.key, core_dist_b);
+    }
+
+    return b;
+  }
+
+  DI void init(value_t* out, value_t maxVal) const { *out = maxVal; }
+  DI void init(KVP* out, value_t maxVal) const
+  {
+    out->key   = -1;
+    out->value = maxVal;
+  }
+
+  DI void init_key(value_t& out, value_idx idx) const { return; }
+  DI void init_key(KVP& out, value_idx idx) const { out.key = idx; }
+
+  DI value_t get_value(KVP& out) const { return out.value; }
+  DI value_t get_value(value_t& out) const { return out; }
+
+  void gather(const raft::resources& handle, value_idx* map)
+  {
+    auto tmp_core_dists = raft::make_device_vector<value_t>(handle, m);
+    thrust::gather(raft::resource::get_thrust_policy(handle),
+                   map,
+                   map + m,
+                   core_dists,
+                   tmp_core_dists.data_handle());
+    raft::copy_async(
+      core_dists, tmp_core_dists.data_handle(), m, raft::resource::get_cuda_stream(handle));
+  }
+
+  void scatter(const raft::resources& handle, value_idx* map)
+  {
+    auto tmp_core_dists = raft::make_device_vector<value_t>(handle, m);
+    thrust::scatter(raft::resource::get_thrust_policy(handle),
+                    core_dists,
+                    core_dists + m,
+                    map,
+                    tmp_core_dists.data_handle());
+    raft::copy_async(
+      core_dists, tmp_core_dists.data_handle(), m, raft::resource::get_cuda_stream(handle));
+  }
+};
+
 /**
  * Assumes 3-iterator tuple containing COO rows, cols, and
  * a cub keyvalue pair object. Sorts the 3 arrays in
diff --git a/cpp/tests/sparse/neighbors/cross_component_nn.cu b/cpp/tests/sparse/neighbors/cross_component_nn.cu
index 7810b0f9cf..b5047b909c 100644
--- a/cpp/tests/sparse/neighbors/cross_component_nn.cu
+++ b/cpp/tests/sparse/neighbors/cross_component_nn.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2018-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -380,93 +380,6 @@ INSTANTIATE_TEST_CASE_P(ConnectComponentsTest,
                         ConnectComponentsTestF_Int,
                         ::testing::ValuesIn(fix_conn_inputsf2));
 
-template <typename value_idx, typename value_t>
-struct MutualReachabilityFixConnectivitiesRedOp {
-  value_t* core_dists;
-  value_idx m;
-
-  DI MutualReachabilityFixConnectivitiesRedOp() : m(0) {}
-
-  MutualReachabilityFixConnectivitiesRedOp(value_t* core_dists_, value_idx m_)
-    : core_dists(core_dists_), m(m_) {};
-
-  typedef typename raft::KeyValuePair<value_idx, value_t> KVP;
-  DI void operator()(value_idx rit, KVP* out, const KVP& other) const
-  {
-    if (rit < m && other.value < std::numeric_limits<value_t>::max()) {
-      value_t core_dist_rit   = core_dists[rit];
-      value_t core_dist_other = max(core_dist_rit, max(core_dists[other.key], other.value));
-
-      value_t core_dist_out;
-      if (out->key > -1) {
-        core_dist_out = max(core_dist_rit, max(core_dists[out->key], out->value));
-      } else {
-        core_dist_out = out->value;
-      }
-
-      bool smaller = core_dist_other < core_dist_out;
-      out->key     = smaller ? other.key : out->key;
-      out->value   = smaller ? core_dist_other : core_dist_out;
-    }
-  }
-
-  DI KVP operator()(value_idx rit, const KVP& a, const KVP& b) const
-  {
-    if (rit < m && a.key > -1) {
-      value_t core_dist_rit = core_dists[rit];
-      value_t core_dist_a   = max(core_dist_rit, max(core_dists[a.key], a.value));
-
-      value_t core_dist_b;
-      if (b.key > -1) {
-        core_dist_b = max(core_dist_rit, max(core_dists[b.key], b.value));
-      } else {
-        core_dist_b = b.value;
-      }
-
-      return core_dist_a < core_dist_b ? KVP(a.key, core_dist_a) : KVP(b.key, core_dist_b);
-    }
-
-    return b;
-  }
-
-  DI void init(value_t* out, value_t maxVal) const { *out = maxVal; }
-  DI void init(KVP* out, value_t maxVal) const
-  {
-    out->key   = -1;
-    out->value = maxVal;
-  }
-
-  DI void init_key(value_t& out, value_idx idx) const { return; }
-  DI void init_key(KVP& out, value_idx idx) const { out.key = idx; }
-
-  DI value_t get_value(KVP& out) const { return out.value; }
-  DI value_t get_value(value_t& out) const { return out; }
-
-  void gather(const raft::resources& handle, value_idx* map)
-  {
-    auto tmp_core_dists = raft::make_device_vector<value_t>(handle, m);
-    thrust::gather(raft::resource::get_thrust_policy(handle),
-                   map,
-                   map + m,
-                   core_dists,
-                   tmp_core_dists.data_handle());
-    raft::copy_async(
-      core_dists, tmp_core_dists.data_handle(), m, raft::resource::get_cuda_stream(handle));
-  }
-
-  void scatter(const raft::resources& handle, value_idx* map)
-  {
-    auto tmp_core_dists = raft::make_device_vector<value_t>(handle, m);
-    thrust::scatter(raft::resource::get_thrust_policy(handle),
-                    core_dists,
-                    core_dists + m,
-                    map,
-                    tmp_core_dists.data_handle());
-    raft::copy_async(
-      core_dists, tmp_core_dists.data_handle(), m, raft::resource::get_cuda_stream(handle));
-  }
-};
-
 template <typename value_t, typename value_idx>
 struct ConnectComponentsMutualReachabilityInputs {
   value_idx n_row;
@@ -514,8 +427,8 @@ class ConnectComponentsEdgesTest
     /**
      * 3. cross_component_nn to fix connectivities
      */
-    MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t> red_op(core_dists.data(),
-                                                                        params.n_row);
+    cuvs::sparse::neighbors::MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t> red_op(
+      core_dists.data(), params.n_row);
 
     cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
                                                                     out_edges_unbatched,

From f17ada800f5bbeddcaa4cb12e8a16254ac9e5ace Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Tue, 1 Jul 2025 13:56:25 -0700
Subject: [PATCH 462/623] [ANN_BENCH] Fix OpenMP Sections in Wrappers (#911)

Fixes OpenMP num thread setting + dispatching search workloads in DiskANN and Faiss CPU cuvs-bench wrappers.
Hnswlib wrapper taken care of in https://github.com/rapidsai/cuvs/pull/724

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/911
---
 cpp/bench/ann/src/diskann/diskann_wrapper.h   | 14 +++++++-------
 .../ann/src/faiss/faiss_cpu_benchmark.cpp     |  3 +--
 cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h   | 19 ++-----------------
 3 files changed, 10 insertions(+), 26 deletions(-)

diff --git a/cpp/bench/ann/src/diskann/diskann_wrapper.h b/cpp/bench/ann/src/diskann/diskann_wrapper.h
index 7f57b1a321..c8219c40cb 100644
--- a/cpp/bench/ann/src/diskann/diskann_wrapper.h
+++ b/cpp/bench/ann/src/diskann/diskann_wrapper.h
@@ -52,13 +52,13 @@ class diskann_memory : public algo<T> {
     uint32_t L_build;
     uint32_t build_pq_bytes = 0;
     float alpha             = 1.2;
-    int num_threads         = omp_get_num_procs();
+    int num_threads         = omp_get_max_threads();
   };
 
   using search_param_base = typename algo<T>::search_param;
   struct search_param : public search_param_base {
     uint32_t L_search;
-    uint32_t num_threads = omp_get_num_procs();
+    uint32_t num_threads = omp_get_max_threads() / 2;
     // Mode metric_objective;
   };
 
@@ -160,7 +160,7 @@ template <typename T>
 void diskann_memory<T>::search(
   const T* queries, int batch_size, int k, algo_base::index_type* indices, float* distances) const
 {
-#pragma omp parallel for schedule(dynamic, 1)
+#pragma omp parallel for if (batch_size > 1) schedule(dynamic, 1) num_threads(num_search_threads_)
   for (int i = 0; i < batch_size; i++) {
     mem_index_->search(queries + i * this->dim_,
                        static_cast<size_t>(k),
@@ -191,7 +191,7 @@ class diskann_ssd : public algo<T> {
     uint32_t L_build;
     uint32_t build_pq_bytes       = 0;
     float alpha                   = 1.2;
-    int num_threads               = omp_get_num_procs();
+    int num_threads               = omp_get_max_threads();
     uint32_t QD                   = 192;
     std::string dataset_base_file = "";
     std::string index_file        = "";
@@ -200,7 +200,7 @@ class diskann_ssd : public algo<T> {
 
   struct search_param : public search_param_base {
     uint32_t L_search;
-    uint32_t num_threads        = omp_get_num_procs() / 2;
+    uint32_t num_threads        = omp_get_max_threads() / 2;
     uint32_t num_nodes_to_cache = 10000;
     int beam_width              = 2;
     // Mode metric_objective;
@@ -241,7 +241,7 @@ class diskann_ssd : public algo<T> {
   uint32_t max_points_;
   // for safe scratch space allocs, set the default to half the number of procs for loading the
   // index. User must ensure that the number of search threads is less than or equal to this value
-  int num_search_threads_ = omp_get_num_procs() / 2;
+  int num_search_threads_ = omp_get_max_threads() / 2;
   // L_search is hardcoded to the maximum visited list size in the search params. This default is
   // for loading the index
   uint32_t L_search_ = 384;
@@ -303,7 +303,7 @@ template <typename T>
 void diskann_ssd<T>::search(
   const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
 {
-#pragma omp parallel for schedule(dynamic, 1)
+#pragma omp parallel for if (batch_size > 1) schedule(dynamic, 1) num_threads(num_search_threads_)
   for (int64_t i = 0; i < (int64_t)batch_size; i++) {
     p_flash_index_->cached_beam_search(queries + (i * this->dim_),
                                        static_cast<size_t>(k),
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
index c34d840132..c65f7110f8 100644
--- a/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_benchmark.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -86,7 +86,6 @@ void parse_search_param(const nlohmann::json& conf,
 {
   param.nprobe = conf.at("nprobe");
   if (conf.contains("refine_ratio")) { param.refine_ratio = conf.at("refine_ratio"); }
-  if (conf.contains("numThreads")) { param.num_threads = conf.at("numThreads"); }
   if (conf.contains("parallel_mode")) { param.parallel_mode = conf.at("parallel_mode"); }
 }
 
diff --git a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
index 649a24c8d5..b5094f111f 100644
--- a/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_cpu_wrapper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,7 +16,6 @@
 #pragma once
 
 #include "../common/ann_types.hpp"
-#include "../common/thread_pool.hpp"
 #include "../common/util.hpp"
 
 #include <faiss/IndexFlat.h>
@@ -28,7 +27,6 @@
 #include <faiss/index_io.h>
 
 #include <cassert>
-#include <iostream>
 #include <memory>
 #include <stdexcept>
 #include <string>
@@ -57,7 +55,6 @@ class faiss_cpu : public algo<T> {
   struct search_param : public search_param_base {
     int nprobe;
     float refine_ratio = 1.0;
-    int num_threads    = omp_get_num_procs();
     /**
      * parallel_mode determines how queries are parallelized with OpenMP.
      *
@@ -127,9 +124,6 @@ class faiss_cpu : public algo<T> {
   faiss::MetricType metric_type_;
   int nlist_;
   double training_sample_fraction_;
-
-  int num_threads_;
-  std::shared_ptr<fixed_thread_pool> thread_pool_;
 };
 
 template <typename T>
@@ -178,11 +172,6 @@ void faiss_cpu<T>::set_search_param(const search_param_base& param, const void*
   }
 
   if (sp.refine_ratio > 1.0) { this->index_refine_.get()->k_factor = sp.refine_ratio; }
-
-  if (!thread_pool_ || num_threads_ != sp.num_threads) {
-    num_threads_ = sp.num_threads;
-    thread_pool_ = std::make_shared<fixed_thread_pool>(num_threads_);
-  }
 }
 
 template <typename T>
@@ -320,11 +309,7 @@ class faiss_cpu_flat : public faiss_cpu<T> {
   {
     if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
     auto search_param = dynamic_cast<const typename faiss_cpu<T>::search_param&>(param);
-    if (!this->thread_pool_ || this->num_threads_ != search_param.num_threads) {
-      this->num_threads_ = search_param.num_threads;
-      this->thread_pool_ = std::make_shared<fixed_thread_pool>(this->num_threads_);
-    }
-  };
+  }
 
   void save(const std::string& file) const override
   {

From 3f40c068abda4ac01e3997411ef6115d9f536e0d Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Tue, 1 Jul 2025 17:18:17 -0500
Subject: [PATCH 463/623] add docs on CI workflow inputs (#1075)

Contributes to https://github.com/rapidsai/shared-workflows/issues/376

* adds descriptions for all inputs to workflows triggered by `workflow_dispatch`

Contributes to https://github.com/rapidsai/shared-workflows/issues/379

* removes input `enable_check_symbols` for `conda-cpp-checks` workflow... no longer needed as of https://github.com/rapidsai/shared-workflows/pull/382

## Notes for Reviewers

### Motivation

The input descriptions show up in the UI when you go to trigger these workflows. Like this:

![image](https://github.com/user-attachments/assets/fc62d1ff-39eb-47c7-9a21-57aab959e64f)

I'm hoping that will make it easier for developers to manually trigger workflows. Inspired by being asked multiple times "what format is `date` supposed to be in?".

Removing `enable_check_symbols` here is a step towards completely removing that always-true input, as part of https://github.com/rapidsai/shared-workflows/issues/379

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: https://github.com/rapidsai/cuvs/pull/1075
---
 .github/workflows/build.yaml | 6 ++++++
 .github/workflows/pr.yaml    | 1 -
 .github/workflows/test.yaml  | 7 ++++++-
 3 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 7730e5b64d..9c487f2bdf 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -9,15 +9,21 @@ on:
   workflow_dispatch:
     inputs:
       branch:
+        description: |
+          branch: git branch the workflow run targets.
+          Required even when 'sha' is provided because it is also used for organizing artifacts.
         required: true
         type: string
       date:
+        description: "date: Date (YYYY-MM-DD) this run is for. Used to organize artifacts produced by nightly builds"
         required: true
         type: string
       sha:
+        description: "sha: full git commit SHA to check out"
         required: true
         type: string
       build_type:
+        description: "build_type: one of [branch, nightly, pull-request]"
         type: string
         default: nightly
 
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 34fc4ed0b3..55d936b204 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -135,7 +135,6 @@ jobs:
     uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.08
     with:
       build_type: pull-request
-      enable_check_symbols: true
       symbol_exclusions: (void (thrust::|cub::))
   conda-python-build:
     needs: conda-cpp-build
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index fa7a1ae3a7..7eaf54f7e5 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -4,15 +4,21 @@ on:
   workflow_dispatch:
     inputs:
       branch:
+        description: |
+          branch: git branch the workflow run targets.
+          Required even when 'sha' is provided because it is also used for organizing artifacts.
         required: true
         type: string
       date:
+        description: "date: Date (YYYY-MM-DD) this run is for. Used to organize artifacts produced by nightly builds"
         required: true
         type: string
       sha:
+        description: "sha: full git commit SHA to check out"
         required: true
         type: string
       build_type:
+        description: "build_type: one of [branch, nightly, pull-request]"
         type: string
         default: nightly
 
@@ -25,7 +31,6 @@ jobs:
       branch: ${{ inputs.branch }}
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
-      enable_check_symbols: true
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit

From 3fcaf8d6e55fb1a516d3ed5a6e49dca86273b5e3 Mon Sep 17 00:00:00 2001
From: Anupam <54245698+aamijar@users.noreply.github.com>
Date: Tue, 1 Jul 2025 20:20:01 -0700
Subject: [PATCH 464/623] Instantiate only specific RAFT linewise kernels
 (#1018)

Reduces libcuvs binary size by 4.21 MB
- (1213.42 MB) Nightly https://downloads.rapids.ai/nightly/cuvs/2025-07-01/fb969cc/cuda12_x86_64.compile_lib.html
- (1209.21 MB) This PR https://downloads.rapids.ai/ci/cuvs/pull-request/1018/3bd8a66/cuda12_x86_64.compile_lib.html

Depends on https://github.com/rapidsai/raft/pull/2701 and Resolves https://github.com/rapidsai/raft/issues/2682

Authors:
  - Anupam (https://github.com/aamijar)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1018
---
 cpp/src/cluster/detail/kmeans.cuh          | 33 +++++++++-------------
 cpp/src/cluster/detail/kmeans_balanced.cuh | 23 ++++++++-------
 cpp/src/cluster/detail/kmeans_mg.cuh       | 18 +++++-------
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh  | 14 ++++-----
 cpp/src/stats/detail/silhouette_score.cuh  | 30 ++++++++++++--------
 cpp/tests/neighbors/ann_cagra.cuh          |  5 ++--
 6 files changed, 60 insertions(+), 63 deletions(-)

diff --git a/cpp/src/cluster/detail/kmeans.cuh b/cpp/src/cluster/detail/kmeans.cuh
index 5ed432a7f2..d5808d6480 100644
--- a/cpp/src/cluster/detail/kmeans.cuh
+++ b/cpp/src/cluster/detail/kmeans.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -27,6 +27,7 @@
 #include <raft/core/kvp.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/core/mdarray.hpp>
+#include <raft/core/mdspan.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
@@ -199,15 +200,12 @@ void kmeansPlusPlus(raft::resources const& handle,
     // Outputs minDistanceBuf[n_trials x n_samples] where minDistance[i, :] contains updated
     // minClusterDistance that includes candidate-i
     auto minDistBuf = distBuffer.view();
-    raft::linalg::matrixVectorOp(minDistBuf.data_handle(),
-                                 pwd.data_handle(),
-                                 minClusterDistance.data_handle(),
-                                 pwd.extent(1),
-                                 pwd.extent(0),
-                                 true,
-                                 true,
-                                 raft::min_op{},
-                                 stream);
+    raft::linalg::matrix_vector_op<raft::Apply::ALONG_ROWS>(
+      handle,
+      raft::make_const_mdspan(pwd),
+      raft::make_const_mdspan(minClusterDistance.view()),
+      minDistBuf,
+      raft::min_op{});
 
     // Calculate costPerCandidate[n_trials] where costPerCandidate[i] is the cluster cost when using
     // centroid candidate-i
@@ -325,15 +323,12 @@ void update_centroids(raft::resources const& handle,
   //   weight_per_cluster[n_clusters] - 1D array, weight_per_cluster[i] contains sum of weights in
   //   cluster-i.
   // Note - when weight_per_cluster[i] is 0, new_centroids[i] is reset to 0
-  raft::linalg::matrixVectorOp(new_centroids.data_handle(),
-                               new_centroids.data_handle(),
-                               weight_per_cluster.data_handle(),
-                               new_centroids.extent(1),
-                               new_centroids.extent(0),
-                               true,
-                               false,
-                               raft::div_checkzero_op{},
-                               raft::resource::get_cuda_stream(handle));
+  raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
+    handle,
+    raft::make_const_mdspan(new_centroids),
+    raft::make_const_mdspan(weight_per_cluster),
+    new_centroids,
+    raft::div_checkzero_op{});
 
   // copy centroids[i] to new_centroids[i] when weight_per_cluster[i] is 0
   cub::ArgIndexInputIterator<DataT*> itr_wt(weight_per_cluster.data_handle());
diff --git a/cpp/src/cluster/detail/kmeans_balanced.cuh b/cpp/src/cluster/detail/kmeans_balanced.cuh
index c93eb45c3c..d48e99126a 100644
--- a/cpp/src/cluster/detail/kmeans_balanced.cuh
+++ b/cpp/src/cluster/detail/kmeans_balanced.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -314,9 +314,12 @@ void calc_centers_and_sizes(const raft::resources& handle,
 {
   auto stream = raft::resource::get_cuda_stream(handle);
 
+  auto centersView      = raft::make_device_matrix_view<MathT>(centers, n_clusters, dim);
+  auto clusterSizesView = raft::make_device_vector_view<const CounterT>(cluster_sizes, n_clusters);
+
   if (!reset_counters) {
-    raft::linalg::matrixVectorOp(
-      centers, centers, cluster_sizes, dim, n_clusters, true, false, raft::mul_op(), stream);
+    raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
+      handle, raft::make_const_mdspan(centersView), clusterSizesView, centersView, raft::mul_op{});
   }
 
   rmm::device_uvector<char> workspace(0, stream, mr);
@@ -350,15 +353,11 @@ void calc_centers_and_sizes(const raft::resources& handle,
     raft::linalg::add(cluster_sizes, cluster_sizes, temp_sizes, n_clusters, stream);
   }
 
-  raft::linalg::matrixVectorOp(centers,
-                               centers,
-                               cluster_sizes,
-                               dim,
-                               n_clusters,
-                               true,
-                               false,
-                               raft::div_checkzero_op(),
-                               stream);
+  raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(handle,
+                                                             raft::make_const_mdspan(centersView),
+                                                             clusterSizesView,
+                                                             centersView,
+                                                             raft::div_checkzero_op{});
 }
 
 /** Computes the L2 norm of the dataset, converting to MathT if necessary */
diff --git a/cpp/src/cluster/detail/kmeans_mg.cuh b/cpp/src/cluster/detail/kmeans_mg.cuh
index c0168ad0f7..8a39f91900 100644
--- a/cpp/src/cluster/detail/kmeans_mg.cuh
+++ b/cpp/src/cluster/detail/kmeans_mg.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -654,21 +654,17 @@ void fit(const raft::resources& handle,
     //   samples in cluster-i.
     // Note - when wtInCluster[i] is 0, newCentroid[i] is reset to 0
 
-    raft::linalg::matrixVectorOp(
-      newCentroids.data_handle(),
-      newCentroids.data_handle(),
-      wtInCluster.data_handle(),
-      newCentroids.extent(1),
-      newCentroids.extent(0),
-      true,
-      false,
+    raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
+      handle,
+      raft::make_const_mdspan(newCentroids.view()),
+      raft::make_const_mdspan(wtInCluster.view()),
+      newCentroids.view(),
       cuda::proclaim_return_type<DataT>([=] __device__(DataT mat, DataT vec) {
         if (vec == 0)
           return DataT(0);
         else
           return mat / vec;
-      }),
-      stream);
+      }));
 
     // copy the centroids[i] to newCentroids[i] when wtInCluster[i] is 0
     cub::ArgIndexInputIterator<DataT*> itr_wt(wtInCluster.data_handle());
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index d4f1b6e686..403eaf81a7 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -101,12 +101,12 @@ void select_residuals(raft::resources const& handle,
     mapping_itr(dataset, utils::mapping<float>{});
   raft::matrix::gather(mapping_itr, (IdxT)dim, n_rows, row_ids, n_rows, tmp.data(), stream);
 
-  raft::matrix::linewise_op(handle,
-                            raft::make_device_matrix_view<const T, IdxT>(tmp.data(), n_rows, dim),
-                            raft::make_device_matrix_view<T, IdxT>(tmp.data(), n_rows, dim),
-                            true,
-                            raft::sub_op{},
-                            raft::make_device_vector_view<const T, IdxT>(center, dim));
+  raft::matrix::linewise_op<raft::Apply::ALONG_ROWS>(
+    handle,
+    raft::make_device_matrix_view<const T, IdxT>(tmp.data(), n_rows, dim),
+    raft::make_device_matrix_view<T, IdxT>(tmp.data(), n_rows, dim),
+    raft::sub_op{},
+    raft::make_device_vector_view<const T, IdxT>(center, dim));
 
   float alpha = 1.0;
   float beta  = 0.0;
diff --git a/cpp/src/stats/detail/silhouette_score.cuh b/cpp/src/stats/detail/silhouette_score.cuh
index 88cb051f15..f323d9c2ed 100644
--- a/cpp/src/stats/detail/silhouette_score.cuh
+++ b/cpp/src/stats/detail/silhouette_score.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -274,16 +274,24 @@ DataT silhouette_score(
   RAFT_CUDA_TRY(cudaMemsetAsync(
     averageDistanceBetweenSampleAndCluster.data(), 0, nRows * nLabels * sizeof(DataT), stream));
 
-  raft::linalg::matrixVectorOp(averageDistanceBetweenSampleAndCluster.data(),
-                               sampleToClusterSumOfDistances.data(),
-                               binCountArray.data(),
-                               binCountArray.data(),
-                               nLabels,
-                               nRows,
-                               true,
-                               true,
-                               DivOp<DataT>(),
-                               stream);
+  auto averageDistanceBetweenSampleAndClusterView = raft::make_device_matrix_view<DataT>(
+    averageDistanceBetweenSampleAndCluster.data(), nRows, nLabels);
+  auto sampleToClusterSumOfDistancesView = raft::make_device_matrix_view<const DataT>(
+    sampleToClusterSumOfDistances.data(), nRows, nLabels);
+  auto binCountArrayView =
+    raft::make_device_vector_view<const DataT>(binCountArray.data(), nLabels);
+
+  raft::linalg::matrix_vector_op<raft::Apply::ALONG_ROWS>(
+    handle,
+    sampleToClusterSumOfDistancesView,
+    binCountArrayView,
+    averageDistanceBetweenSampleAndClusterView,
+    [] __device__(DataT a, DataT b) {
+      if (b == 0)
+        return static_cast<DataT>(ULLONG_MAX);
+      else
+        return a / b;
+    });
 
   // calculating row-wise minimum
   raft::linalg::reduce<true, true, DataT, DataT, int, raft::identity_op, raft::min_op>(
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index aac2c067d5..03c37455f9 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -233,12 +233,11 @@ void InitDataset(const raft::resources& handle,
                                        raft::sq_op(),
                                        raft::add_op(),
                                        raft::sqrt_op());
-      raft::linalg::matrix_vector_op(
+      raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
         handle,
         raft::make_const_mdspan(dataset_view),
         raft::make_const_mdspan(dev_row_norm.view()),
         dataset_view,
-        raft::Apply::ALONG_COLUMNS,
         [normalized_norm] __device__(DataT elm, ComputeT norm) {
           const ComputeT v           = elm / norm * normalized_norm;
           const ComputeT max_v_range = std::numeric_limits<DataT>::max();

From dc8b9457d0109122e3e9f4a5f37f0e9b543b99bf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Fri, 4 Jul 2025 01:50:42 +0200
Subject: [PATCH 465/623] [Review][Java] Fix random segabort/segfault/double
 free problems (#1045)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The cuVS C API we are leveraging uses a common C pattern for "object creation": you pass down a pointer, and that gets populated. Often, this is a **, and memory gets allocated on the other side:
```
create(voi**ptrPtr) {
   *ptrPtr = new Something;
}
void** ptrPtr = // allocate space for a pointer
create(ptrPtr);
void* ptr = *ptrPtr;
```
In Panama, this means
- allocating a small pointer size MemorySegment, very short lived
- call `create`
- extract a MemorySegment from the MemorySegment

Existing code sometimes skipped the extraction bit (the dereference), and/or created the structure directly instead of using the C `create` functions; this lead to some segfaults (addressing the wrong memory/wrong size) and to some double frees (the JVM freed MemorySegments we allocated via an arena, but we also called the `destroy` functions, so the C side tried to free them too).

Plus, this PR tries to reduce the lifetime of some allocations, avoiding to use the `CuVSResources` arena when it is clearly not needed -- but I did not want to expand the scope too much, so I just did the more obvious ones. Optimizing memory allocation is still a WIP, especially on the search side, but this I think will deserve its own PR.

These changes makes the test suite pass reliably - I have not seen a segmentation fault over hundreds of executions locally, and lowers the native memory occupied a little.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1045
---
 .../cuvs/internal/BruteForceIndexImpl.java    | 176 +++++++-------
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 118 ++++-----
 .../cuvs/internal/CuVSResourcesImpl.java      |  32 +--
 .../nvidia/cuvs/internal/HnswIndexImpl.java   | 129 +++++-----
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 226 +++++++++++++-----
 5 files changed, 381 insertions(+), 300 deletions(-)

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 362b946e3a..7b70bd0bb3 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -37,7 +37,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSerialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
@@ -49,10 +48,7 @@
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
-import com.nvidia.cuvs.internal.panama.cuvsBruteForceIndex;
 import com.nvidia.cuvs.internal.panama.cuvsFilter;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.lang.foreign.Arena;
@@ -94,8 +90,7 @@ private BruteForceIndexImpl(
       float[][] vectors,
       Dataset dataset,
       CuVSResourcesImpl resources,
-      BruteForceIndexParams bruteForceIndexParams)
-      throws Throwable {
+      BruteForceIndexParams bruteForceIndexParams) {
     this.vectors = vectors;
     this.dataset = dataset;
     this.resources = resources;
@@ -132,8 +127,17 @@ private void checkNotDestroyed() {
   public void destroyIndex() throws Throwable {
     checkNotDestroyed();
     try {
-      int returnValue = cuvsBruteForceIndexDestroy(bruteForceIndexReference.getMemorySegment());
+      int returnValue = cuvsBruteForceIndexDestroy(bruteForceIndexReference.indexPtr);
       checkCuVSError(returnValue, "cuvsBruteForceIndexDestroy");
+
+      if (bruteForceIndexReference.datasetBytes > 0) {
+        returnValue =
+            cuvsRMMFree(
+                resources.getHandle(),
+                bruteForceIndexReference.datasetPtr,
+                bruteForceIndexReference.datasetBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+      }
     } finally {
       destroyed = true;
     }
@@ -147,7 +151,7 @@ public void destroyIndex() throws Throwable {
    * @return an instance of {@link IndexReference} that holds the pointer to the
    *         index
    */
-  private IndexReference build() throws Throwable {
+  private IndexReference build() {
     try (var localArena = Arena.ofConfined()) {
       long rows = dataset != null ? dataset.size() : vectors.length;
       long cols = dataset != null ? dataset.dimensions() : (rows > 0 ? vectors[0].length : 0);
@@ -158,17 +162,14 @@ private IndexReference build() throws Throwable {
               ? ((DatasetImpl) dataset).seg
               : Util.buildMemorySegment(resources.getArena(), vectors);
 
-      long cuvsResources = resources.getMemorySegment().get(cuvsResources_t, 0);
-      MemorySegment stream = arena.allocate(cudaStream_t);
-      var returnValue = cuvsStreamGet(cuvsResources, stream);
-      checkCuVSError(returnValue, "cuvsStreamGet");
+      long cuvsResources = resources.getHandle();
 
       omp_set_num_threads(bruteForceIndexParams.getNumWriterThreads());
 
-      MemorySegment datasetMemorySegment = arena.allocate(C_POINTER);
+      MemorySegment datasetMemorySegment = localArena.allocate(C_POINTER);
 
       long datasetBytes = C_FLOAT_BYTE_SIZE * rows * cols;
-      returnValue = cuvsRMMAlloc(cuvsResources, datasetMemorySegment, datasetBytes);
+      var returnValue = cuvsRMMAlloc(cuvsResources, datasetMemorySegment, datasetBytes);
       checkCuVSError(returnValue, "cuvsRMMAlloc");
 
       // IMPORTANT: this should only come AFTER cuvsRMMAlloc call
@@ -176,19 +177,18 @@ private IndexReference build() throws Throwable {
 
       cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
 
-      long datasetShape[] = {rows, cols};
+      long[] datasetShape = {rows, cols};
       MemorySegment datasetTensor =
           prepareTensor(arena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
 
-      MemorySegment index = arena.allocate(cuvsBruteForceIndex_t);
-
-      returnValue = cuvsBruteForceIndexCreate(index);
-      checkCuVSError(returnValue, "cuvsBruteForceIndexCreate");
+      var indexReference =
+          new IndexReference(datasetMemorySegmentP, datasetBytes, createBruteForceIndex());
 
       returnValue = cuvsStreamSync(cuvsResources);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, index);
+      returnValue =
+          cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, indexReference.indexPtr);
       checkCuVSError(returnValue, "cuvsBruteForceBuild");
 
       returnValue = cuvsStreamSync(cuvsResources);
@@ -196,7 +196,7 @@ private IndexReference build() throws Throwable {
 
       omp_set_num_threads(1);
 
-      return new IndexReference(index);
+      return indexReference;
     }
   }
 
@@ -228,30 +228,27 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       BitSet[] prefilters = cuvsQuery.getPrefilters();
       if (prefilters != null && prefilters.length > 0) {
         BitSet concatenatedFilters = concatenate(prefilters, cuvsQuery.getNumDocs());
-        long filters[] = concatenatedFilters.toLongArray();
+        long[] filters = concatenatedFilters.toLongArray();
         prefilterDataMemorySegment = buildMemorySegment(arena, filters);
-        prefilterDataLength = cuvsQuery.getNumDocs() * prefilters.length;
+        prefilterDataLength = (long) cuvsQuery.getNumDocs() * prefilters.length;
       }
 
       MemorySegment querySeg = buildMemorySegment(arena, cuvsQuery.getQueryVectors());
 
       int topk = cuvsQuery.getTopK();
-      long cuvsResources = resources.getMemorySegment().get(cuvsResources_t, 0);
+      long cuvsResources = resources.getHandle();
       MemorySegment stream = arena.allocate(cudaStream_t);
       var returnValue = cuvsStreamGet(cuvsResources, stream);
       checkCuVSError(returnValue, "cuvsStreamGet");
 
-      MemorySegment queriesD = arena.allocate(C_POINTER);
-      MemorySegment neighborsD = arena.allocate(C_POINTER);
-      MemorySegment distancesD = arena.allocate(C_POINTER);
-      MemorySegment prefilterD = arena.allocate(C_POINTER);
-      MemorySegment prefilterDP = MemorySegment.NULL;
-      long prefilterLen = 0;
+      MemorySegment queriesD = localArena.allocate(C_POINTER);
+      MemorySegment neighborsD = localArena.allocate(C_POINTER);
+      MemorySegment distancesD = localArena.allocate(C_POINTER);
 
       long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
       long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
       long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
-      long prefilterBytes = 0; // size assigned later
+      long prefilterBytes = 0;
 
       returnValue = cuvsRMMAlloc(cuvsResources, queriesD, queriesBytes);
       checkCuVSError(returnValue, "cuvsRMMAlloc");
@@ -264,15 +261,16 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       MemorySegment queriesDP = queriesD.get(C_POINTER, 0);
       MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
       MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
+      MemorySegment prefilterDP = MemorySegment.NULL;
 
       cudaMemcpy(queriesDP, querySeg, queriesBytes, INFER_DIRECTION);
 
-      long queriesShape[] = {numQueries, vectorDimension};
+      long[] queriesShape = {numQueries, vectorDimension};
       MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
-      long neighborsShape[] = {numQueries, topk};
+      long[] neighborsShape = {numQueries, topk};
       MemorySegment neighborsTensor =
           prepareTensor(arena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
-      long distancesShape[] = {numQueries, topk};
+      long[] distancesShape = {numQueries, topk};
       MemorySegment distancesTensor =
           prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
 
@@ -283,13 +281,14 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
         cuvsFilter.type(prefilter, 0); // NO_FILTER
         cuvsFilter.addr(prefilter, 0);
       } else {
-        long prefilterShape[] = {(prefilterDataLength + 31) / 32};
-        prefilterLen = prefilterShape[0];
+        long[] prefilterShape = {(prefilterDataLength + 31) / 32};
+
+        MemorySegment prefilterD = localArena.allocate(C_POINTER);
+        long prefilterLen = prefilterShape[0];
         prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
         returnValue = cuvsRMMAlloc(cuvsResources, prefilterD, prefilterBytes);
         checkCuVSError(returnValue, "cuvsRMMAlloc");
-
         prefilterDP = prefilterD.get(C_POINTER, 0);
 
         cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
@@ -306,7 +305,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       returnValue =
           cuvsBruteForceSearch(
               cuvsResources,
-              bruteForceIndexReference.getMemorySegment(),
+              bruteForceIndexReference.indexPtr,
               queriesTensor,
               neighborsTensor,
               distancesTensor,
@@ -325,8 +324,10 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       checkCuVSError(returnValue, "cuvsRMMFree");
       returnValue = cuvsRMMFree(cuvsResources, queriesDP, queriesBytes);
       checkCuVSError(returnValue, "cuvsRMMFree");
-      returnValue = cuvsRMMFree(cuvsResources, prefilterDP, prefilterBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
+      if (prefilterBytes > 0) {
+        returnValue = cuvsRMMFree(cuvsResources, prefilterDP, prefilterBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+      }
 
       return new BruteForceSearchResults(
           neighborsSequenceLayout,
@@ -351,21 +352,40 @@ public void serialize(OutputStream outputStream, Path tempFile) throws Throwable
     checkNotDestroyed();
     tempFile = tempFile.toAbsolutePath();
 
-    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-    int returnValue =
-        cuvsBruteForceSerialize(
-            cuvsRes,
-            resources.getArena().allocateFrom(tempFile.toString()),
-            bruteForceIndexReference.getMemorySegment());
-    checkCuVSError(returnValue, "cuvsBruteForceSerialize");
+    long cuvsRes = resources.getHandle();
+    try (var localArena = Arena.ofConfined()) {
+      int returnValue =
+          cuvsBruteForceSerialize(
+              cuvsRes,
+              localArena.allocateFrom(tempFile.toString()),
+              bruteForceIndexReference.indexPtr);
+      checkCuVSError(returnValue, "cuvsBruteForceSerialize");
+    }
 
-    try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
-      fileInputStream.transferTo(outputStream);
+    try (var inputStream = Files.newInputStream(tempFile)) {
+      inputStream.transferTo(outputStream);
     } finally {
       Files.deleteIfExists(tempFile);
     }
   }
 
+  private static MemorySegment createBruteForceIndex() {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment indexPtrPtr = localArena.allocate(cuvsBruteForceIndex_t);
+      // cuvsBruteForceIndexCreate gets a pointer to a cuvsBruteForceIndex_t, which is defined as a
+      // pointer to
+      // cuvsBruteForceIndex.
+      // It's basically a "out" parameter: the C functions will create the index and "return back" a
+      // pointer to it.
+      // The "out parameter" pointer is needed only for the duration of the function invocation (it
+      // could be a stack
+      // pointer, in C) so we allocate it from our localArena, unwrap it and return it.
+      var returnValue = cuvsBruteForceIndexCreate(indexPtrPtr);
+      checkCuVSError(returnValue, "cuvsBruteForceIndexCreate");
+      return indexPtrPtr.get(cuvsBruteForceIndex_t, 0);
+    }
+  }
+
   /**
    * Gets an instance of {@link IndexReference} by deserializing a BRUTEFORCE
    * index using an {@link InputStream}.
@@ -376,20 +396,19 @@ public void serialize(OutputStream outputStream, Path tempFile) throws Throwable
   private IndexReference deserialize(InputStream inputStream) throws Throwable {
     checkNotDestroyed();
     Path tmpIndexFile =
-        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf");
-    tmpIndexFile = tmpIndexFile.toAbsolutePath();
-    IndexReference indexReference = new IndexReference(resources);
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".bf")
+            .toAbsolutePath();
+    IndexReference indexReference = new IndexReference(createBruteForceIndex());
 
-    try (var in = inputStream;
-        FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
-      in.transferTo(fileOutputStream);
+    try (inputStream;
+        var outputStream = Files.newOutputStream(tmpIndexFile);
+        var arena = Arena.ofConfined()) {
+      inputStream.transferTo(outputStream);
 
-      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      long cuvsRes = resources.getHandle();
       int returnValue =
           cuvsBruteForceDeserialize(
-              cuvsRes,
-              resources.getArena().allocateFrom(tmpIndexFile.toString()),
-              indexReference.getMemorySegment());
+              cuvsRes, arena.allocateFrom(tmpIndexFile.toString()), indexReference.indexPtr);
       checkCuVSError(returnValue, "cuvsBruteForceDeserialize");
 
     } finally {
@@ -492,37 +511,24 @@ public BruteForceIndexImpl build() throws Throwable {
   }
 
   /**
-   * Holds the memory reference to a BRUTEFORCE index.
+   * Holds the memory reference to a BRUTEFORCE index and its associated dataset
    */
-  protected static class IndexReference {
-
-    private final MemorySegment memorySegment;
+  private static class IndexReference {
 
-    /**
-     * Constructs CagraIndexReference and allocate the MemorySegment.
-     */
-    protected IndexReference(CuVSResourcesImpl resources) {
-      memorySegment = cuvsBruteForceIndex.allocate(resources.getArena());
-    }
+    private final MemorySegment datasetPtr;
+    private final long datasetBytes;
+    private final MemorySegment indexPtr;
 
-    /**
-     * Constructs BruteForceIndexReference with an instance of MemorySegment passed
-     * as a parameter.
-     *
-     * @param indexMemorySegment the MemorySegment instance to use for containing
-     *                           index reference
-     */
-    protected IndexReference(MemorySegment indexMemorySegment) {
-      this.memorySegment = indexMemorySegment;
+    private IndexReference(MemorySegment datasetPtr, long datasetBytes, MemorySegment indexPtr) {
+      this.datasetPtr = datasetPtr;
+      this.datasetBytes = datasetBytes;
+      this.indexPtr = indexPtr;
     }
 
-    /**
-     * Gets the instance of index MemorySegment.
-     *
-     * @return index MemorySegment
-     */
-    protected MemorySegment getMemorySegment() {
-      return memorySegment;
+    private IndexReference(MemorySegment indexPtr) {
+      this.datasetPtr = MemorySegment.NULL;
+      this.datasetBytes = 0;
+      this.indexPtr = indexPtr;
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 43bb92ff5c..f4c10cc295 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -40,7 +40,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerializeToHnswlib;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
@@ -59,7 +58,6 @@
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
-import com.nvidia.cuvs.internal.panama.cuvsCagraIndex;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraMergeParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
@@ -68,7 +66,6 @@
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqParams;
 import com.nvidia.cuvs.internal.panama.cuvsIvfPqSearchParams;
 import java.io.FileInputStream;
-import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.io.OutputStream;
 import java.lang.foreign.Arena;
@@ -165,12 +162,9 @@ private void checkNotDestroyed() {
   @Override
   public void destroyIndex() throws Throwable {
     checkNotDestroyed();
-    try (var arena = Arena.ofConfined()) {
-      int returnValue = cuvsCagraIndexDestroy(cagraIndexReference.getMemorySegment());
-      checkCuVSError(returnValue, "cuvsCagraIndexDestroy");
-    } finally {
-      destroyed = true;
-    }
+    int returnValue = cuvsCagraIndexDestroy(cagraIndexReference.getMemorySegment());
+    checkCuVSError(returnValue, "cuvsCagraIndexDestroy");
+    destroyed = true;
     if (dataset != null) dataset.close();
   }
 
@@ -188,7 +182,7 @@ private IndexReference build() throws Throwable {
 
       MemorySegment indexParamsMemorySegment =
           cagraIndexParameters != null
-              ? segmentFromIndexParams(resources, cagraIndexParameters)
+              ? segmentFromIndexParams(localArena, cagraIndexParameters)
               : MemorySegment.NULL;
 
       int numWriterThreads =
@@ -200,18 +194,13 @@ private IndexReference build() throws Throwable {
               ? ((DatasetImpl) dataset).seg
               : Util.buildMemorySegment(resources.getArena(), vectors);
 
-      Arena arena = resources.getArena();
-      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-      MemorySegment stream = arena.allocate(cudaStream_t);
-      var returnValue = cuvsStreamGet(cuvsRes, stream);
-      checkCuVSError(returnValue, "cuvsStreamGet");
+      long cuvsRes = resources.getHandle();
 
       long datasetShape[] = {rows, cols};
-      MemorySegment datasetTensor = prepareTensor(arena, dataSeg, datasetShape, 2, 32, 2, 2, 1);
+      MemorySegment datasetTensor =
+          prepareTensor(resources.getArena(), dataSeg, datasetShape, 2, 32, 2, 2, 1);
 
-      MemorySegment index = arena.allocate(cuvsCagraIndex_t);
-      returnValue = cuvsCagraIndexCreate(index);
-      checkCuVSError(returnValue, "cuvsCagraIndexCreate");
+      var indexReference = createCagraIndex();
 
       if (cuvsCagraIndexParams.build_algo(indexParamsMemorySegment)
           == 1) { // when build algo is IVF_PQ
@@ -224,10 +213,12 @@ private IndexReference build() throws Throwable {
             cuvsIvfPqIndexParamsMS, (int) (rows < n_lists ? rows : n_lists));
       }
 
-      returnValue = cuvsStreamSync(cuvsRes);
+      var returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsCagraBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, index);
+      returnValue =
+          cuvsCagraBuild(
+              cuvsRes, indexParamsMemorySegment, datasetTensor, indexReference.getMemorySegment());
       checkCuVSError(returnValue, "cuvsCagraBuild");
 
       returnValue = cuvsStreamSync(cuvsRes);
@@ -235,7 +226,24 @@ private IndexReference build() throws Throwable {
 
       omp_set_num_threads(1);
 
-      return new IndexReference(index);
+      return indexReference;
+    }
+  }
+
+  private static IndexReference createCagraIndex() {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment indexPtrPtr = localArena.allocate(cuvsCagraIndex_t);
+      // cuvsCagraIndexCreate gets a pointer to a cuvsCagraIndex_t, which is defined as a pointer to
+      // cuvsCagraIndex.
+      // It's basically a "out" parameter: the C functions will create the index and "return back" a
+      // pointer to it:
+      // (*index = new cuvsCagraIndex{};
+      // The "out parameter" pointer is needed only for the duration of the function invocation (it
+      // could be a stack
+      // pointer, in C) so we allocate it from our localArena
+      var returnValue = cuvsCagraIndexCreate(indexPtrPtr);
+      checkCuVSError(returnValue, "cuvsCagraIndexCreate");
+      return new IndexReference(indexPtrPtr.get(cuvsCagraIndex_t, 0));
     }
   }
 
@@ -266,7 +274,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
       MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
       MemorySegment floatsSeg = buildMemorySegment(arena, query.getQueryVectors());
 
-      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      long cuvsRes = resources.getHandle();
       MemorySegment stream = arena.allocate(cudaStream_t);
       int returnValue = cuvsStreamGet(cuvsRes, stream);
       checkCuVSError(returnValue, "cuvsStreamGet");
@@ -407,16 +415,16 @@ public void serialize(OutputStream outputStream, Path tempFile, int bufferLength
     tempFile = tempFile.toAbsolutePath();
     try (var localArena = Arena.ofConfined()) {
 
-      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      long cuvsRes = resources.getHandle();
       var returnValue =
           cuvsCagraSerialize(
               cuvsRes,
-              resources.getArena().allocateFrom(tempFile.toString()),
+              localArena.allocateFrom(tempFile.toString()),
               cagraIndexReference.getMemorySegment(),
               true);
       checkCuVSError(returnValue, "cuvsCagraSerialize");
 
-      try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
+      try (var fileInputStream = Files.newInputStream(tempFile)) {
         byte[] chunk = new byte[bufferLength];
         int chunkLength = 0;
         while ((chunkLength = fileInputStream.read(chunk)) != -1) {
@@ -449,7 +457,7 @@ public void serializeToHNSW(OutputStream outputStream, Path tempFile, int buffer
     tempFile = tempFile.toAbsolutePath();
     MemorySegment pathSeg = buildMemorySegment(resources.getArena(), tempFile.toString());
 
-    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+    long cuvsRes = resources.getHandle();
     int returnValue =
         cuvsCagraSerializeToHnswlib(cuvsRes, pathSeg, cagraIndexReference.getMemorySegment());
     checkCuVSError(returnValue, "cuvsCagraSerializeToHnswlib");
@@ -465,41 +473,29 @@ public void serializeToHNSW(OutputStream outputStream, Path tempFile, int buffer
     }
   }
 
-  /**
-   * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
-   * using an {@link InputStream}.
-   *
-   * @param inputStream an instance of {@link InputStream}
-   * @return an instance of {@link IndexReference}.
-   */
-  private IndexReference deserialize(InputStream inputStream) throws Throwable {
-    return deserialize(inputStream, 1024);
-  }
-
   /**
    * Gets an instance of {@link IndexReference} by deserializing a CAGRA index
    * using an {@link InputStream}.
    *
    * @param inputStream  an instance of {@link InputStream}
-   * @param bufferLength the length of the buffer to use while reading the bytes
-   *                     from the stream. Default value is 1024.
    * @return an instance of {@link IndexReference}.
    */
-  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
+  private IndexReference deserialize(InputStream inputStream) throws Throwable {
     Path tmpIndexFile =
-        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag");
-    tmpIndexFile = tmpIndexFile.toAbsolutePath();
-    IndexReference indexReference = new IndexReference(resources);
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag")
+            .toAbsolutePath();
+    IndexReference indexReference = createCagraIndex();
 
-    try (var in = inputStream;
-        FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
-      in.transferTo(fileOutputStream);
+    try (inputStream;
+        var outputStream = Files.newOutputStream(tmpIndexFile);
+        var arena = Arena.ofConfined()) {
+      inputStream.transferTo(outputStream);
 
-      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      long cuvsRes = resources.getHandle();
       var returnValue =
           cuvsCagraDeserialize(
               cuvsRes,
-              resources.getArena().allocateFrom(tmpIndexFile.toString()),
+              arena.allocateFrom(tmpIndexFile.toString()),
               indexReference.getMemorySegment());
       checkCuVSError(returnValue, "cuvsCagraDeserialize");
 
@@ -532,9 +528,7 @@ public CuVSResourcesImpl getCuVSResources() {
   /**
    * Allocates the configured index parameters in the MemorySegment.
    */
-  private static MemorySegment segmentFromIndexParams(
-      CuVSResourcesImpl resources, CagraIndexParams params) {
-    Arena arena = resources.getArena();
+  private static MemorySegment segmentFromIndexParams(Arena arena, CagraIndexParams params) {
     MemorySegment seg = cuvsCagraIndexParams.allocate(arena);
     cuvsCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
     cuvsCagraIndexParams.graph_degree(seg, params.getGraphDegree());
@@ -684,8 +678,8 @@ public static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
   public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams)
       throws Throwable {
     CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
-    IndexReference mergedIndexReference = new IndexReference(resources);
-    long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+    IndexReference mergedIndexReference = createCagraIndex();
+    long cuvsRes = resources.getHandle();
 
     try (var arena = Arena.ofConfined()) {
       MemorySegment indexesSegment =
@@ -725,12 +719,13 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
    */
   private static MemorySegment createMergeParamsSegment(
       CagraMergeParams mergeParams, CuVSResourcesImpl resources) {
-    MemorySegment seg = cuvsCagraMergeParams.allocate(resources.getArena());
+    var arena = resources.getArena();
+    MemorySegment seg = cuvsCagraMergeParams.allocate(arena);
 
     if (mergeParams != null) {
       if (mergeParams.getOutputIndexParams() != null) {
         MemorySegment outputIndexParamsSeg =
-            segmentFromIndexParams(resources, mergeParams.getOutputIndexParams());
+            segmentFromIndexParams(arena, mergeParams.getOutputIndexParams());
         cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
       } else {
         cuvsCagraMergeParams.output_index_params(seg, MemorySegment.NULL);
@@ -739,7 +734,7 @@ private static MemorySegment createMergeParamsSegment(
       cuvsCagraMergeParams.strategy(seg, mergeParams.getStrategy().value);
     } else {
       MemorySegment outputIndexParamsSeg =
-          segmentFromIndexParams(resources, new CagraIndexParams.Builder().build());
+          segmentFromIndexParams(arena, new CagraIndexParams.Builder().build());
       cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
     }
 
@@ -806,13 +801,6 @@ public static class IndexReference {
 
     private final MemorySegment memorySegment;
 
-    /**
-     * Constructs CagraIndexReference and allocate the MemorySegment.
-     */
-    protected IndexReference(CuVSResourcesImpl resources) {
-      memorySegment = cuvsCagraIndex.allocate(resources.getArena());
-    }
-
     /**
      * Constructs CagraIndexReference with an instance of MemorySegment passed as a
      * parameter.
@@ -820,7 +808,7 @@ protected IndexReference(CuVSResourcesImpl resources) {
      * @param indexMemorySegment the MemorySegment instance to use for containing
      *                           index reference
      */
-    protected IndexReference(MemorySegment indexMemorySegment) {
+    private IndexReference(MemorySegment indexMemorySegment) {
       this.memorySegment = indexMemorySegment;
     }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
index 0270d31795..3b0c2afdbd 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -22,7 +22,6 @@
 
 import com.nvidia.cuvs.CuVSResources;
 import java.lang.foreign.Arena;
-import java.lang.foreign.MemorySegment;
 import java.nio.file.Path;
 
 /**
@@ -34,7 +33,7 @@ public class CuVSResourcesImpl implements CuVSResources {
 
   private final Path tempDirectory;
   private final Arena arena;
-  private final MemorySegment resourcesMemorySegment;
+  private final long resourceHandle;
   private boolean destroyed;
 
   /**
@@ -44,19 +43,22 @@ public class CuVSResourcesImpl implements CuVSResources {
    */
   public CuVSResourcesImpl(Path tempDirectory) throws Throwable {
     this.tempDirectory = tempDirectory;
-    arena = Arena.ofShared();
-    resourcesMemorySegment = arena.allocate(cuvsResources_t);
-    int returnValue = cuvsResourcesCreate(resourcesMemorySegment);
-    checkCuVSError(returnValue, "cuvsResourcesCreate");
+    this.arena = Arena.ofShared();
+    try (var localArena = Arena.ofConfined()) {
+      var resourcesMemorySegment = localArena.allocate(cuvsResources_t);
+      int returnValue = cuvsResourcesCreate(resourcesMemorySegment);
+      checkCuVSError(returnValue, "cuvsResourcesCreate");
+      resourceHandle = resourcesMemorySegment.get(cuvsResources_t, 0);
+    }
   }
 
   @Override
   public void close() {
-    checkNotDestroyed();
-    int returnValue = cuvsResourcesDestroy(resourcesMemorySegment.get(cuvsResources_t, 0));
-    checkCuVSError(returnValue, "cuvsResourcesDestroy");
-    destroyed = true;
-    if (!arena.scope().isAlive()) {
+    synchronized (this) {
+      checkNotDestroyed();
+      int returnValue = cuvsResourcesDestroy(resourceHandle);
+      checkCuVSError(returnValue, "cuvsResourcesDestroy");
+      destroyed = true;
       arena.close();
     }
   }
@@ -73,13 +75,13 @@ private void checkNotDestroyed() {
   }
 
   /**
-   * Gets the reference to the cuvsResources MemorySegment.
+   * Gets the opaque CuVSResources handle, to be used whenever we need to pass a cuvsResources_t parameter
    *
-   * @return cuvsResources MemorySegment
+   * @return the CuVSResources handle
    */
-  protected MemorySegment getMemorySegment() {
+  long getHandle() {
     checkNotDestroyed();
-    return resourcesMemorySegment;
+    return resourceHandle;
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index a068e6eee8..be3890b186 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -23,8 +23,8 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswDeserialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndexCreate;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndexDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndex_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswSearch;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 
 import com.nvidia.cuvs.CuVSResources;
@@ -37,7 +37,6 @@
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndex;
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsHnswSearchParams;
-import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemoryLayout;
@@ -78,7 +77,7 @@ private HnswIndexImpl(
    * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
    */
   @Override
-  public void destroyIndex() throws Throwable {
+  public void destroyIndex() {
     int returnValue = cuvsHnswIndexDestroy(hnswIndexReference.getMemorySegment());
     checkCuVSError(returnValue, "cuvsHnswIndexDestroy");
   }
@@ -97,7 +96,7 @@ public SearchResults search(HnswQuery query) throws Throwable {
       int topK = query.getTopK();
       float[][] queryVectors = query.getQueryVectors();
       int numQueries = queryVectors.length;
-      long numBlocks = topK * numQueries;
+      long numBlocks = (long) topK * numQueries;
       int vectorDimension = numQueries > 0 ? queryVectors[0].length : 0;
       Arena arena = resources.getArena();
 
@@ -107,14 +106,14 @@ public SearchResults search(HnswQuery query) throws Throwable {
       MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
       MemorySegment querySeg = buildMemorySegment(arena, queryVectors);
 
-      long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
+      long cuvsRes = resources.getHandle();
 
-      long queriesShape[] = {numQueries, vectorDimension};
+      long[] queriesShape = {numQueries, vectorDimension};
       MemorySegment queriesTensor = prepareTensor(arena, querySeg, queriesShape, 2, 32, 2, 1, 1);
-      long neighborsShape[] = {numQueries, topK};
+      long[] neighborsShape = {numQueries, topK};
       MemorySegment neighborsTensor =
           prepareTensor(arena, neighborsMemorySegment, neighborsShape, 1, 64, 2, 1, 1);
-      long distancesShape[] = {numQueries, topK};
+      long[] distancesShape = {numQueries, topK};
       MemorySegment distancesTensor =
           prepareTensor(arena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
 
@@ -145,6 +144,22 @@ public SearchResults search(HnswQuery query) throws Throwable {
     }
   }
 
+  private static IndexReference createHnswIndex() {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment indexPtrPtr = localArena.allocate(cuvsHnswIndex_t);
+      // cuvsHnswIndexCreate gets a pointer to a cuvsHnswIndex_t, which is defined as a pointer to
+      // cuvsHnswIndex.
+      // It's basically a "out" parameter: the C functions will create the index and "return back" a
+      // pointer to it.
+      // The "out parameter" pointer is needed only for the duration of the function invocation (it
+      // could be a stack
+      // pointer, in C) so we allocate it from our localArena.
+      var returnValue = cuvsHnswIndexCreate(indexPtrPtr);
+      checkCuVSError(returnValue, "cuvsHnswIndexCreate");
+      return new IndexReference(indexPtrPtr.get(cuvsHnswIndex_t, 0));
+    }
+  }
+
   /**
    * Gets an instance of {@link IndexReference} by deserializing a HNSW index
    * using an {@link InputStream}.
@@ -153,62 +168,43 @@ public SearchResults search(HnswQuery query) throws Throwable {
    * @return an instance of {@link IndexReference}.
    */
   private IndexReference deserialize(InputStream inputStream) throws Throwable {
-    return deserialize(inputStream, 1024);
-  }
+    Path tmpIndexFile =
+        Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw")
+            .toAbsolutePath();
 
-  /**
-   * Gets an instance of {@link IndexReference} by deserializing a HNSW index
-   * using an {@link InputStream}.
-   *
-   * @param inputStream  an instance of {@link InputStream}
-   * @param bufferLength the length of the buffer to use while reading the bytes
-   *                     from the stream. Default value is 1024.
-   * @return an instance of {@link IndexReference}.
-   */
-  private IndexReference deserialize(InputStream inputStream, int bufferLength) throws Throwable {
-    try (var localArena = Arena.ofConfined()) {
-      Path tmpIndexFile =
-          Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".hnsw");
-      tmpIndexFile = tmpIndexFile.toAbsolutePath();
-
-      try (var in = inputStream;
-          FileOutputStream fileOutputStream = new FileOutputStream(tmpIndexFile.toFile())) {
-        byte[] chunk = new byte[bufferLength];
-        int chunkLength;
-        while ((chunkLength = in.read(chunk)) != -1) {
-          fileOutputStream.write(chunk, 0, chunkLength);
-        }
-
-        Arena arena = resources.getArena();
-        MemorySegment pathSeg = buildMemorySegment(arena, tmpIndexFile.toString());
-
-        long cuvsRes = resources.getMemorySegment().get(cuvsResources_t, 0);
-        MemorySegment hnswIndex = cuvsHnswIndex.allocate(arena);
-        int returnValue = cuvsHnswIndexCreate(hnswIndex);
-        checkCuVSError(returnValue, "cuvsHnswIndexCreate");
-
-        MemorySegment dtype = DLDataType.allocate(arena);
-        DLDataType.bits(dtype, (byte) 32);
-        DLDataType.code(dtype, (byte) 2); // kDLFloat
-        DLDataType.lanes(dtype, (byte) 1);
-
-        cuvsHnswIndex.dtype(hnswIndex, dtype);
-
-        returnValue =
-            cuvsHnswDeserialize(
-                cuvsRes,
-                segmentFromIndexParams(hnswIndexParams),
-                pathSeg,
-                hnswIndexParams.getVectorDimension(),
-                0,
-                hnswIndex);
-        checkCuVSError(returnValue, "cuvsHnswDeserialize");
-
-        return new IndexReference(hnswIndex);
-
-      } finally {
-        Files.deleteIfExists(tmpIndexFile);
-      }
+    try (inputStream;
+        var outputStream = Files.newOutputStream(tmpIndexFile);
+        var localArena = Arena.ofConfined()) {
+      inputStream.transferTo(outputStream);
+
+      Arena arena = resources.getArena();
+      MemorySegment pathSeg = buildMemorySegment(localArena, tmpIndexFile.toString());
+
+      long cuvsRes = resources.getHandle();
+
+      var indexReference = createHnswIndex();
+
+      MemorySegment dtype = DLDataType.allocate(arena);
+      DLDataType.bits(dtype, (byte) 32);
+      DLDataType.code(dtype, (byte) 2); // kDLFloat
+      DLDataType.lanes(dtype, (byte) 1);
+
+      cuvsHnswIndex.dtype(indexReference.memorySegment, dtype);
+
+      var returnValue =
+          cuvsHnswDeserialize(
+              cuvsRes,
+              segmentFromIndexParams(hnswIndexParams),
+              pathSeg,
+              hnswIndexParams.getVectorDimension(),
+              0,
+              indexReference.memorySegment);
+      checkCuVSError(returnValue, "cuvsHnswDeserialize");
+
+      return indexReference;
+
+    } finally {
+      Files.deleteIfExists(tmpIndexFile);
     }
   }
 
@@ -302,13 +298,6 @@ protected static class IndexReference {
 
     private final MemorySegment memorySegment;
 
-    /**
-     * Constructs CagraIndexReference and allocate the MemorySegment.
-     */
-    protected IndexReference(CuVSResourcesImpl resources) {
-      memorySegment = cuvsHnswIndex.allocate(resources.getArena());
-    }
-
     /**
      * Constructs CagraIndexReference with an instance of MemorySegment passed as a
      * parameter.
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 54e3815ef3..6b802de1d0 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -29,6 +29,8 @@
 import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.invoke.MethodHandles;
+import java.nio.file.Files;
+import java.nio.file.Path;
 import java.util.Arrays;
 import java.util.BitSet;
 import java.util.List;
@@ -70,6 +72,7 @@ private static void runConcurrently(int nThreads, Supplier<Runnable> runnableSup
       CompletableFuture.allOf(futures)
           .exceptionally(
               t -> {
+                log.error("Exception while executing runnable", t);
                 fail("Exception while executing runnable: " + t);
                 return null;
               })
@@ -124,7 +127,11 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     int numTestsRuns = 5;
     try (CuVSResources resources = CuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
-        indexAndQueryOnce(dataset, map, queries, expectedResults, resources);
+        var index = indexOnce(dataset, resources);
+        var indexPath = serializeOnce(index);
+        var loadedIndex = deserializeOnce(indexPath, resources);
+        queryOnce(index, loadedIndex, map, queries, expectedResults, resources);
+        cleanup(indexPath, index, loadedIndex);
       }
     }
   }
@@ -144,7 +151,17 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
     try (CuVSResources resources = CuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
         runInAnotherThread(
-            () -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
+            () -> {
+              try {
+                var index = indexOnce(dataset, resources);
+                var indexPath = serializeOnce(index);
+                var loadedIndex = deserializeOnce(indexPath, resources);
+                queryOnce(index, loadedIndex, map, queries, expectedResults, resources);
+                cleanup(indexPath, index, loadedIndex);
+              } catch (Throwable e) {
+                throw new RuntimeException(e);
+              }
+            });
       }
     }
   }
@@ -164,14 +181,87 @@ public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
     try (CuVSResources resources = CuVSResources.create()) {
       runConcurrently(
           numTestsRuns,
-          () -> () -> indexAndQueryOnce(dataset, map, queries, expectedResults, resources));
+          () ->
+              () -> {
+                try {
+                  var index = indexOnce(dataset, resources);
+                  var indexPath = serializeOnce(index);
+                  var loadedIndex = deserializeOnce(indexPath, resources);
+                  queryOnce(index, loadedIndex, map, queries, expectedResults, resources);
+                  cleanup(indexPath, index, loadedIndex);
+                } catch (Throwable e) {
+                  throw new RuntimeException(e);
+                }
+              });
+    }
+  }
+
+  @Test
+  public void testIndexing() throws Throwable {
+    for (int i = 0; i < 100; ++i) {
+      float[][] dataset = createSampleData();
+      int numTestsRuns = 10;
+      runConcurrently(
+          numTestsRuns,
+          () ->
+              () -> {
+                try (CuVSResources resources = CuVSResources.create()) {
+                  var index = indexOnce(dataset, resources);
+                  index.destroyIndex();
+                } catch (Throwable e) {
+                  throw new RuntimeException(e);
+                }
+              });
+    }
+  }
+
+  @Test
+  public void testSerialization() throws Throwable {
+    for (int i = 0; i < 100; ++i) {
+      float[][] dataset = createSampleData();
+      int numTestsRuns = 10;
+      runConcurrently(
+          numTestsRuns,
+          () ->
+              () -> {
+                try (CuVSResources resources = CuVSResources.create()) {
+                  var index = indexOnce(dataset, resources);
+                  var indexPath = serializeOnce(index);
+                  Files.deleteIfExists(indexPath);
+                  index.destroyIndex();
+                } catch (Throwable e) {
+                  throw new RuntimeException(e);
+                }
+              });
+    }
+  }
+
+  @Test
+  public void testDeserialization() throws Throwable {
+    float[][] dataset = createSampleData();
+    try (CuVSResources resources = CuVSResources.create()) {
+      var index = indexOnce(dataset, resources);
+      var indexPath = serializeOnce(index);
+      for (int i = 0; i < 100; ++i) {
+        int numTestsRuns = 10;
+        runConcurrently(
+            numTestsRuns,
+            () ->
+                () -> {
+                  try {
+                    deserializeOnce(indexPath, resources).destroyIndex();
+                  } catch (Throwable e) {
+                    throw new RuntimeException(e);
+                  }
+                });
+      }
+      Files.deleteIfExists(indexPath);
+      index.destroyIndex();
     }
   }
 
   /**
    * A test that checks the pre-filtering feature.
-   *
-   * @throws Throwable
    */
   @Test
   public void testPrefilteringReducesResults() throws Throwable {
@@ -238,75 +328,81 @@ public void testPrefilteringReducesResults() throws Throwable {
     }
   }
 
-  private void indexAndQueryOnce(
-      float[][] dataset,
-      List<Integer> map,
-      float[][] queries,
-      List<Map<Integer, Float>> expectedResults,
-      CuVSResources resources) {
-
-    try {
-
-      // Configure index parameters
-      CagraIndexParams indexParams =
-          new CagraIndexParams.Builder()
-              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-              .withGraphDegree(1)
-              .withIntermediateGraphDegree(2)
-              .withNumWriterThreads(32)
-              .withMetric(CuvsDistanceType.L2Expanded)
-              .build();
-
-      // Create the index with the dataset
-      CagraIndex index =
-          CagraIndex.newBuilder(resources)
-              .withDataset(dataset)
-              .withIndexParams(indexParams)
-              .build();
+  private CagraIndex indexOnce(float[][] dataset, CuVSResources resources) throws Throwable {
+    // Configure index parameters
+    CagraIndexParams indexParams =
+        new CagraIndexParams.Builder()
+            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+            .withGraphDegree(1)
+            .withIntermediateGraphDegree(2)
+            .withNumWriterThreads(32)
+            .withMetric(CuvsDistanceType.L2Expanded)
+            .build();
 
-      // Saving the index on to the disk.
-      String indexFileName = UUID.randomUUID() + ".cag";
-      index.serialize(new FileOutputStream(indexFileName));
+    // Create the index with the dataset
+    return CagraIndex.newBuilder(resources)
+        .withDataset(dataset)
+        .withIndexParams(indexParams)
+        .build();
+  }
 
-      // Loading a CAGRA index from disk.
-      File indexFile = new File(indexFileName);
-      InputStream inputStream = new FileInputStream(indexFile);
-      CagraIndex loadedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
+  private Path serializeOnce(CagraIndex index) throws Throwable {
+    // Saving the index on to the disk.
+    var indexFilePath = Path.of(UUID.randomUUID() + ".cag");
+    try (var outputStream = Files.newOutputStream(indexFilePath)) {
+      index.serialize(outputStream);
+    }
+    return indexFilePath;
+  }
 
-      // Configure search parameters
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+  private CagraIndex deserializeOnce(Path indexFilePath, CuVSResources resources) throws Throwable {
+    // Loading a CAGRA index from disk.
+    try (var inputStream = Files.newInputStream(indexFilePath)) {
+      return CagraIndex.newBuilder(resources).from(inputStream).build();
+    }
+  }
 
-      // Create a query object with the query vectors
-      CagraQuery cuvsQuery =
-          new CagraQuery.Builder()
-              .withTopK(3)
-              .withSearchParams(searchParams)
-              .withQueryVectors(queries)
-              .withMapping(map)
-              .build();
+  private void queryOnce(
+      CagraIndex index,
+      CagraIndex loadedIndex,
+      List<Integer> map,
+      float[][] queries,
+      List<Map<Integer, Float>> expectedResults,
+      CuVSResources resources)
+      throws Throwable {
+    // Configure search parameters
+    CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+
+    // Create a query object with the query vectors
+    CagraQuery cuvsQuery =
+        new CagraQuery.Builder()
+            .withTopK(3)
+            .withSearchParams(searchParams)
+            .withQueryVectors(queries)
+            .withMapping(map)
+            .build();
 
-      // Perform the search
-      SearchResults results = index.search(cuvsQuery);
+    // Perform the search
+    SearchResults results = index.search(cuvsQuery);
 
-      // Check results
-      log.info(results.getResults().toString());
-      assertEquals(expectedResults, results.getResults());
+    // Check results
+    log.info(results.getResults().toString());
+    assertEquals(expectedResults, results.getResults());
 
-      // Search from deserialized index
-      results = loadedIndex.search(cuvsQuery);
+    // Search from deserialized index
+    results = loadedIndex.search(cuvsQuery);
 
-      // Check results
-      log.info(results.getResults().toString());
-      assertEquals(expectedResults, results.getResults());
+    // Check results
+    log.info(results.getResults().toString());
+    assertEquals(expectedResults, results.getResults());
+  }
 
-      // Cleanup
-      if (indexFile.exists()) {
-        indexFile.delete();
-      }
-      index.destroyIndex();
-    } catch (Throwable ex) {
-      throw new RuntimeException("Exception during indexing/querying", ex);
-    }
+  private void cleanup(Path indexFilePath, CagraIndex index, CagraIndex loadedIndex)
+      throws Throwable {
+    // Cleanup
+    Files.deleteIfExists(indexFilePath);
+    index.destroyIndex();
+    loadedIndex.destroyIndex();
   }
 
   @Test

From 4118dd68762a8159d839f3d13562f9855cd4da16 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 4 Jul 2025 11:34:46 -0700
Subject: [PATCH 466/623] Minor correction for Java test instructions (#1081)

This is a minor correction to the instructions for running Java tests.

The instructions as updated in https://github.com/rapidsai/cuvs/pull/1004 indicates the following:
```
mvn verify -Dintegration-test=com.nvidia.cuvs.CagraBuildAndSearchIT
```

Per the documentation for the [`failsafe`](https://maven.apache.org/surefire/maven-failsafe-plugin/examples/single-test.html), plugin, this commit changes this to the following:
```
mvn verify -Dit.test=com.nvidia.cuvs.CagraBuildAndSearchIT
```

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1081
---
 java/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/java/README.md b/java/README.md
index b568a1ffa0..d7d9e3858e 100644
--- a/java/README.md
+++ b/java/README.md
@@ -28,7 +28,7 @@ Run `./build.sh --run-java-tests` from this directory.
 To run a single test:
 ```sh
 cd cuvs-java/
-mvn verify -Dintegration-test=com.nvidia.cuvs.CagraBuildAndSearchIT
+mvn verify -Dit.test=com.nvidia.cuvs.CagraBuildAndSearchIT
 ```
 Be sure to set (manually, if needed) your `LD_LIBRARY_PATH` to include the directory with the appropriate (matching)
 version of `libcuvs.so`.

From 52ff6af801312f72df74fcd2a257a0605f97418a Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Fri, 4 Jul 2025 22:55:02 +0200
Subject: [PATCH 467/623] hnsw::from_cagra: avoid allocating the graph twice
 (#1057)

When CAGRA graph is not host accessible, HNSW::from_cagra routine copies the data from it to the base layer of HNSW in two stages: first to a host matrix, then to the HNSW index object. As a result, the graph, which can be huge, is allocated in the host memory twice.
For big enough graphs this manifests itself in the program being killed by the linux kernel at the end of the index building process due to not enough system memory.

This PR avoids the staging graph buffer by using cudaMemcpy2DAsync to copy the data in an appropriate format in one go.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/1057
---
 cpp/src/neighbors/detail/hnsw.hpp | 53 +++++++++++++++++++------------
 1 file changed, 32 insertions(+), 21 deletions(-)

diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index 403655cc28..f5a1486f58 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -472,35 +472,46 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
     }
   }
 
-  // move cagra graph to host or access it from host if available
-  auto host_graph_view = cagra_index.graph();
-  auto host_graph      = raft::make_host_matrix<uint32_t, int64_t>(0, 0);
-  if (!raft::is_host_accessible(raft::memory_type_from_pointer(host_graph_view.data_handle()))) {
-    // copy cagra graph to host
-    host_graph = raft::make_host_matrix<uint32_t, int64_t>(host_graph_view.extent(0),
-                                                           host_graph_view.extent(1));
-    raft::copy(host_graph.data_handle(),
-               host_graph_view.data_handle(),
-               host_graph_view.size(),
-               raft::resource::get_cuda_stream(res));
-    raft::resource::sync_stream(res);
-    host_graph_view = host_graph.view();
+  auto graph_ptr = cagra_index.graph().data_handle();
+  cudaPointerAttributes attr;
+  RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, graph_ptr));
+  bool is_host_accessible = false;
+  int64_t degree          = cagra_index.graph().extent(1);
+  if (attr.type == cudaMemoryTypeUnregistered) {
+    is_host_accessible = true;
+  } else if (attr.hostPointer != nullptr) {
+    graph_ptr          = static_cast<uint32_t*>(attr.hostPointer);
+    is_host_accessible = true;
   }
 
-  {
-    common::nvtx::range<common::nvtx::domain::cuvs> copy_scope("get_linklist0");
-// copy cagra graph to hnswlib base layer
+  // copy cagra graph to hnswlib base layer
+  if (is_host_accessible) {
+    common::nvtx::range<common::nvtx::domain::cuvs> copy_scope("get_linklist0<host>");
 #pragma omp parallel for num_threads(num_threads)
-    for (size_t i = 0; i < static_cast<size_t>(host_graph_view.extent(0)); ++i) {
+    for (int64_t i = 0; i < n_rows; i++) {
       auto ll_i = appr_algo->get_linklist0(i);
-      appr_algo->setListCount(ll_i, host_graph_view.extent(1));
+      appr_algo->setListCount(ll_i, degree);
       auto* data = (uint32_t*)(ll_i + 1);
-      for (size_t j = 0; j < static_cast<size_t>(host_graph_view.extent(1)); ++j) {
-        data[j] = host_graph_view(i, j);
+      for (int64_t j = 0; j < degree; j++) {
+        data[j] = graph_ptr[i * degree + j];
       }
     }
+  } else {
+    common::nvtx::range<common::nvtx::domain::cuvs> copy_scope("get_linklist0<device>");
+    RAFT_CUDA_TRY(cudaMemcpy2DAsync(appr_algo->get_linklist0(0) + 1,
+                                    appr_algo->size_data_per_element_,
+                                    graph_ptr,
+                                    degree * sizeof(uint32_t),
+                                    degree * sizeof(uint32_t),
+                                    n_rows,
+                                    cudaMemcpyDefault,
+                                    raft::resource::get_cuda_stream(res)));
+#pragma omp parallel for num_threads(num_threads)
+    for (int64_t i = 0; i < n_rows; i++) {
+      appr_algo->setListCount(appr_algo->get_linklist0(i), degree);
+    }
+    raft::resource::sync_stream(res);
   }
-
   hnsw_index->set_index(std::move(appr_algo));
   return hnsw_index;
 }

From f02ee48967d4277875b205950b22a59712262880 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Sun, 6 Jul 2025 03:44:00 +0200
Subject: [PATCH 468/623] [Java] Encapsulate on-heap float arrays into
 `Dataset` (#1024)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR is a follow-up from https://github.com/rapidsai/cuvs/pull/902.
Still WIP (see self-comments on the changes) but I'd like some early feedback.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1024
---
 java/README.md                                |   8 +-
 .../main/java/com/nvidia/cuvs/CagraIndex.java |   7 -
 .../main/java/com/nvidia/cuvs/Dataset.java    |  29 ++-
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |   7 +-
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |   7 +-
 .../cuvs/internal/BruteForceIndexImpl.java    |  45 ++---
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 167 ++++++------------
 .../com/nvidia/cuvs/internal/DatasetImpl.java |  32 ++--
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  44 ++++-
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |   8 +-
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   8 +-
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |   8 +-
 12 files changed, 177 insertions(+), 193 deletions(-)

diff --git a/java/README.md b/java/README.md
index d7d9e3858e..b913ebe709 100644
--- a/java/README.md
+++ b/java/README.md
@@ -25,10 +25,14 @@ do `./build.sh java` in the top level directory or just do `./build.sh` in this
 
 Run `./build.sh --run-java-tests` from this directory.
 
-To run a single test:
+To run a single test suite:
 ```sh
 cd cuvs-java/
-mvn verify -Dit.test=com.nvidia.cuvs.CagraBuildAndSearchIT
+mvn clean integration-test -Dit.test=com.nvidia.cuvs.CagraBuildAndSearchIT
+```
+or, for a single test:
+```sh
+mvn clean integration-test -Dit.test=com.nvidia.cuvs.CagraBuildAndSearchIT#testMergeStrategies
 ```
 Be sure to set (manually, if needed) your `LD_LIBRARY_PATH` to include the directory with the appropriate (matching)
 version of `libcuvs.so`.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index e878f77109..a6fc4775c3 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -143,13 +143,6 @@ default void serializeToHNSW(OutputStream outputStream, Path tempFile) throws Th
    */
   void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength) throws Throwable;
 
-  /**
-   * Gets an instance of {@link CagraIndexParams}
-   *
-   * @return an instance of {@link CagraIndexParams}
-   */
-  CagraIndexParams getCagraIndexParameters();
-
   /**
    * Gets an instance of {@link CuVSResources}
    *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
index 7bc7dbd797..9eaad71861 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
@@ -28,21 +28,34 @@
 public interface Dataset extends AutoCloseable {
 
   /**
-   * Add a single vector to the dataset.
+   * Creates a dataset from a on-heap array of vectors
    *
-   * @param vector A float array of as many elements as the dimensions
+   * @since 25.08
    */
-  public void addVector(float[] vector);
+  static Dataset ofArray(float[][] vectors) {
+    return CuVSProvider.provider().newArrayDataset(vectors);
+  }
+
+  interface Builder {
+    /**
+     * Add a single vector to the dataset.
+     *
+     * @param vector A float array of as many elements as the dimensions
+     */
+    void addVector(float[] vector);
+
+    Dataset build();
+  }
 
   /**
-   * Create a new instance of a dataset
+   * Returns a builder to create a new instance of a dataset
    *
    * @param size       Number of vectors in the dataset
    * @param dimensions Size of each vector in the dataset
    * @return new instance of {@link Dataset}
    */
-  static Dataset create(int size, int dimensions) {
-    return CuVSProvider.provider().newDataset(size, dimensions);
+  static Dataset.Builder builder(int size, int dimensions) {
+    return CuVSProvider.provider().newDatasetBuilder(size, dimensions);
   }
 
   /**
@@ -50,12 +63,12 @@ static Dataset create(int size, int dimensions) {
    *
    * @return Size of the dataset
    */
-  public int size();
+  int size();
 
   /**
    * Gets the dimensions of the vectors in this dataset
    *
    * @return Dimensions of the vectors in the dataset
    */
-  public int dimensions();
+  int dimensions();
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 12c0a99902..458fa36a48 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -49,8 +49,11 @@ default Path nativeLibraryPath() {
   /** Creates a new CuVSResources. */
   CuVSResources newCuVSResources(Path tempDirectory) throws Throwable;
 
-  /** Create a {@link Dataset} instance **/
-  Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException;
+  /** Create a {@link Dataset.Builder} instance **/
+  Dataset.Builder newDatasetBuilder(int size, int dimensions);
+
+  /** Create a {@link Dataset} backed by a on-heap array **/
+  Dataset newArrayDataset(float[][] vectors);
 
   /** Creates a new BruteForceIndex Builder. */
   BruteForceIndex.Builder newBruteForceIndexBuilder(CuVSResources cuVSResources)
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index b9b5997905..e1168f9d92 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -53,7 +53,12 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
   }
 
   @Override
-  public Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException {
+  public Dataset.Builder newDatasetBuilder(int size, int dimensions) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public Dataset newArrayDataset(float[][] vectors) {
     throw new UnsupportedOperationException();
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 7b70bd0bb3..932d89ee33 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -47,7 +47,6 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
-import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsFilter;
 import java.io.InputStream;
 import java.io.OutputStream;
@@ -70,11 +69,8 @@
  */
 public class BruteForceIndexImpl implements BruteForceIndex {
 
-  private final float[][] vectors;
-  private final Dataset dataset;
   private final CuVSResourcesImpl resources;
   private final IndexReference bruteForceIndexReference;
-  private final BruteForceIndexParams bruteForceIndexParams;
   private boolean destroyed;
 
   /**
@@ -87,15 +83,14 @@ public class BruteForceIndexImpl implements BruteForceIndex {
    *                              holding the index parameters
    */
   private BruteForceIndexImpl(
-      float[][] vectors,
-      Dataset dataset,
-      CuVSResourcesImpl resources,
-      BruteForceIndexParams bruteForceIndexParams) {
-    this.vectors = vectors;
-    this.dataset = dataset;
-    this.resources = resources;
-    this.bruteForceIndexParams = bruteForceIndexParams;
-    this.bruteForceIndexReference = build();
+      Dataset dataset, CuVSResourcesImpl resources, BruteForceIndexParams bruteForceIndexParams)
+      throws Exception {
+    Objects.requireNonNull(dataset);
+    try (dataset) {
+      this.resources = resources;
+      assert dataset instanceof DatasetImpl;
+      this.bruteForceIndexReference = build((DatasetImpl) dataset, bruteForceIndexParams);
+    }
   }
 
   /**
@@ -106,9 +101,6 @@ private BruteForceIndexImpl(
    */
   private BruteForceIndexImpl(InputStream inputStream, CuVSResourcesImpl resources)
       throws Throwable {
-    this.bruteForceIndexParams = null;
-    this.vectors = null;
-    this.dataset = null;
     this.resources = resources;
     this.bruteForceIndexReference = deserialize(inputStream);
   }
@@ -124,7 +116,7 @@ private void checkNotDestroyed() {
    * BRUTEFORCE index
    */
   @Override
-  public void destroyIndex() throws Throwable {
+  public void destroyIndex() {
     checkNotDestroyed();
     try {
       int returnValue = cuvsBruteForceIndexDestroy(bruteForceIndexReference.indexPtr);
@@ -141,7 +133,6 @@ public void destroyIndex() throws Throwable {
     } finally {
       destroyed = true;
     }
-    if (dataset != null) dataset.close();
   }
 
   /**
@@ -151,16 +142,13 @@ public void destroyIndex() throws Throwable {
    * @return an instance of {@link IndexReference} that holds the pointer to the
    *         index
    */
-  private IndexReference build() {
+  private IndexReference build(DatasetImpl dataset, BruteForceIndexParams bruteForceIndexParams) {
     try (var localArena = Arena.ofConfined()) {
-      long rows = dataset != null ? dataset.size() : vectors.length;
-      long cols = dataset != null ? dataset.dimensions() : (rows > 0 ? vectors[0].length : 0);
+      long rows = dataset.size();
+      long cols = dataset.dimensions();
 
       Arena arena = resources.getArena();
-      MemorySegment datasetMemSegment =
-          dataset != null
-              ? ((DatasetImpl) dataset).seg
-              : Util.buildMemorySegment(resources.getArena(), vectors);
+      MemorySegment datasetMemSegment = dataset.asMemorySegment();
 
       long cuvsResources = resources.getHandle();
 
@@ -248,7 +236,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
       long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
       long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
-      long prefilterBytes = 0;
+      long prefilterBytes = 0; // size assigned later
 
       returnValue = cuvsRMMAlloc(cuvsResources, queriesD, queriesBytes);
       checkCuVSError(returnValue, "cuvsRMMAlloc");
@@ -430,7 +418,6 @@ public static BruteForceIndex.Builder newBuilder(CuVSResources cuvsResources) {
    */
   public static class Builder implements BruteForceIndex.Builder {
 
-    private float[][] vectors;
     private Dataset dataset;
     private final CuVSResourcesImpl cuvsResources;
     private BruteForceIndexParams bruteForceIndexParams;
@@ -479,7 +466,7 @@ public Builder from(InputStream inputStream) {
      */
     @Override
     public Builder withDataset(float[][] vectors) {
-      this.vectors = vectors;
+      this.dataset = Dataset.ofArray(vectors);
       return this;
     }
 
@@ -505,7 +492,7 @@ public BruteForceIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new BruteForceIndexImpl(inputStream, cuvsResources);
       } else {
-        return new BruteForceIndexImpl(vectors, dataset, cuvsResources, bruteForceIndexParams);
+        return new BruteForceIndexImpl(dataset, cuvsResources, bruteForceIndexParams);
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index f4c10cc295..7d41dbb9a9 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -31,10 +31,8 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraDeserialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexCreate;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexGetDims;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndex_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraMerge;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraMergeParams_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSearch;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerializeToHnswlib;
@@ -56,7 +54,6 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
-import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraMergeParams;
@@ -92,10 +89,7 @@
  * @since 25.02
  */
 public class CagraIndexImpl implements CagraIndex {
-  private final float[][] vectors;
-  private final Dataset dataset;
   private final CuVSResourcesImpl resources;
-  private final CagraIndexParams cagraIndexParameters;
   private final IndexReference cagraIndexReference;
   private boolean destroyed;
 
@@ -108,16 +102,11 @@ public class CagraIndexImpl implements CagraIndex {
    * @param resources       an instance of {@link CuVSResources}
    */
   private CagraIndexImpl(
-      CagraIndexParams indexParameters,
-      float[][] vectors,
-      Dataset dataset,
-      CuVSResourcesImpl resources)
-      throws Throwable {
-    this.cagraIndexParameters = indexParameters;
-    this.vectors = vectors;
-    this.dataset = dataset;
+      CagraIndexParams indexParameters, Dataset dataset, CuVSResourcesImpl resources) {
+    Objects.requireNonNull(dataset);
     this.resources = resources;
-    this.cagraIndexReference = build();
+    assert dataset instanceof DatasetImpl;
+    this.cagraIndexReference = build(indexParameters, (DatasetImpl) dataset);
   }
 
   /**
@@ -127,9 +116,6 @@ private CagraIndexImpl(
    * @param resources   an instance of {@link CuVSResources}
    */
   private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
-    this.cagraIndexParameters = null;
-    this.vectors = null;
-    this.dataset = null;
     this.resources = resources;
     this.cagraIndexReference = deserialize(inputStream);
   }
@@ -142,9 +128,6 @@ private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) thr
    * @param resources The resources instance
    */
   private CagraIndexImpl(IndexReference indexReference, CuVSResourcesImpl resources) {
-    this.vectors = null;
-    this.cagraIndexParameters = null;
-    this.dataset = null;
     this.resources = resources;
     this.cagraIndexReference = indexReference;
     this.destroyed = false;
@@ -162,10 +145,15 @@ private void checkNotDestroyed() {
   @Override
   public void destroyIndex() throws Throwable {
     checkNotDestroyed();
-    int returnValue = cuvsCagraIndexDestroy(cagraIndexReference.getMemorySegment());
-    checkCuVSError(returnValue, "cuvsCagraIndexDestroy");
-    destroyed = true;
-    if (dataset != null) dataset.close();
+    try {
+      int returnValue = cuvsCagraIndexDestroy(cagraIndexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsCagraIndexDestroy");
+      if (cagraIndexReference.dataset != null) {
+        cagraIndexReference.dataset.close();
+      }
+    } finally {
+      destroyed = true;
+    }
   }
 
   /**
@@ -175,32 +163,28 @@ public void destroyIndex() throws Throwable {
    * @return an instance of {@link IndexReference} that holds the pointer to the
    *         index
    */
-  private IndexReference build() throws Throwable {
+  private IndexReference build(CagraIndexParams indexParameters, DatasetImpl dataset) {
     try (var localArena = Arena.ofConfined()) {
-      long rows = dataset != null ? dataset.size() : vectors.length;
-      long cols = dataset != null ? dataset.dimensions() : (rows > 0 ? vectors[0].length : 0);
+      long rows = dataset.size();
+      long cols = dataset.dimensions();
 
       MemorySegment indexParamsMemorySegment =
-          cagraIndexParameters != null
-              ? segmentFromIndexParams(localArena, cagraIndexParameters)
+          indexParameters != null
+              ? segmentFromIndexParams(localArena, indexParameters)
               : MemorySegment.NULL;
 
-      int numWriterThreads =
-          cagraIndexParameters != null ? cagraIndexParameters.getNumWriterThreads() : 1;
+      int numWriterThreads = indexParameters != null ? indexParameters.getNumWriterThreads() : 1;
       omp_set_num_threads(numWriterThreads);
 
-      MemorySegment dataSeg =
-          dataset != null
-              ? ((DatasetImpl) dataset).seg
-              : Util.buildMemorySegment(resources.getArena(), vectors);
+      MemorySegment dataSeg = dataset.asMemorySegment();
 
       long cuvsRes = resources.getHandle();
 
-      long datasetShape[] = {rows, cols};
+      long[] datasetShape = {rows, cols};
       MemorySegment datasetTensor =
           prepareTensor(resources.getArena(), dataSeg, datasetShape, 2, 32, 2, 2, 1);
 
-      var indexReference = createCagraIndex();
+      var index = createCagraIndex();
 
       if (cuvsCagraIndexParams.build_algo(indexParamsMemorySegment)
           == 1) { // when build algo is IVF_PQ
@@ -216,9 +200,7 @@ private IndexReference build() throws Throwable {
       var returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue =
-          cuvsCagraBuild(
-              cuvsRes, indexParamsMemorySegment, datasetTensor, indexReference.getMemorySegment());
+      returnValue = cuvsCagraBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, index);
       checkCuVSError(returnValue, "cuvsCagraBuild");
 
       returnValue = cuvsStreamSync(cuvsRes);
@@ -226,24 +208,22 @@ private IndexReference build() throws Throwable {
 
       omp_set_num_threads(1);
 
-      return indexReference;
+      return new IndexReference(index, dataset);
     }
   }
 
-  private static IndexReference createCagraIndex() {
+  private static MemorySegment createCagraIndex() {
     try (var localArena = Arena.ofConfined()) {
       MemorySegment indexPtrPtr = localArena.allocate(cuvsCagraIndex_t);
       // cuvsCagraIndexCreate gets a pointer to a cuvsCagraIndex_t, which is defined as a pointer to
       // cuvsCagraIndex.
-      // It's basically a "out" parameter: the C functions will create the index and "return back" a
-      // pointer to it:
-      // (*index = new cuvsCagraIndex{};
+      // It's basically an "out" parameter: the C functions will create the index and "return back"
+      // a pointer to it: (*index = new cuvsCagraIndex{};
       // The "out parameter" pointer is needed only for the duration of the function invocation (it
-      // could be a stack
-      // pointer, in C) so we allocate it from our localArena
+      // could be a stack pointer, in C) so we allocate it from our localArena
       var returnValue = cuvsCagraIndexCreate(indexPtrPtr);
       checkCuVSError(returnValue, "cuvsCagraIndexCreate");
-      return new IndexReference(indexPtrPtr.get(cuvsCagraIndex_t, 0));
+      return indexPtrPtr.get(cuvsCagraIndex_t, 0);
     }
   }
 
@@ -305,12 +285,12 @@ public SearchResults search(CagraQuery query) throws Throwable {
 
       cudaMemcpy(queriesDP, floatsSeg, queriesBytes, INFER_DIRECTION);
 
-      long queriesShape[] = {numQueries, vectorDimension};
+      long[] queriesShape = {numQueries, vectorDimension};
       MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
-      long neighborsShape[] = {numQueries, topK};
+      long[] neighborsShape = {numQueries, topK};
       MemorySegment neighborsTensor =
           prepareTensor(arena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
-      long distancesShape[] = {numQueries, topK};
+      long[] distancesShape = {numQueries, topK};
       MemorySegment distancesTensor =
           prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
 
@@ -324,7 +304,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
       if (query.getPrefilter() != null) {
         prefilters = new BitSet[] {query.getPrefilter()};
         BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
-        long filters[] = concatenatedFilters.toLongArray();
+        long[] filters = concatenatedFilters.toLongArray();
         prefilterDataMemorySegment = buildMemorySegment(arena, filters);
         prefilterDataLength = query.getNumDocs() * prefilters.length;
       }
@@ -336,7 +316,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
         cuvsFilter.type(prefilter, 0); // NO_FILTER
         cuvsFilter.addr(prefilter, 0);
       } else {
-        long prefilterShape[] = {(prefilterDataLength + 31) / 32};
+        long[] prefilterShape = {(prefilterDataLength + 31) / 32};
         prefilterLen = prefilterShape[0];
         prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
@@ -484,7 +464,7 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
     Path tmpIndexFile =
         Files.createTempFile(resources.tempDirectory(), UUID.randomUUID().toString(), ".cag")
             .toAbsolutePath();
-    IndexReference indexReference = createCagraIndex();
+    var index = createCagraIndex();
 
     try (inputStream;
         var outputStream = Files.newOutputStream(tmpIndexFile);
@@ -493,26 +473,13 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
 
       long cuvsRes = resources.getHandle();
       var returnValue =
-          cuvsCagraDeserialize(
-              cuvsRes,
-              arena.allocateFrom(tmpIndexFile.toString()),
-              indexReference.getMemorySegment());
+          cuvsCagraDeserialize(cuvsRes, arena.allocateFrom(tmpIndexFile.toString()), index);
       checkCuVSError(returnValue, "cuvsCagraDeserialize");
 
     } finally {
       Files.deleteIfExists(tmpIndexFile);
     }
-    return indexReference;
-  }
-
-  /**
-   * Gets an instance of {@link CagraIndexParams}
-   *
-   * @return an instance of {@link CagraIndexParams}
-   */
-  @Override
-  public CagraIndexParams getCagraIndexParameters() {
-    return cagraIndexParameters;
+    return new IndexReference(index, null);
   }
 
   /**
@@ -641,29 +608,13 @@ public static CagraIndex.Builder newBuilder(CuVSResources cuvsResources) {
     return new CagraIndexImpl.Builder((CuVSResourcesImpl) cuvsResources);
   }
 
-  /**
-   * Gets the CAGRA index dimensions
-   *
-   * @return the CAGRA index dimensions
-   */
-  public int getIndexDimensions() {
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment dimensionsMemorySegment = localArena.allocate(C_INT);
-      var returnValue =
-          cuvsCagraIndexGetDims(cagraIndexReference.getMemorySegment(), dimensionsMemorySegment);
-      checkCuVSError(returnValue, "cuvsCagraIndexGetDims");
-      return dimensionsMemorySegment.get(C_INT, 0);
-    }
-  }
-
   /**
    * Merges multiple CAGRA indexes into a single index.
    *
    * @param indexes Array of CAGRA indexes to merge
    * @return A new merged CAGRA index
-   * @throws Throwable if an error occurs during the merge operation
    */
-  public static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
+  public static CagraIndex merge(CagraIndex[] indexes) {
     return merge(indexes, null);
   }
 
@@ -673,12 +624,10 @@ public static CagraIndex merge(CagraIndex[] indexes) throws Throwable {
    * @param indexes Array of CAGRA indexes to merge
    * @param mergeParams Parameters to control the merge operation, or null to use defaults
    * @return A new merged CAGRA index
-   * @throws Throwable if an error occurs during the merge operation
    */
-  public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams)
-      throws Throwable {
+  public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) {
     CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
-    IndexReference mergedIndexReference = createCagraIndex();
+    var mergedIndex = createCagraIndex();
     long cuvsRes = resources.getHandle();
 
     try (var arena = Arena.ofConfined()) {
@@ -691,23 +640,14 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
             ValueLayout.ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
       }
 
-      MemorySegment mergeParamsSegment = arena.allocate(cuvsCagraMergeParams_t);
-      int returnValue;
-
-      mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
-
-      returnValue =
-          cuvsCagraMerge(
-              cuvsRes,
-              mergeParamsSegment,
-              indexesSegment,
-              indexes.length,
-              mergedIndexReference.getMemorySegment());
+      MemorySegment mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
+      int returnValue =
+          cuvsCagraMerge(cuvsRes, mergeParamsSegment, indexesSegment, indexes.length, mergedIndex);
 
       checkCuVSError(returnValue, "cuvsCagraMerge");
     }
 
-    return new CagraIndexImpl(mergedIndexReference, resources);
+    return new CagraIndexImpl(new IndexReference(mergedIndex, null), resources);
   }
 
   /**
@@ -746,10 +686,9 @@ private static MemorySegment createMergeParamsSegment(
    */
   public static class Builder implements CagraIndex.Builder {
 
-    private float[][] vectors;
     private Dataset dataset;
     private CagraIndexParams cagraIndexParams;
-    private CuVSResourcesImpl cuvsResources;
+    private final CuVSResourcesImpl cuvsResources;
     private InputStream inputStream;
 
     public Builder(CuVSResourcesImpl cuvsResources) {
@@ -764,7 +703,7 @@ public Builder from(InputStream inputStream) {
 
     @Override
     public Builder withDataset(float[][] vectors) {
-      this.vectors = vectors;
+      this.dataset = Dataset.ofArray(vectors);
       return this;
     }
 
@@ -785,11 +724,7 @@ public CagraIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new CagraIndexImpl(inputStream, cuvsResources);
       } else {
-        if (vectors != null && dataset != null) {
-          throw new IllegalArgumentException(
-              "Please specify only one type of dataset (a float[] or a Dataset instance)");
-        }
-        return new CagraIndexImpl(cagraIndexParams, vectors, dataset, cuvsResources);
+        return new CagraIndexImpl(cagraIndexParams, dataset, cuvsResources);
       }
     }
   }
@@ -800,6 +735,7 @@ public CagraIndexImpl build() throws Throwable {
   public static class IndexReference {
 
     private final MemorySegment memorySegment;
+    private final Dataset dataset;
 
     /**
      * Constructs CagraIndexReference with an instance of MemorySegment passed as a
@@ -807,9 +743,14 @@ public static class IndexReference {
      *
      * @param indexMemorySegment the MemorySegment instance to use for containing
      *                           index reference
+     * @param dataset            the dataset used for indexing; the dataset lifetime
+     *                           matches the lifetime of the index, we need to keep a reference
+     *                           to it so we can close it when the index is closed.
+     *                           Can be null (e.g. from deserialization or merging)
      */
-    private IndexReference(MemorySegment indexMemorySegment) {
+    private IndexReference(MemorySegment indexMemorySegment, Dataset dataset) {
       this.memorySegment = indexMemorySegment;
+      this.dataset = dataset;
     }
 
     /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
index 5e26dcd73a..ee065e632b 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
@@ -15,40 +15,28 @@
  */
 package com.nvidia.cuvs.internal;
 
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-
 import com.nvidia.cuvs.Dataset;
 import java.lang.foreign.Arena;
-import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
+import java.util.concurrent.atomic.AtomicReference;
 
 public class DatasetImpl implements Dataset {
-  private final Arena arena;
-  protected final MemorySegment seg;
+  private final AtomicReference<Arena> arenaReference;
+  private final MemorySegment seg;
   private final int size;
   private final int dimensions;
-  private int current = 0;
 
-  public DatasetImpl(int size, int dimensions) {
+  public DatasetImpl(Arena arena, MemorySegment memorySegment, int size, int dimensions) {
+    this.arenaReference = new AtomicReference<>(arena);
+    this.seg = memorySegment;
     this.size = size;
     this.dimensions = dimensions;
-
-    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(size * dimensions, C_FLOAT);
-
-    this.arena = Arena.ofShared();
-    seg = arena.allocate(dataMemoryLayout);
-  }
-
-  @Override
-  public void addVector(float[] vector) {
-    if (current >= size) throw new ArrayIndexOutOfBoundsException();
-    MemorySegment.copy(
-        vector, 0, seg, C_FLOAT, ((current++) * dimensions * C_FLOAT.byteSize()), (int) dimensions);
   }
 
   @Override
   public void close() {
-    if (!arena.scope().isAlive()) {
+    var arena = arenaReference.getAndSet(null);
+    if (arena != null) {
       arena.close();
     }
   }
@@ -62,4 +50,8 @@ public int size() {
   public int dimensions() {
     return dimensions;
   }
+
+  public MemorySegment asMemorySegment() {
+    return seg;
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index d5a348e1fc..16e54022bc 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -15,6 +15,8 @@
  */
 package com.nvidia.cuvs.spi;
 
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraMergeParams;
@@ -26,6 +28,10 @@
 import com.nvidia.cuvs.internal.CuVSResourcesImpl;
 import com.nvidia.cuvs.internal.DatasetImpl;
 import com.nvidia.cuvs.internal.HnswIndexImpl;
+import com.nvidia.cuvs.internal.common.Util;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.Objects;
@@ -77,7 +83,41 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merge
   }
 
   @Override
-  public Dataset newDataset(int size, int dimensions) throws UnsupportedOperationException {
-    return new DatasetImpl(size, dimensions);
+  public Dataset.Builder newDatasetBuilder(int size, int dimensions)
+      throws UnsupportedOperationException {
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout((long) size * dimensions, C_FLOAT);
+
+    var arena = Arena.ofShared();
+    var seg = arena.allocate(dataMemoryLayout);
+
+    return new Dataset.Builder() {
+      int current = 0;
+
+      @Override
+      public void addVector(float[] vector) {
+        if (current >= size) throw new ArrayIndexOutOfBoundsException();
+        MemorySegment.copy(
+            vector, 0, seg, C_FLOAT, ((current++) * dimensions * C_FLOAT.byteSize()), dimensions);
+      }
+
+      @Override
+      public Dataset build() {
+        return new DatasetImpl(arena, seg, size, dimensions);
+      }
+    };
+  }
+
+  @Override
+  public Dataset newArrayDataset(float[][] vectors) {
+    Objects.requireNonNull(vectors);
+    if (vectors.length == 0) {
+      throw new IllegalArgumentException("vectors should not be empty");
+    }
+    int size = vectors.length;
+    int dimensions = vectors[0].length;
+
+    Arena arena = Arena.ofShared();
+    var memorySegment = Util.buildMemorySegment(arena, vectors);
+    return new DatasetImpl(arena, memorySegment, size, dimensions);
   }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 6f6b24f316..7a7c05fb0f 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -120,11 +120,13 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
       BruteForceIndex index;
       if (useNativeMemoryDataset) {
-        Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
-        for (float[] v : vectors) dataset.addVector(v);
+        var datasetBuilder = Dataset.builder(vectors.length, vectors[0].length);
+        for (float[] v : vectors) {
+          datasetBuilder.addVector(v);
+        }
         index =
             BruteForceIndex.newBuilder(resources)
-                .withDataset(dataset)
+                .withDataset(datasetBuilder.build())
                 .withIndexParams(indexParams)
                 .build();
       } else {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index e29163ba51..3539a1868d 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -122,11 +122,13 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
       CagraIndex index;
       if (useNativeMemoryDataset) {
-        Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
-        for (float[] v : vectors) dataset.addVector(v);
+        var datasetBuilder = Dataset.builder(vectors.length, vectors[0].length);
+        for (float[] v : vectors) {
+          datasetBuilder.addVector(v);
+        }
         index =
             CagraIndex.newBuilder(resources)
-                .withDataset(dataset)
+                .withDataset(datasetBuilder.build())
                 .withIndexParams(indexParams)
                 .build();
       } else {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index ffdf29fe10..ef538f5917 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -115,11 +115,13 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       // Create the index with the dataset
       CagraIndex index;
       if (useNativeMemoryDataset) {
-        Dataset dataset = Dataset.create(vectors.length, vectors[0].length);
-        for (float[] v : vectors) dataset.addVector(v);
+        var datasetBuilder = Dataset.builder(vectors.length, vectors[0].length);
+        for (float[] v : vectors) {
+          datasetBuilder.addVector(v);
+        }
         index =
             CagraIndex.newBuilder(resources)
-                .withDataset(dataset)
+                .withDataset(datasetBuilder.build())
                 .withIndexParams(indexParams)
                 .build();
       } else {

From 7462cbda207cf9347ecbe3104f7417a3ad8eeba4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Sun, 6 Jul 2025 12:00:35 +0200
Subject: [PATCH 469/623] [Java] Using functions for mapping (#1007)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR replaces "array" based ID mappings with a more free-form mapping based on functions.
This should be more efficient both when the number of IDs is huge (you don't need to actually allocate big list for all IDs to map) and in general (as it won't need to perform boxing/unboxing of integers).
It should also be more flexible; as an example, I have introduced a simple helper to wrap a list, so that the previous approach can still be used. The same can be done for maps etc.

Relates to https://github.com/rapidsai/cuvs/issues/699

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Corey J. Nolet (https://github.com/cjnolet)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Ishan Chattopadhyaya (https://github.com/chatman)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1007
---
 .../java/com/nvidia/cuvs/BruteForceQuery.java |  32 +--
 .../main/java/com/nvidia/cuvs/CagraQuery.java |  32 ++-
 .../main/java/com/nvidia/cuvs/HnswQuery.java  |  31 +--
 .../java/com/nvidia/cuvs/SearchResults.java   |  15 ++
 .../internal/BruteForceSearchResults.java     |   6 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |   5 +-
 .../cuvs/internal/CagraSearchResults.java     |   8 +-
 .../cuvs/internal/HnswSearchResults.java      |   6 +-
 .../internal/common/SearchResultsImpl.java    |   5 +-
 .../nvidia/cuvs/BruteForceAndSearchIT.java    | 197 +++++++++++-------
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    |  98 +++++++--
 .../com/nvidia/cuvs/HnswBuildAndSearchIT.java | 185 ++++++++++------
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |  69 +++---
 13 files changed, 433 insertions(+), 256 deletions(-)

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
index 084984b040..93a5218aa2 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
@@ -17,7 +17,7 @@
 
 import java.util.Arrays;
 import java.util.BitSet;
-import java.util.List;
+import java.util.function.LongToIntFunction;
 
 /**
  * BruteForceQuery holds the query vectors to be used while invoking search.
@@ -26,18 +26,18 @@
  */
 public class BruteForceQuery {
 
-  private List<Integer> mapping;
-  private float[][] queryVectors;
-  private BitSet[] prefilters;
+  private final LongToIntFunction mapping;
+  private final float[][] queryVectors;
+  private final BitSet[] prefilters;
   private int numDocs = -1;
-  private int topK;
+  private final int topK;
 
   /**
    * Constructs an instance of {@link BruteForceQuery} using queryVectors,
    * mapping, and topK.
    *
    * @param queryVectors 2D float query vector array
-   * @param mapping      an instance of ID mapping
+   * @param mapping      a function mapping ordinals (neighbor IDs) to custom user IDs
    * @param topK         the top k results to return
    * @param prefilters   the prefilters data to use while searching the BRUTEFORCE
    *                     index
@@ -45,7 +45,11 @@ public class BruteForceQuery {
    *                     Used only when prefilter(s) is/are passed.
    */
   public BruteForceQuery(
-      float[][] queryVectors, List<Integer> mapping, int topK, BitSet[] prefilters, int numDocs) {
+      float[][] queryVectors,
+      LongToIntFunction mapping,
+      int topK,
+      BitSet[] prefilters,
+      int numDocs) {
     this.queryVectors = queryVectors;
     this.mapping = mapping;
     this.topK = topK;
@@ -63,11 +67,9 @@ public float[][] getQueryVectors() {
   }
 
   /**
-   * Gets the passed map instance.
-   *
-   * @return a map of ID mappings
+   * Gets the function mapping ordinals (neighbor IDs) to custom user IDs
    */
-  public List<Integer> getMapping() {
+  public LongToIntFunction getMapping() {
     return mapping;
   }
 
@@ -119,7 +121,7 @@ public static class Builder {
     private float[][] queryVectors;
     private BitSet[] prefilters;
     private int numDocs;
-    private List<Integer> mapping;
+    private LongToIntFunction mapping = SearchResults.IDENTITY_MAPPING;
     private int topK = 2;
 
     /**
@@ -134,12 +136,12 @@ public Builder withQueryVectors(float[][] queryVectors) {
     }
 
     /**
-     * Sets the instance of mapping to be used for ID mapping.
+     * Sets the function used to map ordinals (neighbor IDs) to custom user IDs
      *
-     * @param mapping the ID mapping instance
+     * @param mapping a function mapping ordinals (neighbor IDs) to custom user IDs
      * @return an instance of this Builder
      */
-    public Builder withMapping(List<Integer> mapping) {
+    public Builder withMapping(LongToIntFunction mapping) {
       this.mapping = mapping;
       return this;
     }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
index 7667fe4355..cb318cc2b5 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -17,7 +17,7 @@
 
 import java.util.Arrays;
 import java.util.BitSet;
-import java.util.List;
+import java.util.function.LongToIntFunction;
 
 /**
  * CagraQuery holds the CagraSearchParams and the query vectors to be used while
@@ -27,12 +27,12 @@
  */
 public class CagraQuery {
 
-  private CagraSearchParams cagraSearchParameters;
-  private List<Integer> mapping;
-  private float[][] queryVectors;
-  private int topK;
-  private BitSet prefilter;
-  private int numDocs;
+  private final CagraSearchParams cagraSearchParameters;
+  private final LongToIntFunction mapping;
+  private final float[][] queryVectors;
+  private final int topK;
+  private final BitSet prefilter;
+  private final int numDocs;
 
   /**
    * Constructs an instance of {@link CagraQuery} using cagraSearchParameters,
@@ -41,7 +41,7 @@ public class CagraQuery {
    * @param cagraSearchParameters an instance of {@link CagraSearchParams} holding
    *                              the search parameters
    * @param queryVectors          2D float query vector array
-   * @param mapping               an instance of ID mapping
+   * @param mapping               a function mapping ordinals (neighbor IDs) to custom user IDs
    * @param topK                  the top k results to return
    * @param prefilter             A single BitSet to use as filter while searching the CAGRA index
    * @param numDocs               Total number of dataset vectors; used to align the prefilter correctly
@@ -49,7 +49,7 @@ public class CagraQuery {
   public CagraQuery(
       CagraSearchParams cagraSearchParameters,
       float[][] queryVectors,
-      List<Integer> mapping,
+      LongToIntFunction mapping,
       int topK,
       BitSet prefilter,
       int numDocs) {
@@ -81,11 +81,9 @@ public float[][] getQueryVectors() {
   }
 
   /**
-   * Gets the passed map instance.
-   *
-   * @return a map of ID mappings
+   * Gets the function mapping ordinals (neighbor IDs) to custom user IDs
    */
-  public List<Integer> getMapping() {
+  public LongToIntFunction getMapping() {
     return mapping;
   }
 
@@ -136,7 +134,7 @@ public static class Builder {
 
     private CagraSearchParams cagraSearchParams;
     private float[][] queryVectors;
-    private List<Integer> mapping;
+    private LongToIntFunction mapping = SearchResults.IDENTITY_MAPPING;
     private int topK = 2;
     private BitSet prefilter;
     private int numDocs;
@@ -170,12 +168,12 @@ public Builder withQueryVectors(float[][] queryVectors) {
     }
 
     /**
-     * Sets the instance of mapping to be used for ID mapping.
+     * Sets the function used to map ordinals (neighbor IDs) to custom user IDs
      *
-     * @param mapping the ID mapping instance
+     * @param mapping a function mapping ordinals (neighbor IDs) to custom user IDs
      * @return an instance of this Builder
      */
-    public Builder withMapping(List<Integer> mapping) {
+    public Builder withMapping(LongToIntFunction mapping) {
       this.mapping = mapping;
       return this;
     }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
index 2c3fac6658..9da7fe8814 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
@@ -16,7 +16,7 @@
 package com.nvidia.cuvs;
 
 import java.util.Arrays;
-import java.util.List;
+import java.util.function.LongToIntFunction;
 
 /**
  * HnswQuery holds the query vectors to be used while invoking search on the
@@ -26,10 +26,10 @@
  */
 public class HnswQuery {
 
-  private HnswSearchParams hnswSearchParams;
-  private List<Integer> mapping;
-  private float[][] queryVectors;
-  private int topK;
+  private final HnswSearchParams hnswSearchParams;
+  private final LongToIntFunction mapping;
+  private final float[][] queryVectors;
+  private final int topK;
 
   /**
    * Constructs an instance of {@link HnswQuery} using queryVectors, mapping, and
@@ -37,11 +37,14 @@ public class HnswQuery {
    *
    * @param hnswSearchParams the search parameters to use
    * @param queryVectors     2D float query vector array
-   * @param mapping          an instance of ID mapping
+   * @param mapping          a function mapping ordinals (neighbor IDs) to custom user IDs
    * @param topK             the top k results to return
    */
   private HnswQuery(
-      HnswSearchParams hnswSearchParams, float[][] queryVectors, List<Integer> mapping, int topK) {
+      HnswSearchParams hnswSearchParams,
+      float[][] queryVectors,
+      LongToIntFunction mapping,
+      int topK) {
     this.hnswSearchParams = hnswSearchParams;
     this.queryVectors = queryVectors;
     this.mapping = mapping;
@@ -67,11 +70,9 @@ public float[][] getQueryVectors() {
   }
 
   /**
-   * Gets the passed map instance.
-   *
-   * @return a map of ID mappings
+   * Gets the function mapping ordinals (neighbor IDs) to custom user IDs
    */
-  public List<Integer> getMapping() {
+  public LongToIntFunction getMapping() {
     return mapping;
   }
 
@@ -102,7 +103,7 @@ public static class Builder {
 
     private HnswSearchParams hnswSearchParams;
     private float[][] queryVectors;
-    private List<Integer> mapping;
+    private LongToIntFunction mapping = SearchResults.IDENTITY_MAPPING;
     private int topK = 2;
 
     /**
@@ -129,12 +130,12 @@ public Builder withQueryVectors(float[][] queryVectors) {
     }
 
     /**
-     * Sets the instance of mapping to be used for ID mapping.
+     * Sets the function used to map ordinals (neighbor IDs) to custom user IDs
      *
-     * @param mapping the ID mapping instance
+     * @param mapping a function mapping ordinals (neighbor IDs) to custom user IDs
      * @return an instance of this Builder
      */
-    public Builder withMapping(List<Integer> mapping) {
+    public Builder withMapping(LongToIntFunction mapping) {
       this.mapping = mapping;
       return this;
     }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
index b575500de9..6783905860 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SearchResults.java
@@ -17,9 +17,24 @@
 
 import java.util.List;
 import java.util.Map;
+import java.util.function.LongToIntFunction;
 
 public interface SearchResults {
 
+  /**
+   * The default identity function mapping neighbours IDs to user-defined IDs
+   */
+  LongToIntFunction IDENTITY_MAPPING = l -> (int) l;
+
+  /**
+   * Creates a mapping function from a list lookup of custom user IDs
+   * @param mappingAsList a positional list of custom user IDs
+   * @return a function that maps the input ordinal to a custom user IDs, using the input as an index in the list
+   */
+  static LongToIntFunction mappingsFromList(List<Integer> mappingAsList) {
+    return l -> mappingAsList.get((int) l);
+  }
+
   /**
    * Gets a list results as a map of neighbor IDs to distances.
    *
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
index ce21cbd926..1c7d3752e7 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
@@ -19,8 +19,8 @@
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.util.LinkedHashMap;
-import java.util.List;
 import java.util.Map;
+import java.util.function.LongToIntFunction;
 
 /**
  * SearchResult encapsulates the logic for reading and holding search results.
@@ -35,7 +35,7 @@ protected BruteForceSearchResults(
       MemorySegment neighboursMemorySegment,
       MemorySegment distancesMemorySegment,
       int topK,
-      List<Integer> mapping,
+      LongToIntFunction mapping,
       long numberOfQueries) {
     super(
         neighboursSequenceLayout,
@@ -58,7 +58,7 @@ protected void readResultMemorySegments() {
     for (long i = 0; i < topK * numberOfQueries; i++) {
       long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
       float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
-      intermediateResultMap.put(mapping != null ? mapping.get((int) id) : (int) id, dst);
+      intermediateResultMap.put(mapping.applyAsInt(id), dst);
       count += 1;
       if (count == topK) {
         results.add(intermediateResultMap);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 7d41dbb9a9..d47493b708 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -239,10 +239,7 @@ private static MemorySegment createCagraIndex() {
   public SearchResults search(CagraQuery query) throws Throwable {
     try (var localArena = Arena.ofConfined()) {
       checkNotDestroyed();
-      int topK =
-          query.getMapping() != null
-              ? Math.min(query.getMapping().size(), query.getTopK())
-              : query.getTopK();
+      int topK = query.getTopK();
       long numQueries = query.getQueryVectors().length;
       long numBlocks = topK * numQueries;
       int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
index 6b3d76da84..08c1a042dc 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
@@ -19,8 +19,8 @@
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.util.LinkedHashMap;
-import java.util.List;
 import java.util.Map;
+import java.util.function.LongToIntFunction;
 
 /**
  * SearchResult encapsulates the logic for reading and holding search results.
@@ -35,7 +35,7 @@ protected CagraSearchResults(
       MemorySegment neighboursMemorySegment,
       MemorySegment distancesMemorySegment,
       int topK,
-      List<Integer> mapping,
+      LongToIntFunction mapping,
       long numberOfQueries) {
     super(
         neighboursSequenceLayout,
@@ -56,10 +56,10 @@ protected void readResultMemorySegments() {
     Map<Integer, Float> intermediateResultMap = new LinkedHashMap<Integer, Float>();
     int count = 0;
     for (long i = 0; i < topK * numberOfQueries; i++) {
-      int id = (int) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
+      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
       float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
       if (id != Integer.MAX_VALUE) {
-        intermediateResultMap.put(mapping != null ? mapping.get(id) : id, dst);
+        intermediateResultMap.put(mapping.applyAsInt(id), dst);
       }
       count += 1;
       if (count == topK) {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
index 2e22d27886..265a3d2227 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
@@ -19,8 +19,8 @@
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.util.LinkedHashMap;
-import java.util.List;
 import java.util.Map;
+import java.util.function.LongToIntFunction;
 
 /**
  * SearchResult encapsulates the logic for reading and holding search results.
@@ -35,7 +35,7 @@ protected HnswSearchResults(
       MemorySegment neighboursMemorySegment,
       MemorySegment distancesMemorySegment,
       int topK,
-      List<Integer> mapping,
+      LongToIntFunction mapping,
       long numberOfQueries) {
     super(
         neighboursSequenceLayout,
@@ -58,7 +58,7 @@ protected void readResultMemorySegments() {
     for (long i = 0; i < topK * numberOfQueries; i++) {
       long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
       float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
-      intermediateResultMap.put(mapping != null ? mapping.get((int) id) : (int) id, dst);
+      intermediateResultMap.put(mapping.applyAsInt(id), dst);
       count += 1;
       if (count == topK) {
         results.add(intermediateResultMap);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
index 06ff865f4f..29fd61670b 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
@@ -23,11 +23,12 @@
 import java.util.LinkedList;
 import java.util.List;
 import java.util.Map;
+import java.util.function.LongToIntFunction;
 
 public abstract class SearchResultsImpl implements SearchResults {
 
   protected final List<Map<Integer, Float>> results;
-  protected final List<Integer> mapping; // TODO: Is this performant in a user application?
+  protected final LongToIntFunction mapping;
 
   protected final MemorySegment neighboursMemorySegment;
   protected final MemorySegment distancesMemorySegment;
@@ -42,7 +43,7 @@ protected SearchResultsImpl(
       MemorySegment neighboursMemorySegment,
       MemorySegment distancesMemorySegment,
       int topK,
-      List<Integer> mapping,
+      LongToIntFunction mapping,
       long numberOfQueries) {
     this.topK = topK;
     this.numberOfQueries = numberOfQueries;
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index c7873e39fe..218da3d0d9 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -17,16 +17,13 @@
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.InputStream;
+import java.io.*;
 import java.lang.invoke.MethodHandles;
-import java.util.Arrays;
 import java.util.BitSet;
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
+import java.util.function.LongToIntFunction;
 import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
@@ -41,111 +38,163 @@ public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
   }
 
+  // Sample data and query
+  private static final float[][] dataset = {
+    {0.74021935f, 0.9209938f},
+    {0.03902049f, 0.9689629f},
+    {0.92514056f, 0.4463501f},
+    {0.6673192f, 0.10993068f}
+  };
+  private static final float[][] queries = {
+    {0.48216683f, 0.0428398f},
+    {0.5084142f, 0.6545497f},
+    {0.51260436f, 0.2643005f},
+    {0.05198065f, 0.5789965f}
+  };
+
+  private static void indexAndQueryOnce(
+      CuVSResources resources, BruteForceQuery cuvsQuery, List<Map<Integer, Float>> expectedResults)
+      throws Throwable {
+    // Set index parameters
+    BruteForceIndexParams indexParams =
+        new BruteForceIndexParams.Builder().withNumWriterThreads(32).build();
+
+    // Create the index with the dataset
+    BruteForceIndex index =
+        BruteForceIndex.newBuilder(resources)
+            .withDataset(dataset)
+            .withIndexParams(indexParams)
+            .build();
+
+    // Saving the index on to the disk.
+    String indexFileName = UUID.randomUUID().toString() + ".bf";
+    try (var outputStream = new FileOutputStream(indexFileName)) {
+      index.serialize(outputStream);
+    }
+
+    // Loading a BRUTEFORCE index from disk.
+    File indexFile = new File(indexFileName);
+    InputStream inputStream = new FileInputStream(indexFile);
+    BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources).from(inputStream).build();
+
+    // search the loaded index
+    SearchResults results = loadedIndex.search(cuvsQuery);
+    checkResults(expectedResults, results.getResults());
+
+    // search the first index
+    results = index.search(cuvsQuery);
+    checkResults(expectedResults, results.getResults());
+
+    // Cleanup
+    index.destroyIndex();
+    loadedIndex.destroyIndex();
+
+    if (indexFile.exists()) {
+      indexFile.delete();
+    }
+  }
+
   /**
    * A basic test that checks the whole flow - from indexing to search.
-   *
-   * @throws Throwable
    */
   @Test
   public void testIndexingAndSearchingFlow() throws Throwable {
-
-    // Sample data and query
-    float[][] dataset = {
-      {0.74021935f, 0.9209938f},
-      {0.03902049f, 0.9689629f},
-      {0.92514056f, 0.4463501f},
-      {0.6673192f, 0.10993068f}
-    };
-    List<Integer> map = List.of(0, 1, 2, 3);
-    float[][] queries = {
-      {0.48216683f, 0.0428398f},
-      {0.5084142f, 0.6545497f},
-      {0.51260436f, 0.2643005f},
-      {0.05198065f, 0.5789965f}
-    };
-
     // Expected search results
-    List<Map<Integer, Float>> expectedResults =
-        Arrays.asList(
+    final List<Map<Integer, Float>> expectedResults =
+        List.of(
             Map.of(3, 0.038782537f, 2, 0.35904616f, 0, 0.83774555f),
             Map.of(0, 0.12472606f, 2, 0.21700788f, 1, 0.3191862f),
             Map.of(3, 0.047766685f, 2, 0.20332813f, 0, 0.48305476f),
             Map.of(1, 0.15224183f, 0, 0.5906347f, 3, 0.5986643f));
+    for (int j = 0; j < 10; j++) {
 
-    // pre-filtering
-    BitSet prefilter = new BitSet();
+      try (CuVSResources resources = CuVSResources.create()) {
+        BruteForceQuery cuvsQuery =
+            new BruteForceQuery.Builder()
+                .withTopK(3)
+                .withQueryVectors(queries)
+                .withMapping(SearchResults.IDENTITY_MAPPING)
+                .build();
+        indexAndQueryOnce(resources, cuvsQuery, expectedResults);
+      }
+    }
+  }
+
+  @Test
+  public void testIndexingAndSearchingWithFiltering() throws Throwable {
+    final BitSet prefilter = new BitSet();
     prefilter.set(0);
     prefilter.set(1);
     prefilter.clear(2);
     prefilter.set(3);
 
     final List<Map<Integer, Float>> expectedResultsWithFiltering =
-        Arrays.asList(
+        List.of(
             Map.of(0, 0.83774555f, 1, 1.0540828f, 3, 0.038782537f),
             Map.of(0, 0.12472606f, 1, 0.3191862f, 3, 0.32186073f),
             Map.of(0, 0.48305476f, 1, 0.7208309f, 3, 0.047766685f),
             Map.of(0, 0.5906347f, 1, 0.15224195f, 3, 0.5986643f));
 
     for (int j = 0; j < 10; j++) {
-
       try (CuVSResources resources = CuVSResources.create()) {
-
-        // Set index parameters
-        BruteForceIndexParams indexParams =
-            new BruteForceIndexParams.Builder().withNumWriterThreads(32).build();
-
-        // Create the index with the dataset
-        BruteForceIndex index =
-            BruteForceIndex.newBuilder(resources)
-                .withDataset(dataset)
-                .withIndexParams(indexParams)
+        BruteForceQuery cuvsQuery =
+            new BruteForceQuery.Builder()
+                .withTopK(3)
+                .withQueryVectors(queries)
+                .withPrefilters(
+                    new BitSet[] {prefilter, prefilter, prefilter, prefilter}, dataset.length)
+                .withMapping(SearchResults.IDENTITY_MAPPING)
                 .build();
+        indexAndQueryOnce(resources, cuvsQuery, expectedResultsWithFiltering);
+      }
+    }
+  }
 
-        // Saving the index on to the disk.
-        String indexFileName = UUID.randomUUID().toString() + ".bf";
-        index.serialize(new FileOutputStream(indexFileName));
-
-        // Loading a BRUTEFORCE index from disk.
-        File indexFile = new File(indexFileName);
-        InputStream inputStream = new FileInputStream(indexFile);
-        BruteForceIndex loadedIndex =
-            BruteForceIndex.newBuilder(resources).from(inputStream).build();
+  @Test
+  public void testIndexingAndSearchingWithFunctionMapping() throws Throwable {
+    // Expected search results
+    final List<Map<Integer, Float>> expectedResults =
+        List.of(
+            Map.of(0, 0.038782537f, 3, 0.35904616f, 1, 0.83774555f),
+            Map.of(1, 0.12472606f, 3, 0.21700788f, 2, 0.3191862f),
+            Map.of(0, 0.047766685f, 3, 0.20332813f, 1, 0.48305476f),
+            Map.of(2, 0.15224183f, 1, 0.5906347f, 0, 0.5986643f));
+    LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.length);
 
-        // Perform the search
+    for (int j = 0; j < 10; j++) {
+      try (CuVSResources resources = CuVSResources.create()) {
         BruteForceQuery cuvsQuery =
             new BruteForceQuery.Builder()
                 .withTopK(3)
                 .withQueryVectors(queries)
-                .withMapping(map)
+                .withMapping(rotate)
                 .build();
-        BruteForceQuery cuvsQueryWithFiltering =
+        indexAndQueryOnce(resources, cuvsQuery, expectedResults);
+      }
+    }
+  }
+
+  @Test
+  public void testIndexingAndSearchingWithListMapping() throws Throwable {
+    // Expected search results
+    final List<Map<Integer, Float>> expectedResults =
+        List.of(
+            Map.of(1, 0.038782537f, 2, 0.35904616f, 4, 0.83774555f),
+            Map.of(4, 0.12472606f, 2, 0.21700788f, 3, 0.3191862f),
+            Map.of(1, 0.047766685f, 2, 0.20332813f, 4, 0.48305476f),
+            Map.of(3, 0.15224183f, 4, 0.5906347f, 1, 0.5986643f));
+    var mappings = List.of(4, 3, 2, 1);
+    LongToIntFunction rotate = SearchResults.mappingsFromList(mappings);
+
+    for (int j = 0; j < 10; j++) {
+      try (CuVSResources resources = CuVSResources.create()) {
+        BruteForceQuery cuvsQuery =
             new BruteForceQuery.Builder()
                 .withTopK(3)
                 .withQueryVectors(queries)
-                .withPrefilters(
-                    new BitSet[] {prefilter, prefilter, prefilter, prefilter}, dataset.length)
-                .withMapping(map)
+                .withMapping(rotate)
                 .build();
-
-        // search the loaded index
-        SearchResults results = loadedIndex.search(cuvsQuery);
-        checkResults(expectedResults, results.getResults());
-
-        // search the first index
-        results = index.search(cuvsQuery);
-        checkResults(expectedResults, results.getResults());
-
-        // search with pre-filtering
-        results = index.search(cuvsQueryWithFiltering);
-        checkResults(expectedResultsWithFiltering, results.getResults());
-
-        // Cleanup
-        index.destroyIndex();
-        loadedIndex.destroyIndex();
-
-        if (indexFile.exists()) {
-          indexFile.delete();
-        }
+        indexAndQueryOnce(resources, cuvsQuery, expectedResults);
       }
     }
   }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 6b802de1d0..9145bfe1b4 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -42,6 +42,7 @@
 import java.util.concurrent.Executors;
 import java.util.concurrent.TimeUnit;
 import java.util.concurrent.TimeoutException;
+import java.util.function.LongToIntFunction;
 import java.util.function.Supplier;
 import org.junit.Before;
 import org.junit.Test;
@@ -73,13 +74,21 @@ private static void runConcurrently(int nThreads, Supplier<Runnable> runnableSup
           .exceptionally(
               t -> {
                 log.error("Exception while executing runnable", t);
-                fail("Exception while executing runnable: " + t);
+                fail("Exception while executing runnable: " + unwrap(t));
                 return null;
               })
           .get(2000, TimeUnit.SECONDS);
     }
   }
 
+  private static Throwable unwrap(Throwable t) {
+    var root = t;
+    while (root.getCause() != null) {
+      root = root.getCause();
+    }
+    return root;
+  }
+
   private static void runInAnotherThread(Runnable runnable)
       throws ExecutionException, InterruptedException, TimeoutException {
     try (ExecutorService singleExecutor = Executors.newSingleThreadExecutor()) {
@@ -122,15 +131,19 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     float[][] queries = createSampleQueries();
     List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
-    List<Integer> map = List.of(0, 1, 2, 3);
-
     int numTestsRuns = 5;
     try (CuVSResources resources = CuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
         var index = indexOnce(dataset, resources);
         var indexPath = serializeOnce(index);
         var loadedIndex = deserializeOnce(indexPath, resources);
-        queryOnce(index, loadedIndex, map, queries, expectedResults, resources);
+        queryOnce(
+            index,
+            loadedIndex,
+            SearchResults.IDENTITY_MAPPING,
+            queries,
+            expectedResults,
+            resources);
         cleanup(indexPath, index, loadedIndex);
       }
     }
@@ -145,8 +158,6 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
     float[][] queries = createSampleQueries();
     List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
-    List<Integer> map = List.of(0, 1, 2, 3);
-
     int numTestsRuns = 5;
     try (CuVSResources resources = CuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
@@ -156,7 +167,13 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
                 var index = indexOnce(dataset, resources);
                 var indexPath = serializeOnce(index);
                 var loadedIndex = deserializeOnce(indexPath, resources);
-                queryOnce(index, loadedIndex, map, queries, expectedResults, resources);
+                queryOnce(
+                    index,
+                    loadedIndex,
+                    SearchResults.IDENTITY_MAPPING,
+                    queries,
+                    expectedResults,
+                    resources);
                 cleanup(indexPath, index, loadedIndex);
               } catch (Throwable e) {
                 throw new RuntimeException(e);
@@ -175,8 +192,6 @@ public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
     float[][] queries = createSampleQueries();
     List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
-    List<Integer> map = List.of(0, 1, 2, 3);
-
     int numTestsRuns = 10;
     try (CuVSResources resources = CuVSResources.create()) {
       runConcurrently(
@@ -187,7 +202,13 @@ public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
                   var index = indexOnce(dataset, resources);
                   var indexPath = serializeOnce(index);
                   var loadedIndex = deserializeOnce(indexPath, resources);
-                  queryOnce(index, loadedIndex, map, queries, expectedResults, resources);
+                  queryOnce(
+                      index,
+                      loadedIndex,
+                      SearchResults.IDENTITY_MAPPING,
+                      queries,
+                      expectedResults,
+                      resources);
                   cleanup(indexPath, index, loadedIndex);
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
@@ -260,6 +281,49 @@ public void testDeserialization() throws Throwable {
     }
   }
 
+  @Test
+  public void testIndexingAndSearchingFlowWithCustomMappingFunction() throws Throwable {
+    float[][] dataset = createSampleData();
+    float[][] queries = createSampleQueries();
+    var expectedResults =
+        List.of(
+            Map.of(0, 0.038782578f, 3, 0.3590463f, 1, 0.83774555f),
+            Map.of(1, 0.12472608f, 3, 0.21700792f, 2, 0.31918612f),
+            Map.of(0, 0.047766715f, 3, 0.20332818f, 1, 0.48305473f),
+            Map.of(2, 0.15224178f, 1, 0.59063464f, 0, 0.5986642f));
+
+    LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.length);
+    try (CuVSResources resources = CuVSResources.create()) {
+      var index = indexOnce(dataset, resources);
+      var indexPath = serializeOnce(index);
+      var loadedIndex = deserializeOnce(indexPath, resources);
+      queryOnce(index, loadedIndex, rotate, queries, expectedResults, resources);
+      cleanup(indexPath, index, loadedIndex);
+    }
+  }
+
+  @Test
+  public void testIndexingAndSearchingFlowWithCustomMappingList() throws Throwable {
+    float[][] dataset = createSampleData();
+    float[][] queries = createSampleQueries();
+    var mappings = List.of(4, 3, 2, 1);
+    var expectedResults =
+        List.of(
+            Map.of(1, 0.038782578f, 2, 0.3590463f, 4, 0.83774555f),
+            Map.of(4, 0.12472608f, 2, 0.21700792f, 3, 0.31918612f),
+            Map.of(1, 0.047766715f, 2, 0.20332818f, 4, 0.48305473f),
+            Map.of(3, 0.15224178f, 4, 0.59063464f, 1, 0.5986642f));
+
+    LongToIntFunction rotate = SearchResults.mappingsFromList(mappings);
+    try (CuVSResources resources = CuVSResources.create()) {
+      var index = indexOnce(dataset, resources);
+      var indexPath = serializeOnce(index);
+      var loadedIndex = deserializeOnce(indexPath, resources);
+      queryOnce(index, loadedIndex, rotate, queries, expectedResults, resources);
+      cleanup(indexPath, index, loadedIndex);
+    }
+  }
+
   /**
    * A test that checks the pre-filtering feature.
    */
@@ -365,7 +429,7 @@ private CagraIndex deserializeOnce(Path indexFilePath, CuVSResources resources)
   private void queryOnce(
       CagraIndex index,
       CagraIndex loadedIndex,
-      List<Integer> map,
+      LongToIntFunction mapping,
       float[][] queries,
       List<Map<Integer, Float>> expectedResults,
       CuVSResources resources)
@@ -379,7 +443,7 @@ private void queryOnce(
             .withTopK(3)
             .withSearchParams(searchParams)
             .withQueryVectors(queries)
-            .withMapping(map)
+            .withMapping(mapping)
             .build();
 
     // Perform the search
@@ -387,14 +451,14 @@ private void queryOnce(
 
     // Check results
     log.info(results.getResults().toString());
-    assertEquals(expectedResults, results.getResults());
+    checkResults(expectedResults, results.getResults());
 
     // Search from deserialized index
     results = loadedIndex.search(cuvsQuery);
 
     // Check results
     log.info(results.getResults().toString());
-    assertEquals(expectedResults, results.getResults());
+    checkResults(expectedResults, results.getResults());
   }
 
   private void cleanup(Path indexFilePath, CagraIndex index, CagraIndex loadedIndex)
@@ -460,13 +524,12 @@ public void testMergingIndexes() throws Throwable {
 
       CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
 
-      List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
       CagraQuery query =
           new CagraQuery.Builder()
               .withTopK(3)
               .withSearchParams(searchParams)
               .withQueryVectors(queries)
-              .withMapping(mergedMap)
+              .withMapping(SearchResults.IDENTITY_MAPPING)
               .build();
 
       log.info("Searching merged index...");
@@ -567,13 +630,12 @@ public void testMergeStrategies() throws Throwable {
 
       CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
 
-      List<Integer> mergedMap = Arrays.asList(0, 1, 2, 3);
       CagraQuery query =
           new CagraQuery.Builder()
               .withTopK(3)
               .withSearchParams(searchParams)
               .withQueryVectors(queries)
-              .withMapping(mergedMap)
+              .withMapping(SearchResults.IDENTITY_MAPPING)
               .build();
 
       log.info("Searching physically merged index...");
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index 6c51ac85e0..e6d16e0a13 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -16,19 +16,17 @@
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-import static org.junit.Assert.assertEquals;
 
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.InputStream;
 import java.lang.invoke.MethodHandles;
+import java.nio.file.Files;
+import java.nio.file.Path;
 import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
+import java.util.function.LongToIntFunction;
 import org.junit.Before;
 import org.junit.Test;
 import org.slf4j.Logger;
@@ -43,31 +41,77 @@ public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
   }
 
+  // Sample data and query
+  private static final float[][] dataset = {
+    {0.74021935f, 0.9209938f},
+    {0.03902049f, 0.9689629f},
+    {0.92514056f, 0.4463501f},
+    {0.6673192f, 0.10993068f}
+  };
+
+  private static final float[][] queries = {
+    {0.48216683f, 0.0428398f},
+    {0.5084142f, 0.6545497f},
+    {0.51260436f, 0.2643005f},
+    {0.05198065f, 0.5789965f}
+  };
+
+  private static void indexAndQueryOnce(
+      CuVSResources resources, HnswQuery hnswQuery, List<Map<Integer, Float>> expectedResults)
+      throws Throwable {
+    // Configure index parameters
+    CagraIndexParams indexParams =
+        new CagraIndexParams.Builder()
+            .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+            .withGraphDegree(64)
+            .withIntermediateGraphDegree(128)
+            .withNumWriterThreads(32)
+            .withMetric(CuvsDistanceType.L2Expanded)
+            .build();
+
+    // Create the index with the dataset
+    CagraIndex index =
+        CagraIndex.newBuilder(resources).withDataset(dataset).withIndexParams(indexParams).build();
+
+    // Saving the HNSW index on to the disk.
+    String hnswIndexFileName = UUID.randomUUID() + ".hnsw";
+    var hnswIndexPath = Path.of(hnswIndexFileName);
+    try {
+      try (var outputStream = Files.newOutputStream(hnswIndexPath)) {
+        index.serializeToHNSW(outputStream);
+      }
+
+      HnswIndexParams hnswIndexParams =
+          new HnswIndexParams.Builder().withVectorDimension(2).build();
+      try (var inputStreamHNSW = Files.newInputStream(hnswIndexPath)) {
+        var hnswIndex =
+            HnswIndex.newBuilder(resources)
+                .from(inputStreamHNSW)
+                .withIndexParams(hnswIndexParams)
+                .build();
+
+        SearchResults results = hnswIndex.search(hnswQuery);
+
+        // Check results
+        log.info(results.getResults().toString());
+        checkResults(expectedResults, results.getResults());
+
+        // Cleanup
+        hnswIndex.destroyIndex();
+      }
+    } finally {
+      index.destroyIndex();
+      Files.deleteIfExists(hnswIndexPath);
+    }
+  }
+
   /**
    * A basic test that checks the whole flow - from indexing to search.
-   *
-   * @throws Throwable
    */
   @Test
   public void testIndexingAndSearchingFlow() throws Throwable {
-
-    // Sample data and query
-    float[][] dataset = {
-      {0.74021935f, 0.9209938f},
-      {0.03902049f, 0.9689629f},
-      {0.92514056f, 0.4463501f},
-      {0.6673192f, 0.10993068f}
-    };
-    List<Integer> map = List.of(0, 1, 2, 3);
-    float[][] queries = {
-      {0.48216683f, 0.0428398f},
-      {0.5084142f, 0.6545497f},
-      {0.51260436f, 0.2643005f},
-      {0.05198065f, 0.5789965f}
-    };
-
     // Expected search results
-    List<Map<Integer, Float>> expectedResults =
+    final List<Map<Integer, Float>> expectedResults =
         Arrays.asList(
             Map.of(3, 0.038782578f, 2, 0.35904628f, 0, 0.8377455f),
             Map.of(0, 0.12472608f, 2, 0.21700794f, 1, 0.31918612f),
@@ -75,63 +119,66 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.59866416f));
 
     for (int j = 0; j < 10; j++) {
-
       try (CuVSResources resources = CuVSResources.create()) {
-
-        // Configure index parameters
-        CagraIndexParams indexParams =
-            new CagraIndexParams.Builder()
-                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-                .withGraphDegree(64)
-                .withIntermediateGraphDegree(128)
-                .withNumWriterThreads(32)
-                .withMetric(CuvsDistanceType.L2Expanded)
-                .build();
-
-        // Create the index with the dataset
-        CagraIndex index =
-            CagraIndex.newBuilder(resources)
-                .withDataset(dataset)
-                .withIndexParams(indexParams)
+        HnswQuery hnswQuery =
+            new HnswQuery.Builder()
+                .withMapping(SearchResults.IDENTITY_MAPPING)
+                .withQueryVectors(queries)
+                .withSearchParams(new HnswSearchParams.Builder().build())
+                .withTopK(3)
                 .build();
+        indexAndQueryOnce(resources, hnswQuery, expectedResults);
+      }
+    }
+  }
 
-        // Saving the HNSW index on to the disk.
-        String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
-        index.serializeToHNSW(new FileOutputStream(hnswIndexFileName));
-
-        HnswIndexParams hnswIndexParams =
-            new HnswIndexParams.Builder().withVectorDimension(2).build();
-        InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
-        File hnswIndexFile = new File(hnswIndexFileName);
+  @Test
+  public void testIndexingAndSearchingWithFunctionMapping() throws Throwable {
+    // Expected search results
+    final List<Map<Integer, Float>> expectedResults =
+        Arrays.asList(
+            Map.of(0, 0.038782578f, 3, 0.35904628f, 1, 0.8377455f),
+            Map.of(1, 0.12472608f, 3, 0.21700794f, 2, 0.31918612f),
+            Map.of(0, 0.047766715f, 3, 0.20332818f, 1, 0.48305473f),
+            Map.of(2, 0.15224178f, 1, 0.59063464f, 0, 0.59866416f));
+    LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.length);
 
-        HnswIndex hnswIndex =
-            HnswIndex.newBuilder(resources)
-                .from(inputStreamHNSW)
-                .withIndexParams(hnswIndexParams)
+    for (int j = 0; j < 10; j++) {
+      try (CuVSResources resources = CuVSResources.create()) {
+        HnswQuery hnswQuery =
+            new HnswQuery.Builder()
+                .withQueryVectors(queries)
+                .withMapping(rotate)
+                .withSearchParams(new HnswSearchParams.Builder().build())
+                .withTopK(3)
                 .build();
+        indexAndQueryOnce(resources, hnswQuery, expectedResults);
+      }
+    }
+  }
 
-        HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder().build();
+  @Test
+  public void testIndexingAndSearchingWithListMapping() throws Throwable {
+    // Expected search results
+    final List<Map<Integer, Float>> expectedResults =
+        Arrays.asList(
+            Map.of(1, 0.038782578f, 2, 0.35904628f, 4, 0.8377455f),
+            Map.of(4, 0.12472608f, 2, 0.21700794f, 3, 0.31918612f),
+            Map.of(1, 0.047766715f, 2, 0.20332818f, 4, 0.48305473f),
+            Map.of(3, 0.15224178f, 4, 0.59063464f, 1, 0.59866416f));
+    var mappings = List.of(4, 3, 2, 1);
+    LongToIntFunction rotate = SearchResults.mappingsFromList(mappings);
 
+    for (int j = 0; j < 10; j++) {
+      try (CuVSResources resources = CuVSResources.create()) {
         HnswQuery hnswQuery =
             new HnswQuery.Builder()
-                .withMapping(map)
                 .withQueryVectors(queries)
-                .withSearchParams(hnswSearchParams)
+                .withMapping(rotate)
+                .withSearchParams(new HnswSearchParams.Builder().build())
                 .withTopK(3)
                 .build();
-
-        SearchResults results = hnswIndex.search(hnswQuery);
-
-        // Check results
-        log.info(results.getResults().toString());
-        assertEquals(expectedResults, results.getResults());
-
-        // Cleanup
-        if (hnswIndexFile.exists()) {
-          hnswIndexFile.delete();
-        }
-        index.destroyIndex();
-        hnswIndex.destroyIndex();
+        indexAndQueryOnce(resources, hnswQuery, expectedResults);
       }
     }
   }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index ef538f5917..ded2f0019c 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -20,11 +20,9 @@
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.InputStream;
 import java.lang.invoke.MethodHandles;
+import java.nio.file.Files;
+import java.nio.file.Path;
 import java.util.List;
 import java.util.UUID;
 import org.junit.Before;
@@ -47,7 +45,7 @@ public void setup() {
 
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
-    boolean useNativeMemoryDatasets[] = {true, false};
+    boolean[] useNativeMemoryDatasets = {true, false};
     for (int i = 0; i < 10; i++) {
       for (boolean use : useNativeMemoryDatasets) {
         tmpResultsTopKWithRandomValues(use);
@@ -113,7 +111,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
               .build();
 
       // Create the index with the dataset
-      CagraIndex index;
+      final CagraIndex index;
       if (useNativeMemoryDataset) {
         var datasetBuilder = Dataset.builder(vectors.length, vectors[0].length);
         for (float[] v : vectors) {
@@ -133,37 +131,44 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       }
 
       // Saving the HNSW index on to the disk.
-      String hnswIndexFileName = UUID.randomUUID().toString() + ".hnsw";
-      index.serializeToHNSW(new FileOutputStream(hnswIndexFileName)); // fails here
-
-      HnswIndexParams hnswIndexParams =
-          new HnswIndexParams.Builder().withVectorDimension(dimensions).build();
-      InputStream inputStreamHNSW = new FileInputStream(hnswIndexFileName);
-      File hnswIndexFile = new File(hnswIndexFileName);
-
-      HnswIndex hnswIndex =
-          HnswIndex.newBuilder(resources)
-              .from(inputStreamHNSW)
-              .withIndexParams(hnswIndexParams)
-              .build();
+      String hnswIndexFileName = UUID.randomUUID() + ".hnsw";
+      var hnswIndexPath = Path.of(hnswIndexFileName);
 
-      HnswSearchParams hnswSearchParams = new HnswSearchParams.Builder().withNumThreads(32).build();
+      try {
+        try (var outputStream = Files.newOutputStream(hnswIndexPath)) {
+          index.serializeToHNSW(outputStream); // fails here
+        }
 
-      HnswQuery hnswQuery =
-          new HnswQuery.Builder()
-              .withQueryVectors(queries)
-              .withSearchParams(hnswSearchParams)
-              .withTopK(topK)
-              .build();
+        HnswIndexParams hnswIndexParams =
+            new HnswIndexParams.Builder().withVectorDimension(dimensions).build();
 
-      log.info("Index built successfully. Executing search...");
-      SearchResults results = hnswIndex.search(hnswQuery);
+        try (var inputStreamHNSW = Files.newInputStream(hnswIndexPath)) {
+          HnswIndex hnswIndex =
+              HnswIndex.newBuilder(resources)
+                  .from(inputStreamHNSW)
+                  .withIndexParams(hnswIndexParams)
+                  .build();
 
-      if (hnswIndexFile.exists()) {
-        hnswIndexFile.delete();
-      }
+          HnswSearchParams hnswSearchParams =
+              new HnswSearchParams.Builder().withNumThreads(32).build();
 
-      compareResults(results, expected, topK, datasetSize, numQueries);
+          HnswQuery hnswQuery =
+              new HnswQuery.Builder()
+                  .withQueryVectors(queries)
+                  .withSearchParams(hnswSearchParams)
+                  .withTopK(topK)
+                  .build();
+
+          log.info("Index built successfully. Executing search...");
+          SearchResults results = hnswIndex.search(hnswQuery);
+          compareResults(results, expected, topK, datasetSize, numQueries);
+
+          hnswIndex.destroyIndex();
+        }
+      } finally {
+        index.destroyIndex();
+        Files.deleteIfExists(hnswIndexPath);
+      }
     }
   }
 }

From 23c6dc006e0e637e657dda131066c56f126e8981 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Mon, 7 Jul 2025 20:27:07 -0400
Subject: [PATCH 470/623] `ReachabilityPostProcess` distance epilogue for NN
 Descent (#1073)

NN Descent changed to support distance epilogues. Currently supporting `ReachabilityPostProcess` and `identity_op`. A new distance epilogue will need new instantiations.

Mutual reachability computation will eventually be hidden behind the `all_neighbors` API. Basic tests are still added in this PR to ensure the correctness of this feature.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1073
---
 cpp/src/neighbors/detail/nn_descent.cuh       |  40 ++--
 cpp/src/neighbors/detail/nn_descent_gnnd.hpp  |   8 +-
 cpp/src/neighbors/detail/reachability.cuh     |   5 +-
 cpp/src/neighbors/nn_descent_float.cu         |  29 ++-
 cpp/tests/neighbors/ann_nn_descent.cuh        | 175 +++++++++++++++++-
 .../ann_nn_descent/test_float_uint32_t.cu     |   8 +-
 6 files changed, 238 insertions(+), 27 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 5262b64a97..6f6e26238a 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -488,7 +488,7 @@ __device__ __forceinline__ void remove_duplicates(
 // MAX_RESIDENT_THREAD_PER_SM = BLOCK_SIZE * BLOCKS_PER_SM = 2048
 // For architectures 750 and 860 (890), the values for MAX_RESIDENT_THREAD_PER_SM
 // is 1024 and 1536 respectively, which means the bounds don't work anymore
-template <typename Index_t, typename ID_t = InternalID_t<Index_t>>
+template <typename Index_t, typename ID_t = InternalID_t<Index_t>, typename DistEpilogue_t>
 RAFT_KERNEL
 #ifdef __CUDA_ARCH__
 // Use minBlocksPerMultiprocessor = 4 on specific arches
@@ -513,7 +513,8 @@ __launch_bounds__(BLOCK_SIZE)
                     int graph_width,
                     int* locks,
                     DistData_t* l2_norms,
-                    cuvs::distance::DistanceType metric)
+                    cuvs::distance::DistanceType metric,
+                    DistEpilogue_t dist_epilogue)
 {
 #if (__CUDA_ARCH__ >= 700)
   using namespace nvcuda;
@@ -623,20 +624,22 @@ __launch_bounds__(BLOCK_SIZE)
   __syncthreads();
 
   for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
-    if (i % SKEWED_MAX_NUM_BI_SAMPLES < list_new_size &&
-        i / SKEWED_MAX_NUM_BI_SAMPLES < list_new_size) {
+    int row_id = i % SKEWED_MAX_NUM_BI_SAMPLES;
+    int col_id = i / SKEWED_MAX_NUM_BI_SAMPLES;
+
+    if (row_id < list_new_size && col_id < list_new_size) {
       if (metric == cuvs::distance::DistanceType::InnerProduct) {
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
       } else {  // L2Expanded or L2SqrtExpanded
-        s_distances[i] = l2_norms[new_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
-                         l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
-                         2.0 * s_distances[i];
+        s_distances[i] =
+          l2_norms[new_neighbors[row_id]] + l2_norms[new_neighbors[col_id]] - 2.0 * s_distances[i];
         // for fp32 vs fp16 precision differences resulting in negative distances when distance
         // should be 0 related issue: https://github.com/rapidsai/cuvs/issues/991
         s_distances[i] = s_distances[i] < 0.0f ? 0.0f : s_distances[i];
       }
+      s_distances[i] = dist_epilogue(s_distances[i], new_neighbors[row_id], new_neighbors[col_id]);
     } else {
       s_distances[i] = std::numeric_limits<float>::max();
     }
@@ -707,20 +710,21 @@ __launch_bounds__(BLOCK_SIZE)
   __syncthreads();
 
   for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
-    if (i % SKEWED_MAX_NUM_BI_SAMPLES < list_old_size &&
-        i / SKEWED_MAX_NUM_BI_SAMPLES < list_new_size) {
+    int row_id = i % SKEWED_MAX_NUM_BI_SAMPLES;
+    int col_id = i / SKEWED_MAX_NUM_BI_SAMPLES;
+    if (row_id < list_old_size && col_id < list_new_size) {
       if (metric == cuvs::distance::DistanceType::InnerProduct) {
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
       } else {  // L2Expanded or L2SqrtExpanded
-        s_distances[i] = l2_norms[old_neighbors[i % SKEWED_MAX_NUM_BI_SAMPLES]] +
-                         l2_norms[new_neighbors[i / SKEWED_MAX_NUM_BI_SAMPLES]] -
-                         2.0 * s_distances[i];
+        s_distances[i] =
+          l2_norms[old_neighbors[row_id]] + l2_norms[new_neighbors[col_id]] - 2.0 * s_distances[i];
         // for fp32 vs fp16 precision differences resulting in negative distances when distance
         // should be 0 related issue: https://github.com/rapidsai/cuvs/issues/991
         s_distances[i] = s_distances[i] < 0.0f ? 0.0f : s_distances[i];
       }
+      s_distances[i] = dist_epilogue(s_distances[i], old_neighbors[row_id], new_neighbors[col_id]);
     } else {
       s_distances[i] = std::numeric_limits<float>::max();
     }
@@ -1034,7 +1038,8 @@ void GNND<Data_t, Index_t>::add_reverse_edges(Index_t* graph_ptr,
 }
 
 template <typename Data_t, typename Index_t>
-void GNND<Data_t, Index_t>::local_join(cudaStream_t stream)
+template <typename DistEpilogue_t>
+void GNND<Data_t, Index_t>::local_join(cudaStream_t stream, DistEpilogue_t dist_epilogue)
 {
   raft::matrix::fill(res, dists_buffer_.view(), std::numeric_limits<float>::max());
   local_join_kernel<<<nrow_, BLOCK_SIZE, 0, stream>>>(graph_.h_graph_new.data_handle(),
@@ -1051,15 +1056,18 @@ void GNND<Data_t, Index_t>::local_join(cudaStream_t stream)
                                                       DEGREE_ON_DEVICE,
                                                       d_locks_.data_handle(),
                                                       l2_norms_.data_handle(),
-                                                      build_config_.metric);
+                                                      build_config_.metric,
+                                                      dist_epilogue);
 }
 
 template <typename Data_t, typename Index_t>
+template <typename DistEpilogue_t>
 void GNND<Data_t, Index_t>::build(Data_t* data,
                                   const Index_t nrow,
                                   Index_t* output_graph,
                                   bool return_distances,
-                                  DistData_t* output_distances)
+                                  DistData_t* output_distances,
+                                  DistEpilogue_t dist_epilogue)
 {
   using input_t = typename std::remove_const<Data_t>::type;
 
@@ -1154,7 +1162,7 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
       raft::util::arch::SM_range(raft::util::arch::SM_70(), raft::util::arch::SM_future());
 
     if (wmma_range.contains(runtime_arch)) {
-      local_join(stream);
+      local_join(stream, dist_epilogue);
     } else {
       THROW("NN_DESCENT cannot be run for __CUDA_ARCH__ < 700");
     }
diff --git a/cpp/src/neighbors/detail/nn_descent_gnnd.hpp b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
index 4dc1b3e8a5..8c7f2c9f0e 100644
--- a/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
+++ b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
@@ -207,11 +207,13 @@ class GNND {
   GNND(const GNND&)            = delete;
   GNND& operator=(const GNND&) = delete;
 
+  template <typename DistEpilogue_t = raft::identity_op>
   void build(Data_t* data,
              const Index_t nrow,
              Index_t* output_graph,
              bool return_distances,
-             DistData_t* output_distances);
+             DistData_t* output_distances,
+             DistEpilogue_t dist_epilogue = DistEpilogue_t{});
   ~GNND()    = default;
   using ID_t = InternalID_t<Index_t>;
   void reset(raft::resources const& res);
@@ -222,7 +224,9 @@ class GNND {
                          Index_t* d_rev_graph_ptr,
                          int2* list_sizes,
                          cudaStream_t stream = 0);
-  void local_join(cudaStream_t stream = 0);
+
+  template <typename DistEpilogue_t = raft::identity_op>
+  void local_join(cudaStream_t stream = 0, DistEpilogue_t dist_epilogue = DistEpilogue_t{});
 
   raft::resources const& res;
 
diff --git a/cpp/src/neighbors/detail/reachability.cuh b/cpp/src/neighbors/detail/reachability.cuh
index ead63ac866..0f73e014e5 100644
--- a/cpp/src/neighbors/detail/reachability.cuh
+++ b/cpp/src/neighbors/detail/reachability.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -131,6 +131,7 @@ struct ReachabilityPostProcess {
 
   const value_t* core_dists;
   value_t alpha;
+  size_t n;  // size of core_dists array
 };
 
 /**
@@ -163,7 +164,7 @@ void mutual_reachability_knn_l2(const raft::resources& handle,
   // `A type local to a function cannot be used in the template argument of the
   // enclosing parent function (and any parent classes) of an extended __device__
   // or __host__ __device__ lambda`
-  auto epilogue = ReachabilityPostProcess<value_idx, value_t>{core_dists, alpha};
+  auto epilogue = ReachabilityPostProcess<value_idx, value_t>{core_dists, alpha, m};
 
   cuvs::neighbors::detail::
     tiled_brute_force_knn<value_t, value_idx, value_t, ReachabilityPostProcess<value_idx, value_t>>(
diff --git a/cpp/src/neighbors/nn_descent_float.cu b/cpp/src/neighbors/nn_descent_float.cu
index b1e58b2330..2f9d909dc6 100644
--- a/cpp/src/neighbors/nn_descent_float.cu
+++ b/cpp/src/neighbors/nn_descent_float.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,6 +14,8 @@
  * limitations under the License.
  */
 
+#include "./detail/nn_descent_gnnd.hpp"
+#include "./detail/reachability.cuh"
 #include "nn_descent.cuh"
 #include <cuvs/neighbors/nn_descent.hpp>
 
@@ -54,7 +56,30 @@ namespace cuvs::neighbors::nn_descent {
       return idx;                                                                             \
     }                                                                                         \
   };                                                                                          \
-  template class detail::GNND<const T, int>;
+  template class detail::GNND<const T, int>;                                                  \
+                                                                                              \
+  template void detail::GNND<const T, int>::build<                                            \
+    cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int, T>>(                  \
+    const T* data,                                                                            \
+    const int nrow,                                                                           \
+    int* output_graph,                                                                        \
+    bool return_distances,                                                                    \
+    float* output_distances,                                                                  \
+    cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int, T> dist_epilogue);    \
+  template void detail::GNND<const T, int>::local_join<                                       \
+    cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int, T>>(                  \
+    cudaStream_t stream,                                                                      \
+    cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int, T> dist_epilogue);    \
+                                                                                              \
+  template void detail::GNND<const T, int>::build<raft::identity_op>(                         \
+    const T* data,                                                                            \
+    const int nrow,                                                                           \
+    int* output_graph,                                                                        \
+    bool return_distances,                                                                    \
+    float* output_distances,                                                                  \
+    raft::identity_op dist_epilogue);                                                         \
+  template void detail::GNND<const T, int>::local_join<raft::identity_op>(                    \
+    cudaStream_t stream, raft::identity_op dist_epilogue);
 
 CUVS_INST_NN_DESCENT_BUILD(float, uint32_t);
 
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index 3f5c0397f3..53897851d2 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,8 +15,12 @@
  */
 #pragma once
 
+#include "../../src/neighbors/detail/knn_brute_force.cuh"
+#include "../../src/neighbors/detail/nn_descent_gnnd.hpp"
+#include "../../src/neighbors/detail/reachability.cuh"
 #include "../test_utils.cuh"
 #include "ann_utils.cuh"
+#include "naive_knn.cuh"
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
@@ -26,9 +30,6 @@
 #include <raft/util/itertools.hpp>
 #include <rmm/device_uvector.hpp>
 
-#include "naive_knn.cuh"
-#include <cuvs/distance/distance.hpp>
-
 #include <gtest/gtest.h>
 
 #include <cstddef>
@@ -192,6 +193,164 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
   rmm::device_uvector<DataT> database;
 };
 
+// getting mutual reachability using nn descent should be done through all_neighbors
+// API. this is just a simple check that the feature works well before integrating into
+// all_neighbors API.
+template <typename DistanceT, typename DataT, typename IdxT>
+class AnnNNDescentDistEpiTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
+ public:
+  AnnNNDescentDistEpiTest()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<AnnNNDescentInputs>::GetParam()),
+      database(0, stream_)
+  {
+  }
+
+ protected:
+  void testNNDescent()
+  {
+    size_t queries_size = ps.n_rows * ps.graph_degree;
+    std::vector<IdxT> indices_NNDescent(queries_size);
+    std::vector<DistanceT> distances_NNDescent(queries_size);
+    std::vector<IdxT> indices_naive(queries_size);
+    std::vector<DistanceT> distances_naive(queries_size);
+
+    {
+      rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, stream_);
+      rmm::device_uvector<IdxT> indices_naive_dev(queries_size, stream_);
+      naive_knn<DistanceT, DataT, IdxT>(handle_,
+                                        distances_naive_dev.data(),
+                                        indices_naive_dev.data(),
+                                        database.data(),
+                                        database.data(),
+                                        ps.n_rows,
+                                        ps.n_rows,
+                                        ps.dim,
+                                        ps.graph_degree,
+                                        ps.metric);
+      rmm::device_uvector<DistanceT> core_dists_dev(ps.n_rows, stream_);
+
+      cuvs::neighbors::detail::reachability::core_distances<IdxT, DistanceT>(
+        distances_naive_dev.data(),
+        ps.graph_degree,
+        ps.graph_degree,
+        ps.n_rows,
+        core_dists_dev.data(),
+        stream_);
+      auto epilogue =
+        cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, DistanceT>{
+          core_dists_dev.data(), 1.0, static_cast<size_t>(ps.n_rows)};
+
+      cuvs::neighbors::detail::tiled_brute_force_knn<
+        DataT,
+        IdxT,
+        DistanceT,
+        cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, DistanceT>>(
+        handle_,
+        database.data(),
+        database.data(),
+        ps.n_rows,
+        ps.n_rows,
+        ps.dim,
+        ps.graph_degree,
+        distances_naive_dev.data(),
+        indices_naive_dev.data(),
+        ps.metric,
+        2.0,
+        0,
+        0,
+        nullptr,
+        nullptr,
+        nullptr,
+        epilogue);
+      raft::update_host(indices_naive.data(), indices_naive_dev.data(), queries_size, stream_);
+      raft::update_host(distances_naive.data(), distances_naive_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+    {
+      nn_descent::index_params index_params;
+      index_params.metric                    = ps.metric;
+      index_params.graph_degree              = ps.graph_degree;
+      index_params.intermediate_graph_degree = 2 * ps.graph_degree;
+      index_params.max_iterations            = 100;
+      index_params.return_distances          = true;
+
+      auto database_host = raft::make_host_matrix<DataT, int64_t>(ps.n_rows, ps.dim);
+      raft::copy(database_host.data_handle(), database.data(), database.size(), stream_);
+      raft::resource::sync_stream(handle_);
+      auto database_host_view = raft::make_host_matrix_view<const DataT, int64_t>(
+        (const DataT*)database_host.data_handle(), ps.n_rows, ps.dim);
+      auto index = nn_descent::build(handle_, index_params, database_host_view);
+
+      rmm::device_uvector<DistanceT> core_dists_dev(ps.n_rows, stream_);
+      cuvs::neighbors::detail::reachability::core_distances<IdxT, DistanceT>(
+        index.distances().value().data_handle(),
+        ps.graph_degree,
+        ps.graph_degree,
+        ps.n_rows,
+        core_dists_dev.data(),
+        stream_);
+
+      size_t extended_graph_degree, graph_degree;
+      auto build_config = nn_descent::detail::get_build_config(
+        handle_, index_params, ps.n_rows, ps.dim, ps.metric, extended_graph_degree, graph_degree);
+      auto gnnd      = nn_descent::detail::GNND<const DataT, int>(handle_, build_config);
+      auto int_graph = raft::make_host_matrix<int, IdxT, row_major>(
+        ps.n_rows, static_cast<IdxT>(extended_graph_degree));
+
+      auto dist_epilogue =
+        cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int, DataT>{
+          core_dists_dev.data(), 1.0, static_cast<size_t>(ps.n_rows)};
+      rmm::device_uvector<DistanceT> distances_nnd_dev(queries_size, stream_);
+      gnnd.build(database_host.data_handle(),
+                 static_cast<int>(ps.n_rows),
+                 int_graph.data_handle(),
+                 true,
+                 distances_nnd_dev.data(),
+                 dist_epilogue);
+
+      // copy indices and distances graph
+      for (int i = 0; i < ps.n_rows; i++) {
+        for (int j = 0; j < ps.graph_degree; j++) {
+          indices_NNDescent[i * ps.graph_degree + j] = static_cast<IdxT>(int_graph(i, j));
+        }
+      }
+      raft::copy(distances_NNDescent.data(), distances_nnd_dev.data(), queries_size, stream_);
+      raft::resource::sync_stream(handle_);
+    }
+
+    double min_recall = ps.min_recall;
+    EXPECT_TRUE(eval_neighbours(indices_naive,
+                                indices_NNDescent,
+                                distances_naive,
+                                distances_NNDescent,
+                                ps.n_rows,
+                                ps.graph_degree,
+                                0.001,
+                                min_recall));
+  }
+
+  void SetUp() override
+  {
+    database.resize(((size_t)ps.n_rows) * ps.dim, stream_);
+    raft::random::RngState r(1234ULL);
+    raft::random::normal(handle_, r, database.data(), ps.n_rows * ps.dim, DataT(0.1), DataT(2.0));
+    raft::resource::sync_stream(handle_);
+  }
+
+  void TearDown() override
+  {
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  AnnNNDescentInputs ps;
+  rmm::device_uvector<DataT> database;
+};
+
 template <typename DistanceT, typename DataT, typename IdxT>
 class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchInputs> {
  public:
@@ -322,6 +481,14 @@ const std::vector<AnnNNDescentInputs> inputs =
                                                      {false, true},
                                                      {0.90});
 
+const std::vector<AnnNNDescentInputs> inputsDistEpilogue =
+  raft::util::itertools::product<AnnNNDescentInputs>({2000, 4000},  // n_rows
+                                                     {64, 1024},    // dim
+                                                     {32, 64},      // graph_degree
+                                                     {cuvs::distance::DistanceType::L2Expanded},
+                                                     {true},  // data on host
+                                                     {0.90});
+
 const std::vector<AnnNNDescentBatchInputs> inputsBatch =
   raft::util::itertools::product<AnnNNDescentBatchInputs>(
     {std::make_pair(0.9, 3lu), std::make_pair(0.9, 2lu)},  // min_recall, n_clusters
diff --git a/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
index ab59b06a36..01fa769493 100644
--- a/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -23,10 +23,16 @@ namespace cuvs::neighbors::nn_descent {
 typedef AnnNNDescentTest<float, float, std::uint32_t> AnnNNDescentTestF_U32;
 TEST_P(AnnNNDescentTestF_U32, AnnNNDescent) { this->testNNDescent(); }
 
+typedef AnnNNDescentDistEpiTest<float, float, std::uint32_t> AnnNNDescentTestDistEpiF_U32;
+TEST_P(AnnNNDescentTestDistEpiF_U32, AnnNNDescentDistEpi) { this->testNNDescent(); }
+
 typedef AnnNNDescentBatchTest<float, float, std::uint32_t> AnnNNDescentBatchTestF_U32;
 TEST_P(AnnNNDescentBatchTestF_U32, AnnNNDescentBatch) { this->testNNDescentBatch(); }
 
 INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestF_U32, ::testing::ValuesIn(inputs));
+INSTANTIATE_TEST_CASE_P(AnnNNDescentDistEpi,
+                        AnnNNDescentTestDistEpiF_U32,
+                        ::testing::ValuesIn(inputsDistEpilogue));
 INSTANTIATE_TEST_CASE_P(AnnNNDescentBatchTest,
                         AnnNNDescentBatchTestF_U32,
                         ::testing::ValuesIn(inputsBatch));

From 7bde965533c71bc964c552b884927a825d4fe641 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Tue, 8 Jul 2025 10:13:19 -0400
Subject: [PATCH 471/623] Add brute force to `all_neighbors` API (#1062)

Add brute force option to `all_neighbors` API. Closes https://github.com/rapidsai/cuvs/issues/1055

Authors:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1062
---
 cpp/include/cuvs/neighbors/all_neighbors.hpp  |  20 +--
 .../cuvs/neighbors/graph_build_types.hpp      |   6 +
 .../neighbors/all_neighbors/all_neighbors.cuh |  15 +-
 .../all_neighbors/all_neighbors_builder.cuh   | 142 +++++++++++++++++-
 cpp/tests/neighbors/all_neighbors.cuh         |  20 ++-
 5 files changed, 187 insertions(+), 16 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/all_neighbors.hpp b/cpp/include/cuvs/neighbors/all_neighbors.hpp
index 3b7793be10..a4323b67ba 100644
--- a/cpp/include/cuvs/neighbors/all_neighbors.hpp
+++ b/cpp/include/cuvs/neighbors/all_neighbors.hpp
@@ -17,9 +17,6 @@
 #pragma once
 
 #include <cuvs/neighbors/graph_build_types.hpp>
-#include <cuvs/neighbors/ivf_pq.hpp>
-#include <cuvs/neighbors/nn_descent.hpp>
-
 #include <variant>
 
 namespace cuvs::neighbors::all_neighbors {
@@ -30,8 +27,9 @@ namespace graph_build_params = cuvs::neighbors::graph_build_params;
  * @{
  */
 
-using GraphBuildParams =
-  std::variant<graph_build_params::ivf_pq_params, graph_build_params::nn_descent_params>;
+using GraphBuildParams = std::variant<graph_build_params::ivf_pq_params,
+                                      graph_build_params::nn_descent_params,
+                                      graph_build_params::brute_force_params>;
 
 /**
  * @brief Parameters used to build an all-neighbors graph (find nearest neighbors for all the
@@ -46,11 +44,12 @@ using GraphBuildParams =
  */
 struct all_neighbors_params {
   /** Parameters for knn graph building algorithm
-   * Approximate nearest neighbors methods are used to build the knn graph. Currently supported
-   * options are 'IVF-PQ' and 'NN Descent'. IVF-PQ is more accurate, but slower compared to NN
-   * Descent.
+   * Approximate nearest neighbors methods or a brute force approach are supported to build the knn
+   * graph. Currently supported options are 'IVF-PQ', 'NN Descent', or 'Brute Force'. IVF-PQ is more
+   * accurate, but slower compared to NN Descent. Note that 'Brute Force' can also be approximate if
+   * n_clusters > 1.
    *
-   * Set ivf_pq_params, or nn_descent_params to select the graph build
+   * Set ivf_pq_params, nn_descent_params, or brute_force_params to select the graph build
    * algorithm and control their parameters.
    *
    * @code{.cpp}
@@ -61,6 +60,9 @@ struct all_neighbors_params {
    * // 2. Choose NN Descent algorithm for kNN graph construction
    * params.graph_build_params = all_neighbors::graph_build_params::nn_descent_params{};
    *
+   * // 3. Choose Brute Force algorithm for kNN graph construction
+   * params.graph_build_params = all_neighbors::graph_build_params::brute_force_params{};
+   *
    * @endcode
    */
   GraphBuildParams graph_build_params;
diff --git a/cpp/include/cuvs/neighbors/graph_build_types.hpp b/cpp/include/cuvs/neighbors/graph_build_types.hpp
index d63b13386c..23ca3e0c14 100644
--- a/cpp/include/cuvs/neighbors/graph_build_types.hpp
+++ b/cpp/include/cuvs/neighbors/graph_build_types.hpp
@@ -16,6 +16,7 @@
 
 #pragma once
 
+#include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 
@@ -70,6 +71,11 @@ struct ivf_pq_params {
 
 using nn_descent_params = cuvs::neighbors::nn_descent::index_params;
 
+struct brute_force_params {
+  cuvs::neighbors::brute_force::index_params build_params;
+  cuvs::neighbors::brute_force::search_params search_params;
+};
+
 // **** Experimental ****
 using iterative_search_params = cuvs::neighbors::search_params;
 }  // namespace graph_build_params
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
index 1caab57468..66abd5ac5d 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
@@ -24,7 +24,20 @@ using namespace cuvs::neighbors;
 
 void check_metric(const all_neighbors_params& params)
 {
-  if (std::holds_alternative<graph_build_params::nn_descent_params>(params.graph_build_params)) {
+  if (std::holds_alternative<graph_build_params::brute_force_params>(params.graph_build_params)) {
+    auto allowed_metrics_batch = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                                 params.metric == cuvs::distance::DistanceType::L2SqrtExpanded;
+    auto allowed_metrics_single = allowed_metrics_batch ||
+                                  params.metric == cuvs::distance::DistanceType::CosineExpanded ||
+                                  params.metric == cuvs::distance::DistanceType::InnerProduct;
+    // related issue: https://github.com/rapidsai/cuvs/issues/1056
+    RAFT_EXPECTS((params.n_clusters <= 1 && allowed_metrics_single) || allowed_metrics_batch,
+                 "Distance metric supported for for all-neighbors build with brute force depends "
+                 "on params.n_clusters. When params.n_clusters <= 1, supported metrics are "
+                 "L2Expanded, L2SqrtExpanded, CosineExpanded, or InnerProduct. When "
+                 "params.n_clusters > 1, supported metrics are L2Expanded, L2SqrtExpanded.");
+  } else if (std::holds_alternative<graph_build_params::nn_descent_params>(
+               params.graph_build_params)) {
     auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
                            params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
                            params.metric == cuvs::distance::DistanceType::CosineExpanded ||
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
index e17fb4a0aa..af095baf0f 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
@@ -17,7 +17,11 @@
 
 #include "../detail/nn_descent_gnnd.hpp"
 #include "all_neighbors_merge.cuh"
+#include "raft/core/device_mdarray.hpp"
+#include "raft/core/mdspan.hpp"
+#include "raft/util/cudart_utils.hpp"
 #include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 #include <cuvs/neighbors/refine.hpp>
@@ -441,6 +445,124 @@ struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT>
   std::optional<raft::host_matrix<int, IdxT>> int_graph;
 };
 
+template <typename T, typename IdxT = int64_t>
+struct all_neighbors_builder_brute_force : public all_neighbors_builder<T, IdxT> {
+  all_neighbors_builder_brute_force(
+    raft::resources const& res,
+    size_t n_clusters,
+    size_t min_cluster_size,
+    size_t max_cluster_size,
+    size_t k,
+    graph_build_params::brute_force_params& params,
+    std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
+    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+    : all_neighbors_builder<T, IdxT>(
+        res, n_clusters, min_cluster_size, max_cluster_size, k, indices, distances),
+      bf_params{params}
+  {
+  }
+
+  void prepare_build(raft::device_matrix_view<const T, IdxT, row_major> dataset) override {}
+
+  void prepare_build(raft::host_matrix_view<const T, IdxT, row_major> dataset) override
+  {
+    // data needs to be on device for build - search
+    size_t num_cols = dataset.extent(1);
+    data_d.emplace(
+      raft::make_device_matrix<T, IdxT, row_major>(this->res, this->max_cluster_size, num_cols));
+  }
+
+  void build_knn_common(
+    raft::device_matrix_view<const T, IdxT> dataset,
+    std::optional<raft::host_vector_view<IdxT, IdxT>> inverted_indices    = std::nullopt,
+    std::optional<raft::managed_matrix_view<IdxT, IdxT>> global_neighbors = std::nullopt,
+    std::optional<raft::managed_matrix_view<T, IdxT>> global_distances    = std::nullopt)
+  {
+    RAFT_EXPECTS(
+      this->n_clusters <= 1 || (inverted_indices.has_value() && global_neighbors.has_value() &&
+                                global_distances.has_value()),
+      "need valid inverted_indices, global_neighbors, and global_distances for "
+      "build_knn if doing batching.");
+
+    if (this->n_clusters > 1) {
+      auto idx = cuvs::neighbors::brute_force::build(this->res, bf_params.build_params, dataset);
+
+      size_t num_data_in_cluster = dataset.extent(0);
+
+      cuvs::neighbors::brute_force::search(
+        this->res,
+        bf_params.search_params,
+        idx,
+        dataset,
+        raft::make_device_matrix_view<IdxT, IdxT>(
+          this->batch_neighbors_d.value().data_handle(), num_data_in_cluster, this->k),
+        raft::make_device_matrix_view<T, IdxT>(
+          this->batch_distances_d.value().data_handle(), num_data_in_cluster, this->k));
+
+      raft::copy(this->batch_neighbors_h.value().data_handle(),
+                 this->batch_neighbors_d.value().data_handle(),
+                 num_data_in_cluster * this->k,
+                 raft::resource::get_cuda_stream(this->res));
+
+      remap_and_merge_subgraphs<T, IdxT, IdxT>(
+        this->res,
+        this->inverted_indices_d.value().view(),
+        inverted_indices.value(),
+        this->batch_neighbors_h.value().view(),
+        this->batch_neighbors_h.value().view(),
+        this->batch_neighbors_d.value().view(),
+        this->batch_distances_d.value().view(),
+        global_neighbors.value(),
+        global_distances.value(),
+        num_data_in_cluster,
+        this->k,
+        cuvs::distance::is_min_close(bf_params.build_params.metric));
+    } else {
+      auto idx = cuvs::neighbors::brute_force::build(this->res, bf_params.build_params, dataset);
+
+      cuvs::neighbors::brute_force::search(
+        this->res,
+        bf_params.search_params,
+        idx,
+        dataset,
+        this->indices_.value(),
+        this->distances_.has_value()
+          ? this->distances_.value()
+          : raft::make_device_matrix<T, IdxT>(this->res, dataset.extent(0), this->k).view());
+    }
+  }
+
+  void build_knn(
+    raft::host_matrix_view<const T, IdxT> dataset,
+    std::optional<raft::host_vector_view<IdxT, IdxT>> inverted_indices    = std::nullopt,
+    std::optional<raft::managed_matrix_view<IdxT, IdxT>> global_neighbors = std::nullopt,
+    std::optional<raft::managed_matrix_view<T, IdxT>> global_distances    = std::nullopt) override
+  {
+    raft::copy(data_d.value().data_handle(),
+               dataset.data_handle(),
+               dataset.size(),
+               raft::resource::get_cuda_stream(this->res));
+
+    build_knn_common(raft::make_device_matrix_view<const T, IdxT, row_major>(
+                       data_d.value().data_handle(), dataset.extent(0), dataset.extent(1)),
+                     inverted_indices,
+                     global_neighbors,
+                     global_distances);
+  }
+
+  void build_knn(raft::device_matrix_view<const T, IdxT> dataset) override
+  {
+    RAFT_EXPECTS(this->n_clusters <= 1,
+                 "building all-neighbors knn graph with dataset on device is not supported with "
+                 "batching (n_clusters > 1)");
+    build_knn_common(dataset);
+  }
+
+  graph_build_params::brute_force_params bf_params;
+
+  std::optional<raft::device_matrix<T, IdxT, row_major>> data_d;
+};
+
 template <typename T, typename IdxT>
 std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
   const raft::resources& handle,
@@ -451,7 +573,23 @@ std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
   std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
   std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
 {
-  if (std::holds_alternative<graph_build_params::nn_descent_params>(params.graph_build_params)) {
+  if (std::holds_alternative<graph_build_params::brute_force_params>(params.graph_build_params)) {
+    auto brute_force_params =
+      std::get<graph_build_params::brute_force_params>(params.graph_build_params);
+    if (brute_force_params.build_params.metric != params.metric) {
+      RAFT_LOG_WARN("Setting brute_force_params metric to metric given for batching algorithm");
+      brute_force_params.build_params.metric = params.metric;
+    }
+    return std::make_unique<all_neighbors_builder_brute_force<T, IdxT>>(handle,
+                                                                        params.n_clusters,
+                                                                        min_cluster_size,
+                                                                        max_cluster_size,
+                                                                        k,
+                                                                        brute_force_params,
+                                                                        indices,
+                                                                        distances);
+  } else if (std::holds_alternative<graph_build_params::nn_descent_params>(
+               params.graph_build_params)) {
     auto nn_descent_params =
       std::get<graph_build_params::nn_descent_params>(params.graph_build_params);
     if (nn_descent_params.metric != params.metric) {
@@ -481,7 +619,7 @@ std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
                                                                   indices,
                                                                   distances);
   } else {
-    RAFT_FAIL("Batch KNN build algos only supporting NN Descent and IVFPQ");
+    RAFT_FAIL("Batch KNN build algos only supporting Brute Force, NN Descent, and IVFPQ");
   }
 }
 
diff --git a/cpp/tests/neighbors/all_neighbors.cuh b/cpp/tests/neighbors/all_neighbors.cuh
index 65be8ddcb4..1edc93ce26 100644
--- a/cpp/tests/neighbors/all_neighbors.cuh
+++ b/cpp/tests/neighbors/all_neighbors.cuh
@@ -21,6 +21,7 @@
 #include <cstddef>
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 #include <gtest/gtest.h>
@@ -42,7 +43,7 @@
 
 namespace cuvs::neighbors::all_neighbors {
 
-enum knn_build_algo { NN_DESCENT, IVF_PQ };
+enum knn_build_algo { BRUTE_FORCE, NN_DESCENT, IVF_PQ };
 
 struct AllNeighborsInputs {
   std::tuple<knn_build_algo, cuvs::distance::DistanceType, double> build_algo_metric_recall;
@@ -79,7 +80,11 @@ void get_graphs(raft::resources& handle,
 
   auto build_algo = std::get<0>(ps.build_algo_metric_recall);
 
-  if (build_algo == NN_DESCENT) {
+  if (build_algo == BRUTE_FORCE) {
+    auto brute_force_params                = graph_build_params::brute_force_params{};
+    brute_force_params.build_params.metric = params.metric;
+    params.graph_build_params              = brute_force_params;
+  } else if (build_algo == NN_DESCENT) {
     auto nn_descent_params                      = graph_build_params::nn_descent_params{};
     nn_descent_params.max_iterations            = 100;
     nn_descent_params.graph_degree              = ps.k;
@@ -176,6 +181,7 @@ class AllNeighborsTest : public ::testing::TestWithParam<AllNeighborsInputs> {
                queries_size);
 
     double min_recall = std::get<2>(ps.build_algo_metric_recall);
+
     EXPECT_TRUE(eval_recall(indices_bf, indices_allNN, ps.n_rows, ps.k, 0.01, min_recall, true));
   }
 
@@ -204,7 +210,11 @@ class AllNeighborsTest : public ::testing::TestWithParam<AllNeighborsInputs> {
 
 const std::vector<AllNeighborsInputs> inputsSingle =
   raft::util::itertools::product<AllNeighborsInputs>(
-    {std::make_tuple(IVF_PQ, cuvs::distance::DistanceType::L2Expanded, 0.9),
+    {std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::InnerProduct, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::CosineExpanded, 0.9),
+     std::make_tuple(IVF_PQ, cuvs::distance::DistanceType::L2Expanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2Expanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::CosineExpanded, 0.9),
@@ -218,7 +228,9 @@ const std::vector<AllNeighborsInputs> inputsSingle =
 
 const std::vector<AllNeighborsInputs> inputsBatch =
   raft::util::itertools::product<AllNeighborsInputs>(
-    {std::make_tuple(IVF_PQ, cuvs::distance::DistanceType::L2Expanded, 0.9),
+    {std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(IVF_PQ, cuvs::distance::DistanceType::L2Expanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2Expanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::CosineExpanded, 0.9),

From 730524d505cd65ed995dd472b79ab8341b6d6dac Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Tue, 8 Jul 2025 22:36:20 +0200
Subject: [PATCH 472/623] [Java] Add Java API benchmarks (#1033)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR introduces a maven project configured with JMH, so we can write (micro-)benchmarks to test out different implementation choices and alternatives and their relative performances.

As an example, the PR introduces 2 benchmarks for the Cagra index API: one testing the performances of building and serializing, the other the performances of building an index from a dataset in heap memory. I plan to follow up with PRs that introduce different ways to serialize an index, and different way of specify an input dataset (off-heap memory), adding corresponding tests to see what are the performance gains.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Ben Frederickson (https://github.com/benfred)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1033
---
 java/.gitignore                               |   9 +-
 java/benchmarks/README.md                     |  27 +++
 java/benchmarks/pom.xml                       | 154 ++++++++++++++++++
 .../com/nvidia/cuvs/CagraIndexBenchmarks.java | 114 +++++++++++++
 4 files changed, 299 insertions(+), 5 deletions(-)
 create mode 100644 java/benchmarks/README.md
 create mode 100644 java/benchmarks/pom.xml
 create mode 100644 java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java

diff --git a/java/.gitignore b/java/.gitignore
index 2d3b14dd31..a240aca207 100644
--- a/java/.gitignore
+++ b/java/.gitignore
@@ -1,9 +1,8 @@
+*.iml
+target/
+jextract-22/
+openjdk-22-jextract*
 # cuvs-java
-/cuvs-java/target/
 /cuvs-java/bin/
 /cuvs-java/src/main/java22/com/nvidia/cuvs/internal/panama/
 /cuvs-java/*.cag
-jextract-22/
-openjdk-22-jextract*
-# examples
-/examples/target/
diff --git a/java/benchmarks/README.md b/java/benchmarks/README.md
new file mode 100644
index 0000000000..1740e52811
--- /dev/null
+++ b/java/benchmarks/README.md
@@ -0,0 +1,27 @@
+# CuVS Java API benchmarks
+
+This maven project contains JMH benchmarks for the CAGRA Java API.
+
+## Prerequisites
+- [CuVS libraries](https://docs.rapids.ai/api/cuvs/stable/build/#build-from-source)
+- Build the CuVS-Java API (`./build.sh` from the parent directory)
+
+## Run benchmarks
+
+Build:
+```shell
+mvn clean verify
+```
+Run:
+```shell
+export RAFT_DEBUG_LOG_FILE=/dev/null
+java -jar target/benchmarks.jar
+```
+The environment variable is needed to silence RAFT logging; RAFT emits some logs at INFO level when
+building indices and queries, and writing them to stdout (the default) influences benchmark results.
+
+It is possible to change the dataset size and the vectors dimension via 2 parameters:
+```shell
+java -jar target/benchmarks.jar -p size=4 -p dims=4
+```
+Use `java -jar target/benchmarks.jar -h` for details on the options to fine-tune your benchmark runs.
diff --git a/java/benchmarks/pom.xml b/java/benchmarks/pom.xml
new file mode 100644
index 0000000000..f5c77a14bc
--- /dev/null
+++ b/java/benchmarks/pom.xml
@@ -0,0 +1,154 @@
+<!--
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>com.nvidia.cuvs</groupId>
+    <artifactId>benchmarks</artifactId>
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <packaging>jar</packaging>
+
+    <name>cuvs-java-benchmarks</name>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.openjdk.jmh</groupId>
+            <artifactId>jmh-core</artifactId>
+            <version>${jmh.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>org.openjdk.jmh</groupId>
+            <artifactId>jmh-generator-annprocess</artifactId>
+            <version>${jmh.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>com.nvidia.cuvs</groupId>
+            <artifactId>cuvs-java</artifactId>
+            <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+            <type>jar</type>
+        </dependency>
+    </dependencies>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <jmh.version>1.37</jmh.version>
+        <javac.target>22</javac.target>
+        <uberjar.name>benchmarks</uberjar.name>
+    </properties>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>3.8.0</version>
+                <configuration>
+                    <compilerVersion>${javac.target}</compilerVersion>
+                    <source>${javac.target}</source>
+                    <target>${javac.target}</target>
+                    <compilerArgs>
+                        <arg>-proc:full</arg>
+                    </compilerArgs>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-shade-plugin</artifactId>
+                <version>3.2.1</version>
+                <executions>
+                    <execution>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>shade</goal>
+                        </goals>
+                        <configuration>
+                            <finalName>${uberjar.name}</finalName>
+                            <transformers>
+                                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
+                                    <mainClass>org.openjdk.jmh.Main</mainClass>
+                                    <manifestEntries>
+                                        <Multi-Release>true</Multi-Release>
+                                        <addClasspath>true</addClasspath>
+                                    </manifestEntries>
+                                </transformer>
+                                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+                            </transformers>
+                            <filters>
+                                <filter>
+                                    <!--
+                                        Shading signed JARs will fail without this.
+                                        http://stackoverflow.com/questions/999489/invalid-signature-file-when-attempting-to-run-a-jar
+                                    -->
+                                    <artifact>*:*</artifact>
+                                    <excludes>
+                                        <exclude>META-INF/*.SF</exclude>
+                                        <exclude>META-INF/*.DSA</exclude>
+                                        <exclude>META-INF/*.RSA</exclude>
+                                    </excludes>
+                                </filter>
+                            </filters>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+        </plugins>
+        <pluginManagement>
+            <plugins>
+                <plugin>
+                    <artifactId>maven-clean-plugin</artifactId>
+                    <version>2.5</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-deploy-plugin</artifactId>
+                    <version>2.8.1</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-install-plugin</artifactId>
+                    <version>2.5.1</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-jar-plugin</artifactId>
+                    <version>2.4</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-javadoc-plugin</artifactId>
+                    <version>2.9.1</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-resources-plugin</artifactId>
+                    <version>2.6</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-site-plugin</artifactId>
+                    <version>3.3</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-source-plugin</artifactId>
+                    <version>2.2.1</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-surefire-plugin</artifactId>
+                    <version>2.17</version>
+                </plugin>
+            </plugins>
+        </pluginManagement>
+    </build>
+
+</project>
diff --git a/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
new file mode 100644
index 0000000000..15d47168b3
--- /dev/null
+++ b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Param;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.TearDown;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.infra.Blackhole;
+
+import java.lang.foreign.*;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Random;
+import java.util.UUID;
+
+@Fork(value = 1, warmups = 0)
+@State(Scope.Benchmark)
+public class CagraIndexBenchmarks {
+
+    @Param({ "1024" })
+    private int dims;
+
+    @Param({ "100" })
+    private int size;
+
+    private float[][] arrayDataset;
+
+    private static final Random random = new Random();
+
+    private static float[][] createSampleData(int size, int dimensions) {
+        var array = new float[size][dimensions];
+        for (int i = 0; i < size; ++i) {
+            for (int j = 0; j < dimensions; ++j) {
+                array[i][j] = random.nextFloat();
+            }
+        }
+        return array;
+    }
+
+    @Setup
+    public void initialize() {
+        arrayDataset = createSampleData(size, dims);
+    }
+
+    @Benchmark
+    public void testIndexingAndSerializeToFile() throws Throwable {
+        try (CuVSResources resources = CuVSResources.create()) {
+            // Configure index parameters
+            CagraIndexParams indexParams = new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraIndexParams.CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(1)
+                .withIntermediateGraphDegree(2)
+                .withNumWriterThreads(32)
+                .withMetric(CagraIndexParams.CuvsDistanceType.L2Expanded)
+                .build();
+
+            // Create the index with the dataset
+            CagraIndex index = CagraIndex.newBuilder(resources)
+                .withDataset(arrayDataset)
+                .withIndexParams(indexParams)
+                .build();
+
+            // Saving the index on to the disk.
+            var indexFilePath = Path.of(UUID.randomUUID() + ".cag");
+            try (var outputStream = Files.newOutputStream(indexFilePath)) {
+                index.serialize(outputStream);
+            }
+
+            // Cleanup
+            Files.deleteIfExists(indexFilePath);
+            index.destroyIndex();
+        }
+    }
+
+    @Benchmark
+    public void testIndexingFromHeap(Blackhole blackhole) throws Throwable {
+        try (CuVSResources resources = CuVSResources.create()) {
+
+            // Configure index parameters
+            CagraIndexParams indexParams = new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraIndexParams.CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(1)
+                .withIntermediateGraphDegree(2)
+                .withNumWriterThreads(32)
+                .withMetric(CagraIndexParams.CuvsDistanceType.L2Expanded)
+                .build();
+
+            // Create the index with the dataset
+            CagraIndex index = CagraIndex.newBuilder(resources)
+                .withDataset(arrayDataset)
+                .withIndexParams(indexParams)
+                .build();
+            blackhole.consume(index);
+        }
+    }
+}

From 8cc25c3f45324e5eb20291d72647faa2b112be8d Mon Sep 17 00:00:00 2001
From: mayani-nv <67936769+mayani-nv@users.noreply.github.com>
Date: Tue, 8 Jul 2025 16:10:03 -0700
Subject: [PATCH 473/623] fix(cli): Add validation for search-mode parameter
 (#1026)

This minor PR will add validation check for the search-mode parameter. Originally i tried to run the `cuvs_bench` as following
```
python -m cuvs_bench.run --dataset fbv6_50m --algorithms cuvs_cagra --batch-size 100 -k 10
Enter the path to dataset folder [/opt/conda/lib/python3.10/site-packages/cuvs_bench/run/datasets/]: /data/benchmarks/cuvs_experiments/datasets
Enter the comma separated groups of parameters [base]: base
Enter the search mode ("latency" or "throughput") [latency]: througput
/opt/conda/lib/python3.10/site-packages/cuvs_bench/run/run.py:187: UserWarning: Could not load YAML config
```
As you can see, I had the typo in this line `Enter the search mode ("latency" or "throughput") [latency]: througput` missing `h` and it defaulted to `latency`. This PR would print error if there's anything else specified other than `latency` or `throughput`.

Authors:
  - https://github.com/mayani-nv
  - Divye Gala (https://github.com/divyegala)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Tarang Jain (https://github.com/tarang-jain)

URL: https://github.com/rapidsai/cuvs/pull/1026
---
 python/cuvs_bench/cuvs_bench/run/__main__.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/python/cuvs_bench/cuvs_bench/run/__main__.py b/python/cuvs_bench/cuvs_bench/run/__main__.py
index 3246eb9914..93655cb92d 100644
--- a/python/cuvs_bench/cuvs_bench/run/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/run/__main__.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -112,6 +112,7 @@
 @click.option(
     "-m",
     "--search-mode",
+    type=click.Choice(["latency", "throughput"], case_sensitive=False),
     default="latency",
     show_default=True,
     prompt='Enter the search mode ("latency" or "throughput")',
@@ -135,7 +136,7 @@
     "--dry-run",
     is_flag=True,
     help="Dry-run mode will convert the yaml config for the specified "
-    "algorithms and datasets to the json format that’s consumed "
+    "algorithms and datasets to the json format that's consumed "
     "by the lower-level c++ binaries and then print the command to "
     "run execute the benchmarks but will not actually execute "
     "the command.",

From 5500811c3e2da3f294701f1ef5b154f7f261c3b3 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Wed, 9 Jul 2025 13:19:34 -0700
Subject: [PATCH 474/623] [BUG] Fix device access check in build_sorted_mst
 (#1083)

X may be in managed memory.
Fixes https://github.com/rapidsai/cuml/issues/6948

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1083
---
 cpp/src/cluster/detail/mst.cuh | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/cpp/src/cluster/detail/mst.cuh b/cpp/src/cluster/detail/mst.cuh
index a6b5f3c71e..f637426ee6 100644
--- a/cpp/src/cluster/detail/mst.cuh
+++ b/cpp/src/cluster/detail/mst.cuh
@@ -18,6 +18,7 @@
 
 #include "../../sparse/neighbors/cross_component_nn.cuh"
 #include <cuvs/distance/distance.hpp>
+#include <raft/core/memory_type.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/label/classlabels.cuh>
 #include <raft/matrix/detail/gather.cuh>
@@ -299,9 +300,7 @@ void build_sorted_mst(
   int iters        = 1;
   int n_components = cuvs::sparse::neighbors::get_n_components(color, m, stream);
 
-  cudaPointerAttributes attr;
-  RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, X));
-  bool data_on_device = attr.type == cudaMemoryTypeDevice;
+  bool data_on_device = raft::memory_type_from_pointer(X) != raft::memory_type::host;
 
   while (n_components > 1 && iters < max_iter) {
     if (data_on_device) {

From ecea00e701669fb46b9b283ebfb2fe1afc5528ea Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 10 Jul 2025 05:23:28 +0200
Subject: [PATCH 475/623] [Java] Refactor SearchResults implementation classes
 (#1067)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR refactors the `SearchResults` implementation classes, by removing inheritance and replacing it with factory methods to create results from native data.
The current implementation classes use inheritance and polymorphism just during creation (in the ctors); this is confusing as it forces to navigate across derived/base class to understand the mechanism, and forces the base class to hold onto references to MemorySegments -- something we don't need to do.

This is preliminary work towards https://github.com/rapidsai/cuvs/issues/1037, as it makes the lifetime of those MemorySegment clearer.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1067
---
 .../main/java/com/nvidia/cuvs/CagraIndex.java |  1 -
 .../cuvs/internal/BruteForceIndexImpl.java    |  2 +-
 .../internal/BruteForceSearchResults.java     | 31 +------
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |  2 +-
 .../cuvs/internal/CagraSearchResults.java     | 43 +++++-----
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |  2 +-
 .../cuvs/internal/HnswSearchResults.java      | 31 +------
 .../cuvs/internal/SearchResultsImpl.java      | 80 +++++++++++++++++++
 .../internal/common/SearchResultsImpl.java    | 73 -----------------
 9 files changed, 113 insertions(+), 152 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SearchResultsImpl.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index a6fc4775c3..8e6e2fb27d 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -15,7 +15,6 @@
  */
 package com.nvidia.cuvs;
 
-import com.nvidia.cuvs.BruteForceIndex.Builder;
 import com.nvidia.cuvs.spi.CuVSProvider;
 import java.io.InputStream;
 import java.io.OutputStream;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 932d89ee33..736f974e2b 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -317,7 +317,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
         checkCuVSError(returnValue, "cuvsRMMFree");
       }
 
-      return new BruteForceSearchResults(
+      return BruteForceSearchResults.create(
           neighborsSequenceLayout,
           distancesSequenceLayout,
           neighborsMemorySegment,
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
index 1c7d3752e7..fc3e11e9f5 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceSearchResults.java
@@ -15,11 +15,9 @@
  */
 package com.nvidia.cuvs.internal;
 
-import com.nvidia.cuvs.internal.common.SearchResultsImpl;
+import com.nvidia.cuvs.SearchResults;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
-import java.util.LinkedHashMap;
-import java.util.Map;
 import java.util.function.LongToIntFunction;
 
 /**
@@ -27,9 +25,9 @@
  *
  * @since 25.02
  */
-public class BruteForceSearchResults extends SearchResultsImpl {
+class BruteForceSearchResults {
 
-  protected BruteForceSearchResults(
+  static SearchResults create(
       SequenceLayout neighboursSequenceLayout,
       SequenceLayout distancesSequenceLayout,
       MemorySegment neighboursMemorySegment,
@@ -37,7 +35,7 @@ protected BruteForceSearchResults(
       int topK,
       LongToIntFunction mapping,
       long numberOfQueries) {
-    super(
+    return SearchResultsImpl.create(
         neighboursSequenceLayout,
         distancesSequenceLayout,
         neighboursMemorySegment,
@@ -45,26 +43,5 @@ protected BruteForceSearchResults(
         topK,
         mapping,
         numberOfQueries);
-    readResultMemorySegments();
-  }
-
-  /**
-   * Reads neighbors and distances {@link MemorySegment} and loads the values
-   * internally
-   */
-  protected void readResultMemorySegments() {
-    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<Integer, Float>();
-    int count = 0;
-    for (long i = 0; i < topK * numberOfQueries; i++) {
-      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
-      float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
-      intermediateResultMap.put(mapping.applyAsInt(id), dst);
-      count += 1;
-      if (count == topK) {
-        results.add(intermediateResultMap);
-        intermediateResultMap = new LinkedHashMap<Integer, Float>();
-        count = 0;
-      }
-    }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index d47493b708..a408f020d2 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -360,7 +360,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
       returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
       checkCuVSError(returnValue, "cuvsRMMFree");
 
-      return new CagraSearchResults(
+      return CagraSearchResults.create(
           neighborsSequenceLayout,
           distancesSequenceLayout,
           neighborsMemorySegment,
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
index 08c1a042dc..c61e795537 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraSearchResults.java
@@ -15,10 +15,13 @@
  */
 package com.nvidia.cuvs.internal;
 
-import com.nvidia.cuvs.internal.common.SearchResultsImpl;
+import com.nvidia.cuvs.SearchResults;
+import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
 import java.util.LinkedHashMap;
+import java.util.LinkedList;
+import java.util.List;
 import java.util.Map;
 import java.util.function.LongToIntFunction;
 
@@ -27,9 +30,15 @@
  *
  * @since 25.02
  */
-public class CagraSearchResults extends SearchResultsImpl {
+class CagraSearchResults {
 
-  protected CagraSearchResults(
+  /**
+   * Factory method to create an on-heap SearchResults (backed by standard Java data types and containers) from
+   * native/off-heap memory data structures.
+   * This class provides its own implementation for reading from native memory instead of reling on
+   * {@link SearchResultsImpl#create} because it requires special handling of neighbours IDs.
+   */
+  static SearchResults create(
       SequenceLayout neighboursSequenceLayout,
       SequenceLayout distancesSequenceLayout,
       MemorySegment neighboursMemorySegment,
@@ -37,26 +46,17 @@ protected CagraSearchResults(
       int topK,
       LongToIntFunction mapping,
       long numberOfQueries) {
-    super(
-        neighboursSequenceLayout,
-        distancesSequenceLayout,
-        neighboursMemorySegment,
-        distancesMemorySegment,
-        topK,
-        mapping,
-        numberOfQueries);
-    readResultMemorySegments();
-  }
 
-  /**
-   * Reads neighbors and distances {@link MemorySegment} and loads the values
-   * internally
-   */
-  protected void readResultMemorySegments() {
-    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<Integer, Float>();
+    List<Map<Integer, Float>> results = new LinkedList<>();
+    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<>();
+    var neighboursVarHandle =
+        neighboursSequenceLayout.varHandle(MemoryLayout.PathElement.sequenceElement());
+    var distancesVarHandle =
+        distancesSequenceLayout.varHandle(MemoryLayout.PathElement.sequenceElement());
+
     int count = 0;
     for (long i = 0; i < topK * numberOfQueries; i++) {
-      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
+      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0, i);
       float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
       if (id != Integer.MAX_VALUE) {
         intermediateResultMap.put(mapping.applyAsInt(id), dst);
@@ -64,9 +64,10 @@ protected void readResultMemorySegments() {
       count += 1;
       if (count == topK) {
         results.add(intermediateResultMap);
-        intermediateResultMap = new LinkedHashMap<Integer, Float>();
+        intermediateResultMap = new LinkedHashMap<>();
         count = 0;
       }
     }
+    return new SearchResultsImpl(results);
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index be3890b186..c7ddfa4840 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -133,7 +133,7 @@ public SearchResults search(HnswQuery query) throws Throwable {
       returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
-      return new HnswSearchResults(
+      return HnswSearchResults.create(
           neighborsSequenceLayout,
           distancesSequenceLayout,
           neighborsMemorySegment,
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
index 265a3d2227..c1012a01bb 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswSearchResults.java
@@ -15,11 +15,9 @@
  */
 package com.nvidia.cuvs.internal;
 
-import com.nvidia.cuvs.internal.common.SearchResultsImpl;
+import com.nvidia.cuvs.SearchResults;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.SequenceLayout;
-import java.util.LinkedHashMap;
-import java.util.Map;
 import java.util.function.LongToIntFunction;
 
 /**
@@ -27,9 +25,9 @@
  *
  * @since 25.02
  */
-public class HnswSearchResults extends SearchResultsImpl {
+class HnswSearchResults {
 
-  protected HnswSearchResults(
+  static SearchResults create(
       SequenceLayout neighboursSequenceLayout,
       SequenceLayout distancesSequenceLayout,
       MemorySegment neighboursMemorySegment,
@@ -37,7 +35,7 @@ protected HnswSearchResults(
       int topK,
       LongToIntFunction mapping,
       long numberOfQueries) {
-    super(
+    return SearchResultsImpl.create(
         neighboursSequenceLayout,
         distancesSequenceLayout,
         neighboursMemorySegment,
@@ -45,26 +43,5 @@ protected HnswSearchResults(
         topK,
         mapping,
         numberOfQueries);
-    readResultMemorySegments();
-  }
-
-  /**
-   * Reads neighbors and distances {@link MemorySegment} and loads the values
-   * internally
-   */
-  protected void readResultMemorySegments() {
-    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<Integer, Float>();
-    int count = 0;
-    for (long i = 0; i < topK * numberOfQueries; i++) {
-      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
-      float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
-      intermediateResultMap.put(mapping.applyAsInt(id), dst);
-      count += 1;
-      if (count == topK) {
-        results.add(intermediateResultMap);
-        intermediateResultMap = new LinkedHashMap<Integer, Float>();
-        count = 0;
-      }
-    }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SearchResultsImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SearchResultsImpl.java
new file mode 100644
index 0000000000..fbca4904e1
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SearchResultsImpl.java
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.SearchResults;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.util.LinkedHashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.function.LongToIntFunction;
+
+class SearchResultsImpl implements SearchResults {
+
+  private final List<Map<Integer, Float>> results;
+
+  SearchResultsImpl(List<Map<Integer, Float>> results) {
+    this.results = results;
+  }
+
+  /**
+   * Factory method to create an on-heap SearchResults (backed by standard Java data types and containers) from
+   * native/off-heap memory data structures.
+   */
+  static SearchResults create(
+      SequenceLayout neighboursSequenceLayout,
+      SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment,
+      MemorySegment distancesMemorySegment,
+      int topK,
+      LongToIntFunction mapping,
+      long numberOfQueries) {
+    List<Map<Integer, Float>> results = new LinkedList<>();
+    Map<Integer, Float> intermediateResultMap = new LinkedHashMap<>();
+    var neighboursVarHandle =
+        neighboursSequenceLayout.varHandle(MemoryLayout.PathElement.sequenceElement());
+    var distancesVarHandle =
+        distancesSequenceLayout.varHandle(MemoryLayout.PathElement.sequenceElement());
+
+    int count = 0;
+    for (long i = 0; i < topK * numberOfQueries; i++) {
+      long id = (long) neighboursVarHandle.get(neighboursMemorySegment, 0L, i);
+      float dst = (float) distancesVarHandle.get(distancesMemorySegment, 0L, i);
+      intermediateResultMap.put(mapping != null ? mapping.applyAsInt((int) id) : (int) id, dst);
+      count += 1;
+      if (count == topK) {
+        results.add(intermediateResultMap);
+        intermediateResultMap = new LinkedHashMap<>();
+        count = 0;
+      }
+    }
+
+    return new SearchResultsImpl(results);
+  }
+
+  /**
+   * Gets a list results as a map of neighbor IDs to distances.
+   *
+   * @return a list of results for each query as a map of neighbor IDs to distance
+   */
+  @Override
+  public List<Map<Integer, Float>> getResults() {
+    return results;
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
deleted file mode 100644
index 29fd61670b..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/SearchResultsImpl.java
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs.internal.common;
-
-import com.nvidia.cuvs.SearchResults;
-import java.lang.foreign.MemoryLayout.PathElement;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SequenceLayout;
-import java.lang.invoke.VarHandle;
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Map;
-import java.util.function.LongToIntFunction;
-
-public abstract class SearchResultsImpl implements SearchResults {
-
-  protected final List<Map<Integer, Float>> results;
-  protected final LongToIntFunction mapping;
-
-  protected final MemorySegment neighboursMemorySegment;
-  protected final MemorySegment distancesMemorySegment;
-  protected final int topK;
-  protected final long numberOfQueries;
-  protected final VarHandle neighboursVarHandle;
-  protected final VarHandle distancesVarHandle;
-
-  protected SearchResultsImpl(
-      SequenceLayout neighboursSequenceLayout,
-      SequenceLayout distancesSequenceLayout,
-      MemorySegment neighboursMemorySegment,
-      MemorySegment distancesMemorySegment,
-      int topK,
-      LongToIntFunction mapping,
-      long numberOfQueries) {
-    this.topK = topK;
-    this.numberOfQueries = numberOfQueries;
-    this.neighboursMemorySegment = neighboursMemorySegment;
-    this.distancesMemorySegment = distancesMemorySegment;
-    this.mapping = mapping;
-    results = new LinkedList<Map<Integer, Float>>();
-    neighboursVarHandle = neighboursSequenceLayout.varHandle(PathElement.sequenceElement());
-    distancesVarHandle = distancesSequenceLayout.varHandle(PathElement.sequenceElement());
-  }
-
-  /**
-   * Reads neighbors and distances {@link MemorySegment} and loads the values
-   * internally
-   */
-  protected abstract void readResultMemorySegments();
-
-  /**
-   * Gets a list results as a map of neighbor IDs to distances.
-   *
-   * @return a list of results for each query as a map of neighbor IDs to distance
-   */
-  @Override
-  public List<Map<Integer, Float>> getResults() {
-    return results;
-  }
-}

From 26ca5d2ec8f1a968fe1be2cd67050d034ef92fec Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Fri, 11 Jul 2025 04:00:07 +0900
Subject: [PATCH 476/623] Add mean and median threshold modes to binary
 quantization (#716)

The current binary quantization function sets a bit when the corresponding input dataset element is positive. This method (zero threshold mode) does not work well for, for example, a dataset where all elements are positive. This PR adds mean and median modes that set bits when the corresponding elements are larger than the dim-wise mean and median, respectively.

The new `transform` function requires https://github.com/rapidsai/raft/pull/2592 to be merged to support large datasets.

TODO:
- [x] C++
- [x] C
- [x] test
- [x] <del>python</del> The Python support will be added in a later PR, as the Python package build requires the C header file installed by `conda create` to include this update.

Authors:
  - tsuki (https://github.com/enp1s0)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/716
---
 .../cuvs/preprocessing/quantize/binary.h      | 100 ++++++
 .../cuvs/preprocessing/quantize/binary.hpp    | 304 +++++++++++++++++-
 .../cuvs/preprocessing/quantize/scalar.hpp    |  20 +-
 cpp/src/preprocessing/quantize/binary.cu      |  58 +++-
 cpp/src/preprocessing/quantize/binary_c.cpp   | 124 ++++++-
 .../preprocessing/quantize/detail/binary.cuh  | 298 ++++++++++++++++-
 .../preprocessing/binary_quantization.cu      |  58 +++-
 .../source/cpp_api/preprocessing_quantize.rst |  10 +
 .../quantize/binary/CMakeLists.txt            |   2 +-
 .../preprocessing/quantize/binary/__init__.py |   4 +-
 10 files changed, 908 insertions(+), 70 deletions(-)

diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.h b/cpp/include/cuvs/preprocessing/quantize/binary.h
index 28ef6f0a8e..9a5834ef3e 100644
--- a/cpp/include/cuvs/preprocessing/quantize/binary.h
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.h
@@ -24,6 +24,89 @@
 extern "C" {
 #endif
 
+/**
+ * @brief In the cuvsBinaryQuantizerTransform function, a bit is set if the corresponding element in
+ * the dataset vector is greater than the corresponding element in the threshold vector. The mean
+ * and sampling_median thresholds are calculated separately for each dimension.
+ *
+ */
+enum cuvsBinaryQuantizerThreshold { ZERO, MEAN, SAMPLING_MEDIAN };
+
+/**
+ * @brief Binary quantizer parameters.
+ */
+struct cuvsBinaryQuantizerParams {
+  /*
+   * specifies the threshold to set a bit in cuvsBinaryQuantizerTransform
+   */
+  cuvsBinaryQuantizerThreshold threshold = MEAN;
+
+  /*
+   * specifies the sampling ratio
+   */
+  float sampling_ratio = 0.1;
+};
+
+typedef struct cuvsBinaryQuantizerParams* cuvsBinaryQuantizerParams_t;
+
+/**
+ * @brief Allocate Binary Quantizer params, and populate with default values
+ *
+ * @param[in] params cuvsBinaryQuantizerParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsBinaryQuantizerParamsCreate(cuvsBinaryQuantizerParams_t* params);
+
+/**
+ * @brief De-allocate Binary Quantizer params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsBinaryQuantizerParamsDestroy(cuvsBinaryQuantizerParams_t params);
+
+/**
+ * @brief Defines and stores threshold for quantization upon training
+ *
+ * The quantization is performed by a linear mapping of an interval in the
+ * float data type to the full range of the quantized int type.
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} cuvsBinaryQuantizer;
+
+typedef cuvsBinaryQuantizer* cuvsBinaryQuantizer_t;
+
+/**
+ * @brief Allocate Binary Quantizer and populate with default values
+ *
+ * @param[in] quantizer cuvsBinaryQuantizer_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsBinaryQuantizerCreate(cuvsBinaryQuantizer_t* quantizer);
+
+/**
+ * @brief De-allocate Binary Quantizer
+ *
+ * @param[in] quantizer
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsBinaryQuantizerDestroy(cuvsBinaryQuantizer_t quantizer);
+
+/**
+ * @brief Trains a scalar quantizer to be used later for quantizing the dataset.
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure binary quantizer, e.g. threshold
+ * @param[in] dataset a row-major host or device matrix
+ * @param[out] quantizer trained binary quantizer
+ */
+cuvsError_t cuvsBinaryQuantizerTrain(cuvsResources_t res,
+                                     cuvsBinaryQuantizerParams_t params,
+                                     DLManagedTensor* dataset,
+                                     cuvsBinaryQuantizer_t quantizer);
+
 /**
  * @brief Applies binary quantization transform to the given dataset
  *
@@ -39,6 +122,23 @@ cuvsError_t cuvsBinaryQuantizerTransform(cuvsResources_t res,
                                          DLManagedTensor* dataset,
                                          DLManagedTensor* out);
 
+/**
+ * @brief Applies binary quantization transform to the given dataset
+ *
+ * This applies binary quantization to a dataset, changing any values that are larger than the
+ * threshold specified in the param to a bitwise 1. This is useful for searching with the
+ * BitwiseHamming distance type.
+ *
+ * @param[in] res raft resource
+ * @param[in] quantizer binary quantizer
+ * @param[in] dataset a row-major host or device matrix to transform
+ * @param[out] out a row-major host or device matrix to store transformed data
+ */
+cuvsError_t cuvsBinaryQuantizerTransformWithParams(cuvsResources_t res,
+                                                   cuvsBinaryQuantizer_t quantizer,
+                                                   DLManagedTensor* dataset,
+                                                   DLManagedTensor* out);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.hpp b/cpp/include/cuvs/preprocessing/quantize/binary.hpp
index 4f7d36048e..224c30c8b6 100644
--- a/cpp/include/cuvs/preprocessing/quantize/binary.hpp
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -31,6 +31,79 @@ namespace cuvs::preprocessing::quantize::binary {
  * @{
  */
 
+/**
+ * @brief quantizer algorithms. The mean and sampling_median thresholds are calculated separately
+ * for each dimension.
+ */
+enum class bit_threshold { zero, mean, sampling_median };
+
+/**
+ * @brief quantizer parameters.
+ */
+struct params {
+  bit_threshold threshold = bit_threshold::mean;
+
+  /*
+   * specifies the sampling ratio
+   */
+  float sampling_ratio = 0.1;
+};
+
+/**
+ * @brief Store the threshold vector for quantization. In the binary::transform function, a bit is
+ * set if the corresponding element in the dataset vector is greater than the corresponding element
+ * in the threshold vector.
+ *
+ * @tparam T data element type
+ *
+ */
+template <typename T>
+struct quantizer {
+  raft::device_vector<T, int64_t> threshold;
+
+  quantizer(raft::resources const& res) : threshold(raft::make_device_vector<T, int64_t>(res, 0)) {}
+};
+
+/**
+ * @brief Initializes a binary quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure binary quantizer, e.g. threshold
+ * @param[in] dataset a row-major matrix view on device
+ *
+ * @return quantizer
+ */
+quantizer<double> train(raft::resources const& res,
+                        const params params,
+                        raft::device_matrix_view<const double, int64_t> dataset);
+
+/**
+ * @brief Initializes a binary quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure binary quantizer, e.g. threshold
+ * @param[in] dataset a row-major matrix view on host
+ *
+ * @return quantizer
+ */
+quantizer<double> train(raft::resources const& res,
+                        const params params,
+                        raft::host_matrix_view<const double, int64_t> dataset);
+
 /**
  * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
  * set the corresponding bit to 1.
@@ -38,17 +111,23 @@ namespace cuvs::preprocessing::quantize::binary {
  * Usage example:
  * @code{.cpp}
  * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
  * auto quantized_dataset = raft::make_device_matrix<uint8_t, int64_t>(handle, samples,
- * features); cuvs::preprocessing::quantize::binary::transform(handle, dataset,
- * quantized_dataset.view());
+ * features);
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params,
+ * raft::make_const_mdspan(dataset.view()));
+ * cuvs::preprocessing::quantize::binary::transform(handle, quantizer,
+ * raft::make_const_mdspan(dataset.view()), quantized_dataset.view());
  * @endcode
  *
  * @param[in] res raft resource
+ * @param[in] quantizer a binary quantizer
  * @param[in] dataset a row-major matrix view on device
  * @param[out] out a row-major matrix view on device
  *
  */
 void transform(raft::resources const& res,
+               const quantizer<double>& quantizer,
                raft::device_matrix_view<const double, int64_t> dataset,
                raft::device_matrix_view<uint8_t, int64_t> out);
 
@@ -59,20 +138,66 @@ void transform(raft::resources const& res,
  * Usage example:
  * @code{.cpp}
  * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
  * auto quantized_dataset = raft::make_host_matrix<uint8_t, int64_t>(handle, samples,
- * features); cuvs::preprocessing::quantize::binary::transform(handle, dataset,
- * quantized_dataset.view());
+ * features);
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params,
+ * raft::make_const_mdspan(dataset.view()));
+ * cuvs::preprocessing::quantize::binary::transform(handle, quantizer,
+ * raft::make_const_mdspan(dataset.view()), quantized_dataset.view());
  * @endcode
  *
  * @param[in] res raft resource
+ * @param[in] quantizer a binary quantizer
  * @param[in] dataset a row-major matrix view on host
  * @param[out] out a row-major matrix view on host
  *
  */
 void transform(raft::resources const& res,
+               const quantizer<double>& quantizer,
                raft::host_matrix_view<const double, int64_t> dataset,
                raft::host_matrix_view<uint8_t, int64_t> out);
 
+/**
+ * @brief Initializes a binary quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure binary quantizer, e.g. threshold
+ * @param[in] dataset a row-major matrix view on device
+ *
+ * @return quantizer
+ */
+quantizer<float> train(raft::resources const& res,
+                       const params params,
+                       raft::device_matrix_view<const float, int64_t> dataset);
+
+/**
+ * @brief Initializes a binary quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure binary quantizer, e.g. threshold
+ * @param[in] dataset a row-major matrix view on host
+ *
+ * @return quantizer
+ */
+quantizer<float> train(raft::resources const& res,
+                       const params params,
+                       raft::host_matrix_view<const float, int64_t> dataset);
+
 /**
  * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
  * set the corresponding bit to 1.
@@ -80,19 +205,25 @@ void transform(raft::resources const& res,
  * Usage example:
  * @code{.cpp}
  * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
  * raft::device_matrix<float, uint64_t> dataset = read_dataset(filename);
  * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
  * auto quantized_dataset = raft::make_device_matrix<uint8_t, int64_t>(
  *    handle, dataset.extent(0), quantized_dim);
- *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params,
+ * raft::make_const_mdspan(dataset.view()));
+ * cuvs::preprocessing::quantize::binary::transform(handle, quantizer,
+ * raft::make_const_mdspan(dataset.view()), quantized_dataset.view());
  * @endcode
  *
  * @param[in] res raft resource
+ * @param[in] quantizer a binary quantizer
  * @param[in] dataset a row-major matrix view on device
  * @param[out] out a row-major matrix view on device
  *
  */
 void transform(raft::resources const& res,
+               const quantizer<float>& quantizer,
                raft::device_matrix_view<const float, int64_t> dataset,
                raft::device_matrix_view<uint8_t, int64_t> out);
 
@@ -103,68 +234,209 @@ void transform(raft::resources const& res,
  * Usage example:
  * @code{.cpp}
  * raft::handle_t handle;
+ * @param[in] params quantization params
+ * cuvs::preprocessing::quantize::binary::params params;
  * raft::host_matrix<float, uint64_t> dataset = read_dataset(filename);
  * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
  * auto quantized_dataset = raft::make_host_matrix<uint8_t, int64_t>(
- *    handle, dataset.extent(0), quantized_dim);
- *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ *    handle, params, dataset.extent(0), quantized_dim);
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params,
+ * raft::make_const_mdspan(dataset.view()));
+ * cuvs::preprocessing::quantize::binary::transform(handle, quantizer,
+ * raft::make_const_mdspan(dataset.view()), quantized_dataset.view());
  * @endcode
  *
  * @param[in] res raft resource
+ * @param[in] quantizer a binary quantizer
  * @param[in] dataset a row-major matrix view on host
  * @param[out] out a row-major matrix view on host
  *
  */
 void transform(raft::resources const& res,
+               const quantizer<float>& quantizer,
                raft::host_matrix_view<const float, int64_t> dataset,
                raft::host_matrix_view<uint8_t, int64_t> out);
 
 /**
- * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
- * set the corresponding bit to 1.
+ * @brief Initializes a binary quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure binary quantizer, e.g. threshold
+ * @param[in] dataset a row-major matrix view on device
+ *
+ * @return quantizer
+ */
+quantizer<half> train(raft::resources const& res,
+                      const params params,
+                      raft::device_matrix_view<const half, int64_t> dataset);
+
+/**
+ * @brief Initializes a binary quantizer to be used later for quantizing the dataset.
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset);
+ * @endcode
+ *
+ * @param[in] res raft resource
+ * @param[in] params configure binary quantizer, e.g. threshold
+ * @param[in] dataset a row-major matrix view on host
+ *
+ * @return quantizer
+ */
+quantizer<half> train(raft::resources const& res,
+                      const params params,
+                      raft::host_matrix_view<const half, int64_t> dataset);
+
+/**
+ * @brief Applies binary quantization transform to given dataset.
  *
  * Usage example:
  * @code{.cpp}
  * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
  * raft::device_matrix<half, uint64_t> dataset = read_dataset(filename);
  * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
  * auto quantized_dataset = raft::make_device_matrix<uint8_t, int64_t>(
- *    handle, dataset.extent(0), quantized_dim);
- *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ *    handle, params, dataset.extent(0), quantized_dim);
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params,
+ * raft::make_const_mdspan(dataset.view()));
+ * cuvs::preprocessing::quantize::binary::transform(handle, quantizer,
+ * raft::make_const_mdspan(dataset.view()), quantized_dataset.view());
  * @endcode
  *
  * @param[in] res raft resource
+ * @param[in] quantizer a binary quantizer
  * @param[in] dataset a row-major matrix view on device
  * @param[out] out a row-major matrix view on device
  *
  */
 void transform(raft::resources const& res,
+               const quantizer<half>& quantizer,
                raft::device_matrix_view<const half, int64_t> dataset,
                raft::device_matrix_view<uint8_t, int64_t> out);
 
 /**
- * @brief Applies binary quantization transform to given dataset. If a dataset element is positive,
- * set the corresponding bit to 1.
+ * @brief Applies binary quantization transform to given dataset.
  *
  * Usage example:
  * @code{.cpp}
  * raft::handle_t handle;
+ * cuvs::preprocessing::quantize::binary::params params;
  * raft::host_matrix<half, uint64_t> dataset = read_dataset(filename);
  * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
  * auto quantized_dataset = raft::make_host_matrix<uint8_t, int64_t>(
- *    handle, dataset.extent(0), quantized_dim);
- *  cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_dataset.view());
+ *    handle, params, dataset.extent(0), quantized_dim);
+ * auto quantizer = cuvs::preprocessing::quantize::binary::train(handle, params,
+ * raft::make_const_mdspan(dataset.view()));
+ * cuvs::preprocessing::quantize::binary::transform(handle, quantizer,
+ * raft::make_const_mdspan(dataset.view()), quantized_dataset.view());
  * @endcode
  *
  * @param[in] res raft resource
+ * @param[in] quantizer a binary quantizer
  * @param[in] dataset a row-major matrix view on host
  * @param[out] out a row-major matrix view on host
  *
  */
 void transform(raft::resources const& res,
+               const quantizer<half>& quantizer,
                raft::host_matrix_view<const half, int64_t> dataset,
                raft::host_matrix_view<uint8_t, int64_t> out);
 
+/**
+ * @brief [deprecated] Applies binary quantization transform to given dataset. If a dataset element
+ * is positive, set the corresponding bit to 1.
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+[[deprecated("please create and specify a quantizer")]] void transform(
+  raft::resources const& res,
+  raft::device_matrix_view<const double, int64_t> dataset,
+  raft::device_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief [deprecated] Applies binary quantization transform to given dataset. If a dataset element
+ * is positive, set the corresponding bit to 1.
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+[[deprecated("please create and specify a quantizer")]] void transform(
+  raft::resources const& res,
+  raft::host_matrix_view<const double, int64_t> dataset,
+  raft::host_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief [deprecated] Applies binary quantization transform to given dataset. If a dataset element
+ * is positive, set the corresponding bit to 1.
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+[[deprecated("please create and specify a quantizer")]] void transform(
+  raft::resources const& res,
+  raft::device_matrix_view<const float, int64_t> dataset,
+  raft::device_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief [deprecated] Applies binary quantization transform to given dataset. If a dataset element
+ * is positive, set the corresponding bit to 1.
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+[[deprecated("please create and specify a quantizer")]] void transform(
+  raft::resources const& res,
+  raft::host_matrix_view<const float, int64_t> dataset,
+  raft::host_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief [deprecated] Applies binary quantization transform to given dataset. If a dataset element
+ * is positive, set the corresponding bit to 1.
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on device
+ * @param[out] out a row-major matrix view on device
+ *
+ */
+[[deprecated("please create and specify a quantizer")]] void transform(
+  raft::resources const& res,
+  raft::device_matrix_view<const half, int64_t> dataset,
+  raft::device_matrix_view<uint8_t, int64_t> out);
+
+/**
+ * @brief [deprecated] Applies binary quantization transform to given dataset. If a dataset element
+ * is positive, set the corresponding bit to 1.
+ *
+ * @param[in] res raft resource
+ * @param[in] dataset a row-major matrix view on host
+ * @param[out] out a row-major matrix view on host
+ *
+ */
+[[deprecated("please create and specify a quantizer")]] void transform(
+  raft::resources const& res,
+  raft::host_matrix_view<const half, int64_t> dataset,
+  raft::host_matrix_view<uint8_t, int64_t> out);
+
 /** @} */  // end of group binary
 
 }  // namespace cuvs::preprocessing::quantize::binary
diff --git a/cpp/include/cuvs/preprocessing/quantize/scalar.hpp b/cpp/include/cuvs/preprocessing/quantize/scalar.hpp
index 49b4bb7a67..7ba2332dce 100644
--- a/cpp/include/cuvs/preprocessing/quantize/scalar.hpp
+++ b/cpp/include/cuvs/preprocessing/quantize/scalar.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -64,8 +64,7 @@ struct quantizer {
  * @code{.cpp}
  * raft::handle_t handle;
  * cuvs::preprocessing::quantize::scalar::params params;
- * auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
- * dataset);
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train(handle, params, dataset);
  * @endcode
  *
  * @param[in] res raft resource
@@ -85,8 +84,7 @@ quantizer<double> train(raft::resources const& res,
  * @code{.cpp}
  * raft::handle_t handle;
  * cuvs::preprocessing::quantize::scalar::params params;
- * auto quantizer = cuvs::preprocessing::quantize::scalar::train<double, int8_t>(handle, params,
- * dataset);
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train(handle, params, dataset);
  * @endcode
  *
  * @param[in] res raft resource
@@ -207,8 +205,7 @@ void inverse_transform(raft::resources const& res,
  * @code{.cpp}
  * raft::handle_t handle;
  * cuvs::preprocessing::quantize::scalar::params params;
- * auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
- * dataset);
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train(handle, params, dataset);
  * @endcode
  *
  * @param[in] res raft resource
@@ -228,8 +225,7 @@ quantizer<float> train(raft::resources const& res,
  * @code{.cpp}
  * raft::handle_t handle;
  * cuvs::preprocessing::quantize::scalar::params params;
- * auto quantizer = cuvs::preprocessing::quantize::scalar::train<float, int8_t>(handle, params,
- * dataset);
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train(handle, params, dataset);
  * @endcode
  *
  * @param[in] res raft resource
@@ -349,8 +345,7 @@ void inverse_transform(raft::resources const& res,
  * @code{.cpp}
  * raft::handle_t handle;
  * cuvs::preprocessing::quantize::scalar::params params;
- * auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
- * dataset);
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train(handle, params, dataset);
  * @endcode
  *
  * @param[in] res raft resource
@@ -370,8 +365,7 @@ quantizer<half> train(raft::resources const& res,
  * @code{.cpp}
  * raft::handle_t handle;
  * cuvs::preprocessing::quantize::scalar::params params;
- * auto quantizer = cuvs::preprocessing::quantize::scalar::train<half, int8_t>(handle, params,
- * dataset);
+ * auto quantizer = cuvs::preprocessing::quantize::scalar::train(handle, params, dataset);
  * @endcode
  *
  * @param[in] res raft resource
diff --git a/cpp/src/preprocessing/quantize/binary.cu b/cpp/src/preprocessing/quantize/binary.cu
index fd04c7fe49..b5b3d03b06 100644
--- a/cpp/src/preprocessing/quantize/binary.cu
+++ b/cpp/src/preprocessing/quantize/binary.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -20,18 +20,50 @@
 
 namespace cuvs::preprocessing::quantize::binary {
 
-#define CUVS_INST_QUANTIZATION(T, QuantI)                            \
-  void transform(raft::resources const& res,                         \
-                 raft::device_matrix_view<const T, int64_t> dataset, \
-                 raft::device_matrix_view<QuantI, int64_t> out)      \
-  {                                                                  \
-    detail::transform(res, dataset, out);                            \
-  }                                                                  \
-  void transform(raft::resources const& res,                         \
-                 raft::host_matrix_view<const T, int64_t> dataset,   \
-                 raft::host_matrix_view<QuantI, int64_t> out)        \
-  {                                                                  \
-    detail::transform(res, dataset, out);                            \
+#define CUVS_INST_QUANTIZATION(T, QuantI)                                                \
+  auto train(raft::resources const& res,                                                 \
+             const params params,                                                        \
+             raft::host_matrix_view<const T, int64_t> dataset) -> quantizer<T>           \
+  {                                                                                      \
+    return detail::train(res, params, dataset);                                          \
+  }                                                                                      \
+  auto train(raft::resources const& res,                                                 \
+             const params params,                                                        \
+             raft::device_matrix_view<const T, int64_t> dataset) -> quantizer<T>         \
+  {                                                                                      \
+    return detail::train(res, params, dataset);                                          \
+  }                                                                                      \
+  void transform(raft::resources const& res,                                             \
+                 const cuvs::preprocessing::quantize::binary::quantizer<T>& quantizer,   \
+                 raft::device_matrix_view<const T, int64_t> dataset,                     \
+                 raft::device_matrix_view<QuantI, int64_t> out)                          \
+  {                                                                                      \
+    detail::transform(res, quantizer, dataset, out);                                     \
+  }                                                                                      \
+  void transform(raft::resources const& res,                                             \
+                 const cuvs::preprocessing::quantize::binary::quantizer<T>& quantizer,   \
+                 raft::host_matrix_view<const T, int64_t> dataset,                       \
+                 raft::host_matrix_view<QuantI, int64_t> out)                            \
+  {                                                                                      \
+    detail::transform(res, quantizer, dataset, out);                                     \
+  }                                                                                      \
+  void transform(raft::resources const& res,                                             \
+                 raft::device_matrix_view<const T, int64_t> dataset,                     \
+                 raft::device_matrix_view<QuantI, int64_t> out)                          \
+  {                                                                                      \
+    cuvs::preprocessing::quantize::binary::params params{                                \
+      .threshold = cuvs::preprocessing::quantize::binary::bit_threshold::zero};          \
+    auto quantizer = cuvs::preprocessing::quantize::binary::train(res, params, dataset); \
+    detail::transform(res, quantizer, dataset, out);                                     \
+  }                                                                                      \
+  void transform(raft::resources const& res,                                             \
+                 raft::host_matrix_view<const T, int64_t> dataset,                       \
+                 raft::host_matrix_view<QuantI, int64_t> out)                            \
+  {                                                                                      \
+    cuvs::preprocessing::quantize::binary::params params{                                \
+      .threshold = cuvs::preprocessing::quantize::binary::bit_threshold::zero};          \
+    auto quantizer = cuvs::preprocessing::quantize::binary::train(res, params, dataset); \
+    detail::transform(res, quantizer, dataset, out);                                     \
   }
 
 CUVS_INST_QUANTIZATION(double, uint8_t);
diff --git a/cpp/src/preprocessing/quantize/binary_c.cpp b/cpp/src/preprocessing/quantize/binary_c.cpp
index 1045f06b31..2f795cca4d 100644
--- a/cpp/src/preprocessing/quantize/binary_c.cpp
+++ b/cpp/src/preprocessing/quantize/binary_c.cpp
@@ -26,9 +26,13 @@
 namespace {
 
 template <typename T, typename OutputT = uint8_t>
-void _transform(cuvsResources_t res, DLManagedTensor* dataset_tensor, DLManagedTensor* out_tensor)
+void _transform(cuvsResources_t res,
+                cuvsBinaryQuantizer_t quantizer,
+                DLManagedTensor* dataset_tensor,
+                DLManagedTensor* out_tensor)
 {
   auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto q = reinterpret_cast<cuvs::preprocessing::quantize::binary::quantizer<T>*>(quantizer->addr);
 
   auto dataset = dataset_tensor->dl_tensor;
   if (cuvs::core::is_dlpack_device_compatible(dataset)) {
@@ -37,6 +41,7 @@ void _transform(cuvsResources_t res, DLManagedTensor* dataset_tensor, DLManagedT
 
     cuvs::preprocessing::quantize::binary::transform(
       *res_ptr,
+      *q,
       cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
       cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
 
@@ -46,6 +51,7 @@ void _transform(cuvsResources_t res, DLManagedTensor* dataset_tensor, DLManagedT
 
     cuvs::preprocessing::quantize::binary::transform(
       *res_ptr,
+      *q,
       cuvs::core::from_dlpack<mdspan_type>(dataset_tensor),
       cuvs::core::from_dlpack<out_mdspan_type>(out_tensor));
   } else {
@@ -53,20 +59,104 @@ void _transform(cuvsResources_t res, DLManagedTensor* dataset_tensor, DLManagedT
   }
 }
 
+template <typename T>
+void* _train(cuvsResources_t res,
+             cuvsBinaryQuantizerParams_t params,
+             DLManagedTensor* dataset_tensor)
+{
+  auto dataset = dataset_tensor->dl_tensor;
+
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto quantizer_params           = cuvs::preprocessing::quantize::binary::params();
+  quantizer_params.sampling_ratio = params->sampling_ratio;
+  switch (params->threshold) {
+    case ZERO:
+      quantizer_params.threshold = cuvs::preprocessing::quantize::binary::bit_threshold::zero;
+      break;
+    case MEAN:
+      quantizer_params.threshold = cuvs::preprocessing::quantize::binary::bit_threshold::mean;
+      break;
+    case SAMPLING_MEDIAN:
+      quantizer_params.threshold =
+        cuvs::preprocessing::quantize::binary::bit_threshold::sampling_median;
+      break;
+    default: RAFT_FAIL("Unsupported threshold");
+  }
+
+  auto ret = new cuvs::preprocessing::quantize::binary::quantizer<T>(*res_ptr);
+
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    *ret = cuvs::preprocessing::quantize::binary::train(*res_ptr, quantizer_params, mds);
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    *ret = cuvs::preprocessing::quantize::binary::train(*res_ptr, quantizer_params, mds);
+  } else {
+    RAFT_FAIL("dataset must be accessible on host or device memory");
+  }
+  return ret;
+}
+
 }  // namespace
 
-extern "C" cuvsError_t cuvsBinaryQuantizerTransform(cuvsResources_t res,
-                                                    DLManagedTensor* dataset_tensor,
-                                                    DLManagedTensor* out_tensor)
+extern "C" cuvsError_t cuvsBinaryQuantizerParamsCreate(cuvsBinaryQuantizerParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] { *params = new cuvsBinaryQuantizerParams; });
+}
+
+extern "C" cuvsError_t cuvsBinaryQuantizerParamsDestroy(cuvsBinaryQuantizerParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
+
+extern "C" cuvsError_t cuvsBinaryQuantizerCreate(cuvsBinaryQuantizer_t* quantizer)
+{
+  return cuvs::core::translate_exceptions([=] { *quantizer = new cuvsBinaryQuantizer; });
+}
+
+extern "C" cuvsError_t cuvsBinaryQuantizerDestroy(cuvsBinaryQuantizer_t quantizer)
+{
+  return cuvs::core::translate_exceptions([=] { delete quantizer; });
+}
+
+extern "C" cuvsError_t cuvsBinaryQuantizerTrain(cuvsResources_t res,
+                                                cuvsBinaryQuantizerParams_t params,
+                                                DLManagedTensor* dataset_tensor,
+                                                cuvsBinaryQuantizer_t quantizer)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset     = dataset_tensor->dl_tensor;
+    quantizer->dtype = dataset.dtype;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
+      quantizer->addr = reinterpret_cast<uintptr_t>(_train<double>(res, params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      quantizer->addr = reinterpret_cast<uintptr_t>(_train<float>(res, params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      quantizer->addr = reinterpret_cast<uintptr_t>(_train<half>(res, params, dataset_tensor));
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsBinaryQuantizerTransformWithParams(cuvsResources_t res,
+                                                              cuvsBinaryQuantizer_t quantizer,
+                                                              DLManagedTensor* dataset_tensor,
+                                                              DLManagedTensor* out_tensor)
 {
   return cuvs::core::translate_exceptions([=] {
     auto dataset = dataset_tensor->dl_tensor;
     if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
-      _transform<float>(res, dataset_tensor, out_tensor);
+      _transform<float>(res, quantizer, dataset_tensor, out_tensor);
     } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
-      _transform<half>(res, dataset_tensor, out_tensor);
+      _transform<half>(res, quantizer, dataset_tensor, out_tensor);
     } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 64) {
-      _transform<double>(res, dataset_tensor, out_tensor);
+      _transform<double>(res, quantizer, dataset_tensor, out_tensor);
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
@@ -74,3 +164,23 @@ extern "C" cuvsError_t cuvsBinaryQuantizerTransform(cuvsResources_t res,
     }
   });
 }
+
+extern "C" cuvsError_t cuvsBinaryQuantizerTransform(cuvsResources_t res,
+                                                    DLManagedTensor* dataset_tensor,
+                                                    DLManagedTensor* out_tensor)
+{
+  cuvsBinaryQuantizerParams_t params;
+  cuvsBinaryQuantizerParamsCreate(&params);
+  params->threshold = ZERO;
+
+  cuvsBinaryQuantizer_t quantizer;
+  cuvsBinaryQuantizerCreate(&quantizer);
+  cuvsBinaryQuantizerTrain(res, params, dataset_tensor, quantizer);
+
+  const auto result =
+    cuvsBinaryQuantizerTransformWithParams(res, quantizer, dataset_tensor, out_tensor);
+
+  cuvsBinaryQuantizerDestroy(quantizer);
+  cuvsBinaryQuantizerParamsDestroy(params);
+  return result;
+}
diff --git a/cpp/src/preprocessing/quantize/detail/binary.cuh b/cpp/src/preprocessing/quantize/detail/binary.cuh
index 8ddc33c944..7852a3f52b 100644
--- a/cpp/src/preprocessing/quantize/detail/binary.cuh
+++ b/cpp/src/preprocessing/quantize/detail/binary.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,11 +16,14 @@
 
 #pragma once
 
-#include <cuvs/preprocessing/quantize/scalar.hpp>
+#include <cuvs/preprocessing/quantize/binary.hpp>
 #include <raft/core/operators.hpp>
+#include <raft/linalg/transpose.cuh>
 #include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/sample_rows.cuh>
 #include <raft/random/rng.cuh>
 #include <raft/random/sample_without_replacement.cuh>
+#include <raft/stats/mean.cuh>
 #include <thrust/execution_policy.h>
 #include <thrust/sort.h>
 #include <thrust/system/omp/execution_policy.h>
@@ -42,6 +45,7 @@ _RAFT_HOST_DEVICE bool is_positive<half>(const half& a)
 template <class T, uint32_t block_size, class pack_t = uint8_t>
 RAFT_KERNEL binary_quantization_kernel(const T* const in_ptr,
                                        const uint32_t ldi,
+                                       const T* const threshold_ptr,
                                        const size_t dataset_size,
                                        const uint32_t dataset_dim,
                                        pack_t* const out_ptr,
@@ -71,7 +75,8 @@ RAFT_KERNEL binary_quantization_kernel(const T* const in_ptr,
     for (uint32_t bit_offset = 0; bit_offset < bits_per_pack; bit_offset++) {
       const auto j = j_offset + lane_id + bit_offset * warp_size;
       if (j < dataset_dim) {
-        const auto v = in_ptr[vector_id * ldi + j];
+        auto v = in_ptr[static_cast<size_t>(vector_id) * ldi + j];
+        if (threshold_ptr != nullptr) { v -= threshold_ptr[j]; }
         if (is_positive(v)) { pack |= (1u << bit_offset); }
       }
     }
@@ -106,8 +111,252 @@ RAFT_KERNEL binary_quantization_kernel(const T* const in_ptr,
   }
 }
 
-template <typename T, typename QuantI = uint8_t>
+RAFT_KERNEL mean_f16_in_f32_kernel_sum(float* const result_ptr,
+                                       const half* const src_ptr,
+                                       const uint32_t ld,
+                                       const uint32_t dataset_dim,
+                                       const size_t dataset_size,
+                                       const size_t dataset_size_per_cta)
+{
+  const auto dim_i = threadIdx.x + blockIdx.x * blockDim.x;
+  if (dim_i >= dataset_dim) { return; }
+
+  const auto start_dataset_index = blockIdx.y * dataset_size_per_cta;
+  const auto end_dataset_index   = std::min((blockIdx.y + 1) * dataset_size_per_cta, dataset_size);
+
+  if (start_dataset_index >= dataset_size) { return; }
+
+  float sum = 0;
+  for (size_t dataset_index = start_dataset_index; dataset_index < end_dataset_index;
+       dataset_index++) {
+    sum += static_cast<float>(src_ptr[dim_i + dataset_index * ld]);
+  }
+  atomicAdd(result_ptr + dim_i, sum);
+}
+
+RAFT_KERNEL mean_f16_in_f32_kernel_div(half* const result_ptr,
+                                       const float* const src_ptr,
+                                       const uint32_t dataset_dim,
+                                       const size_t dataset_size)
+{
+  const auto dim_i = threadIdx.x + blockIdx.x * blockDim.x;
+  if (dim_i >= dataset_dim) { return; }
+
+  result_ptr[dim_i] = src_ptr[dim_i] / dataset_size;
+}
+
+void mean_f16_in_f32(raft::resources const& res,
+                     half* const result_ptr,
+                     const half* const src_ptr,
+                     const uint32_t ld,
+                     const uint32_t dataset_dim,
+                     const size_t dataset_size,
+                     cudaStream_t cuda_stream)
+{
+  auto mr = raft::resource::get_workspace_resource(res);
+  auto f32_result_vec =
+    raft::make_device_mdarray<float, int64_t>(res, mr, raft::make_extents<int64_t>(dataset_dim));
+  RAFT_CUDA_TRY(
+    cudaMemsetAsync(f32_result_vec.data_handle(), 0, sizeof(float) * dataset_dim, cuda_stream));
+
+  constexpr uint32_t dataset_size_per_cta = 2048;
+  constexpr uint32_t block_size           = 256;
+  const dim3 grid_size(raft::div_rounding_up_safe<size_t>(dataset_dim, block_size),
+                       raft::div_rounding_up_safe<size_t>(dataset_size, dataset_size_per_cta));
+  mean_f16_in_f32_kernel_sum<<<grid_size, block_size, 0, cuda_stream>>>(
+    f32_result_vec.data_handle(), src_ptr, ld, dataset_dim, dataset_size, dataset_size_per_cta);
+  mean_f16_in_f32_kernel_div<<<grid_size.x, block_size, 0, cuda_stream>>>(
+    result_ptr, f32_result_vec.data_handle(), dataset_dim, dataset_size);
+}
+
+template <typename T>
+auto train(raft::resources const& res,
+           const cuvs::preprocessing::quantize::binary::params& params,
+           raft::device_matrix_view<const T, int64_t> dataset)
+  -> cuvs::preprocessing::quantize::binary::quantizer<T>
+{
+  cuvs::preprocessing::quantize::binary::quantizer<T> quantizer(res);
+  if (params.threshold == cuvs::preprocessing::quantize::binary::bit_threshold::zero) {
+    quantizer.threshold = raft::make_device_vector<T, int64_t>(res, 0);
+    return quantizer;
+  }
+
+  const size_t dataset_size  = dataset.extent(0);
+  const uint32_t dataset_dim = dataset.extent(1);
+
+  quantizer.threshold = raft::make_device_vector<T, int64_t>(res, dataset_dim);
+  auto threshold_ptr  = quantizer.threshold.data_handle();
+
+  if (params.threshold == cuvs::preprocessing::quantize::binary::bit_threshold::mean) {
+    // stats::mean does not support F16
+    if constexpr (!std::is_same_v<T, half>) {
+      raft::stats::mean(
+        res, dataset, raft::make_device_vector_view<T, int64_t>(threshold_ptr, dataset_dim), false);
+    } else {
+      // Use a custom CUDA kernel because 1) raft::stats::mean does not support f16 and 2) it is
+      // better to use f32 in accumulation data type for a large dataset.
+      mean_f16_in_f32(res,
+                      threshold_ptr,
+                      dataset.data_handle(),
+                      dataset_dim,
+                      dataset_dim,
+                      dataset_size,
+                      raft::resource::get_cuda_stream(res));
+    }
+  } else if (params.threshold ==
+             cuvs::preprocessing::quantize::binary::bit_threshold::sampling_median) {
+    RAFT_EXPECTS(params.sampling_ratio > 0 && params.sampling_ratio <= 1,
+                 "The sampling ratio must be within the range (0, 1].");
+    // Make the number of samples odd so that the median is calculated by only sort and memcpy
+    const size_t num_samples =
+      std::max(
+        raft::div_rounding_up_safe(static_cast<size_t>(dataset_size * params.sampling_ratio), 2lu) *
+          2lu,
+        2lu) -
+      1;
+    const size_t data_size_per_vector = sizeof(T) * dataset_dim * 2;
+    const uint32_t max_dim_chunk =
+      std::min(std::max(1lu, raft::resource::get_workspace_free_bytes(res) / data_size_per_vector),
+               static_cast<std::size_t>(dataset_dim));
+
+    raft::random::RngState rng(29837lu);
+    auto mr                    = raft::resource::get_workspace_resource(res);
+    auto sampled_dataset_chunk = raft::make_device_mdarray<T, int64_t>(
+      res, mr, raft::make_extents<int64_t>(num_samples, max_dim_chunk));
+    auto transposed_sampled_dataset_chunk = raft::make_device_mdarray<T, int64_t>(
+      res, mr, raft::make_extents<int64_t>(max_dim_chunk, num_samples));
+    for (uint32_t dim_offset = 0; dim_offset < dataset_dim; dim_offset += max_dim_chunk) {
+      const auto dim_chunk = std::min(max_dim_chunk, dataset_dim - dim_offset);
+
+      auto sampled_view =
+        raft::make_device_matrix_view<T, int64_t>(sampled_dataset_chunk.data_handle(),
+                                                  static_cast<int64_t>(num_samples),
+                                                  static_cast<int64_t>(dim_chunk));
+      raft::matrix::sample_rows(
+        res,
+        rng,
+        raft::make_device_strided_matrix_view<const T, int64_t>(
+          dataset.data_handle() + dim_offset, dataset_size, dim_chunk, dataset_dim),
+        sampled_view);
+      auto transposed_sampled_view = raft::make_device_matrix_view<T, int64_t>(
+        transposed_sampled_dataset_chunk.data_handle(), dim_chunk, num_samples);
+      raft::linalg::transpose(res, sampled_view, transposed_sampled_view);
+
+      for (uint32_t i = 0; i < dim_chunk; i++) {
+        auto start_ptr = transposed_sampled_dataset_chunk.data_handle() + i * num_samples;
+        thrust::sort(thrust::device, start_ptr, start_ptr + num_samples);
+      }
+
+      RAFT_CUDA_TRY(cudaMemcpy2DAsync(threshold_ptr + dim_offset,
+                                      sizeof(T),
+                                      sampled_dataset_chunk.data_handle() + (num_samples - 1) / 2,
+                                      num_samples * sizeof(T),
+                                      sizeof(T),
+                                      dim_chunk,
+                                      cudaMemcpyDefault,
+                                      raft::resource::get_cuda_stream(res)));
+    }
+  }
+  return quantizer;
+}
+
+template <typename T>
+auto train(raft::resources const& res,
+           const cuvs::preprocessing::quantize::binary::params& params,
+           raft::host_matrix_view<const T, int64_t> dataset)
+  -> cuvs::preprocessing::quantize::binary::quantizer<T>
+{
+  cuvs::preprocessing::quantize::binary::quantizer<T> quantizer(res);
+  if (params.threshold == cuvs::preprocessing::quantize::binary::bit_threshold::zero) {
+    quantizer.threshold = raft::make_device_vector<T, int64_t>(res, 0);
+    return quantizer;
+  }
+
+  const uint32_t dataset_dim = dataset.extent(1);
+  const size_t dataset_size  = dataset.extent(0);
+  quantizer.threshold        = raft::make_device_vector<T, int64_t>(res, dataset_dim);
+
+  using compute_t = std::conditional_t<std::is_same_v<half, T>, float, T>;
+  std::vector<compute_t> host_threshold_vec(dataset_dim);
+  auto threshold_ptr = host_threshold_vec.data();
+
+  if (params.threshold == cuvs::preprocessing::quantize::binary::bit_threshold::mean) {
+    for (uint32_t j = 0; j < dataset_dim; j++) {
+      threshold_ptr[j] = 0;
+    }
+#pragma omp parallel for reduction(+ : threshold_ptr[ : dataset_dim])
+    for (size_t i = 0; i < dataset_size; i++) {
+      for (uint32_t j = 0; j < dataset_dim; j++) {
+        threshold_ptr[j] += static_cast<compute_t>(dataset.data_handle()[i * dataset_dim + j]);
+      }
+    }
+    for (uint32_t j = 0; j < dataset_dim; j++) {
+      threshold_ptr[j] /= dataset_size;
+    }
+  } else if (params.threshold ==
+             cuvs::preprocessing::quantize::binary::bit_threshold::sampling_median) {
+    RAFT_EXPECTS(params.sampling_ratio > 0 && params.sampling_ratio <= 1,
+                 "The sampling ratio must be within the range (0, 1].");
+
+    // Make the number of samples odd so that the median is calculated by only sort and memcpy
+    const size_t num_samples =
+      std::max(
+        raft::div_rounding_up_safe(static_cast<size_t>(dataset_size * params.sampling_ratio), 2lu) *
+          2lu,
+        2lu) -
+      1;
+
+    // Calculate stride
+    size_t stride_prime_list[] = {611323lu, 611333lu, 611389lu, 611393};
+    uint32_t prime_i           = 0;
+    while (dataset_size % stride_prime_list[prime_i] == 0) {
+      prime_i++;
+    }
+    const auto stride = stride_prime_list[prime_i];
+
+    // Transposed
+    auto sampled_dataset = raft::make_host_matrix<compute_t, int64_t>(dataset_dim, num_samples);
+#pragma omp parallel for
+    for (size_t out_i = 0; out_i < num_samples; out_i++) {
+      const auto in_i = (out_i * stride) % dataset_size;
+      for (uint32_t j = 0; j < dataset_dim; j++) {
+        sampled_dataset.data_handle()[j * num_samples + out_i] =
+          static_cast<compute_t>(dataset.data_handle()[in_i * dataset_dim + j]);
+      }
+    }
+
+#pragma omp parallel for
+    for (uint32_t j = 0; j < dataset_dim; j++) {
+      auto start_ptr = sampled_dataset.data_handle() + j * num_samples;
+      std::sort(start_ptr, start_ptr + num_samples);
+      threshold_ptr[j] = start_ptr[(num_samples - 1) / 2];
+    }
+  }
+
+  if constexpr (std::is_same_v<T, compute_t>) {
+    raft::copy(quantizer.threshold.data_handle(),
+               host_threshold_vec.data(),
+               dataset_dim,
+               raft::resource::get_cuda_stream(res));
+  } else {
+    auto mr         = raft::resource::get_workspace_resource(res);
+    auto casted_vec = raft::make_device_mdarray<compute_t, int64_t>(
+      res, mr, raft::make_extents<int64_t>(dataset_dim));
+    raft::copy(casted_vec.data_handle(),
+               host_threshold_vec.data(),
+               dataset_dim,
+               raft::resource::get_cuda_stream(res));
+    raft::linalg::map(res,
+                      quantizer.threshold.view(),
+                      raft::cast_op<T>{},
+                      raft::make_const_mdspan(casted_vec.view()));
+  }
+  return quantizer;
+}
+
+template <typename T, typename QuantI>
 void transform(raft::resources const& res,
+               const cuvs::preprocessing::quantize::binary::quantizer<T>& quantizer,
                raft::device_matrix_view<const T, int64_t> dataset,
                raft::device_matrix_view<QuantI, int64_t> out)
 {
@@ -128,6 +377,8 @@ void transform(raft::resources const& res,
                "the input dataset size (%lu) but is %lu passed",
                dataset_size,
                out_dataset_size);
+  const T* threshold_ptr = nullptr;
+  if (quantizer.threshold.size() != 0) { threshold_ptr = quantizer.threshold.data_handle(); }
 
   constexpr uint32_t warp_size    = 32;
   constexpr uint32_t block_size   = 256;
@@ -138,14 +389,16 @@ void transform(raft::resources const& res,
   binary_quantization_kernel<T, block_size>
     <<<grid_size, block_size, 0, stream>>>(dataset.data_handle(),
                                            dataset.stride(0),
+                                           threshold_ptr,
                                            dataset_size,
                                            dataset_dim,
                                            out.data_handle(),
                                            out.stride(0));
 }
 
-template <typename T, typename QuantI = uint8_t>
+template <typename T, typename QuantI>
 void transform(raft::resources const& res,
+               const cuvs::preprocessing::quantize::binary::quantizer<T>& quantizer,
                raft::host_matrix_view<const T, int64_t> dataset,
                raft::host_matrix_view<QuantI, int64_t> out)
 {
@@ -166,6 +419,33 @@ void transform(raft::resources const& res,
                dataset_size,
                out_dataset_size);
 
+  // Use `float` for computation when T == half because a runtime error occurs with CUDA 11.8
+  using compute_t          = std::conditional_t<std::is_same_v<half, T>, float, T>;
+  auto threshold_vec       = raft::make_host_vector<compute_t, int64_t>(0);
+  compute_t* threshold_ptr = nullptr;
+
+  if (quantizer.threshold.size() != 0) {
+    threshold_vec = raft::make_host_vector<compute_t, int64_t>(dataset_dim);
+    threshold_ptr = threshold_vec.data_handle();
+
+    if constexpr (std::is_same_v<compute_t, T>) {
+      raft::copy(threshold_ptr,
+                 quantizer.threshold.data_handle(),
+                 dataset_dim,
+                 raft::resource::get_cuda_stream(res));
+    } else {
+      auto mr         = raft::resource::get_workspace_resource(res);
+      auto casted_vec = raft::make_device_mdarray<float, int64_t>(
+        res, mr, raft::make_extents<int64_t>(dataset_dim));
+      raft::linalg::map(res,
+                        casted_vec.view(),
+                        raft::cast_op<compute_t>{},
+                        raft::make_const_mdspan(quantizer.threshold.view()));
+      raft::copy(
+        threshold_ptr, casted_vec.data_handle(), dataset_dim, raft::resource::get_cuda_stream(res));
+    }
+  }
+
 #pragma omp parallel for collapse(2)
   for (size_t i = 0; i < dataset_size; ++i) {
     for (uint32_t out_j = 0; out_j < minimul_out_dim; ++out_j) {
@@ -173,7 +453,13 @@ void transform(raft::resources const& res,
       for (uint32_t pack_j = 0; pack_j < bits_per_pack; ++pack_j) {
         const uint32_t in_j = out_j * bits_per_pack + pack_j;
         if (in_j < dataset_dim) {
-          if (is_positive(dataset(i, in_j))) { pack |= (1u << pack_j); }
+          if (threshold_ptr == nullptr) {
+            if (is_positive(dataset(i, in_j))) { pack |= (1u << pack_j); }
+          } else {
+            if (is_positive(static_cast<compute_t>(dataset(i, in_j)) - threshold_ptr[in_j])) {
+              pack |= (1u << pack_j);
+            }
+          }
         }
       }
       out(i, out_j) = pack;
diff --git a/cpp/tests/preprocessing/binary_quantization.cu b/cpp/tests/preprocessing/binary_quantization.cu
index af1cc07e8b..cdf287fb84 100644
--- a/cpp/tests/preprocessing/binary_quantization.cu
+++ b/cpp/tests/preprocessing/binary_quantization.cu
@@ -20,6 +20,7 @@
 #include <raft/linalg/transpose.cuh>
 #include <raft/matrix/init.cuh>
 #include <raft/stats/stddev.cuh>
+#include <raft/util/itertools.hpp>
 #include <thrust/reduce.h>
 
 namespace cuvs::preprocessing::quantize::binary {
@@ -28,12 +29,23 @@ template <typename T>
 struct BinaryQuantizationInputs {
   int rows;
   int cols;
+  cuvs::preprocessing::quantize::binary::bit_threshold threshold;
+  bool train_host;
 };
 
 template <typename T>
 std::ostream& operator<<(std::ostream& os, const BinaryQuantizationInputs<T>& inputs)
 {
-  return os << "> rows:" << inputs.rows << " cols:" << inputs.cols;
+  os << "> dataset_size:" << inputs.rows << " dataset_dim:" << inputs.cols;
+  os << " threshold: ";
+  switch (inputs.threshold) {
+    case bit_threshold::zero: os << "zero"; break;
+    case bit_threshold::mean: os << "mean"; break;
+    case bit_threshold::sampling_median: os << "sampling_median"; break;
+    default: os << "unknown"; break;
+  }
+  os << " train_host_dataset: " << inputs.train_host;
+  return os;
 }
 
 template <typename T, typename QuantI>
@@ -58,11 +70,24 @@ class BinaryQuantizationTest : public ::testing::TestWithParam<BinaryQuantizatio
     {
       static_assert(std::is_same_v<QuantI, uint8_t>);
 
+      cuvs::preprocessing::quantize::binary::params params;
+      params.threshold = params_.threshold;
+
       const auto col_quantized = raft::div_rounding_up_safe(cols_, 8);
       auto quantized_input_h   = raft::make_host_matrix<QuantI, int64_t>(rows_, cols_);
       auto quantized_input_d   = raft::make_device_matrix<QuantI, int64_t>(handle, rows_, cols_);
-      cuvs::preprocessing::quantize::binary::transform(handle, dataset, quantized_input_d.view());
-      cuvs::preprocessing::quantize::binary::transform(handle, dataset_h, quantized_input_h.view());
+
+      cuvs::preprocessing::quantize::binary::quantizer<T> quantizer(handle);
+      if (train_host_) {
+        quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset_h);
+      } else {
+        quantizer = cuvs::preprocessing::quantize::binary::train(handle, params, dataset);
+      }
+
+      cuvs::preprocessing::quantize::binary::transform(
+        handle, quantizer, dataset, quantized_input_d.view());
+      cuvs::preprocessing::quantize::binary::transform(
+        handle, quantizer, dataset_h, quantized_input_h.view());
 
       ASSERT_TRUE(devArrMatchHost(quantized_input_h.data_handle(),
                                   quantized_input_d.data_handle(),
@@ -81,6 +106,8 @@ class BinaryQuantizationTest : public ::testing::TestWithParam<BinaryQuantizatio
     input_.resize(n_elements, stream);
     host_input_.resize(n_elements);
 
+    train_host_ = params_.train_host;
+
     // random input
     unsigned long long int seed = 1234ULL;
     raft::random::RngState r(seed);
@@ -100,16 +127,21 @@ class BinaryQuantizationTest : public ::testing::TestWithParam<BinaryQuantizatio
   int cols_;
   rmm::device_uvector<T> input_;
   std::vector<T> host_input_;
+  bool train_host_;
 };
 
 template <typename T>
-const std::vector<BinaryQuantizationInputs<T>> inputs = {
-  {5, 5},
-  {100, 7},
-  {100, 128},
-  {100, 1999},
-  {1000, 1999},
-};
+const std::vector<BinaryQuantizationInputs<T>> generate_inputs()
+{
+  const auto inputs = raft::util::itertools::product<BinaryQuantizationInputs<T>>(
+    {5, 100, 1000},
+    {7, 128, 1999},
+    {cuvs::preprocessing::quantize::binary::bit_threshold::zero,
+     cuvs::preprocessing::quantize::binary::bit_threshold::mean,
+     cuvs::preprocessing::quantize::binary::bit_threshold::sampling_median},
+    {true, false});
+  return inputs;
+}
 
 typedef BinaryQuantizationTest<float, uint8_t> QuantizationTest_float_uint8t;
 TEST_P(QuantizationTest_float_uint8t, BinaryQuantizationTest) { this->testBinaryQuantization(); }
@@ -122,12 +154,12 @@ TEST_P(QuantizationTest_half_uint8t, BinaryQuantizationTest) { this->testBinaryQ
 
 INSTANTIATE_TEST_CASE_P(BinaryQuantizationTest,
                         QuantizationTest_float_uint8t,
-                        ::testing::ValuesIn(inputs<float>));
+                        ::testing::ValuesIn(generate_inputs<float>()));
 INSTANTIATE_TEST_CASE_P(BinaryQuantizationTest,
                         QuantizationTest_double_uint8t,
-                        ::testing::ValuesIn(inputs<double>));
+                        ::testing::ValuesIn(generate_inputs<double>()));
 INSTANTIATE_TEST_CASE_P(BinaryQuantizationTest,
                         QuantizationTest_half_uint8t,
-                        ::testing::ValuesIn(inputs<half>));
+                        ::testing::ValuesIn(generate_inputs<half>()));
 
 }  // namespace cuvs::preprocessing::quantize::binary
diff --git a/docs/source/cpp_api/preprocessing_quantize.rst b/docs/source/cpp_api/preprocessing_quantize.rst
index 8a08fb98ba..35bbe711ce 100644
--- a/docs/source/cpp_api/preprocessing_quantize.rst
+++ b/docs/source/cpp_api/preprocessing_quantize.rst
@@ -17,3 +17,13 @@ namespace *cuvs::preprocessing::quantize::scalar*
 
 .. doxygengroup:: scalar
    :project: cuvs
+
+Binary
+------
+
+``#include <cuvs/preprocessing/quantize/binary.hpp>``
+
+namespace *cuvs::preprocessing::quantize::binary*
+
+.. doxygengroup:: binary
+   :project: cuvs
diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt b/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
index 66b319f3bf..bef66fdf31 100644
--- a/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
+++ b/python/cuvs/cuvs/preprocessing/quantize/binary/CMakeLists.txt
@@ -20,5 +20,5 @@ rapids_cython_create_modules(
   CXX
   SOURCE_FILES "${cython_sources}"
   LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cuvs MODULE_PREFIX
-                   preprocessing_quantize_scalar_
+                   preprocessing_quantize_binary_
 )
diff --git a/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py b/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py
index ce1a9ffeeb..7f96813c4a 100644
--- a/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py
+++ b/python/cuvs/cuvs/preprocessing/quantize/binary/__init__.py
@@ -14,4 +14,6 @@
 
 from .binary import transform
 
-__all__ = ["transform"]
+__all__ = [
+    "transform",
+]

From e892a38eb54b99bf04a8153e4727d5e56a092f07 Mon Sep 17 00:00:00 2001
From: Julian Miller <mail@julian-miller.de>
Date: Fri, 11 Jul 2025 16:37:50 +0200
Subject: [PATCH 477/623] [BUG] Disable flaky cpp test
 `AnnIVFFlatTestF_half.AnnIVFFlat/21` (#1092)

Disable flaky `AnnIVFFlat` test. See https://github.com/rapidsai/cuvs/issues/1091 for more details.

Authors:
  - Julian Miller (https://github.com/julianmi)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/1092
---
 cpp/tests/neighbors/ann_ivf_flat.cuh | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/cpp/tests/neighbors/ann_ivf_flat.cuh b/cpp/tests/neighbors/ann_ivf_flat.cuh
index 104ea91956..aab1868473 100644
--- a/cpp/tests/neighbors/ann_ivf_flat.cuh
+++ b/cpp/tests/neighbors/ann_ivf_flat.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -561,7 +561,9 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   {1000, 10000, 2049, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
   {1000, 10000, 2050, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
   {1000, 10000, 2050, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},
-  {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
+  // TODO: Re-enable test after adjusting parameters for higher recall. See
+  // https://github.com/rapidsai/cuvs/issues/1091
+  // {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, true},
   {1000, 10000, 2051, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, true},
   {1000, 10000, 2052, 16, 40, 1024, cuvs::distance::DistanceType::InnerProduct, false},
   {1000, 10000, 2052, 16, 40, 1024, cuvs::distance::DistanceType::CosineExpanded, false},

From 203e54c9ae45af62ee51c124633effb8aa5ee8a4 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Fri, 11 Jul 2025 17:31:52 -0400
Subject: [PATCH 478/623] Enforce java codeowners for managing contents of
 `java/` (#1103)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ray Douglass (https://github.com/raydouglass)

URL: https://github.com/rapidsai/cuvs/pull/1103
---
 .github/CODEOWNERS | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 6d9cfd4530..a24746bbf5 100755
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -1,6 +1,9 @@
 #cpp code owners
 cpp/               @rapidsai/cuvs-cpp-codeowners
 
+#java code owners
+java/              @rapidsai/cuvs-java-codeowners
+
 #python code owners
 python/            @rapidsai/cuvs-python-codeowners
 

From c591e97ac19a40c7e63a93c53f4efc111713090d Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Mon, 14 Jul 2025 17:38:28 -0700
Subject: [PATCH 479/623] Fix go CI failures (#1114)

Fixes #1113.

We are currently seeing CI failures on the go-build: with an error message like `build constraints exclude all Go files in /__w/cuvs/cuvs/go`

Since we have `import "C"`  declarations  we need to make sure that we have CGO_ENABLED set. The difference in the failing vs non-failing CI runs seem to be that we are installing the `nocgo` version of `_go_select` in the failing CI runs, instead of the `cgo` version in the successful runs.

Fix by installing `_go_select *=cgo` in the conda environments

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1114
---
 conda/environments/all_cuda-129_arch-aarch64.yaml | 1 +
 conda/environments/all_cuda-129_arch-x86_64.yaml  | 1 +
 conda/environments/go_cuda-129_arch-aarch64.yaml  | 1 +
 conda/environments/go_cuda-129_arch-x86_64.yaml   | 1 +
 dependencies.yaml                                 | 1 +
 5 files changed, 5 insertions(+)

diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
index b4bde2bd48..61408b951e 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -5,6 +5,7 @@ channels:
 - rapidsai-nightly
 - conda-forge
 dependencies:
+- _go_select *=cgo
 - breathe>=4.35.0
 - c-compiler
 - clang-tools==20.1.4
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
index 249369bd41..64f9c75fd0 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -5,6 +5,7 @@ channels:
 - rapidsai-nightly
 - conda-forge
 dependencies:
+- _go_select *=cgo
 - breathe>=4.35.0
 - c-compiler
 - clang-tools==20.1.4
diff --git a/conda/environments/go_cuda-129_arch-aarch64.yaml b/conda/environments/go_cuda-129_arch-aarch64.yaml
index e72a117827..ed5e82f6cf 100644
--- a/conda/environments/go_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-129_arch-aarch64.yaml
@@ -5,6 +5,7 @@ channels:
 - rapidsai-nightly
 - conda-forge
 dependencies:
+- _go_select *=cgo
 - c-compiler
 - clang-tools==20.1.4
 - clang==20.1.4
diff --git a/conda/environments/go_cuda-129_arch-x86_64.yaml b/conda/environments/go_cuda-129_arch-x86_64.yaml
index cdd23b95a3..8a2f40d01a 100644
--- a/conda/environments/go_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-129_arch-x86_64.yaml
@@ -5,6 +5,7 @@ channels:
 - rapidsai-nightly
 - conda-forge
 dependencies:
+- _go_select *=cgo
 - c-compiler
 - clang-tools==20.1.4
 - clang==20.1.4
diff --git a/dependencies.yaml b/dependencies.yaml
index 236209de2c..0f77bcf3aa 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -398,6 +398,7 @@ dependencies:
       - output_types: [conda]
         packages:
           - go
+          - _go_select *=cgo
           - dlpack>=0.8,<1.0
   build_wheels:
     common:

From 32d6d838cd1c107f6e8e860772033f7fc6cc9211 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Tue, 15 Jul 2025 06:52:05 +0200
Subject: [PATCH 480/623] [Java] Add Dataset based on `MemorySegment` (#1034)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR adds the ability to define a Dataset directly over a MemorySegment, "wrapping" it instead of allocating a new one.

- Depends on https://github.com/rapidsai/cuvs/pull/1033 and https://github.com/rapidsai/cuvs/pull/1024
- ~~The new API has a `Object memorySegment` parameter, as we target Java 21 for the API (but 22 for the implementation); it works but it's definitely a hack and we need to sort this out~~
   - As discussed, we want to keep targeting Java 21 for the API. This means the API will return a `MethodHandle`, and the Java 22 implementation will use it to return a factory method to build a Dataset from a MemorySegment.
   - This factory method can then be used as shown in the tests (see the `DatasetHelper` convenience class/method).
- Benchmarks show a sizeable speedup -- it is still tiny related to the "big picture" (index build time), but there is an improvement and above all we avoid a whole new copy of the input data (halving the memory requirements).

Fixes https://github.com/rapidsai/cuvs/issues/698

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1034
---
 .../com/nvidia/cuvs/CagraIndexBenchmarks.java |  89 ++++++++-
 .../main/java/com/nvidia/cuvs/Dataset.java    |   2 +-
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  18 ++
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |   6 +
 .../cuvs/internal/CuVSResourcesImpl.java      |   3 +-
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  25 +++
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 175 +++++++++++-------
 .../java/com/nvidia/cuvs/DatasetHelper.java   |  40 ++++
 8 files changed, 291 insertions(+), 67 deletions(-)
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java

diff --git a/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
index 15d47168b3..b8a77979b5 100644
--- a/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
+++ b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
@@ -16,13 +16,17 @@
 
 package com.nvidia.cuvs;
 
+import com.nvidia.cuvs.spi.CuVSProvider;
 import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
 import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
 import org.openjdk.jmh.annotations.Param;
 import org.openjdk.jmh.annotations.Scope;
 import org.openjdk.jmh.annotations.Setup;
-import org.openjdk.jmh.annotations.TearDown;
 import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.TearDown;
 import org.openjdk.jmh.infra.Blackhole;
 
 import java.lang.foreign.*;
@@ -30,6 +34,7 @@
 import java.nio.file.Path;
 import java.util.Random;
 import java.util.UUID;
+import java.util.concurrent.TimeUnit;
 
 @Fork(value = 1, warmups = 0)
 @State(Scope.Benchmark)
@@ -43,6 +48,10 @@ public class CagraIndexBenchmarks {
 
     private float[][] arrayDataset;
 
+    private Arena arena;
+
+    private MemorySegment memorySegmentDataset;
+
     private static final Random random = new Random();
 
     private static float[][] createSampleData(int size, int dimensions) {
@@ -55,9 +64,48 @@ private static float[][] createSampleData(int size, int dimensions) {
         return array;
     }
 
+    private static MemorySegment createSampleDataSegment(Arena arena, float[][] array, int size, int dimensions) {
+        final ValueLayout.OfFloat C_FLOAT = (ValueLayout.OfFloat) Linker.nativeLinker().canonicalLayouts().get("float");
+
+        MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout((long)size * dimensions, C_FLOAT);
+
+        var segment = arena.allocate(dataMemoryLayout);
+        for (int i = 0; i < size; ++i) {
+            var vector = array[i];
+            MemorySegment.copy(vector, 0, segment, C_FLOAT, (i * dimensions * C_FLOAT.byteSize()), dimensions);
+        }
+        return segment;
+    }
+
+    private static Dataset fromMemorySegment(MemorySegment memorySegment, int size, int dimensions) {
+        try {
+            return (Dataset)
+                    CuVSProvider.provider()
+                            .newNativeDatasetBuilder()
+                            .invokeExact(memorySegment, size, dimensions);
+        } catch (Throwable e) {
+            if (e instanceof Error err) {
+                throw err;
+            } else if (e instanceof RuntimeException re) {
+                throw re;
+            } else {
+                throw new RuntimeException(e);
+            }
+        }
+    }
+
     @Setup
     public void initialize() {
+        arena = Arena.ofShared();
         arrayDataset = createSampleData(size, dims);
+        memorySegmentDataset = createSampleDataSegment(arena, arrayDataset, size, dims);
+    }
+
+    @TearDown
+    public void cleanUp() {
+        if (arena != null) {
+            arena.close();
+        }
     }
 
     @Benchmark
@@ -111,4 +159,43 @@ public void testIndexingFromHeap(Blackhole blackhole) throws Throwable {
             blackhole.consume(index);
         }
     }
+
+    @Benchmark
+    public void testIndexingFromMemorySegment(Blackhole blackhole) throws Throwable {
+        try (CuVSResources resources = CuVSResources.create()) {
+            // Configure index parameters
+            CagraIndexParams indexParams = new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraIndexParams.CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(1)
+                .withIntermediateGraphDegree(2)
+                .withNumWriterThreads(32)
+                .withMetric(CagraIndexParams.CuvsDistanceType.L2Expanded)
+                .build();
+
+            // Create the index with the dataset
+            CagraIndex index = CagraIndex.newBuilder(resources)
+                .withDataset(fromMemorySegment(memorySegmentDataset, size, dims))
+                .withIndexParams(indexParams)
+                .build();
+            blackhole.consume(index);
+        }
+    }
+
+    @Benchmark
+    @OutputTimeUnit(TimeUnit.NANOSECONDS)
+    @BenchmarkMode(Mode.AverageTime)
+    public void testDatasetFromHeap(Blackhole blackhole) throws Throwable {
+        try (var dataset = Dataset.ofArray(arrayDataset)) {
+            blackhole.consume(dataset);
+        }
+    }
+
+    @Benchmark
+    @OutputTimeUnit(TimeUnit.NANOSECONDS)
+    @BenchmarkMode(Mode.AverageTime)
+    public void testDatasetFromMemorySegment(Blackhole blackhole) throws Throwable {
+        try (var dataset = fromMemorySegment(memorySegmentDataset, size, dims)) {
+            blackhole.consume(dataset);
+        }
+    }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
index 9eaad71861..e13bed45d0 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
@@ -28,7 +28,7 @@
 public interface Dataset extends AutoCloseable {
 
   /**
-   * Creates a dataset from a on-heap array of vectors
+   * Creates a dataset from an on-heap array of vectors
    *
    * @since 25.08
    */
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 458fa36a48..8ce8b3c2a2 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -21,6 +21,8 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodType;
 import java.nio.file.Path;
 
 /**
@@ -52,6 +54,22 @@ default Path nativeLibraryPath() {
   /** Create a {@link Dataset.Builder} instance **/
   Dataset.Builder newDatasetBuilder(int size, int dimensions);
 
+  /**
+   * Returns the factory method used to build a Dataset from native memory.
+   * The factory method will have this signature: {@code Dataset createNativeDataset(memorySegment, size, dimensions)},
+   * where {@code memorySegment} is a {@code java.lang.foreign.MemorySegment} containing {@code int size} vectors of
+   * {@code int dimensions} length.
+   * <p>
+   * In order to expose this factory in a way that is compatible with Java 21, the factory method is returned as a
+   * {@link MethodHandle} with {@link MethodType} equal to
+   * {@code (Dataset.class, MemorySegment.class, int.class, int.class)}.
+   * The caller will need to invoke the factory via the {@link MethodHandle#invokeExact} method:
+   * {@code Dataset dataset = (Dataset)newNativeDatasetBuilder().invokeExact(memorySegment, size, dimensions)}
+   * </p>
+   * @return a MethodHandle which can be invoked to build a Dataset from a {@code MemorySegment}
+   */
+  MethodHandle newNativeDatasetBuilder();
+
   /** Create a {@link Dataset} backed by a on-heap array **/
   Dataset newArrayDataset(float[][] vectors);
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index e1168f9d92..e7bb0ab663 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -20,6 +20,7 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
+import java.lang.invoke.MethodHandle;
 import java.nio.file.Path;
 
 /**
@@ -57,6 +58,11 @@ public Dataset.Builder newDatasetBuilder(int size, int dimensions) {
     throw new UnsupportedOperationException();
   }
 
+  @Override
+  public MethodHandle newNativeDatasetBuilder() {
+    throw new UnsupportedOperationException();
+  }
+
   @Override
   public Dataset newArrayDataset(float[][] vectors) {
     throw new UnsupportedOperationException();
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
index 3b0c2afdbd..c3095e8304 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -39,9 +39,8 @@ public class CuVSResourcesImpl implements CuVSResources {
   /**
    * Constructor that allocates the resources needed for cuVS
    *
-   * @throws Throwable exception thrown when native function is invoked
    */
-  public CuVSResourcesImpl(Path tempDirectory) throws Throwable {
+  public CuVSResourcesImpl(Path tempDirectory) {
     this.tempDirectory = tempDirectory;
     this.arena = Arena.ofShared();
     try (var localArena = Arena.ofConfined()) {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 16e54022bc..2b9913f04f 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -32,12 +32,32 @@
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
+import java.lang.invoke.MethodHandle;
+import java.lang.invoke.MethodHandles;
+import java.lang.invoke.MethodType;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.Objects;
 
 final class JDKProvider implements CuVSProvider {
 
+  private static final MethodHandle createNativeDataset$mh = createNativeDatasetBuilder();
+
+  static MethodHandle createNativeDatasetBuilder() {
+    try {
+      var lookup = MethodHandles.lookup();
+      var mt = MethodType.methodType(Dataset.class, MemorySegment.class, int.class, int.class);
+      return lookup.findStatic(JDKProvider.class, "createNativeDataset", mt);
+    } catch (NoSuchMethodException | IllegalAccessException e) {
+      throw new RuntimeException(e);
+    }
+  }
+
+  private static Dataset createNativeDataset(
+      MemorySegment memorySegment, int size, int dimensions) {
+    return new DatasetImpl(null, memorySegment, size, dimensions);
+  }
+
   @Override
   public CuVSResources newCuVSResources(Path tempDirectory) throws Throwable {
     Objects.requireNonNull(tempDirectory);
@@ -107,6 +127,11 @@ public Dataset build() {
     };
   }
 
+  @Override
+  public MethodHandle newNativeDatasetBuilder() {
+    return createNativeDataset$mh;
+  }
+
   @Override
   public Dataset newArrayDataset(float[][] vectors) {
     Objects.requireNonNull(vectors);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 9145bfe1b4..ee33de8b4e 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -28,6 +28,11 @@
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
 import java.io.InputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.Linker;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.ValueLayout;
 import java.lang.invoke.MethodHandles;
 import java.nio.file.Files;
 import java.nio.file.Path;
@@ -134,17 +139,18 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     int numTestsRuns = 5;
     try (CuVSResources resources = CuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
-        var index = indexOnce(dataset, resources);
+        var index = indexOnce(Dataset.ofArray(dataset), resources);
         var indexPath = serializeOnce(index);
         var loadedIndex = deserializeOnce(indexPath, resources);
-        queryOnce(
+        queryAndCompare(
             index,
             loadedIndex,
             SearchResults.IDENTITY_MAPPING,
             queries,
             expectedResults,
             resources);
-        cleanup(indexPath, index, loadedIndex);
+        cleanup(index, loadedIndex);
+        Files.deleteIfExists(indexPath);
       }
     }
   }
@@ -164,17 +170,18 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
         runInAnotherThread(
             () -> {
               try {
-                var index = indexOnce(dataset, resources);
+                var index = indexOnce(Dataset.ofArray(dataset), resources);
                 var indexPath = serializeOnce(index);
                 var loadedIndex = deserializeOnce(indexPath, resources);
-                queryOnce(
+                queryAndCompare(
                     index,
                     loadedIndex,
                     SearchResults.IDENTITY_MAPPING,
                     queries,
                     expectedResults,
                     resources);
-                cleanup(indexPath, index, loadedIndex);
+                cleanup(index, loadedIndex);
+                Files.deleteIfExists(indexPath);
               } catch (Throwable e) {
                 throw new RuntimeException(e);
               }
@@ -188,28 +195,46 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
    */
   @Test
   public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
-    float[][] dataset = createSampleData();
+    final float[][] dataset = createSampleData();
     float[][] queries = createSampleQueries();
     List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
     int numTestsRuns = 10;
-    try (CuVSResources resources = CuVSResources.create()) {
+    runConcurrently(
+        numTestsRuns,
+        () ->
+            () -> {
+              try (CuVSResources resources = CuVSResources.create()) {
+                var index = indexOnce(Dataset.ofArray(dataset), resources);
+                var indexPath = serializeOnce(index);
+                var loadedIndex = deserializeOnce(indexPath, resources);
+                queryAndCompare(
+                    index,
+                    loadedIndex,
+                    SearchResults.IDENTITY_MAPPING,
+                    queries,
+                    expectedResults,
+                    resources);
+                cleanup(index, loadedIndex);
+                Files.deleteIfExists(indexPath);
+              } catch (Throwable e) {
+                throw new RuntimeException(e);
+              }
+            });
+  }
+
+  @Test
+  public void testIndexing() throws Throwable {
+    for (int i = 0; i < 100; ++i) {
+      final float[][] dataset = createSampleData();
+      int numTestsRuns = 10;
       runConcurrently(
           numTestsRuns,
           () ->
               () -> {
-                try {
-                  var index = indexOnce(dataset, resources);
-                  var indexPath = serializeOnce(index);
-                  var loadedIndex = deserializeOnce(indexPath, resources);
-                  queryOnce(
-                      index,
-                      loadedIndex,
-                      SearchResults.IDENTITY_MAPPING,
-                      queries,
-                      expectedResults,
-                      resources);
-                  cleanup(indexPath, index, loadedIndex);
+                try (CuVSResources resources = CuVSResources.create()) {
+                  var index = indexOnce(Dataset.ofArray(dataset), resources);
+                  index.destroyIndex();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
                 }
@@ -218,16 +243,18 @@ public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
   }
 
   @Test
-  public void testIndexing() throws Throwable {
+  public void testSerialization() throws Throwable {
     for (int i = 0; i < 100; ++i) {
-      float[][] dataset = createSampleData();
+      final float[][] dataset = createSampleData();
       int numTestsRuns = 10;
       runConcurrently(
           numTestsRuns,
           () ->
               () -> {
                 try (CuVSResources resources = CuVSResources.create()) {
-                  var index = indexOnce(dataset, resources);
+                  var index = indexOnce(Dataset.ofArray(dataset), resources);
+                  var indexPath = serializeOnce(index);
+                  Files.deleteIfExists(indexPath);
                   index.destroyIndex();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
@@ -237,53 +264,36 @@ public void testIndexing() throws Throwable {
   }
 
   @Test
-  public void testSerialization() throws Throwable {
+  public void testDeserialization() throws Throwable {
+    var indexPath = createSerializedIndex(Dataset.ofArray(createSampleData()));
     for (int i = 0; i < 100; ++i) {
-      float[][] dataset = createSampleData();
       int numTestsRuns = 10;
       runConcurrently(
           numTestsRuns,
           () ->
               () -> {
                 try (CuVSResources resources = CuVSResources.create()) {
-                  var index = indexOnce(dataset, resources);
-                  var indexPath = serializeOnce(index);
-                  Files.deleteIfExists(indexPath);
-                  index.destroyIndex();
+                  deserializeOnce(indexPath, resources).destroyIndex();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
                 }
               });
     }
+    Files.deleteIfExists(indexPath);
   }
 
-  @Test
-  public void testDeserialization() throws Throwable {
-    float[][] dataset = createSampleData();
+  private Path createSerializedIndex(Dataset dataset) throws Throwable {
     try (CuVSResources resources = CuVSResources.create()) {
       var index = indexOnce(dataset, resources);
       var indexPath = serializeOnce(index);
-      for (int i = 0; i < 100; ++i) {
-        int numTestsRuns = 10;
-        runConcurrently(
-            numTestsRuns,
-            () ->
-                () -> {
-                  try {
-                    deserializeOnce(indexPath, resources).destroyIndex();
-                  } catch (Throwable e) {
-                    throw new RuntimeException(e);
-                  }
-                });
-      }
-      Files.deleteIfExists(indexPath);
       index.destroyIndex();
+      return indexPath;
     }
   }
 
   @Test
   public void testIndexingAndSearchingFlowWithCustomMappingFunction() throws Throwable {
-    float[][] dataset = createSampleData();
+    var dataset = Dataset.ofArray(createSampleData());
     float[][] queries = createSampleQueries();
     var expectedResults =
         List.of(
@@ -292,19 +302,20 @@ public void testIndexingAndSearchingFlowWithCustomMappingFunction() throws Throw
             Map.of(0, 0.047766715f, 3, 0.20332818f, 1, 0.48305473f),
             Map.of(2, 0.15224178f, 1, 0.59063464f, 0, 0.5986642f));
 
-    LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.length);
+    LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.size());
     try (CuVSResources resources = CuVSResources.create()) {
       var index = indexOnce(dataset, resources);
       var indexPath = serializeOnce(index);
       var loadedIndex = deserializeOnce(indexPath, resources);
-      queryOnce(index, loadedIndex, rotate, queries, expectedResults, resources);
-      cleanup(indexPath, index, loadedIndex);
+      queryAndCompare(index, loadedIndex, rotate, queries, expectedResults, resources);
+      cleanup(index, loadedIndex);
+      Files.deleteIfExists(indexPath);
     }
   }
 
   @Test
   public void testIndexingAndSearchingFlowWithCustomMappingList() throws Throwable {
-    float[][] dataset = createSampleData();
+    var dataset = Dataset.ofArray(createSampleData());
     float[][] queries = createSampleQueries();
     var mappings = List.of(4, 3, 2, 1);
     var expectedResults =
@@ -319,8 +330,9 @@ public void testIndexingAndSearchingFlowWithCustomMappingList() throws Throwable
       var index = indexOnce(dataset, resources);
       var indexPath = serializeOnce(index);
       var loadedIndex = deserializeOnce(indexPath, resources);
-      queryOnce(index, loadedIndex, rotate, queries, expectedResults, resources);
-      cleanup(indexPath, index, loadedIndex);
+      queryAndCompare(index, loadedIndex, rotate, queries, expectedResults, resources);
+      cleanup(index, loadedIndex);
+      Files.deleteIfExists(indexPath);
     }
   }
 
@@ -392,7 +404,7 @@ public void testPrefilteringReducesResults() throws Throwable {
     }
   }
 
-  private CagraIndex indexOnce(float[][] dataset, CuVSResources resources) throws Throwable {
+  private CagraIndex indexOnce(Dataset dataset, CuVSResources resources) throws Throwable {
     // Configure index parameters
     CagraIndexParams indexParams =
         new CagraIndexParams.Builder()
@@ -426,9 +438,9 @@ private CagraIndex deserializeOnce(Path indexFilePath, CuVSResources resources)
     }
   }
 
-  private void queryOnce(
-      CagraIndex index,
-      CagraIndex loadedIndex,
+  private void queryAndCompare(
+      CagraIndex index1,
+      CagraIndex index2,
       LongToIntFunction mapping,
       float[][] queries,
       List<Map<Integer, Float>> expectedResults,
@@ -447,28 +459,65 @@ private void queryOnce(
             .build();
 
     // Perform the search
-    SearchResults results = index.search(cuvsQuery);
+    SearchResults results = index1.search(cuvsQuery);
 
     // Check results
     log.info(results.getResults().toString());
     checkResults(expectedResults, results.getResults());
 
-    // Search from deserialized index
-    results = loadedIndex.search(cuvsQuery);
+    // Search from the second index
+    results = index2.search(cuvsQuery);
 
     // Check results
     log.info(results.getResults().toString());
     checkResults(expectedResults, results.getResults());
   }
 
-  private void cleanup(Path indexFilePath, CagraIndex index, CagraIndex loadedIndex)
-      throws Throwable {
+  private void cleanup(CagraIndex index, CagraIndex loadedIndex) throws Throwable {
     // Cleanup
-    Files.deleteIfExists(indexFilePath);
     index.destroyIndex();
     loadedIndex.destroyIndex();
   }
 
+  @Test
+  public void testNativeDatasetEquivalent() throws Throwable {
+    float[][] sampleData = createSampleData();
+    float[][] queries = createSampleQueries();
+    List<Map<Integer, Float>> expectedResults = getExpectedResults();
+
+    ValueLayout.OfFloat C_FLOAT =
+        (ValueLayout.OfFloat) Linker.nativeLinker().canonicalLayouts().get("float");
+
+    int rows = sampleData.length;
+    int cols = sampleData[0].length;
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout((long) rows * cols, C_FLOAT);
+
+    try (Arena arena = Arena.ofShared()) {
+      MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+      for (int r = 0; r < rows; r++) {
+        MemorySegment.copy(
+            sampleData[r], 0, dataMemorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()), cols);
+      }
+
+      try (var resources = CuVSResources.create();
+          var javaDataset = Dataset.ofArray(sampleData);
+          var nativeDataset = DatasetHelper.fromMemorySegment(dataMemorySegment, rows, cols)) {
+
+        // Indexing with an on-heap and native datasets produce the same results
+        var javaIndex = indexOnce(javaDataset, resources);
+        var nativeIndex = indexOnce(nativeDataset, resources);
+        queryAndCompare(
+            javaIndex,
+            nativeIndex,
+            SearchResults.IDENTITY_MAPPING,
+            queries,
+            expectedResults,
+            resources);
+        cleanup(javaIndex, nativeIndex);
+      }
+    }
+  }
+
   @Test
   public void testMergingIndexes() throws Throwable {
     float[][] vector1 = {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
new file mode 100644
index 0000000000..ae62e10c34
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.spi.CuVSProvider;
+import java.lang.foreign.MemorySegment;
+import java.lang.invoke.MethodHandle;
+
+public class DatasetHelper {
+
+  private static final MethodHandle createDataset$mh =
+      CuVSProvider.provider().newNativeDatasetBuilder();
+
+  public static Dataset fromMemorySegment(MemorySegment memorySegment, int size, int dimensions) {
+    try {
+      return (Dataset) createDataset$mh.invokeExact(memorySegment, size, dimensions);
+    } catch (Throwable e) {
+      if (e instanceof Error err) {
+        throw err;
+      } else if (e instanceof RuntimeException re) {
+        throw re;
+      } else {
+        throw new RuntimeException(e);
+      }
+    }
+  }
+}

From c59bc27c58c6dbb5657f51eeae803efde5bd6f0f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Tue, 15 Jul 2025 16:05:00 +0200
Subject: [PATCH 481/623] [Java] Fix POM (#1106)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

As reported in https://github.com/rapidsai/cuvs/issues/1090, we are producing a "broken" "jar with dependecies" today: the Jar contains extra files that make it impossible to consume from a modularized application, as it breaks the module definition.

This PR fixes this by:
- removing `spotless` as a dependency: adding it as a dependency is not needed, as spotless is used at build time
- ~~removing the `jar-with-dependency` build task: we don't need it, as we don't have dependencies today. If we have in the future, we can re-introduce it, with a better handling of the merge process~~
- change the build script to install in the local maven repository the "regular" jar produced by the build process

EDIT: after review, we decided to revert the removal of the task creating  `jar-with-dependency`, as we might need it in the future (will need fixing anyway, but we can defer that until we need it).

Closes #1090

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Ben Frederickson (https://github.com/benfred)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1106
---
 java/build.sh          | 2 +-
 java/cuvs-java/pom.xml | 7 -------
 2 files changed, 1 insertion(+), 8 deletions(-)

diff --git a/java/build.sh b/java/build.sh
index a0577d48e4..89c2b4bef2 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -40,5 +40,5 @@ fi
 export LD_LIBRARY_PATH=${CURDIR}/../cpp/build:${LD_LIBRARY_PATH}
 cd cuvs-java
 mvn verify "${MAVEN_VERIFY_ARGS[@]}" \
-  && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-jar-with-dependencies.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar \
+  && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar \
   && cp pom.xml ./target/
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index adfed375d2..deaeea5755 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -101,13 +101,6 @@
             <version>4.13.1</version>
             <scope>test</scope>
         </dependency>
-
-        <dependency>
-            <groupId>com.diffplug.spotless</groupId>
-            <artifactId>spotless-maven-plugin</artifactId>
-            <version>2.44.5</version>
-        </dependency>
-
     </dependencies>
 
     <build>

From 0719080156294bfd551f8541a650da8b20947d6b Mon Sep 17 00:00:00 2001
From: James Xia <jamxia@nvidia.com>
Date: Tue, 15 Jul 2025 11:20:39 -0700
Subject: [PATCH 482/623] Serialize Vamana index with SSD sector alignment per
 MSFT DiskANN format, generate quantized dataset for integration with DiskANN
 (#846)

(This supersedes [PR!703](https://github.com/rapidsai/cuvs/pull/703))

Added an optional input flag to `cuvs::neighbors::vamana::serialize` to dump an input cuvs Vamana index to file with SSD sector alignment. File format follows [MSFT DiskANN](https://github.com/microsoft/DiskANN/blob/main/src/disk_utils.cpp).

Using the sector-aligned option also writes out the quantized dataset computed using user-supplied PQ codebooks file and rotation matrix file.

Authors:
  - James Xia (https://github.com/jamxia155)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/846
---
 ci/get_test_data.sh                           |  46 +++
 ci/test_cpp.sh                                |   7 +-
 cpp/CMakeLists.txt                            |   1 +
 cpp/include/cuvs/neighbors/vamana.hpp         | 101 +++++-
 .../neighbors/detail/vamana/vamana_build.cuh  | 140 ++++++-
 .../detail/vamana/vamana_codebooks.cuh        | 153 ++++++++
 .../detail/vamana/vamana_serialize.cuh        | 343 +++++++++++++++---
 cpp/src/neighbors/detail/vpq_dataset.cuh      | 108 +++++-
 cpp/src/neighbors/vamana.cuh                  |   8 +
 cpp/src/neighbors/vamana_codebooks_float.cu   |  32 ++
 cpp/src/neighbors/vamana_serialize.cuh        |   5 +-
 cpp/tests/CMakeLists.txt                      |  19 +-
 cpp/tests/neighbors/ann_vamana.cuh            |  35 +-
 examples/cpp/src/vamana_example.cu            |  72 ++--
 14 files changed, 972 insertions(+), 98 deletions(-)
 create mode 100755 ci/get_test_data.sh
 create mode 100644 cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh
 create mode 100644 cpp/src/neighbors/vamana_codebooks_float.cu

diff --git a/ci/get_test_data.sh b/ci/get_test_data.sh
new file mode 100755
index 0000000000..af47cfe340
--- /dev/null
+++ b/ci/get_test_data.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+# Copyright (c) 2025, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -euo pipefail
+
+NUMARGS=$#
+ARGS=$*
+function hasArg {
+    (( NUMARGS != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+}
+
+if hasArg -h || hasArg --help; then
+    echo "Usage:"
+    echo "    $0 [--NEIGHBORS_ANN_VAMANA_TEST]"
+    exit 0
+fi
+
+DEST=${RAPIDS_DATASET_ROOT_DIR:-"$PWD"}
+
+# get test data for NEIGHBORS_ANN_VAMANA_TEST
+if hasArg "--NEIGHBORS_ANN_VAMANA_TEST"; then
+    echo "Downloading test data for NEIGHBORS_ANN_VAMANA_TEST"
+    echo "Destination: ${DEST}"
+    URL_PREFIX=https://data.rapids.ai/cuvs/tests/data
+    SUBDIR=neighbors/ann_vamana/randomized_codebooks
+    FILE_LIST=(
+        384_int8_pq_pivots.bin
+        384_int8_pq_pivots.bin_rotation_matrix.bin
+        64_float_pq_pivots.bin
+        64_float_pq_pivots.bin_rotation_matrix.bin
+    )
+    for f in "${FILE_LIST[@]}"; do
+        wget --no-verbose --directory-prefix="${DEST}/${SUBDIR}" "${URL_PREFIX}/${SUBDIR}/$f"
+    done
+fi
diff --git a/ci/test_cpp.sh b/ci/test_cpp.sh
index 758f39ba3b..477054b813 100755
--- a/ci/test_cpp.sh
+++ b/ci/test_cpp.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
@@ -30,6 +30,11 @@ rapids-print-env
 rapids-logger "Check GPU usage"
 nvidia-smi
 
+# RAPIDS_DATASET_ROOT_DIR is used by test scripts
+RAPIDS_DATASET_ROOT_DIR=${RAPIDS_TESTS_DIR}/dataset
+export RAPIDS_DATASET_ROOT_DIR
+./ci/get_test_data.sh --NEIGHBORS_ANN_VAMANA_TEST
+
 EXITCODE=0
 trap "EXITCODE=1" ERR
 set +e
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index b5d6f51aa9..b8aefd8a4d 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -484,6 +484,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/vamana_build_float.cu
     src/neighbors/vamana_build_uint8.cu
     src/neighbors/vamana_build_int8.cu
+    src/neighbors/vamana_codebooks_float.cu
     src/neighbors/vamana_serialize_float.cu
     src/neighbors/vamana_serialize_uint8.cu
     src/neighbors/vamana_serialize_int8.cu
diff --git a/cpp/include/cuvs/neighbors/vamana.hpp b/cpp/include/cuvs/neighbors/vamana.hpp
index 007337df21..05bcadf796 100644
--- a/cpp/include/cuvs/neighbors/vamana.hpp
+++ b/cpp/include/cuvs/neighbors/vamana.hpp
@@ -53,6 +53,17 @@ namespace cuvs::neighbors::vamana {
  */
 
 struct index_params : cuvs::neighbors::index_params {
+  /**
+   * @brief Parameters used to build quantized DiskANN index; to be generated using
+   * deserialize_codebooks()
+   */
+  template <typename T = float>
+  struct codebook_params {
+    int pq_codebook_size;
+    int pq_dim;
+    std::vector<T> pq_encoding_table;
+    std::vector<T> rotation_matrix;
+  };
   /** Maximum degree of output graph corresponds to the R parameter in the original Vamana
    * literature. */
   uint32_t graph_degree = 32;
@@ -72,6 +83,8 @@ struct index_params : cuvs::neighbors::index_params {
   uint32_t queue_size = 127;
   /** Max batchsize of reverse edge processing (reduces memory footprint) */
   uint32_t reverse_batchsize = 1000000;
+  /** Codebooks and related parameters */
+  std::optional<codebook_params<float>> codebooks = std::nullopt;
 };
 
 /**
@@ -127,6 +140,13 @@ struct index : cuvs::neighbors::index {
     return *dataset_;
   }
 
+  /** Quantized dataset [size, codes_rowlen] */
+  [[nodiscard]] inline auto quantized_data() const noexcept
+    -> raft::device_matrix_view<const uint8_t, int64_t, raft::row_major>
+  {
+    return quantized_dataset_.view();
+  }
+
   /** vamana graph [size, graph-degree] */
   [[nodiscard]] inline auto graph() const noexcept
     -> raft::device_matrix_view<const IdxT, int64_t, raft::row_major>
@@ -150,7 +170,8 @@ struct index : cuvs::neighbors::index {
     : cuvs::neighbors::index(),
       metric_(metric),
       graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
-      dataset_(new cuvs::neighbors::empty_dataset<int64_t>(0))
+      dataset_(new cuvs::neighbors::empty_dataset<int64_t>(0)),
+      quantized_dataset_(raft::make_device_matrix<uint8_t, int64_t>(res, 0, 0))
   {
   }
 
@@ -168,6 +189,7 @@ struct index : cuvs::neighbors::index {
       metric_(metric),
       graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
       dataset_(make_aligned_dataset(res, dataset, 16)),
+      quantized_dataset_(raft::make_device_matrix<uint8_t, int64_t>(res, 0, 0)),
       medoid_id_(medoid_id)
   {
     RAFT_EXPECTS(dataset.extent(0) == vamana_graph.extent(0),
@@ -212,11 +234,42 @@ struct index : cuvs::neighbors::index {
     graph_view_ = graph_.view();
   }
 
+  /**
+   * @brief Replace the current quantized dataset with a new quantized dataset.
+   *
+   * We create a copy of the quantized dataset on the device. The index manages the lifetime of this
+   * copy.
+   *
+   * @param[in] res
+   * @param[in] new_quantized_dataset the new quantized dataset for the index
+   *
+   */
+  void update_quantized_dataset(
+    raft::resources const& res,
+    raft::device_matrix_view<const uint8_t, int64_t, raft::row_major> new_quantized_dataset)
+  {
+    RAFT_LOG_DEBUG("Creating device copy of Vamana quantized dataset");
+    if ((quantized_dataset_.extent(0) != new_quantized_dataset.extent(0)) ||
+        (quantized_dataset_.extent(1) != new_quantized_dataset.extent(1))) {
+      // clear existing memory before allocating to prevent OOM errors on large datasets
+      if (quantized_dataset_.size()) {
+        quantized_dataset_ = raft::make_device_matrix<uint8_t, int64_t>(res, 0, 0);
+      }
+      quantized_dataset_ = raft::make_device_matrix<uint8_t, int64_t>(
+        res, new_quantized_dataset.extent(0), new_quantized_dataset.extent(1));
+    }
+    raft::copy(quantized_dataset_.data_handle(),
+               new_quantized_dataset.data_handle(),
+               new_quantized_dataset.size(),
+               raft::resource::get_cuda_stream(res));
+  }
+
  private:
   cuvs::distance::DistanceType metric_;
   raft::device_matrix<IdxT, int64_t, raft::row_major> graph_;
   raft::device_matrix_view<const IdxT, int64_t, raft::row_major> graph_view_;
   std::unique_ptr<neighbors::dataset<int64_t>> dataset_;
+  raft::device_matrix<uint8_t, int64_t, raft::row_major> quantized_dataset_;
   IdxT medoid_id_;
 };
 /**
@@ -457,13 +510,15 @@ auto build(raft::resources const& res,
  * @param[in] file_prefix prefix of path and name of index files
  * @param[in] index Vamana index
  * @param[in] include_dataset whether or not to serialize the dataset
+ * @param[in] sector_aligned whether output file should be aligned to disk sectors of 4096 bytes
  *
  */
 
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
                const cuvs::neighbors::vamana::index<float, uint32_t>& index,
-               bool include_dataset = true);
+               bool include_dataset = true,
+               bool sector_aligned  = false);
 
 /**
  * Save the index to file.
@@ -486,12 +541,14 @@ void serialize(raft::resources const& handle,
  * @param[in] file_prefix prefix of path and name of index files
  * @param[in] index Vamana index
  * @param[in] include_dataset whether or not to serialize the dataset
+ * @param[in] sector_aligned whether output file should be aligned to disk sectors of 4096 bytes
  *
  */
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
                const cuvs::neighbors::vamana::index<int8_t, uint32_t>& index,
-               bool include_dataset = true);
+               bool include_dataset = true,
+               bool sector_aligned  = false);
 
 /**
  * Save the index to file.
@@ -514,12 +571,48 @@ void serialize(raft::resources const& handle,
  * @param[in] file_prefix prefix of path and name of index files
  * @param[in] index Vamana index
  * @param[in] include_dataset whether or not to serialize the dataset
+ * @param[in] sector_aligned whether output file should be aligned to disk sectors of 4096 bytes
  *
  */
 void serialize(raft::resources const& handle,
                const std::string& file_prefix,
                const cuvs::neighbors::vamana::index<uint8_t, uint32_t>& index,
-               bool include_dataset = true);
+               bool include_dataset = true,
+               bool sector_aligned  = false);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup vamana_cpp_codebook Vamana codebook functions
+ * @{
+ */
+
+/**
+ * @brief Construct codebook parameters from input codebook files
+ *
+ * Expects pq pivots file at
+ * "${codebook_prefix}_pq_pivots.bin" and rotation matrix file at
+ * "${codebook_prefix}_pq_pivots.bin_rotation_matrix.bin".
+ *
+ * @code{.cpp}
+ *   #include <cuvs/neighbors/vamana.hpp>
+ *
+ *   // create a string with a filepath
+ *   std::string codebook_prefix("/path/to/index/prefix");
+ *   // define dimension of vectors in dataset
+ *   int dim = 64;
+ *   // construct codebook parameters from input codebook files
+ *   auto codebooks = cuvs::neighbors::vamana::deserialize_codebooks(codebook_prefix, dim);
+ * @endcode
+ *
+ * @param[in] codebook_prefix path prefix to pq pivots and rotation matrix files
+ * @param[in] dim dimension of vectors in dataset
+ *
+ */
+auto deserialize_codebooks(const std::string& codebook_prefix, const int dim)
+  -> index_params::codebook_params<float>;
 
 /**
  * @}
diff --git a/cpp/src/neighbors/detail/vamana/vamana_build.cuh b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
index 6701d839a0..9586bf63a2 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_build.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,6 +17,7 @@
 #pragma once
 
 #include "../../../sparse/neighbors/cross_component_nn.cuh"
+#include "../../detail/vpq_dataset.cuh"
 #include "greedy_search.cuh"
 #include "robust_prune.cuh"
 #include "vamana_structs.cuh"
@@ -32,8 +33,10 @@
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/logger.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/gemm.hpp>
 #include <raft/matrix/copy.cuh>
 #include <raft/matrix/init.cuh>
+#include <raft/matrix/slice.cuh>
 #include <raft/random/make_blobs.cuh>
 
 #include <thrust/device_vector.h>
@@ -376,6 +379,21 @@ void batched_insert_vamana(
   RAFT_CHECK_CUDA(stream);
 }
 
+template <typename T>
+auto quantize_all_vectors(raft::resources const& res,
+                          raft::device_matrix_view<const T, int64_t> residuals,
+                          raft::device_matrix_view<float, uint32_t, raft::row_major> pq_codebook,
+                          cuvs::neighbors::vpq_params ps)
+  -> raft::device_matrix<uint8_t, int64_t, raft::row_major>
+{
+  auto dim         = residuals.extent(1);
+  auto vq_codebook = raft::make_device_matrix<float, uint32_t, raft::row_major>(res, 1, dim);
+
+  auto codes = cuvs::neighbors::detail::process_and_fill_codes_subspaces<float, int64_t>(
+    res, ps, residuals, raft::make_const_mdspan(vq_codebook.view()), pq_codebook);
+  return codes;
+}
+
 template <typename T,
           typename IdxT     = uint64_t,
           typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
@@ -408,9 +426,127 @@ index<T, IdxT> build(
   batched_insert_vamana<T, float, IdxT, Accessor>(
     res, params, dataset, vamana_graph.view(), &medoid_id, metric);
 
+  std::optional<raft::device_matrix<uint8_t, int64_t, raft::row_major>> quantized_vectors;
+  if (params.codebooks) {
+    // Full codebook should be a raft::matrix of dimension [2^PQ_BITS * PQ_DIM, VEC_DIM / PQ_DIM]
+    // Every row is (VEC_DIM/PQ_DIM) floats representing a group of cluster centroids.
+    // Every consecutive [PQ_DIM] rows is a set.
+
+    // short-hand
+    auto& codebook_params = params.codebooks.value();
+    int pq_codebook_size  = codebook_params.pq_codebook_size;
+    int pq_dim            = codebook_params.pq_dim;
+
+    cuvs::neighbors::vpq_params pq_params;
+    pq_params.pq_bits = raft::log2(pq_codebook_size);
+    pq_params.pq_dim  = pq_dim;
+
+    // transform pq_encoding_table (dimensions: pq_codebook_size x dim_per_subspace * pq_dim ) to
+    // pq_codebook (dimensions: pq_codebook_size * pq_dim, dim_per_subspace)
+    auto pq_encoding_table_device_vec = raft::make_device_vector<float, uint32_t>(
+      res,
+      codebook_params.pq_encoding_table.size());  // logically a 2D matrix with dimensions
+                                                  // pq_codebook_size x dim_per_subspace * pq_dim
+    raft::copy(pq_encoding_table_device_vec.data_handle(),
+               codebook_params.pq_encoding_table.data(),
+               codebook_params.pq_encoding_table.size(),
+               raft::resource::get_cuda_stream(res));
+    int dim_per_subspace = dim / pq_dim;
+    auto pq_codebook =
+      raft::make_device_matrix<float, uint32_t>(res, pq_codebook_size * pq_dim, dim_per_subspace);
+    auto pq_encoding_table_device_vec_view = pq_encoding_table_device_vec.view();
+    raft::linalg::map_offset(
+      res,
+      pq_codebook.view(),
+      [pq_encoding_table_device_vec_view,
+       pq_dim,
+       pq_codebook_size,
+       dim_per_subspace,
+       dim] __device__(size_t i) {
+        int row_idx        = i / dim_per_subspace;
+        int subspace_id    = row_idx / pq_codebook_size;  // idx_pq_dim
+        int codebook_id    = row_idx % pq_codebook_size;  // idx_pq_codebook_size
+        int id_in_subspace = i % dim_per_subspace;        // idx_dim_per_subspace
+
+        return pq_encoding_table_device_vec_view[codebook_id * pq_dim * dim_per_subspace +
+                                                 subspace_id * dim_per_subspace + id_in_subspace];
+      });
+
+    // prepare rotation matrix
+    auto rotation_matrix_device = raft::make_device_matrix<float, int64_t>(res, dim, dim);
+    raft::copy(rotation_matrix_device.data_handle(),
+               codebook_params.rotation_matrix.data(),
+               codebook_params.rotation_matrix.size(),
+               raft::resource::get_cuda_stream(res));
+
+    // process in batches
+    const uint32_t n_rows = dataset.extent(0);
+    // codes_rowlen defined as in cuvs::neighbors::detail::process_and_fill_codes_subspaces()
+    const int64_t codes_rowlen =
+      sizeof(uint32_t) *
+      (1 + raft::div_rounding_up_safe<int64_t>(pq_dim * pq_params.pq_bits, 8 * sizeof(uint32_t)));
+    quantized_vectors = raft::make_device_matrix<uint8_t, int64_t, raft::row_major>(
+      res,
+      n_rows,
+      codes_rowlen - 4);  // first 4 columns of output from quantize_all_vectors() to be discarded
+    // TODO: with scaling workspace we could choose the batch size dynamically
+    constexpr uint32_t kReasonableMaxBatchSize = 65536;
+    const uint32_t max_batch_size              = std::min(n_rows, kReasonableMaxBatchSize);
+    for (const auto& batch : cuvs::spatial::knn::detail::utils::batch_load_iterator<T>(
+           dataset.data_handle(),
+           n_rows,
+           dim,
+           max_batch_size,
+           raft::resource::get_cuda_stream(res),
+           raft::resource::get_workspace_resource(res))) {
+      // perform rotation
+      auto dataset_rotated = raft::make_device_matrix<float, int64_t>(res, batch.size(), dim);
+      if constexpr (std::is_same_v<T, float>) {
+        auto dataset_view = raft::make_device_matrix_view(const_cast<T*>(batch.data()),
+                                                          static_cast<int64_t>(batch.size()),
+                                                          static_cast<int64_t>(dim));
+        raft::linalg::gemm(
+          res, dataset_view, rotation_matrix_device.view(), dataset_rotated.view());
+      } else {
+        // convert dataset to float
+        auto dataset_float = raft::make_device_matrix<float, int64_t>(res, batch.size(), dim);
+        auto dataset_view  = raft::make_device_matrix_view(
+          batch.data(), static_cast<int64_t>(batch.size()), static_cast<int64_t>(dim));
+        raft::linalg::map_offset(
+          res, dataset_float.view(), [dataset_view, dim] __device__(size_t i) {
+            int row_idx = i / dim;
+            int col_idx = i % dim;
+            return static_cast<float>(dataset_view(row_idx, col_idx));
+          });
+        raft::linalg::gemm(
+          res, dataset_float.view(), rotation_matrix_device.view(), dataset_rotated.view());
+      }
+
+      // quantize rotated vectors using codebook
+      auto temp_vectors =
+        quantize_all_vectors<float>(res, dataset_rotated.view(), pq_codebook.view(), pq_params);
+
+      // Remove the vector quantization header values
+      raft::matrix::slice_coordinates<int64_t> slice_coords(
+        0, 4, temp_vectors.extent(0), temp_vectors.extent(1));
+
+      raft::matrix::slice(res,
+                          raft::make_const_mdspan(temp_vectors.view()),
+                          raft::make_device_matrix_view<uint8_t, int64_t>(
+                            quantized_vectors.value().data_handle() +
+                              batch.offset() * quantized_vectors.value().extent(1),
+                            batch.size(),
+                            quantized_vectors.value().extent(1)),
+                          slice_coords);
+    }
+  }
+
   try {
-    return index<T, IdxT>(
+    auto idx = index<T, IdxT>(
       res, params.metric, dataset, raft::make_const_mdspan(vamana_graph.view()), medoid_id);
+    if (quantized_vectors)
+      idx.update_quantized_dataset(res, raft::make_const_mdspan(quantized_vectors.value().view()));
+    return idx;
   } catch (std::bad_alloc& e) {
     RAFT_LOG_DEBUG("Insufficient GPU memory to construct VAMANA index with dataset on GPU");
     // We just add the graph. User is expected to update dataset separately (e.g allocating in
diff --git a/cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh b/cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh
new file mode 100644
index 0000000000..5657a2840c
--- /dev/null
+++ b/cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh
@@ -0,0 +1,153 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/vamana.hpp>
+
+#include <raft/core/error.hpp>
+
+#include <vector>
+
+namespace cuvs::neighbors::vamana::detail {
+
+/* @defgroup vamana_codebooks_detail vamana deserialize_codebooks
+ * @{
+ */
+
+// Parse pq_pivots file.
+inline std::vector<float> parse_pq_pivots_file(const std::string& path,
+                                               const int vector_dim,
+                                               int32_t& pq_codebook_size,
+                                               int32_t& pq_dim)
+{
+  std::ifstream pivots_if(path, std::ios::in | std::ios::binary);
+  RAFT_EXPECTS(pivots_if, "Cannot open file %s", path.c_str());
+
+  // check file size meets minimum for offset data
+  pivots_if.ignore(std::numeric_limits<std::streamsize>::max());
+  uint32_t length = pivots_if.gcount();
+  RAFT_EXPECTS(length >= 40, "pq_pivots file does not contain expected metadata.");
+  pivots_if.clear();  // Since ignore will have set eof.
+  pivots_if.seekg(0, std::ios_base::beg);
+
+  // check metadata
+  int32_t num_offsets, num_dims;
+  pivots_if.read((char*)&num_offsets, sizeof(int32_t));
+  pivots_if.read((char*)&num_dims, sizeof(int32_t));
+  RAFT_EXPECTS(num_offsets == 4,
+               "Error reading pq_pivots file %s. # offsets = %ld; expected 4.",
+               path.c_str(),
+               (long)num_offsets);
+  RAFT_EXPECTS(num_dims == 1,
+               "Error reading pq_pivots file %s. # dimensions = %ld; expected 1.",
+               path.c_str(),
+               (long)num_dims);
+
+  std::vector<int64_t> offset(num_offsets);
+  pivots_if.read((char*)offset.data(), sizeof(int64_t) * num_offsets);
+
+  // check file size meets minimum for all required data and metadata
+  RAFT_EXPECTS(
+    length >= offset[2] + 4,
+    "pq_pivots file doesn't have the minimum expected size. Min. expected: %lld Actual: %lu",
+    (long long)offset[2] + 4,
+    (unsigned long)length);
+
+  pivots_if.seekg(offset[2], std::ios_base::beg);
+  pivots_if.read((char*)&pq_dim, sizeof(int32_t));
+  --pq_dim;
+
+  pivots_if.seekg(offset[0], std::ios_base::beg);
+  pivots_if.read((char*)&pq_codebook_size, sizeof(int32_t));
+  int32_t pq_dim_times_codebookDim;
+  pivots_if.read((char*)&pq_dim_times_codebookDim, sizeof(int32_t));
+
+  int32_t codebookDim = vector_dim / pq_dim;
+  RAFT_EXPECTS(pq_dim * codebookDim == pq_dim_times_codebookDim,
+               "Invalid metadata in pq_pivots file.");
+
+  // parse pq_encoding_table
+  std::vector<float> pq_encoding_table(vector_dim * pq_codebook_size);
+  for (int i = 0; i < vector_dim * pq_codebook_size; i++) {
+    pivots_if.read((char*)&pq_encoding_table[i],
+                   4);  // Reconstruct type of the overall quantizer is int8 but because it's OPQ,
+                        // need to read it as float
+  }
+
+  return pq_encoding_table;
+}
+
+// Parse rotation matrix file.
+inline std::vector<float> parse_rotation_matrix_file(const std::string& path, const int vector_dim)
+{
+  std::ifstream mat_if(path, std::ios::in | std::ios::binary);
+  if (!mat_if) { RAFT_FAIL("Cannot open file %s", path.c_str()); }
+
+  // check file size meets minimum for metadata
+  mat_if.ignore(std::numeric_limits<std::streamsize>::max());
+  uint32_t length = mat_if.gcount();
+  if (length < 8) { RAFT_FAIL("Rotation matrix file does not contain expected metadata."); }
+  mat_if.clear();  // Since ignore will have set eof.
+  mat_if.seekg(0, std::ios_base::beg);
+
+  // check metadata
+  int32_t nr, nc;
+  mat_if.read((char*)&nr, sizeof(int32_t));
+  mat_if.read((char*)&nc, sizeof(int32_t));
+  RAFT_EXPECTS(vector_dim == nr,
+               "Unexpected #rows in rotation matrix file. Expected: %ld Actual: %ld",
+               (long)vector_dim,
+               (long)nr);
+  RAFT_EXPECTS(vector_dim == nc,
+               "Unexpected #cols in rotation matrix file. Expected: %ld Actual: %ld",
+               (long)vector_dim,
+               (long)nc);
+
+  // check exact length
+  uint32_t length_expected = 8 + (4 * nr * nc);
+  RAFT_EXPECTS(length_expected == length,
+               "Rotation matrix file doesn't have expected size. Expected: %lu Actual: %ld",
+               (unsigned long)length_expected,
+               (long)length);
+
+  // read rotation matrix
+  std::vector<float> mat(nr * nc);
+  for (int iRow = 0; iRow < nr; iRow++) {
+    for (int iCol = 0; iCol < nc; iCol++) {
+      mat_if.read((char*)&mat[iRow * vector_dim + iCol], 4);
+    }
+  }
+  return mat;
+}
+
+template <typename T>
+index_params::codebook_params<T> deserialize_codebooks(const std::string& codebook_prefix,
+                                                       const int dim)
+{
+  index_params::codebook_params<T> codebooks;
+  codebooks.pq_encoding_table = parse_pq_pivots_file(
+    codebook_prefix + "_pq_pivots.bin", dim, codebooks.pq_codebook_size, codebooks.pq_dim);
+  codebooks.rotation_matrix =
+    parse_rotation_matrix_file(codebook_prefix + "_pq_pivots.bin_rotation_matrix.bin", dim);
+  return codebooks;
+}
+
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
index 34766b13ca..9a93654910 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_serialize.cuh
@@ -36,6 +36,272 @@
 
 namespace cuvs::neighbors::vamana::detail {
 
+// write matrix containing dataset to file
+template <typename T>
+void to_file(const std::string& dataset_base_file, raft::host_matrix<T, int64_t>& dataset)
+{
+  std::ofstream dataset_of(dataset_base_file, std::ios::out | std::ios::binary);
+  if (!dataset_of) { RAFT_FAIL("Cannot open file %s", dataset_base_file.c_str()); }
+  size_t dataset_file_offset = 0;
+  int size                   = static_cast<int>(dataset.extent(0));
+  int dim                    = static_cast<int>(dataset.extent(1));
+  dataset_of.seekp(dataset_file_offset, dataset_of.beg);
+  dataset_of.write((char*)&size, sizeof(int));
+  dataset_of.write((char*)&dim, sizeof(int));
+  for (int i = 0; i < size; i++) {
+    dataset_of.write((char*)(dataset.data_handle() + i * dataset.extent(1)), dim * sizeof(T));
+  }
+  dataset_of.close();
+  if (!dataset_of) { RAFT_FAIL("Error writing output %s", dataset_base_file.c_str()); }
+}
+
+/**
+ * Save the dataset to file.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res the raft resource handle
+ * @param[in] dataset the raw dataset from which the VAMANA index is built, or the quantized dataset
+ * @param[out] dataset_base_file the path for writing the output file
+ *
+ */
+template <typename T>
+void serialize_dataset(raft::resources const& res,
+                       const cuvs::neighbors::dataset<int64_t>* dataset,
+                       const std::string& dataset_base_file)
+{
+  // try allocating a buffer for the dataset on host
+  try {
+    const auto* strided_dataset =
+      dynamic_cast<const cuvs::neighbors::strided_dataset<T, int64_t>*>(dataset);
+    if (strided_dataset) {
+      auto h_dataset =
+        raft::make_host_matrix<T, int64_t>(strided_dataset->n_rows(), strided_dataset->dim());
+      raft::copy(h_dataset.data_handle(),
+                 strided_dataset->view().data_handle(),
+                 strided_dataset->n_rows() * strided_dataset->dim(),
+                 raft::resource::get_cuda_stream(res));
+      to_file(dataset_base_file, h_dataset);
+    } else {
+      RAFT_LOG_DEBUG("dynamic_cast to strided_dataset failed");
+    }
+  } catch (std::bad_alloc& e) {
+    RAFT_LOG_INFO("Failed to serialize dataset");
+  } catch (raft::logic_error& e) {
+    RAFT_LOG_INFO("Failed to serialize dataset");
+  }
+}
+template <typename T>
+void serialize_dataset(raft::resources const& res,
+                       raft::device_matrix_view<const T, int64_t> dataset,
+                       const std::string& dataset_base_file)
+{
+  // try allocating a buffer for the dataset on host
+  try {
+    auto h_dataset = raft::make_host_matrix<T, int64_t>(dataset.extent(0), dataset.extent(1));
+    raft::copy(h_dataset.data_handle(),
+               dataset.data_handle(),
+               dataset.extent(0) * dataset.extent(1),
+               raft::resource::get_cuda_stream(res));
+    to_file(dataset_base_file, h_dataset);
+  } catch (std::bad_alloc& e) {
+    RAFT_LOG_INFO("Failed to serialize dataset");
+  } catch (raft::logic_error& e) {
+    RAFT_LOG_INFO("Failed to serialize dataset");
+  }
+}
+
+/**
+ * Save the index to file with sector alignment.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ *
+ * @param[in] res the raft resource handle
+ * @param[in] h_graph the host matrix representation of the VAMANA index
+ * @param[in] dataset the raw dataset from which the VAMANA index is built
+ * @param[in] medoid the medoid
+ * @param[out] output_writer the stream object for writing the output file
+ *
+ */
+template <typename T, typename IdxT, typename HostMatT>
+void serialize_sector_aligned(raft::resources const& res,
+                              const HostMatT& h_graph,
+                              const cuvs::neighbors::dataset<int64_t>& dataset,
+                              const uint64_t medoid,
+                              std::ofstream& output_writer)
+{
+  if constexpr (!std::is_same_v<IdxT, uint32_t>) {
+    RAFT_FAIL("serialization is only implemented for uint32_t graph");
+  }
+
+  const uint64_t sector_len = 4096;
+
+  const uint64_t npts(dataset.n_rows()), ndims(dataset.dim());
+
+  // stats for metadata and logging
+  uint32_t max_degree = 0;
+  size_t index_size   = 24;  // Starting metadata in Vamana file
+  size_t total_edges  = 0;
+  size_t num_sparse   = 0;
+  size_t num_single   = 0;
+  for (uint32_t i = 0; i < h_graph.extent(0); i++) {
+    uint32_t node_edges = 0;
+    for (; node_edges < h_graph.extent(1); node_edges++) {
+      if (h_graph(i, node_edges) == raft::upper_bound<IdxT>()) { break; }
+    }
+
+    if (node_edges < 3) num_sparse++;
+    if (node_edges < 2) num_single++;
+    total_edges += node_edges;
+
+    max_degree = max(max_degree, node_edges);
+    index_size += sizeof(uint32_t) * (node_edges + 1);
+  }
+
+  const uint64_t max_node_len =
+    ((static_cast<uint64_t>(max_degree) + 1) * sizeof(IdxT)) + (ndims * sizeof(T));
+  const uint64_t nnodes_per_sector = sector_len / max_node_len;  // 0 if max_node_len > sector_len
+
+  // copy dataset to host
+  auto dataset_strided =
+    dynamic_cast<const cuvs::neighbors::strided_dataset<T, int64_t>*>(&dataset);
+  if (!dataset_strided) { RAFT_FAIL("Invalid dataset"); }
+  auto d_data = dataset_strided->view();
+  auto h_data = raft::make_host_matrix<T, int64_t>(npts, ndims);
+  auto stride = dataset_strided->stride();
+  RAFT_CUDA_TRY(cudaMemcpy2DAsync(h_data.data_handle(),
+                                  sizeof(T) * ndims,
+                                  d_data.data_handle(),
+                                  sizeof(T) * stride,
+                                  sizeof(T) * ndims,
+                                  npts,
+                                  cudaMemcpyDefault,
+                                  raft::resource::get_cuda_stream(res)));
+  raft::resource::sync_stream(res);
+
+  // buffers
+  std::unique_ptr<char[]> sector_buf = std::make_unique<char[]>(sector_len);
+  std::unique_ptr<char[]> multisector_buf =
+    std::make_unique<char[]>(raft::round_up_safe(max_node_len, sector_len));
+  std::unique_ptr<char[]> node_buf = std::make_unique<char[]>(max_node_len);
+  IdxT& nnbrs                      = *(IdxT*)(node_buf.get() + ndims * sizeof(T));
+  IdxT* const nhood_buf            = (IdxT*)(node_buf.get() + (ndims * sizeof(T)) + sizeof(IdxT));
+
+  const uint64_t n_sectors = nnodes_per_sector > 0
+                               ? raft::div_rounding_up_safe(npts, nnodes_per_sector)
+                               : npts * raft::div_rounding_up_safe(max_node_len, sector_len);
+  // metadata
+  const uint64_t disk_index_file_size = (n_sectors + 1) * sector_len;
+  const uint64_t vamana_frozen_num{}, vamana_frozen_loc{};
+  const bool append_reorder_data = false;
+  std::vector<uint64_t> output_meta;
+  output_meta.push_back(npts);
+  output_meta.push_back(ndims);
+  output_meta.push_back(medoid);
+  output_meta.push_back(max_node_len);
+  output_meta.push_back(nnodes_per_sector);
+  output_meta.push_back(vamana_frozen_num);
+  output_meta.push_back(vamana_frozen_loc);
+  output_meta.push_back(static_cast<uint64_t>(append_reorder_data));
+  output_meta.push_back(disk_index_file_size);
+
+  // zero out first sector of output file
+  output_writer.seekp(0, output_writer.beg);
+  output_writer.write(sector_buf.get(), sector_len);
+  // write metadata to first sector
+  output_writer.seekp(0, output_writer.beg);
+  const int metadata_size  = static_cast<int>(output_meta.size());
+  const int metadata_ndims = 1;
+  output_writer.write((char*)&metadata_size, sizeof(int));
+  output_writer.write((char*)&metadata_ndims, sizeof(int));
+  output_writer.write((char*)output_meta.data(), sizeof(uint64_t) * output_meta.size());
+  output_writer.seekp(sector_len, output_writer.beg);
+
+  if (nnodes_per_sector > 0) {
+    uint64_t cur_node_id = 0;
+    // Write multiple nodes per sector
+    for (uint64_t sector = 0; sector < n_sectors; sector++) {
+      if (sector && sector % 100000 == 0)
+        std::cout << "Sector #" << sector << " written" << std::endl;
+      memset(sector_buf.get(), 0, sector_len);
+      for (uint64_t sector_node_id = 0; sector_node_id < nnodes_per_sector && cur_node_id < npts;
+           sector_node_id++) {
+        memset(node_buf.get(), 0, max_node_len);
+        // copy node coords to buffer
+        memcpy(node_buf.get(), &h_data(cur_node_id, uint64_t(0)), ndims * sizeof(T));
+
+        IdxT node_edges = 0;
+        for (; node_edges < h_graph.extent(1); node_edges++) {
+          if (h_graph(cur_node_id, node_edges) == raft::upper_bound<IdxT>()) { break; }
+        }
+        // write nnbrs to buffer
+        nnbrs = node_edges;
+
+        // sanity checks on nnbrs
+        assert(nnbrs > 0);
+        assert(nnbrs <= max_degree);
+
+        // copy neighbors to buffer
+        memcpy(nhood_buf, &h_graph(cur_node_id, 0), nnbrs * sizeof(IdxT));
+
+        // get offset into sector_buf
+        char* sector_node_buf = sector_buf.get() + (sector_node_id * max_node_len);
+
+        // copy node buf into sector_node_buf
+        memcpy(sector_node_buf, node_buf.get(), max_node_len);
+        cur_node_id++;
+      }
+      // write sector to disk
+      output_writer.write(sector_buf.get(), sector_len);
+    }
+  } else {
+    // Write multi-sector nodes
+    uint64_t nsectors_per_node = raft::div_rounding_up_safe(max_node_len, sector_len);
+    for (uint64_t cur_node_id = 0; cur_node_id < npts; cur_node_id++) {
+      if (cur_node_id && (cur_node_id * nsectors_per_node) % 100000 == 0)
+        std::cout << "Sector #" << cur_node_id * nsectors_per_node << " written" << std::endl;
+      memset(multisector_buf.get(), 0, nsectors_per_node * sector_len);
+      // copy node coords to buffer
+      memcpy(multisector_buf.get(), &h_data(cur_node_id, uint64_t(0)), ndims * sizeof(T));
+
+      IdxT node_edges = 0;
+      for (; node_edges < h_graph.extent(1); node_edges++) {
+        if (h_graph(cur_node_id, node_edges) == raft::upper_bound<IdxT>()) { break; }
+      }
+      // write nnbrs to buffer
+      nnbrs = node_edges;
+
+      // sanity checks on nnbrs
+      assert(nnbrs > 0);
+      assert(nnbrs <= max_degree);
+
+      // copy neighbors to buffer
+      memcpy(nhood_buf, &h_graph(cur_node_id, 0), nnbrs * sizeof(IdxT));
+
+      // write nnbrs to buffer
+      *(IdxT*)(multisector_buf.get() + ndims * sizeof(T)) = nnbrs;
+
+      // copy neighbors to buffer
+      memcpy(multisector_buf.get() + ndims * sizeof(T) + sizeof(IdxT),
+             &h_graph(cur_node_id, 0),
+             nnbrs * sizeof(IdxT));
+
+      // write sectors to disk
+      output_writer.write(multisector_buf.get(), nsectors_per_node * sector_len);
+    }
+  }
+
+  RAFT_LOG_DEBUG(
+    "Wrote file out, index size:%lu, max_degree:%u, num_sparse:%ld, num_single:%ld, total "
+    "edges:%ld, avg degree:%f",
+    index_size,
+    max_degree,
+    num_sparse,
+    num_single,
+    total_edges,
+    (float)total_edges / (float)h_graph.extent(0));
+}
+
 /**
  * Save the index to file.
  *
@@ -45,6 +311,7 @@ namespace cuvs::neighbors::vamana::detail {
  * @param[in] file_name the path and name of the DiskAN index file generated
  * @param[in] index_ VAMANA index
  * @param[in] include_dataset whether to include the dataset in the serialized output
+ * @param[in] sector_aligned whether to align output file to SSD sectors
  *
  */
 
@@ -52,11 +319,37 @@ template <typename T, typename IdxT>
 void serialize(raft::resources const& res,
                const std::string& file_name,
                const index<T, IdxT>& index_,
-               bool include_dataset)
+               bool include_dataset,
+               bool sector_aligned)
 {
+  auto d_graph = index_.graph();
+  auto h_graph = raft::make_host_matrix<IdxT, int64_t>(d_graph.extent(0), d_graph.extent(1));
+  raft::copy(h_graph.data_handle(),
+             d_graph.data_handle(),
+             d_graph.size(),
+             raft::resource::get_cuda_stream(res));
+  raft::resource::sync_stream(res);
+
+  // if requested, write sector-aligned file and return
+  if (sector_aligned) {
+    // Write graph to disk index file with file name suffix according to DiskANN build_disk_index
+    const std::string index_file_name = file_name + "_disk.index";
+    std::ofstream index_of(index_file_name, std::ios::out | std::ios::binary);
+    RAFT_EXPECTS(index_of, "Cannot open file %s", index_file_name.c_str());
+
+    serialize_sector_aligned<T, IdxT>(
+      res, h_graph, index_.data(), static_cast<uint64_t>(index_.medoid()), index_of);
+    index_of.close();
+    RAFT_EXPECTS(index_of, "Error writing output %s", index_file_name.c_str());
+
+    if (include_dataset) { serialize_dataset<T>(res, &index_.data(), file_name + ".data"); }
+    serialize_dataset<uint8_t>(res, index_.quantized_data(), file_name + "_pq_compressed.bin");
+    return;
+  }
+
   // Write graph to first index file (format from MSFT DiskANN OSS)
   std::ofstream index_of(file_name, std::ios::out | std::ios::binary);
-  if (!index_of) { RAFT_FAIL("Cannot open file %s", file_name.c_str()); }
+  RAFT_EXPECTS(index_of, "Cannot open file %s", file_name.c_str());
 
   size_t file_offset = 0;
   index_of.seekp(file_offset, index_of.beg);
@@ -71,14 +364,6 @@ void serialize(raft::resources const& res,
   index_of.write((char*)&start, sizeof(uint32_t));
   index_of.write((char*)&num_frozen_points, sizeof(size_t));
 
-  auto d_graph = index_.graph();
-  auto h_graph = raft::make_host_matrix<IdxT, int64_t>(d_graph.extent(0), d_graph.extent(1));
-  raft::copy(h_graph.data_handle(),
-             d_graph.data_handle(),
-             d_graph.size(),
-             raft::resource::get_cuda_stream(res));
-  raft::resource::sync_stream(res);
-
   size_t total_edges = 0;
   size_t num_sparse  = 0;
   size_t num_single  = 0;
@@ -119,43 +404,7 @@ void serialize(raft::resources const& res,
   index_of.close();
   if (!index_of) { RAFT_FAIL("Error writing output %s", file_name.c_str()); }
 
-  if (include_dataset) {
-    // try allocating a buffer for the dataset on host
-    try {
-      const cuvs::neighbors::strided_dataset<T, int64_t>* strided_dataset =
-        dynamic_cast<cuvs::neighbors::strided_dataset<T, int64_t>*>(
-          const_cast<cuvs::neighbors::dataset<int64_t>*>(&index_.data()));
-      if (strided_dataset == nullptr) {
-        RAFT_LOG_DEBUG("dynamic_cast to strided_dataset failed");
-      } else {
-        auto h_dataset =
-          raft::make_host_matrix<T, int64_t>(strided_dataset->n_rows(), strided_dataset->dim());
-        raft::copy(h_dataset.data_handle(),
-                   strided_dataset->view().data_handle(),
-                   strided_dataset->n_rows() * strided_dataset->dim(),
-                   raft::resource::get_cuda_stream(res));
-        std::string dataset_base_file = file_name + ".data";
-        std::ofstream dataset_of(dataset_base_file, std::ios::out | std::ios::binary);
-        if (!dataset_of) { RAFT_FAIL("Cannot open file %s", dataset_base_file.c_str()); }
-        size_t dataset_file_offset = 0;
-        int size                   = static_cast<int>(index_.size());
-        int dim                    = static_cast<int>(index_.dim());
-        dataset_of.seekp(dataset_file_offset, dataset_of.beg);
-        dataset_of.write((char*)&size, sizeof(int));
-        dataset_of.write((char*)&dim, sizeof(int));
-        for (int i = 0; i < size; i++) {
-          dataset_of.write((char*)(h_dataset.data_handle() + i * h_dataset.extent(1)),
-                           dim * sizeof(T));
-        }
-        dataset_of.close();
-        if (!dataset_of) { RAFT_FAIL("Error writing output %s", dataset_base_file.c_str()); }
-      }
-    } catch (std::bad_alloc& e) {
-      RAFT_LOG_INFO("Failed to serialize dataset");
-    } catch (raft::logic_error& e) {
-      RAFT_LOG_INFO("Failed to serialize dataset");
-    }
-  }
+  if (include_dataset) { serialize_dataset<T>(res, &index_.data(), file_name + ".data"); }
 }
 
 }  // namespace cuvs::neighbors::vamana::detail
diff --git a/cpp/src/neighbors/detail/vpq_dataset.cuh b/cpp/src/neighbors/detail/vpq_dataset.cuh
index 0d7882b4b0..02addada8f 100644
--- a/cpp/src/neighbors/detail/vpq_dataset.cuh
+++ b/cpp/src/neighbors/detail/vpq_dataset.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -408,6 +408,112 @@ auto vpq_convert_math_type(const raft::resources& res, vpq_dataset<OldMathT, Idx
     std::move(vq_code_book), std::move(pq_code_book), std::move(src.data)};
 }
 
+template <uint32_t BlockSize,
+          uint32_t PqBits,
+          typename DataT,
+          typename MathT,
+          typename IdxT,
+          typename LabelT>
+__launch_bounds__(BlockSize) RAFT_KERNEL process_and_fill_codes_subspaces_kernel(
+  raft::device_matrix_view<uint8_t, IdxT, raft::row_major> out_codes,
+  raft::device_matrix_view<const DataT, IdxT, raft::row_major> dataset,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> vq_centers,
+  raft::device_vector_view<const LabelT, IdxT, raft::row_major> vq_labels,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> pq_centers)
+{
+  constexpr uint32_t kSubWarpSize = std::min<uint32_t>(raft::WarpSize, 1u << PqBits);
+  using subwarp_align             = raft::Pow2<kSubWarpSize>;
+  const IdxT row_ix = subwarp_align::div(IdxT{threadIdx.x} + IdxT{BlockSize} * IdxT{blockIdx.x});
+  if (row_ix >= out_codes.extent(0)) { return; }
+
+  const uint32_t pq_dim = raft::div_rounding_up_unsafe(vq_centers.extent(1), pq_centers.extent(1));
+
+  const uint32_t lane_id = raft::Pow2<kSubWarpSize>::mod(threadIdx.x);
+  const LabelT vq_label  = vq_labels(row_ix);
+
+  // write label
+  auto* out_label_ptr = reinterpret_cast<LabelT*>(&out_codes(row_ix, 0));
+  if (lane_id == 0) { *out_label_ptr = vq_label; }
+
+  auto* out_codes_ptr = reinterpret_cast<uint8_t*>(out_label_ptr + 1);
+  cuvs::neighbors::ivf_pq::detail::bitfield_view_t<PqBits> code_view{out_codes_ptr};
+  for (uint32_t j = 0; j < pq_dim; j++) {
+    // find PQ label
+    int subspace_offset   = j * pq_centers.extent(1) * (1 << PqBits);
+    auto pq_subspace_view = raft::make_device_matrix_view(
+      pq_centers.data_handle() + subspace_offset, (uint32_t)(1 << PqBits), pq_centers.extent(1));
+    uint8_t code =
+      compute_code<kSubWarpSize>(dataset, vq_centers, pq_subspace_view, row_ix, j, vq_label);
+    // TODO: this writes in global memory one byte per warp, which is very slow.
+    //  It's better to keep the codes in the shared memory or registers and dump them at once.
+    if (lane_id == 0) { code_view[j] = code; }
+  }
+}
+
+template <typename MathT, typename IdxT, typename DatasetT>
+auto process_and_fill_codes_subspaces(
+  const raft::resources& res,
+  const vpq_params& params,
+  const DatasetT& dataset,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> vq_centers,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> pq_centers)
+  -> raft::device_matrix<uint8_t, IdxT, raft::row_major>
+{
+  using data_t     = typename DatasetT::value_type;
+  using cdataset_t = vpq_dataset<MathT, IdxT>;
+  using label_t    = uint32_t;
+
+  const ix_t n_rows       = dataset.extent(0);
+  const ix_t dim          = dataset.extent(1);
+  const ix_t pq_dim       = params.pq_dim;
+  const ix_t pq_bits      = params.pq_bits;
+  const ix_t pq_n_centers = ix_t{1} << pq_bits;
+  // NB: codes must be aligned at least to sizeof(label_t) to be able to read labels.
+  const ix_t codes_rowlen =
+    sizeof(label_t) * (1 + raft::div_rounding_up_safe<ix_t>(pq_dim * pq_bits, 8 * sizeof(label_t)));
+
+  auto codes = raft::make_device_matrix<uint8_t, IdxT, raft::row_major>(res, n_rows, codes_rowlen);
+
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  // TODO: with scaling workspace we could choose the batch size dynamically
+  constexpr ix_t kReasonableMaxBatchSize = 65536;
+  constexpr ix_t kBlockSize              = 256;
+  const ix_t threads_per_vec             = std::min<ix_t>(raft::WarpSize, pq_n_centers);
+  dim3 threads(kBlockSize, 1, 1);
+  ix_t max_batch_size = std::min<ix_t>(n_rows, kReasonableMaxBatchSize);
+  auto kernel         = [](uint32_t pq_bits) {
+    switch (pq_bits) {
+      case 4:
+        return process_and_fill_codes_subspaces_kernel<kBlockSize, 4, data_t, MathT, IdxT, label_t>;
+      case 8:
+        return process_and_fill_codes_subspaces_kernel<kBlockSize, 8, data_t, MathT, IdxT, label_t>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be 4 or 8", pq_bits);
+    }
+  }(pq_bits);
+  for (const auto& batch : cuvs::spatial::knn::detail::utils::batch_load_iterator(
+         dataset.data_handle(),
+         n_rows,
+         dim,
+         max_batch_size,
+         stream,
+         rmm::mr::get_current_device_resource())) {
+    auto batch_view = raft::make_device_matrix_view(batch.data(), ix_t(batch.size()), dim);
+    auto labels     = predict_vq<label_t>(res, batch_view, vq_centers);
+    dim3 blocks(raft::div_rounding_up_safe<ix_t>(n_rows, kBlockSize / threads_per_vec), 1, 1);
+    kernel<<<blocks, threads, 0, stream>>>(
+      raft::make_device_matrix_view<uint8_t, IdxT>(
+        codes.data_handle() + batch.offset() * codes_rowlen, batch.size(), codes_rowlen),
+      batch_view,
+      vq_centers,
+      raft::make_const_mdspan(labels.view()),
+      pq_centers);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+  }
+
+  return codes;
+}
+
 template <typename DatasetT, typename MathT, typename IdxT>
 auto vpq_build(const raft::resources& res, const vpq_params& params, const DatasetT& dataset)
   -> vpq_dataset<MathT, IdxT>
diff --git a/cpp/src/neighbors/vamana.cuh b/cpp/src/neighbors/vamana.cuh
index 5b66d0525c..4cad294854 100644
--- a/cpp/src/neighbors/vamana.cuh
+++ b/cpp/src/neighbors/vamana.cuh
@@ -17,6 +17,7 @@
 #pragma once
 
 #include "detail/vamana/vamana_build.cuh"
+#include "detail/vamana/vamana_codebooks.cuh"
 #include "detail/vamana/vamana_serialize.cuh"
 
 #include <raft/core/device_mdspan.hpp>
@@ -96,6 +97,13 @@ void serialize(raft::resources const& res,
   cuvs::neighbors::vamana::detail::serialize<T, IdxT>(res, file_prefix, index_);
 }
 
+template <typename T>
+index_params::codebook_params<T> deserialize_codebooks(const std::string& codebook_prefix,
+                                                       const int dim)
+{
+  return cuvs::neighbors::vamana::detail::deserialize_codebooks<T>(codebook_prefix, dim);
+}
+
 /** @} */  // end group vamana
 
 }  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_codebooks_float.cu b/cpp/src/neighbors/vamana_codebooks_float.cu
new file mode 100644
index 0000000000..e630675908
--- /dev/null
+++ b/cpp/src/neighbors/vamana_codebooks_float.cu
@@ -0,0 +1,32 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "vamana.cuh"
+
+namespace cuvs::neighbors::vamana {
+
+#define CUVS_INST_VAMANA_CODEBOOKS(T)                                               \
+  auto deserialize_codebooks(const std::string& codebook_prefix, const int dim)     \
+    -> cuvs::neighbors::vamana::index_params::codebook_params<T>                    \
+  {                                                                                 \
+    return cuvs::neighbors::vamana::deserialize_codebooks<T>(codebook_prefix, dim); \
+  }
+
+CUVS_INST_VAMANA_CODEBOOKS(float);
+
+#undef CUVS_INST_VAMANA_CODEBOOKS
+
+}  // namespace cuvs::neighbors::vamana
diff --git a/cpp/src/neighbors/vamana_serialize.cuh b/cpp/src/neighbors/vamana_serialize.cuh
index 44f69efc99..5ef86121fb 100644
--- a/cpp/src/neighbors/vamana_serialize.cuh
+++ b/cpp/src/neighbors/vamana_serialize.cuh
@@ -29,10 +29,11 @@ namespace cuvs::neighbors::vamana {
   void serialize(raft::resources const& handle,                                 \
                  const std::string& file_prefix,                                \
                  const cuvs::neighbors::vamana::index<DTYPE, uint32_t>& index_, \
-                 bool include_dataset)                                          \
+                 bool include_dataset,                                          \
+                 bool sector_aligned)                                           \
   {                                                                             \
     cuvs::neighbors::vamana::detail::serialize<DTYPE, uint32_t>(                \
-      handle, file_prefix, index_, include_dataset);                            \
+      handle, file_prefix, index_, include_dataset, sector_aligned);            \
   };
 
 /** @} */  // end group vamana
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index f2ca71347b..9945877b47 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -21,7 +21,7 @@ rapids_test_init()
 
 function(ConfigureTest)
 
-  set(options OPTIONAL NOCUDA C_LIB)
+  set(options OPTIONAL NOCUDA C_LIB FETCH_CODEBOOKS)
   set(oneValueArgs NAME GPUS PERCENT ADDITIONAL_DEP)
   set(multiValueArgs PATH TARGETS CONFIGURATIONS)
 
@@ -76,6 +76,22 @@ function(ConfigureTest)
     target_compile_definitions(${TEST_NAME} PRIVATE "CUVS_DISABLE_CUDA")
   endif()
 
+  if(_CUVS_TEST_FETCH_CODEBOOKS)
+    set(CODEBOOKS 384_int8_pq_pivots.bin 384_int8_pq_pivots.bin_rotation_matrix.bin
+                  64_float_pq_pivots.bin 64_float_pq_pivots.bin_rotation_matrix.bin
+    )
+
+    foreach(CODEBOOK ${CODEBOOKS})
+      file(
+        DOWNLOAD
+        https://data.rapids.ai/cuvs/tests/data/neighbors/ann_vamana/randomized_codebooks/${CODEBOOK}
+        ${CUVS_BINARY_DIR}/tests/data/neighbors/ann_vamana/randomized_codebooks/${CODEBOOK}
+      )
+    endforeach()
+
+    target_compile_definitions(${TEST_NAME} PRIVATE TEST_DATA_DIR="${CUVS_BINARY_DIR}/tests/data/")
+  endif()
+
   target_include_directories(${TEST_NAME} PUBLIC "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/tests>")
 
   rapids_test_add(
@@ -185,6 +201,7 @@ if(BUILD_TESTS)
     1
     PERCENT
     100
+    FETCH_CODEBOOKS
   )
 
   ConfigureTest(NAME NEIGHBORS_BALL_COVER_TEST PATH neighbors/ball_cover.cu GPUS 1 PERCENT 100)
diff --git a/cpp/tests/neighbors/ann_vamana.cuh b/cpp/tests/neighbors/ann_vamana.cuh
index 0bb6d3340e..37a9447e95 100644
--- a/cpp/tests/neighbors/ann_vamana.cuh
+++ b/cpp/tests/neighbors/ann_vamana.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -41,6 +41,7 @@
 #include <thrust/sequence.h>
 
 #include <cstddef>
+#include <filesystem>
 #include <iostream>
 #include <optional>
 #include <string>
@@ -126,6 +127,14 @@ class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
       database(0, stream_),
       search_queries(0, stream_)
   {
+    const char* ci = std::getenv("CI");
+    if (ci && std::string(ci) == "true") {
+      const char* rapids_dataset_root_dir = std::getenv("RAPIDS_DATASET_ROOT_DIR");
+      EXPECT_TRUE(rapids_dataset_root_dir);
+      test_data_dir_ = std::string(rapids_dataset_root_dir);
+    } else {
+      test_data_dir_ = std::string(TEST_DATA_DIR);
+    }
   }
 
  protected:
@@ -137,6 +146,13 @@ class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
     index_params.visited_size      = ps.visited_size;
     index_params.max_fraction      = ps.max_fraction;
     index_params.reverse_batchsize = ps.reverse_batchsize;
+    // use randomized codebooks to test serialization & quantization code path
+    if (ps.dim == 384 && std::is_same_v<DataT, int8_t>)
+      index_params.codebooks = vamana::deserialize_codebooks(
+        test_data_dir_ + "/neighbors/ann_vamana/randomized_codebooks/384_int8", ps.dim);
+    if (ps.dim == 64 && std::is_same_v<DataT, float>)
+      index_params.codebooks = vamana::deserialize_codebooks(
+        test_data_dir_ + "/neighbors/ann_vamana/randomized_codebooks/64_float", ps.dim);
 
     auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
       (const DataT*)database.data(), ps.n_rows, ps.dim);
@@ -157,6 +173,14 @@ class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
 
     tmp_index_file index_file;
     vamana::serialize(handle_, index_file.filename, index);
+    if (index_params.codebooks) {
+      vamana::serialize(handle_, index_file.filename + "_sector_aligned", index, true, true);
+      EXPECT_TRUE(std::filesystem::file_size(index_file.filename + "_sector_aligned_disk.index") >
+                  0u);
+      EXPECT_TRUE(std::filesystem::file_size(index_file.filename + "_sector_aligned.data") > 0u);
+      EXPECT_TRUE(
+        std::filesystem::file_size(index_file.filename + "_sector_aligned_pq_compressed.bin") > 0u);
+    }
 
     // Test recall by searching with CAGRA search
     if (ps.graph_degree < 256) {  // CAGRA search result buffer cannot support larger graph degree
@@ -261,13 +285,14 @@ class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
   AnnVamanaInputs ps;
   rmm::device_uvector<DataT> database;
   rmm::device_uvector<DataT> search_queries;
+  std::string test_data_dir_;
 };
 
 inline std::vector<AnnVamanaInputs> generate_inputs()
 {
   std::vector<AnnVamanaInputs> inputs = raft::util::itertools::product<AnnVamanaInputs>(
     {1000},
-    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 384, 512, 619, 1024},
     {32},       // graph degree
     {64, 256},  // visited_size
     {0.06, 0.1},
@@ -284,7 +309,7 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
 
   std::vector<AnnVamanaInputs> inputs2 = raft::util::itertools::product<AnnVamanaInputs>(
     {1000},
-    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 384, 512, 619, 1024},
     {64},        // graph degree
     {128, 512},  // visited_size
     {0.06},
@@ -302,7 +327,7 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
 
   inputs2 = raft::util::itertools::product<AnnVamanaInputs>(
     {1000},
-    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 384, 512, 619, 1024},
     {128},  // graph degree
     {256},  // visited_size
     {0.06},
@@ -320,7 +345,7 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
 
   inputs2 = raft::util::itertools::product<AnnVamanaInputs>(
     {1000},
-    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 512, 619, 1024},
+    {1, 3, 5, 7, 8, 17, 64, 128, 137, 192, 256, 384, 512, 619, 1024},
     {256},        // graph degree
     {512, 1024},  // visited_size
     {0.06},
diff --git a/examples/cpp/src/vamana_example.cu b/examples/cpp/src/vamana_example.cu
index dbda6f032f..a213b4c465 100644
--- a/examples/cpp/src/vamana_example.cu
+++ b/examples/cpp/src/vamana_example.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -28,14 +28,11 @@
 #include "common.cuh"
 
 template <typename T>
-void vamana_build_and_write(raft::device_resources const& dev_resources,
+void vamana_build_and_write(raft::device_resources const &dev_resources,
                             raft::device_matrix_view<const T, int64_t> dataset,
-                            std::string out_fname,
-                            int degree,
-                            int visited_size,
-                            float max_fraction,
-                            int iters)
-{
+                            std::string out_fname, int degree, int visited_size,
+                            float max_fraction, int iters,
+                            std::string codebook_prefix) {
   using namespace cuvs::neighbors;
 
   // use default index parameters
@@ -44,45 +41,49 @@ void vamana_build_and_write(raft::device_resources const& dev_resources,
   index_params.visited_size = visited_size;
   index_params.graph_degree = degree;
   index_params.vamana_iters = iters;
+  index_params.codebooks = vamana::deserialize_codebooks(codebook_prefix, dataset.extent(1));
 
   std::cout << "Building Vamana index (search graph)" << std::endl;
 
   auto start = std::chrono::system_clock::now();
   auto index = vamana::build(dev_resources, index_params, dataset);
-  auto end   = std::chrono::system_clock::now();
+  auto end = std::chrono::system_clock::now();
   std::chrono::duration<double> elapsed_seconds = end - start;
 
   std::cout << "Vamana index has " << index.size() << " vectors" << std::endl;
-  std::cout << "Vamana graph has degree " << index.graph_degree() << ", graph size ["
-            << index.graph().extent(0) << ", " << index.graph().extent(1) << "]" << std::endl;
+  std::cout << "Vamana graph has degree " << index.graph_degree()
+            << ", graph size [" << index.graph().extent(0) << ", "
+            << index.graph().extent(1) << "]" << std::endl;
 
   std::cout << "Time to build index: " << elapsed_seconds.count() << "s\n";
 
-  // Output index to file
+  // Output index to file (in-memory format)
   serialize(dev_resources, out_fname, index);
+
+  // Output index to file (disk sector-aligned format)
+  serialize(dev_resources, out_fname + ".sector_aligned", index, false, true);
 }
 
-void usage()
-{
-  printf(
-    "Usage: ./vamana_example <data filename> <output filename> <graph "
-    "degree> <visited_size> <max_fraction> <iterations> \n");
+void usage() {
+  printf("Usage: ./vamana_example <data filename> <output filename> <graph "
+         "degree> <visited_size> <max_fraction> <iterations> <(optional) "
+         "codebook prefix>\n");
   printf("Input file expected to be binary file of fp32 vectors.\n");
   printf("Graph degree sizes supported: 32, 64, 128, 256\n");
   printf("Visited_size must be > degree and a power of 2.\n");
   printf("max_fraction > 0 and <= 1. Typical values are 0.06 or 0.1.\n");
   printf("Default iterations = 1, increase for better quality graph.\n");
+  printf("Optional path prefix to pq pivots and rotation matrix files. Expects pq pivots file at ${codebook_prefix}_pq_pivots.bin and rotation matrix file at ${codebook_prefix}_pq_pivots.bin_rotation_matrix.bin.\n");
   exit(1);
 }
 
-int main(int argc, char* argv[])
-{
+int main(int argc, char *argv[]) {
   raft::device_resources dev_resources;
 
   // Set pool memory resource with 1 GiB initial pool size. All allocations use
   // the same pool.
   rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
-    rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+      rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
   rmm::mr::set_current_device_resource(&pool_mr);
 
   // Alternatively, one could define a pool allocator for temporary arrays (used
@@ -92,24 +93,25 @@ int main(int argc, char* argv[])
   // limit. raft::resource::set_workspace_to_pool_resource(dev_resources, 2 *
   // 1024 * 1024 * 1024ull);
 
-  if (argc != 7) usage();
+  if (argc != 7 && argc != 8)
+    usage();
 
-  std::string data_fname = (std::string)(argv[1]);  // Input filename
-  std::string out_fname  = (std::string)argv[2];    // Output index filename
-  int degree             = atoi(argv[3]);
-  int max_visited        = atoi(argv[4]);
-  float max_fraction     = atof(argv[5]);
-  int iters              = atoi(argv[6]);
+  std::string data_fname = (std::string)(argv[1]); // Input filename
+  std::string out_fname = (std::string)argv[2];    // Output index filename
+  int degree = atoi(argv[3]);
+  int max_visited = atoi(argv[4]);
+  float max_fraction = atof(argv[5]);
+  int iters = atoi(argv[6]);
+  std::string codebook_prefix = "";
+  if (argc >= 8)
+    codebook_prefix = (std::string)argv[7]; // Path prefix to pq pivots and rotation matrix files
 
   // Read in binary dataset file
-  auto dataset = read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
+  auto dataset =
+      read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
 
   // Simple build example to create graph and write to a file
-  vamana_build_and_write<uint8_t>(dev_resources,
-                                  raft::make_const_mdspan(dataset.view()),
-                                  out_fname,
-                                  degree,
-                                  max_visited,
-                                  max_fraction,
-                                  iters);
+  vamana_build_and_write<uint8_t>(
+      dev_resources, raft::make_const_mdspan(dataset.view()), out_fname, degree,
+      max_visited, max_fraction, iters, codebook_prefix);
 }

From f61a27d8a3f47789c2cb19ef51f9e654ea1d3a7a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Tue, 15 Jul 2025 23:45:53 +0200
Subject: [PATCH 483/623] [Java] Tidy up `MemorySegment`s lifecycle (#1069)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR tidies up the lifecycle of `MemorySegment`s by using specific confined `Arena`s where possible, and specific index-bound `Arena`s where the lifetime needs to be bound to the lifetime of an index.

It addresses all remaining usages of `CuVSResources#arena` after https://github.com/rapidsai/cuvs/pull/1024 and https://github.com/rapidsai/cuvs/pull/1045; as such, we can remove `CuVSResources#arena` completely, and force native memory usages to be tracked and dealt with specifically.

This would towards the goal of https://github.com/rapidsai/cuvs/issues/1037

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1069
---
 .../cuvs/internal/BruteForceIndexImpl.java    | 139 ++++++++----------
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 105 ++++++-------
 .../cuvs/internal/CuVSResourcesImpl.java      |  11 --
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |  30 ++--
 .../com/nvidia/cuvs/internal/common/Util.java |  13 ++
 5 files changed, 132 insertions(+), 166 deletions(-)

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 736f974e2b..f9e3b9ba93 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -20,14 +20,14 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG_BYTE_SIZE;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
-import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.HOST_TO_DEVICE;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.INFER_DIRECTION;
+import static com.nvidia.cuvs.internal.common.Util.allocateRMMSegment;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
 import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceBuild;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceDeserialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndexCreate;
@@ -35,9 +35,7 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndex_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSearch;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSerialize;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
 
@@ -130,6 +128,9 @@ public void destroyIndex() {
                 bruteForceIndexReference.datasetBytes);
         checkCuVSError(returnValue, "cuvsRMMFree");
       }
+      if (bruteForceIndexReference.tensorDataArena != null) {
+        bruteForceIndexReference.tensorDataArena.close();
+      }
     } finally {
       destroyed = true;
     }
@@ -143,49 +144,41 @@ public void destroyIndex() {
    *         index
    */
   private IndexReference build(DatasetImpl dataset, BruteForceIndexParams bruteForceIndexParams) {
-    try (var localArena = Arena.ofConfined()) {
-      long rows = dataset.size();
-      long cols = dataset.dimensions();
-
-      Arena arena = resources.getArena();
-      MemorySegment datasetMemSegment = dataset.asMemorySegment();
-
-      long cuvsResources = resources.getHandle();
-
-      omp_set_num_threads(bruteForceIndexParams.getNumWriterThreads());
+    long rows = dataset.size();
+    long cols = dataset.dimensions();
 
-      MemorySegment datasetMemorySegment = localArena.allocate(C_POINTER);
+    MemorySegment datasetMemSegment = dataset.asMemorySegment();
 
-      long datasetBytes = C_FLOAT_BYTE_SIZE * rows * cols;
-      var returnValue = cuvsRMMAlloc(cuvsResources, datasetMemorySegment, datasetBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
+    long cuvsResources = resources.getHandle();
 
-      // IMPORTANT: this should only come AFTER cuvsRMMAlloc call
-      MemorySegment datasetMemorySegmentP = datasetMemorySegment.get(C_POINTER, 0);
+    omp_set_num_threads(bruteForceIndexParams.getNumWriterThreads());
+    long datasetBytes = C_FLOAT_BYTE_SIZE * rows * cols;
+    MemorySegment datasetMemorySegmentP = allocateRMMSegment(cuvsResources, datasetBytes);
 
-      cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
+    cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
 
-      long[] datasetShape = {rows, cols};
-      MemorySegment datasetTensor =
-          prepareTensor(arena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
+    long[] datasetShape = {rows, cols};
+    var tensorDataArena = Arena.ofShared();
+    MemorySegment datasetTensor =
+        prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
 
-      var indexReference =
-          new IndexReference(datasetMemorySegmentP, datasetBytes, createBruteForceIndex());
+    var indexReference =
+        new IndexReference(
+            datasetMemorySegmentP, datasetBytes, tensorDataArena, createBruteForceIndex());
 
-      returnValue = cuvsStreamSync(cuvsResources);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+    var returnValue = cuvsStreamSync(cuvsResources);
+    checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue =
-          cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, indexReference.indexPtr);
-      checkCuVSError(returnValue, "cuvsBruteForceBuild");
+    returnValue =
+        cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, indexReference.indexPtr);
+    checkCuVSError(returnValue, "cuvsBruteForceBuild");
 
-      returnValue = cuvsStreamSync(cuvsResources);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+    returnValue = cuvsStreamSync(cuvsResources);
+    checkCuVSError(returnValue, "cuvsStreamSync");
 
-      omp_set_num_threads(1);
+    omp_set_num_threads(1);
 
-      return indexReference;
-    }
+    return indexReference;
   }
 
   /**
@@ -203,12 +196,11 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       long numQueries = cuvsQuery.getQueryVectors().length;
       long numBlocks = cuvsQuery.getTopK() * numQueries;
       int vectorDimension = numQueries > 0 ? cuvsQuery.getQueryVectors()[0].length : 0;
-      Arena arena = resources.getArena();
 
       SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
       SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
-      MemorySegment neighborsMemorySegment = arena.allocate(neighborsSequenceLayout);
-      MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
+      MemorySegment neighborsMemorySegment = localArena.allocate(neighborsSequenceLayout);
+      MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
 
       // prepare the prefiltering data
       long prefilterDataLength = 0;
@@ -217,52 +209,38 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       if (prefilters != null && prefilters.length > 0) {
         BitSet concatenatedFilters = concatenate(prefilters, cuvsQuery.getNumDocs());
         long[] filters = concatenatedFilters.toLongArray();
-        prefilterDataMemorySegment = buildMemorySegment(arena, filters);
+        prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
         prefilterDataLength = (long) cuvsQuery.getNumDocs() * prefilters.length;
       }
 
-      MemorySegment querySeg = buildMemorySegment(arena, cuvsQuery.getQueryVectors());
+      MemorySegment querySeg = buildMemorySegment(localArena, cuvsQuery.getQueryVectors());
 
       int topk = cuvsQuery.getTopK();
       long cuvsResources = resources.getHandle();
-      MemorySegment stream = arena.allocate(cudaStream_t);
-      var returnValue = cuvsStreamGet(cuvsResources, stream);
-      checkCuVSError(returnValue, "cuvsStreamGet");
-
-      MemorySegment queriesD = localArena.allocate(C_POINTER);
-      MemorySegment neighborsD = localArena.allocate(C_POINTER);
-      MemorySegment distancesD = localArena.allocate(C_POINTER);
 
       long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
       long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
       long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
       long prefilterBytes = 0; // size assigned later
 
-      returnValue = cuvsRMMAlloc(cuvsResources, queriesD, queriesBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-      returnValue = cuvsRMMAlloc(cuvsResources, neighborsD, neighborsBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-      returnValue = cuvsRMMAlloc(cuvsResources, distancesD, distanceBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-
-      // IMPORTANT: these three should only come AFTER cuvsRMMAlloc calls
-      MemorySegment queriesDP = queriesD.get(C_POINTER, 0);
-      MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
-      MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
+      MemorySegment queriesDP = allocateRMMSegment(cuvsResources, queriesBytes);
+      MemorySegment neighborsDP = allocateRMMSegment(cuvsResources, neighborsBytes);
+      MemorySegment distancesDP = allocateRMMSegment(cuvsResources, distanceBytes);
       MemorySegment prefilterDP = MemorySegment.NULL;
 
       cudaMemcpy(queriesDP, querySeg, queriesBytes, INFER_DIRECTION);
 
       long[] queriesShape = {numQueries, vectorDimension};
-      MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+      MemorySegment queriesTensor =
+          prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
       long[] neighborsShape = {numQueries, topk};
       MemorySegment neighborsTensor =
-          prepareTensor(arena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
+          prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
       long[] distancesShape = {numQueries, topk};
       MemorySegment distancesTensor =
-          prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+          prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
 
-      MemorySegment prefilter = cuvsFilter.allocate(arena);
+      MemorySegment prefilter = cuvsFilter.allocate(localArena);
       MemorySegment prefilterTensor;
 
       if (prefilterDataMemorySegment == MemorySegment.NULL) {
@@ -270,24 +248,20 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
         cuvsFilter.addr(prefilter, 0);
       } else {
         long[] prefilterShape = {(prefilterDataLength + 31) / 32};
-
-        MemorySegment prefilterD = localArena.allocate(C_POINTER);
         long prefilterLen = prefilterShape[0];
         prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
-        returnValue = cuvsRMMAlloc(cuvsResources, prefilterD, prefilterBytes);
-        checkCuVSError(returnValue, "cuvsRMMAlloc");
-        prefilterDP = prefilterD.get(C_POINTER, 0);
+        prefilterDP = allocateRMMSegment(cuvsResources, prefilterBytes);
 
         cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
-        prefilterTensor = prepareTensor(arena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+        prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
 
         cuvsFilter.type(prefilter, 2);
         cuvsFilter.addr(prefilter, prefilterTensor.address());
       }
 
-      returnValue = cuvsStreamSync(cuvsResources);
+      var returnValue = cuvsStreamSync(cuvsResources);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
       returnValue =
@@ -361,13 +335,12 @@ private static MemorySegment createBruteForceIndex() {
     try (var localArena = Arena.ofConfined()) {
       MemorySegment indexPtrPtr = localArena.allocate(cuvsBruteForceIndex_t);
       // cuvsBruteForceIndexCreate gets a pointer to a cuvsBruteForceIndex_t, which is defined as a
-      // pointer to
-      // cuvsBruteForceIndex.
-      // It's basically a "out" parameter: the C functions will create the index and "return back" a
-      // pointer to it.
+      // pointer to cuvsBruteForceIndex.
+      // It's basically an "out" parameter: the C functions will create the index and "return back"
+      // a pointer to it.
       // The "out parameter" pointer is needed only for the duration of the function invocation (it
-      // could be a stack
-      // pointer, in C) so we allocate it from our localArena, unwrap it and return it.
+      // could be a stack pointer, in C) so we allocate it from our localArena, unwrap it and return
+      // it.
       var returnValue = cuvsBruteForceIndexCreate(indexPtrPtr);
       checkCuVSError(returnValue, "cuvsBruteForceIndexCreate");
       return indexPtrPtr.get(cuvsBruteForceIndex_t, 0);
@@ -498,23 +471,31 @@ public BruteForceIndexImpl build() throws Throwable {
   }
 
   /**
-   * Holds the memory reference to a BRUTEFORCE index and its associated dataset
+   * Holds the memory reference to a BRUTEFORCE index, its associated dataset, and the arena used to allocate
+   * input data
    */
   private static class IndexReference {
 
     private final MemorySegment datasetPtr;
     private final long datasetBytes;
+    private final Arena tensorDataArena;
     private final MemorySegment indexPtr;
 
-    private IndexReference(MemorySegment datasetPtr, long datasetBytes, MemorySegment indexPtr) {
+    private IndexReference(
+        MemorySegment datasetPtr,
+        long datasetBytes,
+        Arena tensorDataArena,
+        MemorySegment indexPtr) {
       this.datasetPtr = datasetPtr;
       this.datasetBytes = datasetBytes;
+      this.tensorDataArena = tensorDataArena;
       this.indexPtr = indexPtr;
     }
 
     private IndexReference(MemorySegment indexPtr) {
       this.datasetPtr = MemorySegment.NULL;
       this.datasetBytes = 0;
+      this.tensorDataArena = null;
       this.indexPtr = indexPtr;
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index a408f020d2..49be7104e8 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -19,14 +19,14 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
-import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.HOST_TO_DEVICE;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.INFER_DIRECTION;
+import static com.nvidia.cuvs.internal.common.Util.allocateRMMSegment;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
 import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaStream_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraBuild;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraDeserialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexCreate;
@@ -36,9 +36,7 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSearch;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerialize;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerializeToHnswlib;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
 
@@ -182,7 +180,7 @@ private IndexReference build(CagraIndexParams indexParameters, DatasetImpl datas
 
       long[] datasetShape = {rows, cols};
       MemorySegment datasetTensor =
-          prepareTensor(resources.getArena(), dataSeg, datasetShape, 2, 32, 2, 2, 1);
+          prepareTensor(localArena, dataSeg, datasetShape, 2, 32, 2, 2, 1);
 
       var index = createCagraIndex();
 
@@ -243,55 +241,39 @@ public SearchResults search(CagraQuery query) throws Throwable {
       long numQueries = query.getQueryVectors().length;
       long numBlocks = topK * numQueries;
       int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
-      Arena arena = resources.getArena();
 
       SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_INT);
       SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
-      MemorySegment neighborsMemorySegment = arena.allocate(neighborsSequenceLayout);
-      MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
-      MemorySegment floatsSeg = buildMemorySegment(arena, query.getQueryVectors());
+      MemorySegment neighborsMemorySegment = localArena.allocate(neighborsSequenceLayout);
+      MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
+      MemorySegment floatsSeg = buildMemorySegment(localArena, query.getQueryVectors());
 
       long cuvsRes = resources.getHandle();
-      MemorySegment stream = arena.allocate(cudaStream_t);
-      int returnValue = cuvsStreamGet(cuvsRes, stream);
-      checkCuVSError(returnValue, "cuvsStreamGet");
-
-      MemorySegment queriesD = arena.allocate(C_POINTER);
-      MemorySegment neighborsD = arena.allocate(C_POINTER);
-      MemorySegment distancesD = arena.allocate(C_POINTER);
 
       long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
       long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
       long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
       long prefilterBytes = 0; // size assigned later
 
-      returnValue = cuvsRMMAlloc(cuvsRes, queriesD, queriesBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-      returnValue = cuvsRMMAlloc(cuvsRes, neighborsD, neighborsBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-      returnValue = cuvsRMMAlloc(cuvsRes, distancesD, distancesBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-
-      // IMPORTANT: these three should only come AFTER cuvsRMMAlloc calls
-      MemorySegment queriesDP = queriesD.get(C_POINTER, 0);
-      MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
-      MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
-      MemorySegment prefilterD = arena.allocate(C_POINTER);
+      MemorySegment queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
+      MemorySegment neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
+      MemorySegment distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
       MemorySegment prefilterDP = MemorySegment.NULL;
       long prefilterLen = 0;
 
       cudaMemcpy(queriesDP, floatsSeg, queriesBytes, INFER_DIRECTION);
 
       long[] queriesShape = {numQueries, vectorDimension};
-      MemorySegment queriesTensor = prepareTensor(arena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+      MemorySegment queriesTensor =
+          prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
       long[] neighborsShape = {numQueries, topK};
       MemorySegment neighborsTensor =
-          prepareTensor(arena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
+          prepareTensor(localArena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
       long[] distancesShape = {numQueries, topK};
       MemorySegment distancesTensor =
-          prepareTensor(arena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+          prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
 
-      returnValue = cuvsStreamSync(cuvsRes);
+      var returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
 
       // prepare the prefiltering data
@@ -302,11 +284,11 @@ public SearchResults search(CagraQuery query) throws Throwable {
         prefilters = new BitSet[] {query.getPrefilter()};
         BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
         long[] filters = concatenatedFilters.toLongArray();
-        prefilterDataMemorySegment = buildMemorySegment(arena, filters);
+        prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
         prefilterDataLength = query.getNumDocs() * prefilters.length;
       }
 
-      MemorySegment prefilter = cuvsFilter.allocate(arena);
+      MemorySegment prefilter = cuvsFilter.allocate(localArena);
       MemorySegment prefilterTensor;
 
       if (prefilterDataMemorySegment == MemorySegment.NULL) {
@@ -317,14 +299,11 @@ public SearchResults search(CagraQuery query) throws Throwable {
         prefilterLen = prefilterShape[0];
         prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
-        returnValue = cuvsRMMAlloc(cuvsRes, prefilterD, prefilterBytes);
-        checkCuVSError(returnValue, "cuvsRMMAlloc");
-
-        prefilterDP = prefilterD.get(C_POINTER, 0);
+        prefilterDP = allocateRMMSegment(cuvsRes, prefilterBytes);
 
         cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
-        prefilterTensor = prepareTensor(arena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+        prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
 
         cuvsFilter.type(prefilter, 1);
         cuvsFilter.addr(prefilter, prefilterTensor.address());
@@ -336,7 +315,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
       returnValue =
           cuvsCagraSearch(
               cuvsRes,
-              segmentFromSearchParams(query.getCagraSearchParameters()),
+              segmentFromSearchParams(localArena, query.getCagraSearchParameters()),
               cagraIndexReference.getMemorySegment(),
               queriesTensor,
               neighborsTensor,
@@ -357,8 +336,10 @@ public SearchResults search(CagraQuery query) throws Throwable {
       returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
       checkCuVSError(returnValue, "cuvsRMMFree");
 
-      returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
+      if (prefilterLen > 0) {
+        returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+      }
 
       return CagraSearchResults.create(
           neighborsSequenceLayout,
@@ -432,12 +413,15 @@ public void serializeToHNSW(OutputStream outputStream, Path tempFile, int buffer
       throws Throwable {
     checkNotDestroyed();
     tempFile = tempFile.toAbsolutePath();
-    MemorySegment pathSeg = buildMemorySegment(resources.getArena(), tempFile.toString());
 
-    long cuvsRes = resources.getHandle();
-    int returnValue =
-        cuvsCagraSerializeToHnswlib(cuvsRes, pathSeg, cagraIndexReference.getMemorySegment());
-    checkCuVSError(returnValue, "cuvsCagraSerializeToHnswlib");
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment pathSeg = buildMemorySegment(localArena, tempFile.toString());
+
+      long cuvsRes = resources.getHandle();
+      int returnValue =
+          cuvsCagraSerializeToHnswlib(cuvsRes, pathSeg, cagraIndexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsCagraSerializeToHnswlib");
+    }
 
     try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
       byte[] chunk = new byte[bufferLength];
@@ -576,8 +560,8 @@ private static MemorySegment segmentFromIndexParams(Arena arena, CagraIndexParam
   /**
    * Allocates the configured search parameters in the MemorySegment.
    */
-  private MemorySegment segmentFromSearchParams(CagraSearchParams params) {
-    MemorySegment seg = cuvsCagraSearchParams.allocate(resources.getArena());
+  private MemorySegment segmentFromSearchParams(Arena arena, CagraSearchParams params) {
+    MemorySegment seg = cuvsCagraSearchParams.allocate(arena);
     cuvsCagraSearchParams.max_queries(seg, params.getMaxQueries());
     cuvsCagraSearchParams.itopk_size(seg, params.getITopKSize());
     cuvsCagraSearchParams.max_iterations(seg, params.getMaxIterations());
@@ -627,9 +611,9 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
     var mergedIndex = createCagraIndex();
     long cuvsRes = resources.getHandle();
 
-    try (var arena = Arena.ofConfined()) {
+    try (var localArena = Arena.ofConfined()) {
       MemorySegment indexesSegment =
-          arena.allocate(indexes.length * ValueLayout.ADDRESS.byteSize());
+          localArena.allocate(indexes.length * ValueLayout.ADDRESS.byteSize());
 
       for (int i = 0; i < indexes.length; i++) {
         CagraIndexImpl indexImpl = (CagraIndexImpl) indexes[i];
@@ -637,8 +621,11 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
             ValueLayout.ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
       }
 
-      MemorySegment mergeParamsSegment = createMergeParamsSegment(mergeParams, resources);
-      int returnValue =
+      // TODO: we should call cuvsCreateMergeParams here, instead of allocating this ourselves
+      // See https://github.com/rapidsai/cuvs/pull/1109
+      var mergeParamsSegment = createMergeParamsSegment(localArena, mergeParams);
+
+      var returnValue =
           cuvsCagraMerge(cuvsRes, mergeParamsSegment, indexesSegment, indexes.length, mergedIndex);
 
       checkCuVSError(returnValue, "cuvsCagraMerge");
@@ -648,15 +635,13 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
   }
 
   /**
-   * Creates a memory segment for merge parameters.
+   * Creates (allocates and fill) native memory version of merge parameters.
    *
+   * @param arena       The arena to use to allocate the MemorySegment(s) that will hold the merge parameters data
    * @param mergeParams The merge parameters
-   * @param resources The CuVS resources
-   * @return A memory segment with the merge parameters
+   * @return A memory segment pointing to the native structure for merge parameters
    */
-  private static MemorySegment createMergeParamsSegment(
-      CagraMergeParams mergeParams, CuVSResourcesImpl resources) {
-    var arena = resources.getArena();
+  private static MemorySegment createMergeParamsSegment(Arena arena, CagraMergeParams mergeParams) {
     MemorySegment seg = cuvsCagraMergeParams.allocate(arena);
 
     if (mergeParams != null) {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
index c3095e8304..5acb2cb9bd 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -32,7 +32,6 @@
 public class CuVSResourcesImpl implements CuVSResources {
 
   private final Path tempDirectory;
-  private final Arena arena;
   private final long resourceHandle;
   private boolean destroyed;
 
@@ -42,7 +41,6 @@ public class CuVSResourcesImpl implements CuVSResources {
    */
   public CuVSResourcesImpl(Path tempDirectory) {
     this.tempDirectory = tempDirectory;
-    this.arena = Arena.ofShared();
     try (var localArena = Arena.ofConfined()) {
       var resourcesMemorySegment = localArena.allocate(cuvsResources_t);
       int returnValue = cuvsResourcesCreate(resourcesMemorySegment);
@@ -58,7 +56,6 @@ public void close() {
       int returnValue = cuvsResourcesDestroy(resourceHandle);
       checkCuVSError(returnValue, "cuvsResourcesDestroy");
       destroyed = true;
-      arena.close();
     }
   }
 
@@ -82,12 +79,4 @@ long getHandle() {
     checkNotDestroyed();
     return resourceHandle;
   }
-
-  /**
-   * The allocation arena used by this resources.
-   */
-  protected Arena getArena() {
-    checkNotDestroyed();
-    return arena;
-  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index c7ddfa4840..178ed21b87 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -98,24 +98,24 @@ public SearchResults search(HnswQuery query) throws Throwable {
       int numQueries = queryVectors.length;
       long numBlocks = (long) topK * numQueries;
       int vectorDimension = numQueries > 0 ? queryVectors[0].length : 0;
-      Arena arena = resources.getArena();
 
       SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
       SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
-      MemorySegment neighborsMemorySegment = arena.allocate(neighborsSequenceLayout);
-      MemorySegment distancesMemorySegment = arena.allocate(distancesSequenceLayout);
-      MemorySegment querySeg = buildMemorySegment(arena, queryVectors);
+      MemorySegment neighborsMemorySegment = localArena.allocate(neighborsSequenceLayout);
+      MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
+      MemorySegment querySeg = buildMemorySegment(localArena, queryVectors);
 
       long cuvsRes = resources.getHandle();
 
       long[] queriesShape = {numQueries, vectorDimension};
-      MemorySegment queriesTensor = prepareTensor(arena, querySeg, queriesShape, 2, 32, 2, 1, 1);
+      MemorySegment queriesTensor =
+          prepareTensor(localArena, querySeg, queriesShape, 2, 32, 2, 1, 1);
       long[] neighborsShape = {numQueries, topK};
       MemorySegment neighborsTensor =
-          prepareTensor(arena, neighborsMemorySegment, neighborsShape, 1, 64, 2, 1, 1);
+          prepareTensor(localArena, neighborsMemorySegment, neighborsShape, 1, 64, 2, 1, 1);
       long[] distancesShape = {numQueries, topK};
       MemorySegment distancesTensor =
-          prepareTensor(arena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
+          prepareTensor(localArena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
 
       int returnValue = cuvsStreamSync(cuvsRes);
       checkCuVSError(returnValue, "cuvsStreamSync");
@@ -123,7 +123,7 @@ public SearchResults search(HnswQuery query) throws Throwable {
       returnValue =
           cuvsHnswSearch(
               cuvsRes,
-              segmentFromSearchParams(query.getHnswSearchParams()),
+              segmentFromSearchParams(localArena, query.getHnswSearchParams()),
               hnswIndexReference.getMemorySegment(),
               queriesTensor,
               neighborsTensor,
@@ -176,15 +176,13 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
         var outputStream = Files.newOutputStream(tmpIndexFile);
         var localArena = Arena.ofConfined()) {
       inputStream.transferTo(outputStream);
-
-      Arena arena = resources.getArena();
       MemorySegment pathSeg = buildMemorySegment(localArena, tmpIndexFile.toString());
 
       long cuvsRes = resources.getHandle();
 
       var indexReference = createHnswIndex();
 
-      MemorySegment dtype = DLDataType.allocate(arena);
+      MemorySegment dtype = DLDataType.allocate(localArena);
       DLDataType.bits(dtype, (byte) 32);
       DLDataType.code(dtype, (byte) 2); // kDLFloat
       DLDataType.lanes(dtype, (byte) 1);
@@ -194,7 +192,7 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
       var returnValue =
           cuvsHnswDeserialize(
               cuvsRes,
-              segmentFromIndexParams(hnswIndexParams),
+              segmentFromIndexParams(localArena, hnswIndexParams),
               pathSeg,
               hnswIndexParams.getVectorDimension(),
               0,
@@ -211,8 +209,8 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
   /**
    * Allocates the configured search parameters in the MemorySegment.
    */
-  private MemorySegment segmentFromIndexParams(HnswIndexParams params) {
-    MemorySegment seg = cuvsHnswIndexParams.allocate(resources.getArena());
+  private static MemorySegment segmentFromIndexParams(Arena arena, HnswIndexParams params) {
+    MemorySegment seg = cuvsHnswIndexParams.allocate(arena);
     cuvsHnswIndexParams.ef_construction(seg, params.getEfConstruction());
     cuvsHnswIndexParams.num_threads(seg, params.getNumThreads());
     return seg;
@@ -221,8 +219,8 @@ private MemorySegment segmentFromIndexParams(HnswIndexParams params) {
   /**
    * Allocates the configured search parameters in the MemorySegment.
    */
-  private MemorySegment segmentFromSearchParams(HnswSearchParams params) {
-    MemorySegment seg = cuvsHnswSearchParams.allocate(resources.getArena());
+  private static MemorySegment segmentFromSearchParams(Arena arena, HnswSearchParams params) {
+    MemorySegment seg = cuvsHnswSearchParams.allocate(arena);
     cuvsHnswSearchParams.ef(seg, params.ef());
     cuvsHnswSearchParams.num_threads(seg, params.numThreads());
     return seg;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index af882564c0..576b182588 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -19,10 +19,12 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceCount;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceProperties_v2;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemGetInfo;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaSetDevice;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
 import static com.nvidia.cuvs.internal.panama.headers_h.size_t;
 
 import com.nvidia.cuvs.GPUInfo;
@@ -329,4 +331,15 @@ public static MemorySegment prepareTensor(
 
     return tensor;
   }
+
+  public static MemorySegment allocateRMMSegment(long resourceHandle, long datasetBytes) {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment datasetMemorySegment = localArena.allocate(C_POINTER);
+
+      var returnValue = cuvsRMMAlloc(resourceHandle, datasetMemorySegment, datasetBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+      return datasetMemorySegment.get(C_POINTER, 0);
+    }
+  }
 }

From c04ad9bc35f27223e8e1b478125643d58353c86c Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 16 Jul 2025 18:29:09 +0200
Subject: [PATCH 484/623] IVF-PQ coarse search: fix integer overflow and avoid
 excessive batch sizes (#999)

When the IVF-PQ batch size and the number of cluster are big enough, the GEMM output matrix size may exceed the `uint32_t` limits. This manifests as an illegal memory access popping up in further stages of IVF-PQ (due to everything being as async as possible).

This PR fixes the bug by converting the input arguments to 64-bits before multiplying them.

On top of the overflow fix, this PR also limits the batch size when it may hurt performance: (1) within IVF-PQ to avoid out-of-memory error (2) in CAGRA graph build to avoid switching to the large memory resource (which is typically backed by managed memory).

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/999
---
 .../neighbors/detail/cagra/cagra_build.cuh    | 50 +++++++---
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    | 97 +++++++++++++------
 2 files changed, 108 insertions(+), 39 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 6a8e916774..27ad7f296f 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -166,25 +166,51 @@ void build_knn_graph(
   const auto num_queries = dataset.extent(0);
 
   // Use the same maximum batch size as the ivf_pq::search to avoid allocating more than needed.
-  const uint32_t max_queries = pq.search_params.max_internal_batch_size;
+  uint32_t max_queries = pq.search_params.max_internal_batch_size;
 
   // Heuristic: the build_knn_graph code should use only a fraction of the workspace memory; the
   // rest should be used by the ivf_pq::search. Here we say that the workspace size should be a good
   // multiple of what is required for the I/O batching below.
   constexpr size_t kMinWorkspaceRatio = 5;
-  auto desired_workspace_size         = max_queries * kMinWorkspaceRatio *
-                                (sizeof(DataT) * dataset.extent(1)  // queries (dataset batch)
-                                 + sizeof(float) * gpu_top_k        // distances
-                                 + sizeof(int64_t) * gpu_top_k      // neighbors
-                                 + sizeof(float) * top_k            // refined_distances
-                                 + sizeof(int64_t) * top_k          // refined_neighbors
-                                );
+  constexpr size_t kMinLargeBatchSize = 512;
+  auto desired_workspace_size =
+    max_queries * (sizeof(DataT) * dataset.extent(1)  // queries (dataset batch)
+                   + sizeof(float) * gpu_top_k        // distances
+                   + sizeof(int64_t) * gpu_top_k      // neighbors
+                   + sizeof(float) * top_k            // refined_distances
+                   + sizeof(int64_t) * top_k          // refined_neighbors
+                  );
+  auto free_space_ratio    = raft::resource::get_workspace_free_bytes(res) / desired_workspace_size;
+  bool use_large_workspace = false;
+  if (free_space_ratio < kMinWorkspaceRatio) {
+    auto adjusted_max_queries =
+      static_cast<uint32_t>(max_queries * free_space_ratio / kMinWorkspaceRatio);
+    if (adjusted_max_queries >= kMinLargeBatchSize) {
+      // adjust max_queries, so that the ratio free_space_ratio gets not larger than
+      // kMinWorkspaceRatio.
+      RAFT_LOG_INFO(
+        "CAGRA graph build: reducing IVF-PQ search max_internal_batch_size from %u -> %u to fit "
+        "the workspace",
+        max_queries,
+        adjusted_max_queries);
+      max_queries                              = adjusted_max_queries;
+      pq.search_params.max_internal_batch_size = adjusted_max_queries;
+    } else {
+      // adjusting max_queries to a very small value isn't practical, so we use the large workspace
+      // instead.
+      use_large_workspace = true;
+      RAFT_LOG_WARN(
+        "Using large workspace memory for IVF-PQ search during CAGRA graph build. Desired "
+        "workspace size: %zu, free workspace size: %zu",
+        desired_workspace_size * kMinWorkspaceRatio,
+        raft::resource::get_workspace_free_bytes(res));
+    }
+  }
 
   // If the workspace is smaller than desired, put the I/O buffers into the large workspace.
   rmm::device_async_resource_ref workspace_mr =
-    desired_workspace_size <= raft::resource::get_workspace_free_bytes(res)
-      ? raft::resource::get_workspace_resource(res)
-      : raft::resource::get_large_workspace_resource(res);
+    use_large_workspace ? raft::resource::get_large_workspace_resource(res)
+                        : raft::resource::get_workspace_resource(res);
 
   RAFT_LOG_DEBUG(
     "IVF-PQ search node_degree: %d, top_k: %d,  gpu_top_k: %d,  max_batch_size:: %d, n_probes: %u",
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index 3d86ff7a37..8d9e606671 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -151,7 +151,7 @@ void select_clusters(raft::resources const& handle,
     } break;
     default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
   }
-  rmm::device_uvector<float> qc_distances(n_queries * n_lists, stream, mr);
+  rmm::device_uvector<float> qc_distances(size_t(n_queries) * size_t(n_lists), stream, mr);
   raft::linalg::gemm(handle,
                      true,
                      false,
@@ -169,7 +169,7 @@ void select_clusters(raft::resources const& handle,
                      stream);
 
   // Select neighbor clusters for each query.
-  rmm::device_uvector<float> cluster_dists(n_queries * n_probes, stream, mr);
+  rmm::device_uvector<float> cluster_dists(size_t(n_queries) * size_t(n_probes), stream, mr);
   cuvs::selection::select_k(
     handle,
     raft::make_device_matrix_view<const float, int64_t>(qc_distances.data(), n_queries, n_lists),
@@ -237,7 +237,7 @@ void select_clusters(raft::resources const& handle,
     } break;
     default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
   }
-  rmm::device_uvector<dist_type> qc_distances(n_queries * n_lists, stream, mr);
+  rmm::device_uvector<dist_type> qc_distances(size_t(n_queries) * size_t(n_lists), stream, mr);
   raft::linalg::gemm(handle,
                      true,
                      false,
@@ -255,7 +255,7 @@ void select_clusters(raft::resources const& handle,
                      stream);
 
   // Select neighbor clusters for each query.
-  rmm::device_uvector<dist_type> cluster_dists(n_queries * n_probes, stream, mr);
+  rmm::device_uvector<dist_type> cluster_dists(size_t(n_queries) * size_t(n_probes), stream, mr);
   // cuvs::selection::select_k lacks uint32_t-as-a-value support at the moment
   raft::matrix::select_k<dist_type, uint32_t>(
     handle,
@@ -322,7 +322,7 @@ void select_clusters(raft::resources const& handle,
     } break;
     default: RAFT_FAIL("Unsupported distance type %d.", int(metric));
   }
-  rmm::device_uvector<dist_type> qc_distances(n_queries * n_lists, stream, mr);
+  rmm::device_uvector<dist_type> qc_distances(size_t(n_queries) * size_t(n_lists), stream, mr);
   raft::linalg::gemm(handle,
                      true,
                      false,
@@ -340,7 +340,7 @@ void select_clusters(raft::resources const& handle,
                      stream);
 
   // Select neighbor clusters for each query.
-  rmm::device_uvector<dist_type> cluster_dists(n_queries * n_probes, stream, mr);
+  rmm::device_uvector<dist_type> cluster_dists(size_t(n_queries) * size_t(n_probes), stream, mr);
   cuvs::selection::select_k(
     handle,
     raft::make_device_matrix_view<const dist_type, int64_t>(
@@ -730,8 +730,9 @@ struct ivfpq_search {
 };
 
 /**
- * A heuristic for bounding the number of queries per batch, to improve GPU utilization.
- * (based on the number of SMs and the work size).
+ * A heuristic for bounding the number of queries in compute similarity kernel batch, to improve GPU
+ * utilization. (based on the number of SMs and the work size). A major restriction here is the
+ * max_samples - how many intermediate results may be kept in memory.
  *
  * @param res is used to query the workspace size
  * @param k top-k
@@ -742,11 +743,11 @@ struct ivfpq_search {
  *
  * @return maximum recommended batch size.
  */
-inline auto get_max_batch_size(raft::resources const& res,
-                               uint32_t k,
-                               uint32_t n_probes,
-                               uint32_t n_queries,
-                               uint32_t max_samples) -> uint32_t
+inline auto get_max_fine_batch_size(raft::resources const& res,
+                                    uint32_t k,
+                                    uint32_t n_probes,
+                                    uint32_t n_queries,
+                                    uint32_t max_samples) -> uint32_t
 {
   uint32_t max_batch_size = n_queries;
   uint32_t n_ctas_total   = raft::resource::get_device_properties(res).multiProcessorCount * 2;
@@ -778,6 +779,44 @@ inline auto get_max_batch_size(raft::resources const& res,
   return max_batch_size;
 }
 
+/**
+ * A heuristic for bounding the number of queries per batch for the outer loop of the search,
+ * to improve GPU utilization and memory usage.
+ *
+ * @param res is used to query the workspace size
+ * @param data_size size of each data element in bytes
+ * @param n_probes number of selected clusters per query
+ * @param n_lists total number of clusters
+ * @param n_queries number of queries to process
+ * @param max_queries maximum number of queries that can be processed at once
+ *
+ * @return maximum recommended batch size for the outer loop
+ */
+inline auto get_max_coarse_batch_size(raft::resources const& res,
+                                      const search_params& params,
+                                      uint32_t n_probes,
+                                      uint32_t n_lists,
+                                      uint32_t n_queries) -> uint32_t
+{
+  size_t data_size = 4;
+  switch (params.coarse_search_dtype) {
+    case CUDA_R_32F: data_size = 4; break;
+    case CUDA_R_16F: data_size = 2; break;
+    case CUDA_R_8I: data_size = 1; break;
+    default: RAFT_FAIL("Unexpected coarse_search_dtype (%d)", int(params.coarse_search_dtype));
+  }
+  // How much data we allocate for coarse GEMM.
+  // This is NOT all memory we need, as a rule of thumb max it out to half of the workspace.
+  // We don't reach this limit by default, but only when we increase the max_internal_batch_size by
+  // a lot.
+  auto bytes_per_query = static_cast<size_t>(n_probes + n_lists) * data_size;
+  auto max_per_ws      = raft::resource::get_workspace_free_bytes(res) / bytes_per_query;
+  return std::max<uint32_t>(
+    1,
+    std::min<uint32_t>(max_per_ws / 2,
+                       std::min<uint32_t>(params.max_internal_batch_size, n_queries)));
+}
+
 template <typename T, typename IdxT>
 inline auto get_rotation_matrix(const raft::resources& res, const index<IdxT>& index)
   -> raft::device_matrix_view<const T, uint32_t, raft::row_major>
@@ -867,33 +906,37 @@ inline void search(raft::resources const& handle,
   auto mr = raft::resource::get_workspace_resource(handle);
 
   // Maximum number of query vectors to search at the same time.
-  const auto max_queries =
-    std::min<uint32_t>(std::max<uint32_t>(n_queries, 1), params.max_internal_batch_size);
-  auto max_batch_size = get_max_batch_size(handle, k, n_probes, max_queries, max_samples);
+  // Number of queries in the outer loop, which includes query transform and coarse search.
+  const auto max_bs_outer =
+    get_max_coarse_batch_size(handle, params, n_probes, index.n_lists(), n_queries);
+  // Number of queries in the inner loop, which includes the fine search;
+  // This is usually smaller than the outer loop when the non-fused kernel has to keep intermediate
+  // results in the device memory.
+  auto max_bs_inner = get_max_fine_batch_size(handle, k, n_probes, max_bs_outer, max_samples);
 
   using some_query_t = std::
     variant<rmm::device_uvector<float>, rmm::device_uvector<half>, rmm::device_uvector<int8_t>>;
   some_query_t gemm_queries(
     params.coarse_search_dtype == CUDA_R_32F
       ? std::move(some_query_t{
-          std::in_place_type_t<rmm::device_uvector<float>>{}, max_queries * dim_ext, stream, mr})
+          std::in_place_type_t<rmm::device_uvector<float>>{}, max_bs_outer * dim_ext, stream, mr})
     : params.coarse_search_dtype == CUDA_R_16F
       ? std::move(some_query_t{
-          std::in_place_type_t<rmm::device_uvector<half>>{}, max_queries * dim_ext, stream, mr})
+          std::in_place_type_t<rmm::device_uvector<half>>{}, max_bs_outer * dim_ext, stream, mr})
     : params.coarse_search_dtype == CUDA_R_8I
       ? std::move(some_query_t{
-          std::in_place_type_t<rmm::device_uvector<int8_t>>{}, max_queries * dim_ext, stream, mr})
+          std::in_place_type_t<rmm::device_uvector<int8_t>>{}, max_bs_outer * dim_ext, stream, mr})
       : throw raft::logic_error("Unsupported coarse_search_dtype (only CUDA_R_32F, "
                                 "CUDA_R_16F, and CUDA_R_8I are supported)"));
-  rmm::device_uvector<float> rot_queries(max_queries * index.rot_dim(), stream, mr);
-  rmm::device_uvector<uint32_t> clusters_to_probe(max_queries * n_probes, stream, mr);
+  rmm::device_uvector<float> rot_queries(max_bs_outer * index.rot_dim(), stream, mr);
+  rmm::device_uvector<uint32_t> clusters_to_probe(max_bs_outer * n_probes, stream, mr);
 
   auto filter_adapter = cuvs::neighbors::filtering::ivf_to_sample_filter(
     index.inds_ptrs().data_handle(), sample_filter);
   auto search_instance = ivfpq_search<IdxT, decltype(filter_adapter)>::fun(params, index.metric());
 
-  for (uint32_t offset_q = 0; offset_q < n_queries; offset_q += max_queries) {
-    uint32_t queries_batch = min(max_queries, n_queries - offset_q);
+  for (uint32_t offset_q = 0; offset_q < n_queries; offset_q += max_bs_outer) {
+    uint32_t queries_batch = min(max_bs_outer, n_queries - offset_q);
     raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> batch_scope(
       "ivf_pq::search-batch(queries: %u - %u)", offset_q, offset_q + queries_batch);
 
@@ -942,12 +985,12 @@ inline void search(raft::resources const& handle,
       gemm_queries);
     if (index.metric() == distance::DistanceType::CosineExpanded) {
       auto rot_queries_view = raft::make_device_matrix_view<float, uint32_t>(
-        rot_queries.data(), max_queries, index.rot_dim());
+        rot_queries.data(), max_bs_outer, index.rot_dim());
       raft::linalg::row_normalize<raft::linalg::L2Norm>(
         handle, raft::make_const_mdspan(rot_queries_view), rot_queries_view);
     }
-    for (uint32_t offset_b = 0; offset_b < queries_batch; offset_b += max_batch_size) {
-      uint32_t batch_size = min(max_batch_size, queries_batch - offset_b);
+    for (uint32_t offset_b = 0; offset_b < queries_batch; offset_b += max_bs_inner) {
+      uint32_t batch_size = min(max_bs_inner, queries_batch - offset_b);
       /* The distance calculation is done in the rotated/transformed space;
          as long as `index.rotation_matrix()` is orthogonal, the distances and thus results are
          preserved.

From 74186884f1e5665c0ffcacc3805b34f73dba36f1 Mon Sep 17 00:00:00 2001
From: Jake Awe <jawe@nvidia.com>
Date: Thu, 17 Jul 2025 12:20:44 -0700
Subject: [PATCH 485/623] DOC v25.10 Updates [skip ci]

---
 .../cuda12.9-conda/devcontainer.json          |  6 ++--
 .devcontainer/cuda12.9-pip/devcontainer.json  |  8 ++---
 .github/workflows/build.yaml                  | 22 ++++++-------
 .github/workflows/pr.yaml                     | 32 +++++++++----------
 .github/workflows/test.yaml                   | 10 +++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 README.md                                     |  6 ++--
 VERSION                                       |  2 +-
 .../all_cuda-129_arch-aarch64.yaml            |  4 +--
 .../all_cuda-129_arch-x86_64.yaml             |  4 +--
 .../bench_ann_cuda-129_arch-aarch64.yaml      |  8 ++---
 .../bench_ann_cuda-129_arch-x86_64.yaml       |  8 ++---
 .../go_cuda-129_arch-aarch64.yaml             |  4 +--
 .../environments/go_cuda-129_arch-x86_64.yaml |  4 +--
 .../rust_cuda-129_arch-aarch64.yaml           |  4 +--
 .../rust_cuda-129_arch-x86_64.yaml            |  4 +--
 dependencies.yaml                             | 24 +++++++-------
 docs/source/developer_guide.md                |  4 +--
 examples/go/README.md                         |  2 +-
 java/benchmarks/pom.xml                       |  4 +--
 java/build.sh                                 |  2 +-
 java/cuvs-java/pom.xml                        |  2 +-
 java/examples/README.md                       |  6 ++--
 java/examples/pom.xml                         |  4 +--
 python/cuvs/pyproject.toml                    | 10 +++---
 python/cuvs_bench/cuvs_bench/plot/__main__.py |  2 +-
 python/cuvs_bench/pyproject.toml              |  2 +-
 python/libcuvs/pyproject.toml                 |  8 ++---
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 30 files changed, 101 insertions(+), 101 deletions(-)

diff --git a/.devcontainer/cuda12.9-conda/devcontainer.json b/.devcontainer/cuda12.9-conda/devcontainer.json
index ce601856f1..ef5383cd80 100644
--- a/.devcontainer/cuda12.9-conda/devcontainer.json
+++ b/.devcontainer/cuda12.9-conda/devcontainer.json
@@ -5,17 +5,17 @@
     "args": {
       "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.08-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.10-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.9-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.9-pip/devcontainer.json b/.devcontainer/cuda12.9-pip/devcontainer.json
index be81ad6c64..5902c948f3 100644
--- a/.devcontainer/cuda12.9-pip/devcontainer.json
+++ b/.devcontainer/cuda12.9-pip/devcontainer.json
@@ -5,24 +5,24 @@
     "args": {
       "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.08-cpp-cuda12.9-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda12.9-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.08-cuda12.9-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.8": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.10": {
       "version": "12.9",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.8": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 9c487f2bdf..d0836565a9 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -34,7 +34,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -44,7 +44,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -57,7 +57,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -70,7 +70,7 @@ jobs:
   java-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -83,7 +83,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -93,7 +93,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -108,7 +108,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -120,7 +120,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -134,7 +134,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -145,7 +145,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -157,7 +157,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 55d936b204..54c0144b0e 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -30,7 +30,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.10
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -57,7 +57,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.10
     with:
       files_yaml: |
         test_cpp:
@@ -109,14 +109,14 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.10
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
     with:
       build_type: pull-request
       node_type: cpu16
@@ -124,7 +124,7 @@ jobs:
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
@@ -132,21 +132,21 @@ jobs:
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
     with:
       build_type: pull-request
       symbol_exclusions: (void (thrust::|cub::))
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
     with:
       build_type: pull-request
       script: ci/build_python.sh
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -154,7 +154,7 @@ jobs:
   conda-java-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_java
     with:
       build_type: pull-request
@@ -166,7 +166,7 @@ jobs:
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -176,7 +176,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -186,7 +186,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -196,7 +196,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -207,7 +207,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -216,7 +216,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -224,7 +224,7 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.10
     with:
       arch: '["amd64"]'
       cuda: '["12.9"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 7eaf54f7e5..64153d1809 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -25,7 +25,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -43,7 +43,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -52,7 +52,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-java-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -64,7 +64,7 @@ jobs:
       script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 593fcb1086..48bf37afc4 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.08
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.10
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/README.md b/README.md
index bef1a8af6e..1604428492 100755
--- a/README.md
+++ b/README.md
@@ -104,7 +104,7 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-conda install -c rapidsai-nightly -c conda-forge cuvs=25.08
+conda install -c rapidsai-nightly -c conda-forge cuvs=25.10
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
@@ -164,7 +164,7 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
-For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.08/examples/c)
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.10/examples/c)
 
 ### Rust API
 
@@ -227,7 +227,7 @@ fn cagra_example() -> Result<()> {
 }
 ```
 
-For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.08/examples/rust).
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.10/examples/rust).
 
 ## Contributing
 
diff --git a/VERSION b/VERSION
index 3af4bda020..296e35288d 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-25.08.00
+25.10.00
diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
index 61408b951e..796d166f6c 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.8.*,>=0.0.0a0
+- librmm==25.10.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.8.*,>=0.0.0a0
+- pylibraft==25.10.*,>=0.0.0a0
 - pytest
 - pytest-cov
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
index 64f9c75fd0..57675489b2 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.8.*,>=0.0.0a0
+- librmm==25.10.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.8.*,>=0.0.0a0
+- pylibraft==25.10.*,>=0.0.0a0
 - pytest
 - pytest-cov
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
diff --git a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index d3c0fae8c9..cf9a064f32 100644
--- a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.9
 - cupy>=12.0.0
-- cuvs==25.8.*,>=0.0.0a0
+- cuvs==25.10.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -30,15 +30,15 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.8.*,>=0.0.0a0
-- librmm==25.8.*,>=0.0.0a0
+- libcuvs==25.10.*,>=0.0.0a0
+- librmm==25.10.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.8.*,>=0.0.0a0
+- pylibraft==25.10.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index 4686f6b2bb..8b8bf10a58 100644
--- a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-python>=12.6.2,<13.0a0
 - cuda-version=12.9
 - cupy>=12.0.0
-- cuvs==25.8.*,>=0.0.0a0
+- cuvs==25.10.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,8 +32,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.8.*,>=0.0.0a0
-- librmm==25.8.*,>=0.0.0a0
+- libcuvs==25.10.*,>=0.0.0a0
+- librmm==25.10.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - mkl-devel=2023
 - nccl>=2.19
@@ -41,7 +41,7 @@ dependencies:
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.8.*,>=0.0.0a0
+- pylibraft==25.10.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.3.0,<0.4.0.dev0
 - requests
diff --git a/conda/environments/go_cuda-129_arch-aarch64.yaml b/conda/environments/go_cuda-129_arch-aarch64.yaml
index ed5e82f6cf..1a0bbb0003 100644
--- a/conda/environments/go_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-129_arch-aarch64.yaml
@@ -24,8 +24,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.8.*,>=0.0.0a0
-- libraft==25.8.*,>=0.0.0a0
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
diff --git a/conda/environments/go_cuda-129_arch-x86_64.yaml b/conda/environments/go_cuda-129_arch-x86_64.yaml
index 8a2f40d01a..c73052a292 100644
--- a/conda/environments/go_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-129_arch-x86_64.yaml
@@ -24,8 +24,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.8.*,>=0.0.0a0
-- libraft==25.8.*,>=0.0.0a0
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
diff --git a/conda/environments/rust_cuda-129_arch-aarch64.yaml b/conda/environments/rust_cuda-129_arch-aarch64.yaml
index f25258a1af..a0e6b868d7 100644
--- a/conda/environments/rust_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-129_arch-aarch64.yaml
@@ -21,8 +21,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.8.*,>=0.0.0a0
-- libraft==25.8.*,>=0.0.0a0
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/conda/environments/rust_cuda-129_arch-x86_64.yaml b/conda/environments/rust_cuda-129_arch-x86_64.yaml
index c6f36470a1..2e845864db 100644
--- a/conda/environments/rust_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-129_arch-x86_64.yaml
@@ -21,8 +21,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.8.*,>=0.0.0a0
-- libraft==25.8.*,>=0.0.0a0
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/dependencies.yaml b/dependencies.yaml
index 0f77bcf3aa..88469b62c3 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -489,7 +489,7 @@ dependencies:
       - output_types: [conda, pyproject, requirements]
         packages:
           - click
-          - cuvs==25.8.*,>=0.0.0a0
+          - cuvs==25.10.*,>=0.0.0a0
           - pandas
           - pyyaml
           - requests
@@ -504,17 +504,17 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - cuvs==25.8.*,>=0.0.0a0
+          - cuvs==25.10.*,>=0.0.0a0
   depends_on_cuvs_bench:
     common:
       - output_types: conda
         packages:
-          - cuvs-bench==25.8.*,>=0.0.0a0
+          - cuvs-bench==25.10.*,>=0.0.0a0
   depends_on_libcuvs:
     common:
       - output_types: conda
         packages:
-          - &libcuvs_unsuffixed libcuvs==25.8.*,>=0.0.0a0
+          - &libcuvs_unsuffixed libcuvs==25.10.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -527,18 +527,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu12==25.8.*,>=0.0.0a0
+              - libcuvs-cu12==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
   depends_on_libcuvs_tests:
     common:
       - output_types: conda
         packages:
-          - libcuvs-tests==25.8.*,>=0.0.0a0
+          - libcuvs-tests==25.10.*,>=0.0.0a0
   depends_on_libraft:
     common:
       - output_types: conda
         packages:
-          - &libraft_unsuffixed libraft==25.8.*,>=0.0.0a0
+          - &libraft_unsuffixed libraft==25.10.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -551,13 +551,13 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu12==25.8.*,>=0.0.0a0
+              - libraft-cu12==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
       - output_types: conda
         packages:
-          - &librmm_unsuffixed librmm==25.8.*,>=0.0.0a0
+          - &librmm_unsuffixed librmm==25.10.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -570,13 +570,13 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu12==25.8.*,>=0.0.0a0
+              - librmm-cu12==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
       - output_types: conda
         packages:
-          - &pylibraft_unsuffixed pylibraft==25.8.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==25.10.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -589,7 +589,7 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu12==25.8.*,>=0.0.0a0
+              - pylibraft-cu12==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
   depends_on_nccl:
     common:
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index 8e8cc28017..b4bc021e3d 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.08/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.10/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.08/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.10/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
diff --git a/examples/go/README.md b/examples/go/README.md
index ebeb012308..bcbea09642 100644
--- a/examples/go/README.md
+++ b/examples/go/README.md
@@ -24,7 +24,7 @@ export CC=clang
 
 2. Install the Go module:
 ```bash
-go get github.com/rapidsai/cuvs/go@v25.08.00 # 25.02.00 being your desired version, selected from https://github.com/rapidsai/cuvs/tags
+go get github.com/rapidsai/cuvs/go@v25.10.00 # 25.02.00 being your desired version, selected from https://github.com/rapidsai/cuvs/tags
 ```
 Then you can build your project with the usual `go build`.
 
diff --git a/java/benchmarks/pom.xml b/java/benchmarks/pom.xml
index f5c77a14bc..f80e197c70 100644
--- a/java/benchmarks/pom.xml
+++ b/java/benchmarks/pom.xml
@@ -21,7 +21,7 @@
 
     <groupId>com.nvidia.cuvs</groupId>
     <artifactId>benchmarks</artifactId>
-    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     <packaging>jar</packaging>
 
     <name>cuvs-java-benchmarks</name>
@@ -41,7 +41,7 @@
         <dependency>
             <groupId>com.nvidia.cuvs</groupId>
             <artifactId>cuvs-java</artifactId>
-            <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+            <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
             <type>jar</type>
         </dependency>
     </dependencies>
diff --git a/java/build.sh b/java/build.sh
index 89c2b4bef2..7d2628d7e5 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -8,7 +8,7 @@ ARGS="$*"
 NUMARGS=$#
 
 CURDIR=$(cd "$(dirname "$0")"; pwd)
-VERSION="25.08.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
+VERSION="25.10.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
 
 if [ -z "${CMAKE_PREFIX_PATH:=}" ]; then
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index deaeea5755..b3b862d291 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -22,7 +22,7 @@
     <groupId>com.nvidia.cuvs</groupId>
     <artifactId>cuvs-java</artifactId>
     <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     <name>cuvs-java</name>
     <description>
         This project provides Java bindings for cuVS, enabling approximate nearest neighbors search and clustering
diff --git a/java/examples/README.md b/java/examples/README.md
index 8477a3859f..29826e96a6 100644
--- a/java/examples/README.md
+++ b/java/examples/README.md
@@ -11,17 +11,17 @@ This maven project contains examples for CAGRA, HNSW, and Bruteforce algorithms.
 ### CAGRA Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.08.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.08.0/cuvs-java-25.08.0.jar com.nvidia.cuvs.examples.CagraExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.10.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.10.0/cuvs-java-25.10.0.jar com.nvidia.cuvs.examples.CagraExample
 ```
 
 ### HNSW Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.08.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.08.0/cuvs-java-25.08.0.jar com.nvidia.cuvs.examples.HnswExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.10.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.10.0/cuvs-java-25.10.0.jar com.nvidia.cuvs.examples.HnswExample
 ```
 
 ### Bruteforce Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.08.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.08.0/cuvs-java-25.08.0.jar com.nvidia.cuvs.examples.BruteForceExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.10.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.10.0/cuvs-java-25.10.0.jar com.nvidia.cuvs.examples.BruteForceExample
 ```
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
index 4c18fd5a28..0c52685672 100644
--- a/java/examples/pom.xml
+++ b/java/examples/pom.xml
@@ -5,7 +5,7 @@
   <groupId>com.nvidia.cuvs.examples</groupId>
   <artifactId>cuvs-java-examples</artifactId>
   <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
   <name>cuvs-java-examples</name>
 
   <properties>
@@ -18,7 +18,7 @@
       <groupId>com.nvidia.cuvs</groupId>
       <artifactId>cuvs-java</artifactId>
       <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.08.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     </dependency>
 
     <dependency>
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 7a00b824df..373e313492 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -32,9 +32,9 @@ license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-python",
-    "libcuvs==25.8.*,>=0.0.0a0",
+    "libcuvs==25.10.*,>=0.0.0a0",
     "numpy>=1.23,<3.0a0",
-    "pylibraft==25.8.*,>=0.0.0a0",
+    "pylibraft==25.10.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -119,9 +119,9 @@ requires = [
     "cmake>=3.30.4",
     "cuda-python",
     "cython>=3.0.0",
-    "libcuvs==25.8.*,>=0.0.0a0",
-    "libraft==25.8.*,>=0.0.0a0",
-    "librmm==25.8.*,>=0.0.0a0",
+    "libcuvs==25.10.*,>=0.0.0a0",
+    "libraft==25.10.*,>=0.0.0a0",
+    "librmm==25.10.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
diff --git a/python/cuvs_bench/cuvs_bench/plot/__main__.py b/python/cuvs_bench/cuvs_bench/plot/__main__.py
index e0dbc27837..1c8a3005f6 100644
--- a/python/cuvs_bench/cuvs_bench/plot/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/plot/__main__.py
@@ -17,7 +17,7 @@
 # 1: https://github.com/erikbern/ann-benchmarks/blob/main/plot.py
 # 2: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/utils.py  # noqa: E501
 # 3: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/metrics.py  # noqa: E501
-# License: https://github.com/rapidsai/cuvs/blob/branch-25.08/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
+# License: https://github.com/rapidsai/cuvs/blob/branch-25.10/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
 
 import itertools
 import os
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 59c3829317..901a37e74d 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -19,7 +19,7 @@ license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "click",
-    "cuvs==25.8.*,>=0.0.0a0",
+    "cuvs==25.10.*,>=0.0.0a0",
     "matplotlib>=3.9",
     "pandas",
     "pyyaml",
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 9d357e6928..00e49155da 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -30,8 +30,8 @@ authors = [
 license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
-    "libraft==25.8.*,>=0.0.0a0",
-    "librmm==25.8.*,>=0.0.0a0",
+    "libraft==25.10.*,>=0.0.0a0",
+    "librmm==25.10.*,>=0.0.0a0",
     "nvidia-cublas",
     "nvidia-curand",
     "nvidia-cusolver",
@@ -93,8 +93,8 @@ regex = "(?P<value>.*)"
 build-backend = "scikit_build_core.build"
 requires = [
     "cmake>=3.30.4",
-    "libraft==25.8.*,>=0.0.0a0",
-    "librmm==25.8.*,>=0.0.0a0",
+    "libraft==25.10.*,>=0.0.0a0",
+    "librmm==25.10.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 dependencies-file = "../../dependencies.yaml"
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index 98c2c94018..d8a98b521f 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "25.8.0"
+version = "25.10.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index d2774e9140..c7b713f696 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.8.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.10.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From c194f72069c59e4405ecd52c19d0a2ad3a2752ce Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Thu, 17 Jul 2025 15:25:04 -0700
Subject: [PATCH 486/623] fix(docker): use versioned `-latest` tag for all
 `rapidsai` images (#1129)

In rapidsai/build-planning#187 we switched the docker image tagging scheme
over to include the CalVer information.  This was done to allow us to make
changes to the images during burndown without breaking release pipelines.

This PR moves all of the existing `latest` tags to the newer versioned tag
`25.08-latest` and also modifies the `update_version.sh` script to bump
that version at branch creation time.

xref: https://github.com/rapidsai/build-planning/issues/187

Authors:
  - Gil Forsyth (https://github.com/gforsyth)
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/1129
---
 .github/workflows/build.yaml        | 8 ++++----
 .github/workflows/pr.yaml           | 8 ++++----
 .github/workflows/publish-rust.yaml | 2 +-
 .github/workflows/test.yaml         | 2 +-
 ci/release/update-version.sh        | 1 +
 5 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 9c487f2bdf..7a3f8f29ee 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -50,7 +50,7 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
@@ -63,7 +63,7 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_go.sh"
       sha: ${{ inputs.sha }}
@@ -76,7 +76,7 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       script: "ci/build_java.sh"
       file_to_upload: "java/cuvs-java/target/"
       sha: ${{ inputs.sha }}
@@ -113,7 +113,7 @@ jobs:
       arch: "amd64"
       branch: ${{ inputs.branch }}
       build_type: ${{ inputs.build_type || 'branch' }}
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       date: ${{ inputs.date }}
       node_type: "gpu-l4-latest-1"
       script: "ci/build_docs.sh"
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 55d936b204..d817082b67 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -160,7 +160,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       script: "ci/test_java.sh"
       file_to_upload: "java/cuvs-java/target/"
   docs-build:
@@ -171,7 +171,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       script: "ci/build_docs.sh"
   rust-build:
     needs: conda-cpp-build
@@ -181,7 +181,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       script: "ci/build_rust.sh"
   go-build:
     needs: conda-cpp-build
@@ -191,7 +191,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       script: "ci/build_go.sh"
   wheel-build-libcuvs:
     needs: checks
diff --git a/.github/workflows/publish-rust.yaml b/.github/workflows/publish-rust.yaml
index 86fb14712e..6b9846ec24 100644
--- a/.github/workflows/publish-rust.yaml
+++ b/.github/workflows/publish-rust.yaml
@@ -11,7 +11,7 @@ jobs:
   rust-publish:
     runs-on: ubuntu-latest
     container:
-      image: rapidsai/ci-conda:latest
+      image: rapidsai/ci-conda:25.08-latest
     steps:
       - uses: actions/checkout@v4
       - name: Check if release build
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 7eaf54f7e5..93c8c80b03 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -60,7 +60,7 @@ jobs:
       sha: ${{ inputs.sha }}
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:latest"
+      container_image: "rapidsai/ci-conda:25.08-latest"
       script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 16cd845eed..50be522c6d 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -73,6 +73,7 @@ done
 
 for FILE in .github/workflows/*.yaml; do
   sed_runner "/shared-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}"
+  sed_runner "s/:[0-9]*\\.[0-9]*-/:${NEXT_SHORT_TAG}-/g" "${FILE}"
 done
 
 sed_runner "/rapidsai\/raft/ s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/developer_guide.md

From 35b6cadbf535d3af1bc91c6598d0374b4792ddd0 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Thu, 17 Jul 2025 16:48:59 -0700
Subject: [PATCH 487/623] Add Golang build instructions to build.rst (#1116)

This commit adds instructions to build cuVS Golang bindings.

This is a follow-up to #1114.  When testing the fix in #1114, it became apparent that the Golang build was not documented.  It wasn't immediately evident that `CGO_CFLAGS` (etc.) needed to be set for the right header files to be included.

This has now been documented in the build steps.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1116
---
 docs/source/build.rst | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/docs/source/build.rst b/docs/source/build.rst
index b5a7eead27..4aaf8815c4 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -159,6 +159,21 @@ The Python packages can also be uninstalled using the `build.sh` script:
 
     ./build.sh python --uninstall
 
+Go library
+^^^^^^^^^^
+
+After building the C and C++ libraries, the Golang library can be built with the following command:
+
+.. code-block:: bash
+
+    export CUDA_HOME="/usr/local/cuda" # or wherever your CUDA installation is.
+    export CGO_CFLAGS="-I${CONDA_PREFIX}/include -I${CUDA_HOME}/include"
+    export CGO_LDFLAGS="-L${CONDA_PREFIX}/lib -lcudart -lcuvs -lcuvs_c"
+    export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+    export CC=clang
+
+    ./build.sh go
+
 Rust library
 ^^^^^^^^^^^^
 

From 28d2de677e766d345bebb89c3351a38f48e9dcd5 Mon Sep 17 00:00:00 2001
From: Puneet Ahuja <167976912+punAhuja@users.noreply.github.com>
Date: Fri, 18 Jul 2025 10:21:06 +0530
Subject: [PATCH 488/623] [Java] Support for tiered index (#1028)

TieredIndex exposed in the Java layer.

 Code is written and tested for CAGRA algorithm only.

 Serialization, Deserialization and merge is still not supported in the C layer.

Fixes: #1014

Authors:
  - Puneet Ahuja (https://github.com/punAhuja)
  - Vivek Narang (https://github.com/narangvivek10)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1028
---
 .../java/com/nvidia/cuvs/TieredIndex.java     | 184 +++++
 .../com/nvidia/cuvs/TieredIndexParams.java    | 173 +++++
 .../com/nvidia/cuvs/TieredIndexQuery.java     | 237 +++++++
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |   5 +
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |   6 +
 .../nvidia/cuvs/internal/TieredIndexImpl.java | 644 ++++++++++++++++++
 .../internal/TieredSearchResultsImpl.java     |  62 ++
 .../com/nvidia/cuvs/spi/JDKProvider.java      |   7 +
 .../java/com/nvidia/cuvs/TieredIndexIT.java   | 259 +++++++
 java/panama-bindings/headers.h                |   1 +
 10 files changed, 1578 insertions(+)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexParams.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredSearchResultsImpl.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
new file mode 100644
index 0000000000..a6bd7e1f6e
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
@@ -0,0 +1,184 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.spi.CuVSProvider;
+import java.io.InputStream;
+import java.util.Objects;
+
+/**
+ * {@link TieredIndex} encapsulates a Tiered index, along with methods to
+ * interact with it.
+ */
+public interface TieredIndex {
+
+  /**
+   * Destroys the underlying native TieredIndex object and releases associated
+   * resources.
+   *
+   * @throws Throwable if an error occurs during index destruction
+   */
+  void destroyIndex() throws Throwable;
+
+  /**
+   * Searches the index with the specified query and search parameters.
+   *
+   * @param query An instance of {@link TieredIndexQuery} describing the queries
+   *              and search parameters
+   * @return An instance of {@link SearchResults} containing the k-nearest
+   *         neighbors and their distances for each query
+   * @throws Throwable if an error occurs during the search operation
+   */
+  SearchResults search(TieredIndexQuery query) throws Throwable;
+
+  /**
+   * Returns the algorithm type backing this TieredIndex.
+   *
+   * @return The {@link TieredIndexType} indicating the underlying algorithm
+   *         (e.g., CAGRA)
+   */
+  TieredIndexType getIndexType();
+
+  /**
+   * Returns the resources handle associated with this TieredIndex.
+   *
+   * @return The {@link CuVSResources} instance used by this index
+   */
+  CuVSResources getCuVSResources();
+
+  /**
+   * Creates a new Builder with an instance of {@link CuVSResources}.
+   *
+   * @param cuvsResources An instance of {@link CuVSResources}
+   * @return A new {@link Builder} instance for constructing a TieredIndex
+   * @throws NullPointerException if cuvsResources is null
+   */
+  static Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    return CuVSProvider.provider().newTieredIndexBuilder(cuvsResources);
+  }
+
+  /**
+   * Returns an ExtendBuilder to add new data to the existing index.
+   *
+   * @return An {@link ExtendBuilder} instance for extending the index
+   */
+  ExtendBuilder extend();
+
+  /**
+   * Builder interface for constructing {@link TieredIndex} instances.
+   */
+  interface Builder {
+
+    /**
+     *
+     * @param inputStream The input stream containing serialized index data
+     * @return This Builder instance for method chaining
+     * @throws UnsupportedOperationException as deserialization is not yet
+     *                                       supported
+     */
+    Builder from(InputStream inputStream);
+
+    /**
+     * Sets the dataset vectors for building the TieredIndex.
+     *
+     * @param vectors A two-dimensional float array containing the dataset
+     *                vectors [n_vectors, dimensions]
+     * @return This Builder instance for method chaining
+     */
+    Builder withDataset(float[][] vectors);
+
+    /**
+     * Sets the dataset for building the TieredIndex.
+     *
+     * @param dataset A {@link Dataset} instance containing the vectors
+     * @return This Builder instance for method chaining
+     */
+    Builder withDataset(Dataset dataset);
+
+    /**
+     * Registers TieredIndex parameters with this Builder.
+     *
+     * @param params An instance of {@link TieredIndexParams} containing the
+     *               index configuration
+     * @return This Builder instance for method chaining
+     */
+    Builder withIndexParams(TieredIndexParams params);
+
+    /**
+     * Sets the index type for the TieredIndex.
+     *
+     * @param indexType The {@link TieredIndexType} to use (currently only CAGRA
+     *                  is supported)
+     * @return This Builder instance for method chaining
+     */
+    Builder withIndexType(TieredIndexType indexType);
+
+    /**
+     * Builds and returns an instance of TieredIndex with the configured
+     * parameters.
+     *
+     * @return A new {@link TieredIndex} instance
+     * @throws Throwable                if an error occurs during index
+     *                                  construction
+     * @throws IllegalArgumentException if both vectors and dataset are provided,
+     *                                  or if required parameters are missing
+     */
+    TieredIndex build() throws Throwable;
+  }
+
+  /**
+   * Enumeration of supported TieredIndex algorithm types.
+   */
+  enum TieredIndexType {
+    CAGRA
+  }
+
+  /**
+   * Builder interface for extending existing {@link TieredIndex} instances with
+   * new data.
+   */
+  interface ExtendBuilder {
+
+    /**
+     * Sets the vectors to add to the existing index.
+     *
+     * @param vectors A two-dimensional float array containing the new vectors to
+     *                add [n_new_vectors, dimensions]
+     * @return This ExtendBuilder instance for method chaining
+     */
+    ExtendBuilder withDataset(float[][] vectors);
+
+    /**
+     * Sets the dataset to add to the existing index.
+     *
+     * @param dataset A {@link Dataset} instance containing the new vectors to
+     *                add
+     * @return This ExtendBuilder instance for method chaining
+     */
+    ExtendBuilder withDataset(Dataset dataset);
+
+    /**
+     * Executes the extend operation, adding the specified data to the index.
+     *
+     * @throws Throwable                if an error occurs during the extend
+     *                                  operation
+     * @throws IllegalArgumentException if both vectors and dataset are provided,
+     *                                  or if no data is provided
+     */
+    void execute() throws Throwable;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexParams.java
new file mode 100644
index 0000000000..f62892b2c1
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexParams.java
@@ -0,0 +1,173 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import java.util.Objects;
+
+/**
+ * Configuration parameters for building a {@link TieredIndex}.
+ * Only CAGRA is currently supported as the underlying ANN algorithm.
+ *
+ */
+public final class TieredIndexParams {
+
+  /**
+   * Enumeration of supported distance metrics for TieredIndex.
+   */
+  public enum Metric {
+    /** L2 (Euclidean) distance metric */
+    L2,
+    /** Inner product (cosine similarity) distance metric */
+    INNER_PRODUCT
+  }
+
+  private final Metric metric;
+  private final int minAnnRows;
+  private final boolean createAnnIndexOnExtend;
+  private final CagraIndexParams cagraParams;
+
+  /**
+   * Private constructor used by the Builder.
+   *
+   * @param builder The Builder instance containing the configuration
+   */
+  private TieredIndexParams(Builder builder) {
+    this.metric = builder.metric;
+    this.minAnnRows = builder.minAnnRows;
+    this.createAnnIndexOnExtend = builder.createAnnIndexOnExtend;
+    this.cagraParams = builder.cagraParams;
+  }
+
+  /**
+   * Returns the distance metric used for similarity computation.
+   *
+   * @return The {@link Metric} (L2 or Inner Product)
+   */
+  public Metric getMetric() {
+    return metric;
+  }
+
+  /**
+   * Returns the minimum number of rows required to use the ANN algorithm.
+   *
+   * @return The minimum row count threshold for ANN algorithm usage
+   */
+  public int getMinAnnRows() {
+    return minAnnRows;
+  }
+
+  /**
+   * Returns whether to create an ANN index when extending the dataset.
+   *
+   * @return true if ANN index should be created on extend, false otherwise
+   */
+  public boolean isCreateAnnIndexOnExtend() {
+    return createAnnIndexOnExtend;
+  }
+
+  /**
+   * Returns the CAGRA-specific parameters for the ANN algorithm.
+   *
+   * @return The {@link CagraIndexParams} configuration, or null if not using
+   *         CAGRA
+   */
+  public CagraIndexParams getCagraParams() {
+    return cagraParams;
+  }
+
+  /**
+   * Creates a new Builder for constructing TieredIndexParams.
+   *
+   * @return A new Builder instance
+   */
+  public static Builder newBuilder() {
+    return new Builder();
+  }
+
+  /**
+   * Builder class for constructing {@link TieredIndexParams} instances.
+   */
+  public static final class Builder {
+    private Metric metric = Metric.L2;
+    private int minAnnRows = 4096;
+    private boolean createAnnIndexOnExtend = true;
+    private CagraIndexParams cagraParams = null;
+
+    /**
+     * Sets the distance metric for similarity computation.
+     *
+     * @param metric The {@link Metric} to use (L2 or Inner Product)
+     * @return This Builder instance for method chaining
+     * @throws NullPointerException if metric is null
+     */
+    public Builder metric(Metric metric) {
+      this.metric = Objects.requireNonNull(metric);
+      return this;
+    }
+
+    /**
+     * Sets the minimum number of rows required to use the ANN algorithm.
+     *
+     * @param minAnnRows The minimum row count threshold (must be positive)
+     * @return This Builder instance for method chaining
+     * @throws IllegalArgumentException if minAnnRows is not positive
+     */
+    public Builder minAnnRows(int minAnnRows) {
+      if (minAnnRows <= 0) {
+        throw new IllegalArgumentException("minAnnRows must be positive, got: " + minAnnRows);
+      }
+      this.minAnnRows = minAnnRows;
+      return this;
+    }
+
+    /**
+     * Sets whether to create an ANN index when extending the dataset.
+     *
+     * @param val true to create ANN index on extend, false otherwise
+     * @return This Builder instance for method chaining
+     */
+    public Builder createAnnIndexOnExtend(boolean val) {
+      this.createAnnIndexOnExtend = val;
+      return this;
+    }
+
+    /**
+     * Sets the CAGRA-specific parameters for the ANN algorithm.
+     *
+     * @param params The {@link CagraIndexParams} configuration for CAGRA
+     *               algorithm
+     * @return This Builder instance for method chaining
+     * @throws NullPointerException if params is null
+     */
+    public Builder withCagraParams(CagraIndexParams params) {
+      this.cagraParams = Objects.requireNonNull(params);
+      return this;
+    }
+
+    /**
+     * Builds and returns a {@link TieredIndexParams} instance with the
+     * configured parameters.
+     *
+     * @return A new TieredIndexParams instance
+     * @throws IllegalStateException if CAGRA params are required but not
+     *                               provided
+     */
+    public TieredIndexParams build() {
+      if (cagraParams == null) throw new IllegalStateException("CAGRA params required");
+      return new TieredIndexParams(this);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java
new file mode 100644
index 0000000000..03bfae070e
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java
@@ -0,0 +1,237 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.TieredIndex.TieredIndexType;
+import java.util.Arrays;
+import java.util.BitSet;
+import java.util.List;
+
+/**
+ * TieredIndexQuery holds the search parameters and query vectors to be used
+ * while
+ * invoking search. Currently only supports CAGRA index type.
+ *
+ * @since 25.02
+ */
+public class TieredIndexQuery {
+  private TieredIndexType indexType;
+  private CagraSearchParams cagraSearchParameters;
+  private List<Integer> mapping;
+  private float[][] queryVectors;
+  private int topK;
+  private BitSet prefilter;
+  private long numDocs;
+
+  private TieredIndexQuery(
+      TieredIndexType indexType,
+      CagraSearchParams cagraSearchParameters,
+      List<Integer> mapping,
+      float[][] queryVectors,
+      int topK,
+      BitSet prefilter,
+      long numDocs) {
+    super();
+    this.indexType = indexType;
+    this.cagraSearchParameters = cagraSearchParameters;
+    this.mapping = mapping;
+    this.queryVectors = queryVectors;
+    this.topK = topK;
+    this.prefilter = prefilter;
+    this.numDocs = numDocs;
+  }
+
+  /**
+   * Gets the index type for this query.
+   *
+   * @return the TieredIndexType
+   */
+  public TieredIndexType getIndexType() {
+    return indexType;
+  }
+
+  /**
+   * Gets the instance of CagraSearchParams initially set.
+   *
+   * @return an instance CagraSearchParams
+   */
+  public CagraSearchParams getCagraSearchParameters() {
+    return cagraSearchParameters;
+  }
+
+  /**
+   * Gets the query vector 2D float array.
+   *
+   * @return 2D float array
+   */
+  public float[][] getQueryVectors() {
+    return queryVectors;
+  }
+
+  /**
+   * Gets the passed map instance.
+   *
+   * @return a map of ID mappings
+   */
+  public List<Integer> getMapping() {
+    return mapping;
+  }
+
+  /**
+   * Gets the topK value.
+   *
+   * @return the topK value
+   */
+  public int getTopK() {
+    return topK;
+  }
+
+  /**
+   * Gets the prefilter BitSet.
+   *
+   * @return a BitSet object representing the prefilter
+   */
+  public BitSet getPrefilter() {
+    return prefilter;
+  }
+
+  /**
+   * Gets the number of documents in this index, as used for prefilter.
+   *
+   * @return number of documents as an integer
+   */
+  public long getNumDocs() {
+    return numDocs;
+  }
+
+  @Override
+  public String toString() {
+    return "TieredIndexQuery [indexType="
+        + indexType
+        + ", cagraSearchParameters="
+        + cagraSearchParameters
+        + ", queryVectors="
+        + Arrays.toString(queryVectors)
+        + ", mapping="
+        + mapping
+        + ", topK="
+        + topK
+        + "]";
+  }
+
+  /**
+   * Creates a new Builder instance.
+   *
+   * @return a new Builder instance
+   */
+  public static Builder newBuilder() {
+    return new Builder();
+  }
+
+  /**
+   * Builder helps configure and create an instance of TieredIndexQuery.
+   */
+  public static class Builder {
+    private TieredIndexType indexType = TieredIndexType.CAGRA;
+    private CagraSearchParams cagraSearchParams;
+    private float[][] queryVectors;
+    private List<Integer> mapping;
+    private int topK = 2;
+    private BitSet prefilter;
+    private long numDocs;
+
+    /**
+     * Sets the index type for this query.
+     *
+     * @param indexType the index type
+     * @return an instance of this Builder
+     */
+    public Builder withIndexType(TieredIndexType indexType) {
+      this.indexType = indexType;
+      return this;
+    }
+
+    /**
+     * Sets the instance of configured CagraSearchParams to be passed for search.
+     *
+     * @param cagraSearchParams an instance of the configured CagraSearchParams to
+     *                          be used for this query
+     * @return an instance of this Builder
+     */
+    public Builder withSearchParams(CagraSearchParams cagraSearchParams) {
+      this.cagraSearchParams = cagraSearchParams;
+      return this;
+    }
+
+    /**
+     * Registers the query vectors to be passed in the search call.
+     *
+     * @param queryVectors 2D float query vector array
+     * @return an instance of this Builder
+     */
+    public Builder withQueryVectors(float[][] queryVectors) {
+      this.queryVectors = queryVectors;
+      return this;
+    }
+
+    /**
+     * Sets the instance of mapping to be used for ID mapping.
+     *
+     * @param mapping the ID mapping instance
+     * @return an instance of this Builder
+     */
+    public Builder withMapping(List<Integer> mapping) {
+      this.mapping = mapping;
+      return this;
+    }
+
+    /**
+     * Registers the topK value.
+     *
+     * @param topK the topK value used to retrieve the topK results
+     * @return an instance of this Builder
+     */
+    public Builder withTopK(int topK) {
+      this.topK = topK;
+      return this;
+    }
+
+    /**
+     * Sets a BitSet to use as prefilter while searching.
+     *
+     * @param prefilter the BitSet to use as prefilter
+     * @param numDocs   Total number of dataset vectors; used to align the prefilter
+     *                  correctly
+     * @return an instance of this Builder
+     */
+    public Builder withPrefilter(BitSet prefilter, int numDocs) {
+      this.prefilter = prefilter;
+      this.numDocs = numDocs;
+      return this;
+    }
+
+    /**
+     * Builds an instance of TieredIndexQuery.
+     *
+     * @return an instance of TieredIndexQuery
+     * @throws IllegalStateException if required parameters are missing
+     */
+    public TieredIndexQuery build() {
+      return new TieredIndexQuery(
+          indexType, cagraSearchParams, mapping, queryVectors, topK, prefilter, numDocs);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 8ce8b3c2a2..4dd7fa8eb8 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -21,6 +21,7 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.TieredIndex;
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.MethodType;
 import java.nio.file.Path;
@@ -85,6 +86,10 @@ CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources)
   HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources)
       throws UnsupportedOperationException;
 
+  /** Creates a new TieredIndex Builder. */
+  TieredIndex.Builder newTieredIndexBuilder(CuVSResources cuVSResources)
+      throws UnsupportedOperationException;
+
   /**
    * Merges multiple CAGRA indexes into a single index.
    *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index e7bb0ab663..7e5d1a7e64 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -20,6 +20,7 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.TieredIndex;
 import java.lang.invoke.MethodHandle;
 import java.nio.file.Path;
 
@@ -48,6 +49,11 @@ public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
     throw new UnsupportedOperationException();
   }
 
+  @Override
+  public TieredIndex.Builder newTieredIndexBuilder(CuVSResources cuVSResources) {
+    throw new UnsupportedOperationException();
+  }
+
   @Override
   public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
     throw new UnsupportedOperationException();
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
new file mode 100644
index 0000000000..035bbe2eef
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -0,0 +1,644 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.*;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
+import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
+import static com.nvidia.cuvs.internal.common.Util.concatenate;
+import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexBuild;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexExtend;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexSearch;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndex_t;
+
+import com.nvidia.cuvs.CagraIndexParams;
+import com.nvidia.cuvs.CagraSearchParams;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.Dataset;
+import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.TieredIndex;
+import com.nvidia.cuvs.TieredIndexParams;
+import com.nvidia.cuvs.TieredIndexQuery;
+import com.nvidia.cuvs.internal.common.Util;
+import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
+import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
+import com.nvidia.cuvs.internal.panama.cuvsFilter;
+import com.nvidia.cuvs.internal.panama.cuvsTieredIndexParams;
+import java.io.InputStream;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.util.BitSet;
+import java.util.Objects;
+
+/**
+ * {@link TieredIndex} encapscaps a Tiered index, along with methods to interact
+ * with it.
+ * <p>
+ * TieredIndex is a hybrid index that combines brute force search for small datasets
+ * with ANN algorithms (like CAGRA) for larger datasets, providing optimal performance
+ * across different data sizes.
+ *
+ * @since 25.02
+ */
+public class TieredIndexImpl implements TieredIndex {
+  private final float[][] vectors;
+  private final Dataset dataset;
+  private final CuVSResourcesImpl resources;
+  private final TieredIndexParams tieredIndexParameters;
+  private final IndexReference tieredIndexReference;
+  private boolean destroyed;
+
+  /**
+   * Constructor for building the index using specified dataset
+   */
+  private TieredIndexImpl(
+      TieredIndexParams indexParameters,
+      float[][] vectors,
+      Dataset dataset,
+      CuVSResourcesImpl resources)
+      throws Throwable {
+    this.tieredIndexParameters = indexParameters;
+    this.vectors = vectors;
+    this.dataset = dataset;
+    this.resources = resources;
+    this.tieredIndexReference = build();
+    this.destroyed = false;
+  }
+
+  /**
+   * Constructor for loading the index from an {@link InputStream}
+   */
+  private TieredIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
+    throw new UnsupportedOperationException("Deserialization of TieredIndex is not yet supported");
+  }
+
+  /**
+   * Constructor for creating an index from an existing index reference.
+   */
+  private TieredIndexImpl(IndexReference indexReference, CuVSResourcesImpl resources) {
+    this.vectors = null;
+    this.tieredIndexParameters = null;
+    this.dataset = null;
+    this.resources = resources;
+    this.tieredIndexReference = indexReference;
+    this.destroyed = false;
+  }
+
+  private void checkNotDestroyed() {
+    if (destroyed) {
+      throw new IllegalStateException("destroyed");
+    }
+  }
+
+  /**
+   * Invokes the native destroy_tiered_index to de-allocate the Tiered index
+   */
+  @Override
+  public void destroyIndex() throws Throwable {
+    checkNotDestroyed();
+    try {
+      int returnValue = cuvsTieredIndexDestroy(tieredIndexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsTieredIndexDestroy");
+    } finally {
+      destroyed = true;
+    }
+  }
+
+  /**
+   * Translates C build_tiered_index function to Java
+   * Invokes the native build_tiered_index function via the Panama API to build the
+   * {@link TieredIndex}
+   *
+   * @return an instance of {@link IndexReference} that holds the pointer to the
+   *         index
+   */
+  private IndexReference build() throws Throwable {
+    try (var localArena = Arena.ofConfined()) {
+      long rows = dataset != null ? dataset.size() : vectors.length;
+      long cols = dataset != null ? dataset.dimensions() : (rows > 0 ? vectors[0].length : 0);
+
+      MemorySegment indexParamsMemorySegment =
+          tieredIndexParameters != null
+              ? segmentFromIndexParams(localArena, tieredIndexParameters)
+              : MemorySegment.NULL;
+
+      // Get host data
+      MemorySegment hostDataSeg =
+          dataset != null
+              ? ((DatasetImpl) dataset).asMemorySegment()
+              : Util.buildMemorySegment(localArena, vectors);
+
+      long cuvsRes = resources.getHandle();
+
+      // TieredIndex REQUIRES device memory - allocate it
+      MemorySegment datasetD = localArena.allocate(C_POINTER);
+      long datasetSize = C_FLOAT_BYTE_SIZE * rows * cols;
+      int returnValue = cuvsRMMAlloc(cuvsRes, datasetD, datasetSize);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+      MemorySegment datasetDP = datasetD.get(C_POINTER, 0);
+
+      // Copy host to device
+      Util.cudaMemcpy(datasetDP, hostDataSeg, datasetSize, HOST_TO_DEVICE);
+
+      // Create tensor from device memory
+      long[] datasetShape = {rows, cols};
+      MemorySegment datasetTensor =
+          prepareTensor(localArena, datasetDP, datasetShape, 2, 32, 2, 2, 1);
+
+      MemorySegment index = localArena.allocate(cuvsTieredIndex_t);
+      returnValue = cuvsTieredIndexCreate(index);
+      checkCuVSError(returnValue, "cuvsTieredIndexCreate");
+
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      // Extract the actual index pointer that was written by Create
+      MemorySegment actualIndexPtr = index.get(C_POINTER, 0);
+
+      returnValue =
+          cuvsTieredIndexBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, actualIndexPtr);
+      checkCuVSError(returnValue, "cuvsTieredIndexBuild");
+
+      // Clean up device memory after build
+      returnValue = cuvsRMMFree(cuvsRes, datasetDP, datasetSize);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+
+      return new IndexReference(actualIndexPtr);
+    }
+  }
+
+  /**
+   * Translates C search_tiered_index function to Java
+   * Invokes the native search_tiered_index via the Panama API for searching a
+   * Tiered index.
+   *
+   * @param query an instance of {@link TieredIndexQuery} holding the query vectors and
+   *              other parameters
+   * @return an instance of {@link SearchResults} containing the results
+   */
+  @Override
+  public SearchResults search(TieredIndexQuery query) throws Throwable {
+    try (var localArena = Arena.ofConfined()) {
+      checkNotDestroyed();
+      int topK =
+          query.getMapping() != null
+              ? Math.min(query.getMapping().size(), query.getTopK())
+              : query.getTopK();
+      long numQueries = query.getQueryVectors().length;
+      long numBlocks = (long) topK * numQueries;
+      int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
+
+      SequenceLayout neighborsLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
+      SequenceLayout distancesLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+      MemorySegment neighborsSeg = localArena.allocate(neighborsLayout);
+      MemorySegment distancesSeg = localArena.allocate(distancesLayout);
+
+      // Get host query data
+      MemorySegment hostQueriesSeg = Util.buildMemorySegment(localArena, query.getQueryVectors());
+
+      long cuvsRes = resources.getHandle();
+
+      // Allocate DEVICE memory for all data
+      MemorySegment queriesD = localArena.allocate(C_POINTER);
+      MemorySegment neighborsD = localArena.allocate(C_POINTER);
+      MemorySegment distancesD = localArena.allocate(C_POINTER);
+
+      long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+      long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topK; // 64-bit for tiered index
+      long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+
+      int returnValue = cuvsRMMAlloc(cuvsRes, queriesD, queriesBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+      returnValue = cuvsRMMAlloc(cuvsRes, neighborsD, neighborsBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+      returnValue = cuvsRMMAlloc(cuvsRes, distancesD, distancesBytes);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+      // Get device pointers
+      MemorySegment queriesDP = queriesD.get(C_POINTER, 0);
+      MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
+      MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
+
+      // Copy queries from host to device
+      returnValue =
+          cudaMemcpy(queriesDP, hostQueriesSeg, queriesBytes, 1); // cudaMemcpyHostToDevice
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      // Create tensors from device memory
+      long queriesShape[] = {numQueries, vectorDimension};
+      MemorySegment queriesTensor =
+          prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+      long neighborsShape[] = {numQueries, topK};
+      MemorySegment neighborsTensor =
+          prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1); // 64-bit int
+      long distancesShape[] = {numQueries, topK};
+      MemorySegment distancesTensor =
+          prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+
+      // Sync before prefilter setup
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      // Handle prefilter
+      MemorySegment prefilter = cuvsFilter.allocate(localArena);
+      MemorySegment prefilterD = localArena.allocate(C_POINTER);
+      MemorySegment prefilterDP = MemorySegment.NULL;
+      long prefilterBytes = 0;
+
+      if (query.getPrefilter() != null) {
+        BitSet[] prefilters = new BitSet[] {query.getPrefilter()};
+        BitSet concatenatedFilters = concatenate(prefilters, (int) query.getNumDocs());
+        long filters[] = concatenatedFilters.toLongArray();
+        MemorySegment hostPrefilterSeg = buildMemorySegment(localArena, filters);
+
+        long prefilterDataLength = query.getNumDocs() * prefilters.length;
+        long prefilterShape[] = {(prefilterDataLength + 31) / 32};
+        long prefilterLen = prefilterShape[0];
+        prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
+
+        // Allocate device memory for prefilter
+        returnValue = cuvsRMMAlloc(cuvsRes, prefilterD, prefilterBytes);
+        checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+        prefilterDP = prefilterD.get(C_POINTER, 0);
+
+        // Copy prefilter to device
+        returnValue = cudaMemcpy(prefilterDP, hostPrefilterSeg, prefilterBytes, 1);
+        checkCudaError(returnValue, "cudaMemcpy");
+
+        MemorySegment prefilterTensor =
+            prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+
+        cuvsFilter.type(prefilter, 1); // BITSET
+        cuvsFilter.addr(prefilter, prefilterTensor.address());
+      } else {
+        cuvsFilter.type(prefilter, 0); // NO_FILTER
+        cuvsFilter.addr(prefilter, 0);
+      }
+
+      // Perform search
+      returnValue =
+          cuvsTieredIndexSearch(
+              cuvsRes,
+              segmentFromSearchParams(query.getCagraSearchParameters(), localArena),
+              tieredIndexReference.getMemorySegment(),
+              queriesTensor,
+              neighborsTensor,
+              distancesTensor,
+              prefilter);
+      checkCuVSError(returnValue, "cuvsTieredIndexSearch");
+
+      // Copy results from device to host
+      returnValue =
+          cudaMemcpy(neighborsSeg, neighborsDP, neighborsBytes, 2); // cudaMemcpyDeviceToHost
+      checkCudaError(returnValue, "cudaMemcpy");
+      returnValue = cudaMemcpy(distancesSeg, distancesDP, distancesBytes, 2);
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      // Clean up device memory
+      returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+      returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+      returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+
+      if (prefilterDP != MemorySegment.NULL) {
+        returnValue = cuvsRMMFree(cuvsRes, prefilterDP, prefilterBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+      }
+
+      return TieredSearchResultsImpl.create(
+          neighborsLayout,
+          distancesLayout,
+          neighborsSeg,
+          distancesSeg,
+          topK,
+          query.getMapping(),
+          numQueries);
+    }
+  }
+
+  @Override
+  public ExtendBuilder extend() {
+    checkNotDestroyed();
+    return new ExtendBuilder(this);
+  }
+
+  /**
+   * Performs the actual extend operation
+   */
+  private void performExtend(float[][] extendVectors, Dataset extendDataset) throws Throwable {
+    try (var localArena = Arena.ofConfined()) {
+      long rows = extendDataset != null ? extendDataset.size() : extendVectors.length;
+      long cols = extendDataset != null ? extendDataset.dimensions() : extendVectors[0].length;
+
+      // Get host data
+      MemorySegment hostDataSeg =
+          extendDataset != null
+              ? ((DatasetImpl) extendDataset).asMemorySegment()
+              : Util.buildMemorySegment(localArena, extendVectors);
+
+      long cuvsRes = resources.getHandle();
+
+      // Allocate device memory for extend data
+      MemorySegment datasetD = localArena.allocate(C_POINTER);
+      long dataSize = C_FLOAT_BYTE_SIZE * rows * cols;
+      int returnValue = cuvsRMMAlloc(cuvsRes, datasetD, dataSize);
+      checkCuVSError(returnValue, "cuvsRMMAlloc");
+
+      MemorySegment datasetDP = datasetD.get(C_POINTER, 0);
+
+      // Copy host to device
+      returnValue = cudaMemcpy(datasetDP, hostDataSeg, dataSize, 1); // cudaMemcpyHostToDevice
+      checkCudaError(returnValue, "cudaMemcpy");
+
+      // Create tensor from device memory
+      long datasetShape[] = {rows, cols};
+      MemorySegment datasetTensor =
+          prepareTensor(localArena, datasetDP, datasetShape, 2, 32, 2, 2, 1);
+
+      returnValue = cuvsStreamSync(cuvsRes);
+      checkCuVSError(returnValue, "cuvsStreamSync");
+
+      returnValue =
+          cuvsTieredIndexExtend(cuvsRes, datasetTensor, tieredIndexReference.getMemorySegment());
+      checkCuVSError(returnValue, "cuvsTieredIndexExtend");
+
+      // Clean up device memory
+      returnValue = cuvsRMMFree(cuvsRes, datasetDP, dataSize);
+      checkCuVSError(returnValue, "cuvsRMMFree");
+    }
+  }
+
+  /**
+   * ExtendBuilder implementation
+   */
+  public static class ExtendBuilder implements TieredIndex.ExtendBuilder {
+    private final TieredIndexImpl index;
+    private float[][] vectors;
+    private Dataset dataset;
+
+    private ExtendBuilder(TieredIndexImpl index) {
+      this.index = index;
+    }
+
+    @Override
+    public ExtendBuilder withDataset(float[][] vectors) {
+      this.vectors = vectors;
+      return this;
+    }
+
+    @Override
+    public ExtendBuilder withDataset(Dataset dataset) {
+      this.dataset = dataset;
+      return this;
+    }
+
+    @Override
+    public void execute() throws Throwable {
+      if (vectors != null && dataset != null) {
+        throw new IllegalArgumentException(
+            "Please specify only one type of dataset (a float[][] or a Dataset instance)");
+      }
+      if (vectors == null && dataset == null) {
+        throw new IllegalArgumentException("Must provide vectors or dataset");
+      }
+
+      index.performExtend(vectors, dataset);
+    }
+  }
+
+  /**
+   * Allocates the configured index parameters in the MemorySegment.
+   */
+  private static MemorySegment segmentFromIndexParams(Arena arena, TieredIndexParams params) {
+    MemorySegment seg = cuvsTieredIndexParams.allocate(arena);
+
+    // Get the metric from CagraParams if available, otherwise use TieredIndex metric
+    int metric;
+    if (params.getCagraParams() != null) {
+      // Use the metric from CagraParams to ensure consistency
+      metric = params.getCagraParams().getCuvsDistanceType().value;
+    } else {
+      // Fallback to TieredIndex metric
+      metric =
+          switch (params.getMetric()) {
+            case L2 -> 0;
+            case INNER_PRODUCT -> 1;
+            default ->
+                throw new IllegalArgumentException("Unsupported metric: " + params.getMetric());
+          };
+    }
+
+    cuvsTieredIndexParams.metric(seg, metric);
+
+    int algo = 0; // CUVS_TIERED_INDEX_ALGO_CAGRA
+    cuvsTieredIndexParams.algo(seg, algo);
+
+    cuvsTieredIndexParams.min_ann_rows(seg, params.getMinAnnRows());
+    cuvsTieredIndexParams.create_ann_index_on_extend(seg, params.isCreateAnnIndexOnExtend());
+
+    CagraIndexParams cagraParams = params.getCagraParams();
+    if (cagraParams != null) {
+      MemorySegment cagraParamsSeg = cuvsCagraIndexParams.allocate(arena);
+
+      cuvsCagraIndexParams.intermediate_graph_degree(
+          cagraParamsSeg, cagraParams.getIntermediateGraphDegree());
+      cuvsCagraIndexParams.graph_degree(cagraParamsSeg, cagraParams.getGraphDegree());
+      cuvsCagraIndexParams.build_algo(cagraParamsSeg, cagraParams.getCagraGraphBuildAlgo().value);
+      cuvsCagraIndexParams.nn_descent_niter(
+          cagraParamsSeg, cagraParams.getNNDescentNumIterations());
+      cuvsCagraIndexParams.metric(cagraParamsSeg, metric);
+
+      cuvsTieredIndexParams.cagra_params(seg, cagraParamsSeg);
+    }
+
+    cuvsTieredIndexParams.ivf_flat_params(seg, MemorySegment.NULL);
+    cuvsTieredIndexParams.ivf_pq_params(seg, MemorySegment.NULL);
+
+    return seg;
+  }
+
+  /**
+   * Allocates the configured search parameters in the MemorySegment.
+   */
+  private MemorySegment segmentFromSearchParams(CagraSearchParams params, Arena arena) {
+    MemorySegment seg = cuvsCagraSearchParams.allocate(arena);
+    cuvsCagraSearchParams.max_queries(seg, params.getMaxQueries());
+    cuvsCagraSearchParams.itopk_size(seg, params.getITopKSize());
+    cuvsCagraSearchParams.max_iterations(seg, params.getMaxIterations());
+    if (params.getCagraSearchAlgo() != null) {
+      cuvsCagraSearchParams.algo(seg, params.getCagraSearchAlgo().value);
+    }
+    cuvsCagraSearchParams.team_size(seg, params.getTeamSize());
+    cuvsCagraSearchParams.search_width(seg, params.getSearchWidth());
+    cuvsCagraSearchParams.min_iterations(seg, params.getMinIterations());
+    cuvsCagraSearchParams.thread_block_size(seg, params.getThreadBlockSize());
+    if (params.getHashMapMode() != null) {
+      cuvsCagraSearchParams.hashmap_mode(seg, params.getHashMapMode().value);
+    }
+    cuvsCagraSearchParams.hashmap_max_fill_rate(seg, params.getHashMapMaxFillRate());
+    cuvsCagraSearchParams.num_random_samplings(seg, params.getNumRandomSamplings());
+    cuvsCagraSearchParams.rand_xor_mask(seg, params.getRandXORMask());
+    return seg;
+  }
+
+  /**
+   * Gets an instance of {@link CuVSResources}
+   *
+   * @return an instance of {@link CuVSResources}
+   */
+  @Override
+  public CuVSResources getCuVSResources() {
+    return resources;
+  }
+
+  /**
+   * Gets the index type
+   *
+   * @return the index type
+   */
+  @Override
+  public TieredIndexType getIndexType() {
+    TieredIndexType indexType = TieredIndexType.CAGRA; // Default to CAGRA for now
+    return indexType;
+  }
+
+  /**
+   * Static method to create a new builder
+   */
+  public static TieredIndex.Builder newBuilder(CuVSResources cuvsResources) {
+    Objects.requireNonNull(cuvsResources);
+    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
+      throw new IllegalArgumentException("Unsupported " + cuvsResources);
+    }
+    return new TieredIndexImpl.Builder((CuVSResourcesImpl) cuvsResources);
+  }
+
+  /**
+   * Builder helps configure and create an instance of {@link TieredIndex}.
+   */
+  public static class Builder implements TieredIndex.Builder {
+    private CuVSResourcesImpl resources;
+    private float[][] vectors;
+    private Dataset dataset;
+    private TieredIndexParams params;
+    private TieredIndexType indexType = TieredIndexType.CAGRA;
+    private InputStream inputStream;
+
+    private Builder(CuVSResourcesImpl resources) {
+      this.resources = resources;
+    }
+
+    @Override
+    public Builder from(InputStream inputStream) {
+      this.inputStream = inputStream;
+      return this;
+    }
+
+    @Override
+    public Builder withDataset(float[][] vectors) {
+      this.vectors = vectors;
+      return this;
+    }
+
+    @Override
+    public Builder withDataset(Dataset dataset) {
+      this.dataset = dataset;
+      return this;
+    }
+
+    @Override
+    public Builder withIndexParams(TieredIndexParams params) {
+      this.params = params;
+      return this;
+    }
+
+    @Override
+    public Builder withIndexType(TieredIndexType indexType) {
+      this.indexType = indexType;
+      return this;
+    }
+
+    @Override
+    public TieredIndex build() throws Throwable {
+      if (inputStream != null) {
+        return new TieredIndexImpl(inputStream, resources);
+      } else {
+        if (vectors != null && dataset != null) {
+          throw new IllegalArgumentException(
+              "Please specify only one type of dataset (a float[][] or a Dataset instance)");
+        }
+        if (vectors == null && dataset == null) {
+          throw new IllegalArgumentException("Must provide vectors or dataset");
+        }
+        if (params == null) {
+          throw new IllegalStateException("Index parameters must be provided");
+        }
+        return new TieredIndexImpl(params, vectors, dataset, resources);
+      }
+    }
+  }
+
+  /**
+   * Holds the memory reference to a Tiered index.
+   */
+  public static class IndexReference {
+    private final MemorySegment memorySegment;
+
+    /**
+     * Constructs TieredIndexReference and allocate the MemorySegment.
+     */
+    protected IndexReference(CuVSResourcesImpl resources) {
+      // Don't allocate here - the C function will allocate
+      memorySegment = MemorySegment.NULL;
+    }
+
+    /**
+     * Constructs TieredIndexReference with an instance of MemorySegment passed as a
+     * parameter.
+     */
+    protected IndexReference(MemorySegment indexMemorySegment) {
+      this.memorySegment = indexMemorySegment;
+    }
+
+    /**
+     * Gets the instance of index MemorySegment.
+     */
+    protected MemorySegment getMemorySegment() {
+      return memorySegment;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredSearchResultsImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredSearchResultsImpl.java
new file mode 100644
index 0000000000..13c1ee93fa
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredSearchResultsImpl.java
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.SearchResults;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.util.List;
+import java.util.Map;
+import java.util.function.LongToIntFunction;
+
+class TieredSearchResultsImpl implements SearchResults {
+  private final List<Map<Integer, Float>> results;
+
+  private TieredSearchResultsImpl(List<Map<Integer, Float>> results) {
+    this.results = results;
+  }
+
+  public static TieredSearchResultsImpl create(
+      SequenceLayout neighboursSequenceLayout,
+      SequenceLayout distancesSequenceLayout,
+      MemorySegment neighboursMemorySegment,
+      MemorySegment distancesMemorySegment,
+      int topK,
+      List<Integer> mapping,
+      long numberOfQueries) {
+
+    // Process the data immediately while the memory segments are still valid
+    LongToIntFunction mappingFunction = mapping != null ? (long id) -> mapping.get((int) id) : null;
+
+    List<Map<Integer, Float>> processedResults =
+        SearchResultsImpl.create(
+                neighboursSequenceLayout,
+                distancesSequenceLayout,
+                neighboursMemorySegment,
+                distancesMemorySegment,
+                topK,
+                mappingFunction,
+                numberOfQueries)
+            .getResults();
+
+    return new TieredSearchResultsImpl(processedResults);
+  }
+
+  @Override
+  public List<Map<Integer, Float>> getResults() {
+    return results;
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 2b9913f04f..5940e3db48 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -23,11 +23,13 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
+import com.nvidia.cuvs.TieredIndex;
 import com.nvidia.cuvs.internal.BruteForceIndexImpl;
 import com.nvidia.cuvs.internal.CagraIndexImpl;
 import com.nvidia.cuvs.internal.CuVSResourcesImpl;
 import com.nvidia.cuvs.internal.DatasetImpl;
 import com.nvidia.cuvs.internal.HnswIndexImpl;
+import com.nvidia.cuvs.internal.TieredIndexImpl;
 import com.nvidia.cuvs.internal.common.Util;
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemoryLayout;
@@ -85,6 +87,11 @@ public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
     return HnswIndexImpl.newBuilder(Objects.requireNonNull(cuVSResources));
   }
 
+  @Override
+  public TieredIndex.Builder newTieredIndexBuilder(CuVSResources cuVSResources) {
+    return TieredIndexImpl.newBuilder(Objects.requireNonNull(cuVSResources));
+  }
+
   @Override
   public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
     if (indexes == null || indexes.length == 0) {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
new file mode 100644
index 0000000000..ffe1dddd08
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
@@ -0,0 +1,259 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertTrue;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import java.lang.invoke.MethodHandles;
+import java.util.Arrays;
+import java.util.BitSet;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+@RunWith(RandomizedRunner.class)
+public class TieredIndexIT extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  @Before
+  public void setup() {
+    initializeRandom();
+    log.debug("Random context initialized for test");
+  }
+
+  @Test
+  public void testBasicOperations() throws Throwable {
+    float[][] initialDataset = {
+      {0.0f, 0.0f},
+      {1.0f, 1.0f},
+      {2.0f, 2.0f}
+    };
+
+    float[][] queries = {
+      {0.1f, 0.1f},
+      {1.9f, 1.9f}
+    };
+
+    float[][] extensionVectors = {
+      {3.0f, 3.0f},
+      {4.0f, 4.0f}
+    };
+
+    List<Map<Integer, Float>> expectedInitialResults =
+        Arrays.asList(Map.of(0, 0.02f, 1, 1.62f, 2, 7.22f), Map.of(2, 0.02f, 1, 1.62f, 0, 7.22f));
+
+    List<Map<Integer, Float>> expectedExtendedResults =
+        Arrays.asList(Map.of(0, 0.02f, 1, 1.62f, 2, 7.22f), Map.of(2, 0.02f, 3, 2.42f, 1, 1.62f));
+
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndexParams cagraParams =
+          new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
+
+      TieredIndexParams indexParams =
+          new TieredIndexParams.Builder()
+              .minAnnRows(2)
+              .createAnnIndexOnExtend(true)
+              .withCagraParams(cagraParams)
+              .build();
+
+      log.debug("Building initial TieredIndex with {} vectors", initialDataset.length);
+      TieredIndex index =
+          TieredIndex.newBuilder(resources)
+              .withDataset(initialDataset)
+              .withIndexParams(indexParams)
+              .build();
+      log.debug("Initial TieredIndex built successfully");
+
+      CagraSearchParams searchParams =
+          new CagraSearchParams.Builder(resources).withMaxIterations(20).build();
+
+      TieredIndexQuery query =
+          new TieredIndexQuery.Builder()
+              .withTopK(3)
+              .withQueryVectors(queries)
+              .withSearchParams(searchParams)
+              .build();
+
+      log.debug("Searching initial index with {} queries", queries.length);
+      SearchResults initialResults = index.search(query);
+      log.debug("Initial search completed, validating results");
+      assertEquals(expectedInitialResults, roundResults(initialResults.getResults()));
+
+      log.debug("Extending index with {} additional vectors", extensionVectors.length);
+      index.extend().withDataset(extensionVectors).execute();
+      log.debug("Index extension completed");
+
+      log.debug("Searching extended index");
+      SearchResults extendedResults = index.search(query);
+      log.debug("Extended search completed, validating results");
+      assertEquals(expectedExtendedResults, roundResults(extendedResults.getResults()));
+    }
+  }
+
+  @Test(expected = IllegalArgumentException.class)
+  public void testErrorHandling() throws Throwable {
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndexParams cagraParams =
+          new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
+
+      TieredIndexParams indexParams =
+          new TieredIndexParams.Builder().minAnnRows(2).withCagraParams(cagraParams).build();
+
+      log.debug("Testing error handling with null dataset");
+      TieredIndex.newBuilder(resources)
+          .withIndexParams(indexParams)
+          .withDataset((float[][]) null)
+          .build();
+    }
+  }
+
+  @Test
+  public void testDifferentKValues() throws Throwable {
+    float[][] dataset = {
+      {0.0f, 0.0f},
+      {1.0f, 1.0f},
+      {2.0f, 2.0f},
+      {3.0f, 3.0f},
+      {4.0f, 4.0f}
+    };
+
+    float[][] queries = {{0.1f, 0.1f}};
+
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndexParams cagraParams =
+          new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
+
+      TieredIndexParams indexParams =
+          new TieredIndexParams.Builder().minAnnRows(2).withCagraParams(cagraParams).build();
+
+      log.debug("Building TieredIndex for K-value testing with {} vectors", dataset.length);
+      TieredIndex index =
+          TieredIndex.newBuilder(resources)
+              .withDataset(dataset)
+              .withIndexParams(indexParams)
+              .build();
+      log.debug("TieredIndex built for K-value testing");
+
+      TieredIndexQuery query1 =
+          new TieredIndexQuery.Builder()
+              .withTopK(1)
+              .withQueryVectors(queries)
+              .withSearchParams(
+                  new CagraSearchParams.Builder(resources).withMaxIterations(20).build())
+              .build();
+
+      log.debug("Searching with K=1");
+      SearchResults results1 = index.search(query1);
+      Map<Integer, Float> firstResult = results1.getResults().get(0);
+      log.debug("K=1 search completed, found {} results", firstResult.size());
+
+      assertEquals(1, firstResult.size());
+      assertTrue("Should contain index 0 (closest vector)", firstResult.containsKey(0));
+      assertEquals("Distance to closest vector should be ~0.02", 0.02f, firstResult.get(0), 0.01f);
+
+      TieredIndexQuery query3 =
+          new TieredIndexQuery.Builder()
+              .withTopK(3)
+              .withQueryVectors(queries)
+              .withSearchParams(
+                  new CagraSearchParams.Builder(resources).withMaxIterations(20).build())
+              .build();
+
+      log.debug("Searching with K=3");
+      SearchResults results3 = index.search(query3);
+      Map<Integer, Float> thirdResult = results3.getResults().get(0);
+      log.debug("K=3 search completed, found {} results", thirdResult.size());
+
+      assertEquals(3, thirdResult.size());
+      assertTrue("Should contain index 0", thirdResult.containsKey(0));
+      assertTrue("Should contain index 1", thirdResult.containsKey(1));
+      assertTrue("Should contain index 2", thirdResult.containsKey(2));
+
+      float dist0 = thirdResult.get(0);
+      float dist1 = thirdResult.get(1);
+      float dist2 = thirdResult.get(2);
+
+      assertTrue("Distance to index 0 should be smallest", dist0 <= dist1 && dist0 <= dist2);
+    }
+  }
+
+  @Test
+  public void testPrefilter() throws Throwable {
+    float[][] dataset = {{0.0f, 0.0f}, {1.0f, 1.0f}, {2.0f, 2.0f}, {3.0f, 3.0f}};
+    float[][] queryVectors = {{0.1f, 0.1f}};
+
+    try (CuVSResources resources = CuVSResources.create()) {
+      CagraIndexParams cagraParams =
+          new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
+
+      TieredIndexParams indexParams =
+          new TieredIndexParams.Builder().minAnnRows(2).withCagraParams(cagraParams).build();
+
+      log.debug("Building TieredIndex for prefilter testing with {} vectors", dataset.length);
+      TieredIndex index =
+          TieredIndex.newBuilder(resources)
+              .withDataset(dataset)
+              .withIndexParams(indexParams)
+              .build();
+      log.debug("TieredIndex built for prefilter testing");
+
+      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+
+      BitSet prefilter = new BitSet(4);
+      prefilter.set(1, true);
+      prefilter.set(2, true);
+      log.debug("Created prefilter allowing indices 1 and 2, excluding 0 and 3");
+
+      TieredIndexQuery queryWithFilter =
+          new TieredIndexQuery.Builder()
+              .withTopK(3)
+              .withQueryVectors(queryVectors)
+              .withSearchParams(searchParams)
+              .withPrefilter(prefilter, 4)
+              .build();
+
+      log.debug("Searching with prefilter applied");
+      SearchResults resultsWithFilter = index.search(queryWithFilter);
+      Map<Integer, Float> result = resultsWithFilter.getResults().get(0);
+      log.debug("Prefilter search completed, validating filtered results");
+
+      assertFalse("Index 0 should be filtered out", result.containsKey(0));
+      assertFalse("Index 3 should be filtered out", result.containsKey(3));
+      assertTrue("Index 1 or 2 should be present", result.containsKey(1) || result.containsKey(2));
+    }
+  }
+
+  private List<Map<Integer, Float>> roundResults(List<Map<Integer, Float>> results) {
+    return results.stream()
+        .map(
+            queryResult ->
+                queryResult.entrySet().stream()
+                    .collect(
+                        Collectors.toMap(
+                            Map.Entry::getKey,
+                            entry -> Math.round(entry.getValue() * 100.0f) / 100.0f)))
+        .collect(Collectors.toList());
+  }
+}
diff --git a/java/panama-bindings/headers.h b/java/panama-bindings/headers.h
index 08a381af14..57a13cf1b1 100644
--- a/java/panama-bindings/headers.h
+++ b/java/panama-bindings/headers.h
@@ -22,6 +22,7 @@
 #include <cuvs/neighbors/hnsw.h>
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/tiered_index.h>
 #include <cuda_runtime.h>
 
 /**

From a8249e3b7d7d72d5d93bedb5f511847de148a0d2 Mon Sep 17 00:00:00 2001
From: Jake Awe <jawe@nvidia.com>
Date: Fri, 18 Jul 2025 00:07:36 -0500
Subject: [PATCH 489/623] 25.08 -> 25.10 version updates

---
 .github/workflows/build.yaml        | 8 ++++----
 .github/workflows/pr.yaml           | 8 ++++----
 .github/workflows/publish-rust.yaml | 2 +-
 .github/workflows/test.yaml         | 2 +-
 docs/source/cuvs_bench/index.rst    | 8 ++++----
 5 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 3ebc825f6f..210ade79ca 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -50,7 +50,7 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
@@ -63,7 +63,7 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_go.sh"
       sha: ${{ inputs.sha }}
@@ -76,7 +76,7 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       script: "ci/build_java.sh"
       file_to_upload: "java/cuvs-java/target/"
       sha: ${{ inputs.sha }}
@@ -113,7 +113,7 @@ jobs:
       arch: "amd64"
       branch: ${{ inputs.branch }}
       build_type: ${{ inputs.build_type || 'branch' }}
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       date: ${{ inputs.date }}
       node_type: "gpu-l4-latest-1"
       script: "ci/build_docs.sh"
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 2083429482..eb4bd6b094 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -160,7 +160,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       script: "ci/test_java.sh"
       file_to_upload: "java/cuvs-java/target/"
   docs-build:
@@ -171,7 +171,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       script: "ci/build_docs.sh"
   rust-build:
     needs: conda-cpp-build
@@ -181,7 +181,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       script: "ci/build_rust.sh"
   go-build:
     needs: conda-cpp-build
@@ -191,7 +191,7 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       script: "ci/build_go.sh"
   wheel-build-libcuvs:
     needs: checks
diff --git a/.github/workflows/publish-rust.yaml b/.github/workflows/publish-rust.yaml
index 6b9846ec24..7834e6bc96 100644
--- a/.github/workflows/publish-rust.yaml
+++ b/.github/workflows/publish-rust.yaml
@@ -11,7 +11,7 @@ jobs:
   rust-publish:
     runs-on: ubuntu-latest
     container:
-      image: rapidsai/ci-conda:25.08-latest
+      image: rapidsai/ci-conda:25.10-latest
     steps:
       - uses: actions/checkout@v4
       - name: Check if release build
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 643b5744d2..8479ce38a4 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -60,7 +60,7 @@ jobs:
       sha: ${{ inputs.sha }}
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.08-latest"
+      container_image: "rapidsai/ci-conda:25.10-latest"
       script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 1c231c6e56..26f862c868 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -89,7 +89,7 @@ The following command pulls the nightly container for Python version 3.10, CUDA
 
 .. code-block:: bash
 
-   docker pull rapidsai/cuvs-bench:24.12a-cuda12.5-py3.10 # substitute cuvs-bench for the exact desired container.
+   docker pull rapidsai/cuvs-bench:25.10a-cuda12.5-py3.10 # substitute cuvs-bench for the exact desired container.
 
 The CUDA and python versions can be changed for the supported values:
 - Supported CUDA versions: 11.8 and 12.5
@@ -236,7 +236,7 @@ For GPU-enabled systems, the `DATA_FOLDER` variable should be a local folder whe
     export DATA_FOLDER=path/to/store/datasets/and/results
     docker run --gpus all --rm -it -u $(id -u)                      \
         -v $DATA_FOLDER:/data/benchmarks                            \
-        rapidsai/cuvs-bench:25.08-cuda12.9-py3.13              \
+        rapidsai/cuvs-bench:25.10-cuda12.9-py3.13              \
         "--dataset deep-image-96-angular"                           \
         "--normalize"                                               \
         "--algorithms cuvs_cagra,cuvs_ivf_pq --batch-size 10 -k 10" \
@@ -249,7 +249,7 @@ Usage of the above command is as follows:
  * - Argument
    - Description
 
- * - `rapidsai/cuvs-bench:25.08-cuda12.9-py3.13`
+ * - `rapidsai/cuvs-bench:25.10-cuda12.9-py3.13`
    - Image to use. Can be either `cuvs-bench` or `cuvs-bench-datasets`
 
  * - `"--dataset deep-image-96-angular"`
@@ -296,7 +296,7 @@ All of the `cuvs-bench` images contain the Conda packages, so they can be used d
         --entrypoint /bin/bash                          \
         --workdir /data/benchmarks                      \
         -v $DATA_FOLDER:/data/benchmarks                \
-        rapidsai/cuvs-bench:25.08-cuda12.9-py3.13
+        rapidsai/cuvs-bench:25.10-cuda12.9-py3.13
 
 This will drop you into a command line in the container, with the `cuvs-bench` python package ready to use, as described in the [Running the benchmarks](#running-the-benchmarks) section above:
 

From 0538f34fbf59c04ff3c7dfa78099b2a386e2d88f Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Fri, 18 Jul 2025 09:02:43 -0500
Subject: [PATCH 490/623] Fix update-version (#1135)

Include missed refs in `update-version.sh`
---
 ci/release/update-version.sh | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 50be522c6d..e6056f7332 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -78,6 +78,9 @@ done
 
 sed_runner "/rapidsai\/raft/ s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" docs/source/developer_guide.md
 
+# Update cuvs-bench Docker image references
+sed_runner "s|rapidsai/cuvs-bench:[0-9][0-9].[0-9][0-9]|rapidsai/cuvs-bench:${NEXT_SHORT_TAG}|g" docs/source/cuvs_bench/index.rst
+
 sed_runner "s|=[0-9][0-9].[0-9][0-9]|=${NEXT_SHORT_TAG}|g" README.md
 sed_runner "s|branch-[0-9][0-9].[0-9][0-9]|branch-${NEXT_SHORT_TAG}|g" README.md
 sed_runner "s|@v[0-9][0-9].[0-9][0-9].[0-9][0-9]|@v${NEXT_FULL_TAG}|g" examples/go/README.md

From 722b7e6959c8408508af579b14e3b6da9ae97536 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Fri, 18 Jul 2025 19:05:29 -0400
Subject: [PATCH 491/623] BitwiseHamming distance for NN Descent (#1101)

Adding bitwise hamming distance for NN Descent

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Tarang Jain (https://github.com/tarang-jain)

URL: https://github.com/rapidsai/cuvs/pull/1101
---
 cpp/src/neighbors/detail/nn_descent.cuh      | 173 ++++++++++++-------
 cpp/src/neighbors/detail/nn_descent_gnnd.hpp |   7 +-
 cpp/tests/neighbors/ann_nn_descent.cuh       |  14 +-
 cpp/tests/neighbors/naive_knn.cuh            |   4 +-
 4 files changed, 125 insertions(+), 73 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 6f6e26238a..0b00903524 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -18,6 +18,7 @@
 
 #include "ann_utils.cuh"
 #include "cagra/device_common.hpp"
+#include "cuvs/distance/distance.h"
 #include "nn_descent_gnnd.hpp"
 
 #include <cuvs/distance/distance.hpp>
@@ -285,6 +286,10 @@ RAFT_KERNEL preprocess_data_kernel(
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         output_data[list_id * dim + idx] =
           (float)input_data[(size_t)blockIdx.x * dim + idx] / sqrt(l2_norm);
+      } else if (metric == cuvs::distance::DistanceType::BitwiseHamming) {
+        int idx_for_byte           = list_id * dim + idx;  // uint8 or int8 data
+        uint8_t* output_bytes      = reinterpret_cast<uint8_t*>(output_data);
+        output_bytes[idx_for_byte] = input_data[idx_for_byte];
       } else {  // L2Expanded or L2SqrtExpanded
         output_data[list_id * dim + idx] = input_data[(size_t)blockIdx.x * dim + idx];
         if (idx == 0) { l2_norms[list_id] = l2_norm; }
@@ -588,39 +593,44 @@ __launch_bounds__(BLOCK_SIZE)
   wmma::fragment<wmma::matrix_a, WMMA_M, WMMA_N, WMMA_K, half, wmma::row_major> a_frag;
   wmma::fragment<wmma::matrix_b, WMMA_M, WMMA_N, WMMA_K, half, wmma::col_major> b_frag;
   wmma::fragment<wmma::accumulator, WMMA_M, WMMA_N, WMMA_K, float> c_frag;
-  wmma::fill_fragment(c_frag, 0.0);
-  for (int step = 0; step < raft::ceildiv(data_dim, TILE_COL_WIDTH); step++) {
-    int num_load_elems = (step == raft::ceildiv(data_dim, TILE_COL_WIDTH) - 1)
-                           ? data_dim - step * TILE_COL_WIDTH
-                           : TILE_COL_WIDTH;
+  if (metric != cuvs::distance::DistanceType::BitwiseHamming) {
+    wmma::fill_fragment(c_frag, 0.0);
+
+    for (int step = 0; step < raft::ceildiv(data_dim, TILE_COL_WIDTH); step++) {
+      int num_load_elems = (step == raft::ceildiv(data_dim, TILE_COL_WIDTH) - 1)
+                             ? data_dim - step * TILE_COL_WIDTH
+                             : TILE_COL_WIDTH;
 #pragma unroll
-    for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
-      int idx = i * num_warps + warp_id;
-      if (idx < list_new_size) {
-        size_t neighbor_id = new_neighbors[idx];
-        size_t idx_in_data = neighbor_id * data_dim;
-        load_vec(s_nv[idx],
-                 data + idx_in_data + step * TILE_COL_WIDTH,
-                 num_load_elems,
-                 TILE_COL_WIDTH,
-                 lane_id);
+      for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
+        int idx = i * num_warps + warp_id;
+        if (idx < list_new_size) {
+          size_t neighbor_id = new_neighbors[idx];
+          size_t idx_in_data = neighbor_id * data_dim;
+          load_vec(s_nv[idx],
+                   data + idx_in_data + step * TILE_COL_WIDTH,
+                   num_load_elems,
+                   TILE_COL_WIDTH,
+                   lane_id);
+        }
       }
-    }
-    __syncthreads();
-
-    for (int i = 0; i < TILE_COL_WIDTH / WMMA_K; i++) {
-      wmma::load_matrix_sync(a_frag, s_nv[warp_id_y * WMMA_M] + i * WMMA_K, TILE_COL_WIDTH + APAD);
-      wmma::load_matrix_sync(b_frag, s_nv[warp_id_x * WMMA_N] + i * WMMA_K, TILE_COL_WIDTH + BPAD);
-      wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
       __syncthreads();
+
+      for (int i = 0; i < TILE_COL_WIDTH / WMMA_K; i++) {
+        wmma::load_matrix_sync(
+          a_frag, s_nv[warp_id_y * WMMA_M] + i * WMMA_K, TILE_COL_WIDTH + APAD);
+        wmma::load_matrix_sync(
+          b_frag, s_nv[warp_id_x * WMMA_N] + i * WMMA_K, TILE_COL_WIDTH + BPAD);
+        wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
+        __syncthreads();
+      }
     }
-  }
 
-  wmma::store_matrix_sync(
-    s_distances + warp_id_y * WMMA_M * SKEWED_MAX_NUM_BI_SAMPLES + warp_id_x * WMMA_N,
-    c_frag,
-    SKEWED_MAX_NUM_BI_SAMPLES,
-    wmma::mem_row_major);
+    wmma::store_matrix_sync(
+      s_distances + warp_id_y * WMMA_M * SKEWED_MAX_NUM_BI_SAMPLES + warp_id_x * WMMA_N,
+      c_frag,
+      SKEWED_MAX_NUM_BI_SAMPLES,
+      wmma::mem_row_major);
+  }
   __syncthreads();
 
   for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
@@ -632,6 +642,16 @@ __launch_bounds__(BLOCK_SIZE)
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
+      } else if (metric == cuvs::distance::DistanceType::BitwiseHamming) {
+        s_distances[i] = 0.0;
+        int n1         = new_neighbors[row_id];
+        int n2         = new_neighbors[col_id];
+        // TODO: https://github.com/rapidsai/cuvs/issues/1127
+        const uint8_t* data_n1 = reinterpret_cast<const uint8_t*>(data) + n1 * data_dim;
+        const uint8_t* data_n2 = reinterpret_cast<const uint8_t*>(data) + n2 * data_dim;
+        for (int d = 0; d < data_dim; d++) {
+          s_distances[i] += __popc(static_cast<uint32_t>(data_n1[d] ^ data_n2[d]) & 0xff);
+        }
       } else {  // L2Expanded or L2SqrtExpanded
         s_distances[i] =
           l2_norms[new_neighbors[row_id]] + l2_norms[new_neighbors[col_id]] - 2.0 * s_distances[i];
@@ -659,56 +679,60 @@ __launch_bounds__(BLOCK_SIZE)
 
   __syncthreads();
 
-  wmma::fill_fragment(c_frag, 0.0);
-  for (int step = 0; step < raft::ceildiv(data_dim, TILE_COL_WIDTH); step++) {
-    int num_load_elems = (step == raft::ceildiv(data_dim, TILE_COL_WIDTH) - 1)
-                           ? data_dim - step * TILE_COL_WIDTH
-                           : TILE_COL_WIDTH;
-    if (TILE_COL_WIDTH < data_dim) {
+  if (metric != cuvs::distance::DistanceType::BitwiseHamming) {
+    wmma::fill_fragment(c_frag, 0.0);
+    for (int step = 0; step < raft::ceildiv(data_dim, TILE_COL_WIDTH); step++) {
+      int num_load_elems = (step == raft::ceildiv(data_dim, TILE_COL_WIDTH) - 1)
+                             ? data_dim - step * TILE_COL_WIDTH
+                             : TILE_COL_WIDTH;
+      if (TILE_COL_WIDTH < data_dim) {
+#pragma unroll
+        for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
+          int idx = i * num_warps + warp_id;
+          if (idx < list_new_size) {
+            size_t neighbor_id = new_neighbors[idx];
+            size_t idx_in_data = neighbor_id * data_dim;
+            load_vec(s_nv[idx],
+                     data + idx_in_data + step * TILE_COL_WIDTH,
+                     num_load_elems,
+                     TILE_COL_WIDTH,
+                     lane_id);
+          }
+        }
+      }
 #pragma unroll
       for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
         int idx = i * num_warps + warp_id;
-        if (idx < list_new_size) {
-          size_t neighbor_id = new_neighbors[idx];
+        if (idx < list_old_size) {
+          size_t neighbor_id = old_neighbors[idx];
           size_t idx_in_data = neighbor_id * data_dim;
-          load_vec(s_nv[idx],
+          load_vec(s_ov[idx],
                    data + idx_in_data + step * TILE_COL_WIDTH,
                    num_load_elems,
                    TILE_COL_WIDTH,
                    lane_id);
         }
       }
-    }
-#pragma unroll
-    for (int i = 0; i < MAX_NUM_BI_SAMPLES / num_warps; i++) {
-      int idx = i * num_warps + warp_id;
-      if (idx < list_old_size) {
-        size_t neighbor_id = old_neighbors[idx];
-        size_t idx_in_data = neighbor_id * data_dim;
-        load_vec(s_ov[idx],
-                 data + idx_in_data + step * TILE_COL_WIDTH,
-                 num_load_elems,
-                 TILE_COL_WIDTH,
-                 lane_id);
+      __syncthreads();
+
+      for (int i = 0; i < TILE_COL_WIDTH / WMMA_K; i++) {
+        wmma::load_matrix_sync(
+          a_frag, s_nv[warp_id_y * WMMA_M] + i * WMMA_K, TILE_COL_WIDTH + APAD);
+        wmma::load_matrix_sync(
+          b_frag, s_ov[warp_id_x * WMMA_N] + i * WMMA_K, TILE_COL_WIDTH + BPAD);
+        wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
+        __syncthreads();
       }
     }
-    __syncthreads();
 
-    for (int i = 0; i < TILE_COL_WIDTH / WMMA_K; i++) {
-      wmma::load_matrix_sync(a_frag, s_nv[warp_id_y * WMMA_M] + i * WMMA_K, TILE_COL_WIDTH + APAD);
-      wmma::load_matrix_sync(b_frag, s_ov[warp_id_x * WMMA_N] + i * WMMA_K, TILE_COL_WIDTH + BPAD);
-      wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
-      __syncthreads();
-    }
+    wmma::store_matrix_sync(
+      s_distances + warp_id_y * WMMA_M * SKEWED_MAX_NUM_BI_SAMPLES + warp_id_x * WMMA_N,
+      c_frag,
+      SKEWED_MAX_NUM_BI_SAMPLES,
+      wmma::mem_row_major);
+    __syncthreads();
   }
 
-  wmma::store_matrix_sync(
-    s_distances + warp_id_y * WMMA_M * SKEWED_MAX_NUM_BI_SAMPLES + warp_id_x * WMMA_N,
-    c_frag,
-    SKEWED_MAX_NUM_BI_SAMPLES,
-    wmma::mem_row_major);
-  __syncthreads();
-
   for (int i = threadIdx.x; i < MAX_NUM_BI_SAMPLES * SKEWED_MAX_NUM_BI_SAMPLES; i += blockDim.x) {
     int row_id = i % SKEWED_MAX_NUM_BI_SAMPLES;
     int col_id = i / SKEWED_MAX_NUM_BI_SAMPLES;
@@ -717,6 +741,16 @@ __launch_bounds__(BLOCK_SIZE)
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
+      } else if (metric == cuvs::distance::DistanceType::BitwiseHamming) {
+        s_distances[i] = 0.0;
+        int n1         = old_neighbors[row_id];
+        int n2         = new_neighbors[col_id];
+        // TODO: https://github.com/rapidsai/cuvs/issues/1127
+        const uint8_t* data_n1 = reinterpret_cast<const uint8_t*>(data) + n1 * data_dim;
+        const uint8_t* data_n2 = reinterpret_cast<const uint8_t*>(data) + n2 * data_dim;
+        for (int d = 0; d < data_dim; d++) {
+          s_distances[i] += __popc(static_cast<uint32_t>(data_n1[d] ^ data_n2[d]) & 0xff);
+        }
       } else {  // L2Expanded or L2SqrtExpanded
         s_distances[i] =
           l2_norms[old_neighbors[row_id]] + l2_norms[new_neighbors[col_id]] - 2.0 * s_distances[i];
@@ -980,7 +1014,11 @@ GNND<Data_t, Index_t>::GNND(raft::resources const& res, const BuildConfig& build
     nrow_(build_config.max_dataset_size),
     ndim_(build_config.dataset_dim),
     d_data_{raft::make_device_matrix<__half, size_t, raft::row_major>(
-      res, nrow_, build_config.dataset_dim)},
+      res,
+      nrow_,
+      build_config.metric == cuvs::distance::DistanceType::BitwiseHamming
+        ? (build_config.dataset_dim + 1) / 2
+        : build_config.dataset_dim)},
     l2_norms_{raft::make_device_vector<DistData_t, size_t>(res, 0)},
     graph_buffer_{
       raft::make_device_matrix<ID_t, size_t, raft::row_major>(res, nrow_, DEGREE_ON_DEVICE)},
@@ -1071,12 +1109,19 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
 {
   using input_t = typename std::remove_const<Data_t>::type;
 
+  if (build_config_.metric == cuvsDistanceType::BitwiseHamming &&
+      !(std::is_same_v<input_t, uint8_t> || std::is_same_v<input_t, int8_t>)) {
+    RAFT_FAIL(
+      "Data type needs to be int8 or uint8 for NN Descent to run with BitwiseHamming distance.");
+  }
+
   cudaStream_t stream = raft::resource::get_cuda_stream(res);
   nrow_               = nrow;
   graph_.nrow         = nrow;
   graph_.bloom_filter.set_nrow(nrow);
   update_counter_ = 0;
   graph_.h_graph  = (InternalID_t<Index_t>*)output_graph;
+  raft::matrix::fill(res, d_data_.view(), static_cast<__half>(0));
 
   cudaPointerAttributes data_ptr_attr;
   RAFT_CUDA_TRY(cudaPointerGetAttributes(&data_ptr_attr, data));
diff --git a/cpp/src/neighbors/detail/nn_descent_gnnd.hpp b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
index 8c7f2c9f0e..ce4ae6b20a 100644
--- a/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
+++ b/cpp/src/neighbors/detail/nn_descent_gnnd.hpp
@@ -271,10 +271,11 @@ inline BuildConfig get_build_config(raft::resources const& res,
   auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
                          params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
                          params.metric == cuvs::distance::DistanceType::CosineExpanded ||
-                         params.metric == cuvs::distance::DistanceType::InnerProduct;
+                         params.metric == cuvs::distance::DistanceType::InnerProduct ||
+                         params.metric == cuvs::distance::DistanceType::BitwiseHamming;
   RAFT_EXPECTS(allowed_metrics,
-               "The metric for NN Descent should be L2Expanded, L2SqrtExpanded, CosineExpanded or "
-               "InnerProduct");
+               "The metric for NN Descent should be L2Expanded, L2SqrtExpanded, CosineExpanded, "
+               "InnerProduct or BitwiseHamming");
   RAFT_EXPECTS(
     metric == params.metric,
     "The metrics set in nn_descent::index_params and nn_descent::index are inconsistent");
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index 53897851d2..61fa7665a8 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -25,6 +25,7 @@
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 
+#include <raft/core/host_mdarray.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/util/cudart_utils.hpp>
 #include <raft/util/itertools.hpp>
@@ -86,6 +87,10 @@ class AnnNNDescentTest : public ::testing::TestWithParam<AnnNNDescentInputs> {
  protected:
   void testNNDescent()
   {
+    if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
+        !(std::is_same_v<DataT, uint8_t> || std::is_same_v<DataT, int8_t>)) {
+      GTEST_SKIP();
+    }
     size_t queries_size = ps.n_rows * ps.graph_degree;
     std::vector<IdxT> indices_NNDescent(queries_size);
     std::vector<DistanceT> distances_NNDescent(queries_size);
@@ -471,10 +476,11 @@ class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchI
 };
 
 const std::vector<AnnNNDescentInputs> inputs =
-  raft::util::itertools::product<AnnNNDescentInputs>({2000, 4000},            // n_rows
-                                                     {4, 16, 64, 256, 1024},  // dim
-                                                     {32, 64},                // graph_degree
-                                                     {cuvs::distance::DistanceType::L2Expanded,
+  raft::util::itertools::product<AnnNNDescentInputs>({2000, 4000},                // n_rows
+                                                     {4, 16, 31, 64, 256, 1024},  // dim
+                                                     {32, 64},                    // graph_degree
+                                                     {cuvs::distance::DistanceType::BitwiseHamming,
+                                                      cuvs::distance::DistanceType::L2Expanded,
                                                       cuvs::distance::DistanceType::L2SqrtExpanded,
                                                       cuvs::distance::DistanceType::InnerProduct,
                                                       cuvs::distance::DistanceType::CosineExpanded},
diff --git a/cpp/tests/neighbors/naive_knn.cuh b/cpp/tests/neighbors/naive_knn.cuh
index 484022c795..fc80581da1 100644
--- a/cpp/tests/neighbors/naive_knn.cuh
+++ b/cpp/tests/neighbors/naive_knn.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -66,7 +66,7 @@ RAFT_KERNEL naive_distance_kernel(EvalT* dist,
           acc += diff * diff;
         } break;
         case cuvs::distance::DistanceType::BitwiseHamming: {
-          if constexpr (std::is_same_v<uint8_t, DataT>) {
+          if constexpr (std::is_same_v<uint8_t, DataT> || std::is_same_v<int8_t, DataT>) {
             acc += __popc(static_cast<uint32_t>(xv ^ yv) & 0xff);
           }
         } break;

From 533ce9cf5eb42af12c5dcaf10d4cdb7d92d3e968 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Sat, 19 Jul 2025 06:46:46 +0200
Subject: [PATCH 492/623] [Java] Introduce scoped resource access (#1089)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR wraps access to the cuVS resources handle into a autocloseable accessor. The reason is to provide a clear scope for resource access, with the possibility to add application logic to the scope begin/end.

An example of this logic is the `CheckedCuVSResources` decorator introduced in the test suite, which checks that the `CuVSResources` it decorates has not been destroyed yet, and that access to the underlying cuVS resources handle is not concurrent (is not done by more than 1 thread at every time).

I preferred to write it as a decorator, as this gives the API consumers more freedom: if they want the check, they can use `CheckedCuVSResources` (or a similar class); if they want direct, straight, fast access, they can just use the accessor; if they need more logic (e.g. logging or anything else), that is possible too.

Tests are updated to use  `CheckedCuVSResources`, so we can spot issues quickly in tests without burdening production code with additional logic.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1089
---
 .../java/com/nvidia/cuvs/CuVSResources.java   |  26 +-
 .../cuvs/internal/BruteForceIndexImpl.java    | 238 +++++-----
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 255 +++++-----
 .../cuvs/internal/CuVSResourcesImpl.java      |  37 +-
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |  74 ++-
 .../nvidia/cuvs/internal/TieredIndexImpl.java | 439 ++++++++----------
 .../nvidia/cuvs/BruteForceAndSearchIT.java    |   8 +-
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |   2 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    |  24 +-
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   2 +-
 .../com/nvidia/cuvs/CheckedCuVSResources.java |  74 +++
 .../java/com/nvidia/cuvs/CuVSResourcesIT.java |  98 ++++
 .../com/nvidia/cuvs/HnswBuildAndSearchIT.java |   6 +-
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |   2 +-
 14 files changed, 719 insertions(+), 566 deletions(-)
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
index 6a84f737b1..2d362b6c4c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
@@ -26,7 +26,31 @@
 public interface CuVSResources extends AutoCloseable {
 
   /**
-   * Closes this resources and releases any resources associated with it.
+   * Provide scoped access to the native resources object.
+   */
+  interface ScopedAccess extends AutoCloseable {
+    /**
+     * Gets the opaque CuVSResources handle, to be used whenever we need to pass a cuvsResources_t parameter
+     *
+     * @return the CuVSResources handle
+     */
+    long handle();
+
+    @Override
+    void close();
+  }
+
+  /**
+   * Gets scoped access to the native resources object.
+   * The native resource object is not thread safe: only a single thread at every time should access
+   * concurrently the same native resources. Calling this method from multiple thread is OK, but the
+   * returned {@link ScopedAccess} object must be closed before calling {@code access()} again from a
+   * different thread.
+   */
+  ScopedAccess access();
+
+  /**
+   * Closes this CuVSResources object and releases any resources associated with it.
    */
   @Override
   void close();
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index f9e3b9ba93..163205b830 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -67,7 +67,7 @@
  */
 public class BruteForceIndexImpl implements BruteForceIndex {
 
-  private final CuVSResourcesImpl resources;
+  private final CuVSResources resources;
   private final IndexReference bruteForceIndexReference;
   private boolean destroyed;
 
@@ -76,12 +76,12 @@ public class BruteForceIndexImpl implements BruteForceIndex {
    *
    * @param dataset               the dataset used for creating the BRUTEFORCE
    *                              index
-   * @param resources             an instance of {@link CuVSResourcesImpl}
+   * @param resources             an instance of {@link CuVSResources}
    * @param bruteForceIndexParams an instance of {@link BruteForceIndexParams}
    *                              holding the index parameters
    */
   private BruteForceIndexImpl(
-      Dataset dataset, CuVSResourcesImpl resources, BruteForceIndexParams bruteForceIndexParams)
+      Dataset dataset, CuVSResources resources, BruteForceIndexParams bruteForceIndexParams)
       throws Exception {
     Objects.requireNonNull(dataset);
     try (dataset) {
@@ -95,10 +95,9 @@ private BruteForceIndexImpl(
    * Constructor for loading the index from an {@link InputStream}
    *
    * @param inputStream an instance of stream to read the index bytes from
-   * @param resources   an instance of {@link CuVSResourcesImpl}
+   * @param resources   an instance of {@link CuVSResources}
    */
-  private BruteForceIndexImpl(InputStream inputStream, CuVSResourcesImpl resources)
-      throws Throwable {
+  private BruteForceIndexImpl(InputStream inputStream, CuVSResources resources) throws Throwable {
     this.resources = resources;
     this.bruteForceIndexReference = deserialize(inputStream);
   }
@@ -121,12 +120,14 @@ public void destroyIndex() {
       checkCuVSError(returnValue, "cuvsBruteForceIndexDestroy");
 
       if (bruteForceIndexReference.datasetBytes > 0) {
-        returnValue =
-            cuvsRMMFree(
-                resources.getHandle(),
-                bruteForceIndexReference.datasetPtr,
-                bruteForceIndexReference.datasetBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
+        try (var resourcesAccessor = resources.access()) {
+          checkCuVSError(
+              cuvsRMMFree(
+                  resourcesAccessor.handle(),
+                  bruteForceIndexReference.datasetPtr,
+                  bruteForceIndexReference.datasetBytes),
+              "cuvsRMMFree");
+        }
       }
       if (bruteForceIndexReference.tensorDataArena != null) {
         bruteForceIndexReference.tensorDataArena.close();
@@ -141,7 +142,7 @@ public void destroyIndex() {
    * build the {@link BruteForceIndex}
    *
    * @return an instance of {@link IndexReference} that holds the pointer to the
-   *         index
+   * index
    */
   private IndexReference build(DatasetImpl dataset, BruteForceIndexParams bruteForceIndexParams) {
     long rows = dataset.size();
@@ -149,36 +150,35 @@ private IndexReference build(DatasetImpl dataset, BruteForceIndexParams bruteFor
 
     MemorySegment datasetMemSegment = dataset.asMemorySegment();
 
-    long cuvsResources = resources.getHandle();
-
     omp_set_num_threads(bruteForceIndexParams.getNumWriterThreads());
-    long datasetBytes = C_FLOAT_BYTE_SIZE * rows * cols;
-    MemorySegment datasetMemorySegmentP = allocateRMMSegment(cuvsResources, datasetBytes);
 
-    cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
+    long datasetBytes = C_FLOAT_BYTE_SIZE * rows * cols;
+    var index = createBruteForceIndex();
 
-    long[] datasetShape = {rows, cols};
-    var tensorDataArena = Arena.ofShared();
-    MemorySegment datasetTensor =
-        prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
+    try (var resourcesAccessor = resources.access()) {
+      long cuvsResources = resourcesAccessor.handle();
+      MemorySegment datasetMemorySegmentP = allocateRMMSegment(cuvsResources, datasetBytes);
 
-    var indexReference =
-        new IndexReference(
-            datasetMemorySegmentP, datasetBytes, tensorDataArena, createBruteForceIndex());
+      cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
 
-    var returnValue = cuvsStreamSync(cuvsResources);
-    checkCuVSError(returnValue, "cuvsStreamSync");
+      long[] datasetShape = {rows, cols};
+      var tensorDataArena = Arena.ofShared();
+      MemorySegment datasetTensor =
+          prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
 
-    returnValue =
-        cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, indexReference.indexPtr);
-    checkCuVSError(returnValue, "cuvsBruteForceBuild");
+      var returnValue = cuvsStreamSync(cuvsResources);
+      checkCuVSError(returnValue, "cuvsStreamSync");
 
-    returnValue = cuvsStreamSync(cuvsResources);
-    checkCuVSError(returnValue, "cuvsStreamSync");
+      returnValue = cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, index);
+      checkCuVSError(returnValue, "cuvsBruteForceBuild");
 
-    omp_set_num_threads(1);
+      returnValue = cuvsStreamSync(cuvsResources);
+      checkCuVSError(returnValue, "cuvsStreamSync");
 
-    return indexReference;
+      return new IndexReference(datasetMemorySegmentP, datasetBytes, tensorDataArena, index);
+    } finally {
+      omp_set_num_threads(1);
+    }
   }
 
   /**
@@ -203,94 +203,98 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
 
       // prepare the prefiltering data
-      long prefilterDataLength = 0;
-      MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
+      final long prefilterDataLength;
+      final MemorySegment prefilterDataMemorySegment;
       BitSet[] prefilters = cuvsQuery.getPrefilters();
       if (prefilters != null && prefilters.length > 0) {
         BitSet concatenatedFilters = concatenate(prefilters, cuvsQuery.getNumDocs());
         long[] filters = concatenatedFilters.toLongArray();
         prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
         prefilterDataLength = (long) cuvsQuery.getNumDocs() * prefilters.length;
+      } else {
+        prefilterDataLength = 0;
+        prefilterDataMemorySegment = MemorySegment.NULL;
       }
 
       MemorySegment querySeg = buildMemorySegment(localArena, cuvsQuery.getQueryVectors());
 
       int topk = cuvsQuery.getTopK();
-      long cuvsResources = resources.getHandle();
-
-      long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
-      long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
-      long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
-      long prefilterBytes = 0; // size assigned later
-
-      MemorySegment queriesDP = allocateRMMSegment(cuvsResources, queriesBytes);
-      MemorySegment neighborsDP = allocateRMMSegment(cuvsResources, neighborsBytes);
-      MemorySegment distancesDP = allocateRMMSegment(cuvsResources, distanceBytes);
-      MemorySegment prefilterDP = MemorySegment.NULL;
-
-      cudaMemcpy(queriesDP, querySeg, queriesBytes, INFER_DIRECTION);
-
-      long[] queriesShape = {numQueries, vectorDimension};
-      MemorySegment queriesTensor =
-          prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
-      long[] neighborsShape = {numQueries, topk};
-      MemorySegment neighborsTensor =
-          prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
-      long[] distancesShape = {numQueries, topk};
-      MemorySegment distancesTensor =
-          prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
-
-      MemorySegment prefilter = cuvsFilter.allocate(localArena);
-      MemorySegment prefilterTensor;
-
-      if (prefilterDataMemorySegment == MemorySegment.NULL) {
-        cuvsFilter.type(prefilter, 0); // NO_FILTER
-        cuvsFilter.addr(prefilter, 0);
-      } else {
-        long[] prefilterShape = {(prefilterDataLength + 31) / 32};
-        long prefilterLen = prefilterShape[0];
-        prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
+      try (var resourcesAccessor = resources.access()) {
+        long cuvsResources = resourcesAccessor.handle();
 
-        prefilterDP = allocateRMMSegment(cuvsResources, prefilterBytes);
+        long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+        long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
+        long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
+        long prefilterBytes = 0; // size assigned later
 
-        cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
+        MemorySegment queriesDP = allocateRMMSegment(cuvsResources, queriesBytes);
+        MemorySegment neighborsDP = allocateRMMSegment(cuvsResources, neighborsBytes);
+        MemorySegment distancesDP = allocateRMMSegment(cuvsResources, distanceBytes);
+        MemorySegment prefilterDP = MemorySegment.NULL;
 
-        prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+        cudaMemcpy(queriesDP, querySeg, queriesBytes, INFER_DIRECTION);
 
-        cuvsFilter.type(prefilter, 2);
-        cuvsFilter.addr(prefilter, prefilterTensor.address());
-      }
+        long[] queriesShape = {numQueries, vectorDimension};
+        MemorySegment queriesTensor =
+            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+        long[] neighborsShape = {numQueries, topk};
+        MemorySegment neighborsTensor =
+            prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
+        long[] distancesShape = {numQueries, topk};
+        MemorySegment distancesTensor =
+            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
 
-      var returnValue = cuvsStreamSync(cuvsResources);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+        MemorySegment prefilter = cuvsFilter.allocate(localArena);
+        MemorySegment prefilterTensor;
 
-      returnValue =
-          cuvsBruteForceSearch(
-              cuvsResources,
-              bruteForceIndexReference.indexPtr,
-              queriesTensor,
-              neighborsTensor,
-              distancesTensor,
-              prefilter);
-      checkCuVSError(returnValue, "cuvsBruteForceSearch");
+        if (prefilterDataMemorySegment == MemorySegment.NULL) {
+          cuvsFilter.type(prefilter, 0); // NO_FILTER
+          cuvsFilter.addr(prefilter, 0);
+        } else {
+          long[] prefilterShape = {(prefilterDataLength + 31) / 32};
+          long prefilterLen = prefilterShape[0];
+          prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
-      returnValue = cuvsStreamSync(cuvsResources);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+          prefilterDP = allocateRMMSegment(cuvsResources, prefilterBytes);
+
+          cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
+
+          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+
+          cuvsFilter.type(prefilter, 2);
+          cuvsFilter.addr(prefilter, prefilterTensor.address());
+        }
 
-      cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
-      cudaMemcpy(distancesMemorySegment, distancesDP, distanceBytes, INFER_DIRECTION);
-
-      returnValue = cuvsRMMFree(cuvsResources, neighborsDP, neighborsBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-      returnValue = cuvsRMMFree(cuvsResources, distancesDP, distanceBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-      returnValue = cuvsRMMFree(cuvsResources, queriesDP, queriesBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-      if (prefilterBytes > 0) {
-        returnValue = cuvsRMMFree(cuvsResources, prefilterDP, prefilterBytes);
+        var returnValue = cuvsStreamSync(cuvsResources);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        returnValue =
+            cuvsBruteForceSearch(
+                cuvsResources,
+                bruteForceIndexReference.indexPtr,
+                queriesTensor,
+                neighborsTensor,
+                distancesTensor,
+                prefilter);
+        checkCuVSError(returnValue, "cuvsBruteForceSearch");
+
+        returnValue = cuvsStreamSync(cuvsResources);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
+        cudaMemcpy(distancesMemorySegment, distancesDP, distanceBytes, INFER_DIRECTION);
+
+        returnValue = cuvsRMMFree(cuvsResources, neighborsDP, neighborsBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+        returnValue = cuvsRMMFree(cuvsResources, distancesDP, distanceBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+        returnValue = cuvsRMMFree(cuvsResources, queriesDP, queriesBytes);
         checkCuVSError(returnValue, "cuvsRMMFree");
+        if (prefilterBytes > 0) {
+          returnValue = cuvsRMMFree(cuvsResources, prefilterDP, prefilterBytes);
+          checkCuVSError(returnValue, "cuvsRMMFree");
+        }
       }
-
       return BruteForceSearchResults.create(
           neighborsSequenceLayout,
           distancesSequenceLayout,
@@ -312,19 +316,19 @@ public void serialize(OutputStream outputStream) throws Throwable {
   @Override
   public void serialize(OutputStream outputStream, Path tempFile) throws Throwable {
     checkNotDestroyed();
-    tempFile = tempFile.toAbsolutePath();
+    var tempFilePath = tempFile.toAbsolutePath();
 
-    long cuvsRes = resources.getHandle();
-    try (var localArena = Arena.ofConfined()) {
+    try (var localArena = Arena.ofConfined();
+        var resourcesAccessor = resources.access()) {
       int returnValue =
           cuvsBruteForceSerialize(
-              cuvsRes,
-              localArena.allocateFrom(tempFile.toString()),
+              resourcesAccessor.handle(),
+              localArena.allocateFrom(tempFilePath.toString()),
               bruteForceIndexReference.indexPtr);
       checkCuVSError(returnValue, "cuvsBruteForceSerialize");
     }
 
-    try (var inputStream = Files.newInputStream(tempFile)) {
+    try (var inputStream = Files.newInputStream(tempFilePath)) {
       inputStream.transferTo(outputStream);
     } finally {
       Files.deleteIfExists(tempFile);
@@ -363,13 +367,15 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
 
     try (inputStream;
         var outputStream = Files.newOutputStream(tmpIndexFile);
-        var arena = Arena.ofConfined()) {
+        var arena = Arena.ofConfined();
+        var resourcesAccessor = resources.access()) {
       inputStream.transferTo(outputStream);
 
-      long cuvsRes = resources.getHandle();
       int returnValue =
           cuvsBruteForceDeserialize(
-              cuvsRes, arena.allocateFrom(tmpIndexFile.toString()), indexReference.indexPtr);
+              resourcesAccessor.handle(),
+              arena.allocateFrom(tmpIndexFile.toString()),
+              indexReference.indexPtr);
       checkCuVSError(returnValue, "cuvsBruteForceDeserialize");
 
     } finally {
@@ -379,11 +385,7 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
   }
 
   public static BruteForceIndex.Builder newBuilder(CuVSResources cuvsResources) {
-    Objects.requireNonNull(cuvsResources);
-    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
-      throw new IllegalArgumentException("Unsupported " + cuvsResources);
-    }
-    return new Builder((CuVSResourcesImpl) cuvsResources);
+    return new Builder(Objects.requireNonNull(cuvsResources));
   }
 
   /**
@@ -392,16 +394,16 @@ public static BruteForceIndex.Builder newBuilder(CuVSResources cuvsResources) {
   public static class Builder implements BruteForceIndex.Builder {
 
     private Dataset dataset;
-    private final CuVSResourcesImpl cuvsResources;
+    private final CuVSResources cuvsResources;
     private BruteForceIndexParams bruteForceIndexParams;
     private InputStream inputStream;
 
     /**
-     * Constructs this Builder with an instance of {@link CuVSResourcesImpl}.
+     * Constructs this Builder with an instance of {@link CuVSResources}.
      *
      * @param cuvsResources an instance of {@link CuVSResources}
      */
-    public Builder(CuVSResourcesImpl cuvsResources) {
+    public Builder(CuVSResources cuvsResources) {
       this.cuvsResources = cuvsResources;
     }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 49be7104e8..0e1fcb9048 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -87,7 +87,7 @@
  * @since 25.02
  */
 public class CagraIndexImpl implements CagraIndex {
-  private final CuVSResourcesImpl resources;
+  private final CuVSResources resources;
   private final IndexReference cagraIndexReference;
   private boolean destroyed;
 
@@ -100,7 +100,7 @@ public class CagraIndexImpl implements CagraIndex {
    * @param resources       an instance of {@link CuVSResources}
    */
   private CagraIndexImpl(
-      CagraIndexParams indexParameters, Dataset dataset, CuVSResourcesImpl resources) {
+      CagraIndexParams indexParameters, Dataset dataset, CuVSResources resources) {
     Objects.requireNonNull(dataset);
     this.resources = resources;
     assert dataset instanceof DatasetImpl;
@@ -113,7 +113,7 @@ private CagraIndexImpl(
    * @param inputStream an instance of stream to read the index bytes from
    * @param resources   an instance of {@link CuVSResources}
    */
-  private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
+  private CagraIndexImpl(InputStream inputStream, CuVSResources resources) throws Throwable {
     this.resources = resources;
     this.cagraIndexReference = deserialize(inputStream);
   }
@@ -125,7 +125,7 @@ private CagraIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) thr
    * @param indexReference The reference to the existing index
    * @param resources The resources instance
    */
-  private CagraIndexImpl(IndexReference indexReference, CuVSResourcesImpl resources) {
+  private CagraIndexImpl(IndexReference indexReference, CuVSResources resources) {
     this.resources = resources;
     this.cagraIndexReference = indexReference;
     this.destroyed = false;
@@ -176,8 +176,6 @@ private IndexReference build(CagraIndexParams indexParameters, DatasetImpl datas
 
       MemorySegment dataSeg = dataset.asMemorySegment();
 
-      long cuvsRes = resources.getHandle();
-
       long[] datasetShape = {rows, cols};
       MemorySegment datasetTensor =
           prepareTensor(localArena, dataSeg, datasetShape, 2, 32, 2, 2, 1);
@@ -194,16 +192,18 @@ private IndexReference build(CagraIndexParams indexParameters, DatasetImpl datas
         cuvsIvfPqIndexParams.n_lists(
             cuvsIvfPqIndexParamsMS, (int) (rows < n_lists ? rows : n_lists));
       }
+      try (var resourcesAccessor = resources.access()) {
+        var cuvsRes = resourcesAccessor.handle();
 
-      var returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
-
-      returnValue = cuvsCagraBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, index);
-      checkCuVSError(returnValue, "cuvsCagraBuild");
+        var returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+        returnValue = cuvsCagraBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, index);
+        checkCuVSError(returnValue, "cuvsCagraBuild");
 
+        returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+      }
       omp_set_num_threads(1);
 
       return new IndexReference(index, dataset);
@@ -248,97 +248,101 @@ public SearchResults search(CagraQuery query) throws Throwable {
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
       MemorySegment floatsSeg = buildMemorySegment(localArena, query.getQueryVectors());
 
-      long cuvsRes = resources.getHandle();
-
       long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
       long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
       long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
-      long prefilterBytes = 0; // size assigned later
-
-      MemorySegment queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
-      MemorySegment neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
-      MemorySegment distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
-      MemorySegment prefilterDP = MemorySegment.NULL;
-      long prefilterLen = 0;
-
-      cudaMemcpy(queriesDP, floatsSeg, queriesBytes, INFER_DIRECTION);
-
-      long[] queriesShape = {numQueries, vectorDimension};
-      MemorySegment queriesTensor =
-          prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
-      long[] neighborsShape = {numQueries, topK};
-      MemorySegment neighborsTensor =
-          prepareTensor(localArena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
-      long[] distancesShape = {numQueries, topK};
-      MemorySegment distancesTensor =
-          prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
-
-      var returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
-
-      // prepare the prefiltering data
-      long prefilterDataLength = 0;
-      MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
-      BitSet[] prefilters;
-      if (query.getPrefilter() != null) {
-        prefilters = new BitSet[] {query.getPrefilter()};
-        BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
-        long[] filters = concatenatedFilters.toLongArray();
-        prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
-        prefilterDataLength = query.getNumDocs() * prefilters.length;
-      }
 
-      MemorySegment prefilter = cuvsFilter.allocate(localArena);
-      MemorySegment prefilterTensor;
+      try (var resourcesAccessor = resources.access()) {
+        var cuvsRes = resourcesAccessor.handle();
+
+        MemorySegment queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
+        MemorySegment neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
+        MemorySegment distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
+        MemorySegment prefilterDP = MemorySegment.NULL;
+        long prefilterLen = 0;
+
+        cudaMemcpy(queriesDP, floatsSeg, queriesBytes, INFER_DIRECTION);
+
+        long[] queriesShape = {numQueries, vectorDimension};
+        MemorySegment queriesTensor =
+            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+        long[] neighborsShape = {numQueries, topK};
+        MemorySegment neighborsTensor =
+            prepareTensor(localArena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
+        long[] distancesShape = {numQueries, topK};
+        MemorySegment distancesTensor =
+            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+
+        var returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        // prepare the prefiltering data
+        long prefilterDataLength = 0;
+        MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
+        BitSet[] prefilters;
+        if (query.getPrefilter() != null) {
+          prefilters = new BitSet[] {query.getPrefilter()};
+          BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
+          long[] filters = concatenatedFilters.toLongArray();
+          prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
+          prefilterDataLength = query.getNumDocs() * prefilters.length;
+        }
 
-      if (prefilterDataMemorySegment == MemorySegment.NULL) {
-        cuvsFilter.type(prefilter, 0); // NO_FILTER
-        cuvsFilter.addr(prefilter, 0);
-      } else {
-        long[] prefilterShape = {(prefilterDataLength + 31) / 32};
-        prefilterLen = prefilterShape[0];
-        prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
+        MemorySegment prefilter = cuvsFilter.allocate(localArena);
+        MemorySegment prefilterTensor;
 
-        prefilterDP = allocateRMMSegment(cuvsRes, prefilterBytes);
+        final long prefilterBytes;
 
-        cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
+        if (prefilterDataMemorySegment == MemorySegment.NULL) {
+          cuvsFilter.type(prefilter, 0); // NO_FILTER
+          cuvsFilter.addr(prefilter, 0);
+          prefilterBytes = 0;
+        } else {
+          long[] prefilterShape = {(prefilterDataLength + 31) / 32};
+          prefilterLen = prefilterShape[0];
+          prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
-        prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+          prefilterDP = allocateRMMSegment(cuvsRes, prefilterBytes);
 
-        cuvsFilter.type(prefilter, 1);
-        cuvsFilter.addr(prefilter, prefilterTensor.address());
-      }
+          cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
-      returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
 
-      returnValue =
-          cuvsCagraSearch(
-              cuvsRes,
-              segmentFromSearchParams(localArena, query.getCagraSearchParameters()),
-              cagraIndexReference.getMemorySegment(),
-              queriesTensor,
-              neighborsTensor,
-              distancesTensor,
-              prefilter);
-      checkCuVSError(returnValue, "cuvsCagraSearch");
-
-      returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
-
-      cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
-      cudaMemcpy(distancesMemorySegment, distancesDP, distancesBytes, INFER_DIRECTION);
-
-      returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-      returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-      returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-
-      if (prefilterLen > 0) {
-        returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
+          cuvsFilter.type(prefilter, 1);
+          cuvsFilter.addr(prefilter, prefilterTensor.address());
+        }
+
+        returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        returnValue =
+            cuvsCagraSearch(
+                cuvsRes,
+                segmentFromSearchParams(localArena, query.getCagraSearchParameters()),
+                cagraIndexReference.getMemorySegment(),
+                queriesTensor,
+                neighborsTensor,
+                distancesTensor,
+                prefilter);
+        checkCuVSError(returnValue, "cuvsCagraSearch");
+
+        returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
+        cudaMemcpy(distancesMemorySegment, distancesDP, distancesBytes, INFER_DIRECTION);
+
+        returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+        returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
         checkCuVSError(returnValue, "cuvsRMMFree");
+        returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+
+        if (prefilterLen > 0) {
+          returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
+          checkCuVSError(returnValue, "cuvsRMMFree");
+        }
       }
 
       return CagraSearchResults.create(
@@ -370,26 +374,27 @@ public void serialize(OutputStream outputStream, int bufferLength) throws Throwa
   public void serialize(OutputStream outputStream, Path tempFile, int bufferLength)
       throws Throwable {
     checkNotDestroyed();
-    tempFile = tempFile.toAbsolutePath();
-    try (var localArena = Arena.ofConfined()) {
+    final var tempFilePath = tempFile.toAbsolutePath();
+    try (var localArena = Arena.ofConfined();
+        var resourcesAccessor = resources.access()) {
 
-      long cuvsRes = resources.getHandle();
+      long cuvsRes = resourcesAccessor.handle();
       var returnValue =
           cuvsCagraSerialize(
               cuvsRes,
-              localArena.allocateFrom(tempFile.toString()),
+              localArena.allocateFrom(tempFilePath.toString()),
               cagraIndexReference.getMemorySegment(),
               true);
       checkCuVSError(returnValue, "cuvsCagraSerialize");
 
-      try (var fileInputStream = Files.newInputStream(tempFile)) {
+      try (var fileInputStream = Files.newInputStream(tempFilePath)) {
         byte[] chunk = new byte[bufferLength];
         int chunkLength = 0;
         while ((chunkLength = fileInputStream.read(chunk)) != -1) {
           outputStream.write(chunk, 0, chunkLength);
         }
       } finally {
-        Files.deleteIfExists(tempFile);
+        Files.deleteIfExists(tempFilePath);
       }
     }
   }
@@ -412,25 +417,27 @@ public void serializeToHNSW(OutputStream outputStream, int bufferLength) throws
   public void serializeToHNSW(OutputStream outputStream, Path tempFile, int bufferLength)
       throws Throwable {
     checkNotDestroyed();
-    tempFile = tempFile.toAbsolutePath();
+    final var tempFilePath = tempFile.toAbsolutePath();
 
     try (var localArena = Arena.ofConfined()) {
       MemorySegment pathSeg = buildMemorySegment(localArena, tempFile.toString());
 
-      long cuvsRes = resources.getHandle();
-      int returnValue =
-          cuvsCagraSerializeToHnswlib(cuvsRes, pathSeg, cagraIndexReference.getMemorySegment());
-      checkCuVSError(returnValue, "cuvsCagraSerializeToHnswlib");
+      try (var resourcesAccessor = resources.access()) {
+        checkCuVSError(
+            cuvsCagraSerializeToHnswlib(
+                resourcesAccessor.handle(), pathSeg, cagraIndexReference.getMemorySegment()),
+            "cuvsCagraSerializeToHnswlib");
+      }
     }
 
-    try (FileInputStream fileInputStream = new FileInputStream(tempFile.toFile())) {
+    try (FileInputStream fileInputStream = new FileInputStream(tempFilePath.toFile())) {
       byte[] chunk = new byte[bufferLength];
       int chunkLength;
       while ((chunkLength = fileInputStream.read(chunk)) != -1) {
         outputStream.write(chunk, 0, chunkLength);
       }
     } finally {
-      Files.deleteIfExists(tempFile);
+      Files.deleteIfExists(tempFilePath);
     }
   }
 
@@ -452,11 +459,12 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
         var arena = Arena.ofConfined()) {
       inputStream.transferTo(outputStream);
 
-      long cuvsRes = resources.getHandle();
-      var returnValue =
-          cuvsCagraDeserialize(cuvsRes, arena.allocateFrom(tmpIndexFile.toString()), index);
-      checkCuVSError(returnValue, "cuvsCagraDeserialize");
-
+      try (var resourcesAccessor = resources.access()) {
+        var cuvsRes = resourcesAccessor.handle();
+        var returnValue =
+            cuvsCagraDeserialize(cuvsRes, arena.allocateFrom(tmpIndexFile.toString()), index);
+        checkCuVSError(returnValue, "cuvsCagraDeserialize");
+      }
     } finally {
       Files.deleteIfExists(tmpIndexFile);
     }
@@ -469,7 +477,7 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
    * @return an instance of {@link CuVSResources}
    */
   @Override
-  public CuVSResourcesImpl getCuVSResources() {
+  public CuVSResources getCuVSResources() {
     return resources;
   }
 
@@ -582,11 +590,7 @@ private MemorySegment segmentFromSearchParams(Arena arena, CagraSearchParams par
   }
 
   public static CagraIndex.Builder newBuilder(CuVSResources cuvsResources) {
-    Objects.requireNonNull(cuvsResources);
-    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
-      throw new IllegalArgumentException("Unsupported " + cuvsResources);
-    }
-    return new CagraIndexImpl.Builder((CuVSResourcesImpl) cuvsResources);
+    return new CagraIndexImpl.Builder(Objects.requireNonNull(cuvsResources));
   }
 
   /**
@@ -607,9 +611,8 @@ public static CagraIndex merge(CagraIndex[] indexes) {
    * @return A new merged CAGRA index
    */
   public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParams) {
-    CuVSResourcesImpl resources = (CuVSResourcesImpl) indexes[0].getCuVSResources();
+    CuVSResources resources = indexes[0].getCuVSResources();
     var mergedIndex = createCagraIndex();
-    long cuvsRes = resources.getHandle();
 
     try (var localArena = Arena.ofConfined()) {
       MemorySegment indexesSegment =
@@ -624,11 +627,13 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
       // TODO: we should call cuvsCreateMergeParams here, instead of allocating this ourselves
       // See https://github.com/rapidsai/cuvs/pull/1109
       var mergeParamsSegment = createMergeParamsSegment(localArena, mergeParams);
-
-      var returnValue =
-          cuvsCagraMerge(cuvsRes, mergeParamsSegment, indexesSegment, indexes.length, mergedIndex);
-
-      checkCuVSError(returnValue, "cuvsCagraMerge");
+      try (var resourcesAccessor = resources.access()) {
+        var cuvsRes = resourcesAccessor.handle();
+        var returnValue =
+            cuvsCagraMerge(
+                cuvsRes, mergeParamsSegment, indexesSegment, indexes.length, mergedIndex);
+        checkCuVSError(returnValue, "cuvsCagraMerge");
+      }
     }
 
     return new CagraIndexImpl(new IndexReference(mergedIndex, null), resources);
@@ -670,10 +675,10 @@ public static class Builder implements CagraIndex.Builder {
 
     private Dataset dataset;
     private CagraIndexParams cagraIndexParams;
-    private final CuVSResourcesImpl cuvsResources;
+    private final CuVSResources cuvsResources;
     private InputStream inputStream;
 
-    public Builder(CuVSResourcesImpl cuvsResources) {
+    public Builder(CuVSResources cuvsResources) {
       this.cuvsResources = cuvsResources;
     }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
index 5acb2cb9bd..8f9e1d94d0 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -33,7 +33,7 @@ public class CuVSResourcesImpl implements CuVSResources {
 
   private final Path tempDirectory;
   private final long resourceHandle;
-  private boolean destroyed;
+  private final ScopedAccess access;
 
   /**
    * Constructor that allocates the resources needed for cuVS
@@ -45,17 +45,30 @@ public CuVSResourcesImpl(Path tempDirectory) {
       var resourcesMemorySegment = localArena.allocate(cuvsResources_t);
       int returnValue = cuvsResourcesCreate(resourcesMemorySegment);
       checkCuVSError(returnValue, "cuvsResourcesCreate");
-      resourceHandle = resourcesMemorySegment.get(cuvsResources_t, 0);
+      this.resourceHandle = resourcesMemorySegment.get(cuvsResources_t, 0);
+      this.access =
+          new ScopedAccess() {
+            @Override
+            public long handle() {
+              return resourceHandle;
+            }
+
+            @Override
+            public void close() {}
+          };
     }
   }
 
+  @Override
+  public ScopedAccess access() {
+    return this.access;
+  }
+
   @Override
   public void close() {
     synchronized (this) {
-      checkNotDestroyed();
       int returnValue = cuvsResourcesDestroy(resourceHandle);
       checkCuVSError(returnValue, "cuvsResourcesDestroy");
-      destroyed = true;
     }
   }
 
@@ -63,20 +76,4 @@ public void close() {
   public Path tempDirectory() {
     return tempDirectory;
   }
-
-  private void checkNotDestroyed() {
-    if (destroyed) {
-      throw new IllegalStateException("destroyed");
-    }
-  }
-
-  /**
-   * Gets the opaque CuVSResources handle, to be used whenever we need to pass a cuvsResources_t parameter
-   *
-   * @return the CuVSResources handle
-   */
-  long getHandle() {
-    checkNotDestroyed();
-    return resourceHandle;
-  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index 178ed21b87..0d8997dca8 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -55,7 +55,7 @@
  */
 public class HnswIndexImpl implements HnswIndex {
 
-  private final CuVSResourcesImpl resources;
+  private final CuVSResources resources;
   private final HnswIndexParams hnswIndexParams;
   private final IndexReference hnswIndexReference;
 
@@ -63,10 +63,10 @@ public class HnswIndexImpl implements HnswIndex {
    * Constructor for loading the index from an {@link InputStream}
    *
    * @param inputStream an instance of stream to read the index bytes from
-   * @param resources   an instance of {@link CuVSResourcesImpl}
+   * @param resources   an instance of {@link CuVSResources}
    */
   private HnswIndexImpl(
-      InputStream inputStream, CuVSResourcesImpl resources, HnswIndexParams hnswIndexParams)
+      InputStream inputStream, CuVSResources resources, HnswIndexParams hnswIndexParams)
       throws Throwable {
     this.hnswIndexParams = hnswIndexParams;
     this.resources = resources;
@@ -105,8 +105,6 @@ public SearchResults search(HnswQuery query) throws Throwable {
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
       MemorySegment querySeg = buildMemorySegment(localArena, queryVectors);
 
-      long cuvsRes = resources.getHandle();
-
       long[] queriesShape = {numQueries, vectorDimension};
       MemorySegment queriesTensor =
           prepareTensor(localArena, querySeg, queriesShape, 2, 32, 2, 1, 1);
@@ -117,21 +115,24 @@ public SearchResults search(HnswQuery query) throws Throwable {
       MemorySegment distancesTensor =
           prepareTensor(localArena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
 
-      int returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
-
-      returnValue =
-          cuvsHnswSearch(
-              cuvsRes,
-              segmentFromSearchParams(localArena, query.getHnswSearchParams()),
-              hnswIndexReference.getMemorySegment(),
-              queriesTensor,
-              neighborsTensor,
-              distancesTensor);
-      checkCuVSError(returnValue, "cuvsHnswSearch");
-
-      returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+      try (var resourcesAccessor = resources.access()) {
+        var cuvsRes = resourcesAccessor.handle();
+        int returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        returnValue =
+            cuvsHnswSearch(
+                cuvsRes,
+                segmentFromSearchParams(localArena, query.getHnswSearchParams()),
+                hnswIndexReference.getMemorySegment(),
+                queriesTensor,
+                neighborsTensor,
+                distancesTensor);
+        checkCuVSError(returnValue, "cuvsHnswSearch");
+
+        returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+      }
 
       return HnswSearchResults.create(
           neighborsSequenceLayout,
@@ -178,8 +179,6 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
       inputStream.transferTo(outputStream);
       MemorySegment pathSeg = buildMemorySegment(localArena, tmpIndexFile.toString());
 
-      long cuvsRes = resources.getHandle();
-
       var indexReference = createHnswIndex();
 
       MemorySegment dtype = DLDataType.allocate(localArena);
@@ -189,15 +188,18 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
 
       cuvsHnswIndex.dtype(indexReference.memorySegment, dtype);
 
-      var returnValue =
-          cuvsHnswDeserialize(
-              cuvsRes,
-              segmentFromIndexParams(localArena, hnswIndexParams),
-              pathSeg,
-              hnswIndexParams.getVectorDimension(),
-              0,
-              indexReference.memorySegment);
-      checkCuVSError(returnValue, "cuvsHnswDeserialize");
+      try (var resourcesAccessor = resources.access()) {
+        var cuvsRes = resourcesAccessor.handle();
+        var returnValue =
+            cuvsHnswDeserialize(
+                cuvsRes,
+                segmentFromIndexParams(localArena, hnswIndexParams),
+                pathSeg,
+                hnswIndexParams.getVectorDimension(),
+                0,
+                indexReference.memorySegment);
+        checkCuVSError(returnValue, "cuvsHnswDeserialize");
+      }
 
       return indexReference;
 
@@ -227,11 +229,7 @@ private static MemorySegment segmentFromSearchParams(Arena arena, HnswSearchPara
   }
 
   public static HnswIndex.Builder newBuilder(CuVSResources cuvsResources) {
-    Objects.requireNonNull(cuvsResources);
-    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
-      throw new IllegalArgumentException("Unsupported " + cuvsResources);
-    }
-    return new HnswIndexImpl.Builder((CuVSResourcesImpl) cuvsResources);
+    return new HnswIndexImpl.Builder(Objects.requireNonNull(cuvsResources));
   }
 
   /**
@@ -239,7 +237,7 @@ public static HnswIndex.Builder newBuilder(CuVSResources cuvsResources) {
    */
   public static class Builder implements HnswIndex.Builder {
 
-    private final CuVSResourcesImpl cuvsResources;
+    private final CuVSResources cuvsResources;
     private InputStream inputStream;
     private HnswIndexParams hnswIndexParams;
 
@@ -248,7 +246,7 @@ public static class Builder implements HnswIndex.Builder {
      *
      * @param cuvsResources an instance of {@link CuVSResources}
      */
-    public Builder(CuVSResourcesImpl cuvsResources) {
+    public Builder(CuVSResources cuvsResources) {
       this.cuvsResources = cuvsResources;
     }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index 035bbe2eef..f5dcb5eff0 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -28,16 +28,7 @@
 import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexBuild;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexExtend;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndexSearch;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsTieredIndex_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CagraIndexParams;
 import com.nvidia.cuvs.CagraSearchParams;
@@ -71,9 +62,8 @@
  * @since 25.02
  */
 public class TieredIndexImpl implements TieredIndex {
-  private final float[][] vectors;
   private final Dataset dataset;
-  private final CuVSResourcesImpl resources;
+  private final CuVSResources resources;
   private final TieredIndexParams tieredIndexParameters;
   private final IndexReference tieredIndexReference;
   private boolean destroyed;
@@ -82,13 +72,8 @@ public class TieredIndexImpl implements TieredIndex {
    * Constructor for building the index using specified dataset
    */
   private TieredIndexImpl(
-      TieredIndexParams indexParameters,
-      float[][] vectors,
-      Dataset dataset,
-      CuVSResourcesImpl resources)
-      throws Throwable {
+      TieredIndexParams indexParameters, Dataset dataset, CuVSResources resources) {
     this.tieredIndexParameters = indexParameters;
-    this.vectors = vectors;
     this.dataset = dataset;
     this.resources = resources;
     this.tieredIndexReference = build();
@@ -98,22 +83,10 @@ private TieredIndexImpl(
   /**
    * Constructor for loading the index from an {@link InputStream}
    */
-  private TieredIndexImpl(InputStream inputStream, CuVSResourcesImpl resources) throws Throwable {
+  private TieredIndexImpl(InputStream inputStream, CuVSResources resources) {
     throw new UnsupportedOperationException("Deserialization of TieredIndex is not yet supported");
   }
 
-  /**
-   * Constructor for creating an index from an existing index reference.
-   */
-  private TieredIndexImpl(IndexReference indexReference, CuVSResourcesImpl resources) {
-    this.vectors = null;
-    this.tieredIndexParameters = null;
-    this.dataset = null;
-    this.resources = resources;
-    this.tieredIndexReference = indexReference;
-    this.destroyed = false;
-  }
-
   private void checkNotDestroyed() {
     if (destroyed) {
       throw new IllegalStateException("destroyed");
@@ -129,6 +102,10 @@ public void destroyIndex() throws Throwable {
     try {
       int returnValue = cuvsTieredIndexDestroy(tieredIndexReference.getMemorySegment());
       checkCuVSError(returnValue, "cuvsTieredIndexDestroy");
+      if (dataset != null) {
+        dataset.close();
+      }
+
     } finally {
       destroyed = true;
     }
@@ -142,10 +119,10 @@ public void destroyIndex() throws Throwable {
    * @return an instance of {@link IndexReference} that holds the pointer to the
    *         index
    */
-  private IndexReference build() throws Throwable {
+  private IndexReference build() {
     try (var localArena = Arena.ofConfined()) {
-      long rows = dataset != null ? dataset.size() : vectors.length;
-      long cols = dataset != null ? dataset.dimensions() : (rows > 0 ? vectors[0].length : 0);
+      long rows = dataset.size();
+      long cols = dataset.dimensions();
 
       MemorySegment indexParamsMemorySegment =
           tieredIndexParameters != null
@@ -153,48 +130,43 @@ private IndexReference build() throws Throwable {
               : MemorySegment.NULL;
 
       // Get host data
-      MemorySegment hostDataSeg =
-          dataset != null
-              ? ((DatasetImpl) dataset).asMemorySegment()
-              : Util.buildMemorySegment(localArena, vectors);
+      MemorySegment hostDataSeg = ((DatasetImpl) dataset).asMemorySegment();
 
-      long cuvsRes = resources.getHandle();
+      try (var resourceAccess = resources.access()) {
+        long cuvsRes = resourceAccess.handle();
 
-      // TieredIndex REQUIRES device memory - allocate it
-      MemorySegment datasetD = localArena.allocate(C_POINTER);
-      long datasetSize = C_FLOAT_BYTE_SIZE * rows * cols;
-      int returnValue = cuvsRMMAlloc(cuvsRes, datasetD, datasetSize);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
+        // TieredIndex REQUIRES device memory - allocate it
+        long datasetSize = C_FLOAT_BYTE_SIZE * rows * cols;
+        MemorySegment datasetDP = allocateRMMSegment(cuvsRes, datasetSize);
 
-      MemorySegment datasetDP = datasetD.get(C_POINTER, 0);
+        // Copy host to device
+        Util.cudaMemcpy(datasetDP, hostDataSeg, datasetSize, HOST_TO_DEVICE);
 
-      // Copy host to device
-      Util.cudaMemcpy(datasetDP, hostDataSeg, datasetSize, HOST_TO_DEVICE);
+        // Create tensor from device memory
+        long[] datasetShape = {rows, cols};
+        MemorySegment datasetTensor =
+            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
 
-      // Create tensor from device memory
-      long[] datasetShape = {rows, cols};
-      MemorySegment datasetTensor =
-          prepareTensor(localArena, datasetDP, datasetShape, 2, 32, 2, 2, 1);
+        MemorySegment index = localArena.allocate(cuvsTieredIndex_t);
+        var returnValue = cuvsTieredIndexCreate(index);
+        checkCuVSError(returnValue, "cuvsTieredIndexCreate");
 
-      MemorySegment index = localArena.allocate(cuvsTieredIndex_t);
-      returnValue = cuvsTieredIndexCreate(index);
-      checkCuVSError(returnValue, "cuvsTieredIndexCreate");
+        returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+        // Extract the actual index pointer that was written by Create
+        MemorySegment actualIndexPtr = index.get(C_POINTER, 0);
 
-      // Extract the actual index pointer that was written by Create
-      MemorySegment actualIndexPtr = index.get(C_POINTER, 0);
+        returnValue =
+            cuvsTieredIndexBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, actualIndexPtr);
+        checkCuVSError(returnValue, "cuvsTieredIndexBuild");
 
-      returnValue =
-          cuvsTieredIndexBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, actualIndexPtr);
-      checkCuVSError(returnValue, "cuvsTieredIndexBuild");
-
-      // Clean up device memory after build
-      returnValue = cuvsRMMFree(cuvsRes, datasetDP, datasetSize);
-      checkCuVSError(returnValue, "cuvsRMMFree");
+        // Clean up device memory after build
+        returnValue = cuvsRMMFree(cuvsRes, datasetDP, datasetSize);
+        checkCuVSError(returnValue, "cuvsRMMFree");
 
-      return new IndexReference(actualIndexPtr);
+        return new IndexReference(actualIndexPtr);
+      }
     }
   }
 
@@ -227,126 +199,125 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
       // Get host query data
       MemorySegment hostQueriesSeg = Util.buildMemorySegment(localArena, query.getQueryVectors());
 
-      long cuvsRes = resources.getHandle();
-
-      // Allocate DEVICE memory for all data
-      MemorySegment queriesD = localArena.allocate(C_POINTER);
-      MemorySegment neighborsD = localArena.allocate(C_POINTER);
-      MemorySegment distancesD = localArena.allocate(C_POINTER);
-
-      long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
-      long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topK; // 64-bit for tiered index
-      long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
-
-      int returnValue = cuvsRMMAlloc(cuvsRes, queriesD, queriesBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-      returnValue = cuvsRMMAlloc(cuvsRes, neighborsD, neighborsBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-      returnValue = cuvsRMMAlloc(cuvsRes, distancesD, distancesBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-
-      // Get device pointers
-      MemorySegment queriesDP = queriesD.get(C_POINTER, 0);
-      MemorySegment neighborsDP = neighborsD.get(C_POINTER, 0);
-      MemorySegment distancesDP = distancesD.get(C_POINTER, 0);
-
-      // Copy queries from host to device
-      returnValue =
-          cudaMemcpy(queriesDP, hostQueriesSeg, queriesBytes, 1); // cudaMemcpyHostToDevice
-      checkCudaError(returnValue, "cudaMemcpy");
-
-      // Create tensors from device memory
-      long queriesShape[] = {numQueries, vectorDimension};
-      MemorySegment queriesTensor =
-          prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
-      long neighborsShape[] = {numQueries, topK};
-      MemorySegment neighborsTensor =
-          prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1); // 64-bit int
-      long distancesShape[] = {numQueries, topK};
-      MemorySegment distancesTensor =
-          prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
-
-      // Sync before prefilter setup
-      returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
-
-      // Handle prefilter
-      MemorySegment prefilter = cuvsFilter.allocate(localArena);
-      MemorySegment prefilterD = localArena.allocate(C_POINTER);
-      MemorySegment prefilterDP = MemorySegment.NULL;
-      long prefilterBytes = 0;
-
-      if (query.getPrefilter() != null) {
-        BitSet[] prefilters = new BitSet[] {query.getPrefilter()};
-        BitSet concatenatedFilters = concatenate(prefilters, (int) query.getNumDocs());
-        long filters[] = concatenatedFilters.toLongArray();
-        MemorySegment hostPrefilterSeg = buildMemorySegment(localArena, filters);
-
-        long prefilterDataLength = query.getNumDocs() * prefilters.length;
-        long prefilterShape[] = {(prefilterDataLength + 31) / 32};
-        long prefilterLen = prefilterShape[0];
-        prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
-
-        // Allocate device memory for prefilter
-        returnValue = cuvsRMMAlloc(cuvsRes, prefilterD, prefilterBytes);
-        checkCuVSError(returnValue, "cuvsRMMAlloc");
-
-        prefilterDP = prefilterD.get(C_POINTER, 0);
-
-        // Copy prefilter to device
-        returnValue = cudaMemcpy(prefilterDP, hostPrefilterSeg, prefilterBytes, 1);
+      try (var resourceAccess = resources.access()) {
+        long cuvsRes = resourceAccess.handle();
+
+        long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+        long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topK; // 64-bit for tiered index
+        long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+
+        // Allocate DEVICE memory for all data
+        MemorySegment queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
+        MemorySegment neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
+        MemorySegment distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
+
+        // Copy queries from host to device
+        var returnValue =
+            cudaMemcpy(queriesDP, hostQueriesSeg, queriesBytes, cudaMemcpyHostToDevice());
         checkCudaError(returnValue, "cudaMemcpy");
 
-        MemorySegment prefilterTensor =
-            prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+        // Create tensors from device memory
+        long[] queriesShape = {numQueries, vectorDimension};
+        MemorySegment queriesTensor =
+            prepareTensor(localArena, queriesDP, queriesShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
+        long[] neighborsShape = {numQueries, topK};
+        MemorySegment neighborsTensor =
+            prepareTensor(
+                localArena,
+                neighborsDP,
+                neighborsShape,
+                kDLInt(),
+                64,
+                2,
+                kDLCUDA(),
+                1); // 64-bit int
+        long[] distancesShape = {numQueries, topK};
+        MemorySegment distancesTensor =
+            prepareTensor(localArena, distancesDP, distancesShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
+
+        // Sync before prefilter setup
+        returnValue = cuvsStreamSync(cuvsRes);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        // Handle prefilter
+        MemorySegment prefilter = cuvsFilter.allocate(localArena);
+        final MemorySegment prefilterDP;
+        final long prefilterBytes;
+
+        if (query.getPrefilter() != null) {
+          BitSet[] prefilters = new BitSet[] {query.getPrefilter()};
+          BitSet concatenatedFilters = concatenate(prefilters, (int) query.getNumDocs());
+          long[] filters = concatenatedFilters.toLongArray();
+          MemorySegment hostPrefilterSeg = buildMemorySegment(localArena, filters);
+
+          long prefilterDataLength = query.getNumDocs() * prefilters.length;
+          long[] prefilterShape = {(prefilterDataLength + 31) / 32};
+          long prefilterLen = prefilterShape[0];
+          prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
+
+          // Allocate device memory for prefilter
+          prefilterDP = allocateRMMSegment(cuvsRes, prefilterBytes);
+
+          // Copy prefilter to device
+          checkCudaError(
+              cudaMemcpy(prefilterDP, hostPrefilterSeg, prefilterBytes, cudaMemcpyHostToDevice()),
+              "cudaMemcpy");
+
+          MemorySegment prefilterTensor =
+              prepareTensor(
+                  localArena, prefilterDP, prefilterShape, kDLUInt(), 32, 1, kDLCUDA(), 1);
+
+          cuvsFilter.type(prefilter, 1); // BITSET
+          cuvsFilter.addr(prefilter, prefilterTensor.address());
+        } else {
+          prefilterDP = MemorySegment.NULL;
+          prefilterBytes = 0;
+          cuvsFilter.type(prefilter, 0); // NO_FILTER
+          cuvsFilter.addr(prefilter, 0);
+        }
 
-        cuvsFilter.type(prefilter, 1); // BITSET
-        cuvsFilter.addr(prefilter, prefilterTensor.address());
-      } else {
-        cuvsFilter.type(prefilter, 0); // NO_FILTER
-        cuvsFilter.addr(prefilter, 0);
-      }
+        // Perform search
+        returnValue =
+            cuvsTieredIndexSearch(
+                cuvsRes,
+                segmentFromSearchParams(query.getCagraSearchParameters(), localArena),
+                tieredIndexReference.getMemorySegment(),
+                queriesTensor,
+                neighborsTensor,
+                distancesTensor,
+                prefilter);
+        checkCuVSError(returnValue, "cuvsTieredIndexSearch");
+
+        // Copy results from device to host
+        returnValue =
+            cudaMemcpy(neighborsSeg, neighborsDP, neighborsBytes, cudaMemcpyDeviceToHost());
+        checkCudaError(returnValue, "cudaMemcpy");
+        returnValue =
+            cudaMemcpy(distancesSeg, distancesDP, distancesBytes, cudaMemcpyDeviceToHost());
+        checkCudaError(returnValue, "cudaMemcpy");
 
-      // Perform search
-      returnValue =
-          cuvsTieredIndexSearch(
-              cuvsRes,
-              segmentFromSearchParams(query.getCagraSearchParameters(), localArena),
-              tieredIndexReference.getMemorySegment(),
-              queriesTensor,
-              neighborsTensor,
-              distancesTensor,
-              prefilter);
-      checkCuVSError(returnValue, "cuvsTieredIndexSearch");
-
-      // Copy results from device to host
-      returnValue =
-          cudaMemcpy(neighborsSeg, neighborsDP, neighborsBytes, 2); // cudaMemcpyDeviceToHost
-      checkCudaError(returnValue, "cudaMemcpy");
-      returnValue = cudaMemcpy(distancesSeg, distancesDP, distancesBytes, 2);
-      checkCudaError(returnValue, "cudaMemcpy");
-
-      // Clean up device memory
-      returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-      returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-      returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
-      checkCuVSError(returnValue, "cuvsRMMFree");
-
-      if (prefilterDP != MemorySegment.NULL) {
-        returnValue = cuvsRMMFree(cuvsRes, prefilterDP, prefilterBytes);
+        // Clean up device memory
+        returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
         checkCuVSError(returnValue, "cuvsRMMFree");
-      }
+        returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+        returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
+        checkCuVSError(returnValue, "cuvsRMMFree");
+
+        if (prefilterDP != MemorySegment.NULL) {
+          returnValue = cuvsRMMFree(cuvsRes, prefilterDP, prefilterBytes);
+          checkCuVSError(returnValue, "cuvsRMMFree");
+        }
 
-      return TieredSearchResultsImpl.create(
-          neighborsLayout,
-          distancesLayout,
-          neighborsSeg,
-          distancesSeg,
-          topK,
-          query.getMapping(),
-          numQueries);
+        return TieredSearchResultsImpl.create(
+            neighborsLayout,
+            distancesLayout,
+            neighborsSeg,
+            distancesSeg,
+            topK,
+            query.getMapping(),
+            numQueries);
+      }
     }
   }
 
@@ -359,46 +330,39 @@ public ExtendBuilder extend() {
   /**
    * Performs the actual extend operation
    */
-  private void performExtend(float[][] extendVectors, Dataset extendDataset) throws Throwable {
+  private void performExtend(Dataset extendDataset) throws Throwable {
     try (var localArena = Arena.ofConfined()) {
-      long rows = extendDataset != null ? extendDataset.size() : extendVectors.length;
-      long cols = extendDataset != null ? extendDataset.dimensions() : extendVectors[0].length;
+      long rows = extendDataset.size();
+      long cols = extendDataset.dimensions();
 
       // Get host data
-      MemorySegment hostDataSeg =
-          extendDataset != null
-              ? ((DatasetImpl) extendDataset).asMemorySegment()
-              : Util.buildMemorySegment(localArena, extendVectors);
-
-      long cuvsRes = resources.getHandle();
+      MemorySegment hostDataSeg = ((DatasetImpl) extendDataset).asMemorySegment();
 
-      // Allocate device memory for extend data
-      MemorySegment datasetD = localArena.allocate(C_POINTER);
-      long dataSize = C_FLOAT_BYTE_SIZE * rows * cols;
-      int returnValue = cuvsRMMAlloc(cuvsRes, datasetD, dataSize);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
+      try (var resourceAccess = resources.access()) {
+        long cuvsRes = resourceAccess.handle();
 
-      MemorySegment datasetDP = datasetD.get(C_POINTER, 0);
+        // Allocate device memory for extend data
+        long dataSize = C_FLOAT_BYTE_SIZE * rows * cols;
+        MemorySegment datasetDP = allocateRMMSegment(cuvsRes, dataSize);
 
-      // Copy host to device
-      returnValue = cudaMemcpy(datasetDP, hostDataSeg, dataSize, 1); // cudaMemcpyHostToDevice
-      checkCudaError(returnValue, "cudaMemcpy");
+        // Copy host to device
+        checkCudaError(
+            cudaMemcpy(datasetDP, hostDataSeg, dataSize, cudaMemcpyHostToDevice()), "cudaMemcpy");
 
-      // Create tensor from device memory
-      long datasetShape[] = {rows, cols};
-      MemorySegment datasetTensor =
-          prepareTensor(localArena, datasetDP, datasetShape, 2, 32, 2, 2, 1);
+        // Create tensor from device memory
+        long[] datasetShape = {rows, cols};
+        MemorySegment datasetTensor =
+            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
 
-      returnValue = cuvsStreamSync(cuvsRes);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
 
-      returnValue =
-          cuvsTieredIndexExtend(cuvsRes, datasetTensor, tieredIndexReference.getMemorySegment());
-      checkCuVSError(returnValue, "cuvsTieredIndexExtend");
+        checkCuVSError(
+            cuvsTieredIndexExtend(cuvsRes, datasetTensor, tieredIndexReference.getMemorySegment()),
+            "cuvsTieredIndexExtend");
 
-      // Clean up device memory
-      returnValue = cuvsRMMFree(cuvsRes, datasetDP, dataSize);
-      checkCuVSError(returnValue, "cuvsRMMFree");
+        // Clean up device memory
+        checkCuVSError(cuvsRMMFree(cuvsRes, datasetDP, dataSize), "cuvsRMMFree");
+      }
     }
   }
 
@@ -407,7 +371,6 @@ private void performExtend(float[][] extendVectors, Dataset extendDataset) throw
    */
   public static class ExtendBuilder implements TieredIndex.ExtendBuilder {
     private final TieredIndexImpl index;
-    private float[][] vectors;
     private Dataset dataset;
 
     private ExtendBuilder(TieredIndexImpl index) {
@@ -416,7 +379,7 @@ private ExtendBuilder(TieredIndexImpl index) {
 
     @Override
     public ExtendBuilder withDataset(float[][] vectors) {
-      this.vectors = vectors;
+      this.dataset = Dataset.ofArray(vectors);
       return this;
     }
 
@@ -428,15 +391,11 @@ public ExtendBuilder withDataset(Dataset dataset) {
 
     @Override
     public void execute() throws Throwable {
-      if (vectors != null && dataset != null) {
-        throw new IllegalArgumentException(
-            "Please specify only one type of dataset (a float[][] or a Dataset instance)");
-      }
-      if (vectors == null && dataset == null) {
-        throw new IllegalArgumentException("Must provide vectors or dataset");
+      if (dataset == null) {
+        throw new IllegalArgumentException("Must provide a dataset");
       }
 
-      index.performExtend(vectors, dataset);
+      index.performExtend(dataset);
     }
   }
 
@@ -541,24 +500,20 @@ public TieredIndexType getIndexType() {
    */
   public static TieredIndex.Builder newBuilder(CuVSResources cuvsResources) {
     Objects.requireNonNull(cuvsResources);
-    if (!(cuvsResources instanceof CuVSResourcesImpl)) {
-      throw new IllegalArgumentException("Unsupported " + cuvsResources);
-    }
-    return new TieredIndexImpl.Builder((CuVSResourcesImpl) cuvsResources);
+    return new TieredIndexImpl.Builder(cuvsResources);
   }
 
   /**
    * Builder helps configure and create an instance of {@link TieredIndex}.
    */
   public static class Builder implements TieredIndex.Builder {
-    private CuVSResourcesImpl resources;
-    private float[][] vectors;
+    private final CuVSResources resources;
     private Dataset dataset;
     private TieredIndexParams params;
     private TieredIndexType indexType = TieredIndexType.CAGRA;
     private InputStream inputStream;
 
-    private Builder(CuVSResourcesImpl resources) {
+    private Builder(CuVSResources resources) {
       this.resources = resources;
     }
 
@@ -570,12 +525,24 @@ public Builder from(InputStream inputStream) {
 
     @Override
     public Builder withDataset(float[][] vectors) {
-      this.vectors = vectors;
+      if (this.dataset != null) {
+        throw new IllegalArgumentException("An input dataset can only be specified once");
+      }
+      if (vectors == null || vectors.length == 0 || vectors[0].length == 0) {
+        throw new IllegalArgumentException("The input vectors cannot be null or empty");
+      }
+      this.dataset = Dataset.ofArray(vectors);
       return this;
     }
 
     @Override
     public Builder withDataset(Dataset dataset) {
+      if (this.dataset != null) {
+        throw new IllegalArgumentException("An input dataset can only be specified once");
+      }
+      if (dataset == null) {
+        throw new IllegalArgumentException("An input dataset cannot be null");
+      }
       this.dataset = dataset;
       return this;
     }
@@ -597,17 +564,13 @@ public TieredIndex build() throws Throwable {
       if (inputStream != null) {
         return new TieredIndexImpl(inputStream, resources);
       } else {
-        if (vectors != null && dataset != null) {
-          throw new IllegalArgumentException(
-              "Please specify only one type of dataset (a float[][] or a Dataset instance)");
-        }
-        if (vectors == null && dataset == null) {
-          throw new IllegalArgumentException("Must provide vectors or dataset");
+        if (dataset == null) {
+          throw new IllegalArgumentException("Must provide a dataset");
         }
         if (params == null) {
           throw new IllegalStateException("Index parameters must be provided");
         }
-        return new TieredIndexImpl(params, vectors, dataset, resources);
+        return new TieredIndexImpl(params, dataset, resources);
       }
     }
   }
@@ -618,14 +581,6 @@ public TieredIndex build() throws Throwable {
   public static class IndexReference {
     private final MemorySegment memorySegment;
 
-    /**
-     * Constructs TieredIndexReference and allocate the MemorySegment.
-     */
-    protected IndexReference(CuVSResourcesImpl resources) {
-      // Don't allocate here - the C function will allocate
-      memorySegment = MemorySegment.NULL;
-    }
-
     /**
      * Constructs TieredIndexReference with an instance of MemorySegment passed as a
      * parameter.
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index 218da3d0d9..c39c3c0dfa 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -108,7 +108,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             Map.of(1, 0.15224183f, 0, 0.5906347f, 3, 0.5986643f));
     for (int j = 0; j < 10; j++) {
 
-      try (CuVSResources resources = CuVSResources.create()) {
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
             new BruteForceQuery.Builder()
                 .withTopK(3)
@@ -136,7 +136,7 @@ public void testIndexingAndSearchingWithFiltering() throws Throwable {
             Map.of(0, 0.5906347f, 1, 0.15224195f, 3, 0.5986643f));
 
     for (int j = 0; j < 10; j++) {
-      try (CuVSResources resources = CuVSResources.create()) {
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
             new BruteForceQuery.Builder()
                 .withTopK(3)
@@ -162,7 +162,7 @@ public void testIndexingAndSearchingWithFunctionMapping() throws Throwable {
     LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.length);
 
     for (int j = 0; j < 10; j++) {
-      try (CuVSResources resources = CuVSResources.create()) {
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
             new BruteForceQuery.Builder()
                 .withTopK(3)
@@ -187,7 +187,7 @@ public void testIndexingAndSearchingWithListMapping() throws Throwable {
     LongToIntFunction rotate = SearchResults.mappingsFromList(mappings);
 
     for (int j = 0; j < 10; j++) {
-      try (CuVSResources resources = CuVSResources.create()) {
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
             new BruteForceQuery.Builder()
                 .withTopK(3)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 7a7c05fb0f..a998b3ee9e 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -106,7 +106,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, prefilters, log);
 
     // Create CuVS index and query
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
 
       BruteForceQuery query =
           new BruteForceQuery.Builder()
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index ee33de8b4e..bfbb4e2d1b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -137,7 +137,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
     int numTestsRuns = 5;
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
         var index = indexOnce(Dataset.ofArray(dataset), resources);
         var indexPath = serializeOnce(index);
@@ -165,7 +165,7 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
     List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
     int numTestsRuns = 5;
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
         runInAnotherThread(
             () -> {
@@ -204,7 +204,7 @@ public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
         numTestsRuns,
         () ->
             () -> {
-              try (CuVSResources resources = CuVSResources.create()) {
+              try (CuVSResources resources = CheckedCuVSResources.create()) {
                 var index = indexOnce(Dataset.ofArray(dataset), resources);
                 var indexPath = serializeOnce(index);
                 var loadedIndex = deserializeOnce(indexPath, resources);
@@ -232,7 +232,7 @@ public void testIndexing() throws Throwable {
           numTestsRuns,
           () ->
               () -> {
-                try (CuVSResources resources = CuVSResources.create()) {
+                try (CuVSResources resources = CheckedCuVSResources.create()) {
                   var index = indexOnce(Dataset.ofArray(dataset), resources);
                   index.destroyIndex();
                 } catch (Throwable e) {
@@ -251,7 +251,7 @@ public void testSerialization() throws Throwable {
           numTestsRuns,
           () ->
               () -> {
-                try (CuVSResources resources = CuVSResources.create()) {
+                try (CuVSResources resources = CheckedCuVSResources.create()) {
                   var index = indexOnce(Dataset.ofArray(dataset), resources);
                   var indexPath = serializeOnce(index);
                   Files.deleteIfExists(indexPath);
@@ -272,7 +272,7 @@ public void testDeserialization() throws Throwable {
           numTestsRuns,
           () ->
               () -> {
-                try (CuVSResources resources = CuVSResources.create()) {
+                try (CuVSResources resources = CheckedCuVSResources.create()) {
                   deserializeOnce(indexPath, resources).destroyIndex();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
@@ -283,7 +283,7 @@ public void testDeserialization() throws Throwable {
   }
 
   private Path createSerializedIndex(Dataset dataset) throws Throwable {
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       var index = indexOnce(dataset, resources);
       var indexPath = serializeOnce(index);
       index.destroyIndex();
@@ -303,7 +303,7 @@ public void testIndexingAndSearchingFlowWithCustomMappingFunction() throws Throw
             Map.of(2, 0.15224178f, 1, 0.59063464f, 0, 0.5986642f));
 
     LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.size());
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       var index = indexOnce(dataset, resources);
       var indexPath = serializeOnce(index);
       var loadedIndex = deserializeOnce(indexPath, resources);
@@ -326,7 +326,7 @@ public void testIndexingAndSearchingFlowWithCustomMappingList() throws Throwable
             Map.of(3, 0.15224178f, 4, 0.59063464f, 1, 0.5986642f));
 
     LongToIntFunction rotate = SearchResults.mappingsFromList(mappings);
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       var index = indexOnce(dataset, resources);
       var indexPath = serializeOnce(index);
       var loadedIndex = deserializeOnce(indexPath, resources);
@@ -362,7 +362,7 @@ public void testPrefilteringReducesResults() throws Throwable {
             .withMetric(CuvsDistanceType.L2Expanded)
             .build();
 
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndex index =
           CagraIndex.newBuilder(resources)
               .withDataset(dataset)
@@ -543,7 +543,7 @@ public void testMergingIndexes() throws Throwable {
             Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
             Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f));
 
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndexParams indexParams =
           new CagraIndexParams.Builder()
               .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
@@ -633,7 +633,7 @@ public void testMergeStrategies() throws Throwable {
             Map.of(2, 0.5f, 3, 0.5f, 1, 180.5f),
             Map.of(0, 0.0f, 1, 2.0f, 2, 200.0f));
 
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndexParams indexParams =
           new CagraIndexParams.Builder()
               .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 3539a1868d..cb81f8464b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -114,7 +114,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, prefilters, log);
 
     // Create CuVS index and query
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndexParams indexParams =
           new CagraIndexParams.Builder()
               .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
new file mode 100644
index 0000000000..a58d9c04e7
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import java.nio.file.Path;
+import java.util.concurrent.atomic.AtomicLong;
+
+public class CheckedCuVSResources implements CuVSResources {
+
+  private volatile boolean destroyed;
+  private final AtomicLong currentThreadId = new AtomicLong(0);
+
+  private final CuVSResources inner;
+
+  private CheckedCuVSResources(CuVSResources inner) {
+    this.inner = inner;
+  }
+
+  static CuVSResources create() throws Throwable {
+    return new CheckedCuVSResources(CuVSResources.create());
+  }
+
+  private void checkNotDestroyed() {
+    if (destroyed) {
+      throw new IllegalStateException("Already destroyed");
+    }
+  }
+
+  @Override
+  public ScopedAccess access() {
+    checkNotDestroyed();
+    var previousThreadId = currentThreadId.compareAndExchange(0, Thread.currentThread().threadId());
+    if (previousThreadId != 0) {
+      throw new IllegalStateException(
+          "This resource is already accessed by thread [" + previousThreadId + "]");
+    }
+    return new ScopedAccess() {
+      @Override
+      public long handle() {
+        checkNotDestroyed();
+        return inner.access().handle();
+      }
+
+      @Override
+      public void close() {
+        currentThreadId.set(0);
+      }
+    };
+  }
+
+  @Override
+  public void close() {
+    destroyed = true;
+    inner.close();
+  }
+
+  @Override
+  public Path tempDirectory() {
+    return inner.tempDirectory();
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
new file mode 100644
index 0000000000..a599089dce
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
@@ -0,0 +1,98 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static org.junit.Assert.*;
+
+import java.lang.invoke.MethodHandles;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.Executors;
+import org.junit.Before;
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class CuVSResourcesIT extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+  }
+
+  @Test
+  public void testConcurrentAccessViaCheckedCuVSResourcesIsForbidden() throws Throwable {
+    var expectedError =
+        "This resource is already accessed by thread [" + Thread.currentThread().threadId() + "]";
+    try (var resources = CheckedCuVSResources.create();
+        var executor = Executors.newFixedThreadPool(1)) {
+
+      try (var access1 = resources.access()) {
+        log.info(
+            "Outer access to resource {} from {}",
+            access1.handle(),
+            Thread.currentThread().threadId());
+
+        var exception =
+            assertThrows(
+                ExecutionException.class,
+                () -> {
+                  var future =
+                      executor.submit(
+                          () -> {
+                            try (var access2 = resources.access()) {
+                              log.info(
+                                  "Nested access to resource {} from {}",
+                                  access2.handle(),
+                                  Thread.currentThread().threadId());
+                              log.info("Nested access finished");
+                            }
+                          });
+                  future.get();
+                });
+        assertEquals(IllegalStateException.class, exception.getCause().getClass());
+        assertEquals(expectedError, exception.getCause().getMessage());
+        log.info("Outer access finished");
+      }
+    }
+  }
+
+  @Test
+  public void testSequentialAccessViaCheckedCuVSResourcesIsAllowed() throws Throwable {
+    try (var resources = CheckedCuVSResources.create();
+        var executor = Executors.newFixedThreadPool(1)) {
+
+      try (var access1 = resources.access()) {
+        log.info(
+            "Access 1 to resource {} from {}", access1.handle(), Thread.currentThread().threadId());
+      }
+
+      var future =
+          executor.submit(
+              () -> {
+                try (var access2 = resources.access()) {
+                  log.info(
+                      "Access 2 to resource {} from {}",
+                      access2.handle(),
+                      Thread.currentThread().threadId());
+                }
+              });
+      future.get();
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index e6d16e0a13..dd0926d200 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -119,7 +119,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
             Map.of(1, 0.15224178f, 0, 0.59063464f, 3, 0.59866416f));
 
     for (int j = 0; j < 10; j++) {
-      try (CuVSResources resources = CuVSResources.create()) {
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
         HnswQuery hnswQuery =
             new HnswQuery.Builder()
                 .withMapping(SearchResults.IDENTITY_MAPPING)
@@ -144,7 +144,7 @@ public void testIndexingAndSearchingWithFunctionMapping() throws Throwable {
     LongToIntFunction rotate = l -> (int) ((l + 1) % dataset.length);
 
     for (int j = 0; j < 10; j++) {
-      try (CuVSResources resources = CuVSResources.create()) {
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
         HnswQuery hnswQuery =
             new HnswQuery.Builder()
                 .withQueryVectors(queries)
@@ -170,7 +170,7 @@ public void testIndexingAndSearchingWithListMapping() throws Throwable {
     LongToIntFunction rotate = SearchResults.mappingsFromList(mappings);
 
     for (int j = 0; j < 10; j++) {
-      try (CuVSResources resources = CuVSResources.create()) {
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
         HnswQuery hnswQuery =
             new HnswQuery.Builder()
                 .withQueryVectors(queries)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index ded2f0019c..d2e198738b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -98,7 +98,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     List<List<Integer>> expected = generateExpectedResults(topK, vectors, queries, null, log);
 
     // Create CuVS index and query
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
 
       // Configure index parameters
       CagraIndexParams indexParams =

From 2ac1dcb12470fab31577f6ba6c12cc990daa6040 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Tue, 22 Jul 2025 12:56:18 -0700
Subject: [PATCH 493/623] Binary CAGRA with NN Descent (#1133)

NN Descent can be used as the graph building algo for CAGRA with the BitwiseHamming metric.

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1133
---
 .../neighbors/detail/cagra/cagra_build.cuh    | 15 ++++++++++++---
 cpp/src/neighbors/detail/cagra/graph_core.cuh | 19 +++++++++++++++----
 cpp/tests/neighbors/ann_cagra.cuh             | 17 +++++++----------
 3 files changed, 34 insertions(+), 17 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 27ad7f296f..651e6bbb85 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -42,6 +42,7 @@
 
 #include <chrono>
 #include <cstdio>
+#include <type_traits>
 #include <vector>
 
 #include <sys/mman.h>
@@ -704,9 +705,17 @@ index<T, IdxT> build(
   }
   RAFT_EXPECTS(
     params.metric != BitwiseHamming ||
-      std::holds_alternative<cagra::graph_build_params::iterative_search_params>(knn_build_params),
-    "IVF_PQ and NN_DESCENT for CAGRA graph build do not support BitwiseHamming as a metric. Please "
-    "use the iterative CAGRA search build.");
+      std::holds_alternative<cagra::graph_build_params::iterative_search_params>(
+        knn_build_params) ||
+      std::holds_alternative<cagra::graph_build_params::nn_descent_params>(knn_build_params),
+    "IVF_PQ for CAGRA graph build does not support BitwiseHamming as a metric. Please "
+    "use nn-descent or the iterative CAGRA search build.");
+
+  // Validate data type for BitwiseHamming metric
+  RAFT_EXPECTS(params.metric != cuvs::distance::DistanceType::BitwiseHamming ||
+                 (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>),
+               "BitwiseHamming distance is only supported for int8_t and uint8_t data types. "
+               "Current data type is not supported.");
 
   auto cagra_graph = raft::make_host_matrix<IdxT, int64_t>(0, 0);
 
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 5dbee191c5..809e3b6dd4 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -107,7 +107,7 @@ __global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size,
           norm2_dst += elem_b * elem_b;
         }
       }
-    } else {
+    } else if (metric == cuvs::distance::DistanceType::L2Expanded) {
       // L2Expanded
       for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
         float diff = cuvs::spatial::knn::detail::utils::mapping<float>{}(
@@ -116,6 +116,15 @@ __global__ void kern_sort(const DATA_T* const dataset,  // [dataset_chunk_size,
                        dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]);
         dist += diff * diff;
       }
+    } else if (metric == cuvs::distance::DistanceType::BitwiseHamming) {
+      if constexpr (std::is_integral_v<DATA_T>) {
+        for (int d = lane_id; d < dataset_dim; d += raft::WarpSize) {
+          dist += __popc(
+            static_cast<uint32_t>(dataset[d + static_cast<uint64_t>(dataset_dim) * srcNode] ^
+                                  dataset[d + static_cast<uint64_t>(dataset_dim) * dstNode]) &
+            0xffu);
+        }
+      }
     }
     dist += __shfl_xor_sync(0xffffffff, dist, 1);
     dist += __shfl_xor_sync(0xffffffff, dist, 2);
@@ -508,8 +517,10 @@ void sort_knn_graph(
   RAFT_EXPECTS(
     metric == cuvs::distance::DistanceType::InnerProduct ||
       metric == cuvs::distance::DistanceType::CosineExpanded ||
-      metric == cuvs::distance::DistanceType::L2Expanded,
-    "Unsupported metric. Only InnerProduct, CosineExpanded, and L2Expanded are supported");
+      metric == cuvs::distance::DistanceType::L2Expanded ||
+      metric == cuvs::distance::DistanceType::BitwiseHamming,
+    "Unsupported metric. Only InnerProduct, CosineExpanded, L2Expanded and BitwiseHamming are "
+    "supported");
   const uint64_t dataset_size = dataset.extent(0);
   const uint64_t dataset_dim  = dataset.extent(1);
   const DataT* dataset_ptr    = dataset.data_handle();
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 03c37455f9..e10e1300ca 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -333,10 +333,9 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
   template <typename SearchIdxT = IdxT>
   void testCagra()
   {
-    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    // IVF_PQ graph build does not support BitwiseHamming
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
-        ((!std::is_same_v<DataT, uint8_t>) ||
-         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+        ((!std::is_same_v<DataT, uint8_t>) || (ps.build_algo == graph_build_algo::IVF_PQ)))
       GTEST_SKIP();
     // If the dataset dimension is small and the dataset size is large, there can be a lot of
     // dataset vectors that have the same distance to the query, especially in the binary Hamming
@@ -525,10 +524,9 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
     // issue: https://github.com/rapidsai/raft/issues/2276
     if (ps.metric == InnerProduct && ps.build_algo == graph_build_algo::NN_DESCENT) GTEST_SKIP();
     if (ps.compression != std::nullopt) GTEST_SKIP();
-    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    // IVF_PQ graph build does not support BitwiseHamming
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
-        ((!std::is_same_v<DataT, uint8_t>) ||
-         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+        ((!std::is_same_v<DataT, uint8_t>) || (ps.build_algo == graph_build_algo::IVF_PQ)))
       GTEST_SKIP();
     // If the dataset dimension is small and the dataset size is large, there can be a lot of
     // dataset vectors that have the same distance to the query, especially in the binary Hamming
@@ -735,7 +733,7 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
     if (ps.metric == cuvs::distance::DistanceType::InnerProduct &&
         ps.build_algo == graph_build_algo::NN_DESCENT)
       GTEST_SKIP();
-    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    // IVF_PQ graph build does not support BitwiseHamming
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
         ((!std::is_same_v<DataT, uint8_t>) ||
          (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
@@ -946,10 +944,9 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
     // issue: https://github.com/rapidsai/raft/issues/2276
     if (ps.metric == InnerProduct && ps.build_algo == graph_build_algo::NN_DESCENT) GTEST_SKIP();
     if (ps.compression != std::nullopt) GTEST_SKIP();
-    // IVF_PQ and NN_DESCENT graph builds do not support BitwiseHamming
+    // IVF_PQ graph build does not support BitwiseHamming
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
-        ((!std::is_same_v<DataT, uint8_t>) ||
-         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+        ((!std::is_same_v<DataT, uint8_t>) || (ps.build_algo == graph_build_algo::IVF_PQ)))
       GTEST_SKIP();
     // If the dataset dimension is small and the dataset size is large, there can be a lot of
     // dataset vectors that have the same distance to the query, especially in the binary Hamming

From 989a20ab8a2132157178f8758ef24feff6fcb385 Mon Sep 17 00:00:00 2001
From: Mike Sarahan <msarahan@gmail.com>
Date: Tue, 22 Jul 2025 16:14:55 -0500
Subject: [PATCH 494/623] Adding GH_TOKEN pass-through to summarize job (#1011)

This adds an env var to pass the github token through to the telemetry summary shared action. The token is necessary to check if the base artifact exists. See https://github.com/rapidsai/shared-actions/pull/56 for more information.

The whitespace changes here were introduced from using yq with rapids-reviser to add this field. If the whitespace changes are undesirable, I will revert them.

Authors:
  - Mike Sarahan (https://github.com/msarahan)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1011
---
 .github/workflows/pr.yaml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index d817082b67..71e8675fae 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -1,14 +1,11 @@
 name: pr
-
 on:
   push:
     branches:
       - "pull-request/[0-9]+"
-
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
-
 jobs:
   pr-builder:
     needs:
@@ -232,7 +229,6 @@ jobs:
         sccache -z;
         build-all --verbose;
         sccache -s;
-
   telemetry-summarize:
     # This job must use a self-hosted runner to record telemetry traces.
     runs-on: linux-amd64-cpu4
@@ -242,3 +238,5 @@ jobs:
     steps:
       - name: Telemetry summarize
         uses: rapidsai/shared-actions/telemetry-dispatch-summarize@main
+    env:
+      GH_TOKEN: ${{ github.token }}

From 2ca4d3a9ff1972199a0463bf963cd66ef398cdfe Mon Sep 17 00:00:00 2001
From: Anupam <54245698+aamijar@users.noreply.github.com>
Date: Tue, 22 Jul 2025 14:28:20 -0700
Subject: [PATCH 495/623] Spectral Embedding (#871)

Depends on https://github.com/rapidsai/raft/pull/2662 and Resolves #1025

Authors:
  - Anupam (https://github.com/aamijar)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/871
---
 cpp/CMakeLists.txt                            |   1 +
 .../cuvs/preprocessing/spectral_embedding.hpp |  51 ++++
 .../spectral/spectral_embedding.cu            | 260 ++++++++++++++++++
 cpp/tests/CMakeLists.txt                      |   2 +-
 cpp/tests/preprocessing/spectral_embedding.cu | 223 +++++++++++++++
 5 files changed, 536 insertions(+), 1 deletion(-)
 create mode 100644 cpp/include/cuvs/preprocessing/spectral_embedding.hpp
 create mode 100644 cpp/src/preprocessing/spectral/spectral_embedding.cu
 create mode 100644 cpp/tests/preprocessing/spectral_embedding.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index b8aefd8a4d..08074d57a4 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -490,6 +490,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/vamana_serialize_int8.cu
     src/preprocessing/quantize/scalar.cu
     src/preprocessing/quantize/binary.cu
+    src/preprocessing/spectral/spectral_embedding.cu
     src/selection/select_k_float_int64_t.cu
     src/selection/select_k_float_int32_t.cu
     src/selection/select_k_float_uint32_t.cu
diff --git a/cpp/include/cuvs/preprocessing/spectral_embedding.hpp b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
new file mode 100644
index 0000000000..90197e1bd5
--- /dev/null
+++ b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/device_coo_matrix.hpp>
+#include <raft/core/device_csr_matrix.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::preprocessing::spectral_embedding {
+
+/**
+ * @brief Parameters for spectral embedding algorithm
+ */
+struct params {
+  /** @brief The number of components to reduce the data to. */
+  int n_components;
+
+  /** @brief The number of neighbors to use for the nearest neighbors graph. */
+  int n_neighbors;
+
+  /** @brief Whether to normalize the Laplacian matrix. */
+  bool norm_laplacian;
+
+  /** @brief Whether to drop the first eigenvector. */
+  bool drop_first;
+
+  /** @brief Random seed for reproducibility */
+  uint64_t seed;
+};
+
+void transform(raft::resources const& handle,
+               params config,
+               raft::device_matrix_view<float, int, raft::row_major> dataset,
+               raft::device_matrix_view<float, int, raft::col_major> embedding);
+
+}  // namespace cuvs::preprocessing::spectral_embedding
diff --git a/cpp/src/preprocessing/spectral/spectral_embedding.cu b/cpp/src/preprocessing/spectral/spectral_embedding.cu
new file mode 100644
index 0000000000..d3466802d1
--- /dev/null
+++ b/cpp/src/preprocessing/spectral/spectral_embedding.cu
@@ -0,0 +1,260 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/brute_force.hpp>
+#include <cuvs/preprocessing/spectral_embedding.hpp>
+
+#include <raft/core/device_coo_matrix.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/matrix/gather.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/sparse/coo.hpp>
+#include <raft/sparse/linalg/laplacian.cuh>
+#include <raft/sparse/linalg/symmetrize.cuh>
+#include <raft/sparse/op/filter.cuh>
+#include <raft/sparse/solver/lanczos.cuh>
+#include <raft/sparse/solver/lanczos_types.hpp>
+#include <raft/util/cudart_utils.hpp>
+#include <raft/util/integer_utils.hpp>
+
+#include <thrust/sequence.h>
+#include <thrust/tabulate.h>
+
+namespace cuvs::preprocessing::spectral_embedding {
+
+void create_connectivity_graph(raft::resources const& handle,
+                               params spectral_embedding_config,
+                               raft::device_matrix_view<float, int, raft::row_major> dataset,
+                               raft::device_matrix_view<float, int, raft::col_major> embedding,
+                               raft::device_coo_matrix<float, int, int, int>& connectivity_graph)
+{
+  const int n_samples  = dataset.extent(0);
+  const int n_features = dataset.extent(1);
+  const int k_search   = spectral_embedding_config.n_neighbors;
+  const size_t nnz     = n_samples * k_search;
+
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  cuvs::neighbors::brute_force::search_params search_params;
+  cuvs::neighbors::brute_force::index_params index_params;
+  index_params.metric = cuvs::distance::DistanceType::L2SqrtExpanded;
+
+  auto d_indices   = raft::make_device_matrix<int64_t>(handle, n_samples, k_search);
+  auto d_distances = raft::make_device_matrix<float>(handle, n_samples, k_search);
+
+  auto index =
+    cuvs::neighbors::brute_force::build(handle, index_params, raft::make_const_mdspan(dataset));
+
+  cuvs::neighbors::brute_force::search(
+    handle, search_params, index, dataset, d_indices.view(), d_distances.view());
+
+  auto knn_rows = raft::make_device_vector<int>(handle, nnz);
+  auto knn_cols = raft::make_device_vector<int>(handle, nnz);
+
+  raft::linalg::unary_op(
+    handle, make_const_mdspan(d_indices.view()), knn_cols.view(), [] __device__(int64_t x) {
+      return static_cast<int>(x);
+    });
+
+  thrust::tabulate(raft::resource::get_thrust_policy(handle),
+                   knn_rows.data_handle(),
+                   knn_rows.data_handle() + nnz,
+                   [k_search] __device__(int idx) { return idx / k_search; });
+
+  // set all distances to 1.0f (connectivity KNN graph)
+  raft::matrix::fill(handle, raft::make_device_vector_view(d_distances.data_handle(), nnz), 1.0f);
+
+  auto coo_matrix_view = raft::make_device_coo_matrix_view<const float, int, int, int>(
+    d_distances.data_handle(),
+    raft::make_device_coordinate_structure_view<int, int, int>(
+      knn_rows.data_handle(), knn_cols.data_handle(), n_samples, n_samples, nnz));
+
+  auto sym_coo1_matrix =
+    raft::make_device_coo_matrix<float, int, int, int>(handle, n_samples, n_samples);
+  raft::sparse::linalg::coo_symmetrize<128, float, int, int>(
+    handle, coo_matrix_view, sym_coo1_matrix, [] __device__(int row, int col, float a, float b) {
+      return 0.5f * (a + b);
+    });
+
+  raft::sparse::op::coo_sort<float>(n_samples,
+                                    n_samples,
+                                    sym_coo1_matrix.structure_view().get_nnz(),
+                                    sym_coo1_matrix.structure_view().get_rows().data(),
+                                    sym_coo1_matrix.structure_view().get_cols().data(),
+                                    sym_coo1_matrix.get_elements().data(),
+                                    stream);
+
+  raft::sparse::op::coo_remove_scalar<128, float, int, int>(
+    handle,
+    raft::make_device_coo_matrix_view<const float, int, int, int>(
+      sym_coo1_matrix.get_elements().data(), sym_coo1_matrix.structure_view()),
+    raft::make_host_scalar<float>(0.0f).view(),
+    connectivity_graph);
+}
+
+raft::device_csr_matrix_view<float, int, int, int> coo_to_csr_matrix(
+  raft::resources const& handle,
+  const int n_samples,
+  raft::device_vector_view<int> sym_coo_row_ind,
+  raft::device_coo_matrix<float, int, int, int>& sym_coo_matrix)
+{
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  raft::sparse::op::coo_sort<float>(n_samples,
+                                    n_samples,
+                                    sym_coo_matrix.structure_view().get_nnz(),
+                                    sym_coo_matrix.structure_view().get_rows().data(),
+                                    sym_coo_matrix.structure_view().get_cols().data(),
+                                    sym_coo_matrix.get_elements().data(),
+                                    stream);
+
+  raft::sparse::convert::sorted_coo_to_csr(sym_coo_matrix.structure_view().get_rows().data(),
+                                           sym_coo_matrix.structure_view().get_nnz(),
+                                           sym_coo_row_ind.data_handle(),
+                                           n_samples,
+                                           stream);
+
+  auto sym_coo_nnz = sym_coo_matrix.structure_view().get_nnz();
+  raft::copy(sym_coo_row_ind.data_handle() + sym_coo_row_ind.size() - 1, &sym_coo_nnz, 1, stream);
+
+  auto csr_matrix_view = raft::make_device_csr_matrix_view<float, int, int, int>(
+    const_cast<float*>(sym_coo_matrix.get_elements().data()),
+    raft::make_device_compressed_structure_view<int, int, int>(
+      const_cast<int*>(sym_coo_row_ind.data_handle()),
+      const_cast<int*>(sym_coo_matrix.structure_view().get_cols().data()),
+      n_samples,
+      n_samples,
+      sym_coo_matrix.structure_view().get_nnz()));
+  return csr_matrix_view;
+}
+
+raft::device_csr_matrix<float, int, int, int> create_laplacian(
+  raft::resources const& handle,
+  params spectral_embedding_config,
+  raft::device_csr_matrix_view<float, int, int, int> csr_matrix_view,
+  raft::device_vector_view<float, int> diagonal)
+{
+  auto laplacian = spectral_embedding_config.norm_laplacian
+                     ? raft::sparse::linalg::laplacian_normalized(handle, csr_matrix_view, diagonal)
+                     : raft::sparse::linalg::compute_graph_laplacian(handle, csr_matrix_view);
+
+  auto laplacian_elements_view = raft::make_device_vector_view<float, int>(
+    laplacian.get_elements().data(), laplacian.structure_view().get_nnz());
+
+  raft::linalg::unary_op(handle,
+                         raft::make_const_mdspan(laplacian_elements_view),
+                         laplacian_elements_view,
+                         [] __device__(float x) { return -x; });
+
+  return laplacian;
+}
+
+void compute_eigenpairs(raft::resources const& handle,
+                        params spectral_embedding_config,
+                        const int n_samples,
+                        raft::device_csr_matrix<float, int, int, int> laplacian,
+                        raft::device_vector_view<float, int> diagonal,
+                        raft::device_matrix_view<float, int, raft::col_major> embedding)
+{
+  auto config           = raft::sparse::solver::lanczos_solver_config<float>();
+  config.n_components   = spectral_embedding_config.n_components;
+  config.max_iterations = 1000;
+  config.ncv            = std::min(n_samples, std::max(2 * config.n_components + 1, 20));
+  config.tolerance      = 1e-5;
+  config.which          = raft::sparse::solver::LANCZOS_WHICH::LA;
+  config.seed           = spectral_embedding_config.seed;
+
+  auto eigenvalues =
+    raft::make_device_vector<float, int, raft::col_major>(handle, config.n_components);
+  auto eigenvectors =
+    raft::make_device_matrix<float, int, raft::col_major>(handle, n_samples, config.n_components);
+
+  raft::sparse::solver::lanczos_compute_smallest_eigenvectors<int, float>(
+    handle,
+    config,
+    raft::make_device_csr_matrix_view<float, int, int, int>(laplacian.get_elements().data(),
+                                                            laplacian.structure_view()),
+    std::nullopt,
+    eigenvalues.view(),
+    eigenvectors.view());
+
+  if (spectral_embedding_config.norm_laplacian) {
+    raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
+      handle,
+      raft::make_const_mdspan(eigenvectors.view()),  // input matrix view
+      raft::make_const_mdspan(diagonal),             // input vector view
+      eigenvectors.view(),                           // output matrix view (in-place)
+      [] __device__(float elem, float diag) { return elem / diag; });
+  }
+
+  // Create a sequence of reversed column indices
+  config.n_components =
+    spectral_embedding_config.drop_first ? config.n_components - 1 : config.n_components;
+  auto col_indices = raft::make_device_vector<int>(handle, config.n_components);
+
+  // TODO: https://github.com/rapidsai/raft/issues/2661
+  thrust::sequence(thrust::device,
+                   col_indices.data_handle(),
+                   col_indices.data_handle() + config.n_components,
+                   config.n_components - 1,  // Start from the last column index
+                   -1                        // Decrement (move backward)
+  );
+
+  // Create row-major views of the column-major matrices
+  // This is just a view re-interpretation, no data movement
+  auto eigenvectors_row_view = raft::make_device_matrix_view<float, int, raft::row_major>(
+    eigenvectors.data_handle(),
+    eigenvectors.extent(1),  // Swap dimensions for the view
+    eigenvectors.extent(0));
+
+  auto embedding_row_view = raft::make_device_matrix_view<float, int, raft::row_major>(
+    embedding.data_handle(),
+    embedding.extent(1),  // Swap dimensions for the view
+    embedding.extent(0));
+
+  raft::matrix::gather<float, int, int>(
+    handle,
+    raft::make_const_mdspan(eigenvectors_row_view),  // Source matrix (as row-major view)
+    raft::make_const_mdspan(col_indices.view()),     // Column indices to gather
+    embedding_row_view                               // Destination matrix (as row-major view)
+  );
+}
+
+void transform(raft::resources const& handle,
+               params spectral_embedding_config,
+               raft::device_matrix_view<float, int, raft::row_major> dataset,
+               raft::device_matrix_view<float, int, raft::col_major> embedding)
+{
+  const int n_samples = dataset.extent(0);
+
+  auto sym_coo_matrix =
+    raft::make_device_coo_matrix<float, int, int, int>(handle, n_samples, n_samples);
+  auto sym_coo_row_ind = raft::make_device_vector<int>(handle, n_samples + 1);
+  auto diagonal        = raft::make_device_vector<float, int>(handle, n_samples);
+
+  create_connectivity_graph(handle, spectral_embedding_config, dataset, embedding, sym_coo_matrix);
+  auto csr_matrix_view =
+    coo_to_csr_matrix(handle, n_samples, sym_coo_row_ind.view(), sym_coo_matrix);
+  auto laplacian =
+    create_laplacian(handle, spectral_embedding_config, csr_matrix_view, diagonal.view());
+  compute_eigenpairs(
+    handle, spectral_embedding_config, n_samples, laplacian, diagonal.view(), embedding);
+}
+
+}  // namespace cuvs::preprocessing::spectral_embedding
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index 9945877b47..77bafb5eb2 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -273,7 +273,7 @@ if(BUILD_TESTS)
 
   ConfigureTest(
     NAME PREPROCESSING_TEST PATH preprocessing/scalar_quantization.cu
-    preprocessing/binary_quantization.cu GPUS 1 PERCENT 100
+    preprocessing/binary_quantization.cu preprocessing/spectral_embedding.cu GPUS 1 PERCENT 100
   )
 
   ConfigureTest(
diff --git a/cpp/tests/preprocessing/spectral_embedding.cu b/cpp/tests/preprocessing/spectral_embedding.cu
new file mode 100644
index 0000000000..e031648c56
--- /dev/null
+++ b/cpp/tests/preprocessing/spectral_embedding.cu
@@ -0,0 +1,223 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+#include <cuvs/preprocessing/spectral_embedding.hpp>
+#include <cuvs/stats/trustworthiness_score.hpp>  // Add trustworthiness header
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+namespace cuvs::preprocessing::spectral_embedding {
+
+template <typename T>
+struct SpectralEmbeddingInputs {
+  int n_samples;        // Number of samples in the dataset
+  int n_features;       // Number of features in the dataset
+  int n_clusters;       // Number of clusters to generate
+  int n_components;     // Number of components in the embedding
+  int n_neighbors;      // Number of neighbors for KNN
+  T cluster_std;        // Standard deviation of clusters
+  bool norm_laplacian;  // Whether to use normalized Laplacian
+  bool drop_first;      // Whether to drop the first eigenvector
+  uint64_t seed;        // Random seed
+};
+
+template <typename T>
+std::ostream& operator<<(std::ostream& os, const SpectralEmbeddingInputs<T>& inputs)
+{
+  return os << "n_samples:" << inputs.n_samples << " n_features:" << inputs.n_features
+            << " n_clusters:" << inputs.n_clusters << " n_components:" << inputs.n_components
+            << " n_neighbors:" << inputs.n_neighbors << " cluster_std:" << inputs.cluster_std
+            << " norm_laplacian:" << inputs.norm_laplacian << " drop_first:" << inputs.drop_first
+            << " seed:" << inputs.seed;
+}
+
+template <typename T>
+class SpectralEmbeddingTest : public ::testing::TestWithParam<SpectralEmbeddingInputs<T>> {
+ public:
+  SpectralEmbeddingTest()
+    : params_(::testing::TestWithParam<SpectralEmbeddingInputs<T>>::GetParam()),
+      stream(raft::resource::get_cuda_stream(handle)),
+      input_(raft::make_device_matrix<T, int, raft::row_major>(
+        handle, params_.n_samples, params_.n_features)),
+      labels_(raft::make_device_vector<int, int, raft::row_major>(handle, params_.n_samples)),
+      embedding_(raft::make_device_matrix<T, int, raft::col_major>(
+        handle, params_.n_samples, params_.n_components))
+  {
+  }
+
+ protected:
+  void SetUp() override
+  {
+    n_samples_    = params_.n_samples;
+    n_features_   = params_.n_features;
+    n_clusters_   = params_.n_clusters;
+    n_components_ = params_.n_components;
+
+    // Generate clusters using make_blobs
+    raft::random::make_blobs<T, int, raft::row_major>(
+      handle,
+      input_.view(),
+      labels_.view(),
+      n_clusters_,                          // Number of clusters
+      std::nullopt,                         // Generate random centers
+      std::nullopt,                         // Use scalar std
+      static_cast<T>(params_.cluster_std),  // Cluster std
+      true,                                 // Shuffle
+      static_cast<T>(-10.0),                // Center box min
+      static_cast<T>(10.0),                 // Center box max
+      params_.seed);                        // Random seed
+
+    // Output embedding matrix
+    embedding_ =
+      raft::make_device_matrix<T, int, raft::col_major>(handle, n_samples_, n_components_);
+
+    // Configure spectral embedding
+    config_.n_neighbors    = params_.n_neighbors;
+    config_.norm_laplacian = params_.norm_laplacian;
+    config_.drop_first     = params_.drop_first;
+    config_.seed           = params_.seed;
+
+    config_.n_components = params_.drop_first ? n_components_ + 1 : n_components_;
+  }
+
+  void TearDown() override {}
+
+  void testSpectralEmbedding()
+  {
+    // Call the spectral embedding function
+    transform(handle, config_, input_.view(), embedding_.view());
+
+    // Basic sanity checks on the output embedding
+
+    // 1. Check embedding dimensions - should match the input settings
+    ASSERT_EQ(embedding_.extent(0), n_samples_);
+    int expected_components = n_components_;
+    ASSERT_EQ(embedding_.extent(1), expected_components);
+
+    // 2. Verify that the embedding is not all zeros or NaNs
+    auto h_embedding =
+      raft::make_host_matrix<T, int, raft::col_major>(embedding_.extent(0), embedding_.extent(1));
+    raft::update_host(h_embedding.data_handle(),
+                      embedding_.data_handle(),
+                      embedding_.extent(0) * embedding_.extent(1),
+                      stream);
+    raft::resource::sync_stream(handle, stream);
+
+    bool all_zeros = true;
+    bool has_nan   = false;
+
+    for (int i = 0; i < h_embedding.extent(0) * h_embedding.extent(1); i++) {
+      if (h_embedding.data_handle()[i] != 0) { all_zeros = false; }
+      if (std::isnan(h_embedding.data_handle()[i])) {
+        has_nan = true;
+        break;
+      }
+    }
+
+    ASSERT_FALSE(all_zeros) << "Embedding contains all zeros";
+    ASSERT_FALSE(has_nan) << "Embedding contains NaN values";
+
+    // 3. Compute trustworthiness score to evaluate the quality of embedding
+    // Create views with int64_t indexing for trustworthiness_score function
+    auto input_view_i64 = raft::make_device_matrix_view<const float, int64_t, raft::row_major>(
+      input_.data_handle(), input_.extent(0), input_.extent(1));
+
+    // Need to convert embedding to row-major format for trustworthiness calculation
+    auto embedding_row_major = raft::make_device_matrix<float, int, raft::row_major>(
+      handle, embedding_.extent(0), embedding_.extent(1));
+
+    // Copy and transpose from col-major to row-major
+    raft::copy(embedding_row_major.data_handle(),
+               embedding_.data_handle(),
+               embedding_.extent(0) * embedding_.extent(1),
+               stream);
+
+    auto embedding_view_i64 = raft::make_device_matrix_view<const float, int64_t, raft::row_major>(
+      embedding_row_major.data_handle(),
+      embedding_row_major.extent(0),
+      embedding_row_major.extent(1));
+
+    // Calculate trustworthiness score
+    double trust_score = cuvs::stats::trustworthiness_score(
+      handle,
+      input_view_i64,
+      embedding_view_i64,
+      params_.n_neighbors,  // use same number of neighbors as the embedding
+      cuvs::distance::DistanceType::L2SqrtUnexpanded);
+
+    // Print the trustworthiness score (for reporting purposes)
+    std::cout << "Trustworthiness score: " << trust_score << std::endl;
+
+    // Check that trustworthiness score is reasonable (should be between 0 and 1, with higher being
+    // better)
+    ASSERT_GE(trust_score, 0.0) << "Trustworthiness score should be non-negative";
+    ASSERT_LE(trust_score, 1.0) << "Trustworthiness score should be <= 1.0";
+
+    // For good embeddings, we expect a reasonably high trustworthiness score
+    // This threshold can be adjusted based on empirical results
+    ASSERT_GT(trust_score, 0.7)
+      << "Trustworthiness score is too low, indicating poor embedding quality";
+  }
+
+ private:
+  raft::resources handle;
+  cudaStream_t stream;
+
+  SpectralEmbeddingInputs<T> params_;
+  int n_samples_;
+  int n_features_;
+  int n_clusters_;
+  int n_components_;
+
+  raft::device_matrix<T, int, raft::row_major> input_;
+  raft::device_vector<int, int, raft::row_major> labels_;
+  raft::device_matrix<T, int, raft::col_major> embedding_;
+
+  params config_;
+};
+
+// Define test cases with different parameters
+template <typename T>
+const std::vector<SpectralEmbeddingInputs<T>> inputs = {
+  // Small dataset with 2 components
+  {100, 10, 3, 2, 10, 1.0f, true, false, 42ULL},
+
+  // Medium dataset with 3 components
+  {500, 20, 5, 3, 15, 1.0f, true, false, 42ULL},
+
+  // Small dataset with varying cluster standard deviation
+  {100, 10, 3, 2, 10, 0.5f, true, false, 42ULL},
+
+  // Test with different Laplacian normalization
+  {100, 10, 3, 2, 10, 1.0f, false, false, 42ULL},
+
+  // Test with dropping first eigenvector
+  {100, 10, 3, 2, 10, 1.0f, true, true, 42ULL},
+
+  // Larger dataset
+  {10000, 20, 8, 2, 12, 1.0f, true, true, 42ULL}};
+
+typedef SpectralEmbeddingTest<float> SpectralEmbeddingTestF;
+TEST_P(SpectralEmbeddingTestF, Result) { this->testSpectralEmbedding(); }
+
+INSTANTIATE_TEST_CASE_P(SpectralEmbeddingTests,
+                        SpectralEmbeddingTestF,
+                        ::testing::ValuesIn(inputs<float>));
+
+}  // namespace cuvs::preprocessing::spectral_embedding

From cf9b256146c52c5ceb3ac182d5c200ecea07e3f6 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 22 Jul 2025 16:37:03 -0700
Subject: [PATCH 496/623] Expose graph and dataset accessors for CAGRA to
 C/Python (#1086)

Add the ability to get the graph and dataset from a CAGRA index to the c-api and python apis, as well as being able to reconstruct the cagra index from a graph and dataset.

The eventual goal here is to be more flexible in terms of allowing other serialization formats. Rather than supporting every format inside of cuvs, by exposing the raw data needed to recreate a cagra index - we can let consumers of cuvs decide how they want to serialize an index.

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1086
---
 .pre-commit-config.yaml                       |   2 +-
 cpp/include/cuvs/cluster/kmeans.h             |   4 +-
 cpp/include/cuvs/core/c_api.h                 |  29 ++++
 cpp/include/cuvs/core/detail/interop.hpp      |  24 ++-
 cpp/include/cuvs/neighbors/cagra.h            | 108 ++++++++++++-
 cpp/src/core/c_api.cpp                        | 115 ++++++++++++++
 cpp/src/neighbors/cagra_c.cpp                 | 133 ++++++++++++++++
 python/cuvs/cuvs/common/CMakeLists.txt        |   4 +-
 python/cuvs/cuvs/common/c_api.pxd             |  13 +-
 python/cuvs/cuvs/common/cydlpack.pyx          |  39 ++++-
 .../cuvs/cuvs/common/device_tensor_view.pyx   | 145 ++++++++++++++++++
 python/cuvs/cuvs/neighbors/cagra/__init__.py  |   4 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |  15 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  95 +++++++++++-
 python/cuvs/cuvs/tests/test_cagra.py          |  33 +++-
 .../cuvs/tests/test_device_tensor_view.py     |  40 +++++
 16 files changed, 780 insertions(+), 23 deletions(-)
 create mode 100644 python/cuvs/cuvs/common/device_tensor_view.pyx
 create mode 100644 python/cuvs/cuvs/tests/test_device_tensor_view.py

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 22db1342d1..57674fa1ac 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -108,7 +108,7 @@ repos:
           - id: verify-copyright
             files: |
               (?x)
-                  [.](cmake|cpp|cu|cuh|h|hpp|sh|pxd|py|pyx|rs)$|
+                  [.](cmake|cpp|cu|cuh|h|hpp|sh|pxd|py|pyx|rs|java)$|
                   CMakeLists[.]txt$|
                   CMakeLists_standalone[.]txt$|
                   meta[.]yaml$
diff --git a/cpp/include/cuvs/cluster/kmeans.h b/cpp/include/cuvs/cluster/kmeans.h
index 99849b9a96..203ef6b76e 100644
--- a/cpp/include/cuvs/cluster/kmeans.h
+++ b/cpp/include/cuvs/cluster/kmeans.h
@@ -30,7 +30,7 @@ extern "C" {
  * @{
  */
 
-enum cuvsKMeansInitMethod {
+typedef enum {
   /**
    * Sample the centroids using the kmeans++ strategy
    */
@@ -45,7 +45,7 @@ enum cuvsKMeansInitMethod {
    * User provides the array of initial centroids
    */
   Array
-};
+} cuvsKMeansInitMethod;
 
 /**
  * @brief Hyper-parameters for the kmeans algorithm
diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index f1c94e1f5f..d7b6673990 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -17,6 +17,7 @@
 #pragma once
 
 #include <cuda_runtime.h>
+#include <dlpack/dlpack.h>
 #include <stdbool.h>
 #include <stdint.h>
 
@@ -176,6 +177,34 @@ cuvsError_t cuvsRMMHostFree(void* ptr, size_t bytes);
  */
 cuvsError_t cuvsVersionGet(uint16_t* major, uint16_t* minor, uint16_t* patch);
 
+/**
+ * @brief Copy a matrix
+ *
+ * This function copies a matrix from dst to src. This lets you copy a matrix
+ * from device memory to host memory (or vice versa), while accounting for
+ * differences in strides.
+ *
+ * Both src and dst must have the same shape and dtype, but can have different
+ * strides and device type. The memory for the output dst tensor must already be
+ * allocated and the tensor initialized.
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] src Pointer to DLManagedTensor to copy
+ * @param[out] dst Pointer to DLManagedTensor to receive copy of data
+ */
+cuvsError_t cuvsMatrixCopy(cuvsResources_t res, DLManagedTensor* src, DLManagedTensor* dst);
+
+/**
+ * @brief Slices rows from a matrix
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] src Pointer to DLManagedTensor to copy
+ * @param[in] start First row index to include in the output
+ * @param[in] end Last row index to include in the output
+ * @param[out] dst Pointer to DLManagedTensor to receive slice from matrix
+ */
+cuvsError_t cuvsMatrixSliceRows(
+  cuvsResources_t res, DLManagedTensor* src, int64_t start, int64_t end, DLManagedTensor* dst);
 /** @} */
 
 #ifdef __cplusplus
diff --git a/cpp/include/cuvs/core/detail/interop.hpp b/cpp/include/cuvs/core/detail/interop.hpp
index 205d493962..8558fcf1c3 100644
--- a/cpp/include/cuvs/core/detail/interop.hpp
+++ b/cpp/include/cuvs/core/detail/interop.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -143,9 +143,10 @@ inline bool is_c_contiguous(DLManagedTensor* managed_tensor)
 
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-function"
-static void free_dlmanaged_tensor_shape(DLManagedTensor* tensor)
+static void free_dlmanaged_tensor_metadata(DLManagedTensor* tensor)
 {
   delete[] tensor->dl_tensor.shape;
+  delete[] tensor->dl_tensor.strides;
 }
 #pragma GCC diagnostic pop
 
@@ -157,14 +158,21 @@ static void to_dlpack(MdspanType src, DLManagedTensor* dst)
   tensor->dtype  = data_type_to_DLDataType<typename MdspanType::value_type>();
   tensor->device = accessor_type_to_DLDevice<typename MdspanType::accessor_type>();
   tensor->ndim   = MdspanType::extents_type::rank();
-  tensor->data   = src.data_handle();
-
-  tensor->shape = new int64_t[tensor->ndim];
-  dst->deleter  = free_dlmanaged_tensor_shape;
-
+  tensor->data   = const_cast<typename MdspanType::value_type*>(src.data_handle());
+  tensor->shape  = new int64_t[tensor->ndim];
   for (int64_t i = 0; i < tensor->ndim; ++i) {
     tensor->shape[i] = src.extent(i);
   }
-}
 
+  if constexpr (std::is_same_v<typename MdspanType::layout_type, raft::row_major>) {
+    tensor->strides = nullptr;
+  } else {
+    tensor->strides = new int64_t[tensor->ndim];
+    for (int64_t i = 0; i < tensor->ndim; ++i) {
+      tensor->strides[i] = src.stride(i);
+    }
+  }
+
+  dst->deleter = free_dlmanaged_tensor_metadata;
+}
 }  // namespace cuvs::core::detail
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 5959124870..bd0548d4ba 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -358,6 +358,66 @@ cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index);
  */
 cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim);
 
+/**
+ * @brief Get size of the CAGRA index
+ *
+ * @param[in] index CAGRA index
+ * @param[out] size return number of vectors in the index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* size);
+
+/**
+ * @brief Get graph degree of the CAGRA index
+ *
+ * @param[in] index CAGRA index
+ * @param[out] graph_degree return graph degree
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index, uint32_t* graph_degree);
+
+/**
+ * @brief Returns a view of the CAGRA dataset
+ *
+ * This function returns a non-owning view of the CAGRA dataset.
+ * The output will be referencing device memory that is directly used
+ * in CAGRA, without copying the dataset at all. This means that the
+ * output is only valid as long as the CAGRA index is alive, and once
+ * cuvsCagraIndexDestroy is called on the cagra index - the returned
+ * dataset view will be invalid.
+ *
+ * Note that the DLManagedTensor dataset returned will have an associated
+ * 'deleter' function that must be called when the dataset is no longer
+ * needed. This will free up host memory that stores the shape of the
+ * dataset view.
+ *
+ * @param[in] index CAGRA index
+ * @param[out] dataset the dataset used in cagra
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraIndexGetDataset(cuvsCagraIndex_t index, DLManagedTensor* dataset);
+
+/**
+ * @brief Returns a view of the CAGRA graph
+ *
+ * This function returns a non-owning view of the CAGRA graph.
+ * The output will be referencing device memory that is directly used
+ * in CAGRA, without copying the graph at all. This means that the
+ * output is only valid as long as the CAGRA index is alive, and once
+ * cuvsCagraIndexDestroy is called on the cagra index - the returned
+ * graph view will be invalid.
+ *
+ * Note that the DLManagedTensor graph returned will have an associated
+ * 'deleter' function that must be called when the graph is no longer
+ * needed. This will free up host memory that stores the metadata for the
+ * graph view.
+ *
+ * @param[in] index CAGRA index
+ * @param[out] graph the output knn graph.
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsCagraIndexGetGraph(cuvsCagraIndex_t index, DLManagedTensor* graph);
+
 /**
  * @}
  */
@@ -434,7 +494,8 @@ cuvsError_t cuvsCagraMergeParamsDestroy(cuvsCagraMergeParams_t params);
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] params cuvsCagraIndexParams_t used to build CAGRA index
  * @param[in] dataset DLManagedTensor* training dataset
- * @param[out] index cuvsCagraIndex_t Newly built CAGRA index
+ * @param[inout] index cuvsCagraIndex_t Newly built CAGRA index. This index needs to be already
+ *                                      created with cuvsCagraIndexCreate.
  * @return cuvsError_t
  */
 cuvsError_t cuvsCagraBuild(cuvsResources_t res,
@@ -609,10 +670,51 @@ cuvsError_t cuvsCagraSerializeToHnswlib(cuvsResources_t res,
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[in] filename the name of the file that stores the index
- * @param[out] index CAGRA index loaded disk
+ * @param[inout] index cuvsCagraIndex_t CAGRA index loaded from disk. This index needs to be already
+ *                                      created with cuvsCagraIndexCreate.
  */
 cuvsError_t cuvsCagraDeserialize(cuvsResources_t res, const char* filename, cuvsCagraIndex_t index);
 
+/**
+ * Load index from a dataset and graph
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] metric cuvsDistanceType to use in the index
+ * @param[in] graph the knn graph to use, shape (size, graph_degree)
+ * @param[in] dataset the dataset to use, shape (size, dim)
+ * @param[inout] index cuvsCagraIndex_t CAGRA index populated with the graph and dataset.
+ *                                      This index needs to be already created with
+ *                                      cuvsCagraIndexCreate.
+ *
+ * @code {.c}
+ * #include <cuvs/core/c_api.h>
+ * #include <cuvs/neighbors/cagra.h>
+ *
+ * // Create cuvsResources_t
+ * cuvsResources_t res;
+ * cuvsError_t res_create_status = cuvsResourcesCreate(&res);
+ *
+ * // Create CAGRA index
+ * cuvsCagraIndex_t index;
+ * cuvsError_t index_create_status = cuvsCagraIndexCreate(&index);
+ *
+ * // Assume a populated `DLManagedTensor` type here for the graph and dataset
+ * DLManagedTensor dataset;
+ * DLManagedTensor graph;
+ *
+ * cuvsDistanceType metric = L2Expanded;
+ *
+ * // Build the CAGRA Index from the graph/dataset
+ * cuvsError_t status = cuvsCagraIndexFromArgs(res, metric, &graph, &dataset, index);
+ *
+ * @endcode
+ */
+cuvsError_t cuvsCagraIndexFromArgs(cuvsResources_t res,
+                                   cuvsDistanceType metric,
+                                   DLManagedTensor* graph,
+                                   DLManagedTensor* dataset,
+                                   cuvsCagraIndex_t index);
+
 /**
  * @brief Merge multiple CAGRA indices into a single CAGRA index.
  *
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index c820ebecbe..940aa1746f 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -20,6 +20,7 @@
 
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/util/cudart_utils.hpp>
 #include <rmm/cuda_stream_view.hpp>
 #include <rmm/mr/device/device_memory_resource.hpp>
 #include <rmm/mr/device/managed_memory_resource.hpp>
@@ -165,3 +166,117 @@ extern "C" cuvsError_t cuvsVersionGet(uint16_t* major, uint16_t* minor, uint16_t
   *patch = CUVS_VERSION_PATCH;
   return CUVS_SUCCESS;
 }
+
+namespace {
+template <typename T>
+void _copy_matrix(cuvsResources_t res, DLManagedTensor* src_managed, DLManagedTensor* dst_managed)
+{
+  DLTensor& src = src_managed->dl_tensor;
+  DLTensor& dst = dst_managed->dl_tensor;
+
+  int64_t src_row_stride = src.strides == nullptr ? src.shape[1] : src.strides[0];
+  int64_t dst_row_stride = dst.strides == nullptr ? dst.shape[1] : dst.strides[0];
+  auto res_ptr           = reinterpret_cast<raft::resources*>(res);
+
+  raft::copy_matrix<T>(static_cast<T*>(dst.data),
+                       dst_row_stride,
+                       static_cast<const T*>(src.data),
+                       src_row_stride,
+                       src.shape[1],
+                       src.shape[0],
+                       raft::resource::get_cuda_stream(*res_ptr));
+}
+}  // namespace
+
+extern "C" cuvsError_t cuvsMatrixCopy(cuvsResources_t res,
+                                      DLManagedTensor* src_managed,
+                                      DLManagedTensor* dst_managed)
+{
+  return cuvs::core::translate_exceptions([=] {
+    DLTensor& src = src_managed->dl_tensor;
+    DLTensor& dst = dst_managed->dl_tensor;
+
+    RAFT_EXPECTS(src.ndim == 2, "src should be a 2 dimensional tensor");
+    RAFT_EXPECTS(dst.ndim == 2, "dst should be a 2 dimensional tensor");
+
+    for (int64_t i = 0; i < src.ndim; ++i) {
+      RAFT_EXPECTS(src.shape[i] == dst.shape[i], "shape mismatch between src and dst tensors");
+    }
+    RAFT_EXPECTS(src.dtype.code == dst.dtype.code, "dtype mismatch between src and dst tensors");
+
+    // at some point we could probably copy from a float32 to a float16 here, but for the
+    // moment this isn't supported
+    RAFT_EXPECTS(src.dtype.bits == dst.dtype.bits,
+                 "dtype bits width mismatch between src and dst tensors");
+
+    if (src.dtype.code == kDLFloat && src.dtype.bits == 32) {
+      _copy_matrix<float>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLFloat && src.dtype.bits == 16) {
+      _copy_matrix<half>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLFloat && src.dtype.bits == 64) {
+      _copy_matrix<double>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLInt && src.dtype.bits == 8) {
+      _copy_matrix<int8_t>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLInt && src.dtype.bits == 16) {
+      _copy_matrix<int16_t>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLInt && src.dtype.bits == 32) {
+      _copy_matrix<int32_t>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLInt && src.dtype.bits == 64) {
+      _copy_matrix<int64_t>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLUInt && src.dtype.bits == 8) {
+      _copy_matrix<uint8_t>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLUInt && src.dtype.bits == 16) {
+      _copy_matrix<uint16_t>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLUInt && src.dtype.bits == 32) {
+      _copy_matrix<uint32_t>(res, src_managed, dst_managed);
+    } else if (src.dtype.code == kDLUInt && src.dtype.bits == 64) {
+      _copy_matrix<uint64_t>(res, src_managed, dst_managed);
+    } else {
+      RAFT_FAIL("Unsupported dtype: %d and bits: %d", src.dtype.code, src.dtype.bits);
+    }
+  });
+}
+
+extern "C" void cuvsMatrixDestroy(DLManagedTensor* tensor)
+{
+  if (tensor->dl_tensor.shape != nullptr) {
+    delete[] tensor->dl_tensor.shape;
+    tensor->dl_tensor.shape = nullptr;
+  }
+  if (tensor->dl_tensor.strides != nullptr) {
+    delete[] tensor->dl_tensor.strides;
+    tensor->dl_tensor.strides = nullptr;
+  }
+}
+
+extern "C" cuvsError_t cuvsMatrixSliceRows(cuvsResources_t res,
+                                           DLManagedTensor* src_managed,
+                                           int64_t start,
+                                           int64_t end,
+                                           DLManagedTensor* dst_managed)
+{
+  return cuvs::core::translate_exceptions([=] {
+    RAFT_EXPECTS(end >= start, "end index must be greater than start index");
+
+    DLTensor& src = src_managed->dl_tensor;
+    DLTensor& dst = dst_managed->dl_tensor;
+    RAFT_EXPECTS(src.ndim == 2, "src should be a 2 dimensional tensor");
+
+    dst.dtype    = src.dtype;
+    dst.device   = src.device;
+    dst.ndim     = 2;
+    dst.shape    = new int64_t[2];
+    dst.shape[0] = end - start;
+    dst.shape[1] = src.shape[1];
+
+    int64_t row_strides = dst.shape[1];
+    if (src.strides) {
+      dst.strides = new int64_t[2];
+      row_strides = dst.strides[0] = src.strides[0];
+      dst.strides[1]               = src.strides[1];
+    }
+
+    dst.data = static_cast<char*>(src.data) + start * row_strides * (dst.dtype.bits / 8);
+    dst_managed->deleter = cuvsMatrixDestroy;
+  });
+}
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 656724826e..7c3061bf93 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -120,6 +120,46 @@ void* _build(cuvsResources_t res, cuvsCagraIndexParams params, DLManagedTensor*
   return index;
 }
 
+template <typename T>
+void* _from_args(cuvsResources_t res,
+                 cuvsDistanceType _metric,
+                 DLManagedTensor* graph_tensor,
+                 DLManagedTensor* dataset_tensor)
+{
+  auto metric  = static_cast<cuvs::distance::DistanceType>((int)_metric);
+  auto dataset = dataset_tensor->dl_tensor;
+  auto graph   = graph_tensor->dl_tensor;
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  void* index = NULL;
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    if (cuvs::core::is_dlpack_device_compatible(graph)) {
+      using graph_mdspan_type = raft::device_matrix_view<uint32_t const, int64_t, raft::row_major>;
+      auto graph_mds          = cuvs::core::from_dlpack<graph_mdspan_type>(graph_tensor);
+      index = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr, metric, mds, graph_mds);
+    } else {
+      using graph_mdspan_type = raft::host_matrix_view<uint32_t const, int64_t, raft::row_major>;
+      auto graph_mds          = cuvs::core::from_dlpack<graph_mdspan_type>(graph_tensor);
+      index = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr, metric, mds, graph_mds);
+    }
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    if (cuvs::core::is_dlpack_device_compatible(graph)) {
+      using graph_mdspan_type = raft::device_matrix_view<uint32_t const, int64_t, raft::row_major>;
+      auto graph_mds          = cuvs::core::from_dlpack<graph_mdspan_type>(graph_tensor);
+      index = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr, metric, mds, graph_mds);
+    } else {
+      using graph_mdspan_type = raft::host_matrix_view<uint32_t const, int64_t, raft::row_major>;
+      auto graph_mds          = cuvs::core::from_dlpack<graph_mdspan_type>(graph_tensor);
+      index = new cuvs::neighbors::cagra::index<T, uint32_t>(*res_ptr, metric, mds, graph_mds);
+    }
+  }
+  return index;
+}
+
 template <typename T>
 void _extend(cuvsResources_t res,
              cuvsCagraExtendParams params,
@@ -297,6 +337,20 @@ void* _merge(cuvsResources_t res,
 
   return merged_index;
 }
+
+template <typename T, typename IdxT>
+void get_dataset_view(cuvsCagraIndex_t index, DLManagedTensor* dataset)
+{
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, IdxT>*>(index->addr);
+  cuvs::core::to_dlpack(index_ptr->dataset(), dataset);
+}
+
+template <typename T, typename IdxT>
+void get_graph_view(cuvsCagraIndex_t index, DLManagedTensor* graph)
+{
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, IdxT>*>(index->addr);
+  cuvs::core::to_dlpack(index_ptr->graph(), graph);
+}
 }  // namespace
 
 namespace cuvs::neighbors::cagra {
@@ -380,6 +434,56 @@ extern "C" cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim)
   });
 }
 
+extern "C" cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* size)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<float, uint32_t>*>(index->addr);
+    *size          = index_ptr->size();
+  });
+}
+
+extern "C" cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index, uint32_t* graph_degree)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<float, uint32_t>*>(index->addr);
+    *graph_degree  = index_ptr->graph_degree();
+  });
+}
+
+extern "C" cuvsError_t cuvsCagraIndexGetDataset(cuvsCagraIndex_t index, DLManagedTensor* dataset)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      get_dataset_view<float, uint32_t>(index, dataset);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      get_dataset_view<half, uint32_t>(index, dataset);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      get_dataset_view<int8_t, uint32_t>(index, dataset);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      get_dataset_view<uint8_t, uint32_t>(index, dataset);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsCagraIndexGetGraph(cuvsCagraIndex_t index, DLManagedTensor* graph)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      get_graph_view<float, uint32_t>(index, graph);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      get_graph_view<half, uint32_t>(index, graph);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      get_graph_view<int8_t, uint32_t>(index, graph);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      get_graph_view<uint8_t, uint32_t>(index, graph);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
 extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                                       cuvsCagraIndexParams_t params,
                                       DLManagedTensor* dataset_tensor,
@@ -404,6 +508,35 @@ extern "C" cuvsError_t cuvsCagraBuild(cuvsResources_t res,
   });
 }
 
+extern "C" cuvsError_t cuvsCagraIndexFromArgs(cuvsResources_t res,
+                                              cuvsDistanceType metric,
+                                              DLManagedTensor* graph_tensor,
+                                              DLManagedTensor* dataset_tensor,
+                                              cuvsCagraIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = dataset_tensor->dl_tensor;
+    index->dtype = dataset.dtype;
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_from_args<float>(res, metric, graph_tensor, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_from_args<half>(res, metric, graph_tensor, dataset_tensor));
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_from_args<int8_t>(res, metric, graph_tensor, dataset_tensor));
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      index->addr =
+        reinterpret_cast<uintptr_t>(_from_args<uint8_t>(res, metric, graph_tensor, dataset_tensor));
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
 extern "C" cuvsError_t cuvsCagraExtend(cuvsResources_t res,
                                        cuvsCagraExtendParams_t params,
                                        DLManagedTensor* additional_dataset_tensor,
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index f03632c662..ba3ee12bcb 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -13,7 +13,7 @@
 # =============================================================================
 
 # Set the list of Cython files to build
-set(cython_sources cydlpack.pyx exceptions.pyx resources.pyx)
+set(cython_sources cydlpack.pyx exceptions.pyx resources.pyx device_tensor_view.pyx)
 set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
diff --git a/python/cuvs/cuvs/common/c_api.pxd b/python/cuvs/cuvs/common/c_api.pxd
index dae93d7503..0f9e93fc7c 100644
--- a/python/cuvs/cuvs/common/c_api.pxd
+++ b/python/cuvs/cuvs/common/c_api.pxd
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,7 +17,9 @@
 
 
 from cuda.bindings.cyruntime cimport cudaStream_t
-from libc.stdint cimport uintptr_t
+from libc.stdint cimport int64_t, uintptr_t
+
+from cuvs.common.cydlpack cimport DLManagedTensor
 
 
 cdef extern from "cuvs/core/c_api.h":
@@ -32,3 +34,10 @@ cdef extern from "cuvs/core/c_api.h":
     cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream)
     cuvsError_t cuvsStreamSync(cuvsResources_t res)
     const char * cuvsGetLastErrorText()
+
+    cuvsError_t cuvsMatrixCopy(cuvsResources_t res, DLManagedTensor * src,
+                               DLManagedTensor * dst)
+
+    cuvsError_t cuvsMatrixSliceRows(cuvsResources_t res, DLManagedTensor* src,
+                                    int64_t start, int64_t end,
+                                    DLManagedTensor* dst)
diff --git a/python/cuvs/cuvs/common/cydlpack.pyx b/python/cuvs/cuvs/common/cydlpack.pyx
index bee8d9afa7..fac9e41d6b 100644
--- a/python/cuvs/cuvs/common/cydlpack.pyx
+++ b/python/cuvs/cuvs/common/cydlpack.pyx
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -31,6 +31,43 @@ cdef void deleter(DLManagedTensor* tensor) noexcept:
     stdlib.free(tensor)
 
 
+def dl_data_type_to_numpy(DLDataType dtype):
+    """ Converts a DLDataType dtype to a numpy dtype """
+    if dtype.code == DLDataTypeCode.kDLFloat:
+        if dtype.bits == 32:
+            return np.float32
+        elif dtype.bits == 64:
+            return np.float64
+        elif dtype.bits == 16:
+            return np.float16
+        else:
+            raise ValueError(f"unknown float dtype bits: {dtype.bits}")
+    elif dtype.code == DLDataTypeCode.kDLInt:
+        if dtype.bits == 32:
+            return np.int32
+        elif dtype.bits == 64:
+            return np.int64
+        elif dtype.bits == 16:
+            return np.int16
+        elif dtype.bits == 8:
+            return np.int8
+        else:
+            raise ValueError(f"unknown int dtype bits: {dtype.bits}")
+    elif dtype.code == DLDataTypeCode.kDLUInt:
+        if dtype.bits == 32:
+            return np.uint32
+        elif dtype.bits == 64:
+            return np.uint64
+        elif dtype.bits == 16:
+            return np.uint16
+        elif dtype.bits == 8:
+            return np.uint8
+        else:
+            raise ValueError(f"unknown uint dtype bits: {dtype.bits}")
+    else:
+        raise ValueError(f"unknown DLDataTypeCode.code: {dtype.code}")
+
+
 cdef DLManagedTensor* dlpack_c(ary):
     # todo(dgd): add checking options/parameters
     cdef DLDeviceType dev_type
diff --git a/python/cuvs/cuvs/common/device_tensor_view.pyx b/python/cuvs/cuvs/common/device_tensor_view.pyx
new file mode 100644
index 0000000000..e5c88473f8
--- /dev/null
+++ b/python/cuvs/cuvs/common/device_tensor_view.pyx
@@ -0,0 +1,145 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from cython.operator cimport dereference as deref
+
+import numpy as np
+
+from cuvs.common.c_api cimport (
+    cuvsMatrixCopy,
+    cuvsMatrixSliceRows,
+    cuvsResources_t,
+)
+from cuvs.common.cydlpack cimport DLManagedTensor, dlpack_c
+
+from pylibraft.common.cai_wrapper import wrap_array
+
+from cuvs.common.cydlpack import dl_data_type_to_numpy
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.resources import auto_sync_resources
+
+
+cdef class DeviceTensorView:
+    """A __cuda_array_interface__ compatible tensor on device memory
+
+    This aims to be a lightweight wrapper around device_matrix_view and
+    device_vector_view objects in the C++ layer of cuvs - and lets us
+    access memory internals of cuvs without copying into temporary
+    storage.
+
+    Since this support the `__cuda_array_interface__` protocol, this object
+    gives us easy interopability with other libraries such as cupy, pytorch,
+    and cudf - without adding any dependencies on these libraries inside cuvs.
+
+    Since cuvs also understands CAI, DeviceTensorView objects can also be
+    passed directly to cuvs python functions.
+    """
+
+    cdef DLManagedTensor tensor
+    cdef public object parent
+
+    def __init__(self, array_like=None):
+        if array_like is not None:
+            ai = wrap_array(array_like)
+            self.tensor = deref(dlpack_c(ai))
+
+    def __cinit__(self):
+        self.parent = None
+
+    def __dealloc__(self):
+        if self.tensor.deleter is not NULL:
+            self.tensor.deleter(&self.tensor)
+
+    def get_handle(self):
+        return <size_t>&self.tensor
+
+    @auto_sync_resources
+    def copy_to_host(self, resources=None):
+        """ copies a device matrix to a numpy array
+
+        Parameters
+        ----------
+        {resources_docstring}
+
+        Returns
+        -------
+        tensor: np.array
+            This tensor copied into host memory
+        """
+        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+        output = np.empty(self.shape, dtype=self.dtype)
+        ai = wrap_array(output)
+        cdef DLManagedTensor* output_dlpack = dlpack_c(ai)
+        check_cuvs(cuvsMatrixCopy(res, &self.tensor, output_dlpack))
+        return output
+
+    @auto_sync_resources
+    def slice_rows(self, start, end, resources=None):
+        """ Slices rows from this tensor
+
+        Slices a subset, and returns in a new DeviceTensorView without
+        copying data.
+
+        Parameters
+        ----------
+        start: int
+            the index of the first row to slice
+        end: int
+            the index of the last row to slice
+        {resources_docstring}
+
+        Returns
+        -------
+        tensor: DeviceTensorView
+            A non-owning view of the rows sliced from this tensor
+        """
+        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+        output = DeviceTensorView()
+        cdef DLManagedTensor* output_dlpack = \
+            <DLManagedTensor*><size_t>output.get_handle()
+        check_cuvs(cuvsMatrixSliceRows(res, &self.tensor, start, end,
+                                       output_dlpack))
+        output.parent = self  # keep memory alive on returned slice
+        return output
+
+    @property
+    def __cuda_array_interface__(self):
+        return {
+            "shape": self.shape,
+            "typestr": np.dtype(self.dtype).str,
+            "data": (<size_t>self.tensor.dl_tensor.data, True),
+            "strides": self.strides,
+            # we don't need the 'stream' capability added in cai v3 so
+            # to maximize compatibility with users use v2 for now
+            "version": 2,
+        }
+
+    @property
+    def shape(self):
+        tensor = self.tensor.dl_tensor
+        return tuple(tensor.shape[dim] for dim in range(tensor.ndim))
+
+    @property
+    def strides(self):
+        tensor = self.tensor.dl_tensor
+        if tensor.strides is NULL:
+            return None
+        return tuple(tensor.strides[dim] for dim in range(tensor.ndim))
+
+    @property
+    def dtype(self):
+        return dl_data_type_to_numpy(self.tensor.dl_tensor.dtype)
diff --git a/python/cuvs/cuvs/neighbors/cagra/__init__.py b/python/cuvs/cuvs/neighbors/cagra/__init__.py
index 889425f950..b32f9ca2d5 100644
--- a/python/cuvs/cuvs/neighbors/cagra/__init__.py
+++ b/python/cuvs/cuvs/neighbors/cagra/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -19,6 +19,7 @@
     IndexParams,
     SearchParams,
     build,
+    from_graph,
     load,
     save,
     search,
@@ -30,6 +31,7 @@
     "IndexParams",
     "SearchParams",
     "build",
+    "from_graph",
     "load",
     "save",
     "search",
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index 9503897f39..29e47a5014 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -135,6 +135,13 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
     cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index)
 
     cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int32_t* dim)
+    cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* size)
+    cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index,
+                                             uint32_t* degree)
+    cuvsError_t cuvsCagraIndexGetGraph(cuvsCagraIndex_t index,
+                                       DLManagedTensor * graph)
+    cuvsError_t cuvsCagraIndexGetDataset(cuvsCagraIndex_t index,
+                                         DLManagedTensor * dataset)
 
     cuvsError_t cuvsCagraBuild(cuvsResources_t res,
                                cuvsCagraIndexParams* params,
@@ -162,6 +169,12 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
                                      const char * filename,
                                      cuvsCagraIndex_t index)
 
+    cuvsError_t cuvsCagraIndexFromArgs(cuvsResources_t res,
+                                       cuvsDistanceType metric,
+                                       DLManagedTensor * graph,
+                                       DLManagedTensor * dataset,
+                                       cuvsCagraIndex_t index)
+
 cdef class Index:
     """
     CAGRA index object. This object stores the trained CAGRA index state
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index f395efe684..7fa73a2cff 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -28,12 +28,14 @@ from libcpp cimport bool, cast
 from libcpp.string cimport string
 
 from cuvs.common cimport cydlpack
+from cuvs.common.device_tensor_view import DeviceTensorView
 from cuvs.distance_type cimport cuvsDistanceType
 
 from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
+from cuvs.common.cydlpack import dl_data_type_to_numpy
 from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
 
@@ -273,6 +275,51 @@ cdef class Index:
         check_cuvs(cuvsCagraIndexGetDims(self.index, &dim))
         return dim
 
+    @property
+    def graph_degree(self):
+        cdef uint32_t degree
+        check_cuvs(cuvsCagraIndexGetGraphDegree(self.index, &degree))
+        return degree
+
+    def __len__(self):
+        cdef uint32_t size
+        check_cuvs(cuvsCagraIndexGetSize(self.index, &size))
+        return size
+
+    @property
+    def dtype(self):
+        return dl_data_type_to_numpy(self.index.dtype)
+
+    @property
+    def dataset(self):
+        if not self.trained:
+            raise ValueError("Index needs to be built before getting dataset")
+
+        # get the cagra dataset from the index without copying as dlpack
+        output = DeviceTensorView()
+        cdef cydlpack.DLManagedTensor * tensor = \
+            <cydlpack.DLManagedTensor*><size_t>output.get_handle()
+        check_cuvs(cuvsCagraIndexGetDataset(self.index, tensor))
+
+        # since we're referencing memory internal to this cagra index in the
+        # output view, keep the cagra index alive as long as the output view
+        # is to avoid segfaulting
+        output.parent = self
+
+        return output
+
+    @property
+    def graph(self):
+        if not self.trained:
+            raise ValueError("Index needs to be built before getting graph")
+
+        output = DeviceTensorView()
+        cdef cydlpack.DLManagedTensor * tensor = \
+            <cydlpack.DLManagedTensor*><size_t>output.get_handle()
+        check_cuvs(cuvsCagraIndexGetGraph(self.index, tensor))
+        output.parent = self
+        return output
+
     def __repr__(self):
         # todo(dgd): update repr as we expose data through C API
         attr_str = []
@@ -734,3 +781,49 @@ def load(filename, resources=None):
     ))
     idx.trained = True
     return idx
+
+
+@auto_sync_resources
+def from_graph(graph, dataset, metric="sqeuclidean", resources=None):
+    """
+    Construct a cagra index from an existing graph and dataset
+
+    Parameters
+    ----------
+    graph : Array interface compliant matrix with shape (n_samples,
+        graph_degree)
+    dataset : Array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32, float16, int8, uint8]
+    metric : str
+    {resources_docstring}
+
+    Returns
+    -------
+    index: cuvs.cagra.Index
+    """
+    cdef cuvsDistanceType c_metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+
+    cdef Index idx = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'),
+                                    np.dtype('float16'),
+                                    np.dtype('byte'),
+                                    np.dtype('ubyte')])
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+
+    graph_ai = wrap_array(graph)
+    _check_input_array(graph_ai, [np.dtype('uint32')])
+    cdef cydlpack.DLManagedTensor* graph_dlpack = \
+        cydlpack.dlpack_c(graph_ai)
+
+    check_cuvs(cuvsCagraIndexFromArgs(
+        res,
+        c_metric,
+        graph_dlpack,
+        dataset_dlpack,
+        idx.index))
+    idx.trained = True
+    return idx
diff --git a/python/cuvs/cuvs/tests/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
index 1e577e343d..0b08d48325 100644
--- a/python/cuvs/cuvs/tests/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,7 @@
 # limitations under the License.
 #
 
+import cupy as cp
 import numpy as np
 import pytest
 from pylibraft.common import device_ndarray
@@ -123,6 +124,36 @@ def run_cagra_build_search_test(
     recall = calc_recall(out_idx, skl_idx)
     assert recall > 0.7
 
+    # test that we can get the cagra graph from the index
+    graph = index.graph
+    assert graph.shape == (n_rows, graph_degree)
+
+    # make sure we can convert the graph to cupy, and access it
+    cp_graph = cp.array(graph)
+    assert cp_graph.shape == (n_rows, graph_degree)
+
+    if compression is None:
+        # make sure we can get the dataset from the cagra index
+        dataset_from_index = index.dataset
+
+        dataset_from_index_host = dataset_from_index.copy_to_host()
+        assert np.allclose(dataset, dataset_from_index_host)
+
+        # make sure we can reconstruct the index from the graph
+        # Note that we can't actually use the dataset from the index itself
+        # - since that is a strided matrix (and we expect non-strided inputs
+        # in the C++ cagra::build api), so we are using the host version
+        # which will have been copied into a non-strided layout
+        reloaded_index = cagra.from_graph(
+            graph, dataset_from_index_host, metric=metric
+        )
+
+        dist_device, idx_device = cagra.search(
+            search_params, reloaded_index, queries_device, k
+        )
+        recall = calc_recall(idx_device.copy_to_host(), skl_idx)
+        assert recall > 0.7
+
 
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
diff --git a/python/cuvs/cuvs/tests/test_device_tensor_view.py b/python/cuvs/cuvs/tests/test_device_tensor_view.py
new file mode 100644
index 0000000000..43888b63e6
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_device_tensor_view.py
@@ -0,0 +1,40 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import cupy as cp
+import numpy as np
+import pytest
+
+from cuvs.common.device_tensor_view import DeviceTensorView
+from cuvs.tests.ann_utils import generate_data
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.int32])
+def test_device_tensor_view(dtype):
+    n_rows, n_cols = 1000, 64
+    dataset = generate_data((n_rows, n_cols), dtype)
+    dataset_device = cp.array(dataset)
+    tensor_view = DeviceTensorView(dataset_device)
+
+    # test out converting tensor back to cupy via CAI
+    assert cp.allclose(cp.array(tensor_view), dataset_device)
+
+    # test out converting to host memory
+    assert np.allclose(tensor_view.copy_to_host(), dataset)
+
+    # test out slice_rows functionality
+    assert cp.allclose(
+        cp.array(tensor_view.slice_rows(10, 100)), dataset_device[10:100, :]
+    )

From b230ebe73f3fe7430a9e56199e8ffb54d2325c05 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Tue, 22 Jul 2025 23:26:10 -0400
Subject: [PATCH 497/623] Add warning for unused GPU when `n_clusters <
 n_ranks` in batch `all_neighbors` (#1072)

In MG all_neighbors, some GPUs stay idle when `n_clusters < n_ranks` (this is the expected behavior). Adding warning to let users know that they are not fully using all GPUs and providing directions for better utilization.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1072
---
 .../all_neighbors/all_neighbors_batched.cuh          | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
index 6254b448dd..468d4ee0df 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
@@ -16,6 +16,7 @@
 
 #pragma once
 #include "all_neighbors_builder.cuh"
+#include "raft/core/logger_macros.hpp"
 #include <cuvs/cluster/kmeans.hpp>
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/all_neighbors.hpp>
@@ -336,6 +337,17 @@ void multi_gpu_batch_build(const raft::resources& handle,
     size_t base_cluster_idx       = rank * clusters_per_rank + std::min((size_t)rank, rem);
     size_t num_clusters_for_this_rank =
       (size_t)rank < rem ? clusters_per_rank + 1 : clusters_per_rank;
+    if (num_clusters_for_this_rank == 0) {
+      // Happens in the case when num_ranks > n_clusters.
+      RAFT_LOG_WARN(
+        "Rank %d is not used for computation. This happens because the total number of ranks (%d) "
+        "> n_clusters (%lu). Consider increasing n_clusters or reduce the number of GPUs for "
+        "better utilization.",
+        rank,
+        num_ranks,
+        params.n_clusters);
+      continue;
+    }
     for (size_t p = 0; p < num_clusters_for_this_rank; p++) {
       num_data_for_this_rank += cluster_sizes(base_cluster_idx + p);
     }

From abbbe95ec7514c3efe5b6f170b236026b5d92fc8 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 23 Jul 2025 14:58:43 -0400
Subject: [PATCH 498/623] Deprecation notice for batching code specific to NN
 Descent (#1052)

Planning to deprecate batching code specific to NN Descent, now that we have the more general `all_neighbors` API.
Related issue: https://github.com/rapidsai/cuvs/issues/1051

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1052
---
 cpp/src/neighbors/nn_descent.cuh | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/cpp/src/neighbors/nn_descent.cuh b/cpp/src/neighbors/nn_descent.cuh
index ed91dac919..c4da01c561 100644
--- a/cpp/src/neighbors/nn_descent.cuh
+++ b/cpp/src/neighbors/nn_descent.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -67,6 +67,10 @@ auto build(raft::resources const& res,
            raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
 {
   if (params.n_clusters > 1) {
+    // related issue: https://github.com/rapidsai/cuvs/issues/1051
+    RAFT_LOG_WARN(
+      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
+      "release. Please use cuvs::all_neighbors::build(...) instead.");
     if constexpr (std::is_same_v<T, float>) {
       return detail::experimental::batch_build<T, IdxT>(res, params, dataset);
     } else {
@@ -114,6 +118,10 @@ void build(raft::resources const& res,
            index<IdxT>& idx)
 {
   if (params.n_clusters > 1) {
+    // related issue: https://github.com/rapidsai/cuvs/issues/1051
+    RAFT_LOG_WARN(
+      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
+      "release. Please use cuvs::all_neighbors::build(...) instead.");
     if constexpr (std::is_same_v<T, float>) {
       detail::experimental::batch_build<T, IdxT>(res, params, dataset, idx);
     } else {
@@ -157,6 +165,10 @@ auto build(raft::resources const& res,
            raft::host_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
 {
   if (params.n_clusters > 1) {
+    // related issue: https://github.com/rapidsai/cuvs/issues/1051
+    RAFT_LOG_WARN(
+      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
+      "release. Please use cuvs::all_neighbors::build(...) instead.");
     if constexpr (std::is_same_v<T, float>) {
       return detail::experimental::batch_build<T, IdxT>(res, params, dataset);
     } else {
@@ -204,6 +216,10 @@ void build(raft::resources const& res,
            index<IdxT>& idx)
 {
   if (params.n_clusters > 1) {
+    // related issue: https://github.com/rapidsai/cuvs/issues/1051
+    RAFT_LOG_WARN(
+      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
+      "release. Please use cuvs::all_neighbors::build(...) instead.");
     if constexpr (std::is_same_v<T, float>) {
       detail::experimental::batch_build<T, IdxT>(res, params, dataset, idx);
     } else {

From 7a49356cb589dd69cfd0d03a1e2c2b69082ce95d Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Wed, 23 Jul 2025 21:24:26 +0200
Subject: [PATCH 499/623] Fix for `hellinger` metric (#1128)

The implementation of the distance function for the `hellinger` metric had two issues :
1) It did not restore data integrity correctly : double `sqrt` operation
2) It did not check for data overlap (two data chunks/tiles in the pairwise operation have overlapping data).

Authors:
  - Victor Lafargue (https://github.com/viclafargue)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1128
---
 cpp/src/distance/detail/distance.cuh | 41 ++++++++++++++++++++++------
 1 file changed, 33 insertions(+), 8 deletions(-)

diff --git a/cpp/src/distance/detail/distance.cuh b/cpp/src/distance/detail/distance.cuh
index 034598be4e..e4cc50d482 100644
--- a/cpp/src/distance/detail/distance.cuh
+++ b/cpp/src/distance/detail/distance.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2018-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -298,13 +298,28 @@ void distance_impl(raft::resources const& handle,
 {
   cudaStream_t stream = raft::resource::get_cuda_stream(handle);
 
-  // First sqrt x and y
+  // Check if arrays overlap
+  const DataT* x_end  = x + m * k;
+  const DataT* y_end  = y + n * k;
+  bool arrays_overlap = (x < y_end) && (y < x_end);
+
   const auto raft_sqrt = raft::linalg::unaryOp<DataT, raft::sqrt_op, IdxT>;
+  const auto raft_sq   = raft::linalg::unaryOp<DataT, raft::sq_op, IdxT>;
+
+  if (!arrays_overlap) {
+    // Arrays don't overlap: sqrt each array independently
+    raft_sqrt((DataT*)x, x, m * k, raft::sqrt_op{}, stream);
+    raft_sqrt((DataT*)y, y, n * k, raft::sqrt_op{}, stream);
+  } else {
+    // Arrays overlap: sqrt the union of both arrays exactly once
+    const DataT* start = (x < y) ? x : y;
+    const DataT* end   = (x_end > y_end) ? x_end : y_end;
+    IdxT union_size    = end - start;
 
-  raft_sqrt((DataT*)x, x, m * k, raft::sqrt_op{}, stream);
-  if (x != y) { raft_sqrt((DataT*)y, y, n * k, raft::sqrt_op{}, stream); }
+    raft_sqrt((DataT*)start, start, union_size, raft::sqrt_op{}, stream);
+  }
 
-  // Then calculate Hellinger distance
+  // Calculate Hellinger distance
   ops::hellinger_distance_op<DataT, AccT, IdxT> distance_op{};
 
   const OutT* x_norm = nullptr;
@@ -313,9 +328,19 @@ void distance_impl(raft::resources const& handle,
   pairwise_matrix_dispatch<decltype(distance_op), DataT, AccT, OutT, FinOpT, IdxT>(
     distance_op, m, n, k, x, y, x_norm, y_norm, out, fin_op, stream, is_row_major);
 
-  // Finally revert sqrt of x and y
-  raft_sqrt((DataT*)x, x, m * k, raft::sqrt_op{}, stream);
-  if (x != y) { raft_sqrt((DataT*)y, y, n * k, raft::sqrt_op{}, stream); }
+  // Restore arrays by squaring back
+  if (!arrays_overlap) {
+    // Arrays don't overlap: square each array independently
+    raft_sq((DataT*)x, x, m * k, raft::sq_op{}, stream);
+    raft_sq((DataT*)y, y, n * k, raft::sq_op{}, stream);
+  } else {
+    // Arrays overlap: square the union back
+    const DataT* start = (x < y) ? x : y;
+    const DataT* end   = (x_end > y_end) ? x_end : y_end;
+    IdxT union_size    = end - start;
+
+    raft_sq((DataT*)start, start, union_size, raft::sq_op{}, stream);
+  }
 
   RAFT_CUDA_TRY(cudaGetLastError());
 }

From df0fefc65e6e58cd99e8e1721916ce84252a5af8 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Wed, 23 Jul 2025 14:43:41 -0700
Subject: [PATCH 500/623] [BUG] Fix Large Batch Preprocessing for NN Descent
 (#1166)

Bug fix for illegal memory access while preprocessing batches in nn descent with BitwiseHamming.

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Jinsol Park (https://github.com/jinsolp)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1166
---
 cpp/src/neighbors/detail/nn_descent.cuh                |  2 +-
 cpp/tests/neighbors/ann_nn_descent.cuh                 | 10 ++++++++++
 .../neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu  |  9 ++++++++-
 3 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 0b00903524..49dd767a1c 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -289,7 +289,7 @@ RAFT_KERNEL preprocess_data_kernel(
       } else if (metric == cuvs::distance::DistanceType::BitwiseHamming) {
         int idx_for_byte           = list_id * dim + idx;  // uint8 or int8 data
         uint8_t* output_bytes      = reinterpret_cast<uint8_t*>(output_data);
-        output_bytes[idx_for_byte] = input_data[idx_for_byte];
+        output_bytes[idx_for_byte] = input_data[(size_t)blockIdx.x * dim + idx];
       } else {  // L2Expanded or L2SqrtExpanded
         output_data[list_id * dim + idx] = input_data[(size_t)blockIdx.x * dim + idx];
         if (idx == 0) { l2_norms[list_id] = l2_norm; }
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index 61fa7665a8..d9800dbcd1 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -487,6 +487,16 @@ const std::vector<AnnNNDescentInputs> inputs =
                                                      {false, true},
                                                      {0.90});
 
+// Additional test cases for large datasets to test batching
+const std::vector<AnnNNDescentInputs> inputsLargeBatch =
+  raft::util::itertools::product<AnnNNDescentInputs>(
+    {150000},  // n_rows > 100000 to trigger batching
+    {64},
+    {32},
+    {cuvs::distance::DistanceType::BitwiseHamming},
+    {true},
+    {0.90});
+
 const std::vector<AnnNNDescentInputs> inputsDistEpilogue =
   raft::util::itertools::product<AnnNNDescentInputs>({2000, 4000},  // n_rows
                                                      {64, 1024},    // dim
diff --git a/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
index 2348573466..16159ecc0a 100644
--- a/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,4 +25,11 @@ TEST_P(AnnNNDescentTestUI8_U32, AnnNNDescent) { this->testNNDescent(); }
 
 INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestUI8_U32, ::testing::ValuesIn(inputs));
 
+typedef AnnNNDescentTest<float, uint8_t, std::uint32_t> AnnNNDescentTestUI8_U32_LargeBatch;
+TEST_P(AnnNNDescentTestUI8_U32_LargeBatch, AnnNNDescent) { this->testNNDescent(); }
+
+INSTANTIATE_TEST_CASE_P(AnnNNDescentTestLargeBatch,
+                        AnnNNDescentTestUI8_U32_LargeBatch,
+                        ::testing::ValuesIn(inputsLargeBatch));
+
 }  // namespace   cuvs::neighbors::nn_descent

From 7659031fa8e48e73ca66fc35ee699d69295f4060 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 23 Jul 2025 18:01:55 -0400
Subject: [PATCH 501/623] Conditionally post process distances in NN Descent
 for use with distance epilogue (#1108)

Current implementation of nn descent post processes distances for `L2SqrtExpanded` and `InnerProduct` for efficiency.
However, this may cause issues when using a distance epilogue.

This PR checks if distances can be postprocessed based on the distance epilogue type, and if not, attempts to compute the full accurate distance instead of postprocessing.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1108
---
 cpp/src/neighbors/detail/nn_descent.cuh | 21 +++++++++++++++++----
 cpp/tests/neighbors/ann_nn_descent.cuh  | 13 +++++++------
 2 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 49dd767a1c..096f071aa9 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -53,6 +53,7 @@
 #include <optional>
 #include <queue>
 #include <random>
+#include <type_traits>
 
 namespace cuvs::neighbors::nn_descent::detail {
 
@@ -566,6 +567,10 @@ __launch_bounds__(BLOCK_SIZE)
 
   __syncthreads();
 
+  // if we have a distance epilogue, distances need to be fully calculated instead of postprocessing
+  // them.
+  bool can_postprocess_dist = std::is_same_v<DistEpilogue_t, raft::identity_op>;
+
   remove_duplicates(new_neighbors,
                     list_new_size2.x,
                     new_neighbors + list_new_size2.x,
@@ -638,7 +643,7 @@ __launch_bounds__(BLOCK_SIZE)
     int col_id = i / SKEWED_MAX_NUM_BI_SAMPLES;
 
     if (row_id < list_new_size && col_id < list_new_size) {
-      if (metric == cuvs::distance::DistanceType::InnerProduct) {
+      if (metric == cuvs::distance::DistanceType::InnerProduct && can_postprocess_dist) {
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
@@ -658,6 +663,9 @@ __launch_bounds__(BLOCK_SIZE)
         // for fp32 vs fp16 precision differences resulting in negative distances when distance
         // should be 0 related issue: https://github.com/rapidsai/cuvs/issues/991
         s_distances[i] = s_distances[i] < 0.0f ? 0.0f : s_distances[i];
+        if (!can_postprocess_dist && metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+          s_distances[i] = sqrtf(s_distances[i]);
+        }
       }
       s_distances[i] = dist_epilogue(s_distances[i], new_neighbors[row_id], new_neighbors[col_id]);
     } else {
@@ -737,7 +745,7 @@ __launch_bounds__(BLOCK_SIZE)
     int row_id = i % SKEWED_MAX_NUM_BI_SAMPLES;
     int col_id = i / SKEWED_MAX_NUM_BI_SAMPLES;
     if (row_id < list_old_size && col_id < list_new_size) {
-      if (metric == cuvs::distance::DistanceType::InnerProduct) {
+      if (metric == cuvs::distance::DistanceType::InnerProduct && can_postprocess_dist) {
         s_distances[i] = -s_distances[i];
       } else if (metric == cuvs::distance::DistanceType::CosineExpanded) {
         s_distances[i] = 1.0 - s_distances[i];
@@ -757,6 +765,9 @@ __launch_bounds__(BLOCK_SIZE)
         // for fp32 vs fp16 precision differences resulting in negative distances when distance
         // should be 0 related issue: https://github.com/rapidsai/cuvs/issues/991
         s_distances[i] = s_distances[i] < 0.0f ? 0.0f : s_distances[i];
+        if (!can_postprocess_dist && metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+          s_distances[i] = sqrtf(s_distances[i]);
+        }
       }
       s_distances[i] = dist_epilogue(s_distances[i], old_neighbors[row_id], new_neighbors[col_id]);
     } else {
@@ -1257,10 +1268,12 @@ void GNND<Data_t, Index_t>::build(Data_t* data,
     auto output_dist_view = raft::make_device_matrix_view<DistData_t, int64_t, raft::row_major>(
       output_distances, nrow_, build_config_.output_graph_degree);
     // distance post-processing
-    if (build_config_.metric == cuvs::distance::DistanceType::L2SqrtExpanded) {
+    bool can_postprocess_dist = std::is_same_v<DistEpilogue_t, raft::identity_op>;
+    if (build_config_.metric == cuvs::distance::DistanceType::L2SqrtExpanded &&
+        can_postprocess_dist) {
       raft::linalg::map(
         res, output_dist_view, raft::sqrt_op{}, raft::make_const_mdspan(output_dist_view));
-    } else if (!cuvs::distance::is_min_close(build_config_.metric)) {
+    } else if (!cuvs::distance::is_min_close(build_config_.metric) && can_postprocess_dist) {
       // revert negated innerproduct
       raft::linalg::map(res,
                         output_dist_view,
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index d9800dbcd1..6edae3d6d5 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -498,12 +498,13 @@ const std::vector<AnnNNDescentInputs> inputsLargeBatch =
     {0.90});
 
 const std::vector<AnnNNDescentInputs> inputsDistEpilogue =
-  raft::util::itertools::product<AnnNNDescentInputs>({2000, 4000},  // n_rows
-                                                     {64, 1024},    // dim
-                                                     {32, 64},      // graph_degree
-                                                     {cuvs::distance::DistanceType::L2Expanded},
-                                                     {true},  // data on host
-                                                     {0.90});
+  raft::util::itertools::product<AnnNNDescentInputs>(
+    {2000, 4000},  // n_rows
+    {64, 1024},    // dim
+    {32, 64},      // graph_degree
+    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::L2SqrtExpanded},
+    {true},  // data on host
+    {0.90});
 
 const std::vector<AnnNNDescentBatchInputs> inputsBatch =
   raft::util::itertools::product<AnnNNDescentBatchInputs>(

From 15a98216ad58fc52de219e24d45d31c010f2200b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 24 Jul 2025 14:08:09 +0200
Subject: [PATCH 502/623] [Java] Fix CAGRA params allocation (#1109)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The current code allocates data structures for various "params" CuVS objects manually, i.e. through a Java Arena. This is not supposed to happen: the CuVS C API provides an API for creating ad destroying these objects. At present, the code works as it is mimicking what the C API is doing, but if the C API changes (e.g. allocating more nested objects, or initializing the data structures with default values etc.) the Java API could break in a way that is difficult to track.

This PR replaces direct allocations with calls to the appropriate `cuvsXxxCreate` and `cuvsXxxDestroy` functions; tracking usage is more difficult than using an Arena (which will claim all the allocated memory when closed), but I think we need to ensure correctness here. To alleviate the difficulties, this PR also introduces some helpers and a `CloseableHandle` type to keep track of the created params and destroy all of them when no longer needed.

CC @benfred

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Chris Hegarty (https://github.com/ChrisHegarty)

URL: https://github.com/rapidsai/cuvs/pull/1109
---
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 133 +++++++++--------
 .../cuvs/internal/CuVSParamsHelper.java       | 135 ++++++++++++++++++
 .../cuvs/internal/common/CloseableHandle.java |  47 ++++++
 .../common/CompositeCloseableHandle.java      |  46 ++++++
 4 files changed, 298 insertions(+), 63 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableHandle.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CompositeCloseableHandle.java

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 0e1fcb9048..028296d270 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -15,6 +15,7 @@
  */
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.CuVSParamsHelper.*;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
@@ -27,18 +28,7 @@
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
 import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraBuild;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraDeserialize;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndexDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraIndex_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraMerge;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSearch;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerialize;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsCagraSerializeToHnswlib;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
-import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CagraCompressionParams;
 import com.nvidia.cuvs.CagraIndex;
@@ -52,6 +42,8 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.internal.common.CloseableHandle;
+import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
 import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraMergeParams;
@@ -70,9 +62,7 @@
 import java.lang.foreign.ValueLayout;
 import java.nio.file.Files;
 import java.nio.file.Path;
-import java.util.BitSet;
-import java.util.Objects;
-import java.util.UUID;
+import java.util.*;
 
 /**
  * {@link CagraIndex} encapsulates a CAGRA index, along with methods to interact
@@ -162,14 +152,12 @@ public void destroyIndex() throws Throwable {
    *         index
    */
   private IndexReference build(CagraIndexParams indexParameters, DatasetImpl dataset) {
-    try (var localArena = Arena.ofConfined()) {
-      long rows = dataset.size();
-      long cols = dataset.dimensions();
+    long rows = dataset.size();
+    long cols = dataset.dimensions();
 
-      MemorySegment indexParamsMemorySegment =
-          indexParameters != null
-              ? segmentFromIndexParams(localArena, indexParameters)
-              : MemorySegment.NULL;
+    try (var indexParams = segmentFromIndexParams(indexParameters);
+        var localArena = Arena.ofConfined()) {
+      MemorySegment indexParamsMemorySegment = indexParams.handle();
 
       int numWriterThreads = indexParameters != null ? indexParameters.getNumWriterThreads() : 1;
       omp_set_num_threads(numWriterThreads);
@@ -482,20 +470,39 @@ public CuVSResources getCuVSResources() {
   }
 
   /**
-   * Allocates the configured index parameters in the MemorySegment.
+   * Allocates the native CagraIndexParams data structures and fills the configured index parameters in.
    */
-  private static MemorySegment segmentFromIndexParams(Arena arena, CagraIndexParams params) {
-    MemorySegment seg = cuvsCagraIndexParams.allocate(arena);
-    cuvsCagraIndexParams.intermediate_graph_degree(seg, params.getIntermediateGraphDegree());
-    cuvsCagraIndexParams.graph_degree(seg, params.getGraphDegree());
-    cuvsCagraIndexParams.build_algo(seg, params.getCagraGraphBuildAlgo().value);
-    cuvsCagraIndexParams.nn_descent_niter(seg, params.getNNDescentNumIterations());
-    cuvsCagraIndexParams.metric(seg, params.getCuvsDistanceType().value);
+  private static CloseableHandle segmentFromIndexParams(CagraIndexParams params) {
+
+    if (params == null) {
+      return CloseableHandle.NULL;
+    }
+
+    var handles = new ArrayList<CloseableHandle>();
+
+    var indexParams = createCagraIndexParams();
+    handles.add(indexParams);
+    var indexPtr = indexParams.handle();
+
+    populateNativeIndexParams(indexPtr, params, handles);
+
+    return new CompositeCloseableHandle(indexPtr, handles);
+  }
+
+  private static void populateNativeIndexParams(
+      MemorySegment indexPtr, CagraIndexParams params, List<CloseableHandle> handles) {
+
+    cuvsCagraIndexParams.intermediate_graph_degree(indexPtr, params.getIntermediateGraphDegree());
+    cuvsCagraIndexParams.graph_degree(indexPtr, params.getGraphDegree());
+    cuvsCagraIndexParams.build_algo(indexPtr, params.getCagraGraphBuildAlgo().value);
+    cuvsCagraIndexParams.nn_descent_niter(indexPtr, params.getNNDescentNumIterations());
+    cuvsCagraIndexParams.metric(indexPtr, params.getCuvsDistanceType().value);
 
     CagraCompressionParams cagraCompressionParams = params.getCagraCompressionParams();
     if (cagraCompressionParams != null) {
-      MemorySegment cuvsCagraCompressionParamsMemorySegment =
-          cuvsCagraCompressionParams.allocate(arena);
+      var compressionParams = createCagraCompressionParams();
+      handles.add(compressionParams);
+      MemorySegment cuvsCagraCompressionParamsMemorySegment = compressionParams.handle();
       cuvsCagraCompressionParams.pq_bits(
           cuvsCagraCompressionParamsMemorySegment, cagraCompressionParams.getPqBits());
       cuvsCagraCompressionParams.pq_dim(
@@ -510,12 +517,14 @@ private static MemorySegment segmentFromIndexParams(Arena arena, CagraIndexParam
       cuvsCagraCompressionParams.pq_kmeans_trainset_fraction(
           cuvsCagraCompressionParamsMemorySegment,
           cagraCompressionParams.getPqKmeansTrainsetFraction());
-      cuvsCagraIndexParams.compression(seg, cuvsCagraCompressionParamsMemorySegment);
+      cuvsCagraIndexParams.compression(indexPtr, cuvsCagraCompressionParamsMemorySegment);
     }
 
     if (params.getCagraGraphBuildAlgo().equals(CagraGraphBuildAlgo.IVF_PQ)) {
 
-      MemorySegment ivfpqIndexParamsMemorySegment = cuvsIvfPqIndexParams.allocate(arena);
+      var ivfPqIndexParams = createIvfPqIndexParams();
+      handles.add(ivfPqIndexParams);
+      MemorySegment ivfpqIndexParamsMemorySegment = ivfPqIndexParams.handle();
       CuVSIvfPqIndexParams cuVSIvfPqIndexParams = params.getCuVSIvfPqParams().getIndexParams();
 
       cuvsIvfPqIndexParams.metric(
@@ -540,7 +549,9 @@ private static MemorySegment segmentFromIndexParams(Arena arena, CagraIndexParam
       cuvsIvfPqIndexParams.max_train_points_per_pq_code(
           ivfpqIndexParamsMemorySegment, cuVSIvfPqIndexParams.getMaxTrainPointsPerPqCode());
 
-      MemorySegment ivfpqSearchParamsMemorySegment = cuvsIvfPqSearchParams.allocate(arena);
+      var ivfPqSearchParams = createIvfPqSearchParams();
+      handles.add(ivfPqSearchParams);
+      MemorySegment ivfpqSearchParamsMemorySegment = ivfPqSearchParams.handle();
       CuVSIvfPqSearchParams cuVSIvfPqSearchParams = params.getCuVSIvfPqParams().getSearchParams();
       cuvsIvfPqSearchParams.n_probes(
           ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getnProbes());
@@ -551,7 +562,10 @@ private static MemorySegment segmentFromIndexParams(Arena arena, CagraIndexParam
       cuvsIvfPqSearchParams.preferred_shmem_carveout(
           ivfpqSearchParamsMemorySegment, cuVSIvfPqSearchParams.getPreferredShmemCarveout());
 
-      MemorySegment cuvsIvfPqParamsMemorySegment = cuvsIvfPqParams.allocate(arena);
+      // This is already allocated by cuvsCagraIndexParamsCreate,
+      // we just need to populate it.
+      MemorySegment cuvsIvfPqParamsMemorySegment =
+          cuvsCagraIndexParams.graph_build_params(indexPtr);
       cuvsIvfPqParams.ivf_pq_build_params(
           cuvsIvfPqParamsMemorySegment, ivfpqIndexParamsMemorySegment);
       cuvsIvfPqParams.ivf_pq_search_params(
@@ -559,10 +573,8 @@ private static MemorySegment segmentFromIndexParams(Arena arena, CagraIndexParam
       cuvsIvfPqParams.refinement_rate(
           cuvsIvfPqParamsMemorySegment, params.getCuVSIvfPqParams().getRefinementRate());
 
-      cuvsCagraIndexParams.graph_build_params(seg, cuvsIvfPqParamsMemorySegment);
+      cuvsCagraIndexParams.graph_build_params(indexPtr, cuvsIvfPqParamsMemorySegment);
     }
-
-    return seg;
   }
 
   /**
@@ -624,15 +636,13 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
             ValueLayout.ADDRESS, i, indexImpl.cagraIndexReference.getMemorySegment());
       }
 
-      // TODO: we should call cuvsCreateMergeParams here, instead of allocating this ourselves
-      // See https://github.com/rapidsai/cuvs/pull/1109
-      var mergeParamsSegment = createMergeParamsSegment(localArena, mergeParams);
-      try (var resourcesAccessor = resources.access()) {
+      try (var nativeMergeParams = createMergeParamsSegment(mergeParams);
+          var resourcesAccessor = resources.access()) {
         var cuvsRes = resourcesAccessor.handle();
-        var returnValue =
+        checkCuVSError(
             cuvsCagraMerge(
-                cuvsRes, mergeParamsSegment, indexesSegment, indexes.length, mergedIndex);
-        checkCuVSError(returnValue, "cuvsCagraMerge");
+                cuvsRes, nativeMergeParams.handle(), indexesSegment, indexes.length, mergedIndex),
+            "cuvsCagraMerge");
       }
     }
 
@@ -642,30 +652,27 @@ public static CagraIndex merge(CagraIndex[] indexes, CagraMergeParams mergeParam
   /**
    * Creates (allocates and fill) native memory version of merge parameters.
    *
-   * @param arena       The arena to use to allocate the MemorySegment(s) that will hold the merge parameters data
-   * @param mergeParams The merge parameters
-   * @return A memory segment pointing to the native structure for merge parameters
+   * @return A memory segment with the merge parameters
    */
-  private static MemorySegment createMergeParamsSegment(Arena arena, CagraMergeParams mergeParams) {
-    MemorySegment seg = cuvsCagraMergeParams.allocate(arena);
+  private static CloseableHandle createMergeParamsSegment(CagraMergeParams mergeParams) {
+    var handles = new ArrayList<CloseableHandle>();
 
-    if (mergeParams != null) {
-      if (mergeParams.getOutputIndexParams() != null) {
-        MemorySegment outputIndexParamsSeg =
-            segmentFromIndexParams(arena, mergeParams.getOutputIndexParams());
-        cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
-      } else {
-        cuvsCagraMergeParams.output_index_params(seg, MemorySegment.NULL);
-      }
+    var nativeMergeParams = createCagraMergeParams();
+    handles.add(nativeMergeParams);
+    var seg = nativeMergeParams.handle();
 
+    // The output index params are already allocated by cuvsCagraMergeParamsCreate,
+    // we just need to populate it.
+    var outputIndexParamsPtr = cuvsCagraMergeParams.output_index_params(seg);
+    if (mergeParams != null) {
+      populateNativeIndexParams(outputIndexParamsPtr, mergeParams.getOutputIndexParams(), handles);
       cuvsCagraMergeParams.strategy(seg, mergeParams.getStrategy().value);
     } else {
-      MemorySegment outputIndexParamsSeg =
-          segmentFromIndexParams(arena, new CagraIndexParams.Builder().build());
-      cuvsCagraMergeParams.output_index_params(seg, outputIndexParamsSeg);
+      populateNativeIndexParams(
+          outputIndexParamsPtr, new CagraIndexParams.Builder().build(), handles);
     }
 
-    return seg;
+    return new CompositeCloseableHandle(seg, handles);
   }
 
   /**
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
new file mode 100644
index 0000000000..9d4c759530
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
@@ -0,0 +1,135 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
+
+import com.nvidia.cuvs.internal.common.CloseableHandle;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+
+/**
+ * A set of helpers to create various types of native CuVS "params" data structures.
+ * The CuVS API encapsulates and hides these data structures behind opaque handles
+ * (e.g. {@code cuvsCagraIndexParams_t}, and provides functions to create (allocate)
+ * and destroy (free) them.
+ * The helpers wrap the Create calls and return a {@link CloseableHandle} to hold the
+ * opaque handle (to access the params data structure) and pair them with their respective
+ * Destroy calls, so that the params native resources will be cleared when
+ * {@link AutoCloseable#close()} is called.
+ */
+final class CuVSParamsHelper {
+
+  private CuVSParamsHelper() {}
+
+  static CloseableHandle createCagraIndexParams() {
+    try (var localArena = Arena.ofConfined()) {
+      var paramsPtrPtr = localArena.allocate(cuvsCagraIndexParams_t);
+      checkCuVSError(cuvsCagraIndexParamsCreate(paramsPtrPtr), "cuvsCagraIndexParamsCreate");
+      var paramsPtr = paramsPtrPtr.get(cuvsCagraIndexParams_t, 0L);
+      return new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return paramsPtr;
+        }
+
+        @Override
+        public void close() {
+          checkCuVSError(cuvsCagraIndexParamsDestroy(paramsPtr), "cuvsCagraIndexParamsDestroy");
+        }
+      };
+    }
+  }
+
+  static CloseableHandle createCagraCompressionParams() {
+    try (var localArena = Arena.ofConfined()) {
+      var paramsPtrPtr = localArena.allocate(cuvsCagraCompressionParams_t);
+      checkCuVSError(
+          cuvsCagraCompressionParamsCreate(paramsPtrPtr), "cuvsCagraCompressionParamsCreate");
+      var paramsPtr = paramsPtrPtr.get(cuvsCagraCompressionParams_t, 0L);
+      return new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return paramsPtr;
+        }
+
+        @Override
+        public void close() {
+          checkCuVSError(
+              cuvsCagraCompressionParamsDestroy(paramsPtr), "cuvsCagraCompressionParamsDestroy");
+        }
+      };
+    }
+  }
+
+  static CloseableHandle createIvfPqIndexParams() {
+    try (var localArena = Arena.ofConfined()) {
+      var paramsPtrPtr = localArena.allocate(cuvsIvfPqIndexParams_t);
+      checkCuVSError(cuvsIvfPqIndexParamsCreate(paramsPtrPtr), "cuvsIvfPqIndexParamsCreate");
+      var paramsPtr = paramsPtrPtr.get(cuvsIvfPqIndexParams_t, 0L);
+      return new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return paramsPtr;
+        }
+
+        @Override
+        public void close() {
+          checkCuVSError(cuvsIvfPqIndexParamsDestroy(paramsPtr), "cuvsIvfPqIndexParamsDestroy");
+        }
+      };
+    }
+  }
+
+  static CloseableHandle createIvfPqSearchParams() {
+    try (var localArena = Arena.ofConfined()) {
+      var paramsPtrPtr = localArena.allocate(cuvsIvfPqSearchParams_t);
+      checkCuVSError(cuvsIvfPqSearchParamsCreate(paramsPtrPtr), "cuvsIvfPqSearchParamsCreate");
+      var paramsPtr = paramsPtrPtr.get(cuvsIvfPqSearchParams_t, 0L);
+      return new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return paramsPtr;
+        }
+
+        @Override
+        public void close() {
+          checkCuVSError(cuvsIvfPqSearchParamsDestroy(paramsPtr), "cuvsIvfPqSearchParamsDestroy");
+        }
+      };
+    }
+  }
+
+  static CloseableHandle createCagraMergeParams() {
+    try (var localArena = Arena.ofConfined()) {
+      var paramsPtrPtr = localArena.allocate(cuvsCagraMergeParams_t);
+      checkCuVSError(cuvsCagraMergeParamsCreate(paramsPtrPtr), "cuvsCagraMergeParamsCreate");
+      var paramsPtr = paramsPtrPtr.get(cuvsCagraMergeParams_t, 0L);
+      return new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return paramsPtr;
+        }
+
+        @Override
+        public void close() {
+          checkCuVSError(cuvsCagraMergeParamsDestroy(paramsPtr), "cuvsCagraMergeParamsDestroy");
+        }
+      };
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableHandle.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableHandle.java
new file mode 100644
index 0000000000..3b6d6fd32b
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableHandle.java
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal.common;
+
+import java.lang.foreign.MemorySegment;
+
+/**
+ * An interface holding a "handle" (C pointer type) for a native resource that needs to
+ * be manually freed. It is used to pair {@code cuvsXxxCreate} calls (which create a CuVS
+ * native object) to their respective {@code cuvsXxxDestroy} calls (which destroys it,
+ * freeing any associated resources), while holding the "handle" (represented as
+ * a {@link MemorySegment}) used to access the CuVS native object.
+ */
+public interface CloseableHandle extends AutoCloseable {
+
+  /**
+   * A "null" handle, associated to no native object. {@code close()} is a no-op.
+   */
+  CloseableHandle NULL =
+      new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return MemorySegment.NULL;
+        }
+
+        @Override
+        public void close() {}
+      };
+
+  MemorySegment handle();
+
+  @Override
+  void close();
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CompositeCloseableHandle.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CompositeCloseableHandle.java
new file mode 100644
index 0000000000..855907a15a
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CompositeCloseableHandle.java
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal.common;
+
+import java.lang.foreign.MemorySegment;
+import java.util.ArrayList;
+
+/**
+ * A composite {@link CloseableHandle}. A native object might need the creation of nested native objects,
+ * which handle is then embedded within the outer object(s) structures. This composite helps in keeping
+ * track of the nested native objects, so when the parent is cleared, the child objects are cleared too.
+ */
+public class CompositeCloseableHandle implements CloseableHandle {
+  private final MemorySegment indexPtr;
+  private final ArrayList<CloseableHandle> handles;
+
+  public CompositeCloseableHandle(MemorySegment indexPtr, ArrayList<CloseableHandle> handles) {
+    this.indexPtr = indexPtr;
+    this.handles = handles;
+  }
+
+  @Override
+  public MemorySegment handle() {
+    return indexPtr;
+  }
+
+  @Override
+  public void close() {
+    for (var closeable : handles) {
+      closeable.close();
+    }
+  }
+}

From ed32f0fb6689de6a346f7d551662d58e2965588e Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 24 Jul 2025 07:10:36 -0700
Subject: [PATCH 503/623] Add error checking for our C example code (#1171)

We weren't consistently checking the return value of cuda and cuvs functions in our C examples.

This caused problems when we tried to adapt the SQ code to an example, and it was returning invalid results. The root cause was because a function was failing, but we didn't notice because the pattern has been to ignore the error codes.

Enforce best practices and check the result of every cuvs and cuda function call in our c examples.

Also:
* enable -Wall and -Werror to the c examples for extra compile time checks
* add examples to CODEOWNERS
*  ensure that clang-format runs on the example files

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/1171
---
 .github/CODEOWNERS                           |   4 +
 examples/.clang-format                       |   1 +
 examples/.clang-tidy                         |   1 +
 examples/.clangd                             |   1 +
 examples/c/CMakeLists.txt                    |   4 +-
 examples/c/src/L2_c_example.c                |  79 ++--
 examples/c/src/cagra_c_example.c             | 136 +++---
 examples/c/src/common.h                      | 122 +++--
 examples/c/src/ivf_flat_c_example.c          | 469 +++++++++----------
 examples/c/src/ivf_pq_c_example.c            | 333 ++++++-------
 examples/cpp/src/brute_force_bitmap.cu       |  27 +-
 examples/cpp/src/cagra_persistent_example.cu | 183 ++++----
 examples/cpp/src/common.cuh                  |  89 ++--
 examples/cpp/src/dynamic_batching_example.cu | 238 +++++-----
 examples/cpp/src/ivf_flat_example.cu         |   8 +-
 examples/cpp/src/ivf_pq_example.cu           |   9 +-
 examples/cpp/src/vamana_example.cu           |  72 +--
 17 files changed, 907 insertions(+), 869 deletions(-)
 create mode 120000 examples/.clang-format
 create mode 120000 examples/.clang-tidy
 create mode 120000 examples/.clangd

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index a24746bbf5..73320903c6 100755
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -1,14 +1,18 @@
 #cpp code owners
 cpp/               @rapidsai/cuvs-cpp-codeowners
+examples/cpp/      @rapidsai/cuvs-cpp-codeowners
+examples/c/        @rapidsai/cuvs-cpp-codeowners
 
 #java code owners
 java/              @rapidsai/cuvs-java-codeowners
+examples/java/     @rapidsai/cuvs-java-codeowners
 
 #python code owners
 python/            @rapidsai/cuvs-python-codeowners
 
 #rust code owners
 rust/              @rapidsai/cuvs-rust-codeowners
+examples/rust/     @rapidsai/cuvs-rust-codeowners
 
 #docs code owners
 docs/              @rapidsai/cuvs-docs-codeowners
diff --git a/examples/.clang-format b/examples/.clang-format
new file mode 120000
index 0000000000..39499f29f9
--- /dev/null
+++ b/examples/.clang-format
@@ -0,0 +1 @@
+../cpp/.clang-format
\ No newline at end of file
diff --git a/examples/.clang-tidy b/examples/.clang-tidy
new file mode 120000
index 0000000000..f91470a293
--- /dev/null
+++ b/examples/.clang-tidy
@@ -0,0 +1 @@
+../cpp/.clang-tidy
\ No newline at end of file
diff --git a/examples/.clangd b/examples/.clangd
new file mode 120000
index 0000000000..4133acbac3
--- /dev/null
+++ b/examples/.clangd
@@ -0,0 +1 @@
+../cpp/.clangd
\ No newline at end of file
diff --git a/examples/c/CMakeLists.txt b/examples/c/CMakeLists.txt
index a897207d0e..603f34626a 100644
--- a/examples/c/CMakeLists.txt
+++ b/examples/c/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -35,6 +35,8 @@ set(BUILD_CUVS_C_LIBRARY ON)
 include(../cmake/thirdparty/get_dlpack.cmake)
 include(../cmake/thirdparty/get_cuvs.cmake)
 
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Wextra -Werror")
+
 add_executable(CAGRA_C_EXAMPLE src/cagra_c_example.c)
 target_include_directories(CAGRA_C_EXAMPLE PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>")
 target_link_libraries(CAGRA_C_EXAMPLE PRIVATE cuvs::c_api $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/c/src/L2_c_example.c b/examples/c/src/L2_c_example.c
index 0b621798e3..7d84afe834 100644
--- a/examples/c/src/L2_c_example.c
+++ b/examples/c/src/L2_c_example.c
@@ -24,18 +24,21 @@
 #include <stdio.h>
 #include <stdlib.h>
 
-#define DIM 4
+#include "common.h"
+
+#define DIM    4
 #define N_ROWS 1
 
-float PointA[N_ROWS][DIM] = {1.0,2.0,3.0,4.0};
-float PointB[N_ROWS][DIM] = {2.0,3.0,4.0,5.0};
+float PointA[N_ROWS][DIM] = {{1.0, 2.0, 3.0, 4.0}};
+float PointB[N_ROWS][DIM] = {{2.0, 3.0, 4.0, 5.0}};
 
 cuvsResources_t res;
 
-void outputVector(float * Vec) {
+void outputVector(float* Vec)
+{
   printf("Vector is ");
-  for (int i = 0; i < DIM; ++i){
-    printf(" %f",Vec[i]);
+  for (int i = 0; i < DIM; ++i) {
+    printf(" %f", Vec[i]);
   }
   printf("\n");
 }
@@ -44,18 +47,20 @@ void outputVector(float * Vec) {
  * @brief Initialize Tensor.
  *
  * @param[in] x_d Pointer to a vector
- * @param[in] x_shape[] Two-dimensional array, which stores the number of rows and columns of vectors.
+ * @param[in] x_shape[] Two-dimensional array, which stores the number of rows
+ * and columns of vectors.
  * @param[out] x_tensor Stores the initialized DLManagedTensor.
  */
-void tensor_initialize(float* x_d, int64_t x_shape[2], DLManagedTensor* x_tensor) {
-  x_tensor->dl_tensor.data = x_d;
+void tensor_initialize(float* x_d, int64_t x_shape[2], DLManagedTensor* x_tensor)
+{
+  x_tensor->dl_tensor.data               = x_d;
   x_tensor->dl_tensor.device.device_type = kDLCUDA;
-  x_tensor->dl_tensor.ndim = 2;
-  x_tensor->dl_tensor.dtype.code = kDLFloat;
-  x_tensor->dl_tensor.dtype.bits = 32;
-  x_tensor->dl_tensor.dtype.lanes = 1;
-  x_tensor->dl_tensor.shape = x_shape;
-  x_tensor->dl_tensor.strides = NULL;
+  x_tensor->dl_tensor.ndim               = 2;
+  x_tensor->dl_tensor.dtype.code         = kDLFloat;
+  x_tensor->dl_tensor.dtype.bits         = 32;
+  x_tensor->dl_tensor.dtype.lanes        = 1;
+  x_tensor->dl_tensor.shape              = x_shape;
+  x_tensor->dl_tensor.strides            = NULL;
 }
 
 /**
@@ -66,16 +71,17 @@ void tensor_initialize(float* x_d, int64_t x_shape[2], DLManagedTensor* x_tensor
  * @param[in] y[] Pointer to another vector
  * @param[out] ret will store the result about the euclidean distance
  */
-void l2_distance_calc(int64_t n_cols,float x[], float y[], float *ret) {
+void l2_distance_calc(int64_t n_cols, float x[], float y[], float* ret)
+{
   float *x_d, *y_d;
-  float *distance_d;
-  cuvsRMMAlloc(res, (void**) &x_d, sizeof(float) * N_ROWS * n_cols);
-  cuvsRMMAlloc(res, (void**) &y_d, sizeof(float) * N_ROWS * n_cols);
-  cuvsRMMAlloc(res, (void**) &distance_d, sizeof(float) * N_ROWS * N_ROWS);
+  float* distance_d;
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&x_d, sizeof(float) * N_ROWS * n_cols));
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&y_d, sizeof(float) * N_ROWS * n_cols));
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&distance_d, sizeof(float) * N_ROWS * N_ROWS));
 
   // Use DLPack to represent x[] and y[] as tensors
-  cudaMemcpy(x_d, x, sizeof(float) * N_ROWS * n_cols, cudaMemcpyDefault);
-  cudaMemcpy(y_d, y, sizeof(float) * N_ROWS * n_cols, cudaMemcpyDefault);
+  CHECK_CUDA(cudaMemcpy(x_d, x, sizeof(float) * N_ROWS * n_cols, cudaMemcpyDefault));
+  CHECK_CUDA(cudaMemcpy(y_d, y, sizeof(float) * N_ROWS * n_cols, cudaMemcpyDefault));
 
   DLManagedTensor x_tensor;
   int64_t x_shape[2] = {N_ROWS, n_cols};
@@ -90,34 +96,35 @@ void l2_distance_calc(int64_t n_cols,float x[], float y[], float *ret) {
   tensor_initialize(distance_d, distances_shape, &dist_tensor);
 
   // metric_arg default value is 2.0,used for Minkowski distance
-  cuvsPairwiseDistance(res, &x_tensor, &y_tensor, &dist_tensor, L2SqrtUnexpanded, 2.0);
-
-  cudaMemcpy(ret, distance_d, sizeof(float) * N_ROWS * N_ROWS, cudaMemcpyDefault);
+  CHECK_CUVS(cuvsPairwiseDistance(res, &x_tensor, &y_tensor, &dist_tensor, L2SqrtUnexpanded, 2.0));
 
-  cuvsRMMFree(res, distance_d, sizeof(float) * N_ROWS * N_ROWS);
-  cuvsRMMFree(res, x_d, sizeof(float) * N_ROWS * n_cols);
-  cuvsRMMFree(res, y_d, sizeof(float) * N_ROWS * n_cols);
+  CHECK_CUDA(cudaMemcpy(ret, distance_d, sizeof(float) * N_ROWS * N_ROWS, cudaMemcpyDefault));
 
+  CHECK_CUVS(cuvsRMMFree(res, distance_d, sizeof(float) * N_ROWS * N_ROWS));
+  CHECK_CUVS(cuvsRMMFree(res, x_d, sizeof(float) * N_ROWS * n_cols));
+  CHECK_CUVS(cuvsRMMFree(res, y_d, sizeof(float) * N_ROWS * n_cols));
 }
 
-int euclidean_distance_calculation_example() {
+int euclidean_distance_calculation_example()
+{
   // Create a cuvsResources_t object
   cuvsResourcesCreate(&res);
 
-  outputVector((float *)PointA);
-  outputVector((float *)PointB);
+  outputVector((float*)PointA);
+  outputVector((float*)PointB);
 
   float ret;
 
-  l2_distance_calc(DIM, (float *)PointA, (float *)PointB, &ret);
+  l2_distance_calc(DIM, (float*)PointA, (float*)PointB, &ret);
   printf("L2 distance is %f.\n", ret);
 
-  cuvsResourcesDestroy(res);
+  CHECK_CUVS(cuvsResourcesDestroy(res));
 
   return 0;
 }
 
-int main() {
-    euclidean_distance_calculation_example();
-    return 0;
+int main()
+{
+  euclidean_distance_calculation_example();
+  return 0;
 }
diff --git a/examples/c/src/cagra_c_example.c b/examples/c/src/cagra_c_example.c
index 83957aabca..6bc37a9931 100644
--- a/examples/c/src/cagra_c_example.c
+++ b/examples/c/src/cagra_c_example.c
@@ -23,6 +23,8 @@
 #include <stdio.h>
 #include <stdlib.h>
 
+#include "common.h"
+
 float dataset[4][2] = {{0.74021935, 0.9209938},
                        {0.03902049, 0.9689629},
                        {0.92514056, 0.4463501},
@@ -32,121 +34,119 @@ float queries[4][2] = {{0.48216683, 0.0428398},
                        {0.51260436, 0.2643005},
                        {0.05198065, 0.5789965}};
 
-void cagra_build_search_simple() {
-
-  int64_t n_rows = 4;
-  int64_t n_cols = 2;
-  int64_t topk = 2;
+void cagra_build_search_simple()
+{
+  int64_t n_rows    = 4;
+  int64_t n_cols    = 2;
+  int64_t topk      = 2;
   int64_t n_queries = 4;
 
   // Create a cuvsResources_t object
   cuvsResources_t res;
-  cuvsResourcesCreate(&res);
+  CHECK_CUVS(cuvsResourcesCreate(&res));
 
   // Use DLPack to represent `dataset` as a tensor
   DLManagedTensor dataset_tensor;
-  dataset_tensor.dl_tensor.data = dataset;
+  dataset_tensor.dl_tensor.data               = dataset;
   dataset_tensor.dl_tensor.device.device_type = kDLCPU;
-  dataset_tensor.dl_tensor.ndim = 2;
-  dataset_tensor.dl_tensor.dtype.code = kDLFloat;
-  dataset_tensor.dl_tensor.dtype.bits = 32;
-  dataset_tensor.dl_tensor.dtype.lanes = 1;
-  int64_t dataset_shape[2] = {n_rows, n_cols};
-  dataset_tensor.dl_tensor.shape = dataset_shape;
-  dataset_tensor.dl_tensor.strides = NULL;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {n_rows, n_cols};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
 
   // Build the CAGRA index
   cuvsCagraIndexParams_t index_params;
-  cuvsCagraIndexParamsCreate(&index_params);
+  CHECK_CUVS(cuvsCagraIndexParamsCreate(&index_params));
 
   cuvsCagraIndex_t index;
-  cuvsCagraIndexCreate(&index);
+  CHECK_CUVS(cuvsCagraIndexCreate(&index));
 
-  cuvsCagraBuild(res, index_params, &dataset_tensor, index);
+  CHECK_CUVS(cuvsCagraBuild(res, index_params, &dataset_tensor, index));
 
   // Allocate memory for `queries`, `neighbors` and `distances` output
-  uint32_t *neighbors;
+  uint32_t* neighbors;
   float *distances, *queries_d;
-  cuvsRMMAlloc(res, (void **)&queries_d, sizeof(float) * n_queries * n_cols);
-  cuvsRMMAlloc(res, (void **)&neighbors, sizeof(uint32_t) * n_queries * topk);
-  cuvsRMMAlloc(res, (void **)&distances, sizeof(float) * n_queries * topk);
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&queries_d, sizeof(float) * n_queries * n_cols));
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&neighbors, sizeof(uint32_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&distances, sizeof(float) * n_queries * topk));
 
   // Use DLPack to represent `queries`, `neighbors` and `distances` as tensors
-  cudaMemcpy(queries_d, queries, sizeof(float) * 4 * 2, cudaMemcpyDefault);
+  CHECK_CUDA(cudaMemcpy(queries_d, queries, sizeof(float) * 4 * 2, cudaMemcpyDefault));
 
   DLManagedTensor queries_tensor;
-  queries_tensor.dl_tensor.data = queries_d;
+  queries_tensor.dl_tensor.data               = queries_d;
   queries_tensor.dl_tensor.device.device_type = kDLCUDA;
-  queries_tensor.dl_tensor.ndim = 2;
-  queries_tensor.dl_tensor.dtype.code = kDLFloat;
-  queries_tensor.dl_tensor.dtype.bits = 32;
-  queries_tensor.dl_tensor.dtype.lanes = 1;
-  int64_t queries_shape[2] = {n_queries, n_cols};
-  queries_tensor.dl_tensor.shape = queries_shape;
-  queries_tensor.dl_tensor.strides = NULL;
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {n_queries, n_cols};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
 
   DLManagedTensor neighbors_tensor;
-  neighbors_tensor.dl_tensor.data = neighbors;
+  neighbors_tensor.dl_tensor.data               = neighbors;
   neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
-  neighbors_tensor.dl_tensor.ndim = 2;
-  neighbors_tensor.dl_tensor.dtype.code = kDLUInt;
-  neighbors_tensor.dl_tensor.dtype.bits = 32;
-  neighbors_tensor.dl_tensor.dtype.lanes = 1;
-  int64_t neighbors_shape[2] = {n_queries, topk};
-  neighbors_tensor.dl_tensor.shape = neighbors_shape;
-  neighbors_tensor.dl_tensor.strides = NULL;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLUInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 32;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {n_queries, topk};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
 
   DLManagedTensor distances_tensor;
-  distances_tensor.dl_tensor.data = distances;
+  distances_tensor.dl_tensor.data               = distances;
   distances_tensor.dl_tensor.device.device_type = kDLCUDA;
-  distances_tensor.dl_tensor.ndim = 2;
-  distances_tensor.dl_tensor.dtype.code = kDLFloat;
-  distances_tensor.dl_tensor.dtype.bits = 32;
-  distances_tensor.dl_tensor.dtype.lanes = 1;
-  int64_t distances_shape[2] = {n_queries, topk};
-  distances_tensor.dl_tensor.shape = distances_shape;
-  distances_tensor.dl_tensor.strides = NULL;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {n_queries, topk};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
 
   // Search the CAGRA index
   cuvsCagraSearchParams_t search_params;
-  cuvsCagraSearchParamsCreate(&search_params);
+  CHECK_CUVS(cuvsCagraSearchParamsCreate(&search_params));
 
   cuvsFilter filter;
   filter.type = NO_FILTER;
   filter.addr = (uintptr_t)NULL;
 
-  cuvsCagraSearch(res, search_params, index, &queries_tensor, &neighbors_tensor,
-                  &distances_tensor, filter);
+  CHECK_CUVS(cuvsCagraSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor, filter));
 
   // print results
-  uint32_t *neighbors_h =
-      (uint32_t *)malloc(sizeof(uint32_t) * n_queries * topk);
-  float *distances_h = (float *)malloc(sizeof(float) * n_queries * topk);
-  cudaMemcpy(neighbors_h, neighbors, sizeof(uint32_t) * n_queries * topk,
-             cudaMemcpyDefault);
-  cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk,
-             cudaMemcpyDefault);
-  printf("Query 0 neighbor indices: =[%d, %d]\n", neighbors_h[0],
-         neighbors_h[1]);
-  printf("Query 0 neighbor distances: =[%f, %f]\n", distances_h[0],
-         distances_h[1]);
+  uint32_t* neighbors_h = (uint32_t*)malloc(sizeof(uint32_t) * n_queries * topk);
+  float* distances_h    = (float*)malloc(sizeof(float) * n_queries * topk);
+  CHECK_CUDA(
+    cudaMemcpy(neighbors_h, neighbors, sizeof(uint32_t) * n_queries * topk, cudaMemcpyDefault));
+  CHECK_CUDA(
+    cudaMemcpy(distances_h, distances, sizeof(float) * n_queries * topk, cudaMemcpyDefault));
+  printf("Query 0 neighbor indices: =[%d, %d]\n", neighbors_h[0], neighbors_h[1]);
+  printf("Query 0 neighbor distances: =[%f, %f]\n", distances_h[0], distances_h[1]);
 
   // Free or destroy all allocations
   free(neighbors_h);
   free(distances_h);
 
-  cuvsCagraSearchParamsDestroy(search_params);
+  CHECK_CUVS(cuvsCagraSearchParamsDestroy(search_params));
 
-  cuvsRMMFree(res, distances, sizeof(float) * n_queries * topk);
-  cuvsRMMFree(res, neighbors, sizeof(uint32_t) * n_queries * topk);
-  cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_cols);
+  CHECK_CUVS(cuvsRMMFree(res, distances, sizeof(float) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMFree(res, neighbors, sizeof(uint32_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_cols));
 
-  cuvsCagraIndexDestroy(index);
-  cuvsCagraIndexParamsDestroy(index_params);
-  cuvsResourcesDestroy(res);
+  CHECK_CUVS(cuvsCagraIndexDestroy(index));
+  CHECK_CUVS(cuvsCagraIndexParamsDestroy(index_params));
+  CHECK_CUVS(cuvsResourcesDestroy(res));
 }
 
-int main() {
+int main()
+{
   // Simple build and search example.
   cagra_build_search_simple();
 }
diff --git a/examples/c/src/common.h b/examples/c/src/common.h
index e5b8d01768..d690f6ecf0 100644
--- a/examples/c/src/common.h
+++ b/examples/c/src/common.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,43 +19,71 @@
 #include <stdint.h>
 #include <stdio.h>
 #include <stdlib.h>
-#include <time.h>
 #include <string.h>
+#include <time.h>
+
+inline void check_cuvs(cuvsError_t code, const char* file, int line)
+{
+  if (code != CUVS_SUCCESS) {
+    fprintf(stderr, "CuVS Error @ (%s: %i): %s", file, line, cuvsGetLastErrorText());
+    exit(1);
+  }
+}
+#define CHECK_CUVS(code)                  \
+  {                                       \
+    check_cuvs(code, __FILE__, __LINE__); \
+  }
+
+inline void check_cuda(cudaError_t code, const char* file, int line)
+{
+  if (code != cudaSuccess) {
+    fprintf(stderr, "CUDA Error @ (%s: %i): %s", file, line, cudaGetErrorString(code));
+    exit(1);
+  }
+}
+#define CHECK_CUDA(code)                  \
+  {                                       \
+    check_cuda(code, __FILE__, __LINE__); \
+  }
 
 /**
  * @brief Initialize Tensor for kDLFloat.
  *
  * @param[in] t_d Pointer to a vector
- * @param[in] t_shape[] Two-dimensional array, which stores the number of rows and columns of vectors.
+ * @param[in] t_shape[] Two-dimensional array, which stores the number of rows
+ * and columns of vectors.
  * @param[out] t_tensor Stores the initialized DLManagedTensor.
  */
-void float_tensor_initialize(float* t_d, int64_t t_shape[2], DLManagedTensor* t_tensor) {
-  t_tensor->dl_tensor.data = t_d;
+void float_tensor_initialize(float* t_d, int64_t t_shape[2], DLManagedTensor* t_tensor)
+{
+  t_tensor->dl_tensor.data               = t_d;
   t_tensor->dl_tensor.device.device_type = kDLCUDA;
-  t_tensor->dl_tensor.ndim = 2;
-  t_tensor->dl_tensor.dtype.code = kDLFloat;
-  t_tensor->dl_tensor.dtype.bits = 32;
-  t_tensor->dl_tensor.dtype.lanes = 1;
-  t_tensor->dl_tensor.shape = t_shape;
-  t_tensor->dl_tensor.strides = NULL;
+  t_tensor->dl_tensor.ndim               = 2;
+  t_tensor->dl_tensor.dtype.code         = kDLFloat;
+  t_tensor->dl_tensor.dtype.bits         = 32;
+  t_tensor->dl_tensor.dtype.lanes        = 1;
+  t_tensor->dl_tensor.shape              = t_shape;
+  t_tensor->dl_tensor.strides            = NULL;
 }
 
 /**
  * @brief Initialize Tensor for kDLInt.
  *
  * @param[in] t_d Pointer to a vector
- * @param[in] t_shape[] Two-dimensional array, which stores the number of rows and columns of vectors.
+ * @param[in] t_shape[] Two-dimensional array, which stores the number of rows
+ * and columns of vectors.
  * @param[out] t_tensor Stores the initialized DLManagedTensor.
  */
-void int_tensor_initialize(int64_t* t_d, int64_t t_shape[], DLManagedTensor* t_tensor) {
-  t_tensor->dl_tensor.data = t_d;
+void int_tensor_initialize(int64_t* t_d, int64_t t_shape[], DLManagedTensor* t_tensor)
+{
+  t_tensor->dl_tensor.data               = t_d;
   t_tensor->dl_tensor.device.device_type = kDLCUDA;
-  t_tensor->dl_tensor.ndim = 2;
-  t_tensor->dl_tensor.dtype.code = kDLInt;
-  t_tensor->dl_tensor.dtype.bits = 64;
-  t_tensor->dl_tensor.dtype.lanes = 1;
-  t_tensor->dl_tensor.shape = t_shape;
-  t_tensor->dl_tensor.strides = NULL;
+  t_tensor->dl_tensor.ndim               = 2;
+  t_tensor->dl_tensor.dtype.code         = kDLInt;
+  t_tensor->dl_tensor.dtype.bits         = 64;
+  t_tensor->dl_tensor.dtype.lanes        = 1;
+  t_tensor->dl_tensor.shape              = t_shape;
+  t_tensor->dl_tensor.strides            = NULL;
 }
 
 /**
@@ -67,17 +95,18 @@ void int_tensor_initialize(int64_t* t_d, int64_t t_shape[], DLManagedTensor* t_t
  * @param[in] min Minimum value among random values.
  * @param[in] max Maximum value among random values.
  */
-void generate_dataset(float * Vec,int n_rows, int n_cols, float min, float max) {
-    float scale;
-    float * ptr = Vec;
-    srand((unsigned int)time(NULL));
-    for (int i = 0; i < n_rows; i++) {
-        for (int j = 0; j < n_cols; j++) {
-            scale = rand()/(float)RAND_MAX;
-            ptr = Vec + i * n_cols + j;
-            *ptr = min + scale * (max - min);
-        }
+void generate_dataset(float* Vec, int n_rows, int n_cols, float min, float max)
+{
+  float scale;
+  float* ptr = Vec;
+  srand((unsigned int)time(NULL));
+  for (int i = 0; i < n_rows; i++) {
+    for (int j = 0; j < n_cols; j++) {
+      scale = rand() / (float)RAND_MAX;
+      ptr   = Vec + i * n_cols + j;
+      *ptr  = min + scale * (max - min);
     }
+  }
 }
 
 /**
@@ -88,21 +117,22 @@ void generate_dataset(float * Vec,int n_rows, int n_cols, float min, float max)
  * @param[in] n_rows the number of rows in the matrix.
  * @param[in] n_cols the number of columns in the matrix.
  */
-void print_results(int64_t * neighbor, float* distances,int n_rows, int n_cols) {
-    int64_t * pn = neighbor;
-    float * pd = distances;
-    for (int i = 0; i < n_rows; ++i) {
-        printf("Query %d neighbor indices: =[", i);
-        for (int j = 0; j < n_cols; ++j) {
-            pn = neighbor + i * n_cols + j;
-            printf(" %ld", *pn);
-        }
-        printf("]\n");
-        printf("Query %d neighbor distances: =[", i);
-        for (int j = 0; j < n_cols; ++j) {
-            pd = distances + i * n_cols + j;
-            printf(" %f", *pd);
-        }
-        printf("]\n");
+void print_results(int64_t* neighbor, float* distances, int n_rows, int n_cols)
+{
+  int64_t* pn = neighbor;
+  float* pd   = distances;
+  for (int i = 0; i < n_rows; ++i) {
+    printf("Query %d neighbor indices: =[", i);
+    for (int j = 0; j < n_cols; ++j) {
+      pn = neighbor + i * n_cols + j;
+      printf(" %ld", *pn);
+    }
+    printf("]\n");
+    printf("Query %d neighbor distances: =[", i);
+    for (int j = 0; j < n_cols; ++j) {
+      pd = distances + i * n_cols + j;
+      printf(" %f", *pd);
     }
+    printf("]\n");
+  }
 }
diff --git a/examples/c/src/ivf_flat_c_example.c b/examples/c/src/ivf_flat_c_example.c
index 510d624f3f..70a8470f3a 100644
--- a/examples/c/src/ivf_flat_c_example.c
+++ b/examples/c/src/ivf_flat_c_example.c
@@ -17,250 +17,241 @@
 #include <cuvs/core/c_api.h>
 #include <cuvs/neighbors/ivf_flat.h>
 
-#include <cuda_runtime.h>
 #include "common.h"
+#include <cuda_runtime.h>
 
-void ivf_flat_build_search_simple(cuvsResources_t *res, DLManagedTensor * dataset_tensor, DLManagedTensor * queries_tensor) {
-    // Create default index params
-    cuvsIvfFlatIndexParams_t index_params;
-    cuvsIvfFlatIndexParamsCreate(&index_params);
-    index_params->n_lists                  = 1024; // default value
-    index_params->kmeans_n_iters = 20; // default value
-    index_params->kmeans_trainset_fraction = 0.1;
-    //index_params->metric default is L2Expanded
-
-    // Create IVF-Flat index
-    cuvsIvfFlatIndex_t index;
-    cuvsIvfFlatIndexCreate(&index);
-
-    printf("Building IVF-Flat index\n");
-    // Build the IVF-Flat Index
-    cuvsError_t build_status = cuvsIvfFlatBuild(*res, index_params, dataset_tensor, index);
-    if (build_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-        cuvsIvfFlatIndexDestroy(index);
-        cuvsIvfFlatIndexParamsDestroy(index_params);
-        return;
-    }
-
-    // Create output arrays.
-    int64_t topk      = 10;
-    int64_t n_queries = queries_tensor->dl_tensor.shape[0];
-
-    //Allocate memory for `neighbors` and `distances` output
-    int64_t *neighbors_d;
-    float *distances_d;
-    cuvsRMMAlloc(*res, (void**) &neighbors_d, sizeof(int64_t) * n_queries * topk);
-    cuvsRMMAlloc(*res, (void**) &distances_d, sizeof(float) * n_queries * topk);
-
-    DLManagedTensor neighbors_tensor;
-    int64_t neighbors_shape[2] = {n_queries, topk};
-    int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
-
-    DLManagedTensor distances_tensor;
-    int64_t distances_shape[2] = {n_queries, topk};
-    float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
-
-    // Create default search params
-    cuvsIvfFlatSearchParams_t search_params;
-    cuvsIvfFlatSearchParamsCreate(&search_params);
-    search_params->n_probes = 50;
-
-    // Search the `index` built using `ivfFlatBuild`
-    cuvsFilter filter;
-    filter.type = NO_FILTER;
-    filter.addr = (uintptr_t)NULL;
-
-    cuvsError_t search_status = cuvsIvfFlatSearch(*res, search_params, index,
-     queries_tensor, &neighbors_tensor, &distances_tensor, filter);
-    if (build_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-    }
-
-    int64_t *neighbors = (int64_t *)malloc(n_queries * topk * sizeof(int64_t));
-    float *distances = (float *)malloc(n_queries * topk * sizeof(float));
-    memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
-    memset(distances, 0, n_queries * topk * sizeof(float));
-
-    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
-    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
-
-    print_results(neighbors, distances, 2, topk);
-
-    free(distances);
-    free(neighbors);
-
-    cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk);
-    cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk);
-
-    cuvsIvfFlatSearchParamsDestroy(search_params);
-    cuvsIvfFlatIndexDestroy(index);
-    cuvsIvfFlatIndexParamsDestroy(index_params);
+void ivf_flat_build_search_simple(cuvsResources_t* res,
+                                  DLManagedTensor* dataset_tensor,
+                                  DLManagedTensor* queries_tensor)
+{
+  // Create default index params
+  cuvsIvfFlatIndexParams_t index_params;
+  CHECK_CUVS(cuvsIvfFlatIndexParamsCreate(&index_params));
+  index_params->n_lists                  = 1024;  // default value
+  index_params->kmeans_n_iters           = 20;    // default value
+  index_params->kmeans_trainset_fraction = 0.1;
+  // index_params->metric default is L2Expanded
+
+  // Create IVF-Flat index
+  cuvsIvfFlatIndex_t index;
+  CHECK_CUVS(cuvsIvfFlatIndexCreate(&index));
+
+  printf("Building IVF-Flat index\n");
+  // Build the IVF-Flat Index
+  CHECK_CUVS(cuvsIvfFlatBuild(*res, index_params, dataset_tensor, index));
+
+  // Create output arrays.
+  int64_t topk      = 10;
+  int64_t n_queries = queries_tensor->dl_tensor.shape[0];
+
+  // Allocate memory for `neighbors` and `distances` output
+  int64_t* neighbors_d;
+  float* distances_d;
+  CHECK_CUVS(cuvsRMMAlloc(*res, (void**)&neighbors_d, sizeof(int64_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMAlloc(*res, (void**)&distances_d, sizeof(float) * n_queries * topk));
+
+  DLManagedTensor neighbors_tensor;
+  int64_t neighbors_shape[2] = {n_queries, topk};
+  int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
+
+  DLManagedTensor distances_tensor;
+  int64_t distances_shape[2] = {n_queries, topk};
+  float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
+
+  // Create default search params
+  cuvsIvfFlatSearchParams_t search_params;
+  CHECK_CUVS(cuvsIvfFlatSearchParamsCreate(&search_params));
+  search_params->n_probes = 50;
+
+  // Search the `index` built using `ivfFlatBuild`
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+
+  CHECK_CUVS(cuvsIvfFlatSearch(
+    *res, search_params, index, queries_tensor, &neighbors_tensor, &distances_tensor, filter));
+
+  int64_t* neighbors = (int64_t*)malloc(n_queries * topk * sizeof(int64_t));
+  float* distances   = (float*)malloc(n_queries * topk * sizeof(float));
+  memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
+  memset(distances, 0, n_queries * topk * sizeof(float));
+
+  CHECK_CUDA(
+    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault));
+  CHECK_CUDA(
+    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault));
+
+  print_results(neighbors, distances, 2, topk);
+
+  free(distances);
+  free(neighbors);
+
+  CHECK_CUVS(cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk));
+
+  CHECK_CUVS(cuvsIvfFlatSearchParamsDestroy(search_params));
+  CHECK_CUVS(cuvsIvfFlatIndexDestroy(index));
+  CHECK_CUVS(cuvsIvfFlatIndexParamsDestroy(index_params));
 }
 
-void ivf_flat_build_extend_search(cuvsResources_t *res, DLManagedTensor * trainset_tensor, DLManagedTensor * dataset_tensor, DLManagedTensor * queries_tensor) {
-    int64_t *data_indices_d;
-    int64_t n_dataset = dataset_tensor->dl_tensor.shape[0];
-    cuvsRMMAlloc(*res, (void**) &data_indices_d, sizeof(int64_t) * n_dataset);
-    DLManagedTensor data_indices_tensor;
-    int64_t data_indices_shape[1] = {n_dataset};
-    int_tensor_initialize(data_indices_d, data_indices_shape, &data_indices_tensor);
-    data_indices_tensor.dl_tensor.ndim = 1;
-
-    printf("\nRun k-means clustering using the training set\n");
-
-    int64_t *data_indices = (int64_t *)malloc(n_dataset * sizeof(int64_t));
-    int64_t * ptr = data_indices;
-    for (int i = 0; i < n_dataset; i++) {
-        *ptr = i;
-        ptr++;
-    }
-    ptr = NULL;
-    cudaMemcpy(data_indices_d, data_indices, sizeof(int64_t) * n_dataset, cudaMemcpyDefault);
-
-    // Create default index params
-    cuvsIvfFlatIndexParams_t index_params;
-    cuvsIvfFlatIndexParamsCreate(&index_params);
-    index_params->n_lists                  = 100;
-    index_params->add_data_on_build = false;
-    //index_params->metric default is L2Expanded
-
-    // Create IVF-Flat index
-    cuvsIvfFlatIndex_t index;
-    cuvsIvfFlatIndexCreate(&index);
-
-    // Build the IVF-Flat Index
-    cuvsError_t build_status = cuvsIvfFlatBuild(*res, index_params, trainset_tensor, index);
-    if (build_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-        cuvsIvfFlatIndexDestroy(index);
-        cuvsIvfFlatIndexParamsDestroy(index_params);
-        return;
-    }
-
-    printf("Filling index with the dataset vectors\n");
-    cuvsError_t extend_status = cuvsIvfFlatExtend(*res, dataset_tensor, &data_indices_tensor, index);
-    if (extend_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-        return;
-    }
-
-    // Create output arrays.
-    int64_t topk      = 10;
-    int64_t n_queries = queries_tensor->dl_tensor.shape[0];
-
-    //Allocate memory for `neighbors` and `distances` output
-    int64_t *neighbors_d;
-    float *distances_d;
-    cuvsRMMAlloc(*res, (void**) &neighbors_d, sizeof(int64_t) * n_queries * topk);
-    cuvsRMMAlloc(*res, (void**) &distances_d, sizeof(float) * n_queries * topk);
-
-    DLManagedTensor neighbors_tensor;
-    int64_t neighbors_shape[2] = {n_queries, topk};
-    int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
-
-    DLManagedTensor distances_tensor;
-    int64_t distances_shape[2] = {n_queries, topk};
-    float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
-
-    // Create default search params
-    cuvsIvfFlatSearchParams_t search_params;
-    cuvsIvfFlatSearchParamsCreate(&search_params);
-    search_params->n_probes = 10;
-
-    // Search the `index` built using `ivfFlatBuild`
-    cuvsFilter filter;
-    filter.type = NO_FILTER;
-    filter.addr = (uintptr_t)NULL;
-    cuvsError_t search_status = cuvsIvfFlatSearch(*res, search_params, index,
-     queries_tensor, &neighbors_tensor, &distances_tensor, filter);
-    if (search_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-        exit(-1);
-    }
-
-    int64_t *neighbors = (int64_t *)malloc(n_queries * topk * sizeof(int64_t));
-    float *distances = (float *)malloc(n_queries * topk * sizeof(float));
-    memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
-    memset(distances, 0, n_queries * topk * sizeof(float));
-
-    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
-    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
-
-    print_results(neighbors, distances, 2, topk);
-
-    free(distances);
-    free(neighbors);
-    free(data_indices);
-    cuvsRMMFree(*res, data_indices_d, sizeof(int64_t) * n_dataset);
-    cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk);
-    cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk);
-
-    cuvsIvfFlatSearchParamsDestroy(search_params);
-    cuvsIvfFlatIndexDestroy(index);
-    cuvsIvfFlatIndexParamsDestroy(index_params);
+void ivf_flat_build_extend_search(cuvsResources_t* res,
+                                  DLManagedTensor* trainset_tensor,
+                                  DLManagedTensor* dataset_tensor,
+                                  DLManagedTensor* queries_tensor)
+{
+  int64_t* data_indices_d;
+  int64_t n_dataset = dataset_tensor->dl_tensor.shape[0];
+  CHECK_CUVS(cuvsRMMAlloc(*res, (void**)&data_indices_d, sizeof(int64_t) * n_dataset));
+  DLManagedTensor data_indices_tensor;
+  int64_t data_indices_shape[1] = {n_dataset};
+  int_tensor_initialize(data_indices_d, data_indices_shape, &data_indices_tensor);
+  data_indices_tensor.dl_tensor.ndim = 1;
+
+  printf("\nRun k-means clustering using the training set\n");
+
+  int64_t* data_indices = (int64_t*)malloc(n_dataset * sizeof(int64_t));
+  int64_t* ptr          = data_indices;
+  for (int i = 0; i < n_dataset; i++) {
+    *ptr = i;
+    ptr++;
+  }
+  ptr = NULL;
+  cudaMemcpy(data_indices_d, data_indices, sizeof(int64_t) * n_dataset, cudaMemcpyDefault);
+
+  // Create default index params
+  cuvsIvfFlatIndexParams_t index_params;
+  CHECK_CUVS(cuvsIvfFlatIndexParamsCreate(&index_params));
+  index_params->n_lists           = 100;
+  index_params->add_data_on_build = false;
+  // index_params->metric default is L2Expanded
+
+  // Create IVF-Flat index
+  cuvsIvfFlatIndex_t index;
+  CHECK_CUVS(cuvsIvfFlatIndexCreate(&index));
+
+  // Build the IVF-Flat Index
+  CHECK_CUVS(cuvsIvfFlatBuild(*res, index_params, trainset_tensor, index));
+
+  printf("Filling index with the dataset vectors\n");
+  CHECK_CUVS(cuvsIvfFlatExtend(*res, dataset_tensor, &data_indices_tensor, index));
+
+  // Create output arrays.
+  int64_t topk      = 10;
+  int64_t n_queries = queries_tensor->dl_tensor.shape[0];
+
+  // Allocate memory for `neighbors` and `distances` output
+  int64_t* neighbors_d;
+  float* distances_d;
+  CHECK_CUVS(cuvsRMMAlloc(*res, (void**)&neighbors_d, sizeof(int64_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMAlloc(*res, (void**)&distances_d, sizeof(float) * n_queries * topk));
+
+  DLManagedTensor neighbors_tensor;
+  int64_t neighbors_shape[2] = {n_queries, topk};
+  int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
+
+  DLManagedTensor distances_tensor;
+  int64_t distances_shape[2] = {n_queries, topk};
+  float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
+
+  // Create default search params
+  cuvsIvfFlatSearchParams_t search_params;
+  CHECK_CUVS(cuvsIvfFlatSearchParamsCreate(&search_params));
+  search_params->n_probes = 10;
+
+  // Search the `index` built using `ivfFlatBuild`
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+  CHECK_CUVS(cuvsIvfFlatSearch(
+    *res, search_params, index, queries_tensor, &neighbors_tensor, &distances_tensor, filter));
+
+  int64_t* neighbors = (int64_t*)malloc(n_queries * topk * sizeof(int64_t));
+  float* distances   = (float*)malloc(n_queries * topk * sizeof(float));
+  memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
+  memset(distances, 0, n_queries * topk * sizeof(float));
+
+  CHECK_CUDA(
+    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault));
+  CHECK_CUDA(
+    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault));
+
+  print_results(neighbors, distances, 2, topk);
+
+  free(distances);
+  free(neighbors);
+  free(data_indices);
+  CHECK_CUVS(cuvsRMMFree(*res, data_indices_d, sizeof(int64_t) * n_dataset));
+  CHECK_CUVS(cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk));
+
+  CHECK_CUVS(cuvsIvfFlatSearchParamsDestroy(search_params));
+  CHECK_CUVS(cuvsIvfFlatIndexDestroy(index));
+
+  CHECK_CUVS(cuvsIvfFlatIndexParamsDestroy(index_params));
 }
 
-int main() {
-    // Create input arrays.
-    int64_t n_samples = 10000;
-    int64_t n_dim     = 3;
-    int64_t n_queries = 10;
-    float *dataset = (float *)malloc(n_samples * n_dim * sizeof(float));
-    float *queries = (float *)malloc(n_queries * n_dim * sizeof(float));
-    generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
-    generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
-
-    // Create a cuvsResources_t object
-    cuvsResources_t res;
-    cuvsResourcesCreate(&res);
-
-    // Allocate memory for `queries`
-    float *dataset_d;
-    cuvsRMMAlloc(res, (void**) &dataset_d, sizeof(float) * n_samples * n_dim);
-    // Use DLPack to represent `dataset_d` as a tensor
-    cudaMemcpy(dataset_d, dataset, sizeof(float) * n_samples * n_dim, cudaMemcpyDefault);
-
-    DLManagedTensor dataset_tensor;
-    int64_t dataset_shape[2] = {n_samples,n_dim};
-    float_tensor_initialize(dataset_d, dataset_shape, &dataset_tensor);
-
-    // Allocate memory for `queries`
-    float *queries_d;
-    cuvsRMMAlloc(res, (void**) &queries_d, sizeof(float) * n_queries * n_dim);
-
-    // Use DLPack to represent `queries` as tensors
-    cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * n_dim, cudaMemcpyDefault);
-
-    DLManagedTensor queries_tensor;
-    int64_t queries_shape[2] = {n_queries, n_dim};
-    float_tensor_initialize(queries_d, queries_shape, &queries_tensor);
-
-    // Simple build and search example.
-    ivf_flat_build_search_simple(&res, &dataset_tensor, &queries_tensor);
-
-    float *trainset_d;
-    int64_t n_trainset = n_samples * 0.1;
-    float *trainset = (float *)malloc(n_trainset * n_dim * sizeof(float));
-    for (int i = 0; i < n_trainset; i++) {
-        for (int j = 0; j < n_dim; j++) {
-            *(trainset + i * n_dim + j)  = *(dataset + i * n_dim + j);
-        }
+int main()
+{
+  // Create input arrays.
+  int64_t n_samples = 10000;
+  int64_t n_dim     = 3;
+  int64_t n_queries = 10;
+  float* dataset    = (float*)malloc(n_samples * n_dim * sizeof(float));
+  float* queries    = (float*)malloc(n_queries * n_dim * sizeof(float));
+  generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
+  generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
+
+  // Create a cuvsResources_t object
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // Allocate memory for `queries`
+  float* dataset_d;
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&dataset_d, sizeof(float) * n_samples * n_dim));
+  // Use DLPack to represent `dataset_d` as a tensor
+  CHECK_CUDA(cudaMemcpy(dataset_d, dataset, sizeof(float) * n_samples * n_dim, cudaMemcpyDefault));
+
+  DLManagedTensor dataset_tensor;
+  int64_t dataset_shape[2] = {n_samples, n_dim};
+  float_tensor_initialize(dataset_d, dataset_shape, &dataset_tensor);
+
+  // Allocate memory for `queries`
+  float* queries_d;
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&queries_d, sizeof(float) * n_queries * n_dim));
+
+  // Use DLPack to represent `queries` as tensors
+  cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * n_dim, cudaMemcpyDefault);
+
+  DLManagedTensor queries_tensor;
+  int64_t queries_shape[2] = {n_queries, n_dim};
+  float_tensor_initialize(queries_d, queries_shape, &queries_tensor);
+
+  // Simple build and search example.
+  ivf_flat_build_search_simple(&res, &dataset_tensor, &queries_tensor);
+
+  float* trainset_d;
+  int64_t n_trainset = n_samples * 0.1;
+  float* trainset    = (float*)malloc(n_trainset * n_dim * sizeof(float));
+  for (int i = 0; i < n_trainset; i++) {
+    for (int j = 0; j < n_dim; j++) {
+      *(trainset + i * n_dim + j) = *(dataset + i * n_dim + j);
     }
-    cuvsRMMAlloc(res, (void**) &trainset_d, sizeof(float) * n_trainset * n_dim);
-    cudaMemcpy(trainset_d, trainset, sizeof(float) * n_trainset * n_dim, cudaMemcpyDefault);
-    DLManagedTensor trainset_tensor;
-    int64_t trainset_shape[2] = {n_trainset, n_dim};
-    float_tensor_initialize(trainset_d, trainset_shape, &trainset_tensor);
-
-    // Build and extend example.
-    ivf_flat_build_extend_search(&res, &trainset_tensor, &dataset_tensor, &queries_tensor);
-
-    cuvsRMMFree(res, trainset_d, sizeof(float) * n_trainset * n_dim);
-    cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_dim);
-    cuvsRMMFree(res, dataset_d, sizeof(float) * n_samples * n_dim);
-    cuvsResourcesDestroy(res);
-    free(trainset);
-    free(dataset);
-    free(queries);
+  }
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&trainset_d, sizeof(float) * n_trainset * n_dim));
+  CHECK_CUDA(
+    cudaMemcpy(trainset_d, trainset, sizeof(float) * n_trainset * n_dim, cudaMemcpyDefault));
+  DLManagedTensor trainset_tensor;
+  int64_t trainset_shape[2] = {n_trainset, n_dim};
+  float_tensor_initialize(trainset_d, trainset_shape, &trainset_tensor);
+
+  // Build and extend example.
+  ivf_flat_build_extend_search(&res, &trainset_tensor, &dataset_tensor, &queries_tensor);
+
+  CHECK_CUVS(cuvsRMMFree(res, trainset_d, sizeof(float) * n_trainset * n_dim));
+  CHECK_CUVS(cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_dim));
+  CHECK_CUVS(cuvsRMMFree(res, dataset_d, sizeof(float) * n_samples * n_dim));
+  CHECK_CUVS(cuvsResourcesDestroy(res));
+  free(trainset);
+  free(dataset);
+  free(queries);
 }
diff --git a/examples/c/src/ivf_pq_c_example.c b/examples/c/src/ivf_pq_c_example.c
index c3235f2d45..dc7e09849d 100644
--- a/examples/c/src/ivf_pq_c_example.c
+++ b/examples/c/src/ivf_pq_c_example.c
@@ -18,172 +18,177 @@
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/refine.h>
 
-#include <cuda_runtime.h>
 #include "common.h"
+#include <cuda_runtime.h>
 
-void ivf_pq_build_search(cuvsResources_t *res, DLManagedTensor * dataset_tensor, DLManagedTensor * queries_tensor) {
-    // Create default index params
-    cuvsIvfPqIndexParams_t index_params;
-    cuvsIvfPqIndexParamsCreate(&index_params);
-    index_params->n_lists                  = 1024; // default value
-    index_params->kmeans_trainset_fraction = 0.1;
-    //index_params->metric default is L2Expanded
-    index_params->pq_bits = 8;
-    index_params->pq_dim = 2;
-
-    // Create IVF-PQ index
-    cuvsIvfPqIndex_t index;
-    cuvsIvfPqIndexCreate(&index);
-
-    printf("Building IVF-PQ index\n");
-
-    // Build the IVF-PQ Index
-    cuvsError_t build_status = cuvsIvfPqBuild(*res, index_params, dataset_tensor, index);
-    if (build_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-        cuvsIvfPqIndexDestroy(index);
-        cuvsIvfPqIndexParamsDestroy(index_params);
-        return;
-    }
-
-    // Create output arrays.
-    int64_t topk      = 10;
-    int64_t n_queries = queries_tensor->dl_tensor.shape[0];
-
-    //Allocate memory for `neighbors` and `distances` output
-    int64_t *neighbors_d;
-    float *distances_d;
-    cuvsRMMAlloc(*res, (void**) &neighbors_d, sizeof(int64_t) * n_queries * topk);
-    cuvsRMMAlloc(*res, (void**) &distances_d, sizeof(float) * n_queries * topk);
-
-    DLManagedTensor neighbors_tensor;
-    int64_t neighbors_shape[2] = {n_queries, topk};
-    int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
-
-    DLManagedTensor distances_tensor;
-    int64_t distances_shape[2] = {n_queries, topk};
-    float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
-
-    // Create default search params
-    cuvsIvfPqSearchParams_t search_params;
-    cuvsIvfPqSearchParamsCreate(&search_params);
-    search_params->n_probes = 50;
-    search_params->internal_distance_dtype = CUDA_R_16F;
-    search_params->lut_dtype = CUDA_R_16F;
-
-    // Search the `index` built using `cuvsIvfPqBuild`
-    cuvsError_t search_status = cuvsIvfPqSearch(*res, search_params, index,
-     queries_tensor, &neighbors_tensor, &distances_tensor);
-    if (search_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-        exit(-1);
-    }
-
-    int64_t *neighbors = (int64_t *)malloc(n_queries * topk * sizeof(int64_t));
-    float *distances = (float *)malloc(n_queries * topk * sizeof(float));
-    memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
-    memset(distances, 0, n_queries * topk * sizeof(float));
-
-    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault);
-    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault);
-
-    printf("\nOriginal results:\n");
-    print_results(neighbors, distances, 2, topk);
-
-    // Re-ranking operation: refine the initial search results by computing exact distances
-    int64_t topk_refined = 7;
-    int64_t *neighbors_refined_d;
-    float *distances_refined_d;
-    cuvsRMMAlloc(*res, (void**) &neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined);
-    cuvsRMMAlloc(*res, (void**) &distances_refined_d, sizeof(float) * n_queries * topk_refined);
-
-    DLManagedTensor neighbors_refined_tensor;
-    int64_t neighbors_refined_shape[2] = {n_queries, topk_refined};
-    int_tensor_initialize(neighbors_refined_d, neighbors_refined_shape, &neighbors_refined_tensor);
-
-    DLManagedTensor distances_refined_tensor;
-    int64_t distances_refined_shape[2] = {n_queries, topk_refined};
-    float_tensor_initialize(distances_refined_d, distances_refined_shape, &distances_refined_tensor);
-
-    // Note, refinement requires the original dataset and the queries.
-    // Don't forget to specify the same distance metric as used by the index.
-    cuvsError_t refine_status = cuvsRefine(*res, dataset_tensor, queries_tensor,
-                       &neighbors_tensor, index_params->metric,
-                       &neighbors_refined_tensor, &distances_refined_tensor);
-    if (refine_status != CUVS_SUCCESS) {
-        printf("%s.\n", cuvsGetLastErrorText());
-        exit(-1);
-    }
-
-    int64_t *neighbors_refine = (int64_t *)malloc(n_queries * topk_refined * sizeof(int64_t));
-    float *distances_refine = (float *)malloc(n_queries * topk_refined * sizeof(float));
-    memset(neighbors_refine, 0, n_queries * topk_refined * sizeof(int64_t));
-    memset(distances_refine, 0, n_queries * topk_refined * sizeof(float));
-
-    cudaMemcpy(neighbors_refine, neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined, cudaMemcpyDefault);
-    cudaMemcpy(distances_refine, distances_refined_d, sizeof(float) * n_queries * topk_refined, cudaMemcpyDefault);
-
-    printf("\nRefined results:\n");
-    print_results(neighbors, distances, 2, topk_refined);
-
-    free(distances_refine);
-    free(neighbors_refine);
-
-    free(distances);
-    free(neighbors);
-
-    cuvsRMMFree(*res, neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined);
-    cuvsRMMFree(*res, distances_refined_d, sizeof(float) * n_queries * topk_refined);
-
-    cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk);
-    cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk);
-
-    cuvsIvfPqSearchParamsDestroy(search_params);
-    cuvsIvfPqIndexDestroy(index);
-    cuvsIvfPqIndexParamsDestroy(index_params);
+void ivf_pq_build_search(cuvsResources_t* res,
+                         DLManagedTensor* dataset_tensor,
+                         DLManagedTensor* queries_tensor)
+{
+  // Create default index params
+  cuvsIvfPqIndexParams_t index_params;
+  CHECK_CUVS(cuvsIvfPqIndexParamsCreate(&index_params));
+  index_params->n_lists                  = 1024;  // default value
+  index_params->kmeans_trainset_fraction = 0.1;
+  // index_params->metric default is L2Expanded
+  index_params->pq_bits = 8;
+  index_params->pq_dim  = 2;
+
+  // Create IVF-PQ index
+  cuvsIvfPqIndex_t index;
+  CHECK_CUVS(cuvsIvfPqIndexCreate(&index));
+
+  printf("Building IVF-PQ index\n");
+
+  // Build the IVF-PQ Index
+  CHECK_CUVS(cuvsIvfPqBuild(*res, index_params, dataset_tensor, index));
+
+  // Create output arrays.
+  int64_t topk      = 10;
+  int64_t n_queries = queries_tensor->dl_tensor.shape[0];
+
+  // Allocate memory for `neighbors` and `distances` output
+  int64_t* neighbors_d;
+  float* distances_d;
+  CHECK_CUVS(cuvsRMMAlloc(*res, (void**)&neighbors_d, sizeof(int64_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMAlloc(*res, (void**)&distances_d, sizeof(float) * n_queries * topk));
+
+  DLManagedTensor neighbors_tensor;
+  int64_t neighbors_shape[2] = {n_queries, topk};
+  int_tensor_initialize(neighbors_d, neighbors_shape, &neighbors_tensor);
+
+  DLManagedTensor distances_tensor;
+  int64_t distances_shape[2] = {n_queries, topk};
+  float_tensor_initialize(distances_d, distances_shape, &distances_tensor);
+
+  // Create default search params
+  cuvsIvfPqSearchParams_t search_params;
+  CHECK_CUVS(cuvsIvfPqSearchParamsCreate(&search_params));
+  search_params->n_probes                = 50;
+  search_params->internal_distance_dtype = CUDA_R_16F;
+  search_params->lut_dtype               = CUDA_R_16F;
+
+  // Search the `index` built using `cuvsIvfPqBuild`
+  CHECK_CUVS(cuvsIvfPqSearch(
+    *res, search_params, index, queries_tensor, &neighbors_tensor, &distances_tensor));
+
+  int64_t* neighbors = (int64_t*)malloc(n_queries * topk * sizeof(int64_t));
+  float* distances   = (float*)malloc(n_queries * topk * sizeof(float));
+  memset(neighbors, 0, n_queries * topk * sizeof(int64_t));
+  memset(distances, 0, n_queries * topk * sizeof(float));
+
+  CHECK_CUDA(
+    cudaMemcpy(neighbors, neighbors_d, sizeof(int64_t) * n_queries * topk, cudaMemcpyDefault));
+  CHECK_CUDA(
+    cudaMemcpy(distances, distances_d, sizeof(float) * n_queries * topk, cudaMemcpyDefault));
+
+  printf("\nOriginal results:\n");
+  print_results(neighbors, distances, 2, topk);
+
+  // Re-ranking operation: refine the initial search results by computing exact
+  // distances
+  int64_t topk_refined = 7;
+  int64_t* neighbors_refined_d;
+  float* distances_refined_d;
+  CHECK_CUVS(
+    cuvsRMMAlloc(*res, (void**)&neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined));
+  CHECK_CUVS(
+    cuvsRMMAlloc(*res, (void**)&distances_refined_d, sizeof(float) * n_queries * topk_refined));
+
+  DLManagedTensor neighbors_refined_tensor;
+  int64_t neighbors_refined_shape[2] = {n_queries, topk_refined};
+  int_tensor_initialize(neighbors_refined_d, neighbors_refined_shape, &neighbors_refined_tensor);
+
+  DLManagedTensor distances_refined_tensor;
+  int64_t distances_refined_shape[2] = {n_queries, topk_refined};
+  float_tensor_initialize(distances_refined_d, distances_refined_shape, &distances_refined_tensor);
+
+  // Note, refinement requires the original dataset and the queries.
+  // Don't forget to specify the same distance metric as used by the index.
+  CHECK_CUVS(cuvsRefine(*res,
+                        dataset_tensor,
+                        queries_tensor,
+                        &neighbors_tensor,
+                        index_params->metric,
+                        &neighbors_refined_tensor,
+                        &distances_refined_tensor));
+
+  int64_t* neighbors_refine = (int64_t*)malloc(n_queries * topk_refined * sizeof(int64_t));
+  float* distances_refine   = (float*)malloc(n_queries * topk_refined * sizeof(float));
+  memset(neighbors_refine, 0, n_queries * topk_refined * sizeof(int64_t));
+  memset(distances_refine, 0, n_queries * topk_refined * sizeof(float));
+
+  CHECK_CUDA(cudaMemcpy(neighbors_refine,
+                        neighbors_refined_d,
+                        sizeof(int64_t) * n_queries * topk_refined,
+                        cudaMemcpyDefault));
+  CHECK_CUDA(cudaMemcpy(distances_refine,
+                        distances_refined_d,
+                        sizeof(float) * n_queries * topk_refined,
+                        cudaMemcpyDefault));
+
+  printf("\nRefined results:\n");
+  print_results(neighbors, distances, 2, topk_refined);
+
+  free(distances_refine);
+  free(neighbors_refine);
+
+  free(distances);
+  free(neighbors);
+
+  CHECK_CUVS(cuvsRMMFree(*res, neighbors_refined_d, sizeof(int64_t) * n_queries * topk_refined));
+  CHECK_CUVS(cuvsRMMFree(*res, distances_refined_d, sizeof(float) * n_queries * topk_refined));
+
+  CHECK_CUVS(cuvsRMMFree(*res, neighbors_d, sizeof(int64_t) * n_queries * topk));
+  CHECK_CUVS(cuvsRMMFree(*res, distances_d, sizeof(float) * n_queries * topk));
+
+  CHECK_CUVS(cuvsIvfPqSearchParamsDestroy(search_params));
+  CHECK_CUVS(cuvsIvfPqIndexDestroy(index));
+  CHECK_CUVS(cuvsIvfPqIndexParamsDestroy(index_params));
 }
 
-int main() {
-    // Create input arrays.
-    int64_t n_samples = 10000;
-    int64_t n_dim     = 3;
-    int64_t n_queries = 10;
-    float *dataset = (float *)malloc(n_samples * n_dim * sizeof(float));
-    float *queries = (float *)malloc(n_queries * n_dim * sizeof(float));
-    generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
-    generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
-
-    // Create a cuvsResources_t object
-    cuvsResources_t res;
-    cuvsResourcesCreate(&res);
-
-    // Allocate memory for `queries`
-    float *dataset_d;
-    cuvsRMMAlloc(res, (void**) &dataset_d, sizeof(float) * n_samples * n_dim);
-    // Use DLPack to represent `dataset_d` as a tensor
-    cudaMemcpy(dataset_d, dataset, sizeof(float) * n_samples * n_dim, cudaMemcpyDefault);
-
-    DLManagedTensor dataset_tensor;
-    int64_t dataset_shape[2] = {n_samples,n_dim};
-    float_tensor_initialize(dataset_d, dataset_shape, &dataset_tensor);
-
-    // Allocate memory for `queries`
-    float *queries_d;
-    cuvsRMMAlloc(res, (void**) &queries_d, sizeof(float) * n_queries * n_dim);
-
-    // Use DLPack to represent `queries` as tensors
-    cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * n_dim, cudaMemcpyDefault);
-
-    DLManagedTensor queries_tensor;
-    int64_t queries_shape[2] = {n_queries, n_dim};
-    float_tensor_initialize(queries_d, queries_shape, &queries_tensor);
-
-    // Simple build and search example.
-    ivf_pq_build_search(&res, &dataset_tensor, &queries_tensor);
-
-    cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_dim);
-    cuvsRMMFree(res, dataset_d, sizeof(float) * n_samples * n_dim);
-    cuvsResourcesDestroy(res);
-    free(dataset);
-    free(queries);
+int main()
+{
+  // Create input arrays.
+  int64_t n_samples = 10000;
+  int64_t n_dim     = 3;
+  int64_t n_queries = 10;
+  float* dataset    = (float*)malloc(n_samples * n_dim * sizeof(float));
+  float* queries    = (float*)malloc(n_queries * n_dim * sizeof(float));
+  generate_dataset(dataset, n_samples, n_dim, -10.0, 10.0);
+  generate_dataset(queries, n_queries, n_dim, -1.0, 1.0);
+
+  // Create a cuvsResources_t object
+  cuvsResources_t res;
+  CHECK_CUVS(cuvsResourcesCreate(&res));
+
+  // Allocate memory for `queries`
+  float* dataset_d;
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&dataset_d, sizeof(float) * n_samples * n_dim));
+  // Use DLPack to represent `dataset_d` as a tensor
+  CHECK_CUDA(cudaMemcpy(dataset_d, dataset, sizeof(float) * n_samples * n_dim, cudaMemcpyDefault));
+
+  DLManagedTensor dataset_tensor;
+  int64_t dataset_shape[2] = {n_samples, n_dim};
+  float_tensor_initialize(dataset_d, dataset_shape, &dataset_tensor);
+
+  // Allocate memory for `queries`
+  float* queries_d;
+  CHECK_CUVS(cuvsRMMAlloc(res, (void**)&queries_d, sizeof(float) * n_queries * n_dim));
+
+  // Use DLPack to represent `queries` as tensors
+  CHECK_CUDA(cudaMemcpy(queries_d, queries, sizeof(float) * n_queries * n_dim, cudaMemcpyDefault));
+
+  DLManagedTensor queries_tensor;
+  int64_t queries_shape[2] = {n_queries, n_dim};
+  float_tensor_initialize(queries_d, queries_shape, &queries_tensor);
+
+  // Simple build and search example.
+  ivf_pq_build_search(&res, &dataset_tensor, &queries_tensor);
+
+  CHECK_CUVS(cuvsRMMFree(res, queries_d, sizeof(float) * n_queries * n_dim));
+  CHECK_CUVS(cuvsRMMFree(res, dataset_d, sizeof(float) * n_samples * n_dim));
+  CHECK_CUVS(cuvsResourcesDestroy(res));
+  free(dataset);
+  free(queries);
 }
diff --git a/examples/cpp/src/brute_force_bitmap.cu b/examples/cpp/src/brute_force_bitmap.cu
index 69e4df60b8..a50549d52d 100644
--- a/examples/cpp/src/brute_force_bitmap.cu
+++ b/examples/cpp/src/brute_force_bitmap.cu
@@ -21,25 +21,24 @@
 #include <raft/core/device_resources.hpp>
 #include <raft/random/make_blobs.cuh>
 
-#include <rmm/mr/device/device_memory_resource.hpp>
 #include <iostream>
+#include <rmm/mr/device/device_memory_resource.hpp>
 
 void load_dataset(const raft::device_resources& res, float* data_ptr, int n_vectors, int dim)
 {
   raft::random::RngState rng(1234ULL);
-  raft::random::uniform(
-    res, rng, data_ptr, n_vectors * dim, 0.1f, 2.0f);
+  raft::random::uniform(res, rng, data_ptr, n_vectors * dim, 0.1f, 2.0f);
 }
 
 int main()
 {
   using namespace cuvs::neighbors;
-  using dataset_dtype = float;
+  using dataset_dtype  = float;
   using indexing_dtype = int64_t;
-  auto dim = 128;
-  auto n_vectors = 90;
-  auto n_queries = 100;
-  auto k = 5;
+  auto dim             = 128;
+  auto n_vectors       = 90;
+  auto n_queries       = 100;
+  auto k               = 5;
 
   // ... build index ...
   raft::device_resources res;
@@ -55,16 +54,18 @@ int main()
   // Load a list of all the samples that will get filtered
   std::vector<indexing_dtype> removed_indices_host = {2, 13, 21, 8};
   auto removed_indices_device =
-        raft::make_device_vector<indexing_dtype, indexing_dtype>(res, removed_indices_host.size());
+    raft::make_device_vector<indexing_dtype, indexing_dtype>(res, removed_indices_host.size());
   // Copy this list to device
-  raft::copy(removed_indices_device.data_handle(), removed_indices_host.data(),
-             removed_indices_host.size(), raft::resource::get_cuda_stream(res));
+  raft::copy(removed_indices_device.data_handle(),
+             removed_indices_host.data(),
+             removed_indices_host.size(),
+             raft::resource::get_cuda_stream(res));
 
   // Create a bitmap with the list of samples to filter.
   cuvs::core::bitset<uint32_t, indexing_dtype> removed_indices_bitset(
-      res, removed_indices_device.view(), n_queries * n_vectors);
+    res, removed_indices_device.view(), n_queries * n_vectors);
   cuvs::core::bitmap_view<const uint32_t, indexing_dtype> removed_indices_bitmap(
-      removed_indices_bitset.data(), n_queries, n_vectors);
+    removed_indices_bitset.data(), n_queries, n_vectors);
 
   // Use a `bitmap_filter` in the `brute_force::search` function call.
   auto bitmap_filter = cuvs::neighbors::filtering::bitmap_filter(removed_indices_bitmap);
diff --git a/examples/cpp/src/cagra_persistent_example.cu b/examples/cpp/src/cagra_persistent_example.cu
index 9258a7311c..e65e10e725 100644
--- a/examples/cpp/src/cagra_persistent_example.cu
+++ b/examples/cpp/src/cagra_persistent_example.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -32,30 +32,30 @@
 template <typename DeviceMatrixOrView>
 auto slice_matrix(DeviceMatrixOrView source,
                   typename DeviceMatrixOrView::index_type offset_rows,
-                  typename DeviceMatrixOrView::index_type count_rows) {
+                  typename DeviceMatrixOrView::index_type count_rows)
+{
   auto n_cols = source.extent(1);
-  return raft::make_device_matrix_view<
-      typename DeviceMatrixOrView::element_type,
-      typename DeviceMatrixOrView::index_type>(
-      source.data_handle() + offset_rows * n_cols, count_rows, n_cols);
+  return raft::make_device_matrix_view<typename DeviceMatrixOrView::element_type,
+                                       typename DeviceMatrixOrView::index_type>(
+    source.data_handle() + offset_rows * n_cols, count_rows, n_cols);
 }
 
 // A helper to measure the execution time of a function
 template <typename F, typename... Args>
-void time_it(std::string label, F f, Args &&...xs) {
+void time_it(std::string label, F f, Args&&... xs)
+{
   auto start = std::chrono::system_clock::now();
   f(std::forward<Args>(xs)...);
-  auto end = std::chrono::system_clock::now();
-  auto t = std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+  auto end  = std::chrono::system_clock::now();
+  auto t    = std::chrono::duration_cast<std::chrono::microseconds>(end - start);
   auto t_ms = double(t.count()) / 1000.0;
-  std::cout << "[" << label << "] execution time: " << t_ms << " ms"
-            << std::endl;
+  std::cout << "[" << label << "] execution time: " << t_ms << " ms" << std::endl;
 }
 
-void cagra_build_search_variants(
-    raft::device_resources const &res,
-    raft::device_matrix_view<const float, int64_t> dataset,
-    raft::device_matrix_view<const float, int64_t> queries) {
+void cagra_build_search_variants(raft::device_resources const& res,
+                                 raft::device_matrix_view<const float, int64_t> dataset,
+                                 raft::device_matrix_view<const float, int64_t> queries)
+{
   using namespace cuvs::neighbors;
 
   // Number of neighbors to search
@@ -69,10 +69,8 @@ void cagra_build_search_variants(
   auto queries_b = slice_matrix(queries, n_queries_a, n_queries_b);
 
   // create output arrays
-  auto neighbors =
-      raft::make_device_matrix<uint32_t>(res, queries.extent(0), topk);
-  auto distances =
-      raft::make_device_matrix<float>(res, queries.extent(0), topk);
+  auto neighbors = raft::make_device_matrix<uint32_t>(res, queries.extent(0), topk);
+  auto distances = raft::make_device_matrix<float>(res, queries.extent(0), topk);
   // slice them same as queries
   auto neighbors_a = slice_matrix(neighbors, 0, n_queries_a);
   auto distances_a = slice_matrix(distances, 0, n_queries_a);
@@ -86,9 +84,8 @@ void cagra_build_search_variants(
   auto index = cagra::build(res, index_params, dataset);
 
   std::cout << "CAGRA index has " << index.size() << " vectors" << std::endl;
-  std::cout << "CAGRA graph has degree " << index.graph_degree()
-            << ", graph size [" << index.graph().extent(0) << ", "
-            << index.graph().extent(1) << "]" << std::endl;
+  std::cout << "CAGRA graph has degree " << index.graph_degree() << ", graph size ["
+            << index.graph().extent(0) << ", " << index.graph().extent(1) << "]" << std::endl;
 
   // use default search parameters
   cagra::search_params search_params;
@@ -97,7 +94,7 @@ void cagra_build_search_variants(
 
   // Another copy of search parameters to enable persistent kernel
   cagra::search_params search_params_persistent = search_params;
-  search_params_persistent.persistent = true;
+  search_params_persistent.persistent           = true;
   // Persistent kernel only support single-cta search algorithm for now.
   search_params_persistent.algo = cagra::search_algo::SINGLE_CTA;
   // Slightly reduce the kernel grid size to make this example program work
@@ -112,23 +109,21 @@ void cagra_build_search_variants(
   implementation interchangeably: the index stays the same, only search
   parameters need some adjustment.
   */
-  auto search_batch =
-      [&res, &index](bool needs_sync, const cagra::search_params &ps,
-                     raft::device_matrix_view<const float, int64_t> queries,
-                     raft::device_matrix_view<uint32_t, int64_t> neighbors,
-                     raft::device_matrix_view<float, int64_t> distances) {
-        cagra::search(res, ps, index, queries, neighbors, distances);
-        /*
-        To make a fair comparison, standard implementation needs to synchronize
-        with the device to make sure the kernel has finished the work.
-        Persistent kernel does not make any use of CUDA streams and blocks till
-        the results are available. Hence, synchronizing with the stream is a
-        waste of time in this case.
-         */
-        if (needs_sync) {
-          raft::resource::sync_stream(res);
-        }
-      };
+  auto search_batch = [&res, &index](bool needs_sync,
+                                     const cagra::search_params& ps,
+                                     raft::device_matrix_view<const float, int64_t> queries,
+                                     raft::device_matrix_view<uint32_t, int64_t> neighbors,
+                                     raft::device_matrix_view<float, int64_t> distances) {
+    cagra::search(res, ps, index, queries, neighbors, distances);
+    /*
+    To make a fair comparison, standard implementation needs to synchronize
+    with the device to make sure the kernel has finished the work.
+    Persistent kernel does not make any use of CUDA streams and blocks till
+    the results are available. Hence, synchronizing with the stream is a
+    waste of time in this case.
+     */
+    if (needs_sync) { raft::resource::sync_stream(res); }
+  };
 
   /*
   Define the asynchronous small-batch search setting.
@@ -157,55 +152,64 @@ void cagra_build_search_variants(
        This way, you can save some GPU resources by not manually copying the
        data in cuda streams.
   */
-  auto search_async =
-      [&res, &index](bool needs_sync, const cagra::search_params &ps,
-                     raft::device_matrix_view<const float, int64_t> queries,
-                     raft::device_matrix_view<uint32_t, int64_t> neighbors,
-                     raft::device_matrix_view<float, int64_t> distances) {
-        auto work_size = queries.extent(0);
-        using index_type = typeof(work_size);
-        // Limit the maximum number of concurrent jobs
-        constexpr index_type kMaxJobs = 1000;
-        std::array<std::future<void>, kMaxJobs> futures;
-        for (index_type i = 0; i < work_size + kMaxJobs; i++) {
-          // wait for previous job in the same slot to finish
-          if (i >= kMaxJobs) {
-            futures[i % kMaxJobs].wait();
-          }
-          // submit a new job
-          if (i < work_size) {
-            futures[i % kMaxJobs] = std::async(std::launch::async, [&]() {
-              cagra::search(res, ps, index, slice_matrix(queries, i, 1),
-                            slice_matrix(neighbors, i, 1),
-                            slice_matrix(distances, i, 1));
-            });
-          }
-        }
-        /* See the remark for search_batch */
-        if (needs_sync) {
-          raft::resource::sync_stream(res);
-        }
-      };
+  auto search_async = [&res, &index](bool needs_sync,
+                                     const cagra::search_params& ps,
+                                     raft::device_matrix_view<const float, int64_t> queries,
+                                     raft::device_matrix_view<uint32_t, int64_t> neighbors,
+                                     raft::device_matrix_view<float, int64_t> distances) {
+    auto work_size   = queries.extent(0);
+    using index_type = typeof(work_size);
+    // Limit the maximum number of concurrent jobs
+    constexpr index_type kMaxJobs = 1000;
+    std::array<std::future<void>, kMaxJobs> futures;
+    for (index_type i = 0; i < work_size + kMaxJobs; i++) {
+      // wait for previous job in the same slot to finish
+      if (i >= kMaxJobs) { futures[i % kMaxJobs].wait(); }
+      // submit a new job
+      if (i < work_size) {
+        futures[i % kMaxJobs] = std::async(std::launch::async, [&]() {
+          cagra::search(res,
+                        ps,
+                        index,
+                        slice_matrix(queries, i, 1),
+                        slice_matrix(neighbors, i, 1),
+                        slice_matrix(distances, i, 1));
+        });
+      }
+    }
+    /* See the remark for search_batch */
+    if (needs_sync) { raft::resource::sync_stream(res); }
+  };
 
   // Launch the baseline search: check the big-batch performance
-  time_it("standard/batch A", search_batch, true, search_params, queries_a,
-          neighbors_a, distances_a);
-  time_it("standard/batch B", search_batch, true, search_params, queries_b,
-          neighbors_b, distances_b);
+  time_it(
+    "standard/batch A", search_batch, true, search_params, queries_a, neighbors_a, distances_a);
+  time_it(
+    "standard/batch B", search_batch, true, search_params, queries_b, neighbors_b, distances_b);
 
   // Try to handle the same amount of work in the async setting using the
   // standard implementation.
   // (Warning: suboptimal - it uses a single stream for all async jobs)
-  time_it("standard/async A", search_async, true, search_params, queries_a,
-          neighbors_a, distances_a);
-  time_it("standard/async B", search_async, true, search_params, queries_b,
-          neighbors_b, distances_b);
+  time_it(
+    "standard/async A", search_async, true, search_params, queries_a, neighbors_a, distances_a);
+  time_it(
+    "standard/async B", search_async, true, search_params, queries_b, neighbors_b, distances_b);
 
   // Do the same using persistent kernel.
-  time_it("persistent/async A", search_async, false, search_params_persistent,
-          queries_a, neighbors_a, distances_a);
-  time_it("persistent/async B", search_async, false, search_params_persistent,
-          queries_b, neighbors_b, distances_b);
+  time_it("persistent/async A",
+          search_async,
+          false,
+          search_params_persistent,
+          queries_a,
+          neighbors_a,
+          distances_a);
+  time_it("persistent/async B",
+          search_async,
+          false,
+          search_params_persistent,
+          queries_b,
+          neighbors_b,
+          distances_b);
   /*
 Here's an example output, which shows the wall time of processing the same
 amount of data in a single batch vs in async mode (1 query per job):
@@ -233,26 +237,25 @@ for you. This increases the latency of individual jobs though.
   */
 }
 
-int main() {
+int main()
+{
   raft::device_resources res;
 
   // Set pool memory resource with 1 GiB initial pool size. All allocations use
   // the same pool.
   rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
-      rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+    rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
   rmm::mr::set_current_device_resource(&pool_mr);
 
   // Create input arrays.
   int64_t n_samples = 1000000;
-  int64_t n_dim = 128;
+  int64_t n_dim     = 128;
   int64_t n_queries = 100000;
-  auto dataset =
-      raft::make_device_matrix<float, int64_t>(res, n_samples, n_dim);
-  auto queries =
-      raft::make_device_matrix<float, int64_t>(res, n_queries, n_dim);
+  auto dataset      = raft::make_device_matrix<float, int64_t>(res, n_samples, n_dim);
+  auto queries      = raft::make_device_matrix<float, int64_t>(res, n_queries, n_dim);
   generate_dataset(res, dataset.view(), queries.view());
 
   // run the interesting part of the program
-  cagra_build_search_variants(res, raft::make_const_mdspan(dataset.view()),
-                              raft::make_const_mdspan(queries.view()));
+  cagra_build_search_variants(
+    res, raft::make_const_mdspan(dataset.view()), raft::make_const_mdspan(queries.view()));
 }
diff --git a/examples/cpp/src/common.cuh b/examples/cpp/src/common.cuh
index a743ff802d..9ad6ae80c7 100644
--- a/examples/cpp/src/common.cuh
+++ b/examples/cpp/src/common.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -33,36 +33,34 @@
 #include <fstream>
 
 // Fill dataset and queries with synthetic data.
-void generate_dataset(raft::device_resources const &dev_resources,
+void generate_dataset(raft::device_resources const& dev_resources,
                       raft::device_matrix_view<float, int64_t> dataset,
-                      raft::device_matrix_view<float, int64_t> queries) {
-  auto labels = raft::make_device_vector<int64_t, int64_t>(dev_resources,
-                                                           dataset.extent(0));
+                      raft::device_matrix_view<float, int64_t> queries)
+{
+  auto labels = raft::make_device_vector<int64_t, int64_t>(dev_resources, dataset.extent(0));
   raft::random::make_blobs(dev_resources, dataset, labels.view());
   raft::random::RngState r(1234ULL);
-  raft::random::uniform(
-      dev_resources, r,
-      raft::make_device_vector_view(queries.data_handle(), queries.size()),
-      -1.0f, 1.0f);
+  raft::random::uniform(dev_resources,
+                        r,
+                        raft::make_device_vector_view(queries.data_handle(), queries.size()),
+                        -1.0f,
+                        1.0f);
 }
 
 // Copy the results to host and print a few samples
 template <typename IdxT>
-void print_results(raft::device_resources const &dev_resources,
+void print_results(raft::device_resources const& dev_resources,
                    raft::device_matrix_view<IdxT, int64_t> neighbors,
-                   raft::device_matrix_view<float, int64_t> distances) {
-  int64_t topk = neighbors.extent(1);
-  auto neighbors_host =
-      raft::make_host_matrix<IdxT, int64_t>(neighbors.extent(0), topk);
-  auto distances_host =
-      raft::make_host_matrix<float, int64_t>(distances.extent(0), topk);
+                   raft::device_matrix_view<float, int64_t> distances)
+{
+  int64_t topk        = neighbors.extent(1);
+  auto neighbors_host = raft::make_host_matrix<IdxT, int64_t>(neighbors.extent(0), topk);
+  auto distances_host = raft::make_host_matrix<float, int64_t>(distances.extent(0), topk);
 
   cudaStream_t stream = raft::resource::get_cuda_stream(dev_resources);
 
-  raft::copy(neighbors_host.data_handle(), neighbors.data_handle(),
-             neighbors.size(), stream);
-  raft::copy(distances_host.data_handle(), distances.data_handle(),
-             distances.size(), stream);
+  raft::copy(neighbors_host.data_handle(), neighbors.data_handle(), neighbors.size(), stream);
+  raft::copy(distances_host.data_handle(), distances.data_handle(), distances.size(), stream);
 
   // The calls to RAFT algorithms and  raft::copy is asynchronous.
   // We need to sync the stream before accessing the data.
@@ -77,37 +75,35 @@ void print_results(raft::device_resources const &dev_resources,
 }
 
 /** Subsample the dataset to create a training set*/
-raft::device_matrix<float, int64_t>
-subsample(raft::device_resources const &dev_resources,
-          raft::device_matrix_view<const float, int64_t> dataset,
-          raft::device_vector_view<const int64_t, int64_t> data_indices,
-          float fraction) {
+raft::device_matrix<float, int64_t> subsample(
+  raft::device_resources const& dev_resources,
+  raft::device_matrix_view<const float, int64_t> dataset,
+  raft::device_vector_view<const int64_t, int64_t> data_indices,
+  float fraction)
+{
   int64_t n_samples = dataset.extent(0);
-  int64_t n_dim = dataset.extent(1);
-  int64_t n_train = n_samples * fraction;
-  auto trainset =
-      raft::make_device_matrix<float, int64_t>(dev_resources, n_train, n_dim);
+  int64_t n_dim     = dataset.extent(1);
+  int64_t n_train   = n_samples * fraction;
+  auto trainset     = raft::make_device_matrix<float, int64_t>(dev_resources, n_train, n_dim);
 
   int seed = 137;
   raft::random::RngState rng(seed);
-  auto train_indices =
-      raft::make_device_vector<int64_t>(dev_resources, n_train);
+  auto train_indices = raft::make_device_vector<int64_t>(dev_resources, n_train);
 
-  raft::random::sample_without_replacement(dev_resources, rng, data_indices,
-                                           std::nullopt, train_indices.view(),
-                                           std::nullopt);
+  raft::random::sample_without_replacement(
+    dev_resources, rng, data_indices, std::nullopt, train_indices.view(), std::nullopt);
 
-  raft::matrix::copy_rows(dev_resources, dataset, trainset.view(),
-                          raft::make_const_mdspan(train_indices.view()));
+  raft::matrix::copy_rows(
+    dev_resources, dataset, trainset.view(), raft::make_const_mdspan(train_indices.view()));
 
   return trainset;
 }
 
-template<typename T, typename idxT>
-raft::device_matrix<T,idxT> read_bin_dataset(raft::device_resources const &dev_resources,
-		       std::string fname,
-                       int max_N = INT_MAX) {
-
+template <typename T, typename idxT>
+raft::device_matrix<T, idxT> read_bin_dataset(raft::device_resources const& dev_resources,
+                                              std::string fname,
+                                              int max_N = INT_MAX)
+{
   // Read datafile in
   std::ifstream datafile(fname, std::ifstream::binary);
   uint32_t N;
@@ -115,15 +111,18 @@ raft::device_matrix<T,idxT> read_bin_dataset(raft::device_resources const &dev_r
   datafile.read((char*)&N, sizeof(uint32_t));
   datafile.read((char*)&dim, sizeof(uint32_t));
 
-  if(N > max_N) N = max_N;
+  if (N > max_N) N = max_N;
   printf("Read in file - N:%u, dim:%u\n", N, dim);
   std::vector<T> data;
-  data.resize((size_t)N*(size_t)dim);
-  datafile.read(reinterpret_cast<char*>(data.data()), (size_t)N*(size_t)dim*sizeof(T));
+  data.resize((size_t)N * (size_t)dim);
+  datafile.read(reinterpret_cast<char*>(data.data()), (size_t)N * (size_t)dim * sizeof(T));
   datafile.close();
 
   auto dataset = raft::make_device_matrix<T, idxT>(dev_resources, N, dim);
-  raft::copy(dataset.data_handle(), data.data(), data.size(), raft::resource::get_cuda_stream(dev_resources));
+  raft::copy(dataset.data_handle(),
+             data.data(),
+             data.size(),
+             raft::resource::get_cuda_stream(dev_resources));
 
   return dataset;
 }
diff --git a/examples/cpp/src/dynamic_batching_example.cu b/examples/cpp/src/dynamic_batching_example.cu
index 95f66a454a..f462ecb7e5 100644
--- a/examples/cpp/src/dynamic_batching_example.cu
+++ b/examples/cpp/src/dynamic_batching_example.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -35,29 +35,29 @@
 
 // A helper to split the dataset into chunks
 template <typename DeviceMatrixOrView>
-auto slice_matrix(const DeviceMatrixOrView &source,
+auto slice_matrix(const DeviceMatrixOrView& source,
                   typename DeviceMatrixOrView::index_type offset_rows,
-                  typename DeviceMatrixOrView::index_type count_rows) {
+                  typename DeviceMatrixOrView::index_type count_rows)
+{
   auto n_cols = source.extent(1);
-  return raft::make_device_matrix_view<
-      typename DeviceMatrixOrView::element_type,
-      typename DeviceMatrixOrView::index_type>(
-      const_cast<typename DeviceMatrixOrView::element_type *>(
-          source.data_handle()) +
-          offset_rows * n_cols,
-      count_rows, n_cols);
+  return raft::make_device_matrix_view<typename DeviceMatrixOrView::element_type,
+                                       typename DeviceMatrixOrView::index_type>(
+    const_cast<typename DeviceMatrixOrView::element_type*>(source.data_handle()) +
+      offset_rows * n_cols,
+    count_rows,
+    n_cols);
 }
 
 // A helper to measure the execution time of a function
 template <typename F, typename... Args>
-void time_it(std::string label, F f, Args &&...xs) {
+void time_it(std::string label, F f, Args&&... xs)
+{
   auto start = std::chrono::system_clock::now();
   f(std::forward<Args>(xs)...);
-  auto end = std::chrono::system_clock::now();
-  auto t = std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+  auto end  = std::chrono::system_clock::now();
+  auto t    = std::chrono::duration_cast<std::chrono::microseconds>(end - start);
   auto t_ms = double(t.count()) / 1000.0;
-  std::cout << "[" << label << "] execution time: " << t_ms << " ms"
-            << std::endl;
+  std::cout << "[" << label << "] execution time: " << t_ms << " ms" << std::endl;
 }
 
 /**
@@ -65,12 +65,11 @@ void time_it(std::string label, F f, Args &&...xs) {
  * This is similar to recording and waiting on CUDA events, but in C++11 API.
  */
 struct cuda_work_completion_promise {
-
-  cuda_work_completion_promise(const raft::resources &res) {
-    auto *promise = new std::promise<void>;
-    RAFT_CUDA_TRY(cudaLaunchHostFunc(raft::resource::get_cuda_stream(res),
-                                     completion_callback,
-                                     reinterpret_cast<void *>(promise)));
+  cuda_work_completion_promise(const raft::resources& res)
+  {
+    auto* promise = new std::promise<void>;
+    RAFT_CUDA_TRY(cudaLaunchHostFunc(
+      raft::resource::get_cuda_stream(res), completion_callback, reinterpret_cast<void*>(promise)));
     value_ = promise->get_future();
   }
 
@@ -79,22 +78,23 @@ struct cuda_work_completion_promise {
    * cudaEventSynchronize if an event was recorded at the time of creation of
    * this promise object.
    */
-  auto get_future() -> std::future<void> && { return std::move(value_); }
+  auto get_future() -> std::future<void>&& { return std::move(value_); }
 
-private:
+ private:
   std::future<void> value_;
 
-  static void completion_callback(void *ptr) {
-    auto *promise = reinterpret_cast<std::promise<void> *>(ptr);
+  static void completion_callback(void* ptr)
+  {
+    auto* promise = reinterpret_cast<std::promise<void>*>(ptr);
     promise->set_value();
     delete promise;
   }
 };
 
-void dynamic_batching_example(
-    raft::resources const &res,
-    raft::device_matrix_view<const float, int64_t> dataset,
-    raft::device_matrix_view<const float, int64_t> queries) {
+void dynamic_batching_example(raft::resources const& res,
+                              raft::device_matrix_view<const float, int64_t> dataset,
+                              raft::device_matrix_view<const float, int64_t> queries)
+{
   using namespace cuvs::neighbors;
 
   // Number of neighbors to search
@@ -114,10 +114,8 @@ void dynamic_batching_example(
   auto queries_b = slice_matrix(queries, n_queries_a, n_queries_b);
 
   // create output arrays
-  auto neighbors =
-      raft::make_device_matrix<uint32_t>(res, queries.extent(0), topk);
-  auto distances =
-      raft::make_device_matrix<float>(res, queries.extent(0), topk);
+  auto neighbors = raft::make_device_matrix<uint32_t>(res, queries.extent(0), topk);
+  auto distances = raft::make_device_matrix<float>(res, queries.extent(0), topk);
   // slice them same as queries
   auto neighbors_a = slice_matrix(neighbors, 0, n_queries_a);
   auto distances_a = slice_matrix(distances, 0, n_queries_a);
@@ -130,33 +128,32 @@ void dynamic_batching_example(
   std::cout << "Building CAGRA index (search graph)" << std::endl;
   auto orig_index = cagra::build(res, orig_index_params, dataset);
 
-  std::cout << "CAGRA index has " << orig_index.size() << " vectors"
+  std::cout << "CAGRA index has " << orig_index.size() << " vectors" << std::endl;
+  std::cout << "CAGRA graph has degree " << orig_index.graph_degree() << ", graph size ["
+            << orig_index.graph().extent(0) << ", " << orig_index.graph().extent(1) << "]"
             << std::endl;
-  std::cout << "CAGRA graph has degree " << orig_index.graph_degree()
-            << ", graph size [" << orig_index.graph().extent(0) << ", "
-            << orig_index.graph().extent(1) << "]" << std::endl;
 
   // use default search parameters
   cagra::search_params orig_search_params;
   // get a decent recall by increasing the internal topk list
   orig_search_params.itopk_size = 512;
-  orig_search_params.algo = cagra::search_algo::SINGLE_CTA;
+  orig_search_params.algo       = cagra::search_algo::SINGLE_CTA;
 
   // Set up dynamic batching parameters
   dynamic_batching::index_params dynb_index_params{
-      /* default-initializing the parent `neighbors::index_params`
-         (not used anyway) */
-      {},
-      /* Set the K in advance (the batcher needs to allocate buffers) */
-      topk,
-      /* Configure the number and the size of IO buffers */
-      64,
-      kNumWorkerStreams};
+    /* default-initializing the parent `neighbors::index_params`
+       (not used anyway) */
+    {},
+    /* Set the K in advance (the batcher needs to allocate buffers) */
+    topk,
+    /* Configure the number and the size of IO buffers */
+    64,
+    kNumWorkerStreams};
 
   // "build" the index (it's a low-cost index wrapping),
   //  that is we need to pass the original index and its search params here
   dynamic_batching::index<float, uint32_t> dynb_index(
-      res, dynb_index_params, orig_index, orig_search_params);
+    res, dynb_index_params, orig_index, orig_search_params);
 
   // You can implement job priorities by varying the deadlines of individual
   // requests
@@ -164,119 +161,104 @@ void dynamic_batching_example(
   dynb_search_params.dispatch_timeout_ms = 0.1;
 
   // Define the big-batch setting as a baseline for measuring the throughput.
-  auto search_batch_orig =
-      [&res, &orig_index, &orig_search_params](
-          raft::device_matrix_view<const float, int64_t> queries,
-          raft::device_matrix_view<uint32_t, int64_t> neighbors,
-          raft::device_matrix_view<float, int64_t> distances) {
-        cagra::search(res, orig_search_params, orig_index, queries, neighbors,
-                      distances);
-        raft::resource::sync_stream(res);
-      };
+  auto search_batch_orig = [&res, &orig_index, &orig_search_params](
+                             raft::device_matrix_view<const float, int64_t> queries,
+                             raft::device_matrix_view<uint32_t, int64_t> neighbors,
+                             raft::device_matrix_view<float, int64_t> distances) {
+    cagra::search(res, orig_search_params, orig_index, queries, neighbors, distances);
+    raft::resource::sync_stream(res);
+  };
 
   // Launch the baseline search: check the big-batch performance
-  time_it("standard/batch A", search_batch_orig, queries_a, neighbors_a,
-          distances_a);
-  time_it("standard/batch B", search_batch_orig, queries_b, neighbors_b,
-          distances_b);
+  time_it("standard/batch A", search_batch_orig, queries_a, neighbors_a, distances_a);
+  time_it("standard/batch B", search_batch_orig, queries_b, neighbors_b, distances_b);
 
   // Streaming scenario: prepare concurrent resources
   rmm::cuda_stream_pool worker_streams{kNumWorkerStreams};
   std::vector<raft::resources> resource_pool(0);
   for (int64_t i = 0; i < kNumWorkerStreams; i++) {
     resource_pool.push_back(res);
-    raft::resource::set_cuda_stream(resource_pool[i],
-                                    worker_streams.get_stream(i));
+    raft::resource::set_cuda_stream(resource_pool[i], worker_streams.get_stream(i));
   }
 
   // Streaming scenario:
   // send queries one-by-one, with a maximum kMaxJobs in-flight
-  auto search_async_orig =
-      [&resource_pool, &orig_index, &orig_search_params](
-          raft::device_matrix_view<const float, int64_t> queries,
-          raft::device_matrix_view<uint32_t, int64_t> neighbors,
-          raft::device_matrix_view<float, int64_t> distances) {
-        auto work_size = queries.extent(0);
-        std::array<std::future<void>, kMaxJobs> futures;
-        for (int64_t i = 0; i < work_size + kMaxJobs; i++) {
-          // wait for previous job in the same slot to finish
-          if (i >= kMaxJobs) {
-            futures[i % kMaxJobs].wait();
-          }
-          // submit a new job
-          if (i < work_size) {
-            auto &res = resource_pool[i % kNumWorkerStreams];
-            cagra::search(res, orig_search_params, orig_index,
-                          slice_matrix(queries, i, 1),
-                          slice_matrix(neighbors, i, 1),
-                          slice_matrix(distances, i, 1));
-            futures[i % kMaxJobs] =
-                cuda_work_completion_promise(res).get_future();
-          }
-        }
-      };
+  auto search_async_orig = [&resource_pool, &orig_index, &orig_search_params](
+                             raft::device_matrix_view<const float, int64_t> queries,
+                             raft::device_matrix_view<uint32_t, int64_t> neighbors,
+                             raft::device_matrix_view<float, int64_t> distances) {
+    auto work_size = queries.extent(0);
+    std::array<std::future<void>, kMaxJobs> futures;
+    for (int64_t i = 0; i < work_size + kMaxJobs; i++) {
+      // wait for previous job in the same slot to finish
+      if (i >= kMaxJobs) { futures[i % kMaxJobs].wait(); }
+      // submit a new job
+      if (i < work_size) {
+        auto& res = resource_pool[i % kNumWorkerStreams];
+        cagra::search(res,
+                      orig_search_params,
+                      orig_index,
+                      slice_matrix(queries, i, 1),
+                      slice_matrix(neighbors, i, 1),
+                      slice_matrix(distances, i, 1));
+        futures[i % kMaxJobs] = cuda_work_completion_promise(res).get_future();
+      }
+    }
+  };
 
   // Streaming scenario with dynamic batching:
   // send queries one-by-one, with a maximum kMaxJobs in-flight,
   // yet allow grouping the sequential requests (subject to deadlines)
-  auto search_async_dynb =
-      [&resource_pool, &dynb_index, &dynb_search_params](
-          raft::device_matrix_view<const float, int64_t> queries,
-          raft::device_matrix_view<uint32_t, int64_t> neighbors,
-          raft::device_matrix_view<float, int64_t> distances) {
-        auto work_size = queries.extent(0);
-        std::array<std::future<void>, kMaxJobs> futures;
-        for (int64_t i = 0; i < work_size + kMaxJobs; i++) {
-          // wait for previous job in the same slot to finish
-          if (i >= kMaxJobs) {
-            futures[i % kMaxJobs].wait();
-          }
-          // submit a new job
-          if (i < work_size) {
-            auto &res = resource_pool[i % kNumWorkerStreams];
-            dynamic_batching::search(res, dynb_search_params, dynb_index,
-                                     slice_matrix(queries, i, 1),
-                                     slice_matrix(neighbors, i, 1),
-                                     slice_matrix(distances, i, 1));
-            futures[i % kMaxJobs] =
-                cuda_work_completion_promise(res).get_future();
-          }
-        }
-      };
+  auto search_async_dynb = [&resource_pool, &dynb_index, &dynb_search_params](
+                             raft::device_matrix_view<const float, int64_t> queries,
+                             raft::device_matrix_view<uint32_t, int64_t> neighbors,
+                             raft::device_matrix_view<float, int64_t> distances) {
+    auto work_size = queries.extent(0);
+    std::array<std::future<void>, kMaxJobs> futures;
+    for (int64_t i = 0; i < work_size + kMaxJobs; i++) {
+      // wait for previous job in the same slot to finish
+      if (i >= kMaxJobs) { futures[i % kMaxJobs].wait(); }
+      // submit a new job
+      if (i < work_size) {
+        auto& res = resource_pool[i % kNumWorkerStreams];
+        dynamic_batching::search(res,
+                                 dynb_search_params,
+                                 dynb_index,
+                                 slice_matrix(queries, i, 1),
+                                 slice_matrix(neighbors, i, 1),
+                                 slice_matrix(distances, i, 1));
+        futures[i % kMaxJobs] = cuda_work_completion_promise(res).get_future();
+      }
+    }
+  };
 
   // Try to handle the same amount of work in the async setting using the
   // standard implementation.
-  time_it("standard/async A", search_async_orig, queries_a, neighbors_a,
-          distances_a);
-  time_it("standard/async B", search_async_orig, queries_b, neighbors_b,
-          distances_b);
+  time_it("standard/async A", search_async_orig, queries_a, neighbors_a, distances_a);
+  time_it("standard/async B", search_async_orig, queries_b, neighbors_b, distances_b);
 
   // Do the same using dynamic batching
-  time_it("dynamic_batching/async A", search_async_dynb, queries_a, neighbors_a,
-          distances_a);
-  time_it("dynamic_batching/async B", search_async_dynb, queries_b, neighbors_b,
-          distances_b);
+  time_it("dynamic_batching/async A", search_async_dynb, queries_a, neighbors_a, distances_a);
+  time_it("dynamic_batching/async B", search_async_dynb, queries_b, neighbors_b, distances_b);
 }
 
-int main() {
+int main()
+{
   raft::device_resources res;
 
   // Set the raft resource to use a pool for internal memory allocations
   // (workspace) and limit the available workspace size.
-  raft::resource::set_workspace_to_pool_resource(res,
-                                                 12ull * 1024 * 1024 * 1024ull);
+  raft::resource::set_workspace_to_pool_resource(res, 12ull * 1024 * 1024 * 1024ull);
 
   // Create input arrays.
   int64_t n_samples = 1000000;
-  int64_t n_dim = 128;
+  int64_t n_dim     = 128;
   int64_t n_queries = 10000;
-  auto dataset =
-      raft::make_device_matrix<float, int64_t>(res, n_samples, n_dim);
-  auto queries =
-      raft::make_device_matrix<float, int64_t>(res, n_queries, n_dim);
+  auto dataset      = raft::make_device_matrix<float, int64_t>(res, n_samples, n_dim);
+  auto queries      = raft::make_device_matrix<float, int64_t>(res, n_queries, n_dim);
   generate_dataset(res, dataset.view(), queries.view());
 
   // run the interesting part of the program
-  dynamic_batching_example(res, raft::make_const_mdspan(dataset.view()),
-                           raft::make_const_mdspan(queries.view()));
+  dynamic_batching_example(
+    res, raft::make_const_mdspan(dataset.view()), raft::make_const_mdspan(queries.view()));
 }
diff --git a/examples/cpp/src/ivf_flat_example.cu b/examples/cpp/src/ivf_flat_example.cu
index 0480fa1b9a..4744d3e42d 100644
--- a/examples/cpp/src/ivf_flat_example.cu
+++ b/examples/cpp/src/ivf_flat_example.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,10 +16,10 @@
 
 #include "common.cuh"
 
+#include <cuvs/neighbors/ivf_flat.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_resources.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
-#include <cuvs/neighbors/ivf_flat.hpp>
 #include <raft/util/cudart_utils.hpp>
 
 #include <rmm/mr/device/device_memory_resource.hpp>
@@ -64,7 +64,7 @@ void ivf_flat_build_search_simple(raft::device_resources const& dev_resources,
     dev_resources, search_params, index, queries, neighbors.view(), distances.view());
 
   // The call to ivf_flat::search is asynchronous. Before accessing the data, sync by calling
-   raft::resource::sync_stream(dev_resources);
+  raft::resource::sync_stream(dev_resources);
 
   print_results(dev_resources, neighbors.view(), distances.view());
 }
@@ -121,7 +121,7 @@ void ivf_flat_build_extend_search(raft::device_resources const& dev_resources,
     dev_resources, search_params, index, queries, neighbors.view(), distances.view());
 
   // The call to ivf_flat::search is asynchronous. Before accessing the data, sync using:
-   raft::resource::sync_stream(dev_resources);
+  raft::resource::sync_stream(dev_resources);
 
   print_results(dev_resources, neighbors.view(), distances.view());
 }
diff --git a/examples/cpp/src/ivf_pq_example.cu b/examples/cpp/src/ivf_pq_example.cu
index bef9787141..d19ceabc81 100644
--- a/examples/cpp/src/ivf_pq_example.cu
+++ b/examples/cpp/src/ivf_pq_example.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,10 +16,10 @@
 
 #include "common.cuh"
 
-#include <raft/core/device_mdarray.hpp>
-#include <raft/core/device_resources.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/refine.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
 
 #include <rmm/mr/device/device_memory_resource.hpp>
 #include <rmm/mr/device/pool_memory_resource.hpp>
@@ -60,8 +60,7 @@ void ivf_pq_build_search(raft::device_resources const& dev_resources,
   auto distances    = raft::make_device_matrix<float>(dev_resources, n_queries, topk);
 
   // Search K nearest neighbors for each of the queries.
-  ivf_pq::search(
-    dev_resources, search_params, index, queries, neighbors.view(), distances.view());
+  ivf_pq::search(dev_resources, search_params, index, queries, neighbors.view(), distances.view());
 
   // Re-ranking operation: refine the initial search results by computing exact distances
   int64_t topk_refined = 7;
diff --git a/examples/cpp/src/vamana_example.cu b/examples/cpp/src/vamana_example.cu
index a213b4c465..bb8085b27f 100644
--- a/examples/cpp/src/vamana_example.cu
+++ b/examples/cpp/src/vamana_example.cu
@@ -28,11 +28,15 @@
 #include "common.cuh"
 
 template <typename T>
-void vamana_build_and_write(raft::device_resources const &dev_resources,
+void vamana_build_and_write(raft::device_resources const& dev_resources,
                             raft::device_matrix_view<const T, int64_t> dataset,
-                            std::string out_fname, int degree, int visited_size,
-                            float max_fraction, int iters,
-                            std::string codebook_prefix) {
+                            std::string out_fname,
+                            int degree,
+                            int visited_size,
+                            float max_fraction,
+                            int iters,
+                            std::string codebook_prefix)
+{
   using namespace cuvs::neighbors;
 
   // use default index parameters
@@ -41,19 +45,18 @@ void vamana_build_and_write(raft::device_resources const &dev_resources,
   index_params.visited_size = visited_size;
   index_params.graph_degree = degree;
   index_params.vamana_iters = iters;
-  index_params.codebooks = vamana::deserialize_codebooks(codebook_prefix, dataset.extent(1));
+  index_params.codebooks    = vamana::deserialize_codebooks(codebook_prefix, dataset.extent(1));
 
   std::cout << "Building Vamana index (search graph)" << std::endl;
 
   auto start = std::chrono::system_clock::now();
   auto index = vamana::build(dev_resources, index_params, dataset);
-  auto end = std::chrono::system_clock::now();
+  auto end   = std::chrono::system_clock::now();
   std::chrono::duration<double> elapsed_seconds = end - start;
 
   std::cout << "Vamana index has " << index.size() << " vectors" << std::endl;
-  std::cout << "Vamana graph has degree " << index.graph_degree()
-            << ", graph size [" << index.graph().extent(0) << ", "
-            << index.graph().extent(1) << "]" << std::endl;
+  std::cout << "Vamana graph has degree " << index.graph_degree() << ", graph size ["
+            << index.graph().extent(0) << ", " << index.graph().extent(1) << "]" << std::endl;
 
   std::cout << "Time to build index: " << elapsed_seconds.count() << "s\n";
 
@@ -64,26 +67,32 @@ void vamana_build_and_write(raft::device_resources const &dev_resources,
   serialize(dev_resources, out_fname + ".sector_aligned", index, false, true);
 }
 
-void usage() {
-  printf("Usage: ./vamana_example <data filename> <output filename> <graph "
-         "degree> <visited_size> <max_fraction> <iterations> <(optional) "
-         "codebook prefix>\n");
+void usage()
+{
+  printf(
+    "Usage: ./vamana_example <data filename> <output filename> <graph "
+    "degree> <visited_size> <max_fraction> <iterations> <(optional) "
+    "codebook prefix>\n");
   printf("Input file expected to be binary file of fp32 vectors.\n");
   printf("Graph degree sizes supported: 32, 64, 128, 256\n");
   printf("Visited_size must be > degree and a power of 2.\n");
   printf("max_fraction > 0 and <= 1. Typical values are 0.06 or 0.1.\n");
   printf("Default iterations = 1, increase for better quality graph.\n");
-  printf("Optional path prefix to pq pivots and rotation matrix files. Expects pq pivots file at ${codebook_prefix}_pq_pivots.bin and rotation matrix file at ${codebook_prefix}_pq_pivots.bin_rotation_matrix.bin.\n");
+  printf(
+    "Optional path prefix to pq pivots and rotation matrix files. Expects pq pivots file at "
+    "${codebook_prefix}_pq_pivots.bin and rotation matrix file at "
+    "${codebook_prefix}_pq_pivots.bin_rotation_matrix.bin.\n");
   exit(1);
 }
 
-int main(int argc, char *argv[]) {
+int main(int argc, char* argv[])
+{
   raft::device_resources dev_resources;
 
   // Set pool memory resource with 1 GiB initial pool size. All allocations use
   // the same pool.
   rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
-      rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+    rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
   rmm::mr::set_current_device_resource(&pool_mr);
 
   // Alternatively, one could define a pool allocator for temporary arrays (used
@@ -93,25 +102,28 @@ int main(int argc, char *argv[]) {
   // limit. raft::resource::set_workspace_to_pool_resource(dev_resources, 2 *
   // 1024 * 1024 * 1024ull);
 
-  if (argc != 7 && argc != 8)
-    usage();
+  if (argc != 7 && argc != 8) usage();
 
-  std::string data_fname = (std::string)(argv[1]); // Input filename
-  std::string out_fname = (std::string)argv[2];    // Output index filename
-  int degree = atoi(argv[3]);
-  int max_visited = atoi(argv[4]);
-  float max_fraction = atof(argv[5]);
-  int iters = atoi(argv[6]);
+  std::string data_fname      = (std::string)(argv[1]);  // Input filename
+  std::string out_fname       = (std::string)argv[2];    // Output index filename
+  int degree                  = atoi(argv[3]);
+  int max_visited             = atoi(argv[4]);
+  float max_fraction          = atof(argv[5]);
+  int iters                   = atoi(argv[6]);
   std::string codebook_prefix = "";
   if (argc >= 8)
-    codebook_prefix = (std::string)argv[7]; // Path prefix to pq pivots and rotation matrix files
+    codebook_prefix = (std::string)argv[7];  // Path prefix to pq pivots and rotation matrix files
 
   // Read in binary dataset file
-  auto dataset =
-      read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
+  auto dataset = read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
 
   // Simple build example to create graph and write to a file
-  vamana_build_and_write<uint8_t>(
-      dev_resources, raft::make_const_mdspan(dataset.view()), out_fname, degree,
-      max_visited, max_fraction, iters, codebook_prefix);
+  vamana_build_and_write<uint8_t>(dev_resources,
+                                  raft::make_const_mdspan(dataset.view()),
+                                  out_fname,
+                                  degree,
+                                  max_visited,
+                                  max_fraction,
+                                  iters,
+                                  codebook_prefix);
 }

From 09193514a820256b0db8ca92c8a628b08f97ce00 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 24 Jul 2025 10:40:50 -0500
Subject: [PATCH 504/623] Allow latest OS in devcontainers (#1169)

This PR removes the OS suffix from devcontainers, allowing the upstream devcontainer images to determine the OS version.

Contributes to https://github.com/rapidsai/build-planning/issues/200.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/1169
---
 .devcontainer/cuda12.9-conda/devcontainer.json | 2 +-
 .devcontainer/cuda12.9-pip/devcontainer.json   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.devcontainer/cuda12.9-conda/devcontainer.json b/.devcontainer/cuda12.9-conda/devcontainer.json
index ef5383cd80..3c1da8fe73 100644
--- a/.devcontainer/cuda12.9-conda/devcontainer.json
+++ b/.devcontainer/cuda12.9-conda/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.10-cpp-mambaforge-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.10-cpp-mambaforge"
     }
   },
   "runArgs": [
diff --git a/.devcontainer/cuda12.9-pip/devcontainer.json b/.devcontainer/cuda12.9-pip/devcontainer.json
index 5902c948f3..0305705fb9 100644
--- a/.devcontainer/cuda12.9-pip/devcontainer.json
+++ b/.devcontainer/cuda12.9-pip/devcontainer.json
@@ -5,7 +5,7 @@
     "args": {
       "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda12.9-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda12.9-ucx1.18.0-openmpi5.0.7"
     }
   },
   "runArgs": [

From 1248cee9e0d3bea76990f58a85b707b3749db566 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Thu, 24 Jul 2025 20:40:33 -0400
Subject: [PATCH 505/623] Reducing binary size in `ivf_pq` by deduplicating
 kernels (#1130)

A few kernels (`unpack_contiguous_list_data_kernel`, `pack_contiguous_list_data_kernel`, and `process_and_fill_codes_kernel`) were being compiled multiple times.

This PR refactors the code so that we have pre-compiled code for launching these kernels.

- `.cuh`: with declarations for including in other files
- `*impl.cuh`: actual implementation
- `*.cu`: instantiation

From the build metrics report:
(latest commit in `branch-25.08`) 1218.81 MB -> 1209.90 MB

Related issue: https://github.com/rapidsai/cuvs/issues/1174

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1130
---
 cpp/CMakeLists.txt                            |   2 +
 .../detail/ivf_pq_contiguous_list_data.cu     |  48 +++
 .../detail/ivf_pq_process_and_fill_codes.cu   |  29 ++
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     | 382 +-----------------
 .../ivf_pq/ivf_pq_contiguous_list_data.cuh    |  79 ++++
 .../ivf_pq_contiguous_list_data_impl.cuh      | 184 +++++++++
 .../ivf_pq/ivf_pq_process_and_fill_codes.cuh  | 148 +++++++
 .../ivf_pq_process_and_fill_codes_impl.cuh    | 108 +++++
 8 files changed, 602 insertions(+), 378 deletions(-)
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_contiguous_list_data.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_process_and_fill_codes.cu
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data_impl.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes.cuh
 create mode 100644 cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes_impl.cuh

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 08074d57a4..bff0668532 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -458,6 +458,8 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
     src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
     src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_contiguous_list_data.cu
+    src/neighbors/ivf_pq/detail/ivf_pq_process_and_fill_codes.cu
     src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
     src/neighbors/ivf_pq/detail/ivf_pq_search_half_int64_t.cu
     src/neighbors/ivf_pq/detail/ivf_pq_search_int8_t_int64_t.cu
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_contiguous_list_data.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_contiguous_list_data.cu
new file mode 100644
index 0000000000..fbfc85e965
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_contiguous_list_data.cu
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ivf_pq_contiguous_list_data_impl.cuh"
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+void unpack_contiguous_list_data(
+  uint8_t* codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  unpack_contiguous_list_data_impl(
+    codes, list_data, n_rows, pq_dim, offset_or_indices, pq_bits, stream);
+};
+
+void pack_contiguous_list_data(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  pack_contiguous_list_data_impl(
+    list_data, codes, n_rows, pq_dim, offset_or_indices, pq_bits, stream);
+};
+};  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_process_and_fill_codes.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_process_and_fill_codes.cu
new file mode 100644
index 0000000000..b26bb9921d
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_process_and_fill_codes.cu
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ivf_pq_process_and_fill_codes_impl.cuh"
+#include <cuvs/neighbors/ivf_pq.hpp>
+
+#define instantiate_cuvs_neighbors_ivf_pq_detail_process_and_fill_codes(IdxT)                \
+  template void cuvs::neighbors::ivf_pq::detail::launch_process_and_fill_codes_kernel<IdxT>( \
+    raft::resources const& handle,                                                           \
+    cuvs::neighbors::ivf_pq::index<IdxT>& index,                                             \
+    raft::device_matrix_view<float> new_vectors_residual,                                    \
+    std::variant<IdxT, const IdxT*> src_offset_or_indices,                                   \
+    const uint32_t* new_labels,                                                              \
+    IdxT n_rows);
+
+instantiate_cuvs_neighbors_ivf_pq_detail_process_and_fill_codes(int64_t);
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index 403eaf81a7..a2556dc651 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -20,6 +20,8 @@
 #include "../ivf_common.cuh"
 #include "../ivf_list.cuh"
 #include "ivf_pq_codepacking.cuh"
+#include "ivf_pq_contiguous_list_data.cuh"
+#include "ivf_pq_process_and_fill_codes.cuh"
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
@@ -482,32 +484,6 @@ void train_per_cluster(raft::resources const& handle,
   transpose_pq_centers(handle, index, pq_centers_tmp.data());
 }
 
-/**
- * A helper function: given the dataset in the rotated space
- *  [n_rows, rot_dim] = [n_rows, pq_dim * pq_len],
- * reinterpret the last dimension as two: [n_rows, pq_dim, pq_len]
- *
- * @tparam T
- * @tparam IdxT
- *
- * @param vectors input data [n_rows, rot_dim]
- * @param pq_centers codebook (used to infer the structure - pq_len)
- * @return reinterpreted vectors [n_rows, pq_dim, pq_len]
- */
-template <typename T, typename IdxT>
-static __device__ auto reinterpret_vectors(
-  raft::device_matrix_view<T, IdxT, raft::row_major> vectors,
-  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers)
-  -> raft::device_mdspan<T, raft::extent_3d<IdxT>, raft::row_major>
-{
-  const uint32_t pq_len = pq_centers.extent(1);
-  const uint32_t pq_dim = vectors.extent(1) / pq_len;
-  using layout_t        = typename decltype(vectors)::layout_type;
-  using accessor_t      = typename decltype(vectors)::accessor_type;
-  return raft::mdspan<T, raft::extent_3d<IdxT>, layout_t, accessor_t>(
-    vectors.data_handle(), raft::extent_3d<IdxT>{vectors.extent(0), pq_dim, pq_len});
-}
-
 /**
  * A consumer for the `run_on_list` and `run_on_vector` that just flattens PQ codes
  * one-per-byte. That is, independent of the code width (pq_bits), one code uses
@@ -598,102 +574,6 @@ void unpack_list_data(raft::resources const& res,
                    raft::resource::get_cuda_stream(res));
 }
 
-/**
- * A consumer for the `run_on_vector` that just flattens PQ codes
- * into a tightly packed matrix. That is, the codes are not expanded to one code-per-byte.
- */
-template <uint32_t PqBits>
-struct unpack_contiguous {
-  uint8_t* codes;
-  uint32_t code_size;
-
-  /**
-   * Create a callable to be passed to `run_on_vector`.
-   *
-   * @param[in] codes flat compressed PQ codes
-   */
-  __host__ __device__ inline unpack_contiguous(uint8_t* codes, uint32_t pq_dim)
-    : codes{codes}, code_size{raft::ceildiv<uint32_t>(pq_dim * PqBits, 8)}
-  {
-  }
-
-  /**  Write j-th component (code) of the i-th vector into the output array. */
-  __host__ __device__ inline void operator()(uint8_t code, uint32_t i, uint32_t j)
-  {
-    bitfield_view_t<PqBits> code_view{codes + i * code_size};
-    code_view[j] = code;
-  }
-};
-
-template <uint32_t BlockSize, uint32_t PqBits>
-__launch_bounds__(BlockSize) static __global__ void unpack_contiguous_list_data_kernel(
-  uint8_t* out_codes,
-  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
-    in_list_data,
-  uint32_t n_rows,
-  uint32_t pq_dim,
-  std::variant<uint32_t, const uint32_t*> offset_or_indices)
-{
-  run_on_list<PqBits>(
-    in_list_data, offset_or_indices, n_rows, pq_dim, unpack_contiguous<PqBits>(out_codes, pq_dim));
-}
-
-/**
- * Unpack flat PQ codes from an existing list by the given offset.
- *
- * @param[out] codes flat compressed PQ codes [n_rows, ceildiv(pq_dim * pq_bits, 8)]
- * @param[in] list_data the packed ivf::list data.
- * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
- * @param[in] pq_bits codebook size (1 << pq_bits)
- * @param[in] stream
- */
-inline void unpack_contiguous_list_data(
-  uint8_t* codes,
-  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
-    list_data,
-  uint32_t n_rows,
-  uint32_t pq_dim,
-  std::variant<uint32_t, const uint32_t*> offset_or_indices,
-  uint32_t pq_bits,
-  rmm::cuda_stream_view stream)
-{
-  if (n_rows == 0) { return; }
-
-  constexpr uint32_t kBlockSize = 256;
-  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
-  dim3 threads(kBlockSize, 1, 1);
-  auto kernel = [pq_bits]() {
-    switch (pq_bits) {
-      case 4: return unpack_contiguous_list_data_kernel<kBlockSize, 4>;
-      case 5: return unpack_contiguous_list_data_kernel<kBlockSize, 5>;
-      case 6: return unpack_contiguous_list_data_kernel<kBlockSize, 6>;
-      case 7: return unpack_contiguous_list_data_kernel<kBlockSize, 7>;
-      case 8: return unpack_contiguous_list_data_kernel<kBlockSize, 8>;
-      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
-    }
-  }();
-  kernel<<<blocks, threads, 0, stream>>>(codes, list_data, n_rows, pq_dim, offset_or_indices);
-  RAFT_CUDA_TRY(cudaPeekAtLastError());
-}
-
-/** Unpack the list data; see the public interface for the api and usage. */
-template <typename IdxT>
-void unpack_contiguous_list_data(raft::resources const& res,
-                                 const index<IdxT>& index,
-                                 uint8_t* out_codes,
-                                 uint32_t n_rows,
-                                 uint32_t label,
-                                 std::variant<uint32_t, const uint32_t*> offset_or_indices)
-{
-  unpack_contiguous_list_data(out_codes,
-                              index.lists()[label]->data.view(),
-                              n_rows,
-                              index.pq_dim(),
-                              offset_or_indices,
-                              index.pq_bits(),
-                              raft::resource::get_cuda_stream(res));
-}
-
 /** A consumer for the `run_on_list` and `run_on_vector` that approximates the original input data.
  */
 struct reconstruct_vectors {
@@ -939,238 +819,6 @@ void pack_list_data(raft::resources const& res,
                  raft::resource::get_cuda_stream(res));
 }
 
-/**
- * A producer for the `write_vector` reads tightly packed flat codes. That is,
- * the codes are not expanded to one code-per-byte.
- */
-template <uint32_t PqBits>
-struct pack_contiguous {
-  const uint8_t* codes;
-  uint32_t code_size;
-
-  /**
-   * Create a callable to be passed to `write_vector`.
-   *
-   * @param[in] codes flat compressed PQ codes
-   */
-  __host__ __device__ inline pack_contiguous(const uint8_t* codes, uint32_t pq_dim)
-    : codes{codes}, code_size{raft::ceildiv<uint32_t>(pq_dim * PqBits, 8)}
-  {
-  }
-
-  /** Read j-th component (code) of the i-th vector from the source. */
-  __host__ __device__ inline auto operator()(uint32_t i, uint32_t j) -> uint8_t
-  {
-    bitfield_view_t<PqBits> code_view{const_cast<uint8_t*>(codes + i * code_size)};
-    return uint8_t(code_view[j]);
-  }
-};
-
-template <uint32_t BlockSize, uint32_t PqBits>
-__launch_bounds__(BlockSize) static __global__ void pack_contiguous_list_data_kernel(
-  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
-    list_data,
-  const uint8_t* codes,
-  uint32_t n_rows,
-  uint32_t pq_dim,
-  std::variant<uint32_t, const uint32_t*> offset_or_indices)
-{
-  write_list<PqBits, 1>(
-    list_data, offset_or_indices, n_rows, pq_dim, pack_contiguous<PqBits>(codes, pq_dim));
-}
-
-/**
- * Write flat PQ codes into an existing list by the given offset.
- *
- * NB: no memory allocation happens here; the list must fit the data (offset + n_rows).
- *
- * @param[out] list_data the packed ivf::list data.
- * @param[in] codes flat compressed PQ codes [n_rows, ceildiv(pq_dim * pq_bits, 8)]
- * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
- * @param[in] pq_bits codebook size (1 << pq_bits)
- * @param[in] stream
- */
-inline void pack_contiguous_list_data(
-  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
-    list_data,
-  const uint8_t* codes,
-  uint32_t n_rows,
-  uint32_t pq_dim,
-  std::variant<uint32_t, const uint32_t*> offset_or_indices,
-  uint32_t pq_bits,
-  rmm::cuda_stream_view stream)
-{
-  if (n_rows == 0) { return; }
-
-  constexpr uint32_t kBlockSize = 256;
-  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
-  dim3 threads(kBlockSize, 1, 1);
-  auto kernel = [pq_bits]() {
-    switch (pq_bits) {
-      case 4: return pack_contiguous_list_data_kernel<kBlockSize, 4>;
-      case 5: return pack_contiguous_list_data_kernel<kBlockSize, 5>;
-      case 6: return pack_contiguous_list_data_kernel<kBlockSize, 6>;
-      case 7: return pack_contiguous_list_data_kernel<kBlockSize, 7>;
-      case 8: return pack_contiguous_list_data_kernel<kBlockSize, 8>;
-      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
-    }
-  }();
-  kernel<<<blocks, threads, 0, stream>>>(list_data, codes, n_rows, pq_dim, offset_or_indices);
-  RAFT_CUDA_TRY(cudaPeekAtLastError());
-}
-
-template <typename IdxT>
-void pack_contiguous_list_data(raft::resources const& res,
-                               index<IdxT>* index,
-                               const uint8_t* new_codes,
-                               uint32_t n_rows,
-                               uint32_t label,
-                               std::variant<uint32_t, const uint32_t*> offset_or_indices)
-{
-  pack_contiguous_list_data(index->lists()[label]->data.view(),
-                            new_codes,
-                            n_rows,
-                            index->pq_dim(),
-                            offset_or_indices,
-                            index->pq_bits(),
-                            raft::resource::get_cuda_stream(res));
-}
-
-/**
- *
- * A producer for the `write_list` and `write_vector` that encodes level-1 input vector residuals
- * into lvl-2 PQ codes.
- * Computing a PQ code means finding the closest cluster in a pq_dim-subspace.
- *
- * @tparam SubWarpSize
- *   how many threads work on a single vector;
- *   bounded by either WarpSize or pq_book_size.
- *
- * @param pq_centers
- *   - codebook_gen::PER_SUBSPACE: [pq_dim , pq_len, pq_book_size]
- *   - codebook_gen::PER_CLUSTER:  [n_lists, pq_len, pq_book_size]
- * @param new_vector a single input of length rot_dim, reinterpreted as [pq_dim, pq_len].
- *   the input must be already transformed to floats, rotated, and the level 1 cluster
- *   center must be already substructed (i.e. this is the residual of a single input vector).
- * @param codebook_kind
- * @param j index along pq_dim "dimension"
- * @param cluster_ix is used for PER_CLUSTER codebooks.
- */
-/**
- */
-template <uint32_t SubWarpSize, typename IdxT>
-struct encode_vectors {
-  codebook_gen codebook_kind;
-  uint32_t cluster_ix;
-  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers;
-  raft::device_mdspan<const float, raft::extent_3d<IdxT>, raft::row_major> in_vectors;
-
-  __device__ inline encode_vectors(
-    raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
-    raft::device_matrix_view<const float, IdxT, raft::row_major> in_vectors,
-    codebook_gen codebook_kind,
-    uint32_t cluster_ix)
-    : codebook_kind{codebook_kind},
-      cluster_ix{cluster_ix},
-      pq_centers{pq_centers},
-      in_vectors{reinterpret_vectors(in_vectors, pq_centers)}
-  {
-  }
-
-  /**
-   * Decode j-th component of the i-th vector by its code and write it into a chunk of the output
-   * vectors (pq_len elements).
-   */
-  __device__ inline auto operator()(IdxT i, uint32_t j) -> uint8_t
-  {
-    uint32_t lane_id = raft::Pow2<SubWarpSize>::mod(raft::laneId());
-    uint32_t partition_ix;
-    switch (codebook_kind) {
-      case codebook_gen::PER_CLUSTER: {
-        partition_ix = cluster_ix;
-      } break;
-      case codebook_gen::PER_SUBSPACE: {
-        partition_ix = j;
-      } break;
-      default: __builtin_unreachable();
-    }
-
-    const uint32_t pq_book_size = pq_centers.extent(2);
-    const uint32_t pq_len       = pq_centers.extent(1);
-    float min_dist              = std::numeric_limits<float>::infinity();
-    uint8_t code                = 0;
-    // calculate the distance for each PQ cluster, find the minimum for each thread
-    for (uint32_t l = lane_id; l < pq_book_size; l += SubWarpSize) {
-      // NB: the L2 quantifiers on residuals are always trained on L2 metric.
-      float d = 0.0f;
-      for (uint32_t k = 0; k < pq_len; k++) {
-        auto t = in_vectors(i, j, k) - pq_centers(partition_ix, k, l);
-        d += t * t;
-      }
-      if (d < min_dist) {
-        min_dist = d;
-        code     = uint8_t(l);
-      }
-    }
-    // reduce among threads
-#pragma unroll
-    for (uint32_t stride = SubWarpSize >> 1; stride > 0; stride >>= 1) {
-      const auto other_dist = raft::shfl_xor(min_dist, stride, SubWarpSize);
-      const auto other_code = raft::shfl_xor(code, stride, SubWarpSize);
-      if (other_dist < min_dist) {
-        min_dist = other_dist;
-        code     = other_code;
-      }
-    }
-    return code;
-  }
-};
-
-template <uint32_t BlockSize, uint32_t PqBits, typename IdxT>
-__launch_bounds__(BlockSize) static __global__ void process_and_fill_codes_kernel(
-  raft::device_matrix_view<const float, IdxT, raft::row_major> new_vectors,
-  std::variant<IdxT, const IdxT*> src_offset_or_indices,
-  const uint32_t* new_labels,
-  raft::device_vector_view<uint32_t, uint32_t, raft::row_major> list_sizes,
-  raft::device_vector_view<IdxT*, uint32_t, raft::row_major> inds_ptrs,
-  raft::device_vector_view<uint8_t*, uint32_t, raft::row_major> data_ptrs,
-  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
-  codebook_gen codebook_kind)
-{
-  constexpr uint32_t kSubWarpSize = std::min<uint32_t>(raft::WarpSize, 1u << PqBits);
-  using subwarp_align             = raft::Pow2<kSubWarpSize>;
-  const uint32_t lane_id          = subwarp_align::mod(threadIdx.x);
-  const IdxT row_ix = subwarp_align::div(IdxT{threadIdx.x} + IdxT{BlockSize} * IdxT{blockIdx.x});
-  if (row_ix >= new_vectors.extent(0)) { return; }
-
-  const uint32_t cluster_ix = new_labels[row_ix];
-  uint32_t out_ix;
-  if (lane_id == 0) { out_ix = atomicAdd(&list_sizes(cluster_ix), 1); }
-  out_ix = raft::shfl(out_ix, 0, kSubWarpSize);
-
-  // write the label  (one record per subwarp)
-  auto pq_indices = inds_ptrs(cluster_ix);
-  if (lane_id == 0) {
-    if (std::holds_alternative<IdxT>(src_offset_or_indices)) {
-      pq_indices[out_ix] = std::get<IdxT>(src_offset_or_indices) + row_ix;
-    } else {
-      pq_indices[out_ix] = std::get<const IdxT*>(src_offset_or_indices)[row_ix];
-    }
-  }
-
-  // write the codes (one record per subwarp):
-  const uint32_t pq_dim = new_vectors.extent(1) / pq_centers.extent(1);
-  auto pq_extents = list_spec<uint32_t, IdxT>{PqBits, pq_dim, true}.make_list_extents(out_ix + 1);
-  auto pq_dataset = raft::make_mdspan<uint8_t, uint32_t, raft::row_major, false, true>(
-    data_ptrs[cluster_ix], pq_extents);
-  write_vector<PqBits, kSubWarpSize>(
-    pq_dataset,
-    out_ix,
-    row_ix,
-    pq_dim,
-    encode_vectors<kSubWarpSize, IdxT>{pq_centers, new_vectors, codebook_kind, cluster_ix});
-}
-
 template <uint32_t BlockSize, uint32_t PqBits>
 __launch_bounds__(BlockSize) static __global__ void encode_list_data_kernel(
   raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
@@ -1285,30 +933,8 @@ void process_and_fill_codes(raft::resources const& handle,
                                   new_labels,
                                   mr);
 
-  constexpr uint32_t kBlockSize  = 256;
-  const uint32_t threads_per_vec = std::min<uint32_t>(raft::WarpSize, index.pq_book_size());
-  dim3 blocks(raft::div_rounding_up_safe<IdxT>(n_rows, kBlockSize / threads_per_vec), 1, 1);
-  dim3 threads(kBlockSize, 1, 1);
-  auto kernel = [](uint32_t pq_bits) {
-    switch (pq_bits) {
-      case 4: return process_and_fill_codes_kernel<kBlockSize, 4, IdxT>;
-      case 5: return process_and_fill_codes_kernel<kBlockSize, 5, IdxT>;
-      case 6: return process_and_fill_codes_kernel<kBlockSize, 6, IdxT>;
-      case 7: return process_and_fill_codes_kernel<kBlockSize, 7, IdxT>;
-      case 8: return process_and_fill_codes_kernel<kBlockSize, 8, IdxT>;
-      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
-    }
-  }(index.pq_bits());
-  kernel<<<blocks, threads, 0, raft::resource::get_cuda_stream(handle)>>>(
-    new_vectors_residual.view(),
-    src_offset_or_indices,
-    new_labels,
-    index.list_sizes(),
-    index.inds_ptrs(),
-    index.data_ptrs(),
-    index.pq_centers(),
-    index.codebook_kind());
-  RAFT_CUDA_TRY(cudaPeekAtLastError());
+  launch_process_and_fill_codes_kernel(
+    handle, index, new_vectors_residual.view(), src_offset_or_indices, new_labels, n_rows);
 }
 
 /**
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data.cuh
new file mode 100644
index 0000000000..5cccf097e5
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data.cuh
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <variant>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+void unpack_contiguous_list_data(
+  uint8_t* codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream);
+
+template <typename IdxT>
+void unpack_contiguous_list_data(raft::resources const& res,
+                                 const index<IdxT>& index,
+                                 uint8_t* out_codes,
+                                 uint32_t n_rows,
+                                 uint32_t label,
+                                 std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  unpack_contiguous_list_data(out_codes,
+                              index.lists()[label]->data.view(),
+                              n_rows,
+                              index.pq_dim(),
+                              offset_or_indices,
+                              index.pq_bits(),
+                              raft::resource::get_cuda_stream(res));
+};
+
+void pack_contiguous_list_data(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream);
+
+template <typename IdxT>
+void pack_contiguous_list_data(raft::resources const& res,
+                               index<IdxT>* index,
+                               const uint8_t* new_codes,
+                               uint32_t n_rows,
+                               uint32_t label,
+                               std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  pack_contiguous_list_data(index->lists()[label]->data.view(),
+                            new_codes,
+                            n_rows,
+                            index->pq_dim(),
+                            offset_or_indices,
+                            index->pq_bits(),
+                            raft::resource::get_cuda_stream(res));
+};
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data_impl.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data_impl.cuh
new file mode 100644
index 0000000000..227cdb3558
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_contiguous_list_data_impl.cuh
@@ -0,0 +1,184 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "ivf_pq_codepacking.cuh"
+#include <cstdint>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/util/cuda_dev_essentials.cuh>
+#include <raft/util/pow2_utils.cuh>
+#include <variant>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+/**
+ * A consumer for the `run_on_vector` that just flattens PQ codes
+ * into a tightly packed matrix. That is, the codes are not expanded to one code-per-byte.
+ */
+template <uint32_t PqBits>
+struct unpack_contiguous {
+  uint8_t* codes;
+  uint32_t code_size;
+
+  /**
+   * Create a callable to be passed to `run_on_vector`.
+   *
+   * @param[in] codes flat compressed PQ codes
+   */
+  __host__ __device__ inline unpack_contiguous(uint8_t* codes, uint32_t pq_dim)
+    : codes{codes}, code_size{raft::ceildiv<uint32_t>(pq_dim * PqBits, 8)}
+  {
+  }
+
+  /**  Write j-th component (code) of the i-th vector into the output array. */
+  __host__ __device__ inline void operator()(uint8_t code, uint32_t i, uint32_t j)
+  {
+    bitfield_view_t<PqBits> code_view{codes + i * code_size};
+    code_view[j] = code;
+  }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void unpack_contiguous_list_data_kernel(
+  uint8_t* out_codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    in_list_data,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  run_on_list<PqBits>(
+    in_list_data, offset_or_indices, n_rows, pq_dim, unpack_contiguous<PqBits>(out_codes, pq_dim));
+}
+
+/**
+ * Unpack flat PQ codes from an existing list by the given offset.
+ *
+ * @param[out] codes flat compressed PQ codes [n_rows, ceildiv(pq_dim * pq_bits, 8)]
+ * @param[in] list_data the packed ivf::list data.
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ * @param[in] pq_bits codebook size (1 << pq_bits)
+ * @param[in] stream
+ */
+inline void unpack_contiguous_list_data_impl(
+  uint8_t* codes,
+  raft::device_mdspan<const uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  if (n_rows == 0) { return; }
+
+  constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [pq_bits]() {
+    switch (pq_bits) {
+      case 4: return unpack_contiguous_list_data_kernel<kBlockSize, 4>;
+      case 5: return unpack_contiguous_list_data_kernel<kBlockSize, 5>;
+      case 6: return unpack_contiguous_list_data_kernel<kBlockSize, 6>;
+      case 7: return unpack_contiguous_list_data_kernel<kBlockSize, 7>;
+      case 8: return unpack_contiguous_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }();
+  kernel<<<blocks, threads, 0, stream>>>(codes, list_data, n_rows, pq_dim, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+
+/**
+ * A producer for the `write_vector` reads tightly packed flat codes. That is,
+ * the codes are not expanded to one code-per-byte.
+ */
+template <uint32_t PqBits>
+struct pack_contiguous {
+  const uint8_t* codes;
+  uint32_t code_size;
+
+  /**
+   * Create a callable to be passed to `write_vector`.
+   *
+   * @param[in] codes flat compressed PQ codes
+   */
+  __host__ __device__ inline pack_contiguous(const uint8_t* codes, uint32_t pq_dim)
+    : codes{codes}, code_size{raft::ceildiv<uint32_t>(pq_dim * PqBits, 8)}
+  {
+  }
+
+  /** Read j-th component (code) of the i-th vector from the source. */
+  __host__ __device__ inline auto operator()(uint32_t i, uint32_t j) -> uint8_t
+  {
+    bitfield_view_t<PqBits> code_view{const_cast<uint8_t*>(codes + i * code_size)};
+    return uint8_t(code_view[j]);
+  }
+};
+
+template <uint32_t BlockSize, uint32_t PqBits>
+__launch_bounds__(BlockSize) static __global__ void pack_contiguous_list_data_kernel(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices)
+{
+  write_list<PqBits, 1>(
+    list_data, offset_or_indices, n_rows, pq_dim, pack_contiguous<PqBits>(codes, pq_dim));
+}
+
+/**
+ * Write flat PQ codes into an existing list by the given offset.
+ *
+ * NB: no memory allocation happens here; the list must fit the data (offset + n_rows).
+ *
+ * @param[out] list_data the packed ivf::list data.
+ * @param[in] codes flat compressed PQ codes [n_rows, ceildiv(pq_dim * pq_bits, 8)]
+ * @param[in] offset_or_indices how many records in the list to skip or the exact indices.
+ * @param[in] pq_bits codebook size (1 << pq_bits)
+ * @param[in] stream
+ */
+inline void pack_contiguous_list_data_impl(
+  raft::device_mdspan<uint8_t, list_spec<uint32_t, uint32_t>::list_extents, raft::row_major>
+    list_data,
+  const uint8_t* codes,
+  uint32_t n_rows,
+  uint32_t pq_dim,
+  std::variant<uint32_t, const uint32_t*> offset_or_indices,
+  uint32_t pq_bits,
+  rmm::cuda_stream_view stream)
+{
+  if (n_rows == 0) { return; }
+
+  constexpr uint32_t kBlockSize = 256;
+  dim3 blocks(raft::div_rounding_up_safe<uint32_t>(n_rows, kBlockSize), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [pq_bits]() {
+    switch (pq_bits) {
+      case 4: return pack_contiguous_list_data_kernel<kBlockSize, 4>;
+      case 5: return pack_contiguous_list_data_kernel<kBlockSize, 5>;
+      case 6: return pack_contiguous_list_data_kernel<kBlockSize, 6>;
+      case 7: return pack_contiguous_list_data_kernel<kBlockSize, 7>;
+      case 8: return pack_contiguous_list_data_kernel<kBlockSize, 8>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }();
+  kernel<<<blocks, threads, 0, stream>>>(list_data, codes, n_rows, pq_dim, offset_or_indices);
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes.cuh
new file mode 100644
index 0000000000..e534b72a1a
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes.cuh
@@ -0,0 +1,148 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <variant>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+/**
+ * A helper function: given the dataset in the rotated space
+ *  [n_rows, rot_dim] = [n_rows, pq_dim * pq_len],
+ * reinterpret the last dimension as two: [n_rows, pq_dim, pq_len]
+ *
+ * @tparam T
+ * @tparam IdxT
+ *
+ * @param vectors input data [n_rows, rot_dim]
+ * @param pq_centers codebook (used to infer the structure - pq_len)
+ * @return reinterpreted vectors [n_rows, pq_dim, pq_len]
+ */
+template <typename T, typename IdxT>
+static __device__ auto reinterpret_vectors(
+  raft::device_matrix_view<T, IdxT, raft::row_major> vectors,
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers)
+  -> raft::device_mdspan<T, raft::extent_3d<IdxT>, raft::row_major>
+{
+  const uint32_t pq_len = pq_centers.extent(1);
+  const uint32_t pq_dim = vectors.extent(1) / pq_len;
+  using layout_t        = typename decltype(vectors)::layout_type;
+  using accessor_t      = typename decltype(vectors)::accessor_type;
+  return raft::mdspan<T, raft::extent_3d<IdxT>, layout_t, accessor_t>(
+    vectors.data_handle(), raft::extent_3d<IdxT>{vectors.extent(0), pq_dim, pq_len});
+}
+
+/**
+ *
+ * A producer for the `write_list` and `write_vector` that encodes level-1 input vector residuals
+ * into lvl-2 PQ codes.
+ * Computing a PQ code means finding the closest cluster in a pq_dim-subspace.
+ *
+ * @tparam SubWarpSize
+ *   how many threads work on a single vector;
+ *   bounded by either WarpSize or pq_book_size.
+ *
+ * @param pq_centers
+ *   - codebook_gen::PER_SUBSPACE: [pq_dim , pq_len, pq_book_size]
+ *   - codebook_gen::PER_CLUSTER:  [n_lists, pq_len, pq_book_size]
+ * @param new_vector a single input of length rot_dim, reinterpreted as [pq_dim, pq_len].
+ *   the input must be already transformed to floats, rotated, and the level 1 cluster
+ *   center must be already substructed (i.e. this is the residual of a single input vector).
+ * @param codebook_kind
+ * @param j index along pq_dim "dimension"
+ * @param cluster_ix is used for PER_CLUSTER codebooks.
+ */
+/**
+ */
+template <uint32_t SubWarpSize, typename IdxT>
+struct encode_vectors {
+  codebook_gen codebook_kind;
+  uint32_t cluster_ix;
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers;
+  raft::device_mdspan<const float, raft::extent_3d<IdxT>, raft::row_major> in_vectors;
+
+  __device__ inline encode_vectors(
+    raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
+    raft::device_matrix_view<const float, IdxT, raft::row_major> in_vectors,
+    codebook_gen codebook_kind,
+    uint32_t cluster_ix)
+    : codebook_kind{codebook_kind},
+      cluster_ix{cluster_ix},
+      pq_centers{pq_centers},
+      in_vectors{reinterpret_vectors(in_vectors, pq_centers)}
+  {
+  }
+
+  /**
+   * Decode j-th component of the i-th vector by its code and write it into a chunk of the output
+   * vectors (pq_len elements).
+   */
+  __device__ inline auto operator()(IdxT i, uint32_t j) -> uint8_t
+  {
+    uint32_t lane_id = raft::Pow2<SubWarpSize>::mod(raft::laneId());
+    uint32_t partition_ix;
+    switch (codebook_kind) {
+      case codebook_gen::PER_CLUSTER: {
+        partition_ix = cluster_ix;
+      } break;
+      case codebook_gen::PER_SUBSPACE: {
+        partition_ix = j;
+      } break;
+      default: __builtin_unreachable();
+    }
+
+    const uint32_t pq_book_size = pq_centers.extent(2);
+    const uint32_t pq_len       = pq_centers.extent(1);
+    float min_dist              = std::numeric_limits<float>::infinity();
+    uint8_t code                = 0;
+    // calculate the distance for each PQ cluster, find the minimum for each thread
+    for (uint32_t l = lane_id; l < pq_book_size; l += SubWarpSize) {
+      // NB: the L2 quantifiers on residuals are always trained on L2 metric.
+      float d = 0.0f;
+      for (uint32_t k = 0; k < pq_len; k++) {
+        auto t = in_vectors(i, j, k) - pq_centers(partition_ix, k, l);
+        d += t * t;
+      }
+      if (d < min_dist) {
+        min_dist = d;
+        code     = uint8_t(l);
+      }
+    }
+    // reduce among threads
+#pragma unroll
+    for (uint32_t stride = SubWarpSize >> 1; stride > 0; stride >>= 1) {
+      const auto other_dist = raft::shfl_xor(min_dist, stride, SubWarpSize);
+      const auto other_code = raft::shfl_xor(code, stride, SubWarpSize);
+      if (other_dist < min_dist) {
+        min_dist = other_dist;
+        code     = other_code;
+      }
+    }
+    return code;
+  }
+};
+
+template <typename IdxT>
+void launch_process_and_fill_codes_kernel(raft::resources const& handle,
+                                          index<IdxT>& index,
+                                          raft::device_matrix_view<float> new_vectors_residual,
+                                          std::variant<IdxT, const IdxT*> src_offset_or_indices,
+                                          const uint32_t* new_labels,
+                                          IdxT n_rows);
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes_impl.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes_impl.cuh
new file mode 100644
index 0000000000..634c6c9887
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_process_and_fill_codes_impl.cuh
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "ivf_pq_codepacking.cuh"
+#include "ivf_pq_process_and_fill_codes.cuh"
+#include <cstdint>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/util/cuda_dev_essentials.cuh>
+#include <raft/util/pow2_utils.cuh>
+#include <variant>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+template <uint32_t BlockSize, uint32_t PqBits, typename IdxT>
+__launch_bounds__(BlockSize) static __global__ void process_and_fill_codes_kernel(
+  raft::device_matrix_view<const float, IdxT, raft::row_major> new_vectors,
+  std::variant<IdxT, const IdxT*> src_offset_or_indices,
+  const uint32_t* new_labels,
+  raft::device_vector_view<uint32_t, uint32_t, raft::row_major> list_sizes,
+  raft::device_vector_view<IdxT*, uint32_t, raft::row_major> inds_ptrs,
+  raft::device_vector_view<uint8_t*, uint32_t, raft::row_major> data_ptrs,
+  raft::device_mdspan<const float, raft::extent_3d<uint32_t>, raft::row_major> pq_centers,
+  codebook_gen codebook_kind)
+{
+  constexpr uint32_t kSubWarpSize = std::min<uint32_t>(raft::WarpSize, 1u << PqBits);
+  using subwarp_align             = raft::Pow2<kSubWarpSize>;
+  const uint32_t lane_id          = subwarp_align::mod(threadIdx.x);
+  const IdxT row_ix = subwarp_align::div(IdxT{threadIdx.x} + IdxT{BlockSize} * IdxT{blockIdx.x});
+  if (row_ix >= new_vectors.extent(0)) { return; }
+
+  const uint32_t cluster_ix = new_labels[row_ix];
+  uint32_t out_ix;
+  if (lane_id == 0) { out_ix = atomicAdd(&list_sizes(cluster_ix), 1); }
+  out_ix = raft::shfl(out_ix, 0, kSubWarpSize);
+
+  // write the label  (one record per subwarp)
+  auto pq_indices = inds_ptrs(cluster_ix);
+  if (lane_id == 0) {
+    if (std::holds_alternative<IdxT>(src_offset_or_indices)) {
+      pq_indices[out_ix] = std::get<IdxT>(src_offset_or_indices) + row_ix;
+    } else {
+      pq_indices[out_ix] = std::get<const IdxT*>(src_offset_or_indices)[row_ix];
+    }
+  }
+
+  // write the codes (one record per subwarp):
+  const uint32_t pq_dim = new_vectors.extent(1) / pq_centers.extent(1);
+  auto pq_extents = list_spec<uint32_t, IdxT>{PqBits, pq_dim, true}.make_list_extents(out_ix + 1);
+  auto pq_dataset = raft::make_mdspan<uint8_t, uint32_t, raft::row_major, false, true>(
+    data_ptrs[cluster_ix], pq_extents);
+  write_vector<PqBits, kSubWarpSize>(
+    pq_dataset,
+    out_ix,
+    row_ix,
+    pq_dim,
+    encode_vectors<kSubWarpSize, IdxT>{pq_centers, new_vectors, codebook_kind, cluster_ix});
+}
+
+template <typename IdxT>
+void launch_process_and_fill_codes_kernel(raft::resources const& handle,
+                                          index<IdxT>& index,
+                                          raft::device_matrix_view<float> new_vectors_residual,
+                                          std::variant<IdxT, const IdxT*> src_offset_or_indices,
+                                          const uint32_t* new_labels,
+                                          IdxT n_rows)
+{
+  constexpr uint32_t kBlockSize  = 256;
+  const uint32_t threads_per_vec = std::min<uint32_t>(raft::WarpSize, index.pq_book_size());
+  dim3 blocks(raft::div_rounding_up_safe<IdxT>(n_rows, kBlockSize / threads_per_vec), 1, 1);
+  dim3 threads(kBlockSize, 1, 1);
+  auto kernel = [](uint32_t pq_bits) {
+    switch (pq_bits) {
+      case 4: return process_and_fill_codes_kernel<kBlockSize, 4, IdxT>;
+      case 5: return process_and_fill_codes_kernel<kBlockSize, 5, IdxT>;
+      case 6: return process_and_fill_codes_kernel<kBlockSize, 6, IdxT>;
+      case 7: return process_and_fill_codes_kernel<kBlockSize, 7, IdxT>;
+      case 8: return process_and_fill_codes_kernel<kBlockSize, 8, IdxT>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be within [4, 8]", pq_bits);
+    }
+  }(index.pq_bits());
+
+  kernel<<<blocks, threads, 0, raft::resource::get_cuda_stream(handle)>>>(new_vectors_residual,
+                                                                          src_offset_or_indices,
+                                                                          new_labels,
+                                                                          index.list_sizes(),
+                                                                          index.inds_ptrs(),
+                                                                          index.data_ptrs(),
+                                                                          index.pq_centers(),
+                                                                          index.codebook_kind());
+
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+}
+}  // namespace cuvs::neighbors::ivf_pq::detail

From bade5ee3c4ff5352fefe361de88caa9a3146c54e Mon Sep 17 00:00:00 2001
From: Puneet Ahuja <167976912+punAhuja@users.noreply.github.com>
Date: Fri, 25 Jul 2025 09:01:16 +0530
Subject: [PATCH 506/623] Fix TieredIndexIT,  Scoped resource access change was
 missed (#1156)

#1089 Scoped Resource access changes were missed in the TieredIndex test causing them to fail.
 - Using CheckedCuVSResources.create() now
 - Skip TieredIndex tests if CuVS native support is unavailable

Authors:
  - Puneet Ahuja (https://github.com/punAhuja)

Approvers:
  - Mayya Sharipova (https://github.com/mayya-sharipova)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1156
---
 .../src/test/java/com/nvidia/cuvs/TieredIndexIT.java   | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
index ffe1dddd08..f8cb70b563 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
@@ -18,6 +18,7 @@
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertFalse;
 import static org.junit.Assert.assertTrue;
+import static org.junit.Assume.assumeTrue;
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import java.lang.invoke.MethodHandles;
@@ -39,6 +40,7 @@ public class TieredIndexIT extends CuVSTestCase {
 
   @Before
   public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
     initializeRandom();
     log.debug("Random context initialized for test");
   }
@@ -67,7 +69,7 @@ public void testBasicOperations() throws Throwable {
     List<Map<Integer, Float>> expectedExtendedResults =
         Arrays.asList(Map.of(0, 0.02f, 1, 1.62f, 2, 7.22f), Map.of(2, 0.02f, 3, 2.42f, 1, 1.62f));
 
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndexParams cagraParams =
           new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
 
@@ -114,7 +116,7 @@ public void testBasicOperations() throws Throwable {
 
   @Test(expected = IllegalArgumentException.class)
   public void testErrorHandling() throws Throwable {
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndexParams cagraParams =
           new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
 
@@ -141,7 +143,7 @@ public void testDifferentKValues() throws Throwable {
 
     float[][] queries = {{0.1f, 0.1f}};
 
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndexParams cagraParams =
           new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
 
@@ -204,7 +206,7 @@ public void testPrefilter() throws Throwable {
     float[][] dataset = {{0.0f, 0.0f}, {1.0f, 1.0f}, {2.0f, 2.0f}, {3.0f, 3.0f}};
     float[][] queryVectors = {{0.1f, 0.1f}};
 
-    try (CuVSResources resources = CuVSResources.create()) {
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
       CagraIndexParams cagraParams =
           new CagraIndexParams.Builder().withGraphDegree(4).withIntermediateGraphDegree(8).build();
 

From 549d9706653d37cc5ccf1eb76feeef1b429ea6b8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Fri, 25 Jul 2025 16:50:56 +0200
Subject: [PATCH 507/623] [Java] Fix HNSW params allocation (#1110)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Similar to https://github.com/rapidsai/cuvs/pull/1109, but for HNSW index type (a bit simpler).

#1109 replaces direct allocations of CAGRA index params (via Memory Arenas) with calls to the `cuvsCagraIndexParamsCreate()` and `cuvsCagraIndexParamsDestroy()` in the C-API.  This ensures that the Java API is insulated from changes to show the C-API is implemented.

The changes in this PR (#1110) make similar changes, but for the HNSW index, and reuses the `CloseableHandle` to ensure cleanup.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Chris Hegarty (https://github.com/ChrisHegarty)

URL: https://github.com/rapidsai/cuvs/pull/1110
---
 .../cuvs/internal/CuVSParamsHelper.java       | 19 ++++++++++
 .../nvidia/cuvs/internal/HnswIndexImpl.java   | 35 +++++++++----------
 2 files changed, 35 insertions(+), 19 deletions(-)

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
index 9d4c759530..388f6afc05 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
@@ -132,4 +132,23 @@ public void close() {
       };
     }
   }
+
+  static CloseableHandle createHnswIndexParams() {
+    try (var localArena = Arena.ofConfined()) {
+      var paramsPtrPtr = localArena.allocate(cuvsHnswIndexParams_t);
+      checkCuVSError(cuvsHnswIndexParamsCreate(paramsPtrPtr), "cuvsHnswIndexParamsCreate");
+      var paramsPtr = paramsPtrPtr.get(cuvsHnswIndexParams_t, 0L);
+      return new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return paramsPtr;
+        }
+
+        @Override
+        public void close() {
+          checkCuVSError(cuvsHnswIndexParamsDestroy(paramsPtr), "cuvsHnswIndexParamsDestroy");
+        }
+      };
+    }
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index 0d8997dca8..a4231450e2 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -15,17 +15,13 @@
  */
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.CuVSParamsHelper.createHnswIndexParams;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswDeserialize;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndexCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndexDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswIndex_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsHnswSearch;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.HnswIndex;
@@ -33,6 +29,7 @@
 import com.nvidia.cuvs.HnswQuery;
 import com.nvidia.cuvs.HnswSearchParams;
 import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.internal.common.CloseableHandle;
 import com.nvidia.cuvs.internal.panama.DLDataType;
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndex;
 import com.nvidia.cuvs.internal.panama.cuvsHnswIndexParams;
@@ -183,22 +180,22 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
 
       MemorySegment dtype = DLDataType.allocate(localArena);
       DLDataType.bits(dtype, (byte) 32);
-      DLDataType.code(dtype, (byte) 2); // kDLFloat
+      DLDataType.code(dtype, (byte) kDLFloat());
       DLDataType.lanes(dtype, (byte) 1);
 
       cuvsHnswIndex.dtype(indexReference.memorySegment, dtype);
 
-      try (var resourcesAccessor = resources.access()) {
-        var cuvsRes = resourcesAccessor.handle();
-        var returnValue =
+      try (var params = segmentFromIndexParams(hnswIndexParams);
+          var cuvsResourcesAccessor = resources.access()) {
+        checkCuVSError(
             cuvsHnswDeserialize(
-                cuvsRes,
-                segmentFromIndexParams(localArena, hnswIndexParams),
+                cuvsResourcesAccessor.handle(),
+                params.handle(),
                 pathSeg,
                 hnswIndexParams.getVectorDimension(),
                 0,
-                indexReference.memorySegment);
-        checkCuVSError(returnValue, "cuvsHnswDeserialize");
+                indexReference.memorySegment),
+            "cuvsHnswDeserialize");
       }
 
       return indexReference;
@@ -211,11 +208,11 @@ private IndexReference deserialize(InputStream inputStream) throws Throwable {
   /**
    * Allocates the configured search parameters in the MemorySegment.
    */
-  private static MemorySegment segmentFromIndexParams(Arena arena, HnswIndexParams params) {
-    MemorySegment seg = cuvsHnswIndexParams.allocate(arena);
-    cuvsHnswIndexParams.ef_construction(seg, params.getEfConstruction());
-    cuvsHnswIndexParams.num_threads(seg, params.getNumThreads());
-    return seg;
+  private CloseableHandle segmentFromIndexParams(HnswIndexParams params) {
+    var hnswParams = createHnswIndexParams();
+    cuvsHnswIndexParams.ef_construction(hnswParams.handle(), params.getEfConstruction());
+    cuvsHnswIndexParams.num_threads(hnswParams.handle(), params.getNumThreads());
+    return hnswParams;
   }
 
   /**

From 42068e57eded36d3c5b57463b7c6acfaec2e190d Mon Sep 17 00:00:00 2001
From: Vyas Ramasubramani <vyasr@nvidia.com>
Date: Fri, 25 Jul 2025 08:43:55 -0700
Subject: [PATCH 508/623] Use GCC 14 in conda builds. (#1030)

conda-forge is migrating to gcc 14, so this PR is updating for alignment.

See https://github.com/rapidsai/build-planning/issues/188

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Jake Awe (https://github.com/AyodeAwe)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1030
---
 ci/build_cpp.sh                                        |  6 +++++-
 conda/environments/all_cuda-129_arch-aarch64.yaml      |  2 +-
 conda/environments/all_cuda-129_arch-x86_64.yaml       |  2 +-
 .../environments/bench_ann_cuda-129_arch-aarch64.yaml  |  2 +-
 conda/environments/bench_ann_cuda-129_arch-x86_64.yaml |  2 +-
 conda/environments/go_cuda-129_arch-aarch64.yaml       |  2 +-
 conda/environments/go_cuda-129_arch-x86_64.yaml        |  2 +-
 conda/environments/rust_cuda-129_arch-aarch64.yaml     |  2 +-
 conda/environments/rust_cuda-129_arch-x86_64.yaml      |  2 +-
 conda/recipes/cuvs-bench-cpu/conda_build_config.yaml   |  4 ++--
 conda/recipes/cuvs/conda_build_config.yaml             |  4 ++--
 conda/recipes/libcuvs/conda_build_config.yaml          |  4 ++--
 cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h   | 10 +++++++---
 cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu         |  4 ++--
 dependencies.yaml                                      |  4 ++--
 15 files changed, 30 insertions(+), 22 deletions(-)

diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
index e55ba04fdb..f8fb90dbb9 100755
--- a/ci/build_cpp.sh
+++ b/ci/build_cpp.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
@@ -19,6 +19,10 @@ sccache --zero-stats
 RAPIDS_PACKAGE_VERSION=$(rapids-generate-version)
 export RAPIDS_PACKAGE_VERSION
 
+RAPIDS_ARTIFACTS_DIR=${RAPIDS_ARTIFACTS_DIR:-"${PWD}/artifacts"}
+mkdir -p "${RAPIDS_ARTIFACTS_DIR}"
+export RAPIDS_ARTIFACTS_DIR
+
 # populates `RATTLER_CHANNELS` array and `RATTLER_ARGS` array
 source rapids-rattler-channel-string
 
diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
index 796d166f6c..04fe7a5c21 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -22,7 +22,7 @@ dependencies:
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
-- gcc_linux-aarch64=13.*
+- gcc_linux-aarch64=14.*
 - go
 - graphviz
 - ipython
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
index 57675489b2..643dbfe04c 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -22,7 +22,7 @@ dependencies:
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
 - doxygen>=1.8.20
-- gcc_linux-64=13.*
+- gcc_linux-64=14.*
 - go
 - graphviz
 - ipython
diff --git a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index cf9a064f32..7e5efd0693 100644
--- a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -22,7 +22,7 @@ dependencies:
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
-- gcc_linux-aarch64=13.*
+- gcc_linux-aarch64=14.*
 - glog>=0.6.0
 - h5py>=3.8.0
 - libclang==20.1.4
diff --git a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index 8b8bf10a58..5604f56252 100644
--- a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -22,7 +22,7 @@ dependencies:
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
-- gcc_linux-64=13.*
+- gcc_linux-64=14.*
 - glog>=0.6.0
 - h5py>=3.8.0
 - libaio
diff --git a/conda/environments/go_cuda-129_arch-aarch64.yaml b/conda/environments/go_cuda-129_arch-aarch64.yaml
index 1a0bbb0003..a23a3e636b 100644
--- a/conda/environments/go_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-129_arch-aarch64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-version=12.9
 - cxx-compiler
 - dlpack>=0.8,<1.0
-- gcc_linux-aarch64=13.*
+- gcc_linux-aarch64=14.*
 - go
 - libclang==20.1.4
 - libcublas-dev
diff --git a/conda/environments/go_cuda-129_arch-x86_64.yaml b/conda/environments/go_cuda-129_arch-x86_64.yaml
index c73052a292..a6bdbe0159 100644
--- a/conda/environments/go_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-129_arch-x86_64.yaml
@@ -17,7 +17,7 @@ dependencies:
 - cuda-version=12.9
 - cxx-compiler
 - dlpack>=0.8,<1.0
-- gcc_linux-64=13.*
+- gcc_linux-64=14.*
 - go
 - libclang==20.1.4
 - libcublas-dev
diff --git a/conda/environments/rust_cuda-129_arch-aarch64.yaml b/conda/environments/rust_cuda-129_arch-aarch64.yaml
index a0e6b868d7..3a94cc2488 100644
--- a/conda/environments/rust_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-129_arch-aarch64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-profiler-api
 - cuda-version=12.9
 - cxx-compiler
-- gcc_linux-aarch64=13.*
+- gcc_linux-aarch64=14.*
 - libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
diff --git a/conda/environments/rust_cuda-129_arch-x86_64.yaml b/conda/environments/rust_cuda-129_arch-x86_64.yaml
index 2e845864db..98545cc195 100644
--- a/conda/environments/rust_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-129_arch-x86_64.yaml
@@ -15,7 +15,7 @@ dependencies:
 - cuda-profiler-api
 - cuda-version=12.9
 - cxx-compiler
-- gcc_linux-64=13.*
+- gcc_linux-64=14.*
 - libclang==20.1.4
 - libcublas-dev
 - libcurand-dev
diff --git a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
index 8bad5c58e9..b4e099cde8 100644
--- a/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
+++ b/conda/recipes/cuvs-bench-cpu/conda_build_config.yaml
@@ -1,8 +1,8 @@
 c_compiler_version:
-  - 13
+  - 14
 
 cxx_compiler_version:
-  - 13
+  - 14
 
 c_stdlib:
   - sysroot
diff --git a/conda/recipes/cuvs/conda_build_config.yaml b/conda/recipes/cuvs/conda_build_config.yaml
index caf5489e1d..1082f0d214 100644
--- a/conda/recipes/cuvs/conda_build_config.yaml
+++ b/conda/recipes/cuvs/conda_build_config.yaml
@@ -1,8 +1,8 @@
 c_compiler_version:
-  - 13
+  - 14
 
 cxx_compiler_version:
-  - 13
+  - 14
 
 cuda_compiler:
   - cuda-nvcc
diff --git a/conda/recipes/libcuvs/conda_build_config.yaml b/conda/recipes/libcuvs/conda_build_config.yaml
index 65b9e3e8da..21989d9803 100644
--- a/conda/recipes/libcuvs/conda_build_config.yaml
+++ b/conda/recipes/libcuvs/conda_build_config.yaml
@@ -1,8 +1,8 @@
 c_compiler_version:
-  - 13
+  - 14
 
 cxx_compiler_version:
-  - 13
+  - 14
 
 cuda_compiler:
   - cuda-nvcc
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 7ad8ef91b5..fa462252e3 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -83,7 +83,9 @@ void parse_build_param(const nlohmann::json& conf,
 {
   param.n_lists = conf.at("nlist");
   if (conf.contains("niter")) { param.kmeans_n_iters = conf.at("niter"); }
-  if (conf.contains("ratio")) { param.kmeans_trainset_fraction = 1.0 / (double)conf.at("ratio"); }
+  if (conf.contains("ratio")) {
+    param.kmeans_trainset_fraction = 1.0 / static_cast<double>(conf.at("ratio"));
+  }
 }
 
 template <typename T, typename IdxT>
@@ -102,7 +104,9 @@ void parse_build_param(const nlohmann::json& conf,
 {
   if (conf.contains("nlist")) { param.n_lists = conf.at("nlist"); }
   if (conf.contains("niter")) { param.kmeans_n_iters = conf.at("niter"); }
-  if (conf.contains("ratio")) { param.kmeans_trainset_fraction = 1.0 / (double)conf.at("ratio"); }
+  if (conf.contains("ratio")) {
+    param.kmeans_trainset_fraction = 1.0 / static_cast<double>(conf.at("ratio"));
+  }
   if (conf.contains("pq_bits")) { param.pq_bits = conf.at("pq_bits"); }
   if (conf.contains("pq_dim")) { param.pq_dim = conf.at("pq_dim"); }
   if (conf.contains("codebook_kind")) {
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
index f9ad76dc59..fbe042979d 100644
--- a/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_benchmark.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -132,7 +132,7 @@ void parse_build_param(const nlohmann::json& conf,
       ivf_pq_build_p.kmeans_n_iters = ivf_pq_build_conf.at("niter");
     }
     if (ivf_pq_build_conf.contains("ratio")) {
-      ivf_pq_build_p.kmeans_trainset_fraction = 1.0 / (double)conf.at("ratio");
+      ivf_pq_build_p.kmeans_trainset_fraction = 1.0 / static_cast<double>(conf.at("ratio"));
     }
     if (ivf_pq_build_conf.contains("pq_bits")) {
       ivf_pq_build_p.pq_bits = ivf_pq_build_conf.at("pq_bits");
diff --git a/dependencies.yaml b/dependencies.yaml
index 88469b62c3..960614a891 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -247,13 +247,13 @@ dependencies:
               arch: x86_64
               cuda: "12.*"
             packages:
-              - gcc_linux-64=13.*
+              - gcc_linux-64=14.*
               - sysroot_linux-64==2.28
           - matrix:
               arch: aarch64
               cuda: "12.*"
             packages:
-              - gcc_linux-aarch64=13.*
+              - gcc_linux-aarch64=14.*
               - sysroot_linux-aarch64==2.28
       - output_types: conda
         matrices:

From b627c245b0b889f3e0ff8c1152775a857afcaa78 Mon Sep 17 00:00:00 2001
From: "Corey J. Nolet" <cjnolet@gmail.com>
Date: Fri, 25 Jul 2025 15:18:48 -0400
Subject: [PATCH 509/623] Removing all references to CUDA 11 from codebase
 (#1150)

_Edit by @jakirkham_ - Fixes https://github.com/rapidsai/cuvs/issues/1118

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)
  - https://github.com/LizYou

Approvers:
  - https://github.com/jakirkham
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1150
---
 cpp/bench/ann/src/common/util.hpp             |  2 -
 cpp/cmake/modules/FindSVE.cmake               | 45 +++++++++++++++++++
 cpp/cmake/thirdparty/get_faiss.cmake          |  3 +-
 .../preprocessing/quantize/detail/binary.cuh  |  1 -
 docs/source/build.rst                         | 11 +----
 docs/source/cuvs_bench/build.rst              |  2 +-
 docs/source/cuvs_bench/index.rst              |  2 +-
 7 files changed, 51 insertions(+), 15 deletions(-)
 create mode 100644 cpp/cmake/modules/FindSVE.cmake

diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
index e3d55cb83d..ebcae4f73b 100644
--- a/cpp/bench/ann/src/common/util.hpp
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -449,10 +449,8 @@ inline auto cuda_info()
   props.emplace_back("gpu_pageableMemoryAccess", std::to_string(device_prop.pageableMemoryAccess));
   props.emplace_back("gpu_pageableMemoryAccessUsesHostPageTables",
                      std::to_string(device_prop.pageableMemoryAccessUsesHostPageTables));
-#if defined(CUDART_VERSION) && CUDART_VERSION >= 12000
   props.emplace_back("gpu_gpuDirectRDMASupported",
                      std::to_string(device_prop.gpuDirectRDMASupported));
-#endif
 #endif
   return props;
 }
diff --git a/cpp/cmake/modules/FindSVE.cmake b/cpp/cmake/modules/FindSVE.cmake
new file mode 100644
index 0000000000..6533c7f069
--- /dev/null
+++ b/cpp/cmake/modules/FindSVE.cmake
@@ -0,0 +1,45 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+# =============================================================================
+# FindSVE.cmake This module finds the SVE (Scalable Vector Extension) support in the compiler.
+# =============================================================================
+
+INCLUDE(CheckCXXSourceRuns)
+
+set(SVE_CODE
+    "
+  #include <arm_sve.h>
+
+  int main()
+  {
+    svfloat32_t a = svdup_f32(0);
+    return 0;
+  }
+"
+)
+
+# Check for SVE support
+message("Checking for SVE support")
+set(CMAKE_REQUIRED_FLAGS "-march=armv9-a+sve")
+check_cxx_source_runs("${SVE_CODE}" CXX_HAS_SVE)
+
+if(CXX_HAS_SVE)
+  set(CXX_SVE_FOUND
+      TRUE
+      CACHE BOOL "SVE support found"
+  )
+  set(CXX_SVE_FLAGS
+      "-march=armv9-a+sve"
+      CACHE STRING "Flags for SVE support"
+  )
+else()
+  set(CXX_SVE_FOUND
+      FALSE
+      CACHE BOOL "SVE support not found"
+  )
+  set(CXX_SVE_FLAGS
+      ""
+      CACHE STRING "Flags for SVE support"
+  )
+endif()
+
+mark_as_advanced(CXX_SVE_FOUND CXX_SVE_FLAGS)
diff --git a/cpp/cmake/thirdparty/get_faiss.cmake b/cpp/cmake/thirdparty/get_faiss.cmake
index 26965273e2..8075abe050 100644
--- a/cpp/cmake/thirdparty/get_faiss.cmake
+++ b/cpp/cmake/thirdparty/get_faiss.cmake
@@ -1,5 +1,5 @@
 #=============================================================================
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -46,6 +46,7 @@ function(find_and_configure_faiss)
     set(CUVS_FAISS_OPT_LEVEL "avx2")
   endif()
 
+
   rapids_cpm_find(faiss ${version} ${build_patch_only}
     GLOBAL_TARGETS faiss faiss_avx2 faiss_gpu_objs faiss::faiss faiss::faiss_avx2
     CPM_ARGS
diff --git a/cpp/src/preprocessing/quantize/detail/binary.cuh b/cpp/src/preprocessing/quantize/detail/binary.cuh
index 7852a3f52b..26c9bf5481 100644
--- a/cpp/src/preprocessing/quantize/detail/binary.cuh
+++ b/cpp/src/preprocessing/quantize/detail/binary.cuh
@@ -419,7 +419,6 @@ void transform(raft::resources const& res,
                dataset_size,
                out_dataset_size);
 
-  // Use `float` for computation when T == half because a runtime error occurs with CUDA 11.8
   using compute_t          = std::conditional_t<std::is_same_v<half, T>, float, T>;
   auto threshold_vec       = raft::make_host_vector<compute_t, int64_t>(0);
   compute_t* threshold_ptr = nullptr;
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 4aaf8815c4..4acf3bea54 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -76,8 +76,8 @@ Prerequisites
 
 - CMake 3.26.4+
 - GCC 9.3+ (11.4+ recommended)
-- CUDA Toolkit 11.4+
-- Volta architecture or better (compute capability >= 7.0)
+- CUDA Toolkit 12.0+
+- Ampere architecture or better (compute capability >= 8.0)
 
 Create a build environment
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -89,13 +89,6 @@ Conda environment scripts are provided for installing the necessary dependencies
     conda env create --name cuvs -f conda/environments/all_cuda-128_arch-x86_64.yaml
     conda activate cuvs
 
-The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
-
-.. code-block:: bash
-
-    conda env create --name cuvs -f conda/environments/all_cuda-118_arch-x86_64.yaml
-    conda activate cuvs
-
 The recommended way to build and install cuVS from source is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides CMake options for building and installing the headers, tests, benchmarks, and the pre-compiled shared library.
 
 
diff --git a/docs/source/cuvs_bench/build.rst b/docs/source/cuvs_bench/build.rst
index 0c2dc98b8c..56feda986b 100644
--- a/docs/source/cuvs_bench/build.rst
+++ b/docs/source/cuvs_bench/build.rst
@@ -5,7 +5,7 @@ Build cuVS Bench From Source
 Dependencies
 ============
 
-CUDA 11 and a GPU with Volta architecture or later are required to run the benchmarks.
+CUDA 12 and a GPU with Volta architecture or later are required to run the benchmarks.
 
 Please refer to the  :doc:`installation docs <../build>` for the base requirements to build cuVS.
 
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 1c231c6e56..6e1a6a1978 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -92,7 +92,7 @@ The following command pulls the nightly container for Python version 3.10, CUDA
    docker pull rapidsai/cuvs-bench:24.12a-cuda12.5-py3.10 # substitute cuvs-bench for the exact desired container.
 
 The CUDA and python versions can be changed for the supported values:
-- Supported CUDA versions: 11.8 and 12.5
+- Supported CUDA versions: 12
 - Supported Python versions: 3.10 and 3.11.
 
 You can see the exact versions as well in the dockerhub site:

From 1155a3a427cd1d1bfaf8fe74a937ed6dfa797ec7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Sat, 26 Jul 2025 03:58:49 +0200
Subject: [PATCH 510/623] [Java] Extend `Dataset` to work as an output data
 container (#1111)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

In #902 and https://github.com/rapidsai/cuvs/pull/1034 we introduced a `Dataset` interface to support on-heap and off-heap ("native") memory seamlessly as inputs for cagra and bruteforce index building.

As we expand the functionality of cuvs-java, we realized we have similar needs for outputs (see e.g. https://github.com/rapidsai/cuvs/pull/1105 / https://github.com/rapidsai/cuvs/pull/1102 or https://github.com/rapidsai/cuvs/pull/1104).

This PR extends `Dataset` to support being used as an output, wrapping native (off-heap) memory in a convenient and efficient way, and providing common utilities to transform to and from on-heap memory.
This work is inspired by the existing raft `mdspan` and `DLTensor` data structures, but tailored to our needs (2d only, just 3 data types, etc.). The PR keeps the current implementation simple and minimal on purpose, but structured in a way that is simple to extend.

By itself, the PR is just a refactoring to extend the `Dataset` implementation and reorganize the implementation classes; its real usefulness will be in using it in the PRs mentioned above (in fact, this PR has been extracted from https://github.com/rapidsai/cuvs/pull/1105).
The implementation class hierarchy is implemented with future extensions in mind: atm we have one `HostMemoryDatasetImpl`, but we are already thinking to have a corresponding `DeviceMemoryDatasetImpl` that will wrap and manage (views) on GPU memory to avoid (in some cases) extra copies of data from GPU memory to CPU memory only to process them or forward them to another algorithm (e.g quantization followed by indexing).

Future work will also include add support/refactoring to allocate and manage GPU memory and DLTensors (e.g. working better with/refactoring `prepareTensor`).

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1111
---
 .../com/nvidia/cuvs/CagraIndexBenchmarks.java |  10 +-
 .../java/com/nvidia/cuvs/BruteForceIndex.java |   4 +-
 .../main/java/com/nvidia/cuvs/CagraIndex.java |   4 +-
 .../com/nvidia/cuvs/CuVSDeviceMatrix.java     |  21 ++
 .../java/com/nvidia/cuvs/CuVSHostMatrix.java  |  23 ++
 .../main/java/com/nvidia/cuvs/CuVSMatrix.java | 156 ++++++++++
 .../main/java/com/nvidia/cuvs/Dataset.java    |  74 -----
 .../main/java/com/nvidia/cuvs/RowView.java    |  74 +++++
 .../java/com/nvidia/cuvs/TieredIndex.java     |   8 +-
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  29 +-
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |  19 +-
 .../cuvs/internal/BruteForceIndexImpl.java    |  33 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |  35 ++-
 ...Impl.java => CuVSHostMatrixArenaImpl.java} |  49 +--
 .../cuvs/internal/CuVSHostMatrixImpl.java     | 198 ++++++++++++
 .../cuvs/internal/CuVSMatrixBaseImpl.java     |  48 +++
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |   7 +-
 .../nvidia/cuvs/internal/TieredIndexImpl.java |  63 ++--
 .../com/nvidia/cuvs/internal/common/Util.java |  30 +-
 .../com/nvidia/cuvs/spi/JDKProvider.java      | 108 ++++---
 .../nvidia/cuvs/BruteForceAndSearchIT.java    |  38 ++-
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |   3 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    |  31 +-
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   8 +-
 .../java/com/nvidia/cuvs/CuVSMatrixIT.java    | 286 ++++++++++++++++++
 .../java/com/nvidia/cuvs/DatasetHelper.java   |   7 +-
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |   3 +-
 27 files changed, 1081 insertions(+), 288 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
 delete mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java
 rename java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/{DatasetImpl.java => CuVSHostMatrixArenaImpl.java} (54%)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java

diff --git a/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
index b8a77979b5..18d8495424 100644
--- a/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
+++ b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
@@ -77,12 +77,12 @@ private static MemorySegment createSampleDataSegment(Arena arena, float[][] arra
         return segment;
     }
 
-    private static Dataset fromMemorySegment(MemorySegment memorySegment, int size, int dimensions) {
+    private static CuVSMatrix fromMemorySegment(MemorySegment memorySegment, int size, int dimensions) {
         try {
-            return (Dataset)
+            return (CuVSMatrix)
                     CuVSProvider.provider()
-                            .newNativeDatasetBuilder()
-                            .invokeExact(memorySegment, size, dimensions);
+                            .newNativeMatrixBuilder()
+                            .invokeExact(memorySegment, size, dimensions, CuVSMatrix.DataType.FLOAT);
         } catch (Throwable e) {
             if (e instanceof Error err) {
                 throw err;
@@ -185,7 +185,7 @@ public void testIndexingFromMemorySegment(Blackhole blackhole) throws Throwable
     @OutputTimeUnit(TimeUnit.NANOSECONDS)
     @BenchmarkMode(Mode.AverageTime)
     public void testDatasetFromHeap(Blackhole blackhole) throws Throwable {
-        try (var dataset = Dataset.ofArray(arrayDataset)) {
+        try (var dataset = CuVSMatrix.ofArray(arrayDataset)) {
             blackhole.consume(dataset);
         }
     }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
index b21109dc9e..2b50a0b741 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
@@ -111,10 +111,10 @@ interface Builder {
     /**
      * Sets the dataset for building the {@link BruteForceIndex}.
      *
-     * @param dataset a {@link Dataset} object containing the vectors
+     * @param dataset a {@link CuVSMatrix} object containing the vectors
      * @return an instance of this Builder
      */
-    Builder withDataset(Dataset dataset);
+    Builder withDataset(CuVSMatrix dataset);
 
     /**
      * Builds and returns an instance of {@link BruteForceIndex}.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index 8e6e2fb27d..84487857c8 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -219,10 +219,10 @@ interface Builder {
     /**
      * Sets the dataset for building the {@link CagraIndex}.
      *
-     * @param dataset a {@link Dataset} object containing the vectors
+     * @param dataset a {@link CuVSMatrix} object containing the vectors
      * @return an instance of this Builder
      */
-    Builder withDataset(Dataset dataset);
+    Builder withDataset(CuVSMatrix dataset);
 
     /**
      * Registers an instance of configured {@link CagraIndexParams} with this
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
new file mode 100644
index 0000000000..23d29fd479
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
@@ -0,0 +1,21 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+/**
+ * A Dataset implementation backed by device (GPU) memory.
+ */
+public interface CuVSDeviceMatrix extends CuVSMatrix {}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
new file mode 100644
index 0000000000..237dfa39f7
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+/**
+ * A Dataset implementation backed by host (CPU) memory.
+ */
+public interface CuVSHostMatrix extends CuVSMatrix {
+  int get(int row, int col);
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
new file mode 100644
index 0000000000..0b892fbc5f
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
@@ -0,0 +1,156 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.spi.CuVSProvider;
+
+/**
+ * This represents a wrapper for a dataset to be used for index construction.
+ * The purpose is to allow a caller to place the vectors into native memory
+ * directly, instead of requiring the caller to load all the vectors into the heap
+ * (e.g. with a float[][]).
+ *
+ * @since 25.06
+ */
+public interface CuVSMatrix extends AutoCloseable {
+
+  enum DataType {
+    FLOAT,
+    INT,
+    BYTE
+  }
+
+  enum MemoryKind {
+    HOST,
+    DEVICE
+  }
+
+  /**
+   * Creates a dataset from an on-heap array of vectors.
+   * This method will allocate an additional MemorySegment to hold the graph data.
+   *
+   * @since 25.08
+   */
+  static CuVSMatrix ofArray(float[][] vectors) {
+    return CuVSProvider.provider().newMatrixFromArray(vectors);
+  }
+
+  /**
+   * Creates a dataset from an on-heap array of vectors.
+   * This method will allocate an additional MemorySegment to hold the graph data.
+   *
+   * @since 25.08
+   */
+  static CuVSMatrix ofArray(int[][] vectors) {
+    return CuVSProvider.provider().newMatrixFromArray(vectors);
+  }
+
+  /**
+   * Creates a dataset from an on-heap array of vectors.
+   * This method will allocate an additional MemorySegment to hold the graph data.
+   *
+   * @since 25.08
+   */
+  static CuVSMatrix ofArray(byte[][] vectors) {
+    return CuVSProvider.provider().newMatrixFromArray(vectors);
+  }
+
+  interface Builder {
+    /**
+     * Add a single vector to the dataset.
+     *
+     * @param vector A float array of as many elements as the dimensions
+     */
+    void addVector(float[] vector);
+
+    /**
+     * Add a single vector to the dataset.
+     *
+     * @param vector A byte array of as many elements as the dimensions
+     */
+    void addVector(byte[] vector);
+
+    /**
+     * Add a single vector to the dataset.
+     *
+     * @param vector A int array of as many elements as the dimensions
+     */
+    void addVector(int[] vector);
+
+    CuVSMatrix build();
+  }
+
+  /**
+   * Returns a builder to create a new instance of a dataset
+   *
+   * @param size     Number of vectors in the dataset
+   * @param columns  Size of each vector in the dataset
+   * @param dataType The data type of the dataset elements
+   * @return new instance of {@link CuVSMatrix}
+   */
+  static CuVSMatrix.Builder builder(int size, int columns, DataType dataType) {
+    return CuVSProvider.provider().newMatrixBuilder(size, columns, dataType);
+  }
+
+  /**
+   * Gets the size of the dataset
+   *
+   * @return Size of the dataset
+   */
+  long size();
+
+  /**
+   * Gets the number of columns in the Dataset (e.g. the dimensions of the vectors in this dataset,
+   * or the graph degree for the graph represented as a list of neighbours
+   *
+   * @return Dimensions of the vectors in the dataset
+   */
+  long columns();
+
+  /**
+   * Get a view (0-copy) of the row data, as a list of integers (32 bit)
+   *
+   * @param row the row for which to return the data
+   */
+  RowView getRow(long row);
+
+  /**
+   * Copies the content of this dataset to an on-heap Java matrix (array of arrays).
+   *
+   * @param array the destination array. Must be of length {@link CuVSMatrix#size()} or bigger,
+   *              and each element must be of length {@link CuVSMatrix#columns()} or bigger.
+   */
+  void toArray(int[][] array);
+
+  /**
+   * Copies the content of this dataset to an on-heap Java matrix (array of arrays).
+   *
+   * @param array the destination array. Must be of length {@link CuVSMatrix#size()} or bigger,
+   *              and each element must be of length {@link CuVSMatrix#columns()} or bigger.
+   */
+  void toArray(float[][] array);
+
+  /**
+   * Copies the content of this dataset to an on-heap Java matrix (array of arrays).
+   *
+   * @param array the destination array. Must be of length {@link CuVSMatrix#size()} or bigger,
+   *              and each element must be of length {@link CuVSMatrix#columns()} or bigger.
+   */
+  void toArray(byte[][] array);
+
+  @Override
+  void close();
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
deleted file mode 100644
index e13bed45d0..0000000000
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Dataset.java
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs;
-
-import com.nvidia.cuvs.spi.CuVSProvider;
-
-/**
- * This represents a wrapper for a dataset to be used for index construction.
- * The purpose is to allow a caller to place the vectors into native memory
- * directly, instead of requiring the caller to load all the vectors into the heap
- * (e.g. with a float[][]).
- *
- * @since 25.06
- */
-public interface Dataset extends AutoCloseable {
-
-  /**
-   * Creates a dataset from an on-heap array of vectors
-   *
-   * @since 25.08
-   */
-  static Dataset ofArray(float[][] vectors) {
-    return CuVSProvider.provider().newArrayDataset(vectors);
-  }
-
-  interface Builder {
-    /**
-     * Add a single vector to the dataset.
-     *
-     * @param vector A float array of as many elements as the dimensions
-     */
-    void addVector(float[] vector);
-
-    Dataset build();
-  }
-
-  /**
-   * Returns a builder to create a new instance of a dataset
-   *
-   * @param size       Number of vectors in the dataset
-   * @param dimensions Size of each vector in the dataset
-   * @return new instance of {@link Dataset}
-   */
-  static Dataset.Builder builder(int size, int dimensions) {
-    return CuVSProvider.provider().newDatasetBuilder(size, dimensions);
-  }
-
-  /**
-   * Gets the size of the dataset
-   *
-   * @return Size of the dataset
-   */
-  int size();
-
-  /**
-   * Gets the dimensions of the vectors in this dataset
-   *
-   * @return Dimensions of the vectors in the dataset
-   */
-  int dimensions();
-}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java
new file mode 100644
index 0000000000..cbace82ae3
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+/**
+ * Represent a contiguous list of integers (32-bit) backed by off-heap memory.
+ *
+ * @since 25.08
+ */
+public interface RowView {
+
+  long size();
+
+  /**
+   * Returns the integer element at the given position. Asserts that the
+   * data type of the dataset on top of which this view is instantiates is
+   * {@link CuVSMatrix.DataType#INT}
+   *
+   * @param index the element index
+   */
+  int getAsInt(long index);
+
+  /**
+   * Returns the integer element at the given position. Asserts that the
+   * data type of the dataset on top of which this view is instantiates is
+   * {@link CuVSMatrix.DataType#FLOAT}
+   *
+   * @param index the element index
+   */
+  float getAsFloat(long index);
+
+  /**
+   * Returns the integer element at the given position. Asserts that the
+   * data type of the dataset on top of which this view is instantiates is
+   * {@link CuVSMatrix.DataType#BYTE}
+   *
+   * @param index the element index
+   */
+  byte getAsByte(long index);
+
+  /**
+   * Copies the content of this row to an on-heap Java array.
+   *
+   * @param array the destination array. Must be of length {@link RowView#size()} or bigger.
+   */
+  void toArray(int[] array);
+
+  /**
+   * Copies the content of this row to an on-heap Java array.
+   *
+   * @param array the destination array. Must be of length {@link RowView#size()} or bigger.
+   */
+  void toArray(float[] array);
+
+  /**
+   * Copies the content of this row to an on-heap Java array.
+   *
+   * @param array the destination array. Must be of length {@link RowView#size()} or bigger.
+   */
+  void toArray(byte[] array);
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
index a6bd7e1f6e..2726382f23 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
@@ -104,10 +104,10 @@ interface Builder {
     /**
      * Sets the dataset for building the TieredIndex.
      *
-     * @param dataset A {@link Dataset} instance containing the vectors
+     * @param dataset A {@link CuVSMatrix} instance containing the vectors
      * @return This Builder instance for method chaining
      */
-    Builder withDataset(Dataset dataset);
+    Builder withDataset(CuVSMatrix dataset);
 
     /**
      * Registers TieredIndex parameters with this Builder.
@@ -165,11 +165,11 @@ interface ExtendBuilder {
     /**
      * Sets the dataset to add to the existing index.
      *
-     * @param dataset A {@link Dataset} instance containing the new vectors to
+     * @param dataset A {@link CuVSMatrix} instance containing the new vectors to
      *                add
      * @return This ExtendBuilder instance for method chaining
      */
-    ExtendBuilder withDataset(Dataset dataset);
+    ExtendBuilder withDataset(CuVSMatrix dataset);
 
     /**
      * Executes the extend operation, adding the specified data to the index.
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 4dd7fa8eb8..e528beff1a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -18,8 +18,8 @@
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraMergeParams;
+import com.nvidia.cuvs.CuVSMatrix;
 import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
 import com.nvidia.cuvs.TieredIndex;
 import java.lang.invoke.MethodHandle;
@@ -52,27 +52,34 @@ default Path nativeLibraryPath() {
   /** Creates a new CuVSResources. */
   CuVSResources newCuVSResources(Path tempDirectory) throws Throwable;
 
-  /** Create a {@link Dataset.Builder} instance **/
-  Dataset.Builder newDatasetBuilder(int size, int dimensions);
+  /** Create a {@link CuVSMatrix.Builder} instance **/
+  CuVSMatrix.Builder newMatrixBuilder(int size, int dimensions, CuVSMatrix.DataType dataType);
 
   /**
    * Returns the factory method used to build a Dataset from native memory.
-   * The factory method will have this signature: {@code Dataset createNativeDataset(memorySegment, size, dimensions)},
+   * The factory method will have this signature:
+   * {@code Dataset createNativeDataset(memorySegment, size, dimensions, dataType)},
    * where {@code memorySegment} is a {@code java.lang.foreign.MemorySegment} containing {@code int size} vectors of
-   * {@code int dimensions} length.
+   * {@code int dimensions} length of type {@link CuVSMatrix.DataType}.
    * <p>
    * In order to expose this factory in a way that is compatible with Java 21, the factory method is returned as a
    * {@link MethodHandle} with {@link MethodType} equal to
-   * {@code (Dataset.class, MemorySegment.class, int.class, int.class)}.
+   * {@code (Dataset.class, MemorySegment.class, int.class, int.class, Dataset.DataType.class)}.
    * The caller will need to invoke the factory via the {@link MethodHandle#invokeExact} method:
-   * {@code Dataset dataset = (Dataset)newNativeDatasetBuilder().invokeExact(memorySegment, size, dimensions)}
+   * {@code Dataset dataset = (Dataset)newNativeDatasetBuilder().invokeExact(memorySegment, size, dimensions, dataType)}
    * </p>
-   * @return a MethodHandle which can be invoked to build a Dataset from a {@code MemorySegment}
+   * @return a MethodHandle which can be invoked to build a Dataset from an external {@code MemorySegment}
    */
-  MethodHandle newNativeDatasetBuilder();
+  MethodHandle newNativeMatrixBuilder();
 
-  /** Create a {@link Dataset} backed by a on-heap array **/
-  Dataset newArrayDataset(float[][] vectors);
+  /** Create a {@link CuVSMatrix} from an on-heap array **/
+  CuVSMatrix newMatrixFromArray(float[][] vectors);
+
+  /** Create a {@link CuVSMatrix} from an on-heap array **/
+  CuVSMatrix newMatrixFromArray(int[][] vectors);
+
+  /** Create a {@link CuVSMatrix} from an on-heap array **/
+  CuVSMatrix newMatrixFromArray(byte[][] vectors);
 
   /** Creates a new BruteForceIndex Builder. */
   BruteForceIndex.Builder newBruteForceIndexBuilder(CuVSResources cuVSResources)
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 7e5d1a7e64..8f65bf7068 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -17,8 +17,8 @@
 
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
+import com.nvidia.cuvs.CuVSMatrix;
 import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.HnswIndex;
 import com.nvidia.cuvs.TieredIndex;
 import java.lang.invoke.MethodHandle;
@@ -60,17 +60,28 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
   }
 
   @Override
-  public Dataset.Builder newDatasetBuilder(int size, int dimensions) {
+  public CuVSMatrix.Builder newMatrixBuilder(
+      int size, int dimensions, CuVSMatrix.DataType dataType) {
     throw new UnsupportedOperationException();
   }
 
   @Override
-  public MethodHandle newNativeDatasetBuilder() {
+  public MethodHandle newNativeMatrixBuilder() {
     throw new UnsupportedOperationException();
   }
 
   @Override
-  public Dataset newArrayDataset(float[][] vectors) {
+  public CuVSMatrix newMatrixFromArray(float[][] vectors) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public CuVSMatrix newMatrixFromArray(int[][] vectors) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     throw new UnsupportedOperationException();
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 163205b830..f32e311f77 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -42,8 +42,8 @@
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.BruteForceIndexParams;
 import com.nvidia.cuvs.BruteForceQuery;
+import com.nvidia.cuvs.CuVSMatrix;
 import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.panama.cuvsFilter;
 import java.io.InputStream;
@@ -81,13 +81,13 @@ public class BruteForceIndexImpl implements BruteForceIndex {
    *                              holding the index parameters
    */
   private BruteForceIndexImpl(
-      Dataset dataset, CuVSResources resources, BruteForceIndexParams bruteForceIndexParams)
+      CuVSMatrix dataset, CuVSResources resources, BruteForceIndexParams bruteForceIndexParams)
       throws Exception {
     Objects.requireNonNull(dataset);
     try (dataset) {
       this.resources = resources;
-      assert dataset instanceof DatasetImpl;
-      this.bruteForceIndexReference = build((DatasetImpl) dataset, bruteForceIndexParams);
+      assert dataset instanceof CuVSMatrixBaseImpl;
+      this.bruteForceIndexReference = build((CuVSMatrixBaseImpl) dataset, bruteForceIndexParams);
     }
   }
 
@@ -144,11 +144,12 @@ public void destroyIndex() {
    * @return an instance of {@link IndexReference} that holds the pointer to the
    * index
    */
-  private IndexReference build(DatasetImpl dataset, BruteForceIndexParams bruteForceIndexParams) {
+  private IndexReference build(
+      CuVSMatrixBaseImpl dataset, BruteForceIndexParams bruteForceIndexParams) {
     long rows = dataset.size();
-    long cols = dataset.dimensions();
+    long cols = dataset.columns();
 
-    MemorySegment datasetMemSegment = dataset.asMemorySegment();
+    MemorySegment datasetMemSegment = dataset.memorySegment();
 
     omp_set_num_threads(bruteForceIndexParams.getNumWriterThreads());
 
@@ -164,7 +165,7 @@ private IndexReference build(DatasetImpl dataset, BruteForceIndexParams bruteFor
       long[] datasetShape = {rows, cols};
       var tensorDataArena = Arena.ofShared();
       MemorySegment datasetTensor =
-          prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 2, 1);
+          prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 1);
 
       var returnValue = cuvsStreamSync(cuvsResources);
       checkCuVSError(returnValue, "cuvsStreamSync");
@@ -236,13 +237,13 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
 
         long[] queriesShape = {numQueries, vectorDimension};
         MemorySegment queriesTensor =
-            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 1);
         long[] neighborsShape = {numQueries, topk};
         MemorySegment neighborsTensor =
-            prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 2, 1);
+            prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 1);
         long[] distancesShape = {numQueries, topk};
         MemorySegment distancesTensor =
-            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 1);
 
         MemorySegment prefilter = cuvsFilter.allocate(localArena);
         MemorySegment prefilterTensor;
@@ -259,7 +260,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
 
           cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
-          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 2, 1);
 
           cuvsFilter.type(prefilter, 2);
           cuvsFilter.addr(prefilter, prefilterTensor.address());
@@ -393,7 +394,7 @@ public static BruteForceIndex.Builder newBuilder(CuVSResources cuvsResources) {
    */
   public static class Builder implements BruteForceIndex.Builder {
 
-    private Dataset dataset;
+    private CuVSMatrix dataset;
     private final CuVSResources cuvsResources;
     private BruteForceIndexParams bruteForceIndexParams;
     private InputStream inputStream;
@@ -441,18 +442,18 @@ public Builder from(InputStream inputStream) {
      */
     @Override
     public Builder withDataset(float[][] vectors) {
-      this.dataset = Dataset.ofArray(vectors);
+      this.dataset = CuVSMatrix.ofArray(vectors);
       return this;
     }
 
     /**
      * Sets the dataset for building the {@link BruteForceIndex}.
      *
-     * @param dataset a {@link Dataset} object containing the vectors
+     * @param dataset a {@link CuVSMatrix} object containing the vectors
      * @return an instance of this Builder
      */
     @Override
-    public Builder withDataset(Dataset dataset) {
+    public Builder withDataset(CuVSMatrix dataset) {
       this.dataset = dataset;
       return this;
     }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 028296d270..4fc373e8ca 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -39,8 +39,8 @@
 import com.nvidia.cuvs.CagraSearchParams;
 import com.nvidia.cuvs.CuVSIvfPqIndexParams;
 import com.nvidia.cuvs.CuVSIvfPqSearchParams;
+import com.nvidia.cuvs.CuVSMatrix;
 import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.Dataset;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.CloseableHandle;
 import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
@@ -90,11 +90,11 @@ public class CagraIndexImpl implements CagraIndex {
    * @param resources       an instance of {@link CuVSResources}
    */
   private CagraIndexImpl(
-      CagraIndexParams indexParameters, Dataset dataset, CuVSResources resources) {
+      CagraIndexParams indexParameters, CuVSMatrix dataset, CuVSResources resources) {
     Objects.requireNonNull(dataset);
     this.resources = resources;
-    assert dataset instanceof DatasetImpl;
-    this.cagraIndexReference = build(indexParameters, (DatasetImpl) dataset);
+    assert dataset instanceof CuVSMatrixBaseImpl;
+    this.cagraIndexReference = build(indexParameters, (CuVSMatrixBaseImpl) dataset);
   }
 
   /**
@@ -151,9 +151,9 @@ public void destroyIndex() throws Throwable {
    * @return an instance of {@link IndexReference} that holds the pointer to the
    *         index
    */
-  private IndexReference build(CagraIndexParams indexParameters, DatasetImpl dataset) {
+  private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImpl dataset) {
     long rows = dataset.size();
-    long cols = dataset.dimensions();
+    long cols = dataset.columns();
 
     try (var indexParams = segmentFromIndexParams(indexParameters);
         var localArena = Arena.ofConfined()) {
@@ -162,11 +162,10 @@ private IndexReference build(CagraIndexParams indexParameters, DatasetImpl datas
       int numWriterThreads = indexParameters != null ? indexParameters.getNumWriterThreads() : 1;
       omp_set_num_threads(numWriterThreads);
 
-      MemorySegment dataSeg = dataset.asMemorySegment();
+      MemorySegment dataSeg = dataset.memorySegment();
 
       long[] datasetShape = {rows, cols};
-      MemorySegment datasetTensor =
-          prepareTensor(localArena, dataSeg, datasetShape, 2, 32, 2, 2, 1);
+      MemorySegment datasetTensor = prepareTensor(localArena, dataSeg, datasetShape, 2, 32, 2, 1);
 
       var index = createCagraIndex();
 
@@ -253,13 +252,13 @@ public SearchResults search(CagraQuery query) throws Throwable {
 
         long[] queriesShape = {numQueries, vectorDimension};
         MemorySegment queriesTensor =
-            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 2, 1);
+            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 1);
         long[] neighborsShape = {numQueries, topK};
         MemorySegment neighborsTensor =
-            prepareTensor(localArena, neighborsDP, neighborsShape, 1, 32, 2, 2, 1);
+            prepareTensor(localArena, neighborsDP, neighborsShape, 1, 32, 2, 1);
         long[] distancesShape = {numQueries, topK};
         MemorySegment distancesTensor =
-            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 2, 1);
+            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 1);
 
         var returnValue = cuvsStreamSync(cuvsRes);
         checkCuVSError(returnValue, "cuvsStreamSync");
@@ -294,7 +293,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
 
           cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
-          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 1, 2, 1);
+          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 2, 1);
 
           cuvsFilter.type(prefilter, 1);
           cuvsFilter.addr(prefilter, prefilterTensor.address());
@@ -680,7 +679,7 @@ private static CloseableHandle createMergeParamsSegment(CagraMergeParams mergePa
    */
   public static class Builder implements CagraIndex.Builder {
 
-    private Dataset dataset;
+    private CuVSMatrix dataset;
     private CagraIndexParams cagraIndexParams;
     private final CuVSResources cuvsResources;
     private InputStream inputStream;
@@ -697,12 +696,12 @@ public Builder from(InputStream inputStream) {
 
     @Override
     public Builder withDataset(float[][] vectors) {
-      this.dataset = Dataset.ofArray(vectors);
+      this.dataset = CuVSMatrix.ofArray(vectors);
       return this;
     }
 
     @Override
-    public Builder withDataset(Dataset dataset) {
+    public Builder withDataset(CuVSMatrix dataset) {
       this.dataset = dataset;
       return this;
     }
@@ -729,7 +728,7 @@ public CagraIndexImpl build() throws Throwable {
   public static class IndexReference {
 
     private final MemorySegment memorySegment;
-    private final Dataset dataset;
+    private final CuVSMatrix dataset;
 
     /**
      * Constructs CagraIndexReference with an instance of MemorySegment passed as a
@@ -742,7 +741,7 @@ public static class IndexReference {
      *                           to it so we can close it when the index is closed.
      *                           Can be null (e.g. from deserialization or merging)
      */
-    private IndexReference(MemorySegment indexMemorySegment, Dataset dataset) {
+    private IndexReference(MemorySegment indexMemorySegment, CuVSMatrix dataset) {
       this.memorySegment = indexMemorySegment;
       this.dataset = dataset;
     }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixArenaImpl.java
similarity index 54%
rename from java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
rename to java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixArenaImpl.java
index ee065e632b..1dc2350638 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/DatasetImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixArenaImpl.java
@@ -15,22 +15,37 @@
  */
 package com.nvidia.cuvs.internal;
 
-import com.nvidia.cuvs.Dataset;
 import java.lang.foreign.Arena;
-import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout;
 import java.util.concurrent.atomic.AtomicReference;
 
-public class DatasetImpl implements Dataset {
+/**
+ * A Dataset implementation backed by host (CPU) memory.
+ * Memory is allocated and managed by the implementation, via a Java shared {@link Arena}
+ */
+public class CuVSHostMatrixArenaImpl extends CuVSHostMatrixImpl {
   private final AtomicReference<Arena> arenaReference;
-  private final MemorySegment seg;
-  private final int size;
-  private final int dimensions;
 
-  public DatasetImpl(Arena arena, MemorySegment memorySegment, int size, int dimensions) {
+  public CuVSHostMatrixArenaImpl(long size, long columns, DataType dataType) {
+    this(
+        size,
+        columns,
+        dataType,
+        valueLayoutFromType(dataType),
+        sequenceLayoutFromType(size, columns, dataType),
+        Arena.ofShared());
+  }
+
+  private CuVSHostMatrixArenaImpl(
+      long size,
+      long columns,
+      DataType dataType,
+      ValueLayout valueLayout,
+      SequenceLayout layout,
+      Arena arena) {
+    super(arena.allocate(layout), size, columns, dataType, valueLayout, layout);
     this.arenaReference = new AtomicReference<>(arena);
-    this.seg = memorySegment;
-    this.size = size;
-    this.dimensions = dimensions;
   }
 
   @Override
@@ -40,18 +55,4 @@ public void close() {
       arena.close();
     }
   }
-
-  @Override
-  public int size() {
-    return size;
-  }
-
-  @Override
-  public int dimensions() {
-    return dimensions;
-  }
-
-  public MemorySegment asMemorySegment() {
-    return seg;
-  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
new file mode 100644
index 0000000000..a7096e207c
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -0,0 +1,198 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+
+import com.nvidia.cuvs.CuVSHostMatrix;
+import com.nvidia.cuvs.RowView;
+import java.lang.foreign.MemoryLayout;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SequenceLayout;
+import java.lang.foreign.ValueLayout;
+import java.lang.invoke.VarHandle;
+
+/**
+ * A Dataset implementation backed by host (CPU) memory.
+ */
+public class CuVSHostMatrixImpl extends CuVSMatrixBaseImpl implements CuVSHostMatrix {
+  private final ValueLayout valueLayout;
+  protected final VarHandle accessor$vh;
+
+  public CuVSHostMatrixImpl(
+      MemorySegment memorySegment, long size, long columns, DataType dataType) {
+    this(
+        memorySegment,
+        size,
+        columns,
+        dataType,
+        valueLayoutFromType(dataType),
+        MemoryLayout.sequenceLayout(size * columns, valueLayoutFromType(dataType))
+            .withByteAlignment(32));
+  }
+
+  protected CuVSHostMatrixImpl(
+      MemorySegment memorySegment,
+      long size,
+      long columns,
+      DataType dataType,
+      ValueLayout valueLayout,
+      MemoryLayout sequenceLayout) {
+    super(memorySegment, dataType, size, columns);
+    this.accessor$vh = sequenceLayout.varHandle(MemoryLayout.PathElement.sequenceElement());
+    this.valueLayout = valueLayout;
+  }
+
+  protected static ValueLayout valueLayoutFromType(DataType dataType) {
+    return switch (dataType) {
+      case FLOAT -> C_FLOAT;
+      case INT -> C_INT;
+      case BYTE -> C_CHAR;
+    };
+  }
+
+  protected static SequenceLayout sequenceLayoutFromType(
+      long size, long columns, DataType dataType) {
+    return MemoryLayout.sequenceLayout(size * columns, valueLayoutFromType(dataType))
+        .withByteAlignment(32);
+  }
+
+  @Override
+  public RowView getRow(long nodeIndex) {
+    var valueByteSize = valueLayout.byteSize();
+    return new SliceRowView(
+        memorySegment.asSlice(nodeIndex * columns * valueByteSize, columns * valueByteSize),
+        columns,
+        valueLayout,
+        dataType,
+        valueByteSize);
+  }
+
+  @Override
+  public void toArray(int[][] array) {
+    assert dataType == DataType.INT;
+    assert (array.length >= size) : "Input array is not large enough";
+    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    var valueByteSize = valueLayout.byteSize();
+    for (int r = 0; r < size; ++r) {
+      MemorySegment.copy(
+          memorySegment, valueLayout, r * columns * valueByteSize, array[r], 0, (int) columns);
+    }
+  }
+
+  @Override
+  public void toArray(float[][] array) {
+    assert dataType == DataType.FLOAT;
+    assert (array.length >= size) : "Input array is not large enough";
+    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    var valueByteSize = valueLayout.byteSize();
+    for (int r = 0; r < size; ++r) {
+      MemorySegment.copy(
+          memorySegment, valueLayout, r * columns * valueByteSize, array[r], 0, (int) columns);
+    }
+  }
+
+  @Override
+  public void toArray(byte[][] array) {
+    assert dataType == DataType.BYTE;
+    assert (array.length >= size) : "Input array is not large enough";
+    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    var valueByteSize = valueLayout.byteSize();
+    for (int r = 0; r < size; ++r) {
+      MemorySegment.copy(
+          memorySegment, valueLayout, r * columns * valueByteSize, array[r], 0, (int) columns);
+    }
+  }
+
+  @Override
+  public void close() {}
+
+  @Override
+  public int get(int row, int col) {
+    return (int) accessor$vh.get(memorySegment, 0L, (long) row * columns + col);
+  }
+
+  public ValueLayout valueLayout() {
+    return valueLayout;
+  }
+
+  private static class SliceRowView implements RowView {
+    private final MemorySegment memorySegment;
+    private final long size;
+    private final ValueLayout valueLayout;
+    private final DataType dataType;
+    private final long valueByteSize;
+
+    SliceRowView(
+        MemorySegment slice,
+        long size,
+        ValueLayout valueLayout,
+        DataType dataType,
+        long valueByteSize) {
+      this.memorySegment = slice;
+      this.size = size;
+      this.valueLayout = valueLayout;
+      this.dataType = dataType;
+      this.valueByteSize = valueByteSize;
+    }
+
+    @Override
+    public long size() {
+      return size;
+    }
+
+    @Override
+    public float getAsFloat(long index) {
+      assert dataType == DataType.FLOAT;
+      return memorySegment.get((ValueLayout.OfFloat) valueLayout, index * valueByteSize);
+    }
+
+    @Override
+    public byte getAsByte(long index) {
+      assert dataType == DataType.BYTE;
+      return memorySegment.get((ValueLayout.OfByte) valueLayout, index * valueByteSize);
+    }
+
+    @Override
+    public int getAsInt(long index) {
+      assert dataType == DataType.INT;
+      return memorySegment.get((ValueLayout.OfInt) valueLayout, index * valueByteSize);
+    }
+
+    @Override
+    public void toArray(int[] array) {
+      assert (array.length >= size) : "Input array is not large enough";
+      assert dataType == DataType.INT;
+      MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
+    }
+
+    @Override
+    public void toArray(float[] array) {
+      assert (array.length >= size) : "Input array is not large enough";
+      assert dataType == DataType.FLOAT;
+      MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
+    }
+
+    @Override
+    public void toArray(byte[] array) {
+      assert (array.length >= size) : "Input array is not large enough";
+      assert dataType == DataType.BYTE;
+      MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
new file mode 100644
index 0000000000..fc9daf9387
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.CuVSMatrix;
+import java.lang.foreign.MemorySegment;
+
+public abstract class CuVSMatrixBaseImpl implements CuVSMatrix {
+  protected final MemorySegment memorySegment;
+  protected final DataType dataType;
+  protected final long size;
+  protected final long columns;
+
+  protected CuVSMatrixBaseImpl(
+      MemorySegment memorySegment, DataType dataType, long size, long columns) {
+    this.memorySegment = memorySegment;
+    this.dataType = dataType;
+    this.size = size;
+    this.columns = columns;
+  }
+
+  @Override
+  public long size() {
+    return size;
+  }
+
+  @Override
+  public long columns() {
+    return columns;
+  }
+
+  public MemorySegment memorySegment() {
+    return memorySegment;
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index a4231450e2..1c97422d15 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -103,14 +103,13 @@ public SearchResults search(HnswQuery query) throws Throwable {
       MemorySegment querySeg = buildMemorySegment(localArena, queryVectors);
 
       long[] queriesShape = {numQueries, vectorDimension};
-      MemorySegment queriesTensor =
-          prepareTensor(localArena, querySeg, queriesShape, 2, 32, 2, 1, 1);
+      MemorySegment queriesTensor = prepareTensor(localArena, querySeg, queriesShape, 2, 32, 1, 1);
       long[] neighborsShape = {numQueries, topK};
       MemorySegment neighborsTensor =
-          prepareTensor(localArena, neighborsMemorySegment, neighborsShape, 1, 64, 2, 1, 1);
+          prepareTensor(localArena, neighborsMemorySegment, neighborsShape, 1, 64, 1, 1);
       long[] distancesShape = {numQueries, topK};
       MemorySegment distancesTensor =
-          prepareTensor(localArena, distancesMemorySegment, distancesShape, 2, 32, 2, 1, 1);
+          prepareTensor(localArena, distancesMemorySegment, distancesShape, 2, 32, 1, 1);
 
       try (var resourcesAccessor = resources.access()) {
         var cuvsRes = resourcesAccessor.handle();
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index f5dcb5eff0..9dfbf5a843 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -30,14 +30,8 @@
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
-import com.nvidia.cuvs.CagraIndexParams;
-import com.nvidia.cuvs.CagraSearchParams;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.Dataset;
-import com.nvidia.cuvs.SearchResults;
-import com.nvidia.cuvs.TieredIndex;
-import com.nvidia.cuvs.TieredIndexParams;
-import com.nvidia.cuvs.TieredIndexQuery;
+import com.nvidia.cuvs.*;
+import com.nvidia.cuvs.CuVSMatrix;
 import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
@@ -62,7 +56,7 @@
  * @since 25.02
  */
 public class TieredIndexImpl implements TieredIndex {
-  private final Dataset dataset;
+  private final CuVSMatrix dataset;
   private final CuVSResources resources;
   private final TieredIndexParams tieredIndexParameters;
   private final IndexReference tieredIndexReference;
@@ -72,7 +66,7 @@ public class TieredIndexImpl implements TieredIndex {
    * Constructor for building the index using specified dataset
    */
   private TieredIndexImpl(
-      TieredIndexParams indexParameters, Dataset dataset, CuVSResources resources) {
+      TieredIndexParams indexParameters, CuVSMatrix dataset, CuVSResources resources) {
     this.tieredIndexParameters = indexParameters;
     this.dataset = dataset;
     this.resources = resources;
@@ -97,7 +91,7 @@ private void checkNotDestroyed() {
    * Invokes the native destroy_tiered_index to de-allocate the Tiered index
    */
   @Override
-  public void destroyIndex() throws Throwable {
+  public void destroyIndex() {
     checkNotDestroyed();
     try {
       int returnValue = cuvsTieredIndexDestroy(tieredIndexReference.getMemorySegment());
@@ -105,7 +99,6 @@ public void destroyIndex() throws Throwable {
       if (dataset != null) {
         dataset.close();
       }
-
     } finally {
       destroyed = true;
     }
@@ -121,8 +114,9 @@ public void destroyIndex() throws Throwable {
    */
   private IndexReference build() {
     try (var localArena = Arena.ofConfined()) {
+      assert dataset != null;
       long rows = dataset.size();
-      long cols = dataset.dimensions();
+      long cols = dataset.columns();
 
       MemorySegment indexParamsMemorySegment =
           tieredIndexParameters != null
@@ -130,7 +124,7 @@ private IndexReference build() {
               : MemorySegment.NULL;
 
       // Get host data
-      MemorySegment hostDataSeg = ((DatasetImpl) dataset).asMemorySegment();
+      MemorySegment hostDataSeg = ((CuVSHostMatrixImpl) dataset).memorySegment();
 
       try (var resourceAccess = resources.access()) {
         long cuvsRes = resourceAccess.handle();
@@ -145,7 +139,7 @@ private IndexReference build() {
         // Create tensor from device memory
         long[] datasetShape = {rows, cols};
         MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
+            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
 
         MemorySegment index = localArena.allocate(cuvsTieredIndex_t);
         var returnValue = cuvsTieredIndexCreate(index);
@@ -219,21 +213,14 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
         // Create tensors from device memory
         long[] queriesShape = {numQueries, vectorDimension};
         MemorySegment queriesTensor =
-            prepareTensor(localArena, queriesDP, queriesShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
+            prepareTensor(localArena, queriesDP, queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
         long[] neighborsShape = {numQueries, topK};
         MemorySegment neighborsTensor =
             prepareTensor(
-                localArena,
-                neighborsDP,
-                neighborsShape,
-                kDLInt(),
-                64,
-                2,
-                kDLCUDA(),
-                1); // 64-bit int
+                localArena, neighborsDP, neighborsShape, kDLInt(), 64, kDLCUDA(), 1); // 64-bit int
         long[] distancesShape = {numQueries, topK};
         MemorySegment distancesTensor =
-            prepareTensor(localArena, distancesDP, distancesShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
+            prepareTensor(localArena, distancesDP, distancesShape, kDLFloat(), 32, kDLCUDA(), 1);
 
         // Sync before prefilter setup
         returnValue = cuvsStreamSync(cuvsRes);
@@ -264,8 +251,7 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
               "cudaMemcpy");
 
           MemorySegment prefilterTensor =
-              prepareTensor(
-                  localArena, prefilterDP, prefilterShape, kDLUInt(), 32, 1, kDLCUDA(), 1);
+              prepareTensor(localArena, prefilterDP, prefilterShape, kDLUInt(), 32, kDLCUDA(), 1);
 
           cuvsFilter.type(prefilter, 1); // BITSET
           cuvsFilter.addr(prefilter, prefilterTensor.address());
@@ -330,13 +316,14 @@ public ExtendBuilder extend() {
   /**
    * Performs the actual extend operation
    */
-  private void performExtend(Dataset extendDataset) throws Throwable {
+  private void performExtend(CuVSMatrix extendDataset) {
     try (var localArena = Arena.ofConfined()) {
+      assert extendDataset != null;
       long rows = extendDataset.size();
-      long cols = extendDataset.dimensions();
+      long cols = extendDataset.columns();
 
       // Get host data
-      MemorySegment hostDataSeg = ((DatasetImpl) extendDataset).asMemorySegment();
+      MemorySegment hostDataSeg = ((CuVSMatrixBaseImpl) extendDataset).memorySegment();
 
       try (var resourceAccess = resources.access()) {
         long cuvsRes = resourceAccess.handle();
@@ -352,7 +339,7 @@ private void performExtend(Dataset extendDataset) throws Throwable {
         // Create tensor from device memory
         long[] datasetShape = {rows, cols};
         MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, 2, kDLCUDA(), 1);
+            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
 
         checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
 
@@ -371,7 +358,7 @@ private void performExtend(Dataset extendDataset) throws Throwable {
    */
   public static class ExtendBuilder implements TieredIndex.ExtendBuilder {
     private final TieredIndexImpl index;
-    private Dataset dataset;
+    private CuVSMatrix dataset;
 
     private ExtendBuilder(TieredIndexImpl index) {
       this.index = index;
@@ -379,18 +366,18 @@ private ExtendBuilder(TieredIndexImpl index) {
 
     @Override
     public ExtendBuilder withDataset(float[][] vectors) {
-      this.dataset = Dataset.ofArray(vectors);
+      this.dataset = CuVSMatrix.ofArray(vectors);
       return this;
     }
 
     @Override
-    public ExtendBuilder withDataset(Dataset dataset) {
+    public ExtendBuilder withDataset(CuVSMatrix dataset) {
       this.dataset = dataset;
       return this;
     }
 
     @Override
-    public void execute() throws Throwable {
+    public void execute() {
       if (dataset == null) {
         throw new IllegalArgumentException("Must provide a dataset");
       }
@@ -508,7 +495,7 @@ public static TieredIndex.Builder newBuilder(CuVSResources cuvsResources) {
    */
   public static class Builder implements TieredIndex.Builder {
     private final CuVSResources resources;
-    private Dataset dataset;
+    private CuVSMatrix dataset;
     private TieredIndexParams params;
     private TieredIndexType indexType = TieredIndexType.CAGRA;
     private InputStream inputStream;
@@ -531,12 +518,12 @@ public Builder withDataset(float[][] vectors) {
       if (vectors == null || vectors.length == 0 || vectors[0].length == 0) {
         throw new IllegalArgumentException("The input vectors cannot be null or empty");
       }
-      this.dataset = Dataset.ofArray(vectors);
+      this.dataset = CuVSMatrix.ofArray(vectors);
       return this;
     }
 
     @Override
-    public Builder withDataset(Dataset dataset) {
+    public Builder withDataset(CuVSMatrix dataset) {
       if (this.dataset != null) {
         throw new IllegalArgumentException("An input dataset can only be specified once");
       }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 576b182588..6d08878650 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -264,11 +264,32 @@ public static MemorySegment buildMemorySegment(Arena arena, float[][] data) {
     long cols = rows > 0 ? data[0].length : 0;
     MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout(rows * cols, C_FLOAT);
     MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+    copy(dataMemorySegment, data);
+    return dataMemorySegment;
+  }
+
+  public static void copy(MemorySegment memorySegment, float[][] data) {
+    int rows = data.length;
+    int cols = rows > 0 ? data[0].length : 0;
     for (int r = 0; r < rows; r++) {
-      MemorySegment.copy(
-          data[r], 0, dataMemorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()), (int) cols);
+      MemorySegment.copy(data[r], 0, memorySegment, C_FLOAT, (r * cols * C_FLOAT.byteSize()), cols);
+    }
+  }
+
+  public static void copy(MemorySegment memorySegment, int[][] data) {
+    int rows = data.length;
+    int cols = rows > 0 ? data[0].length : 0;
+    for (int r = 0; r < rows; r++) {
+      MemorySegment.copy(data[r], 0, memorySegment, C_INT, (r * cols * C_INT.byteSize()), cols);
+    }
+  }
+
+  public static void copy(MemorySegment memorySegment, byte[][] data) {
+    int rows = data.length;
+    int cols = rows > 0 ? data[0].length : 0;
+    for (int r = 0; r < rows; r++) {
+      MemorySegment.copy(data[r], 0, memorySegment, C_CHAR, (r * cols * C_CHAR.byteSize()), cols);
     }
-    return dataMemorySegment;
   }
 
   public static BitSet concatenate(BitSet[] arr, int maxSizeOfEachBitSet) {
@@ -293,7 +314,6 @@ public static BitSet concatenate(BitSet[] arr, int maxSizeOfEachBitSet) {
    * @param[in] shape the shape of the tensor
    * @param[in] code the type code of base types
    * @param[in] bits the shape of the tensor
-   * @param[in] ndim the number of dimensions
    * @return DLManagedTensor
    */
   public static MemorySegment prepareTensor(
@@ -302,7 +322,6 @@ public static MemorySegment prepareTensor(
       long[] shape,
       int code,
       int bits,
-      int ndim,
       int deviceType,
       int lanes) {
 
@@ -315,6 +334,7 @@ public static MemorySegment prepareTensor(
     DLDevice.device_type(dlDevice, deviceType);
     DLTensor.device(dlTensor, dlDevice);
 
+    var ndim = shape.length;
     DLTensor.ndim(dlTensor, ndim);
 
     MemorySegment dtype = DLDataType.allocate(arena);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 5940e3db48..668befe236 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -15,24 +15,9 @@
  */
 package com.nvidia.cuvs.spi;
 
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-
-import com.nvidia.cuvs.BruteForceIndex;
-import com.nvidia.cuvs.CagraIndex;
-import com.nvidia.cuvs.CagraMergeParams;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.Dataset;
-import com.nvidia.cuvs.HnswIndex;
-import com.nvidia.cuvs.TieredIndex;
-import com.nvidia.cuvs.internal.BruteForceIndexImpl;
-import com.nvidia.cuvs.internal.CagraIndexImpl;
-import com.nvidia.cuvs.internal.CuVSResourcesImpl;
-import com.nvidia.cuvs.internal.DatasetImpl;
-import com.nvidia.cuvs.internal.HnswIndexImpl;
-import com.nvidia.cuvs.internal.TieredIndexImpl;
+import com.nvidia.cuvs.*;
+import com.nvidia.cuvs.internal.*;
 import com.nvidia.cuvs.internal.common.Util;
-import java.lang.foreign.Arena;
-import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.MethodHandles;
@@ -48,20 +33,26 @@ final class JDKProvider implements CuVSProvider {
   static MethodHandle createNativeDatasetBuilder() {
     try {
       var lookup = MethodHandles.lookup();
-      var mt = MethodType.methodType(Dataset.class, MemorySegment.class, int.class, int.class);
+      var mt =
+          MethodType.methodType(
+              CuVSMatrix.class,
+              MemorySegment.class,
+              int.class,
+              int.class,
+              CuVSMatrix.DataType.class);
       return lookup.findStatic(JDKProvider.class, "createNativeDataset", mt);
     } catch (NoSuchMethodException | IllegalAccessException e) {
       throw new RuntimeException(e);
     }
   }
 
-  private static Dataset createNativeDataset(
-      MemorySegment memorySegment, int size, int dimensions) {
-    return new DatasetImpl(null, memorySegment, size, dimensions);
+  private static CuVSMatrix createNativeDataset(
+      MemorySegment memorySegment, int size, int dimensions, CuVSMatrix.DataType dataType) {
+    return new CuVSHostMatrixImpl(memorySegment, size, dimensions, dataType);
   }
 
   @Override
-  public CuVSResources newCuVSResources(Path tempDirectory) throws Throwable {
+  public CuVSResources newCuVSResources(Path tempDirectory) {
     Objects.requireNonNull(tempDirectory);
     if (Files.notExists(tempDirectory)) {
       throw new IllegalArgumentException("does not exist:" + tempDirectory);
@@ -110,46 +101,91 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merge
   }
 
   @Override
-  public Dataset.Builder newDatasetBuilder(int size, int dimensions)
+  public CuVSMatrix.Builder newMatrixBuilder(int size, int dimensions, CuVSMatrix.DataType dataType)
       throws UnsupportedOperationException {
-    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout((long) size * dimensions, C_FLOAT);
 
-    var arena = Arena.ofShared();
-    var seg = arena.allocate(dataMemoryLayout);
+    var dataset = new CuVSHostMatrixArenaImpl(size, dimensions, dataType);
 
-    return new Dataset.Builder() {
+    return new CuVSMatrix.Builder() {
       int current = 0;
 
       @Override
       public void addVector(float[] vector) {
+        internalAddVector(vector);
+      }
+
+      @Override
+      public void addVector(byte[] vector) {
+        internalAddVector(vector);
+      }
+
+      @Override
+      public void addVector(int[] vector) {
+        internalAddVector(vector);
+      }
+
+      private void internalAddVector(Object vector) {
         if (current >= size) throw new ArrayIndexOutOfBoundsException();
         MemorySegment.copy(
-            vector, 0, seg, C_FLOAT, ((current++) * dimensions * C_FLOAT.byteSize()), dimensions);
+            vector,
+            0,
+            dataset.memorySegment(),
+            dataset.valueLayout(),
+            ((current++) * dimensions * dataset.valueLayout().byteSize()),
+            dimensions);
       }
 
       @Override
-      public Dataset build() {
-        return new DatasetImpl(arena, seg, size, dimensions);
+      public CuVSMatrix build() {
+        return dataset;
       }
     };
   }
 
   @Override
-  public MethodHandle newNativeDatasetBuilder() {
+  public MethodHandle newNativeMatrixBuilder() {
     return createNativeDataset$mh;
   }
 
   @Override
-  public Dataset newArrayDataset(float[][] vectors) {
+  public CuVSMatrix newMatrixFromArray(float[][] vectors) {
+    Objects.requireNonNull(vectors);
+    if (vectors.length == 0) {
+      throw new IllegalArgumentException("vectors should not be empty");
+    }
+    int size = vectors.length;
+    int columns = vectors[0].length;
+
+    var dataset = new CuVSHostMatrixArenaImpl(size, columns, CuVSMatrix.DataType.FLOAT);
+    Util.copy(dataset.memorySegment(), vectors);
+    return dataset;
+  }
+
+  @Override
+  public CuVSMatrix newMatrixFromArray(int[][] vectors) {
+    Objects.requireNonNull(vectors);
+    if (vectors.length == 0) {
+      throw new IllegalArgumentException("vectors should not be empty");
+    }
+    int size = vectors.length;
+    int columns = vectors[0].length;
+
+    var dataset = new CuVSHostMatrixArenaImpl(size, columns, CuVSMatrix.DataType.INT);
+    Util.copy(dataset.memorySegment(), vectors);
+    return dataset;
+  }
+
+  @Override
+  public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     Objects.requireNonNull(vectors);
     if (vectors.length == 0) {
       throw new IllegalArgumentException("vectors should not be empty");
     }
     int size = vectors.length;
-    int dimensions = vectors[0].length;
+    int columns = vectors[0].length;
 
-    Arena arena = Arena.ofShared();
-    var memorySegment = Util.buildMemorySegment(arena, vectors);
-    return new DatasetImpl(arena, memorySegment, size, dimensions);
+    var dataset = new CuVSHostMatrixArenaImpl(size, columns, CuVSMatrix.DataType.BYTE);
+    Util.copy(dataset.memorySegment(), vectors);
+    return dataset;
   }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index c39c3c0dfa..92f215165b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -18,7 +18,8 @@
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 
 import java.io.*;
-import java.lang.invoke.MethodHandles;
+import java.nio.file.Files;
+import java.nio.file.Path;
 import java.util.BitSet;
 import java.util.List;
 import java.util.Map;
@@ -26,13 +27,9 @@
 import java.util.function.LongToIntFunction;
 import org.junit.Before;
 import org.junit.Test;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
 
 public class BruteForceAndSearchIT extends CuVSTestCase {
 
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
-
   @Before
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
@@ -67,31 +64,30 @@ private static void indexAndQueryOnce(
             .build();
 
     // Saving the index on to the disk.
-    String indexFileName = UUID.randomUUID().toString() + ".bf";
+    String indexFileName = UUID.randomUUID() + ".bf";
     try (var outputStream = new FileOutputStream(indexFileName)) {
       index.serialize(outputStream);
     }
 
     // Loading a BRUTEFORCE index from disk.
-    File indexFile = new File(indexFileName);
-    InputStream inputStream = new FileInputStream(indexFile);
-    BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources).from(inputStream).build();
-
-    // search the loaded index
-    SearchResults results = loadedIndex.search(cuvsQuery);
-    checkResults(expectedResults, results.getResults());
+    Path indexFile = Path.of(indexFileName);
+    try (var inputStream = Files.newInputStream(indexFile)) {
+      BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources).from(inputStream).build();
 
-    // search the first index
-    results = index.search(cuvsQuery);
-    checkResults(expectedResults, results.getResults());
+      // search the loaded index
+      SearchResults results = loadedIndex.search(cuvsQuery);
+      checkResults(expectedResults, results.getResults());
 
-    // Cleanup
-    index.destroyIndex();
-    loadedIndex.destroyIndex();
+      // search the first index
+      results = index.search(cuvsQuery);
+      checkResults(expectedResults, results.getResults());
 
-    if (indexFile.exists()) {
-      indexFile.delete();
+      // Cleanup
+      index.destroyIndex();
+      loadedIndex.destroyIndex();
     }
+
+    Files.deleteIfExists(indexFile);
   }
 
   /**
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index a998b3ee9e..fb338106f2 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -120,7 +120,8 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
       BruteForceIndex index;
       if (useNativeMemoryDataset) {
-        var datasetBuilder = Dataset.builder(vectors.length, vectors[0].length);
+        var datasetBuilder =
+            CuVSMatrix.builder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
         for (float[] v : vectors) {
           datasetBuilder.addVector(v);
         }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index bfbb4e2d1b..10ea10bcf5 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -16,9 +16,7 @@
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-import static org.junit.Assert.fail;
+import static org.junit.Assert.*;
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
@@ -139,7 +137,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     int numTestsRuns = 5;
     try (CuVSResources resources = CheckedCuVSResources.create()) {
       for (int j = 0; j < numTestsRuns; j++) {
-        var index = indexOnce(Dataset.ofArray(dataset), resources);
+        var index = indexOnce(CuVSMatrix.ofArray(dataset), resources);
         var indexPath = serializeOnce(index);
         var loadedIndex = deserializeOnce(indexPath, resources);
         queryAndCompare(
@@ -170,7 +168,7 @@ public void testIndexingAndSearchingFlowInDifferentThreads() throws Throwable {
         runInAnotherThread(
             () -> {
               try {
-                var index = indexOnce(Dataset.ofArray(dataset), resources);
+                var index = indexOnce(CuVSMatrix.ofArray(dataset), resources);
                 var indexPath = serializeOnce(index);
                 var loadedIndex = deserializeOnce(indexPath, resources);
                 queryAndCompare(
@@ -200,12 +198,13 @@ public void testIndexingAndSearchingFlowConcurrently() throws Throwable {
     List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
     int numTestsRuns = 10;
+
     runConcurrently(
         numTestsRuns,
         () ->
             () -> {
               try (CuVSResources resources = CheckedCuVSResources.create()) {
-                var index = indexOnce(Dataset.ofArray(dataset), resources);
+                var index = indexOnce(CuVSMatrix.ofArray(dataset), resources);
                 var indexPath = serializeOnce(index);
                 var loadedIndex = deserializeOnce(indexPath, resources);
                 queryAndCompare(
@@ -233,7 +232,7 @@ public void testIndexing() throws Throwable {
           () ->
               () -> {
                 try (CuVSResources resources = CheckedCuVSResources.create()) {
-                  var index = indexOnce(Dataset.ofArray(dataset), resources);
+                  var index = indexOnce(CuVSMatrix.ofArray(dataset), resources);
                   index.destroyIndex();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
@@ -252,7 +251,7 @@ public void testSerialization() throws Throwable {
           () ->
               () -> {
                 try (CuVSResources resources = CheckedCuVSResources.create()) {
-                  var index = indexOnce(Dataset.ofArray(dataset), resources);
+                  var index = indexOnce(CuVSMatrix.ofArray(dataset), resources);
                   var indexPath = serializeOnce(index);
                   Files.deleteIfExists(indexPath);
                   index.destroyIndex();
@@ -265,7 +264,7 @@ public void testSerialization() throws Throwable {
 
   @Test
   public void testDeserialization() throws Throwable {
-    var indexPath = createSerializedIndex(Dataset.ofArray(createSampleData()));
+    var indexPath = createSerializedIndex(CuVSMatrix.ofArray(createSampleData()));
     for (int i = 0; i < 100; ++i) {
       int numTestsRuns = 10;
       runConcurrently(
@@ -282,7 +281,7 @@ public void testDeserialization() throws Throwable {
     Files.deleteIfExists(indexPath);
   }
 
-  private Path createSerializedIndex(Dataset dataset) throws Throwable {
+  private Path createSerializedIndex(CuVSMatrix dataset) throws Throwable {
     try (CuVSResources resources = CheckedCuVSResources.create()) {
       var index = indexOnce(dataset, resources);
       var indexPath = serializeOnce(index);
@@ -293,7 +292,7 @@ private Path createSerializedIndex(Dataset dataset) throws Throwable {
 
   @Test
   public void testIndexingAndSearchingFlowWithCustomMappingFunction() throws Throwable {
-    var dataset = Dataset.ofArray(createSampleData());
+    var dataset = CuVSMatrix.ofArray(createSampleData());
     float[][] queries = createSampleQueries();
     var expectedResults =
         List.of(
@@ -315,7 +314,7 @@ public void testIndexingAndSearchingFlowWithCustomMappingFunction() throws Throw
 
   @Test
   public void testIndexingAndSearchingFlowWithCustomMappingList() throws Throwable {
-    var dataset = Dataset.ofArray(createSampleData());
+    var dataset = CuVSMatrix.ofArray(createSampleData());
     float[][] queries = createSampleQueries();
     var mappings = List.of(4, 3, 2, 1);
     var expectedResults =
@@ -404,7 +403,7 @@ public void testPrefilteringReducesResults() throws Throwable {
     }
   }
 
-  private CagraIndex indexOnce(Dataset dataset, CuVSResources resources) throws Throwable {
+  private CagraIndex indexOnce(CuVSMatrix dataset, CuVSResources resources) throws Throwable {
     // Configure index parameters
     CagraIndexParams indexParams =
         new CagraIndexParams.Builder()
@@ -500,8 +499,10 @@ public void testNativeDatasetEquivalent() throws Throwable {
       }
 
       try (var resources = CuVSResources.create();
-          var javaDataset = Dataset.ofArray(sampleData);
-          var nativeDataset = DatasetHelper.fromMemorySegment(dataMemorySegment, rows, cols)) {
+          var javaDataset = CuVSMatrix.ofArray(sampleData);
+          var nativeDataset =
+              DatasetHelper.fromMemorySegment(
+                  dataMemorySegment, rows, cols, CuVSMatrix.DataType.FLOAT)) {
 
         // Indexing with an on-heap and native datasets produce the same results
         var javaIndex = indexOnce(javaDataset, resources);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index cb81f8464b..af4965159b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -19,7 +19,6 @@
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
-import java.lang.invoke.MethodHandles;
 import java.util.BitSet;
 import java.util.List;
 import org.junit.Before;
@@ -31,7 +30,7 @@
 @RunWith(RandomizedRunner.class)
 public class CagraRandomizedIT extends CuVSTestCase {
 
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+  private static final Logger log = LoggerFactory.getLogger(CagraRandomizedIT.class);
 
   @Before
   public void setup() {
@@ -42,7 +41,7 @@ public void setup() {
 
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
-    boolean useNativeMemoryDatasets[] = {true, false};
+    boolean[] useNativeMemoryDatasets = {true, false};
     for (int i = 0; i < 100; i++) {
       for (boolean use : useNativeMemoryDatasets) {
         tmpResultsTopKWithRandomValues(use);
@@ -122,7 +121,8 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
       CagraIndex index;
       if (useNativeMemoryDataset) {
-        var datasetBuilder = Dataset.builder(vectors.length, vectors[0].length);
+        var datasetBuilder =
+            CuVSMatrix.builder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
         for (float[] v : vectors) {
           datasetBuilder.addVector(v);
         }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
new file mode 100644
index 0000000000..3518b8acd6
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
@@ -0,0 +1,286 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import static com.carrotsearch.randomizedtesting.RandomizedTest.*;
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+@RunWith(RandomizedRunner.class)
+public class CuVSMatrixIT extends CuVSTestCase {
+
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+    initializeRandom();
+  }
+
+  private static final float DELTA = 1e-9f;
+
+  private static final byte[][] byteData = {
+    {1, 2, 3},
+    {0, 2, 3},
+    {4, 1, 3},
+    {3, 0, 2},
+    {0, 4, 2}
+  };
+
+  private int[][] createIntMatrix() {
+    int rows = randomIntBetween(1, 32);
+    int cols = randomIntBetween(1, 100);
+
+    int[][] result = new int[rows][cols];
+
+    for (int r = 0; r < rows; ++r) {
+      for (int c = 0; c < cols; ++c) {
+        result[r][c] = randomInt();
+      }
+    }
+    return result;
+  }
+
+  private float[][] createFloatMatrix() {
+    int rows = randomIntBetween(1, 32);
+    int cols = randomIntBetween(1, 100);
+
+    float[][] result = new float[rows][cols];
+
+    for (int r = 0; r < rows; ++r) {
+      for (int c = 0; c < cols; ++c) {
+        result[r][c] = randomFloat();
+      }
+    }
+    return result;
+  }
+
+  @Test
+  public void testByteDatasetRowGetAccess() {
+    try (var dataset = CuVSMatrix.ofArray(byteData)) {
+      for (int n = 0; n < dataset.size(); ++n) {
+        var row = dataset.getRow(n);
+        assertEquals(dataset.columns(), row.size());
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(byteData[n][i], row.getAsByte(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testByteDatasetRowCopy() {
+    try (var dataset = CuVSMatrix.ofArray(byteData)) {
+      for (int n = 0; n < dataset.size(); ++n) {
+        var row = dataset.getRow(n);
+        assertEquals(dataset.columns(), row.size());
+
+        var rowCopy = new byte[(int) row.size()];
+        row.toArray(rowCopy);
+        assertArrayEquals(byteData[n], rowCopy);
+      }
+    }
+  }
+
+  @Test
+  public void testByteDatasetCopy() {
+    try (var dataset = CuVSMatrix.ofArray(byteData)) {
+      var dataCopy = new byte[(int) dataset.size()][(int) dataset.columns()];
+      dataset.toArray(dataCopy);
+      for (int n = 0; n < dataset.size(); ++n) {
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(byteData[n][i], dataCopy[n][i]);
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testIntDatasetRowGetAccess() {
+    var intData = createIntMatrix();
+    try (var dataset = CuVSMatrix.ofArray(intData)) {
+      for (int n = 0; n < dataset.size(); ++n) {
+        var row = dataset.getRow(n);
+        assertEquals(dataset.columns(), row.size());
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(intData[n][i], row.getAsInt(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testIntDatasetRowCopy() {
+    var intData = createIntMatrix();
+    try (var dataset = CuVSMatrix.ofArray(intData)) {
+      for (int n = 0; n < dataset.size(); ++n) {
+        var row = dataset.getRow(n);
+        assertEquals(dataset.columns(), row.size());
+
+        var rowCopy = new int[(int) row.size()];
+        row.toArray(rowCopy);
+        assertArrayEquals(intData[n], rowCopy);
+      }
+    }
+  }
+
+  @Test
+  public void testIntDatasetCopy() {
+    var intData = createIntMatrix();
+    try (var dataset = CuVSMatrix.ofArray(intData)) {
+      var intDataCopy = new int[(int) dataset.size()][(int) dataset.columns()];
+      dataset.toArray(intDataCopy);
+      for (int n = 0; n < dataset.size(); ++n) {
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(intData[n][i], intDataCopy[n][i]);
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testFloatDatasetRowGetAccess() {
+    var floatData = createFloatMatrix();
+    try (var dataset = CuVSMatrix.ofArray(floatData)) {
+      for (int n = 0; n < dataset.size(); ++n) {
+        var row = dataset.getRow(n);
+        assertEquals(dataset.columns(), row.size());
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(floatData[n][i], row.getAsFloat(i), DELTA);
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testFloatDatasetRowCopy() {
+    var floatData = createFloatMatrix();
+    try (var dataset = CuVSMatrix.ofArray(floatData)) {
+      for (int n = 0; n < dataset.size(); ++n) {
+        var row = dataset.getRow(n);
+        assertEquals(dataset.columns(), row.size());
+
+        var rowCopy = new float[(int) row.size()];
+        row.toArray(rowCopy);
+        assertArrayEquals(floatData[n], rowCopy, DELTA);
+      }
+    }
+  }
+
+  @Test
+  public void testFloatDatasetCopy() {
+    var floatData = createFloatMatrix();
+    try (var dataset = CuVSMatrix.ofArray(floatData)) {
+      var dataCopy = new float[(int) dataset.size()][(int) dataset.columns()];
+      dataset.toArray(dataCopy);
+      for (int n = 0; n < dataset.size(); ++n) {
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(floatData[n][i], dataCopy[n][i], DELTA);
+        }
+      }
+    }
+  }
+
+  public void testFloatDatasetBuilder() {
+    int rows = randomIntBetween(1, 32);
+    int cols = randomIntBetween(1, 100);
+
+    float[][] data = new float[rows][cols];
+    for (int r = 0; r < rows; ++r) {
+      for (int c = 0; c < cols; ++c) {
+        data[c][r] = randomFloat();
+      }
+    }
+
+    var builder = CuVSMatrix.builder(rows, cols, CuVSMatrix.DataType.FLOAT);
+    for (int r = 0; r < rows; ++r) {
+      builder.addVector(data[r]);
+    }
+
+    float[][] roundTripData = new float[rows][cols];
+
+    try (var dataset = builder.build()) {
+      dataset.toArray(roundTripData);
+
+      for (int n = 0; n < dataset.size(); ++n) {
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(data[n][i], roundTripData[n][i], DELTA);
+        }
+      }
+    }
+  }
+
+  public void testIntDatasetBuilder() {
+    int rows = randomIntBetween(1, 32);
+    int cols = randomIntBetween(1, 100);
+
+    var data = new int[rows][cols];
+    for (int r = 0; r < rows; ++r) {
+      for (int c = 0; c < cols; ++c) {
+        data[c][r] = randomInt();
+      }
+    }
+
+    var builder = CuVSMatrix.builder(rows, cols, CuVSMatrix.DataType.INT);
+    for (int r = 0; r < rows; ++r) {
+      builder.addVector(data[r]);
+    }
+
+    var roundTripData = new int[rows][cols];
+
+    try (var dataset = builder.build()) {
+      dataset.toArray(roundTripData);
+
+      for (int n = 0; n < dataset.size(); ++n) {
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(data[n][i], roundTripData[n][i]);
+        }
+      }
+    }
+  }
+
+  public void testByteDatasetBuilder() {
+    int rows = randomIntBetween(1, 32);
+    int cols = randomIntBetween(1, 100);
+
+    var data = new byte[rows][cols];
+    for (int r = 0; r < rows; ++r) {
+      for (int c = 0; c < cols; ++c) {
+        data[c][r] = randomByte();
+      }
+    }
+
+    var builder = CuVSMatrix.builder(rows, cols, CuVSMatrix.DataType.BYTE);
+    for (int r = 0; r < rows; ++r) {
+      builder.addVector(data[r]);
+    }
+
+    var roundTripData = new byte[rows][cols];
+
+    try (var dataset = builder.build()) {
+      dataset.toArray(roundTripData);
+
+      for (int n = 0; n < dataset.size(); ++n) {
+        for (int i = 0; i < dataset.columns(); ++i) {
+          assertEquals(data[n][i], roundTripData[n][i]);
+        }
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
index ae62e10c34..9138f9952a 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
@@ -22,11 +22,12 @@
 public class DatasetHelper {
 
   private static final MethodHandle createDataset$mh =
-      CuVSProvider.provider().newNativeDatasetBuilder();
+      CuVSProvider.provider().newNativeMatrixBuilder();
 
-  public static Dataset fromMemorySegment(MemorySegment memorySegment, int size, int dimensions) {
+  public static CuVSMatrix fromMemorySegment(
+      MemorySegment memorySegment, int size, int dimensions, CuVSMatrix.DataType dataType) {
     try {
-      return (Dataset) createDataset$mh.invokeExact(memorySegment, size, dimensions);
+      return (CuVSMatrix) createDataset$mh.invokeExact(memorySegment, size, dimensions, dataType);
     } catch (Throwable e) {
       if (e instanceof Error err) {
         throw err;
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index d2e198738b..ded5bda54f 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -113,7 +113,8 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       // Create the index with the dataset
       final CagraIndex index;
       if (useNativeMemoryDataset) {
-        var datasetBuilder = Dataset.builder(vectors.length, vectors[0].length);
+        var datasetBuilder =
+            CuVSMatrix.builder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
         for (float[] v : vectors) {
           datasetBuilder.addVector(v);
         }

From 5bd425dc2a3d2a7f0871030b4716a4fb35a70e49 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 28 Jul 2025 10:11:15 -0400
Subject: [PATCH 511/623] Update build infra to support new branching strategy
 (#1143)

rapids_config will use `RAPIDS_BRANCH` contents to determine what branch to use

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1143
---
 RAPIDS_BRANCH               |  1 +
 cmake/RAPIDS.cmake          |  6 +++---
 cmake/rapids_config.cmake   | 10 ++++++++++
 rust/cuvs-sys/RAPIDS_BRANCH |  1 +
 4 files changed, 15 insertions(+), 3 deletions(-)
 create mode 100644 RAPIDS_BRANCH
 create mode 120000 rust/cuvs-sys/RAPIDS_BRANCH

diff --git a/RAPIDS_BRANCH b/RAPIDS_BRANCH
new file mode 100644
index 0000000000..9b1c52d941
--- /dev/null
+++ b/RAPIDS_BRANCH
@@ -0,0 +1 @@
+branch-25.10
diff --git a/cmake/RAPIDS.cmake b/cmake/RAPIDS.cmake
index d112951d3c..40de7cefcd 100644
--- a/cmake/RAPIDS.cmake
+++ b/cmake/RAPIDS.cmake
@@ -18,9 +18,9 @@
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 # Allow users to control which version is used
-if(NOT rapids-cmake-version OR NOT rapids-cmake-version MATCHES [[^([0-9][0-9])\.([0-9][0-9])$]])
+if(NOT rapids-cmake-branch OR NOT rapids-cmake-version)
   message(
-    FATAL_ERROR "The CMake variable rapids-cmake-version must be defined in the format MAJOR.MINOR."
+    FATAL_ERROR "The CMake variable `rapids-cmake-branch` or `rapids-cmake-version` must be defined"
   )
 endif()
 
@@ -33,7 +33,7 @@ endif()
 # Allow users to control which branch is fetched
 if(NOT rapids-cmake-branch)
   # Define a default branch if the user doesn't set one
-  set(rapids-cmake-branch "branch-${rapids-cmake-version}")
+  set(rapids-cmake-branch "release/${rapids-cmake-version}")
 endif()
 
 # Allow users to control the exact URL passed to FetchContent
diff --git a/cmake/rapids_config.cmake b/cmake/rapids_config.cmake
index abe468dce8..b706c926e7 100644
--- a/cmake/rapids_config.cmake
+++ b/cmake/rapids_config.cmake
@@ -26,5 +26,15 @@ else()
   )
 endif()
 
+# Use STRINGS to trim whitespace/newlines
+file(STRINGS "${CMAKE_CURRENT_LIST_DIR}/../RAPIDS_BRANCH" _rapids_branch)
+if(NOT _rapids_branch)
+  message(
+    FATAL_ERROR
+      "Could not determine branch name to use for checking out rapids-cmake. The file \"${CMAKE_CURRENT_LIST_DIR}/../RAPIDS_BRANCH\" is missing."
+  )
+endif()
+
 set(rapids-cmake-version "${RAPIDS_VERSION_MAJOR_MINOR}")
+set(rapids-cmake-branch "${_rapids_branch}")
 include("${CMAKE_CURRENT_LIST_DIR}/RAPIDS.cmake")
diff --git a/rust/cuvs-sys/RAPIDS_BRANCH b/rust/cuvs-sys/RAPIDS_BRANCH
new file mode 120000
index 0000000000..15b9ac7c23
--- /dev/null
+++ b/rust/cuvs-sys/RAPIDS_BRANCH
@@ -0,0 +1 @@
+../../RAPIDS_BRANCH
\ No newline at end of file

From 921db94a9ffbc90dfb15e5ceeb2d03b6f7c9c8fe Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Tue, 29 Jul 2025 14:15:19 -0700
Subject: [PATCH 512/623] [ANN_BENCH] [DOCS] Add Vamana / DiskANN to cuvs-bench
 Docs (#1164)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1164
---
 .../ann/src/diskann/diskann_benchmark.cpp     |  8 +-
 docs/source/cuvs_bench/index.rst              |  9 +-
 docs/source/cuvs_bench/param_tuning.rst       | 94 +++++++++++++++++++
 3 files changed, 104 insertions(+), 7 deletions(-)

diff --git a/cpp/bench/ann/src/diskann/diskann_benchmark.cpp b/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
index c410995334..fe47215341 100644
--- a/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
+++ b/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
@@ -35,8 +35,8 @@ template <typename T>
 void parse_build_param(const nlohmann::json& conf,
                        typename cuvs::bench::diskann_memory<T>::build_param& param)
 {
-  param.R = conf.at("R");
-  if (conf.contains("L_build")) { param.L_build = conf.at("L_build"); }
+  param.R       = conf.at("R");
+  param.L_build = conf.at("L_build");
   if (conf.contains("alpha")) { param.num_threads = conf.at("alpha"); }
   if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
 }
@@ -45,8 +45,8 @@ template <typename T>
 void parse_build_param(const nlohmann::json& conf,
                        typename cuvs::bench::diskann_ssd<T>::build_param& param)
 {
-  param.R = conf.at("R");
-  if (conf.contains("L_build")) { param.L_build = conf.at("L_build"); }
+  param.R       = conf.at("R");
+  param.L_build = conf.at("L_build");
   if (conf.contains("alpha")) { param.num_threads = conf.at("alpha"); }
   if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
   if (conf.contains("QD")) { param.QD = conf.at("QD"); }
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 6e1a6a1978..c299a96144 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -432,10 +432,10 @@ The table below contains all algorithms supported by cuVS. Each unique algorithm
    - Algorithms
 
  * - FAISS_GPU
-   - `faiss_gpu_flat`, `faiss_gpu_ivf_flat`, `faiss_gpu_ivf_pq`
+   - `faiss_gpu_flat`, `faiss_gpu_ivf_flat`, `faiss_gpu_ivf_pq`, `faiss_gpu_cagra`
 
  * - FAISS_CPU
-   - `faiss_cpu_flat`, `faiss_cpu_ivf_flat`, `faiss_cpu_ivf_pq`
+   - `faiss_cpu_flat`, `faiss_cpu_ivf_flat`, `faiss_cpu_ivf_pq`, `faiss_cpu_hnsw_flat`
 
  * - GGNN
    - `ggnn`
@@ -443,8 +443,11 @@ The table below contains all algorithms supported by cuVS. Each unique algorithm
  * - HNSWLIB
    - `hnswlib`
 
+ * - DiskANN
+   - `diskann_memory`, `diskann_ssd`
+
  * - cuVS
-   - `cuvs_brute_force`, `cuvs_cagra`, `cuvs_ivf_flat`, `cuvs_ivf_pq`, `cuvs_cagra_hnswlib`
+   - `cuvs_brute_force`, `cuvs_cagra`, `cuvs_ivf_flat`, `cuvs_ivf_pq`, `cuvs_cagra_hnswlib`, `cuvs_vamana`
 
 
 Multi-GPU benchmarks
diff --git a/docs/source/cuvs_bench/param_tuning.rst b/docs/source/cuvs_bench/param_tuning.rst
index c85504e967..9e22aed7d1 100644
--- a/docs/source/cuvs_bench/param_tuning.rst
+++ b/docs/source/cuvs_bench/param_tuning.rst
@@ -379,6 +379,48 @@ This is a benchmark that enables interoperability between `CAGRA` built `HNSW` s
 
 `search` : Same as `search` of Hnswlib
 
+cuvs_vamana
+-----------
+
+Benchmark for building an in-memory Vamana graph based index on the GPU and interoperability with DiskANN for search.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `graph_degree`
+   - `build`
+   - N
+   - Positive integer >0
+   - 32
+   - Maximum degree of the graph index
+
+ * - `visited_size`
+   - `build`
+   - N
+   - Positive integer >0
+   - 64
+   - Maximum number of visited nodes per search corresponds to the L parameter in the Vamana literature
+
+ * - `alpha`
+   - `build`
+   -  N
+   - Positive float >0
+   - 1.2
+   - Alpha for pruning parameter
+
+ * - `L_search`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - Maximum number of visited nodes per search corresponds to the L parameter in the Vamana literature. Larger values improve recall at the cost of search time.
+
 FAISS Indexes
 =============
 
@@ -672,3 +714,55 @@ hnswlib
    - Number of threads to use for queries.
 
 Please refer to `HNSW algorithm parameters guide <https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md>`_ from `hnswlib` to learn more about these arguments.
+
+DiskANN
+=======
+
+diskann_memory
+--------------
+
+Use DiskANN in-memory index for approximate search.
+
+.. list-table::
+
+ * - Parameter
+   - Type
+   - Required
+   - Data Type
+   - Default
+   - Description
+
+ * - `R`
+   - `build`
+   - Y
+   - Positive integer >0
+   -
+   - Maximum degree of the graph index
+
+ * - `L_build`
+   - `build`
+   - Y
+   - Positive integer >0
+   -
+   - number of visited nodes per greedy search during graph construction
+
+ * - `alpha`
+   - `build`
+   - N
+   - Positive number >=1
+   - 1.2
+   - controls the pruning parameter of the graph construction
+
+ * - `num_threads`
+   - `build`
+   - N
+   - Positive integer >0
+   - omp_get_max_threads()
+   - Number of CPU threads to use to build the index.
+
+ * - `L_search`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - visited list size during search

From 1b0965b3ed9541c8a428f027a6058fec39aa3f72 Mon Sep 17 00:00:00 2001
From: Puneet Ahuja <167976912+punAhuja@users.noreply.github.com>
Date: Wed, 30 Jul 2025 05:00:28 +0530
Subject: [PATCH 513/623] [Java]Fixed TieredIndexParams creation/destruction,
 calling cuvsTieredIndexParamsCreate (#1147)

Using `cuvsTieredIndexParamsCreate` and `cuvsTieredIndexParamsDestroy` now instead of allocating arena in Java.
  -Used CloseableHandle and CuvsParamsHelper as used in #1109 and #1110 for consistency

  This fixes #1138

Authors:
  - Puneet Ahuja (https://github.com/punAhuja)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1147
---
 .../cuvs/internal/CuVSParamsHelper.java       | 19 ++++++++++
 .../nvidia/cuvs/internal/TieredIndexImpl.java | 35 ++++++++++---------
 2 files changed, 37 insertions(+), 17 deletions(-)

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
index 388f6afc05..6774cc4619 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSParamsHelper.java
@@ -151,4 +151,23 @@ public void close() {
       };
     }
   }
+
+  static CloseableHandle createTieredIndexParams() {
+    try (var localArena = Arena.ofConfined()) {
+      var paramsPtrPtr = localArena.allocate(cuvsTieredIndexParams_t);
+      checkCuVSError(cuvsTieredIndexParamsCreate(paramsPtrPtr), "cuvsTieredIndexParamsCreate");
+      var paramsPtr = paramsPtrPtr.get(cuvsTieredIndexParams_t, 0L);
+      return new CloseableHandle() {
+        @Override
+        public MemorySegment handle() {
+          return paramsPtr;
+        }
+
+        @Override
+        public void close() {
+          checkCuVSError(cuvsTieredIndexParamsDestroy(paramsPtr), "cuvsTieredIndexParamsDestroy");
+        }
+      };
+    }
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index 9dfbf5a843..fd94287787 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -15,6 +15,7 @@
  */
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.CuVSParamsHelper.createTieredIndexParams;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
@@ -32,6 +33,7 @@
 
 import com.nvidia.cuvs.*;
 import com.nvidia.cuvs.CuVSMatrix;
+import com.nvidia.cuvs.internal.common.CloseableHandle;
 import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
@@ -118,15 +120,16 @@ private IndexReference build() {
       long rows = dataset.size();
       long cols = dataset.columns();
 
-      MemorySegment indexParamsMemorySegment =
-          tieredIndexParameters != null
-              ? segmentFromIndexParams(localArena, tieredIndexParameters)
-              : MemorySegment.NULL;
-
       // Get host data
       MemorySegment hostDataSeg = ((CuVSHostMatrixImpl) dataset).memorySegment();
 
-      try (var resourceAccess = resources.access()) {
+      try (var resourceAccess = resources.access();
+          var indexParamsHandle =
+              tieredIndexParameters != null
+                  ? segmentFromIndexParams(localArena, tieredIndexParameters)
+                  : CloseableHandle.NULL) {
+
+        MemorySegment indexParamsMemorySegment = indexParamsHandle.handle();
         long cuvsRes = resourceAccess.handle();
 
         // TieredIndex REQUIRES device memory - allocate it
@@ -389,16 +392,20 @@ public void execute() {
   /**
    * Allocates the configured index parameters in the MemorySegment.
    */
-  private static MemorySegment segmentFromIndexParams(Arena arena, TieredIndexParams params) {
-    MemorySegment seg = cuvsTieredIndexParams.allocate(arena);
+
+  /**
+   * Allocates the configured index parameters in a MemorySegment and returns a CloseableHandle
+   * for safe resource management.
+   */
+  private static CloseableHandle segmentFromIndexParams(Arena arena, TieredIndexParams params) {
+    CloseableHandle paramsHandle = createTieredIndexParams();
+    MemorySegment seg = paramsHandle.handle();
 
     // Get the metric from CagraParams if available, otherwise use TieredIndex metric
     int metric;
     if (params.getCagraParams() != null) {
-      // Use the metric from CagraParams to ensure consistency
       metric = params.getCagraParams().getCuvsDistanceType().value;
     } else {
-      // Fallback to TieredIndex metric
       metric =
           switch (params.getMetric()) {
             case L2 -> 0;
@@ -407,19 +414,15 @@ private static MemorySegment segmentFromIndexParams(Arena arena, TieredIndexPara
                 throw new IllegalArgumentException("Unsupported metric: " + params.getMetric());
           };
     }
-
     cuvsTieredIndexParams.metric(seg, metric);
-
     int algo = 0; // CUVS_TIERED_INDEX_ALGO_CAGRA
     cuvsTieredIndexParams.algo(seg, algo);
-
     cuvsTieredIndexParams.min_ann_rows(seg, params.getMinAnnRows());
     cuvsTieredIndexParams.create_ann_index_on_extend(seg, params.isCreateAnnIndexOnExtend());
 
     CagraIndexParams cagraParams = params.getCagraParams();
     if (cagraParams != null) {
       MemorySegment cagraParamsSeg = cuvsCagraIndexParams.allocate(arena);
-
       cuvsCagraIndexParams.intermediate_graph_degree(
           cagraParamsSeg, cagraParams.getIntermediateGraphDegree());
       cuvsCagraIndexParams.graph_degree(cagraParamsSeg, cagraParams.getGraphDegree());
@@ -427,14 +430,12 @@ private static MemorySegment segmentFromIndexParams(Arena arena, TieredIndexPara
       cuvsCagraIndexParams.nn_descent_niter(
           cagraParamsSeg, cagraParams.getNNDescentNumIterations());
       cuvsCagraIndexParams.metric(cagraParamsSeg, metric);
-
       cuvsTieredIndexParams.cagra_params(seg, cagraParamsSeg);
     }
-
     cuvsTieredIndexParams.ivf_flat_params(seg, MemorySegment.NULL);
     cuvsTieredIndexParams.ivf_pq_params(seg, MemorySegment.NULL);
 
-    return seg;
+    return paramsHandle;
   }
 
   /**

From 2b3fc1b2d08aa38feef876c553a3a1d9f758712e Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 29 Jul 2025 20:11:05 -0700
Subject: [PATCH 514/623] Check shape is initialized in cuvsMatrixSliceRows
 (#1193)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1193
---
 cpp/src/core/c_api.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 940aa1746f..8a9116f4cb 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -261,6 +261,7 @@ extern "C" cuvsError_t cuvsMatrixSliceRows(cuvsResources_t res,
     DLTensor& src = src_managed->dl_tensor;
     DLTensor& dst = dst_managed->dl_tensor;
     RAFT_EXPECTS(src.ndim == 2, "src should be a 2 dimensional tensor");
+    RAFT_EXPECTS(src.shape != nullptr, "shape should be initialized in the src tensor");
 
     dst.dtype    = src.dtype;
     dst.device   = src.device;

From 4f7910614ea9175890fa0adf01ce9d1a8434d170 Mon Sep 17 00:00:00 2001
From: rhdong <rhdong2017@gmail.com>
Date: Tue, 29 Jul 2025 22:11:39 -0700
Subject: [PATCH 515/623] [Feat] Add Dockerfile for reproducible installation
 (#1195)

Authors:
  - rhdong (https://github.com/rhdong)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1195
---
 Dockerfile | 88 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 Dockerfile

diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000..2dbdecf9d8
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,88 @@
+# syntax=docker/dockerfile:1.5
+
+# ==============================================================================
+# RAPIDS cuVS Docker Image - Vector Search and Clustering on GPU
+#
+# Usage:
+#   docker build -t cuvs:latest .
+#   docker run --gpus all -it cuvs:latest
+#
+# Customizable Build Arguments:
+#   docker build --build-arg CUDA_VER=12.8 --build-arg RAPIDS_VER=25.06 -t cuvs:custom .
+# ==============================================================================
+
+# Configurable build arguments - override with --build-arg during build
+ARG CUDA_VER=12.9.1
+
+# Python version for the conda environment (supported: 3.10, 3.11, 3.12)
+ARG PYTHON_VER=3.12
+
+# RAPIDS/cuVS version
+ARG RAPIDS_VER=25.06
+
+FROM nvidia/cuda:${CUDA_VER}-devel-ubuntu24.04
+
+# Display build configuration for verification
+RUN echo "  Building cuVS Docker image with:" && \
+    echo "   CUDA Version: ${CUDA_VER}" && \
+    echo "   Python Version: ${PYTHON_VER}" && \
+    echo "   RAPIDS Version: ${RAPIDS_VER}"
+
+# Container metadata
+LABEL maintainer="RAPIDS cuVS Team"
+LABEL description="RAPIDS cuVS - Vector Search and Clustering on GPU"
+LABEL org.opencontainers.image.source="https://github.com/rapidsai/cuvs"
+LABEL org.opencontainers.image.usage="docker run --gpus all -it <image>"
+
+# Environment setup
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+ENV CONDA_DIR=/opt/conda
+ENV PATH=${CONDA_DIR}/bin:$PATH
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    wget \
+    ca-certificates \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Miniforge (lightweight conda with conda-forge defaults)
+RUN wget --quiet https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -O /tmp/miniforge.sh && \
+    /bin/bash /tmp/miniforge.sh -b -p ${CONDA_DIR} && \
+    rm /tmp/miniforge.sh && \
+    ${CONDA_DIR}/bin/conda clean -afy
+
+# Create conda environment and install cuVS packages with pinned dependencies
+RUN conda create -n cuvs python=${PYTHON_VER} -y && \
+    conda run -n cuvs conda install -c rapidsai -c conda-forge -c nvidia \
+        cuvs=${RAPIDS_VER} \
+        libcuvs=${RAPIDS_VER} \
+        cuda-version=${CUDA_VER} \
+        "numpy>=1.23,<3.0a0" \
+        "cupy>=12.0.0" \
+        jupyter \
+        ipython \
+        -y && \
+    conda clean -afy
+
+# Create non-root user
+RUN useradd -m -s /bin/bash cuvs
+USER cuvs
+WORKDIR /home/cuvs
+
+# Initialize conda for the user and configure environment activation
+RUN ${CONDA_DIR}/bin/conda init bash && \
+    echo "conda activate cuvs" >> ~/.bashrc && \
+    echo "conda activate cuvs" >> ~/.bash_profile
+
+# Set environment variables for automatic cuvs environment activation
+ENV PATH=${CONDA_DIR}/envs/cuvs/bin:$PATH
+ENV CONDA_DEFAULT_ENV=cuvs
+ENV CONDA_PREFIX=${CONDA_DIR}/envs/cuvs
+
+# Verify installation and show version info
+RUN python -c "import cuvs; print('cuVS version:', cuvs.__version__)"
+
+# Default command
+CMD ["bash"]

From 1bc0fa580d9f53eddf6f0f9689384fa1e06f57bc Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Wed, 30 Jul 2025 12:07:00 +0200
Subject: [PATCH 516/623] CAGRA build heuristics for HNSW (#1125)

Add helper functions to construct a reasonable default CAGRA build parameters based on HNSW parameters.
The goal is to build a CAGRA graph that can be converted to an HNSW graph which has very similar search performance as the corresponding HNSW-built graph.

Additionally, this PR refactors CAGRA benchmark wrapper to store build parameters in a single place and flexibly set defaults based on the dataset shape.

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/1125
---
 .../src/cuvs/cuvs_ann_bench_param_parser.h    | 122 ++++++++++++------
 cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu  |  47 +++++--
 .../ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h |  17 ++-
 cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h   |  70 ++++------
 .../ann/src/cuvs/cuvs_mg_cagra_wrapper.h      |  11 +-
 .../cuvs/neighbors/graph_build_types.hpp      |  33 ++++-
 cpp/include/cuvs/neighbors/hnsw.hpp           |  35 ++++-
 cpp/src/neighbors/hnsw.cpp                    |  17 +++
 8 files changed, 242 insertions(+), 110 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 7ad8ef91b5..5237409f5a 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -229,55 +229,84 @@ nlohmann::json collect_conf_with_prefix(const nlohmann::json& conf,
 }
 
 template <typename T, typename IdxT>
-void parse_build_param(const nlohmann::json& conf,
-                       typename cuvs::bench::cuvs_cagra<T, IdxT>::build_param& param)
+void parse_build_param(const nlohmann::json& conf, cuvs::neighbors::cagra::index_params& params)
 {
-  if (conf.contains("graph_degree")) {
-    param.cagra_params.graph_degree              = conf.at("graph_degree");
-    param.cagra_params.intermediate_graph_degree = param.cagra_params.graph_degree * 2;
-  }
+  // NB: try to avoid setting parameters implicitly, because this parsing may be used after
+  // parameter heuristics are applied (e.g. in cuvs_cagra_hnswlib.cu).
+  if (conf.contains("graph_degree")) { params.graph_degree = conf.at("graph_degree"); }
   if (conf.contains("intermediate_graph_degree")) {
-    param.cagra_params.intermediate_graph_degree = conf.at("intermediate_graph_degree");
+    params.intermediate_graph_degree = conf.at("intermediate_graph_degree");
+  } else {
+    // Only update the intermediate graph degree if it's invalid.
+    params.intermediate_graph_degree =
+      std::max(params.graph_degree, params.intermediate_graph_degree);
   }
+
+  nlohmann::json comp_search_conf = collect_conf_with_prefix(conf, "compression_");
+  if (!comp_search_conf.empty()) {
+    auto vpq_pams = params.compression.value_or(cuvs::neighbors::vpq_params{});
+    parse_build_param(comp_search_conf, vpq_pams);
+    params.compression.emplace(vpq_pams);
+  }
+
+  if (conf.contains("guarantee_connectivity")) {
+    params.guarantee_connectivity = conf.at("guarantee_connectivity");
+  }
+
+  // Override the graph_build_algo if requested explicitly
   if (conf.contains("graph_build_algo")) {
     if (conf.at("graph_build_algo") == "IVF_PQ") {
-      param.algo = cuvs::bench::CagraBuildAlgo::kIvfPq;
+      if (!std::holds_alternative<cuvs::neighbors::graph_build_params::ivf_pq_params>(
+            params.graph_build_params)) {
+        params.graph_build_params = cuvs::neighbors::graph_build_params::ivf_pq_params{};
+      }
     } else if (conf.at("graph_build_algo") == "NN_DESCENT") {
-      param.algo = cuvs::bench::CagraBuildAlgo::kNnDescent;
-    } else {
-      param.algo = cuvs::bench::CagraBuildAlgo::kAuto;
+      if (!std::holds_alternative<cuvs::neighbors::graph_build_params::nn_descent_params>(
+            params.graph_build_params)) {
+        params.graph_build_params = cuvs::neighbors::graph_build_params::nn_descent_params{};
+      }
     }
   }
-  nlohmann::json ivf_pq_build_conf = collect_conf_with_prefix(conf, "ivf_pq_build_");
-  if (!ivf_pq_build_conf.empty()) {
-    cuvs::neighbors::ivf_pq::index_params bparam;
-    parse_build_param<T, IdxT>(ivf_pq_build_conf, bparam);
-    param.ivf_pq_build_params = bparam;
-  }
+
+  // Parse build-algo-specific parameters and use them to decide on the algo type
+  nlohmann::json ivf_pq_build_conf  = collect_conf_with_prefix(conf, "ivf_pq_build_");
   nlohmann::json ivf_pq_search_conf = collect_conf_with_prefix(conf, "ivf_pq_search_");
-  if (!ivf_pq_search_conf.empty()) {
-    typename cuvs::bench::cuvs_ivf_pq<T, IdxT>::search_param sparam;
-    parse_search_param<T, IdxT>(ivf_pq_search_conf, sparam);
-    param.ivf_pq_search_params = sparam.pq_param;
-    param.ivf_pq_refine_rate   = sparam.refine_ratio;
-  }
-  nlohmann::json nn_descent_conf = collect_conf_with_prefix(conf, "nn_descent_");
-  if (!nn_descent_conf.empty()) {
-    cuvs::neighbors::nn_descent::index_params nn_param;
-    nn_param.intermediate_graph_degree = 1.5 * param.cagra_params.intermediate_graph_degree;
-    parse_build_param<T, IdxT>(nn_descent_conf, nn_param);
-    if (nn_param.graph_degree != param.cagra_params.intermediate_graph_degree) {
-      nn_param.graph_degree = param.cagra_params.intermediate_graph_degree;
+  nlohmann::json nn_descent_conf    = collect_conf_with_prefix(conf, "nn_descent_");
+
+  if (std::holds_alternative<std::monostate>(params.graph_build_params)) {
+    if (!ivf_pq_build_conf.empty() || !ivf_pq_search_conf.empty()) {
+      params.graph_build_params = cuvs::neighbors::graph_build_params::ivf_pq_params{};
+    } else if (!nn_descent_conf.empty()) {
+      params.graph_build_params = cuvs::neighbors::graph_build_params::nn_descent_params{};
+    } else {
+      params.graph_build_params = cuvs::neighbors::graph_build_params::iterative_search_params{};
     }
-    param.nn_descent_params = nn_param;
-  }
-  nlohmann::json comp_search_conf = collect_conf_with_prefix(conf, "compression_");
-  if (!comp_search_conf.empty()) {
-    cuvs::neighbors::vpq_params vpq_pams;
-    parse_build_param(comp_search_conf, vpq_pams);
-    param.cagra_params.compression.emplace(vpq_pams);
   }
 
+  // Apply build-algo-specific parameters
+  std::visit(
+    [&](auto& arg) {
+      using U = std::decay_t<decltype(arg)>;
+      if constexpr (std::is_same_v<U, cuvs::neighbors::graph_build_params::ivf_pq_params>) {
+        parse_build_param<T, IdxT>(ivf_pq_build_conf, arg.build_params);
+        typename cuvs::bench::cuvs_ivf_pq<T, IdxT>::search_param sparam;
+        sparam.pq_param     = arg.search_params;
+        sparam.refine_ratio = arg.refinement_rate;
+        parse_search_param<T, IdxT>(ivf_pq_search_conf, sparam);
+        arg.search_params   = sparam.pq_param;
+        arg.refinement_rate = sparam.refine_ratio;
+      } else if constexpr (std::is_same_v<U,
+                                          cuvs::neighbors::graph_build_params::nn_descent_params>) {
+        parse_build_param<T, IdxT>(nn_descent_conf, arg);
+      }
+    },
+    params.graph_build_params);
+}
+
+template <typename T, typename IdxT>
+void parse_build_param(const nlohmann::json& conf,
+                       typename cuvs::bench::cuvs_cagra<T, IdxT>::build_param& param)
+{
   if (conf.contains("num_dataset_splits")) {
     param.num_dataset_splits = conf.at("num_dataset_splits");
   }
@@ -291,6 +320,23 @@ void parse_build_param(const nlohmann::json& conf,
       throw std::runtime_error("invalid value for merge_type");
     }
   }
+  param.cagra_params = [conf](raft::matrix_extent<int64_t> extents,
+                              cuvs::distance::DistanceType dist_type) {
+    // Delayed parsing/initialization of cagra_params - it's called once the dataset shape is known
+    auto cagra_params   = cuvs::neighbors::cagra::index_params{};
+    cagra_params.metric = dist_type;
+    if (conf.value("graph_build_algo", "") == "IVF_PQ") {
+      cagra_params.graph_build_params =
+        cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(extents, dist_type);
+    } else if (conf.value("graph_build_algo", "") == "NN_DESCENT") {
+      cagra_params.graph_build_params =
+        cuvs::neighbors::cagra::graph_build_params::nn_descent_params(
+          conf.value("intermediate_graph_degree", cagra_params.intermediate_graph_degree),
+          dist_type);
+    }
+    ::parse_build_param<T, IdxT>(conf, cagra_params);
+    return cagra_params;
+  };
 }
 
 cuvs::bench::AllocatorType parse_allocator(std::string mem_type)
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
index 0c14658357..d27a350dfa 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,26 +25,49 @@
 namespace cuvs::bench {
 
 template <typename T, typename IdxT>
-void parse_build_param(const nlohmann::json& conf,
-                       typename cuvs::bench::cuvs_cagra_hnswlib<T, IdxT>::build_param& param)
+auto parse_build_param(const nlohmann::json& conf) ->
+  typename cuvs::bench::cuvs_cagra_hnswlib<T, IdxT>::build_param
 {
+  typename cuvs::bench::cuvs_cagra_hnswlib<T, IdxT>::build_param param;
+  auto& hnsw_params  = param.hnsw_index_params;
+  auto& cagra_params = param.cagra_build_params;
   if (conf.contains("hierarchy")) {
     if (conf.at("hierarchy") == "none") {
-      param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::NONE;
+      hnsw_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::NONE;
     } else if (conf.at("hierarchy") == "cpu") {
-      param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::CPU;
+      hnsw_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::CPU;
     } else if (conf.at("hierarchy") == "gpu") {
-      param.hnsw_index_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::GPU;
+      hnsw_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::GPU;
     } else {
       THROW("Invalid value for hierarchy: %s", conf.at("hierarchy").get<std::string>().c_str());
     }
+  } else {
+    hnsw_params.hierarchy = cuvs::neighbors::hnsw::HnswHierarchy::GPU;
   }
   if (conf.contains("ef_construction")) {
-    param.hnsw_index_params.ef_construction = conf.at("ef_construction");
+    hnsw_params.ef_construction = conf.at("ef_construction");
   }
-  if (conf.contains("num_threads")) {
-    param.hnsw_index_params.num_threads = conf.at("num_threads");
+  if (conf.contains("num_threads")) { hnsw_params.num_threads = conf.at("num_threads"); }
+
+  // Reuse the CAGRA wrapper params parser
+  ::parse_build_param<T, IdxT>(conf, cagra_params);
+  // If the users provides parameter M, we can use the CAGRA-HNSW heuristics to find optimal
+  // parameters for the dataset and HNSW reference.
+  if (conf.contains("M")) {
+    // Postpone the parsing of the CAGRA build params until the dataset extents are known.
+    // We the default parameters depend on the dataset extents; and we still would like to be able
+    // to override them.
+    cagra_params.cagra_params = [conf, hnsw_params](raft::matrix_extent<int64_t> extents,
+                                                    cuvs::distance::DistanceType dist_type) {
+      auto ps = cuvs::neighbors::hnsw::to_cagra_params(
+        extents, conf.at("M"), hnsw_params.ef_construction, dist_type);
+      ps.metric = dist_type;
+      // NB: above, we only provide the defaults. Below we parse the explicit parameters as usual.
+      ::parse_build_param<T, uint32_t>(conf, ps);
+      return ps;
+    };
   }
+  return param;
 }
 
 template <typename T, typename IdxT>
@@ -69,10 +92,8 @@ auto create_algo(const std::string& algo_name,
   if constexpr (std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, int8_t> ||
                 std::is_same_v<T, uint8_t>) {
     if (algo_name == "raft_cagra_hnswlib" || algo_name == "cuvs_cagra_hnswlib") {
-      typename cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>::build_param bparam;
-      ::parse_build_param<T, uint32_t>(conf, bparam.cagra_build_param);
-      parse_build_param<T, uint32_t>(conf, bparam);
-      a = std::make_unique<cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>>(metric, dim, bparam);
+      auto params = parse_build_param<T, uint32_t>(conf);
+      a = std::make_unique<cuvs::bench::cuvs_cagra_hnswlib<T, uint32_t>>(metric, dim, params);
     }
   }
 
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
index ed5075fc1d..600061dead 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_hnswlib_wrapper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -30,7 +30,8 @@ class cuvs_cagra_hnswlib : public algo<T>, public algo_gpu {
   using search_param_base = typename algo<T>::search_param;
 
   struct build_param {
-    typename cuvs_cagra<T, IdxT>::build_param cagra_build_param;
+    using cagra_wrapper_params = typename cuvs_cagra<T, IdxT>::build_param;
+    cagra_wrapper_params cagra_build_params;
     cuvs::neighbors::hnsw::index_params hnsw_index_params;
   };
 
@@ -94,8 +95,16 @@ void cuvs_cagra_hnswlib<T, IdxT>::build(const T* dataset, size_t nrow)
   bool dataset_is_on_host = raft::get_device_for_address(dataset) == -1;
 
   // re-use the CAGRA wrapper to parse build params
-  auto bps                                 = build_param_.cagra_build_param;
-  bps.cagra_params.attach_dataset_on_build = !dataset_is_on_host;
+  auto bps = build_param_.cagra_build_params;
+  // Not very conveniently, the CAGRA wrapper resolves parameters after the dataset shape is known,
+  // so it takes a lambda to do it. Even though we know the shape, we want to use the wrapper as-is,
+  // so we just modify that lambda.
+  bps.cagra_params = [dataset_is_on_host, orig_cagra_params = bps.cagra_params](
+                       auto dataset_extents, auto metric) {
+    auto params                    = orig_cagra_params(dataset_extents, metric);
+    params.attach_dataset_on_build = !dataset_is_on_host;
+    return params;
+  };
   cuvs_cagra<T, IdxT> cagra_wrapper{this->metric_, this->dim_, bps};
 
   // build the CAGRA index
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
index ed21179b5e..eb4e3ec3a0 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_wrapper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -61,6 +61,8 @@ template <typename T, typename IdxT>
 class cuvs_cagra : public algo<T>, public algo_gpu {
  public:
   using search_param_base = typename algo<T>::search_param;
+  using algo<T>::dim_;
+  using algo<T>::metric_;
 
   struct search_param : public search_param_base {
     cuvs::neighbors::cagra::search_params p;
@@ -78,37 +80,18 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
   };
 
   struct build_param {
-    cuvs::neighbors::cagra::index_params cagra_params;
-    CagraBuildAlgo algo;
-    std::optional<cuvs::neighbors::nn_descent::index_params> nn_descent_params = std::nullopt;
-    std::optional<float> ivf_pq_refine_rate                                    = std::nullopt;
-    std::optional<cuvs::neighbors::ivf_pq::index_params> ivf_pq_build_params   = std::nullopt;
-    std::optional<cuvs::neighbors::ivf_pq::search_params> ivf_pq_search_params = std::nullopt;
-    size_t num_dataset_splits                                                  = 1;
+    // The optimal defaults depend on the dataset shape and thus only available once the build
+    // function is called.
+    using dataset_dependent_params = std::function<cuvs::neighbors::cagra::index_params(
+      raft::matrix_extent<int64_t>, cuvs::distance::DistanceType)>;
+    dataset_dependent_params cagra_params;
+    size_t num_dataset_splits = 1;
     CagraMergeType merge_type = CagraMergeType::kPhysical;
-
-    void prepare_build_params(const raft::extent_2d<IdxT>& dataset_extents)
-    {
-      if (algo == CagraBuildAlgo::kIvfPq) {
-        auto pq_params = cuvs::neighbors::cagra::graph_build_params::ivf_pq_params(
-          dataset_extents, cagra_params.metric);
-        if (ivf_pq_build_params) { pq_params.build_params = *ivf_pq_build_params; }
-        if (ivf_pq_search_params) { pq_params.search_params = *ivf_pq_search_params; }
-        if (ivf_pq_refine_rate) { pq_params.refinement_rate = *ivf_pq_refine_rate; }
-        cagra_params.graph_build_params = pq_params;
-      } else if (algo == CagraBuildAlgo::kNnDescent) {
-        auto nn_params = cuvs::neighbors::cagra::graph_build_params::nn_descent_params(
-          cagra_params.intermediate_graph_degree);
-        if (nn_descent_params) { nn_params = *nn_descent_params; }
-        cagra_params.graph_build_params = nn_params;
-      }
-    }
   };
 
   cuvs_cagra(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
     : algo<T>(metric, dim),
       index_params_(param),
-      dimension_(dim),
 
       dataset_(std::make_shared<raft::device_matrix<T, int64_t, raft::row_major>>(
         std::move(raft::make_device_matrix<T, int64_t>(handle_, 0, 0)))),
@@ -119,8 +102,6 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
           nullptr, 0, 0))
 
   {
-    index_params_.cagra_params.metric         = parse_metric_type(metric);
-    index_params_.ivf_pq_build_params->metric = parse_metric_type(metric);
   }
 
   void build(const T* dataset, size_t nrow) final;
@@ -181,7 +162,6 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
   bool need_dataset_update_{true};
   cuvs::neighbors::cagra::search_params search_params_;
   std::shared_ptr<cuvs::neighbors::cagra::index<T, IdxT>> index_;
-  int dimension_;
   std::shared_ptr<raft::device_matrix<IdxT, int64_t, raft::row_major>> graph_;
   std::shared_ptr<raft::device_matrix<T, int64_t, raft::row_major>> dataset_;
   std::shared_ptr<raft::device_matrix_view<const T, int64_t, raft::row_major>> input_dataset_v_;
@@ -209,10 +189,9 @@ class cuvs_cagra : public algo<T>, public algo_gpu {
 template <typename T, typename IdxT>
 void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
 {
-  auto dataset_extents = raft::make_extents<IdxT>(nrow, dimension_);
-  index_params_.prepare_build_params(dataset_extents);
+  auto dataset_extents = raft::make_extents<IdxT>(nrow, dim_);
+  auto params          = index_params_.cagra_params(dataset_extents, parse_metric_type(metric_));
 
-  auto& params = index_params_.cagra_params;
   auto dataset_view_host =
     raft::make_mdspan<const T, IdxT, raft::row_major, true, false>(dataset, dataset_extents);
   auto dataset_view_device =
@@ -229,14 +208,13 @@ void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
       IdxT start = static_cast<IdxT>(i * rows_per_split);
       if (start >= nrow) break;
       IdxT rows        = std::min(rows_per_split, static_cast<IdxT>(nrow) - start);
-      const T* sub_ptr = dataset + static_cast<size_t>(start) * dimension_;
+      const T* sub_ptr = dataset + static_cast<size_t>(start) * dim_;
       auto sub_host =
-        raft::make_host_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+        raft::make_host_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dim_);
       auto sub_dev =
-        raft::make_device_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+        raft::make_device_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dim_);
 
-      auto sub_index =
-        cuvs::neighbors::cagra::index<T, IdxT>(handle_, index_params_.cagra_params.metric);
+      auto sub_index = cuvs::neighbors::cagra::index<T, IdxT>(handle_, params.metric);
       if (index_params_.merge_type == CagraMergeType::kPhysical) {
         if (dataset_is_on_host) {
           sub_index.update_dataset(handle_, sub_host);
@@ -256,7 +234,7 @@ void cuvs_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
       sub_indices_.push_back(std::move(sub_index_shared));
     }
     if (index_params_.merge_type == CagraMergeType::kPhysical) {
-      cuvs::neighbors::cagra::merge_params merge_params{index_params_.cagra_params};
+      cuvs::neighbors::cagra::merge_params merge_params{params};
       merge_params.merge_strategy = cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL;
 
       std::vector<cuvs::neighbors::cagra::index<T, IdxT>*> indices;
@@ -377,11 +355,11 @@ void cuvs_cagra<T, IdxT>::set_search_dataset(const T* dataset, size_t nrow)
       IdxT start = static_cast<IdxT>(i * rows_per_split);
       if (start >= nrow) break;
       IdxT rows        = std::min(rows_per_split, static_cast<IdxT>(nrow) - start);
-      const T* sub_ptr = dataset + static_cast<size_t>(start) * dimension_;
+      const T* sub_ptr = dataset + static_cast<size_t>(start) * dim_;
       auto sub_host =
-        raft::make_host_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+        raft::make_host_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dim_);
       auto sub_dev =
-        raft::make_device_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dimension_);
+        raft::make_device_matrix_view<const T, int64_t, raft::row_major>(sub_ptr, rows, dim_);
       auto sub_index = sub_indices_[i].get();
       if (index_params_.merge_type == CagraMergeType::kLogical) {
         if (dataset_is_on_host) {
@@ -471,8 +449,7 @@ void cuvs_cagra<T, IdxT>::search_base(
   static_assert(std::is_integral_v<algo_base::index_type>);
   static_assert(std::is_integral_v<IdxT>);
 
-  auto queries_view =
-    raft::make_device_matrix_view<const T, int64_t>(queries, batch_size, dimension_);
+  auto queries_view = raft::make_device_matrix_view<const T, int64_t>(queries, batch_size, dim_);
   auto neighbors_view =
     raft::make_device_matrix_view<algo_base::index_type, int64_t>(neighbors, batch_size, k);
   auto distances_view = raft::make_device_matrix_view<float, int64_t>(distances, batch_size, k);
@@ -491,7 +468,8 @@ void cuvs_cagra<T, IdxT>::search_base(
         handle_, search_params_, *index_, queries_view, neighbors_view, distances_view, *filter_);
     } else {
       if (index_params_.merge_type == CagraMergeType::kLogical) {
-        cuvs::neighbors::cagra::merge_params merge_params{index_params_.cagra_params};
+        // TODO: index merge must happen outside of search, otherwise what are we benchmarking?
+        cuvs::neighbors::cagra::merge_params merge_params{cuvs::neighbors::cagra::index_params{}};
         merge_params.merge_strategy = cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL;
 
         // Create wrapped indices for composite merge
@@ -579,8 +557,8 @@ void cuvs_cagra<T, IdxT>::search(
     auto candidate_ixs =
       raft::make_device_matrix_view<const algo_base::index_type, algo_base::index_type>(
         candidates_ptr, batch_size, k0);
-    auto queries_v = raft::make_device_matrix_view<const T, algo_base::index_type>(
-      queries, batch_size, dimension_);
+    auto queries_v =
+      raft::make_device_matrix_view<const T, algo_base::index_type>(queries, batch_size, dim_);
     refine_helper(
       res, *input_dataset_v_, queries_v, candidate_ixs, k, neighbors, distances, index_->metric());
   }
diff --git a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
index f086ae7b89..7e76900865 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_mg_cagra_wrapper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -31,6 +31,7 @@ class cuvs_mg_cagra : public algo<T>, public algo_gpu {
  public:
   using search_param_base = typename algo<T>::search_param;
   using algo<T>::dim_;
+  using algo<T>::metric_;
 
   struct build_param : public cuvs::bench::cuvs_cagra<T, IdxT>::build_param {
     cuvs::neighbors::distribution_mode mode;
@@ -43,9 +44,6 @@ class cuvs_mg_cagra : public algo<T>, public algo_gpu {
   cuvs_mg_cagra(Metric metric, int dim, const build_param& param, int concurrent_searches = 1)
     : algo<T>(metric, dim), index_params_(param), clique_()
   {
-    index_params_.cagra_params.metric         = parse_metric_type(metric);
-    index_params_.ivf_pq_build_params->metric = parse_metric_type(metric);
-
     clique_.set_memory_pool(80);
   }
 
@@ -98,8 +96,9 @@ template <typename T, typename IdxT>
 void cuvs_mg_cagra<T, IdxT>::build(const T* dataset, size_t nrow)
 {
   auto dataset_extents = raft::make_extents<IdxT>(nrow, dim_);
-  index_params_.prepare_build_params(dataset_extents);
-  cuvs::neighbors::mg_index_params<cagra::index_params> build_params = index_params_.cagra_params;
+  auto params          = index_params_.cagra_params(dataset_extents, parse_metric_type(metric_));
+
+  cuvs::neighbors::mg_index_params<cagra::index_params> build_params = params;
   build_params.mode                                                  = index_params_.mode;
 
   auto dataset_view =
diff --git a/cpp/include/cuvs/neighbors/graph_build_types.hpp b/cpp/include/cuvs/neighbors/graph_build_types.hpp
index 23ca3e0c14..0711b5c2fb 100644
--- a/cpp/include/cuvs/neighbors/graph_build_types.hpp
+++ b/cpp/include/cuvs/neighbors/graph_build_types.hpp
@@ -56,10 +56,39 @@ struct ivf_pq_params {
   ivf_pq_params(raft::matrix_extent<int64_t> dataset_extents,
                 cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
   {
-    build_params = cuvs::neighbors::ivf_pq::index_params::from_dataset(dataset_extents, metric);
+    build_params    = ivf_pq::index_params::from_dataset(dataset_extents, metric);
+    auto n_rows     = dataset_extents.extent(0);
+    auto n_features = dataset_extents.extent(1);
+    if (n_features <= 32) {
+      build_params.pq_dim  = 16;
+      build_params.pq_bits = 8;
+    } else {
+      build_params.pq_bits = 4;
+      if (n_features <= 64) {
+        build_params.pq_dim = 32;
+      } else if (n_features <= 128) {
+        build_params.pq_dim = 64;
+      } else if (n_features <= 192) {
+        build_params.pq_dim = 96;
+      } else {
+        build_params.pq_dim = raft::round_up_safe<uint32_t>(n_features / 2, 128);
+      }
+    }
+
+    build_params.n_lists        = std::max<uint32_t>(1, n_rows / 2000);
+    build_params.kmeans_n_iters = 10;
+
+    const double kMinPointsPerCluster         = 32;
+    const double min_kmeans_trainset_points   = kMinPointsPerCluster * build_params.n_lists;
+    const double max_kmeans_trainset_fraction = 1.0;
+    const double min_kmeans_trainset_fraction =
+      std::min(max_kmeans_trainset_fraction, min_kmeans_trainset_points / n_rows);
+    build_params.kmeans_trainset_fraction = std::clamp(
+      1.0 / std::sqrt(n_rows * 1e-5), min_kmeans_trainset_fraction, max_kmeans_trainset_fraction);
+    build_params.codebook_kind = ivf_pq::codebook_gen::PER_SUBSPACE;
 
     search_params                         = cuvs::neighbors::ivf_pq::search_params{};
-    search_params.n_probes                = std::max<uint32_t>(10, build_params.n_lists * 0.01);
+    search_params.n_probes                = std::round(std::sqrt(build_params.n_lists) / 20 + 4);
     search_params.lut_dtype               = CUDA_R_16F;
     search_params.internal_distance_dtype = CUDA_R_16F;
     search_params.coarse_search_dtype     = CUDA_R_16F;
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 4605dfc4cf..7db6d549eb 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -64,6 +64,39 @@ struct index_params : cuvs::neighbors::index_params {
   int num_threads = 0;
 };
 
+/**
+ * @brief Create a CAGRA index parameters compatible with HNSW index
+ *
+ * @param dataset The shape of the input dataset.
+ * @param M HNSW index parameter M.
+ * @param ef_construction HNSW index parameter ef_construction.
+ * @param metric The distance metric to search.
+ *
+ *
+ * * IMPORTANT NOTE *
+ *
+ * The reference HNSW index and the corresponding from-CAGRA generated HNSW index will NOT produce
+ * the same recalls and QPS for the same parameter `ef`. The graphs are different internally. For
+ * the same `ef`, the from-CAGRA index likely has a slightly higher recall and slightly lower QPS.
+ * However, the Recall-QPS curves should be similar (i.e. the points are just shifted along the
+ * curve).
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   raft::resources res;
+ *   auto dataset = raft::make_device_matrix<float, int64_t>(res, N, D);
+ *   auto cagra_params = to_cagra_params(dataset.extents(), M, efc);
+ *   auto cagra_index = cagra::build(res, cagra_params, dataset);
+ *   auto hnsw_index = hnsw::from_cagra(res, hnsw_params, cagra_index);
+ * @endcode
+ */
+cuvs::neighbors::cagra::index_params to_cagra_params(
+  raft::matrix_extent<int64_t> dataset,
+  int M,
+  int ef_construction,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded);
+
 /**@}*/
 
 /**
diff --git a/cpp/src/neighbors/hnsw.cpp b/cpp/src/neighbors/hnsw.cpp
index b12e3217e4..738ddc181e 100644
--- a/cpp/src/neighbors/hnsw.cpp
+++ b/cpp/src/neighbors/hnsw.cpp
@@ -21,6 +21,23 @@
 
 namespace cuvs::neighbors::hnsw {
 
+auto to_cagra_params(raft::matrix_extent<int64_t> dataset,
+                     int M,
+                     int ef_construction,
+                     cuvs::distance::DistanceType metric) -> cuvs::neighbors::cagra::index_params
+{
+  auto ivf_pq_params = cuvs::neighbors::graph_build_params::ivf_pq_params(dataset, metric);
+  ivf_pq_params.search_params.n_probes =
+    std::round(std::sqrt(ivf_pq_params.build_params.n_lists) / 20 + ef_construction / 16);
+
+  cagra::index_params params;
+  params.graph_build_params        = ivf_pq_params;
+  params.graph_degree              = M * 2;
+  params.intermediate_graph_degree = M * 3;
+
+  return params;
+}
+
 #define CUVS_INST_HNSW_FROM_CAGRA(T)                                                  \
   std::unique_ptr<index<T>> from_cagra(                                               \
     raft::resources const& res,                                                       \

From 7ecac66f2bf358b361148ffe3f95394d38077ead Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Wed, 30 Jul 2025 11:04:55 -0400
Subject: [PATCH 517/623] cuvs RAPIDS dependency now use the new rapids-cmake
 branch info (#1189)

This is required to handle when we switch RAPIDS branching strategy

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1189
---
 cpp/cmake/thirdparty/get_cuvs.cmake      | 6 +++---
 cpp/cmake/thirdparty/get_raft.cmake      | 6 +++---
 examples/cmake/thirdparty/get_cuvs.cmake | 4 ++--
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/cpp/cmake/thirdparty/get_cuvs.cmake b/cpp/cmake/thirdparty/get_cuvs.cmake
index b37249745b..6ef4791474 100644
--- a/cpp/cmake/thirdparty/get_cuvs.cmake
+++ b/cpp/cmake/thirdparty/get_cuvs.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -14,14 +14,14 @@
 # Use RAPIDS_VERSION_MAJOR_MINOR from cmake/rapids_config.cmake
 set(CUVS_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
 set(CUVS_FORK "rapidsai")
-set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
+set(CUVS_PINNED_TAG "${rapids-cmake-checkout-tag}")
 
 function(find_and_configure_cuvs)
     set(oneValueArgs VERSION FORK PINNED_TAG ENABLE_NVTX CLONE_ON_PIN BUILD_CPU_ONLY BUILD_SHARED_LIBS)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
-    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "branch-${CUVS_VERSION}")
+    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "${rapids-cmake-checkout-tag}")
         message(STATUS "cuVS: pinned tag found: ${PKG_PINNED_TAG}. Cloning cuVS locally.")
         set(CPM_DOWNLOAD_cuvs ON)
     endif()
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index a71dc4cb43..424e829cb6 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -14,14 +14,14 @@
 # Use RAPIDS_VERSION_MAJOR_MINOR from rapids_config.cmake
 set(RAFT_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
 set(RAFT_FORK "rapidsai")
-set(RAFT_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
+set(RAFT_PINNED_TAG "${rapids-cmake-checkout-tag}")
 
 function(find_and_configure_raft)
     set(oneValueArgs VERSION FORK PINNED_TAG USE_RAFT_STATIC ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES CLONE_ON_PIN)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
-    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "branch-${RAFT_VERSION}")
+    if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "${rapids-cmake-checkout-tag}")
         message(STATUS "cuVS: RAFT pinned tag found: ${PKG_PINNED_TAG}. Cloning raft locally.")
         set(CPM_DOWNLOAD_raft ON)
     elseif(PKG_USE_RAFT_STATIC AND (NOT CPM_raft_SOURCE))
diff --git a/examples/cmake/thirdparty/get_cuvs.cmake b/examples/cmake/thirdparty/get_cuvs.cmake
index 49f0ab7017..911adbfa4a 100644
--- a/examples/cmake/thirdparty/get_cuvs.cmake
+++ b/examples/cmake/thirdparty/get_cuvs.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -14,7 +14,7 @@
 # Use RAPIDS_VERSION_MAJOR_MINOR from cmake/rapids_config.cmake
 set(CUVS_VERSION "${RAPIDS_VERSION_MAJOR_MINOR}")
 set(CUVS_FORK "rapidsai")
-set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION_MAJOR_MINOR}")
+set(CUVS_PINNED_TAG "${rapids-cmake-branch}")
 
 function(find_and_configure_cuvs)
     set(oneValueArgs VERSION FORK PINNED_TAG ENABLE_NVTX BUILD_CUVS_C_LIBRARY)

From 10e07955281c12b4ae7ea160ff2d2558d84e83a7 Mon Sep 17 00:00:00 2001
From: Tamas Bela Feher <tfeher@nvidia.com>
Date: Wed, 30 Jul 2025 17:57:41 +0200
Subject: [PATCH 518/623] Reduce binary size of refine functions (#1095)

The refine functions that work with GPU data use IVF-Flat under the hood to perform the refinement operation. This PR adds extern template declarations for `ivfflat_interleaved_scan` and uses these in the refine functions. This way we avoid recompiling the IVF-Flat search kernels, and save binary size.

Before this PR `ivfflat_interleaved_scan` was compiled through the `ivf_flat::search()` function instantiations. But the function symbols were not available due to inlining. This PR also add explicit instantiations for `ivfflat_interleaved_scan`, and now both `ivf_flat::search` and `refine` can use the same interleaved scan function.

Authors:
  - Tamas Bela Feher (https://github.com/tfeher)

Approvers:
  - Artem M. Chirkin (https://github.com/achirkin)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1095
---
 cpp/CMakeLists.txt                            |  8 ++
 cpp/src/neighbors/ivf_common.cuh              | 16 ++--
 cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh | 20 ++--
 .../ivf_flat/ivf_flat_interleaved_scan.cuh    |  7 +-
 ...vf_flat_interleaved_scan_explicit_inst.cuh | 49 ++++++++++
 .../ivf_flat_interleaved_scan_ext.cuh         | 96 +++++++++++++++++++
 ...ivf_flat_interleaved_scan_float_int64_t.cu | 21 ++++
 ...t_interleaved_scan_float_int64_t_bitset.cu | 23 +++++
 .../ivf_flat_interleaved_scan_half_int64_t.cu | 24 +++++
 ...at_interleaved_scan_half_int64_t_bitset.cu | 26 +++++
 ...vf_flat_interleaved_scan_int8_t_int64_t.cu | 23 +++++
 ..._interleaved_scan_int8_t_int64_t_bitset.cu | 25 +++++
 ...f_flat_interleaved_scan_uint8_t_int64_t.cu | 23 +++++
 ...interleaved_scan_uint8_t_int64_t_bitset.cu | 25 +++++
 .../neighbors/ivf_flat/ivf_flat_search.cuh    | 16 +++-
 cpp/src/neighbors/refine/refine_device.cuh    | 48 +++++-----
 16 files changed, 400 insertions(+), 50 deletions(-)
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_explicit_inst.cuh
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_ext.cuh
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t_bitset.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t_bitset.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t_bitset.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t.cu
 create mode 100644 cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t_bitset.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index bff0668532..f422276fe1 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -432,6 +432,14 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/ivf_flat/ivf_flat_search_half_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_search_int8_t_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_search_uint8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t_bitset.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t_bitset.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t_bitset.cu
+    src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t_bitset.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_float_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_half_int64_t.cu
     src/neighbors/ivf_flat/ivf_flat_serialize_int8_t_int64_t.cu
diff --git a/cpp/src/neighbors/ivf_common.cuh b/cpp/src/neighbors/ivf_common.cuh
index 2169f72998..7e3664d519 100644
--- a/cpp/src/neighbors/ivf_common.cuh
+++ b/cpp/src/neighbors/ivf_common.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -148,17 +148,19 @@ __device__ inline auto find_chunk_ix(uint32_t& sample_ix,  // NOLINT
   return ix_min;
 }
 
-template <int BlockDim, typename IdxT>
+template <int BlockDim, typename IdxT, typename DbIdxT>
 __launch_bounds__(BlockDim) RAFT_KERNEL
   postprocess_neighbors_kernel(IdxT* neighbors_out,                // [n_queries, topk]
                                const uint32_t* neighbors_in,       // [n_queries, topk]
-                               const IdxT* const* db_indices,      // [n_clusters][..]
+                               const DbIdxT* const* db_indices,    // [n_clusters][..]
                                const uint32_t* clusters_to_probe,  // [n_queries, n_probes]
                                const uint32_t* chunk_indices,      // [n_queries, n_probes]
                                uint32_t n_queries,
                                uint32_t n_probes,
                                uint32_t topk)
 {
+  static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
+                "IdxT must be able to represent all values of uint32_t");
   const uint64_t i        = threadIdx.x + BlockDim * uint64_t(blockIdx.x);
   const uint32_t query_ix = i / uint64_t(topk);
   if (query_ix >= n_queries) { return; }
@@ -170,8 +172,8 @@ __launch_bounds__(BlockDim) RAFT_KERNEL
   uint32_t data_ix        = neighbors_in[k];
   const uint32_t chunk_ix = find_chunk_ix(data_ix, n_probes, chunk_indices);
   const bool valid        = chunk_ix < n_probes;
-  neighbors_out[k] =
-    valid ? db_indices[clusters_to_probe[chunk_ix]][data_ix] : kOutOfBoundsRecord<IdxT>;
+  neighbors_out[k] = valid ? static_cast<IdxT>(db_indices[clusters_to_probe[chunk_ix]][data_ix])
+                           : kOutOfBoundsRecord<IdxT>;
 }
 
 /**
@@ -181,10 +183,10 @@ __launch_bounds__(BlockDim) RAFT_KERNEL
  * probed clusters / defined by the `chunk_indices`.
  * We assume the searched sample sizes (for a single query) fit into `uint32_t`.
  */
-template <typename IdxT>
+template <typename IdxT, typename DbIdxT>
 void postprocess_neighbors(IdxT* neighbors_out,                // [n_queries, topk]
                            const uint32_t* neighbors_in,       // [n_queries, topk]
-                           const IdxT* const* db_indices,      // [n_clusters][..]
+                           const DbIdxT* const* db_indices,    // [n_clusters][..]
                            const uint32_t* clusters_to_probe,  // [n_queries, n_probes]
                            const uint32_t* chunk_indices,      // [n_queries, n_probes]
                            uint32_t n_queries,
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
index 00fc9d4779..8769255c0a 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_build.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -100,8 +100,9 @@ auto clone(const raft::resources& res, const index<T, IdxT>& source) -> index<T,
  *   there are no dependencies between threads, hence no constraints on the block size.
  *
  * @tparam T      element type.
- * @tparam IdxT   type of the indices in the source source_vecs
+ * @tparam IdxT   type of the vector ids in the index (corresponds to second arg ofindex<T, IdxT>)
  * @tparam LabelT label type
+ * @tparam SourceIndexT input index type (usually same as IdxT)
  * @tparam gather_src if false, then we build the index from vectors source_vecs[i,:], otherwise
  *     we use source_vecs[source_ixs[i],:]. In both cases i=0..n_rows-1.
  *
@@ -118,10 +119,10 @@ auto clone(const raft::resources& res, const index<T, IdxT>& source) -> index<T,
  * @param veclen size of vectorized loads/stores; must satisfy `dim % veclen == 0`.
  *
  */
-template <typename T, typename IdxT, typename LabelT, bool gather_src = false>
+template <typename T, typename IdxT, typename LabelT, typename SourceIdxT, bool gather_src = false>
 RAFT_KERNEL build_index_kernel(const LabelT* labels,
                                const T* source_vecs,
-                               const IdxT* source_ixs,
+                               const SourceIdxT* source_ixs,
                                T** list_data_ptrs,
                                IdxT** list_index_ptrs,
                                uint32_t* list_sizes_ptr,
@@ -135,7 +136,10 @@ RAFT_KERNEL build_index_kernel(const LabelT* labels,
   auto source_ix = source_ixs == nullptr ? i + batch_offset : source_ixs[i];
   // In the context of refinement, some indices may be invalid (the generating NN algorithm does
   // not return enough valid items). Do not add the item to the index in this case.
-  if (source_ix == ivf::kInvalidRecord<IdxT> || source_ix == raft::upper_bound<IdxT>()) { return; }
+  if (source_ix == ivf::kInvalidRecord<SourceIdxT> ||
+      source_ix == raft::upper_bound<SourceIdxT>()) {
+    return;
+  }
 
   auto list_id     = labels[i];
   auto inlist_id   = atomicAdd(list_sizes_ptr + list_id, 1);
@@ -460,11 +464,11 @@ inline auto build(raft::resources const& handle,
  * @param[in] candidate_idx device pointer to neighbor candidates, size [n_queries, n_candidates]
  * @param[in] n_candidates  of neighbor_candidates
  */
-template <typename T, typename IdxT>
+template <typename T, typename IdxT, typename CandidateIdxT>
 inline void fill_refinement_index(raft::resources const& handle,
                                   index<T, IdxT>* refinement_index,
                                   const T* dataset,
-                                  const IdxT* candidate_idx,
+                                  const CandidateIdxT* candidate_idx,
                                   IdxT n_queries,
                                   uint32_t n_candidates)
 {
@@ -500,7 +504,7 @@ inline void fill_refinement_index(raft::resources const& handle,
 
   const dim3 block_dim(256);
   const dim3 grid_dim(raft::ceildiv<IdxT>(n_queries * n_candidates, block_dim.x));
-  build_index_kernel<T, IdxT, LabelT, true>
+  build_index_kernel<T, IdxT, LabelT, CandidateIdxT, true>
     <<<grid_dim, block_dim, 0, stream>>>(new_labels.data(),
                                          dataset,
                                          candidate_idx,
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
index 9bf4ae6784..e3286ba548 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -40,11 +40,6 @@ using namespace cuvs::spatial::knn::detail;  // NOLINT
 
 constexpr int kThreadsPerBlock = 128;
 
-auto RAFT_WEAK_FUNCTION is_local_topk_feasible(uint32_t k) -> bool
-{
-  return k <= raft::matrix::detail::select::warpsort::kMaxCapacity;
-}
-
 /**
  * @brief Copy `n` elements per block from one place to another.
  *
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_explicit_inst.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_explicit_inst.cuh
new file mode 100644
index 0000000000..3265a622b9
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_explicit_inst.cuh
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../detail/ann_utils.cuh"
+#include "ivf_flat_interleaved_scan.cuh"
+#include <cstdint>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+
+#define CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(T, IdxT, SampleFilterT)                        \
+  template void                                                                            \
+  ivfflat_interleaved_scan<T,                                                              \
+                           typename cuvs::spatial::knn::detail::utils::config<T>::value_t, \
+                           IdxT,                                                           \
+                           SampleFilterT>(const index<T, IdxT>& index,                     \
+                                          const T* queries,                                \
+                                          const uint32_t* coarse_query_results,            \
+                                          const uint32_t n_queries,                        \
+                                          const uint32_t queries_offset,                   \
+                                          const cuvs::distance::DistanceType metric,       \
+                                          const uint32_t n_probes,                         \
+                                          const uint32_t k,                                \
+                                          const uint32_t max_samples,                      \
+                                          const uint32_t* chunk_indices,                   \
+                                          const bool select_min,                           \
+                                          SampleFilterT sample_filter,                     \
+                                          uint32_t* neighbors,                             \
+                                          float* distances,                                \
+                                          uint32_t& grid_dim_x,                            \
+                                          rmm::cuda_stream_view stream);
+
+#define COMMA ,
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_ext.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_ext.cuh
new file mode 100644
index 0000000000..65a537b983
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_ext.cuh
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdint>
+#include <cuda_fp16.h>
+
+#include "../detail/ann_utils.cuh"
+#include <cuvs/neighbors/common.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/raft_explicit.hpp>
+
+namespace cuvs::neighbors::ivf_flat::detail {
+template <typename T, typename AccT, typename IdxT, typename IvfSampleFilterT>
+void ivfflat_interleaved_scan(const index<T, IdxT>& index,
+                              const T* queries,
+                              const uint32_t* coarse_query_results,
+                              const uint32_t n_queries,
+                              const uint32_t queries_offset,
+                              const cuvs::distance::DistanceType metric,
+                              const uint32_t n_probes,
+                              const uint32_t k,
+                              const uint32_t max_samples,
+                              const uint32_t* chunk_indices,
+                              const bool select_min,
+                              IvfSampleFilterT sample_filter,
+                              uint32_t* neighbors,
+                              float* distances,
+                              uint32_t& grid_dim_x,
+                              rmm::cuda_stream_view stream) RAFT_EXPLICIT;
+
+#define CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(T, IdxT, SampleFilterT)                        \
+  extern template void                                                                     \
+  ivfflat_interleaved_scan<T,                                                              \
+                           typename cuvs::spatial::knn::detail::utils::config<T>::value_t, \
+                           IdxT,                                                           \
+                           SampleFilterT>(const index<T, IdxT>& index,                     \
+                                          const T* queries,                                \
+                                          const uint32_t* coarse_query_results,            \
+                                          const uint32_t n_queries,                        \
+                                          const uint32_t queries_offset,                   \
+                                          const cuvs::distance::DistanceType metric,       \
+                                          const uint32_t n_probes,                         \
+                                          const uint32_t k,                                \
+                                          const uint32_t max_samples,                      \
+                                          const uint32_t* chunk_indices,                   \
+                                          const bool select_min,                           \
+                                          SampleFilterT sample_filter,                     \
+                                          uint32_t* neighbors,                             \
+                                          float* distances,                                \
+                                          uint32_t& grid_dim_x,                            \
+                                          rmm::cuda_stream_view stream);
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(float, int64_t, cuvs::neighbors::filtering::none_sample_filter);
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(half, int64_t, cuvs::neighbors::filtering::none_sample_filter);
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(int8_t,
+                                    int64_t,
+                                    cuvs::neighbors::filtering::none_sample_filter);
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(uint8_t,
+                                    int64_t,
+                                    cuvs::neighbors::filtering::none_sample_filter);
+
+#define COMMA ,
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(
+  float, int64_t, cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>);
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(
+  half, int64_t, cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>);
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(
+  int8_t, int64_t, cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>);
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(
+  uint8_t, int64_t, cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>);
+#undef COMMA
+#undef CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t.cu
new file mode 100644
index 0000000000..427b2b21b7
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t.cu
@@ -0,0 +1,21 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(float, int64_t, filtering::none_sample_filter);
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t_bitset.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t_bitset.cu
new file mode 100644
index 0000000000..ae41155af3
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_float_int64_t_bitset.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(float,
+                                    int64_t,
+                                    filtering::bitset_filter<uint32_t COMMA int64_t>);
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t.cu
new file mode 100644
index 0000000000..b1507263b0
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t.cu
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+#include <cuda_fp16.h>
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(half, int64_t, filtering::none_sample_filter);
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t_bitset.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t_bitset.cu
new file mode 100644
index 0000000000..d0fce261cb
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_half_int64_t_bitset.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+#include <cuda_fp16.h>
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(half,
+                                    int64_t,
+                                    filtering::bitset_filter<uint32_t COMMA int64_t>);
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t.cu
new file mode 100644
index 0000000000..9cffc30510
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(int8_t, int64_t, filtering::none_sample_filter);
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t_bitset.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t_bitset.cu
new file mode 100644
index 0000000000..f44c0523f0
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_int8_t_int64_t_bitset.cu
@@ -0,0 +1,25 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(int8_t,
+                                    int64_t,
+                                    filtering::bitset_filter<uint32_t COMMA int64_t>);
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t.cu
new file mode 100644
index 0000000000..42d429c8c0
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t.cu
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(uint8_t, int64_t, filtering::none_sample_filter);
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t_bitset.cu b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t_bitset.cu
new file mode 100644
index 0000000000..2f3924e199
--- /dev/null
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_interleaved_scan_uint8_t_int64_t_bitset.cu
@@ -0,0 +1,25 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_flat_interleaved_scan_explicit_inst.cuh"
+
+namespace cuvs::neighbors::ivf_flat::detail {
+
+CUVS_INST_IVF_FLAT_INTERLEAVED_SCAN(uint8_t,
+                                    int64_t,
+                                    filtering::bitset_filter<uint32_t COMMA int64_t>);
+
+}  // namespace cuvs::neighbors::ivf_flat::detail
diff --git a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
index d36043ad45..c0289b66d0 100644
--- a/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
+++ b/cpp/src/neighbors/ivf_flat/ivf_flat_search.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -18,10 +18,10 @@
 
 #include "../../core/nvtx.hpp"
 #include "../detail/ann_utils.cuh"
-#include "../ivf_common.cuh"              // cuvs::neighbors::detail::ivf
-#include "ivf_flat_interleaved_scan.cuh"  // interleaved_scan
-#include <cuvs/neighbors/common.hpp>      // none_sample_filter
-#include <cuvs/neighbors/ivf_flat.hpp>    // raft::neighbors::ivf_flat::index
+#include "../ivf_common.cuh"                  // cuvs::neighbors::detail::ivf
+#include "ivf_flat_interleaved_scan_ext.cuh"  // interleaved_scan
+#include <cuvs/neighbors/common.hpp>          // none_sample_filter
+#include <cuvs/neighbors/ivf_flat.hpp>        // raft::neighbors::ivf_flat::index
 
 #include "../detail/ann_utils.cuh"      // utils::mapping
 #include <cuvs/distance/distance.hpp>   // is_min_close, DistanceType
@@ -33,6 +33,7 @@
 #include <raft/linalg/gemm.cuh>      // raft::linalg::gemm
 #include <raft/linalg/norm.cuh>      // raft::linalg::norm
 #include <raft/linalg/unary_op.cuh>  // raft::linalg::unary_op
+#include <raft/matrix/detail/select_warpsort.cuh>
 
 #include <rmm/resource_ref.hpp>
 
@@ -40,6 +41,11 @@ namespace cuvs::neighbors::ivf_flat::detail {
 
 using namespace cuvs::spatial::knn::detail;  // NOLINT
 
+auto RAFT_WEAK_FUNCTION is_local_topk_feasible(uint32_t k) -> bool
+{
+  return k <= raft::matrix::detail::select::warpsort::kMaxCapacity;
+}
+
 template <typename T, typename AccT, typename IdxT, typename IvfSampleFilterT>
 void search_impl(raft::resources const& handle,
                  const cuvs::neighbors::ivf_flat::index<T, IdxT>& index,
diff --git a/cpp/src/neighbors/refine/refine_device.cuh b/cpp/src/neighbors/refine/refine_device.cuh
index 52ae8c409e..2e98c368fd 100644
--- a/cpp/src/neighbors/refine/refine_device.cuh
+++ b/cpp/src/neighbors/refine/refine_device.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@
 #include "../../core/nvtx.hpp"
 #include "../detail/ann_utils.cuh"
 #include "../ivf_flat/ivf_flat_build.cuh"
-#include "../ivf_flat/ivf_flat_interleaved_scan.cuh"
+#include "../ivf_flat/ivf_flat_interleaved_scan_ext.cuh"
 #include "refine_common.hpp"
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
@@ -81,15 +81,15 @@ void refine_device(
                    fake_coarse_idx.data(),
                    fake_coarse_idx.data() + n_queries);
 
-  cuvs::neighbors::ivf_flat::index<data_t, idx_t> refinement_index(
+  cuvs::neighbors::ivf_flat::index<data_t, int64_t> refinement_index(
     handle, metric, n_queries, false, true, dim);
 
-  cuvs::neighbors::ivf_flat::detail::fill_refinement_index<data_t, idx_t>(
+  cuvs::neighbors::ivf_flat::detail::fill_refinement_index<data_t, int64_t>(
     handle,
     &refinement_index,
     dataset.data_handle(),
     neighbor_candidates.data_handle(),
-    static_cast<idx_t>(n_queries),
+    static_cast<int64_t>(n_queries),
     static_cast<uint32_t>(n_candidates));
   uint32_t grid_dim_x = 1;
 
@@ -113,25 +113,25 @@ void refine_device(
     neighbors_uint32 = neighbors_uint32_buf.data();
   }
 
-  cuvs::neighbors::ivf_flat::detail::ivfflat_interleaved_scan<
-    data_t,
-    typename cuvs::spatial::knn::detail::utils::config<data_t>::value_t,
-    idx_t>(refinement_index,
-           queries.data_handle(),
-           fake_coarse_idx.data(),
-           static_cast<uint32_t>(n_queries),
-           0,
-           metric,
-           1,
-           k,
-           0,
-           chunk_index.data(),
-           cuvs::distance::is_min_close(metric),
-           cuvs::neighbors::filtering::none_sample_filter(),
-           neighbors_uint32,
-           distances.data_handle(),
-           grid_dim_x,
-           raft::resource::get_cuda_stream(handle));
+  using acc_t = typename cuvs::spatial::knn::detail::utils::config<data_t>::value_t;
+
+  cuvs::neighbors::ivf_flat::detail::ivfflat_interleaved_scan<data_t, acc_t, int64_t>(
+    refinement_index,
+    queries.data_handle(),
+    fake_coarse_idx.data(),
+    static_cast<uint32_t>(n_queries),
+    0,
+    metric,
+    1,
+    k,
+    0,
+    chunk_index.data(),
+    cuvs::distance::is_min_close(metric),
+    cuvs::neighbors::filtering::none_sample_filter(),
+    neighbors_uint32,
+    distances.data_handle(),
+    grid_dim_x,
+    raft::resource::get_cuda_stream(handle));
 
   // postprocessing -- neighbors from position to actual id
   cuvs::neighbors::ivf::detail::postprocess_neighbors(indices.data_handle(),

From 35cda392c859dc2a799efe035c508d80759047e2 Mon Sep 17 00:00:00 2001
From: rmaschal <robertmaschal@gmail.com>
Date: Wed, 30 Jul 2025 12:28:15 -0700
Subject: [PATCH 519/623] SCaNN Index build (#1120)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR gives a proof-of-concept implementation of GPU-based index build for the ScaNN index. The ScaNN index defined here is similar to IVF-PQ index  in structure (a tree structure coming from kmeans, plus product quantization of vectors assigned to leaf nodes), together with “AVQ update” of the kmeans centroids and a spilled cluster assignment from the “SOAR” loss.

Other features, optimizations, and customizability options to appear in subsequent PRs.

* scann_build.cuh

This file contains the implementation for build(..). The general pipeline looks like:
Train kmeans centers on sampled data
Assign all dataset vectors to kmeans clusters by minimizing L2 loss
Update kmeans centers with AVQ
Train PQ codebook on sampled residual vectors (here we use VPQ, slightly modified to perform product quantization on individual subspaces, e.g. each subspace has its own codebook)
Quantization loop (batched):
Compute spilled SOAR labels (performed here to minimize HtoD copies)
Compute and quantize residuals/soar residuals using trained pq codebook
If enabled, compute bf16 quantization of dataset vectors (performed here to minimize HtoD copies).

* scann_avq.cuh

This file contains apply_avq(..), which recomputes cluster centers using AVQ. The main technique is a single application of Theorem 4.2 in https://arxiv.org/pdf/1908.10396 to each cluster, using parameters:
h_i_parallel = eta * || x_i || ^ (eta - 1)
h_i_orthogonal = ||x _i || ^ (eta -1)
The implementation of Theorem 4.2 is in compute_avq_centroid(..)

The overall pipeline for apply_avq(..) is:
Build clusters from kmeans cluster assignments
For each cluster:
Gather cluster vectors into single matrix
Update kmeans center via compute_avq_centroid
Rescale updated centroids (I need to add more details about this step).

* scann_quantize.cuh

This file contains helpers for PQ. Codebooks are created from residual vectors using train_pq from vpq_dataset.cuh (using a single vq center which is set to zero). Unlike in VPQ, codebooks are generated separately for each subspace, rather than collapsing all subspaces into a single space and computing a global codebook.



* scann_soar.cuh

The main function is compute_soar_labels(..), which computes a second, spilled cluster assignment by minimizing the SOAR loss function (Theorem 3.1 in https://arxiv.org/pdf/2404.00774)

* scann_serialize.cuh

Contains the implementation of serialize(..). The goal is to serialize ScaNN artifacts in a way that is usable with open-source ScaNN search with minimal additional post-processing. The cluster assignments, quantized vectors (for both the primary and spilled SOAR assignments), and bf16 dataset are all stored in separate .npy files for direct consumption by open-source ScaNN. The codebook and cluster centers are also serialized separately, but require additional post-processing into the correct Protobuf structs (not included in this PR).

Test Plan:
This code is mostly tested via CPU search with open-source ScaNN. Additional protobuf artifacts are created from the cuVS serialized index via an external tool. A pareto for OpenAI 5M is shown here:

Authors:
  - https://github.com/rmaschal

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/1120
---
 cpp/CMakeLists.txt                            |   2 +
 cpp/include/cuvs/neighbors/scann.hpp          | 311 ++++++++++++
 cpp/src/neighbors/scann/detail/scann_avq.cuh  | 460 ++++++++++++++++++
 .../neighbors/scann/detail/scann_build.cuh    | 380 +++++++++++++++
 .../neighbors/scann/detail/scann_common.cuh   |  68 +++
 .../neighbors/scann/detail/scann_quantize.cuh | 325 +++++++++++++
 .../scann/detail/scann_serialize.cuh          | 155 ++++++
 cpp/src/neighbors/scann/detail/scann_soar.cuh | 175 +++++++
 cpp/src/neighbors/scann/scann.cuh             |  89 ++++
 cpp/src/neighbors/scann/scann_build_float.cu  |  43 ++
 .../neighbors/scann/scann_serialize_float.cu  |  40 ++
 cpp/tests/CMakeLists.txt                      |   4 +
 cpp/tests/neighbors/ann_scann.cuh             | 274 +++++++++++
 .../neighbors/ann_scann/test_float_int64_t.cu |  31 ++
 examples/cpp/CMakeLists.txt                   |   4 +-
 examples/cpp/src/scann_example.cu             |  86 ++++
 16 files changed, 2446 insertions(+), 1 deletion(-)
 create mode 100644 cpp/include/cuvs/neighbors/scann.hpp
 create mode 100644 cpp/src/neighbors/scann/detail/scann_avq.cuh
 create mode 100644 cpp/src/neighbors/scann/detail/scann_build.cuh
 create mode 100644 cpp/src/neighbors/scann/detail/scann_common.cuh
 create mode 100644 cpp/src/neighbors/scann/detail/scann_quantize.cuh
 create mode 100644 cpp/src/neighbors/scann/detail/scann_serialize.cuh
 create mode 100644 cpp/src/neighbors/scann/detail/scann_soar.cuh
 create mode 100644 cpp/src/neighbors/scann/scann.cuh
 create mode 100644 cpp/src/neighbors/scann/scann_build_float.cu
 create mode 100644 cpp/src/neighbors/scann/scann_serialize_float.cu
 create mode 100644 cpp/tests/neighbors/ann_scann.cuh
 create mode 100644 cpp/tests/neighbors/ann_scann/test_float_int64_t.cu
 create mode 100644 examples/cpp/src/scann_example.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index f422276fe1..7fb6141f9b 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -488,6 +488,8 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/refine/detail/refine_host_half_float.cpp
     src/neighbors/refine/detail/refine_host_int8_t_float.cpp
     src/neighbors/refine/detail/refine_host_uint8_t_float.cpp
+    src/neighbors/scann/scann_build_float.cu
+    src/neighbors/scann/scann_serialize_float.cu
     src/neighbors/sample_filter.cu
     src/neighbors/tiered_index.cu
     src/neighbors/sparse_brute_force.cu
diff --git a/cpp/include/cuvs/neighbors/scann.hpp b/cpp/include/cuvs/neighbors/scann.hpp
new file mode 100644
index 0000000000..7215817746
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/scann.hpp
@@ -0,0 +1,311 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resource/stream_view.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/util/integer_utils.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+#include <variant>
+
+namespace cuvs::neighbors::experimental::scann {
+/**
+ * @defgroup scann_cpp_index_params ScaNN index build parameters
+ * @{
+ */
+
+/**
+ * @brief ANN parameters used by ScaNN to build index
+ *
+ */
+struct index_params : cuvs::neighbors::index_params {
+  // partitioning parameters
+
+  /** the number of leaves in the tree **/
+  uint32_t n_leaves = 1000;
+  /** the number of rows for training the tree structures **/
+  int64_t kmeans_n_rows_train = 100000;
+
+  /** the max number of iterations for training the tree structure **/
+  uint32_t kmeans_n_iters = 24;
+
+  /** the value of eta for AVQ adjustment during partitioning **/
+  float partitioning_eta = 1;
+
+  /** the value of lambda for SOAR spilling **/
+  float soar_lambda = 1;
+
+  // Residual quanitzation params
+  /** the dimension of pq subspaces (must divide dataset dimension)**/
+  uint32_t pq_dim = 8;
+
+  /** the number of bits for pq codes (must be 4 or 8, for 16 and 256 codes respectively) **/
+  uint32_t pq_bits = 8;
+
+  /** the number of rows for PQ training (internally capped to 100k) **/
+  int64_t pq_n_rows_train = 100000;
+
+  /** the max number of iterations for PQ training **/
+  uint32_t pq_train_iters = 10;
+
+  /** whether to apply bf16 quantization of dataset vectors **/
+  bool bf16_enabled = false;
+
+  // TODO - add other scann build params
+};
+
+/**
+ * @}
+ */
+
+static_assert(std::is_aggregate_v<index_params>);
+
+/**
+ * @defgroup scann_cpp_index ScaNN index type
+ * @{
+ */
+
+/**
+ * @brief ScaNN index.
+ *
+ * The index stores the dataset and the ScaNN graph in device memory.
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the vector indices (represent dataset.extent(0))
+ *
+ */
+template <typename T, typename IdxT>
+struct index : cuvs::neighbors::index {
+  static_assert(!raft::is_narrowing_v<uint32_t, IdxT>,
+                "IdxT must be able to represent all values of uint32_t");
+
+ public:
+  /** Distance metric used for clustering. */
+  [[nodiscard]] constexpr inline auto metric() const noexcept -> cuvs::distance::DistanceType
+  {
+    return metric_;
+  }
+
+  /** Total length of the index (number of vectors). */
+  IdxT size() const noexcept;
+
+  /** Dimensionality of the data. */
+  [[nodiscard]] constexpr inline auto dim() const noexcept -> uint32_t { return dim_; }
+
+  // Don't allow copying the index for performance reasons (try avoiding copying data)
+  index(const index&)                    = delete;
+  index(index&&)                         = default;
+  auto operator=(const index&) -> index& = delete;
+  auto operator=(index&&) -> index&      = default;
+  ~index()                               = default;
+
+  /** Construct an empty index. It will need to be trained and populated with `build`*/
+  //  index(raft::resources const& res) {}
+
+  index(raft::resources const& res,
+        cuvs::distance::DistanceType metric,
+        uint32_t n_leaves,
+        uint32_t pq_bits,
+        uint32_t pq_dim,
+        IdxT n_rows,
+        IdxT dim,
+        uint32_t pq_clusters,
+        uint32_t pq_num_subspaces,
+        bool bf16_enabled)
+    : cuvs::neighbors::index(),
+      metric_(metric),
+      pq_dim_(pq_dim),
+      pq_bits_(pq_bits),
+      n_leaves_(n_leaves),
+      centers_(raft::make_device_matrix<float, IdxT>(res, n_leaves, dim)),
+      labels_(raft::make_device_vector<uint32_t, IdxT>(res, n_rows)),
+      soar_labels_(raft::make_device_vector<uint32_t, IdxT>(res, n_rows)),
+      pq_codebook_(
+        raft::make_device_matrix<float, uint32_t, raft::row_major>(res, pq_clusters, dim)),
+      quantized_residuals_(
+        raft::make_host_matrix<uint8_t, IdxT, raft::row_major>(n_rows, pq_num_subspaces)),
+      quantized_soar_residuals_(
+        raft::make_host_matrix<uint8_t, IdxT, raft::row_major>(n_rows, pq_num_subspaces)),
+      n_rows_(n_rows),
+      dim_(dim),
+      bf16_dataset_(raft::make_host_matrix<int16_t, IdxT, raft::row_major>(
+        bf16_enabled ? n_rows : 0, bf16_enabled ? dim : 0))
+
+  {
+  }
+
+  index(raft::resources const& res, const index_params& params, IdxT n_rows, IdxT dim)
+    : index(res,
+            params.metric,
+            params.n_leaves,
+            params.pq_bits,
+            params.pq_dim,
+            n_rows,
+            dim,
+            1 << params.pq_bits,
+            dim / params.pq_dim,
+            params.bf16_enabled)
+  {
+    RAFT_EXPECTS(params.pq_bits == 4 || params.pq_bits == 8, "ScaNN only supports 4 or 8 bit PQ");
+    RAFT_EXPECTS(dim >= params.pq_dim,
+                 "PQ subspace dimension (pq_dim) should be smaller than the dataset dimension");
+    RAFT_EXPECTS(dim % params.pq_dim == 0,
+                 "PQ subspace dimension (pq_dim) must divide the dataset dimension");
+  }
+
+  raft::device_matrix_view<float, IdxT> centers() noexcept { return centers_.view(); }
+
+  raft::device_matrix_view<const float, IdxT> centers() const noexcept
+  {
+    return raft::make_const_mdspan(centers_.view());
+  }
+
+  raft::device_vector_view<uint32_t, IdxT> labels() noexcept { return labels_.view(); }
+
+  raft::device_vector_view<const uint32_t, IdxT> labels() const noexcept
+  {
+    return raft::make_const_mdspan(labels_.view());
+  }
+
+  raft::device_vector_view<uint32_t, IdxT> soar_labels() noexcept { return soar_labels_.view(); }
+
+  raft::device_vector_view<const uint32_t, IdxT> soar_labels() const noexcept
+  {
+    return raft::make_const_mdspan(soar_labels_.view());
+  }
+
+  uint32_t n_rows() const noexcept { return n_rows_; }
+
+  uint32_t n_leaves() const noexcept { return n_leaves_; }
+
+  uint32_t pq_dim() const noexcept { return pq_dim_; }
+
+  raft::device_matrix_view<const float, uint32_t, raft::row_major> pq_codebook() const noexcept
+  {
+    return raft::make_const_mdspan(pq_codebook_.view());
+  }
+
+  raft::device_matrix_view<float, uint32_t, raft::row_major> pq_codebook() noexcept
+  {
+    return pq_codebook_.view();
+  }
+
+  raft::host_matrix_view<const uint8_t, IdxT, raft::row_major> quantized_residuals() const noexcept
+  {
+    return raft::make_const_mdspan(quantized_residuals_.view());
+  }
+
+  raft::host_matrix_view<uint8_t, IdxT, raft::row_major> quantized_residuals() noexcept
+  {
+    return quantized_residuals_.view();
+  }
+
+  raft::host_matrix_view<const uint8_t, IdxT, raft::row_major> quantized_soar_residuals()
+    const noexcept
+  {
+    return raft::make_const_mdspan(quantized_soar_residuals_.view());
+  }
+
+  raft::host_matrix_view<uint8_t, IdxT, raft::row_major> quantized_soar_residuals() noexcept
+  {
+    return quantized_soar_residuals_.view();
+  }
+
+  raft::host_matrix_view<int16_t, IdxT, raft::row_major> bf16_dataset() noexcept
+  {
+    return bf16_dataset_.view();
+  }
+
+  raft::host_matrix_view<const int16_t, IdxT, raft::row_major> bf16_dataset() const noexcept
+  {
+    return raft::make_const_mdspan(bf16_dataset_.view());
+  }
+
+ private:
+  cuvs::distance::DistanceType metric_;
+  IdxT dim_;
+  IdxT n_rows_;
+  uint32_t pq_dim_;
+  uint32_t pq_bits_;
+  uint32_t n_leaves_;
+
+  raft::device_matrix<float, IdxT, raft::row_major> centers_;
+  raft::device_vector<uint32_t, IdxT> labels_;
+  raft::device_vector<uint32_t, IdxT> soar_labels_;
+  raft::device_matrix<float, uint32_t, raft::row_major> pq_codebook_;
+  raft::host_matrix<uint8_t, IdxT, raft::row_major> quantized_residuals_;
+  raft::host_matrix<uint8_t, IdxT, raft::row_major> quantized_soar_residuals_;
+  raft::host_matrix<int16_t, IdxT, raft::row_major> bf16_dataset_;
+  // TODO - add any data, pointers or structures needed
+};
+/**
+ * @}
+ */
+
+/**
+ * @defgroup scann_cpp_index_build ScaNN index build functions
+ * @{
+ */
+/**
+ * @brief Build the index from the dataset for efficient search.
+ *
+ */
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::scann::index_params& params,
+           raft::device_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::scann::index<float, int64_t>;
+
+auto build(raft::resources const& handle,
+           const cuvs::neighbors::experimental::scann::index_params& params,
+           raft::host_matrix_view<const float, int64_t, raft::row_major> dataset)
+  -> cuvs::neighbors::experimental::scann::index<float, int64_t>;
+/**
+ * @defgroup scann_cpp_serialize ScaNN serialize functions
+ * @{
+ */
+/**
+ * @brief Save the index to files in a directory
+ *
+ * This serializes the index into a list of files for integration into
+ * OSS ScaNN for use with search
+ *
+ * NOTE: the implementation of ScaNN index build is EXPERIMENTAL and currently
+ * not subject to comprehensive, automated testing. Accuracy and performance
+ * are not guaranteed, and could diverge without warning.
+ *
+ */
+
+void serialize(raft::resources const& handle,
+               const std::string& file_prefix,
+               const cuvs::neighbors::experimental::scann::index<float, int64_t>& index);
+
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::experimental::scann
diff --git a/cpp/src/neighbors/scann/detail/scann_avq.cuh b/cpp/src/neighbors/scann/detail/scann_avq.cuh
new file mode 100644
index 0000000000..cdfd4fbf28
--- /dev/null
+++ b/cpp/src/neighbors/scann/detail/scann_avq.cuh
@@ -0,0 +1,460 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cub/cub.cuh>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/coalesced_reduction.cuh>
+#include <raft/linalg/detail/cublas_wrappers.hpp>
+#include <raft/linalg/detail/cusolver_wrappers.hpp>
+#include <raft/linalg/dot.cuh>
+#include <raft/linalg/gemm.cuh>
+#include <raft/linalg/gemv.cuh>
+#include <raft/linalg/linalg_types.hpp>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/matrix_vector.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/multiply.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/power.cuh>
+#include <raft/linalg/reduce.cuh>
+#include <raft/linalg/transpose.cuh>
+#include <raft/matrix/argmin.cuh>
+#include <raft/matrix/copy.cuh>
+#include <raft/matrix/diagonal.cuh>
+#include <raft/matrix/init.cuh>
+
+#include "scann_common.cuh"
+
+namespace cuvs::neighbors::experimental::scann::detail {
+
+namespace {
+
+template <typename LabelT, typename IdxT>
+__global__ void build_clusters(
+  LabelT const* node_to_cluster, LabelT* clusters, IdxT* cluster_start, int nodes, int n_clusters)
+{
+  size_t node = blockDim.x * blockIdx.x + threadIdx.x;
+
+  if (node < nodes) {
+    LabelT cluster        = node_to_cluster[node];
+    LabelT cluster_ptr    = atomicAdd(&cluster_start[cluster], 1);
+    clusters[cluster_ptr] = node;
+  }
+}
+}  // namespace
+
+// Compute cluster sizes/offsets
+template <typename LabelT, typename IdxT>
+void compute_cluster_offsets(raft::resources const& dev_resources,
+                             raft::device_vector_view<const LabelT, IdxT> clusters,
+                             raft::device_vector_view<LabelT, int64_t> cluster_sizes)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(dev_resources);
+  rmm::device_async_resource_ref device_memory =
+    raft::resource::get_workspace_resource(dev_resources);
+
+  // Histrogram to compute cluster sizes
+  int num_levels  = cluster_sizes.extent(0) + 1;
+  int lower_level = 0;
+  int upper_level = cluster_sizes.extent(0);
+
+  size_t temp_storage_bytes = 0;
+
+  cub::DeviceHistogram::HistogramEven(nullptr,
+                                      temp_storage_bytes,
+                                      clusters.data_handle(),
+                                      cluster_sizes.data_handle(),
+                                      num_levels,
+                                      lower_level,
+                                      upper_level,
+                                      clusters.extent(0),
+                                      stream);
+
+  rmm::device_uvector<char> temp_storage_hist(temp_storage_bytes, stream, device_memory);
+
+  cub::DeviceHistogram::HistogramEven(temp_storage_hist.data(),
+                                      temp_storage_bytes,
+                                      clusters.data_handle(),
+                                      cluster_sizes.data_handle(),
+                                      num_levels,
+                                      lower_level,
+                                      upper_level,
+                                      clusters.extent(0),
+                                      stream);
+
+  temp_storage_bytes = 0;
+  // Scan to sum cluster sizes and get cluster start ptrs in flat array
+  // Done in place
+  int num_items = cluster_sizes.extent(0);
+
+  cub::DeviceScan::ExclusiveSum(nullptr,
+                                temp_storage_bytes,
+                                cluster_sizes.data_handle(),
+                                cluster_sizes.data_handle(),
+                                num_items);
+
+  rmm::device_uvector<char> temp_storage_sum(temp_storage_bytes, stream, device_memory);
+
+  cub::DeviceScan::ExclusiveSum(temp_storage_sum.data(),
+                                temp_storage_bytes,
+                                cluster_sizes.data_handle(),
+                                cluster_sizes.data_handle(),
+                                num_items);
+}
+
+// Sum elements of device vector into device scalar
+template <typename T>
+void sum_reduce_vector(raft::resources const& dev_resources,
+                       raft::device_vector_view<T, int64_t> v,
+                       raft::device_scalar_view<T> s)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(dev_resources);
+  rmm::device_async_resource_ref device_memory =
+    raft::resource::get_workspace_resource(dev_resources);
+
+  size_t temp_storage_bytes = 0;
+
+  cub::DeviceReduce::Sum(
+    nullptr, temp_storage_bytes, v.data_handle(), s.data_handle(), v.extent(0), stream);
+
+  rmm::device_uvector<char> temp_storage(temp_storage_bytes, stream, device_memory);
+  // cudaMalloc(&d_temp_storage, temp_storage_bytes);
+
+  cub::DeviceReduce::Sum(
+    temp_storage.data(), temp_storage_bytes, v.data_handle(), s.data_handle(), v.extent(0), stream);
+
+  // raft::resource::sync_stream(dev_resources, stream);
+
+  // cudaFree(d_temp_storage);
+}
+
+// Solve Ax = b for a symmetric, pos-def matrix A via cholesky factorization
+template <typename T>
+void cholesky_solver(raft::resources const& dev_resources,
+                     raft::device_matrix_view<T, int64_t, raft::col_major> A,
+                     raft::device_vector_view<T, int64_t> b,
+                     raft::device_vector_view<T, int64_t> x)
+{
+  cudaStream_t stream          = raft::resource::get_cuda_stream(dev_resources);
+  cusolverDnHandle_t cusolverH = raft::resource::get_cusolver_dn_handle(dev_resources);
+  rmm::device_async_resource_ref device_memory =
+    raft::resource::get_workspace_resource(dev_resources);
+
+  // RAFT_CUSOLVER_TRY(cusolverDnSetStream(cusolverH, stream));
+
+  int n                 = A.extent(0);
+  int lda               = n;
+  int lwork             = 0;
+  cublasFillMode_t uplo = CUBLAS_FILL_MODE_LOWER;
+
+  // compute bufferszie for potrf
+  RAFT_CUSOLVER_TRY(raft::linalg::detail::cusolverDnpotrf_bufferSize(
+    cusolverH, uplo, n, A.data_handle(), lda, &lwork));
+  // compute cholesky factorization w/ potrf
+  auto devInfo = raft::make_device_scalar(dev_resources, 0);
+  rmm::device_uvector<T> d_work(lwork, stream, device_memory);
+
+  RAFT_CUSOLVER_TRY(raft::linalg::detail::cusolverDnpotrf(
+    cusolverH, uplo, n, A.data_handle(), lda, d_work.data(), lwork, devInfo.data_handle(), stream));
+
+  // solve Ax = b
+  int ldb  = b.extent(0);
+  int nrhs = 1;
+
+  RAFT_CUSOLVER_TRY(raft::linalg::detail::cusolverDnpotrs(cusolverH,
+                                                          uplo,
+                                                          n,
+                                                          nrhs,
+                                                          A.data_handle(),
+                                                          lda,
+                                                          b.data_handle(),
+                                                          ldb,
+                                                          devInfo.data_handle(),
+                                                          stream));
+}
+
+// Apply Anisotropic Vector Quantization to a single cluster
+// via Theorem 4.2 in https://arxiv.org/abs/1908.10396 with
+//   h_i_parallel = eta * || x_i || ^ (eta - 1)
+//   h_i_orthogonal = ||x _i || ^ (eta -1)
+// as taken from the open source ScaNN implementation
+template <typename T>
+void compute_avq_centroid(raft::resources const& dev_resources,
+                          raft::device_matrix_view<T, int64_t> x,
+                          raft::device_vector_view<T, int64_t> avq_centroid,
+                          raft::device_scalar_view<T> rescale_num,
+                          raft::device_scalar_view<T> rescale_denom,
+                          float eta)
+{
+  // Compute and scale norms
+  auto norms = raft::make_device_vector<float, int64_t>(dev_resources, x.extent(0));
+
+  raft::linalg::norm<raft::linalg::NormType::L2Norm, raft::Apply::ALONG_ROWS>(
+    dev_resources, raft::make_const_mdspan(x), norms.view());
+
+  // Compute || x_i || ^ 0.5 * (eta -3)
+  auto norms_eta_3 = raft::make_device_vector<float, int64_t>(dev_resources, x.extent(0));
+  auto eta_3       = raft::make_host_scalar<float>((eta - 3) / 2);
+
+  raft::linalg::power_scalar(dev_resources,
+                             raft::make_const_mdspan(norms.view()),
+                             norms_eta_3.view(),
+                             raft::make_const_mdspan(eta_3.view()));
+
+  // Compute || x_i || ^ (eta - 1)
+  auto norms_eta_1 = raft::make_device_vector<float, int64_t>(dev_resources, x.extent(0));
+  auto eta_1       = raft::make_host_scalar<float>(eta - 1);
+
+  raft::linalg::power_scalar(dev_resources,
+                             raft::make_const_mdspan(norms.view()),
+                             norms_eta_1.view(),
+                             raft::make_const_mdspan(eta_1.view()));
+
+  auto sum_norms_eta_1 = raft::make_device_scalar<float>(dev_resources, 0.0);
+
+  sum_reduce_vector(dev_resources, norms_eta_1.view(), sum_norms_eta_1.view());
+
+  auto x_eta_1 = raft::make_device_matrix<float, int64_t>(dev_resources, x.extent(0), x.extent(1));
+
+  raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
+    dev_resources,
+    raft::make_const_mdspan(x),
+    raft::make_const_mdspan(norms_eta_1.view()),
+    x_eta_1.view(),
+    raft::mul_op());
+
+  raft::linalg::reduce<true, false>(avq_centroid.data_handle(),
+                                    raft::make_const_mdspan(x_eta_1.view()).data_handle(),
+                                    x_eta_1.extent(1),
+                                    x_eta_1.extent(0),
+                                    0.0f,
+                                    raft::resource::get_cuda_stream(dev_resources),
+                                    false,
+                                    raft::identity_op(),
+                                    raft::add_op(),
+                                    raft::identity_op());
+
+  // scale x
+  // skipping zero elements in the vector should be ok, since they are norms
+  // of dataset vectors
+  raft::linalg::binary_mult_skip_zero<raft::Apply::ALONG_COLUMNS>(
+    dev_resources, x, raft::make_const_mdspan(norms_eta_3.view()));
+
+  // x^T x
+  auto x_trans = raft::make_device_matrix<float, int64_t>(dev_resources, x.extent(1), x.extent(0));
+
+  raft::linalg::transpose(dev_resources, x, x_trans.view());
+
+  // Solving a system of linear equations via QR decomp only supports col_major
+  // for some reason
+  auto x_trans_x = raft::make_device_matrix<float, int64_t, raft::col_major>(
+    dev_resources, x.extent(1), x.extent(1));
+
+  raft::matrix::eye(dev_resources, x_trans_x.view());
+
+  auto eta_m_1       = raft::make_device_scalar<float>(dev_resources, eta - 1);
+  auto cublas_handle = raft::resource::get_cublas_handle(dev_resources);
+
+  raft::linalg::detail::cublas_device_pointer_mode<true> pm(cublas_handle);
+
+  RAFT_CUBLAS_TRY(cublasSetStream(cublas_handle, raft::resource::get_cuda_stream(dev_resources)));
+
+  RAFT_CUBLAS_TRY(cublasSgemm(cublas_handle,
+                              cublasOperation_t::CUBLAS_OP_T,
+                              cublasOperation_t::CUBLAS_OP_T,
+                              x.extent(1),
+                              x.extent(1),
+                              x.extent(0),
+                              eta_m_1.data_handle(),
+                              x_trans.view().data_handle(),
+                              x.extent(0),
+                              x.data_handle(),
+                              x.extent(1),
+                              sum_norms_eta_1.data_handle(),
+                              x_trans_x.view().data_handle(),
+                              x.extent(1)));
+
+  cholesky_solver(dev_resources, x_trans_x.view(), avq_centroid, avq_centroid);
+
+  auto h_eta = raft::make_host_scalar<float>(eta);
+
+  raft::linalg::multiply_scalar(dev_resources,
+                                raft::make_const_mdspan(avq_centroid),
+                                avq_centroid,
+                                raft::make_const_mdspan(h_eta.view()));
+
+  auto dots = raft::make_device_vector<float, int64_t>(dev_resources, x.extent(1));
+
+  raft::linalg::reduce<true, false>(dots.data_handle(),
+                                    raft::make_const_mdspan(x).data_handle(),
+                                    x.extent(1),
+                                    x.extent(0),
+                                    0.0f,
+                                    raft::resource::get_cuda_stream(dev_resources),
+                                    false,
+                                    raft::identity_op(),
+                                    raft::add_op(),
+                                    raft::identity_op());
+
+  raft::linalg::dot(dev_resources,
+                    raft::make_const_mdspan(dots.view()),
+                    raft::make_const_mdspan(avq_centroid),
+                    rescale_num);
+
+  raft::linalg::dot(dev_resources,
+                    raft::make_const_mdspan(avq_centroid),
+                    raft::make_const_mdspan(avq_centroid),
+                    rescale_denom);
+}
+
+template <typename T>
+void rescale_avq_centroids(raft::resources const& dev_resources,
+                           raft::device_matrix_view<T, int64_t> centroids,
+                           raft::device_vector_view<T, int64_t> rescale_num_v,
+                           raft::device_vector_view<T, int64_t> rescale_denom_v,
+                           raft::device_vector_view<uint32_t, int64_t> cluster_sizes,
+                           uint32_t dataset_size)
+{
+  auto rescale_num   = raft::make_device_scalar<float>(dev_resources, 0);
+  auto rescale_denom = raft::make_device_scalar<float>(dev_resources, 0);
+
+  sum_reduce_vector(dev_resources, rescale_num_v, rescale_num.view());
+
+  raft::linalg::map_offset(dev_resources,
+                           raft::make_const_mdspan(rescale_denom_v),
+                           rescale_denom_v,
+                           [cluster_sizes, dataset_size] __device__(size_t i, float x) {
+                             uint32_t cluster_size = i + 1 < cluster_sizes.extent(0)
+                                                       ? cluster_sizes[i + 1] - cluster_sizes[i]
+                                                       : dataset_size - cluster_sizes[i];
+
+                             return x * cluster_size;
+                           });
+
+  sum_reduce_vector(dev_resources, rescale_denom_v, rescale_denom.view());
+
+  auto rescale_num_ptr   = rescale_num.data_handle();
+  auto rescale_denom_ptr = rescale_denom.data_handle();
+
+  raft::linalg::map_offset(dev_resources,
+                           raft::make_const_mdspan(centroids),
+                           centroids,
+                           [rescale_num_ptr, rescale_denom_ptr] __device__(size_t i, float x) {
+                             // should probably check the denominator is nonzero
+                             float rescale = (*rescale_num_ptr) / (*rescale_denom_ptr);
+
+                             return x * rescale;
+                           });
+}
+
+/**
+ * @brief Perform AVQ adjustment on cluster centers
+ *
+ * Apply Anisotropic Vector Quantization to recompute cluster centers
+ * via Theorem 4.2 in https://arxiv.org/abs/1908.10396 with
+ *  h_i_parallel = eta * || x_i || ^ (eta - 1)
+ * h_i_orthogonal = ||x _i || ^ (eta -1)
+ * as taken from the open source ScaNN implementation
+ *
+ * @tparam T
+ * @tparam IdxT
+ * @tparam LabelT
+ * @tparam Accessor
+ * @param res raft resources
+ * @param dataset the dataset (host or device), size [n_rows, dim]
+ * @param centroids_view cluster centers, size [n_clusters, dim]
+ * @param labels_view nearest cluster idx for each dataset vector, size [n_rows]
+ * @param eta the weight for the parallel component of the residual in the avq update
+ */
+template <typename T,
+          typename IdxT     = int64_t,
+          typename LabelT   = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+void apply_avq(raft::resources const& res,
+               raft::mdspan<const T, raft::matrix_extent<IdxT>, raft::row_major, Accessor> dataset,
+               raft::device_matrix_view<T, IdxT> centroids_view,
+               raft::device_vector_view<const LabelT, IdxT> labels_view,
+               float eta)
+{
+  // Compute clusters
+
+  cudaStream_t stream = raft::resource::get_cuda_stream(res);
+  auto cluster_ptrs   = raft::make_device_vector<uint32_t, int64_t>(res, centroids_view.extent(0));
+  auto clusters       = raft::make_device_vector<uint32_t, int64_t>(res, dataset.extent(0));
+
+  compute_cluster_offsets(res, labels_view, cluster_ptrs.view());
+
+  auto h_cluster_ptrs = raft::make_host_vector<uint32_t, int64_t>(cluster_ptrs.extent(0));
+
+  raft::copy(h_cluster_ptrs.data_handle(), cluster_ptrs.data_handle(), cluster_ptrs.size(), stream);
+
+  dim3 block(32, 1, 1);
+  dim3 grid((dataset.extent(0) + block.x - 1) / block.x, 1, 1);
+
+  build_clusters<uint32_t, uint32_t><<<grid, block>>>(labels_view.data_handle(),
+                                                      clusters.view().data_handle(),
+                                                      cluster_ptrs.view().data_handle(),
+                                                      dataset.extent(0),
+                                                      labels_view.extent(0));
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+  auto rescale_num   = raft::make_device_vector<float, int64_t>(res, centroids_view.extent(0));
+  auto rescale_denom = raft::make_device_vector<float, int64_t>(res, centroids_view.extent(0));
+
+  raft::resource::sync_stream(res);
+
+  RAFT_LOG_DEBUG("Compute AVQ centroids\n");
+
+  for (int i = 0; i < h_cluster_ptrs.extent(0); i++) {
+    int cluster_size = i + 1 < h_cluster_ptrs.extent(0) ? h_cluster_ptrs(i + 1) - h_cluster_ptrs(i)
+                                                        : dataset.extent(0) - h_cluster_ptrs(i);
+
+    if (cluster_size == 0) { continue; }
+    auto cluster_ids = raft::make_device_vector_view<const uint32_t, int64_t>(
+      clusters.data_handle() + h_cluster_ptrs(i), cluster_size);
+    auto cluster_vectors =
+      raft::make_device_matrix<float, int64_t>(res, cluster_size, dataset.extent(1));
+    auto avq_centroid = raft::make_device_vector_view<float, int64_t>(
+      centroids_view.data_handle() + i * dataset.extent(1), dataset.extent(1));
+    auto rescale_num_view   = raft::make_device_scalar_view<float>(rescale_num.data_handle() + i);
+    auto rescale_denom_view = raft::make_device_scalar_view<float>(rescale_denom.data_handle() + i);
+
+    gather_functor<float, uint32_t>{}(
+      res, dataset, cluster_ids, cluster_vectors.view(), raft::resource::get_cuda_stream(res));
+
+    compute_avq_centroid(
+      res, cluster_vectors.view(), avq_centroid, rescale_num_view, rescale_denom_view, eta);
+  }
+
+  rescale_avq_centroids(res,
+                        centroids_view,
+                        rescale_num.view(),
+                        rescale_denom.view(),
+                        cluster_ptrs.view(),
+                        dataset.extent(0));
+
+  raft::resource::sync_stream(res);
+}
+}  // namespace cuvs::neighbors::experimental::scann::detail
diff --git a/cpp/src/neighbors/scann/detail/scann_build.cuh b/cpp/src/neighbors/scann/detail/scann_build.cuh
new file mode 100644
index 0000000000..5520684f4c
--- /dev/null
+++ b/cpp/src/neighbors/scann/detail/scann_build.cuh
@@ -0,0 +1,380 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../../../cluster/kmeans_balanced.cuh"
+#include "../../detail/ann_utils.cuh"
+#include <cuvs/cluster/kmeans.hpp>
+#include <cuvs/neighbors/scann.hpp>
+
+#include <cub/cub.cuh>
+#include <cuda_bf16.h>
+#include <nvtx3/nvtx3.hpp>
+#include <raft/cluster/kmeans.cuh>
+#include <raft/cluster/kmeans_types.hpp>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/coalesced_reduction.cuh>
+#include <raft/linalg/dot.cuh>
+#include <raft/linalg/gemm.hpp>
+#include <raft/linalg/gemv.cuh>
+#include <raft/linalg/linalg_types.hpp>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/matrix_vector.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/multiply.cuh>
+#include <raft/linalg/normalize.cuh>
+#include <raft/linalg/power.cuh>
+#include <raft/linalg/reduce.cuh>
+#include <raft/linalg/transpose.cuh>
+#include <raft/linalg/unary_op.cuh>
+#include <raft/matrix/argmin.cuh>
+#include <raft/matrix/copy.cuh>
+#include <raft/matrix/diagonal.cuh>
+#include <raft/matrix/init.cuh>
+#include <raft/matrix/sample_rows.cuh>
+#include <raft/matrix/slice.cuh>
+#include <raft/random/make_blobs.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/sparse/neighbors/cross_component_nn.cuh>
+#include <raft/util/memory_type_dispatcher.cuh>
+
+#include <thrust/device_vector.h>
+#include <thrust/sequence.h>
+#include <thrust/unique.h>
+
+#include <cuvs/distance/distance.hpp>
+
+#include "scann_avq.cuh"
+#include "scann_common.cuh"
+#include "scann_quantize.cuh"
+#include "scann_soar.cuh"
+
+#include <chrono>
+#include <cstdio>
+#include <vector>
+
+namespace cuvs::neighbors::experimental::scann::detail {
+using namespace cuvs::spatial::knn::detail;  // NOLINT
+
+/* @defgroup scann_build_detail scann build
+ * @{
+ */
+
+static const std::string RAFT_NAME = "raft";
+
+template <typename T,
+          typename IdxT     = int64_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+index<T, IdxT> build(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<IdxT>, raft::row_major, Accessor> dataset)
+{
+  cudaStream_t stream = raft::resource::get_cuda_stream(res);
+  IdxT dim            = dataset.extent(1);
+
+  RAFT_LOG_DEBUG("Creating empty index");
+
+  index<T, IdxT> idx(res, params, dataset.extent(0), dataset.extent(1));
+  raft::device_matrix_view<T, int64_t> centroids_view = idx.centers();
+  raft::random::RngState random_state{137};
+  // sample/project
+
+  cuvs::cluster::kmeans::balanced_params kmeans_params;
+  kmeans_params.n_iters = params.kmeans_n_iters;
+
+  {
+    auto trainset = raft::make_device_matrix<T, int64_t>(res, 0, 0);
+
+    try {
+      trainset =
+        raft::make_device_matrix<T, int64_t>(res, params.kmeans_n_rows_train, dataset.extent(1));
+    } catch (raft::logic_error& e) {
+      RAFT_LOG_ERROR(
+        "Insufficient device memory for kmeans training set allocation. Please "
+        "reduce kmeans_n_rows_train.");
+      throw;
+    }
+
+    RAFT_LOG_DEBUG("Sampling rows.\n");
+    raft::matrix::sample_rows(res, random_state, dataset, trainset.view());
+
+    raft::resource::sync_stream(res);
+
+    // fit kmean
+
+    RAFT_LOG_DEBUG("Fitting Kmeans");
+    cuvs::cluster::kmeans_balanced::fit(
+      res, kmeans_params, raft::make_const_mdspan(trainset.view()), centroids_view);
+  }
+  raft::resource::sync_stream(res);
+
+  raft::device_vector_view<uint32_t, int64_t> labels_view = idx.labels();
+
+  // setup batching for kmeans prediction + quantization
+  auto* device_memory = raft::resource::get_workspace_resource(res);
+
+  constexpr size_t kReasonableMaxBatchSize = 65536;
+  size_t max_batch_size = std::min<size_t>(dataset.extent(0), kReasonableMaxBatchSize);
+  auto copy_stream      = raft::resource::get_cuda_stream(res);
+  bool enable_prefetch  = false;
+
+  if (res.has_resource_factory(raft::resource::resource_type::CUDA_STREAM_POOL)) {
+    if (raft::resource::get_stream_pool_size(res) >= 1) {
+      enable_prefetch = true;
+
+      copy_stream = raft::resource::get_stream_from_stream_pool(res);
+    }
+  }
+
+  utils::batch_load_iterator<T> dataset_vec_batches(dataset.data_handle(),
+                                                    dataset.extent(0),
+                                                    dataset.extent(1),
+                                                    max_batch_size,
+                                                    copy_stream,
+                                                    device_memory,
+                                                    enable_prefetch);
+
+  dataset_vec_batches.reset();
+  dataset_vec_batches.prefetch_next_batch();
+
+  RAFT_LOG_DEBUG("Batched kmeans prediction.\n");
+  for (const auto& batch : dataset_vec_batches) {
+    auto batch_view = raft::make_device_matrix_view<const T, int64_t>(
+      batch.data(), batch.size(), dataset.extent(1));
+
+    auto batch_labels_view = raft::make_device_vector_view<uint32_t, int64_t>(
+      labels_view.data_handle() + batch.offset(), batch.size());
+
+    cuvs::cluster::kmeans_balanced::predict(
+      res, kmeans_params, batch_view, raft::make_const_mdspan(centroids_view), batch_labels_view);
+
+    dataset_vec_batches.prefetch_next_batch();
+  }
+
+  // AVQ update of KMeans centroids
+  apply_avq(
+    res, dataset, centroids_view, raft::make_const_mdspan(labels_view), params.partitioning_eta);
+
+  raft::device_vector_view<uint32_t, int64_t> soar_labels_view = idx.soar_labels();
+
+  // Train PQ codebooks
+  RAFT_LOG_DEBUG("Train PQ Codebooks\n");
+
+  // Limit PQ training rows to 100k. IVF-PQ/VPQ limit to 65k as recall
+  // doesn't increase significantly. ScaNN configs in common benchmarks use 100k,
+  // so staying consistent with that.
+  constexpr int64_t kMaxPQTrainRows = 100000;
+  int64_t pq_n_rows_train           = std::min(params.pq_n_rows_train, kMaxPQTrainRows);
+
+  auto trainset_residuals = sample_training_residuals(res,
+                                                      random_state,
+                                                      dataset,
+                                                      raft::make_const_mdspan(centroids_view),
+                                                      raft::make_const_mdspan(labels_view),
+                                                      pq_n_rows_train);
+
+  int num_subspaces    = dataset.extent(1) / params.pq_dim;
+  int dim_per_subspace = params.pq_dim;
+  int num_clusters     = 1 << params.pq_bits;
+
+  auto full_codebook =
+    raft::make_device_matrix<T, uint32_t>(res, num_clusters * num_subspaces, dim_per_subspace);
+
+  // Loop each subspace, training codebooks for each
+  for (int subspace = 0; subspace < num_subspaces; subspace++) {
+    int sub_dim_start = subspace * dim_per_subspace;
+    int sub_dim_end   = (subspace + 1) * dim_per_subspace;
+
+    auto sub_trainset = raft::make_device_matrix<T, int64_t>(
+      res, trainset_residuals.extent(0), (int64_t)dim_per_subspace);
+    raft::matrix::slice_coordinates<int64_t> avq_sub_coords(
+      0, sub_dim_start, trainset_residuals.extent(0), sub_dim_end);
+    raft::matrix::slice(
+      res, raft::make_const_mdspan(trainset_residuals.view()), sub_trainset.view(), avq_sub_coords);
+
+    // Set up quantization bits and params
+    cuvs::neighbors::vpq_params pq_params;
+    pq_params.pq_bits = params.pq_bits;
+    // For VPQ, pq_dim is the number of subspaces, not the dimension of the subspaces
+    pq_params.pq_dim = 1;
+    // We handle sampling/training set construction above, so use the full set in VPQ
+    pq_params.pq_kmeans_trainset_fraction = 1.0;
+    pq_params.kmeans_n_iters              = params.pq_train_iters;
+
+    // Create pq codebook for this subspace
+    auto sub_pq_codebook =
+      create_pq_codebook<T>(res, raft::make_const_mdspan(sub_trainset.view()), pq_params);
+
+    raft::copy(full_codebook.data_handle() + (subspace * sub_pq_codebook.size()),
+               sub_pq_codebook.data_handle(),
+               sub_pq_codebook.size(),
+               stream);
+  }
+  raft::resource::sync_stream(res);
+
+  // Set up quantization bits and params
+  cuvs::neighbors::vpq_params pq_params;
+  pq_params.pq_bits = params.pq_bits;
+  pq_params.pq_dim  = dataset.extent(1) / params.pq_dim;
+
+  dataset_vec_batches.reset();
+  dataset_vec_batches.prefetch_next_batch();
+
+  // Quantize residuals for both normal and SOAR assignments
+  RAFT_LOG_DEBUG("Quantize residuals");
+  for (const auto& batch : dataset_vec_batches) {
+    auto batch_view = raft::make_device_matrix_view<const T, int64_t>(
+      batch.data(), batch.size(), dataset.extent(1));
+
+    auto batch_labels_view = raft::make_device_vector_view<const uint32_t, int64_t>(
+      labels_view.data_handle() + batch.offset(), batch.size());
+
+    auto batch_soar_labels_view = raft::make_device_vector_view<uint32_t, int64_t>(
+      soar_labels_view.data_handle() + batch.offset(), batch.size());
+
+    // Compute residuals for use in SOAR + quantization
+    auto avq_residuals = compute_residuals<T, uint32_t>(
+      res, batch_view, raft::make_const_mdspan(centroids_view), batch_labels_view);
+
+    // Compute SOAR labels.
+    // We compute SOAR labels in this loop to eliminate one HtoD copy of the full dataset.
+    compute_soar_labels<T, uint32_t>(res,
+                                     batch_view,
+                                     raft::make_const_mdspan(avq_residuals.view()),
+                                     centroids_view,
+                                     batch_labels_view,
+                                     batch_soar_labels_view,
+                                     params.soar_lambda);
+
+    // Compute and quantize residuals
+    auto avq_quant = quantize_residuals<T, IdxT, uint32_t>(
+      res, raft::make_const_mdspan(avq_residuals.view()), full_codebook.view(), pq_params);
+
+    // Compute and quantize SOAR residuals
+    auto soar_residuals =
+      compute_residuals<T, uint32_t>(res,
+                                     batch_view,
+                                     raft::make_const_mdspan(centroids_view),
+                                     raft::make_const_mdspan(batch_soar_labels_view));
+
+    auto soar_quant = quantize_residuals<T, IdxT, uint32_t>(
+      res, raft::make_const_mdspan(soar_residuals.view()), full_codebook.view(), pq_params);
+
+    // unpack codes
+    auto quantized_residuals =
+      raft::make_device_matrix<uint8_t, IdxT>(res, batch.size(), num_subspaces);
+    auto quantized_soar_residuals =
+      raft::make_device_matrix<uint8_t, IdxT>(res, batch.size(), num_subspaces);
+
+    unpack_codes(res,
+                 quantized_residuals.view(),
+                 raft::make_const_mdspan(avq_quant.view()),
+                 params.pq_bits,
+                 num_subspaces);
+    unpack_codes(res,
+                 quantized_soar_residuals.view(),
+                 raft::make_const_mdspan(soar_quant.view()),
+                 params.pq_bits,
+                 num_subspaces);
+
+    // quantize dataset to bfloat16, if enabled. Similar to SOAR, quantization
+    // is performed in this loop to improve locality
+    // TODO (rmaschal): Might be more efficient to do on CPU, to avoid DtoH copy
+
+    auto bf16_dataset = raft::make_device_matrix<int16_t, int64_t>(res, batch_view.extent(0), dim);
+
+    if (params.bf16_enabled) {
+      // raft::linalg::map_offset(res, bf16_dataset.view(), [batch_view, dim] __device__(size_t i) {
+      //   int64_t row_idx = i / dim;
+      //   int64_t col_idx = i % dim;
+
+      //  nv_bfloat16 val = __float2bfloat16(batch_view(row_idx, col_idx));
+
+      //  return reinterpret_cast<int16_t&>(val);
+      //});
+      raft::linalg::unaryOp(
+        bf16_dataset.data_handle(),
+        batch_view.data_handle(),
+        batch_view.size(),
+        [] __device__(T x) {
+          nv_bfloat16 val = __float2bfloat16(x);
+          return reinterpret_cast<int16_t&>(val);
+        },
+        resource::get_cuda_stream(res));
+    }
+
+    // Prefetch next batch
+    dataset_vec_batches.prefetch_next_batch();
+
+    // Copy unpacked codes to host
+    // TODO (rmaschal): these copies are blocking and not overlapped
+    raft::copy(idx.quantized_residuals().data_handle() + batch.offset() * num_subspaces,
+               quantized_residuals.data_handle(),
+               quantized_residuals.size(),
+               stream);
+
+    raft::copy(idx.quantized_soar_residuals().data_handle() + batch.offset() * num_subspaces,
+               quantized_soar_residuals.data_handle(),
+               quantized_soar_residuals.size(),
+               stream);
+
+    if (params.bf16_enabled) {
+      raft::copy(idx.bf16_dataset().data_handle() + batch.offset() * dim,
+                 bf16_dataset.data_handle(),
+                 bf16_dataset.size(),
+                 stream);
+    }
+  }
+
+  // Codebooks from VPQ have the shape [subspace idx, subspace dim, code]
+  // This converts the codebook into matrix format for easy interoperability
+  // with open-source ScaNN search
+  auto full_codebook_view = full_codebook.view();
+
+  raft::linalg::map_offset(
+    res,
+    idx.pq_codebook(),
+    [full_codebook_view, num_subspaces, dim_per_subspace, num_clusters] __device__(size_t i) {
+      int codebook_dim   = num_subspaces * dim_per_subspace;
+      int row_idx        = i / codebook_dim;
+      int el_idx         = i % codebook_dim;
+      int subspace_id    = el_idx / dim_per_subspace;
+      int id_in_subspace = el_idx % dim_per_subspace;
+
+      return full_codebook_view(row_idx + subspace_id * num_clusters, id_in_subspace);
+    });
+
+  raft::resource::sync_stream(res);
+
+  return idx;
+}
+
+/**
+ * @}
+ */
+
+}  // namespace cuvs::neighbors::experimental::scann::detail
diff --git a/cpp/src/neighbors/scann/detail/scann_common.cuh b/cpp/src/neighbors/scann/detail/scann_common.cuh
new file mode 100644
index 0000000000..87621af6e4
--- /dev/null
+++ b/cpp/src/neighbors/scann/detail/scann_common.cuh
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/matrix/gather.cuh>
+
+namespace cuvs::neighbors::experimental::scann::detail {
+
+template <typename T, typename IdxT>
+struct gather_functor {
+  auto operator()(raft::resources const& res,
+                  raft::device_matrix_view<const T, int64_t> dataset,
+                  raft::device_vector_view<const IdxT, int64_t> cluster_ids,
+                  raft::device_matrix_view<T, int64_t> cluster_vecs,
+                  cudaStream_t stream)
+  {
+    raft::matrix::gather(
+      res, raft::make_const_mdspan(dataset), raft::make_const_mdspan(cluster_ids), cluster_vecs);
+  }
+
+  auto operator()(raft::resources const& res,
+                  raft::host_matrix_view<const T, int64_t> dataset,
+                  raft::device_vector_view<const IdxT, int64_t> cluster_ids,
+                  raft::device_matrix_view<T, int64_t> cluster_vecs,
+                  cudaStream_t stream)
+  {
+    auto h_cluster_ids = raft::make_host_vector<IdxT, int64_t>(cluster_ids.extent(0));
+
+    raft::copy(h_cluster_ids.data_handle(), cluster_ids.data_handle(), cluster_ids.size(), stream);
+
+    auto pinned_cluster =
+      raft::make_host_matrix<T, int64_t>(cluster_vecs.extent(0), cluster_vecs.extent(1));
+
+    raft::resource::sync_stream(res, stream);
+
+    int n_threads = std::min<int>(omp_get_max_threads(), 32);
+
+#pragma omp parallel for num_threads(n_threads)
+    for (int i = 0; i < h_cluster_ids.extent(0); i++) {
+      // std::
+      memcpy(pinned_cluster.data_handle() + i * pinned_cluster.extent(1),
+             dataset.data_handle() + h_cluster_ids(i) * dataset.extent(1),
+             sizeof(T) * dataset.extent(1));
+    }
+
+    raft::copy(
+      cluster_vecs.data_handle(), pinned_cluster.data_handle(), pinned_cluster.size(), stream);
+    raft::resource::sync_stream(res, stream);
+  }
+};
+
+}  // namespace cuvs::neighbors::experimental::scann::detail
diff --git a/cpp/src/neighbors/scann/detail/scann_quantize.cuh b/cpp/src/neighbors/scann/detail/scann_quantize.cuh
new file mode 100644
index 0000000000..3cc0f40ebf
--- /dev/null
+++ b/cpp/src/neighbors/scann/detail/scann_quantize.cuh
@@ -0,0 +1,325 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../../detail/vpq_dataset.cuh"
+#include <chrono>
+#include <cuvs/neighbors/common.hpp>
+#include <raft/matrix/gather.cuh>
+
+#include "scann_common.cuh"
+using namespace cuvs::neighbors;
+
+namespace cuvs::neighbors::experimental::scann::detail {
+
+/** Fix the internal indexing type to avoid integer underflows/overflows */
+using ix_t = int64_t;
+
+template <uint32_t BlockSize,
+          uint32_t PqBits,
+          typename DataT,
+          typename MathT,
+          typename IdxT,
+          typename LabelT>
+__launch_bounds__(BlockSize) RAFT_KERNEL process_and_fill_codes_subspaces_kernel(
+  raft::device_matrix_view<uint8_t, IdxT, raft::row_major> out_codes,
+  raft::device_matrix_view<const DataT, IdxT, raft::row_major> dataset,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> vq_centers,
+  raft::device_vector_view<const LabelT, IdxT, raft::row_major> vq_labels,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> pq_centers)
+{
+  constexpr uint32_t kSubWarpSize = std::min<uint32_t>(raft::WarpSize, 1u << PqBits);
+  using subwarp_align             = raft::Pow2<kSubWarpSize>;
+  const IdxT row_ix = subwarp_align::div(IdxT{threadIdx.x} + IdxT{BlockSize} * IdxT{blockIdx.x});
+  if (row_ix >= out_codes.extent(0)) { return; }
+
+  const uint32_t pq_dim = raft::div_rounding_up_unsafe(vq_centers.extent(1), pq_centers.extent(1));
+
+  const uint32_t lane_id = raft::Pow2<kSubWarpSize>::mod(threadIdx.x);
+  const LabelT vq_label  = vq_labels(row_ix);
+
+  // write label
+  auto* out_label_ptr = reinterpret_cast<LabelT*>(&out_codes(row_ix, 0));
+  if (lane_id == 0) { *out_label_ptr = vq_label; }
+
+  auto* out_codes_ptr = reinterpret_cast<uint8_t*>(out_label_ptr + 1);
+  cuvs::neighbors::ivf_pq::detail::bitfield_view_t<PqBits> code_view{out_codes_ptr};
+  for (uint32_t j = 0; j < pq_dim; j++) {
+    // find PQ label
+    int subspace_offset   = j * pq_centers.extent(1) * (1 << PqBits);
+    auto pq_subspace_view = raft::make_device_matrix_view(
+      pq_centers.data_handle() + subspace_offset, (uint32_t)(1 << PqBits), pq_centers.extent(1));
+    uint8_t code = cuvs::neighbors::detail::compute_code<kSubWarpSize>(
+      dataset, vq_centers, pq_subspace_view, row_ix, j, vq_label);
+    // TODO: this writes in global memory one byte per warp, which is very slow.
+    //  It's better to keep the codes in the shared memory or registers and dump them at once.
+    if (lane_id == 0) { code_view[j] = code; }
+  }
+}
+
+template <typename MathT, typename IdxT, typename DatasetT>
+auto process_and_fill_codes_subspaces(
+  const raft::resources& res,
+  const vpq_params& params,
+  const DatasetT& dataset,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> vq_centers,
+  raft::device_matrix_view<const MathT, uint32_t, raft::row_major> pq_centers)
+  -> raft::device_matrix<uint8_t, IdxT, raft::row_major>
+{
+  using data_t     = typename DatasetT::value_type;
+  using cdataset_t = vpq_dataset<MathT, IdxT>;
+  using label_t    = uint32_t;
+
+  const ix_t n_rows       = dataset.extent(0);
+  const ix_t dim          = dataset.extent(1);
+  const ix_t pq_dim       = params.pq_dim;
+  const ix_t pq_bits      = params.pq_bits;
+  const ix_t pq_n_centers = ix_t{1} << pq_bits;
+  // NB: codes must be aligned at least to sizeof(label_t) to be able to read labels.
+  const ix_t codes_rowlen =
+    sizeof(label_t) * (1 + raft::div_rounding_up_safe<ix_t>(pq_dim * pq_bits, 8 * sizeof(label_t)));
+
+  auto codes = raft::make_device_matrix<uint8_t, IdxT, raft::row_major>(res, n_rows, codes_rowlen);
+
+  auto stream = raft::resource::get_cuda_stream(res);
+
+  // TODO: with scaling workspace we could choose the batch size dynamically
+  constexpr ix_t kBlockSize  = 256;
+  const ix_t threads_per_vec = std::min<ix_t>(raft::WarpSize, pq_n_centers);
+  dim3 threads(kBlockSize, 1, 1);
+
+  auto kernel = [](uint32_t pq_bits) {
+    switch (pq_bits) {
+      case 4:
+        return process_and_fill_codes_subspaces_kernel<kBlockSize, 4, data_t, MathT, IdxT, label_t>;
+      case 8:
+        return process_and_fill_codes_subspaces_kernel<kBlockSize, 8, data_t, MathT, IdxT, label_t>;
+      default: RAFT_FAIL("Invalid pq_bits (%u), the value must be 4 or 8", pq_bits);
+    }
+  }(pq_bits);
+
+  auto labels = cuvs::neighbors::detail::predict_vq<label_t>(res, dataset, vq_centers);
+
+  dim3 blocks(raft::div_rounding_up_safe<ix_t>(n_rows, kBlockSize / threads_per_vec), 1, 1);
+
+  kernel<<<blocks, threads, 0, stream>>>(
+    raft::make_device_matrix_view<uint8_t, IdxT>(codes.data_handle(), n_rows, codes_rowlen),
+    dataset,
+    vq_centers,
+    raft::make_const_mdspan(labels.view()),
+    pq_centers);
+
+  RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+  return codes;
+}
+
+template <typename T>
+auto create_pq_codebook(raft::resources const& res,
+                        raft::device_matrix_view<const T, int64_t> residuals,
+                        cuvs::neighbors::vpq_params ps)
+  -> raft::device_matrix<T, uint32_t, raft::row_major>
+{
+  // Create codebooks (vq initialized to 0s since we don't need here)
+  auto vq_code_book =
+    raft::make_device_matrix<T, uint32_t, raft::row_major>(res, 1, residuals.extent(1));
+  raft::linalg::map_offset(res, vq_code_book.view(), [] __device__(size_t i) { return 0; });
+
+  auto pq_code_book = cuvs::neighbors::detail::train_pq<T>(
+    res, ps, residuals, raft::make_const_mdspan(vq_code_book.view()));
+
+  return pq_code_book;
+}
+
+/**
+ * @brief Subtract cluster center coordinates from each dataset vector.
+ *
+ * residual[i, k] = dataset[i ,k] - centers[l, k],
+ * where l = labels[i], the cluster label corresponding to vector i.
+ *
+ * @tparam T
+ * @tparam LabelT
+ * @param res raft resources
+ * @param dataset dataset vectors, size [n_rows, dim]
+ * @param centers cluster center coordinates, size [n_clusters, dim]
+ * @param labels cluster labels, size [n_rows]
+ * @return device matrix with the residuals, size [n_rows, dim]
+ */
+template <typename T, typename LabelT>
+auto compute_residuals(raft::resources const& res,
+                       raft::device_matrix_view<const T, int64_t> dataset,
+                       raft::device_matrix_view<const T, int64_t> centers,
+                       raft::device_vector_view<const LabelT, int64_t> labels)
+  -> raft::device_matrix<T, int64_t, raft::row_major>
+{
+  auto dim       = dataset.extent(1);
+  auto residuals = raft::make_device_matrix<T, int64_t>(res, labels.extent(0), dim);
+
+  // compute residuals for AVQ assignments
+  raft::linalg::map_offset(
+    res, residuals.view(), [dataset, centers, labels, dim] __device__(size_t i) {
+      int row_idx = i / dim;
+      int el_idx  = i % dim;
+      return dataset(row_idx, el_idx) - centers(labels(row_idx), el_idx);
+    });
+
+  return residuals;
+}
+
+/**}
+ * @brief Generate PQ codes for residual vectors using codebook
+ *
+ * For each subspace, minimize L2 norm between residual vectors and
+ * PQ centers to generate codes for residual vectors
+ *
+ * @tparam T
+ * @tparam IdxT
+ * @tparam LabelT
+ * @param res raft resources
+ * @param residuals the residual vectors we're quantizing, size [n_rows, dim]
+ * @param pq_codebook the codebook of PQ centers size [dim, 1 << pq_bits]
+ * @oaran ps parameters used with vpq_dataset for pq quantization
+ * @return device matrix with (packed) codes from vpq, size [n_rows, 1 +ceil((dim / pq_dim *
+ * pq_bits) /( 8 * sizeof(LabelT)))]
+ */
+template <typename T, typename IdxT, typename LabelT>
+auto quantize_residuals(raft::resources const& res,
+                        raft::device_matrix_view<const T, int64_t> residuals,
+                        raft::device_matrix_view<T, uint32_t, raft::row_major> pq_codebook,
+                        cuvs::neighbors::vpq_params ps)
+  -> raft::device_matrix<uint8_t, IdxT, raft::row_major>
+{
+  auto dim = residuals.extent(1);
+
+  // Using a single 0 vector for the vq_codebook, since we already have
+  // vq centers and computed residuals w.r.t those centers
+  auto vq_codebook = raft::make_device_matrix<T, uint32_t, raft::row_major>(res, 1, dim);
+
+  RAFT_CUDA_TRY(cudaMemsetAsync(vq_codebook.data_handle(),
+                                0,
+                                vq_codebook.size() * sizeof(T),
+                                raft::resource::get_cuda_stream(res)));
+
+  auto codes = process_and_fill_codes_subspaces<T, IdxT>(
+    res, ps, residuals, raft::make_const_mdspan(vq_codebook.view()), pq_codebook);
+
+  return codes;
+}
+
+/**
+ * @brief Unpack VPQ codes into 1-byte per code
+ *
+ * VPQ gives codes in a "packed" form. The first 4 bytes give the code for
+ * vector quantization, and the remaining bytes the codes for subspace product
+ * quantization. In the case of 4 bit PQ, each byte stores codes for 2 subspaces
+ * in a packed form.
+ *
+ * This function unpacks the codes by discarding the VQ code (which we don't need,
+ * since we use VPQ only for residual quantization) and (in the case of 4-bit PQ)
+ * unpackes the subspace codes into one byte each. This is for interoperability
+ * with open source ScaNN, which doesn't pack codes
+ *
+ * @tparam IdxT
+ * @param res raft resources
+ * @param unpacked_codes_view matrix of unpacked codes, size  [n_rows, dim / pq_dim]
+ * @param codes_view packed codes from vpq, size [n_rows, 1 +ceil((dim / pq_dim * pq_bits) /( 8 *
+ * sizeof(LabelT)))]
+ * @param pq_bits number of bits used for PQ
+ * @param num_subspaces the number of pq_subspaces (dim / pq_dim)
+ */
+template <typename IdxT>
+void unpack_codes(raft::resources const& res,
+                  raft::device_matrix_view<uint8_t, IdxT> unpacked_codes_view,
+                  raft::device_matrix_view<const uint8_t, IdxT> codes_view,
+                  int pq_bits,
+                  int num_subspaces)
+{
+  if (pq_bits == 4) {
+    raft::linalg::map_offset(
+      res, unpacked_codes_view, [codes_view, num_subspaces] __device__(size_t i) {
+        int64_t row_idx             = i / num_subspaces;
+        int64_t subspace_idx        = i % num_subspaces;
+        int64_t packed_subspace_idx = 4 + subspace_idx / 2;
+        uint8_t mask                = subspace_idx % 2;
+
+        uint8_t packed_labels = codes_view(row_idx, packed_subspace_idx);
+        uint8_t first         = packed_labels >> 4;
+        uint8_t second        = (packed_labels & 15);
+
+        return (mask)*first + (1 - mask) * second;
+      });
+
+  } else {
+    raft::matrix::slice_coordinates<IdxT> coords(0, 4, codes_view.extent(0), 4 + num_subspaces);
+    raft::matrix::slice(res, raft::make_const_mdspan(codes_view), unpacked_codes_view, coords);
+  }
+}
+
+/**
+ * @brief sample dataset vectors/labels and compute their residuals for PQ training
+ *
+ * @tparam T
+ * @tparms LabelT
+ * @tparam Accessor
+ * @param res raft resources
+ * @param random_state state for random generator
+ * @param dataset the dataset (host or device), size [n_rows, dim]
+ * @param centroids the centers from kmeans training, size [n_clusters, dim]
+ * @param labels the idx of the nearest center for each dataset vector, size [n_rows]
+ * @param n_samples number of samples
+ * @return the sampled residuals for PQ training, size [n_samples, dim]
+ */
+template <typename T,
+          typename LabelT,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+auto sample_training_residuals(
+  raft::resources const& res,
+  random::RngState random_state,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  raft::device_matrix_view<const T, int64_t> centroids,
+  raft::device_vector_view<const LabelT, int64_t> labels,
+  int64_t n_samples) -> raft::device_matrix<T, int64_t, raft::row_major>
+{
+  int64_t n_dim = dataset.extent(1);
+
+  raft::device_vector<int64_t, int64_t> train_indices =
+    raft::random::excess_subsample<int64_t, int64_t>(
+      res, random_state, dataset.extent(0), n_samples);
+
+  auto trainset = raft::make_device_matrix<T, int64_t>(res, n_samples, n_dim);
+
+  gather_functor<T, int64_t>{}(res,
+                               dataset,
+                               raft::make_const_mdspan(train_indices.view()),
+                               trainset.view(),
+                               raft::resource::get_cuda_stream(res));
+
+  // Considering labels as a single column matrix for use in gather
+  auto labels_view =
+    raft::make_device_matrix_view<const LabelT, int64_t>(labels.data_handle(), labels.extent(0), 1);
+  auto trainset_labels = raft::make_device_matrix<LabelT, int64_t>(res, n_samples, 1);
+
+  raft::matrix::gather(
+    res, labels_view, raft::make_const_mdspan(train_indices.view()), trainset_labels.view());
+
+  return compute_residuals<T, LabelT>(
+    res,
+    raft::make_const_mdspan(trainset.view()),
+    centroids,
+    raft::make_device_vector_view<const LabelT, int64_t>(trainset_labels.data_handle(), n_samples));
+}
+
+}  // namespace cuvs::neighbors::experimental::scann::detail
diff --git a/cpp/src/neighbors/scann/detail/scann_serialize.cuh b/cpp/src/neighbors/scann/detail/scann_serialize.cuh
new file mode 100644
index 0000000000..4a8788d679
--- /dev/null
+++ b/cpp/src/neighbors/scann/detail/scann_serialize.cuh
@@ -0,0 +1,155 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/scann.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/mdarray.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/nvtx.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/serialize.hpp>
+#include <raft/linalg/map.cuh>
+
+#include <cstddef>
+#include <cstdint>
+#include <filesystem>
+#include <fstream>
+#include <type_traits>
+
+namespace cuvs::neighbors::experimental::scann::detail {
+
+constexpr int kSerializationVersion = 1;
+
+namespace {
+// Helper for opening an ofstream
+std::ofstream open_file(std::string file_name)
+{
+  std::ofstream file_of(file_name, std::ios::out | std::ios::binary);
+
+  if (!file_of) { RAFT_FAIL("Cannot open file %s", file_name.c_str()); }
+
+  return file_of;
+}
+}  // namespace
+
+// Helper for serializing device/host matrix to a given file
+template <typename T,
+          typename IdxT,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+void serialize_matrix(
+  raft::resources const& res,
+  std::filesystem::path file_path,
+  raft::mdspan<const T, raft::matrix_extent<IdxT>, raft::row_major, Accessor> mat)
+{
+  auto mat_of = open_file(file_path);
+
+  raft::serialize_mdspan(res, mat_of, mat);
+
+  mat_of.close();
+}
+
+// ScaNN stores primary and soar cluster assignments interleaved in a single vector.
+// When the primary and soar assignment are equal, the soar assignment should be -1
+// This is a helper for combining the primary and soar assignments into this format
+// and serializing it
+template <typename T, typename IdxT>
+void save_labels(raft::resources const& res,
+                 std::filesystem::path labels_path,
+                 const index<T, IdxT>& index_)
+{
+  auto combined_labels  = raft::make_device_vector<int, IdxT>(res, 2 * index_.labels().extent(0));
+  auto labels_view      = index_.labels();
+  auto soar_labels_view = index_.soar_labels();
+
+  raft::linalg::map_offset(
+    res, combined_labels.view(), [labels_view, soar_labels_view] __device__(size_t i) {
+      size_t label_type = i % 2;
+      size_t idx        = i / 2;
+
+      int label      = labels_view(idx);
+      int soar_label = soar_labels_view(idx);
+
+      if (label == soar_label) { soar_label = -1; }
+
+      if (label_type == 0) { return label; }
+
+      return soar_label;
+    });
+
+  auto labels_of = open_file(labels_path);
+
+  raft::serialize_mdspan(res, labels_of, combined_labels.view());
+
+  labels_of.close();
+}
+
+/**
+ * Save the ScaNN index into multiple files.
+ *
+ * Experimental, both the API and the serialization format are subject to change.
+ * The format is meant to ease the integration into OSS ScaNN for search.
+ * Labels and quantized vectors are stored directly in .npy files, as required by OSS ScaNN.
+ * Cluster centers and the pq_codebook are also stored in .npy files, for later
+ * conversion into the correct protobuf structs by an external tool. Additional metadata
+ * required for this conversion are also serialized.
+ *
+ * @param[in] res the raft resource handle
+ * @param[in] scann_assets_dir:  the directory where ScaNN index assets will be saved
+ * @param[in] index_ ScaNN index
+ *
+ */
+template <typename T, typename IdxT>
+void serialize(raft::resources const& res,
+               const std::string& scann_assets_dir,
+               const index<T, IdxT>& index_)
+{
+  // Metadata
+  std::filesystem::path scann_path(scann_assets_dir);
+
+  auto metadata_of = open_file(scann_path / "cuvs_metadata.bin");
+
+  raft::serialize_scalar(res, metadata_of, kSerializationVersion);
+  raft::serialize_scalar(res, metadata_of, index_.dim());
+  raft::serialize_scalar(res, metadata_of, index_.pq_dim());
+
+  metadata_of.close();
+
+  // kmeans cluster centers
+  serialize_matrix(res, scann_path / "centers.npy", index_.centers());
+
+  // cluster assignments
+  save_labels(res, scann_path / "datapoint_to_token.npy", index_);
+
+  // codebook
+
+  serialize_matrix(res, scann_path / "pq_codebook.npy", index_.pq_codebook());
+
+  // quantized residuals
+  serialize_matrix(res, scann_path / "hashed_dataset.npy", index_.quantized_residuals());
+
+  // quantized SOAR residuals
+  serialize_matrix(res, scann_path / "hashed_dataset_soar.npy", index_.quantized_soar_residuals());
+
+  // bf16 dataset
+  if (index_.bf16_dataset().extent(0) > 0) {
+    serialize_matrix(res, scann_path / "bf16_dataset.npy", index_.bf16_dataset());
+  }
+}
+
+}  // namespace cuvs::neighbors::experimental::scann::detail
diff --git a/cpp/src/neighbors/scann/detail/scann_soar.cuh b/cpp/src/neighbors/scann/detail/scann_soar.cuh
new file mode 100644
index 0000000000..8dca364850
--- /dev/null
+++ b/cpp/src/neighbors/scann/detail/scann_soar.cuh
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/host_mdarray.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/linalg/coalesced_reduction.cuh>
+#include <raft/linalg/dot.cuh>
+#include <raft/linalg/gemm.hpp>
+#include <raft/linalg/gemv.cuh>
+#include <raft/linalg/linalg_types.hpp>
+#include <raft/linalg/map.cuh>
+#include <raft/linalg/matrix_vector.cuh>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/multiply.cuh>
+#include <raft/linalg/normalize.cuh>
+#include <raft/linalg/power.cuh>
+#include <raft/linalg/reduce.cuh>
+#include <raft/linalg/transpose.cuh>
+#include <raft/matrix/argmin.cuh>
+#include <raft/matrix/copy.cuh>
+#include <raft/matrix/diagonal.cuh>
+#include <raft/matrix/init.cuh>
+
+/**
+ * @brief Compute SOAR labels for each dataset vector
+ *
+ * Compute a second, spilled cluster for each dataset vector by minimizing
+ * the loss function in Theorem 3.1 of https://arxiv.org/abs/2404.00774
+ *
+ * @tparam T
+ * @tparam LavelT
+ * @param res raft resources
+ * @param dataset the dataset, size [n_rows, dim]
+ * @param residuals the residual vectors r, size [n_rows, dim]
+ * @param centers the cluster centers, size [n_clusters, dim]
+ * @param labels the cluster assignments, size [n_rows]
+ * @param soar_labels the computed soar labels
+ * @param lambda the weight for the projection of a residual r' onto r in the SOAR loss
+ */
+template <typename T, typename LabelT>
+void compute_soar_labels(raft::resources const& dev_resources,
+                         raft::device_matrix_view<const T, int64_t> dataset,
+                         raft::device_matrix_view<const T, int64_t> residuals,
+                         raft::device_matrix_view<T, int64_t> centers,
+                         raft::device_vector_view<const LabelT, int64_t> labels,
+                         raft::device_vector_view<LabelT, int64_t> soar_labels,
+                         float lambda)
+{
+  auto dim = dataset.extent(1);
+
+  // compute SOAR metric for each center
+  auto soar_scores =
+    raft::make_device_matrix<float, int64_t>(dev_resources, dataset.extent(0), centers.extent(0));
+  auto n_centers = centers.extent(0);
+
+  auto residuals_norm = raft::make_device_matrix<float, int64_t>(
+    dev_resources, residuals.extent(0), residuals.extent(1));
+
+  raft::linalg::row_normalize(dev_resources,
+                              residuals,
+                              residuals_norm.view(),
+                              0.0f,
+                              raft::sq_op(),
+                              raft::add_op(),
+                              raft::sqrt_op());
+
+  int64_t n_rows = dataset.extent(0);
+
+  auto cublas_handle = raft::resource::get_cublas_handle(dev_resources);
+
+  auto alpha = raft::make_host_scalar<float>(1.0);
+  auto beta  = raft::make_host_scalar<float>(0.0);
+
+  auto dataset_dot_residual = raft::make_device_vector<float, int64_t>(dev_resources, n_rows);
+
+  RAFT_CUBLAS_TRY(cublasSgemvStridedBatched(cublas_handle,
+                                            CUBLAS_OP_N,
+                                            1,
+                                            dim,
+                                            alpha.data_handle(),
+                                            dataset.data_handle(),
+                                            1,
+                                            dim,
+                                            residuals_norm.data_handle(),
+                                            1,
+                                            dim,
+                                            beta.data_handle(),
+                                            dataset_dot_residual.data_handle(),
+                                            1,
+                                            1,
+                                            n_rows));
+
+  auto centers_norm = raft::make_device_vector<float, int64_t>(dev_resources, centers.extent(0));
+  auto centers_transpose =
+    raft::make_device_matrix<T, int64_t>(dev_resources, centers.extent(1), centers.extent(0));
+
+  raft::linalg::reduce<true, true>(centers_norm.data_handle(),
+                                   centers.data_handle(),
+                                   centers.extent(1),
+                                   centers.extent(0),
+                                   0.0f,
+                                   raft::resource::get_cuda_stream(dev_resources),
+                                   false,
+                                   raft::sq_op(),
+                                   raft::add_op(),
+                                   raft::identity_op());
+
+  raft::linalg::transpose(dev_resources, centers, centers_transpose.view());
+
+  raft::linalg::gemm(
+    dev_resources, residuals_norm.view(), centers_transpose.view(), soar_scores.view());
+
+  raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
+    dev_resources,
+    raft::make_const_mdspan(soar_scores.view()),
+    raft::make_const_mdspan(dataset_dot_residual.view()),
+    soar_scores.view(),
+    raft::sub_op());
+
+  raft::linalg::map(
+    dev_resources, raft::make_const_mdspan(soar_scores.view()), soar_scores.view(), raft::sq_op());
+
+  raft::linalg::map(dev_resources,
+                    raft::make_const_mdspan(soar_scores.view()),
+                    soar_scores.view(),
+                    raft::mul_const_op<float>(lambda));
+
+  auto nc_dataset = raft::make_device_matrix_view<float, int64_t>(
+    const_cast<float*>(dataset.data_handle()), dataset.extent(0), dataset.extent(1));
+
+  alpha(0) = -2.0;
+  beta(0)  = 1.0;
+
+  raft::linalg::gemm<float,
+                     int64_t,
+                     raft::row_major,
+                     raft::row_major,
+                     raft::row_major,
+                     uint32_t,
+                     raft::host_scalar_view<float, uint32_t>>(dev_resources,
+                                                              nc_dataset,
+                                                              centers_transpose.view(),
+                                                              soar_scores.view(),
+                                                              alpha.view(),
+                                                              beta.view());
+
+  raft::linalg::matrix_vector_op<raft::Apply::ALONG_ROWS>(
+    dev_resources,
+    raft::make_const_mdspan(soar_scores.view()),
+    raft::make_const_mdspan(centers_norm.view()),
+    soar_scores.view(),
+    raft::add_op());
+
+  raft::matrix::argmin(dev_resources, raft::make_const_mdspan(soar_scores.view()), soar_labels);
+}
diff --git a/cpp/src/neighbors/scann/scann.cuh b/cpp/src/neighbors/scann/scann.cuh
new file mode 100644
index 0000000000..d45790aa75
--- /dev/null
+++ b/cpp/src/neighbors/scann/scann.cuh
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "detail/scann_build.cuh"
+#include "detail/scann_serialize.cuh"
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_device_accessor.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/scann.hpp>
+
+#include <cuvs/neighbors/common.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cuvs::neighbors::experimental::scann {
+
+/**
+ * @defgroup ScaNN ANN Graph-based nearest neighbor search
+ * @{
+ */
+
+/**
+ * @brief Build the ScaNN / DiskANN index from the dataset.
+ *
+ * TODO - add brief explanation of the algo
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   using namespace cuvs::neighbors;
+ *   // use default index parameters
+ *   scann::index_params index_params;
+ *   // create and fill the index from a [N, D] dataset
+ *   auto index = scann::build(res, index_params, dataset);
+ *   // write graph to file for later use.
+     scann.serialize(res, filename, index);
+ * @endcode
+ *
+ * @tparam T data element type
+ * @tparam IdxT type of the indices in the source dataset
+ *
+ * @param[in] res
+ * @param[in] params parameters for building the index
+ * @param[in] dataset a matrix view (host or device) to a row-major matrix [n_rows, dim]
+ *
+ * @return the constructed scann index
+ */
+template <typename T,
+          typename IdxT     = uint32_t,
+          typename Accessor = raft::host_device_accessor<std::experimental::default_accessor<T>,
+                                                         raft::memory_type::host>>
+index<T, IdxT> build(
+  raft::resources const& res,
+  const index_params& params,
+  raft::mdspan<const T, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
+{
+  return cuvs::neighbors::experimental::scann::detail::build<T, IdxT, Accessor>(
+    res, params, dataset);
+}
+
+template <typename T, typename IdxT>
+void serialize(raft::resources const& res,
+               const std::string& file_prefix,
+               const index<T, IdxT>& index_)
+{
+  cuvs::neighbors::experimental::scann::detail::serialize<T, IdxT>(res, file_prefix, index_);
+}
+
+/** @} */  // end group scann
+
+}  // namespace cuvs::neighbors::experimental::scann
diff --git a/cpp/src/neighbors/scann/scann_build_float.cu b/cpp/src/neighbors/scann/scann_build_float.cu
new file mode 100644
index 0000000000..448e29e685
--- /dev/null
+++ b/cpp/src/neighbors/scann/scann_build_float.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "scann.cuh"
+#include <cuvs/neighbors/scann.hpp>
+
+namespace cuvs::neighbors::experimental::scann {
+
+#define CUVS_INST_SCANN_BUILD(T, IdxT)                                                    \
+  auto build(raft::resources const& handle,                                               \
+             const cuvs::neighbors::experimental::scann::index_params& params,            \
+             raft::device_matrix_view<const T, IdxT, raft::row_major> dataset)            \
+    -> cuvs::neighbors::experimental::scann::index<T, IdxT>                               \
+  {                                                                                       \
+    return cuvs::neighbors::experimental::scann::build<T, IdxT>(handle, params, dataset); \
+  }                                                                                       \
+                                                                                          \
+  auto build(raft::resources const& handle,                                               \
+             const cuvs::neighbors::experimental::scann::index_params& params,            \
+             raft::host_matrix_view<const T, IdxT, raft::row_major> dataset)              \
+    -> cuvs::neighbors::experimental::scann::index<T, IdxT>                               \
+  {                                                                                       \
+    return cuvs::neighbors::experimental::scann::build<T, IdxT>(handle, params, dataset); \
+  }
+
+CUVS_INST_SCANN_BUILD(float, int64_t);
+
+#undef CUVS_INST_SCANN_BUILD
+
+}  // namespace cuvs::neighbors::experimental::scann
diff --git a/cpp/src/neighbors/scann/scann_serialize_float.cu b/cpp/src/neighbors/scann/scann_serialize_float.cu
new file mode 100644
index 0000000000..8a00e1d825
--- /dev/null
+++ b/cpp/src/neighbors/scann/scann_serialize_float.cu
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "scann.cuh"
+#include <cuvs/neighbors/scann.hpp>
+
+namespace cuvs::neighbors::experimental::scann {
+
+/**
+ * @defgroup ScaNN graph serialize/derserialize
+ * @{
+ */
+
+#define CUVS_INST_SCANN_SERIALIZE(DTYPE, IdxT)                                           \
+  void serialize(raft::resources const& handle,                                          \
+                 const std::string& file_prefix,                                         \
+                 const cuvs::neighbors::experimental::scann::index<DTYPE, IdxT>& index_) \
+  {                                                                                      \
+    cuvs::neighbors::experimental::scann::detail::serialize<DTYPE, IdxT>(                \
+      handle, file_prefix, index_);                                                      \
+  };
+
+CUVS_INST_SCANN_SERIALIZE(float, int64_t);
+
+/** @} */  // end group scann
+
+}  // namespace cuvs::neighbors::experimental::scann
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index 77bafb5eb2..a9ec6dfd00 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -147,6 +147,10 @@ if(BUILD_TESTS)
     100
   )
 
+  ConfigureTest(
+    NAME NEIGHBORS_ANN_SCANN_TEST PATH neighbors/ann_scann/test_float_int64_t.cu GPUS 1 PERCENT 1
+  )
+
   ConfigureTest(
     NAME NEIGHBORS_ANN_BRUTE_FORCE_TEST PATH neighbors/ann_brute_force/test_float.cu
     neighbors/ann_brute_force/test_half.cu GPUS 1 PERCENT 100
diff --git a/cpp/tests/neighbors/ann_scann.cuh b/cpp/tests/neighbors/ann_scann.cuh
new file mode 100644
index 0000000000..082f3bb659
--- /dev/null
+++ b/cpp/tests/neighbors/ann_scann.cuh
@@ -0,0 +1,274 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "../test_utils.cuh"
+#include "ann_utils.cuh"
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/scann.hpp>
+
+#include <raft/core/resource/cuda_stream_pool.hpp>
+#include <raft/linalg/add.cuh>
+#include <raft/matrix/gather.cuh>
+#include <rmm/cuda_stream_pool.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+#include <thrust/sequence.h>
+
+namespace cuvs::neighbors::experimental::scann {
+
+struct scann_inputs {
+  uint32_t num_db_vecs = 4096;
+  uint32_t dim         = 64;
+
+  cuvs::neighbors::experimental::scann::index_params index_params;
+
+  scann_inputs()
+  {
+    index_params.n_leaves            = max(32u, min(1024u, num_db_vecs / 128u));
+    index_params.kmeans_n_rows_train = num_db_vecs;
+    index_params.pq_n_rows_train     = num_db_vecs;
+  }
+};
+
+template <typename DataT, typename IdxT>
+class scann_test : public ::testing::TestWithParam<scann_inputs> {
+ public:
+  scann_test()
+    : stream_(raft::resource::get_cuda_stream(handle_)),
+      ps(::testing::TestWithParam<scann_inputs>::GetParam()),
+      database(0, stream_)
+  {
+  }
+
+  void gen_data()
+  {
+    database.resize(size_t{ps.num_db_vecs} * size_t{ps.dim}, stream_);
+
+    raft::random::RngState r(1234ULL);
+
+    if constexpr (std::is_same<DataT, float>{}) {
+      raft::random::uniform(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(0.1), DataT(2.0));
+    } else {
+      raft::random::uniformInt(
+        handle_, r, database.data(), ps.num_db_vecs * ps.dim, DataT(1), DataT(20));
+    }
+
+    raft::resource::sync_stream(handle_);
+  }
+
+  auto build_only()
+  {
+    auto ipams = ps.index_params;
+
+    auto db_view =
+      raft::make_device_matrix_view<const DataT, int64_t>(database.data(), ps.num_db_vecs, ps.dim);
+
+    return cuvs::neighbors::experimental::scann::build(handle_, ipams, db_view);
+  }
+
+  auto build_only_host_input()
+  {
+    auto ipams = ps.index_params;
+
+    auto h_database = raft::make_host_matrix<DataT, int64_t>(ps.num_db_vecs, ps.dim);
+
+    raft::copy(h_database.data_handle(), database.data(), ps.num_db_vecs * ps.dim, stream_);
+
+    auto db_view = raft::make_host_matrix_view<const DataT, int64_t>(
+      h_database.data_handle(), ps.num_db_vecs, ps.dim);
+
+    return cuvs::neighbors::experimental::scann::build(handle_, ipams, db_view);
+  }
+
+  auto build_only_host_input_overlap()
+  {
+    auto ipams = ps.index_params;
+
+    // additional stream for overlapping HtoD copy
+    size_t n_streams = 2;
+    raft::resource::set_cuda_stream_pool(handle_,
+                                         std::make_shared<rmm::cuda_stream_pool>(n_streams));
+
+    auto h_database = raft::make_host_matrix<DataT, int64_t>(ps.num_db_vecs, ps.dim);
+
+    raft::copy(h_database.data_handle(), database.data(), ps.num_db_vecs * ps.dim, stream_);
+
+    auto db_view = raft::make_host_matrix_view<const DataT, int64_t>(
+      h_database.data_handle(), ps.num_db_vecs, ps.dim);
+
+    return cuvs::neighbors::experimental::scann::build(handle_, ipams, db_view);
+  }
+
+  template <typename BuildIndex>
+  void run(BuildIndex build_index)
+  {
+    index<DataT, IdxT> index = build_index();
+
+    // Simple checking of dimensions of index artifacts
+    auto num_subspaces   = ps.dim / ps.index_params.pq_dim;
+    auto num_pq_clusters = 1 << ps.index_params.pq_bits;
+
+    ASSERT_EQ(index.quantized_residuals().extent(0), ps.num_db_vecs);
+    ASSERT_EQ(index.quantized_residuals().extent(1), num_subspaces);
+
+    ASSERT_EQ(index.quantized_soar_residuals().extent(0), ps.num_db_vecs);
+    ASSERT_EQ(index.quantized_soar_residuals().extent(1), num_subspaces);
+
+    ASSERT_EQ(index.pq_codebook().extent(0), num_pq_clusters);
+    ASSERT_EQ(index.pq_codebook().extent(1), ps.dim);
+
+    IdxT expected_bf16_size = ps.index_params.bf16_enabled ? ps.dim * ps.num_db_vecs : 0;
+
+    ASSERT_EQ(index.bf16_dataset().size(), expected_bf16_size);
+  }
+
+  void SetUp() override  // NOLINT
+  {
+    gen_data();
+  }
+
+  void TearDown() override  // NOLINT
+  {
+    cudaGetLastError();
+    raft::resource::sync_stream(handle_);
+    database.resize(0, stream_);
+  }
+
+ private:
+  raft::resources handle_;
+  rmm::cuda_stream_view stream_;
+  scann_inputs ps;                      // NOLINT
+  rmm::device_uvector<DataT> database;  // NOLINT
+};
+
+/* Test cases */
+using test_cases_t = std::vector<scann_inputs>;
+
+// concatenate parameter sets for different type
+template <typename T>
+auto operator+(const std::vector<T>& a, const std::vector<T>& b) -> std::vector<T>
+{
+  std::vector<T> res = a;
+  res.insert(res.end(), b.begin(), b.end());
+  return res;
+}
+
+template <typename B, typename A, typename F>
+auto map(const std::vector<A>& xs, F f) -> std::vector<B>
+{
+  std::vector<B> ys(xs.size());
+  std::transform(xs.begin(), xs.end(), ys.begin(), f);
+  return ys;
+}
+
+inline auto with_dims(const std::vector<uint32_t>& dims) -> test_cases_t
+{
+  return map<scann_inputs>(dims, [](uint32_t d) {
+    scann_inputs x;
+    x.dim = d;
+    return x;
+  });
+}
+
+inline auto defaults() -> test_cases_t { return {scann_inputs{}}; }
+
+inline auto small_dims_all_pq_bits() -> test_cases_t
+{
+  auto four_bit_ts = with_dims({2, 4, 6, 8, 10, 12, 14, 16, 18});
+
+  for (auto& ts : four_bit_ts) {
+    ts.index_params.pq_dim  = 2;
+    ts.index_params.pq_bits = 4;
+  }
+
+  auto eight_bit_ts = with_dims({2, 4, 6, 8, 10, 12, 14, 16, 18});
+
+  for (auto& ts : eight_bit_ts) {
+    ts.index_params.pq_dim  = 2;
+    ts.index_params.pq_bits = 8;
+  }
+
+  return four_bit_ts + eight_bit_ts;
+}
+
+inline auto big_dims_all_pq_bits() -> test_cases_t
+{
+  auto four_bit_ts = with_dims({64, 128, 256, 512, 1024, 2048});
+
+  for (auto& ts : four_bit_ts) {
+    ts.index_params.pq_dim  = 8;
+    ts.index_params.pq_bits = 4;
+  }
+
+  auto eight_bit_ts = with_dims({64, 128, 256, 512, 1024, 2048});
+
+  for (auto& ts : eight_bit_ts) {
+    ts.index_params.pq_dim  = 8;
+    ts.index_params.pq_bits = 8;
+  }
+
+  return four_bit_ts + eight_bit_ts;
+}
+
+inline auto bf16() -> test_cases_t
+{
+  scann_inputs ts;
+  ts.index_params.bf16_enabled = true;
+
+  return {ts};
+}
+
+inline auto avq() -> test_cases_t
+{
+  scann_inputs ts;
+  ts.index_params.partitioning_eta = 2;
+
+  return {ts};
+}
+
+inline auto soar() -> test_cases_t
+{
+  scann_inputs ts;
+  ts.index_params.soar_lambda = 1.5;
+
+  return {ts};
+}
+
+/* Test instantiations */
+
+#define TEST_BUILD(type)                                \
+  TEST_P(type, build) /* NOLINT */                      \
+  {                                                     \
+    this->run([this]() { return this->build_only(); }); \
+  }
+
+#define TEST_BUILD_HOST_INPUT(type)                                \
+  TEST_P(type, build_host_input) /* NOLINT */                      \
+  {                                                                \
+    this->run([this]() { return this->build_only_host_input(); }); \
+  }
+
+#define TEST_BUILD_HOST_INPUT_OVERLAP(type)                                \
+  TEST_P(type, build_host_input_overlap) /* NOLINT */                      \
+  {                                                                        \
+    this->run([this]() { return this->build_only_host_input_overlap(); }); \
+  }
+
+#define INSTANTIATE(type, vals) \
+  INSTANTIATE_TEST_SUITE_P(ScaNN, type, ::testing::ValuesIn(vals)); /* NOLINT */
+
+}  // namespace cuvs::neighbors::experimental::scann
diff --git a/cpp/tests/neighbors/ann_scann/test_float_int64_t.cu b/cpp/tests/neighbors/ann_scann/test_float_int64_t.cu
new file mode 100644
index 0000000000..67013e94de
--- /dev/null
+++ b/cpp/tests/neighbors/ann_scann/test_float_int64_t.cu
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../ann_scann.cuh"
+
+namespace cuvs::neighbors::experimental::scann {
+
+using f32_i64 = scann_test<float, int64_t>;
+
+TEST_BUILD(f32_i64)
+TEST_BUILD_HOST_INPUT(f32_i64)
+TEST_BUILD_HOST_INPUT_OVERLAP(f32_i64);
+
+INSTANTIATE(f32_i64,
+            defaults() + small_dims_all_pq_bits() + big_dims_all_pq_bits() + bf16() + avq() +
+              soar());
+
+}  // namespace cuvs::neighbors::experimental::scann
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 2d6f1d3665..d1b012c960 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -43,6 +43,7 @@ add_executable(DYNAMIC_BATCHING_EXAMPLE src/dynamic_batching_example.cu)
 add_executable(IVF_FLAT_EXAMPLE src/ivf_flat_example.cu)
 add_executable(IVF_PQ_EXAMPLE src/ivf_pq_example.cu)
 add_executable(VAMANA_EXAMPLE src/vamana_example.cu)
+add_executable(SCANN_EXAMPLE src/scann_example.cu)
 
 # `$<TARGET_NAME_IF_EXISTS:conda_env>` is a generator expression that ensures that targets are
 # installed in a conda environment, if one exists
@@ -57,3 +58,4 @@ target_link_libraries(
 target_link_libraries(IVF_PQ_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(IVF_FLAT_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
 target_link_libraries(VAMANA_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
+target_link_libraries(SCANN_EXAMPLE PRIVATE cuvs::cuvs $<TARGET_NAME_IF_EXISTS:conda_env>)
diff --git a/examples/cpp/src/scann_example.cu b/examples/cpp/src/scann_example.cu
new file mode 100644
index 0000000000..48cf970278
--- /dev/null
+++ b/examples/cpp/src/scann_example.cu
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <cstdlib>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/device_resources.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/sparse/neighbors/cross_component_nn.cuh>
+
+#include <cuvs/neighbors/scann.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include "common.cuh"
+
+template <typename T>
+void scann_build_and_write(raft::device_resources const& dev_resources,
+                           raft::device_matrix_view<const T, int64_t> dataset)
+{
+  using namespace cuvs::neighbors::experimental;
+
+  // use default index parameters
+  scann::index_params index_params;
+
+  index_params.n_leaves            = 1000;
+  index_params.kmeans_n_rows_train = 10000;
+  index_params.partitioning_eta    = 2;
+  index_params.soar_lambda         = 1.5;
+
+  index_params.pq_dim          = 2;
+  index_params.pq_bits         = 4;
+  index_params.pq_n_rows_train = 10000;
+
+  std::cout << "Building ScaNN index" << std::endl;
+
+  auto start = std::chrono::system_clock::now();
+  auto index = scann::build(dev_resources, index_params, dataset);
+  auto end   = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end - start;
+
+  // TODO - output statistics about the index
+
+  std::cout << "Time to build index: " << elapsed_seconds.count() << "s\n";
+
+  // Output index to files in /tmp directory
+  serialize(dev_resources, "/tmp", index);
+}
+
+int main(int argc, char* argv[])
+{
+  raft::device_resources dev_resources;
+
+  // Set pool memory resource with 1 GiB initial pool size. All allocations use
+  // the same pool.
+  rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> pool_mr(
+    rmm::mr::get_current_device_resource(), 1024 * 1024 * 1024ull);
+  rmm::mr::set_current_device_resource(&pool_mr);
+
+  // Create input arrays.
+  int64_t n_samples = 10000;
+  int64_t n_dim     = 64;
+  int64_t n_queries = 10;
+
+  auto dataset = raft::make_device_matrix<float, int64_t>(dev_resources, n_samples, n_dim);
+  auto queries = raft::make_device_matrix<float, int64_t>(dev_resources, n_queries, n_dim);
+
+  generate_dataset(dev_resources, dataset.view(), queries.view());
+
+  // Simple build example to create ScaNN index and write to file
+  scann_build_and_write<float>(dev_resources, raft::make_const_mdspan(dataset.view()));
+}

From 02d834623d11a4cda6f971043539711aaf53e281 Mon Sep 17 00:00:00 2001
From: Anupam <54245698+aamijar@users.noreply.github.com>
Date: Thu, 31 Jul 2025 08:31:50 -0700
Subject: [PATCH 520/623] Spectral Embedding precomputed graph api (#1197)

Resolves #1184

This API is needed so that we have the option to pass in a precomputed connectivity graph. This allows users to build a connectivity graph using their preferred method whether it is through brute_force or other means and then pass the input to the spectral embedding computation.

Authors:
  - Anupam (https://github.com/aamijar)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1197
---
 .../cuvs/preprocessing/spectral_embedding.hpp |  5 +++
 .../spectral/spectral_embedding.cu            | 42 +++++++++++++------
 2 files changed, 35 insertions(+), 12 deletions(-)

diff --git a/cpp/include/cuvs/preprocessing/spectral_embedding.hpp b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
index 90197e1bd5..3d935bfd9a 100644
--- a/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
+++ b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
@@ -48,4 +48,9 @@ void transform(raft::resources const& handle,
                raft::device_matrix_view<float, int, raft::row_major> dataset,
                raft::device_matrix_view<float, int, raft::col_major> embedding);
 
+void transform(raft::resources const& handle,
+               params config,
+               raft::device_coo_matrix_view<float, int, int, int> connectivity_graph,
+               raft::device_matrix_view<float, int, raft::col_major> embedding);
+
 }  // namespace cuvs::preprocessing::spectral_embedding
diff --git a/cpp/src/preprocessing/spectral/spectral_embedding.cu b/cpp/src/preprocessing/spectral/spectral_embedding.cu
index d3466802d1..7565ae63b1 100644
--- a/cpp/src/preprocessing/spectral/spectral_embedding.cu
+++ b/cpp/src/preprocessing/spectral/spectral_embedding.cu
@@ -112,35 +112,35 @@ raft::device_csr_matrix_view<float, int, int, int> coo_to_csr_matrix(
   raft::resources const& handle,
   const int n_samples,
   raft::device_vector_view<int> sym_coo_row_ind,
-  raft::device_coo_matrix<float, int, int, int>& sym_coo_matrix)
+  raft::device_coo_matrix_view<float, int, int, int> sym_coo_matrix_view)
 {
   auto stream = raft::resource::get_cuda_stream(handle);
 
   raft::sparse::op::coo_sort<float>(n_samples,
                                     n_samples,
-                                    sym_coo_matrix.structure_view().get_nnz(),
-                                    sym_coo_matrix.structure_view().get_rows().data(),
-                                    sym_coo_matrix.structure_view().get_cols().data(),
-                                    sym_coo_matrix.get_elements().data(),
+                                    sym_coo_matrix_view.structure_view().get_nnz(),
+                                    sym_coo_matrix_view.structure_view().get_rows().data(),
+                                    sym_coo_matrix_view.structure_view().get_cols().data(),
+                                    sym_coo_matrix_view.get_elements().data(),
                                     stream);
 
-  raft::sparse::convert::sorted_coo_to_csr(sym_coo_matrix.structure_view().get_rows().data(),
-                                           sym_coo_matrix.structure_view().get_nnz(),
+  raft::sparse::convert::sorted_coo_to_csr(sym_coo_matrix_view.structure_view().get_rows().data(),
+                                           sym_coo_matrix_view.structure_view().get_nnz(),
                                            sym_coo_row_ind.data_handle(),
                                            n_samples,
                                            stream);
 
-  auto sym_coo_nnz = sym_coo_matrix.structure_view().get_nnz();
+  auto sym_coo_nnz = sym_coo_matrix_view.structure_view().get_nnz();
   raft::copy(sym_coo_row_ind.data_handle() + sym_coo_row_ind.size() - 1, &sym_coo_nnz, 1, stream);
 
   auto csr_matrix_view = raft::make_device_csr_matrix_view<float, int, int, int>(
-    const_cast<float*>(sym_coo_matrix.get_elements().data()),
+    const_cast<float*>(sym_coo_matrix_view.get_elements().data()),
     raft::make_device_compressed_structure_view<int, int, int>(
       const_cast<int*>(sym_coo_row_ind.data_handle()),
-      const_cast<int*>(sym_coo_matrix.structure_view().get_cols().data()),
+      const_cast<int*>(sym_coo_matrix_view.structure_view().get_cols().data()),
       n_samples,
       n_samples,
-      sym_coo_matrix.structure_view().get_nnz()));
+      sym_coo_matrix_view.structure_view().get_nnz()));
   return csr_matrix_view;
 }
 
@@ -250,7 +250,25 @@ void transform(raft::resources const& handle,
 
   create_connectivity_graph(handle, spectral_embedding_config, dataset, embedding, sym_coo_matrix);
   auto csr_matrix_view =
-    coo_to_csr_matrix(handle, n_samples, sym_coo_row_ind.view(), sym_coo_matrix);
+    coo_to_csr_matrix(handle, n_samples, sym_coo_row_ind.view(), sym_coo_matrix.view());
+  auto laplacian =
+    create_laplacian(handle, spectral_embedding_config, csr_matrix_view, diagonal.view());
+  compute_eigenpairs(
+    handle, spectral_embedding_config, n_samples, laplacian, diagonal.view(), embedding);
+}
+
+void transform(raft::resources const& handle,
+               params spectral_embedding_config,
+               raft::device_coo_matrix_view<float, int, int, int> connectivity_graph,
+               raft::device_matrix_view<float, int, raft::col_major> embedding)
+{
+  const int n_samples = connectivity_graph.structure_view().get_n_rows();
+
+  auto sym_coo_row_ind = raft::make_device_vector<int>(handle, n_samples + 1);
+  auto diagonal        = raft::make_device_vector<float, int>(handle, n_samples);
+
+  auto csr_matrix_view =
+    coo_to_csr_matrix(handle, n_samples, sym_coo_row_ind.view(), connectivity_graph);
   auto laplacian =
     create_laplacian(handle, spectral_embedding_config, csr_matrix_view, diagonal.view());
   compute_eigenpairs(

From 99a6b753c9db848fecf0e69cdd0788c6cdd6f40a Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Thu, 31 Jul 2025 21:00:11 +0200
Subject: [PATCH 521/623] Add error handling for CPU-only bench (#1203)

Authors:
  - Micka (https://github.com/lowener)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1203
---
 cpp/bench/ann/src/common/util.hpp       | 17 +++++++++++++++--
 python/cuvs_bench/cuvs_bench/run/run.py |  5 ++++-
 2 files changed, 19 insertions(+), 3 deletions(-)

diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
index ebcae4f73b..b3bcc77950 100644
--- a/cpp/bench/ann/src/common/util.hpp
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -431,8 +431,21 @@ inline auto cuda_info()
   cudaRuntimeGetVersion(&runtime);
 
   cudaDeviceProp device_prop;
-  cudaGetDevice(&dev);
-  cudaGetDeviceProperties(&device_prop, dev);
+  auto err_code = cudaGetDevice(&dev);
+  if (err_code == cudaErrorNoDevice || err_code == cudaErrorInvalidDevice) {
+    throw std::runtime_error{"cuda_info: call to cudaGetDevice failed with code " +
+                             std::to_string(err_code) +
+                             ". If you are running on a CPU-only machine, please "
+                             "use the CPU package instead."};
+  } else if (err_code != cudaSuccess) {
+    throw std::runtime_error{"cuda_info: call to cudaGetDevice failed with code " +
+                             std::to_string(err_code)};
+  }
+  err_code = cudaGetDeviceProperties(&device_prop, dev);
+  if (err_code != cudaSuccess) {
+    throw std::runtime_error{"cuda_info: call to cudaGetDeviceProperties failed with code " +
+                             std::to_string(err_code)};
+  }
   props.emplace_back("gpu_name", std::string(device_prop.name));
   props.emplace_back("gpu_sm_count", std::to_string(device_prop.multiProcessorCount));
   props.emplace_back("gpu_sm_freq", std::to_string(device_prop.clockRate * 1e3));
diff --git a/python/cuvs_bench/cuvs_bench/run/run.py b/python/cuvs_bench/cuvs_bench/run/run.py
index bec7766793..8821286951 100644
--- a/python/cuvs_bench/cuvs_bench/run/run.py
+++ b/python/cuvs_bench/cuvs_bench/run/run.py
@@ -140,7 +140,10 @@ def gather_algorithm_configs(
     algos_conf_fs = [
         os.path.join(scripts_path, "../config", "algos", f)
         for f in algos_conf_fs
-        if ".json" not in f and "constraint" not in f and ".py" not in f
+        if ".json" not in f
+        and "constraint" not in f
+        and ".py" not in f
+        and "__pycache__" not in f
     ]
 
     if configuration:

From d29a06a31d31bcd393de7875ffbc9b94f6a0f708 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Thu, 31 Jul 2025 23:16:34 +0200
Subject: [PATCH 522/623] Fix single GPU sharded search merge (#1094)

https://github.com/rapidsai/cuvs/issues/904

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1094
---
 cpp/src/neighbors/mg/snmg.cuh | 28 +++++++++++++++++++++++++---
 1 file changed, 25 insertions(+), 3 deletions(-)

diff --git a/cpp/src/neighbors/mg/snmg.cuh b/cpp/src/neighbors/mg/snmg.cuh
index 16aec08b37..abff1f82b6 100644
--- a/cpp/src/neighbors/mg/snmg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -602,7 +602,7 @@ void search(const raft::resources& clique,
     int64_t n_batches = raft::ceildiv(n_rows, (int64_t)n_rows_per_batch);
     if (n_batches <= 1) n_rows_per_batch = n_rows;
 
-    if (merge_mode == MERGE_ON_ROOT_RANK) {
+    if (merge_mode == MERGE_ON_ROOT_RANK && index.num_ranks_ > 1) {
       RAFT_LOG_DEBUG("SHARDED SEARCH WITH MERGE_ON_ROOT_RANK MERGE MODE: %d*%drows",
                      n_batches,
                      n_rows_per_batch);
@@ -617,7 +617,7 @@ void search(const raft::resources& clique,
                                        n_cols,
                                        n_neighbors,
                                        n_batches);
-    } else if (merge_mode == TREE_MERGE) {
+    } else if (merge_mode == TREE_MERGE && index.num_ranks_ > 1) {
       RAFT_LOG_DEBUG(
         "SHARDED SEARCH WITH TREE_MERGE MERGE MODE %d*%drows", n_batches, n_rows_per_batch);
       sharded_search_with_tree_merge(clique,
@@ -631,6 +631,28 @@ void search(const raft::resources& clique,
                                      n_cols,
                                      n_neighbors,
                                      n_batches);
+    } else {
+      const int rank = 0;
+#pragma omp parallel for
+      for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+        int64_t offset                  = batch_idx * n_rows_per_batch;
+        int64_t query_offset            = offset * n_cols;
+        int64_t output_offset           = offset * n_neighbors;
+        int64_t n_rows_of_current_batch = std::min(n_rows_per_batch, n_rows - offset);
+
+        run_search_batch(clique,
+                         index,
+                         rank,
+                         search_params,
+                         queries,
+                         neighbors,
+                         distances,
+                         query_offset,
+                         output_offset,
+                         n_rows_of_current_batch,
+                         n_cols,
+                         n_neighbors);
+      }
     }
   }
 }

From 60e8af26cc72a5336f8d9c19dd95a25462913018 Mon Sep 17 00:00:00 2001
From: Vinay Deshpande <vinayd@nvidia.com>
Date: Thu, 31 Jul 2025 22:30:37 +0100
Subject: [PATCH 523/623] Remove unused templated instances (#1204)

The `fusedDistanceNNMinReduce` is not documented or exposed downstream, so removing it. This reduced libcuvs.so size by ~0.5 MB, when I compiled for single arch.

Authors:
  - Vinay Deshpande (https://github.com/vinaydes)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1204
---
 cpp/CMakeLists.txt                           |  1 -
 cpp/src/distance/detail/fused_distance_nn.cu | 53 --------------------
 2 files changed, 54 deletions(-)
 delete mode 100644 cpp/src/distance/detail/fused_distance_nn.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 7fb6141f9b..50ee1a0ce2 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -377,7 +377,6 @@ if(BUILD_SHARED_LIBS)
     src/distance/detail/pairwise_matrix/dispatch_rbf.cu
     src/distance/detail/pairwise_matrix/dispatch_l2_expanded_double_double_double_int64_t.cu
     src/distance/detail/pairwise_matrix/dispatch_l2_expanded_float_float_float_int64_t.cu
-    src/distance/detail/fused_distance_nn.cu
     src/distance/distance.cu
     src/distance/pairwise_distance.cu
     src/distance/sparse_distance.cu
diff --git a/cpp/src/distance/detail/fused_distance_nn.cu b/cpp/src/distance/detail/fused_distance_nn.cu
deleted file mode 100644
index 6d2e0f3cdf..0000000000
--- a/cpp/src/distance/detail/fused_distance_nn.cu
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "../fused_distance_nn.cuh"
-#include <raft/core/kvp.hpp>  // raft::KeyValuePair
-
-#include <cstdint>  // int64_t
-
-#define instantiate_cuvs_distance_fusedDistanceNNMinReduce(DataT, OutT, IdxT) \
-  template void cuvs::distance::fusedDistanceNNMinReduce<DataT, OutT, IdxT>(  \
-    OutT * min,                                                               \
-    const DataT* x,                                                           \
-    const DataT* y,                                                           \
-    const DataT* xn,                                                          \
-    const DataT* yn,                                                          \
-    IdxT m,                                                                   \
-    IdxT n,                                                                   \
-    IdxT k,                                                                   \
-    void* workspace,                                                          \
-    bool sqrt,                                                                \
-    bool initOutBuffer,                                                       \
-    bool isRowMajor,                                                          \
-    cuvs::distance::DistanceType metric,                                      \
-    float metric_arg,                                                         \
-    cudaStream_t stream)
-
-instantiate_cuvs_distance_fusedDistanceNNMinReduce(float, float, int);
-instantiate_cuvs_distance_fusedDistanceNNMinReduce(float, float, int64_t);
-
-// We can't have comma's in the macro expansion, so we use the COMMA macro:
-#define COMMA ,
-
-instantiate_cuvs_distance_fusedDistanceNNMinReduce(float, raft::KeyValuePair<int COMMA float>, int);
-instantiate_cuvs_distance_fusedDistanceNNMinReduce(float,
-                                                   raft::KeyValuePair<int64_t COMMA float>,
-                                                   int64_t);
-
-#undef COMMA
-
-#undef instantiate_cuvs_distance_fusedDistanceNNMinReduce

From 81e22d3fcaf7a1205135efbdf5be6bbb2abe03a2 Mon Sep 17 00:00:00 2001
From: Ishan Chattopadhyaya <ichattopadhyaya@gmail.com>
Date: Fri, 1 Aug 2025 09:30:50 +0530
Subject: [PATCH 524/623] [Java][Fix] Multithreaded querying fails without
 synchronization (#1082)

Fix CuVSResources usage for multithreaded queries

    * Added resources as a constructor argument for Builder classes in CagraIndex, HNSWIndex, TieredIndex and BruteForceIndex
    * Added a multithreaded CAGRA stability test (which fails with improper resources usage). This test uses CheckedCuVSResources.

Authors:
  - Ishan Chattopadhyaya (https://github.com/chatman)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1082
---
 .../java/com/nvidia/cuvs/BruteForceQuery.java |  36 +++-
 .../main/java/com/nvidia/cuvs/CagraQuery.java |  36 +++-
 .../com/nvidia/cuvs/CagraSearchParams.java    |  12 +-
 .../main/java/com/nvidia/cuvs/HnswQuery.java  |  38 +++-
 .../com/nvidia/cuvs/TieredIndexQuery.java     |  43 +++-
 .../cuvs/internal/BruteForceIndexImpl.java    |   2 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |   2 +-
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |   2 +-
 .../nvidia/cuvs/internal/TieredIndexImpl.java |   2 +-
 .../nvidia/cuvs/BruteForceAndSearchIT.java    |   8 +-
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |   2 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    |  18 +-
 .../cuvs/CagraMultiThreadStabilityIT.java     | 199 ++++++++++++++++++
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   4 +-
 .../com/nvidia/cuvs/HnswBuildAndSearchIT.java |   6 +-
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |   2 +-
 .../java/com/nvidia/cuvs/TieredIndexIT.java   |  18 +-
 17 files changed, 371 insertions(+), 59 deletions(-)
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
index 93a5218aa2..20e9b8e576 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceQuery.java
@@ -17,11 +17,16 @@
 
 import java.util.Arrays;
 import java.util.BitSet;
+import java.util.Objects;
 import java.util.function.LongToIntFunction;
 
 /**
  * BruteForceQuery holds the query vectors to be used while invoking search.
  *
+ * <p><strong>Thread Safety:</strong> Each BruteForceQuery instance should use its own
+ * CuVSResources object that is not shared with other threads. Sharing CuVSResources
+ * between threads can lead to memory allocation errors or JVM crashes.
+ *
  * @since 25.02
  */
 public class BruteForceQuery {
@@ -31,6 +36,7 @@ public class BruteForceQuery {
   private final BitSet[] prefilters;
   private int numDocs = -1;
   private final int topK;
+  private final CuVSResources resources;
 
   /**
    * Constructs an instance of {@link BruteForceQuery} using queryVectors,
@@ -43,18 +49,21 @@ public class BruteForceQuery {
    *                     index
    * @param numDocs      Maximum of bits in each prefilter, representing number of documents in this index.
    *                     Used only when prefilter(s) is/are passed.
+   * @param resources    CuVSResources instance to use for this query
    */
   public BruteForceQuery(
       float[][] queryVectors,
       LongToIntFunction mapping,
       int topK,
       BitSet[] prefilters,
-      int numDocs) {
+      int numDocs,
+      CuVSResources resources) {
     this.queryVectors = queryVectors;
     this.mapping = mapping;
     this.topK = topK;
     this.prefilters = prefilters;
     this.numDocs = numDocs;
+    this.resources = resources;
   }
 
   /**
@@ -100,6 +109,15 @@ public int getNumDocs() {
     return numDocs;
   }
 
+  /**
+   * Gets the CuVSResources instance for this query.
+   *
+   * @return the CuVSResources instance
+   */
+  public CuVSResources getResources() {
+    return resources;
+  }
+
   @Override
   public String toString() {
     return "BruteForceQuery [mapping="
@@ -123,6 +141,20 @@ public static class Builder {
     private int numDocs;
     private LongToIntFunction mapping = SearchResults.IDENTITY_MAPPING;
     private int topK = 2;
+    private final CuVSResources resources;
+
+    /**
+     * Constructor that requires CuVSResources.
+     *
+     * <p><strong>Important:</strong> The provided CuVSResources instance should not be
+     * shared with other threads. Each thread performing searches should create its own
+     * CuVSResources instance to avoid memory allocation conflicts and potential JVM crashes.
+     *
+     * @param resources the CuVSResources instance to use for this query (must not be shared between threads)
+     */
+    public Builder(CuVSResources resources) {
+      this.resources = Objects.requireNonNull(resources, "resources cannot be null");
+    }
 
     /**
      * Registers the query vectors to be passed in the search call.
@@ -176,7 +208,7 @@ public Builder withPrefilters(BitSet[] prefilters, int numDocs) {
      * @return an instance of {@link BruteForceQuery}
      */
     public BruteForceQuery build() {
-      return new BruteForceQuery(queryVectors, mapping, topK, prefilters, numDocs);
+      return new BruteForceQuery(queryVectors, mapping, topK, prefilters, numDocs, resources);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
index cb318cc2b5..f81ec3191c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -17,12 +17,17 @@
 
 import java.util.Arrays;
 import java.util.BitSet;
+import java.util.Objects;
 import java.util.function.LongToIntFunction;
 
 /**
  * CagraQuery holds the CagraSearchParams and the query vectors to be used while
  * invoking search.
  *
+ * <p><strong>Thread Safety:</strong> Each CagraQuery instance should use its own
+ * CuVSResources object that is not shared with other threads. Sharing CuVSResources
+ * between threads can lead to memory allocation errors or JVM crashes.
+ *
  * @since 25.02
  */
 public class CagraQuery {
@@ -33,6 +38,7 @@ public class CagraQuery {
   private final int topK;
   private final BitSet prefilter;
   private final int numDocs;
+  private final CuVSResources resources;
 
   /**
    * Constructs an instance of {@link CagraQuery} using cagraSearchParameters,
@@ -45,6 +51,7 @@ public class CagraQuery {
    * @param topK                  the top k results to return
    * @param prefilter             A single BitSet to use as filter while searching the CAGRA index
    * @param numDocs               Total number of dataset vectors; used to align the prefilter correctly
+   * @param resources             CuVSResources instance to use for this query
    */
   public CagraQuery(
       CagraSearchParams cagraSearchParameters,
@@ -52,7 +59,8 @@ public CagraQuery(
       LongToIntFunction mapping,
       int topK,
       BitSet prefilter,
-      int numDocs) {
+      int numDocs,
+      CuVSResources resources) {
     super();
     this.cagraSearchParameters = cagraSearchParameters;
     this.queryVectors = queryVectors;
@@ -60,6 +68,7 @@ public CagraQuery(
     this.topK = topK;
     this.prefilter = prefilter;
     this.numDocs = numDocs;
+    this.resources = resources;
   }
 
   /**
@@ -114,6 +123,15 @@ public int getNumDocs() {
     return numDocs;
   }
 
+  /**
+   * Gets the CuVSResources instance for this query.
+   *
+   * @return the CuVSResources instance
+   */
+  public CuVSResources getResources() {
+    return resources;
+  }
+
   @Override
   public String toString() {
     return "CuVSQuery [cagraSearchParameters="
@@ -138,11 +156,20 @@ public static class Builder {
     private int topK = 2;
     private BitSet prefilter;
     private int numDocs;
+    private final CuVSResources resources;
 
     /**
-     * Default constructor.
+     * Constructor that requires CuVSResources.
+     *
+     * <p><strong>Important:</strong> The provided CuVSResources instance should not be
+     * shared with other threads. Each thread performing searches should create its own
+     * CuVSResources instance to avoid memory allocation conflicts and potential JVM crashes.
+     *
+     * @param resources the CuVSResources instance to use for this query (must not be shared between threads)
      */
-    public Builder() {}
+    public Builder(CuVSResources resources) {
+      this.resources = Objects.requireNonNull(resources, "resources cannot be null");
+    }
 
     /**
      * Sets the instance of configured CagraSearchParams to be passed for search.
@@ -211,7 +238,8 @@ public Builder withPrefilter(BitSet prefilter, int numDocs) {
      * @return an instance of CuVSQuery
      */
     public CagraQuery build() {
-      return new CagraQuery(cagraSearchParams, queryVectors, mapping, topK, prefilter, numDocs);
+      return new CagraQuery(
+          cagraSearchParams, queryVectors, mapping, topK, prefilter, numDocs, resources);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
index 83211d8cf8..15073db468 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
@@ -98,7 +98,6 @@ private HashMapMode(int value) {
   /**
    * Constructs an instance of CagraSearchParams with passed search parameters.
    *
-   * @param resources          the resources instance to use
    * @param maxQueries         the maximum number of queries to search at the same
    *                           time (batch size)
    * @param iTopKSize          the number of intermediate search results retained
@@ -120,7 +119,6 @@ private HashMapMode(int value) {
    *                           selection
    */
   private CagraSearchParams(
-      CuVSResources resources,
       int maxQueries,
       int iTopKSize,
       int maxIterations,
@@ -303,7 +301,6 @@ public String toString() {
    */
   public static class Builder {
 
-    private CuVSResources resources;
     private int maxQueries;
     private int iTopKSize = 64;
     private int maxIterations;
@@ -319,13 +316,9 @@ public static class Builder {
     private HashMapMode hashMapMode;
 
     /**
-     * Constructs this Builder with an instance of Arena.
-     *
-     * @param resources the {@link CuVSResources} instance to use
+     * Default constructor.
      */
-    public Builder(CuVSResources resources) {
-      this.resources = resources;
-    }
+    public Builder() {}
 
     /**
      * Sets the maximum number of queries to search at the same time (batch size).
@@ -486,7 +479,6 @@ public Builder withRandXorMask(long randXORMask) {
      */
     public CagraSearchParams build() {
       return new CagraSearchParams(
-          resources,
           maxQueries,
           iTopKSize,
           maxIterations,
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
index 9da7fe8814..28fe4cb6f2 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswQuery.java
@@ -16,12 +16,17 @@
 package com.nvidia.cuvs;
 
 import java.util.Arrays;
+import java.util.Objects;
 import java.util.function.LongToIntFunction;
 
 /**
  * HnswQuery holds the query vectors to be used while invoking search on the
  * HNSW index.
  *
+ * <p><strong>Thread Safety:</strong> Each HnswQuery instance should use its own
+ * CuVSResources object that is not shared with other threads. Sharing CuVSResources
+ * between threads can lead to memory allocation errors or JVM crashes.
+ *
  * @since 25.02
  */
 public class HnswQuery {
@@ -30,6 +35,7 @@ public class HnswQuery {
   private final LongToIntFunction mapping;
   private final float[][] queryVectors;
   private final int topK;
+  private final CuVSResources resources;
 
   /**
    * Constructs an instance of {@link HnswQuery} using queryVectors, mapping, and
@@ -39,16 +45,19 @@ public class HnswQuery {
    * @param queryVectors     2D float query vector array
    * @param mapping          a function mapping ordinals (neighbor IDs) to custom user IDs
    * @param topK             the top k results to return
+   * @param resources        CuVSResources instance to use for this query
    */
   private HnswQuery(
       HnswSearchParams hnswSearchParams,
       float[][] queryVectors,
       LongToIntFunction mapping,
-      int topK) {
+      int topK,
+      CuVSResources resources) {
     this.hnswSearchParams = hnswSearchParams;
     this.queryVectors = queryVectors;
     this.mapping = mapping;
     this.topK = topK;
+    this.resources = resources;
   }
 
   /**
@@ -85,6 +94,15 @@ public int getTopK() {
     return topK;
   }
 
+  /**
+   * Gets the CuVSResources instance for this query.
+   *
+   * @return the CuVSResources instance
+   */
+  public CuVSResources getResources() {
+    return resources;
+  }
+
   @Override
   public String toString() {
     return "HnswQuery [mapping="
@@ -97,7 +115,7 @@ public String toString() {
   }
 
   /**
-   * Builder helps configure and create an instance of BruteForceQuery.
+   * Builder helps configure and create an instance of HnswQuery.
    */
   public static class Builder {
 
@@ -105,6 +123,20 @@ public static class Builder {
     private float[][] queryVectors;
     private LongToIntFunction mapping = SearchResults.IDENTITY_MAPPING;
     private int topK = 2;
+    private final CuVSResources resources;
+
+    /**
+     * Constructor that requires CuVSResources.
+     *
+     * <p><strong>Important:</strong> The provided CuVSResources instance should not be
+     * shared with other threads. Each thread performing searches should create its own
+     * CuVSResources instance to avoid memory allocation conflicts and potential JVM crashes.
+     *
+     * @param resources the CuVSResources instance to use for this query (must not be shared between threads)
+     */
+    public Builder(CuVSResources resources) {
+      this.resources = Objects.requireNonNull(resources, "resources cannot be null");
+    }
 
     /**
      * Sets the instance of configured HnswSearchParams to be passed for search.
@@ -157,7 +189,7 @@ public Builder withTopK(int topK) {
      * @return an instance of {@link HnswQuery}
      */
     public HnswQuery build() {
-      return new HnswQuery(hnswSearchParams, queryVectors, mapping, topK);
+      return new HnswQuery(hnswSearchParams, queryVectors, mapping, topK, resources);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java
index 03bfae070e..7aea2d7139 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndexQuery.java
@@ -19,11 +19,15 @@
 import java.util.Arrays;
 import java.util.BitSet;
 import java.util.List;
+import java.util.Objects;
 
 /**
  * TieredIndexQuery holds the search parameters and query vectors to be used
- * while
- * invoking search. Currently only supports CAGRA index type.
+ * while invoking search. Currently only supports CAGRA index type.
+ *
+ * <p><strong>Thread Safety:</strong> Each TieredIndexQuery instance should use its own
+ * CuVSResources object that is not shared with other threads. Sharing CuVSResources
+ * between threads can lead to memory allocation errors or JVM crashes.
  *
  * @since 25.02
  */
@@ -35,6 +39,7 @@ public class TieredIndexQuery {
   private int topK;
   private BitSet prefilter;
   private long numDocs;
+  private final CuVSResources resources;
 
   private TieredIndexQuery(
       TieredIndexType indexType,
@@ -43,7 +48,8 @@ private TieredIndexQuery(
       float[][] queryVectors,
       int topK,
       BitSet prefilter,
-      long numDocs) {
+      long numDocs,
+      CuVSResources resources) {
     super();
     this.indexType = indexType;
     this.cagraSearchParameters = cagraSearchParameters;
@@ -52,6 +58,7 @@ private TieredIndexQuery(
     this.topK = topK;
     this.prefilter = prefilter;
     this.numDocs = numDocs;
+    this.resources = resources;
   }
 
   /**
@@ -117,6 +124,15 @@ public long getNumDocs() {
     return numDocs;
   }
 
+  /**
+   * Gets the CuVSResources instance for this query.
+   *
+   * @return the CuVSResources instance
+   */
+  public CuVSResources getResources() {
+    return resources;
+  }
+
   @Override
   public String toString() {
     return "TieredIndexQuery [indexType="
@@ -135,10 +151,11 @@ public String toString() {
   /**
    * Creates a new Builder instance.
    *
+   * @param resources the CuVSResources instance to use for this query
    * @return a new Builder instance
    */
-  public static Builder newBuilder() {
-    return new Builder();
+  public static Builder newBuilder(CuVSResources resources) {
+    return new Builder(resources);
   }
 
   /**
@@ -152,6 +169,20 @@ public static class Builder {
     private int topK = 2;
     private BitSet prefilter;
     private long numDocs;
+    private final CuVSResources resources;
+
+    /**
+     * Constructor that requires CuVSResources.
+     *
+     * <p><strong>Important:</strong> The provided CuVSResources instance should not be
+     * shared with other threads. Each thread performing searches should create its own
+     * CuVSResources instance to avoid memory allocation conflicts and potential JVM crashes.
+     *
+     * @param resources the CuVSResources instance to use for this query (must not be shared between threads)
+     */
+    public Builder(CuVSResources resources) {
+      this.resources = Objects.requireNonNull(resources, "resources cannot be null");
+    }
 
     /**
      * Sets the index type for this query.
@@ -231,7 +262,7 @@ public Builder withPrefilter(BitSet prefilter, int numDocs) {
      */
     public TieredIndexQuery build() {
       return new TieredIndexQuery(
-          indexType, cagraSearchParams, mapping, queryVectors, topK, prefilter, numDocs);
+          indexType, cagraSearchParams, mapping, queryVectors, topK, prefilter, numDocs, resources);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index f32e311f77..4dfef90261 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -220,7 +220,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       MemorySegment querySeg = buildMemorySegment(localArena, cuvsQuery.getQueryVectors());
 
       int topk = cuvsQuery.getTopK();
-      try (var resourcesAccessor = resources.access()) {
+      try (var resourcesAccessor = cuvsQuery.getResources().access()) {
         long cuvsResources = resourcesAccessor.handle();
 
         long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 4fc373e8ca..704bc72b34 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -239,7 +239,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
       long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
       long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
 
-      try (var resourcesAccessor = resources.access()) {
+      try (var resourcesAccessor = query.getResources().access()) {
         var cuvsRes = resourcesAccessor.handle();
 
         MemorySegment queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index 1c97422d15..ed25b5cc3f 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -111,7 +111,7 @@ public SearchResults search(HnswQuery query) throws Throwable {
       MemorySegment distancesTensor =
           prepareTensor(localArena, distancesMemorySegment, distancesShape, 2, 32, 1, 1);
 
-      try (var resourcesAccessor = resources.access()) {
+      try (var resourcesAccessor = query.getResources().access()) {
         var cuvsRes = resourcesAccessor.handle();
         int returnValue = cuvsStreamSync(cuvsRes);
         checkCuVSError(returnValue, "cuvsStreamSync");
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index fd94287787..ef9d33cd43 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -196,7 +196,7 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
       // Get host query data
       MemorySegment hostQueriesSeg = Util.buildMemorySegment(localArena, query.getQueryVectors());
 
-      try (var resourceAccess = resources.access()) {
+      try (var resourceAccess = query.getResources().access()) {
         long cuvsRes = resourceAccess.handle();
 
         long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index 92f215165b..ddd1be0b5d 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -106,7 +106,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
 
       try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
-            new BruteForceQuery.Builder()
+            new BruteForceQuery.Builder(resources)
                 .withTopK(3)
                 .withQueryVectors(queries)
                 .withMapping(SearchResults.IDENTITY_MAPPING)
@@ -134,7 +134,7 @@ public void testIndexingAndSearchingWithFiltering() throws Throwable {
     for (int j = 0; j < 10; j++) {
       try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
-            new BruteForceQuery.Builder()
+            new BruteForceQuery.Builder(resources)
                 .withTopK(3)
                 .withQueryVectors(queries)
                 .withPrefilters(
@@ -160,7 +160,7 @@ public void testIndexingAndSearchingWithFunctionMapping() throws Throwable {
     for (int j = 0; j < 10; j++) {
       try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
-            new BruteForceQuery.Builder()
+            new BruteForceQuery.Builder(resources)
                 .withTopK(3)
                 .withQueryVectors(queries)
                 .withMapping(rotate)
@@ -185,7 +185,7 @@ public void testIndexingAndSearchingWithListMapping() throws Throwable {
     for (int j = 0; j < 10; j++) {
       try (CuVSResources resources = CheckedCuVSResources.create()) {
         BruteForceQuery cuvsQuery =
-            new BruteForceQuery.Builder()
+            new BruteForceQuery.Builder(resources)
                 .withTopK(3)
                 .withQueryVectors(queries)
                 .withMapping(rotate)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index fb338106f2..4a446c249c 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -109,7 +109,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     try (CuVSResources resources = CheckedCuVSResources.create()) {
 
       BruteForceQuery query =
-          new BruteForceQuery.Builder()
+          new BruteForceQuery.Builder(resources)
               .withTopK(topK)
               .withQueryVectors(queries)
               .withPrefilters(prefilters, vectors.length)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 10ea10bcf5..39e8226250 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -369,9 +369,9 @@ public void testPrefilteringReducesResults() throws Throwable {
               .build();
 
       // No prefilter (all points allowed)
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
       CagraQuery fullQuery =
-          new CagraQuery.Builder()
+          new CagraQuery.Builder(resources)
               .withTopK(2)
               .withSearchParams(searchParams)
               .withQueryVectors(queries)
@@ -387,7 +387,7 @@ public void testPrefilteringReducesResults() throws Throwable {
       prefilter.set(2);
 
       CagraQuery filteredQuery =
-          new CagraQuery.Builder()
+          new CagraQuery.Builder(resources)
               .withTopK(2)
               .withSearchParams(searchParams)
               .withQueryVectors(queries)
@@ -446,11 +446,11 @@ private void queryAndCompare(
       CuVSResources resources)
       throws Throwable {
     // Configure search parameters
-    CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+    CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
     // Create a query object with the query vectors
     CagraQuery cuvsQuery =
-        new CagraQuery.Builder()
+        new CagraQuery.Builder(resources)
             .withTopK(3)
             .withSearchParams(searchParams)
             .withQueryVectors(queries)
@@ -572,10 +572,10 @@ public void testMergingIndexes() throws Throwable {
       CagraIndex mergedIndex = CagraIndex.merge(new CagraIndex[] {index1, index2});
       log.info("Merge completed successfully");
 
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
       CagraQuery query =
-          new CagraQuery.Builder()
+          new CagraQuery.Builder(resources)
               .withTopK(3)
               .withSearchParams(searchParams)
               .withQueryVectors(queries)
@@ -678,10 +678,10 @@ public void testMergeStrategies() throws Throwable {
           CagraIndex.merge(new CagraIndex[] {index1, index2}, physicalMergeParams);
       log.info("Physical merge completed successfully");
 
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
       CagraQuery query =
-          new CagraQuery.Builder()
+          new CagraQuery.Builder(resources)
               .withTopK(3)
               .withSearchParams(searchParams)
               .withQueryVectors(queries)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
new file mode 100644
index 0000000000..50e8f12a5a
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
@@ -0,0 +1,199 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
+import static org.junit.Assert.fail;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import java.lang.invoke.MethodHandles;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+import java.util.concurrent.CountDownLatch;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.TimeUnit;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.concurrent.atomic.AtomicReference;
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+@RunWith(RandomizedRunner.class)
+public class CagraMultiThreadStabilityIT extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+    initializeRandom();
+    log.info("Multi-threaded stability test initialized");
+  }
+
+  @Test
+  public void testQueryingUsingMultipleThreads() throws Throwable {
+    final int dataSize = 10000;
+    final int dimensions = 256;
+    final int numThreads = 16; // High thread count to increase contention
+    final int queriesPerThread = 500;
+    final int queryBatchSize = 1; // Small batch size to increase frequency of calls
+    final int topK = 10;
+
+    log.info("  Dataset: {}x{}", dataSize, dimensions);
+    log.info("  Threads: {}, Queries per thread: {}", numThreads, queriesPerThread);
+
+    float[][] dataset = generateRandomDataset(dataSize, dimensions);
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      log.info("Creating CAGRA index for MultiThreaded stability test...");
+
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(64)
+              .withIntermediateGraphDegree(128)
+              .withNumWriterThreads(8)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(dataset)
+              .withIndexParams(indexParams)
+              .build();
+
+      log.info("CAGRA index created, starting high-contention multi-threaded search...");
+
+      // Create high contention scenario that would fail without using separate resources in every thread
+      ExecutorService executor = Executors.newFixedThreadPool(numThreads);
+      List<Future<?>> futures = new ArrayList<>();
+      CountDownLatch startLatch = new CountDownLatch(1);
+      AtomicInteger successfulQueries = new AtomicInteger(0);
+      AtomicReference<Throwable> firstError = new AtomicReference<>();
+
+      for (int threadId = 0; threadId < numThreads; threadId++) {
+        final int finalThreadId = threadId;
+
+        Future<?> future =
+            executor.submit(
+                () -> {
+                  try {
+                    // Wait for all threads to start simultaneously
+                    startLatch.await();
+
+                    for (int queryId = 0; queryId < queriesPerThread; queryId++) {
+                      float[][] queries = generateRandomDataset(queryBatchSize, dimensions);
+
+                      try (CuVSResources threadResources = CheckedCuVSResources.create()) {
+                        CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+                        CagraQuery query =
+                            new CagraQuery.Builder(threadResources)
+                                .withTopK(topK)
+                                .withSearchParams(searchParams)
+                                .withQueryVectors(queries)
+                                .build();
+
+                        // This call should now work with per-thread resources
+                        SearchResults results = index.search(query);
+                        assertNotNull("Query should return results", results);
+                        assertTrue(
+                            "Query should return some results", !results.getResults().isEmpty());
+
+                        successfulQueries.incrementAndGet();
+                      }
+
+                      // No Thread.yield() - maximize contention
+                    }
+
+                    log.info("Thread {} completed successfully", finalThreadId);
+
+                  } catch (Throwable t) {
+                    log.error("Thread {} failed: {}", finalThreadId, t.getMessage(), t);
+                    firstError.compareAndSet(null, t);
+                    throw new RuntimeException("Thread failed", t);
+                  }
+                });
+
+        futures.add(future);
+      }
+
+      // Start all threads simultaneously to maximize contention
+      log.info("Starting all {} threads simultaneously...", numThreads);
+      startLatch.countDown();
+
+      // Wait for all threads to complete
+      boolean allCompleted = true;
+      for (Future<?> future : futures) {
+        try {
+          future.get(120, TimeUnit.SECONDS); // Longer timeout for stress test
+        } catch (Exception e) {
+          allCompleted = false;
+          log.error("Thread failed: {}", e.getMessage(), e);
+          if (firstError.get() == null) {
+            firstError.set(e);
+          }
+        }
+      }
+
+      executor.shutdown();
+      if (!executor.awaitTermination(10, TimeUnit.SECONDS)) {
+        executor.shutdownNow();
+      }
+
+      // Verify results
+      int expectedTotalQueries = numThreads * queriesPerThread;
+      int actualSuccessfulQueries = successfulQueries.get();
+
+      log.info("  Successful queries: {} / {}", actualSuccessfulQueries, expectedTotalQueries);
+
+      if (firstError.get() != null) {
+        fail(
+            "MultiThreaded stablity test failed:"
+                + " "
+                + firstError.get().getMessage());
+      }
+
+      assertTrue("All threads should complete successfully", allCompleted);
+      assertTrue(
+          "All queries should complete successfully",
+          actualSuccessfulQueries == expectedTotalQueries);
+
+      index.destroyIndex();
+    }
+  }
+
+  private float[][] generateRandomDataset(int size, int dimensions) {
+    Random random = new Random(42 + System.nanoTime());
+    float[][] data = new float[size][dimensions];
+
+    for (int i = 0; i < size; i++) {
+      for (int j = 0; j < dimensions; j++) {
+        data[i][j] = random.nextFloat() * 2.0f - 1.0f;
+      }
+    }
+
+    return data;
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index af4965159b..02b5a6526a 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -143,10 +143,10 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       try {
         // Execute search and retrieve results
         CagraQuery.Builder queryBuilder =
-            new CagraQuery.Builder()
+            new CagraQuery.Builder(resources)
                 .withQueryVectors(queries)
                 .withTopK(topK)
-                .withSearchParams(new CagraSearchParams.Builder(resources).build());
+                .withSearchParams(new CagraSearchParams.Builder().build());
 
         if (sharedPrefilter != null) {
           queryBuilder.withPrefilter(sharedPrefilter, datasetSize);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index dd0926d200..ab041ec24b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -121,7 +121,7 @@ public void testIndexingAndSearchingFlow() throws Throwable {
     for (int j = 0; j < 10; j++) {
       try (CuVSResources resources = CheckedCuVSResources.create()) {
         HnswQuery hnswQuery =
-            new HnswQuery.Builder()
+            new HnswQuery.Builder(resources)
                 .withMapping(SearchResults.IDENTITY_MAPPING)
                 .withQueryVectors(queries)
                 .withSearchParams(new HnswSearchParams.Builder().build())
@@ -146,7 +146,7 @@ public void testIndexingAndSearchingWithFunctionMapping() throws Throwable {
     for (int j = 0; j < 10; j++) {
       try (CuVSResources resources = CheckedCuVSResources.create()) {
         HnswQuery hnswQuery =
-            new HnswQuery.Builder()
+            new HnswQuery.Builder(resources)
                 .withQueryVectors(queries)
                 .withMapping(rotate)
                 .withSearchParams(new HnswSearchParams.Builder().build())
@@ -172,7 +172,7 @@ public void testIndexingAndSearchingWithListMapping() throws Throwable {
     for (int j = 0; j < 10; j++) {
       try (CuVSResources resources = CheckedCuVSResources.create()) {
         HnswQuery hnswQuery =
-            new HnswQuery.Builder()
+            new HnswQuery.Builder(resources)
                 .withQueryVectors(queries)
                 .withMapping(rotate)
                 .withSearchParams(new HnswSearchParams.Builder().build())
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index ded5bda54f..3000c125a3 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -154,7 +154,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
               new HnswSearchParams.Builder().withNumThreads(32).build();
 
           HnswQuery hnswQuery =
-              new HnswQuery.Builder()
+              new HnswQuery.Builder(resources)
                   .withQueryVectors(queries)
                   .withSearchParams(hnswSearchParams)
                   .withTopK(topK)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
index f8cb70b563..89465be53d 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/TieredIndexIT.java
@@ -89,10 +89,10 @@ public void testBasicOperations() throws Throwable {
       log.debug("Initial TieredIndex built successfully");
 
       CagraSearchParams searchParams =
-          new CagraSearchParams.Builder(resources).withMaxIterations(20).build();
+          new CagraSearchParams.Builder().withMaxIterations(20).build();
 
       TieredIndexQuery query =
-          new TieredIndexQuery.Builder()
+          TieredIndexQuery.newBuilder(resources)
               .withTopK(3)
               .withQueryVectors(queries)
               .withSearchParams(searchParams)
@@ -159,11 +159,10 @@ public void testDifferentKValues() throws Throwable {
       log.debug("TieredIndex built for K-value testing");
 
       TieredIndexQuery query1 =
-          new TieredIndexQuery.Builder()
+          TieredIndexQuery.newBuilder(resources)
               .withTopK(1)
               .withQueryVectors(queries)
-              .withSearchParams(
-                  new CagraSearchParams.Builder(resources).withMaxIterations(20).build())
+              .withSearchParams(new CagraSearchParams.Builder().withMaxIterations(20).build())
               .build();
 
       log.debug("Searching with K=1");
@@ -176,11 +175,10 @@ public void testDifferentKValues() throws Throwable {
       assertEquals("Distance to closest vector should be ~0.02", 0.02f, firstResult.get(0), 0.01f);
 
       TieredIndexQuery query3 =
-          new TieredIndexQuery.Builder()
+          TieredIndexQuery.newBuilder(resources)
               .withTopK(3)
               .withQueryVectors(queries)
-              .withSearchParams(
-                  new CagraSearchParams.Builder(resources).withMaxIterations(20).build())
+              .withSearchParams(new CagraSearchParams.Builder().withMaxIterations(20).build())
               .build();
 
       log.debug("Searching with K=3");
@@ -221,7 +219,7 @@ public void testPrefilter() throws Throwable {
               .build();
       log.debug("TieredIndex built for prefilter testing");
 
-      CagraSearchParams searchParams = new CagraSearchParams.Builder(resources).build();
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
       BitSet prefilter = new BitSet(4);
       prefilter.set(1, true);
@@ -229,7 +227,7 @@ public void testPrefilter() throws Throwable {
       log.debug("Created prefilter allowing indices 1 and 2, excluding 0 and 3");
 
       TieredIndexQuery queryWithFilter =
-          new TieredIndexQuery.Builder()
+          TieredIndexQuery.newBuilder(resources)
               .withTopK(3)
               .withQueryVectors(queryVectors)
               .withSearchParams(searchParams)

From 53d483966763bc2e2cf4a1a674ccdb30d0414cf8 Mon Sep 17 00:00:00 2001
From: Ben Karsin <bkarsin@nvidia.com>
Date: Mon, 4 Aug 2025 08:56:25 -1000
Subject: [PATCH 525/623] Improve Vamana index build performance and recall
 (#1032)

Series of optimizations for the Vamana index build implementation including:
- Reduce shared memory usage by the main kernels: GreedySearch and RobustPrune
- Replace cub::blockSort with manual merge that reduces shared memory and improves perf
- Remove other cub Sort calls to separate kernel to improve resource utilization
- Other changes to resource allocation and management to improve performance

Other changes that significantly improve recall (closing most of the gap with CPU build and giving the option to further improve recall with parameters):
- Re-worked the RobustPrune kernel to use a multi-pass "occlusion" approach. This closes 60-80% of the recall gap seen between previous GPU Vamana and CPU DiskANN.
- Changed the "vamana_iters" parameter to a float to allow fractional iterations. This lets us precisely control recall vs. performance to close the gap (or surpass CPU recall).

Further optimizations are underway, but this first round address many needs of users to both improve performance and recall.

Authors:
  - Ben Karsin (https://github.com/bkarsin)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - James Xia (https://github.com/jamxia155)
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/1032
---
 cpp/include/cuvs/neighbors/vamana.hpp         |   3 +-
 .../neighbors/detail/vamana/greedy_search.cuh |  35 +-
 cpp/src/neighbors/detail/vamana/macros.cuh    |  58 +-
 .../neighbors/detail/vamana/robust_prune.cuh  | 251 ++++----
 .../neighbors/detail/vamana/vamana_build.cuh  | 556 ++++++++++++------
 .../detail/vamana/vamana_structs.cuh          |  51 +-
 cpp/tests/neighbors/ann_vamana.cuh            |   7 +
 examples/cpp/src/vamana_example.cu            |  63 +-
 8 files changed, 618 insertions(+), 406 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/vamana.hpp b/cpp/include/cuvs/neighbors/vamana.hpp
index 05bcadf796..733f9c4e71 100644
--- a/cpp/include/cuvs/neighbors/vamana.hpp
+++ b/cpp/include/cuvs/neighbors/vamana.hpp
@@ -71,7 +71,7 @@ struct index_params : cuvs::neighbors::index_params {
    * literature **/
   uint32_t visited_size = 64;
   /** Number of Vamana vector insertion iterations (each iteration inserts all vectors). */
-  uint32_t vamana_iters = 1;
+  float vamana_iters = 1.0;
   /** Alpha for pruning parameter */
   float alpha = 1.2;
   /** Maximum fraction of dataset inserted per batch.              *
@@ -83,6 +83,7 @@ struct index_params : cuvs::neighbors::index_params {
   uint32_t queue_size = 127;
   /** Max batchsize of reverse edge processing (reduces memory footprint) */
   uint32_t reverse_batchsize = 1000000;
+
   /** Codebooks and related parameters */
   std::optional<codebook_params<float>> codebooks = std::nullopt;
 };
diff --git a/cpp/src/neighbors/detail/vamana/greedy_search.cuh b/cpp/src/neighbors/detail/vamana/greedy_search.cuh
index 4d94bbaa77..9e884fc3ea 100644
--- a/cpp/src/neighbors/detail/vamana/greedy_search.cuh
+++ b/cpp/src/neighbors/detail/vamana/greedy_search.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,10 +16,11 @@
 
 #pragma once
 
+#include <cub/cub.cuh>
+
 #include "macros.cuh"
 #include "priority_queue.cuh"
 #include "vamana_structs.cuh"
-#include <cub/cub.cuh>
 #include <cuvs/neighbors/vamana.hpp>
 
 #include <cuvs/distance/distance.hpp>
@@ -52,7 +53,7 @@ __forceinline__ __device__ void sort_visited(
   }
 
   __syncthreads();
-  BlockSortT(*sort_mem).Sort(tmp, CmpDist());
+  BlockSortT(*sort_mem).Sort(tmp, CmpDist<IdxT, accT>());
   __syncthreads();
 
   for (int i = 0; i < ELTS; i++) {
@@ -64,6 +65,23 @@ __forceinline__ __device__ void sort_visited(
 
 namespace {
 
+template <typename T, typename accT, typename IdxT = uint32_t>
+__global__ void SortPairsKernel(void* query_list_ptr, int num_queries, int topk)
+{
+  union ShmemLayout {
+    typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, 1>::TempStorage sort_mem;
+  };
+  extern __shared__ __align__(alignof(ShmemLayout)) char smem[];
+
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  for (int i = blockIdx.x; i < num_queries; i += gridDim.x) {
+    __syncthreads();
+    SEARCH_SELECT_SORT(topk);
+  }
+}
+
 /********************************************************************************************
   GPU kernel to perform a batched GreedySearch on a graph. Since this is used for
   Vamana construction, the entire visited list is kept and stored within the query_list.
@@ -87,7 +105,7 @@ __global__ void GreedySearchKernel(
   int topk,
   cuvs::distance::DistanceType metric,
   int max_queue_size,
-  int sort_smem_size)
+  Node<accT>* topk_pq_mem)
 {
   int n      = dataset.extent(0);
   int dim    = dataset.extent(1);
@@ -105,9 +123,7 @@ __global__ void GreedySearchKernel(
 
   union ShmemLayout {
     // All blocksort sizes have same alignment (16)
-    typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, 1>::TempStorage sort_mem;
     T coords;
-    Node<accT> topk_pq;
     int neighborhood_arr;
     DistPair<IdxT, accT> candidate_queue;
   };
@@ -117,13 +133,12 @@ __global__ void GreedySearchKernel(
   // Dynamic shared memory used for blocksort, temp vector storage, and neighborhood list
   extern __shared__ __align__(alignof(ShmemLayout)) char smem[];
 
-  size_t smem_offset = sort_smem_size;  // temp sorting memory takes first chunk
+  size_t smem_offset = 0;
 
   T* s_coords = reinterpret_cast<T*>(&smem[smem_offset]);
   smem_offset += (dim + align_padding) * sizeof(T);
 
-  Node<accT>* topk_pq = reinterpret_cast<Node<accT>*>(&smem[smem_offset]);
-  smem_offset += topk * sizeof(Node<accT>);
+  Node<accT>* topk_pq = &topk_pq_mem[blockIdx.x * topk];
 
   int* neighbor_array = reinterpret_cast<int*>(&smem[smem_offset]);
   smem_offset += degree * sizeof(int);
@@ -272,8 +287,6 @@ __global__ void GreedySearchKernel(
 
     __syncthreads();
     if (self_found) query_list[i].size--;
-
-    SEARCH_SELECT_SORT(topk);
   }
 
   return;
diff --git a/cpp/src/neighbors/detail/vamana/macros.cuh b/cpp/src/neighbors/detail/vamana/macros.cuh
index c290413a20..4fddfe4522 100644
--- a/cpp/src/neighbors/detail/vamana/macros.cuh
+++ b/cpp/src/neighbors/detail/vamana/macros.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,49 +19,25 @@
 namespace cuvs::neighbors::vamana::detail {
 
 /* Macros to compute the shared memory requirements for CUB primitives used by search and prune */
-#define COMPUTE_SMEM_SIZES(degree, visited_size, DEG, CANDS)                                     \
-  if (degree == DEG && visited_size <= CANDS && visited_size > CANDS / 2) {                      \
-    search_smem_sort_size = static_cast<int>(                                                    \
-      sizeof(typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, CANDS / 32>::TempStorage));  \
-                                                                                                 \
-    prune_smem_sort_size = static_cast<int>(sizeof(                                              \
-      typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, (CANDS + DEG) / 32>::TempStorage)); \
+#define COMPUTE_SMEM_SIZE(degree, visited_size, DEG, CANDS)                                     \
+  if (degree == DEG && visited_size <= CANDS && visited_size > CANDS / 2) {                     \
+    sort_smem_size = static_cast<int>(                                                          \
+      sizeof(typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, CANDS / 32>::TempStorage)); \
   }
 
 // Current supported sizes for degree and visited_size. Note that visited_size must be > degree
-#define SELECT_SMEM_SIZES(degree, visited_size)       \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 64);   \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 128);  \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 256);  \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 32, 512);  \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 64, 128);  \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 64, 256);  \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 64, 512);  \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 128, 256); \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 128, 512); \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 256, 512); \
-  COMPUTE_SMEM_SIZES(degree, visited_size, 256, 1024);
-
-/* Macros to call the CUB BlockSort primitives for supported sizes for ROBUST_PRUNE*/
-#define PRUNE_CALL_SORT(degree, visited_list, DEG, CANDS)                                  \
-  if (degree == DEG && visited_list <= CANDS && visited_list > CANDS / 2) {                \
-    using BlockSortT = cub::BlockMergeSort<DistPair<IdxT, accT>, 32, (DEG + CANDS) / 32>;  \
-    auto& sort_mem   = reinterpret_cast<typename BlockSortT::TempStorage&>(smem);          \
-    sort_edges_and_cands<accT, IdxT, DEG, CANDS>(new_nbh_list, &query_list[i], &sort_mem); \
-  }
-
-#define PRUNE_SELECT_SORT(degree, visited_list)    \
-  PRUNE_CALL_SORT(degree, visited_size, 32, 64);   \
-  PRUNE_CALL_SORT(degree, visited_size, 32, 128);  \
-  PRUNE_CALL_SORT(degree, visited_size, 32, 256);  \
-  PRUNE_CALL_SORT(degree, visited_size, 32, 512);  \
-  PRUNE_CALL_SORT(degree, visited_size, 64, 128);  \
-  PRUNE_CALL_SORT(degree, visited_size, 64, 256);  \
-  PRUNE_CALL_SORT(degree, visited_size, 64, 512);  \
-  PRUNE_CALL_SORT(degree, visited_size, 128, 256); \
-  PRUNE_CALL_SORT(degree, visited_size, 128, 512); \
-  PRUNE_CALL_SORT(degree, visited_size, 256, 512); \
-  PRUNE_CALL_SORT(degree, visited_size, 256, 1024);
+#define SELECT_SORT_SMEM_SIZE(degree, visited_size)  \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 32, 64);   \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 32, 128);  \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 32, 256);  \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 32, 512);  \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 64, 128);  \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 64, 256);  \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 64, 512);  \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 128, 256); \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 128, 512); \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 256, 512); \
+  COMPUTE_SMEM_SIZE(degree, visited_size, 256, 1024);
 
 /* Macros to call the CUB BlockSort primitives for supported sizes for GREEDY SEARCH */
 #define SEARCH_CALL_SORT(topk, CANDS)                                             \
diff --git a/cpp/src/neighbors/detail/vamana/robust_prune.cuh b/cpp/src/neighbors/detail/vamana/robust_prune.cuh
index 182d20c887..0a76093444 100644
--- a/cpp/src/neighbors/detail/vamana/robust_prune.cuh
+++ b/cpp/src/neighbors/detail/vamana/robust_prune.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -49,62 +49,6 @@ __forceinline__ __device__ void load_to_registers(DistPair<IdxT, accT>* tmp,
   }
 }
 
-/* Combines edge and candidate lists, removes duplicates, and sorts by distance
- * Uses CUB primitives, so needs to be templated. Called with Macros for supported sizes above */
-template <typename accT, typename IdxT, int DEG, int CANDS>
-__forceinline__ __device__ void sort_edges_and_cands(
-  DistPair<IdxT, accT>* new_nbh_list,
-  QueryCandidates<IdxT, accT>* query,
-  typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, (DEG + CANDS) / 32>::TempStorage* sort_mem)
-{
-  const int ELTS   = (DEG + CANDS) / 32;
-  using BlockSortT = cub::BlockMergeSort<DistPair<IdxT, accT>, 32, ELTS>;
-  DistPair<IdxT, accT> tmp[ELTS];
-
-  load_to_registers<accT, IdxT, DEG, CANDS>(tmp, query, new_nbh_list);
-
-  __syncthreads();
-  BlockSortT(*sort_mem).Sort(tmp, CmpDist());
-  __syncthreads();
-
-  // Mark duplicates and re-sort
-  // Copy last element over and shuffle to check for duplicate between threads
-  new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)] = tmp[ELTS - 1];
-  if (tmp[ELTS - 1].idx == tmp[ELTS - 2].idx) {
-    new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].idx  = raft::upper_bound<IdxT>();
-    new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].dist = raft::upper_bound<accT>();
-  }
-  __shfl_up_sync(0xffffffff, tmp[ELTS - 1].idx, 1);
-  __syncthreads();
-
-  for (int i = ELTS - 2; i > 0; i--) {
-    if (tmp[i].idx == tmp[i - 1].idx) {
-      tmp[i].idx  = raft::upper_bound<IdxT>();
-      tmp[i].dist = raft::upper_bound<accT>();
-    }
-  }
-  if (threadIdx.x == 0) {
-    if (tmp[0].idx == tmp[ELTS - 1].idx) {
-      tmp[0].idx  = raft::upper_bound<IdxT>();
-      tmp[0].dist = raft::upper_bound<accT>();
-    }
-  }
-
-  tmp[ELTS - 1].idx =
-    new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].idx;  // copy back to tmp for re-shuffling
-  tmp[ELTS - 1].dist = new_nbh_list[ELTS * threadIdx.x + (ELTS - 1)].dist;
-
-  __syncthreads();
-  BlockSortT(*sort_mem).Sort(tmp, CmpDist());
-  __syncthreads();
-
-  for (int i = 0; i < ELTS; i++) {
-    new_nbh_list[ELTS * threadIdx.x + i].idx  = tmp[i].idx;
-    new_nbh_list[ELTS * threadIdx.x + i].dist = tmp[i].dist;
-  }
-  __syncthreads();
-}
-
 namespace {
 
 /********************************************************************************************
@@ -129,7 +73,7 @@ __global__ void RobustPruneKernel(
   int visited_size,
   cuvs::distance::DistanceType metric,
   float alpha,
-  int sort_smem_size)
+  T* s_coords_mem)
 {
   int n      = dataset.extent(0);
   int dim    = dataset.extent(1);
@@ -139,8 +83,7 @@ __global__ void RobustPruneKernel(
 
   union ShmemLayout {
     // All blocksort sizes have same alignment (16)
-    typename cub::BlockMergeSort<DistPair<IdxT, accT>, 32, 3>::TempStorage sort_mem;
-    T coords;
+    float occlusion;
     DistPair<IdxT, accT> nbh_list;
   };
 
@@ -149,101 +92,161 @@ __global__ void RobustPruneKernel(
 
   int align_padding = raft::alignTo<int>(dim, alignof(ShmemLayout)) - dim;
 
-  T* s_coords                        = reinterpret_cast<T*>(&smem[sort_smem_size]);
-  DistPair<IdxT, accT>* new_nbh_list = reinterpret_cast<DistPair<IdxT, accT>*>(
-    &smem[(dim + align_padding) * sizeof(T) + sort_smem_size]);
+  float* occlusion_list = reinterpret_cast<float*>(smem);
+  DistPair<IdxT, accT>* new_nbh_list =
+    reinterpret_cast<DistPair<IdxT, accT>*>(&smem[(degree + visited_size) * sizeof(float)]);
 
   static __shared__ Point<T, accT> s_query;
-  s_query.coords = s_coords;
+  s_query.coords = &s_coords_mem[blockIdx.x * (dim + align_padding)];
   s_query.Dim    = dim;
+  static __shared__ int prev_edges;
+  static __shared__ accT graphDist;
 
   for (int i = blockIdx.x; i < num_queries; i += gridDim.x) {
     int queryId = query_list[i].queryId;
 
-    update_shared_point<T, accT>(&s_query, &dataset(0, 0), query_list[i].queryId, dim);
+    update_shared_point<T, accT>(&s_query, &dataset(0, 0), queryId, dim, i);
 
-    // Load new neighbors to be sorted with candidates
-    for (int j = threadIdx.x; j < degree; j += blockDim.x) {
-      new_nbh_list[j].idx = graph(queryId, j);
-    }
+    int graphIdx = 0;
+    int listIdx  = 0;
+    int res_size = degree + visited_size;
+
+    // Count total valid edge candidates
     __syncthreads();
-    for (int j = 0; j < degree; j++) {
-      if (new_nbh_list[j].idx != raft::upper_bound<IdxT>()) {
-        new_nbh_list[j].dist =
-          dist<T, accT>(s_query.coords, &dataset((size_t)new_nbh_list[j].idx, 0), dim, metric);
-      } else {
-        new_nbh_list[j].dist = raft::upper_bound<accT>();
+    if (threadIdx.x == 0) {
+      prev_edges = degree;
+      for (int j = 0; j < degree; j++) {
+        if (graph(queryId, j) == raft::upper_bound<IdxT>()) {
+          prev_edges = j;
+          break;
+        }
       }
     }
+    for (int j = threadIdx.x; j < degree + visited_size; j += blockDim.x) {
+      occlusion_list[j] = 0.0;
+    }
     __syncthreads();
 
-    // combine and sort candidates and existing edges (and removes duplicates)
-    // Resulting list is stored in new_nbh_list
-    PRUNE_SELECT_SORT(degree, visited_size);
-
-    __syncthreads();
-
-    // If less than degree total neighbors, don't need to prune
-    if (new_nbh_list[degree].idx == raft::upper_bound<IdxT>()) {
-      if (threadIdx.x == 0) {
-        int writeId = 0;
-        for (; new_nbh_list[writeId].idx != raft::upper_bound<IdxT>(); writeId++) {
-          query_list[i].ids[writeId]   = new_nbh_list[writeId].idx;
-          query_list[i].dists[writeId] = new_nbh_list[writeId].dist;
-        }
-        query_list[i].size = writeId;
-        for (; writeId < degree; writeId++) {
-          query_list[i].ids[writeId]   = raft::upper_bound<IdxT>();
-          query_list[i].dists[writeId] = raft::upper_bound<accT>();
-        }
+    DistPair<IdxT, accT> next_cand;
+    // Merge graph and candidate list
+    for (int outIdx = 0; outIdx < degree + visited_size; outIdx++) {
+      // Check if no more valid elements from graph or list
+      if (graphIdx < degree && graph(queryId, graphIdx) == raft::upper_bound<IdxT>()) {
+        graphIdx = degree;
       }
-    } else {
-      // loop through list, writing nearest to visited_list,
-      // while nulling out violating neighbors in shared memory
-      if (threadIdx.x == 0) {
-        query_list[i].ids[0]   = new_nbh_list[0].idx;
-        query_list[i].dists[0] = new_nbh_list[0].dist;
+      if (listIdx < visited_size && query_list[i].ids[listIdx] == raft::upper_bound<IdxT>()) {
+        listIdx = visited_size;
       }
 
-      int writeId = 1;
-      for (int j = 1; j < degree + query_list[i].size && writeId < degree; j++) {
-        __syncthreads();
-        if (new_nbh_list[j].idx == queryId || new_nbh_list[j].idx == raft::upper_bound<IdxT>()) {
+      // Get next candidate vector for list
+      if (graphIdx >= degree) {
+        if (listIdx >= visited_size) {               // Fill remaining list if no candidates
+          if (res_size > outIdx) res_size = outIdx;  // Set result size
+          new_nbh_list[outIdx].idx  = raft::upper_bound<IdxT>();
+          new_nbh_list[outIdx].dist = raft::upper_bound<accT>();
+          __syncthreads();
           continue;
+        } else {
+          next_cand.idx  = query_list[i].ids[listIdx];
+          next_cand.dist = query_list[i].dists[listIdx];
+          listIdx++;
         }
+      } else if (listIdx >= visited_size) {
+        next_cand.idx = graph(queryId, graphIdx);
+        accT tempDist =
+          dist<T, accT>(s_query.coords, &dataset((size_t)graph(queryId, graphIdx), 0), dim, metric);
+        if (threadIdx.x == 0) graphDist = tempDist;
         __syncthreads();
-        if (threadIdx.x == 0) {
-          query_list[i].ids[writeId]   = new_nbh_list[j].idx;
-          query_list[i].dists[writeId] = new_nbh_list[j].dist;
-        }
-        writeId++;
+        next_cand.dist = graphDist;
+        graphIdx++;
+      } else {
+        accT listDist = query_list[i].dists[listIdx];
+
+        accT tempDist =
+          dist<T, accT>(s_query.coords, &dataset((size_t)graph(queryId, graphIdx), 0), dim, metric);
+        if (threadIdx.x == 0) graphDist = tempDist;
         __syncthreads();
 
-        update_shared_point<T, accT>(&s_query, &dataset(0, 0), new_nbh_list[j].idx, dim);
+        if (listDist <= graphDist) {
+          next_cand.idx  = query_list[i].ids[listIdx];
+          next_cand.dist = listDist;
+
+          if (graph(queryId, graphIdx) == query_list[i].ids[listIdx]) {  // Duplicate found!
+            graphIdx++;                                                  // Skip the duplicate
+          }
+          listIdx++;
+        } else {
+          next_cand.idx  = graph(queryId, graphIdx);
+          next_cand.dist = graphDist;
+          graphIdx++;
+        }
+      }
+
+      new_nbh_list[outIdx].idx  = next_cand.idx;
+      new_nbh_list[outIdx].dist = next_cand.dist;
+    }
+
+    // If we need to prune at all...
+    if (res_size > degree) {
+      int accept_count = 0;
+
+      // Go through different alpha values. These constants are hard-coded in the MSFT DiskANN code
+      for (float cur_alpha = 1.0; cur_alpha <= alpha && accept_count < degree; cur_alpha *= 1.2) {
+        for (int pass_start = 0; pass_start < res_size && accept_count < degree; pass_start++) {
+          // pick next non-occluded element
+          if (occlusion_list[pass_start] == raft::lower_bound<float>() ||
+              occlusion_list[pass_start] > cur_alpha) {
+            continue;  // Skip over elements already pruned or already accepted
+          }
+
+          if (new_nbh_list[pass_start].idx == queryId) { continue; }
+
+          T* cand_ptr = const_cast<T*>(&dataset((size_t)(new_nbh_list[pass_start].idx), 0));
 
-        int tot_size = degree + query_list[i].size;
-        for (int k = j + 1; k < tot_size; k++) {
-          T* mem_ptr = const_cast<T*>(&dataset((size_t)new_nbh_list[k].idx, 0));
-          if (new_nbh_list[k].idx != raft::upper_bound<IdxT>()) {
-            accT dist_starprime = dist<T, accT>(s_query.coords, mem_ptr, dim, metric);
-            // TODO - create cosine and selector fcn
+          occlusion_list[pass_start] = raft::lower_bound<float>();  // Mark as "accepted"
+          accept_count++;
 
-            if (threadIdx.x == 0 && alpha * dist_starprime <= new_nbh_list[k].dist) {
-              new_nbh_list[k].idx = raft::upper_bound<IdxT>();
+          // Update rest of the occlusion list
+          for (int occId = pass_start + 1; occId < res_size; occId++) {
+            if (occlusion_list[occId] <= alpha &&
+                occlusion_list[occId] != raft::lower_bound<float>()) {
+              T* k_ptr     = const_cast<T*>(&dataset((size_t)(new_nbh_list[occId].idx), 0));
+              accT djk     = dist<T, accT>(cand_ptr, k_ptr, dim, metric);
+              accT new_occ = (float)(new_nbh_list[occId].dist / djk);
+
+              occlusion_list[occId] = std::max(occlusion_list[occId], new_occ);
             }
           }
         }
       }
-      __syncthreads();
-      if (threadIdx.x == 0) { query_list[i].size = writeId; }
 
+      // Move all "accepted" candidates to front of list and zero out the rest
+      if (threadIdx.x == 0) {
+        int out_idx = 1;
+        for (int read_idx = 1; out_idx < accept_count; read_idx++) {
+          if (occlusion_list[read_idx] == raft::lower_bound<float>()) {  // If it is "accepted"
+            new_nbh_list[out_idx].idx  = new_nbh_list[read_idx].idx;
+            new_nbh_list[out_idx].dist = new_nbh_list[read_idx].dist;
+            out_idx++;
+          }
+        }
+      }
       __syncthreads();
-      for (int j = writeId + threadIdx.x; j < degree;
-           j += blockDim.x) {  // Zero out any unfilled neighbors
-        query_list[i].ids[j]   = raft::upper_bound<IdxT>();
-        query_list[i].dists[j] = raft::upper_bound<accT>();
+      for (int out_idx = accept_count + threadIdx.x; out_idx < degree; out_idx++) {
+        new_nbh_list[out_idx].idx  = raft::upper_bound<IdxT>();
+        new_nbh_list[out_idx].dist = raft::upper_bound<accT>();
       }
+
+      if (threadIdx.x == 0) { res_size = accept_count; }
+      __syncthreads();
+    }
+
+    // Copy results out to graph
+    for (int j = threadIdx.x; j < degree; j += blockDim.x) {
+      query_list[i].ids[j]   = new_nbh_list[j].idx;
+      query_list[i].dists[j] = new_nbh_list[j].dist;
     }
+    if (threadIdx.x == 0) { query_list[i].size = res_size; }
   }
 }
 
diff --git a/cpp/src/neighbors/detail/vamana/vamana_build.cuh b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
index 9586bf63a2..25c842947f 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_build.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
@@ -75,6 +75,29 @@ void create_insert_permutation(std::vector<IdxT>& insert_order, uint32_t N)
   }
 }
 
+template <typename IdxT>
+__global__ void print_mtx(raft::device_vector_view<IdxT, int64_t> vec)
+{
+  printf("extents:%ld\n", vec.extent(0));
+  for (int i = 0; i < vec.extent(0); i++) {
+    printf("%d, ", vec(i));
+  }
+  printf("\n");
+}
+
+template <typename IdxT, typename accT>
+__global__ void print_queryIds(void* query_list_ptr)
+{
+  QueryCandidates<IdxT, accT>* query_list =
+    static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
+
+  for (int i = 0; i < 50; i++) {
+    printf("queryId:%d\n", query_list[i].queryId);
+  }
+}
+
+#define KERNEL_TIMING (RAFT_LOG_ACTIVE_LEVEL <= RAPIDS_LOGGER_LOG_LEVEL_DEBUG)
+
 /********************************************************************************************
  * Main Vamana building function - insert vectors into empty graph in batches
  * Pre - dataset contains the vector data, host matrix allocated to store the graph
@@ -92,7 +115,6 @@ void batched_insert_vamana(
   raft::host_matrix_view<IdxT, int64_t> graph,
   IdxT* medoid_id,
   cuvs::distance::DistanceType metric)
-//  int dim)
 {
   auto stream = raft::resource::get_cuda_stream(res);
   int N       = dataset.extent(0);
@@ -101,18 +123,19 @@ void batched_insert_vamana(
 
   // Algorithm params
   int max_batchsize = (int)(params.max_fraction * (float)N);
+
   if (max_batchsize > (int)dataset.extent(0)) {
     RAFT_LOG_WARN(
       "Max fraction is the fraction of the total dataset, so it cannot be larger 1.0, reducing it "
       "to 1.0");
     max_batchsize = (int)dataset.extent(0);
   }
-  int insert_iters  = (int)(params.vamana_iters);
-  double base       = (double)(params.batch_base);
-  float alpha       = (float)(params.alpha);
-  int visited_size  = params.visited_size;
-  int queue_size    = params.queue_size;
-  int reverse_batch = params.reverse_batchsize;
+  float insert_iters = (float)(params.vamana_iters);
+  double base        = (double)(params.batch_base);
+  float alpha        = (float)(params.alpha);
+  int visited_size   = params.visited_size;
+  int queue_size     = params.queue_size;
+  int reverse_batch  = params.reverse_batchsize;
 
   if ((visited_size & (visited_size - 1)) != 0) {
     RAFT_LOG_WARN("visited_size must be a power of 2, rounding up.");
@@ -122,7 +145,11 @@ void batched_insert_vamana(
     visited_size = power;
   }
 
-  // create gpu graph and set to all -1s
+#if KERNEL_TIMING
+  auto start_t = std::chrono::system_clock::now();
+#endif
+
+  // Initialize graph with invalid neighbor indices (raft::upper_bound<IdxT>()).
   auto d_graph = raft::make_device_matrix<IdxT, int64_t>(res, graph.extent(0), graph.extent(1));
   raft::linalg::map(res, d_graph.view(), raft::const_op<IdxT>{raft::upper_bound<IdxT>()});
 
@@ -150,36 +177,56 @@ void batched_insert_vamana(
                                                                     visited_ids.data_handle(),
                                                                     visited_dists.data_handle(),
                                                                     (int)max_batchsize,
-                                                                    visited_size);
+                                                                    visited_size,
+                                                                    1);
+  auto topk_pq_mem =
+    raft::make_device_mdarray<Node<accT>>(res,
+                                          raft::resource::get_large_workspace_resource(res),
+                                          raft::make_extents<int64_t>(max_batchsize, visited_size));
+
+  int align_padding = raft::alignTo(dim, 16) - dim;
+
+  auto s_coords_mem = raft::make_device_mdarray<T>(
+    res,
+    raft::resource::get_large_workspace_resource(res),
+    raft::make_extents<int64_t>(min(maxBlocks, max(max_batchsize, reverse_batch)),
+                                dim + align_padding));
 
   // Create random permutation for order of node inserts into graph
   std::vector<IdxT> insert_order;
   create_insert_permutation<IdxT>(insert_order, (uint32_t)N);
 
   // Calculate the shared memory sizes of each kernel
-  int search_smem_sort_size = 0;
-  int prune_smem_sort_size  = 0;
-  SELECT_SMEM_SIZES(degree, visited_size);  // Sets above 2 variables to appropriate sizes
+  int sort_smem_size = 0;
+  SELECT_SORT_SMEM_SIZE(degree, visited_size);  // Sets sort_smem_size based on dataset
 
   // Total dynamic shared memory used by GreedySearch
-  int align_padding          = raft::alignTo(dim, 16) - dim;
-  int search_smem_total_size = static_cast<int>(
-    search_smem_sort_size + (dim + align_padding) * sizeof(T) + visited_size * sizeof(Node<accT>) +
-    degree * sizeof(int) + queue_size * sizeof(DistPair<IdxT, accT>));
+  int search_smem_total_size =
+    static_cast<int>((dim + align_padding) * sizeof(T) +  // visited_size * sizeof(Node<accT>) +
+                     degree * sizeof(int) + queue_size * sizeof(DistPair<IdxT, accT>));
 
   // Total dynamic shared memory size needed by both RobustPrune calls
-  int prune_smem_total_size = prune_smem_sort_size + (dim + align_padding) * sizeof(T) +
+  int prune_smem_total_size = (degree + visited_size) * sizeof(float) +  // Occlusion list
                               (degree + visited_size) * sizeof(DistPair<IdxT, accT>);
 
-  RAFT_LOG_DEBUG("Dynamic shared memory usage (bytes): GreedySearch: %d, RobustPrune: %d",
-                 search_smem_total_size,
-                 prune_smem_total_size);
-
-  if (prune_smem_sort_size == 0) {  // If sizes not supported, smem sizes will be 0
-    RAFT_FAIL("Vamana graph parameters not supported: graph_degree=%d, visited_size:%d\n",
-              degree,
-              visited_size);
-  }
+  RAFT_LOG_DEBUG(
+    "Dynamic shared memory usage (bytes): GreedySearch: %d, Segment Sort: %d, Robust Prune: %d",
+    search_smem_total_size,
+    sort_smem_size,
+    prune_smem_total_size);
+
+#if KERNEL_TIMING
+  auto end_t                                    = std::chrono::system_clock::now();
+  std::chrono::duration<double> elapsed_seconds = end_t - start_t;
+  double alloc_time                             = elapsed_seconds.count();
+
+  double search_time       = 0.0;
+  double segment_sort_time = 0.0;
+  double prune1_time       = 0.0;
+  double write1_time       = 0.0;
+  double rev_time          = 0.0;
+  double batch_prune       = 0.0;
+#endif
 
   // Random medoid has minor impact on recall
   // TODO: use heuristic for better medoid selection, issue:
@@ -188,88 +235,146 @@ void batched_insert_vamana(
 
   // size of current batch of inserts, increases logarithmically until max_batchsize
   int step_size = 1;
-  // Number of passes over dataset (default 1)
-  for (int iter = 0; iter < insert_iters; iter++) {
-    // Loop through batches and call the insert and prune kernels
-    for (int start = 0; start < N;) {
-      if (start + step_size > N) {
-        int new_size = N - start;
-        step_size    = new_size;
-      }
-      RAFT_LOG_DEBUG("Starting batch of inserts indices_start:%d, batch_size:%d", start, step_size);
-
-      int num_blocks = min(maxBlocks, step_size);
-
-      // Copy ids to be inserted for this batch
-      raft::copy(query_ids.data_handle(), &insert_order.data()[start], step_size, stream);
-      set_query_ids<IdxT, accT><<<num_blocks, blockD, 0, stream>>>(
-        query_list_ptr.data_handle(), query_ids.data_handle(), step_size);
-
-      // Call greedy search to get candidates for every vector being inserted
-      GreedySearchKernel<T, accT, IdxT, Accessor>
-        <<<num_blocks, blockD, search_smem_total_size, stream>>>(d_graph.view(),
-                                                                 dataset,
-                                                                 query_list_ptr.data_handle(),
-                                                                 step_size,
-                                                                 *medoid_id,
-                                                                 visited_size,
-                                                                 metric,
-                                                                 queue_size,
-                                                                 search_smem_sort_size);
-      // Run on candidates of vectors being inserted
-      RobustPruneKernel<T, accT, IdxT>
-        <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
-                                                                dataset,
-                                                                query_list_ptr.data_handle(),
-                                                                step_size,
-                                                                visited_size,
-                                                                metric,
-                                                                alpha,
-                                                                prune_smem_sort_size);
-
-      // Write results from first prune to graph edge list
-      write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
-        d_graph.view(), query_list_ptr.data_handle(), degree, step_size);
-
-      // compute prefix sums of query_list sizes - TODO parallelize prefix sums
-      auto d_total_edges = raft::make_device_mdarray<int>(
-        res, raft::resource::get_workspace_resource(res), raft::make_extents<int64_t>(1));
-      prefix_sums_sizes<accT, IdxT>
-        <<<1, 1, 0, stream>>>(query_list, step_size, d_total_edges.data_handle());
-
-      int total_edges;
-      raft::copy(&total_edges, d_total_edges.data_handle(), 1, stream);
-      RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
-
-      auto edge_dest =
-        raft::make_device_mdarray<IdxT>(res,
-                                        raft::resource::get_large_workspace_resource(res),
-                                        raft::make_extents<int64_t>(total_edges));
-      auto edge_src =
-        raft::make_device_mdarray<IdxT>(res,
-                                        raft::resource::get_large_workspace_resource(res),
-                                        raft::make_extents<int64_t>(total_edges));
-
-      // Create reverse edge list
-      create_reverse_edge_list<accT, IdxT>
-        <<<num_blocks, blockD, 0, stream>>>(query_list_ptr.data_handle(),
-                                            step_size,
-                                            degree,
-                                            edge_src.data_handle(),
-                                            edge_dest.data_handle());
-
+  // Loop through batches and call the insert and prune kernels - can insert > N times based on
+  // iters parameter
+  for (int start = 0; start < (int)(insert_iters * (float)N);) {
+#if KERNEL_TIMING
+    start_t = std::chrono::system_clock::now();
+#endif
+
+    if (start + step_size > (int)(insert_iters * (float)N)) {
+      step_size = (int)(insert_iters * (float)N) - start;
+    }
+    if (start + step_size > N) { step_size = N - start; }
+    RAFT_LOG_DEBUG("Starting batch of inserts indices_start:%d, batch_size:%d", start, step_size);
+
+    int num_blocks = min(maxBlocks, step_size);
+
+    // Copy ids to be inserted for this batch
+    raft::copy(query_ids.data_handle(), &insert_order.data()[start], step_size, stream);
+    set_query_ids<IdxT, accT><<<num_blocks, blockD, 0, stream>>>(
+      query_list_ptr.data_handle(), query_ids.data_handle(), step_size);
+
+    // Call greedy search to get candidates for every vector being inserted
+    GreedySearchKernel<T, accT, IdxT, Accessor>
+      <<<num_blocks, blockD, search_smem_total_size, stream>>>(d_graph.view(),
+                                                               dataset,
+                                                               query_list_ptr.data_handle(),
+                                                               step_size,
+                                                               *medoid_id,
+                                                               visited_size,
+                                                               metric,
+                                                               queue_size,
+                                                               topk_pq_mem.data_handle());
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+#if KERNEL_TIMING
+    RAFT_CUDA_TRY(cudaDeviceSynchronize());
+    end_t           = std::chrono::system_clock::now();
+    elapsed_seconds = end_t - start_t;
+    search_time += elapsed_seconds.count();
+    start_t = std::chrono::system_clock::now();
+#endif
+
+    // Segmented sort on query list
+    SortPairsKernel<T, accT, IdxT><<<num_blocks, blockD, sort_smem_size, stream>>>(
+      query_list_ptr.data_handle(), step_size, visited_size);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+#if KERNEL_TIMING
+    RAFT_CUDA_TRY(cudaDeviceSynchronize());
+    end_t           = std::chrono::system_clock::now();
+    elapsed_seconds = end_t - start_t;
+    segment_sort_time += elapsed_seconds.count();
+    start_t = std::chrono::system_clock::now();
+#endif
+
+    // Run on candidates of vectors being inserted
+    RobustPruneKernel<T, accT, IdxT>
+      <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
+                                                              dataset,
+                                                              query_list_ptr.data_handle(),
+                                                              step_size,
+                                                              visited_size,
+                                                              metric,
+                                                              alpha,
+                                                              s_coords_mem.data_handle());
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+    // Segmented sort on query list
+    SortPairsKernel<T, accT, IdxT><<<num_blocks, blockD, sort_smem_size, stream>>>(
+      query_list_ptr.data_handle(), step_size, degree);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+#if KERNEL_TIMING
+    RAFT_CUDA_TRY(cudaDeviceSynchronize());
+    end_t           = std::chrono::system_clock::now();
+    elapsed_seconds = end_t - start_t;
+    prune1_time += elapsed_seconds.count();
+    start_t = std::chrono::system_clock::now();
+#endif
+
+    // Write results from first prune to graph edge list
+    write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
+      d_graph.view(), query_list_ptr.data_handle(), degree, step_size);
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+#if KERNEL_TIMING
+    RAFT_CUDA_TRY(cudaDeviceSynchronize());
+    end_t           = std::chrono::system_clock::now();
+    elapsed_seconds = end_t - start_t;
+    write1_time += elapsed_seconds.count();
+    start_t = std::chrono::system_clock::now();
+#endif
+
+    // compute prefix sums of query_list sizes - TODO parallelize prefix sums
+    //    auto d_total_edges = raft::make_device_mdarray<int>(
+    //      res, raft::resource::get_workspace_resource(res), raft::make_extents<int64_t>(1));
+    rmm::device_scalar<int> d_total_edges(stream);
+    prefix_sums_sizes<accT, IdxT><<<1, 1, 0, stream>>>(query_list, step_size, d_total_edges.data());
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+    int total_edges = d_total_edges.value(stream);
+    //    raft::copy(&total_edges, d_total_edges.data_handle(), 1, stream);
+    //    RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+
+    auto edge_dist_pair = raft::make_device_mdarray<DistPair<IdxT, accT>>(
+      res,
+      raft::resource::get_large_workspace_resource(res),
+      raft::make_extents<int64_t>(total_edges));
+
+    auto edge_dest =
+      raft::make_device_mdarray<IdxT>(res,
+                                      raft::resource::get_large_workspace_resource(res),
+                                      raft::make_extents<int64_t>(total_edges));
+    auto edge_src =
+      raft::make_device_mdarray<IdxT>(res,
+                                      raft::resource::get_large_workspace_resource(res),
+                                      raft::make_extents<int64_t>(total_edges));
+
+    // Create reverse edge list
+    create_reverse_edge_list<accT, IdxT>
+      <<<num_blocks, blockD, 0, stream>>>(query_list_ptr.data_handle(),
+                                          step_size,
+                                          degree,
+                                          edge_src.data_handle(),
+                                          edge_dist_pair.data_handle());
+    RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+    {
+      // Sort by dists first so final edge lists are each sorted by dist
       void* d_temp_storage      = nullptr;
       size_t temp_storage_bytes = 0;
 
       cub::DeviceMergeSort::SortPairs(d_temp_storage,
                                       temp_storage_bytes,
-                                      edge_dest.data_handle(),
+                                      edge_dist_pair.data_handle(),
                                       edge_src.data_handle(),
                                       total_edges,
-                                      CmpEdge<IdxT>(),
+                                      CmpDist<IdxT, accT>(),
                                       stream);
 
-      RAFT_LOG_DEBUG("Temp storage needed for sorting (bytes): %lu", temp_storage_bytes);
+      RAFT_LOG_DEBUG("Temp storage needed for sorting dist (bytes): %lu", temp_storage_bytes);
 
       auto temp_sort_storage = raft::make_device_mdarray<IdxT>(
         res,
@@ -279,100 +384,177 @@ void batched_insert_vamana(
       // Sort to group reverse edges by destination
       cub::DeviceMergeSort::SortPairs(temp_sort_storage.data_handle(),
                                       temp_storage_bytes,
-                                      edge_dest.data_handle(),
+                                      edge_dist_pair.data_handle(),
                                       edge_src.data_handle(),
                                       total_edges,
-                                      CmpEdge<IdxT>(),
+                                      CmpDist<IdxT, accT>(),
                                       stream);
+    }
 
-      // Get number of unique node destinations
-      IdxT unique_dests =
-        cuvs::sparse::neighbors::get_n_components(edge_dest.data_handle(), total_edges, stream);
-
-      // Find which node IDs have reverse edges and their indices in the reverse edge list
-      thrust::device_vector<IdxT> edge_dest_vec(edge_dest.data_handle(),
-                                                edge_dest.data_handle() + total_edges);
-      auto unique_indices = raft::make_device_vector<int>(res, total_edges);
-      raft::linalg::map_offset(res, unique_indices.view(), raft::identity_op{});
-
-      thrust::unique_by_key(
-        edge_dest_vec.begin(), edge_dest_vec.end(), unique_indices.data_handle());
-
-      edge_dest_vec.clear();
-      edge_dest_vec.shrink_to_fit();
-
-      // Batch execution of reverse edge creation/application
-      reverse_batch = params.reverse_batchsize;
-      for (int rev_start = 0; rev_start < (int)unique_dests; rev_start += reverse_batch) {
-        if (rev_start + reverse_batch > (int)unique_dests) {
-          reverse_batch = (int)unique_dests - rev_start;
-        }
-
-        // Allocate reverse QueryCandidate list based on number of unique destinations
-        auto reverse_list_ptr = raft::make_device_mdarray<QueryCandidates<IdxT, accT>>(
-          res,
-          raft::resource::get_large_workspace_resource(res),
-          raft::make_extents<int64_t>(reverse_batch));
-        auto rev_ids =
-          raft::make_device_mdarray<IdxT>(res,
-                                          raft::resource::get_large_workspace_resource(res),
-                                          raft::make_extents<int64_t>(reverse_batch, visited_size));
-        auto rev_dists =
-          raft::make_device_mdarray<accT>(res,
-                                          raft::resource::get_large_workspace_resource(res),
-                                          raft::make_extents<int64_t>(reverse_batch, visited_size));
-
-        QueryCandidates<IdxT, accT>* reverse_list =
-          static_cast<QueryCandidates<IdxT, accT>*>(reverse_list_ptr.data_handle());
-
-        init_query_candidate_list<IdxT, accT><<<256, blockD, 0, stream>>>(reverse_list,
-                                                                          rev_ids.data_handle(),
-                                                                          rev_dists.data_handle(),
-                                                                          (int)reverse_batch,
-                                                                          visited_size);
-
-        // May need more blocks for reverse list
-        num_blocks = min(maxBlocks, reverse_batch);
-
-        // Populate reverse list ids and candidate lists from edge_src and edge_dest
-        populate_reverse_list_struct<T, accT, IdxT>
-          <<<num_blocks, blockD, 0, stream>>>(reverse_list,
-                                              edge_src.data_handle(),
-                                              edge_dest.data_handle(),
-                                              unique_indices.data_handle(),
-                                              unique_dests,
-                                              total_edges,
-                                              dataset.extent(0),
-                                              rev_start,
-                                              reverse_batch);
-
-        // Recompute distances (avoided keeping it during sorting)
-        recompute_reverse_dists<T, accT, IdxT>
-          <<<num_blocks, blockD, 0, stream>>>(reverse_list, dataset, reverse_batch, metric);
-
-        // Call 2nd RobustPrune on reverse query_list
-        RobustPruneKernel<T, accT, IdxT>
-          <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
-                                                                  raft::make_const_mdspan(dataset),
-                                                                  reverse_list_ptr.data_handle(),
-                                                                  reverse_batch,
-                                                                  visited_size,
-                                                                  metric,
-                                                                  alpha,
-                                                                  prune_smem_sort_size);
-
-        // Write new edge lists to graph
-        write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
-          d_graph.view(), reverse_list_ptr.data_handle(), degree, reverse_batch);
+    /*
+    DistPair<IdxT, accT>* temp_ptr = edge_dist_pair.data_handle();
+    raft::linalg::map_offset(
+      res, edge_dest.view(), [temp_ptr] __device__(size_t i) { return temp_ptr[i].idx; });
+      */
+    raft::linalg::map(
+      res,
+      edge_dest.view(),
+      [] __device__(auto x) { return x.idx; },
+      raft::make_const_mdspan(edge_dist_pair.view()));
+
+    void* d_temp_storage      = nullptr;
+    size_t temp_storage_bytes = 0;
+
+    cub::DeviceMergeSort::SortPairs(d_temp_storage,
+                                    temp_storage_bytes,
+                                    edge_dest.data_handle(),
+                                    edge_src.data_handle(),
+                                    total_edges,
+                                    CmpEdge<IdxT>(),
+                                    stream);
+
+    RAFT_LOG_DEBUG("Temp storage needed for sorting (bytes): %lu", temp_storage_bytes);
+
+    auto temp_sort_storage = raft::make_device_mdarray<IdxT>(
+      res,
+      raft::resource::get_large_workspace_resource(res),
+      raft::make_extents<int64_t>(temp_storage_bytes / sizeof(IdxT)));
+
+    // Sort to group reverse edges by destination
+    cub::DeviceMergeSort::SortPairs(temp_sort_storage.data_handle(),
+                                    temp_storage_bytes,
+                                    edge_dest.data_handle(),
+                                    edge_src.data_handle(),
+                                    total_edges,
+                                    CmpEdge<IdxT>(),
+                                    stream);
+
+    // Get number of unique node destinations
+    IdxT unique_dests =
+      cuvs::sparse::neighbors::get_n_components(edge_dest.data_handle(), total_edges, stream);
+
+    // Find which node IDs have reverse edges and their indices in the reverse edge list
+    thrust::device_vector<IdxT> edge_dest_vec(edge_dest.data_handle(),
+                                              edge_dest.data_handle() + total_edges);
+    auto unique_indices = raft::make_device_vector<int>(res, total_edges);
+    raft::linalg::map_offset(res, unique_indices.view(), raft::identity_op{});
+
+    thrust::unique_by_key(edge_dest_vec.begin(), edge_dest_vec.end(), unique_indices.data_handle());
+
+    edge_dest_vec.clear();
+    edge_dest_vec.shrink_to_fit();
+
+#if KERNEL_TIMING
+    RAFT_CUDA_TRY(cudaDeviceSynchronize());
+    end_t           = std::chrono::system_clock::now();
+    elapsed_seconds = end_t - start_t;
+    rev_time += elapsed_seconds.count();
+    start_t = std::chrono::system_clock::now();
+#endif
+
+    // Batch execution of reverse edge creation/application
+    reverse_batch = params.reverse_batchsize;
+    for (int rev_start = 0; rev_start < (int)unique_dests; rev_start += reverse_batch) {
+      if (rev_start + reverse_batch > (int)unique_dests) {
+        reverse_batch = (int)unique_dests - rev_start;
       }
 
-      start += step_size;
-      step_size *= base;
-      if (step_size > max_batchsize) step_size = max_batchsize;
+      // Allocate reverse QueryCandidate list based on number of unique destinations
+      auto reverse_list_ptr = raft::make_device_mdarray<QueryCandidates<IdxT, accT>>(
+        res,
+        raft::resource::get_large_workspace_resource(res),
+        raft::make_extents<int64_t>(reverse_batch));
+      auto rev_ids =
+        raft::make_device_mdarray<IdxT>(res,
+                                        raft::resource::get_large_workspace_resource(res),
+                                        raft::make_extents<int64_t>(reverse_batch, visited_size));
+
+      auto rev_dists =
+        raft::make_device_mdarray<accT>(res,
+                                        raft::resource::get_large_workspace_resource(res),
+                                        raft::make_extents<int64_t>(reverse_batch, visited_size));
+
+      QueryCandidates<IdxT, accT>* reverse_list =
+        static_cast<QueryCandidates<IdxT, accT>*>(reverse_list_ptr.data_handle());
+
+      init_query_candidate_list<IdxT, accT><<<256, blockD, 0, stream>>>(reverse_list,
+                                                                        rev_ids.data_handle(),
+                                                                        rev_dists.data_handle(),
+                                                                        (int)reverse_batch,
+                                                                        visited_size);
+
+      // May need more blocks for reverse list
+      num_blocks = min(maxBlocks, reverse_batch);
+
+      // Populate reverse list ids and candidate lists from edge_src and edge_dest
+      populate_reverse_list_struct<T, accT, IdxT>
+        <<<num_blocks, blockD, 0, stream>>>(reverse_list,
+                                            edge_src.data_handle(),
+                                            edge_dest.data_handle(),
+                                            unique_indices.data_handle(),
+                                            unique_dests,
+                                            total_edges,
+                                            dataset.extent(0),
+                                            rev_start,
+                                            reverse_batch);
+      RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+      // Recompute distances (avoided keeping it during sorting)
+      recompute_reverse_dists<T, accT, IdxT>
+        <<<num_blocks, blockD, 0, stream>>>(reverse_list, dataset, reverse_batch, metric);
+      RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+      // Call 2nd RobustPrune on reverse query_list
+      RobustPruneKernel<T, accT, IdxT>
+        <<<num_blocks, blockD, prune_smem_total_size, stream>>>(d_graph.view(),
+                                                                raft::make_const_mdspan(dataset),
+                                                                reverse_list_ptr.data_handle(),
+                                                                reverse_batch,
+                                                                visited_size,
+                                                                metric,
+                                                                alpha,
+                                                                s_coords_mem.data_handle());
+      RAFT_CUDA_TRY(cudaPeekAtLastError());
+
+      // Segmented sort on reverse_list
+      SortPairsKernel<T, accT, IdxT><<<num_blocks, blockD, sort_smem_size, stream>>>(
+        reverse_list_ptr.data_handle(), reverse_batch, degree);
+      RAFT_CUDA_TRY(cudaPeekAtLastError());
 
-    }  // Batch of inserts
+      // Write new edge lists to graph
+      write_graph_edges_kernel<accT, IdxT><<<num_blocks, blockD, 0, stream>>>(
+        d_graph.view(), reverse_list_ptr.data_handle(), degree, reverse_batch);
+      RAFT_CUDA_TRY(cudaPeekAtLastError());
+    }
 
-  }  // insert iterations
+#if KERNEL_TIMING
+    RAFT_CUDA_TRY(cudaDeviceSynchronize());
+    end_t           = std::chrono::system_clock::now();
+    elapsed_seconds = end_t - start_t;
+    batch_prune += elapsed_seconds.count();
+#endif
+
+    start += step_size;
+    if (start >= N) {
+      start = 0;
+      insert_iters -= 1.0;
+      step_size = max_batchsize;
+    }
+    step_size *= base;
+    step_size = min(step_size, max_batchsize);
+
+  }  // Batch of inserts
+
+#if KERNEL_TIMING
+  printf("intro:%lf\ngreedy:%lf\nseg_sort:%lf\nprune1:%lf\nwrite1:%lf\nrev:%lf\nbatch_prune:%lf\n",
+         alloc_time,
+         search_time,
+         segment_sort_time,
+         prune1_time,
+         write1_time,
+         rev_time,
+         batch_prune);
+#endif
 
   raft::copy(graph.data_handle(), d_graph.data_handle(), d_graph.size(), stream);
 
@@ -413,6 +595,9 @@ index<T, IdxT> build(
 
   RAFT_EXPECTS(params.visited_size > graph_degree, "visited_size must be > graph_degree");
 
+  RAFT_EXPECTS(params.vamana_iters >= 1.0,
+               "vamana_iters must be at least 1.0 to insert the entire input dataset");
+
   int dim = dataset.extent(1);
 
   RAFT_LOG_DEBUG("Creating empty graph structure");
@@ -421,7 +606,6 @@ index<T, IdxT> build(
   RAFT_LOG_DEBUG("Running Vamana batched insert algorithm");
 
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded;
-
   IdxT medoid_id;
   batched_insert_vamana<T, float, IdxT, Accessor>(
     res, params, dataset, vamana_graph.view(), &medoid_id, metric);
diff --git a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
index 0cf29a5b42..d5812e1ff6 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_structs.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -50,20 +50,6 @@ template <typename IdxT, typename accT>
 struct __align__(16) DistPair {
   accT dist;
   IdxT idx;
-
-  __device__ __host__ DistPair& operator=(const DistPair& other)
-  {
-    dist = other.dist;
-    idx  = other.idx;
-    return *this;
-  }
-
-  __device__ __host__ DistPair& operator=(const volatile DistPair& other)
-  {
-    dist = other.dist;
-    idx  = other.idx;
-    return *this;
-  }
 };
 
 // Swap the values of two DistPair<SUMTYPE> objects
@@ -80,8 +66,8 @@ __device__ __host__ void swap(DistPair<IdxT, accT>* a, DistPair<IdxT, accT>* b)
 }
 
 // Structure to sort by distance
+template <typename IdxT, typename accT>
 struct CmpDist {
-  template <typename IdxT, typename accT>
   __device__ bool operator()(const DistPair<IdxT, accT>& lhs, const DistPair<IdxT, accT>& rhs)
   {
     return lhs.dist < rhs.dist;
@@ -309,19 +295,20 @@ __global__ void init_query_candidate_list(QueryCandidates<IdxT, accT>* query_lis
                                           IdxT* visited_id_ptr,
                                           accT* visited_dist_ptr,
                                           int num_queries,
-                                          int maxSize)
+                                          int maxSize,
+                                          int extra_queries_in_list = 0)
 {
   IdxT* ids_ptr  = static_cast<IdxT*>(visited_id_ptr);
   accT* dist_ptr = static_cast<accT*>(visited_dist_ptr);
 
   for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num_queries * maxSize;
-       i += blockDim.x + gridDim.x) {
+       i += blockDim.x * gridDim.x) {
     ids_ptr[i]  = raft::upper_bound<IdxT>();
     dist_ptr[i] = raft::upper_bound<accT>();
   }
 
-  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_queries;
-       i += blockDim.x + gridDim.x) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_queries + extra_queries_in_list;
+       i += blockDim.x * gridDim.x) {
     query_list[i].maxSize = maxSize;
     query_list[i].size    = 0;
     query_list[i].ids     = &ids_ptr[i * (size_t)(maxSize)];
@@ -359,6 +346,18 @@ __global__ void prefix_sums_sizes(QueryCandidates<IdxT, accT>* query_list,
   }
 }
 
+// Device fcn to have a threadblock copy coordinates into shared memory
+template <typename T, typename accT>
+__device__ void update_shared_point(
+  Point<T, accT>* shared_point, const T* data_ptr, int id, int dim, int idx)
+{
+  shared_point->id  = id;
+  shared_point->Dim = dim;
+  for (size_t i = threadIdx.x; i < dim; i += blockDim.x) {
+    shared_point->coords[i] = data_ptr[(size_t)(id) * (size_t)(dim) + i];
+  }
+}
+
 // Device fcn to have a threadblock copy coordinates into shared memory
 template <typename T, typename accT>
 __device__ void update_shared_point(Point<T, accT>* shared_point,
@@ -392,8 +391,11 @@ __global__ void write_graph_edges_kernel(raft::device_matrix_view<IdxT, int64_t>
 
 // Create src and dest edge lists used to sort and create reverse edges
 template <typename accT, typename IdxT = uint32_t>
-__global__ void create_reverse_edge_list(
-  void* query_list_ptr, int num_queries, int degree, IdxT* edge_src, IdxT* edge_dest)
+__global__ void create_reverse_edge_list(void* query_list_ptr,
+                                         int num_queries,
+                                         int degree,
+                                         IdxT* edge_src,
+                                         DistPair<IdxT, accT>* edge_dest)
 {
   QueryCandidates<IdxT, accT>* query_list =
     static_cast<QueryCandidates<IdxT, accT>*>(query_list_ptr);
@@ -404,8 +406,9 @@ __global__ void create_reverse_edge_list(
     int cand_count = query_list[i + 1].size - query_list[i].size;
 
     for (int j = 0; j < cand_count; j++) {
-      edge_src[query_list[i].size + j]  = query_list[i].queryId;
-      edge_dest[query_list[i].size + j] = query_list[i].ids[j];
+      edge_src[query_list[i].size + j]       = query_list[i].queryId;
+      edge_dest[query_list[i].size + j].idx  = query_list[i].ids[j];
+      edge_dest[query_list[i].size + j].dist = query_list[i].dists[j];
     }
   }
 }
diff --git a/cpp/tests/neighbors/ann_vamana.cuh b/cpp/tests/neighbors/ann_vamana.cuh
index 37a9447e95..bb8cd9bd72 100644
--- a/cpp/tests/neighbors/ann_vamana.cuh
+++ b/cpp/tests/neighbors/ann_vamana.cuh
@@ -66,6 +66,7 @@ struct AnnVamanaInputs {
   cuvs::distance::DistanceType metric;
   bool host_dataset;
   int reverse_batchsize;
+  double insert_iters;
 
   // cagra search params
   int n_queries;
@@ -146,6 +147,8 @@ class AnnVamanaTest : public ::testing::TestWithParam<AnnVamanaInputs> {
     index_params.visited_size      = ps.visited_size;
     index_params.max_fraction      = ps.max_fraction;
     index_params.reverse_batchsize = ps.reverse_batchsize;
+    index_params.vamana_iters      = ps.insert_iters;
+
     // use randomized codebooks to test serialization & quantization code path
     if (ps.dim == 384 && std::is_same_v<DataT, int8_t>)
       index_params.codebooks = vamana::deserialize_codebooks(
@@ -299,6 +302,7 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded},
     {false},
     {100, 1000000},
+    {1.0, 1.5},
     {100},
     {10},
     {cagra::search_algo::AUTO},
@@ -316,6 +320,7 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded},
     {false},
     {1000000},
+    {1.0},
     {100},
     {10},
     {cagra::search_algo::AUTO},
@@ -334,6 +339,7 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded},
     {false},
     {1000000},
+    {1.0},
     {100},
     {10},
     {cagra::search_algo::AUTO},
@@ -352,6 +358,7 @@ inline std::vector<AnnVamanaInputs> generate_inputs()
     {cuvs::distance::DistanceType::L2Expanded},
     {false},
     {1000000},
+    {1.0},
     {100},
     {10},
     {cagra::search_algo::AUTO},
diff --git a/examples/cpp/src/vamana_example.cu b/examples/cpp/src/vamana_example.cu
index bb8085b27f..9715d0dc38 100644
--- a/examples/cpp/src/vamana_example.cu
+++ b/examples/cpp/src/vamana_example.cu
@@ -34,7 +34,7 @@ void vamana_build_and_write(raft::device_resources const& dev_resources,
                             int degree,
                             int visited_size,
                             float max_fraction,
-                            int iters,
+                            float iters,
                             std::string codebook_prefix)
 {
   using namespace cuvs::neighbors;
@@ -45,7 +45,10 @@ void vamana_build_and_write(raft::device_resources const& dev_resources,
   index_params.visited_size = visited_size;
   index_params.graph_degree = degree;
   index_params.vamana_iters = iters;
-  index_params.codebooks    = vamana::deserialize_codebooks(codebook_prefix, dataset.extent(1));
+
+  if (codebook_prefix != "") {
+    index_params.codebooks = vamana::deserialize_codebooks(codebook_prefix, dataset.extent(1));
+  }
 
   std::cout << "Building Vamana index (search graph)" << std::endl;
 
@@ -64,20 +67,24 @@ void vamana_build_and_write(raft::device_resources const& dev_resources,
   serialize(dev_resources, out_fname, index);
 
   // Output index to file (disk sector-aligned format)
-  serialize(dev_resources, out_fname + ".sector_aligned", index, false, true);
+  if (codebook_prefix != "") {
+    serialize(dev_resources, out_fname + ".sector_aligned", index, false, true);
+  }
 }
 
 void usage()
 {
   printf(
-    "Usage: ./vamana_example <data filename> <output filename> <graph "
-    "degree> <visited_size> <max_fraction> <iterations> <(optional) "
+    "Usage: ./vamana_example <data filename> <output filename> <datatype> "
+    "<graph degree> <visited_size> <max_fraction> <iterations> <(optional) "
     "codebook prefix>\n");
+
   printf("Input file expected to be binary file of fp32 vectors.\n");
   printf("Graph degree sizes supported: 32, 64, 128, 256\n");
+  printf("Datatype of input dataset (int8 or float)\n");
   printf("Visited_size must be > degree and a power of 2.\n");
   printf("max_fraction > 0 and <= 1. Typical values are 0.06 or 0.1.\n");
-  printf("Default iterations = 1, increase for better quality graph.\n");
+  printf("Default iterations = 1.0, increase for better quality graph.\n");
   printf(
     "Optional path prefix to pq pivots and rotation matrix files. Expects pq pivots file at "
     "${codebook_prefix}_pq_pivots.bin and rotation matrix file at "
@@ -102,23 +109,38 @@ int main(int argc, char* argv[])
   // limit. raft::resource::set_workspace_to_pool_resource(dev_resources, 2 *
   // 1024 * 1024 * 1024ull);
 
-  if (argc != 7 && argc != 8) usage();
+  if (argc != 8 && argc != 9) usage();
 
   std::string data_fname      = (std::string)(argv[1]);  // Input filename
   std::string out_fname       = (std::string)argv[2];    // Output index filename
-  int degree                  = atoi(argv[3]);
-  int max_visited             = atoi(argv[4]);
-  float max_fraction          = atof(argv[5]);
-  int iters                   = atoi(argv[6]);
+  std::string dtype           = (std::string)argv[3];
+  int degree                  = atoi(argv[4]);
+  int max_visited             = atoi(argv[5]);
+  float max_fraction          = atof(argv[6]);
+  float iters                 = atof(argv[7]);
   std::string codebook_prefix = "";
-  if (argc >= 8)
-    codebook_prefix = (std::string)argv[7];  // Path prefix to pq pivots and rotation matrix files
-
-  // Read in binary dataset file
-  auto dataset = read_bin_dataset<uint8_t, int64_t>(dev_resources, data_fname, INT_MAX);
-
-  // Simple build example to create graph and write to a file
-  vamana_build_and_write<uint8_t>(dev_resources,
+  if (argc >= 9)
+    codebook_prefix = (std::string)argv[8];  // Path prefix to pq pivots and rotation matrix files
+
+  if (dtype == "int8") {
+    // Read in binary dataset file
+    auto dataset = read_bin_dataset<int8_t, int64_t>(dev_resources, data_fname, INT_MAX);
+
+    // Simple build example to create graph and write to a file
+    vamana_build_and_write<int8_t>(dev_resources,
+                                   raft::make_const_mdspan(dataset.view()),
+                                   out_fname,
+                                   degree,
+                                   max_visited,
+                                   max_fraction,
+                                   iters,
+                                   codebook_prefix);
+  } else if (dtype == "float") {
+    // Read in binary dataset file
+    auto dataset = read_bin_dataset<float, int64_t>(dev_resources, data_fname, INT_MAX);
+
+    // Simple build example to create graph and write to a file
+    vamana_build_and_write<float>(dev_resources,
                                   raft::make_const_mdspan(dataset.view()),
                                   out_fname,
                                   degree,
@@ -126,4 +148,7 @@ int main(int argc, char* argv[])
                                   max_fraction,
                                   iters,
                                   codebook_prefix);
+  } else {
+    usage();
+  }
 }

From 0a6d5b7feb7ed03b85b2bc45bfedd887ebe804c7 Mon Sep 17 00:00:00 2001
From: Gil Forsyth <gforsyth@users.noreply.github.com>
Date: Mon, 4 Aug 2025 17:21:43 -0400
Subject: [PATCH 526/623] ci(labeler): update labeler action to @v5 (#1210)

Bumps the version of `actions/labeler` to `@v5` and updates the syntax in
the `labeler.yml` file to account for breaking changes in that version bump.

xref: rapidsai/ops#2968

Authors:
  - Gil Forsyth (https://github.com/gforsyth)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/1210
---
 .github/labeler.yml           | 21 +++++++++++++--------
 .github/workflows/labeler.yml |  2 +-
 2 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/.github/labeler.yml b/.github/labeler.yml
index 56f77e69c0..bebde421e8 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -3,14 +3,19 @@
 # Labels culled from https://github.com/rapidsai/raft/labels
 
 python:
- - 'python/**'
-
+  - changed-files:
+      any-glob-to-any-file:
+        - 'python/**'
 cpp:
- - 'cpp/**'
-
+  - changed-files:
+      any-glob-to-any-file:
+        - 'cpp/**'
 CMake:
- - '**/CMakeLists.txt'
- - '**/cmake/**'
-
+  - changed-files:
+      any-glob-to-any-file:
+        - '**/CMakeLists.txt'
+        - '**/cmake/**'
 ci:
-  - 'ci/**'
+  - changed-files:
+      any-glob-to-any-file:
+        - 'ci/**'
diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml
index 1ddd5b5cc3..65e68275d3 100644
--- a/.github/workflows/labeler.yml
+++ b/.github/workflows/labeler.yml
@@ -6,6 +6,6 @@ jobs:
  triage:
    runs-on: ubuntu-latest
    steps:
-   - uses: actions/labeler@v4
+   - uses: actions/labeler@v5
      with:
        repo-token: "${{ secrets.GITHUB_TOKEN }}"

From ad4dba2fa33bdee4b607b9fdb68cfa847d9c812c Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 5 Aug 2025 09:49:35 -0400
Subject: [PATCH 527/623] Update rapids-build-backend to 0.4.1 (#1214)

Issue: https://github.com/rapidsai/build-planning/issues/207

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1214
---
 conda/environments/all_cuda-129_arch-aarch64.yaml | 2 +-
 conda/environments/all_cuda-129_arch-x86_64.yaml  | 2 +-
 conda/recipes/cuvs-bench-cpu/recipe.yaml          | 2 +-
 conda/recipes/cuvs-bench/recipe.yaml              | 2 +-
 conda/recipes/cuvs/recipe.yaml                    | 2 +-
 dependencies.yaml                                 | 2 +-
 python/cuvs/pyproject.toml                        | 2 +-
 python/libcuvs/pyproject.toml                     | 2 +-
 8 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
index 04fe7a5c21..45bb00e8df 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -42,7 +42,7 @@ dependencies:
 - pylibraft==25.10.*,>=0.0.0a0
 - pytest
 - pytest-cov
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
 - recommonmark
 - rust
 - scikit-build-core>=0.10.0
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
index 643dbfe04c..46a0f234c7 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -42,7 +42,7 @@ dependencies:
 - pylibraft==25.10.*,>=0.0.0a0
 - pytest
 - pytest-cov
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
 - recommonmark
 - rust
 - scikit-build-core>=0.10.0
diff --git a/conda/recipes/cuvs-bench-cpu/recipe.yaml b/conda/recipes/cuvs-bench-cpu/recipe.yaml
index fe9d26618e..e856382c68 100644
--- a/conda/recipes/cuvs-bench-cpu/recipe.yaml
+++ b/conda/recipes/cuvs-bench-cpu/recipe.yaml
@@ -63,7 +63,7 @@ requirements:
     - openblas
     - pip
     - python =${{ py_version }}
-    - rapids-build-backend>=0.3.0,<0.4.0.dev0
+    - rapids-build-backend>=0.4.0,<0.5.0.dev0
     - setuptools >=64.0.0
     - if: linux64
       then:
diff --git a/conda/recipes/cuvs-bench/recipe.yaml b/conda/recipes/cuvs-bench/recipe.yaml
index b5e001b62d..a823877926 100644
--- a/conda/recipes/cuvs-bench/recipe.yaml
+++ b/conda/recipes/cuvs-bench/recipe.yaml
@@ -32,7 +32,7 @@ requirements:
     - libcuvs-bench-ann =${{ version }}
     - python =${{ py_version }}
     - pip
-    - rapids-build-backend >=0.3.0,<0.4.0.dev0
+    - rapids-build-backend >=0.4.0,<0.5.0.dev0
     - rmm =${{ minor_version }}
     - setuptools >=64.0.0
   run:
diff --git a/conda/recipes/cuvs/recipe.yaml b/conda/recipes/cuvs/recipe.yaml
index 0c3610771a..e7b9f6de99 100644
--- a/conda/recipes/cuvs/recipe.yaml
+++ b/conda/recipes/cuvs/recipe.yaml
@@ -56,7 +56,7 @@ requirements:
     - pip
     - pylibraft =${{ minor_version }}
     - python =${{ py_version }}
-    - rapids-build-backend >=0.3.0,<0.4.0.dev0
+    - rapids-build-backend >=0.4.0,<0.5.0.dev0
     - scikit-build-core >=0.10.0
     - cuda-python >=12.6.2,<13.0a0
     - cuda-cudart-dev
diff --git a/dependencies.yaml b/dependencies.yaml
index 960614a891..dd25ae9afb 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -218,7 +218,7 @@ dependencies:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - rapids-build-backend>=0.3.0,<0.4.0.dev0
+          - rapids-build-backend>=0.4.0,<0.5.0.dev0
       - output_types: [conda]
         packages:
           - scikit-build-core>=0.10.0
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 373e313492..99e31fc620 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -15,7 +15,7 @@
 [build-system]
 
 requires = [
-    "rapids-build-backend>=0.3.0,<0.4.0.dev0",
+    "rapids-build-backend>=0.4.0,<0.5.0.dev0",
     "scikit-build-core[pyproject]>=0.10.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "rapids_build_backend.build"
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 00e49155da..51bb46316b 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -14,7 +14,7 @@
 
 [build-system]
 requires = [
-    "rapids-build-backend>=0.3.0,<0.4.0.dev0",
+    "rapids-build-backend>=0.4.0,<0.5.0.dev0",
     "scikit-build-core[pyproject]>=0.10.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "rapids_build_backend.build"

From c22b7774954d69464cacbd2d647dfe1e84344c7e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Tue, 5 Aug 2025 16:37:12 +0200
Subject: [PATCH 528/623] [Java] Add decorator for synchronized CuVSResource
 access (#1209)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

As a follow up to #1089 and #1082, this PR introduces a decorator to ensure that access to a shared CuVSResource is synchronized. This could be useful in cases where application logic is complex and it's not easy to efficiently and cleanly guarantee that access to a CuVSResources is done by at most one thread at each given time.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1209
---
 .../cuvs/SynchronizedCuVSResources.java       |  63 +++++
 .../cuvs/CagraMultiThreadStabilityIT.java     | 219 ++++++++++--------
 2 files changed, 191 insertions(+), 91 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java
new file mode 100644
index 0000000000..bbba262bb0
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import java.nio.file.Path;
+import java.util.concurrent.locks.ReentrantLock;
+
+/**
+ * A decorator for CuVSResources that guarantees synchronized (blocking) access to the wrapped CuVSResource
+ */
+public class SynchronizedCuVSResources implements CuVSResources {
+
+  private final CuVSResources inner;
+  private final ReentrantLock lock;
+
+  private SynchronizedCuVSResources(CuVSResources inner) {
+    this.inner = inner;
+    this.lock = new ReentrantLock();
+  }
+
+  static CuVSResources create() throws Throwable {
+    return new SynchronizedCuVSResources(CuVSResources.create());
+  }
+
+  @Override
+  public ScopedAccess access() {
+    lock.lock();
+    return new ScopedAccess() {
+      @Override
+      public long handle() {
+        return inner.access().handle();
+      }
+
+      @Override
+      public void close() {
+        lock.unlock();
+      }
+    };
+  }
+
+  @Override
+  public void close() {
+    inner.close();
+  }
+
+  @Override
+  public Path tempDirectory() {
+    return inner.tempDirectory();
+  }
+}
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
index 50e8f12a5a..3bcef6bb94 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
@@ -16,9 +16,7 @@
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-import static org.junit.Assert.assertNotNull;
-import static org.junit.Assert.assertTrue;
-import static org.junit.Assert.fail;
+import static org.junit.Assert.*;
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
@@ -45,6 +43,16 @@ public class CagraMultiThreadStabilityIT extends CuVSTestCase {
 
   private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
 
+  private final int dimensions = 256;
+  private final int queriesPerThread = 500;
+  private final int queryBatchSize = 1; // Small batch size to increase frequency of calls
+  private final int topK = 10;
+
+  @FunctionalInterface
+  private interface QueryAction {
+    void run(CagraIndex index) throws Throwable;
+  }
+
   @Before
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
@@ -53,18 +61,27 @@ public void setup() {
   }
 
   @Test
-  public void testQueryingUsingMultipleThreads() throws Throwable {
+  public void testQueryingUsingMultipleThreadsWithSharedSynchronizedResources() throws Throwable {
+    try (CuVSResources sharedResources = SynchronizedCuVSResources.create()) {
+      testQueryingUsingMultipleThreads(
+          index -> performQueryWithSharedSynchronizedResource(sharedResources, index));
+    }
+  }
+
+  @Test
+  public void testQueryingUsingMultipleThreadsWithPrivateResources() throws Throwable {
+    testQueryingUsingMultipleThreads(this::performQueryWithPrivateResource);
+  }
+
+  private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Throwable {
     final int dataSize = 10000;
-    final int dimensions = 256;
-    final int numThreads = 16; // High thread count to increase contention
-    final int queriesPerThread = 500;
-    final int queryBatchSize = 1; // Small batch size to increase frequency of calls
-    final int topK = 10;
+    final int numThreads = 16;
 
     log.info("  Dataset: {}x{}", dataSize, dimensions);
+    // High thread count to increase contention
     log.info("  Threads: {}, Queries per thread: {}", numThreads, queriesPerThread);
 
-    float[][] dataset = generateRandomDataset(dataSize, dimensions);
+    float[][] dataset = generateRandomDataset(dataSize);
 
     try (CuVSResources resources = CheckedCuVSResources.create()) {
       log.info("Creating CAGRA index for MultiThreaded stability test...");
@@ -86,105 +103,125 @@ public void testQueryingUsingMultipleThreads() throws Throwable {
 
       log.info("CAGRA index created, starting high-contention multi-threaded search...");
 
-      // Create high contention scenario that would fail without using separate resources in every thread
-      ExecutorService executor = Executors.newFixedThreadPool(numThreads);
-      List<Future<?>> futures = new ArrayList<>();
-      CountDownLatch startLatch = new CountDownLatch(1);
-      AtomicInteger successfulQueries = new AtomicInteger(0);
-      AtomicReference<Throwable> firstError = new AtomicReference<>();
-
-      for (int threadId = 0; threadId < numThreads; threadId++) {
-        final int finalThreadId = threadId;
-
-        Future<?> future =
-            executor.submit(
-                () -> {
-                  try {
-                    // Wait for all threads to start simultaneously
-                    startLatch.await();
-
-                    for (int queryId = 0; queryId < queriesPerThread; queryId++) {
-                      float[][] queries = generateRandomDataset(queryBatchSize, dimensions);
-
-                      try (CuVSResources threadResources = CheckedCuVSResources.create()) {
-                        CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-                        CagraQuery query =
-                            new CagraQuery.Builder(threadResources)
-                                .withTopK(topK)
-                                .withSearchParams(searchParams)
-                                .withQueryVectors(queries)
-                                .build();
-
-                        // This call should now work with per-thread resources
-                        SearchResults results = index.search(query);
-                        assertNotNull("Query should return results", results);
-                        assertTrue(
-                            "Query should return some results", !results.getResults().isEmpty());
-
+      // Create high contention scenario that would fail without using separate resources in every
+      // thread
+      try (ExecutorService executor = Executors.newFixedThreadPool(numThreads)) {
+        List<Future<?>> futures = new ArrayList<>();
+        CountDownLatch startLatch = new CountDownLatch(1);
+        AtomicInteger successfulQueries = new AtomicInteger(0);
+        AtomicReference<Throwable> firstError = new AtomicReference<>();
+
+        for (int threadId = 0; threadId < numThreads; threadId++) {
+          final int finalThreadId = threadId;
+
+          Future<?> future =
+              executor.submit(
+                  () -> {
+                    try {
+                      // Wait for all threads to start simultaneously
+                      startLatch.await();
+
+                      for (int queryId = 0; queryId < queriesPerThread; queryId++) {
+                        queryAction.run(index);
                         successfulQueries.incrementAndGet();
-                      }
 
-                      // No Thread.yield() - maximize contention
-                    }
+                        // No Thread.yield() - maximize contention
+                      }
 
-                    log.info("Thread {} completed successfully", finalThreadId);
+                      log.info("Thread {} completed successfully", finalThreadId);
 
-                  } catch (Throwable t) {
-                    log.error("Thread {} failed: {}", finalThreadId, t.getMessage(), t);
-                    firstError.compareAndSet(null, t);
-                    throw new RuntimeException("Thread failed", t);
-                  }
-                });
+                    } catch (Throwable t) {
+                      log.error("Thread {} failed: {}", finalThreadId, t.getMessage(), t);
+                      firstError.compareAndSet(null, t);
+                      throw new RuntimeException("Thread failed", t);
+                    }
+                  });
 
-        futures.add(future);
-      }
+          futures.add(future);
+        }
 
-      // Start all threads simultaneously to maximize contention
-      log.info("Starting all {} threads simultaneously...", numThreads);
-      startLatch.countDown();
-
-      // Wait for all threads to complete
-      boolean allCompleted = true;
-      for (Future<?> future : futures) {
-        try {
-          future.get(120, TimeUnit.SECONDS); // Longer timeout for stress test
-        } catch (Exception e) {
-          allCompleted = false;
-          log.error("Thread failed: {}", e.getMessage(), e);
-          if (firstError.get() == null) {
-            firstError.set(e);
+        // Start all threads simultaneously to maximize contention
+        log.info("Starting all {} threads simultaneously...", numThreads);
+        startLatch.countDown();
+
+        // Wait for all threads to complete
+        boolean allCompleted = true;
+        for (Future<?> future : futures) {
+          try {
+            future.get(120, TimeUnit.SECONDS); // Longer timeout for stress test
+          } catch (Exception e) {
+            allCompleted = false;
+            log.error("Thread failed: {}", e.getMessage(), e);
+            if (firstError.get() == null) {
+              firstError.set(e);
+            }
           }
         }
-      }
 
-      executor.shutdown();
-      if (!executor.awaitTermination(10, TimeUnit.SECONDS)) {
-        executor.shutdownNow();
-      }
+        executor.shutdown();
+        if (!executor.awaitTermination(10, TimeUnit.SECONDS)) {
+          executor.shutdownNow();
+        }
 
-      // Verify results
-      int expectedTotalQueries = numThreads * queriesPerThread;
-      int actualSuccessfulQueries = successfulQueries.get();
+        // Verify results
+        int expectedTotalQueries = numThreads * queriesPerThread;
+        int actualSuccessfulQueries = successfulQueries.get();
 
-      log.info("  Successful queries: {} / {}", actualSuccessfulQueries, expectedTotalQueries);
+        log.info("  Successful queries: {} / {}", actualSuccessfulQueries, expectedTotalQueries);
 
-      if (firstError.get() != null) {
-        fail(
-            "MultiThreaded stablity test failed:"
-                + " "
-                + firstError.get().getMessage());
-      }
+        if (firstError.get() != null) {
+          fail("MultiThreaded stablity test failed:" + " " + firstError.get().getMessage());
+        }
 
-      assertTrue("All threads should complete successfully", allCompleted);
-      assertTrue(
-          "All queries should complete successfully",
-          actualSuccessfulQueries == expectedTotalQueries);
+        assertTrue("All threads should complete successfully", allCompleted);
+        assertEquals(
+            "All queries should complete successfully",
+            expectedTotalQueries,
+            actualSuccessfulQueries);
+      }
 
       index.destroyIndex();
     }
   }
 
-  private float[][] generateRandomDataset(int size, int dimensions) {
+  private void performQueryWithPrivateResource(CagraIndex index) throws Throwable {
+    float[][] queries = generateRandomDataset(queryBatchSize);
+
+    try (CuVSResources threadResources = CheckedCuVSResources.create()) {
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+      CagraQuery query =
+          new CagraQuery.Builder(threadResources)
+              .withTopK(topK)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queries)
+              .build();
+
+      // This call should now work with per-thread resources
+      SearchResults results = index.search(query);
+      assertNotNull("Query should return results", results);
+      assertFalse("Query should return some results", results.getResults().isEmpty());
+    }
+  }
+
+  private void performQueryWithSharedSynchronizedResource(
+      CuVSResources threadResources, CagraIndex index) throws Throwable {
+    float[][] queries = generateRandomDataset(queryBatchSize);
+
+    CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+    CagraQuery query =
+        new CagraQuery.Builder(threadResources)
+            .withTopK(topK)
+            .withSearchParams(searchParams)
+            .withQueryVectors(queries)
+            .build();
+
+    // This call should now work with per-thread resources
+    SearchResults results = index.search(query);
+    assertNotNull("Query should return results", results);
+    assertFalse("Query should return some results", results.getResults().isEmpty());
+  }
+
+  private float[][] generateRandomDataset(int size) {
     Random random = new Random(42 + System.nanoTime());
     float[][] data = new float[size][dimensions];
 

From 6e734d5eef728c101762233c7b85a7ee8552ef4c Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Tue, 5 Aug 2025 22:26:22 +0200
Subject: [PATCH 529/623] Add NN.extend and kmeans to Python doc (#1217)

All the nearest neighbors `extend` functions were missing.

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1217
---
 docs/source/python_api.rst                    |  1 +
 docs/source/python_api/cluster.rst            | 12 +++++++++
 docs/source/python_api/cluster_kmeans.rst     | 27 +++++++++++++++++++
 docs/source/python_api/neighbors_cagra.rst    |  5 ++++
 docs/source/python_api/neighbors_hnsw.rst     |  5 ++++
 docs/source/python_api/neighbors_ivf_flat.rst |  5 ++++
 docs/source/python_api/neighbors_ivf_pq.rst   |  5 ++++
 7 files changed, 60 insertions(+)
 create mode 100644 docs/source/python_api/cluster.rst
 create mode 100644 docs/source/python_api/cluster_kmeans.rst

diff --git a/docs/source/python_api.rst b/docs/source/python_api.rst
index aa75160c1f..4c8fc47820 100644
--- a/docs/source/python_api.rst
+++ b/docs/source/python_api.rst
@@ -7,6 +7,7 @@ Python API Documentation
 .. toctree::
    :maxdepth: 4
 
+   python_api/cluster.rst
    python_api/distance.rst
    python_api/neighbors.rst
    python_api/preprocessing.rst
diff --git a/docs/source/python_api/cluster.rst b/docs/source/python_api/cluster.rst
new file mode 100644
index 0000000000..b5c0ab957c
--- /dev/null
+++ b/docs/source/python_api/cluster.rst
@@ -0,0 +1,12 @@
+Cluster
+========
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+   cluster_kmeans.rst
diff --git a/docs/source/python_api/cluster_kmeans.rst b/docs/source/python_api/cluster_kmeans.rst
new file mode 100644
index 0000000000..8fda17f80d
--- /dev/null
+++ b/docs/source/python_api/cluster_kmeans.rst
@@ -0,0 +1,27 @@
+K-Means
+=======
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+K-Means Parameters
+##################
+
+.. autoclass:: cuvs.cluster.kmeans.KMeansParams
+    :members:
+
+K-Means Fit
+###########
+
+.. autofunction:: cuvs.cluster.kmeans.fit
+
+K-Means Predict
+###############
+
+.. autofunction:: cuvs.cluster.kmeans.predict
+
+K-Means Cluster Cost
+####################
+
+.. autofunction:: cuvs.cluster.kmeans.cluster_cost
diff --git a/docs/source/python_api/neighbors_cagra.rst b/docs/source/python_api/neighbors_cagra.rst
index e7155efb83..76564ce5e7 100644
--- a/docs/source/python_api/neighbors_cagra.rst
+++ b/docs/source/python_api/neighbors_cagra.rst
@@ -44,3 +44,8 @@ Index load
 ##########
 
 .. autofunction:: cuvs.neighbors.cagra.load
+
+Index extend
+###########
+
+.. autofunction:: cuvs.neighbors.cagra.extend
diff --git a/docs/source/python_api/neighbors_hnsw.rst b/docs/source/python_api/neighbors_hnsw.rst
index 64fe5493ba..fe46a8e4b6 100644
--- a/docs/source/python_api/neighbors_hnsw.rst
+++ b/docs/source/python_api/neighbors_hnsw.rst
@@ -38,3 +38,8 @@ Index load
 ##########
 
 .. autofunction:: cuvs.neighbors.hnsw.load
+
+Index extend
+###########
+
+.. autofunction:: cuvs.neighbors.hnsw.extend
diff --git a/docs/source/python_api/neighbors_ivf_flat.rst b/docs/source/python_api/neighbors_ivf_flat.rst
index f2c21e68a1..d0846b0d67 100644
--- a/docs/source/python_api/neighbors_ivf_flat.rst
+++ b/docs/source/python_api/neighbors_ivf_flat.rst
@@ -42,3 +42,8 @@ Index load
 ##########
 
 .. autofunction:: cuvs.neighbors.ivf_flat.load
+
+Index extend
+############
+
+.. autofunction:: cuvs.neighbors.ivf_flat.extend
diff --git a/docs/source/python_api/neighbors_ivf_pq.rst b/docs/source/python_api/neighbors_ivf_pq.rst
index 57668fbc33..ec4cfdff6a 100644
--- a/docs/source/python_api/neighbors_ivf_pq.rst
+++ b/docs/source/python_api/neighbors_ivf_pq.rst
@@ -42,3 +42,8 @@ Index load
 ##########
 
 .. autofunction:: cuvs.neighbors.ivf_pq.load
+
+Index extend
+############
+
+.. autofunction:: cuvs.neighbors.ivf_pq.extend

From 94c281993bfb05dbe82310acecda671b44a997ee Mon Sep 17 00:00:00 2001
From: Jake Awe <jawe@nvidia.com>
Date: Wed, 6 Aug 2025 09:17:49 -0500
Subject: [PATCH 530/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 106 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 04d849764c..2cf7bc0f62 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,109 @@
+# cuvs 25.08.00 (6 Aug 2025)
+
+## 🚨 Breaking Changes
+
+- [Java] Using functions for mapping ([#1007](https://github.com/rapidsai/cuvs/pull/1007)) [@ldematte](https://github.com/ldematte)
+- Remove CUDA 11 from dependencies.yaml ([#962](https://github.com/rapidsai/cuvs/pull/962)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Common knn graph build params ([#949](https://github.com/rapidsai/cuvs/pull/949)) [@jinsolp](https://github.com/jinsolp)
+- stop uploading packages to downloads.rapids.ai ([#940](https://github.com/rapidsai/cuvs/pull/940)) [@jameslamb](https://github.com/jameslamb)
+
+## 🐛 Bug Fixes
+
+- [BUG] Fix Large Batch Preprocessing for NN Descent ([#1166](https://github.com/rapidsai/cuvs/pull/1166)) [@tarang-jain](https://github.com/tarang-jain)
+- Fix TieredIndexIT,  Scoped resource access change was missed ([#1156](https://github.com/rapidsai/cuvs/pull/1156)) [@punAhuja](https://github.com/punAhuja)
+- Fix for `hellinger` metric ([#1128](https://github.com/rapidsai/cuvs/pull/1128)) [@viclafargue](https://github.com/viclafargue)
+- Fix go CI failures ([#1114](https://github.com/rapidsai/cuvs/pull/1114)) [@benfred](https://github.com/benfred)
+- Conditionally post process distances in NN Descent for use with distance epilogue ([#1108](https://github.com/rapidsai/cuvs/pull/1108)) [@jinsolp](https://github.com/jinsolp)
+- [BUG] Disable flaky cpp test `AnnIVFFlatTestF_half.AnnIVFFlat/21` ([#1092](https://github.com/rapidsai/cuvs/pull/1092)) [@julianmi](https://github.com/julianmi)
+- [BUG] Fix device access check in build_sorted_mst ([#1083](https://github.com/rapidsai/cuvs/pull/1083)) [@tarang-jain](https://github.com/tarang-jain)
+- Pin Faiss Version to Last Stable Release ([#1029](https://github.com/rapidsai/cuvs/pull/1029)) [@tarang-jain](https://github.com/tarang-jain)
+- Fix verify-copyright precommit hook ([#1017](https://github.com/rapidsai/cuvs/pull/1017)) [@benfred](https://github.com/benfred)
+- Adding GH_TOKEN pass-through to summarize job ([#1011](https://github.com/rapidsai/cuvs/pull/1011)) [@msarahan](https://github.com/msarahan)
+- Reuse `&lt;version_config.h&gt;` instead of separate version constants in `c_api.h` ([#1003](https://github.com/rapidsai/cuvs/pull/1003)) [@mythrocks](https://github.com/mythrocks)
+- IVF-PQ coarse search: fix integer overflow and avoid excessive batch sizes ([#999](https://github.com/rapidsai/cuvs/pull/999)) [@achirkin](https://github.com/achirkin)
+- Fix C compile error in `tiered_index.h` ([#996](https://github.com/rapidsai/cuvs/pull/996)) [@mythrocks](https://github.com/mythrocks)
+- Fix for NN Descent negative or NaN distances ([#994](https://github.com/rapidsai/cuvs/pull/994)) [@jinsolp](https://github.com/jinsolp)
+- Adding int64 search for MG CAGRA ([#975](https://github.com/rapidsai/cuvs/pull/975)) [@viclafargue](https://github.com/viclafargue)
+- [ANN_BENCH] Fix OpenMP Sections in Wrappers ([#911](https://github.com/rapidsai/cuvs/pull/911)) [@tarang-jain](https://github.com/tarang-jain)
+- [BUG] cuvs-bench data_export fixes ([#863](https://github.com/rapidsai/cuvs/pull/863)) [@tarang-jain](https://github.com/tarang-jain)
+
+## 📖 Documentation
+
+- [ANN_BENCH] [DOCS] Add Vamana / DiskANN to cuvs-bench Docs ([#1164](https://github.com/rapidsai/cuvs/pull/1164)) [@tarang-jain](https://github.com/tarang-jain)
+- Add Golang build instructions to build.rst ([#1116](https://github.com/rapidsai/cuvs/pull/1116)) [@mythrocks](https://github.com/mythrocks)
+- Minor correction for Java test instructions ([#1081](https://github.com/rapidsai/cuvs/pull/1081)) [@mythrocks](https://github.com/mythrocks)
+- add docs on CI workflow inputs ([#1075](https://github.com/rapidsai/cuvs/pull/1075)) [@jameslamb](https://github.com/jameslamb)
+- [REVIEW][Java] Add instructions on how to run a single test ([#1004](https://github.com/rapidsai/cuvs/pull/1004)) [@ldematte](https://github.com/ldematte)
+
+## 🚀 New Features
+
+- Binary CAGRA with NN Descent ([#1133](https://github.com/rapidsai/cuvs/pull/1133)) [@tarang-jain](https://github.com/tarang-jain)
+- CAGRA build heuristics for HNSW ([#1125](https://github.com/rapidsai/cuvs/pull/1125)) [@achirkin](https://github.com/achirkin)
+- BitwiseHamming distance for NN Descent ([#1101](https://github.com/rapidsai/cuvs/pull/1101)) [@jinsolp](https://github.com/jinsolp)
+- `ReachabilityPostProcess` distance epilogue for NN Descent ([#1073](https://github.com/rapidsai/cuvs/pull/1073)) [@jinsolp](https://github.com/jinsolp)
+- Add brute force to `all_neighbors` API ([#1062](https://github.com/rapidsai/cuvs/pull/1062)) [@jinsolp](https://github.com/jinsolp)
+- [Java] Support for tiered index ([#1028](https://github.com/rapidsai/cuvs/pull/1028)) [@punAhuja](https://github.com/punAhuja)
+- Enable `build_sorted_mst` with data on host memory ([#997](https://github.com/rapidsai/cuvs/pull/997)) [@jinsolp](https://github.com/jinsolp)
+- Add C-API method to fetch cuVS version from Java. ([#935](https://github.com/rapidsai/cuvs/pull/935)) [@mythrocks](https://github.com/mythrocks)
+- Spectral Embedding ([#871](https://github.com/rapidsai/cuvs/pull/871)) [@aamijar](https://github.com/aamijar)
+- [FEA] Build Single Linkage API ([#820](https://github.com/rapidsai/cuvs/pull/820)) [@tarang-jain](https://github.com/tarang-jain)
+
+## 🛠️ Improvements
+
+- Check shape is initialized in cuvsMatrixSliceRows ([#1193](https://github.com/rapidsai/cuvs/pull/1193)) [@benfred](https://github.com/benfred)
+- Add error checking for our C example code ([#1171](https://github.com/rapidsai/cuvs/pull/1171)) [@benfred](https://github.com/benfred)
+- Removing all references to CUDA 11 from codebase ([#1150](https://github.com/rapidsai/cuvs/pull/1150)) [@cjnolet](https://github.com/cjnolet)
+- [Java]Fixed TieredIndexParams creation/destruction, calling cuvsTieredIndexParamsCreate ([#1147](https://github.com/rapidsai/cuvs/pull/1147)) [@punAhuja](https://github.com/punAhuja)
+- Fix update-version ([#1135](https://github.com/rapidsai/cuvs/pull/1135)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Reducing binary size in `ivf_pq` by deduplicating kernels ([#1130](https://github.com/rapidsai/cuvs/pull/1130)) [@jinsolp](https://github.com/jinsolp)
+- fix(docker): use versioned `-latest` tag for all `rapidsai` images ([#1129](https://github.com/rapidsai/cuvs/pull/1129)) [@gforsyth](https://github.com/gforsyth)
+- SCaNN Index build ([#1120](https://github.com/rapidsai/cuvs/pull/1120)) [@rmaschal](https://github.com/rmaschal)
+- [Java] Extend `Dataset` to work as an output data container ([#1111](https://github.com/rapidsai/cuvs/pull/1111)) [@ldematte](https://github.com/ldematte)
+- [Java] Fix HNSW params allocation ([#1110](https://github.com/rapidsai/cuvs/pull/1110)) [@ldematte](https://github.com/ldematte)
+- [Java] Fix CAGRA params allocation ([#1109](https://github.com/rapidsai/cuvs/pull/1109)) [@ldematte](https://github.com/ldematte)
+- [Java] Fix POM ([#1106](https://github.com/rapidsai/cuvs/pull/1106)) [@ldematte](https://github.com/ldematte)
+- Enforce java codeowners for managing contents of `java/` ([#1103](https://github.com/rapidsai/cuvs/pull/1103)) [@cjnolet](https://github.com/cjnolet)
+- Reduce binary size of refine functions ([#1095](https://github.com/rapidsai/cuvs/pull/1095)) [@tfeher](https://github.com/tfeher)
+- [Java] Introduce scoped resource access ([#1089](https://github.com/rapidsai/cuvs/pull/1089)) [@ldematte](https://github.com/ldematte)
+- Expose graph and dataset accessors for CAGRA to C/Python ([#1086](https://github.com/rapidsai/cuvs/pull/1086)) [@benfred](https://github.com/benfred)
+- Add warning for unused GPU when `n_clusters &lt; n_ranks` in batch `all_neighbors` ([#1072](https://github.com/rapidsai/cuvs/pull/1072)) [@jinsolp](https://github.com/jinsolp)
+- [Java] Tidy up `MemorySegment`s lifecycle ([#1069](https://github.com/rapidsai/cuvs/pull/1069)) [@ldematte](https://github.com/ldematte)
+- [Java] Refactor SearchResults implementation classes ([#1067](https://github.com/rapidsai/cuvs/pull/1067)) [@ldematte](https://github.com/ldematte)
+- Use CUDA 12.9 in Conda, Devcontainers, Spark, GHA, etc. ([#1063](https://github.com/rapidsai/cuvs/pull/1063)) [@jakirkham](https://github.com/jakirkham)
+- Exporting changed `graph_build_params` namespace into `all_neighbors` ([#1060](https://github.com/rapidsai/cuvs/pull/1060)) [@jinsolp](https://github.com/jinsolp)
+- hnsw::from_cagra: avoid allocating the graph twice ([#1057](https://github.com/rapidsai/cuvs/pull/1057)) [@achirkin](https://github.com/achirkin)
+- Improve memory efficiency for returning NN Descent distances ([#1053](https://github.com/rapidsai/cuvs/pull/1053)) [@jinsolp](https://github.com/jinsolp)
+- Deprecation notice for batching code specific to NN Descent ([#1052](https://github.com/rapidsai/cuvs/pull/1052)) [@jinsolp](https://github.com/jinsolp)
+- Remove nvidia and dask channels ([#1050](https://github.com/rapidsai/cuvs/pull/1050)) [@vyasr](https://github.com/vyasr)
+- [Java] Enforce Java code format standard ([#1049](https://github.com/rapidsai/cuvs/pull/1049)) [@narangvivek10](https://github.com/narangvivek10)
+- Make rust publish run after conda upload ([#1047](https://github.com/rapidsai/cuvs/pull/1047)) [@AyodeAwe](https://github.com/AyodeAwe)
+- refactor(cuda11): remove cuda11-only conda channels, cleanup docs ([#1046](https://github.com/rapidsai/cuvs/pull/1046)) [@gforsyth](https://github.com/gforsyth)
+- [Review][Java] Fix random segabort/segfault/double free problems ([#1045](https://github.com/rapidsai/cuvs/pull/1045)) [@ldematte](https://github.com/ldematte)
+- [Java] Add Dataset based on `MemorySegment` ([#1034](https://github.com/rapidsai/cuvs/pull/1034)) [@ldematte](https://github.com/ldematte)
+- [Java] Add Java API benchmarks ([#1033](https://github.com/rapidsai/cuvs/pull/1033)) [@ldematte](https://github.com/ldematte)
+- fix(cli): Add validation for search-mode parameter ([#1026](https://github.com/rapidsai/cuvs/pull/1026)) [@mayani-nv](https://github.com/mayani-nv)
+- [Java] Encapsulate on-heap float arrays into `Dataset` ([#1024](https://github.com/rapidsai/cuvs/pull/1024)) [@ldematte](https://github.com/ldematte)
+- refactor(shellcheck): fix all remaining warnings/errors ([#1019](https://github.com/rapidsai/cuvs/pull/1019)) [@gforsyth](https://github.com/gforsyth)
+- Instantiate only specific RAFT linewise kernels ([#1018](https://github.com/rapidsai/cuvs/pull/1018)) [@aamijar](https://github.com/aamijar)
+- Update nightly CI check to allow 30 days of failing nightly build. ([#1008](https://github.com/rapidsai/cuvs/pull/1008)) [@cjnolet](https://github.com/cjnolet)
+- [Java] Using functions for mapping ([#1007](https://github.com/rapidsai/cuvs/pull/1007)) [@ldematte](https://github.com/ldematte)
+- [REVIEW][Java] Refactor CagraBuildAndSearchIT to explicitly express different execution modes ([#1006](https://github.com/rapidsai/cuvs/pull/1006)) [@ldematte](https://github.com/ldematte)
+- [REVIEW][Java] Add jextract artifacts to gitignore ([#1005](https://github.com/rapidsai/cuvs/pull/1005)) [@ldematte](https://github.com/ldematte)
+- Remove pytest pin ([#998](https://github.com/rapidsai/cuvs/pull/998)) [@vyasr](https://github.com/vyasr)
+- [java] Utility function for `cudaMemcpy` ([#983](https://github.com/rapidsai/cuvs/pull/983)) [@mythrocks](https://github.com/mythrocks)
+- [java] Copy `pom.xml` to `target/` on build ([#981](https://github.com/rapidsai/cuvs/pull/981)) [@mythrocks](https://github.com/mythrocks)
+- Remove CUDA 11 from dependencies.yaml ([#962](https://github.com/rapidsai/cuvs/pull/962)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- refactor(rattler): remove cuda11 options and general cleanup ([#961](https://github.com/rapidsai/cuvs/pull/961)) [@gforsyth](https://github.com/gforsyth)
+- Remove CUDA 11 devcontainers and update CI scripts ([#960](https://github.com/rapidsai/cuvs/pull/960)) [@bdice](https://github.com/bdice)
+- Common knn graph build params ([#949](https://github.com/rapidsai/cuvs/pull/949)) [@jinsolp](https://github.com/jinsolp)
+- stop uploading packages to downloads.rapids.ai ([#940](https://github.com/rapidsai/cuvs/pull/940)) [@jameslamb](https://github.com/jameslamb)
+- Instantiate only specific RAFT reduction kernels ([#925](https://github.com/rapidsai/cuvs/pull/925)) [@divyegala](https://github.com/divyegala)
+- [Java] Replace the C wrapper layer with the Panama API-based Java implementation ([#916](https://github.com/rapidsai/cuvs/pull/916)) [@narangvivek10](https://github.com/narangvivek10)
+- Forward-merge branch-25.06 into branch-25.08 ([#909](https://github.com/rapidsai/cuvs/pull/909)) [@gforsyth](https://github.com/gforsyth)
+- Forward-merge branch-25.06 into branch-25.08 ([#897](https://github.com/rapidsai/cuvs/pull/897)) [@gforsyth](https://github.com/gforsyth)
+- Serialize Vamana index with SSD sector alignment per MSFT DiskANN format, generate quantized dataset for integration with DiskANN ([#846](https://github.com/rapidsai/cuvs/pull/846)) [@jamxia155](https://github.com/jamxia155)
+- Add mean and median threshold modes to binary quantization ([#716](https://github.com/rapidsai/cuvs/pull/716)) [@enp1s0](https://github.com/enp1s0)
+
 # cuvs 25.06.00 (5 Jun 2025)
 
 ## 🚨 Breaking Changes

From 222362f5f2630f445e783f48aab1529f63e547ac Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 6 Aug 2025 12:43:59 -0700
Subject: [PATCH 531/623] Processing mutual reachability dist in
 connect_knn_graph for host data (#1093)

Closes https://github.com/rapidsai/cuvs/issues/1096

Existing `connect_knn_graph` function for host data does not consider the reduction op.

This PR adds a fix to calculate mutual reachability distances for selected random cross-component neighbors instead of simple pairwise distances when `reduction_op == cuvs::sparse::neighbors::MutualReachabilityFixConnectivitiesRedOp`.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)

URL: https://github.com/rapidsai/cuvs/pull/1093
---
 cpp/src/cluster/detail/mst.cuh   | 84 +++++++++++++++++++++++---------
 cpp/tests/cluster/connect_knn.cu | 79 ++++++++++++++++++++++++------
 2 files changed, 127 insertions(+), 36 deletions(-)

diff --git a/cpp/src/cluster/detail/mst.cuh b/cpp/src/cluster/detail/mst.cuh
index f637426ee6..e73c54ac3c 100644
--- a/cpp/src/cluster/detail/mst.cuh
+++ b/cpp/src/cluster/detail/mst.cuh
@@ -17,10 +17,13 @@
 #pragma once
 
 #include "../../sparse/neighbors/cross_component_nn.cuh"
+#include "raft/core/device_mdspan.hpp"
+#include "raft/core/operators.hpp"
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/memory_type.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/label/classlabels.cuh>
+#include <raft/linalg/map.cuh>
 #include <raft/matrix/detail/gather.cuh>
 #include <raft/matrix/diagonal.cuh>
 #include <raft/sparse/op/sort.cuh>
@@ -74,7 +77,7 @@ void merge_msts(raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>&
 template <typename value_idx, typename value_t, typename red_op>
 void connect_knn_graph(
   raft::resources const& handle,
-  const value_t* X,
+  raft::device_matrix_view<const value_t, value_idx> X,
   raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>& msf,
   size_t m,
   size_t n,
@@ -93,7 +96,7 @@ void connect_knn_graph(
 
   cuvs::sparse::neighbors::cross_component_nn<value_idx, value_t>(handle,
                                                                   connected_edges,
-                                                                  X,
+                                                                  X.data_handle(),
                                                                   color,
                                                                   m,
                                                                   n,
@@ -132,22 +135,30 @@ void connect_knn_graph(
  * @param[inout] msf edge list containing the mst result
  * @param[in] m number of rows in X
  * @param[in] n number of columns in X
- * @param[in] n_components number of components in color
  * @param[inout] color the color labels array returned from the mst invocation
  * @return updated MST edge list
  */
-template <typename value_idx, typename value_t>
+template <typename value_idx, typename value_t, typename red_op = raft::identity_op>
 void connect_knn_graph(
   raft::resources const& handle,
-  const value_t* X,
+  raft::host_matrix_view<const value_t, value_idx> X,
   raft::sparse::solver::Graph_COO<value_idx, value_idx, value_t>& msf,
   size_t m,
   size_t n,
-  int n_components,
   value_idx* color,
+  red_op reduction_op                 = red_op{},
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2SqrtExpanded)
 {
-  auto stream = raft::resource::get_cuda_stream(handle);
+  using namespace cuvs::sparse::neighbors;
+  static_assert(
+    std::is_same_v<red_op, raft::identity_op> ||
+      std::is_same_v<red_op, MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t>> ||
+      std::is_same_v<red_op, FixConnectivitiesRedOp<value_idx, value_t>>,
+    "reduction_op must be identity_op, MutualReachabilityFixConnectivitiesRedOp, or "
+    "FixConnectivitiesRedOp");
+
+  auto stream      = raft::resource::get_cuda_stream(handle);
+  int n_components = get_n_components(color, m, stream);
 
   rmm::device_uvector<value_idx> d_color_remapped(m, stream);
   raft::label::make_monotonic(d_color_remapped.data(), color, m, stream, true);
@@ -190,34 +201,48 @@ void connect_knn_graph(
     host_v_indices.push_back(v);
   }
 
-  auto device_u_indices = raft::make_device_vector<value_idx, int64_t>(handle, n_components - 1);
-  auto device_v_indices = raft::make_device_vector<value_idx, int64_t>(handle, n_components - 1);
+  size_t new_nnz = n_components - 1;
+
+  auto device_u_indices = raft::make_device_vector<value_idx, value_idx>(handle, new_nnz);
+  auto device_v_indices = raft::make_device_vector<value_idx, value_idx>(handle, new_nnz);
 
-  raft::copy(device_u_indices.data_handle(), host_u_indices.data(), n_components - 1, stream);
-  raft::copy(device_v_indices.data_handle(), host_v_indices.data(), n_components - 1, stream);
+  raft::copy(device_u_indices.data_handle(), host_u_indices.data(), new_nnz, stream);
+  raft::copy(device_v_indices.data_handle(), host_v_indices.data(), new_nnz, stream);
 
-  auto X_view = raft::make_host_matrix_view<const value_t, int64_t>(X, m, n);
-  auto data_u = raft::make_device_matrix<value_t, int64_t>(handle, n_components - 1, n);
-  auto data_v = raft::make_device_matrix<value_t, int64_t>(handle, n_components - 1, n);
+  auto data_u = raft::make_device_matrix<value_t, value_idx>(handle, new_nnz, n);
+  auto data_v = raft::make_device_matrix<value_t, value_idx>(handle, new_nnz, n);
 
   raft::matrix::detail::gather(
-    handle, X_view, raft::make_const_mdspan(device_u_indices.view()), data_u.view());
+    handle, X, raft::make_const_mdspan(device_u_indices.view()), data_u.view());
   raft::matrix::detail::gather(
-    handle, X_view, raft::make_const_mdspan(device_v_indices.view()), data_v.view());
+    handle, X, raft::make_const_mdspan(device_v_indices.view()), data_v.view());
 
-  auto pairwise_dist =
-    raft::make_device_matrix<value_t, int64_t>(handle, n_components - 1, n_components - 1);
+  auto pairwise_dist = raft::make_device_matrix<value_t, value_idx>(handle, new_nnz, new_nnz);
   cuvs::distance::pairwise_distance(handle,
                                     raft::make_const_mdspan(data_u.view()),
                                     raft::make_const_mdspan(data_v.view()),
                                     pairwise_dist.view(),
                                     metric);
 
-  auto pairwise_dist_vec = raft::make_device_vector<value_t, int64_t>(handle, n_components - 1);
+  auto pairwise_dist_vec = raft::make_device_vector<value_t, value_idx>(handle, n_components - 1);
   raft::matrix::get_diagonal(
     handle, raft::make_const_mdspan(pairwise_dist.view()), pairwise_dist_vec.view());
 
-  size_t new_nnz = n_components - 1;
+  if constexpr (std::is_same<red_op,
+                             MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t>>::value) {
+    raft::linalg::map_offset(
+      handle,
+      pairwise_dist_vec.view(),
+      [pairwise_dist_ptr    = pairwise_dist_vec.data_handle(),
+       core_dist_ptr        = reduction_op.core_dists,
+       device_u_indices_ptr = device_u_indices.data_handle(),
+       device_v_indices_ptr = device_v_indices.data_handle()] __device__(auto i) {
+        float dist        = pairwise_dist_ptr[i];
+        float u_core_dist = core_dist_ptr[device_u_indices_ptr[i]];
+        float v_core_dist = core_dist_ptr[device_v_indices_ptr[i]];
+        return fmaxf(dist, fmaxf(u_core_dist, v_core_dist));
+      });
+  }
 
   // sort in order of rows to run sorted_coo_to_csr
   auto rows_begin = thrust::device_pointer_cast(device_u_indices.data_handle());
@@ -304,9 +329,24 @@ void build_sorted_mst(
 
   while (n_components > 1 && iters < max_iter) {
     if (data_on_device) {
-      connect_knn_graph<value_idx, value_t>(handle, X, mst_coo, m, n, color, reduction_op);
+      connect_knn_graph<value_idx, value_t>(
+        handle,
+        raft::make_device_matrix_view<const value_t, value_idx>(X, m, n),
+        mst_coo,
+        m,
+        n,
+        color,
+        reduction_op);
     } else {
-      connect_knn_graph<value_idx, value_t>(handle, X, mst_coo, m, n, n_components, color, metric);
+      connect_knn_graph<value_idx, value_t>(
+        handle,
+        raft::make_host_matrix_view<const value_t, value_idx>(X, m, n),
+        mst_coo,
+        m,
+        n,
+        color,
+        reduction_op,
+        metric);
     }
 
     iters++;
diff --git a/cpp/tests/cluster/connect_knn.cu b/cpp/tests/cluster/connect_knn.cu
index 70e999406d..b4fd55e5b6 100644
--- a/cpp/tests/cluster/connect_knn.cu
+++ b/cpp/tests/cluster/connect_knn.cu
@@ -25,7 +25,12 @@
 #include <raft/util/itertools.hpp>
 
 #include "../../src/cluster/detail/mst.cuh"
+#include "../../src/neighbors/detail/knn_brute_force.cuh"
+#include "../../src/neighbors/detail/reachability.cuh"
+#include "../../src/sparse/neighbors/cross_component_nn.cuh"
 #include "../neighbors/naive_knn.cuh"
+#include <raft/core/mdspan.hpp>
+#include <raft/core/operators.hpp>
 #include <rmm/device_uvector.hpp>
 
 #include <gtest/gtest.h>
@@ -41,6 +46,7 @@ struct ConnectKNNInputs {
   int n_clusters;
   int k;
   cuvs::distance::DistanceType metric;
+  bool mutual_reach;
 };
 
 template <typename T>
@@ -69,6 +75,36 @@ class ConnectKNNTest : public ::testing::TestWithParam<ConnectKNNInputs> {
                                               ps.k,
                                               ps.metric);
 
+    rmm::device_uvector<T> core_dists(ps.n_rows, stream);
+    if (ps.mutual_reach) {
+      cuvs::neighbors::detail::reachability::core_distances<int64_t, T>(
+        dists.data(), ps.k, ps.k, (size_t)ps.n_rows, core_dists.data(), stream);
+
+      auto epilogue = cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int64_t, T>{
+        core_dists.data(), 1.0};
+      cuvs::neighbors::detail::tiled_brute_force_knn<
+        T,
+        int64_t,
+        T,
+        cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int64_t, T>>(handle,
+                                                                                    database.data(),
+                                                                                    database.data(),
+                                                                                    ps.n_rows,
+                                                                                    ps.n_rows,
+                                                                                    ps.dim,
+                                                                                    ps.k,
+                                                                                    dists.data(),
+                                                                                    inds.data(),
+                                                                                    ps.metric,
+                                                                                    2.0,
+                                                                                    0,
+                                                                                    0,
+                                                                                    nullptr,
+                                                                                    nullptr,
+                                                                                    nullptr,
+                                                                                    epilogue);
+    }
+
     rmm::device_uvector<int64_t> coo_rows(queries_size, stream);
 
     raft::sparse::COO<T, int64_t> knn_coo(stream, queries_size * 2);
@@ -116,14 +152,29 @@ class ConnectKNNTest : public ::testing::TestWithParam<ConnectKNNInputs> {
     auto database_h = raft::make_host_matrix<T, int64_t>(ps.n_rows, ps.dim);
     raft::copy(database_h.data_handle(), database.data(), ps.n_rows * ps.dim, stream);
 
-    cuvs::cluster::agglomerative::detail::connect_knn_graph<int64_t, T>(handle,
-                                                                        database_h.data_handle(),
-                                                                        mst_coo,
-                                                                        ps.n_rows,
-                                                                        ps.dim,
-                                                                        n_components,
-                                                                        color.data(),
-                                                                        ps.metric);
+    if (ps.mutual_reach) {
+      cuvs::sparse::neighbors::MutualReachabilityFixConnectivitiesRedOp<int64_t, T> reduction_op(
+        core_dists.data(), (int64_t)ps.n_rows);
+      cuvs::cluster::agglomerative::detail::connect_knn_graph<int64_t, T>(
+        handle,
+        raft::make_const_mdspan(database_h.view()),
+        mst_coo,
+        ps.n_rows,
+        ps.dim,
+        color.data(),
+        reduction_op,
+        ps.metric);
+    } else {
+      cuvs::cluster::agglomerative::detail::connect_knn_graph<int64_t, T>(
+        handle,
+        raft::make_const_mdspan(database_h.view()),
+        mst_coo,
+        ps.n_rows,
+        ps.dim,
+        color.data(),
+        raft::identity_op{},
+        ps.metric);
+    }
     n_components = cuvs::sparse::neighbors::get_n_components(color.data(), ps.n_rows, stream);
 
     ASSERT_TRUE(n_components == 1);
@@ -147,12 +198,12 @@ class ConnectKNNTest : public ::testing::TestWithParam<ConnectKNNInputs> {
 };
 
 const std::vector<ConnectKNNInputs> inputs = raft::util::itertools::product<ConnectKNNInputs>(
-  {5000, 7151},                                   // n_rows
-  {64, 137},                                      // dim
-  {5, 10},                                        // n_clusters of make_blobs data
-  {16},                                           // k
-  {cuvs::distance::DistanceType::L2SqrtExpanded}  // metric
-);
+  {5000, 7151},                                    // n_rows
+  {64, 137},                                       // dim
+  {5, 10},                                         // n_clusters of make_blobs data
+  {16},                                            // k
+  {cuvs::distance::DistanceType::L2SqrtExpanded},  // metric
+  {true, false});                                  // mutual_reach
 
 typedef ConnectKNNTest<float> ConnectKNNTestF;
 TEST_P(ConnectKNNTestF, ConnectKNN) { this->basicTest(); }

From 1b29b0408f9e2772195d22a48d2f179440f97f2d Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Wed, 6 Aug 2025 17:30:19 -0400
Subject: [PATCH 532/623] Update another instance of rapids-build-backend
 (#1225)

Issue: https://github.com/rapidsai/build-planning/issues/207

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/1225
---
 conda/environments/bench_ann_cuda-129_arch-aarch64.yaml | 2 +-
 conda/environments/bench_ann_cuda-129_arch-x86_64.yaml  | 2 +-
 dependencies.yaml                                       | 4 ++--
 python/cuvs_bench/pyproject.toml                        | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index 7e5efd0693..7ffb09777b 100644
--- a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -40,7 +40,7 @@ dependencies:
 - pandas
 - pylibraft==25.10.*,>=0.0.0a0
 - pyyaml
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
 - requests
 - scikit-learn
 - setuptools
diff --git a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index 5604f56252..fcd0f89053 100644
--- a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -43,7 +43,7 @@ dependencies:
 - pandas
 - pylibraft==25.10.*,>=0.0.0a0
 - pyyaml
-- rapids-build-backend>=0.3.0,<0.4.0.dev0
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
 - requests
 - scikit-learn
 - setuptools
diff --git a/dependencies.yaml b/dependencies.yaml
index dd25ae9afb..7570ce1973 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -218,7 +218,7 @@ dependencies:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - rapids-build-backend>=0.4.0,<0.5.0.dev0
+          - &rapids_build_backend rapids-build-backend>=0.4.0,<0.5.0.dev0
       - output_types: [conda]
         packages:
           - scikit-build-core>=0.10.0
@@ -263,7 +263,7 @@ dependencies:
     common:
       - output_types: [conda, requirements, pyproject]
         packages:
-          - &rapids_build_backend rapids-build-backend>=0.3.0,<0.4.0.dev0
+          - *rapids_build_backend
           - setuptools
           - wheel
   build_py_cuvs:
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 901a37e74d..0e052c46e1 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -3,7 +3,7 @@
 [build-system]
 build-backend = "rapids_build_backend.build"
 requires = [
-    "rapids-build-backend>=0.3.0,<0.4.0.dev0",
+    "rapids-build-backend>=0.4.0,<0.5.0.dev0",
     "setuptools",
     "wheel",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.

From 2cdb85d82956bcdefc594c1f835c7a497f1a9234 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Thu, 7 Aug 2025 09:49:46 -0400
Subject: [PATCH 533/623] Updates needed when building with CUDA 13 (#1219)

cuvs also requires https://github.com/rapidsai/raft/pull/2774 to be merged

Authors:
  - Robert Maynard (https://github.com/robertmaynard)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1219
---
 cpp/include/cuvs/neighbors/common.hpp | 2 +-
 cpp/tests/CMakeLists.txt              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 8b2edaa424..2e91f3c15f 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -485,7 +485,7 @@ namespace filtering {
 enum class FilterType { None, Bitmap, Bitset };
 
 struct base_filter {
-  virtual ~base_filter()                     = default;
+  ~base_filter()                             = default;
   virtual FilterType get_filter_type() const = 0;
 };
 
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index a9ec6dfd00..a1b037f706 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -212,7 +212,7 @@ if(BUILD_TESTS)
 
   ConfigureTest(
     NAME NEIGHBORS_ALL_NEIGHBORS_TEST PATH neighbors/all_neighbors/test_float.cu GPUS 1 PERCENT 100
-    ADDITIONAL_DEP nccl
+    ADDITIONAL_DEP NCCL::NCCL
   )
 
   ConfigureTest(

From 3cd48dc5a24999a6def8fcf79cde81160fc7d061 Mon Sep 17 00:00:00 2001
From: rmaschal <rmaschal@nvidia.com>
Date: Thu, 7 Aug 2025 10:01:45 -0700
Subject: [PATCH 534/623] Fix missing sync_stream in ScaNN build (#1224)

As title. For larger number of clusters (or faster PCI-E), the work required to find cluster assignments would take longer than the HtoD copy for each batch. This would lead to overwriting of one of the batch_load_iterator device buffers before kmeans predict completed, and thus lower recall.

This adds a raft::resource::sync_stream at the end of the kmeans predict loop to avoid this. Although not technically necessary (as the prior DtoH copy into pageable memory is synchronous), sync_stream is also added to the end of the quantization loop for consistency/correctness.

Also removed one unused comment.

Authors:
  - https://github.com/rmaschal

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Tarang Jain (https://github.com/tarang-jain)

URL: https://github.com/rapidsai/cuvs/pull/1224
---
 cpp/src/neighbors/scann/detail/scann_build.cuh | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/cpp/src/neighbors/scann/detail/scann_build.cuh b/cpp/src/neighbors/scann/detail/scann_build.cuh
index 5520684f4c..da723d56e9 100644
--- a/cpp/src/neighbors/scann/detail/scann_build.cuh
+++ b/cpp/src/neighbors/scann/detail/scann_build.cuh
@@ -173,6 +173,9 @@ index<T, IdxT> build(
       res, kmeans_params, batch_view, raft::make_const_mdspan(centroids_view), batch_labels_view);
 
     dataset_vec_batches.prefetch_next_batch();
+
+    // Make sure work on device is finished before swapping buffers
+    raft::resource::sync_stream(res);
   }
 
   // AVQ update of KMeans centroids
@@ -304,18 +307,9 @@ index<T, IdxT> build(
     // quantize dataset to bfloat16, if enabled. Similar to SOAR, quantization
     // is performed in this loop to improve locality
     // TODO (rmaschal): Might be more efficient to do on CPU, to avoid DtoH copy
-
     auto bf16_dataset = raft::make_device_matrix<int16_t, int64_t>(res, batch_view.extent(0), dim);
 
     if (params.bf16_enabled) {
-      // raft::linalg::map_offset(res, bf16_dataset.view(), [batch_view, dim] __device__(size_t i) {
-      //   int64_t row_idx = i / dim;
-      //   int64_t col_idx = i % dim;
-
-      //  nv_bfloat16 val = __float2bfloat16(batch_view(row_idx, col_idx));
-
-      //  return reinterpret_cast<int16_t&>(val);
-      //});
       raft::linalg::unaryOp(
         bf16_dataset.data_handle(),
         batch_view.data_handle(),
@@ -348,6 +342,9 @@ index<T, IdxT> build(
                  bf16_dataset.size(),
                  stream);
     }
+
+    // Make sure work on device is finished before swapping buffers
+    raft::resource::sync_stream(res);
   }
 
   // Codebooks from VPQ have the shape [subspace idx, subspace dim, code]

From e2ec0c80399d1559eaa66071bee45485b1bb20f0 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Thu, 7 Aug 2025 17:36:10 -0400
Subject: [PATCH 535/623] Update rapids_config to handle user defined branch
 name (#1227)

rapids_config will use a user defined branch over `RAPIDS_BRANCH` contents

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/1227
---
 cmake/RAPIDS.cmake        | 2 +-
 cmake/rapids_config.cmake | 8 ++++++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/cmake/RAPIDS.cmake b/cmake/RAPIDS.cmake
index 40de7cefcd..ddef819498 100644
--- a/cmake/RAPIDS.cmake
+++ b/cmake/RAPIDS.cmake
@@ -18,7 +18,7 @@
 cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
 
 # Allow users to control which version is used
-if(NOT rapids-cmake-branch OR NOT rapids-cmake-version)
+if(NOT (rapids-cmake-branch OR rapids-cmake-version))
   message(
     FATAL_ERROR "The CMake variable `rapids-cmake-branch` or `rapids-cmake-version` must be defined"
   )
diff --git a/cmake/rapids_config.cmake b/cmake/rapids_config.cmake
index b706c926e7..b2c54a3f27 100644
--- a/cmake/rapids_config.cmake
+++ b/cmake/rapids_config.cmake
@@ -35,6 +35,10 @@ if(NOT _rapids_branch)
   )
 endif()
 
-set(rapids-cmake-version "${RAPIDS_VERSION_MAJOR_MINOR}")
-set(rapids-cmake-branch "${_rapids_branch}")
+if(NOT rapids-cmake-version)
+  set(rapids-cmake-version "${RAPIDS_VERSION_MAJOR_MINOR}")
+endif()
+if(NOT rapids-cmake-branch)
+  set(rapids-cmake-branch "${_rapids_branch}")
+endif()
 include("${CMAKE_CURRENT_LIST_DIR}/RAPIDS.cmake")

From afc24ee8302a57a5c7192a38fc2d74a1a47aa95f Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Thu, 7 Aug 2025 17:58:21 -0700
Subject: [PATCH 536/623] Make duplicate removal in all neighbors robust to
 distance drift across batches (#1185)

This is to fix an edge case that happens and the root cause is in issue: https://github.com/rapidsai/cuvs/issues/1056, which is about different distance results from `raft::linalg::gemm` based on the input sizes.

Right now, when merging two knn graphs from different batches, we sort by distances (i.e. keys), and if the distances are same we sort by indices (i.e. values). After doing so, we compare indices right next to each other to check for duplicates under assumption that same vectors end up with same distances.

However, due to the problem stated in issue 1056, distance for same index can be slightly different based on the size of the input matrix to gemm (or where the vector is in the entire matrix).

For example, say we are calculating nearest neighbors for vector 0.
we could end up with
```
indices = [1, 2, 3, 2, ....]
distances = [0.023, 0.02355981, 0.02355983, 0.02355987]
```
because distance between vector 0 and vector 2 is calculated as 0.02355981 in the first batch, and 0.02355987 in the second batch.

This PR fixes this issue by checking 4 neighbors to its left for duplicates, instead of checking the one next to itself.

Authors:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1185
---
 .../all_neighbors/all_neighbors_merge.cuh     | 77 ++++++++++++++-----
 1 file changed, 56 insertions(+), 21 deletions(-)

diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh
index 1ce8576f5a..a766a7fcad 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_merge.cuh
@@ -30,7 +30,7 @@
 namespace cuvs::neighbors::all_neighbors::detail {
 using namespace cuvs::neighbors;
 
-template <typename IdxT, int BLOCK_SIZE, int ITEMS_PER_THREAD>
+template <typename IdxT, int BLOCK_SIZE, int ITEMS_PER_THREAD, bool SweepAll = false>
 RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
                                    size_t graph_degree,
                                    size_t num_cluster_in_batch,
@@ -102,11 +102,12 @@ RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
 
     __syncthreads();
 
+    size_t limit = 2 * graph_degree;
     // load sorted result into shared memory to get unique values
     idxBase = threadIdx.x * ITEMS_PER_THREAD;
     for (int i = 0; i < ITEMS_PER_THREAD; i++) {
       size_t colId = idxBase + i;
-      if (colId < 2 * graph_degree) {
+      if (colId < limit) {
         blockKeys[colId]   = threadKeyValuePair[i].key;
         blockValues[colId] = threadKeyValuePair[i].value;
       }
@@ -118,8 +119,39 @@ RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
     if (threadIdx.x == 0) { uniqueMask[0] = 1; }
     for (int i = 0; i < ITEMS_PER_THREAD; i++) {
       size_t colId = idxBase + i;
-      if (colId > 0 && colId < 2 * graph_degree) {
-        uniqueMask[colId] = static_cast<int16_t>(blockValues[colId] != blockValues[colId - 1]);
+      if (colId > 0 && colId < limit) {
+        // this assumes same distance between vector from two different batches. however, currently
+        // there are subtle differences in the result based on the matrix size used to call gemm.
+        // This makes it difficult to remove duplicates, because they might no longer be right next
+        // to each other after sorting by distances. Thus, for now we sweep a neighboring window of
+        // size 4 or sweep the entire row to check for duplicates, and keep the first occurrence
+        // only.
+        // related issue: https://github.com/rapidsai/cuvs/issues/1056
+        // uniqueMask[colId] = static_cast<int16_t>(blockValues[colId] != blockValues[colId - 1]);
+
+        int is_unique = 1;
+
+        if constexpr (SweepAll) {  // sweep whole row for better deduplication
+          for (int j = 0; j < limit; ++j) {
+            if (j < colId && blockValues[j] == blockValues[colId]) {
+              is_unique = 0;
+              break;
+            }
+          }
+        } else {  // otherwise sweep a small window
+          IdxT curr_val = blockValues[colId];
+#pragma unroll
+          for (int offset = -4; offset < 0; offset++) {
+            int neighbor_idx = static_cast<int>(colId) + offset;
+            if (neighbor_idx >= 0) {
+              if (blockValues[neighbor_idx] == curr_val) {
+                is_unique = 0;
+                break;
+              }
+            }
+          }
+        }
+        uniqueMask[colId] = static_cast<int16_t>(is_unique);
       }
     }
 
@@ -127,7 +159,7 @@ RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
 
     // prefix sum
     if (threadIdx.x == 0) {
-      for (int i = 1; i < 2 * graph_degree; i++) {
+      for (int i = 1; i < limit; i++) {
         uniqueMask[i] += uniqueMask[i - 1];
       }
     }
@@ -141,7 +173,7 @@ RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
 
     for (int i = 0; i < ITEMS_PER_THREAD; i++) {
       size_t colId = idxBase + i;
-      if (colId > 0 && colId < 2 * graph_degree) {
+      if (colId > 0 && colId < limit) {
         bool is_unique       = uniqueMask[colId] != uniqueMask[colId - 1];
         int16_t global_colId = uniqueMask[colId] - 1;
         if (is_unique && static_cast<size_t>(global_colId) < graph_degree) {
@@ -153,7 +185,7 @@ RAFT_KERNEL merge_subgraphs_kernel(IdxT* cluster_data_indices,
   }
 }
 
-template <typename T, typename IdxT = int64_t>
+template <typename T, typename IdxT = int64_t, bool SweepAll = false>
 void merge_subgraphs(raft::resources const& res,
                      size_t k,
                      size_t num_data_in_cluster,
@@ -170,7 +202,7 @@ void merge_subgraphs(raft::resources const& res,
 #pragma omp critical  // for omp-using multi-gpu purposes
   {
     if (num_elems <= 128) {
-      merge_subgraphs_kernel<IdxT, 32, 4>
+      merge_subgraphs_kernel<IdxT, 32, 4, SweepAll>
         <<<num_data_in_cluster, 32, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
           inverted_indices_d,
           k,
@@ -181,7 +213,7 @@ void merge_subgraphs(raft::resources const& res,
           batch_neighbors_d,
           select_min);
     } else if (num_elems <= 512) {
-      merge_subgraphs_kernel<IdxT, 128, 4>
+      merge_subgraphs_kernel<IdxT, 128, 4, SweepAll>
         <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
           inverted_indices_d,
           k,
@@ -192,7 +224,7 @@ void merge_subgraphs(raft::resources const& res,
           batch_neighbors_d,
           select_min);
     } else if (num_elems <= 1024) {
-      merge_subgraphs_kernel<IdxT, 128, 8>
+      merge_subgraphs_kernel<IdxT, 128, 8, SweepAll>
         <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
           inverted_indices_d,
           k,
@@ -203,7 +235,7 @@ void merge_subgraphs(raft::resources const& res,
           batch_neighbors_d,
           select_min);
     } else if (num_elems <= 2048) {
-      merge_subgraphs_kernel<IdxT, 256, 8>
+      merge_subgraphs_kernel<IdxT, 256, 8, SweepAll>
         <<<num_data_in_cluster, 256, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
           inverted_indices_d,
           k,
@@ -221,7 +253,10 @@ void merge_subgraphs(raft::resources const& res,
   }
 }
 
-template <typename T, typename IdxT = int64_t, typename BeforeRemapT = int64_t>
+template <typename T,
+          typename IdxT         = int64_t,
+          typename BeforeRemapT = int64_t,
+          bool SweepAll         = false>
 void remap_and_merge_subgraphs(raft::resources const& res,
                                raft::device_vector_view<IdxT, IdxT> inverted_indices_d,
                                raft::host_vector_view<IdxT, IdxT> inverted_indices,
@@ -254,15 +289,15 @@ void remap_and_merge_subgraphs(raft::resources const& res,
              num_data_in_cluster * k,
              raft::resource::get_cuda_stream(res));
 
-  merge_subgraphs(res,
-                  k,
-                  num_data_in_cluster,
-                  inverted_indices_d.data_handle(),
-                  global_distances.data_handle(),
-                  batch_distances_d.data_handle(),
-                  global_neighbors.data_handle(),
-                  batch_neighbors_d.data_handle(),
-                  select_min);
+  merge_subgraphs<T, IdxT, SweepAll>(res,
+                                     k,
+                                     num_data_in_cluster,
+                                     inverted_indices_d.data_handle(),
+                                     global_distances.data_handle(),
+                                     batch_distances_d.data_handle(),
+                                     global_neighbors.data_handle(),
+                                     batch_neighbors_d.data_handle(),
+                                     select_min);
 }
 
 }  // namespace cuvs::neighbors::all_neighbors::detail

From 5e3a5a6391c5ac4366279430d3ac778e60b900db Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 8 Aug 2025 10:28:40 -0700
Subject: [PATCH 537/623] [Java] Exception-safe RMM Allocations (#1215)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This commit introduces exception safety for RMM allocations.

Previously, device memory allocated through `cuvsRmmAlloc()` was freed manually using `cuvsRmmFree()`, in all the index impl classes.  The problem there is that if an exception is thrown in the intervening time between alloc and free, it would lead to a leak of device memory.

This commit extends the `CloseableHandle` class to encapsulate the allocation of device memory.  This new class is used in try-with-resources blocks, to make device memory allocations exception-safe.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1215
---
 .../cuvs/internal/BruteForceIndexImpl.java    | 206 ++++++------
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 177 +++++------
 .../nvidia/cuvs/internal/TieredIndexImpl.java | 296 +++++++++---------
 .../common/CloseableRMMAllocation.java        |  86 +++++
 .../com/nvidia/cuvs/internal/common/Util.java |  13 -
 5 files changed, 424 insertions(+), 354 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 4dfef90261..83c08fedc6 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -15,6 +15,7 @@
  */
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
@@ -22,7 +23,6 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.HOST_TO_DEVICE;
 import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.INFER_DIRECTION;
-import static com.nvidia.cuvs.internal.common.Util.allocateRMMSegment;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
@@ -35,7 +35,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndex_t;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSearch;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSerialize;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
 import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
 
@@ -45,6 +44,7 @@
 import com.nvidia.cuvs.CuVSMatrix;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.SearchResults;
+import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.panama.cuvsFilter;
 import java.io.InputStream;
 import java.io.OutputStream;
@@ -118,20 +118,7 @@ public void destroyIndex() {
     try {
       int returnValue = cuvsBruteForceIndexDestroy(bruteForceIndexReference.indexPtr);
       checkCuVSError(returnValue, "cuvsBruteForceIndexDestroy");
-
-      if (bruteForceIndexReference.datasetBytes > 0) {
-        try (var resourcesAccessor = resources.access()) {
-          checkCuVSError(
-              cuvsRMMFree(
-                  resourcesAccessor.handle(),
-                  bruteForceIndexReference.datasetPtr,
-                  bruteForceIndexReference.datasetBytes),
-              "cuvsRMMFree");
-        }
-      }
-      if (bruteForceIndexReference.tensorDataArena != null) {
-        bruteForceIndexReference.tensorDataArena.close();
-      }
+      bruteForceIndexReference.close(resources);
     } finally {
       destroyed = true;
     }
@@ -158,25 +145,31 @@ private IndexReference build(
 
     try (var resourcesAccessor = resources.access()) {
       long cuvsResources = resourcesAccessor.handle();
-      MemorySegment datasetMemorySegmentP = allocateRMMSegment(cuvsResources, datasetBytes);
+      try (var closeableDataMemorySegmentP = allocateRMMSegment(cuvsResources, datasetBytes)) {
+        MemorySegment datasetMemorySegmentP = closeableDataMemorySegmentP.handle();
 
-      cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
+        cudaMemcpy(datasetMemorySegmentP, datasetMemSegment, datasetBytes, INFER_DIRECTION);
 
-      long[] datasetShape = {rows, cols};
-      var tensorDataArena = Arena.ofShared();
-      MemorySegment datasetTensor =
-          prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 1);
+        long[] datasetShape = {rows, cols};
+        var tensorDataArena = Arena.ofShared();
+        MemorySegment datasetTensor =
+            prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 1);
 
-      var returnValue = cuvsStreamSync(cuvsResources);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+        var returnValue = cuvsStreamSync(cuvsResources);
+        checkCuVSError(returnValue, "cuvsStreamSync");
 
-      returnValue = cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, index);
-      checkCuVSError(returnValue, "cuvsBruteForceBuild");
+        returnValue = cuvsBruteForceBuild(cuvsResources, datasetTensor, 0, 0.0f, index);
+        checkCuVSError(returnValue, "cuvsBruteForceBuild");
 
-      returnValue = cuvsStreamSync(cuvsResources);
-      checkCuVSError(returnValue, "cuvsStreamSync");
+        returnValue = cuvsStreamSync(cuvsResources);
+        checkCuVSError(returnValue, "cuvsStreamSync");
 
-      return new IndexReference(datasetMemorySegmentP, datasetBytes, tensorDataArena, index);
+        return new IndexReference(
+            new CloseableRMMAllocation(closeableDataMemorySegmentP),
+            datasetBytes,
+            tensorDataArena,
+            index);
+      }
     } finally {
       omp_set_num_threads(1);
     }
@@ -205,6 +198,7 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
 
       // prepare the prefiltering data
       final long prefilterDataLength;
+      final long prefilterBytes;
       final MemorySegment prefilterDataMemorySegment;
       BitSet[] prefilters = cuvsQuery.getPrefilters();
       if (prefilters != null && prefilters.length > 0) {
@@ -212,8 +206,11 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
         long[] filters = concatenatedFilters.toLongArray();
         prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
         prefilterDataLength = (long) cuvsQuery.getNumDocs() * prefilters.length;
+        long[] prefilterShape = {(prefilterDataLength + 31) / 32};
+        prefilterBytes = C_INT_BYTE_SIZE * prefilterShape[0];
       } else {
         prefilterDataLength = 0;
+        prefilterBytes = 0;
         prefilterDataMemorySegment = MemorySegment.NULL;
       }
 
@@ -223,77 +220,66 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
       try (var resourcesAccessor = cuvsQuery.getResources().access()) {
         long cuvsResources = resourcesAccessor.handle();
 
-        long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
-        long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
-        long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
-        long prefilterBytes = 0; // size assigned later
-
-        MemorySegment queriesDP = allocateRMMSegment(cuvsResources, queriesBytes);
-        MemorySegment neighborsDP = allocateRMMSegment(cuvsResources, neighborsBytes);
-        MemorySegment distancesDP = allocateRMMSegment(cuvsResources, distanceBytes);
-        MemorySegment prefilterDP = MemorySegment.NULL;
-
-        cudaMemcpy(queriesDP, querySeg, queriesBytes, INFER_DIRECTION);
-
-        long[] queriesShape = {numQueries, vectorDimension};
-        MemorySegment queriesTensor =
-            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 1);
-        long[] neighborsShape = {numQueries, topk};
-        MemorySegment neighborsTensor =
-            prepareTensor(localArena, neighborsDP, neighborsShape, 0, 64, 2, 1);
-        long[] distancesShape = {numQueries, topk};
-        MemorySegment distancesTensor =
-            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 1);
-
-        MemorySegment prefilter = cuvsFilter.allocate(localArena);
-        MemorySegment prefilterTensor;
-
-        if (prefilterDataMemorySegment == MemorySegment.NULL) {
-          cuvsFilter.type(prefilter, 0); // NO_FILTER
-          cuvsFilter.addr(prefilter, 0);
-        } else {
-          long[] prefilterShape = {(prefilterDataLength + 31) / 32};
-          long prefilterLen = prefilterShape[0];
-          prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
-
-          prefilterDP = allocateRMMSegment(cuvsResources, prefilterBytes);
-
-          cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
-
-          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 2, 1);
-
-          cuvsFilter.type(prefilter, 2);
-          cuvsFilter.addr(prefilter, prefilterTensor.address());
-        }
-
-        var returnValue = cuvsStreamSync(cuvsResources);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        returnValue =
-            cuvsBruteForceSearch(
-                cuvsResources,
-                bruteForceIndexReference.indexPtr,
-                queriesTensor,
-                neighborsTensor,
-                distancesTensor,
-                prefilter);
-        checkCuVSError(returnValue, "cuvsBruteForceSearch");
-
-        returnValue = cuvsStreamSync(cuvsResources);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
-        cudaMemcpy(distancesMemorySegment, distancesDP, distanceBytes, INFER_DIRECTION);
-
-        returnValue = cuvsRMMFree(cuvsResources, neighborsDP, neighborsBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-        returnValue = cuvsRMMFree(cuvsResources, distancesDP, distanceBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-        returnValue = cuvsRMMFree(cuvsResources, queriesDP, queriesBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-        if (prefilterBytes > 0) {
-          returnValue = cuvsRMMFree(cuvsResources, prefilterDP, prefilterBytes);
-          checkCuVSError(returnValue, "cuvsRMMFree");
+        final long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+        final long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topk;
+        final long distanceBytes = C_FLOAT_BYTE_SIZE * numQueries * topk;
+
+        try (var queriesDP = allocateRMMSegment(cuvsResources, queriesBytes);
+            var neighborsDP = allocateRMMSegment(cuvsResources, neighborsBytes);
+            var distancesDP = allocateRMMSegment(cuvsResources, distanceBytes);
+            var prefilterDP =
+                prefilterBytes > 0
+                    ? allocateRMMSegment(cuvsResources, prefilterBytes)
+                    : CloseableRMMAllocation.EMPTY) {
+
+          cudaMemcpy(queriesDP.handle(), querySeg, queriesBytes, INFER_DIRECTION);
+
+          long[] queriesShape = {numQueries, vectorDimension};
+          MemorySegment queriesTensor =
+              prepareTensor(localArena, queriesDP.handle(), queriesShape, 2, 32, 2, 1);
+          long[] neighborsShape = {numQueries, topk};
+          MemorySegment neighborsTensor =
+              prepareTensor(localArena, neighborsDP.handle(), neighborsShape, 0, 64, 2, 1);
+          long[] distancesShape = {numQueries, topk};
+          MemorySegment distancesTensor =
+              prepareTensor(localArena, distancesDP.handle(), distancesShape, 2, 32, 2, 1);
+
+          MemorySegment prefilter = cuvsFilter.allocate(localArena);
+          MemorySegment prefilterTensor;
+
+          if (prefilterDataMemorySegment == MemorySegment.NULL) {
+            cuvsFilter.type(prefilter, 0); // NO_FILTER
+            cuvsFilter.addr(prefilter, 0);
+          } else {
+            long[] prefilterShape = {(prefilterDataLength + 31) / 32};
+            cudaMemcpy(
+                prefilterDP.handle(), prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
+
+            prefilterTensor =
+                prepareTensor(localArena, prefilterDP.handle(), prefilterShape, 1, 32, 2, 1);
+
+            cuvsFilter.type(prefilter, 2);
+            cuvsFilter.addr(prefilter, prefilterTensor.address());
+          }
+
+          var returnValue = cuvsStreamSync(cuvsResources);
+          checkCuVSError(returnValue, "cuvsStreamSync");
+
+          returnValue =
+              cuvsBruteForceSearch(
+                  cuvsResources,
+                  bruteForceIndexReference.indexPtr,
+                  queriesTensor,
+                  neighborsTensor,
+                  distancesTensor,
+                  prefilter);
+          checkCuVSError(returnValue, "cuvsBruteForceSearch");
+
+          returnValue = cuvsStreamSync(cuvsResources);
+          checkCuVSError(returnValue, "cuvsStreamSync");
+
+          cudaMemcpy(neighborsMemorySegment, neighborsDP.handle(), neighborsBytes, INFER_DIRECTION);
+          cudaMemcpy(distancesMemorySegment, distancesDP.handle(), distanceBytes, INFER_DIRECTION);
         }
       }
       return BruteForceSearchResults.create(
@@ -479,27 +465,39 @@ public BruteForceIndexImpl build() throws Throwable {
    */
   private static class IndexReference {
 
-    private final MemorySegment datasetPtr;
+    private final CloseableRMMAllocation datasetAllocationHandle;
     private final long datasetBytes;
     private final Arena tensorDataArena;
     private final MemorySegment indexPtr;
 
     private IndexReference(
-        MemorySegment datasetPtr,
+        CloseableRMMAllocation datasetAllocationHandle,
         long datasetBytes,
         Arena tensorDataArena,
         MemorySegment indexPtr) {
-      this.datasetPtr = datasetPtr;
+      this.datasetAllocationHandle = datasetAllocationHandle;
       this.datasetBytes = datasetBytes;
       this.tensorDataArena = tensorDataArena;
       this.indexPtr = indexPtr;
     }
 
     private IndexReference(MemorySegment indexPtr) {
-      this.datasetPtr = MemorySegment.NULL;
+      this.datasetAllocationHandle = CloseableRMMAllocation.EMPTY;
       this.datasetBytes = 0;
       this.tensorDataArena = null;
       this.indexPtr = indexPtr;
     }
+
+    /**
+     * Free up the memory used for dataset, tensor-data.
+     */
+    private void close(CuVSResources resources) {
+      try (var resourcesAccessor = resources.access()) {
+        datasetAllocationHandle.close();
+      }
+      if (tensorDataArena != null) {
+        tensorDataArena.close();
+      }
+    }
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 704bc72b34..ac3e9ba43a 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -16,13 +16,13 @@
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.CuVSParamsHelper.*;
+import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.HOST_TO_DEVICE;
 import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.INFER_DIRECTION;
-import static com.nvidia.cuvs.internal.common.Util.allocateRMMSegment;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
@@ -43,6 +43,7 @@
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.CloseableHandle;
+import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
 import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
@@ -212,6 +213,8 @@ private static MemorySegment createCagraIndex() {
     }
   }
 
+  private static final BitSet[] EMPTY_PREFILTER_BITSET = new BitSet[0];
+
   /**
    * Invokes the native search_cagra_index via the Panama API for searching a
    * CAGRA index.
@@ -235,100 +238,92 @@ public SearchResults search(CagraQuery query) throws Throwable {
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
       MemorySegment floatsSeg = buildMemorySegment(localArena, query.getQueryVectors());
 
-      long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
-      long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
-      long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+      final long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+      final long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
+      final long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+      final boolean hasPreFilter = query.getPrefilter() != null;
+      final BitSet[] prefilters =
+          hasPreFilter ? new BitSet[] {query.getPrefilter()} : EMPTY_PREFILTER_BITSET;
+      final long prefilterDataLength = hasPreFilter ? query.getNumDocs() * prefilters.length : 0;
+      final long prefilterLen = hasPreFilter ? (prefilterDataLength + 31) / 32 : 0;
+      final long prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
       try (var resourcesAccessor = query.getResources().access()) {
         var cuvsRes = resourcesAccessor.handle();
 
-        MemorySegment queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
-        MemorySegment neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
-        MemorySegment distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
-        MemorySegment prefilterDP = MemorySegment.NULL;
-        long prefilterLen = 0;
-
-        cudaMemcpy(queriesDP, floatsSeg, queriesBytes, INFER_DIRECTION);
-
-        long[] queriesShape = {numQueries, vectorDimension};
-        MemorySegment queriesTensor =
-            prepareTensor(localArena, queriesDP, queriesShape, 2, 32, 2, 1);
-        long[] neighborsShape = {numQueries, topK};
-        MemorySegment neighborsTensor =
-            prepareTensor(localArena, neighborsDP, neighborsShape, 1, 32, 2, 1);
-        long[] distancesShape = {numQueries, topK};
-        MemorySegment distancesTensor =
-            prepareTensor(localArena, distancesDP, distancesShape, 2, 32, 2, 1);
-
-        var returnValue = cuvsStreamSync(cuvsRes);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        // prepare the prefiltering data
-        long prefilterDataLength = 0;
-        MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
-        BitSet[] prefilters;
-        if (query.getPrefilter() != null) {
-          prefilters = new BitSet[] {query.getPrefilter()};
-          BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
-          long[] filters = concatenatedFilters.toLongArray();
-          prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
-          prefilterDataLength = query.getNumDocs() * prefilters.length;
-        }
-
-        MemorySegment prefilter = cuvsFilter.allocate(localArena);
-        MemorySegment prefilterTensor;
-
-        final long prefilterBytes;
-
-        if (prefilterDataMemorySegment == MemorySegment.NULL) {
-          cuvsFilter.type(prefilter, 0); // NO_FILTER
-          cuvsFilter.addr(prefilter, 0);
-          prefilterBytes = 0;
-        } else {
-          long[] prefilterShape = {(prefilterDataLength + 31) / 32};
-          prefilterLen = prefilterShape[0];
-          prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
-
-          prefilterDP = allocateRMMSegment(cuvsRes, prefilterBytes);
-
-          cudaMemcpy(prefilterDP, prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
-
-          prefilterTensor = prepareTensor(localArena, prefilterDP, prefilterShape, 1, 32, 2, 1);
-
-          cuvsFilter.type(prefilter, 1);
-          cuvsFilter.addr(prefilter, prefilterTensor.address());
-        }
-
-        returnValue = cuvsStreamSync(cuvsRes);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        returnValue =
-            cuvsCagraSearch(
-                cuvsRes,
-                segmentFromSearchParams(localArena, query.getCagraSearchParameters()),
-                cagraIndexReference.getMemorySegment(),
-                queriesTensor,
-                neighborsTensor,
-                distancesTensor,
-                prefilter);
-        checkCuVSError(returnValue, "cuvsCagraSearch");
-
-        returnValue = cuvsStreamSync(cuvsRes);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        cudaMemcpy(neighborsMemorySegment, neighborsDP, neighborsBytes, INFER_DIRECTION);
-        cudaMemcpy(distancesMemorySegment, distancesDP, distancesBytes, INFER_DIRECTION);
-
-        returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-        returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-        returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-
-        if (prefilterLen > 0) {
-          returnValue = cuvsRMMFree(cuvsRes, prefilterDP, C_INT_BYTE_SIZE * prefilterBytes);
-          checkCuVSError(returnValue, "cuvsRMMFree");
+        try (var queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
+            var neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
+            var distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
+            var prefilterDP =
+                hasPreFilter
+                    ? allocateRMMSegment(cuvsRes, prefilterBytes)
+                    : CloseableRMMAllocation.EMPTY) {
+
+          cudaMemcpy(queriesDP.handle(), floatsSeg, queriesBytes, INFER_DIRECTION);
+
+          long[] queriesShape = {numQueries, vectorDimension};
+          MemorySegment queriesTensor =
+              prepareTensor(
+                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
+          long[] neighborsShape = {numQueries, topK};
+          MemorySegment neighborsTensor =
+              prepareTensor(
+                  localArena, neighborsDP.handle(), neighborsShape, kDLUInt(), 32, kDLCUDA(), 1);
+          long[] distancesShape = {numQueries, topK};
+          MemorySegment distancesTensor =
+              prepareTensor(
+                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA(), 1);
+
+          var returnValue = cuvsStreamSync(cuvsRes);
+          checkCuVSError(returnValue, "cuvsStreamSync");
+
+          // prepare the prefiltering data
+          MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
+          if (hasPreFilter) {
+            BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
+            long[] filters = concatenatedFilters.toLongArray();
+            prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
+          }
+
+          MemorySegment prefilter = cuvsFilter.allocate(localArena);
+          MemorySegment prefilterTensor;
+
+          if (!hasPreFilter) {
+            cuvsFilter.type(prefilter, 0); // NO_FILTER
+            cuvsFilter.addr(prefilter, 0);
+          } else {
+            long[] prefilterShape = {prefilterLen};
+
+            cudaMemcpy(
+                prefilterDP.handle(), prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
+
+            prefilterTensor =
+                prepareTensor(
+                    localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA(), 1);
+
+            cuvsFilter.type(prefilter, 1);
+            cuvsFilter.addr(prefilter, prefilterTensor.address());
+          }
+
+          returnValue = cuvsStreamSync(cuvsRes);
+          checkCuVSError(returnValue, "cuvsStreamSync");
+
+          returnValue =
+              cuvsCagraSearch(
+                  cuvsRes,
+                  segmentFromSearchParams(localArena, query.getCagraSearchParameters()),
+                  cagraIndexReference.getMemorySegment(),
+                  queriesTensor,
+                  neighborsTensor,
+                  distancesTensor,
+                  prefilter);
+          checkCuVSError(returnValue, "cuvsCagraSearch");
+
+          returnValue = cuvsStreamSync(cuvsRes);
+          checkCuVSError(returnValue, "cuvsStreamSync");
+
+          cudaMemcpy(neighborsMemorySegment, neighborsDP.handle(), neighborsBytes, INFER_DIRECTION);
+          cudaMemcpy(distancesMemorySegment, distancesDP.handle(), distancesBytes, INFER_DIRECTION);
         }
       }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index ef9d33cd43..31d8cd4bec 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -16,13 +16,13 @@
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.CuVSParamsHelper.createTieredIndexParams;
+import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
-import static com.nvidia.cuvs.internal.common.Util.*;
 import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
@@ -34,6 +34,7 @@
 import com.nvidia.cuvs.*;
 import com.nvidia.cuvs.CuVSMatrix;
 import com.nvidia.cuvs.internal.common.CloseableHandle;
+import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
@@ -48,7 +49,7 @@
 import java.util.Objects;
 
 /**
- * {@link TieredIndex} encapscaps a Tiered index, along with methods to interact
+ * {@link TieredIndex} encapsulates a Tiered index, along with methods to interact
  * with it.
  * <p>
  * TieredIndex is a hybrid index that combines brute force search for small datasets
@@ -134,39 +135,39 @@ private IndexReference build() {
 
         // TieredIndex REQUIRES device memory - allocate it
         long datasetSize = C_FLOAT_BYTE_SIZE * rows * cols;
-        MemorySegment datasetDP = allocateRMMSegment(cuvsRes, datasetSize);
+        try (var datasetDP = allocateRMMSegment(cuvsRes, datasetSize)) {
+          // Copy host to device
+          Util.cudaMemcpy(datasetDP.handle(), hostDataSeg, datasetSize, HOST_TO_DEVICE);
 
-        // Copy host to device
-        Util.cudaMemcpy(datasetDP, hostDataSeg, datasetSize, HOST_TO_DEVICE);
+          // Create tensor from device memory
+          long[] datasetShape = {rows, cols};
+          MemorySegment datasetTensor =
+              prepareTensor(
+                  localArena, datasetDP.handle(), datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
 
-        // Create tensor from device memory
-        long[] datasetShape = {rows, cols};
-        MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+          MemorySegment index = localArena.allocate(cuvsTieredIndex_t);
+          var returnValue = cuvsTieredIndexCreate(index);
+          checkCuVSError(returnValue, "cuvsTieredIndexCreate");
 
-        MemorySegment index = localArena.allocate(cuvsTieredIndex_t);
-        var returnValue = cuvsTieredIndexCreate(index);
-        checkCuVSError(returnValue, "cuvsTieredIndexCreate");
+          returnValue = cuvsStreamSync(cuvsRes);
+          checkCuVSError(returnValue, "cuvsStreamSync");
 
-        returnValue = cuvsStreamSync(cuvsRes);
-        checkCuVSError(returnValue, "cuvsStreamSync");
+          // Extract the actual index pointer that was written by Create
+          MemorySegment actualIndexPtr = index.get(C_POINTER, 0);
 
-        // Extract the actual index pointer that was written by Create
-        MemorySegment actualIndexPtr = index.get(C_POINTER, 0);
+          returnValue =
+              cuvsTieredIndexBuild(
+                  cuvsRes, indexParamsMemorySegment, datasetTensor, actualIndexPtr);
+          checkCuVSError(returnValue, "cuvsTieredIndexBuild");
 
-        returnValue =
-            cuvsTieredIndexBuild(cuvsRes, indexParamsMemorySegment, datasetTensor, actualIndexPtr);
-        checkCuVSError(returnValue, "cuvsTieredIndexBuild");
-
-        // Clean up device memory after build
-        returnValue = cuvsRMMFree(cuvsRes, datasetDP, datasetSize);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-
-        return new IndexReference(actualIndexPtr);
+          return new IndexReference(actualIndexPtr);
+        }
       }
     }
   }
 
+  private static final BitSet[] EMPTY_PREFILTER_BITSET = new BitSet[0];
+
   /**
    * Translates C search_tiered_index function to Java
    * Invokes the native search_tiered_index via the Panama API for searching a
@@ -196,116 +197,119 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
       // Get host query data
       MemorySegment hostQueriesSeg = Util.buildMemorySegment(localArena, query.getQueryVectors());
 
+      final long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+      final long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topK; // 64-bit for tiered index
+      final long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+      final boolean hasPreFilter = query.getPrefilter() != null;
+      final BitSet[] prefilters =
+          hasPreFilter ? new BitSet[] {query.getPrefilter()} : EMPTY_PREFILTER_BITSET;
+      final long prefilterDataLength = hasPreFilter ? query.getNumDocs() * prefilters.length : 0;
+      final long prefilterLen = hasPreFilter ? (prefilterDataLength + 31) / 32 : 0;
+      final long prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
+
       try (var resourceAccess = query.getResources().access()) {
         long cuvsRes = resourceAccess.handle();
 
-        long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
-        long neighborsBytes = C_LONG_BYTE_SIZE * numQueries * topK; // 64-bit for tiered index
-        long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
-
         // Allocate DEVICE memory for all data
-        MemorySegment queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
-        MemorySegment neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
-        MemorySegment distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
-
-        // Copy queries from host to device
-        var returnValue =
-            cudaMemcpy(queriesDP, hostQueriesSeg, queriesBytes, cudaMemcpyHostToDevice());
-        checkCudaError(returnValue, "cudaMemcpy");
-
-        // Create tensors from device memory
-        long[] queriesShape = {numQueries, vectorDimension};
-        MemorySegment queriesTensor =
-            prepareTensor(localArena, queriesDP, queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
-        long[] neighborsShape = {numQueries, topK};
-        MemorySegment neighborsTensor =
-            prepareTensor(
-                localArena, neighborsDP, neighborsShape, kDLInt(), 64, kDLCUDA(), 1); // 64-bit int
-        long[] distancesShape = {numQueries, topK};
-        MemorySegment distancesTensor =
-            prepareTensor(localArena, distancesDP, distancesShape, kDLFloat(), 32, kDLCUDA(), 1);
-
-        // Sync before prefilter setup
-        returnValue = cuvsStreamSync(cuvsRes);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        // Handle prefilter
-        MemorySegment prefilter = cuvsFilter.allocate(localArena);
-        final MemorySegment prefilterDP;
-        final long prefilterBytes;
-
-        if (query.getPrefilter() != null) {
-          BitSet[] prefilters = new BitSet[] {query.getPrefilter()};
-          BitSet concatenatedFilters = concatenate(prefilters, (int) query.getNumDocs());
-          long[] filters = concatenatedFilters.toLongArray();
-          MemorySegment hostPrefilterSeg = buildMemorySegment(localArena, filters);
-
-          long prefilterDataLength = query.getNumDocs() * prefilters.length;
-          long[] prefilterShape = {(prefilterDataLength + 31) / 32};
-          long prefilterLen = prefilterShape[0];
-          prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
-
-          // Allocate device memory for prefilter
-          prefilterDP = allocateRMMSegment(cuvsRes, prefilterBytes);
-
-          // Copy prefilter to device
-          checkCudaError(
-              cudaMemcpy(prefilterDP, hostPrefilterSeg, prefilterBytes, cudaMemcpyHostToDevice()),
-              "cudaMemcpy");
-
-          MemorySegment prefilterTensor =
-              prepareTensor(localArena, prefilterDP, prefilterShape, kDLUInt(), 32, kDLCUDA(), 1);
-
-          cuvsFilter.type(prefilter, 1); // BITSET
-          cuvsFilter.addr(prefilter, prefilterTensor.address());
-        } else {
-          prefilterDP = MemorySegment.NULL;
-          prefilterBytes = 0;
-          cuvsFilter.type(prefilter, 0); // NO_FILTER
-          cuvsFilter.addr(prefilter, 0);
-        }
-
-        // Perform search
-        returnValue =
-            cuvsTieredIndexSearch(
-                cuvsRes,
-                segmentFromSearchParams(query.getCagraSearchParameters(), localArena),
-                tieredIndexReference.getMemorySegment(),
-                queriesTensor,
-                neighborsTensor,
-                distancesTensor,
-                prefilter);
-        checkCuVSError(returnValue, "cuvsTieredIndexSearch");
-
-        // Copy results from device to host
-        returnValue =
-            cudaMemcpy(neighborsSeg, neighborsDP, neighborsBytes, cudaMemcpyDeviceToHost());
-        checkCudaError(returnValue, "cudaMemcpy");
-        returnValue =
-            cudaMemcpy(distancesSeg, distancesDP, distancesBytes, cudaMemcpyDeviceToHost());
-        checkCudaError(returnValue, "cudaMemcpy");
-
-        // Clean up device memory
-        returnValue = cuvsRMMFree(cuvsRes, queriesDP, queriesBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-        returnValue = cuvsRMMFree(cuvsRes, neighborsDP, neighborsBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-        returnValue = cuvsRMMFree(cuvsRes, distancesDP, distancesBytes);
-        checkCuVSError(returnValue, "cuvsRMMFree");
-
-        if (prefilterDP != MemorySegment.NULL) {
-          returnValue = cuvsRMMFree(cuvsRes, prefilterDP, prefilterBytes);
-          checkCuVSError(returnValue, "cuvsRMMFree");
+        try (var queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
+            var neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
+            var distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
+            var prefilterDP =
+                hasPreFilter
+                    ? allocateRMMSegment(cuvsRes, prefilterBytes)
+                    : CloseableRMMAllocation.EMPTY) {
+
+          // Copy queries from host to device
+          var returnValue =
+              cudaMemcpy(
+                  queriesDP.handle(), hostQueriesSeg, queriesBytes, cudaMemcpyHostToDevice());
+          checkCudaError(returnValue, "cudaMemcpy");
+
+          // Create tensors from device memory
+          long[] queriesShape = {numQueries, vectorDimension};
+          MemorySegment queriesTensor =
+              prepareTensor(
+                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
+          long[] neighborsShape = {numQueries, topK};
+          MemorySegment neighborsTensor =
+              prepareTensor(
+                  localArena,
+                  neighborsDP.handle(),
+                  neighborsShape,
+                  kDLInt(),
+                  64,
+                  kDLCUDA(),
+                  1); // 64-bit int
+          long[] distancesShape = {numQueries, topK};
+          MemorySegment distancesTensor =
+              prepareTensor(
+                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA(), 1);
+
+          // Sync before prefilter setup
+          returnValue = cuvsStreamSync(cuvsRes);
+          checkCuVSError(returnValue, "cuvsStreamSync");
+
+          // Handle prefilter
+          MemorySegment prefilter = cuvsFilter.allocate(localArena);
+
+          if (hasPreFilter) {
+            BitSet concatenatedFilters = concatenate(prefilters, (int) query.getNumDocs());
+            long[] filters = concatenatedFilters.toLongArray();
+            MemorySegment hostPrefilterSeg = buildMemorySegment(localArena, filters);
+
+            long[] prefilterShape = {prefilterLen};
+
+            // Copy prefilter to device
+            checkCudaError(
+                cudaMemcpy(
+                    prefilterDP.handle(),
+                    hostPrefilterSeg,
+                    prefilterBytes,
+                    cudaMemcpyHostToDevice()),
+                "cudaMemcpy");
+
+            MemorySegment prefilterTensor =
+                prepareTensor(
+                    localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA(), 1);
+
+            cuvsFilter.type(prefilter, 1); // BITSET
+            cuvsFilter.addr(prefilter, prefilterTensor.address());
+          } else {
+            cuvsFilter.type(prefilter, 0); // NO_FILTER
+            cuvsFilter.addr(prefilter, 0);
+          }
+
+          // Perform search
+          returnValue =
+              cuvsTieredIndexSearch(
+                  cuvsRes,
+                  segmentFromSearchParams(query.getCagraSearchParameters(), localArena),
+                  tieredIndexReference.getMemorySegment(),
+                  queriesTensor,
+                  neighborsTensor,
+                  distancesTensor,
+                  prefilter);
+          checkCuVSError(returnValue, "cuvsTieredIndexSearch");
+
+          // Copy results from device to host
+          returnValue =
+              cudaMemcpy(
+                  neighborsSeg, neighborsDP.handle(), neighborsBytes, cudaMemcpyDeviceToHost());
+          checkCudaError(returnValue, "cudaMemcpy");
+          returnValue =
+              cudaMemcpy(
+                  distancesSeg, distancesDP.handle(), distancesBytes, cudaMemcpyDeviceToHost());
+          checkCudaError(returnValue, "cudaMemcpy");
+
+          return TieredSearchResultsImpl.create(
+              neighborsLayout,
+              distancesLayout,
+              neighborsSeg,
+              distancesSeg,
+              topK,
+              query.getMapping(),
+              numQueries);
         }
-
-        return TieredSearchResultsImpl.create(
-            neighborsLayout,
-            distancesLayout,
-            neighborsSeg,
-            distancesSeg,
-            topK,
-            query.getMapping(),
-            numQueries);
       }
     }
   }
@@ -333,25 +337,25 @@ private void performExtend(CuVSMatrix extendDataset) {
 
         // Allocate device memory for extend data
         long dataSize = C_FLOAT_BYTE_SIZE * rows * cols;
-        MemorySegment datasetDP = allocateRMMSegment(cuvsRes, dataSize);
-
-        // Copy host to device
-        checkCudaError(
-            cudaMemcpy(datasetDP, hostDataSeg, dataSize, cudaMemcpyHostToDevice()), "cudaMemcpy");
-
-        // Create tensor from device memory
-        long[] datasetShape = {rows, cols};
-        MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetDP, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+        try (var datasetDP = allocateRMMSegment(cuvsRes, dataSize)) {
+          // Copy host to device
+          checkCudaError(
+              cudaMemcpy(datasetDP.handle(), hostDataSeg, dataSize, cudaMemcpyHostToDevice()),
+              "cudaMemcpy");
 
-        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
+          // Create tensor from device memory
+          long[] datasetShape = {rows, cols};
+          MemorySegment datasetTensor =
+              prepareTensor(
+                  localArena, datasetDP.handle(), datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
 
-        checkCuVSError(
-            cuvsTieredIndexExtend(cuvsRes, datasetTensor, tieredIndexReference.getMemorySegment()),
-            "cuvsTieredIndexExtend");
+          checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
 
-        // Clean up device memory
-        checkCuVSError(cuvsRMMFree(cuvsRes, datasetDP, dataSize), "cuvsRMMFree");
+          checkCuVSError(
+              cuvsTieredIndexExtend(
+                  cuvsRes, datasetTensor, tieredIndexReference.getMemorySegment()),
+              "cuvsTieredIndexExtend");
+        }
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java
new file mode 100644
index 0000000000..6dcc4c892f
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal.common;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMFree;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+
+/**
+ * A closeable handle for RMM allocations that can be used with try-with-resources.
+ * This class manages the lifecycle of a memory allocation made with RMM (RAPIDS Memory Manager).
+ * It ensures that the allocated memory is properly released when no longer needed.
+ */
+public class CloseableRMMAllocation implements CloseableHandle {
+
+  private final long cuvsResourceHandle;
+  private long numBytes;
+  private MemorySegment pointer;
+
+  private CloseableRMMAllocation(long cuvsResourceHandle, long numBytes, MemorySegment pointer) {
+    this.cuvsResourceHandle = cuvsResourceHandle;
+    this.numBytes = numBytes;
+    this.pointer = pointer;
+  }
+
+  /**
+   * Copy constructor transfers the ownership of the argument's MemorySegment to this object.
+   */
+  public CloseableRMMAllocation(CloseableRMMAllocation other) {
+    this.cuvsResourceHandle = other.cuvsResourceHandle;
+    this.numBytes = other.numBytes;
+    this.pointer = other.release();
+  }
+
+  public static CloseableRMMAllocation allocateRMMSegment(long cuvsResourceHandle, long numBytes) {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment datasetMemorySegment = localArena.allocate(C_POINTER);
+      checkCuVSError(
+          cuvsRMMAlloc(cuvsResourceHandle, datasetMemorySegment, numBytes), "cuvsRMMAlloc");
+      return new CloseableRMMAllocation(
+          cuvsResourceHandle, numBytes, datasetMemorySegment.get(C_POINTER, 0));
+    }
+  }
+
+  @Override
+  public MemorySegment handle() {
+    return pointer;
+  }
+
+  private MemorySegment release() {
+    var oldPointer = pointer;
+    pointer = MemorySegment.NULL;
+    numBytes = 0;
+    return oldPointer;
+  }
+
+  private boolean mustClose() {
+    return pointer != MemorySegment.NULL;
+  }
+
+  @Override
+  public void close() {
+    if (mustClose()) {
+      checkCuVSError(cuvsRMMFree(cuvsResourceHandle, pointer, numBytes), "cuvsRMMFree");
+    }
+  }
+
+  public static CloseableRMMAllocation EMPTY = new CloseableRMMAllocation(0, 0, MemorySegment.NULL);
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 6d08878650..d5972ecca7 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -19,12 +19,10 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceCount;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceProperties_v2;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemGetInfo;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaSetDevice;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsRMMAlloc;
 import static com.nvidia.cuvs.internal.panama.headers_h.size_t;
 
 import com.nvidia.cuvs.GPUInfo;
@@ -351,15 +349,4 @@ public static MemorySegment prepareTensor(
 
     return tensor;
   }
-
-  public static MemorySegment allocateRMMSegment(long resourceHandle, long datasetBytes) {
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment datasetMemorySegment = localArena.allocate(C_POINTER);
-
-      var returnValue = cuvsRMMAlloc(resourceHandle, datasetMemorySegment, datasetBytes);
-      checkCuVSError(returnValue, "cuvsRMMAlloc");
-
-      return datasetMemorySegment.get(C_POINTER, 0);
-    }
-  }
 }

From 6947ac41166a5511b28e023999aa093f0b54e424 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 8 Aug 2025 15:16:17 -0700
Subject: [PATCH 538/623] Drop log level for CAGRA trace messages (#1229)

This is a follow-up to #1226, which aims to reduce the amount of noise in the Java test logs.

In the course of making the changes in #1226, one observes that there exist some `TRACE` messages from `libcuvs` that are being logged at the `INFO` level.  Thousands of these fill up the Java logs, obscuring some legitimate warnings. For instance:
1. `Graph optimized, creating index` and `optimizing graph` appear 4574 times.
2. `Saving CAGRA index with dataset` appears 1025 times.

There isn't any further information in the messages, beyond indicating that position in code.

This commit changes the level of the first message to `TRACE`, and the second to `DEBUG` (to match the logs around it).

AFAICT, this shouldn't have an effect on the tests, or have impact on user-logs, beyond reducing log volume.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1229
---
 cpp/src/neighbors/detail/cagra/cagra_build.cuh     | 4 ++--
 cpp/src/neighbors/detail/cagra/cagra_serialize.cuh | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 651e6bbb85..07c9157b72 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -769,14 +769,14 @@ index<T, IdxT> build(
 
     cagra_graph = raft::make_host_matrix<IdxT, int64_t>(dataset.extent(0), graph_degree);
 
-    RAFT_LOG_INFO("optimizing graph");
+    RAFT_LOG_TRACE("optimizing graph");
     optimize<IdxT>(res, knn_graph->view(), cagra_graph.view(), params.guarantee_connectivity);
 
     // free intermediate graph before trying to create the index
     knn_graph.reset();
   }
 
-  RAFT_LOG_INFO("Graph optimized, creating index");
+  RAFT_LOG_TRACE("Graph optimized, creating index");
 
   // Construct an index from dataset and optimized knn graph.
   if (params.compression.has_value()) {
diff --git a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
index 4bd761dc60..8b0990ac6f 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -74,7 +74,7 @@ void serialize(raft::resources const& res,
 
   raft::serialize_scalar(res, os, include_dataset);
   if (include_dataset) {
-    RAFT_LOG_INFO("Saving CAGRA index with dataset");
+    RAFT_LOG_DEBUG("Saving CAGRA index with dataset");
     neighbors::detail::serialize(res, os, index_.data());
   } else {
     RAFT_LOG_DEBUG("Saving CAGRA index WITHOUT dataset");

From 811bc0068e5023d9988afbf3b6fd424f8c1423f3 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Mon, 11 Aug 2025 07:03:04 -0700
Subject: [PATCH 539/623] Reduce noise in Java tests (#1226)

When the Java tests run currently, they produce a large amount of logs to the console.  This includes contents of the test datasets, and various messages that ought to be trace/debug messages.

This commit changes a lot of these logs to trace/debug messages, to reduce the console log noise.

Note that this doesn't completely eliminate unnecessary noise.  There will need to be follow-up changes to `libcuvs` itself.  We might also need to investigate some of the warnings that were previously obscured by the log volume.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1226
---
 .../nvidia/cuvs/BruteForceRandomizedIT.java   | 12 +++----
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 32 +++++++++----------
 .../cuvs/CagraMultiThreadStabilityIT.java     | 16 +++++-----
 .../com/nvidia/cuvs/CagraRandomizedIT.java    | 14 ++++----
 .../java/com/nvidia/cuvs/CuVSResourcesIT.java | 12 +++----
 .../java/com/nvidia/cuvs/CuVSTestCase.java    | 10 +++---
 .../com/nvidia/cuvs/HnswBuildAndSearchIT.java |  2 +-
 .../com/nvidia/cuvs/HnswRandomizedIT.java     | 12 +++----
 8 files changed, 55 insertions(+), 55 deletions(-)

diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 4a446c249c..6bfbf4d2c7 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -36,7 +36,7 @@ public class BruteForceRandomizedIT extends CuVSTestCase {
   public void setup() {
     assumeTrue(isLinuxAmd64());
     initializeRandom();
-    log.info("Random context initialized for test.");
+    log.trace("Random context initialized for test.");
   }
 
   @Test
@@ -80,10 +80,10 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     // Generate random query vectors
     float[][] queries = generateData(random, numQueries, dimensions);
 
-    log.info("Dataset size: {}x{}", datasetSize, dimensions);
-    log.info("Query size: {}x{}", numQueries, dimensions);
-    log.info("TopK: {}", topK);
-    log.info("Use native memory dataset? " + useNativeMemoryDataset);
+    log.debug("Dataset size: {}x{}", datasetSize, dimensions);
+    log.debug("Query size: {}x{}", numQueries, dimensions);
+    log.debug("TopK: {}", topK);
+    log.debug("Use native memory dataset? " + useNativeMemoryDataset);
 
     // Debugging: Log dataset and queries
     if (log.isDebugEnabled()) {
@@ -138,7 +138,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
                 .build();
       }
 
-      log.info("Index built successfully. Executing search...");
+      log.trace("Index built successfully. Executing search...");
       SearchResults results = index.search(query);
 
       compareResults(results, expected, topK, datasetSize, numQueries);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 39e8226250..cc7879a43f 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -62,7 +62,7 @@ public class CagraBuildAndSearchIT extends CuVSTestCase {
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
     initializeRandom();
-    log.info("Random context initialized for test.");
+    log.trace("Random context initialized for test.");
   }
 
   private static void runConcurrently(int nThreads, Supplier<Runnable> runnableSupplier)
@@ -379,7 +379,7 @@ public void testPrefilteringReducesResults() throws Throwable {
 
       SearchResults fullSearchResults = index.search(fullQuery);
       List<Map<Integer, Float>> fullResults = fullSearchResults.getResults();
-      log.info("Full results: {}", fullResults);
+      log.debug("Full results: {}", fullResults);
 
       // Apply prefilter: only allow ids 0 and 2 (bitset: 1100)
       BitSet prefilter = new BitSet(4);
@@ -396,7 +396,7 @@ public void testPrefilteringReducesResults() throws Throwable {
 
       SearchResults filteredSearchResults = index.search(filteredQuery);
       List<Map<Integer, Float>> filteredResults = filteredSearchResults.getResults();
-      log.info("Filtered results: {}", filteredResults);
+      log.debug("Filtered results: {}", filteredResults);
 
       assertEquals(expectedResults, fullResults);
       assertEquals(expectedFilteredResults, filteredResults);
@@ -461,14 +461,14 @@ private void queryAndCompare(
     SearchResults results = index1.search(cuvsQuery);
 
     // Check results
-    log.info(results.getResults().toString());
+    log.debug(results.getResults().toString());
     checkResults(expectedResults, results.getResults());
 
     // Search from the second index
     results = index2.search(cuvsQuery);
 
     // Check results
-    log.info(results.getResults().toString());
+    log.debug(results.getResults().toString());
     checkResults(expectedResults, results.getResults());
   }
 
@@ -554,23 +554,23 @@ public void testMergingIndexes() throws Throwable {
               .withMetric(CuvsDistanceType.L2Expanded)
               .build();
 
-      log.info("Building first index...");
+      log.trace("Building first index...");
       CagraIndex index1 =
           CagraIndex.newBuilder(resources)
               .withDataset(vector1)
               .withIndexParams(indexParams)
               .build();
 
-      log.info("Building second index...");
+      log.trace("Building second index...");
       CagraIndex index2 =
           CagraIndex.newBuilder(resources)
               .withDataset(vector2)
               .withIndexParams(indexParams)
               .build();
 
-      log.info("Merging indexes...");
+      log.trace("Merging indexes...");
       CagraIndex mergedIndex = CagraIndex.merge(new CagraIndex[] {index1, index2});
-      log.info("Merge completed successfully");
+      log.trace("Merge completed successfully");
 
       CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
@@ -582,9 +582,9 @@ public void testMergingIndexes() throws Throwable {
               .withMapping(SearchResults.IDENTITY_MAPPING)
               .build();
 
-      log.info("Searching merged index...");
+      log.trace("Searching merged index...");
       SearchResults results = mergedIndex.search(query);
-      log.info("Search results: " + results.getResults().toString());
+      log.debug("Search results: " + results.getResults().toString());
 
       assertEquals(expectedResults, results.getResults());
 
@@ -644,14 +644,14 @@ public void testMergeStrategies() throws Throwable {
               .withMetric(CuvsDistanceType.L2Expanded)
               .build();
 
-      log.info("Building first index...");
+      log.trace("Building first index...");
       CagraIndex index1 =
           CagraIndex.newBuilder(resources)
               .withDataset(vector1)
               .withIndexParams(indexParams)
               .build();
 
-      log.info("Building second index...");
+      log.trace("Building second index...");
       CagraIndex index2 =
           CagraIndex.newBuilder(resources)
               .withDataset(vector2)
@@ -673,10 +673,10 @@ public void testMergeStrategies() throws Throwable {
               .withStrategy(MergeStrategy.PHYSICAL)
               .build();
 
-      log.info("Merging indexes with PHYSICAL strategy...");
+      log.trace("Merging indexes with PHYSICAL strategy...");
       CagraIndex physicalMergedIndex =
           CagraIndex.merge(new CagraIndex[] {index1, index2}, physicalMergeParams);
-      log.info("Physical merge completed successfully");
+      log.trace("Physical merge completed successfully");
 
       CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
@@ -688,7 +688,7 @@ public void testMergeStrategies() throws Throwable {
               .withMapping(SearchResults.IDENTITY_MAPPING)
               .build();
 
-      log.info("Searching physically merged index...");
+      log.trace("Searching physically merged index...");
       SearchResults physicalResults = physicalMergedIndex.search(query);
       assertNotNull("Physical merge search results should not be null", physicalResults);
       assertEquals(
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
index 3bcef6bb94..1df57d1e0b 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
@@ -57,7 +57,7 @@ private interface QueryAction {
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
     initializeRandom();
-    log.info("Multi-threaded stability test initialized");
+    log.trace("Multi-threaded stability test initialized");
   }
 
   @Test
@@ -77,14 +77,14 @@ private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Th
     final int dataSize = 10000;
     final int numThreads = 16;
 
-    log.info("  Dataset: {}x{}", dataSize, dimensions);
+    log.debug("  Dataset: {}x{}", dataSize, dimensions);
     // High thread count to increase contention
-    log.info("  Threads: {}, Queries per thread: {}", numThreads, queriesPerThread);
+    log.debug("  Threads: {}, Queries per thread: {}", numThreads, queriesPerThread);
 
     float[][] dataset = generateRandomDataset(dataSize);
 
     try (CuVSResources resources = CheckedCuVSResources.create()) {
-      log.info("Creating CAGRA index for MultiThreaded stability test...");
+      log.trace("Creating CAGRA index for MultiThreaded stability test...");
 
       CagraIndexParams indexParams =
           new CagraIndexParams.Builder()
@@ -101,7 +101,7 @@ private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Th
               .withIndexParams(indexParams)
               .build();
 
-      log.info("CAGRA index created, starting high-contention multi-threaded search...");
+      log.trace("CAGRA index created, starting high-contention multi-threaded search...");
 
       // Create high contention scenario that would fail without using separate resources in every
       // thread
@@ -128,7 +128,7 @@ private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Th
                         // No Thread.yield() - maximize contention
                       }
 
-                      log.info("Thread {} completed successfully", finalThreadId);
+                      log.trace("Thread {} completed successfully", finalThreadId);
 
                     } catch (Throwable t) {
                       log.error("Thread {} failed: {}", finalThreadId, t.getMessage(), t);
@@ -141,7 +141,7 @@ private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Th
         }
 
         // Start all threads simultaneously to maximize contention
-        log.info("Starting all {} threads simultaneously...", numThreads);
+        log.debug("Starting all {} threads simultaneously...", numThreads);
         startLatch.countDown();
 
         // Wait for all threads to complete
@@ -167,7 +167,7 @@ private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Th
         int expectedTotalQueries = numThreads * queriesPerThread;
         int actualSuccessfulQueries = successfulQueries.get();
 
-        log.info("  Successful queries: {} / {}", actualSuccessfulQueries, expectedTotalQueries);
+        log.debug("  Successful queries: {} / {}", actualSuccessfulQueries, expectedTotalQueries);
 
         if (firstError.get() != null) {
           fail("MultiThreaded stablity test failed:" + " " + firstError.get().getMessage());
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 02b5a6526a..69e28d20aa 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -36,7 +36,7 @@ public class CagraRandomizedIT extends CuVSTestCase {
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
     initializeRandom();
-    log.info("Random context initialized for test.");
+    log.trace("Random context initialized for test.");
   }
 
   @Test
@@ -87,10 +87,10 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     // Generate random query vectors
     float[][] queries = generateData(random, numQueries, dimensions);
 
-    log.info("Dataset size: {}x{}", datasetSize, dimensions);
-    log.info("Query size: {}x{}", numQueries, dimensions);
-    log.info("TopK: {}", topK);
-    log.info("Use native memory dataset? " + useNativeMemoryDataset);
+    log.debug("Dataset size: {}x{}", datasetSize, dimensions);
+    log.debug("Query size: {}x{}", numQueries, dimensions);
+    log.debug("TopK: {}", topK);
+    log.debug("Use native memory dataset? " + useNativeMemoryDataset);
 
     // Debugging: Log dataset and queries
     if (log.isDebugEnabled()) {
@@ -138,7 +138,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
                 .withIndexParams(indexParams)
                 .build();
       }
-      log.info("Index built successfully.");
+      log.trace("Index built successfully.");
 
       try {
         // Execute search and retrieve results
@@ -153,7 +153,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
         }
 
         CagraQuery query = queryBuilder.build();
-        log.info("Query built successfully. Executing search...");
+        log.trace("Query built successfully. Executing search...");
         SearchResults results = index.search(query);
 
         compareResults(results, expected, topK, datasetSize, numQueries);
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
index a599089dce..e479a6ebda 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
@@ -43,7 +43,7 @@ public void testConcurrentAccessViaCheckedCuVSResourcesIsForbidden() throws Thro
         var executor = Executors.newFixedThreadPool(1)) {
 
       try (var access1 = resources.access()) {
-        log.info(
+        log.debug(
             "Outer access to resource {} from {}",
             access1.handle(),
             Thread.currentThread().threadId());
@@ -56,18 +56,18 @@ public void testConcurrentAccessViaCheckedCuVSResourcesIsForbidden() throws Thro
                       executor.submit(
                           () -> {
                             try (var access2 = resources.access()) {
-                              log.info(
+                              log.debug(
                                   "Nested access to resource {} from {}",
                                   access2.handle(),
                                   Thread.currentThread().threadId());
-                              log.info("Nested access finished");
+                              log.debug("Nested access finished");
                             }
                           });
                   future.get();
                 });
         assertEquals(IllegalStateException.class, exception.getCause().getClass());
         assertEquals(expectedError, exception.getCause().getMessage());
-        log.info("Outer access finished");
+        log.debug("Outer access finished");
       }
     }
   }
@@ -78,7 +78,7 @@ public void testSequentialAccessViaCheckedCuVSResourcesIsAllowed() throws Throwa
         var executor = Executors.newFixedThreadPool(1)) {
 
       try (var access1 = resources.access()) {
-        log.info(
+        log.debug(
             "Access 1 to resource {} from {}", access1.handle(), Thread.currentThread().threadId());
       }
 
@@ -86,7 +86,7 @@ public void testSequentialAccessViaCheckedCuVSResourcesIsAllowed() throws Throwa
           executor.submit(
               () -> {
                 try (var access2 = resources.access()) {
-                  log.info(
+                  log.debug(
                       "Access 2 to resource {} from {}",
                       access2.handle(),
                       Thread.currentThread().threadId());
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
index 17fe7d5206..fee3c87cca 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSTestCase.java
@@ -35,7 +35,7 @@ public abstract class CuVSTestCase {
 
   protected void initializeRandom() {
     random = RandomizedContext.current().getRandom();
-    log.info("Test seed: " + RandomizedContext.current().getRunnerSeedAsString());
+    log.debug("Test seed: " + RandomizedContext.current().getRunnerSeedAsString());
   }
 
   protected float[][] generateData(Random random, int rows, int cols) {
@@ -77,7 +77,7 @@ protected List<List<Integer>> generateExpectedResults(
       neighborsResult.add(neighbors.subList(0, Math.min(topK * 2 + 10, dataset.length)));
     }
 
-    log.info("Expected results generated successfully.");
+    log.trace("Expected results generated successfully.");
     return neighborsResult;
   }
 
@@ -89,8 +89,8 @@ protected void compareResults(
       int numQueries) {
 
     for (int i = 0; i < numQueries; i++) {
-      log.info("Results returned for query " + i + ": " + results.getResults().get(i).keySet());
-      log.info(
+      log.debug("Results returned for query " + i + ": " + results.getResults().get(i).keySet());
+      log.debug(
           "Expected results for query "
               + i
               + ": "
@@ -109,7 +109,7 @@ protected void compareResults(
               .toList();
 
       // just make sure that the first 5 results are in the expected list (which
-      // comprises of 2*topK results)
+      // consists of 2*topK results)
       for (int j = 0; j < Math.min(5, sortedResultKeys.size()); j++) {
         assertTrue(
             "Not found in expected list: " + sortedResultKeys.get(j),
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index ab041ec24b..b2b86c37ad 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -93,7 +93,7 @@ private static void indexAndQueryOnce(
         SearchResults results = hnswIndex.search(hnswQuery);
 
         // Check results
-        log.info(results.getResults().toString());
+        log.debug(results.getResults().toString());
         checkResults(expectedResults, results.getResults());
 
         // Cleanup
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index 3000c125a3..8cda810c6c 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -40,7 +40,7 @@ public class HnswRandomizedIT extends CuVSTestCase {
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
     initializeRandom();
-    log.info("Random context initialized for test.");
+    log.trace("Random context initialized for test.");
   }
 
   @Test
@@ -72,10 +72,10 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     // Generate random query vectors
     float[][] queries = generateData(random, numQueries, dimensions);
 
-    log.info("Dataset size: {}x{}", datasetSize, dimensions);
-    log.info("Query size: {}x{}", numQueries, dimensions);
-    log.info("TopK: {}", topK);
-    log.info("Use native memory dataset? " + useNativeMemoryDataset);
+    log.debug("Dataset size: {}x{}", datasetSize, dimensions);
+    log.debug("Query size: {}x{}", numQueries, dimensions);
+    log.debug("TopK: {}", topK);
+    log.debug("Use native memory dataset? " + useNativeMemoryDataset);
 
     // Debugging: Log dataset and queries
     if (log.isDebugEnabled()) {
@@ -160,7 +160,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
                   .withTopK(topK)
                   .build();
 
-          log.info("Index built successfully. Executing search...");
+          log.trace("Index built successfully. Executing search...");
           SearchResults results = hnswIndex.search(hnswQuery);
           compareResults(results, expected, topK, datasetSize, numQueries);
 

From aff1e3b4291b735fa1838dccd57b5ec491141ec4 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 11 Aug 2025 20:20:33 -0400
Subject: [PATCH 540/623] Use rapids_cuda_enable_fatbin_compression (#1239)

Standardize compression flags via rapids_cuda_enable_fatbin_compression

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1239
---
 cpp/cmake/modules/ConfigureCUDA.cmake | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index 0b6ebbaad2..5ce9b918b4 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -48,13 +48,8 @@ list(APPEND CUVS_CUDA_FLAGS --expt-extended-lambda --expt-relaxed-constexpr)
 list(APPEND CUVS_CXX_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
 list(APPEND CUVS_CUDA_FLAGS "-DCUDA_API_PER_THREAD_DEFAULT_STREAM")
 # make sure we produce smallest binary size
-list(APPEND CUVS_CUDA_FLAGS -Xfatbin=-compress-all)
-if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA"
-   AND (CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9 AND CMAKE_CUDA_COMPILER_VERSION
-                                                                   VERSION_LESS 13.0)
-)
-  list(APPEND CUVS_CUDA_FLAGS -Xfatbin=--compress-level=3)
-endif()
+include(${rapids-cmake-dir}/cuda/enable_fatbin_compression.cmake)
+rapids_cuda_enable_fatbin_compression(VARIABLE CUVS_CUDA_FLAGS TUNE_FOR rapids)
 
 # Option to enable line info in CUDA device compilation to allow introspection when profiling /
 # memchecking

From 34032a813c9c38573a6cc091d98669870ee2f053 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Tue, 12 Aug 2025 07:50:12 +0200
Subject: [PATCH 541/623] [Java] Add CAGRA index graph accessor/build from
 graph (host memory) (#1216)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR leverages the functions introduced by #1086 and the data structures introduced by #1111 to access, copy, and re-create an index to/from a CAGRA graph.

Supersedes #1105

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1216
---
 .../main/java/com/nvidia/cuvs/CagraIndex.java |  13 ++
 .../main/java/com/nvidia/cuvs/CuVSMatrix.java |   1 +
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 125 +++++++++++++++++-
 .../cuvs/internal/CuVSHostMatrixImpl.java     |  15 ++-
 .../cuvs/internal/CuVSMatrixBaseImpl.java     |  20 +++
 .../com/nvidia/cuvs/internal/common/Util.java |  13 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    |  42 ++++++
 7 files changed, 217 insertions(+), 12 deletions(-)

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index 84487857c8..427b5e22d2 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -50,6 +50,13 @@ public interface CagraIndex {
    */
   SearchResults search(CagraQuery query) throws Throwable;
 
+  /** Returns the CAGRA graph
+   *
+   * @return a {@link CuVSMatrix} encapsulating the native int (uint32_t) array used to represent
+   * the cagra graph
+   */
+  CuVSMatrix getGraph();
+
   /**
    * A method to persist a CAGRA index using an instance of {@link OutputStream}
    * for writing index bytes.
@@ -208,6 +215,12 @@ interface Builder {
      */
     Builder from(InputStream inputStream);
 
+    /**
+     * Sets a CAGRA graph instance to re-create an index from a
+     * previously built graph.
+     */
+    Builder from(CuVSMatrix graph);
+
     /**
      * Sets the dataset vectors for building the {@link CagraIndex}.
      *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
index 0b892fbc5f..f8e60c5555 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
@@ -30,6 +30,7 @@ public interface CuVSMatrix extends AutoCloseable {
   enum DataType {
     FLOAT,
     INT,
+    UINT,
     BYTE
   }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index ac3e9ba43a..489213f4da 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -45,6 +45,7 @@
 import com.nvidia.cuvs.internal.common.CloseableHandle;
 import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
+import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
 import com.nvidia.cuvs.internal.panama.cuvsCagraMergeParams;
@@ -122,6 +123,31 @@ private CagraIndexImpl(IndexReference indexReference, CuVSResources resources) {
     this.destroyed = false;
   }
 
+  /**
+   * Constructor for creating an index from a pre-build CAGRA graph
+   *
+   * @param metric      the distance type used
+   * @param graph       a previously built CAGRA graph
+   * @param dataset     the dataset used for indexing
+   * @param resources   an instance of {@link CuVSResources}
+   */
+  private CagraIndexImpl(
+      CagraIndexParams.CuvsDistanceType metric,
+      CuVSMatrix graph,
+      CuVSMatrix dataset,
+      CuVSResources resources) {
+    Objects.requireNonNull(graph);
+    Objects.requireNonNull(dataset);
+
+    this.resources = resources;
+
+    assert graph instanceof CuVSMatrixBaseImpl;
+    assert dataset instanceof CuVSMatrixBaseImpl;
+
+    this.cagraIndexReference =
+        fromGraph(metric, (CuVSMatrixBaseImpl) graph, (CuVSMatrixBaseImpl) dataset);
+  }
+
   private void checkNotDestroyed() {
     if (destroyed) {
       throw new IllegalStateException("destroyed");
@@ -164,9 +190,12 @@ private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImp
       omp_set_num_threads(numWriterThreads);
 
       MemorySegment dataSeg = dataset.memorySegment();
+      // TODO: type kDLCPU()/kDLCUDA() should be aligned with the CuVSMatrixBaseImpl type (host or
+      // device?)
 
       long[] datasetShape = {rows, cols};
-      MemorySegment datasetTensor = prepareTensor(localArena, dataSeg, datasetShape, 2, 32, 2, 1);
+      MemorySegment datasetTensor =
+          prepareTensor(localArena, dataSeg, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
 
       var index = createCagraIndex();
 
@@ -381,6 +410,81 @@ public void serialize(OutputStream outputStream, Path tempFile, int bufferLength
     }
   }
 
+  @Override
+  public CuVSMatrix getGraph() {
+    try (var localArena = Arena.ofConfined()) {
+      var outPtr = localArena.allocate(uint32_t);
+      checkCuVSError(
+          cuvsCagraIndexGetGraphDegree(cagraIndexReference.getMemorySegment(), outPtr),
+          "cuvsCagraIndexGetGraphDegree");
+      long graphDegree = Util.dereferenceUnsignedInt(outPtr);
+
+      checkCuVSError(
+          cuvsCagraIndexGetSize(cagraIndexReference.getMemorySegment(), outPtr),
+          "cuvsCagraIndexGetSize");
+      long size = Util.dereferenceUnsignedInt(outPtr);
+
+      // TODO: use a "device" graph + tensor, avoid (defer) copy
+      var graph = new CuVSHostMatrixArenaImpl(size, graphDegree, CuVSMatrix.DataType.UINT);
+      var graphHostTensor = graph.toTensor(localArena);
+      var graphDeviceTensor =
+          prepareTensor(
+              localArena,
+              MemorySegment.NULL,
+              new long[] {size, graphDegree},
+              kDLUInt(),
+              32,
+              kDLCUDA(),
+              1);
+      checkCuVSError(
+          cuvsCagraIndexGetGraph(cagraIndexReference.getMemorySegment(), graphDeviceTensor),
+          "cuvsCagraIndexGetGraph");
+
+      try (var resourceAccess = resources.access()) {
+        var cuvsRes = resourceAccess.handle();
+        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
+
+        checkCuVSError(
+            cuvsMatrixCopy(cuvsRes, graphDeviceTensor, graphHostTensor), "cuvsMatrixCopy");
+
+        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
+      }
+
+      return graph;
+    }
+  }
+
+  private IndexReference fromGraph(
+      CagraIndexParams.CuvsDistanceType metric,
+      CuVSMatrixBaseImpl graph,
+      CuVSMatrixBaseImpl dataset) {
+    try (var localArena = Arena.ofConfined()) {
+      long rows = dataset.size();
+      long cols = dataset.columns();
+
+      var index = createCagraIndex();
+      try (var resourcesAccess = resources.access()) {
+        long cuvsRes = resourcesAccess.handle();
+
+        long[] datasetShape = {rows, cols};
+        MemorySegment datasetTensor =
+            prepareTensor(
+                localArena, dataset.memorySegment(), datasetShape, kDLFloat(), 32, kDLCPU(), 1);
+
+        long[] graphShape = {graph.size(), graph.columns()};
+        MemorySegment graphTensor =
+            prepareTensor(
+                localArena, graph.memorySegment(), graphShape, kDLUInt(), 32, kDLCPU(), 1);
+
+        checkCuVSError(
+            cuvsCagraIndexFromArgs(cuvsRes, metric.value, graphTensor, datasetTensor, index),
+            "cuvsCagraIndexFromArgs");
+      }
+
+      return new IndexReference(index, dataset);
+    }
+  }
+
   @Override
   public void serializeToHNSW(OutputStream outputStream) throws Throwable {
     Path path =
@@ -678,6 +782,7 @@ public static class Builder implements CagraIndex.Builder {
     private CagraIndexParams cagraIndexParams;
     private final CuVSResources cuvsResources;
     private InputStream inputStream;
+    private CuVSMatrix graph;
 
     public Builder(CuVSResources cuvsResources) {
       this.cuvsResources = cuvsResources;
@@ -689,6 +794,12 @@ public Builder from(InputStream inputStream) {
       return this;
     }
 
+    @Override
+    public Builder from(CuVSMatrix graph) {
+      this.graph = graph;
+      return this;
+    }
+
     @Override
     public Builder withDataset(float[][] vectors) {
       this.dataset = CuVSMatrix.ofArray(vectors);
@@ -712,7 +823,17 @@ public CagraIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new CagraIndexImpl(inputStream, cuvsResources);
       } else {
-        return new CagraIndexImpl(cagraIndexParams, dataset, cuvsResources);
+        if (graph != null) {
+          if (cagraIndexParams == null || dataset == null) {
+            throw new IllegalArgumentException(
+                "In order to reconstruct a CAGRA index from a graph, "
+                    + "you must specify the original dataset and the metric used.");
+          }
+          return new CagraIndexImpl(
+              cagraIndexParams.getCuvsDistanceType(), graph, dataset, cuvsResources);
+        } else {
+          return new CagraIndexImpl(cagraIndexParams, dataset, cuvsResources);
+        }
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
index a7096e207c..c2895282ca 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -18,13 +18,12 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSHostMatrix;
 import com.nvidia.cuvs.RowView;
-import java.lang.foreign.MemoryLayout;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.SequenceLayout;
-import java.lang.foreign.ValueLayout;
+import java.lang.foreign.*;
 import java.lang.invoke.VarHandle;
 
 /**
@@ -61,7 +60,7 @@ protected CuVSHostMatrixImpl(
   protected static ValueLayout valueLayoutFromType(DataType dataType) {
     return switch (dataType) {
       case FLOAT -> C_FLOAT;
-      case INT -> C_INT;
+      case INT, UINT -> C_INT;
       case BYTE -> C_CHAR;
     };
   }
@@ -131,6 +130,12 @@ public ValueLayout valueLayout() {
     return valueLayout;
   }
 
+  @Override
+  public MemorySegment toTensor(Arena arena) {
+    return prepareTensor(
+        arena, memorySegment, new long[] {size, columns}, code(), bits(), kDLCPU(), 1);
+  }
+
   private static class SliceRowView implements RowView {
     private final MemorySegment memorySegment;
     private final long size;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
index fc9daf9387..120960cfc4 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -15,7 +15,10 @@
  */
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
+
 import com.nvidia.cuvs.CuVSMatrix;
+import java.lang.foreign.Arena;
 import java.lang.foreign.MemorySegment;
 
 public abstract class CuVSMatrixBaseImpl implements CuVSMatrix {
@@ -45,4 +48,21 @@ public long columns() {
   public MemorySegment memorySegment() {
     return memorySegment;
   }
+
+  protected int bits() {
+    return switch (dataType) {
+      case FLOAT, INT, UINT -> 32;
+      case BYTE -> 8;
+    };
+  }
+
+  protected int code() {
+    return switch (dataType) {
+      case FLOAT -> kDLFloat();
+      case INT -> kDLInt();
+      case UINT, BYTE -> kDLUInt();
+    };
+  }
+
+  public abstract MemorySegment toTensor(Arena arena);
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index d5972ecca7..8125497078 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -19,11 +19,7 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceCount;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaGetDeviceProperties_v2;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemGetInfo;
-import static com.nvidia.cuvs.internal.panama.headers_h.cudaSetDevice;
-import static com.nvidia.cuvs.internal.panama.headers_h.size_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.GPUInfo;
 import com.nvidia.cuvs.internal.panama.DLDataType;
@@ -73,6 +69,13 @@ public static void checkCudaError(int value, String caller) {
     }
   }
 
+  private static final long UNSIGNED_INT_MASK = 0xFFFFFFFFL;
+
+  public static long dereferenceUnsignedInt(MemorySegment ptr) {
+    assert ptr.byteSize() == 4;
+    return ptr.get(uint32_t, 0) & UNSIGNED_INT_MASK;
+  }
+
   /**
    * Java analog to CUDA's cudaMemcpyKind, used for cudaMemcpy() calls.
    * @see <a href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html">CUDA Runtime API</a>
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index cc7879a43f..8223e199f6 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -290,6 +290,48 @@ private Path createSerializedIndex(CuVSMatrix dataset) throws Throwable {
     }
   }
 
+  @Test
+  public void testReconstructIndexFromGraph() throws Throwable {
+    try (var dataset = CuVSMatrix.ofArray(createSampleData())) {
+      var queries = createSampleQueries();
+      List<Map<Integer, Float>> expectedResults = getExpectedResults();
+
+      try (CuVSResources resources = CuVSResources.create()) {
+        var index = indexOnce(dataset, resources);
+        var graph = index.getGraph();
+
+        var reconstructedIndex =
+            CagraIndex.newBuilder(resources)
+                .from(graph)
+                .withDataset(dataset)
+                .withIndexParams(
+                    new CagraIndexParams.Builder().withMetric(CuvsDistanceType.L2Expanded).build())
+                .build();
+        queryAndCompare(
+            index,
+            reconstructedIndex,
+            SearchResults.IDENTITY_MAPPING,
+            queries,
+            expectedResults,
+            resources);
+
+        var originalIndexPath = serializeOnce(index);
+        var reconstructedIndexPath = serializeOnce(reconstructedIndex);
+
+        var originalBytes = Files.readAllBytes(originalIndexPath);
+        var reconstructedBytes = Files.readAllBytes(reconstructedIndexPath);
+
+        assertArrayEquals(originalBytes, reconstructedBytes);
+
+        index.destroyIndex();
+        reconstructedIndex.destroyIndex();
+
+        Files.deleteIfExists(originalIndexPath);
+        Files.deleteIfExists(reconstructedIndexPath);
+      }
+    }
+  }
+
   @Test
   public void testIndexingAndSearchingFlowWithCustomMappingFunction() throws Throwable {
     var dataset = CuVSMatrix.ofArray(createSampleData());

From 690610ce9b023a491e0c69a4b0549a65bc8daec7 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Wed, 13 Aug 2025 08:10:02 -0700
Subject: [PATCH 542/623] Pin Latest Faiss Version (#1247)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1247
---
 cpp/cmake/patches/faiss.diff          | 19 -------------------
 cpp/cmake/patches/faiss_override.json | 11 ++---------
 2 files changed, 2 insertions(+), 28 deletions(-)
 delete mode 100644 cpp/cmake/patches/faiss.diff

diff --git a/cpp/cmake/patches/faiss.diff b/cpp/cmake/patches/faiss.diff
deleted file mode 100644
index bf92f89d77..0000000000
--- a/cpp/cmake/patches/faiss.diff
+++ /dev/null
@@ -1,19 +0,0 @@
-diff --git a/faiss/gpu/impl/CuvsIVFFlat.cu b/faiss/gpu/impl/CuvsIVFFlat.cu
-index 2cccee860..c4cb13f48 100644
---- a/faiss/gpu/impl/CuvsIVFFlat.cu
-+++ b/faiss/gpu/impl/CuvsIVFFlat.cu
-@@ -427,13 +427,11 @@ void CuvsIVFFlat::copyInvertedListsFrom(const InvertedLists* ivf) {
-     // Precompute the centers vector norms for L2Expanded distance
-     if (this->metric_ == faiss::METRIC_L2) {
-         cuvs_index->allocate_center_norms(raft_handle);
--        raft::linalg::rowNorm(
-+        raft::linalg::rowNorm<raft::linalg::L2Norm, true>(
-                 cuvs_index->center_norms().value().data_handle(),
-                 cuvs_index->centers().data_handle(),
-                 cuvs_index->dim(),
-                 (uint32_t)nlist,
--                raft::linalg::L2Norm,
--                true,
-                 raft_handle.get_stream());
-     }
- }
diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
index 0252cf3103..f3a1aaf795 100644
--- a/cpp/cmake/patches/faiss_override.json
+++ b/cpp/cmake/patches/faiss_override.json
@@ -1,16 +1,9 @@
 {
     "packages" : {
       "faiss" : {
-        "version": "1.11.0",
+        "version": "1.12.0",
         "git_url": "https://github.com/facebookresearch/faiss.git",
-        "git_tag": "v1.11.0",
-        "patches" : [
-          {
-            "file" : "${current_json_dir}/faiss.diff",
-            "issue" : "Apply RAFT breaking changes",
-            "fixed_in" : ""
-          }
-        ]
+        "git_tag": "v1.12.0"
       }
     }
   }

From 378440e36b023812daf7e741034d67db72f45299 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Wed, 13 Aug 2025 14:35:27 -0700
Subject: [PATCH 543/623] [Improvement] Replace Calls to thrust::transform with
 raft::linalg (#1238)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Jinsol Park (https://github.com/jinsolp)
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/1238
---
 cpp/src/cluster/detail/connectivities.cuh     | 62 ++++++++---------
 cpp/src/neighbors/ball_cover/ball_cover.cuh   | 48 +++++---------
 cpp/src/neighbors/ball_cover/registers.cuh    | 16 +++--
 cpp/src/neighbors/detail/reachability.cuh     | 66 +++++++++----------
 cpp/tests/cluster/connect_knn.cu              | 15 ++---
 cpp/tests/neighbors/ann_ivf_flat.cuh          | 14 ++--
 cpp/tests/neighbors/ann_nn_descent.cuh        |  8 +--
 cpp/tests/neighbors/ball_cover.cu             | 24 ++-----
 .../neighbors/brute_force_prefiltered.cu      | 53 +++++----------
 examples/cpp/src/ivf_flat_example.cu          | 11 +---
 10 files changed, 131 insertions(+), 186 deletions(-)

diff --git a/cpp/src/cluster/detail/connectivities.cuh b/cpp/src/cluster/detail/connectivities.cuh
index 39be707bb7..1847da325b 100644
--- a/cpp/src/cluster/detail/connectivities.cuh
+++ b/cpp/src/cluster/detail/connectivities.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2021-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -23,6 +23,7 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/linalg/map.cuh>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/sparse/convert/csr.cuh>
 #include <raft/sparse/coo.hpp>
@@ -31,10 +32,6 @@
 
 #include <rmm/device_uvector.hpp>
 
-#include <thrust/iterator/zip_iterator.h>
-#include <thrust/transform.h>
-#include <thrust/tuple.h>
-
 #include <limits>
 
 namespace cuvs::cluster::agglomerative::detail {
@@ -83,18 +80,25 @@ struct distance_graph_impl<Linkage::KNN_GRAPH, value_idx, value_t> {
     data.resize(knn_graph_coo.nnz, stream);
 
     // self-loops get max distance
-    auto transform_in = thrust::make_zip_iterator(
-      thrust::make_tuple(knn_graph_coo.rows(), knn_graph_coo.cols(), knn_graph_coo.vals()));
-
-    thrust::transform(thrust_policy,
-                      transform_in,
-                      transform_in + knn_graph_coo.nnz,
-                      knn_graph_coo.vals(),
-                      [=] __device__(const thrust::tuple<value_idx, value_idx, value_t>& tup) {
-                        bool self_loop = thrust::get<0>(tup) == thrust::get<1>(tup);
-                        return (self_loop * std::numeric_limits<value_t>::max()) +
-                               (!self_loop * thrust::get<2>(tup));
-                      });
+    auto rows_view = raft::make_device_vector_view<const value_idx, value_idx>(knn_graph_coo.rows(),
+                                                                               knn_graph_coo.nnz);
+    auto cols_view = raft::make_device_vector_view<const value_idx, value_idx>(knn_graph_coo.cols(),
+                                                                               knn_graph_coo.nnz);
+    auto vals_in_view = raft::make_device_vector_view<const value_t, value_idx>(
+      knn_graph_coo.vals(), knn_graph_coo.nnz);
+    auto vals_out_view =
+      raft::make_device_vector_view<value_t, value_idx>(knn_graph_coo.vals(), knn_graph_coo.nnz);
+
+    raft::linalg::map(
+      handle,
+      vals_out_view,
+      [=] __device__(const value_idx row, const value_idx col, const value_t val) {
+        bool self_loop = row == col;
+        return (self_loop * std::numeric_limits<value_t>::max()) + (!self_loop * val);
+      },
+      rows_view,
+      cols_view,
+      vals_in_view);
 
     raft::sparse::convert::sorted_coo_to_csr(
       knn_graph_coo.rows(), knn_graph_coo.nnz, indptr.data(), m + 1, stream);
@@ -147,7 +151,9 @@ void pairwise_distances(const raft::resources& handle,
   value_idx blocks = raft::ceildiv(nnz, (value_idx)256);
   fill_indices2<value_idx><<<blocks, 256, 0, stream>>>(indices, m, nnz);
 
-  thrust::sequence(exec_policy, indptr, indptr + m, 0, (int)m);
+  raft::linalg::map_offset(handle,
+                           raft::make_device_vector_view<value_idx, value_idx>(indptr, m),
+                           [=] __device__(value_idx idx) { return idx * m; });
 
   raft::update_device(indptr + m, &nnz, 1, stream);
 
@@ -160,19 +166,13 @@ void pairwise_distances(const raft::resources& handle,
     handle, X_view, X_view, raft::make_device_matrix_view<value_t, value_idx>(data, m, m), metric);
 
   // self-loops get max distance
-  auto transform_in =
-    thrust::make_zip_iterator(thrust::make_tuple(thrust::make_counting_iterator(0), data));
-
-  thrust::transform(exec_policy,
-                    transform_in,
-                    transform_in + nnz,
-                    data,
-                    [=] __device__(const thrust::tuple<value_idx, value_t>& tup) {
-                      value_idx idx  = thrust::get<0>(tup);
-                      bool self_loop = idx % m == idx / m;
-                      return (self_loop * std::numeric_limits<value_t>::max()) +
-                             (!self_loop * thrust::get<1>(tup));
-                    });
+  auto data_view = raft::make_device_vector_view<value_t, value_idx>(data, nnz);
+
+  raft::linalg::map_offset(handle, data_view, [=] __device__(value_idx idx) {
+    value_t val    = data[idx];
+    bool self_loop = idx % m == idx / m;
+    return (self_loop * std::numeric_limits<value_t>::max()) + (!self_loop * val);
+  });
 }
 
 /**
diff --git a/cpp/src/neighbors/ball_cover/ball_cover.cuh b/cpp/src/neighbors/ball_cover/ball_cover.cuh
index b0c44c3526..99c1de882e 100644
--- a/cpp/src/neighbors/ball_cover/ball_cover.cuh
+++ b/cpp/src/neighbors/ball_cover/ball_cover.cuh
@@ -25,6 +25,7 @@
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/linalg/map.cuh>
 #include <raft/matrix/copy.cuh>
 #include <raft/random/rng.cuh>
 #include <raft/sparse/convert/csr.cuh>
@@ -34,12 +35,9 @@
 #include <rmm/exec_policy.hpp>
 
 #include <thrust/fill.h>
-#include <thrust/for_each.h>
 #include <thrust/functional.h>
-#include <thrust/iterator/counting_iterator.h>
 #include <thrust/iterator/zip_iterator.h>
 #include <thrust/reduce.h>
-#include <thrust/sequence.h>
 #include <thrust/sort.h>
 #include <thrust/tuple.h>
 
@@ -68,10 +66,7 @@ void sample_landmarks(raft::resources const& handle,
   rmm::device_uvector<value_idx> R_indices(index.n_landmarks,
                                            raft::resource::get_cuda_stream(handle));
 
-  thrust::sequence(raft::resource::get_thrust_policy(handle),
-                   index.get_R_1nn_cols().data_handle(),
-                   index.get_R_1nn_cols().data_handle() + index.m,
-                   (value_idx)0);
+  raft::linalg::map_offset(handle, index.get_R_1nn_cols(), raft::identity_op{});
 
   thrust::fill(raft::resource::get_thrust_policy(handle),
                R_1nn_ones.data(),
@@ -124,25 +119,23 @@ void construct_landmark_1nn(raft::resources const& handle,
                             int64_t k,
                             cuvs::neighbors::ball_cover::index<value_idx, value_t>& index)
 {
-  rmm::device_uvector<value_idx> R_1nn_inds(index.m, raft::resource::get_cuda_stream(handle));
+  auto R_1nn_inds = raft::make_device_vector<value_idx, value_idx>(handle, index.m);
 
   thrust::fill(raft::resource::get_thrust_policy(handle),
-               R_1nn_inds.data(),
-               R_1nn_inds.data() + index.m,
+               R_1nn_inds.data_handle(),
+               R_1nn_inds.data_handle() + index.m,
                std::numeric_limits<value_idx>::max());
 
-  value_idx* R_1nn_inds_ptr = R_1nn_inds.data();
-  value_t* R_1nn_dists_ptr  = index.get_R_1nn_dists().data_handle();
-
-  auto idxs = thrust::make_counting_iterator<value_idx>(0);
-  thrust::for_each(
-    raft::resource::get_thrust_policy(handle), idxs, idxs + index.m, [=] __device__(value_idx i) {
-      R_1nn_inds_ptr[i]  = R_knn_inds_ptr[i * k];
-      R_1nn_dists_ptr[i] = R_knn_dists_ptr[i * k];
+  raft::linalg::map_offset(handle, R_1nn_inds.view(), [R_knn_inds_ptr, k] __device__(value_idx i) {
+    return R_knn_inds_ptr[i * k];
+  });
+  raft::linalg::map_offset(
+    handle, index.get_R_1nn_dists(), [R_knn_dists_ptr, k] __device__(value_idx i) {
+      return R_knn_dists_ptr[i * k];
     });
 
   auto keys = thrust::make_zip_iterator(
-    thrust::make_tuple(R_1nn_inds.data(), index.get_R_1nn_dists().data_handle()));
+    thrust::make_tuple(R_1nn_inds.data_handle(), index.get_R_1nn_dists().data_handle()));
 
   // group neighborhoods for each reference landmark and sort each group by distance
   thrust::sort_by_key(raft::resource::get_thrust_policy(handle),
@@ -152,7 +145,7 @@ void construct_landmark_1nn(raft::resources const& handle,
                       NNComp());
 
   // convert to CSR for fast lookup
-  raft::sparse::convert::sorted_coo_to_csr(R_1nn_inds.data(),
+  raft::sparse::convert::sorted_coo_to_csr(R_1nn_inds.data_handle(),
                                            index.m,
                                            index.get_R_indptr().data_handle(),
                                            index.n_landmarks + 1,
@@ -212,18 +205,13 @@ template <typename value_idx, typename value_t>
 void compute_landmark_radii(raft::resources const& handle,
                             cuvs::neighbors::ball_cover::index<value_idx, value_t>& index)
 {
-  auto entries = thrust::make_counting_iterator<value_idx>(0);
-
   const value_idx* R_indptr_ptr  = index.get_R_indptr().data_handle();
   const value_t* R_1nn_dists_ptr = index.get_R_1nn_dists().data_handle();
-  value_t* R_radius_ptr          = index.get_R_radius().data_handle();
-  thrust::for_each(raft::resource::get_thrust_policy(handle),
-                   entries,
-                   entries + index.n_landmarks,
-                   [=] __device__(value_idx input) {
-                     value_idx last_row_idx = R_indptr_ptr[input + 1] - 1;
-                     R_radius_ptr[input]    = R_1nn_dists_ptr[last_row_idx];
-                   });
+  raft::linalg::map_offset(
+    handle, index.get_R_radius(), [R_indptr_ptr, R_1nn_dists_ptr] __device__(value_idx input) {
+      value_idx last_row_idx = R_indptr_ptr[input + 1] - 1;
+      return R_1nn_dists_ptr[last_row_idx];
+    });
 }
 
 /**
diff --git a/cpp/src/neighbors/ball_cover/registers.cuh b/cpp/src/neighbors/ball_cover/registers.cuh
index 9931275f33..191dfc1fb5 100644
--- a/cpp/src/neighbors/ball_cover/registers.cuh
+++ b/cpp/src/neighbors/ball_cover/registers.cuh
@@ -24,6 +24,7 @@
 
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
+#include <raft/linalg/unary_op.cuh>
 #include <raft/neighbors/detail/faiss_select/key_value_block_select.cuh>
 #include <raft/util/cuda_utils.cuh>
 
@@ -1458,13 +1459,14 @@ void rbc_eps_pass(raft::resources const& handle,
 
     if (actual_max > max_k_in) {
       // ceil vd to max_k
-      thrust::transform(raft::resource::get_thrust_policy(handle),
-                        vd_ptr,
-                        vd_ptr + n_query_rows,
-                        vd_ptr,
-                        [max_k_in] __device__(value_idx vd_count) {
-                          return vd_count > max_k_in ? max_k_in : vd_count;
-                        });
+      raft::linalg::unaryOp(
+        vd_ptr,
+        vd_ptr,
+        n_query_rows,
+        [max_k_in] __device__(value_idx vd_count) {
+          return vd_count > max_k_in ? max_k_in : vd_count;
+        },
+        raft::resource::get_cuda_stream(handle));
     }
 
     thrust::exclusive_scan(raft::resource::get_thrust_policy(handle),
diff --git a/cpp/src/neighbors/detail/reachability.cuh b/cpp/src/neighbors/detail/reachability.cuh
index 0f73e014e5..4a73179b6a 100644
--- a/cpp/src/neighbors/detail/reachability.cuh
+++ b/cpp/src/neighbors/detail/reachability.cuh
@@ -17,6 +17,9 @@
 #pragma once
 #include "./knn_brute_force.cuh"
 
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/map.cuh>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/sparse/convert/csr.cuh>
 #include <raft/sparse/linalg/symmetrize.cuh>
@@ -24,12 +27,6 @@
 #include <raft/util/cudart_utils.hpp>
 
 #include <rmm/device_uvector.hpp>
-#include <rmm/exec_policy.hpp>
-
-#include <thrust/iterator/counting_iterator.h>
-#include <thrust/iterator/zip_iterator.h>
-#include <thrust/transform.h>
-#include <thrust/tuple.h>
 
 namespace cuvs::neighbors::detail::reachability {
 
@@ -47,17 +44,19 @@ namespace cuvs::neighbors::detail::reachability {
  * @param[in] stream stream for which to order cuda operations
  */
 template <typename value_idx, typename value_t, int tpb = 256>
-void core_distances(
-  value_t* knn_dists, int min_samples, int n_neighbors, size_t n, value_t* out, cudaStream_t stream)
+void core_distances(raft::resources const& handle,
+                    value_t* knn_dists,
+                    int min_samples,
+                    int n_neighbors,
+                    size_t n,
+                    value_t* out)
 {
   ASSERT(n_neighbors >= min_samples,
          "the size of the neighborhood should be greater than or equal to min_samples");
 
-  auto exec_policy = rmm::exec_policy(stream);
-
-  auto indices = thrust::make_counting_iterator<value_idx>(0);
+  auto out_view = raft::make_device_vector_view<value_t, value_idx>(out, n);
 
-  thrust::transform(exec_policy, indices, indices + n, out, [=] __device__(value_idx row) {
+  raft::linalg::map_offset(handle, out_view, [=] __device__(value_idx row) {
     return knn_dists[row * n_neighbors + (min_samples - 1)];
   });
 }
@@ -118,7 +117,7 @@ void _compute_core_dists(const raft::resources& handle,
   compute_knn(handle, X, inds.data(), dists.data(), m, n, X, m, min_samples, metric);
 
   // Slice core distances (distances to kth nearest neighbor)
-  core_distances<value_idx>(dists.data(), min_samples, min_samples, m, core_dists, stream);
+  core_distances<value_idx>(handle, dists.data(), min_samples, min_samples, m, core_dists);
 }
 
 //  Functor to post-process distances into reachability space
@@ -202,8 +201,7 @@ void mutual_reachability_graph(const raft::resources& handle,
   RAFT_EXPECTS(metric == cuvs::distance::DistanceType::L2SqrtExpanded,
                "Currently only L2 expanded distance is supported");
 
-  auto stream      = raft::resource::get_cuda_stream(handle);
-  auto exec_policy = raft::resource::get_thrust_policy(handle);
+  auto stream = raft::resource::get_cuda_stream(handle);
 
   rmm::device_uvector<value_idx> coo_rows(min_samples * m, stream);
   rmm::device_uvector<value_idx> inds(min_samples * m, stream);
@@ -213,7 +211,7 @@ void mutual_reachability_graph(const raft::resources& handle,
   compute_knn(handle, X, inds.data(), dists.data(), m, n, X, m, min_samples, metric);
 
   // Slice core distances (distances to kth nearest neighbor)
-  core_distances<value_idx>(dists.data(), min_samples, min_samples, m, core_dists, stream);
+  core_distances<value_idx>(handle, dists.data(), min_samples, min_samples, m, core_dists);
 
   /**
    * Compute L2 norm
@@ -222,12 +220,12 @@ void mutual_reachability_graph(const raft::resources& handle,
     handle, inds.data(), dists.data(), X, m, n, min_samples, core_dists, (value_t)1.0 / alpha);
 
   // self-loops get max distance
-  auto coo_rows_counting_itr = thrust::make_counting_iterator<value_idx>(0);
-  thrust::transform(exec_policy,
-                    coo_rows_counting_itr,
-                    coo_rows_counting_itr + (m * min_samples),
-                    coo_rows.data(),
-                    [min_samples] __device__(value_idx c) -> value_idx { return c / min_samples; });
+  auto coo_rows_view =
+    raft::make_device_vector_view<value_idx, value_idx>(coo_rows.data(), m * min_samples);
+  raft::linalg::map_offset(
+    handle, coo_rows_view, [min_samples] __device__(value_idx c) -> value_idx {
+      return c / min_samples;
+    });
 
   raft::sparse::linalg::symmetrize(handle,
                                    coo_rows.data(),
@@ -241,18 +239,20 @@ void mutual_reachability_graph(const raft::resources& handle,
   raft::sparse::convert::sorted_coo_to_csr(out.rows(), out.nnz, indptr, m + 1, stream);
 
   // self-loops get max distance
-  auto transform_in =
-    thrust::make_zip_iterator(thrust::make_tuple(out.rows(), out.cols(), out.vals()));
+  auto rows_view     = raft::make_device_vector_view<const value_idx, nnz_t>(out.rows(), out.nnz);
+  auto cols_view     = raft::make_device_vector_view<const value_idx, nnz_t>(out.cols(), out.nnz);
+  auto vals_in_view  = raft::make_device_vector_view<const value_t, nnz_t>(out.vals(), out.nnz);
+  auto vals_out_view = raft::make_device_vector_view<value_t, nnz_t>(out.vals(), out.nnz);
 
-  thrust::transform(exec_policy,
-                    transform_in,
-                    transform_in + out.nnz,
-                    out.vals(),
-                    [=] __device__(const thrust::tuple<value_idx, value_idx, value_t>& tup) {
-                      return thrust::get<0>(tup) == thrust::get<1>(tup)
-                               ? std::numeric_limits<value_t>::max()
-                               : thrust::get<2>(tup);
-                    });
+  raft::linalg::map(
+    handle,
+    vals_out_view,
+    [=] __device__(const value_idx row, const value_idx col, const value_t val) {
+      return row == col ? std::numeric_limits<value_t>::max() : val;
+    },
+    rows_view,
+    cols_view,
+    vals_in_view);
 }
 
 }  // namespace cuvs::neighbors::detail::reachability
diff --git a/cpp/tests/cluster/connect_knn.cu b/cpp/tests/cluster/connect_knn.cu
index b4fd55e5b6..ecca492489 100644
--- a/cpp/tests/cluster/connect_knn.cu
+++ b/cpp/tests/cluster/connect_knn.cu
@@ -16,6 +16,7 @@
 
 #include <cuvs/distance/distance.hpp>
 #include <raft/core/handle.hpp>
+#include <raft/linalg/map.cuh>
 #include <raft/random/make_blobs.cuh>
 #include <raft/sparse/convert/csr.cuh>
 #include <raft/sparse/coo.hpp>
@@ -78,7 +79,7 @@ class ConnectKNNTest : public ::testing::TestWithParam<ConnectKNNInputs> {
     rmm::device_uvector<T> core_dists(ps.n_rows, stream);
     if (ps.mutual_reach) {
       cuvs::neighbors::detail::reachability::core_distances<int64_t, T>(
-        dists.data(), ps.k, ps.k, (size_t)ps.n_rows, core_dists.data(), stream);
+        handle, dists.data(), ps.k, ps.k, (size_t)ps.n_rows, core_dists.data());
 
       auto epilogue = cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int64_t, T>{
         core_dists.data(), 1.0};
@@ -111,13 +112,11 @@ class ConnectKNNTest : public ::testing::TestWithParam<ConnectKNNInputs> {
     rmm::device_uvector<int64_t> indptr(ps.n_rows + 1, stream);
 
     // changing inds and dists to sparse format
-    int64_t k                  = ps.k;
-    auto coo_rows_counting_itr = thrust::make_counting_iterator<int64_t>(0);
-    thrust::transform(raft::resource::get_thrust_policy(handle),
-                      coo_rows_counting_itr,
-                      coo_rows_counting_itr + (ps.n_rows * ps.k),
-                      coo_rows.data(),
-                      [k] __device__(int64_t c) -> int64_t { return c / k; });
+    int64_t k = ps.k;
+    auto coo_rows_view =
+      raft::make_device_vector_view<int64_t, int64_t>(coo_rows.data(), ps.n_rows * ps.k);
+    raft::linalg::map_offset(
+      handle, coo_rows_view, [k] __device__(int64_t c) -> int64_t { return c / k; });
 
     raft::sparse::linalg::symmetrize(handle,
                                      coo_rows.data(),
diff --git a/cpp/tests/neighbors/ann_ivf_flat.cuh b/cpp/tests/neighbors/ann_ivf_flat.cuh
index aab1868473..bb8c7d1cd7 100644
--- a/cpp/tests/neighbors/ann_ivf_flat.cuh
+++ b/cpp/tests/neighbors/ann_ivf_flat.cuh
@@ -25,7 +25,6 @@
 #include <raft/linalg/normalize.cuh>
 #include <raft/stats/mean.cuh>
 #include <thrust/reduce.h>
-#include <thrust/sequence.h>
 
 #include <raft/core/resource/cuda_stream_pool.hpp>
 #include <raft/linalg/add.cuh>
@@ -135,10 +134,8 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
           auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
             (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
           idx = cuvs::neighbors::ivf_flat::build(handle_, index_params, database_view);
-          rmm::device_uvector<IdxT> vector_indices(ps.num_db_vecs, stream_);
-          thrust::sequence(raft::resource::get_thrust_policy(handle_),
-                           thrust::device_pointer_cast(vector_indices.data()),
-                           thrust::device_pointer_cast(vector_indices.data() + ps.num_db_vecs));
+          auto vector_indices = raft::make_device_vector<IdxT, IdxT>(handle_, ps.num_db_vecs);
+          raft::linalg::map_offset(handle_, vector_indices.view(), raft::identity_op{});
           raft::resource::sync_stream(handle_);
 
           IdxT half_of_data = ps.num_db_vecs / 2;
@@ -153,7 +150,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
             database.data() + half_of_data * ps.dim, IdxT(ps.num_db_vecs) - half_of_data, ps.dim);
 
           auto new_half_of_data_indices_view = raft::make_device_vector_view<const IdxT, IdxT>(
-            vector_indices.data() + half_of_data, IdxT(ps.num_db_vecs) - half_of_data);
+            vector_indices.data_handle() + half_of_data, IdxT(ps.num_db_vecs) - half_of_data);
 
           cuvs::neighbors::ivf_flat::extend(
             handle_,
@@ -461,10 +458,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
         // Create Bitset filter
         auto removed_indices =
           raft::make_device_vector<IdxT, int64_t>(handle_, test_ivf_sample_filter::offset);
-        thrust::sequence(raft::resource::get_thrust_policy(handle_),
-                         thrust::device_pointer_cast(removed_indices.data_handle()),
-                         thrust::device_pointer_cast(removed_indices.data_handle() +
-                                                     test_ivf_sample_filter::offset));
+        raft::linalg::map_offset(handle_, removed_indices.view(), raft::identity_op{});
         raft::resource::sync_stream(handle_);
 
         cuvs::core::bitset<std::uint32_t, IdxT> removed_indices_bitset(
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index 6edae3d6d5..e1c27dc48c 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -236,12 +236,12 @@ class AnnNNDescentDistEpiTest : public ::testing::TestWithParam<AnnNNDescentInpu
       rmm::device_uvector<DistanceT> core_dists_dev(ps.n_rows, stream_);
 
       cuvs::neighbors::detail::reachability::core_distances<IdxT, DistanceT>(
+        handle_,
         distances_naive_dev.data(),
         ps.graph_degree,
         ps.graph_degree,
         ps.n_rows,
-        core_dists_dev.data(),
-        stream_);
+        core_dists_dev.data());
       auto epilogue =
         cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, DistanceT>{
           core_dists_dev.data(), 1.0, static_cast<size_t>(ps.n_rows)};
@@ -289,12 +289,12 @@ class AnnNNDescentDistEpiTest : public ::testing::TestWithParam<AnnNNDescentInpu
 
       rmm::device_uvector<DistanceT> core_dists_dev(ps.n_rows, stream_);
       cuvs::neighbors::detail::reachability::core_distances<IdxT, DistanceT>(
+        handle_,
         index.distances().value().data_handle(),
         ps.graph_degree,
         ps.graph_degree,
         ps.n_rows,
-        core_dists_dev.data(),
-        stream_);
+        core_dists_dev.data());
 
       size_t extended_graph_degree, graph_degree;
       auto build_config = nn_descent::detail::get_build_config(
diff --git a/cpp/tests/neighbors/ball_cover.cu b/cpp/tests/neighbors/ball_cover.cu
index 429c939d71..5e7fb51bac 100644
--- a/cpp/tests/neighbors/ball_cover.cu
+++ b/cpp/tests/neighbors/ball_cover.cu
@@ -23,15 +23,14 @@
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/thrust_policy.hpp>
+#include <raft/linalg/unary_op.cuh>
 #include <raft/random/make_blobs.cuh>
 #include <raft/util/cudart_utils.hpp>
 
 #include <rmm/device_uvector.hpp>
 #include <rmm/exec_policy.hpp>
 
-#include <thrust/count.h>
 #include <thrust/fill.h>
-#include <thrust/transform.h>
 
 #include <gtest/gtest.h>
 
@@ -193,16 +192,10 @@ class BallCoverKNNQueryTest : public ::testing::TestWithParam<BallCoverInputs<va
                                          raft::resource::get_cuda_stream(handle));
 
     if (metric == cuvs::distance::DistanceType::Haversine) {
-      thrust::transform(raft::resource::get_thrust_policy(handle),
-                        X.data(),
-                        X.data() + X.size(),
-                        X.data(),
-                        ToRadians());
-      thrust::transform(raft::resource::get_thrust_policy(handle),
-                        X2.data(),
-                        X2.data() + X2.size(),
-                        X2.data(),
-                        ToRadians());
+      raft::linalg::unaryOp(
+        X.data(), X.data(), X.size(), ToRadians(), raft::resource::get_cuda_stream(handle));
+      raft::linalg::unaryOp(
+        X2.data(), X2.data(), X2.size(), ToRadians(), raft::resource::get_cuda_stream(handle));
     }
 
     compute_bfknn(handle,
@@ -305,11 +298,8 @@ class BallCoverAllKNNTest : public ::testing::TestWithParam<BallCoverInputs<valu
       (const value_t*)X.data(), params.n_rows, params.n_cols);
 
     if (metric == cuvs::distance::DistanceType::Haversine) {
-      thrust::transform(raft::resource::get_thrust_policy(handle),
-                        X.data(),
-                        X.data() + X.size(),
-                        X.data(),
-                        ToRadians());
+      raft::linalg::unaryOp(
+        X.data(), X.data(), X.size(), ToRadians(), raft::resource::get_cuda_stream(handle));
     }
 
     compute_bfknn(handle,
diff --git a/cpp/tests/neighbors/brute_force_prefiltered.cu b/cpp/tests/neighbors/brute_force_prefiltered.cu
index 7f2c1f195b..630f537473 100644
--- a/cpp/tests/neighbors/brute_force_prefiltered.cu
+++ b/cpp/tests/neighbors/brute_force_prefiltered.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -21,6 +21,7 @@
 #include <cuvs/neighbors/brute_force.hpp>
 
 #include <raft/core/host_mdspan.hpp>
+#include <raft/linalg/unary_op.cuh>
 #include <raft/matrix/copy.cuh>
 #include <raft/random/make_blobs.cuh>
 #include <raft/random/rmat_rectangular_generator.cuh>
@@ -32,10 +33,6 @@
 #include <gtest/gtest.h>
 
 #include <cuda_fp16.h>
-#include <thrust/device_vector.h>
-#include <thrust/iterator/transform_iterator.h>
-#include <thrust/reduce.h>
-#include <thrust/transform.h>
 
 #include <algorithm>
 #include <cmath>
@@ -431,28 +428,19 @@ class PrefilteredBruteForceOnBitmapTest
     raft::copy(dataset_h.data(), blobs_in_val.data_handle(), dataset_size, stream);
 
     if constexpr (std::is_same_v<value_t, half>) {
-      thrust::device_ptr<dist_t> d_output_ptr =
-        thrust::device_pointer_cast(blobs_in_val.data_handle());
-      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(dataset_d.data());
-      thrust::transform(thrust::cuda::par.on(stream),
-                        d_output_ptr,
-                        d_output_ptr + dataset_size,
-                        d_value_ptr,
-                        float_to_half());
+      raft::linalg::unaryOp(
+        dataset_d.data(), blobs_in_val.data_handle(), dataset_size, float_to_half(), stream);
     } else {
       raft::copy(dataset_d.data(), blobs_in_val.data_handle(), dataset_size, stream);
     }
 
     raft::copy(queries_h.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
     if constexpr (std::is_same_v<value_t, half>) {
-      thrust::device_ptr<dist_t> d_output_ptr =
-        thrust::device_pointer_cast(blobs_in_val.data_handle() + dataset_size);
-      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(queries_d.data());
-      thrust::transform(thrust::cuda::par.on(stream),
-                        d_output_ptr,
-                        d_output_ptr + queries_size,
-                        d_value_ptr,
-                        float_to_half());
+      raft::linalg::unaryOp(queries_d.data(),
+                            blobs_in_val.data_handle() + dataset_size,
+                            queries_size,
+                            float_to_half(),
+                            stream);
     } else {
       raft::copy(queries_d.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
     }
@@ -861,28 +849,19 @@ class PrefilteredBruteForceOnBitsetTest
     raft::copy(dataset_h.data(), blobs_in_val.data_handle(), dataset_size, stream);
 
     if constexpr (std::is_same_v<value_t, half>) {
-      thrust::device_ptr<dist_t> d_output_ptr =
-        thrust::device_pointer_cast(blobs_in_val.data_handle());
-      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(dataset_d.data());
-      thrust::transform(thrust::cuda::par.on(stream),
-                        d_output_ptr,
-                        d_output_ptr + dataset_size,
-                        d_value_ptr,
-                        float_to_half());
+      raft::linalg::unaryOp(
+        dataset_d.data(), blobs_in_val.data_handle(), dataset_size, float_to_half(), stream);
     } else {
       raft::copy(dataset_d.data(), blobs_in_val.data_handle(), dataset_size, stream);
     }
 
     raft::copy(queries_h.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
     if constexpr (std::is_same_v<value_t, half>) {
-      thrust::device_ptr<dist_t> d_output_ptr =
-        thrust::device_pointer_cast(blobs_in_val.data_handle() + dataset_size);
-      thrust::device_ptr<value_t> d_value_ptr = thrust::device_pointer_cast(queries_d.data());
-      thrust::transform(thrust::cuda::par.on(stream),
-                        d_output_ptr,
-                        d_output_ptr + queries_size,
-                        d_value_ptr,
-                        float_to_half());
+      raft::linalg::unaryOp(queries_d.data(),
+                            blobs_in_val.data_handle() + dataset_size,
+                            queries_size,
+                            float_to_half(),
+                            stream);
     } else {
       raft::copy(queries_d.data(), blobs_in_val.data_handle() + dataset_size, queries_size, stream);
     }
diff --git a/examples/cpp/src/ivf_flat_example.cu b/examples/cpp/src/ivf_flat_example.cu
index 4744d3e42d..8f1d8c44f6 100644
--- a/examples/cpp/src/ivf_flat_example.cu
+++ b/examples/cpp/src/ivf_flat_example.cu
@@ -19,16 +19,12 @@
 #include <cuvs/neighbors/ivf_flat.hpp>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_resources.hpp>
-#include <raft/core/resource/thrust_policy.hpp>
+#include <raft/linalg/map.cuh>
 #include <raft/util/cudart_utils.hpp>
 
 #include <rmm/mr/device/device_memory_resource.hpp>
 #include <rmm/mr/device/pool_memory_resource.hpp>
 
-#include <thrust/copy.h>
-#include <thrust/device_ptr.h>
-#include <thrust/iterator/counting_iterator.h>
-
 #include <cstdint>
 #include <optional>
 
@@ -77,10 +73,7 @@ void ivf_flat_build_extend_search(raft::device_resources const& dev_resources,
 
   // Define dataset indices.
   auto data_indices = raft::make_device_vector<int64_t, int64_t>(dev_resources, dataset.extent(0));
-  thrust::counting_iterator<int64_t> first(0);
-  thrust::device_ptr<int64_t> ptr(data_indices.data_handle());
-  thrust::copy(
-    raft::resource::get_thrust_policy(dev_resources), first, first + dataset.extent(0), ptr);
+  raft::linalg::map_offset(dev_resources, data_indices.view(), raft::identity_op{});
 
   // Sub-sample the dataset to create a training set.
   auto trainset =

From e3a61265c9eeeb64a883d9f3d13a21809b552d08 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 13 Aug 2025 17:03:24 -0700
Subject: [PATCH 544/623] Improve NN Descent documentation (#1246)

Closing https://github.com/rapidsai/cuvs/issues/1245

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Micka (https://github.com/lowener)

URL: https://github.com/rapidsai/cuvs/pull/1246
---
 cpp/include/cuvs/neighbors/nn_descent.hpp     | 70 +++++++++++++------
 cpp/src/neighbors/nn_descent.cuh              | 38 +++++++---
 .../cuvs/neighbors/nn_descent/nn_descent.pyx  |  4 +-
 3 files changed, 79 insertions(+), 33 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index 94300e2737..5f6260b0af 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -37,26 +37,25 @@ namespace cuvs::neighbors::nn_descent {
 
 /**
  * @brief Parameters used to build an nn-descent index
- *
- * `graph_degree`: For an input dataset of dimensions (N, D),
+ * - `graph_degree`: For an input dataset of dimensions (N, D),
  * determines the final dimensions of the all-neighbors knn graph
  * which turns out to be of dimensions (N, graph_degree)
- * `intermediate_graph_degree`: Internally, nn-descent builds an
+ * - `intermediate_graph_degree`: Internally, nn-descent builds an
  * all-neighbors knn graph of dimensions (N, intermediate_graph_degree)
  * before selecting the final `graph_degree` neighbors. It's recommended
  * that `intermediate_graph_degree` >= 1.5 * graph_degree
- * `max_iterations`: The number of iterations that nn-descent will refine
+ * - `max_iterations`: The number of iterations that nn-descent will refine
  * the graph for. More iterations produce a better quality graph at cost of performance
- * `termination_threshold`: The delta at which nn-descent will terminate its iterations
- *
+ * - `termination_threshold`: The delta at which nn-descent will terminate its iterations
+ * - `return_distances`: Boolean to decide whether to return distances array
  */
 struct index_params : cuvs::neighbors::index_params {
-  size_t graph_degree              = 64;      // Degree of output graph.
-  size_t intermediate_graph_degree = 128;     // Degree of input graph for pruning.
-  size_t max_iterations            = 20;      // Number of nn-descent iterations.
-  float termination_threshold      = 0.0001;  // Termination threshold of nn-descent.
-  bool return_distances            = true;    // return distances if true
-  size_t n_clusters                = 1;       // defaults to not using any batching
+  size_t graph_degree              = 64;
+  size_t intermediate_graph_degree = 128;
+  size_t max_iterations            = 20;
+  float termination_threshold      = 0.0001;
+  bool return_distances            = true;
+  size_t n_clusters                = 1;
 
   /** @brief Construct NN descent parameters for a specific kNN graph degree
    *
@@ -211,7 +210,10 @@ struct index : cuvs::neighbors::index {
  * @brief Build nn-descent Index with dataset in device memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
  *
  * Usage example:
  * @code{.cpp}
@@ -244,7 +246,10 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in host memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
  *
  * Usage example:
  * @code{.cpp}
@@ -279,7 +284,10 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in device memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
  *
  * Usage example:
  * @code{.cpp}
@@ -312,7 +320,10 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in host memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
  *
  * Usage example:
  * @code{.cpp}
@@ -347,7 +358,11 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in device memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming
  *
  * Usage example:
  * @code{.cpp}
@@ -380,7 +395,11 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in host memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming
  *
  * Usage example:
  * @code{.cpp}
@@ -415,7 +434,11 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in device memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming
  *
  * Usage example:
  * @code{.cpp}
@@ -448,7 +471,11 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in host memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming
  *
  * Usage example:
  * @code{.cpp}
@@ -479,6 +506,7 @@ auto build(raft::resources const& res,
            std::optional<raft::host_matrix_view<uint32_t, int64_t, raft::row_major>> graph =
              std::nullopt) -> cuvs::neighbors::nn_descent::index<uint32_t>;
 
+/** @} */
 /**
  * @brief Test if we have enough GPU memory to run NN descent algorithm.
  *
diff --git a/cpp/src/neighbors/nn_descent.cuh b/cpp/src/neighbors/nn_descent.cuh
index c4da01c561..78c63dc31e 100644
--- a/cpp/src/neighbors/nn_descent.cuh
+++ b/cpp/src/neighbors/nn_descent.cuh
@@ -38,15 +38,19 @@ namespace cuvs::neighbors::nn_descent {
  * @brief Build nn-descent Index with dataset in device memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming (when T == int8, uint8)
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors::experimental;
+ *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   nn_descent::index_params index_params;
  *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
- *   auto index = cagra::build(res, index_params, dataset);
+ *   auto index = nn_descent::build(res, index_params, dataset);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
@@ -85,17 +89,21 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in device memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming (when T == int8, uint8)
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors::experimental;
+ *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   nn_descent::index_params index_params;
  *   // create and fill the index from a [N, D] raft::device_matrix_view dataset
  *   auto knn_graph = raft::make_host_matrix<uint32_t, int64_t>(N, D);
  *   auto index = nn_descent::index{res, knn_graph.view()};
- *   cagra::build(res, index_params, dataset, index);
+ *   nn_descent::build(res, index_params, dataset, index);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
@@ -136,15 +144,19 @@ void build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in host memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming (when T == int8, uint8)
  *
  * Usage example:
  * @code{.cpp}
- *   using namespace cuvs::neighbors::experimental;
+ *   using namespace cuvs::neighbors;
  *   // use default index parameters
  *   nn_descent::index_params index_params;
  *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
- *   auto index = cagra::build(res, index_params, dataset);
+ *   auto index = nn_descent::build(res, index_params, dataset);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
@@ -183,7 +195,11 @@ auto build(raft::resources const& res,
  * @brief Build nn-descent Index with dataset in host memory
  *
  * The following distance metrics are supported:
- * - L2
+ * - L2Expanded
+ * - L2SqrtExpanded
+ * - CosineExpanded
+ * - InnerProduct
+ * - BitwiseHamming (when T == int8, uint8)
  *
  * Usage example:
  * @code{.cpp}
@@ -193,7 +209,7 @@ auto build(raft::resources const& res,
  *   // create and fill the index from a [N, D] raft::host_matrix_view dataset
  *   auto knn_graph = raft::make_host_matrix<uint32_t, int64_t>(N, D);
  *   auto index = nn_descent::index{res, knn_graph.view()};
- *   cagra::build(res, index_params, dataset, index);
+ *   nn_descent::build(res, index_params, dataset, index);
  *   // index.graph() provides a raft::host_matrix_view of an
  *   // all-neighbors knn graph of dimensions [N, k] of the input
  *   // dataset
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
index a6277b911d..0f0589eced 100644
--- a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
@@ -55,7 +55,9 @@ cdef class IndexParams:
     ----------
     metric : str, default = "sqeuclidean"
         String denoting the metric type.
-        distribution of the newly added data.
+        Supported metrics are `l2`, `euclidean`, `sqeuclidean`,
+        `inner_product`, `cosine`, and `bitwise_hamming`
+        (`bitwise_hamming` is for int8 and uint8 data types only)
     graph_degree :  int
         For an input dataset of dimensions (N, D), determines the final
         dimensions of the all-neighbors knn graph which turns out to be of

From 279afdb041b4e7f201f21345231673dcd833bf60 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 14 Aug 2025 05:45:14 +0200
Subject: [PATCH 545/623] [REVIEW][Java] Rename destroyIndex() to close(),
 extend AutoCloseable (#1252)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The various `Index` classes in Java all have a `destroyIndex` method that must be called in order to free resources associated with that index, when done.
Other classes in the repository use `close()` instead, and extend or implement `AutoCloseable`; this have the advantage of support in language (try-with-resources) and tooling (e.g. IntelliJ warnings).
Additionally, having a single pattern is better for ease of use and consistency.

This PR renames all instances of `destroyIndex` to `close` and make the declaring interfaces extend `AutoCloseable`

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1252
---
 .../com/nvidia/cuvs/CagraIndexBenchmarks.java |  18 +--
 .../java/com/nvidia/cuvs/BruteForceIndex.java |   5 +-
 .../main/java/com/nvidia/cuvs/CagraIndex.java |   5 +-
 .../main/java/com/nvidia/cuvs/HnswIndex.java  |   5 +-
 .../java/com/nvidia/cuvs/TieredIndex.java     |   7 +-
 .../cuvs/internal/BruteForceIndexImpl.java    |   2 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |   2 +-
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |   2 +-
 .../nvidia/cuvs/internal/TieredIndexImpl.java |   2 +-
 .../nvidia/cuvs/BruteForceAndSearchIT.java    |  43 +++---
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    |  85 ++++++------
 .../cuvs/CagraMultiThreadStabilityIT.java     | 129 +++++++++---------
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   2 +-
 .../com/nvidia/cuvs/HnswBuildAndSearchIT.java |   4 +-
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |   4 +-
 15 files changed, 155 insertions(+), 160 deletions(-)

diff --git a/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
index 18d8495424..9b3a7dffc3 100644
--- a/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
+++ b/java/benchmarks/src/main/java/com/nvidia/cuvs/CagraIndexBenchmarks.java
@@ -121,20 +121,20 @@ public void testIndexingAndSerializeToFile() throws Throwable {
                 .build();
 
             // Create the index with the dataset
-            CagraIndex index = CagraIndex.newBuilder(resources)
+            try (CagraIndex index = CagraIndex.newBuilder(resources)
                 .withDataset(arrayDataset)
                 .withIndexParams(indexParams)
-                .build();
+                .build()) {
 
-            // Saving the index on to the disk.
-            var indexFilePath = Path.of(UUID.randomUUID() + ".cag");
-            try (var outputStream = Files.newOutputStream(indexFilePath)) {
+              // Saving the index on to the disk.
+              var indexFilePath = Path.of(UUID.randomUUID() + ".cag");
+              try (var outputStream = Files.newOutputStream(indexFilePath)) {
                 index.serialize(outputStream);
-            }
+              }
 
-            // Cleanup
-            Files.deleteIfExists(indexFilePath);
-            index.destroyIndex();
+              // Cleanup
+              Files.deleteIfExists(indexFilePath);
+            }
         }
     }
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
index 2b50a0b741..47a5f64c2a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BruteForceIndex.java
@@ -28,13 +28,14 @@
  *
  * @since 25.02
  */
-public interface BruteForceIndex {
+public interface BruteForceIndex extends AutoCloseable {
 
   /**
    * Invokes the native destroy_brute_force_index function to de-allocate
    * BRUTEFORCE index
    */
-  void destroyIndex() throws Throwable;
+  @Override
+  void close() throws Exception;
 
   /**
    * Invokes the native search_brute_force_index via the Panama API for searching
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index 427b5e22d2..e3b885a333 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -33,12 +33,13 @@
  *
  * @since 25.02
  */
-public interface CagraIndex {
+public interface CagraIndex extends AutoCloseable {
 
   /**
    * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
    */
-  void destroyIndex() throws Throwable;
+  @Override
+  void close() throws Exception;
 
   /**
    * Invokes the native search_cagra_index via the Panama API for searching a
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
index 746dc05fd1..cda73c9ea4 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/HnswIndex.java
@@ -25,12 +25,13 @@
  *
  * @since 25.02
  */
-public interface HnswIndex {
+public interface HnswIndex extends AutoCloseable {
 
   /**
    * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
    */
-  void destroyIndex() throws Throwable;
+  @Override
+  void close() throws Exception;
 
   /**
    * Invokes the native search_hnsw_index via the Panama API for searching a HNSW
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
index 2726382f23..3e8d1e0752 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/TieredIndex.java
@@ -23,15 +23,16 @@
  * {@link TieredIndex} encapsulates a Tiered index, along with methods to
  * interact with it.
  */
-public interface TieredIndex {
+public interface TieredIndex extends AutoCloseable {
 
   /**
    * Destroys the underlying native TieredIndex object and releases associated
    * resources.
    *
-   * @throws Throwable if an error occurs during index destruction
+   * @throws Exception if an error occurs during index destruction
    */
-  void destroyIndex() throws Throwable;
+  @Override
+  void close() throws Exception;
 
   /**
    * Searches the index with the specified query and search parameters.
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index 83c08fedc6..e5f98ad2dd 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -113,7 +113,7 @@ private void checkNotDestroyed() {
    * BRUTEFORCE index
    */
   @Override
-  public void destroyIndex() {
+  public void close() {
     checkNotDestroyed();
     try {
       int returnValue = cuvsBruteForceIndexDestroy(bruteForceIndexReference.indexPtr);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 489213f4da..0d5fc8d90f 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -158,7 +158,7 @@ private void checkNotDestroyed() {
    * Invokes the native destroy_cagra_index to de-allocate the CAGRA index
    */
   @Override
-  public void destroyIndex() throws Throwable {
+  public void close() {
     checkNotDestroyed();
     try {
       int returnValue = cuvsCagraIndexDestroy(cagraIndexReference.getMemorySegment());
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index ed25b5cc3f..736b22f567 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -74,7 +74,7 @@ private HnswIndexImpl(
    * Invokes the native destroy_hnsw_index to de-allocate the HNSW index
    */
   @Override
-  public void destroyIndex() {
+  public void close() {
     int returnValue = cuvsHnswIndexDestroy(hnswIndexReference.getMemorySegment());
     checkCuVSError(returnValue, "cuvsHnswIndexDestroy");
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index 31d8cd4bec..c507045326 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -94,7 +94,7 @@ private void checkNotDestroyed() {
    * Invokes the native destroy_tiered_index to de-allocate the Tiered index
    */
   @Override
-  public void destroyIndex() {
+  public void close() {
     checkNotDestroyed();
     try {
       int returnValue = cuvsTieredIndexDestroy(tieredIndexReference.getMemorySegment());
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
index ddd1be0b5d..900b16ba1a 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceAndSearchIT.java
@@ -57,37 +57,34 @@ private static void indexAndQueryOnce(
         new BruteForceIndexParams.Builder().withNumWriterThreads(32).build();
 
     // Create the index with the dataset
-    BruteForceIndex index =
+    try (BruteForceIndex index =
         BruteForceIndex.newBuilder(resources)
             .withDataset(dataset)
             .withIndexParams(indexParams)
-            .build();
+            .build()) {
 
-    // Saving the index on to the disk.
-    String indexFileName = UUID.randomUUID() + ".bf";
-    try (var outputStream = new FileOutputStream(indexFileName)) {
-      index.serialize(outputStream);
-    }
-
-    // Loading a BRUTEFORCE index from disk.
-    Path indexFile = Path.of(indexFileName);
-    try (var inputStream = Files.newInputStream(indexFile)) {
-      BruteForceIndex loadedIndex = BruteForceIndex.newBuilder(resources).from(inputStream).build();
+      // Saving the index on to the disk.
+      String indexFileName = UUID.randomUUID() + ".bf";
+      try (var outputStream = new FileOutputStream(indexFileName)) {
+        index.serialize(outputStream);
+      }
 
-      // search the loaded index
-      SearchResults results = loadedIndex.search(cuvsQuery);
-      checkResults(expectedResults, results.getResults());
+      // Loading a BRUTEFORCE index from disk.
+      Path indexFile = Path.of(indexFileName);
+      try (var inputStream = Files.newInputStream(indexFile);
+          BruteForceIndex loadedIndex =
+              BruteForceIndex.newBuilder(resources).from(inputStream).build()) {
 
-      // search the first index
-      results = index.search(cuvsQuery);
-      checkResults(expectedResults, results.getResults());
+        // search the loaded index
+        SearchResults results = loadedIndex.search(cuvsQuery);
+        checkResults(expectedResults, results.getResults());
 
-      // Cleanup
-      index.destroyIndex();
-      loadedIndex.destroyIndex();
+        // search the first index
+        results = index.search(cuvsQuery);
+        checkResults(expectedResults, results.getResults());
+      }
+      Files.deleteIfExists(indexFile);
     }
-
-    Files.deleteIfExists(indexFile);
   }
 
   /**
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 8223e199f6..a4133c4dc5 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -233,7 +233,7 @@ public void testIndexing() throws Throwable {
               () -> {
                 try (CuVSResources resources = CheckedCuVSResources.create()) {
                   var index = indexOnce(CuVSMatrix.ofArray(dataset), resources);
-                  index.destroyIndex();
+                  index.close();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
                 }
@@ -250,11 +250,10 @@ public void testSerialization() throws Throwable {
           numTestsRuns,
           () ->
               () -> {
-                try (CuVSResources resources = CheckedCuVSResources.create()) {
-                  var index = indexOnce(CuVSMatrix.ofArray(dataset), resources);
+                try (CuVSResources resources = CheckedCuVSResources.create();
+                    var index = indexOnce(CuVSMatrix.ofArray(dataset), resources)) {
                   var indexPath = serializeOnce(index);
                   Files.deleteIfExists(indexPath);
-                  index.destroyIndex();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
                 }
@@ -272,7 +271,7 @@ public void testDeserialization() throws Throwable {
           () ->
               () -> {
                 try (CuVSResources resources = CheckedCuVSResources.create()) {
-                  deserializeOnce(indexPath, resources).destroyIndex();
+                  deserializeOnce(indexPath, resources).close();
                 } catch (Throwable e) {
                   throw new RuntimeException(e);
                 }
@@ -282,11 +281,9 @@ public void testDeserialization() throws Throwable {
   }
 
   private Path createSerializedIndex(CuVSMatrix dataset) throws Throwable {
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      var index = indexOnce(dataset, resources);
-      var indexPath = serializeOnce(index);
-      index.destroyIndex();
-      return indexPath;
+    try (CuVSResources resources = CheckedCuVSResources.create();
+        var index = indexOnce(dataset, resources)) {
+      return serializeOnce(index);
     }
   }
 
@@ -296,38 +293,36 @@ public void testReconstructIndexFromGraph() throws Throwable {
       var queries = createSampleQueries();
       List<Map<Integer, Float>> expectedResults = getExpectedResults();
 
-      try (CuVSResources resources = CuVSResources.create()) {
-        var index = indexOnce(dataset, resources);
+      try (CuVSResources resources = CuVSResources.create();
+          var index = indexOnce(dataset, resources)) {
         var graph = index.getGraph();
 
-        var reconstructedIndex =
+        try (var reconstructedIndex =
             CagraIndex.newBuilder(resources)
                 .from(graph)
                 .withDataset(dataset)
                 .withIndexParams(
                     new CagraIndexParams.Builder().withMetric(CuvsDistanceType.L2Expanded).build())
-                .build();
-        queryAndCompare(
-            index,
-            reconstructedIndex,
-            SearchResults.IDENTITY_MAPPING,
-            queries,
-            expectedResults,
-            resources);
-
-        var originalIndexPath = serializeOnce(index);
-        var reconstructedIndexPath = serializeOnce(reconstructedIndex);
-
-        var originalBytes = Files.readAllBytes(originalIndexPath);
-        var reconstructedBytes = Files.readAllBytes(reconstructedIndexPath);
-
-        assertArrayEquals(originalBytes, reconstructedBytes);
-
-        index.destroyIndex();
-        reconstructedIndex.destroyIndex();
-
-        Files.deleteIfExists(originalIndexPath);
-        Files.deleteIfExists(reconstructedIndexPath);
+                .build()) {
+          queryAndCompare(
+              index,
+              reconstructedIndex,
+              SearchResults.IDENTITY_MAPPING,
+              queries,
+              expectedResults,
+              resources);
+
+          var originalIndexPath = serializeOnce(index);
+          var reconstructedIndexPath = serializeOnce(reconstructedIndex);
+
+          var originalBytes = Files.readAllBytes(originalIndexPath);
+          var reconstructedBytes = Files.readAllBytes(reconstructedIndexPath);
+
+          assertArrayEquals(originalBytes, reconstructedBytes);
+
+          Files.deleteIfExists(originalIndexPath);
+          Files.deleteIfExists(reconstructedIndexPath);
+        }
       }
     }
   }
@@ -516,8 +511,8 @@ private void queryAndCompare(
 
   private void cleanup(CagraIndex index, CagraIndex loadedIndex) throws Throwable {
     // Cleanup
-    index.destroyIndex();
-    loadedIndex.destroyIndex();
+    index.close();
+    loadedIndex.close();
   }
 
   @Test
@@ -644,10 +639,10 @@ public void testMergingIndexes() throws Throwable {
       if (indexFile.exists()) {
         indexFile.delete();
       }
-      index1.destroyIndex();
-      index2.destroyIndex();
-      mergedIndex.destroyIndex();
-      loadedMergedIndex.destroyIndex();
+      index1.close();
+      index2.close();
+      mergedIndex.close();
+      loadedMergedIndex.close();
     }
   }
 
@@ -756,10 +751,10 @@ public void testMergeStrategies() throws Throwable {
       if (physicalIndexFile.exists()) {
         physicalIndexFile.delete();
       }
-      index1.destroyIndex();
-      index2.destroyIndex();
-      physicalMergedIndex.destroyIndex();
-      loadedPhysicalIndex.destroyIndex();
+      index1.close();
+      index2.close();
+      physicalMergedIndex.close();
+      loadedPhysicalIndex.close();
     }
   }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
index 1df57d1e0b..32f3d86fe0 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
@@ -95,92 +95,91 @@ private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Th
               .withMetric(CuvsDistanceType.L2Expanded)
               .build();
 
-      CagraIndex index =
+      try (CagraIndex index =
           CagraIndex.newBuilder(resources)
               .withDataset(dataset)
               .withIndexParams(indexParams)
-              .build();
+              .build()) {
 
-      log.trace("CAGRA index created, starting high-contention multi-threaded search...");
+        log.trace("CAGRA index created, starting high-contention multi-threaded search...");
 
-      // Create high contention scenario that would fail without using separate resources in every
-      // thread
-      try (ExecutorService executor = Executors.newFixedThreadPool(numThreads)) {
-        List<Future<?>> futures = new ArrayList<>();
-        CountDownLatch startLatch = new CountDownLatch(1);
-        AtomicInteger successfulQueries = new AtomicInteger(0);
-        AtomicReference<Throwable> firstError = new AtomicReference<>();
+        // Create high contention scenario that would fail without using separate resources in every
+        // thread
+        try (ExecutorService executor = Executors.newFixedThreadPool(numThreads)) {
+          List<Future<?>> futures = new ArrayList<>();
+          CountDownLatch startLatch = new CountDownLatch(1);
+          AtomicInteger successfulQueries = new AtomicInteger(0);
+          AtomicReference<Throwable> firstError = new AtomicReference<>();
 
-        for (int threadId = 0; threadId < numThreads; threadId++) {
-          final int finalThreadId = threadId;
+          for (int threadId = 0; threadId < numThreads; threadId++) {
+            final int finalThreadId = threadId;
 
-          Future<?> future =
-              executor.submit(
-                  () -> {
-                    try {
-                      // Wait for all threads to start simultaneously
-                      startLatch.await();
+            Future<?> future =
+                executor.submit(
+                    () -> {
+                      try {
+                        // Wait for all threads to start simultaneously
+                        startLatch.await();
 
-                      for (int queryId = 0; queryId < queriesPerThread; queryId++) {
-                        queryAction.run(index);
-                        successfulQueries.incrementAndGet();
+                        for (int queryId = 0; queryId < queriesPerThread; queryId++) {
+                          queryAction.run(index);
+                          successfulQueries.incrementAndGet();
 
-                        // No Thread.yield() - maximize contention
-                      }
+                          // No Thread.yield() - maximize contention
+                        }
 
-                      log.trace("Thread {} completed successfully", finalThreadId);
+                        log.trace("Thread {} completed successfully", finalThreadId);
 
-                    } catch (Throwable t) {
-                      log.error("Thread {} failed: {}", finalThreadId, t.getMessage(), t);
-                      firstError.compareAndSet(null, t);
-                      throw new RuntimeException("Thread failed", t);
-                    }
-                  });
+                      } catch (Throwable t) {
+                        log.error("Thread {} failed: {}", finalThreadId, t.getMessage(), t);
+                        firstError.compareAndSet(null, t);
+                        throw new RuntimeException("Thread failed", t);
+                      }
+                    });
 
-          futures.add(future);
-        }
+            futures.add(future);
+          }
 
-        // Start all threads simultaneously to maximize contention
-        log.debug("Starting all {} threads simultaneously...", numThreads);
-        startLatch.countDown();
-
-        // Wait for all threads to complete
-        boolean allCompleted = true;
-        for (Future<?> future : futures) {
-          try {
-            future.get(120, TimeUnit.SECONDS); // Longer timeout for stress test
-          } catch (Exception e) {
-            allCompleted = false;
-            log.error("Thread failed: {}", e.getMessage(), e);
-            if (firstError.get() == null) {
-              firstError.set(e);
+          // Start all threads simultaneously to maximize contention
+          log.debug("Starting all {} threads simultaneously...", numThreads);
+          startLatch.countDown();
+
+          // Wait for all threads to complete
+          boolean allCompleted = true;
+          for (Future<?> future : futures) {
+            try {
+              future.get(120, TimeUnit.SECONDS); // Longer timeout for stress test
+            } catch (Exception e) {
+              allCompleted = false;
+              log.error("Thread failed: {}", e.getMessage(), e);
+              if (firstError.get() == null) {
+                firstError.set(e);
+              }
             }
           }
-        }
 
-        executor.shutdown();
-        if (!executor.awaitTermination(10, TimeUnit.SECONDS)) {
-          executor.shutdownNow();
-        }
+          executor.shutdown();
+          if (!executor.awaitTermination(10, TimeUnit.SECONDS)) {
+            executor.shutdownNow();
+          }
 
-        // Verify results
-        int expectedTotalQueries = numThreads * queriesPerThread;
-        int actualSuccessfulQueries = successfulQueries.get();
+          // Verify results
+          int expectedTotalQueries = numThreads * queriesPerThread;
+          int actualSuccessfulQueries = successfulQueries.get();
 
-        log.debug("  Successful queries: {} / {}", actualSuccessfulQueries, expectedTotalQueries);
+          log.debug("  Successful queries: {} / {}", actualSuccessfulQueries, expectedTotalQueries);
 
-        if (firstError.get() != null) {
-          fail("MultiThreaded stablity test failed:" + " " + firstError.get().getMessage());
-        }
+          if (firstError.get() != null) {
+            fail("MultiThreaded stablity test failed:" + " " + firstError.get().getMessage());
+          }
 
-        assertTrue("All threads should complete successfully", allCompleted);
-        assertEquals(
-            "All queries should complete successfully",
-            expectedTotalQueries,
-            actualSuccessfulQueries);
+          assertTrue("All threads should complete successfully", allCompleted);
+          assertEquals(
+              "All queries should complete successfully",
+              expectedTotalQueries,
+              actualSuccessfulQueries);
+        }
       }
-
-      index.destroyIndex();
     }
   }
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 69e28d20aa..144090866c 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -158,7 +158,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
 
         compareResults(results, expected, topK, datasetSize, numQueries);
       } finally {
-        index.destroyIndex();
+        index.close();
       }
     }
   }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
index b2b86c37ad..c72ccd8aec 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswBuildAndSearchIT.java
@@ -97,10 +97,10 @@ private static void indexAndQueryOnce(
         checkResults(expectedResults, results.getResults());
 
         // Cleanup
-        hnswIndex.destroyIndex();
+        hnswIndex.close();
       }
     } finally {
-      index.destroyIndex();
+      index.close();
       Files.deleteIfExists(hnswIndexPath);
     }
   }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index 8cda810c6c..02ac3c3a14 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -164,10 +164,10 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
           SearchResults results = hnswIndex.search(hnswQuery);
           compareResults(results, expected, topK, datasetSize, numQueries);
 
-          hnswIndex.destroyIndex();
+          hnswIndex.close();
         }
       } finally {
-        index.destroyIndex();
+        index.close();
         Files.deleteIfExists(hnswIndexPath);
       }
     }

From 699d32c8be9a344c83456232851430e4c3840484 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 14 Aug 2025 12:21:49 -0700
Subject: [PATCH 546/623] Merge support for tiered index (#1155)

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1155
---
 cpp/include/cuvs/neighbors/cagra.hpp          |   3 +
 cpp/include/cuvs/neighbors/tiered_index.h     |  24 ++++
 cpp/include/cuvs/neighbors/tiered_index.hpp   |  30 +++++
 .../neighbors/detail/cagra/cagra_merge.cuh    |   6 +-
 cpp/src/neighbors/detail/tiered_index.cuh     |  73 ++++++++++
 cpp/src/neighbors/tiered_index.cu             |  27 ++++
 cpp/src/neighbors/tiered_index_c.cpp          | 126 +++++++++++++++---
 cpp/tests/neighbors/tiered_index.cu           |  79 +++++++----
 8 files changed, 322 insertions(+), 46 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 48ee128271..6c7d1a1979 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1975,6 +1975,9 @@ void serialize_to_hnswlib(
  * @note: When device memory is sufficient, the dataset attached to the returned index is allocated
  * in device memory by default; otherwise, host memory is used automatically.
  *
+ * @note: This API only supports physical merge (`merge_strategy = MERGE_STRATEGY_PHYSICAL`), and
+ * attempting a logical merge here will throw an error.
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace raft::neighbors;
diff --git a/cpp/include/cuvs/neighbors/tiered_index.h b/cpp/include/cuvs/neighbors/tiered_index.h
index c72f929d95..6c54d474b6 100644
--- a/cpp/include/cuvs/neighbors/tiered_index.h
+++ b/cpp/include/cuvs/neighbors/tiered_index.h
@@ -246,6 +246,30 @@ cuvsError_t cuvsTieredIndexSearch(cuvsResources_t res,
 cuvsError_t cuvsTieredIndexExtend(cuvsResources_t res,
                                   DLManagedTensor* new_vectors,
                                   cuvsTieredIndex_t index);
+/**
+ * @}
+ */
+
+/**
+ * @defgroup tiered_c_index_merge Tiered index merge
+ * @{
+ */
+/**
+ * @brief Merge multiple indices together into a single index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index_params Index parameters to use when merging
+ * @param[in] indices pointers to indices to merge together
+ * @param[in] num_indices the number of indices to merge
+ * @param[out] output_index the merged index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsTieredIndexMerge(cuvsResources_t res,
+                                 cuvsTieredIndexParams_t index_params,
+                                 cuvsTieredIndex_t* indices,
+                                 size_t num_indices,
+                                 cuvsTieredIndex_t output_index);
+
 /**
  * @}
  */
diff --git a/cpp/include/cuvs/neighbors/tiered_index.hpp b/cpp/include/cuvs/neighbors/tiered_index.hpp
index 1037955309..4d62773fc5 100644
--- a/cpp/include/cuvs/neighbors/tiered_index.hpp
+++ b/cpp/include/cuvs/neighbors/tiered_index.hpp
@@ -197,4 +197,34 @@ void search(raft::resources const& res,
             raft::device_matrix_view<float, int64_t, raft::row_major> distances,
             const cuvs::neighbors::filtering::base_filter& sample_filter =
               cuvs::neighbors::filtering::none_sample_filter{});
+
+/** @brief Merge multiple tiered indices into a single index.
+ *
+ * This function merges multiple tiered indices into one, combining both the datasets and graph
+ * structures.
+ *
+ * @param[in] res
+ * @param[in] index_params configure the index building
+ * @param[in] indices A vector of pointers to the indices to merge. All indices should
+ *                    be of the same type, and have datasets with the same dimensionality
+ *
+ * @return A new tiered index containing the merged indices
+ */
+auto merge(raft::resources const& res,
+           const index_params<cagra::index_params>& index_params,
+           const std::vector<tiered_index::index<cagra::index<float, uint32_t>>*>& indices)
+  -> tiered_index::index<cagra::index<float, uint32_t>>;
+
+/** @copydoc merge */
+auto merge(raft::resources const& res,
+           const index_params<ivf_flat::index_params>& index_params,
+           const std::vector<tiered_index::index<ivf_flat::index<float, int64_t>>*>& indices)
+  -> tiered_index::index<ivf_flat::index<float, int64_t>>;
+
+/** @copydoc merge */
+auto merge(raft::resources const& res,
+           const index_params<ivf_pq::index_params>& index_params,
+           const std::vector<tiered_index::index<ivf_pq::typed_index<float, int64_t>>*>& indices)
+  -> tiered_index::index<ivf_pq::typed_index<float, int64_t>>;
+
 }  // namespace cuvs::neighbors::tiered_index
diff --git a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
index 192f7f1632..86faad02c0 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_merge.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -44,6 +44,10 @@ index<T, IdxT> merge(raft::resources const& handle,
                      const cagra::merge_params& params,
                      std::vector<cuvs::neighbors::cagra::index<T, IdxT>*>& indices)
 {
+  // we're doing a physical merge here, make sure that this matches the merge_params
+  RAFT_EXPECTS(params.merge_strategy == cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
+               "cagra::merge only supports merge_strategy=MERGE_STRATEGY_PHYSICAL");
+
   using cagra_index_t = cuvs::neighbors::cagra::index<T, IdxT>;
   using ds_idx_type   = typename cagra_index_t::dataset_index_type;
 
diff --git a/cpp/src/neighbors/detail/tiered_index.cuh b/cpp/src/neighbors/detail/tiered_index.cuh
index ef7fd7d015..47f1ff349c 100644
--- a/cpp/src/neighbors/detail/tiered_index.cuh
+++ b/cpp/src/neighbors/detail/tiered_index.cuh
@@ -297,6 +297,79 @@ auto build(
   return std::shared_ptr<index_state<UpstreamT>>(ret);
 }
 
+/**
+ * @brief merge multiple indices together
+ */
+template <typename UpstreamT>
+auto merge(raft::resources const& res,
+           const index_params<typename UpstreamT::index_params_type>& index_params,
+           const std::vector<tiered_index::index<UpstreamT>*>& indices)
+  -> std::shared_ptr<index_state<UpstreamT>>
+{
+  using value_type = typename UpstreamT::value_type;
+
+  RAFT_EXPECTS(indices.size() > 0, "Must pass at least one index to merge");
+
+  for (auto index : indices) {
+    RAFT_EXPECTS(index != nullptr,
+                 "Null pointer detected in 'indices'. Ensure all elements are valid before usage.");
+  }
+
+  // handle simple case of only one index being merged
+  if (indices.size() == 1) { return indices[0]->state; }
+
+  auto dim           = indices[0]->state->dim();
+  auto include_norms = indices[0]->state->storage->include_norms;
+
+  // validate data and check what needs to be merged
+  size_t bfknn_rows = 0, ann_rows = 0;
+  for (auto index : indices) {
+    RAFT_EXPECTS(dim == index->state->dim(), "Each index must have the same dimensionality");
+    bfknn_rows += index->state->bfknn_rows();
+    ann_rows += index->state->ann_rows();
+  }
+
+  // degenerate case - all indices are empty, just re-use the state from the first index
+  if (!bfknn_rows && !ann_rows) { return indices[0]->state; }
+
+  // concatenate all the storages together
+  auto to_allocate = bfknn_rows + ann_rows;
+  auto new_storage =
+    std::make_shared<brute_force_storage<value_type>>(res, to_allocate, dim, include_norms);
+
+  for (auto index : indices) {
+    auto storage = index->state->storage;
+
+    // copy over dataset to new storage
+    raft::copy(res,
+               raft::make_device_matrix_view<value_type, int64_t, raft::row_major>(
+                 new_storage->dataset.data() + new_storage->num_rows_used * dim,
+                 storage->num_rows_used,
+                 dim),
+               raft::make_device_matrix_view<const value_type, int64_t, raft::row_major>(
+                 storage->dataset.data(), storage->num_rows_used, dim));
+
+    // copy over precalculated norms
+    if (include_norms) {
+      raft::copy(res,
+                 raft::make_device_vector_view<value_type, int64_t, raft::row_major>(
+                   new_storage->norms->data() + new_storage->num_rows_used, storage->num_rows_used),
+                 raft::make_device_vector_view<const value_type, int64_t, raft::row_major>(
+                   storage->norms->data(), storage->num_rows_used));
+    }
+    new_storage->num_rows_used += storage->num_rows_used;
+  }
+
+  auto next_state          = std::make_shared<index_state<UpstreamT>>(*indices[0]->state);
+  next_state->storage      = new_storage;
+  next_state->build_params = index_params;
+
+  if (next_state->bfknn_rows() > static_cast<size_t>(next_state->build_params.min_ann_rows)) {
+    next_state = compact(res, *next_state);
+  }
+  return next_state;
+}
+
 template <typename UpstreamT>
 auto extend(raft::resources const& res,
             const index_state<UpstreamT>& current,
diff --git a/cpp/src/neighbors/tiered_index.cu b/cpp/src/neighbors/tiered_index.cu
index 1a3a410419..ba109defcb 100644
--- a/cpp/src/neighbors/tiered_index.cu
+++ b/cpp/src/neighbors/tiered_index.cu
@@ -175,6 +175,33 @@ void search(raft::resources const& res,
     res, search_params, ivf_pq::typed_search, queries, neighbors, distances, sample_filter);
 }
 
+auto merge(raft::resources const& res,
+           const index_params<cagra::index_params>& index_params,
+           const std::vector<tiered_index::index<cagra::index<float, uint32_t>>*>& indices)
+  -> tiered_index::index<cagra::index<float, uint32_t>>
+{
+  auto state = detail::merge(res, index_params, indices);
+  return cuvs::neighbors::tiered_index::index<cagra::index<float, uint32_t>>(state);
+}
+
+auto merge(raft::resources const& res,
+           const index_params<ivf_flat::index_params>& index_params,
+           const std::vector<tiered_index::index<ivf_flat::index<float, int64_t>>*>& indices)
+  -> tiered_index::index<ivf_flat::index<float, int64_t>>
+{
+  auto state = detail::merge(res, index_params, indices);
+  return cuvs::neighbors::tiered_index::index<ivf_flat::index<float, int64_t>>(state);
+}
+
+auto merge(raft::resources const& res,
+           const index_params<ivf_pq::index_params>& index_params,
+           const std::vector<tiered_index::index<ivf_pq::typed_index<float, int64_t>>*>& indices)
+  -> tiered_index::index<ivf_pq::typed_index<float, int64_t>>
+{
+  auto state = detail::merge(res, index_params, indices);
+  return cuvs::neighbors::tiered_index::index<ivf_pq::typed_index<float, int64_t>>(state);
+}
+
 template <typename UpstreamT>
 int64_t index<UpstreamT>::size() const noexcept
 {
diff --git a/cpp/src/neighbors/tiered_index_c.cpp b/cpp/src/neighbors/tiered_index_c.cpp
index 196b199e10..267c7ba5f0 100644
--- a/cpp/src/neighbors/tiered_index_c.cpp
+++ b/cpp/src/neighbors/tiered_index_c.cpp
@@ -37,6 +37,33 @@
 namespace {
 using namespace cuvs::neighbors;
 
+template <typename T>
+void convert_c_index_params(cuvsTieredIndexParams params,
+                            int64_t n_rows,
+                            int64_t dim,
+                            tiered_index::index_params<T>* out)
+{
+  out->min_ann_rows               = params.min_ann_rows;
+  out->create_ann_index_on_extend = params.create_ann_index_on_extend;
+  out->metric                     = params.metric;
+
+  if constexpr (std::is_same_v<T, cagra::index_params>) {
+    if (params.cagra_params != NULL) {
+      cagra::convert_c_index_params(*params.cagra_params, n_rows, dim, out);
+    }
+  } else if constexpr (std::is_same_v<T, ivf_flat::index_params>) {
+    if (params.ivf_flat_params != NULL) {
+      ivf_flat::convert_c_index_params(*params.ivf_flat_params, out);
+    }
+  } else if constexpr (std::is_same_v<T, ivf_pq::index_params>) {
+    if (params.ivf_pq_params != NULL) {
+      ivf_pq::convert_c_index_params(*params.ivf_pq_params, out);
+    }
+  } else {
+    RAFT_FAIL("unhandled index params type");
+  }
+}
+
 template <typename T>
 void* _build(cuvsResources_t res, cuvsTieredIndexParams params, DLManagedTensor* dataset_tensor)
 {
@@ -44,40 +71,26 @@ void* _build(cuvsResources_t res, cuvsTieredIndexParams params, DLManagedTensor*
   using mdspan_type = raft::device_matrix_view<const T, int64_t, raft::row_major>;
   auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
 
+  auto dataset = dataset_tensor->dl_tensor;
+  RAFT_EXPECTS(dataset.ndim == 2, "dataset should be a 2-dimensional tensor");
+  RAFT_EXPECTS(dataset.shape != nullptr, "dataset should have an initialized shape");
+
   switch (params.algo) {
     case CUVS_TIERED_INDEX_ALGO_CAGRA: {
       auto build_params = tiered_index::index_params<cagra::index_params>();
-      if (params.cagra_params != NULL) {
-        auto dataset = dataset_tensor->dl_tensor;
-        cagra::convert_c_index_params(
-          *params.cagra_params, dataset.shape[0], dataset.shape[1], &build_params);
-      }
-      build_params.min_ann_rows               = params.min_ann_rows;
-      build_params.create_ann_index_on_extend = params.create_ann_index_on_extend;
-      build_params.metric                     = params.metric;
+      convert_c_index_params(params, dataset.shape[0], dataset.shape[1], &build_params);
       return new tiered_index::index<cagra::index<T, uint32_t>>(
         tiered_index::build(*res_ptr, build_params, mds));
     }
     case CUVS_TIERED_INDEX_ALGO_IVF_FLAT: {
       auto build_params = tiered_index::index_params<ivf_flat::index_params>();
-      if (params.ivf_flat_params != NULL) {
-        ivf_flat::convert_c_index_params(*params.ivf_flat_params, &build_params);
-      }
-      build_params.metric                     = params.metric;
-      build_params.min_ann_rows               = params.min_ann_rows;
-      build_params.create_ann_index_on_extend = params.create_ann_index_on_extend;
+      convert_c_index_params(params, dataset.shape[0], dataset.shape[1], &build_params);
       return new tiered_index::index<ivf_flat::index<T, int64_t>>(
         tiered_index::build(*res_ptr, build_params, mds));
     }
     case CUVS_TIERED_INDEX_ALGO_IVF_PQ: {
-      auto build_params   = tiered_index::index_params<ivf_pq::index_params>();
-      build_params.metric = params.metric;
-      if (params.ivf_pq_params != NULL) {
-        ivf_pq::convert_c_index_params(*params.ivf_pq_params, &build_params);
-      }
-      build_params.metric                     = params.metric;
-      build_params.min_ann_rows               = params.min_ann_rows;
-      build_params.create_ann_index_on_extend = params.create_ann_index_on_extend;
+      auto build_params = tiered_index::index_params<ivf_pq::index_params>();
+      convert_c_index_params(params, dataset.shape[0], dataset.shape[1], &build_params);
       return new tiered_index::index<ivf_pq::typed_index<T, int64_t>>(
         tiered_index::build(*res_ptr, build_params, mds));
     }
@@ -157,6 +170,47 @@ void _extend(cuvsResources_t res, DLManagedTensor* new_vectors, cuvsTieredIndex
 
   tiered_index::extend(*res_ptr, vectors_mds, index_ptr);
 }
+template <typename UpstreamT>
+void _merge(cuvsResources_t res,
+            cuvsTieredIndexParams params,
+            cuvsTieredIndex_t* indices,
+            size_t num_indices,
+            cuvsTieredIndex_t output_index)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  std::vector<cuvs::neighbors::tiered_index::index<UpstreamT>*> cpp_indices;
+
+  int64_t n_rows = 0, dim = 0;
+  for (size_t i = 0; i < num_indices; ++i) {
+    RAFT_EXPECTS(indices[i]->dtype.code == indices[0]->dtype.code,
+                 "indices must all have the same dtype");
+    RAFT_EXPECTS(indices[i]->dtype.bits == indices[0]->dtype.bits,
+                 "indices must all have the same dtype");
+    RAFT_EXPECTS(indices[i]->algo == indices[0]->algo,
+                 "indices must all have the same index algorithm");
+
+    auto idx_ptr =
+      reinterpret_cast<cuvs::neighbors::tiered_index::index<UpstreamT>*>(indices[i]->addr);
+    n_rows += idx_ptr->size();
+    if (dim) {
+      RAFT_EXPECTS(dim == idx_ptr->dim(), "indices must all have the same dimensionality");
+    } else {
+      dim = idx_ptr->dim();
+    }
+    cpp_indices.push_back(idx_ptr);
+  }
+
+  auto build_params = tiered_index::index_params<typename UpstreamT::index_params_type>();
+  convert_c_index_params(params, n_rows, dim, &build_params);
+
+  auto ptr = new cuvs::neighbors::tiered_index::index<UpstreamT>(
+    cuvs::neighbors::tiered_index::merge(*res_ptr, build_params, cpp_indices));
+
+  output_index->addr  = reinterpret_cast<uintptr_t>(ptr);
+  output_index->dtype = indices[0]->dtype;
+  output_index->algo  = indices[0]->algo;
+}
 
 }  // namespace
 
@@ -305,3 +359,31 @@ extern "C" cuvsError_t cuvsTieredIndexExtend(cuvsResources_t res,
     }
   });
 }
+
+extern "C" cuvsError_t cuvsTieredIndexMerge(cuvsResources_t res,
+                                            cuvsTieredIndexParams_t params,
+                                            cuvsTieredIndex_t* indices,
+                                            size_t num_indices,
+                                            cuvsTieredIndex_t output_index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    RAFT_EXPECTS(num_indices >= 1, "must have at least one index to merge");
+
+    switch (indices[0]->algo) {
+      case CUVS_TIERED_INDEX_ALGO_CAGRA: {
+        _merge<cagra::index<float, uint32_t>>(res, *params, indices, num_indices, output_index);
+        break;
+      }
+      case CUVS_TIERED_INDEX_ALGO_IVF_FLAT: {
+        _merge<ivf_flat::index<float, int64_t>>(res, *params, indices, num_indices, output_index);
+        break;
+      }
+      case CUVS_TIERED_INDEX_ALGO_IVF_PQ: {
+        _merge<ivf_pq::typed_index<float, int64_t>>(
+          res, *params, indices, num_indices, output_index);
+        break;
+      }
+      default: RAFT_FAIL("unsupported tiered index algorithm");
+    }
+  });
+}
diff --git a/cpp/tests/neighbors/tiered_index.cu b/cpp/tests/neighbors/tiered_index.cu
index 4aab6ba6ff..b1ed2b79ea 100644
--- a/cpp/tests/neighbors/tiered_index.cu
+++ b/cpp/tests/neighbors/tiered_index.cu
@@ -34,18 +34,30 @@
 
 namespace cuvs::neighbors::tiered_index {
 
+enum TieredIndexTestStrategy { TEST_EXTEND, TEST_MERGE };
+
 struct AnnTieredIndexInputs {
   int n_rows;
   int dim;
   cuvs::distance::DistanceType metric;
   int k;
   int n_queries;
-  double min_recall;
+  TieredIndexTestStrategy test_strategy;
 };
 
+inline ::std::ostream& operator<<(::std::ostream& os, const TieredIndexTestStrategy& p)
+{
+  switch (p) {
+    case TEST_EXTEND: os << "TEST_EXTEND"; break;
+    case TEST_MERGE: os << "TEST_MERGE"; break;
+  }
+  return os;
+}
+
 inline ::std::ostream& operator<<(::std::ostream& os, const AnnTieredIndexInputs& p)
 {
   os << "dataset shape=" << p.n_rows << "x" << p.dim << ", metric=" << print_metric{p.metric}
+     << ", k=" << p.k << ", n_queries=" << p.n_queries << ", test_strategy=" << p.test_strategy
      << std::endl;
   return os;
 }
@@ -95,25 +107,57 @@ class ANNTieredIndexTest : public ::testing::TestWithParam<AnnTieredIndexInputs>
     // Calculate tiered index
     std::vector<int64_t> indices_tiered(ps.n_queries * ps.k);
     std::vector<value_type> distances_tiered(ps.n_queries * ps.k);
+    double min_recall = 0.85;
     {
       index_params<typename UpstreamT::index_params_type> build_params;
       build_params.metric                     = ps.metric;
       build_params.min_ann_rows               = 2000;
       build_params.create_ann_index_on_extend = true;
 
-      // include 50% of the rows in the initial build - and then incrementally add
-      // the rest row by row
+      typename UpstreamT::search_params_type search_params;
+      if constexpr (std::is_same_v<ivf_flat::search_params,
+                                   typename UpstreamT::search_params_type>) {
+        // min_recall logic copied from ivf_flat unittest
+        min_recall =
+          static_cast<double>(search_params.n_probes) / static_cast<double>(build_params.n_lists);
+      }
+      if constexpr (std::is_same_v<ivf_pq::search_params, typename UpstreamT::search_params_type>) {
+        min_recall =
+          static_cast<double>(search_params.n_probes) / static_cast<double>(build_params.n_lists);
+      }
+
+      // include 50% of the rows in the initial build
       auto initial_rows  = ps.n_rows / 2;
       auto database_view = raft::make_device_matrix_view<const value_type, int64_t>(
         (const value_type*)database.data(), initial_rows, ps.dim);
-
       auto index = cuvs::neighbors::tiered_index::build(handle_, build_params, database_view);
-      for (int i = initial_rows; i < ps.n_rows; ++i) {
-        cuvs::neighbors::tiered_index::extend(
-          handle_,
-          raft::make_device_matrix_view<const value_type, int64_t>(
-            (const value_type*)database.data() + i * ps.dim, 1, ps.dim),
-          &index);
+
+      std::optional<cuvs::neighbors::tiered_index::index<UpstreamT>> final_index;
+
+      if (ps.test_strategy == TEST_EXTEND) {
+        // test extend functionality by adding remaining vectors one by one
+        for (int i = initial_rows; i < ps.n_rows; ++i) {
+          cuvs::neighbors::tiered_index::extend(
+            handle_,
+            raft::make_device_matrix_view<const value_type, int64_t>(
+              (const value_type*)database.data() + i * ps.dim, 1, ps.dim),
+            &index);
+        }
+        final_index.emplace(index);
+      } else if (ps.test_strategy == TEST_MERGE) {
+        // test merge by creating a new index with the remaining vectors and then merge into the
+        // main index
+        auto database_view2 = raft::make_device_matrix_view<const value_type, int64_t>(
+          (const value_type*)database.data() + initial_rows * ps.dim,
+          ps.n_rows - initial_rows,
+          ps.dim);
+        auto index2 = cuvs::neighbors::tiered_index::build(handle_, build_params, database_view2);
+
+        std::vector<cuvs::neighbors::tiered_index::index<UpstreamT>*> indices = {&index, &index2};
+        final_index.emplace(cuvs::neighbors::tiered_index::merge(handle_, build_params, indices));
+
+      } else {
+        RAFT_FAIL("unknown test_strategy");
       }
       raft::resource::sync_stream(handle_);
 
@@ -122,23 +166,13 @@ class ANNTieredIndexTest : public ::testing::TestWithParam<AnnTieredIndexInputs>
       rmm::device_uvector<value_type> distances_tiered_dev(ps.n_queries * ps.k, stream_);
       rmm::device_uvector<int64_t> indices_tiered_dev(ps.n_queries * ps.k, stream_);
 
-      typename UpstreamT::search_params_type search_params;
-
-      if constexpr (std::is_same_v<ivf_flat::search_params,
-                                   typename UpstreamT::search_params_type>) {
-        search_params.n_probes = 128;
-      }
-      if constexpr (std::is_same_v<ivf_pq::search_params, typename UpstreamT::search_params_type>) {
-        search_params.n_probes = 128;
-      }
-
       auto distances_view = raft::make_device_matrix_view<value_type, int64_t>(
         (value_type*)distances_tiered_dev.data(), ps.n_queries, ps.k);
       auto indices_view = raft::make_device_matrix_view<int64_t, int64_t>(
         (int64_t*)indices_tiered_dev.data(), ps.n_queries, ps.k);
 
       cuvs::neighbors::tiered_index::search(
-        handle_, search_params, index, queries_view, indices_view, distances_view);
+        handle_, search_params, *final_index, queries_view, indices_view, distances_view);
 
       raft::update_host(
         distances_tiered.data(), distances_tiered_dev.data(), ps.n_queries * ps.k, stream_);
@@ -147,7 +181,6 @@ class ANNTieredIndexTest : public ::testing::TestWithParam<AnnTieredIndexInputs>
       raft::resource::sync_stream(handle_);
     }
 
-    double min_recall = ps.min_recall;
     EXPECT_TRUE(eval_neighbours(indices_naive,
                                 indices_tiered,
                                 distances_naive,
@@ -193,7 +226,7 @@ const std::vector<AnnTieredIndexInputs> inputs =
      cuvs::distance::DistanceType::InnerProduct},  // metric
     {10},                                          // k
     {10},                                          // n_queries
-    {0.85}                                         // min_recall
+    {TEST_EXTEND, TEST_MERGE}                      // test_strategy
   );
 typedef ANNTieredIndexTest<cagra::index<float, uint32_t>> CAGRA_F;
 TEST_P(CAGRA_F, AnnTieredIndex) { this->testTieredIndex(); }

From 77d6b6e2eb012c38e547afd2c48204f04a506b4d Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Fri, 15 Aug 2025 15:02:02 -0700
Subject: [PATCH 547/623] Run `cargo fmt` in the pre-commit hooks (#1255)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/1255
---
 .pre-commit-config.yaml                |  6 ++++++
 rust/cuvs-sys/build.rs                 |  5 ++---
 rust/cuvs/src/brute_force.rs           |  4 ++--
 rust/cuvs/src/distance/mod.rs          | 14 ++++++++++----
 rust/cuvs/src/ivf_flat/index.rs        |  8 ++++----
 rust/cuvs/src/ivf_flat/index_params.rs |  4 ++--
 rust/cuvs/src/ivf_pq/index_params.rs   |  4 ++--
 rust/cuvs/src/lib.rs                   |  4 ++--
 8 files changed, 30 insertions(+), 19 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 57674fa1ac..05489ee94d 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -92,6 +92,12 @@ repos:
                 pass_filenames: false
                 language: python
                 additional_dependencies: [gitpython]
+              - id: cargo-fmt
+                name: cargo-fmt
+                entry: cargo fmt --manifest-path rust/Cargo.toml --all
+                pass_filenames: false
+                files: rust/.*
+                language: rust
       - repo: https://github.com/codespell-project/codespell
         rev: v2.2.2
         hooks:
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
index bcb0a88c7d..2b9a98cee4 100644
--- a/rust/cuvs-sys/build.rs
+++ b/rust/cuvs-sys/build.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -20,8 +20,7 @@ use std::path::PathBuf;
 
 fn main() {
     // build the cuvs c-api library with cmake, and link it into this crate
-    let cuvs_build = cmake::Config::new(".")
-        .build();
+    let cuvs_build = cmake::Config::new(".").build();
 
     println!(
         "cargo:rustc-link-search=native={}/lib",
diff --git a/rust/cuvs/src/brute_force.rs b/rust/cuvs/src/brute_force.rs
index 63752ee9c1..dd5e93d204 100644
--- a/rust/cuvs/src/brute_force.rs
+++ b/rust/cuvs/src/brute_force.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -109,10 +109,10 @@ impl Drop for Index {
 #[cfg(test)]
 mod tests {
     use super::*;
+    use mark_flaky_tests::flaky;
     use ndarray::s;
     use ndarray_rand::rand_distr::Uniform;
     use ndarray_rand::RandomExt;
-    use mark_flaky_tests::flaky;
 
     fn test_bfknn(metric: DistanceType) {
         let res = Resources::new().unwrap();
diff --git a/rust/cuvs/src/distance/mod.rs b/rust/cuvs/src/distance/mod.rs
index f6911c6836..c29a4dd350 100644
--- a/rust/cuvs/src/distance/mod.rs
+++ b/rust/cuvs/src/distance/mod.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,7 +14,6 @@
  * limitations under the License.
  */
 
-
 use crate::distance_type::DistanceType;
 use crate::dlpack::ManagedTensor;
 use crate::error::{check_cuvs, Result};
@@ -72,8 +71,15 @@ mod tests {
             .to_device(&res)
             .unwrap();
 
-        pairwise_distance(&res, &dataset_device, &dataset_device, &distances, DistanceType::L2Expanded,
-        None).unwrap();
+        pairwise_distance(
+            &res,
+            &dataset_device,
+            &dataset_device,
+            &distances,
+            DistanceType::L2Expanded,
+            None,
+        )
+        .unwrap();
 
         // Copy back to host memory
         distances.to_host(&res, &mut distances_host).unwrap();
diff --git a/rust/cuvs/src/ivf_flat/index.rs b/rust/cuvs/src/ivf_flat/index.rs
index f66904142b..5892959557 100644
--- a/rust/cuvs/src/ivf_flat/index.rs
+++ b/rust/cuvs/src/ivf_flat/index.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,9 +16,9 @@
 
 use std::io::{stderr, Write};
 
-use crate::ivf_flat::{IndexParams, SearchParams};
 use crate::dlpack::ManagedTensor;
 use crate::error::{check_cuvs, Result};
+use crate::ivf_flat::{IndexParams, SearchParams};
 use crate::resources::Resources;
 
 /// Ivf-Flat ANN Index
@@ -127,8 +127,8 @@ mod tests {
         let dataset_device = ManagedTensor::from(&dataset).to_device(&res).unwrap();
 
         // build the ivf-flat index
-        let index =
-            Index::build(&res, &build_params, dataset_device).expect("failed to create ivf-flat index");
+        let index = Index::build(&res, &build_params, dataset_device)
+            .expect("failed to create ivf-flat index");
 
         // use the first 4 points from the dataset as queries : will test that we get them back
         // as their own nearest neighbor
diff --git a/rust/cuvs/src/ivf_flat/index_params.rs b/rust/cuvs/src/ivf_flat/index_params.rs
index 6c86ef527f..266de3bdb0 100644
--- a/rust/cuvs/src/ivf_flat/index_params.rs
+++ b/rust/cuvs/src/ivf_flat/index_params.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-use crate::error::{check_cuvs, Result};
 use crate::distance_type::DistanceType;
+use crate::error::{check_cuvs, Result};
 use std::fmt;
 use std::io::{stderr, Write};
 
diff --git a/rust/cuvs/src/ivf_pq/index_params.rs b/rust/cuvs/src/ivf_pq/index_params.rs
index 321821bc32..46d1f81d3e 100644
--- a/rust/cuvs/src/ivf_pq/index_params.rs
+++ b/rust/cuvs/src/ivf_pq/index_params.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -129,7 +129,7 @@ impl IndexParams {
     /// time. The parameter is applied to both PQ codebook generation methods, i.e., PER_SUBSPACE and
     /// PER_CLUSTER. In both cases, we will use `pq_book_size * max_train_points_per_pq_code` training
     /// points to train each codebook.
-    pub fn set_max_train_points_per_pq_code(self, max_pq_points: u32)-> IndexParams {
+    pub fn set_max_train_points_per_pq_code(self, max_pq_points: u32) -> IndexParams {
         unsafe {
             (*self.0).max_train_points_per_pq_code = max_pq_points;
         }
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
index 71a387b2c6..c2059b6097 100644
--- a/rust/cuvs/src/lib.rs
+++ b/rust/cuvs/src/lib.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,9 +22,9 @@ pub mod brute_force;
 pub mod cagra;
 pub mod distance;
 pub mod distance_type;
-pub mod ivf_flat;
 mod dlpack;
 mod error;
+pub mod ivf_flat;
 pub mod ivf_pq;
 mod resources;
 

From 2dcf6d8b245669c905c37170dfa004198cb928ee Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Mon, 18 Aug 2025 12:23:45 -0700
Subject: [PATCH 548/623] Removing deprecated batching code specific to NN
 Descent (#1249)

Closing https://github.com/rapidsai/cuvs/issues/1051

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1249
---
 cpp/include/cuvs/neighbors/nn_descent.h       |   3 +-
 cpp/include/cuvs/neighbors/nn_descent.hpp     |   1 -
 cpp/src/neighbors/detail/nn_descent_batch.cuh | 737 ------------------
 cpp/src/neighbors/nn_descent.cuh              |  59 +-
 cpp/src/neighbors/nn_descent_c.cpp            |   4 +-
 cpp/tests/neighbors/ann_nn_descent.cuh        |  20 -
 .../ann_nn_descent/test_float_uint32_t.cu     |   6 -
 .../ann_nn_descent/test_uint8_t_uint32_t.cu   |   8 -
 .../cuvs/neighbors/nn_descent/nn_descent.pxd  |   1 -
 .../cuvs/neighbors/nn_descent/nn_descent.pyx  |   9 +-
 10 files changed, 8 insertions(+), 840 deletions(-)
 delete mode 100644 cpp/src/neighbors/detail/nn_descent_batch.cuh

diff --git a/cpp/include/cuvs/neighbors/nn_descent.h b/cpp/include/cuvs/neighbors/nn_descent.h
index 7dd87f2248..118b1c26aa 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.h
+++ b/cpp/include/cuvs/neighbors/nn_descent.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -54,7 +54,6 @@ struct cuvsNNDescentIndexParams {
   size_t max_iterations;
   float termination_threshold;
   bool return_distances;
-  size_t n_clusters;
 };
 
 typedef struct cuvsNNDescentIndexParams* cuvsNNDescentIndexParams_t;
diff --git a/cpp/include/cuvs/neighbors/nn_descent.hpp b/cpp/include/cuvs/neighbors/nn_descent.hpp
index 5f6260b0af..ba1d417587 100644
--- a/cpp/include/cuvs/neighbors/nn_descent.hpp
+++ b/cpp/include/cuvs/neighbors/nn_descent.hpp
@@ -55,7 +55,6 @@ struct index_params : cuvs::neighbors::index_params {
   size_t max_iterations            = 20;
   float termination_threshold      = 0.0001;
   bool return_distances            = true;
-  size_t n_clusters                = 1;
 
   /** @brief Construct NN descent parameters for a specific kNN graph degree
    *
diff --git a/cpp/src/neighbors/detail/nn_descent_batch.cuh b/cpp/src/neighbors/detail/nn_descent_batch.cuh
deleted file mode 100644
index c0726409a2..0000000000
--- a/cpp/src/neighbors/detail/nn_descent_batch.cuh
+++ /dev/null
@@ -1,737 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <raft/core/resources.hpp>
-#include <raft/util/cudart_utils.hpp>
-#include <sys/types.h>
-#undef RAFT_EXPLICIT_INSTANTIATE_ONLY
-
-#include "nn_descent_gnnd.hpp"
-#include <cuvs/neighbors/brute_force.hpp>
-#include <cuvs/neighbors/nn_descent.hpp>
-
-#include <cuvs/cluster/kmeans.hpp>
-#include <raft/core/device_mdspan.hpp>
-#include <raft/core/host_mdspan.hpp>
-#include <raft/core/managed_mdarray.hpp>
-#include <raft/core/mdspan.hpp>
-#include <raft/core/mdspan_types.hpp>
-#include <raft/core/resource/cuda_stream.hpp>
-#include <raft/matrix/detail/gather_inplace.cuh>
-#include <raft/matrix/init.cuh>
-#include <raft/matrix/sample_rows.cuh>
-
-#include <thrust/copy.h>
-
-#include <vector_types.h>
-
-#include <cstddef>
-#include <cstdint>
-#include <limits>
-#include <optional>
-#include <random>
-#include <type_traits>
-
-namespace cuvs::neighbors::nn_descent::detail::experimental {
-
-//
-// Run balanced kmeans on a subsample of the dataset to get centroids
-//
-template <typename T,
-          typename IdxT = uint32_t,
-          typename Accessor =
-            host_device_accessor<std::experimental::default_accessor<T>, memory_type::host>>
-void get_balanced_kmeans_centroids(
-  raft::resources const& res,
-  cuvs::distance::DistanceType metric,
-  mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset,
-  raft::device_matrix_view<T, IdxT> centroids)
-{
-  size_t num_rows   = static_cast<size_t>(dataset.extent(0));
-  size_t num_cols   = static_cast<size_t>(dataset.extent(1));
-  size_t n_clusters = centroids.extent(0);
-  size_t num_subsamples =
-    std::min(static_cast<size_t>(num_rows / n_clusters), static_cast<size_t>(num_rows * 0.1));
-
-  auto d_subsample_dataset =
-    raft::make_device_matrix<T, int64_t, raft::row_major>(res, num_subsamples, num_cols);
-  raft::matrix::sample_rows<T, int64_t, Accessor>(
-    res, raft::random::RngState{0}, dataset, d_subsample_dataset.view());
-
-  cuvs::cluster::kmeans::balanced_params kmeans_params;
-  kmeans_params.metric = metric;
-
-  auto d_subsample_dataset_const_view =
-    raft::make_device_matrix_view<const T, int, raft::row_major>(
-      d_subsample_dataset.data_handle(), num_subsamples, num_cols);
-  auto centroids_view = raft::make_device_matrix_view<T, int, raft::row_major>(
-    centroids.data_handle(), n_clusters, num_cols);
-  cuvs::cluster::kmeans::fit(res, kmeans_params, d_subsample_dataset_const_view, centroids_view);
-}
-
-//
-// Get the top k closest centroid indices for each data point
-// Loads the data in batches onto device if data is on host for memory efficiency
-//
-template <typename T, typename IdxT = uint32_t>
-void get_global_nearest_k(
-  raft::resources const& res,
-  size_t k,
-  size_t num_rows,
-  size_t n_clusters,
-  const T* dataset,
-  raft::host_matrix_view<IdxT, IdxT, raft::row_major> global_nearest_cluster,
-  raft::device_matrix_view<T, IdxT, raft::row_major> centroids,
-  cuvs::distance::DistanceType metric)
-{
-  size_t num_cols     = centroids.extent(1);
-  auto centroids_view = raft::make_device_matrix_view<const T, int64_t, raft::row_major>(
-    centroids.data_handle(), n_clusters, num_cols);
-
-  cudaPointerAttributes attr;
-  RAFT_CUDA_TRY(cudaPointerGetAttributes(&attr, dataset));
-  float* ptr = reinterpret_cast<float*>(attr.devicePointer);
-
-  size_t num_batches = n_clusters;
-  size_t batch_size  = (num_rows + n_clusters) / n_clusters;
-  if (ptr == nullptr) {  // data on host
-
-    auto d_dataset_batch =
-      raft::make_device_matrix<T, int64_t, raft::row_major>(res, batch_size, num_cols);
-
-    auto nearest_clusters_idx =
-      raft::make_device_matrix<int64_t, int64_t, raft::row_major>(res, batch_size, k);
-    auto nearest_clusters_idxt =
-      raft::make_device_matrix<IdxT, int64_t, raft::row_major>(res, batch_size, k);
-    auto nearest_clusters_dist =
-      raft::make_device_matrix<T, int64_t, raft::row_major>(res, batch_size, k);
-
-    for (size_t i = 0; i < num_batches; i++) {
-      size_t batch_size_ = batch_size;
-
-      if (i == num_batches - 1) { batch_size_ = num_rows - batch_size * i; }
-      raft::copy(d_dataset_batch.data_handle(),
-                 dataset + i * batch_size * num_cols,
-                 batch_size_ * num_cols,
-                 resource::get_cuda_stream(res));
-
-      std::optional<raft::device_vector_view<const T, int64_t>> norms_view;
-      cuvs::neighbors::brute_force::index<T> brute_force_index(
-        res, centroids_view, norms_view, metric);
-      cuvs::neighbors::brute_force::search(res,
-                                           brute_force_index,
-                                           raft::make_const_mdspan(d_dataset_batch.view()),
-                                           nearest_clusters_idx.view(),
-                                           nearest_clusters_dist.view());
-
-      thrust::copy(raft::resource::get_thrust_policy(res),
-                   nearest_clusters_idx.data_handle(),
-                   nearest_clusters_idx.data_handle() + nearest_clusters_idx.size(),
-                   nearest_clusters_idxt.data_handle());
-      raft::copy(global_nearest_cluster.data_handle() + i * batch_size * k,
-                 nearest_clusters_idxt.data_handle(),
-                 batch_size_ * k,
-                 resource::get_cuda_stream(res));
-    }
-  } else {  // data on device
-    auto nearest_clusters_idx =
-      raft::make_device_matrix<int64_t, int64_t, raft::row_major>(res, num_rows, k);
-    auto nearest_clusters_dist =
-      raft::make_device_matrix<T, int64_t, raft::row_major>(res, num_rows, k);
-
-    std::optional<raft::device_vector_view<const T, int64_t>> norms_view;
-    cuvs::neighbors::brute_force::index<T> brute_force_index(
-      res, centroids_view, norms_view, metric);
-    auto dataset_view =
-      raft::make_device_matrix_view<const T, int64_t, raft::row_major>(dataset, num_rows, num_cols);
-    cuvs::neighbors::brute_force::search(res,
-                                         brute_force_index,
-                                         dataset_view,
-                                         nearest_clusters_idx.view(),
-                                         nearest_clusters_dist.view());
-
-    auto nearest_clusters_idxt =
-      raft::make_device_matrix<IdxT, int64_t, raft::row_major>(res, batch_size, k);
-    for (size_t i = 0; i < num_batches; i++) {
-      size_t batch_size_ = batch_size;
-
-      if (i == num_batches - 1) { batch_size_ = num_rows - batch_size * i; }
-      thrust::copy(raft::resource::get_thrust_policy(res),
-                   nearest_clusters_idx.data_handle() + i * batch_size * k,
-                   nearest_clusters_idx.data_handle() + (i * batch_size + batch_size_) * k,
-                   nearest_clusters_idxt.data_handle());
-      raft::copy(global_nearest_cluster.data_handle() + i * batch_size * k,
-                 nearest_clusters_idxt.data_handle(),
-                 batch_size_ * k,
-                 resource::get_cuda_stream(res));
-    }
-  }
-}
-
-//
-// global_nearest_cluster [num_rows X k=2] : top 2 closest clusters for each data point
-// inverted_indices [num_rows x k vector] : sparse vector for data indices for each cluster
-// cluster_size [n_cluster] : cluster size for each cluster
-// offset [n_cluster] : offset in inverted_indices for each cluster
-// Loads the data in batches onto device if data is on host for memory efficiency
-//
-template <typename IdxT = uint32_t>
-void get_inverted_indices(raft::resources const& res,
-                          size_t n_clusters,
-                          size_t& max_cluster_size,
-                          size_t& min_cluster_size,
-                          raft::host_matrix_view<IdxT, IdxT> global_nearest_cluster,
-                          raft::host_vector_view<IdxT, IdxT> inverted_indices,
-                          raft::host_vector_view<IdxT, IdxT> cluster_size,
-                          raft::host_vector_view<IdxT, IdxT> offset)
-{
-  // build sparse inverted indices and get number of data points for each cluster
-  size_t num_rows = global_nearest_cluster.extent(0);
-  size_t k        = global_nearest_cluster.extent(1);
-
-  auto local_offset = raft::make_host_vector<IdxT>(n_clusters);
-
-  max_cluster_size = 0;
-  min_cluster_size = std::numeric_limits<size_t>::max();
-
-  std::fill(cluster_size.data_handle(), cluster_size.data_handle() + n_clusters, 0);
-  std::fill(local_offset.data_handle(), local_offset.data_handle() + n_clusters, 0);
-
-  // TODO: this part isn't really a bottleneck but maybe worth trying omp parallel
-  // for with atomic add
-  for (size_t i = 0; i < num_rows; i++) {
-    for (size_t j = 0; j < k; j++) {
-      IdxT cluster_id = global_nearest_cluster(i, j);
-      cluster_size(cluster_id) += 1;
-    }
-  }
-
-  offset(0) = 0;
-  for (size_t i = 1; i < n_clusters; i++) {
-    offset(i) = offset(i - 1) + cluster_size(i - 1);
-  }
-  for (size_t i = 0; i < num_rows; i++) {
-    for (size_t j = 0; j < k; j++) {
-      IdxT cluster_id = global_nearest_cluster(i, j);
-      inverted_indices(offset(cluster_id) + local_offset(cluster_id)) = i;
-      local_offset(cluster_id) += 1;
-    }
-  }
-
-  max_cluster_size = static_cast<size_t>(
-    *std::max_element(cluster_size.data_handle(), cluster_size.data_handle() + n_clusters));
-  min_cluster_size = static_cast<size_t>(
-    *std::min_element(cluster_size.data_handle(), cluster_size.data_handle() + n_clusters));
-}
-
-template <typename KeyType, typename ValueType>
-struct KeyValuePair {
-  KeyType key;
-  ValueType value;
-};
-
-template <typename KeyType, typename ValueType>
-struct CustomKeyComparator {
-  __device__ bool operator()(const KeyValuePair<KeyType, ValueType>& a,
-                             const KeyValuePair<KeyType, ValueType>& b) const
-  {
-    if (a.key == b.key) { return a.value < b.value; }
-    return a.key < b.key;
-  }
-};
-
-template <typename IdxT, int BLOCK_SIZE, int ITEMS_PER_THREAD>
-RAFT_KERNEL merge_subgraphs(IdxT* cluster_data_indices,
-                            size_t graph_degree,
-                            size_t num_cluster_in_batch,
-                            float* global_distances,
-                            float* batch_distances,
-                            IdxT* global_indices,
-                            IdxT* batch_indices)
-{
-  size_t batch_row = blockIdx.x;
-  typedef cub::BlockMergeSort<KeyValuePair<float, IdxT>, BLOCK_SIZE, ITEMS_PER_THREAD>
-    BlockMergeSortType;
-  __shared__ typename cub::BlockMergeSort<KeyValuePair<float, IdxT>, BLOCK_SIZE, ITEMS_PER_THREAD>::
-    TempStorage tmpSmem;
-
-  extern __shared__ char sharedMem[];
-  float* blockKeys  = reinterpret_cast<float*>(sharedMem);
-  IdxT* blockValues = reinterpret_cast<IdxT*>(&sharedMem[graph_degree * 2 * sizeof(float)]);
-  int16_t* uniqueMask =
-    reinterpret_cast<int16_t*>(&sharedMem[graph_degree * 2 * (sizeof(float) + sizeof(IdxT))]);
-
-  if (batch_row < num_cluster_in_batch) {
-    // load batch or global depending on threadIdx
-    size_t global_row = cluster_data_indices[batch_row];
-
-    KeyValuePair<float, IdxT> threadKeyValuePair[ITEMS_PER_THREAD];
-
-    size_t halfway   = BLOCK_SIZE / 2;
-    size_t do_global = threadIdx.x < halfway;
-
-    float* distances;
-    IdxT* indices;
-
-    if (do_global) {
-      distances = global_distances;
-      indices   = global_indices;
-    } else {
-      distances = batch_distances;
-      indices   = batch_indices;
-    }
-
-    size_t idxBase = (threadIdx.x * do_global + (threadIdx.x - halfway) * (1lu - do_global)) *
-                     static_cast<size_t>(ITEMS_PER_THREAD);
-    size_t arrIdxBase = (global_row * do_global + batch_row * (1lu - do_global)) * graph_degree;
-    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
-      size_t colId = idxBase + i;
-      if (colId < graph_degree) {
-        threadKeyValuePair[i].key   = distances[arrIdxBase + colId];
-        threadKeyValuePair[i].value = indices[arrIdxBase + colId];
-      } else {
-        threadKeyValuePair[i].key   = std::numeric_limits<float>::max();
-        threadKeyValuePair[i].value = std::numeric_limits<IdxT>::max();
-      }
-    }
-
-    __syncthreads();
-
-    BlockMergeSortType(tmpSmem).Sort(threadKeyValuePair, CustomKeyComparator<float, IdxT>{});
-
-    // load sorted result into shared memory to get unique values
-    idxBase = threadIdx.x * ITEMS_PER_THREAD;
-    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
-      size_t colId = idxBase + i;
-      if (colId < 2 * graph_degree) {
-        blockKeys[colId]   = threadKeyValuePair[i].key;
-        blockValues[colId] = threadKeyValuePair[i].value;
-      }
-    }
-
-    __syncthreads();
-
-    // get unique mask
-    if (threadIdx.x == 0) { uniqueMask[0] = 1; }
-    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
-      size_t colId = idxBase + i;
-      if (colId > 0 && colId < 2 * graph_degree) {
-        uniqueMask[colId] = static_cast<int16_t>(blockValues[colId] != blockValues[colId - 1]);
-      }
-    }
-
-    __syncthreads();
-
-    // prefix sum
-    if (threadIdx.x == 0) {
-      for (int i = 1; i < 2 * graph_degree; i++) {
-        uniqueMask[i] += uniqueMask[i - 1];
-      }
-    }
-
-    __syncthreads();
-    // load unique values to global memory
-    if (threadIdx.x == 0) {
-      global_distances[global_row * graph_degree] = blockKeys[0];
-      global_indices[global_row * graph_degree]   = blockValues[0];
-    }
-
-    for (int i = 0; i < ITEMS_PER_THREAD; i++) {
-      size_t colId = idxBase + i;
-      if (colId > 0 && colId < 2 * graph_degree) {
-        bool is_unique       = uniqueMask[colId] != uniqueMask[colId - 1];
-        int16_t global_colId = uniqueMask[colId] - 1;
-        if (is_unique && static_cast<size_t>(global_colId) < graph_degree) {
-          global_distances[global_row * graph_degree + global_colId] = blockKeys[colId];
-          global_indices[global_row * graph_degree + global_colId]   = blockValues[colId];
-        }
-      }
-    }
-  }
-}
-
-//
-// builds knn graph using NN Descent and merge with global graph
-//
-template <typename T,
-          typename IdxT = uint32_t,
-          typename Accessor =
-            host_device_accessor<std::experimental::default_accessor<T>, memory_type::host>>
-void build_and_merge(raft::resources const& res,
-                     const index_params& params,
-                     size_t num_data_in_cluster,
-                     size_t graph_degree,
-                     size_t int_graph_node_degree,
-                     T* cluster_data,
-                     IdxT* cluster_data_indices,
-                     int* int_graph,
-                     IdxT* inverted_indices,
-                     IdxT* global_indices_d,
-                     float* global_distances_d,
-                     IdxT* batch_indices_h,
-                     IdxT* batch_indices_d,
-                     float* batch_distances_d,
-                     GNND<const T, int>& nnd)
-{
-  nnd.build(cluster_data, num_data_in_cluster, int_graph, true, batch_distances_d);
-
-  // remap indices
-#pragma omp parallel for
-  for (size_t i = 0; i < num_data_in_cluster; i++) {
-    for (size_t j = 0; j < graph_degree; j++) {
-      size_t local_idx                      = int_graph[i * int_graph_node_degree + j];
-      batch_indices_h[i * graph_degree + j] = inverted_indices[local_idx];
-    }
-  }
-
-  raft::copy(batch_indices_d,
-             batch_indices_h,
-             num_data_in_cluster * graph_degree,
-             raft::resource::get_cuda_stream(res));
-
-  size_t num_elems     = graph_degree * 2;
-  size_t sharedMemSize = num_elems * (sizeof(float) + sizeof(IdxT) + sizeof(int16_t));
-
-  if (num_elems <= 128) {
-    merge_subgraphs<IdxT, 32, 4>
-      <<<num_data_in_cluster, 32, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
-        cluster_data_indices,
-        graph_degree,
-        num_data_in_cluster,
-        global_distances_d,
-        batch_distances_d,
-        global_indices_d,
-        batch_indices_d);
-  } else if (num_elems <= 512) {
-    merge_subgraphs<IdxT, 128, 4>
-      <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
-        cluster_data_indices,
-        graph_degree,
-        num_data_in_cluster,
-        global_distances_d,
-        batch_distances_d,
-        global_indices_d,
-        batch_indices_d);
-  } else if (num_elems <= 1024) {
-    merge_subgraphs<IdxT, 128, 8>
-      <<<num_data_in_cluster, 128, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
-        cluster_data_indices,
-        graph_degree,
-        num_data_in_cluster,
-        global_distances_d,
-        batch_distances_d,
-        global_indices_d,
-        batch_indices_d);
-  } else if (num_elems <= 2048) {
-    merge_subgraphs<IdxT, 256, 8>
-      <<<num_data_in_cluster, 256, sharedMemSize, raft::resource::get_cuda_stream(res)>>>(
-        cluster_data_indices,
-        graph_degree,
-        num_data_in_cluster,
-        global_distances_d,
-        batch_distances_d,
-        global_indices_d,
-        batch_indices_d);
-  } else {
-    // this is as far as we can get due to the shared mem usage of cub::BlockMergeSort
-    RAFT_FAIL("The degree of knn is too large (%lu). It must be smaller than 1024", graph_degree);
-  }
-  raft::resource::sync_stream(res);
-}
-
-//
-// For each cluster, gather the data samples that belong to that cluster, and
-// call build_and_merge
-//
-template <typename T, typename IdxT = uint32_t>
-void cluster_nnd(raft::resources const& res,
-                 const index_params& params,
-                 size_t graph_degree,
-                 size_t extended_graph_degree,
-                 size_t max_cluster_size,
-                 raft::host_matrix_view<const T, int64_t> dataset,
-                 IdxT* offsets,
-                 IdxT* cluster_size,
-                 IdxT* cluster_data_indices,
-                 int* int_graph,
-                 IdxT* inverted_indices,
-                 IdxT* global_indices_h,
-                 float* global_distances_h,
-                 IdxT* batch_indices_h,
-                 IdxT* batch_indices_d,
-                 float* batch_distances_d,
-                 const BuildConfig& build_config)
-{
-  size_t num_rows = dataset.extent(0);
-  size_t num_cols = dataset.extent(1);
-
-  GNND<const T, int> nnd(res, build_config);
-
-  auto cluster_data_matrix =
-    raft::make_host_matrix<T, int64_t, row_major>(max_cluster_size, num_cols);
-
-  for (size_t cluster_id = 0; cluster_id < params.n_clusters; cluster_id++) {
-    RAFT_LOG_DEBUG(
-      "# Data on host. Running clusters: %lu / %lu", cluster_id + 1, params.n_clusters);
-    size_t num_data_in_cluster = cluster_size[cluster_id];
-    size_t offset              = offsets[cluster_id];
-
-#pragma omp parallel for
-    for (size_t i = 0; i < num_data_in_cluster; i++) {
-      for (size_t j = 0; j < num_cols; j++) {
-        size_t global_row         = (inverted_indices + offset)[i];
-        cluster_data_matrix(i, j) = dataset(global_row, j);
-      }
-    }
-
-    build_and_merge<T, IdxT>(res,
-                             params,
-                             num_data_in_cluster,
-                             graph_degree,
-                             extended_graph_degree,
-                             cluster_data_matrix.data_handle(),
-                             cluster_data_indices + offset,
-                             int_graph,
-                             inverted_indices + offset,
-                             global_indices_h,
-                             global_distances_h,
-                             batch_indices_h,
-                             batch_indices_d,
-                             batch_distances_d,
-                             nnd);
-    nnd.reset(res);
-  }
-}
-
-template <typename T, typename IdxT = uint32_t>
-void cluster_nnd(raft::resources const& res,
-                 const index_params& params,
-                 size_t graph_degree,
-                 size_t extended_graph_degree,
-                 size_t max_cluster_size,
-                 raft::device_matrix_view<const T, int64_t> dataset,
-                 IdxT* offsets,
-                 IdxT* cluster_size,
-                 IdxT* cluster_data_indices,
-                 int* int_graph,
-                 IdxT* inverted_indices,
-                 IdxT* global_indices_h,
-                 float* global_distances_h,
-                 IdxT* batch_indices_h,
-                 IdxT* batch_indices_d,
-                 float* batch_distances_d,
-                 const BuildConfig& build_config)
-{
-  size_t num_rows = dataset.extent(0);
-  size_t num_cols = dataset.extent(1);
-
-  GNND<const T, int> nnd(res, build_config);
-
-  auto cluster_data_matrix =
-    raft::make_device_matrix<T, int64_t, row_major>(res, max_cluster_size, num_cols);
-
-  for (size_t cluster_id = 0; cluster_id < params.n_clusters; cluster_id++) {
-    RAFT_LOG_DEBUG(
-      "# Data on device. Running clusters: %lu / %lu", cluster_id + 1, params.n_clusters);
-    size_t num_data_in_cluster = cluster_size[cluster_id];
-    size_t offset              = offsets[cluster_id];
-
-    auto cluster_data_view = raft::make_device_matrix_view<T, IdxT>(
-      cluster_data_matrix.data_handle(), num_data_in_cluster, num_cols);
-    auto cluster_data_indices_view = raft::make_device_vector_view<const IdxT, IdxT>(
-      cluster_data_indices + offset, num_data_in_cluster);
-
-    auto dataset_IdxT =
-      raft::make_device_matrix_view<const T, IdxT>(dataset.data_handle(), num_rows, num_cols);
-    raft::matrix::gather(res, dataset_IdxT, cluster_data_indices_view, cluster_data_view);
-
-    build_and_merge<T, IdxT>(res,
-                             params,
-                             num_data_in_cluster,
-                             graph_degree,
-                             extended_graph_degree,
-                             cluster_data_view.data_handle(),
-                             cluster_data_indices + offset,
-                             int_graph,
-                             inverted_indices + offset,
-                             global_indices_h,
-                             global_distances_h,
-                             batch_indices_h,
-                             batch_indices_d,
-                             batch_distances_d,
-                             nnd);
-    nnd.reset(res);
-  }
-}
-
-template <typename T,
-          typename IdxT = uint32_t,
-          typename Accessor =
-            host_device_accessor<std::experimental::default_accessor<float>, memory_type::host>>
-void batch_build(raft::resources const& res,
-                 const index_params& params,
-                 mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset,
-                 index<IdxT>& global_idx)
-{
-  size_t graph_degree        = params.graph_degree;
-  size_t intermediate_degree = params.intermediate_graph_degree;
-
-  size_t num_rows = static_cast<size_t>(dataset.extent(0));
-  size_t num_cols = static_cast<size_t>(dataset.extent(1));
-
-  auto centroids =
-    raft::make_device_matrix<T, IdxT, raft::row_major>(res, params.n_clusters, num_cols);
-  get_balanced_kmeans_centroids<T, IdxT>(res, params.metric, dataset, centroids.view());
-
-  size_t k                    = 2;
-  auto global_nearest_cluster = raft::make_host_matrix<IdxT, IdxT, raft::row_major>(num_rows, k);
-  get_global_nearest_k<T, IdxT>(res,
-                                k,
-                                num_rows,
-                                params.n_clusters,
-                                dataset.data_handle(),
-                                global_nearest_cluster.view(),
-                                centroids.view(),
-                                params.metric);
-
-  auto inverted_indices = raft::make_host_vector<IdxT, IdxT, raft::row_major>(num_rows * k);
-  auto cluster_size     = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
-  auto offset           = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
-
-  size_t max_cluster_size, min_cluster_size;
-  get_inverted_indices(res,
-                       params.n_clusters,
-                       max_cluster_size,
-                       min_cluster_size,
-                       global_nearest_cluster.view(),
-                       inverted_indices.view(),
-                       cluster_size.view(),
-                       offset.view());
-
-  if (intermediate_degree >= min_cluster_size) {
-    RAFT_LOG_WARN(
-      "Intermediate graph degree cannot be larger than minimum cluster size, reducing it to %lu",
-      dataset.extent(0));
-    intermediate_degree = min_cluster_size - 1;
-  }
-  if (intermediate_degree < graph_degree) {
-    RAFT_LOG_WARN(
-      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
-      "graph_degree.",
-      graph_degree,
-      intermediate_degree);
-    graph_degree = intermediate_degree;
-  }
-
-  size_t extended_graph_degree =
-    roundUp32(static_cast<size_t>(graph_degree * (graph_degree <= 32 ? 1.0 : 1.3)));
-  size_t extended_intermediate_degree =
-    roundUp32(static_cast<size_t>(intermediate_degree * (intermediate_degree <= 32 ? 1.0 : 1.3)));
-
-  auto int_graph = raft::make_host_matrix<int, int64_t, row_major>(
-    max_cluster_size, static_cast<int64_t>(extended_graph_degree));
-
-  BuildConfig build_config{.max_dataset_size      = max_cluster_size,
-                           .dataset_dim           = num_cols,
-                           .node_degree           = extended_graph_degree,
-                           .internal_node_degree  = extended_intermediate_degree,
-                           .max_iterations        = params.max_iterations,
-                           .termination_threshold = params.termination_threshold,
-                           .output_graph_degree   = graph_degree,
-                           .metric                = params.metric};
-
-  auto global_indices_h   = raft::make_managed_matrix<IdxT, int64_t>(res, num_rows, graph_degree);
-  auto global_distances_h = raft::make_managed_matrix<float, int64_t>(res, num_rows, graph_degree);
-
-  std::fill(global_indices_h.data_handle(),
-            global_indices_h.data_handle() + num_rows * graph_degree,
-            std::numeric_limits<IdxT>::max());
-  std::fill(global_distances_h.data_handle(),
-            global_distances_h.data_handle() + num_rows * graph_degree,
-            std::numeric_limits<float>::max());
-
-  auto batch_indices_h =
-    raft::make_host_matrix<IdxT, int64_t, row_major>(max_cluster_size, graph_degree);
-  auto batch_indices_d =
-    raft::make_device_matrix<IdxT, int64_t, row_major>(res, max_cluster_size, graph_degree);
-  auto batch_distances_d =
-    raft::make_device_matrix<float, int64_t, row_major>(res, max_cluster_size, graph_degree);
-
-  auto cluster_data_indices = raft::make_device_vector<IdxT, IdxT>(res, num_rows * k);
-  raft::copy(cluster_data_indices.data_handle(),
-             inverted_indices.data_handle(),
-             num_rows * k,
-             resource::get_cuda_stream(res));
-
-  cluster_nnd<T, IdxT>(res,
-                       params,
-                       graph_degree,
-                       extended_graph_degree,
-                       max_cluster_size,
-                       dataset,
-                       offset.data_handle(),
-                       cluster_size.data_handle(),
-                       cluster_data_indices.data_handle(),
-                       int_graph.data_handle(),
-                       inverted_indices.data_handle(),
-                       global_indices_h.data_handle(),
-                       global_distances_h.data_handle(),
-                       batch_indices_h.data_handle(),
-                       batch_indices_d.data_handle(),
-                       batch_distances_d.data_handle(),
-                       build_config);
-
-  raft::copy(global_idx.graph().data_handle(),
-             global_indices_h.data_handle(),
-             num_rows * graph_degree,
-             raft::resource::get_cuda_stream(res));
-  if (params.return_distances && global_idx.distances().has_value()) {
-    raft::copy(global_idx.distances().value().data_handle(),
-               global_distances_h.data_handle(),
-               num_rows * graph_degree,
-               raft::resource::get_cuda_stream(res));
-  }
-}
-
-template <typename T,
-          typename IdxT = uint32_t,
-          typename Accessor =
-            host_device_accessor<std::experimental::default_accessor<float>, memory_type::host>>
-index<IdxT> batch_build(raft::resources const& res,
-                        const index_params& params,
-                        mdspan<const T, matrix_extent<int64_t>, row_major, Accessor> dataset)
-{
-  size_t intermediate_degree = params.intermediate_graph_degree;
-  size_t graph_degree        = params.graph_degree;
-
-  if (intermediate_degree < graph_degree) {
-    RAFT_LOG_WARN(
-      "Graph degree (%lu) cannot be larger than intermediate graph degree (%lu), reducing "
-      "graph_degree.",
-      graph_degree,
-      intermediate_degree);
-    graph_degree = intermediate_degree;
-  }
-
-  index<IdxT> idx{
-    res, dataset.extent(0), static_cast<int64_t>(graph_degree), params.return_distances};
-
-  batch_build(res, params, dataset, idx);
-
-  return idx;
-}
-
-}  // namespace cuvs::neighbors::nn_descent::detail::experimental
diff --git a/cpp/src/neighbors/nn_descent.cuh b/cpp/src/neighbors/nn_descent.cuh
index 78c63dc31e..4f0bf01cfa 100644
--- a/cpp/src/neighbors/nn_descent.cuh
+++ b/cpp/src/neighbors/nn_descent.cuh
@@ -17,14 +17,11 @@
 #pragma once
 
 #include "detail/nn_descent.cuh"
-#include "detail/nn_descent_batch.cuh"
 
-#include <cmath>
 #include <cstdint>
 #include <cuvs/neighbors/nn_descent.hpp>
 
 #include <raft/core/device_mdspan.hpp>
-#include <raft/core/error.hpp>
 #include <raft/core/host_mdspan.hpp>
 
 namespace cuvs::neighbors::nn_descent {
@@ -70,19 +67,7 @@ auto build(raft::resources const& res,
            index_params const& params,
            raft::device_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
 {
-  if (params.n_clusters > 1) {
-    // related issue: https://github.com/rapidsai/cuvs/issues/1051
-    RAFT_LOG_WARN(
-      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
-      "release. Please use cuvs::all_neighbors::build(...) instead.");
-    if constexpr (std::is_same_v<T, float>) {
-      return detail::experimental::batch_build<T, IdxT>(res, params, dataset);
-    } else {
-      RAFT_FAIL("Batched nn-descent is only supported for float precision");
-    }
-  } else {
-    return detail::build<T, IdxT>(res, params, dataset);
-  }
+  return detail::build<T, IdxT>(res, params, dataset);
 }
 
 /**
@@ -125,19 +110,7 @@ void build(raft::resources const& res,
            raft::device_matrix_view<const T, int64_t, raft::row_major> dataset,
            index<IdxT>& idx)
 {
-  if (params.n_clusters > 1) {
-    // related issue: https://github.com/rapidsai/cuvs/issues/1051
-    RAFT_LOG_WARN(
-      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
-      "release. Please use cuvs::all_neighbors::build(...) instead.");
-    if constexpr (std::is_same_v<T, float>) {
-      detail::experimental::batch_build<T, IdxT>(res, params, dataset, idx);
-    } else {
-      RAFT_FAIL("Batched nn-descent is only supported for float precision");
-    }
-  } else {
-    detail::build<T, IdxT>(res, params, dataset, idx);
-  }
+  detail::build<T, IdxT>(res, params, dataset, idx);
 }
 
 /**
@@ -176,19 +149,7 @@ auto build(raft::resources const& res,
            index_params const& params,
            raft::host_matrix_view<const T, int64_t, raft::row_major> dataset) -> index<IdxT>
 {
-  if (params.n_clusters > 1) {
-    // related issue: https://github.com/rapidsai/cuvs/issues/1051
-    RAFT_LOG_WARN(
-      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
-      "release. Please use cuvs::all_neighbors::build(...) instead.");
-    if constexpr (std::is_same_v<T, float>) {
-      return detail::experimental::batch_build<T, IdxT>(res, params, dataset);
-    } else {
-      RAFT_FAIL("Batched nn-descent is only supported for float precision");
-    }
-  } else {
-    return detail::build<T, IdxT>(res, params, dataset);
-  }
+  return detail::build<T, IdxT>(res, params, dataset);
 }
 
 /**
@@ -231,19 +192,7 @@ void build(raft::resources const& res,
            raft::host_matrix_view<const T, int64_t, raft::row_major> dataset,
            index<IdxT>& idx)
 {
-  if (params.n_clusters > 1) {
-    // related issue: https://github.com/rapidsai/cuvs/issues/1051
-    RAFT_LOG_WARN(
-      "NN Descent batch build (using n_clusters > 1) is deprecated and will be removed in a future "
-      "release. Please use cuvs::all_neighbors::build(...) instead.");
-    if constexpr (std::is_same_v<T, float>) {
-      detail::experimental::batch_build<T, IdxT>(res, params, dataset, idx);
-    } else {
-      RAFT_FAIL("Batched nn-descent is only supported for float precision");
-    }
-  } else {
-    detail::build<T, IdxT>(res, params, dataset, idx);
-  }
+  detail::build<T, IdxT>(res, params, dataset, idx);
 }
 
 /** @} */  // end group nn-descent
diff --git a/cpp/src/neighbors/nn_descent_c.cpp b/cpp/src/neighbors/nn_descent_c.cpp
index ddebb11e73..b0a19879c4 100644
--- a/cpp/src/neighbors/nn_descent_c.cpp
+++ b/cpp/src/neighbors/nn_descent_c.cpp
@@ -52,7 +52,6 @@ void* _build(cuvsResources_t res,
   build_params.max_iterations            = params.max_iterations;
   build_params.termination_threshold     = params.termination_threshold;
   build_params.return_distances          = params.return_distances;
-  build_params.n_clusters                = params.n_clusters;
 
   using graph_type = raft::host_matrix_view<IdxT, int64_t, raft::row_major>;
   std::optional<graph_type> graph;
@@ -187,8 +186,7 @@ extern "C" cuvsError_t cuvsNNDescentIndexParamsCreate(cuvsNNDescentIndexParams_t
       .intermediate_graph_degree = cpp_params.intermediate_graph_degree,
       .max_iterations            = cpp_params.max_iterations,
       .termination_threshold     = cpp_params.termination_threshold,
-      .return_distances          = cpp_params.return_distances,
-      .n_clusters                = cpp_params.n_clusters};
+      .return_distances          = cpp_params.return_distances};
   });
 }
 
diff --git a/cpp/tests/neighbors/ann_nn_descent.cuh b/cpp/tests/neighbors/ann_nn_descent.cuh
index e1c27dc48c..ad0bf617d8 100644
--- a/cpp/tests/neighbors/ann_nn_descent.cuh
+++ b/cpp/tests/neighbors/ann_nn_descent.cuh
@@ -400,7 +400,6 @@ class AnnNNDescentBatchTest : public ::testing::TestWithParam<AnnNNDescentBatchI
         index_params.intermediate_graph_degree = 2 * ps.graph_degree;
         index_params.max_iterations            = 100;
         index_params.return_distances          = true;
-        index_params.n_clusters                = ps.recall_cluster.second;
 
         auto database_view = raft::make_device_matrix_view<const DataT, int64_t>(
           (const DataT*)database.data(), ps.n_rows, ps.dim);
@@ -487,16 +486,6 @@ const std::vector<AnnNNDescentInputs> inputs =
                                                      {false, true},
                                                      {0.90});
 
-// Additional test cases for large datasets to test batching
-const std::vector<AnnNNDescentInputs> inputsLargeBatch =
-  raft::util::itertools::product<AnnNNDescentInputs>(
-    {150000},  // n_rows > 100000 to trigger batching
-    {64},
-    {32},
-    {cuvs::distance::DistanceType::BitwiseHamming},
-    {true},
-    {0.90});
-
 const std::vector<AnnNNDescentInputs> inputsDistEpilogue =
   raft::util::itertools::product<AnnNNDescentInputs>(
     {2000, 4000},  // n_rows
@@ -506,13 +495,4 @@ const std::vector<AnnNNDescentInputs> inputsDistEpilogue =
     {true},  // data on host
     {0.90});
 
-const std::vector<AnnNNDescentBatchInputs> inputsBatch =
-  raft::util::itertools::product<AnnNNDescentBatchInputs>(
-    {std::make_pair(0.9, 3lu), std::make_pair(0.9, 2lu)},  // min_recall, n_clusters
-    {4000, 5000},                                          // n_rows
-    {192, 512},                                            // dim
-    {32, 64},                                              // graph_degree
-    {cuvs::distance::DistanceType::L2Expanded},
-    {false, true});
-
 }  // namespace cuvs::neighbors::nn_descent
diff --git a/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
index 01fa769493..aff8a2e62f 100644
--- a/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_nn_descent/test_float_uint32_t.cu
@@ -26,15 +26,9 @@ TEST_P(AnnNNDescentTestF_U32, AnnNNDescent) { this->testNNDescent(); }
 typedef AnnNNDescentDistEpiTest<float, float, std::uint32_t> AnnNNDescentTestDistEpiF_U32;
 TEST_P(AnnNNDescentTestDistEpiF_U32, AnnNNDescentDistEpi) { this->testNNDescent(); }
 
-typedef AnnNNDescentBatchTest<float, float, std::uint32_t> AnnNNDescentBatchTestF_U32;
-TEST_P(AnnNNDescentBatchTestF_U32, AnnNNDescentBatch) { this->testNNDescentBatch(); }
-
 INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestF_U32, ::testing::ValuesIn(inputs));
 INSTANTIATE_TEST_CASE_P(AnnNNDescentDistEpi,
                         AnnNNDescentTestDistEpiF_U32,
                         ::testing::ValuesIn(inputsDistEpilogue));
-INSTANTIATE_TEST_CASE_P(AnnNNDescentBatchTest,
-                        AnnNNDescentBatchTestF_U32,
-                        ::testing::ValuesIn(inputsBatch));
 
 }  // namespace   cuvs::neighbors::nn_descent
diff --git a/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu b/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
index 16159ecc0a..92207b8db7 100644
--- a/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
+++ b/cpp/tests/neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
@@ -24,12 +24,4 @@ typedef AnnNNDescentTest<float, uint8_t, std::uint32_t> AnnNNDescentTestUI8_U32;
 TEST_P(AnnNNDescentTestUI8_U32, AnnNNDescent) { this->testNNDescent(); }
 
 INSTANTIATE_TEST_CASE_P(AnnNNDescentTest, AnnNNDescentTestUI8_U32, ::testing::ValuesIn(inputs));
-
-typedef AnnNNDescentTest<float, uint8_t, std::uint32_t> AnnNNDescentTestUI8_U32_LargeBatch;
-TEST_P(AnnNNDescentTestUI8_U32_LargeBatch, AnnNNDescent) { this->testNNDescent(); }
-
-INSTANTIATE_TEST_CASE_P(AnnNNDescentTestLargeBatch,
-                        AnnNNDescentTestUI8_U32_LargeBatch,
-                        ::testing::ValuesIn(inputsLargeBatch));
-
 }  // namespace   cuvs::neighbors::nn_descent
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
index 6753d9f016..5d82b7e7b3 100644
--- a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pxd
@@ -33,7 +33,6 @@ cdef extern from "cuvs/neighbors/nn_descent.h" nogil:
         size_t max_iterations
         float termination_threshold
         bool return_distances
-        size_t n_clusters
 
     ctypedef cuvsNNDescentIndexParams* cuvsNNDescentIndexParams_t
 
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
index 0f0589eced..5c9d0d8155 100644
--- a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
@@ -72,6 +72,8 @@ cdef class IndexParams:
         More iterations produce a better quality graph at cost of performance
     termination_threshold : float
         The delta at which nn-descent will terminate its iterations
+    return_distances : bool
+        Whether to return distances array
     """
 
     cdef cuvsNNDescentIndexParams* params
@@ -90,7 +92,6 @@ cdef class IndexParams:
                  intermediate_graph_degree=None,
                  max_iterations=None,
                  termination_threshold=None,
-                 n_clusters=None,
                  return_distances=None
                  ):
         if metric is not None:
@@ -103,8 +104,6 @@ cdef class IndexParams:
             self.params.max_iterations = max_iterations
         if termination_threshold is not None:
             self.params.termination_threshold = termination_threshold
-        if n_clusters is not None:
-            self.params.n_clusters = n_clusters
         if return_distances is not None:
             self.params.return_distances = return_distances
 
@@ -132,10 +131,6 @@ cdef class IndexParams:
     def termination_threshold(self):
         return self.params.termination_threshold
 
-    @property
-    def n_clusters(self):
-        return self.params.n_clusters
-
 cdef class Index:
     """
     NN-Descent index object. This object stores the trained NN-Descent index,

From 2922045b7beaf180940ec5e3732fdd70f336dbd0 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Mon, 18 Aug 2025 16:41:47 -0700
Subject: [PATCH 549/623] Mutual Reachability in all-neighbors API (#1016)

Closing https://github.com/rapidsai/cuvs/issues/856
Making mutual reachability part of all-neighbors.
If `core_distances` optional matrix is given, automatically calculates distances and indices in mutual reachability space.

Authors:
  - Jinsol Park (https://github.com/jinsolp)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1016
---
 cpp/include/cuvs/neighbors/all_neighbors.hpp  |  16 +-
 .../cuvs/neighbors/graph_build_types.hpp      |   2 +-
 .../neighbors/all_neighbors/all_neighbors.cu  |  40 +--
 .../neighbors/all_neighbors/all_neighbors.cuh | 197 +++++++++---
 .../all_neighbors/all_neighbors_batched.cuh   | 188 +++++++++---
 .../all_neighbors/all_neighbors_builder.cuh   | 287 +++++++++++++-----
 cpp/tests/neighbors/all_neighbors.cuh         |  93 +++++-
 .../neighbors/all_neighbors/test_float.cu     |   8 +
 8 files changed, 655 insertions(+), 176 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/all_neighbors.hpp b/cpp/include/cuvs/neighbors/all_neighbors.hpp
index a4323b67ba..b52a4ca294 100644
--- a/cpp/include/cuvs/neighbors/all_neighbors.hpp
+++ b/cpp/include/cuvs/neighbors/all_neighbors.hpp
@@ -126,13 +126,19 @@ struct all_neighbors_params {
  *                in host memory
  * @param[out] indices nearest neighbor indices of shape [n_row x k]
  * @param[out] distances nearest neighbor distances [n_row x k]
+ * @param[out] core_distances array for core distances of size [n_row]. Requires distances matrix to
+ * compute core_distances. If core_distances is given, the resulting indices and distances will be
+ * mutual reachability space.
+ * @param[in] alpha distance scaling parameter as used in robust single linkage.
  */
 void build(
   const raft::resources& handle,
   const all_neighbors_params& params,
   raft::host_matrix_view<const float, int64_t, row_major> dataset,
   raft::device_matrix_view<int64_t, int64_t, row_major> indices,
-  std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances = std::nullopt);
+  std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances      = std::nullopt,
+  std::optional<raft::device_vector_view<float, int64_t, row_major>> core_distances = std::nullopt,
+  float alpha                                                                       = 1.0);
 
 /**
  * @brief Builds an approximate all-neighbors knn graph (find nearest neighbors for all the training
@@ -156,13 +162,19 @@ void build(
  *                in device memory
  * @param[out] indices nearest neighbor indices of shape [n_row x k]
  * @param[out] distances nearest neighbor distances [n_row x k]
+ * @param[out] core_distances array for core distances of size [n_row]. Requires distances matrix to
+ * compute core_distances. If core_distances is given, the resulting indices and distances will be
+ * mutual reachability space.
+ * @param[in] alpha distance scaling parameter as used in robust single linkage.
  */
 void build(
   const raft::resources& handle,
   const all_neighbors_params& params,
   raft::device_matrix_view<const float, int64_t, row_major> dataset,
   raft::device_matrix_view<int64_t, int64_t, row_major> indices,
-  std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances = std::nullopt);
+  std::optional<raft::device_matrix_view<float, int64_t, row_major>> distances      = std::nullopt,
+  std::optional<raft::device_vector_view<float, int64_t, row_major>> core_distances = std::nullopt,
+  float alpha                                                                       = 1.0);
 
 /** @} */
 }  // namespace cuvs::neighbors::all_neighbors
diff --git a/cpp/include/cuvs/neighbors/graph_build_types.hpp b/cpp/include/cuvs/neighbors/graph_build_types.hpp
index 0711b5c2fb..dc6fdb45bd 100644
--- a/cpp/include/cuvs/neighbors/graph_build_types.hpp
+++ b/cpp/include/cuvs/neighbors/graph_build_types.hpp
@@ -27,7 +27,7 @@ namespace cuvs::neighbors {
  * @{
  */
 
-enum GRAPH_BUILD_ALGO { BRUTE_FORCE = 0, IVF_PQ = 1, NN_DESCENT = 1 };
+enum GRAPH_BUILD_ALGO { BRUTE_FORCE = 0, IVF_PQ = 1, NN_DESCENT = 2 };
 
 namespace graph_build_params {
 
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors.cu b/cpp/src/neighbors/all_neighbors/all_neighbors.cu
index 8ba57df398..2e9bf93688 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors.cu
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors.cu
@@ -18,23 +18,29 @@
 
 namespace cuvs::neighbors::all_neighbors {
 
-#define CUVS_INST_ALL_NEIGHBORS(T, IdxT)                                                       \
-  void build(const raft::resources& handle,                                                    \
-             const all_neighbors_params& params,                                               \
-             raft::host_matrix_view<const T, IdxT, row_major> dataset,                         \
-             raft::device_matrix_view<IdxT, IdxT, row_major> indices,                          \
-             std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances)            \
-  {                                                                                            \
-    return all_neighbors::detail::build<T, IdxT>(handle, params, dataset, indices, distances); \
-  }                                                                                            \
-                                                                                               \
-  void build(const raft::resources& handle,                                                    \
-             const all_neighbors_params& params,                                               \
-             raft::device_matrix_view<const T, IdxT, row_major> dataset,                       \
-             raft::device_matrix_view<IdxT, IdxT, row_major> indices,                          \
-             std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances)            \
-  {                                                                                            \
-    return all_neighbors::detail::build<T, IdxT>(handle, params, dataset, indices, distances); \
+#define CUVS_INST_ALL_NEIGHBORS(T, IdxT)                                                 \
+  void build(const raft::resources& handle,                                              \
+             const all_neighbors_params& params,                                         \
+             raft::host_matrix_view<const T, IdxT, row_major> dataset,                   \
+             raft::device_matrix_view<IdxT, IdxT, row_major> indices,                    \
+             std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances,      \
+             std::optional<raft::device_vector_view<T, IdxT, row_major>> core_distances, \
+             T alpha)                                                                    \
+  {                                                                                      \
+    return all_neighbors::detail::build<T, IdxT>(                                        \
+      handle, params, dataset, indices, distances, core_distances, alpha);               \
+  }                                                                                      \
+                                                                                         \
+  void build(const raft::resources& handle,                                              \
+             const all_neighbors_params& params,                                         \
+             raft::device_matrix_view<const T, IdxT, row_major> dataset,                 \
+             raft::device_matrix_view<IdxT, IdxT, row_major> indices,                    \
+             std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances,      \
+             std::optional<raft::device_vector_view<T, IdxT, row_major>> core_distances, \
+             T alpha)                                                                    \
+  {                                                                                      \
+    return all_neighbors::detail::build<T, IdxT>(                                        \
+      handle, params, dataset, indices, distances, core_distances, alpha);               \
   }
 
 CUVS_INST_ALL_NEIGHBORS(float, int64_t);
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
index 66abd5ac5d..50f0de104c 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors.cuh
@@ -15,71 +15,104 @@
  */
 
 #pragma once
+#include "../detail/reachability.cuh"
 #include "all_neighbors_batched.cuh"
 #include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/graph_build_types.hpp>
+#include <raft/matrix/shift.cuh>
 #include <raft/util/cudart_utils.hpp>
 
 namespace cuvs::neighbors::all_neighbors::detail {
 using namespace cuvs::neighbors;
 
-void check_metric(const all_neighbors_params& params)
+GRAPH_BUILD_ALGO check_params_validity(const all_neighbors_params& params,
+                                       bool do_mutual_reachability_dist)
 {
   if (std::holds_alternative<graph_build_params::brute_force_params>(params.graph_build_params)) {
-    auto allowed_metrics_batch = params.metric == cuvs::distance::DistanceType::L2Expanded ||
-                                 params.metric == cuvs::distance::DistanceType::L2SqrtExpanded;
-    auto allowed_metrics_single = allowed_metrics_batch ||
-                                  params.metric == cuvs::distance::DistanceType::CosineExpanded ||
-                                  params.metric == cuvs::distance::DistanceType::InnerProduct;
-    // related issue: https://github.com/rapidsai/cuvs/issues/1056
-    RAFT_EXPECTS((params.n_clusters <= 1 && allowed_metrics_single) || allowed_metrics_batch,
-                 "Distance metric supported for for all-neighbors build with brute force depends "
-                 "on params.n_clusters. When params.n_clusters <= 1, supported metrics are "
-                 "L2Expanded, L2SqrtExpanded, CosineExpanded, or InnerProduct. When "
-                 "params.n_clusters > 1, supported metrics are L2Expanded, L2SqrtExpanded.");
+    if (do_mutual_reachability_dist) {
+      // InnerProduct is not supported for mutual reachability distance, because mutual reachability
+      // distance takes "max" of core distances and pairwise distance.
+      auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                             params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                             params.metric == cuvs::distance::DistanceType::CosineExpanded;
+      RAFT_EXPECTS(
+        allowed_metrics,
+        "Distance metric for all-neighbors build with brute force for computing mutual "
+        "reachability distance should be L2Expanded, L2SqrtExpanded, or CosineExpanded.");
+    } else {
+      auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                             params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                             params.metric == cuvs::distance::DistanceType::CosineExpanded ||
+                             params.metric == cuvs::distance::DistanceType::InnerProduct;
+      RAFT_EXPECTS(allowed_metrics,
+                   "Distance metric for all-neighbors build with brute force should be L2Expanded, "
+                   "L2SqrtExpanded, CosineExpanded, or InnerProduct.");
+    }
+    return GRAPH_BUILD_ALGO::BRUTE_FORCE;
   } else if (std::holds_alternative<graph_build_params::nn_descent_params>(
                params.graph_build_params)) {
-    auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
-                           params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
-                           params.metric == cuvs::distance::DistanceType::CosineExpanded ||
-                           params.metric == cuvs::distance::DistanceType::InnerProduct;
-    RAFT_EXPECTS(allowed_metrics,
-                 "Distance metric for all-neighbors build with NN Descent should be L2Expanded, "
-                 "L2SqrtExpanded, CosineExpanded, or InnerProduct");
+    if (do_mutual_reachability_dist) {
+      // InnerProduct is not supported for mutual reachability distance, because mutual reachability
+      // distance takes "max" of core distances and pairwise distance.
+      auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                             params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                             params.metric == cuvs::distance::DistanceType::CosineExpanded;
+      RAFT_EXPECTS(
+        allowed_metrics,
+        "Distance metric for all-neighbors build with NN Descent for computing mutual reachability "
+        "distance should be L2Expanded, L2SqrtExpanded, or CosineExpanded.");
+    } else {
+      auto allowed_metrics = params.metric == cuvs::distance::DistanceType::L2Expanded ||
+                             params.metric == cuvs::distance::DistanceType::L2SqrtExpanded ||
+                             params.metric == cuvs::distance::DistanceType::CosineExpanded ||
+                             params.metric == cuvs::distance::DistanceType::InnerProduct;
+      RAFT_EXPECTS(allowed_metrics,
+                   "Distance metric for all-neighbors build with NN Descent should be L2Expanded, "
+                   "L2SqrtExpanded, CosineExpanded, or InnerProduct.");
+    }
+    return GRAPH_BUILD_ALGO::NN_DESCENT;
   } else if (std::holds_alternative<graph_build_params::ivf_pq_params>(params.graph_build_params)) {
     RAFT_EXPECTS(params.metric == cuvs::distance::DistanceType::L2Expanded,
                  "Distance metric for all-neighbors build with IVFPQ should be L2Expanded");
+    RAFT_EXPECTS(!do_mutual_reachability_dist,
+                 "mutual reachability distance cannot be calculated using IVFPQ");
+    return GRAPH_BUILD_ALGO::IVF_PQ;
   } else {
     RAFT_FAIL("Invalid all-neighbors build algo");
   }
 }
 
 // Single build (i.e. no batching) supports both host and device datasets
-template <typename T, typename IdxT, typename Accessor>
+template <typename T, typename IdxT, typename Accessor, typename DistEpilogueT = raft::identity_op>
 void single_build(
   const raft::resources& handle,
   const all_neighbors_params& params,
   mdspan<const T, matrix_extent<IdxT>, row_major, Accessor> dataset,
   raft::device_matrix_view<IdxT, IdxT, row_major> indices,
-  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt,
+  DistEpilogueT dist_epilogue                                           = DistEpilogueT{})
 {
   size_t num_rows = static_cast<size_t>(dataset.extent(0));
   size_t num_cols = static_cast<size_t>(dataset.extent(1));
 
   auto knn_builder = get_knn_builder<T, IdxT>(
-    handle, params, num_rows, num_rows, indices.extent(1), indices, distances);
+    handle, params, num_rows, num_rows, indices.extent(1), indices, distances, dist_epilogue);
 
   knn_builder->prepare_build(dataset);
   knn_builder->build_knn(dataset);
 }
 
 template <typename T, typename IdxT>
-void build(const raft::resources& handle,
-           const all_neighbors_params& params,
-           raft::host_matrix_view<const T, IdxT, row_major> dataset,
-           raft::device_matrix_view<IdxT, IdxT, row_major> indices,
-           std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+void build(
+  const raft::resources& handle,
+  const all_neighbors_params& params,
+  raft::host_matrix_view<const T, IdxT, row_major> dataset,
+  raft::device_matrix_view<IdxT, IdxT, row_major> indices,
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances      = std::nullopt,
+  std::optional<raft::device_vector_view<T, IdxT, row_major>> core_distances = std::nullopt,
+  T alpha                                                                    = 1.0)
 {
-  check_metric(params);
+  auto build_algo = check_params_validity(params, core_distances.has_value());
 
   RAFT_EXPECTS(dataset.extent(0) == indices.extent(0),
                "number of rows in dataset should be the same as number of rows in indices matrix");
@@ -90,21 +123,75 @@ void build(const raft::resources& handle,
                  "indices matrix and distances matrix has to be the same shape.");
   }
 
+  if (core_distances.has_value()) {
+    RAFT_EXPECTS(distances.has_value(),
+                 "distances matrix should be allocated to get mutual reachability distance.");
+  }
+
+  std::unique_ptr<BatchBuildAux<IdxT>> aux_vectors;
   if (params.n_clusters == 1) {
     single_build(handle, params, dataset, indices, distances);
   } else {
-    batch_build(handle, params, dataset, indices, distances);
+    if (core_distances.has_value()) {
+      aux_vectors = std::make_unique<BatchBuildAux<IdxT>>(
+        params.n_clusters, dataset.extent(0), params.overlap_factor);
+      batch_build(handle, params, dataset, indices, distances, aux_vectors.get());
+    } else {
+      batch_build(handle, params, dataset, indices, distances);
+    }
+  }
+
+  // NN Descent doesn't include self loops. Shifted to keep it consistent with brute force and ivfpq
+  bool need_shift = (build_algo == GRAPH_BUILD_ALGO::NN_DESCENT) &&
+                    (params.metric != cuvs::distance::DistanceType::InnerProduct);
+
+  if (need_shift) {
+    raft::matrix::shift(handle, indices, 1);
+    if (distances.has_value()) {
+      raft::matrix::shift(handle, distances.value(), 1, std::make_optional<T>(0.0));
+    }
+  }
+
+  if (core_distances.has_value()) {  // calculate mutual reachability distances
+    size_t k        = indices.extent(1);
+    size_t num_rows = core_distances.value().size();
+    cuvs::neighbors::detail::reachability::core_distances<IdxT, T>(
+      handle,
+      distances.value().data_handle(),
+      k,
+      k,
+      num_rows,
+      core_distances.value().data_handle());
+
+    using ReachabilityPP = cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, T>;
+    auto dist_epilogue   = ReachabilityPP{core_distances.value().data_handle(), alpha, num_rows};
+    if (params.n_clusters == 1) {
+      single_build(handle, params, dataset, indices, distances, dist_epilogue);
+    } else {
+      batch_build(handle, params, dataset, indices, distances, aux_vectors.get(), dist_epilogue);
+    }
+
+    if (need_shift) {
+      raft::matrix::shift(handle, indices, 1);
+      raft::matrix::shift(handle,
+                          distances.value(),
+                          raft::make_device_matrix_view<const T, IdxT>(
+                            core_distances.value().data_handle(), num_rows, 1));
+    }
   }
 }
 
 template <typename T, typename IdxT>
-void build(const raft::resources& handle,
-           const all_neighbors_params& params,
-           raft::device_matrix_view<const T, IdxT, row_major> dataset,
-           raft::device_matrix_view<IdxT, IdxT, row_major> indices,
-           std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+void build(
+  const raft::resources& handle,
+  const all_neighbors_params& params,
+  raft::device_matrix_view<const T, IdxT, row_major> dataset,
+  raft::device_matrix_view<IdxT, IdxT, row_major> indices,
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances      = std::nullopt,
+  std::optional<raft::device_vector_view<T, IdxT, row_major>> core_distances = std::nullopt,
+  T alpha                                                                    = 1.0)
 {
-  check_metric(params);
+  auto build_algo = check_params_validity(params, core_distances.has_value());
 
   RAFT_EXPECTS(dataset.extent(0) == indices.extent(0),
                "number of rows in dataset should be the same as number of rows in indices matrix");
@@ -115,6 +202,11 @@ void build(const raft::resources& handle,
                  "indices matrix and distances matrix has to be the same shape.");
   }
 
+  if (core_distances.has_value()) {
+    RAFT_EXPECTS(distances.has_value(),
+                 "distances matrix should be allocated to get mutual reachability distance.");
+  }
+
   if (params.n_clusters > 1) {
     RAFT_FAIL(
       "Batched all-neighbors build is not supported with data on device. Put data on host for "
@@ -122,5 +214,40 @@ void build(const raft::resources& handle,
   } else {
     single_build(handle, params, dataset, indices, distances);
   }
+
+  // NN Descent doesn't include self loops. Shifted to keep it consistent with brute force and ivfpq
+  bool need_shift = (build_algo == GRAPH_BUILD_ALGO::NN_DESCENT) &&
+                    (params.metric != cuvs::distance::DistanceType::InnerProduct);
+
+  if (need_shift) {
+    raft::matrix::shift(handle, indices, 1);
+    if (distances.has_value()) {
+      raft::matrix::shift(handle, distances.value(), 1, std::make_optional<T>(0.0));
+    }
+  }
+
+  if (core_distances.has_value()) {
+    size_t k        = indices.extent(1);
+    size_t num_rows = core_distances.value().size();
+    cuvs::neighbors::detail::reachability::core_distances<IdxT, T>(
+      handle,
+      distances.value().data_handle(),
+      k,
+      k,
+      num_rows,
+      core_distances.value().data_handle());
+
+    using ReachabilityPP = cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, T>;
+    auto dist_epilogue   = ReachabilityPP{core_distances.value().data_handle(), alpha, num_rows};
+    single_build(handle, params, dataset, indices, distances, dist_epilogue);
+
+    if (need_shift) {
+      raft::matrix::shift(handle, indices, 1);
+      raft::matrix::shift(handle,
+                          distances.value(),
+                          raft::make_device_matrix_view<const T, IdxT>(
+                            core_distances.value().data_handle(), num_rows, 1));
+    }
+  }
 }
 }  // namespace cuvs::neighbors::all_neighbors::detail
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
index 468d4ee0df..75eec6d50f 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
@@ -15,6 +15,7 @@
  */
 
 #pragma once
+#include "../detail/reachability.cuh"
 #include "all_neighbors_builder.cuh"
 #include "raft/core/logger_macros.hpp"
 #include <cuvs/cluster/kmeans.hpp>
@@ -23,11 +24,13 @@
 #include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/refine.hpp>
+#include <optional>
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/managed_mdarray.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/multi_gpu.hpp>
+#include <raft/matrix/init.cuh>
 #include <raft/matrix/sample_rows.cuh>
 #include <raft/util/cudart_utils.hpp>
 #include <variant>
@@ -35,6 +38,31 @@
 namespace cuvs::neighbors::all_neighbors::detail {
 using namespace cuvs::neighbors;
 
+template <typename T, typename IdxT>
+void reset_global_matrices(raft::resources const& res,
+                           cuvs::distance::DistanceType metric,
+                           raft::managed_matrix_view<IdxT, IdxT> global_neighbors,
+                           raft::managed_matrix_view<T, IdxT> global_distances)
+{
+  size_t num_rows = static_cast<size_t>(global_neighbors.extent(0));
+  size_t k        = static_cast<size_t>(global_neighbors.extent(1));
+
+  bool select_min = cuvs::distance::is_min_close(metric);
+  IdxT global_neighbors_fill_value =
+    select_min ? std::numeric_limits<IdxT>::max() : std::numeric_limits<IdxT>::min();
+  T global_distances_fill_value =
+    select_min ? std::numeric_limits<T>::max() : std::numeric_limits<T>::min();
+
+  raft::matrix::fill(
+    res,
+    raft::make_device_matrix_view<IdxT, IdxT>(global_neighbors.data_handle(), num_rows, k),
+    global_neighbors_fill_value);
+  raft::matrix::fill(
+    res,
+    raft::make_device_matrix_view<T, IdxT>(global_distances.data_handle(), num_rows, k),
+    global_distances_fill_value);
+}
+
 /**
  * Run balanced kmeans on a subsample of the dataset to get centroids.
  * Arguments:
@@ -307,15 +335,16 @@ void single_gpu_batch_build(const raft::resources& handle,
   }
 }
 
-template <typename T, typename IdxT>
+template <typename T, typename IdxT, typename DistEpilogueT = raft::identity_op>
 void multi_gpu_batch_build(const raft::resources& handle,
                            const all_neighbors_params& params,
                            raft::host_matrix_view<const T, IdxT, row_major> dataset,
                            raft::managed_matrix_view<IdxT, IdxT> global_neighbors,
                            raft::managed_matrix_view<T, IdxT> global_distances,
                            raft::host_vector_view<IdxT, IdxT, row_major> cluster_sizes,
-                           raft::host_vector_view<IdxT, IdxT, row_major> cluster_offsets,
-                           raft::host_vector_view<IdxT, IdxT, row_major> inverted_indices)
+                           raft::host_vector_view<const IdxT, IdxT, row_major> cluster_offsets_c,
+                           raft::host_vector_view<IdxT, IdxT, row_major> inverted_indices,
+                           DistEpilogueT dist_epilogue = DistEpilogueT{})
 {
   size_t num_rows = dataset.extent(0);
   size_t num_cols = dataset.extent(1);
@@ -326,6 +355,23 @@ void multi_gpu_batch_build(const raft::resources& handle,
   size_t clusters_per_rank = params.n_clusters / num_ranks;
   size_t rem               = params.n_clusters - clusters_per_rank * num_ranks;
 
+  auto cluster_offsets = raft::make_host_vector<IdxT, IdxT>(cluster_offsets_c.size());
+  raft::copy(cluster_offsets.data_handle(),
+             cluster_offsets_c.data_handle(),
+             cluster_offsets_c.size(),
+             raft::resource::get_cuda_stream(handle));
+
+  using ReachabilityPP = cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, T>;
+  const bool mutual_reach_dist = std::is_same_v<DistEpilogueT, ReachabilityPP>;
+  std::optional<raft::host_vector<T, IdxT>> core_distances_h;
+  if constexpr (mutual_reach_dist) {
+    core_distances_h.emplace(raft::make_host_vector<T, IdxT>(num_rows));
+    raft::copy(core_distances_h.value().data_handle(),
+               dist_epilogue.core_dists,
+               num_rows,
+               raft::resource::get_cuda_stream(handle));
+  }
+
 #pragma omp parallel for num_threads(num_ranks)
   for (int rank = 0; rank < num_ranks; rank++) {
     auto dev_res = raft::resource::set_current_device_to_rank(handle, rank);
@@ -369,8 +415,30 @@ void multi_gpu_batch_build(const raft::resources& handle,
     size_t max_cluster_size, min_cluster_size;
     get_min_max_cluster_size(k, max_cluster_size, min_cluster_size, cluster_sizes_for_this_rank);
 
+    std::optional<raft::device_vector<T, IdxT>> core_distances_d_for_rank;
+    auto dist_epilgogue_for_rank = [&]() {
+      if constexpr (mutual_reach_dist) {
+        core_distances_d_for_rank.emplace(raft::make_device_vector<T, IdxT>(dev_res, num_rows));
+        raft::copy(core_distances_d_for_rank.value().data_handle(),
+                   core_distances_h.value().data_handle(),
+                   num_rows,
+                   raft::resource::get_cuda_stream(dev_res));
+        return ReachabilityPP{
+          core_distances_d_for_rank.value().data_handle(), dist_epilogue.alpha, num_rows};
+      } else {
+        return dist_epilogue;
+      }
+    }();
+
     std::unique_ptr<all_neighbors_builder<T, IdxT>> knn_builder =
-      get_knn_builder<T, IdxT>(dev_res, params, min_cluster_size, max_cluster_size, k);
+      get_knn_builder<T, IdxT>(dev_res,
+                               params,
+                               min_cluster_size,
+                               max_cluster_size,
+                               k,
+                               std::nullopt,
+                               std::nullopt,
+                               dist_epilgogue_for_rank);
 
     single_gpu_batch_build(dev_res,
                            dataset,
@@ -384,13 +452,31 @@ void multi_gpu_batch_build(const raft::resources& handle,
   }
 }
 
-template <typename T, typename IdxT>
+/* Holds necessary vectors to avoid recomputation when calculating mutual rechability distances */
+template <typename IdxT>
+struct BatchBuildAux {
+  raft::host_vector<IdxT, IdxT> cluster_sizes;
+  raft::host_vector<IdxT, IdxT> cluster_offsets;
+  raft::host_vector<IdxT, IdxT> inverted_indices;
+  bool is_computed = false;
+
+  BatchBuildAux(IdxT n_clusters, IdxT num_rows, IdxT overlap_factor)
+    : cluster_sizes(raft::make_host_vector<IdxT, IdxT>(n_clusters)),
+      cluster_offsets(raft::make_host_vector<IdxT, IdxT>(n_clusters)),
+      inverted_indices(raft::make_host_vector<IdxT, IdxT>(num_rows * overlap_factor))
+  {
+  }
+};
+
+template <typename T, typename IdxT, typename DistEpilogueT = raft::identity_op>
 void batch_build(
   const raft::resources& handle,
   const all_neighbors_params& params,
   raft::host_matrix_view<const T, IdxT, row_major> dataset,
   raft::device_matrix_view<IdxT, IdxT, row_major> indices,
-  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt)
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances = std::nullopt,
+  BatchBuildAux<IdxT>* aux_vectors                                      = nullptr,
+  DistEpilogueT dist_epilogue                                           = DistEpilogueT{})
 {
   if (raft::resource::is_multi_gpu(handle)) {
     // For efficient CPU-computation of omp parallel for regions per GPU
@@ -405,37 +491,49 @@ void batch_build(
                "overlap_factor should be smaller than n_clusters. We recommend starting from "
                "overlap_factor=2 and gradually increase it for better knn graph recall.");
 
-  auto centroids = raft::make_device_matrix<T, IdxT>(handle, params.n_clusters, num_cols);
-  get_centroids_on_data_subsample<T, IdxT>(handle, params.metric, dataset, centroids.view());
-
-  auto global_nearest_cluster = raft::make_host_matrix<IdxT, IdxT>(num_rows, params.overlap_factor);
-  assign_clusters<T, IdxT>(
-    handle, params, dataset, centroids.view(), global_nearest_cluster.view());
-
-  auto inverted_indices =
-    raft::make_host_vector<IdxT, IdxT, raft::row_major>(num_rows * params.overlap_factor);
-  auto cluster_sizes   = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
-  auto cluster_offsets = raft::make_host_vector<IdxT, IdxT, raft::row_major>(params.n_clusters);
-  get_inverted_indices(handle,
-                       global_nearest_cluster.view(),
-                       inverted_indices.view(),
-                       cluster_sizes.view(),
-                       cluster_offsets.view());
+  std::optional<raft::host_vector<IdxT, IdxT>> local_inverted_indices;
+  std::optional<raft::host_vector<IdxT, IdxT>> local_cluster_sizes;
+  std::optional<raft::host_vector<IdxT, IdxT>> local_cluster_offsets;
+
+  auto inverted_indices_view =
+    aux_vectors != nullptr
+      ? aux_vectors->inverted_indices.view()
+      : local_inverted_indices
+          .emplace(raft::make_host_vector<IdxT, IdxT>(num_rows * params.overlap_factor))
+          .view();
+
+  auto cluster_sizes_view =
+    aux_vectors != nullptr
+      ? aux_vectors->cluster_sizes.view()
+      : local_cluster_sizes.emplace(raft::make_host_vector<IdxT, IdxT>(params.n_clusters)).view();
+
+  auto cluster_offsets_view =
+    aux_vectors != nullptr
+      ? aux_vectors->cluster_offsets.view()
+      : local_cluster_offsets.emplace(raft::make_host_vector<IdxT, IdxT>(params.n_clusters)).view();
+
+  if (aux_vectors == nullptr || !aux_vectors->is_computed) {
+    // clustering step is only computed when needed
+    auto centroids = raft::make_device_matrix<T, IdxT>(handle, params.n_clusters, num_cols);
+    get_centroids_on_data_subsample<T, IdxT>(handle, params.metric, dataset, centroids.view());
+
+    auto global_nearest_cluster =
+      raft::make_host_matrix<IdxT, IdxT>(num_rows, params.overlap_factor);
+    assign_clusters<T, IdxT>(
+      handle, params, dataset, centroids.view(), global_nearest_cluster.view());
+
+    get_inverted_indices(handle,
+                         global_nearest_cluster.view(),
+                         inverted_indices_view,
+                         cluster_sizes_view,
+                         cluster_offsets_view);
+    if (aux_vectors != nullptr) { aux_vectors->is_computed = true; }
+  }
 
   auto global_neighbors = raft::make_managed_matrix<IdxT, IdxT>(handle, num_rows, k);
   auto global_distances = raft::make_managed_matrix<T, IdxT>(handle, num_rows, k);
 
-  bool select_min = cuvs::distance::is_min_close(params.metric);
-  IdxT global_neighbors_fill_value =
-    select_min ? std::numeric_limits<IdxT>::max() : std::numeric_limits<IdxT>::min();
-  T global_distances_fill_value =
-    select_min ? std::numeric_limits<T>::max() : std::numeric_limits<T>::min();
-  std::fill(global_neighbors.data_handle(),
-            global_neighbors.data_handle() + num_rows * k,
-            global_neighbors_fill_value);
-  std::fill(global_distances.data_handle(),
-            global_distances.data_handle() + num_rows * k,
-            global_distances_fill_value);
+  reset_global_matrices(handle, params.metric, global_neighbors.view(), global_distances.view());
 
   if (raft::resource::is_multi_gpu(handle)) {
     multi_gpu_batch_build(handle,
@@ -443,23 +541,31 @@ void batch_build(
                           dataset,
                           global_neighbors.view(),
                           global_distances.view(),
-                          cluster_sizes.view(),
-                          cluster_offsets.view(),
-                          inverted_indices.view());
+                          cluster_sizes_view,
+                          raft::make_const_mdspan(cluster_offsets_view),
+                          inverted_indices_view,
+                          dist_epilogue);
   } else {
     size_t max_cluster_size, min_cluster_size;
-    get_min_max_cluster_size(k, max_cluster_size, min_cluster_size, cluster_sizes.view());
+    get_min_max_cluster_size(k, max_cluster_size, min_cluster_size, cluster_sizes_view);
     std::unique_ptr<all_neighbors_builder<T, IdxT>> knn_builder =
-      get_knn_builder<T, IdxT>(handle, params, min_cluster_size, max_cluster_size, k);
+      get_knn_builder<T, IdxT>(handle,
+                               params,
+                               min_cluster_size,
+                               max_cluster_size,
+                               k,
+                               std::nullopt,
+                               std::nullopt,
+                               dist_epilogue);
     single_gpu_batch_build(handle,
                            dataset,
                            *knn_builder,
                            params.n_clusters,
                            global_neighbors.view(),
                            global_distances.view(),
-                           cluster_sizes.view(),
-                           cluster_offsets.view(),
-                           inverted_indices.view());
+                           cluster_sizes_view,
+                           cluster_offsets_view,
+                           inverted_indices_view);
   }
 
   raft::copy(indices.data_handle(),
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
index af095baf0f..123d7b5401 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_builder.cuh
@@ -15,22 +15,27 @@
  */
 #pragma once
 
+#include "../detail/knn_brute_force.cuh"
 #include "../detail/nn_descent_gnnd.hpp"
+#include "../detail/reachability.cuh"
 #include "all_neighbors_merge.cuh"
-#include "raft/core/device_mdarray.hpp"
-#include "raft/core/mdspan.hpp"
-#include "raft/util/cudart_utils.hpp"
+
 #include <cuvs/neighbors/all_neighbors.hpp>
 #include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 #include <cuvs/neighbors/refine.hpp>
 
+#include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_mdarray.hpp>
 #include <raft/core/managed_mdspan.hpp>
+#include <raft/core/mdspan.hpp>
 #include <raft/core/mdspan_types.hpp>
+#include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/matrix/gather.cuh>
+#include <raft/util/cudart_utils.hpp>
 
 namespace cuvs::neighbors::all_neighbors::detail {
 using namespace cuvs::neighbors;
@@ -304,7 +309,7 @@ struct all_neighbors_builder_ivfpq : public all_neighbors_builder<T, IdxT> {
   std::optional<raft::host_matrix<T, IdxT>> refined_distances_h;
 };
 
-template <typename T, typename IdxT = int64_t>
+template <typename T, typename IdxT = int64_t, typename DistEpilogueT = raft::identity_op>
 struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT> {
   all_neighbors_builder_nn_descent(
     raft::resources const& res,
@@ -314,10 +319,12 @@ struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT>
     size_t k,
     graph_build_params::nn_descent_params& params,
     std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
-    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt,
+    DistEpilogueT dist_epilogue                                            = DistEpilogueT{})
     : all_neighbors_builder<T, IdxT>(
         res, n_clusters, min_cluster_size, max_cluster_size, k, indices, distances),
-      nnd_params{params}
+      nnd_params{params},
+      dist_epilogue{dist_epilogue}
   {
   }
 
@@ -346,6 +353,13 @@ struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT>
     nnd_builder.emplace(this->res, build_config);
     int_graph.emplace(raft::make_host_matrix<int, IdxT, row_major>(
       this->max_cluster_size, static_cast<IdxT>(extended_graph_degree)));
+
+    if constexpr (std::is_same_v<
+                    DistEpilogueT,
+                    cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, T>>) {
+      batch_core_distances.emplace(
+        raft::make_device_vector<T, IdxT>(this->res, this->max_cluster_size));
+    }
   }
 
   void prepare_build(raft::device_matrix_view<const T, IdxT, row_major> dataset) override
@@ -371,37 +385,78 @@ struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT>
       "need valid inverted_indices, global_neighbors, and global_distances for "
       "build_knn if doing batching.");
 
+    using ReachabilityPP = cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, T>;
+
     if (this->n_clusters > 1) {
       bool return_distances      = true;
       size_t num_data_in_cluster = dataset.extent(0);
-      nnd_builder.value().build(dataset.data_handle(),
-                                static_cast<int>(num_data_in_cluster),
-                                int_graph.value().data_handle(),
-                                return_distances,
-                                this->batch_distances_d.value().data_handle());
-
-      remap_and_merge_subgraphs<T, IdxT, int>(this->res,
-                                              this->inverted_indices_d.value().view(),
-                                              inverted_indices.value(),
-                                              int_graph.value().view(),
-                                              this->batch_neighbors_h.value().view(),
-                                              this->batch_neighbors_d.value().view(),
-                                              this->batch_distances_d.value().view(),
-                                              global_neighbors.value(),
-                                              global_distances.value(),
-                                              num_data_in_cluster,
-                                              this->k,
-                                              cuvs::distance::is_min_close(nnd_params.metric));
+      if constexpr (std::is_same_v<DistEpilogueT, ReachabilityPP>) {
+        // gather core dists
+        raft::copy(this->inverted_indices_d.value().data_handle(),
+                   inverted_indices.value().data_handle(),
+                   num_data_in_cluster,
+                   raft::resource::get_cuda_stream(this->res));
+
+        raft::matrix::gather(this->res,
+                             raft::make_device_matrix_view<const T, IdxT>(
+                               dist_epilogue.core_dists, dist_epilogue.n, 1),
+                             raft::make_device_vector_view<const IdxT, IdxT>(
+                               this->inverted_indices_d.value().data_handle(), num_data_in_cluster),
+                             raft::make_device_matrix_view<T, IdxT>(
+                               batch_core_distances.value().data_handle(), num_data_in_cluster, 1));
+
+        nnd_builder.value().build(
+          dataset.data_handle(),
+          static_cast<int>(num_data_in_cluster),
+          int_graph.value().data_handle(),
+          return_distances,
+          this->batch_distances_d.value().data_handle(),
+          cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int, T>{
+            batch_core_distances.value().data_handle(), dist_epilogue.alpha, num_data_in_cluster});
+      } else {
+        nnd_builder.value().build(dataset.data_handle(),
+                                  static_cast<int>(num_data_in_cluster),
+                                  int_graph.value().data_handle(),
+                                  return_distances,
+                                  this->batch_distances_d.value().data_handle());
+      }
+
+      remap_and_merge_subgraphs<T, IdxT, int, std::is_same_v<DistEpilogueT, ReachabilityPP>>(
+        this->res,
+        this->inverted_indices_d.value().view(),
+        inverted_indices.value(),
+        int_graph.value().view(),
+        this->batch_neighbors_h.value().view(),
+        this->batch_neighbors_d.value().view(),
+        this->batch_distances_d.value().view(),
+        global_neighbors.value(),
+        global_distances.value(),
+        num_data_in_cluster,
+        this->k,
+        cuvs::distance::is_min_close(nnd_params.metric));
     } else {
       size_t num_rows = dataset.extent(0);
 
-      nnd_builder.value().build(
-        dataset.data_handle(),
-        static_cast<int>(num_rows),
-        int_graph.value().data_handle(),
-        this->distances_.has_value(),
-        this->distances_.value_or(raft::make_device_matrix<T, IdxT>(this->res, 0, 0).view())
-          .data_handle());
+      if constexpr (std::is_same_v<DistEpilogueT, ReachabilityPP>) {
+        nnd_builder.value().build(
+          dataset.data_handle(),
+          static_cast<int>(num_rows),
+          int_graph.value().data_handle(),
+          this->distances_.has_value(),
+          this->distances_.value_or(raft::make_device_matrix<T, IdxT>(this->res, 0, 0).view())
+            .data_handle(),
+          cuvs::neighbors::detail::reachability::ReachabilityPostProcess<int, T>{
+            dist_epilogue.core_dists, dist_epilogue.alpha, dist_epilogue.n});
+      } else {
+        nnd_builder.value().build(
+          dataset.data_handle(),
+          static_cast<int>(num_rows),
+          int_graph.value().data_handle(),
+          this->distances_.has_value(),
+          this->distances_.value_or(raft::make_device_matrix<T, IdxT>(this->res, 0, 0).view())
+            .data_handle(),
+          dist_epilogue);
+      }
 
       auto tmp_indices = raft::make_host_matrix<IdxT, IdxT>(int_graph.value().extent(0), this->k);
 
@@ -443,9 +498,12 @@ struct all_neighbors_builder_nn_descent : public all_neighbors_builder<T, IdxT>
 
   std::optional<nn_descent::detail::GNND<const T, int>> nnd_builder;
   std::optional<raft::host_matrix<int, IdxT>> int_graph;
+
+  DistEpilogueT dist_epilogue;
+  std::optional<raft::device_vector<T, IdxT>> batch_core_distances;
 };
 
-template <typename T, typename IdxT = int64_t>
+template <typename T, typename IdxT = int64_t, typename DistEpilogueT = raft::identity_op>
 struct all_neighbors_builder_brute_force : public all_neighbors_builder<T, IdxT> {
   all_neighbors_builder_brute_force(
     raft::resources const& res,
@@ -455,10 +513,12 @@ struct all_neighbors_builder_brute_force : public all_neighbors_builder<T, IdxT>
     size_t k,
     graph_build_params::brute_force_params& params,
     std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
-    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+    std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt,
+    DistEpilogueT dist_epilogue                                            = DistEpilogueT{})
     : all_neighbors_builder<T, IdxT>(
         res, n_clusters, min_cluster_size, max_cluster_size, k, indices, distances),
-      bf_params{params}
+      bf_params{params},
+      dist_epilogue{dist_epilogue}
   {
   }
 
@@ -470,6 +530,12 @@ struct all_neighbors_builder_brute_force : public all_neighbors_builder<T, IdxT>
     size_t num_cols = dataset.extent(1);
     data_d.emplace(
       raft::make_device_matrix<T, IdxT, row_major>(this->res, this->max_cluster_size, num_cols));
+    if constexpr (std::is_same_v<
+                    DistEpilogueT,
+                    cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, T>>) {
+      batch_core_distances.emplace(
+        raft::make_device_vector<T, IdxT>(this->res, this->max_cluster_size));
+    }
   }
 
   void build_knn_common(
@@ -485,26 +551,63 @@ struct all_neighbors_builder_brute_force : public all_neighbors_builder<T, IdxT>
       "build_knn if doing batching.");
 
     if (this->n_clusters > 1) {
-      auto idx = cuvs::neighbors::brute_force::build(this->res, bf_params.build_params, dataset);
-
       size_t num_data_in_cluster = dataset.extent(0);
+      using ReachabilityPP =
+        cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, T>;
+
+      if constexpr (std::is_same_v<DistEpilogueT, ReachabilityPP>) {
+        // gather core dists
+        raft::copy(this->inverted_indices_d.value().data_handle(),
+                   inverted_indices.value().data_handle(),
+                   num_data_in_cluster,
+                   raft::resource::get_cuda_stream(this->res));
 
-      cuvs::neighbors::brute_force::search(
-        this->res,
-        bf_params.search_params,
-        idx,
-        dataset,
-        raft::make_device_matrix_view<IdxT, IdxT>(
-          this->batch_neighbors_d.value().data_handle(), num_data_in_cluster, this->k),
-        raft::make_device_matrix_view<T, IdxT>(
-          this->batch_distances_d.value().data_handle(), num_data_in_cluster, this->k));
-
+        raft::matrix::gather(this->res,
+                             raft::make_device_matrix_view<const T, IdxT>(
+                               dist_epilogue.core_dists, dist_epilogue.n, 1),
+                             raft::make_device_vector_view<const IdxT, IdxT>(
+                               this->inverted_indices_d.value().data_handle(), num_data_in_cluster),
+                             raft::make_device_matrix_view<T, IdxT>(
+                               batch_core_distances.value().data_handle(), num_data_in_cluster, 1));
+
+        cuvs::neighbors::detail::tiled_brute_force_knn<T, IdxT, T, DistEpilogueT>(
+          this->res,
+          dataset.data_handle(),
+          dataset.data_handle(),
+          dataset.extent(0),
+          dataset.extent(0),
+          dataset.extent(1),
+          this->k,
+          this->batch_distances_d.value().data_handle(),
+          this->batch_neighbors_d.value().data_handle(),
+          bf_params.build_params.metric,
+          2.0,
+          0,
+          0,
+          nullptr,
+          nullptr,
+          nullptr,
+          ReachabilityPP{
+            batch_core_distances.value().data_handle(), dist_epilogue.alpha, num_data_in_cluster});
+      } else {
+        auto idx = cuvs::neighbors::brute_force::build(this->res, bf_params.build_params, dataset);
+
+        cuvs::neighbors::brute_force::search(
+          this->res,
+          bf_params.search_params,
+          idx,
+          dataset,
+          raft::make_device_matrix_view<IdxT, IdxT>(
+            this->batch_neighbors_d.value().data_handle(), num_data_in_cluster, this->k),
+          raft::make_device_matrix_view<T, IdxT>(
+            this->batch_distances_d.value().data_handle(), num_data_in_cluster, this->k));
+      }
       raft::copy(this->batch_neighbors_h.value().data_handle(),
                  this->batch_neighbors_d.value().data_handle(),
                  num_data_in_cluster * this->k,
                  raft::resource::get_cuda_stream(this->res));
 
-      remap_and_merge_subgraphs<T, IdxT, IdxT>(
+      remap_and_merge_subgraphs<T, IdxT, IdxT, std::is_same_v<DistEpilogueT, ReachabilityPP>>(
         this->res,
         this->inverted_indices_d.value().view(),
         inverted_indices.value(),
@@ -518,17 +621,41 @@ struct all_neighbors_builder_brute_force : public all_neighbors_builder<T, IdxT>
         this->k,
         cuvs::distance::is_min_close(bf_params.build_params.metric));
     } else {
-      auto idx = cuvs::neighbors::brute_force::build(this->res, bf_params.build_params, dataset);
-
-      cuvs::neighbors::brute_force::search(
-        this->res,
-        bf_params.search_params,
-        idx,
-        dataset,
-        this->indices_.value(),
-        this->distances_.has_value()
-          ? this->distances_.value()
-          : raft::make_device_matrix<T, IdxT>(this->res, dataset.extent(0), this->k).view());
+      if constexpr (std::is_same_v<DistEpilogueT, raft::identity_op>) {
+        auto idx = cuvs::neighbors::brute_force::build(this->res, bf_params.build_params, dataset);
+
+        cuvs::neighbors::brute_force::search(
+          this->res,
+          bf_params.search_params,
+          idx,
+          dataset,
+          this->indices_.value(),
+          this->distances_.has_value()
+            ? this->distances_.value()
+            : raft::make_device_matrix<T, IdxT>(this->res, dataset.extent(0), this->k).view());
+      } else {
+        cuvs::neighbors::detail::tiled_brute_force_knn<T, IdxT, T, DistEpilogueT>(
+          this->res,
+          dataset.data_handle(),
+          dataset.data_handle(),
+          dataset.extent(0),
+          dataset.extent(0),
+          dataset.extent(1),
+          this->k,
+          this->distances_.has_value()
+            ? this->distances_.value().data_handle()
+            : raft::make_device_matrix<T, IdxT>(this->res, dataset.extent(0), this->k)
+                .data_handle(),
+          this->indices_.value().data_handle(),
+          bf_params.build_params.metric,
+          2.0,
+          0,
+          0,
+          nullptr,
+          nullptr,
+          nullptr,
+          dist_epilogue);
+      }
     }
   }
 
@@ -559,11 +686,13 @@ struct all_neighbors_builder_brute_force : public all_neighbors_builder<T, IdxT>
   }
 
   graph_build_params::brute_force_params bf_params;
+  DistEpilogueT dist_epilogue;
 
   std::optional<raft::device_matrix<T, IdxT, row_major>> data_d;
+  std::optional<raft::device_vector<T, IdxT>> batch_core_distances;
 };
 
-template <typename T, typename IdxT>
+template <typename T, typename IdxT, typename DistEpilogueT = raft::identity_op>
 std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
   const raft::resources& handle,
   const all_neighbors_params& params,
@@ -571,7 +700,8 @@ std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
   size_t max_cluster_size,
   size_t k,
   std::optional<raft::device_matrix_view<IdxT, IdxT, row_major>> indices = std::nullopt,
-  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt)
+  std::optional<raft::device_matrix_view<T, IdxT, row_major>> distances  = std::nullopt,
+  DistEpilogueT dist_epilogue                                            = DistEpilogueT{})
 {
   if (std::holds_alternative<graph_build_params::brute_force_params>(params.graph_build_params)) {
     auto brute_force_params =
@@ -580,14 +710,17 @@ std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
       RAFT_LOG_WARN("Setting brute_force_params metric to metric given for batching algorithm");
       brute_force_params.build_params.metric = params.metric;
     }
-    return std::make_unique<all_neighbors_builder_brute_force<T, IdxT>>(handle,
-                                                                        params.n_clusters,
-                                                                        min_cluster_size,
-                                                                        max_cluster_size,
-                                                                        k,
-                                                                        brute_force_params,
-                                                                        indices,
-                                                                        distances);
+    return std::make_unique<all_neighbors_builder_brute_force<T, IdxT, DistEpilogueT>>(
+      handle,
+      params.n_clusters,
+      min_cluster_size,
+      max_cluster_size,
+      k,
+      brute_force_params,
+      indices,
+      distances,
+      dist_epilogue);
+
   } else if (std::holds_alternative<graph_build_params::nn_descent_params>(
                params.graph_build_params)) {
     auto nn_descent_params =
@@ -596,14 +729,16 @@ std::unique_ptr<all_neighbors_builder<T, IdxT>> get_knn_builder(
       RAFT_LOG_WARN("Setting nnd_params metric to metric given for batching algorithm");
       nn_descent_params.metric = params.metric;
     }
-    return std::make_unique<all_neighbors_builder_nn_descent<T, IdxT>>(handle,
-                                                                       params.n_clusters,
-                                                                       min_cluster_size,
-                                                                       max_cluster_size,
-                                                                       k,
-                                                                       nn_descent_params,
-                                                                       indices,
-                                                                       distances);
+    return std::make_unique<all_neighbors_builder_nn_descent<T, IdxT, DistEpilogueT>>(
+      handle,
+      params.n_clusters,
+      min_cluster_size,
+      max_cluster_size,
+      k,
+      nn_descent_params,
+      indices,
+      distances,
+      dist_epilogue);
   } else if (std::holds_alternative<graph_build_params::ivf_pq_params>(params.graph_build_params)) {
     auto ivf_pq_params = std::get<graph_build_params::ivf_pq_params>(params.graph_build_params);
     if (ivf_pq_params.build_params.metric != params.metric) {
diff --git a/cpp/tests/neighbors/all_neighbors.cuh b/cpp/tests/neighbors/all_neighbors.cuh
index 1edc93ce26..fddfeb61af 100644
--- a/cpp/tests/neighbors/all_neighbors.cuh
+++ b/cpp/tests/neighbors/all_neighbors.cuh
@@ -15,6 +15,8 @@
  */
 #pragma once
 
+#include "../../src/neighbors/detail/knn_brute_force.cuh"
+#include "../../src/neighbors/detail/reachability.cuh"
 #include "../test_utils.cuh"
 #include "ann_utils.cuh"
 #include "naive_knn.cuh"
@@ -52,6 +54,7 @@ struct AllNeighborsInputs {
   int dim;
   int k;
   bool data_on_host;
+  bool mutual_reach;
 };
 
 inline ::std::ostream& operator<<(::std::ostream& os, const AllNeighborsInputs& p)
@@ -101,6 +104,7 @@ void get_graphs(raft::resources& handle,
     params.graph_build_params = ivfpq_build_params;
   }
 
+  auto metric      = std::get<1>(ps.build_algo_metric_recall);
   auto cuda_stream = raft::resource::get_cuda_stream(handle);
   {
     rmm::device_uvector<DistanceT> distances_naive_dev(queries_size, cuda_stream);
@@ -115,6 +119,41 @@ void get_graphs(raft::resources& handle,
                                       ps.dim,
                                       ps.k,
                                       std::get<1>(ps.build_algo_metric_recall));
+
+    if (ps.mutual_reach) {
+      rmm::device_uvector<DistanceT> core_dists_dev(ps.n_rows, cuda_stream);
+
+      cuvs::neighbors::detail::reachability::core_distances<IdxT, DistanceT>(
+        handle, distances_naive_dev.data(), ps.k, ps.k, ps.n_rows, core_dists_dev.data());
+
+      auto epilogue =
+        cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, DistanceT>{
+          core_dists_dev.data(), 1.0, static_cast<size_t>(ps.n_rows)};
+
+      cuvs::neighbors::detail::tiled_brute_force_knn<
+        DataT,
+        IdxT,
+        DistanceT,
+        cuvs::neighbors::detail::reachability::ReachabilityPostProcess<IdxT, DistanceT>>(
+        handle,
+        database.data(),
+        database.data(),
+        ps.n_rows,
+        ps.n_rows,
+        ps.dim,
+        ps.k,
+        distances_naive_dev.data(),
+        indices_naive_dev.data(),
+        metric,
+        2.0,
+        0,
+        0,
+        nullptr,
+        nullptr,
+        nullptr,
+        epilogue);
+    }
+
     raft::update_host(indices_bf.data(), indices_naive_dev.data(), queries_size, cuda_stream);
     raft::update_host(distances_bf.data(), distances_naive_dev.data(), queries_size, cuda_stream);
 
@@ -134,7 +173,10 @@ void get_graphs(raft::resources& handle,
         params,
         raft::make_const_mdspan(database_h.view()),
         raft::make_device_matrix_view<IdxT>(indices_allNN_dev.data(), ps.n_rows, ps.k),
-        raft::make_device_matrix_view<DistanceT>(distances_allNN_dev.data(), ps.n_rows, ps.k));
+        raft::make_device_matrix_view<DistanceT>(distances_allNN_dev.data(), ps.n_rows, ps.k),
+        ps.mutual_reach
+          ? std::make_optional(raft::make_device_vector<DistanceT>(handle, ps.n_rows).view())
+          : std::nullopt);
 
     } else {
       all_neighbors::build(
@@ -142,7 +184,10 @@ void get_graphs(raft::resources& handle,
         params,
         raft::make_device_matrix_view<const DataT, IdxT>(database.data(), ps.n_rows, ps.dim),
         raft::make_device_matrix_view<IdxT>(indices_allNN_dev.data(), ps.n_rows, ps.k),
-        raft::make_device_matrix_view<DistanceT>(distances_allNN_dev.data(), ps.n_rows, ps.k));
+        raft::make_device_matrix_view<DistanceT>(distances_allNN_dev.data(), ps.n_rows, ps.k),
+        ps.mutual_reach
+          ? std::make_optional(raft::make_device_vector<DistanceT>(handle, ps.n_rows).view())
+          : std::nullopt);
     }
 
     raft::copy(indices_allNN.data(), indices_allNN_dev.data(), queries_size, cuda_stream);
@@ -223,13 +268,16 @@ const std::vector<AllNeighborsInputs> inputsSingle =
     {5000, 7151},                 // n_rows
     {64, 137},                    // dim
     {16, 23},                     // graph_degree
-    {false, true}                 // data on host
+    {false, true},                // data on host
+    {false}                       // mutual_reach
   );
 
 const std::vector<AllNeighborsInputs> inputsBatch =
   raft::util::itertools::product<AllNeighborsInputs>(
     {std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2Expanded, 0.9),
      std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::CosineExpanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::InnerProduct, 0.9),
      std::make_tuple(IVF_PQ, cuvs::distance::DistanceType::L2Expanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2Expanded, 0.9),
      std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
@@ -243,7 +291,44 @@ const std::vector<AllNeighborsInputs> inputsBatch =
     {5000, 7151},  // n_rows
     {64, 137},     // dim
     {16, 23},      // graph_degree
-    {true}         // data on host
+    {true},        // data on host
+    {false}        // mutual_reach
+  );
+
+const std::vector<AllNeighborsInputs> mutualReachSingle =
+  raft::util::itertools::product<AllNeighborsInputs>(
+    {std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::CosineExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::CosineExpanded, 0.9)},
+    {std::make_tuple(1lu, 2lu)},  // n_clusters, overlap_factor
+    {5000, 7151},                 // n_rows
+    {64, 137},                    // dim
+    {16, 23},                     // graph_degree
+    {false, true},                // data on host
+    {true}                        // mutual_reach
+  );
+
+const std::vector<AllNeighborsInputs> mutualReachBatch =
+  raft::util::itertools::product<AllNeighborsInputs>(
+    {std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(BRUTE_FORCE, cuvs::distance::DistanceType::CosineExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2Expanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::L2SqrtExpanded, 0.9),
+     std::make_tuple(NN_DESCENT, cuvs::distance::DistanceType::CosineExpanded, 0.9)},
+    {
+      std::make_tuple(4lu, 2lu),
+      std::make_tuple(7lu, 2lu),
+      std::make_tuple(10lu, 2lu),
+    },             // n_clusters, overlap_factor
+    {5000, 7151},  // n_rows
+    {64, 137},     // dim
+    {16, 23},      // graph_degree
+    {true},        // data on host
+    {true}         // mutual_reach
   );
 
 }  // namespace cuvs::neighbors::all_neighbors
diff --git a/cpp/tests/neighbors/all_neighbors/test_float.cu b/cpp/tests/neighbors/all_neighbors/test_float.cu
index 3fa41efd5c..88af4c2253 100644
--- a/cpp/tests/neighbors/all_neighbors/test_float.cu
+++ b/cpp/tests/neighbors/all_neighbors/test_float.cu
@@ -28,4 +28,12 @@ INSTANTIATE_TEST_CASE_P(AllNeighborsSingleTest,
                         ::testing::ValuesIn(inputsSingle));
 
 INSTANTIATE_TEST_CASE_P(AllNeighborsBatchTest, AllNeighborsTestF, ::testing::ValuesIn(inputsBatch));
+
+INSTANTIATE_TEST_CASE_P(AllNeighborsSingleMutualTest,
+                        AllNeighborsTestF,
+                        ::testing::ValuesIn(mutualReachSingle));
+
+INSTANTIATE_TEST_CASE_P(AllNeighborsBatchMutualTest,
+                        AllNeighborsTestF,
+                        ::testing::ValuesIn(mutualReachBatch));
 }  // namespace cuvs::neighbors::all_neighbors

From e0d413380cb9ca17145b2ca2e4b0c80d463d83be Mon Sep 17 00:00:00 2001
From: Rui Lan <abc99lr@gmail.com>
Date: Mon, 18 Aug 2025 19:53:11 -0700
Subject: [PATCH 550/623] [REVIEW] Add a public API for CAGRA graph optimize
 (#1260)

This change exposes CAGRA's graph optimization functionality through a new public API in the `cuvs::neighbors::cagra::helpers` namespace. The optimize function allows users to optimize a custom KNN graphs to create CAGRA search graphs.

Key Changes:

- Added `cpp/include/cuvs/neighbors/cagra_optimize.hpp` with a public API declaration. As cagra_optimize assumes both input and output are host matrices, this PR exposes the API with host inputs only. Given this, users need to explicitly manage device-to-host transfers if KNN graph is not a RAFT host matrix
- Modified previously unused `cpp/src/neighbors/cagra_optimize.cu` with implementation that forwards to internal CAGRA optimize. Removed the version using device matrix for `knn_graph`, which generates runtime segfaults
- Added unit tests for the new API

This PR partially resolves https://github.com/rapidsai/cuvs/issues/456 and completes one of the steps for https://github.com/rapidsai/cuvs/issues/1146

Authors:
  - Rui Lan (https://github.com/abc99lr)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1260
---
 cpp/include/cuvs/neighbors/cagra.hpp          |  1 +
 cpp/include/cuvs/neighbors/cagra_optimize.hpp | 49 ++++++++++++++
 cpp/src/neighbors/cagra_optimize.cu           | 22 ++----
 cpp/tests/CMakeLists.txt                      |  5 ++
 .../ann_cagra/test_optimize_uint32_t.cu       | 67 +++++++++++++++++++
 5 files changed, 127 insertions(+), 17 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/cagra_optimize.hpp
 create mode 100644 cpp/tests/neighbors/ann_cagra/test_optimize_uint32_t.cu

diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 6c7d1a1979..fd1fe72b34 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -2686,3 +2686,4 @@ auto distribute(const raft::resources& clique, const std::string& filename)
 }  // namespace cuvs::neighbors::cagra
 
 #include <cuvs/neighbors/cagra_index_wrapper.hpp>
+#include <cuvs/neighbors/cagra_optimize.hpp>
diff --git a/cpp/include/cuvs/neighbors/cagra_optimize.hpp b/cpp/include/cuvs/neighbors/cagra_optimize.hpp
new file mode 100644
index 0000000000..ef0ae90061
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/cagra_optimize.hpp
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+
+namespace cuvs::neighbors::cagra::helpers {
+
+/**
+ * @brief Optimize a KNN graph into a CAGRA graph.
+ *
+ * This function optimizes a k-NN graph to create a CAGRA graph.
+ * The input/output graphs must be on host memory.
+ *
+ * Usage example:
+ * @code{.cpp}
+ *   raft::resources res;
+ *   auto h_knn = raft::make_host_matrix<uint32_t, int64_t>(N, K_in);
+ *   // Fill h_knn with KNN graph
+ *   auto h_out = raft::make_host_matrix<uint32_t, int64_t>(N, K_out);
+ *   cuvs::neighbors::cagra::helpers::optimize(res, h_knn.view(), h_out.view());
+ * @endcode
+ *
+ * @param[in] handle RAFT resources
+ * @param[in] knn_graph Input KNN graph on host [n_rows, k_in]
+ * @param[out] new_graph Output CAGRA graph on host [n_rows, k_out]
+ */
+void optimize(raft::resources const& handle,
+              raft::host_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
+              raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph);
+
+}  // namespace cuvs::neighbors::cagra::helpers
diff --git a/cpp/src/neighbors/cagra_optimize.cu b/cpp/src/neighbors/cagra_optimize.cu
index 436d5d321b..b26239f2d3 100644
--- a/cpp/src/neighbors/cagra_optimize.cu
+++ b/cpp/src/neighbors/cagra_optimize.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,27 +15,15 @@
  */
 
 #include "cagra.cuh"
-#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/cagra_optimize.hpp>
 
-namespace cuvs::neighbors::cagra {
+namespace cuvs::neighbors::cagra::helpers {
 
-void optimize(raft::resources const& handle,
-              raft::device_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
-              raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
-{
-  cuvs::neighbors::cagra::optimize<
-    uint32_t,
-    raft::host_device_accessor<std::experimental::default_accessor<uint32_t>,
-                               raft::memory_type::device>>(handle, knn_graph, new_graph);
-}
 void optimize(raft::resources const& handle,
               raft::host_matrix_view<uint32_t, int64_t, raft::row_major> knn_graph,
               raft::host_matrix_view<uint32_t, int64_t, raft::row_major> new_graph)
 {
-  cuvs::neighbors::cagra::optimize<
-    uint32_t,
-    raft::host_device_accessor<std::experimental::default_accessor<uint32_t>,
-                               raft::memory_type::host>>(handle, knn_graph, new_graph);
+  cuvs::neighbors::cagra::optimize(handle, knn_graph, new_graph);
 }
 
-}  // namespace cuvs::neighbors::cagra
+}  // namespace cuvs::neighbors::cagra::helpers
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index a1b037f706..a266b577ed 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -166,6 +166,11 @@ if(BUILD_TESTS)
     1 PERCENT 100
   )
 
+  ConfigureTest(
+    NAME NEIGHBORS_ANN_CAGRA_HELPERS_TEST PATH neighbors/ann_cagra/test_optimize_uint32_t.cu GPUS 1
+    PERCENT 100
+  )
+
   ConfigureTest(
     NAME NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST PATH neighbors/ann_cagra/test_half_uint32_t.cu GPUS 1
     PERCENT 100
diff --git a/cpp/tests/neighbors/ann_cagra/test_optimize_uint32_t.cu b/cpp/tests/neighbors/ann_cagra/test_optimize_uint32_t.cu
new file mode 100644
index 0000000000..55058affee
--- /dev/null
+++ b/cpp/tests/neighbors/ann_cagra/test_optimize_uint32_t.cu
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/cagra.hpp>
+#include <gtest/gtest.h>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resources.hpp>
+
+// This test targets public API exposure and basic invariants only (shapes, in-range indices).
+// Detailed optimization correctness is exercised by CAGRA build tests.
+
+namespace {
+
+using IdxT = uint32_t;
+
+// Helper to create a simple synthetic KNN graph (ring-like neighbors)
+auto make_ring_knn_host(int64_t num_rows, int64_t kin)
+{
+  auto knn_graph = raft::make_host_matrix<IdxT, int64_t>(num_rows, kin);
+  for (int64_t i = 0; i < num_rows; ++i) {
+    for (int64_t j = 0; j < kin; ++j) {
+      knn_graph(i, j) = static_cast<IdxT>((i + j + 1) % num_rows);
+    }
+  }
+  return knn_graph;
+}
+
+TEST(CagraOptimize, HostToHostOptimizesGraph)
+{
+  raft::resources res;
+
+  constexpr int64_t num_rows = 8;
+  constexpr int64_t kin      = 8;
+  constexpr int64_t kout     = 4;
+
+  auto knn_graph       = make_ring_knn_host(num_rows, kin);
+  auto optimized_graph = raft::make_host_matrix<IdxT, int64_t>(num_rows, kout);
+
+  // Test the optimize API
+  cuvs::neighbors::cagra::helpers::optimize(res, knn_graph.view(), optimized_graph.view());
+
+  // Check basic invariants
+  ASSERT_EQ(optimized_graph.extent(0), num_rows);
+  ASSERT_EQ(optimized_graph.extent(1), kout);
+
+  // Check that all neighbors are valid indices
+  for (int64_t i = 0; i < num_rows; ++i) {
+    for (int64_t j = 0; j < kout; ++j) {
+      EXPECT_LT(optimized_graph(i, j), static_cast<IdxT>(num_rows));
+    }
+  }
+}
+
+}  // namespace

From d0a83d43f7be934f0c4dcef585302a7fad287df0 Mon Sep 17 00:00:00 2001
From: Paul Taylor <178183+trxcllnt@users.noreply.github.com>
Date: Tue, 19 Aug 2025 11:14:50 -0700
Subject: [PATCH 551/623] Use build cluster in devcontainers (#1240)

RAPIDS has deployed an autoscaling cloud build cluster that can be used to accelerate building large RAPIDS projects. This contributes to https://github.com/rapidsai/build-planning/issues/209.

Authors:
  - Paul Taylor (https://github.com/trxcllnt)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1240
---
 .devcontainer/Dockerfile                      | 34 +++++++++++++++++--
 .../cuda12.9-conda/devcontainer.json          |  4 ++-
 .devcontainer/cuda12.9-pip/devcontainer.json  |  6 ++--
 .github/workflows/pr.yaml                     | 15 +++++---
 4 files changed, 50 insertions(+), 9 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 77b90fa205..31e3837840 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -21,6 +21,8 @@ ENV DEFAULT_CONDA_ENV=rapids
 
 FROM ${PYTHON_PACKAGE_MANAGER}-base
 
+ARG TARGETARCH
+
 ARG CUDA
 ENV CUDAARCHS="RAPIDS"
 ENV CUDA_VERSION="${CUDA_VERSION:-${CUDA}}"
@@ -32,7 +34,35 @@ ENV PYTHONSAFEPATH="1"
 ENV PYTHONUNBUFFERED="1"
 ENV PYTHONDONTWRITEBYTECODE="1"
 
+ENV HISTFILE="/home/coder/.cache/._bash_history"
+
+###
+# sccache configuration
+###
+ENV AWS_ROLE_ARN="arn:aws:iam::279114543810:role/nv-gha-token-sccache-devs"
 ENV SCCACHE_REGION="us-east-2"
 ENV SCCACHE_BUCKET="rapids-sccache-devs"
-ENV VAULT_HOST="https://vault.ops.k8s.rapids.ai"
-ENV HISTFILE="/home/coder/.cache/._bash_history"
+# 2hr (1 minute longer than sccache-dist request timeout)
+ENV SCCACHE_IDLE_TIMEOUT=7200
+
+###
+# sccache-dist configuration
+###
+# Enable sccache-dist by default
+ENV DEVCONTAINER_UTILS_ENABLE_SCCACHE_DIST=1
+# Compile locally if max retries exceeded
+ENV SCCACHE_DIST_FALLBACK_TO_LOCAL_COMPILE=true
+# Retry transient errors 4 times (for a total of 5 attempts)
+ENV SCCACHE_DIST_MAX_RETRIES=4
+ENV SCCACHE_DIST_CONNECT_TIMEOUT=30
+ENV SCCACHE_DIST_CONNECTION_POOL=false
+# 1hr 59min (to accommodate debug builds)
+ENV SCCACHE_DIST_REQUEST_TIMEOUT=7140
+ENV SCCACHE_DIST_KEEPALIVE_ENABLED=true
+ENV SCCACHE_DIST_KEEPALIVE_INTERVAL=20
+ENV SCCACHE_DIST_KEEPALIVE_TIMEOUT=600
+ENV SCCACHE_DIST_URL="https://${TARGETARCH}.linux.sccache.rapids.nvidia.com"
+
+# Build as much in parallel as possible
+ENV INFER_NUM_DEVICE_ARCHITECTURES=1
+ENV MAX_DEVICE_OBJ_TO_COMPILE_IN_PARALLEL=20
diff --git a/.devcontainer/cuda12.9-conda/devcontainer.json b/.devcontainer/cuda12.9-conda/devcontainer.json
index 3c1da8fe73..be50d56c15 100644
--- a/.devcontainer/cuda12.9-conda/devcontainer.json
+++ b/.devcontainer/cuda12.9-conda/devcontainer.json
@@ -11,7 +11,9 @@
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-conda",
+    "--ulimit",
+    "nofile=500000"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
diff --git a/.devcontainer/cuda12.9-pip/devcontainer.json b/.devcontainer/cuda12.9-pip/devcontainer.json
index 0305705fb9..abb88bb356 100644
--- a/.devcontainer/cuda12.9-pip/devcontainer.json
+++ b/.devcontainer/cuda12.9-pip/devcontainer.json
@@ -5,13 +5,15 @@
     "args": {
       "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda12.9-ucx1.18.0-openmpi5.0.7"
+      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda12.9-ucx1.19.0-openmpi5.0.7"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-pip",
+    "--ulimit",
+    "nofile=500000"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 89795ed967..41fbc93a84 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -223,12 +223,19 @@ jobs:
     needs: telemetry-setup
     uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.10
     with:
-      arch: '["amd64"]'
+      arch: '["amd64", "arm64"]'
       cuda: '["12.9"]'
+      node_type: "cpu8"
+      rapids-aux-secret-1: GIST_REPO_READ_ORG_GITHUB_TOKEN
+      env: |
+        SCCACHE_DIST_MAX_RETRIES=inf
+        SCCACHE_SERVER_LOG=sccache=debug
+        SCCACHE_DIST_FALLBACK_TO_LOCAL_COMPILE=false
+        SCCACHE_DIST_AUTH_TOKEN_VAR=RAPIDS_AUX_SECRET_1
       build_command: |
-        sccache -z;
-        build-all --verbose;
-        sccache -s;
+        sccache --zero-stats;
+        build-all -j0 --verbose 2>&1 | tee telemetry-artifacts/build.log;
+        sccache --show-adv-stats | tee telemetry-artifacts/sccache-stats.txt;
   telemetry-summarize:
     # This job must use a self-hosted runner to record telemetry traces.
     runs-on: linux-amd64-cpu4

From 989c994e3d15d3b81c86aa1d5d10cb95a9cb9e88 Mon Sep 17 00:00:00 2001
From: Puneet Ahuja <167976912+punAhuja@users.noreply.github.com>
Date: Fri, 22 Aug 2025 09:33:41 +0530
Subject: [PATCH 552/623] [Java]Binary and scalar quantization (#1104)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Java support for Binary and Scalar Quantization
  -Incorporated changes for indexing and searching on quantized dataset
  -Added tests to test quantization, indexing and searching on quantized dataset, with quantized query

Authors:
  - Puneet Ahuja (https://github.com/punAhuja)
  - Lorenzo Dematté (https://github.com/ldematte)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1104
---
 .../cuvs/preprocessing/quantize/binary.h      |   6 +-
 .../java/com/nvidia/cuvs/BinaryQuantizer.java | 125 ++++
 .../main/java/com/nvidia/cuvs/CagraIndex.java |   8 +
 .../main/java/com/nvidia/cuvs/CagraQuery.java | 108 +++-
 .../main/java/com/nvidia/cuvs/CuVSMatrix.java |  18 +
 .../java/com/nvidia/cuvs/CuVSQuantizer.java   |  71 +++
 .../com/nvidia/cuvs/Scalar8BitQuantizer.java  | 123 ++++
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  27 +
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |  45 +-
 .../cuvs/internal/BinaryQuantizerImpl.java    | 324 ++++++++++
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 130 +++-
 .../cuvs/internal/CuVSHostMatrixImpl.java     |   7 +
 .../cuvs/internal/CuVSMatrixBaseImpl.java     |   6 +
 .../internal/Scalar8BitQuantizerImpl.java     | 461 ++++++++++++++
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  41 ++
 .../java/com/nvidia/cuvs/QuantizationIT.java  | 588 ++++++++++++++++++
 java/panama-bindings/headers.h                |   2 +
 17 files changed, 2053 insertions(+), 37 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java

diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.h b/cpp/include/cuvs/preprocessing/quantize/binary.h
index 9a5834ef3e..ec58bae3bd 100644
--- a/cpp/include/cuvs/preprocessing/quantize/binary.h
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.h
@@ -30,7 +30,7 @@ extern "C" {
  * and sampling_median thresholds are calculated separately for each dimension.
  *
  */
-enum cuvsBinaryQuantizerThreshold { ZERO, MEAN, SAMPLING_MEDIAN };
+typedef enum { ZERO, MEAN, SAMPLING_MEDIAN } cuvsBinaryQuantizerThreshold;
 
 /**
  * @brief Binary quantizer parameters.
@@ -39,12 +39,12 @@ struct cuvsBinaryQuantizerParams {
   /*
    * specifies the threshold to set a bit in cuvsBinaryQuantizerTransform
    */
-  cuvsBinaryQuantizerThreshold threshold = MEAN;
+  cuvsBinaryQuantizerThreshold threshold;
 
   /*
    * specifies the sampling ratio
    */
-  float sampling_ratio = 0.1;
+  float sampling_ratio;
 };
 
 typedef struct cuvsBinaryQuantizerParams* cuvsBinaryQuantizerParams_t;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java
new file mode 100644
index 0000000000..82584d6cac
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+import com.nvidia.cuvs.spi.CuVSProvider;
+
+/** Binary quantizer that transforms float datasets into packed binary datasets. */
+public class BinaryQuantizer implements CuVSQuantizer {
+  private final CuVSResources resources;
+  private final ThresholdType thresholdType;
+
+  /** Output data type (BYTE for packed binary data). */
+  private final DataType outputDataType = DataType.BYTE;
+
+  private final Object impl;
+
+  /** Creates a binary quantizer with training data. */
+  public BinaryQuantizer(CuVSResources resources, CuVSMatrix trainingDataset) throws Throwable {
+    this(resources, trainingDataset, ThresholdType.MEAN);
+  }
+
+  /** Creates a binary quantizer with specified threshold type and training data. */
+  public BinaryQuantizer(
+      CuVSResources resources, CuVSMatrix trainingDataset, ThresholdType thresholdType)
+      throws Throwable {
+    if (trainingDataset == null) {
+      throw new IllegalArgumentException("Training dataset cannot be null");
+    }
+    if (trainingDataset.dataType() != DataType.FLOAT) {
+      throw new IllegalArgumentException(
+          "Training dataset must have FLOAT data type, got " + trainingDataset.dataType());
+    }
+    this.resources = resources;
+    this.thresholdType = thresholdType;
+    this.impl =
+        CuVSProvider.provider()
+            .createBinaryQuantizerImpl(resources, trainingDataset, thresholdType);
+  }
+
+  /** Threshold types for binary quantization. */
+  public enum ThresholdType {
+    /** Zero threshold */
+    ZERO(0),
+    /** Mean threshold */
+    MEAN(1),
+    /** Sampling median threshold */
+    SAMPLING_MEDIAN(2);
+
+    private final int value;
+
+    ThresholdType(int value) {
+      this.value = value;
+    }
+
+    public int getValue() {
+      return value;
+    }
+  }
+
+  /** Returns the output data type (BYTE). */
+  @Override
+  public DataType outputDataType() {
+    return outputDataType;
+  }
+
+  /** Gets the threshold type used for binary quantization. */
+  public ThresholdType getThresholdType() {
+    return thresholdType;
+  }
+
+  @Override
+  public CuVSMatrix transform(CuVSMatrix input) throws Throwable {
+    if (input.dataType() != DataType.FLOAT) {
+      throw new IllegalArgumentException(
+          "BinaryQuantizer requires FLOAT input, got " + input.dataType());
+    }
+
+    CuVSMatrix result = CuVSProvider.provider().transformBinaryWithImpl(impl, input);
+
+    if (result.dataType() != DataType.BYTE) {
+      throw new IllegalStateException(
+          "Expected BYTE output from binary quantization, got " + result.dataType());
+    }
+
+    return result;
+  }
+
+  @Override
+  public void train(CuVSMatrix trainingData) throws Throwable {
+    throw new UnsupportedOperationException("Training handled during construction");
+  }
+
+  @Override
+  public CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
+    throw new UnsupportedOperationException(
+        "Binary quantization does not support inverse transformation");
+  }
+
+  @Override
+  public void close() throws Exception {
+    try {
+      CuVSProvider.provider().closeBinaryQuantizer(impl);
+    } catch (Throwable t) {
+      if (t instanceof Exception) {
+        throw (Exception) t;
+      } else {
+        throw new RuntimeException("Error closing BinaryQuantizer", t);
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index e3b885a333..c0032f7ea4 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -247,6 +247,14 @@ interface Builder {
      */
     Builder withIndexParams(CagraIndexParams cagraIndexParameters);
 
+    /**
+     * Sets the quantizer to use for building the index with quantized data.
+     *
+     * @param quantizer The quantizer to apply to the dataset during index construction
+     * @return An instance of this Builder
+     */
+    Builder withQuantizer(CuVSQuantizer quantizer);
+
     /**
      * Builds and returns an instance of CagraIndex.
      *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
index f81ec3191c..c976949bf7 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -15,14 +15,15 @@
  */
 package com.nvidia.cuvs;
 
+import com.nvidia.cuvs.CuVSMatrix.DataType;
 import java.util.Arrays;
 import java.util.BitSet;
 import java.util.Objects;
 import java.util.function.LongToIntFunction;
 
 /**
- * CagraQuery holds the CagraSearchParams and the query vectors to be used while
- * invoking search.
+ * CagraQuery holds the search parameters plus either raw float[][] vectors
+ * or a quantized {@link CuVSMatrix} for querying a CAGRA index.
  *
  * <p><strong>Thread Safety:</strong> Each CagraQuery instance should use its own
  * CuVSResources object that is not shared with other threads. Sharing CuVSResources
@@ -33,8 +34,9 @@
 public class CagraQuery {
 
   private final CagraSearchParams cagraSearchParameters;
-  private final LongToIntFunction mapping;
   private final float[][] queryVectors;
+  private final CuVSMatrix quantizedQueries;
+  private final LongToIntFunction mapping;
   private final int topK;
   private final BitSet prefilter;
   private final int numDocs;
@@ -47,23 +49,25 @@ public class CagraQuery {
    * @param cagraSearchParameters an instance of {@link CagraSearchParams} holding
    *                              the search parameters
    * @param queryVectors          2D float query vector array
+   * @param quantizedQueries      2D quantized query vector array
    * @param mapping               a function mapping ordinals (neighbor IDs) to custom user IDs
    * @param topK                  the top k results to return
    * @param prefilter             A single BitSet to use as filter while searching the CAGRA index
    * @param numDocs               Total number of dataset vectors; used to align the prefilter correctly
    * @param resources             CuVSResources instance to use for this query
    */
-  public CagraQuery(
+  private CagraQuery(
       CagraSearchParams cagraSearchParameters,
       float[][] queryVectors,
+      CuVSMatrix quantizedQueries,
       LongToIntFunction mapping,
       int topK,
       BitSet prefilter,
       int numDocs,
       CuVSResources resources) {
-    super();
     this.cagraSearchParameters = cagraSearchParameters;
     this.queryVectors = queryVectors;
+    this.quantizedQueries = quantizedQueries;
     this.mapping = mapping;
     this.topK = topK;
     this.prefilter = prefilter;
@@ -71,6 +75,11 @@ public CagraQuery(
     this.resources = resources;
   }
 
+  /** Start building a new CagraQuery. */
+  public static Builder newBuilder(CuVSResources resources) {
+    return new Builder(resources);
+  }
+
   /**
    * Gets the instance of CagraSearchParams initially set.
    *
@@ -81,14 +90,35 @@ public CagraSearchParams getCagraSearchParameters() {
   }
 
   /**
-   * Gets the query vector 2D float array.
-   *
-   * @return 2D float array
+   * If this query was built without a quantizer, returns the original float vectors.
+   * Otherwise returns null.
    */
   public float[][] getQueryVectors() {
     return queryVectors;
   }
 
+  /**
+   * If this query was built with a quantizer, returns the quantized Dataset.
+   * Otherwise returns null.
+   */
+  public CuVSMatrix getQuantizedQueries() {
+    return quantizedQueries;
+  }
+
+  /** True if this query carries a quantized Dataset instead of float[][] */
+  public boolean hasQuantizedQueries() {
+    return quantizedQueries != null;
+  }
+
+  /**
+   * Returns the data type of the query payload:
+   * - 32 for float32 queries
+   * - 8 for quantized queries
+   */
+  public DataType getQueryDataType() {
+    return quantizedQueries != null ? quantizedQueries.dataType() : DataType.FLOAT;
+  }
+
   /**
    * Gets the function mapping ordinals (neighbor IDs) to custom user IDs
    */
@@ -134,10 +164,13 @@ public CuVSResources getResources() {
 
   @Override
   public String toString() {
-    return "CuVSQuery [cagraSearchParameters="
+    return "CagraQuery["
+        + "params="
         + cagraSearchParameters
-        + ", queryVectors="
-        + Arrays.toString(queryVectors)
+        + ", floatVectors="
+        + (queryVectors != null ? Arrays.toString(queryVectors) : "null")
+        + ", quantized="
+        + (quantizedQueries != null ? ("Dataset@" + quantizedQueries.dataType() + "-bit") : "false")
         + ", mapping="
         + mapping
         + ", topK="
@@ -156,6 +189,7 @@ public static class Builder {
     private int topK = 2;
     private BitSet prefilter;
     private int numDocs;
+    private CuVSQuantizer quantizer;
     private final CuVSResources resources;
 
     /**
@@ -174,12 +208,12 @@ public Builder(CuVSResources resources) {
     /**
      * Sets the instance of configured CagraSearchParams to be passed for search.
      *
-     * @param cagraSearchParams an instance of the configured CagraSearchParams to
-     *                          be used for this query
+     * @param params an instance of the configured CagraSearchParams to
+     *               be used for this query
      * @return an instance of this Builder
      */
-    public Builder withSearchParams(CagraSearchParams cagraSearchParams) {
-      this.cagraSearchParams = cagraSearchParams;
+    public Builder withSearchParams(CagraSearchParams params) {
+      this.cagraSearchParams = params;
       return this;
     }
 
@@ -233,13 +267,47 @@ public Builder withPrefilter(BitSet prefilter, int numDocs) {
     }
 
     /**
-     * Builds an instance of CuVSQuery.
-     *
-     * @return an instance of CuVSQuery
+     * Specify a quantizer to automatically transform the float[][] queryVectors
+     * into a quantized {@link CuVSMatrix} using the same quantizer used for training.
+     */
+    public Builder withQuantizer(CuVSQuantizer quantizer) {
+      this.quantizer = quantizer;
+      return this;
+    }
+
+    /**
+     * Builds the CagraQuery. If a quantizer was provided, queryVectors is ignored
+     * and a quantized Dataset is produced instead.
      */
-    public CagraQuery build() {
+    public CagraQuery build() throws Throwable {
+      if (queryVectors == null) {
+        throw new IllegalArgumentException("Query vectors must be provided");
+      }
+
+      CuVSMatrix quantized = null;
+      float[][] floatsForQuery = queryVectors;
+
+      if (quantizer != null) {
+        // wrap float[][] in a CuVSMatrix and quantize
+        try (CuVSMatrix tmp = CuVSMatrix.ofArray(queryVectors)) {
+          if (tmp.dataType() != DataType.FLOAT) {
+            throw new IllegalArgumentException(
+                "Query quantization requires FLOAT input, got " + tmp.dataType());
+          }
+          quantized = quantizer.transform(tmp);
+        }
+        floatsForQuery = null;
+      }
+
       return new CagraQuery(
-          cagraSearchParams, queryVectors, mapping, topK, prefilter, numDocs, resources);
+          cagraSearchParams,
+          floatsForQuery,
+          quantized,
+          mapping,
+          topK,
+          prefilter,
+          numDocs,
+          resources);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
index f8e60c5555..5143c8bbc7 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
@@ -69,6 +69,17 @@ static CuVSMatrix ofArray(byte[][] vectors) {
     return CuVSProvider.provider().newMatrixFromArray(vectors);
   }
 
+  /**
+   * Returns the data type of this matrix.
+   *
+   * <p>DataType provides the fundamental type information needed for all operations.
+   * FLOAT represents 32-bit floating point data, INT represents 32-bit integer data,
+   * and BYTE represents 8-bit data (used for quantized values).
+   *
+   * @return The DataType of this matrix
+   */
+  DataType dataType();
+
   interface Builder {
     /**
      * Add a single vector to the dataset.
@@ -152,6 +163,13 @@ static CuVSMatrix.Builder builder(int size, int columns, DataType dataType) {
    */
   void toArray(byte[][] array);
 
+  /**
+   * Gets where the content is stored
+   *
+   * @return MemoryKind whether HOST or DEVICE
+   */
+  CuVSMatrix.MemoryKind memoryKind();
+
   @Override
   void close();
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java
new file mode 100644
index 0000000000..2c86eff0f1
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+
+/**
+ * Base interface for all cuVS quantizers providing unified quantization operations.
+ *
+ */
+public interface CuVSQuantizer extends AutoCloseable {
+
+  /**
+   * Returns the data type of quantized data produced by this quantizer.
+   *
+   * @return The DataType of the quantized output (BYTE for quantized data)
+   */
+  DataType outputDataType();
+
+  /**
+   * Transforms a float32 dataset into a quantized dataset.
+   *
+   * @param input The input dataset of float32 vectors (must have precision() == 32)
+   * @return A new Dataset containing the quantized vectors with this quantizer's precision
+   */
+  CuVSMatrix transform(CuVSMatrix input) throws Throwable;
+
+  /**
+   * Optional training method - only applies to quantizers that require training.
+   *
+   * @param trainingData The dataset to train on (must have precision() == 32)
+   * @throws Throwable if an error occurs during training
+   */
+  default void train(CuVSMatrix trainingData) throws Throwable {
+    throw new UnsupportedOperationException(
+        "Training not supported for " + getClass().getSimpleName());
+  }
+
+  /**
+   * Optional inverse transformation - only applies to quantizers that support reconstruction
+   *
+   * @param quantizedData The quantized dataset to inverse transform
+   *                      (must have precision() matching this quantizer's precision())
+   * @return A float[][] array containing the de-quantized vectors
+   */
+  default CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
+    throw new UnsupportedOperationException(
+        "Inverse transform not supported for " + getClass().getSimpleName());
+  }
+
+  /**
+   * Closes this quantizer and releases any associated resources.
+   *
+   * @throws Exception if an error occurs while closing
+   */
+  @Override
+  void close() throws Exception;
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java
new file mode 100644
index 0000000000..056a7ad468
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+import com.nvidia.cuvs.spi.CuVSProvider;
+
+/**
+ * Scalar 8-bit quantizer implementation that transforms 32-bit float datasets into 8-bit integer datasets.
+ *
+ * <p>This quantizer reduces each float32 value to an 8-bit signed integer, providing significant
+ * compression while maintaining reasonable precision. The quantizer requires training on a representative
+ * dataset to compute optimal quantization parameters (min/max values per dimension).
+ *
+ * <p>The quantizer supports both transformation and inverse transformation, allowing approximate
+ * recovery of the original float32 values from the quantized representation.
+ *
+ * @since 25.08
+ */
+public class Scalar8BitQuantizer implements CuVSQuantizer {
+  private final Object impl;
+
+  /**
+   * The data type used by this quantizer (BYTE for 8-bit signed integers).
+   */
+  private final DataType outputDataType = DataType.BYTE;
+
+  /**
+   * Creates a new scalar 8-bit quantizer with CuVSMatrix training data.
+   *
+   * <p>The training dataset is used to compute quantization parameters (min/max values)
+   * for each dimension. The quantizer will be immediately ready for use after construction.
+   *
+   * @param resources The CuVS resources to use for quantization operations
+   * @param trainingDataset A CuVSMatrix containing training vectors (must have FLOAT data type)
+   * @throws Throwable if an error occurs during quantizer training
+   * @throws IllegalArgumentException if trainingDataset is null or doesn't have FLOAT data type
+   */
+  public Scalar8BitQuantizer(CuVSResources resources, CuVSMatrix trainingDataset) throws Throwable {
+    if (trainingDataset == null) {
+      throw new IllegalArgumentException("Training dataset cannot be null");
+    }
+    if (trainingDataset.dataType() != DataType.FLOAT) {
+      throw new IllegalArgumentException(
+          "Training dataset must have FLOAT data type, got " + trainingDataset.dataType());
+    }
+    this.impl = CuVSProvider.provider().createScalar8BitQuantizerImpl(resources, trainingDataset);
+  }
+
+  /**
+   * Returns the data type of quantized data produced by this quantizer.
+   *
+   * @return The DataType (BYTE for this scalar quantizer)
+   */
+  @Override
+  public DataType outputDataType() {
+    return outputDataType;
+  }
+
+  @Override
+  public CuVSMatrix transform(CuVSMatrix input) throws Throwable {
+    // Validate input data type
+    if (input.dataType() != DataType.FLOAT) {
+      throw new IllegalArgumentException(
+          "Scalar8BitQuantizer requires FLOAT input, got " + input.dataType());
+    }
+
+    CuVSMatrix result = CuVSProvider.provider().transformScalar8Bit(impl, input);
+
+    // Validate output data type
+    if (result.dataType() != DataType.BYTE) {
+      throw new IllegalStateException(
+          "Expected BYTE output from scalar quantization, got " + result.dataType());
+    }
+
+    return result;
+  }
+
+  @Override
+  public void train(CuVSMatrix trainingData) throws Throwable {
+    // Training is handled during construction for ScalarQuantizer
+    // This method could be implemented if you want to support re-training
+    throw new UnsupportedOperationException("Training handled during construction");
+  }
+
+  @Override
+  public CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
+    // Validate input data type for inverse transform
+    if (quantizedData.dataType() != DataType.BYTE) {
+      throw new IllegalArgumentException(
+          "Inverse transform requires BYTE input, got " + quantizedData.dataType());
+    }
+
+    return CuVSProvider.provider().inverseTransformScalar8Bit(impl, quantizedData);
+  }
+
+  @Override
+  public void close() throws Exception {
+    try {
+      CuVSProvider.provider().closeScalar8BitQuantizer(impl);
+    } catch (Throwable t) {
+      if (t instanceof Exception) {
+        throw (Exception) t;
+      } else {
+        // Wrap non-Exception throwables (like Error) in RuntimeException
+        throw new RuntimeException("Error closing Scalar8BitQuantizer", t);
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index e528beff1a..316406b24e 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -15,6 +15,7 @@
  */
 package com.nvidia.cuvs.spi;
 
+import com.nvidia.cuvs.BinaryQuantizer;
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraMergeParams;
@@ -120,6 +121,32 @@ default CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merg
     return mergeCagraIndexes(indexes);
   }
 
+  /** Creates a new Scalar8BitQuantizer implementation. */
+  Object createScalar8BitQuantizerImpl(CuVSResources resources, CuVSMatrix trainingDataset)
+      throws Throwable;
+
+  /** Performs inverse transform using a Scalar8BitQuantizer. */
+  CuVSMatrix inverseTransformScalar8Bit(Object impl, CuVSMatrix quantizedData) throws Throwable;
+
+  /** Transforms dataset using Scalar8BitQuantizer */
+  CuVSMatrix transformScalar8Bit(Object impl, CuVSMatrix input) throws Throwable;
+
+  /** Closes Scalar8BitQuantizer implementation */
+  void closeScalar8BitQuantizer(Object impl) throws Throwable;
+
+  /** Creates a new BinaryQuantizer implementation. */
+  Object createBinaryQuantizerImpl(
+      CuVSResources resources,
+      CuVSMatrix trainingDataset,
+      BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable;
+
+  /** Performs transform using a BinaryQuantizer. */
+  CuVSMatrix transformBinaryWithImpl(Object impl, CuVSMatrix input) throws Throwable;
+
+  /** Closes BinaryQuantizer implementation. */
+  void closeBinaryQuantizer(Object impl) throws Throwable;
+
   /** Retrieves the system-wide provider. */
   static CuVSProvider provider() {
     return CuVSServiceProvider.Holder.INSTANCE;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 8f65bf7068..cd6604663b 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -15,6 +15,7 @@
  */
 package com.nvidia.cuvs.spi;
 
+import com.nvidia.cuvs.BinaryQuantizer;
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CuVSMatrix;
@@ -25,7 +26,8 @@
 import java.nio.file.Path;
 
 /**
- * A provider that unconditionally throws UnsupportedOperationException.
+ * A provider that throws UnsupportedOperationException for all operations.
+ * Used as a fallback when no proper implementation is available.
  */
 final class UnsupportedProvider implements CuVSProvider {
 
@@ -84,4 +86,45 @@ public CuVSMatrix newMatrixFromArray(int[][] vectors) {
   public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     throw new UnsupportedOperationException();
   }
+
+  @Override
+  public Object createScalar8BitQuantizerImpl(CuVSResources resources, CuVSMatrix trainingDataset)
+      throws Throwable {
+    throw new UnsupportedOperationException("CuVS is not supported on this platform");
+  }
+
+  @Override
+  public CuVSMatrix inverseTransformScalar8Bit(Object impl, CuVSMatrix quantizedData)
+      throws Throwable {
+    throw new UnsupportedOperationException("CuVS is not supported on this platform");
+  }
+
+  @Override
+  public Object createBinaryQuantizerImpl(
+      CuVSResources resources,
+      CuVSMatrix trainingDataset,
+      BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable {
+    throw new UnsupportedOperationException("CuVS is not supported on this platform");
+  }
+
+  @Override
+  public CuVSMatrix transformBinaryWithImpl(Object impl, CuVSMatrix input) throws Throwable {
+    throw new UnsupportedOperationException("CuVS is not supported on this platform");
+  }
+
+  @Override
+  public void closeBinaryQuantizer(Object impl) throws Throwable {
+    throw new UnsupportedOperationException("CuVS is not supported on this platform");
+  }
+
+  @Override
+  public CuVSMatrix transformScalar8Bit(Object impl, CuVSMatrix input) throws Throwable {
+    throw new UnsupportedOperationException("CuVS is not supported on this platform");
+  }
+
+  @Override
+  public void closeScalar8BitQuantizer(Object impl) throws Throwable {
+    throw new UnsupportedOperationException("CuVS is not supported on this platform");
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java
new file mode 100644
index 0000000000..704fb45738
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java
@@ -0,0 +1,324 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
+import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
+import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerParamsCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerParamsDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerParams_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerTrain;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerTransformWithParams;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizer_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLCPU;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLCUDA;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLFloat;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLUInt;
+
+import com.nvidia.cuvs.BinaryQuantizer;
+import com.nvidia.cuvs.CuVSMatrix;
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+import com.nvidia.cuvs.CuVSMatrix.MemoryKind;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.internal.common.Util;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.ValueLayout;
+
+/**
+ * Implementation of binary quantizer functionality using Panama Foreign Function Interface.
+ */
+public class BinaryQuantizerImpl {
+
+  public static CuVSMatrix transform(
+      CuVSResources cuvsResources, float[][] dataset, BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable {
+
+    try (var localArena = Arena.ofConfined();
+        var resourcesAccessor = cuvsResources.access()) {
+
+      long rows = dataset.length;
+      long cols = rows > 0 ? dataset[0].length : 0;
+
+      MemorySegment datasetMemSegment = buildMemorySegment(localArena, dataset);
+      long cuvsResourcesPtr = resourcesAccessor.handle();
+
+      return performTransformGPU(
+          cuvsResourcesPtr,
+          localArena,
+          rows,
+          cols,
+          datasetMemSegment,
+          HOST_TO_DEVICE,
+          thresholdType);
+    }
+  }
+
+  public static CuVSMatrix transform(
+      CuVSResources cuvsResources, CuVSMatrix dataset, BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable {
+    if (dataset.dataType() != DataType.FLOAT) {
+      throw new IllegalArgumentException(
+          "BinaryQuantizer requires FLOAT input, got " + dataset.dataType());
+    }
+
+    try (var localArena = Arena.ofConfined();
+        var resourcesAccessor = cuvsResources.access()) {
+
+      long rows = dataset.size();
+      long cols = dataset.columns();
+
+      MemorySegment datasetMemSegment = ((CuVSMatrixBaseImpl) dataset).memorySegment();
+      long cuvsResourcesPtr = resourcesAccessor.handle();
+
+      // TODO: Currently all datasets are HOST memory kind, preventing testing of GPU quantization
+      // flow.
+      // Future improvement: Support GPU-native dataset creation in Java API to enable direct GPU
+      // quantization testing.
+      if (dataset.memoryKind() == MemoryKind.HOST) {
+        return performTransformHost(
+            cuvsResourcesPtr, localArena, rows, cols, datasetMemSegment, thresholdType);
+      } else {
+        CuVSMatrix result =
+            performTransformGPU(
+                cuvsResourcesPtr,
+                localArena,
+                rows,
+                cols,
+                datasetMemSegment,
+                INFER_DIRECTION,
+                thresholdType);
+
+        if (result.dataType() != DataType.BYTE) {
+          throw new IllegalStateException(
+              "Expected BYTE output from binary quantization, got " + result.dataType());
+        }
+        return result;
+      }
+    }
+  }
+
+  private static CuVSMatrix performTransformGPU(
+      long cuvsResourcesPtr,
+      Arena localArena,
+      long rows,
+      long cols,
+      MemorySegment datasetMemSegment,
+      Util.CudaMemcpyKind memcpyDirection,
+      BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable {
+
+    final long BUFFER_SIZE_BYTES = 1024 * 1024;
+    final long FLOAT_SIZE = C_FLOAT_BYTE_SIZE;
+    final long FLOATS_PER_BUFFER = BUFFER_SIZE_BYTES / FLOAT_SIZE;
+    final long ROWS_PER_BUFFER = Math.max(1, FLOATS_PER_BUFFER / cols);
+
+    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
+
+    long maxDatasetBytes = FLOAT_SIZE * ROWS_PER_BUFFER * cols;
+    long maxOutputBytes = ROWS_PER_BUFFER * cols;
+
+    // Allocate quantizer objects once
+    MemorySegment paramsSegment = localArena.allocate(cuvsBinaryQuantizerParams_t);
+    int returnValue = cuvsBinaryQuantizerParamsCreate(paramsSegment);
+    checkCuVSError(returnValue, "cuvsBinaryQuantizerParamsCreate");
+    MemorySegment paramsPtr = paramsSegment.get(C_POINTER, 0);
+    setBinaryQuantizerThreshold(paramsPtr, thresholdType);
+
+    MemorySegment quantizerSegment = localArena.allocate(cuvsBinaryQuantizer_t);
+    returnValue = cuvsBinaryQuantizerCreate(quantizerSegment);
+    checkCuVSError(returnValue, "cuvsBinaryQuantizerCreate");
+    MemorySegment quantizerPtr = quantizerSegment.get(C_POINTER, 0);
+
+    try (var datasetDP = allocateRMMSegment(cuvsResourcesPtr, maxDatasetBytes);
+        var outputDP = allocateRMMSegment(cuvsResourcesPtr, maxOutputBytes)) {
+
+      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
+        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
+        long chunkRows = endRow - startRow;
+        long actualDatasetBytes = FLOAT_SIZE * chunkRows * cols;
+        long actualOutputBytes = chunkRows * cols;
+
+        MemorySegment datasetPtr = datasetDP.handle().asSlice(0, actualDatasetBytes);
+        MemorySegment outputPtr = outputDP.handle().asSlice(0, actualOutputBytes);
+        MemorySegment chunkData =
+            datasetMemSegment.asSlice(startRow * cols * FLOAT_SIZE, actualDatasetBytes);
+
+        cudaMemcpy(datasetPtr, chunkData, actualDatasetBytes, memcpyDirection);
+
+        returnValue = cuvsStreamSync(cuvsResourcesPtr);
+        checkCuVSError(returnValue, "cuvsStreamSync before transform");
+
+        long datasetShape[] = {chunkRows, cols};
+        long outputShape[] = {chunkRows, cols};
+
+        MemorySegment datasetTensor =
+            prepareTensor(localArena, datasetPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+        MemorySegment outputTensor =
+            prepareTensor(localArena, outputPtr, outputShape, kDLUInt(), 8, kDLCUDA(), 1);
+
+        returnValue =
+            cuvsBinaryQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
+        checkCuVSError(returnValue, "cuvsBinaryQuantizerTrain (GPU)");
+
+        returnValue =
+            cuvsBinaryQuantizerTransformWithParams(
+                cuvsResourcesPtr, quantizerPtr, datasetTensor, outputTensor);
+        checkCuVSError(returnValue, "cuvsBinaryQuantizerTransformWithParams (GPU)");
+
+        returnValue = cuvsStreamSync(cuvsResourcesPtr);
+        checkCuVSError(returnValue, "cuvsStreamSync after transform");
+
+        MemorySegment resultChunk =
+            ((CuVSMatrixBaseImpl) result)
+                .memorySegment()
+                .asSlice(startRow * cols, actualOutputBytes);
+        cudaMemcpy(resultChunk, outputPtr, actualOutputBytes, DEVICE_TO_HOST);
+      }
+    } finally {
+      cuvsBinaryQuantizerDestroy(quantizerPtr);
+      cuvsBinaryQuantizerParamsDestroy(paramsPtr);
+    }
+
+    return result;
+  }
+
+  private static CuVSMatrix performTransformHost(
+      long cuvsResourcesPtr,
+      Arena localArena,
+      long rows,
+      long cols,
+      MemorySegment datasetMemSegment,
+      BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable {
+
+    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
+
+    MemorySegment paramsSegment = localArena.allocate(cuvsBinaryQuantizerParams_t);
+    int returnValue = cuvsBinaryQuantizerParamsCreate(paramsSegment);
+    checkCuVSError(returnValue, "cuvsBinaryQuantizerParamsCreate");
+
+    MemorySegment paramsPtr = paramsSegment.get(C_POINTER, 0);
+    setBinaryQuantizerThreshold(paramsPtr, thresholdType);
+
+    MemorySegment quantizerSegment = localArena.allocate(cuvsBinaryQuantizer_t);
+    returnValue = cuvsBinaryQuantizerCreate(quantizerSegment);
+    checkCuVSError(returnValue, "cuvsBinaryQuantizerCreate");
+
+    MemorySegment quantizerPtr = quantizerSegment.get(C_POINTER, 0);
+
+    try {
+      long[] datasetShape = {rows, cols};
+      MemorySegment datasetTensor =
+          prepareTensor(localArena, datasetMemSegment, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
+
+      returnValue =
+          cuvsBinaryQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
+      checkCuVSError(returnValue, "cuvsBinaryQuantizerTrain (host)");
+
+      MemorySegment outputTensor =
+          prepareTensor(
+              localArena,
+              ((CuVSMatrixBaseImpl) result).memorySegment(),
+              datasetShape,
+              kDLUInt(),
+              8,
+              kDLCPU(),
+              1);
+
+      returnValue =
+          cuvsBinaryQuantizerTransformWithParams(
+              cuvsResourcesPtr, quantizerPtr, datasetTensor, outputTensor);
+      checkCuVSError(returnValue, "cuvsBinaryQuantizerTransformWithParams (host)");
+
+      return result;
+
+    } finally {
+      cuvsBinaryQuantizerDestroy(quantizerPtr);
+      cuvsBinaryQuantizerParamsDestroy(paramsPtr);
+    }
+  }
+
+  private static void setBinaryQuantizerThreshold(
+      MemorySegment paramsPtr, BinaryQuantizer.ThresholdType thresholdType) {
+    paramsPtr.set(ValueLayout.JAVA_INT, 0, thresholdType.getValue());
+  }
+
+  /** Creates a new BinaryQuantizer implementation. */
+  public static Object create(
+      CuVSResources resources,
+      CuVSMatrix trainingDataset,
+      BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable {
+    return new BinaryQuantizerImpl(resources, trainingDataset, thresholdType);
+  }
+
+  /** Performs transform using a BinaryQuantizer implementation. */
+  public static CuVSMatrix transformWithImpl(Object impl, CuVSMatrix input) throws Throwable {
+    if (impl instanceof BinaryQuantizerImpl) {
+      return ((BinaryQuantizerImpl) impl).transform(input);
+    } else {
+      throw new IllegalArgumentException("Invalid implementation object");
+    }
+  }
+
+  /** Closes BinaryQuantizer implementation. */
+  public static void close(Object impl) throws Throwable {
+    if (impl instanceof BinaryQuantizerImpl) {
+      ((BinaryQuantizerImpl) impl).close();
+    }
+  }
+
+  private final CuVSResources resources;
+  private final CuVSMatrix trainingDataset;
+  private final BinaryQuantizer.ThresholdType thresholdType;
+  private boolean isClosed = false;
+
+  private BinaryQuantizerImpl(
+      CuVSResources resources,
+      CuVSMatrix trainingDataset,
+      BinaryQuantizer.ThresholdType thresholdType) {
+    this.resources = resources;
+    this.trainingDataset = trainingDataset;
+    this.thresholdType = thresholdType;
+  }
+
+  private CuVSMatrix transform(CuVSMatrix input) throws Throwable {
+    if (isClosed) {
+      throw new IllegalStateException("BinaryQuantizerImpl has been closed");
+    }
+
+    if (input.dataType() != DataType.FLOAT) {
+      throw new IllegalArgumentException(
+          "BinaryQuantizer requires FLOAT input, got " + input.dataType());
+    }
+
+    return transform(resources, input, thresholdType);
+  }
+
+  private void close() throws Throwable {
+    isClosed = true;
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 0d5fc8d90f..b97c65cf6d 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -30,6 +30,7 @@
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
+import com.nvidia.cuvs.BinaryQuantizer;
 import com.nvidia.cuvs.CagraCompressionParams;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
@@ -40,6 +41,8 @@
 import com.nvidia.cuvs.CuVSIvfPqIndexParams;
 import com.nvidia.cuvs.CuVSIvfPqSearchParams;
 import com.nvidia.cuvs.CuVSMatrix;
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+import com.nvidia.cuvs.CuVSQuantizer;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.CloseableHandle;
@@ -81,6 +84,7 @@
 public class CagraIndexImpl implements CagraIndex {
   private final CuVSResources resources;
   private final IndexReference cagraIndexReference;
+  private final CuVSQuantizer indexQuantizer;
   private boolean destroyed;
 
   /**
@@ -95,6 +99,7 @@ private CagraIndexImpl(
       CagraIndexParams indexParameters, CuVSMatrix dataset, CuVSResources resources) {
     Objects.requireNonNull(dataset);
     this.resources = resources;
+    this.indexQuantizer = null;
     assert dataset instanceof CuVSMatrixBaseImpl;
     this.cagraIndexReference = build(indexParameters, (CuVSMatrixBaseImpl) dataset);
   }
@@ -107,6 +112,7 @@ private CagraIndexImpl(
    */
   private CagraIndexImpl(InputStream inputStream, CuVSResources resources) throws Throwable {
     this.resources = resources;
+    this.indexQuantizer = null;
     this.cagraIndexReference = deserialize(inputStream);
   }
 
@@ -119,6 +125,7 @@ private CagraIndexImpl(InputStream inputStream, CuVSResources resources) throws
    */
   private CagraIndexImpl(IndexReference indexReference, CuVSResources resources) {
     this.resources = resources;
+    this.indexQuantizer = null;
     this.cagraIndexReference = indexReference;
     this.destroyed = false;
   }
@@ -135,11 +142,13 @@ private CagraIndexImpl(
       CagraIndexParams.CuvsDistanceType metric,
       CuVSMatrix graph,
       CuVSMatrix dataset,
-      CuVSResources resources) {
+      CuVSResources resources,
+      CuVSQuantizer quantizer) {
     Objects.requireNonNull(graph);
     Objects.requireNonNull(dataset);
 
     this.resources = resources;
+    this.indexQuantizer = quantizer;
 
     assert graph instanceof CuVSMatrixBaseImpl;
     assert dataset instanceof CuVSMatrixBaseImpl;
@@ -181,6 +190,7 @@ public void close() {
   private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImpl dataset) {
     long rows = dataset.size();
     long cols = dataset.columns();
+    DataType datasetDataType = dataset.dataType();
 
     try (var indexParams = segmentFromIndexParams(indexParameters);
         var localArena = Arena.ofConfined()) {
@@ -195,7 +205,14 @@ private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImp
 
       long[] datasetShape = {rows, cols};
       MemorySegment datasetTensor =
-          prepareTensor(localArena, dataSeg, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
+          prepareTensor(
+              localArena,
+              dataSeg,
+              datasetShape,
+              getDataTypeCode(datasetDataType, this.indexQuantizer),
+              getPrecisionFromDataType(datasetDataType),
+              kDLCPU(),
+              1);
 
       var index = createCagraIndex();
 
@@ -256,20 +273,39 @@ private static MemorySegment createCagraIndex() {
   public SearchResults search(CagraQuery query) throws Throwable {
     try (var localArena = Arena.ofConfined()) {
       checkNotDestroyed();
+
+      // Extract query information upfront to avoid "mutually-null" fields
+      long numQueries;
+      int vectorDimension;
+      DataType queryDataType;
+      MemorySegment floatsSeg;
+
+      if (query.hasQuantizedQueries()) {
+        CuVSMatrix quantizedQueries = query.getQuantizedQueries();
+        numQueries = quantizedQueries.size();
+        vectorDimension = (int) quantizedQueries.columns();
+        queryDataType = quantizedQueries.dataType();
+        floatsSeg = ((CuVSMatrixBaseImpl) quantizedQueries).memorySegment();
+      } else {
+        float[][] queryVectors = query.getQueryVectors();
+        numQueries = queryVectors.length;
+        vectorDimension = numQueries > 0 ? queryVectors[0].length : 0;
+        queryDataType = DataType.FLOAT; // Float queries are always FLOAT
+        floatsSeg = buildMemorySegment(localArena, queryVectors);
+      }
+
       int topK = query.getTopK();
-      long numQueries = query.getQueryVectors().length;
+
+      // Calculate bytes based on actual data type
+      long queriesBytes = getQueryBytesSize(numQueries, vectorDimension, queryDataType);
       long numBlocks = topK * numQueries;
-      int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
+      long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
+      long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
 
       SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_INT);
       SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
       MemorySegment neighborsMemorySegment = localArena.allocate(neighborsSequenceLayout);
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
-      MemorySegment floatsSeg = buildMemorySegment(localArena, query.getQueryVectors());
-
-      final long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
-      final long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
-      final long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
       final boolean hasPreFilter = query.getPrefilter() != null;
       final BitSet[] prefilters =
           hasPreFilter ? new BitSet[] {query.getPrefilter()} : EMPTY_PREFILTER_BITSET;
@@ -293,7 +329,13 @@ public SearchResults search(CagraQuery query) throws Throwable {
           long[] queriesShape = {numQueries, vectorDimension};
           MemorySegment queriesTensor =
               prepareTensor(
-                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
+                  localArena,
+                  queriesDP.handle(),
+                  queriesShape,
+                  getDataTypeCode(queryDataType, this.indexQuantizer),
+                  getPrecisionFromDataType(queryDataType),
+                  kDLCUDA(),
+                  1);
           long[] neighborsShape = {numQueries, topK};
           MemorySegment neighborsTensor =
               prepareTensor(
@@ -773,6 +815,51 @@ private static CloseableHandle createMergeParamsSegment(CagraMergeParams mergePa
     return new CompositeCloseableHandle(seg, handles);
   }
 
+  // Helper method to map DataType to data type codes
+  private int getDataTypeCode(DataType dataType, CuVSQuantizer quantizer) {
+    switch (dataType) {
+      case FLOAT:
+        return kDLFloat();
+      case BYTE:
+        if (quantizer instanceof BinaryQuantizer) {
+          return kDLUInt();
+        } else {
+          return kDLInt();
+        }
+      case INT:
+        return kDLInt();
+      default:
+        throw new IllegalArgumentException("Unsupported data type: " + dataType);
+    }
+  }
+
+  // Helper method to convert DataType to precision
+  private int getPrecisionFromDataType(DataType dataType) {
+    switch (dataType) {
+      case FLOAT:
+        return 32;
+      case BYTE:
+        return 8;
+      case INT:
+        return 32;
+      default:
+        throw new IllegalArgumentException("Unsupported data type: " + dataType);
+    }
+  }
+
+  private long getQueryBytesSize(long numQueries, int vectorDimension, DataType dataType) {
+    switch (dataType) {
+      case FLOAT:
+        return C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+      case BYTE:
+        return numQueries * vectorDimension;
+      case INT:
+        return C_INT_BYTE_SIZE * numQueries * vectorDimension;
+      default:
+        throw new IllegalArgumentException("Unsupported query data type: " + dataType);
+    }
+  }
+
   /**
    * Builder helps configure and create an instance of {@link CagraIndex}.
    */
@@ -782,6 +869,7 @@ public static class Builder implements CagraIndex.Builder {
     private CagraIndexParams cagraIndexParams;
     private final CuVSResources cuvsResources;
     private InputStream inputStream;
+    private CuVSQuantizer quantizer;
     private CuVSMatrix graph;
 
     public Builder(CuVSResources cuvsResources) {
@@ -818,11 +906,28 @@ public Builder withIndexParams(CagraIndexParams cagraIndexParameters) {
       return this;
     }
 
+    @Override
+    public Builder withQuantizer(CuVSQuantizer quantizer) {
+      this.quantizer = quantizer;
+      return this;
+    }
+
     @Override
     public CagraIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new CagraIndexImpl(inputStream, cuvsResources);
       } else {
+        if (quantizer != null && dataset != null) {
+          if (dataset.dataType() != DataType.FLOAT) {
+            throw new IllegalArgumentException(
+                "Quantizer input requires FLOAT data type, dataset has " + dataset.dataType());
+          }
+
+          CuVSMatrix quantizedDataset = quantizer.transform(dataset);
+
+          return new CagraIndexImpl(cagraIndexParams, quantizedDataset, cuvsResources);
+        }
+
         if (graph != null) {
           if (cagraIndexParams == null || dataset == null) {
             throw new IllegalArgumentException(
@@ -830,10 +935,9 @@ public CagraIndexImpl build() throws Throwable {
                     + "you must specify the original dataset and the metric used.");
           }
           return new CagraIndexImpl(
-              cagraIndexParams.getCuvsDistanceType(), graph, dataset, cuvsResources);
-        } else {
-          return new CagraIndexImpl(cagraIndexParams, dataset, cuvsResources);
+              cagraIndexParams.getCuvsDistanceType(), graph, dataset, cuvsResources, null);
         }
+        return new CagraIndexImpl(cagraIndexParams, dataset, cuvsResources);
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
index c2895282ca..ffc4efcd95 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -22,6 +22,8 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSHostMatrix;
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+import com.nvidia.cuvs.CuVSMatrix.MemoryKind;
 import com.nvidia.cuvs.RowView;
 import java.lang.foreign.*;
 import java.lang.invoke.VarHandle;
@@ -118,6 +120,11 @@ public void toArray(byte[][] array) {
     }
   }
 
+  @Override
+  public MemoryKind memoryKind() {
+    return MemoryKind.HOST;
+  }
+
   @Override
   public void close() {}
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
index 120960cfc4..fc0461ef22 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -18,6 +18,7 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSMatrix;
+import com.nvidia.cuvs.CuVSMatrix.DataType;
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemorySegment;
 
@@ -49,6 +50,11 @@ public MemorySegment memorySegment() {
     return memorySegment;
   }
 
+  @Override
+  public DataType dataType() {
+    return dataType;
+  }
+
   protected int bits() {
     return switch (dataType) {
       case FLOAT, INT, UINT -> 32;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java
new file mode 100644
index 0000000000..de49005cb2
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java
@@ -0,0 +1,461 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
+import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
+import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerInverseTransform;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerParamsCreate;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerParamsDestroy;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerParams_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerTrain;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerTransform;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizer_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLCPU;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLCUDA;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLFloat;
+import static com.nvidia.cuvs.internal.panama.headers_h.kDLInt;
+
+import com.nvidia.cuvs.CuVSMatrix;
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+import com.nvidia.cuvs.CuVSMatrix.MemoryKind;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.internal.panama.cuvsScalarQuantizerParams;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+import java.util.Objects;
+
+/**
+ * Implementation of Scalar8BitQuantizer using Panama Foreign Function Interface.
+ */
+public class Scalar8BitQuantizerImpl {
+
+  private static final long C_BYTE_SIZE = C_CHAR.byteSize();
+
+  private final CuVSResources resources;
+  private final MemorySegment quantizerSegment;
+  private boolean destroyed;
+
+  private Scalar8BitQuantizerImpl(CuVSResources resources, MemorySegment quantizerSegment) {
+    this.resources = resources;
+    this.quantizerSegment = quantizerSegment;
+  }
+
+  private void checkNotDestroyed() {
+    if (destroyed) {
+      throw new IllegalStateException("Scalar8BitQuantizer has been destroyed");
+    }
+  }
+
+  public void destroy() throws Throwable {
+    checkNotDestroyed();
+    try {
+      int returnValue = cuvsScalarQuantizerDestroy(quantizerSegment);
+      checkCuVSError(returnValue, "cuvsScalarQuantizerDestroy");
+    } finally {
+      destroyed = true;
+    }
+  }
+
+  public void close() throws Throwable {
+    destroy();
+  }
+
+  public CuVSMatrix transform(CuVSMatrix dataset) throws Throwable {
+    checkNotDestroyed();
+
+    if (dataset.dataType() != DataType.FLOAT) {
+      throw new IllegalArgumentException(
+          "Scalar8BitQuantizer requires FLOAT input, got " + dataset.dataType());
+    }
+
+    try (Arena resultArena = Arena.ofShared();
+        var localArena = Arena.ofConfined();
+        var resourcesAccessor = resources.access()) {
+
+      long rows = dataset.size();
+      long cols = dataset.columns();
+
+      MemorySegment datasetMemSegment = ((CuVSMatrixBaseImpl) dataset).memorySegment();
+      long cuvsResourcesPtr = resourcesAccessor.handle();
+
+      // TODO: Currently all datasets are HOST memory kind, preventing testing of GPU quantization
+      // flow.
+      // Future improvement: Support GPU-native dataset creation in Java API to enable direct GPU
+      // quantization testing.
+      if (dataset.memoryKind() == MemoryKind.HOST) {
+        return performTransformHost(
+            cuvsResourcesPtr, quantizerSegment, localArena, rows, cols, datasetMemSegment);
+      }
+
+      return performTransformGPU(
+          cuvsResourcesPtr,
+          quantizerSegment,
+          localArena,
+          resultArena,
+          rows,
+          cols,
+          datasetMemSegment);
+    } catch (Throwable t) {
+      throw t;
+    }
+  }
+
+  /**
+   * GPU transform with buffered processing for large datasets.
+   */
+  private CuVSMatrix performTransformGPU(
+      long cuvsResourcesPtr,
+      MemorySegment quantizerSeg,
+      Arena localArena,
+      Arena resultArena,
+      long rows,
+      long cols,
+      MemorySegment datasetMemSegment)
+      throws Throwable {
+
+    final long BUFFER_SIZE_BYTES = 1024 * 1024;
+    final long FLOAT_SIZE = C_FLOAT_BYTE_SIZE;
+    final long FLOATS_PER_BUFFER = BUFFER_SIZE_BYTES / FLOAT_SIZE;
+    final long ROWS_PER_BUFFER = Math.max(1, FLOATS_PER_BUFFER / cols);
+
+    CuVSMatrix result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
+
+    // Allocate buffers outside the loop (addressing reviewer feedback)
+    long maxDatasetBytes = FLOAT_SIZE * ROWS_PER_BUFFER * cols;
+    long maxOutputBytes = C_BYTE_SIZE * ROWS_PER_BUFFER * cols;
+
+    try (var datasetDP = allocateRMMSegment(cuvsResourcesPtr, maxDatasetBytes);
+        var outputDP = allocateRMMSegment(cuvsResourcesPtr, maxOutputBytes)) {
+
+      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
+        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
+        long chunkRows = endRow - startRow;
+        long actualDatasetBytes = FLOAT_SIZE * chunkRows * cols;
+        long actualOutputBytes = C_BYTE_SIZE * chunkRows * cols;
+
+        // Get slices of pre-allocated buffers
+        MemorySegment datasetPtr = datasetDP.handle().asSlice(0, actualDatasetBytes);
+        MemorySegment outputPtr = outputDP.handle().asSlice(0, actualOutputBytes);
+
+        MemorySegment chunkData =
+            datasetMemSegment.asSlice(startRow * cols * FLOAT_SIZE, actualDatasetBytes);
+
+        cudaMemcpy(datasetPtr, chunkData, actualDatasetBytes, INFER_DIRECTION);
+
+        long datasetShape[] = {chunkRows, cols};
+        MemorySegment datasetTensor =
+            prepareTensor(localArena, datasetPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+        MemorySegment outputTensor =
+            prepareTensor(localArena, outputPtr, datasetShape, kDLInt(), 8, kDLCUDA(), 1);
+
+        int returnValue =
+            cuvsScalarQuantizerTransform(
+                cuvsResourcesPtr, quantizerSeg, datasetTensor, outputTensor);
+        checkCuVSError(returnValue, "cuvsScalarQuantizerTransform");
+
+        returnValue = cuvsStreamSync(cuvsResourcesPtr);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        MemorySegment resultChunk =
+            ((CuVSMatrixBaseImpl) result)
+                .memorySegment()
+                .asSlice(startRow * cols, actualOutputBytes);
+        cudaMemcpy(resultChunk, outputPtr, actualOutputBytes, DEVICE_TO_HOST);
+      }
+    } // CloseableRMMAllocation automatically closes here
+
+    if (result.dataType() != DataType.BYTE) {
+      throw new IllegalStateException(
+          "Expected BYTE output from scalar quantization, got " + result.dataType());
+    }
+
+    return result;
+  }
+
+  private MemorySegment buildMemorySegmentFromBytes(Arena arena, byte[][] data) {
+    long rows = data.length;
+    long cols = rows > 0 ? data[0].length : 0;
+    long totalBytes = rows * cols * C_BYTE_SIZE;
+
+    MemorySegment segment = arena.allocate(totalBytes);
+    for (int i = 0; i < rows; i++) {
+      for (int j = 0; j < cols; j++) {
+        segment.set(C_CHAR, (i * cols + j) * C_BYTE_SIZE, data[i][j]);
+      }
+    }
+    return segment;
+  }
+
+  public static Scalar8BitQuantizerImpl create(CuVSResources resources, CuVSMatrix trainingDataset)
+      throws Throwable {
+    Objects.requireNonNull(resources);
+    Objects.requireNonNull(trainingDataset);
+
+    try (var localArena = Arena.ofConfined();
+        var resourcesAccessor = resources.access()) {
+
+      long rows = trainingDataset.size();
+      long cols = trainingDataset.columns();
+      MemorySegment datasetMemSegment = ((CuVSMatrixBaseImpl) trainingDataset).memorySegment();
+      long cuvsResourcesPtr = resourcesAccessor.handle();
+
+      MemorySegment paramsSegment = localArena.allocate(cuvsScalarQuantizerParams_t);
+      int returnValue = cuvsScalarQuantizerParamsCreate(paramsSegment);
+      checkCuVSError(returnValue, "cuvsScalarQuantizerParamsCreate");
+
+      MemorySegment paramsPtr = paramsSegment.get(C_POINTER, 0);
+      cuvsScalarQuantizerParams.quantile(paramsPtr, 0.99f);
+
+      MemorySegment quantizerSegment = localArena.allocate(cuvsScalarQuantizer_t);
+      returnValue = cuvsScalarQuantizerCreate(quantizerSegment);
+      checkCuVSError(returnValue, "cuvsScalarQuantizerCreate");
+
+      MemorySegment quantizerPtr = quantizerSegment.get(C_POINTER, 0);
+
+      if (trainingDataset.memoryKind() == MemoryKind.HOST) {
+        long datasetShape[] = {rows, cols};
+        MemorySegment datasetTensor =
+            prepareTensor(localArena, datasetMemSegment, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
+
+        returnValue =
+            cuvsScalarQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
+        checkCuVSError(returnValue, "cuvsScalarQuantizerTrain");
+      } else {
+        performGPUTraining(
+            cuvsResourcesPtr, paramsPtr, quantizerPtr, localArena, rows, cols, datasetMemSegment);
+      }
+
+      returnValue = cuvsScalarQuantizerParamsDestroy(paramsPtr);
+      checkCuVSError(returnValue, "cuvsScalarQuantizerParamsDestroy");
+
+      return new Scalar8BitQuantizerImpl(resources, quantizerPtr);
+    }
+  }
+
+  public CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
+    checkNotDestroyed();
+
+    if (quantizedData.dataType() != DataType.BYTE) {
+      throw new IllegalArgumentException(
+          "Inverse transform requires BYTE input, got " + quantizedData.dataType());
+    }
+
+    try (Arena resultArena = Arena.ofShared();
+        var localArena = Arena.ofConfined();
+        var resourcesAccessor = resources.access()) {
+
+      long rows = quantizedData.size();
+      long cols = quantizedData.columns();
+      MemorySegment quantizedMemSegment = ((CuVSMatrixBaseImpl) quantizedData).memorySegment();
+      long cuvsResourcesPtr = resourcesAccessor.handle();
+
+      // TODO: Currently all datasets are HOST memory kind, preventing testing of GPU quantization
+      // flow.
+      // Future improvement: Support GPU-native dataset creation in Java API to enable direct GPU
+      // quantization testing.
+      if (quantizedData.memoryKind() == MemoryKind.HOST) {
+        return performInverseTransformHost(
+            cuvsResourcesPtr, quantizerSegment, localArena, rows, cols, quantizedMemSegment);
+      }
+
+      return performInverseTransformGPU(
+          cuvsResourcesPtr,
+          quantizerSegment,
+          localArena,
+          resultArena,
+          rows,
+          cols,
+          quantizedMemSegment);
+    }
+  }
+
+  private CuVSMatrix performInverseTransformGPU(
+      long cuvsResourcesPtr,
+      MemorySegment quantizerSeg,
+      Arena localArena,
+      Arena resultArena,
+      long rows,
+      long cols,
+      MemorySegment quantizedMemSegment)
+      throws Throwable {
+
+    final long BUFFER_SIZE_BYTES = 1024 * 1024;
+    final long BYTE_SIZE = C_BYTE_SIZE;
+    final long BYTES_PER_BUFFER = BUFFER_SIZE_BYTES;
+    final long ROWS_PER_BUFFER = Math.max(1, BYTES_PER_BUFFER / (cols * BYTE_SIZE));
+
+    CuVSMatrix result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.FLOAT);
+
+    // Allocate buffers outside the loop (addressing reviewer feedback)
+    long maxQuantizedBytes = BYTE_SIZE * ROWS_PER_BUFFER * cols;
+    long maxOutputBytes = C_FLOAT_BYTE_SIZE * ROWS_PER_BUFFER * cols;
+
+    try (var quantizedDP = allocateRMMSegment(cuvsResourcesPtr, maxQuantizedBytes);
+        var outputDP = allocateRMMSegment(cuvsResourcesPtr, maxOutputBytes)) {
+
+      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
+        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
+        long chunkRows = endRow - startRow;
+        long actualQuantizedBytes = BYTE_SIZE * chunkRows * cols;
+        long actualOutputBytes = C_FLOAT_BYTE_SIZE * chunkRows * cols;
+
+        // Get slices of pre-allocated buffers
+        MemorySegment quantizedPtr = quantizedDP.handle().asSlice(0, actualQuantizedBytes);
+        MemorySegment outputPtr = outputDP.handle().asSlice(0, actualOutputBytes);
+
+        MemorySegment chunkData =
+            quantizedMemSegment.asSlice(startRow * cols * BYTE_SIZE, actualQuantizedBytes);
+        cudaMemcpy(quantizedPtr, chunkData, actualQuantizedBytes, INFER_DIRECTION);
+
+        long datasetShape[] = {chunkRows, cols};
+        MemorySegment quantizedTensor =
+            prepareTensor(localArena, quantizedPtr, datasetShape, kDLInt(), 8, kDLCUDA(), 1);
+        MemorySegment outputTensor =
+            prepareTensor(localArena, outputPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+
+        int returnValue =
+            cuvsScalarQuantizerInverseTransform(
+                cuvsResourcesPtr, quantizerSeg, quantizedTensor, outputTensor);
+        checkCuVSError(returnValue, "cuvsScalarQuantizerInverseTransform");
+
+        returnValue = cuvsStreamSync(cuvsResourcesPtr);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+
+        MemorySegment resultChunk =
+            ((CuVSMatrixBaseImpl) result)
+                .memorySegment()
+                .asSlice(startRow * cols * C_FLOAT_BYTE_SIZE, actualOutputBytes);
+        cudaMemcpy(resultChunk, outputPtr, actualOutputBytes, DEVICE_TO_HOST);
+      }
+    } // CloseableRMMAllocation automatically closes here
+
+    return result;
+  }
+
+  private static void performGPUTraining(
+      long cuvsResourcesPtr,
+      MemorySegment paramsPtr,
+      MemorySegment quantizerPtr,
+      Arena localArena,
+      long rows,
+      long cols,
+      MemorySegment datasetMemSegment)
+      throws Throwable {
+
+    final long BUFFER_SIZE_BYTES = 1024 * 1024;
+    final long FLOAT_SIZE = C_FLOAT_BYTE_SIZE;
+    final long FLOATS_PER_BUFFER = BUFFER_SIZE_BYTES / FLOAT_SIZE;
+    final long ROWS_PER_BUFFER = Math.max(1, FLOATS_PER_BUFFER / cols);
+
+    // Allocate buffer outside the loop (addressing reviewer feedback)
+    long maxDatasetBytes = FLOAT_SIZE * ROWS_PER_BUFFER * cols;
+
+    try (var datasetDP = allocateRMMSegment(cuvsResourcesPtr, maxDatasetBytes)) {
+      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
+        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
+        long chunkRows = endRow - startRow;
+        long actualDatasetBytes = FLOAT_SIZE * chunkRows * cols;
+
+        // Get slice of pre-allocated buffer
+        MemorySegment datasetPtr = datasetDP.handle().asSlice(0, actualDatasetBytes);
+
+        MemorySegment chunkData =
+            datasetMemSegment.asSlice(startRow * cols * FLOAT_SIZE, actualDatasetBytes);
+        cudaMemcpy(datasetPtr, chunkData, actualDatasetBytes, INFER_DIRECTION);
+
+        long datasetShape[] = {chunkRows, cols};
+        MemorySegment datasetTensor =
+            prepareTensor(localArena, datasetPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+
+        int returnValue =
+            cuvsScalarQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
+        checkCuVSError(returnValue, "cuvsScalarQuantizerTrain");
+
+        returnValue = cuvsStreamSync(cuvsResourcesPtr);
+        checkCuVSError(returnValue, "cuvsStreamSync");
+      }
+    } // CloseableRMMAllocation automatically closes here
+  }
+
+  private static CuVSMatrix performTransformHost(
+      long resPtr,
+      MemorySegment quantizerSeg,
+      Arena localArena,
+      long rows,
+      long cols,
+      MemorySegment dataSeg)
+      throws Throwable {
+
+    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
+
+    long[] shape = {rows, cols};
+    MemorySegment inTensor = prepareTensor(localArena, dataSeg, shape, kDLFloat(), 32, kDLCPU(), 1);
+    MemorySegment outTensor =
+        prepareTensor(
+            localArena,
+            ((CuVSMatrixBaseImpl) result).memorySegment(),
+            shape,
+            kDLInt(),
+            8,
+            kDLCPU(),
+            1);
+
+    int rv = cuvsScalarQuantizerTransform(resPtr, quantizerSeg, inTensor, outTensor);
+    checkCuVSError(rv, "cuvsScalarQuantizerTransform (host)");
+
+    return result;
+  }
+
+  private static CuVSMatrix performInverseTransformHost(
+      long resPtr,
+      MemorySegment quantizerSeg,
+      Arena localArena,
+      long rows,
+      long cols,
+      MemorySegment quantizedSeg)
+      throws Throwable {
+
+    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.FLOAT);
+
+    long[] shape = {rows, cols};
+    MemorySegment inTensor =
+        prepareTensor(localArena, quantizedSeg, shape, kDLInt(), 8, kDLCPU(), 1);
+    MemorySegment outTensor =
+        prepareTensor(
+            localArena,
+            ((CuVSMatrixBaseImpl) result).memorySegment(),
+            shape,
+            kDLFloat(),
+            32,
+            kDLCPU(),
+            1);
+
+    int rv = cuvsScalarQuantizerInverseTransform(resPtr, quantizerSeg, inTensor, outTensor);
+    checkCuVSError(rv, "cuvsScalarQuantizerInverseTransform (host)");
+
+    return result;
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 668befe236..0c29188456 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -188,4 +188,45 @@ public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     Util.copy(dataset.memorySegment(), vectors);
     return dataset;
   }
+
+  @Override
+  public Object createScalar8BitQuantizerImpl(CuVSResources resources, CuVSMatrix trainingDataset)
+      throws Throwable {
+    return Scalar8BitQuantizerImpl.create(resources, trainingDataset);
+  }
+
+  @Override
+  public CuVSMatrix transformScalar8Bit(Object impl, CuVSMatrix input) throws Throwable {
+    return ((Scalar8BitQuantizerImpl) impl).transform(input);
+  }
+
+  @Override
+  public CuVSMatrix inverseTransformScalar8Bit(Object impl, CuVSMatrix quantizedData)
+      throws Throwable {
+    return ((Scalar8BitQuantizerImpl) impl).inverseTransform(quantizedData);
+  }
+
+  @Override
+  public Object createBinaryQuantizerImpl(
+      CuVSResources resources,
+      CuVSMatrix trainingDataset,
+      BinaryQuantizer.ThresholdType thresholdType)
+      throws Throwable {
+    return BinaryQuantizerImpl.create(resources, trainingDataset, thresholdType);
+  }
+
+  @Override
+  public CuVSMatrix transformBinaryWithImpl(Object impl, CuVSMatrix input) throws Throwable {
+    return BinaryQuantizerImpl.transformWithImpl(impl, input);
+  }
+
+  @Override
+  public void closeBinaryQuantizer(Object impl) throws Throwable {
+    BinaryQuantizerImpl.close(impl);
+  }
+
+  @Override
+  public void closeScalar8BitQuantizer(Object impl) throws Throwable {
+    ((Scalar8BitQuantizerImpl) impl).close();
+  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java
new file mode 100644
index 0000000000..8189fd9139
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java
@@ -0,0 +1,588 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static org.junit.Assert.*;
+
+import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
+import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
+import com.nvidia.cuvs.CuVSMatrix.DataType;
+import java.lang.invoke.MethodHandles;
+import java.util.Map;
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+@RunWith(RandomizedRunner.class)
+public class QuantizationIT extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+    initializeRandom();
+    log.info("Random context initialized for quantization test.");
+  }
+
+  private static float[][] createSimpleDataset() {
+    return new float[][] {
+      {1.0f, 2.0f, 3.0f},
+      {4.0f, 5.0f, 6.0f},
+      {7.0f, 8.0f, 9.0f},
+      {10.0f, 11.0f, 12.0f},
+      {13.0f, 14.0f, 15.0f}
+    };
+  }
+
+  private static float[][] createSimpleQueries() {
+    return new float[][] {
+      {1.1f, 2.1f, 3.1f},
+      {7.1f, 8.1f, 9.1f}
+    };
+  }
+
+  @Test
+  public void testScalarQuantizerBasicFlow() throws Throwable {
+    float[][] dataset = createSimpleDataset();
+    float[][] queries = createSimpleQueries();
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
+      assertEquals(DataType.FLOAT, trainingDataset.dataType());
+      assertEquals(dataset.length, trainingDataset.size());
+      assertEquals(dataset[0].length, trainingDataset.columns());
+
+      Scalar8BitQuantizer quantizer = new Scalar8BitQuantizer(resources, trainingDataset);
+      assertEquals(DataType.BYTE, quantizer.outputDataType());
+      log.info("Created scalar quantizer with BYTE data type");
+
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(2)
+              .withIntermediateGraphDegree(3)
+              .withNumWriterThreads(1)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(trainingDataset)
+              .withQuantizer(quantizer)
+              .withIndexParams(indexParams)
+              .build();
+
+      log.info("Built index with quantized dataset");
+
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+
+      CagraQuery query =
+          CagraQuery.newBuilder(resources)
+              .withQueryVectors(queries)
+              .withQuantizer(quantizer)
+              .withSearchParams(searchParams)
+              .withTopK(3)
+              .build();
+
+      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
+      assertEquals(DataType.BYTE, query.getQueryDataType());
+      log.info("Created quantized query");
+
+      // Perform search
+      SearchResults results = index.search(query);
+      assertNotNull("Search results should not be null", results);
+
+      // Verify results
+      assertEquals(
+          "Should have results for all queries", queries.length, results.getResults().size());
+
+      for (int i = 0; i < results.getResults().size(); i++) {
+        Map<Integer, Float> queryResults = results.getResults().get(i);
+        assertFalse("Query " + i + " should return some neighbors", queryResults.isEmpty());
+
+        // Verify all returned IDs are within valid range
+        for (Integer id : queryResults.keySet()) {
+          assertTrue("Returned ID should be valid", id >= 0 && id < dataset.length);
+        }
+      }
+
+      log.info(
+          "Search completed successfully with {} queries returning neighbors",
+          results.getResults().size());
+
+      // Cleanup
+      index.close();
+      quantizer.close();
+      trainingDataset.close();
+    }
+  }
+
+  @Test
+  public void testBinaryQuantizerBasicFlow() throws Throwable {
+    float[][] dataset = createSimpleDataset();
+    float[][] queries = createSimpleQueries();
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
+      assertEquals(DataType.FLOAT, trainingDataset.dataType());
+
+      BinaryQuantizer quantizer = new BinaryQuantizer(resources, trainingDataset);
+      assertEquals(DataType.BYTE, quantizer.outputDataType());
+      log.info("Created binary quantizer");
+
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(2)
+              .withIntermediateGraphDegree(3)
+              .withNumWriterThreads(1)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(trainingDataset)
+              .withQuantizer(quantizer)
+              .withIndexParams(indexParams)
+              .build();
+
+      log.info("Built index with binary quantized dataset");
+
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+
+      CagraQuery query =
+          CagraQuery.newBuilder(resources)
+              .withQueryVectors(queries)
+              .withQuantizer(quantizer)
+              .withSearchParams(searchParams)
+              .withTopK(3)
+              .build();
+
+      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
+      assertEquals(DataType.BYTE, query.getQueryDataType());
+
+      // Perform search
+      SearchResults results = index.search(query);
+      assertNotNull("Search results should not be null", results);
+
+      // Verify results
+      assertEquals(
+          "Should have results for all queries", queries.length, results.getResults().size());
+
+      for (int i = 0; i < results.getResults().size(); i++) {
+        Map<Integer, Float> queryResults = results.getResults().get(i);
+        assertFalse("Query " + i + " should return some neighbors", queryResults.isEmpty());
+
+        // Verify all returned IDs are within valid range
+        for (Integer id : queryResults.keySet()) {
+          assertTrue("Returned ID should be valid", id >= 0 && id < dataset.length);
+        }
+      }
+
+      log.info("Binary quantized search completed successfully");
+
+      CuVSMatrix quantizedQueries = query.getQuantizedQueries();
+      try {
+        quantizer.inverseTransform(quantizedQueries);
+        fail("Expected UnsupportedOperationException to be thrown");
+      } catch (UnsupportedOperationException e) {
+        assertTrue(
+            "Exception message should mention inverse transform",
+            e.getMessage().contains("inverse"));
+      }
+
+      // Cleanup
+      index.close();
+      quantizer.close();
+      trainingDataset.close();
+      quantizedQueries.close();
+    }
+  }
+
+  @Test
+  public void testScalarQuantizerInverseTransform() throws Throwable {
+    float[][] dataset = createSimpleDataset();
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
+      Scalar8BitQuantizer quantizer = new Scalar8BitQuantizer(resources, trainingDataset);
+
+      // Transform and inverse transform
+      CuVSMatrix quantized = quantizer.transform(trainingDataset);
+      assertEquals(DataType.BYTE, quantized.dataType());
+
+      CuVSMatrix recovered = quantizer.inverseTransform(quantized);
+      assertEquals(DataType.FLOAT, recovered.dataType());
+      assertEquals(trainingDataset.size(), recovered.size());
+      assertEquals(trainingDataset.columns(), recovered.columns());
+
+      log.info("Inverse transform completed successfully");
+
+      // Verify quantization worked
+      assertEquals(DataType.BYTE, quantized.dataType());
+      assertEquals(DataType.FLOAT, recovered.dataType());
+
+      // Cleanup
+      quantizer.close();
+      trainingDataset.close();
+      quantized.close();
+      recovered.close();
+    }
+  }
+
+  @Test
+  public void testCPUBinaryQuantization() throws Throwable {
+    float[][] dataset = createSimpleDataset();
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      CuVSMatrix inputDataset = CuVSMatrix.ofArray(dataset);
+      assertEquals(CuVSMatrix.MemoryKind.HOST, inputDataset.memoryKind());
+
+      BinaryQuantizer quantizer = new BinaryQuantizer(resources, inputDataset);
+
+      CuVSMatrix quantized = quantizer.transform(inputDataset);
+      assertEquals(DataType.BYTE, quantized.dataType());
+      assertEquals(CuVSMatrix.MemoryKind.HOST, quantized.memoryKind());
+
+      try {
+        quantizer.inverseTransform(quantized);
+        fail("Expected UnsupportedOperationException to be thrown");
+      } catch (UnsupportedOperationException e) {
+        assertTrue(
+            "Exception message should mention inverse transform",
+            e.getMessage().contains("inverse"));
+      }
+
+      quantizer.close();
+      inputDataset.close();
+      quantized.close();
+
+      log.info("✓ Binary CPU quantization test passed");
+    }
+  }
+
+  @Test
+  public void testRandomizedScalarQuantizedIndexingAndSearch() throws Throwable {
+    int maxDatasetSize = 60;
+    int maxDimensions = 20;
+    int maxQueries = 5;
+    int maxTopK = 4;
+
+    for (int iteration = 0; iteration < 5; iteration++) {
+      int datasetSize = random.nextInt(maxDatasetSize) + maxTopK;
+      int dims = random.nextInt(maxDimensions) + 2;
+      int numQueries = random.nextInt(maxQueries) + 1;
+      int topK = Math.min(random.nextInt(maxTopK) + 1, datasetSize);
+
+      // Generate dataset and query
+      float[][] dataset = new float[datasetSize][dims];
+      float[][] queries = new float[numQueries][dims];
+      for (int i = 0; i < datasetSize; i++)
+        for (int d = 0; d < dims; d++) dataset[i][d] = (float) random.nextGaussian();
+      for (int i = 0; i < numQueries; i++)
+        for (int d = 0; d < dims; d++) queries[i][d] = (float) random.nextGaussian();
+
+      try (CuVSResources resources = CheckedCuVSResources.create()) {
+        CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
+        Scalar8BitQuantizer quantizer = new Scalar8BitQuantizer(resources, trainingDataset);
+
+        CagraIndexParams indexParams =
+            new CagraIndexParams.Builder()
+                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+                .withGraphDegree(Math.min(4, datasetSize - 1))
+                .withIntermediateGraphDegree(Math.min(5, datasetSize))
+                .withNumWriterThreads(1)
+                .withMetric(CuvsDistanceType.L2Expanded)
+                .build();
+
+        CagraIndex index =
+            CagraIndex.newBuilder(resources)
+                .withDataset(trainingDataset)
+                .withQuantizer(quantizer)
+                .withIndexParams(indexParams)
+                .build();
+
+        CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+        CagraQuery query =
+            CagraQuery.newBuilder(resources)
+                .withQueryVectors(queries)
+                .withQuantizer(quantizer)
+                .withSearchParams(searchParams)
+                .withTopK(topK)
+                .build();
+
+        SearchResults results = index.search(query);
+        assertNotNull("Results must not be null", results);
+        assertEquals("One result per query", numQueries, results.getResults().size());
+
+        for (int qi = 0; qi < numQueries; qi++) {
+          Map<Integer, Float> result = results.getResults().get(qi);
+          // It is possible for result to have < topK entries if datasetSize == topK
+          assertFalse("Each query should return non-empty result", result.isEmpty());
+          for (Integer id : result.keySet()) {
+            assertTrue("ID in result should be in range", id >= 0 && id < datasetSize);
+          }
+        }
+
+        quantizer.close();
+        trainingDataset.close();
+        index.close();
+      }
+    }
+  }
+
+  @Test
+  public void testBinaryQuantizationWithZeroThreshold() throws Throwable {
+    float[][] dataset =
+        new float[][] {
+          {-2.0f, -1.0f, 0.0f, 1.0f, 2.0f}, // crosses zero
+          {0.1f, 0.5f, 1.0f, 1.5f, 2.0f}, // all positive
+          {-2.0f, -1.5f, -1.0f, -0.5f, 0.0f}, // mostly negative
+          {-1.0f, 0.0f, 1.0f, 2.0f, 3.0f} // mixed
+        };
+
+    float[][] queries =
+        new float[][] {
+          {0.5f, 0.5f, 0.5f, 0.5f, 0.5f},
+          {-0.5f, -0.5f, -0.5f, -0.5f, -0.5f}
+        };
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
+
+      BinaryQuantizer quantizer =
+          new BinaryQuantizer(resources, trainingDataset, BinaryQuantizer.ThresholdType.ZERO);
+      assertEquals(DataType.BYTE, quantizer.outputDataType());
+      assertEquals(BinaryQuantizer.ThresholdType.ZERO, quantizer.getThresholdType());
+
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(2)
+              .withIntermediateGraphDegree(3)
+              .withNumWriterThreads(1)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(trainingDataset)
+              .withQuantizer(quantizer)
+              .withIndexParams(indexParams)
+              .build();
+
+      log.info("Built index with ZERO threshold binary quantization");
+
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+      CagraQuery query =
+          CagraQuery.newBuilder(resources)
+              .withQueryVectors(queries)
+              .withQuantizer(quantizer)
+              .withSearchParams(searchParams)
+              .withTopK(2)
+              .build();
+
+      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
+      assertEquals(DataType.BYTE, query.getQueryDataType());
+
+      SearchResults results = index.search(query);
+      assertNotNull("Search results should not be null", results);
+      assertEquals(
+          "Should have results for all queries", queries.length, results.getResults().size());
+
+      CuVSMatrix quantizedDataset = quantizer.transform(trainingDataset);
+      byte[][] result = new byte[(int) quantizedDataset.size()][(int) quantizedDataset.columns()];
+      quantizedDataset.toArray(result);
+
+      byte firstByte = result[0][0];
+      int[] unpackedBits = new int[5];
+      for (int i = 0; i < 5; i++) {
+        unpackedBits[i] = (firstByte >> i) & 1;
+      }
+
+      int[] expectedRow0 = {0, 0, 0, 1, 1};
+      assertArrayEquals("ZERO threshold failed for row 0", expectedRow0, unpackedBits);
+
+      index.close();
+      quantizer.close();
+      trainingDataset.close();
+      quantizedDataset.close();
+
+      log.info("✓ ZERO threshold binary quantization test passed");
+    }
+  }
+
+  @Test
+  public void testBinaryQuantizationWithMeanThreshold() throws Throwable {
+    float[][] dataset =
+        new float[][] {
+          {1.0f, 2.0f, 3.0f, 4.0f, 5.0f}, // mean = 3.0
+          {0.0f, 1.0f, 2.0f, 3.0f, 4.0f}, // mean = 2.0
+          {2.0f, 4.0f, 6.0f, 8.0f, 10.0f}, // mean = 6.0
+          {-1.0f, 0.0f, 1.0f, 2.0f, 3.0f} // mean = 1.0
+        };
+
+    float[][] queries =
+        new float[][] {
+          {2.5f, 2.5f, 2.5f, 2.5f, 2.5f},
+          {1.0f, 3.0f, 5.0f, 7.0f, 9.0f}
+        };
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
+
+      BinaryQuantizer quantizer =
+          new BinaryQuantizer(resources, trainingDataset, BinaryQuantizer.ThresholdType.MEAN);
+      assertEquals(BinaryQuantizer.ThresholdType.MEAN, quantizer.getThresholdType());
+
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(2)
+              .withIntermediateGraphDegree(3)
+              .withNumWriterThreads(1)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(trainingDataset)
+              .withQuantizer(quantizer)
+              .withIndexParams(indexParams)
+              .build();
+
+      log.info("Built index with MEAN threshold binary quantization");
+
+      // Perform search
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+      CagraQuery query =
+          CagraQuery.newBuilder(resources)
+              .withQueryVectors(queries)
+              .withQuantizer(quantizer)
+              .withSearchParams(searchParams)
+              .withTopK(2)
+              .build();
+
+      SearchResults results = index.search(query);
+      assertNotNull("Search results should not be null", results);
+      assertEquals(
+          "Should have results for all queries", queries.length, results.getResults().size());
+
+      // Calculate overall mean: (3.0 + 2.0 + 6.0 + 1.0) / 4 = 3.0
+      // Verify quantization behavior - values > mean should become 1, values <= mean should become
+      // 0
+      CuVSMatrix quantizedDataset = quantizer.transform(trainingDataset);
+      byte[][] result1 = new byte[(int) quantizedDataset.size()][(int) quantizedDataset.columns()];
+
+      // Copy data from CuVSMatrix to array
+      quantizedDataset.toArray(result1);
+
+      // Verify that MEAN threshold produces different results than ZERO threshold
+      BinaryQuantizer zeroQuantizer =
+          new BinaryQuantizer(resources, trainingDataset, BinaryQuantizer.ThresholdType.ZERO);
+      CuVSMatrix zeroQuantized = zeroQuantizer.transform(trainingDataset);
+      byte[][] zeroData = new byte[(int) zeroQuantized.size()][(int) zeroQuantized.columns()];
+      zeroQuantized.toArray(zeroData);
+
+      boolean isDifferent = false;
+      for (int i = 0; i < result1.length && !isDifferent; i++) {
+        for (int j = 0; j < result1[i].length && !isDifferent; j++) {
+          if (result1[i][j] != zeroData[i][j]) {
+            isDifferent = true;
+          }
+        }
+      }
+      assertTrue("MEAN and ZERO thresholds should produce different results", isDifferent);
+
+      // Cleanup
+      index.close();
+      quantizer.close();
+      zeroQuantizer.close();
+      trainingDataset.close();
+      quantizedDataset.close();
+      zeroQuantized.close();
+
+      log.info("✓ MEAN threshold binary quantization test passed");
+    }
+  }
+
+  @Test
+  public void testBinaryQuantizationWithSamplingMedianThreshold() throws Throwable {
+    float[][] dataset = createSimpleDataset();
+    float[][] queries = createSimpleQueries();
+
+    try (CuVSResources resources = CheckedCuVSResources.create()) {
+      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
+      assertEquals(DataType.FLOAT, trainingDataset.dataType());
+
+      BinaryQuantizer quantizer =
+          new BinaryQuantizer(
+              resources, trainingDataset, BinaryQuantizer.ThresholdType.SAMPLING_MEDIAN);
+      assertEquals(DataType.BYTE, quantizer.outputDataType());
+      log.info("Created binary quantizer with sampling median threshold");
+
+      CagraIndexParams indexParams =
+          new CagraIndexParams.Builder()
+              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
+              .withGraphDegree(2)
+              .withIntermediateGraphDegree(3)
+              .withNumWriterThreads(1)
+              .withMetric(CuvsDistanceType.L2Expanded)
+              .build();
+
+      CagraIndex index =
+          CagraIndex.newBuilder(resources)
+              .withDataset(trainingDataset)
+              .withQuantizer(quantizer)
+              .withIndexParams(indexParams)
+              .build();
+
+      log.info("Built index with binary quantized dataset");
+
+      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+
+      CagraQuery query =
+          CagraQuery.newBuilder(resources)
+              .withQueryVectors(queries)
+              .withQuantizer(quantizer)
+              .withSearchParams(searchParams)
+              .withTopK(3)
+              .build();
+
+      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
+      assertEquals(DataType.BYTE, query.getQueryDataType());
+
+      SearchResults results = index.search(query);
+      assertNotNull("Search results should not be null", results);
+
+      assertEquals(
+          "Should return results for each query", queries.length, results.getResults().size());
+
+      for (int i = 0; i < results.getResults().size(); i++) {
+        Map<Integer, Float> result = results.getResults().get(i);
+        assertFalse("Each query should return non-empty result", result.isEmpty());
+        assertTrue("Should return at most topK results", result.size() <= 3);
+      }
+
+      log.info("✓ Binary quantization with sampling median threshold test passed");
+    }
+  }
+}
diff --git a/java/panama-bindings/headers.h b/java/panama-bindings/headers.h
index 57a13cf1b1..ba4cf8c203 100644
--- a/java/panama-bindings/headers.h
+++ b/java/panama-bindings/headers.h
@@ -22,6 +22,8 @@
 #include <cuvs/neighbors/hnsw.h>
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/common.h>
+#include <cuvs/preprocessing/quantize/binary.h>
+#include <cuvs/preprocessing/quantize/scalar.h>
 #include <cuvs/neighbors/tiered_index.h>
 #include <cuda_runtime.h>
 

From 3f953364fb7aa6162adafbd70070c1fd39e6f80f Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Thu, 21 Aug 2025 21:40:07 -0700
Subject: [PATCH 553/623] Add rust bindings for kmeans (#1266)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1266
---
 rust/cuvs-sys/cuvs_c_wrapper.h         |   3 +-
 rust/cuvs/src/cluster/kmeans/mod.rs    | 212 +++++++++++++++++++++++++
 rust/cuvs/src/cluster/kmeans/params.rs | 152 ++++++++++++++++++
 rust/cuvs/src/cluster/mod.rs           |  17 ++
 rust/cuvs/src/dlpack.rs                |   8 +-
 rust/cuvs/src/lib.rs                   |   1 +
 6 files changed, 390 insertions(+), 3 deletions(-)
 create mode 100644 rust/cuvs/src/cluster/kmeans/mod.rs
 create mode 100644 rust/cuvs/src/cluster/kmeans/params.rs
 create mode 100644 rust/cuvs/src/cluster/mod.rs

diff --git a/rust/cuvs-sys/cuvs_c_wrapper.h b/rust/cuvs-sys/cuvs_c_wrapper.h
index 0c66928bcf..aeac04f54e 100644
--- a/rust/cuvs-sys/cuvs_c_wrapper.h
+++ b/rust/cuvs-sys/cuvs_c_wrapper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,6 +17,7 @@
 // wrapper file containing all the C-API's we should automatically be creating rust
 // bindings for
 #include <cuvs/core/c_api.h>
+#include <cuvs/cluster/kmeans.h>
 #include <cuvs/distance/pairwise_distance.h>
 #include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/ivf_flat.h>
diff --git a/rust/cuvs/src/cluster/kmeans/mod.rs b/rust/cuvs/src/cluster/kmeans/mod.rs
new file mode 100644
index 0000000000..2ebac1cdf6
--- /dev/null
+++ b/rust/cuvs/src/cluster/kmeans/mod.rs
@@ -0,0 +1,212 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//! Kmeans clustering API's
+//!
+//! Example:
+//! ```
+//!
+//! use cuvs::cluster::kmeans;
+//! use cuvs::{ManagedTensor, Resources, Result};
+//!
+//! use ndarray_rand::rand_distr::Uniform;
+//! use ndarray_rand::RandomExt;
+//!
+//! fn kmeans_example() -> Result<()> {
+//!     let res = Resources::new()?;
+//!
+//!     // Create a new random dataset to index
+//!     let n_datapoints = 65536;
+//!     let n_features = 512;
+//!     let n_clusters = 8;
+//!     let dataset =
+//!         ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+//!     let dataset = ManagedTensor::from(&dataset).to_device(&res)?;
+//!
+//!     let centroids_host = ndarray::Array::<f32, _>::zeros((n_clusters, n_features));
+//!     let mut centroids = ManagedTensor::from(&centroids_host).to_device(&res)?;
+//!
+//!     // find the centroids with the kmeans index
+//!     let kmeans_params = kmeans::Params::new()?.set_n_clusters(n_clusters as i32);
+//!     let (inertia, n_iter) = kmeans::fit(&res, &kmeans_params, &dataset, &None, &mut centroids)?;
+//!     Ok(())
+//! }
+//! ```
+
+mod params;
+
+pub use params::Params;
+
+use crate::dlpack::ManagedTensor;
+use crate::error::{check_cuvs, Result};
+use crate::resources::Resources;
+
+/// Find clusters with the k-means algorithm
+///
+/// # Arguments
+///
+/// * `res` - Resources to use
+/// * `params` - Parameters to use to fit KMeans model
+/// * `x` - A matrix in device memory - shape (m, k)
+/// * `sample_weight` - Optional device matrix shape (n_clusters, 1)
+/// * `centroids` - Output device matrix, that has the centroids for each cluster
+///   shape (n_clusters, k)
+pub fn fit(
+    res: &Resources,
+    params: &Params,
+    x: &ManagedTensor,
+    sample_weight: &Option<ManagedTensor>,
+    centroids: &mut ManagedTensor,
+) -> Result<(f64, i32)> {
+    let mut inertia: f64 = 0.0;
+    let mut niter: i32 = 0;
+
+    unsafe {
+        let sample_weight_dlpack = match sample_weight {
+            Some(tensor) => tensor.as_ptr(),
+            None => std::ptr::null_mut(),
+        };
+        check_cuvs(ffi::cuvsKMeansFit(
+            res.0,
+            params.0,
+            x.as_ptr(),
+            sample_weight_dlpack,
+            centroids.as_ptr(),
+            &mut inertia as *mut f64,
+            &mut niter as *mut i32,
+        ))?;
+    }
+    Ok((inertia, niter))
+}
+
+/// Predict clusters with the k-means algorithm
+///
+/// # Arguments
+///
+/// * `res` - Resources to use
+/// * `params` - Parameters to use to fit KMeans model
+/// * `x` - Input matrix in device memory - shape (m, k)
+/// * `sample_weight` - Optional device matrix shape (n_clusters, 1)
+/// * `centroids` - Centroids calculated by fit in device memory, shape (n_clusters, k)
+/// * `labels` - preallocated CUDA array interface matrix shape (m, 1) to hold the output labels
+/// * `normalize_weight` - whether or not to normalize the weights
+pub fn predict(
+    res: &Resources,
+    params: &Params,
+    x: &ManagedTensor,
+    sample_weight: &Option<ManagedTensor>,
+    centroids: &ManagedTensor,
+    labels: &mut ManagedTensor,
+    normalize_weight: bool,
+) -> Result<f64> {
+    let mut inertia: f64 = 0.0;
+
+    unsafe {
+        let sample_weight_dlpack = match sample_weight {
+            Some(tensor) => tensor.as_ptr(),
+            None => std::ptr::null_mut(),
+        };
+        check_cuvs(ffi::cuvsKMeansPredict(
+            res.0,
+            params.0,
+            x.as_ptr(),
+            sample_weight_dlpack,
+            centroids.as_ptr(),
+            labels.as_ptr(),
+            normalize_weight,
+            &mut inertia as *mut f64,
+        ))?;
+    }
+    Ok(inertia)
+}
+
+/// Compute cluster cost given an input matrix and existing centroids
+/// # Arguments
+///
+/// * `res` - Resources to use
+/// * `x` - Input matrix in device memory - shape (m, k)
+/// * `centroids` - Centroids calculated by fit in device memory, shape (n_clusters, k)
+pub fn cluster_cost(res: &Resources, x: &ManagedTensor, centroids: &ManagedTensor) -> Result<f64> {
+    let mut inertia: f64 = 0.0;
+
+    unsafe {
+        check_cuvs(ffi::cuvsKMeansClusterCost(
+            res.0,
+            x.as_ptr(),
+            centroids.as_ptr(),
+            &mut inertia as *mut f64,
+        ))?;
+    }
+    Ok(inertia)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray_rand::rand_distr::Uniform;
+    use ndarray_rand::RandomExt;
+
+    #[test]
+    fn test_kmeans() {
+        let res = Resources::new().unwrap();
+
+        let n_clusters = 4;
+
+        // Create a new random dataset to index
+        let n_datapoints = 256;
+        let n_features = 16;
+        let dataset =
+            ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
+        let dataset = ManagedTensor::from(&dataset).to_device(&res).unwrap();
+
+        let centroids_host = ndarray::Array::<f32, _>::zeros((n_clusters, n_features));
+        let mut centroids = ManagedTensor::from(&centroids_host)
+            .to_device(&res)
+            .unwrap();
+
+        let params = Params::new().unwrap().set_n_clusters(n_clusters as i32);
+
+        // compute the inertia, before fitting centroids
+        let original_inertia = cluster_cost(&res, &dataset, &centroids).unwrap();
+
+        // fit the centroids, make sure that inertia has gone down
+        let (inertia, n_iter) = fit(&res, &params, &dataset, &None, &mut centroids).unwrap();
+
+        assert!(inertia < original_inertia);
+        assert!(n_iter >= 1);
+
+        let mut labels_host = ndarray::Array::<i32, _>::zeros((n_clusters,));
+        let mut labels = ManagedTensor::from(&labels_host).to_device(&res).unwrap();
+
+        // make sure the prediction for each centroid is the centroid itself
+        predict(
+            &res,
+            &params,
+            &centroids,
+            &None,
+            &centroids,
+            &mut labels,
+            false,
+        )
+        .unwrap();
+
+        labels.to_host(&res, &mut labels_host).unwrap();
+        assert_eq!(labels_host[[0,]], 0);
+        assert_eq!(labels_host[[1,]], 1);
+        assert_eq!(labels_host[[2,]], 2);
+        assert_eq!(labels_host[[3,]], 3);
+    }
+}
diff --git a/rust/cuvs/src/cluster/kmeans/params.rs b/rust/cuvs/src/cluster/kmeans/params.rs
new file mode 100644
index 0000000000..bbefe19351
--- /dev/null
+++ b/rust/cuvs/src/cluster/kmeans/params.rs
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+use crate::distance_type::DistanceType;
+use crate::error::{check_cuvs, Result};
+use std::fmt;
+use std::io::{stderr, Write};
+
+pub struct Params(pub ffi::cuvsKMeansParams_t);
+
+impl Params {
+    /// Returns a new Params
+    pub fn new() -> Result<Params> {
+        unsafe {
+            let mut params = std::mem::MaybeUninit::<ffi::cuvsKMeansParams_t>::uninit();
+            check_cuvs(ffi::cuvsKMeansParamsCreate(params.as_mut_ptr()))?;
+            Ok(Params(params.assume_init()))
+        }
+    }
+
+    /// DistanceType to use for fitting kmeans
+    pub fn set_metric(self, metric: DistanceType) -> Params {
+        unsafe {
+            (*self.0).metric = metric;
+        }
+        self
+    }
+
+    /// The number of clusters to form as well as the number of centroids to generate (default:8).
+    pub fn set_n_clusters(self, n_clusters: i32) -> Params {
+        unsafe {
+            (*self.0).n_clusters = n_clusters;
+        }
+        self
+    }
+
+    /// Maximum number of iterations of the k-means algorithm for a single run.
+    pub fn set_max_iter(self, max_iter: i32) -> Params {
+        unsafe {
+            (*self.0).max_iter = max_iter;
+        }
+        self
+    }
+
+    /// Relative tolerance with regards to inertia to declare convergence.
+    pub fn set_tol(self, tol: f64) -> Params {
+        unsafe {
+            (*self.0).tol = tol;
+        }
+        self
+    }
+
+    /// Number of instance k-means algorithm will be run with different seeds.
+    pub fn set_n_init(self, n_init: i32) -> Params {
+        unsafe {
+            (*self.0).n_init = n_init;
+        }
+        self
+    }
+
+    /// Oversampling factor for use in the k-means|| algorithm
+    pub fn set_oversampling_factor(self, oversampling_factor: f64) -> Params {
+        unsafe {
+            (*self.0).oversampling_factor = oversampling_factor;
+        }
+        self
+    }
+
+    /**
+     * batch_samples and batch_centroids are used to tile 1NN computation which is
+     * useful to optimize/control the memory footprint
+     * Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0
+     * then don't tile the centroids.
+     */
+    pub fn set_batch_samples(self, batch_samples: i32) -> Params {
+        unsafe {
+            (*self.0).batch_samples = batch_samples;
+        }
+        self
+    }
+    /// if 0 then batch_centroids = n_clusters
+    pub fn set_batch_centroids(self, batch_centroids: i32) -> Params {
+        unsafe {
+            (*self.0).batch_centroids = batch_centroids;
+        }
+        self
+    }
+
+    /// Whether to use hierarchical (balanced) kmeans or not
+    pub fn set_hierarchical(self, hierarchical: bool) -> Params {
+        unsafe {
+            (*self.0).hierarchical = hierarchical;
+        }
+        self
+    }
+
+    /// For hierarchical k-means , defines the number of training iterations
+    pub fn set_hierarchical_n_iters(self, hierarchical_n_iters: i32) -> Params {
+        unsafe {
+            (*self.0).hierarchical_n_iters = hierarchical_n_iters;
+        }
+        self
+    }
+}
+
+impl fmt::Debug for Params {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        // custom debug trait here, default value will show the pointer address
+        // for the inner params object which isn't that useful.
+        write!(f, "Params({:?})", unsafe { *self.0 })
+    }
+}
+
+impl Drop for Params {
+    fn drop(&mut self) {
+        if let Err(e) = check_cuvs(unsafe { ffi::cuvsKMeansParamsDestroy(self.0) }) {
+            write!(stderr(), "failed to call cuvsKMeansParamsDestroy {:?}", e)
+                .expect("failed to write to stderr");
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_params() {
+        let params = Params::new()
+            .unwrap()
+            .set_n_clusters(128)
+            .set_hierarchical(true);
+
+        unsafe {
+            assert_eq!((*params.0).n_clusters, 128);
+            assert_eq!((*params.0).hierarchical, true);
+        }
+    }
+}
diff --git a/rust/cuvs/src/cluster/mod.rs b/rust/cuvs/src/cluster/mod.rs
new file mode 100644
index 0000000000..2972b3431d
--- /dev/null
+++ b/rust/cuvs/src/cluster/mod.rs
@@ -0,0 +1,17 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+pub mod kmeans;
diff --git a/rust/cuvs/src/dlpack.rs b/rust/cuvs/src/dlpack.rs
index b74484a5c9..dfc730c24d 100644
--- a/rust/cuvs/src/dlpack.rs
+++ b/rust/cuvs/src/dlpack.rs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -51,7 +51,7 @@ impl ManagedTensor {
                 res.get_cuda_stream()?,
             ))?;
 
-            let mut ret = self.0.clone();
+            let mut ret = self.0;
             ret.dl_tensor.data = device_data;
             ret.deleter = Some(rmm_free_tensor);
             ret.dl_tensor.device.device_type = ffi::DLDeviceType::kDLCUDA;
@@ -205,5 +205,9 @@ mod tests {
         // make sure we can get the shape ok
         assert_eq!(unsafe { *tensor.shape }, 8);
         assert_eq!(unsafe { *tensor.shape.add(1) }, 4);
+
+        let arr = ndarray::Array::<f32, _>::zeros((8,));
+        let tensor = unsafe { (*(ManagedTensor::from(&arr).as_ptr())).dl_tensor };
+        assert_eq!(tensor.ndim, 1);
     }
 }
diff --git a/rust/cuvs/src/lib.rs b/rust/cuvs/src/lib.rs
index c2059b6097..085bbbb801 100644
--- a/rust/cuvs/src/lib.rs
+++ b/rust/cuvs/src/lib.rs
@@ -20,6 +20,7 @@
 //! approximate nearest neighbors search on the GPU.
 pub mod brute_force;
 pub mod cagra;
+pub mod cluster;
 pub mod distance;
 pub mod distance_type;
 mod dlpack;

From 8a1f2f6a137bbcfb1b3ce4120783df327dfcf285 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Fri, 22 Aug 2025 12:02:58 -0700
Subject: [PATCH 554/623] Revert "[Java]Binary and scalar quantization (#1104)"
 (#1274)

This reverts commit 989c994e3d15d3b81c86aa1d5d10cb95a9cb9e88.

The changes in #1104 were merged by accident, ahead of higher priority features.  This change is to revert #1104, to reduce rework on the other PRs.

Java support for Quantization will be revisited when prioritized. The changes in #1104 can be rebased then.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1274
---
 .../cuvs/preprocessing/quantize/binary.h      |   6 +-
 .../java/com/nvidia/cuvs/BinaryQuantizer.java | 125 ----
 .../main/java/com/nvidia/cuvs/CagraIndex.java |   8 -
 .../main/java/com/nvidia/cuvs/CagraQuery.java | 108 +---
 .../main/java/com/nvidia/cuvs/CuVSMatrix.java |  18 -
 .../java/com/nvidia/cuvs/CuVSQuantizer.java   |  71 ---
 .../com/nvidia/cuvs/Scalar8BitQuantizer.java  | 123 ----
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  27 -
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |  45 +-
 .../cuvs/internal/BinaryQuantizerImpl.java    | 324 ----------
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 130 +---
 .../cuvs/internal/CuVSHostMatrixImpl.java     |   7 -
 .../cuvs/internal/CuVSMatrixBaseImpl.java     |   6 -
 .../internal/Scalar8BitQuantizerImpl.java     | 461 --------------
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  41 --
 .../java/com/nvidia/cuvs/QuantizationIT.java  | 588 ------------------
 java/panama-bindings/headers.h                |   2 -
 17 files changed, 37 insertions(+), 2053 deletions(-)
 delete mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java
 delete mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java
 delete mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java
 delete mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java

diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.h b/cpp/include/cuvs/preprocessing/quantize/binary.h
index ec58bae3bd..9a5834ef3e 100644
--- a/cpp/include/cuvs/preprocessing/quantize/binary.h
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.h
@@ -30,7 +30,7 @@ extern "C" {
  * and sampling_median thresholds are calculated separately for each dimension.
  *
  */
-typedef enum { ZERO, MEAN, SAMPLING_MEDIAN } cuvsBinaryQuantizerThreshold;
+enum cuvsBinaryQuantizerThreshold { ZERO, MEAN, SAMPLING_MEDIAN };
 
 /**
  * @brief Binary quantizer parameters.
@@ -39,12 +39,12 @@ struct cuvsBinaryQuantizerParams {
   /*
    * specifies the threshold to set a bit in cuvsBinaryQuantizerTransform
    */
-  cuvsBinaryQuantizerThreshold threshold;
+  cuvsBinaryQuantizerThreshold threshold = MEAN;
 
   /*
    * specifies the sampling ratio
    */
-  float sampling_ratio;
+  float sampling_ratio = 0.1;
 };
 
 typedef struct cuvsBinaryQuantizerParams* cuvsBinaryQuantizerParams_t;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java
deleted file mode 100644
index 82584d6cac..0000000000
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/BinaryQuantizer.java
+++ /dev/null
@@ -1,125 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs;
-
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-import com.nvidia.cuvs.spi.CuVSProvider;
-
-/** Binary quantizer that transforms float datasets into packed binary datasets. */
-public class BinaryQuantizer implements CuVSQuantizer {
-  private final CuVSResources resources;
-  private final ThresholdType thresholdType;
-
-  /** Output data type (BYTE for packed binary data). */
-  private final DataType outputDataType = DataType.BYTE;
-
-  private final Object impl;
-
-  /** Creates a binary quantizer with training data. */
-  public BinaryQuantizer(CuVSResources resources, CuVSMatrix trainingDataset) throws Throwable {
-    this(resources, trainingDataset, ThresholdType.MEAN);
-  }
-
-  /** Creates a binary quantizer with specified threshold type and training data. */
-  public BinaryQuantizer(
-      CuVSResources resources, CuVSMatrix trainingDataset, ThresholdType thresholdType)
-      throws Throwable {
-    if (trainingDataset == null) {
-      throw new IllegalArgumentException("Training dataset cannot be null");
-    }
-    if (trainingDataset.dataType() != DataType.FLOAT) {
-      throw new IllegalArgumentException(
-          "Training dataset must have FLOAT data type, got " + trainingDataset.dataType());
-    }
-    this.resources = resources;
-    this.thresholdType = thresholdType;
-    this.impl =
-        CuVSProvider.provider()
-            .createBinaryQuantizerImpl(resources, trainingDataset, thresholdType);
-  }
-
-  /** Threshold types for binary quantization. */
-  public enum ThresholdType {
-    /** Zero threshold */
-    ZERO(0),
-    /** Mean threshold */
-    MEAN(1),
-    /** Sampling median threshold */
-    SAMPLING_MEDIAN(2);
-
-    private final int value;
-
-    ThresholdType(int value) {
-      this.value = value;
-    }
-
-    public int getValue() {
-      return value;
-    }
-  }
-
-  /** Returns the output data type (BYTE). */
-  @Override
-  public DataType outputDataType() {
-    return outputDataType;
-  }
-
-  /** Gets the threshold type used for binary quantization. */
-  public ThresholdType getThresholdType() {
-    return thresholdType;
-  }
-
-  @Override
-  public CuVSMatrix transform(CuVSMatrix input) throws Throwable {
-    if (input.dataType() != DataType.FLOAT) {
-      throw new IllegalArgumentException(
-          "BinaryQuantizer requires FLOAT input, got " + input.dataType());
-    }
-
-    CuVSMatrix result = CuVSProvider.provider().transformBinaryWithImpl(impl, input);
-
-    if (result.dataType() != DataType.BYTE) {
-      throw new IllegalStateException(
-          "Expected BYTE output from binary quantization, got " + result.dataType());
-    }
-
-    return result;
-  }
-
-  @Override
-  public void train(CuVSMatrix trainingData) throws Throwable {
-    throw new UnsupportedOperationException("Training handled during construction");
-  }
-
-  @Override
-  public CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
-    throw new UnsupportedOperationException(
-        "Binary quantization does not support inverse transformation");
-  }
-
-  @Override
-  public void close() throws Exception {
-    try {
-      CuVSProvider.provider().closeBinaryQuantizer(impl);
-    } catch (Throwable t) {
-      if (t instanceof Exception) {
-        throw (Exception) t;
-      } else {
-        throw new RuntimeException("Error closing BinaryQuantizer", t);
-      }
-    }
-  }
-}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index c0032f7ea4..e3b885a333 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -247,14 +247,6 @@ interface Builder {
      */
     Builder withIndexParams(CagraIndexParams cagraIndexParameters);
 
-    /**
-     * Sets the quantizer to use for building the index with quantized data.
-     *
-     * @param quantizer The quantizer to apply to the dataset during index construction
-     * @return An instance of this Builder
-     */
-    Builder withQuantizer(CuVSQuantizer quantizer);
-
     /**
      * Builds and returns an instance of CagraIndex.
      *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
index c976949bf7..f81ec3191c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -15,15 +15,14 @@
  */
 package com.nvidia.cuvs;
 
-import com.nvidia.cuvs.CuVSMatrix.DataType;
 import java.util.Arrays;
 import java.util.BitSet;
 import java.util.Objects;
 import java.util.function.LongToIntFunction;
 
 /**
- * CagraQuery holds the search parameters plus either raw float[][] vectors
- * or a quantized {@link CuVSMatrix} for querying a CAGRA index.
+ * CagraQuery holds the CagraSearchParams and the query vectors to be used while
+ * invoking search.
  *
  * <p><strong>Thread Safety:</strong> Each CagraQuery instance should use its own
  * CuVSResources object that is not shared with other threads. Sharing CuVSResources
@@ -34,9 +33,8 @@
 public class CagraQuery {
 
   private final CagraSearchParams cagraSearchParameters;
-  private final float[][] queryVectors;
-  private final CuVSMatrix quantizedQueries;
   private final LongToIntFunction mapping;
+  private final float[][] queryVectors;
   private final int topK;
   private final BitSet prefilter;
   private final int numDocs;
@@ -49,25 +47,23 @@ public class CagraQuery {
    * @param cagraSearchParameters an instance of {@link CagraSearchParams} holding
    *                              the search parameters
    * @param queryVectors          2D float query vector array
-   * @param quantizedQueries      2D quantized query vector array
    * @param mapping               a function mapping ordinals (neighbor IDs) to custom user IDs
    * @param topK                  the top k results to return
    * @param prefilter             A single BitSet to use as filter while searching the CAGRA index
    * @param numDocs               Total number of dataset vectors; used to align the prefilter correctly
    * @param resources             CuVSResources instance to use for this query
    */
-  private CagraQuery(
+  public CagraQuery(
       CagraSearchParams cagraSearchParameters,
       float[][] queryVectors,
-      CuVSMatrix quantizedQueries,
       LongToIntFunction mapping,
       int topK,
       BitSet prefilter,
       int numDocs,
       CuVSResources resources) {
+    super();
     this.cagraSearchParameters = cagraSearchParameters;
     this.queryVectors = queryVectors;
-    this.quantizedQueries = quantizedQueries;
     this.mapping = mapping;
     this.topK = topK;
     this.prefilter = prefilter;
@@ -75,11 +71,6 @@ private CagraQuery(
     this.resources = resources;
   }
 
-  /** Start building a new CagraQuery. */
-  public static Builder newBuilder(CuVSResources resources) {
-    return new Builder(resources);
-  }
-
   /**
    * Gets the instance of CagraSearchParams initially set.
    *
@@ -90,35 +81,14 @@ public CagraSearchParams getCagraSearchParameters() {
   }
 
   /**
-   * If this query was built without a quantizer, returns the original float vectors.
-   * Otherwise returns null.
+   * Gets the query vector 2D float array.
+   *
+   * @return 2D float array
    */
   public float[][] getQueryVectors() {
     return queryVectors;
   }
 
-  /**
-   * If this query was built with a quantizer, returns the quantized Dataset.
-   * Otherwise returns null.
-   */
-  public CuVSMatrix getQuantizedQueries() {
-    return quantizedQueries;
-  }
-
-  /** True if this query carries a quantized Dataset instead of float[][] */
-  public boolean hasQuantizedQueries() {
-    return quantizedQueries != null;
-  }
-
-  /**
-   * Returns the data type of the query payload:
-   * - 32 for float32 queries
-   * - 8 for quantized queries
-   */
-  public DataType getQueryDataType() {
-    return quantizedQueries != null ? quantizedQueries.dataType() : DataType.FLOAT;
-  }
-
   /**
    * Gets the function mapping ordinals (neighbor IDs) to custom user IDs
    */
@@ -164,13 +134,10 @@ public CuVSResources getResources() {
 
   @Override
   public String toString() {
-    return "CagraQuery["
-        + "params="
+    return "CuVSQuery [cagraSearchParameters="
         + cagraSearchParameters
-        + ", floatVectors="
-        + (queryVectors != null ? Arrays.toString(queryVectors) : "null")
-        + ", quantized="
-        + (quantizedQueries != null ? ("Dataset@" + quantizedQueries.dataType() + "-bit") : "false")
+        + ", queryVectors="
+        + Arrays.toString(queryVectors)
         + ", mapping="
         + mapping
         + ", topK="
@@ -189,7 +156,6 @@ public static class Builder {
     private int topK = 2;
     private BitSet prefilter;
     private int numDocs;
-    private CuVSQuantizer quantizer;
     private final CuVSResources resources;
 
     /**
@@ -208,12 +174,12 @@ public Builder(CuVSResources resources) {
     /**
      * Sets the instance of configured CagraSearchParams to be passed for search.
      *
-     * @param params an instance of the configured CagraSearchParams to
-     *               be used for this query
+     * @param cagraSearchParams an instance of the configured CagraSearchParams to
+     *                          be used for this query
      * @return an instance of this Builder
      */
-    public Builder withSearchParams(CagraSearchParams params) {
-      this.cagraSearchParams = params;
+    public Builder withSearchParams(CagraSearchParams cagraSearchParams) {
+      this.cagraSearchParams = cagraSearchParams;
       return this;
     }
 
@@ -267,47 +233,13 @@ public Builder withPrefilter(BitSet prefilter, int numDocs) {
     }
 
     /**
-     * Specify a quantizer to automatically transform the float[][] queryVectors
-     * into a quantized {@link CuVSMatrix} using the same quantizer used for training.
-     */
-    public Builder withQuantizer(CuVSQuantizer quantizer) {
-      this.quantizer = quantizer;
-      return this;
-    }
-
-    /**
-     * Builds the CagraQuery. If a quantizer was provided, queryVectors is ignored
-     * and a quantized Dataset is produced instead.
+     * Builds an instance of CuVSQuery.
+     *
+     * @return an instance of CuVSQuery
      */
-    public CagraQuery build() throws Throwable {
-      if (queryVectors == null) {
-        throw new IllegalArgumentException("Query vectors must be provided");
-      }
-
-      CuVSMatrix quantized = null;
-      float[][] floatsForQuery = queryVectors;
-
-      if (quantizer != null) {
-        // wrap float[][] in a CuVSMatrix and quantize
-        try (CuVSMatrix tmp = CuVSMatrix.ofArray(queryVectors)) {
-          if (tmp.dataType() != DataType.FLOAT) {
-            throw new IllegalArgumentException(
-                "Query quantization requires FLOAT input, got " + tmp.dataType());
-          }
-          quantized = quantizer.transform(tmp);
-        }
-        floatsForQuery = null;
-      }
-
+    public CagraQuery build() {
       return new CagraQuery(
-          cagraSearchParams,
-          floatsForQuery,
-          quantized,
-          mapping,
-          topK,
-          prefilter,
-          numDocs,
-          resources);
+          cagraSearchParams, queryVectors, mapping, topK, prefilter, numDocs, resources);
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
index 5143c8bbc7..f8e60c5555 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
@@ -69,17 +69,6 @@ static CuVSMatrix ofArray(byte[][] vectors) {
     return CuVSProvider.provider().newMatrixFromArray(vectors);
   }
 
-  /**
-   * Returns the data type of this matrix.
-   *
-   * <p>DataType provides the fundamental type information needed for all operations.
-   * FLOAT represents 32-bit floating point data, INT represents 32-bit integer data,
-   * and BYTE represents 8-bit data (used for quantized values).
-   *
-   * @return The DataType of this matrix
-   */
-  DataType dataType();
-
   interface Builder {
     /**
      * Add a single vector to the dataset.
@@ -163,13 +152,6 @@ static CuVSMatrix.Builder builder(int size, int columns, DataType dataType) {
    */
   void toArray(byte[][] array);
 
-  /**
-   * Gets where the content is stored
-   *
-   * @return MemoryKind whether HOST or DEVICE
-   */
-  CuVSMatrix.MemoryKind memoryKind();
-
   @Override
   void close();
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java
deleted file mode 100644
index 2c86eff0f1..0000000000
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSQuantizer.java
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs;
-
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-
-/**
- * Base interface for all cuVS quantizers providing unified quantization operations.
- *
- */
-public interface CuVSQuantizer extends AutoCloseable {
-
-  /**
-   * Returns the data type of quantized data produced by this quantizer.
-   *
-   * @return The DataType of the quantized output (BYTE for quantized data)
-   */
-  DataType outputDataType();
-
-  /**
-   * Transforms a float32 dataset into a quantized dataset.
-   *
-   * @param input The input dataset of float32 vectors (must have precision() == 32)
-   * @return A new Dataset containing the quantized vectors with this quantizer's precision
-   */
-  CuVSMatrix transform(CuVSMatrix input) throws Throwable;
-
-  /**
-   * Optional training method - only applies to quantizers that require training.
-   *
-   * @param trainingData The dataset to train on (must have precision() == 32)
-   * @throws Throwable if an error occurs during training
-   */
-  default void train(CuVSMatrix trainingData) throws Throwable {
-    throw new UnsupportedOperationException(
-        "Training not supported for " + getClass().getSimpleName());
-  }
-
-  /**
-   * Optional inverse transformation - only applies to quantizers that support reconstruction
-   *
-   * @param quantizedData The quantized dataset to inverse transform
-   *                      (must have precision() matching this quantizer's precision())
-   * @return A float[][] array containing the de-quantized vectors
-   */
-  default CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
-    throw new UnsupportedOperationException(
-        "Inverse transform not supported for " + getClass().getSimpleName());
-  }
-
-  /**
-   * Closes this quantizer and releases any associated resources.
-   *
-   * @throws Exception if an error occurs while closing
-   */
-  @Override
-  void close() throws Exception;
-}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java
deleted file mode 100644
index 056a7ad468..0000000000
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/Scalar8BitQuantizer.java
+++ /dev/null
@@ -1,123 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs;
-
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-import com.nvidia.cuvs.spi.CuVSProvider;
-
-/**
- * Scalar 8-bit quantizer implementation that transforms 32-bit float datasets into 8-bit integer datasets.
- *
- * <p>This quantizer reduces each float32 value to an 8-bit signed integer, providing significant
- * compression while maintaining reasonable precision. The quantizer requires training on a representative
- * dataset to compute optimal quantization parameters (min/max values per dimension).
- *
- * <p>The quantizer supports both transformation and inverse transformation, allowing approximate
- * recovery of the original float32 values from the quantized representation.
- *
- * @since 25.08
- */
-public class Scalar8BitQuantizer implements CuVSQuantizer {
-  private final Object impl;
-
-  /**
-   * The data type used by this quantizer (BYTE for 8-bit signed integers).
-   */
-  private final DataType outputDataType = DataType.BYTE;
-
-  /**
-   * Creates a new scalar 8-bit quantizer with CuVSMatrix training data.
-   *
-   * <p>The training dataset is used to compute quantization parameters (min/max values)
-   * for each dimension. The quantizer will be immediately ready for use after construction.
-   *
-   * @param resources The CuVS resources to use for quantization operations
-   * @param trainingDataset A CuVSMatrix containing training vectors (must have FLOAT data type)
-   * @throws Throwable if an error occurs during quantizer training
-   * @throws IllegalArgumentException if trainingDataset is null or doesn't have FLOAT data type
-   */
-  public Scalar8BitQuantizer(CuVSResources resources, CuVSMatrix trainingDataset) throws Throwable {
-    if (trainingDataset == null) {
-      throw new IllegalArgumentException("Training dataset cannot be null");
-    }
-    if (trainingDataset.dataType() != DataType.FLOAT) {
-      throw new IllegalArgumentException(
-          "Training dataset must have FLOAT data type, got " + trainingDataset.dataType());
-    }
-    this.impl = CuVSProvider.provider().createScalar8BitQuantizerImpl(resources, trainingDataset);
-  }
-
-  /**
-   * Returns the data type of quantized data produced by this quantizer.
-   *
-   * @return The DataType (BYTE for this scalar quantizer)
-   */
-  @Override
-  public DataType outputDataType() {
-    return outputDataType;
-  }
-
-  @Override
-  public CuVSMatrix transform(CuVSMatrix input) throws Throwable {
-    // Validate input data type
-    if (input.dataType() != DataType.FLOAT) {
-      throw new IllegalArgumentException(
-          "Scalar8BitQuantizer requires FLOAT input, got " + input.dataType());
-    }
-
-    CuVSMatrix result = CuVSProvider.provider().transformScalar8Bit(impl, input);
-
-    // Validate output data type
-    if (result.dataType() != DataType.BYTE) {
-      throw new IllegalStateException(
-          "Expected BYTE output from scalar quantization, got " + result.dataType());
-    }
-
-    return result;
-  }
-
-  @Override
-  public void train(CuVSMatrix trainingData) throws Throwable {
-    // Training is handled during construction for ScalarQuantizer
-    // This method could be implemented if you want to support re-training
-    throw new UnsupportedOperationException("Training handled during construction");
-  }
-
-  @Override
-  public CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
-    // Validate input data type for inverse transform
-    if (quantizedData.dataType() != DataType.BYTE) {
-      throw new IllegalArgumentException(
-          "Inverse transform requires BYTE input, got " + quantizedData.dataType());
-    }
-
-    return CuVSProvider.provider().inverseTransformScalar8Bit(impl, quantizedData);
-  }
-
-  @Override
-  public void close() throws Exception {
-    try {
-      CuVSProvider.provider().closeScalar8BitQuantizer(impl);
-    } catch (Throwable t) {
-      if (t instanceof Exception) {
-        throw (Exception) t;
-      } else {
-        // Wrap non-Exception throwables (like Error) in RuntimeException
-        throw new RuntimeException("Error closing Scalar8BitQuantizer", t);
-      }
-    }
-  }
-}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 316406b24e..e528beff1a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -15,7 +15,6 @@
  */
 package com.nvidia.cuvs.spi;
 
-import com.nvidia.cuvs.BinaryQuantizer;
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraMergeParams;
@@ -121,32 +120,6 @@ default CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merg
     return mergeCagraIndexes(indexes);
   }
 
-  /** Creates a new Scalar8BitQuantizer implementation. */
-  Object createScalar8BitQuantizerImpl(CuVSResources resources, CuVSMatrix trainingDataset)
-      throws Throwable;
-
-  /** Performs inverse transform using a Scalar8BitQuantizer. */
-  CuVSMatrix inverseTransformScalar8Bit(Object impl, CuVSMatrix quantizedData) throws Throwable;
-
-  /** Transforms dataset using Scalar8BitQuantizer */
-  CuVSMatrix transformScalar8Bit(Object impl, CuVSMatrix input) throws Throwable;
-
-  /** Closes Scalar8BitQuantizer implementation */
-  void closeScalar8BitQuantizer(Object impl) throws Throwable;
-
-  /** Creates a new BinaryQuantizer implementation. */
-  Object createBinaryQuantizerImpl(
-      CuVSResources resources,
-      CuVSMatrix trainingDataset,
-      BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable;
-
-  /** Performs transform using a BinaryQuantizer. */
-  CuVSMatrix transformBinaryWithImpl(Object impl, CuVSMatrix input) throws Throwable;
-
-  /** Closes BinaryQuantizer implementation. */
-  void closeBinaryQuantizer(Object impl) throws Throwable;
-
   /** Retrieves the system-wide provider. */
   static CuVSProvider provider() {
     return CuVSServiceProvider.Holder.INSTANCE;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index cd6604663b..8f65bf7068 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -15,7 +15,6 @@
  */
 package com.nvidia.cuvs.spi;
 
-import com.nvidia.cuvs.BinaryQuantizer;
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CuVSMatrix;
@@ -26,8 +25,7 @@
 import java.nio.file.Path;
 
 /**
- * A provider that throws UnsupportedOperationException for all operations.
- * Used as a fallback when no proper implementation is available.
+ * A provider that unconditionally throws UnsupportedOperationException.
  */
 final class UnsupportedProvider implements CuVSProvider {
 
@@ -86,45 +84,4 @@ public CuVSMatrix newMatrixFromArray(int[][] vectors) {
   public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     throw new UnsupportedOperationException();
   }
-
-  @Override
-  public Object createScalar8BitQuantizerImpl(CuVSResources resources, CuVSMatrix trainingDataset)
-      throws Throwable {
-    throw new UnsupportedOperationException("CuVS is not supported on this platform");
-  }
-
-  @Override
-  public CuVSMatrix inverseTransformScalar8Bit(Object impl, CuVSMatrix quantizedData)
-      throws Throwable {
-    throw new UnsupportedOperationException("CuVS is not supported on this platform");
-  }
-
-  @Override
-  public Object createBinaryQuantizerImpl(
-      CuVSResources resources,
-      CuVSMatrix trainingDataset,
-      BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable {
-    throw new UnsupportedOperationException("CuVS is not supported on this platform");
-  }
-
-  @Override
-  public CuVSMatrix transformBinaryWithImpl(Object impl, CuVSMatrix input) throws Throwable {
-    throw new UnsupportedOperationException("CuVS is not supported on this platform");
-  }
-
-  @Override
-  public void closeBinaryQuantizer(Object impl) throws Throwable {
-    throw new UnsupportedOperationException("CuVS is not supported on this platform");
-  }
-
-  @Override
-  public CuVSMatrix transformScalar8Bit(Object impl, CuVSMatrix input) throws Throwable {
-    throw new UnsupportedOperationException("CuVS is not supported on this platform");
-  }
-
-  @Override
-  public void closeScalar8BitQuantizer(Object impl) throws Throwable {
-    throw new UnsupportedOperationException("CuVS is not supported on this platform");
-  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java
deleted file mode 100644
index 704fb45738..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BinaryQuantizerImpl.java
+++ /dev/null
@@ -1,324 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs.internal;
-
-import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
-import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
-import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
-import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
-import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
-import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerParamsCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerParamsDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerParams_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerTrain;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizerTransformWithParams;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBinaryQuantizer_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLCPU;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLCUDA;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLFloat;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLUInt;
-
-import com.nvidia.cuvs.BinaryQuantizer;
-import com.nvidia.cuvs.CuVSMatrix;
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-import com.nvidia.cuvs.CuVSMatrix.MemoryKind;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.internal.common.Util;
-import java.lang.foreign.Arena;
-import java.lang.foreign.MemorySegment;
-import java.lang.foreign.ValueLayout;
-
-/**
- * Implementation of binary quantizer functionality using Panama Foreign Function Interface.
- */
-public class BinaryQuantizerImpl {
-
-  public static CuVSMatrix transform(
-      CuVSResources cuvsResources, float[][] dataset, BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable {
-
-    try (var localArena = Arena.ofConfined();
-        var resourcesAccessor = cuvsResources.access()) {
-
-      long rows = dataset.length;
-      long cols = rows > 0 ? dataset[0].length : 0;
-
-      MemorySegment datasetMemSegment = buildMemorySegment(localArena, dataset);
-      long cuvsResourcesPtr = resourcesAccessor.handle();
-
-      return performTransformGPU(
-          cuvsResourcesPtr,
-          localArena,
-          rows,
-          cols,
-          datasetMemSegment,
-          HOST_TO_DEVICE,
-          thresholdType);
-    }
-  }
-
-  public static CuVSMatrix transform(
-      CuVSResources cuvsResources, CuVSMatrix dataset, BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable {
-    if (dataset.dataType() != DataType.FLOAT) {
-      throw new IllegalArgumentException(
-          "BinaryQuantizer requires FLOAT input, got " + dataset.dataType());
-    }
-
-    try (var localArena = Arena.ofConfined();
-        var resourcesAccessor = cuvsResources.access()) {
-
-      long rows = dataset.size();
-      long cols = dataset.columns();
-
-      MemorySegment datasetMemSegment = ((CuVSMatrixBaseImpl) dataset).memorySegment();
-      long cuvsResourcesPtr = resourcesAccessor.handle();
-
-      // TODO: Currently all datasets are HOST memory kind, preventing testing of GPU quantization
-      // flow.
-      // Future improvement: Support GPU-native dataset creation in Java API to enable direct GPU
-      // quantization testing.
-      if (dataset.memoryKind() == MemoryKind.HOST) {
-        return performTransformHost(
-            cuvsResourcesPtr, localArena, rows, cols, datasetMemSegment, thresholdType);
-      } else {
-        CuVSMatrix result =
-            performTransformGPU(
-                cuvsResourcesPtr,
-                localArena,
-                rows,
-                cols,
-                datasetMemSegment,
-                INFER_DIRECTION,
-                thresholdType);
-
-        if (result.dataType() != DataType.BYTE) {
-          throw new IllegalStateException(
-              "Expected BYTE output from binary quantization, got " + result.dataType());
-        }
-        return result;
-      }
-    }
-  }
-
-  private static CuVSMatrix performTransformGPU(
-      long cuvsResourcesPtr,
-      Arena localArena,
-      long rows,
-      long cols,
-      MemorySegment datasetMemSegment,
-      Util.CudaMemcpyKind memcpyDirection,
-      BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable {
-
-    final long BUFFER_SIZE_BYTES = 1024 * 1024;
-    final long FLOAT_SIZE = C_FLOAT_BYTE_SIZE;
-    final long FLOATS_PER_BUFFER = BUFFER_SIZE_BYTES / FLOAT_SIZE;
-    final long ROWS_PER_BUFFER = Math.max(1, FLOATS_PER_BUFFER / cols);
-
-    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
-
-    long maxDatasetBytes = FLOAT_SIZE * ROWS_PER_BUFFER * cols;
-    long maxOutputBytes = ROWS_PER_BUFFER * cols;
-
-    // Allocate quantizer objects once
-    MemorySegment paramsSegment = localArena.allocate(cuvsBinaryQuantizerParams_t);
-    int returnValue = cuvsBinaryQuantizerParamsCreate(paramsSegment);
-    checkCuVSError(returnValue, "cuvsBinaryQuantizerParamsCreate");
-    MemorySegment paramsPtr = paramsSegment.get(C_POINTER, 0);
-    setBinaryQuantizerThreshold(paramsPtr, thresholdType);
-
-    MemorySegment quantizerSegment = localArena.allocate(cuvsBinaryQuantizer_t);
-    returnValue = cuvsBinaryQuantizerCreate(quantizerSegment);
-    checkCuVSError(returnValue, "cuvsBinaryQuantizerCreate");
-    MemorySegment quantizerPtr = quantizerSegment.get(C_POINTER, 0);
-
-    try (var datasetDP = allocateRMMSegment(cuvsResourcesPtr, maxDatasetBytes);
-        var outputDP = allocateRMMSegment(cuvsResourcesPtr, maxOutputBytes)) {
-
-      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
-        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
-        long chunkRows = endRow - startRow;
-        long actualDatasetBytes = FLOAT_SIZE * chunkRows * cols;
-        long actualOutputBytes = chunkRows * cols;
-
-        MemorySegment datasetPtr = datasetDP.handle().asSlice(0, actualDatasetBytes);
-        MemorySegment outputPtr = outputDP.handle().asSlice(0, actualOutputBytes);
-        MemorySegment chunkData =
-            datasetMemSegment.asSlice(startRow * cols * FLOAT_SIZE, actualDatasetBytes);
-
-        cudaMemcpy(datasetPtr, chunkData, actualDatasetBytes, memcpyDirection);
-
-        returnValue = cuvsStreamSync(cuvsResourcesPtr);
-        checkCuVSError(returnValue, "cuvsStreamSync before transform");
-
-        long datasetShape[] = {chunkRows, cols};
-        long outputShape[] = {chunkRows, cols};
-
-        MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
-        MemorySegment outputTensor =
-            prepareTensor(localArena, outputPtr, outputShape, kDLUInt(), 8, kDLCUDA(), 1);
-
-        returnValue =
-            cuvsBinaryQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
-        checkCuVSError(returnValue, "cuvsBinaryQuantizerTrain (GPU)");
-
-        returnValue =
-            cuvsBinaryQuantizerTransformWithParams(
-                cuvsResourcesPtr, quantizerPtr, datasetTensor, outputTensor);
-        checkCuVSError(returnValue, "cuvsBinaryQuantizerTransformWithParams (GPU)");
-
-        returnValue = cuvsStreamSync(cuvsResourcesPtr);
-        checkCuVSError(returnValue, "cuvsStreamSync after transform");
-
-        MemorySegment resultChunk =
-            ((CuVSMatrixBaseImpl) result)
-                .memorySegment()
-                .asSlice(startRow * cols, actualOutputBytes);
-        cudaMemcpy(resultChunk, outputPtr, actualOutputBytes, DEVICE_TO_HOST);
-      }
-    } finally {
-      cuvsBinaryQuantizerDestroy(quantizerPtr);
-      cuvsBinaryQuantizerParamsDestroy(paramsPtr);
-    }
-
-    return result;
-  }
-
-  private static CuVSMatrix performTransformHost(
-      long cuvsResourcesPtr,
-      Arena localArena,
-      long rows,
-      long cols,
-      MemorySegment datasetMemSegment,
-      BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable {
-
-    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
-
-    MemorySegment paramsSegment = localArena.allocate(cuvsBinaryQuantizerParams_t);
-    int returnValue = cuvsBinaryQuantizerParamsCreate(paramsSegment);
-    checkCuVSError(returnValue, "cuvsBinaryQuantizerParamsCreate");
-
-    MemorySegment paramsPtr = paramsSegment.get(C_POINTER, 0);
-    setBinaryQuantizerThreshold(paramsPtr, thresholdType);
-
-    MemorySegment quantizerSegment = localArena.allocate(cuvsBinaryQuantizer_t);
-    returnValue = cuvsBinaryQuantizerCreate(quantizerSegment);
-    checkCuVSError(returnValue, "cuvsBinaryQuantizerCreate");
-
-    MemorySegment quantizerPtr = quantizerSegment.get(C_POINTER, 0);
-
-    try {
-      long[] datasetShape = {rows, cols};
-      MemorySegment datasetTensor =
-          prepareTensor(localArena, datasetMemSegment, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
-
-      returnValue =
-          cuvsBinaryQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
-      checkCuVSError(returnValue, "cuvsBinaryQuantizerTrain (host)");
-
-      MemorySegment outputTensor =
-          prepareTensor(
-              localArena,
-              ((CuVSMatrixBaseImpl) result).memorySegment(),
-              datasetShape,
-              kDLUInt(),
-              8,
-              kDLCPU(),
-              1);
-
-      returnValue =
-          cuvsBinaryQuantizerTransformWithParams(
-              cuvsResourcesPtr, quantizerPtr, datasetTensor, outputTensor);
-      checkCuVSError(returnValue, "cuvsBinaryQuantizerTransformWithParams (host)");
-
-      return result;
-
-    } finally {
-      cuvsBinaryQuantizerDestroy(quantizerPtr);
-      cuvsBinaryQuantizerParamsDestroy(paramsPtr);
-    }
-  }
-
-  private static void setBinaryQuantizerThreshold(
-      MemorySegment paramsPtr, BinaryQuantizer.ThresholdType thresholdType) {
-    paramsPtr.set(ValueLayout.JAVA_INT, 0, thresholdType.getValue());
-  }
-
-  /** Creates a new BinaryQuantizer implementation. */
-  public static Object create(
-      CuVSResources resources,
-      CuVSMatrix trainingDataset,
-      BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable {
-    return new BinaryQuantizerImpl(resources, trainingDataset, thresholdType);
-  }
-
-  /** Performs transform using a BinaryQuantizer implementation. */
-  public static CuVSMatrix transformWithImpl(Object impl, CuVSMatrix input) throws Throwable {
-    if (impl instanceof BinaryQuantizerImpl) {
-      return ((BinaryQuantizerImpl) impl).transform(input);
-    } else {
-      throw new IllegalArgumentException("Invalid implementation object");
-    }
-  }
-
-  /** Closes BinaryQuantizer implementation. */
-  public static void close(Object impl) throws Throwable {
-    if (impl instanceof BinaryQuantizerImpl) {
-      ((BinaryQuantizerImpl) impl).close();
-    }
-  }
-
-  private final CuVSResources resources;
-  private final CuVSMatrix trainingDataset;
-  private final BinaryQuantizer.ThresholdType thresholdType;
-  private boolean isClosed = false;
-
-  private BinaryQuantizerImpl(
-      CuVSResources resources,
-      CuVSMatrix trainingDataset,
-      BinaryQuantizer.ThresholdType thresholdType) {
-    this.resources = resources;
-    this.trainingDataset = trainingDataset;
-    this.thresholdType = thresholdType;
-  }
-
-  private CuVSMatrix transform(CuVSMatrix input) throws Throwable {
-    if (isClosed) {
-      throw new IllegalStateException("BinaryQuantizerImpl has been closed");
-    }
-
-    if (input.dataType() != DataType.FLOAT) {
-      throw new IllegalArgumentException(
-          "BinaryQuantizer requires FLOAT input, got " + input.dataType());
-    }
-
-    return transform(resources, input, thresholdType);
-  }
-
-  private void close() throws Throwable {
-    isClosed = true;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index b97c65cf6d..0d5fc8d90f 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -30,7 +30,6 @@
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
-import com.nvidia.cuvs.BinaryQuantizer;
 import com.nvidia.cuvs.CagraCompressionParams;
 import com.nvidia.cuvs.CagraIndex;
 import com.nvidia.cuvs.CagraIndexParams;
@@ -41,8 +40,6 @@
 import com.nvidia.cuvs.CuVSIvfPqIndexParams;
 import com.nvidia.cuvs.CuVSIvfPqSearchParams;
 import com.nvidia.cuvs.CuVSMatrix;
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-import com.nvidia.cuvs.CuVSQuantizer;
 import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.CloseableHandle;
@@ -84,7 +81,6 @@
 public class CagraIndexImpl implements CagraIndex {
   private final CuVSResources resources;
   private final IndexReference cagraIndexReference;
-  private final CuVSQuantizer indexQuantizer;
   private boolean destroyed;
 
   /**
@@ -99,7 +95,6 @@ private CagraIndexImpl(
       CagraIndexParams indexParameters, CuVSMatrix dataset, CuVSResources resources) {
     Objects.requireNonNull(dataset);
     this.resources = resources;
-    this.indexQuantizer = null;
     assert dataset instanceof CuVSMatrixBaseImpl;
     this.cagraIndexReference = build(indexParameters, (CuVSMatrixBaseImpl) dataset);
   }
@@ -112,7 +107,6 @@ private CagraIndexImpl(
    */
   private CagraIndexImpl(InputStream inputStream, CuVSResources resources) throws Throwable {
     this.resources = resources;
-    this.indexQuantizer = null;
     this.cagraIndexReference = deserialize(inputStream);
   }
 
@@ -125,7 +119,6 @@ private CagraIndexImpl(InputStream inputStream, CuVSResources resources) throws
    */
   private CagraIndexImpl(IndexReference indexReference, CuVSResources resources) {
     this.resources = resources;
-    this.indexQuantizer = null;
     this.cagraIndexReference = indexReference;
     this.destroyed = false;
   }
@@ -142,13 +135,11 @@ private CagraIndexImpl(
       CagraIndexParams.CuvsDistanceType metric,
       CuVSMatrix graph,
       CuVSMatrix dataset,
-      CuVSResources resources,
-      CuVSQuantizer quantizer) {
+      CuVSResources resources) {
     Objects.requireNonNull(graph);
     Objects.requireNonNull(dataset);
 
     this.resources = resources;
-    this.indexQuantizer = quantizer;
 
     assert graph instanceof CuVSMatrixBaseImpl;
     assert dataset instanceof CuVSMatrixBaseImpl;
@@ -190,7 +181,6 @@ public void close() {
   private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImpl dataset) {
     long rows = dataset.size();
     long cols = dataset.columns();
-    DataType datasetDataType = dataset.dataType();
 
     try (var indexParams = segmentFromIndexParams(indexParameters);
         var localArena = Arena.ofConfined()) {
@@ -205,14 +195,7 @@ private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImp
 
       long[] datasetShape = {rows, cols};
       MemorySegment datasetTensor =
-          prepareTensor(
-              localArena,
-              dataSeg,
-              datasetShape,
-              getDataTypeCode(datasetDataType, this.indexQuantizer),
-              getPrecisionFromDataType(datasetDataType),
-              kDLCPU(),
-              1);
+          prepareTensor(localArena, dataSeg, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
 
       var index = createCagraIndex();
 
@@ -273,39 +256,20 @@ private static MemorySegment createCagraIndex() {
   public SearchResults search(CagraQuery query) throws Throwable {
     try (var localArena = Arena.ofConfined()) {
       checkNotDestroyed();
-
-      // Extract query information upfront to avoid "mutually-null" fields
-      long numQueries;
-      int vectorDimension;
-      DataType queryDataType;
-      MemorySegment floatsSeg;
-
-      if (query.hasQuantizedQueries()) {
-        CuVSMatrix quantizedQueries = query.getQuantizedQueries();
-        numQueries = quantizedQueries.size();
-        vectorDimension = (int) quantizedQueries.columns();
-        queryDataType = quantizedQueries.dataType();
-        floatsSeg = ((CuVSMatrixBaseImpl) quantizedQueries).memorySegment();
-      } else {
-        float[][] queryVectors = query.getQueryVectors();
-        numQueries = queryVectors.length;
-        vectorDimension = numQueries > 0 ? queryVectors[0].length : 0;
-        queryDataType = DataType.FLOAT; // Float queries are always FLOAT
-        floatsSeg = buildMemorySegment(localArena, queryVectors);
-      }
-
       int topK = query.getTopK();
-
-      // Calculate bytes based on actual data type
-      long queriesBytes = getQueryBytesSize(numQueries, vectorDimension, queryDataType);
+      long numQueries = query.getQueryVectors().length;
       long numBlocks = topK * numQueries;
-      long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
-      long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+      int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
 
       SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_INT);
       SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
       MemorySegment neighborsMemorySegment = localArena.allocate(neighborsSequenceLayout);
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
+      MemorySegment floatsSeg = buildMemorySegment(localArena, query.getQueryVectors());
+
+      final long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
+      final long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
+      final long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
       final boolean hasPreFilter = query.getPrefilter() != null;
       final BitSet[] prefilters =
           hasPreFilter ? new BitSet[] {query.getPrefilter()} : EMPTY_PREFILTER_BITSET;
@@ -329,13 +293,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
           long[] queriesShape = {numQueries, vectorDimension};
           MemorySegment queriesTensor =
               prepareTensor(
-                  localArena,
-                  queriesDP.handle(),
-                  queriesShape,
-                  getDataTypeCode(queryDataType, this.indexQuantizer),
-                  getPrecisionFromDataType(queryDataType),
-                  kDLCUDA(),
-                  1);
+                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
           long[] neighborsShape = {numQueries, topK};
           MemorySegment neighborsTensor =
               prepareTensor(
@@ -815,51 +773,6 @@ private static CloseableHandle createMergeParamsSegment(CagraMergeParams mergePa
     return new CompositeCloseableHandle(seg, handles);
   }
 
-  // Helper method to map DataType to data type codes
-  private int getDataTypeCode(DataType dataType, CuVSQuantizer quantizer) {
-    switch (dataType) {
-      case FLOAT:
-        return kDLFloat();
-      case BYTE:
-        if (quantizer instanceof BinaryQuantizer) {
-          return kDLUInt();
-        } else {
-          return kDLInt();
-        }
-      case INT:
-        return kDLInt();
-      default:
-        throw new IllegalArgumentException("Unsupported data type: " + dataType);
-    }
-  }
-
-  // Helper method to convert DataType to precision
-  private int getPrecisionFromDataType(DataType dataType) {
-    switch (dataType) {
-      case FLOAT:
-        return 32;
-      case BYTE:
-        return 8;
-      case INT:
-        return 32;
-      default:
-        throw new IllegalArgumentException("Unsupported data type: " + dataType);
-    }
-  }
-
-  private long getQueryBytesSize(long numQueries, int vectorDimension, DataType dataType) {
-    switch (dataType) {
-      case FLOAT:
-        return C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
-      case BYTE:
-        return numQueries * vectorDimension;
-      case INT:
-        return C_INT_BYTE_SIZE * numQueries * vectorDimension;
-      default:
-        throw new IllegalArgumentException("Unsupported query data type: " + dataType);
-    }
-  }
-
   /**
    * Builder helps configure and create an instance of {@link CagraIndex}.
    */
@@ -869,7 +782,6 @@ public static class Builder implements CagraIndex.Builder {
     private CagraIndexParams cagraIndexParams;
     private final CuVSResources cuvsResources;
     private InputStream inputStream;
-    private CuVSQuantizer quantizer;
     private CuVSMatrix graph;
 
     public Builder(CuVSResources cuvsResources) {
@@ -906,28 +818,11 @@ public Builder withIndexParams(CagraIndexParams cagraIndexParameters) {
       return this;
     }
 
-    @Override
-    public Builder withQuantizer(CuVSQuantizer quantizer) {
-      this.quantizer = quantizer;
-      return this;
-    }
-
     @Override
     public CagraIndexImpl build() throws Throwable {
       if (inputStream != null) {
         return new CagraIndexImpl(inputStream, cuvsResources);
       } else {
-        if (quantizer != null && dataset != null) {
-          if (dataset.dataType() != DataType.FLOAT) {
-            throw new IllegalArgumentException(
-                "Quantizer input requires FLOAT data type, dataset has " + dataset.dataType());
-          }
-
-          CuVSMatrix quantizedDataset = quantizer.transform(dataset);
-
-          return new CagraIndexImpl(cagraIndexParams, quantizedDataset, cuvsResources);
-        }
-
         if (graph != null) {
           if (cagraIndexParams == null || dataset == null) {
             throw new IllegalArgumentException(
@@ -935,9 +830,10 @@ public CagraIndexImpl build() throws Throwable {
                     + "you must specify the original dataset and the metric used.");
           }
           return new CagraIndexImpl(
-              cagraIndexParams.getCuvsDistanceType(), graph, dataset, cuvsResources, null);
+              cagraIndexParams.getCuvsDistanceType(), graph, dataset, cuvsResources);
+        } else {
+          return new CagraIndexImpl(cagraIndexParams, dataset, cuvsResources);
         }
-        return new CagraIndexImpl(cagraIndexParams, dataset, cuvsResources);
       }
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
index ffc4efcd95..c2895282ca 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -22,8 +22,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSHostMatrix;
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-import com.nvidia.cuvs.CuVSMatrix.MemoryKind;
 import com.nvidia.cuvs.RowView;
 import java.lang.foreign.*;
 import java.lang.invoke.VarHandle;
@@ -120,11 +118,6 @@ public void toArray(byte[][] array) {
     }
   }
 
-  @Override
-  public MemoryKind memoryKind() {
-    return MemoryKind.HOST;
-  }
-
   @Override
   public void close() {}
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
index fc0461ef22..120960cfc4 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -18,7 +18,6 @@
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSMatrix;
-import com.nvidia.cuvs.CuVSMatrix.DataType;
 import java.lang.foreign.Arena;
 import java.lang.foreign.MemorySegment;
 
@@ -50,11 +49,6 @@ public MemorySegment memorySegment() {
     return memorySegment;
   }
 
-  @Override
-  public DataType dataType() {
-    return dataType;
-  }
-
   protected int bits() {
     return switch (dataType) {
       case FLOAT, INT, UINT -> 32;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java
deleted file mode 100644
index de49005cb2..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/Scalar8BitQuantizerImpl.java
+++ /dev/null
@@ -1,461 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs.internal;
-
-import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
-import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.*;
-import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
-import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
-import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerInverseTransform;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerParamsCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerParamsDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerParams_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerTrain;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizerTransform;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsScalarQuantizer_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLCPU;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLCUDA;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLFloat;
-import static com.nvidia.cuvs.internal.panama.headers_h.kDLInt;
-
-import com.nvidia.cuvs.CuVSMatrix;
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-import com.nvidia.cuvs.CuVSMatrix.MemoryKind;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.internal.panama.cuvsScalarQuantizerParams;
-import java.lang.foreign.Arena;
-import java.lang.foreign.MemorySegment;
-import java.util.Objects;
-
-/**
- * Implementation of Scalar8BitQuantizer using Panama Foreign Function Interface.
- */
-public class Scalar8BitQuantizerImpl {
-
-  private static final long C_BYTE_SIZE = C_CHAR.byteSize();
-
-  private final CuVSResources resources;
-  private final MemorySegment quantizerSegment;
-  private boolean destroyed;
-
-  private Scalar8BitQuantizerImpl(CuVSResources resources, MemorySegment quantizerSegment) {
-    this.resources = resources;
-    this.quantizerSegment = quantizerSegment;
-  }
-
-  private void checkNotDestroyed() {
-    if (destroyed) {
-      throw new IllegalStateException("Scalar8BitQuantizer has been destroyed");
-    }
-  }
-
-  public void destroy() throws Throwable {
-    checkNotDestroyed();
-    try {
-      int returnValue = cuvsScalarQuantizerDestroy(quantizerSegment);
-      checkCuVSError(returnValue, "cuvsScalarQuantizerDestroy");
-    } finally {
-      destroyed = true;
-    }
-  }
-
-  public void close() throws Throwable {
-    destroy();
-  }
-
-  public CuVSMatrix transform(CuVSMatrix dataset) throws Throwable {
-    checkNotDestroyed();
-
-    if (dataset.dataType() != DataType.FLOAT) {
-      throw new IllegalArgumentException(
-          "Scalar8BitQuantizer requires FLOAT input, got " + dataset.dataType());
-    }
-
-    try (Arena resultArena = Arena.ofShared();
-        var localArena = Arena.ofConfined();
-        var resourcesAccessor = resources.access()) {
-
-      long rows = dataset.size();
-      long cols = dataset.columns();
-
-      MemorySegment datasetMemSegment = ((CuVSMatrixBaseImpl) dataset).memorySegment();
-      long cuvsResourcesPtr = resourcesAccessor.handle();
-
-      // TODO: Currently all datasets are HOST memory kind, preventing testing of GPU quantization
-      // flow.
-      // Future improvement: Support GPU-native dataset creation in Java API to enable direct GPU
-      // quantization testing.
-      if (dataset.memoryKind() == MemoryKind.HOST) {
-        return performTransformHost(
-            cuvsResourcesPtr, quantizerSegment, localArena, rows, cols, datasetMemSegment);
-      }
-
-      return performTransformGPU(
-          cuvsResourcesPtr,
-          quantizerSegment,
-          localArena,
-          resultArena,
-          rows,
-          cols,
-          datasetMemSegment);
-    } catch (Throwable t) {
-      throw t;
-    }
-  }
-
-  /**
-   * GPU transform with buffered processing for large datasets.
-   */
-  private CuVSMatrix performTransformGPU(
-      long cuvsResourcesPtr,
-      MemorySegment quantizerSeg,
-      Arena localArena,
-      Arena resultArena,
-      long rows,
-      long cols,
-      MemorySegment datasetMemSegment)
-      throws Throwable {
-
-    final long BUFFER_SIZE_BYTES = 1024 * 1024;
-    final long FLOAT_SIZE = C_FLOAT_BYTE_SIZE;
-    final long FLOATS_PER_BUFFER = BUFFER_SIZE_BYTES / FLOAT_SIZE;
-    final long ROWS_PER_BUFFER = Math.max(1, FLOATS_PER_BUFFER / cols);
-
-    CuVSMatrix result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
-
-    // Allocate buffers outside the loop (addressing reviewer feedback)
-    long maxDatasetBytes = FLOAT_SIZE * ROWS_PER_BUFFER * cols;
-    long maxOutputBytes = C_BYTE_SIZE * ROWS_PER_BUFFER * cols;
-
-    try (var datasetDP = allocateRMMSegment(cuvsResourcesPtr, maxDatasetBytes);
-        var outputDP = allocateRMMSegment(cuvsResourcesPtr, maxOutputBytes)) {
-
-      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
-        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
-        long chunkRows = endRow - startRow;
-        long actualDatasetBytes = FLOAT_SIZE * chunkRows * cols;
-        long actualOutputBytes = C_BYTE_SIZE * chunkRows * cols;
-
-        // Get slices of pre-allocated buffers
-        MemorySegment datasetPtr = datasetDP.handle().asSlice(0, actualDatasetBytes);
-        MemorySegment outputPtr = outputDP.handle().asSlice(0, actualOutputBytes);
-
-        MemorySegment chunkData =
-            datasetMemSegment.asSlice(startRow * cols * FLOAT_SIZE, actualDatasetBytes);
-
-        cudaMemcpy(datasetPtr, chunkData, actualDatasetBytes, INFER_DIRECTION);
-
-        long datasetShape[] = {chunkRows, cols};
-        MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
-        MemorySegment outputTensor =
-            prepareTensor(localArena, outputPtr, datasetShape, kDLInt(), 8, kDLCUDA(), 1);
-
-        int returnValue =
-            cuvsScalarQuantizerTransform(
-                cuvsResourcesPtr, quantizerSeg, datasetTensor, outputTensor);
-        checkCuVSError(returnValue, "cuvsScalarQuantizerTransform");
-
-        returnValue = cuvsStreamSync(cuvsResourcesPtr);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        MemorySegment resultChunk =
-            ((CuVSMatrixBaseImpl) result)
-                .memorySegment()
-                .asSlice(startRow * cols, actualOutputBytes);
-        cudaMemcpy(resultChunk, outputPtr, actualOutputBytes, DEVICE_TO_HOST);
-      }
-    } // CloseableRMMAllocation automatically closes here
-
-    if (result.dataType() != DataType.BYTE) {
-      throw new IllegalStateException(
-          "Expected BYTE output from scalar quantization, got " + result.dataType());
-    }
-
-    return result;
-  }
-
-  private MemorySegment buildMemorySegmentFromBytes(Arena arena, byte[][] data) {
-    long rows = data.length;
-    long cols = rows > 0 ? data[0].length : 0;
-    long totalBytes = rows * cols * C_BYTE_SIZE;
-
-    MemorySegment segment = arena.allocate(totalBytes);
-    for (int i = 0; i < rows; i++) {
-      for (int j = 0; j < cols; j++) {
-        segment.set(C_CHAR, (i * cols + j) * C_BYTE_SIZE, data[i][j]);
-      }
-    }
-    return segment;
-  }
-
-  public static Scalar8BitQuantizerImpl create(CuVSResources resources, CuVSMatrix trainingDataset)
-      throws Throwable {
-    Objects.requireNonNull(resources);
-    Objects.requireNonNull(trainingDataset);
-
-    try (var localArena = Arena.ofConfined();
-        var resourcesAccessor = resources.access()) {
-
-      long rows = trainingDataset.size();
-      long cols = trainingDataset.columns();
-      MemorySegment datasetMemSegment = ((CuVSMatrixBaseImpl) trainingDataset).memorySegment();
-      long cuvsResourcesPtr = resourcesAccessor.handle();
-
-      MemorySegment paramsSegment = localArena.allocate(cuvsScalarQuantizerParams_t);
-      int returnValue = cuvsScalarQuantizerParamsCreate(paramsSegment);
-      checkCuVSError(returnValue, "cuvsScalarQuantizerParamsCreate");
-
-      MemorySegment paramsPtr = paramsSegment.get(C_POINTER, 0);
-      cuvsScalarQuantizerParams.quantile(paramsPtr, 0.99f);
-
-      MemorySegment quantizerSegment = localArena.allocate(cuvsScalarQuantizer_t);
-      returnValue = cuvsScalarQuantizerCreate(quantizerSegment);
-      checkCuVSError(returnValue, "cuvsScalarQuantizerCreate");
-
-      MemorySegment quantizerPtr = quantizerSegment.get(C_POINTER, 0);
-
-      if (trainingDataset.memoryKind() == MemoryKind.HOST) {
-        long datasetShape[] = {rows, cols};
-        MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetMemSegment, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
-
-        returnValue =
-            cuvsScalarQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
-        checkCuVSError(returnValue, "cuvsScalarQuantizerTrain");
-      } else {
-        performGPUTraining(
-            cuvsResourcesPtr, paramsPtr, quantizerPtr, localArena, rows, cols, datasetMemSegment);
-      }
-
-      returnValue = cuvsScalarQuantizerParamsDestroy(paramsPtr);
-      checkCuVSError(returnValue, "cuvsScalarQuantizerParamsDestroy");
-
-      return new Scalar8BitQuantizerImpl(resources, quantizerPtr);
-    }
-  }
-
-  public CuVSMatrix inverseTransform(CuVSMatrix quantizedData) throws Throwable {
-    checkNotDestroyed();
-
-    if (quantizedData.dataType() != DataType.BYTE) {
-      throw new IllegalArgumentException(
-          "Inverse transform requires BYTE input, got " + quantizedData.dataType());
-    }
-
-    try (Arena resultArena = Arena.ofShared();
-        var localArena = Arena.ofConfined();
-        var resourcesAccessor = resources.access()) {
-
-      long rows = quantizedData.size();
-      long cols = quantizedData.columns();
-      MemorySegment quantizedMemSegment = ((CuVSMatrixBaseImpl) quantizedData).memorySegment();
-      long cuvsResourcesPtr = resourcesAccessor.handle();
-
-      // TODO: Currently all datasets are HOST memory kind, preventing testing of GPU quantization
-      // flow.
-      // Future improvement: Support GPU-native dataset creation in Java API to enable direct GPU
-      // quantization testing.
-      if (quantizedData.memoryKind() == MemoryKind.HOST) {
-        return performInverseTransformHost(
-            cuvsResourcesPtr, quantizerSegment, localArena, rows, cols, quantizedMemSegment);
-      }
-
-      return performInverseTransformGPU(
-          cuvsResourcesPtr,
-          quantizerSegment,
-          localArena,
-          resultArena,
-          rows,
-          cols,
-          quantizedMemSegment);
-    }
-  }
-
-  private CuVSMatrix performInverseTransformGPU(
-      long cuvsResourcesPtr,
-      MemorySegment quantizerSeg,
-      Arena localArena,
-      Arena resultArena,
-      long rows,
-      long cols,
-      MemorySegment quantizedMemSegment)
-      throws Throwable {
-
-    final long BUFFER_SIZE_BYTES = 1024 * 1024;
-    final long BYTE_SIZE = C_BYTE_SIZE;
-    final long BYTES_PER_BUFFER = BUFFER_SIZE_BYTES;
-    final long ROWS_PER_BUFFER = Math.max(1, BYTES_PER_BUFFER / (cols * BYTE_SIZE));
-
-    CuVSMatrix result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.FLOAT);
-
-    // Allocate buffers outside the loop (addressing reviewer feedback)
-    long maxQuantizedBytes = BYTE_SIZE * ROWS_PER_BUFFER * cols;
-    long maxOutputBytes = C_FLOAT_BYTE_SIZE * ROWS_PER_BUFFER * cols;
-
-    try (var quantizedDP = allocateRMMSegment(cuvsResourcesPtr, maxQuantizedBytes);
-        var outputDP = allocateRMMSegment(cuvsResourcesPtr, maxOutputBytes)) {
-
-      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
-        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
-        long chunkRows = endRow - startRow;
-        long actualQuantizedBytes = BYTE_SIZE * chunkRows * cols;
-        long actualOutputBytes = C_FLOAT_BYTE_SIZE * chunkRows * cols;
-
-        // Get slices of pre-allocated buffers
-        MemorySegment quantizedPtr = quantizedDP.handle().asSlice(0, actualQuantizedBytes);
-        MemorySegment outputPtr = outputDP.handle().asSlice(0, actualOutputBytes);
-
-        MemorySegment chunkData =
-            quantizedMemSegment.asSlice(startRow * cols * BYTE_SIZE, actualQuantizedBytes);
-        cudaMemcpy(quantizedPtr, chunkData, actualQuantizedBytes, INFER_DIRECTION);
-
-        long datasetShape[] = {chunkRows, cols};
-        MemorySegment quantizedTensor =
-            prepareTensor(localArena, quantizedPtr, datasetShape, kDLInt(), 8, kDLCUDA(), 1);
-        MemorySegment outputTensor =
-            prepareTensor(localArena, outputPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
-
-        int returnValue =
-            cuvsScalarQuantizerInverseTransform(
-                cuvsResourcesPtr, quantizerSeg, quantizedTensor, outputTensor);
-        checkCuVSError(returnValue, "cuvsScalarQuantizerInverseTransform");
-
-        returnValue = cuvsStreamSync(cuvsResourcesPtr);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-
-        MemorySegment resultChunk =
-            ((CuVSMatrixBaseImpl) result)
-                .memorySegment()
-                .asSlice(startRow * cols * C_FLOAT_BYTE_SIZE, actualOutputBytes);
-        cudaMemcpy(resultChunk, outputPtr, actualOutputBytes, DEVICE_TO_HOST);
-      }
-    } // CloseableRMMAllocation automatically closes here
-
-    return result;
-  }
-
-  private static void performGPUTraining(
-      long cuvsResourcesPtr,
-      MemorySegment paramsPtr,
-      MemorySegment quantizerPtr,
-      Arena localArena,
-      long rows,
-      long cols,
-      MemorySegment datasetMemSegment)
-      throws Throwable {
-
-    final long BUFFER_SIZE_BYTES = 1024 * 1024;
-    final long FLOAT_SIZE = C_FLOAT_BYTE_SIZE;
-    final long FLOATS_PER_BUFFER = BUFFER_SIZE_BYTES / FLOAT_SIZE;
-    final long ROWS_PER_BUFFER = Math.max(1, FLOATS_PER_BUFFER / cols);
-
-    // Allocate buffer outside the loop (addressing reviewer feedback)
-    long maxDatasetBytes = FLOAT_SIZE * ROWS_PER_BUFFER * cols;
-
-    try (var datasetDP = allocateRMMSegment(cuvsResourcesPtr, maxDatasetBytes)) {
-      for (long startRow = 0; startRow < rows; startRow += ROWS_PER_BUFFER) {
-        long endRow = Math.min(startRow + ROWS_PER_BUFFER, rows);
-        long chunkRows = endRow - startRow;
-        long actualDatasetBytes = FLOAT_SIZE * chunkRows * cols;
-
-        // Get slice of pre-allocated buffer
-        MemorySegment datasetPtr = datasetDP.handle().asSlice(0, actualDatasetBytes);
-
-        MemorySegment chunkData =
-            datasetMemSegment.asSlice(startRow * cols * FLOAT_SIZE, actualDatasetBytes);
-        cudaMemcpy(datasetPtr, chunkData, actualDatasetBytes, INFER_DIRECTION);
-
-        long datasetShape[] = {chunkRows, cols};
-        MemorySegment datasetTensor =
-            prepareTensor(localArena, datasetPtr, datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
-
-        int returnValue =
-            cuvsScalarQuantizerTrain(cuvsResourcesPtr, paramsPtr, datasetTensor, quantizerPtr);
-        checkCuVSError(returnValue, "cuvsScalarQuantizerTrain");
-
-        returnValue = cuvsStreamSync(cuvsResourcesPtr);
-        checkCuVSError(returnValue, "cuvsStreamSync");
-      }
-    } // CloseableRMMAllocation automatically closes here
-  }
-
-  private static CuVSMatrix performTransformHost(
-      long resPtr,
-      MemorySegment quantizerSeg,
-      Arena localArena,
-      long rows,
-      long cols,
-      MemorySegment dataSeg)
-      throws Throwable {
-
-    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.BYTE);
-
-    long[] shape = {rows, cols};
-    MemorySegment inTensor = prepareTensor(localArena, dataSeg, shape, kDLFloat(), 32, kDLCPU(), 1);
-    MemorySegment outTensor =
-        prepareTensor(
-            localArena,
-            ((CuVSMatrixBaseImpl) result).memorySegment(),
-            shape,
-            kDLInt(),
-            8,
-            kDLCPU(),
-            1);
-
-    int rv = cuvsScalarQuantizerTransform(resPtr, quantizerSeg, inTensor, outTensor);
-    checkCuVSError(rv, "cuvsScalarQuantizerTransform (host)");
-
-    return result;
-  }
-
-  private static CuVSMatrix performInverseTransformHost(
-      long resPtr,
-      MemorySegment quantizerSeg,
-      Arena localArena,
-      long rows,
-      long cols,
-      MemorySegment quantizedSeg)
-      throws Throwable {
-
-    var result = new CuVSHostMatrixArenaImpl(rows, cols, DataType.FLOAT);
-
-    long[] shape = {rows, cols};
-    MemorySegment inTensor =
-        prepareTensor(localArena, quantizedSeg, shape, kDLInt(), 8, kDLCPU(), 1);
-    MemorySegment outTensor =
-        prepareTensor(
-            localArena,
-            ((CuVSMatrixBaseImpl) result).memorySegment(),
-            shape,
-            kDLFloat(),
-            32,
-            kDLCPU(),
-            1);
-
-    int rv = cuvsScalarQuantizerInverseTransform(resPtr, quantizerSeg, inTensor, outTensor);
-    checkCuVSError(rv, "cuvsScalarQuantizerInverseTransform (host)");
-
-    return result;
-  }
-}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 0c29188456..668befe236 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -188,45 +188,4 @@ public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     Util.copy(dataset.memorySegment(), vectors);
     return dataset;
   }
-
-  @Override
-  public Object createScalar8BitQuantizerImpl(CuVSResources resources, CuVSMatrix trainingDataset)
-      throws Throwable {
-    return Scalar8BitQuantizerImpl.create(resources, trainingDataset);
-  }
-
-  @Override
-  public CuVSMatrix transformScalar8Bit(Object impl, CuVSMatrix input) throws Throwable {
-    return ((Scalar8BitQuantizerImpl) impl).transform(input);
-  }
-
-  @Override
-  public CuVSMatrix inverseTransformScalar8Bit(Object impl, CuVSMatrix quantizedData)
-      throws Throwable {
-    return ((Scalar8BitQuantizerImpl) impl).inverseTransform(quantizedData);
-  }
-
-  @Override
-  public Object createBinaryQuantizerImpl(
-      CuVSResources resources,
-      CuVSMatrix trainingDataset,
-      BinaryQuantizer.ThresholdType thresholdType)
-      throws Throwable {
-    return BinaryQuantizerImpl.create(resources, trainingDataset, thresholdType);
-  }
-
-  @Override
-  public CuVSMatrix transformBinaryWithImpl(Object impl, CuVSMatrix input) throws Throwable {
-    return BinaryQuantizerImpl.transformWithImpl(impl, input);
-  }
-
-  @Override
-  public void closeBinaryQuantizer(Object impl) throws Throwable {
-    BinaryQuantizerImpl.close(impl);
-  }
-
-  @Override
-  public void closeScalar8BitQuantizer(Object impl) throws Throwable {
-    ((Scalar8BitQuantizerImpl) impl).close();
-  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java
deleted file mode 100644
index 8189fd9139..0000000000
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/QuantizationIT.java
+++ /dev/null
@@ -1,588 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs;
-
-import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
-import static org.junit.Assert.*;
-
-import com.carrotsearch.randomizedtesting.RandomizedRunner;
-import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
-import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
-import com.nvidia.cuvs.CuVSMatrix.DataType;
-import java.lang.invoke.MethodHandles;
-import java.util.Map;
-import org.junit.Before;
-import org.junit.Test;
-import org.junit.runner.RunWith;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-@RunWith(RandomizedRunner.class)
-public class QuantizationIT extends CuVSTestCase {
-
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
-
-  @Before
-  public void setup() {
-    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
-    initializeRandom();
-    log.info("Random context initialized for quantization test.");
-  }
-
-  private static float[][] createSimpleDataset() {
-    return new float[][] {
-      {1.0f, 2.0f, 3.0f},
-      {4.0f, 5.0f, 6.0f},
-      {7.0f, 8.0f, 9.0f},
-      {10.0f, 11.0f, 12.0f},
-      {13.0f, 14.0f, 15.0f}
-    };
-  }
-
-  private static float[][] createSimpleQueries() {
-    return new float[][] {
-      {1.1f, 2.1f, 3.1f},
-      {7.1f, 8.1f, 9.1f}
-    };
-  }
-
-  @Test
-  public void testScalarQuantizerBasicFlow() throws Throwable {
-    float[][] dataset = createSimpleDataset();
-    float[][] queries = createSimpleQueries();
-
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
-      assertEquals(DataType.FLOAT, trainingDataset.dataType());
-      assertEquals(dataset.length, trainingDataset.size());
-      assertEquals(dataset[0].length, trainingDataset.columns());
-
-      Scalar8BitQuantizer quantizer = new Scalar8BitQuantizer(resources, trainingDataset);
-      assertEquals(DataType.BYTE, quantizer.outputDataType());
-      log.info("Created scalar quantizer with BYTE data type");
-
-      CagraIndexParams indexParams =
-          new CagraIndexParams.Builder()
-              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-              .withGraphDegree(2)
-              .withIntermediateGraphDegree(3)
-              .withNumWriterThreads(1)
-              .withMetric(CuvsDistanceType.L2Expanded)
-              .build();
-
-      CagraIndex index =
-          CagraIndex.newBuilder(resources)
-              .withDataset(trainingDataset)
-              .withQuantizer(quantizer)
-              .withIndexParams(indexParams)
-              .build();
-
-      log.info("Built index with quantized dataset");
-
-      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-
-      CagraQuery query =
-          CagraQuery.newBuilder(resources)
-              .withQueryVectors(queries)
-              .withQuantizer(quantizer)
-              .withSearchParams(searchParams)
-              .withTopK(3)
-              .build();
-
-      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
-      assertEquals(DataType.BYTE, query.getQueryDataType());
-      log.info("Created quantized query");
-
-      // Perform search
-      SearchResults results = index.search(query);
-      assertNotNull("Search results should not be null", results);
-
-      // Verify results
-      assertEquals(
-          "Should have results for all queries", queries.length, results.getResults().size());
-
-      for (int i = 0; i < results.getResults().size(); i++) {
-        Map<Integer, Float> queryResults = results.getResults().get(i);
-        assertFalse("Query " + i + " should return some neighbors", queryResults.isEmpty());
-
-        // Verify all returned IDs are within valid range
-        for (Integer id : queryResults.keySet()) {
-          assertTrue("Returned ID should be valid", id >= 0 && id < dataset.length);
-        }
-      }
-
-      log.info(
-          "Search completed successfully with {} queries returning neighbors",
-          results.getResults().size());
-
-      // Cleanup
-      index.close();
-      quantizer.close();
-      trainingDataset.close();
-    }
-  }
-
-  @Test
-  public void testBinaryQuantizerBasicFlow() throws Throwable {
-    float[][] dataset = createSimpleDataset();
-    float[][] queries = createSimpleQueries();
-
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
-      assertEquals(DataType.FLOAT, trainingDataset.dataType());
-
-      BinaryQuantizer quantizer = new BinaryQuantizer(resources, trainingDataset);
-      assertEquals(DataType.BYTE, quantizer.outputDataType());
-      log.info("Created binary quantizer");
-
-      CagraIndexParams indexParams =
-          new CagraIndexParams.Builder()
-              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-              .withGraphDegree(2)
-              .withIntermediateGraphDegree(3)
-              .withNumWriterThreads(1)
-              .withMetric(CuvsDistanceType.L2Expanded)
-              .build();
-
-      CagraIndex index =
-          CagraIndex.newBuilder(resources)
-              .withDataset(trainingDataset)
-              .withQuantizer(quantizer)
-              .withIndexParams(indexParams)
-              .build();
-
-      log.info("Built index with binary quantized dataset");
-
-      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-
-      CagraQuery query =
-          CagraQuery.newBuilder(resources)
-              .withQueryVectors(queries)
-              .withQuantizer(quantizer)
-              .withSearchParams(searchParams)
-              .withTopK(3)
-              .build();
-
-      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
-      assertEquals(DataType.BYTE, query.getQueryDataType());
-
-      // Perform search
-      SearchResults results = index.search(query);
-      assertNotNull("Search results should not be null", results);
-
-      // Verify results
-      assertEquals(
-          "Should have results for all queries", queries.length, results.getResults().size());
-
-      for (int i = 0; i < results.getResults().size(); i++) {
-        Map<Integer, Float> queryResults = results.getResults().get(i);
-        assertFalse("Query " + i + " should return some neighbors", queryResults.isEmpty());
-
-        // Verify all returned IDs are within valid range
-        for (Integer id : queryResults.keySet()) {
-          assertTrue("Returned ID should be valid", id >= 0 && id < dataset.length);
-        }
-      }
-
-      log.info("Binary quantized search completed successfully");
-
-      CuVSMatrix quantizedQueries = query.getQuantizedQueries();
-      try {
-        quantizer.inverseTransform(quantizedQueries);
-        fail("Expected UnsupportedOperationException to be thrown");
-      } catch (UnsupportedOperationException e) {
-        assertTrue(
-            "Exception message should mention inverse transform",
-            e.getMessage().contains("inverse"));
-      }
-
-      // Cleanup
-      index.close();
-      quantizer.close();
-      trainingDataset.close();
-      quantizedQueries.close();
-    }
-  }
-
-  @Test
-  public void testScalarQuantizerInverseTransform() throws Throwable {
-    float[][] dataset = createSimpleDataset();
-
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
-      Scalar8BitQuantizer quantizer = new Scalar8BitQuantizer(resources, trainingDataset);
-
-      // Transform and inverse transform
-      CuVSMatrix quantized = quantizer.transform(trainingDataset);
-      assertEquals(DataType.BYTE, quantized.dataType());
-
-      CuVSMatrix recovered = quantizer.inverseTransform(quantized);
-      assertEquals(DataType.FLOAT, recovered.dataType());
-      assertEquals(trainingDataset.size(), recovered.size());
-      assertEquals(trainingDataset.columns(), recovered.columns());
-
-      log.info("Inverse transform completed successfully");
-
-      // Verify quantization worked
-      assertEquals(DataType.BYTE, quantized.dataType());
-      assertEquals(DataType.FLOAT, recovered.dataType());
-
-      // Cleanup
-      quantizer.close();
-      trainingDataset.close();
-      quantized.close();
-      recovered.close();
-    }
-  }
-
-  @Test
-  public void testCPUBinaryQuantization() throws Throwable {
-    float[][] dataset = createSimpleDataset();
-
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CuVSMatrix inputDataset = CuVSMatrix.ofArray(dataset);
-      assertEquals(CuVSMatrix.MemoryKind.HOST, inputDataset.memoryKind());
-
-      BinaryQuantizer quantizer = new BinaryQuantizer(resources, inputDataset);
-
-      CuVSMatrix quantized = quantizer.transform(inputDataset);
-      assertEquals(DataType.BYTE, quantized.dataType());
-      assertEquals(CuVSMatrix.MemoryKind.HOST, quantized.memoryKind());
-
-      try {
-        quantizer.inverseTransform(quantized);
-        fail("Expected UnsupportedOperationException to be thrown");
-      } catch (UnsupportedOperationException e) {
-        assertTrue(
-            "Exception message should mention inverse transform",
-            e.getMessage().contains("inverse"));
-      }
-
-      quantizer.close();
-      inputDataset.close();
-      quantized.close();
-
-      log.info("✓ Binary CPU quantization test passed");
-    }
-  }
-
-  @Test
-  public void testRandomizedScalarQuantizedIndexingAndSearch() throws Throwable {
-    int maxDatasetSize = 60;
-    int maxDimensions = 20;
-    int maxQueries = 5;
-    int maxTopK = 4;
-
-    for (int iteration = 0; iteration < 5; iteration++) {
-      int datasetSize = random.nextInt(maxDatasetSize) + maxTopK;
-      int dims = random.nextInt(maxDimensions) + 2;
-      int numQueries = random.nextInt(maxQueries) + 1;
-      int topK = Math.min(random.nextInt(maxTopK) + 1, datasetSize);
-
-      // Generate dataset and query
-      float[][] dataset = new float[datasetSize][dims];
-      float[][] queries = new float[numQueries][dims];
-      for (int i = 0; i < datasetSize; i++)
-        for (int d = 0; d < dims; d++) dataset[i][d] = (float) random.nextGaussian();
-      for (int i = 0; i < numQueries; i++)
-        for (int d = 0; d < dims; d++) queries[i][d] = (float) random.nextGaussian();
-
-      try (CuVSResources resources = CheckedCuVSResources.create()) {
-        CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
-        Scalar8BitQuantizer quantizer = new Scalar8BitQuantizer(resources, trainingDataset);
-
-        CagraIndexParams indexParams =
-            new CagraIndexParams.Builder()
-                .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-                .withGraphDegree(Math.min(4, datasetSize - 1))
-                .withIntermediateGraphDegree(Math.min(5, datasetSize))
-                .withNumWriterThreads(1)
-                .withMetric(CuvsDistanceType.L2Expanded)
-                .build();
-
-        CagraIndex index =
-            CagraIndex.newBuilder(resources)
-                .withDataset(trainingDataset)
-                .withQuantizer(quantizer)
-                .withIndexParams(indexParams)
-                .build();
-
-        CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-        CagraQuery query =
-            CagraQuery.newBuilder(resources)
-                .withQueryVectors(queries)
-                .withQuantizer(quantizer)
-                .withSearchParams(searchParams)
-                .withTopK(topK)
-                .build();
-
-        SearchResults results = index.search(query);
-        assertNotNull("Results must not be null", results);
-        assertEquals("One result per query", numQueries, results.getResults().size());
-
-        for (int qi = 0; qi < numQueries; qi++) {
-          Map<Integer, Float> result = results.getResults().get(qi);
-          // It is possible for result to have < topK entries if datasetSize == topK
-          assertFalse("Each query should return non-empty result", result.isEmpty());
-          for (Integer id : result.keySet()) {
-            assertTrue("ID in result should be in range", id >= 0 && id < datasetSize);
-          }
-        }
-
-        quantizer.close();
-        trainingDataset.close();
-        index.close();
-      }
-    }
-  }
-
-  @Test
-  public void testBinaryQuantizationWithZeroThreshold() throws Throwable {
-    float[][] dataset =
-        new float[][] {
-          {-2.0f, -1.0f, 0.0f, 1.0f, 2.0f}, // crosses zero
-          {0.1f, 0.5f, 1.0f, 1.5f, 2.0f}, // all positive
-          {-2.0f, -1.5f, -1.0f, -0.5f, 0.0f}, // mostly negative
-          {-1.0f, 0.0f, 1.0f, 2.0f, 3.0f} // mixed
-        };
-
-    float[][] queries =
-        new float[][] {
-          {0.5f, 0.5f, 0.5f, 0.5f, 0.5f},
-          {-0.5f, -0.5f, -0.5f, -0.5f, -0.5f}
-        };
-
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
-
-      BinaryQuantizer quantizer =
-          new BinaryQuantizer(resources, trainingDataset, BinaryQuantizer.ThresholdType.ZERO);
-      assertEquals(DataType.BYTE, quantizer.outputDataType());
-      assertEquals(BinaryQuantizer.ThresholdType.ZERO, quantizer.getThresholdType());
-
-      CagraIndexParams indexParams =
-          new CagraIndexParams.Builder()
-              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-              .withGraphDegree(2)
-              .withIntermediateGraphDegree(3)
-              .withNumWriterThreads(1)
-              .withMetric(CuvsDistanceType.L2Expanded)
-              .build();
-
-      CagraIndex index =
-          CagraIndex.newBuilder(resources)
-              .withDataset(trainingDataset)
-              .withQuantizer(quantizer)
-              .withIndexParams(indexParams)
-              .build();
-
-      log.info("Built index with ZERO threshold binary quantization");
-
-      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-      CagraQuery query =
-          CagraQuery.newBuilder(resources)
-              .withQueryVectors(queries)
-              .withQuantizer(quantizer)
-              .withSearchParams(searchParams)
-              .withTopK(2)
-              .build();
-
-      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
-      assertEquals(DataType.BYTE, query.getQueryDataType());
-
-      SearchResults results = index.search(query);
-      assertNotNull("Search results should not be null", results);
-      assertEquals(
-          "Should have results for all queries", queries.length, results.getResults().size());
-
-      CuVSMatrix quantizedDataset = quantizer.transform(trainingDataset);
-      byte[][] result = new byte[(int) quantizedDataset.size()][(int) quantizedDataset.columns()];
-      quantizedDataset.toArray(result);
-
-      byte firstByte = result[0][0];
-      int[] unpackedBits = new int[5];
-      for (int i = 0; i < 5; i++) {
-        unpackedBits[i] = (firstByte >> i) & 1;
-      }
-
-      int[] expectedRow0 = {0, 0, 0, 1, 1};
-      assertArrayEquals("ZERO threshold failed for row 0", expectedRow0, unpackedBits);
-
-      index.close();
-      quantizer.close();
-      trainingDataset.close();
-      quantizedDataset.close();
-
-      log.info("✓ ZERO threshold binary quantization test passed");
-    }
-  }
-
-  @Test
-  public void testBinaryQuantizationWithMeanThreshold() throws Throwable {
-    float[][] dataset =
-        new float[][] {
-          {1.0f, 2.0f, 3.0f, 4.0f, 5.0f}, // mean = 3.0
-          {0.0f, 1.0f, 2.0f, 3.0f, 4.0f}, // mean = 2.0
-          {2.0f, 4.0f, 6.0f, 8.0f, 10.0f}, // mean = 6.0
-          {-1.0f, 0.0f, 1.0f, 2.0f, 3.0f} // mean = 1.0
-        };
-
-    float[][] queries =
-        new float[][] {
-          {2.5f, 2.5f, 2.5f, 2.5f, 2.5f},
-          {1.0f, 3.0f, 5.0f, 7.0f, 9.0f}
-        };
-
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
-
-      BinaryQuantizer quantizer =
-          new BinaryQuantizer(resources, trainingDataset, BinaryQuantizer.ThresholdType.MEAN);
-      assertEquals(BinaryQuantizer.ThresholdType.MEAN, quantizer.getThresholdType());
-
-      CagraIndexParams indexParams =
-          new CagraIndexParams.Builder()
-              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-              .withGraphDegree(2)
-              .withIntermediateGraphDegree(3)
-              .withNumWriterThreads(1)
-              .withMetric(CuvsDistanceType.L2Expanded)
-              .build();
-
-      CagraIndex index =
-          CagraIndex.newBuilder(resources)
-              .withDataset(trainingDataset)
-              .withQuantizer(quantizer)
-              .withIndexParams(indexParams)
-              .build();
-
-      log.info("Built index with MEAN threshold binary quantization");
-
-      // Perform search
-      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-      CagraQuery query =
-          CagraQuery.newBuilder(resources)
-              .withQueryVectors(queries)
-              .withQuantizer(quantizer)
-              .withSearchParams(searchParams)
-              .withTopK(2)
-              .build();
-
-      SearchResults results = index.search(query);
-      assertNotNull("Search results should not be null", results);
-      assertEquals(
-          "Should have results for all queries", queries.length, results.getResults().size());
-
-      // Calculate overall mean: (3.0 + 2.0 + 6.0 + 1.0) / 4 = 3.0
-      // Verify quantization behavior - values > mean should become 1, values <= mean should become
-      // 0
-      CuVSMatrix quantizedDataset = quantizer.transform(trainingDataset);
-      byte[][] result1 = new byte[(int) quantizedDataset.size()][(int) quantizedDataset.columns()];
-
-      // Copy data from CuVSMatrix to array
-      quantizedDataset.toArray(result1);
-
-      // Verify that MEAN threshold produces different results than ZERO threshold
-      BinaryQuantizer zeroQuantizer =
-          new BinaryQuantizer(resources, trainingDataset, BinaryQuantizer.ThresholdType.ZERO);
-      CuVSMatrix zeroQuantized = zeroQuantizer.transform(trainingDataset);
-      byte[][] zeroData = new byte[(int) zeroQuantized.size()][(int) zeroQuantized.columns()];
-      zeroQuantized.toArray(zeroData);
-
-      boolean isDifferent = false;
-      for (int i = 0; i < result1.length && !isDifferent; i++) {
-        for (int j = 0; j < result1[i].length && !isDifferent; j++) {
-          if (result1[i][j] != zeroData[i][j]) {
-            isDifferent = true;
-          }
-        }
-      }
-      assertTrue("MEAN and ZERO thresholds should produce different results", isDifferent);
-
-      // Cleanup
-      index.close();
-      quantizer.close();
-      zeroQuantizer.close();
-      trainingDataset.close();
-      quantizedDataset.close();
-      zeroQuantized.close();
-
-      log.info("✓ MEAN threshold binary quantization test passed");
-    }
-  }
-
-  @Test
-  public void testBinaryQuantizationWithSamplingMedianThreshold() throws Throwable {
-    float[][] dataset = createSimpleDataset();
-    float[][] queries = createSimpleQueries();
-
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CuVSMatrix trainingDataset = CuVSMatrix.ofArray(dataset);
-      assertEquals(DataType.FLOAT, trainingDataset.dataType());
-
-      BinaryQuantizer quantizer =
-          new BinaryQuantizer(
-              resources, trainingDataset, BinaryQuantizer.ThresholdType.SAMPLING_MEDIAN);
-      assertEquals(DataType.BYTE, quantizer.outputDataType());
-      log.info("Created binary quantizer with sampling median threshold");
-
-      CagraIndexParams indexParams =
-          new CagraIndexParams.Builder()
-              .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
-              .withGraphDegree(2)
-              .withIntermediateGraphDegree(3)
-              .withNumWriterThreads(1)
-              .withMetric(CuvsDistanceType.L2Expanded)
-              .build();
-
-      CagraIndex index =
-          CagraIndex.newBuilder(resources)
-              .withDataset(trainingDataset)
-              .withQuantizer(quantizer)
-              .withIndexParams(indexParams)
-              .build();
-
-      log.info("Built index with binary quantized dataset");
-
-      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-
-      CagraQuery query =
-          CagraQuery.newBuilder(resources)
-              .withQueryVectors(queries)
-              .withQuantizer(quantizer)
-              .withSearchParams(searchParams)
-              .withTopK(3)
-              .build();
-
-      assertTrue("Query should have quantized vectors", query.hasQuantizedQueries());
-      assertEquals(DataType.BYTE, query.getQueryDataType());
-
-      SearchResults results = index.search(query);
-      assertNotNull("Search results should not be null", results);
-
-      assertEquals(
-          "Should return results for each query", queries.length, results.getResults().size());
-
-      for (int i = 0; i < results.getResults().size(); i++) {
-        Map<Integer, Float> result = results.getResults().get(i);
-        assertFalse("Each query should return non-empty result", result.isEmpty());
-        assertTrue("Should return at most topK results", result.size() <= 3);
-      }
-
-      log.info("✓ Binary quantization with sampling median threshold test passed");
-    }
-  }
-}
diff --git a/java/panama-bindings/headers.h b/java/panama-bindings/headers.h
index ba4cf8c203..57a13cf1b1 100644
--- a/java/panama-bindings/headers.h
+++ b/java/panama-bindings/headers.h
@@ -22,8 +22,6 @@
 #include <cuvs/neighbors/hnsw.h>
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/common.h>
-#include <cuvs/preprocessing/quantize/binary.h>
-#include <cuvs/preprocessing/quantize/scalar.h>
 #include <cuvs/neighbors/tiered_index.h>
 #include <cuda_runtime.h>
 

From 50ed7109abc2bb579e323404a6a7525adc7da275 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Fri, 22 Aug 2025 23:50:08 +0200
Subject: [PATCH 555/623] [Java] CuVSMatrix for device memory (#1232)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR introduces implementation classes for `CuVSDeviceMatrix` (a `CuVSMatrix` backed by device memory).
It reworks the base implementation classes a bit to increase reuse, and adds benchmarks and tests for the new classes.

Benchmarks were used to try out different implementations so the best one could be chosen:
- Row access is backed by a buffer of pinned memory
- Builders for device memory use `cudaMemcpyAsync` with the `critical` linker option to directly access heap-based memory
- `cuvsMatrixCopy` is used across the board, as it has the same performances of the various `cudaMemcpy*` functions.

There are some places in the codebase that will benefit from refactoring to use `CuVSDeviceMatrix` (or a generic  `CuVSMatrix` plus `toHost`/`toTensor`/`fromTensor` functions); replacing these multiple ad-hoc implementations with `CuVSDeviceMatrix` will be addressed in a follow-up PR.

Final numbers:
```
Benchmark                                            (dims)  (size)   Mode  Cnt   Score   Error  Units
CuVSDeviceMatrixBenchmarks.matrixCopyDeviceToHost      2048   16384  thrpt    5  70.531 ± 0.322  ops/s
CuVSDeviceMatrixBenchmarks.matrixDeviceBuilder         2048   16384  thrpt    5  35.493 ± 0.772  ops/s
CuVSDeviceMatrixBenchmarks.matrixReadRowsFromDevice    2048   16384  thrpt    5  83.616 ± 0.745  ops/s
```
With theoretical max for the PCI-E bus of 15.7 GB/s and a data size of 128MB, we get close to 2/3 of the maximum theoretical throughput (see comments for details).

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1232
---
 java/.gitignore                               |   1 +
 .../cuvs/CuVSDeviceMatrixBenchmarks.java      | 107 ++++++++
 .../main/java/com/nvidia/cuvs/CagraIndex.java |   4 +-
 .../com/nvidia/cuvs/CuVSDeviceMatrix.java     |  23 +-
 .../main/java/com/nvidia/cuvs/CuVSMatrix.java |  43 ++-
 .../main/java/com/nvidia/cuvs/RowView.java    |   2 +-
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  36 ++-
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |  30 ++-
 .../cuvs/internal/BruteForceIndexImpl.java    |  26 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 100 ++-----
 .../cuvs/internal/CuVSDeviceMatrixImpl.java   | 246 ++++++++++++++++++
 .../internal/CuVSDeviceMatrixRMMImpl.java     |  97 +++++++
 .../cuvs/internal/CuVSHostMatrixImpl.java     | 157 ++++-------
 .../cuvs/internal/CuVSMatrixBaseImpl.java     | 139 +++++++++-
 .../nvidia/cuvs/internal/HnswIndexImpl.java   |  11 +-
 .../nvidia/cuvs/internal/SliceRowView.java    | 126 +++++++++
 .../nvidia/cuvs/internal/TieredIndexImpl.java |  14 +-
 .../com/nvidia/cuvs/internal/common/Util.java | 131 ++++++++--
 .../com/nvidia/cuvs/spi/JDKProvider.java      | 149 ++++++++++-
 .../nvidia/cuvs/BruteForceRandomizedIT.java   |   2 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    |   3 +-
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   2 +-
 .../java/com/nvidia/cuvs/CuVSMatrixIT.java    | 212 ++++++++++++---
 .../com/nvidia/cuvs/HnswRandomizedIT.java     |   2 +-
 24 files changed, 1337 insertions(+), 326 deletions(-)
 create mode 100644 java/benchmarks/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrixBenchmarks.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SliceRowView.java

diff --git a/java/.gitignore b/java/.gitignore
index a240aca207..2e98aff7ba 100644
--- a/java/.gitignore
+++ b/java/.gitignore
@@ -1,4 +1,5 @@
 *.iml
+hs_err*.log
 target/
 jextract-22/
 openjdk-22-jextract*
diff --git a/java/benchmarks/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrixBenchmarks.java b/java/benchmarks/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrixBenchmarks.java
new file mode 100644
index 0000000000..2a0c430fb4
--- /dev/null
+++ b/java/benchmarks/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrixBenchmarks.java
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.cuvs;
+
+import org.openjdk.jmh.annotations.*;
+import org.openjdk.jmh.infra.Blackhole;
+
+import java.util.Random;
+
+@Fork(value = 1, warmups = 0)
+@State(Scope.Benchmark)
+public class CuVSDeviceMatrixBenchmarks {
+
+  @Param({"2048"})
+  private int dims;
+
+  @Param({"16384"})
+  private int size;
+
+  private static final Random random = new Random();
+
+  private float[][] data;
+
+  private CuVSResources resources;
+  private CuVSDeviceMatrix deviceMatrix;
+  private CuVSHostMatrix hostMatrix;
+
+  private float[][] createRandomData() {
+    var array = new float[size][dims];
+
+    for (int i = 0; i < size; ++i) {
+      for (int j = 0; j < dims; ++j) {
+        array[i][j] = random.nextFloat();
+      }
+    }
+    return array;
+  }
+
+  @Setup
+  public void initialize() throws Throwable {
+    data = createRandomData();
+    resources = CuVSResources.create();
+
+    var builder0 = CuVSMatrix.deviceBuilder(resources, size, dims, CuVSMatrix.DataType.FLOAT);
+
+    for (int i = 0; i < size; ++i) {
+      var array = data[i];
+      builder0.addVector(array);
+    }
+
+    deviceMatrix = builder0.build();
+    hostMatrix = CuVSMatrix.hostBuilder(size, dims, CuVSMatrix.DataType.FLOAT).build();
+  }
+
+  @TearDown
+  public void cleanUp() {
+    if (deviceMatrix != null) {
+      deviceMatrix.close();
+    }
+    if (hostMatrix != null) {
+      hostMatrix.close();
+    }
+    if (resources != null) {
+      resources.close();
+    }
+  }
+
+  @Benchmark
+  public void matrixReadRowsFromDevice(Blackhole bh) {
+    for (int i = 0; i < size; ++i) {
+      bh.consume(deviceMatrix.getRow(i));
+    }
+  }
+
+  @Benchmark
+  public void matrixCopyDeviceToHost() {
+    deviceMatrix.toHost(hostMatrix);
+  }
+
+  @Benchmark
+  public void matrixDeviceBuilder() throws Throwable {
+    try (CuVSResources resources = CuVSResources.create()) {
+      var builder = CuVSMatrix.deviceBuilder(resources, size, dims, CuVSMatrix.DataType.FLOAT);
+
+      for (int i = 0; i < size; ++i) {
+        var array = data[i];
+        builder.addVector(array);
+      }
+      CuVSDeviceMatrix matrix = builder.build();
+      matrix.close();
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
index e3b885a333..a3cb68448c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraIndex.java
@@ -53,10 +53,10 @@ public interface CagraIndex extends AutoCloseable {
 
   /** Returns the CAGRA graph
    *
-   * @return a {@link CuVSMatrix} encapsulating the native int (uint32_t) array used to represent
+   * @return a {@link CuVSDeviceMatrix} encapsulating the native int (uint32_t) array used to represent
    * the cagra graph
    */
-  CuVSMatrix getGraph();
+  CuVSDeviceMatrix getGraph();
 
   /**
    * A method to persist a CAGRA index using an instance of {@link OutputStream}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
index 23d29fd479..b654163a63 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
@@ -18,4 +18,25 @@
 /**
  * A Dataset implementation backed by device (GPU) memory.
  */
-public interface CuVSDeviceMatrix extends CuVSMatrix {}
+public interface CuVSDeviceMatrix extends CuVSMatrix {
+
+  /**
+   * Fills the provided, pre-allocated host matrix with data from this device matrix.
+   * The content of the provided host matrix will be overwritten; the 2 matrices must have the
+   * same element type and dimension.
+   *
+   * @param hostMatrix  the host-memory-backed matrix to fill.
+   */
+  void toHost(CuVSHostMatrix hostMatrix);
+
+  /**
+   * Returns a new, host matrix with data from this device matrix.
+   * The returned host matrix will need to be managed by the caller, which will be
+   * responsible to call {@link CuVSMatrix#close()} to free its resources when done.
+   */
+  default CuVSHostMatrix toHost() {
+    var hostMatrix = CuVSMatrix.hostBuilder(size(), columns(), dataType()).build();
+    toHost(hostMatrix);
+    return hostMatrix;
+  }
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
index f8e60c5555..708009be02 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
@@ -69,29 +69,33 @@ static CuVSMatrix ofArray(byte[][] vectors) {
     return CuVSProvider.provider().newMatrixFromArray(vectors);
   }
 
-  interface Builder {
+  /**
+   * A builder to construct a new matrix one row at a time
+   * @param <T> the CuVSMatrix type to build
+   */
+  interface Builder<T extends CuVSMatrix> {
     /**
-     * Add a single vector to the dataset.
+     * Adds a single vector to the matrix.
      *
      * @param vector A float array of as many elements as the dimensions
      */
     void addVector(float[] vector);
 
     /**
-     * Add a single vector to the dataset.
+     * Adds a single vector to the matrix.
      *
      * @param vector A byte array of as many elements as the dimensions
      */
     void addVector(byte[] vector);
 
     /**
-     * Add a single vector to the dataset.
+     * Adds a single vector to the matrix.
      *
-     * @param vector A int array of as many elements as the dimensions
+     * @param vector An int array of as many elements as the dimensions
      */
     void addVector(int[] vector);
 
-    CuVSMatrix build();
+    T build();
   }
 
   /**
@@ -100,10 +104,24 @@ interface Builder {
    * @param size     Number of vectors in the dataset
    * @param columns  Size of each vector in the dataset
    * @param dataType The data type of the dataset elements
-   * @return new instance of {@link CuVSMatrix}
+   * @return a builder for creating a {@link CuVSHostMatrix}
+   */
+  static Builder<CuVSHostMatrix> hostBuilder(long size, long columns, DataType dataType) {
+    return CuVSProvider.provider().newHostMatrixBuilder(size, columns, dataType);
+  }
+
+  /**
+   * Returns a builder to create a new instance of a dataset
+   *
+   * @param resources CuVS resources used to allocate the device memory needed
+   * @param size      Number of vectors in the dataset
+   * @param columns   Size of each vector in the dataset
+   * @param dataType  The data type of the dataset elements
+   * @return a builder for creating a {@link CuVSDeviceMatrix}
    */
-  static CuVSMatrix.Builder builder(int size, int columns, DataType dataType) {
-    return CuVSProvider.provider().newMatrixBuilder(size, columns, dataType);
+  static Builder<CuVSDeviceMatrix> deviceBuilder(
+      CuVSResources resources, long size, long columns, DataType dataType) {
+    return CuVSProvider.provider().newDeviceMatrixBuilder(resources, size, columns, dataType);
   }
 
   /**
@@ -121,6 +139,13 @@ static CuVSMatrix.Builder builder(int size, int columns, DataType dataType) {
    */
   long columns();
 
+  /**
+   * Gets the element type
+   *
+   * @return a {@link DataType} describing the matrix element type
+   */
+  DataType dataType();
+
   /**
    * Get a view (0-copy) of the row data, as a list of integers (32 bit)
    *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java
index cbace82ae3..676a652427 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/RowView.java
@@ -16,7 +16,7 @@
 package com.nvidia.cuvs;
 
 /**
- * Represent a contiguous list of integers (32-bit) backed by off-heap memory.
+ * Represent a contiguous list of elements backed by off-heap memory.
  *
  * @since 25.08
  */
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index e528beff1a..2ee3fb373a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -15,13 +15,7 @@
  */
 package com.nvidia.cuvs.spi;
 
-import com.nvidia.cuvs.BruteForceIndex;
-import com.nvidia.cuvs.CagraIndex;
-import com.nvidia.cuvs.CagraMergeParams;
-import com.nvidia.cuvs.CuVSMatrix;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.HnswIndex;
-import com.nvidia.cuvs.TieredIndex;
+import com.nvidia.cuvs.*;
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.MethodType;
 import java.nio.file.Path;
@@ -52,23 +46,37 @@ default Path nativeLibraryPath() {
   /** Creates a new CuVSResources. */
   CuVSResources newCuVSResources(Path tempDirectory) throws Throwable;
 
-  /** Create a {@link CuVSMatrix.Builder} instance **/
-  CuVSMatrix.Builder newMatrixBuilder(int size, int dimensions, CuVSMatrix.DataType dataType);
+  /** Create a {@link CuVSMatrix.Builder} instance for a host memory matrix **/
+  CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
+      long size, long dimensions, CuVSMatrix.DataType dataType);
+
+  /** Create a {@link CuVSMatrix.Builder} instance for a device memory matrix **/
+  CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
+      CuVSResources cuVSResources, long size, long dimensions, CuVSMatrix.DataType dataType);
+
+  /** Create a {@link CuVSMatrix.Builder} instance for a device memory matrix **/
+  CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
+      CuVSResources cuVSResources,
+      long size,
+      long dimensions,
+      int rowStride,
+      int columnStride,
+      CuVSMatrix.DataType dataType);
 
   /**
-   * Returns the factory method used to build a Dataset from native memory.
+   * Returns the factory method used to build a CuVSMatrix from native memory.
    * The factory method will have this signature:
-   * {@code Dataset createNativeDataset(memorySegment, size, dimensions, dataType)},
+   * {@code CuVSMatrix createNativeMatrix(memorySegment, size, dimensions, dataType)},
    * where {@code memorySegment} is a {@code java.lang.foreign.MemorySegment} containing {@code int size} vectors of
    * {@code int dimensions} length of type {@link CuVSMatrix.DataType}.
    * <p>
    * In order to expose this factory in a way that is compatible with Java 21, the factory method is returned as a
    * {@link MethodHandle} with {@link MethodType} equal to
-   * {@code (Dataset.class, MemorySegment.class, int.class, int.class, Dataset.DataType.class)}.
+   * {@code (CuVSMatrix.class, MemorySegment.class, int.class, int.class, CuVSMatrix.DataType.class)}.
    * The caller will need to invoke the factory via the {@link MethodHandle#invokeExact} method:
-   * {@code Dataset dataset = (Dataset)newNativeDatasetBuilder().invokeExact(memorySegment, size, dimensions, dataType)}
+   * {@code var matrix = (CuVSMatrix)newNativeMatrixBuilder().invokeExact(memorySegment, size, dimensions, dataType)}
    * </p>
-   * @return a MethodHandle which can be invoked to build a Dataset from an external {@code MemorySegment}
+   * @return a MethodHandle which can be invoked to build a CuVSMatrix from an external {@code MemorySegment}
    */
   MethodHandle newNativeMatrixBuilder();
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 8f65bf7068..499ea22fc5 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -15,12 +15,7 @@
  */
 package com.nvidia.cuvs.spi;
 
-import com.nvidia.cuvs.BruteForceIndex;
-import com.nvidia.cuvs.CagraIndex;
-import com.nvidia.cuvs.CuVSMatrix;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.HnswIndex;
-import com.nvidia.cuvs.TieredIndex;
+import com.nvidia.cuvs.*;
 import java.lang.invoke.MethodHandle;
 import java.nio.file.Path;
 
@@ -55,13 +50,30 @@ public TieredIndex.Builder newTieredIndexBuilder(CuVSResources cuVSResources) {
   }
 
   @Override
-  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
+  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) {
     throw new UnsupportedOperationException();
   }
 
   @Override
-  public CuVSMatrix.Builder newMatrixBuilder(
-      int size, int dimensions, CuVSMatrix.DataType dataType) {
+  public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
+      long size, long dimensions, CuVSMatrix.DataType dataType) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
+      CuVSResources cuVSResources, long size, long dimensions, CuVSMatrix.DataType dataType) {
+    throw new UnsupportedOperationException();
+  }
+
+  @Override
+  public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
+      CuVSResources cuVSResources,
+      long size,
+      long dimensions,
+      int rowStride,
+      int columnStride,
+      CuVSMatrix.DataType dataType) {
     throw new UnsupportedOperationException();
   }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index e5f98ad2dd..bc7d6b0a8b 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -28,15 +28,7 @@
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
 import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceBuild;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceDeserialize;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndexCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndexDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceIndex_t;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSearch;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsBruteForceSerialize;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsStreamSync;
-import static com.nvidia.cuvs.internal.panama.headers_h.omp_set_num_threads;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.BruteForceIndex;
 import com.nvidia.cuvs.BruteForceIndexParams;
@@ -153,7 +145,8 @@ private IndexReference build(
         long[] datasetShape = {rows, cols};
         var tensorDataArena = Arena.ofShared();
         MemorySegment datasetTensor =
-            prepareTensor(tensorDataArena, datasetMemorySegmentP, datasetShape, 2, 32, 2, 1);
+            prepareTensor(
+                tensorDataArena, datasetMemorySegmentP, datasetShape, kDLFloat(), 32, kDLCUDA());
 
         var returnValue = cuvsStreamSync(cuvsResources);
         checkCuVSError(returnValue, "cuvsStreamSync");
@@ -236,13 +229,16 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
 
           long[] queriesShape = {numQueries, vectorDimension};
           MemorySegment queriesTensor =
-              prepareTensor(localArena, queriesDP.handle(), queriesShape, 2, 32, 2, 1);
+              prepareTensor(
+                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA());
           long[] neighborsShape = {numQueries, topk};
           MemorySegment neighborsTensor =
-              prepareTensor(localArena, neighborsDP.handle(), neighborsShape, 0, 64, 2, 1);
+              prepareTensor(
+                  localArena, neighborsDP.handle(), neighborsShape, kDLInt(), 64, kDLCUDA());
           long[] distancesShape = {numQueries, topk};
           MemorySegment distancesTensor =
-              prepareTensor(localArena, distancesDP.handle(), distancesShape, 2, 32, 2, 1);
+              prepareTensor(
+                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA());
 
           MemorySegment prefilter = cuvsFilter.allocate(localArena);
           MemorySegment prefilterTensor;
@@ -255,8 +251,10 @@ public SearchResults search(BruteForceQuery cuvsQuery) throws Throwable {
             cudaMemcpy(
                 prefilterDP.handle(), prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
 
+            // TODO: check if this is correct: filters is a long[] (64 bits)
             prefilterTensor =
-                prepareTensor(localArena, prefilterDP.handle(), prefilterShape, 1, 32, 2, 1);
+                prepareTensor(
+                    localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA());
 
             cuvsFilter.type(prefilter, 2);
             cuvsFilter.addr(prefilter, prefilterTensor.address());
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 0d5fc8d90f..aa082482dc 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -30,30 +30,12 @@
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
-import com.nvidia.cuvs.CagraCompressionParams;
-import com.nvidia.cuvs.CagraIndex;
-import com.nvidia.cuvs.CagraIndexParams;
+import com.nvidia.cuvs.*;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
-import com.nvidia.cuvs.CagraMergeParams;
-import com.nvidia.cuvs.CagraQuery;
-import com.nvidia.cuvs.CagraSearchParams;
-import com.nvidia.cuvs.CuVSIvfPqIndexParams;
-import com.nvidia.cuvs.CuVSIvfPqSearchParams;
-import com.nvidia.cuvs.CuVSMatrix;
-import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.SearchResults;
 import com.nvidia.cuvs.internal.common.CloseableHandle;
 import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
-import com.nvidia.cuvs.internal.common.Util;
-import com.nvidia.cuvs.internal.panama.cuvsCagraCompressionParams;
-import com.nvidia.cuvs.internal.panama.cuvsCagraIndexParams;
-import com.nvidia.cuvs.internal.panama.cuvsCagraMergeParams;
-import com.nvidia.cuvs.internal.panama.cuvsCagraSearchParams;
-import com.nvidia.cuvs.internal.panama.cuvsFilter;
-import com.nvidia.cuvs.internal.panama.cuvsIvfPqIndexParams;
-import com.nvidia.cuvs.internal.panama.cuvsIvfPqParams;
-import com.nvidia.cuvs.internal.panama.cuvsIvfPqSearchParams;
+import com.nvidia.cuvs.internal.panama.*;
 import java.io.FileInputStream;
 import java.io.InputStream;
 import java.io.OutputStream;
@@ -180,7 +162,6 @@ public void close() {
    */
   private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImpl dataset) {
     long rows = dataset.size();
-    long cols = dataset.columns();
 
     try (var indexParams = segmentFromIndexParams(indexParameters);
         var localArena = Arena.ofConfined()) {
@@ -189,14 +170,7 @@ private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImp
       int numWriterThreads = indexParameters != null ? indexParameters.getNumWriterThreads() : 1;
       omp_set_num_threads(numWriterThreads);
 
-      MemorySegment dataSeg = dataset.memorySegment();
-      // TODO: type kDLCPU()/kDLCUDA() should be aligned with the CuVSMatrixBaseImpl type (host or
-      // device?)
-
-      long[] datasetShape = {rows, cols};
-      MemorySegment datasetTensor =
-          prepareTensor(localArena, dataSeg, datasetShape, kDLFloat(), 32, kDLCPU(), 1);
-
+      var datasetTensor = dataset.toTensor(localArena);
       var index = createCagraIndex();
 
       if (cuvsCagraIndexParams.build_algo(indexParamsMemorySegment)
@@ -212,6 +186,7 @@ private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImp
       try (var resourcesAccessor = resources.access()) {
         var cuvsRes = resourcesAccessor.handle();
 
+        // TODO: do we need a stream sync here?
         var returnValue = cuvsStreamSync(cuvsRes);
         checkCuVSError(returnValue, "cuvsStreamSync");
 
@@ -293,15 +268,15 @@ public SearchResults search(CagraQuery query) throws Throwable {
           long[] queriesShape = {numQueries, vectorDimension};
           MemorySegment queriesTensor =
               prepareTensor(
-                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
+                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA());
           long[] neighborsShape = {numQueries, topK};
           MemorySegment neighborsTensor =
               prepareTensor(
-                  localArena, neighborsDP.handle(), neighborsShape, kDLUInt(), 32, kDLCUDA(), 1);
+                  localArena, neighborsDP.handle(), neighborsShape, kDLUInt(), 32, kDLCUDA());
           long[] distancesShape = {numQueries, topK};
           MemorySegment distancesTensor =
               prepareTensor(
-                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA(), 1);
+                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA());
 
           var returnValue = cuvsStreamSync(cuvsRes);
           checkCuVSError(returnValue, "cuvsStreamSync");
@@ -328,7 +303,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
 
             prefilterTensor =
                 prepareTensor(
-                    localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA(), 1);
+                    localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA());
 
             cuvsFilter.type(prefilter, 1);
             cuvsFilter.addr(prefilter, prefilterTensor.address());
@@ -411,46 +386,24 @@ public void serialize(OutputStream outputStream, Path tempFile, int bufferLength
   }
 
   @Override
-  public CuVSMatrix getGraph() {
+  public CuVSDeviceMatrix getGraph() {
     try (var localArena = Arena.ofConfined()) {
-      var outPtr = localArena.allocate(uint32_t);
-      checkCuVSError(
-          cuvsCagraIndexGetGraphDegree(cagraIndexReference.getMemorySegment(), outPtr),
-          "cuvsCagraIndexGetGraphDegree");
-      long graphDegree = Util.dereferenceUnsignedInt(outPtr);
+      // Use a "device" graph + tensor, avoid (defer) copy
+      MemorySegment graphDeviceTensor = DLManagedTensor.allocate(localArena);
+      DLManagedTensor.dl_tensor(graphDeviceTensor, DLTensor.allocate(localArena));
 
-      checkCuVSError(
-          cuvsCagraIndexGetSize(cagraIndexReference.getMemorySegment(), outPtr),
-          "cuvsCagraIndexGetSize");
-      long size = Util.dereferenceUnsignedInt(outPtr);
-
-      // TODO: use a "device" graph + tensor, avoid (defer) copy
-      var graph = new CuVSHostMatrixArenaImpl(size, graphDegree, CuVSMatrix.DataType.UINT);
-      var graphHostTensor = graph.toTensor(localArena);
-      var graphDeviceTensor =
-          prepareTensor(
-              localArena,
-              MemorySegment.NULL,
-              new long[] {size, graphDegree},
-              kDLUInt(),
-              32,
-              kDLCUDA(),
-              1);
       checkCuVSError(
           cuvsCagraIndexGetGraph(cagraIndexReference.getMemorySegment(), graphDeviceTensor),
           "cuvsCagraIndexGetGraph");
 
-      try (var resourceAccess = resources.access()) {
-        var cuvsRes = resourceAccess.handle();
-        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
-
-        checkCuVSError(
-            cuvsMatrixCopy(cuvsRes, graphDeviceTensor, graphHostTensor), "cuvsMatrixCopy");
+      assert DLTensor.ndim(DLManagedTensor.dl_tensor(graphDeviceTensor)) == 2;
+      assert DLTensor.shape(DLManagedTensor.dl_tensor(graphDeviceTensor)).get(int64_t, 0) > 0;
+      assert DLTensor.shape(DLManagedTensor.dl_tensor(graphDeviceTensor)).getAtIndex(int64_t, 1)
+          > 0;
 
-        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
-      }
-
-      return graph;
+      var graph = CuVSMatrixBaseImpl.fromTensor(graphDeviceTensor, resources);
+      assert graph instanceof CuVSDeviceMatrix;
+      return (CuVSDeviceMatrix) graph;
     }
   }
 
@@ -459,28 +412,17 @@ private IndexReference fromGraph(
       CuVSMatrixBaseImpl graph,
       CuVSMatrixBaseImpl dataset) {
     try (var localArena = Arena.ofConfined()) {
-      long rows = dataset.size();
-      long cols = dataset.columns();
-
       var index = createCagraIndex();
       try (var resourcesAccess = resources.access()) {
         long cuvsRes = resourcesAccess.handle();
 
-        long[] datasetShape = {rows, cols};
-        MemorySegment datasetTensor =
-            prepareTensor(
-                localArena, dataset.memorySegment(), datasetShape, kDLFloat(), 32, kDLCPU(), 1);
-
-        long[] graphShape = {graph.size(), graph.columns()};
-        MemorySegment graphTensor =
-            prepareTensor(
-                localArena, graph.memorySegment(), graphShape, kDLUInt(), 32, kDLCPU(), 1);
+        MemorySegment datasetTensor = dataset.toTensor(localArena);
+        MemorySegment graphTensor = graph.toTensor(localArena);
 
         checkCuVSError(
             cuvsCagraIndexFromArgs(cuvsRes, metric.value, graphTensor, datasetTensor, index),
             "cuvsCagraIndexFromArgs");
       }
-
       return new IndexReference(index, dataset);
     }
   }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
new file mode 100644
index 0000000000..50b914c76b
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
@@ -0,0 +1,246 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.*;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
+
+import com.nvidia.cuvs.*;
+import com.nvidia.cuvs.internal.panama.DLManagedTensor;
+import com.nvidia.cuvs.internal.panama.DLTensor;
+import java.lang.foreign.*;
+
+public class CuVSDeviceMatrixImpl extends CuVSMatrixBaseImpl implements CuVSDeviceMatrix {
+
+  private static final int CHUNK_BYTES =
+      8 * 1024 * 1024; // Based on benchmarks, 8MB seems the minimum size to optimize PCIe bandwidth
+  private final long hostBufferBytes;
+
+  private long bufferedMatrixRowStart = 0;
+  private long bufferedMatrixRowEnd = 0;
+
+  private final CuVSResources resources;
+
+  private final long rowStride;
+  private final long columnStride;
+
+  private MemorySegment hostBuffer = MemorySegment.NULL;
+
+  protected CuVSDeviceMatrixImpl(
+      CuVSResources resources,
+      MemorySegment deviceMemorySegment,
+      long size,
+      long columns,
+      DataType dataType,
+      ValueLayout valueLayout) {
+    this(resources, deviceMemorySegment, size, columns, -1, -1, dataType, valueLayout);
+  }
+
+  protected CuVSDeviceMatrixImpl(
+      CuVSResources resources,
+      MemorySegment deviceMemorySegment,
+      long size,
+      long columns,
+      long rowStride,
+      long columnStride,
+      DataType dataType,
+      ValueLayout valueLayout) {
+    super(deviceMemorySegment, dataType, valueLayout, size, columns);
+    this.resources = resources;
+    this.rowStride = rowStride;
+    this.columnStride = columnStride;
+
+    long rowBytes = columns * valueLayout.byteSize();
+    long matrixBytes = size * rowBytes;
+    if (matrixBytes < CHUNK_BYTES) {
+      this.hostBufferBytes = matrixBytes;
+    } else if (rowBytes > CHUNK_BYTES) {
+      // We need to buffer at least one row at time
+      this.hostBufferBytes = rowBytes;
+    } else {
+      var rowCount = (CHUNK_BYTES / rowBytes);
+      this.hostBufferBytes = rowBytes * rowCount;
+    }
+  }
+
+  @Override
+  public MemorySegment toTensor(Arena arena) {
+    var strides = rowStride >= 0 ? new long[] {rowStride, columnStride} : null;
+    return prepareTensor(
+        arena, memorySegment, new long[] {size, columns}, strides, code(), bits(), kDLCUDA());
+  }
+
+  private static MemorySegment createPinnedBuffer(long bufferBytes) {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment pointer = localArena.allocate(C_POINTER);
+      checkCudaError(cudaMallocHost(pointer, bufferBytes), "cudaMallocHost");
+      return pointer.get(C_POINTER, 0);
+    }
+  }
+
+  private static void destroyPinnedBuffer(MemorySegment bufferSegment) {
+    checkCudaError(cudaFreeHost(bufferSegment), "cudaFreeHost");
+  }
+
+  private void populateBuffer(long startRow) {
+    if (hostBuffer == MemorySegment.NULL) {
+      //      System.out.println("Creating a buffer of size " + hostBufferBytes);
+      hostBuffer = createPinnedBuffer(hostBufferBytes);
+    }
+
+    try (var localArena = Arena.ofConfined()) {
+      long rowBytes = columns * valueLayout.byteSize();
+      var endRow = Math.min(startRow + (hostBufferBytes / rowBytes), size);
+      var rowCount = endRow - startRow;
+
+      //      System.out.printf(
+      //          Locale.ROOT, "startRow: %d, endRow %d, count: %d\n", startRow, endRow, rowCount);
+
+      MemorySegment sliceManagedTensor = DLManagedTensor.allocate(localArena);
+      DLManagedTensor.dl_tensor(sliceManagedTensor, DLTensor.allocate(localArena));
+
+      checkCuVSError(
+          cuvsMatrixSliceRows(0, toTensor(localArena), startRow, endRow, sliceManagedTensor),
+          "cuvsMatrixSliceRows");
+      assert DLTensor.shape(DLManagedTensor.dl_tensor(sliceManagedTensor)).get(C_LONG, 0)
+          == rowCount;
+      assert DLTensor.shape(DLManagedTensor.dl_tensor(sliceManagedTensor)).getAtIndex(C_LONG, 1)
+          == columns;
+
+      MemorySegment bufferTensor =
+          prepareTensor(
+              localArena, hostBuffer, new long[] {rowCount, columns}, code(), bits(), kDLCPU());
+
+      try (var resourceAccess = resources.access()) {
+        checkCuVSError(
+            cuvsMatrixCopy(resourceAccess.handle(), sliceManagedTensor, bufferTensor),
+            "cuvsMatrixCopy");
+        checkCuVSError(cuvsStreamSync(resourceAccess.handle()), "cuvsStreamSync");
+
+        bufferedMatrixRowStart = startRow;
+        bufferedMatrixRowEnd = endRow;
+      }
+    }
+  }
+
+  @Override
+  public RowView getRow(long row) {
+    if (row < bufferedMatrixRowStart || row >= bufferedMatrixRowEnd) {
+      populateBuffer(row);
+    }
+    var valueByteSize = valueLayout.byteSize();
+    var startRow = row - bufferedMatrixRowStart;
+
+    return new SliceRowView(
+        hostBuffer.asSlice(startRow * columns * valueByteSize, columns * valueByteSize),
+        columns,
+        valueLayout,
+        dataType,
+        valueByteSize);
+  }
+
+  @Override
+  public void toArray(int[][] array) {
+    assert dataType == DataType.INT || dataType == DataType.UINT;
+    assert (array.length >= size) : "Input array is not large enough";
+    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    try (var localArena = Arena.ofConfined()) {
+      var rowBytes = columns * valueLayout.byteSize();
+      var tmpRowSegment = localArena.allocate(rowBytes);
+      for (int r = 0; r < size; ++r) {
+        copyRow(array[r], localArena, r, tmpRowSegment);
+      }
+    }
+  }
+
+  @Override
+  public void toArray(float[][] array) {
+    assert dataType == DataType.FLOAT;
+    assert (array.length >= size) : "Input array is not large enough";
+    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    try (var localArena = Arena.ofConfined()) {
+      var rowBytes = columns * valueLayout.byteSize();
+      var tmpRowSegment = localArena.allocate(rowBytes);
+      for (int r = 0; r < size; ++r) {
+        copyRow(array[r], localArena, r, tmpRowSegment);
+      }
+    }
+  }
+
+  @Override
+  public void toArray(byte[][] array) {
+    assert dataType == DataType.BYTE;
+    assert (array.length >= size) : "Input array is not large enough";
+    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    try (var localArena = Arena.ofConfined()) {
+      var rowSegmentLayout = MemoryLayout.sequenceLayout(columns, valueLayout);
+      var tmpRowSegment = localArena.allocate(rowSegmentLayout);
+      for (int r = 0; r < size; ++r) {
+        copyRow(array[r], localArena, r, tmpRowSegment);
+      }
+    }
+  }
+
+  private void copyRow(Object array, Arena localArena, int r, MemorySegment tmpRowSegment) {
+    MemorySegment sliceManagedTensor = DLManagedTensor.allocate(localArena);
+    DLManagedTensor.dl_tensor(sliceManagedTensor, DLTensor.allocate(localArena));
+
+    checkCuVSError(
+        cuvsMatrixSliceRows(0, toTensor(localArena), r, r + 1, sliceManagedTensor),
+        "cuvsMatrixSliceRows");
+
+    MemorySegment bufferTensor =
+        prepareTensor(
+            localArena, tmpRowSegment, new long[] {1, columns}, code(), bits(), kDLCUDA());
+    try (var resourceAccess = resources.access()) {
+      checkCuVSError(
+          cuvsMatrixCopy(resourceAccess.handle(), sliceManagedTensor, bufferTensor),
+          "cuvsMatrixCopy");
+      checkCuVSError(cuvsStreamSync(resourceAccess.handle()), "cuvsStreamSync");
+    }
+    MemorySegment.copy(tmpRowSegment, valueLayout, 0L, array, 0, (int) columns);
+  }
+
+  @Override
+  public void toHost(CuVSHostMatrix hostMatrix) {
+    if (hostMatrix.columns() != columns || hostMatrix.size() != size) {
+      throw new IllegalArgumentException("[hostMatrix] must have the same dimensions");
+    }
+    if (hostMatrix.dataType() != dataType) {
+      throw new IllegalArgumentException("[hostMatrix] must have the same dataType");
+    }
+    try (var localArena = Arena.ofConfined()) {
+      var hostMatrixTensor = ((CuVSHostMatrixImpl) hostMatrix).toTensor(localArena);
+
+      try (var resourceAccess = resources.access()) {
+        var cuvsRes = resourceAccess.handle();
+        var deviceMatrixTensor = toTensor(localArena);
+        checkCuVSError(
+            cuvsMatrixCopy(cuvsRes, deviceMatrixTensor, hostMatrixTensor), "cuvsMatrixCopy");
+        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
+      }
+    }
+  }
+
+  @Override
+  public void close() {
+    if (hostBuffer != MemorySegment.NULL) {
+      destroyPinnedBuffer(hostBuffer);
+      hostBuffer = MemorySegment.NULL;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java
new file mode 100644
index 0000000000..7ad84a0e1f
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.CuVSDeviceMatrix;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
+import java.lang.foreign.ValueLayout;
+
+/**
+ * A Dataset implementation backed by device (GPU) memory, allocated
+ * using RMM functions from {@link CuVSResources} pools (via {@link CloseableRMMAllocation}).
+ */
+public class CuVSDeviceMatrixRMMImpl extends CuVSDeviceMatrixImpl implements CuVSDeviceMatrix {
+
+  private final CloseableRMMAllocation rmmAllocation;
+
+  private CuVSDeviceMatrixRMMImpl(
+      CuVSResources resources,
+      CloseableRMMAllocation rmmAllocation,
+      long size,
+      long columns,
+      DataType dataType,
+      ValueLayout valueLayout) {
+    super(resources, rmmAllocation.handle(), size, columns, dataType, valueLayout);
+    this.rmmAllocation = rmmAllocation;
+  }
+
+  private CuVSDeviceMatrixRMMImpl(
+      CuVSResources resources,
+      CloseableRMMAllocation rmmAllocation,
+      long size,
+      long columns,
+      long rowStride,
+      long columnStride,
+      DataType dataType,
+      ValueLayout valueLayout) {
+    super(
+        resources,
+        rmmAllocation.handle(),
+        size,
+        columns,
+        rowStride,
+        columnStride,
+        dataType,
+        valueLayout);
+    this.rmmAllocation = rmmAllocation;
+  }
+
+  public static CuVSDeviceMatrixImpl create(
+      CuVSResources resources, long size, long columns, DataType dataType) {
+    try (var resourcesAccess = resources.access()) {
+      var valueLayout = valueLayoutFromType(dataType);
+      var rmmAllocation =
+          CloseableRMMAllocation.allocateRMMSegment(
+              resourcesAccess.handle(), size * columns * valueLayout.byteSize());
+      return new CuVSDeviceMatrixRMMImpl(
+          resources, rmmAllocation, size, columns, dataType, valueLayout);
+    }
+  }
+
+  public static CuVSDeviceMatrixImpl create(
+      CuVSResources resources,
+      long size,
+      long columns,
+      long rowStride,
+      long columnStride,
+      DataType dataType) {
+    try (var resourcesAccess = resources.access()) {
+      var valueLayout = valueLayoutFromType(dataType);
+      var rmmAllocation =
+          CloseableRMMAllocation.allocateRMMSegment(
+              resourcesAccess.handle(), size * columns * valueLayout.byteSize());
+      return new CuVSDeviceMatrixRMMImpl(
+          resources, rmmAllocation, size, columns, rowStride, columnStride, dataType, valueLayout);
+    }
+  }
+
+  @Override
+  public void close() {
+    super.close();
+    rmmAllocation.close();
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
index c2895282ca..317b315c77 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -15,9 +15,6 @@
  */
 package com.nvidia.cuvs.internal;
 
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
@@ -25,12 +22,12 @@
 import com.nvidia.cuvs.RowView;
 import java.lang.foreign.*;
 import java.lang.invoke.VarHandle;
+import java.util.Locale;
 
 /**
  * A Dataset implementation backed by host (CPU) memory.
  */
 public class CuVSHostMatrixImpl extends CuVSMatrixBaseImpl implements CuVSHostMatrix {
-  private final ValueLayout valueLayout;
   protected final VarHandle accessor$vh;
 
   public CuVSHostMatrixImpl(
@@ -52,30 +49,18 @@ protected CuVSHostMatrixImpl(
       DataType dataType,
       ValueLayout valueLayout,
       MemoryLayout sequenceLayout) {
-    super(memorySegment, dataType, size, columns);
+    super(memorySegment, dataType, valueLayout, size, columns);
     this.accessor$vh = sequenceLayout.varHandle(MemoryLayout.PathElement.sequenceElement());
-    this.valueLayout = valueLayout;
-  }
-
-  protected static ValueLayout valueLayoutFromType(DataType dataType) {
-    return switch (dataType) {
-      case FLOAT -> C_FLOAT;
-      case INT, UINT -> C_INT;
-      case BYTE -> C_CHAR;
-    };
-  }
-
-  protected static SequenceLayout sequenceLayoutFromType(
-      long size, long columns, DataType dataType) {
-    return MemoryLayout.sequenceLayout(size * columns, valueLayoutFromType(dataType))
-        .withByteAlignment(32);
   }
 
   @Override
-  public RowView getRow(long nodeIndex) {
+  public RowView getRow(long index) {
+    assert (index < size)
+        : String.format(Locale.ROOT, "Index out of bound ([%d], size [%d])", index, size);
+
     var valueByteSize = valueLayout.byteSize();
     return new SliceRowView(
-        memorySegment.asSlice(nodeIndex * columns * valueByteSize, columns * valueByteSize),
+        memorySegment.asSlice(index * columns * valueByteSize, columns * valueByteSize),
         columns,
         valueLayout,
         dataType,
@@ -84,9 +69,22 @@ public RowView getRow(long nodeIndex) {
 
   @Override
   public void toArray(int[][] array) {
-    assert dataType == DataType.INT;
-    assert (array.length >= size) : "Input array is not large enough";
-    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    assert (array.length >= size)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not large enough (required: [%d], actual [%d])",
+            size,
+            array.length);
+    assert (array.length == 0 || array[0].length >= columns)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not wide enough (required: [%d], actual [%d])",
+            columns,
+            array[0].length);
+    assert dataType == DataType.INT || dataType == DataType.UINT
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+
     var valueByteSize = valueLayout.byteSize();
     for (int r = 0; r < size; ++r) {
       MemorySegment.copy(
@@ -96,9 +94,22 @@ public void toArray(int[][] array) {
 
   @Override
   public void toArray(float[][] array) {
-    assert dataType == DataType.FLOAT;
-    assert (array.length >= size) : "Input array is not large enough";
-    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    assert (array.length >= size)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not large enough (required: [%d], actual [%d])",
+            size,
+            array.length);
+    assert (array.length == 0 || array[0].length >= columns)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not wide enough (required: [%d], actual [%d])",
+            columns,
+            array[0].length);
+    assert dataType == DataType.FLOAT
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+
     var valueByteSize = valueLayout.byteSize();
     for (int r = 0; r < size; ++r) {
       MemorySegment.copy(
@@ -108,9 +119,22 @@ public void toArray(float[][] array) {
 
   @Override
   public void toArray(byte[][] array) {
-    assert dataType == DataType.BYTE;
-    assert (array.length >= size) : "Input array is not large enough";
-    assert (array.length == 0 || array[0].length >= columns) : "Input array is not large enough";
+    assert (array.length >= size)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not large enough (required: [%d], actual [%d])",
+            size,
+            array.length);
+    assert (array.length == 0 || array[0].length >= columns)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not wide enough (required: [%d], actual [%d])",
+            columns,
+            array[0].length);
+    assert dataType == DataType.BYTE
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+
     var valueByteSize = valueLayout.byteSize();
     for (int r = 0; r < size; ++r) {
       MemorySegment.copy(
@@ -126,78 +150,9 @@ public int get(int row, int col) {
     return (int) accessor$vh.get(memorySegment, 0L, (long) row * columns + col);
   }
 
-  public ValueLayout valueLayout() {
-    return valueLayout;
-  }
-
   @Override
   public MemorySegment toTensor(Arena arena) {
     return prepareTensor(
-        arena, memorySegment, new long[] {size, columns}, code(), bits(), kDLCPU(), 1);
-  }
-
-  private static class SliceRowView implements RowView {
-    private final MemorySegment memorySegment;
-    private final long size;
-    private final ValueLayout valueLayout;
-    private final DataType dataType;
-    private final long valueByteSize;
-
-    SliceRowView(
-        MemorySegment slice,
-        long size,
-        ValueLayout valueLayout,
-        DataType dataType,
-        long valueByteSize) {
-      this.memorySegment = slice;
-      this.size = size;
-      this.valueLayout = valueLayout;
-      this.dataType = dataType;
-      this.valueByteSize = valueByteSize;
-    }
-
-    @Override
-    public long size() {
-      return size;
-    }
-
-    @Override
-    public float getAsFloat(long index) {
-      assert dataType == DataType.FLOAT;
-      return memorySegment.get((ValueLayout.OfFloat) valueLayout, index * valueByteSize);
-    }
-
-    @Override
-    public byte getAsByte(long index) {
-      assert dataType == DataType.BYTE;
-      return memorySegment.get((ValueLayout.OfByte) valueLayout, index * valueByteSize);
-    }
-
-    @Override
-    public int getAsInt(long index) {
-      assert dataType == DataType.INT;
-      return memorySegment.get((ValueLayout.OfInt) valueLayout, index * valueByteSize);
-    }
-
-    @Override
-    public void toArray(int[] array) {
-      assert (array.length >= size) : "Input array is not large enough";
-      assert dataType == DataType.INT;
-      MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
-    }
-
-    @Override
-    public void toArray(float[] array) {
-      assert (array.length >= size) : "Input array is not large enough";
-      assert dataType == DataType.FLOAT;
-      MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
-    }
-
-    @Override
-    public void toArray(byte[] array) {
-      assert (array.length >= size) : "Input array is not large enough";
-      assert dataType == DataType.BYTE;
-      MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
-    }
+        arena, memorySegment, new long[] {size, columns}, code(), bits(), kDLCPU());
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
index 120960cfc4..d629645e61 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -15,22 +15,36 @@
  */
 package com.nvidia.cuvs.internal;
 
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSMatrix;
-import java.lang.foreign.Arena;
-import java.lang.foreign.MemorySegment;
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.internal.panama.DLDataType;
+import com.nvidia.cuvs.internal.panama.DLDevice;
+import com.nvidia.cuvs.internal.panama.DLManagedTensor;
+import com.nvidia.cuvs.internal.panama.DLTensor;
+import java.lang.foreign.*;
+import java.util.Locale;
 
 public abstract class CuVSMatrixBaseImpl implements CuVSMatrix {
   protected final MemorySegment memorySegment;
   protected final DataType dataType;
+  protected final ValueLayout valueLayout;
   protected final long size;
   protected final long columns;
 
   protected CuVSMatrixBaseImpl(
-      MemorySegment memorySegment, DataType dataType, long size, long columns) {
+      MemorySegment memorySegment,
+      DataType dataType,
+      ValueLayout valueLayout,
+      long size,
+      long columns) {
     this.memorySegment = memorySegment;
     this.dataType = dataType;
+    this.valueLayout = valueLayout;
     this.size = size;
     this.columns = columns;
   }
@@ -45,17 +59,29 @@ public long columns() {
     return columns;
   }
 
+  @Override
+  public DataType dataType() {
+    return dataType;
+  }
+
   public MemorySegment memorySegment() {
     return memorySegment;
   }
 
+  public ValueLayout valueLayout() {
+    return valueLayout;
+  }
+
+  /**
+   * Size (in bits) for the element type of this matrix
+   */
   protected int bits() {
-    return switch (dataType) {
-      case FLOAT, INT, UINT -> 32;
-      case BYTE -> 8;
-    };
+    return (int) (valueLayout.byteSize() * 8);
   }
 
+  /**
+   * DLTensor data type {@code code} for the element type of this matrix
+   */
   protected int code() {
     return switch (dataType) {
       case FLOAT -> kDLFloat();
@@ -64,5 +90,104 @@ protected int code() {
     };
   }
 
+  protected static ValueLayout valueLayoutFromType(DataType dataType) {
+    return switch (dataType) {
+      case FLOAT -> C_FLOAT;
+      case INT, UINT -> C_INT;
+      case BYTE -> C_CHAR;
+    };
+  }
+
+  protected static SequenceLayout sequenceLayoutFromType(
+      long size, long columns, DataType dataType) {
+    return MemoryLayout.sequenceLayout(size * columns, valueLayoutFromType(dataType))
+        .withByteAlignment(32);
+  }
+
+  /**
+   * Creates a {@link DLManagedTensor} representing the matrix data and shape, to be
+   * passed to the CuVS C API.
+   * @param arena The Arena to use to allocate DL data structures
+   * @return a {@link MemorySegment} for the newly allocated DLManagedTensor
+   */
   public abstract MemorySegment toTensor(Arena arena);
+
+  /**
+   * Creates a {@link CuVSMatrix} from data and infos from a {@link DLManagedTensor}
+   *
+   * @param dlManagedTensor a {@link MemorySegment} representing the source DLManagedTensor
+   * @param resources       {@link CuVSResources} to allocate the resulting matrix
+   * @return a {@link CuVSMatrix} encapsulating the same data as the input {@link DLManagedTensor}
+   */
+  public static CuVSMatrix fromTensor(MemorySegment dlManagedTensor, CuVSResources resources) {
+    var dlTensor = DLManagedTensor.dl_tensor(dlManagedTensor);
+    var dlDevice = DLTensor.device(dlTensor);
+
+    var deviceType = DLDevice.device_type(dlDevice);
+
+    var data = DLTensor.data(dlTensor);
+    if (data.equals(MemorySegment.NULL)) {
+      throw new IllegalArgumentException("[data] must not be NULL");
+    }
+
+    var ndim = DLTensor.ndim(dlTensor);
+    if (ndim != 2) {
+      throw new IllegalArgumentException("CuVSMatrix only supports 2D data");
+    }
+
+    var dtype = DLTensor.dtype(dlTensor);
+    var code = DLDataType.code(dtype);
+    var bits = DLDataType.bits(dtype);
+
+    final DataType dataType = dataTypeFromTensor(code, bits);
+
+    var shape = DLTensor.shape(dlTensor);
+    if (shape.equals(MemorySegment.NULL)) {
+      throw new IllegalArgumentException("[shape] must not be NULL");
+    }
+
+    var rows = shape.get(int64_t, 0);
+    var cols = shape.getAtIndex(int64_t, 1);
+
+    if (deviceType == kDLCUDA()) {
+      var strides = DLTensor.strides(dlTensor);
+      if (strides.equals(MemorySegment.NULL)) {
+        return new CuVSDeviceMatrixImpl(
+            resources, data, rows, cols, dataType, valueLayoutFromType(dataType));
+      } else {
+        var rowStride = strides.get(int64_t, 0);
+        var colStride = strides.getAtIndex(int64_t, 1);
+        return new CuVSDeviceMatrixImpl(
+            resources,
+            data,
+            rows,
+            cols,
+            rowStride,
+            colStride,
+            dataType,
+            valueLayoutFromType(dataType));
+      }
+    } else if (deviceType == kDLCPU()) {
+      return new CuVSHostMatrixImpl(data, rows, cols, dataType);
+    } else {
+      throw new IllegalArgumentException("Unsupported device type: " + deviceType);
+    }
+  }
+
+  private static DataType dataTypeFromTensor(byte code, byte bits) {
+    final DataType dataType;
+    if (code == kDLUInt() && bits == 32) {
+      dataType = DataType.UINT;
+    } else if (code == kDLInt() && bits == 32) {
+      dataType = DataType.INT;
+    } else if (code == kDLFloat() && bits == 32) {
+      dataType = DataType.FLOAT;
+    } else if ((code == kDLInt() || code == kDLUInt()) && bits == 8) {
+      dataType = DataType.BYTE;
+    } else {
+      throw new IllegalArgumentException(
+          String.format(Locale.ROOT, "Unsupported data type (code=%d, bits=%d)", code, bits));
+    }
+    return dataType;
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
index 736b22f567..9428d2eb6f 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/HnswIndexImpl.java
@@ -98,18 +98,23 @@ public SearchResults search(HnswQuery query) throws Throwable {
 
       SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_LONG);
       SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+      // TODO: these could be CuVSHostMatrix
       MemorySegment neighborsMemorySegment = localArena.allocate(neighborsSequenceLayout);
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
       MemorySegment querySeg = buildMemorySegment(localArena, queryVectors);
 
       long[] queriesShape = {numQueries, vectorDimension};
-      MemorySegment queriesTensor = prepareTensor(localArena, querySeg, queriesShape, 2, 32, 1, 1);
+      MemorySegment queriesTensor =
+          prepareTensor(localArena, querySeg, queriesShape, kDLFloat(), 32, kDLCPU());
       long[] neighborsShape = {numQueries, topK};
+      // TODO: check type code and bits across all implementations -- they are inconsistent
       MemorySegment neighborsTensor =
-          prepareTensor(localArena, neighborsMemorySegment, neighborsShape, 1, 64, 1, 1);
+          prepareTensor(
+              localArena, neighborsMemorySegment, neighborsShape, kDLUInt(), 64, kDLCPU());
       long[] distancesShape = {numQueries, topK};
       MemorySegment distancesTensor =
-          prepareTensor(localArena, distancesMemorySegment, distancesShape, 2, 32, 1, 1);
+          prepareTensor(
+              localArena, distancesMemorySegment, distancesShape, kDLFloat(), 32, kDLCPU());
 
       try (var resourcesAccessor = query.getResources().access()) {
         var cuvsRes = resourcesAccessor.handle();
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SliceRowView.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SliceRowView.java
new file mode 100644
index 0000000000..3620527449
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/SliceRowView.java
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import com.nvidia.cuvs.CuVSMatrix;
+import com.nvidia.cuvs.RowView;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.ValueLayout;
+import java.util.Locale;
+
+/**
+ * A {@link RowView} over a {@link MemorySegment} slice.
+ */
+class SliceRowView implements RowView {
+  private final MemorySegment memorySegment;
+  private final long size;
+  private final ValueLayout valueLayout;
+  private final CuVSMatrix.DataType dataType;
+  private final long valueByteSize;
+
+  SliceRowView(
+      MemorySegment slice,
+      long size,
+      ValueLayout valueLayout,
+      CuVSMatrix.DataType dataType,
+      long valueByteSize) {
+    this.memorySegment = slice;
+    this.size = size;
+    this.valueLayout = valueLayout;
+    this.dataType = dataType;
+    this.valueByteSize = valueByteSize;
+  }
+
+  @Override
+  public long size() {
+    return size;
+  }
+
+  @Override
+  public float getAsFloat(long index) {
+    assert (index < size)
+        : String.format(Locale.ROOT, "Index out of bound ([%d], size [%d])", index, size);
+    assert dataType == CuVSMatrix.DataType.FLOAT
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+
+    return memorySegment.get((ValueLayout.OfFloat) valueLayout, index * valueByteSize);
+  }
+
+  @Override
+  public byte getAsByte(long index) {
+    assert (index < size)
+        : String.format(Locale.ROOT, "Index out of bound ([%d], size [%d])", index, size);
+    assert dataType == CuVSMatrix.DataType.BYTE
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+
+    return memorySegment.get((ValueLayout.OfByte) valueLayout, index * valueByteSize);
+  }
+
+  @Override
+  public int getAsInt(long index) {
+    assert (index < size)
+        : String.format(Locale.ROOT, "Index out of bound ([%d], size [%d])", index, size);
+    assert dataType == CuVSMatrix.DataType.INT || dataType == CuVSMatrix.DataType.UINT
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+
+    return memorySegment.get((ValueLayout.OfInt) valueLayout, index * valueByteSize);
+  }
+
+  @Override
+  public void toArray(int[] array) {
+    assert (array.length >= size)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not large enough (required: [%d], actual [%d])",
+            size,
+            array.length);
+    assert dataType == CuVSMatrix.DataType.INT || dataType == CuVSMatrix.DataType.UINT
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+    MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
+  }
+
+  @Override
+  public void toArray(float[] array) {
+    assert (array.length >= size)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not large enough (required: [%d], actual [%d])",
+            size,
+            array.length);
+    assert dataType == CuVSMatrix.DataType.FLOAT
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+    MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
+  }
+
+  @Override
+  public void toArray(byte[] array) {
+    assert (array.length >= size)
+        : String.format(
+            Locale.ROOT,
+            "Input array is not large enough (required: [%d], actual [%d])",
+            size,
+            array.length);
+    assert dataType == CuVSMatrix.DataType.BYTE
+        : String.format(
+            Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
+    MemorySegment.copy(memorySegment, valueLayout, 0, array, 0, (int) size);
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index c507045326..5edbcabd71 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -143,7 +143,7 @@ private IndexReference build() {
           long[] datasetShape = {rows, cols};
           MemorySegment datasetTensor =
               prepareTensor(
-                  localArena, datasetDP.handle(), datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+                  localArena, datasetDP.handle(), datasetShape, kDLFloat(), 32, kDLCUDA());
 
           MemorySegment index = localArena.allocate(cuvsTieredIndex_t);
           var returnValue = cuvsTieredIndexCreate(index);
@@ -205,6 +205,7 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
           hasPreFilter ? new BitSet[] {query.getPrefilter()} : EMPTY_PREFILTER_BITSET;
       final long prefilterDataLength = hasPreFilter ? query.getNumDocs() * prefilters.length : 0;
       final long prefilterLen = hasPreFilter ? (prefilterDataLength + 31) / 32 : 0;
+      // TODO: is this correct? prefilters is a LONG array
       final long prefilterBytes = C_INT_BYTE_SIZE * prefilterLen;
 
       try (var resourceAccess = query.getResources().access()) {
@@ -229,7 +230,7 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
           long[] queriesShape = {numQueries, vectorDimension};
           MemorySegment queriesTensor =
               prepareTensor(
-                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA(), 1);
+                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA());
           long[] neighborsShape = {numQueries, topK};
           MemorySegment neighborsTensor =
               prepareTensor(
@@ -238,12 +239,11 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
                   neighborsShape,
                   kDLInt(),
                   64,
-                  kDLCUDA(),
-                  1); // 64-bit int
+                  kDLCUDA()); // 64-bit int
           long[] distancesShape = {numQueries, topK};
           MemorySegment distancesTensor =
               prepareTensor(
-                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA(), 1);
+                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA());
 
           // Sync before prefilter setup
           returnValue = cuvsStreamSync(cuvsRes);
@@ -270,7 +270,7 @@ public SearchResults search(TieredIndexQuery query) throws Throwable {
 
             MemorySegment prefilterTensor =
                 prepareTensor(
-                    localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA(), 1);
+                    localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA());
 
             cuvsFilter.type(prefilter, 1); // BITSET
             cuvsFilter.addr(prefilter, prefilterTensor.address());
@@ -347,7 +347,7 @@ private void performExtend(CuVSMatrix extendDataset) {
           long[] datasetShape = {rows, cols};
           MemorySegment datasetTensor =
               prepareTensor(
-                  localArena, datasetDP.handle(), datasetShape, kDLFloat(), 32, kDLCUDA(), 1);
+                  localArena, datasetDP.handle(), datasetShape, kDLFloat(), 32, kDLCUDA());
 
           checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 8125497078..d62450ece0 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -20,7 +20,9 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_LONG;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
+import static com.nvidia.cuvs.internal.panama.headers_h_1.cudaStream_t;
 
+import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.GPUInfo;
 import com.nvidia.cuvs.internal.panama.DLDataType;
 import com.nvidia.cuvs.internal.panama.DLDevice;
@@ -29,9 +31,11 @@
 import com.nvidia.cuvs.internal.panama.cudaDeviceProp;
 import com.nvidia.cuvs.internal.panama.headers_h;
 import java.lang.foreign.Arena;
+import java.lang.foreign.Linker;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemoryLayout.PathElement;
 import java.lang.foreign.MemorySegment;
+import java.lang.invoke.MethodHandle;
 import java.lang.invoke.VarHandle;
 import java.util.ArrayList;
 import java.util.BitSet;
@@ -39,11 +43,25 @@
 
 public class Util {
 
-  public static final int CUVS_SUCCESS = 1;
+  public static final int CUVS_SUCCESS = headers_h.CUVS_SUCCESS();
   public static final int CUDA_SUCCESS = 0;
 
   private Util() {}
 
+  private static final Linker LINKER = Linker.nativeLinker();
+
+  /**
+   * Bindings for {@code cudaMemcpyAsync}; differently from the {@code headers_h} bindings (which are
+   * automatically generated by {@code jextract}), these bindings specify the {@code critical} linker option,
+   * which allows to directly access heap-based {@link MemorySegment}s.
+   * {@snippet lang=c :
+   * extern cudaError_t cudaMemcpyAsync(void *dst, const void *src, size_t count, enum cudaMemcpyKind kind, cudaStream_t stream)
+   * }
+   */
+  private static final MethodHandle cudaMemcpyAsync$mh =
+      LINKER.downcallHandle(
+          cudaMemcpyAsync$address(), cudaMemcpyAsync$descriptor(), Linker.Option.critical(true));
+
   /**
    * Checks the result value of a (CuVS) native method handle call.
    *
@@ -81,10 +99,10 @@ public static long dereferenceUnsignedInt(MemorySegment ptr) {
    * @see <a href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html">CUDA Runtime API</a>
    */
   public enum CudaMemcpyKind {
-    HOST_TO_HOST(0),
-    HOST_TO_DEVICE(1),
-    DEVICE_TO_HOST(2),
-    DEVICE_TO_DEVICE(3),
+    HOST_TO_HOST(cudaMemcpyHostToHost()),
+    HOST_TO_DEVICE(cudaMemcpyHostToDevice()),
+    DEVICE_TO_HOST(cudaMemcpyDeviceToHost()),
+    DEVICE_TO_DEVICE(cudaMemcpyDeviceToDevice()),
     INFER_DIRECTION(4);
 
     CudaMemcpyKind(int k) {
@@ -120,6 +138,41 @@ public static void cudaMemcpy(MemorySegment dest, MemorySegment src, long numByt
     Util.cudaMemcpy(dest, src, numBytes, CudaMemcpyKind.INFER_DIRECTION);
   }
 
+  /**
+   * Helper to invoke cudaMemcpyAsync CUDA runtime function to copy data between host/device memory.
+   *
+   * @param dst Destination address for data copy
+   * @param src Source address for data copy
+   * @param numBytes Number of bytes to be copied
+   * @param kind "Direction" of data copy (Host->Device, Device->Host, etc.)
+   * @throws RuntimeException on failure of copy
+   */
+  public static void cudaMemcpyAsync(
+      MemorySegment dst,
+      MemorySegment src,
+      long numBytes,
+      CudaMemcpyKind kind,
+      MemorySegment stream) {
+    try {
+      int returnValue = (int) cudaMemcpyAsync$mh.invokeExact(dst, src, numBytes, kind.kind, stream);
+      checkCudaError(returnValue, "cudaMemcpyAsync");
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
+  /**
+   * Helper to get the CUDA stream associated with a {@link CuVSResources}
+   */
+  public static MemorySegment getStream(CuVSResources resources) {
+    try (var resourcesAccess = resources.access();
+        var localArena = Arena.ofConfined()) {
+      var streamPointer = localArena.allocate(cudaStream_t);
+      checkCuVSError(cuvsStreamGet(resourcesAccess.handle(), streamPointer), "cuvsStreamGet");
+      return streamPointer.get(cudaStream_t, 0);
+    }
+  }
+
   static final long MAX_ERROR_TEXT = 1_000_000L;
 
   static String getLastErrorText() {
@@ -309,47 +362,71 @@ public static BitSet concatenate(BitSet[] arr, int maxSizeOfEachBitSet) {
   }
 
   /**
-   * @brief Helper function for creating DLManagedTensor instance
+   * Helper function for creating a DLManagedTensor instance
    *
-   * @param[in] data the data pointer points to the allocated data
-   * @param[in] shape the shape of the tensor
-   * @param[in] code the type code of base types
-   * @param[in] bits the shape of the tensor
+   * @param arena      the Arena to use to allocate native memory for this data structure
+   * @param data       the data pointer points to the allocated data
+   * @param shape      the shape of the tensor
+   * @param code       the type code of the matrix elements (e.g. kDLFloat())
+   * @param bits       the size in bits of the matrix elements
+   * @param deviceType the device where {@code data} is held (kDLCPU() or kDLCUDA())
+   * @return DLManagedTensor
+   */
+  public static MemorySegment prepareTensor(
+      Arena arena, MemorySegment data, long[] shape, int code, int bits, int deviceType) {
+    return prepareTensor(arena, data, shape, null, code, bits, deviceType);
+  }
+
+  /**
+   * Helper function for creating a DLManagedTensor instance
+   *
+   * @param arena      the Arena to use to allocate native memory for this data structure
+   * @param data       the data pointer points to the allocated data
+   * @param shape      the shape of the tensor
+   * @param strides    the strides for each dimension (can be null)
+   * @param code       the type code of the matrix elements (e.g. kDLFloat())
+   * @param bits       the size in bits of the matrix elements
+   * @param deviceType the device where {@code data} is held (kDLCPU() or kDLCUDA())
    * @return DLManagedTensor
    */
   public static MemorySegment prepareTensor(
       Arena arena,
       MemorySegment data,
       long[] shape,
+      long[] strides,
       int code,
       int bits,
-      int deviceType,
-      int lanes) {
+      int deviceType) {
 
-    MemorySegment tensor = DLManagedTensor.allocate(arena);
-    MemorySegment dlTensor = DLTensor.allocate(arena);
+    MemorySegment managedTensor = DLManagedTensor.allocate(arena);
+    MemorySegment tensor = DLTensor.allocate(arena);
 
-    DLTensor.data(dlTensor, data);
+    DLTensor.data(tensor, data);
 
     MemorySegment dlDevice = DLDevice.allocate(arena);
     DLDevice.device_type(dlDevice, deviceType);
-    DLTensor.device(dlTensor, dlDevice);
-
-    var ndim = shape.length;
-    DLTensor.ndim(dlTensor, ndim);
+    DLTensor.device(tensor, dlDevice);
 
     MemorySegment dtype = DLDataType.allocate(arena);
     DLDataType.code(dtype, (byte) code);
     DLDataType.bits(dtype, (byte) bits);
-    DLDataType.lanes(dtype, (short) lanes);
-    DLTensor.dtype(dlTensor, dtype);
-
-    DLTensor.shape(dlTensor, Util.buildMemorySegment(arena, shape));
-
-    DLTensor.strides(dlTensor, MemorySegment.NULL);
+    // Number of lanes for vectorized types (1 for scalar types)
+    DLDataType.lanes(dtype, (short) 1);
+    DLTensor.dtype(tensor, dtype);
+
+    DLTensor.ndim(tensor, shape.length);
+    DLTensor.shape(tensor, Util.buildMemorySegment(arena, shape));
+    if (strides != null) {
+      assert shape.length == strides.length;
+      DLTensor.strides(tensor, Util.buildMemorySegment(arena, strides));
+    } else {
+      DLTensor.strides(tensor, MemorySegment.NULL);
+    }
 
-    DLManagedTensor.dl_tensor(tensor, dlTensor);
+    // Copy tensor information into the DLManagedTensor struct
+    DLManagedTensor.dl_tensor(managedTensor, tensor);
 
-    return tensor;
+    assert bits == DLDataType.bits(DLTensor.dtype(DLManagedTensor.dl_tensor(managedTensor)));
+    return managedTensor;
   }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 668befe236..01eb72ed69 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -15,6 +15,8 @@
  */
 package com.nvidia.cuvs.spi;
 
+import static com.nvidia.cuvs.internal.common.Util.*;
+
 import com.nvidia.cuvs.*;
 import com.nvidia.cuvs.internal.*;
 import com.nvidia.cuvs.internal.common.Util;
@@ -24,6 +26,7 @@
 import java.lang.invoke.MethodType;
 import java.nio.file.Files;
 import java.nio.file.Path;
+import java.util.Locale;
 import java.util.Objects;
 
 final class JDKProvider implements CuVSProvider {
@@ -84,7 +87,7 @@ public TieredIndex.Builder newTieredIndexBuilder(CuVSResources cuVSResources) {
   }
 
   @Override
-  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
+  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) {
     if (indexes == null || indexes.length == 0) {
       throw new IllegalArgumentException("At least one index must be provided for merging");
     }
@@ -92,8 +95,7 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) throws Throwable {
   }
 
   @Override
-  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams)
-      throws Throwable {
+  public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams mergeParams) {
     if (indexes == null || indexes.length == 0) {
       throw new IllegalArgumentException("At least one index must be provided for merging");
     }
@@ -101,26 +103,40 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merge
   }
 
   @Override
-  public CuVSMatrix.Builder newMatrixBuilder(int size, int dimensions, CuVSMatrix.DataType dataType)
-      throws UnsupportedOperationException {
+  public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
+      long size, long columns, CuVSMatrix.DataType dataType) throws UnsupportedOperationException {
 
-    var dataset = new CuVSHostMatrixArenaImpl(size, dimensions, dataType);
-
-    return new CuVSMatrix.Builder() {
+    return new CuVSMatrix.Builder<>() {
+      final CuVSHostMatrixArenaImpl matrix = new CuVSHostMatrixArenaImpl(size, columns, dataType);
       int current = 0;
 
       @Override
       public void addVector(float[] vector) {
+        if (vector.length != columns) {
+          throw new IllegalArgumentException(
+              String.format(
+                  Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
+        }
         internalAddVector(vector);
       }
 
       @Override
       public void addVector(byte[] vector) {
+        if (vector.length != columns) {
+          throw new IllegalArgumentException(
+              String.format(
+                  Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
+        }
         internalAddVector(vector);
       }
 
       @Override
       public void addVector(int[] vector) {
+        if (vector.length != columns) {
+          throw new IllegalArgumentException(
+              String.format(
+                  Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
+        }
         internalAddVector(vector);
       }
 
@@ -129,19 +145,37 @@ private void internalAddVector(Object vector) {
         MemorySegment.copy(
             vector,
             0,
-            dataset.memorySegment(),
-            dataset.valueLayout(),
-            ((current++) * dimensions * dataset.valueLayout().byteSize()),
-            dimensions);
+            matrix.memorySegment(),
+            matrix.valueLayout(),
+            ((current++) * columns * matrix.valueLayout().byteSize()),
+            (int) columns);
       }
 
       @Override
-      public CuVSMatrix build() {
-        return dataset;
+      public CuVSHostMatrix build() {
+        return matrix;
       }
     };
   }
 
+  @Override
+  public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
+      CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType)
+      throws UnsupportedOperationException {
+    return new HeapSegmentBuilder(resources, size, columns, dataType);
+  }
+
+  @Override
+  public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
+      CuVSResources resources,
+      long size,
+      long columns,
+      int rowStride,
+      int columnStride,
+      CuVSMatrix.DataType dataType) {
+    return new HeapSegmentBuilder(resources, size, columns, rowStride, columnStride, dataType);
+  }
+
   @Override
   public MethodHandle newNativeMatrixBuilder() {
     return createNativeDataset$mh;
@@ -188,4 +222,91 @@ public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     Util.copy(dataset.memorySegment(), vectors);
     return dataset;
   }
+
+  /**
+   * This {@link CuVSDeviceMatrix} builder implementation returns a {@link CuVSDeviceMatrix} backed by managed RMM
+   * device memory. It uses a non-native {@link MemorySegment} created directly from on-heap java arrays to avoid
+   * an intermediate allocation and copy to a native (off-heap) segment.
+   * It requires the copy function ({@code cudaMemcpyAsync}) to have the {@code Critical} linker option in order
+   * to allow the access to on-heap memory (see {@link Util#cudaMemcpyAsync}).
+   */
+  private static class HeapSegmentBuilder implements CuVSMatrix.Builder<CuVSDeviceMatrix> {
+    private final long columns;
+    private final long size;
+    private final CuVSDeviceMatrixImpl matrix;
+    private final MemorySegment stream;
+    private int current;
+
+    private HeapSegmentBuilder(
+        CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType) {
+      this.columns = columns;
+      this.size = size;
+      this.matrix = CuVSDeviceMatrixRMMImpl.create(resources, size, columns, dataType);
+      this.stream = Util.getStream(resources);
+      this.current = 0;
+    }
+
+    private HeapSegmentBuilder(
+        CuVSResources resources,
+        long size,
+        long columns,
+        int rowStride,
+        int columnStride,
+        CuVSMatrix.DataType dataType) {
+      this.columns = columns;
+      this.size = size;
+      this.matrix =
+          CuVSDeviceMatrixRMMImpl.create(
+              resources, size, columns, rowStride, columnStride, dataType);
+      this.stream = Util.getStream(resources);
+      this.current = 0;
+    }
+
+    @Override
+    public void addVector(float[] vector) {
+      if (vector.length != columns) {
+        throw new IllegalArgumentException(
+            String.format(
+                Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
+      }
+      internalAddVector(MemorySegment.ofArray(vector));
+    }
+
+    @Override
+    public void addVector(byte[] vector) {
+      if (vector.length != columns) {
+        throw new IllegalArgumentException(
+            String.format(
+                Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
+      }
+      internalAddVector(MemorySegment.ofArray(vector));
+    }
+
+    @Override
+    public void addVector(int[] vector) {
+      if (vector.length != columns) {
+        throw new IllegalArgumentException(
+            String.format(
+                Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
+      }
+      internalAddVector(MemorySegment.ofArray(vector));
+    }
+
+    private void internalAddVector(MemorySegment vector) {
+      if (current >= size) {
+        throw new ArrayIndexOutOfBoundsException();
+      }
+
+      long rowBytes = columns * matrix.valueLayout().byteSize();
+
+      var dstOffset = ((current++) * rowBytes);
+      var dst = matrix.memorySegment().asSlice(dstOffset);
+      cudaMemcpyAsync(dst, vector, rowBytes, CudaMemcpyKind.HOST_TO_DEVICE, stream);
+    }
+
+    @Override
+    public CuVSDeviceMatrix build() {
+      return matrix;
+    }
+  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
index 6bfbf4d2c7..f65680d631 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/BruteForceRandomizedIT.java
@@ -121,7 +121,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       BruteForceIndex index;
       if (useNativeMemoryDataset) {
         var datasetBuilder =
-            CuVSMatrix.builder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
+            CuVSMatrix.hostBuilder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
         for (float[] v : vectors) {
           datasetBuilder.addVector(v);
         }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index a4133c4dc5..c0c0c8a37a 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -31,7 +31,6 @@
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemorySegment;
 import java.lang.foreign.ValueLayout;
-import java.lang.invoke.MethodHandles;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.Arrays;
@@ -56,7 +55,7 @@
 @RunWith(RandomizedRunner.class)
 public class CagraBuildAndSearchIT extends CuVSTestCase {
 
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+  private static final Logger log = LoggerFactory.getLogger(CagraBuildAndSearchIT.class);
 
   @Before
   public void setup() {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 144090866c..6e02793478 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -122,7 +122,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       CagraIndex index;
       if (useNativeMemoryDataset) {
         var datasetBuilder =
-            CuVSMatrix.builder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
+            CuVSMatrix.hostBuilder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
         for (float[] v : vectors) {
           datasetBuilder.addVector(v);
         }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
index 3518b8acd6..0eaf666d15 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
@@ -23,10 +23,14 @@
 import org.junit.Before;
 import org.junit.Test;
 import org.junit.runner.RunWith;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 @RunWith(RandomizedRunner.class)
 public class CuVSMatrixIT extends CuVSTestCase {
 
+  private static final Logger log = LoggerFactory.getLogger(CuVSMatrixIT.class);
+
   @Before
   public void setup() {
     assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
@@ -61,6 +65,10 @@ private float[][] createFloatMatrix() {
     int rows = randomIntBetween(1, 32);
     int cols = randomIntBetween(1, 100);
 
+    return createFloatMatrix(rows, cols);
+  }
+
+  private float[][] createFloatMatrix(int rows, int cols) {
     float[][] result = new float[rows][cols];
 
     for (int r = 0; r < rows; ++r) {
@@ -71,42 +79,99 @@ private float[][] createFloatMatrix() {
     return result;
   }
 
+  private void testByteDatasetRowGetAccess(CuVSMatrix dataset) {
+    for (int n = 0; n < dataset.size(); ++n) {
+      var row = dataset.getRow(n);
+      assertEquals(dataset.columns(), row.size());
+      for (int i = 0; i < dataset.columns(); ++i) {
+        assertEquals(byteData[n][i], row.getAsByte(i));
+      }
+    }
+  }
+
   @Test
-  public void testByteDatasetRowGetAccess() {
-    try (var dataset = CuVSMatrix.ofArray(byteData)) {
-      for (int n = 0; n < dataset.size(); ++n) {
-        var row = dataset.getRow(n);
-        assertEquals(dataset.columns(), row.size());
-        for (int i = 0; i < dataset.columns(); ++i) {
-          assertEquals(byteData[n][i], row.getAsByte(i));
-        }
+  public void testByteHostDatasetRowGetAccess() {
+    try (var matrix = CuVSMatrix.ofArray(byteData)) {
+      testByteDatasetRowGetAccess(matrix);
+    }
+  }
+
+  @Test
+  public void testByteDeviceDatasetRowGetAccess() throws Throwable {
+    try (var resources = CheckedCuVSResources.create()) {
+      var builder =
+          CuVSMatrix.deviceBuilder(
+              resources, byteData.length, byteData[0].length, CuVSMatrix.DataType.BYTE);
+      for (int i = 0; i < byteData.length; i++) {
+        builder.addVector(byteData[i]);
+      }
+      try (var matrix = builder.build()) {
+        testByteDatasetRowGetAccess(matrix);
       }
     }
   }
 
+  private void testByteDatasetRowCopy(CuVSMatrix dataset) {
+    for (int n = 0; n < dataset.size(); ++n) {
+      var row = dataset.getRow(n);
+      assertEquals(dataset.columns(), row.size());
+
+      var rowCopy = new byte[(int) row.size()];
+      row.toArray(rowCopy);
+      assertArrayEquals(byteData[n], rowCopy);
+    }
+  }
+
   @Test
-  public void testByteDatasetRowCopy() {
+  public void testByteHostDatasetRowCopy() {
     try (var dataset = CuVSMatrix.ofArray(byteData)) {
-      for (int n = 0; n < dataset.size(); ++n) {
-        var row = dataset.getRow(n);
-        assertEquals(dataset.columns(), row.size());
+      testByteDatasetRowCopy(dataset);
+    }
+  }
 
-        var rowCopy = new byte[(int) row.size()];
-        row.toArray(rowCopy);
-        assertArrayEquals(byteData[n], rowCopy);
+  @Test
+  public void testByteDeviceDatasetRowCopy() throws Throwable {
+    try (var resources = CheckedCuVSResources.create()) {
+      var builder =
+          CuVSMatrix.deviceBuilder(
+              resources, byteData.length, byteData[0].length, CuVSMatrix.DataType.BYTE);
+      for (int i = 0; i < byteData.length; i++) {
+        builder.addVector(byteData[i]);
+      }
+      try (var matrix = builder.build()) {
+        testByteDatasetRowCopy(matrix);
+      }
+    }
+  }
+
+  private void testByteDatasetCopy(CuVSMatrix dataset) {
+    var dataCopy = new byte[(int) dataset.size()][(int) dataset.columns()];
+    dataset.toArray(dataCopy);
+    for (int n = 0; n < dataset.size(); ++n) {
+      for (int i = 0; i < dataset.columns(); ++i) {
+        assertEquals(byteData[n][i], dataCopy[n][i]);
       }
     }
   }
 
   @Test
-  public void testByteDatasetCopy() {
+  public void testByteHostDatasetCopy() {
     try (var dataset = CuVSMatrix.ofArray(byteData)) {
-      var dataCopy = new byte[(int) dataset.size()][(int) dataset.columns()];
-      dataset.toArray(dataCopy);
-      for (int n = 0; n < dataset.size(); ++n) {
-        for (int i = 0; i < dataset.columns(); ++i) {
-          assertEquals(byteData[n][i], dataCopy[n][i]);
-        }
+      testByteDatasetCopy(dataset);
+    }
+  }
+
+  @Test
+  public void testByteDeviceDatasetCopy() throws Throwable {
+    try (var resources = CheckedCuVSResources.create()) {
+      var builder =
+          CuVSMatrix.deviceBuilder(
+              resources, byteData.length, byteData[0].length, CuVSMatrix.DataType.BYTE);
+      for (int i = 0; i < byteData.length; i++) {
+        builder.addVector(byteData[i]);
+      }
+      try (var matrix = builder.build()) {
+        testByteDatasetCopy(matrix);
       }
     }
   }
@@ -197,18 +262,15 @@ public void testFloatDatasetCopy() {
     }
   }
 
-  public void testFloatDatasetBuilder() {
-    int rows = randomIntBetween(1, 32);
-    int cols = randomIntBetween(1, 100);
+  private void testFloatDatasetBuilder(int rows, int cols, CuVSMatrix.Builder builder) {
 
     float[][] data = new float[rows][cols];
     for (int r = 0; r < rows; ++r) {
       for (int c = 0; c < cols; ++c) {
-        data[c][r] = randomFloat();
+        data[r][c] = randomFloat();
       }
     }
 
-    var builder = CuVSMatrix.builder(rows, cols, CuVSMatrix.DataType.FLOAT);
     for (int r = 0; r < rows; ++r) {
       builder.addVector(data[r]);
     }
@@ -226,18 +288,33 @@ public void testFloatDatasetBuilder() {
     }
   }
 
-  public void testIntDatasetBuilder() {
+  @Test
+  public void testFloatDatasetHostBuilder() {
     int rows = randomIntBetween(1, 32);
     int cols = randomIntBetween(1, 100);
+    testFloatDatasetBuilder(
+        rows, cols, CuVSMatrix.hostBuilder(rows, cols, CuVSMatrix.DataType.FLOAT));
+  }
+
+  @Test
+  public void testFloatDatasetDeviceBuilder() throws Throwable {
+    int rows = randomIntBetween(1, 1024);
+    int cols = randomIntBetween(1, 2048);
+    try (var resources = CheckedCuVSResources.create()) {
+      testFloatDatasetBuilder(
+          rows, cols, CuVSMatrix.deviceBuilder(resources, rows, cols, CuVSMatrix.DataType.FLOAT));
+    }
+  }
+
+  private void testIntDatasetBuilder(int rows, int cols, CuVSMatrix.Builder builder) {
 
     var data = new int[rows][cols];
     for (int r = 0; r < rows; ++r) {
       for (int c = 0; c < cols; ++c) {
-        data[c][r] = randomInt();
+        data[r][c] = randomInt();
       }
     }
 
-    var builder = CuVSMatrix.builder(rows, cols, CuVSMatrix.DataType.INT);
     for (int r = 0; r < rows; ++r) {
       builder.addVector(data[r]);
     }
@@ -255,18 +332,32 @@ public void testIntDatasetBuilder() {
     }
   }
 
-  public void testByteDatasetBuilder() {
+  @Test
+  public void testIntDatasetHostBuilder() {
     int rows = randomIntBetween(1, 32);
     int cols = randomIntBetween(1, 100);
+    testIntDatasetBuilder(rows, cols, CuVSMatrix.hostBuilder(rows, cols, CuVSMatrix.DataType.INT));
+  }
+
+  @Test
+  public void testIntDatasetDeviceBuilder() throws Throwable {
+    int rows = randomIntBetween(1, 1024);
+    int cols = randomIntBetween(1, 2048);
+    try (var resources = CheckedCuVSResources.create()) {
+      testIntDatasetBuilder(
+          rows, cols, CuVSMatrix.deviceBuilder(resources, rows, cols, CuVSMatrix.DataType.INT));
+    }
+  }
+
+  private void testByteDatasetBuilder(int rows, int cols, CuVSMatrix.Builder builder) {
 
     var data = new byte[rows][cols];
     for (int r = 0; r < rows; ++r) {
       for (int c = 0; c < cols; ++c) {
-        data[c][r] = randomByte();
+        data[r][c] = randomByte();
       }
     }
 
-    var builder = CuVSMatrix.builder(rows, cols, CuVSMatrix.DataType.BYTE);
     for (int r = 0; r < rows; ++r) {
       builder.addVector(data[r]);
     }
@@ -283,4 +374,59 @@ public void testByteDatasetBuilder() {
       }
     }
   }
+
+  @Test
+  public void testByteDatasetHostBuilder() {
+    int rows = randomIntBetween(1, 32);
+    int cols = randomIntBetween(1, 100);
+    testByteDatasetBuilder(
+        rows, cols, CuVSMatrix.hostBuilder(rows, cols, CuVSMatrix.DataType.BYTE));
+  }
+
+  @Test
+  public void testByteDatasetDeviceBuilder() throws Throwable {
+    int rows = randomIntBetween(1, 1024);
+    int cols = randomIntBetween(1, 2048);
+    try (var resources = CheckedCuVSResources.create()) {
+      testByteDatasetBuilder(
+          rows, cols, CuVSMatrix.deviceBuilder(resources, rows, cols, CuVSMatrix.DataType.BYTE));
+    }
+  }
+
+  @Test
+  public void testDeviceToHost() throws Throwable {
+
+    final int size = 16 * 1024;
+    final int columns = 2048;
+    final float[][] data = createFloatMatrix(size, columns);
+
+    try (var resources = CuVSResources.create()) {
+
+      var builder = CuVSMatrix.deviceBuilder(resources, size, columns, CuVSMatrix.DataType.FLOAT);
+      for (int i = 0; i < size; ++i) {
+        var array = data[i];
+        builder.addVector(array);
+      }
+
+      try (var deviceMatrix = (CuVSDeviceMatrix) builder.build();
+          var hostMatrix = deviceMatrix.toHost()) {
+
+        assertEquals(data.length, deviceMatrix.size());
+        assertEquals(data[0].length, deviceMatrix.columns());
+
+        assertEquals(deviceMatrix.size(), hostMatrix.size());
+        assertEquals(deviceMatrix.columns(), hostMatrix.columns());
+
+        var roundTripData = new float[size][columns];
+
+        hostMatrix.toArray(roundTripData);
+
+        for (int n = 0; n < hostMatrix.size(); ++n) {
+          for (int i = 0; i < hostMatrix.columns(); ++i) {
+            assertEquals(data[n][i], roundTripData[n][i], 1e-9);
+          }
+        }
+      }
+    }
+  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
index 02ac3c3a14..ac4a491cff 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/HnswRandomizedIT.java
@@ -114,7 +114,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
       final CagraIndex index;
       if (useNativeMemoryDataset) {
         var datasetBuilder =
-            CuVSMatrix.builder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
+            CuVSMatrix.hostBuilder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
         for (float[] v : vectors) {
           datasetBuilder.addVector(v);
         }

From 0b1b1ec47ff5364e40148ae9ae4ff375242c05be Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Tue, 26 Aug 2025 01:57:10 +0900
Subject: [PATCH 556/623] Fix balanced kmeans API (#1268)

This PR updates cluster::balanced_kmeans to:
1. use `int64_t` as the `mdspan` index type to support a large dataset
2. fix the `fit` function for `data_t` = `int8_t`
3. test `fit` and `predict` in the unit test.

## 1. Use `int64_t` as the `mdspan` index type to support a large dataset

In the current implementation, the mdspan index type is `int32_t`. Even if `int64_t` is specified, it is cast to `int32_t`.

e.g.
```cpp
auto dataset = raft::make_device_matrix<float, int64_t>(handle, n, d);
cuvs::cluster::kmeans::fit(handle, params, mkconst(dataset.view()), centroids_view); // Here, the index type of the dataset view is cast to int32_t since there is only an API for int32_t.
```

This cast results in an unexpected error in this check. `IndexT` is `int32_t` even if the input index type is `int64_t`.
https://github.com/rapidsai/cuvs/blob/d0a83d43f7be934f0c4dcef585302a7fad287df0/cpp/src/cluster/kmeans_balanced.cuh#L86-L88

This PR changes the index type to `int64_t`. If `int32_t` is specified, it is cast to `int64_t`. Therefore, this change is not breaking.

## 2. Fix the `fit` function for `data_t` = `int8_t`
The centroid data type of the `predict` and `fit_predict` functions is `float`, but `int8_t` for the `fit` function (`fit_predict` is a function that sequentially calls the `fit` and `predict` functions internally).
This PR changes it to `float` since this data type must be the same as the one of `predict`.

## 3. Test `fit` and `predict` in the unit test.
In the current unit test, only the `fit_predict` function is tested. This PR adds tests for the `fit` and `predict` functions.

## Discussion
There is no consistency in the label data types for `predict` and `fit_predict`: `predict` is `int32_t`, while `fit_predict` is `uint32_t`. Would it be better to:
1. Support both types in both functions (e.g., by reinterpret_cast-ing int32_t* to uint32*),
2. Unify on one type, or
3. Keep the inconsistency?

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1268
---
 cpp/include/cuvs/cluster/kmeans.hpp           | 113 ++++++++++++------
 cpp/src/cluster/kmeans_balanced_fit_float.cu  |   6 +-
 cpp/src/cluster/kmeans_balanced_fit_int8.cu   |   6 +-
 .../kmeans_balanced_fit_predict_float.cu      |   8 +-
 .../kmeans_balanced_fit_predict_int8.cu       |  18 ++-
 .../cluster/kmeans_balanced_predict_float.cu  |   8 +-
 .../cluster/kmeans_balanced_predict_int8.cu   |  18 ++-
 cpp/tests/cluster/kmeans_balanced.cu          |  53 +++++---
 8 files changed, 159 insertions(+), 71 deletions(-)

diff --git a/cpp/include/cuvs/cluster/kmeans.hpp b/cpp/include/cuvs/cluster/kmeans.hpp
index ad08cd1394..43d2db5a5c 100644
--- a/cpp/include/cuvs/cluster/kmeans.hpp
+++ b/cpp/include/cuvs/cluster/kmeans.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -407,9 +407,9 @@ void fit(raft::resources const& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15;
- *   int n_clusters = 8;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *   int64_t n_features = 15;
+ *   int64_t n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
@@ -429,8 +429,8 @@ void fit(raft::resources const& handle,
  */
 void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
-         raft::device_matrix_view<const float, int> X,
-         raft::device_matrix_view<float, int> centroids);
+         raft::device_matrix_view<const float, int64_t> X,
+         raft::device_matrix_view<float, int64_t> centroids);
 
 /**
  * @brief Find balanced clusters with k-means algorithm.
@@ -442,8 +442,8 @@ void fit(const raft::resources& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15, n_clusters = 8;
- *   auto centroids = raft::make_device_matrix<int8_t, int>(handle, n_clusters, n_features);
+ *   int64_t n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
@@ -463,8 +463,9 @@ void fit(const raft::resources& handle,
  */
 void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
-         raft::device_matrix_view<const int8_t, int> X,
-         raft::device_matrix_view<int8_t, int> centroids);
+         raft::device_matrix_view<const int8_t, int64_t> X,
+         raft::device_matrix_view<float, int64_t> centroids);
+
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
  *
@@ -707,15 +708,15 @@ void predict(raft::resources const& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15, n_clusters = 8;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *   int64_t n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
  *               X,
  *               centroids.view());
  *   ...
- *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *   auto labels = raft::make_device_vector<uint32_t, int64_t>(handle, X.extent(0));
  *
  *   kmeans::predict(handle,
  *                   params,
@@ -737,9 +738,9 @@ void predict(raft::resources const& handle,
  */
 void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
-             raft::device_matrix_view<const int8_t, int> X,
-             raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<uint32_t, int> labels);
+             raft::device_matrix_view<const int8_t, int64_t> X,
+             raft::device_matrix_view<const float, int64_t> centroids,
+             raft::device_vector_view<uint32_t, int64_t> labels);
 
 /**
  * @brief Predict the closest cluster each sample in X belongs to.
@@ -751,15 +752,15 @@ void predict(const raft::resources& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15, n_clusters = 8;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
+ *   int64_t n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, n_clusters, n_features);
  *
  *   kmeans::fit(handle,
  *               params,
  *               X,
  *               centroids.view());
  *   ...
- *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *   auto labels = raft::make_device_vector<int, int64_t>(handle, X.extent(0));
  *
  *   kmeans::predict(handle,
  *                   params,
@@ -781,9 +782,53 @@ void predict(const raft::resources& handle,
  */
 void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
-             raft::device_matrix_view<const float, int> X,
-             raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<int, int> labels);
+             raft::device_matrix_view<const int8_t, int64_t> X,
+             raft::device_matrix_view<const float, int64_t> centroids,
+             raft::device_vector_view<int, int64_t> labels);
+
+/**
+ * @brief Predict the closest cluster each sample in X belongs to.
+ *
+ * @code{.cpp}
+ *   #include <raft/core/resources.hpp>
+ *   #include <cuvs/cluster/kmeans.hpp>
+ *   using namespace  cuvs::cluster;
+ *   ...
+ *   raft::resources handle;
+ *   cuvs::cluster::kmeans::balanced_params params;
+ *   int64_t n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, n_clusters, n_features);
+ *
+ *   kmeans::fit(handle,
+ *               params,
+ *               X,
+ *               centroids.view());
+ *   ...
+ *   auto labels = raft::make_device_vector<int, int64_t>(handle, X.extent(0));
+ *
+ *   kmeans::predict(handle,
+ *                   params,
+ *                   X,
+ *                   centroids.view(),
+ *                   labels.view());
+ * @endcode
+ *
+ * @param[in]     handle           The raft handle.
+ * @param[in]     params           Parameters for KMeans model.
+ * @param[in]     X                New data to predict.
+ *                                 [dim = n_samples x n_features]
+ * @param[in]     centroids        Cluster centroids. The data must be in
+ *                                 row-major format.
+ *                                 [dim = n_clusters x n_features]
+ * @param[out]    labels           Index of the cluster each sample in X
+ *                                 belongs to.
+ *                                 [len = n_samples]
+ */
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const float, int64_t> X,
+             raft::device_matrix_view<const float, int64_t> centroids,
+             raft::device_vector_view<int, int64_t> labels);
 
 /**
  * @brief Compute k-means clustering and predicts cluster index for each sample
@@ -1016,9 +1061,9 @@ void fit_predict(raft::resources const& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15, n_clusters = 8;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
- *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *   int64_t n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int64_t>(handle, X.extent(0));
  *
  *   kmeans::fit_predict(handle,
  *                       params,
@@ -1045,9 +1090,9 @@ void fit_predict(raft::resources const& handle,
  */
 void fit_predict(const raft::resources& handle,
                  cuvs::cluster::kmeans::balanced_params const& params,
-                 raft::device_matrix_view<const float, int> X,
-                 raft::device_matrix_view<float, int> centroids,
-                 raft::device_vector_view<uint32_t, int> labels);
+                 raft::device_matrix_view<const float, int64_t> X,
+                 raft::device_matrix_view<float, int64_t> centroids,
+                 raft::device_vector_view<uint32_t, int64_t> labels);
 
 /**
  * @brief Compute balanced k-means clustering and predicts cluster index for each sample
@@ -1060,9 +1105,9 @@ void fit_predict(const raft::resources& handle,
  *   ...
  *   raft::resources handle;
  *   cuvs::cluster::kmeans::balanced_params params;
- *   int n_features = 15, n_clusters = 8;
- *   auto centroids = raft::make_device_matrix<float, int>(handle, n_clusters, n_features);
- *   auto labels = raft::make_device_vector<int, int>(handle, X.extent(0));
+ *   int64_t n_features = 15, n_clusters = 8;
+ *   auto centroids = raft::make_device_matrix<float, int64_t>(handle, n_clusters, n_features);
+ *   auto labels = raft::make_device_vector<int, int64_t>(handle, X.extent(0));
  *
  *   kmeans::fit_predict(handle,
  *                       params,
@@ -1089,9 +1134,9 @@ void fit_predict(const raft::resources& handle,
  */
 void fit_predict(const raft::resources& handle,
                  cuvs::cluster::kmeans::balanced_params const& params,
-                 raft::device_matrix_view<const int8_t, int> X,
-                 raft::device_matrix_view<float, int> centroids,
-                 raft::device_vector_view<uint32_t, int> labels);
+                 raft::device_matrix_view<const int8_t, int64_t> X,
+                 raft::device_matrix_view<float, int64_t> centroids,
+                 raft::device_vector_view<uint32_t, int64_t> labels);
 
 /**
  * @brief Transform X to a cluster-distance space.
diff --git a/cpp/src/cluster/kmeans_balanced_fit_float.cu b/cpp/src/cluster/kmeans_balanced_fit_float.cu
index ac5a664929..41e13d9f93 100644
--- a/cpp/src/cluster/kmeans_balanced_fit_float.cu
+++ b/cpp/src/cluster/kmeans_balanced_fit_float.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,8 +24,8 @@ namespace cuvs::cluster::kmeans {
 
 void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
-         raft::device_matrix_view<const float, int> X,
-         raft::device_matrix_view<float, int> centroids)
+         raft::device_matrix_view<const float, int64_t> X,
+         raft::device_matrix_view<float, int64_t> centroids)
 {
   cuvs::cluster::kmeans_balanced::fit(
     handle, params, X, centroids, cuvs::spatial::knn::detail::utils::mapping<float>{});
diff --git a/cpp/src/cluster/kmeans_balanced_fit_int8.cu b/cpp/src/cluster/kmeans_balanced_fit_int8.cu
index ad9c118684..fd74c2dc69 100644
--- a/cpp/src/cluster/kmeans_balanced_fit_int8.cu
+++ b/cpp/src/cluster/kmeans_balanced_fit_int8.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,8 +24,8 @@ namespace cuvs::cluster::kmeans {
 
 void fit(const raft::resources& handle,
          cuvs::cluster::kmeans::balanced_params const& params,
-         raft::device_matrix_view<const int8_t, int> X,
-         raft::device_matrix_view<float, int> centroids)
+         raft::device_matrix_view<const int8_t, int64_t> X,
+         raft::device_matrix_view<float, int64_t> centroids)
 {
   cuvs::cluster::kmeans_balanced::fit(
     handle, params, X, centroids, cuvs::spatial::knn::detail::utils::mapping<float>{});
diff --git a/cpp/src/cluster/kmeans_balanced_fit_predict_float.cu b/cpp/src/cluster/kmeans_balanced_fit_predict_float.cu
index f6f177121f..5c0109ffb4 100644
--- a/cpp/src/cluster/kmeans_balanced_fit_predict_float.cu
+++ b/cpp/src/cluster/kmeans_balanced_fit_predict_float.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,9 +24,9 @@ namespace cuvs::cluster::kmeans {
 
 void fit_predict(const raft::resources& handle,
                  cuvs::cluster::kmeans::balanced_params const& params,
-                 raft::device_matrix_view<const float, int> X,
-                 raft::device_matrix_view<float, int> centroids,
-                 raft::device_vector_view<uint32_t, int> labels)
+                 raft::device_matrix_view<const float, int64_t> X,
+                 raft::device_matrix_view<float, int64_t> centroids,
+                 raft::device_vector_view<uint32_t, int64_t> labels)
 {
   cuvs::cluster::kmeans_balanced::fit_predict(
     handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
diff --git a/cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu b/cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu
index b6171ece5a..007f50df9b 100644
--- a/cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu
+++ b/cpp/src/cluster/kmeans_balanced_fit_predict_int8.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,9 +24,19 @@ namespace cuvs::cluster::kmeans {
 
 void fit_predict(const raft::resources& handle,
                  cuvs::cluster::kmeans::balanced_params const& params,
-                 raft::device_matrix_view<const int8_t, int> X,
-                 raft::device_matrix_view<float, int> centroids,
-                 raft::device_vector_view<uint32_t, int> labels)
+                 raft::device_matrix_view<const int8_t, int64_t> X,
+                 raft::device_matrix_view<float, int64_t> centroids,
+                 raft::device_vector_view<uint32_t, int64_t> labels)
+{
+  cuvs::cluster::kmeans_balanced::fit_predict(
+    handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+
+void fit_predict(const raft::resources& handle,
+                 cuvs::cluster::kmeans::balanced_params const& params,
+                 raft::device_matrix_view<const int8_t, int64_t> X,
+                 raft::device_matrix_view<float, int64_t> centroids,
+                 raft::device_vector_view<int, int64_t> labels)
 {
   cuvs::cluster::kmeans_balanced::fit_predict(
     handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
diff --git a/cpp/src/cluster/kmeans_balanced_predict_float.cu b/cpp/src/cluster/kmeans_balanced_predict_float.cu
index e951c20748..2b4086bcfc 100644
--- a/cpp/src/cluster/kmeans_balanced_predict_float.cu
+++ b/cpp/src/cluster/kmeans_balanced_predict_float.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,9 +24,9 @@ namespace cuvs::cluster::kmeans {
 
 void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
-             raft::device_matrix_view<const float, int> X,
-             raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<int, int> labels)
+             raft::device_matrix_view<const float, int64_t> X,
+             raft::device_matrix_view<const float, int64_t> centroids,
+             raft::device_vector_view<int, int64_t> labels)
 {
   cuvs::cluster::kmeans_balanced::predict(
     handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
diff --git a/cpp/src/cluster/kmeans_balanced_predict_int8.cu b/cpp/src/cluster/kmeans_balanced_predict_int8.cu
index e08d8fc87e..c91c6f35ba 100644
--- a/cpp/src/cluster/kmeans_balanced_predict_int8.cu
+++ b/cpp/src/cluster/kmeans_balanced_predict_int8.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,9 +24,19 @@ namespace cuvs::cluster::kmeans {
 
 void predict(const raft::resources& handle,
              cuvs::cluster::kmeans::balanced_params const& params,
-             raft::device_matrix_view<const int8_t, int> X,
-             raft::device_matrix_view<const float, int> centroids,
-             raft::device_vector_view<uint32_t, int> labels)
+             raft::device_matrix_view<const int8_t, int64_t> X,
+             raft::device_matrix_view<const float, int64_t> centroids,
+             raft::device_vector_view<uint32_t, int64_t> labels)
+{
+  cuvs::cluster::kmeans_balanced::predict(
+    handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
+}
+
+void predict(const raft::resources& handle,
+             cuvs::cluster::kmeans::balanced_params const& params,
+             raft::device_matrix_view<const int8_t, int64_t> X,
+             raft::device_matrix_view<const float, int64_t> centroids,
+             raft::device_vector_view<int, int64_t> labels)
 {
   cuvs::cluster::kmeans_balanced::predict(
     handle, params, X, centroids, labels, cuvs::spatial::knn::detail::utils::mapping<float>{});
diff --git a/cpp/tests/cluster/kmeans_balanced.cu b/cpp/tests/cluster/kmeans_balanced.cu
index f273dd92fa..339f8d9124 100644
--- a/cpp/tests/cluster/kmeans_balanced.cu
+++ b/cpp/tests/cluster/kmeans_balanced.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -60,7 +60,12 @@ template <typename MathT, typename IdxT>
   return os;
 }
 
-template <typename DataT, typename MathT, typename LabelT, typename IdxT, typename MappingOpT>
+template <typename DataT,
+          typename MathT,
+          typename LabelT,
+          typename IdxT,
+          typename MappingOpT,
+          bool SeparateFitPredict>
 class KmeansBalancedTest : public ::testing::TestWithParam<KmeansBalancedInputs<MathT, IdxT>> {
  protected:
   KmeansBalancedTest()
@@ -123,8 +128,14 @@ class KmeansBalancedTest : public ::testing::TestWithParam<KmeansBalancedInputs<
       raft::make_device_matrix_view<MathT, IdxT>(d_centroids.data(), p.n_clusters, p.n_cols);
     auto d_labels_view = raft::make_device_vector_view<LabelT, IdxT>(d_labels.data(), p.n_rows);
 
-    cuvs::cluster::kmeans::fit_predict(
-      handle, p.kb_params, X_view, d_centroids_view, d_labels_view);
+    if constexpr (SeparateFitPredict) {
+      cuvs::cluster::kmeans::fit(handle, p.kb_params, X_view, d_centroids_view);
+      cuvs::cluster::kmeans::predict(
+        handle, p.kb_params, X_view, raft::make_const_mdspan(d_centroids_view), d_labels_view);
+    } else {
+      cuvs::cluster::kmeans::fit_predict(
+        handle, p.kb_params, X_view, d_centroids_view, d_labels_view);
+    }
 
     raft::resource::sync_stream(handle, stream);
 
@@ -161,12 +172,15 @@ std::vector<KmeansBalancedInputs<MathT, IdxT>> get_kmeans_balanced_inputs()
   p.kb_params.n_iters = 20;
   p.kb_params.metric  = cuvs::distance::DistanceType::L2Expanded;
   p.tol               = MathT{0.0001};
-  std::vector<std::tuple<size_t, size_t, size_t>> row_cols_k = {{1000, 32, 5},
-                                                                {1000, 100, 20},
-                                                                {10000, 32, 10},
-                                                                {10000, 100, 50},
-                                                                {10000, 500, 100},
-                                                                {1000000, 128, 10}};
+  std::vector<std::tuple<size_t, size_t, size_t>> row_cols_k = {
+    {1000, 32, 5},
+    {1000, 100, 20},
+    {10000, 32, 10},
+    {10000, 100, 50},
+    {10000, 500, 100},
+    {1000000, 128, 10},
+    {10000000, 128, 10},
+  };
   for (auto& rck : row_cols_k) {
     p.n_rows     = static_cast<IdxT>(std::get<0>(rck));
     p.n_cols     = static_cast<IdxT>(std::get<1>(rck));
@@ -190,15 +204,21 @@ const auto inputsf_i64 = get_kmeans_balanced_inputs<float, int64_t>();
  * First set of tests: no conversion
  */
 
-KB_TEST((KmeansBalancedTest<float, float, uint32_t, int, raft::identity_op>),
+KB_TEST((KmeansBalancedTest<float, float, uint32_t, int, raft::identity_op, false>),
         KmeansBalancedTestFFU32I32,
         inputsf_i32);
+KB_TEST((KmeansBalancedTest<float, float, int, int, raft::identity_op, true>),
+        KmeansBalancedTestFFI32I32_SEP,
+        inputsf_i32);
 // KB_TEST((KmeansBalancedTest<double, double, uint32_t, int, raft::identity_op>),
 //         KmeansBalancedTestDDU32I32,
 //         inputsd_i32);
-// KB_TEST((KmeansBalancedTest<float, float, uint32_t, int64_t, raft::identity_op>),
-//         KmeansBalancedTestFFU32I64,
-//         inputsf_i64);
+KB_TEST((KmeansBalancedTest<float, float, uint32_t, int64_t, raft::identity_op, false>),
+        KmeansBalancedTestFFU32I64,
+        inputsf_i64);
+KB_TEST((KmeansBalancedTest<float, float, int, int64_t, raft::identity_op, true>),
+        KmeansBalancedTestFFI32I64_SEP,
+        inputsf_i64);
 // KB_TEST((KmeansBalancedTest<double, double, uint32_t, int64_t, raft::identity_op>),
 //         KmeansBalancedTestDDU32I64,
 //         inputsd_i64);
@@ -231,8 +251,11 @@ struct i2f_scaler {
   RAFT_INLINE_FUNCTION auto operator()(const DataT& x) const { return op(x); };
 };
 
-KB_TEST((KmeansBalancedTest<int8_t, float, uint32_t, int, i2f_scaler<int8_t, float>>),
+KB_TEST((KmeansBalancedTest<int8_t, float, uint32_t, int, i2f_scaler<int8_t, float>, false>),
         KmeansBalancedTestFI8U32I32,
         inputsf_i32);
+KB_TEST((KmeansBalancedTest<int8_t, float, int, int, i2f_scaler<int8_t, float>, true>),
+        KmeansBalancedTestFI8I32I32_SEP,
+        inputsf_i32);
 
 }  // namespace cuvs

From 73672634c3841df358d15da5dea647cf6472b199 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Tue, 26 Aug 2025 16:23:51 -0400
Subject: [PATCH 557/623] Update rapids-dependency-file-generator (#1285)

This PR updates the rapids-dependency-file-generator hook to get https://github.com/rapidsai/dependency-file-generator/pull/163.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/1285
---
 .pre-commit-config.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 05489ee94d..c135ff4ff4 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -126,10 +126,10 @@ repos:
           - id: verify-codeowners
             args: [--fix, --project-prefix=cuvs]
       - repo: https://github.com/rapidsai/dependency-file-generator
-        rev: v1.19.0
+        rev: v1.20.0
         hooks:
             - id: rapids-dependency-file-generator
-              args: ["--clean"]
+              args: ["--clean", "--warn-all", "--strict"]
       - repo: https://github.com/shellcheck-py/shellcheck-py
         rev: v0.10.0.1
         hooks:

From 6e0f85997d86e0e9be0744dbb3674b2f58484c08 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Wed, 27 Aug 2025 08:56:51 +0200
Subject: [PATCH 558/623] [Java][C] Expose GPUInfo (#1267)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

cuvs-java already contains a public `GPUInfo` class, but methods to retrieve the information and fill it are internal.
This PR exposes them through and interface, `GPUInfoProvider`. It also separates immutable data related to a GPU (which is kept in `GPUInfo`) from transient resources-related data and counters (at the moment, only the amount of free memory, which is kept in the new `CuVSResourcesInfo`).

The change let you query transient data at a later moment; to do this, we need to find the device ID associated with a `CuVSResource` object. The change to the C API exposes the raft function that does it.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1267
---
 cpp/include/cuvs/core/c_api.h                 |  11 +-
 cpp/src/core/c_api.cpp                        |  10 ++
 .../java/com/nvidia/cuvs/CuVSResources.java   |   7 +
 .../com/nvidia/cuvs/CuVSResourcesInfo.java    |  26 ++++
 .../main/java/com/nvidia/cuvs/GPUInfo.java    |  22 +++-
 .../java/com/nvidia/cuvs/GPUInfoProvider.java |  48 +++++++
 .../cuvs/SynchronizedCuVSResources.java       |   5 +
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |   3 +
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |   5 +
 .../cuvs/internal/CuVSResourcesImpl.java      |  17 ++-
 .../cuvs/internal/GPUInfoProviderImpl.java    | 121 ++++++++++++++++++
 .../com/nvidia/cuvs/internal/common/Util.java |  83 ------------
 .../com/nvidia/cuvs/spi/JDKProvider.java      |   5 +
 .../com/nvidia/cuvs/CheckedCuVSResources.java |   5 +
 .../java/com/nvidia/cuvs/CuVSResourcesIT.java |   3 +-
 .../test/java/com/nvidia/cuvs/GPUInfoIT.java  |  78 +++++++++++
 16 files changed, 352 insertions(+), 97 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResourcesInfo.java
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfoProvider.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java
 create mode 100644 java/cuvs-java/src/test/java/com/nvidia/cuvs/GPUInfoIT.java

diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index d7b6673990..e3cd9bd40b 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -86,7 +86,7 @@ cuvsError_t cuvsResourcesDestroy(cuvsResources_t res);
 cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream);
 
 /**
- * @brief Get the cudaStream_t from a cuvsResources_t t
+ * @brief Get the cudaStream_t from a cuvsResources_t
  *
  * @param[in] res cuvsResources_t opaque C handle
  * @param[out] stream cudaStream_t stream to queue CUDA kernels
@@ -101,6 +101,15 @@ cuvsError_t cuvsStreamGet(cuvsResources_t res, cudaStream_t* stream);
  * @return cuvsError_t
  */
 cuvsError_t cuvsStreamSync(cuvsResources_t res);
+
+/**
+ * @brief Get the id of the device associated with this cuvsResources_t
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[out] device_id int the id of the device associated with res
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsDeviceIdGet(cuvsResources_t res, int* device_id);
 /** @} */
 
 /**
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 8a9116f4cb..2f66adf416 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -19,6 +19,8 @@
 #include <cuvs/version_config.h>
 
 #include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_id.hpp>
+#include <raft/core/resource/resource_types.hpp>
 #include <raft/core/resources.hpp>
 #include <raft/util/cudart_utils.hpp>
 #include <rmm/cuda_stream_view.hpp>
@@ -73,6 +75,14 @@ extern "C" cuvsError_t cuvsStreamSync(cuvsResources_t res)
   });
 }
 
+extern "C" cuvsError_t cuvsDeviceIdGet(cuvsResources_t res, int* device_id)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto res_ptr = reinterpret_cast<raft::resources*>(res);
+    *device_id   = raft::resource::get_device_id(*res_ptr);
+  });
+}
+
 extern "C" cuvsError_t cuvsRMMAlloc(cuvsResources_t res, void** ptr, size_t bytes)
 {
   return cuvs::core::translate_exceptions([=] {
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
index 2d362b6c4c..b44e50e890 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResources.java
@@ -49,6 +49,13 @@ interface ScopedAccess extends AutoCloseable {
    */
   ScopedAccess access();
 
+  /**
+   * Get the logical id of the device associated with this resources object.
+   * Information about the device id is immutable, so it is safe to expose it without getting {@link ScopedAccess}
+   * to the enclosing resources.
+   */
+  int deviceId();
+
   /**
    * Closes this CuVSResources object and releases any resources associated with it.
    */
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResourcesInfo.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResourcesInfo.java
new file mode 100644
index 0000000000..a100091f12
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSResourcesInfo.java
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+/**
+ * Contains performance-related information associated to a {@link CuVSResources} and its GPU.
+ * Can be extended to report different types of GPU memory linked to the resources,
+ * e.g. the type and capacity of the underlying RMM {@code device_memory_resource}
+ *
+ * @param freeDeviceMemoryInBytes   free memory in bytes, as reported by the device driver
+ * @param totalDeviceMemoryInBytes  total device memory in bytes
+ */
+public record CuVSResourcesInfo(long freeDeviceMemoryInBytes, long totalDeviceMemoryInBytes) {}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
index c0ccaf99c3..8d24df9aae 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfo.java
@@ -18,11 +18,21 @@
 /**
  * Contains GPU information
  *
- * @param gpuId             id of the GPU starting from 0
- * @param name              ASCII string identifying device
- * @param freeMemory        returned free memory in bytes
- * @param totalMemory       returned total memory in bytes
- * @param computeCapability the compute capability of the device
+ * @param gpuId                     id of the GPU starting from 0
+ * @param name                      ASCII string identifying device
+ * @param totalDeviceMemoryInBytes  total device memory in bytes
+ * @param computeCapabilityMajor    the compute capability of the device (major)
+ * @param computeCapabilityMinor    the compute capability of the device (minor)
+ * @param supportsConcurrentCopy    whether the device can concurrently copy memory between host and device while
+ *                                  executing a kernel
+ * @param supportsConcurrentKernels whether the device supports executing multiple kernels within the same context
+ *                                  simultaneously
  */
 public record GPUInfo(
-    int gpuId, String name, long freeMemory, long totalMemory, float computeCapability) {}
+    int gpuId,
+    String name,
+    long totalDeviceMemoryInBytes,
+    int computeCapabilityMajor,
+    int computeCapabilityMinor,
+    boolean supportsConcurrentCopy,
+    boolean supportsConcurrentKernels) {}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfoProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfoProvider.java
new file mode 100644
index 0000000000..fe03934df0
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/GPUInfoProvider.java
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import java.util.List;
+
+public interface GPUInfoProvider {
+
+  int MIN_COMPUTE_CAPABILITY_MAJOR = 7;
+  int MIN_COMPUTE_CAPABILITY_MINOR = 0;
+
+  int MIN_DEVICE_MEMORY_IN_MB = 8192;
+
+  /**
+   * Gets all the available GPUs
+   *
+   * @return a list of {@link GPUInfo} objects with GPU details
+   */
+  List<GPUInfo> availableGPUs();
+
+  /**
+   * Get the list of compatible GPUs based on compute capability >= 7.0 and total
+   * memory >= 8GB
+   *
+   * @return a list of compatible GPUs. See {@link GPUInfo}
+   */
+  List<GPUInfo> compatibleGPUs();
+
+  /**
+   * Gets memory information relative to a {@link CuVSResources}
+   * @param resources from which to obtain memory information
+   * @return a {@link CuVSResourcesInfo} record containing the memory information
+   */
+  CuVSResourcesInfo getCurrentInfo(CuVSResources resources);
+}
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java
index bbba262bb0..6f17d27317 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/SynchronizedCuVSResources.java
@@ -51,6 +51,11 @@ public void close() {
     };
   }
 
+  @Override
+  public int deviceId() {
+    return inner.deviceId();
+  }
+
   @Override
   public void close() {
     inner.close();
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index 2ee3fb373a..f12389d226 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -128,6 +128,9 @@ default CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merg
     return mergeCagraIndexes(indexes);
   }
 
+  /** Returns a {@link GPUInfoProvider} to query the system for GPU related information */
+  GPUInfoProvider gpuInfoProvider();
+
   /** Retrieves the system-wide provider. */
   static CuVSProvider provider() {
     return CuVSServiceProvider.Holder.INSTANCE;
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 499ea22fc5..60509004c4 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -66,6 +66,11 @@ public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
     throw new UnsupportedOperationException();
   }
 
+  @Override
+  public GPUInfoProvider gpuInfoProvider() {
+    throw new UnsupportedOperationException();
+  }
+
   @Override
   public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       CuVSResources cuVSResources,
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
index 8f9e1d94d0..ef1b9cdab7 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSResourcesImpl.java
@@ -16,9 +16,8 @@
 package com.nvidia.cuvs.internal;
 
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResourcesCreate;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResourcesDestroy;
-import static com.nvidia.cuvs.internal.panama.headers_h.cuvsResources_t;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
+import static com.nvidia.cuvs.internal.panama.headers_h_1.C_INT;
 
 import com.nvidia.cuvs.CuVSResources;
 import java.lang.foreign.Arena;
@@ -34,6 +33,7 @@ public class CuVSResourcesImpl implements CuVSResources {
   private final Path tempDirectory;
   private final long resourceHandle;
   private final ScopedAccess access;
+  private final int deviceId;
 
   /**
    * Constructor that allocates the resources needed for cuVS
@@ -43,9 +43,11 @@ public CuVSResourcesImpl(Path tempDirectory) {
     this.tempDirectory = tempDirectory;
     try (var localArena = Arena.ofConfined()) {
       var resourcesMemorySegment = localArena.allocate(cuvsResources_t);
-      int returnValue = cuvsResourcesCreate(resourcesMemorySegment);
-      checkCuVSError(returnValue, "cuvsResourcesCreate");
+      checkCuVSError(cuvsResourcesCreate(resourcesMemorySegment), "cuvsResourcesCreate");
       this.resourceHandle = resourcesMemorySegment.get(cuvsResources_t, 0);
+      var deviceIdPtr = localArena.allocate(C_INT);
+      checkCuVSError(cuvsDeviceIdGet(resourceHandle, deviceIdPtr), "cuvsDeviceIdGet");
+      this.deviceId = deviceIdPtr.get(C_INT, 0);
       this.access =
           new ScopedAccess() {
             @Override
@@ -64,6 +66,11 @@ public ScopedAccess access() {
     return this.access;
   }
 
+  @Override
+  public int deviceId() {
+    return this.deviceId;
+  }
+
   @Override
   public void close() {
     synchronized (this) {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java
new file mode 100644
index 0000000000..c2422318ac
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java
@@ -0,0 +1,121 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemGetInfo;
+import static com.nvidia.cuvs.internal.panama.headers_h_1.*;
+
+import com.nvidia.cuvs.CuVSResources;
+import com.nvidia.cuvs.CuVSResourcesInfo;
+import com.nvidia.cuvs.GPUInfo;
+import com.nvidia.cuvs.GPUInfoProvider;
+import com.nvidia.cuvs.internal.panama.cudaDeviceProp;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+import java.util.ArrayList;
+import java.util.List;
+
+public class GPUInfoProviderImpl implements GPUInfoProvider {
+
+  // Lazy initialization for list of available GPUs.
+  private static class AvailableGpuInitializer {
+
+    // Available GPUs are initialized only once when first accessed.
+    // This is assumed to be invariant for the lifetime of the program.
+    static final List<GPUInfo> AVAILABLE_GPUS = getAvailableGpusInfo();
+
+    private static List<GPUInfo> getAvailableGpusInfo() {
+      try (var localArena = Arena.ofConfined()) {
+
+        MemorySegment numGpus = localArena.allocate(C_INT);
+        int returnValue = cudaGetDeviceCount(numGpus);
+        checkCudaError(returnValue, "cudaGetDeviceCount");
+
+        int numGpuCount = numGpus.get(C_INT, 0);
+        List<GPUInfo> gpuInfoArr = new ArrayList<GPUInfo>();
+
+        MemorySegment deviceProp = cudaDeviceProp.allocate(localArena);
+
+        for (int i = 0; i < numGpuCount; i++) {
+          returnValue = cudaGetDeviceProperties_v2(deviceProp, i);
+          checkCudaError(returnValue, "cudaGetDeviceProperties_v2");
+
+          GPUInfo gpuInfo =
+              new GPUInfo(
+                  i,
+                  cudaDeviceProp.name(deviceProp).getString(0),
+                  cudaDeviceProp.totalGlobalMem(deviceProp),
+                  cudaDeviceProp.major(deviceProp),
+                  cudaDeviceProp.minor(deviceProp),
+                  cudaDeviceProp.asyncEngineCount(deviceProp) > 0,
+                  cudaDeviceProp.concurrentKernels(deviceProp) > 0);
+
+          gpuInfoArr.add(gpuInfo);
+        }
+        return gpuInfoArr;
+      }
+    }
+  }
+
+  private static boolean hasMinimumCapability(GPUInfo gpuInfo) {
+    return gpuInfo.computeCapabilityMajor() > GPUInfoProvider.MIN_COMPUTE_CAPABILITY_MAJOR
+        || (gpuInfo.computeCapabilityMajor() == GPUInfoProvider.MIN_COMPUTE_CAPABILITY_MAJOR
+            && gpuInfo.computeCapabilityMinor() >= GPUInfoProvider.MIN_COMPUTE_CAPABILITY_MINOR);
+  }
+
+  @Override
+  public List<GPUInfo> availableGPUs() {
+    return AvailableGpuInitializer.AVAILABLE_GPUS;
+  }
+
+  @Override
+  public List<GPUInfo> compatibleGPUs() {
+    List<GPUInfo> compatibleGPUs = new ArrayList<>();
+    long minDeviceMemoryInBytes = 1024L * 1024L * GPUInfoProvider.MIN_DEVICE_MEMORY_IN_MB;
+    for (GPUInfo gpuInfo : AvailableGpuInitializer.AVAILABLE_GPUS) {
+      if (hasMinimumCapability(gpuInfo)
+          && gpuInfo.totalDeviceMemoryInBytes() >= minDeviceMemoryInBytes) {
+        compatibleGPUs.add(gpuInfo);
+      }
+    }
+    return compatibleGPUs;
+  }
+
+  @Override
+  public CuVSResourcesInfo getCurrentInfo(CuVSResources resources) {
+    try (var localArena = Arena.ofConfined()) {
+      var deviceIdPtr = localArena.allocate(C_INT);
+      checkCudaError(cudaGetDevice(deviceIdPtr), "cudaGetDevice");
+      var currentDeviceId = deviceIdPtr.get(C_INT, 0);
+
+      if (resources.deviceId() != currentDeviceId) {
+        checkCudaError(cudaSetDevice(resources.deviceId()), "cudaSetDevice");
+      }
+
+      MemorySegment freeMemoryPtr = localArena.allocate(size_t);
+      MemorySegment totalMemoryPtr = localArena.allocate(size_t);
+      checkCudaError(cudaMemGetInfo(freeMemoryPtr, totalMemoryPtr), "cudaMemGetInfo");
+
+      if (resources.deviceId() != currentDeviceId) {
+        checkCudaError(cudaSetDevice(currentDeviceId), "cudaSetDevice");
+      }
+
+      return new CuVSResourcesInfo(freeMemoryPtr.get(size_t, 0), totalMemoryPtr.get(size_t, 0));
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index d62450ece0..09adf59309 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -23,12 +23,10 @@
 import static com.nvidia.cuvs.internal.panama.headers_h_1.cudaStream_t;
 
 import com.nvidia.cuvs.CuVSResources;
-import com.nvidia.cuvs.GPUInfo;
 import com.nvidia.cuvs.internal.panama.DLDataType;
 import com.nvidia.cuvs.internal.panama.DLDevice;
 import com.nvidia.cuvs.internal.panama.DLManagedTensor;
 import com.nvidia.cuvs.internal.panama.DLTensor;
-import com.nvidia.cuvs.internal.panama.cudaDeviceProp;
 import com.nvidia.cuvs.internal.panama.headers_h;
 import java.lang.foreign.Arena;
 import java.lang.foreign.Linker;
@@ -37,9 +35,7 @@
 import java.lang.foreign.MemorySegment;
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.VarHandle;
-import java.util.ArrayList;
 import java.util.BitSet;
-import java.util.List;
 
 public class Util {
 
@@ -187,85 +183,6 @@ static String getLastErrorText() {
     }
   }
 
-  /**
-   * Get the list of compatible GPUs based on compute capability >= 7.0 and total
-   * memory >= 8GB
-   *
-   * @return a list of compatible GPUs. See {@link GPUInfo}
-   */
-  public static List<GPUInfo> compatibleGPUs() throws Throwable {
-    return compatibleGPUs(7.0, 8192);
-  }
-
-  /**
-   * Get the list of compatible GPUs based on given compute capability and total
-   * memory
-   *
-   * @param minComputeCapability the minimum compute capability
-   * @param minDeviceMemoryMB    the minimum total available memory in MB
-   * @return a list of compatible GPUs. See {@link GPUInfo}
-   */
-  public static List<GPUInfo> compatibleGPUs(double minComputeCapability, int minDeviceMemoryMB)
-      throws Throwable {
-    List<GPUInfo> compatibleGPUs = new ArrayList<GPUInfo>();
-    double minDeviceMemoryB = Math.pow(2, 20) * minDeviceMemoryMB;
-    for (GPUInfo gpuInfo : availableGPUs()) {
-      if (gpuInfo.computeCapability() >= minComputeCapability
-          && gpuInfo.totalMemory() >= minDeviceMemoryB) {
-        compatibleGPUs.add(gpuInfo);
-      }
-    }
-    return compatibleGPUs;
-  }
-
-  /**
-   * Gets all the available GPUs
-   *
-   * @return a list of {@link GPUInfo} objects with GPU details
-   */
-  public static List<GPUInfo> availableGPUs() throws Throwable {
-    try (var localArena = Arena.ofConfined()) {
-
-      MemorySegment numGpus = localArena.allocate(C_INT);
-      int returnValue = cudaGetDeviceCount(numGpus);
-      checkCudaError(returnValue, "cudaGetDeviceCount");
-
-      int numGpuCount = numGpus.get(C_INT, 0);
-      List<GPUInfo> gpuInfoArr = new ArrayList<GPUInfo>();
-
-      MemorySegment free = localArena.allocate(size_t);
-      MemorySegment total = localArena.allocate(size_t);
-      MemorySegment deviceProp = cudaDeviceProp.allocate(localArena);
-
-      for (int i = 0; i < numGpuCount; i++) {
-
-        returnValue = cudaSetDevice(i);
-        checkCudaError(returnValue, "cudaSetDevice");
-
-        returnValue = cudaGetDeviceProperties_v2(deviceProp, i);
-        checkCudaError(returnValue, "cudaGetDeviceProperties_v2");
-
-        returnValue = cudaMemGetInfo(free, total);
-        checkCudaError(returnValue, "cudaMemGetInfo");
-
-        float computeCapability =
-            Float.parseFloat(
-                cudaDeviceProp.major(deviceProp) + "." + cudaDeviceProp.minor(deviceProp));
-
-        GPUInfo gpuInfo =
-            new GPUInfo(
-                i,
-                cudaDeviceProp.name(deviceProp).getString(0),
-                free.get(C_LONG, 0),
-                total.get(C_LONG, 0),
-                computeCapability);
-
-        gpuInfoArr.add(gpuInfo);
-      }
-      return gpuInfoArr;
-    }
-  }
-
   /**
    * A utility method for getting an instance of {@link MemorySegment} for a
    * {@link String}.
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 01eb72ed69..0ee0cf1d10 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -102,6 +102,11 @@ public CagraIndex mergeCagraIndexes(CagraIndex[] indexes, CagraMergeParams merge
     return CagraIndexImpl.merge(indexes, mergeParams);
   }
 
+  @Override
+  public GPUInfoProvider gpuInfoProvider() {
+    return new GPUInfoProviderImpl();
+  }
+
   @Override
   public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
       long size, long columns, CuVSMatrix.DataType dataType) throws UnsupportedOperationException {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
index a58d9c04e7..581e29973c 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
@@ -61,6 +61,11 @@ public void close() {
     };
   }
 
+  @Override
+  public int deviceId() {
+    return inner.deviceId();
+  }
+
   @Override
   public void close() {
     destroyed = true;
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
index e479a6ebda..bc012f6a0e 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
@@ -18,7 +18,6 @@
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
 import static org.junit.Assert.*;
 
-import java.lang.invoke.MethodHandles;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.Executors;
 import org.junit.Before;
@@ -28,7 +27,7 @@
 
 public class CuVSResourcesIT extends CuVSTestCase {
 
-  private static final Logger log = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
+  private static final Logger log = LoggerFactory.getLogger(CuVSResourcesIT.class);
 
   @Before
   public void setup() {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/GPUInfoIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/GPUInfoIT.java
new file mode 100644
index 0000000000..1284df2d31
--- /dev/null
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/GPUInfoIT.java
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs;
+
+import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static org.junit.Assert.*;
+
+import com.nvidia.cuvs.spi.CuVSProvider;
+import org.junit.Before;
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class GPUInfoIT extends CuVSTestCase {
+
+  private static final Logger log = LoggerFactory.getLogger(GPUInfoIT.class);
+
+  @Before
+  public void setup() {
+    assumeTrue("not supported on " + System.getProperty("os.name"), isLinuxAmd64());
+  }
+
+  @Test
+  public void testAvailableAndCompatibleGpus() throws Throwable {
+    var gpuInfoProvider = CuVSProvider.provider().gpuInfoProvider();
+    var availableGpus = gpuInfoProvider.availableGPUs();
+    var compatibleGpus = gpuInfoProvider.compatibleGPUs();
+    assertFalse(availableGpus.isEmpty());
+    assertTrue(availableGpus.get(0).gpuId() >= 0);
+    for (var gpuInfo : availableGpus) {
+      log.trace(
+          "Available GPU with name [{}], memory [{}MB], compute [{}.{}]",
+          gpuInfo.name(),
+          gpuInfo.totalDeviceMemoryInBytes() / (1024L * 1024L),
+          gpuInfo.computeCapabilityMajor(),
+          gpuInfo.computeCapabilityMinor());
+    }
+
+    assertTrue(availableGpus.size() >= compatibleGpus.size());
+    log.trace("Compatible GPUs: [{}]", compatibleGpus.size());
+    for (var gpuInfo : compatibleGpus) {
+      log.trace(
+          "Compatible GPU with name [{}], memory [{}MB], compute [{}.{}]",
+          gpuInfo.name(),
+          gpuInfo.totalDeviceMemoryInBytes() / (1024L * 1024L),
+          gpuInfo.computeCapabilityMajor(),
+          gpuInfo.computeCapabilityMinor());
+      assertTrue(gpuInfo.computeCapabilityMajor() >= GPUInfoProvider.MIN_COMPUTE_CAPABILITY_MAJOR);
+      assertTrue(
+          gpuInfo.totalDeviceMemoryInBytes()
+              >= GPUInfoProvider.MIN_DEVICE_MEMORY_IN_MB * 1024L * 1024L);
+    }
+  }
+
+  @Test
+  public void testMemoryInfo() throws Throwable {
+    try (var resources = CheckedCuVSResources.create()) {
+      var gpuInfoProvider = CuVSProvider.provider().gpuInfoProvider();
+      var memoryInfo = gpuInfoProvider.getCurrentInfo(resources);
+      assertNotNull(memoryInfo);
+      log.trace("Free memory: {}", memoryInfo.freeDeviceMemoryInBytes());
+      assertTrue(memoryInfo.freeDeviceMemoryInBytes() > 0);
+    }
+  }
+}

From 4f710ecfcd5a054179151655721aa6b47ec711aa Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Wed, 27 Aug 2025 12:40:49 -0700
Subject: [PATCH 559/623] Removing filter templates in ivfpq for binary size
 reduction (#1211)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

### Summary

This PR removed filters from templates in ivfpq. Only applied to `compute_similarity_kernel` in this PR, but can be applied elsewhere (e.g. `ivfflat_interleaved_scan`) which will be a follow-up PR to this one.


### Perf check
Ran the default setting with bitset flter 20 times to check if this slows anything down.
All numbers are averaged across the 20 runs. Profiing done with nsys.

`compute_similarity` kernel runtime
<img width="688" height="79" alt="Screenshot 2025-08-04 at 1 10 31 PM" src="https://github.com/user-attachments/assets/7dce103d-a537-4b57-9646-7d5fe3aa4533" />

Think the extra time is coming from the `switch` statements inside `ivf_to_sample_filter_dev`'s `operator()`. But not too much of an overhead.


### Binary size reduction
From CI reports: 1107.27 MB ->1040.44 MB

Authors:
  - Jinsol Park (https://github.com/jinsolp)
  - Divye Gala (https://github.com/divyegala)

Approvers:
  - Micka (https://github.com/lowener)
  - Divye Gala (https://github.com/divyegala)
  - Artem M. Chirkin (https://github.com/achirkin)

URL: https://github.com/rapidsai/cuvs/pull/1211
---
 cpp/CMakeLists.txt                            |   7 -
 cpp/include/cuvs/neighbors/common.hpp         |  14 +-
 .../ivf_pq_compute_similarity_float_float.cu  |  83 ++++-----
 ...compute_similarity_float_float_bitset64.cu |  73 --------
 ...f_pq_compute_similarity_float_fp8_false.cu |  82 ++++-----
 ...ute_similarity_float_fp8_false_bitset64.cu |  73 --------
 ...vf_pq_compute_similarity_float_fp8_true.cu |  82 ++++-----
 ...pute_similarity_float_fp8_true_bitset64.cu |  73 --------
 .../ivf_pq_compute_similarity_float_half.cu   |  83 ++++-----
 ..._compute_similarity_float_half_bitset64.cu |  73 --------
 ...vf_pq_compute_similarity_half_fp8_false.cu |  82 ++++-----
 ...pute_similarity_half_fp8_false_bitset64.cu |  73 --------
 ...ivf_pq_compute_similarity_half_fp8_true.cu |  82 ++++-----
 ...mpute_similarity_half_fp8_true_bitset64.cu |  73 --------
 .../ivf_pq_compute_similarity_half_half.cu    |  83 ++++-----
 ...q_compute_similarity_half_half_bitset64.cu |  73 --------
 .../ivf_pq/ivf_pq_compute_similarity.cuh      | 114 +++---------
 .../ivf_pq/ivf_pq_compute_similarity_impl.cuh | 169 +++++++++---------
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |  29 ++-
 cpp/src/neighbors/sample_filter.cuh           |  55 +++++-
 20 files changed, 449 insertions(+), 1027 deletions(-)
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
 delete mode 100644 cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 50ee1a0ce2..5606ff85f0 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -458,13 +458,6 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
     src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
     src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
-    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
-    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
-    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
-    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
-    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
-    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
-    src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
     src/neighbors/ivf_pq/detail/ivf_pq_contiguous_list_data.cu
     src/neighbors/ivf_pq/detail/ivf_pq_process_and_fill_codes.cu
     src/neighbors/ivf_pq/detail/ivf_pq_search_float_int64_t.cu
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 2e91f3c15f..70da22a09d 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -485,13 +485,13 @@ namespace filtering {
 enum class FilterType { None, Bitmap, Bitset };
 
 struct base_filter {
-  ~base_filter()                             = default;
+  virtual ~base_filter()                     = default;
   virtual FilterType get_filter_type() const = 0;
 };
 
 /* A filter that filters nothing. This is the default behavior. */
 struct none_sample_filter : public base_filter {
-  inline _RAFT_HOST_DEVICE bool operator()(
+  constexpr __forceinline__ _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
     // the current inverted list index
@@ -499,7 +499,7 @@ struct none_sample_filter : public base_filter {
     // the index of the current sample inside the current inverted list
     const uint32_t sample_ix) const;
 
-  inline _RAFT_HOST_DEVICE bool operator()(
+  constexpr __forceinline__ _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
     // the index of the current sample
@@ -517,7 +517,7 @@ struct none_sample_filter : public base_filter {
  * @tparam filter_t
  */
 template <typename index_t, typename filter_t>
-struct ivf_to_sample_filter {
+struct ivf_to_sample_filter : public base_filter {
   const index_t* const* inds_ptrs_;
   const filter_t next_filter_;
 
@@ -534,6 +534,8 @@ struct ivf_to_sample_filter {
     const uint32_t cluster_ix,
     // the index of the current sample inside the current inverted list
     const uint32_t sample_ix) const;
+
+  FilterType get_filter_type() const override { return next_filter_.get_filter_type(); }
 };
 
 /**
@@ -577,8 +579,8 @@ struct bitset_filter : public base_filter {
   // View of the bitset to use as a filter
   const view_t bitset_view_;
 
-  bitset_filter(const view_t bitset_for_filtering);
-  inline _RAFT_HOST_DEVICE bool operator()(
+  _RAFT_HOST_DEVICE bitset_filter(const view_t bitset_for_filtering);
+  constexpr __forceinline__ _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
     // the index of the current sample
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
index 33e7067a70..a5c726b6ee 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,50 +24,43 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)  \
+  template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
+    const cudaDeviceProp& dev_props,                                                    \
+    bool manage_local_topk,                                                             \
+    int locality_hint,                                                                  \
+    double preferred_shmem_carveout,                                                    \
+    uint32_t pq_bits,                                                                   \
+    uint32_t pq_dim,                                                                    \
+    uint32_t precomp_data_count,                                                        \
+    uint32_t n_queries,                                                                 \
+    uint32_t n_probes,                                                                  \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;            \
+                                                                                        \
+  template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                            \
+    rmm::cuda_stream_view stream,                                                       \
+    uint32_t dim,                                                                       \
+    uint32_t n_probes,                                                                  \
+    uint32_t pq_dim,                                                                    \
+    uint32_t n_queries,                                                                 \
+    uint32_t queries_offset,                                                            \
+    cuvs::distance::DistanceType metric,                                                \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                \
+    uint32_t topk,                                                                      \
+    uint32_t max_samples,                                                               \
+    const float* cluster_centers,                                                       \
+    const float* pq_centers,                                                            \
+    const uint8_t* const* pq_dataset,                                                   \
+    const uint32_t* cluster_labels,                                                     \
+    const uint32_t* _chunk_indices,                                                     \
+    const float* queries,                                                               \
+    const uint32_t* index_list,                                                         \
+    float* query_kths,                                                                  \
+    const cuvs::neighbors::filtering::base_filter& sample_filter,                       \
+    LutT* lut_scores,                                                                   \
+    OutT* _out_scores,                                                                  \
     uint32_t* _out_indices);
 
 #define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  float,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(float, float);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
deleted file mode 100644
index 8c28e2c5b9..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_float_bitset64.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
- * Make changes there and run in this directory:
- * > python generate_ivf_pq_compute_similarity.py
- */
-
-#include "../../sample_filter.cuh"
-#include "../ivf_pq_compute_similarity_impl.cuh"
-#include "../ivf_pq_fp_8bit.cuh"
-
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
-    uint32_t* _out_indices);
-
-#define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  float,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
index 4cb3424cb5..b65b26fc8c 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,50 +24,44 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)  \
+  template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
+    const cudaDeviceProp& dev_props,                                                    \
+    bool manage_local_topk,                                                             \
+    int locality_hint,                                                                  \
+    double preferred_shmem_carveout,                                                    \
+    uint32_t pq_bits,                                                                   \
+    uint32_t pq_dim,                                                                    \
+    uint32_t precomp_data_count,                                                        \
+    uint32_t n_queries,                                                                 \
+    uint32_t n_probes,                                                                  \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;            \
+                                                                                        \
+  template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                            \
+    rmm::cuda_stream_view stream,                                                       \
+    uint32_t dim,                                                                       \
+    uint32_t n_probes,                                                                  \
+    uint32_t pq_dim,                                                                    \
+    uint32_t n_queries,                                                                 \
+    uint32_t queries_offset,                                                            \
+    cuvs::distance::DistanceType metric,                                                \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                \
+    uint32_t topk,                                                                      \
+    uint32_t max_samples,                                                               \
+    const float* cluster_centers,                                                       \
+    const float* pq_centers,                                                            \
+    const uint8_t* const* pq_dataset,                                                   \
+    const uint32_t* cluster_labels,                                                     \
+    const uint32_t* _chunk_indices,                                                     \
+    const float* queries,                                                               \
+    const uint32_t* index_list,                                                         \
+    float* query_kths,                                                                  \
+    const cuvs::neighbors::filtering::base_filter& sample_filter,                       \
+    LutT* lut_scores,                                                                   \
+    OutT* _out_scores,                                                                  \
     uint32_t* _out_indices);
 
 #define COMMA ,
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+  float, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
deleted file mode 100644
index e3f330dccb..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_false_bitset64.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
- * Make changes there and run in this directory:
- * > python generate_ivf_pq_compute_similarity.py
- */
-
-#include "../../sample_filter.cuh"
-#include "../ivf_pq_compute_similarity_impl.cuh"
-#include "../ivf_pq_fp_8bit.cuh"
-
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
-    uint32_t* _out_indices);
-
-#define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
index 75f8f2cf8a..30f7eaa30c 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,50 +24,44 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)  \
+  template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
+    const cudaDeviceProp& dev_props,                                                    \
+    bool manage_local_topk,                                                             \
+    int locality_hint,                                                                  \
+    double preferred_shmem_carveout,                                                    \
+    uint32_t pq_bits,                                                                   \
+    uint32_t pq_dim,                                                                    \
+    uint32_t precomp_data_count,                                                        \
+    uint32_t n_queries,                                                                 \
+    uint32_t n_probes,                                                                  \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;            \
+                                                                                        \
+  template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                            \
+    rmm::cuda_stream_view stream,                                                       \
+    uint32_t dim,                                                                       \
+    uint32_t n_probes,                                                                  \
+    uint32_t pq_dim,                                                                    \
+    uint32_t n_queries,                                                                 \
+    uint32_t queries_offset,                                                            \
+    cuvs::distance::DistanceType metric,                                                \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                \
+    uint32_t topk,                                                                      \
+    uint32_t max_samples,                                                               \
+    const float* cluster_centers,                                                       \
+    const float* pq_centers,                                                            \
+    const uint8_t* const* pq_dataset,                                                   \
+    const uint32_t* cluster_labels,                                                     \
+    const uint32_t* _chunk_indices,                                                     \
+    const float* queries,                                                               \
+    const uint32_t* index_list,                                                         \
+    float* query_kths,                                                                  \
+    const cuvs::neighbors::filtering::base_filter& sample_filter,                       \
+    LutT* lut_scores,                                                                   \
+    OutT* _out_scores,                                                                  \
     uint32_t* _out_indices);
 
 #define COMMA ,
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+  float, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
deleted file mode 100644
index 3d0c610dd2..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_fp8_true_bitset64.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
- * Make changes there and run in this directory:
- * > python generate_ivf_pq_compute_similarity.py
- */
-
-#include "../../sample_filter.cuh"
-#include "../ivf_pq_compute_similarity_impl.cuh"
-#include "../ivf_pq_fp_8bit.cuh"
-
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
-    uint32_t* _out_indices);
-
-#define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
index 9a288a1c7c..1545374419 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,50 +24,43 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)  \
+  template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
+    const cudaDeviceProp& dev_props,                                                    \
+    bool manage_local_topk,                                                             \
+    int locality_hint,                                                                  \
+    double preferred_shmem_carveout,                                                    \
+    uint32_t pq_bits,                                                                   \
+    uint32_t pq_dim,                                                                    \
+    uint32_t precomp_data_count,                                                        \
+    uint32_t n_queries,                                                                 \
+    uint32_t n_probes,                                                                  \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;            \
+                                                                                        \
+  template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                            \
+    rmm::cuda_stream_view stream,                                                       \
+    uint32_t dim,                                                                       \
+    uint32_t n_probes,                                                                  \
+    uint32_t pq_dim,                                                                    \
+    uint32_t n_queries,                                                                 \
+    uint32_t queries_offset,                                                            \
+    cuvs::distance::DistanceType metric,                                                \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                \
+    uint32_t topk,                                                                      \
+    uint32_t max_samples,                                                               \
+    const float* cluster_centers,                                                       \
+    const float* pq_centers,                                                            \
+    const uint8_t* const* pq_dataset,                                                   \
+    const uint32_t* cluster_labels,                                                     \
+    const uint32_t* _chunk_indices,                                                     \
+    const float* queries,                                                               \
+    const uint32_t* index_list,                                                         \
+    float* query_kths,                                                                  \
+    const cuvs::neighbors::filtering::base_filter& sample_filter,                       \
+    LutT* lut_scores,                                                                   \
+    OutT* _out_scores,                                                                  \
     uint32_t* _out_indices);
 
 #define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(float, half);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
deleted file mode 100644
index 07af7815e2..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_float_half_bitset64.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
- * Make changes there and run in this directory:
- * > python generate_ivf_pq_compute_similarity.py
- */
-
-#include "../../sample_filter.cuh"
-#include "../ivf_pq_compute_similarity_impl.cuh"
-#include "../ivf_pq_fp_8bit.cuh"
-
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
-    uint32_t* _out_indices);
-
-#define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
index 8886b01bef..00707e5ecc 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,50 +24,44 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)  \
+  template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
+    const cudaDeviceProp& dev_props,                                                    \
+    bool manage_local_topk,                                                             \
+    int locality_hint,                                                                  \
+    double preferred_shmem_carveout,                                                    \
+    uint32_t pq_bits,                                                                   \
+    uint32_t pq_dim,                                                                    \
+    uint32_t precomp_data_count,                                                        \
+    uint32_t n_queries,                                                                 \
+    uint32_t n_probes,                                                                  \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;            \
+                                                                                        \
+  template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                            \
+    rmm::cuda_stream_view stream,                                                       \
+    uint32_t dim,                                                                       \
+    uint32_t n_probes,                                                                  \
+    uint32_t pq_dim,                                                                    \
+    uint32_t n_queries,                                                                 \
+    uint32_t queries_offset,                                                            \
+    cuvs::distance::DistanceType metric,                                                \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                \
+    uint32_t topk,                                                                      \
+    uint32_t max_samples,                                                               \
+    const float* cluster_centers,                                                       \
+    const float* pq_centers,                                                            \
+    const uint8_t* const* pq_dataset,                                                   \
+    const uint32_t* cluster_labels,                                                     \
+    const uint32_t* _chunk_indices,                                                     \
+    const float* queries,                                                               \
+    const uint32_t* index_list,                                                         \
+    float* query_kths,                                                                  \
+    const cuvs::neighbors::filtering::base_filter& sample_filter,                       \
+    LutT* lut_scores,                                                                   \
+    OutT* _out_scores,                                                                  \
     uint32_t* _out_indices);
 
 #define COMMA ,
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+  half, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
deleted file mode 100644
index a921545f80..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_false_bitset64.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
- * Make changes there and run in this directory:
- * > python generate_ivf_pq_compute_similarity.py
- */
-
-#include "../../sample_filter.cuh"
-#include "../ivf_pq_compute_similarity_impl.cuh"
-#include "../ivf_pq_fp_8bit.cuh"
-
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
-    uint32_t* _out_indices);
-
-#define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
index 05241135b5..7de8719957 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,50 +24,44 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)  \
+  template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
+    const cudaDeviceProp& dev_props,                                                    \
+    bool manage_local_topk,                                                             \
+    int locality_hint,                                                                  \
+    double preferred_shmem_carveout,                                                    \
+    uint32_t pq_bits,                                                                   \
+    uint32_t pq_dim,                                                                    \
+    uint32_t precomp_data_count,                                                        \
+    uint32_t n_queries,                                                                 \
+    uint32_t n_probes,                                                                  \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;            \
+                                                                                        \
+  template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                            \
+    rmm::cuda_stream_view stream,                                                       \
+    uint32_t dim,                                                                       \
+    uint32_t n_probes,                                                                  \
+    uint32_t pq_dim,                                                                    \
+    uint32_t n_queries,                                                                 \
+    uint32_t queries_offset,                                                            \
+    cuvs::distance::DistanceType metric,                                                \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                \
+    uint32_t topk,                                                                      \
+    uint32_t max_samples,                                                               \
+    const float* cluster_centers,                                                       \
+    const float* pq_centers,                                                            \
+    const uint8_t* const* pq_dataset,                                                   \
+    const uint32_t* cluster_labels,                                                     \
+    const uint32_t* _chunk_indices,                                                     \
+    const float* queries,                                                               \
+    const uint32_t* index_list,                                                         \
+    float* query_kths,                                                                  \
+    const cuvs::neighbors::filtering::base_filter& sample_filter,                       \
+    LutT* lut_scores,                                                                   \
+    OutT* _out_scores,                                                                  \
     uint32_t* _out_indices);
 
 #define COMMA ,
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+  half, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
deleted file mode 100644
index 2ebd3d5270..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_fp8_true_bitset64.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
- * Make changes there and run in this directory:
- * > python generate_ivf_pq_compute_similarity.py
- */
-
-#include "../../sample_filter.cuh"
-#include "../ivf_pq_compute_similarity_impl.cuh"
-#include "../ivf_pq_fp_8bit.cuh"
-
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
-    uint32_t* _out_indices);
-
-#define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
index 95a6f0bedd..94406c844a 100644
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
+++ b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,50 +24,43 @@
 #include "../ivf_pq_compute_similarity_impl.cuh"
 #include "../ivf_pq_fp_8bit.cuh"
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)  \
+  template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
+    const cudaDeviceProp& dev_props,                                                    \
+    bool manage_local_topk,                                                             \
+    int locality_hint,                                                                  \
+    double preferred_shmem_carveout,                                                    \
+    uint32_t pq_bits,                                                                   \
+    uint32_t pq_dim,                                                                    \
+    uint32_t precomp_data_count,                                                        \
+    uint32_t n_queries,                                                                 \
+    uint32_t n_probes,                                                                  \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;            \
+                                                                                        \
+  template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                            \
+    rmm::cuda_stream_view stream,                                                       \
+    uint32_t dim,                                                                       \
+    uint32_t n_probes,                                                                  \
+    uint32_t pq_dim,                                                                    \
+    uint32_t n_queries,                                                                 \
+    uint32_t queries_offset,                                                            \
+    cuvs::distance::DistanceType metric,                                                \
+    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                \
+    uint32_t topk,                                                                      \
+    uint32_t max_samples,                                                               \
+    const float* cluster_centers,                                                       \
+    const float* pq_centers,                                                            \
+    const uint8_t* const* pq_dataset,                                                   \
+    const uint32_t* cluster_labels,                                                     \
+    const uint32_t* _chunk_indices,                                                     \
+    const float* queries,                                                               \
+    const uint32_t* index_list,                                                         \
+    float* query_kths,                                                                  \
+    const cuvs::neighbors::filtering::base_filter& sample_filter,                       \
+    LutT* lut_scores,                                                                   \
+    OutT* _out_scores,                                                                  \
     uint32_t* _out_indices);
 
 #define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(half, half);
diff --git a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu b/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
deleted file mode 100644
index a0be0b392f..0000000000
--- a/cpp/src/neighbors/ivf_pq/detail/ivf_pq_compute_similarity_half_half_bitset64.cu
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
- * NOTE: this file is generated by generate_ivf_pq_compute_similarity.py
- * Make changes there and run in this directory:
- * > python generate_ivf_pq_compute_similarity.py
- */
-
-#include "../../sample_filter.cuh"
-#include "../ivf_pq_compute_similarity_impl.cuh"
-#include "../ivf_pq_fp_8bit.cuh"
-
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  template auto                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
-    const cudaDeviceProp& dev_props,                                                           \
-    bool manage_local_topk,                                                                    \
-    int locality_hint,                                                                         \
-    double preferred_shmem_carveout,                                                           \
-    uint32_t pq_bits,                                                                          \
-    uint32_t pq_dim,                                                                           \
-    uint32_t precomp_data_count,                                                               \
-    uint32_t n_queries,                                                                        \
-    uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
-                                                                                               \
-  template void                                                                                \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
-    rmm::cuda_stream_view stream,                                                              \
-    uint32_t dim,                                                                              \
-    uint32_t n_probes,                                                                         \
-    uint32_t pq_dim,                                                                           \
-    uint32_t n_queries,                                                                        \
-    uint32_t queries_offset,                                                                   \
-    cuvs::distance::DistanceType metric,                                                       \
-    cuvs::neighbors::ivf_pq::codebook_gen codebook_kind,                                       \
-    uint32_t topk,                                                                             \
-    uint32_t max_samples,                                                                      \
-    const float* cluster_centers,                                                              \
-    const float* pq_centers,                                                                   \
-    const uint8_t* const* pq_dataset,                                                          \
-    const uint32_t* cluster_labels,                                                            \
-    const uint32_t* _chunk_indices,                                                            \
-    const float* queries,                                                                      \
-    const uint32_t* index_list,                                                                \
-    float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
-    LutT* lut_scores,                                                                          \
-    OutT* _out_scores,                                                                         \
-    uint32_t* _out_indices);
-
-#define COMMA ,
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
index 235f0aacb3..6f9d97dbf1 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,7 +19,8 @@
 #include "../sample_filter.cuh"  // none_sample_filter
 #include "ivf_pq_fp_8bit.cuh"    // cuvs::neighbors::ivf_pq::detail::fp_8bit
 
-#include <cuvs/distance/distance.hpp>   // cuvs::distance::DistanceType
+#include <cuvs/distance/distance.hpp>  // cuvs::distance::DistanceType
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>    // cuvs::neighbors::ivf_pq::codebook_gen
 #include <raft/core/detail/macros.hpp>  // RAFT_WEAK_FUNCTION
 #include <rmm/cuda_stream_view.hpp>     // rmm::cuda_stream_view
@@ -36,7 +37,6 @@ auto RAFT_WEAK_FUNCTION is_local_topk_feasible(uint32_t k, uint32_t n_probes, ui
 
 template <typename OutT,
           typename LutT,
-          typename IvfSampleFilterT,
           uint32_t PqBits,
           int Capacity,
           bool PrecompBaseDiff,
@@ -58,27 +58,27 @@ RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
                                       const float* queries,
                                       const uint32_t* index_list,
                                       float* query_kths,
-                                      IvfSampleFilterT sample_filter,
+                                      filtering::ivf_filter_dev sample_filter,
                                       LutT* lut_scores,
                                       OutT* _out_scores,
                                       uint32_t* _out_indices);
 
 // The signature of the kernel defined by a minimal set of template parameters
-template <typename OutT, typename LutT, typename IvfSampleFilterT>
+template <typename OutT, typename LutT>
 using compute_similarity_kernel_t =
-  decltype(&compute_similarity_kernel<OutT, LutT, IvfSampleFilterT, 8, 0, true, true>);
+  decltype(&compute_similarity_kernel<OutT, LutT, 8, 0, true, true>);
 
-template <typename OutT, typename LutT, typename IvfSampleFilterT>
+template <typename OutT, typename LutT>
 struct selected {
-  compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT> kernel;
+  compute_similarity_kernel_t<OutT, LutT> kernel;
   dim3 grid_dim;
   dim3 block_dim;
   size_t smem_size;
   size_t device_lut_size;
 };
 
-template <typename OutT, typename LutT, typename IvfSampleFilterT>
-void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
+template <typename OutT, typename LutT>
+void compute_similarity_run(selected<OutT, LutT> s,
                             rmm::cuda_stream_view stream,
                             uint32_t dim,
                             uint32_t n_probes,
@@ -97,7 +97,7 @@ void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
                             const float* queries,
                             const uint32_t* index_list,
                             float* query_kths,
-                            IvfSampleFilterT sample_filter,
+                            const filtering::base_filter& sample_filter,
                             LutT* lut_scores,
                             OutT* _out_scores,
                             uint32_t* _out_indices);
@@ -116,7 +116,7 @@ void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
  *    beyond this limit do not consider increasing the number of active blocks per SM
  *    would improve locality anymore.
  */
-template <typename OutT, typename LutT, typename IvfSampleFilterT>
+template <typename OutT, typename LutT>
 auto compute_similarity_select(const cudaDeviceProp& dev_props,
                                bool manage_local_topk,
                                int locality_hint,
@@ -126,14 +126,12 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
                                uint32_t precomp_data_count,
                                uint32_t n_queries,
                                uint32_t n_probes,
-                               uint32_t topk) -> selected<OutT, LutT, IvfSampleFilterT>;
+                               uint32_t topk) -> selected<OutT, LutT>;
 
 }  // namespace cuvs::neighbors::ivf_pq::detail
 
-#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(                    \
-  OutT, LutT, IvfSampleFilterT)                                                                \
-  extern template auto                                                                         \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT, IvfSampleFilterT>(    \
+#define instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(OutT, LutT)         \
+  extern template auto cuvs::neighbors::ivf_pq::detail::compute_similarity_select<OutT, LutT>( \
     const cudaDeviceProp& dev_props,                                                           \
     bool manage_local_topk,                                                                    \
     int locality_hint,                                                                         \
@@ -143,11 +141,10 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
     uint32_t precomp_data_count,                                                               \
     uint32_t n_queries,                                                                        \
     uint32_t n_probes,                                                                         \
-    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT>; \
+    uint32_t topk) -> cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT>;                   \
                                                                                                \
-  extern template void                                                                         \
-  cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT, IvfSampleFilterT>(       \
-    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT, IvfSampleFilterT> s,                 \
+  extern template void cuvs::neighbors::ivf_pq::detail::compute_similarity_run<OutT, LutT>(    \
+    cuvs::neighbors::ivf_pq::detail::selected<OutT, LutT> s,                                   \
     rmm::cuda_stream_view stream,                                                              \
     uint32_t dim,                                                                              \
     uint32_t n_probes,                                                                         \
@@ -166,82 +163,23 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
     const float* queries,                                                                      \
     const uint32_t* index_list,                                                                \
     float* query_kths,                                                                         \
-    IvfSampleFilterT sample_filter,                                                            \
+    const filtering::base_filter& sample_filter,                                               \
     LutT* lut_scores,                                                                          \
     OutT* _out_scores,                                                                         \
     uint32_t* _out_indices);
 
 #define COMMA ,
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+  half, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+  half, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(half, half);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(float, half);
+instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(float, float);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
+  float, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>);
 instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  float,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::none_sample_filter>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  half,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  half,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  float,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA false>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
-instantiate_cuvs_neighbors_ivf_pq_detail_compute_similarity_select(
-  float,
-  cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>,
-  cuvs::neighbors::filtering::ivf_to_sample_filter<
-    int64_t COMMA cuvs::neighbors::filtering::bitset_filter<uint32_t COMMA int64_t>>);
+  float, cuvs::neighbors::ivf_pq::detail::fp_8bit<5u COMMA true>);
 
 #undef COMMA
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
index fbbdd06c24..2bb5b342e8 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_compute_similarity_impl.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,9 +16,10 @@
 
 #pragma once
 
-#include "../ivf_common.cuh"                       // dummy_block_sort_t
-#include "../sample_filter.cuh"                    // none_sample_filter
-#include <cuvs/distance/distance.hpp>              // cuvs::distance::DistanceType
+#include "../ivf_common.cuh"           // dummy_block_sort_t
+#include "../sample_filter.cuh"        // none_sample_filter
+#include <cuvs/distance/distance.hpp>  // cuvs::distance::DistanceType
+#include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_pq.hpp>               // codebook_gen
 #include <raft/matrix/detail/select_warpsort.cuh>  // matrix::detail::select::warpsort::warp_sort_distributed
 #include <raft/util/cuda_rt_essentials.hpp>  // RAFT_CUDA_TRY
@@ -247,8 +248,7 @@ __device__ auto ivfpq_compute_score(uint32_t pq_dim,
  *   query_kths keep the current state of the filtering - atomically updated distances to the
  *   k-th closest neighbors for each query [n_queries].
  * @param sample_filter
- *   A filter that selects samples for a given query. Use an instance of none_sample_filter to
- *   provide a green light for every sample.
+ *   A filter that selects samples for a given query.
  * @param lut_scores
  *   The device pointer for storing the lookup table globally [gridDim.x, pq_dim << PqBits].
  *   Ignored when `EnableSMemLut == true`.
@@ -264,7 +264,6 @@ __device__ auto ivfpq_compute_score(uint32_t pq_dim,
  */
 template <typename OutT,
           typename LutT,
-          typename IvfSampleFilterT,
           uint32_t PqBits,
           int Capacity,
           bool PrecompBaseDiff,
@@ -286,7 +285,7 @@ RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
                                       const float* queries,
                                       const uint32_t* index_list,
                                       float* query_kths,
-                                      IvfSampleFilterT sample_filter,
+                                      filtering::ivf_filter_dev sample_filter,
                                       LutT* lut_scores,
                                       OutT* _out_scores,
                                       uint32_t* _out_indices)
@@ -514,29 +513,22 @@ RAFT_KERNEL compute_similarity_kernel(uint32_t dim,
 }
 
 // The signature of the kernel defined by a minimal set of template parameters
-template <typename OutT,
-          typename LutT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
+template <typename OutT, typename LutT>
 using compute_similarity_kernel_t =
-  decltype(&compute_similarity_kernel<OutT, LutT, IvfSampleFilterT, 8, 0, true, true>);
+  decltype(&compute_similarity_kernel<OutT, LutT, 8, 0, true, true>);
 
 // The config struct lifts the runtime parameters to the template parameters
-template <typename OutT,
-          typename LutT,
-          bool PrecompBaseDiff,
-          bool EnableSMemLut,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
+template <typename OutT, typename LutT, bool PrecompBaseDiff, bool EnableSMemLut>
 struct compute_similarity_kernel_config {
  public:
-  static auto get(uint32_t pq_bits, uint32_t k_max)
-    -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+  static auto get(uint32_t pq_bits, uint32_t k_max) -> compute_similarity_kernel_t<OutT, LutT>
   {
     return kernel_choose_bits(pq_bits, k_max);
   }
 
  private:
   static auto kernel_choose_bits(uint32_t pq_bits, uint32_t k_max)
-    -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+    -> compute_similarity_kernel_t<OutT, LutT>
   {
     switch (pq_bits) {
       case 4: return kernel_try_capacity<4, kMaxCapacity>(k_max);
@@ -549,8 +541,7 @@ struct compute_similarity_kernel_config {
   }
 
   template <uint32_t PqBits, int Capacity>
-  static auto kernel_try_capacity(uint32_t k_max)
-    -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+  static auto kernel_try_capacity(uint32_t k_max) -> compute_similarity_kernel_t<OutT, LutT>
   {
     if constexpr (Capacity > 0) {
       if (k_max == 0 || k_max > Capacity) { return kernel_try_capacity<PqBits, 0>(k_max); }
@@ -558,36 +549,23 @@ struct compute_similarity_kernel_config {
     if constexpr (Capacity > 1) {
       if (k_max * 2 <= Capacity) { return kernel_try_capacity<PqBits, (Capacity / 2)>(k_max); }
     }
-    return compute_similarity_kernel<OutT,
-                                     LutT,
-                                     IvfSampleFilterT,
-                                     PqBits,
-                                     Capacity,
-                                     PrecompBaseDiff,
-                                     EnableSMemLut>;
+    return compute_similarity_kernel<OutT, LutT, PqBits, Capacity, PrecompBaseDiff, EnableSMemLut>;
   }
 };
 
 // A standalone accessor function was necessary to make sure template
 // instantiation work correctly. This accessor function is not used anymore and
 // may be removed.
-template <typename OutT,
-          typename LutT,
-          bool PrecompBaseDiff,
-          bool EnableSMemLut,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
+template <typename OutT, typename LutT, bool PrecompBaseDiff, bool EnableSMemLut>
 auto get_compute_similarity_kernel(uint32_t pq_bits, uint32_t k_max)
-  -> compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT>
+  -> compute_similarity_kernel_t<OutT, LutT>
 {
-  return compute_similarity_kernel_config<OutT,
-                                          LutT,
-                                          PrecompBaseDiff,
-                                          EnableSMemLut,
-                                          IvfSampleFilterT>::get(pq_bits, k_max);
+  return compute_similarity_kernel_config<OutT, LutT, PrecompBaseDiff, EnableSMemLut>::get(pq_bits,
+                                                                                           k_max);
 }
 
 /** Estimate the occupancy for the given kernel on the given device. */
-template <typename OutT, typename LutT, typename IvfSampleFilterT>
+template <typename OutT, typename LutT>
 struct occupancy_t {
   using shmem_unit = raft::Pow2<128>;
 
@@ -598,7 +576,7 @@ struct occupancy_t {
   inline occupancy_t() = default;
   inline occupancy_t(size_t smem,
                      uint32_t n_threads,
-                     compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT> kernel,
+                     compute_similarity_kernel_t<OutT, LutT> kernel,
                      const cudaDeviceProp& dev_props)
   {
     RAFT_CUDA_TRY(
@@ -609,19 +587,17 @@ struct occupancy_t {
   }
 };
 
-template <typename OutT, typename LutT, typename IvfSampleFilterT>
+template <typename OutT, typename LutT>
 struct selected {
-  compute_similarity_kernel_t<OutT, LutT, IvfSampleFilterT> kernel;
+  compute_similarity_kernel_t<OutT, LutT> kernel;
   dim3 grid_dim;
   dim3 block_dim;
   size_t smem_size;
   size_t device_lut_size;
 };
 
-template <typename OutT,
-          typename LutT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
-void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
+template <typename OutT, typename LutT>
+void compute_similarity_run(selected<OutT, LutT> s,
                             rmm::cuda_stream_view stream,
                             uint32_t dim,
                             uint32_t n_probes,
@@ -640,33 +616,63 @@ void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
                             const float* queries,
                             const uint32_t* index_list,
                             float* query_kths,
-                            IvfSampleFilterT sample_filter,
+                            const filtering::base_filter& sample_filter_ref,
                             LutT* lut_scores,
                             OutT* _out_scores,
                             uint32_t* _out_indices)
 {
-  s.kernel<<<s.grid_dim, s.block_dim, s.smem_size, stream>>>(dim,
-                                                             n_probes,
-                                                             pq_dim,
-                                                             n_queries,
-                                                             queries_offset,
-                                                             metric,
-                                                             codebook_kind,
-                                                             topk,
-                                                             max_samples,
-                                                             cluster_centers,
-                                                             pq_centers,
-                                                             pq_dataset,
-                                                             cluster_labels,
-                                                             _chunk_indices,
-                                                             queries,
-                                                             index_list,
-                                                             query_kths,
-                                                             sample_filter,
-                                                             lut_scores,
-                                                             _out_scores,
-                                                             _out_indices);
-  RAFT_CHECK_CUDA(stream);
+  auto launch_kernel = [&](filtering::ivf_filter_dev sample_filter) {
+    s.kernel<<<s.grid_dim, s.block_dim, s.smem_size, stream>>>(dim,
+                                                               n_probes,
+                                                               pq_dim,
+                                                               n_queries,
+                                                               queries_offset,
+                                                               metric,
+                                                               codebook_kind,
+                                                               topk,
+                                                               max_samples,
+                                                               cluster_centers,
+                                                               pq_centers,
+                                                               pq_dataset,
+                                                               cluster_labels,
+                                                               _chunk_indices,
+                                                               queries,
+                                                               index_list,
+                                                               query_kths,
+                                                               sample_filter,
+                                                               lut_scores,
+                                                               _out_scores,
+                                                               _out_indices);
+    RAFT_CHECK_CUDA(stream);
+  };
+
+  switch (sample_filter_ref.get_filter_type()) {
+    case filtering::FilterType::None: {
+      try {
+        auto& typed_sample_filter = dynamic_cast<
+          const filtering::ivf_to_sample_filter<int64_t, filtering::none_sample_filter>&>(
+          sample_filter_ref);
+        filtering::ivf_filter_dev sample_filter{filtering::none_filter_args_t{}};
+        launch_kernel(sample_filter);
+      } catch (const std::bad_cast& e) {
+      }
+      break;
+    }
+    case filtering::FilterType::Bitset: {
+      try {
+        auto& typed_sample_filter = dynamic_cast<
+          const filtering::ivf_to_sample_filter<int64_t,
+                                                filtering::bitset_filter<uint32_t, int64_t>>&>(
+          sample_filter_ref);
+        filtering::ivf_filter_dev sample_filter{filtering::bitset_filter_args_t{
+          typed_sample_filter.inds_ptrs_, typed_sample_filter.next_filter_.bitset_view_}};
+        launch_kernel(sample_filter);
+      } catch (const std::bad_cast& e) {
+      }
+      break;
+    }
+    default: RAFT_FAIL("Unsupported filter type");
+  }
 }
 
 /**
@@ -683,9 +689,7 @@ void compute_similarity_run(selected<OutT, LutT, IvfSampleFilterT> s,
  *    beyond this limit do not consider increasing the number of active blocks per SM
  *    would improve locality anymore.
  */
-template <typename OutT,
-          typename LutT,
-          typename IvfSampleFilterT = cuvs::neighbors::filtering::none_sample_filter>
+template <typename OutT, typename LutT>
 auto compute_similarity_select(const cudaDeviceProp& dev_props,
                                bool manage_local_topk,
                                int locality_hint,
@@ -695,7 +699,7 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
                                uint32_t precomp_data_count,
                                uint32_t n_queries,
                                uint32_t n_probes,
-                               uint32_t topk) -> selected<OutT, LutT, IvfSampleFilterT>
+                               uint32_t topk) -> selected<OutT, LutT>
 {
   // Shared memory for storing the lookup table
   size_t lut_mem = sizeof(LutT) * (pq_dim << pq_bits);
@@ -796,9 +800,9 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
    the minimum number of blocks (just one, really). Then, we tweak the `n_threads` to further
    optimize occupancy and data locality for the L1 cache.
    */
-  auto conf_fast        = get_compute_similarity_kernel<OutT, LutT, true, true, IvfSampleFilterT>;
-  auto conf_no_basediff = get_compute_similarity_kernel<OutT, LutT, false, true, IvfSampleFilterT>;
-  auto conf_no_smem_lut = get_compute_similarity_kernel<OutT, LutT, true, false, IvfSampleFilterT>;
+  auto conf_fast        = get_compute_similarity_kernel<OutT, LutT, true, true>;
+  auto conf_no_basediff = get_compute_similarity_kernel<OutT, LutT, false, true>;
+  auto conf_no_smem_lut = get_compute_similarity_kernel<OutT, LutT, true, false>;
   auto topk_or_zero     = manage_local_topk ? topk : 0u;
   std::array candidates{
     std::make_tuple(conf_fast(pq_bits, topk_or_zero),
@@ -814,8 +818,8 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
   // we may allow slightly lower than 100% occupancy;
   constexpr double kTargetOccupancy = 0.75;
   // This struct is used to select the better candidate
-  occupancy_t<OutT, LutT, IvfSampleFilterT> selected_perf{};
-  selected<OutT, LutT, IvfSampleFilterT> selected_config;
+  occupancy_t<OutT, LutT> selected_perf{};
+  selected<OutT, LutT> selected_config;
   for (auto [kernel, smem_size_f, lut_is_in_shmem] : candidates) {
     if (smem_size_f(raft::WarpSize) > dev_props.sharedMemPerBlockOptin) {
       // Even a single block cannot fit into an SM due to shmem requirements. Skip the candidate.
@@ -852,7 +856,7 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
       continue;
     }
 
-    occupancy_t<OutT, LutT, IvfSampleFilterT> cur(smem_size, n_threads, kernel, dev_props);
+    occupancy_t<OutT, LutT> cur(smem_size, n_threads, kernel, dev_props);
     if (cur.blocks_per_sm <= 0) {
       // For some reason, we still cannot make this kernel run. Skip the candidate.
       continue;
@@ -867,8 +871,7 @@ auto compute_similarity_select(const cudaDeviceProp& dev_props,
       if (n_threads_tmp < n_threads) {
         while (n_threads_tmp >= n_threads_min) {
           auto smem_size_tmp = smem_size_f(n_threads_tmp);
-          occupancy_t<OutT, LutT, IvfSampleFilterT> tmp(
-            smem_size_tmp, n_threads_tmp, kernel, dev_props);
+          occupancy_t<OutT, LutT> tmp(smem_size_tmp, n_threads_tmp, kernel, dev_props);
           bool select_it = false;
           if (lut_is_in_shmem && locality_hint >= tmp.blocks_per_sm) {
             // Normally, the smaller the block the better for L1 cache hit rate.
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index 8d9e606671..15ab026cee 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -417,10 +417,7 @@ template <typename OutT, typename LutT, typename IvfSampleFilterT>
 struct search_kernel_cache {
   /** Number of matmul invocations to cache. */
   static constexpr size_t kDefaultSize = 100;
-  raft::cache::lru<search_kernel_key,
-                   search_kernel_key_hash,
-                   std::equal_to<>,
-                   selected<OutT, LutT, IvfSampleFilterT>>
+  raft::cache::lru<search_kernel_key, search_kernel_key_hash, std::equal_to<>, selected<OutT, LutT>>
     value{kDefaultSize};
 };
 
@@ -557,7 +554,7 @@ void ivfpq_search_worker(raft::resources const& handle,
     } break;
   }
 
-  selected<ScoreT, LutT, IvfSampleFilterT> search_instance;
+  selected<ScoreT, LutT> search_instance;
   search_kernel_key search_key{manage_local_topk,
                                coresidency,
                                preferred_shmem_carveout,
@@ -571,17 +568,17 @@ void ivfpq_search_worker(raft::resources const& handle,
     raft::resource::get_custom_resource<search_kernel_cache<ScoreT, LutT, IvfSampleFilterT>>(handle)
       ->value;
   if (!cache.get(search_key, &search_instance)) {
-    search_instance = compute_similarity_select<ScoreT, LutT, IvfSampleFilterT>(
-      raft::resource::get_device_properties(handle),
-      manage_local_topk,
-      coresidency,
-      preferred_shmem_carveout,
-      index.pq_bits(),
-      index.pq_dim(),
-      precomp_data_count,
-      n_queries,
-      n_probes,
-      topK);
+    search_instance =
+      compute_similarity_select<ScoreT, LutT>(raft::resource::get_device_properties(handle),
+                                              manage_local_topk,
+                                              coresidency,
+                                              preferred_shmem_carveout,
+                                              index.pq_bits(),
+                                              index.pq_dim(),
+                                              precomp_data_count,
+                                              n_queries,
+                                              n_probes,
+                                              topK);
     cache.set(search_key, search_instance);
   }
 
diff --git a/cpp/src/neighbors/sample_filter.cuh b/cpp/src/neighbors/sample_filter.cuh
index b0c61f924b..4df4794206 100644
--- a/cpp/src/neighbors/sample_filter.cuh
+++ b/cpp/src/neighbors/sample_filter.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -28,7 +28,7 @@
 namespace cuvs::neighbors::filtering {
 
 /* A filter that filters nothing. This is the default behavior. */
-inline _RAFT_HOST_DEVICE bool none_sample_filter::operator()(
+constexpr __forceinline__ _RAFT_HOST_DEVICE bool none_sample_filter::operator()(
   // query index
   const uint32_t query_ix,
   // the current inverted list index
@@ -40,7 +40,7 @@ inline _RAFT_HOST_DEVICE bool none_sample_filter::operator()(
 }
 
 /* A filter that filters nothing. This is the default behavior. */
-inline _RAFT_HOST_DEVICE bool none_sample_filter::operator()(
+constexpr __forceinline__ _RAFT_HOST_DEVICE bool none_sample_filter::operator()(
   // query index
   const uint32_t query_ix,
   // the index of the current sample
@@ -93,14 +93,14 @@ inline _RAFT_HOST_DEVICE bool ivf_to_sample_filter<index_t, filter_t>::operator(
 }
 
 template <typename bitset_t, typename index_t>
-bitset_filter<bitset_t, index_t>::bitset_filter(
+_RAFT_HOST_DEVICE bitset_filter<bitset_t, index_t>::bitset_filter(
   const cuvs::core::bitset_view<bitset_t, index_t> bitset_for_filtering)
   : bitset_view_{bitset_for_filtering}
 {
 }
 
 template <typename bitset_t, typename index_t>
-inline _RAFT_HOST_DEVICE bool bitset_filter<bitset_t, index_t>::operator()(
+constexpr __forceinline__ _RAFT_HOST_DEVICE bool bitset_filter<bitset_t, index_t>::operator()(
   // query index
   const uint32_t query_ix,
   // the index of the current sample
@@ -140,4 +140,49 @@ void bitmap_filter<bitmap_t, index_t>::to_csr(raft::resources const& handle, csr
   raft::sparse::convert::bitmap_to_csr(handle, bitmap_view_, csr);
 }
 
+struct none_filter_args_t {};
+using bitset_filter_args_t =
+  std::tuple<const int64_t* const*, cuvs::core::bitset_view<uint32_t, int64_t>>;
+
+struct ivf_filter_dev {
+  filtering::FilterType tag_;
+
+  union ivf_filter_dev_args_variant {
+    none_filter_args_t none_filter_args;
+    bitset_filter_args_t bitset_filter_args;
+
+    _RAFT_HOST_DEVICE explicit ivf_filter_dev_args_variant(const none_filter_args_t& args)
+      : none_filter_args(args)
+    {
+    }
+
+    _RAFT_HOST_DEVICE explicit ivf_filter_dev_args_variant(const bitset_filter_args_t& args)
+      : bitset_filter_args(args)
+    {
+    }
+  } args_;
+
+  _RAFT_HOST_DEVICE ivf_filter_dev(none_filter_args_t args = {})
+    : tag_(FilterType::None), args_(args) {};
+
+  _RAFT_HOST_DEVICE ivf_filter_dev(bitset_filter_args_t args)
+    : tag_(FilterType::Bitset), args_(args) {};
+
+  constexpr __forceinline__ _RAFT_HOST_DEVICE bool operator()(const uint32_t query_ix,
+                                                              const uint32_t cluster_ix,
+                                                              const uint32_t sample_ix)
+  {
+    switch (tag_) {
+      case FilterType::None:
+        return filtering::none_sample_filter{}(query_ix, cluster_ix, sample_ix);
+      case FilterType::Bitset: {
+        auto& [inds_ptrs_, bitset_view_] = args_.bitset_filter_args;
+        return filtering::bitset_filter<uint32_t, int64_t>(bitset_view_)(
+          query_ix, inds_ptrs_[cluster_ix][sample_ix]);
+      }
+      default: return true;
+    }
+  }
+};
+
 }  // namespace cuvs::neighbors::filtering

From e3a2d0c1b6967c6b990af7dfb36b165745a3c2b2 Mon Sep 17 00:00:00 2001
From: Anupam <54245698+aamijar@users.noreply.github.com>
Date: Wed, 27 Aug 2025 19:06:21 -0700
Subject: [PATCH 560/623] Port `raft::neighbors::epsilon_neighborhood` to cuvs
 (#1294)

Resolves https://github.com/rapidsai/cuvs/issues/1284

Authors:
  - Anupam (https://github.com/aamijar)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1294
---
 cpp/CMakeLists.txt                            |   1 +
 .../cuvs/neighbors/epsilon_neighborhood.hpp   |  80 ++++
 .../neighbors/detail/epsilon_neighborhood.cuh | 237 ++++++++++
 cpp/src/neighbors/epsilon_neighborhood.cu     |  99 ++++
 cpp/tests/CMakeLists.txt                      |   5 +
 cpp/tests/neighbors/epsilon_neighborhood.cu   | 433 ++++++++++++++++++
 docs/source/cpp_api/neighbors.rst             |   1 +
 .../neighbors_epsilon_neighborhood.rst        |  20 +
 8 files changed, 876 insertions(+)
 create mode 100644 cpp/include/cuvs/neighbors/epsilon_neighborhood.hpp
 create mode 100644 cpp/src/neighbors/detail/epsilon_neighborhood.cuh
 create mode 100644 cpp/src/neighbors/epsilon_neighborhood.cu
 create mode 100644 cpp/tests/neighbors/epsilon_neighborhood.cu
 create mode 100644 docs/source/cpp_api/neighbors_epsilon_neighborhood.rst

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 5606ff85f0..7830004c8b 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -383,6 +383,7 @@ if(BUILD_SHARED_LIBS)
     src/embed/spectral.cu
     src/neighbors/all_neighbors/all_neighbors.cu
     src/neighbors/ball_cover.cu
+    src/neighbors/epsilon_neighborhood.cu
     src/neighbors/ball_cover/detail/ball_cover/registers_eps_pass_euclidean.cu
     src/neighbors/ball_cover/detail/ball_cover/registers_pass_one.cu
     src/neighbors/ball_cover/detail/ball_cover/registers_pass_two.cu
diff --git a/cpp/include/cuvs/neighbors/epsilon_neighborhood.hpp b/cpp/include/cuvs/neighbors/epsilon_neighborhood.hpp
new file mode 100644
index 0000000000..ef884193e4
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/epsilon_neighborhood.hpp
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/distance/distance.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+
+namespace cuvs::neighbors::epsilon_neighborhood {
+
+/**
+ * @defgroup epsilon_neighborhood_cpp_l2 Epsilon Neighborhood L2 Operations
+ * @{
+ */
+
+/**
+ * @brief Computes epsilon neighborhood for the given distance metric and ball size.
+ * The epsilon neighbors is represented by a dense boolean adjacency matrix of size m * n and
+ * an array of degrees for each vertex, which can be used as a compressed sparse row (CSR)
+ * indptr array.
+ *
+ * Currently, only L2Unexpanded (L2-squared) distance metric is supported. Other metrics will
+ * throw an exception.
+ *
+ * @code{.cpp}
+ *  #include <cuvs/neighbors/epsilon_neighborhood.hpp>
+ *  #include <raft/core/resources.hpp>
+ *  #include <raft/core/device_mdarray.hpp>
+ *  using namespace cuvs::neighbors;
+ *  raft::resources handle;
+ *  ...
+ *  auto x = raft::make_device_matrix<float, int64_t>(handle, m, k);
+ *  auto y = raft::make_device_matrix<float, int64_t>(handle, n, k);
+ *  auto adj = raft::make_device_matrix<bool, int64_t>(handle, m, n);
+ *  auto vd = raft::make_device_vector<int, int64_t>(handle, m + 1);
+ *  epsilon_neighborhood::compute(handle, x.view(), y.view(), adj.view(), vd.view(),
+ *                                eps, cuvs::distance::DistanceType::L2Unexpanded);
+ * @endcode
+ *
+ * @tparam value_t   IO and math type
+ * @tparam idx_t    Index type
+ * @tparam matrix_idx_t matrix indexing type
+ *
+ * @param[in]  handle raft handle to manage library resources
+ * @param[in]  x      first matrix [row-major] [on device] [dim = m x k]
+ * @param[in]  y      second matrix [row-major] [on device] [dim = n x k]
+ * @param[out] adj    adjacency matrix [row-major] [on device] [dim = m x n]
+ * @param[out] vd     vertex degree array [on device] [len = m + 1]
+ *                    `vd + m` stores the total number of edges in the adjacency
+ *                    matrix. Pass a nullptr if you don't need this info.
+ * @param[in]  eps    defines epsilon neighborhood radius (should be passed as
+ *                    squared when using L2Unexpanded metric)
+ * @param[in]  metric distance metric to use. Currently only L2Unexpanded is supported.
+ */
+template <typename value_t, typename idx_t, typename matrix_idx_t>
+void compute(raft::resources const& handle,
+             raft::device_matrix_view<const value_t, matrix_idx_t, raft::row_major> x,
+             raft::device_matrix_view<const value_t, matrix_idx_t, raft::row_major> y,
+             raft::device_matrix_view<bool, matrix_idx_t, raft::row_major> adj,
+             raft::device_vector_view<idx_t, matrix_idx_t> vd,
+             value_t eps,
+             cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Unexpanded);
+
+/** @} */  // end group epsilon_neighborhood_cpp_l2
+
+}  // namespace cuvs::neighbors::epsilon_neighborhood
diff --git a/cpp/src/neighbors/detail/epsilon_neighborhood.cuh b/cpp/src/neighbors/detail/epsilon_neighborhood.cuh
new file mode 100644
index 0000000000..d1ba86e1b2
--- /dev/null
+++ b/cpp/src/neighbors/detail/epsilon_neighborhood.cuh
@@ -0,0 +1,237 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/neighbors/epsilon_neighborhood.hpp>
+#include <raft/linalg/contractions.cuh>
+#include <raft/util/device_utils.cuh>
+
+namespace cuvs::neighbors::epsilon_neighborhood::detail {
+
+template <typename DataT,
+          typename IdxT,
+          typename Policy,
+          typename BaseClass = raft::linalg::Contractions_NT<DataT, IdxT, Policy>>
+struct EpsUnexpL2SqNeighborhood : public BaseClass {
+ private:
+  typedef Policy P;
+
+  bool* adj;
+  DataT eps;
+  IdxT* vd;
+
+  char* smem;  // for final reductions
+
+  DataT acc[P::AccRowsPerTh][P::AccColsPerTh];
+
+ public:
+  DI EpsUnexpL2SqNeighborhood(bool* _adj,
+                              IdxT* _vd,
+                              const DataT* _x,
+                              const DataT* _y,
+                              IdxT _m,
+                              IdxT _n,
+                              IdxT _k,
+                              DataT _eps,
+                              char* _smem)
+    : BaseClass(_x, _y, _m, _n, _k, _smem), adj(_adj), eps(_eps), vd(_vd), smem(_smem)
+  {
+  }
+
+  DI void run()
+  {
+    prolog();
+    loop();
+    epilog();
+  }
+
+ private:
+  DI void prolog()
+  {
+    this->ldgXY(IdxT(blockIdx.x) * P::Mblk, IdxT(blockIdx.y) * P::Nblk, 0);
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        acc[i][j] = BaseClass::Zero();
+      }
+    }
+    this->stsXY();
+    __syncthreads();
+    this->switch_write_buffer();
+  }
+
+  DI void loop()
+  {
+    for (int kidx = P::Kblk; kidx < this->k; kidx += P::Kblk) {
+      this->ldgXY(IdxT(blockIdx.x) * P::Mblk, IdxT(blockIdx.y) * P::Nblk, kidx);
+      accumulate();  // on the previous k-block
+      this->stsXY();
+      __syncthreads();
+      this->switch_write_buffer();
+      this->switch_read_buffer();
+    }
+    accumulate();  // last iteration
+  }
+
+  DI void epilog()
+  {
+    IdxT startx = blockIdx.x * P::Mblk + this->accrowid;
+    IdxT starty = blockIdx.y * P::Nblk + this->acccolid;
+    auto lid    = raft::laneId();
+    IdxT sums[P::AccRowsPerTh];
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+      sums[i]  = 0;
+      auto xid = startx + i * P::AccThRows;
+#pragma unroll
+      for (int j = 0; j < P::AccColsPerTh; ++j) {
+        auto yid      = starty + j * P::AccThCols;
+        auto is_neigh = acc[i][j] <= eps;
+        ///@todo: fix uncoalesced writes using shared mem
+        if (xid < this->m && yid < this->n) {
+          adj[xid * this->n + yid] = is_neigh;
+          sums[i] += is_neigh;
+        }
+      }
+    }
+    // perform reduction of adjacency values to compute vertex degrees
+    if (vd != nullptr) { updateVertexDegree(sums); }
+  }
+
+  DI void accumulate()
+  {
+#pragma unroll
+    for (int ki = 0; ki < P::Kblk; ki += P::Veclen) {
+      this->ldsXY(ki);
+#pragma unroll
+      for (int i = 0; i < P::AccRowsPerTh; ++i) {
+#pragma unroll
+        for (int j = 0; j < P::AccColsPerTh; ++j) {
+#pragma unroll
+          for (int v = 0; v < P::Veclen; ++v) {
+            auto diff = this->regx[i][v] - this->regy[j][v];
+            acc[i][j] += diff * diff;
+          }
+        }
+      }
+    }
+  }
+
+  DI void updateVertexDegree(IdxT (&sums)[P::AccRowsPerTh])
+  {
+    __syncthreads();  // so that we can safely reuse smem
+    int gid       = this->accrowid;
+    int lid       = this->acccolid;
+    auto cidx     = IdxT(blockIdx.x) * P::Mblk + gid;
+    IdxT totalSum = 0;
+    // update the individual vertex degrees
+#pragma unroll
+    for (int i = 0; i < P::AccRowsPerTh; ++i) {
+      // P::AccThCols neighboring threads need to reduce
+      // -> we have P::Nblk/P::AccThCols individual reductions
+      auto cid = cidx + i * P::AccThRows;
+      sums[i]  = raft::logicalWarpReduce<P::AccThCols>(sums[i], raft::add_op());
+      if (lid == 0 && cid < this->m) {
+        atomicUpdate(cid, sums[i]);
+        totalSum += sums[i];
+      }
+      __syncthreads();  // for safe smem reuse
+    }
+    // update the total edge count
+    totalSum = raft::blockReduce<IdxT>(totalSum, smem);
+    if (threadIdx.x == 0) { atomicUpdate(this->m, totalSum); }
+  }
+
+  DI void atomicUpdate(IdxT addrId, IdxT val)
+  {
+    if (sizeof(IdxT) == 4) {
+      raft::myAtomicAdd<unsigned>((unsigned*)(vd + addrId), val);
+    } else if (sizeof(IdxT) == 8) {
+      raft::myAtomicAdd<unsigned long long>((unsigned long long*)(vd + addrId), val);
+    }
+  }
+};  // struct EpsUnexpL2SqNeighborhood
+
+template <typename DataT, typename IdxT, typename Policy>
+__launch_bounds__(Policy::Nthreads, 2) RAFT_KERNEL epsUnexpL2SqNeighKernel(
+  bool* adj, IdxT* vd, const DataT* x, const DataT* y, IdxT m, IdxT n, IdxT k, DataT eps)
+{
+  extern __shared__ char smem[];
+  EpsUnexpL2SqNeighborhood<DataT, IdxT, Policy> obj(adj, vd, x, y, m, n, k, eps, smem);
+  obj.run();
+}
+
+template <typename DataT, typename IdxT, int VecLen>
+void epsUnexpL2SqNeighImpl(bool* adj,
+                           IdxT* vd,
+                           const DataT* x,
+                           const DataT* y,
+                           IdxT m,
+                           IdxT n,
+                           IdxT k,
+                           DataT eps,
+                           cudaStream_t stream)
+{
+  typedef typename raft::linalg::Policy4x4<DataT, VecLen>::Policy Policy;
+  dim3 grid(raft::ceildiv<int>(m, Policy::Mblk), raft::ceildiv<int>(n, Policy::Nblk));
+  dim3 blk(Policy::Nthreads);
+  epsUnexpL2SqNeighKernel<DataT, IdxT, Policy>
+    <<<grid, blk, Policy::SmemSize, stream>>>(adj, vd, x, y, m, n, k, eps);
+  RAFT_CUDA_TRY(cudaGetLastError());
+}
+
+/**
+ * @brief Computes epsilon neighborhood for the L2-Squared distance metric
+ *
+ * @tparam DataT   IO and math type
+ * @tparam IdxT    Index type
+ *
+ * @param[out] adj    adjacency matrix [row-major] [on device] [dim = m x n]
+ * @param[out] vd     vertex degree array [on device] [len = m + 1]
+ *                    `vd + m` stores the total number of edges in the adjacency
+ *                    matrix. Pass a nullptr if you don't need this info.
+ * @param[in]  x      first matrix [row-major] [on device] [dim = m x k]
+ * @param[in]  y      second matrix [row-major] [on device] [dim = n x k]
+ * @param[in]  eps    defines epsilon neighborhood radius (should be passed as
+ *                    squared as we compute L2-squared distance in this method)
+ * @param[in]  fop    device lambda to do any other custom functions
+ * @param[in]  stream cuda stream
+ */
+template <typename DataT, typename IdxT>
+void epsUnexpL2SqNeighborhood(bool* adj,
+                              IdxT* vd,
+                              const DataT* x,
+                              const DataT* y,
+                              IdxT m,
+                              IdxT n,
+                              IdxT k,
+                              DataT eps,
+                              cudaStream_t stream)
+{
+  if (vd != nullptr) { RAFT_CUDA_TRY(cudaMemsetAsync(vd, 0, (m + 1) * sizeof(IdxT), stream)); }
+
+  size_t bytes = sizeof(DataT) * k;
+  if (16 % sizeof(DataT) == 0 && bytes % 16 == 0) {
+    epsUnexpL2SqNeighImpl<DataT, IdxT, 16 / sizeof(DataT)>(adj, vd, x, y, m, n, k, eps, stream);
+  } else if (8 % sizeof(DataT) == 0 && bytes % 8 == 0) {
+    epsUnexpL2SqNeighImpl<DataT, IdxT, 8 / sizeof(DataT)>(adj, vd, x, y, m, n, k, eps, stream);
+  } else {
+    epsUnexpL2SqNeighImpl<DataT, IdxT, 1>(adj, vd, x, y, m, n, k, eps, stream);
+  }
+}
+}  // namespace cuvs::neighbors::epsilon_neighborhood::detail
diff --git a/cpp/src/neighbors/epsilon_neighborhood.cu b/cpp/src/neighbors/epsilon_neighborhood.cu
new file mode 100644
index 0000000000..bfe2c93000
--- /dev/null
+++ b/cpp/src/neighbors/epsilon_neighborhood.cu
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/epsilon_neighborhood.cuh"
+#include <cuvs/neighbors/epsilon_neighborhood.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+namespace cuvs::neighbors::epsilon_neighborhood {
+
+template <typename value_t, typename idx_t>
+void epsUnexpL2SqNeighborhood(bool* adj,
+                              idx_t* vd,
+                              const value_t* x,
+                              const value_t* y,
+                              idx_t m,
+                              idx_t n,
+                              idx_t k,
+                              value_t eps,
+                              cudaStream_t stream)
+{
+  detail::epsUnexpL2SqNeighborhood<value_t, idx_t>(adj, vd, x, y, m, n, k, eps, stream);
+}
+
+template <typename value_t, typename idx_t, typename matrix_idx_t>
+void compute(raft::resources const& handle,
+             raft::device_matrix_view<const value_t, matrix_idx_t, raft::row_major> x,
+             raft::device_matrix_view<const value_t, matrix_idx_t, raft::row_major> y,
+             raft::device_matrix_view<bool, matrix_idx_t, raft::row_major> adj,
+             raft::device_vector_view<idx_t, matrix_idx_t> vd,
+             value_t eps,
+             cuvs::distance::DistanceType metric)
+{
+  // Currently only L2Unexpanded metric is supported
+  RAFT_EXPECTS(metric == cuvs::distance::DistanceType::L2Unexpanded,
+               "Currently only L2Unexpanded distance metric is supported. "
+               "Other metrics will be supported in future versions.");
+
+  epsUnexpL2SqNeighborhood<value_t, idx_t>(adj.data_handle(),
+                                           vd.data_handle(),
+                                           x.data_handle(),
+                                           y.data_handle(),
+                                           x.extent(0),
+                                           y.extent(0),
+                                           x.extent(1),
+                                           eps,
+                                           raft::resource::get_cuda_stream(handle));
+}
+
+// Explicit template instantiations
+template void compute<float, int64_t, int64_t>(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> x,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> y,
+  raft::device_matrix_view<bool, int64_t, raft::row_major> adj,
+  raft::device_vector_view<int64_t, int64_t> vd,
+  float eps,
+  cuvs::distance::DistanceType metric);
+
+template void compute<float, int, int64_t>(
+  raft::resources const& handle,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> x,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> y,
+  raft::device_matrix_view<bool, int64_t, raft::row_major> adj,
+  raft::device_vector_view<int, int64_t> vd,
+  float eps,
+  cuvs::distance::DistanceType metric);
+
+template void compute<double, int, int64_t>(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> x,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> y,
+  raft::device_matrix_view<bool, int64_t, raft::row_major> adj,
+  raft::device_vector_view<int, int64_t> vd,
+  double eps,
+  cuvs::distance::DistanceType metric);
+
+template void compute<double, int64_t, int64_t>(
+  raft::resources const& handle,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> x,
+  raft::device_matrix_view<const double, int64_t, raft::row_major> y,
+  raft::device_matrix_view<bool, int64_t, raft::row_major> adj,
+  raft::device_vector_view<int64_t, int64_t> vd,
+  double eps,
+  cuvs::distance::DistanceType metric);
+
+}  // namespace cuvs::neighbors::epsilon_neighborhood
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index a266b577ed..ac9db70b17 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -215,6 +215,11 @@ if(BUILD_TESTS)
 
   ConfigureTest(NAME NEIGHBORS_BALL_COVER_TEST PATH neighbors/ball_cover.cu GPUS 1 PERCENT 100)
 
+  ConfigureTest(
+    NAME NEIGHBORS_EPSILON_NEIGHBORHOOD_TEST PATH neighbors/epsilon_neighborhood.cu GPUS 1 PERCENT
+    100
+  )
+
   ConfigureTest(
     NAME NEIGHBORS_ALL_NEIGHBORS_TEST PATH neighbors/all_neighbors/test_float.cu GPUS 1 PERCENT 100
     ADDITIONAL_DEP NCCL::NCCL
diff --git a/cpp/tests/neighbors/epsilon_neighborhood.cu b/cpp/tests/neighbors/epsilon_neighborhood.cu
new file mode 100644
index 0000000000..97db16c99e
--- /dev/null
+++ b/cpp/tests/neighbors/epsilon_neighborhood.cu
@@ -0,0 +1,433 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../test_utils.cuh"
+
+#include <cuvs/neighbors/ball_cover.hpp>
+#include <cuvs/neighbors/epsilon_neighborhood.hpp>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/host_mdspan.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/sparse/convert/csr.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <gtest/gtest.h>
+
+#include <memory>
+
+namespace cuvs::neighbors::epsilon_neighborhood {
+
+template <typename T, typename IdxT>
+struct EpsInputs {
+  IdxT n_row, n_col, n_centers, n_batches;
+  T eps;
+};
+
+template <typename T, typename IdxT>
+::std::ostream& operator<<(::std::ostream& os, const EpsInputs<T, IdxT>& p)
+{
+  return os;
+}
+
+template <typename T, typename IdxT>
+class EpsNeighTest : public ::testing::TestWithParam<EpsInputs<T, IdxT>> {
+ protected:
+  EpsNeighTest()
+    : data(0, raft::resource::get_cuda_stream(handle)),
+      adj(0, raft::resource::get_cuda_stream(handle)),
+      labels(0, raft::resource::get_cuda_stream(handle)),
+      vd(0, raft::resource::get_cuda_stream(handle))
+  {
+  }
+
+  void SetUp() override
+  {
+    auto stream = raft::resource::get_cuda_stream(handle);
+    param       = ::testing::TestWithParam<EpsInputs<T, IdxT>>::GetParam();
+    data.resize(param.n_row * param.n_col, stream);
+    labels.resize(param.n_row, stream);
+    batchSize = param.n_row / param.n_batches;
+    adj.resize(param.n_row * batchSize, stream);
+    vd.resize(batchSize + 1, stream);
+    RAFT_CUDA_TRY(cudaMemsetAsync(vd.data(), 0, vd.size() * sizeof(IdxT), stream));
+    raft::random::make_blobs<T, IdxT>(data.data(),
+                                      labels.data(),
+                                      param.n_row,
+                                      param.n_col,
+                                      param.n_centers,
+                                      stream,
+                                      true,
+                                      nullptr,
+                                      nullptr,
+                                      T(0.01),
+                                      false);
+  }
+
+  const raft::resources handle;
+  EpsInputs<T, IdxT> param;
+  cudaStream_t stream = 0;
+  rmm::device_uvector<T> data;
+  rmm::device_uvector<bool> adj;
+  rmm::device_uvector<IdxT> labels, vd;
+  IdxT batchSize;
+};  // class EpsNeighTest
+
+const std::vector<EpsInputs<float, int64_t>> inputsfi = {
+  {100, 16, 5, 2, 2.f},
+  {1500, 16, 5, 3, 2.f},
+  {15000, 16, 5, 1, 2.f},
+  {15000, 3, 5, 1, 2.f},
+  {14000, 16, 5, 1, 2.f},
+  {15000, 17, 5, 1, 2.f},
+  {14000, 17, 5, 1, 2.f},
+  {15000, 18, 5, 1, 2.f},
+  {14000, 18, 5, 1, 2.f},
+  {15000, 32, 5, 1, 2.f},
+  {14000, 32, 5, 1, 2.f},
+  {14000, 32, 5, 10, 2.f},
+  {20000, 10000, 10, 1, 2.f},
+  {20000, 10000, 10, 2, 2.f},
+};
+
+typedef EpsNeighTest<float, int64_t> EpsNeighTestFI;
+
+TEST_P(EpsNeighTestFI, ResultBruteForce)
+{
+  for (int i = 0; i < param.n_batches; ++i) {
+    RAFT_CUDA_TRY(cudaMemsetAsync(adj.data(), 0, sizeof(bool) * param.n_row * batchSize, stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(vd.data(), 0, sizeof(int64_t) * (batchSize + 1), stream));
+
+    auto adj_view =
+      raft::make_device_matrix_view<bool, int64_t>(adj.data(), batchSize, param.n_row);
+    auto vd_view = raft::make_device_vector_view<int64_t, int64_t>(vd.data(), batchSize + 1);
+    auto x_view  = raft::make_device_matrix_view<float, int64_t>(
+      data.data() + (i * batchSize * param.n_col), batchSize, param.n_col);
+    auto y_view =
+      raft::make_device_matrix_view<float, int64_t>(data.data(), param.n_row, param.n_col);
+
+    cuvs::neighbors::epsilon_neighborhood::compute<float, int64_t, int64_t>(
+      handle,
+      x_view,
+      y_view,
+      adj_view,
+      vd_view,
+      param.eps * param.eps,
+      cuvs::distance::DistanceType::L2Unexpanded);
+
+    ASSERT_TRUE(cuvs::devArrMatch(
+      param.n_row / param.n_centers, vd.data(), batchSize, cuvs::Compare<int64_t>(), stream));
+  }
+}
+
+INSTANTIATE_TEST_CASE_P(EpsNeighTests, EpsNeighTestFI, ::testing::ValuesIn(inputsfi));
+
+// rbc examples take fewer points as correctness checks are very costly
+const std::vector<EpsInputs<float, int64_t>> inputsfi_rbc = {
+  {100, 16, 5, 2, 2.f},
+  {1500, 16, 5, 3, 2.f},
+  {1500, 16, 5, 1, 2.f},
+  {1500, 3, 5, 1, 2.f},
+  {1400, 16, 5, 1, 2.f},
+  {1500, 17, 5, 1, 2.f},
+  {1400, 17, 5, 1, 2.f},
+  {1500, 18, 5, 1, 2.f},
+  {1400, 18, 5, 1, 2.f},
+  {1500, 32, 5, 1, 2.f},
+  {1400, 32, 5, 1, 2.f},
+  {1400, 32, 5, 10, 2.f},
+  {2000, 1000, 10, 1, 2.f},
+  {2000, 1000, 10, 2, 2.f},
+};
+
+typedef EpsNeighTest<float, int64_t> EpsNeighRbcTestFI;
+
+TEST_P(EpsNeighRbcTestFI, DenseRbc)
+{
+  auto adj_baseline = raft::make_device_matrix<bool, int64_t>(handle, batchSize, param.n_row);
+
+  auto data_view = raft::make_device_matrix_view<const float, int64_t, raft::row_major>(
+    data.data(), param.n_row, param.n_col);
+  cuvs::neighbors::ball_cover::index<int64_t, float> rbc_index(
+    handle, data_view, cuvs::distance::DistanceType::L2SqrtUnexpanded);
+  cuvs::neighbors::ball_cover::build(handle, rbc_index);
+
+  for (int i = 0; i < param.n_batches; ++i) {
+    // invalidate
+    RAFT_CUDA_TRY(cudaMemsetAsync(adj.data(), 1, sizeof(bool) * param.n_row * batchSize, stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(vd.data(), 1, sizeof(int64_t) * (batchSize + 1), stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(
+      adj_baseline.data_handle(), 1, sizeof(bool) * param.n_row * batchSize, stream));
+
+    float* query = data.data() + (i * batchSize * param.n_col);
+
+    cuvs::neighbors::ball_cover::eps_nn(
+      handle,
+      rbc_index,
+      raft::make_device_matrix_view<bool, int64_t>(adj.data(), batchSize, param.n_row),
+      raft::make_device_vector_view<int64_t, int64_t>(vd.data(), batchSize + 1),
+      raft::make_device_matrix_view<float, int64_t>(query, batchSize, param.n_col),
+      param.eps * param.eps);
+
+    ASSERT_TRUE(cuvs::devArrMatch(
+      param.n_row / param.n_centers, vd.data(), batchSize, cuvs::Compare<int64_t>(), stream));
+
+    // compute baseline via brute force + compare
+    cuvs::neighbors::epsilon_neighborhood::compute<float, int64_t, int64_t>(
+      handle,
+      raft::make_device_matrix_view<const float, int64_t, raft::row_major>(
+        query, batchSize, param.n_col),
+      raft::make_device_matrix_view<const float, int64_t, raft::row_major>(
+        data.data(), param.n_row, param.n_col),
+      raft::make_device_matrix_view<bool, int64_t, raft::row_major>(
+        adj_baseline.data_handle(), batchSize, param.n_row),
+      raft::make_device_vector_view<int64_t, int64_t>(vd.data(), batchSize + 1),
+      param.eps * param.eps,
+      cuvs::distance::DistanceType::L2Unexpanded);
+
+    ASSERT_TRUE(cuvs::devArrMatch(adj_baseline.data_handle(),
+                                  adj.data(),
+                                  batchSize,
+                                  param.n_row,
+                                  cuvs::Compare<bool>(),
+                                  stream));
+  }
+}
+
+template <typename T>
+testing::AssertionResult assertCsrEqualUnordered(
+  T* ia_exp, T* ja_exp, T* ia_act, T* ja_act, size_t rows, size_t cols, cudaStream_t stream)
+{
+  std::unique_ptr<T[]> ia_exp_h(new T[rows + 1]);
+  std::unique_ptr<T[]> ia_act_h(new T[rows + 1]);
+  raft::update_host<T>(ia_exp_h.get(), ia_exp, rows + 1, stream);
+  raft::update_host<T>(ia_act_h.get(), ia_act, rows + 1, stream);
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+
+  size_t nnz = ia_exp_h.get()[rows];
+  std::unique_ptr<T[]> ja_exp_h(new T[nnz]);
+  std::unique_ptr<T[]> ja_act_h(new T[nnz]);
+  raft::update_host<T>(ja_exp_h.get(), ja_exp, nnz, stream);
+  raft::update_host<T>(ja_act_h.get(), ja_act, nnz, stream);
+  RAFT_CUDA_TRY(cudaStreamSynchronize(stream));
+
+  for (size_t i(0); i < rows; ++i) {
+    auto row_start = ia_exp_h.get()[i];
+    auto row_end   = ia_exp_h.get()[i + 1];
+
+    // sort ja's
+    std::sort(ja_exp_h.get() + row_start, ja_exp_h.get() + row_end);
+    std::sort(ja_act_h.get() + row_start, ja_act_h.get() + row_end);
+
+    for (size_t idx(row_start); idx < (size_t)row_end; ++idx) {
+      auto exp = ja_exp_h.get()[idx];
+      auto act = ja_act_h.get()[idx];
+      if (exp != act) {
+        return testing::AssertionFailure()
+               << "actual=" << act << " != expected=" << exp << " @" << i << "," << idx;
+      }
+    }
+  }
+  return testing::AssertionSuccess();
+}
+
+TEST_P(EpsNeighRbcTestFI, SparseRbc)
+{
+  auto adj_ia          = raft::make_device_vector<int64_t>(handle, batchSize + 1);
+  auto adj_ja          = raft::make_device_vector<int64_t>(handle, param.n_row * batchSize);
+  auto vd_baseline     = raft::make_device_vector<int64_t>(handle, batchSize + 1);
+  auto adj_ia_baseline = raft::make_device_vector<int64_t>(handle, batchSize + 1);
+  auto adj_ja_baseline = raft::make_device_vector<int64_t>(handle, param.n_row * batchSize);
+
+  auto data_view = raft::make_device_matrix_view<const float, int64_t, raft::row_major>(
+    data.data(), param.n_row, param.n_col);
+  cuvs::neighbors::ball_cover::index<int64_t, float> rbc_index(
+    handle, data_view, cuvs::distance::DistanceType::L2SqrtUnexpanded);
+  cuvs::neighbors::ball_cover::build(handle, rbc_index);
+
+  for (int i = 0; i < param.n_batches; ++i) {
+    // reset full array -- that way we can compare the full size
+    RAFT_CUDA_TRY(
+      cudaMemsetAsync(adj_ja.data_handle(), 0, sizeof(int64_t) * param.n_row * batchSize, stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(
+      adj_ja_baseline.data_handle(), 0, sizeof(int64_t) * param.n_row * batchSize, stream));
+
+    float* query = data.data() + (i * batchSize * param.n_col);
+
+    // compute dense baseline and convert adj to csr
+    {
+      cuvs::neighbors::ball_cover::eps_nn(
+        handle,
+        rbc_index,
+        raft::make_device_matrix_view<bool, int64_t>(adj.data(), batchSize, param.n_row),
+        raft::make_device_vector_view<int64_t, int64_t>(vd_baseline.data_handle(), batchSize + 1),
+        raft::make_device_matrix_view<float, int64_t>(query, batchSize, param.n_col),
+        param.eps * param.eps);
+      thrust::exclusive_scan(raft::resource::get_thrust_policy(handle),
+                             vd_baseline.data_handle(),
+                             vd_baseline.data_handle() + batchSize + 1,
+                             adj_ia_baseline.data_handle());
+      raft::sparse::convert::adj_to_csr(handle,
+                                        adj.data(),
+                                        adj_ia_baseline.data_handle(),
+                                        batchSize,
+                                        param.n_row,
+                                        labels.data(),
+                                        adj_ja_baseline.data_handle());
+    }
+
+    // exact computation with 2 passes
+    {
+      cuvs::neighbors::ball_cover::eps_nn(
+        handle,
+        rbc_index,
+        raft::make_device_vector_view<int64_t, int64_t>(adj_ia.data_handle(), batchSize + 1),
+        raft::make_device_vector_view<int64_t, int64_t>(nullptr, 0),
+        raft::make_device_vector_view<int64_t, int64_t>(vd.data(), batchSize + 1),
+        raft::make_device_matrix_view<float, int64_t>(query, batchSize, param.n_col),
+        param.eps * param.eps);
+      cuvs::neighbors::ball_cover::eps_nn(
+        handle,
+        rbc_index,
+        raft::make_device_vector_view<int64_t, int64_t>(adj_ia.data_handle(), batchSize + 1),
+        raft::make_device_vector_view<int64_t, int64_t>(adj_ja.data_handle(),
+                                                        batchSize * param.n_row),
+        raft::make_device_vector_view<int64_t, int64_t>(vd.data(), batchSize + 1),
+        raft::make_device_matrix_view<float, int64_t>(query, batchSize, param.n_col),
+        param.eps * param.eps);
+      ASSERT_TRUE(cuvs::devArrMatch(adj_ia_baseline.data_handle(),
+                                    adj_ia.data_handle(),
+                                    batchSize + 1,
+                                    cuvs::Compare<int64_t>(),
+                                    stream));
+      ASSERT_TRUE(assertCsrEqualUnordered(adj_ia_baseline.data_handle(),
+                                          adj_ja_baseline.data_handle(),
+                                          adj_ia.data_handle(),
+                                          adj_ja.data_handle(),
+                                          batchSize,
+                                          param.n_row,
+                                          stream));
+    }
+  }
+}
+
+TEST_P(EpsNeighRbcTestFI, SparseRbcMaxK)
+{
+  auto adj_ia          = raft::make_device_vector<int64_t>(handle, batchSize + 1);
+  auto adj_ja          = raft::make_device_vector<int64_t>(handle, param.n_row * batchSize);
+  auto vd_baseline     = raft::make_device_vector<int64_t>(handle, batchSize + 1);
+  auto adj_ia_baseline = raft::make_device_vector<int64_t>(handle, batchSize + 1);
+  auto adj_ja_baseline = raft::make_device_vector<int64_t>(handle, param.n_row * batchSize);
+
+  auto data_view = raft::make_device_matrix_view<const float, int64_t, raft::row_major>(
+    data.data(), param.n_row, param.n_col);
+  cuvs::neighbors::ball_cover::index<int64_t, float> rbc_index(
+    handle, data_view, cuvs::distance::DistanceType::L2SqrtUnexpanded);
+  cuvs::neighbors::ball_cover::build(handle, rbc_index);
+
+  int64_t expected_max_k = param.n_row / param.n_centers;
+
+  for (int i = 0; i < param.n_batches; ++i) {
+    // reset full array -- that way we can compare the full size
+    RAFT_CUDA_TRY(
+      cudaMemsetAsync(adj_ja.data_handle(), 0, sizeof(int64_t) * param.n_row * batchSize, stream));
+    RAFT_CUDA_TRY(cudaMemsetAsync(
+      adj_ja_baseline.data_handle(), 0, sizeof(int64_t) * param.n_row * batchSize, stream));
+
+    float* query = data.data() + (i * batchSize * param.n_col);
+
+    // compute dense baseline and convert adj to csr
+    {
+      cuvs::neighbors::ball_cover::eps_nn(
+        handle,
+        rbc_index,
+        raft::make_device_matrix_view<bool, int64_t>(adj.data(), batchSize, param.n_row),
+        raft::make_device_vector_view<int64_t, int64_t>(vd_baseline.data_handle(), batchSize + 1),
+        raft::make_device_matrix_view<float, int64_t>(query, batchSize, param.n_col),
+        param.eps * param.eps);
+      thrust::exclusive_scan(raft::resource::get_thrust_policy(handle),
+                             vd_baseline.data_handle(),
+                             vd_baseline.data_handle() + batchSize + 1,
+                             adj_ia_baseline.data_handle());
+      raft::sparse::convert::adj_to_csr(handle,
+                                        adj.data(),
+                                        adj_ia_baseline.data_handle(),
+                                        batchSize,
+                                        param.n_row,
+                                        labels.data(),
+                                        adj_ja_baseline.data_handle());
+    }
+
+    // exact computation with 1 pass
+    {
+      int64_t max_k = expected_max_k;
+      cuvs::neighbors::ball_cover::eps_nn(
+        handle,
+        rbc_index,
+        raft::make_device_vector_view<int64_t, int64_t>(adj_ia.data_handle(), batchSize + 1),
+        raft::make_device_vector_view<int64_t, int64_t>(adj_ja.data_handle(),
+                                                        batchSize * param.n_row),
+        raft::make_device_vector_view<int64_t, int64_t>(vd.data(), batchSize + 1),
+        raft::make_device_matrix_view<float, int64_t>(query, batchSize, param.n_col),
+        param.eps * param.eps,
+        raft::make_host_scalar_view<int64_t, int64_t>(&max_k));
+      ASSERT_TRUE(cuvs::devArrMatch(adj_ia_baseline.data_handle(),
+                                    adj_ia.data_handle(),
+                                    batchSize + 1,
+                                    cuvs::Compare<int64_t>(),
+                                    stream));
+      ASSERT_TRUE(assertCsrEqualUnordered(adj_ia_baseline.data_handle(),
+                                          adj_ja_baseline.data_handle(),
+                                          adj_ia.data_handle(),
+                                          adj_ja.data_handle(),
+                                          batchSize,
+                                          param.n_row,
+                                          stream));
+      ASSERT_TRUE(cuvs::devArrMatch(
+        vd_baseline.data_handle(), vd.data(), batchSize + 1, cuvs::Compare<int64_t>(), stream));
+      ASSERT_TRUE(max_k == expected_max_k);
+    }
+
+    // k-limited computation with 1 pass
+    {
+      int64_t max_k = expected_max_k / 2;
+      cuvs::neighbors::ball_cover::eps_nn(
+        handle,
+        rbc_index,
+        raft::make_device_vector_view<int64_t, int64_t>(adj_ia.data_handle(), batchSize + 1),
+        raft::make_device_vector_view<int64_t, int64_t>(adj_ja.data_handle(),
+                                                        batchSize * param.n_row),
+        raft::make_device_vector_view<int64_t, int64_t>(vd.data(), batchSize + 1),
+        raft::make_device_matrix_view<float, int64_t>(query, batchSize, param.n_col),
+        param.eps * param.eps,
+        raft::make_host_scalar_view<int64_t, int64_t>(&max_k));
+      ASSERT_TRUE(max_k == expected_max_k);
+      ASSERT_TRUE(cuvs::devArrMatch(
+        expected_max_k / 2, vd.data(), batchSize, cuvs::Compare<int64_t>(), stream));
+      ASSERT_TRUE(cuvs::devArrMatch(expected_max_k / 2 * batchSize,
+                                    vd.data() + batchSize,
+                                    1,
+                                    cuvs::Compare<int64_t>(),
+                                    stream));
+    }
+  }
+}
+
+INSTANTIATE_TEST_CASE_P(EpsNeighTests, EpsNeighRbcTestFI, ::testing::ValuesIn(inputsfi_rbc));
+
+};  // namespace cuvs::neighbors::epsilon_neighborhood
diff --git a/docs/source/cpp_api/neighbors.rst b/docs/source/cpp_api/neighbors.rst
index 0032e17e2b..0c6e9cfd86 100644
--- a/docs/source/cpp_api/neighbors.rst
+++ b/docs/source/cpp_api/neighbors.rst
@@ -13,6 +13,7 @@ Nearest Neighbors
    neighbors_bruteforce.rst
    neighbors_cagra.rst
    neighbors_dynamic_batching.rst
+   neighbors_epsilon_neighborhood.rst
    neighbors_filter.rst
    neighbors_hnsw.rst
    neighbors_ivf_flat.rst
diff --git a/docs/source/cpp_api/neighbors_epsilon_neighborhood.rst b/docs/source/cpp_api/neighbors_epsilon_neighborhood.rst
new file mode 100644
index 0000000000..1ca957bfed
--- /dev/null
+++ b/docs/source/cpp_api/neighbors_epsilon_neighborhood.rst
@@ -0,0 +1,20 @@
+Epsilon Neighborhood
+====================
+
+Epsilon neighborhood finds all neighbors within a given radius (epsilon) for each point in a dataset. Unlike k-nearest neighbors which finds a fixed number of neighbors, epsilon neighborhood finds all points within a specified distance threshold, making it particularly useful for density-based algorithms and graph construction.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+``#include <cuvs/neighbors/epsilon_neighborhood.hpp>``
+
+namespace *cuvs::neighbors::epsilon_neighborhood*
+
+L2-Squared Distance Operations
+------------------------------
+
+.. doxygengroup:: epsilon_neighborhood_cpp_l2
+    :project: cuvs
+    :members:
+    :content-only:

From e02b7d1280264cad19885b309590e5cdddbe0efe Mon Sep 17 00:00:00 2001
From: tsuki <12711693+enp1s0@users.noreply.github.com>
Date: Thu, 28 Aug 2025 21:04:32 +0900
Subject: [PATCH 561/623] [BUG] NEIGHBORS_ALL_NEIGHBORS_TEST build ignores
 --no-mg (#1230)

The following error occurs when building the unit tests on a machine where NCCL is not installed, even though the `--no-mg` option is specified.
```
CMake Error at tests/CMakeLists.txt:47 (target_link_libraries):
  Target "NEIGHBORS_ALL_NEIGHBORS_TEST" links to:

    NCCL::NCCL

  but the target was not found.  Possible reasons include:

    * There is a typo in the target name.
    * A find_package call is missing for an IMPORTED target.
    * An ALIAS target is missing.

Call Stack (most recent call first):
  tests/CMakeLists.txt:213 (ConfigureTest)
```

This is because `NCCL::NCCL` is specified in tests/CmakeLists.txt
https://github.com/rapidsai/cuvs/blob/afc24ee8302a57a5c7192a38fc2d74a1a47aa95f/cpp/tests/CMakeLists.txt#L213-L216

I could build the unit tests without the NCCL dependency. Is this dependency really necessary even when the `--no-mg` option is specified?
This PR modifies the CMakeLists.txt file so that the NCCL dependency is set only when the `--no-mg` option is not specified.

Rel: https://github.com/rapidsai/cuvs/pull/785 vis: @jinsolp

Authors:
  - tsuki (https://github.com/enp1s0)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Robert Maynard (https://github.com/robertmaynard)

URL: https://github.com/rapidsai/cuvs/pull/1230
---
 cpp/tests/CMakeLists.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index ac9db70b17..a865aeab89 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -222,7 +222,6 @@ if(BUILD_TESTS)
 
   ConfigureTest(
     NAME NEIGHBORS_ALL_NEIGHBORS_TEST PATH neighbors/all_neighbors/test_float.cu GPUS 1 PERCENT 100
-    ADDITIONAL_DEP NCCL::NCCL
   )
 
   ConfigureTest(

From 55985fed716cf376a9ec6c55444acc768cea1152 Mon Sep 17 00:00:00 2001
From: James Lamb <jlamb@nvidia.com>
Date: Fri, 29 Aug 2025 08:37:38 -0500
Subject: [PATCH 562/623] Build and test with CUDA 13.0.0 (#1273)

Contributes to https://github.com/rapidsai/build-planning/issues/208

* uses CUDA 13.0.0 to build and test
* adds CUDA 13 devcontainers
* moves some dependency pins:
  - `cuda-python`: `>=12.9.2` (CUDA 12), `>=13.0.1` (CUDA 13)
  - `cupy`: `>=13.6.0`

Contributes to https://github.com/rapidsai/build-planning/issues/68

* updates to CUDA 13 dependencies in fallback entries in `dependencies.yaml` matrices (i.e., the ones that get written to `pyproject.toml` in source control)

## Notes for Reviewers

This switches GitHub Actions workflows to the `cuda13.0` branch from here: https://github.com/rapidsai/shared-workflows/pull/413

A future round of PRs will revert that back to `branch-25.10`, once all of RAPIDS supports CUDA 13.

### What about Go, Java, and Rust?

This PR expands building / testing for those bindings to cover CUDA 13, but more changes probably need to be made to support distributing packages for those.

Proposing deferring that to follow-ups:

* #1275
* #1276
* #1277

Authors:
  - James Lamb (https://github.com/jameslamb)
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Mike Sarahan (https://github.com/msarahan)
  - MithunR (https://github.com/mythrocks)
  - Ben Frederickson (https://github.com/benfred)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1273
---
 .../cuda13.0-conda/devcontainer.json          |  44 ++++++
 .devcontainer/cuda13.0-pip/devcontainer.json  |  52 ++++++++
 .github/workflows/build.yaml                  |  53 ++++++--
 .github/workflows/pr.yaml                     |  65 ++++++---
 .github/workflows/publish-rust.yaml           |   7 +-
 .github/workflows/test.yaml                   |  20 ++-
 .../trigger-breaking-change-alert.yaml        |   2 +-
 README.md                                     |  11 +-
 ci/build_java.sh                              |   3 +
 ci/build_python.sh                            |   4 +-
 ci/test_java.sh                               |   3 +
 .../all_cuda-129_arch-aarch64.yaml            |   4 +-
 .../all_cuda-129_arch-x86_64.yaml             |   4 +-
 .../all_cuda-130_arch-aarch64.yaml            |  56 ++++++++
 .../all_cuda-130_arch-x86_64.yaml             |  56 ++++++++
 .../bench_ann_cuda-129_arch-aarch64.yaml      |   4 +-
 .../bench_ann_cuda-129_arch-x86_64.yaml       |   4 +-
 .../bench_ann_cuda-130_arch-aarch64.yaml      |  49 +++++++
 .../bench_ann_cuda-130_arch-x86_64.yaml       |  52 ++++++++
 .../go_cuda-130_arch-aarch64.yaml             |  32 +++++
 .../environments/go_cuda-130_arch-x86_64.yaml |  32 +++++
 .../rust_cuda-130_arch-aarch64.yaml           |  31 +++++
 .../rust_cuda-130_arch-x86_64.yaml            |  31 +++++
 conda/recipes/cuvs-bench/recipe.yaml          |   2 +-
 conda/recipes/cuvs/recipe.yaml                |   8 +-
 cpp/bench/ann/src/common/util.hpp             |   9 +-
 cpp/include/cuvs/neighbors/common.hpp         |   2 +-
 dependencies.yaml                             | 126 +++++++++++-------
 docs/source/build.rst                         |  16 ++-
 docs/source/cuvs_bench/index.rst              |   2 +-
 examples/go/README.md                         |   2 +-
 examples/rust/README.md                       |   2 +-
 .../cuvs/internal/GPUInfoProviderImpl.java    |   5 +-
 .../com/nvidia/cuvs/internal/common/Util.java |  27 ++++
 python/cuvs/pyproject.toml                    |   6 +-
 35 files changed, 708 insertions(+), 118 deletions(-)
 create mode 100644 .devcontainer/cuda13.0-conda/devcontainer.json
 create mode 100644 .devcontainer/cuda13.0-pip/devcontainer.json
 create mode 100644 conda/environments/all_cuda-130_arch-aarch64.yaml
 create mode 100644 conda/environments/all_cuda-130_arch-x86_64.yaml
 create mode 100644 conda/environments/bench_ann_cuda-130_arch-aarch64.yaml
 create mode 100644 conda/environments/bench_ann_cuda-130_arch-x86_64.yaml
 create mode 100644 conda/environments/go_cuda-130_arch-aarch64.yaml
 create mode 100644 conda/environments/go_cuda-130_arch-x86_64.yaml
 create mode 100644 conda/environments/rust_cuda-130_arch-aarch64.yaml
 create mode 100644 conda/environments/rust_cuda-130_arch-x86_64.yaml

diff --git a/.devcontainer/cuda13.0-conda/devcontainer.json b/.devcontainer/cuda13.0-conda/devcontainer.json
new file mode 100644
index 0000000000..e69607dc06
--- /dev/null
+++ b/.devcontainer/cuda13.0-conda/devcontainer.json
@@ -0,0 +1,44 @@
+{
+  "build": {
+    "context": "${localWorkspaceFolder}/.devcontainer",
+    "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
+    "args": {
+      "CUDA": "13.0",
+      "PYTHON_PACKAGE_MANAGER": "conda",
+      "BASE": "rapidsai/devcontainers:25.10-cpp-mambaforge"
+    }
+  },
+  "runArgs": [
+    "--rm",
+    "--name",
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda13.0-conda",
+    "--ulimit",
+    "nofile=500000"
+  ],
+  "hostRequirements": {"gpu": "optional"},
+  "features": {
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
+  },
+  "overrideFeatureInstallOrder": [
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
+  ],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda13.0-envs}"],
+  "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
+  "workspaceFolder": "/home/coder",
+  "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
+  "mounts": [
+    "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
+    "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
+    "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
+    "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda13.0-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+  ],
+  "customizations": {
+    "vscode": {
+      "extensions": [
+        "ms-python.flake8",
+        "nvidia.nsight-vscode-edition"
+      ]
+    }
+  }
+}
diff --git a/.devcontainer/cuda13.0-pip/devcontainer.json b/.devcontainer/cuda13.0-pip/devcontainer.json
new file mode 100644
index 0000000000..27f0acd3f4
--- /dev/null
+++ b/.devcontainer/cuda13.0-pip/devcontainer.json
@@ -0,0 +1,52 @@
+{
+  "build": {
+    "context": "${localWorkspaceFolder}/.devcontainer",
+    "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
+    "args": {
+      "CUDA": "13.0",
+      "PYTHON_PACKAGE_MANAGER": "pip",
+      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda13.0-ucx1.19.0-openmpi5.0.7"
+    }
+  },
+  "runArgs": [
+    "--rm",
+    "--name",
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda13.0-pip",
+    "--ulimit",
+    "nofile=500000"
+  ],
+  "hostRequirements": {"gpu": "optional"},
+  "features": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.10": {
+      "version": "13.0",
+      "installcuBLAS": true,
+      "installcuSOLVER": true,
+      "installcuRAND": true,
+      "installcuSPARSE": true
+    },
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
+  },
+  "overrideFeatureInstallOrder": [
+    "ghcr.io/rapidsai/devcontainers/features/ucx",
+    "ghcr.io/rapidsai/devcontainers/features/cuda",
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
+  ],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda13.0-venvs}"],
+  "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
+  "workspaceFolder": "/home/coder",
+  "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cuvs,type=bind,consistency=consistent",
+  "mounts": [
+    "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
+    "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
+    "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda13.0-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+  ],
+  "customizations": {
+    "vscode": {
+      "extensions": [
+        "ms-python.flake8",
+        "nvidia.nsight-vscode-edition"
+      ]
+    }
+  }
+}
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 210ade79ca..ccb75dbdb7 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -34,7 +34,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -44,46 +44,71 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
+    # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
+          - '13.0.0'
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
+    # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
+          - '13.0.0'
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_go.sh"
       sha: ${{ inputs.sha }}
   java-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
+    # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
+          - '13.0.0'
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/build_java.sh"
+      artifact-name: "cuvs-java-cuda${{ matrix.cuda_version }}"
       file_to_upload: "java/cuvs-java/target/"
       sha: ${{ inputs.sha }}
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -93,7 +118,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -108,7 +133,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -120,7 +145,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -134,7 +159,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -145,7 +170,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -157,7 +182,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 41fbc93a84..2c7a61fd39 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda13.0
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -54,7 +54,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@cuda13.0
     with:
       files_yaml: |
         test_cpp:
@@ -106,14 +106,14 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda13.0
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda13.0
     with:
       build_type: pull-request
       node_type: cpu16
@@ -121,7 +121,7 @@ jobs:
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda13.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
@@ -129,21 +129,21 @@ jobs:
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda13.0
     with:
       build_type: pull-request
       symbol_exclusions: (void (thrust::|cub::))
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda13.0
     with:
       build_type: pull-request
       script: ci/build_python.sh
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda13.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -151,19 +151,28 @@ jobs:
   conda-java-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_java
+    # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
+    # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
+          - '13.0.0'
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/test_java.sh"
+      artifact-name: "cuvs-java-cuda${{ matrix.cuda_version }}"
       file_to_upload: "java/cuvs-java/target/"
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -173,27 +182,43 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
+    # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
+          - '13.0.0'
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/build_rust.sh"
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
+    # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
+          - '13.0.0'
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/build_go.sh"
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -204,7 +229,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -213,7 +238,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda13.0
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -221,10 +246,10 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda13.0
     with:
       arch: '["amd64", "arm64"]'
-      cuda: '["12.9"]'
+      cuda: '["13.0"]'
       node_type: "cpu8"
       rapids-aux-secret-1: GIST_REPO_READ_ORG_GITHUB_TOKEN
       env: |
diff --git a/.github/workflows/publish-rust.yaml b/.github/workflows/publish-rust.yaml
index 7834e6bc96..c74af7e7f1 100644
--- a/.github/workflows/publish-rust.yaml
+++ b/.github/workflows/publish-rust.yaml
@@ -10,8 +10,13 @@ defaults:
 jobs:
   rust-publish:
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
     container:
-      image: rapidsai/ci-conda:25.10-latest
+      image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
     steps:
       - uses: actions/checkout@v4
       - name: Check if release build
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 8479ce38a4..51418cbd22 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -25,7 +25,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -43,7 +43,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -52,7 +52,15 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-java-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
+    # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_version:
+          - '12.9.1'
+          - '13.0.0'
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -60,11 +68,11 @@ jobs:
       sha: ${{ inputs.sha }}
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda13.0
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 48bf37afc4..72751d071b 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@cuda13.0
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/README.md b/README.md
index 1604428492..a81dd1b64f 100755
--- a/README.md
+++ b/README.md
@@ -95,8 +95,11 @@ conda install -c rapidsai -c conda-forge cuvs
 
 The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.
 
-For CUDA 12 packages:
 ```bash
+# CUDA 13
+pip install cuvs-cu13 --extra-index-url=https://pypi.nvidia.com
+
+# CUDA 12
 pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 ```
 
@@ -104,7 +107,11 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:
 
 ```bash
-conda install -c rapidsai-nightly -c conda-forge cuvs=25.10
+# CUDA 13
+conda install -c rapidsai-nightly -c conda-forge cuvs=25.10 cuda-version=13.0
+
+# CUDA 12
+conda install -c rapidsai-nightly -c conda-forge cuvs=25.10 cuda-version=12.9
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
diff --git a/ci/build_java.sh b/ci/build_java.sh
index 175cc9a559..b692bbbffd 100755
--- a/ci/build_java.sh
+++ b/ci/build_java.sh
@@ -41,6 +41,9 @@ set +e
 
 rapids-logger "Run Java build"
 
+RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
+export RAPIDS_CUDA_MAJOR
+
 bash ./build.sh java "${EXTRA_BUILD_ARGS[@]}"
 
 rapids-logger "Test script exiting with value: $EXITCODE"
diff --git a/ci/build_python.sh b/ci/build_python.sh
index 8ee818341c..d4956cce78 100755
--- a/ci/build_python.sh
+++ b/ci/build_python.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
@@ -54,7 +54,7 @@ sccache --zero-stats
 # Build cuvs-bench-cpu only in one CUDA major version since it only depends on
 # python version
 RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
-if [[ ${RAPIDS_CUDA_MAJOR} == "12" ]]; then
+if [[ ${RAPIDS_CUDA_MAJOR} == "13" ]]; then
   rattler-build build --recipe conda/recipes/cuvs-bench-cpu \
                       "${RATTLER_ARGS[@]}" \
                       "${RATTLER_CHANNELS[@]}"
diff --git a/ci/test_java.sh b/ci/test_java.sh
index 7c7d77860a..20c19b6dee 100755
--- a/ci/test_java.sh
+++ b/ci/test_java.sh
@@ -12,6 +12,9 @@ nvidia-smi
 
 rapids-logger "Run Java build and tests"
 
+RAPIDS_CUDA_MAJOR="${RAPIDS_CUDA_VERSION%%.*}"
+export RAPIDS_CUDA_MAJOR
+
 # TODO: switch to installing pre-built artifacts instead of rebuilding in test jobs
 #       ref: https://github.com/rapidsai/cuvs/issues/868
 ci/build_java.sh --run-java-tests
diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
index 45bb00e8df..a877f09e23 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -15,9 +15,9 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.6.2,<13.0a0
+- cuda-python>=12.9.2,<13.0a0
 - cuda-version=12.9
-- cupy>=12.0.0
+- cupy>=13.6.0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
index 46a0f234c7..588bfa8d64 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -15,9 +15,9 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.6.2,<13.0a0
+- cuda-python>=12.9.2,<13.0a0
 - cuda-version=12.9
-- cupy>=12.0.0
+- cupy>=13.6.0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
diff --git a/conda/environments/all_cuda-130_arch-aarch64.yaml b/conda/environments/all_cuda-130_arch-aarch64.yaml
new file mode 100644
index 0000000000..ebf59a3f99
--- /dev/null
+++ b/conda/environments/all_cuda-130_arch-aarch64.yaml
@@ -0,0 +1,56 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- _go_select *=cgo
+- breathe>=4.35.0
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-python>=13.0.1,<14.0a0
+- cuda-version=13.0
+- cupy>=13.6.0
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- doxygen>=1.8.20
+- gcc_linux-aarch64=14.*
+- go
+- graphviz
+- ipython
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- librmm==25.10.*,>=0.0.0a0
+- make
+- nccl>=2.19
+- ninja
+- numpy>=1.23,<3.0a0
+- numpydoc
+- openblas
+- pre-commit
+- pylibraft==25.10.*,>=0.0.0a0
+- pytest
+- pytest-cov
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
+- recommonmark
+- rust
+- scikit-build-core>=0.10.0
+- scikit-learn
+- sphinx-copybutton
+- sphinx-markdown-tables
+- sphinx>=8.0.0
+- sysroot_linux-aarch64==2.28
+- pip:
+  - nvidia-sphinx-theme
+name: all_cuda-130_arch-aarch64
diff --git a/conda/environments/all_cuda-130_arch-x86_64.yaml b/conda/environments/all_cuda-130_arch-x86_64.yaml
new file mode 100644
index 0000000000..8433f4adff
--- /dev/null
+++ b/conda/environments/all_cuda-130_arch-x86_64.yaml
@@ -0,0 +1,56 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- _go_select *=cgo
+- breathe>=4.35.0
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-python>=13.0.1,<14.0a0
+- cuda-version=13.0
+- cupy>=13.6.0
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- doxygen>=1.8.20
+- gcc_linux-64=14.*
+- go
+- graphviz
+- ipython
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- librmm==25.10.*,>=0.0.0a0
+- make
+- nccl>=2.19
+- ninja
+- numpy>=1.23,<3.0a0
+- numpydoc
+- openblas
+- pre-commit
+- pylibraft==25.10.*,>=0.0.0a0
+- pytest
+- pytest-cov
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
+- recommonmark
+- rust
+- scikit-build-core>=0.10.0
+- scikit-learn
+- sphinx-copybutton
+- sphinx-markdown-tables
+- sphinx>=8.0.0
+- sysroot_linux-64==2.28
+- pip:
+  - nvidia-sphinx-theme
+name: all_cuda-130_arch-x86_64
diff --git a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index 7ffb09777b..7735923261 100644
--- a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -15,9 +15,9 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.6.2,<13.0a0
+- cuda-python>=12.9.2,<13.0a0
 - cuda-version=12.9
-- cupy>=12.0.0
+- cupy>=13.6.0
 - cuvs==25.10.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index fcd0f89053..96e2c34d5d 100644
--- a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -15,9 +15,9 @@ dependencies:
 - cuda-nvcc
 - cuda-nvtx-dev
 - cuda-profiler-api
-- cuda-python>=12.6.2,<13.0a0
+- cuda-python>=12.9.2,<13.0a0
 - cuda-version=12.9
-- cupy>=12.0.0
+- cupy>=13.6.0
 - cuvs==25.10.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
diff --git a/conda/environments/bench_ann_cuda-130_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-130_arch-aarch64.yaml
new file mode 100644
index 0000000000..939a24b445
--- /dev/null
+++ b/conda/environments/bench_ann_cuda-130_arch-aarch64.yaml
@@ -0,0 +1,49 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- benchmark>=1.8.2
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- click
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-python>=13.0.1,<14.0a0
+- cuda-version=13.0
+- cupy>=13.6.0
+- cuvs==25.10.*,>=0.0.0a0
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- gcc_linux-aarch64=14.*
+- glog>=0.6.0
+- h5py>=3.8.0
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.10.*,>=0.0.0a0
+- librmm==25.10.*,>=0.0.0a0
+- matplotlib-base>=3.9
+- nccl>=2.19
+- ninja
+- nlohmann_json>=3.11.2
+- openblas
+- pandas
+- pylibraft==25.10.*,>=0.0.0a0
+- pyyaml
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
+- requests
+- scikit-learn
+- setuptools
+- sysroot_linux-aarch64==2.28
+- wheel
+name: bench_ann_cuda-130_arch-aarch64
diff --git a/conda/environments/bench_ann_cuda-130_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-130_arch-x86_64.yaml
new file mode 100644
index 0000000000..296c1fc03a
--- /dev/null
+++ b/conda/environments/bench_ann_cuda-130_arch-x86_64.yaml
@@ -0,0 +1,52 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- benchmark>=1.8.2
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- click
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-python>=13.0.1,<14.0a0
+- cuda-version=13.0
+- cupy>=13.6.0
+- cuvs==25.10.*,>=0.0.0a0
+- cxx-compiler
+- cython>=3.0.0
+- dlpack>=0.8,<1.0
+- gcc_linux-64=14.*
+- glog>=0.6.0
+- h5py>=3.8.0
+- libaio
+- libboost-devel=1.87
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.10.*,>=0.0.0a0
+- librmm==25.10.*,>=0.0.0a0
+- matplotlib-base>=3.9
+- mkl-devel=2023
+- nccl>=2.19
+- ninja
+- nlohmann_json>=3.11.2
+- openblas
+- pandas
+- pylibraft==25.10.*,>=0.0.0a0
+- pyyaml
+- rapids-build-backend>=0.4.0,<0.5.0.dev0
+- requests
+- scikit-learn
+- setuptools
+- sysroot_linux-64==2.28
+- wheel
+name: bench_ann_cuda-130_arch-x86_64
diff --git a/conda/environments/go_cuda-130_arch-aarch64.yaml b/conda/environments/go_cuda-130_arch-aarch64.yaml
new file mode 100644
index 0000000000..3359dd084a
--- /dev/null
+++ b/conda/environments/go_cuda-130_arch-aarch64.yaml
@@ -0,0 +1,32 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- _go_select *=cgo
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=13.0
+- cxx-compiler
+- dlpack>=0.8,<1.0
+- gcc_linux-aarch64=14.*
+- go
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
+- nccl>=2.19
+- ninja
+- sysroot_linux-aarch64==2.28
+name: go_cuda-130_arch-aarch64
diff --git a/conda/environments/go_cuda-130_arch-x86_64.yaml b/conda/environments/go_cuda-130_arch-x86_64.yaml
new file mode 100644
index 0000000000..a9a6a90cf1
--- /dev/null
+++ b/conda/environments/go_cuda-130_arch-x86_64.yaml
@@ -0,0 +1,32 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- _go_select *=cgo
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=13.0
+- cxx-compiler
+- dlpack>=0.8,<1.0
+- gcc_linux-64=14.*
+- go
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
+- nccl>=2.19
+- ninja
+- sysroot_linux-64==2.28
+name: go_cuda-130_arch-x86_64
diff --git a/conda/environments/rust_cuda-130_arch-aarch64.yaml b/conda/environments/rust_cuda-130_arch-aarch64.yaml
new file mode 100644
index 0000000000..751e44f36b
--- /dev/null
+++ b/conda/environments/rust_cuda-130_arch-aarch64.yaml
@@ -0,0 +1,31 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=13.0
+- cxx-compiler
+- gcc_linux-aarch64=14.*
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
+- make
+- nccl>=2.19
+- ninja
+- rust
+- sysroot_linux-aarch64==2.28
+name: rust_cuda-130_arch-aarch64
diff --git a/conda/environments/rust_cuda-130_arch-x86_64.yaml b/conda/environments/rust_cuda-130_arch-x86_64.yaml
new file mode 100644
index 0000000000..56f7c9830e
--- /dev/null
+++ b/conda/environments/rust_cuda-130_arch-x86_64.yaml
@@ -0,0 +1,31 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- conda-forge
+dependencies:
+- c-compiler
+- clang-tools==20.1.4
+- clang==20.1.4
+- cmake>=3.30.4
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvtx-dev
+- cuda-profiler-api
+- cuda-version=13.0
+- cxx-compiler
+- gcc_linux-64=14.*
+- libclang==20.1.4
+- libcublas-dev
+- libcurand-dev
+- libcusolver-dev
+- libcusparse-dev
+- libcuvs==25.10.*,>=0.0.0a0
+- libraft==25.10.*,>=0.0.0a0
+- make
+- nccl>=2.19
+- ninja
+- rust
+- sysroot_linux-64==2.28
+name: rust_cuda-130_arch-x86_64
diff --git a/conda/recipes/cuvs-bench/recipe.yaml b/conda/recipes/cuvs-bench/recipe.yaml
index a823877926..fe12bea9ae 100644
--- a/conda/recipes/cuvs-bench/recipe.yaml
+++ b/conda/recipes/cuvs-bench/recipe.yaml
@@ -40,7 +40,7 @@ requirements:
     - benchmark
     - click
     - cuda-cudart
-    - cupy >=12.0.0
+    - cupy >=13.6.0
     - cuvs =${{ version }}
     - glog ${{ glog_version }}
     - h5py ${{ h5py_version }}
diff --git a/conda/recipes/cuvs/recipe.yaml b/conda/recipes/cuvs/recipe.yaml
index e7b9f6de99..f3fd05f8f3 100644
--- a/conda/recipes/cuvs/recipe.yaml
+++ b/conda/recipes/cuvs/recipe.yaml
@@ -58,7 +58,9 @@ requirements:
     - python =${{ py_version }}
     - rapids-build-backend >=0.4.0,<0.5.0.dev0
     - scikit-build-core >=0.10.0
-    - cuda-python >=12.6.2,<13.0a0
+    - if: cuda_major == "12"
+      then: cuda-python >=12.9.2,<13.0a0
+      else: cuda-python >=13.0.1,<14.0a0
     - cuda-cudart-dev
   run:
     - ${{ pin_compatible("cuda-version", upper_bound="x", lower_bound="x") }}
@@ -66,7 +68,9 @@ requirements:
     - pylibraft =${{ minor_version }}
     - python
     - numpy >=1.23,<3.0a0
-    - cuda-python >=12.6.2,<13.0a0
+    - if: cuda_major == "12"
+      then: cuda-python >=12.9.2,<13.0a0
+      else: cuda-python >=13.0.1,<14.0a0
     - cuda-cudart
   ignore_run_exports:
     by_name:
diff --git a/cpp/bench/ann/src/common/util.hpp b/cpp/bench/ann/src/common/util.hpp
index b3bcc77950..11361c88fe 100644
--- a/cpp/bench/ann/src/common/util.hpp
+++ b/cpp/bench/ann/src/common/util.hpp
@@ -446,10 +446,15 @@ inline auto cuda_info()
     throw std::runtime_error{"cuda_info: call to cudaGetDeviceProperties failed with code " +
                              std::to_string(err_code)};
   }
+  int clockRate       = 0;
+  int memoryClockRate = 0;
+  err_code            = cudaDeviceGetAttribute(&clockRate, cudaDevAttrClockRate, dev);
+  err_code            = cudaDeviceGetAttribute(&memoryClockRate, cudaDevAttrMemoryClockRate, dev);
+
   props.emplace_back("gpu_name", std::string(device_prop.name));
   props.emplace_back("gpu_sm_count", std::to_string(device_prop.multiProcessorCount));
-  props.emplace_back("gpu_sm_freq", std::to_string(device_prop.clockRate * 1e3));
-  props.emplace_back("gpu_mem_freq", std::to_string(device_prop.memoryClockRate * 1e3));
+  props.emplace_back("gpu_sm_freq", std::to_string(clockRate * 1e3));
+  props.emplace_back("gpu_mem_freq", std::to_string(memoryClockRate * 1e3));
   props.emplace_back("gpu_mem_bus_width", std::to_string(device_prop.memoryBusWidth));
   props.emplace_back("gpu_mem_global_size", std::to_string(device_prop.totalGlobalMem));
   props.emplace_back("gpu_mem_shared_size", std::to_string(device_prop.sharedMemPerMultiprocessor));
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index 70da22a09d..b49b7a4e8a 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -485,7 +485,7 @@ namespace filtering {
 enum class FilterType { None, Bitmap, Bitset };
 
 struct base_filter {
-  virtual ~base_filter()                     = default;
+  ~base_filter()                             = default;
   virtual FilterType get_filter_type() const = 0;
 };
 
diff --git a/dependencies.yaml b/dependencies.yaml
index 7570ce1973..c370e90f88 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -3,7 +3,7 @@ files:
   all:
     output: conda
     matrix:
-      cuda: ["12.9"]
+      cuda: ["12.9", "13.0"]
       arch: [x86_64, aarch64]
     includes:
       - build
@@ -14,6 +14,7 @@ files:
       - clang
       - cuda
       - cuda_version
+      - depends_on_cuda_python
       - depends_on_cupy
       - depends_on_librmm
       - depends_on_pylibraft
@@ -29,7 +30,7 @@ files:
   bench_ann:
     output: conda
     matrix:
-      cuda: ["12.9"]
+      cuda: ["12.9", "13.0"]
       arch: [x86_64, aarch64]
     includes:
       - bench
@@ -39,6 +40,7 @@ files:
       - clang
       - cuda
       - cuda_version
+      - depends_on_cuda_python
       - depends_on_cupy
       - depends_on_pylibraft
       - depends_on_libcuvs
@@ -85,7 +87,7 @@ files:
   rust:
     output: conda
     matrix:
-      cuda: ["12.9"]
+      cuda: ["12.9", "13.0"]
       arch: [x86_64, aarch64]
     includes:
       # clang/libclang only needed for bindgen support
@@ -100,7 +102,7 @@ files:
   go:
     output: conda
     matrix:
-      cuda: ["12.9"]
+      cuda: ["12.9", "13.0"]
       arch: [x86_64, aarch64]
     includes:
       - clang
@@ -165,6 +167,7 @@ files:
     includes:
       - build_cython
       - build_py_cuvs
+      - depends_on_cuda_python
       - depends_on_libcuvs
       - depends_on_libraft
       - depends_on_librmm
@@ -175,6 +178,7 @@ files:
     extras:
       table: project
     includes:
+      - depends_on_cuda_python
       - depends_on_libcuvs
       - depends_on_pylibraft
       - run_py_cuvs
@@ -239,26 +243,21 @@ dependencies:
       - output_types: [conda]
         packages:
           - c-compiler
+          - cuda-nvcc
           - cxx-compiler
     specific:
       - output_types: conda
         matrices:
           - matrix:
               arch: x86_64
-              cuda: "12.*"
             packages:
               - gcc_linux-64=14.*
               - sysroot_linux-64==2.28
           - matrix:
               arch: aarch64
-              cuda: "12.*"
             packages:
               - gcc_linux-aarch64=14.*
               - sysroot_linux-aarch64==2.28
-      - output_types: conda
-        matrices:
-          - matrix: {cuda: "12.*"}
-            packages: [cuda-nvcc]
   rapids_build_setuptools:
     common:
       - output_types: [conda, requirements, pyproject]
@@ -271,16 +270,6 @@ dependencies:
       - output_types: [conda]
         packages:
           - dlpack>=0.8,<1.0
-    specific:
-      - output_types: [conda, requirements, pyproject]
-        matrices:
-          - matrix:
-              cuda: "12.*"
-            packages:
-              - &cuda_python12 cuda-python>=12.6.2,<13.0a0
-          - matrix:
-            packages:
-              - &cuda_python cuda-python
   checks:
     common:
       - output_types: [conda, requirements]
@@ -317,20 +306,21 @@ dependencies:
               cuda: "12.9"
             packages:
               - cuda-version=12.9
-  cuda:
-    specific:
-      - output_types: conda
-        matrices:
           - matrix:
-              cuda: "12.*"
+              cuda: "13.0"
             packages:
-              - cuda-nvtx-dev
-              - cuda-cudart-dev
-              - cuda-profiler-api
-              - libcublas-dev
-              - libcurand-dev
-              - libcusolver-dev
-              - libcusparse-dev
+              - cuda-version=13.0
+  cuda:
+    common:
+      - output_types: [conda]
+        packages:
+          - cuda-nvtx-dev
+          - cuda-cudart-dev
+          - cuda-profiler-api
+          - libcublas-dev
+          - libcurand-dev
+          - libcusolver-dev
+          - libcusparse-dev
   cuda_wheels:
     specific:
       - output_types: [requirements, pyproject]
@@ -343,6 +333,14 @@ dependencies:
               - nvidia-curand-cu12
               - nvidia-cusolver-cu12
               - nvidia-cusparse-cu12
+          - matrix:
+              cuda: "13.*"
+              use_cuda_wheels: "true"
+            packages:
+              - nvidia-cublas-cu13
+              - nvidia-curand-cu13
+              - nvidia-cusolver-cu13
+              - nvidia-cusparse-cu13
           - matrix:
               use_cuda_wheels: "false"
             packages:
@@ -358,14 +356,21 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - cupy>=12.0.0
+          - cupy>=13.6.0
+    # NOTE: This is intentionally not broken into groups by a 'cuda_suffixed' selector like
+    #       other packages with -cu{nn}x suffixes in this file.
+    #       All RAPIDS wheel builds (including in devcontainers) expect cupy to be suffixed.
     specific:
       - output_types: [requirements, pyproject]
         matrices:
-          - matrix: {cuda: "12.*"}
+          - matrix:
+              cuda: "12.*"
             packages:
-              - cupy-cuda12x>=12.0.0
-          - {matrix: null, packages: [cupy-cuda12x>=12.0.0]}
+              - cupy-cuda12x>=13.6.0
+          # fallback to CUDA 13 versions if 'cuda' is '13.*' or not provided
+          - matrix:
+            packages:
+              - &cupy_cu13 cupy-cuda13x>=13.6.0
   test_libcuvs:
     common:
       - output_types: [conda]
@@ -434,16 +439,6 @@ dependencies:
       - output_types: [conda, requirements, pyproject]
         packages:
           - &numpy numpy>=1.23,<3.0a0
-    specific:
-      - output_types: [conda, requirements, pyproject]
-        matrices:
-          - matrix:
-              cuda: "12.*"
-            packages:
-              - *cuda_python12
-          - matrix:
-            packages:
-              - *cuda_python
   test_python_common:
     common:
       - output_types: [conda, requirements, pyproject]
@@ -500,6 +495,18 @@ dependencies:
       - output_types: [requirements, pyproject]
         packages:
           - matplotlib>=3.9
+  depends_on_cuda_python:
+    specific:
+      - output_types: [conda, requirements, pyproject]
+        matrices:
+          - matrix:
+              cuda: "12.*"
+            packages:
+              - cuda-python>=12.9.2,<13.0a0
+          # fallback to CUDA 13 versions if 'cuda' is '13.*' or not provided
+          - matrix:
+            packages:
+              - cuda-python>=13.0.1,<14.0a0
   depends_on_cuvs:
     common:
       - output_types: conda
@@ -528,6 +535,11 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - libcuvs-cu12==25.10.*,>=0.0.0a0
+          - matrix:
+              cuda: "13.*"
+              cuda_suffixed: "true"
+            packages:
+              - libcuvs-cu13==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
   depends_on_libcuvs_tests:
     common:
@@ -552,6 +564,11 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - libraft-cu12==25.10.*,>=0.0.0a0
+          - matrix:
+              cuda: "13.*"
+              cuda_suffixed: "true"
+            packages:
+              - libraft-cu13==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
@@ -571,6 +588,11 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - librmm-cu12==25.10.*,>=0.0.0a0
+          - matrix:
+              cuda: "13.*"
+              cuda_suffixed: "true"
+            packages:
+              - librmm-cu13==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
@@ -590,12 +612,17 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - pylibraft-cu12==25.10.*,>=0.0.0a0
+          - matrix:
+              cuda: "13.*"
+              cuda_suffixed: "true"
+            packages:
+              - pylibraft-cu13==25.10.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
   depends_on_nccl:
     common:
       - output_types: conda
         packages:
-          - &nccl_unsuffixed nccl>=2.19
+          - nccl>=2.19
     specific:
       - output_types: [pyproject, requirements]
         matrices:
@@ -604,5 +631,10 @@ dependencies:
               cuda_suffixed: "true"
             packages:
               - nvidia-nccl-cu12>=2.19
+          - matrix:
+              cuda: "13.*"
+              cuda_suffixed: "true"
+            packages:
+              - nvidia-nccl-cu13>=2.19
           - matrix:
             packages:
diff --git a/docs/source/build.rst b/docs/source/build.rst
index 4acf3bea54..26c361dfbc 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -43,6 +43,10 @@ C/C++ Package
 
 .. code-block:: bash
 
+   # CUDA 13
+   conda install -c rapidsai -c conda-forge libcuvs cuda-version=13.0
+
+   # CUDA 12
    conda install -c rapidsai -c conda-forge libcuvs cuda-version=12.9
 
 Python Package
@@ -50,6 +54,10 @@ Python Package
 
 .. code-block:: bash
 
+   # CUDA 13
+   conda install -c rapidsai -c conda-forge cuvs cuda-version=13.0
+
+   # CUDA 12
    conda install -c rapidsai -c conda-forge cuvs cuda-version=12.9
 
 Python through Pip
@@ -57,10 +65,12 @@ Python through Pip
 
 The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.
 
-For CUDA 12 packages:
-
 .. code-block:: bash
 
+    # CUDA 13
+    pip install cuvs-cu13 --extra-index-url=https://pypi.nvidia.com
+
+    # CUDA 12
     pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
 
 Note: these packages statically link the C and C++ libraries so the `libcuvs` and `libcuvs_c` shared libraries won't be readily available to use in your code.
@@ -86,7 +96,7 @@ Conda environment scripts are provided for installing the necessary dependencies
 
 .. code-block:: bash
 
-    conda env create --name cuvs -f conda/environments/all_cuda-128_arch-x86_64.yaml
+    conda env create --name cuvs -f conda/environments/all_cuda-130_arch-x86_64.yaml
     conda activate cuvs
 
 The recommended way to build and install cuVS from source is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides CMake options for building and installing the headers, tests, benchmarks, and the pre-compiled shared library.
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 5a6f0f17e2..cfaa30acb2 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -65,7 +65,7 @@ Conda
    conda activate cuvs_benchmarks
 
    # to install GPU package:
-   conda install -c rapidsai -c conda-forge cuvs-bench=<rapids_version> cuda-version=12.9*
+   conda install -c rapidsai -c conda-forge cuvs-bench=<rapids_version> cuda-version=13.0*
 
    # to install CPU package for usage in CPU-only systems:
    conda install -c rapidsai -c conda-forge  cuvs-bench-cpu
diff --git a/examples/go/README.md b/examples/go/README.md
index bcbea09642..093e87d17d 100644
--- a/examples/go/README.md
+++ b/examples/go/README.md
@@ -7,7 +7,7 @@ This package provides Go bindings for the cuVS (CUDA Vector Search) library.
 The required dependencies can be installed with a simple command (which creates your build environment):
 
 ```bash
-conda env create --name go -f conda/environments/go_cuda-128_arch-x86_64.yaml
+conda env create --name go -f conda/environments/go_cuda-130_arch-x86_64.yaml
 conda activate go
 ```
 You may prefer to use `mamba`, as it provides significant speedup over `conda`.
diff --git a/examples/rust/README.md b/examples/rust/README.md
index 98fe526975..1d2671de34 100644
--- a/examples/rust/README.md
+++ b/examples/rust/README.md
@@ -9,7 +9,7 @@ Once the minimum requirements are satisfied, this example template application c
 You may follow these steps to quickly get set up:
 
 ```bash
-conda env create --name rust -f conda/environments/rust_cuda-128_arch-x86_64.yaml
+conda env create --name rust -f conda/environments/rust_cuda-130_arch-x86_64.yaml
 conda activate rust
 ```
 You may prefer to use `mamba`, as it provides significant speedup over `conda`.
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java
index c2422318ac..ab4b0dde1d 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/GPUInfoProviderImpl.java
@@ -17,6 +17,7 @@
 
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
+import static com.nvidia.cuvs.internal.common.Util.cudaGetDeviceProperties;
 import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemGetInfo;
 import static com.nvidia.cuvs.internal.panama.headers_h_1.*;
 
@@ -52,8 +53,8 @@ private static List<GPUInfo> getAvailableGpusInfo() {
         MemorySegment deviceProp = cudaDeviceProp.allocate(localArena);
 
         for (int i = 0; i < numGpuCount; i++) {
-          returnValue = cudaGetDeviceProperties_v2(deviceProp, i);
-          checkCudaError(returnValue, "cudaGetDeviceProperties_v2");
+          returnValue = cudaGetDeviceProperties(deviceProp, i);
+          checkCudaError(returnValue, "cudaGetDeviceProperties");
 
           GPUInfo gpuInfo =
               new GPUInfo(
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 09adf59309..41f6b3736d 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -29,10 +29,12 @@
 import com.nvidia.cuvs.internal.panama.DLTensor;
 import com.nvidia.cuvs.internal.panama.headers_h;
 import java.lang.foreign.Arena;
+import java.lang.foreign.FunctionDescriptor;
 import java.lang.foreign.Linker;
 import java.lang.foreign.MemoryLayout;
 import java.lang.foreign.MemoryLayout.PathElement;
 import java.lang.foreign.MemorySegment;
+import java.lang.foreign.SymbolLookup;
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.VarHandle;
 import java.util.BitSet;
@@ -46,6 +48,11 @@ private Util() {}
 
   private static final Linker LINKER = Linker.nativeLinker();
 
+  static final SymbolLookup SYMBOL_LOOKUP =
+      SymbolLookup.libraryLookup(System.mapLibraryName("cuvs_c"), Arena.ofAuto())
+          .or(SymbolLookup.loaderLookup())
+          .or(Linker.nativeLinker().defaultLookup());
+
   /**
    * Bindings for {@code cudaMemcpyAsync}; differently from the {@code headers_h} bindings (which are
    * automatically generated by {@code jextract}), these bindings specify the {@code critical} linker option,
@@ -58,6 +65,26 @@ private Util() {}
       LINKER.downcallHandle(
           cudaMemcpyAsync$address(), cudaMemcpyAsync$descriptor(), Linker.Option.critical(true));
 
+  private static final String cudaGetDevicePropertiesSymbolName =
+      "12".equals(System.getenv("RAPIDS_CUDA_MAJOR"))
+          ? "cudaGetDeviceProperties_v2"
+          : "cudaGetDeviceProperties";
+
+  private static final MethodHandle cudaGetDeviceProperties$mh =
+      LINKER.downcallHandle(
+          SYMBOL_LOOKUP
+              .find(cudaGetDevicePropertiesSymbolName)
+              .orElseThrow(UnsatisfiedLinkError::new),
+          FunctionDescriptor.of(headers_h.C_INT, headers_h.C_POINTER, headers_h.C_INT));
+
+  public static int cudaGetDeviceProperties(MemorySegment prop, int device) {
+    try {
+      return (int) cudaGetDeviceProperties$mh.invokeExact(prop, device);
+    } catch (Throwable ex$) {
+      throw new AssertionError("should not reach here", ex$);
+    }
+  }
+
   /**
    * Checks the result value of a (CuVS) native method handle call.
    *
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index 99e31fc620..b5b7dba8cf 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -31,7 +31,7 @@ authors = [
 license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
-    "cuda-python",
+    "cuda-python>=13.0.1,<14.0a0",
     "libcuvs==25.10.*,>=0.0.0a0",
     "numpy>=1.23,<3.0a0",
     "pylibraft==25.10.*,>=0.0.0a0",
@@ -47,7 +47,7 @@ classifiers = [
 
 [project.optional-dependencies]
 test = [
-    "cupy-cuda12x>=12.0.0",
+    "cupy-cuda13x>=13.6.0",
     "pytest",
     "pytest-cov",
     "scikit-learn",
@@ -117,7 +117,7 @@ regex = "(?P<value>.*)"
 [tool.rapids-build-backend]
 requires = [
     "cmake>=3.30.4",
-    "cuda-python",
+    "cuda-python>=13.0.1,<14.0a0",
     "cython>=3.0.0",
     "libcuvs==25.10.*,>=0.0.0a0",
     "libraft==25.10.*,>=0.0.0a0",

From 6254dd9993bd9e3afc503b82a599147933c7eed5 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Tue, 2 Sep 2025 16:57:11 +0200
Subject: [PATCH 563/623] MG C API (#1160)

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1160
---
 cpp/CMakeLists.txt                            |  10 +
 cpp/include/cuvs/core/c_api.h                 |  17 +
 cpp/include/cuvs/neighbors/ivf_flat.hpp       | 100 ++-
 cpp/include/cuvs/neighbors/mg_cagra.h         | 285 +++++++++
 cpp/include/cuvs/neighbors/mg_common.h        |  67 ++
 cpp/include/cuvs/neighbors/mg_ivf_flat.h      | 285 +++++++++
 cpp/include/cuvs/neighbors/mg_ivf_pq.h        | 285 +++++++++
 cpp/src/core/c_api.cpp                        |  17 +
 cpp/src/neighbors/iface/generate_iface.py     |   5 +-
 .../iface/iface_flat_half_int64_t.cu          |  96 +++
 cpp/src/neighbors/mg/generate_mg.py           |  13 +-
 .../neighbors/mg/mg_cagra_float_uint32_t.cu   |  10 +-
 .../neighbors/mg/mg_cagra_half_uint32_t.cu    |  10 +-
 .../neighbors/mg/mg_cagra_int8_t_uint32_t.cu  |  10 +-
 .../neighbors/mg/mg_cagra_uint8_t_uint32_t.cu |  10 +-
 cpp/src/neighbors/mg/mg_flat_half_int64_t.cu  |  96 +++
 cpp/src/neighbors/mg/snmg.cuh                 |  15 +-
 cpp/src/neighbors/mg_cagra_c.cpp              | 419 +++++++++++++
 cpp/src/neighbors/mg_ivf_flat_c.cpp           | 416 +++++++++++++
 cpp/src/neighbors/mg_ivf_pq_c.cpp             | 408 +++++++++++++
 cpp/tests/CMakeLists.txt                      |   2 +
 cpp/tests/neighbors/ann_mg_c.cu               | 341 +++++++++++
 cpp/tests/neighbors/run_mg_c.c                | 576 ++++++++++++++++++
 docs/source/c_api/neighbors.rst               |   1 +
 docs/source/c_api/neighbors_mg.rst            | 257 ++++++++
 25 files changed, 3739 insertions(+), 12 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/mg_cagra.h
 create mode 100644 cpp/include/cuvs/neighbors/mg_common.h
 create mode 100644 cpp/include/cuvs/neighbors/mg_ivf_flat.h
 create mode 100644 cpp/include/cuvs/neighbors/mg_ivf_pq.h
 create mode 100644 cpp/src/neighbors/iface/iface_flat_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg/mg_flat_half_int64_t.cu
 create mode 100644 cpp/src/neighbors/mg_cagra_c.cpp
 create mode 100644 cpp/src/neighbors/mg_ivf_flat_c.cpp
 create mode 100644 cpp/src/neighbors/mg_ivf_pq_c.cpp
 create mode 100644 cpp/tests/neighbors/ann_mg_c.cu
 create mode 100644 cpp/tests/neighbors/run_mg_c.c
 create mode 100644 docs/source/c_api/neighbors_mg.rst

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 7830004c8b..04621aaffd 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -295,6 +295,7 @@ if(BUILD_SHARED_LIBS)
   if(BUILD_MG_ALGOS)
     set(CUVS_MG_ALGOS
         src/neighbors/mg/mg_flat_float_int64_t.cu
+        src/neighbors/mg/mg_flat_half_int64_t.cu
         src/neighbors/mg/mg_flat_int8_t_int64_t.cu
         src/neighbors/mg/mg_flat_uint8_t_int64_t.cu
         src/neighbors/mg/mg_pq_float_int64_t.cu
@@ -410,6 +411,7 @@ if(BUILD_SHARED_LIBS)
     src/neighbors/iface/iface_cagra_int8_t_uint32_t.cu
     src/neighbors/iface/iface_cagra_uint8_t_uint32_t.cu
     src/neighbors/iface/iface_flat_float_int64_t.cu
+    src/neighbors/iface/iface_flat_half_int64_t.cu
     src/neighbors/iface/iface_flat_int8_t_int64_t.cu
     src/neighbors/iface/iface_flat_uint8_t_int64_t.cu
     src/neighbors/iface/iface_pq_float_int64_t.cu
@@ -712,6 +714,13 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
 
   # ################################################################################################
   # * cuvs_c -------------------------------------------------------------------------------
+
+  if(BUILD_MG_ALGOS)
+    set(CUVS_C_MG_ALGOS src/neighbors/mg_ivf_pq_c.cpp src/neighbors/mg_ivf_flat_c.cpp
+                        src/neighbors/mg_cagra_c.cpp
+    )
+  endif()
+
   if(BUILD_C_LIBRARY)
     add_library(
       cuvs_c SHARED
@@ -728,6 +737,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       src/preprocessing/quantize/binary_c.cpp
       src/preprocessing/quantize/scalar_c.cpp
       src/distance/pairwise_distance_c.cpp
+      ${CUVS_C_MG_ALGOS}
     )
 
     if(BUILD_CAGRA_HNSWLIB)
diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index e3cd9bd40b..e2fc4ebc94 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -75,6 +75,23 @@ cuvsError_t cuvsResourcesCreate(cuvsResources_t* res);
  */
 cuvsError_t cuvsResourcesDestroy(cuvsResources_t res);
 
+/**
+ * @brief Create an Initialized opaque C handle for C++ type `raft::device_resources_snmg`
+ *        for multi-GPU operations
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuResourcesCreate(cuvsResources_t* res);
+
+/**
+ * @brief Destroy and de-allocate opaque C handle for C++ type `raft::device_resources_snmg`
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuResourcesDestroy(cuvsResources_t res);
+
 /**
  * @brief Set cudaStream_t on cuvsResources_t to queue CUDA kernels on APIs
  *        that accept a cuvsResources_t handle
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.hpp b/cpp/include/cuvs/neighbors/ivf_flat.hpp
index c206fefde1..445cd527bc 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_flat.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -2042,6 +2042,28 @@ auto build(const raft::resources& clique,
            raft::host_matrix_view<const float, int64_t, row_major> index_dataset)
   -> cuvs::neighbors::mg_index<ivf_flat::index<float, int64_t>, float, int64_t>;
 
+/// \ingroup mg_cpp_index_build
+/**
+ * @brief Builds a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index_params configure the index building
+ * @param[in] index_dataset a row-major matrix on host [n_rows, dim]
+ *
+ * @return the constructed IVF-Flat MG index
+ */
+auto build(const raft::resources& clique,
+           const cuvs::neighbors::mg_index_params<ivf_flat::index_params>& index_params,
+           raft::host_matrix_view<const half, int64_t, row_major> index_dataset)
+  -> cuvs::neighbors::mg_index<ivf_flat::index<half, int64_t>, half, int64_t>;
+
 /// \ingroup mg_cpp_index_build
 /**
  * @brief Builds a multi-GPU index
@@ -2112,6 +2134,30 @@ void extend(const raft::resources& clique,
             raft::host_matrix_view<const float, int64_t, row_major> new_vectors,
             std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
 
+/// \ingroup mg_cpp_index_extend
+/**
+ * @brief Extends a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::ivf_flat::extend(clique, index, new_vectors, std::nullopt);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] new_vectors a row-major matrix on host [n_rows, dim]
+ * @param[in] new_indices optional vector on host [n_rows],
+ * `std::nullopt` means default continuous range `[0...n_rows)`
+ *
+ */
+void extend(const raft::resources& clique,
+            cuvs::neighbors::mg_index<ivf_flat::index<half, int64_t>, half, int64_t>& index,
+            raft::host_matrix_view<const half, int64_t, row_major> new_vectors,
+            std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices);
+
 /// \ingroup mg_cpp_index_extend
 /**
  * @brief Extends a multi-GPU index
@@ -2191,6 +2237,35 @@ void search(const raft::resources& clique,
             raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
             raft::host_matrix_view<float, int64_t, row_major> distances);
 
+/// \ingroup mg_cpp_index_search
+/**
+ * @brief Searches a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * cuvs::neighbors::mg_search_params<ivf_flat::search_params> search_params;
+ * cuvs::neighbors::ivf_flat::search(clique, index, search_params, queries, neighbors,
+ * distances);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] search_params configure the index search
+ * @param[in] queries a row-major matrix on host [n_rows, dim]
+ * @param[out] neighbors a row-major matrix on host [n_rows, n_neighbors]
+ * @param[out] distances a row-major matrix on host [n_rows, n_neighbors]
+ *
+ */
+void search(const raft::resources& clique,
+            const cuvs::neighbors::mg_index<ivf_flat::index<half, int64_t>, half, int64_t>& index,
+            const cuvs::neighbors::mg_search_params<ivf_flat::search_params>& search_params,
+            raft::host_matrix_view<const half, int64_t, row_major> queries,
+            raft::host_matrix_view<int64_t, int64_t, row_major> neighbors,
+            raft::host_matrix_view<float, int64_t, row_major> distances);
+
 /// \ingroup mg_cpp_index_search
 /**
  * @brief Searches a multi-GPU index
@@ -2276,6 +2351,29 @@ void serialize(
   const cuvs::neighbors::mg_index<ivf_flat::index<float, int64_t>, float, int64_t>& index,
   const std::string& filename);
 
+/// \ingroup mg_cpp_serialize
+/**
+ * @brief Serializes a multi-GPU index
+ *
+ * Usage example:
+ * @code{.cpp}
+ * raft::device_resources_snmg clique;
+ * cuvs::neighbors::mg_index_params<ivf_flat::index_params> index_params;
+ * auto index = cuvs::neighbors::ivf_flat::build(clique, index_params, index_dataset);
+ * const std::string filename = "mg_index.cuvs";
+ * cuvs::neighbors::ivf_flat::serialize(clique, index, filename);
+ * @endcode
+ *
+ * @param[in] clique a `raft::resources` object specifying the NCCL clique configuration
+ * @param[in] index the pre-built index
+ * @param[in] filename path to the file to be serialized
+ *
+ */
+void serialize(
+  const raft::resources& clique,
+  const cuvs::neighbors::mg_index<ivf_flat::index<half, int64_t>, half, int64_t>& index,
+  const std::string& filename);
+
 /// \ingroup mg_cpp_serialize
 /**
  * @brief Serializes a multi-GPU index
diff --git a/cpp/include/cuvs/neighbors/mg_cagra.h b/cpp/include/cuvs/neighbors/mg_cagra.h
new file mode 100644
index 0000000000..8680ba9202
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/mg_cagra.h
@@ -0,0 +1,285 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/mg_common.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup mg_cagra_c_index_params Multi-GPU CAGRA index build parameters
+ * @{
+ */
+
+/**
+ * @brief Multi-GPU parameters to build CAGRA Index
+ *
+ * This structure extends the base CAGRA index parameters with multi-GPU specific settings.
+ */
+struct cuvsMultiGpuCagraIndexParams {
+  /** Base CAGRA index parameters */
+  cuvsCagraIndexParams_t base_params;
+  /** Distribution mode for multi-GPU setup */
+  cuvsMultiGpuDistributionMode mode;
+};
+
+typedef struct cuvsMultiGpuCagraIndexParams* cuvsMultiGpuCagraIndexParams_t;
+
+/**
+ * @brief Allocate Multi-GPU CAGRA Index params, and populate with default values
+ *
+ * @param[in] index_params cuvsMultiGpuCagraIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraIndexParamsCreate(cuvsMultiGpuCagraIndexParams_t* index_params);
+
+/**
+ * @brief De-allocate Multi-GPU CAGRA Index params
+ *
+ * @param[in] index_params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraIndexParamsDestroy(cuvsMultiGpuCagraIndexParams_t index_params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_search_params Multi-GPU CAGRA index search parameters
+ * @{
+ */
+
+/**
+ * @brief Multi-GPU parameters to search CAGRA index
+ *
+ * This structure extends the base CAGRA search parameters with multi-GPU specific settings.
+ */
+struct cuvsMultiGpuCagraSearchParams {
+  /** Base CAGRA search parameters */
+  cuvsCagraSearchParams_t base_params;
+  /** Replicated search mode */
+  cuvsMultiGpuReplicatedSearchMode search_mode;
+  /** Sharded merge mode */
+  cuvsMultiGpuShardedMergeMode merge_mode;
+  /** Number of rows per batch */
+  int64_t n_rows_per_batch;
+};
+
+typedef struct cuvsMultiGpuCagraSearchParams* cuvsMultiGpuCagraSearchParams_t;
+
+/**
+ * @brief Allocate Multi-GPU CAGRA search params, and populate with default values
+ *
+ * @param[in] params cuvsMultiGpuCagraSearchParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraSearchParamsCreate(cuvsMultiGpuCagraSearchParams_t* params);
+
+/**
+ * @brief De-allocate Multi-GPU CAGRA search params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraSearchParamsDestroy(cuvsMultiGpuCagraSearchParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_index Multi-GPU CAGRA index
+ * @{
+ */
+
+/**
+ * @brief Struct to hold address of cuvs::neighbors::mg_index<cagra::index> and its active trained
+ * dtype
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} cuvsMultiGpuCagraIndex;
+
+typedef cuvsMultiGpuCagraIndex* cuvsMultiGpuCagraIndex_t;
+
+/**
+ * @brief Allocate Multi-GPU CAGRA index
+ *
+ * @param[in] index cuvsMultiGpuCagraIndex_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraIndexCreate(cuvsMultiGpuCagraIndex_t* index);
+
+/**
+ * @brief De-allocate Multi-GPU CAGRA index
+ *
+ * @param[in] index cuvsMultiGpuCagraIndex_t to de-allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraIndexDestroy(cuvsMultiGpuCagraIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_index_build Multi-GPU CAGRA index build
+ * @{
+ */
+
+/**
+ * @brief Build a Multi-GPU CAGRA index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params Multi-GPU CAGRA index parameters
+ * @param[in] dataset_tensor DLManagedTensor* training dataset
+ * @param[out] index Multi-GPU CAGRA index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraBuild(cuvsResources_t res,
+                                   cuvsMultiGpuCagraIndexParams_t params,
+                                   DLManagedTensor* dataset_tensor,
+                                   cuvsMultiGpuCagraIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_index_search Multi-GPU CAGRA index search
+ * @{
+ */
+
+/**
+ * @brief Search a Multi-GPU CAGRA index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params Multi-GPU CAGRA search parameters
+ * @param[in] index Multi-GPU CAGRA index
+ * @param[in] queries_tensor DLManagedTensor* queries dataset
+ * @param[out] neighbors_tensor DLManagedTensor* output neighbors
+ * @param[out] distances_tensor DLManagedTensor* output distances
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraSearch(cuvsResources_t res,
+                                    cuvsMultiGpuCagraSearchParams_t params,
+                                    cuvsMultiGpuCagraIndex_t index,
+                                    DLManagedTensor* queries_tensor,
+                                    DLManagedTensor* neighbors_tensor,
+                                    DLManagedTensor* distances_tensor);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_index_extend Multi-GPU CAGRA index extend
+ * @{
+ */
+
+/**
+ * @brief Extend a Multi-GPU CAGRA index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in,out] index Multi-GPU CAGRA index to extend
+ * @param[in] new_vectors_tensor DLManagedTensor* new vectors to add
+ * @param[in] new_indices_tensor DLManagedTensor* new indices (optional, can be NULL)
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraExtend(cuvsResources_t res,
+                                    cuvsMultiGpuCagraIndex_t index,
+                                    DLManagedTensor* new_vectors_tensor,
+                                    DLManagedTensor* new_indices_tensor);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_index_serialize Multi-GPU CAGRA index serialize
+ * @{
+ */
+
+/**
+ * @brief Serialize a Multi-GPU CAGRA index to file
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index Multi-GPU CAGRA index to serialize
+ * @param[in] filename Path to the output file
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraSerialize(cuvsResources_t res,
+                                       cuvsMultiGpuCagraIndex_t index,
+                                       const char* filename);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_index_deserialize Multi-GPU CAGRA index deserialize
+ * @{
+ */
+
+/**
+ * @brief Deserialize a Multi-GPU CAGRA index from file
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename Path to the input file
+ * @param[out] index Multi-GPU CAGRA index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraDeserialize(cuvsResources_t res,
+                                         const char* filename,
+                                         cuvsMultiGpuCagraIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_cagra_c_index_distribute Multi-GPU CAGRA index distribute
+ * @{
+ */
+
+/**
+ * @brief Distribute a local CAGRA index to create a Multi-GPU index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename Path to the local index file
+ * @param[out] index Multi-GPU CAGRA index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuCagraDistribute(cuvsResources_t res,
+                                        const char* filename,
+                                        cuvsMultiGpuCagraIndex_t index);
+
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/mg_common.h b/cpp/include/cuvs/neighbors/mg_common.h
new file mode 100644
index 0000000000..06b6771573
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/mg_common.h
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup mg_c_common_types Multi-GPU common types and enums
+ * @{
+ */
+
+/**
+ * @brief Distribution mode for multi-GPU indexes
+ */
+typedef enum {
+  /** Index is replicated on each device, favors throughput */
+  CUVS_NEIGHBORS_MG_REPLICATED,
+  /** Index is split on several devices, favors scaling */
+  CUVS_NEIGHBORS_MG_SHARDED
+} cuvsMultiGpuDistributionMode;
+
+/**
+ * @brief Search mode when using a replicated index
+ */
+typedef enum {
+  /** Search queries are split to maintain equal load on GPUs */
+  CUVS_NEIGHBORS_MG_LOAD_BALANCER,
+  /** Each search query is processed by a single GPU in a round-robin fashion */
+  CUVS_NEIGHBORS_MG_ROUND_ROBIN
+} cuvsMultiGpuReplicatedSearchMode;
+
+/**
+ * @brief Merge mode when using a sharded index
+ */
+typedef enum {
+  /** Search batches are merged on the root rank */
+  CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK,
+  /** Search batches are merged in a tree reduction fashion */
+  CUVS_NEIGHBORS_MG_TREE_MERGE
+} cuvsMultiGpuShardedMergeMode;
+
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/mg_ivf_flat.h b/cpp/include/cuvs/neighbors/mg_ivf_flat.h
new file mode 100644
index 0000000000..b6ead7e108
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/mg_ivf_flat.h
@@ -0,0 +1,285 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/ivf_flat.h>
+#include <cuvs/neighbors/mg_common.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup mg_ivf_flat_c_index_params Multi-GPU IVF-Flat index build parameters
+ * @{
+ */
+
+/**
+ * @brief Multi-GPU parameters to build IVF-Flat Index
+ *
+ * This structure extends the base IVF-Flat index parameters with multi-GPU specific settings.
+ */
+struct cuvsMultiGpuIvfFlatIndexParams {
+  /** Base IVF-Flat index parameters */
+  cuvsIvfFlatIndexParams_t base_params;
+  /** Distribution mode for multi-GPU setup */
+  cuvsMultiGpuDistributionMode mode;
+};
+
+typedef struct cuvsMultiGpuIvfFlatIndexParams* cuvsMultiGpuIvfFlatIndexParams_t;
+
+/**
+ * @brief Allocate Multi-GPU IVF-Flat Index params, and populate with default values
+ *
+ * @param[in] index_params cuvsMultiGpuIvfFlatIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatIndexParamsCreate(cuvsMultiGpuIvfFlatIndexParams_t* index_params);
+
+/**
+ * @brief De-allocate Multi-GPU IVF-Flat Index params
+ *
+ * @param[in] index_params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatIndexParamsDestroy(cuvsMultiGpuIvfFlatIndexParams_t index_params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_search_params Multi-GPU IVF-Flat index search parameters
+ * @{
+ */
+
+/**
+ * @brief Multi-GPU parameters to search IVF-Flat index
+ *
+ * This structure extends the base IVF-Flat search parameters with multi-GPU specific settings.
+ */
+struct cuvsMultiGpuIvfFlatSearchParams {
+  /** Base IVF-Flat search parameters */
+  cuvsIvfFlatSearchParams_t base_params;
+  /** Replicated search mode */
+  cuvsMultiGpuReplicatedSearchMode search_mode;
+  /** Sharded merge mode */
+  cuvsMultiGpuShardedMergeMode merge_mode;
+  /** Number of rows per batch */
+  int64_t n_rows_per_batch;
+};
+
+typedef struct cuvsMultiGpuIvfFlatSearchParams* cuvsMultiGpuIvfFlatSearchParams_t;
+
+/**
+ * @brief Allocate Multi-GPU IVF-Flat search params, and populate with default values
+ *
+ * @param[in] params cuvsMultiGpuIvfFlatSearchParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatSearchParamsCreate(cuvsMultiGpuIvfFlatSearchParams_t* params);
+
+/**
+ * @brief De-allocate Multi-GPU IVF-Flat search params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatSearchParamsDestroy(cuvsMultiGpuIvfFlatSearchParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_index Multi-GPU IVF-Flat index
+ * @{
+ */
+
+/**
+ * @brief Struct to hold address of cuvs::neighbors::mg_index<ivf_flat::index> and its active
+ * trained dtype
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} cuvsMultiGpuIvfFlatIndex;
+
+typedef cuvsMultiGpuIvfFlatIndex* cuvsMultiGpuIvfFlatIndex_t;
+
+/**
+ * @brief Allocate Multi-GPU IVF-Flat index
+ *
+ * @param[in] index cuvsMultiGpuIvfFlatIndex_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatIndexCreate(cuvsMultiGpuIvfFlatIndex_t* index);
+
+/**
+ * @brief De-allocate Multi-GPU IVF-Flat index
+ *
+ * @param[in] index cuvsMultiGpuIvfFlatIndex_t to de-allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatIndexDestroy(cuvsMultiGpuIvfFlatIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_index_build Multi-GPU IVF-Flat index build
+ * @{
+ */
+
+/**
+ * @brief Build a Multi-GPU IVF-Flat index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params Multi-GPU IVF-Flat index parameters
+ * @param[in] dataset_tensor DLManagedTensor* training dataset
+ * @param[out] index Multi-GPU IVF-Flat index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatBuild(cuvsResources_t res,
+                                     cuvsMultiGpuIvfFlatIndexParams_t params,
+                                     DLManagedTensor* dataset_tensor,
+                                     cuvsMultiGpuIvfFlatIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_index_search Multi-GPU IVF-Flat index search
+ * @{
+ */
+
+/**
+ * @brief Search a Multi-GPU IVF-Flat index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params Multi-GPU IVF-Flat search parameters
+ * @param[in] index Multi-GPU IVF-Flat index
+ * @param[in] queries_tensor DLManagedTensor* queries dataset
+ * @param[out] neighbors_tensor DLManagedTensor* output neighbors
+ * @param[out] distances_tensor DLManagedTensor* output distances
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatSearch(cuvsResources_t res,
+                                      cuvsMultiGpuIvfFlatSearchParams_t params,
+                                      cuvsMultiGpuIvfFlatIndex_t index,
+                                      DLManagedTensor* queries_tensor,
+                                      DLManagedTensor* neighbors_tensor,
+                                      DLManagedTensor* distances_tensor);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_index_extend Multi-GPU IVF-Flat index extend
+ * @{
+ */
+
+/**
+ * @brief Extend a Multi-GPU IVF-Flat index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in,out] index Multi-GPU IVF-Flat index to extend
+ * @param[in] new_vectors_tensor DLManagedTensor* new vectors to add
+ * @param[in] new_indices_tensor DLManagedTensor* new indices (optional, can be NULL)
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatExtend(cuvsResources_t res,
+                                      cuvsMultiGpuIvfFlatIndex_t index,
+                                      DLManagedTensor* new_vectors_tensor,
+                                      DLManagedTensor* new_indices_tensor);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_index_serialize Multi-GPU IVF-Flat index serialize
+ * @{
+ */
+
+/**
+ * @brief Serialize a Multi-GPU IVF-Flat index to file
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index Multi-GPU IVF-Flat index to serialize
+ * @param[in] filename Path to the output file
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatSerialize(cuvsResources_t res,
+                                         cuvsMultiGpuIvfFlatIndex_t index,
+                                         const char* filename);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_index_deserialize Multi-GPU IVF-Flat index deserialize
+ * @{
+ */
+
+/**
+ * @brief Deserialize a Multi-GPU IVF-Flat index from file
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename Path to the input file
+ * @param[out] index Multi-GPU IVF-Flat index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatDeserialize(cuvsResources_t res,
+                                           const char* filename,
+                                           cuvsMultiGpuIvfFlatIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_flat_c_index_distribute Multi-GPU IVF-Flat index distribute
+ * @{
+ */
+
+/**
+ * @brief Distribute a local IVF-Flat index to create a Multi-GPU index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename Path to the local index file
+ * @param[out] index Multi-GPU IVF-Flat index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfFlatDistribute(cuvsResources_t res,
+                                          const char* filename,
+                                          cuvsMultiGpuIvfFlatIndex_t index);
+
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/include/cuvs/neighbors/mg_ivf_pq.h b/cpp/include/cuvs/neighbors/mg_ivf_pq.h
new file mode 100644
index 0000000000..6654bc3a89
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/mg_ivf_pq.h
@@ -0,0 +1,285 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/mg_common.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup mg_ivf_pq_c_index_params Multi-GPU IVF-PQ index build parameters
+ * @{
+ */
+
+/**
+ * @brief Multi-GPU parameters to build IVF-PQ Index
+ *
+ * This structure extends the base IVF-PQ index parameters with multi-GPU specific settings.
+ */
+struct cuvsMultiGpuIvfPqIndexParams {
+  /** Base IVF-PQ index parameters */
+  cuvsIvfPqIndexParams_t base_params;
+  /** Distribution mode for multi-GPU setup */
+  cuvsMultiGpuDistributionMode mode;
+};
+
+typedef struct cuvsMultiGpuIvfPqIndexParams* cuvsMultiGpuIvfPqIndexParams_t;
+
+/**
+ * @brief Allocate Multi-GPU IVF-PQ Index params, and populate with default values
+ *
+ * @param[in] index_params cuvsMultiGpuIvfPqIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqIndexParamsCreate(cuvsMultiGpuIvfPqIndexParams_t* index_params);
+
+/**
+ * @brief De-allocate Multi-GPU IVF-PQ Index params
+ *
+ * @param[in] index_params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqIndexParamsDestroy(cuvsMultiGpuIvfPqIndexParams_t index_params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_search_params Multi-GPU IVF-PQ index search parameters
+ * @{
+ */
+
+/**
+ * @brief Multi-GPU parameters to search IVF-PQ index
+ *
+ * This structure extends the base IVF-PQ search parameters with multi-GPU specific settings.
+ */
+struct cuvsMultiGpuIvfPqSearchParams {
+  /** Base IVF-PQ search parameters */
+  cuvsIvfPqSearchParams_t base_params;
+  /** Replicated search mode */
+  cuvsMultiGpuReplicatedSearchMode search_mode;
+  /** Sharded merge mode */
+  cuvsMultiGpuShardedMergeMode merge_mode;
+  /** Number of rows per batch */
+  int64_t n_rows_per_batch;
+};
+
+typedef struct cuvsMultiGpuIvfPqSearchParams* cuvsMultiGpuIvfPqSearchParams_t;
+
+/**
+ * @brief Allocate Multi-GPU IVF-PQ search params, and populate with default values
+ *
+ * @param[in] params cuvsMultiGpuIvfPqSearchParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqSearchParamsCreate(cuvsMultiGpuIvfPqSearchParams_t* params);
+
+/**
+ * @brief De-allocate Multi-GPU IVF-PQ search params
+ *
+ * @param[in] params
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqSearchParamsDestroy(cuvsMultiGpuIvfPqSearchParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_index Multi-GPU IVF-PQ index
+ * @{
+ */
+
+/**
+ * @brief Struct to hold address of cuvs::neighbors::mg_index<ivf_pq::index> and its active trained
+ * dtype
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+} cuvsMultiGpuIvfPqIndex;
+
+typedef cuvsMultiGpuIvfPqIndex* cuvsMultiGpuIvfPqIndex_t;
+
+/**
+ * @brief Allocate Multi-GPU IVF-PQ index
+ *
+ * @param[in] index cuvsMultiGpuIvfPqIndex_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqIndexCreate(cuvsMultiGpuIvfPqIndex_t* index);
+
+/**
+ * @brief De-allocate Multi-GPU IVF-PQ index
+ *
+ * @param[in] index cuvsMultiGpuIvfPqIndex_t to de-allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqIndexDestroy(cuvsMultiGpuIvfPqIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_index_build Multi-GPU IVF-PQ index build
+ * @{
+ */
+
+/**
+ * @brief Build a Multi-GPU IVF-PQ index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params Multi-GPU IVF-PQ index parameters
+ * @param[in] dataset_tensor DLManagedTensor* training dataset
+ * @param[out] index Multi-GPU IVF-PQ index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqBuild(cuvsResources_t res,
+                                   cuvsMultiGpuIvfPqIndexParams_t params,
+                                   DLManagedTensor* dataset_tensor,
+                                   cuvsMultiGpuIvfPqIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_index_search Multi-GPU IVF-PQ index search
+ * @{
+ */
+
+/**
+ * @brief Search a Multi-GPU IVF-PQ index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params Multi-GPU IVF-PQ search parameters
+ * @param[in] index Multi-GPU IVF-PQ index
+ * @param[in] queries_tensor DLManagedTensor* queries dataset
+ * @param[out] neighbors_tensor DLManagedTensor* output neighbors
+ * @param[out] distances_tensor DLManagedTensor* output distances
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqSearch(cuvsResources_t res,
+                                    cuvsMultiGpuIvfPqSearchParams_t params,
+                                    cuvsMultiGpuIvfPqIndex_t index,
+                                    DLManagedTensor* queries_tensor,
+                                    DLManagedTensor* neighbors_tensor,
+                                    DLManagedTensor* distances_tensor);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_index_extend Multi-GPU IVF-PQ index extend
+ * @{
+ */
+
+/**
+ * @brief Extend a Multi-GPU IVF-PQ index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in,out] index Multi-GPU IVF-PQ index to extend
+ * @param[in] new_vectors_tensor DLManagedTensor* new vectors to add
+ * @param[in] new_indices_tensor DLManagedTensor* new indices (optional, can be NULL)
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqExtend(cuvsResources_t res,
+                                    cuvsMultiGpuIvfPqIndex_t index,
+                                    DLManagedTensor* new_vectors_tensor,
+                                    DLManagedTensor* new_indices_tensor);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_index_serialize Multi-GPU IVF-PQ index serialize
+ * @{
+ */
+
+/**
+ * @brief Serialize a Multi-GPU IVF-PQ index to file
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] index Multi-GPU IVF-PQ index to serialize
+ * @param[in] filename Path to the output file
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqSerialize(cuvsResources_t res,
+                                       cuvsMultiGpuIvfPqIndex_t index,
+                                       const char* filename);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_index_deserialize Multi-GPU IVF-PQ index deserialize
+ * @{
+ */
+
+/**
+ * @brief Deserialize a Multi-GPU IVF-PQ index from file
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename Path to the input file
+ * @param[out] index Multi-GPU IVF-PQ index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqDeserialize(cuvsResources_t res,
+                                         const char* filename,
+                                         cuvsMultiGpuIvfPqIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup mg_ivf_pq_c_index_distribute Multi-GPU IVF-PQ index distribute
+ * @{
+ */
+
+/**
+ * @brief Distribute a local IVF-PQ index to create a Multi-GPU index
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename Path to the local index file
+ * @param[out] index Multi-GPU IVF-PQ index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuIvfPqDistribute(cuvsResources_t res,
+                                        const char* filename,
+                                        cuvsMultiGpuIvfPqIndex_t index);
+
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index 2f66adf416..a36bb472b6 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -18,6 +18,7 @@
 #include <cuvs/core/exceptions.hpp>
 #include <cuvs/version_config.h>
 
+#include <raft/core/device_resources_snmg.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/core/resource/device_id.hpp>
 #include <raft/core/resource/resource_types.hpp>
@@ -51,6 +52,22 @@ extern "C" cuvsError_t cuvsResourcesDestroy(cuvsResources_t res)
   });
 }
 
+extern "C" cuvsError_t cuvsMultiGpuResourcesCreate(cuvsResources_t* res)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto res_ptr = new raft::device_resources_snmg{};
+    *res         = reinterpret_cast<uintptr_t>(res_ptr);
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuResourcesDestroy(cuvsResources_t res)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto res_ptr = reinterpret_cast<raft::device_resources_snmg*>(res);
+    delete res_ptr;
+  });
+}
+
 extern "C" cuvsError_t cuvsStreamSet(cuvsResources_t res, cudaStream_t stream)
 {
   return cuvs::core::translate_exceptions([=] {
diff --git a/cpp/src/neighbors/iface/generate_iface.py b/cpp/src/neighbors/iface/generate_iface.py
index d5fa6f56ca..66209e3f47 100644
--- a/cpp/src/neighbors/iface/generate_iface.py
+++ b/cpp/src/neighbors/iface/generate_iface.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 header = """/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -253,6 +253,7 @@
 
 flat_types = dict(
     float_int64_t=("float", "int64_t"),
+    half_int64_t=("half", "int64_t"),
     int8_t_int64_t=("int8_t", "int64_t"),
     uint8_t_int64_t=("uint8_t", "int64_t"),
 )
diff --git a/cpp/src/neighbors/iface/iface_flat_half_int64_t.cu b/cpp/src/neighbors/iface/iface_flat_half_int64_t.cu
new file mode 100644
index 0000000000..4907e11063
--- /dev/null
+++ b/cpp/src/neighbors/iface/iface_flat_half_int64_t.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_iface.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_iface.py
+ *
+ */
+
+#include "iface.hpp"
+
+namespace cuvs::neighbors {
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                                 \
+  using T_ha    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                             raft::memory_type::device>;                           \
+  using T_da    = raft::host_device_accessor<std::experimental::default_accessor<const T>,         \
+                                             raft::memory_type::host>;                             \
+  using IdxT_ha = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::device>;                           \
+  using IdxT_da = raft::host_device_accessor<std::experimental::default_accessor<const IdxT>,      \
+                                             raft::memory_type::host>;                             \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::resources& handle,                                                                 \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> index_dataset);                 \
+                                                                                                   \
+  template void build(                                                                             \
+    const raft::resources& handle,                                                                 \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    const cuvs::neighbors::index_params* index_params,                                             \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> index_dataset);                 \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::resources& handle,                                                                 \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_ha> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_ha>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void extend(                                                                            \
+    const raft::resources& handle,                                                                 \
+    cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                          \
+    raft::mdspan<const T, matrix_extent<int64_t>, row_major, T_da> new_vectors,                    \
+    std::optional<raft::mdspan<const IdxT, vector_extent<int64_t>, layout_c_contiguous, IdxT_da>>  \
+      new_indices);                                                                                \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::device_matrix_view<const T, int64_t, row_major> queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> neighbors,               \
+                       raft::device_matrix_view<float, int64_t, row_major> distances);             \
+                                                                                                   \
+  template void search(const raft::resources& handle,                                              \
+                       const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface, \
+                       const cuvs::neighbors::search_params* search_params,                        \
+                       raft::host_matrix_view<const T, int64_t, row_major> h_queries,              \
+                       raft::device_matrix_view<IdxT, int64_t, row_major> d_neighbors,             \
+                       raft::device_matrix_view<float, int64_t, row_major> d_distances);           \
+                                                                                                   \
+  template void serialize(                                                                         \
+    const raft::resources& handle,                                                                 \
+    const cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,                    \
+    std::ostream& os);                                                                             \
+                                                                                                   \
+  template void deserialize(const raft::resources& handle,                                         \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            std::istream& is);                                                     \
+                                                                                                   \
+  template void deserialize(const raft::resources& handle,                                         \
+                            cuvs::neighbors::iface<ivf_flat::index<T, IdxT>, T, IdxT>& interface,  \
+                            const std::string& filename);
+CUVS_INST_MG_FLAT(half, int64_t);
+
+#undef CUVS_INST_MG_FLAT
+
+}  // namespace cuvs::neighbors
diff --git a/cpp/src/neighbors/mg/generate_mg.py b/cpp/src/neighbors/mg/generate_mg.py
index f6ee47213e..895c1a31fc 100644
--- a/cpp/src/neighbors/mg/generate_mg.py
+++ b/cpp/src/neighbors/mg/generate_mg.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 header = """/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -190,6 +190,14 @@
     return index;                                                                                                 \\
   }                                                                                                               \\
                                                                                                                   \\
+  void extend(const raft::resources& res,                                                                         \\
+              cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,                                   \\
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                                    \\
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)                             \\
+  {                                                                                                               \\
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);                                  \\
+  }                                                                                                               \\
+                                                                                                                  \\
   void search(const raft::resources& res,                                                                         \\
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,                             \\
               const mg_search_params<cagra::search_params>& search_params,                                        \\
@@ -268,6 +276,7 @@
 
 flat_types = dict(
     float_int64_t=("float", "int64_t"),
+    half_int64_t=("half", "int64_t"),
     int8_t_int64_t=("int8_t", "int64_t"),
     uint8_t_int64_t=("uint8_t", "int64_t"),
 )
diff --git a/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
index 19d6ccc4f9..cbde5ef929 100644
--- a/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_float_uint32_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -43,6 +43,14 @@
     return index;                                                                            \
   }                                                                                          \
                                                                                              \
+  void extend(const raft::resources& res,                                                    \
+              cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,               \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)        \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);             \
+  }                                                                                          \
+                                                                                             \
   void search(const raft::resources& res,                                                    \
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
diff --git a/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
index 8b6b6a5c8c..4483f39178 100644
--- a/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_half_uint32_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -43,6 +43,14 @@
     return index;                                                                            \
   }                                                                                          \
                                                                                              \
+  void extend(const raft::resources& res,                                                    \
+              cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,               \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)        \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);             \
+  }                                                                                          \
+                                                                                             \
   void search(const raft::resources& res,                                                    \
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
diff --git a/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
index 4050a013a2..64282e531f 100644
--- a/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -43,6 +43,14 @@
     return index;                                                                            \
   }                                                                                          \
                                                                                              \
+  void extend(const raft::resources& res,                                                    \
+              cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,               \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)        \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);             \
+  }                                                                                          \
+                                                                                             \
   void search(const raft::resources& res,                                                    \
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
diff --git a/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
index 76d6a44aa7..64464034fa 100644
--- a/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
+++ b/cpp/src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -43,6 +43,14 @@
     return index;                                                                            \
   }                                                                                          \
                                                                                              \
+  void extend(const raft::resources& res,                                                    \
+              cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,               \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)        \
+  {                                                                                          \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);             \
+  }                                                                                          \
+                                                                                             \
   void search(const raft::resources& res,                                                    \
               const cuvs::neighbors::mg_index<cagra::index<T, IdxT>, T, IdxT>& index,        \
               const mg_search_params<cagra::search_params>& search_params,                   \
diff --git a/cpp/src/neighbors/mg/mg_flat_half_int64_t.cu b/cpp/src/neighbors/mg/mg_flat_half_int64_t.cu
new file mode 100644
index 0000000000..12446aee03
--- /dev/null
+++ b/cpp/src/neighbors/mg/mg_flat_half_int64_t.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by generate_mg.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python generate_mg.py
+ *
+ */
+
+#include "snmg.cuh"
+
+#define CUVS_INST_MG_FLAT(T, IdxT)                                                              \
+  namespace cuvs::neighbors::ivf_flat {                                                         \
+  using namespace cuvs::neighbors;                                                              \
+                                                                                                \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> build(                           \
+    const raft::resources& res,                                                                 \
+    const mg_index_params<ivf_flat::index_params>& index_params,                                \
+    raft::host_matrix_view<const T, int64_t, row_major> index_dataset)                          \
+  {                                                                                             \
+    cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> index(res, index_params.mode); \
+    cuvs::neighbors::snmg::detail::build(                                                       \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::index_params*>(&index_params),                         \
+      index_dataset);                                                                           \
+    return index;                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void extend(const raft::resources& res,                                                       \
+              cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,              \
+              raft::host_matrix_view<const T, int64_t, row_major> new_vectors,                  \
+              std::optional<raft::host_vector_view<const IdxT, int64_t>> new_indices)           \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::extend(res, index, new_vectors, new_indices);                \
+  }                                                                                             \
+                                                                                                \
+  void search(const raft::resources& res,                                                       \
+              const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,        \
+              const mg_search_params<ivf_flat::search_params>& search_params,                   \
+              raft::host_matrix_view<const T, int64_t, row_major> queries,                      \
+              raft::host_matrix_view<IdxT, int64_t, row_major> neighbors,                       \
+              raft::host_matrix_view<float, int64_t, row_major> distances)                      \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::search(                                                      \
+      res,                                                                                      \
+      index,                                                                                    \
+      static_cast<const cuvs::neighbors::search_params*>(&search_params),                       \
+      queries,                                                                                  \
+      neighbors,                                                                                \
+      distances);                                                                               \
+  }                                                                                             \
+                                                                                                \
+  void serialize(const raft::resources& res,                                                    \
+                 const cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>& index,     \
+                 const std::string& filename)                                                   \
+  {                                                                                             \
+    cuvs::neighbors::snmg::detail::serialize(res, index, filename);                             \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> deserialize<T, IdxT>(            \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, filename);     \
+    return idx;                                                                                 \
+  }                                                                                             \
+                                                                                                \
+  template <>                                                                                   \
+  cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT> distribute<T, IdxT>(             \
+    const raft::resources& res, const std::string& filename)                                    \
+  {                                                                                             \
+    auto idx = cuvs::neighbors::mg_index<ivf_flat::index<T, IdxT>, T, IdxT>(res, REPLICATED);   \
+    cuvs::neighbors::snmg::detail::deserialize_and_distribute(res, idx, filename);              \
+    return idx;                                                                                 \
+  }                                                                                             \
+  }  // namespace cuvs::neighbors::ivf_flat
+CUVS_INST_MG_FLAT(half, int64_t);
+
+#undef CUVS_INST_MG_FLAT
diff --git a/cpp/src/neighbors/mg/snmg.cuh b/cpp/src/neighbors/mg/snmg.cuh
index abff1f82b6..b5658825f0 100644
--- a/cpp/src/neighbors/mg/snmg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -72,9 +72,13 @@ void deserialize(const raft::resources& clique,
   std::ifstream is(filename, std::ios::in | std::ios::binary);
   if (!is) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
 
+  char dtype_string[4];
+  is.read(dtype_string, 4);
+
   const auto& handle = raft::resource::set_current_device_to_root_rank(clique);
-  index.mode_        = (cuvs::neighbors::distribution_mode)deserialize_scalar<int>(handle, is);
-  index.num_ranks_   = deserialize_scalar<int>(handle, is);
+  index.mode_ =
+    static_cast<cuvs::neighbors::distribution_mode>(deserialize_scalar<int>(handle, is));
+  index.num_ranks_ = deserialize_scalar<int>(handle, is);
 
   if (index.num_ranks_ != raft::resource::get_num_ranks(clique)) {
     RAFT_FAIL("Serialized index has %d ranks whereas NCCL clique has %d ranks",
@@ -665,9 +669,14 @@ void serialize(const raft::resources& clique,
   std::ofstream of(filename, std::ios::out | std::ios::binary);
   if (!of) { RAFT_FAIL("Cannot open file %s", filename.c_str()); }
 
+  std::string dtype_string = raft::detail::numpy_serializer::get_numpy_dtype<T>().to_string();
+  dtype_string.resize(4);
+  of << dtype_string;
+
   const auto& handle = raft::resource::set_current_device_to_root_rank(clique);
+
   serialize_scalar(handle, of, (int)index.mode_);
-  serialize_scalar(handle, of, index.num_ranks_);
+  serialize_scalar(handle, of, (int)index.num_ranks_);
 
   for (int rank = 0; rank < index.num_ranks_; rank++) {
     const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
diff --git a/cpp/src/neighbors/mg_cagra_c.cpp b/cpp/src/neighbors/mg_cagra_c.cpp
new file mode 100644
index 0000000000..c6d05605d7
--- /dev/null
+++ b/cpp/src/neighbors/mg_cagra_c.cpp
@@ -0,0 +1,419 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cagra_c.hpp"
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/cagra.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/mg_cagra.h>
+#include <dlpack/dlpack.h>
+#include <raft/core/error.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <fstream>
+
+extern "C" cuvsError_t cuvsMultiGpuCagraIndexParamsCreate(
+  cuvsMultiGpuCagraIndexParams_t* index_params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Create base CAGRA parameters
+    cuvsCagraIndexParams_t base_params;
+    cuvsCagraIndexParamsCreate(&base_params);
+
+    // Create MG wrapper with default values
+    *index_params = new cuvsMultiGpuCagraIndexParams{
+      .base_params = base_params,
+      .mode        = CUVS_NEIGHBORS_MG_SHARDED  // Default to sharded mode
+    };
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraIndexParamsDestroy(
+  cuvsMultiGpuCagraIndexParams_t index_params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index_params) {
+      cuvsCagraIndexParamsDestroy(index_params->base_params);
+      delete index_params;
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraSearchParamsCreate(cuvsMultiGpuCagraSearchParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Create base CAGRA search parameters
+    cuvsCagraSearchParams_t base_params;
+    cuvsCagraSearchParamsCreate(&base_params);
+
+    // Create MG wrapper with default values
+    *params = new cuvsMultiGpuCagraSearchParams{
+      .base_params      = base_params,
+      .search_mode      = CUVS_NEIGHBORS_MG_LOAD_BALANCER,  // Default to load balancer
+      .merge_mode       = CUVS_NEIGHBORS_MG_TREE_MERGE,     // Default to tree merge
+      .n_rows_per_batch = 1LL << 20                         // Default to 1M rows per batch
+    };
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraSearchParamsDestroy(cuvsMultiGpuCagraSearchParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (params) {
+      cuvsCagraSearchParamsDestroy(params->base_params);
+      delete params;
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraIndexCreate(cuvsMultiGpuCagraIndex_t* index)
+{
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsMultiGpuCagraIndex{}; });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraIndexDestroy(cuvsMultiGpuCagraIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index) {
+      // Properly clean up the templated inner object based on dtype, like single GPU API
+      if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+        auto mg_index_ptr =
+          reinterpret_cast<cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<float, uint32_t>,
+                                                     float,
+                                                     uint32_t>*>(index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+        auto mg_index_ptr =
+          reinterpret_cast<cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<half, uint32_t>,
+                                                     half,
+                                                     uint32_t>*>(index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::
+            mg_index<cuvs::neighbors::cagra::index<int8_t, uint32_t>, int8_t, uint32_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::
+            mg_index<cuvs::neighbors::cagra::index<uint8_t, uint32_t>, uint8_t, uint32_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      }
+      delete index;
+    }
+  });
+}
+
+namespace cuvs::neighbors::cagra {
+
+void convert_c_mg_index_params(
+  cuvsMultiGpuCagraIndexParams params,
+  int64_t n_rows,
+  int64_t dim,
+  cuvs::neighbors::mg_index_params<cuvs::neighbors::cagra::index_params>* out)
+{
+  convert_c_index_params(*params.base_params, n_rows, dim, out);
+  out->mode = (params.mode == CUVS_NEIGHBORS_MG_SHARDED)
+                ? cuvs::neighbors::distribution_mode::SHARDED
+                : cuvs::neighbors::distribution_mode::REPLICATED;
+}
+
+void convert_c_mg_search_params(
+  cuvsMultiGpuCagraSearchParams params,
+  cuvs::neighbors::mg_search_params<cuvs::neighbors::cagra::search_params>* out)
+{
+  convert_c_search_params(*params.base_params, out);
+  out->search_mode      = (params.search_mode == CUVS_NEIGHBORS_MG_LOAD_BALANCER)
+                            ? cuvs::neighbors::replicated_search_mode::LOAD_BALANCER
+                            : cuvs::neighbors::replicated_search_mode::ROUND_ROBIN;
+  out->merge_mode       = (params.merge_mode == CUVS_NEIGHBORS_MG_TREE_MERGE)
+                            ? cuvs::neighbors::sharded_merge_mode::TREE_MERGE
+                            : cuvs::neighbors::sharded_merge_mode::MERGE_ON_ROOT_RANK;
+  out->n_rows_per_batch = params.n_rows_per_batch;
+}
+}  // namespace cuvs::neighbors::cagra
+
+namespace {
+
+template <typename T>
+void* _mg_build(cuvsResources_t res,
+                cuvsMultiGpuCagraIndexParams params,
+                DLManagedTensor* dataset_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto dataset = dataset_tensor->dl_tensor;
+
+  auto mg_params = cuvs::neighbors::mg_index_params<cuvs::neighbors::cagra::index_params>();
+  cuvs::neighbors::cagra::convert_c_mg_index_params(
+    params, dataset.shape[0], dataset.shape[1], &mg_params);
+
+  using mdspan_type = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, uint32_t>, T, uint32_t>(
+      cuvs::neighbors::cagra::build(*res_ptr, mg_params, mds));
+
+  return mg_index;
+}
+
+template <typename T>
+void _mg_search(cuvsResources_t res,
+                cuvsMultiGpuCagraSearchParams params,
+                cuvsMultiGpuCagraIndex index,
+                DLManagedTensor* queries_tensor,
+                DLManagedTensor* neighbors_tensor,
+                DLManagedTensor* distances_tensor)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, uint32_t>, T, uint32_t>*>(
+    index.addr);
+
+  auto mg_search_params =
+    cuvs::neighbors::mg_search_params<cuvs::neighbors::cagra::search_params>();
+  cuvs::neighbors::cagra::convert_c_mg_search_params(params, &mg_search_params);
+
+  using queries_mdspan_type   = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  using neighbors_mdspan_type = raft::host_matrix_view<int64_t, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
+
+  auto queries_mds   = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  cuvs::neighbors::cagra::search(
+    *res_ptr, *mg_index_ptr, mg_search_params, queries_mds, neighbors_mds, distances_mds);
+}
+
+template <typename T>
+void _mg_extend(cuvsResources_t res,
+                cuvsMultiGpuCagraIndex index,
+                DLManagedTensor* new_vectors_tensor,
+                DLManagedTensor* new_indices_tensor)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, uint32_t>, T, uint32_t>*>(
+    index.addr);
+
+  using vectors_mdspan_type = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  auto new_vectors_mds      = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors_tensor);
+
+  std::optional<raft::host_vector_view<const uint32_t, int64_t>> new_indices_mds = std::nullopt;
+  if (new_indices_tensor != nullptr) {
+    using indices_mdspan_type = raft::host_vector_view<const uint32_t, int64_t>;
+    new_indices_mds           = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices_tensor);
+  }
+
+  cuvs::neighbors::cagra::extend(*res_ptr, *mg_index_ptr, new_vectors_mds, new_indices_mds);
+}
+
+template <typename T>
+void _mg_serialize(cuvsResources_t res, cuvsMultiGpuCagraIndex index, const char* filename)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, uint32_t>, T, uint32_t>*>(
+    index.addr);
+
+  cuvs::neighbors::cagra::serialize(*res_ptr, *mg_index_ptr, std::string(filename));
+}
+
+template <typename T>
+void* _mg_deserialize(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, uint32_t>, T, uint32_t>(
+      cuvs::neighbors::cagra::deserialize<T, uint32_t>(*res_ptr, std::string(filename)));
+
+  return mg_index;
+}
+
+template <typename T>
+void* _mg_distribute(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::cagra::index<T, uint32_t>, T, uint32_t>(
+      cuvs::neighbors::cagra::distribute<T, uint32_t>(*res_ptr, std::string(filename)));
+
+  return mg_index;
+}
+
+}  // anonymous namespace
+
+extern "C" cuvsError_t cuvsMultiGpuCagraBuild(cuvsResources_t res,
+                                              cuvsMultiGpuCagraIndexParams_t params,
+                                              DLManagedTensor* dataset_tensor,
+                                              cuvsMultiGpuCagraIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset      = dataset_tensor->dl_tensor;
+    index->dtype.code = dataset.dtype.code;
+    index->dtype.bits = dataset.dtype.bits;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<float>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<half>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<int8_t>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<uint8_t>(res, *params, dataset_tensor));
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraSearch(cuvsResources_t res,
+                                               cuvsMultiGpuCagraSearchParams_t params,
+                                               cuvsMultiGpuCagraIndex_t index,
+                                               DLManagedTensor* queries_tensor,
+                                               DLManagedTensor* neighbors_tensor,
+                                               DLManagedTensor* distances_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto queries = queries_tensor->dl_tensor;
+
+    if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
+      _mg_search<float>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      _mg_search<half>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
+      _mg_search<int8_t>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
+      _mg_search<uint8_t>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
+                queries.dtype.code,
+                queries.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraExtend(cuvsResources_t res,
+                                               cuvsMultiGpuCagraIndex_t index,
+                                               DLManagedTensor* new_vectors_tensor,
+                                               DLManagedTensor* new_indices_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto vectors = new_vectors_tensor->dl_tensor;
+
+    if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
+      _mg_extend<float>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 16) {
+      _mg_extend<half>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLInt && vectors.dtype.bits == 8) {
+      _mg_extend<int8_t>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLUInt && vectors.dtype.bits == 8) {
+      _mg_extend<uint8_t>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else {
+      RAFT_FAIL("Unsupported new_vectors DLtensor dtype: %d and bits: %d",
+                vectors.dtype.code,
+                vectors.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraSerialize(cuvsResources_t res,
+                                                  cuvsMultiGpuCagraIndex_t index,
+                                                  const char* filename)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _mg_serialize<float>(res, *index, filename);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _mg_serialize<half>(res, *index, filename);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _mg_serialize<int8_t>(res, *index, filename);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _mg_serialize<uint8_t>(res, *index, filename);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraDeserialize(cuvsResources_t res,
+                                                    const char* filename,
+                                                    cuvsMultiGpuCagraIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+    is.close();
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<float>(res, filename));
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<half>(res, filename));
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->dtype.code = kDLInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<int8_t>(res, filename));
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->dtype.code = kDLUInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<uint8_t>(res, filename));
+    } else {
+      RAFT_FAIL("Unsupported index dtype");
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuCagraDistribute(cuvsResources_t res,
+                                                   const char* filename,
+                                                   cuvsMultiGpuCagraIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+    is.close();
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<float>(res, filename));
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<half>(res, filename));
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->dtype.code = kDLInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<int8_t>(res, filename));
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->dtype.code = kDLUInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<uint8_t>(res, filename));
+    } else {
+      RAFT_FAIL("Unsupported index dtype");
+    }
+  });
+}
diff --git a/cpp/src/neighbors/mg_ivf_flat_c.cpp b/cpp/src/neighbors/mg_ivf_flat_c.cpp
new file mode 100644
index 0000000000..c012cb4c7c
--- /dev/null
+++ b/cpp/src/neighbors/mg_ivf_flat_c.cpp
@@ -0,0 +1,416 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_flat_c.hpp"
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_flat.h>
+#include <cuvs/neighbors/ivf_flat.hpp>
+#include <cuvs/neighbors/mg_ivf_flat.h>
+#include <dlpack/dlpack.h>
+#include <raft/core/error.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <fstream>
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatIndexParamsCreate(
+  cuvsMultiGpuIvfFlatIndexParams_t* index_params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Create base IVF-Flat parameters
+    cuvsIvfFlatIndexParams_t base_params;
+    cuvsIvfFlatIndexParamsCreate(&base_params);
+
+    // Create MG wrapper with default values
+    *index_params = new cuvsMultiGpuIvfFlatIndexParams{
+      .base_params = base_params,
+      .mode        = CUVS_NEIGHBORS_MG_SHARDED  // Default to sharded mode
+    };
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatIndexParamsDestroy(
+  cuvsMultiGpuIvfFlatIndexParams_t index_params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index_params) {
+      cuvsIvfFlatIndexParamsDestroy(index_params->base_params);
+      delete index_params;
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatSearchParamsCreate(
+  cuvsMultiGpuIvfFlatSearchParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Create base IVF-Flat search parameters
+    cuvsIvfFlatSearchParams_t base_params;
+    cuvsIvfFlatSearchParamsCreate(&base_params);
+
+    // Create MG wrapper with default values
+    *params = new cuvsMultiGpuIvfFlatSearchParams{
+      .base_params      = base_params,
+      .search_mode      = CUVS_NEIGHBORS_MG_LOAD_BALANCER,  // Default to load balancer
+      .merge_mode       = CUVS_NEIGHBORS_MG_TREE_MERGE,     // Default to tree merge
+      .n_rows_per_batch = 1LL << 20                         // Default to 1M rows per batch
+    };
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatSearchParamsDestroy(
+  cuvsMultiGpuIvfFlatSearchParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (params) {
+      cuvsIvfFlatSearchParamsDestroy(params->base_params);
+      delete params;
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatIndexCreate(cuvsMultiGpuIvfFlatIndex_t* index)
+{
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsMultiGpuIvfFlatIndex{}; });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatIndexDestroy(cuvsMultiGpuIvfFlatIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index) {
+      // Properly clean up the templated inner object based on dtype, like single GPU API
+      if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::
+            mg_index<cuvs::neighbors::ivf_flat::index<float, int64_t>, float, int64_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::
+            mg_index<cuvs::neighbors::ivf_flat::index<half, int64_t>, half, int64_t>*>(index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::
+            mg_index<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>, int8_t, int64_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::
+            mg_index<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>, uint8_t, int64_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      }
+      delete index;
+    }
+  });
+}
+
+namespace cuvs::neighbors::ivf_flat {
+
+void convert_c_mg_index_params(
+  cuvsMultiGpuIvfFlatIndexParams params,
+  cuvs::neighbors::mg_index_params<cuvs::neighbors::ivf_flat::index_params>* out)
+{
+  convert_c_index_params(*params.base_params, out);
+  out->mode = (params.mode == CUVS_NEIGHBORS_MG_SHARDED)
+                ? cuvs::neighbors::distribution_mode::SHARDED
+                : cuvs::neighbors::distribution_mode::REPLICATED;
+}
+
+void convert_c_mg_search_params(
+  cuvsMultiGpuIvfFlatSearchParams params,
+  cuvs::neighbors::mg_search_params<cuvs::neighbors::ivf_flat::search_params>* out)
+{
+  convert_c_search_params(*params.base_params, out);
+  out->search_mode      = (params.search_mode == CUVS_NEIGHBORS_MG_LOAD_BALANCER)
+                            ? cuvs::neighbors::replicated_search_mode::LOAD_BALANCER
+                            : cuvs::neighbors::replicated_search_mode::ROUND_ROBIN;
+  out->merge_mode       = (params.merge_mode == CUVS_NEIGHBORS_MG_TREE_MERGE)
+                            ? cuvs::neighbors::sharded_merge_mode::TREE_MERGE
+                            : cuvs::neighbors::sharded_merge_mode::MERGE_ON_ROOT_RANK;
+  out->n_rows_per_batch = params.n_rows_per_batch;
+}
+}  // namespace cuvs::neighbors::ivf_flat
+
+namespace {
+
+template <typename T>
+void* _mg_build(cuvsResources_t res,
+                cuvsMultiGpuIvfFlatIndexParams params,
+                DLManagedTensor* dataset_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto mg_params = cuvs::neighbors::mg_index_params<cuvs::neighbors::ivf_flat::index_params>();
+  cuvs::neighbors::ivf_flat::convert_c_mg_index_params(params, &mg_params);
+
+  using mdspan_type = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, int64_t>, T, int64_t>(
+      cuvs::neighbors::ivf_flat::build(*res_ptr, mg_params, mds));
+
+  return mg_index;
+}
+
+template <typename T>
+void _mg_search(cuvsResources_t res,
+                cuvsMultiGpuIvfFlatSearchParams params,
+                cuvsMultiGpuIvfFlatIndex index,
+                DLManagedTensor* queries_tensor,
+                DLManagedTensor* neighbors_tensor,
+                DLManagedTensor* distances_tensor)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, int64_t>, T, int64_t>*>(
+    index.addr);
+
+  auto mg_search_params =
+    cuvs::neighbors::mg_search_params<cuvs::neighbors::ivf_flat::search_params>();
+  cuvs::neighbors::ivf_flat::convert_c_mg_search_params(params, &mg_search_params);
+
+  using queries_mdspan_type   = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  using neighbors_mdspan_type = raft::host_matrix_view<int64_t, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
+
+  auto queries_mds   = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  cuvs::neighbors::ivf_flat::search(
+    *res_ptr, *mg_index_ptr, mg_search_params, queries_mds, neighbors_mds, distances_mds);
+}
+
+template <typename T>
+void _mg_extend(cuvsResources_t res,
+                cuvsMultiGpuIvfFlatIndex index,
+                DLManagedTensor* new_vectors_tensor,
+                DLManagedTensor* new_indices_tensor)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, int64_t>, T, int64_t>*>(
+    index.addr);
+
+  using vectors_mdspan_type = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  auto new_vectors_mds      = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors_tensor);
+
+  std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices_mds = std::nullopt;
+  if (new_indices_tensor != nullptr) {
+    using indices_mdspan_type = raft::host_vector_view<const int64_t, int64_t>;
+    new_indices_mds           = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices_tensor);
+  }
+
+  cuvs::neighbors::ivf_flat::extend(*res_ptr, *mg_index_ptr, new_vectors_mds, new_indices_mds);
+}
+
+template <typename T>
+void _mg_serialize(cuvsResources_t res, cuvsMultiGpuIvfFlatIndex index, const char* filename)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, int64_t>, T, int64_t>*>(
+    index.addr);
+
+  cuvs::neighbors::ivf_flat::serialize(*res_ptr, *mg_index_ptr, std::string(filename));
+}
+
+template <typename T>
+void* _mg_deserialize(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, int64_t>, T, int64_t>(
+      cuvs::neighbors::ivf_flat::deserialize<T, int64_t>(*res_ptr, std::string(filename)));
+
+  return mg_index;
+}
+
+template <typename T>
+void* _mg_distribute(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::ivf_flat::index<T, int64_t>, T, int64_t>(
+      cuvs::neighbors::ivf_flat::distribute<T, int64_t>(*res_ptr, std::string(filename)));
+
+  return mg_index;
+}
+
+}  // anonymous namespace
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatBuild(cuvsResources_t res,
+                                                cuvsMultiGpuIvfFlatIndexParams_t params,
+                                                DLManagedTensor* dataset_tensor,
+                                                cuvsMultiGpuIvfFlatIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset      = dataset_tensor->dl_tensor;
+    index->dtype.code = dataset.dtype.code;
+    index->dtype.bits = dataset.dtype.bits;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<float>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<half>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<int8_t>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<uint8_t>(res, *params, dataset_tensor));
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatSearch(cuvsResources_t res,
+                                                 cuvsMultiGpuIvfFlatSearchParams_t params,
+                                                 cuvsMultiGpuIvfFlatIndex_t index,
+                                                 DLManagedTensor* queries_tensor,
+                                                 DLManagedTensor* neighbors_tensor,
+                                                 DLManagedTensor* distances_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto queries = queries_tensor->dl_tensor;
+
+    if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
+      _mg_search<float>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      _mg_search<half>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
+      _mg_search<int8_t>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
+      _mg_search<uint8_t>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
+                queries.dtype.code,
+                queries.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatExtend(cuvsResources_t res,
+                                                 cuvsMultiGpuIvfFlatIndex_t index,
+                                                 DLManagedTensor* new_vectors_tensor,
+                                                 DLManagedTensor* new_indices_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto vectors = new_vectors_tensor->dl_tensor;
+
+    if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
+      _mg_extend<float>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 16) {
+      _mg_extend<half>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLInt && vectors.dtype.bits == 8) {
+      _mg_extend<int8_t>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLUInt && vectors.dtype.bits == 8) {
+      _mg_extend<uint8_t>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else {
+      RAFT_FAIL("Unsupported new_vectors DLtensor dtype: %d and bits: %d",
+                vectors.dtype.code,
+                vectors.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatSerialize(cuvsResources_t res,
+                                                    cuvsMultiGpuIvfFlatIndex_t index,
+                                                    const char* filename)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _mg_serialize<float>(res, *index, filename);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _mg_serialize<half>(res, *index, filename);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _mg_serialize<int8_t>(res, *index, filename);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _mg_serialize<uint8_t>(res, *index, filename);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatDeserialize(cuvsResources_t res,
+                                                      const char* filename,
+                                                      cuvsMultiGpuIvfFlatIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+    is.close();
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<float>(res, filename));
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<half>(res, filename));
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->dtype.code = kDLInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<int8_t>(res, filename));
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->dtype.code = kDLUInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<uint8_t>(res, filename));
+    } else {
+      RAFT_FAIL("Unsupported index dtype");
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfFlatDistribute(cuvsResources_t res,
+                                                     const char* filename,
+                                                     cuvsMultiGpuIvfFlatIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+    is.close();
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<float>(res, filename));
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<half>(res, filename));
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->dtype.code = kDLInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<int8_t>(res, filename));
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->dtype.code = kDLUInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<uint8_t>(res, filename));
+    } else {
+      RAFT_FAIL("Unsupported index dtype");
+    }
+  });
+}
diff --git a/cpp/src/neighbors/mg_ivf_pq_c.cpp b/cpp/src/neighbors/mg_ivf_pq_c.cpp
new file mode 100644
index 0000000000..57d11f5264
--- /dev/null
+++ b/cpp/src/neighbors/mg_ivf_pq_c.cpp
@@ -0,0 +1,408 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ivf_pq_c.hpp"
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/ivf_pq.hpp>
+#include <cuvs/neighbors/mg_ivf_pq.h>
+#include <dlpack/dlpack.h>
+#include <raft/core/error.hpp>
+#include <raft/core/serialize.hpp>
+
+#include <fstream>
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqIndexParamsCreate(
+  cuvsMultiGpuIvfPqIndexParams_t* index_params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Create base IVF-PQ parameters
+    cuvsIvfPqIndexParams_t base_params;
+    cuvsIvfPqIndexParamsCreate(&base_params);
+
+    // Create MG wrapper with default values
+    *index_params = new cuvsMultiGpuIvfPqIndexParams{
+      .base_params = base_params,
+      .mode        = CUVS_NEIGHBORS_MG_SHARDED  // Default to sharded mode
+    };
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqIndexParamsDestroy(
+  cuvsMultiGpuIvfPqIndexParams_t index_params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index_params) {
+      cuvsIvfPqIndexParamsDestroy(index_params->base_params);
+      delete index_params;
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqSearchParamsCreate(cuvsMultiGpuIvfPqSearchParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Create base IVF-PQ search parameters
+    cuvsIvfPqSearchParams_t base_params;
+    cuvsIvfPqSearchParamsCreate(&base_params);
+
+    // Create MG wrapper with default values
+    *params = new cuvsMultiGpuIvfPqSearchParams{
+      .base_params      = base_params,
+      .search_mode      = CUVS_NEIGHBORS_MG_LOAD_BALANCER,  // Default to load balancer
+      .merge_mode       = CUVS_NEIGHBORS_MG_TREE_MERGE,     // Default to tree merge
+      .n_rows_per_batch = 1LL << 20                         // Default to 1M rows per batch
+    };
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqSearchParamsDestroy(cuvsMultiGpuIvfPqSearchParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (params) {
+      cuvsIvfPqSearchParamsDestroy(params->base_params);
+      delete params;
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqIndexCreate(cuvsMultiGpuIvfPqIndex_t* index)
+{
+  return cuvs::core::translate_exceptions([=] { *index = new cuvsMultiGpuIvfPqIndex{}; });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqIndexDestroy(cuvsMultiGpuIvfPqIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index) {
+      // Properly clean up the templated inner object based on dtype, like single GPU API
+      if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, float, int64_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, half, int64_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, int8_t, int64_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+        auto mg_index_ptr = reinterpret_cast<
+          cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, uint8_t, int64_t>*>(
+          index->addr);
+        delete mg_index_ptr;
+      }
+      delete index;
+    }
+  });
+}
+
+namespace cuvs::neighbors::ivf_pq {
+
+void convert_c_mg_index_params(
+  cuvsMultiGpuIvfPqIndexParams params,
+  cuvs::neighbors::mg_index_params<cuvs::neighbors::ivf_pq::index_params>* out)
+{
+  convert_c_index_params(*params.base_params, out);
+  out->mode = (params.mode == CUVS_NEIGHBORS_MG_SHARDED)
+                ? cuvs::neighbors::distribution_mode::SHARDED
+                : cuvs::neighbors::distribution_mode::REPLICATED;
+}
+
+void convert_c_mg_search_params(
+  cuvsMultiGpuIvfPqSearchParams params,
+  cuvs::neighbors::mg_search_params<cuvs::neighbors::ivf_pq::search_params>* out)
+{
+  convert_c_search_params(*params.base_params, out);
+  out->search_mode      = (params.search_mode == CUVS_NEIGHBORS_MG_LOAD_BALANCER)
+                            ? cuvs::neighbors::replicated_search_mode::LOAD_BALANCER
+                            : cuvs::neighbors::replicated_search_mode::ROUND_ROBIN;
+  out->merge_mode       = (params.merge_mode == CUVS_NEIGHBORS_MG_TREE_MERGE)
+                            ? cuvs::neighbors::sharded_merge_mode::TREE_MERGE
+                            : cuvs::neighbors::sharded_merge_mode::MERGE_ON_ROOT_RANK;
+  out->n_rows_per_batch = params.n_rows_per_batch;
+}
+}  // namespace cuvs::neighbors::ivf_pq
+
+namespace {
+
+template <typename T>
+void* _mg_build(cuvsResources_t res,
+                cuvsMultiGpuIvfPqIndexParams params,
+                DLManagedTensor* dataset_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto mg_params = cuvs::neighbors::mg_index_params<cuvs::neighbors::ivf_pq::index_params>();
+  cuvs::neighbors::ivf_pq::convert_c_mg_index_params(params, &mg_params);
+
+  using mdspan_type = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, T, int64_t>(
+      cuvs::neighbors::ivf_pq::build(*res_ptr, mg_params, mds));
+
+  return mg_index;
+}
+
+template <typename T>
+void _mg_search(cuvsResources_t res,
+                cuvsMultiGpuIvfPqSearchParams params,
+                cuvsMultiGpuIvfPqIndex index,
+                DLManagedTensor* queries_tensor,
+                DLManagedTensor* neighbors_tensor,
+                DLManagedTensor* distances_tensor)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, T, int64_t>*>(index.addr);
+
+  auto mg_search_params =
+    cuvs::neighbors::mg_search_params<cuvs::neighbors::ivf_pq::search_params>();
+  cuvs::neighbors::ivf_pq::convert_c_mg_search_params(params, &mg_search_params);
+
+  using queries_mdspan_type   = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  using neighbors_mdspan_type = raft::host_matrix_view<int64_t, int64_t, raft::row_major>;
+  using distances_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
+
+  auto queries_mds   = cuvs::core::from_dlpack<queries_mdspan_type>(queries_tensor);
+  auto neighbors_mds = cuvs::core::from_dlpack<neighbors_mdspan_type>(neighbors_tensor);
+  auto distances_mds = cuvs::core::from_dlpack<distances_mdspan_type>(distances_tensor);
+
+  cuvs::neighbors::ivf_pq::search(
+    *res_ptr, *mg_index_ptr, mg_search_params, queries_mds, neighbors_mds, distances_mds);
+}
+
+template <typename T>
+void _mg_extend(cuvsResources_t res,
+                cuvsMultiGpuIvfPqIndex index,
+                DLManagedTensor* new_vectors_tensor,
+                DLManagedTensor* new_indices_tensor)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, T, int64_t>*>(index.addr);
+
+  using vectors_mdspan_type = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  auto new_vectors_mds      = cuvs::core::from_dlpack<vectors_mdspan_type>(new_vectors_tensor);
+
+  std::optional<raft::host_vector_view<const int64_t, int64_t>> new_indices_mds = std::nullopt;
+  if (new_indices_tensor != nullptr) {
+    using indices_mdspan_type = raft::host_vector_view<const int64_t, int64_t>;
+    new_indices_mds           = cuvs::core::from_dlpack<indices_mdspan_type>(new_indices_tensor);
+  }
+
+  cuvs::neighbors::ivf_pq::extend(*res_ptr, *mg_index_ptr, new_vectors_mds, new_indices_mds);
+}
+
+template <typename T>
+void _mg_serialize(cuvsResources_t res, cuvsMultiGpuIvfPqIndex index, const char* filename)
+{
+  auto res_ptr      = reinterpret_cast<raft::resources*>(res);
+  auto mg_index_ptr = reinterpret_cast<
+    cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, T, int64_t>*>(index.addr);
+
+  cuvs::neighbors::ivf_pq::serialize(*res_ptr, *mg_index_ptr, std::string(filename));
+}
+
+template <typename T>
+void* _mg_deserialize(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, T, int64_t>(
+      cuvs::neighbors::ivf_pq::deserialize<T, int64_t>(*res_ptr, std::string(filename)));
+
+  return mg_index;
+}
+
+template <typename T>
+void* _mg_distribute(cuvsResources_t res, const char* filename)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+  auto mg_index =
+    new cuvs::neighbors::mg_index<cuvs::neighbors::ivf_pq::index<int64_t>, T, int64_t>(
+      cuvs::neighbors::ivf_pq::distribute<T, int64_t>(*res_ptr, std::string(filename)));
+
+  return mg_index;
+}
+
+}  // anonymous namespace
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqBuild(cuvsResources_t res,
+                                              cuvsMultiGpuIvfPqIndexParams_t params,
+                                              DLManagedTensor* dataset_tensor,
+                                              cuvsMultiGpuIvfPqIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset      = dataset_tensor->dl_tensor;
+    index->dtype.code = dataset.dtype.code;
+    index->dtype.bits = dataset.dtype.bits;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<float>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 16) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<half>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<int8_t>(res, *params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_mg_build<uint8_t>(res, *params, dataset_tensor));
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqSearch(cuvsResources_t res,
+                                               cuvsMultiGpuIvfPqSearchParams_t params,
+                                               cuvsMultiGpuIvfPqIndex_t index,
+                                               DLManagedTensor* queries_tensor,
+                                               DLManagedTensor* neighbors_tensor,
+                                               DLManagedTensor* distances_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto queries = queries_tensor->dl_tensor;
+
+    if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
+      _mg_search<float>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLFloat && queries.dtype.bits == 16) {
+      _mg_search<half>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLInt && queries.dtype.bits == 8) {
+      _mg_search<int8_t>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else if (queries.dtype.code == kDLUInt && queries.dtype.bits == 8) {
+      _mg_search<uint8_t>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
+    } else {
+      RAFT_FAIL("Unsupported queries DLtensor dtype: %d and bits: %d",
+                queries.dtype.code,
+                queries.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqExtend(cuvsResources_t res,
+                                               cuvsMultiGpuIvfPqIndex_t index,
+                                               DLManagedTensor* new_vectors_tensor,
+                                               DLManagedTensor* new_indices_tensor)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto vectors = new_vectors_tensor->dl_tensor;
+
+    if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
+      _mg_extend<float>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 16) {
+      _mg_extend<half>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLInt && vectors.dtype.bits == 8) {
+      _mg_extend<int8_t>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else if (vectors.dtype.code == kDLUInt && vectors.dtype.bits == 8) {
+      _mg_extend<uint8_t>(res, *index, new_vectors_tensor, new_indices_tensor);
+    } else {
+      RAFT_FAIL("Unsupported new_vectors DLtensor dtype: %d and bits: %d",
+                vectors.dtype.code,
+                vectors.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqSerialize(cuvsResources_t res,
+                                                  cuvsMultiGpuIvfPqIndex_t index,
+                                                  const char* filename)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _mg_serialize<float>(res, *index, filename);
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      _mg_serialize<half>(res, *index, filename);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _mg_serialize<int8_t>(res, *index, filename);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _mg_serialize<uint8_t>(res, *index, filename);
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqDeserialize(cuvsResources_t res,
+                                                    const char* filename,
+                                                    cuvsMultiGpuIvfPqIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+    is.close();
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<float>(res, filename));
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<half>(res, filename));
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->dtype.code = kDLInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<int8_t>(res, filename));
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->dtype.code = kDLUInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<uint8_t>(res, filename));
+    } else {
+      RAFT_FAIL("Unsupported index dtype");
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsMultiGpuIvfPqDistribute(cuvsResources_t res,
+                                                   const char* filename,
+                                                   cuvsMultiGpuIvfPqIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    std::ifstream is(filename, std::ios::in | std::ios::binary);
+    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
+    char dtype_string[4];
+    is.read(dtype_string, 4);
+    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
+    is.close();
+
+    index->dtype.bits = dtype.itemsize * 8;
+    if (dtype.kind == 'f' && dtype.itemsize == 4) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<float>(res, filename));
+    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+      index->dtype.code = kDLFloat;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<half>(res, filename));
+    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
+      index->dtype.code = kDLInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<int8_t>(res, filename));
+    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
+      index->dtype.code = kDLUInt;
+      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<uint8_t>(res, filename));
+    } else {
+      RAFT_FAIL("Unsupported index dtype");
+    }
+  });
+}
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index a865aeab89..c6bb7ce8e4 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -315,6 +315,8 @@ if(TARGET cuvs::c_api)
 
   ConfigureTest(NAME CAGRA_C_TEST PATH neighbors/ann_cagra_c.cu C_LIB)
 
+  ConfigureTest(NAME MG_C_TEST PATH neighbors/run_mg_c.c neighbors/ann_mg_c.cu C_LIB)
+
   if(BUILD_CAGRA_HNSWLIB)
     ConfigureTest(NAME HNSW_C_TEST PATH neighbors/ann_hnsw_c.cu C_LIB)
     target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
diff --git a/cpp/tests/neighbors/ann_mg_c.cu b/cpp/tests/neighbors/ann_mg_c.cu
new file mode 100644
index 0000000000..8a82982951
--- /dev/null
+++ b/cpp/tests/neighbors/ann_mg_c.cu
@@ -0,0 +1,341 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#include <gtest/gtest.h>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/random/rng.cuh>
+
+#include "ann_utils.cuh"
+#include "naive_knn.cuh"
+#include <cuvs/neighbors/brute_force.h>
+#include <cuvs/neighbors/mg_cagra.h>
+#include <cuvs/neighbors/mg_ivf_flat.h>
+#include <cuvs/neighbors/mg_ivf_pq.h>
+
+extern "C" {
+
+typedef enum { MG_ALGO_IVF_FLAT, MG_ALGO_IVF_PQ, MG_ALGO_CAGRA } mg_algo_t;
+
+typedef enum { MG_MODE_REPLICATED, MG_MODE_SHARDED, MG_MODE_LOCAL_THEN_DISTRIBUTED } mg_mode_t;
+
+typedef struct {
+  int64_t num_queries;
+  int64_t num_db_vecs;
+  int64_t dim;
+  int64_t k;
+  mg_mode_t mode;
+  mg_algo_t algo;
+  int64_t nprobe;
+  int64_t nlist;
+  cuvsDistanceType metric;
+} mg_test_params;
+
+int run_mg_ivf_flat_test(mg_test_params params,
+                         float* index_data,
+                         float* query_data,
+                         float* distances_data,
+                         int64_t* neighbors_data,
+                         float* ref_distances_data,
+                         int64_t* ref_neighbors_data);
+
+int run_mg_ivf_pq_test(mg_test_params params,
+                       float* index_data,
+                       float* query_data,
+                       float* distances_data,
+                       int64_t* neighbors_data,
+                       float* ref_distances_data,
+                       int64_t* ref_neighbors_data);
+
+int run_mg_cagra_test(mg_test_params params,
+                      float* index_data,
+                      float* query_data,
+                      float* distances_data,
+                      int64_t* neighbors_data,
+                      float* ref_distances_data,
+                      int64_t* ref_neighbors_data);
+
+int generate_reference_results(mg_test_params params,
+                               float* index_data,
+                               float* query_data,
+                               float* ref_distances_data,
+                               int64_t* ref_neighbors_data);
+}
+
+template <typename T>
+void generate_random_data(T* devPtr, size_t size)
+{
+  raft::handle_t handle;
+  raft::random::RngState r(1234ULL);
+  if constexpr (std::is_same_v<T, float>) {
+    raft::random::uniform(handle, r, devPtr, size, T(0.1), T(2.0));
+  } else {
+    raft::random::uniformInt(handle, r, devPtr, size, T(1), T(20));
+  }
+}
+
+template <typename T, typename IdxT>
+bool eval_recall(T* query_data,
+                 T* index_data,
+                 IdxT* neighbors,
+                 T* distances,
+                 IdxT* ref_neighbors,
+                 T* ref_distances,
+                 size_t n_queries,
+                 size_t n_rows,
+                 size_t n_dim,
+                 size_t n_neighbors,
+                 cuvsDistanceType metric,
+                 double min_recall = 0.9)
+{
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  size_t size = n_queries * n_neighbors;
+  std::vector<IdxT> neighbors_h(size);
+  std::vector<T> distances_h(size);
+  std::vector<IdxT> neighbors_ref_h(size);
+  std::vector<T> distances_ref_h(size);
+
+  raft::copy(neighbors_h.data(), neighbors, size, stream);
+  raft::copy(distances_h.data(), distances, size, stream);
+  raft::copy(neighbors_ref_h.data(), ref_neighbors, size, stream);
+  raft::copy(distances_ref_h.data(), ref_distances, size, stream);
+
+  raft::resource::sync_stream(handle);
+
+  // Evaluate recall
+  return cuvs::neighbors::eval_neighbours(neighbors_ref_h,
+                                          neighbors_h,
+                                          distances_ref_h,
+                                          distances_h,
+                                          n_queries,
+                                          n_neighbors,
+                                          0.001,
+                                          min_recall);
+}
+
+class MgCTest : public ::testing::TestWithParam<mg_test_params> {
+ public:
+  void SetUp() override
+  {
+    params = GetParam();
+
+    raft::handle_t handle;
+    auto stream = raft::resource::get_cuda_stream(handle);
+
+    // Allocate device memory
+    index_data.resize(params.num_db_vecs * params.dim, stream);
+    query_data.resize(params.num_queries * params.dim, stream);
+    neighbors_data.resize(params.num_queries * params.k, stream);
+    distances_data.resize(params.num_queries * params.k, stream);
+    ref_neighbors_data.resize(params.num_queries * params.k, stream);
+    ref_distances_data.resize(params.num_queries * params.k, stream);
+
+    // Generate random test data
+    generate_random_data(index_data.data(), params.num_db_vecs * params.dim);
+    generate_random_data(query_data.data(), params.num_queries * params.dim);
+
+    raft::resource::sync_stream(handle);
+
+    // Allocate host memory for multi-GPU tests
+    index_data_host.resize(params.num_db_vecs * params.dim);
+    query_data_host.resize(params.num_queries * params.dim);
+    neighbors_data_host.resize(params.num_queries * params.k);
+    distances_data_host.resize(params.num_queries * params.k);
+
+    // Copy data from device to host for multi-GPU functions
+    raft::copy(index_data_host.data(), index_data.data(), index_data.size(), stream);
+    raft::copy(query_data_host.data(), query_data.data(), query_data.size(), stream);
+
+    raft::resource::sync_stream(handle);
+  }
+
+  void TearDown() override {}
+
+ protected:
+  mg_test_params params;
+  rmm::device_uvector<float> index_data{0, rmm::cuda_stream_default};
+  rmm::device_uvector<float> query_data{0, rmm::cuda_stream_default};
+  rmm::device_uvector<int64_t> neighbors_data{0, rmm::cuda_stream_default};
+  rmm::device_uvector<float> distances_data{0, rmm::cuda_stream_default};
+  rmm::device_uvector<int64_t> ref_neighbors_data{0, rmm::cuda_stream_default};
+  rmm::device_uvector<float> ref_distances_data{0, rmm::cuda_stream_default};
+
+  // Host memory for multi-GPU tests
+  std::vector<float> index_data_host;
+  std::vector<float> query_data_host;
+  std::vector<int64_t> neighbors_data_host;
+  std::vector<float> distances_data_host;
+};
+
+TEST_P(MgCTest, MgIvfFlatTest)
+{
+  auto params = GetParam();
+  if (params.algo != MG_ALGO_IVF_FLAT) return;
+
+  // Generate reference results using brute force
+  int ref_result = generate_reference_results(params,
+                                              index_data.data(),
+                                              query_data.data(),
+                                              ref_distances_data.data(),
+                                              ref_neighbors_data.data());
+  ASSERT_EQ(ref_result, 0) << "Failed to generate reference results";
+
+  // Run MG IVF-Flat test (use host data for multi-GPU)
+  int test_result = run_mg_ivf_flat_test(params,
+                                         index_data_host.data(),
+                                         query_data_host.data(),
+                                         distances_data_host.data(),
+                                         neighbors_data_host.data(),
+                                         ref_distances_data.data(),
+                                         ref_neighbors_data.data());
+  ASSERT_EQ(test_result, 0) << "MG IVF-Flat test failed";
+
+  // Copy results back from host to device for evaluation
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+  raft::copy(neighbors_data.data(), neighbors_data_host.data(), neighbors_data_host.size(), stream);
+  raft::copy(distances_data.data(), distances_data_host.data(), distances_data_host.size(), stream);
+  raft::resource::sync_stream(handle);
+
+  // Evaluate recall compared to reference
+  double min_recall = static_cast<double>(params.nprobe) / static_cast<double>(params.nlist);
+  bool recall_ok    = eval_recall(query_data.data(),
+                               index_data.data(),
+                               neighbors_data.data(),
+                               distances_data.data(),
+                               ref_neighbors_data.data(),
+                               ref_distances_data.data(),
+                               params.num_queries,
+                               params.num_db_vecs,
+                               params.dim,
+                               params.k,
+                               params.metric,
+                               min_recall);
+  ASSERT_TRUE(recall_ok) << "Recall evaluation failed for MG IVF-Flat";
+}
+
+TEST_P(MgCTest, MgIvfPqTest)
+{
+  auto params = GetParam();
+  if (params.algo != MG_ALGO_IVF_PQ) return;
+
+  // Generate reference results using brute force
+  int ref_result = generate_reference_results(params,
+                                              index_data.data(),
+                                              query_data.data(),
+                                              ref_distances_data.data(),
+                                              ref_neighbors_data.data());
+  ASSERT_EQ(ref_result, 0) << "Failed to generate reference results";
+
+  // Run MG IVF-PQ test (use host data for multi-GPU)
+  int test_result = run_mg_ivf_pq_test(params,
+                                       index_data_host.data(),
+                                       query_data_host.data(),
+                                       distances_data_host.data(),
+                                       neighbors_data_host.data(),
+                                       ref_distances_data.data(),
+                                       ref_neighbors_data.data());
+  ASSERT_EQ(test_result, 0) << "MG IVF-PQ test failed";
+
+  // Copy results back from host to device for evaluation
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+  raft::copy(neighbors_data.data(), neighbors_data_host.data(), neighbors_data_host.size(), stream);
+  raft::copy(distances_data.data(), distances_data_host.data(), distances_data_host.size(), stream);
+  raft::resource::sync_stream(handle);
+
+  // Evaluate recall compared to reference
+  double min_recall = static_cast<double>(params.nprobe) / static_cast<double>(params.nlist);
+  bool recall_ok    = eval_recall(query_data.data(),
+                               index_data.data(),
+                               neighbors_data.data(),
+                               distances_data.data(),
+                               ref_neighbors_data.data(),
+                               ref_distances_data.data(),
+                               params.num_queries,
+                               params.num_db_vecs,
+                               params.dim,
+                               params.k,
+                               params.metric,
+                               min_recall);
+  ASSERT_TRUE(recall_ok) << "Recall evaluation failed for MG IVF-PQ";
+}
+
+TEST_P(MgCTest, MgCagraTest)
+{
+  auto params = GetParam();
+  if (params.algo != MG_ALGO_CAGRA) return;
+
+  // Generate reference results using brute force
+  int ref_result = generate_reference_results(params,
+                                              index_data.data(),
+                                              query_data.data(),
+                                              ref_distances_data.data(),
+                                              ref_neighbors_data.data());
+  ASSERT_EQ(ref_result, 0) << "Failed to generate reference results";
+
+  // Run MG CAGRA test (use host data for multi-GPU)
+  int test_result = run_mg_cagra_test(params,
+                                      index_data_host.data(),
+                                      query_data_host.data(),
+                                      distances_data_host.data(),
+                                      neighbors_data_host.data(),
+                                      ref_distances_data.data(),
+                                      ref_neighbors_data.data());
+  ASSERT_EQ(test_result, 0) << "MG CAGRA test failed";
+
+  // Copy results back from host to device for evaluation
+  raft::handle_t handle;
+  auto stream = raft::resource::get_cuda_stream(handle);
+  raft::copy(neighbors_data.data(), neighbors_data_host.data(), neighbors_data_host.size(), stream);
+  raft::copy(distances_data.data(), distances_data_host.data(), distances_data_host.size(), stream);
+  raft::resource::sync_stream(handle);
+
+  // Evaluate recall compared to reference
+  bool recall_ok = eval_recall(query_data.data(),
+                               index_data.data(),
+                               neighbors_data.data(),
+                               distances_data.data(),
+                               ref_neighbors_data.data(),
+                               ref_distances_data.data(),
+                               params.num_queries,
+                               params.num_db_vecs,
+                               params.dim,
+                               params.k,
+                               params.metric,
+                               0.9);
+  ASSERT_TRUE(recall_ok) << "Recall evaluation failed for MG CAGRA";
+}
+
+// Test parameters that mirror the C++ test cases
+const std::vector<mg_test_params> test_inputs = {
+  // IVF-Flat tests
+  {1000, 5000, 8, 16, MG_MODE_REPLICATED, MG_ALGO_IVF_FLAT, 40, 256, L2Expanded},
+  {1000, 5000, 8, 16, MG_MODE_SHARDED, MG_ALGO_IVF_FLAT, 40, 256, L2Expanded},
+
+  // IVF-PQ tests
+  {1000, 5000, 8, 16, MG_MODE_REPLICATED, MG_ALGO_IVF_PQ, 40, 256, L2Expanded},
+  {1000, 5000, 8, 16, MG_MODE_SHARDED, MG_ALGO_IVF_PQ, 40, 256, L2Expanded},
+
+  // CAGRA tests
+  {1000, 5000, 8, 16, MG_MODE_REPLICATED, MG_ALGO_CAGRA, 40, 256, L2Expanded},
+  {1000, 5000, 8, 16, MG_MODE_SHARDED, MG_ALGO_CAGRA, 40, 256, L2Expanded},
+};
+
+INSTANTIATE_TEST_SUITE_P(MgCTests, MgCTest, ::testing::ValuesIn(test_inputs));
diff --git a/cpp/tests/neighbors/run_mg_c.c b/cpp/tests/neighbors/run_mg_c.c
new file mode 100644
index 0000000000..e5d35b82ca
--- /dev/null
+++ b/cpp/tests/neighbors/run_mg_c.c
@@ -0,0 +1,576 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/brute_force.h>
+#include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/mg_cagra.h>
+#include <cuvs/neighbors/mg_ivf_flat.h>
+#include <cuvs/neighbors/mg_ivf_pq.h>
+#include <stdio.h>
+
+typedef enum { MG_ALGO_IVF_FLAT, MG_ALGO_IVF_PQ, MG_ALGO_CAGRA } mg_algo_t;
+
+typedef enum { MG_MODE_REPLICATED, MG_MODE_SHARDED, MG_MODE_LOCAL_THEN_DISTRIBUTED } mg_mode_t;
+
+typedef struct {
+  int64_t num_queries;
+  int64_t num_db_vecs;
+  int64_t dim;
+  int64_t k;
+  mg_mode_t mode;
+  mg_algo_t algo;
+  int64_t nprobe;
+  int64_t nlist;
+  cuvsDistanceType metric;
+} mg_test_params;
+
+// Test IVF-Flat multi-GPU functionality
+int run_mg_ivf_flat_test(mg_test_params params,
+                         float* index_data,
+                         float* query_data,
+                         float* distances_data,
+                         int64_t* neighbors_data,
+                         float* ref_distances_data,
+                         int64_t* ref_neighbors_data)
+{
+  printf("Running MG IVF-Flat test (mode=%d, queries=%ld, db_vecs=%ld, dim=%ld, k=%ld)\n",
+         params.mode,
+         params.num_queries,
+         params.num_db_vecs,
+         params.dim,
+         params.k);
+
+  // Create multi-GPU resources
+  cuvsResources_t res;
+  cuvsMultiGpuResourcesCreate(&res);
+
+  // Create dataset tensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {params.num_db_vecs, params.dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // Create index
+  cuvsMultiGpuIvfFlatIndex_t index;
+  cuvsMultiGpuIvfFlatIndexCreate(&index);
+
+  // Build index
+  cuvsMultiGpuIvfFlatIndexParams_t build_params;
+  cuvsMultiGpuIvfFlatIndexParamsCreate(&build_params);
+  build_params->base_params->metric                   = params.metric;
+  build_params->base_params->n_lists                  = params.nlist;
+  build_params->base_params->add_data_on_build        = false;
+  build_params->base_params->kmeans_trainset_fraction = 1.0;
+  build_params->base_params->metric_arg               = 0;
+
+  if (params.mode == MG_MODE_REPLICATED) {
+    build_params->mode = CUVS_NEIGHBORS_MG_REPLICATED;
+  } else {
+    build_params->mode = CUVS_NEIGHBORS_MG_SHARDED;
+  }
+
+  cuvsError_t build_result = cuvsMultiGpuIvfFlatBuild(res, build_params, &dataset_tensor, index);
+  if (build_result != CUVS_SUCCESS) {
+    const char* error_msg = cuvsGetLastErrorText();
+    printf("MG IVF-Flat build failed: %s\n", error_msg ? error_msg : "Unknown error");
+    goto cleanup;
+  }
+
+  // Extend index with data
+  cuvsError_t extend_result = cuvsMultiGpuIvfFlatExtend(res, index, &dataset_tensor, NULL);
+  if (extend_result != CUVS_SUCCESS) {
+    printf("MG IVF-Flat extend failed\n");
+    goto cleanup;
+  }
+
+  // Serialize index to test serialization/deserialization
+  const char* serialize_filename = "/tmp/mg_ivf_flat_test_index.bin";
+  cuvsError_t serialize_result   = cuvsMultiGpuIvfFlatSerialize(res, index, serialize_filename);
+  if (serialize_result != CUVS_SUCCESS) {
+    printf("MG IVF-Flat serialize failed\n");
+    goto cleanup;
+  }
+
+  // Destroy current index and recreate it
+  cuvsMultiGpuIvfFlatIndexDestroy(index);
+  cuvsMultiGpuIvfFlatIndexCreate(&index);
+
+  // Deserialize index from file
+  cuvsError_t deserialize_result = cuvsMultiGpuIvfFlatDeserialize(res, serialize_filename, index);
+  if (deserialize_result != CUVS_SUCCESS) {
+    printf("MG IVF-Flat deserialize failed\n");
+    goto cleanup;
+  }
+
+  // Create queries tensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {params.num_queries, params.dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // Create neighbors tensor
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = neighbors_data;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {params.num_queries, params.k};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
+
+  // Create distances tensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {params.num_queries, params.k};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // Search index
+  cuvsMultiGpuIvfFlatSearchParams_t search_params;
+  cuvsMultiGpuIvfFlatSearchParamsCreate(&search_params);
+  search_params->base_params->n_probes = params.nprobe;
+  search_params->search_mode           = CUVS_NEIGHBORS_MG_LOAD_BALANCER;
+  search_params->merge_mode            = CUVS_NEIGHBORS_MG_TREE_MERGE;
+  search_params->n_rows_per_batch      = 3000;
+
+  cuvsError_t search_result = cuvsMultiGpuIvfFlatSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  if (search_result != CUVS_SUCCESS) {
+    printf("MG IVF-Flat search failed\n");
+    goto cleanup;
+  }
+
+  printf("MG IVF-Flat test completed successfully\n");
+
+cleanup:
+  cuvsMultiGpuIvfFlatSearchParamsDestroy(search_params);
+  cuvsMultiGpuIvfFlatIndexParamsDestroy(build_params);
+  cuvsMultiGpuIvfFlatIndexDestroy(index);
+  cuvsMultiGpuResourcesDestroy(res);
+
+  // Clean up temporary serialization file
+  remove("/tmp/mg_ivf_flat_test_index.bin");
+
+  return (build_result == CUVS_SUCCESS && extend_result == CUVS_SUCCESS &&
+          serialize_result == CUVS_SUCCESS && deserialize_result == CUVS_SUCCESS &&
+          search_result == CUVS_SUCCESS)
+           ? 0
+           : 1;
+}
+
+// Test IVF-PQ multi-GPU functionality
+int run_mg_ivf_pq_test(mg_test_params params,
+                       float* index_data,
+                       float* query_data,
+                       float* distances_data,
+                       int64_t* neighbors_data,
+                       float* ref_distances_data,
+                       int64_t* ref_neighbors_data)
+{
+  printf("Running MG IVF-PQ test (mode=%d, queries=%ld, db_vecs=%ld, dim=%ld, k=%ld)\n",
+         params.mode,
+         params.num_queries,
+         params.num_db_vecs,
+         params.dim,
+         params.k);
+
+  // Create multi-GPU resources
+  cuvsResources_t res;
+  cuvsMultiGpuResourcesCreate(&res);
+
+  // Create dataset tensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {params.num_db_vecs, params.dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // Create index
+  cuvsMultiGpuIvfPqIndex_t index;
+  cuvsMultiGpuIvfPqIndexCreate(&index);
+
+  // Build index
+  cuvsMultiGpuIvfPqIndexParams_t build_params;
+  cuvsMultiGpuIvfPqIndexParamsCreate(&build_params);
+  build_params->base_params->metric                   = params.metric;
+  build_params->base_params->n_lists                  = params.nlist;
+  build_params->base_params->add_data_on_build        = false;
+  build_params->base_params->kmeans_trainset_fraction = 1.0;
+  build_params->base_params->metric_arg               = 0;
+
+  if (params.mode == MG_MODE_REPLICATED) {
+    build_params->mode = CUVS_NEIGHBORS_MG_REPLICATED;
+  } else {
+    build_params->mode = CUVS_NEIGHBORS_MG_SHARDED;
+  }
+
+  cuvsError_t build_result = cuvsMultiGpuIvfPqBuild(res, build_params, &dataset_tensor, index);
+  if (build_result != CUVS_SUCCESS) {
+    const char* error_msg = cuvsGetLastErrorText();
+    printf("MG IVF-PQ build failed: %s\n", error_msg ? error_msg : "Unknown error");
+    goto cleanup;
+  }
+
+  // Extend index with data
+  cuvsError_t extend_result = cuvsMultiGpuIvfPqExtend(res, index, &dataset_tensor, NULL);
+  if (extend_result != CUVS_SUCCESS) {
+    printf("MG IVF-PQ extend failed\n");
+    goto cleanup;
+  }
+
+  // Serialize index to test serialization/deserialization
+  const char* serialize_filename = "/tmp/mg_ivf_pq_test_index.bin";
+  cuvsError_t serialize_result   = cuvsMultiGpuIvfPqSerialize(res, index, serialize_filename);
+  if (serialize_result != CUVS_SUCCESS) {
+    printf("MG IVF-PQ serialize failed\n");
+    goto cleanup;
+  }
+
+  // Destroy current index and recreate it
+  cuvsMultiGpuIvfPqIndexDestroy(index);
+  cuvsMultiGpuIvfPqIndexCreate(&index);
+
+  // Deserialize index from file
+  cuvsError_t deserialize_result = cuvsMultiGpuIvfPqDeserialize(res, serialize_filename, index);
+  if (deserialize_result != CUVS_SUCCESS) {
+    printf("MG IVF-PQ deserialize failed\n");
+    goto cleanup;
+  }
+
+  // Create queries tensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {params.num_queries, params.dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // Create neighbors tensor
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = neighbors_data;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {params.num_queries, params.k};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
+
+  // Create distances tensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {params.num_queries, params.k};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // Search index
+  cuvsMultiGpuIvfPqSearchParams_t search_params;
+  cuvsMultiGpuIvfPqSearchParamsCreate(&search_params);
+  search_params->base_params->n_probes = params.nprobe;
+  search_params->search_mode           = CUVS_NEIGHBORS_MG_LOAD_BALANCER;
+  search_params->merge_mode            = CUVS_NEIGHBORS_MG_TREE_MERGE;
+  search_params->n_rows_per_batch      = 3000;
+
+  cuvsError_t search_result = cuvsMultiGpuIvfPqSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  if (search_result != CUVS_SUCCESS) {
+    printf("MG IVF-PQ search failed\n");
+    goto cleanup;
+  }
+
+  printf("MG IVF-PQ test completed successfully\n");
+
+cleanup:
+  cuvsMultiGpuIvfPqSearchParamsDestroy(search_params);
+  cuvsMultiGpuIvfPqIndexParamsDestroy(build_params);
+  cuvsMultiGpuIvfPqIndexDestroy(index);
+  cuvsMultiGpuResourcesDestroy(res);
+
+  // Clean up temporary serialization file
+  remove("/tmp/mg_ivf_pq_test_index.bin");
+
+  return (build_result == CUVS_SUCCESS && extend_result == CUVS_SUCCESS &&
+          serialize_result == CUVS_SUCCESS && deserialize_result == CUVS_SUCCESS &&
+          search_result == CUVS_SUCCESS)
+           ? 0
+           : 1;
+}
+
+// Test CAGRA multi-GPU functionality
+int run_mg_cagra_test(mg_test_params params,
+                      float* index_data,
+                      float* query_data,
+                      float* distances_data,
+                      int64_t* neighbors_data,
+                      float* ref_distances_data,
+                      int64_t* ref_neighbors_data)
+{
+  printf("Running MG CAGRA test (mode=%d, queries=%ld, db_vecs=%ld, dim=%ld, k=%ld)\n",
+         params.mode,
+         params.num_queries,
+         params.num_db_vecs,
+         params.dim,
+         params.k);
+
+  // Create multi-GPU resources
+  cuvsResources_t res;
+  cuvsMultiGpuResourcesCreate(&res);
+
+  // Create dataset tensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {params.num_db_vecs, params.dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // Create index
+  cuvsMultiGpuCagraIndex_t index;
+  cuvsMultiGpuCagraIndexCreate(&index);
+
+  // Build index
+  cuvsMultiGpuCagraIndexParams_t build_params;
+  cuvsMultiGpuCagraIndexParamsCreate(&build_params);
+
+  if (params.mode == MG_MODE_REPLICATED) {
+    build_params->mode = CUVS_NEIGHBORS_MG_REPLICATED;
+  } else {
+    build_params->mode = CUVS_NEIGHBORS_MG_SHARDED;
+  }
+
+  cuvsError_t build_result = cuvsMultiGpuCagraBuild(res, build_params, &dataset_tensor, index);
+  if (build_result != CUVS_SUCCESS) {
+    const char* error_msg = cuvsGetLastErrorText();
+    printf("MG CAGRA build failed: %s\n", error_msg ? error_msg : "Unknown error");
+    goto cleanup;
+  }
+
+  // Serialize index to test serialization/deserialization
+  const char* serialize_filename = "/tmp/mg_cagra_test_index.bin";
+  cuvsError_t serialize_result   = cuvsMultiGpuCagraSerialize(res, index, serialize_filename);
+  if (serialize_result != CUVS_SUCCESS) {
+    printf("MG CAGRA serialize failed\n");
+    goto cleanup;
+  }
+
+  // Destroy current index and recreate it
+  cuvsMultiGpuCagraIndexDestroy(index);
+  cuvsMultiGpuCagraIndexCreate(&index);
+
+  // Deserialize index from file
+  cuvsError_t deserialize_result = cuvsMultiGpuCagraDeserialize(res, serialize_filename, index);
+  if (deserialize_result != CUVS_SUCCESS) {
+    printf("MG CAGRA deserialize failed\n");
+    goto cleanup;
+  }
+
+  // Create queries tensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {params.num_queries, params.dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // Create neighbors tensor
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = neighbors_data;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {params.num_queries, params.k};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
+
+  // Create distances tensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCPU;  // Multi-GPU requires host memory
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {params.num_queries, params.k};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // Search index
+  cuvsMultiGpuCagraSearchParams_t search_params;
+  cuvsMultiGpuCagraSearchParamsCreate(&search_params);
+  search_params->search_mode      = CUVS_NEIGHBORS_MG_LOAD_BALANCER;
+  search_params->merge_mode       = CUVS_NEIGHBORS_MG_TREE_MERGE;
+  search_params->n_rows_per_batch = 3000;
+
+  cuvsError_t search_result = cuvsMultiGpuCagraSearch(
+    res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor);
+  if (search_result != CUVS_SUCCESS) {
+    printf("MG CAGRA search failed\n");
+    goto cleanup;
+  }
+
+  printf("MG CAGRA test completed successfully\n");
+
+cleanup:
+  cuvsMultiGpuCagraSearchParamsDestroy(search_params);
+  cuvsMultiGpuCagraIndexParamsDestroy(build_params);
+  cuvsMultiGpuCagraIndexDestroy(index);
+  cuvsMultiGpuResourcesDestroy(res);
+
+  // Clean up temporary serialization file
+  remove("/tmp/mg_cagra_test_index.bin");
+
+  return (build_result == CUVS_SUCCESS && serialize_result == CUVS_SUCCESS &&
+          deserialize_result == CUVS_SUCCESS && search_result == CUVS_SUCCESS)
+           ? 0
+           : 1;
+}
+
+// Generate reference results using brute force
+int generate_reference_results(mg_test_params params,
+                               float* index_data,
+                               float* query_data,
+                               float* ref_distances_data,
+                               int64_t* ref_neighbors_data)
+{
+  printf("Generating reference results using brute force\n");
+
+  // Create resources
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // Create dataset tensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = index_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCUDA;  // Brute force can use device memory
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {params.num_db_vecs, params.dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+
+  // Create queries tensor
+  DLManagedTensor queries_tensor;
+  queries_tensor.dl_tensor.data               = query_data;
+  queries_tensor.dl_tensor.device.device_type = kDLCUDA;  // Brute force can use device memory
+  queries_tensor.dl_tensor.ndim               = 2;
+  queries_tensor.dl_tensor.dtype.code         = kDLFloat;
+  queries_tensor.dl_tensor.dtype.bits         = 32;
+  queries_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t queries_shape[2]                    = {params.num_queries, params.dim};
+  queries_tensor.dl_tensor.shape              = queries_shape;
+  queries_tensor.dl_tensor.strides            = NULL;
+
+  // Create neighbors tensor
+  DLManagedTensor neighbors_tensor;
+  neighbors_tensor.dl_tensor.data               = ref_neighbors_data;
+  neighbors_tensor.dl_tensor.device.device_type = kDLCUDA;
+  neighbors_tensor.dl_tensor.ndim               = 2;
+  neighbors_tensor.dl_tensor.dtype.code         = kDLInt;
+  neighbors_tensor.dl_tensor.dtype.bits         = 64;
+  neighbors_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t neighbors_shape[2]                    = {params.num_queries, params.k};
+  neighbors_tensor.dl_tensor.shape              = neighbors_shape;
+  neighbors_tensor.dl_tensor.strides            = NULL;
+
+  // Create distances tensor
+  DLManagedTensor distances_tensor;
+  distances_tensor.dl_tensor.data               = ref_distances_data;
+  distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+  distances_tensor.dl_tensor.ndim               = 2;
+  distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+  distances_tensor.dl_tensor.dtype.bits         = 32;
+  distances_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t distances_shape[2]                    = {params.num_queries, params.k};
+  distances_tensor.dl_tensor.shape              = distances_shape;
+  distances_tensor.dl_tensor.strides            = NULL;
+
+  // Build brute force index
+  cuvsBruteForceIndex_t index;
+  cuvsBruteForceIndexCreate(&index);
+
+  cuvsError_t build_result = cuvsBruteForceBuild(res, &dataset_tensor, params.metric, 0.0f, index);
+  if (build_result != CUVS_SUCCESS) {
+    printf("Brute force build failed\n");
+    goto cleanup;
+  }
+
+  // Search with brute force
+  cuvsFilter filter;
+  filter.type = NO_FILTER;
+  filter.addr = (uintptr_t)NULL;
+
+  cuvsError_t search_result =
+    cuvsBruteForceSearch(res, index, &queries_tensor, &neighbors_tensor, &distances_tensor, filter);
+  if (search_result != CUVS_SUCCESS) {
+    printf("Brute force search failed\n");
+    goto cleanup;
+  }
+
+  printf("Reference results generated successfully\n");
+
+cleanup:
+  cuvsBruteForceIndexDestroy(index);
+  cuvsResourcesDestroy(res);
+
+  return (build_result == CUVS_SUCCESS && search_result == CUVS_SUCCESS) ? 0 : 1;
+}
diff --git a/docs/source/c_api/neighbors.rst b/docs/source/c_api/neighbors.rst
index 9c3fce672a..9de9c40a96 100644
--- a/docs/source/c_api/neighbors.rst
+++ b/docs/source/c_api/neighbors.rst
@@ -14,3 +14,4 @@ Nearest Neighbors
    neighbors_ivf_pq_c.rst
    neighbors_cagra_c.rst
    neighbors_hnsw_c.rst
+   neighbors_mg.rst
diff --git a/docs/source/c_api/neighbors_mg.rst b/docs/source/c_api/neighbors_mg.rst
new file mode 100644
index 0000000000..bffe3fc4c5
--- /dev/null
+++ b/docs/source/c_api/neighbors_mg.rst
@@ -0,0 +1,257 @@
+Multi-GPU Nearest Neighbors
+===========================
+
+The Multi-GPU (SNMG - single-node multi-GPUs) C API provides a set of functions to deploy ANN indexes across multiple GPUs for improved performance and scalability.
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+Common Types and Enums
+======================
+
+Common types and enums used across multi-GPU ANN algorithms.
+
+``#include <cuvs/neighbors/mg_common.h>``
+
+.. doxygengroup:: mg_c_common_types
+    :project: cuvs
+    :members:
+    :content-only:
+
+Multi-GPU IVF-Flat
+==================
+
+The Multi-GPU IVF-Flat method extends the IVF-Flat ANN algorithm to work across multiple GPUs. It provides two distribution modes: replicated (for higher throughput) and sharded (for handling larger datasets).
+
+``#include <cuvs/neighbors/mg_ivf_flat.h>``
+
+IVF-Flat Index Build Parameters
+-------------------------------
+
+.. doxygengroup:: mg_ivf_flat_c_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index Search Parameters
+--------------------------------
+
+.. doxygengroup:: mg_ivf_flat_c_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index
+--------------
+
+.. doxygengroup:: mg_ivf_flat_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index Build
+--------------------
+
+.. doxygengroup:: mg_ivf_flat_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index Search
+---------------------
+
+.. doxygengroup:: mg_ivf_flat_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index Extend
+---------------------
+
+.. doxygengroup:: mg_ivf_flat_c_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index Serialize
+------------------------
+
+.. doxygengroup:: mg_ivf_flat_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index Deserialize
+---------------------------
+
+.. doxygengroup:: mg_ivf_flat_c_index_deserialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-Flat Index Distribute
+--------------------------
+
+.. doxygengroup:: mg_ivf_flat_c_index_distribute
+    :project: cuvs
+    :members:
+    :content-only:
+
+Multi-GPU IVF-PQ
+=================
+
+The Multi-GPU IVF-PQ method extends the IVF-PQ ANN algorithm to work across multiple GPUs. It provides two distribution modes: replicated (for higher throughput) and sharded (for handling larger datasets).
+
+``#include <cuvs/neighbors/mg_ivf_pq.h>``
+
+IVF-PQ Index Build Parameters
+-----------------------------
+
+.. doxygengroup:: mg_ivf_pq_c_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index Search Parameters
+------------------------------
+
+.. doxygengroup:: mg_ivf_pq_c_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index
+------------
+
+.. doxygengroup:: mg_ivf_pq_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index Build
+------------------
+
+.. doxygengroup:: mg_ivf_pq_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index Search
+-------------------
+
+.. doxygengroup:: mg_ivf_pq_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index Extend
+-------------------
+
+.. doxygengroup:: mg_ivf_pq_c_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index Serialize
+----------------------
+
+.. doxygengroup:: mg_ivf_pq_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index Deserialize
+------------------------
+
+.. doxygengroup:: mg_ivf_pq_c_index_deserialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+IVF-PQ Index Distribute
+-----------------------
+
+.. doxygengroup:: mg_ivf_pq_c_index_distribute
+    :project: cuvs
+    :members:
+    :content-only:
+
+Multi-GPU CAGRA
+================
+
+The Multi-GPU CAGRA method extends the CAGRA graph-based ANN algorithm to work across multiple GPUs. It provides two distribution modes: replicated (for higher throughput) and sharded (for handling larger datasets).
+
+``#include <cuvs/neighbors/mg_cagra.h>``
+
+CAGRA Index Build Parameters
+----------------------------
+
+.. doxygengroup:: mg_cagra_c_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index Search Parameters
+-----------------------------
+
+.. doxygengroup:: mg_cagra_c_search_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index
+-----------
+
+.. doxygengroup:: mg_cagra_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index Build
+-----------------
+
+.. doxygengroup:: mg_cagra_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index Search
+------------------
+
+.. doxygengroup:: mg_cagra_c_index_search
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index Extend
+------------------
+
+.. doxygengroup:: mg_cagra_c_index_extend
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index Serialize
+---------------------
+
+.. doxygengroup:: mg_cagra_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index Deserialize
+-----------------------
+
+.. doxygengroup:: mg_cagra_c_index_deserialize
+    :project: cuvs
+    :members:
+    :content-only:
+
+CAGRA Index Distribute
+----------------------
+
+.. doxygengroup:: mg_cagra_c_index_distribute
+    :project: cuvs
+    :members:
+    :content-only:

From 3ca1ed9096f32112d3ceeabd558fc137ee10e2e9 Mon Sep 17 00:00:00 2001
From: Vinay Deshpande <vinayd@nvidia.com>
Date: Tue, 2 Sep 2025 16:50:12 +0100
Subject: [PATCH 564/623] Fix redundant memset (#1305)

The workspace is already being reset in the calling function `fusedDistanceNNImpl`, see https://github.com/rapidsai/cuvs/blob/branch-25.10/cpp/src/distance/detail/fused_distance_nn.cuh#L73
Therefore removing the memset in `fusedL2NNImpl`

Authors:
  - Vinay Deshpande (https://github.com/vinaydes)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/1305
---
 cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh b/cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh
index 59fe10ea00..0d194f3833 100644
--- a/cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh
+++ b/cpp/src/distance/detail/fused_distance_nn/fused_l2_nn.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2021-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -64,7 +64,6 @@ void fusedL2NNImpl(OutT* min,
   constexpr auto maxVal = std::numeric_limits<DataT>::max();
   typedef raft::KeyValuePair<IdxT, DataT> KVPair;
 
-  RAFT_CUDA_TRY(cudaMemsetAsync(workspace, 0, sizeof(int) * m, stream));
   if (initOutBuffer) {
     initKernel<DataT, OutT, IdxT, ReduceOpT>
       <<<nblks, P::Nthreads, 0, stream>>>(min, m, maxVal, redOp);

From 1f482cac6ac3f934eba42a4929d1b34b1af77b61 Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 3 Sep 2025 13:39:33 -0700
Subject: [PATCH 565/623] Expose pq-centers to C and Python (#1303)

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1303
---
 cpp/include/cuvs/neighbors/ivf_flat.h         |  7 +--
 cpp/include/cuvs/neighbors/ivf_pq.h           | 22 +++++++---
 cpp/src/neighbors/ivf_flat_c.cpp              | 41 ++++--------------
 cpp/src/neighbors/ivf_pq_c.cpp                | 43 +++++++++++--------
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |  5 +--
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx | 17 +++-----
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd  |  8 ++--
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  | 30 ++++++++-----
 python/cuvs/cuvs/tests/test_ivf_pq.py         |  7 ++-
 9 files changed, 91 insertions(+), 89 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index e274af93b9..7d647829ad 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -174,14 +174,11 @@ uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index);
 /**
  * @brief Get the cluster centers corresponding to the lists [n_lists, dim]
  *
- * @param[in] res cuvsResources_t opaque C handle
  * @param[in] index cuvsIvfFlatIndex_t Built Ivf-Flat Index
  * @param[out] centers Preallocated array on host or device memory to store output, [n_lists, dim]
  * @return cuvsError_t
  */
-cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsResources_t res,
-                                       cuvsIvfFlatIndex_t index,
-                                       DLManagedTensor* centers);
+cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsIvfFlatIndex_t index, DLManagedTensor* centers);
 
 /**
  * @}
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index 40b9e9b839..5c349be5cb 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -270,15 +270,23 @@ uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index);
 /**
  * @brief Get the cluster centers corresponding to the lists in the original space
  *
- * @param[in] res cuvsResources_t opaque C handle
  * @param[in] index cuvsIvfPqIndex_t Built Ivf-Pq index
- * @param[out] centers Preallocated array on host or device memory to store output,
- * dimensions [n_lists, dim]
+ * @param[out] centers Output tensor that will be populated with a non-owning view of the data
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsIvfPqIndexGetCenters(cuvsIvfPqIndex_t index, DLManagedTensor* centers);
+
+/**
+ * @brief Get the PQ cluster centers
+ *
+ *   - codebook_gen::PER_SUBSPACE: [pq_dim , pq_len, pq_book_size]
+ *   - codebook_gen::PER_CLUSTER:  [n_lists, pq_len, pq_book_size]
+ *
+ * @param[in] index cuvsIvfPqIndex_t Built Ivf-Pq index
+ * @param[out] pq_centers Output tensor that will be populated with a non-owning view of the data
  * @return cuvsError_t
  */
-cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
-                                     cuvsIvfPqIndex_t index,
-                                     DLManagedTensor* centers);
+cuvsError_t cuvsIvfPqIndexGetPqCenters(cuvsIvfPqIndex_t index, DLManagedTensor* pq_centers);
 /**
  * @}
  */
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index 2e660f6377..91ad9abbed 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -1,6 +1,6 @@
 
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -154,33 +154,11 @@ void _extend(cuvsResources_t res,
   cuvs::neighbors::ivf_flat::extend(*res_ptr, vectors_mds, indices_mds, index_ptr);
 }
 
-template <typename output_mdspan_type, typename T, typename IdxT>
-void _get_centers(cuvsResources_t res, cuvsIvfFlatIndex index, DLManagedTensor* centers)
-{
-  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
-  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
-  auto dst       = cuvs::core::from_dlpack<output_mdspan_type>(centers);
-  auto src       = index_ptr->centers();
-
-  RAFT_EXPECTS(src.extent(0) == dst.extent(0), "Output centers has incorrect number of rows");
-  RAFT_EXPECTS(src.extent(1) == dst.extent(1), "Output centers has incorrect number of cols");
-
-  raft::copy(dst.data_handle(),
-             src.data_handle(),
-             dst.extent(0) * dst.extent(1),
-             raft::resource::get_cuda_stream(*res_ptr));
-}
-
 template <typename T, typename IdxT>
-void get_centers(cuvsResources_t res, cuvsIvfFlatIndex index, DLManagedTensor* centers)
+void get_centers(cuvsIvfFlatIndex index, DLManagedTensor* centers)
 {
-  if (cuvs::core::is_dlpack_device_compatible(centers->dl_tensor)) {
-    using output_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
-    _get_centers<output_mdspan_type, T, IdxT>(res, index, centers);
-  } else {
-    using output_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
-    _get_centers<output_mdspan_type, T, IdxT>(res, index, centers);
-  }
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_flat::index<T, IdxT>*>(index.addr);
+  cuvs::core::to_dlpack(index_ptr->centers(), centers);
 }
 }  // namespace
 
@@ -441,19 +419,18 @@ extern "C" uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index)
   }
 }
 
-extern "C" cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsResources_t res,
-                                                  cuvsIvfFlatIndex_t index,
+extern "C" cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsIvfFlatIndex_t index,
                                                   DLManagedTensor* centers)
 {
   return cuvs::core::translate_exceptions([=] {
     if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
-      get_centers<float, int64_t>(res, *index, centers);
+      get_centers<float, int64_t>(*index, centers);
     } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
-      get_centers<half, int64_t>(res, *index, centers);
+      get_centers<half, int64_t>(*index, centers);
     } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
-      get_centers<int8_t, int64_t>(res, *index, centers);
+      get_centers<int8_t, int64_t>(*index, centers);
     } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
-      get_centers<uint8_t, int64_t>(res, *index, centers);
+      get_centers<uint8_t, int64_t>(*index, centers);
     } else {
       RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
     }
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 40871f7ef8..4abd080582 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -157,14 +157,25 @@ void _extend(cuvsResources_t res,
   }
 }
 
-template <typename output_mdspan_type, typename IdxT>
-void _get_centers(cuvsResources_t res, cuvsIvfPqIndex index, DLManagedTensor* centers)
+template <typename IdxT>
+void _get_centers(cuvsIvfPqIndex index, DLManagedTensor* output)
 {
-  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
   auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
-  auto dst       = cuvs::core::from_dlpack<output_mdspan_type>(centers);
 
-  cuvs::neighbors::ivf_pq::helpers::extract_centers(*res_ptr, *index_ptr, dst);
+  auto centers = index_ptr->centers();
+
+  // mimic the behaviour of `extract_centers` without performing a copy of the data
+  auto strided_centers = raft::make_device_strided_matrix_view(
+    centers.data_handle(), centers.extent(0), index_ptr->dim(), index_ptr->dim_ext());
+
+  cuvs::core::to_dlpack(strided_centers, output);
+}
+
+template <typename IdxT>
+void _get_pq_centers(cuvsIvfPqIndex index, DLManagedTensor* centers)
+{
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<IdxT>*>(index.addr);
+  cuvs::core::to_dlpack(index_ptr->pq_centers(), centers);
 }
 }  // namespace
 
@@ -348,17 +359,13 @@ extern "C" uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index)
   return index_ptr->dim();
 }
 
-extern "C" cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
-                                                cuvsIvfPqIndex_t index,
-                                                DLManagedTensor* centers)
+extern "C" cuvsError_t cuvsIvfPqIndexGetCenters(cuvsIvfPqIndex_t index, DLManagedTensor* centers)
 {
-  return cuvs::core::translate_exceptions([=] {
-    if (cuvs::core::is_dlpack_device_compatible(centers->dl_tensor)) {
-      using output_mdspan_type = raft::device_matrix_view<float, int64_t, raft::row_major>;
-      _get_centers<output_mdspan_type, int64_t>(res, *index, centers);
-    } else {
-      using output_mdspan_type = raft::host_matrix_view<float, int64_t, raft::row_major>;
-      _get_centers<output_mdspan_type, int64_t>(res, *index, centers);
-    }
-  });
+  return cuvs::core::translate_exceptions([=] { _get_centers<int64_t>(*index, centers); });
+}
+
+extern "C" cuvsError_t cuvsIvfPqIndexGetPqCenters(cuvsIvfPqIndex_t index,
+                                                  DLManagedTensor* pq_centers)
+{
+  return cuvs::core::translate_exceptions([=] { _get_pq_centers<int64_t>(*index, pq_centers); });
 }
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
index b97faa1ad9..6e30ac0a07 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -66,8 +66,7 @@ cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
 
     uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index)
 
-    cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsResources_t res,
-                                           cuvsIvfFlatIndex_t index,
+    cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsIvfFlatIndex_t index,
                                            DLManagedTensor * centers)
 
     cuvsError_t cuvsIvfFlatBuild(cuvsResources_t res,
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 1ad8167cdc..6fb4279db1 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -32,6 +32,7 @@ from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
+from cuvs.common.device_tensor_view import DeviceTensorView
 from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
 from cuvs.neighbors.filters import no_filter
@@ -196,19 +197,15 @@ cdef class Index:
     def centers(self):
         """ Get the cluster centers corresponding to the lists in the
         original space """
-        return self._get_centers()
-
-    @auto_sync_resources
-    def _get_centers(self, resources=None):
         if not self.trained:
             raise ValueError("Index needs to be built before getting centers")
 
-        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+        output = DeviceTensorView()
+        cdef cydlpack.DLManagedTensor * tensor = \
+            <cydlpack.DLManagedTensor*><size_t>output.get_handle()
+        check_cuvs(cuvsIvfFlatIndexGetCenters(self.index, tensor))
+        output.parent = self
 
-        output = np.empty((self.n_lists, self.dim), dtype='float32')
-        ai = wrap_array(output)
-        cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
-        check_cuvs(cuvsIvfFlatIndexGetCenters(res, self.index, output_dlpack))
         return output
 
 
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index 06932ac601..897e4951a9 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -88,10 +88,12 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
 
     uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index)
 
-    cuvsError_t cuvsIvfPqIndexGetCenters(cuvsResources_t res,
-                                         cuvsIvfPqIndex_t index,
+    cuvsError_t cuvsIvfPqIndexGetCenters(cuvsIvfPqIndex_t index,
                                          DLManagedTensor * centers)
 
+    cuvsError_t cuvsIvfPqIndexGetPqCenters(cuvsIvfPqIndex_t index,
+                                           DLManagedTensor * centers)
+
     cuvsError_t cuvsIvfPqBuild(cuvsResources_t res,
                                cuvsIvfPqIndexParams* params,
                                DLManagedTensor* dataset,
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index a2208b5148..40f22c236b 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -32,6 +32,7 @@ from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
 from pylibraft.common.cai_wrapper import wrap_array
 from pylibraft.common.interruptible import cuda_interruptible
 
+from cuvs.common.device_tensor_view import DeviceTensorView
 from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
 from cuvs.neighbors.common import _check_input_array
 
@@ -257,19 +258,28 @@ cdef class Index:
     def centers(self):
         """ Get the cluster centers corresponding to the lists in the
         original space """
-        return self._get_centers()
-
-    @auto_sync_resources
-    def _get_centers(self, resources=None):
         if not self.trained:
             raise ValueError("Index needs to be built before getting centers")
 
-        cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+        output = DeviceTensorView()
+        cdef cydlpack.DLManagedTensor * tensor = \
+            <cydlpack.DLManagedTensor*><size_t>output.get_handle()
+        check_cuvs(cuvsIvfPqIndexGetCenters(self.index, tensor))
+        output.parent = self
+        return output
 
-        output = np.empty((self.n_lists, self.dim), dtype='float32')
-        ai = wrap_array(output)
-        cdef cydlpack.DLManagedTensor* output_dlpack = cydlpack.dlpack_c(ai)
-        check_cuvs(cuvsIvfPqIndexGetCenters(res, self.index, output_dlpack))
+    @property
+    def pq_centers(self):
+        """ Get the PQ cluster centers """
+        if not self.trained:
+            raise ValueError("Index needs to be built before getting"
+                             " pq centers")
+
+        output = DeviceTensorView()
+        cdef cydlpack.DLManagedTensor * tensor = \
+            <cydlpack.DLManagedTensor*><size_t>output.get_handle()
+        check_cuvs(cuvsIvfPqIndexGetPqCenters(self.index, tensor))
+        output.parent = self
         return output
 
 
diff --git a/python/cuvs/cuvs/tests/test_ivf_pq.py b/python/cuvs/cuvs/tests/test_ivf_pq.py
index b6365c5485..a7367b9736 100644
--- a/python/cuvs/cuvs/tests/test_ivf_pq.py
+++ b/python/cuvs/cuvs/tests/test_ivf_pq.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -112,6 +112,11 @@ def run_ivf_pq_build_search_test(
 
     centers = index.centers
     assert centers.shape[0] == n_lists
+    assert centers.shape[1] == index.dim
+
+    pq_centers = index.pq_centers
+    assert len(pq_centers.shape) == 3
+    assert pq_centers.shape[2] == 1 << pq_bits
 
     if not compare:
         return

From d24564a0a8e0dd6fbd4bcec46b04e19dfc8f2564 Mon Sep 17 00:00:00 2001
From: stic <1974622+stic@users.noreply.github.com>
Date: Fri, 5 Sep 2025 16:28:31 +0200
Subject: [PATCH 566/623] Add note about Linux-only support for cuVS
 pre-compiled packages (#1306)

As I've dived in into cuVS from outside RAPIDS project I wasn't aware of target platform. Hence lost coffee break trying to troubleshoot (along hallucinating GPT insisting it's issue with latest CUDA toolkit).

Added one liner to reference to RAPIDS docs on WSL2 support

Authors:
  - https://github.com/stic

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1306
---
 docs/source/build.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/source/build.rst b/docs/source/build.rst
index 26c361dfbc..aee49bbf20 100644
--- a/docs/source/build.rst
+++ b/docs/source/build.rst
@@ -31,6 +31,8 @@ The cuVS software development kit provides APIs for C, C++, Python, and Rust lan
 Installing Pre-compiled Packages
 --------------------------------
 
+**Note:** The cuVS pre-compiled packages are available for **Linux** only (x86_64 and aarch64 architectures). Native Windows support is not available at this time. On Windows, use **WSL2** with GPU passthrough. See the `RAPIDS WSL2 guide <https://rapids.ai/start.html#wsl2>`_.
+
 C, C++, and Python through Conda
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

From 2c0e124e3d38575f7f8d920679166dcf7258474c Mon Sep 17 00:00:00 2001
From: James Lamb <jaylamb20@gmail.com>
Date: Fri, 5 Sep 2025 14:58:27 -0500
Subject: [PATCH 567/623] Use branch-25.10 again (#1319)

Contributes to https://github.com/rapidsai/build-planning/issues/208

Now that https://github.com/rapidsai/shared-workflows/pull/413 is
merged, this converts all GitHub Actions references from `@cuda13.0`
back to `branch-25.10`.

## Notes for Reviewers

This is safe to admin-merge because the change is a no-op... configs on
those 2 branches are identical.
---
 .github/workflows/build.yaml                  | 22 ++++++-------
 .github/workflows/pr.yaml                     | 32 +++++++++----------
 .github/workflows/test.yaml                   | 10 +++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 4 files changed, 33 insertions(+), 33 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index ccb75dbdb7..892764844b 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -34,7 +34,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -44,7 +44,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -65,7 +65,7 @@ jobs:
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -86,7 +86,7 @@ jobs:
   java-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -108,7 +108,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -118,7 +118,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -133,7 +133,7 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
@@ -145,7 +145,7 @@ jobs:
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -159,7 +159,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -170,7 +170,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -182,7 +182,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 2c7a61fd39..15b3f35efb 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.10
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -54,7 +54,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.10
     with:
       files_yaml: |
         test_cpp:
@@ -106,14 +106,14 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.10
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
     with:
       build_type: pull-request
       node_type: cpu16
@@ -121,7 +121,7 @@ jobs:
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
@@ -129,21 +129,21 @@ jobs:
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
     with:
       build_type: pull-request
       symbol_exclusions: (void (thrust::|cub::))
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
     with:
       build_type: pull-request
       script: ci/build_python.sh
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -151,7 +151,7 @@ jobs:
   conda-java-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_java
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
@@ -172,7 +172,7 @@ jobs:
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -182,7 +182,7 @@ jobs:
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -200,7 +200,7 @@ jobs:
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -218,7 +218,7 @@ jobs:
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -229,7 +229,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -238,7 +238,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -246,7 +246,7 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.10
     with:
       arch: '["amd64", "arm64"]'
       cuda: '["13.0"]'
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 51418cbd22..c5892ca7ea 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -25,7 +25,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -43,7 +43,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -52,7 +52,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-java-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -72,7 +72,7 @@ jobs:
       script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 72751d071b..48bf37afc4 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@cuda13.0
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.10
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}

From 03d62f663d8f9dbed85dacbb353bed8cd3d38dc9 Mon Sep 17 00:00:00 2001
From: rmaschal <rmaschal@nvidia.com>
Date: Tue, 9 Sep 2025 22:44:59 -0700
Subject: [PATCH 568/623] ScaNN: Overlapped gather for AVQ (#1286)

Adds a class cluster_loader for AVQ that enables overlapping the gather operation and HtoD copy with GPU computation.

There are two scenarios:
1. dataset on device: This is identical to the previous code, using raft::matrix::gather to perform the gather on device.
2. dataset on host: cluster_loader allocates to pinned buffers in host for fast (and possibly async) copies of cluster vectors DtoH. The actual gather operation is performed on cpu, into the pinned buffer. Copies can be overlapped with GPU work (namely AVQ update of the previous cluster) if scheduled on a separate stream.

Authors:
  - https://github.com/rmaschal

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Ben Karsin (https://github.com/bkarsin)

URL: https://github.com/rapidsai/cuvs/pull/1286
---
 cpp/src/neighbors/scann/detail/scann_avq.cuh  | 253 ++++++++++++++++--
 .../neighbors/scann/detail/scann_build.cuh    |   8 +-
 2 files changed, 235 insertions(+), 26 deletions(-)

diff --git a/cpp/src/neighbors/scann/detail/scann_avq.cuh b/cpp/src/neighbors/scann/detail/scann_avq.cuh
index cdfd4fbf28..1c42f6d2b9 100644
--- a/cpp/src/neighbors/scann/detail/scann_avq.cuh
+++ b/cpp/src/neighbors/scann/detail/scann_avq.cuh
@@ -68,7 +68,8 @@ __global__ void build_clusters(
 template <typename LabelT, typename IdxT>
 void compute_cluster_offsets(raft::resources const& dev_resources,
                              raft::device_vector_view<const LabelT, IdxT> clusters,
-                             raft::device_vector_view<LabelT, int64_t> cluster_sizes)
+                             raft::device_vector_view<LabelT, int64_t> cluster_sizes,
+                             int64_t& max_cluster_size)
 {
   cudaStream_t stream = raft::resource::get_cuda_stream(dev_resources);
   rmm::device_async_resource_ref device_memory =
@@ -103,10 +104,30 @@ void compute_cluster_offsets(raft::resources const& dev_resources,
                                       clusters.extent(0),
                                       stream);
 
+  int num_items = cluster_sizes.extent(0);
+
+  // Compute max cluster size
+  // auto d_max_cluster_size = rmmo::make_device_scalar<int64_t>(dev_resources, 0);
+  rmm::device_scalar<int64_t> d_max_cluster_size(stream);
+
   temp_storage_bytes = 0;
+
+  cub::DeviceReduce::Max(
+    nullptr, temp_storage_bytes, cluster_sizes.data_handle(), d_max_cluster_size.data(), num_items);
+
+  rmm::device_uvector<int64_t> temp_storage_max(temp_storage_bytes, stream, device_memory);
+
+  cub::DeviceReduce::Max(temp_storage_max.data(),
+                         temp_storage_bytes,
+                         cluster_sizes.data_handle(),
+                         d_max_cluster_size.data(),
+                         num_items);
+
+  max_cluster_size = d_max_cluster_size.value(stream);
+
   // Scan to sum cluster sizes and get cluster start ptrs in flat array
   // Done in place
-  int num_items = cluster_sizes.extent(0);
+  temp_storage_bytes = 0;
 
   cub::DeviceScan::ExclusiveSum(nullptr,
                                 temp_storage_bytes,
@@ -368,6 +389,190 @@ void rescale_avq_centroids(raft::resources const& dev_resources,
                            });
 }
 
+/**
+ * A class for loading clusters into a compact matrix (sparse gather)
+ * for use in AVQ.
+ *
+ * There are two possible scenarios:
+ * 1. Dataset is stored in device memory: No host buffers are allocated,
+ *    and the gather is performed on device
+ * 2. Dataset is stored in host memory: Two pinned buffers are allocated
+ *    in host for fast DtoH copies of cluster ids, and fast HtoD copy of the
+ *    cluster matrix, while amortizing the cost of allocating pinned memory.
+ *    The gather is performed on cpu, overlapping with GPU compute. Copies are
+ * 	 allocated on the provided stream, allowing for overlapping with
+ *		 other work on other streams.
+ */
+template <typename T, typename LabelT>
+class cluster_loader {
+ private:
+  raft::pinned_matrix<T, int64_t> cluster_buf_;
+  raft::pinned_vector<LabelT, int64_t> cluster_ids_buf_;
+  raft::device_matrix<T, int64_t> d_cluster_buf_;
+  raft::device_matrix<T, int64_t> d_cluster_copy_buf_;
+  const T* dataset_ptr_;
+  raft::host_vector_view<const LabelT> h_cluster_offsets_;
+  raft::device_vector_view<const LabelT> cluster_ids_;
+  cudaStream_t stream_;
+  int64_t dim_;
+  int64_t n_rows_;
+  bool needs_copy_;
+
+  int64_t cur_idx_  = -1;
+  int64_t copy_idx_ = -1;
+
+  size_t cluster_size(LabelT idx)
+  {
+    if (idx + 1 < h_cluster_offsets_.extent(0)) {
+      return h_cluster_offsets_(idx + 1) - h_cluster_offsets_(idx);
+    }
+    return n_rows_ - h_cluster_offsets_(idx);
+  }
+
+  cluster_loader(raft::resources const& res,
+                 const T* dataset_ptr,
+                 int64_t dim,
+                 int64_t n_rows,
+                 int64_t max_cluster_size,
+                 int64_t h_buf_size,
+                 raft::host_vector_view<LabelT> h_cluster_offsets,
+                 raft::device_vector_view<LabelT> cluster_ids,
+                 bool needs_copy,
+                 cudaStream_t stream)
+    : dim_(dim),
+      n_rows_(n_rows),
+      dataset_ptr_(dataset_ptr),
+      cluster_buf_(raft::make_pinned_matrix<T, int64_t>(res, h_buf_size, dim)),
+      cluster_ids_buf_(raft::make_pinned_vector<LabelT, int64_t>(res, h_buf_size)),
+      d_cluster_buf_(raft::make_device_matrix<T, int64_t>(res, max_cluster_size, dim)),
+      d_cluster_copy_buf_(raft::make_device_matrix<T, int64_t>(res, max_cluster_size, dim)),
+      h_cluster_offsets_(h_cluster_offsets),
+      cluster_ids_(cluster_ids),
+      needs_copy_(needs_copy),
+      stream_(stream)
+  {
+  }
+
+ public:
+  cluster_loader(raft::resources const& res,
+                 raft::device_matrix_view<const T, int64_t> dataset_view,
+                 raft::host_vector_view<LabelT> h_cluster_offsets,
+                 raft::device_vector_view<LabelT> cluster_ids,
+                 int64_t max_cluster_size,
+                 cudaStream_t stream)
+    : cluster_loader(res,
+                     dataset_view.data_handle(),
+                     dataset_view.extent(1),
+                     dataset_view.extent(0),
+                     max_cluster_size,
+                     0,
+                     h_cluster_offsets,
+                     cluster_ids,
+                     false,
+                     stream)
+
+  {
+  }
+
+  cluster_loader(raft::resources const& res,
+                 raft::host_matrix_view<const T, int64_t> dataset_view,
+                 raft::host_vector_view<LabelT> h_cluster_offsets,
+                 raft::device_vector_view<LabelT> cluster_ids,
+                 int64_t max_cluster_size,
+                 cudaStream_t stream)
+    : cluster_loader(res,
+                     dataset_view.data_handle(),
+                     dataset_view.extent(1),
+                     dataset_view.extent(0),
+                     max_cluster_size,
+                     max_cluster_size,
+                     h_cluster_offsets,
+                     cluster_ids,
+                     true,
+                     stream)
+
+  {
+  }
+
+  /**
+   * @brief load and return a view of the provided cluster
+   *
+   * @param res: the raft resources;
+   * @param cluster_idx: the index of the cluster to be loaded
+   * @return device_matrix_view of the cluster vectors
+   */
+  raft::device_matrix_view<T, int64_t> load_cluster(raft::resources const& res, LabelT cluster_idx)
+  {
+    size_t size = cluster_size(cluster_idx);
+
+    // Check if cluster is already loaded
+    if (cur_idx_ != cluster_idx) {
+      // If not, load the cluster
+      if (copy_idx_ != cluster_idx) { prefetch_cluster(res, cluster_idx); }
+
+      // swap buffers
+      std::swap(d_cluster_buf_, d_cluster_copy_buf_);
+      std::swap(cur_idx_, copy_idx_);
+    }
+
+    return raft::make_device_matrix_view<T, int64_t>(d_cluster_buf_.data_handle(), size, dim_);
+  }
+
+  /** @brief Perform gather operation on stream_
+   *
+   * @param res: the raft resources
+   * @param cluster_idx: the index of the cluster
+   */
+  void prefetch_cluster(raft::resources const& res, LabelT cluster_idx)
+  {
+    if (cluster_idx >= h_cluster_offsets_.extent(0)) { return; }
+
+    size_t size = cluster_size(cluster_idx);
+
+    auto cluster_ids = raft::make_device_vector_view<const LabelT, int64_t>(
+      cluster_ids_.data_handle() + h_cluster_offsets_(cluster_idx), size);
+
+    auto cluster_vectors =
+      raft::make_device_matrix_view<float, int64_t>(d_cluster_copy_buf_.data_handle(), size, dim_);
+
+    if (needs_copy_) {
+      // htod
+      auto h_cluster_ids =
+        raft::make_pinned_vector_view<LabelT, int64_t>(cluster_ids_buf_.data_handle(), size);
+
+      raft::copy(
+        h_cluster_ids.data_handle(), cluster_ids.data_handle(), cluster_ids.size(), stream_);
+      raft::resource::sync_stream(res, stream_);
+
+      auto pinned_cluster = raft::make_pinned_matrix_view<T, int64_t>(
+        cluster_buf_.data_handle(), cluster_vectors.extent(0), cluster_vectors.extent(1));
+
+      int n_threads = std::min<int>(omp_get_max_threads(), 32);
+#pragma omp parallel for num_threads(n_threads)
+      for (int i = 0; i < h_cluster_ids.extent(0); i++) {
+        memcpy(pinned_cluster.data_handle() + i * pinned_cluster.extent(1),
+               dataset_ptr_ + h_cluster_ids(i) * dim_,
+               sizeof(T) * dim_);
+      }
+
+      raft::copy(cluster_vectors.data_handle(),
+                 pinned_cluster.data_handle(),
+                 pinned_cluster.size(),
+                 stream_);
+      raft::resource::sync_stream(res, stream_);
+
+    } else {
+      // dtod
+      auto dataset_view =
+        raft::make_device_matrix_view<const T, int64_t>(dataset_ptr_, n_rows_, dim_);
+
+      raft::matrix::gather(res, dataset_view, cluster_ids, cluster_vectors);
+    }
+
+    copy_idx_ = cluster_idx;
+  }
+};
+
 /**
  * @brief Perform AVQ adjustment on cluster centers
  *
@@ -396,26 +601,28 @@ void apply_avq(raft::resources const& res,
                raft::mdspan<const T, raft::matrix_extent<IdxT>, raft::row_major, Accessor> dataset,
                raft::device_matrix_view<T, IdxT> centroids_view,
                raft::device_vector_view<const LabelT, IdxT> labels_view,
-               float eta)
+               float eta,
+               cudaStream_t copy_stream)
 {
   // Compute clusters
 
-  cudaStream_t stream = raft::resource::get_cuda_stream(res);
-  auto cluster_ptrs   = raft::make_device_vector<uint32_t, int64_t>(res, centroids_view.extent(0));
-  auto clusters       = raft::make_device_vector<uint32_t, int64_t>(res, dataset.extent(0));
-
-  compute_cluster_offsets(res, labels_view, cluster_ptrs.view());
+  cudaStream_t stream  = raft::resource::get_cuda_stream(res);
+  auto cluster_offsets = raft::make_device_vector<uint32_t, int64_t>(res, centroids_view.extent(0));
+  auto clusters        = raft::make_device_vector<uint32_t, int64_t>(res, dataset.extent(0));
+  int64_t max_cluster_size = 0;
 
-  auto h_cluster_ptrs = raft::make_host_vector<uint32_t, int64_t>(cluster_ptrs.extent(0));
+  compute_cluster_offsets(res, labels_view, cluster_offsets.view(), max_cluster_size);
+  auto h_cluster_offsets = raft::make_host_vector<uint32_t, int64_t>(cluster_offsets.extent(0));
 
-  raft::copy(h_cluster_ptrs.data_handle(), cluster_ptrs.data_handle(), cluster_ptrs.size(), stream);
+  raft::copy(
+    h_cluster_offsets.data_handle(), cluster_offsets.data_handle(), cluster_offsets.size(), stream);
 
   dim3 block(32, 1, 1);
   dim3 grid((dataset.extent(0) + block.x - 1) / block.x, 1, 1);
 
   build_clusters<uint32_t, uint32_t><<<grid, block>>>(labels_view.data_handle(),
                                                       clusters.view().data_handle(),
-                                                      cluster_ptrs.view().data_handle(),
+                                                      cluster_offsets.view().data_handle(),
                                                       dataset.extent(0),
                                                       labels_view.extent(0));
   RAFT_CUDA_TRY(cudaPeekAtLastError());
@@ -423,36 +630,34 @@ void apply_avq(raft::resources const& res,
   auto rescale_num   = raft::make_device_vector<float, int64_t>(res, centroids_view.extent(0));
   auto rescale_denom = raft::make_device_vector<float, int64_t>(res, centroids_view.extent(0));
 
+  cluster_loader<T, LabelT> loader(
+    res, dataset, h_cluster_offsets.view(), clusters.view(), max_cluster_size, copy_stream);
   raft::resource::sync_stream(res);
 
   RAFT_LOG_DEBUG("Compute AVQ centroids\n");
 
-  for (int i = 0; i < h_cluster_ptrs.extent(0); i++) {
-    int cluster_size = i + 1 < h_cluster_ptrs.extent(0) ? h_cluster_ptrs(i + 1) - h_cluster_ptrs(i)
-                                                        : dataset.extent(0) - h_cluster_ptrs(i);
+  for (int i = 0; i < h_cluster_offsets.extent(0); i++) {
+    auto cluster_vectors = loader.load_cluster(res, i);
 
-    if (cluster_size == 0) { continue; }
-    auto cluster_ids = raft::make_device_vector_view<const uint32_t, int64_t>(
-      clusters.data_handle() + h_cluster_ptrs(i), cluster_size);
-    auto cluster_vectors =
-      raft::make_device_matrix<float, int64_t>(res, cluster_size, dataset.extent(1));
     auto avq_centroid = raft::make_device_vector_view<float, int64_t>(
       centroids_view.data_handle() + i * dataset.extent(1), dataset.extent(1));
     auto rescale_num_view   = raft::make_device_scalar_view<float>(rescale_num.data_handle() + i);
     auto rescale_denom_view = raft::make_device_scalar_view<float>(rescale_denom.data_handle() + i);
 
-    gather_functor<float, uint32_t>{}(
-      res, dataset, cluster_ids, cluster_vectors.view(), raft::resource::get_cuda_stream(res));
-
     compute_avq_centroid(
-      res, cluster_vectors.view(), avq_centroid, rescale_num_view, rescale_denom_view, eta);
+      res, cluster_vectors, avq_centroid, rescale_num_view, rescale_denom_view, eta);
+
+    loader.prefetch_cluster(res, i + 1);
+
+    // make sure work is done before swapping buffers in cluster_loader
+    raft::resource::sync_stream(res);
   }
 
   rescale_avq_centroids(res,
                         centroids_view,
                         rescale_num.view(),
                         rescale_denom.view(),
-                        cluster_ptrs.view(),
+                        cluster_offsets.view(),
                         dataset.extent(0));
 
   raft::resource::sync_stream(res);
diff --git a/cpp/src/neighbors/scann/detail/scann_build.cuh b/cpp/src/neighbors/scann/detail/scann_build.cuh
index da723d56e9..62ab8a317f 100644
--- a/cpp/src/neighbors/scann/detail/scann_build.cuh
+++ b/cpp/src/neighbors/scann/detail/scann_build.cuh
@@ -179,8 +179,12 @@ index<T, IdxT> build(
   }
 
   // AVQ update of KMeans centroids
-  apply_avq(
-    res, dataset, centroids_view, raft::make_const_mdspan(labels_view), params.partitioning_eta);
+  apply_avq(res,
+            dataset,
+            centroids_view,
+            raft::make_const_mdspan(labels_view),
+            params.partitioning_eta,
+            copy_stream);
 
   raft::device_vector_view<uint32_t, int64_t> soar_labels_view = idx.soar_labels();
 

From 21357feac3742c246af187ac30ae2b35178789b0 Mon Sep 17 00:00:00 2001
From: James Lamb <jaylamb20@gmail.com>
Date: Wed, 10 Sep 2025 13:40:55 -0500
Subject: [PATCH 569/623] update dependencies: use cuda-toolkit wheels (#1326)

Contributes to https://github.com/rapidsai/build-planning/issues/213

* switches uses of `nvidia-cuda-*-cu{12,13}` wheels to https://pypi.org/project/cuda-toolkit/

## Notes for Reviewers

### Benefits of these changes

Using the `cuda-toolkit` metapackage means we don't need to maintain our own pins for mutually compatible versions of CTK sub-components (which each follow their own different versioning conventions).

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Mike Sarahan (https://github.com/msarahan)

URL: https://github.com/rapidsai/cuvs/pull/1326
---
 dependencies.yaml             | 17 ++++-------------
 python/libcuvs/CMakeLists.txt | 19 ++++++++++++-------
 python/libcuvs/pyproject.toml |  5 +----
 3 files changed, 17 insertions(+), 24 deletions(-)

diff --git a/dependencies.yaml b/dependencies.yaml
index c370e90f88..adb923afab 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -329,29 +329,20 @@ dependencies:
               cuda: "12.*"
               use_cuda_wheels: "true"
             packages:
-              - nvidia-cublas-cu12
-              - nvidia-curand-cu12
-              - nvidia-cusolver-cu12
-              - nvidia-cusparse-cu12
+              - cuda-toolkit[cublas,curand,cusolver,cusparse]==12.*
           - matrix:
               cuda: "13.*"
               use_cuda_wheels: "true"
             packages:
-              - nvidia-cublas-cu13
-              - nvidia-curand-cu13
-              - nvidia-cusolver-cu13
-              - nvidia-cusparse-cu13
+              - cuda-toolkit[cublas,curand,cusolver,cusparse]==13.*
           - matrix:
               use_cuda_wheels: "false"
             packages:
-          # if no matching matrix selectors passed, list the unsuffixed packages
+          # if no matching matrix selectors passed, list a range
           # (just as a source of documentation, as this populates pyproject.toml in source control)
           - matrix:
             packages:
-              - nvidia-cublas
-              - nvidia-curand
-              - nvidia-cusolver
-              - nvidia-cusparse
+              - cuda-toolkit[cublas,curand,cusolver,cusparse]>=12,<14
   depends_on_cupy:
     common:
       - output_types: conda
diff --git a/python/libcuvs/CMakeLists.txt b/python/libcuvs/CMakeLists.txt
index da43cb48a8..02574ac104 100644
--- a/python/libcuvs/CMakeLists.txt
+++ b/python/libcuvs/CMakeLists.txt
@@ -52,13 +52,18 @@ set(CUVS_COMPILE_DYNAMIC_ONLY ON)
 add_subdirectory(../../cpp cuvs-cpp)
 
 # assumes libcuvs.so is installed 2 levels deep, e.g. site-packages/libcuvs/lib64/libcuvs.so
-set(rpaths
-    "$ORIGIN/../../nvidia/cublas/lib"
-    "$ORIGIN/../../nvidia/curand/lib"
-    "$ORIGIN/../../nvidia/cusolver/lib"
-    "$ORIGIN/../../nvidia/cusparse/lib"
-    "$ORIGIN/../../nvidia/nvjitlink/lib"
-)
+if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0)
+  # starting with CTK 13 wheels, all libraries are grouped in this 'nvidia/cu13/lib' directory
+  set(rpaths "$ORIGIN/../../nvidia/cu13/lib")
+else()
+  set(rpaths
+      "$ORIGIN/../../nvidia/cublas/lib"
+      "$ORIGIN/../../nvidia/curand/lib"
+      "$ORIGIN/../../nvidia/cusolver/lib"
+      "$ORIGIN/../../nvidia/cusparse/lib"
+      "$ORIGIN/../../nvidia/nvjitlink/lib"
+  )
+endif()
 
 if(USE_NCCL_RUNTIME_WHEEL)
   list(APPEND rpaths "$ORIGIN/../../nvidia/nccl/lib")
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index 51bb46316b..f19f2d33df 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -30,12 +30,9 @@ authors = [
 license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
+    "cuda-toolkit[cublas,curand,cusolver,cusparse]>=12,<14",
     "libraft==25.10.*,>=0.0.0a0",
     "librmm==25.10.*,>=0.0.0a0",
-    "nvidia-cublas",
-    "nvidia-curand",
-    "nvidia-cusolver",
-    "nvidia-cusparse",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",

From 60244ca8201119ea6de0e947ed9bf4f5862f3712 Mon Sep 17 00:00:00 2001
From: MithunR <mithunr@nvidia.com>
Date: Wed, 10 Sep 2025 13:56:52 -0700
Subject: [PATCH 570/623] [REVIEW] [Java] Option to build fat-jars with native
 dependencies included (#1296)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This commit introduces an _option_ to include the native libraries as part of a new Java JAR artifact.

In addition, this commit also adds scripts to build the libraries included in the fat-jars using `gcc-toolset`, to allow the libraries to be portable across several Linux / `libstdc++` versions.  (This was earlier attempted in #1264, but will now reside in this commit.)

Note that for the initial cut, the "fat" jars will include only the following libraries:
1. `libcuvs.so`
2. `libcuvs_c.so`
3. `librmm.so`
4. `librapids_logger.so`

The resultant JARs will still be dependent on `LD_LIBRARY_PATH` for other dependencies (`cublas`, `cusparse`, `cusolver`, `nccl`, etc.).

Two new profiles have been introduced in the `pom.xml`:
1. `x86_64-cuda12`
5. `x86_64-cuda13` (Although this is more of an example than anything.)

The main JAR artifact (`cuvs-java-25.x.x.jar`) remains unmodified.  But when `-P x86_64-cuda12` is employed, an additional `cuvs-java-25.x.x-x86_64-cuda12.jar` is produced, containing `libcuvs.so`, `libcuvs_c.so`, and some (minimal) additional dependencies.

The idea is that the JAR artifact build for `x86_64` + `cuda12` would look something like:
```bash
# On an x86 build box, using the cuda-12 conda env, from the project root directory:

# Build `libcuvs.so` first.
LIBCUVS_BUILD_DIR=`pwd`/cpp/build/cuda12 ./build.sh libcuvs

# Now the Java build.
cd java/
CMAKE_PREFIX_PATH=`pwd`/../cpp/build/cuda12 ./build.sh
```

The `java/build.sh` detects the CPU platform and the CUDA version to automatically choose the profile (`x86-cuda12`).

Note that there are tangential changes to the `pom.xml`:
1. Fixes #1293: The Java 22 portion of the build will now follow the Java 21, to prevent races between the builds.
2. The `maven-compiler-plugin` version has been dropped to `3.11.0`, to prevent build errors regarding a "0-byte module-info.class".  This is apparently a known issue in `3.13`, to be fixed in `3.14`.

Authors:
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Mike Sarahan (https://github.com/msarahan)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1296
---
 java/build.sh                                 |  18 +-
 java/cuvs-java/pom.xml                        | 198 +++++++++++++++---
 .../src/assembly/native-with-deps.xml         |  52 +++++
 .../com/nvidia/cuvs/spi/JDKProvider.java      |   4 +
 .../spi/OptionalNativeDependencyLoader.java   |  81 +++++++
 java/docker-build/Dockerfile                  | 104 +++++++++
 java/docker-build/README.md                   | 152 ++++++++++++++
 java/docker-build/build-in-docker             |  44 ++++
 java/docker-build/run-in-docker               | 161 ++++++++++++++
 9 files changed, 785 insertions(+), 29 deletions(-)
 create mode 100644 java/cuvs-java/src/assembly/native-with-deps.xml
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java
 create mode 100644 java/docker-build/Dockerfile
 create mode 100644 java/docker-build/README.md
 create mode 100755 java/docker-build/build-in-docker
 create mode 100755 java/docker-build/run-in-docker

diff --git a/java/build.sh b/java/build.sh
index 7d2628d7e5..cbb1b9ed48 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -7,10 +7,18 @@ set -e -u -o pipefail
 ARGS="$*"
 NUMARGS=$#
 
-CURDIR=$(cd "$(dirname "$0")"; pwd)
 VERSION="25.10.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
 
+# Identify CUDA major version.
+CUDA_VERSION_FROM_NVCC=$(nvcc --version | grep -oP 'release [0-9]+' | awk '{print $2}')
+CUDA_MAJOR_VERSION=${CUDA_VERSION_FROM_NVCC:-12}
+
+# Identify architecture.
+ARCH=$(uname -m)
+
+BUILD_PROFILE="$ARCH-cuda$CUDA_MAJOR_VERSION"
+
 if [ -z "${CMAKE_PREFIX_PATH:=}" ]; then
   CMAKE_PREFIX_PATH="$(pwd)/../cpp/build"
   export CMAKE_PREFIX_PATH
@@ -33,12 +41,12 @@ fi
 
 # Build the java layer
 if [ -z ${LD_LIBRARY_PATH+x} ]
-then export LD_LIBRARY_PATH=${CURDIR}/../cpp/build
-else export LD_LIBRARY_PATH=${CURDIR}/../cpp/build:${LD_LIBRARY_PATH}
+then export LD_LIBRARY_PATH=$CMAKE_PREFIX_PATH
+else export LD_LIBRARY_PATH=$CMAKE_PREFIX_PATH:${LD_LIBRARY_PATH}
 fi
 
-export LD_LIBRARY_PATH=${CURDIR}/../cpp/build:${LD_LIBRARY_PATH}
 cd cuvs-java
-mvn verify "${MAVEN_VERIFY_ARGS[@]}" \
+mvn clean verify "${MAVEN_VERIFY_ARGS[@]}" -P "$BUILD_PROFILE" \
   && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION.jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dpackaging=jar \
+  && mvn install:install-file -Dfile=./target/cuvs-java-$VERSION-"$BUILD_PROFILE".jar -DgroupId=$GROUP_ID -DartifactId=cuvs-java -Dversion=$VERSION -Dclassifier="$BUILD_PROFILE" -Dpackaging=jar \
   && cp pom.xml ./target/
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index b3b862d291..ca27c67635 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -62,6 +62,7 @@
         <maven.compiler.source>22</maven.compiler.source>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
+        <native.build.path>${project.build.directory}/../../../cpp/build</native.build.path>
     </properties>
 
     <distributionManagement>
@@ -108,10 +109,11 @@
             <plugin>
                 <groupId>org.apache.maven.plugins</groupId>
                 <artifactId>maven-compiler-plugin</artifactId>
-                <version>3.13.0</version>
+                <version>3.11.0</version>
                 <executions>
                     <execution>
                         <id>default-compile</id>
+                        <phase>compile</phase>
                         <goals>
                             <goal>compile</goal>
                         </goals>
@@ -134,6 +136,9 @@
                                 <compileSourceRoot>${project.basedir}/src/main/java22</compileSourceRoot>
                             </compileSourceRoots>
                             <multiReleaseOutput>true</multiReleaseOutput>
+                            <excludes>
+                                <exclude>module-info.java</exclude>
+                            </excludes>
                         </configuration>
                     </execution>
                 </executions>
@@ -155,29 +160,6 @@
                     </execution>
                 </executions>
             </plugin>
-            <plugin>
-                <groupId>org.apache.maven.plugins</groupId>
-                <artifactId>maven-assembly-plugin</artifactId>
-                <version>3.4.2</version>
-                <configuration>
-                    <descriptorRefs>
-                        <descriptorRef>jar-with-dependencies</descriptorRef>
-                    </descriptorRefs>
-                    <mergeManifestMode>merge</mergeManifestMode>
-                    <archiverConfig>
-                        <duplicateBehavior>add</duplicateBehavior>
-                    </archiverConfig>
-                </configuration>
-                <executions>
-                    <execution>
-                        <id>assemble-all</id>
-                        <phase>package</phase>
-                        <goals>
-                            <goal>single</goal>
-                        </goals>
-                    </execution>
-                </executions>
-            </plugin>
             <plugin>
                 <groupId>org.apache.maven.plugins</groupId>
                 <artifactId>maven-jar-plugin</artifactId>
@@ -277,4 +259,172 @@
             </plugin>
         </plugins>
     </build>
+
+    <profiles>
+        <profile>
+            <id>x86_64-cuda12</id>
+            <activation>
+                <property>
+                    <name>cuda.version</name>
+                    <value>12</value>
+                </property>
+            </activation>
+            <properties>
+                <native.classifier>x86_64-cuda12</native.classifier>
+                <native.lib.path>${native.build.path}/cuda12</native.lib.path>
+            </properties>
+            <build>
+                <plugins>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-resources-plugin</artifactId>
+                        <version>3.3.1</version>
+                        <executions>
+                            <execution>
+                                <id>copy-native-libs</id>
+                                <phase>prepare-package</phase>
+                                <goals>
+                                    <goal>copy-resources</goal>
+                                </goals>
+                                <configuration>
+                                    <overwrite>true</overwrite>
+                                    <outputDirectory>${project.build.directory}/native-libs/${os.arch}/${os.name}</outputDirectory>
+                                    <resources>
+                                        <resource>
+                                            <directory>${native.lib.path}</directory>
+                                            <includes>
+                                                <include>libcuvs.so</include>
+                                                <include>libcuvs_c.so</include>
+                                            </includes>
+                                        </resource>
+                                        <resource>
+                                            <directory>${native.lib.path}/_deps/rmm-build</directory>
+                                            <includes>
+                                                <include>librmm.so</include>
+                                            </includes>
+                                        </resource>
+                                        <resource>
+                                            <directory>${native.lib.path}/_deps/rapids_logger-build</directory>
+                                            <includes>
+                                                <include>librapids_logger.so</include>
+                                            </includes>
+                                        </resource>
+                                    </resources>
+                                </configuration>
+                            </execution>
+                        </executions>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-assembly-plugin</artifactId>
+                        <version>3.4.2</version>
+                        <configuration>
+                            <descriptors>
+                                <descriptor>src/assembly/native-with-deps.xml</descriptor>
+                            </descriptors>
+                            <archive>
+                                <manifestEntries>
+                                    <Multi-Release>true</Multi-Release>
+                                    <addClasspath>true</addClasspath>
+                                    <mainClass>com.nvidia.cuvs.examples.CagraExample</mainClass>
+                                </manifestEntries>
+                            </archive>
+                        </configuration>
+                        <executions>
+                            <execution>
+                                <id>assemble-native</id>
+                                <phase>package</phase>
+                                <goals>
+                                    <goal>single</goal>
+                                </goals>
+                            </execution>
+                        </executions>
+                    </plugin>
+                </plugins>
+            </build>
+        </profile>
+
+        <profile>
+            <id>x86_64-cuda13</id>
+            <activation>
+                <property>
+                    <name>cuda.version</name>
+                    <value>13</value>
+                </property>
+            </activation>
+            <properties>
+                <native.classifier>x86_64-cuda13</native.classifier>
+                <native.lib.path>${native.build.path}/cuda13</native.lib.path>
+            </properties>
+            <build>
+                <plugins>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-resources-plugin</artifactId>
+                        <version>3.3.1</version>
+                        <executions>
+                            <execution>
+                                <id>copy-native-libs</id>
+                                <phase>prepare-package</phase>
+                                <goals>
+                                    <goal>copy-resources</goal>
+                                </goals>
+                                <configuration>
+                                    <overwrite>true</overwrite>
+                                    <outputDirectory>${project.build.directory}/native-libs/${os.arch}/${os.name}</outputDirectory>
+                                    <resources>
+                                        <resource>
+                                            <directory>${native.lib.path}</directory>
+                                            <includes>
+                                                <include>libcuvs.so</include>
+                                                <include>libcuvs_c.so</include>
+                                            </includes>
+                                        </resource>
+                                        <resource>
+                                            <directory>${native.lib.path}/_deps/rmm-build</directory>
+                                            <includes>
+                                                <include>librmm.so</include>
+                                            </includes>
+                                        </resource>
+                                        <resource>
+                                            <directory>${native.lib.path}/_deps/rapids_logger-build</directory>
+                                            <includes>
+                                                <include>librapids_logger.so</include>
+                                            </includes>
+                                        </resource>
+                                    </resources>
+                                </configuration>
+                            </execution>
+                        </executions>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-assembly-plugin</artifactId>
+                        <version>3.4.2</version>
+                        <configuration>
+                            <descriptors>
+                                <descriptor>src/assembly/native-with-deps.xml</descriptor>
+                            </descriptors>
+                            <archive>
+                                <manifestEntries>
+                                    <Multi-Release>true</Multi-Release>
+                                    <addClasspath>true</addClasspath>
+                                    <mainClass>com.nvidia.cuvs.examples.CagraExample</mainClass>
+                                </manifestEntries>
+                            </archive>
+                        </configuration>
+                        <executions>
+                            <execution>
+                                <id>assemble-native</id>
+                                <phase>package</phase>
+                                <goals>
+                                    <goal>single</goal>
+                                </goals>
+                            </execution>
+                        </executions>
+                    </plugin>
+                </plugins>
+            </build>
+        </profile>
+    </profiles>
 </project>
diff --git a/java/cuvs-java/src/assembly/native-with-deps.xml b/java/cuvs-java/src/assembly/native-with-deps.xml
new file mode 100644
index 0000000000..91e1ad85de
--- /dev/null
+++ b/java/cuvs-java/src/assembly/native-with-deps.xml
@@ -0,0 +1,52 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+ -->
+<assembly xmlns="http://maven.apache.org/ASSEMBLY/2.1.0"
+          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+          xsi:schemaLocation="http://maven.apache.org/ASSEMBLY/2.1.0
+                              http://maven.apache.org/xsd/assembly-2.1.0.xsd">
+
+    <id>${native.classifier}</id>
+    <formats>
+        <format>jar</format>
+    </formats>
+
+    <includeBaseDirectory>false</includeBaseDirectory>
+
+    <!-- Include all dependencies -->
+    <dependencySets>
+        <dependencySet>
+            <outputDirectory>/</outputDirectory>
+            <useProjectArtifact>true</useProjectArtifact>
+            <unpack>true</unpack>
+            <scope>runtime</scope>
+        </dependencySet>
+    </dependencySets>
+
+    <!-- Include native libraries from separate directory -->
+    <fileSets>
+        <fileSet>
+            <directory>${project.build.directory}/native-libs</directory>
+            <outputDirectory>/</outputDirectory>
+            <includes>
+                <include>**/*</include>
+            </includes>
+        </fileSet>
+    </fileSets>
+
+</assembly>
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 0ee0cf1d10..cfbc8acefa 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -31,6 +31,10 @@
 
 final class JDKProvider implements CuVSProvider {
 
+  static {
+    OptionalNativeDependencyLoader.loadLibraries();
+  }
+
   private static final MethodHandle createNativeDataset$mh = createNativeDatasetBuilder();
 
   static MethodHandle createNativeDatasetBuilder() {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java
new file mode 100644
index 0000000000..facbb670a3
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.spi;
+
+import java.io.*;
+import java.net.URL;
+import java.util.stream.*;
+
+/**
+ * A class that loads native dependencies if they are available in the jar.
+ */
+public class OptionalNativeDependencyLoader {
+
+  private static final ClassLoader loader = JDKProvider.class.getClassLoader();
+
+  private static boolean loaded = false;
+
+  private static final String[] FILES_TO_LOAD = {
+    "rapids_logger", "rmm", "cuvs", "cuvs_c",
+  };
+
+  public static void loadLibraries() {
+    if (!loaded) {
+      String os = System.getProperty("os.name");
+      String arch = System.getProperty("os.arch");
+
+      Stream.of(FILES_TO_LOAD)
+          .forEach(
+              file -> {
+                // Uncomment the following line to trace the loading of native dependencies.
+                // System.out.println("Loading native dependency: " + file);
+                try {
+                  System.load(createFile(os, arch, file).getAbsolutePath());
+                } catch (Throwable t) {
+                  System.err.println(
+                      "Continuing despite failure to load native dependency: "
+                          + System.mapLibraryName(file)
+                          + ".so: "
+                          + t.getMessage());
+                }
+              });
+
+      loaded = true;
+    }
+  }
+
+  /** Extract the contents of a library resource into a temporary file */
+  private static File createFile(String os, String arch, String baseName) throws IOException {
+    String path = arch + "/" + os + "/" + System.mapLibraryName(baseName);
+    File loc;
+    URL resource = loader.getResource(path);
+    if (resource == null) {
+      throw new FileNotFoundException("Could not locate native dependency " + path);
+    }
+    try (InputStream in = resource.openStream()) {
+      loc = File.createTempFile(baseName, ".so");
+      loc.deleteOnExit();
+      try (OutputStream out = new FileOutputStream(loc)) {
+        byte[] buffer = new byte[1024 * 16];
+        int read = 0;
+        while ((read = in.read(buffer)) >= 0) {
+          out.write(buffer, 0, read);
+        }
+      }
+    }
+    return loc;
+  }
+}
diff --git a/java/docker-build/Dockerfile b/java/docker-build/Dockerfile
new file mode 100644
index 0000000000..84e3d1f712
--- /dev/null
+++ b/java/docker-build/Dockerfile
@@ -0,0 +1,104 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+###
+# Build the image for cuVS Java API development environment.
+#
+# Arguments: CUDA_VERSION=[12.X.Y], OS_RELEASE=[8, 9], TARGETPLATFORM=[linux/amd64, linux/arm64]
+#
+###
+ARG CUDA_VERSION=12.9.1
+ARG OS_RELEASE=9
+ARG TARGETPLATFORM=linux/amd64
+
+# multi-platform build with: docker buildx build --platform linux/arm64,linux/amd64 <ARGS> on either amd64 or arm64 host
+# check available official arm-based docker images at https://hub.docker.com/r/nvidia/cuda/tags (OS/ARCH)
+FROM --platform=$TARGETPLATFORM nvidia/cuda:$CUDA_VERSION-devel-rockylinux$OS_RELEASE
+
+# Re-declare ARGs after FROM since they don't persist across FROM boundary
+ARG CUDA_VERSION=12.9.1
+ARG OS_RELEASE=9
+ARG TARGETPLATFORM=linux/amd64
+
+# If DEV_BUILD is ON, the gcc-toolset will be enabled by default for bash shell
+ARG DEV_BUILD=OFF
+
+# Dependency versions
+# Act as default GCC toolset in the image
+ARG TOOLSET_VERSION=14
+ARG CMAKE_VERSION=3.30.4
+ARG CCACHE_VERSION=4.11.2
+
+# Default x86_64 from x86 build, aarch64 cmake for arm build
+ARG CMAKE_ARCH=x86_64
+
+### Install basic requirements
+RUN if [ "$OS_RELEASE" = "9" ]; then \
+    dnf --enablerepo=crb install -y scl-utils gcc-toolset-${TOOLSET_VERSION} python39 \
+      zlib-devel maven tar wget patch ninja-build git zip; \
+  elif [ "$OS_RELEASE" = "8" ]; then \
+    dnf --enablerepo=devel install -y scl-utils gcc-toolset-${TOOLSET_VERSION} python39 \
+      zlib-devel maven tar wget patch ninja-build git zip; \
+  else \
+    echo "Unsupported OS version: $OS_RELEASE"; \
+  fi
+
+# Enable the gcc-toolset by default for bash shell if DEV_BUILD is ON
+RUN if [ "$DEV_BUILD" = "ON" ]; then \
+        echo "source scl_source enable gcc-toolset-${TOOLSET_VERSION}" >> /etc/bashrc; \
+    fi
+
+# Execute every time a new non-interactive bash shell is started
+ENV BASH_ENV=/etc/bashrc
+
+## pre-create the CMAKE_INSTALL_PREFIX folder, set writable by any user for Jenkins
+RUN mkdir -m 777 /usr/local/rapids /rapids
+
+# Fetch and install CMake.
+RUN cd /usr/local && wget --quiet https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-${CMAKE_ARCH}.tar.gz && \
+   tar zxf cmake-${CMAKE_VERSION}-linux-${CMAKE_ARCH}.tar.gz && \
+   rm cmake-${CMAKE_VERSION}-linux-${CMAKE_ARCH}.tar.gz
+
+# Make version-less alias for external reference such as when cmake is called by an IDE outside of the container
+RUN ln -s /usr/local/cmake-${CMAKE_VERSION}-linux-${CMAKE_ARCH}/bin/cmake /usr/local/bin/cmake
+
+# ccache for interactive builds
+RUN cd /tmp && wget --quiet https://github.com/ccache/ccache/releases/download/v${CCACHE_VERSION}/ccache-${CCACHE_VERSION}.tar.gz && \
+   tar zxf ccache-${CCACHE_VERSION}.tar.gz && \
+   rm ccache-${CCACHE_VERSION}.tar.gz && \
+   cd ccache-${CCACHE_VERSION} && \
+   mkdir build && \
+   cd build && \
+   scl enable gcc-toolset-${TOOLSET_VERSION} \
+      "cmake .. \
+         -DCMAKE_BUILD_TYPE=Release \
+         -DZSTD_FROM_INTERNET=ON \
+         -DREDIS_STORAGE_BACKEND=OFF && \
+      cmake --build . --parallel ${PARALLEL_LEVEL} --target install" && \
+   cd ../.. && \
+   rm -rf ccache-${CCACHE_VERSION}
+
+# Fetch and install JDK 22.
+RUN cd /usr/local && wget --quiet https://download.oracle.com/java/22/archive/jdk-22.0.2_linux-x64_bin.tar.gz && \
+   tar zxf jdk-22.0.2_linux-x64_bin.tar.gz && \
+   rm jdk-22.0.2_linux-x64_bin.tar.gz
+
+# Make Java22 available in path.
+RUN ln -sf /usr/local/jdk-22.0.2/bin/java /usr/local/bin/java
+ENV JAVA_HOME="/usr/local/jdk-22.0.2"
+
+# disable cuda container constraints to allow running w/ older drivers on datacenter GPUs
+ENV NVIDIA_DISABLE_REQUIRE="true"
diff --git a/java/docker-build/README.md b/java/docker-build/README.md
new file mode 100644
index 0000000000..92cb616bf1
--- /dev/null
+++ b/java/docker-build/README.md
@@ -0,0 +1,152 @@
+# Docker Build Environment for cuVS Java API
+
+This directory contains the Docker-based build system for the cuVS Java API, providing a containerized environment with all necessary dependencies for building the project across different CUDA versions and architectures.
+
+## Overview
+
+The Docker build system provides:
+- Consistent build environment across different host systems
+- Support for multiple CUDA versions (12.x and 13.x)
+- Cross-platform builds (x86_64 and aarch64)
+- Pre-configured development tools (GCC toolset, CMake, Maven, JDK 22)
+
+## Quick Start
+
+### Default Build (CUDA 12, Rocky Linux 9, All GPU Architectures)
+
+```bash
+./build-in-docker
+```
+
+This builds using the defaults:
+- CUDA version: 12.9.1
+- OS version: Rocky Linux 9
+- Target: libcuvs java with all GPU architectures (`--allgpuarch`)
+
+### Building for CUDA 13, All GPU Architectures
+
+```bash
+CUDA_VERSION=13.0.0 ./build-in-docker
+```
+
+### Building for Local GPU Architecture Only
+
+```bash
+./build-in-docker libcuvs java
+```
+
+## Environment Variables
+
+### Core Configuration
+
+- **`CUDA_VERSION`**: CUDA toolkit version to use (default: `12.9.1`)
+  - Examples: `12.9.1`, `13.0.0`, `13.1.0`
+- **`OS_VERSION`**: Rocky Linux OS version to use (default: `9`)
+  - Supported values: `8`, `9`
+- **`CMAKE_GENERATOR`**: CMake generator to use (default: `Ninja`)
+- **`LOCAL_MAVEN_REPO`**: Local Maven repository path (default: `$HOME/.m2/repository`)
+
+### Docker Configuration
+
+- **`JNI_DOCKER_DEV_BUILD`**: Set to `ON` for development builds with gcc-toolset enabled by default (default: `OFF`)
+- **`DOCKER_CMD`**: Docker command to use (default: `docker`)
+- **`DOCKER_BUILD_EXTRA_ARGS`**: Additional arguments for `docker build`
+- **`DOCKER_RUN_EXTRA_ARGS`**: Additional arguments for `docker run`
+- **`DOCKER_GPU_OPTS`**: GPU options for Docker (default: `--gpus all`)
+
+### Build Optimization
+
+- **`LOCAL_CCACHE_DIR`**: ccache directory for build acceleration (default: `$HOME/.ccache`)
+- **`PARALLEL_LEVEL`**: Number of parallel build jobs
+- **`VERBOSE`**: Enable verbose build output
+
+## Architecture Support
+
+The build system automatically detects the host architecture:
+- **x86_64**: Uses `linux/amd64` platform and x86_64 CMake binaries
+- **aarch64**: Uses `linux/arm64` platform and aarch64 CMake binaries
+
+## Files in This Directory
+
+- **`build-in-docker`**: Main entry point script for Docker-based builds
+- **`run-in-docker`**: Lower-level script that handles Docker container execution
+- **`Dockerfile`**: Multi-stage Docker image definition with CUDA, development tools, and dependencies
+- **`env.sh`**: Environment configuration script
+
+## Examples
+
+### Development Build with Custom CUDA Version (Default: All GPU Architectures)
+
+```bash
+CUDA_VERSION=13.1.0 JNI_DOCKER_DEV_BUILD=ON ./build-in-docker
+```
+
+### Production Build with Rocky Linux 8
+
+```bash
+OS_VERSION=8 ./build-in-docker
+```
+
+### Build with Custom Maven Repository and ccache
+
+```bash
+LOCAL_MAVEN_REPO=/custom/maven/repo LOCAL_CCACHE_DIR=/custom/ccache CUDA_VERSION=13.0.0 ./build-in-docker
+```
+
+### Build with Additional Docker Arguments
+
+```bash
+DOCKER_BUILD_EXTRA_ARGS="--no-cache" ./build-in-docker
+```
+
+### Interactive Development Session
+
+```bash
+CUDA_VERSION=12.9.1 JNI_DOCKER_DEV_BUILD=ON ./docker-build/run-in-docker
+```
+
+This starts an interactive bash shell in the container for development work.
+
+## Docker Image Details
+
+The Docker image is based on `nvidia/cuda:{CUDA_VERSION}-devel-rockylinux9` and includes:
+
+- **CUDA Development Tools**: Complete CUDA toolkit for the specified version
+- **GCC Toolset 14**: Modern C++ compiler with C++20 support
+- **CMake 3.30.4**: Build system generator
+- **JDK 22**: Java Development Kit with Panama FFM support
+- **Maven**: Java project management and build tool
+- **ccache**: Compiler cache for faster incremental builds
+- **Additional Tools**: git, ninja-build, wget, tar, zip, patch
+
+## Troubleshooting
+
+### CUDA Version Compatibility
+
+Ensure your target CUDA version is supported by checking available tags at:
+- [NVIDIA CUDA Docker Hub](https://hub.docker.com/r/nvidia/cuda/tags)
+
+### GPU Access Issues
+
+If you encounter GPU access problems, verify:
+1. NVIDIA Docker runtime is installed (`nvidia-docker2` or `nvidia-container-toolkit`)
+2. Your user has permissions to access Docker
+3. GPU drivers are compatible with the CUDA version
+
+### Build Performance
+
+For tweaking build performance:
+1. Use ccache: Ensure `LOCAL_CCACHE_DIR` is set to a persistent directory. (By default, the build uses ccache.)
+2. Change parallelism: Set `PARALLEL_LEVEL` to match your CPU cores. (This happens by default.)
+3. Use development builds: Set `JNI_DOCKER_DEV_BUILD=ON` for development
+
+### Memory Requirements
+
+The Docker build process requires significant memory. For large projects, ensure:
+- At least 8GB of available RAM
+- Sufficient disk space for Docker images and build artifacts
+
+## Related Documentation
+
+- [cuVS Java API README](../README.md): Main Java API documentation
+- [cuVS Build Instructions](https://docs.rapids.ai/api/cuvs/stable/build/): Native build documentation
diff --git a/java/docker-build/build-in-docker b/java/docker-build/build-in-docker
new file mode 100755
index 0000000000..1c61429fa8
--- /dev/null
+++ b/java/docker-build/build-in-docker
@@ -0,0 +1,44 @@
+#!/bin/bash
+
+#
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Build the cuVS Java API artifact in a Docker container with devtoolset
+
+set -e
+
+# Base paths relative to this script's location
+SCRIPTDIR=$(cd "$(dirname "$0")"; pwd)
+
+LOCAL_MAVEN_REPO=${LOCAL_MAVEN_REPO:-"$HOME/.m2/repository"}
+export CMAKE_GENERATOR=${CMAKE_GENERATOR:-"Ninja"}
+export CUDA_VERSION=${CUDA_VERSION:-12.9.1}
+# CUDA_CLASSIFIER=cuda${CUDA_VERSION%%.*}  # Currently unused, but may be needed for future CUDA version handling
+
+# Set env for arm64 build, The possible values of 'uname -m' : [x86_64/i386/aarch64/mips/...]
+if [ "$(uname -m)" == "aarch64" ]; then
+  # ARM-specific settings.
+  echo "Set ARM Specific settings"
+fi
+
+# TODO: Settings to build for different CUDA versions, different GPU archs, CPU archs, etc.
+
+# Default to building libcuvs java with all GPU architectures if no arguments provided
+if [ $# -eq 0 ]; then
+  "$SCRIPTDIR/run-in-docker" "./build.sh" "libcuvs" "java" "--allgpuarch"
+else
+  "$SCRIPTDIR/run-in-docker" "./build.sh" "$@"
+fi
diff --git a/java/docker-build/run-in-docker b/java/docker-build/run-in-docker
new file mode 100755
index 0000000000..9c102b64a3
--- /dev/null
+++ b/java/docker-build/run-in-docker
@@ -0,0 +1,161 @@
+#!/bin/bash
+
+#
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Run a command in a Docker container with devtoolset
+
+set -e
+
+# Convert sclCMD to array for proper argument handling
+if [[ -n "${sclCMD:-}" ]]; then
+  # Convert existing string to array by splitting on whitespace
+  read -ra sclCMD <<< "$sclCMD"
+else
+  sclCMD=(scl enable gcc-toolset-14)
+fi
+
+# Set this environment variable to ON to build the docker image for local development,
+# with gcc-toolset enabled by default.
+# This is necessary especially if the docker container is used for local development with an IDE.
+JNI_DOCKER_DEV_BUILD=${JNI_DOCKER_DEV_BUILD:-OFF}
+
+REPODIR_REL=$(git rev-parse --show-toplevel)
+REPODIR=$(realpath "$REPODIR_REL")
+GIT_COMMON_DIR_REL=$(git rev-parse --git-common-dir)
+GIT_COMMON_DIR=$(realpath "$GIT_COMMON_DIR_REL")
+WORKDIR=${WORKDIR:-$REPODIR}
+TZ=${TZ:-UTC}
+
+CUDA_VERSION=${CUDA_VERSION:-12.9.1}
+CUDA_CLASSIFIER=cuda${CUDA_VERSION%%.*}
+OS_VERSION=${OS_VERSION:-9}  # Default to 9
+
+DOCKER_CMD=${DOCKER_CMD:-docker}
+
+# Initialize DOCKER_BUILD_EXTRA_ARGS as an array
+if [[ -n "${DOCKER_BUILD_EXTRA_ARGS:-}" ]]; then
+    # Convert existing string to array by splitting on whitespace
+    read -ra DOCKER_BUILD_EXTRA_ARGS <<< "$DOCKER_BUILD_EXTRA_ARGS"
+else
+    DOCKER_BUILD_EXTRA_ARGS=()
+fi
+
+if [ "$(uname -m)" == "aarch64" ]; then
+    DOCKER_BUILD_EXTRA_ARGS=(--build-arg TARGETPLATFORM=linux/arm64 --build-arg CMAKE_ARCH=aarch64 "${DOCKER_BUILD_EXTRA_ARGS[@]}")
+else
+    DOCKER_BUILD_EXTRA_ARGS=(--build-arg TARGETPLATFORM=linux/amd64 --build-arg CMAKE_ARCH=x86_64 "${DOCKER_BUILD_EXTRA_ARGS[@]}")
+fi
+# Initialize DOCKER_RUN_EXTRA_ARGS as an array
+if [[ -n "${DOCKER_RUN_EXTRA_ARGS:-}" ]]; then
+  # Convert existing string to array by splitting on whitespace
+  read -ra DOCKER_RUN_EXTRA_ARGS <<< "$DOCKER_RUN_EXTRA_ARGS"
+else
+  DOCKER_RUN_EXTRA_ARGS=()
+fi
+LOCAL_CCACHE_DIR=${LOCAL_CCACHE_DIR:-"$HOME/.ccache"}
+LOCAL_MAVEN_REPO=${LOCAL_MAVEN_REPO:-"$HOME/.m2/repository"}
+
+if [ "$JNI_DOCKER_DEV_BUILD" == "ON" ]; then
+  echo "Building docker image for local development, gcc-toolset is enabled by default..."
+  JNI_DOCKER_IMAGE="cuvs-jni-build:${CUDA_VERSION}-devel-rockylinux${OS_VERSION}"
+else
+  echo "Building docker image for production, gcc-toolset is NOT enabled by default..."
+  JNI_DOCKER_IMAGE="cuvs-jni-build:${CUDA_VERSION}-rockylinux${OS_VERSION}"
+fi
+
+# ensure directories exist
+mkdir -p "$LOCAL_CCACHE_DIR" "$LOCAL_MAVEN_REPO"
+
+$DOCKER_CMD build "${DOCKER_BUILD_EXTRA_ARGS[@]}" -f "$REPODIR/java/docker-build/Dockerfile" \
+  --build-arg CUDA_VERSION="$CUDA_VERSION" \
+  --build-arg OS_RELEASE="$OS_VERSION" \
+  --build-arg DEV_BUILD="$JNI_DOCKER_DEV_BUILD" \
+  -t "$JNI_DOCKER_IMAGE" \
+  "$REPODIR/java/docker-build"
+
+if [[ "$DOCKER_CMD" == "docker" ]]; then
+  # Initialize DOCKER_GPU_OPTS as an array
+  if [[ -n "${DOCKER_GPU_OPTS:-}" ]]; then
+    # Convert existing string to array by splitting on whitespace
+    read -ra DOCKER_GPU_OPTS <<< "$DOCKER_GPU_OPTS"
+  else
+    DOCKER_GPU_OPTS=(--gpus all)
+  fi
+else
+  DOCKER_GPU_OPTS=()
+fi
+
+# Initialize DOCKER_OPTS as an array
+if [[ -n "${DOCKER_OPTS:-}" ]]; then
+  # Convert existing string to array by splitting on whitespace
+  read -ra DOCKER_OPTS <<< "$DOCKER_OPTS"
+else
+  DOCKER_OPTS=()
+fi
+
+if (( $# == 0 )); then
+  # no arguments gets an interactive shell
+  DOCKER_OPTS+=(-it)
+  RUN_CMD=("/bin/bash")
+else
+  RUN_CMD=("$@")
+fi
+
+MNT_ARGS=()
+
+RO_SRC=(
+  "/etc/group"
+  "/etc/passwd"
+  "/etc/shadow"
+  "/etc/sudoers.d"
+)
+RO_DST=("${RO_SRC[@]}")
+if [[ "$HOST_CUDA_PATH" != "" ]]; then
+  RO_SRC+=("$HOST_CUDA_PATH")
+  RO_DST+=("/usr/local/cuda")
+fi
+for (( i=0; i<${#RO_SRC[@]}; i++)); do
+  MNT_ARGS+=(--mount "type=bind,src=${RO_SRC[$i]},dst=${RO_DST[$i]},ro")
+done
+
+RW_SRC=(
+  "$GIT_COMMON_DIR"
+  "$WORKDIR"
+  "$LOCAL_CCACHE_DIR"
+  "$LOCAL_MAVEN_REPO"
+)
+for (( i=0; i<${#RW_SRC[@]}; i++)); do
+  MNT_ARGS+=(--mount "type=bind,src=${RW_SRC[$i]},dst=${RW_SRC[$i]}")
+done
+
+$DOCKER_CMD run "${DOCKER_GPU_OPTS[@]}" "${DOCKER_RUN_EXTRA_ARGS[@]}" -u "$(id -u):$(id -g)" --rm \
+  "${MNT_ARGS[@]}" \
+  --workdir "$WORKDIR" \
+  -e CCACHE_DIR="$LOCAL_CCACHE_DIR" \
+  -e CMAKE_C_COMPILER_LAUNCHER="ccache" \
+  -e CMAKE_CXX_COMPILER_LAUNCHER="ccache" \
+  -e CMAKE_CUDA_COMPILER_LAUNCHER="ccache" \
+  -e CMAKE_CXX_LINKER_LAUNCHER="ccache" \
+  -e CMAKE_PREFIX_PATH="${WORKDIR}/cpp/build/$CUDA_CLASSIFIER" \
+  -e LIBCUVS_BUILD_DIR="${WORKDIR}/cpp/build/$CUDA_CLASSIFIER" \
+  -e CMAKE_GENERATOR \
+  -e CUDA_VISIBLE_DEVICES \
+  -e PARALLEL_LEVEL \
+  -e VERBOSE \
+  "${DOCKER_OPTS[@]}" \
+  "$JNI_DOCKER_IMAGE" \
+  "${sclCMD[@]}" -- "${RUN_CMD[@]}"

From 792730c89e0c955442faab4d2755e7c0b45e3589 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 11 Sep 2025 16:46:03 +0200
Subject: [PATCH 571/623] [Java] Add reason(s) to
 UnsupportedProvider/UnsupportedOperationExceptions (#1314)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR adds some precise and readable messages specifying why a CuVSProvider is unsupported (it throws UnsupportedOperationException), to help with diagnosing cuvs-java startup issues.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1314
---
 .../nvidia/cuvs/spi/CuVSServiceProvider.java  | 27 +++++++++------
 .../nvidia/cuvs/spi/UnsupportedProvider.java  | 34 +++++++++++--------
 2 files changed, 37 insertions(+), 24 deletions(-)

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
index c8c886dc13..a4a5843adf 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
@@ -17,6 +17,8 @@
 
 import java.lang.invoke.MethodHandles;
 import java.lang.invoke.MethodType;
+import java.util.ArrayList;
+import java.util.List;
 import java.util.ServiceLoader;
 
 /**
@@ -43,7 +45,10 @@ private static CuVSProvider loadProvider() {
     }
 
     static CuVSProvider builtinProvider() {
-      if (Runtime.version().feature() > 21 && isLinuxAmd64()) {
+      var supportedJavaRuntime = Runtime.version().feature() > 21;
+      var supportedOs = System.getProperty("os.name").startsWith("Linux");
+      var supportedArchitecture = System.getProperty("os.arch").equals("amd64");
+      if (supportedJavaRuntime && supportedOs && supportedArchitecture) {
         try {
           var cls = Class.forName("com.nvidia.cuvs.spi.JDKProvider");
           var ctr = MethodHandles.lookup().findConstructor(cls, MethodType.methodType(void.class));
@@ -52,16 +57,18 @@ static CuVSProvider builtinProvider() {
           throw new AssertionError(e);
         }
       }
-      return new UnsupportedProvider();
-    }
+      List<String> unsupportedReasons = new ArrayList<>();
+      if (!supportedJavaRuntime) {
+        unsupportedReasons.add("cuvs-java requires Java Runtime version 22 or greater");
+      }
+      if (!supportedOs) {
+        unsupportedReasons.add("cuvs-java supports only Linux");
+      }
+      if (!supportedArchitecture) {
+        unsupportedReasons.add("cuvs-java supports only x86");
+      }
 
-    /**
-     * Returns true iff the architecture is x64 (amd64) and the OS Linux
-     * (the * OS we currently support for the native lib).
-     */
-    static boolean isLinuxAmd64() {
-      String name = System.getProperty("os.name");
-      return (name.startsWith("Linux")) && System.getProperty("os.arch").equals("amd64");
+      return new UnsupportedProvider(String.join("; ", unsupportedReasons));
     }
   }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 60509004c4..0f1edda99c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -24,51 +24,57 @@
  */
 final class UnsupportedProvider implements CuVSProvider {
 
+  private final String reasons;
+
+  public UnsupportedProvider(String reasons) {
+    this.reasons = reasons;
+  }
+
   @Override
   public CuVSResources newCuVSResources(Path tempDirectory) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public BruteForceIndex.Builder newBruteForceIndexBuilder(CuVSResources cuVSResources) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public CagraIndex.Builder newCagraIndexBuilder(CuVSResources cuVSResources) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public HnswIndex.Builder newHnswIndexBuilder(CuVSResources cuVSResources) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public TieredIndex.Builder newTieredIndexBuilder(CuVSResources cuVSResources) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public CagraIndex mergeCagraIndexes(CagraIndex[] indexes) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
       long size, long dimensions, CuVSMatrix.DataType dataType) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       CuVSResources cuVSResources, long size, long dimensions, CuVSMatrix.DataType dataType) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public GPUInfoProvider gpuInfoProvider() {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
@@ -79,26 +85,26 @@ public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       int rowStride,
       int columnStride,
       CuVSMatrix.DataType dataType) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public MethodHandle newNativeMatrixBuilder() {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public CuVSMatrix newMatrixFromArray(float[][] vectors) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public CuVSMatrix newMatrixFromArray(int[][] vectors) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 
   @Override
   public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
-    throw new UnsupportedOperationException();
+    throw new UnsupportedOperationException(reasons);
   }
 }

From 607e9ebc4b32ae703bf7fb7d62609a27c5fc577d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Fri, 12 Sep 2025 00:27:34 +0200
Subject: [PATCH 572/623] [Java] Make `cudaGetDeviceProperties` compatible with
 CUDA 12 and 13 based on symbol presence (#1323)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

In #1273 we addressed a signature change in CUDA 13 by binding different symbols based on a environment variable, `RAPIDS_CUDA_MAJOR`. That works, but forces users of cuvs-java with CUDA 12 to define this environment variable.

This PR improves on it by making the symbol lookup dynamic, looking for the CUDA 13 symbol name, and falling back to the CUDA 12 exported name if we fail to locate the first.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1323
---
 .../main/java22/com/nvidia/cuvs/internal/common/Util.java | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 41f6b3736d..28dfc5c020 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -65,15 +65,11 @@ private Util() {}
       LINKER.downcallHandle(
           cudaMemcpyAsync$address(), cudaMemcpyAsync$descriptor(), Linker.Option.critical(true));
 
-  private static final String cudaGetDevicePropertiesSymbolName =
-      "12".equals(System.getenv("RAPIDS_CUDA_MAJOR"))
-          ? "cudaGetDeviceProperties_v2"
-          : "cudaGetDeviceProperties";
-
   private static final MethodHandle cudaGetDeviceProperties$mh =
       LINKER.downcallHandle(
           SYMBOL_LOOKUP
-              .find(cudaGetDevicePropertiesSymbolName)
+              .find("cudaGetDeviceProperties") // CUDA 13+ symbol name
+              .or(() -> SYMBOL_LOOKUP.find("cudaGetDeviceProperties_v2")) // CUDA 12 symbol name
               .orElseThrow(UnsatisfiedLinkError::new),
           FunctionDescriptor.of(headers_h.C_INT, headers_h.C_POINTER, headers_h.C_INT));
 

From e292a09efe2341e91da7d74e5f807563d52b2317 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Sun, 14 Sep 2025 02:45:13 +0200
Subject: [PATCH 573/623] [Java] Add `libcuvs` <-> cuvs-java version check
 (#1327)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR extends https://github.com/rapidsai/cuvs/pull/1314 to add version check (version reported by libcuvs vs. cuvs-java build version)

It replaces #1315 which was already reviewed and approved, but got closed before I could rebase it.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1327
---
 java/cuvs-java/pom.xml                        |  5 ++-
 .../nvidia/cuvs/spi/CuVSServiceProvider.java  |  6 ++-
 .../spi/ProviderInitializationException.java  | 26 +++++++++++
 .../com/nvidia/cuvs/spi/JDKProvider.java      | 45 +++++++++++++++++++
 4 files changed, 79 insertions(+), 3 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/ProviderInitializationException.java

diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index ca27c67635..57da752660 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -166,11 +166,12 @@
                 <version>3.4.2</version>
                 <configuration>
                     <archive>
+                        <manifest>
+                            <addDefaultImplementationEntries>true</addDefaultImplementationEntries>
+                        </manifest>
                         <manifestEntries>
                             <Multi-Release>true</Multi-Release>
                             <addClasspath>true</addClasspath>
-                            <mainClass>
-                                com.nvidia.cuvs.examples.CagraExample</mainClass>
                         </manifestEntries>
                     </archive>
                 </configuration>
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
index a4a5843adf..ae4c9b083a 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
@@ -51,8 +51,12 @@ static CuVSProvider builtinProvider() {
       if (supportedJavaRuntime && supportedOs && supportedArchitecture) {
         try {
           var cls = Class.forName("com.nvidia.cuvs.spi.JDKProvider");
-          var ctr = MethodHandles.lookup().findConstructor(cls, MethodType.methodType(void.class));
+          var ctr =
+              MethodHandles.lookup()
+                  .findStatic(cls, "create", MethodType.methodType(CuVSProvider.class));
           return (CuVSProvider) ctr.invoke();
+        } catch (ProviderInitializationException e) {
+          return new UnsupportedProvider("cannot create JDKProvider: " + e.getMessage());
         } catch (Throwable e) {
           throw new AssertionError(e);
         }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/ProviderInitializationException.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/ProviderInitializationException.java
new file mode 100644
index 0000000000..1a6c34a2fd
--- /dev/null
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/ProviderInitializationException.java
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.spi;
+
+class ProviderInitializationException extends Exception {
+  ProviderInitializationException(String message, Throwable cause) {
+    super(message, cause);
+  }
+
+  public ProviderInitializationException(String message) {
+    super(message);
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index cfbc8acefa..77eb246e80 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -16,10 +16,14 @@
 package com.nvidia.cuvs.spi;
 
 import static com.nvidia.cuvs.internal.common.Util.*;
+import static com.nvidia.cuvs.internal.panama.headers_h.cuvsVersionGet;
+import static com.nvidia.cuvs.internal.panama.headers_h.uint16_t;
 
 import com.nvidia.cuvs.*;
 import com.nvidia.cuvs.internal.*;
 import com.nvidia.cuvs.internal.common.Util;
+import java.io.IOException;
+import java.lang.foreign.Arena;
 import java.lang.foreign.MemorySegment;
 import java.lang.invoke.MethodHandle;
 import java.lang.invoke.MethodHandles;
@@ -28,6 +32,8 @@
 import java.nio.file.Path;
 import java.util.Locale;
 import java.util.Objects;
+import java.util.jar.JarFile;
+import java.util.jar.Manifest;
 
 final class JDKProvider implements CuVSProvider {
 
@@ -37,6 +43,45 @@ final class JDKProvider implements CuVSProvider {
 
   private static final MethodHandle createNativeDataset$mh = createNativeDatasetBuilder();
 
+  static CuVSProvider create() throws Throwable {
+    var mavenVersion = readCuVSVersionFromManifest();
+
+    try (var localArena = Arena.ofConfined()) {
+      var majorPtr = localArena.allocate(uint16_t);
+      var minorPtr = localArena.allocate(uint16_t);
+      var patchPtr = localArena.allocate(uint16_t);
+      checkCuVSError(cuvsVersionGet(majorPtr, minorPtr, patchPtr), "cuvsVersionGet");
+      var major = majorPtr.get(uint16_t, 0);
+      var minor = minorPtr.get(uint16_t, 0);
+      var patch = patchPtr.get(uint16_t, 0);
+
+      var cuvsVersionString = String.format(Locale.ROOT, "%02d.%02d.%d", major, minor, patch);
+      if (mavenVersion != null && !cuvsVersionString.equals(mavenVersion)) {
+        throw new ProviderInitializationException(
+            String.format(
+                Locale.ROOT,
+                "libcuvs_c version mismatch: expected [%s], found [%s]",
+                mavenVersion,
+                cuvsVersionString));
+      }
+    }
+    return new JDKProvider();
+  }
+
+  /**
+   * Read cuvs-java version from this Jar Manifest, or null if these are not available
+   */
+  private static String readCuVSVersionFromManifest() {
+    try (var jarFile =
+        new JarFile(
+            JDKProvider.class.getProtectionDomain().getCodeSource().getLocation().getPath())) {
+      Manifest manifest = jarFile.getManifest();
+      return manifest.getMainAttributes().getValue("Implementation-Version");
+    } catch (IOException e) {
+      return null;
+    }
+  }
+
   static MethodHandle createNativeDatasetBuilder() {
     try {
       var lookup = MethodHandles.lookup();

From 757b02a4293f25ade7f0cf3194f1f374007cee51 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Mon, 15 Sep 2025 17:38:19 +0200
Subject: [PATCH 574/623] [Review][Java] Add detailed error message for
 `libcuvs` load failure to UnsupportedProvider/UnsupportedOperationExceptions
 (#1316)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR further extends #1296 and #1314 to give meaningful error messages in case libcuvs fails to load.

The `jextract` generated bindings we use in cuvs-java use `SymbolLookup#libraryLookup` to load the `cuvs_c` dynamic library; this uses `RawNativeLibraries#load` (see https://github.com/openjdk/jdk/blob/master/src/java.base/share/native/libjava/RawNativeLibraries.c#L58); `RawNativeLibraries#load` in turn calls `JVM_LoadLibrary`.

`JVM_LoadLibrary` does a good job to put together a good error message (e.g. calling `dlerror`, trying to locate and inspect the file for platform mismatch, etc. Unfortunately, `RawNativeLibraries#load` calls it passing false to the `throwException` parameter, which means that the detailed error messages are not surfaced.

This PR follows the pattern introduced in #1296 and preloads libcuvs (and dependencies) using `JVM_LoadLibrary` directly with `throwException` true; preloading it will also cause the OS to look for and load all dependencies.
In case of error we can see what's broken in better detail; e.g. if `libcuvs_c.so` is present, but `librmm.so` is missing:

```
java.lang.UnsupportedOperationException: cannot create JDKProvider: libcuvs_c.so: librmm.so: cannot open shared object file: No such file or directory
        at com.nvidia.cuvs@25.10.0/com.nvidia.cuvs.spi.UnsupportedProvider.newCuVSResources(UnsupportedProvider.java:35)
        at com.nvidia.cuvs@25.10.0/com.nvidia.cuvs.CuVSResources.create(CuVSResources.java:90)
        at com.nvidia.cuvs@25.10.0/com.nvidia.cuvs.CuVSResources.create(CuVSResources.java:79)
```

Fixes https://github.com/rapidsai/cuvs/issues/1321

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)

Approvers:
  - Ishan Chattopadhyaya (https://github.com/chatman)
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1316
---
 java/cuvs-java/pom.xml                        |   4 +-
 .../nvidia/cuvs/spi/CuVSServiceProvider.java  |   2 +-
 .../internal/common/NativeLibraryUtils.java   |  43 +++++
 .../com/nvidia/cuvs/spi/JDKProvider.java      |  10 +-
 .../cuvs/spi/NativeDependencyLoader.java      | 163 ++++++++++++++++++
 .../spi/OptionalNativeDependencyLoader.java   |  81 ---------
 6 files changed, 214 insertions(+), 89 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/NativeLibraryUtils.java
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/NativeDependencyLoader.java
 delete mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java

diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 57da752660..5e79d0c595 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -327,7 +327,7 @@
                                 <manifestEntries>
                                     <Multi-Release>true</Multi-Release>
                                     <addClasspath>true</addClasspath>
-                                    <mainClass>com.nvidia.cuvs.examples.CagraExample</mainClass>
+                                    <Embedded-Libraries-Cuda-Version>12</Embedded-Libraries-Cuda-Version>
                                 </manifestEntries>
                             </archive>
                         </configuration>
@@ -410,7 +410,7 @@
                                 <manifestEntries>
                                     <Multi-Release>true</Multi-Release>
                                     <addClasspath>true</addClasspath>
-                                    <mainClass>com.nvidia.cuvs.examples.CagraExample</mainClass>
+                                    <Embedded-Libraries-Cuda-Version>13</Embedded-Libraries-Cuda-Version>
                                 </manifestEntries>
                             </archive>
                         </configuration>
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
index ae4c9b083a..56db88098d 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSServiceProvider.java
@@ -56,7 +56,7 @@ static CuVSProvider builtinProvider() {
                   .findStatic(cls, "create", MethodType.methodType(CuVSProvider.class));
           return (CuVSProvider) ctr.invoke();
         } catch (ProviderInitializationException e) {
-          return new UnsupportedProvider("cannot create JDKProvider: " + e.getMessage());
+          return new UnsupportedProvider("Cannot create JDKProvider: " + e.getMessage());
         } catch (Throwable e) {
           throw new AssertionError(e);
         }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/NativeLibraryUtils.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/NativeLibraryUtils.java
new file mode 100644
index 0000000000..b09d528224
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/NativeLibraryUtils.java
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal.common;
+
+import java.lang.foreign.*;
+import java.lang.invoke.MethodHandle;
+
+public class NativeLibraryUtils {
+
+  private NativeLibraryUtils() {}
+
+  private static final SymbolLookup LOOKUP =
+      SymbolLookup.libraryLookup(System.mapLibraryName("jvm"), Arena.ofAuto())
+          .or(SymbolLookup.loaderLookup())
+          .or(Linker.nativeLinker().defaultLookup());
+
+  // void * JVM_LoadLibrary(const char *name, jboolean throwException);
+  public static MethodHandle JVM_LoadLibrary$mh =
+      Linker.nativeLinker()
+          .downcallHandle(
+              LOOKUP.find("JVM_LoadLibrary").orElseThrow(),
+              FunctionDescriptor.of(
+                  ValueLayout.ADDRESS, ValueLayout.ADDRESS, ValueLayout.JAVA_BOOLEAN));
+  // void JVM_UnloadLibrary(void * handle);
+  public static MethodHandle JVM_UnloadLibrary$mh =
+      Linker.nativeLinker()
+          .downcallHandle(
+              LOOKUP.find("JVM_UnloadLibrary").orElseThrow(),
+              FunctionDescriptor.ofVoid(ValueLayout.ADDRESS));
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 77eb246e80..6f3d3fa6e3 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -37,13 +37,13 @@
 
 final class JDKProvider implements CuVSProvider {
 
-  static {
-    OptionalNativeDependencyLoader.loadLibraries();
-  }
-
   private static final MethodHandle createNativeDataset$mh = createNativeDatasetBuilder();
 
-  static CuVSProvider create() throws Throwable {
+  private JDKProvider() {}
+
+  static CuVSProvider create() throws ProviderInitializationException {
+    NativeDependencyLoader.loadLibraries();
+
     var mavenVersion = readCuVSVersionFromManifest();
 
     try (var localArena = Arena.ofConfined()) {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/NativeDependencyLoader.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/NativeDependencyLoader.java
new file mode 100644
index 0000000000..436008ed52
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/NativeDependencyLoader.java
@@ -0,0 +1,163 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.spi;
+
+import static com.nvidia.cuvs.internal.common.NativeLibraryUtils.JVM_LoadLibrary$mh;
+
+import java.io.*;
+import java.lang.foreign.Arena;
+import java.net.URL;
+import java.nio.file.Files;
+import java.nio.file.StandardCopyOption;
+import java.util.jar.JarFile;
+import java.util.jar.Manifest;
+
+/**
+ * A class that loads native dependencies if they are available in the jar.
+ */
+class NativeDependencyLoader {
+
+  interface NativeDependencyLoaderStrategy {
+    void loadLibraries() throws ProviderInitializationException;
+  }
+
+  private static final NativeDependencyLoaderStrategy LOADER_STRATEGY = createLoaderStrategy();
+
+  private static NativeDependencyLoaderStrategy createLoaderStrategy() {
+    if (jarHasNativeDependencies()) {
+      return new EmbeddedNativeDependencyLoaderStrategy();
+    } else {
+      return new SystemNativeDependencyLoaderStrategy();
+    }
+  }
+
+  private static boolean jarHasNativeDependencies() {
+    try (var jarFile =
+        new JarFile(
+            JDKProvider.class.getProtectionDomain().getCodeSource().getLocation().getPath())) {
+      Manifest manifest = jarFile.getManifest();
+      // TODO: use this variable to add a check on the installed CUDA version
+      // (which will be system-loaded in any case, even with the fat-jar)
+      var embeddedLibrariesCudaVersion =
+          manifest.getMainAttributes().getValue("Embedded-Libraries-Cuda-Version");
+      return embeddedLibrariesCudaVersion != null;
+    } catch (IOException e) {
+      return false;
+    }
+  }
+
+  private static boolean loaded = false;
+
+  static void loadLibraries() throws ProviderInitializationException {
+    if (!loaded) {
+      try {
+        LOADER_STRATEGY.loadLibraries();
+      } finally {
+        loaded = true;
+      }
+    }
+  }
+
+  private static class EmbeddedNativeDependencyLoaderStrategy
+      implements NativeDependencyLoaderStrategy {
+
+    private static final String OS = System.getProperty("os.name");
+    private static final String ARCH = System.getProperty("os.arch");
+    private static final ClassLoader CLASS_LOADER = JDKProvider.class.getClassLoader();
+
+    private static final String[] FILES_TO_LOAD = {
+      "rapids_logger", "rmm", "cuvs", "cuvs_c",
+    };
+
+    @Override
+    public void loadLibraries() throws ProviderInitializationException {
+      for (String file : FILES_TO_LOAD) {
+        // Uncomment the following line to trace the loading of native dependencies.
+        // System.out.println("Loading native dependency: " + file);
+        try {
+          System.load(createFile(file).getAbsolutePath());
+        } catch (Throwable t) {
+          throw new ProviderInitializationException(
+              "Failed to load native dependency: "
+                  + System.mapLibraryName(file)
+                  + ".so: "
+                  + t.getMessage(),
+              t);
+        }
+      }
+    }
+
+    /**
+     * Extract the contents of a library resource into a temporary file
+     */
+    private static File createFile(String baseName) throws IOException {
+      String path =
+          EmbeddedNativeDependencyLoaderStrategy.ARCH
+              + "/"
+              + EmbeddedNativeDependencyLoaderStrategy.OS
+              + "/"
+              + System.mapLibraryName(baseName);
+      File loc;
+      URL resource = CLASS_LOADER.getResource(path);
+      if (resource == null) {
+        throw new FileNotFoundException("Could not locate native dependency " + path);
+      }
+      try (InputStream in = resource.openStream()) {
+        loc = File.createTempFile(baseName, ".so");
+        loc.deleteOnExit();
+
+        Files.copy(in, loc.toPath(), StandardCopyOption.REPLACE_EXISTING);
+      }
+      return loc;
+    }
+  }
+
+  private static class SystemNativeDependencyLoaderStrategy
+      implements NativeDependencyLoaderStrategy {
+
+    @Override
+    public void loadLibraries() throws ProviderInitializationException {
+      // Try load libcuvs using directly JVM_LoadLibrary with the correct flags for in-depth failure
+      // diagnosis.
+      //
+      // jextract loads the dynamic libraries it references with SymbolLookup.libraryLookup; this
+      // uses
+      // RawNativeLibraries::load
+      // https://github.com/openjdk/jdk/blob/master/src/java.base/share/native/libjava/RawNativeLibraries.c#L58
+      // RawNativeLibraries::load in turn calls JVM_LoadLibrary. Unfortunately, it calls it with a
+      // JNI_FALSE parameter for throwException, which means that the detailed error messages are
+      // not surfaced.
+      //
+      // Here we invoke it with throwException true, so in case of error we can see what's broken
+      String cuvsLibraryName = System.mapLibraryName("cuvs_c");
+
+      final Object lib;
+      try (var localArena = Arena.ofConfined()) {
+        var name = localArena.allocateFrom(cuvsLibraryName);
+        lib = JVM_LoadLibrary$mh.invoke(name, true);
+      } catch (Throwable ex) {
+        if (ex instanceof UnsatisfiedLinkError ulex) {
+          throw new ProviderInitializationException(ulex.getMessage(), ulex);
+        } else {
+          throw new ProviderInitializationException("Error while loading " + cuvsLibraryName, ex);
+        }
+      }
+      if (lib == null) {
+        throw new ProviderInitializationException("Unspecified failure loading " + cuvsLibraryName);
+      }
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java
deleted file mode 100644
index facbb670a3..0000000000
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/OptionalNativeDependencyLoader.java
+++ /dev/null
@@ -1,81 +0,0 @@
-/*
- * Copyright (c) 2025, NVIDIA CORPORATION.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package com.nvidia.cuvs.spi;
-
-import java.io.*;
-import java.net.URL;
-import java.util.stream.*;
-
-/**
- * A class that loads native dependencies if they are available in the jar.
- */
-public class OptionalNativeDependencyLoader {
-
-  private static final ClassLoader loader = JDKProvider.class.getClassLoader();
-
-  private static boolean loaded = false;
-
-  private static final String[] FILES_TO_LOAD = {
-    "rapids_logger", "rmm", "cuvs", "cuvs_c",
-  };
-
-  public static void loadLibraries() {
-    if (!loaded) {
-      String os = System.getProperty("os.name");
-      String arch = System.getProperty("os.arch");
-
-      Stream.of(FILES_TO_LOAD)
-          .forEach(
-              file -> {
-                // Uncomment the following line to trace the loading of native dependencies.
-                // System.out.println("Loading native dependency: " + file);
-                try {
-                  System.load(createFile(os, arch, file).getAbsolutePath());
-                } catch (Throwable t) {
-                  System.err.println(
-                      "Continuing despite failure to load native dependency: "
-                          + System.mapLibraryName(file)
-                          + ".so: "
-                          + t.getMessage());
-                }
-              });
-
-      loaded = true;
-    }
-  }
-
-  /** Extract the contents of a library resource into a temporary file */
-  private static File createFile(String os, String arch, String baseName) throws IOException {
-    String path = arch + "/" + os + "/" + System.mapLibraryName(baseName);
-    File loc;
-    URL resource = loader.getResource(path);
-    if (resource == null) {
-      throw new FileNotFoundException("Could not locate native dependency " + path);
-    }
-    try (InputStream in = resource.openStream()) {
-      loc = File.createTempFile(baseName, ".so");
-      loc.deleteOnExit();
-      try (OutputStream out = new FileOutputStream(loc)) {
-        byte[] buffer = new byte[1024 * 16];
-        int read = 0;
-        while ((read = in.read(buffer)) >= 0) {
-          out.write(buffer, 0, read);
-        }
-      }
-    }
-    return loc;
-  }
-}

From 703a1028e2b5c1eec8ef805a74ac718ac9a26086 Mon Sep 17 00:00:00 2001
From: Ben Karsin <bkarsin@nvidia.com>
Date: Mon, 15 Sep 2025 18:10:18 -1000
Subject: [PATCH 575/623] Fix pq_compressed file created by SSD Vamana build
 (#1313)

Fix issue with pq_compressed files being incorrect when GPU vamana build was being provided opq codebooks as an input to the build. Bug was caused by vq_centers not being set to 0s, despite OPQ compression not using vq centers, and only using pq_centers during quantized vector encoding.

Also cleaned up vamana_example file to support other datatypes and function correctly when building the DiskANN index without quantization.

Authors:
  - Ben Karsin (https://github.com/bkarsin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Tarang Jain (https://github.com/tarang-jain)

URL: https://github.com/rapidsai/cuvs/pull/1313
---
 cpp/src/neighbors/detail/vamana/vamana_build.cuh |  1 +
 examples/cpp/src/vamana_example.cu               | 12 ++++++------
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/cpp/src/neighbors/detail/vamana/vamana_build.cuh b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
index 25c842947f..629319e0e3 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_build.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_build.cuh
@@ -570,6 +570,7 @@ auto quantize_all_vectors(raft::resources const& res,
 {
   auto dim         = residuals.extent(1);
   auto vq_codebook = raft::make_device_matrix<float, uint32_t, raft::row_major>(res, 1, dim);
+  raft::matrix::fill<float>(res, vq_codebook.view(), 0.0);
 
   auto codes = cuvs::neighbors::detail::process_and_fill_codes_subspaces<float, int64_t>(
     res, ps, residuals, raft::make_const_mdspan(vq_codebook.view()), pq_codebook);
diff --git a/examples/cpp/src/vamana_example.cu b/examples/cpp/src/vamana_example.cu
index 9715d0dc38..c708020831 100644
--- a/examples/cpp/src/vamana_example.cu
+++ b/examples/cpp/src/vamana_example.cu
@@ -63,12 +63,12 @@ void vamana_build_and_write(raft::device_resources const& dev_resources,
 
   std::cout << "Time to build index: " << elapsed_seconds.count() << "s\n";
 
-  // Output index to file (in-memory format)
-  serialize(dev_resources, out_fname, index);
-
-  // Output index to file (disk sector-aligned format)
   if (codebook_prefix != "") {
-    serialize(dev_resources, out_fname + ".sector_aligned", index, false, true);
+    // Output index to file (disk sector-aligned format)
+    serialize(dev_resources, out_fname, index, false, true);
+  } else {
+    // Output index to file (in-memory format)
+    serialize(dev_resources, out_fname, index);
   }
 }
 
@@ -80,8 +80,8 @@ void usage()
     "codebook prefix>\n");
 
   printf("Input file expected to be binary file of fp32 vectors.\n");
-  printf("Graph degree sizes supported: 32, 64, 128, 256\n");
   printf("Datatype of input dataset (int8 or float)\n");
+  printf("Graph degree sizes supported: 32, 64, 128, 256\n");
   printf("Visited_size must be > degree and a power of 2.\n");
   printf("max_fraction > 0 and <= 1. Typical values are 0.06 or 0.1.\n");
   printf("Default iterations = 1.0, increase for better quality graph.\n");

From 07e422b54d1f2ed43172949d0ef40d989229dbca Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 16 Sep 2025 06:28:36 -0700
Subject: [PATCH 576/623] Documentation Fixes (#1301)

This fixes all the warnings in the docs build, and enables warnings as errors to prevent future regressions

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/1301
---
 build.sh                                      |    2 +-
 cpp/doxygen/Doxyfile                          | 1209 +++++++++++------
 cpp/include/cuvs/neighbors/brute_force.h      |    4 +-
 cpp/include/cuvs/neighbors/cagra.h            |    2 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |    2 +
 cpp/include/cuvs/neighbors/common.hpp         |    9 +-
 cpp/include/cuvs/neighbors/hnsw.h             |    4 +-
 cpp/include/cuvs/neighbors/hnsw.hpp           |   34 +-
 cpp/include/cuvs/neighbors/ivf_flat.h         |    2 +-
 cpp/include/cuvs/neighbors/ivf_pq.h           |    2 +-
 cpp/include/cuvs/neighbors/vamana.hpp         |   27 +-
 .../cuvs/preprocessing/quantize/binary.hpp    |    1 -
 .../detail/vamana/vamana_codebooks.cuh        |    5 +-
 cpp/src/neighbors/vamana.cuh                  |    3 +-
 cpp/src/neighbors/vamana_codebooks_float.cu   |    2 +-
 docs/source/api_basics.rst                    |    4 +-
 docs/source/api_interoperability.rst          |    4 +-
 docs/source/c_api/neighbors_cagra_c.rst       |    2 +-
 docs/source/conf.py                           |    4 +-
 docs/source/cpp_api.rst                       |    1 +
 docs/source/cpp_api/distance.rst              |    4 +-
 .../cpp_api/neighbors_all_neighbors.rst       |    6 +-
 docs/source/cpp_api/neighbors_cagra.rst       |    8 -
 docs/source/cuvs_bench/index.rst              |   17 +-
 docs/source/cuvs_bench/param_tuning.rst       |   56 +-
 docs/source/cuvs_bench/wiki_all_dataset.rst   |    1 +
 docs/source/filtering.rst                     |    2 +-
 docs/source/getting_started.rst               |    2 +-
 docs/source/index.rst                         |    1 +
 docs/source/indexes/ivfpq.rst                 |    2 +-
 docs/source/indexes/vamana.rst                |    2 +-
 docs/source/python_api/neighbors_cagra.rst    |    5 -
 docs/source/python_api/neighbors_hnsw.rst     |    2 +-
 .../source/python_api/neighbors_nn_decent.rst |    2 +-
 docs/source/python_api/preprocessing.rst      |   13 +-
 docs/source/sphinxext/github_link.py          |   68 +-
 docs/source/working_with_ann_indexes_c.rst    |    2 -
 docs/source/working_with_ann_indexes_cpp.rst  |    2 -
 .../working_with_ann_indexes_python.rst       |    2 -
 docs/source/working_with_ann_indexes_rust.rst |   39 +-
 .../neighbors/brute_force/brute_force.pyx     |    8 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |   46 +-
 python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx      |   23 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |    2 +
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  |    2 +
 45 files changed, 1047 insertions(+), 593 deletions(-)

diff --git a/build.sh b/build.sh
index 2caec74ca7..328c64b43b 100755
--- a/build.sh
+++ b/build.sh
@@ -517,7 +517,7 @@ if hasArg docs; then
     cd "${DOXYGEN_BUILD_DIR}"
     doxygen Doxyfile
     cd "${SPHINX_BUILD_DIR}"
-    sphinx-build -b html source _html
+    sphinx-build -W -b html source _html
     cd "${REPODIR}"/rust
     cargo doc -p cuvs --no-deps
     rsync -av "${RUST_BUILD_DIR}"/doc/ "${SPHINX_BUILD_DIR}"/_html/_static/rust
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index ce6741c1f2..f9ffb39a20 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -1,7 +1,7 @@
-# Doxyfile 1.8.20
+# Doxyfile 1.13.2
 
 # This file describes the settings to be used by the documentation system
-# doxygen (www.doxygen.org) for a project.
+# Doxygen (www.doxygen.org) for a project.
 #
 # All text after a double hash (##) is considered a comment and is placed in
 # front of the TAG it is preceding.
@@ -12,6 +12,16 @@
 # For lists, items can also be appended using:
 # TAG += value [value, ...]
 # Values that contain spaces should be placed between quotes (\" \").
+#
+# Note:
+#
+# Use Doxygen to compare the used configuration file with the template
+# configuration file:
+# doxygen -x [configFile]
+# Use Doxygen to compare the used configuration file with the template
+# configuration file without replacing the environment variables or CMake type
+# replacement variables:
+# doxygen -x_noenv [configFile]
 
 #---------------------------------------------------------------------------
 # Project related configuration options
@@ -38,10 +48,10 @@ PROJECT_NAME           = "cuVS C++ API"
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = "$(RAPIDS_VERSION_MAJOR_MINOR)"
+PROJECT_NUMBER         = $(RAPIDS_VERSION_MAJOR_MINOR)
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
-# for a project that appears at the top of each page and should give viewer a
+# for a project that appears at the top of each page and should give viewers a
 # quick idea about the purpose of the project. Keep the description short.
 
 PROJECT_BRIEF          =
@@ -53,24 +63,42 @@ PROJECT_BRIEF          =
 
 PROJECT_LOGO           =
 
+# With the PROJECT_ICON tag one can specify an icon that is included in the tabs
+# when the HTML document is shown. Doxygen will copy the logo to the output
+# directory.
+
+PROJECT_ICON           =
+
 # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
 # into which the generated documentation will be written. If a relative path is
-# entered, it will be relative to the location where doxygen was started. If
+# entered, it will be relative to the location where Doxygen was started. If
 # left blank the current directory will be used.
 
 OUTPUT_DIRECTORY       =
 
-# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
-# directories (in 2 levels) under the output directory of each output format and
-# will distribute the generated files over these directories. Enabling this
-# option can be useful when feeding doxygen a huge amount of source files, where
-# putting all generated files in the same directory would otherwise causes
-# performance problems for the file system.
+# If the CREATE_SUBDIRS tag is set to YES then Doxygen will create up to 4096
+# sub-directories (in 2 levels) under the output directory of each output format
+# and will distribute the generated files over these directories. Enabling this
+# option can be useful when feeding Doxygen a huge amount of source files, where
+# putting all generated files in the same directory would otherwise cause
+# performance problems for the file system. Adapt CREATE_SUBDIRS_LEVEL to
+# control the number of sub-directories.
 # The default value is: NO.
 
 CREATE_SUBDIRS         = NO
 
-# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
+# Controls the number of sub-directories that will be created when
+# CREATE_SUBDIRS tag is set to YES. Level 0 represents 16 directories, and every
+# level increment doubles the number of directories, resulting in 4096
+# directories at level 8 which is the default and also the maximum value. The
+# sub-directories are organized in 2 levels, the first level always has a fixed
+# number of 16 directories.
+# Minimum value: 0, maximum value: 8, default value: 8.
+# This tag requires that the tag CREATE_SUBDIRS is set to YES.
+
+CREATE_SUBDIRS_LEVEL   = 8
+
+# If the ALLOW_UNICODE_NAMES tag is set to YES, Doxygen will allow non-ASCII
 # characters to appear in the names of generated files. If set to NO, non-ASCII
 # characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
 # U+3044.
@@ -79,36 +107,28 @@ CREATE_SUBDIRS         = NO
 ALLOW_UNICODE_NAMES    = NO
 
 # The OUTPUT_LANGUAGE tag is used to specify the language in which all
-# documentation generated by doxygen is written. Doxygen will use this
+# documentation generated by Doxygen is written. Doxygen will use this
 # information to generate all constant output in the proper language.
-# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
-# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
-# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
-# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
-# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
-# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
-# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
-# Ukrainian and Vietnamese.
+# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Bulgarian,
+# Catalan, Chinese, Chinese-Traditional, Croatian, Czech, Danish, Dutch, English
+# (United States), Esperanto, Farsi (Persian), Finnish, French, German, Greek,
+# Hindi, Hungarian, Indonesian, Italian, Japanese, Japanese-en (Japanese with
+# English messages), Korean, Korean-en (Korean with English messages), Latvian,
+# Lithuanian, Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese,
+# Romanian, Russian, Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish,
+# Swedish, Turkish, Ukrainian and Vietnamese.
 # The default value is: English.
 
 OUTPUT_LANGUAGE        = English
 
-# The OUTPUT_TEXT_DIRECTION tag is used to specify the direction in which all
-# documentation generated by doxygen is written. Doxygen will use this
-# information to generate all generated output in the proper direction.
-# Possible values are: None, LTR, RTL and Context.
-# The default value is: None.
-
-OUTPUT_TEXT_DIRECTION  = None
-
-# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member
+# If the BRIEF_MEMBER_DESC tag is set to YES, Doxygen will include brief member
 # descriptions after the members that are listed in the file and class
 # documentation (similar to Javadoc). Set to NO to disable this.
 # The default value is: YES.
 
 BRIEF_MEMBER_DESC      = YES
 
-# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief
+# If the REPEAT_BRIEF tag is set to YES, Doxygen will prepend the brief
 # description of a member or function before the detailed description
 #
 # Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
@@ -129,13 +149,13 @@ REPEAT_BRIEF           = YES
 ABBREVIATE_BRIEF       =
 
 # If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
-# doxygen will generate a detailed section even if there is only a brief
+# Doxygen will generate a detailed section even if there is only a brief
 # description.
 # The default value is: NO.
 
 ALWAYS_DETAILED_SEC    = NO
 
-# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
+# If the INLINE_INHERITED_MEMB tag is set to YES, Doxygen will show all
 # inherited members of a class in the documentation of that class as if those
 # members were ordinary class members. Constructors, destructors and assignment
 # operators of the base classes will not be shown.
@@ -143,7 +163,7 @@ ALWAYS_DETAILED_SEC    = NO
 
 INLINE_INHERITED_MEMB  = NO
 
-# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path
+# If the FULL_PATH_NAMES tag is set to YES, Doxygen will prepend the full path
 # before files name in the file list and in the header files. If set to NO the
 # shortest path that makes the file name unique will be used
 # The default value is: YES.
@@ -153,11 +173,11 @@ FULL_PATH_NAMES        = YES
 # The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
 # Stripping is only done if one of the specified strings matches the left-hand
 # part of the path. The tag can be used to show relative paths in the file list.
-# If left blank the directory from which doxygen is run is used as the path to
+# If left blank the directory from which Doxygen is run is used as the path to
 # strip.
 #
 # Note that you can specify absolute paths here, but also relative paths, which
-# will be relative from the directory where doxygen is started.
+# will be relative from the directory where Doxygen is started.
 # This tag requires that the tag FULL_PATH_NAMES is set to YES.
 
 STRIP_FROM_PATH        =
@@ -171,41 +191,42 @@ STRIP_FROM_PATH        =
 
 STRIP_FROM_INC_PATH    =
 
-# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
-# less readable) file names. This can be useful is your file systems doesn't
+# If the SHORT_NAMES tag is set to YES, Doxygen will generate much shorter (but
+# less readable) file names. This can be useful if your file system doesn't
 # support long names like on DOS, Mac, or CD-ROM.
 # The default value is: NO.
 
 SHORT_NAMES            = NO
 
-# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
-# first line (until the first dot) of a Javadoc-style comment as the brief
-# description. If set to NO, the Javadoc-style will behave just like regular Qt-
-# style comments (thus requiring an explicit @brief command for a brief
-# description.)
+# If the JAVADOC_AUTOBRIEF tag is set to YES then Doxygen will interpret the
+# first line (until the first dot, question mark or exclamation mark) of a
+# Javadoc-style comment as the brief description. If set to NO, the Javadoc-
+# style will behave just like regular Qt-style comments (thus requiring an
+# explicit @brief command for a brief description.)
 # The default value is: NO.
 
 JAVADOC_AUTOBRIEF      = NO
 
-# If the JAVADOC_BANNER tag is set to YES then doxygen will interpret a line
+# If the JAVADOC_BANNER tag is set to YES then Doxygen will interpret a line
 # such as
 # /***************
 # as being the beginning of a Javadoc-style comment "banner". If set to NO, the
 # Javadoc-style will behave just like regular comments and it will not be
-# interpreted by doxygen.
+# interpreted by Doxygen.
 # The default value is: NO.
 
 JAVADOC_BANNER         = NO
 
-# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
-# line (until the first dot) of a Qt-style comment as the brief description. If
-# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
-# requiring an explicit \brief command for a brief description.)
+# If the QT_AUTOBRIEF tag is set to YES then Doxygen will interpret the first
+# line (until the first dot, question mark or exclamation mark) of a Qt-style
+# comment as the brief description. If set to NO, the Qt-style will behave just
+# like regular Qt-style comments (thus requiring an explicit \brief command for
+# a brief description.)
 # The default value is: NO.
 
 QT_AUTOBRIEF           = NO
 
-# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
+# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make Doxygen treat a
 # multi-line C++ special comment block (i.e. a block of //! or /// comments) as
 # a brief description. This used to be the default behavior. The new default is
 # to treat a multi-line C++ comment block as a detailed description. Set this
@@ -217,10 +238,10 @@ QT_AUTOBRIEF           = NO
 
 MULTILINE_CPP_IS_BRIEF = NO
 
-# By default Python docstrings are displayed as preformatted text and doxygen's
+# By default Python docstrings are displayed as preformatted text and Doxygen's
 # special commands cannot be used. By setting PYTHON_DOCSTRING to NO the
-# doxygen's special commands can be used and the contents of the docstring
-# documentation blocks is shown as doxygen documentation.
+# Doxygen's special commands can be used and the contents of the docstring
+# documentation blocks is shown as Doxygen documentation.
 # The default value is: YES.
 
 PYTHON_DOCSTRING       = YES
@@ -231,7 +252,7 @@ PYTHON_DOCSTRING       = YES
 
 INHERIT_DOCS           = YES
 
-# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new
+# If the SEPARATE_MEMBER_PAGES tag is set to YES then Doxygen will produce a new
 # page for each member. If set to NO, the documentation of a member will be part
 # of the file/class/namespace that contains it.
 # The default value is: NO.
@@ -248,16 +269,16 @@ TAB_SIZE               = 4
 # the documentation. An alias has the form:
 # name=value
 # For example adding
-# "sideeffect=@par Side Effects:\n"
+# "sideeffect=@par Side Effects:^^"
 # will allow you to put the command \sideeffect (or @sideeffect) in the
 # documentation, which will result in a user-defined paragraph with heading
-# "Side Effects:". You can put \n's in the value part of an alias to insert
-# newlines (in the resulting output). You can put ^^ in the value part of an
-# alias to insert a newline as if a physical newline was in the original file.
-# When you need a literal { or } or , in the value part of an alias you have to
-# escape them by means of a backslash (\), this can lead to conflicts with the
-# commands \{ and \} for these it is advised to use the version @{ and @} or use
-# a double escape (\\{ and \\})
+# "Side Effects:". Note that you cannot put \n's in the value part of an alias
+# to insert newlines (in the resulting output). You can put ^^ in the value part
+# of an alias to insert a newline as if a physical newline was in the original
+# file. When you need a literal { or } or , in the value part of an alias you
+# have to escape them by means of a backslash (\), this can lead to conflicts
+# with the commands \{ and \} for these it is advised to use the version @{ and
+# @} or use a double escape (\\{ and \\})
 
 ALIASES                =
 
@@ -301,28 +322,31 @@ OPTIMIZE_OUTPUT_SLICE  = NO
 # parses. With this tag you can assign which parser to use for a given
 # extension. Doxygen has a built-in mapping, but you can override or extend it
 # using this tag. The format is ext=language, where ext is a file extension, and
-# language is one of the parsers supported by doxygen: IDL, Java, JavaScript,
-# Csharp (C#), C, C++, D, PHP, md (Markdown), Objective-C, Python, Slice, VHDL,
-# Fortran (fixed format Fortran: FortranFixed, free formatted Fortran:
+# language is one of the parsers supported by Doxygen: IDL, Java, JavaScript,
+# Csharp (C#), C, C++, Lex, D, PHP, md (Markdown), Objective-C, Python, Slice,
+# VHDL, Fortran (fixed format Fortran: FortranFixed, free formatted Fortran:
 # FortranFree, unknown formatted Fortran: Fortran. In the later case the parser
 # tries to guess whether the code is fixed or free formatted code, this is the
-# default for Fortran type files). For instance to make doxygen treat .inc files
+# default for Fortran type files). For instance to make Doxygen treat .inc files
 # as Fortran files (default is PHP), and .f files as C (default is Fortran),
 # use: inc=Fortran f=C.
 #
 # Note: For files without extension you can use no_extension as a placeholder.
 #
 # Note that for custom extensions you also need to set FILE_PATTERNS otherwise
-# the files are not read by doxygen.
+# the files are not read by Doxygen. When specifying no_extension you should add
+# * to the FILE_PATTERNS.
+#
+# Note see also the list of default file extension mappings.
 
 EXTENSION_MAPPING      = cu=C++ \
                          cuh=C++
 
-# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
+# If the MARKDOWN_SUPPORT tag is enabled then Doxygen pre-processes all comments
 # according to the Markdown format, which allows for more readable
 # documentation. See https://daringfireball.net/projects/markdown/ for details.
-# The output of markdown processing is further processed by doxygen, so you can
-# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
+# The output of markdown processing is further processed by Doxygen, so you can
+# mix Doxygen, HTML, and XML commands with Markdown formatting. Disable only in
 # case of backward compatibilities issues.
 # The default value is: YES.
 
@@ -332,25 +356,45 @@ MARKDOWN_SUPPORT       = YES
 # to that level are automatically included in the table of contents, even if
 # they do not have an id attribute.
 # Note: This feature currently applies only to Markdown headings.
-# Minimum value: 0, maximum value: 99, default value: 5.
+# Minimum value: 0, maximum value: 99, default value: 6.
 # This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
 
 TOC_INCLUDE_HEADINGS   = 5
 
-# When enabled doxygen tries to link words that correspond to documented
+# The MARKDOWN_ID_STYLE tag can be used to specify the algorithm used to
+# generate identifiers for the Markdown headings. Note: Every identifier is
+# unique.
+# Possible values are: DOXYGEN use a fixed 'autotoc_md' string followed by a
+# sequence number starting at 0 and GITHUB use the lower case version of title
+# with any whitespace replaced by '-' and punctuation characters removed.
+# The default value is: DOXYGEN.
+# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
+
+MARKDOWN_ID_STYLE      = DOXYGEN
+
+# When enabled Doxygen tries to link words that correspond to documented
 # classes, or namespaces to their corresponding documentation. Such a link can
 # be prevented in individual cases by putting a % sign in front of the word or
-# globally by setting AUTOLINK_SUPPORT to NO.
+# globally by setting AUTOLINK_SUPPORT to NO. Words listed in the
+# AUTOLINK_IGNORE_WORDS tag are excluded from automatic linking.
 # The default value is: YES.
 
 AUTOLINK_SUPPORT       = YES
 
+# This tag specifies a list of words that, when matching the start of a word in
+# the documentation, will suppress auto links generation, if it is enabled via
+# AUTOLINK_SUPPORT. This list does not affect affect links explicitly created
+# using \# or the \link or commands.
+# This tag requires that the tag AUTOLINK_SUPPORT is set to YES.
+
+AUTOLINK_IGNORE_WORDS  =
+
 # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
 # to include (a tag file for) the STL sources as input, then you should set this
-# tag to YES in order to let doxygen match functions declarations and
+# tag to YES in order to let Doxygen match functions declarations and
 # definitions whose arguments contain STL classes (e.g. func(std::string);
-# versus func(std::string) {}). This also make the inheritance and collaboration
-# diagrams that involve STL classes more complete and accurate.
+# versus func(std::string) {}). This also makes the inheritance and
+# collaboration diagrams that involve STL classes more complete and accurate.
 # The default value is: NO.
 
 BUILTIN_STL_SUPPORT    = NO
@@ -362,16 +406,16 @@ BUILTIN_STL_SUPPORT    = NO
 CPP_CLI_SUPPORT        = NO
 
 # Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
-# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen
-# will parse them like normal C++ but will assume all classes use public instead
-# of private inheritance when no explicit protection keyword is present.
+# https://www.riverbankcomputing.com/software) sources only. Doxygen will parse
+# them like normal C++ but will assume all classes use public instead of private
+# inheritance when no explicit protection keyword is present.
 # The default value is: NO.
 
 SIP_SUPPORT            = NO
 
 # For Microsoft's IDL there are propget and propput attributes to indicate
 # getter and setter methods for a property. Setting this option to YES will make
-# doxygen to replace the get and set methods by a property in the documentation.
+# Doxygen to replace the get and set methods by a property in the documentation.
 # This will only work if the methods are indeed getting or setting a simple
 # type. If this is not the case, or you want to show the methods anyway, you
 # should set this option to NO.
@@ -380,7 +424,7 @@ SIP_SUPPORT            = NO
 IDL_PROPERTY_SUPPORT   = YES
 
 # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
-# tag is set to YES then doxygen will reuse the documentation of the first
+# tag is set to YES then Doxygen will reuse the documentation of the first
 # member in the group (if any) for the other members of the group. By default
 # all members of a group must be documented explicitly.
 # The default value is: NO.
@@ -438,18 +482,18 @@ TYPEDEF_HIDES_STRUCT   = NO
 # The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
 # cache is used to resolve symbols given their name and scope. Since this can be
 # an expensive process and often the same symbol appears multiple times in the
-# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
-# doxygen will become slower. If the cache is too large, memory is wasted. The
+# code, Doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# Doxygen will become slower. If the cache is too large, memory is wasted. The
 # cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
 # is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
-# symbols. At the end of a run doxygen will report the cache usage and suggest
+# symbols. At the end of a run Doxygen will report the cache usage and suggest
 # the optimal cache size from a speed point of view.
 # Minimum value: 0, maximum value: 9, default value: 0.
 
 LOOKUP_CACHE_SIZE      = 0
 
-# The NUM_PROC_THREADS specifies the number threads doxygen is allowed to use
-# during processing. When set to 0 doxygen will based this on the number of
+# The NUM_PROC_THREADS specifies the number of threads Doxygen is allowed to use
+# during processing. When set to 0 Doxygen will based this on the number of
 # cores available in the system. You can set it explicitly to a value larger
 # than 0 to get more control over the balance between CPU load and processing
 # speed. At this moment only the input processing can be done using multiple
@@ -461,11 +505,19 @@ LOOKUP_CACHE_SIZE      = 0
 
 NUM_PROC_THREADS       = 0
 
+# If the TIMESTAMP tag is set different from NO then each generated page will
+# contain the date or date and time when the page was generated. Setting this to
+# NO can help when comparing the output of multiple runs.
+# Possible values are: YES, NO, DATETIME and DATE.
+# The default value is: NO.
+
+TIMESTAMP              = NO
+
 #---------------------------------------------------------------------------
 # Build related configuration options
 #---------------------------------------------------------------------------
 
-# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
+# If the EXTRACT_ALL tag is set to YES, Doxygen will assume all entities in
 # documentation are documented, even if no documentation was available. Private
 # class members and static file members will be hidden unless the
 # EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
@@ -524,7 +576,14 @@ EXTRACT_LOCAL_METHODS  = NO
 
 EXTRACT_ANON_NSPACES   = NO
 
-# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# If this flag is set to YES, the name of an unnamed parameter in a declaration
+# will be determined by the corresponding definition. By default unnamed
+# parameters remain unnamed in the output.
+# The default value is: YES.
+
+RESOLVE_UNNAMED_PARAMS = YES
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all
 # undocumented members inside documented classes or files. If set to NO these
 # members will be included in the various overviews, but no documentation
 # section is generated. This option has no effect if EXTRACT_ALL is enabled.
@@ -532,22 +591,31 @@ EXTRACT_ANON_NSPACES   = NO
 
 HIDE_UNDOC_MEMBERS     = NO
 
-# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all
 # undocumented classes that are normally visible in the class hierarchy. If set
 # to NO, these classes will be included in the various overviews. This option
-# has no effect if EXTRACT_ALL is enabled.
+# will also hide undocumented C++ concepts if enabled. This option has no effect
+# if EXTRACT_ALL is enabled.
 # The default value is: NO.
 
 HIDE_UNDOC_CLASSES     = NO
 
-# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# If the HIDE_UNDOC_NAMESPACES tag is set to YES, Doxygen will hide all
+# undocumented namespaces that are normally visible in the namespace hierarchy.
+# If set to NO, these namespaces will be included in the various overviews. This
+# option has no effect if EXTRACT_ALL is enabled.
+# The default value is: YES.
+
+HIDE_UNDOC_NAMESPACES  = YES
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all friend
 # declarations. If set to NO, these declarations will be included in the
 # documentation.
 # The default value is: NO.
 
 HIDE_FRIEND_COMPOUNDS  = NO
 
-# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any
 # documentation blocks found inside the body of a function. If set to NO, these
 # blocks will be appended to the function's detailed documentation block.
 # The default value is: NO.
@@ -561,30 +629,44 @@ HIDE_IN_BODY_DOCS      = NO
 
 INTERNAL_DOCS          = NO
 
-# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
-# names in lower-case letters. If set to YES, upper-case letters are also
-# allowed. This is useful if you have classes or files whose names only differ
-# in case and if your file system supports case sensitive file names. Windows
-# (including Cygwin) and Mac users are advised to set this option to NO.
-# The default value is: system dependent.
+# With the correct setting of option CASE_SENSE_NAMES Doxygen will better be
+# able to match the capabilities of the underlying filesystem. In case the
+# filesystem is case sensitive (i.e. it supports files in the same directory
+# whose names only differ in casing), the option must be set to YES to properly
+# deal with such files in case they appear in the input. For filesystems that
+# are not case sensitive the option should be set to NO to properly deal with
+# output files written for symbols that only differ in casing, such as for two
+# classes, one named CLASS and the other named Class, and to also support
+# references to files without having to specify the exact matching casing. On
+# Windows (including Cygwin) and macOS, users should typically set this option
+# to NO, whereas on Linux or other Unix flavors it should typically be set to
+# YES.
+# Possible values are: SYSTEM, NO and YES.
+# The default value is: SYSTEM.
 
 CASE_SENSE_NAMES       = YES
 
-# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# If the HIDE_SCOPE_NAMES tag is set to NO then Doxygen will show members with
 # their full class and namespace scopes in the documentation. If set to YES, the
 # scope will be hidden.
 # The default value is: NO.
 
 HIDE_SCOPE_NAMES       = NO
 
-# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
+# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then Doxygen will
 # append additional text to a page's title, such as Class Reference. If set to
 # YES the compound reference will be hidden.
 # The default value is: NO.
 
 HIDE_COMPOUND_REFERENCE= NO
 
-# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# If the SHOW_HEADERFILE tag is set to YES then the documentation for a class
+# will show which file needs to be included to use the class.
+# The default value is: YES.
+
+SHOW_HEADERFILE        = YES
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then Doxygen will put a list of
 # the files that are included by a file in the documentation of that file.
 # The default value is: YES.
 
@@ -597,7 +679,7 @@ SHOW_INCLUDE_FILES     = YES
 
 SHOW_GROUPED_MEMB_INC  = NO
 
-# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then Doxygen will list include
 # files with double quotes in the documentation rather than with sharp brackets.
 # The default value is: NO.
 
@@ -609,14 +691,14 @@ FORCE_LOCAL_INCLUDES   = NO
 
 INLINE_INFO            = YES
 
-# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# If the SORT_MEMBER_DOCS tag is set to YES then Doxygen will sort the
 # (detailed) documentation of file and class members alphabetically by member
 # name. If set to NO, the members will appear in declaration order.
 # The default value is: YES.
 
 SORT_MEMBER_DOCS       = YES
 
-# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# If the SORT_BRIEF_DOCS tag is set to YES then Doxygen will sort the brief
 # descriptions of file, namespace and class members alphabetically by member
 # name. If set to NO, the members will appear in declaration order. Note that
 # this will also influence the order of the classes in the class list.
@@ -624,7 +706,7 @@ SORT_MEMBER_DOCS       = YES
 
 SORT_BRIEF_DOCS        = NO
 
-# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then Doxygen will sort the
 # (brief and detailed) documentation of class members so that constructors and
 # destructors are listed first. If set to NO the constructors will appear in the
 # respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
@@ -636,7 +718,7 @@ SORT_BRIEF_DOCS        = NO
 
 SORT_MEMBERS_CTORS_1ST = NO
 
-# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# If the SORT_GROUP_NAMES tag is set to YES then Doxygen will sort the hierarchy
 # of group names into alphabetical order. If set to NO the group names will
 # appear in their defined order.
 # The default value is: NO.
@@ -653,11 +735,11 @@ SORT_GROUP_NAMES       = NO
 
 SORT_BY_SCOPE_NAME     = NO
 
-# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# If the STRICT_PROTO_MATCHING option is enabled and Doxygen fails to do proper
 # type resolution of all parameters of a function it will reject a match between
 # the prototype and the implementation of a member function even if there is
 # only one candidate or it is obvious which candidate to choose by doing a
-# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# simple string match. By disabling STRICT_PROTO_MATCHING Doxygen will still
 # accept a match between prototype and implementation in such cases.
 # The default value is: NO.
 
@@ -727,24 +809,25 @@ SHOW_FILES             = YES
 SHOW_NAMESPACES        = YES
 
 # The FILE_VERSION_FILTER tag can be used to specify a program or script that
-# doxygen should invoke to get the current version for each file (typically from
+# Doxygen should invoke to get the current version for each file (typically from
 # the version control system). Doxygen will invoke the program by executing (via
 # popen()) the command command input-file, where command is the value of the
 # FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
-# by doxygen. Whatever the program writes to standard output is used as the file
+# by Doxygen. Whatever the program writes to standard output is used as the file
 # version. For an example see the documentation.
 
 FILE_VERSION_FILTER    =
 
 # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
-# by doxygen. The layout file controls the global structure of the generated
+# by Doxygen. The layout file controls the global structure of the generated
 # output files in an output format independent way. To create the layout file
-# that represents doxygen's defaults, run doxygen with the -l option. You can
+# that represents Doxygen's defaults, run Doxygen with the -l option. You can
 # optionally specify a file name after the option, if omitted DoxygenLayout.xml
-# will be used as the name of the layout file.
+# will be used as the name of the layout file. See also section "Changing the
+# layout of pages" for information.
 #
-# Note that if you run doxygen from a directory containing a file called
-# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# Note that if you run Doxygen from a directory containing a file called
+# DoxygenLayout.xml, Doxygen will parse it automatically even if the LAYOUT_FILE
 # tag is left empty.
 
 LAYOUT_FILE            =
@@ -759,19 +842,35 @@ LAYOUT_FILE            =
 
 CITE_BIB_FILES         =
 
+# The EXTERNAL_TOOL_PATH tag can be used to extend the search path (PATH
+# environment variable) so that external tools such as latex and gs can be
+# found.
+# Note: Directories specified with EXTERNAL_TOOL_PATH are added in front of the
+# path already specified by the PATH variable, and are added in the order
+# specified.
+# Note: This option is particularly useful for macOS version 14 (Sonoma) and
+# higher, when running Doxygen from Doxywizard, because in this case any user-
+# defined changes to the PATH are ignored. A typical example on macOS is to set
+# EXTERNAL_TOOL_PATH = /Library/TeX/texbin /usr/local/bin
+# together with the standard path, the full search path used by doxygen when
+# launching external tools will then become
+# PATH=/Library/TeX/texbin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin
+
+EXTERNAL_TOOL_PATH     =
+
 #---------------------------------------------------------------------------
 # Configuration options related to warning and progress messages
 #---------------------------------------------------------------------------
 
 # The QUIET tag can be used to turn on/off the messages that are generated to
-# standard output by doxygen. If QUIET is set to YES this implies that the
+# standard output by Doxygen. If QUIET is set to YES this implies that the
 # messages are off.
 # The default value is: NO.
 
 QUIET                  = NO
 
 # The WARNINGS tag can be used to turn on/off the warning messages that are
-# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
+# generated to standard error (stderr) by Doxygen. If WARNINGS is set to YES
 # this implies that the warnings are on.
 #
 # Tip: Turn warnings on while writing the documentation.
@@ -779,49 +878,97 @@ QUIET                  = NO
 
 WARNINGS               = YES
 
-# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
+# If the WARN_IF_UNDOCUMENTED tag is set to YES then Doxygen will generate
 # warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
 # will automatically be disabled.
 # The default value is: YES.
 
 WARN_IF_UNDOCUMENTED   = YES
 
-# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
-# potential errors in the documentation, such as not documenting some parameters
-# in a documented function, or documenting parameters that don't exist or using
-# markup commands wrongly.
+# If the WARN_IF_DOC_ERROR tag is set to YES, Doxygen will generate warnings for
+# potential errors in the documentation, such as documenting some parameters in
+# a documented function twice, or documenting parameters that don't exist or
+# using markup commands wrongly.
 # The default value is: YES.
 
 WARN_IF_DOC_ERROR      = YES
 
+# If WARN_IF_INCOMPLETE_DOC is set to YES, Doxygen will warn about incomplete
+# function parameter documentation. If set to NO, Doxygen will accept that some
+# parameters have no documentation without warning.
+# The default value is: YES.
+
+WARN_IF_INCOMPLETE_DOC = YES
+
 # This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
 # are documented, but have no documentation for their parameters or return
-# value. If set to NO, doxygen will only warn about wrong or incomplete
-# parameter documentation, but not about the absence of documentation. If
-# EXTRACT_ALL is set to YES then this flag will automatically be disabled.
+# value. If set to NO, Doxygen will only warn about wrong parameter
+# documentation, but not about the absence of documentation. If EXTRACT_ALL is
+# set to YES then this flag will automatically be disabled. See also
+# WARN_IF_INCOMPLETE_DOC
 # The default value is: NO.
 
 WARN_NO_PARAMDOC       = YES
 
-# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
-# a warning is encountered.
+# If WARN_IF_UNDOC_ENUM_VAL option is set to YES, Doxygen will warn about
+# undocumented enumeration values. If set to NO, Doxygen will accept
+# undocumented enumeration values. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: NO.
+
+WARN_IF_UNDOC_ENUM_VAL = NO
+
+# If WARN_LAYOUT_FILE option is set to YES, Doxygen will warn about issues found
+# while parsing the user defined layout file, such as missing or wrong elements.
+# See also LAYOUT_FILE for details. If set to NO, problems with the layout file
+# will be suppressed.
+# The default value is: YES.
+
+WARN_LAYOUT_FILE       = YES
+
+# If the WARN_AS_ERROR tag is set to YES then Doxygen will immediately stop when
+# a warning is encountered. If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS
+# then Doxygen will continue running as if WARN_AS_ERROR tag is set to NO, but
+# at the end of the Doxygen process Doxygen will return with a non-zero status.
+# If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS_PRINT then Doxygen behaves
+# like FAIL_ON_WARNINGS but in case no WARN_LOGFILE is defined Doxygen will not
+# write the warning messages in between other messages but write them at the end
+# of a run, in case a WARN_LOGFILE is defined the warning messages will be
+# besides being in the defined file also be shown at the end of a run, unless
+# the WARN_LOGFILE is defined as - i.e. standard output (stdout) in that case
+# the behavior will remain as with the setting FAIL_ON_WARNINGS.
+# Possible values are: NO, YES, FAIL_ON_WARNINGS and FAIL_ON_WARNINGS_PRINT.
 # The default value is: NO.
 
 WARN_AS_ERROR          = YES
 
-# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# The WARN_FORMAT tag determines the format of the warning messages that Doxygen
 # can produce. The string should contain the $file, $line, and $text tags, which
 # will be replaced by the file and line number from which the warning originated
 # and the warning text. Optionally the format may contain $version, which will
 # be replaced by the version of the file (if it could be obtained via
 # FILE_VERSION_FILTER)
+# See also: WARN_LINE_FORMAT
 # The default value is: $file:$line: $text.
 
 WARN_FORMAT            = "$file:$line: $text"
 
+# In the $text part of the WARN_FORMAT command it is possible that a reference
+# to a more specific place is given. To make it easier to jump to this place
+# (outside of Doxygen) the user can define a custom "cut" / "paste" string.
+# Example:
+# WARN_LINE_FORMAT = "'vi $file +$line'"
+# See also: WARN_FORMAT
+# The default value is: at line $line of file $file.
+
+WARN_LINE_FORMAT       = "at line $line of file $file"
+
 # The WARN_LOGFILE tag can be used to specify a file to which warning and error
 # messages should be written. If left blank the output is written to standard
-# error (stderr).
+# error (stderr). In case the file specified cannot be opened for writing the
+# warning and error messages are written to standard error. When as file - is
+# specified the warning and error messages are written to standard output
+# (stdout).
 
 WARN_LOGFILE           =
 
@@ -839,29 +986,42 @@ INPUT                  = main_page.md \
                          ../include
 
 # This tag can be used to specify the character encoding of the source files
-# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# that Doxygen parses. Internally Doxygen uses the UTF-8 encoding. Doxygen uses
 # libiconv (or the iconv built into libc) for the transcoding. See the libiconv
-# documentation (see: https://www.gnu.org/software/libiconv/) for the list of
-# possible encodings.
+# documentation (see:
+# https://www.gnu.org/software/libiconv/) for the list of possible encodings.
+# See also: INPUT_FILE_ENCODING
 # The default value is: UTF-8.
 
 INPUT_ENCODING         = UTF-8
 
+# This tag can be used to specify the character encoding of the source files
+# that Doxygen parses. The INPUT_FILE_ENCODING tag can be used to specify
+# character encoding on a per file pattern basis. Doxygen will compare the file
+# name with each pattern and apply the encoding instead of the default
+# INPUT_ENCODING if there is a match. The character encodings are a list of the
+# form: pattern=encoding (like *.php=ISO-8859-1).
+# See also: INPUT_ENCODING for further information on supported encodings.
+
+INPUT_FILE_ENCODING    =
+
 # If the value of the INPUT tag contains directories, you can use the
 # FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
 # *.h) to filter out the source-files in the directories.
 #
 # Note that for custom extensions or not directly supported extensions you also
 # need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# read by doxygen.
+# read by Doxygen.
 #
-# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
-# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
-# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
-# *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C comment),
-# *.doc (to be provided as doxygen C comment), *.txt (to be provided as doxygen
-# C comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f18, *.f, *.for, *.vhd,
-# *.vhdl, *.ucf, *.qsf and *.ice.
+# Note the list of default checked file patterns might differ from the list of
+# default file extension mappings.
+#
+# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cxxm,
+# *.cpp, *.cppm, *.ccm, *.c++, *.c++m, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl,
+# *.idl, *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp, *.h++, *.ixx, *.l, *.cs, *.d,
+# *.php, *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown, *.md, *.mm, *.dox (to
+# be provided as Doxygen C comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,
+# *.f18, *.f, *.for, *.vhd, *.vhdl, *.ucf, *.qsf and *.ice.
 
 FILE_PATTERNS          = *.hpp \
                          *.h \
@@ -877,10 +1037,13 @@ RECURSIVE              = YES
 # excluded from the INPUT source files. This way you can easily exclude a
 # subdirectory from a directory tree whose root is specified with the INPUT tag.
 #
-# Note that relative paths are relative to the directory from which doxygen is
+# Note that relative paths are relative to the directory from which Doxygen is
 # run.
 
-EXCLUDE                = ../include/cuvs/sparse/selection
+EXCLUDE                = ../include/cuvs/sparse/selection \
+                         ../build \
+                         ../src
+
 
 # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
 # directories that are symbolic links (a Unix file system feature) are excluded
@@ -896,7 +1059,6 @@ EXCLUDE_SYMLINKS       = NO
 # Note that the wildcards are matched against the file with absolute path, so to
 # exclude all test directories for example use the pattern */test/*
 
-# TODO: remove specializations from exclude patterns when headers have been removed.
 EXCLUDE_PATTERNS       = */detail/* \
                          */specializations/* \
                          */thirdparty/*
@@ -905,10 +1067,7 @@ EXCLUDE_PATTERNS       = */detail/* \
 # (namespaces, classes, functions, etc.) that should be excluded from the
 # output. The symbol name can be a fully qualified name, a word, or if the
 # wildcard * is used, a substring. Examples: ANamespace, AClass,
-# AClass::ANamespace, ANamespace::*Test
-#
-# Note that the wildcards are matched against the file with absolute path, so to
-# exclude all test directories use the pattern */test/*
+# ANamespace::AClass, ANamespace::*Test
 
 EXCLUDE_SYMBOLS        = detail
 
@@ -938,7 +1097,7 @@ EXAMPLE_RECURSIVE      = NO
 
 IMAGE_PATH             =
 
-# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# The INPUT_FILTER tag can be used to specify a program that Doxygen should
 # invoke to filter for each input file. Doxygen will invoke the filter program
 # by executing (via popen()) the command:
 #
@@ -953,9 +1112,14 @@ IMAGE_PATH             =
 # code is scanned, but not when the output code is generated. If lines are added
 # or removed, the anchors will not be placed correctly.
 #
+# Note that Doxygen will use the data processed and written to standard output
+# for further processing, therefore nothing else, like debug statements or used
+# commands (so in case of a Windows batch file always use @echo OFF), should be
+# written to standard output.
+#
 # Note that for custom extensions or not directly supported extensions you also
 # need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# properly processed by doxygen.
+# properly processed by Doxygen.
 
 INPUT_FILTER           =
 
@@ -968,7 +1132,7 @@ INPUT_FILTER           =
 #
 # Note that for custom extensions or not directly supported extensions you also
 # need to set EXTENSION_MAPPING for the extension otherwise the files are not
-# properly processed by doxygen.
+# properly processed by Doxygen.
 
 FILTER_PATTERNS        =
 
@@ -990,10 +1154,28 @@ FILTER_SOURCE_PATTERNS =
 # If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
 # is part of the input, its contents will be placed on the main page
 # (index.html). This can be useful if you have a project on for instance GitHub
-# and want to reuse the introduction page also for the doxygen output.
+# and want to reuse the introduction page also for the Doxygen output.
 
 USE_MDFILE_AS_MAINPAGE = main_page.md
 
+# If the IMPLICIT_DIR_DOCS tag is set to YES, any README.md file found in sub-
+# directories of the project's root, is used as the documentation for that sub-
+# directory, except when the README.md starts with a \dir, \page or \mainpage
+# command. If set to NO, the README.md file needs to start with an explicit \dir
+# command in order to be used as directory documentation.
+# The default value is: YES.
+
+IMPLICIT_DIR_DOCS      = YES
+
+# The Fortran standard specifies that for fixed formatted Fortran code all
+# characters from position 72 are to be considered as comment. A common
+# extension is to allow longer lines before the automatic comment starts. The
+# setting FORTRAN_COMMENT_AFTER will also make it possible that longer lines can
+# be processed before the automatic comment starts.
+# Minimum value: 7, maximum value: 10000, default value: 72.
+
+FORTRAN_COMMENT_AFTER  = 72
+
 #---------------------------------------------------------------------------
 # Configuration options related to source browsing
 #---------------------------------------------------------------------------
@@ -1008,12 +1190,13 @@ USE_MDFILE_AS_MAINPAGE = main_page.md
 SOURCE_BROWSER         = NO
 
 # Setting the INLINE_SOURCES tag to YES will include the body of functions,
-# classes and enums directly into the documentation.
+# multi-line macros, enums or list initialized variables directly into the
+# documentation.
 # The default value is: NO.
 
 INLINE_SOURCES         = NO
 
-# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct Doxygen to hide any
 # special comment blocks from generated source code fragments. Normal C, C++ and
 # Fortran comments will always remain visible.
 # The default value is: YES.
@@ -1051,7 +1234,7 @@ REFERENCES_LINK_SOURCE = YES
 SOURCE_TOOLTIPS        = YES
 
 # If the USE_HTAGS tag is set to YES then the references to source code will
-# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# point to the HTML generated by the htags(1) tool instead of Doxygen built-in
 # source browser. The htags tool is part of GNU's global source tagging system
 # (see https://www.gnu.org/software/global/global.html). You will need version
 # 4.8.6 or higher.
@@ -1065,14 +1248,14 @@ SOURCE_TOOLTIPS        = YES
 # Doxygen will invoke htags (and that will in turn invoke gtags), so these
 # tools must be available from the command line (i.e. in the search path).
 #
-# The result: instead of the source browser generated by doxygen, the links to
+# The result: instead of the source browser generated by Doxygen, the links to
 # source code will now point to the output of htags.
 # The default value is: NO.
 # This tag requires that the tag SOURCE_BROWSER is set to YES.
 
 USE_HTAGS              = NO
 
-# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# If the VERBATIM_HEADERS tag is set the YES then Doxygen will generate a
 # verbatim copy of the header file for each class for which an include is
 # specified. Set to NO to disable this.
 # See also: Section \class.
@@ -1091,17 +1274,11 @@ VERBATIM_HEADERS       = YES
 
 ALPHABETICAL_INDEX     = YES
 
-# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
-# which the alphabetical index list will be split.
-# Minimum value: 1, maximum value: 20, default value: 5.
-# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
-
-COLS_IN_ALPHA_INDEX    = 5
-
-# In case all classes in a project start with a common prefix, all classes will
-# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
-# can be used to specify a prefix (or a list of prefixes) that should be ignored
-# while generating the index headers.
+# The IGNORE_PREFIX tag can be used to specify a prefix (or a list of prefixes)
+# that should be ignored while generating the index headers. The IGNORE_PREFIX
+# tag works for classes, function and member names. The entity will be placed in
+# the alphabetical list under the first letter of the entity name that remains
+# after removing the prefix.
 # This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
 
 IGNORE_PREFIX          =
@@ -1110,7 +1287,7 @@ IGNORE_PREFIX          =
 # Configuration options related to the HTML output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
+# If the GENERATE_HTML tag is set to YES, Doxygen will generate HTML output
 # The default value is: YES.
 
 GENERATE_HTML          = NO
@@ -1131,40 +1308,40 @@ HTML_OUTPUT            = html
 HTML_FILE_EXTENSION    = .html
 
 # The HTML_HEADER tag can be used to specify a user-defined HTML header file for
-# each generated HTML page. If the tag is left blank doxygen will generate a
+# each generated HTML page. If the tag is left blank Doxygen will generate a
 # standard header.
 #
 # To get valid HTML the header file that includes any scripts and style sheets
-# that doxygen needs, which is dependent on the configuration options used (e.g.
+# that Doxygen needs, which is dependent on the configuration options used (e.g.
 # the setting GENERATE_TREEVIEW). It is highly recommended to start with a
 # default header using
 # doxygen -w html new_header.html new_footer.html new_stylesheet.css
 # YourConfigFile
 # and then modify the file new_header.html. See also section "Doxygen usage"
-# for information on how to generate the default header that doxygen normally
+# for information on how to generate the default header that Doxygen normally
 # uses.
 # Note: The header is subject to change so you typically have to regenerate the
-# default header when upgrading to a newer version of doxygen. For a description
+# default header when upgrading to a newer version of Doxygen. For a description
 # of the possible markers and block names see the documentation.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_HEADER            = header.html
 
 # The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
-# generated HTML page. If the tag is left blank doxygen will generate a standard
+# generated HTML page. If the tag is left blank Doxygen will generate a standard
 # footer. See HTML_HEADER for more information on how to generate a default
 # footer and what special commands can be used inside the footer. See also
 # section "Doxygen usage" for information on how to generate the default footer
-# that doxygen normally uses.
+# that Doxygen normally uses.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_FOOTER            =
 
 # The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
 # sheet that is used by each HTML page. It can be used to fine-tune the look of
-# the HTML output. If left blank doxygen will generate a default style sheet.
+# the HTML output. If left blank Doxygen will generate a default style sheet.
 # See also section "Doxygen usage" for information on how to generate the style
-# sheet that doxygen normally uses.
+# sheet that Doxygen normally uses.
 # Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
 # it is more robust and this tag (HTML_STYLESHEET) will in the future become
 # obsolete.
@@ -1174,13 +1351,18 @@ HTML_STYLESHEET        =
 
 # The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
 # cascading style sheets that are included after the standard style sheets
-# created by doxygen. Using this option one can overrule certain style aspects.
+# created by Doxygen. Using this option one can overrule certain style aspects.
 # This is preferred over using HTML_STYLESHEET since it does not replace the
 # standard style sheet and is therefore more robust against future updates.
 # Doxygen will copy the style sheet files to the output directory.
 # Note: The order of the extra style sheet files is of importance (e.g. the last
 # style sheet in the list overrules the setting of the previous ones in the
-# list). For an example see the documentation.
+# list).
+# Note: Since the styling of scrollbars can currently not be overruled in
+# Webkit/Chromium, the styling will be left out of the default doxygen.css if
+# one or more extra stylesheets have been specified. So if scrollbar
+# customization is desired it has to be added explicitly. For an example see the
+# documentation.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_EXTRA_STYLESHEET  =
@@ -1195,9 +1377,22 @@ HTML_EXTRA_STYLESHEET  =
 
 HTML_EXTRA_FILES       =
 
+# The HTML_COLORSTYLE tag can be used to specify if the generated HTML output
+# should be rendered with a dark or light theme.
+# Possible values are: LIGHT always generates light mode output, DARK always
+# generates dark mode output, AUTO_LIGHT automatically sets the mode according
+# to the user preference, uses light mode if no preference is set (the default),
+# AUTO_DARK automatically sets the mode according to the user preference, uses
+# dark mode if no preference is set and TOGGLE allows a user to switch between
+# light and dark mode via a button.
+# The default value is: AUTO_LIGHT.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE        = AUTO_LIGHT
+
 # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
 # will adjust the colors in the style sheet and background images according to
-# this color. Hue is specified as an angle on a colorwheel, see
+# this color. Hue is specified as an angle on a color-wheel, see
 # https://en.wikipedia.org/wiki/Hue for more information. For instance the value
 # 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
 # purple, and 360 is red again.
@@ -1207,7 +1402,7 @@ HTML_EXTRA_FILES       =
 HTML_COLORSTYLE_HUE    = 266
 
 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
-# in the HTML output. For a value of 0 the output will use grayscales only. A
+# in the HTML output. For a value of 0 the output will use gray-scales only. A
 # value of 255 will produce the most vivid colors.
 # Minimum value: 0, maximum value: 255, default value: 100.
 # This tag requires that the tag GENERATE_HTML is set to YES.
@@ -1225,15 +1420,6 @@ HTML_COLORSTYLE_SAT    = 255
 
 HTML_COLORSTYLE_GAMMA  = 52
 
-# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
-# page will contain the date and time when the page was generated. Setting this
-# to YES can help to show when doxygen was last run and thus if the
-# documentation is up to date.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-HTML_TIMESTAMP         = NO
-
 # If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
 # documentation will contain a main index with vertical navigation menus that
 # are dynamically created via JavaScript. If disabled, the navigation index will
@@ -1253,6 +1439,33 @@ HTML_DYNAMIC_MENUS     = YES
 
 HTML_DYNAMIC_SECTIONS  = NO
 
+# If the HTML_CODE_FOLDING tag is set to YES then classes and functions can be
+# dynamically folded and expanded in the generated HTML source code.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_CODE_FOLDING      = YES
+
+# If the HTML_COPY_CLIPBOARD tag is set to YES then Doxygen will show an icon in
+# the top right corner of code and text fragments that allows the user to copy
+# its content to the clipboard. Note this only works if supported by the browser
+# and the web page is served via a secure context (see:
+# https://www.w3.org/TR/secure-contexts/), i.e. using the https: or file:
+# protocol.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COPY_CLIPBOARD    = YES
+
+# Doxygen stores a couple of settings persistently in the browser (via e.g.
+# cookies). By default these settings apply to all HTML pages generated by
+# Doxygen across all projects. The HTML_PROJECT_COOKIE tag can be used to store
+# the settings under a project specific key, such that the user preferences will
+# be stored separately.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_PROJECT_COOKIE    =
+
 # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
 # shown in the various tree structured indices initially; the user can expand
 # and collapse entries dynamically later on. Doxygen will expand the tree to
@@ -1268,10 +1481,11 @@ HTML_INDEX_NUM_ENTRIES = 100
 
 # If the GENERATE_DOCSET tag is set to YES, additional index files will be
 # generated that can be used as input for Apple's Xcode 3 integrated development
-# environment (see: https://developer.apple.com/xcode/), introduced with OSX
-# 10.5 (Leopard). To create a documentation set, doxygen will generate a
-# Makefile in the HTML output directory. Running make will produce the docset in
-# that directory and running make install will install the docset in
+# environment (see:
+# https://developer.apple.com/xcode/), introduced with OSX 10.5 (Leopard). To
+# create a documentation set, Doxygen will generate a Makefile in the HTML
+# output directory. Running make will produce the docset in that directory and
+# running make install will install the docset in
 # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
 # startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy
 # genXcode/_index.html for more information.
@@ -1288,6 +1502,13 @@ GENERATE_DOCSET        = NO
 
 DOCSET_FEEDNAME        = "Doxygen generated docs"
 
+# This tag determines the URL of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDURL         =
+
 # This tag specifies a string that should uniquely identify the documentation
 # set bundle. This should be a reverse domain-name style string, e.g.
 # com.mycompany.MyDocSet. Doxygen will append .docset to the name.
@@ -1310,14 +1531,18 @@ DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
 
 DOCSET_PUBLISHER_NAME  = Publisher
 
-# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# If the GENERATE_HTMLHELP tag is set to YES then Doxygen generates three
 # additional HTML index files: index.hhp, index.hhc, and index.hhk. The
 # index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
-# (see: https://www.microsoft.com/en-us/download/details.aspx?id=21138) on
-# Windows.
+# on Windows. In the beginning of 2021 Microsoft took the original page, with
+# a.o. the download links, offline (the HTML help workshop was already many
+# years in maintenance mode). You can download the HTML help workshop from the
+# web archives at Installation executable (see:
+# http://web.archive.org/web/20160201063255/http://download.microsoft.com/downlo
+# ad/0/A/9/0A939EF6-E31C-430F-A3DF-DFAE7960D564/htmlhelp.exe).
 #
 # The HTML Help Workshop contains a compiler that can convert all HTML output
-# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# generated by Doxygen into a single compiled HTML file (.chm). Compiled HTML
 # files are now used as the Windows 98 help format, and will replace the old
 # Windows help format (.hlp) on all Windows platforms in the future. Compressed
 # HTML files also contain an index, a table of contents, and you can search for
@@ -1337,7 +1562,7 @@ CHM_FILE               =
 
 # The HHC_LOCATION tag can be used to specify the location (absolute path
 # including file name) of the HTML help compiler (hhc.exe). If non-empty,
-# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# Doxygen will try to run the HTML help compiler on the generated index.hhp.
 # The file has to be specified with full path.
 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
@@ -1371,6 +1596,16 @@ BINARY_TOC             = NO
 
 TOC_EXPAND             = NO
 
+# The SITEMAP_URL tag is used to specify the full URL of the place where the
+# generated documentation will be placed on the server by the user during the
+# deployment of the documentation. The generated sitemap is called sitemap.xml
+# and placed on the directory specified by HTML_OUTPUT. In case no SITEMAP_URL
+# is specified no sitemap is generated. For information about the sitemap
+# protocol see https://www.sitemaps.org
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SITEMAP_URL            =
+
 # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
 # QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
 # can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
@@ -1389,7 +1624,8 @@ QCH_FILE               =
 
 # The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
 # Project output. For more information please see Qt Help Project / Namespace
-# (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
+# (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
 # The default value is: org.doxygen.Project.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
@@ -1397,8 +1633,8 @@ QHP_NAMESPACE          = org.doxygen.Project
 
 # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
 # Help Project output. For more information please see Qt Help Project / Virtual
-# Folders (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-
-# folders).
+# Folders (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-folders).
 # The default value is: doc.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
@@ -1406,16 +1642,16 @@ QHP_VIRTUAL_FOLDER     = doc
 
 # If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
 # filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
-# filters).
+# Filters (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_NAME   =
 
 # The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
 # custom filter to add. For more information please see Qt Help Project / Custom
-# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
-# filters).
+# Filters (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_ATTRS  =
@@ -1427,9 +1663,9 @@ QHP_CUST_FILTER_ATTRS  =
 
 QHP_SECT_FILTER_ATTRS  =
 
-# The QHG_LOCATION tag can be used to specify the location of Qt's
-# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
-# generated .qhp file.
+# The QHG_LOCATION tag can be used to specify the location (absolute path
+# including file name) of Qt's qhelpgenerator. If non-empty Doxygen will try to
+# run qhelpgenerator on the generated .qhp file.
 # This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHG_LOCATION           =
@@ -1460,7 +1696,7 @@ ECLIPSE_DOC_ID         = org.doxygen.Project
 # of each HTML page. A value of NO enables the index and the value YES disables
 # it. Since the tabs in the index contain the same information as the navigation
 # tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
-# The default value is: NO.
+# The default value is: YES.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 DISABLE_INDEX          = NO
@@ -1472,18 +1708,30 @@ DISABLE_INDEX          = NO
 # to work a browser that supports JavaScript, DHTML, CSS and frames is required
 # (i.e. any modern browser). Windows users are probably better off using the
 # HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
-# further fine-tune the look of the index. As an example, the default style
-# sheet generated by doxygen has an example that shows how to put an image at
-# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
-# the same information as the tab index, you could consider setting
-# DISABLE_INDEX to YES when enabling this option.
-# The default value is: NO.
+# further fine tune the look of the index (see "Fine-tuning the output"). As an
+# example, the default style sheet generated by Doxygen has an example that
+# shows how to put an image at the root of the tree instead of the PROJECT_NAME.
+# Since the tree basically has the same information as the tab index, you could
+# consider setting DISABLE_INDEX to YES when enabling this option.
+# The default value is: YES.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 GENERATE_TREEVIEW      = NO
 
+# When both GENERATE_TREEVIEW and DISABLE_INDEX are set to YES, then the
+# FULL_SIDEBAR option determines if the side bar is limited to only the treeview
+# area (value NO) or if it should extend to the full height of the window (value
+# YES). Setting this to YES gives a layout similar to
+# https://docs.readthedocs.io with more room for contents, but less room for the
+# project logo, title, and description. If either GENERATE_TREEVIEW or
+# DISABLE_INDEX is set to NO, this option has no effect.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FULL_SIDEBAR           = NO
+
 # The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
-# doxygen will group on one line in the generated HTML documentation.
+# Doxygen will group on one line in the generated HTML documentation.
 #
 # Note that a value of 0 will completely suppress the enum values from appearing
 # in the overview section.
@@ -1492,6 +1740,12 @@ GENERATE_TREEVIEW      = NO
 
 ENUM_VALUES_PER_LINE   = 4
 
+# When the SHOW_ENUM_VALUES tag is set doxygen will show the specified
+# enumeration values besides the enumeration mnemonics.
+# The default value is: NO.
+
+SHOW_ENUM_VALUES       = NO
+
 # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
 # to set the initial width (in pixels) of the frame in which the tree is shown.
 # Minimum value: 0, maximum value: 1500, default value: 250.
@@ -1499,14 +1753,21 @@ ENUM_VALUES_PER_LINE   = 4
 
 TREEVIEW_WIDTH         = 250
 
-# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
+# If the EXT_LINKS_IN_WINDOW option is set to YES, Doxygen will open links to
 # external symbols imported via tag files in a separate window.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 EXT_LINKS_IN_WINDOW    = NO
 
-# If the HTML_FORMULA_FORMAT option is set to svg, doxygen will use the pdf2svg
+# If the OBFUSCATE_EMAILS tag is set to YES, Doxygen will obfuscate email
+# addresses.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+OBFUSCATE_EMAILS       = YES
+
+# If the HTML_FORMULA_FORMAT option is set to svg, Doxygen will use the pdf2svg
 # tool (see https://github.com/dawbarton/pdf2svg) or inkscape (see
 # https://inkscape.org) to generate formulas as SVG images instead of PNGs for
 # the HTML output. These images will generally look nicer at scaled resolutions.
@@ -1519,24 +1780,13 @@ HTML_FORMULA_FORMAT    = png
 
 # Use this tag to change the font size of LaTeX formulas included as images in
 # the HTML documentation. When you change the font size after a successful
-# doxygen run you need to manually remove any form_*.png images from the HTML
+# Doxygen run you need to manually remove any form_*.png images from the HTML
 # output directory to force them to be regenerated.
 # Minimum value: 8, maximum value: 50, default value: 10.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
 FORMULA_FONTSIZE       = 10
 
-# Use the FORMULA_TRANSPARENT tag to determine whether or not the images
-# generated for formulas are transparent PNGs. Transparent PNGs are not
-# supported properly for IE 6.0, but are supported on all modern browsers.
-#
-# Note that when changing this option you need to delete any form_*.png files in
-# the HTML output directory before the changes have effect.
-# The default value is: YES.
-# This tag requires that the tag GENERATE_HTML is set to YES.
-
-FORMULA_TRANSPARENT    = YES
-
 # The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands
 # to create new LaTeX commands to be used in formulas as building blocks. See
 # the section "Including formulas" for details.
@@ -1554,11 +1804,29 @@ FORMULA_MACROFILE      =
 
 USE_MATHJAX            = YES
 
+# With MATHJAX_VERSION it is possible to specify the MathJax version to be used.
+# Note that the different versions of MathJax have different requirements with
+# regards to the different settings, so it is possible that also other MathJax
+# settings have to be changed when switching between the different MathJax
+# versions.
+# Possible values are: MathJax_2 and MathJax_3.
+# The default value is: MathJax_2.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_VERSION        = MathJax_2
+
 # When MathJax is enabled you can set the default output format to be used for
-# the MathJax output. See the MathJax site (see:
-# http://docs.mathjax.org/en/latest/output.html) for more details.
+# the MathJax output. For more details about the output format see MathJax
+# version 2 (see:
+# http://docs.mathjax.org/en/v2.7-latest/output.html) and MathJax version 3
+# (see:
+# http://docs.mathjax.org/en/latest/web/components/output.html).
 # Possible values are: HTML-CSS (which is slower, but has the best
-# compatibility), NativeMML (i.e. MathML) and SVG.
+# compatibility. This is the name for Mathjax version 2, for MathJax version 3
+# this will be translated into chtml), NativeMML (i.e. MathML. Only supported
+# for MathJax 2. For MathJax version 3 chtml will be used instead.), chtml (This
+# is the name for Mathjax version 3, for MathJax version 2 this will be
+# translated into HTML-CSS) and SVG.
 # The default value is: HTML-CSS.
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
@@ -1571,33 +1839,40 @@ MATHJAX_FORMAT         = HTML-CSS
 # MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
 # Content Delivery Network so you can quickly see the result without installing
 # MathJax. However, it is strongly recommended to install a local copy of
-# MathJax from https://www.mathjax.org before deployment.
-# The default value is: https://cdn.jsdelivr.net/npm/mathjax@2.
+# MathJax from https://www.mathjax.org before deployment. The default value is:
+# - in case of MathJax version 2: https://cdn.jsdelivr.net/npm/mathjax@2
+# - in case of MathJax version 3: https://cdn.jsdelivr.net/npm/mathjax@3
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
 MATHJAX_RELPATH        = http://cdn.mathjax.org/mathjax/latest
 
 # The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
 # extension names that should be enabled during MathJax rendering. For example
+# for MathJax version 2 (see
+# https://docs.mathjax.org/en/v2.7-latest/tex.html#tex-and-latex-extensions):
 # MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# For example for MathJax version 3 (see
+# http://docs.mathjax.org/en/latest/input/tex/extensions/index.html):
+# MATHJAX_EXTENSIONS = ams
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
 MATHJAX_EXTENSIONS     =
 
-# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# The MATHJAX_CODEFILE tag can be used to specify a file with JavaScript pieces
 # of code that will be used on startup of the MathJax code. See the MathJax site
-# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# (see:
+# http://docs.mathjax.org/en/v2.7-latest/output.html) for more details. For an
 # example see the documentation.
 # This tag requires that the tag USE_MATHJAX is set to YES.
 
 MATHJAX_CODEFILE       =
 
-# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
-# the HTML output. The underlying search engine uses javascript and DHTML and
+# When the SEARCHENGINE tag is enabled Doxygen will generate a search box for
+# the HTML output. The underlying search engine uses JavaScript and DHTML and
 # should work on any modern browser. Note that when using HTML help
 # (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
 # there is already a search function so this one should typically be disabled.
-# For large projects the javascript based search engine can be slow, then
+# For large projects the JavaScript based search engine can be slow, then
 # enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
 # search using the keyboard; to jump to the search box use <access key> + S
 # (what the <access key> is depends on the OS and browser, but it is typically
@@ -1616,7 +1891,7 @@ SEARCHENGINE           = YES
 # When the SERVER_BASED_SEARCH tag is enabled the search engine will be
 # implemented using a web server instead of a web client using JavaScript. There
 # are two flavors of web server based searching depending on the EXTERNAL_SEARCH
-# setting. When disabled, doxygen will generate a PHP script for searching and
+# setting. When disabled, Doxygen will generate a PHP script for searching and
 # an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
 # and searching needs to be provided by external tools. See the section
 # "External Indexing and Searching" for details.
@@ -1625,7 +1900,7 @@ SEARCHENGINE           = YES
 
 SERVER_BASED_SEARCH    = NO
 
-# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# When EXTERNAL_SEARCH tag is enabled Doxygen will no longer generate the PHP
 # script for searching. Instead the search results are written to an XML file
 # which needs to be processed by an external indexer. Doxygen will invoke an
 # external search engine pointed to by the SEARCHENGINE_URL option to obtain the
@@ -1633,7 +1908,8 @@ SERVER_BASED_SEARCH    = NO
 #
 # Doxygen ships with an example indexer (doxyindexer) and search engine
 # (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: https://xapian.org/).
+# Xapian (see:
+# https://xapian.org/).
 #
 # See the section "External Indexing and Searching" for details.
 # The default value is: NO.
@@ -1646,8 +1922,9 @@ EXTERNAL_SEARCH        = NO
 #
 # Doxygen ships with an example indexer (doxyindexer) and search engine
 # (doxysearch.cgi) which are based on the open source search engine library
-# Xapian (see: https://xapian.org/). See the section "External Indexing and
-# Searching" for details.
+# Xapian (see:
+# https://xapian.org/). See the section "External Indexing and Searching" for
+# details.
 # This tag requires that the tag SEARCHENGINE is set to YES.
 
 SEARCHENGINE_URL       =
@@ -1668,7 +1945,7 @@ SEARCHDATA_FILE        = searchdata.xml
 
 EXTERNAL_SEARCH_ID     =
 
-# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through Doxygen
 # projects other than the one defined by this configuration file, but that are
 # all added to the same external search index. Each project needs to have a
 # unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
@@ -1682,7 +1959,7 @@ EXTRA_SEARCH_MAPPINGS  =
 # Configuration options related to the LaTeX output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
+# If the GENERATE_LATEX tag is set to YES, Doxygen will generate LaTeX output.
 # The default value is: YES.
 
 GENERATE_LATEX         = NO
@@ -1727,7 +2004,7 @@ MAKEINDEX_CMD_NAME     = makeindex
 
 LATEX_MAKEINDEX_CMD    = makeindex
 
-# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
+# If the COMPACT_LATEX tag is set to YES, Doxygen generates more compact LaTeX
 # documents. This may be useful for small projects and may help to save some
 # trees in general.
 # The default value is: NO.
@@ -1756,36 +2033,38 @@ PAPER_TYPE             = a4
 
 EXTRA_PACKAGES         =
 
-# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
-# generated LaTeX document. The header should contain everything until the first
-# chapter. If it is left blank doxygen will generate a standard header. See
-# section "Doxygen usage" for information on how to let doxygen write the
-# default header to a separate file.
+# The LATEX_HEADER tag can be used to specify a user-defined LaTeX header for
+# the generated LaTeX document. The header should contain everything until the
+# first chapter. If it is left blank Doxygen will generate a standard header. It
+# is highly recommended to start with a default header using
+# doxygen -w latex new_header.tex new_footer.tex new_stylesheet.sty
+# and then modify the file new_header.tex. See also section "Doxygen usage" for
+# information on how to generate the default header that Doxygen normally uses.
 #
-# Note: Only use a user-defined header if you know what you are doing! The
-# following commands have a special meaning inside the header: $title,
-# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
-# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
-# string, for the replacement values of the other commands the user is referred
-# to HTML_HEADER.
+# Note: Only use a user-defined header if you know what you are doing!
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of Doxygen. The following
+# commands have a special meaning inside the header (and footer): For a
+# description of the possible markers and block names see the documentation.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_HEADER           =
 
-# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
-# generated LaTeX document. The footer should contain everything after the last
-# chapter. If it is left blank doxygen will generate a standard footer. See
+# The LATEX_FOOTER tag can be used to specify a user-defined LaTeX footer for
+# the generated LaTeX document. The footer should contain everything after the
+# last chapter. If it is left blank Doxygen will generate a standard footer. See
 # LATEX_HEADER for more information on how to generate a default footer and what
-# special commands can be used inside the footer.
-#
-# Note: Only use a user-defined footer if you know what you are doing!
+# special commands can be used inside the footer. See also section "Doxygen
+# usage" for information on how to generate the default footer that Doxygen
+# normally uses. Note: Only use a user-defined footer if you know what you are
+# doing!
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_FOOTER           =
 
 # The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
 # LaTeX style sheets that are included after the standard style sheets created
-# by doxygen. Using this option one can overrule certain style aspects. Doxygen
+# by Doxygen. Using this option one can overrule certain style aspects. Doxygen
 # will copy the style sheet files to the output directory.
 # Note: The order of the extra style sheet files is of importance (e.g. the last
 # style sheet in the list overrules the setting of the previous ones in the
@@ -1811,7 +2090,7 @@ LATEX_EXTRA_FILES      =
 
 PDF_HYPERLINKS         = YES
 
-# If the USE_PDFLATEX tag is set to YES, doxygen will use the engine as
+# If the USE_PDFLATEX tag is set to YES, Doxygen will use the engine as
 # specified with LATEX_CMD_NAME to generate the PDF file directly from the LaTeX
 # files. Set this option to YES, to get a higher quality PDF documentation.
 #
@@ -1821,48 +2100,36 @@ PDF_HYPERLINKS         = YES
 
 USE_PDFLATEX           = YES
 
-# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
-# command to the generated LaTeX files. This will instruct LaTeX to keep running
-# if errors occur, instead of asking the user for help. This option is also used
-# when generating formulas in HTML.
+# The LATEX_BATCHMODE tag signals the behavior of LaTeX in case of an error.
+# Possible values are: NO same as ERROR_STOP, YES same as BATCH, BATCH In batch
+# mode nothing is printed on the terminal, errors are scrolled as if <return> is
+# hit at every error; missing files that TeX tries to input or request from
+# keyboard input (\read on a not open input stream) cause the job to abort,
+# NON_STOP In nonstop mode the diagnostic message will appear on the terminal,
+# but there is no possibility of user interaction just like in batch mode,
+# SCROLL In scroll mode, TeX will stop only for missing files to input or if
+# keyboard input is necessary and ERROR_STOP In errorstop mode, TeX will stop at
+# each error, asking for user intervention.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_BATCHMODE        = NO
 
-# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# If the LATEX_HIDE_INDICES tag is set to YES then Doxygen will not include the
 # index chapters (such as File Index, Compound Index, etc.) in the output.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_HIDE_INDICES     = NO
 
-# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
-# code with syntax highlighting in the LaTeX output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_SOURCE_CODE      = NO
-
 # The LATEX_BIB_STYLE tag can be used to specify the style to use for the
 # bibliography, e.g. plainnat, or ieeetr. See
 # https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
-# The default value is: plain.
+# The default value is: plainnat.
 # This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_BIB_STYLE        = plain
 
-# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
-# page will contain the date and time when the page was generated. Setting this
-# to NO can help when comparing the output of multiple runs.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_LATEX is set to YES.
-
-LATEX_TIMESTAMP        = NO
-
 # The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
 # path from which the emoji images will be read. If a relative path is entered,
 # it will be relative to the LATEX_OUTPUT directory. If left blank the
@@ -1875,7 +2142,7 @@ LATEX_EMOJI_DIRECTORY  =
 # Configuration options related to the RTF output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
+# If the GENERATE_RTF tag is set to YES, Doxygen will generate RTF output. The
 # RTF output is optimized for Word 97 and may not look too pretty with other RTF
 # readers/editors.
 # The default value is: NO.
@@ -1890,7 +2157,7 @@ GENERATE_RTF           = NO
 
 RTF_OUTPUT             = rtf
 
-# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
+# If the COMPACT_RTF tag is set to YES, Doxygen generates more compact RTF
 # documents. This may be useful for small projects and may help to save some
 # trees in general.
 # The default value is: NO.
@@ -1910,38 +2177,36 @@ COMPACT_RTF            = NO
 
 RTF_HYPERLINKS         = NO
 
-# Load stylesheet definitions from file. Syntax is similar to doxygen's
+# Load stylesheet definitions from file. Syntax is similar to Doxygen's
 # configuration file, i.e. a series of assignments. You only have to provide
 # replacements, missing definitions are set to their default value.
 #
 # See also section "Doxygen usage" for information on how to generate the
-# default style sheet that doxygen normally uses.
+# default style sheet that Doxygen normally uses.
 # This tag requires that the tag GENERATE_RTF is set to YES.
 
 RTF_STYLESHEET_FILE    =
 
 # Set optional variables used in the generation of an RTF document. Syntax is
-# similar to doxygen's configuration file. A template extensions file can be
+# similar to Doxygen's configuration file. A template extensions file can be
 # generated using doxygen -e rtf extensionFile.
 # This tag requires that the tag GENERATE_RTF is set to YES.
 
 RTF_EXTENSIONS_FILE    =
 
-# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
-# with syntax highlighting in the RTF output.
-#
-# Note that which sources are shown also depends on other settings such as
-# SOURCE_BROWSER.
-# The default value is: NO.
+# The RTF_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the RTF_OUTPUT output directory.
+# Note that the files will be copied as-is; there are no commands or markers
+# available.
 # This tag requires that the tag GENERATE_RTF is set to YES.
 
-RTF_SOURCE_CODE        = NO
+RTF_EXTRA_FILES        =
 
 #---------------------------------------------------------------------------
 # Configuration options related to the man page output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
+# If the GENERATE_MAN tag is set to YES, Doxygen will generate man pages for
 # classes and files.
 # The default value is: NO.
 
@@ -1972,7 +2237,7 @@ MAN_EXTENSION          = .3
 
 MAN_SUBDIR             =
 
-# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# If the MAN_LINKS tag is set to YES and Doxygen generates man output, then it
 # will generate one additional man file for each entity documented in the real
 # man page(s). These additional files only source the real man page, but without
 # them the man command would be unable to find the correct page.
@@ -1985,7 +2250,7 @@ MAN_LINKS              = NO
 # Configuration options related to the XML output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
+# If the GENERATE_XML tag is set to YES, Doxygen will generate an XML file that
 # captures the structure of the code including all documentation.
 # The default value is: NO.
 
@@ -1999,7 +2264,7 @@ GENERATE_XML           = YES
 
 XML_OUTPUT             = _xml
 
-# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
+# If the XML_PROGRAMLISTING tag is set to YES, Doxygen will dump the program
 # listings (including syntax highlighting and cross-referencing information) to
 # the XML output. Note that enabling this will significantly increase the size
 # of the XML output.
@@ -2008,7 +2273,7 @@ XML_OUTPUT             = _xml
 
 XML_PROGRAMLISTING     = YES
 
-# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
+# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, Doxygen will include
 # namespace members in file scope as well, matching the HTML output.
 # The default value is: NO.
 # This tag requires that the tag GENERATE_XML is set to YES.
@@ -2019,7 +2284,7 @@ XML_NS_MEMB_FILE_SCOPE = NO
 # Configuration options related to the DOCBOOK output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
+# If the GENERATE_DOCBOOK tag is set to YES, Doxygen will generate Docbook files
 # that can be used to generate PDF.
 # The default value is: NO.
 
@@ -2033,32 +2298,49 @@ GENERATE_DOCBOOK       = NO
 
 DOCBOOK_OUTPUT         = docbook
 
-# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
-# program listings (including syntax highlighting and cross-referencing
-# information) to the DOCBOOK output. Note that enabling this will significantly
-# increase the size of the DOCBOOK output.
-# The default value is: NO.
-# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
-
-DOCBOOK_PROGRAMLISTING = NO
-
 #---------------------------------------------------------------------------
 # Configuration options for the AutoGen Definitions output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
-# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
+# If the GENERATE_AUTOGEN_DEF tag is set to YES, Doxygen will generate an
+# AutoGen Definitions (see https://autogen.sourceforge.net/) file that captures
 # the structure of the code including all documentation. Note that this feature
 # is still experimental and incomplete at the moment.
 # The default value is: NO.
 
 GENERATE_AUTOGEN_DEF   = NO
 
+#---------------------------------------------------------------------------
+# Configuration options related to Sqlite3 output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_SQLITE3 tag is set to YES Doxygen will generate a Sqlite3
+# database with symbols found by Doxygen stored in tables.
+# The default value is: NO.
+
+GENERATE_SQLITE3       = NO
+
+# The SQLITE3_OUTPUT tag is used to specify where the Sqlite3 database will be
+# put. If a relative path is entered the value of OUTPUT_DIRECTORY will be put
+# in front of it.
+# The default directory is: sqlite3.
+# This tag requires that the tag GENERATE_SQLITE3 is set to YES.
+
+SQLITE3_OUTPUT         = sqlite3
+
+# The SQLITE3_RECREATE_DB tag is set to YES, the existing doxygen_sqlite3.db
+# database file will be recreated with each Doxygen run. If set to NO, Doxygen
+# will warn if a database file is already found and not modify it.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_SQLITE3 is set to YES.
+
+SQLITE3_RECREATE_DB    = YES
+
 #---------------------------------------------------------------------------
 # Configuration options related to the Perl module output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
+# If the GENERATE_PERLMOD tag is set to YES, Doxygen will generate a Perl module
 # file that captures the structure of the code including all documentation.
 #
 # Note that this feature is still experimental and incomplete at the moment.
@@ -2066,7 +2348,7 @@ GENERATE_AUTOGEN_DEF   = NO
 
 GENERATE_PERLMOD       = NO
 
-# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
+# If the PERLMOD_LATEX tag is set to YES, Doxygen will generate the necessary
 # Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
 # output from the Perl module output.
 # The default value is: NO.
@@ -2096,13 +2378,13 @@ PERLMOD_MAKEVAR_PREFIX =
 # Configuration options related to the preprocessor
 #---------------------------------------------------------------------------
 
-# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
+# If the ENABLE_PREPROCESSING tag is set to YES, Doxygen will evaluate all
 # C-preprocessor directives found in the sources and include files.
 # The default value is: YES.
 
 ENABLE_PREPROCESSING   = YES
 
-# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
+# If the MACRO_EXPANSION tag is set to YES, Doxygen will expand all macro names
 # in the source code. If set to NO, only conditional compilation will be
 # performed. Macro expansion can be done in a controlled way by setting
 # EXPAND_ONLY_PREDEF to YES.
@@ -2128,7 +2410,8 @@ SEARCH_INCLUDES        = YES
 
 # The INCLUDE_PATH tag can be used to specify one or more directories that
 # contain include files that are not input files but should be processed by the
-# preprocessor.
+# preprocessor. Note that the INCLUDE_PATH is not recursive, so the setting of
+# RECURSIVE has no effect here.
 # This tag requires that the tag SEARCH_INCLUDES is set to YES.
 
 INCLUDE_PATH           =
@@ -2149,7 +2432,8 @@ INCLUDE_FILE_PATTERNS  =
 # recursively expanded use the := operator instead of the = operator.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-PREDEFINED             = "CUVS_BUILD_MG_ALGOS=1"
+PREDEFINED             = "CUVS_BUILD_MG_ALGOS=1" \
+                         "CUVS_BUILD_CAGRA_HNSWLIB=1"
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The
@@ -2160,7 +2444,7 @@ PREDEFINED             = "CUVS_BUILD_MG_ALGOS=1"
 
 EXPAND_AS_DEFINED      =
 
-# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# If the SKIP_FUNCTION_MACROS tag is set to YES then Doxygen's preprocessor will
 # remove all references to function-like macros that are alone on a line, have
 # an all uppercase name, and do not end with a semicolon. Such function macros
 # are typically used for boiler-plate code, and will confuse the parser if not
@@ -2184,26 +2468,26 @@ SKIP_FUNCTION_MACROS   = YES
 # section "Linking to external documentation" for more information about the use
 # of tag files.
 # Note: Each tag file must have a unique name (where the name does NOT include
-# the path). If a tag file is not located in the directory in which doxygen is
+# the path). If a tag file is not located in the directory in which Doxygen is
 # run, you must also specify the path to the tagfile here.
 
 TAGFILES               =
 
-# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
+# When a file name is specified after GENERATE_TAGFILE, Doxygen will create a
 # tag file that is based on the input files it reads. See section "Linking to
 # external documentation" for more information about the usage of tag files.
 
 GENERATE_TAGFILE       =
 
-# If the ALLEXTERNALS tag is set to YES, all external class will be listed in
-# the class index. If set to NO, only the inherited external classes will be
-# listed.
+# If the ALLEXTERNALS tag is set to YES, all external classes and namespaces
+# will be listed in the class and namespace index. If set to NO, only the
+# inherited external classes will be listed.
 # The default value is: NO.
 
 ALLEXTERNALS           = NO
 
 # If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
-# in the modules index. If set to NO, only the current project's groups will be
+# in the topic index. If set to NO, only the current project's groups will be
 # listed.
 # The default value is: YES.
 
@@ -2217,42 +2501,26 @@ EXTERNAL_GROUPS        = YES
 EXTERNAL_PAGES         = YES
 
 #---------------------------------------------------------------------------
-# Configuration options related to the dot tool
+# Configuration options related to diagram generator tools
 #---------------------------------------------------------------------------
 
-# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
-# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
-# NO turns the diagrams off. Note that this option also works with HAVE_DOT
-# disabled, but it is recommended to install and use dot, since it yields more
-# powerful graphs.
-# The default value is: YES.
-
-CLASS_DIAGRAMS         = YES
-
-# You can include diagrams made with dia in doxygen documentation. Doxygen will
-# then run dia to produce the diagram and insert it in the documentation. The
-# DIA_PATH tag allows you to specify the directory where the dia binary resides.
-# If left empty dia is assumed to be found in the default search path.
-
-DIA_PATH               =
-
 # If set to YES the inheritance and collaboration graphs will hide inheritance
 # and usage relations if the target is undocumented or is not a class.
 # The default value is: YES.
 
 HIDE_UNDOC_RELATIONS   = YES
 
-# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
+# If you set the HAVE_DOT tag to YES then Doxygen will assume the dot tool is
 # available from the path. This tool is part of Graphviz (see:
-# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# https://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
 # Bell Labs. The other options in this section have no effect if this option is
 # set to NO
 # The default value is: NO.
 
-HAVE_DOT               =
+HAVE_DOT               = NO
 
-# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
-# to run in parallel. When set to 0 doxygen will base this on the number of
+# The DOT_NUM_THREADS specifies the number of dot invocations Doxygen is allowed
+# to run in parallel. When set to 0 Doxygen will base this on the number of
 # processors available in the system. You can set it explicitly to a value
 # larger than 0 to get control over the balance between CPU load and processing
 # speed.
@@ -2261,55 +2529,83 @@ HAVE_DOT               =
 
 DOT_NUM_THREADS        = 0
 
-# When you want a differently looking font in the dot files that doxygen
-# generates you can specify the font name using DOT_FONTNAME. You need to make
-# sure dot is able to find the font, which can be done by putting it in a
-# standard location or by setting the DOTFONTPATH environment variable or by
-# setting DOT_FONTPATH to the directory containing the font.
-# The default value is: Helvetica.
+# DOT_COMMON_ATTR is common attributes for nodes, edges and labels of
+# subgraphs. When you want a differently looking font in the dot files that
+# Doxygen generates you can specify fontname, fontcolor and fontsize attributes.
+# For details please see <a href=https://graphviz.org/doc/info/attrs.html>Node,
+# Edge and Graph Attributes specification</a> You need to make sure dot is able
+# to find the font, which can be done by putting it in a standard location or by
+# setting the DOTFONTPATH environment variable or by setting DOT_FONTPATH to the
+# directory containing the font. Default graphviz fontsize is 14.
+# The default value is: fontname=Helvetica,fontsize=10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_COMMON_ATTR        = "fontname=Helvetica,fontsize=10"
+
+# DOT_EDGE_ATTR is concatenated with DOT_COMMON_ATTR. For elegant style you can
+# add 'arrowhead=open, arrowtail=open, arrowsize=0.5'. <a
+# href=https://graphviz.org/doc/info/arrows.html>Complete documentation about
+# arrows shapes.</a>
+# The default value is: labelfontname=Helvetica,labelfontsize=10.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_FONTNAME           = Helvetica
+DOT_EDGE_ATTR          = "labelfontname=Helvetica,labelfontsize=10"
 
-# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
-# dot graphs.
-# Minimum value: 4, maximum value: 24, default value: 10.
+# DOT_NODE_ATTR is concatenated with DOT_COMMON_ATTR. For view without boxes
+# around nodes set 'shape=plain' or 'shape=plaintext' <a
+# href=https://www.graphviz.org/doc/info/shapes.html>Shapes specification</a>
+# The default value is: shape=box,height=0.2,width=0.4.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_FONTSIZE           = 10
+DOT_NODE_ATTR          = "shape=box,height=0.2,width=0.4"
 
-# By default doxygen will tell dot to use the default font as specified with
-# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
-# the path where dot can find it using this tag.
+# You can set the path where dot can find font specified with fontname in
+# DOT_COMMON_ATTR and others dot attributes.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_FONTPATH           =
 
-# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
-# each documented class showing the direct and indirect inheritance relations.
-# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# If the CLASS_GRAPH tag is set to YES or GRAPH or BUILTIN then Doxygen will
+# generate a graph for each documented class showing the direct and indirect
+# inheritance relations. In case the CLASS_GRAPH tag is set to YES or GRAPH and
+# HAVE_DOT is enabled as well, then dot will be used to draw the graph. In case
+# the CLASS_GRAPH tag is set to YES and HAVE_DOT is disabled or if the
+# CLASS_GRAPH tag is set to BUILTIN, then the built-in generator will be used.
+# If the CLASS_GRAPH tag is set to TEXT the direct and indirect inheritance
+# relations will be shown as texts / links. Explicit enabling an inheritance
+# graph or choosing a different representation for an inheritance graph of a
+# specific class, can be accomplished by means of the command \inheritancegraph.
+# Disabling an inheritance graph can be accomplished by means of the command
+# \hideinheritancegraph.
+# Possible values are: NO, YES, TEXT, GRAPH and BUILTIN.
 # The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
 
 CLASS_GRAPH            = YES
 
-# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
+# If the COLLABORATION_GRAPH tag is set to YES then Doxygen will generate a
 # graph for each documented class showing the direct and indirect implementation
 # dependencies (inheritance, containment, and class references variables) of the
-# class with other documented classes.
+# class with other documented classes. Explicit enabling a collaboration graph,
+# when COLLABORATION_GRAPH is set to NO, can be accomplished by means of the
+# command \collaborationgraph. Disabling a collaboration graph can be
+# accomplished by means of the command \hidecollaborationgraph.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 COLLABORATION_GRAPH    = YES
 
-# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
-# groups, showing the direct groups dependencies.
+# If the GROUP_GRAPHS tag is set to YES then Doxygen will generate a graph for
+# groups, showing the direct groups dependencies. Explicit enabling a group
+# dependency graph, when GROUP_GRAPHS is set to NO, can be accomplished by means
+# of the command \groupgraph. Disabling a directory graph can be accomplished by
+# means of the command \hidegroupgraph. See also the chapter Grouping in the
+# manual.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 GROUP_GRAPHS           = YES
 
-# If the UML_LOOK tag is set to YES, doxygen will generate inheritance and
+# If the UML_LOOK tag is set to YES, Doxygen will generate inheritance and
 # collaboration diagrams in a style similar to the OMG's Unified Modeling
 # Language.
 # The default value is: NO.
@@ -2326,10 +2622,32 @@ UML_LOOK               = YES
 # but if the number exceeds 15, the total amount of fields shown is limited to
 # 10.
 # Minimum value: 0, maximum value: 100, default value: 10.
-# This tag requires that the tag HAVE_DOT is set to YES.
+# This tag requires that the tag UML_LOOK is set to YES.
 
 UML_LIMIT_NUM_FIELDS   = 10
 
+# If the DOT_UML_DETAILS tag is set to NO, Doxygen will show attributes and
+# methods without types and arguments in the UML graphs. If the DOT_UML_DETAILS
+# tag is set to YES, Doxygen will add type and arguments for attributes and
+# methods in the UML graphs. If the DOT_UML_DETAILS tag is set to NONE, Doxygen
+# will not generate fields with class member information in the UML graphs. The
+# class diagrams will look similar to the default class diagrams but using UML
+# notation for the relationships.
+# Possible values are: NO, YES and NONE.
+# The default value is: NO.
+# This tag requires that the tag UML_LOOK is set to YES.
+
+DOT_UML_DETAILS        = NO
+
+# The DOT_WRAP_THRESHOLD tag can be used to set the maximum number of characters
+# to display on a single line. If the actual line length exceeds this threshold
+# significantly it will be wrapped across multiple lines. Some heuristics are
+# applied to avoid ugly line breaks.
+# Minimum value: 0, maximum value: 1000, default value: 17.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_WRAP_THRESHOLD     = 17
+
 # If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
 # collaboration graphs will show the relations between templates and their
 # instances.
@@ -2339,24 +2657,29 @@ UML_LIMIT_NUM_FIELDS   = 10
 TEMPLATE_RELATIONS     = NO
 
 # If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
-# YES then doxygen will generate a graph for each documented file showing the
+# YES then Doxygen will generate a graph for each documented file showing the
 # direct and indirect include dependencies of the file with other documented
-# files.
+# files. Explicit enabling an include graph, when INCLUDE_GRAPH is is set to NO,
+# can be accomplished by means of the command \includegraph. Disabling an
+# include graph can be accomplished by means of the command \hideincludegraph.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 INCLUDE_GRAPH          = YES
 
 # If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
-# set to YES then doxygen will generate a graph for each documented file showing
+# set to YES then Doxygen will generate a graph for each documented file showing
 # the direct and indirect include dependencies of the file with other documented
-# files.
+# files. Explicit enabling an included by graph, when INCLUDED_BY_GRAPH is set
+# to NO, can be accomplished by means of the command \includedbygraph. Disabling
+# an included by graph can be accomplished by means of the command
+# \hideincludedbygraph.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 INCLUDED_BY_GRAPH      = YES
 
-# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
+# If the CALL_GRAPH tag is set to YES then Doxygen will generate a call
 # dependency graph for every global function or class method.
 #
 # Note that enabling this option will significantly increase the time of a run.
@@ -2368,7 +2691,7 @@ INCLUDED_BY_GRAPH      = YES
 
 CALL_GRAPH             = NO
 
-# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
+# If the CALLER_GRAPH tag is set to YES then Doxygen will generate a caller
 # dependency graph for every global function or class method.
 #
 # Note that enabling this option will significantly increase the time of a run.
@@ -2380,44 +2703,59 @@ CALL_GRAPH             = NO
 
 CALLER_GRAPH           = NO
 
-# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
+# If the GRAPHICAL_HIERARCHY tag is set to YES then Doxygen will graphical
 # hierarchy of all classes instead of a textual one.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 GRAPHICAL_HIERARCHY    = YES
 
-# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
+# If the DIRECTORY_GRAPH tag is set to YES then Doxygen will show the
 # dependencies a directory has on other directories in a graphical way. The
 # dependency relations are determined by the #include relations between the
-# files in the directories.
+# files in the directories. Explicit enabling a directory graph, when
+# DIRECTORY_GRAPH is set to NO, can be accomplished by means of the command
+# \directorygraph. Disabling a directory graph can be accomplished by means of
+# the command \hidedirectorygraph.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 DIRECTORY_GRAPH        = YES
 
+# The DIR_GRAPH_MAX_DEPTH tag can be used to limit the maximum number of levels
+# of child directories generated in directory dependency graphs by dot.
+# Minimum value: 1, maximum value: 25, default value: 1.
+# This tag requires that the tag DIRECTORY_GRAPH is set to YES.
+
+DIR_GRAPH_MAX_DEPTH    = 1
+
 # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
 # generated by dot. For an explanation of the image formats see the section
 # output formats in the documentation of the dot tool (Graphviz (see:
-# http://www.graphviz.org/)).
-# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
-# to make the SVG files visible in IE 9+ (other browsers do not have this
-# requirement).
+# https://www.graphviz.org/)).
+#
+# Note the formats svg:cairo and svg:cairo:cairo cannot be used in combination
+# with INTERACTIVE_SVG (the INTERACTIVE_SVG will be set to NO).
 # Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,
-# png:cairo:gd, png:cairo:cairo, png:cairo:gdiplus, png:gdiplus and
-# png:gdiplus:gdiplus.
+# png:cairo:gd, png:cairo:cairo, png:cairo:gdiplus, png:gdiplus,
+# png:gdiplus:gdiplus, svg:cairo, svg:cairo:cairo, svg:svg, svg:svg:core,
+# gif:cairo, gif:cairo:gd, gif:cairo:gdiplus, gif:gdiplus, gif:gdiplus:gdiplus,
+# gif:gd, gif:gd:gd, jpg:cairo, jpg:cairo:gd, jpg:cairo:gdiplus, jpg:gd,
+# jpg:gd:gd, jpg:gdiplus and jpg:gdiplus:gdiplus.
 # The default value is: png.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_IMAGE_FORMAT       = png
 
-# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
-# enable generation of interactive SVG images that allow zooming and panning.
+# If DOT_IMAGE_FORMAT is set to svg or svg:svg or svg:svg:core, then this option
+# can be set to YES to enable generation of interactive SVG images that allow
+# zooming and panning.
 #
 # Note that this requires a modern browser other than Internet Explorer. Tested
 # and working are Firefox, Chrome, Safari, and Opera.
-# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
-# the SVG files visible. Older versions of IE do not have SVG support.
+#
+# Note This option will be automatically disabled when DOT_IMAGE_FORMAT is set
+# to svg:cairo or svg:cairo:cairo.
 # The default value is: NO.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
@@ -2436,11 +2774,12 @@ DOT_PATH               =
 
 DOTFILE_DIRS           =
 
-# The MSCFILE_DIRS tag can be used to specify one or more directories that
-# contain msc files that are included in the documentation (see the \mscfile
-# command).
+# You can include diagrams made with dia in Doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
 
-MSCFILE_DIRS           =
+DIA_PATH               =
 
 # The DIAFILE_DIRS tag can be used to specify one or more directories that
 # contain dia files that are included in the documentation (see the \diafile
@@ -2448,28 +2787,34 @@ MSCFILE_DIRS           =
 
 DIAFILE_DIRS           =
 
-# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
-# path where java can find the plantuml.jar file. If left blank, it is assumed
-# PlantUML is not used or called during a preprocessing step. Doxygen will
-# generate a warning when it encounters a \startuml command in this case and
-# will not generate output for the diagram.
+# When using PlantUML, the PLANTUML_JAR_PATH tag should be used to specify the
+# path where java can find the plantuml.jar file or to the filename of jar file
+# to be used. If left blank, it is assumed PlantUML is not used or called during
+# a preprocessing step. Doxygen will generate a warning when it encounters a
+# \startuml command in this case and will not generate output for the diagram.
 
 PLANTUML_JAR_PATH      =
 
-# When using plantuml, the PLANTUML_CFG_FILE tag can be used to specify a
-# configuration file for plantuml.
+# When using PlantUML, the PLANTUML_CFG_FILE tag can be used to specify a
+# configuration file for PlantUML.
 
 PLANTUML_CFG_FILE      =
 
-# When using plantuml, the specified paths are searched for files specified by
-# the !include statement in a plantuml block.
+# When using PlantUML, the specified paths are searched for files specified by
+# the !include statement in a PlantUML block.
 
 PLANTUML_INCLUDE_PATH  =
 
+# The PLANTUMLFILE_DIRS tag can be used to specify one or more directories that
+# contain PlantUml files that are included in the documentation (see the
+# \plantumlfile command).
+
+PLANTUMLFILE_DIRS      =
+
 # The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
 # that will be shown in the graph. If the number of nodes in a graph becomes
-# larger than this value, doxygen will truncate the graph, which is visualized
-# by representing a node as a red box. Note that doxygen if the number of direct
+# larger than this value, Doxygen will truncate the graph, which is visualized
+# by representing a node as a red box. Note that if the number of direct
 # children of the root node in a graph is already larger than
 # DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
 # the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
@@ -2490,18 +2835,6 @@ DOT_GRAPH_MAX_NODES    = 100
 
 MAX_DOT_GRAPH_DEPTH    = 0
 
-# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
-# background. This is disabled by default, because dot on Windows does not seem
-# to support this out of the box.
-#
-# Warning: Depending on the platform used, enabling this option may lead to
-# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
-# read).
-# The default value is: NO.
-# This tag requires that the tag HAVE_DOT is set to YES.
-
-DOT_TRANSPARENT        = NO
-
 # Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output
 # files in one run (i.e. multiple -o and -T options on the command line). This
 # makes dot run faster, but since only newer versions of dot (>1.8.10) support
@@ -2511,17 +2844,37 @@ DOT_TRANSPARENT        = NO
 
 DOT_MULTI_TARGETS      = NO
 
-# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
+# If the GENERATE_LEGEND tag is set to YES Doxygen will generate a legend page
 # explaining the meaning of the various boxes and arrows in the dot generated
 # graphs.
+# Note: This tag requires that UML_LOOK isn't set, i.e. the Doxygen internal
+# graphical representation for inheritance and collaboration diagrams is used.
 # The default value is: YES.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
 GENERATE_LEGEND        = YES
 
-# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate dot
+# If the DOT_CLEANUP tag is set to YES, Doxygen will remove the intermediate
 # files that are used to generate the various graphs.
+#
+# Note: This setting is not only used for dot files but also for msc temporary
+# files.
 # The default value is: YES.
-# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_CLEANUP            = YES
+
+# You can define message sequence charts within Doxygen comments using the \msc
+# command. If the MSCGEN_TOOL tag is left empty (the default), then Doxygen will
+# use a built-in version of mscgen tool to produce the charts. Alternatively,
+# the MSCGEN_TOOL tag can also specify the name an external tool. For instance,
+# specifying prog as the value, Doxygen will call the tool as prog -T
+# <outfile_format> -o <outputfile> <inputfile>. The external tool should support
+# output file formats "png", "eps", "svg", and "ismap".
+
+MSCGEN_TOOL            =
+
+# The MSCFILE_DIRS tag can be used to specify one or more directories that
+# contain msc files that are included in the documentation (see the \mscfile
+# command).
+
+MSCFILE_DIRS           =
diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/cpp/include/cuvs/neighbors/brute_force.h
index 91893e7d93..93cdffd7da 100644
--- a/cpp/include/cuvs/neighbors/brute_force.h
+++ b/cpp/include/cuvs/neighbors/brute_force.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -167,7 +167,7 @@ cuvsError_t cuvsBruteForceSearch(cuvsResources_t res,
  */
 
 /**
- * @defgroup bruteforce_c_serialize BRUTEFORCE C-API serialize functions
+ * @defgroup bruteforce_c_index_serialize BRUTEFORCE C-API serialize functions
  * @{
  */
 /**
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index bd0548d4ba..e892b5304c 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -605,7 +605,7 @@ cuvsError_t cuvsCagraSearch(cuvsResources_t res,
  */
 
 /**
- * @defgroup cagra_c_serialize CAGRA C-API serialize functions
+ * @defgroup cagra_c_index_serialize CAGRA C-API serialize functions
  * @{
  */
 /**
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index fd1fe72b34..ac86492ac6 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -340,11 +340,13 @@ struct index : cuvs::neighbors::index {
   }
 
   // Don't allow copying the index for performance reasons (try avoiding copying data)
+  /** \cond */
   index(const index&)                    = delete;
   index(index&&)                         = default;
   auto operator=(const index&) -> index& = delete;
   auto operator=(index&&) -> index&      = default;
   ~index()                               = default;
+  /** \endcond */
 
   /** Construct an empty index. */
   index(raft::resources const& res,
diff --git a/cpp/include/cuvs/neighbors/common.hpp b/cpp/include/cuvs/neighbors/common.hpp
index b49b7a4e8a..4ed79dd695 100644
--- a/cpp/include/cuvs/neighbors/common.hpp
+++ b/cpp/include/cuvs/neighbors/common.hpp
@@ -491,6 +491,7 @@ struct base_filter {
 
 /* A filter that filters nothing. This is the default behavior. */
 struct none_sample_filter : public base_filter {
+  /** \cond */
   constexpr __forceinline__ _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
@@ -504,7 +505,7 @@ struct none_sample_filter : public base_filter {
     const uint32_t query_ix,
     // the index of the current sample
     const uint32_t sample_ix) const;
-
+  /** \endcond */
   FilterType get_filter_type() const override { return FilterType::None; }
 };
 
@@ -523,6 +524,7 @@ struct ivf_to_sample_filter : public base_filter {
 
   ivf_to_sample_filter(const index_t* const* inds_ptrs, const filter_t next_filter);
 
+  /** \cond */
   /** If the original filter takes three arguments, then don't modify the arguments.
    * If the original filter takes two arguments, then we are using `inds_ptr_` to obtain the sample
    * index.
@@ -536,6 +538,7 @@ struct ivf_to_sample_filter : public base_filter {
     const uint32_t sample_ix) const;
 
   FilterType get_filter_type() const override { return next_filter_.get_filter_type(); }
+  /** \endcond */
 };
 
 /**
@@ -552,11 +555,13 @@ struct bitmap_filter : public base_filter {
   const view_t bitmap_view_;
 
   bitmap_filter(const view_t bitmap_for_filtering);
+  /** \cond */
   inline _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
     // the index of the current sample
     const uint32_t sample_ix) const;
+  /** \endcond */
 
   FilterType get_filter_type() const override { return FilterType::Bitmap; }
 
@@ -579,12 +584,14 @@ struct bitset_filter : public base_filter {
   // View of the bitset to use as a filter
   const view_t bitset_view_;
 
+  /** \cond */
   _RAFT_HOST_DEVICE bitset_filter(const view_t bitset_for_filtering);
   constexpr __forceinline__ _RAFT_HOST_DEVICE bool operator()(
     // query index
     const uint32_t query_ix,
     // the index of the current sample
     const uint32_t sample_ix) const;
+  /** \endcond */
 
   FilterType get_filter_type() const override { return FilterType::Bitset; }
 
diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/cpp/include/cuvs/neighbors/hnsw.h
index b5f129e96d..c584f41529 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/cpp/include/cuvs/neighbors/hnsw.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -369,7 +369,7 @@ cuvsError_t cuvsHnswSearch(cuvsResources_t res,
  */
 
 /**
- * @defgroup hnsw_c_serialize HNSW C-API serialize functions
+ * @defgroup hnsw_c_index_serialize HNSW C-API serialize functions
  * @{
  */
 
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 7db6d549eb..7be7ccff54 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -97,7 +97,9 @@ cuvs::neighbors::cagra::index_params to_cagra_params(
   int ef_construction,
   cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded);
 
-/**@}*/
+/**
+ * @}
+ */
 
 /**
  * @defgroup hnsw_cpp_index hnswlib index wrapper
@@ -127,7 +129,7 @@ struct index : cuvs::neighbors::index {
   /**
   @brief Get underlying index
   */
-  virtual auto get_index() const -> void const* = 0;
+  virtual void const* get_index() const = 0;
 
   auto dim() const -> int const { return dim_; }
 
@@ -146,7 +148,9 @@ struct index : cuvs::neighbors::index {
   HnswHierarchy hierarchy_;
 };
 
-/**@}*/
+/**
+ * @}
+ */
 
 /**
  * @defgroup hnsw_cpp_extend_params HNSW index extend parameters
@@ -308,7 +312,9 @@ std::unique_ptr<index<int8_t>> from_cagra(
   std::optional<raft::host_matrix_view<const int8_t, int64_t, raft::row_major>> dataset =
     std::nullopt);
 
-/**@}*/
+/**
+ * @}
+ */
 
 /**
  * @defgroup hnsw_cpp_index_extend Extend HNSW index with additional vectors
@@ -342,6 +348,7 @@ std::unique_ptr<index<int8_t>> from_cagra(
  *   auto additional_dataset = raft::make_host_matrix<float>(res, add_size, index->dim());
  *   hnsw::extend_params extend_params;
  *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ * @endcode
  */
 void extend(raft::resources const& res,
             const extend_params& params,
@@ -375,6 +382,7 @@ void extend(raft::resources const& res,
  *   auto additional_dataset = raft::make_host_matrix<half>(res, add_size, index->dim());
  *   hnsw::extend_params extend_params;
  *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ * @endcode
  */
 void extend(raft::resources const& res,
             const extend_params& params,
@@ -408,6 +416,7 @@ void extend(raft::resources const& res,
  *   auto additional_dataset = raft::make_host_matrix<uint8_t>(res, add_size, index->dim());
  *   hnsw::extend_params extend_params;
  *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ * @endcode
  */
 void extend(raft::resources const& res,
             const extend_params& params,
@@ -441,13 +450,16 @@ void extend(raft::resources const& res,
  *   auto additional_dataset = raft::make_host_matrix<int8_t>(res, add_size, index->dim());
  *   hnsw::extend_params extend_params;
  *   hnsw::extend(res, extend_params, additional_dataset, *hnsw_index.get());
+ * @endcode
  */
 void extend(raft::resources const& res,
             const extend_params& params,
             raft::host_matrix_view<const int8_t, int64_t, raft::row_major> additional_dataset,
             index<int8_t>& idx);
 
-/**@} */
+/**
+ * @}
+ */
 
 /**
  * @defgroup hnsw_cpp_search_params Build CAGRA index and search with hnswlib
@@ -460,7 +472,9 @@ struct search_params : cuvs::neighbors::search_params {
                         // automatically maximizes parallelism
 };
 
-/**@}*/
+/**
+ * @}
+ */
 
 // TODO: Filtered Search APIs: https://github.com/rapidsai/cuvs/issues/363
 
@@ -645,7 +659,9 @@ void search(raft::resources const& res,
             raft::host_matrix_view<uint64_t, int64_t, raft::row_major> neighbors,
             raft::host_matrix_view<float, int64_t, raft::row_major> distances);
 
-/**@}*/
+/**
+ * @}
+ */
 
 /**
  * @defgroup hnsw_cpp_index_serialize Deserialize CAGRA index as hnswlib index
@@ -940,7 +956,9 @@ void deserialize(raft::resources const& res,
                  cuvs::distance::DistanceType metric,
                  index<int8_t>** index);
 
-/**@}*/
+/**
+ * @}
+ */
 
 }  // namespace cuvs::neighbors::hnsw
 
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 7d647829ad..9e89ea354d 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -300,7 +300,7 @@ cuvsError_t cuvsIvfFlatSearch(cuvsResources_t res,
  */
 
 /**
- * @defgroup ivf_flat_c_serialize IVF-Flat C-API serialize functions
+ * @defgroup ivf_flat_c_index_serialize IVF-Flat C-API serialize functions
  * @{
  */
 /**
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index 5c349be5cb..eef10290c7 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -405,7 +405,7 @@ cuvsError_t cuvsIvfPqSearch(cuvsResources_t res,
  */
 
 /**
- * @defgroup ivf_pq_c_serialize IVF-PQ C-API serialize functions
+ * @defgroup ivf_pq_c_index_serialize IVF-PQ C-API serialize functions
  * @{
  */
 /**
diff --git a/cpp/include/cuvs/neighbors/vamana.hpp b/cpp/include/cuvs/neighbors/vamana.hpp
index 733f9c4e71..25b3d4f7d6 100644
--- a/cpp/include/cuvs/neighbors/vamana.hpp
+++ b/cpp/include/cuvs/neighbors/vamana.hpp
@@ -38,6 +38,18 @@ namespace cuvs::neighbors::vamana {
  * @{
  */
 
+/**
+ * @brief Parameters used to build quantized DiskANN index; to be generated using
+ * deserialize_codebooks()
+ */
+template <typename T = float>
+struct codebook_params {
+  int pq_codebook_size;
+  int pq_dim;
+  std::vector<T> pq_encoding_table;
+  std::vector<T> rotation_matrix;
+};
+
 /**
  * @brief Parameters used to build DiskANN index
  *
@@ -53,17 +65,6 @@ namespace cuvs::neighbors::vamana {
  */
 
 struct index_params : cuvs::neighbors::index_params {
-  /**
-   * @brief Parameters used to build quantized DiskANN index; to be generated using
-   * deserialize_codebooks()
-   */
-  template <typename T = float>
-  struct codebook_params {
-    int pq_codebook_size;
-    int pq_dim;
-    std::vector<T> pq_encoding_table;
-    std::vector<T> rotation_matrix;
-  };
   /** Maximum degree of output graph corresponds to the R parameter in the original Vamana
    * literature. */
   uint32_t graph_degree = 32;
@@ -159,11 +160,13 @@ struct index : cuvs::neighbors::index {
   [[nodiscard]] inline auto medoid() const noexcept -> IdxT { return medoid_id_; }
 
   // Don't allow copying the index for performance reasons (try avoiding copying data)
+  /** \cond */
   index(const index&)                    = delete;
   index(index&&)                         = default;
   auto operator=(const index&) -> index& = delete;
   auto operator=(index&&) -> index&      = default;
   ~index()                               = default;
+  /** \endcond */
 
   /** Construct an empty index. */
   index(raft::resources const& res,
@@ -613,7 +616,7 @@ void serialize(raft::resources const& handle,
  *
  */
 auto deserialize_codebooks(const std::string& codebook_prefix, const int dim)
-  -> index_params::codebook_params<float>;
+  -> codebook_params<float>;
 
 /**
  * @}
diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.hpp b/cpp/include/cuvs/preprocessing/quantize/binary.hpp
index 224c30c8b6..94cdb5ce5b 100644
--- a/cpp/include/cuvs/preprocessing/quantize/binary.hpp
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.hpp
@@ -234,7 +234,6 @@ void transform(raft::resources const& res,
  * Usage example:
  * @code{.cpp}
  * raft::handle_t handle;
- * @param[in] params quantization params
  * cuvs::preprocessing::quantize::binary::params params;
  * raft::host_matrix<float, uint64_t> dataset = read_dataset(filename);
  * int64_t quantized_dim = raft::div_rounding_up_safe(dataset.extent(1), sizeof(uint8_t) * 8);
diff --git a/cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh b/cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh
index 5657a2840c..ae918af304 100644
--- a/cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh
+++ b/cpp/src/neighbors/detail/vamana/vamana_codebooks.cuh
@@ -135,10 +135,9 @@ inline std::vector<float> parse_rotation_matrix_file(const std::string& path, co
 }
 
 template <typename T>
-index_params::codebook_params<T> deserialize_codebooks(const std::string& codebook_prefix,
-                                                       const int dim)
+codebook_params<T> deserialize_codebooks(const std::string& codebook_prefix, const int dim)
 {
-  index_params::codebook_params<T> codebooks;
+  codebook_params<T> codebooks;
   codebooks.pq_encoding_table = parse_pq_pivots_file(
     codebook_prefix + "_pq_pivots.bin", dim, codebooks.pq_codebook_size, codebooks.pq_dim);
   codebooks.rotation_matrix =
diff --git a/cpp/src/neighbors/vamana.cuh b/cpp/src/neighbors/vamana.cuh
index 4cad294854..af8cc432d1 100644
--- a/cpp/src/neighbors/vamana.cuh
+++ b/cpp/src/neighbors/vamana.cuh
@@ -98,8 +98,7 @@ void serialize(raft::resources const& res,
 }
 
 template <typename T>
-index_params::codebook_params<T> deserialize_codebooks(const std::string& codebook_prefix,
-                                                       const int dim)
+codebook_params<T> deserialize_codebooks(const std::string& codebook_prefix, const int dim)
 {
   return cuvs::neighbors::vamana::detail::deserialize_codebooks<T>(codebook_prefix, dim);
 }
diff --git a/cpp/src/neighbors/vamana_codebooks_float.cu b/cpp/src/neighbors/vamana_codebooks_float.cu
index e630675908..458c05ebb0 100644
--- a/cpp/src/neighbors/vamana_codebooks_float.cu
+++ b/cpp/src/neighbors/vamana_codebooks_float.cu
@@ -20,7 +20,7 @@ namespace cuvs::neighbors::vamana {
 
 #define CUVS_INST_VAMANA_CODEBOOKS(T)                                               \
   auto deserialize_codebooks(const std::string& codebook_prefix, const int dim)     \
-    -> cuvs::neighbors::vamana::index_params::codebook_params<T>                    \
+    -> cuvs::neighbors::vamana::codebook_params<T>                                  \
   {                                                                                 \
     return cuvs::neighbors::vamana::deserialize_codebooks<T>(codebook_prefix, dim); \
   }
diff --git a/docs/source/api_basics.rst b/docs/source/api_basics.rst
index d3e2c8a6ff..b085d22de3 100644
--- a/docs/source/api_basics.rst
+++ b/docs/source/api_basics.rst
@@ -14,7 +14,7 @@ RMM currently has APIs for C++ and Python.
 C++
 ^^^
 
-Here's an example of configuring RMM to use a pool allocator in C++ (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#example>`_):
+Here's an example of configuring RMM to use a pool allocator in C++ (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#example>`__):
 
 .. code-block:: c++
 
@@ -29,7 +29,7 @@ Here's an example of configuring RMM to use a pool allocator in C++ (derived fro
 Python
 ^^^^^^
 
-And the corresponding code in Python (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#memoryresource-objects>`_):
+And the corresponding code in Python (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#memoryresource-objects>`__):
 
 .. code-block:: python
 
diff --git a/docs/source/api_interoperability.rst b/docs/source/api_interoperability.rst
index 519660036f..92e19de1fa 100644
--- a/docs/source/api_interoperability.rst
+++ b/docs/source/api_interoperability.rst
@@ -36,7 +36,7 @@ Here's an example on how to represent device memory using `DLManagedTensor`:
     // free memory after use
     cuvsRMMFree(dataset_dev);
 
-Please refer to cuVS C API `documentation <c_api.rst>`_ to learn more.
+Please refer to `cuVS C API documentation <c_api.rst>`_ to learn more.
 
 Multi-dimensional span (C++)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -99,7 +99,7 @@ Of course, RAFT's `mdspan`/`mdarray` APIs aren't just limited to the `device`. Y
     auto vector_view = raft::make_host_vector_view(vector.data_handle(), vector.extent(0));
     auto matrix_view = raft::make_host_matrix_view(matrix.data_handle(), matrix.extent(0), matrix.extent(1));
 
-Please refer to RAFT's `mdspan` `documentation <https://docs.rapids.ai/api/raft/stable/cpp_api/mdspan/>`_ to learn more.
+Please refer to RAFT's `mdspan documentation <https://docs.rapids.ai/api/raft/stable/cpp_api/mdspan/>`_ to learn more.
 
 
 CUDA array interface (Python)
diff --git a/docs/source/c_api/neighbors_cagra_c.rst b/docs/source/c_api/neighbors_cagra_c.rst
index a5ffc45b9c..85b34d4947 100644
--- a/docs/source/c_api/neighbors_cagra_c.rst
+++ b/docs/source/c_api/neighbors_cagra_c.rst
@@ -51,7 +51,7 @@ Index search
     :content-only:
 
 Index serialize
-------------
+---------------
 
 .. doxygengroup:: cagra_c_index_serialize
     :project: cuvs
diff --git a/docs/source/conf.py b/docs/source/conf.py
index ca7330279d..a7247ed719 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2018-2024, NVIDIA CORPORATION.
+# Copyright (c) 2018-2025, NVIDIA CORPORATION.
 
 import os
 import sys
@@ -206,3 +206,5 @@ def setup(app):
 # backticks`) to be a python object. See
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-default_role
 default_role = "py:obj"
+
+suppress_warnings = ["duplicate_declaration.cpp"]
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index 34f48a88f6..131858ff34 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -13,3 +13,4 @@ C++ API Documentation
    cpp_api/preprocessing.rst
    cpp_api/selection.rst
    cpp_api/stats.rst
+   cpp_api/core_interop
diff --git a/docs/source/cpp_api/distance.rst b/docs/source/cpp_api/distance.rst
index 2e83da7b35..6fcf373d48 100644
--- a/docs/source/cpp_api/distance.rst
+++ b/docs/source/cpp_api/distance.rst
@@ -11,11 +11,11 @@ distances have been highly optimized and support a wide assortment of different
 Distance Types
 --------------
 
-``#include <cuvs/distance/distance.hpp>``
+``#include <cuvs/distance/distance.h>``
 
 namespace *cuvs::distance*
 
-.. doxygenenum:: cuvs::distance::DistanceType
+.. doxygenenum:: cuvsDistanceType
    :project: cuvs
 
 
diff --git a/docs/source/cpp_api/neighbors_all_neighbors.rst b/docs/source/cpp_api/neighbors_all_neighbors.rst
index 50ba35bca2..e11e374f8d 100644
--- a/docs/source/cpp_api/neighbors_all_neighbors.rst
+++ b/docs/source/cpp_api/neighbors_all_neighbors.rst
@@ -1,5 +1,5 @@
 All-neighbors
-==========
+=============
 
 All-neighbors allows building an approximate all-neighbors knn graph. Given a full dataset, it finds nearest neighbors for all the training vectors in the dataset.
 
@@ -12,7 +12,7 @@ All-neighbors allows building an approximate all-neighbors knn graph. Given a fu
 namespace *cuvs::neighbors::all_neighbors*
 
 All neighbors knn graph build parameters
-----------------------
+----------------------------------------
 
 .. doxygengroup:: all_neighbors_cpp_params
     :project: cuvs
@@ -21,7 +21,7 @@ All neighbors knn graph build parameters
 
 
 Build
------------
+-----
 
 .. doxygengroup:: all_neighbors_cpp_build
     :project: cuvs
diff --git a/docs/source/cpp_api/neighbors_cagra.rst b/docs/source/cpp_api/neighbors_cagra.rst
index d9f5038718..b4cef43162 100644
--- a/docs/source/cpp_api/neighbors_cagra.rst
+++ b/docs/source/cpp_api/neighbors_cagra.rst
@@ -35,14 +35,6 @@ Index extend parameters
     :members:
     :content-only:
 
-Index extend memory buffers
----------------------------
-
-.. doxygengroup:: cagra_cpp_extend_memory_buffers
-    :project: cuvs
-    :members:
-    :content-only:
-
 Index
 -----
 
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index cfaa30acb2..0ce5ac8fff 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -181,7 +181,7 @@ All of the datasets above contain ground test datasets with 100 neighbors. Thus
 End-to-end: large-scale benchmarks (>10M vectors)
 -------------------------------------------------
 
-`cuvs_bench.get_dataset` cannot be used to download the `billion-scale datasets`_ due to their size. You should instead use our billion-scale datasets guide to download and prepare them.
+`cuvs_bench.get_dataset` cannot be used to download the billion-scale datasets due to their size. You should instead use our billion-scale datasets guide to download and prepare them.
 All other python commands mentioned below work as intended once the billion-scale dataset has been downloaded.
 
 To download billion-scale datasets, visit `big-ann-benchmarks <http://big-ann-benchmarks.com/neurips21.html>`_
@@ -212,6 +212,7 @@ The steps below demonstrate how to download, install, and run benchmarks on a su
 The usage of `python -m cuvs_bench.split_groundtruth` is:
 
 .. code-block:: bash
+
     usage: split_groundtruth.py [-h] --groundtruth GROUNDTRUTH
 
     options:
@@ -481,6 +482,7 @@ Implementation of a new algorithm should be a C++ class that inherits `class ANN
 In addition, it should define two `struct`s for building and searching parameters. The searching parameter class should inherit `struct ANN<T>::AnnSearchParam`. Take `class HnswLib` as an example, its definition is:
 
 .. code-block:: c++
+
     template<typename T>
     class HnswLib : public ANN<T> {
     public:
@@ -503,6 +505,7 @@ In addition, it should define two `struct`s for building and searching parameter
 The benchmark program uses JSON format natively in a configuration file to specify indexes to build, along with the build and search parameters. However the JSON config files are overly verbose and are not meant to be used directly. Instead, the Python scripts parse YAML and create these json files automatically. It's important to realize that these json objects align with the yaml objects for `build_param`, whose value is a JSON object, and `search_param`, whose value is an array of JSON objects. Take the json configuration for `HnswLib` as an example of the json after it's been parsed from yaml:
 
 .. code-block:: json
+
     {
       "name" : "hnswlib.M12.ef500.th32",
       "algo" : "hnswlib",
@@ -546,6 +549,7 @@ The build and search params are ultimately passed to the C++ layer as json objec
 2. Next, add corresponding `if` case to functions `create_algo()` (in `cpp/bench/ann/) and `create_search_param()` by calling parsing functions. The string literal in `if` condition statement must be the same as the value of `algo` in configuration file. For example,
 
 .. code-block:: c++
+
       // JSON configuration file contains a line like:  "algo" : "hnswlib"
       if (algo == "hnswlib") {
          // ...
@@ -558,6 +562,7 @@ In `cuvs/cpp/bench/ann/CMakeLists.txt`, we provide a `CMake` function to configu
 
 
 .. code-block:: cmake
+
     ConfigureAnnBench(
       NAME <algo_name>
       PATH </path/to/algo/benchmark/source/file>
@@ -580,9 +585,19 @@ This will create an executable called `HNSWLIB_ANN_BENCH`, which can then be use
 Add a new entry to `algos.yaml` to map the name of the algorithm to its binary executable and specify whether the algorithm requires GPU support.
 
 .. code-block:: yaml
+
     cuvs_ivf_pq:
       executable: CUVS_IVF_PQ_ANN_BENCH
       requires_gpu: true
 
 `executable` : specifies the name of the binary that will build/search the index. It is assumed to be available in `cuvs/cpp/build/`.
 `requires_gpu` : denotes whether an algorithm requires GPU to run.
+
+
+.. toctree::
+   :maxdepth: 4
+
+   build.rst
+   datasets.rst
+   param_tuning.rst
+   wiki_all_dataset.rst
diff --git a/docs/source/cuvs_bench/param_tuning.rst b/docs/source/cuvs_bench/param_tuning.rst
index 9e22aed7d1..276fb35a47 100644
--- a/docs/source/cuvs_bench/param_tuning.rst
+++ b/docs/source/cuvs_bench/param_tuning.rst
@@ -1,6 +1,6 @@
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 cuVS Bench Parameter Tuning Guide
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This guide outlines the various parameter settings that can be specified in :doc:`cuVS Benchmarks <index>` yaml configuration files and explains the impact they have on corresponding algorithms to help inform their settings for benchmarking across desired levels of recall.
 
@@ -32,6 +32,7 @@ IVF-flat is a simple algorithm which won't save any space, but it provides compe
    - `build`
    - Y
    - Positive integer >0
+   - 1024
    - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
 
  * - `niter`
@@ -48,25 +49,27 @@ IVF-flat is a simple algorithm which won't save any space, but it provides compe
    - 2
    - `1/ratio` is the number of training points which should be used to train the clusters.
 
-  * - `dataset_memory_type`
-    - `build`
-    - N
-    - [`device`, `host`, `mmap`]
-    - `mmap`
-    - Where should the dataset reside?
-
-  * - `query_memory_type`
-    - `search`
-    - [`device`, `host`, `mmap`]
-    - `device`
-    - Where should the queries reside?
-
-  * - `nprobe`
-    - `search`
-    - Y
-    - Positive integer >0
-    -
-    - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+ * - `dataset_memory_type`
+   - `build`
+   - N
+   - [`device`, `host`, `mmap`]
+   - `mmap`
+   - Where should the dataset reside?
+
+ * - `query_memory_type`
+   - `search`
+   - N
+   - [`device`, `host`, `mmap`]
+   - `device`
+   - Where should the queries reside?
+
+ * - `nprobe`
+   - `search`
+   - Y
+   - Positive integer >0
+   -
+   - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
+
 
 cuvs_ivf_pq
 -----------
@@ -86,6 +89,7 @@ IVF-pq is an inverted-file index, which partitions the vectors into a series of
    - `build`
    - Y
    - Positive integer >0
+   - 1024
    - Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
 
  * - `niter`
@@ -132,6 +136,7 @@ IVF-pq is an inverted-file index, which partitions the vectors into a series of
 
  * - `query_memory_type`
    - `search`
+   - N
    - [`device`, `host`, `mmap`]
    - `device`
    - Where should the queries reside?
@@ -140,7 +145,7 @@ IVF-pq is an inverted-file index, which partitions the vectors into a series of
    - `search`
    - Y
    - Positive integer >0
-   -
+   - 20
    - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
 
  * - `internalDistanceDtype`
@@ -195,8 +200,8 @@ CAGRA uses a graph-based index, which creates an intermediate, approximate kNN g
 
  * - `graph_build_algo`
    - `build`
-   - `N
-   - [`IVF_PQ`, NN_DESCENT`]
+   - `N`
+   - [`IVF_PQ`, `NN_DESCENT`]
    - `IVF_PQ`
    - Algorithm to use for building the initial kNN graph, from which CAGRA will optimize into the navigable CAGRA graph
 
@@ -209,6 +214,7 @@ CAGRA uses a graph-based index, which creates an intermediate, approximate kNN g
 
  * - `query_memory_type`
    - `search`
+   - N
    - [`device`, `host`, `mmap`]
    - `device`
    - Where should the queries reside?
@@ -356,7 +362,7 @@ Alternatively, if `graph_build_algo == "NN_DESCENT"`, then we can customize the
    - 20
    - Number of nn-descent iterations
 
- * - `nn_descent_intermediate_graph_degree
+ * - `nn_descent_intermediate_graph_degree`
    - `build`
    - N
    - Positive integer >0
@@ -463,7 +469,7 @@ IVF-flat is a simple algorithm which won't save any space, but it provides compe
    - `search`
    - Y
    - Positive integer >0
-   -
+   - 20
    - The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
 
 faiss_gpu_ivf_pq
diff --git a/docs/source/cuvs_bench/wiki_all_dataset.rst b/docs/source/cuvs_bench/wiki_all_dataset.rst
index cedeeb93f5..38b72ae3f5 100644
--- a/docs/source/cuvs_bench/wiki_all_dataset.rst
+++ b/docs/source/cuvs_bench/wiki_all_dataset.rst
@@ -22,6 +22,7 @@ A version of the dataset is made available in the binary format that can be used
 The following will download all 10 the parts and untar them to a `wiki_all_88M` directory:
 
 .. code-block:: bash
+
     curl -s https://data.rapids.ai/raft/datasets/wiki_all/wiki_all.tar.{00..9} | tar -xf - -C wiki_all_88M/
 
 The above has the unfortunate drawback that if the command should fail for any reason, all the parts need to be re-downloaded. The files can also be downloaded individually and then untarred to the directory. Each file is ~27GB and there are 10 of them.
diff --git a/docs/source/filtering.rst b/docs/source/filtering.rst
index 35805c5def..7a62270264 100644
--- a/docs/source/filtering.rst
+++ b/docs/source/filtering.rst
@@ -24,7 +24,7 @@ A bitmap is based on the same principle as a bitset, but in two dimensions. This
 being searched. Check out RAFT's `bitmap API documentation <https://docs.rapids.ai/api/raft/stable/cpp_api/core_bitmap/>`.
 
 Examples
-=======
+========
 
 Using a Bitset filter on a CAGRA index
 --------------------------------------
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
index c4706e5101..f6cd919adc 100644
--- a/docs/source/getting_started.rst
+++ b/docs/source/getting_started.rst
@@ -43,7 +43,7 @@ Getting Started
 New to vector search?
 =====================
 
-If you are unfamiliar with the basics of vector search or how vector search differs from vector databases, then :doc:`this primer on vector search guide <choosing_and_configuring_index>` should provide some good insight. Another good resource for the uninitiated is our :doc:`vector databases vs vector search <vector_databases_vs_vector_search>` guide. As outlined in the primer, vector search as used in vector databases is often closer to machine learning than to traditional databases. This means that while traditional databases can often be slow without any performance tuning, they will usually still yield the correct results. Unfortunately, vector search indexes, like other machine learning models, can yield garbage results of not tuned correctly.
+If you are unfamiliar with the basics of vector search or how vector search differs from vector databases, then :doc:`this primer on vector search guide <choosing_and_configuring_indexes>` should provide some good insight. Another good resource for the uninitiated is our :doc:`vector databases vs vector search <vector_databases_vs_vector_search>` guide. As outlined in the primer, vector search as used in vector databases is often closer to machine learning than to traditional databases. This means that while traditional databases can often be slow without any performance tuning, they will usually still yield the correct results. Unfortunately, vector search indexes, like other machine learning models, can yield garbage results of not tuned correctly.
 
 Fortunately, this opens up the whole world of hyperparamer optimization to improve vector search performance and quality. Please see our :doc:`index tuning guide <tuning_guide>` for more information.
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 4dcf59112d..4c7665c162 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -88,3 +88,4 @@ Contents
    cuvs_bench/index.rst
    api_docs.rst
    contributing.md
+   developer_guide.md
diff --git a/docs/source/indexes/ivfpq.rst b/docs/source/indexes/ivfpq.rst
index ad973adf28..e243a5b562 100644
--- a/docs/source/indexes/ivfpq.rst
+++ b/docs/source/indexes/ivfpq.rst
@@ -58,7 +58,7 @@ Build parameters
 
 
 Search parameters
-~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~
 
 .. list-table::
    :widths: 25 25 50
diff --git a/docs/source/indexes/vamana.rst b/docs/source/indexes/vamana.rst
index 7e0a79fb84..4f5c2eb5f0 100644
--- a/docs/source/indexes/vamana.rst
+++ b/docs/source/indexes/vamana.rst
@@ -11,7 +11,7 @@ There are many algorithmic details that are outlined in the `paper <https://pape
 
 The current implementation of DiskANN in cuVS only includes the 'in-memory' graph construction and a serialization step that writes the index to a file. This index file can be then used by the `open-source DiskANN <https://github.com/microsoft/DiskANN>` library to perform efficient search. Additional DiskANN functionality, including GPU-accelerated search and 'ssd' index build are planned for future cuVS releases.
 
-[ :doc:`C++ API <../cpp_api/neighbors_vamana>` | :doc:`Python API <../python_api/neighbors_vamana>` ]
+[ :doc:`C++ API <../cpp_api/neighbors_vamana>` ]
 
 Interoperability with CPU DiskANN
 ---------------------------------
diff --git a/docs/source/python_api/neighbors_cagra.rst b/docs/source/python_api/neighbors_cagra.rst
index 76564ce5e7..e7155efb83 100644
--- a/docs/source/python_api/neighbors_cagra.rst
+++ b/docs/source/python_api/neighbors_cagra.rst
@@ -44,8 +44,3 @@ Index load
 ##########
 
 .. autofunction:: cuvs.neighbors.cagra.load
-
-Index extend
-###########
-
-.. autofunction:: cuvs.neighbors.cagra.extend
diff --git a/docs/source/python_api/neighbors_hnsw.rst b/docs/source/python_api/neighbors_hnsw.rst
index fe46a8e4b6..40f3a1de7e 100644
--- a/docs/source/python_api/neighbors_hnsw.rst
+++ b/docs/source/python_api/neighbors_hnsw.rst
@@ -40,6 +40,6 @@ Index load
 .. autofunction:: cuvs.neighbors.hnsw.load
 
 Index extend
-###########
+############
 
 .. autofunction:: cuvs.neighbors.hnsw.extend
diff --git a/docs/source/python_api/neighbors_nn_decent.rst b/docs/source/python_api/neighbors_nn_decent.rst
index df872d7e99..01e9e196c9 100644
--- a/docs/source/python_api/neighbors_nn_decent.rst
+++ b/docs/source/python_api/neighbors_nn_decent.rst
@@ -1,5 +1,5 @@
 NN-Descent
-======
+==========
 
 .. role:: py(code)
    :language: python
diff --git a/docs/source/python_api/preprocessing.rst b/docs/source/python_api/preprocessing.rst
index 679eabc16a..f0e3edac81 100644
--- a/docs/source/python_api/preprocessing.rst
+++ b/docs/source/python_api/preprocessing.rst
@@ -8,4 +8,15 @@ Preprocessing
 Scalar Quantizer
 ################
 
-.. autofunction:: cuvs.preprocessing.quantize.scalar
+
+.. autoclass:: cuvs.preprocessing.quantize.scalar.Quantizer
+    :members:
+
+.. autoclass:: cuvs.preprocessing.quantize.scalar.QuantizerParams
+    :members:
+
+.. autofunction:: cuvs.preprocessing.quantize.scalar.train
+
+.. autofunction:: cuvs.preprocessing.quantize.scalar.transform
+
+.. autofunction:: cuvs.preprocessing.quantize.scalar.inverse_transform
diff --git a/docs/source/sphinxext/github_link.py b/docs/source/sphinxext/github_link.py
index 75acfbd6e1..75a609d454 100644
--- a/docs/source/sphinxext/github_link.py
+++ b/docs/source/sphinxext/github_link.py
@@ -34,7 +34,7 @@ def isfunction(obj):
 
     new_val = hasattr(type(obj), "__code__")
 
-    if (orig_val != new_val):
+    if orig_val != new_val:
         return new_val
 
     return orig_val
@@ -42,19 +42,20 @@ def isfunction(obj):
 
 inspect.isfunction = isfunction
 
-REVISION_CMD = 'git rev-parse --short HEAD'
+REVISION_CMD = "git rev-parse --short HEAD"
 
-source_regex = re.compile(r"^File: (.*?) \(starting at line ([0-9]*?)\)$",
-                          re.MULTILINE)
+source_regex = re.compile(
+    r"^File: (.*?) \(starting at line ([0-9]*?)\)$", re.MULTILINE
+)
 
 
 def _get_git_revision():
     try:
         revision = subprocess.check_output(REVISION_CMD.split()).strip()
     except (subprocess.CalledProcessError, OSError):
-        print('Failed to execute git to get revision')
+        print("Failed to execute git to get revision")
         return None
-    return revision.decode('utf-8')
+    return revision.decode("utf-8")
 
 
 def _linkcode_resolve(domain, info, package, url_fmt, revision):
@@ -74,14 +75,14 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision):
 
     if revision is None:
         return
-    if domain not in ('py', 'pyx'):
+    if domain not in ("py", "pyx"):
         return
-    if not info.get('module') or not info.get('fullname'):
+    if not info.get("module") or not info.get("fullname"):
         return
 
-    class_name = info['fullname'].split('.')[0]
-    module = __import__(info['module'], fromlist=[class_name])
-    obj = attrgetter(info['fullname'])(module)
+    class_name = info["fullname"].split(".")[0]
+    module = __import__(info["module"], fromlist=[class_name])
+    obj = attrgetter(info["fullname"])(module)
 
     # Unwrap the object to get the correct source
     # file in case that is wrapped by a decorator
@@ -104,42 +105,46 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision):
         # Possibly Cython code. Search docstring for source
         m = source_regex.search(obj.__doc__)
 
-        if (m is not None):
+        if m is not None:
             source_file = m.group(1)
             lineno = m.group(2)
 
             # fn is expected to be the absolute path.
             fn = os.path.relpath(source_file, start=package)
-            print("{}:{}".format(
-                os.path.abspath(os.path.join("..", "python", "cuvs", fn)),
-                lineno))
+            print(
+                "{}:{}".format(
+                    os.path.abspath(os.path.join("..", "python", "cuvs", fn)),
+                    lineno,
+                )
+            )
         else:
             return
     else:
         if fn.endswith(".pyx"):
-            sp_path = next(x for x in sys.path if re.match(".*site-packages$", x))
+            sp_path = next(
+                x for x in sys.path if re.match(".*site-packages$", x)
+            )
             fn = fn.replace("/opt/conda/conda-bld/work/python/cuvs", sp_path)
 
         # Convert to relative from module root
-        fn = os.path.relpath(fn,
-                             start=os.path.dirname(
-                                 __import__(package).__file__))
+        fn = os.path.relpath(
+            fn, start=os.path.dirname(__import__(package).__file__)
+        )
 
     # Get the line number if we need it. (Can work without it)
-    if (lineno is None):
+    if lineno is None:
         try:
             lineno = inspect.getsourcelines(obj)[1]
         except Exception:
 
             # Can happen if its a cyfunction. See if it has `__code__`
-            if (hasattr(obj, "__code__")):
+            if hasattr(obj, "__code__"):
                 lineno = obj.__code__.co_firstlineno
             else:
-                lineno = ''
-    return url_fmt.format(revision=revision,
-                          package=package,
-                          path=fn,
-                          lineno=lineno)
+                lineno = ""
+    return url_fmt.format(
+        revision=revision, package=package, path=fn, lineno=lineno
+    )
 
 
 def make_linkcode_resolve(package, url_fmt):
@@ -154,7 +159,10 @@ def make_linkcode_resolve(package, url_fmt):
                                    '{path}#L{lineno}')
     """
     revision = _get_git_revision()
-    return partial(_linkcode_resolve,
-                   revision=revision,
-                   package=package,
-                   url_fmt=url_fmt)
+
+    def linkcode_resolve(domain, info):
+        return _linkcode_resolve(
+            domain, info, revision=revision, package=package, url_fmt=url_fmt
+        )
+
+    return linkcode_resolve
diff --git a/docs/source/working_with_ann_indexes_c.rst b/docs/source/working_with_ann_indexes_c.rst
index 9d0c17a1e3..1e84141a86 100644
--- a/docs/source/working_with_ann_indexes_c.rst
+++ b/docs/source/working_with_ann_indexes_c.rst
@@ -3,8 +3,6 @@ Working with ANN Indexes in C
 
 - `Building an index`_
 - `Searching an index`_
-- `CPU/GPU Interoperability`_
-- `Serializing an index`_
 
 Building an index
 -----------------
diff --git a/docs/source/working_with_ann_indexes_cpp.rst b/docs/source/working_with_ann_indexes_cpp.rst
index ead64107e9..68578bf848 100644
--- a/docs/source/working_with_ann_indexes_cpp.rst
+++ b/docs/source/working_with_ann_indexes_cpp.rst
@@ -3,8 +3,6 @@ Working with ANN Indexes in C++
 
 - `Building an index`_
 - `Searching an index`_
-- `CPU/GPU Interoperability`_
-- `Serializing an index`_
 
 Building an index
 -----------------
diff --git a/docs/source/working_with_ann_indexes_python.rst b/docs/source/working_with_ann_indexes_python.rst
index b2ab47a854..0419c47beb 100644
--- a/docs/source/working_with_ann_indexes_python.rst
+++ b/docs/source/working_with_ann_indexes_python.rst
@@ -3,8 +3,6 @@ Working with ANN Indexes in Python
 
 - `Building an index`_
 - `Searching an index`_
-- `CPU/GPU Interoperability`_
-- `Serializing an index`_
 
 Building an index
 -----------------
diff --git a/docs/source/working_with_ann_indexes_rust.rst b/docs/source/working_with_ann_indexes_rust.rst
index ba284d1688..487ad0964b 100644
--- a/docs/source/working_with_ann_indexes_rust.rst
+++ b/docs/source/working_with_ann_indexes_rust.rst
@@ -1,13 +1,10 @@
 Working with ANN Indexes in Rust
 ================================
 
-- `Building an index`_
-- `Searching an index`_
-- `CPU/GPU Interoperability`_
-- `Serializing an index`_
+- `Building and Searching an index`_
 
-Building an index
------------------
+Building and Searching an index
+-------------------------------
 
 .. code-block:: rust
 
@@ -31,5 +28,35 @@ Building an index
         let build_params = IndexParams::new()?;
         let index = Index::build(&res, &build_params, &dataset)?;
 
+        // use the first 4 points from the dataset as queries : will test that we get them back
+        // as their own nearest neighbor
+        let n_queries = 4;
+        let queries = dataset.slice(s![0..n_queries, ..]);
+
+        let k = 10;
+
+        // CAGRA search API requires queries and outputs to be on device memory
+        // copy query data over, and allocate new device memory for the distances/ neighbors
+        // outputs
+        let queries = ManagedTensor::from(&queries).to_device(&res)?;
+        let mut neighbors_host = ndarray::Array::<u32, _>::zeros((n_queries, k));
+        let neighbors = ManagedTensor::from(&neighbors_host).to_device(&res)?;
+
+        let mut distances_host = ndarray::Array::<f32, _>::zeros((n_queries, k));
+        let distances = ManagedTensor::from(&distances_host).to_device(&res)?;
+
+        let search_params = SearchParams::new()?;
+
+        index.search(&res, &search_params, &queries, &neighbors, &distances)?;
+
+        // Copy back to host memory
+        distances.to_host(&res, &mut distances_host)?;
+        neighbors.to_host(&res, &mut neighbors_host)?;
+
+        // nearest neighbors should be themselves, since queries are from the
+        // dataset
+        println!("Neighbors {:?}", neighbors_host);
+        println!("Distances {:?}", distances_host);
+
         Ok(())
     }
diff --git a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
index 08690d0932..b271145b6c 100644
--- a/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
+++ b/python/cuvs/cuvs/neighbors/brute_force/brute_force.pyx
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -161,11 +161,12 @@ def search(Index index,
                 - `n_prefilter_rows == 1` when using a bitset prefilter.
                 Each bit in `n_samples` determines whether `queries[i]`
                 should be considered for distance computation with the index.
-        (default None)
+                (default None)
     {resources_docstring}
 
     Examples
     --------
+
     >>> # Example without pre-filter
     >>> import cupy as cp
     >>> from cuvs.neighbors import brute_force
@@ -187,8 +188,6 @@ def search(Index index,
     >>> neighbors = cp.asarray(neighbors)
     >>> distances = cp.asarray(distances)
 
-    Examples
-    --------
     >>> # Example with pre-filter
     >>> import numpy as np
     >>> import cupy as cp
@@ -223,6 +222,7 @@ def search(Index index,
     ...                                           prefilter=bitmap_prefilter)
     >>> neighbors = cp.asarray(neighbors)
     >>> distances = cp.asarray(distances)
+
     """
     if not index.trained:
         raise ValueError("Index needs to be built before calling search.")
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 7fa73a2cff..0a8fec17b4 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -136,26 +136,29 @@ cdef class IndexParams:
 
     Parameters
     ----------
-    metric : string denoting the metric type, default="sqeuclidean"
-        Valid values for metric: ["sqeuclidean", "inner_product"], where
+
+    metric : str, default = "sqeuclidean"
+        String denoting the metric type, valid values for metric are
+        ["sqeuclidean", "inner_product"], where:
+
             - sqeuclidean is the euclidean distance without the square root
               operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2
             - inner_product distance is defined as
               distance(a, b) = \\sum_i a_i * b_i.
-    intermediate_graph_degree : int, default = 128
 
+    intermediate_graph_degree : int, default = 128
     graph_degree : int, default = 64
+    build_algo: str, default = "ivf_pq"
+        string denoting the graph building algorithm to use. Valid values for
+        algo: ["ivf_pq", "nn_descent", "iterative_cagra_search"], where
 
-    build_algo: string denoting the graph building algorithm to use, \
-                default = "ivf_pq"
-        Valid values for algo: ["ivf_pq", "nn_descent",
-                                "iterative_cagra_search"], where
             - ivf_pq will use the IVF-PQ algorithm for building the knn graph
             - nn_descent (experimental) will use the NN-Descent algorithm for
               building the knn graph. It is expected to be generally
               faster than ivf_pq.
             - iterative_cagra_search will iteratively build the knn graph using
               CAGRA's search() and optimize()
+
     compression: CompressionParams, optional
         If compression is desired should be a CompressionParams object. If None
         compression will be disabled.
@@ -166,6 +169,7 @@ cdef class IndexParams:
         Parameters for IVF-PQ search. If provided, it will be used for
         searching the graph.
     refinement_rate: float, default = 1.0
+
     """
 
     cdef cuvsCagraIndexParams* params
@@ -413,6 +417,7 @@ cdef class SearchParams:
 
     Parameters
     ----------
+
     max_queries: int, default = 0
         Maximum number of queries to search at the same time (batch size).
         Auto select when 0.
@@ -422,12 +427,15 @@ cdef class SearchParams:
         search speed. Higher values improve the search accuracy.
     max_iterations: int, default = 0
         Upper limit of search iterations. Auto select when 0.
-    algo: string denoting the search algorithm to use, default = "auto"
-        Valid values for algo: ["auto", "single_cta", "multi_cta"], where
+    algo: str, default = "auto"
+        String denoting the search algorithm to use
+        Valid values for algo: ["auto", "single_cta", "multi_cta"], where:
+
             - auto will automatically select the best value based on query size
             - single_cta is better when query contains larger number of
               vectors (e.g >10)
             - multi_cta is better when query contains only a few vectors
+
     team_size: int, default = 0
         Number of threads used to calculate a single distance. 4, 8, 16,
         or 32.
@@ -439,13 +447,15 @@ cdef class SearchParams:
     thread_block_size: int, default = 0
         Thread block size. 0, 64, 128, 256, 512, 1024.
         Auto selection when 0.
-    hashmap_mode: string denoting the type of hash map to use.
+    hashmap_mode: str, default = "auto"
+        String denoting the type of hash map to use.
         It's usually better to allow the algorithm to select this value,
-        default = "auto".
-        Valid values for hashmap_mode: ["auto", "small", "hash"], where
+        Valid values for hashmap_mode: ["auto", "small", "hash"], where:
+
             - auto will automatically select the best value based on algo
             - small will use the small shared memory hash table with resetting.
             - hash will use a single hash table in global memory.
+
     hashmap_min_bitlen: int, default = 0
         Upper limit of hashmap fill rate. More than 0.1, less than 0.9.
     hashmap_max_fill_rate: float, default = 0.5
@@ -456,12 +466,13 @@ cdef class SearchParams:
     rand_xor_mask: int, default = 0x128394
         Bit mask used for initial random seed node selection.
     persistent: bool, default = false
-         Whether to use the persistent version of the kernel
+        Whether to use the persistent version of the kernel
     persistent_lifetime: float
-         Persistent kernel: time in seconds before the kernel stops if no
-         requests are received.
+        Persistent kernel: time in seconds before the kernel stops if no
+        requests are received.
     persistent_device_usage : float
         Sets the fraction of maximum grid size used by persistent kernel.
+
     """
 
     cdef cuvsCagraSearchParams * params
@@ -608,6 +619,7 @@ def search(SearchParams search_params,
 
     Parameters
     ----------
+
     search_params : SearchParams
     index : Index
         Trained CAGRA index.
@@ -623,11 +635,12 @@ def search(SearchParams search_params,
                 neighbors will be written here in-place. (default None)
     filter:     Optional cuvs.neighbors.cuvsFilter can be used to filter
                 neighbors based on a given bitset.
-        (default None)
+                (default None)
     {resources_docstring}
 
     Examples
     --------
+
     >>> import cupy as cp
     >>> from cuvs.neighbors import cagra
     >>> n_samples = 50000
@@ -652,6 +665,7 @@ def search(SearchParams search_params,
     ...                                     k)
     >>> neighbors = cp.asarray(neighbors)
     >>> distances = cp.asarray(distances)
+
     """
     if not index.trained:
         raise ValueError("Index needs to be built before calling search.")
diff --git a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
index 35bf2c8d95..a0f46f59e8 100644
--- a/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
+++ b/python/cuvs/cuvs/neighbors/hnsw/hnsw.pyx
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -303,21 +303,22 @@ def from_cagra(IndexParams index_params, cagra.Index cagra_index,
     Returns an HNSW index from a CAGRA index.
 
     NOTE: When `index_params.hierarchy` is:
-          1. `NONE`: This method uses the filesystem to write the CAGRA index
-                     in `/tmp/<random_number>.bin` before reading it as an
-                     hnswlib index, then deleting the temporary file. The
-                     returned index is immutable and can only be searched by
-                     the hnswlib wrapper in cuVS, as the format is not
-                    compatible with the original hnswlib.
-          2. `CPU`: The returned index is mutable and can be extended with
-                    additional vectors. The serialized index is also compatible
-                    with the original hnswlib library.
+
+    1. `NONE`: This method uses the filesystem to write the CAGRA index in
+    `/tmp/<random_number>.bin` before reading it as an hnswlib index, then
+    deleting the temporary file. The returned index is immutable and can only
+    be searched by the hnswlib wrapper in cuVS, as the format is not
+    compatible with the original hnswlib.
+    2. `CPU`: The returned index is mutable and can be extended with additional
+    vectors. The serialized index is also compatible with the original hnswlib
+    library.
 
     Saving / loading the index is experimental. The serialization format is
     subject to change.
 
     Parameters
     ----------
+
     index_params : IndexParams
         Parameters to convert the CAGRA index to HNSW index.
     cagra_index : cagra.Index
@@ -329,6 +330,7 @@ def from_cagra(IndexParams index_params, cagra.Index cagra_index,
 
     Examples
     --------
+
     >>> import cupy as cp
     >>> from cuvs.neighbors import cagra
     >>> from cuvs.neighbors import hnsw
@@ -340,6 +342,7 @@ def from_cagra(IndexParams index_params, cagra.Index cagra_index,
     >>> index = cagra.build(cagra.IndexParams(), dataset)
     >>> # Serialize the CAGRA index to hnswlib base layer only index format
     >>> hnsw_index = hnsw.from_cagra(hnsw.IndexParams(), index)
+
     """
 
     cdef Index hnsw_index = Index()
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 6fb4279db1..3a803e5599 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -61,6 +61,7 @@ cdef class IndexParams:
         String denoting the metric type.
         Valid values for metric: ["sqeuclidean", "inner_product",
         "euclidean", "cosine"], where
+
             - sqeuclidean is the euclidean distance without the square root
               operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2,
             - euclidean is the euclidean distance
@@ -68,6 +69,7 @@ cdef class IndexParams:
               distance(a, b) = \\sum_i a_i * b_i.
             - cosine distance is defined as
               distance(a, b) = 1 - \\sum_i a_i * b_i / ( ||a||_2 * ||b||_2).
+
     kmeans_n_iters : int, default = 20
         The number of iterations searching for kmeans centers during index
         building.
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 40f22c236b..f784327d33 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -60,11 +60,13 @@ cdef class IndexParams:
         String denoting the metric type.
         Valid values for metric: ["sqeuclidean", "inner_product", "euclidean"],
         where:
+
             - sqeuclidean is the euclidean distance without the square root
               operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2,
             - euclidean is the euclidean distance
             - inner product distance is defined as
               distance(a, b) = \\sum_i a_i * b_i.
+
     kmeans_n_iters : int, default = 20
         The number of iterations searching for kmeans centers during index
         building.

From 9667a576a3a534a44fec24d7c23d4926eb6a5173 Mon Sep 17 00:00:00 2001
From: Peter Andreas Entschev <peter@entschev.com>
Date: Tue, 16 Sep 2025 17:01:47 +0200
Subject: [PATCH 577/623] Remove UCX-Py/UCXX from release script (#1331)

Contributes to https://github.com/rapidsai/build-planning/issues/198

There are no direct dependencies on either UCX-Py (deprecated) or UCXX but the release script still has code to handle that which may be removed.

Authors:
  - Peter Andreas Entschev (https://github.com/pentschev)

Approvers:
  - James Lamb (https://github.com/jameslamb)

URL: https://github.com/rapidsai/cuvs/pull/1331
---
 ci/release/update-version.sh | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index e6056f7332..29cfe0e1a3 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -22,11 +22,9 @@ NEXT_MAJOR=$(echo "$NEXT_FULL_TAG" | awk '{split($0, a, "."); print a[1]}')
 NEXT_MINOR=$(echo "$NEXT_FULL_TAG" | awk '{split($0, a, "."); print a[2]}')
 NEXT_PATCH=$(echo "$NEXT_FULL_TAG" | awk '{split($0, a, "."); print a[3]}')
 NEXT_SHORT_TAG=${NEXT_MAJOR}.${NEXT_MINOR}
-NEXT_UCXX_SHORT_TAG="$(curl -sL https://version.gpuci.io/rapids/"${NEXT_SHORT_TAG}")"
 
 # Need to distutils-normalize the original version
 NEXT_SHORT_TAG_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_SHORT_TAG}'))")
-NEXT_UCXX_SHORT_TAG_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_UCXX_SHORT_TAG}'))")
 PATCH_PEP440=$(python -c "from packaging.version import Version; print(Version('${NEXT_PATCH}'))")
 
 echo "Preparing release $CURRENT_TAG => $NEXT_FULL_TAG"
@@ -51,24 +49,15 @@ DEPENDENCIES=(
   rmm
   rapids-dask-dependency
 )
-UCXX_DEPENDENCIES=(
-  ucx-py
-)
 for FILE in dependencies.yaml conda/environments/*.yaml; do
   for DEP in "${DEPENDENCIES[@]}"; do
     sed_runner "/-.* ${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0/g" "${FILE}"
   done
-  for DEP in "${UCXX_DEPENDENCIES[@]}"; do
-    sed_runner "/-.* ${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}==/ s/==.*/==${NEXT_UCXX_SHORT_TAG_PEP440}.*,>=0.0.0a0/g" "${FILE}"
-  done
 done
 for FILE in python/*/pyproject.toml; do
   for DEP in "${DEPENDENCIES[@]}"; do
     sed_runner "/\"${DEP}==/ s/==.*\"/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" "${FILE}"
   done
-  for DEP in "${UCXX_DEPENDENCIES[@]}"; do
-    sed_runner "/\"${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}==/ s/==.*\"/==${NEXT_UCXX_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" "${FILE}"
-  done
 done
 
 for FILE in .github/workflows/*.yaml; do

From 439620ad2dec561dbb182cc8d30f671d16aa225f Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Tue, 16 Sep 2025 22:34:13 +0200
Subject: [PATCH 578/623] Fix inadvertent uses of copy constructor in mdarrays
 across cuVS (#1330)

As of current date, raft's device mdarray is copy-constructible. This has resulted in cuVS doing accidental mdarray (and its data) copies in a few places across cuVS codebase. In some cases, this leads to segfaults (use-after-destruction of the temporary copy). In other cases, this leads to unnecessary copying of data.

This PR fixes the problematic occurrences (found by removing the copy constructor in a local copy of raft and following the compiler errors).

Fixes https://github.com/rapidsai/cuvs/pull/1263

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1330
---
 cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h      |  8 ++++----
 cpp/src/neighbors/ivf_pq_index.cu                  |  4 ++--
 .../preprocessing/spectral/spectral_embedding.cu   |  2 +-
 cpp/tests/distance/masked_nn.cu                    | 14 +++++++-------
 cpp/tests/neighbors/ann_ivf_flat.cuh               |  6 +++---
 examples/cpp/src/cagra_persistent_example.cu       |  2 +-
 6 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
index 37d1190e66..3aef8a64a4 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_utils.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -192,9 +192,9 @@ inline auto configured_raft_resources::operator=(configured_raft_resources&&)
 /** A helper to refine the neighbors when the data is on device or on host. */
 template <typename DatasetT, typename QueriesT, typename CandidatesT>
 void refine_helper(const raft::resources& res,
-                   DatasetT dataset,
-                   QueriesT queries,
-                   CandidatesT candidates,
+                   const DatasetT& dataset,
+                   const QueriesT& queries,
+                   const CandidatesT& candidates,
                    int k,
                    algo_base::index_type* neighbors,
                    float* distances,
diff --git a/cpp/src/neighbors/ivf_pq_index.cu b/cpp/src/neighbors/ivf_pq_index.cu
index 1bd6b6291d..7711fd63b2 100644
--- a/cpp/src/neighbors/ivf_pq_index.cu
+++ b/cpp/src/neighbors/ivf_pq_index.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -302,7 +302,7 @@ uint32_t index<IdxT>::get_list_size_in_bytes(uint32_t label)
 {
   RAFT_EXPECTS(label < this->n_lists(),
                "Expected label to be less than number of lists in the index");
-  auto list_data = this->lists()[label]->data;
+  auto& list_data = this->lists()[label]->data;
   return list_data.size();
 }
 
diff --git a/cpp/src/preprocessing/spectral/spectral_embedding.cu b/cpp/src/preprocessing/spectral/spectral_embedding.cu
index 7565ae63b1..0f288d2b13 100644
--- a/cpp/src/preprocessing/spectral/spectral_embedding.cu
+++ b/cpp/src/preprocessing/spectral/spectral_embedding.cu
@@ -168,7 +168,7 @@ raft::device_csr_matrix<float, int, int, int> create_laplacian(
 void compute_eigenpairs(raft::resources const& handle,
                         params spectral_embedding_config,
                         const int n_samples,
-                        raft::device_csr_matrix<float, int, int, int> laplacian,
+                        raft::device_csr_matrix<float, int, int, int>& laplacian,
                         raft::device_vector_view<float, int> diagonal,
                         raft::device_matrix_view<float, int, raft::col_major> embedding)
 {
diff --git a/cpp/tests/distance/masked_nn.cu b/cpp/tests/distance/masked_nn.cu
index 63fcee1e31..0d6372f3d9 100644
--- a/cpp/tests/distance/masked_nn.cu
+++ b/cpp/tests/distance/masked_nn.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -89,10 +89,10 @@ RAFT_KERNEL init_adj(AdjacencyPattern pattern,
 
 template <typename DataT, typename ReduceOpT, int NWARPS>
 __launch_bounds__(32 * NWARPS, 2) RAFT_KERNEL referenceKernel(raft::KeyValuePair<int, DataT>* min,
-                                                              DataT* x,
-                                                              DataT* y,
-                                                              bool* adj,
-                                                              int* group_idxs,
+                                                              const DataT* x,
+                                                              const DataT* y,
+                                                              const bool* adj,
+                                                              const int* group_idxs,
                                                               int m,
                                                               int n,
                                                               int k,
@@ -196,7 +196,7 @@ struct Inputs {
 };
 
 template <typename DataT, typename OutT = raft::KeyValuePair<int, DataT>>
-auto reference(const raft::handle_t& handle, Inputs<DataT> inp, const Params& p)
+auto reference(const raft::handle_t& handle, const Inputs<DataT>& inp, const Params& p)
   -> raft::device_vector<OutT, int>
 {
   int m          = inp.x.extent(0);
@@ -244,7 +244,7 @@ auto reference(const raft::handle_t& handle, Inputs<DataT> inp, const Params& p)
 }
 
 template <typename DataT, typename OutT = raft::KeyValuePair<int, DataT>>
-auto run_masked_nn(const raft::handle_t& handle, Inputs<DataT> inp, const Params& p)
+auto run_masked_nn(const raft::handle_t& handle, const Inputs<DataT>& inp, const Params& p)
   -> raft::device_vector<OutT, int>
 {
   // Compute norms:
diff --git a/cpp/tests/neighbors/ann_ivf_flat.cuh b/cpp/tests/neighbors/ann_ivf_flat.cuh
index bb8c7d1cd7..1bb2609873 100644
--- a/cpp/tests/neighbors/ann_ivf_flat.cuh
+++ b/cpp/tests/neighbors/ann_ivf_flat.cuh
@@ -314,8 +314,8 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
       if (list_size > 0) {
         uint32_t padded_list_size = interleaved_group::roundUp(list_size);
         uint32_t n_elems          = padded_list_size * idx.dim();
-        auto list_data            = lists[label]->data;
-        auto list_inds            = extend_index.lists()[label]->indices;
+        auto& list_data           = lists[label]->data;
+        auto& list_inds           = extend_index.lists()[label]->indices;
 
         // fetch the flat codes
         auto flat_codes = raft::make_device_matrix<DataT, uint32_t>(handle_, list_size, idx.dim());
@@ -364,7 +364,7 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
                                      return DataT{0};
                                    });
 
-          auto extend_data          = extend_index.lists()[label]->data;
+          auto& extend_data         = extend_index.lists()[label]->data;
           auto extend_data_filtered = raft::make_device_vector<DataT, uint32_t>(handle_, n_elems);
           raft::linalg::map_offset(
             handle_,
diff --git a/examples/cpp/src/cagra_persistent_example.cu b/examples/cpp/src/cagra_persistent_example.cu
index e65e10e725..a0234e2472 100644
--- a/examples/cpp/src/cagra_persistent_example.cu
+++ b/examples/cpp/src/cagra_persistent_example.cu
@@ -30,7 +30,7 @@
 
 // A helper to split the dataset into chunks
 template <typename DeviceMatrixOrView>
-auto slice_matrix(DeviceMatrixOrView source,
+auto slice_matrix(DeviceMatrixOrView& source,
                   typename DeviceMatrixOrView::index_type offset_rows,
                   typename DeviceMatrixOrView::index_type count_rows)
 {

From 890929ece6cd03251019fe688b7d4659932d4a12 Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Tue, 16 Sep 2025 22:46:58 +0200
Subject: [PATCH 579/623] Fix debug build (#1258)

Closes #462
Fix for the error when building in debug. It will also help downstream libraries that want to build in debug (https://github.com/zilliztech/knowhere/issues/1123)
```
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISA_EEPjPKNSA_7INDEX_TEjjmSL_jPSJ_jjjjSI_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS0_9filtering18none_sample_filterEEEvPNT2_7INDEX_TEPNSA_10DISTANCE_TEjPKSA_PKNSA_6DATA_TEPKSB_jjmSL_jSC_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj64ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj128ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj64ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj128ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj256ELj1ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj256ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search15search_kernel_pILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPKT2_PNS3_15worker_handle_tEPNS3_10job_desc_tISD_EEPjPKNSD_7INDEX_TEjjmSO_jPSM_jjjjSL_jjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
ptxas error   : Entry function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search13search_kernelILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjT3_' with max regcount of 64 calls function '_ZN4cuvs9neighbors5cagra6detail17single_cta_search11search_coreILj512ELj32ELj0ENS2_25dataset_descriptor_base_tI6__halfjfEENS2_34CagraSampleFilterWithQueryIdOffsetINS0_9filtering13bitset_filterIjlEEEEEEvPNT2_7INDEX_TEPNSD_10DISTANCE_TEjPKSD_PKNSD_6DATA_TEPKSE_jjmSO_jSF_jjjjPjjjjjT3_' with regcount of 255
```

Authors:
  - Micka (https://github.com/lowener)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1258
---
 cpp/cmake/modules/ConfigureCUDA.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index 5ce9b918b4..0550aa0b11 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -64,6 +64,6 @@ endif()
 # Debug options
 if(CMAKE_BUILD_TYPE MATCHES Debug)
   message(VERBOSE "cuVS: Building with debugging flags")
-  list(APPEND CUVS_CUDA_FLAGS -G -Xcompiler=-rdynamic)
+  list(APPEND CUVS_CUDA_FLAGS -G -Xcompiler=-rdynamic --maxrregcount=64)
   list(APPEND CUVS_CUDA_FLAGS -Xptxas --suppress-stack-size-warning)
 endif()

From bcf9025fbf619aa174b688ed5d2545b59271cd74 Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Tue, 16 Sep 2025 19:21:10 -0400
Subject: [PATCH 580/623] CAGRA Build + DiskANN Search cuvs-bench Wrapper
 (#899)

cuvs-bench wrapper for CAGRA build + Vamana style serialize. Search the index as a Diskann in-memory index.

This PR also brings a bunch of fixes for already existing algos:
- `cuvs_vamana` wrapper cleanup via default destructor gives SIGFPE (fixed by declaring raft handle first, so that it is destroyed last)
- Low throughput for diskann in-memory and ssd indexes (fixed via removing OpenMP reliance entirely during search and using the default benchmarking methods for multi-threading)
- `cuvs_vamana` was using the incorrect template params for device mdspan for the dataset
- DiskANN indexes to load the index using the correct number of threads using the `benchmark_n_threads` variable
- Faiss CAGRA + HNSW wrapper supports throughput mode search by resetting the CAGRA index `shared_ptr`, which is not used during search (this allows using different CPU threads)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/899
---
 cpp/bench/ann/CMakeLists.txt                  |  17 +-
 .../src/cuvs/cuvs_ann_bench_param_parser.h    |  12 +-
 cpp/bench/ann/src/cuvs/cuvs_cagra_diskann.cu  |  88 +++++++
 .../ann/src/cuvs/cuvs_cagra_diskann_wrapper.h | 227 ++++++++++++++++++
 cpp/bench/ann/src/cuvs/cuvs_vamana.cu         |   1 -
 cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h  |   6 +-
 .../ann/src/diskann/diskann_benchmark.cpp     |   8 +-
 cpp/bench/ann/src/diskann/diskann_wrapper.h   |  48 ++--
 cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h   |  14 +-
 .../cuvs_bench/config/algorithms.yaml         |   3 +
 .../config/algos/cuvs_cagra_diskann.yaml      |  11 +
 .../cuvs_bench/config/algos/cuvs_vamana.yaml  |   4 +-
 .../config/algos/diskann_memory.yaml          |   3 +-
 .../cuvs_bench/config/algos/diskann_ssd.yaml  |   1 -
 14 files changed, 391 insertions(+), 52 deletions(-)
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_diskann.cu
 create mode 100644 cpp/bench/ann/src/cuvs/cuvs_cagra_diskann_wrapper.h
 create mode 100644 python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_diskann.yaml

diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 7fc49c9e80..9f366e7f83 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -39,9 +39,13 @@ option(CUVS_ANN_BENCH_USE_HNSWLIB "Include hnsw algorithm in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_GGNN "Include ggnn algorithm in benchmark" OFF)
 option(CUVS_ANN_BENCH_USE_DISKANN "Include DISKANN search in benchmark" ON)
 option(CUVS_ANN_BENCH_USE_CUVS_VAMANA "Include cuVS Vamana with DiskANN search in benchmark" ON)
+option(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN "Include cuVS CAGRA with DISKANN search in benchmark"
+       ON
+)
 if(CMAKE_SYSTEM_PROCESSOR MATCHES "(ARM|arm|aarch64)")
   set(CUVS_ANN_BENCH_USE_DISKANN OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_VAMANA OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN OFF)
 endif()
 option(CUVS_ANN_BENCH_USE_CUVS_MG "Include cuVS ann mg algorithm in benchmark" ${BUILD_MG_ALGOS})
 option(CUVS_ANN_BENCH_SINGLE_EXE
@@ -69,6 +73,7 @@ if(BUILD_CPU_ONLY)
   set(CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_MG OFF)
   set(CUVS_ANN_BENCH_USE_CUVS_VAMANA OFF)
+  set(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN OFF)
 else()
   set(CUVS_FAISS_ENABLE_GPU ON)
 endif()
@@ -82,6 +87,7 @@ if(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
    OR CUVS_KNN_BENCH_USE_CUVS_BRUTE_FORCE
    OR CUVS_ANN_BENCH_USE_CUVS_MG
    OR CUVS_ANN_BENCH_USE_CUVS_VAMANA
+   OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN
 )
   set(CUVS_ANN_BENCH_USE_CUVS ON)
 endif()
@@ -99,7 +105,10 @@ if(CUVS_ANN_BENCH_USE_GGNN)
   include(cmake/thirdparty/get_ggnn)
 endif()
 
-if(CUVS_ANN_BENCH_USE_DISKANN OR CUVS_ANN_BENCH_USE_CUVS_VAMANA)
+if(CUVS_ANN_BENCH_USE_DISKANN
+   OR CUVS_ANN_BENCH_USE_CUVS_VAMANA
+   OR CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN
+)
   include(cmake/thirdparty/get_diskann)
 endif()
 
@@ -341,6 +350,12 @@ if(CUVS_ANN_BENCH_USE_CUVS_VAMANA)
   ConfigureAnnBench(NAME CUVS_VAMANA PATH src/cuvs/cuvs_vamana.cu LINKS cuvs diskann::diskann aio)
 endif()
 
+if(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN)
+  ConfigureAnnBench(
+    NAME CUVS_CAGRA_DISKANN PATH src/cuvs/cuvs_cagra_diskann.cu LINKS cuvs diskann::diskann aio
+  )
+endif()
+
 # ##################################################################################################
 # * Dynamically-loading ANN_BENCH executable -------------------------------------------------------
 if(CUVS_ANN_BENCH_SINGLE_EXE)
diff --git a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
index 3ddf026046..3ad01dc2b4 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_ann_bench_param_parser.h
@@ -27,7 +27,7 @@ extern template class cuvs::bench::cuvs_ivf_flat<uint8_t, int64_t>;
 extern template class cuvs::bench::cuvs_ivf_flat<int8_t, int64_t>;
 #endif
 #if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || \
-  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN)
 #include "cuvs_ivf_pq_wrapper.h"
 #endif
 #ifdef CUVS_ANN_BENCH_USE_CUVS_IVF_PQ
@@ -35,7 +35,8 @@ extern template class cuvs::bench::cuvs_ivf_pq<float, int64_t>;
 extern template class cuvs::bench::cuvs_ivf_pq<uint8_t, int64_t>;
 extern template class cuvs::bench::cuvs_ivf_pq<int8_t, int64_t>;
 #endif
-#if defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB)
+#if defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB) || \
+  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN)
 #include "cuvs_cagra_wrapper.h"
 #endif
 #ifdef CUVS_ANN_BENCH_USE_CUVS_CAGRA
@@ -96,8 +97,9 @@ void parse_search_param(const nlohmann::json& conf,
 }
 #endif
 
-#if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || \
-  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB) || defined(CUVS_ANN_BENCH_USE_CUVS_MG)
+#if defined(CUVS_ANN_BENCH_USE_CUVS_IVF_PQ) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) ||   \
+  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB) || defined(CUVS_ANN_BENCH_USE_CUVS_MG) || \
+  defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN)
 template <typename T, typename IdxT>
 void parse_build_param(const nlohmann::json& conf,
                        typename cuvs::bench::cuvs_ivf_pq<T, IdxT>::build_param& param)
@@ -188,7 +190,7 @@ void parse_search_param(const nlohmann::json& conf,
 #endif
 
 #if defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_HNSWLIB) || \
-  defined(CUVS_ANN_BENCH_USE_CUVS_MG)
+  defined(CUVS_ANN_BENCH_USE_CUVS_MG) || defined(CUVS_ANN_BENCH_USE_CUVS_CAGRA_DISKANN)
 template <typename T, typename IdxT>
 void parse_build_param(const nlohmann::json& conf, cuvs::neighbors::nn_descent::index_params& param)
 {
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_diskann.cu b/cpp/bench/ann/src/cuvs/cuvs_cagra_diskann.cu
new file mode 100644
index 0000000000..8719d83867
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_diskann.cu
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "../common/ann_types.hpp"
+#include "cuvs_ann_bench_param_parser.h"
+#include "cuvs_cagra_diskann_wrapper.h"
+
+#include <rmm/cuda_device.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+#include <rmm/resource_ref.hpp>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+void parse_search_param(const nlohmann::json& conf,
+                        typename cuvs::bench::cuvs_cagra_diskann<T, IdxT>::search_param& param)
+{
+  if (conf.contains("L_search")) { param.L_search = conf.at("L_search"); }
+}
+
+template <typename T>
+auto create_algo(const std::string& algo_name,
+                 const std::string& distance,
+                 int dim,
+                 const nlohmann::json& conf) -> std::unique_ptr<cuvs::bench::algo<T>>
+{
+  [[maybe_unused]] cuvs::bench::Metric metric = parse_metric(distance);
+  std::unique_ptr<cuvs::bench::algo<T>> a;
+
+  if constexpr (std::is_same_v<T, float> || std::is_same_v<T, std::uint8_t> ||
+                std::is_same_v<T, std::int8_t>) {
+    if (algo_name == "cuvs_cagra_diskann") {
+      typename cuvs::bench::cuvs_cagra_diskann<T, uint32_t>::build_param param;
+      ::parse_build_param<T, uint32_t>(conf, param);
+      a = std::make_unique<cuvs::bench::cuvs_cagra_diskann<T, uint32_t>>(metric, dim, param);
+    }
+  }
+
+  if (!a) { throw std::runtime_error("invalid algo: '" + algo_name + "'"); }
+
+  return a;
+}
+
+template <typename T>
+auto create_search_param(const std::string& algo_name, const nlohmann::json& conf)
+  -> std::unique_ptr<typename cuvs::bench::algo<T>::search_param>
+{
+  if (algo_name == "cuvs_cagra_diskann") {
+    auto param =
+      std::make_unique<typename cuvs::bench::cuvs_cagra_diskann<T, uint32_t>::search_param>();
+    parse_search_param<T, uint32_t>(conf, *param);
+    return param;
+  }
+
+  throw std::runtime_error("invalid algo: '" + algo_name + "'");
+}
+
+}  // namespace cuvs::bench
+
+REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
+
+#ifdef ANN_BENCH_BUILD_MAIN
+#include "../common/benchmark.hpp"
+/*
+[NOTE] Dear developer,
+
+Please don't modify the content of the `main` function; this will make the behavior of the benchmark
+executable differ depending on the cmake flags and will complicate the debugging. In particular,
+don't try to setup an RMM memory resource here; it will anyway be modified by the memory resource
+set on per-algorithm basis. For example, see `cuvs/cuvs_ann_bench_utils.h`.
+*/
+int main(int argc, char** argv) { return cuvs::bench::run_main(argc, argv); }
+#endif
diff --git a/cpp/bench/ann/src/cuvs/cuvs_cagra_diskann_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_cagra_diskann_wrapper.h
new file mode 100644
index 0000000000..9cf5b50930
--- /dev/null
+++ b/cpp/bench/ann/src/cuvs/cuvs_cagra_diskann_wrapper.h
@@ -0,0 +1,227 @@
+/*
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "cuvs_cagra_wrapper.h"
+#include <cuvs/neighbors/hnsw.hpp>
+#include <raft/core/logger.hpp>
+
+#include <chrono>
+#include <memory>
+
+#include "../common/ann_types.hpp"
+#include "../diskann/diskann_wrapper.h"
+#include "cuvs_ann_bench_utils.h"
+#include <cuvs/neighbors/vamana.hpp>
+#include <utils.h>
+
+#include <memory>
+#include <raft/core/device_mdspan.hpp>
+#include <raft/core/mdspan.hpp>
+
+namespace cuvs::bench {
+
+template <typename T, typename IdxT>
+class cuvs_cagra_diskann : public algo<T>, public algo_gpu {
+ public:
+  using build_param       = typename cuvs_cagra<T, IdxT>::build_param;
+  using search_param_base = typename algo<T>::search_param;
+  using search_param      = typename diskann_memory<T>::search_param;
+
+  cuvs_cagra_diskann(Metric metric, int dim, const build_param& param);
+
+  void build(const T* dataset, size_t nrow) final;
+
+  void set_search_param(const search_param_base& param, const void* filter_bitset) override;
+
+  void search(const T* queries,
+              int batch_size,
+              int k,
+              algo_base::index_type* neighbors,
+              float* distances) const override;
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    return cagra_build_.get_sync_stream();
+  }
+
+  // to enable dataset access from GPU memory
+  [[nodiscard]] auto get_preference() const -> algo_property override
+  {
+    algo_property property;
+    property.dataset_memory_type = MemoryType::kHostMmap;
+    property.query_memory_type   = MemoryType::kHost;
+    return property;
+  }
+
+  void save(const std::string& file) const override;
+  void load(const std::string&) override;
+  std::unique_ptr<algo<T>> copy() override
+  {
+    return std::make_unique<cuvs_cagra_diskann<T, IdxT>>(*this);
+  }
+
+ private:
+  raft::resources handle_{};
+  build_param build_param_;
+  search_param search_param_;
+  cuvs_cagra<T, IdxT> cagra_build_;
+  std::shared_ptr<diskann_memory<T>> diskann_memory_search_;
+};
+
+template <typename T, typename IdxT>
+cuvs_cagra_diskann<T, IdxT>::cuvs_cagra_diskann(Metric metric, int dim, const build_param& param)
+  : algo<T>(metric, dim), build_param_{param}, cagra_build_{metric, dim, param, 1}
+{
+  diskann_memory_search_ = std::make_shared<cuvs::bench::diskann_memory<T>>(
+    metric,
+    dim,
+    typename diskann_memory<T>::build_param{static_cast<uint32_t>(32), static_cast<uint32_t>(32)});
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_diskann<T, IdxT>::build(const T* dataset, size_t nrow)
+{
+  cagra_build_.build(dataset, nrow);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_diskann<T, IdxT>::set_search_param(const search_param_base& param,
+                                                   const void* filter_bitset)
+{
+  if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
+  diskann_memory_search_->set_search_param(param, nullptr);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_diskann<T, IdxT>::save(const std::string& file) const
+{
+  // Write graph to first index file (format from MSFT DiskANN OSS)
+  std::ofstream index_of(file, std::ios::out | std::ios::binary);
+  if (!index_of) { RAFT_FAIL("Cannot open file %s", file.c_str()); }
+
+  size_t file_offset = 0;
+  index_of.seekp(file_offset, index_of.beg);
+  uint32_t max_degree = 0;
+  size_t index_size   = 24;
+  uint32_t start = static_cast<uint32_t>(rand() % (cagra_build_.get_index()->graph().extent(0)));
+  size_t num_frozen_points     = 0;
+  uint32_t max_observed_degree = 0;
+
+  index_of.write((char*)&index_size, sizeof(uint64_t));
+  index_of.write((char*)&max_observed_degree, sizeof(uint32_t));
+  index_of.write((char*)&start, sizeof(uint32_t));
+  index_of.write((char*)&num_frozen_points, sizeof(size_t));
+
+  auto d_graph = cagra_build_.get_index()->graph();
+  auto h_graph = raft::make_host_matrix<IdxT, int64_t>(d_graph.extent(0), d_graph.extent(1));
+  raft::copy(h_graph.data_handle(),
+             d_graph.data_handle(),
+             d_graph.size(),
+             raft::resource::get_cuda_stream(handle_));
+  raft::resource::sync_stream(handle_);
+
+  size_t total_edges = 0;
+  size_t num_sparse  = 0;
+  size_t num_single  = 0;
+
+  for (uint32_t i = 0; i < h_graph.extent(0); i++) {
+    uint32_t node_edges = 0;
+    for (; node_edges < h_graph.extent(1); node_edges++) {
+      if (h_graph(i, node_edges) == raft::upper_bound<IdxT>()) { break; }
+    }
+
+    if (node_edges < 3) num_sparse++;
+    if (node_edges < 2) num_single++;
+    total_edges += node_edges;
+
+    index_of.write((char*)&node_edges, sizeof(uint32_t));
+    if constexpr (!std::is_same_v<IdxT, uint32_t>) {
+      RAFT_FAIL("serialization is only implemented for uint32_t graph");
+    }
+    index_of.write((char*)&h_graph(i, 0), node_edges * sizeof(uint32_t));
+
+    max_degree = node_edges > max_degree ? (uint32_t)node_edges : max_degree;
+    index_size += (size_t)(sizeof(uint32_t) * (node_edges + 1));
+  }
+  index_of.seekp(file_offset, index_of.beg);
+  index_of.write((char*)&index_size, sizeof(uint64_t));
+  index_of.write((char*)&max_degree, sizeof(uint32_t));
+
+  RAFT_LOG_DEBUG(
+    "Wrote file out, index size:%lu, max_degree:%u, num_sparse:%ld, num_single:%ld, total "
+    "edges:%ld, avg degree:%f",
+    index_size,
+    max_degree,
+    num_sparse,
+    num_single,
+    total_edges,
+    (float)total_edges / (float)h_graph.extent(0));
+
+  index_of.close();
+  if (!index_of) { RAFT_FAIL("Error writing output %s", file.c_str()); }
+
+  // try allocating a buffer for the dataset on host
+  try {
+    const cuvs::neighbors::strided_dataset<T, int64_t>* strided_dataset =
+      dynamic_cast<cuvs::neighbors::strided_dataset<T, int64_t>*>(
+        const_cast<cuvs::neighbors::dataset<int64_t>*>(&cagra_build_.get_index()->data()));
+    if (strided_dataset == nullptr) {
+      RAFT_LOG_DEBUG("dynamic_cast to strided_dataset failed");
+    } else {
+      auto h_dataset =
+        raft::make_host_matrix<T, int64_t>(strided_dataset->n_rows(), strided_dataset->dim());
+      raft::copy(h_dataset.data_handle(),
+                 strided_dataset->view().data_handle(),
+                 strided_dataset->n_rows() * strided_dataset->dim(),
+                 raft::resource::get_cuda_stream(handle_));
+      std::string dataset_base_file = file + ".data";
+      std::ofstream dataset_of(dataset_base_file, std::ios::out | std::ios::binary);
+      if (!dataset_of) { RAFT_FAIL("Cannot open file %s", dataset_base_file.c_str()); }
+      size_t dataset_file_offset = 0;
+      int size                   = static_cast<int>(cagra_build_.get_index()->size());
+      int dim                    = static_cast<int>(cagra_build_.get_index()->dim());
+      dataset_of.seekp(dataset_file_offset, dataset_of.beg);
+      dataset_of.write((char*)&size, sizeof(int));
+      dataset_of.write((char*)&dim, sizeof(int));
+      for (int i = 0; i < size; i++) {
+        dataset_of.write((char*)(h_dataset.data_handle() + i * h_dataset.extent(1)),
+                         dim * sizeof(T));
+      }
+      dataset_of.close();
+      if (!dataset_of) { RAFT_FAIL("Error writing output %s", dataset_base_file.c_str()); }
+    }
+  } catch (std::bad_alloc& e) {
+    RAFT_LOG_INFO("Failed to serialize dataset");
+  } catch (raft::logic_error& e) {
+    RAFT_LOG_INFO("Failed to serialize dataset");
+  }
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_diskann<T, IdxT>::load(const std::string& file)
+{
+  diskann_memory_search_->load(file);
+}
+
+template <typename T, typename IdxT>
+void cuvs_cagra_diskann<T, IdxT>::search(
+  const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
+{
+  diskann_memory_search_->search(queries, batch_size, k, neighbors, distances);
+}
+
+}  // namespace cuvs::bench
diff --git a/cpp/bench/ann/src/cuvs/cuvs_vamana.cu b/cpp/bench/ann/src/cuvs/cuvs_vamana.cu
index 2bc0ca9596..524a61e0b4 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_vamana.cu
+++ b/cpp/bench/ann/src/cuvs/cuvs_vamana.cu
@@ -37,7 +37,6 @@ void parse_search_param(const nlohmann::json& conf,
                         typename cuvs::bench::cuvs_vamana<T, IdxT>::search_param& param)
 {
   if (conf.contains("L_search")) { param.L_search = conf.at("L_search"); }
-  if (conf.contains("num_threads")) { param.num_threads = conf.at("num_threads"); }
 }
 
 template <typename T>
diff --git a/cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h b/cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h
index 410f5c2a02..882483853a 100644
--- a/cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h
+++ b/cpp/bench/ann/src/cuvs/cuvs_vamana_wrapper.h
@@ -65,10 +65,10 @@ class cuvs_vamana : public algo<T>, public algo_gpu {
   std::unique_ptr<algo<T>> copy() override { return std::make_unique<cuvs_vamana<T, IdxT>>(*this); }
 
  private:
-  std::shared_ptr<cuvs::neighbors::vamana::index<T, IdxT>> vamana_index_;
-  std::shared_ptr<diskann_memory<T>> diskann_memory_search_;
   configured_raft_resources handle_{};
   build_param vamana_index_params_;
+  std::shared_ptr<cuvs::neighbors::vamana::index<T, IdxT>> vamana_index_;
+  std::shared_ptr<diskann_memory<T>> diskann_memory_search_;
 };
 
 template <typename T, typename IdxT>
@@ -85,7 +85,7 @@ void cuvs_vamana<T, IdxT>::build(const T* dataset, size_t nrow)
 {
   auto dataset_view_host = raft::make_mdspan<const T, int64_t, raft::row_major, true, false>(
     dataset, raft::make_extents<int64_t>(nrow, this->dim_));
-  auto dataset_view_device = raft::make_mdspan<const T, int64_t, raft::row_major, true, false>(
+  auto dataset_view_device = raft::make_mdspan<const T, int64_t, raft::row_major, false, true>(
     dataset, raft::make_extents<int64_t>(nrow, this->dim_));
   bool dataset_is_on_host = raft::get_device_for_address(dataset) == -1;
 
diff --git a/cpp/bench/ann/src/diskann/diskann_benchmark.cpp b/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
index fe47215341..3c1e81c3ad 100644
--- a/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
+++ b/cpp/bench/ann/src/diskann/diskann_benchmark.cpp
@@ -65,16 +65,14 @@ template <typename T>
 void parse_search_param(const nlohmann::json& conf,
                         typename cuvs::bench::diskann_memory<T>::search_param& param)
 {
-  param.L_search    = conf.at("L_search");
-  param.num_threads = conf.at("num_threads");
+  param.L_search = conf.at("L_search");
 }
 
 template <typename T>
 void parse_search_param(const nlohmann::json& conf,
                         typename cuvs::bench::diskann_ssd<T>::search_param& param)
 {
-  param.L_search    = conf.at("L_search");
-  param.num_threads = conf.at("num_threads");
+  param.L_search = conf.at("L_search");
   if (conf.contains("num_nodes_to_cache")) {
     param.num_nodes_to_cache = conf.at("num_nodes_to_cache");
   }
@@ -132,6 +130,8 @@ std::unique_ptr<typename cuvs::bench::algo<T>::search_param> create_search_param
 };  // namespace cuvs::bench
 
 REGISTER_ALGO_INSTANCE(float);
+REGISTER_ALGO_INSTANCE(std::int8_t);
+REGISTER_ALGO_INSTANCE(std::uint8_t);
 
 #ifdef ANN_BENCH_BUILD_MAIN
 #include "../common/benchmark.hpp"
diff --git a/cpp/bench/ann/src/diskann/diskann_wrapper.h b/cpp/bench/ann/src/diskann/diskann_wrapper.h
index c8219c40cb..6e7e0dc1c7 100644
--- a/cpp/bench/ann/src/diskann/diskann_wrapper.h
+++ b/cpp/bench/ann/src/diskann/diskann_wrapper.h
@@ -16,6 +16,7 @@
 #pragma once
 
 #include "../common/ann_types.hpp"
+#include "../common/util.hpp"
 
 #include <limits>
 
@@ -58,8 +59,6 @@ class diskann_memory : public algo<T> {
   using search_param_base = typename algo<T>::search_param;
   struct search_param : public search_param_base {
     uint32_t L_search;
-    uint32_t num_threads = omp_get_max_threads() / 2;
-    // Mode metric_objective;
   };
 
   diskann_memory(Metric metric, int dim, const build_param& param);
@@ -94,7 +93,6 @@ class diskann_memory : public algo<T> {
   int num_threads_;
   uint32_t L_search_;
   Mode bench_mode_;
-  int num_search_threads_;
   std::string index_path_prefix_;
   std::shared_ptr<diskann::Index<T>> mem_index_{nullptr};
   void initialize_index_(size_t max_points);
@@ -143,24 +141,14 @@ template <typename T>
 void diskann_memory<T>::set_search_param(const search_param_base& param, const void* filter_bitset)
 {
   if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
-  auto sp             = dynamic_cast<const search_param&>(param);
-  L_search_           = sp.L_search;
-  num_search_threads_ = sp.num_threads;
-
-  // only latency mode supported. Use the num_threads search param to run search with multiple
-  // threads
-  bench_mode_ = Mode::kLatency;
-
-  // Create a pool if multiple query threads have been set and the pool hasn't been created already
-  initialize_index_(0);
-  this->mem_index_->load(index_path_prefix_.c_str(), num_search_threads_, L_search_);
+  auto sp   = dynamic_cast<const search_param&>(param);
+  L_search_ = sp.L_search;
 }
 
 template <typename T>
 void diskann_memory<T>::search(
   const T* queries, int batch_size, int k, algo_base::index_type* indices, float* distances) const
 {
-#pragma omp parallel for if (batch_size > 1) schedule(dynamic, 1) num_threads(num_search_threads_)
   for (int i = 0; i < batch_size; i++) {
     mem_index_->search(queries + i * this->dim_,
                        static_cast<size_t>(k),
@@ -179,8 +167,14 @@ void diskann_memory<T>::save(const std::string& index_file) const
 template <typename T>
 void diskann_memory<T>::load(const std::string& index_file)
 {
-  // only save the index path prefix here
   index_path_prefix_ = index_file;
+
+  bench_mode_ = (cuvs::bench::benchmark_n_threads > 1) ? Mode::kThroughput : Mode::kLatency;
+
+  initialize_index_(0);
+
+  int load_threads = (bench_mode_ == Mode::kThroughput) ? cuvs::bench::benchmark_n_threads : 1;
+  this->mem_index_->load(index_path_prefix_.c_str(), load_threads, 2000);
 }
 
 template <typename T>
@@ -200,10 +194,8 @@ class diskann_ssd : public algo<T> {
 
   struct search_param : public search_param_base {
     uint32_t L_search;
-    uint32_t num_threads        = omp_get_max_threads() / 2;
     uint32_t num_nodes_to_cache = 10000;
     int beam_width              = 2;
-    // Mode metric_objective;
   };
 
   diskann_ssd(Metric metric, int dim, const build_param& param);
@@ -226,7 +218,7 @@ class diskann_ssd : public algo<T> {
   [[nodiscard]] auto get_preference() const -> algo_property override
   {
     algo_property property;
-    property.dataset_memory_type = MemoryType::kHost;
+    property.dataset_memory_type = MemoryType::kHostMmap;
     property.query_memory_type   = MemoryType::kHost;
     return property;
   }
@@ -239,11 +231,6 @@ class diskann_ssd : public algo<T> {
   // in-memory index params
   uint32_t build_pq_bytes_ = 0;
   uint32_t max_points_;
-  // for safe scratch space allocs, set the default to half the number of procs for loading the
-  // index. User must ensure that the number of search threads is less than or equal to this value
-  int num_search_threads_ = omp_get_max_threads() / 2;
-  // L_search is hardcoded to the maximum visited list size in the search params. This default is
-  // for loading the index
   uint32_t L_search_ = 384;
   Mode bench_mode_;
   std::string base_file_;
@@ -291,19 +278,14 @@ void diskann_ssd<T>::set_search_param(const search_param_base& param, const void
   if (filter_bitset != nullptr) { throw std::runtime_error("Filtering is not supported yet."); }
   auto sp             = dynamic_cast<const search_param&>(param);
   L_search_           = sp.L_search;
-  num_search_threads_ = sp.num_threads;
   num_nodes_to_cache_ = sp.num_nodes_to_cache;
   beam_width_         = sp.beam_width;
-
-  // only latency mode supported with thread pool
-  bench_mode_ = Mode::kLatency;
 }
 
 template <typename T>
 void diskann_ssd<T>::search(
   const T* queries, int batch_size, int k, algo_base::index_type* neighbors, float* distances) const
 {
-#pragma omp parallel for if (batch_size > 1) schedule(dynamic, 1) num_threads(num_search_threads_)
   for (int64_t i = 0; i < (int64_t)batch_size; i++) {
     p_flash_index_->cached_beam_search(queries + (i * this->dim_),
                                        static_cast<size_t>(k),
@@ -328,10 +310,16 @@ void diskann_ssd<T>::save(const std::string& index_file) const
 template <typename T>
 void diskann_ssd<T>::load(const std::string& index_file)
 {
+  index_path_prefix_ = index_file;
+
+  bench_mode_ = (cuvs::bench::benchmark_n_threads > 1) ? Mode::kThroughput : Mode::kLatency;
+
+  int load_threads = (bench_mode_ == Mode::kThroughput) ? cuvs::bench::benchmark_n_threads : 1;
+
   reader.reset(new LinuxAlignedFileReader());
   p_flash_index_ =
     std::make_shared<diskann::PQFlashIndex<T>>(reader, parse_metric_to_diskann(this->metric_));
-  int result = p_flash_index_->load(num_search_threads_, index_file.c_str());
+  int result = p_flash_index_->load(load_threads, index_path_prefix_.c_str());
   std::vector<uint32_t> node_list;
   p_flash_index_->cache_bfs_levels(num_nodes_to_cache_, node_list);
   p_flash_index_->load_cache_list(node_list);
diff --git a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
index 54f685fe79..b4bd384e13 100644
--- a/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
+++ b/cpp/bench/ann/src/faiss/faiss_gpu_wrapper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -550,7 +550,7 @@ class faiss_gpu_cagra : public faiss_gpu<T> {
     config.graph_degree              = param.graph_degree;
     config.intermediate_graph_degree = param.intermediate_graph_degree;
     config.device                    = this->device_;
-    config.store_dataset             = false;
+    config.store_dataset             = true;
     if (param.cagra_build_algo == "IVF_PQ") {
       config.build_algo           = faiss::gpu::graph_build_algo::IVF_PQ;
       this->ivf_pq_build_params_  = param.ivf_pq_build_params;
@@ -655,9 +655,17 @@ class faiss_gpu_cagra_hnsw : public faiss_gpu<T> {
     omp_single_thread_scope omp_single_thread;
     this->search_index_.reset(static_cast<faiss::IndexHNSWCagra*>(faiss::read_index(file.c_str())));
   }
+
+  [[nodiscard]] auto get_sync_stream() const noexcept -> cudaStream_t override
+  {
+    if (this->gpu_resource_ == nullptr) { return 0; }
+    return this->gpu_resource_->getDefaultStream(this->device_);
+  }
   std::unique_ptr<algo<T>> copy() override
   {
-    return std::make_unique<faiss_gpu_cagra_hnsw<T>>(*this);
+    auto new_instance          = std::make_unique<faiss_gpu_cagra_hnsw<T>>(*this);
+    new_instance->build_index_ = nullptr;
+    return new_instance;
   };
 
  private:
diff --git a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
index 191c640c00..fa2195fc61 100644
--- a/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algorithms.yaml
@@ -67,3 +67,6 @@ diskann_ssd:
 cuvs_vamana:
   executable: CUVS_VAMANA_ANN_BENCH
   requires_gpu: true
+cuvs_cagra_diskann:
+  executable: CUVS_CAGRA_DISKANN_ANN_BENCH
+  requires_gpu: true
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_diskann.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_diskann.yaml
new file mode 100644
index 0000000000..e086e8230b
--- /dev/null
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_cagra_diskann.yaml
@@ -0,0 +1,11 @@
+name: cuvs_cagra_diskann
+constraints:
+  build: cuvs_bench.config.algos.constraints.cuvs_cagra_build
+groups:
+  base:
+    build:
+      graph_degree: [32, 64, 96]
+      intermediate_graph_degree: [32, 64, 128]
+      graph_build_algo: ["NN_DESCENT"]
+    search:
+      L_search: [10, 20, 30, 40, 50, 100, 200, 300]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml
index 5252f818c5..b840962d92 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/cuvs_vamana.yaml
@@ -2,8 +2,8 @@ name: cuvs_vamana
 groups:
   base:
     build:
-      graph_degree: [32, 64]
-      visited_size: [128, 256]
+      graph_degree: [32, 64, 96]
+      visited_size: [128, 256, 512]
       alpha: [1.2]
     search:
       L_search: [10, 20, 30, 40, 50, 100, 200, 300]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml b/python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml
index faf1224656..b62f057084 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/diskann_memory.yaml
@@ -4,10 +4,9 @@ constraints:
 groups:
   base:
     build:
-      R: [64, 96]
+      R: [32, 64, 96]
       L_build: [128, 256, 384]
       alpha: [1.2]
       num_threads: [32]
     search:
-      num_threads: [32]
       L_search: [10, 20, 30, 40, 50, 100, 200, 300]
diff --git a/python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml b/python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml
index 40afc6fce8..a8eb52abea 100644
--- a/python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml
+++ b/python/cuvs_bench/cuvs_bench/config/algos/diskann_ssd.yaml
@@ -10,4 +10,3 @@ groups:
       num_threads: [32]
     search:
       L_search: [10, 20, 30, 40, 50, 100, 200, 300]
-      num_threads: [32]

From 587f975305ebce2c5e904008b423f169a146c48c Mon Sep 17 00:00:00 2001
From: Anupam <54245698+aamijar@users.noreply.github.com>
Date: Tue, 16 Sep 2025 20:46:02 -0700
Subject: [PATCH 581/623] Docs for spectral embedding (#1299)

Resolves https://github.com/rapidsai/cuvs/issues/1298

Authors:
  - Anupam (https://github.com/aamijar)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1299
---
 .../cuvs/preprocessing/spectral_embedding.hpp | 127 +++++++++++++++++-
 docs/source/cpp_api/preprocessing.rst         |   1 +
 .../preprocessing_spectral_embedding.rst      | 108 +++++++++++++++
 3 files changed, 233 insertions(+), 3 deletions(-)
 create mode 100644 docs/source/cpp_api/preprocessing_spectral_embedding.rst

diff --git a/cpp/include/cuvs/preprocessing/spectral_embedding.hpp b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
index 3d935bfd9a..3162f74f02 100644
--- a/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
+++ b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
@@ -23,8 +23,18 @@
 
 namespace cuvs::preprocessing::spectral_embedding {
 
+/**
+ * @defgroup spectral_embedding Spectral Embedding
+ * @{
+ */
+
 /**
  * @brief Parameters for spectral embedding algorithm
+ *
+ * Spectral embedding is a dimensionality reduction technique that uses the
+ * eigenvectors of the graph Laplacian to embed data points into a lower-dimensional
+ * space. This technique is particularly useful for non-linear dimensionality
+ * reduction and clustering tasks.
  */
 struct params {
   /** @brief The number of components to reduce the data to. */
@@ -33,24 +43,135 @@ struct params {
   /** @brief The number of neighbors to use for the nearest neighbors graph. */
   int n_neighbors;
 
-  /** @brief Whether to normalize the Laplacian matrix. */
+  /**
+   * @brief Whether to normalize the Laplacian matrix.
+   *
+   * If true, uses the normalized graph Laplacian (D^(-1/2) L D^(-1/2)).
+   * If false, uses the unnormalized graph Laplacian (L = D - W).
+   * Normalized Laplacian often leads to better results for clustering tasks.
+   */
   bool norm_laplacian;
 
-  /** @brief Whether to drop the first eigenvector. */
+  /**
+   * @brief Whether to drop the first eigenvector.
+   *
+   * The first eigenvector of the normalized Laplacian is constant and
+   * uninformative. Setting this to true drops it from the embedding.
+   * This is typically set to true when norm_laplacian is true.
+   */
   bool drop_first;
 
-  /** @brief Random seed for reproducibility */
+  /**
+   * @brief Random seed for reproducibility.
+   *
+   * Controls the random number generation for k-NN graph construction
+   * and eigenvalue solver initialization. Use the same seed value to
+   * ensure reproducible results across runs.
+   */
   uint64_t seed;
 };
 
+/**
+ * @brief Perform spectral embedding on input dataset
+ *
+ * This function computes the spectral embedding of the input dataset by:
+ * 1. Constructing a k-nearest neighbors graph from the input data
+ * 2. Computing the graph Laplacian (normalized or unnormalized)
+ * 3. Finding the eigenvectors corresponding to the smallest eigenvalues
+ * 4. Using these eigenvectors as the embedding coordinates
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/preprocessing/spectral_embedding.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // Set up parameters
+ * cuvs::preprocessing::spectral_embedding::params params;
+ * params.n_components = 2;
+ * params.n_neighbors = 15;
+ * params.norm_laplacian = true;
+ * params.drop_first = true;
+ * params.seed = 42;
+ *
+ * // Create input dataset (n_samples x n_features)
+ * auto dataset = raft::make_device_matrix<float, int>(handle, n_samples, n_features);
+ * // ... fill dataset ...
+ *
+ * // Create output embedding matrix (n_samples x n_components)
+ * auto embedding = raft::make_device_matrix<float, int, raft::col_major>(
+ *     handle, n_samples, params.n_components);
+ *
+ * // Perform spectral embedding
+ * cuvs::preprocessing::spectral_embedding::transform(
+ *     handle, params, dataset.view(), embedding.view());
+ * @endcode
+ *
+ * @param[in] handle RAFT resource handle for managing CUDA resources
+ * @param[in] config Parameters controlling the spectral embedding algorithm
+ * @param[in] dataset Input dataset in row-major format [n_samples x n_features]
+ * @param[out] embedding Output embedding in column-major format [n_samples x n_components]
+ *
+ */
 void transform(raft::resources const& handle,
                params config,
                raft::device_matrix_view<float, int, raft::row_major> dataset,
                raft::device_matrix_view<float, int, raft::col_major> embedding);
 
+/**
+ * @brief Perform spectral embedding using a precomputed connectivity graph
+ *
+ * This function computes the spectral embedding from a precomputed sparse
+ * connectivity graph (e.g., from a k-NN search or custom similarity matrix).
+ * This is useful when you want to use a custom graph construction method
+ * or when you have a precomputed similarity/affinity matrix.
+ *
+ * The function:
+ * 1. Converts the COO matrix to the graph Laplacian
+ * 2. Computes eigenvectors of the Laplacian
+ * 3. Returns the eigenvectors as the embedding
+ *
+ * @code{.cpp}
+ * #include <raft/core/resources.hpp>
+ * #include <cuvs/preprocessing/spectral_embedding.hpp>
+ *
+ * raft::resources handle;
+ *
+ * // Set up parameters
+ * cuvs::preprocessing::spectral_embedding::params params;
+ * params.n_components = 2;
+ * params.norm_laplacian = true;
+ * params.drop_first = true;
+ * params.seed = 42;
+ *
+ * // Assume we have a precomputed connectivity graph as COO matrix
+ * // connectivity_graph represents weighted edges between samples
+ * raft::device_coo_matrix<float, int, int, int> connectivity_graph(...);
+ *
+ * // Create output embedding matrix (n_samples x n_components)
+ * auto embedding = raft::make_device_matrix<float, int, raft::col_major>(
+ *     handle, n_samples, params.n_components);
+ *
+ * // Perform spectral embedding
+ * cuvs::preprocessing::spectral_embedding::transform(
+ *     handle, params, connectivity_graph.view(), embedding.view());
+ * @endcode
+ *
+ * @param[in] handle RAFT resource handle for managing CUDA resources
+ * @param[in] config Parameters controlling the spectral embedding algorithm
+ *                   (n_neighbors parameter is ignored when using precomputed graph)
+ * @param[in] connectivity_graph Precomputed sparse connectivity/affinity graph in COO format
+ *                               representing weighted connections between samples
+ * @param[out] embedding Output embedding in column-major format [n_samples x n_components]
+ *
+ */
 void transform(raft::resources const& handle,
                params config,
                raft::device_coo_matrix_view<float, int, int, int> connectivity_graph,
                raft::device_matrix_view<float, int, raft::col_major> embedding);
 
+/**
+ * @}
+ */
+
 }  // namespace cuvs::preprocessing::spectral_embedding
diff --git a/docs/source/cpp_api/preprocessing.rst b/docs/source/cpp_api/preprocessing.rst
index 1c2b0f0513..5fd8cfe778 100644
--- a/docs/source/cpp_api/preprocessing.rst
+++ b/docs/source/cpp_api/preprocessing.rst
@@ -10,3 +10,4 @@ Preprocessing
    :caption: Contents:
 
    preprocessing_quantize.rst
+   preprocessing_spectral_embedding.rst
diff --git a/docs/source/cpp_api/preprocessing_spectral_embedding.rst b/docs/source/cpp_api/preprocessing_spectral_embedding.rst
new file mode 100644
index 0000000000..6ab0785836
--- /dev/null
+++ b/docs/source/cpp_api/preprocessing_spectral_embedding.rst
@@ -0,0 +1,108 @@
+Spectral Embedding
+==================
+
+Spectral embedding is a powerful dimensionality reduction technique that uses the eigenvectors
+of the graph Laplacian to embed high-dimensional data into a lower-dimensional space. This
+method is particularly effective for discovering non-linear manifold structures in data and
+is widely used in clustering, visualization, and feature extraction tasks.
+
+.. role:: py(code)
+   :language: c++
+   :class: highlight
+
+Overview
+--------
+
+The spectral embedding algorithm works by:
+
+1. **Graph Construction**: Building a k-nearest neighbors graph from the input data
+2. **Laplacian Computation**: Computing the graph Laplacian matrix (normalized or unnormalized)
+3. **Eigendecomposition**: Finding the eigenvectors corresponding to the smallest eigenvalues
+4. **Embedding**: Using these eigenvectors as coordinates in the lower-dimensional space
+
+Parameters
+----------
+
+``#include <cuvs/preprocessing/spectral_embedding.hpp>``
+
+namespace *cuvs::preprocessing::spectral_embedding*
+
+.. doxygenstruct:: cuvs::preprocessing::spectral_embedding::params
+   :project: cuvs
+   :members:
+
+Functions
+---------
+
+``#include <cuvs/preprocessing/spectral_embedding.hpp>``
+
+namespace *cuvs::preprocessing::spectral_embedding*
+
+.. doxygengroup:: spectral_embedding
+   :project: cuvs
+   :content-only:
+
+Example Usage
+-------------
+
+Basic Usage with Dataset
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: cpp
+
+   #include <raft/core/resources.hpp>
+   #include <cuvs/preprocessing/spectral_embedding.hpp>
+
+   // Initialize RAFT resources
+   raft::resources handle;
+
+   // Configure spectral embedding parameters
+   cuvs::preprocessing::spectral_embedding::params params;
+   params.n_components = 2;        // Reduce to 2D for visualization
+   params.n_neighbors = 15;        // Local neighborhood size
+   params.norm_laplacian = true;   // Use normalized Laplacian
+   params.drop_first = true;       // Drop constant eigenvector
+   params.seed = 42;               // For reproducibility
+
+   // Create input dataset (n_samples x n_features)
+   int n_samples = 1000;
+   int n_features = 50;
+   auto dataset = raft::make_device_matrix<float, int>(handle, n_samples, n_features);
+   // ... populate dataset with your data ...
+
+   // Allocate output embedding matrix (n_samples x n_components)
+   auto embedding = raft::make_device_matrix<float, int, raft::col_major>(
+       handle, n_samples, params.n_components);
+
+   // Perform spectral embedding
+   cuvs::preprocessing::spectral_embedding::transform(
+       handle, params, dataset.view(), embedding.view());
+
+Using Precomputed Graph
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: cpp
+
+   #include <raft/core/resources.hpp>
+   #include <cuvs/preprocessing/spectral_embedding.hpp>
+
+   raft::resources handle;
+
+   // Configure parameters (n_neighbors is ignored with precomputed graph)
+   cuvs::preprocessing::spectral_embedding::params params;
+   params.n_components = 3;
+   params.norm_laplacian = true;
+   params.drop_first = true;
+   params.seed = 42;
+
+   // Assume we have a precomputed connectivity graph
+   // This could be from custom similarity computation or k-NN search
+   raft::device_coo_matrix<float, int, int, int> connectivity_graph(...);
+
+   // Allocate output embedding
+   auto embedding = raft::make_device_matrix<float, int, raft::col_major>(
+       handle, n_samples, params.n_components);
+
+   // Perform spectral embedding with precomputed graph
+   cuvs::preprocessing::spectral_embedding::transform(
+       handle, params, connectivity_graph.view(), embedding.view());

From 659b6644a29a596dd77aef6254b859254e483c0c Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 16 Sep 2025 22:18:55 -0700
Subject: [PATCH 582/623] Add cagra.extend bindings for python (#1324)

Add the cagra.extend functionality for python.

Note that this PR also removes the `return_dataset` parameter from the C-API in favour of the cuvsCagraIndexGetDataset function.  This makes it optional to get the extended dataset in c/python, and also doesn't perform a copy.

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1324
---
 cpp/include/cuvs/neighbors/cagra.h           |  4 +-
 cpp/src/neighbors/cagra_c.cpp                | 41 +++++------
 cpp/tests/neighbors/ann_cagra_c.cu           | 28 +-------
 docs/source/python_api/neighbors_cagra.rst   |  5 ++
 go/cagra/cagra.go                            |  5 +-
 python/cuvs/cuvs/neighbors/cagra/__init__.py |  4 ++
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd   | 13 ++++
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx   | 72 +++++++++++++++++++-
 python/cuvs/cuvs/tests/test_cagra.py         | 35 +++++-----
 9 files changed, 131 insertions(+), 76 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index e892b5304c..2532bbd3df 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -525,14 +525,12 @@ cuvsError_t cuvsCagraBuild(cuvsResources_t res,
  * @param[in] params cuvsCagraExtendParams_t used to extend CAGRA index
  * @param[in] additional_dataset DLManagedTensor* additional dataset
  * @param[in,out] index cuvsCagraIndex_t CAGRA index
- * @param[out] return_dataset DLManagedTensor* extended dataset
  * @return cuvsError_t
  */
 cuvsError_t cuvsCagraExtend(cuvsResources_t res,
                             cuvsCagraExtendParams_t params,
                             DLManagedTensor* additional_dataset,
-                            cuvsCagraIndex_t index,
-                            DLManagedTensor* return_dataset);
+                            cuvsCagraIndex_t index);
 
 /**
  * @}
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 7c3061bf93..92d20f85df 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -164,32 +164,24 @@ template <typename T>
 void _extend(cuvsResources_t res,
              cuvsCagraExtendParams params,
              cuvsCagraIndex index,
-             DLManagedTensor* additional_dataset_tensor,
-             DLManagedTensor* return_tensor)
+             DLManagedTensor* additional_dataset_tensor)
 {
-  auto dataset          = additional_dataset_tensor->dl_tensor;
-  auto return_dl_tensor = return_tensor->dl_tensor;
-  auto index_ptr        = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index.addr);
-  auto res_ptr          = reinterpret_cast<raft::resources*>(res);
+  auto dataset   = additional_dataset_tensor->dl_tensor;
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<T, uint32_t>*>(index.addr);
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
 
   // TODO: use C struct here (see issue #487)
   auto extend_params           = cuvs::neighbors::cagra::extend_params();
   extend_params.max_chunk_size = params.max_chunk_size;
 
-  if (cuvs::core::is_dlpack_device_compatible(dataset) &&
-      cuvs::core::is_dlpack_device_compatible(return_dl_tensor)) {
-    using mdspan_type        = raft::device_matrix_view<T const, int64_t, raft::row_major>;
-    using mdspan_return_type = raft::device_matrix_view<T, int64_t, raft::row_major>;
-    auto mds                 = cuvs::core::from_dlpack<mdspan_type>(additional_dataset_tensor);
-    auto return_mds          = cuvs::core::from_dlpack<mdspan_return_type>(return_tensor);
-    cuvs::neighbors::cagra::extend(*res_ptr, extend_params, mds, *index_ptr, return_mds);
-  } else if (cuvs::core::is_dlpack_host_compatible(dataset) &&
-             cuvs::core::is_dlpack_host_compatible(return_dl_tensor)) {
-    using mdspan_type        = raft::host_matrix_view<T const, int64_t, raft::row_major>;
-    using mdspan_return_type = raft::device_matrix_view<T, int64_t, raft::row_major>;
-    auto mds                 = cuvs::core::from_dlpack<mdspan_type>(additional_dataset_tensor);
-    auto return_mds          = cuvs::core::from_dlpack<mdspan_return_type>(return_tensor);
-    cuvs::neighbors::cagra::extend(*res_ptr, extend_params, mds, *index_ptr, return_mds);
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(additional_dataset_tensor);
+    cuvs::neighbors::cagra::extend(*res_ptr, extend_params, mds, *index_ptr);
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(additional_dataset_tensor);
+    cuvs::neighbors::cagra::extend(*res_ptr, extend_params, mds, *index_ptr);
   } else {
     RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
               dataset.dtype.code,
@@ -540,19 +532,18 @@ extern "C" cuvsError_t cuvsCagraIndexFromArgs(cuvsResources_t res,
 extern "C" cuvsError_t cuvsCagraExtend(cuvsResources_t res,
                                        cuvsCagraExtendParams_t params,
                                        DLManagedTensor* additional_dataset_tensor,
-                                       cuvsCagraIndex_t index_c_ptr,
-                                       DLManagedTensor* return_dataset_tensor)
+                                       cuvsCagraIndex_t index_c_ptr)
 {
   return cuvs::core::translate_exceptions([=] {
     auto dataset = additional_dataset_tensor->dl_tensor;
     auto index   = *index_c_ptr;
 
     if ((dataset.dtype.code == kDLFloat) && (dataset.dtype.bits == 32)) {
-      _extend<float>(res, *params, index, additional_dataset_tensor, return_dataset_tensor);
+      _extend<float>(res, *params, index, additional_dataset_tensor);
     } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
-      _extend<int8_t>(res, *params, index, additional_dataset_tensor, return_dataset_tensor);
+      _extend<int8_t>(res, *params, index, additional_dataset_tensor);
     } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
-      _extend<uint8_t>(res, *params, index, additional_dataset_tensor, return_dataset_tensor);
+      _extend<uint8_t>(res, *params, index, additional_dataset_tensor);
     } else {
       RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
                 dataset.dtype.code,
diff --git a/cpp/tests/neighbors/ann_cagra_c.cu b/cpp/tests/neighbors/ann_cagra_c.cu
index ae80cc8986..3e73f545d4 100644
--- a/cpp/tests/neighbors/ann_cagra_c.cu
+++ b/cpp/tests/neighbors/ann_cagra_c.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -210,20 +210,6 @@ TEST(CagraC, BuildExtendSearch)
   additional_dataset_tensor.dl_tensor.shape              = additional_dataset_shape;
   additional_dataset_tensor.dl_tensor.strides            = nullptr;
 
-  // create tensor for that points to the extended dataset
-  rmm::device_uvector<float> extend_return_d((additional_data_size + main_data_size) * dimensions,
-                                             stream);
-  DLManagedTensor additional_dataset_return_tensor;
-  additional_dataset_return_tensor.dl_tensor.data               = extend_return_d.data();
-  additional_dataset_return_tensor.dl_tensor.device.device_type = kDLCUDA;
-  additional_dataset_return_tensor.dl_tensor.ndim               = 2;
-  additional_dataset_return_tensor.dl_tensor.dtype.code         = kDLFloat;
-  additional_dataset_return_tensor.dl_tensor.dtype.bits         = 32;
-  additional_dataset_return_tensor.dl_tensor.dtype.lanes        = 1;
-  int64_t additional_return_dataset_shape[2] = {additional_data_size + main_data_size, dimensions};
-  additional_dataset_return_tensor.dl_tensor.shape   = additional_return_dataset_shape;
-  additional_dataset_return_tensor.dl_tensor.strides = nullptr;
-
   // create index
   cuvsCagraIndex_t index;
   cuvsCagraIndexCreate(&index);
@@ -238,8 +224,7 @@ TEST(CagraC, BuildExtendSearch)
   // extend index
   cuvsCagraExtendParams_t extend_params;
   cuvsCagraExtendParamsCreate(&extend_params);
-  cuvsCagraExtend(
-    res, extend_params, &additional_dataset_tensor, index, &additional_dataset_return_tensor);
+  cuvsCagraExtend(res, extend_params, &additional_dataset_tensor, index);
 
   // create queries DLTensor
   rmm::device_uvector<float> queries_d(num_queries * dimensions, stream);
@@ -342,15 +327,6 @@ TEST(CagraC, BuildExtendSearch)
   cuvsCagraSearch(
     res, search_params, index, &queries_tensor, &neighbors_tensor, &distances_tensor, filter);
 
-  // make sure that extend_return_d points to the extended dataset
-  ASSERT_TRUE(cuvs::devArrMatch(
-    main_d.data(), extend_return_d.data(), main_d.size(), cuvs::Compare<float>()));
-
-  ASSERT_TRUE(cuvs::devArrMatch(additional_d.data(),
-                                extend_return_d.data() + main_d.size(),
-                                additional_d.size(),
-                                cuvs::Compare<float>()));
-
   // check neighbors
   ASSERT_TRUE(
     cuvs::devArrMatch(min_cols.data_handle(), neighbors_d.data(), 4, cuvs::Compare<uint32_t>()));
diff --git a/docs/source/python_api/neighbors_cagra.rst b/docs/source/python_api/neighbors_cagra.rst
index e7155efb83..42647914f2 100644
--- a/docs/source/python_api/neighbors_cagra.rst
+++ b/docs/source/python_api/neighbors_cagra.rst
@@ -44,3 +44,8 @@ Index load
 ##########
 
 .. autofunction:: cuvs.neighbors.cagra.load
+
+Index extend
+############
+
+.. autofunction:: cuvs.neighbors.cagra.extend
diff --git a/go/cagra/cagra.go b/go/cagra/cagra.go
index 61b9e71d6e..c68e85e956 100644
--- a/go/cagra/cagra.go
+++ b/go/cagra/cagra.go
@@ -51,13 +51,12 @@ func BuildIndex[T any](Resources cuvs.Resource, params *IndexParams, dataset *cu
 // * `Resources` - Resources to use
 // * `params` - Parameters for extending the index
 // * `additional_dataset` - A row-major Tensor on the device to extend the index with
-// * `return_dataset` - A row-major Tensor on the device that will receive the extended dataset
 // * `index` - CagraIndex to extend
-func ExtendIndex[T any](Resources cuvs.Resource, params *ExtendParams, additional_dataset *cuvs.Tensor[T], return_dataset *cuvs.Tensor[T], index *CagraIndex) error {
+func ExtendIndex[T any](Resources cuvs.Resource, params *ExtendParams, additional_dataset *cuvs.Tensor[T], index *CagraIndex) error {
 	if !index.trained {
 		return errors.New("index needs to be built before calling extend")
 	}
-	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraExtend(C.ulong(Resources.Resource), params.params, (*C.DLManagedTensor)(unsafe.Pointer(additional_dataset.C_tensor)), index.index, (*C.DLManagedTensor)(unsafe.Pointer(return_dataset.C_tensor)))))
+	err := cuvs.CheckCuvs(cuvs.CuvsError(C.cuvsCagraExtend(C.ulong(Resources.Resource), params.params, (*C.DLManagedTensor)(unsafe.Pointer(additional_dataset.C_tensor)), index.index)))
 	if err != nil {
 		return err
 	}
diff --git a/python/cuvs/cuvs/neighbors/cagra/__init__.py b/python/cuvs/cuvs/neighbors/cagra/__init__.py
index b32f9ca2d5..8d21e09022 100644
--- a/python/cuvs/cuvs/neighbors/cagra/__init__.py
+++ b/python/cuvs/cuvs/neighbors/cagra/__init__.py
@@ -15,10 +15,12 @@
 
 from .cagra import (
     CompressionParams,
+    ExtendParams,
     Index,
     IndexParams,
     SearchParams,
     build,
+    extend,
     from_graph,
     load,
     save,
@@ -27,10 +29,12 @@
 
 __all__ = [
     "CompressionParams",
+    "ExtendParams",
     "Index",
     "IndexParams",
     "SearchParams",
     "build",
+    "extend",
     "from_graph",
     "load",
     "save",
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index 29e47a5014..bb01c047cd 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -175,6 +175,19 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
                                        DLManagedTensor * dataset,
                                        cuvsCagraIndex_t index)
 
+    ctypedef struct cuvsCagraExtendParams:
+        uint32_t max_chunk_size
+
+    ctypedef cuvsCagraExtendParams* cuvsCagraExtendParams_t
+
+    cuvsError_t cuvsCagraExtendParamsCreate(cuvsCagraExtendParams_t* params)
+    cuvsError_t cuvsCagraExtendParamsDestroy(cuvsCagraExtendParams_t params)
+    cuvsError_t cuvsCagraExtend(cuvsResources_t res,
+                                cuvsCagraExtendParams_t params,
+                                DLManagedTensor* additional_dataset,
+                                cuvsCagraIndex_t index)
+
+
 cdef class Index:
     """
     CAGRA index object. This object stores the trained CAGRA index state
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 0a8fec17b4..e09970222f 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -350,7 +350,7 @@ def build(IndexParams index_params, dataset, resources=None):
     ----------
     index_params : IndexParams object
     dataset : CUDA array interface compliant matrix shape (n_samples, dim)
-        Supported dtype [float, int8, uint8]
+        Supported dtype [float, half, int8, uint8]
     {resources_docstring}
 
     Returns
@@ -841,3 +841,73 @@ def from_graph(graph, dataset, metric="sqeuclidean", resources=None):
         idx.index))
     idx.trained = True
     return idx
+
+
+cdef class ExtendParams:
+    """ Supplemental parameters to extend CAGRA Index
+
+    Parameters
+    ----------
+    max_chunk_size : int
+        The additional dataset is divided into chunks and added to the graph.
+        This is the knob to adjust the tradeoff between the recall and
+        operation throughput. Large chunk sizes can result in high throughput,
+        but use more working memory (O(max_chunk_size*degree^2)). This can also
+        degrade recall because no edges are added between the nodes in the same
+        chunk. Auto select when 0.
+    """
+
+    cdef cuvsCagraExtendParams* params
+
+    def __cinit__(self):
+        check_cuvs(cuvsCagraExtendParamsCreate(&self.params))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsCagraExtendParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 max_chunk_size=None):
+        if max_chunk_size is not None:
+            self.params.max_chunk_size = max_chunk_size
+
+    @property
+    def max_chunk_size(self):
+        return self.params.max_chunk_size
+
+
+@auto_sync_resources
+def extend(ExtendParams params, Index index, additional_dataset,
+           resources=None):
+    """
+    Extend a CAGRA index with additional vectors
+
+    Parameters
+    ----------
+    params : ExtendParams object
+    index: Index
+       Existing cagra index to extend
+    additional_dataset : CUDA array interface compliant matrix shape
+        Supported dtype [float, half, int8, uint8]
+    {resources_docstring}
+
+    """
+    dataset_ai = wrap_array(additional_dataset)
+    _check_input_array(dataset_ai, [np.dtype("float32"),
+                                    np.dtype("float16"),
+                                    np.dtype("byte"),
+                                    np.dtype("ubyte")])
+
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsCagraExtend(
+            res,
+            params.params,
+            dataset_dlpack,
+            index.index
+        ))
+
+    return index
diff --git a/python/cuvs/cuvs/tests/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
index 0b08d48325..359cec02f5 100644
--- a/python/cuvs/cuvs/tests/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -41,7 +41,7 @@ def run_cagra_build_search_test(
     array_type="device",
     compare=True,
     inplace=True,
-    add_data_on_build=True,
+    test_extend=False,
     search_params={},
     compression=None,
 ):
@@ -60,26 +60,24 @@ def run_cagra_build_search_test(
         compression=compression,
     )
 
-    if array_type == "device":
-        index = cagra.build(build_params, dataset_device)
-    else:
-        index = cagra.build(build_params, dataset)
-
-    if not add_data_on_build:
+    if test_extend:
         dataset_1 = dataset[: n_rows // 2, :]
         dataset_2 = dataset[n_rows // 2 :, :]
-        indices_1 = np.arange(n_rows // 2, dtype=np.uint32)
-        indices_2 = np.arange(n_rows // 2, n_rows, dtype=np.uint32)
+        extend_params = cagra.ExtendParams()
         if array_type == "device":
             dataset_1_device = device_ndarray(dataset_1)
             dataset_2_device = device_ndarray(dataset_2)
-            indices_1_device = device_ndarray(indices_1)
-            indices_2_device = device_ndarray(indices_2)
-            index = cagra.extend(index, dataset_1_device, indices_1_device)
-            index = cagra.extend(index, dataset_2_device, indices_2_device)
+
+            index = cagra.build(build_params, dataset_1_device)
+            index = cagra.extend(extend_params, index, dataset_2_device)
+        else:
+            index = cagra.build(build_params, dataset_1)
+            index = cagra.extend(index, dataset_2)
+    else:
+        if array_type == "device":
+            index = cagra.build(build_params, dataset_device)
         else:
-            index = cagra.extend(index, dataset_1, indices_1)
-            index = cagra.extend(index, dataset_2, indices_2)
+            index = cagra.build(build_params, dataset)
 
     queries = generate_data((n_queries, n_cols), dtype)
     out_idx = np.zeros((n_queries, k), dtype=np.uint32)
@@ -185,7 +183,7 @@ def test_filtered_cagra(sparsity):
         {
             "intermediate_graph_degree": 64,
             "graph_degree": 32,
-            "add_data_on_build": True,
+            "test_extend": False,
             "k": 1,
             "metric": "sqeuclidean",
             "build_algo": "ivf_pq",
@@ -193,7 +191,7 @@ def test_filtered_cagra(sparsity):
         {
             "intermediate_graph_degree": 32,
             "graph_degree": 16,
-            "add_data_on_build": False,
+            "test_extend": True,
             "k": 5,
             "metric": "sqeuclidean",
             "build_algo": "ivf_pq",
@@ -201,7 +199,7 @@ def test_filtered_cagra(sparsity):
         {
             "intermediate_graph_degree": 128,
             "graph_degree": 32,
-            "add_data_on_build": True,
+            "test_extend": False,
             "k": 10,
             "metric": "inner_product",
             "build_algo": "nn_descent",
@@ -212,6 +210,7 @@ def test_cagra_index_params(params):
     # Note that inner_product tests use normalized input which we cannot
     # represent in int8, therefore we test only sqeuclidean metric here.
     run_cagra_build_search_test(
+        test_extend=params["test_extend"],
         k=params["k"],
         metric=params["metric"],
         graph_degree=params["graph_degree"],

From b3a4fd80878f0233b74f36b6c91b275f3e980e0e Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Tue, 16 Sep 2025 22:37:05 -0700
Subject: [PATCH 583/623] Use int64_t for getters in the cagra/ivf_flat c-api
 (#1272)

Java doesn't support unsigned types well, and this is leading to us only supporting 2^31 items in the cagra index - instead of 2^32.

Fix by changing the getters in the c-api to return int64_t values for the size/dim etc, instead of uint32_t.

We were also not consistent on returning error codes in ivf_flat/ivf_pq for these getters - with cagra returning an error code, and the ivf* methods just returning the value directly. Change to always return an error code to be consistent. (Note that #1123 is also making this change for ivf-pq - and I'm skipping ivf-pq to avoid merge conflicts).

Authors:
  - Ben Frederickson (https://github.com/benfred)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - MithunR (https://github.com/mythrocks)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1272
---
 cpp/include/cuvs/neighbors/cagra.h            |  6 +-
 cpp/include/cuvs/neighbors/ivf_flat.h         |  4 +-
 cpp/include/cuvs/neighbors/ivf_pq.h           |  7 +-
 cpp/src/neighbors/cagra_c.cpp                 |  6 +-
 cpp/src/neighbors/ivf_flat_c.cpp              | 84 ++++++++++---------
 cpp/src/neighbors/ivf_pq_c.cpp                | 24 ++++--
 cpp/tests/neighbors/ann_cagra_c.cu            |  2 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |  1 +
 .../com/nvidia/cuvs/internal/common/Util.java |  7 --
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |  6 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  6 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |  7 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |  8 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd  |  9 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  | 13 ++-
 15 files changed, 110 insertions(+), 80 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/cagra.h b/cpp/include/cuvs/neighbors/cagra.h
index 2532bbd3df..ddf3207aa0 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/cpp/include/cuvs/neighbors/cagra.h
@@ -356,7 +356,7 @@ cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index);
  * @param[out] dim return dimension of the index
  * @return cuvsError_t
  */
-cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim);
+cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int64_t* dim);
 
 /**
  * @brief Get size of the CAGRA index
@@ -365,7 +365,7 @@ cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim);
  * @param[out] size return number of vectors in the index
  * @return cuvsError_t
  */
-cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* size);
+cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, int64_t* size);
 
 /**
  * @brief Get graph degree of the CAGRA index
@@ -374,7 +374,7 @@ cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* size);
  * @param[out] graph_degree return graph degree
  * @return cuvsError_t
  */
-cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index, uint32_t* graph_degree);
+cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index, int64_t* graph_degree);
 
 /**
  * @brief Returns a view of the CAGRA dataset
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/cpp/include/cuvs/neighbors/ivf_flat.h
index 9e89ea354d..8211f1ef58 100644
--- a/cpp/include/cuvs/neighbors/ivf_flat.h
+++ b/cpp/include/cuvs/neighbors/ivf_flat.h
@@ -166,10 +166,10 @@ cuvsError_t cuvsIvfFlatIndexCreate(cuvsIvfFlatIndex_t* index);
 cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index);
 
 /** Get the number of clusters/inverted lists */
-uint32_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index);
+cuvsError_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index, int64_t* n_lists);
 
 /** Get the dimensionality of the data */
-uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index);
+cuvsError_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index, int64_t* dim);
 
 /**
  * @brief Get the cluster centers corresponding to the lists [n_lists, dim]
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/cpp/include/cuvs/neighbors/ivf_pq.h
index eef10290c7..3ef7b8b7af 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.h
+++ b/cpp/include/cuvs/neighbors/ivf_pq.h
@@ -262,10 +262,13 @@ cuvsError_t cuvsIvfPqIndexCreate(cuvsIvfPqIndex_t* index);
 cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index);
 
 /** Get the number of clusters/inverted lists */
-uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index);
+cuvsError_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index, int64_t* n_lists);
 
 /** Get the dimensionality */
-uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index);
+cuvsError_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index, int64_t* dim);
+
+/** Get the size of the index */
+cuvsError_t cuvsIvfPqIndexGetSize(cuvsIvfPqIndex_t index, int64_t* size);
 
 /**
  * @brief Get the cluster centers corresponding to the lists in the original space
diff --git a/cpp/src/neighbors/cagra_c.cpp b/cpp/src/neighbors/cagra_c.cpp
index 92d20f85df..b3c8a93a91 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/cpp/src/neighbors/cagra_c.cpp
@@ -418,7 +418,7 @@ extern "C" cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index_c_ptr)
   });
 }
 
-extern "C" cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim)
+extern "C" cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int64_t* dim)
 {
   return cuvs::core::translate_exceptions([=] {
     auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<float, uint32_t>*>(index->addr);
@@ -426,7 +426,7 @@ extern "C" cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int* dim)
   });
 }
 
-extern "C" cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* size)
+extern "C" cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, int64_t* size)
 {
   return cuvs::core::translate_exceptions([=] {
     auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<float, uint32_t>*>(index->addr);
@@ -434,7 +434,7 @@ extern "C" cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* s
   });
 }
 
-extern "C" cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index, uint32_t* graph_degree)
+extern "C" cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index, int64_t* graph_degree)
 {
   return cuvs::core::translate_exceptions([=] {
     auto index_ptr = reinterpret_cast<cuvs::neighbors::cagra::index<float, uint32_t>*>(index->addr);
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/cpp/src/neighbors/ivf_flat_c.cpp
index 91ad9abbed..5d8f7462fa 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/cpp/src/neighbors/ivf_flat_c.cpp
@@ -373,50 +373,54 @@ extern "C" cuvsError_t cuvsIvfFlatExtend(cuvsResources_t res,
   });
 }
 
-extern "C" uint32_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index)
+extern "C" cuvsError_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index, int64_t* n_lists)
 {
-  if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index->addr);
-    return index_ptr->n_lists();
-  } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<half, int64_t>*>(index->addr);
-    return index_ptr->n_lists();
-  } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index->addr);
-    return index_ptr->n_lists();
-  } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>*>(index->addr);
-    return index_ptr->n_lists();
-  } else {
-    return 0;
-  }
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index->addr);
+      *n_lists = index_ptr->n_lists();
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<half, int64_t>*>(index->addr);
+      *n_lists = index_ptr->n_lists();
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index->addr);
+      *n_lists = index_ptr->n_lists();
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>*>(index->addr);
+      *n_lists = index_ptr->n_lists();
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
 }
 
-extern "C" uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index)
+extern "C" cuvsError_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index, int64_t* dim)
 {
-  if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index->addr);
-    return index_ptr->dim();
-  } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<half, int64_t>*>(index->addr);
-    return index_ptr->dim();
-  } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index->addr);
-    return index_ptr->dim();
-  } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
-    auto index_ptr =
-      reinterpret_cast<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>*>(index->addr);
-    return index_ptr->dim();
-  } else {
-    return 0;
-  }
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<float, int64_t>*>(index->addr);
+      *dim = index_ptr->dim();
+    } else if (index->dtype.code == kDLFloat && index->dtype.bits == 16) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<half, int64_t>*>(index->addr);
+      *dim = index_ptr->dim();
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<int8_t, int64_t>*>(index->addr);
+      *dim = index_ptr->dim();
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::ivf_flat::index<uint8_t, int64_t>*>(index->addr);
+      *dim = index_ptr->dim();
+    } else {
+      RAFT_FAIL("Unsupported index dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsIvfFlatIndex_t index,
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/cpp/src/neighbors/ivf_pq_c.cpp
index 4abd080582..5c10d4111b 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/cpp/src/neighbors/ivf_pq_c.cpp
@@ -347,16 +347,28 @@ extern "C" cuvsError_t cuvsIvfPqExtend(cuvsResources_t res,
   });
 }
 
-extern "C" uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index)
+extern "C" cuvsError_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index, int64_t* n_lists)
 {
-  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
-  return index_ptr->n_lists();
+  return cuvs::core::translate_exceptions([=] {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
+    *n_lists       = index_ptr->n_lists();
+  });
 }
 
-extern "C" uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index)
+extern "C" cuvsError_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index, int64_t* dim)
 {
-  auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
-  return index_ptr->dim();
+  return cuvs::core::translate_exceptions([=] {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
+    *dim           = index_ptr->dim();
+  });
+}
+
+extern "C" cuvsError_t cuvsIvfPqIndexGetSize(cuvsIvfPqIndex_t index, int64_t* size)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index_ptr = reinterpret_cast<cuvs::neighbors::ivf_pq::index<int64_t>*>(index->addr);
+    *size          = index_ptr->size();
+  });
 }
 
 extern "C" cuvsError_t cuvsIvfPqIndexGetCenters(cuvsIvfPqIndex_t index, DLManagedTensor* centers)
diff --git a/cpp/tests/neighbors/ann_cagra_c.cu b/cpp/tests/neighbors/ann_cagra_c.cu
index 3e73f545d4..3123e2b3b3 100644
--- a/cpp/tests/neighbors/ann_cagra_c.cu
+++ b/cpp/tests/neighbors/ann_cagra_c.cu
@@ -517,7 +517,7 @@ TEST(CagraC, BuildMergeSearch)
   cuvsCagraIndex_t index_array[2] = {index_main, index_add};
   ASSERT_EQ(cuvsCagraMerge(res, merge_params, index_array, 2, index_merged), CUVS_SUCCESS);
 
-  int merged_dim = -1;
+  int64_t merged_dim = -1;
   ASSERT_EQ(cuvsCagraIndexGetDims(index_merged, &merged_dim), CUVS_SUCCESS);
   EXPECT_EQ(merged_dim, 2);
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index aa082482dc..99b0ecc71e 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -36,6 +36,7 @@
 import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
 import com.nvidia.cuvs.internal.panama.*;
+
 import java.io.FileInputStream;
 import java.io.InputStream;
 import java.io.OutputStream;
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 28dfc5c020..1ffb5ad559 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -106,13 +106,6 @@ public static void checkCudaError(int value, String caller) {
     }
   }
 
-  private static final long UNSIGNED_INT_MASK = 0xFFFFFFFFL;
-
-  public static long dereferenceUnsignedInt(MemorySegment ptr) {
-    assert ptr.byteSize() == 4;
-    return ptr.get(uint32_t, 0) & UNSIGNED_INT_MASK;
-  }
-
   /**
    * Java analog to CUDA's cudaMemcpyKind, used for cudaMemcpy() calls.
    * @see <a href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html">CUDA Runtime API</a>
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index bb01c047cd..b498cf1681 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -134,10 +134,10 @@ cdef extern from "cuvs/neighbors/cagra.h" nogil:
 
     cuvsError_t cuvsCagraIndexDestroy(cuvsCagraIndex_t index)
 
-    cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int32_t* dim)
-    cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, uint32_t* size)
+    cuvsError_t cuvsCagraIndexGetDims(cuvsCagraIndex_t index, int64_t* dim)
+    cuvsError_t cuvsCagraIndexGetSize(cuvsCagraIndex_t index, int64_t* size)
     cuvsError_t cuvsCagraIndexGetGraphDegree(cuvsCagraIndex_t index,
-                                             uint32_t* degree)
+                                             int64_t* degree)
     cuvsError_t cuvsCagraIndexGetGraph(cuvsCagraIndex_t index,
                                        DLManagedTensor * graph)
     cuvsError_t cuvsCagraIndexGetDataset(cuvsCagraIndex_t index,
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index e09970222f..d82ea99ffa 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -275,18 +275,18 @@ cdef class Index:
 
     @property
     def dim(self):
-        cdef int32_t dim
+        cdef int64_t dim
         check_cuvs(cuvsCagraIndexGetDims(self.index, &dim))
         return dim
 
     @property
     def graph_degree(self):
-        cdef uint32_t degree
+        cdef int64_t degree
         check_cuvs(cuvsCagraIndexGetGraphDegree(self.index, &degree))
         return degree
 
     def __len__(self):
-        cdef uint32_t size
+        cdef int64_t size
         check_cuvs(cuvsCagraIndexGetSize(self.index, &size))
         return size
 
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
index 6e30ac0a07..470234d9cf 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -15,7 +15,7 @@
 #
 # cython: language_level=3
 
-from libc.stdint cimport uint32_t, uintptr_t
+from libc.stdint cimport int64_t, uint32_t, uintptr_t
 from libcpp cimport bool
 
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
@@ -62,9 +62,10 @@ cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
 
     cuvsError_t cuvsIvfFlatIndexDestroy(cuvsIvfFlatIndex_t index)
 
-    uint32_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index)
+    cuvsError_t cuvsIvfFlatIndexGetNLists(cuvsIvfFlatIndex_t index,
+                                          int64_t * n_lists)
 
-    uint32_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index)
+    cuvsError_t cuvsIvfFlatIndexGetDim(cuvsIvfFlatIndex_t index, int64_t * dim)
 
     cuvsError_t cuvsIvfFlatIndexGetCenters(cuvsIvfFlatIndex_t index,
                                            DLManagedTensor * centers)
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 3a803e5599..996a39d4fe 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -188,12 +188,16 @@ cdef class Index:
     @property
     def n_lists(self):
         """ The number of inverted lists (clusters) """
-        return cuvsIvfFlatIndexGetNLists(self.index)
+        cdef int64_t n_lists = 0
+        cuvsIvfFlatIndexGetNLists(self.index, &n_lists)
+        return n_lists
 
     @property
     def dim(self):
         """ dimensionality of the cluster centers """
-        return cuvsIvfFlatIndexGetDim(self.index)
+        cdef int64_t dim = 0
+        cuvsIvfFlatIndexGetDim(self.index, &dim)
+        return dim
 
     @property
     def centers(self):
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index 897e4951a9..5ca7b97602 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -15,7 +15,7 @@
 #
 # cython: language_level=3
 
-from libc.stdint cimport uint32_t, uintptr_t
+from libc.stdint cimport int64_t, uint32_t, uintptr_t
 from libcpp cimport bool
 
 from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
@@ -84,9 +84,12 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
 
     cuvsError_t cuvsIvfPqIndexDestroy(cuvsIvfPqIndex_t index)
 
-    uint32_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index)
+    cuvsError_t cuvsIvfPqIndexGetNLists(cuvsIvfPqIndex_t index,
+                                        int64_t * n_lists)
 
-    uint32_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index)
+    cuvsError_t cuvsIvfPqIndexGetDim(cuvsIvfPqIndex_t index, int64_t * dim)
+
+    cuvsError_t cuvsIvfPqIndexGetSize(cuvsIvfPqIndex_t index, int64_t * size)
 
     cuvsError_t cuvsIvfPqIndexGetCenters(cuvsIvfPqIndex_t index,
                                          DLManagedTensor * centers)
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index f784327d33..5ffa0b0c88 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -249,12 +249,21 @@ cdef class Index:
     @property
     def n_lists(self):
         """ The number of inverted lists (clusters) """
-        return cuvsIvfPqIndexGetNLists(self.index)
+        cdef int64_t n_lists
+        check_cuvs(cuvsIvfPqIndexGetNLists(self.index, &n_lists))
+        return n_lists
 
     @property
     def dim(self):
         """ dimensionality of the cluster centers """
-        return cuvsIvfPqIndexGetDim(self.index)
+        cdef int64_t dim
+        check_cuvs(cuvsIvfPqIndexGetDim(self.index, &dim))
+        return dim
+
+    def __len__(self):
+        cdef int64_t size
+        check_cuvs(cuvsIvfPqIndexGetSize(self.index, &size))
+        return size
 
     @property
     def centers(self):

From d5edf1298ed2ab2b01d5941ed7d35b22ff61e65f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Wed, 17 Sep 2025 16:36:38 +0200
Subject: [PATCH 584/623] [Java] Uniform toHost/toDevice to work across all
 CuVSMatrix classes (#1328)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR moves `toHost`/`toDevice` operations to `CuVSMatrix` in order to uniform them; implementations then do not need to worry or perform checks on the input type, but simply accept any `CuVSMatrix` and then "request" the type they want or need (no ifs, custom cudaAlloc or cudaMemcpy operations needed):
```
CuVSDeviceMatrix matrix = input.toDevice(resources);
```
If the source matrix is a `CuVSHostMatrix`, device memory will be allocated and data will be copied; if the source matrix is a already a `CuVSDeviceMatrix`, a (wrapped) reference to the original data is returned (very fast, no copies, no additional allocations).

The wrapper is currently implemented to follow C++ `weak_ptr` semantics: it delegates everything to the wrapped matrix, the only difference is on `close()` (which is a no-op in the wrapper, as you'd expect). The lifecycle of the data follows the one of the original matrix, the "weak" delegate does not influence it in any way. This way the caller can handle it uniformly (e.g. with try-with-resources), without side effects on the original matrix (which lifecycle will be already handled by its owner).

An alternative would be to give this C++ `shared_ptr` semantics, making it reference counted. I think this would be more complex and not necessary, but let me know.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1328
---
 .../com/nvidia/cuvs/CuVSDeviceMatrix.java     |  74 +++++++++++--
 .../java/com/nvidia/cuvs/CuVSHostMatrix.java  |  73 ++++++++++++
 .../main/java/com/nvidia/cuvs/CuVSMatrix.java |  36 ++++++
 .../cuvs/internal/CuVSDeviceMatrixImpl.java   |   5 +
 .../cuvs/internal/CuVSHostMatrixImpl.java     |  15 +++
 .../cuvs/internal/CuVSMatrixBaseImpl.java     |  24 ++++
 .../java/com/nvidia/cuvs/CuVSMatrixIT.java    | 104 +++++++++++++++++-
 7 files changed, 317 insertions(+), 14 deletions(-)

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
index b654163a63..ec34a3de1c 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
@@ -21,16 +21,7 @@
 public interface CuVSDeviceMatrix extends CuVSMatrix {
 
   /**
-   * Fills the provided, pre-allocated host matrix with data from this device matrix.
-   * The content of the provided host matrix will be overwritten; the 2 matrices must have the
-   * same element type and dimension.
-   *
-   * @param hostMatrix  the host-memory-backed matrix to fill.
-   */
-  void toHost(CuVSHostMatrix hostMatrix);
-
-  /**
-   * Returns a new, host matrix with data from this device matrix.
+   * Returns a new host matrix with data from this device matrix.
    * The returned host matrix will need to be managed by the caller, which will be
    * responsible to call {@link CuVSMatrix#close()} to free its resources when done.
    */
@@ -39,4 +30,67 @@ default CuVSHostMatrix toHost() {
     toHost(hostMatrix);
     return hostMatrix;
   }
+
+  default CuVSDeviceMatrix toDevice(CuVSResources resources) {
+    return new CuVSDeviceMatrixDelegate(this);
+  }
+
+  class CuVSDeviceMatrixDelegate implements CuVSDeviceMatrix {
+
+    private final CuVSDeviceMatrix deviceMatrix;
+
+    private CuVSDeviceMatrixDelegate(CuVSDeviceMatrix deviceMatrix) {
+      this.deviceMatrix = deviceMatrix;
+    }
+
+    @Override
+    public long size() {
+      return deviceMatrix.size();
+    }
+
+    @Override
+    public long columns() {
+      return deviceMatrix.columns();
+    }
+
+    @Override
+    public DataType dataType() {
+      return deviceMatrix.dataType();
+    }
+
+    @Override
+    public RowView getRow(long row) {
+      return deviceMatrix.getRow(row);
+    }
+
+    @Override
+    public void toArray(int[][] array) {
+      deviceMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(float[][] array) {
+      deviceMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(byte[][] array) {
+      deviceMatrix.toArray(array);
+    }
+
+    @Override
+    public void toHost(CuVSHostMatrix hostMatrix) {
+      deviceMatrix.toHost(hostMatrix);
+    }
+
+    @Override
+    public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
+      this.deviceMatrix.toDevice(deviceMatrix, cuVSResources);
+    }
+
+    @Override
+    public void close() {
+      // Do nothing
+    }
+  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
index 237dfa39f7..91ff9012f3 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
@@ -20,4 +20,77 @@
  */
 public interface CuVSHostMatrix extends CuVSMatrix {
   int get(int row, int col);
+
+  default CuVSHostMatrix toHost() {
+    return new CuVSHostMatrixDelegate(this);
+  }
+
+  default CuVSDeviceMatrix toDevice(CuVSResources resources) {
+    var deviceMatrix = CuVSMatrix.deviceBuilder(resources, size(), columns(), dataType()).build();
+    toDevice(deviceMatrix, resources);
+    return deviceMatrix;
+  }
+
+  class CuVSHostMatrixDelegate implements CuVSHostMatrix {
+    private final CuVSHostMatrix hostMatrix;
+
+    public CuVSHostMatrixDelegate(CuVSHostMatrix cuVSHostMatrix) {
+      this.hostMatrix = cuVSHostMatrix;
+    }
+
+    @Override
+    public int get(int row, int col) {
+      return hostMatrix.get(row, col);
+    }
+
+    @Override
+    public long size() {
+      return hostMatrix.size();
+    }
+
+    @Override
+    public long columns() {
+      return hostMatrix.columns();
+    }
+
+    @Override
+    public DataType dataType() {
+      return hostMatrix.dataType();
+    }
+
+    @Override
+    public RowView getRow(long row) {
+      return hostMatrix.getRow(row);
+    }
+
+    @Override
+    public void toArray(int[][] array) {
+      hostMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(float[][] array) {
+      hostMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(byte[][] array) {
+      hostMatrix.toArray(array);
+    }
+
+    @Override
+    public void toHost(CuVSHostMatrix hostMatrix) {
+      this.hostMatrix.toHost(hostMatrix);
+    }
+
+    @Override
+    public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
+      hostMatrix.toDevice(deviceMatrix, cuVSResources);
+    }
+
+    @Override
+    public void close() {
+      // Do nothing
+    }
+  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
index 708009be02..3ccdb9ae28 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
@@ -177,6 +177,42 @@ static Builder<CuVSDeviceMatrix> deviceBuilder(
    */
   void toArray(byte[][] array);
 
+  /**
+   * Fills the provided, pre-allocated host matrix with data from this matrix.
+   * The content of the provided host matrix will be overwritten; the 2 matrices must have the
+   * same element type and dimension.
+   *
+   * @param hostMatrix  the host-memory-backed matrix to fill.
+   */
+  void toHost(CuVSHostMatrix hostMatrix);
+
+  /**
+   * Returns a host matrix; if the matrix is already a host matrix, a "weak" reference to the same host memory
+   * is returned. If the matrix is a device matrix, a newly allocated matrix will be populated with data from
+   * the device matrix.
+   * The returned host matrix will need to be managed by the caller, which will be
+   * responsible to call {@link CuVSMatrix#close()} to free its resources when done.
+   */
+  CuVSHostMatrix toHost();
+
+  /**
+   * Fills the provided, pre-allocated device matrix with data from this matrix.
+   * The content of the provided device matrix will be overwritten; the 2 matrices must have the
+   * same element type and dimension.
+   *
+   * @param deviceMatrix  the device-memory-backed matrix to fill.
+   */
+  void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources);
+
+  /**
+   * Returns a device matrix; if this matrix is already a device matrix, a "weak" reference to the same host memory
+   * is returned. If the matrix is a host matrix, a newly allocated matrix will be populated with data from
+   * the host matrix.
+   * The returned device matrix will need to be managed by the caller, which will be
+   * responsible to call {@link CuVSMatrix#close()} to free its resources when done.
+   */
+  CuVSDeviceMatrix toDevice(CuVSResources cuVSResources);
+
   @Override
   void close();
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
index 50b914c76b..0f97608ae1 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
@@ -236,6 +236,11 @@ public void toHost(CuVSHostMatrix hostMatrix) {
     }
   }
 
+  @Override
+  public void toDevice(CuVSDeviceMatrix targetMatrix, CuVSResources cuVSResources) {
+    copyMatrix(this, (CuVSMatrixBaseImpl) targetMatrix, cuVSResources);
+  }
+
   @Override
   public void close() {
     if (hostBuffer != MemorySegment.NULL) {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
index 317b315c77..408feda2bf 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -18,7 +18,9 @@
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
+import com.nvidia.cuvs.CuVSDeviceMatrix;
 import com.nvidia.cuvs.CuVSHostMatrix;
+import com.nvidia.cuvs.CuVSResources;
 import com.nvidia.cuvs.RowView;
 import java.lang.foreign.*;
 import java.lang.invoke.VarHandle;
@@ -142,6 +144,19 @@ public void toArray(byte[][] array) {
     }
   }
 
+  @Override
+  public void toHost(CuVSHostMatrix hostMatrix) {
+    var targetMatrix = (CuVSHostMatrixImpl) hostMatrix;
+    var valueByteSize = valueLayout.byteSize();
+    MemorySegment.copy(
+        this.memorySegment, 0L, targetMatrix.memorySegment, 0L, size * columns * valueByteSize);
+  }
+
+  @Override
+  public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
+    copyMatrix(this, (CuVSMatrixBaseImpl) deviceMatrix, cuVSResources);
+  }
+
   @Override
   public void close() {}
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
index d629645e61..b46ab63c9f 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -18,6 +18,7 @@
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_CHAR;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
+import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.CuVSMatrix;
@@ -49,6 +50,29 @@ protected CuVSMatrixBaseImpl(
     this.columns = columns;
   }
 
+  protected static void copyMatrix(
+      CuVSMatrixBaseImpl sourceMatrix, CuVSMatrixBaseImpl targetMatrix, CuVSResources resources) {
+    if (targetMatrix.columns() != sourceMatrix.columns
+        || targetMatrix.size() != sourceMatrix.size) {
+      throw new IllegalArgumentException(
+          "Source and target matrices must have the same dimensions");
+    }
+    if (targetMatrix.dataType() != sourceMatrix.dataType) {
+      throw new IllegalArgumentException("Source and target matrices must have the same dataType");
+    }
+
+    try (var localArena = Arena.ofConfined()) {
+      var targetTensor = targetMatrix.toTensor(localArena);
+
+      try (var resourceAccess = resources.access()) {
+        var cuvsRes = resourceAccess.handle();
+        var sourceTensor = sourceMatrix.toTensor(localArena);
+        checkCuVSError(cuvsMatrixCopy(cuvsRes, sourceTensor, targetTensor), "cuvsMatrixCopy");
+        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
+      }
+    }
+  }
+
   @Override
   public long size() {
     return size;
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
index 0eaf666d15..499c7d366f 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
@@ -262,7 +262,7 @@ public void testFloatDatasetCopy() {
     }
   }
 
-  private void testFloatDatasetBuilder(int rows, int cols, CuVSMatrix.Builder builder) {
+  private void testFloatDatasetBuilder(int rows, int cols, CuVSMatrix.Builder<?> builder) {
 
     float[][] data = new float[rows][cols];
     for (int r = 0; r < rows; ++r) {
@@ -306,7 +306,7 @@ public void testFloatDatasetDeviceBuilder() throws Throwable {
     }
   }
 
-  private void testIntDatasetBuilder(int rows, int cols, CuVSMatrix.Builder builder) {
+  private void testIntDatasetBuilder(int rows, int cols, CuVSMatrix.Builder<?> builder) {
 
     var data = new int[rows][cols];
     for (int r = 0; r < rows; ++r) {
@@ -349,7 +349,7 @@ public void testIntDatasetDeviceBuilder() throws Throwable {
     }
   }
 
-  private void testByteDatasetBuilder(int rows, int cols, CuVSMatrix.Builder builder) {
+  private void testByteDatasetBuilder(int rows, int cols, CuVSMatrix.Builder<?> builder) {
 
     var data = new byte[rows][cols];
     for (int r = 0; r < rows; ++r) {
@@ -408,7 +408,7 @@ public void testDeviceToHost() throws Throwable {
         builder.addVector(array);
       }
 
-      try (var deviceMatrix = (CuVSDeviceMatrix) builder.build();
+      try (var deviceMatrix = builder.build();
           var hostMatrix = deviceMatrix.toHost()) {
 
         assertEquals(data.length, deviceMatrix.size());
@@ -429,4 +429,100 @@ public void testDeviceToHost() throws Throwable {
       }
     }
   }
+
+  @Test
+  public void testHostToDevice() throws Throwable {
+
+    final int size = 16 * 1024;
+    final int columns = 2048;
+    final float[][] data = createFloatMatrix(size, columns);
+
+    try (var resources = CuVSResources.create()) {
+
+      try (var hostMatrix = CuVSMatrix.ofArray(data);
+          var deviceMatrix = hostMatrix.toDevice(resources)) {
+
+        assertEquals(data.length, deviceMatrix.size());
+        assertEquals(data[0].length, deviceMatrix.columns());
+
+        assertEquals(deviceMatrix.size(), hostMatrix.size());
+        assertEquals(deviceMatrix.columns(), hostMatrix.columns());
+
+        var roundTripData = new float[size][columns];
+
+        deviceMatrix.toArray(roundTripData);
+
+        for (int n = 0; n < deviceMatrix.size(); ++n) {
+          for (int i = 0; i < deviceMatrix.columns(); ++i) {
+            assertEquals(data[n][i], roundTripData[n][i], 1e-9);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testHostToHostReturnsWeakReferenceSameData() {
+
+    final int size = 16 * 1024;
+    final int columns = 2048;
+    final float[][] data = createFloatMatrix(size, columns);
+
+    try (var hostMatrix = CuVSMatrix.ofArray(data);
+        var hostMatrix2 = hostMatrix.toHost()) {
+
+      assertEquals(data.length, hostMatrix2.size());
+      assertEquals(data[0].length, hostMatrix2.columns());
+
+      assertEquals(hostMatrix2.size(), hostMatrix.size());
+      assertEquals(hostMatrix2.columns(), hostMatrix.columns());
+
+      var roundTripData = new float[size][columns];
+
+      hostMatrix2.toArray(roundTripData);
+
+      for (int n = 0; n < hostMatrix2.size(); ++n) {
+        for (int i = 0; i < hostMatrix2.columns(); ++i) {
+          assertEquals(data[n][i], roundTripData[n][i], 1e-9);
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testDeviceToDeviceReturnsWeakReferenceSameData() throws Throwable {
+
+    final int size = 16 * 1024;
+    final int columns = 2048;
+    final float[][] data = createFloatMatrix(size, columns);
+
+    try (var resources = CuVSResources.create()) {
+
+      var builder = CuVSMatrix.deviceBuilder(resources, size, columns, CuVSMatrix.DataType.FLOAT);
+      for (int i = 0; i < size; ++i) {
+        var array = data[i];
+        builder.addVector(array);
+      }
+
+      try (var deviceMatrix = builder.build();
+          var deviceMatrix2 = deviceMatrix.toDevice(resources)) {
+
+        assertEquals(data.length, deviceMatrix.size());
+        assertEquals(data[0].length, deviceMatrix.columns());
+
+        assertEquals(deviceMatrix.size(), deviceMatrix2.size());
+        assertEquals(deviceMatrix.columns(), deviceMatrix2.columns());
+
+        var roundTripData = new float[size][columns];
+
+        deviceMatrix2.toArray(roundTripData);
+
+        for (int n = 0; n < deviceMatrix2.size(); ++n) {
+          for (int i = 0; i < deviceMatrix2.columns(); ++i) {
+            assertEquals(data[n][i], roundTripData[n][i], 1e-9);
+          }
+        }
+      }
+    }
+  }
 }

From 5492a061f7124485c146fbbf4173d8b6d8afc072 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Wed, 17 Sep 2025 10:58:10 -0400
Subject: [PATCH 585/623] Refactor CMakeLists to support static only builds of
 libcuvs (#1317)

Fixes: https://github.com/rapidsai/cuvs/issues/1312
Fixes: https://github.com/rapidsai/cuvs/issues/1322

Supports: https://github.com/rapidsai/cuvs/issues/1241

If you want a shared libcuvs but static RAPIDS dependencies ( raft, rmm, logger, .... ). You can use `CUVS_STATIC_RAPIDS_LIBRARIES=ON` with `CUVS_COMPILE_DYNAMIC_ONLY=ON`. If you want a fully static cuvs and dependencies you can use `BUILD_SHARED_LIBS=OFF` ( `CUVS_STATIC_RAPIDS_LIBRARIES` is not needed but can also be set to `ON` )

Authors:
  - Robert Maynard (https://github.com/robertmaynard)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1317
---
 cpp/CMakeLists.txt                  | 281 ++++++++++++++--------------
 cpp/cmake/thirdparty/get_raft.cmake |   7 +-
 python/libcuvs/CMakeLists.txt       |   2 +-
 3 files changed, 143 insertions(+), 147 deletions(-)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 04621aaffd..d21f130ee8 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -63,7 +63,7 @@ option(CUDA_ENABLE_LINEINFO
 )
 option(CUDA_STATIC_RUNTIME "Statically link the CUDA runtime" OFF)
 option(CUDA_STATIC_MATH_LIBRARIES "Statically link the CUDA math libraries" OFF)
-option(CUVS_USE_RAFT_STATIC "Build and statically link the RAFT libraries" OFF)
+option(CUVS_STATIC_RAPIDS_LIBRARIES "Build and statically link RAPIDS libraries" OFF)
 option(CUDA_LOG_COMPILE_TIME "Write a log of compilation times to nvcc_compile_log.csv" OFF)
 option(DETECT_CONDA_ENV "Enable detection of conda environment for dependencies" ON)
 option(DISABLE_DEPRECATION_WARNINGS "Disable deprecaction warnings " ON)
@@ -83,6 +83,23 @@ elseif(NOT BUILD_SHARED_LIBS)
   set(BUILD_CAGRA_HNSWLIB OFF)
 endif()
 
+if(NOT BUILD_SHARED_LIBS AND CUVS_COMPILE_DYNAMIC_ONLY)
+  message(
+    VERBOSE
+    "cuVS: Ignoring `BUILD_SHARED_LIBS` and forcing shared library due to `CUVS_COMPILE_DYNAMIC_ONLY`"
+  )
+endif()
+
+set(cuvs_compile_mode "both")
+set(_cuvs_lib_targets cuvs cuvs_static)
+if(CUVS_COMPILE_DYNAMIC_ONLY)
+  set(cuvs_compile_mode "shared_only")
+  set(_cuvs_lib_targets cuvs)
+elseif(NOT BUILD_SHARED_LIBS)
+  set(cuvs_compile_mode "static_only")
+  set(_cuvs_lib_targets cuvs_static)
+endif()
+
 # Needed because GoogleBenchmark changes the state of FindThreads.cmake, causing subsequent runs to
 # have different values for the `Threads::Threads` target. Setting this flag ensures
 # `Threads::Threads` is the same value across all builds so that cache hits occur
@@ -104,7 +121,7 @@ message(VERBOSE "cuVS: Enable lineinfo in nvcc: ${CUDA_ENABLE_LINEINFO}")
 message(VERBOSE "cuVS: Enable nvtx markers: ${CUVS_NVTX}")
 message(VERBOSE "cuVS: Statically link the CUDA runtime: ${CUDA_STATIC_RUNTIME}")
 message(VERBOSE "cuVS: Statically link the CUDA math libraries: ${CUDA_STATIC_MATH_LIBRARIES}")
-message(VERBOSE "cuVS: Build and statically link RAFT libraries: ${CUVS_USE_RAFT_STATIC}")
+message(VERBOSE "cuVS: Build and statically link RAPIDS libraries: ${CUVS_STATIC_RAPIDS_LIBRARIES}")
 
 # Set RMM logging level
 set(RMM_LOGGING_LEVEL
@@ -200,7 +217,7 @@ endif()
 
 # ##################################################################################################
 # * cuvs ---------------------------------------------------------------------
-if(BUILD_SHARED_LIBS)
+if(NOT BUILD_CPU_ONLY)
   add_library(
     cuvs-cagra-search STATIC
     src/neighbors/cagra_search_float.cu
@@ -519,25 +536,116 @@ if(BUILD_SHARED_LIBS)
     cuvs_objs PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
                       "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
   )
+
+  target_compile_definitions(
+    cuvs_objs PRIVATE $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>
+                      $<$<BOOL:${CUVS_NVTX}>:NVTX_ENABLED>
+  )
+
   target_link_libraries(
-    cuvs_objs PUBLIC raft::raft rmm::rmm ${CUVS_CTK_MATH_DEPENDENCIES}
-                     $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+    cuvs_objs
+    PRIVATE raft::raft rmm::rmm ${CUVS_CTK_MATH_DEPENDENCIES}
+            $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX> $<TARGET_NAME_IF_EXISTS:hnswlib::hnswlib>
   )
 
-  add_library(cuvs SHARED $<TARGET_OBJECTS:cuvs_objs>)
+  target_include_directories(
+    cuvs_objs
+    PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+           "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+    INTERFACE "$<INSTALL_INTERFACE:include>"
+  )
 
-  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
-    add_library(cuvs_static STATIC $<TARGET_OBJECTS:cuvs_objs>)
+  # Endian detection
+  include(TestBigEndian)
+  test_big_endian(BIG_ENDIAN)
+  if(BIG_ENDIAN)
+    target_compile_definitions(cuvs_objs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=0)
+  else()
+    target_compile_definitions(cuvs_objs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=1)
   endif()
 
-  target_compile_options(
-    cuvs INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
-                   --expt-relaxed-constexpr>
+  if(BUILD_MG_ALGOS)
+    rapids_find_generate_module(
+      NCCL
+      HEADER_NAMES nccl.h
+      LIBRARY_NAMES nccl
+    )
+    find_package(NCCL REQUIRED)
+    target_link_libraries(cuvs_objs PUBLIC $<BUILD_LOCAL_INTERFACE:NCCL::NCCL>)
+
+    target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_MG_ALGOS)
+    target_compile_definitions(cuvs-cagra-search PUBLIC CUVS_BUILD_MG_ALGOS)
+  endif()
+
+  set(CUVS_CUSOLVER_DEPENDENCY CUDA::cusolver${_ctk_static_suffix})
+  set(CUVS_CUBLAS_DEPENDENCY CUDA::cublas${_ctk_static_suffix})
+  set(CUVS_CURAND_DEPENDENCY CUDA::curand${_ctk_static_suffix})
+  set(CUVS_CUSPARSE_DEPENDENCY CUDA::cusparse${_ctk_static_suffix})
+
+  set(CUVS_CTK_MATH_DEPENDENCIES ${CUVS_CUBLAS_DEPENDENCY} ${CUVS_CUSOLVER_DEPENDENCY}
+                                 ${CUVS_CUSPARSE_DEPENDENCY} ${CUVS_CURAND_DEPENDENCY}
   )
 
-  add_library(cuvs::cuvs ALIAS cuvs)
+  if(NOT cuvs_compile_mode STREQUAL "static_only")
+    add_library(cuvs SHARED $<TARGET_OBJECTS:cuvs_objs>)
+    add_library(cuvs::cuvs ALIAS cuvs)
+    set_target_properties(
+      cuvs
+      PROPERTIES BUILD_RPATH "\$ORIGIN"
+                 INSTALL_RPATH "\$ORIGIN"
+                 CXX_STANDARD 17
+                 CXX_STANDARD_REQUIRED ON
+                 CUDA_STANDARD 17
+                 CUDA_STANDARD_REQUIRED ON
+                 INTERFACE_POSITION_INDEPENDENT_CODE ON
+                 POSITION_INDEPENDENT_CODE ON
+    )
+    target_compile_options(
+      cuvs
+      INTERFACE $<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:--expt-extended-lambda
+                --expt-relaxed-constexpr>
+      PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
+              "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
+    )
+    target_compile_definitions(
+      cuvs PUBLIC $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>
+                  $<$<BOOL:${CUVS_NVTX}>:NVTX_ENABLED>
+    )
+
+    target_include_directories(
+      cuvs PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+                  "$<INSTALL_INTERFACE:include>"
+    )
 
-  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
+    target_link_libraries(
+      cuvs
+      PUBLIC rmm::rmm
+             raft::raft
+             ${CUVS_CTK_MATH_DEPENDENCIES}
+             $<BUILD_LOCAL_INTERFACE:$<TARGET_NAME_IF_EXISTS:NCCL::NCCL>>
+             $<BUILD_LOCAL_INTERFACE:$<TARGET_NAME_IF_EXISTS:hnswlib::hnswlib>>
+             $<$<BOOL:${CUVS_NVTX}>:CUDA::nvtx3>
+      PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
+              cuvs-cagra-search
+    )
+
+    # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
+    file(
+      WRITE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld"
+      [=[
+SECTIONS
+{
+.nvFatBinSegment : { *(.nvFatBinSegment) }
+.nv_fatbin : { *(.nv_fatbin) }
+}
+]=]
+    )
+    target_link_options(cuvs PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
+
+  endif()
+
+  if(NOT cuvs_compile_mode STREQUAL "shared_only")
+    add_library(cuvs_static STATIC $<TARGET_OBJECTS:cuvs_objs>)
     add_library(cuvs::cuvs_static ALIAS cuvs_static)
 
     set_target_properties(
@@ -552,16 +660,11 @@ if(BUILD_SHARED_LIBS)
     )
 
     target_compile_options(cuvs_static PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
-  endif()
-
-  target_include_directories(
-    cuvs_objs
-    PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-           "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-    INTERFACE "$<INSTALL_INTERFACE:include>"
-  )
+    target_compile_definitions(
+      cuvs_static PUBLIC $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>
+                         $<$<BOOL:${CUVS_NVTX}>:NVTX_ENABLED>
+    )
 
-  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
     target_include_directories(
       cuvs_static
       PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
@@ -575,102 +678,21 @@ if(BUILD_SHARED_LIBS)
       cuvs_static PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
                          "$<INSTALL_INTERFACE:include>"
     )
-  endif()
-
-  target_include_directories(
-    cuvs PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-                "$<INSTALL_INTERFACE:include>"
-  )
-
-  rapids_find_package(
-    OpenMP REQUIRED
-    BUILD_EXPORT_SET cuvs-exports
-    INSTALL_EXPORT_SET cuvs-exports
-  )
-
-  if(NOT BUILD_CPU_ONLY)
-
-    set(CUVS_CUSOLVER_DEPENDENCY CUDA::cusolver${_ctk_static_suffix})
-    set(CUVS_CUBLAS_DEPENDENCY CUDA::cublas${_ctk_static_suffix})
-    set(CUVS_CURAND_DEPENDENCY CUDA::curand${_ctk_static_suffix})
-    set(CUVS_CUSPARSE_DEPENDENCY CUDA::cusparse${_ctk_static_suffix})
-
-    set(CUVS_CTK_MATH_DEPENDENCIES ${CUVS_CUBLAS_DEPENDENCY} ${CUVS_CUSOLVER_DEPENDENCY}
-                                   ${CUVS_CUSPARSE_DEPENDENCY} ${CUVS_CURAND_DEPENDENCY}
-    )
-
-    if(BUILD_MG_ALGOS)
-      rapids_find_generate_module(
-        NCCL
-        HEADER_NAMES nccl.h
-        LIBRARY_NAMES nccl
-      )
-      find_package(NCCL REQUIRED)
-      target_link_libraries(cuvs_objs PRIVATE NCCL::NCCL)
-      target_link_libraries(cuvs PRIVATE NCCL::NCCL)
-    endif()
-
-    # Keep cuVS as lightweight as possible. Only CUDA libs and rmm should be used in global target.
     target_link_libraries(
-      cuvs
-      PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
+      cuvs_static
+      PUBLIC rmm::rmm
+             raft::raft
+             ${CUVS_CTK_MATH_DEPENDENCIES}
+             $<BUILD_LOCAL_INTERFACE:$<TARGET_NAME_IF_EXISTS:NCCL::NCCL>>
+             $<BUILD_LOCAL_INTERFACE:$<TARGET_NAME_IF_EXISTS:hnswlib::hnswlib>>
+             $<$<BOOL:${CUVS_NVTX}>:CUDA::nvtx3>
       PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-              cuvs-cagra-search
     )
-
-    if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
-      target_link_libraries(
-        cuvs_static
-        PUBLIC rmm::rmm raft::raft ${CUVS_CTK_MATH_DEPENDENCIES}
-        PRIVATE nvidia::cutlass::cutlass $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
-      )
-    endif()
-  endif()
-
-  if(BUILD_MG_ALGOS)
-    target_compile_definitions(cuvs PUBLIC CUVS_BUILD_MG_ALGOS)
-    target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_MG_ALGOS)
-    target_compile_definitions(cuvs-cagra-search PUBLIC CUVS_BUILD_MG_ALGOS)
-  endif()
-
-  if(BUILD_CAGRA_HNSWLIB)
-    target_link_libraries(cuvs_objs PRIVATE hnswlib::hnswlib)
-    target_compile_definitions(cuvs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
-    target_compile_definitions(cuvs_objs PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
-  endif()
-
-  # Endian detection
-  include(TestBigEndian)
-  test_big_endian(BIG_ENDIAN)
-  if(BIG_ENDIAN)
-    target_compile_definitions(cuvs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=0)
-  else()
-    target_compile_definitions(cuvs PRIVATE CUVS_SYSTEM_LITTLE_ENDIAN=1)
   endif()
 
-  file(
-    WRITE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld"
-    [=[
-SECTIONS
-{
-.nvFatBinSegment : { *(.nvFatBinSegment) }
-.nv_fatbin : { *(.nv_fatbin) }
-}
-]=]
-  )
-
   # ################################################################################################
   # * NVTX support in cuvs -----------------------------------------------------
-
-  if(CUVS_NVTX)
-    # This enables NVTX within the project with no option to disable it downstream.
-    target_link_libraries(cuvs PUBLIC CUDA::nvtx3)
-    target_compile_definitions(cuvs_objs PUBLIC NVTX_ENABLED)
-    target_compile_definitions(cuvs PUBLIC NVTX_ENABLED)
-
-    target_link_libraries(cuvs-cagra-search PUBLIC CUDA::nvtx3)
-    target_compile_definitions(cuvs-cagra-search PUBLIC NVTX_ENABLED)
-  else()
+  if(NOT CUVS_NVTX)
     # Allow enable NVTX downstream if not set here. This creates a new option at build/install time,
     # which is set by default to OFF, but can be enabled in the dependent project.
     get_property(
@@ -693,25 +715,6 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
     )
   endif()
 
-  set_target_properties(
-    cuvs
-    PROPERTIES BUILD_RPATH "\$ORIGIN"
-               INSTALL_RPATH "\$ORIGIN"
-               CXX_STANDARD 17
-               CXX_STANDARD_REQUIRED ON
-               CUDA_STANDARD 17
-               CUDA_STANDARD_REQUIRED ON
-               INTERFACE_POSITION_INDEPENDENT_CODE ON
-               POSITION_INDEPENDENT_CODE ON
-  )
-
-  target_compile_options(
-    cuvs PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
-                 "$<$<COMPILE_LANGUAGE:CUDA>:${CUVS_CUDA_FLAGS}>"
-  )
-  # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
-  target_link_options(cuvs PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
-
   # ################################################################################################
   # * cuvs_c -------------------------------------------------------------------------------
 
@@ -739,14 +742,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       src/distance/pairwise_distance_c.cpp
       ${CUVS_C_MG_ALGOS}
     )
-
-    if(BUILD_CAGRA_HNSWLIB)
-      target_link_libraries(cuvs_c PRIVATE hnswlib::hnswlib)
-      target_compile_definitions(cuvs_c PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
-    endif()
-
     add_library(cuvs::c_api ALIAS cuvs_c)
-
     set_target_properties(
       cuvs_c
       PROPERTIES BUILD_RPATH "\$ORIGIN"
@@ -758,6 +754,10 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
                  EXPORT_NAME c_api
     )
 
+    target_compile_definitions(
+      cuvs_c PUBLIC $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>
+    )
+
     target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
 
     target_include_directories(
@@ -770,7 +770,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
     target_link_libraries(
       cuvs_c
       PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
-      PRIVATE raft::raft
+      PRIVATE raft::raft $<TARGET_NAME_IF_EXISTS:hnswlib::hnswlib>
     )
 
     # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
@@ -783,11 +783,6 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
   include(GNUInstallDirs)
   include(CPack)
 
-  set(_cuvs_lib_targets cuvs)
-  if(NOT CUVS_COMPILE_DYNAMIC_ONLY)
-    list(APPEND _cuvs_lib_targets cuvs_static)
-  endif()
-
   install(
     TARGETS ${_cuvs_lib_targets}
     DESTINATION ${lib_dir}
diff --git a/cpp/cmake/thirdparty/get_raft.cmake b/cpp/cmake/thirdparty/get_raft.cmake
index 424e829cb6..6a08edb2ab 100644
--- a/cpp/cmake/thirdparty/get_raft.cmake
+++ b/cpp/cmake/thirdparty/get_raft.cmake
@@ -17,16 +17,17 @@ set(RAFT_FORK "rapidsai")
 set(RAFT_PINNED_TAG "${rapids-cmake-checkout-tag}")
 
 function(find_and_configure_raft)
-    set(oneValueArgs VERSION FORK PINNED_TAG USE_RAFT_STATIC ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES CLONE_ON_PIN)
+    set(oneValueArgs VERSION FORK PINNED_TAG BUILD_STATIC_DEPS ENABLE_NVTX ENABLE_MNMG_DEPENDENCIES CLONE_ON_PIN)
     cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
             "${multiValueArgs}" ${ARGN} )
 
     if(PKG_CLONE_ON_PIN AND NOT PKG_PINNED_TAG STREQUAL "${rapids-cmake-checkout-tag}")
         message(STATUS "cuVS: RAFT pinned tag found: ${PKG_PINNED_TAG}. Cloning raft locally.")
         set(CPM_DOWNLOAD_raft ON)
-    elseif(PKG_USE_RAFT_STATIC AND (NOT CPM_raft_SOURCE))
+    elseif(PKG_BUILD_STATIC_DEPS AND (NOT CPM_raft_SOURCE))
         message(STATUS "cuVS: Cloning raft locally to build static libraries.")
         set(CPM_DOWNLOAD_raft ON)
+        set(BUILD_SHARED_LIBS OFF)
     endif()
 
     set(RAFT_COMPONENTS "")
@@ -65,7 +66,7 @@ find_and_configure_raft(VERSION  ${RAFT_VERSION}.00
         PINNED_TAG               ${RAFT_PINNED_TAG}
         ENABLE_MNMG_DEPENDENCIES OFF
         ENABLE_NVTX              OFF
-        USE_RAFT_STATIC ${CUVS_USE_RAFT_STATIC}
+        BUILD_STATIC_DEPS ${CUVS_STATIC_RAPIDS_LIBRARIES}
         # When PINNED_TAG above doesn't match the default rapids branch,
         # force local raft clone in build directory
         # even if it's already installed.
diff --git a/python/libcuvs/CMakeLists.txt b/python/libcuvs/CMakeLists.txt
index 02574ac104..aec24b9ca0 100644
--- a/python/libcuvs/CMakeLists.txt
+++ b/python/libcuvs/CMakeLists.txt
@@ -42,7 +42,7 @@ set(CUDA_STATIC_RUNTIME ON)
 set(CUDA_STATIC_MATH_LIBRARIES OFF)
 
 # --- RAFT ---#
-set(CUVS_USE_RAFT_STATIC OFF)
+set(CUVS_STATIC_RAPIDS_LIBRARIES OFF)
 
 # --- cuVS ---#
 set(BUILD_TESTS OFF)

From 5e8c0396d83b9c8270bf0058b1e374587c74cd26 Mon Sep 17 00:00:00 2001
From: Jake Awe <jawe@nvidia.com>
Date: Thu, 18 Sep 2025 16:22:33 +0000
Subject: [PATCH 586/623] DOC v25.12 Updates [skip ci]

---
 .../cuda12.9-conda/devcontainer.json          |  6 +--
 .devcontainer/cuda12.9-pip/devcontainer.json  |  8 ++--
 .../cuda13.0-conda/devcontainer.json          |  6 +--
 .devcontainer/cuda13.0-pip/devcontainer.json  |  8 ++--
 .github/workflows/build.yaml                  | 30 +++++++-------
 .github/workflows/pr.yaml                     | 40 +++++++++----------
 .github/workflows/publish-rust.yaml           |  2 +-
 .github/workflows/test.yaml                   | 12 +++---
 .../trigger-breaking-change-alert.yaml        |  2 +-
 RAPIDS_BRANCH                                 |  2 +-
 README.md                                     |  8 ++--
 VERSION                                       |  2 +-
 .../all_cuda-129_arch-aarch64.yaml            |  4 +-
 .../all_cuda-129_arch-x86_64.yaml             |  4 +-
 .../all_cuda-130_arch-aarch64.yaml            |  4 +-
 .../all_cuda-130_arch-x86_64.yaml             |  4 +-
 .../bench_ann_cuda-129_arch-aarch64.yaml      |  8 ++--
 .../bench_ann_cuda-129_arch-x86_64.yaml       |  8 ++--
 .../bench_ann_cuda-130_arch-aarch64.yaml      |  8 ++--
 .../bench_ann_cuda-130_arch-x86_64.yaml       |  8 ++--
 .../go_cuda-129_arch-aarch64.yaml             |  4 +-
 .../environments/go_cuda-129_arch-x86_64.yaml |  4 +-
 .../go_cuda-130_arch-aarch64.yaml             |  4 +-
 .../environments/go_cuda-130_arch-x86_64.yaml |  4 +-
 .../rust_cuda-129_arch-aarch64.yaml           |  4 +-
 .../rust_cuda-129_arch-x86_64.yaml            |  4 +-
 .../rust_cuda-130_arch-aarch64.yaml           |  4 +-
 .../rust_cuda-130_arch-x86_64.yaml            |  4 +-
 dependencies.yaml                             | 32 +++++++--------
 docs/source/cuvs_bench/index.rst              |  8 ++--
 docs/source/developer_guide.md                |  4 +-
 examples/go/README.md                         |  2 +-
 java/benchmarks/pom.xml                       |  4 +-
 java/build.sh                                 |  2 +-
 java/cuvs-java/pom.xml                        |  2 +-
 java/examples/README.md                       |  6 +--
 java/examples/pom.xml                         |  4 +-
 python/cuvs/pyproject.toml                    | 10 ++---
 python/cuvs_bench/cuvs_bench/plot/__main__.py |  2 +-
 python/cuvs_bench/pyproject.toml              |  2 +-
 python/libcuvs/pyproject.toml                 |  8 ++--
 rust/Cargo.toml                               |  2 +-
 rust/cuvs/Cargo.toml                          |  2 +-
 43 files changed, 148 insertions(+), 148 deletions(-)

diff --git a/.devcontainer/cuda12.9-conda/devcontainer.json b/.devcontainer/cuda12.9-conda/devcontainer.json
index be50d56c15..f7565bbeaa 100644
--- a/.devcontainer/cuda12.9-conda/devcontainer.json
+++ b/.devcontainer/cuda12.9-conda/devcontainer.json
@@ -5,19 +5,19 @@
     "args": {
       "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.10-cpp-mambaforge"
+      "BASE": "rapidsai/devcontainers:25.12-cpp-mambaforge"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-conda",
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.12-cuda12.9-conda",
     "--ulimit",
     "nofile=500000"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda12.9-pip/devcontainer.json b/.devcontainer/cuda12.9-pip/devcontainer.json
index abb88bb356..b7b43b9b45 100644
--- a/.devcontainer/cuda12.9-pip/devcontainer.json
+++ b/.devcontainer/cuda12.9-pip/devcontainer.json
@@ -5,26 +5,26 @@
     "args": {
       "CUDA": "12.9",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda12.9-ucx1.19.0-openmpi5.0.7"
+      "BASE": "rapidsai/devcontainers:25.12-cpp-cuda12.9-ucx1.19.0-openmpi5.0.7"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda12.9-pip",
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.12-cuda12.9-pip",
     "--ulimit",
     "nofile=500000"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.10": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.12": {
       "version": "12.9",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.devcontainer/cuda13.0-conda/devcontainer.json b/.devcontainer/cuda13.0-conda/devcontainer.json
index e69607dc06..f4e2e662eb 100644
--- a/.devcontainer/cuda13.0-conda/devcontainer.json
+++ b/.devcontainer/cuda13.0-conda/devcontainer.json
@@ -5,19 +5,19 @@
     "args": {
       "CUDA": "13.0",
       "PYTHON_PACKAGE_MANAGER": "conda",
-      "BASE": "rapidsai/devcontainers:25.10-cpp-mambaforge"
+      "BASE": "rapidsai/devcontainers:25.12-cpp-mambaforge"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda13.0-conda",
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.12-cuda13.0-conda",
     "--ulimit",
     "nofile=500000"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
diff --git a/.devcontainer/cuda13.0-pip/devcontainer.json b/.devcontainer/cuda13.0-pip/devcontainer.json
index 27f0acd3f4..1fd011180e 100644
--- a/.devcontainer/cuda13.0-pip/devcontainer.json
+++ b/.devcontainer/cuda13.0-pip/devcontainer.json
@@ -5,26 +5,26 @@
     "args": {
       "CUDA": "13.0",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.10-cpp-cuda13.0-ucx1.19.0-openmpi5.0.7"
+      "BASE": "rapidsai/devcontainers:25.12-cpp-cuda13.0-ucx1.19.0-openmpi5.0.7"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.10-cuda13.0-pip",
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.12-cuda13.0-pip",
     "--ulimit",
     "nofile=500000"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
-    "ghcr.io/rapidsai/devcontainers/features/cuda:25.10": {
+    "ghcr.io/rapidsai/devcontainers/features/cuda:25.12": {
       "version": "13.0",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
       "installcuSPARSE": true
     },
-    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.10": {}
+    "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:25.12": {}
   },
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/ucx",
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 892764844b..353481d326 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -34,7 +34,7 @@ concurrency:
 jobs:
   cpp-build:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -44,7 +44,7 @@ jobs:
   rust-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -58,14 +58,14 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      container_image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_rust.sh"
       sha: ${{ inputs.sha }}
   go-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -79,14 +79,14 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      container_image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       node_type: "gpu-l4-latest-1"
       script: "ci/build_go.sh"
       sha: ${{ inputs.sha }}
   java-build:
     needs: cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -100,7 +100,7 @@ jobs:
       branch: ${{ inputs.branch }}
       arch: "amd64"
       date: ${{ inputs.date }}
-      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      container_image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/build_java.sh"
       artifact-name: "cuvs-java-cuda${{ matrix.cuda_version }}"
       file_to_upload: "java/cuvs-java/target/"
@@ -108,7 +108,7 @@ jobs:
   python-build:
     needs: [cpp-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -118,7 +118,7 @@ jobs:
   upload-conda:
     needs: [cpp-build, python-build]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -133,19 +133,19 @@ jobs:
     if: github.ref_type == 'branch'
     needs: python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     with:
       arch: "amd64"
       branch: ${{ inputs.branch }}
       build_type: ${{ inputs.build_type || 'branch' }}
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.12-latest"
       date: ${{ inputs.date }}
       node_type: "gpu-l4-latest-1"
       script: "ci/build_docs.sh"
       sha: ${{ inputs.sha }}
   wheel-build-libcuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -159,7 +159,7 @@ jobs:
   wheel-publish-libcuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -170,7 +170,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -182,7 +182,7 @@ jobs:
   wheel-publish-cuvs:
     needs: wheel-build-cuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 15b3f35efb..64d0c756c6 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -27,7 +27,7 @@ jobs:
       - devcontainer
       - telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-25.12
     if: always()
     with:
       needs: ${{ toJSON(needs) }}
@@ -54,7 +54,7 @@ jobs:
   changed-files:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/changed-files.yaml@branch-25.12
     with:
       files_yaml: |
         test_cpp:
@@ -106,14 +106,14 @@ jobs:
   checks:
     needs: telemetry-setup
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-25.12
     with:
       enable_check_generated_files: false
       ignored_pr_jobs: "telemetry-summarize"
   conda-cpp-build:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-25.12
     with:
       build_type: pull-request
       node_type: cpu16
@@ -121,7 +121,7 @@ jobs:
   conda-cpp-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.12
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_cpp
     with:
       build_type: pull-request
@@ -129,21 +129,21 @@ jobs:
   conda-cpp-checks:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.12
     with:
       build_type: pull-request
       symbol_exclusions: (void (thrust::|cub::))
   conda-python-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-25.12
     with:
       build_type: pull-request
       script: ci/build_python.sh
   conda-python-tests:
     needs: [conda-python-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.12
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -151,7 +151,7 @@ jobs:
   conda-java-tests:
     needs: [conda-cpp-build, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_java
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
@@ -165,24 +165,24 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      container_image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/test_java.sh"
       artifact-name: "cuvs-java-cuda${{ matrix.cuda_version }}"
       file_to_upload: "java/cuvs-java/target/"
   docs-build:
     needs: conda-python-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-latest"
+      container_image: "rapidsai/ci-conda:25.12-latest"
       script: "ci/build_docs.sh"
   rust-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -195,12 +195,12 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      container_image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/build_rust.sh"
   go-build:
     needs: conda-cpp-build
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -213,12 +213,12 @@ jobs:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      container_image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/build_go.sh"
   wheel-build-libcuvs:
     needs: checks
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.12
     with:
       build_type: pull-request
       script: ci/build_wheel_libcuvs.sh
@@ -229,7 +229,7 @@ jobs:
   wheel-build-cuvs:
     needs: wheel-build-libcuvs
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-25.12
     with:
       build_type: pull-request
       script: ci/build_wheel_cuvs.sh
@@ -238,7 +238,7 @@ jobs:
   wheel-tests-cuvs:
     needs: [wheel-build-cuvs, changed-files]
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.12
     if: fromJSON(needs.changed-files.outputs.changed_file_groups).test_python
     with:
       build_type: pull-request
@@ -246,7 +246,7 @@ jobs:
   devcontainer:
     secrets: inherit
     needs: telemetry-setup
-    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-25.12
     with:
       arch: '["amd64", "arm64"]'
       cuda: '["13.0"]'
diff --git a/.github/workflows/publish-rust.yaml b/.github/workflows/publish-rust.yaml
index c74af7e7f1..aa9438e55e 100644
--- a/.github/workflows/publish-rust.yaml
+++ b/.github/workflows/publish-rust.yaml
@@ -16,7 +16,7 @@ jobs:
         cuda_version:
           - '12.9.1'
     container:
-      image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
     steps:
       - uses: actions/checkout@v4
       - name: Check if release build
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index c5892ca7ea..eacd720a18 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -25,7 +25,7 @@ on:
 jobs:
   conda-cpp-checks:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-post-build-checks.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -34,7 +34,7 @@ jobs:
       symbol_exclusions: (void (thrust::|cub::))
   conda-cpp-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -43,7 +43,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-python-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
@@ -52,7 +52,7 @@ jobs:
       sha: ${{ inputs.sha }}
   conda-java-tests:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-25.12
     # Artifacts are not published from these jobs, so it's safe to run for multiple CUDA versions.
     # If these jobs start producing artifacts, the names will have to differentiate between CUDA versions.
     strategy:
@@ -68,11 +68,11 @@ jobs:
       sha: ${{ inputs.sha }}
       node_type: "gpu-l4-latest-1"
       arch: "amd64"
-      container_image: "rapidsai/ci-conda:25.10-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
+      container_image: "rapidsai/ci-conda:25.12-cuda${{ matrix.cuda_version }}-ubuntu24.04-py3.13"
       script: "ci/test_java.sh"
   wheel-tests-cuvs:
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-25.12
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/trigger-breaking-change-alert.yaml b/.github/workflows/trigger-breaking-change-alert.yaml
index 48bf37afc4..c3cce1c34f 100644
--- a/.github/workflows/trigger-breaking-change-alert.yaml
+++ b/.github/workflows/trigger-breaking-change-alert.yaml
@@ -12,7 +12,7 @@ jobs:
   trigger-notifier:
     if: contains(github.event.pull_request.labels.*.name, 'breaking')
     secrets: inherit
-    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.10
+    uses: rapidsai/shared-workflows/.github/workflows/breaking-change-alert.yaml@branch-25.12
     with:
       sender_login: ${{ github.event.sender.login }}
       sender_avatar: ${{ github.event.sender.avatar_url }}
diff --git a/RAPIDS_BRANCH b/RAPIDS_BRANCH
index 9b1c52d941..a2b5a91fc4 100644
--- a/RAPIDS_BRANCH
+++ b/RAPIDS_BRANCH
@@ -1 +1 @@
-branch-25.10
+branch-25.12
diff --git a/README.md b/README.md
index a81dd1b64f..a54466aa88 100755
--- a/README.md
+++ b/README.md
@@ -108,10 +108,10 @@ If installing a version that has not yet been released, the `rapidsai` channel c
 
 ```bash
 # CUDA 13
-conda install -c rapidsai-nightly -c conda-forge cuvs=25.10 cuda-version=13.0
+conda install -c rapidsai-nightly -c conda-forge cuvs=25.12 cuda-version=13.0
 
 # CUDA 12
-conda install -c rapidsai-nightly -c conda-forge cuvs=25.10 cuda-version=12.9
+conda install -c rapidsai-nightly -c conda-forge cuvs=25.12 cuda-version=12.9
 ```
 
 cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
@@ -171,7 +171,7 @@ cuvsCagraIndexParamsDestroy(index_params);
 cuvsResourcesDestroy(res);
 ```
 
-For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.10/examples/c)
+For more code examples of the C APIs, including drop-in Cmake project templates, please refer to the [C examples](https://github.com/rapidsai/cuvs/tree/branch-25.12/examples/c)
 
 ### Rust API
 
@@ -234,7 +234,7 @@ fn cagra_example() -> Result<()> {
 }
 ```
 
-For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.10/examples/rust).
+For more code examples of the Rust APIs, including a drop-in project templates, please refer to the [Rust examples](https://github.com/rapidsai/cuvs/tree/branch-25.12/examples/rust).
 
 ## Contributing
 
diff --git a/VERSION b/VERSION
index 296e35288d..7924af6192 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-25.10.00
+25.12.00
diff --git a/conda/environments/all_cuda-129_arch-aarch64.yaml b/conda/environments/all_cuda-129_arch-aarch64.yaml
index a877f09e23..af56842a11 100644
--- a/conda/environments/all_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-129_arch-aarch64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.10.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pytest
 - pytest-cov
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
diff --git a/conda/environments/all_cuda-129_arch-x86_64.yaml b/conda/environments/all_cuda-129_arch-x86_64.yaml
index 588bfa8d64..44739ffd6a 100644
--- a/conda/environments/all_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-129_arch-x86_64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.10.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pytest
 - pytest-cov
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
diff --git a/conda/environments/all_cuda-130_arch-aarch64.yaml b/conda/environments/all_cuda-130_arch-aarch64.yaml
index ebf59a3f99..3a81210868 100644
--- a/conda/environments/all_cuda-130_arch-aarch64.yaml
+++ b/conda/environments/all_cuda-130_arch-aarch64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.10.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pytest
 - pytest-cov
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
diff --git a/conda/environments/all_cuda-130_arch-x86_64.yaml b/conda/environments/all_cuda-130_arch-x86_64.yaml
index 8433f4adff..2e93cf1489 100644
--- a/conda/environments/all_cuda-130_arch-x86_64.yaml
+++ b/conda/environments/all_cuda-130_arch-x86_64.yaml
@@ -31,7 +31,7 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- librmm==25.10.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
@@ -39,7 +39,7 @@ dependencies:
 - numpydoc
 - openblas
 - pre-commit
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pytest
 - pytest-cov
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
diff --git a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
index 7735923261..5fe174ab6a 100644
--- a/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-aarch64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-python>=12.9.2,<13.0a0
 - cuda-version=12.9
 - cupy>=13.6.0
-- cuvs==25.10.*,>=0.0.0a0
+- cuvs==25.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -30,15 +30,15 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- librmm==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
index 96e2c34d5d..098e1765fa 100644
--- a/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-129_arch-x86_64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-python>=12.9.2,<13.0a0
 - cuda-version=12.9
 - cupy>=13.6.0
-- cuvs==25.10.*,>=0.0.0a0
+- cuvs==25.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,8 +32,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- librmm==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - mkl-devel=2023
 - nccl>=2.19
@@ -41,7 +41,7 @@ dependencies:
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-130_arch-aarch64.yaml b/conda/environments/bench_ann_cuda-130_arch-aarch64.yaml
index 939a24b445..3a54fecedb 100644
--- a/conda/environments/bench_ann_cuda-130_arch-aarch64.yaml
+++ b/conda/environments/bench_ann_cuda-130_arch-aarch64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-python>=13.0.1,<14.0a0
 - cuda-version=13.0
 - cupy>=13.6.0
-- cuvs==25.10.*,>=0.0.0a0
+- cuvs==25.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -30,15 +30,15 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- librmm==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - nccl>=2.19
 - ninja
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
 - requests
diff --git a/conda/environments/bench_ann_cuda-130_arch-x86_64.yaml b/conda/environments/bench_ann_cuda-130_arch-x86_64.yaml
index 296c1fc03a..95aa4d7a13 100644
--- a/conda/environments/bench_ann_cuda-130_arch-x86_64.yaml
+++ b/conda/environments/bench_ann_cuda-130_arch-x86_64.yaml
@@ -18,7 +18,7 @@ dependencies:
 - cuda-python>=13.0.1,<14.0a0
 - cuda-version=13.0
 - cupy>=13.6.0
-- cuvs==25.10.*,>=0.0.0a0
+- cuvs==25.12.*,>=0.0.0a0
 - cxx-compiler
 - cython>=3.0.0
 - dlpack>=0.8,<1.0
@@ -32,8 +32,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- librmm==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- librmm==25.12.*,>=0.0.0a0
 - matplotlib-base>=3.9
 - mkl-devel=2023
 - nccl>=2.19
@@ -41,7 +41,7 @@ dependencies:
 - nlohmann_json>=3.11.2
 - openblas
 - pandas
-- pylibraft==25.10.*,>=0.0.0a0
+- pylibraft==25.12.*,>=0.0.0a0
 - pyyaml
 - rapids-build-backend>=0.4.0,<0.5.0.dev0
 - requests
diff --git a/conda/environments/go_cuda-129_arch-aarch64.yaml b/conda/environments/go_cuda-129_arch-aarch64.yaml
index a23a3e636b..b8bf557877 100644
--- a/conda/environments/go_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-129_arch-aarch64.yaml
@@ -24,8 +24,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
diff --git a/conda/environments/go_cuda-129_arch-x86_64.yaml b/conda/environments/go_cuda-129_arch-x86_64.yaml
index a6bdbe0159..adc12d644b 100644
--- a/conda/environments/go_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-129_arch-x86_64.yaml
@@ -24,8 +24,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
diff --git a/conda/environments/go_cuda-130_arch-aarch64.yaml b/conda/environments/go_cuda-130_arch-aarch64.yaml
index 3359dd084a..ca450a317c 100644
--- a/conda/environments/go_cuda-130_arch-aarch64.yaml
+++ b/conda/environments/go_cuda-130_arch-aarch64.yaml
@@ -24,8 +24,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-aarch64==2.28
diff --git a/conda/environments/go_cuda-130_arch-x86_64.yaml b/conda/environments/go_cuda-130_arch-x86_64.yaml
index a9a6a90cf1..5873836633 100644
--- a/conda/environments/go_cuda-130_arch-x86_64.yaml
+++ b/conda/environments/go_cuda-130_arch-x86_64.yaml
@@ -24,8 +24,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - nccl>=2.19
 - ninja
 - sysroot_linux-64==2.28
diff --git a/conda/environments/rust_cuda-129_arch-aarch64.yaml b/conda/environments/rust_cuda-129_arch-aarch64.yaml
index 3a94cc2488..28d7701d68 100644
--- a/conda/environments/rust_cuda-129_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-129_arch-aarch64.yaml
@@ -21,8 +21,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/conda/environments/rust_cuda-129_arch-x86_64.yaml b/conda/environments/rust_cuda-129_arch-x86_64.yaml
index 98545cc195..a21932185b 100644
--- a/conda/environments/rust_cuda-129_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-129_arch-x86_64.yaml
@@ -21,8 +21,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/conda/environments/rust_cuda-130_arch-aarch64.yaml b/conda/environments/rust_cuda-130_arch-aarch64.yaml
index 751e44f36b..7533f45e23 100644
--- a/conda/environments/rust_cuda-130_arch-aarch64.yaml
+++ b/conda/environments/rust_cuda-130_arch-aarch64.yaml
@@ -21,8 +21,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/conda/environments/rust_cuda-130_arch-x86_64.yaml b/conda/environments/rust_cuda-130_arch-x86_64.yaml
index 56f7c9830e..0b4dbd7b09 100644
--- a/conda/environments/rust_cuda-130_arch-x86_64.yaml
+++ b/conda/environments/rust_cuda-130_arch-x86_64.yaml
@@ -21,8 +21,8 @@ dependencies:
 - libcurand-dev
 - libcusolver-dev
 - libcusparse-dev
-- libcuvs==25.10.*,>=0.0.0a0
-- libraft==25.10.*,>=0.0.0a0
+- libcuvs==25.12.*,>=0.0.0a0
+- libraft==25.12.*,>=0.0.0a0
 - make
 - nccl>=2.19
 - ninja
diff --git a/dependencies.yaml b/dependencies.yaml
index adb923afab..fcb83485f7 100644
--- a/dependencies.yaml
+++ b/dependencies.yaml
@@ -475,7 +475,7 @@ dependencies:
       - output_types: [conda, pyproject, requirements]
         packages:
           - click
-          - cuvs==25.10.*,>=0.0.0a0
+          - cuvs==25.12.*,>=0.0.0a0
           - pandas
           - pyyaml
           - requests
@@ -502,17 +502,17 @@ dependencies:
     common:
       - output_types: conda
         packages:
-          - cuvs==25.10.*,>=0.0.0a0
+          - cuvs==25.12.*,>=0.0.0a0
   depends_on_cuvs_bench:
     common:
       - output_types: conda
         packages:
-          - cuvs-bench==25.10.*,>=0.0.0a0
+          - cuvs-bench==25.12.*,>=0.0.0a0
   depends_on_libcuvs:
     common:
       - output_types: conda
         packages:
-          - &libcuvs_unsuffixed libcuvs==25.10.*,>=0.0.0a0
+          - &libcuvs_unsuffixed libcuvs==25.12.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -525,23 +525,23 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu12==25.10.*,>=0.0.0a0
+              - libcuvs-cu12==25.12.*,>=0.0.0a0
           - matrix:
               cuda: "13.*"
               cuda_suffixed: "true"
             packages:
-              - libcuvs-cu13==25.10.*,>=0.0.0a0
+              - libcuvs-cu13==25.12.*,>=0.0.0a0
           - {matrix: null, packages: [*libcuvs_unsuffixed]}
   depends_on_libcuvs_tests:
     common:
       - output_types: conda
         packages:
-          - libcuvs-tests==25.10.*,>=0.0.0a0
+          - libcuvs-tests==25.12.*,>=0.0.0a0
   depends_on_libraft:
     common:
       - output_types: conda
         packages:
-          - &libraft_unsuffixed libraft==25.10.*,>=0.0.0a0
+          - &libraft_unsuffixed libraft==25.12.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -554,18 +554,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu12==25.10.*,>=0.0.0a0
+              - libraft-cu12==25.12.*,>=0.0.0a0
           - matrix:
               cuda: "13.*"
               cuda_suffixed: "true"
             packages:
-              - libraft-cu13==25.10.*,>=0.0.0a0
+              - libraft-cu13==25.12.*,>=0.0.0a0
           - {matrix: null, packages: [*libraft_unsuffixed]}
   depends_on_librmm:
     common:
       - output_types: conda
         packages:
-          - &librmm_unsuffixed librmm==25.10.*,>=0.0.0a0
+          - &librmm_unsuffixed librmm==25.12.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -578,18 +578,18 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu12==25.10.*,>=0.0.0a0
+              - librmm-cu12==25.12.*,>=0.0.0a0
           - matrix:
               cuda: "13.*"
               cuda_suffixed: "true"
             packages:
-              - librmm-cu13==25.10.*,>=0.0.0a0
+              - librmm-cu13==25.12.*,>=0.0.0a0
           - {matrix: null, packages: [*librmm_unsuffixed]}
   depends_on_pylibraft:
     common:
       - output_types: conda
         packages:
-          - &pylibraft_unsuffixed pylibraft==25.10.*,>=0.0.0a0
+          - &pylibraft_unsuffixed pylibraft==25.12.*,>=0.0.0a0
       - output_types: requirements
         packages:
           # pip recognizes the index as a global option for the requirements.txt file
@@ -602,12 +602,12 @@ dependencies:
               cuda: "12.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu12==25.10.*,>=0.0.0a0
+              - pylibraft-cu12==25.12.*,>=0.0.0a0
           - matrix:
               cuda: "13.*"
               cuda_suffixed: "true"
             packages:
-              - pylibraft-cu13==25.10.*,>=0.0.0a0
+              - pylibraft-cu13==25.12.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibraft_unsuffixed]}
   depends_on_nccl:
     common:
diff --git a/docs/source/cuvs_bench/index.rst b/docs/source/cuvs_bench/index.rst
index 0ce5ac8fff..16914ac596 100644
--- a/docs/source/cuvs_bench/index.rst
+++ b/docs/source/cuvs_bench/index.rst
@@ -89,7 +89,7 @@ The following command pulls the nightly container for Python version 3.10, CUDA
 
 .. code-block:: bash
 
-   docker pull rapidsai/cuvs-bench:25.10a-cuda12.5-py3.10 # substitute cuvs-bench for the exact desired container.
+   docker pull rapidsai/cuvs-bench:25.12a-cuda12.5-py3.10 # substitute cuvs-bench for the exact desired container.
 
 The CUDA and python versions can be changed for the supported values:
 - Supported CUDA versions: 12
@@ -237,7 +237,7 @@ For GPU-enabled systems, the `DATA_FOLDER` variable should be a local folder whe
     export DATA_FOLDER=path/to/store/datasets/and/results
     docker run --gpus all --rm -it -u $(id -u)                      \
         -v $DATA_FOLDER:/data/benchmarks                            \
-        rapidsai/cuvs-bench:25.10-cuda12.9-py3.13              \
+        rapidsai/cuvs-bench:25.12-cuda12.9-py3.13              \
         "--dataset deep-image-96-angular"                           \
         "--normalize"                                               \
         "--algorithms cuvs_cagra,cuvs_ivf_pq --batch-size 10 -k 10" \
@@ -250,7 +250,7 @@ Usage of the above command is as follows:
  * - Argument
    - Description
 
- * - `rapidsai/cuvs-bench:25.10-cuda12.9-py3.13`
+ * - `rapidsai/cuvs-bench:25.12-cuda12.9-py3.13`
    - Image to use. Can be either `cuvs-bench` or `cuvs-bench-datasets`
 
  * - `"--dataset deep-image-96-angular"`
@@ -297,7 +297,7 @@ All of the `cuvs-bench` images contain the Conda packages, so they can be used d
         --entrypoint /bin/bash                          \
         --workdir /data/benchmarks                      \
         -v $DATA_FOLDER:/data/benchmarks                \
-        rapidsai/cuvs-bench:25.10-cuda12.9-py3.13
+        rapidsai/cuvs-bench:25.12-cuda12.9-py3.13
 
 This will drop you into a command line in the container, with the `cuvs-bench` python package ready to use, as described in the [Running the benchmarks](#running-the-benchmarks) section above:
 
diff --git a/docs/source/developer_guide.md b/docs/source/developer_guide.md
index b4bc021e3d..48eafc2f62 100644
--- a/docs/source/developer_guide.md
+++ b/docs/source/developer_guide.md
@@ -187,7 +187,7 @@ RAFT relies on `clang-format` to enforce code style across all C++ and CUDA sour
 1. Do not split empty functions/records/namespaces.
 2. Two-space indentation everywhere, including the line continuations.
 3. Disable reflowing of comments.
-   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.10/cpp/.clang-format).
+   The reasons behind these deviations from the Google style guide are given in comments [here](https://github.com/rapidsai/raft/blob/branch-25.12/cpp/.clang-format).
 
 [`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
 In order to run doxygen as a linter on C++/CUDA code, run
@@ -205,7 +205,7 @@ you can run  `codespell -i 3 -w .` from the repository root directory.
 This will bring up an interactive prompt to select which spelling fixes to apply.
 
 ### #include style
-[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.10/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
+[include_checker.py](https://github.com/rapidsai/raft/blob/branch-25.12/cpp/scripts/include_checker.py) is used to enforce the include style as follows:
 1. `#include "..."` should be used for referencing local files only. It is acceptable to be used for referencing files in a sub-folder/parent-folder of the same algorithm, but should never be used to include files in other algorithms or between algorithms and the primitives or other dependencies.
 2. `#include <...>` should be used for referencing everything else
 
diff --git a/examples/go/README.md b/examples/go/README.md
index 093e87d17d..e4242de8c5 100644
--- a/examples/go/README.md
+++ b/examples/go/README.md
@@ -24,7 +24,7 @@ export CC=clang
 
 2. Install the Go module:
 ```bash
-go get github.com/rapidsai/cuvs/go@v25.10.00 # 25.02.00 being your desired version, selected from https://github.com/rapidsai/cuvs/tags
+go get github.com/rapidsai/cuvs/go@v25.12.00 # 25.02.00 being your desired version, selected from https://github.com/rapidsai/cuvs/tags
 ```
 Then you can build your project with the usual `go build`.
 
diff --git a/java/benchmarks/pom.xml b/java/benchmarks/pom.xml
index f80e197c70..bb2fe45941 100644
--- a/java/benchmarks/pom.xml
+++ b/java/benchmarks/pom.xml
@@ -21,7 +21,7 @@
 
     <groupId>com.nvidia.cuvs</groupId>
     <artifactId>benchmarks</artifactId>
-    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.12.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     <packaging>jar</packaging>
 
     <name>cuvs-java-benchmarks</name>
@@ -41,7 +41,7 @@
         <dependency>
             <groupId>com.nvidia.cuvs</groupId>
             <artifactId>cuvs-java</artifactId>
-            <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+            <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.12.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
             <type>jar</type>
         </dependency>
     </dependencies>
diff --git a/java/build.sh b/java/build.sh
index cbb1b9ed48..b8ef3374f3 100755
--- a/java/build.sh
+++ b/java/build.sh
@@ -7,7 +7,7 @@ set -e -u -o pipefail
 ARGS="$*"
 NUMARGS=$#
 
-VERSION="25.10.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
+VERSION="25.12.0" # Note: The version is updated automatically when ci/release/update-version.sh is invoked
 GROUP_ID="com.nvidia.cuvs"
 
 # Identify CUDA major version.
diff --git a/java/cuvs-java/pom.xml b/java/cuvs-java/pom.xml
index 5e79d0c595..bbea6cd252 100644
--- a/java/cuvs-java/pom.xml
+++ b/java/cuvs-java/pom.xml
@@ -22,7 +22,7 @@
     <groupId>com.nvidia.cuvs</groupId>
     <artifactId>cuvs-java</artifactId>
     <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+    <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.12.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     <name>cuvs-java</name>
     <description>
         This project provides Java bindings for cuVS, enabling approximate nearest neighbors search and clustering
diff --git a/java/examples/README.md b/java/examples/README.md
index 29826e96a6..9a48ad6ea1 100644
--- a/java/examples/README.md
+++ b/java/examples/README.md
@@ -11,17 +11,17 @@ This maven project contains examples for CAGRA, HNSW, and Bruteforce algorithms.
 ### CAGRA Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.10.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.10.0/cuvs-java-25.10.0.jar com.nvidia.cuvs.examples.CagraExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.12.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.12.0/cuvs-java-25.12.0.jar com.nvidia.cuvs.examples.CagraExample
 ```
 
 ### HNSW Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.10.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.10.0/cuvs-java-25.10.0.jar com.nvidia.cuvs.examples.HnswExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.12.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.12.0/cuvs-java-25.12.0.jar com.nvidia.cuvs.examples.HnswExample
 ```
 
 ### Bruteforce Example
 In the current directory do:
 ```
-mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.10.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.10.0/cuvs-java-25.10.0.jar com.nvidia.cuvs.examples.BruteForceExample
+mvn package && java --enable-native-access=ALL-UNNAMED -cp target/cuvs-java-examples-25.12.0.jar:$HOME/.m2/repository/com/nvidia/cuvs/cuvs-java/25.12.0/cuvs-java-25.12.0.jar com.nvidia.cuvs.examples.BruteForceExample
 ```
diff --git a/java/examples/pom.xml b/java/examples/pom.xml
index 0c52685672..8ab8a7a560 100644
--- a/java/examples/pom.xml
+++ b/java/examples/pom.xml
@@ -5,7 +5,7 @@
   <groupId>com.nvidia.cuvs.examples</groupId>
   <artifactId>cuvs-java-examples</artifactId>
   <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+  <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.12.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
   <name>cuvs-java-examples</name>
 
   <properties>
@@ -18,7 +18,7 @@
       <groupId>com.nvidia.cuvs</groupId>
       <artifactId>cuvs-java</artifactId>
       <!-- NOTE: The version automatically gets updated when ci/release/update-version.sh is invoked. -->
-      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.10.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
+      <!--CUVS_JAVA#VERSION_UPDATE_MARKER_START--><version>25.12.0</version><!--CUVS_JAVA#VERSION_UPDATE_MARKER_END-->
     </dependency>
 
     <dependency>
diff --git a/python/cuvs/pyproject.toml b/python/cuvs/pyproject.toml
index b5b7dba8cf..98c097fc2e 100644
--- a/python/cuvs/pyproject.toml
+++ b/python/cuvs/pyproject.toml
@@ -32,9 +32,9 @@ license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-python>=13.0.1,<14.0a0",
-    "libcuvs==25.10.*,>=0.0.0a0",
+    "libcuvs==25.12.*,>=0.0.0a0",
     "numpy>=1.23,<3.0a0",
-    "pylibraft==25.10.*,>=0.0.0a0",
+    "pylibraft==25.12.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -119,9 +119,9 @@ requires = [
     "cmake>=3.30.4",
     "cuda-python>=13.0.1,<14.0a0",
     "cython>=3.0.0",
-    "libcuvs==25.10.*,>=0.0.0a0",
-    "libraft==25.10.*,>=0.0.0a0",
-    "librmm==25.10.*,>=0.0.0a0",
+    "libcuvs==25.12.*,>=0.0.0a0",
+    "libraft==25.12.*,>=0.0.0a0",
+    "librmm==25.12.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 build-backend = "scikit_build_core.build"
diff --git a/python/cuvs_bench/cuvs_bench/plot/__main__.py b/python/cuvs_bench/cuvs_bench/plot/__main__.py
index 1c8a3005f6..ea0ab22e1a 100644
--- a/python/cuvs_bench/cuvs_bench/plot/__main__.py
+++ b/python/cuvs_bench/cuvs_bench/plot/__main__.py
@@ -17,7 +17,7 @@
 # 1: https://github.com/erikbern/ann-benchmarks/blob/main/plot.py
 # 2: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/utils.py  # noqa: E501
 # 3: https://github.com/erikbern/ann-benchmarks/blob/main/ann_benchmarks/plotting/metrics.py  # noqa: E501
-# License: https://github.com/rapidsai/cuvs/blob/branch-25.10/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
+# License: https://github.com/rapidsai/cuvs/blob/branch-25.12/thirdparty/LICENSES/LICENSE.ann-benchmark # noqa: E501
 
 import itertools
 import os
diff --git a/python/cuvs_bench/pyproject.toml b/python/cuvs_bench/pyproject.toml
index 0e052c46e1..15795d2751 100644
--- a/python/cuvs_bench/pyproject.toml
+++ b/python/cuvs_bench/pyproject.toml
@@ -19,7 +19,7 @@ license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "click",
-    "cuvs==25.10.*,>=0.0.0a0",
+    "cuvs==25.12.*,>=0.0.0a0",
     "matplotlib>=3.9",
     "pandas",
     "pyyaml",
diff --git a/python/libcuvs/pyproject.toml b/python/libcuvs/pyproject.toml
index f19f2d33df..09ad1f8778 100644
--- a/python/libcuvs/pyproject.toml
+++ b/python/libcuvs/pyproject.toml
@@ -31,8 +31,8 @@ license = { text = "Apache-2.0" }
 requires-python = ">=3.10"
 dependencies = [
     "cuda-toolkit[cublas,curand,cusolver,cusparse]>=12,<14",
-    "libraft==25.10.*,>=0.0.0a0",
-    "librmm==25.10.*,>=0.0.0a0",
+    "libraft==25.12.*,>=0.0.0a0",
+    "librmm==25.12.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Intended Audience :: Developers",
@@ -90,8 +90,8 @@ regex = "(?P<value>.*)"
 build-backend = "scikit_build_core.build"
 requires = [
     "cmake>=3.30.4",
-    "libraft==25.10.*,>=0.0.0a0",
-    "librmm==25.10.*,>=0.0.0a0",
+    "libraft==25.12.*,>=0.0.0a0",
+    "librmm==25.12.*,>=0.0.0a0",
     "ninja",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 dependencies-file = "../../dependencies.yaml"
diff --git a/rust/Cargo.toml b/rust/Cargo.toml
index d8a98b521f..3e45ac65ba 100644
--- a/rust/Cargo.toml
+++ b/rust/Cargo.toml
@@ -6,7 +6,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "25.10.0"
+version = "25.12.0"
 edition = "2021"
 repository = "https://github.com/rapidsai/cuvs"
 homepage = "https://github.com/rapidsai/cuvs"
diff --git a/rust/cuvs/Cargo.toml b/rust/cuvs/Cargo.toml
index c7b713f696..30429f814c 100644
--- a/rust/cuvs/Cargo.toml
+++ b/rust/cuvs/Cargo.toml
@@ -9,7 +9,7 @@ authors.workspace = true
 license.workspace = true
 
 [dependencies]
-ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.10.0" }
+ffi = { package = "cuvs-sys", path = "../cuvs-sys", version = "25.12.0" }
 ndarray = "0.15"
 
 [dev-dependencies]

From f33de2a8b499ca805082e3dc8e9617fc3d7e92a5 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Thu, 18 Sep 2025 18:43:31 +0200
Subject: [PATCH 587/623] All neighbors C and Python bindings (#1282)

Answers https://github.com/rapidsai/cuvs/issues/1279

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Corey J. Nolet (https://github.com/cjnolet)
  - Jinsol Park (https://github.com/jinsolp)

URL: https://github.com/rapidsai/cuvs/pull/1282
---
 cpp/CMakeLists.txt                            |   1 +
 cpp/include/cuvs/core/c_api.h                 |  28 ++
 cpp/include/cuvs/neighbors/all_neighbors.h    | 122 ++++++
 cpp/src/core/c_api.cpp                        |  28 ++
 cpp/src/neighbors/all_neighbors_c.cpp         | 287 ++++++++++++++
 cpp/tests/CMakeLists.txt                      |   5 +
 cpp/tests/neighbors/all_neighbors_c.cu        | 301 ++++++++++++++
 cpp/tests/neighbors/c_api.c                   |  16 +
 cpp/tests/neighbors/run_all_neighbors_c.c     | 124 ++++++
 docs/source/c_api/neighbors.rst               |   1 +
 .../c_api/neighbors_all_neighbors_c.rst       |  18 +
 docs/source/cuvs_bench/param_tuning.rst       |   4 +-
 docs/source/getting_started.rst               |   6 +-
 docs/source/neighbors/all_neighbors.rst       |  71 ++++
 .../{indexes => neighbors}/bruteforce.rst     |   0
 docs/source/{indexes => neighbors}/cagra.rst  |   0
 .../source/{indexes => neighbors}/ivfflat.rst |   0
 docs/source/{indexes => neighbors}/ivfpq.rst  |   0
 .../indexes.rst => neighbors/neighbors.rst}   |   5 +-
 docs/source/{indexes => neighbors}/vamana.rst |   0
 docs/source/python_api/neighbors.rst          |   1 +
 .../python_api/neighbors_all_neighbors.rst    |  19 +
 docs/source/tuning_guide.rst                  |   2 +-
 python/cuvs/cuvs/common/CMakeLists.txt        |   6 +-
 python/cuvs/cuvs/common/__init__.py           |  10 +-
 python/cuvs/cuvs/common/c_api.pxd             |   6 +
 python/cuvs/cuvs/common/mg_resources.pxd      |  24 ++
 python/cuvs/cuvs/common/mg_resources.pyx      | 153 +++++++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   3 +-
 python/cuvs/cuvs/neighbors/__init__.py        |   4 +-
 .../neighbors/all_neighbors/CMakeLists.txt    |  24 ++
 .../cuvs/neighbors/all_neighbors/__init__.pxd |   0
 .../cuvs/neighbors/all_neighbors/__init__.py  |  17 +
 .../neighbors/all_neighbors/all_neighbors.pxd |  52 +++
 .../neighbors/all_neighbors/all_neighbors.pyx | 373 ++++++++++++++++++
 .../cuvs/neighbors/nn_descent/nn_descent.pyx  |   4 +
 python/cuvs/cuvs/tests/test_all_neighbors.py  | 219 ++++++++++
 37 files changed, 1920 insertions(+), 14 deletions(-)
 create mode 100644 cpp/include/cuvs/neighbors/all_neighbors.h
 create mode 100644 cpp/src/neighbors/all_neighbors_c.cpp
 create mode 100644 cpp/tests/neighbors/all_neighbors_c.cu
 create mode 100644 cpp/tests/neighbors/run_all_neighbors_c.c
 create mode 100644 docs/source/c_api/neighbors_all_neighbors_c.rst
 create mode 100644 docs/source/neighbors/all_neighbors.rst
 rename docs/source/{indexes => neighbors}/bruteforce.rst (100%)
 rename docs/source/{indexes => neighbors}/cagra.rst (100%)
 rename docs/source/{indexes => neighbors}/ivfflat.rst (100%)
 rename docs/source/{indexes => neighbors}/ivfpq.rst (100%)
 rename docs/source/{indexes/indexes.rst => neighbors/neighbors.rst} (80%)
 rename docs/source/{indexes => neighbors}/vamana.rst (100%)
 create mode 100644 docs/source/python_api/neighbors_all_neighbors.rst
 create mode 100644 python/cuvs/cuvs/common/mg_resources.pxd
 create mode 100644 python/cuvs/cuvs/common/mg_resources.pyx
 create mode 100644 python/cuvs/cuvs/neighbors/all_neighbors/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/all_neighbors/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/all_neighbors/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pyx
 create mode 100644 python/cuvs/cuvs/tests/test_all_neighbors.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d21f130ee8..85e1c171ab 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -737,6 +737,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       src/neighbors/nn_descent_c.cpp
       src/neighbors/refine/refine_c.cpp
       src/neighbors/tiered_index_c.cpp
+      src/neighbors/all_neighbors_c.cpp
       src/preprocessing/quantize/binary_c.cpp
       src/preprocessing/quantize/scalar_c.cpp
       src/distance/pairwise_distance_c.cpp
diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index e2fc4ebc94..b6319fe3b0 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -127,6 +127,34 @@ cuvsError_t cuvsStreamSync(cuvsResources_t res);
  * @return cuvsError_t
  */
 cuvsError_t cuvsDeviceIdGet(cuvsResources_t res, int* device_id);
+
+/**
+ * @brief Create an Initialized opaque C handle for C++ type `raft::device_resources_snmg`
+ *        for multi-GPU operations
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuResourcesCreate(cuvsResources_t* res);
+
+/**
+ * @brief Create an Initialized opaque C handle for C++ type `raft::device_resources_snmg`
+ *        for multi-GPU operations with specific device IDs
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] device_ids DLManagedTensor* containing device IDs to use
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuResourcesCreateWithDeviceIds(cuvsResources_t* res,
+                                                     DLManagedTensor* device_ids);
+
+/**
+ * @brief Destroy and de-allocate opaque C handle for C++ type `raft::device_resources_snmg`
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsMultiGpuResourcesDestroy(cuvsResources_t res);
 /** @} */
 
 /**
diff --git a/cpp/include/cuvs/neighbors/all_neighbors.h b/cpp/include/cuvs/neighbors/all_neighbors.h
new file mode 100644
index 0000000000..08d509c825
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/all_neighbors.h
@@ -0,0 +1,122 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/nn_descent.h>
+#include <dlpack/dlpack.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup all_neighbors_c_build All-neighbors C-API build
+ * @{
+ *
+ * All-neighbors constructs an approximate k-NN graph for all vectors in a dataset.
+ * The All-neighbors API can either be used in single-GPU or multi-GPU mode.
+ * For multi-GPU deployment, please pass a multi-GPU resources handle and
+ * provide the dataset on host.
+ *
+ * Notes:
+ * - Outputs (indices, distances, core_distances) are expected to be on device memory.
+ * - Host variant accepts host-resident dataset; device variant accepts device-resident dataset.
+ * - For batching, `overlap_factor < n_clusters` must hold.
+ * - When `core_distances` is provided, mutual-reachability distances are produced (see alpha).
+ */
+
+/**
+ * @brief Graph build algorithm selection.
+ */
+typedef enum {
+  CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE = 0,  ///< Use Brute Force for local kNN subgraphs
+  CUVS_ALL_NEIGHBORS_ALGO_IVF_PQ = 1,  ///< Use IVF-PQ for local kNN subgraphs (host dataset only)
+  CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT = 2  ///< Use NN-Descent for local kNN subgraphs
+} cuvsAllNeighborsAlgo;
+
+/**
+ * @brief Parameters controlling SNMG all-neighbors build.
+ */
+struct cuvsAllNeighborsIndexParams {
+  cuvsAllNeighborsAlgo algo;  ///< Local kNN graph build algorithm
+  size_t overlap_factor;  ///< Number of clusters each point is assigned to (must be < n_clusters)
+  size_t
+    n_clusters;  ///< Number of clusters/batches to partition the dataset into (> overlap_factor)
+  cuvsDistanceType metric;  ///< Distance metric used for graph construction
+
+  // Algorithm-specific parameters (only one should be set based on algo)
+  cuvsIvfPqIndexParams_t ivf_pq_params;          ///< Parameters for IVF-PQ algorithm (when algo ==
+                                                 ///< CUVS_ALL_NEIGHBORS_ALGO_IVF_PQ)
+  cuvsNNDescentIndexParams_t nn_descent_params;  ///< Parameters for NN-Descent algorithm (when algo
+                                                 ///< == CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT)
+};
+
+typedef struct cuvsAllNeighborsIndexParams* cuvsAllNeighborsIndexParams_t;
+
+/**
+ * @brief Create a default all-neighbors index parameters struct.
+ *
+ * @param[out] index_params  Pointer to allocated index_params struct
+ *
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsAllNeighborsIndexParamsCreate(cuvsAllNeighborsIndexParams_t* index_params);
+
+/**
+ * @brief Destroy an all-neighbors index parameters struct.
+ *
+ * @param[in] index_params  Index parameters struct to destroy
+ *
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsAllNeighborsIndexParamsDestroy(cuvsAllNeighborsIndexParams_t index_params);
+
+/**
+ * @brief Build an all-neighbors k-NN graph automatically detecting host vs device dataset.
+ *
+ * @param[in] res             Can be a SNMG multi-GPU resources (`cuvsResources_t`) or single-GPU
+ * resources
+ * @param[in] params          Build parameters (see cuvsAllNeighborsIndexParams)
+ * @param[in] dataset         2D tensor [num_rows x dim] on host or device (auto-detected)
+ * @param[out] indices        2D tensor [num_rows x k] on device (int64)
+ * @param[out] distances      Optional 2D tensor [num_rows x k] on device (float32); can be NULL
+ * @param[out] core_distances Optional 1D tensor [num_rows] on device (float32); can be NULL
+ * @param[in] alpha           Mutual-reachability scaling; used only when core_distances is provided
+ *
+ * The function automatically detects whether the dataset is host-resident or device-resident
+ * and calls the appropriate implementation. For host datasets, it partitions data into
+ * `n_clusters` clusters and assigns each row to `overlap_factor` nearest clusters. For device
+ * datasets, `n_clusters` must be 1 (no batching); `overlap_factor` is ignored.
+ * Outputs always reside in device memory.
+ */
+cuvsError_t cuvsAllNeighborsBuild(cuvsResources_t res,
+                                  cuvsAllNeighborsIndexParams_t params,
+                                  DLManagedTensor* dataset,
+                                  DLManagedTensor* indices,
+                                  DLManagedTensor* distances,
+                                  DLManagedTensor* core_distances,
+                                  float alpha);
+
+/** @} */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/core/c_api.cpp b/cpp/src/core/c_api.cpp
index a36bb472b6..3cf6db457b 100644
--- a/cpp/src/core/c_api.cpp
+++ b/cpp/src/core/c_api.cpp
@@ -60,6 +60,34 @@ extern "C" cuvsError_t cuvsMultiGpuResourcesCreate(cuvsResources_t* res)
   });
 }
 
+extern "C" cuvsError_t cuvsMultiGpuResourcesCreateWithDeviceIds(cuvsResources_t* res,
+                                                                DLManagedTensor* device_ids)
+{
+  return cuvs::core::translate_exceptions([=] {
+    // Basic validation
+    if (device_ids == nullptr || device_ids->dl_tensor.data == nullptr) {
+      throw std::invalid_argument("device_ids cannot be null");
+    }
+
+    // Check data type is int32
+    if (device_ids->dl_tensor.dtype.code != kDLInt || device_ids->dl_tensor.dtype.bits != 32) {
+      throw std::invalid_argument("device_ids must be int32");
+    }
+
+    // Check data is on host
+    if (device_ids->dl_tensor.device.device_type != kDLCPU) {
+      throw std::invalid_argument("device_ids must be on host memory");
+    }
+
+    // Cast void* to int* to perform pointer arithmetic
+    int* data_ptr = static_cast<int*>(device_ids->dl_tensor.data);
+    std::vector<int> ids(data_ptr, data_ptr + device_ids->dl_tensor.shape[0]);
+
+    auto res_ptr = new raft::device_resources_snmg{ids};
+    *res         = reinterpret_cast<uintptr_t>(res_ptr);
+  });
+}
+
 extern "C" cuvsError_t cuvsMultiGpuResourcesDestroy(cuvsResources_t res)
 {
   return cuvs::core::translate_exceptions([=] {
diff --git a/cpp/src/neighbors/all_neighbors_c.cpp b/cpp/src/neighbors/all_neighbors_c.cpp
new file mode 100644
index 0000000000..38f6b5c2a2
--- /dev/null
+++ b/cpp/src/neighbors/all_neighbors_c.cpp
@@ -0,0 +1,287 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/util/cudart_utils.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/all_neighbors.h>
+#include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/graph_build_types.hpp>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/nn_descent.h>
+
+#include "ivf_pq_c.hpp"
+
+namespace {
+
+void convert_nn_descent_params(cuvsNNDescentIndexParams params,
+                               cuvs::neighbors::nn_descent::index_params* out)
+{
+  out->metric                    = static_cast<cuvs::distance::DistanceType>((int)params.metric);
+  out->metric_arg                = params.metric_arg;
+  out->graph_degree              = params.graph_degree;
+  out->intermediate_graph_degree = params.intermediate_graph_degree;
+  out->max_iterations            = params.max_iterations;
+  out->termination_threshold     = params.termination_threshold;
+  out->return_distances          = params.return_distances;
+}
+
+static cuvs::neighbors::all_neighbors::all_neighbors_params convert_params(
+  cuvsAllNeighborsIndexParams_t params_ptr, int64_t n_rows, int64_t n_cols)
+{
+  using namespace cuvs::neighbors;
+  cuvs::neighbors::all_neighbors::all_neighbors_params out{};
+  auto& p            = *params_ptr;
+  out.metric         = static_cast<cuvs::distance::DistanceType>((int)p.metric);
+  out.overlap_factor = p.overlap_factor;
+  out.n_clusters     = p.n_clusters;
+
+  switch (p.algo) {
+    case CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE: {
+      graph_build_params::brute_force_params b{};
+      b.build_params.metric  = out.metric;
+      out.graph_build_params = b;
+      break;
+    }
+    case CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT: {
+      graph_build_params::nn_descent_params n{};
+      n.metric = out.metric;
+      // Use nn_descent_params if provided, otherwise use defaults
+      if (p.nn_descent_params != nullptr) { convert_nn_descent_params(*p.nn_descent_params, &n); }
+      out.graph_build_params = n;
+      break;
+    }
+    case CUVS_ALL_NEIGHBORS_ALGO_IVF_PQ: {
+      auto dataset_extents = raft::matrix_extent<int64_t>{n_rows, n_cols};
+      graph_build_params::ivf_pq_params ivf(dataset_extents, out.metric);
+      // Use ivf_pq_params if provided, otherwise use defaults
+      if (p.ivf_pq_params != nullptr) {
+        cuvs::neighbors::ivf_pq::convert_c_index_params(*p.ivf_pq_params, &ivf.build_params);
+      }
+      out.graph_build_params = ivf;
+      break;
+    }
+    default: RAFT_FAIL("Unsupported all-neighbors algo");
+  }
+
+  return out;
+}
+
+static void ensure_indices_dtype_and_device_compatibility(DLManagedTensor* indices)
+{
+  auto dtype = indices->dl_tensor.dtype;
+  RAFT_EXPECTS(dtype.code == kDLInt && dtype.bits == 64, "indices must be int64 output tensor");
+  RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(indices->dl_tensor),
+               "indices tensor must be device-compatible");
+}
+
+static void ensure_optional_distance_dtype_and_device_compatibility(DLManagedTensor* distances)
+{
+  if (distances == nullptr) { return; }
+  auto dtype = distances->dl_tensor.dtype;
+  RAFT_EXPECTS(dtype.code == kDLFloat && dtype.bits == 32,
+               "distances must be float32 output tensor");
+  RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(distances->dl_tensor),
+               "distances tensor must be device-compatible");
+}
+
+static void ensure_optional_core_distance_dtype_and_device_compatibility(
+  DLManagedTensor* core_distances)
+{
+  if (core_distances == nullptr) { return; }
+  auto dtype = core_distances->dl_tensor.dtype;
+  RAFT_EXPECTS(dtype.code == kDLFloat && dtype.bits == 32,
+               "core_distances must be float32 output tensor");
+  RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(core_distances->dl_tensor),
+               "core_distances tensor must be device-compatible");
+}
+
+template <typename T>
+void _build_host(cuvsResources_t res,
+                 cuvsAllNeighborsIndexParams_t params,
+                 DLManagedTensor* dataset_tensor,
+                 DLManagedTensor* indices_tensor,
+                 DLManagedTensor* distances_tensor,
+                 DLManagedTensor* core_distances_tensor,
+                 float alpha)
+{
+  auto& cpp_res = *reinterpret_cast<raft::device_resources*>(res);
+
+  auto& dlt = dataset_tensor->dl_tensor;
+  RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(dlt),
+               "Host build expects host-compatible dataset tensor");
+
+  ensure_indices_dtype_and_device_compatibility(indices_tensor);
+  ensure_optional_distance_dtype_and_device_compatibility(distances_tensor);
+  ensure_optional_core_distance_dtype_and_device_compatibility(core_distances_tensor);
+
+  // Check dependencies between parameters
+  if (core_distances_tensor != nullptr && distances_tensor == nullptr) {
+    RAFT_FAIL("distances tensor must be provided when core_distances tensor is provided");
+  }
+
+  int64_t n_rows = dlt.shape[0];
+  int64_t n_cols = dlt.shape[1];
+
+  auto cpp_params = convert_params(params, n_rows, n_cols);
+
+  using dataset_mdspan_t   = raft::host_matrix_view<const T, int64_t, raft::row_major>;
+  using indices_mdspan_t   = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
+  using distances_mdspan_t = raft::device_matrix_view<float, int64_t, raft::row_major>;
+  using core_mdspan_t      = raft::device_vector_view<float, int64_t>;
+
+  auto dataset = cuvs::core::from_dlpack<dataset_mdspan_t>(dataset_tensor);
+  auto indices = cuvs::core::from_dlpack<indices_mdspan_t>(indices_tensor);
+
+  std::optional<distances_mdspan_t> distances = std::nullopt;
+  if (distances_tensor) {
+    distances = cuvs::core::from_dlpack<distances_mdspan_t>(distances_tensor);
+  }
+
+  std::optional<core_mdspan_t> core_distances = std::nullopt;
+  if (core_distances_tensor) {
+    core_distances = cuvs::core::from_dlpack<core_mdspan_t>(core_distances_tensor);
+  }
+
+  cuvs::neighbors::all_neighbors::build(
+    cpp_res, cpp_params, dataset, indices, distances, core_distances, alpha);
+}
+
+template <typename T>
+void _build_device(cuvsResources_t device_res,
+                   cuvsAllNeighborsIndexParams_t params,
+                   DLManagedTensor* dataset_tensor,
+                   DLManagedTensor* indices_tensor,
+                   DLManagedTensor* distances_tensor,
+                   DLManagedTensor* core_distances_tensor,
+                   float alpha)
+{
+  auto& cpp_res = *reinterpret_cast<raft::device_resources*>(device_res);
+
+  auto& dlt = dataset_tensor->dl_tensor;
+  RAFT_EXPECTS(cuvs::core::is_dlpack_device_compatible(dlt),
+               "Device build expects device-compatible dataset tensor");
+
+  ensure_indices_dtype_and_device_compatibility(indices_tensor);
+  ensure_optional_distance_dtype_and_device_compatibility(distances_tensor);
+  ensure_optional_core_distance_dtype_and_device_compatibility(core_distances_tensor);
+
+  // Check dependencies between parameters
+  if (core_distances_tensor != nullptr && distances_tensor == nullptr) {
+    RAFT_FAIL("distances tensor must be provided when core_distances tensor is provided");
+  }
+
+  int64_t n_rows = dlt.shape[0];
+  int64_t n_cols = dlt.shape[1];
+
+  auto cpp_params = convert_params(params, n_rows, n_cols);
+
+  using dataset_mdspan_t   = raft::device_matrix_view<const T, int64_t, raft::row_major>;
+  using indices_mdspan_t   = raft::device_matrix_view<int64_t, int64_t, raft::row_major>;
+  using distances_mdspan_t = raft::device_matrix_view<float, int64_t, raft::row_major>;
+  using core_mdspan_t      = raft::device_vector_view<float, int64_t>;
+
+  auto dataset = cuvs::core::from_dlpack<dataset_mdspan_t>(dataset_tensor);
+  auto indices = cuvs::core::from_dlpack<indices_mdspan_t>(indices_tensor);
+
+  std::optional<distances_mdspan_t> distances = std::nullopt;
+  if (distances_tensor) {
+    distances = cuvs::core::from_dlpack<distances_mdspan_t>(distances_tensor);
+  }
+
+  std::optional<core_mdspan_t> core_distances = std::nullopt;
+  if (core_distances_tensor) {
+    core_distances = cuvs::core::from_dlpack<core_mdspan_t>(core_distances_tensor);
+  }
+
+  cuvs::neighbors::all_neighbors::build(
+    cpp_res, cpp_params, dataset, indices, distances, core_distances, alpha);
+}
+
+}  // namespace
+
+extern "C" cuvsError_t cuvsAllNeighborsIndexParamsCreate(cuvsAllNeighborsIndexParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    *params                      = new cuvsAllNeighborsIndexParams{};
+    (*params)->algo              = CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE;
+    (*params)->overlap_factor    = 1;
+    (*params)->n_clusters        = 1;
+    (*params)->metric            = L2Expanded;
+    (*params)->ivf_pq_params     = nullptr;
+    (*params)->nn_descent_params = nullptr;
+  });
+}
+
+extern "C" cuvsError_t cuvsAllNeighborsIndexParamsDestroy(cuvsAllNeighborsIndexParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (params != nullptr) {
+      if (params->ivf_pq_params != nullptr) { cuvsIvfPqIndexParamsDestroy(params->ivf_pq_params); }
+      if (params->nn_descent_params != nullptr) {
+        cuvsNNDescentIndexParamsDestroy(params->nn_descent_params);
+      }
+      delete params;
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsAllNeighborsBuild(cuvsResources_t res,
+                                             cuvsAllNeighborsIndexParams_t params,
+                                             DLManagedTensor* dataset_tensor,
+                                             DLManagedTensor* indices_tensor,
+                                             DLManagedTensor* distances_tensor,
+                                             DLManagedTensor* core_distances_tensor,
+                                             float alpha)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = dataset_tensor->dl_tensor;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      // Check if dataset is host-compatible or device-compatible
+      if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+        _build_host<float>(res,
+                           params,
+                           dataset_tensor,
+                           indices_tensor,
+                           distances_tensor,
+                           core_distances_tensor,
+                           alpha);
+      } else if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+        _build_device<float>(res,
+                             params,
+                             dataset_tensor,
+                             indices_tensor,
+                             distances_tensor,
+                             core_distances_tensor,
+                             alpha);
+      } else {
+        RAFT_FAIL("Dataset tensor must be either host-compatible or device-compatible");
+      }
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index c6bb7ce8e4..33f9a12193 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -317,6 +317,11 @@ if(TARGET cuvs::c_api)
 
   ConfigureTest(NAME MG_C_TEST PATH neighbors/run_mg_c.c neighbors/ann_mg_c.cu C_LIB)
 
+  ConfigureTest(
+    NAME ALL_NEIGHBORS_C_TEST PATH neighbors/run_all_neighbors_c.c neighbors/all_neighbors_c.cu
+    C_LIB
+  )
+
   if(BUILD_CAGRA_HNSWLIB)
     ConfigureTest(NAME HNSW_C_TEST PATH neighbors/ann_hnsw_c.cu C_LIB)
     target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
diff --git a/cpp/tests/neighbors/all_neighbors_c.cu b/cpp/tests/neighbors/all_neighbors_c.cu
new file mode 100644
index 0000000000..a4c79cc6f2
--- /dev/null
+++ b/cpp/tests/neighbors/all_neighbors_c.cu
@@ -0,0 +1,301 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/handle.hpp>
+#include <raft/random/rng.cuh>
+#include <rmm/device_uvector.hpp>
+
+#include "ann_utils.cuh"
+#include "naive_knn.cuh"
+#include <cuvs/neighbors/all_neighbors.h>
+
+#include <algorithm>
+#include <cmath>
+#include <gtest/gtest.h>
+#include <limits>
+#include <unordered_set>
+#include <vector>
+
+extern "C" void run_all_neighbors(int64_t n_rows,
+                                  int64_t n_dim,
+                                  int64_t k,
+                                  float* dataset_data,
+                                  int64_t* indices_data,
+                                  float* distances_data,
+                                  float* core_distances_data,
+                                  cuvsDistanceType metric,
+                                  cuvsAllNeighborsAlgo algo,
+                                  float alpha);
+
+template <typename T>
+void generate_random_data(T* devPtr, size_t size)
+{
+  raft::handle_t handle;
+  raft::random::RngState r(1234ULL);
+  // Generate more diverse data with larger range to ensure different neighbors
+  raft::random::uniform(handle, r, devPtr, size, T(-10.0), T(10.0));
+}
+
+struct AllNeighborsCInputs {
+  int64_t n_rows;
+  int64_t n_dim;
+  int64_t k;
+  cuvsDistanceType metric;
+  cuvsAllNeighborsAlgo algo;
+  float alpha;
+  bool include_distances;
+  bool include_core_distances;
+};
+
+class AllNeighborsCTest : public ::testing::TestWithParam<AllNeighborsCInputs> {
+ public:
+  AllNeighborsCTest()
+    : stream_(0),
+      data_(0, stream_),
+      indices_(0, stream_),
+      distances_(0, stream_),
+      core_distances_(0, stream_)
+  {
+  }
+
+ protected:
+  void SetUp() override
+  {
+    params_ = ::testing::TestWithParam<AllNeighborsCInputs>::GetParam();
+    RAFT_CUDA_TRY(cudaStreamCreate(&stream_));
+  }
+
+  void TearDown() override { RAFT_CUDA_TRY(cudaStreamDestroy(stream_)); }
+
+  void run()
+  {
+    size_t data_size    = params_.n_rows * params_.n_dim;
+    size_t indices_size = params_.n_rows * params_.k;
+
+    data_.resize(data_size, stream_);
+    indices_.resize(indices_size, stream_);
+
+    if (params_.include_distances) { distances_.resize(indices_size, stream_); }
+
+    if (params_.include_core_distances) { core_distances_.resize(params_.n_rows, stream_); }
+
+    generate_random_data(data_.data(), data_size);
+
+    float* distances_ptr      = params_.include_distances ? distances_.data() : nullptr;
+    float* core_distances_ptr = params_.include_core_distances ? core_distances_.data() : nullptr;
+
+    // Test the C API
+    run_all_neighbors(params_.n_rows,
+                      params_.n_dim,
+                      params_.k,
+                      data_.data(),
+                      indices_.data(),
+                      distances_ptr,
+                      core_distances_ptr,
+                      params_.metric,
+                      params_.algo,
+                      params_.alpha);
+
+    RAFT_CUDA_TRY(cudaStreamSynchronize(stream_));
+
+    // Basic sanity checks
+    verify_results();
+  }
+
+ private:
+  template <typename T, typename IdxT>
+  void recall_eval(T* query_data,
+                   T* index_data,
+                   IdxT* neighbors,
+                   T* distances,
+                   size_t n_queries,
+                   size_t n_rows,
+                   size_t n_dim,
+                   size_t n_neighbors,
+                   cuvsDistanceType metric)
+  {
+    raft::handle_t handle;
+    auto distances_ref = raft::make_device_matrix<T, IdxT>(handle, n_queries, n_neighbors);
+    auto neighbors_ref = raft::make_device_matrix<IdxT, IdxT>(handle, n_queries, n_neighbors);
+    cuvs::neighbors::naive_knn<T, T, IdxT>(
+      handle,
+      distances_ref.data_handle(),
+      neighbors_ref.data_handle(),
+      query_data,
+      index_data,
+      n_queries,
+      n_rows,
+      n_dim,
+      n_neighbors,
+      static_cast<cuvs::distance::DistanceType>((uint16_t)metric));
+
+    size_t size = n_queries * n_neighbors;
+    std::vector<IdxT> neighbors_h(size);
+    std::vector<T> distances_h(size);
+    std::vector<IdxT> neighbors_ref_h(size);
+    std::vector<T> distances_ref_h(size);
+
+    auto stream = raft::resource::get_cuda_stream(handle);
+    raft::copy(neighbors_h.data(), neighbors, size, stream);
+    raft::copy(distances_h.data(), distances, size, stream);
+    raft::copy(neighbors_ref_h.data(), neighbors_ref.data_handle(), size, stream);
+    raft::copy(distances_ref_h.data(), distances_ref.data_handle(), size, stream);
+
+    // verify output with algorithm-specific minimum recall
+    double min_recall = 0.8;  // Default for approximate algorithms
+
+    if (params_.algo == CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE) {
+      min_recall = 0.95;  // Brute force should be nearly perfect
+    } else if (params_.algo == CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT) {
+      min_recall = 0.7;  // NN Descent is approximate
+    }
+
+    // Lower recall threshold when using mutual reachability distance (core_distances)
+    if (params_.include_core_distances) {
+      min_recall = std::min(min_recall, 0.8);  // Mutual reachability is more approximate
+    }
+
+    ASSERT_TRUE(cuvs::neighbors::eval_neighbours(neighbors_ref_h,
+                                                 neighbors_h,
+                                                 distances_ref_h,
+                                                 distances_h,
+                                                 n_queries,
+                                                 n_neighbors,
+                                                 0.001,
+                                                 min_recall));
+  }
+
+  void verify_results()
+  {
+    // Copy results to host for verification
+    std::vector<int64_t> indices_host(params_.n_rows * params_.k);
+    RAFT_CUDA_TRY(cudaMemcpy(indices_host.data(),
+                             indices_.data(),
+                             indices_host.size() * sizeof(int64_t),
+                             cudaMemcpyDeviceToHost));
+
+    // Verify that all indices are valid (within bounds) or indicate missing neighbors
+    for (size_t i = 0; i < indices_host.size(); ++i) {
+      // Accept either valid indices or max value (indicating missing neighbors)
+      EXPECT_TRUE(indices_host[i] >= 0 && indices_host[i] < params_.n_rows ||
+                  indices_host[i] == std::numeric_limits<int64_t>::max())
+        << "Index " << i << " has value " << indices_host[i] << " which is out of bounds";
+    }
+
+    // Perform recall evaluation using the same approach as IVF-PQ tests
+    if (params_.include_distances) {
+      // We have distances, so we can do full recall evaluation
+      std::vector<float> distances_host(params_.n_rows * params_.k);
+      RAFT_CUDA_TRY(cudaMemcpy(distances_host.data(),
+                               distances_.data(),
+                               distances_host.size() * sizeof(float),
+                               cudaMemcpyDeviceToHost));
+
+      recall_eval<float, int64_t>(data_.data(),     // query_data (same as index for all_neighbors)
+                                  data_.data(),     // index_data
+                                  indices_.data(),  // neighbors
+                                  distances_.data(),  // distances
+                                  params_.n_rows,     // n_queries
+                                  params_.n_rows,     // n_rows
+                                  params_.n_dim,      // n_dim
+                                  params_.k,          // n_neighbors
+                                  params_.metric);    // metric
+
+      // Verify distances are non-negative for valid neighbors
+      for (size_t i = 0; i < distances_host.size(); ++i) {
+        int64_t corresponding_index = indices_host[i];
+        if (corresponding_index != std::numeric_limits<int64_t>::max()) {
+          // Allow tiny negative distances due to floating-point precision errors
+          EXPECT_GE(distances_host[i], -1e-6f)
+            << "Distance at index " << i << " is significantly negative: " << distances_host[i];
+        }
+      }
+    } else {
+      // No distances available, do basic neighbor validation only
+      for (int64_t row = 0; row < params_.n_rows; ++row) {
+        std::unordered_set<int64_t> unique_neighbors;
+        int64_t valid_neighbors = 0;
+        for (int64_t neighbor_idx = 0; neighbor_idx < params_.k; ++neighbor_idx) {
+          int64_t neighbor = indices_host[row * params_.k + neighbor_idx];
+          if (neighbor != std::numeric_limits<int64_t>::max() && neighbor >= 0 &&
+              neighbor < params_.n_rows) {
+            unique_neighbors.insert(neighbor);
+            valid_neighbors++;
+          }
+        }
+
+        // For all_neighbors, we should have at least some valid neighbors
+        EXPECT_GT(valid_neighbors, 0) << "Row " << row << " has no valid neighbors";
+
+        // The number of unique neighbors should be reasonable
+        EXPECT_GE(unique_neighbors.size(), 1) << "Row " << row << " has no unique neighbors";
+
+        // We should have at most min(k, n_rows) neighbors
+        EXPECT_LE(unique_neighbors.size(), static_cast<size_t>(std::min(params_.k, params_.n_rows)))
+          << "Row " << row << " has more unique neighbors than expected";
+      }
+    }
+
+    // If core distances are computed, verify they are non-negative
+    if (params_.include_core_distances) {
+      std::vector<float> core_distances_host(params_.n_rows);
+      RAFT_CUDA_TRY(cudaMemcpy(core_distances_host.data(),
+                               core_distances_.data(),
+                               core_distances_host.size() * sizeof(float),
+                               cudaMemcpyDeviceToHost));
+
+      for (size_t i = 0; i < core_distances_host.size(); ++i) {
+        EXPECT_GE(core_distances_host[i], 0.0f) << "Core distance at index " << i << " is negative";
+      }
+    }
+  }
+
+  cudaStream_t stream_;
+  AllNeighborsCInputs params_;
+  rmm::device_uvector<float> data_;
+  rmm::device_uvector<int64_t> indices_;
+  rmm::device_uvector<float> distances_;
+  rmm::device_uvector<float> core_distances_;
+};
+
+const std::vector<AllNeighborsCInputs> inputs = {
+  // Basic brute force tests
+  {100, 16, 10, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 1.0f, true, false},
+  {100, 16, 10, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 1.0f, false, false},
+  {100, 16, 10, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 1.0f, true, true},
+
+  // Different metrics
+  {50, 8, 5, InnerProduct, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 1.0f, true, false},
+  {50, 8, 5, CosineExpanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 1.0f, true, false},
+
+  // NN Descent tests
+  {200, 32, 15, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT, 1.0f, true, false},
+  {200, 32, 15, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT, 1.0f, false, false},
+
+  // Different k values
+  {80, 12, 20, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 1.0f, true, false},
+  {120, 24, 30, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 1.0f, true, false},
+
+  // Different alpha values for mutual reachability
+  {60, 10, 8, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 0.5f, true, true},
+  {60, 10, 8, L2Expanded, CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE, 2.0f, true, true},
+};
+
+TEST_P(AllNeighborsCTest, AllNeighborsC) { run(); }
+
+INSTANTIATE_TEST_CASE_P(AllNeighborsCTest, AllNeighborsCTest, ::testing::ValuesIn(inputs));
diff --git a/cpp/tests/neighbors/c_api.c b/cpp/tests/neighbors/c_api.c
index cc71847eaf..75d232cd7f 100644
--- a/cpp/tests/neighbors/c_api.c
+++ b/cpp/tests/neighbors/c_api.c
@@ -15,6 +15,7 @@
  */
 
 #include <cuvs/core/c_api.h>
+#include <cuvs/neighbors/all_neighbors.h>
 #include <cuvs/neighbors/cagra.h>
 #include <cuvs/neighbors/tiered_index.h>
 
@@ -57,12 +58,27 @@ void test_compile_tiered_index()
   cuvsTieredIndexExtend(resources, &dataset, tiered_index);
 }
 
+void test_compile_all_neighbors()
+{
+  // Smoke test to ensure that the all_neighbors.h API compiles correctly
+  // using a c compiler. Not a full test.
+  assert(!"test_compile_all_neighbors is not meant to be run");
+
+  cuvsAllNeighborsIndexParams_t params;
+  cuvsResources_t resources;
+  DLManagedTensor dataset, indices, distances, core_distances;
+  cuvsAllNeighborsIndexParamsCreate(&params);
+  cuvsAllNeighborsIndexParamsDestroy(params);
+  cuvsAllNeighborsBuild(resources, params, &dataset, &indices, &distances, &core_distances, 1.0f);
+}
+
 int main()
 {
   // These are smoke tests that check that the C-APIs compile with a C compiler.
   // These are not meant to be run.
   test_compile_cagra();
   test_compile_tiered_index();
+  test_compile_all_neighbors();
 
   return 0;
 }
diff --git a/cpp/tests/neighbors/run_all_neighbors_c.c b/cpp/tests/neighbors/run_all_neighbors_c.c
new file mode 100644
index 0000000000..796fbb74cb
--- /dev/null
+++ b/cpp/tests/neighbors/run_all_neighbors_c.c
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuvs/neighbors/all_neighbors.h>
+#include <stdint.h>
+
+void run_all_neighbors(int64_t n_rows,
+                       int64_t n_dim,
+                       int64_t k,
+                       float* dataset_data,
+                       int64_t* indices_data,
+                       float* distances_data,
+                       float* core_distances_data,
+                       cuvsDistanceType metric,
+                       cuvsAllNeighborsAlgo algo,
+                       float alpha)
+{
+  // create cuvsResources_t
+  cuvsResources_t res;
+  cuvsResourcesCreate(&res);
+
+  // create all neighbors index parameters
+  cuvsAllNeighborsIndexParams_t params;
+  cuvsAllNeighborsIndexParamsCreate(&params);
+
+  // configure parameters
+  params->algo           = algo;
+  params->metric         = metric;
+  params->overlap_factor = 1;
+  params->n_clusters     = 1;
+
+  // create dataset DLTensor
+  DLManagedTensor dataset_tensor;
+  dataset_tensor.dl_tensor.data               = dataset_data;
+  dataset_tensor.dl_tensor.device.device_type = kDLCUDA;
+  dataset_tensor.dl_tensor.device.device_id   = 0;
+  dataset_tensor.dl_tensor.ndim               = 2;
+  dataset_tensor.dl_tensor.dtype.code         = kDLFloat;
+  dataset_tensor.dl_tensor.dtype.bits         = 32;
+  dataset_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t dataset_shape[2]                    = {n_rows, n_dim};
+  dataset_tensor.dl_tensor.shape              = dataset_shape;
+  dataset_tensor.dl_tensor.strides            = NULL;
+  dataset_tensor.dl_tensor.byte_offset        = 0;
+  dataset_tensor.manager_ctx                  = NULL;
+  dataset_tensor.deleter                      = NULL;
+
+  // create indices DLTensor
+  DLManagedTensor indices_tensor;
+  indices_tensor.dl_tensor.data               = (void*)indices_data;
+  indices_tensor.dl_tensor.device.device_type = kDLCUDA;
+  indices_tensor.dl_tensor.device.device_id   = 0;
+  indices_tensor.dl_tensor.ndim               = 2;
+  indices_tensor.dl_tensor.dtype.code         = kDLInt;
+  indices_tensor.dl_tensor.dtype.bits         = 64;
+  indices_tensor.dl_tensor.dtype.lanes        = 1;
+  int64_t indices_shape[2]                    = {n_rows, k};
+  indices_tensor.dl_tensor.shape              = indices_shape;
+  indices_tensor.dl_tensor.strides            = NULL;
+  indices_tensor.dl_tensor.byte_offset        = 0;
+  indices_tensor.manager_ctx                  = NULL;
+  indices_tensor.deleter                      = NULL;
+
+  // create distances DLTensor (optional)
+  DLManagedTensor* distances_ptr = NULL;
+  DLManagedTensor distances_tensor;
+  int64_t distances_shape[2] = {n_rows, k};  // Moved outside if block
+  if (distances_data != NULL) {
+    distances_tensor.dl_tensor.data               = (void*)distances_data;
+    distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+    distances_tensor.dl_tensor.device.device_id   = 0;
+    distances_tensor.dl_tensor.ndim               = 2;
+    distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+    distances_tensor.dl_tensor.dtype.bits         = 32;
+    distances_tensor.dl_tensor.dtype.lanes        = 1;
+    distances_tensor.dl_tensor.shape              = distances_shape;
+    distances_tensor.dl_tensor.strides            = NULL;
+    distances_tensor.dl_tensor.byte_offset        = 0;
+    distances_tensor.manager_ctx                  = NULL;
+    distances_tensor.deleter                      = NULL;
+    distances_ptr                                 = &distances_tensor;
+  }
+
+  // create core_distances DLTensor (optional)
+  DLManagedTensor* core_distances_ptr = NULL;
+  DLManagedTensor core_distances_tensor;
+  int64_t core_distances_shape[1] = {n_rows};  // Moved outside if block
+  if (core_distances_data != NULL) {
+    core_distances_tensor.dl_tensor.data               = (void*)core_distances_data;
+    core_distances_tensor.dl_tensor.device.device_type = kDLCUDA;
+    core_distances_tensor.dl_tensor.device.device_id   = 0;
+    core_distances_tensor.dl_tensor.ndim               = 1;
+    core_distances_tensor.dl_tensor.dtype.code         = kDLFloat;
+    core_distances_tensor.dl_tensor.dtype.bits         = 32;
+    core_distances_tensor.dl_tensor.dtype.lanes        = 1;
+    core_distances_tensor.dl_tensor.shape              = core_distances_shape;
+    core_distances_tensor.dl_tensor.strides            = NULL;
+    core_distances_tensor.dl_tensor.byte_offset        = 0;
+    core_distances_tensor.manager_ctx                  = NULL;
+    core_distances_tensor.deleter                      = NULL;
+    core_distances_ptr                                 = &core_distances_tensor;
+  }
+
+  // build all neighbors graph
+  cuvsError_t result = cuvsAllNeighborsBuild(
+    res, params, &dataset_tensor, &indices_tensor, distances_ptr, core_distances_ptr, alpha);
+
+  // cleanup
+  cuvsAllNeighborsIndexParamsDestroy(params);
+  cuvsResourcesDestroy(res);
+}
diff --git a/docs/source/c_api/neighbors.rst b/docs/source/c_api/neighbors.rst
index 9de9c40a96..bb6dbec5c2 100644
--- a/docs/source/c_api/neighbors.rst
+++ b/docs/source/c_api/neighbors.rst
@@ -15,3 +15,4 @@ Nearest Neighbors
    neighbors_cagra_c.rst
    neighbors_hnsw_c.rst
    neighbors_mg.rst
+   neighbors_all_neighbors_c.rst
diff --git a/docs/source/c_api/neighbors_all_neighbors_c.rst b/docs/source/c_api/neighbors_all_neighbors_c.rst
new file mode 100644
index 0000000000..f8936384a6
--- /dev/null
+++ b/docs/source/c_api/neighbors_all_neighbors_c.rst
@@ -0,0 +1,18 @@
+All-Neighbors
+=============
+
+The all-neighbors method constructs a k-NN graph for all vectors in a dataset. It supports multiple algorithms including brute force, IVF-PQ (approximate), and NN-Descent (approximate) for building local k-NN subgraphs. The API automatically detects whether the dataset is host-resident or device-resident and applies appropriate optimizations.
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+``#include <cuvs/neighbors/all_neighbors.h>``
+
+Build
+-----
+
+.. doxygengroup:: all_neighbors_c_build
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/docs/source/cuvs_bench/param_tuning.rst b/docs/source/cuvs_bench/param_tuning.rst
index 276fb35a47..a0de6daf09 100644
--- a/docs/source/cuvs_bench/param_tuning.rst
+++ b/docs/source/cuvs_bench/param_tuning.rst
@@ -125,7 +125,7 @@ IVF-pq is an inverted-file index, which partitions the vectors into a series of
    - N
    - [`cluster`, `subspace`]
    - `subspace`
-   - Type of codebook. See :doc:`IVF-PQ index overview <../indexes/ivfpq>` for more detail
+   - Type of codebook. See :doc:`IVF-PQ index overview <../neighbors/ivfpq>` for more detail
 
  * - `dataset_memory_type`
    - `build`
@@ -314,7 +314,7 @@ To fine tune CAGRA index building we can customize IVF-PQ index builder options
    - N
    - [`cluster`, `subspace`]
    - `subspace`
-   - Type of codebook. See :doc:`IVF-PQ index overview <../indexes/ivfpq>` for more detail
+   - Type of codebook. See :doc:`IVF-PQ index overview <../neighbors/ivfpq>` for more detail
 
  * - `ivf_pq_build_nprobe`
    - `search`
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
index f6cd919adc..89be0f1e5b 100644
--- a/docs/source/getting_started.rst
+++ b/docs/source/getting_started.rst
@@ -14,7 +14,7 @@ Getting Started
 
 - `Supported indexes`_
 
-  * :doc:`Vector search index guide <indexes/indexes>`
+  * :doc:`Vector search index guide <neighbors/neighbors>`
 
 - `Using cuVS APIs`_
 
@@ -58,7 +58,7 @@ Please see the :doc:`primer on comparing vector search index performance <compar
 Supported indexes
 =================
 
-cuVS supports many of the standard index types with the list continuing to grow and stay current with the state-of-the-art. Please refer to our :doc:`vector search index guide <indexes/indexes>` for to learn more about each individual index type, when they can be useful on the GPU, the tuning knobs they offer to trade off performance and quality.
+cuVS supports many of the standard index types with the list continuing to grow and stay current with the state-of-the-art. Please refer to our :doc:`vector search index guide <neighbors/neighbors>` for to learn more about each individual index type, when they can be useful on the GPU, the tuning knobs they offer to trade off performance and quality.
 
 The primary goal of cuVS is to enable speed, scale, and flexibility (in that order)- and one of the important value propositions is to enhance existing software deployments with extensible GPU capabilities to improve pain points while not interrupting parts of the system that work well today with CPU.
 
@@ -115,7 +115,7 @@ We always welcome patches for new features and bug fixes. Please read our `contr
    vector_databases_vs_vector_search.rst
    tuning_guide.rst
    comparing_indexes.rst
-   indexes/indexes.rst
+   neighbors/neighbors.rst
    api_basics.rst
    api_interoperability.rst
    working_with_ann_indexes.rst
diff --git a/docs/source/neighbors/all_neighbors.rst b/docs/source/neighbors/all_neighbors.rst
new file mode 100644
index 0000000000..c95d07488e
--- /dev/null
+++ b/docs/source/neighbors/all_neighbors.rst
@@ -0,0 +1,71 @@
+All-neighbors
+=============
+
+All-neighbors is a specialized algorithm for building approximate all-neighbors k-NN graphs. Unlike traditional nearest neighbor indexes that are designed for searching, all-neighbors focuses on constructing complete k-NN graphs for entire datasets.
+
+This algorithm is particularly useful for:
+- Graph construction for visualization algorithms (UMAP, t-SNE)
+- Building connectivity graphs for clustering algorithms
+- Creating similarity graphs for graph-based machine learning
+- Batch processing of large datasets across multiple GPUs
+
+All-neighbors supports multiple underlying algorithms:
+- **Brute Force**: Exact nearest neighbors computation
+- **IVF-PQ**: Approximate nearest neighbors using inverted file with product quantization
+- **NN-Descent**: Approximate nearest neighbors using graph-based descent
+
+The algorithm partitions the dataset into clusters and distributes the work across multiple GPUs when possible, making it suitable for large-scale graph construction tasks.
+
+[ :doc:`C API <../c_api/neighbors_all_neighbors_c>` |:doc:`C++ API <../cpp_api/neighbors_all_neighbors>` | :doc:`Python API <../python_api/neighbors_all_neighbors>` ]
+
+Algorithm Overview
+------------------
+
+All-neighbors works by:
+
+1. **Partitioning**: Dividing the dataset into `n_clusters` clusters/batches
+2. **Assignment**: Assigning each point to `overlap_factor` clusters (must be < n_clusters)
+3. **Local Computation**: Building k-NN graphs within each cluster using the specified algorithm
+4. **Aggregation**: Combining results from all clusters to form the complete graph
+
+This approach enables:
+- **Scalability**: Work distribution across multiple GPUs
+- **Memory Efficiency**: Processing large datasets that don't fit in single GPU memory
+- **Flexibility**: Choice of underlying algorithm based on accuracy vs. speed requirements
+
+Use Cases
+---------
+
+**Data Mining and Machine Learning**
+- Clustering algorithms (K-means, HDBSCAN)
+- Visualization algorithms (UMAP, t-SNE)
+- Sampling and ensemble methods
+
+**Graph Construction**
+- Building similarity graphs for graph neural networks
+- Creating connectivity matrices for spectral clustering
+- Constructing neighborhood graphs for manifold learning
+
+**Large-scale Processing**
+- Processing datasets that exceed single GPU memory
+- Batch processing for distributed computing environments
+- Building graphs for graph databases and analytics
+
+Parameters
+----------
+
+- **algo**: Underlying algorithm (brute_force, ivf_pq, nn_descent)
+- **overlap_factor**: Number of clusters each point is assigned to
+- **n_clusters**: Total number of clusters for work distribution
+- **metric**: Distance metric for graph construction
+- **algorithm-specific parameters**: IVF-PQ or NN-Descent specific settings
+
+Performance Characteristics
+---------------------------
+
+- **Build Time**: Scales with dataset size and chosen algorithm
+- **Memory Usage**: Depends on cluster size and overlap factor
+- **Accuracy**: Varies by algorithm (brute_force, others) and also according to the number of clusters (n_clusters). n_clusters>1 will result in an approximation.
+- **Scalability**: Linear scaling with number of GPUs when n_clusters > 1
+
+The algorithm automatically chooses between single-GPU and multi-GPU execution based on the n_clusters parameter and available resources.
diff --git a/docs/source/indexes/bruteforce.rst b/docs/source/neighbors/bruteforce.rst
similarity index 100%
rename from docs/source/indexes/bruteforce.rst
rename to docs/source/neighbors/bruteforce.rst
diff --git a/docs/source/indexes/cagra.rst b/docs/source/neighbors/cagra.rst
similarity index 100%
rename from docs/source/indexes/cagra.rst
rename to docs/source/neighbors/cagra.rst
diff --git a/docs/source/indexes/ivfflat.rst b/docs/source/neighbors/ivfflat.rst
similarity index 100%
rename from docs/source/indexes/ivfflat.rst
rename to docs/source/neighbors/ivfflat.rst
diff --git a/docs/source/indexes/ivfpq.rst b/docs/source/neighbors/ivfpq.rst
similarity index 100%
rename from docs/source/indexes/ivfpq.rst
rename to docs/source/neighbors/ivfpq.rst
diff --git a/docs/source/indexes/indexes.rst b/docs/source/neighbors/neighbors.rst
similarity index 80%
rename from docs/source/indexes/indexes.rst
rename to docs/source/neighbors/neighbors.rst
index 8746b84acc..f66b73f867 100644
--- a/docs/source/indexes/indexes.rst
+++ b/docs/source/neighbors/neighbors.rst
@@ -1,5 +1,5 @@
-Nearest Neighbor Indexes
-========================
+Nearest Neighbor
+================
 
 .. toctree::
    :maxdepth: 3
@@ -10,6 +10,7 @@ Nearest Neighbor Indexes
    ivfflat.rst
    ivfpq.rst
    vamana.rst
+   all_neighbors.rst
 
 
 Indices and tables
diff --git a/docs/source/indexes/vamana.rst b/docs/source/neighbors/vamana.rst
similarity index 100%
rename from docs/source/indexes/vamana.rst
rename to docs/source/neighbors/vamana.rst
diff --git a/docs/source/python_api/neighbors.rst b/docs/source/python_api/neighbors.rst
index 8e2bad9f14..47e4b2044e 100644
--- a/docs/source/python_api/neighbors.rst
+++ b/docs/source/python_api/neighbors.rst
@@ -15,3 +15,4 @@ Nearest Neighbors
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
    neighbors_nn_decent.rst
+   neighbors_all_neighbors.rst
diff --git a/docs/source/python_api/neighbors_all_neighbors.rst b/docs/source/python_api/neighbors_all_neighbors.rst
new file mode 100644
index 0000000000..241848b7f7
--- /dev/null
+++ b/docs/source/python_api/neighbors_all_neighbors.rst
@@ -0,0 +1,19 @@
+All-neighbors KNN
+=================
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+All-neighbors allows building an approximate all-neighbors knn graph. Given a full dataset, it finds nearest neighbors for all the training vectors in the dataset.
+
+Parameters
+##########
+
+.. autoclass:: cuvs.neighbors.all_neighbors.AllNeighborsParams
+    :members:
+
+Build
+#####
+
+.. autofunction:: cuvs.neighbors.all_neighbors.build
diff --git a/docs/source/tuning_guide.rst b/docs/source/tuning_guide.rst
index 26cb2a7010..0f49802c86 100644
--- a/docs/source/tuning_guide.rst
+++ b/docs/source/tuning_guide.rst
@@ -12,7 +12,7 @@ For more information on the various different types of vector search indexes, pl
 Why automated tuning?
 =====================
 
-As much as 75% of users have told us they will not be able to tune a vector database beyond one or two simple knobs and we suggest that an ideal “knob” would be to balance training time and search time with search quality. The more time, the higher the quality, and the more needed to find an acceptable search performance. Even the 25% of users that want to tune are still asking for simple tools for doing so. These users also ask for some simple guidelines for setting tuning parameters, like :doc:`this guide <indexes/indexes>`.
+As much as 75% of users have told us they will not be able to tune a vector database beyond one or two simple knobs and we suggest that an ideal “knob” would be to balance training time and search time with search quality. The more time, the higher the quality, and the more needed to find an acceptable search performance. Even the 25% of users that want to tune are still asking for simple tools for doing so. These users also ask for some simple guidelines for setting tuning parameters, like :doc:`this guide <neighbors/neighbors>`.
 
 Since vector search indexes are more closely related to machine learning models than traditional databases indexes, one option for easing the parameter tuning burden is to use hyper-parameter optimization tools like `Ray Tune <https://medium.com/rapids-ai/30x-faster-hyperparameter-search-with-raytune-and-rapids-403013fbefc5>`_ and `Optuna <https://docs.rapids.ai/deployment/stable/examples/rapids-optuna-hpo/notebook/>`_. to verify this.
 
diff --git a/python/cuvs/cuvs/common/CMakeLists.txt b/python/cuvs/cuvs/common/CMakeLists.txt
index ba3ee12bcb..13306d0133 100644
--- a/python/cuvs/cuvs/common/CMakeLists.txt
+++ b/python/cuvs/cuvs/common/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2024-2025, NVIDIA CORPORATION.
+# Copyright (c) 2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -13,7 +13,9 @@
 # =============================================================================
 
 # Set the list of Cython files to build
-set(cython_sources cydlpack.pyx exceptions.pyx resources.pyx device_tensor_view.pyx)
+set(cython_sources cydlpack.pyx exceptions.pyx resources.pyx mg_resources.pyx
+                   device_tensor_view.pyx
+)
 set(linked_libraries cuvs::cuvs cuvs::c_api)
 
 # Build all of the Cython targets
diff --git a/python/cuvs/cuvs/common/__init__.py b/python/cuvs/cuvs/common/__init__.py
index e66418306b..52cff6dd9a 100644
--- a/python/cuvs/cuvs/common/__init__.py
+++ b/python/cuvs/cuvs/common/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2025, NVIDIA CORPORATION.
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,12 @@
 # limitations under the License.
 
 
+from .mg_resources import MultiGpuResources, auto_sync_multi_gpu_resources
 from .resources import Resources, auto_sync_resources
 
-__all__ = ["auto_sync_resources", "Resources"]
+__all__ = [
+    "auto_sync_resources",
+    "Resources",
+    "MultiGpuResources",
+    "auto_sync_multi_gpu_resources",
+]
diff --git a/python/cuvs/cuvs/common/c_api.pxd b/python/cuvs/cuvs/common/c_api.pxd
index 0f9e93fc7c..1d125ba851 100644
--- a/python/cuvs/cuvs/common/c_api.pxd
+++ b/python/cuvs/cuvs/common/c_api.pxd
@@ -35,6 +35,12 @@ cdef extern from "cuvs/core/c_api.h":
     cuvsError_t cuvsStreamSync(cuvsResources_t res)
     const char * cuvsGetLastErrorText()
 
+    cuvsError_t cuvsMultiGpuResourcesCreate(cuvsResources_t* res)
+    cuvsError_t cuvsMultiGpuResourcesCreateWithDeviceIds(
+        cuvsResources_t* res,
+        DLManagedTensor* device_ids)
+    cuvsError_t cuvsMultiGpuResourcesDestroy(cuvsResources_t res)
+
     cuvsError_t cuvsMatrixCopy(cuvsResources_t res, DLManagedTensor * src,
                                DLManagedTensor * dst)
 
diff --git a/python/cuvs/cuvs/common/mg_resources.pxd b/python/cuvs/cuvs/common/mg_resources.pxd
new file mode 100644
index 0000000000..ffd9357a9e
--- /dev/null
+++ b/python/cuvs/cuvs/common/mg_resources.pxd
@@ -0,0 +1,24 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuda.bindings.cyruntime cimport cudaStream_t
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+
+
+cdef class MultiGpuResources:
+    cdef cuvsResources_t c_obj
diff --git a/python/cuvs/cuvs/common/mg_resources.pyx b/python/cuvs/cuvs/common/mg_resources.pyx
new file mode 100644
index 0000000000..d1dc34b623
--- /dev/null
+++ b/python/cuvs/cuvs/common/mg_resources.pyx
@@ -0,0 +1,153 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from cuda.bindings.cyruntime cimport cudaStream_t
+
+from cuvs.common.c_api cimport (
+    cuvsMultiGpuResourcesCreate,
+    cuvsMultiGpuResourcesCreateWithDeviceIds,
+    cuvsMultiGpuResourcesDestroy,
+    cuvsResources_t,
+    cuvsStreamSet,
+    cuvsStreamSync,
+)
+from cuvs.common.cydlpack cimport DLManagedTensor, dlpack_c
+
+import numpy as np
+from pylibraft.common.cai_wrapper import wrap_array
+
+from cuvs.common.exceptions import check_cuvs
+
+
+cdef class MultiGpuResources:
+    """
+    Multi-GPU Resources is a lightweight python wrapper around the
+    corresponding C++ class of multi-GPU resources exposed by RAFT's C++
+    interface. This class provides a handle for multi-GPU operations across
+    all available GPUs.
+
+    Parameters
+    ----------
+    stream : int, optional
+        A CUDA stream pointer to use for this resource handle. If None, a
+        default stream will be used.
+    device_ids : list of int, optional
+        A list of device IDs to use for multi-GPU operations. If None, all
+        available GPUs will be used.
+
+    Examples
+    --------
+
+    Basic usage:
+
+    >>> from cuvs.common import MultiGpuResources
+    >>> handle = MultiGpuResources()
+    >>>
+    >>> # call multi-GPU algos here
+    >>>
+    >>> # final sync of all work launched in the stream of this handle
+    >>> handle.sync()
+
+    Using a cuPy stream with cuVS Multi-GPU Resources:
+
+    >>> import cupy
+    >>> from cuvs.common import MultiGpuResources
+    >>>
+    >>> cupy_stream = cupy.cuda.Stream()
+    >>> handle = MultiGpuResources(stream=cupy_stream.ptr)
+
+    Using specific device IDs:
+
+    >>> from cuvs.common import MultiGpuResources
+    >>> handle = MultiGpuResources(device_ids=[0])
+    >>>
+    >>> # call multi-GPU algos here
+    >>>
+    >>> handle.sync()
+    """
+
+    def __cinit__(self, stream=None, device_ids=None):
+        cdef DLManagedTensor* device_ids_dlpack = NULL
+
+        if device_ids is not None:
+            # Convert device_ids list to DLManagedTensor (keep on host/CPU)
+            # NumPy arrays are naturally on the host, which is what we need
+            device_ids_array = np.asarray(device_ids, dtype=np.int32)
+            ai = wrap_array(device_ids_array)
+            device_ids_dlpack = dlpack_c(ai)
+            check_cuvs(cuvsMultiGpuResourcesCreateWithDeviceIds(
+                &self.c_obj, device_ids_dlpack))
+        else:
+            check_cuvs(cuvsMultiGpuResourcesCreate(&self.c_obj))
+
+        if stream:
+            check_cuvs(cuvsStreamSet(self.c_obj, <cudaStream_t>stream))
+
+    def sync(self):
+        check_cuvs(cuvsStreamSync(self.c_obj))
+
+    def get_c_obj(self):
+        """
+        Return the pointer to the underlying c_obj as a size_t
+        """
+        return <size_t> self.c_obj
+
+    def __dealloc__(self):
+        check_cuvs(cuvsMultiGpuResourcesDestroy(self.c_obj))
+
+
+_multi_gpu_resources_param_string = """
+     resources : Optional cuVS Multi-GPU Resource handle for reusing CUDA \
+resources.
+        If Multi-GPU Resources aren't supplied, CUDA resources will be
+        allocated inside this function and synchronized before the
+        function exits. If resources are supplied, you will need to
+        explicitly synchronize yourself by calling `resources.sync()`
+        before accessing the output.
+""".strip()
+
+
+def auto_sync_multi_gpu_resources(f):
+    """Decorator to automatically call sync on a cuVS Multi-GPU Resources
+    object when it isn't passed to a function.
+
+    When a resources=None is passed to the wrapped function, this decorator
+    will automatically create a default multi-GPU resources for the function,
+    and call sync on that resources when the function exits.
+
+    This will also insert the appropriate docstring for the resources
+    parameter
+    """
+    import functools
+
+    @functools.wraps(f)
+    def wrapper(*args, resources=None, **kwargs):
+        sync_resources = resources is None
+        resources = (resources if resources is not None
+                     else MultiGpuResources())
+
+        ret_value = f(*args, resources=resources, **kwargs)
+
+        if sync_resources:
+            resources.sync()
+
+        return ret_value
+
+    wrapper.__doc__ = wrapper.__doc__.format(
+        resources_docstring=_multi_gpu_resources_param_string
+    )
+    return wrapper
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 1b08a32544..0c9196dc43 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -20,6 +20,7 @@ add_subdirectory(ivf_pq)
 add_subdirectory(filters)
 add_subdirectory(nn_descent)
 add_subdirectory(tiered_index)
+add_subdirectory(all_neighbors)
 
 # Set the list of Cython files to build
 set(cython_sources refine.pyx)
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index 1ba7f79ec8..8ae8c5678b 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,6 +14,7 @@
 
 
 from cuvs.neighbors import (
+    all_neighbors,
     brute_force,
     cagra,
     filters,
@@ -31,5 +32,6 @@
     "ivf_flat",
     "ivf_pq",
     "nn_descent",
+    "all_neighbors",
     "refine",
 ]
diff --git a/python/cuvs/cuvs/neighbors/all_neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/all_neighbors/CMakeLists.txt
new file mode 100644
index 0000000000..e38b4ac879
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/all_neighbors/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources all_neighbors.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_all_neighbors_
+)
diff --git a/python/cuvs/cuvs/neighbors/all_neighbors/__init__.pxd b/python/cuvs/cuvs/neighbors/all_neighbors/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/all_neighbors/__init__.py b/python/cuvs/cuvs/neighbors/all_neighbors/__init__.py
new file mode 100644
index 0000000000..511bb013e2
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/all_neighbors/__init__.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .all_neighbors import AllNeighborsParams, build
+
+__all__ = ["AllNeighborsParams", "build"]
diff --git a/python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pxd b/python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pxd
new file mode 100644
index 0000000000..8eef428151
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pxd
@@ -0,0 +1,52 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport int64_t
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+from cuvs.neighbors.ivf_pq.ivf_pq cimport cuvsIvfPqIndexParams_t
+from cuvs.neighbors.nn_descent.nn_descent cimport cuvsNNDescentIndexParams_t
+
+
+cdef extern from "cuvs/neighbors/all_neighbors.h" nogil:
+    ctypedef enum cuvsAllNeighborsAlgo:
+        CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE
+        CUVS_ALL_NEIGHBORS_ALGO_IVF_PQ
+        CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT
+
+    ctypedef struct cuvsAllNeighborsIndexParams:
+        cuvsAllNeighborsAlgo algo
+        size_t overlap_factor
+        size_t n_clusters
+        cuvsDistanceType metric
+
+        cuvsIvfPqIndexParams_t ivf_pq_params
+        cuvsNNDescentIndexParams_t nn_descent_params
+
+    ctypedef cuvsAllNeighborsIndexParams* cuvsAllNeighborsIndexParams_t
+
+    cuvsError_t cuvsAllNeighborsBuild(
+        cuvsResources_t res,
+        cuvsAllNeighborsIndexParams_t params,
+        DLManagedTensor* dataset,
+        DLManagedTensor* indices,
+        DLManagedTensor* distances,
+        DLManagedTensor* core_distances,
+        float alpha
+    )
diff --git a/python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pyx b/python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pyx
new file mode 100644
index 0000000000..a824c1a512
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/all_neighbors/all_neighbors.pyx
@@ -0,0 +1,373 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.mg_resources import MultiGpuResources
+from cuvs.common.resources import Resources
+
+from cython.operator cimport dereference as deref
+
+from cuvs.common cimport cydlpack
+from cuvs.common.c_api cimport cuvsResources_t
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.distance import DISTANCE_TYPES
+from cuvs.neighbors.common import _check_input_array
+
+from libc.stdint cimport int64_t
+
+from cuvs.common.exceptions import check_cuvs
+
+from cuvs.neighbors.ivf_pq.ivf_pq cimport cuvsIvfPqIndexParams_t
+from cuvs.neighbors.nn_descent.nn_descent cimport cuvsNNDescentIndexParams_t
+
+from .all_neighbors cimport (
+    CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE,
+    CUVS_ALL_NEIGHBORS_ALGO_IVF_PQ,
+    CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT,
+    cuvsAllNeighborsAlgo,
+    cuvsAllNeighborsBuild,
+    cuvsAllNeighborsIndexParams,
+    cuvsAllNeighborsIndexParams_t,
+)
+
+from cuvs.neighbors.ivf_pq.ivf_pq import IndexParams as IvfPqIndexParams
+from cuvs.neighbors.nn_descent.nn_descent import (
+    IndexParams as NNDescentIndexParams,
+)
+
+
+cdef inline cuvsAllNeighborsAlgo _algo_from_str(object algo):
+    if isinstance(algo, str):
+        if algo == "brute_force":
+            return CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE
+        elif algo == "ivf_pq":
+            return CUVS_ALL_NEIGHBORS_ALGO_IVF_PQ
+        elif algo == "nn_descent":
+            return CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT
+        else:
+            raise ValueError(f"Invalid algo: {algo}")
+    elif isinstance(algo, int):
+        return <cuvsAllNeighborsAlgo>algo
+    else:
+        raise ValueError(f"Invalid algo type: {type(algo)}")
+
+
+cdef class AllNeighborsParams:
+    """
+    Parameters for all-neighbors k-NN graph building.
+
+    Parameters
+    ----------
+    algo : str or cuvsAllNeighborsAlgo
+        Algorithm to use for local k-NN graph building.
+        Options: "brute_force", "ivf_pq", "nn_descent"
+    overlap_factor : int, default=2
+        Number of clusters each point is assigned to (must be < n_clusters)
+    n_clusters : int, default=1
+        Number of clusters/batches to partition the dataset into
+        (> overlap_factor). Use n_clusters>1 to distribute the work
+        across GPUs.
+    metric : str or cuvsDistanceType, default="sqeuclidean"
+        Distance metric to use for graph construction
+    ivf_pq_params : cuvs.neighbors.ivf_pq.IndexParams, optional
+        IVF-PQ specific parameters (used when algo="ivf_pq")
+    nn_descent_params : cuvs.neighbors.nn_descent.IndexParams, optional
+        NN-Descent specific parameters (used when algo="nn_descent")
+    """
+
+    cdef cuvsAllNeighborsIndexParams params
+    cdef object _ivf_pq_params
+    cdef object _nn_descent_params
+
+    def __init__(self, *,
+                 algo="nn_descent",
+                 overlap_factor=2,
+                 n_clusters=1,
+                 metric="sqeuclidean",
+                 ivf_pq_params=None,
+                 nn_descent_params=None):
+
+        self.params.algo = _algo_from_str(algo)
+        self.params.overlap_factor = overlap_factor
+        self.params.n_clusters = n_clusters
+        self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+
+        if ivf_pq_params is not None:
+            if not isinstance(ivf_pq_params, IvfPqIndexParams):
+                raise TypeError(
+                    "ivf_pq_params must be an instance of "
+                    "cuvs.neighbors.ivf_pq.IndexParams"
+                )
+
+            # Check metric consistency
+            ivf_pq_metric = ivf_pq_params.metric
+            if ivf_pq_metric != metric:
+                raise ValueError(
+                    f"Metric conflict: AllNeighborsParams metric '{metric}' "
+                    f"does not match IVF-PQ metric '{ivf_pq_metric}'. Please "
+                    f"ensure both use the same metric."
+                )
+
+        if nn_descent_params is not None:
+            if not isinstance(nn_descent_params, NNDescentIndexParams):
+                raise TypeError(
+                    "nn_descent_params must be an instance of "
+                    "cuvs.neighbors.nn_descent.IndexParams"
+                )
+
+            # Check metric consistency
+            nn_descent_metric = nn_descent_params.metric
+            if nn_descent_metric != metric:
+                raise ValueError(
+                    f"Metric conflict: AllNeighborsParams metric '{metric}' "
+                    f"does not match NN-Descent metric '{nn_descent_metric}'. "
+                    f"Please ensure both use the same metric."
+                )
+
+        # Store references to prevent garbage collection
+        self._ivf_pq_params = ivf_pq_params
+        self._nn_descent_params = nn_descent_params
+
+        # Set algorithm-specific parameter pointers
+        if ivf_pq_params is not None:
+            self.params.ivf_pq_params = (
+                <cuvsIvfPqIndexParams_t><size_t>ivf_pq_params.get_handle()
+            )
+        else:
+            self.params.ivf_pq_params = <cuvsIvfPqIndexParams_t>NULL
+
+        if nn_descent_params is not None:
+            self.params.nn_descent_params = (
+                <cuvsNNDescentIndexParams_t><size_t>(
+                    nn_descent_params.get_handle()
+                )
+            )
+        else:
+            self.params.nn_descent_params = <cuvsNNDescentIndexParams_t>NULL
+
+    def get_handle(self):
+        """Get a pointer to the underlying C object."""
+        return <size_t>&self.params
+
+    @property
+    def algo(self):
+        """Algorithm used for local k-NN graph building."""
+        if self.params.algo == CUVS_ALL_NEIGHBORS_ALGO_BRUTE_FORCE:
+            return "brute_force"
+        elif self.params.algo == CUVS_ALL_NEIGHBORS_ALGO_IVF_PQ:
+            return "ivf_pq"
+        elif self.params.algo == CUVS_ALL_NEIGHBORS_ALGO_NN_DESCENT:
+            return "nn_descent"
+        else:
+            return self.params.algo
+
+    @property
+    def overlap_factor(self):
+        """Number of clusters each point is assigned to."""
+        return self.params.overlap_factor
+
+    @property
+    def n_clusters(self):
+        """Number of clusters/batches to partition the dataset into."""
+        return self.params.n_clusters
+
+    @property
+    def metric(self):
+        """Distance metric used for graph construction."""
+        # Reverse lookup in DISTANCE_TYPES
+        for name, value in DISTANCE_TYPES.items():
+            if value == self.params.metric:
+                return name
+        return self.params.metric
+
+
+@auto_convert_output
+def build(dataset, k, params, *,
+          indices=None,
+          distances=None,
+          core_distances=None,
+          alpha=1.0,
+          resources=None):
+    """
+    All-neighbors allows building an approximate all-neighbors knn graph.
+    Given a full dataset, it finds nearest neighbors for all the training
+    vectors in the dataset.
+
+    Parameters
+    ----------
+    dataset : array_like
+        Training dataset to build the k-NN graph for. Can be provided
+        on host (for multi-GPU build) or device (for single-GPU build).
+        Host vs device location is automatically detected.
+        Supported dtype: float32
+    k : int
+        Number of nearest neighbors to find for each point
+    params : AllNeighborsParams
+        Parameters object containing all build settings including algorithm
+        choice and algorithm-specific parameters.
+    indices : array_like, optional
+        Optional output buffer for indices [num_rows x k] on device
+        (int64). If not provided, will be allocated automatically.
+    distances : array_like, optional
+        Optional output buffer for distances [num_rows x k] on device
+        (float32)
+    core_distances : array_like, optional
+        Optional output buffer for core distances [num_rows] on device
+        (float32). Requires distances parameter to be provided.
+    alpha : float, default=1.0
+        Mutual-reachability scaling; used only when core_distances is
+        provided
+    resources : Resources or MultiGpuResources, optional
+        CUDA resources to use for the operation. If not provided, a default
+        Resources object will be created. Use MultiGpuResources to enable
+        multi-GPU execution across multiple devices.
+
+    Returns
+    -------
+    indices : array_like
+        k-NN indices for each point [num_rows x k], always on device.
+        If indices buffer was provided, returns the same array filled
+        with results.
+    distances : array_like or None
+        k-NN distances if distances buffer was provided, None otherwise
+    core_distances : array_like or None
+        Core distances if core_distances buffer was provided, None otherwise
+    """
+    if not isinstance(params, AllNeighborsParams):
+        raise TypeError("params must be an instance of AllNeighborsParams")
+
+    # Check if data is on device for validation purposes
+    on_device = hasattr(dataset, "__cuda_array_interface__")
+
+    if on_device and params.n_clusters > 1:
+        raise ValueError(
+            "Batched all-neighbors build is not supported with data on "
+            "device. Put data on host for batch build."
+        )
+
+    if not isinstance(resources, (Resources, MultiGpuResources)):
+        resources = Resources()
+
+    resources.sync()
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32')])
+    n_rows, n_cols = dataset_ai.shape
+
+    # Check dependencies between parameters
+    if core_distances is not None and distances is None:
+        raise ValueError(
+            "distances must be provided when core_distances is provided"
+        )
+
+    # Validate user-provided outputs (must be device arrays if provided)
+    if indices is not None and not hasattr(
+        indices, "__cuda_array_interface__"
+    ):
+        raise ValueError(
+            "indices must be a device array (CUDA array interface)"
+        )
+    if distances is not None and not hasattr(
+        distances, "__cuda_array_interface__"
+    ):
+        raise ValueError(
+            "distances must be a device array (CUDA array interface)"
+        )
+    if core_distances is not None and not hasattr(
+        core_distances, "__cuda_array_interface__"
+    ):
+        raise ValueError(
+            "core_distances must be a device array (CUDA array interface)"
+        )
+
+    # Handle indices array (create if not provided)
+    if indices is None:
+        indices = device_ndarray.empty((n_rows, k), dtype="int64")
+
+    indices_out = wrap_array(indices)
+    _check_input_array(
+        indices_out, [np.dtype("int64")], exp_rows=n_rows, exp_cols=k
+    )
+
+    distances_out = None
+    if distances is not None:
+        distances_out = wrap_array(distances)
+        _check_input_array(
+            distances_out, [np.dtype("float32")], exp_rows=n_rows, exp_cols=k
+        )
+
+    core_out = None
+    if core_distances is not None:
+        core_out = wrap_array(core_distances)
+        _check_input_array(
+            core_out, [np.dtype("float32")], exp_rows=n_rows, exp_cols=None
+        )
+
+    cdef cydlpack.DLManagedTensor* indices_dlpack = cydlpack.dlpack_c(
+        indices_out
+    )
+
+    cdef cydlpack.DLManagedTensor* distances_dlpack = NULL
+    if distances_out is not None:
+        distances_dlpack = cydlpack.dlpack_c(distances_out)
+
+    cdef cydlpack.DLManagedTensor* core_dlpack = NULL
+    if core_out is not None:
+        core_dlpack = cydlpack.dlpack_c(core_out)
+
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = cydlpack.dlpack_c(
+        dataset_ai
+    )
+
+    cdef cuvsAllNeighborsIndexParams_t params_ptr = (
+        <cuvsAllNeighborsIndexParams_t><size_t>params.get_handle()
+    )
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        # Use unified function that auto-detects host vs device
+        check_cuvs(cuvsAllNeighborsBuild(
+            res,
+            params_ptr,
+            dataset_dlpack,
+            indices_dlpack,
+            distances_dlpack,
+            core_dlpack,
+            <float>alpha,
+        ))
+
+    # Build return tuple based on provided parameters
+    result = [indices]
+    if distances is not None:
+        result.append(distances)
+    if core_distances is not None:
+        result.append(core_distances)
+
+    # Return single element if only indices, otherwise return tuple
+    if len(result) == 1:
+        return result[0]
+    else:
+        return tuple(result)
diff --git a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
index 5c9d0d8155..f9edf7cdf2 100644
--- a/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
+++ b/python/cuvs/cuvs/neighbors/nn_descent/nn_descent.pyx
@@ -131,6 +131,10 @@ cdef class IndexParams:
     def termination_threshold(self):
         return self.params.termination_threshold
 
+    def get_handle(self):
+        """Get a pointer to the underlying C object."""
+        return <size_t>self.params
+
 cdef class Index:
     """
     NN-Descent index object. This object stores the trained NN-Descent index,
diff --git a/python/cuvs/cuvs/tests/test_all_neighbors.py b/python/cuvs/cuvs/tests/test_all_neighbors.py
new file mode 100644
index 0000000000..8fff7b8ed9
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_all_neighbors.py
@@ -0,0 +1,219 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import cupy
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+from sklearn.datasets import make_blobs
+
+from cuvs.common import MultiGpuResources, Resources
+from cuvs.neighbors import all_neighbors, brute_force, ivf_pq, nn_descent
+from cuvs.tests.ann_utils import calc_recall
+
+
+def make_cosine(
+    n_samples=100,
+    n_features=2,
+    x_range=(0, 2 * np.pi),
+    noise=0.0,
+    random_state=None,
+):
+    r = np.random.default_rng(random_state)
+    x = r.uniform(x_range[0], x_range[1], n_samples)
+    y = np.cos(x) + r.normal(0, noise, n_samples)
+    X = (
+        y.reshape(-1, 1)
+        if n_features == 1
+        else np.column_stack(
+            (x, y, r.normal(size=(n_samples, max(0, n_features - 2))))
+        )
+    )
+    return X, y
+
+
+@pytest.mark.parametrize("algo", ["nn_descent", "brute_force", "ivf_pq"])
+@pytest.mark.parametrize("cluster", ["single_cluster", "multi_cluster"])
+@pytest.mark.parametrize("metric", ["sqeuclidean", "cosine"])
+def test_all_neighbors_device_build_quality(algo, cluster, metric):
+    """Test device build with quality validation against brute force ground
+    truth.
+    """
+    n_rows, n_cols, k = 7151, 64, 16
+
+    if algo == "ivf_pq" and metric == "cosine":
+        pytest.skip("Skipping IVF-PQ with cosine distance")
+
+    if cluster == "single_cluster":
+        overlap_factor = 0
+    else:
+        overlap_factor = 3
+
+    np.random.seed(42)
+
+    if metric == "cosine":
+        X, _ = make_cosine(
+            n_samples=n_rows, n_features=n_cols, random_state=42
+        )
+    else:
+        X, _ = make_blobs(
+            n_samples=n_rows,
+            n_features=n_cols,
+            centers=10,
+            cluster_std=1.0,
+            center_box=(-10.0, 10.0),
+            random_state=42,
+        )
+    X = X.astype(np.float32)
+    X_device = device_ndarray(X)
+
+    ivf_pq_params = None
+    nn_descent_params = None
+    if algo == "ivf_pq":
+        ivf_pq_params = ivf_pq.IndexParams(
+            metric=metric,
+            n_lists=8 if cluster == "multi_cluster" else 4,
+            pq_bits=8,
+            pq_dim=0,
+            add_data_on_build=True,
+        )
+    elif algo == "nn_descent":
+        nn_descent_params = nn_descent.IndexParams(
+            metric=metric,
+            graph_degree=k,
+            intermediate_graph_degree=k * 2,
+            max_iterations=100,
+            termination_threshold=0.001,
+        )
+
+    params = all_neighbors.AllNeighborsParams(
+        algo=algo,
+        overlap_factor=overlap_factor,
+        n_clusters=1,
+        metric=metric,
+        ivf_pq_params=ivf_pq_params,
+        nn_descent_params=nn_descent_params,
+    )
+
+    res = Resources()
+    indices, distances = all_neighbors.build(
+        X_device,
+        k,
+        params,
+        distances=cupy.empty((n_rows, k), dtype=cupy.float32),
+        resources=res,
+    )
+
+    bf_index = brute_force.build(X_device, metric=metric)
+    bf_distances, bf_indices = brute_force.search(bf_index, X_device, k=k)
+
+    indices_host = cupy.asnumpy(indices)
+    bf_indices_host = cupy.asnumpy(bf_indices)
+
+    assert indices.shape == (n_rows, k)
+    assert indices.dtype == cupy.int64
+    assert distances.shape == (n_rows, k)
+    assert distances.dtype == cupy.float32
+
+    recall = calc_recall(indices_host, bf_indices_host)
+    assert recall > 0.85
+
+
+@pytest.mark.parametrize("algo", ["nn_descent", "brute_force", "ivf_pq"])
+@pytest.mark.parametrize("cluster", ["single_cluster", "multi_cluster"])
+@pytest.mark.parametrize("snmg", [False, True])
+def test_all_neighbors_host_build_quality(algo, cluster, snmg):
+    """Test host build with quality validation against brute force ground
+    truth.
+    """
+    n_rows, n_cols, k = 7151, 64, 16
+
+    if cluster == "single_cluster":
+        n_clusters = 1
+        overlap_factor = 0
+    else:
+        n_clusters = 8
+        overlap_factor = 3
+
+    np.random.seed(42)
+
+    X_host, _ = make_blobs(
+        n_samples=n_rows,
+        n_features=n_cols,
+        centers=10,
+        cluster_std=1.0,
+        center_box=(-10.0, 10.0),
+        random_state=42,
+    )
+    X_host = X_host.astype(np.float32)
+    X_device = device_ndarray(X_host)
+
+    ivf_pq_params = None
+    nn_descent_params = None
+
+    if algo == "ivf_pq":
+        ivf_pq_params = ivf_pq.IndexParams(
+            metric="sqeuclidean",
+            n_lists=8 if cluster == "multi_cluster" else 4,
+            pq_bits=8,
+            pq_dim=0,
+            add_data_on_build=True,
+        )
+    elif algo == "nn_descent":
+        nn_descent_params = nn_descent.IndexParams(
+            metric="sqeuclidean",
+            graph_degree=k,
+            intermediate_graph_degree=k * 2,
+            max_iterations=100,
+            termination_threshold=0.001,
+        )
+
+    params = all_neighbors.AllNeighborsParams(
+        algo=algo,
+        overlap_factor=overlap_factor,
+        n_clusters=n_clusters,
+        metric="sqeuclidean",
+        ivf_pq_params=ivf_pq_params,
+        nn_descent_params=nn_descent_params,
+    )
+
+    if snmg:
+        res = MultiGpuResources()
+    else:
+        res = Resources()
+
+    indices, distances = all_neighbors.build(
+        X_host,
+        k,
+        params,
+        distances=cupy.empty((n_rows, k), dtype=cupy.float32),
+        resources=res,
+    )
+
+    bf_index = brute_force.build(X_device, metric="sqeuclidean")
+    bf_distances, bf_indices = brute_force.search(bf_index, X_device, k=k)
+
+    indices_host = cupy.asnumpy(indices)
+    bf_indices_host = cupy.asnumpy(bf_indices)
+
+    assert indices.shape == (n_rows, k)
+    assert indices.dtype == cupy.int64
+    assert distances.shape == (n_rows, k)
+    assert distances.dtype == cupy.float32
+
+    recall = calc_recall(indices_host, bf_indices_host)
+
+    assert recall > 0.85

From be6024d0eafcdb4f3c0795028a1e3893dc2749f2 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Thu, 18 Sep 2025 09:44:38 -0700
Subject: [PATCH 588/623] Improve performance of assigning clusters in batched
 all_neighbors (#1336)

Turns out we were using the wrong resource to create a per-device vector.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1336
---
 .../neighbors/all_neighbors/all_neighbors_batched.cuh    | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
index 75eec6d50f..2649babdc2 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
@@ -126,13 +126,15 @@ void single_gpu_assign_clusters(
   std::optional<raft::device_vector_view<const T, int64_t>> norms_view;
   cuvs::neighbors::brute_force::index<T> brute_force_index(res, centroids, norms_view, metric);
 
+  auto stream = resource::get_cuda_stream(res);
+
   for (size_t i = 0; i < num_batches; i++) {
     size_t row_offset              = n_rows_per_batch * i + base_row_offset;
     size_t n_rows_of_current_batch = std::min(n_rows_per_batch, num_rows - row_offset);
     raft::copy(dataset_batch_d.data_handle(),
                dataset.data_handle() + row_offset * num_cols,
                n_rows_of_current_batch * num_cols,
-               resource::get_cuda_stream(res));
+               stream);
 
     // n_clusters is usually not large, so okay to do this brute-force
     cuvs::neighbors::brute_force::search(res,
@@ -143,7 +145,7 @@ void single_gpu_assign_clusters(
     raft::copy(global_nearest_cluster.data_handle() + row_offset * overlap_factor,
                nearest_clusters_idx_d.data_handle(),
                n_rows_of_current_batch * overlap_factor,
-               resource::get_cuda_stream(res));
+               stream);
   }
 }
 
@@ -185,7 +187,8 @@ void assign_clusters(raft::resources const& res,
     for (int rank = 0; rank < num_ranks; rank++) {
       auto dev_res = raft::resource::set_current_device_to_rank(res, rank);
 
-      auto centroids_matrix = raft::make_device_matrix<T, IdxT>(res, params.n_clusters, num_cols);
+      auto centroids_matrix =
+        raft::make_device_matrix<T, IdxT>(dev_res, params.n_clusters, num_cols);
       raft::copy(centroids_matrix.data_handle(),
                  centroids_h.data_handle(),
                  params.n_clusters * num_cols,

From cb2fafda82f1de23a6fbe098d8fbc344610d1157 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Fri, 19 Sep 2025 18:12:53 +0200
Subject: [PATCH 589/623] Update SNMG ANN API testing (#1023)

Enabling all tests that are able to be ran through CI and refers to issues otherwise.

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1023
---
 cpp/tests/neighbors/mg.cuh | 16 +---------------
 1 file changed, 1 insertion(+), 15 deletions(-)

diff --git a/cpp/tests/neighbors/mg.cuh b/cpp/tests/neighbors/mg.cuh
index 046968ed2e..9818e0c856 100644
--- a/cpp/tests/neighbors/mg.cuh
+++ b/cpp/tests/neighbors/mg.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -686,8 +686,6 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-
-  /*
   {7000,
    10000,
    8,
@@ -699,9 +697,6 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-  */
-
-  /*
   {7000,
    10000,
    8,
@@ -768,8 +763,6 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-   */
-
   {7000,
    10000,
    8,
@@ -792,8 +785,6 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-
-  /*
   {7000,
    10000,
    8,
@@ -805,8 +796,6 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-  */
-
   {3,
    10000,
    8,
@@ -829,8 +818,6 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-
-  /*
   {3,
    10000,
    8,
@@ -842,6 +829,5 @@ const std::vector<AnnMGInputs> inputs = {
    1024,
    cuvs::distance::DistanceType::L2Expanded,
    true},
-  */
 };
 }  // namespace cuvs::neighbors::mg

From d02e0fc1aabe4e72cc5b0383b1385a0c992cf4f3 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Sat, 20 Sep 2025 00:56:39 +0200
Subject: [PATCH 590/623] MG Python API (#1307)

Authors:
  - Victor Lafargue (https://github.com/viclafargue)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1307
---
 cpp/include/cuvs/core/c_api.h                 |  17 -
 cpp/scripts/gitutils.py                       |   4 +-
 cpp/src/neighbors/mg_cagra_c.cpp              |  50 +-
 cpp/src/neighbors/mg_ivf_flat_c.cpp           |  50 +-
 cpp/src/neighbors/mg_ivf_pq_c.cpp             |  76 +-
 docs/source/python_api/neighbors.rst          |  14 +-
 docs/source/python_api/neighbors_mg_cagra.rst |  55 ++
 .../python_api/neighbors_mg_ivf_flat.rst      |  60 ++
 .../source/python_api/neighbors_mg_ivf_pq.rst |  60 ++
 .../source/python_api/neighbors_multi_gpu.rst | 116 +++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   4 +-
 python/cuvs/cuvs/neighbors/__init__.py        |   2 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pxd    |  10 +
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |  15 +-
 python/cuvs/cuvs/neighbors/common.py          |  44 +-
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd |   7 +
 .../cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx |  10 +-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd  |   8 +
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx  |  11 +-
 python/cuvs/cuvs/neighbors/mg/CMakeLists.txt  |  17 +
 python/cuvs/cuvs/neighbors/mg/__init__.py     |  21 +
 .../cuvs/neighbors/mg/cagra/CMakeLists.txt    |  24 +
 .../cuvs/cuvs/neighbors/mg/cagra/__init__.py  |  37 +
 python/cuvs/cuvs/neighbors/mg/cagra/cagra.pxd | 126 ++++
 python/cuvs/cuvs/neighbors/mg/cagra/cagra.pyx | 571 +++++++++++++++
 .../cuvs/neighbors/mg/ivf_flat/CMakeLists.txt |  24 +
 .../cuvs/neighbors/mg/ivf_flat/__init__.py    |  39 +
 .../cuvs/neighbors/mg/ivf_flat/ivf_flat.pxd   | 128 ++++
 .../cuvs/neighbors/mg/ivf_flat/ivf_flat.pyx   | 575 +++++++++++++++
 .../cuvs/neighbors/mg/ivf_pq/CMakeLists.txt   |  24 +
 .../cuvs/cuvs/neighbors/mg/ivf_pq/__init__.py |  39 +
 .../cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pxd  | 125 ++++
 .../cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pyx  | 572 +++++++++++++++
 python/cuvs/cuvs/tests/test_mg_cagra.py       | 608 ++++++++++++++++
 python/cuvs/cuvs/tests/test_mg_ivf_flat.py    | 650 +++++++++++++++++
 python/cuvs/cuvs/tests/test_mg_ivf_pq.py      | 682 ++++++++++++++++++
 36 files changed, 4799 insertions(+), 76 deletions(-)
 create mode 100644 docs/source/python_api/neighbors_mg_cagra.rst
 create mode 100644 docs/source/python_api/neighbors_mg_ivf_flat.rst
 create mode 100644 docs/source/python_api/neighbors_mg_ivf_pq.rst
 create mode 100644 docs/source/python_api/neighbors_multi_gpu.rst
 create mode 100644 python/cuvs/cuvs/neighbors/mg/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/mg/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/mg/cagra/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/mg/cagra/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/mg/cagra/cagra.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/mg/cagra/cagra.pyx
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_flat/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_flat/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pyx
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_pq/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_pq/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pyx
 create mode 100644 python/cuvs/cuvs/tests/test_mg_cagra.py
 create mode 100644 python/cuvs/cuvs/tests/test_mg_ivf_flat.py
 create mode 100644 python/cuvs/cuvs/tests/test_mg_ivf_pq.py

diff --git a/cpp/include/cuvs/core/c_api.h b/cpp/include/cuvs/core/c_api.h
index b6319fe3b0..b47af2c773 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/cpp/include/cuvs/core/c_api.h
@@ -75,23 +75,6 @@ cuvsError_t cuvsResourcesCreate(cuvsResources_t* res);
  */
 cuvsError_t cuvsResourcesDestroy(cuvsResources_t res);
 
-/**
- * @brief Create an Initialized opaque C handle for C++ type `raft::device_resources_snmg`
- *        for multi-GPU operations
- *
- * @param[in] res cuvsResources_t opaque C handle
- * @return cuvsError_t
- */
-cuvsError_t cuvsMultiGpuResourcesCreate(cuvsResources_t* res);
-
-/**
- * @brief Destroy and de-allocate opaque C handle for C++ type `raft::device_resources_snmg`
- *
- * @param[in] res cuvsResources_t opaque C handle
- * @return cuvsError_t
- */
-cuvsError_t cuvsMultiGpuResourcesDestroy(cuvsResources_t res);
-
 /**
  * @brief Set cudaStream_t on cuvsResources_t to queue CUDA kernels on APIs
  *        that accept a cuvsResources_t handle
diff --git a/cpp/scripts/gitutils.py b/cpp/scripts/gitutils.py
index a7337ed4df..800d7797e8 100644
--- a/cpp/scripts/gitutils.py
+++ b/cpp/scripts/gitutils.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -155,7 +155,7 @@ def uncommittedFiles():
     ret = []
     for f in files.splitlines():
         f = f.strip(" ")
-        f = re.sub("\s+", " ", f)  # noqa: W605
+        f = re.sub(r"\s+", " ", f)  # noqa: W605
         tmp = f.split(" ", 1)
         # only consider staged files or uncommitted files
         # in other words, ignore untracked files
diff --git a/cpp/src/neighbors/mg_cagra_c.cpp b/cpp/src/neighbors/mg_cagra_c.cpp
index c6d05605d7..e661297b97 100644
--- a/cpp/src/neighbors/mg_cagra_c.cpp
+++ b/cpp/src/neighbors/mg_cagra_c.cpp
@@ -267,7 +267,12 @@ extern "C" cuvsError_t cuvsMultiGpuCagraBuild(cuvsResources_t res,
                                               cuvsMultiGpuCagraIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto dataset      = dataset_tensor->dl_tensor;
+    auto dataset = dataset_tensor->dl_tensor;
+
+    // Multi-GPU CAGRA requires dataset to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(dataset),
+                 "Multi-GPU CAGRA build requires dataset to have host compatible memory");
+
     index->dtype.code = dataset.dtype.code;
     index->dtype.bits = dataset.dtype.bits;
 
@@ -295,7 +300,29 @@ extern "C" cuvsError_t cuvsMultiGpuCagraSearch(cuvsResources_t res,
                                                DLManagedTensor* distances_tensor)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto queries = queries_tensor->dl_tensor;
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    // Multi-GPU CAGRA requires all tensors to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(queries),
+                 "Multi-GPU CAGRA search requires queries to have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(neighbors),
+                 "Multi-GPU CAGRA search requires neighbors to have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(distances),
+                 "Multi-GPU CAGRA search requires distances to have host compatible memory");
+
+    // Validate data types
+    RAFT_EXPECTS(neighbors.dtype.code == kDLInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    // Check type compatibility between index and queries
+    RAFT_EXPECTS(queries.dtype.code == index->dtype.code,
+                 "type mismatch between index and queries");
+    RAFT_EXPECTS(queries.dtype.bits == index->dtype.bits,
+                 "type mismatch between index and queries");
 
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
       _mg_search<float>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
@@ -321,6 +348,25 @@ extern "C" cuvsError_t cuvsMultiGpuCagraExtend(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     auto vectors = new_vectors_tensor->dl_tensor;
 
+    // Multi-GPU CAGRA requires vectors to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(vectors),
+                 "Multi-GPU CAGRA extend requires new_vectors to have host compatible memory");
+
+    // Check type compatibility between index and vectors
+    RAFT_EXPECTS(vectors.dtype.code == index->dtype.code,
+                 "type mismatch between index and new_vectors");
+    RAFT_EXPECTS(vectors.dtype.bits == index->dtype.bits,
+                 "type mismatch between index and new_vectors");
+
+    // If indices are provided, they should also be in host memory
+    if (new_indices_tensor != nullptr) {
+      auto indices = new_indices_tensor->dl_tensor;
+      RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(indices),
+                   "Multi-GPU CAGRA extend requires new_indices to have host compatible memory");
+      RAFT_EXPECTS(indices.dtype.code == kDLUInt && indices.dtype.bits == 32,
+                   "new_indices should be of type uint32_t");
+    }
+
     if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
       _mg_extend<float>(res, *index, new_vectors_tensor, new_indices_tensor);
     } else if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 16) {
diff --git a/cpp/src/neighbors/mg_ivf_flat_c.cpp b/cpp/src/neighbors/mg_ivf_flat_c.cpp
index c012cb4c7c..bec2fe8149 100644
--- a/cpp/src/neighbors/mg_ivf_flat_c.cpp
+++ b/cpp/src/neighbors/mg_ivf_flat_c.cpp
@@ -264,7 +264,12 @@ extern "C" cuvsError_t cuvsMultiGpuIvfFlatBuild(cuvsResources_t res,
                                                 cuvsMultiGpuIvfFlatIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto dataset      = dataset_tensor->dl_tensor;
+    auto dataset = dataset_tensor->dl_tensor;
+
+    // Multi-GPU IVF-Flat requires dataset to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(dataset),
+                 "Multi-GPU IVF-Flat build requires dataset to have host compatible memory");
+
     index->dtype.code = dataset.dtype.code;
     index->dtype.bits = dataset.dtype.bits;
 
@@ -292,7 +297,29 @@ extern "C" cuvsError_t cuvsMultiGpuIvfFlatSearch(cuvsResources_t res,
                                                  DLManagedTensor* distances_tensor)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto queries = queries_tensor->dl_tensor;
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    // Multi-GPU IVF-Flat requires all tensors to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(queries),
+                 "Multi-GPU IVF-Flat search requires queries to have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(neighbors),
+                 "Multi-GPU IVF-Flat search requires neighbors to have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(distances),
+                 "Multi-GPU IVF-Flat search requires distances to have host compatible memory");
+
+    // Validate data types
+    RAFT_EXPECTS(neighbors.dtype.code == kDLInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    // Check type compatibility between index and queries
+    RAFT_EXPECTS(queries.dtype.code == index->dtype.code,
+                 "type mismatch between index and queries");
+    RAFT_EXPECTS(queries.dtype.bits == index->dtype.bits,
+                 "type mismatch between index and queries");
 
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
       _mg_search<float>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
@@ -318,6 +345,25 @@ extern "C" cuvsError_t cuvsMultiGpuIvfFlatExtend(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     auto vectors = new_vectors_tensor->dl_tensor;
 
+    // Multi-GPU IVF-Flat requires vectors to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(vectors),
+                 "Multi-GPU IVF-Flat extend requires new_vectors to have host compatible memory");
+
+    // Check type compatibility between index and vectors
+    RAFT_EXPECTS(vectors.dtype.code == index->dtype.code,
+                 "type mismatch between index and new_vectors");
+    RAFT_EXPECTS(vectors.dtype.bits == index->dtype.bits,
+                 "type mismatch between index and new_vectors");
+
+    // If indices are provided, they should also be in host memory
+    if (new_indices_tensor != nullptr) {
+      auto indices = new_indices_tensor->dl_tensor;
+      RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(indices),
+                   "Multi-GPU IVF-Flat extend requires new_indices to have host compatible memory");
+      RAFT_EXPECTS(indices.dtype.code == kDLInt && indices.dtype.bits == 64,
+                   "new_indices should be of type int64_t");
+    }
+
     if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
       _mg_extend<float>(res, *index, new_vectors_tensor, new_indices_tensor);
     } else if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 16) {
diff --git a/cpp/src/neighbors/mg_ivf_pq_c.cpp b/cpp/src/neighbors/mg_ivf_pq_c.cpp
index 57d11f5264..0307a659d1 100644
--- a/cpp/src/neighbors/mg_ivf_pq_c.cpp
+++ b/cpp/src/neighbors/mg_ivf_pq_c.cpp
@@ -256,7 +256,12 @@ extern "C" cuvsError_t cuvsMultiGpuIvfPqBuild(cuvsResources_t res,
                                               cuvsMultiGpuIvfPqIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto dataset      = dataset_tensor->dl_tensor;
+    auto dataset = dataset_tensor->dl_tensor;
+
+    // Multi-GPU IVF-PQ requires dataset to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(dataset),
+                 "Multi-GPU IVF-PQ build requires dataset to have host compatible memory");
+
     index->dtype.code = dataset.dtype.code;
     index->dtype.bits = dataset.dtype.bits;
 
@@ -284,7 +289,29 @@ extern "C" cuvsError_t cuvsMultiGpuIvfPqSearch(cuvsResources_t res,
                                                DLManagedTensor* distances_tensor)
 {
   return cuvs::core::translate_exceptions([=] {
-    auto queries = queries_tensor->dl_tensor;
+    auto queries   = queries_tensor->dl_tensor;
+    auto neighbors = neighbors_tensor->dl_tensor;
+    auto distances = distances_tensor->dl_tensor;
+
+    // Multi-GPU IVF-PQ requires all tensors to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(queries),
+                 "Multi-GPU IVF-PQ search requires queries to have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(neighbors),
+                 "Multi-GPU IVF-PQ search requires neighbors to have host compatible memory");
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(distances),
+                 "Multi-GPU IVF-PQ search requires distances to have host compatible memory");
+
+    // Validate data types
+    RAFT_EXPECTS(neighbors.dtype.code == kDLInt && neighbors.dtype.bits == 64,
+                 "neighbors should be of type int64_t");
+    RAFT_EXPECTS(distances.dtype.code == kDLFloat && distances.dtype.bits == 32,
+                 "distances should be of type float32");
+
+    // Check type compatibility between index and queries
+    RAFT_EXPECTS(queries.dtype.code == index->dtype.code,
+                 "type mismatch between index and queries");
+    RAFT_EXPECTS(queries.dtype.bits == index->dtype.bits,
+                 "type mismatch between index and queries");
 
     if (queries.dtype.code == kDLFloat && queries.dtype.bits == 32) {
       _mg_search<float>(res, *params, *index, queries_tensor, neighbors_tensor, distances_tensor);
@@ -310,6 +337,25 @@ extern "C" cuvsError_t cuvsMultiGpuIvfPqExtend(cuvsResources_t res,
   return cuvs::core::translate_exceptions([=] {
     auto vectors = new_vectors_tensor->dl_tensor;
 
+    // Multi-GPU IVF-PQ requires vectors to be in host memory
+    RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(vectors),
+                 "Multi-GPU IVF-PQ extend requires new_vectors to have host compatible memory");
+
+    // Check type compatibility between index and vectors
+    RAFT_EXPECTS(vectors.dtype.code == index->dtype.code,
+                 "type mismatch between index and new_vectors");
+    RAFT_EXPECTS(vectors.dtype.bits == index->dtype.bits,
+                 "type mismatch between index and new_vectors");
+
+    // If indices are provided, they should also be in host memory
+    if (new_indices_tensor != nullptr) {
+      auto indices = new_indices_tensor->dl_tensor;
+      RAFT_EXPECTS(cuvs::core::is_dlpack_host_compatible(indices),
+                   "Multi-GPU IVF-PQ extend requires new_indices to have host compatible memory");
+      RAFT_EXPECTS(indices.dtype.code == kDLInt && indices.dtype.bits == 64,
+                   "new_indices should be of type int64_t");
+    }
+
     if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 32) {
       _mg_extend<float>(res, *index, new_vectors_tensor, new_indices_tensor);
     } else if (vectors.dtype.code == kDLFloat && vectors.dtype.bits == 16) {
@@ -381,28 +427,8 @@ extern "C" cuvsError_t cuvsMultiGpuIvfPqDistribute(cuvsResources_t res,
                                                    cuvsMultiGpuIvfPqIndex_t index)
 {
   return cuvs::core::translate_exceptions([=] {
-    std::ifstream is(filename, std::ios::in | std::ios::binary);
-    if (!is) { RAFT_FAIL("Cannot open file %s", filename); }
-    char dtype_string[4];
-    is.read(dtype_string, 4);
-    auto dtype = raft::detail::numpy_serializer::parse_descr(std::string(dtype_string, 4));
-    is.close();
-
-    index->dtype.bits = dtype.itemsize * 8;
-    if (dtype.kind == 'f' && dtype.itemsize == 4) {
-      index->dtype.code = kDLFloat;
-      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<float>(res, filename));
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
-      index->dtype.code = kDLFloat;
-      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<half>(res, filename));
-    } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
-      index->dtype.code = kDLInt;
-      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<int8_t>(res, filename));
-    } else if (dtype.kind == 'u' && dtype.itemsize == 1) {
-      index->dtype.code = kDLUInt;
-      index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<uint8_t>(res, filename));
-    } else {
-      RAFT_FAIL("Unsupported index dtype");
-    }
+    index->dtype.code = kDLFloat;
+    index->dtype.bits = 32;
+    index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<float>(res, filename));
   });
 }
diff --git a/docs/source/python_api/neighbors.rst b/docs/source/python_api/neighbors.rst
index 47e4b2044e..909f2013ad 100644
--- a/docs/source/python_api/neighbors.rst
+++ b/docs/source/python_api/neighbors.rst
@@ -5,9 +5,12 @@ Nearest Neighbors
    :language: python
    :class: highlight
 
+Single-GPU Algorithms
+#####################
+
 .. toctree::
    :maxdepth: 2
-   :caption: Contents:
+   :caption: Single-GPU ANN Algorithms:
 
    neighbors_brute_force.rst
    neighbors_cagra.rst
@@ -15,4 +18,13 @@ Nearest Neighbors
    neighbors_ivf_flat.rst
    neighbors_ivf_pq.rst
    neighbors_nn_decent.rst
+
+Multi-GPU Algorithms
+####################
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Multi-GPU Distributed ANN:
+
+   neighbors_multi_gpu.rst
    neighbors_all_neighbors.rst
diff --git a/docs/source/python_api/neighbors_mg_cagra.rst b/docs/source/python_api/neighbors_mg_cagra.rst
new file mode 100644
index 0000000000..2f03fa389b
--- /dev/null
+++ b/docs/source/python_api/neighbors_mg_cagra.rst
@@ -0,0 +1,55 @@
+Multi-GPU CAGRA
+===============
+
+Multi-GPU CAGRA extends the graph-based CAGRA algorithm to work across multiple GPUs, providing improved scalability and performance for large-scale vector search. It supports both replicated and sharded distribution modes.
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+.. note::
+   **IMPORTANT**: Multi-GPU CAGRA requires all data (datasets, queries, output arrays) to be in host memory (CPU).
+   If using CuPy/device arrays, transfer to host with ``array.get()`` or ``cp.asnumpy(array)`` before use.
+
+Index build parameters
+######################
+
+.. autoclass:: cuvs.neighbors.mg_cagra.IndexParams
+    :members:
+
+Index search parameters
+#######################
+
+.. autoclass:: cuvs.neighbors.mg_cagra.SearchParams
+    :members:
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.mg_cagra.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.mg_cagra.build
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.mg_cagra.search
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.mg_cagra.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.mg_cagra.load
+
+Index distribute
+################
+
+.. autofunction:: cuvs.neighbors.mg_cagra.distribute
diff --git a/docs/source/python_api/neighbors_mg_ivf_flat.rst b/docs/source/python_api/neighbors_mg_ivf_flat.rst
new file mode 100644
index 0000000000..37515ce546
--- /dev/null
+++ b/docs/source/python_api/neighbors_mg_ivf_flat.rst
@@ -0,0 +1,60 @@
+Multi-GPU IVF-Flat
+==================
+
+Multi-GPU IVF-Flat extends the IVF-Flat algorithm to work across multiple GPUs, providing improved scalability and performance for large-scale vector search. It supports both replicated and sharded distribution modes.
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+.. note::
+   **IMPORTANT**: Multi-GPU IVF-Flat requires all data (datasets, queries, output arrays) to be in host memory (CPU).
+   If using CuPy/device arrays, transfer to host with ``array.get()`` or ``cp.asnumpy(array)`` before use.
+
+Index build parameters
+######################
+
+.. autoclass:: cuvs.neighbors.mg_ivf_flat.IndexParams
+    :members:
+
+Index search parameters
+#######################
+
+.. autoclass:: cuvs.neighbors.mg_ivf_flat.SearchParams
+    :members:
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.mg_ivf_flat.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.mg_ivf_flat.build
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.mg_ivf_flat.search
+
+Index extend
+############
+
+.. autofunction:: cuvs.neighbors.mg_ivf_flat.extend
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.mg_ivf_flat.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.mg_ivf_flat.load
+
+Index distribute
+################
+
+.. autofunction:: cuvs.neighbors.mg_ivf_flat.distribute
diff --git a/docs/source/python_api/neighbors_mg_ivf_pq.rst b/docs/source/python_api/neighbors_mg_ivf_pq.rst
new file mode 100644
index 0000000000..d7d13b4734
--- /dev/null
+++ b/docs/source/python_api/neighbors_mg_ivf_pq.rst
@@ -0,0 +1,60 @@
+Multi-GPU IVF-PQ
+================
+
+Multi-GPU IVF-PQ extends the IVF-PQ (Inverted File with Product Quantization) algorithm to work across multiple GPUs, providing improved scalability and performance for large-scale vector search. It supports both replicated and sharded distribution modes.
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+.. note::
+   **IMPORTANT**: Multi-GPU IVF-PQ requires all data (datasets, queries, output arrays) to be in host memory (CPU).
+   If using CuPy/device arrays, transfer to host with ``array.get()`` or ``cp.asnumpy(array)`` before use.
+
+Index build parameters
+######################
+
+.. autoclass:: cuvs.neighbors.mg_ivf_pq.IndexParams
+    :members:
+
+Index search parameters
+#######################
+
+.. autoclass:: cuvs.neighbors.mg_ivf_pq.SearchParams
+    :members:
+
+Index
+#####
+
+.. autoclass:: cuvs.neighbors.mg_ivf_pq.Index
+    :members:
+
+Index build
+###########
+
+.. autofunction:: cuvs.neighbors.mg_ivf_pq.build
+
+Index search
+############
+
+.. autofunction:: cuvs.neighbors.mg_ivf_pq.search
+
+Index extend
+############
+
+.. autofunction:: cuvs.neighbors.mg_ivf_pq.extend
+
+Index save
+##########
+
+.. autofunction:: cuvs.neighbors.mg_ivf_pq.save
+
+Index load
+##########
+
+.. autofunction:: cuvs.neighbors.mg_ivf_pq.load
+
+Index distribute
+################
+
+.. autofunction:: cuvs.neighbors.mg_ivf_pq.distribute
diff --git a/docs/source/python_api/neighbors_multi_gpu.rst b/docs/source/python_api/neighbors_multi_gpu.rst
new file mode 100644
index 0000000000..e8230d7695
--- /dev/null
+++ b/docs/source/python_api/neighbors_multi_gpu.rst
@@ -0,0 +1,116 @@
+Multi-GPU Nearest Neighbors
+===========================
+
+Multi-GPU support in cuVS enables scaling ANN (Approximate Nearest Neighbors) algorithms across multiple GPUs on a single node, providing improved performance and the ability to handle larger datasets.
+
+.. role:: py(code)
+   :language: python
+   :class: highlight
+
+Overview
+--------
+
+The multi-GPU implementations extend the single-GPU algorithms to work across multiple GPUs using two main distribution strategies:
+
+- **Replicated Mode**: The entire index is replicated across all GPUs. This mode provides higher query throughput by distributing queries across GPUs while maintaining the full index on each GPU.
+
+- **Sharded Mode**: The index is partitioned (sharded) across GPUs. This mode allows handling larger datasets that don't fit on a single GPU by distributing the data across multiple GPUs.
+
+Important Notes
+---------------
+
+.. warning::
+   **Memory Requirements**: Multi-GPU algorithms require all data to be in host memory (CPU). This is different from single-GPU algorithms that typically work with device memory.
+
+.. note::
+   **Supported Algorithms**: Currently, multi-GPU support is available for:
+
+   - CAGRA (Graph-based ANN)
+   - IVF-Flat (Inverted File with Flat storage)
+   - IVF-PQ (Inverted File with Product Quantization)
+
+Configuration Options
+---------------------
+
+Distribution Modes
+^^^^^^^^^^^^^^^^^^
+
+- **Replicated Mode**
+
+  In replicated mode, the complete index is stored on each GPU. This approach:
+
+  - Maximizes query throughput by processing queries in parallel across all GPUs
+  - Requires each GPU to have enough memory to store the entire index
+  - Is ideal for scenarios where query throughput is more important than index size limitations
+
+- **Sharded Mode**
+
+  In sharded mode, the index is distributed across GPUs. This approach:
+
+  - Enables handling of larger datasets by partitioning across GPUs
+  - Requires coordination between GPUs during search operations
+  - Is ideal for scenarios where the dataset is too large for a single GPU
+
+Search Modes
+^^^^^^^^^^^^
+
+- **Load Balancer**
+
+  Divides each query across multiple GPUs, distributing workload efficiently to maximize performance and throughput.
+
+- **Round Robin**
+
+  Distributes queries evenly across GPUs in a rotating sequence, ensuring balanced workload allocation. This mode is best suited for frequent, small-scale search operations.
+
+Merge Modes
+^^^^^^^^^^^
+
+- **Merge on Root Rank**
+
+  Results from all GPUs are collected and merged on the root rank (typically GPU 0).
+
+- **Tree Merge**
+
+  Results are merged in a tree-like fashion across GPUs to reduce communication overhead.
+
+Usage Examples
+--------------
+
+Basic Multi-GPU Usage
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+   import numpy as np
+   from cuvs.neighbors import mg_cagra
+
+   # Create dataset in host memory
+   n_samples = 100000
+   n_features = 128
+   dataset = np.random.random_sample((n_samples, n_features), dtype=np.float32)
+
+   # Build multi-GPU index
+   build_params = mg_cagra.IndexParams(
+       distribution_mode="sharded",
+       metric="sqeuclidean"
+   )
+   index = mg_cagra.build(build_params, dataset)
+
+   # Search with multi-GPU
+   queries = np.random.random_sample((1000, n_features), dtype=np.float32)
+   search_params = mg_cagra.SearchParams(
+       search_mode="load_balancer",
+       merge_mode="merge_on_root_rank"
+   )
+   distances, neighbors = mg_cagra.search(search_params, index, queries, k=10)
+
+Algorithm-Specific Documentation
+--------------------------------
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Multi-GPU Algorithms:
+
+   neighbors_mg_cagra.rst
+   neighbors_mg_ivf_flat.rst
+   neighbors_mg_ivf_pq.rst
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index 0c9196dc43..ee48687d69 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -10,8 +10,7 @@
 # is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
-# =============================================================================
-
+#
 add_subdirectory(brute_force)
 add_subdirectory(cagra)
 add_subdirectory(hnsw)
@@ -20,6 +19,7 @@ add_subdirectory(ivf_pq)
 add_subdirectory(filters)
 add_subdirectory(nn_descent)
 add_subdirectory(tiered_index)
+add_subdirectory(mg)
 add_subdirectory(all_neighbors)
 
 # Set the list of Cython files to build
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index 8ae8c5678b..b34a3b3598 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -20,6 +20,7 @@
     filters,
     ivf_flat,
     ivf_pq,
+    mg,
     nn_descent,
 )
 
@@ -31,6 +32,7 @@
     "filters",
     "ivf_flat",
     "ivf_pq",
+    "mg",
     "nn_descent",
     "all_neighbors",
     "refine",
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
index b498cf1681..b142a4b33b 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pxd
@@ -197,3 +197,13 @@ cdef class Index:
     cdef cuvsCagraIndex_t index
     cdef bool trained
     cdef str active_index_type
+
+
+cdef class IndexParams:
+    cdef cuvsCagraIndexParams* params
+    cdef public object compression
+    cdef public object ivf_pq_build_params
+    cdef public object ivf_pq_search_params
+
+cdef class SearchParams:
+    cdef cuvsCagraSearchParams * params
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index d82ea99ffa..8738e035c3 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -172,13 +172,6 @@ cdef class IndexParams:
 
     """
 
-    cdef cuvsCagraIndexParams* params
-
-    # hold on to a reference to the compression, to keep from being GC'ed
-    cdef public object compression
-    cdef public object ivf_pq_build_params
-    cdef public object ivf_pq_search_params
-
     def __cinit__(self):
         check_cuvs(cuvsCagraIndexParamsCreate(&self.params))
         self.compression = None
@@ -186,7 +179,8 @@ cdef class IndexParams:
         self.ivf_pq_search_params = None
 
     def __dealloc__(self):
-        check_cuvs(cuvsCagraIndexParamsDestroy(self.params))
+        if self.params != NULL:
+            check_cuvs(cuvsCagraIndexParamsDestroy(self.params))
 
     def __init__(self, *,
                  metric="sqeuclidean",
@@ -475,13 +469,12 @@ cdef class SearchParams:
 
     """
 
-    cdef cuvsCagraSearchParams * params
-
     def __cinit__(self):
         check_cuvs(cuvsCagraSearchParamsCreate(&self.params))
 
     def __dealloc__(self):
-        check_cuvs(cuvsCagraSearchParamsDestroy(self.params))
+        if self.params != NULL:
+            check_cuvs(cuvsCagraSearchParamsDestroy(self.params))
 
     def __init__(self, *,
                  max_queries=0,
diff --git a/python/cuvs/cuvs/neighbors/common.py b/python/cuvs/cuvs/neighbors/common.py
index f49d9eb1f0..4eaaf91d65 100644
--- a/python/cuvs/cuvs/neighbors/common.py
+++ b/python/cuvs/cuvs/neighbors/common.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import numpy as np
+
 
 def _check_input_array(
     cai, exp_dt, exp_rows=None, exp_cols=None, exp_row_major=True
@@ -36,3 +38,43 @@ def _check_input_array(
                 exp_rows, cai.shape[0]
             )
         )
+
+
+def _check_memory_location(array_like, expected_host=True, name="array"):
+    """
+    Check if array is in expected memory location for multi-GPU operations.
+
+    Parameters
+    ----------
+    array_like : array-like
+        Array to check memory location of
+    expected_host : bool, default=True
+        If True, expects host memory. If False, expects device memory.
+    name : str
+        Name of the array for error messages
+
+    Raises
+    ------
+    ValueError
+        If array is not in expected memory location
+    """
+    # Check if array has __cuda_array_interface__ (device memory indicator)
+    has_cuda_interface = hasattr(array_like, "__cuda_array_interface__")
+
+    # Check if array is NumPy array (host memory indicator)
+    is_numpy = isinstance(array_like, np.ndarray)
+
+    if expected_host:
+        if has_cuda_interface and not is_numpy:
+            raise ValueError(
+                f"Multi-GPU IVF-PQ requires {name} to be in host memory "
+                f"(CPU), but received device memory (GPU). Please use "
+                f"array.get() or cp.asnumpy(array) to transfer to host memory."
+            )
+    else:
+        if is_numpy and not has_cuda_interface:
+            raise ValueError(
+                f"Expected {name} to be in device memory (GPU), but received "
+                f"host memory (CPU). Please use cp.asarray(array) to transfer "
+                f"to device memory."
+            )
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
index 470234d9cf..2078210d30 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pxd
@@ -95,3 +95,10 @@ cdef extern from "cuvs/neighbors/ivf_flat.h" nogil:
                                   DLManagedTensor* new_vectors,
                                   DLManagedTensor* new_indices,
                                   cuvsIvfFlatIndex_t index)
+
+
+cdef class IndexParams:
+    cdef cuvsIvfFlatIndexParams* params
+
+cdef class SearchParams:
+    cdef cuvsIvfFlatSearchParams* params
diff --git a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
index 996a39d4fe..c5f5fce5a4 100644
--- a/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_flat/ivf_flat.pyx
@@ -99,13 +99,12 @@ cdef class IndexParams:
         distribution of the newly added data.
     """
 
-    cdef cuvsIvfFlatIndexParams* params
-
     def __cinit__(self):
         cuvsIvfFlatIndexParamsCreate(&self.params)
 
     def __dealloc__(self):
-        check_cuvs(cuvsIvfFlatIndexParamsDestroy(self.params))
+        if self.params != NULL:
+            check_cuvs(cuvsIvfFlatIndexParamsDestroy(self.params))
 
     def __init__(self, *,
                  n_lists=1024,
@@ -284,13 +283,12 @@ cdef class SearchParams:
         The number of clusters to search.
     """
 
-    cdef cuvsIvfFlatSearchParams* params
-
     def __cinit__(self):
         cuvsIvfFlatSearchParamsCreate(&self.params)
 
     def __dealloc__(self):
-        check_cuvs(cuvsIvfFlatSearchParamsDestroy(self.params))
+        if self.params != NULL:
+            check_cuvs(cuvsIvfFlatSearchParamsDestroy(self.params))
 
     def __init__(self, *, n_probes=20):
         self.params.n_probes = n_probes
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
index 5ca7b97602..928a0cba1b 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pxd
@@ -121,3 +121,11 @@ cdef extern from "cuvs/neighbors/ivf_pq.h" nogil:
                                 DLManagedTensor* new_vectors,
                                 DLManagedTensor* new_indices,
                                 cuvsIvfPqIndex_t index)
+
+
+cdef class IndexParams:
+    cdef cuvsIvfPqIndexParams* params
+    cdef object _metric
+
+cdef class SearchParams:
+    cdef cuvsIvfPqSearchParams* params
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index 5ffa0b0c88..dd3b17f949 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -123,14 +123,12 @@ cdef class IndexParams:
         train each codebook.
     """
 
-    cdef cuvsIvfPqIndexParams* params
-    cdef object _metric
-
     def __cinit__(self):
         cuvsIvfPqIndexParamsCreate(&self.params)
 
     def __dealloc__(self):
-        check_cuvs(cuvsIvfPqIndexParamsDestroy(self.params))
+        if self.params != NULL:
+            check_cuvs(cuvsIvfPqIndexParamsDestroy(self.params))
 
     def __init__(self, *,
                  n_lists=1024,
@@ -399,13 +397,12 @@ cdef class SearchParams:
         of larger memory footprint.
     """
 
-    cdef cuvsIvfPqSearchParams* params
-
     def __cinit__(self):
         cuvsIvfPqSearchParamsCreate(&self.params)
 
     def __dealloc__(self):
-        check_cuvs(cuvsIvfPqSearchParamsDestroy(self.params))
+        if self.params != NULL:
+            check_cuvs(cuvsIvfPqSearchParamsDestroy(self.params))
 
     def __init__(self, *, n_probes=20, lut_dtype=np.float32,
                  internal_distance_dtype=np.float32,
diff --git a/python/cuvs/cuvs/neighbors/mg/CMakeLists.txt b/python/cuvs/cuvs/neighbors/mg/CMakeLists.txt
new file mode 100644
index 0000000000..24a2ae01a4
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/CMakeLists.txt
@@ -0,0 +1,17 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+#
+
+add_subdirectory(cagra)
+add_subdirectory(ivf_flat)
+add_subdirectory(ivf_pq)
diff --git a/python/cuvs/cuvs/neighbors/mg/__init__.py b/python/cuvs/cuvs/neighbors/mg/__init__.py
new file mode 100644
index 0000000000..a36b96d653
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/__init__.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from . import cagra, ivf_flat, ivf_pq
+
+__all__ = [
+    "cagra",
+    "ivf_flat",
+    "ivf_pq",
+]
diff --git a/python/cuvs/cuvs/neighbors/mg/cagra/CMakeLists.txt b/python/cuvs/cuvs/neighbors/mg/cagra/CMakeLists.txt
new file mode 100644
index 0000000000..4a9ffb13e0
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/cagra/CMakeLists.txt
@@ -0,0 +1,24 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+#
+
+# Set the list of Cython files to build
+set(cython_sources cagra.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_mg_cagra_
+)
diff --git a/python/cuvs/cuvs/neighbors/mg/cagra/__init__.py b/python/cuvs/cuvs/neighbors/mg/cagra/__init__.py
new file mode 100644
index 0000000000..597f6317fd
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/cagra/__init__.py
@@ -0,0 +1,37 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .cagra import (
+    Index,
+    IndexParams,
+    SearchParams,
+    build,
+    distribute,
+    extend,
+    load,
+    save,
+    search,
+)
+
+__all__ = [
+    "Index",
+    "IndexParams",
+    "SearchParams",
+    "build",
+    "extend",
+    "search",
+    "save",
+    "load",
+    "distribute",
+]
diff --git a/python/cuvs/cuvs/neighbors/mg/cagra/cagra.pxd b/python/cuvs/cuvs/neighbors/mg/cagra/cagra.pxd
new file mode 100644
index 0000000000..bb42c07d4a
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/cagra/cagra.pxd
@@ -0,0 +1,126 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport uint32_t
+from libcpp cimport bool
+
+# Import base single-GPU extension module for subclassing
+cimport cuvs.neighbors.cagra.cagra as _cagra
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLManagedTensor
+from cuvs.neighbors.cagra.cagra cimport (
+    IndexParams as SingleGpuIndexParams,
+    SearchParams as SingleGpuSearchParams,
+    cuvsCagraIndexParams_t,
+    cuvsCagraSearchParams_t,
+)
+
+
+# Multi-GPU distribution modes
+cdef extern from "cuvs/neighbors/mg_common.h" nogil:
+    ctypedef enum cuvsMultiGpuDistributionMode:
+        CUVS_NEIGHBORS_MG_REPLICATED
+        CUVS_NEIGHBORS_MG_SHARDED
+
+    ctypedef enum cuvsMultiGpuReplicatedSearchMode:
+        CUVS_NEIGHBORS_MG_LOAD_BALANCER
+        CUVS_NEIGHBORS_MG_ROUND_ROBIN
+
+    ctypedef enum cuvsMultiGpuShardedMergeMode:
+        CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK
+        CUVS_NEIGHBORS_MG_TREE_MERGE
+
+# Multi-GPU CAGRA structures and functions
+cdef extern from "cuvs/neighbors/mg_cagra.h" nogil:
+
+    cdef struct cuvsMultiGpuCagraIndexParams:
+        cuvsCagraIndexParams_t base_params
+        cuvsMultiGpuDistributionMode mode
+
+    cdef struct cuvsMultiGpuCagraSearchParams:
+        cuvsCagraSearchParams_t base_params
+        cuvsMultiGpuReplicatedSearchMode search_mode
+        cuvsMultiGpuShardedMergeMode merge_mode
+        uint32_t n_rows_per_batch
+
+    cdef struct cuvsMultiGpuCagraIndex:
+        pass
+
+    ctypedef cuvsMultiGpuCagraIndexParams* cuvsMultiGpuCagraIndexParams_t
+    ctypedef cuvsMultiGpuCagraSearchParams* cuvsMultiGpuCagraSearchParams_t
+    ctypedef cuvsMultiGpuCagraIndex* cuvsMultiGpuCagraIndex_t
+
+    cuvsError_t cuvsMultiGpuCagraIndexParamsCreate(
+        cuvsMultiGpuCagraIndexParams_t* index_params)
+
+    cuvsError_t cuvsMultiGpuCagraIndexParamsDestroy(
+        cuvsMultiGpuCagraIndexParams_t index_params)
+
+    cuvsError_t cuvsMultiGpuCagraSearchParamsCreate(
+        cuvsMultiGpuCagraSearchParams_t* params)
+
+    cuvsError_t cuvsMultiGpuCagraSearchParamsDestroy(
+        cuvsMultiGpuCagraSearchParams_t params)
+
+    cuvsError_t cuvsMultiGpuCagraIndexCreate(cuvsMultiGpuCagraIndex_t* index)
+
+    cuvsError_t cuvsMultiGpuCagraIndexDestroy(cuvsMultiGpuCagraIndex_t index)
+
+    cuvsError_t cuvsMultiGpuCagraBuild(cuvsResources_t res,
+                                       cuvsMultiGpuCagraIndexParams_t params,
+                                       DLManagedTensor* dataset_tensor,
+                                       cuvsMultiGpuCagraIndex_t index) except +
+
+    cuvsError_t cuvsMultiGpuCagraSearch(
+        cuvsResources_t res,
+        cuvsMultiGpuCagraSearchParams_t params,
+        cuvsMultiGpuCagraIndex_t index,
+        DLManagedTensor* queries_tensor,
+        DLManagedTensor* neighbors_tensor,
+        DLManagedTensor* distances_tensor) except +
+
+    cuvsError_t cuvsMultiGpuCagraSerialize(
+        cuvsResources_t res,
+        cuvsMultiGpuCagraIndex_t index,
+        const char* filename) except +
+
+    cuvsError_t cuvsMultiGpuCagraDeserialize(
+        cuvsResources_t res,
+        const char* filename,
+        cuvsMultiGpuCagraIndex_t index) except +
+
+    cuvsError_t cuvsMultiGpuCagraDistribute(
+        cuvsResources_t res,
+        const char* filename,
+        cuvsMultiGpuCagraIndex_t index) except +
+
+    cuvsError_t cuvsMultiGpuCagraExtend(
+        cuvsResources_t res,
+        cuvsMultiGpuCagraIndex_t index,
+        DLManagedTensor* new_vectors_tensor,
+        DLManagedTensor* new_indices_tensor) except +
+
+
+cdef class IndexParams(SingleGpuIndexParams):
+    cdef cuvsMultiGpuCagraIndexParams_t mg_params
+
+cdef class SearchParams(SingleGpuSearchParams):
+    cdef cuvsMultiGpuCagraSearchParams_t mg_params
+
+cdef class Index:
+    cdef cuvsMultiGpuCagraIndex_t mg_index
+    cdef bool mg_trained
diff --git a/python/cuvs/cuvs/neighbors/mg/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/mg/cagra/cagra.pyx
new file mode 100644
index 0000000000..6efcd0cd24
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/cagra/cagra.pyx
@@ -0,0 +1,571 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from libc.stdint cimport uint32_t
+from libcpp.string cimport string
+
+from pylibraft.common import auto_convert_output
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.mg_resources import auto_sync_multi_gpu_resources
+from cuvs.neighbors.common import _check_input_array, _check_memory_location
+
+from cuvs.common cimport cydlpack
+from cuvs.common.c_api cimport cuvsResources_t
+from cuvs.neighbors.cagra.cagra cimport (
+    IndexParams as SingleGpuIndexParams,
+    SearchParams as SingleGpuSearchParams,
+    cuvsCagraIndexParams_t,
+    cuvsCagraIndexParamsDestroy,
+    cuvsCagraSearchParams_t,
+    cuvsCagraSearchParamsDestroy,
+)
+
+from .cagra cimport (
+    cuvsMultiGpuCagraBuild,
+    cuvsMultiGpuCagraDeserialize,
+    cuvsMultiGpuCagraDistribute,
+    cuvsMultiGpuCagraExtend,
+    cuvsMultiGpuCagraIndex_t,
+    cuvsMultiGpuCagraIndexCreate,
+    cuvsMultiGpuCagraIndexDestroy,
+    cuvsMultiGpuCagraIndexParams_t,
+    cuvsMultiGpuCagraIndexParamsCreate,
+    cuvsMultiGpuCagraIndexParamsDestroy,
+    cuvsMultiGpuCagraSearch,
+    cuvsMultiGpuCagraSearchParams_t,
+    cuvsMultiGpuCagraSearchParamsCreate,
+    cuvsMultiGpuCagraSearchParamsDestroy,
+    cuvsMultiGpuCagraSerialize,
+    cuvsMultiGpuDistributionMode,
+    cuvsMultiGpuReplicatedSearchMode,
+    cuvsMultiGpuShardedMergeMode,
+)
+
+
+cdef class IndexParams(SingleGpuIndexParams):
+    """
+    Parameters to build multi-GPU CAGRA index for efficient search.
+    Extends single-GPU IndexParams with multi-GPU specific parameters.
+
+    Parameters
+    ----------
+    distribution_mode : str, default = "sharded"
+        Distribution mode for multi-GPU setup.
+        Valid values: ["replicated", "sharded"]
+    **kwargs : Additional parameters passed to single-GPU IndexParams
+
+    Note
+    ----
+    CAGRA currently only supports "sqeuclidean" and "inner_product" metrics.
+    """
+
+    def __cinit__(self):
+        # Base class __cinit__ has already created self.params
+        # We need to destroy it and use our embedded params instead
+        if self.params != NULL:
+            check_cuvs(cuvsCagraIndexParamsDestroy(self.params))
+
+        # Create multi-GPU params which includes embedded base params
+        check_cuvs(cuvsMultiGpuCagraIndexParamsCreate(&self.mg_params))
+        # Replace base pointer with embedded base params
+        self.params = self.mg_params.base_params
+
+    def __dealloc__(self):
+        # Only destroy the mg_params, which will handle base_params cleanup
+        check_cuvs(cuvsMultiGpuCagraIndexParamsDestroy(self.mg_params))
+        self.mg_params = NULL
+        self.params = NULL
+
+    def __init__(self, *, distribution_mode="sharded", **kwargs):
+        super().__init__(**kwargs)
+        if distribution_mode == "replicated":
+            self.mg_params.mode = CUVS_NEIGHBORS_MG_REPLICATED
+        elif distribution_mode == "sharded":
+            self.mg_params.mode = CUVS_NEIGHBORS_MG_SHARDED
+        else:
+            raise ValueError(
+                "distribution_mode must be 'replicated' or 'sharded'")
+
+    def get_handle(self):
+        return <size_t> self.mg_params
+
+    @property
+    def distribution_mode(self):
+        return ("replicated" if self.mg_params.mode ==
+                CUVS_NEIGHBORS_MG_REPLICATED else "sharded")
+
+
+cdef class Index:
+    """
+    Multi-GPU CAGRA index object. Stores the trained multi-GPU CAGRA index
+    state which can be used to perform nearest neighbors searches across
+    multiple GPUs.
+    """
+
+    def __cinit__(self):
+        # Initialize multi-GPU index
+        check_cuvs(cuvsMultiGpuCagraIndexCreate(&self.mg_index))
+        # Initialize multi-GPU trained state
+        self.mg_trained = False
+
+    def __dealloc__(self):
+        check_cuvs(cuvsMultiGpuCagraIndexDestroy(self.mg_index))
+
+    def __repr__(self):
+        return "Index(type=MultiGpuCagra)"
+
+    @property
+    def trained(self):
+        return self.mg_trained
+
+
+@auto_sync_multi_gpu_resources
+def build(IndexParams index_params, dataset, resources=None):
+    """
+    Build the multi-GPU CAGRA index from the dataset for efficient search.
+
+    Parameters
+    ----------
+    index_params : :py:class:`cuvs.neighbors.cagra.IndexParams`
+    dataset : Array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU CAGRA, the dataset MUST be in host
+        memory (CPU). If using CuPy/device arrays, transfer to host with
+        array.get() or cp.asnumpy(array).
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.cagra.Index`
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> # For multi-GPU CAGRA, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = cagra.IndexParams(metric="sqeuclidean")
+    >>> index = cagra.build(build_params, dataset)
+    >>> distances, neighbors = cagra.search(cagra.SearchParams(),
+    ...                                         index, dataset, k)
+    >>> # Results are already in host memory (NumPy arrays)
+    """
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('float16'),
+                                    np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU CAGRA requires dataset in host memory
+    _check_memory_location(dataset, expected_host=True, name="dataset")
+
+    cdef Index idx = Index()
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = (
+        cydlpack.dlpack_c(dataset_ai))
+    cdef cuvsMultiGpuCagraIndexParams_t params = index_params.mg_params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    # Build the multi-GPU index
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuCagraBuild(
+            res, params, dataset_dlpack, idx.mg_index))
+        idx.mg_trained = True
+
+    return idx
+
+
+cdef class SearchParams(SingleGpuSearchParams):
+    """
+    Parameters to search multi-GPU CAGRA index.
+    """
+
+    def __cinit__(self):
+        # Base class __cinit__ has already created self.params
+        # We need to destroy it and use our embedded params instead
+        if self.params != NULL:
+            check_cuvs(cuvsCagraSearchParamsDestroy(self.params))
+
+        # Create multi-GPU search params which includes embedded base params
+        check_cuvs(cuvsMultiGpuCagraSearchParamsCreate(&self.mg_params))
+        # Replace base pointer with embedded base params
+        self.params = self.mg_params.base_params
+
+    def __dealloc__(self):
+        # Only destroy the mg_params, which will handle base_params cleanup
+        check_cuvs(cuvsMultiGpuCagraSearchParamsDestroy(self.mg_params))
+        self.mg_params = NULL
+        self.params = <cuvsCagraSearchParams_t>NULL
+
+    def __init__(self, *, search_mode="load_balancer",
+                 merge_mode="merge_on_root_rank",
+                 n_rows_per_batch=1000, **kwargs):
+        super().__init__(**kwargs)
+        # Use the property setters for consistent validation
+        self.search_mode = search_mode
+        self.merge_mode = merge_mode
+        self.n_rows_per_batch = n_rows_per_batch
+
+    def get_handle(self):
+        return <size_t> self.mg_params
+
+    @property
+    def search_mode(self):
+        """Get the search mode for multi-GPU search."""
+        return ("load_balancer" if self.mg_params.search_mode ==
+                CUVS_NEIGHBORS_MG_LOAD_BALANCER else "round_robin")
+
+    @search_mode.setter
+    def search_mode(self, value):
+        """Set the search mode for multi-GPU search."""
+        if value == "load_balancer":
+            self.mg_params.search_mode = CUVS_NEIGHBORS_MG_LOAD_BALANCER
+        elif value == "round_robin":
+            self.mg_params.search_mode = CUVS_NEIGHBORS_MG_ROUND_ROBIN
+        else:
+            raise ValueError(
+                "search_mode must be 'load_balancer' or 'round_robin'")
+
+    @property
+    def merge_mode(self):
+        """Get the merge mode for multi-GPU search."""
+        return ("merge_on_root_rank" if self.mg_params.merge_mode ==
+                CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK else "tree_merge")
+
+    @merge_mode.setter
+    def merge_mode(self, value):
+        """Set the merge mode for multi-GPU search."""
+        if value == "merge_on_root_rank":
+            self.mg_params.merge_mode = CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK
+        elif value == "tree_merge":
+            self.mg_params.merge_mode = CUVS_NEIGHBORS_MG_TREE_MERGE
+        else:
+            raise ValueError(
+                "merge_mode must be 'merge_on_root_rank' or 'tree_merge'")
+
+    @property
+    def n_rows_per_batch(self):
+        """Get the number of rows per batch for multi-GPU search."""
+        return self.mg_params.n_rows_per_batch
+
+    @n_rows_per_batch.setter
+    def n_rows_per_batch(self, value):
+        """Set the number of rows per batch for multi-GPU search."""
+        if not isinstance(value, int) or value <= 0:
+            raise ValueError("n_rows_per_batch must be a positive integer")
+        self.mg_params.n_rows_per_batch = value
+
+
+@auto_sync_multi_gpu_resources
+@auto_convert_output
+def search(SearchParams search_params, Index index, queries,
+           k, neighbors=None, distances=None, resources=None):
+    """
+    Search the multi-GPU CAGRA index for the k-nearest neighbors of each query.
+
+    Parameters
+    ----------
+    search_params : :py:class:`cuvs.neighbors.cagra.SearchParams`
+    index : :py:class:`cuvs.neighbors.cagra.Index`
+    queries : Array interface compliant matrix shape (n_queries, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU CAGRA, queries MUST be in host memory
+        (CPU). If using CuPy/device arrays, transfer to host with
+        array.get() or cp.asnumpy(array).
+    k : int
+        The number of neighbors to search for each query.
+    neighbors : Array interface compliant matrix shape (n_queries, k), optional
+        If provided, this array will be filled with the indices of
+        the k-nearest neighbors.
+        If not provided, a new host array will be allocated.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU CAGRA.
+        Expected dtype: int64
+    distances : Array interface compliant matrix shape (n_queries, k), optional
+        If provided, this array will be filled with the distances
+        to the k-nearest neighbors.
+        If not provided, a new host array will be allocated.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU CAGRA.
+    {resources_docstring}
+
+    Returns
+    -------
+    distances : numpy.ndarray
+        The distances to the k-nearest neighbors for each query
+        (in host memory).
+    neighbors : numpy.ndarray
+        The indices of the k-nearest neighbors for each query
+        (in host memory).
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> # For multi-GPU CAGRA, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> queries = np.random.random_sample((n_queries, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = cagra.IndexParams(metric="sqeuclidean")
+    >>> index = cagra.build(build_params, dataset)
+    >>> distances, neighbors = cagra.search(cagra.SearchParams(),
+    ...                                         index, queries, k)
+    >>> # Results are already in host memory (NumPy arrays)
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before searching")
+
+    queries_ai = wrap_array(queries)
+    _check_input_array(queries_ai, [np.dtype('float32'), np.dtype('float16'),
+                                    np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU CAGRA requires queries in host memory
+    _check_memory_location(queries, expected_host=True, name="queries")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    # Prepare output arrays
+    cdef uint32_t n_queries = queries.shape[0]
+    if neighbors is None:
+        # For multi-GPU, create host arrays instead of device arrays
+        neighbors = np.empty((n_queries, k), dtype='int64')
+    if distances is None:
+        # For multi-GPU, create host arrays instead of device arrays
+        distances = np.empty((n_queries, k), dtype='float32')
+
+    neighbors_ai = wrap_array(neighbors)
+    _check_input_array(neighbors_ai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+    distances_ai = wrap_array(distances)
+    _check_input_array(distances_ai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    # Multi-GPU CAGRA requires output arrays in host memory
+    _check_memory_location(neighbors, expected_host=True,
+                           name="neighbors")
+    _check_memory_location(distances, expected_host=True,
+                           name="distances")
+
+    cdef cydlpack.DLManagedTensor* queries_dlpack = (
+        cydlpack.dlpack_c(queries_ai))
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = (
+        cydlpack.dlpack_c(neighbors_ai))
+    cdef cydlpack.DLManagedTensor* distances_dlpack = (
+        cydlpack.dlpack_c(distances_ai))
+
+    # Perform search
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuCagraSearch(
+            res, search_params.mg_params, index.mg_index, queries_dlpack,
+            neighbors_dlpack, distances_dlpack))
+
+    return (distances, neighbors)
+
+
+@auto_sync_multi_gpu_resources
+def extend(Index index, new_vectors, new_indices=None, resources=None):
+    """
+    Extend the multi-GPU CAGRA index with new vectors.
+
+    Parameters
+    ----------
+    index : :py:class:`cuvs.neighbors.cagra.Index`
+    new_vectors : Array interface compliant matrix shape (n_new_vectors, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU CAGRA, new_vectors MUST be in host
+        memory (CPU). If using CuPy/device arrays, transfer to host with
+        array.get() or cp.asnumpy(array).
+    new_indices : Array interface compliant matrix shape (n_new_vectors,),
+                  optional
+        If provided, these indices will be used for the new vectors.
+        If not provided, indices will be automatically assigned.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU CAGRA.
+        Expected dtype: uint32
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_new_vectors = 1000
+    >>> # For multi-GPU CAGRA, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> new_vectors = np.random.random_sample(
+    ...     (n_new_vectors, n_features)).astype(np.float32)
+    >>> new_indices = np.arange(n_samples, n_samples + n_new_vectors,
+    ...                         dtype=np.uint32)
+    >>> build_params = cagra.IndexParams(metric="sqeuclidean")
+    >>> index = cagra.build(build_params, dataset)
+    >>> cagra.extend(index, new_vectors, new_indices)  # doctest: +SKIP
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before extending")
+
+    new_vectors_ai = wrap_array(new_vectors)
+    _check_input_array(new_vectors_ai,
+                       [np.dtype('float32'), np.dtype('float16'),
+                        np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU CAGRA requires new_vectors in host memory
+    _check_memory_location(new_vectors, expected_host=True, name="new_vectors")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* new_vectors_dlpack = \
+        cydlpack.dlpack_c(new_vectors_ai)
+    cdef cydlpack.DLManagedTensor* new_indices_dlpack = NULL
+
+    if new_indices is not None:
+        new_indices_ai = wrap_array(new_indices)
+        _check_input_array(new_indices_ai, [np.dtype('uint32')])
+        # Multi-GPU CAGRA requires new_indices in host memory
+        _check_memory_location(new_indices, expected_host=True,
+                               name="new_indices")
+        new_indices_dlpack = cydlpack.dlpack_c(new_indices_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuCagraExtend(res, index.mg_index,
+                                           new_vectors_dlpack,
+                                           new_indices_dlpack))
+
+
+@auto_sync_multi_gpu_resources
+def save(Index index, filename, resources=None):
+    """
+    Serialize the multi-GPU CAGRA index to a file.
+
+    Parameters
+    ----------
+    index : :py:class:`cuvs.neighbors.cagra.Index`
+    filename : str
+        The filename to serialize the index to.
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import cagra
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> # For multi-GPU CAGRA, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = cagra.IndexParams(metric="sqeuclidean")
+    >>> index = cagra.build(build_params, dataset)
+    >>> cagra.save(index, "index.bin")
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before serializing")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuCagraSerialize(
+        res, index.mg_index, filename_str.c_str()))
+
+
+@auto_sync_multi_gpu_resources
+def load(filename, resources=None):
+    """
+    Deserialize the multi-GPU CAGRA index from a file.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to deserialize the index from.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+        The deserialized index.
+
+    Examples
+    --------
+
+    >>> from cuvs.neighbors.mg import cagra
+    >>> index = cagra.load("index.bin")  # doctest: +SKIP
+    """
+
+    cdef Index index = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuCagraDeserialize(
+        res, filename_str.c_str(), index.mg_index))
+    index.mg_trained = True
+
+    return index
+
+
+@auto_sync_multi_gpu_resources
+def distribute(filename, resources=None):
+    """
+    Distribute a single-GPU CAGRA index across multiple GPUs from a file.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to distribute the index from.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+        The distributed index.
+
+    Examples
+    --------
+
+    >>> from cuvs.neighbors.mg import cagra
+    >>> index = cagra.distribute("single_gpu_index.bin")  # doctest: +SKIP
+    """
+
+    cdef Index index = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuCagraDistribute(
+        res, filename_str.c_str(), index.mg_index))
+    index.mg_trained = True
+
+    return index
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_flat/CMakeLists.txt b/python/cuvs/cuvs/neighbors/mg/ivf_flat/CMakeLists.txt
new file mode 100644
index 0000000000..faacbfe2ea
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_flat/CMakeLists.txt
@@ -0,0 +1,24 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+#
+
+# Set the list of Cython files to build
+set(cython_sources ivf_flat.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_mg_ivf_flat_
+)
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_flat/__init__.py b/python/cuvs/cuvs/neighbors/mg/ivf_flat/__init__.py
new file mode 100644
index 0000000000..e4ea5ce643
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_flat/__init__.py
@@ -0,0 +1,39 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .ivf_flat import (
+    Index,
+    IndexParams,
+    SearchParams,
+    build,
+    distribute,
+    extend,
+    load,
+    save,
+    search,
+)
+
+__all__ = [
+    "Index",
+    "IndexParams",
+    "SearchParams",
+    "build",
+    "extend",
+    "search",
+    "save",
+    "load",
+    "distribute",
+]
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pxd b/python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pxd
new file mode 100644
index 0000000000..65dfe0db15
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pxd
@@ -0,0 +1,128 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport int64_t, uintptr_t
+from libcpp cimport bool
+
+# Import base single-GPU extension module for subclassing
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.neighbors.ivf_flat.ivf_flat cimport (
+    IndexParams as SingleGpuIndexParams,
+    SearchParams as SingleGpuSearchParams,
+    cuvsIvfFlatIndexParams_t,
+    cuvsIvfFlatSearchParams_t,
+)
+
+
+# Multi-GPU distribution modes
+cdef extern from "cuvs/neighbors/mg_common.h" nogil:
+    ctypedef enum cuvsMultiGpuDistributionMode:
+        CUVS_NEIGHBORS_MG_REPLICATED
+        CUVS_NEIGHBORS_MG_SHARDED
+
+    ctypedef enum cuvsMultiGpuReplicatedSearchMode:
+        CUVS_NEIGHBORS_MG_LOAD_BALANCER
+        CUVS_NEIGHBORS_MG_ROUND_ROBIN
+
+    ctypedef enum cuvsMultiGpuShardedMergeMode:
+        CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK
+        CUVS_NEIGHBORS_MG_TREE_MERGE
+
+# Multi-GPU IVF-Flat structures and functions
+cdef extern from "cuvs/neighbors/mg_ivf_flat.h" nogil:
+    cdef struct cuvsMultiGpuIvfFlatIndexParams:
+        cuvsIvfFlatIndexParams_t base_params
+        cuvsMultiGpuDistributionMode mode
+
+    cdef struct cuvsMultiGpuIvfFlatSearchParams:
+        cuvsIvfFlatSearchParams_t base_params
+        cuvsMultiGpuReplicatedSearchMode search_mode
+        cuvsMultiGpuShardedMergeMode merge_mode
+        int64_t n_rows_per_batch
+
+    cdef struct cuvsMultiGpuIvfFlatIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsMultiGpuIvfFlatIndexParams* cuvsMultiGpuIvfFlatIndexParams_t
+    ctypedef cuvsMultiGpuIvfFlatSearchParams* cuvsMultiGpuIvfFlatSearchParams_t
+    ctypedef cuvsMultiGpuIvfFlatIndex* cuvsMultiGpuIvfFlatIndex_t
+
+    cuvsError_t cuvsMultiGpuIvfFlatIndexParamsCreate(
+        cuvsMultiGpuIvfFlatIndexParams_t* index_params)
+
+    cuvsError_t cuvsMultiGpuIvfFlatIndexParamsDestroy(
+        cuvsMultiGpuIvfFlatIndexParams_t index_params)
+
+    cuvsError_t cuvsMultiGpuIvfFlatSearchParamsCreate(
+        cuvsMultiGpuIvfFlatSearchParams_t* params)
+
+    cuvsError_t cuvsMultiGpuIvfFlatSearchParamsDestroy(
+        cuvsMultiGpuIvfFlatSearchParams_t params)
+
+    cuvsError_t cuvsMultiGpuIvfFlatIndexCreate(
+        cuvsMultiGpuIvfFlatIndex_t* index)
+
+    cuvsError_t cuvsMultiGpuIvfFlatIndexDestroy(
+        cuvsMultiGpuIvfFlatIndex_t index)
+
+    cuvsError_t cuvsMultiGpuIvfFlatBuild(
+        cuvsResources_t res,
+        cuvsMultiGpuIvfFlatIndexParams_t params,
+        DLManagedTensor* dataset_tensor,
+        cuvsMultiGpuIvfFlatIndex_t index) except +
+
+    cuvsError_t cuvsMultiGpuIvfFlatSearch(
+        cuvsResources_t res,
+        cuvsMultiGpuIvfFlatSearchParams_t params,
+        cuvsMultiGpuIvfFlatIndex_t index,
+        DLManagedTensor* queries_tensor,
+        DLManagedTensor* neighbors_tensor,
+        DLManagedTensor* distances_tensor) except +
+
+    cuvsError_t cuvsMultiGpuIvfFlatExtend(
+        cuvsResources_t res,
+        cuvsMultiGpuIvfFlatIndex_t index,
+        DLManagedTensor* new_vectors_tensor,
+        DLManagedTensor* new_indices_tensor) except +
+
+    cuvsError_t cuvsMultiGpuIvfFlatSerialize(
+        cuvsResources_t res,
+        cuvsMultiGpuIvfFlatIndex_t index,
+        const char* filename) except +
+
+    cuvsError_t cuvsMultiGpuIvfFlatDeserialize(
+        cuvsResources_t res,
+        const char* filename,
+        cuvsMultiGpuIvfFlatIndex_t index) except +
+
+    cuvsError_t cuvsMultiGpuIvfFlatDistribute(
+        cuvsResources_t res,
+        const char* filename,
+        cuvsMultiGpuIvfFlatIndex_t index) except +
+
+
+cdef class IndexParams(SingleGpuIndexParams):
+    cdef cuvsMultiGpuIvfFlatIndexParams_t mg_params
+
+cdef class SearchParams(SingleGpuSearchParams):
+    cdef cuvsMultiGpuIvfFlatSearchParams_t mg_params
+
+cdef class Index:
+    cdef cuvsMultiGpuIvfFlatIndex_t mg_index
+    cdef bool mg_trained
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pyx b/python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pyx
new file mode 100644
index 0000000000..e40b6b82ea
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_flat/ivf_flat.pyx
@@ -0,0 +1,575 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from libc.stdint cimport uint32_t
+from libcpp.string cimport string
+
+from pylibraft.common import auto_convert_output
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.mg_resources import auto_sync_multi_gpu_resources
+from cuvs.neighbors.common import _check_input_array, _check_memory_location
+
+from cuvs.common cimport cydlpack
+from cuvs.common.c_api cimport cuvsResources_t
+from cuvs.neighbors.ivf_flat.ivf_flat cimport (
+    IndexParams as SingleGpuIndexParams,
+    SearchParams as SingleGpuSearchParams,
+    cuvsIvfFlatIndexParams_t,
+    cuvsIvfFlatIndexParamsDestroy,
+    cuvsIvfFlatSearchParams_t,
+    cuvsIvfFlatSearchParamsDestroy,
+)
+
+from .ivf_flat cimport (
+    cuvsMultiGpuDistributionMode,
+    cuvsMultiGpuIvfFlatBuild,
+    cuvsMultiGpuIvfFlatDeserialize,
+    cuvsMultiGpuIvfFlatDistribute,
+    cuvsMultiGpuIvfFlatExtend,
+    cuvsMultiGpuIvfFlatIndex,
+    cuvsMultiGpuIvfFlatIndex_t,
+    cuvsMultiGpuIvfFlatIndexCreate,
+    cuvsMultiGpuIvfFlatIndexDestroy,
+    cuvsMultiGpuIvfFlatIndexParams,
+    cuvsMultiGpuIvfFlatIndexParams_t,
+    cuvsMultiGpuIvfFlatIndexParamsCreate,
+    cuvsMultiGpuIvfFlatIndexParamsDestroy,
+    cuvsMultiGpuIvfFlatSearch,
+    cuvsMultiGpuIvfFlatSearchParams,
+    cuvsMultiGpuIvfFlatSearchParams_t,
+    cuvsMultiGpuIvfFlatSearchParamsCreate,
+    cuvsMultiGpuIvfFlatSearchParamsDestroy,
+    cuvsMultiGpuIvfFlatSerialize,
+    cuvsMultiGpuReplicatedSearchMode,
+    cuvsMultiGpuShardedMergeMode,
+)
+
+
+cdef class IndexParams(SingleGpuIndexParams):
+    """
+    Parameters to build multi-GPU IVF-Flat index for efficient search.
+    Extends single-GPU IndexParams with multi-GPU specific parameters.
+
+    Parameters
+    ----------
+    distribution_mode : str, default = "sharded"
+        Distribution mode for multi-GPU setup.
+        Valid values: ["replicated", "sharded"]
+    **kwargs : Additional parameters passed to single-GPU IndexParams
+    """
+
+    def __cinit__(self):
+        # Base class __cinit__ has already created self.params
+        # We need to destroy it and use our embedded params instead
+        if self.params != NULL:
+            check_cuvs(cuvsIvfFlatIndexParamsDestroy(self.params))
+
+        # Create multi-GPU params which includes embedded base params
+        check_cuvs(cuvsMultiGpuIvfFlatIndexParamsCreate(&self.mg_params))
+        # Replace base pointer with embedded base params
+        self.params = self.mg_params.base_params
+
+    def __dealloc__(self):
+        # Only destroy the mg_params, which will handle base_params cleanup
+        check_cuvs(cuvsMultiGpuIvfFlatIndexParamsDestroy(self.mg_params))
+        self.mg_params = NULL
+        self.params = NULL
+
+    def __init__(self, *, distribution_mode="sharded", **kwargs):
+        super().__init__(**kwargs)
+        if distribution_mode == "replicated":
+            self.mg_params.mode = CUVS_NEIGHBORS_MG_REPLICATED
+        elif distribution_mode == "sharded":
+            self.mg_params.mode = CUVS_NEIGHBORS_MG_SHARDED
+        else:
+            raise ValueError(
+                "distribution_mode must be 'replicated' or 'sharded'")
+
+    def get_handle(self):
+        return <size_t> self.mg_params
+
+    @property
+    def distribution_mode(self):
+        return ("replicated" if self.mg_params.mode ==
+                CUVS_NEIGHBORS_MG_REPLICATED else "sharded")
+
+
+cdef class Index:
+    """
+    Multi-GPU IVF-Flat index object. Stores the trained multi-GPU IVF-Flat
+    index state which can be used to perform nearest neighbors searches
+    across multiple GPUs.
+    """
+
+    def __cinit__(self):
+        # Initialize multi-GPU index
+        check_cuvs(cuvsMultiGpuIvfFlatIndexCreate(&self.mg_index))
+        # Initialize multi-GPU trained state
+        self.mg_trained = False
+
+    def __dealloc__(self):
+        check_cuvs(cuvsMultiGpuIvfFlatIndexDestroy(self.mg_index))
+
+    def __repr__(self):
+        return "Index(type=MultiGpuIvfFlat)"
+
+    @property
+    def trained(self):
+        return self.mg_trained
+
+
+@auto_sync_multi_gpu_resources
+def build(IndexParams index_params, dataset, resources=None):
+    """
+    Build the multi-GPU IVF-Flat index from the dataset for efficient search.
+
+    Parameters
+    ----------
+    index_params : :py:class:`cuvs.neighbors.ivf_flat.\
+IndexParams`
+    dataset : Array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU IVF-Flat, the dataset MUST be in host
+        memory (CPU). If using CuPy/device arrays, transfer to host with
+        array.get() or cp.asnumpy(array).
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.ivf_flat.Index`
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> # For multi-GPU IVF-Flat, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = ivf_flat.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_flat.build(build_params, dataset)
+    >>> distances, neighbors = ivf_flat.search(
+    ...     ivf_flat.SearchParams(),
+    ...     index, dataset, k)
+    >>> # Results are already in host memory (NumPy arrays)
+    """
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('float16'),
+                                    np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU IVF-Flat requires dataset in host memory
+    _check_memory_location(dataset, expected_host=True, name="dataset")
+
+    cdef Index idx = Index()
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = (
+        cydlpack.dlpack_c(dataset_ai))
+    cdef cuvsMultiGpuIvfFlatIndexParams_t params = index_params.mg_params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    # Build the multi-GPU index
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuIvfFlatBuild(
+            res, params, dataset_dlpack, idx.mg_index))
+        idx.mg_trained = True
+
+    return idx
+
+
+cdef class SearchParams(SingleGpuSearchParams):
+    """
+    Parameters to search multi-GPU IVF-Flat index.
+    """
+
+    def __cinit__(self):
+        # Base class __cinit__ has already created self.params
+        # We need to destroy it and use our embedded params instead
+        if self.params != NULL:
+            check_cuvs(cuvsIvfFlatSearchParamsDestroy(self.params))
+
+        # Create multi-GPU search params which includes embedded base params
+        check_cuvs(cuvsMultiGpuIvfFlatSearchParamsCreate(&self.mg_params))
+        # Replace base pointer with embedded base params
+        self.params = self.mg_params.base_params
+
+    def __dealloc__(self):
+        # Only destroy the mg_params, which will handle base_params cleanup
+        check_cuvs(cuvsMultiGpuIvfFlatSearchParamsDestroy(self.mg_params))
+        self.mg_params = NULL
+        self.params = NULL
+
+    def __init__(self, *, n_probes=1, search_mode="load_balancer",
+                 merge_mode="merge_on_root_rank",
+                 n_rows_per_batch=1000, **kwargs):
+        super().__init__(n_probes=n_probes, **kwargs)
+        # Use the property setters for consistent validation
+        self.search_mode = search_mode
+        self.merge_mode = merge_mode
+        self.n_rows_per_batch = n_rows_per_batch
+
+    def get_handle(self):
+        return <size_t> self.mg_params
+
+    @property
+    def search_mode(self):
+        """Get the search mode for multi-GPU search."""
+        return ("load_balancer" if self.mg_params.search_mode ==
+                CUVS_NEIGHBORS_MG_LOAD_BALANCER else "round_robin")
+
+    @search_mode.setter
+    def search_mode(self, value):
+        """Set the search mode for multi-GPU search."""
+        if value == "load_balancer":
+            self.mg_params.search_mode = CUVS_NEIGHBORS_MG_LOAD_BALANCER
+        elif value == "round_robin":
+            self.mg_params.search_mode = CUVS_NEIGHBORS_MG_ROUND_ROBIN
+        else:
+            raise ValueError(
+                "search_mode must be 'load_balancer' or 'round_robin'")
+
+    @property
+    def merge_mode(self):
+        """Get the merge mode for multi-GPU search."""
+        return ("merge_on_root_rank" if self.mg_params.merge_mode ==
+                CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK else "tree_merge")
+
+    @merge_mode.setter
+    def merge_mode(self, value):
+        """Set the merge mode for multi-GPU search."""
+        if value == "merge_on_root_rank":
+            self.mg_params.merge_mode = CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK
+        elif value == "tree_merge":
+            self.mg_params.merge_mode = CUVS_NEIGHBORS_MG_TREE_MERGE
+        else:
+            raise ValueError(
+                "merge_mode must be 'merge_on_root_rank' or 'tree_merge'")
+
+    @property
+    def n_rows_per_batch(self):
+        """Get the number of rows per batch for multi-GPU search."""
+        return self.mg_params.n_rows_per_batch
+
+    @n_rows_per_batch.setter
+    def n_rows_per_batch(self, value):
+        """Set the number of rows per batch for multi-GPU search."""
+        if not isinstance(value, int) or value <= 0:
+            raise ValueError("n_rows_per_batch must be a positive integer")
+        self.mg_params.n_rows_per_batch = value
+
+
+@auto_sync_multi_gpu_resources
+@auto_convert_output
+def search(SearchParams search_params, Index index, queries,
+           k, neighbors=None, distances=None, resources=None):
+    """
+    Search the multi-GPU IVF-Flat index for the k-nearest neighbors
+    of each query.
+
+    Parameters
+    ----------
+    search_params : :py:class:`cuvs.neighbors.ivf_flat.SearchParams`
+    index : :py:class:`cuvs.neighbors.ivf_flat.Index`
+    queries : Array interface compliant matrix shape (n_queries, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU IVF-Flat, queries MUST be
+        in host memory (CPU).
+        If using CuPy/device arrays, transfer to host with array.get()
+        or cp.asnumpy(array).
+    k : int
+        The number of neighbors to search for each query.
+    neighbors : Array interface compliant matrix shape (n_queries, k), optional
+        If provided, this array will be filled with the indices of
+        the k-nearest neighbors.
+        If not provided, a new host array will be allocated.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU IVF-Flat.
+    distances : Array interface compliant matrix shape (n_queries, k), optional
+        If provided, this array will be filled with the distances to
+        the k-nearest neighbors.
+        If not provided, a new host array will be allocated.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU IVF-Flat.
+    {resources_docstring}
+
+    Returns
+    -------
+    distances : numpy.ndarray
+        The distances to the k-nearest neighbors for each query
+        (in host memory).
+    neighbors : numpy.ndarray
+        The indices of the k-nearest neighbors for each query
+        (in host memory).
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> # For multi-GPU IVF-Flat, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> queries = np.random.random_sample((n_queries, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = ivf_flat.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_flat.build(build_params, dataset)
+    >>> distances, neighbors = ivf_flat.search(
+    ...    ivf_flat.SearchParams(),
+    ...    index, queries, k)
+    >>> # Results are already in host memory (NumPy arrays)
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before searching")
+
+    queries_ai = wrap_array(queries)
+    _check_input_array(queries_ai, [np.dtype('float32'), np.dtype('float16'),
+                                    np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU IVF-Flat requires queries in host memory
+    _check_memory_location(queries, expected_host=True, name="queries")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    # Prepare output arrays
+    cdef uint32_t n_queries = queries.shape[0]
+    if neighbors is None:
+        # For multi-GPU, create host arrays instead of device arrays
+        neighbors = np.empty((n_queries, k), dtype='int64')
+    if distances is None:
+        # For multi-GPU, create host arrays instead of device arrays
+        distances = np.empty((n_queries, k), dtype='float32')
+
+    neighbors_ai = wrap_array(neighbors)
+    _check_input_array(neighbors_ai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+    distances_ai = wrap_array(distances)
+    _check_input_array(distances_ai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    # Multi-GPU IVF-Flat requires output arrays in host memory
+    _check_memory_location(neighbors, expected_host=True, name="neighbors")
+    _check_memory_location(distances, expected_host=True, name="distances")
+
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_ai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_ai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_ai)
+
+    # Perform search
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuIvfFlatSearch(res, search_params.mg_params,
+                                             index.mg_index, queries_dlpack,
+                                             neighbors_dlpack,
+                                             distances_dlpack))
+
+    return (distances, neighbors)
+
+
+@auto_sync_multi_gpu_resources
+def extend(Index index, new_vectors, new_indices=None,
+           resources=None):
+    """
+    Extend the multi-GPU IVF-Flat index with new vectors.
+
+    Parameters
+    ----------
+    index : :py:class:`cuvs.neighbors.ivf_flat.Index`
+    new_vectors : Array interface compliant matrix shape (n_new_vectors, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU IVF-Flat, new_vectors MUST be
+        in host memory (CPU). If using CuPy/device arrays, transfer
+        to host with array.get() or cp.asnumpy(array).
+    new_indices : Array interface compliant matrix shape (n_new_vectors,)
+        , optional
+        If provided, these indices will be used for the new vectors.
+        If not provided, indices will be automatically assigned.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU IVF-Flat.
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_new_vectors = 1000
+    >>> # For multi-GPU IVF-Flat, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> new_vectors = np.random.random_sample(
+    ...     (n_new_vectors, n_features)).astype(np.float32)
+    >>> new_indices = np.arange(n_samples, n_new_vectors, dtype=np.int64)
+    >>> build_params = ivf_flat.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_flat.build(build_params, dataset)
+    >>> ivf_flat.extend(index, new_vectors, new_indices)
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before extending")
+
+    new_vectors_ai = wrap_array(new_vectors)
+    _check_input_array(new_vectors_ai,
+                       [np.dtype('float32'), np.dtype('float16'),
+                        np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU IVF-Flat requires new_vectors in host memory
+    _check_memory_location(new_vectors, expected_host=True, name="new_vectors")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* new_vectors_dlpack = \
+        cydlpack.dlpack_c(new_vectors_ai)
+    cdef cydlpack.DLManagedTensor* new_indices_dlpack = NULL
+
+    if new_indices is not None:
+        new_indices_ai = wrap_array(new_indices)
+        _check_input_array(new_indices_ai, [np.dtype('int64')])
+        # Multi-GPU IVF-Flat requires new_indices in host memory
+        _check_memory_location(new_indices, expected_host=True,
+                               name="new_indices")
+        new_indices_dlpack = cydlpack.dlpack_c(new_indices_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuIvfFlatExtend(res, index.mg_index,
+                                             new_vectors_dlpack,
+                                             new_indices_dlpack))
+
+
+@auto_sync_multi_gpu_resources
+def save(Index index, filename, resources=None):
+    """
+    Serialize the multi-GPU IVF-Flat index to a file.
+
+    Parameters
+    ----------
+    index : :py:class:`cuvs.neighbors.ivf_flat.Index`
+    filename : str
+        The filename to serialize the index to.
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_flat
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> # For multi-GPU IVF-Flat, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = ivf_flat.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_flat.build(build_params, dataset)
+    >>> ivf_flat.save(index, "index.bin")
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before serializing")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuIvfFlatSerialize(res,
+                                            index.mg_index,
+                                            filename_str.c_str()))
+
+
+@auto_sync_multi_gpu_resources
+def load(filename, resources=None):
+    """
+    Deserialize the multi-GPU IVF-Flat index from a file.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to deserialize the index from.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+        The deserialized index.
+
+    Examples
+    --------
+
+    >>> from cuvs.neighbors.mg import ivf_flat
+    >>> index = ivf_flat.load("index.bin")  # doctest: +SKIP
+    """
+
+    cdef Index index = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuIvfFlatDeserialize(res,
+                                              filename_str.c_str(),
+                                              index.mg_index))
+    index.mg_trained = True
+
+    return index
+
+
+@auto_sync_multi_gpu_resources
+def distribute(filename, resources=None):
+    """
+    Distribute a single-GPU IVF-Flat index across multiple GPUs from a file.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to distribute the index from.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+        The distributed index.
+
+    Examples
+    --------
+
+    >>> from cuvs.neighbors.mg import ivf_flat
+    >>> index = ivf_flat.distribute("single_gpu_index.bin")  # doctest: +SKIP
+    """
+
+    cdef Index index = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuIvfFlatDistribute(res,
+                                             filename_str.c_str(),
+                                             index.mg_index))
+    index.mg_trained = True
+
+    return index
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_pq/CMakeLists.txt b/python/cuvs/cuvs/neighbors/mg/ivf_pq/CMakeLists.txt
new file mode 100644
index 0000000000..2b5c5a18c8
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_pq/CMakeLists.txt
@@ -0,0 +1,24 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+#
+
+# Set the list of Cython files to build
+set(cython_sources ivf_pq.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_mg_ivf_pq_
+)
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_pq/__init__.py b/python/cuvs/cuvs/neighbors/mg/ivf_pq/__init__.py
new file mode 100644
index 0000000000..c75cedc267
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_pq/__init__.py
@@ -0,0 +1,39 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .ivf_pq import (
+    Index,
+    IndexParams,
+    SearchParams,
+    build,
+    distribute,
+    extend,
+    load,
+    save,
+    search,
+)
+
+__all__ = [
+    "Index",
+    "IndexParams",
+    "SearchParams",
+    "build",
+    "extend",
+    "search",
+    "save",
+    "load",
+    "distribute",
+]
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pxd b/python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pxd
new file mode 100644
index 0000000000..b0a635eb52
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pxd
@@ -0,0 +1,125 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport int64_t, uintptr_t
+from libcpp cimport bool
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.neighbors.ivf_pq.ivf_pq cimport (
+    IndexParams as SingleGpuIndexParams,
+    SearchParams as SingleGpuSearchParams,
+    cuvsIvfPqIndexParams_t,
+    cuvsIvfPqSearchParams_t,
+)
+
+# Import base single-GPU extension module for subclassing
+
+# Multi-GPU distribution modes
+cdef extern from "cuvs/neighbors/mg_common.h" nogil:
+    ctypedef enum cuvsMultiGpuDistributionMode:
+        CUVS_NEIGHBORS_MG_REPLICATED
+        CUVS_NEIGHBORS_MG_SHARDED
+
+    ctypedef enum cuvsMultiGpuReplicatedSearchMode:
+        CUVS_NEIGHBORS_MG_LOAD_BALANCER
+        CUVS_NEIGHBORS_MG_ROUND_ROBIN
+
+    ctypedef enum cuvsMultiGpuShardedMergeMode:
+        CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK
+        CUVS_NEIGHBORS_MG_TREE_MERGE
+
+# Multi-GPU IVF-PQ structures and functions
+cdef extern from "cuvs/neighbors/mg_ivf_pq.h" nogil:
+    cdef struct cuvsMultiGpuIvfPqIndexParams:
+        cuvsIvfPqIndexParams_t base_params
+        cuvsMultiGpuDistributionMode mode
+
+    cdef struct cuvsMultiGpuIvfPqSearchParams:
+        cuvsIvfPqSearchParams_t base_params
+        cuvsMultiGpuReplicatedSearchMode search_mode
+        cuvsMultiGpuShardedMergeMode merge_mode
+        int64_t n_rows_per_batch
+
+    cdef struct cuvsMultiGpuIvfPqIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsMultiGpuIvfPqIndexParams* cuvsMultiGpuIvfPqIndexParams_t
+    ctypedef cuvsMultiGpuIvfPqSearchParams* cuvsMultiGpuIvfPqSearchParams_t
+    ctypedef cuvsMultiGpuIvfPqIndex* cuvsMultiGpuIvfPqIndex_t
+
+    cuvsError_t cuvsMultiGpuIvfPqIndexParamsCreate(
+        cuvsMultiGpuIvfPqIndexParams_t* index_params)
+
+    cuvsError_t cuvsMultiGpuIvfPqIndexParamsDestroy(
+        cuvsMultiGpuIvfPqIndexParams_t index_params)
+
+    cuvsError_t cuvsMultiGpuIvfPqSearchParamsCreate(
+        cuvsMultiGpuIvfPqSearchParams_t* params)
+
+    cuvsError_t cuvsMultiGpuIvfPqSearchParamsDestroy(
+        cuvsMultiGpuIvfPqSearchParams_t params)
+
+    cuvsError_t cuvsMultiGpuIvfPqIndexCreate(cuvsMultiGpuIvfPqIndex_t* index)
+
+    cuvsError_t cuvsMultiGpuIvfPqIndexDestroy(cuvsMultiGpuIvfPqIndex_t index)
+
+    cuvsError_t cuvsMultiGpuIvfPqBuild(cuvsResources_t res,
+                                       cuvsMultiGpuIvfPqIndexParams_t params,
+                                       DLManagedTensor* dataset_tensor,
+                                       cuvsMultiGpuIvfPqIndex_t index) except +
+
+    cuvsError_t cuvsMultiGpuIvfPqSearch(
+        cuvsResources_t res,
+        cuvsMultiGpuIvfPqSearchParams_t params,
+        cuvsMultiGpuIvfPqIndex_t index,
+        DLManagedTensor* queries_tensor,
+        DLManagedTensor* neighbors_tensor,
+        DLManagedTensor* distances_tensor) except +
+
+    cuvsError_t cuvsMultiGpuIvfPqExtend(
+        cuvsResources_t res,
+        cuvsMultiGpuIvfPqIndex_t index,
+        DLManagedTensor* new_vectors_tensor,
+        DLManagedTensor* new_indices_tensor) except +
+
+    cuvsError_t cuvsMultiGpuIvfPqSerialize(
+        cuvsResources_t res,
+        cuvsMultiGpuIvfPqIndex_t index,
+        const char* filename) except +
+
+    cuvsError_t cuvsMultiGpuIvfPqDeserialize(
+        cuvsResources_t res,
+        const char* filename,
+        cuvsMultiGpuIvfPqIndex_t index) except +
+
+    cuvsError_t cuvsMultiGpuIvfPqDistribute(
+        cuvsResources_t res,
+        const char* filename,
+        cuvsMultiGpuIvfPqIndex_t index) except +
+
+
+cdef class IndexParams(SingleGpuIndexParams):
+    cdef cuvsMultiGpuIvfPqIndexParams_t mg_params
+
+cdef class SearchParams(SingleGpuSearchParams):
+    cdef cuvsMultiGpuIvfPqSearchParams_t mg_params
+
+cdef class Index:
+    cdef cuvsMultiGpuIvfPqIndex_t mg_index
+    cdef bool mg_trained
diff --git a/python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pyx
new file mode 100644
index 0000000000..6e137ce492
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/mg/ivf_pq/ivf_pq.pyx
@@ -0,0 +1,572 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import numpy as np
+
+from libc.stdint cimport uint32_t
+from libcpp.string cimport string
+
+from pylibraft.common import auto_convert_output
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.common.exceptions import check_cuvs
+from cuvs.common.mg_resources import auto_sync_multi_gpu_resources
+from cuvs.neighbors.common import _check_input_array, _check_memory_location
+
+from cuvs.common cimport cydlpack
+from cuvs.common.c_api cimport cuvsResources_t
+from cuvs.neighbors.ivf_pq.ivf_pq cimport (
+    IndexParams as SingleGpuIndexParams,
+    SearchParams as SingleGpuSearchParams,
+    cuvsIvfPqIndexParams_t,
+    cuvsIvfPqIndexParamsDestroy,
+    cuvsIvfPqSearchParams_t,
+    cuvsIvfPqSearchParamsDestroy,
+)
+
+from .ivf_pq cimport (
+    cuvsMultiGpuDistributionMode,
+    cuvsMultiGpuIvfPqBuild,
+    cuvsMultiGpuIvfPqDeserialize,
+    cuvsMultiGpuIvfPqDistribute,
+    cuvsMultiGpuIvfPqExtend,
+    cuvsMultiGpuIvfPqIndex,
+    cuvsMultiGpuIvfPqIndex_t,
+    cuvsMultiGpuIvfPqIndexCreate,
+    cuvsMultiGpuIvfPqIndexDestroy,
+    cuvsMultiGpuIvfPqIndexParams,
+    cuvsMultiGpuIvfPqIndexParams_t,
+    cuvsMultiGpuIvfPqIndexParamsCreate,
+    cuvsMultiGpuIvfPqIndexParamsDestroy,
+    cuvsMultiGpuIvfPqSearch,
+    cuvsMultiGpuIvfPqSearchParams,
+    cuvsMultiGpuIvfPqSearchParams_t,
+    cuvsMultiGpuIvfPqSearchParamsCreate,
+    cuvsMultiGpuIvfPqSearchParamsDestroy,
+    cuvsMultiGpuIvfPqSerialize,
+    cuvsMultiGpuReplicatedSearchMode,
+    cuvsMultiGpuShardedMergeMode,
+)
+
+
+cdef class IndexParams(SingleGpuIndexParams):
+    """
+    Parameters to build multi-GPU IVF-PQ index for efficient search.
+    Extends single-GPU IndexParams with multi-GPU specific parameters.
+
+    Parameters
+    ----------
+    distribution_mode : str, default = "sharded"
+        Distribution mode for multi-GPU setup.
+        Valid values: ["replicated", "sharded"]
+    **kwargs : Additional parameters passed to single-GPU IndexParams
+    """
+
+    def __cinit__(self):
+        # Base class __cinit__ has already created self.params
+        # We need to destroy it and use our embedded params instead
+        if self.params != NULL:
+            check_cuvs(cuvsIvfPqIndexParamsDestroy(self.params))
+
+        # Create multi-GPU params which includes embedded base params
+        check_cuvs(cuvsMultiGpuIvfPqIndexParamsCreate(&self.mg_params))
+        # Replace base pointer with embedded base params
+        self.params = self.mg_params.base_params
+
+    def __dealloc__(self):
+        # Only destroy the mg_params, which will handle base_params cleanup
+        check_cuvs(cuvsMultiGpuIvfPqIndexParamsDestroy(self.mg_params))
+        self.mg_params = NULL
+        self.params = <cuvsIvfPqIndexParams_t>NULL
+
+    def __init__(self, *, distribution_mode="sharded", **kwargs):
+        super().__init__(**kwargs)
+        if distribution_mode == "replicated":
+            self.mg_params.mode = CUVS_NEIGHBORS_MG_REPLICATED
+        elif distribution_mode == "sharded":
+            self.mg_params.mode = CUVS_NEIGHBORS_MG_SHARDED
+        else:
+            raise ValueError(
+                "distribution_mode must be 'replicated' or 'sharded'")
+
+    def get_handle(self):
+        return <size_t> self.mg_params
+
+    @property
+    def distribution_mode(self):
+        return ("replicated" if self.mg_params.mode ==
+                CUVS_NEIGHBORS_MG_REPLICATED else "sharded")
+
+
+cdef class Index:
+    """
+    Multi-GPU IVF-PQ index object. Stores the trained multi-GPU IVF-PQ
+    index state which can be used to perform nearest neighbors searches
+    across multiple GPUs.
+    """
+
+    def __cinit__(self):
+        # Initialize multi-GPU index
+        check_cuvs(cuvsMultiGpuIvfPqIndexCreate(&self.mg_index))
+        # Initialize multi-GPU trained state
+        self.mg_trained = False
+
+    def __dealloc__(self):
+        check_cuvs(cuvsMultiGpuIvfPqIndexDestroy(self.mg_index))
+
+    def __repr__(self):
+        return "Index(type=MultiGpuIvfPq)"
+
+    @property
+    def trained(self):
+        return self.mg_trained
+
+
+@auto_sync_multi_gpu_resources
+def build(IndexParams index_params, dataset, resources=None):
+    """
+    Build the multi-GPU IVF-PQ index from the dataset for efficient search.
+
+    Parameters
+    ----------
+    index_params : :py:class:`cuvs.neighbors.ivf_pq.IndexParams`
+    dataset : Array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU IVF-PQ, the dataset MUST be
+        in host memory (CPU).
+        If using CuPy/device arrays, transfer to host with array.get()
+        or cp.asnumpy(array).
+    {resources_docstring}
+
+    Returns
+    -------
+    index: py:class:`cuvs.neighbors.ivf_pq.Index`
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> # For multi-GPU IVF-PQ, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = ivf_pq.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_pq.build(build_params, dataset)
+    >>> distances, neighbors = ivf_pq.search(
+    ...     ivf_pq.SearchParams(),
+    ...     index, dataset, k)
+    >>> # Results are already in host memory (NumPy arrays)
+    """
+
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'), np.dtype('float16'),
+                                    np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU IVF-PQ requires dataset in host memory
+    _check_memory_location(dataset, expected_host=True, name="dataset")
+
+    cdef Index idx = Index()
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cuvsMultiGpuIvfPqIndexParams_t params = index_params.mg_params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    # Build the multi-GPU index
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuIvfPqBuild(res, params,
+                                          dataset_dlpack,
+                                          idx.mg_index))
+        idx.mg_trained = True
+
+    return idx
+
+
+cdef class SearchParams(SingleGpuSearchParams):
+    """
+    Parameters to search multi-GPU IVF-PQ index.
+    """
+
+    def __cinit__(self):
+        # Base class __cinit__ has already created self.params
+        # We need to destroy it and use our embedded params instead
+        if self.params != NULL:
+            check_cuvs(cuvsIvfPqSearchParamsDestroy(self.params))
+
+        # Create multi-GPU search params which includes embedded base params
+        check_cuvs(cuvsMultiGpuIvfPqSearchParamsCreate(&self.mg_params))
+        # Replace base pointer with embedded base params
+        self.params = self.mg_params.base_params
+
+    def __dealloc__(self):
+        # Only destroy the mg_params, which will handle base_params cleanup
+        check_cuvs(cuvsMultiGpuIvfPqSearchParamsDestroy(self.mg_params))
+        self.mg_params = NULL
+        self.params = <cuvsIvfPqSearchParams_t>NULL
+
+    def __init__(self, *, n_probes=20, search_mode="load_balancer",
+                 merge_mode="merge_on_root_rank", n_rows_per_batch=1000,
+                 **kwargs):
+        super().__init__(n_probes=n_probes, **kwargs)
+        # Use the property setters for consistent validation
+        self.search_mode = search_mode
+        self.merge_mode = merge_mode
+        self.n_rows_per_batch = n_rows_per_batch
+
+    def get_handle(self):
+        return <size_t> self.mg_params
+
+    @property
+    def search_mode(self):
+        """Get the search mode for multi-GPU search."""
+        return ("load_balancer" if self.mg_params.search_mode ==
+                CUVS_NEIGHBORS_MG_LOAD_BALANCER else "round_robin")
+
+    @search_mode.setter
+    def search_mode(self, value):
+        """Set the search mode for multi-GPU search."""
+        if value == "load_balancer":
+            self.mg_params.search_mode = CUVS_NEIGHBORS_MG_LOAD_BALANCER
+        elif value == "round_robin":
+            self.mg_params.search_mode = CUVS_NEIGHBORS_MG_ROUND_ROBIN
+        else:
+            raise ValueError(
+                "search_mode must be 'load_balancer' or 'round_robin'")
+
+    @property
+    def merge_mode(self):
+        """Get the merge mode for multi-GPU search."""
+        return ("merge_on_root_rank" if self.mg_params.merge_mode ==
+                CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK else "tree_merge")
+
+    @merge_mode.setter
+    def merge_mode(self, value):
+        """Set the merge mode for multi-GPU search."""
+        if value == "merge_on_root_rank":
+            self.mg_params.merge_mode = CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK
+        elif value == "tree_merge":
+            self.mg_params.merge_mode = CUVS_NEIGHBORS_MG_TREE_MERGE
+        else:
+            raise ValueError(
+                "merge_mode must be 'merge_on_root_rank' or 'tree_merge'")
+
+    @property
+    def n_rows_per_batch(self):
+        """Get the number of rows per batch for multi-GPU search."""
+        return self.mg_params.n_rows_per_batch
+
+    @n_rows_per_batch.setter
+    def n_rows_per_batch(self, value):
+        """Set the number of rows per batch for multi-GPU search."""
+        if not isinstance(value, int) or value <= 0:
+            raise ValueError("n_rows_per_batch must be a positive integer")
+        self.mg_params.n_rows_per_batch = value
+
+
+@auto_sync_multi_gpu_resources
+@auto_convert_output
+def search(SearchParams search_params, Index index, queries,
+           k, neighbors=None, distances=None, resources=None):
+    """
+    Search the multi-GPU IVF-PQ index for the k-nearest neighbors
+    of each query.
+
+    Parameters
+    ----------
+    search_params : :py:class:`cuvs.neighbors.ivf_pq.SearchParams`
+    index : :py:class:`cuvs.neighbors.ivf_pq.Index`
+    queries : Array interface compliant matrix shape (n_queries, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU IVF-PQ, queries MUST be
+        in host memory (CPU).
+        If using CuPy/device arrays, transfer to host with array.get()
+        or cp.asnumpy(array).
+    k : int
+        The number of neighbors to search for each query.
+    neighbors : Array interface compliant matrix shape (n_queries, k), optional
+        If provided, this array will be filled with the indices of
+        the k-nearest neighbors.
+        If not provided, a new host array will be allocated.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU IVF-PQ.
+    distances : Array interface compliant matrix shape (n_queries, k), optional
+        If provided, this array will be filled with the distances to
+        the k-nearest neighbors.
+        If not provided, a new host array will be allocated.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU IVF-PQ.
+    {resources_docstring}
+
+    Returns
+    -------
+    distances : numpy.ndarray
+        The distances to the k-nearest neighbors for each query
+        (in host memory).
+    neighbors : numpy.ndarray
+        The indices of the k-nearest neighbors for each query
+        (in host memory).
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_queries = 1000
+    >>> k = 10
+    >>> # For multi-GPU IVF-PQ, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> queries = np.random.random_sample((n_queries, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = ivf_pq.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_pq.build(build_params, dataset)
+    >>> distances, neighbors = ivf_pq.search(ivf_pq.SearchParams(),
+    ...                                       index, queries, k)
+    >>> # Results are already in host memory (NumPy arrays)
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before searching")
+
+    queries_ai = wrap_array(queries)
+    _check_input_array(queries_ai, [np.dtype('float32'), np.dtype('float16'),
+                                    np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU IVF-PQ requires queries in host memory
+    _check_memory_location(queries, expected_host=True, name="queries")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    # Prepare output arrays
+    cdef uint32_t n_queries = queries.shape[0]
+    if neighbors is None:
+        # For multi-GPU, create host arrays instead of device arrays
+        neighbors = np.empty((n_queries, k), dtype='int64')
+    if distances is None:
+        # For multi-GPU, create host arrays instead of device arrays
+        distances = np.empty((n_queries, k), dtype='float32')
+
+    neighbors_ai = wrap_array(neighbors)
+    _check_input_array(neighbors_ai, [np.dtype('int64')],
+                       exp_rows=n_queries, exp_cols=k)
+    distances_ai = wrap_array(distances)
+    _check_input_array(distances_ai, [np.dtype('float32')],
+                       exp_rows=n_queries, exp_cols=k)
+
+    # Multi-GPU IVF-PQ requires output arrays in host memory
+    _check_memory_location(neighbors, expected_host=True, name="neighbors")
+    _check_memory_location(distances, expected_host=True, name="distances")
+
+    cdef cydlpack.DLManagedTensor* queries_dlpack = \
+        cydlpack.dlpack_c(queries_ai)
+    cdef cydlpack.DLManagedTensor* neighbors_dlpack = \
+        cydlpack.dlpack_c(neighbors_ai)
+    cdef cydlpack.DLManagedTensor* distances_dlpack = \
+        cydlpack.dlpack_c(distances_ai)
+
+    # Perform search
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuIvfPqSearch(res, search_params.mg_params,
+                                           index.mg_index, queries_dlpack,
+                                           neighbors_dlpack, distances_dlpack))
+
+    return (distances, neighbors)
+
+
+@auto_sync_multi_gpu_resources
+def extend(Index index, new_vectors, new_indices=None,
+           resources=None):
+    """
+    Extend the multi-GPU IVF-PQ index with new vectors.
+
+    Parameters
+    ----------
+    index : :py:class:`cuvs.neighbors.ivf_pq.Index`
+    new_vectors : Array interface compliant matrix shape (n_new_vectors, dim)
+        Supported dtype [float32, float16, int8, uint8]
+        **IMPORTANT**: For multi-GPU IVF-PQ, new_vectors MUST be
+        in host memory (CPU).
+        If using CuPy/device arrays, transfer to host with array.get()
+        or cp.asnumpy(array).
+    new_indices : Array interface compliant matrix shape (n_new_vectors,)
+        , optional
+        If provided, these indices will be used for the new vectors.
+        If not provided, indices will be automatically assigned.
+        **IMPORTANT**: Must be in host memory (CPU) for multi-GPU IVF-PQ.
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> n_new_vectors = 1000
+    >>> # For multi-GPU IVF-PQ, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> new_vectors = np.random.random_sample(
+    ...     (n_new_vectors, n_features)).astype(np.float32)
+    >>> new_indices = np.arange(n_samples, n_new_vectors, dtype=np.int64)
+    >>> build_params = ivf_pq.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_pq.build(build_params, dataset)
+    >>> ivf_pq.extend(index, new_vectors, new_indices)
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before extending")
+
+    new_vectors_ai = wrap_array(new_vectors)
+    _check_input_array(new_vectors_ai,
+                       [np.dtype('float32'), np.dtype('float16'),
+                        np.dtype('byte'), np.dtype('ubyte')])
+
+    # Multi-GPU IVF-PQ requires new_vectors in host memory
+    _check_memory_location(new_vectors, expected_host=True, name="new_vectors")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef cydlpack.DLManagedTensor* new_vectors_dlpack = \
+        cydlpack.dlpack_c(new_vectors_ai)
+    cdef cydlpack.DLManagedTensor* new_indices_dlpack = NULL
+
+    if new_indices is not None:
+        new_indices_ai = wrap_array(new_indices)
+        _check_input_array(new_indices_ai, [np.dtype('int64')])
+        # Multi-GPU IVF-PQ requires new_indices in host memory
+        _check_memory_location(new_indices, expected_host=True,
+                               name="new_indices")
+        new_indices_dlpack = cydlpack.dlpack_c(new_indices_ai)
+
+    with cuda_interruptible():
+        check_cuvs(cuvsMultiGpuIvfPqExtend(res, index.mg_index,
+                                           new_vectors_dlpack,
+                                           new_indices_dlpack))
+
+
+@auto_sync_multi_gpu_resources
+def save(Index index, filename, resources=None):
+    """
+    Serialize the multi-GPU IVF-PQ index to a file.
+
+    Parameters
+    ----------
+    index : :py:class:`cuvs.neighbors.ivf_pq.Index`
+    filename : str
+        The filename to serialize the index to.
+    {resources_docstring}
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from cuvs.neighbors.mg import ivf_pq
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> # For multi-GPU IVF-PQ, use host (NumPy) arrays
+    >>> dataset = np.random.random_sample((n_samples, n_features)).astype(
+    ...     np.float32)
+    >>> build_params = ivf_pq.IndexParams(metric="sqeuclidean")
+    >>> index = ivf_pq.build(build_params, dataset)
+    >>> ivf_pq.save(index, "index.bin")
+    """
+
+    if not index.trained:
+        raise ValueError("Index needs to be built before serializing")
+
+    # Get resources
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuIvfPqSerialize(res, index.mg_index,
+                                          filename_str.c_str()))
+
+
+@auto_sync_multi_gpu_resources
+def load(filename, resources=None):
+    """
+    Deserialize the multi-GPU IVF-PQ index from a file.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to deserialize the index from.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+        The deserialized index.
+
+    Examples
+    --------
+
+    >>> from cuvs.neighbors.mg import ivf_pq
+    >>> index = ivf_pq.load("index.bin")  # doctest: +SKIP
+    """
+
+    cdef Index index = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuIvfPqDeserialize(res, filename_str.c_str(),
+                                            index.mg_index))
+    index.mg_trained = True
+
+    return index
+
+
+@auto_sync_multi_gpu_resources
+def distribute(filename, resources=None):
+    """
+    Distribute a single-GPU IVF-PQ index across multiple GPUs from a file.
+
+    Parameters
+    ----------
+    filename : str
+        The filename to distribute the index from.
+    {resources_docstring}
+
+    Returns
+    -------
+    index : Index
+        The distributed index.
+
+    Examples
+    --------
+
+    >>> from cuvs.neighbors.mg import ivf_pq
+    >>> index = ivf_pq.distribute("single_gpu_index.bin")  # doctest: +SKIP
+    """
+
+    cdef Index index = Index()
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    cdef string filename_str = filename.encode('utf-8')
+    check_cuvs(cuvsMultiGpuIvfPqDistribute(res, filename_str.c_str(),
+                                           index.mg_index))
+    index.mg_trained = True
+
+    return index
diff --git a/python/cuvs/cuvs/tests/test_mg_cagra.py b/python/cuvs/cuvs/tests/test_mg_cagra.py
new file mode 100644
index 0000000000..16d40f9c17
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_mg_cagra.py
@@ -0,0 +1,608 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import tempfile
+
+import numpy as np
+import pytest
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+
+from cuvs.common import MultiGpuResources
+from cuvs.neighbors.mg import cagra as mg_cagra
+from cuvs.tests.ann_utils import calc_recall, generate_data
+
+
+# Check if multi-GPU functionality is available
+def has_multiple_gpus():
+    """Check if system has multiple GPUs available."""
+    try:
+        import cupy as cp
+
+        return cp.cuda.runtime.getDeviceCount() > 1
+    except Exception:
+        return False
+
+
+# Mark tests that require multiple GPUs
+requires_multiple_gpus = pytest.mark.skipif(
+    not has_multiple_gpus(), reason="Multi-GPU tests require multiple GPUs"
+)
+
+
+def run_mg_cagra_build_search_test(
+    n_rows=10000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    metric="sqeuclidean",  # CAGRA only supports sqeuclidean and inner_product
+    distribution_mode="sharded",
+    search_mode="load_balancer",
+    merge_mode="tree_merge",
+    n_rows_per_batch=1000,
+    compare=True,
+    search_params=None,
+    graph_degree=64,
+    intermediate_graph_degree=128,
+):
+    """
+    Run a multi-GPU CAGRA build and search test.
+
+    Note: Multi-GPU CAGRA requires host memory arrays (NumPy), not device
+    arrays.
+    """
+    # Generate host memory arrays (NumPy)
+    dataset = generate_data((n_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    if metric == "inner_product":
+        queries = normalize(queries, norm="l2", axis=1)
+
+    # Multi-GPU resources
+    resources = MultiGpuResources()
+
+    # Build parameters
+    build_params = mg_cagra.IndexParams(
+        metric=metric,
+        distribution_mode=distribution_mode,
+        graph_degree=graph_degree,
+        intermediate_graph_degree=intermediate_graph_degree,
+    )
+
+    # Build index
+    index = mg_cagra.build(build_params, dataset, resources=resources)
+    assert index.trained
+
+    # Search parameters
+    if search_params is None:
+        search_params = {}
+    search_params_obj = mg_cagra.SearchParams(
+        search_mode=search_mode,
+        merge_mode=merge_mode,
+        n_rows_per_batch=n_rows_per_batch,
+        **search_params,
+    )
+
+    # Perform search
+    distances, neighbors = mg_cagra.search(
+        search_params_obj,
+        index,
+        queries,
+        k,
+        resources=resources,
+    )
+
+    # Verify results are in host memory (NumPy arrays)
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+
+    if not compare:
+        return distances, neighbors
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+    }[metric]
+
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    recall = calc_recall(neighbors, skl_idx)
+    # Multi-GPU implementation may have lower recall due to data
+    # distribution across GPUs
+    # This is acceptable as long as the functionality works correctly
+    assert recall > 0.3, f"Recall too low: {recall:.3f}"
+
+    return distances, neighbors
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize(
+    "metric", ["sqeuclidean"]
+)  # Start with just sqeuclidean
+@pytest.mark.parametrize(
+    "distribution_mode", ["sharded"]
+)  # Start with just sharded
+def test_mg_cagra_basic(dtype, metric, distribution_mode):
+    """Test basic multi-GPU CAGRA build and search functionality."""
+    run_mg_cagra_build_search_test(
+        n_rows=2000,  # Use smaller dataset for more reliable tests
+        n_cols=8,
+        n_queries=20,
+        k=5,
+        dtype=dtype,
+        metric=metric,
+        distribution_mode=distribution_mode,
+        graph_degree=32,  # Smaller graph for faster tests
+        intermediate_graph_degree=64,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize(
+    "metric", ["inner_product"]
+)  # Only test supported metrics
+@pytest.mark.parametrize("distribution_mode", ["replicated"])
+def test_mg_cagra_additional_metrics(metric, distribution_mode):
+    """Test additional metrics and distribution modes."""
+    run_mg_cagra_build_search_test(
+        n_rows=2000,
+        n_cols=8,
+        n_queries=20,
+        k=5,
+        dtype=np.float32,
+        metric=metric,
+        distribution_mode=distribution_mode,
+        graph_degree=32,
+        intermediate_graph_degree=64,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
+def test_mg_cagra_dtypes(dtype):
+    """Test multi-GPU CAGRA with different data types."""
+    run_mg_cagra_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        dtype=dtype,
+        metric="sqeuclidean",
+        graph_degree=32,
+        intermediate_graph_degree=64,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("distribution_mode", ["sharded", "replicated"])
+def test_mg_cagra_distribution_modes(distribution_mode):
+    """Test different distribution modes for multi-GPU CAGRA."""
+    run_mg_cagra_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        distribution_mode=distribution_mode,
+        graph_degree=32,
+        intermediate_graph_degree=64,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("search_mode", ["load_balancer", "round_robin"])
+@pytest.mark.parametrize("merge_mode", ["merge_on_root_rank", "tree_merge"])
+def test_mg_cagra_search_params(search_mode, merge_mode):
+    """Test different multi-GPU search parameters."""
+    run_mg_cagra_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        search_mode=search_mode,
+        merge_mode=merge_mode,
+        n_rows_per_batch=500,
+        graph_degree=32,
+        intermediate_graph_degree=64,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize(
+    "metric", ["sqeuclidean"]
+)  # Only test supported metrics
+def test_mg_cagra_metrics(metric):
+    """Test different distance metrics for multi-GPU CAGRA."""
+    run_mg_cagra_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        metric=metric,
+        graph_degree=32,
+        intermediate_graph_degree=64,
+    )
+
+
+@requires_multiple_gpus
+def test_mg_cagra_serialize():
+    """Test save/load functionality for multi-GPU CAGRA."""
+    n_rows, n_cols = 2000, 8
+    k = 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((20, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build original index
+    build_params = mg_cagra.IndexParams(
+        graph_degree=32, intermediate_graph_degree=64
+    )
+    original_index = mg_cagra.build(build_params, dataset, resources=resources)
+
+    # Search with original index
+    search_params = mg_cagra.SearchParams(itopk_size=32)
+    orig_distances, orig_neighbors = mg_cagra.search(
+        search_params, original_index, queries, k, resources=resources
+    )
+
+    # Save index to temporary file
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        mg_cagra.save(original_index, temp_filename, resources=resources)
+
+        # Load index from file
+        loaded_index = mg_cagra.load(temp_filename, resources=resources)
+        assert loaded_index.trained
+
+        # Search with loaded index
+        loaded_distances, loaded_neighbors = mg_cagra.search(
+            search_params, loaded_index, queries, k, resources=resources
+        )
+
+        # Results should be identical
+        np.testing.assert_array_equal(orig_neighbors, loaded_neighbors)
+        np.testing.assert_allclose(orig_distances, loaded_distances, rtol=1e-6)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)
+
+
+@requires_multiple_gpus
+def test_mg_cagra_distribute():
+    """Test distribute functionality for multi-GPU CAGRA."""
+    # Note: Distribute is for replicating a single-GPU index across
+    # multiple GPUs.
+    # This test builds a single-GPU index, serializes it, then distributes it.
+
+    n_rows, n_cols = 2000, 8
+    k = 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((20, n_cols), np.float32)
+
+    # Import single-GPU CAGRA to build and serialize a single-GPU index
+    from cuvs.common import Resources
+    from cuvs.neighbors import cagra
+
+    # Build single-GPU index first
+    single_gpu_resources = Resources()
+    single_build_params = cagra.IndexParams(
+        metric="sqeuclidean", graph_degree=32, intermediate_graph_degree=64
+    )
+
+    # Convert to device arrays for single-GPU build
+    try:
+        import cupy as cp
+
+        device_dataset = cp.asarray(dataset)
+        single_index = cagra.build(
+            single_build_params, device_dataset, resources=single_gpu_resources
+        )
+    except ImportError:
+        pytest.skip("CuPy not available for single-GPU index building")
+
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        # Serialize single-GPU index
+        cagra.save(temp_filename, single_index, resources=single_gpu_resources)
+
+        # Now distribute the single-GPU index across multiple GPUs
+        resources = MultiGpuResources()
+        distributed_index = mg_cagra.distribute(
+            temp_filename, resources=resources
+        )
+        assert distributed_index.trained
+
+        # Search should work with distributed index (using host memory arrays)
+        search_params = mg_cagra.SearchParams(itopk_size=32)
+        distances, neighbors = mg_cagra.search(
+            search_params, distributed_index, queries, k, resources=resources
+        )
+
+        assert distances.shape == (20, k)
+        assert neighbors.shape == (20, k)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)
+
+
+def test_memory_location_validation():
+    """Test that multi-GPU CAGRA validates memory locations correctly."""
+    try:
+        import cupy as cp
+    except ImportError:
+        pytest.skip("CuPy not available for memory location tests")
+
+    n_rows, n_cols = 1500, 8
+
+    # Create host and device arrays
+    host_data = generate_data((n_rows, n_cols), np.float32)
+    device_data = cp.asarray(host_data)
+
+    resources = MultiGpuResources()
+    build_params = mg_cagra.IndexParams(
+        graph_degree=32, intermediate_graph_degree=64
+    )
+
+    # Test that device arrays are rejected for build
+    with pytest.raises(ValueError, match="host memory"):
+        mg_cagra.build(build_params, device_data, resources=resources)
+
+    # Test that host arrays work for build
+    index = mg_cagra.build(build_params, host_data, resources=resources)
+
+    # Test that device arrays are rejected for search
+    queries = generate_data((20, n_cols), np.float32)
+    device_queries = cp.asarray(queries)
+    search_params = mg_cagra.SearchParams(itopk_size=32)
+
+    with pytest.raises(ValueError, match="host memory"):
+        mg_cagra.search(
+            search_params, index, device_queries, 5, resources=resources
+        )
+
+    # Test that host arrays work for search
+    distances, neighbors = mg_cagra.search(
+        search_params, index, queries, 5, resources=resources
+    )
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+
+
+def test_parameter_validation():
+    """Test parameter validation for multi-GPU CAGRA."""
+    # Test invalid distribution mode
+    with pytest.raises(ValueError, match="distribution_mode must be"):
+        mg_cagra.IndexParams(distribution_mode="invalid")
+
+    # Test invalid search mode
+    with pytest.raises(ValueError, match="search_mode must be"):
+        mg_cagra.SearchParams(search_mode="invalid")
+
+    # Test invalid merge mode
+    with pytest.raises(ValueError, match="merge_mode must be"):
+        mg_cagra.SearchParams(merge_mode="invalid")
+
+
+def test_parameter_properties():
+    """Test that parameters can be accessed via properties."""
+    # Test IndexParams properties
+    params = mg_cagra.IndexParams(distribution_mode="replicated")
+    assert params.distribution_mode == "replicated"
+
+    params = mg_cagra.IndexParams(distribution_mode="sharded")
+    assert params.distribution_mode == "sharded"
+
+    # Test SearchParams creation with different parameters
+    mg_cagra.SearchParams(
+        search_mode="round_robin",
+        merge_mode="tree_merge",
+        n_rows_per_batch=2000,
+    )
+    # These don't have properties exposed, but creation should work
+
+
+def test_untrained_index_error():
+    """Test that using an untrained index raises appropriate errors."""
+    resources = MultiGpuResources()
+
+    # Create untrained index
+    index = mg_cagra.Index()
+    assert not index.trained
+
+    queries = generate_data((100, 10), np.float32)
+    search_params = mg_cagra.SearchParams()
+
+    # Test that search on untrained index fails
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_cagra.search(search_params, index, queries, 10, resources=resources)
+
+    # Test that save on untrained index fails
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_cagra.save(index, "temp.bin", resources=resources)
+
+
+@requires_multiple_gpus
+def test_mg_cagra_with_prealloc_output():
+    """Test multi-GPU CAGRA search with pre-allocated output arrays."""
+    n_rows, n_cols = 1500, 8
+    n_queries = 20
+    k = 5
+
+    # Generate data in host memory
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build index
+    build_params = mg_cagra.IndexParams(
+        graph_degree=32, intermediate_graph_degree=64
+    )
+    index = mg_cagra.build(build_params, dataset, resources=resources)
+
+    # Pre-allocate output arrays in host memory
+    neighbors = np.empty((n_queries, k), dtype=np.int64)
+    distances = np.empty((n_queries, k), dtype=np.float32)
+
+    # Search with pre-allocated arrays
+    search_params = mg_cagra.SearchParams(itopk_size=32)
+    ret_distances, ret_neighbors = mg_cagra.search(
+        search_params,
+        index,
+        queries,
+        k,
+        neighbors=neighbors,
+        distances=distances,
+        resources=resources,
+    )
+
+    # Should return the same arrays we passed in
+    assert ret_distances is distances
+    assert ret_neighbors is neighbors
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+
+
+def test_index_repr():
+    """Test string representation of Index."""
+    index = mg_cagra.Index()
+    assert repr(index) == "Index(type=MultiGpuCagra)"
+
+
+def test_mg_cagra_simple():
+    """Simple test to validate multi-GPU CAGRA works with very favorable
+    parameters.
+    """
+    if not has_multiple_gpus():
+        pytest.skip("Multi-GPU tests require multiple GPUs")
+
+    # Use simple test case that should definitely work
+    n_rows, n_cols = 1000, 8
+    n_queries, k = 20, 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Use small graph for reliable testing
+    build_params = mg_cagra.IndexParams(
+        metric="sqeuclidean",
+        graph_degree=16,
+        intermediate_graph_degree=32,
+    )
+
+    # Build index
+    index = mg_cagra.build(build_params, dataset, resources=resources)
+
+    # Search with basic parameters
+    search_params = mg_cagra.SearchParams(itopk_size=16)
+    distances, neighbors = mg_cagra.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Basic sanity checks
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+
+    # Check that we get valid neighbors
+    assert np.all(neighbors >= 0)
+    assert np.all(neighbors < n_rows)
+
+    # Distances should be non-negative and sorted
+    assert np.all(distances >= 0)
+    for i in range(n_queries):
+        assert np.all(
+            distances[i, :-1] <= distances[i, 1:]
+        ), f"Distances not sorted for query {i}"
+
+
+# Integration test with multiple operations
+@requires_multiple_gpus
+def test_mg_cagra_integration():
+    """Integration test covering build, search, and serialization."""
+    n_rows, n_cols = 2000, 8
+    k = 5
+
+    # Generate initial dataset
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((20, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build initial index
+    build_params = mg_cagra.IndexParams(
+        distribution_mode="sharded",
+        metric="sqeuclidean",
+        graph_degree=32,
+        intermediate_graph_degree=64,
+    )
+    index = mg_cagra.build(build_params, dataset, resources=resources)
+
+    # Initial search
+    search_params = mg_cagra.SearchParams(
+        itopk_size=32,
+        search_mode="load_balancer",
+        merge_mode="merge_on_root_rank",
+    )
+    distances1, neighbors1 = mg_cagra.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Save and reload
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        mg_cagra.save(index, temp_filename, resources=resources)
+        reloaded_index = mg_cagra.load(temp_filename, resources=resources)
+
+        # Search with reloaded index
+        distances2, neighbors2 = mg_cagra.search(
+            search_params, reloaded_index, queries, k, resources=resources
+        )
+
+        # Results from reloaded index should match
+        np.testing.assert_array_equal(neighbors1, neighbors2)
+        np.testing.assert_allclose(distances1, distances2, rtol=1e-6)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)
diff --git a/python/cuvs/cuvs/tests/test_mg_ivf_flat.py b/python/cuvs/cuvs/tests/test_mg_ivf_flat.py
new file mode 100644
index 0000000000..8bec3663c0
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_mg_ivf_flat.py
@@ -0,0 +1,650 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import tempfile
+
+import numpy as np
+import pytest
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+
+from cuvs.common import MultiGpuResources
+from cuvs.neighbors.mg import ivf_flat as mg_ivf_flat
+from cuvs.tests.ann_utils import calc_recall, generate_data
+
+
+# Check if multi-GPU functionality is available
+def has_multiple_gpus():
+    """Check if system has multiple GPUs available."""
+    try:
+        import cupy as cp
+
+        return cp.cuda.runtime.getDeviceCount() > 1
+    except Exception:
+        return False
+
+
+# Mark tests that require multiple GPUs
+requires_multiple_gpus = pytest.mark.skipif(
+    not has_multiple_gpus(), reason="Multi-GPU tests require multiple GPUs"
+)
+
+
+def run_mg_ivf_flat_build_search_test(
+    n_rows=10000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    metric="euclidean",
+    distribution_mode="sharded",
+    search_mode="load_balancer",
+    merge_mode="tree_merge",
+    n_rows_per_batch=1000,
+    compare=True,
+    add_data_on_build=True,
+    search_params=None,
+    n_lists=None,
+):
+    """
+    Run a multi-GPU IVF-Flat build and search test.
+
+    Note: Multi-GPU IVF-Flat requires host memory arrays (NumPy), not
+    device arrays.
+    """
+    # Generate host memory arrays (NumPy)
+    dataset = generate_data((n_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    if metric == "inner_product":
+        queries = normalize(queries, norm="l2", axis=1)
+
+    # Multi-GPU resources
+    resources = MultiGpuResources()
+
+    # Build parameters - use fewer clusters for better recall
+    # with smaller datasets
+    if n_lists is None:
+        # Use fewer clusters for smaller datasets to ensure enough points
+        # per cluster
+        n_lists = min(1024, max(64, n_rows // 50))
+
+    build_params = mg_ivf_flat.IndexParams(
+        metric=metric,
+        distribution_mode=distribution_mode,
+        add_data_on_build=add_data_on_build,
+        n_lists=n_lists,
+    )
+
+    # Build index
+    index = mg_ivf_flat.build(build_params, dataset, resources=resources)
+    assert index.trained
+
+    # If not adding data on build, extend the index
+    if not add_data_on_build:
+        dataset_1 = dataset[: n_rows // 2, :]
+        dataset_2 = dataset[n_rows // 2 :, :]
+        indices_1 = np.arange(n_rows // 2, dtype=np.int64)
+        indices_2 = np.arange(n_rows // 2, n_rows, dtype=np.int64)
+
+        mg_ivf_flat.extend(index, dataset_1, indices_1, resources=resources)
+        mg_ivf_flat.extend(index, dataset_2, indices_2, resources=resources)
+
+    # Search parameters
+    if search_params is None:
+        search_params = {}
+    # Use higher n_probes for better recall in multi-GPU setting
+    if "n_probes" not in search_params:
+        # Use many clusters for good recall - search majority of clusters
+        search_params["n_probes"] = min(n_lists, max(20, (n_lists * 3) // 4))
+    search_params_obj = mg_ivf_flat.SearchParams(
+        search_mode=search_mode,
+        merge_mode=merge_mode,
+        n_rows_per_batch=n_rows_per_batch,
+        **search_params,
+    )
+
+    # Perform search
+    distances, neighbors = mg_ivf_flat.search(
+        search_params_obj,
+        index,
+        queries,
+        k,
+        resources=resources,
+    )
+
+    # Verify results are in host memory (NumPy arrays)
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+
+    if not compare:
+        return distances, neighbors
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "cosine": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    recall = calc_recall(neighbors, skl_idx)
+    # Multi-GPU implementation may have lower recall due to data distribution
+    # across GPUs
+    # This is acceptable as long as the functionality works correctly
+    assert recall > 0.3, (
+        f"Recall too low: {recall:.3f} (n_lists={n_lists}, "
+        f"n_probes={search_params.get('n_probes', 'default')})"
+    )
+
+    return distances, neighbors
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize(
+    "metric", ["sqeuclidean"]
+)  # Start with just sqeuclidean
+@pytest.mark.parametrize(
+    "distribution_mode", ["sharded"]
+)  # Start with just sharded
+def test_mg_ivf_flat_basic(dtype, metric, distribution_mode):
+    """Test basic multi-GPU IVF-Flat build and search functionality."""
+    run_mg_ivf_flat_build_search_test(
+        n_rows=2000,  # Use smaller dataset for more reliable tests
+        n_cols=8,
+        n_queries=20,
+        k=5,
+        dtype=dtype,
+        metric=metric,
+        distribution_mode=distribution_mode,
+        n_lists=50,  # Fixed small number of clusters
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("metric", ["inner_product", "euclidean", "cosine"])
+@pytest.mark.parametrize("distribution_mode", ["replicated"])
+def test_mg_ivf_flat_additional_metrics(metric, distribution_mode):
+    """Test additional metrics and distribution modes."""
+    run_mg_ivf_flat_build_search_test(
+        n_rows=2000,
+        n_cols=8,
+        n_queries=20,
+        k=5,
+        dtype=np.float32,
+        metric=metric,
+        distribution_mode=distribution_mode,
+        n_lists=50,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
+def test_mg_ivf_flat_dtypes(dtype):
+    """Test multi-GPU IVF-Flat with different data types."""
+    run_mg_ivf_flat_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        dtype=dtype,
+        metric="sqeuclidean",
+        n_lists=30,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("distribution_mode", ["sharded", "replicated"])
+def test_mg_ivf_flat_distribution_modes(distribution_mode):
+    """Test different distribution modes for multi-GPU IVF-Flat."""
+    run_mg_ivf_flat_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        distribution_mode=distribution_mode,
+        n_lists=30,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("search_mode", ["load_balancer", "round_robin"])
+@pytest.mark.parametrize("merge_mode", ["merge_on_root_rank", "tree_merge"])
+def test_mg_ivf_flat_search_params(search_mode, merge_mode):
+    """Test different multi-GPU search parameters."""
+    run_mg_ivf_flat_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        search_mode=search_mode,
+        merge_mode=merge_mode,
+        n_rows_per_batch=500,
+        n_lists=30,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("metric", ["euclidean", "sqeuclidean"])
+def test_mg_ivf_flat_metrics(metric):
+    """Test different distance metrics for multi-GPU IVF-Flat."""
+    run_mg_ivf_flat_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        metric=metric,
+        n_lists=30,
+    )
+
+
+@requires_multiple_gpus
+def test_mg_ivf_flat_extend():
+    """Test extending multi-GPU IVF-Flat index with new vectors."""
+    run_mg_ivf_flat_build_search_test(
+        n_rows=1500,
+        n_cols=8,
+        n_queries=15,
+        k=5,
+        add_data_on_build=False,
+        n_lists=30,
+    )
+
+
+@requires_multiple_gpus
+def test_mg_ivf_flat_serialize():
+    """Test save/load functionality for multi-GPU IVF-Flat."""
+    n_rows, n_cols = 2000, 8
+    k = 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((20, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build original index
+    build_params = mg_ivf_flat.IndexParams(n_lists=50)
+    original_index = mg_ivf_flat.build(
+        build_params, dataset, resources=resources
+    )
+
+    # Search with original index
+    search_params = mg_ivf_flat.SearchParams(n_probes=37)
+    orig_distances, orig_neighbors = mg_ivf_flat.search(
+        search_params, original_index, queries, k, resources=resources
+    )
+
+    # Save index to temporary file
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        mg_ivf_flat.save(original_index, temp_filename, resources=resources)
+
+        # Load index from file
+        loaded_index = mg_ivf_flat.load(temp_filename, resources=resources)
+        assert loaded_index.trained
+
+        # Search with loaded index
+        loaded_distances, loaded_neighbors = mg_ivf_flat.search(
+            search_params, loaded_index, queries, k, resources=resources
+        )
+
+        # Results should be identical
+        np.testing.assert_array_equal(orig_neighbors, loaded_neighbors)
+        np.testing.assert_allclose(orig_distances, loaded_distances, rtol=1e-6)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)
+
+
+@requires_multiple_gpus
+def test_mg_ivf_flat_distribute():
+    """Test distribute functionality for multi-GPU IVF-Flat."""
+    # Note: Distribute is for replicating a single-GPU index
+    # across multiple GPUs.
+    # This test builds a single-GPU index, serializes it, then distributes it.
+
+    n_rows, n_cols = 2000, 8
+    k = 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((20, n_cols), np.float32)
+
+    # Import single-GPU IVF-Flat to build and serialize a single-GPU index
+    from cuvs.common import Resources
+    from cuvs.neighbors import ivf_flat
+
+    # Build single-GPU index first
+    single_gpu_resources = Resources()
+    single_build_params = ivf_flat.IndexParams(
+        metric="sqeuclidean", n_lists=50
+    )
+
+    # Convert to device arrays for single-GPU build
+    try:
+        import cupy as cp
+
+        device_dataset = cp.asarray(dataset)
+        single_index = ivf_flat.build(
+            single_build_params, device_dataset, resources=single_gpu_resources
+        )
+    except ImportError:
+        pytest.skip("CuPy not available for single-GPU index building")
+
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        # Serialize single-GPU index
+        ivf_flat.save(
+            temp_filename, single_index, resources=single_gpu_resources
+        )
+
+        # Now distribute the single-GPU index across multiple GPUs
+        resources = MultiGpuResources()
+        distributed_index = mg_ivf_flat.distribute(
+            temp_filename, resources=resources
+        )
+        assert distributed_index.trained
+
+        # Search should work with distributed index (using host memory arrays)
+        search_params = mg_ivf_flat.SearchParams(n_probes=37)
+        distances, neighbors = mg_ivf_flat.search(
+            search_params, distributed_index, queries, k, resources=resources
+        )
+
+        assert distances.shape == (20, k)
+        assert neighbors.shape == (20, k)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)
+
+
+def test_memory_location_validation():
+    """Test that multi-GPU IVF-Flat validates memory locations correctly."""
+    try:
+        import cupy as cp
+    except ImportError:
+        pytest.skip("CuPy not available for memory location tests")
+
+    n_rows, n_cols = 1500, 8
+
+    # Create host and device arrays
+    host_data = generate_data((n_rows, n_cols), np.float32)
+    device_data = cp.asarray(host_data)
+
+    resources = MultiGpuResources()
+    build_params = mg_ivf_flat.IndexParams(n_lists=30)
+
+    # Test that device arrays are rejected for build
+    with pytest.raises(ValueError, match="host memory"):
+        mg_ivf_flat.build(build_params, device_data, resources=resources)
+
+    # Test that host arrays work for build
+    index = mg_ivf_flat.build(build_params, host_data, resources=resources)
+
+    # Test that device arrays are rejected for search
+    queries = generate_data((20, n_cols), np.float32)
+    device_queries = cp.asarray(queries)
+    search_params = mg_ivf_flat.SearchParams(n_probes=22)
+
+    with pytest.raises(ValueError, match="host memory"):
+        mg_ivf_flat.search(
+            search_params, index, device_queries, 5, resources=resources
+        )
+
+    # Test that host arrays work for search
+    distances, neighbors = mg_ivf_flat.search(
+        search_params, index, queries, 5, resources=resources
+    )
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+
+
+def test_parameter_validation():
+    """Test parameter validation for multi-GPU IVF-Flat."""
+    # Test invalid distribution mode
+    with pytest.raises(ValueError, match="distribution_mode must be"):
+        mg_ivf_flat.IndexParams(distribution_mode="invalid")
+
+    # Test invalid search mode
+    with pytest.raises(ValueError, match="search_mode must be"):
+        mg_ivf_flat.SearchParams(search_mode="invalid")
+
+    # Test invalid merge mode
+    with pytest.raises(ValueError, match="merge_mode must be"):
+        mg_ivf_flat.SearchParams(merge_mode="invalid")
+
+
+def test_parameter_properties():
+    """Test that parameters can be accessed via properties."""
+    # Test IndexParams properties
+    params = mg_ivf_flat.IndexParams(distribution_mode="replicated")
+    assert params.distribution_mode == "replicated"
+
+    params = mg_ivf_flat.IndexParams(distribution_mode="sharded")
+    assert params.distribution_mode == "sharded"
+
+    # Test SearchParams creation with different parameters
+    mg_ivf_flat.SearchParams(
+        search_mode="round_robin",
+        merge_mode="tree_merge",
+        n_rows_per_batch=2000,
+    )
+    # These don't have properties exposed, but creation should work
+
+
+def test_untrained_index_error():
+    """Test that using an untrained index raises appropriate errors."""
+    resources = MultiGpuResources()
+
+    # Create untrained index
+    index = mg_ivf_flat.Index()
+    assert not index.trained
+
+    queries = generate_data((100, 10), np.float32)
+    search_params = mg_ivf_flat.SearchParams(n_probes=20)
+
+    # Test that search on untrained index fails
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_ivf_flat.search(
+            search_params, index, queries, 10, resources=resources
+        )
+
+    # Test that extend on untrained index fails
+    new_vectors = generate_data((50, 10), np.float32)
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_ivf_flat.extend(index, new_vectors, resources=resources)
+
+    # Test that save on untrained index fails
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_ivf_flat.save(index, "temp.bin", resources=resources)
+
+
+@requires_multiple_gpus
+def test_mg_ivf_flat_with_prealloc_output():
+    """Test multi-GPU IVF-Flat search with pre-allocated output arrays."""
+    n_rows, n_cols = 1500, 8  # Ensure n_rows > n_lists
+    n_queries = 20
+    k = 5
+
+    # Generate data in host memory
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build index with fewer clusters to avoid n_rows < n_lists error
+    build_params = mg_ivf_flat.IndexParams(n_lists=30)
+    index = mg_ivf_flat.build(build_params, dataset, resources=resources)
+
+    # Pre-allocate output arrays in host memory
+    neighbors = np.empty((n_queries, k), dtype=np.int64)
+    distances = np.empty((n_queries, k), dtype=np.float32)
+
+    # Search with pre-allocated arrays
+    search_params = mg_ivf_flat.SearchParams(n_probes=20)
+    ret_distances, ret_neighbors = mg_ivf_flat.search(
+        search_params,
+        index,
+        queries,
+        k,
+        neighbors=neighbors,
+        distances=distances,
+        resources=resources,
+    )
+
+    # Should return the same arrays we passed in
+    assert ret_distances is distances
+    assert ret_neighbors is neighbors
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+
+
+def test_index_repr():
+    """Test string representation of Index."""
+    index = mg_ivf_flat.Index()
+    assert repr(index) == "Index(type=MultiGpuIvfFlat)"
+
+
+def test_mg_ivf_flat_simple():
+    """Simple test to validate multi-GPU IVF-Flat works with very favorable
+    parameters.
+    """
+    if not has_multiple_gpus():
+        pytest.skip("Multi-GPU tests require multiple GPUs")
+
+    # Use simple test case that should definitely work
+    n_rows, n_cols = 1000, 8
+    n_queries, k = 20, 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Use very few clusters for high recall
+    build_params = mg_ivf_flat.IndexParams(
+        metric="sqeuclidean",
+        n_lists=32,  # Very few clusters
+    )
+
+    # Build index
+    index = mg_ivf_flat.build(build_params, dataset, resources=resources)
+
+    # Search with many probes for maximum recall
+    search_params = mg_ivf_flat.SearchParams(
+        n_probes=32
+    )  # Search all clusters
+    distances, neighbors = mg_ivf_flat.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Basic sanity checks
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+
+    # Check that we get valid neighbors
+    assert np.all(neighbors >= 0)
+    assert np.all(neighbors < n_rows)
+
+    # Distances should be non-negative and sorted
+    assert np.all(distances >= 0)
+    for i in range(n_queries):
+        assert np.all(
+            distances[i, :-1] <= distances[i, 1:]
+        ), f"Distances not sorted for query {i}"
+
+
+# Integration test with multiple operations
+@requires_multiple_gpus
+def test_mg_ivf_flat_integration():
+    """Integration test covering build, search, extend, and serialization."""
+    n_rows, n_cols = 2000, 8
+    k = 5
+
+    # Generate initial dataset
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((20, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build initial index
+    build_params = mg_ivf_flat.IndexParams(
+        distribution_mode="sharded", metric="sqeuclidean", n_lists=50
+    )
+    index = mg_ivf_flat.build(build_params, dataset, resources=resources)
+
+    # Initial search
+    search_params = mg_ivf_flat.SearchParams(
+        n_probes=37,
+        search_mode="load_balancer",
+        merge_mode="merge_on_root_rank",
+    )
+    distances1, neighbors1 = mg_ivf_flat.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Extend index with new vectors
+    new_vectors = generate_data((200, n_cols), np.float32)
+    # Provide indices for extend operation on non-empty index
+    new_indices = np.arange(n_rows, n_rows + 200, dtype=np.int64)
+    mg_ivf_flat.extend(index, new_vectors, new_indices, resources=resources)
+
+    # Search after extend
+    distances2, neighbors2 = mg_ivf_flat.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Save and reload
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        mg_ivf_flat.save(index, temp_filename, resources=resources)
+        reloaded_index = mg_ivf_flat.load(temp_filename, resources=resources)
+
+        # Search with reloaded index
+        distances3, neighbors3 = mg_ivf_flat.search(
+            search_params, reloaded_index, queries, k, resources=resources
+        )
+
+        # Results from extended and reloaded index should match
+        np.testing.assert_array_equal(neighbors2, neighbors3)
+        np.testing.assert_allclose(distances2, distances3, rtol=1e-6)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)
diff --git a/python/cuvs/cuvs/tests/test_mg_ivf_pq.py b/python/cuvs/cuvs/tests/test_mg_ivf_pq.py
new file mode 100644
index 0000000000..382fb9eed7
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_mg_ivf_pq.py
@@ -0,0 +1,682 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import tempfile
+
+import numpy as np
+import pytest
+from sklearn.neighbors import NearestNeighbors
+from sklearn.preprocessing import normalize
+
+from cuvs.common import MultiGpuResources
+from cuvs.neighbors.mg import ivf_pq as mg_ivf_pq
+from cuvs.tests.ann_utils import calc_recall, generate_data
+
+
+# Check if multi-GPU functionality is available
+def has_multiple_gpus():
+    """Check if system has multiple GPUs available."""
+    try:
+        import cupy as cp
+
+        return cp.cuda.runtime.getDeviceCount() > 1
+    except Exception:
+        return False
+
+
+# Mark tests that require multiple GPUs
+requires_multiple_gpus = pytest.mark.skipif(
+    not has_multiple_gpus(), reason="Multi-GPU tests require multiple GPUs"
+)
+
+
+def run_mg_ivf_pq_build_search_test(
+    n_rows=10000,
+    n_cols=10,
+    n_queries=100,
+    k=10,
+    dtype=np.float32,
+    metric="euclidean",
+    distribution_mode="sharded",
+    search_mode="load_balancer",
+    merge_mode="tree_merge",
+    n_rows_per_batch=1000,
+    compare=True,
+    add_data_on_build=True,
+    search_params=None,
+    n_lists=None,
+    pq_bits=8,
+    pq_dim=0,
+    codebook_kind="subspace",
+):
+    """
+    Run a multi-GPU IVF-PQ build and search test.
+
+    Note: Multi-GPU IVF-PQ requires host memory arrays (NumPy), not device
+    arrays.
+    """
+    # Generate host memory arrays (NumPy)
+    dataset = generate_data((n_rows, n_cols), dtype)
+    if metric == "inner_product":
+        dataset = normalize(dataset, norm="l2", axis=1)
+
+    queries = generate_data((n_queries, n_cols), dtype)
+    if metric == "inner_product":
+        queries = normalize(queries, norm="l2", axis=1)
+
+    # Multi-GPU resources
+    resources = MultiGpuResources()
+
+    # Build parameters - use fewer clusters for better recall with smaller
+    # datasets
+    if n_lists is None:
+        # Use fewer clusters for smaller datasets to ensure enough points per
+        # cluster
+        n_lists = min(1024, max(64, n_rows // 50))
+
+    build_params = mg_ivf_pq.IndexParams(
+        metric=metric,
+        distribution_mode=distribution_mode,
+        add_data_on_build=add_data_on_build,
+        n_lists=n_lists,
+        pq_bits=pq_bits,
+        pq_dim=pq_dim,
+        codebook_kind=codebook_kind,
+    )
+
+    # Build index
+    index = mg_ivf_pq.build(build_params, dataset, resources=resources)
+    assert index.trained
+
+    # If not adding data on build, extend the index
+    if not add_data_on_build:
+        dataset_1 = dataset[: n_rows // 2, :]
+        dataset_2 = dataset[n_rows // 2 :, :]
+        indices_1 = np.arange(n_rows // 2, dtype=np.int64)
+        indices_2 = np.arange(n_rows // 2, n_rows, dtype=np.int64)
+
+        mg_ivf_pq.extend(index, dataset_1, indices_1, resources=resources)
+        mg_ivf_pq.extend(index, dataset_2, indices_2, resources=resources)
+
+    # Search parameters
+    if search_params is None:
+        search_params = {}
+    # Use higher n_probes for better recall in multi-GPU setting
+    if "n_probes" not in search_params:
+        # Use many clusters for good recall - search majority of clusters
+        search_params["n_probes"] = min(n_lists, max(20, (n_lists * 3) // 4))
+    search_params_obj = mg_ivf_pq.SearchParams(
+        search_mode=search_mode,
+        merge_mode=merge_mode,
+        n_rows_per_batch=n_rows_per_batch,
+        **search_params,
+    )
+
+    # Perform search
+    distances, neighbors = mg_ivf_pq.search(
+        search_params_obj,
+        index,
+        queries,
+        k,
+        resources=resources,
+    )
+
+    # Verify results are in host memory (NumPy arrays)
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+
+    if not compare:
+        return distances, neighbors
+
+    # Calculate reference values with sklearn
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "cosine": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
+    nn_skl.fit(dataset)
+    skl_idx = nn_skl.kneighbors(queries, return_distance=False)
+
+    recall = calc_recall(neighbors, skl_idx)
+
+    return distances, neighbors, recall
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize(
+    "metric", ["sqeuclidean"]
+)  # Start with just sqeuclidean
+@pytest.mark.parametrize(
+    "distribution_mode", ["sharded"]
+)  # Start with just sharded
+def test_mg_ivf_pq_basic(dtype, metric, distribution_mode):
+    """Test basic multi-GPU IVF-PQ build and search functionality."""
+    run_mg_ivf_pq_build_search_test(
+        n_rows=2000,  # Use smaller dataset for more reliable tests
+        n_cols=32,
+        n_queries=20,
+        k=5,
+        dtype=dtype,
+        metric=metric,
+        distribution_mode=distribution_mode,
+        n_lists=50,  # Fixed small number of clusters
+        compare=True,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("metric", ["inner_product", "euclidean", "cosine"])
+@pytest.mark.parametrize("distribution_mode", ["replicated"])
+def test_mg_ivf_pq_additional_metrics(metric, distribution_mode):
+    """Test additional metrics and distribution modes for IVF-PQ."""
+    run_mg_ivf_pq_build_search_test(
+        n_rows=2000,
+        n_cols=32,
+        n_queries=20,
+        k=5,
+        dtype=np.float32,
+        metric=metric,
+        distribution_mode=distribution_mode,
+        n_lists=50,
+        compare=False,  # PQ may have lower recall, don't enforce strict recall
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
+def test_mg_ivf_pq_dtypes(dtype):
+    """Test multi-GPU IVF-PQ with different data types."""
+    run_mg_ivf_pq_build_search_test(
+        n_rows=1500,
+        n_cols=32,
+        n_queries=15,
+        k=5,
+        dtype=dtype,
+        metric="sqeuclidean",
+        n_lists=30,
+        compare=False,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("distribution_mode", ["sharded", "replicated"])
+def test_mg_ivf_pq_distribution_modes(distribution_mode):
+    """Test different distribution modes for multi-GPU IVF-PQ."""
+    run_mg_ivf_pq_build_search_test(
+        n_rows=1500,
+        n_cols=32,
+        n_queries=15,
+        k=5,
+        distribution_mode=distribution_mode,
+        n_lists=30,
+        compare=False,
+    )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("search_mode", ["load_balancer", "round_robin"])
+@pytest.mark.parametrize("merge_mode", ["merge_on_root_rank", "tree_merge"])
+def test_mg_ivf_pq_search_params(search_mode, merge_mode):
+    """Test different multi-GPU search parameters for IVF-PQ."""
+    run_mg_ivf_pq_build_search_test(
+        n_rows=1500,
+        n_cols=32,
+        n_queries=15,
+        k=5,
+        search_mode=search_mode,
+        merge_mode=merge_mode,
+        n_rows_per_batch=500,
+        n_lists=30,
+        compare=False,
+    )
+
+
+@requires_multiple_gpus
+def test_mg_ivf_pq_pq_parameters():
+    """Test different PQ-specific parameters."""
+    for pq_bits in [4, 8]:
+        for pq_dim in [0, 8, 16]:  # 0 means auto-select
+            for codebook_kind in ["subspace", "cluster"]:
+                run_mg_ivf_pq_build_search_test(
+                    n_rows=1000,
+                    n_cols=32,
+                    n_queries=100,
+                    k=10,
+                    pq_bits=pq_bits,
+                    pq_dim=pq_dim,
+                    codebook_kind=codebook_kind,
+                    compare=False,
+                )
+
+
+@requires_multiple_gpus
+@pytest.mark.parametrize("metric", ["euclidean", "sqeuclidean"])
+def test_mg_ivf_pq_metrics(metric):
+    """Test different distance metrics for multi-GPU IVF-PQ."""
+    run_mg_ivf_pq_build_search_test(
+        n_rows=1500,
+        n_cols=32,
+        n_queries=15,
+        k=5,
+        metric=metric,
+        n_lists=30,
+        compare=False,
+    )
+
+
+@requires_multiple_gpus
+def test_mg_ivf_pq_extend():
+    """Test extending index with new vectors."""
+    run_mg_ivf_pq_build_search_test(
+        n_rows=1000,
+        n_cols=32,
+        n_queries=100,
+        k=10,
+        add_data_on_build=False,  # This triggers extend functionality
+        compare=False,
+    )
+
+
+@requires_multiple_gpus
+def test_mg_ivf_pq_serialize():
+    """Test serialization and deserialization."""
+    # Generate data
+    n_rows, n_cols = 1000, 32
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((100, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build index
+    build_params = mg_ivf_pq.IndexParams(
+        metric="euclidean",
+        n_lists=100,
+        pq_bits=8,
+        pq_dim=16,
+    )
+    index = mg_ivf_pq.build(build_params, dataset, resources=resources)
+
+    # Search before serialization
+    search_params = mg_ivf_pq.SearchParams(n_probes=50)
+    distances_1, neighbors_1 = mg_ivf_pq.search(
+        search_params, index, queries, 10, resources=resources
+    )
+
+    # Serialize
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        filename = f.name
+
+    try:
+        mg_ivf_pq.save(index, filename, resources=resources)
+
+        # Load index
+        index_loaded = mg_ivf_pq.load(filename, resources=resources)
+        assert index_loaded.trained
+
+        # Search after loading
+        distances_2, neighbors_2 = mg_ivf_pq.search(
+            search_params, index_loaded, queries, 10, resources=resources
+        )
+
+        # Results should be the same
+        assert np.array_equal(distances_1, distances_2)
+        assert np.array_equal(neighbors_1, neighbors_2)
+
+    finally:
+        if os.path.exists(filename):
+            os.unlink(filename)
+
+
+@requires_multiple_gpus
+def test_mg_ivf_pq_distribute():
+    """Test distribute functionality for multi-GPU IVF-PQ."""
+    # Note: Distribute is for replicating a single-GPU index across multiple
+    # GPUs.
+    # This test builds a single-GPU index, serializes it, then distributes it.
+    # Multi-GPU distribute only supports float32 indexes.
+
+    n_rows, n_cols = 2000, 32
+    k = 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((100, n_cols), np.float32)
+
+    # Import single-GPU IVF-PQ to build and serialize a single-GPU index
+    from cuvs.common import Resources
+    from cuvs.neighbors import ivf_pq
+
+    # Build single-GPU index first
+    single_gpu_resources = Resources()
+    single_build_params = ivf_pq.IndexParams(
+        metric="sqeuclidean", n_lists=50, pq_bits=8, pq_dim=16
+    )
+
+    # Convert to device arrays for single-GPU build
+    try:
+        import cupy as cp
+
+        device_dataset = cp.asarray(dataset, dtype=np.float32)
+        single_index = ivf_pq.build(
+            single_build_params, device_dataset, resources=single_gpu_resources
+        )
+    except ImportError:
+        pytest.skip("CuPy not available for single-GPU index building")
+
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        # Serialize single-GPU index
+        ivf_pq.save(
+            temp_filename, single_index, resources=single_gpu_resources
+        )
+
+        # Now distribute the single-GPU index across multiple GPUs
+        resources = MultiGpuResources()
+        distributed_index = mg_ivf_pq.distribute(
+            temp_filename, resources=resources
+        )
+        assert distributed_index.trained
+
+        # Search using the distributed index
+        search_params = mg_ivf_pq.SearchParams(n_probes=25)
+        distances, neighbors = mg_ivf_pq.search(
+            search_params, distributed_index, queries, k, resources=resources
+        )
+
+        # Verify results shape
+        assert distances.shape == (100, k)
+        assert neighbors.shape == (100, k)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)
+
+
+def test_memory_location_validation():
+    """Test that multi-GPU IVF-PQ validates memory locations correctly."""
+    try:
+        import cupy as cp
+    except ImportError:
+        pytest.skip("CuPy not available")
+
+    # Generate device arrays (should fail) - use enough data points for n_lists
+    dataset_gpu = cp.random.random((1000, 32), dtype=cp.float32)
+    queries_gpu = cp.random.random((100, 32), dtype=cp.float32)
+
+    # Create parameters with smaller n_lists for the small dataset
+    build_params = mg_ivf_pq.IndexParams(
+        n_lists=20
+    )  # Smaller n_lists for 1000 points
+    search_params = mg_ivf_pq.SearchParams()
+
+    # These should raise ValueError about memory location
+    with pytest.raises(ValueError, match="host memory"):
+        mg_ivf_pq.build(build_params, dataset_gpu)
+
+    # For search test, we need a valid index first
+    dataset_cpu = cp.asnumpy(dataset_gpu)
+    resources = MultiGpuResources() if has_multiple_gpus() else None
+    if resources:
+        index = mg_ivf_pq.build(build_params, dataset_cpu, resources=resources)
+
+        with pytest.raises(ValueError, match="host memory"):
+            mg_ivf_pq.search(
+                search_params, index, queries_gpu, 5, resources=resources
+            )
+
+
+def test_parameter_validation():
+    """Test parameter validation for multi-GPU IVF-PQ."""
+    # Test invalid distribution mode
+    with pytest.raises(ValueError, match="distribution_mode must be"):
+        mg_ivf_pq.IndexParams(distribution_mode="invalid")
+
+    # Test invalid search mode
+    with pytest.raises(ValueError, match="search_mode must be"):
+        mg_ivf_pq.SearchParams(search_mode="invalid")
+
+    # Test invalid merge mode
+    with pytest.raises(ValueError, match="merge_mode must be"):
+        mg_ivf_pq.SearchParams(merge_mode="invalid")
+
+    # Test invalid codebook kind
+    with pytest.raises(ValueError, match="Incorrect codebook kind"):
+        mg_ivf_pq.IndexParams(codebook_kind="invalid")
+
+
+def test_parameter_properties():
+    """Test that parameters can be accessed via properties."""
+    # Test IndexParams properties
+    params = mg_ivf_pq.IndexParams(distribution_mode="replicated")
+    assert params.distribution_mode == "replicated"
+
+    params = mg_ivf_pq.IndexParams(distribution_mode="sharded")
+    assert params.distribution_mode == "sharded"
+
+    # Test PQ-specific parameters
+    params = mg_ivf_pq.IndexParams(
+        pq_bits=4, pq_dim=16, codebook_kind="cluster"
+    )
+    # These don't have properties exposed, but creation should work
+
+    # Test SearchParams creation with different parameters
+    mg_ivf_pq.SearchParams(
+        search_mode="round_robin",
+        merge_mode="tree_merge",
+        n_rows_per_batch=2000,
+    )
+    # These don't have properties exposed, but creation should work
+
+
+def test_untrained_index_error():
+    """Test that using an untrained index raises appropriate errors."""
+    resources = MultiGpuResources()
+
+    # Create untrained index
+    index = mg_ivf_pq.Index()
+    assert not index.trained
+
+    queries = generate_data((100, 10), np.float32)
+    search_params = mg_ivf_pq.SearchParams(n_probes=20)
+
+    # Test that search on untrained index fails
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_ivf_pq.search(
+            search_params, index, queries, 10, resources=resources
+        )
+
+    # Test that extend on untrained index fails
+    new_vectors = generate_data((50, 10), np.float32)
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_ivf_pq.extend(index, new_vectors, resources=resources)
+
+    # Test that save on untrained index fails
+    with pytest.raises(ValueError, match="Index needs to be built"):
+        mg_ivf_pq.save(index, "temp.bin", resources=resources)
+
+
+@requires_multiple_gpus
+def test_mg_ivf_pq_with_prealloc_output():
+    """Test multi-GPU IVF-PQ search with pre-allocated output arrays."""
+    n_rows, n_cols = 1500, 32  # Ensure n_rows > n_lists
+    n_queries = 20
+    k = 5
+
+    # Generate data in host memory
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build index with fewer clusters to avoid n_rows < n_lists error
+    build_params = mg_ivf_pq.IndexParams(n_lists=30, pq_bits=8, pq_dim=16)
+    index = mg_ivf_pq.build(build_params, dataset, resources=resources)
+
+    # Pre-allocate output arrays in host memory
+    neighbors = np.empty((n_queries, k), dtype=np.int64)
+    distances = np.empty((n_queries, k), dtype=np.float32)
+
+    # Search with pre-allocated arrays
+    search_params = mg_ivf_pq.SearchParams(n_probes=20)
+    ret_distances, ret_neighbors = mg_ivf_pq.search(
+        search_params,
+        index,
+        queries,
+        k,
+        neighbors=neighbors,
+        distances=distances,
+        resources=resources,
+    )
+
+    # Should return the same arrays we passed in
+    assert ret_distances is distances
+    assert ret_neighbors is neighbors
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+
+
+def test_index_repr():
+    """Test string representation of Index."""
+    index = mg_ivf_pq.Index()
+    assert repr(index) == "Index(type=MultiGpuIvfPq)"
+
+
+def test_mg_ivf_pq_simple():
+    """Simple test to validate multi-GPU IVF-PQ works with very favorable
+    parameters.
+    """
+    if not has_multiple_gpus():
+        pytest.skip("Multi-GPU tests require multiple GPUs")
+
+    # Use simple test case that should definitely work
+    n_rows, n_cols = 1000, 32
+    n_queries, k = 20, 5
+
+    # Generate data
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((n_queries, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Use very few clusters for high recall
+    build_params = mg_ivf_pq.IndexParams(
+        metric="sqeuclidean",
+        n_lists=32,  # Very few clusters
+        pq_bits=8,
+        pq_dim=16,
+    )
+
+    # Build index
+    index = mg_ivf_pq.build(build_params, dataset, resources=resources)
+
+    # Search with many probes for maximum recall
+    search_params = mg_ivf_pq.SearchParams(n_probes=32)  # Search all clusters
+    distances, neighbors = mg_ivf_pq.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Basic sanity checks
+    assert distances.shape == (n_queries, k)
+    assert neighbors.shape == (n_queries, k)
+    assert isinstance(distances, np.ndarray)
+    assert isinstance(neighbors, np.ndarray)
+
+    # Check that we get valid neighbors
+    assert np.all(neighbors >= 0)
+    assert np.all(neighbors < n_rows)
+
+    # Distances should be non-negative and sorted
+    assert np.all(distances >= 0)
+    for i in range(n_queries):
+        assert np.all(
+            distances[i, :-1] <= distances[i, 1:]
+        ), f"Distances not sorted for query {i}"
+
+
+# Integration test with multiple operations
+@requires_multiple_gpus
+def test_mg_ivf_pq_integration():
+    """Integration test covering build, search, extend, and serialization."""
+    n_rows, n_cols = 2000, 32
+    k = 5
+
+    # Generate initial dataset
+    dataset = generate_data((n_rows, n_cols), np.float32)
+    queries = generate_data((20, n_cols), np.float32)
+
+    resources = MultiGpuResources()
+
+    # Build initial index
+    build_params = mg_ivf_pq.IndexParams(
+        distribution_mode="sharded",
+        metric="sqeuclidean",
+        n_lists=50,
+        pq_bits=8,
+        pq_dim=16,
+    )
+    index = mg_ivf_pq.build(build_params, dataset, resources=resources)
+
+    # Initial search
+    search_params = mg_ivf_pq.SearchParams(
+        n_probes=37,
+        search_mode="load_balancer",
+        merge_mode="merge_on_root_rank",
+    )
+    distances1, neighbors1 = mg_ivf_pq.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Extend index with new vectors
+    new_vectors = generate_data((200, n_cols), np.float32)
+    # Provide indices for extend operation on non-empty index
+    new_indices = np.arange(n_rows, n_rows + 200, dtype=np.int64)
+    mg_ivf_pq.extend(index, new_vectors, new_indices, resources=resources)
+
+    # Search after extend
+    distances2, neighbors2 = mg_ivf_pq.search(
+        search_params, index, queries, k, resources=resources
+    )
+
+    # Save and reload
+    with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+        temp_filename = f.name
+
+    try:
+        mg_ivf_pq.save(index, temp_filename, resources=resources)
+        reloaded_index = mg_ivf_pq.load(temp_filename, resources=resources)
+
+        # Search with reloaded index
+        distances3, neighbors3 = mg_ivf_pq.search(
+            search_params, reloaded_index, queries, k, resources=resources
+        )
+
+        # Results from extended and reloaded index should match
+        np.testing.assert_array_equal(neighbors2, neighbors3)
+        np.testing.assert_allclose(distances2, distances3, rtol=1e-6)
+
+    finally:
+        if os.path.exists(temp_filename):
+            os.unlink(temp_filename)

From 8fc4a2ad134f2d2d7763b026d0965b328ecc8db8 Mon Sep 17 00:00:00 2001
From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com>
Date: Mon, 22 Sep 2025 09:10:10 -0500
Subject: [PATCH 591/623] Configure repo for automatic release notes generation
 (#1334)

This PR configures this repo (adds a `.github/release.yml` file) for
automatic release notes generation. See
https://docs.github.com/en/repositories/releasing-projects-on-github/automatically-generated-release-notes.
This will fix such issues as hotfix releases including wrong content
(e.g [cugraph
v25.04.01](https://github.com/rapidsai/cugraph/releases/tag/v25.04.01)
contains 50+ PRs from the full v25.04.00 release when it should only
contain the single hotfix PR (#5017).)

Example auto-generated release notes:
https://github.com/rapidsai/literate-octo-potato/releases/tag/v25.10.00a
---
 .github/release.yml | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 .github/release.yml

diff --git a/.github/release.yml b/.github/release.yml
new file mode 100644
index 0000000000..2c9a85805c
--- /dev/null
+++ b/.github/release.yml
@@ -0,0 +1,27 @@
+# GitHub Auto-Generated Release Notes Configuration for RAPIDS
+# This file configures how GitHub automatically generates release notes
+
+changelog:
+  exclude:
+    labels:
+      - ignore-for-release
+      - dependencies
+    authors:
+      - rapids-bot[bot]
+      - dependabot[bot]
+  categories:
+    - title: 🚨 Breaking Changes
+      labels:
+        - breaking
+    - title: 🐛 Bug Fixes
+      labels:
+        - bug
+    - title: 📖 Documentation
+      labels:
+        - doc
+    - title: 🚀 New Features
+      labels:
+        - feature request
+    - title: 🛠️ Improvements
+      labels:
+        - improvement

From b431fda8f93d059567054d2c4ef71541fd4bd859 Mon Sep 17 00:00:00 2001
From: Mike Sarahan <msarahan@nvidia.com>
Date: Tue, 23 Sep 2025 16:19:45 -0500
Subject: [PATCH 592/623] Empty commit to trigger a build (#1363)

This is an empty commit to trigger a build. It is used when builds get
stuck with an old ABI. Rebuilding updates them to the new one.

From b6a09dd8404190a64b385c6b9ce343877d2e6162 Mon Sep 17 00:00:00 2001
From: James Lamb <jaylamb20@gmail.com>
Date: Wed, 24 Sep 2025 12:05:05 -0500
Subject: [PATCH 593/623] use CUDA 13.0.1 CI images (#1353)

RAPIDS recently started building and testing against CUDA 13.0.1:

* https://github.com/rapidsai/ci-imgs/pull/304
* https://github.com/rapidsai/shared-workflows/pull/423

This updates hard-coded `13.0.0` references in some CI jobs to `13.0.1`.

Authors:
  - James Lamb (https://github.com/jameslamb)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1353
---
 .github/workflows/build.yaml | 12 ++++++------
 .github/workflows/pr.yaml    | 12 ++++++------
 .github/workflows/test.yaml  |  2 +-
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 892764844b..9e914fbfcb 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -51,8 +51,8 @@ jobs:
       fail-fast: false
       matrix:
         cuda_version:
-          - '12.9.1'
-          - '13.0.0'
+          - &latest_cuda12 '12.9.1'
+          - &latest_cuda13 '13.0.1'
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -72,8 +72,8 @@ jobs:
       fail-fast: false
       matrix:
         cuda_version:
-          - '12.9.1'
-          - '13.0.0'
+          - *latest_cuda12
+          - *latest_cuda13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
@@ -93,8 +93,8 @@ jobs:
       fail-fast: false
       matrix:
         cuda_version:
-          - '12.9.1'
-          - '13.0.0'
+          - *latest_cuda12
+          - *latest_cuda13
     with:
       build_type: ${{ inputs.build_type || 'branch' }}
       branch: ${{ inputs.branch }}
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 15b3f35efb..80e2107af8 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -159,8 +159,8 @@ jobs:
       fail-fast: false
       matrix:
         cuda_version:
-          - '12.9.1'
-          - '13.0.0'
+          - &latest_cuda12 '12.9.1'
+          - &latest_cuda13 '13.0.1'
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -189,8 +189,8 @@ jobs:
       fail-fast: false
       matrix:
         cuda_version:
-          - '12.9.1'
-          - '13.0.0'
+          - *latest_cuda12
+          - *latest_cuda13
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
@@ -207,8 +207,8 @@ jobs:
       fail-fast: false
       matrix:
         cuda_version:
-          - '12.9.1'
-          - '13.0.0'
+          - *latest_cuda12
+          - *latest_cuda13
     with:
       build_type: pull-request
       node_type: "gpu-l4-latest-1"
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index c5892ca7ea..87f530a672 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -60,7 +60,7 @@ jobs:
       matrix:
         cuda_version:
           - '12.9.1'
-          - '13.0.0'
+          - '13.0.1'
     with:
       build_type: ${{ inputs.build_type }}
       branch: ${{ inputs.branch }}

From 0317f1e96c5770f96f05363aeb9bf9dc58a66268 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 25 Sep 2025 00:26:13 +0200
Subject: [PATCH 594/623] [Review][Java] Refactor: extract interface from
 CuVSMatrixBaseImpl (#1361)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR extract an internal interface that is used as a base to implement all internal CuVSMatrix types; the interface introduces commonly used field accessors like `memorySegment()` and `toTensor()` that we do not want/could not appear on the public interface (e.g. because they expose or require Panama types or internal types).

The new interface is implemented by all concrete matrix types, closing a gap that we had in https://github.com/rapidsai/cuvs/pull/1328 (which I realized while working on separate PRs like https://github.com/rapidsai/cuvs/pull/1283)

Follow-up of https://github.com/rapidsai/cuvs/pull/1328

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1361
---
 .../com/nvidia/cuvs/CuVSDeviceMatrix.java     | 63 ------------
 .../java/com/nvidia/cuvs/CuVSHostMatrix.java  | 67 -------------
 .../cuvs/internal/BruteForceIndexImpl.java    |  6 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  | 17 ++--
 .../cuvs/internal/CuVSDeviceMatrixImpl.java   | 97 ++++++++++++++++++-
 .../cuvs/internal/CuVSHostMatrixImpl.java     | 94 +++++++++++++++++-
 .../cuvs/internal/CuVSMatrixBaseImpl.java     | 38 ++------
 .../cuvs/internal/CuVSMatrixInternal.java     | 61 ++++++++++++
 .../nvidia/cuvs/internal/TieredIndexImpl.java |  4 +-
 9 files changed, 267 insertions(+), 180 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
index ec34a3de1c..6b0782da52 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSDeviceMatrix.java
@@ -30,67 +30,4 @@ default CuVSHostMatrix toHost() {
     toHost(hostMatrix);
     return hostMatrix;
   }
-
-  default CuVSDeviceMatrix toDevice(CuVSResources resources) {
-    return new CuVSDeviceMatrixDelegate(this);
-  }
-
-  class CuVSDeviceMatrixDelegate implements CuVSDeviceMatrix {
-
-    private final CuVSDeviceMatrix deviceMatrix;
-
-    private CuVSDeviceMatrixDelegate(CuVSDeviceMatrix deviceMatrix) {
-      this.deviceMatrix = deviceMatrix;
-    }
-
-    @Override
-    public long size() {
-      return deviceMatrix.size();
-    }
-
-    @Override
-    public long columns() {
-      return deviceMatrix.columns();
-    }
-
-    @Override
-    public DataType dataType() {
-      return deviceMatrix.dataType();
-    }
-
-    @Override
-    public RowView getRow(long row) {
-      return deviceMatrix.getRow(row);
-    }
-
-    @Override
-    public void toArray(int[][] array) {
-      deviceMatrix.toArray(array);
-    }
-
-    @Override
-    public void toArray(float[][] array) {
-      deviceMatrix.toArray(array);
-    }
-
-    @Override
-    public void toArray(byte[][] array) {
-      deviceMatrix.toArray(array);
-    }
-
-    @Override
-    public void toHost(CuVSHostMatrix hostMatrix) {
-      deviceMatrix.toHost(hostMatrix);
-    }
-
-    @Override
-    public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
-      this.deviceMatrix.toDevice(deviceMatrix, cuVSResources);
-    }
-
-    @Override
-    public void close() {
-      // Do nothing
-    }
-  }
 }
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
index 91ff9012f3..7b98d82a60 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSHostMatrix.java
@@ -21,76 +21,9 @@
 public interface CuVSHostMatrix extends CuVSMatrix {
   int get(int row, int col);
 
-  default CuVSHostMatrix toHost() {
-    return new CuVSHostMatrixDelegate(this);
-  }
-
   default CuVSDeviceMatrix toDevice(CuVSResources resources) {
     var deviceMatrix = CuVSMatrix.deviceBuilder(resources, size(), columns(), dataType()).build();
     toDevice(deviceMatrix, resources);
     return deviceMatrix;
   }
-
-  class CuVSHostMatrixDelegate implements CuVSHostMatrix {
-    private final CuVSHostMatrix hostMatrix;
-
-    public CuVSHostMatrixDelegate(CuVSHostMatrix cuVSHostMatrix) {
-      this.hostMatrix = cuVSHostMatrix;
-    }
-
-    @Override
-    public int get(int row, int col) {
-      return hostMatrix.get(row, col);
-    }
-
-    @Override
-    public long size() {
-      return hostMatrix.size();
-    }
-
-    @Override
-    public long columns() {
-      return hostMatrix.columns();
-    }
-
-    @Override
-    public DataType dataType() {
-      return hostMatrix.dataType();
-    }
-
-    @Override
-    public RowView getRow(long row) {
-      return hostMatrix.getRow(row);
-    }
-
-    @Override
-    public void toArray(int[][] array) {
-      hostMatrix.toArray(array);
-    }
-
-    @Override
-    public void toArray(float[][] array) {
-      hostMatrix.toArray(array);
-    }
-
-    @Override
-    public void toArray(byte[][] array) {
-      hostMatrix.toArray(array);
-    }
-
-    @Override
-    public void toHost(CuVSHostMatrix hostMatrix) {
-      this.hostMatrix.toHost(hostMatrix);
-    }
-
-    @Override
-    public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
-      hostMatrix.toDevice(deviceMatrix, cuVSResources);
-    }
-
-    @Override
-    public void close() {
-      // Do nothing
-    }
-  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
index bc7d6b0a8b..76e6003e19 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/BruteForceIndexImpl.java
@@ -78,8 +78,8 @@ private BruteForceIndexImpl(
     Objects.requireNonNull(dataset);
     try (dataset) {
       this.resources = resources;
-      assert dataset instanceof CuVSMatrixBaseImpl;
-      this.bruteForceIndexReference = build((CuVSMatrixBaseImpl) dataset, bruteForceIndexParams);
+      assert dataset instanceof CuVSMatrixInternal;
+      this.bruteForceIndexReference = build((CuVSMatrixInternal) dataset, bruteForceIndexParams);
     }
   }
 
@@ -124,7 +124,7 @@ public void close() {
    * index
    */
   private IndexReference build(
-      CuVSMatrixBaseImpl dataset, BruteForceIndexParams bruteForceIndexParams) {
+      CuVSMatrixInternal dataset, BruteForceIndexParams bruteForceIndexParams) {
     long rows = dataset.size();
     long cols = dataset.columns();
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 99b0ecc71e..4045d8b83d 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -36,7 +36,6 @@
 import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
 import com.nvidia.cuvs.internal.panama.*;
-
 import java.io.FileInputStream;
 import java.io.InputStream;
 import java.io.OutputStream;
@@ -78,8 +77,8 @@ private CagraIndexImpl(
       CagraIndexParams indexParameters, CuVSMatrix dataset, CuVSResources resources) {
     Objects.requireNonNull(dataset);
     this.resources = resources;
-    assert dataset instanceof CuVSMatrixBaseImpl;
-    this.cagraIndexReference = build(indexParameters, (CuVSMatrixBaseImpl) dataset);
+    assert dataset instanceof CuVSMatrixInternal;
+    this.cagraIndexReference = build(indexParameters, (CuVSMatrixInternal) dataset);
   }
 
   /**
@@ -124,11 +123,11 @@ private CagraIndexImpl(
 
     this.resources = resources;
 
-    assert graph instanceof CuVSMatrixBaseImpl;
-    assert dataset instanceof CuVSMatrixBaseImpl;
+    assert graph instanceof CuVSMatrixInternal;
+    assert dataset instanceof CuVSMatrixInternal;
 
     this.cagraIndexReference =
-        fromGraph(metric, (CuVSMatrixBaseImpl) graph, (CuVSMatrixBaseImpl) dataset);
+        fromGraph(metric, (CuVSMatrixInternal) graph, (CuVSMatrixInternal) dataset);
   }
 
   private void checkNotDestroyed() {
@@ -161,7 +160,7 @@ public void close() {
    * @return an instance of {@link IndexReference} that holds the pointer to the
    *         index
    */
-  private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixBaseImpl dataset) {
+  private IndexReference build(CagraIndexParams indexParameters, CuVSMatrixInternal dataset) {
     long rows = dataset.size();
 
     try (var indexParams = segmentFromIndexParams(indexParameters);
@@ -410,8 +409,8 @@ public CuVSDeviceMatrix getGraph() {
 
   private IndexReference fromGraph(
       CagraIndexParams.CuvsDistanceType metric,
-      CuVSMatrixBaseImpl graph,
-      CuVSMatrixBaseImpl dataset) {
+      CuVSMatrixInternal graph,
+      CuVSMatrixInternal dataset) {
     try (var localArena = Arena.ofConfined()) {
       var index = createCagraIndex();
       try (var resourcesAccess = resources.access()) {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
index 0f97608ae1..27f2adbd02 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
@@ -224,7 +224,7 @@ public void toHost(CuVSHostMatrix hostMatrix) {
       throw new IllegalArgumentException("[hostMatrix] must have the same dataType");
     }
     try (var localArena = Arena.ofConfined()) {
-      var hostMatrixTensor = ((CuVSHostMatrixImpl) hostMatrix).toTensor(localArena);
+      var hostMatrixTensor = ((CuVSMatrixInternal) hostMatrix).toTensor(localArena);
 
       try (var resourceAccess = resources.access()) {
         var cuvsRes = resourceAccess.handle();
@@ -236,9 +236,14 @@ public void toHost(CuVSHostMatrix hostMatrix) {
     }
   }
 
+  @Override
+  public CuVSDeviceMatrix toDevice(CuVSResources resources) {
+    return new CuVSDeviceMatrixDelegate(this);
+  }
+
   @Override
   public void toDevice(CuVSDeviceMatrix targetMatrix, CuVSResources cuVSResources) {
-    copyMatrix(this, (CuVSMatrixBaseImpl) targetMatrix, cuVSResources);
+    copyMatrix(this, (CuVSMatrixInternal) targetMatrix, cuVSResources);
   }
 
   @Override
@@ -248,4 +253,92 @@ public void close() {
       hostBuffer = MemorySegment.NULL;
     }
   }
+
+  private static class CuVSDeviceMatrixDelegate implements CuVSDeviceMatrix, CuVSMatrixInternal {
+    private final CuVSDeviceMatrixImpl deviceMatrix;
+
+    private CuVSDeviceMatrixDelegate(CuVSDeviceMatrixImpl deviceMatrix) {
+      this.deviceMatrix = deviceMatrix;
+    }
+
+    @Override
+    public long size() {
+      return deviceMatrix.size();
+    }
+
+    @Override
+    public long columns() {
+      return deviceMatrix.columns();
+    }
+
+    @Override
+    public DataType dataType() {
+      return deviceMatrix.dataType();
+    }
+
+    @Override
+    public RowView getRow(long row) {
+      return deviceMatrix.getRow(row);
+    }
+
+    @Override
+    public void toArray(int[][] array) {
+      deviceMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(float[][] array) {
+      deviceMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(byte[][] array) {
+      deviceMatrix.toArray(array);
+    }
+
+    @Override
+    public void toHost(CuVSHostMatrix hostMatrix) {
+      deviceMatrix.toHost(hostMatrix);
+    }
+
+    @Override
+    public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
+      deviceMatrix.toDevice(deviceMatrix, cuVSResources);
+    }
+
+    @Override
+    public CuVSDeviceMatrix toDevice(CuVSResources cuVSResources) {
+      return this;
+    }
+
+    @Override
+    public MemorySegment memorySegment() {
+      return deviceMatrix.memorySegment();
+    }
+
+    @Override
+    public ValueLayout valueLayout() {
+      return deviceMatrix.valueLayout();
+    }
+
+    @Override
+    public int bits() {
+      return deviceMatrix.bits();
+    }
+
+    @Override
+    public int code() {
+      return 0;
+    }
+
+    @Override
+    public MemorySegment toTensor(Arena arena) {
+      return deviceMatrix.toTensor(arena);
+    }
+
+    @Override
+    public void close() {
+      // Do nothing
+    }
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
index 408feda2bf..86e7e2a059 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -144,17 +144,22 @@ public void toArray(byte[][] array) {
     }
   }
 
+  @Override
+  public CuVSHostMatrix toHost() {
+    return new CuVSHostMatrixDelegate(this);
+  }
+
   @Override
   public void toHost(CuVSHostMatrix hostMatrix) {
-    var targetMatrix = (CuVSHostMatrixImpl) hostMatrix;
+    var targetMatrix = (CuVSMatrixInternal) hostMatrix;
     var valueByteSize = valueLayout.byteSize();
     MemorySegment.copy(
-        this.memorySegment, 0L, targetMatrix.memorySegment, 0L, size * columns * valueByteSize);
+        this.memorySegment, 0L, targetMatrix.memorySegment(), 0L, size * columns * valueByteSize);
   }
 
   @Override
   public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
-    copyMatrix(this, (CuVSMatrixBaseImpl) deviceMatrix, cuVSResources);
+    copyMatrix(this, (CuVSMatrixInternal) deviceMatrix, cuVSResources);
   }
 
   @Override
@@ -170,4 +175,87 @@ public MemorySegment toTensor(Arena arena) {
     return prepareTensor(
         arena, memorySegment, new long[] {size, columns}, code(), bits(), kDLCPU());
   }
+
+  private static class CuVSHostMatrixDelegate implements CuVSHostMatrix, CuVSMatrixInternal {
+    private final CuVSHostMatrixImpl hostMatrix;
+
+    public CuVSHostMatrixDelegate(CuVSHostMatrixImpl cuVSHostMatrix) {
+      this.hostMatrix = cuVSHostMatrix;
+    }
+
+    @Override
+    public int get(int row, int col) {
+      return hostMatrix.get(row, col);
+    }
+
+    @Override
+    public long size() {
+      return hostMatrix.size();
+    }
+
+    @Override
+    public long columns() {
+      return hostMatrix.columns();
+    }
+
+    @Override
+    public DataType dataType() {
+      return hostMatrix.dataType();
+    }
+
+    @Override
+    public RowView getRow(long row) {
+      return hostMatrix.getRow(row);
+    }
+
+    @Override
+    public void toArray(int[][] array) {
+      hostMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(float[][] array) {
+      hostMatrix.toArray(array);
+    }
+
+    @Override
+    public void toArray(byte[][] array) {
+      hostMatrix.toArray(array);
+    }
+
+    @Override
+    public void toHost(CuVSHostMatrix hostMatrix) {
+      this.hostMatrix.toHost(hostMatrix);
+    }
+
+    @Override
+    public CuVSHostMatrix toHost() {
+      return this;
+    }
+
+    @Override
+    public void toDevice(CuVSDeviceMatrix deviceMatrix, CuVSResources cuVSResources) {
+      hostMatrix.toDevice(deviceMatrix, cuVSResources);
+    }
+
+    @Override
+    public MemorySegment memorySegment() {
+      return hostMatrix.memorySegment();
+    }
+
+    @Override
+    public ValueLayout valueLayout() {
+      return hostMatrix.valueLayout();
+    }
+
+    @Override
+    public MemorySegment toTensor(Arena arena) {
+      return hostMatrix.toTensor(arena);
+    }
+
+    @Override
+    public void close() {
+      // Do nothing
+    }
+  }
 }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
index b46ab63c9f..2e510a99b2 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -30,7 +30,7 @@
 import java.lang.foreign.*;
 import java.util.Locale;
 
-public abstract class CuVSMatrixBaseImpl implements CuVSMatrix {
+abstract class CuVSMatrixBaseImpl implements CuVSMatrixInternal {
   protected final MemorySegment memorySegment;
   protected final DataType dataType;
   protected final ValueLayout valueLayout;
@@ -51,13 +51,13 @@ protected CuVSMatrixBaseImpl(
   }
 
   protected static void copyMatrix(
-      CuVSMatrixBaseImpl sourceMatrix, CuVSMatrixBaseImpl targetMatrix, CuVSResources resources) {
-    if (targetMatrix.columns() != sourceMatrix.columns
-        || targetMatrix.size() != sourceMatrix.size) {
+      CuVSMatrixInternal sourceMatrix, CuVSMatrixInternal targetMatrix, CuVSResources resources) {
+    if (targetMatrix.columns() != sourceMatrix.columns()
+        || targetMatrix.size() != sourceMatrix.size()) {
       throw new IllegalArgumentException(
           "Source and target matrices must have the same dimensions");
     }
-    if (targetMatrix.dataType() != sourceMatrix.dataType) {
+    if (targetMatrix.dataType() != sourceMatrix.dataType()) {
       throw new IllegalArgumentException("Source and target matrices must have the same dataType");
     }
 
@@ -88,32 +88,16 @@ public DataType dataType() {
     return dataType;
   }
 
+  @Override
   public MemorySegment memorySegment() {
     return memorySegment;
   }
 
+  @Override
   public ValueLayout valueLayout() {
     return valueLayout;
   }
 
-  /**
-   * Size (in bits) for the element type of this matrix
-   */
-  protected int bits() {
-    return (int) (valueLayout.byteSize() * 8);
-  }
-
-  /**
-   * DLTensor data type {@code code} for the element type of this matrix
-   */
-  protected int code() {
-    return switch (dataType) {
-      case FLOAT -> kDLFloat();
-      case INT -> kDLInt();
-      case UINT, BYTE -> kDLUInt();
-    };
-  }
-
   protected static ValueLayout valueLayoutFromType(DataType dataType) {
     return switch (dataType) {
       case FLOAT -> C_FLOAT;
@@ -128,14 +112,6 @@ protected static SequenceLayout sequenceLayoutFromType(
         .withByteAlignment(32);
   }
 
-  /**
-   * Creates a {@link DLManagedTensor} representing the matrix data and shape, to be
-   * passed to the CuVS C API.
-   * @param arena The Arena to use to allocate DL data structures
-   * @return a {@link MemorySegment} for the newly allocated DLManagedTensor
-   */
-  public abstract MemorySegment toTensor(Arena arena);
-
   /**
    * Creates a {@link CuVSMatrix} from data and infos from a {@link DLManagedTensor}
    *
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
new file mode 100644
index 0000000000..ed53836719
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal;
+
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
+
+import com.nvidia.cuvs.CuVSMatrix;
+import com.nvidia.cuvs.internal.panama.DLManagedTensor;
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.ValueLayout;
+
+/**
+ * Internal interface for {@link CuVSMatrix}, for shared functionality that
+ * require/expose Panama types or internal cuvs types.
+ */
+interface CuVSMatrixInternal extends CuVSMatrix {
+
+  MemorySegment memorySegment();
+
+  ValueLayout valueLayout();
+
+  /**
+   * Size (in bits) for the element type of this matrix
+   */
+  default int bits() {
+    return (int) (valueLayout().byteSize() * 8);
+  }
+
+  /**
+   * DLTensor data type {@code code} for the element type of this matrix
+   */
+  default int code() {
+    return switch (dataType()) {
+      case FLOAT -> kDLFloat();
+      case INT -> kDLInt();
+      case UINT, BYTE -> kDLUInt();
+    };
+  }
+
+  /**
+   * Creates a {@link DLManagedTensor} representing the matrix data and shape, to be
+   * passed to the CuVS C API.
+   * @param arena The Arena to use to allocate DL data structures
+   * @return a {@link MemorySegment} for the newly allocated DLManagedTensor
+   */
+  MemorySegment toTensor(Arena arena);
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
index 5edbcabd71..31967eee13 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/TieredIndexImpl.java
@@ -122,7 +122,7 @@ private IndexReference build() {
       long cols = dataset.columns();
 
       // Get host data
-      MemorySegment hostDataSeg = ((CuVSHostMatrixImpl) dataset).memorySegment();
+      MemorySegment hostDataSeg = ((CuVSMatrixInternal) dataset).memorySegment();
 
       try (var resourceAccess = resources.access();
           var indexParamsHandle =
@@ -330,7 +330,7 @@ private void performExtend(CuVSMatrix extendDataset) {
       long cols = extendDataset.columns();
 
       // Get host data
-      MemorySegment hostDataSeg = ((CuVSMatrixBaseImpl) extendDataset).memorySegment();
+      MemorySegment hostDataSeg = ((CuVSMatrixInternal) extendDataset).memorySegment();
 
       try (var resourceAccess = resources.access()) {
         long cuvsRes = resourceAccess.handle();

From 12ebfa1ee38131cb5dbb1f46ca16d833b98f3bb4 Mon Sep 17 00:00:00 2001
From: Simon Adorf <sadorf@nvidia.com>
Date: Wed, 24 Sep 2025 20:53:01 -0500
Subject: [PATCH 595/623] Fix k-means++ root rank race condition (#1359)

Fixes https://github.com/rapidsai/cuvs/issues/1358

Authors:
  - Simon Adorf (https://github.com/csadorf)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Victor Lafargue (https://github.com/viclafargue)

URL: https://github.com/rapidsai/cuvs/pull/1359
---
 cpp/src/cluster/detail/kmeans_mg.cuh | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/cpp/src/cluster/detail/kmeans_mg.cuh b/cpp/src/cluster/detail/kmeans_mg.cuh
index 8a39f91900..797e531f58 100644
--- a/cpp/src/cluster/detail/kmeans_mg.cuh
+++ b/cpp/src/cluster/detail/kmeans_mg.cuh
@@ -175,9 +175,20 @@ void initKMeansPlusPlus(const raft::resources& handle,
   //          X which will be used as the initial centroid for kmeans++
   //    1.3 - Communicate the initial centroid chosen by rank-r' to all other
   //          ranks
-  std::mt19937 gen(params.rng_state.seed);
-  std::uniform_int_distribution<> dis(0, n_rank - 1);
-  int rp = dis(gen);
+  // Choose rp on rank 0 and broadcast to all ranks to guarantee agreement
+  int rp = 0;
+  if (my_rank == KMEANS_COMM_ROOT) {
+    std::mt19937 gen(params.rng_state.seed);
+    std::uniform_int_distribution<> dis(0, n_rank - 1);
+    rp = dis(gen);
+  }
+  {
+    rmm::device_scalar<int> rp_d(stream);
+    raft::copy(rp_d.data(), &rp, 1, stream);
+    comm.bcast<int>(rp_d.data(), 1, /*root=*/KMEANS_COMM_ROOT, stream);
+    raft::copy(&rp, rp_d.data(), 1, stream);
+    raft::resource::sync_stream(handle);
+  }
 
   // buffer to flag the sample that is chosen as initial centroids
   std::vector<std::uint8_t> h_isSampleCentroid(n_samples);

From c55cfd7f4c8f0ea8a1696b9dc2b86cceb5d7c1b5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 25 Sep 2025 11:48:16 +0200
Subject: [PATCH 596/623] [Java] Adding tests to use CuVSDeviceMatrix (device
 memory) directly as a CagraIndex input dataset (#1340)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

When we introduced `CuVSDeviceMatrix` in #1232, we made it possible to use device-memory-backed dataset as an input for index build: since we accept a `CuVSMatrix`, and we have correct `toTensor` implementations for CPU and GPU, and the underlying functions in libcuvs support different memory types and sizes (through DLManagedTensor information), this became supported "naturally".
However, we never tested this explicitly.

This PR adds tests to check and show that using CuVSDeviceMatrix (device memory) directly as a CagraIndex input dataset works as intended.

(similar tests for other index types will be added as follow-ups)

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1340
---
 .../common/CloseableRMMAllocation.java        |   1 +
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 149 +++++++++++-------
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |  36 ++++-
 .../java/com/nvidia/cuvs/CuVSMatrixIT.java    |  73 ++++-----
 4 files changed, 155 insertions(+), 104 deletions(-)

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java
index 6dcc4c892f..98f269b573 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/CloseableRMMAllocation.java
@@ -79,6 +79,7 @@ private boolean mustClose() {
   public void close() {
     if (mustClose()) {
       checkCuVSError(cuvsRMMFree(cuvsResourceHandle, pointer, numBytes), "cuvsRMMFree");
+      pointer = MemorySegment.NULL;
     }
   }
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index c0c0c8a37a..45076e6482 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -16,15 +16,13 @@
 package com.nvidia.cuvs;
 
 import static com.carrotsearch.randomizedtesting.RandomizedTest.assumeTrue;
+import static com.nvidia.cuvs.CuVSMatrixIT.assertSame2dArray;
 import static org.junit.Assert.*;
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
 import com.nvidia.cuvs.CagraMergeParams.MergeStrategy;
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
 import java.io.InputStream;
 import java.lang.foreign.Arena;
 import java.lang.foreign.Linker;
@@ -397,12 +395,12 @@ public void testPrefilteringReducesResults() throws Throwable {
             .withMetric(CuvsDistanceType.L2Expanded)
             .build();
 
-    try (CuVSResources resources = CheckedCuVSResources.create()) {
-      CagraIndex index =
-          CagraIndex.newBuilder(resources)
-              .withDataset(dataset)
-              .withIndexParams(indexParams)
-              .build();
+    try (CuVSResources resources = CheckedCuVSResources.create();
+        CagraIndex index =
+            CagraIndex.newBuilder(resources)
+                .withDataset(dataset)
+                .withIndexParams(indexParams)
+                .build()) {
 
       // No prefilter (all points allowed)
       CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
@@ -514,6 +512,10 @@ private void cleanup(CagraIndex index, CagraIndex loadedIndex) throws Throwable
     loadedIndex.close();
   }
 
+  /**
+   * Tests that an index built starting from a native MemorySegment is identical to one built from
+   * Java heap arrays
+   */
   @Test
   public void testNativeDatasetEquivalent() throws Throwable {
     float[][] sampleData = createSampleData();
@@ -555,6 +557,37 @@ public void testNativeDatasetEquivalent() throws Throwable {
     }
   }
 
+  /**
+   * Tests that an index built starting from device memory ({@link CuVSDeviceMatrix}) is identical to one
+   * built from Java heap arrays
+   */
+  @Test
+  public void testDeviceDatasetEquivalent() throws Throwable {
+    float[][] sampleData = createSampleData();
+
+    try (var resources = CuVSResources.create();
+        var javaDataset = CuVSMatrix.ofArray(sampleData);
+        var deviceDataset = javaDataset.toDevice(resources)) {
+
+      // Indexing with an on-heap and native datasets produce the same results
+      var javaIndex = indexOnce(javaDataset, resources);
+      var deviceIndex = indexOnce(deviceDataset, resources);
+
+      int size = (int) javaIndex.getGraph().size();
+      assertEquals(size, (int) deviceIndex.getGraph().size());
+
+      int columns = (int) javaIndex.getGraph().columns();
+      assertEquals(columns, (int) deviceIndex.getGraph().columns());
+
+      var javaIndexGraph = new int[size][columns];
+      var deviceIndexGraph = new int[size][columns];
+      javaIndex.getGraph().toArray(javaIndexGraph);
+      deviceIndex.getGraph().toArray(deviceIndexGraph);
+
+      assertSame2dArray(size, columns, javaIndexGraph, deviceIndexGraph);
+    }
+  }
+
   @Test
   public void testMergingIndexes() throws Throwable {
     float[][] vector1 = {
@@ -626,22 +659,23 @@ public void testMergingIndexes() throws Throwable {
 
       // --- Serialization/deserialization check ---
       String indexFileName = UUID.randomUUID() + ".cag";
-      mergedIndex.serialize(new FileOutputStream(indexFileName));
+      var indexFile = Path.of(indexFileName);
 
-      File indexFile = new File(indexFileName);
-      InputStream inputStream = new FileInputStream(indexFile);
-      CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
+      try (var out = Files.newOutputStream(indexFile)) {
+        mergedIndex.serialize(out);
+      }
 
-      SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
-      assertEquals(expectedResults, resultsFromLoaded.getResults());
+      try (InputStream inputStream = Files.newInputStream(indexFile)) {
+        CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
 
-      if (indexFile.exists()) {
-        indexFile.delete();
+        SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
+        assertEquals(expectedResults, resultsFromLoaded.getResults());
+        mergedIndex.close();
+        loadedMergedIndex.close();
       }
+      Files.deleteIfExists(indexFile);
       index1.close();
       index2.close();
-      mergedIndex.close();
-      loadedMergedIndex.close();
     }
   }
 
@@ -710,50 +744,51 @@ public void testMergeStrategies() throws Throwable {
               .build();
 
       log.trace("Merging indexes with PHYSICAL strategy...");
-      CagraIndex physicalMergedIndex =
-          CagraIndex.merge(new CagraIndex[] {index1, index2}, physicalMergeParams);
-      log.trace("Physical merge completed successfully");
+      try (CagraIndex physicalMergedIndex =
+          CagraIndex.merge(new CagraIndex[] {index1, index2}, physicalMergeParams)) {
+        log.trace("Physical merge completed successfully");
+
+        CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+
+        CagraQuery query =
+            new CagraQuery.Builder(resources)
+                .withTopK(3)
+                .withSearchParams(searchParams)
+                .withQueryVectors(queries)
+                .withMapping(SearchResults.IDENTITY_MAPPING)
+                .build();
+
+        log.trace("Searching physically merged index...");
+        SearchResults physicalResults = physicalMergedIndex.search(query);
+        assertNotNull("Physical merge search results should not be null", physicalResults);
+        assertEquals(
+            "Physical merge search results should match expected",
+            expectedResults,
+            physicalResults.getResults());
 
-      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+        // --- Serialization/deserialization check for both merged indexes ---
+        String physicalIndexFileName = UUID.randomUUID() + ".cag";
+        var physicalIndexFile = Path.of(physicalIndexFileName);
 
-      CagraQuery query =
-          new CagraQuery.Builder(resources)
-              .withTopK(3)
-              .withSearchParams(searchParams)
-              .withQueryVectors(queries)
-              .withMapping(SearchResults.IDENTITY_MAPPING)
-              .build();
+        try (var out = Files.newOutputStream(physicalIndexFile)) {
+          physicalMergedIndex.serialize(out);
+        }
 
-      log.trace("Searching physically merged index...");
-      SearchResults physicalResults = physicalMergedIndex.search(query);
-      assertNotNull("Physical merge search results should not be null", physicalResults);
-      assertEquals(
-          "Physical merge search results should match expected",
-          expectedResults,
-          physicalResults.getResults());
-
-      // --- Serialization/deserialization check for both merged indexes ---
-      String physicalIndexFileName = UUID.randomUUID().toString() + ".cag";
-      physicalMergedIndex.serialize(new FileOutputStream(physicalIndexFileName));
-
-      File physicalIndexFile = new File(physicalIndexFileName);
-      InputStream physicalInputStream = new FileInputStream(physicalIndexFile);
-      CagraIndex loadedPhysicalIndex =
-          CagraIndex.newBuilder(resources).from(physicalInputStream).build();
-
-      SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
-      assertEquals(
-          "Loaded physical index search results should match expected",
-          expectedResults,
-          resultsFromLoadedPhysical.getResults());
-
-      if (physicalIndexFile.exists()) {
-        physicalIndexFile.delete();
+        try (InputStream physicalInputStream = Files.newInputStream(physicalIndexFile);
+            CagraIndex loadedPhysicalIndex =
+                CagraIndex.newBuilder(resources).from(physicalInputStream).build()) {
+
+          Files.deleteIfExists(physicalIndexFile);
+
+          SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
+          assertEquals(
+              "Loaded physical index search results should match expected",
+              expectedResults,
+              resultsFromLoadedPhysical.getResults());
+        }
       }
       index1.close();
       index2.close();
-      physicalMergedIndex.close();
-      loadedPhysicalIndex.close();
     }
   }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index 6e02793478..d7d41c8c2f 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -39,17 +39,26 @@ public void setup() {
     log.trace("Random context initialized for test.");
   }
 
+  enum TestDatasetMemoryKind {
+    HEAP,
+    NATIVE,
+    DEVICE
+  }
+
   @Test
   public void testResultsTopKWithRandomValues() throws Throwable {
-    boolean[] useNativeMemoryDatasets = {true, false};
+    TestDatasetMemoryKind[] testDatasetMemoryKinds = {
+      TestDatasetMemoryKind.HEAP, TestDatasetMemoryKind.NATIVE, TestDatasetMemoryKind.DEVICE
+    };
     for (int i = 0; i < 100; i++) {
-      for (boolean use : useNativeMemoryDatasets) {
-        tmpResultsTopKWithRandomValues(use);
+      for (var datasetMemoryKind : testDatasetMemoryKinds) {
+        tmpResultsTopKWithRandomValues(datasetMemoryKind);
       }
     }
   }
 
-  private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) throws Throwable {
+  private void tmpResultsTopKWithRandomValues(TestDatasetMemoryKind datasetMemoryKind)
+      throws Throwable {
     int DATASET_SIZE_LIMIT = 10_000;
     int DIMENSIONS_LIMIT = 2048;
     int NUM_QUERIES_LIMIT = 10;
@@ -90,7 +99,7 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
     log.debug("Dataset size: {}x{}", datasetSize, dimensions);
     log.debug("Query size: {}x{}", numQueries, dimensions);
     log.debug("TopK: {}", topK);
-    log.debug("Use native memory dataset? " + useNativeMemoryDataset);
+    log.debug("Use memory dataset: " + datasetMemoryKind.name());
 
     // Debugging: Log dataset and queries
     if (log.isDebugEnabled()) {
@@ -119,8 +128,8 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
               .withCagraGraphBuildAlgo(CagraGraphBuildAlgo.NN_DESCENT)
               .build();
 
-      CagraIndex index;
-      if (useNativeMemoryDataset) {
+      final CagraIndex index;
+      if (datasetMemoryKind == TestDatasetMemoryKind.NATIVE) {
         var datasetBuilder =
             CuVSMatrix.hostBuilder(vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
         for (float[] v : vectors) {
@@ -131,7 +140,20 @@ private void tmpResultsTopKWithRandomValues(boolean useNativeMemoryDataset) thro
                 .withDataset(datasetBuilder.build())
                 .withIndexParams(indexParams)
                 .build();
+      } else if (datasetMemoryKind == TestDatasetMemoryKind.DEVICE) {
+        var datasetBuilder =
+            CuVSMatrix.deviceBuilder(
+                resources, vectors.length, vectors[0].length, CuVSMatrix.DataType.FLOAT);
+        for (float[] v : vectors) {
+          datasetBuilder.addVector(v);
+        }
+        index =
+            CagraIndex.newBuilder(resources)
+                .withDataset(datasetBuilder.build())
+                .withIndexParams(indexParams)
+                .build();
       } else {
+        assert datasetMemoryKind == TestDatasetMemoryKind.HEAP;
         index =
             CagraIndex.newBuilder(resources)
                 .withDataset(vectors)
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
index 499c7d366f..917acd4fc4 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
@@ -211,11 +211,7 @@ public void testIntDatasetCopy() {
     try (var dataset = CuVSMatrix.ofArray(intData)) {
       var intDataCopy = new int[(int) dataset.size()][(int) dataset.columns()];
       dataset.toArray(intDataCopy);
-      for (int n = 0; n < dataset.size(); ++n) {
-        for (int i = 0; i < dataset.columns(); ++i) {
-          assertEquals(intData[n][i], intDataCopy[n][i]);
-        }
-      }
+      assertSame2dArray(dataset.size(), dataset.columns(), intData, intDataCopy);
     }
   }
 
@@ -254,10 +250,32 @@ public void testFloatDatasetCopy() {
     try (var dataset = CuVSMatrix.ofArray(floatData)) {
       var dataCopy = new float[(int) dataset.size()][(int) dataset.columns()];
       dataset.toArray(dataCopy);
-      for (int n = 0; n < dataset.size(); ++n) {
-        for (int i = 0; i < dataset.columns(); ++i) {
-          assertEquals(floatData[n][i], dataCopy[n][i], DELTA);
-        }
+      assertSame2dArray(dataset.size(), dataset.columns(), floatData, dataCopy);
+    }
+  }
+
+  static void assertSame2dArray(long rows, long cols, float[][] array1, float[][] array2) {
+    assertEquals(rows, array1.length);
+    assertEquals(cols, array1[0].length);
+    assertEquals(rows, array2.length);
+    assertEquals(cols, array2[0].length);
+
+    for (int n = 0; n < rows; ++n) {
+      for (int i = 0; i < cols; ++i) {
+        assertEquals(array1[n][i], array2[n][i], DELTA);
+      }
+    }
+  }
+
+  static void assertSame2dArray(long rows, long cols, int[][] array1, int[][] array2) {
+    assertEquals(rows, array1.length);
+    assertEquals(cols, array1[0].length);
+    assertEquals(rows, array2.length);
+    assertEquals(cols, array2[0].length);
+
+    for (int n = 0; n < rows; ++n) {
+      for (int i = 0; i < cols; ++i) {
+        assertEquals(array1[n][i], array2[n][i]);
       }
     }
   }
@@ -280,11 +298,7 @@ private void testFloatDatasetBuilder(int rows, int cols, CuVSMatrix.Builder<?> b
     try (var dataset = builder.build()) {
       dataset.toArray(roundTripData);
 
-      for (int n = 0; n < dataset.size(); ++n) {
-        for (int i = 0; i < dataset.columns(); ++i) {
-          assertEquals(data[n][i], roundTripData[n][i], DELTA);
-        }
-      }
+      assertSame2dArray(dataset.size(), dataset.columns(), data, roundTripData);
     }
   }
 
@@ -323,12 +337,7 @@ private void testIntDatasetBuilder(int rows, int cols, CuVSMatrix.Builder<?> bui
 
     try (var dataset = builder.build()) {
       dataset.toArray(roundTripData);
-
-      for (int n = 0; n < dataset.size(); ++n) {
-        for (int i = 0; i < dataset.columns(); ++i) {
-          assertEquals(data[n][i], roundTripData[n][i]);
-        }
-      }
+      assertSame2dArray(dataset.size(), dataset.columns(), data, roundTripData);
     }
   }
 
@@ -421,11 +430,7 @@ public void testDeviceToHost() throws Throwable {
 
         hostMatrix.toArray(roundTripData);
 
-        for (int n = 0; n < hostMatrix.size(); ++n) {
-          for (int i = 0; i < hostMatrix.columns(); ++i) {
-            assertEquals(data[n][i], roundTripData[n][i], 1e-9);
-          }
-        }
+        assertSame2dArray(hostMatrix.size(), hostMatrix.columns(), data, roundTripData);
       }
     }
   }
@@ -452,11 +457,7 @@ public void testHostToDevice() throws Throwable {
 
         deviceMatrix.toArray(roundTripData);
 
-        for (int n = 0; n < deviceMatrix.size(); ++n) {
-          for (int i = 0; i < deviceMatrix.columns(); ++i) {
-            assertEquals(data[n][i], roundTripData[n][i], 1e-9);
-          }
-        }
+        assertSame2dArray(deviceMatrix.size(), deviceMatrix.columns(), data, roundTripData);
       }
     }
   }
@@ -481,11 +482,7 @@ public void testHostToHostReturnsWeakReferenceSameData() {
 
       hostMatrix2.toArray(roundTripData);
 
-      for (int n = 0; n < hostMatrix2.size(); ++n) {
-        for (int i = 0; i < hostMatrix2.columns(); ++i) {
-          assertEquals(data[n][i], roundTripData[n][i], 1e-9);
-        }
-      }
+      assertSame2dArray(hostMatrix2.size(), hostMatrix2.columns(), data, roundTripData);
     }
   }
 
@@ -517,11 +514,7 @@ public void testDeviceToDeviceReturnsWeakReferenceSameData() throws Throwable {
 
         deviceMatrix2.toArray(roundTripData);
 
-        for (int n = 0; n < deviceMatrix2.size(); ++n) {
-          for (int i = 0; i < deviceMatrix2.columns(); ++i) {
-            assertEquals(data[n][i], roundTripData[n][i], 1e-9);
-          }
-        }
+        assertSame2dArray(deviceMatrix2.size(), deviceMatrix2.columns(), data, roundTripData);
       }
     }
   }

From c07ab70786d5553d4bb726a9d25aa643352ef946 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Thu, 25 Sep 2025 18:50:17 +0200
Subject: [PATCH 597/623] [Java] Buffered device matrix builder (#1332)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The current `CuVSDeviceMatrix` builder implementation uses a non-native `MemorySegment` created directly from on-heap java arrays to avoid an intermediate allocation and copy to a native (off-heap) segment.

However, this means that copy from host (Java heap) memory to device memory happens row by row (vector by vector), which is not ideal.

This PR introduces a new builder implementation; it still returns a `CuVSDeviceMatrix` backed by managed RMM device memory, but it uses an intermediate buffer (pinned, implemented by a new `PinnedMemoryBuffer` class extracted/refactored from the existing code) to batch data before copying it to the GPU.

Even if this means an intermediate allocation and copy to a native (off-heap) segment (so, data is copied twice), using pinned memory is up to 2x faster; benchmarks reveal that copying data directly from on-heap memory and using this intermediate buffer have similar throughput:

Before (heap to GPU, row-by-row)
```
Benchmark                                            (dims)  (size)   Mode  Cnt   Score   Error  Units
CuVSDeviceMatrixBenchmarks.matrixDeviceBuilder         2048   16384  thrpt    5  34.932 ± 0.574  ops/s
```

After (heap to native memory to GPU, batched)
```
Benchmark                                            (dims)  (size)   Mode  Cnt   Score   Error  Units
CuVSDeviceMatrixBenchmarks.matrixDeviceBuilder         2048   16384  thrpt    5  35.622 ± 0.304  ops/s
```

So essentially identical; however, I think that despite the added complexity the change is still worth it as this method significantly reduces the number of calls to the GPU, reducing the risk of device locking due to syncs.
But I'm open to feedback, comments and suggestions!

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - MithunR (https://github.com/mythrocks)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1332
---
 .../cuvs/internal/CuVSDeviceMatrixImpl.java   | 54 +++----------
 .../internal/common/PinnedMemoryBuffer.java   | 79 +++++++++++++++++++
 .../com/nvidia/cuvs/spi/JDKProvider.java      | 71 +++++++++++++----
 3 files changed, 145 insertions(+), 59 deletions(-)
 create mode 100644 java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/PinnedMemoryBuffer.java

diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
index 27f2adbd02..2e72ea69f5 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
@@ -15,21 +15,17 @@
  */
 package com.nvidia.cuvs.internal;
 
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
 import static com.nvidia.cuvs.internal.common.Util.*;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
 import com.nvidia.cuvs.*;
+import com.nvidia.cuvs.internal.common.PinnedMemoryBuffer;
 import com.nvidia.cuvs.internal.panama.DLManagedTensor;
 import com.nvidia.cuvs.internal.panama.DLTensor;
 import java.lang.foreign.*;
 
 public class CuVSDeviceMatrixImpl extends CuVSMatrixBaseImpl implements CuVSDeviceMatrix {
 
-  private static final int CHUNK_BYTES =
-      8 * 1024 * 1024; // Based on benchmarks, 8MB seems the minimum size to optimize PCIe bandwidth
-  private final long hostBufferBytes;
-
   private long bufferedMatrixRowStart = 0;
   private long bufferedMatrixRowEnd = 0;
 
@@ -38,7 +34,7 @@ public class CuVSDeviceMatrixImpl extends CuVSMatrixBaseImpl implements CuVSDevi
   private final long rowStride;
   private final long columnStride;
 
-  private MemorySegment hostBuffer = MemorySegment.NULL;
+  private final PinnedMemoryBuffer hostBuffer;
 
   protected CuVSDeviceMatrixImpl(
       CuVSResources resources,
@@ -63,18 +59,7 @@ protected CuVSDeviceMatrixImpl(
     this.resources = resources;
     this.rowStride = rowStride;
     this.columnStride = columnStride;
-
-    long rowBytes = columns * valueLayout.byteSize();
-    long matrixBytes = size * rowBytes;
-    if (matrixBytes < CHUNK_BYTES) {
-      this.hostBufferBytes = matrixBytes;
-    } else if (rowBytes > CHUNK_BYTES) {
-      // We need to buffer at least one row at time
-      this.hostBufferBytes = rowBytes;
-    } else {
-      var rowCount = (CHUNK_BYTES / rowBytes);
-      this.hostBufferBytes = rowBytes * rowCount;
-    }
+    this.hostBuffer = new PinnedMemoryBuffer(size, columns, valueLayout);
   }
 
   @Override
@@ -84,27 +69,10 @@ public MemorySegment toTensor(Arena arena) {
         arena, memorySegment, new long[] {size, columns}, strides, code(), bits(), kDLCUDA());
   }
 
-  private static MemorySegment createPinnedBuffer(long bufferBytes) {
-    try (var localArena = Arena.ofConfined()) {
-      MemorySegment pointer = localArena.allocate(C_POINTER);
-      checkCudaError(cudaMallocHost(pointer, bufferBytes), "cudaMallocHost");
-      return pointer.get(C_POINTER, 0);
-    }
-  }
-
-  private static void destroyPinnedBuffer(MemorySegment bufferSegment) {
-    checkCudaError(cudaFreeHost(bufferSegment), "cudaFreeHost");
-  }
-
   private void populateBuffer(long startRow) {
-    if (hostBuffer == MemorySegment.NULL) {
-      //      System.out.println("Creating a buffer of size " + hostBufferBytes);
-      hostBuffer = createPinnedBuffer(hostBufferBytes);
-    }
-
     try (var localArena = Arena.ofConfined()) {
       long rowBytes = columns * valueLayout.byteSize();
-      var endRow = Math.min(startRow + (hostBufferBytes / rowBytes), size);
+      var endRow = Math.min(startRow + (hostBuffer.size() / rowBytes), size);
       var rowCount = endRow - startRow;
 
       //      System.out.printf(
@@ -123,7 +91,12 @@ private void populateBuffer(long startRow) {
 
       MemorySegment bufferTensor =
           prepareTensor(
-              localArena, hostBuffer, new long[] {rowCount, columns}, code(), bits(), kDLCPU());
+              localArena,
+              hostBuffer.address(),
+              new long[] {rowCount, columns},
+              code(),
+              bits(),
+              kDLCPU());
 
       try (var resourceAccess = resources.access()) {
         checkCuVSError(
@@ -146,7 +119,7 @@ public RowView getRow(long row) {
     var startRow = row - bufferedMatrixRowStart;
 
     return new SliceRowView(
-        hostBuffer.asSlice(startRow * columns * valueByteSize, columns * valueByteSize),
+        hostBuffer.address().asSlice(startRow * columns * valueByteSize, columns * valueByteSize),
         columns,
         valueLayout,
         dataType,
@@ -248,10 +221,7 @@ public void toDevice(CuVSDeviceMatrix targetMatrix, CuVSResources cuVSResources)
 
   @Override
   public void close() {
-    if (hostBuffer != MemorySegment.NULL) {
-      destroyPinnedBuffer(hostBuffer);
-      hostBuffer = MemorySegment.NULL;
-    }
+    hostBuffer.close();
   }
 
   private static class CuVSDeviceMatrixDelegate implements CuVSDeviceMatrix, CuVSMatrixInternal {
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/PinnedMemoryBuffer.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/PinnedMemoryBuffer.java
new file mode 100644
index 0000000000..23f4068bad
--- /dev/null
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/PinnedMemoryBuffer.java
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.nvidia.cuvs.internal.common;
+
+import static com.nvidia.cuvs.internal.common.LinkerHelper.C_POINTER;
+import static com.nvidia.cuvs.internal.common.Util.checkCudaError;
+import static com.nvidia.cuvs.internal.panama.headers_h.*;
+
+import java.lang.foreign.Arena;
+import java.lang.foreign.MemorySegment;
+import java.lang.foreign.ValueLayout;
+
+public class PinnedMemoryBuffer implements AutoCloseable {
+
+  private static final int CHUNK_BYTES =
+      8 * 1024 * 1024; // Based on benchmarks, 8MB seems the minimum size to optimize PCIe bandwidth
+  private final long hostBufferBytes;
+
+  private MemorySegment hostBuffer = MemorySegment.NULL;
+
+  public PinnedMemoryBuffer(long rows, long columns, ValueLayout valueLayout) {
+
+    long rowBytes = columns * valueLayout.byteSize();
+    long matrixBytes = rows * rowBytes;
+    if (matrixBytes < CHUNK_BYTES) {
+      this.hostBufferBytes = matrixBytes;
+    } else if (rowBytes > CHUNK_BYTES) {
+      // We need to buffer at least one row at time
+      this.hostBufferBytes = rowBytes;
+    } else {
+      var rowCount = (CHUNK_BYTES / rowBytes);
+      this.hostBufferBytes = rowBytes * rowCount;
+    }
+  }
+
+  private static MemorySegment createPinnedBuffer(long bufferBytes) {
+    try (var localArena = Arena.ofConfined()) {
+      MemorySegment pointer = localArena.allocate(C_POINTER);
+      checkCudaError(cudaMallocHost(pointer, bufferBytes), "cudaMallocHost");
+      return pointer.get(C_POINTER, 0);
+    }
+  }
+
+  private static void destroyPinnedBuffer(MemorySegment bufferSegment) {
+    checkCudaError(cudaFreeHost(bufferSegment), "cudaFreeHost");
+  }
+
+  public MemorySegment address() {
+    if (hostBuffer == MemorySegment.NULL) {
+      hostBuffer = createPinnedBuffer(hostBufferBytes);
+    }
+    return hostBuffer;
+  }
+
+  public long size() {
+    return hostBufferBytes;
+  }
+
+  @Override
+  public void close() {
+    if (hostBuffer != MemorySegment.NULL) {
+      destroyPinnedBuffer(hostBuffer);
+      hostBuffer = MemorySegment.NULL;
+    }
+  }
+}
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index 6f3d3fa6e3..ed8af603e0 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -18,9 +18,11 @@
 import static com.nvidia.cuvs.internal.common.Util.*;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsVersionGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.uint16_t;
+import static com.nvidia.cuvs.internal.panama.headers_h_1.cudaStreamSynchronize;
 
 import com.nvidia.cuvs.*;
 import com.nvidia.cuvs.internal.*;
+import com.nvidia.cuvs.internal.common.PinnedMemoryBuffer;
 import com.nvidia.cuvs.internal.common.Util;
 import java.io.IOException;
 import java.lang.foreign.Arena;
@@ -216,7 +218,7 @@ public CuVSHostMatrix build() {
   public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType)
       throws UnsupportedOperationException {
-    return new HeapSegmentBuilder(resources, size, columns, dataType);
+    return new BufferedSegmentBuilder(resources, size, columns, dataType);
   }
 
   @Override
@@ -227,7 +229,7 @@ public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       int rowStride,
       int columnStride,
       CuVSMatrix.DataType dataType) {
-    return new HeapSegmentBuilder(resources, size, columns, rowStride, columnStride, dataType);
+    return new BufferedSegmentBuilder(resources, size, columns, rowStride, columnStride, dataType);
   }
 
   @Override
@@ -279,28 +281,38 @@ public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
 
   /**
    * This {@link CuVSDeviceMatrix} builder implementation returns a {@link CuVSDeviceMatrix} backed by managed RMM
-   * device memory. It uses a non-native {@link MemorySegment} created directly from on-heap java arrays to avoid
-   * an intermediate allocation and copy to a native (off-heap) segment.
-   * It requires the copy function ({@code cudaMemcpyAsync}) to have the {@code Critical} linker option in order
-   * to allow the access to on-heap memory (see {@link Util#cudaMemcpyAsync}).
+   * device memory. It uses a {@link PinnedMemoryBuffer} to batch data before copying it to the GPU.
    */
-  private static class HeapSegmentBuilder implements CuVSMatrix.Builder<CuVSDeviceMatrix> {
+  private static class BufferedSegmentBuilder implements CuVSMatrix.Builder<CuVSDeviceMatrix> {
+
     private final long columns;
     private final long size;
     private final CuVSDeviceMatrixImpl matrix;
     private final MemorySegment stream;
-    private int current;
 
-    private HeapSegmentBuilder(
+    private final long rowBytes;
+    private int currentRow;
+
+    private final PinnedMemoryBuffer hostBuffer;
+    private final long bufferRowCount;
+    private int currentBufferRow;
+
+    private BufferedSegmentBuilder(
         CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType) {
       this.columns = columns;
       this.size = size;
       this.matrix = CuVSDeviceMatrixRMMImpl.create(resources, size, columns, dataType);
       this.stream = Util.getStream(resources);
-      this.current = 0;
+      this.currentRow = 0;
+
+      this.hostBuffer = new PinnedMemoryBuffer(size, columns, matrix.valueLayout());
+
+      this.rowBytes = columns * matrix.valueLayout().byteSize();
+      this.bufferRowCount = Math.min((hostBuffer.size() / rowBytes), size);
+      this.currentBufferRow = 0;
     }
 
-    private HeapSegmentBuilder(
+    private BufferedSegmentBuilder(
         CuVSResources resources,
         long size,
         long columns,
@@ -313,7 +325,13 @@ private HeapSegmentBuilder(
           CuVSDeviceMatrixRMMImpl.create(
               resources, size, columns, rowStride, columnStride, dataType);
       this.stream = Util.getStream(resources);
-      this.current = 0;
+      this.currentRow = 0;
+
+      this.hostBuffer = new PinnedMemoryBuffer(size, columns, matrix.valueLayout());
+
+      this.rowBytes = columns * matrix.valueLayout().byteSize();
+      this.bufferRowCount = Math.min((hostBuffer.size() / rowBytes), size);
+      this.currentBufferRow = 0;
     }
 
     @Override
@@ -347,19 +365,38 @@ public void addVector(int[] vector) {
     }
 
     private void internalAddVector(MemorySegment vector) {
-      if (current >= size) {
+      if (currentRow >= size) {
         throw new ArrayIndexOutOfBoundsException();
       }
+      var hostBufferOffset = currentBufferRow * rowBytes;
+      MemorySegment.copy(vector, 0, hostBuffer.address(), hostBufferOffset, rowBytes);
 
-      long rowBytes = columns * matrix.valueLayout().byteSize();
+      currentRow++;
+      currentBufferRow++;
+      if (currentBufferRow == bufferRowCount) {
+        flushBuffer();
+      }
+    }
 
-      var dstOffset = ((current++) * rowBytes);
-      var dst = matrix.memorySegment().asSlice(dstOffset);
-      cudaMemcpyAsync(dst, vector, rowBytes, CudaMemcpyKind.HOST_TO_DEVICE, stream);
+    private void flushBuffer() {
+      if (currentBufferRow > 0) {
+        var deviceMemoryOffset = (currentRow - currentBufferRow) * rowBytes;
+        var dst = matrix.memorySegment().asSlice(deviceMemoryOffset);
+        cudaMemcpyAsync(
+            dst,
+            hostBuffer.address(),
+            currentBufferRow * rowBytes,
+            CudaMemcpyKind.HOST_TO_DEVICE,
+            stream);
+        currentBufferRow = 0;
+        checkCudaError(cudaStreamSynchronize(stream), "cudaStreamSynchronize");
+      }
     }
 
     @Override
     public CuVSDeviceMatrix build() {
+      flushBuffer();
+      hostBuffer.close();
       return matrix;
     }
   }

From e32b14dbb9ff3f414998dfcbd56c5aff02df72ed Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Thu, 25 Sep 2025 16:42:12 -0700
Subject: [PATCH 598/623] [DOCS] Update IVF Docs with Supported Distance
 Metrics (#1365)

Fixes #1355

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1365
---
 cpp/include/cuvs/neighbors/ivf_pq.hpp        | 50 +++++++++++++++++++-
 python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx |  5 +-
 2 files changed, 53 insertions(+), 2 deletions(-)

diff --git a/cpp/include/cuvs/neighbors/ivf_pq.hpp b/cpp/include/cuvs/neighbors/ivf_pq.hpp
index 2db1f33eb2..593403f2aa 100644
--- a/cpp/include/cuvs/neighbors/ivf_pq.hpp
+++ b/cpp/include/cuvs/neighbors/ivf_pq.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -570,6 +570,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -617,6 +623,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -663,6 +675,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -710,6 +728,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -763,6 +787,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
@@ -824,6 +854,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Usage example:
  * @code{.cpp}
  *   using namespace cuvs::neighbors;
@@ -871,6 +907,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
@@ -932,6 +974,12 @@ auto build(raft::resources const& handle,
 /**
  * @brief Build the index from the dataset for efficient search.
  *
+ * NB: Currently, the following distance metrics are supported:
+ * - L2Expanded
+ * - L2Unexpanded
+ * - InnerProduct
+ * - CosineExpanded
+ *
  * Note, if index_params.add_data_on_build is set to true, the user can set a
  * stream pool in the input raft::resource with at least one stream to enable kernel and copy
  * overlapping.
diff --git a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
index dd3b17f949..5e13b3bf3b 100644
--- a/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
+++ b/python/cuvs/cuvs/neighbors/ivf_pq/ivf_pq.pyx
@@ -58,7 +58,8 @@ cdef class IndexParams:
         The number of clusters used in the coarse quantizer.
     metric : str, default="sqeuclidean"
         String denoting the metric type.
-        Valid values for metric: ["sqeuclidean", "inner_product", "euclidean"],
+        Valid values for metric: ["sqeuclidean", "inner_product",
+        "euclidean", "cosine"],
         where:
 
             - sqeuclidean is the euclidean distance without the square root
@@ -66,6 +67,8 @@ cdef class IndexParams:
             - euclidean is the euclidean distance
             - inner product distance is defined as
               distance(a, b) = \\sum_i a_i * b_i.
+            - cosine distance is defined as
+              distance(a, b) = 1 - \\sum_i a_i * b_i / ( ||a||_2 * ||b||_2).
 
     kmeans_n_iters : int, default = 20
         The number of iterations searching for kmeans centers during index

From ee1ad84baf748c8e39fffc2a8f7a582d91df07ad Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Fri, 26 Sep 2025 10:50:23 +0200
Subject: [PATCH 599/623] Regroup overloads in docs (#1377)

This PR introduce a custom js script to regroup overloads into a single link.
Another alternative would be to have the brief of every function in there instead of grouping them, so that it could look like
```
- fit(float, int)
- fit(double, int)
- fit(float, int64)
- ...
```
Before:
<img width="273" height="832" alt="grafik" src="https://github.com/user-attachments/assets/e088e597-e442-4338-b2ee-1bd26e4300a6" />

After:
<img width="296" height="295" alt="grafik" src="https://github.com/user-attachments/assets/63279466-1245-43fe-b873-297edb697128" />

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1377
---
 .../cuvs/preprocessing/quantize/binary.h      |  2 +-
 docs/source/_static/collapse_overloads.js     | 20 +++++++++++++++++++
 docs/source/conf.py                           |  1 +
 docs/source/cpp_api/cluster_agglomerative.rst |  4 ++--
 docs/source/cpp_api/cluster_kmeans.rst        |  2 +-
 5 files changed, 25 insertions(+), 4 deletions(-)
 create mode 100644 docs/source/_static/collapse_overloads.js

diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.h b/cpp/include/cuvs/preprocessing/quantize/binary.h
index 9a5834ef3e..7c6e522c85 100644
--- a/cpp/include/cuvs/preprocessing/quantize/binary.h
+++ b/cpp/include/cuvs/preprocessing/quantize/binary.h
@@ -95,7 +95,7 @@ cuvsError_t cuvsBinaryQuantizerCreate(cuvsBinaryQuantizer_t* quantizer);
 cuvsError_t cuvsBinaryQuantizerDestroy(cuvsBinaryQuantizer_t quantizer);
 
 /**
- * @brief Trains a scalar quantizer to be used later for quantizing the dataset.
+ * @brief Trains a binary quantizer to be used later for quantizing the dataset.
  *
  * @param[in] res raft resource
  * @param[in] params configure binary quantizer, e.g. threshold
diff --git a/docs/source/_static/collapse_overloads.js b/docs/source/_static/collapse_overloads.js
new file mode 100644
index 0000000000..ea8135f407
--- /dev/null
+++ b/docs/source/_static/collapse_overloads.js
@@ -0,0 +1,20 @@
+document.addEventListener("DOMContentLoaded", () => {
+    const toc = document.querySelector(".bd-toc-nav");
+    if (!toc) return;
+
+    // Get all TOC links
+    const links = toc.querySelectorAll("a");
+
+    const seen = new Set();
+    links.forEach(link => {
+      let text = link.textContent.trim();
+      let norm = text.replace(/\(\)$/, ""); // strip trailing ()
+
+      if (seen.has(norm)) {
+        // hide duplicate
+        link.parentElement.style.display = "none";
+      } else {
+        seen.add(norm);
+      }
+    });
+  });
diff --git a/docs/source/conf.py b/docs/source/conf.py
index a7247ed719..b5404edd22 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -192,6 +192,7 @@ def setup(app):
     app.add_js_file(
         "https://docs.rapids.ai/assets/js/custom.js", loading_method="defer"
     )
+    app.add_js_file("collapse_overloads.js")
 
 
 # The following is used by sphinx.ext.linkcode to provide links to github
diff --git a/docs/source/cpp_api/cluster_agglomerative.rst b/docs/source/cpp_api/cluster_agglomerative.rst
index 08a811780a..e4bdf0c020 100644
--- a/docs/source/cpp_api/cluster_agglomerative.rst
+++ b/docs/source/cpp_api/cluster_agglomerative.rst
@@ -1,5 +1,5 @@
-Cluster
-=======
+Agglomerative
+=============
 
 .. role:: py(code)
    :language: c++
diff --git a/docs/source/cpp_api/cluster_kmeans.rst b/docs/source/cpp_api/cluster_kmeans.rst
index 6c342baabd..bae14dcb9d 100644
--- a/docs/source/cpp_api/cluster_kmeans.rst
+++ b/docs/source/cpp_api/cluster_kmeans.rst
@@ -1,4 +1,4 @@
-Cluster
+K-Means
 =======
 
 .. role:: py(code)

From f4831b2cec4605a50349323d9ed951209ef3de0b Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Fri, 26 Sep 2025 08:57:54 -0400
Subject: [PATCH 600/623] Update `RAPIDS_BRANCH`, codify changes in
 `update-version.sh` (#1368)

This PR updates `RAPIDS_BRANCH` and modifies `update-version.sh` to make future changes automatically.

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Gil Forsyth (https://github.com/gforsyth)

URL: https://github.com/rapidsai/cuvs/pull/1368
---
 ci/release/update-version.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
index 29cfe0e1a3..b9e701267d 100755
--- a/ci/release/update-version.sh
+++ b/ci/release/update-version.sh
@@ -36,6 +36,7 @@ function sed_runner() {
 
 # Centralized version file update
 echo "${NEXT_FULL_TAG}" > VERSION
+echo "branch-${NEXT_SHORT_TAG}" > RAPIDS_BRANCH
 
 DEPENDENCIES=(
   dask-cuda

From f328ff1e27b2ad65f53c34bf1bff8db6e38b95df Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Fri, 26 Sep 2025 16:15:37 +0200
Subject: [PATCH 601/623] [Review][Java] Expand `CagraIndex#search` to accept
 more types (int8 and int32) (#1283)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

With the introduction of `CuVSMatrix` as the type to provide an input dataset, we expanded the types that can be used in indexing.
With this PR we aim to do the same query/search:
- Use a `CuVSMatrix` for `CagraQuery` too
- Use the correct types for sizes in `CagraIndex#search`

There is more we can do there, e.g. use `CuVSDeviceMatrix` for storing the results so we don't have to explicitly copy them (all duplicated code). That would be follow-up work.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Chris Hegarty (https://github.com/ChrisHegarty)

URL: https://github.com/rapidsai/cuvs/pull/1283
---
 .../main/java/com/nvidia/cuvs/CagraQuery.java |  21 +-
 .../nvidia/cuvs/internal/CagraIndexImpl.java  |  89 +++----
 .../cuvs/internal/CuVSDeviceMatrixImpl.java   |  10 -
 .../cuvs/internal/CuVSMatrixInternal.java     |   6 +-
 .../com/nvidia/cuvs/internal/common/Util.java |  14 +-
 .../nvidia/cuvs/CagraBuildAndSearchIT.java    | 218 +++++++++---------
 .../cuvs/CagraMultiThreadStabilityIT.java     |  31 +--
 .../com/nvidia/cuvs/CagraRandomizedIT.java    |   4 +-
 .../com/nvidia/cuvs/CheckedCuVSResources.java |  13 +-
 .../java/com/nvidia/cuvs/CuVSResourcesIT.java |   2 +-
 10 files changed, 216 insertions(+), 192 deletions(-)

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
index f81ec3191c..78f4de759e 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraQuery.java
@@ -15,7 +15,6 @@
  */
 package com.nvidia.cuvs;
 
-import java.util.Arrays;
 import java.util.BitSet;
 import java.util.Objects;
 import java.util.function.LongToIntFunction;
@@ -34,7 +33,7 @@ public class CagraQuery {
 
   private final CagraSearchParams cagraSearchParameters;
   private final LongToIntFunction mapping;
-  private final float[][] queryVectors;
+  private final CuVSMatrix queryVectors;
   private final int topK;
   private final BitSet prefilter;
   private final int numDocs;
@@ -53,9 +52,9 @@ public class CagraQuery {
    * @param numDocs               Total number of dataset vectors; used to align the prefilter correctly
    * @param resources             CuVSResources instance to use for this query
    */
-  public CagraQuery(
+  private CagraQuery(
       CagraSearchParams cagraSearchParameters,
-      float[][] queryVectors,
+      CuVSMatrix queryVectors,
       LongToIntFunction mapping,
       int topK,
       BitSet prefilter,
@@ -81,11 +80,9 @@ public CagraSearchParams getCagraSearchParameters() {
   }
 
   /**
-   * Gets the query vector 2D float array.
-   *
-   * @return 2D float array
+   * Gets the query vector matrix.
    */
-  public float[][] getQueryVectors() {
+  public CuVSMatrix getQueryVectors() {
     return queryVectors;
   }
 
@@ -137,7 +134,7 @@ public String toString() {
     return "CuVSQuery [cagraSearchParameters="
         + cagraSearchParameters
         + ", queryVectors="
-        + Arrays.toString(queryVectors)
+        + queryVectors.toString()
         + ", mapping="
         + mapping
         + ", topK="
@@ -151,7 +148,7 @@ public String toString() {
   public static class Builder {
 
     private CagraSearchParams cagraSearchParams;
-    private float[][] queryVectors;
+    private CuVSMatrix queryVectors;
     private LongToIntFunction mapping = SearchResults.IDENTITY_MAPPING;
     private int topK = 2;
     private BitSet prefilter;
@@ -186,10 +183,10 @@ public Builder withSearchParams(CagraSearchParams cagraSearchParams) {
     /**
      * Registers the query vectors to be passed in the search call.
      *
-     * @param queryVectors 2D float query vector array
+     * @param queryVectors 2D query vector array
      * @return an instance of this Builder
      */
-    public Builder withQueryVectors(float[][] queryVectors) {
+    public Builder withQueryVectors(CuVSMatrix queryVectors) {
       this.queryVectors = queryVectors;
       return this;
     }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
index 4045d8b83d..a9ac722e17 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CagraIndexImpl.java
@@ -17,16 +17,13 @@
 
 import static com.nvidia.cuvs.internal.CuVSParamsHelper.*;
 import static com.nvidia.cuvs.internal.common.CloseableRMMAllocation.allocateRMMSegment;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT;
-import static com.nvidia.cuvs.internal.common.LinkerHelper.C_FLOAT_BYTE_SIZE;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT;
 import static com.nvidia.cuvs.internal.common.LinkerHelper.C_INT_BYTE_SIZE;
+import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.DEVICE_TO_HOST;
 import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.HOST_TO_DEVICE;
-import static com.nvidia.cuvs.internal.common.Util.CudaMemcpyKind.INFER_DIRECTION;
 import static com.nvidia.cuvs.internal.common.Util.buildMemorySegment;
 import static com.nvidia.cuvs.internal.common.Util.checkCuVSError;
 import static com.nvidia.cuvs.internal.common.Util.concatenate;
-import static com.nvidia.cuvs.internal.common.Util.cudaMemcpy;
 import static com.nvidia.cuvs.internal.common.Util.prepareTensor;
 import static com.nvidia.cuvs.internal.panama.headers_h.*;
 
@@ -35,6 +32,7 @@
 import com.nvidia.cuvs.internal.common.CloseableHandle;
 import com.nvidia.cuvs.internal.common.CloseableRMMAllocation;
 import com.nvidia.cuvs.internal.common.CompositeCloseableHandle;
+import com.nvidia.cuvs.internal.common.Util;
 import com.nvidia.cuvs.internal.panama.*;
 import java.io.FileInputStream;
 import java.io.InputStream;
@@ -232,19 +230,18 @@ public SearchResults search(CagraQuery query) throws Throwable {
     try (var localArena = Arena.ofConfined()) {
       checkNotDestroyed();
       int topK = query.getTopK();
-      long numQueries = query.getQueryVectors().length;
+      var queryVectors = (CuVSMatrixInternal) query.getQueryVectors();
+      long numQueries = queryVectors.size();
       long numBlocks = topK * numQueries;
-      int vectorDimension = numQueries > 0 ? query.getQueryVectors()[0].length : 0;
 
       SequenceLayout neighborsSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_INT);
-      SequenceLayout distancesSequenceLayout = MemoryLayout.sequenceLayout(numBlocks, C_FLOAT);
+      SequenceLayout distancesSequenceLayout =
+          MemoryLayout.sequenceLayout(numBlocks, queryVectors.valueLayout());
       MemorySegment neighborsMemorySegment = localArena.allocate(neighborsSequenceLayout);
       MemorySegment distancesMemorySegment = localArena.allocate(distancesSequenceLayout);
-      MemorySegment floatsSeg = buildMemorySegment(localArena, query.getQueryVectors());
 
-      final long queriesBytes = C_FLOAT_BYTE_SIZE * numQueries * vectorDimension;
       final long neighborsBytes = C_INT_BYTE_SIZE * numQueries * topK;
-      final long distancesBytes = C_FLOAT_BYTE_SIZE * numQueries * topK;
+      final long distancesBytes = queryVectors.valueLayout().byteSize() * numQueries * topK;
       final boolean hasPreFilter = query.getPrefilter() != null;
       final BitSet[] prefilters =
           hasPreFilter ? new BitSet[] {query.getPrefilter()} : EMPTY_PREFILTER_BITSET;
@@ -254,8 +251,10 @@ public SearchResults search(CagraQuery query) throws Throwable {
 
       try (var resourcesAccessor = query.getResources().access()) {
         var cuvsRes = resourcesAccessor.handle();
+        var cuvsStream = Util.getStream(cuvsRes);
 
-        try (var queriesDP = allocateRMMSegment(cuvsRes, queriesBytes);
+        try (var deviceQueryVectors =
+                (CuVSMatrixInternal) queryVectors.toDevice(query.getResources());
             var neighborsDP = allocateRMMSegment(cuvsRes, neighborsBytes);
             var distancesDP = allocateRMMSegment(cuvsRes, distancesBytes);
             var prefilterDP =
@@ -263,12 +262,7 @@ public SearchResults search(CagraQuery query) throws Throwable {
                     ? allocateRMMSegment(cuvsRes, prefilterBytes)
                     : CloseableRMMAllocation.EMPTY) {
 
-          cudaMemcpy(queriesDP.handle(), floatsSeg, queriesBytes, INFER_DIRECTION);
-
-          long[] queriesShape = {numQueries, vectorDimension};
-          MemorySegment queriesTensor =
-              prepareTensor(
-                  localArena, queriesDP.handle(), queriesShape, kDLFloat(), 32, kDLCUDA());
+          var queryTensor = deviceQueryVectors.toTensor(localArena);
           long[] neighborsShape = {numQueries, topK};
           MemorySegment neighborsTensor =
               prepareTensor(
@@ -276,32 +270,34 @@ public SearchResults search(CagraQuery query) throws Throwable {
           long[] distancesShape = {numQueries, topK};
           MemorySegment distancesTensor =
               prepareTensor(
-                  localArena, distancesDP.handle(), distancesShape, kDLFloat(), 32, kDLCUDA());
-
-          var returnValue = cuvsStreamSync(cuvsRes);
-          checkCuVSError(returnValue, "cuvsStreamSync");
+                  localArena,
+                  distancesDP.handle(),
+                  distancesShape,
+                  deviceQueryVectors.code(),
+                  deviceQueryVectors.bits(),
+                  kDLCUDA());
 
           // prepare the prefiltering data
-          MemorySegment prefilterDataMemorySegment = MemorySegment.NULL;
-          if (hasPreFilter) {
-            BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
-            long[] filters = concatenatedFilters.toLongArray();
-            prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
-          }
-
           MemorySegment prefilter = cuvsFilter.allocate(localArena);
-          MemorySegment prefilterTensor;
 
           if (!hasPreFilter) {
             cuvsFilter.type(prefilter, 0); // NO_FILTER
             cuvsFilter.addr(prefilter, 0);
           } else {
+            BitSet concatenatedFilters = concatenate(prefilters, query.getNumDocs());
+            long[] filters = concatenatedFilters.toLongArray();
+            var prefilterDataMemorySegment = buildMemorySegment(localArena, filters);
+
             long[] prefilterShape = {prefilterLen};
 
-            cudaMemcpy(
-                prefilterDP.handle(), prefilterDataMemorySegment, prefilterBytes, HOST_TO_DEVICE);
+            Util.cudaMemcpyAsync(
+                prefilterDP.handle(),
+                prefilterDataMemorySegment,
+                prefilterBytes,
+                HOST_TO_DEVICE,
+                cuvsStream);
 
-            prefilterTensor =
+            MemorySegment prefilterTensor =
                 prepareTensor(
                     localArena, prefilterDP.handle(), prefilterShape, kDLUInt(), 32, kDLCUDA());
 
@@ -309,25 +305,36 @@ public SearchResults search(CagraQuery query) throws Throwable {
             cuvsFilter.addr(prefilter, prefilterTensor.address());
           }
 
-          returnValue = cuvsStreamSync(cuvsRes);
-          checkCuVSError(returnValue, "cuvsStreamSync");
+          // TODO: do we need this stream sync here?
+          checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
 
-          returnValue =
+          var returnValue =
               cuvsCagraSearch(
                   cuvsRes,
                   segmentFromSearchParams(localArena, query.getCagraSearchParameters()),
                   cagraIndexReference.getMemorySegment(),
-                  queriesTensor,
+                  queryTensor,
                   neighborsTensor,
                   distancesTensor,
                   prefilter);
           checkCuVSError(returnValue, "cuvsCagraSearch");
 
-          returnValue = cuvsStreamSync(cuvsRes);
-          checkCuVSError(returnValue, "cuvsStreamSync");
-
-          cudaMemcpy(neighborsMemorySegment, neighborsDP.handle(), neighborsBytes, INFER_DIRECTION);
-          cudaMemcpy(distancesMemorySegment, distancesDP.handle(), distancesBytes, INFER_DIRECTION);
+          // TODO: we can avoid/defer this using CuVSDeviceMatrix for neighborsDP and distancesDP
+          // TODO: also, should we use cuvsMatrixCopy instead?
+          Util.cudaMemcpyAsync(
+              neighborsMemorySegment,
+              neighborsDP.handle(),
+              neighborsBytes,
+              DEVICE_TO_HOST,
+              cuvsStream);
+          Util.cudaMemcpyAsync(
+              distancesMemorySegment,
+              distancesDP.handle(),
+              distancesBytes,
+              DEVICE_TO_HOST,
+              cuvsStream);
+
+          checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
         }
       }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
index 2e72ea69f5..ef1b10941e 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
@@ -291,16 +291,6 @@ public ValueLayout valueLayout() {
       return deviceMatrix.valueLayout();
     }
 
-    @Override
-    public int bits() {
-      return deviceMatrix.bits();
-    }
-
-    @Override
-    public int code() {
-      return 0;
-    }
-
     @Override
     public MemorySegment toTensor(Arena arena) {
       return deviceMatrix.toTensor(arena);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
index ed53836719..f700811d4d 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
@@ -44,7 +44,11 @@ default int bits() {
    * DLTensor data type {@code code} for the element type of this matrix
    */
   default int code() {
-    return switch (dataType()) {
+    return code(dataType());
+  }
+
+  static int code(DataType dataType) {
+    return switch (dataType) {
       case FLOAT -> kDLFloat();
       case INT -> kDLInt();
       case UINT, BYTE -> kDLUInt();
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
index 1ffb5ad559..7510a846f6 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/common/Util.java
@@ -177,10 +177,18 @@ public static void cudaMemcpyAsync(
    * Helper to get the CUDA stream associated with a {@link CuVSResources}
    */
   public static MemorySegment getStream(CuVSResources resources) {
-    try (var resourcesAccess = resources.access();
-        var localArena = Arena.ofConfined()) {
+    try (var resourcesAccess = resources.access()) {
+      return getStream(resourcesAccess.handle());
+    }
+  }
+
+  /**
+   * Helper to get the CUDA stream associated with a {@link CuVSResources} handle
+   */
+  public static MemorySegment getStream(long resourcesHandle) {
+    try (var localArena = Arena.ofConfined()) {
       var streamPointer = localArena.allocate(cudaStream_t);
-      checkCuVSError(cuvsStreamGet(resourcesAccess.handle(), streamPointer), "cuvsStreamGet");
+      checkCuVSError(cuvsStreamGet(resourcesHandle, streamPointer), "cuvsStreamGet");
       return streamPointer.get(cudaStream_t, 0);
     }
   }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
index 45076e6482..c9de5f6eeb 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraBuildAndSearchIT.java
@@ -23,7 +23,6 @@
 import com.nvidia.cuvs.CagraIndexParams.CagraGraphBuildAlgo;
 import com.nvidia.cuvs.CagraIndexParams.CuvsDistanceType;
 import com.nvidia.cuvs.CagraMergeParams.MergeStrategy;
-import java.io.InputStream;
 import java.lang.foreign.Arena;
 import java.lang.foreign.Linker;
 import java.lang.foreign.MemoryLayout;
@@ -404,36 +403,40 @@ public void testPrefilteringReducesResults() throws Throwable {
 
       // No prefilter (all points allowed)
       CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-      CagraQuery fullQuery =
-          new CagraQuery.Builder(resources)
-              .withTopK(2)
-              .withSearchParams(searchParams)
-              .withQueryVectors(queries)
-              .build();
 
-      SearchResults fullSearchResults = index.search(fullQuery);
-      List<Map<Integer, Float>> fullResults = fullSearchResults.getResults();
-      log.debug("Full results: {}", fullResults);
+      // No prefilter (all points allowed)
+      try (var queryVectors = CuVSMatrix.ofArray(queries)) {
+        CagraQuery fullQuery =
+            new CagraQuery.Builder(resources)
+                .withTopK(2)
+                .withSearchParams(searchParams)
+                .withQueryVectors(queryVectors)
+                .build();
+
+        SearchResults fullSearchResults = index.search(fullQuery);
+        List<Map<Integer, Float>> fullResults = fullSearchResults.getResults();
+        log.debug("Full results: {}", fullResults);
 
-      // Apply prefilter: only allow ids 0 and 2 (bitset: 1100)
-      BitSet prefilter = new BitSet(4);
-      prefilter.set(0);
-      prefilter.set(2);
+        // Apply prefilter: only allow ids 0 and 2 (bitset: 1100)
+        BitSet prefilter = new BitSet(4);
+        prefilter.set(0);
+        prefilter.set(2);
 
-      CagraQuery filteredQuery =
-          new CagraQuery.Builder(resources)
-              .withTopK(2)
-              .withSearchParams(searchParams)
-              .withQueryVectors(queries)
-              .withPrefilter(prefilter, 4)
-              .build();
+        CagraQuery filteredQuery =
+            new CagraQuery.Builder(resources)
+                .withTopK(2)
+                .withSearchParams(searchParams)
+                .withQueryVectors(queryVectors)
+                .withPrefilter(prefilter, 4)
+                .build();
 
-      SearchResults filteredSearchResults = index.search(filteredQuery);
-      List<Map<Integer, Float>> filteredResults = filteredSearchResults.getResults();
-      log.debug("Filtered results: {}", filteredResults);
+        SearchResults filteredSearchResults = index.search(filteredQuery);
+        List<Map<Integer, Float>> filteredResults = filteredSearchResults.getResults();
+        log.debug("Filtered results: {}", filteredResults);
 
-      assertEquals(expectedResults, fullResults);
-      assertEquals(expectedFilteredResults, filteredResults);
+        assertEquals(expectedResults, fullResults);
+        assertEquals(expectedFilteredResults, filteredResults);
+      }
     }
   }
 
@@ -483,27 +486,29 @@ private void queryAndCompare(
     CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
     // Create a query object with the query vectors
-    CagraQuery cuvsQuery =
-        new CagraQuery.Builder(resources)
-            .withTopK(3)
-            .withSearchParams(searchParams)
-            .withQueryVectors(queries)
-            .withMapping(mapping)
-            .build();
+    try (var queryVectors = CuVSMatrix.ofArray(queries)) {
+      CagraQuery cuvsQuery =
+          new CagraQuery.Builder(resources)
+              .withTopK(3)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queryVectors)
+              .withMapping(mapping)
+              .build();
 
-    // Perform the search
-    SearchResults results = index1.search(cuvsQuery);
+      // Perform the search
+      SearchResults results = index1.search(cuvsQuery);
 
-    // Check results
-    log.debug(results.getResults().toString());
-    checkResults(expectedResults, results.getResults());
+      // Check results
+      log.debug(results.getResults().toString());
+      checkResults(expectedResults, results.getResults());
 
-    // Search from the second index
-    results = index2.search(cuvsQuery);
+      // Search from the second index
+      results = index2.search(cuvsQuery);
 
-    // Check results
-    log.debug(results.getResults().toString());
-    checkResults(expectedResults, results.getResults());
+      // Check results
+      log.debug(results.getResults().toString());
+      checkResults(expectedResults, results.getResults());
+    }
   }
 
   private void cleanup(CagraIndex index, CagraIndex loadedIndex) throws Throwable {
@@ -643,39 +648,42 @@ public void testMergingIndexes() throws Throwable {
 
       CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
-      CagraQuery query =
-          new CagraQuery.Builder(resources)
-              .withTopK(3)
-              .withSearchParams(searchParams)
-              .withQueryVectors(queries)
-              .withMapping(SearchResults.IDENTITY_MAPPING)
-              .build();
+      try (var queryVectors = CuVSMatrix.ofArray(queries)) {
+        CagraQuery query =
+            new CagraQuery.Builder(resources)
+                .withTopK(3)
+                .withSearchParams(searchParams)
+                .withQueryVectors(queryVectors)
+                .withMapping(SearchResults.IDENTITY_MAPPING)
+                .build();
 
-      log.trace("Searching merged index...");
-      SearchResults results = mergedIndex.search(query);
-      log.debug("Search results: " + results.getResults().toString());
+        log.trace("Searching merged index...");
+        SearchResults results = mergedIndex.search(query);
+        log.debug("Search results: " + results.getResults().toString());
 
-      assertEquals(expectedResults, results.getResults());
+        assertEquals(expectedResults, results.getResults());
 
-      // --- Serialization/deserialization check ---
-      String indexFileName = UUID.randomUUID() + ".cag";
-      var indexFile = Path.of(indexFileName);
+        // --- Serialization/deserialization check ---
+        String indexFileName = UUID.randomUUID() + ".cag";
+        var indexFile = Path.of(indexFileName);
 
-      try (var out = Files.newOutputStream(indexFile)) {
-        mergedIndex.serialize(out);
-      }
+        try (var out = Files.newOutputStream(indexFile)) {
+          mergedIndex.serialize(out);
+        }
 
-      try (InputStream inputStream = Files.newInputStream(indexFile)) {
-        CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
+        try (var inputStream = Files.newInputStream(indexFile)) {
+          CagraIndex loadedMergedIndex = CagraIndex.newBuilder(resources).from(inputStream).build();
 
-        SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
-        assertEquals(expectedResults, resultsFromLoaded.getResults());
-        mergedIndex.close();
-        loadedMergedIndex.close();
+          SearchResults resultsFromLoaded = loadedMergedIndex.search(query);
+          assertEquals(expectedResults, resultsFromLoaded.getResults());
+          mergedIndex.close();
+          loadedMergedIndex.close();
+        } finally {
+          Files.deleteIfExists(indexFile);
+        }
+        index1.close();
+        index2.close();
       }
-      Files.deleteIfExists(indexFile);
-      index1.close();
-      index2.close();
     }
   }
 
@@ -750,45 +758,47 @@ public void testMergeStrategies() throws Throwable {
 
         CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
 
-        CagraQuery query =
-            new CagraQuery.Builder(resources)
-                .withTopK(3)
-                .withSearchParams(searchParams)
-                .withQueryVectors(queries)
-                .withMapping(SearchResults.IDENTITY_MAPPING)
-                .build();
-
-        log.trace("Searching physically merged index...");
-        SearchResults physicalResults = physicalMergedIndex.search(query);
-        assertNotNull("Physical merge search results should not be null", physicalResults);
-        assertEquals(
-            "Physical merge search results should match expected",
-            expectedResults,
-            physicalResults.getResults());
-
-        // --- Serialization/deserialization check for both merged indexes ---
-        String physicalIndexFileName = UUID.randomUUID() + ".cag";
-        var physicalIndexFile = Path.of(physicalIndexFileName);
-
-        try (var out = Files.newOutputStream(physicalIndexFile)) {
-          physicalMergedIndex.serialize(out);
-        }
-
-        try (InputStream physicalInputStream = Files.newInputStream(physicalIndexFile);
-            CagraIndex loadedPhysicalIndex =
-                CagraIndex.newBuilder(resources).from(physicalInputStream).build()) {
-
-          Files.deleteIfExists(physicalIndexFile);
-
-          SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
+        try (var queryVectors = CuVSMatrix.ofArray(queries)) {
+          CagraQuery query =
+              new CagraQuery.Builder(resources)
+                  .withTopK(3)
+                  .withSearchParams(searchParams)
+                  .withQueryVectors(queryVectors)
+                  .withMapping(SearchResults.IDENTITY_MAPPING)
+                  .build();
+
+          log.trace("Searching physically merged index...");
+          SearchResults physicalResults = physicalMergedIndex.search(query);
+          assertNotNull("Physical merge search results should not be null", physicalResults);
           assertEquals(
-              "Loaded physical index search results should match expected",
+              "Physical merge search results should match expected",
               expectedResults,
-              resultsFromLoadedPhysical.getResults());
+              physicalResults.getResults());
+
+          // --- Serialization/deserialization check for both merged indexes ---
+          String physicalIndexFileName = UUID.randomUUID() + ".cag";
+          var physicalIndexFile = Path.of(physicalIndexFileName);
+
+          try (var out = Files.newOutputStream(physicalIndexFile)) {
+            physicalMergedIndex.serialize(out);
+          }
+
+          try (var physicalInputStream = Files.newInputStream(physicalIndexFile);
+              CagraIndex loadedPhysicalIndex =
+                  CagraIndex.newBuilder(resources).from(physicalInputStream).build()) {
+
+            SearchResults resultsFromLoadedPhysical = loadedPhysicalIndex.search(query);
+            assertEquals(
+                "Loaded physical index search results should match expected",
+                expectedResults,
+                resultsFromLoadedPhysical.getResults());
+          } finally {
+            Files.deleteIfExists(physicalIndexFile);
+          }
         }
+        index1.close();
+        index2.close();
       }
-      index1.close();
-      index2.close();
     }
   }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
index 32f3d86fe0..1452411b72 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraMultiThreadStabilityIT.java
@@ -186,13 +186,14 @@ private void testQueryingUsingMultipleThreads(QueryAction queryAction) throws Th
   private void performQueryWithPrivateResource(CagraIndex index) throws Throwable {
     float[][] queries = generateRandomDataset(queryBatchSize);
 
-    try (CuVSResources threadResources = CheckedCuVSResources.create()) {
-      CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+    CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
+    try (CuVSResources threadResources = CheckedCuVSResources.create();
+        var queryVectors = CuVSMatrix.ofArray(queries)) {
       CagraQuery query =
           new CagraQuery.Builder(threadResources)
               .withTopK(topK)
               .withSearchParams(searchParams)
-              .withQueryVectors(queries)
+              .withQueryVectors(queryVectors)
               .build();
 
       // This call should now work with per-thread resources
@@ -207,17 +208,19 @@ private void performQueryWithSharedSynchronizedResource(
     float[][] queries = generateRandomDataset(queryBatchSize);
 
     CagraSearchParams searchParams = new CagraSearchParams.Builder().build();
-    CagraQuery query =
-        new CagraQuery.Builder(threadResources)
-            .withTopK(topK)
-            .withSearchParams(searchParams)
-            .withQueryVectors(queries)
-            .build();
-
-    // This call should now work with per-thread resources
-    SearchResults results = index.search(query);
-    assertNotNull("Query should return results", results);
-    assertFalse("Query should return some results", results.getResults().isEmpty());
+    try (var queryVectors = CuVSMatrix.ofArray(queries)) {
+      CagraQuery query =
+          new CagraQuery.Builder(threadResources)
+              .withTopK(topK)
+              .withSearchParams(searchParams)
+              .withQueryVectors(queryVectors)
+              .build();
+
+      // This call should now work with per-thread resources
+      SearchResults results = index.search(query);
+      assertNotNull("Query should return results", results);
+      assertFalse("Query should return some results", results.getResults().isEmpty());
+    }
   }
 
   private float[][] generateRandomDataset(int size) {
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
index d7d41c8c2f..b18b03adbc 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CagraRandomizedIT.java
@@ -162,11 +162,11 @@ private void tmpResultsTopKWithRandomValues(TestDatasetMemoryKind datasetMemoryK
       }
       log.trace("Index built successfully.");
 
-      try {
+      try (var queryVectors = CuVSMatrix.ofArray(queries)) {
         // Execute search and retrieve results
         CagraQuery.Builder queryBuilder =
             new CagraQuery.Builder(resources)
-                .withQueryVectors(queries)
+                .withQueryVectors(queryVectors)
                 .withTopK(topK)
                 .withSearchParams(new CagraSearchParams.Builder().build());
 
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
index 581e29973c..8b10c7f9b0 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CheckedCuVSResources.java
@@ -42,10 +42,15 @@ private void checkNotDestroyed() {
   @Override
   public ScopedAccess access() {
     checkNotDestroyed();
-    var previousThreadId = currentThreadId.compareAndExchange(0, Thread.currentThread().threadId());
-    if (previousThreadId != 0) {
+    var currentThreadId = Thread.currentThread().threadId();
+    var previousThreadId = this.currentThreadId.compareAndExchange(0, currentThreadId);
+    if (previousThreadId != 0 && previousThreadId != currentThreadId) {
       throw new IllegalStateException(
-          "This resource is already accessed by thread [" + previousThreadId + "]");
+          "This resource is already accessed by thread ["
+              + previousThreadId
+              + "]. Current thread id: ["
+              + currentThreadId
+              + "]");
     }
     return new ScopedAccess() {
       @Override
@@ -56,7 +61,7 @@ public long handle() {
 
       @Override
       public void close() {
-        currentThreadId.set(0);
+        CheckedCuVSResources.this.currentThreadId.set(0);
       }
     };
   }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
index bc012f6a0e..633ecd52b1 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSResourcesIT.java
@@ -65,7 +65,7 @@ public void testConcurrentAccessViaCheckedCuVSResourcesIsForbidden() throws Thro
                   future.get();
                 });
         assertEquals(IllegalStateException.class, exception.getCause().getClass());
-        assertEquals(expectedError, exception.getCause().getMessage());
+        assertTrue(exception.getCause().getMessage().startsWith(expectedError));
         log.debug("Outer access finished");
       }
     }

From 90c812d00b6aa316ce45486ac6ea160c676304b4 Mon Sep 17 00:00:00 2001
From: Kyle Edwards <kyedwards@nvidia.com>
Date: Fri, 26 Sep 2025 16:58:07 -0400
Subject: [PATCH 602/623] Update `cpp/cmake/config.json` for `ConfigureTest()`
 (#1385)

Authors:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

Approvers:
  - Robert Maynard (https://github.com/robertmaynard)

URL: https://github.com/rapidsai/cuvs/pull/1385
---
 cpp/cmake/config.json    |  19 +--
 cpp/tests/CMakeLists.txt | 289 +++++++++++++++++++++++----------------
 2 files changed, 177 insertions(+), 131 deletions(-)

diff --git a/cpp/cmake/config.json b/cpp/cmake/config.json
index 3c568d9766..a10c274cb1 100644
--- a/cpp/cmake/config.json
+++ b/cpp/cmake/config.json
@@ -1,22 +1,15 @@
 {
   "parse": {
     "additional_commands": {
-      "CPMFindPackage": {
+      "ConfigureTest": {
+        "flags": ["NOCUDA", "C_LIB", "FETCH_CODEBOOKS"],
         "kwargs": {
           "NAME": 1,
-          "GITHUB_REPOSITORY": "?",
-          "GIT_TAG": "?",
-          "VERSION": "?",
-          "GIT_SHALLOW": "?",
-          "OPTIONS": "*",
-          "FIND_PACKAGE_ARGUMENTS": "*"
+          "GPUS": "?",
+          "PERCENT": "?",
+          "ADDITIONAL_DEP": "?",
+          "PATH": "*"
         }
-      },
-      "ConfigureTest": {
-        "flags": ["TEST_NAME", "TEST_SRC"]
-      },
-      "ConfigureBench": {
-        "flags": ["BENCH_NAME", "BENCH_SRC"]
       }
     }
   },
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index 33f9a12193..3921bae864 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -21,9 +21,9 @@ rapids_test_init()
 
 function(ConfigureTest)
 
-  set(options OPTIONAL NOCUDA C_LIB FETCH_CODEBOOKS)
+  set(options NOCUDA C_LIB FETCH_CODEBOOKS)
   set(oneValueArgs NAME GPUS PERCENT ADDITIONAL_DEP)
-  set(multiValueArgs PATH TARGETS CONFIGURATIONS)
+  set(multiValueArgs PATH)
 
   cmake_parse_arguments(_CUVS_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
   if(NOT DEFINED _CUVS_TEST_GPUS AND NOT DEFINED _CUVS_TEST_PERCENT)
@@ -109,221 +109,274 @@ endfunction()
 
 if(BUILD_TESTS)
   ConfigureTest(
-    NAME NEIGHBORS_TEST PATH neighbors/brute_force.cu neighbors/brute_force_prefiltered.cu
-    neighbors/sparse_brute_force.cu neighbors/refine.cu GPUS 1 PERCENT 100
+    NAME NEIGHBORS_TEST
+    PATH neighbors/brute_force.cu neighbors/brute_force_prefiltered.cu
+         neighbors/sparse_brute_force.cu neighbors/refine.cu
+    GPUS 1
+    PERCENT 100
   )
 
-  ConfigureTest(NAME NEIGHBORS_TIERED_INDEX_TEST PATH neighbors/tiered_index.cu GPUS 1 PERCENT 100)
+  ConfigureTest(
+    NAME NEIGHBORS_TIERED_INDEX_TEST
+    PATH neighbors/tiered_index.cu
+    GPUS 1
+    PERCENT 100
+  )
 
   ConfigureTest(
-    NAME CLUSTER_TEST PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu
-    cluster/linkage.cu cluster/connect_knn.cu GPUS 1 PERCENT 100
+    NAME CLUSTER_TEST
+    PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu cluster/linkage.cu
+         cluster/connect_knn.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME
-    NEIGHBORS_ANN_IVF_FLAT_TEST
-    PATH
-    neighbors/ann_ivf_flat/test_float_int64_t.cu
-    neighbors/ann_ivf_flat/test_half_int64_t.cu
-    neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
-    neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_ANN_IVF_FLAT_TEST
+    PATH neighbors/ann_ivf_flat/test_float_int64_t.cu
+         neighbors/ann_ivf_flat/test_half_int64_t.cu
+         neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
+         neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME
-    NEIGHBORS_ANN_IVF_PQ_TEST
-    PATH
-    neighbors/ann_ivf_pq/test_float_int64_t.cu
-    neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
-    neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_ANN_IVF_PQ_TEST
+    PATH neighbors/ann_ivf_pq/test_float_int64_t.cu neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+         neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_SCANN_TEST PATH neighbors/ann_scann/test_float_int64_t.cu GPUS 1 PERCENT 1
+    NAME NEIGHBORS_ANN_SCANN_TEST
+    PATH neighbors/ann_scann/test_float_int64_t.cu
+    GPUS 1
+    PERCENT 1
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_BRUTE_FORCE_TEST PATH neighbors/ann_brute_force/test_float.cu
-    neighbors/ann_brute_force/test_half.cu GPUS 1 PERCENT 100
+    NAME NEIGHBORS_ANN_BRUTE_FORCE_TEST
+    PATH neighbors/ann_brute_force/test_float.cu neighbors/ann_brute_force/test_half.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_TEST_BUGS PATH neighbors/ann_cagra/bug_extreme_inputs_oob.cu
-    neighbors/ann_cagra/bug_multi_cta_crash.cu GPUS 1 PERCENT 100
+    NAME NEIGHBORS_ANN_CAGRA_TEST_BUGS
+    PATH neighbors/ann_cagra/bug_extreme_inputs_oob.cu neighbors/ann_cagra/bug_multi_cta_crash.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST PATH neighbors/ann_cagra/test_float_uint32_t.cu GPUS
-    1 PERCENT 100
+    NAME NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST
+    PATH neighbors/ann_cagra/test_float_uint32_t.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_HELPERS_TEST PATH neighbors/ann_cagra/test_optimize_uint32_t.cu GPUS 1
+    NAME NEIGHBORS_ANN_CAGRA_HELPERS_TEST
+    PATH neighbors/ann_cagra/test_optimize_uint32_t.cu
+    GPUS 1
     PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST PATH neighbors/ann_cagra/test_half_uint32_t.cu GPUS 1
+    NAME NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
+    PATH neighbors/ann_cagra/test_half_uint32_t.cu
+    GPUS 1
     PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST PATH neighbors/ann_cagra/test_int8_t_uint32_t.cu GPUS
-    1 PERCENT 100
+    NAME NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST
+    PATH neighbors/ann_cagra/test_int8_t_uint32_t.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST PATH neighbors/ann_cagra/test_uint8_t_uint32_t.cu
-    GPUS 1 PERCENT 100
+    NAME NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST
+    PATH neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME
-    NEIGHBORS_ANN_NN_DESCENT_TEST
-    PATH
-    neighbors/ann_nn_descent/test_float_uint32_t.cu
-    neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
-    neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_ANN_NN_DESCENT_TEST
+    PATH neighbors/ann_nn_descent/test_float_uint32_t.cu
+         neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
+         neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME
-    NEIGHBORS_ANN_VAMANA_TEST
-    PATH
-    neighbors/ann_vamana/test_float_uint32_t.cu
-    neighbors/ann_vamana/test_int8_t_uint32_t.cu
-    neighbors/ann_vamana/test_uint8_t_uint32_t.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_ANN_VAMANA_TEST
+    PATH neighbors/ann_vamana/test_float_uint32_t.cu neighbors/ann_vamana/test_int8_t_uint32_t.cu
+         neighbors/ann_vamana/test_uint8_t_uint32_t.cu
+    GPUS 1
+    PERCENT 100
     FETCH_CODEBOOKS
   )
 
-  ConfigureTest(NAME NEIGHBORS_BALL_COVER_TEST PATH neighbors/ball_cover.cu GPUS 1 PERCENT 100)
+  ConfigureTest(
+    NAME NEIGHBORS_BALL_COVER_TEST
+    PATH neighbors/ball_cover.cu
+    GPUS 1
+    PERCENT 100
+  )
 
   ConfigureTest(
-    NAME NEIGHBORS_EPSILON_NEIGHBORHOOD_TEST PATH neighbors/epsilon_neighborhood.cu GPUS 1 PERCENT
-    100
+    NAME NEIGHBORS_EPSILON_NEIGHBORHOOD_TEST
+    PATH neighbors/epsilon_neighborhood.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME NEIGHBORS_ALL_NEIGHBORS_TEST PATH neighbors/all_neighbors/test_float.cu GPUS 1 PERCENT 100
+    NAME NEIGHBORS_ALL_NEIGHBORS_TEST
+    PATH neighbors/all_neighbors/test_float.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME
-    NEIGHBORS_DYNAMIC_BATCHING_TEST
-    PATH
-    neighbors/dynamic_batching/test_brute_force.cu
-    neighbors/dynamic_batching/test_cagra.cu
-    neighbors/dynamic_batching/test_ivf_flat.cu
-    neighbors/dynamic_batching/test_ivf_pq.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME NEIGHBORS_DYNAMIC_BATCHING_TEST
+    PATH neighbors/dynamic_batching/test_brute_force.cu neighbors/dynamic_batching/test_cagra.cu
+         neighbors/dynamic_batching/test_ivf_flat.cu neighbors/dynamic_batching/test_ivf_pq.cu
+    GPUS 1
+    PERCENT 100
   )
 
   if(BUILD_CAGRA_HNSWLIB)
-    ConfigureTest(NAME NEIGHBORS_HNSW_TEST PATH neighbors/hnsw.cu GPUS 1 PERCENT 100)
+    ConfigureTest(
+      NAME NEIGHBORS_HNSW_TEST
+      PATH neighbors/hnsw.cu
+      GPUS 1
+      PERCENT 100
+    )
     target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
     target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()
 
   if(BUILD_MG_ALGOS)
     ConfigureTest(
-      NAME NEIGHBORS_MG_TEST PATH neighbors/mg/test_float.cu GPUS 1 PERCENT 100 ADDITIONAL_DEP
-      NCCL::NCCL
+      NAME NEIGHBORS_MG_TEST
+      PATH neighbors/mg/test_float.cu
+      GPUS 1
+      PERCENT 100
+      ADDITIONAL_DEP NCCL::NCCL
     )
   endif()
 
   ConfigureTest(
-    NAME
-    DISTANCE_TEST
-    PATH
-    distance/dist_canberra.cu
-    distance/dist_correlation.cu
-    distance/dist_cos.cu
-    distance/dist_hamming.cu
-    distance/dist_hellinger.cu
-    distance/dist_inner_product.cu
-    distance/dist_jensen_shannon.cu
-    distance/dist_kl_divergence.cu
-    distance/dist_l1.cu
-    distance/dist_l2_exp.cu
-    distance/dist_l2_sqrt_exp.cu
-    distance/dist_l_inf.cu
-    distance/dist_lp_unexp.cu
-    distance/dist_russell_rao.cu
-    distance/gram.cu
-    distance/masked_nn.cu
-    distance/sparse_distance.cu
-    sparse/neighbors/cross_component_nn.cu
-    GPUS
-    1
-    PERCENT
-    100
+    NAME DISTANCE_TEST
+    PATH distance/dist_canberra.cu
+         distance/dist_correlation.cu
+         distance/dist_cos.cu
+         distance/dist_hamming.cu
+         distance/dist_hellinger.cu
+         distance/dist_inner_product.cu
+         distance/dist_jensen_shannon.cu
+         distance/dist_kl_divergence.cu
+         distance/dist_l1.cu
+         distance/dist_l2_exp.cu
+         distance/dist_l2_sqrt_exp.cu
+         distance/dist_l_inf.cu
+         distance/dist_lp_unexp.cu
+         distance/dist_russell_rao.cu
+         distance/gram.cu
+         distance/masked_nn.cu
+         distance/sparse_distance.cu
+         sparse/neighbors/cross_component_nn.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME SPARSE_TEST PATH sparse/cluster/cluster_solvers.cu sparse/cluster/eigen_solvers.cu
-    sparse/cluster/spectral.cu GPUS 1 PERCENT 100
+    NAME SPARSE_TEST
+    PATH sparse/cluster/cluster_solvers.cu sparse/cluster/eigen_solvers.cu
+         sparse/cluster/spectral.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME PREPROCESSING_TEST PATH preprocessing/scalar_quantization.cu
-    preprocessing/binary_quantization.cu preprocessing/spectral_embedding.cu GPUS 1 PERCENT 100
+    NAME PREPROCESSING_TEST
+    PATH preprocessing/scalar_quantization.cu preprocessing/binary_quantization.cu
+         preprocessing/spectral_embedding.cu
+    GPUS 1
+    PERCENT 100
   )
 
   ConfigureTest(
-    NAME STATS_TEST PATH stats/trustworthiness.cu stats/silhouette_score.cu GPUS 1 PERCENT 100
+    NAME STATS_TEST
+    PATH stats/trustworthiness.cu stats/silhouette_score.cu
+    GPUS 1
+    PERCENT 100
   )
 endif()
 
 if(TARGET cuvs::c_api)
   enable_language(C)
 
-  ConfigureTest(NAME INTEROP_TEST PATH core/interop.cu C_LIB)
   ConfigureTest(
-    NAME DISTANCE_C_TEST PATH distance/run_pairwise_distance_c.c distance/pairwise_distance_c.cu
+    NAME INTEROP_TEST
+    PATH core/interop.cu
+    C_LIB
+  )
+  ConfigureTest(
+    NAME DISTANCE_C_TEST
+    PATH distance/run_pairwise_distance_c.c distance/pairwise_distance_c.cu
     C_LIB
   )
 
   ConfigureTest(
-    NAME BRUTEFORCE_C_TEST PATH neighbors/run_brute_force_c.c neighbors/brute_force_c.cu C_LIB
+    NAME BRUTEFORCE_C_TEST
+    PATH neighbors/run_brute_force_c.c neighbors/brute_force_c.cu
+    C_LIB
   )
 
   ConfigureTest(
-    NAME IVF_FLAT_C_TEST PATH neighbors/run_ivf_flat_c.c neighbors/ann_ivf_flat_c.cu C_LIB
+    NAME IVF_FLAT_C_TEST
+    PATH neighbors/run_ivf_flat_c.c neighbors/ann_ivf_flat_c.cu
+    C_LIB
   )
 
-  ConfigureTest(NAME IVF_PQ_C_TEST PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu C_LIB)
+  ConfigureTest(
+    NAME IVF_PQ_C_TEST
+    PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu
+    C_LIB
+  )
 
-  ConfigureTest(NAME CAGRA_C_TEST PATH neighbors/ann_cagra_c.cu C_LIB)
+  ConfigureTest(
+    NAME CAGRA_C_TEST
+    PATH neighbors/ann_cagra_c.cu
+    C_LIB
+  )
 
-  ConfigureTest(NAME MG_C_TEST PATH neighbors/run_mg_c.c neighbors/ann_mg_c.cu C_LIB)
+  ConfigureTest(
+    NAME MG_C_TEST
+    PATH neighbors/run_mg_c.c neighbors/ann_mg_c.cu
+    C_LIB
+  )
 
   ConfigureTest(
-    NAME ALL_NEIGHBORS_C_TEST PATH neighbors/run_all_neighbors_c.c neighbors/all_neighbors_c.cu
+    NAME ALL_NEIGHBORS_C_TEST
+    PATH neighbors/run_all_neighbors_c.c neighbors/all_neighbors_c.cu
     C_LIB
   )
 
   if(BUILD_CAGRA_HNSWLIB)
-    ConfigureTest(NAME HNSW_C_TEST PATH neighbors/ann_hnsw_c.cu C_LIB)
+    ConfigureTest(
+      NAME HNSW_C_TEST
+      PATH neighbors/ann_hnsw_c.cu
+      C_LIB
+    )
     target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
     target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
   endif()

From 172c023df8fabf73a0ecabbc0d1edb39f2344dfc Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Fri, 26 Sep 2025 19:02:41 -0700
Subject: [PATCH 603/623] Vamana C / Python API (#1112)

Fixes https://github.com/rapidsai/cuvs/issues/853

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Ben Karsin (https://github.com/bkarsin)

URL: https://github.com/rapidsai/cuvs/pull/1112
---
 cpp/CMakeLists.txt                            |   1 +
 cpp/include/cuvs/neighbors/vamana.h           | 227 ++++++++++++++
 cpp/src/neighbors/vamana_c.cpp                | 186 ++++++++++++
 docs/source/c_api/neighbors_vamana_c.rst      |  43 +++
 python/cuvs/cuvs/neighbors/CMakeLists.txt     |   1 +
 python/cuvs/cuvs/neighbors/__init__.py        |   2 +
 .../cuvs/cuvs/neighbors/vamana/CMakeLists.txt |  24 ++
 .../cuvs/cuvs/neighbors/vamana/__init__.pxd   |   0
 python/cuvs/cuvs/neighbors/vamana/__init__.py |  23 ++
 python/cuvs/cuvs/neighbors/vamana/vamana.pxd  |  80 +++++
 python/cuvs/cuvs/neighbors/vamana/vamana.pyx  | 282 ++++++++++++++++++
 python/cuvs/cuvs/tests/test_vamana.py         |  92 ++++++
 12 files changed, 961 insertions(+)
 create mode 100644 cpp/include/cuvs/neighbors/vamana.h
 create mode 100644 cpp/src/neighbors/vamana_c.cpp
 create mode 100644 docs/source/c_api/neighbors_vamana_c.rst
 create mode 100644 python/cuvs/cuvs/neighbors/vamana/CMakeLists.txt
 create mode 100644 python/cuvs/cuvs/neighbors/vamana/__init__.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/vamana/__init__.py
 create mode 100644 python/cuvs/cuvs/neighbors/vamana/vamana.pxd
 create mode 100644 python/cuvs/cuvs/neighbors/vamana/vamana.pyx
 create mode 100644 python/cuvs/cuvs/tests/test_vamana.py

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 85e1c171ab..d68c8df37a 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -735,6 +735,7 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       src/neighbors/cagra_c.cpp
       $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
       src/neighbors/nn_descent_c.cpp
+      src/neighbors/vamana_c.cpp
       src/neighbors/refine/refine_c.cpp
       src/neighbors/tiered_index_c.cpp
       src/neighbors/all_neighbors_c.cpp
diff --git a/cpp/include/cuvs/neighbors/vamana.h b/cpp/include/cuvs/neighbors/vamana.h
new file mode 100644
index 0000000000..3847aa6d1f
--- /dev/null
+++ b/cpp/include/cuvs/neighbors/vamana.h
@@ -0,0 +1,227 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/distance/distance.h>
+#include <cuvs/neighbors/common.h>
+#include <dlpack/dlpack.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @defgroup vamana_c_index_params C API for Vamana index build
+ * @{
+ */
+
+/**
+ * @brief Supplemental parameters to build Vamana Index
+ *
+ * `graph_degree`: Maximum degree of graph; corresponds to the R parameter of
+ * Vamana algorithm in the literature.
+ * `visited_size`: Maximum number of visited nodes per search during Vamana algorithm.
+ * Loosely corresponds to the L parameter in the literature.
+ * `vamana_iters`: The number of times all vectors are inserted into the graph. If > 1,
+ * all vectors are re-inserted to improve graph quality.
+ * `max_fraction`: The maximum batch size is this fraction of the total dataset size. Larger
+ * gives faster build but lower graph quality.
+ * `alpha`: Used to determine how aggressive the pruning will be.
+ */
+struct cuvsVamanaIndexParams {
+  /** Distance type. */
+  cuvsDistanceType metric;
+  /** Maximum degree of output graph corresponds to the R parameter in the original Vamana
+   * literature. */
+  uint32_t graph_degree;
+  /** Maximum number of visited nodes per search corresponds to the L parameter in the Vamana
+   * literature **/
+  uint32_t visited_size;
+  /** Number of Vamana vector insertion iterations (each iteration inserts all vectors). */
+  float vamana_iters;
+  /** Alpha for pruning parameter */
+  float alpha;
+  /** Maximum fraction of dataset inserted per batch.              *
+   * Larger max batch decreases graph quality, but improves speed */
+  float max_fraction;
+  /** Base of growth rate of batch sizes **/
+  float batch_base;
+  /** Size of candidate queue structure - should be (2^x)-1 */
+  uint32_t queue_size;
+  /** Max batchsize of reverse edge processing (reduces memory footprint) */
+  uint32_t reverse_batchsize;
+};
+
+typedef struct cuvsVamanaIndexParams* cuvsVamanaIndexParams_t;
+
+/**
+ * @brief Allocate Vamana Index params, and populate with default values
+ *
+ * @param[in] params cuvsVamanaIndexParams_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVamanaIndexParamsCreate(cuvsVamanaIndexParams_t* params);
+
+/**
+ * @brief De-allocate Vamana Index params
+ *
+ * @param[in] params cuvsVamanaIndexParams_t to de-allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVamanaIndexParamsDestroy(cuvsVamanaIndexParams_t params);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup vamana_c_index Vamana index
+ * @{
+ */
+
+/**
+ * @brief Struct to hold address of cuvs::neighbors::vamana::index and its active trained dtype
+ *
+ */
+typedef struct {
+  uintptr_t addr;
+  DLDataType dtype;
+
+} cuvsVamanaIndex;
+
+typedef cuvsVamanaIndex* cuvsVamanaIndex_t;
+
+/**
+ * @brief Allocate Vamana index
+ *
+ * @param[in] index cuvsVamanaIndex_t to allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVamanaIndexCreate(cuvsVamanaIndex_t* index);
+
+/**
+ * @brief De-allocate Vamana index
+ *
+ * @param[in] index cuvsVamanaIndex_t to de-allocate
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVamanaIndexDestroy(cuvsVamanaIndex_t index);
+
+/**
+ * @brief Get the dimension of the index
+ *
+ * @param[in] index cuvsVamanaIndex_t to get dimension of
+ * @param[out] dim pointer to dimension to set
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVamanaIndexGetDims(cuvsVamanaIndex_t index, int* dim);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup vamana_c_index_build Vamana index build
+ * @{
+ */
+
+/**
+ * @brief Build Vamana index
+ *
+ * Build the index from the dataset for efficient DiskANN search.
+ *
+ * The build utilities the Vamana insertion-based algorithm to create the graph. The algorithm
+ * starts with an empty graph and iteratively inserts batches of nodes. Each batch involves
+ * performing a greedy search for each vector to be inserted, and inserting it with edges to
+ * all nodes traversed during the search. Reverse edges are also inserted and robustPrune is applied
+ * to improve graph quality. The index_params struct controls the degree of the final graph.
+ *
+ * The following distance metrics are supported:
+ * - L2
+ *
+ * Usage example:
+ * @code{.c}
+ *   // Create cuvsResources_t
+ *   cuvsResources_t res;
+ *   cuvsResourcesCreate(&res);
+ *
+ *   // Assume a row-major dataset [n_rows, n_cols] is defined as `float* dataset`
+ *   cuvsVamanaIndexParams_t index_params;
+ *   cuvsVamanaIndexParamsCreate(&index_params);
+ *   index_params->metric = L2Expanded; // set distance metric
+ *   cuvsVamanaIndex_t index;
+ *   cuvsVamanaIndexCreate(&index);
+ *   cuvsVamanaBuild(res, index_params, dataset, index);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] params cuvsVamanaIndexParams_t used to build Vamana index
+ * @param[in] dataset DLManagedTensor* training dataset
+ * @param[out] index cuvsVamanaIndex_t Vamana index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVamanaBuild(cuvsResources_t res,
+                            cuvsVamanaIndexParams_t params,
+                            DLManagedTensor* dataset,
+                            cuvsVamanaIndex_t index);
+
+/**
+ * @}
+ */
+
+/**
+ * @defgroup vamana_c_index_serialize Vamana index serialize
+ * @{
+ */
+
+/**
+ * @brief Save Vamana index to file
+ *
+ * Matches the file format used by the DiskANN open-source repository, allowing cross-compatibility.
+ *
+ * Serialized Index is to be used by the DiskANN open-source repository for graph search.
+ *
+ * @code{.c}
+ *   // Create cuvsResources_t
+ *   cuvsResources_t res;
+ *   cuvsResourcesCreate(&res);
+ *
+ *   // create an index with `cuvsVamanaBuild`
+ *   cuvsVamanaSerialize(res, "/path/to/index", index, true);
+ * @endcode
+ *
+ * @param[in] res cuvsResources_t opaque C handle
+ * @param[in] filename the file prefix for where the index is saved
+ * @param[in] index cuvsVamanaIndex_t to serialize
+ * @param[in] include_dataset whether to include the dataset in the serialized index
+ * @return cuvsError_t
+ */
+cuvsError_t cuvsVamanaSerialize(cuvsResources_t res,
+                                const char* filename,
+                                cuvsVamanaIndex_t index,
+                                bool include_dataset);
+
+/**
+ * @}
+ */
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/cpp/src/neighbors/vamana_c.cpp b/cpp/src/neighbors/vamana_c.cpp
new file mode 100644
index 0000000000..4ad497d17e
--- /dev/null
+++ b/cpp/src/neighbors/vamana_c.cpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <dlpack/dlpack.h>
+
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan_types.hpp>
+#include <raft/core/resources.hpp>
+
+#include <cuvs/core/c_api.h>
+#include <cuvs/core/exceptions.hpp>
+#include <cuvs/core/interop.hpp>
+#include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/vamana.h>
+#include <cuvs/neighbors/vamana.hpp>
+
+namespace {
+
+template <typename T>
+void* _build(cuvsResources_t res, cuvsVamanaIndexParams* params, DLManagedTensor* dataset_tensor)
+{
+  auto res_ptr = reinterpret_cast<raft::resources*>(res);
+
+  auto dataset = dataset_tensor->dl_tensor;
+  cuvs::neighbors::vamana::index_params index_params;
+  index_params.metric            = static_cast<cuvs::distance::DistanceType>((int)params->metric);
+  index_params.graph_degree      = params->graph_degree;
+  index_params.visited_size      = params->visited_size;
+  index_params.vamana_iters      = params->vamana_iters;
+  index_params.alpha             = params->alpha;
+  index_params.max_fraction      = params->max_fraction;
+  index_params.batch_base        = params->batch_base;
+  index_params.queue_size        = params->queue_size;
+  index_params.reverse_batchsize = params->reverse_batchsize;
+  auto index                     = new cuvs::neighbors::vamana::index<T, uint32_t>(*res_ptr);
+
+  if (cuvs::core::is_dlpack_device_compatible(dataset)) {
+    using mdspan_type = raft::device_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    *index            = cuvs::neighbors::vamana::build(*res_ptr, index_params, mds);
+  } else if (cuvs::core::is_dlpack_host_compatible(dataset)) {
+    using mdspan_type = raft::host_matrix_view<T const, int64_t, raft::row_major>;
+    auto mds          = cuvs::core::from_dlpack<mdspan_type>(dataset_tensor);
+    *index            = cuvs::neighbors::vamana::build(*res_ptr, index_params, mds);
+  }
+
+  return index;
+}
+
+template <typename T>
+void _serialize(cuvsResources_t res,
+                const char* filename,
+                cuvsVamanaIndex_t index,
+                bool include_dataset)
+{
+  auto res_ptr   = reinterpret_cast<raft::resources*>(res);
+  auto index_ptr = reinterpret_cast<cuvs::neighbors::vamana::index<T, uint32_t>*>(index->addr);
+
+  cuvs::neighbors::vamana::serialize(*res_ptr, std::string(filename), *index_ptr, include_dataset);
+}
+
+}  // namespace
+
+extern "C" cuvsError_t cuvsVamanaIndexCreate(cuvsVamanaIndex_t* index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    *index          = new cuvsVamanaIndex{};
+    (*index)->addr  = 0;
+    (*index)->dtype = {};
+  });
+}
+
+extern "C" cuvsError_t cuvsVamanaIndexDestroy(cuvsVamanaIndex_t index_c_ptr)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto index = *index_c_ptr;
+
+    if (index.addr != 0) {
+      if (index.dtype.code == kDLFloat && index.dtype.bits == 32) {
+        delete reinterpret_cast<cuvs::neighbors::vamana::index<float, uint32_t>*>(index.addr);
+      } else if (index.dtype.code == kDLInt && index.dtype.bits == 8) {
+        delete reinterpret_cast<cuvs::neighbors::vamana::index<int8_t, uint32_t>*>(index.addr);
+      } else if (index.dtype.code == kDLUInt && index.dtype.bits == 8) {
+        delete reinterpret_cast<cuvs::neighbors::vamana::index<uint8_t, uint32_t>*>(index.addr);
+      }
+    }
+
+    delete index_c_ptr;
+  });
+}
+
+extern "C" cuvsError_t cuvsVamanaIndexGetDims(cuvsVamanaIndex_t index, int* dim)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::vamana::index<float, uint32_t>*>(index->addr);
+      *dim = index_ptr->dim();
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::vamana::index<int8_t, uint32_t>*>(index->addr);
+      *dim = index_ptr->dim();
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      auto index_ptr =
+        reinterpret_cast<cuvs::neighbors::vamana::index<uint8_t, uint32_t>*>(index->addr);
+      *dim = index_ptr->dim();
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsVamanaBuild(cuvsResources_t res,
+                                       cuvsVamanaIndexParams_t params,
+                                       DLManagedTensor* dataset_tensor,
+                                       cuvsVamanaIndex_t index)
+{
+  return cuvs::core::translate_exceptions([=] {
+    auto dataset = dataset_tensor->dl_tensor;
+    index->dtype = dataset.dtype;
+
+    if (dataset.dtype.code == kDLFloat && dataset.dtype.bits == 32) {
+      index->addr = reinterpret_cast<uintptr_t>(_build<float>(res, params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_build<int8_t>(res, params, dataset_tensor));
+    } else if (dataset.dtype.code == kDLUInt && dataset.dtype.bits == 8) {
+      index->addr = reinterpret_cast<uintptr_t>(_build<uint8_t>(res, params, dataset_tensor));
+    } else {
+      RAFT_FAIL("Unsupported dataset DLtensor dtype: %d and bits: %d",
+                dataset.dtype.code,
+                dataset.dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsVamanaSerialize(cuvsResources_t res,
+                                           const char* filename,
+                                           cuvsVamanaIndex_t index,
+                                           bool include_dataset)
+{
+  return cuvs::core::translate_exceptions([=] {
+    if (index->dtype.code == kDLFloat && index->dtype.bits == 32) {
+      _serialize<float>(res, filename, index, include_dataset);
+    } else if (index->dtype.code == kDLInt && index->dtype.bits == 8) {
+      _serialize<int8_t>(res, filename, index, include_dataset);
+    } else if (index->dtype.code == kDLUInt && index->dtype.bits == 8) {
+      _serialize<uint8_t>(res, filename, index, include_dataset);
+    } else {
+      RAFT_FAIL(
+        "Unsupported index DLtensor dtype: %d and bits: %d", index->dtype.code, index->dtype.bits);
+    }
+  });
+}
+
+extern "C" cuvsError_t cuvsVamanaIndexParamsCreate(cuvsVamanaIndexParams_t* params)
+{
+  return cuvs::core::translate_exceptions([=] {
+    *params                      = new cuvsVamanaIndexParams{};
+    (*params)->metric            = L2Expanded;
+    (*params)->graph_degree      = 32;
+    (*params)->visited_size      = 64;
+    (*params)->vamana_iters      = 1;
+    (*params)->alpha             = 1.2f;
+    (*params)->max_fraction      = 0.06f;
+    (*params)->batch_base        = 2.0f;
+    (*params)->queue_size        = 127;
+    (*params)->reverse_batchsize = 1000000;
+  });
+}
+
+extern "C" cuvsError_t cuvsVamanaIndexParamsDestroy(cuvsVamanaIndexParams_t params)
+{
+  return cuvs::core::translate_exceptions([=] { delete params; });
+}
diff --git a/docs/source/c_api/neighbors_vamana_c.rst b/docs/source/c_api/neighbors_vamana_c.rst
new file mode 100644
index 0000000000..8e489d315a
--- /dev/null
+++ b/docs/source/c_api/neighbors_vamana_c.rst
@@ -0,0 +1,43 @@
+Vamana
+======
+
+Vamana is the graph construction algorithm behind the well-known DiskANN vector search solution. The cuVS implementation of Vamana/DiskANN is a custom GPU-acceleration version of the algorithm that aims to reduce index construction time using NVIDIA GPUs.
+
+
+.. role:: py(code)
+   :language: c
+   :class: highlight
+
+``#include <raft/neighbors/vamana.h>``
+
+Index build parameters
+----------------------
+
+.. doxygengroup:: vamana_c_index_params
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index
+-----
+
+.. doxygengroup:: vamana_c_index
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index build
+-----------
+
+.. doxygengroup:: vamana_c_index_build
+    :project: cuvs
+    :members:
+    :content-only:
+
+Index serialize
+---------------
+
+.. doxygengroup:: vamana_c_index_serialize
+    :project: cuvs
+    :members:
+    :content-only:
diff --git a/python/cuvs/cuvs/neighbors/CMakeLists.txt b/python/cuvs/cuvs/neighbors/CMakeLists.txt
index ee48687d69..a6ef180aeb 100644
--- a/python/cuvs/cuvs/neighbors/CMakeLists.txt
+++ b/python/cuvs/cuvs/neighbors/CMakeLists.txt
@@ -21,6 +21,7 @@ add_subdirectory(nn_descent)
 add_subdirectory(tiered_index)
 add_subdirectory(mg)
 add_subdirectory(all_neighbors)
+add_subdirectory(vamana)
 
 # Set the list of Cython files to build
 set(cython_sources refine.pyx)
diff --git a/python/cuvs/cuvs/neighbors/__init__.py b/python/cuvs/cuvs/neighbors/__init__.py
index b34a3b3598..1e58c21d9c 100644
--- a/python/cuvs/cuvs/neighbors/__init__.py
+++ b/python/cuvs/cuvs/neighbors/__init__.py
@@ -22,6 +22,7 @@
     ivf_pq,
     mg,
     nn_descent,
+    vamana,
 )
 
 from .refine import refine
@@ -36,4 +37,5 @@
     "nn_descent",
     "all_neighbors",
     "refine",
+    "vamana",
 ]
diff --git a/python/cuvs/cuvs/neighbors/vamana/CMakeLists.txt b/python/cuvs/cuvs/neighbors/vamana/CMakeLists.txt
new file mode 100644
index 0000000000..e1bc25af6c
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/vamana/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Set the list of Cython files to build
+set(cython_sources vamana.pyx)
+set(linked_libraries cuvs::cuvs cuvs::c_api)
+
+# Build all of the Cython targets
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX neighbors_vamana_
+)
diff --git a/python/cuvs/cuvs/neighbors/vamana/__init__.pxd b/python/cuvs/cuvs/neighbors/vamana/__init__.pxd
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/cuvs/cuvs/neighbors/vamana/__init__.py b/python/cuvs/cuvs/neighbors/vamana/__init__.py
new file mode 100644
index 0000000000..d12956e21b
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/vamana/__init__.py
@@ -0,0 +1,23 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .vamana import Index, IndexParams, build, save
+
+__all__ = [
+    "Index",
+    "IndexParams",
+    "build",
+    "save",
+]
diff --git a/python/cuvs/cuvs/neighbors/vamana/vamana.pxd b/python/cuvs/cuvs/neighbors/vamana/vamana.pxd
new file mode 100644
index 0000000000..0e0c776e89
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/vamana/vamana.pxd
@@ -0,0 +1,80 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+from libc.stdint cimport int32_t, uint32_t, uintptr_t
+from libcpp cimport bool
+
+from cuvs.common.c_api cimport cuvsError_t, cuvsResources_t
+from cuvs.common.cydlpack cimport DLDataType, DLManagedTensor
+from cuvs.distance_type cimport cuvsDistanceType
+
+
+cdef extern from "cuvs/neighbors/vamana.h" nogil:
+
+    ctypedef struct cuvsVamanaIndexParams:
+        cuvsDistanceType metric
+        uint32_t graph_degree
+        uint32_t visited_size
+        float vamana_iters
+        float alpha
+        float max_fraction
+        float batch_base
+        uint32_t queue_size
+        uint32_t reverse_batchsize
+
+    ctypedef cuvsVamanaIndexParams* cuvsVamanaIndexParams_t
+
+    ctypedef struct cuvsVamanaIndex:
+        uintptr_t addr
+        DLDataType dtype
+
+    ctypedef cuvsVamanaIndex* cuvsVamanaIndex_t
+
+    cuvsError_t cuvsVamanaIndexParamsCreate(cuvsVamanaIndexParams_t* params)
+
+    cuvsError_t cuvsVamanaIndexParamsDestroy(cuvsVamanaIndexParams_t params)
+
+    cuvsError_t cuvsVamanaIndexCreate(cuvsVamanaIndex_t* index)
+
+    cuvsError_t cuvsVamanaIndexDestroy(cuvsVamanaIndex_t index)
+
+    cuvsError_t cuvsVamanaIndexGetDims(cuvsVamanaIndex_t index, int* dim)
+
+    cuvsError_t cuvsVamanaBuild(cuvsResources_t res,
+                                cuvsVamanaIndexParams_t params,
+                                DLManagedTensor* dataset,
+                                cuvsVamanaIndex_t index)
+
+    cuvsError_t cuvsVamanaSerialize(cuvsResources_t res,
+                                    const char* filename,
+                                    cuvsVamanaIndex_t index,
+                                    bool include_dataset)
+
+
+cdef class Index:
+    """
+    Vamana index object. This object stores the trained Vamana index state
+    which can be used to perform nearest neighbors searches.
+    """
+
+    cdef cuvsVamanaIndex_t index
+    cdef bool trained
+    cdef str active_index_type
+
+
+cdef class IndexParams:
+    cdef cuvsVamanaIndexParams* params
diff --git a/python/cuvs/cuvs/neighbors/vamana/vamana.pyx b/python/cuvs/cuvs/neighbors/vamana/vamana.pyx
new file mode 100644
index 0000000000..fc65c228d0
--- /dev/null
+++ b/python/cuvs/cuvs/neighbors/vamana/vamana.pyx
@@ -0,0 +1,282 @@
+#
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# cython: language_level=3
+
+import warnings
+
+import numpy as np
+
+cimport cuvs.common.cydlpack
+
+from cuvs.common.resources import auto_sync_resources
+
+from cython.operator cimport dereference as deref
+from libcpp cimport bool
+from libcpp.string cimport string
+
+from cuvs.common cimport cydlpack
+from cuvs.distance_type cimport cuvsDistanceType
+
+from pylibraft.common import auto_convert_output, cai_wrapper, device_ndarray
+from pylibraft.common.cai_wrapper import wrap_array
+from pylibraft.common.interruptible import cuda_interruptible
+
+from cuvs.distance import DISTANCE_NAMES, DISTANCE_TYPES
+from cuvs.neighbors.common import _check_input_array
+
+from libc.stdint cimport uint32_t, uintptr_t
+
+from cuvs.common.exceptions import check_cuvs
+
+
+cdef class Index:
+    """
+    Vamana index object. This object stores the trained Vamana index state
+    which can be used to perform nearest neighbors searches.
+    """
+
+    def __cinit__(self):
+        self.trained = False
+        check_cuvs(cuvsVamanaIndexCreate(&self.index))
+
+    def __dealloc__(self):
+        if self.index is not NULL:
+            check_cuvs(cuvsVamanaIndexDestroy(self.index))
+
+    @property
+    def trained(self):
+        return self.trained
+
+    def __repr__(self):
+        attr_str = [attr + "=" + str(getattr(self, attr))
+                    for attr in ["trained"]]
+        return "Index(type=Vamana, " + (", ".join(attr_str)) + ")"
+
+
+cdef class IndexParams:
+    """
+    Parameters for building a Vamana index
+
+    Parameters
+    ----------
+    metric : str, default="sqeuclidean"
+        String denoting the metric type. Supported metrics include:
+        - "sqeuclidean"
+        - "l2"
+    graph_degree : int, default=32
+        Maximum degree of graph; corresponds to the R parameter of
+        Vamana algorithm in the literature.
+    visited_size : int, default=64
+        Maximum number of visited nodes per search during Vamana algorithm.
+        Loosely corresponds to the L parameter in the literature.
+    vamana_iters : float, default=1
+        Number of Vamana vector insertion iterations (each iteration inserts
+        all vectors).
+    alpha : float, default=1.2
+        Alpha for pruning parameter. Used to determine how aggressive the
+        pruning will be.
+    max_fraction : float, default=0.06
+        Maximum fraction of dataset inserted per batch. Larger max batch
+        decreases graph quality, but improves speed.
+    batch_base : float, default=2.0
+        Base of growth rate of batch sizes.
+    queue_size : int, default=127
+        Size of candidate queue structure - should be (2^x)-1.
+    reverse_batchsize : int, default=1000000
+        Max batchsize of reverse edge processing (reduces memory footprint).
+    """
+
+    def __cinit__(self):
+        check_cuvs(cuvsVamanaIndexParamsCreate(&self.params))
+
+    def __dealloc__(self):
+        check_cuvs(cuvsVamanaIndexParamsDestroy(self.params))
+
+    def __init__(self, *,
+                 metric="sqeuclidean",
+                 graph_degree=32,
+                 visited_size=64,
+                 vamana_iters=1,
+                 alpha=1.2,
+                 max_fraction=0.06,
+                 batch_base=2.0,
+                 queue_size=127,
+                 reverse_batchsize=1000000):
+        if metric in DISTANCE_TYPES:
+            self.params.metric = <cuvsDistanceType>DISTANCE_TYPES[metric]
+        else:
+            raise ValueError("metric %s not supported" % metric)
+
+        self.params.graph_degree = graph_degree
+        self.params.visited_size = visited_size
+        self.params.vamana_iters = vamana_iters
+        self.params.alpha = alpha
+        self.params.max_fraction = max_fraction
+        self.params.batch_base = batch_base
+        self.params.queue_size = queue_size
+        self.params.reverse_batchsize = reverse_batchsize
+
+    @property
+    def metric(self):
+        return DISTANCE_NAMES[self.params.metric]
+
+    @property
+    def graph_degree(self):
+        return self.params.graph_degree
+
+    @property
+    def visited_size(self):
+        return self.params.visited_size
+
+    @property
+    def vamana_iters(self):
+        return self.params.vamana_iters
+
+    @property
+    def alpha(self):
+        return self.params.alpha
+
+    @property
+    def max_fraction(self):
+        return self.params.max_fraction
+
+    @property
+    def batch_base(self):
+        return self.params.batch_base
+
+    @property
+    def queue_size(self):
+        return self.params.queue_size
+
+    @property
+    def reverse_batchsize(self):
+        return self.params.reverse_batchsize
+
+
+@auto_sync_resources
+def build(IndexParams index_params, dataset, resources=None):
+    """
+    Build the Vamana index from the dataset for efficient search.
+
+    The build utilities the Vamana insertion-based algorithm to create
+    the graph. The algorithm starts with an empty graph and iteratively
+    inserts batches of nodes. Each batch involves performing a greedy
+    search for each vector to be inserted, and inserting it with edges to
+    all nodes traversed during the search. Reverse edges are also inserted
+    and robustPrune is applied to improve graph quality. The index_params
+    struct controls the degree of the final graph.
+
+    The following distance metrics are supported:
+        - L2Expanded
+
+    Parameters
+    ----------
+    index_params : IndexParams object
+    dataset : CUDA array interface compliant matrix shape (n_samples, dim)
+        Supported dtype [float, int8, uint8]
+    {resources_docstring}
+
+    Returns
+    -------
+    index: cuvs.vamana.Index
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import vamana
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> build_params = vamana.IndexParams(metric="sqeuclidean")
+    >>> index = vamana.build(build_params, dataset)
+    >>> # Serialize index to file for later use with CPU DiskANN
+    >>> vamana.save("my_index.bin", index)
+    """
+
+    # todo(dgd): we can make the check of dtype a parameter of wrap_array
+    # in RAFT to make this a single call
+    dataset_ai = wrap_array(dataset)
+    _check_input_array(dataset_ai, [np.dtype('float32'),
+                                    np.dtype('int8'),
+                                    np.dtype('uint8')])
+
+    cdef Index idx = Index()
+    cdef cydlpack.DLManagedTensor* dataset_dlpack = \
+        cydlpack.dlpack_c(dataset_ai)
+    cdef cuvsVamanaIndexParams* params = index_params.params
+
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+
+    with cuda_interruptible():
+        check_cuvs(cuvsVamanaBuild(
+            res,
+            params,
+            dataset_dlpack,
+            idx.index
+        ))
+        idx.trained = True
+        idx.active_index_type = dataset_ai.dtype.name
+
+    return idx
+
+
+@auto_sync_resources
+def save(filename, Index index, bool include_dataset=True, resources=None):
+    """
+    Saves the index to a file.
+
+    Matches the file format used by the DiskANN open-source repository,
+    allowing cross-compatibility.
+
+    Parameters
+    ----------
+    filename : string
+        Name of the file.
+    index : Index
+        Trained Vamana index.
+    include_dataset : bool
+        Whether or not to write out the dataset along with the index. Including
+        the dataset in the serialized index will use extra disk space, and
+        might not be desired if you already have a copy of the dataset on
+        disk.
+    {resources_docstring}
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cuvs.neighbors import vamana
+    >>> n_samples = 50000
+    >>> n_features = 50
+    >>> dataset = cp.random.random_sample((n_samples, n_features),
+    ...                                   dtype=cp.float32)
+    >>> # Build index
+    >>> index = vamana.build(vamana.IndexParams(), dataset)
+    >>> # Serialize and save the vamana index
+    >>> vamana.save("my_index.bin", index)
+    """
+    cdef string c_filename = filename.encode('utf-8')
+    cdef cuvsResources_t res = <cuvsResources_t>resources.get_c_obj()
+    check_cuvs(cuvsVamanaSerialize(res,
+                                   c_filename.c_str(),
+                                   index.index,
+                                   include_dataset))
+
+
+# Note: Vamana index currently only supports build and serialize operations.
+# Search functionality is not yet implemented in cuVS and should be performed
+# using the DiskANN library with the serialized index.
diff --git a/python/cuvs/cuvs/tests/test_vamana.py b/python/cuvs/cuvs/tests/test_vamana.py
new file mode 100644
index 0000000000..236688b543
--- /dev/null
+++ b/python/cuvs/cuvs/tests/test_vamana.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import pytest
+from pylibraft.common import device_ndarray
+
+from cuvs.neighbors import vamana
+
+
+def _gen_data(shape, dtype):
+    rng = np.random.default_rng(12345)
+    if dtype == np.float32:
+        return rng.random(shape, dtype=np.float32)
+    if dtype == np.int8:
+        # keep small magnitude to avoid overflow if used elsewhere
+        return rng.integers(low=-10, high=10, size=shape, dtype=np.int8)
+    if dtype == np.uint8:
+        return rng.integers(low=0, high=20, size=shape, dtype=np.uint8)
+    raise AssertionError("unexpected dtype in test helper")
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+def test_vamana_build_basic(dtype):
+    n_rows, n_cols = 1000, 16
+    data = _gen_data((n_rows, n_cols), dtype)
+    data_dev = device_ndarray(data)
+
+    params = vamana.IndexParams(metric="sqeuclidean")
+    idx = vamana.build(params, data_dev)
+
+    # Basic sanity: object type and flags
+    assert isinstance(idx, vamana.Index)
+    assert idx.trained is True
+    # repr should include trained flag
+    assert "Index(type=Vamana" in repr(idx)
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.int8, np.uint8])
+@pytest.mark.skip(
+    reason="Skipping host build test because of CUDA error "
+    "in C++ API. Reference issue: "
+    "https://github.com/rapidsai/cuvs/issues/1380"
+)
+def test_vamana_build_basic_host(dtype):
+    n_rows, n_cols = 512, 12
+    data = _gen_data((n_rows, n_cols), dtype)  # host array
+
+    params = vamana.IndexParams(metric="sqeuclidean")
+    idx = vamana.build(params, data)
+
+    assert isinstance(idx, vamana.Index)
+    assert idx.trained is True
+
+
+@pytest.mark.parametrize("include_dataset", [True, False])
+def test_vamana_serialize(tmp_path, include_dataset):
+    n_rows, n_cols = 256, 8
+    data = _gen_data((n_rows, n_cols), np.float32)
+    data_dev = device_ndarray(data)
+
+    params = vamana.IndexParams()
+    idx = vamana.build(params, data_dev)
+
+    out_path = tmp_path / "vamana_index.bin"
+    vamana.save(str(out_path), idx, include_dataset=include_dataset)
+
+    assert out_path.exists() and out_path.stat().st_size > 0
+
+
+def test_vamana_build_rejects_unsupported_dtype():
+    # float16 is not in the accepted list in the build wrapper; expect failure
+    n_rows, n_cols = 64, 8
+    data = _gen_data((n_rows, n_cols), np.float32).astype(np.float16)
+    from pylibraft.common import device_ndarray
+
+    data_dev = device_ndarray(data)
+    params = vamana.IndexParams()
+
+    with pytest.raises(Exception):
+        _ = vamana.build(params, data_dev)

From 91d72b170548a59e74572c447439fa94f74b90a1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lorenzo=20Dematt=C3=A9?= <lorenzo.dematte@elastic.co>
Date: Sat, 27 Sep 2025 19:45:22 +0200
Subject: [PATCH 604/623] [Java] Support row strides in CuVSMatrix (#1345)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This PR adds full support for row strides to `CuVSMatrix`.

There was already some support for them in `CuVSDeviceMatrix`, as we needed to at least to save them to handle matrices built from tensors (e.g. resulting from a cuvs computation) and/or to create tensors for that kind of data (again, to pass them to the C API).
With this PR we introduce ways to:
- allocate host and device matrices with row stride != from the number of columns (e.g. padding/additional info at the end of row vectors)
- map a pre-allocated memory region to a `CuVSMatrix` taking row stride into account
- copy between different matrices with different strides
- populate data taking the target matrix stride into account
- read data from matrices (e.g. read rows or copy to heap arrays) taking the source matrix stride into account

The API changes include also the introduction of column strides ("gaps" between elements withing the same vector); however this is not implemented yet. To the best of my knowledge, cuvs functions do not support these _yet_. I wanted to introduce them as we need them in some scenarios (e.g. round-tripping from `DLTensor`s), and I did not want to change the API once again if/when we are going to support them.

However, I am open to drop `columnStride` if we do not want it. Let me know.

Authors:
  - Lorenzo Dematté (https://github.com/ldematte)
  - Ben Frederickson (https://github.com/benfred)
  - MithunR (https://github.com/mythrocks)

Approvers:
  - Chris Hegarty (https://github.com/ChrisHegarty)
  - MithunR (https://github.com/mythrocks)

URL: https://github.com/rapidsai/cuvs/pull/1345
---
 .../main/java/com/nvidia/cuvs/CuVSMatrix.java |  48 ++-
 .../com/nvidia/cuvs/spi/CuVSProvider.java     |  23 ++
 .../nvidia/cuvs/spi/UnsupportedProvider.java  |  11 +
 .../cuvs/internal/CuVSDeviceMatrixImpl.java   |  30 +-
 .../internal/CuVSDeviceMatrixRMMImpl.java     |  22 +-
 .../internal/CuVSHostMatrixArenaImpl.java     |  35 +-
 .../cuvs/internal/CuVSHostMatrixImpl.java     | 101 +++++-
 .../cuvs/internal/CuVSMatrixBaseImpl.java     |   5 +-
 .../cuvs/internal/CuVSMatrixInternal.java     |   4 +-
 .../com/nvidia/cuvs/spi/JDKProvider.java      | 295 ++++++++++-------
 .../java/com/nvidia/cuvs/CuVSMatrixIT.java    | 307 +++++++++++++++---
 .../java/com/nvidia/cuvs/DatasetHelper.java   |  24 ++
 12 files changed, 691 insertions(+), 214 deletions(-)

diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
index 3ccdb9ae28..e37b696308 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CuVSMatrix.java
@@ -99,10 +99,10 @@ interface Builder<T extends CuVSMatrix> {
   }
 
   /**
-   * Returns a builder to create a new instance of a dataset
+   * Returns a builder to create a new instance of a host-memory matrix
    *
-   * @param size     Number of vectors in the dataset
-   * @param columns  Size of each vector in the dataset
+   * @param size      Number of rows (e.g. vectors in a dataset)
+   * @param columns   Number of columns (e.g. dimension of each vector in the dataset)
    * @param dataType The data type of the dataset elements
    * @return a builder for creating a {@link CuVSHostMatrix}
    */
@@ -110,12 +110,28 @@ static Builder<CuVSHostMatrix> hostBuilder(long size, long columns, DataType dat
     return CuVSProvider.provider().newHostMatrixBuilder(size, columns, dataType);
   }
 
+  /**
+   * Returns a builder to create a new instance of a host-memory matrix
+   *
+   * @param size      Number of rows (e.g. vectors in a dataset)
+   * @param columns   Number of columns (e.g. dimension of each vector in the dataset)
+   * @param rowStride The stride (in number of elements) for each row. Must be -1 or > than {@code columns}
+   * @param columnStride The stride for each column. Currently, it is not supported (must be -1)
+   * @param dataType  The data type of the dataset elements
+   * @return a builder for creating a {@link CuVSDeviceMatrix}
+   */
+  static Builder<CuVSHostMatrix> hostBuilder(
+      long size, long columns, int rowStride, int columnStride, DataType dataType) {
+    return CuVSProvider.provider()
+        .newHostMatrixBuilder(size, columns, rowStride, columnStride, dataType);
+  }
+
   /**
    * Returns a builder to create a new instance of a dataset
    *
    * @param resources CuVS resources used to allocate the device memory needed
-   * @param size      Number of vectors in the dataset
-   * @param columns   Size of each vector in the dataset
+   * @param size      Number of rows (e.g. vectors in a dataset)
+   * @param columns   Number of columns (e.g. dimension of each vector in the dataset)
    * @param dataType  The data type of the dataset elements
    * @return a builder for creating a {@link CuVSDeviceMatrix}
    */
@@ -124,6 +140,28 @@ static Builder<CuVSDeviceMatrix> deviceBuilder(
     return CuVSProvider.provider().newDeviceMatrixBuilder(resources, size, columns, dataType);
   }
 
+  /**
+   * Returns a builder to create a new instance of a dataset
+   *
+   * @param resources CuVS resources used to allocate the device memory needed
+   * @param size      Number of rows (e.g. vectors in a dataset)
+   * @param columns   Number of columns (e.g. dimension of each vector in the dataset)
+   * @param rowStride The stride (in number of elements) for each row. Must be -1 or > than {@code columns}
+   * @param columnStride The stride for each column. Currently, it is not supported (must be -1)
+   * @param dataType  The data type of the dataset elements
+   * @return a builder for creating a {@link CuVSDeviceMatrix}
+   */
+  static Builder<CuVSDeviceMatrix> deviceBuilder(
+      CuVSResources resources,
+      long size,
+      long columns,
+      int rowStride,
+      int columnStride,
+      DataType dataType) {
+    return CuVSProvider.provider()
+        .newDeviceMatrixBuilder(resources, size, columns, rowStride, columnStride, dataType);
+  }
+
   /**
    * Gets the size of the dataset
    *
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
index f12389d226..034aaf0d7d 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/CuVSProvider.java
@@ -50,6 +50,10 @@ default Path nativeLibraryPath() {
   CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
       long size, long dimensions, CuVSMatrix.DataType dataType);
 
+  /** Create a {@link CuVSMatrix.Builder} instance for a host memory matrix **/
+  CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
+      long size, long columns, int rowStride, int columnStride, CuVSMatrix.DataType dataType);
+
   /** Create a {@link CuVSMatrix.Builder} instance for a device memory matrix **/
   CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       CuVSResources cuVSResources, long size, long dimensions, CuVSMatrix.DataType dataType);
@@ -80,6 +84,25 @@ CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
    */
   MethodHandle newNativeMatrixBuilder();
 
+  /**
+   * Returns the factory method used to build a CuVSMatrix from native memory, with strides.
+   * The factory method will have this signature:
+   * {@code CuVSMatrix createNativeMatrix(memorySegment, size, dimensions, rowStride, columnStride, dataType)},
+   * where {@code memorySegment} is a {@code java.lang.foreign.MemorySegment} containing {@code int size} vectors of
+   * {@code int dimensions} length of type {@link CuVSMatrix.DataType}. Rows have a stride of {@code rowStride},
+   * where 0 indicates "no stride" (a stride equal to the number of columns), and columns have a stride of
+   * {@code columnStride}
+   * <p>
+   * In order to expose this factory in a way that is compatible with Java 21, the factory method is returned as a
+   * {@link MethodHandle} with {@link MethodType} equal to
+   * {@code (CuVSMatrix.class, MemorySegment.class, int.class, int.class, int.class, int.class, DataType.class)}.
+   * The caller will need to invoke the factory via the {@link MethodHandle#invokeExact} method:
+   * {@code var matrix = (CuVSMatrix)newNativeMatrixBuilder().invokeExact(memorySegment, size, dimensions, rowStride, columnStride, dataType)}
+   * </p>
+   * @return a MethodHandle which can be invoked to build a CuVSMatrix from an external {@code MemorySegment}
+   */
+  MethodHandle newNativeMatrixBuilderWithStrides();
+
   /** Create a {@link CuVSMatrix} from an on-heap array **/
   CuVSMatrix newMatrixFromArray(float[][] vectors);
 
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
index 0f1edda99c..8ba651ce6b 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/spi/UnsupportedProvider.java
@@ -66,6 +66,12 @@ public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
     throw new UnsupportedOperationException(reasons);
   }
 
+  @Override
+  public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
+      long size, long columns, int rowStride, int columnStride, CuVSMatrix.DataType dataType) {
+    throw new UnsupportedOperationException(reasons);
+  }
+
   @Override
   public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       CuVSResources cuVSResources, long size, long dimensions, CuVSMatrix.DataType dataType) {
@@ -93,6 +99,11 @@ public MethodHandle newNativeMatrixBuilder() {
     throw new UnsupportedOperationException(reasons);
   }
 
+  @Override
+  public MethodHandle newNativeMatrixBuilderWithStrides() {
+    throw new UnsupportedOperationException(reasons);
+  }
+
   @Override
   public CuVSMatrix newMatrixFromArray(float[][] vectors) {
     throw new UnsupportedOperationException(reasons);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
index ef1b10941e..de9639c188 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixImpl.java
@@ -62,6 +62,11 @@ protected CuVSDeviceMatrixImpl(
     this.hostBuffer = new PinnedMemoryBuffer(size, columns, valueLayout);
   }
 
+  @Override
+  public long rowStride() {
+    return rowStride;
+  }
+
   @Override
   public MemorySegment toTensor(Arena arena) {
     var strides = rowStride >= 0 ? new long[] {rowStride, columnStride} : null;
@@ -189,24 +194,8 @@ private void copyRow(Object array, Arena localArena, int r, MemorySegment tmpRow
   }
 
   @Override
-  public void toHost(CuVSHostMatrix hostMatrix) {
-    if (hostMatrix.columns() != columns || hostMatrix.size() != size) {
-      throw new IllegalArgumentException("[hostMatrix] must have the same dimensions");
-    }
-    if (hostMatrix.dataType() != dataType) {
-      throw new IllegalArgumentException("[hostMatrix] must have the same dataType");
-    }
-    try (var localArena = Arena.ofConfined()) {
-      var hostMatrixTensor = ((CuVSMatrixInternal) hostMatrix).toTensor(localArena);
-
-      try (var resourceAccess = resources.access()) {
-        var cuvsRes = resourceAccess.handle();
-        var deviceMatrixTensor = toTensor(localArena);
-        checkCuVSError(
-            cuvsMatrixCopy(cuvsRes, deviceMatrixTensor, hostMatrixTensor), "cuvsMatrixCopy");
-        checkCuVSError(cuvsStreamSync(cuvsRes), "cuvsStreamSync");
-      }
-    }
+  public void toHost(CuVSHostMatrix targetMatrix) {
+    copyMatrix(this, (CuVSMatrixInternal) targetMatrix, resources);
   }
 
   @Override
@@ -291,6 +280,11 @@ public ValueLayout valueLayout() {
       return deviceMatrix.valueLayout();
     }
 
+    @Override
+    public long rowStride() {
+      return deviceMatrix.rowStride();
+    }
+
     @Override
     public MemorySegment toTensor(Arena arena) {
       return deviceMatrix.toTensor(arena);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java
index 7ad84a0e1f..97558776f6 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSDeviceMatrixRMMImpl.java
@@ -79,11 +79,27 @@ public static CuVSDeviceMatrixImpl create(
       long rowStride,
       long columnStride,
       DataType dataType) {
+
+    var valueLayout = valueLayoutFromType(dataType);
+    var elementSize = valueLayout.byteSize();
+
+    final long rowSize;
+    if (rowStride <= 0) {
+      rowSize = columns * elementSize;
+    } else if (rowStride >= columns) {
+      rowSize = rowStride * elementSize;
+    } else {
+      throw new IllegalArgumentException("Row stride cannot be less than the number of columns");
+    }
+    if (columnStride != -1) {
+      throw new UnsupportedOperationException(
+          "Stridden columns are currently not supported; columnStride must be equal to -1");
+    }
+
     try (var resourcesAccess = resources.access()) {
-      var valueLayout = valueLayoutFromType(dataType);
+
       var rmmAllocation =
-          CloseableRMMAllocation.allocateRMMSegment(
-              resourcesAccess.handle(), size * columns * valueLayout.byteSize());
+          CloseableRMMAllocation.allocateRMMSegment(resourcesAccess.handle(), size * rowSize);
       return new CuVSDeviceMatrixRMMImpl(
           resources, rmmAllocation, size, columns, rowStride, columnStride, dataType, valueLayout);
     }
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixArenaImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixArenaImpl.java
index 1dc2350638..37708d59a1 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixArenaImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixArenaImpl.java
@@ -31,20 +31,51 @@ public CuVSHostMatrixArenaImpl(long size, long columns, DataType dataType) {
     this(
         size,
         columns,
+        -1,
+        -1,
         dataType,
         valueLayoutFromType(dataType),
-        sequenceLayoutFromType(size, columns, dataType),
+        sequenceLayoutFromType(size, columns, -1, dataType),
+        Arena.ofShared());
+  }
+
+  public CuVSHostMatrixArenaImpl(
+      long size, long columns, int rowStride, int columnStride, DataType dataType) {
+    this(
+        size,
+        columns,
+        rowStride,
+        columnStride,
+        dataType,
+        valueLayoutFromType(dataType),
+        sequenceLayoutFromType(size, columns, rowStride, dataType),
         Arena.ofShared());
   }
 
   private CuVSHostMatrixArenaImpl(
       long size,
       long columns,
+      int rowStride,
+      int columnStride,
       DataType dataType,
       ValueLayout valueLayout,
       SequenceLayout layout,
       Arena arena) {
-    super(arena.allocate(layout), size, columns, dataType, valueLayout, layout);
+    super(
+        arena.allocate(layout),
+        size,
+        columns,
+        rowStride,
+        columnStride,
+        dataType,
+        valueLayout,
+        layout);
+
+    if (columnStride != -1) {
+      throw new UnsupportedOperationException(
+          "Stridden columns are currently not supported; columnStride must be equal to -1");
+    }
+
     this.arenaReference = new AtomicReference<>(arena);
   }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
index 86e7e2a059..0f5945aae5 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSHostMatrixImpl.java
@@ -32,26 +32,65 @@
 public class CuVSHostMatrixImpl extends CuVSMatrixBaseImpl implements CuVSHostMatrix {
   protected final VarHandle accessor$vh;
 
+  private final int rowStride;
+  private final int columnStride;
+  private final long rowBytes;
+  private final long rowSize;
+  private final long elementSize;
+
   public CuVSHostMatrixImpl(
       MemorySegment memorySegment, long size, long columns, DataType dataType) {
     this(
         memorySegment,
         size,
         columns,
+        -1,
+        -1,
         dataType,
         valueLayoutFromType(dataType),
-        MemoryLayout.sequenceLayout(size * columns, valueLayoutFromType(dataType))
-            .withByteAlignment(32));
+        sequenceLayoutFromType(size, columns, -1, dataType));
+  }
+
+  public CuVSHostMatrixImpl(
+      MemorySegment memorySegment,
+      long size,
+      long columns,
+      int rowStride,
+      int columnStride,
+      DataType dataType) {
+    this(
+        memorySegment,
+        size,
+        columns,
+        rowStride,
+        columnStride,
+        dataType,
+        valueLayoutFromType(dataType),
+        sequenceLayoutFromType(size, columns, rowStride, dataType));
   }
 
   protected CuVSHostMatrixImpl(
       MemorySegment memorySegment,
       long size,
       long columns,
+      int rowStride,
+      int columnStride,
       DataType dataType,
       ValueLayout valueLayout,
       MemoryLayout sequenceLayout) {
     super(memorySegment, dataType, valueLayout, size, columns);
+
+    if (rowStride > 0 && rowStride < columns) {
+      throw new IllegalArgumentException("Row stride cannot be less than the number of columns");
+    }
+
+    this.rowStride = rowStride;
+    this.columnStride = columnStride;
+
+    this.elementSize = valueLayout.byteSize();
+    this.rowSize = rowStride > 0 ? rowStride * elementSize : columns * elementSize;
+    this.rowBytes = columns * elementSize;
+
     this.accessor$vh = sequenceLayout.varHandle(MemoryLayout.PathElement.sequenceElement());
   }
 
@@ -87,10 +126,8 @@ public void toArray(int[][] array) {
         : String.format(
             Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
 
-    var valueByteSize = valueLayout.byteSize();
     for (int r = 0; r < size; ++r) {
-      MemorySegment.copy(
-          memorySegment, valueLayout, r * columns * valueByteSize, array[r], 0, (int) columns);
+      MemorySegment.copy(memorySegment, valueLayout, r * rowSize, array[r], 0, (int) columns);
     }
   }
 
@@ -112,10 +149,8 @@ public void toArray(float[][] array) {
         : String.format(
             Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
 
-    var valueByteSize = valueLayout.byteSize();
     for (int r = 0; r < size; ++r) {
-      MemorySegment.copy(
-          memorySegment, valueLayout, r * columns * valueByteSize, array[r], 0, (int) columns);
+      MemorySegment.copy(memorySegment, valueLayout, r * rowSize, array[r], 0, (int) columns);
     }
   }
 
@@ -137,10 +172,8 @@ public void toArray(byte[][] array) {
         : String.format(
             Locale.ROOT, "Input array is of the wrong type for dataType [%s]", dataType.toString());
 
-    var valueByteSize = valueLayout.byteSize();
     for (int r = 0; r < size; ++r) {
-      MemorySegment.copy(
-          memorySegment, valueLayout, r * columns * valueByteSize, array[r], 0, (int) columns);
+      MemorySegment.copy(memorySegment, valueLayout, r * rowSize, array[r], 0, (int) columns);
     }
   }
 
@@ -152,9 +185,33 @@ public CuVSHostMatrix toHost() {
   @Override
   public void toHost(CuVSHostMatrix hostMatrix) {
     var targetMatrix = (CuVSMatrixInternal) hostMatrix;
-    var valueByteSize = valueLayout.byteSize();
-    MemorySegment.copy(
-        this.memorySegment, 0L, targetMatrix.memorySegment(), 0L, size * columns * valueByteSize);
+
+    if (targetMatrix.columns() != this.columns || targetMatrix.size() != this.size) {
+      throw new IllegalArgumentException(
+          "Source and target matrices must have the same dimensions");
+    }
+    if (targetMatrix.dataType() != this.dataType) {
+      throw new IllegalArgumentException("Source and target matrices must have the same dataType");
+    }
+
+    if (this.rowStride <= 0 && targetMatrix.rowStride() <= 0) {
+      // copy whole matrix
+      MemorySegment.copy(this.memorySegment, 0L, targetMatrix.memorySegment(), 0L, size * rowSize);
+    } else {
+      // copy row-by-row
+      long targetRowSize =
+          targetMatrix.rowStride() > 0
+              ? targetMatrix.rowStride() * elementSize
+              : columns * elementSize;
+      for (int r = 0; r < size; ++r) {
+        MemorySegment.copy(
+            this.memorySegment,
+            r * rowSize,
+            targetMatrix.memorySegment(),
+            r * targetRowSize,
+            rowBytes);
+      }
+    }
   }
 
   @Override
@@ -167,13 +224,20 @@ public void close() {}
 
   @Override
   public int get(int row, int col) {
-    return (int) accessor$vh.get(memorySegment, 0L, (long) row * columns + col);
+    var rowPitch = rowStride > 0 ? rowStride : columns;
+    return (int) accessor$vh.get(memorySegment, 0L, (long) row * rowPitch + col);
+  }
+
+  @Override
+  public long rowStride() {
+    return rowStride;
   }
 
   @Override
   public MemorySegment toTensor(Arena arena) {
+    var strides = rowStride >= 0 ? new long[] {rowStride, columnStride} : null;
     return prepareTensor(
-        arena, memorySegment, new long[] {size, columns}, code(), bits(), kDLCPU());
+        arena, memorySegment, new long[] {size, columns}, strides, code(), bits(), kDLCPU());
   }
 
   private static class CuVSHostMatrixDelegate implements CuVSHostMatrix, CuVSMatrixInternal {
@@ -248,6 +312,11 @@ public ValueLayout valueLayout() {
       return hostMatrix.valueLayout();
     }
 
+    @Override
+    public long rowStride() {
+      return hostMatrix.rowStride();
+    }
+
     @Override
     public MemorySegment toTensor(Arena arena) {
       return hostMatrix.toTensor(arena);
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
index 2e510a99b2..934603e3c4 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixBaseImpl.java
@@ -107,8 +107,9 @@ protected static ValueLayout valueLayoutFromType(DataType dataType) {
   }
 
   protected static SequenceLayout sequenceLayoutFromType(
-      long size, long columns, DataType dataType) {
-    return MemoryLayout.sequenceLayout(size * columns, valueLayoutFromType(dataType))
+      long size, long columns, int rowStride, DataType dataType) {
+    var elements = rowStride > 0 ? rowStride * size : columns * size;
+    return MemoryLayout.sequenceLayout(elements, valueLayoutFromType(dataType))
         .withByteAlignment(32);
   }
 
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
index f700811d4d..472caf41d8 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/internal/CuVSMatrixInternal.java
@@ -27,7 +27,7 @@
  * Internal interface for {@link CuVSMatrix}, for shared functionality that
  * require/expose Panama types or internal cuvs types.
  */
-interface CuVSMatrixInternal extends CuVSMatrix {
+public interface CuVSMatrixInternal extends CuVSMatrix {
 
   MemorySegment memorySegment();
 
@@ -55,6 +55,8 @@ static int code(DataType dataType) {
     };
   }
 
+  long rowStride();
+
   /**
    * Creates a {@link DLManagedTensor} representing the matrix data and shape, to be
    * passed to the CuVS C API.
diff --git a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
index ed8af603e0..8a036a62ef 100644
--- a/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
+++ b/java/cuvs-java/src/main/java22/com/nvidia/cuvs/spi/JDKProvider.java
@@ -16,6 +16,7 @@
 package com.nvidia.cuvs.spi;
 
 import static com.nvidia.cuvs.internal.common.Util.*;
+import static com.nvidia.cuvs.internal.panama.headers_h.cudaMemcpy2DAsync;
 import static com.nvidia.cuvs.internal.panama.headers_h.cuvsVersionGet;
 import static com.nvidia.cuvs.internal.panama.headers_h.uint16_t;
 import static com.nvidia.cuvs.internal.panama.headers_h_1.cudaStreamSynchronize;
@@ -39,7 +40,39 @@
 
 final class JDKProvider implements CuVSProvider {
 
-  private static final MethodHandle createNativeDataset$mh = createNativeDatasetBuilder();
+  private static final MethodHandle createNativeDataset$mh;
+  private static final MethodHandle createNativeDatasetWithStrides$mh;
+
+  static {
+    try {
+      var lookup = MethodHandles.lookup();
+      createNativeDataset$mh =
+          lookup.findStatic(
+              JDKProvider.class,
+              "createNativeDataset",
+              MethodType.methodType(
+                  CuVSMatrix.class,
+                  MemorySegment.class,
+                  int.class,
+                  int.class,
+                  CuVSMatrix.DataType.class));
+
+      createNativeDatasetWithStrides$mh =
+          lookup.findStatic(
+              JDKProvider.class,
+              "createNativeDatasetWithStrides",
+              MethodType.methodType(
+                  CuVSMatrix.class,
+                  MemorySegment.class,
+                  int.class,
+                  int.class,
+                  int.class,
+                  int.class,
+                  CuVSMatrix.DataType.class));
+    } catch (NoSuchMethodException | IllegalAccessException e) {
+      throw new RuntimeException(e);
+    }
+  }
 
   private JDKProvider() {}
 
@@ -84,27 +117,22 @@ private static String readCuVSVersionFromManifest() {
     }
   }
 
-  static MethodHandle createNativeDatasetBuilder() {
-    try {
-      var lookup = MethodHandles.lookup();
-      var mt =
-          MethodType.methodType(
-              CuVSMatrix.class,
-              MemorySegment.class,
-              int.class,
-              int.class,
-              CuVSMatrix.DataType.class);
-      return lookup.findStatic(JDKProvider.class, "createNativeDataset", mt);
-    } catch (NoSuchMethodException | IllegalAccessException e) {
-      throw new RuntimeException(e);
-    }
-  }
-
   private static CuVSMatrix createNativeDataset(
       MemorySegment memorySegment, int size, int dimensions, CuVSMatrix.DataType dataType) {
     return new CuVSHostMatrixImpl(memorySegment, size, dimensions, dataType);
   }
 
+  private static CuVSMatrix createNativeDatasetWithStrides(
+      MemorySegment memorySegment,
+      int size,
+      int dimensions,
+      int rowStride,
+      int columnStride,
+      CuVSMatrix.DataType dataType) {
+    return new CuVSHostMatrixImpl(
+        memorySegment, size, dimensions, rowStride, columnStride, dataType);
+  }
+
   @Override
   public CuVSResources newCuVSResources(Path tempDirectory) {
     Objects.requireNonNull(tempDirectory);
@@ -160,65 +188,22 @@ public GPUInfoProvider gpuInfoProvider() {
 
   @Override
   public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
-      long size, long columns, CuVSMatrix.DataType dataType) throws UnsupportedOperationException {
-
-    return new CuVSMatrix.Builder<>() {
-      final CuVSHostMatrixArenaImpl matrix = new CuVSHostMatrixArenaImpl(size, columns, dataType);
-      int current = 0;
-
-      @Override
-      public void addVector(float[] vector) {
-        if (vector.length != columns) {
-          throw new IllegalArgumentException(
-              String.format(
-                  Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
-        }
-        internalAddVector(vector);
-      }
-
-      @Override
-      public void addVector(byte[] vector) {
-        if (vector.length != columns) {
-          throw new IllegalArgumentException(
-              String.format(
-                  Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
-        }
-        internalAddVector(vector);
-      }
+      long size, long columns, CuVSMatrix.DataType dataType) {
 
-      @Override
-      public void addVector(int[] vector) {
-        if (vector.length != columns) {
-          throw new IllegalArgumentException(
-              String.format(
-                  Locale.ROOT, "Expected a vector of size [%d], got [%d]", columns, vector.length));
-        }
-        internalAddVector(vector);
-      }
+    return new HostMatrixBuilder(size, columns, dataType);
+  }
 
-      private void internalAddVector(Object vector) {
-        if (current >= size) throw new ArrayIndexOutOfBoundsException();
-        MemorySegment.copy(
-            vector,
-            0,
-            matrix.memorySegment(),
-            matrix.valueLayout(),
-            ((current++) * columns * matrix.valueLayout().byteSize()),
-            (int) columns);
-      }
+  @Override
+  public CuVSMatrix.Builder<CuVSHostMatrix> newHostMatrixBuilder(
+      long size, long columns, int rowStride, int columnStride, CuVSMatrix.DataType dataType) {
 
-      @Override
-      public CuVSHostMatrix build() {
-        return matrix;
-      }
-    };
+    return new HostMatrixBuilder(size, columns, rowStride, columnStride, dataType);
   }
 
   @Override
   public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
-      CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType)
-      throws UnsupportedOperationException {
-    return new BufferedSegmentBuilder(resources, size, columns, dataType);
+      CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType) {
+    return new DeviceMatrixBuilder(resources, size, columns, dataType);
   }
 
   @Override
@@ -229,7 +214,7 @@ public CuVSMatrix.Builder<CuVSDeviceMatrix> newDeviceMatrixBuilder(
       int rowStride,
       int columnStride,
       CuVSMatrix.DataType dataType) {
-    return new BufferedSegmentBuilder(resources, size, columns, rowStride, columnStride, dataType);
+    return new DeviceMatrixBuilder(resources, size, columns, rowStride, columnStride, dataType);
   }
 
   @Override
@@ -237,6 +222,11 @@ public MethodHandle newNativeMatrixBuilder() {
     return createNativeDataset$mh;
   }
 
+  @Override
+  public MethodHandle newNativeMatrixBuilderWithStrides() {
+    return createNativeDatasetWithStrides$mh;
+  }
+
   @Override
   public CuVSMatrix newMatrixFromArray(float[][] vectors) {
     Objects.requireNonNull(vectors);
@@ -279,62 +269,37 @@ public CuVSMatrix newMatrixFromArray(byte[][] vectors) {
     return dataset;
   }
 
-  /**
-   * This {@link CuVSDeviceMatrix} builder implementation returns a {@link CuVSDeviceMatrix} backed by managed RMM
-   * device memory. It uses a {@link PinnedMemoryBuffer} to batch data before copying it to the GPU.
-   */
-  private static class BufferedSegmentBuilder implements CuVSMatrix.Builder<CuVSDeviceMatrix> {
-
-    private final long columns;
-    private final long size;
-    private final CuVSDeviceMatrixImpl matrix;
-    private final MemorySegment stream;
+  private abstract static class MatrixBuilder<T extends CuVSMatrixInternal> {
 
-    private final long rowBytes;
-    private int currentRow;
+    protected final long columns;
+    protected final long size;
+    protected final T matrix;
+    protected final long elementSize;
+    protected final long rowSize;
+    protected final long rowBytes;
+    protected int currentRow;
 
-    private final PinnedMemoryBuffer hostBuffer;
-    private final long bufferRowCount;
-    private int currentBufferRow;
-
-    private BufferedSegmentBuilder(
-        CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType) {
+    protected MatrixBuilder(T matrix, long size, long columns) {
       this.columns = columns;
       this.size = size;
-      this.matrix = CuVSDeviceMatrixRMMImpl.create(resources, size, columns, dataType);
-      this.stream = Util.getStream(resources);
+      this.matrix = matrix;
+      this.elementSize = matrix.valueLayout().byteSize();
+      this.rowSize = columns * elementSize;
+      this.rowBytes = rowSize;
       this.currentRow = 0;
-
-      this.hostBuffer = new PinnedMemoryBuffer(size, columns, matrix.valueLayout());
-
-      this.rowBytes = columns * matrix.valueLayout().byteSize();
-      this.bufferRowCount = Math.min((hostBuffer.size() / rowBytes), size);
-      this.currentBufferRow = 0;
     }
 
-    private BufferedSegmentBuilder(
-        CuVSResources resources,
-        long size,
-        long columns,
-        int rowStride,
-        int columnStride,
-        CuVSMatrix.DataType dataType) {
+    protected MatrixBuilder(T matrix, long size, long columns, int rowStride) {
       this.columns = columns;
       this.size = size;
-      this.matrix =
-          CuVSDeviceMatrixRMMImpl.create(
-              resources, size, columns, rowStride, columnStride, dataType);
-      this.stream = Util.getStream(resources);
-      this.currentRow = 0;
+      this.matrix = matrix;
+      this.elementSize = matrix.valueLayout().byteSize();
+      this.rowSize = rowStride > 0 ? rowStride * elementSize : columns * elementSize;
+      this.rowBytes = columns * elementSize;
 
-      this.hostBuffer = new PinnedMemoryBuffer(size, columns, matrix.valueLayout());
-
-      this.rowBytes = columns * matrix.valueLayout().byteSize();
-      this.bufferRowCount = Math.min((hostBuffer.size() / rowBytes), size);
-      this.currentBufferRow = 0;
+      this.currentRow = 0;
     }
 
-    @Override
     public void addVector(float[] vector) {
       if (vector.length != columns) {
         throw new IllegalArgumentException(
@@ -344,7 +309,6 @@ public void addVector(float[] vector) {
       internalAddVector(MemorySegment.ofArray(vector));
     }
 
-    @Override
     public void addVector(byte[] vector) {
       if (vector.length != columns) {
         throw new IllegalArgumentException(
@@ -354,7 +318,6 @@ public void addVector(byte[] vector) {
       internalAddVector(MemorySegment.ofArray(vector));
     }
 
-    @Override
     public void addVector(int[] vector) {
       if (vector.length != columns) {
         throw new IllegalArgumentException(
@@ -364,7 +327,55 @@ public void addVector(int[] vector) {
       internalAddVector(MemorySegment.ofArray(vector));
     }
 
-    private void internalAddVector(MemorySegment vector) {
+    protected abstract void internalAddVector(MemorySegment vector);
+  }
+
+  /**
+   * This {@link CuVSDeviceMatrix} builder implementation returns a {@link CuVSDeviceMatrix} backed by managed RMM
+   * device memory. It uses a {@link PinnedMemoryBuffer} to batch data before copying it to the GPU.
+   */
+  private static final class DeviceMatrixBuilder extends MatrixBuilder<CuVSDeviceMatrixImpl>
+      implements CuVSMatrix.Builder<CuVSDeviceMatrix> {
+
+    private final MemorySegment stream;
+
+    private final PinnedMemoryBuffer hostBuffer;
+    private final long bufferRowCount;
+    private int currentBufferRow;
+
+    private DeviceMatrixBuilder(
+        CuVSResources resources, long size, long columns, CuVSMatrix.DataType dataType) {
+      super(CuVSDeviceMatrixRMMImpl.create(resources, size, columns, dataType), size, columns);
+      this.stream = Util.getStream(resources);
+
+      this.hostBuffer = new PinnedMemoryBuffer(size, columns, matrix.valueLayout());
+      this.bufferRowCount = Math.min((hostBuffer.size() / rowBytes), size);
+      this.currentBufferRow = 0;
+    }
+
+    private DeviceMatrixBuilder(
+        CuVSResources resources,
+        long size,
+        long columns,
+        int rowStride,
+        int columnStride,
+        CuVSMatrix.DataType dataType) {
+      super(
+          CuVSDeviceMatrixRMMImpl.create(
+              resources, size, columns, rowStride, columnStride, dataType),
+          size,
+          columns,
+          rowStride);
+
+      this.stream = Util.getStream(resources);
+
+      this.hostBuffer = new PinnedMemoryBuffer(size, columns, matrix.valueLayout());
+      this.bufferRowCount = Math.min((hostBuffer.size() / rowBytes), size);
+      this.currentBufferRow = 0;
+    }
+
+    @Override
+    protected void internalAddVector(MemorySegment vector) {
       if (currentRow >= size) {
         throw new ArrayIndexOutOfBoundsException();
       }
@@ -380,14 +391,20 @@ private void internalAddVector(MemorySegment vector) {
 
     private void flushBuffer() {
       if (currentBufferRow > 0) {
-        var deviceMemoryOffset = (currentRow - currentBufferRow) * rowBytes;
+        var deviceMemoryOffset = (currentRow - currentBufferRow) * rowSize;
         var dst = matrix.memorySegment().asSlice(deviceMemoryOffset);
-        cudaMemcpyAsync(
-            dst,
-            hostBuffer.address(),
-            currentBufferRow * rowBytes,
-            CudaMemcpyKind.HOST_TO_DEVICE,
-            stream);
+        checkCudaError(
+            cudaMemcpy2DAsync(
+                dst,
+                rowSize,
+                hostBuffer.address(),
+                rowBytes,
+                rowBytes,
+                currentBufferRow,
+                CudaMemcpyKind.HOST_TO_DEVICE.kind,
+                stream),
+            "cudaMemcpy2DAsync");
+
         currentBufferRow = 0;
         checkCudaError(cudaStreamSynchronize(stream), "cudaStreamSynchronize");
       }
@@ -400,4 +417,34 @@ public CuVSDeviceMatrix build() {
       return matrix;
     }
   }
+
+  private static class HostMatrixBuilder extends MatrixBuilder<CuVSHostMatrixImpl>
+      implements CuVSMatrix.Builder<CuVSHostMatrix> {
+
+    private HostMatrixBuilder(long size, long columns, CuVSMatrix.DataType dataType) {
+      super(new CuVSHostMatrixArenaImpl(size, columns, dataType), size, columns);
+    }
+
+    private HostMatrixBuilder(
+        long size, long columns, int rowStride, int columnStride, CuVSMatrix.DataType dataType) {
+      super(
+          new CuVSHostMatrixArenaImpl(size, columns, rowStride, columnStride, dataType),
+          size,
+          columns,
+          rowStride);
+    }
+
+    @Override
+    protected void internalAddVector(MemorySegment vector) {
+      if (currentRow >= size) {
+        throw new ArrayIndexOutOfBoundsException();
+      }
+      MemorySegment.copy(vector, 0, matrix.memorySegment(), ((currentRow++) * rowSize), rowBytes);
+    }
+
+    @Override
+    public CuVSHostMatrix build() {
+      return matrix;
+    }
+  }
 }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
index 917acd4fc4..9e46845110 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/CuVSMatrixIT.java
@@ -20,6 +20,8 @@
 import static org.junit.Assert.assertEquals;
 
 import com.carrotsearch.randomizedtesting.RandomizedRunner;
+import java.lang.foreign.*;
+import java.util.Locale;
 import org.junit.Before;
 import org.junit.Test;
 import org.junit.runner.RunWith;
@@ -71,9 +73,11 @@ private float[][] createFloatMatrix() {
   private float[][] createFloatMatrix(int rows, int cols) {
     float[][] result = new float[rows][cols];
 
+    float value = 1;
     for (int r = 0; r < rows; ++r) {
       for (int c = 0; c < cols; ++c) {
-        result[r][c] = randomFloat();
+        result[r][c] = value;
+        value += 1;
       }
     }
     return result;
@@ -419,18 +423,7 @@ public void testDeviceToHost() throws Throwable {
 
       try (var deviceMatrix = builder.build();
           var hostMatrix = deviceMatrix.toHost()) {
-
-        assertEquals(data.length, deviceMatrix.size());
-        assertEquals(data[0].length, deviceMatrix.columns());
-
-        assertEquals(deviceMatrix.size(), hostMatrix.size());
-        assertEquals(deviceMatrix.columns(), hostMatrix.columns());
-
-        var roundTripData = new float[size][columns];
-
-        hostMatrix.toArray(roundTripData);
-
-        assertSame2dArray(hostMatrix.size(), hostMatrix.columns(), data, roundTripData);
+        checkSameData(deviceMatrix, hostMatrix, data, size, columns);
       }
     }
   }
@@ -446,18 +439,7 @@ public void testHostToDevice() throws Throwable {
 
       try (var hostMatrix = CuVSMatrix.ofArray(data);
           var deviceMatrix = hostMatrix.toDevice(resources)) {
-
-        assertEquals(data.length, deviceMatrix.size());
-        assertEquals(data[0].length, deviceMatrix.columns());
-
-        assertEquals(deviceMatrix.size(), hostMatrix.size());
-        assertEquals(deviceMatrix.columns(), hostMatrix.columns());
-
-        var roundTripData = new float[size][columns];
-
-        deviceMatrix.toArray(roundTripData);
-
-        assertSame2dArray(deviceMatrix.size(), deviceMatrix.columns(), data, roundTripData);
+        checkSameData(hostMatrix, deviceMatrix, data, size, columns);
       }
     }
   }
@@ -471,18 +453,7 @@ public void testHostToHostReturnsWeakReferenceSameData() {
 
     try (var hostMatrix = CuVSMatrix.ofArray(data);
         var hostMatrix2 = hostMatrix.toHost()) {
-
-      assertEquals(data.length, hostMatrix2.size());
-      assertEquals(data[0].length, hostMatrix2.columns());
-
-      assertEquals(hostMatrix2.size(), hostMatrix.size());
-      assertEquals(hostMatrix2.columns(), hostMatrix.columns());
-
-      var roundTripData = new float[size][columns];
-
-      hostMatrix2.toArray(roundTripData);
-
-      assertSame2dArray(hostMatrix2.size(), hostMatrix2.columns(), data, roundTripData);
+      checkSameData(hostMatrix, hostMatrix2, data, size, columns);
     }
   }
 
@@ -503,18 +474,268 @@ public void testDeviceToDeviceReturnsWeakReferenceSameData() throws Throwable {
 
       try (var deviceMatrix = builder.build();
           var deviceMatrix2 = deviceMatrix.toDevice(resources)) {
+        checkSameData(deviceMatrix, deviceMatrix2, data, size, columns);
+      }
+    }
+  }
+
+  @Test
+  public void testHostToHostWithDifferentStrides() {
+
+    int size = randomIntBetween(1, 32 * 1024);
+    int columns = randomIntBetween(16, 2048);
+    int rowStride1 = randomIntBetween(columns, columns * 2);
+    int rowStride2 = randomIntBetween(columns, columns * 2);
+    final float[][] data = createFloatMatrix(size, columns);
 
-        assertEquals(data.length, deviceMatrix.size());
-        assertEquals(data[0].length, deviceMatrix.columns());
+    var builder1 = CuVSMatrix.hostBuilder(size, columns, rowStride1, -1, CuVSMatrix.DataType.FLOAT);
+    for (int i = 0; i < size; ++i) {
+      var array = data[i];
+      builder1.addVector(array);
+    }
+
+    var builder2 = CuVSMatrix.hostBuilder(size, columns, rowStride2, -1, CuVSMatrix.DataType.FLOAT);
+
+    try (var matrix1 = builder1.build();
+        var matrix2 = builder2.build()) {
+
+      matrix1.toHost(matrix2);
+      checkSameData(matrix1, matrix2, data, size, columns);
+    }
+  }
 
-        assertEquals(deviceMatrix.size(), deviceMatrix2.size());
-        assertEquals(deviceMatrix.columns(), deviceMatrix2.columns());
+  @Test
+  public void testHostBuilderWithDifferentStrides() throws Throwable {
+
+    int size = randomIntBetween(1, 32 * 1024);
+    int columns = randomIntBetween(16, 2048);
+    int rowStride1 = randomIntBetween(columns, columns * 2);
+    int rowStride2 = randomIntBetween(columns, columns * 2);
+    final float[][] data = createFloatMatrix(size, columns);
+
+    var builder1 = CuVSMatrix.hostBuilder(size, columns, rowStride1, -1, CuVSMatrix.DataType.FLOAT);
+    for (int i = 0; i < size; ++i) {
+      var array = data[i];
+      builder1.addVector(array);
+    }
+
+    var builder2 = CuVSMatrix.hostBuilder(size, columns, rowStride2, -1, CuVSMatrix.DataType.FLOAT);
+    for (int i = 0; i < size; ++i) {
+      var array = data[i];
+      builder2.addVector(array);
+    }
+
+    try (var matrix1 = builder1.build();
+        var matrix2 = builder2.build()) {
+      checkSameData(matrix1, matrix2, data, size, columns);
+    }
+  }
+
+  @Test
+  public void testDeviceToHostWithDifferentStrides() throws Throwable {
+
+    int size = randomIntBetween(1, 32 * 1024);
+    int columns = randomIntBetween(16, 2048);
+    int rowStride1 = randomIntBetween(columns, columns * 2);
+    int rowStride2 = randomIntBetween(columns, columns * 2);
+    final float[][] data = createFloatMatrix(size, columns);
+
+    try (var resources = CuVSResources.create()) {
+      var builder1 =
+          CuVSMatrix.deviceBuilder(
+              resources, size, columns, rowStride1, -1, CuVSMatrix.DataType.FLOAT);
+      for (int i = 0; i < size; ++i) {
+        var array = data[i];
+        builder1.addVector(array);
+      }
+
+      var builder2 =
+          CuVSMatrix.hostBuilder(size, columns, rowStride2, -1, CuVSMatrix.DataType.FLOAT);
+
+      try (var matrix1 = builder1.build();
+          var matrix2 = builder2.build()) {
+
+        matrix1.toHost(matrix2);
+        checkSameData(matrix1, matrix2, data, size, columns);
+      }
+    }
+  }
+
+  @Test
+  public void testDeviceToDeviceWithDifferentStrides() throws Throwable {
+
+    int size = randomIntBetween(1, 32 * 1024);
+    int columns = randomIntBetween(16, 2048);
+    int rowStride1 = randomIntBetween(columns, columns * 2);
+    int rowStride2 = randomIntBetween(columns, columns * 2);
+    final float[][] data = createFloatMatrix(size, columns);
+
+    try (var resources = CuVSResources.create()) {
+      var builder1 =
+          CuVSMatrix.deviceBuilder(
+              resources, size, columns, rowStride1, -1, CuVSMatrix.DataType.FLOAT);
+      for (int i = 0; i < size; ++i) {
+        var array = data[i];
+        builder1.addVector(array);
+      }
+
+      var builder2 =
+          CuVSMatrix.deviceBuilder(
+              resources, size, columns, rowStride2, -1, CuVSMatrix.DataType.FLOAT);
+
+      try (var matrix1 = builder1.build();
+          var matrix2 = builder2.build()) {
+
+        matrix1.toDevice(matrix2, resources);
+        checkSameData(matrix1, matrix2, data, size, columns);
+      }
+    }
+  }
+
+  private static void checkSameData(
+      CuVSMatrix matrix1, CuVSMatrix matrix2, float[][] data, int size, int columns) {
+    assertEquals(data.length, matrix1.size());
+    assertEquals(data[0].length, matrix1.columns());
+
+    assertEquals(matrix1.size(), matrix2.size());
+    assertEquals(matrix1.columns(), matrix2.columns());
+
+    var roundTripData1 = new float[size][columns];
+    var roundTripData2 = new float[size][columns];
+
+    matrix1.toArray(roundTripData1);
+    matrix2.toArray(roundTripData2);
+
+    for (int n = 0; n < matrix2.size(); ++n) {
+      for (int i = 0; i < matrix2.columns(); ++i) {
+        var diff1 = Math.abs(data[n][i] - roundTripData1[n][i]);
+        if (diff1 > DELTA) {
+          throw new AssertionError(
+              String.format(
+                  Locale.ROOT,
+                  "Matrix1 mismatch. Expected:<%f> but was:<%f> at row [%d]of[%d], col [%d]of[%d]",
+                  data[n][i],
+                  roundTripData2[n][i],
+                  n,
+                  size,
+                  i,
+                  columns));
+        }
+        var diff2 = Math.abs(data[n][i] - roundTripData2[n][i]);
+        if (diff2 > DELTA) {
+          throw new AssertionError(
+              String.format(
+                  Locale.ROOT,
+                  "Matrix2 mismatch. Expected:<%f> but was:<%f> at row [%d]of[%d], col [%d]of[%d]",
+                  data[n][i],
+                  roundTripData2[n][i],
+                  n,
+                  size,
+                  i,
+                  columns));
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testDeviceBuilderWithDifferentStrides() throws Throwable {
+
+    int size = randomIntBetween(1, 32 * 1024);
+    int columns = randomIntBetween(16, 2048);
+    int rowStride1 = randomIntBetween(columns, columns * 2);
+    int rowStride2 = randomIntBetween(columns, columns * 2);
+    final float[][] data = createFloatMatrix(size, columns);
+
+    try (var resources = CuVSResources.create()) {
+      var builder1 =
+          CuVSMatrix.deviceBuilder(
+              resources, size, columns, rowStride1, -1, CuVSMatrix.DataType.FLOAT);
+      for (int i = 0; i < size; ++i) {
+        var array = data[i];
+        builder1.addVector(array);
+      }
+
+      var builder2 =
+          CuVSMatrix.deviceBuilder(
+              resources, size, columns, rowStride2, -1, CuVSMatrix.DataType.FLOAT);
+      for (int i = 0; i < size; ++i) {
+        var array = data[i];
+        builder2.addVector(array);
+      }
+
+      try (var matrix1 = builder1.build();
+          var matrix2 = builder2.build()) {
+        checkSameData(matrix1, matrix2, data, size, columns);
+      }
+    }
+  }
+
+  @Test
+  public void testHostMatrixFromNativeDataset() {
+    int size = randomIntBetween(1, 32 * 1024);
+    int columns = randomIntBetween(16, 2048);
+    final float[][] data = createFloatMatrix(size, columns);
+
+    ValueLayout.OfFloat C_FLOAT =
+        (ValueLayout.OfFloat) Linker.nativeLinker().canonicalLayouts().get("float");
+
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout((long) size * columns, C_FLOAT);
+
+    try (Arena arena = Arena.ofShared()) {
+      MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+      for (int r = 0; r < size; r++) {
+        MemorySegment.copy(
+            data[r], 0, dataMemorySegment, C_FLOAT, (r * columns * C_FLOAT.byteSize()), columns);
+      }
+
+      try (var nativeDataset =
+          DatasetHelper.fromMemorySegment(
+              dataMemorySegment, size, columns, CuVSMatrix.DataType.FLOAT)) {
 
         var roundTripData = new float[size][columns];
+        nativeDataset.toArray(roundTripData);
 
-        deviceMatrix2.toArray(roundTripData);
+        for (int n = 0; n < nativeDataset.size(); ++n) {
+          for (int i = 0; i < nativeDataset.columns(); ++i) {
+            assertEquals(data[n][i], roundTripData[n][i], DELTA);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testHostMatrixFromNativeDatasetWithStride() {
+    int size = randomIntBetween(1, 32 * 1024);
+    int columns = randomIntBetween(16, 2048);
+    int rowStride = randomIntBetween(columns, columns * 2);
+    final float[][] data = createFloatMatrix(size, columns);
+
+    ValueLayout.OfFloat C_FLOAT =
+        (ValueLayout.OfFloat) Linker.nativeLinker().canonicalLayouts().get("float");
 
-        assertSame2dArray(deviceMatrix2.size(), deviceMatrix2.columns(), data, roundTripData);
+    MemoryLayout dataMemoryLayout = MemoryLayout.sequenceLayout((long) size * rowStride, C_FLOAT);
+
+    try (Arena arena = Arena.ofShared()) {
+      MemorySegment dataMemorySegment = arena.allocate(dataMemoryLayout);
+      for (int r = 0; r < size; r++) {
+        MemorySegment.copy(
+            data[r], 0, dataMemorySegment, C_FLOAT, (r * rowStride * C_FLOAT.byteSize()), columns);
+      }
+
+      try (var nativeDataset =
+          DatasetHelper.fromMemorySegment(
+              dataMemorySegment, size, columns, rowStride, -1, CuVSMatrix.DataType.FLOAT)) {
+
+        var roundTripData = new float[size][columns];
+        nativeDataset.toArray(roundTripData);
+
+        for (int n = 0; n < nativeDataset.size(); ++n) {
+          for (int i = 0; i < nativeDataset.columns(); ++i) {
+            assertEquals(data[n][i], roundTripData[n][i], DELTA);
+          }
+        }
       }
     }
   }
diff --git a/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java b/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
index 9138f9952a..fd25e2df55 100644
--- a/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
+++ b/java/cuvs-java/src/test/java/com/nvidia/cuvs/DatasetHelper.java
@@ -23,6 +23,8 @@ public class DatasetHelper {
 
   private static final MethodHandle createDataset$mh =
       CuVSProvider.provider().newNativeMatrixBuilder();
+  private static final MethodHandle createDatasetWithStrides$mh =
+      CuVSProvider.provider().newNativeMatrixBuilderWithStrides();
 
   public static CuVSMatrix fromMemorySegment(
       MemorySegment memorySegment, int size, int dimensions, CuVSMatrix.DataType dataType) {
@@ -38,4 +40,26 @@ public static CuVSMatrix fromMemorySegment(
       }
     }
   }
+
+  public static CuVSMatrix fromMemorySegment(
+      MemorySegment memorySegment,
+      int size,
+      int dimensions,
+      int rowStride,
+      int columnStride,
+      CuVSMatrix.DataType dataType) {
+    try {
+      return (CuVSMatrix)
+          createDatasetWithStrides$mh.invokeExact(
+              memorySegment, size, dimensions, rowStride, columnStride, dataType);
+    } catch (Throwable e) {
+      if (e instanceof Error err) {
+        throw err;
+      } else if (e instanceof RuntimeException re) {
+        throw re;
+      } else {
+        throw new RuntimeException(e);
+      }
+    }
+  }
 }

From fdb8bfb8541f680f03fcd2ecd1e76657aff31251 Mon Sep 17 00:00:00 2001
From: Victor Lafargue <viclafargue@nvidia.com>
Date: Mon, 29 Sep 2025 19:06:20 +0200
Subject: [PATCH 605/623] Treat warnings as errors in doc builds + MG ANNs
 Python API doc update (#1350)

This PR :
- updates doc build to treat warnings as errors
- fixes an issue in the MG ANNs Python API doc
- fixes several doc issues that resulted in warnings

Closes https://github.com/rapidsai/cuvs/issues/1382

Authors:
  - Victor Lafargue (https://github.com/viclafargue)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - James Lamb (https://github.com/jameslamb)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1350
---
 build.sh                                       |  4 ++--
 ci/build_docs.sh                               |  8 ++++----
 cpp/include/cuvs/neighbors/cagra.hpp           |  4 ++++
 cpp/include/cuvs/neighbors/hnsw.hpp            |  4 ++++
 cpp/include/cuvs/neighbors/vamana.hpp          |  4 ++++
 .../cuvs/preprocessing/spectral_embedding.hpp  | 10 +++++-----
 docs/Makefile                                  |  2 +-
 docs/source/neighbors/all_neighbors.rst        |  2 +-
 docs/source/python_api/neighbors_mg_cagra.rst  | 16 ++++++++--------
 .../python_api/neighbors_mg_ivf_flat.rst       | 18 +++++++++---------
 docs/source/python_api/neighbors_mg_ivf_pq.rst | 18 +++++++++---------
 11 files changed, 51 insertions(+), 39 deletions(-)

diff --git a/build.sh b/build.sh
index 328c64b43b..35e6fc84fd 100755
--- a/build.sh
+++ b/build.sh
@@ -517,10 +517,10 @@ if hasArg docs; then
     cd "${DOXYGEN_BUILD_DIR}"
     doxygen Doxyfile
     cd "${SPHINX_BUILD_DIR}"
-    sphinx-build -W -b html source _html
+    make html
     cd "${REPODIR}"/rust
     cargo doc -p cuvs --no-deps
-    rsync -av "${RUST_BUILD_DIR}"/doc/ "${SPHINX_BUILD_DIR}"/_html/_static/rust
+    rsync -av "${RUST_BUILD_DIR}"/doc/ "${SPHINX_BUILD_DIR}"/build/html/_static/rust
 fi
 
 ################################################################################
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index c53e6a0e9d..9fe65d1b04 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 
 set -euo pipefail
 
@@ -48,10 +48,10 @@ popd
 
 rapids-logger "Build Python docs"
 pushd docs
-sphinx-build -b dirhtml source _html
-mv ../rust/target/doc ./_html/_static/rust
+make dirhtml
+mv ../rust/target/doc ./build/dirhtml/_static/rust
 mkdir -p "${RAPIDS_DOCS_DIR}/cuvs/"html
-mv _html/* "${RAPIDS_DOCS_DIR}/cuvs/html"
+mv build/dirhtml/* "${RAPIDS_DOCS_DIR}/cuvs/html"
 popd
 
 RAPIDS_VERSION_NUMBER="${RAPIDS_VERSION_MAJOR_MINOR}" rapids-upload-docs
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index ac86492ac6..1c0890b393 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -1964,6 +1964,10 @@ void serialize_to_hnswlib(
   std::optional<raft::host_matrix_view<const uint8_t, int64_t, raft::row_major>> dataset =
     std::nullopt);
 
+/**
+ * @}
+ */
+
 /**
  * @defgroup cagra_cpp_index_merge CAGRA index build functions
  * @{
diff --git a/cpp/include/cuvs/neighbors/hnsw.hpp b/cpp/include/cuvs/neighbors/hnsw.hpp
index 7be7ccff54..b89a434156 100644
--- a/cpp/include/cuvs/neighbors/hnsw.hpp
+++ b/cpp/include/cuvs/neighbors/hnsw.hpp
@@ -163,6 +163,10 @@ struct extend_params {
   int num_threads = 0;
 };
 
+/**
+ * @}
+ */
+
 /**
  * @defgroup hnsw_cpp_index_load Load CAGRA index as hnswlib index
  * @{
diff --git a/cpp/include/cuvs/neighbors/vamana.hpp b/cpp/include/cuvs/neighbors/vamana.hpp
index 25b3d4f7d6..625ceff988 100644
--- a/cpp/include/cuvs/neighbors/vamana.hpp
+++ b/cpp/include/cuvs/neighbors/vamana.hpp
@@ -488,6 +488,10 @@ auto build(raft::resources const& res,
            raft::host_matrix_view<const uint8_t, int64_t, raft::row_major> dataset)
   -> cuvs::neighbors::vamana::index<uint8_t, uint32_t>;
 
+/**
+ * @}
+ */
+
 /**
  * @defgroup vamana_cpp_serialize Vamana serialize functions
  * @{
diff --git a/cpp/include/cuvs/preprocessing/spectral_embedding.hpp b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
index 3162f74f02..58f0880079 100644
--- a/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
+++ b/cpp/include/cuvs/preprocessing/spectral_embedding.hpp
@@ -23,11 +23,6 @@
 
 namespace cuvs::preprocessing::spectral_embedding {
 
-/**
- * @defgroup spectral_embedding Spectral Embedding
- * @{
- */
-
 /**
  * @brief Parameters for spectral embedding algorithm
  *
@@ -71,6 +66,11 @@ struct params {
   uint64_t seed;
 };
 
+/**
+ * @defgroup spectral_embedding Spectral Embedding
+ * @{
+ */
+
 /**
  * @brief Perform spectral embedding on input dataset
  *
diff --git a/docs/Makefile b/docs/Makefile
index ac40850f1f..86a635a54d 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -2,7 +2,7 @@
 #
 
 # You can set these variables from the command line.
-SPHINXOPTS    =
+SPHINXOPTS    = -W
 SPHINXBUILD   = sphinx-build
 SPHINXPROJ    = cuvs
 SOURCEDIR     = source
diff --git a/docs/source/neighbors/all_neighbors.rst b/docs/source/neighbors/all_neighbors.rst
index c95d07488e..a70414fe06 100644
--- a/docs/source/neighbors/all_neighbors.rst
+++ b/docs/source/neighbors/all_neighbors.rst
@@ -16,7 +16,7 @@ All-neighbors supports multiple underlying algorithms:
 
 The algorithm partitions the dataset into clusters and distributes the work across multiple GPUs when possible, making it suitable for large-scale graph construction tasks.
 
-[ :doc:`C API <../c_api/neighbors_all_neighbors_c>` |:doc:`C++ API <../cpp_api/neighbors_all_neighbors>` | :doc:`Python API <../python_api/neighbors_all_neighbors>` ]
+[ :doc:`C API <../c_api/neighbors_all_neighbors_c>` | :doc:`C++ API <../cpp_api/neighbors_all_neighbors>` | :doc:`Python API <../python_api/neighbors_all_neighbors>` ]
 
 Algorithm Overview
 ------------------
diff --git a/docs/source/python_api/neighbors_mg_cagra.rst b/docs/source/python_api/neighbors_mg_cagra.rst
index 2f03fa389b..763e0e2157 100644
--- a/docs/source/python_api/neighbors_mg_cagra.rst
+++ b/docs/source/python_api/neighbors_mg_cagra.rst
@@ -14,42 +14,42 @@ Multi-GPU CAGRA extends the graph-based CAGRA algorithm to work across multiple
 Index build parameters
 ######################
 
-.. autoclass:: cuvs.neighbors.mg_cagra.IndexParams
+.. autoclass:: cuvs.neighbors.mg.cagra.IndexParams
     :members:
 
 Index search parameters
 #######################
 
-.. autoclass:: cuvs.neighbors.mg_cagra.SearchParams
+.. autoclass:: cuvs.neighbors.mg.cagra.SearchParams
     :members:
 
 Index
 #####
 
-.. autoclass:: cuvs.neighbors.mg_cagra.Index
+.. autoclass:: cuvs.neighbors.mg.cagra.Index
     :members:
 
 Index build
 ###########
 
-.. autofunction:: cuvs.neighbors.mg_cagra.build
+.. autofunction:: cuvs.neighbors.mg.cagra.build
 
 Index search
 ############
 
-.. autofunction:: cuvs.neighbors.mg_cagra.search
+.. autofunction:: cuvs.neighbors.mg.cagra.search
 
 Index save
 ##########
 
-.. autofunction:: cuvs.neighbors.mg_cagra.save
+.. autofunction:: cuvs.neighbors.mg.cagra.save
 
 Index load
 ##########
 
-.. autofunction:: cuvs.neighbors.mg_cagra.load
+.. autofunction:: cuvs.neighbors.mg.cagra.load
 
 Index distribute
 ################
 
-.. autofunction:: cuvs.neighbors.mg_cagra.distribute
+.. autofunction:: cuvs.neighbors.mg.cagra.distribute
diff --git a/docs/source/python_api/neighbors_mg_ivf_flat.rst b/docs/source/python_api/neighbors_mg_ivf_flat.rst
index 37515ce546..68eea86fec 100644
--- a/docs/source/python_api/neighbors_mg_ivf_flat.rst
+++ b/docs/source/python_api/neighbors_mg_ivf_flat.rst
@@ -14,47 +14,47 @@ Multi-GPU IVF-Flat extends the IVF-Flat algorithm to work across multiple GPUs,
 Index build parameters
 ######################
 
-.. autoclass:: cuvs.neighbors.mg_ivf_flat.IndexParams
+.. autoclass:: cuvs.neighbors.mg.ivf_flat.IndexParams
     :members:
 
 Index search parameters
 #######################
 
-.. autoclass:: cuvs.neighbors.mg_ivf_flat.SearchParams
+.. autoclass:: cuvs.neighbors.mg.ivf_flat.SearchParams
     :members:
 
 Index
 #####
 
-.. autoclass:: cuvs.neighbors.mg_ivf_flat.Index
+.. autoclass:: cuvs.neighbors.mg.ivf_flat.Index
     :members:
 
 Index build
 ###########
 
-.. autofunction:: cuvs.neighbors.mg_ivf_flat.build
+.. autofunction:: cuvs.neighbors.mg.ivf_flat.build
 
 Index search
 ############
 
-.. autofunction:: cuvs.neighbors.mg_ivf_flat.search
+.. autofunction:: cuvs.neighbors.mg.ivf_flat.search
 
 Index extend
 ############
 
-.. autofunction:: cuvs.neighbors.mg_ivf_flat.extend
+.. autofunction:: cuvs.neighbors.mg.ivf_flat.extend
 
 Index save
 ##########
 
-.. autofunction:: cuvs.neighbors.mg_ivf_flat.save
+.. autofunction:: cuvs.neighbors.mg.ivf_flat.save
 
 Index load
 ##########
 
-.. autofunction:: cuvs.neighbors.mg_ivf_flat.load
+.. autofunction:: cuvs.neighbors.mg.ivf_flat.load
 
 Index distribute
 ################
 
-.. autofunction:: cuvs.neighbors.mg_ivf_flat.distribute
+.. autofunction:: cuvs.neighbors.mg.ivf_flat.distribute
diff --git a/docs/source/python_api/neighbors_mg_ivf_pq.rst b/docs/source/python_api/neighbors_mg_ivf_pq.rst
index d7d13b4734..8343d59753 100644
--- a/docs/source/python_api/neighbors_mg_ivf_pq.rst
+++ b/docs/source/python_api/neighbors_mg_ivf_pq.rst
@@ -14,47 +14,47 @@ Multi-GPU IVF-PQ extends the IVF-PQ (Inverted File with Product Quantization) al
 Index build parameters
 ######################
 
-.. autoclass:: cuvs.neighbors.mg_ivf_pq.IndexParams
+.. autoclass:: cuvs.neighbors.mg.ivf_pq.IndexParams
     :members:
 
 Index search parameters
 #######################
 
-.. autoclass:: cuvs.neighbors.mg_ivf_pq.SearchParams
+.. autoclass:: cuvs.neighbors.mg.ivf_pq.SearchParams
     :members:
 
 Index
 #####
 
-.. autoclass:: cuvs.neighbors.mg_ivf_pq.Index
+.. autoclass:: cuvs.neighbors.mg.ivf_pq.Index
     :members:
 
 Index build
 ###########
 
-.. autofunction:: cuvs.neighbors.mg_ivf_pq.build
+.. autofunction:: cuvs.neighbors.mg.ivf_pq.build
 
 Index search
 ############
 
-.. autofunction:: cuvs.neighbors.mg_ivf_pq.search
+.. autofunction:: cuvs.neighbors.mg.ivf_pq.search
 
 Index extend
 ############
 
-.. autofunction:: cuvs.neighbors.mg_ivf_pq.extend
+.. autofunction:: cuvs.neighbors.mg.ivf_pq.extend
 
 Index save
 ##########
 
-.. autofunction:: cuvs.neighbors.mg_ivf_pq.save
+.. autofunction:: cuvs.neighbors.mg.ivf_pq.save
 
 Index load
 ##########
 
-.. autofunction:: cuvs.neighbors.mg_ivf_pq.load
+.. autofunction:: cuvs.neighbors.mg.ivf_pq.load
 
 Index distribute
 ################
 
-.. autofunction:: cuvs.neighbors.mg_ivf_pq.distribute
+.. autofunction:: cuvs.neighbors.mg.ivf_pq.distribute

From 668e1b5c07a9c077b1dd6ab947745b1ae4b658cc Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Tue, 30 Sep 2025 13:22:18 -0700
Subject: [PATCH 606/623] Add Vamana C rst Docs to toctree (#1399)

Authors:
  - Tarang Jain (https://github.com/tarang-jain)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1399
---
 docs/source/c_api/neighbors.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/c_api/neighbors.rst b/docs/source/c_api/neighbors.rst
index bb6dbec5c2..09bd47e2c8 100644
--- a/docs/source/c_api/neighbors.rst
+++ b/docs/source/c_api/neighbors.rst
@@ -16,3 +16,4 @@ Nearest Neighbors
    neighbors_hnsw_c.rst
    neighbors_mg.rst
    neighbors_all_neighbors_c.rst
+   neighbors_vamana_c.rst

From 815d86dd276ee9f7abf55e5f1bce9176d206a969 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Tue, 30 Sep 2025 18:58:03 -0500
Subject: [PATCH 607/623] Patch FAISS for missing Thrust includes (#1398)

This PR patches FAISS for some missing Thrust includes. This is needed to support building with CCCL 3.1.x. RAPIDS 25.10 will still use CCCL 3.0.x but we want to make it possible to build with 3.1.x in 25.10.

xref: #1329 (This backports a fix from that testing PR, which is targeting 25.12)

I am upstreaming the patch here: https://github.com/facebookresearch/faiss/pull/4597

Authors:
  - Bradley Dice (https://github.com/bdice)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tarang Jain (https://github.com/tarang-jain)
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1398
---
 ...fix-missing-includes-for-thrust-copy.patch | 40 +++++++++++++++++++
 cpp/cmake/patches/faiss_override.json         | 19 ++++++---
 2 files changed, 53 insertions(+), 6 deletions(-)
 create mode 100644 cpp/cmake/patches/faiss/fix-missing-includes-for-thrust-copy.patch

diff --git a/cpp/cmake/patches/faiss/fix-missing-includes-for-thrust-copy.patch b/cpp/cmake/patches/faiss/fix-missing-includes-for-thrust-copy.patch
new file mode 100644
index 0000000000..9f250b88be
--- /dev/null
+++ b/cpp/cmake/patches/faiss/fix-missing-includes-for-thrust-copy.patch
@@ -0,0 +1,40 @@
+From 87c5331dfa6ec30fd9839ecb89af519e65f010e7 Mon Sep 17 00:00:00 2001
+From: Bradley Dice <bdice@bradleydice.com>
+Date: Mon, 29 Sep 2025 15:39:17 -0500
+Subject: [PATCH] Fix missing includes for thrust::copy
+
+---
+ faiss/gpu/impl/BinaryCuvsCagra.cu | 3 +++
+ faiss/gpu/impl/CuvsCagra.cu       | 3 +++
+ 2 files changed, 6 insertions(+)
+
+diff --git a/faiss/gpu/impl/BinaryCuvsCagra.cu b/faiss/gpu/impl/BinaryCuvsCagra.cu
+index 0ca21dc5f..b331fdc8f 100644
+--- a/faiss/gpu/impl/BinaryCuvsCagra.cu
++++ b/faiss/gpu/impl/BinaryCuvsCagra.cu
+@@ -32,6 +32,9 @@
+ #include <raft/core/resource/thrust_policy.hpp>
+ #include <raft/linalg/map.cuh>
+
++#include <thrust/copy.h>
++#include <thrust/device_ptr.h>
++
+ namespace faiss {
+ namespace gpu {
+
+diff --git a/faiss/gpu/impl/CuvsCagra.cu b/faiss/gpu/impl/CuvsCagra.cu
+index 9ac4e1c5a..755817f43 100644
+--- a/faiss/gpu/impl/CuvsCagra.cu
++++ b/faiss/gpu/impl/CuvsCagra.cu
+@@ -31,6 +31,9 @@
+ #include <raft/core/device_resources.hpp>
+ #include <raft/core/resource/thrust_policy.hpp>
+
++#include <thrust/copy.h>
++#include <thrust/device_ptr.h>
++
+ namespace faiss {
+ namespace gpu {
+
+--
+2.49.0
diff --git a/cpp/cmake/patches/faiss_override.json b/cpp/cmake/patches/faiss_override.json
index f3a1aaf795..5af8bd8543 100644
--- a/cpp/cmake/patches/faiss_override.json
+++ b/cpp/cmake/patches/faiss_override.json
@@ -1,9 +1,16 @@
 {
-    "packages" : {
-      "faiss" : {
-        "version": "1.12.0",
-        "git_url": "https://github.com/facebookresearch/faiss.git",
-        "git_tag": "v1.12.0"
-      }
+  "packages" : {
+    "faiss" : {
+      "version": "1.12.0",
+      "git_url": "https://github.com/facebookresearch/faiss.git",
+      "git_tag": "v1.12.0",
+      "patches" : [
+        {
+          "file" : "${current_json_dir}/faiss/fix-missing-includes-for-thrust-copy.patch",
+          "issue" : "Fix missing Thrust includes. https://github.com/facebookresearch/faiss/pull/4597",
+          "fixed_in" : ""
+        }
+      ]
     }
   }
+}

From bd9458661ad148f7413d6b2d9408ae8260d98c61 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Wed, 1 Oct 2025 14:46:35 -0400
Subject: [PATCH 608/623] Allow compilation when OpenMP is disabled (#1346)

Since we compile CUVS with warnings as errors, we need to disable the unknown pragma warning generated by our OpenMP pragmas so we can still compile.

Authors:
  - Robert Maynard (https://github.com/robertmaynard)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)

URL: https://github.com/rapidsai/cuvs/pull/1346
---
 cpp/cmake/modules/ConfigureCUDA.cmake    | 5 +++++
 cpp/src/neighbors/detail/hnsw.hpp        | 6 ++++--
 cpp/src/neighbors/refine/refine_host.hpp | 8 +++++---
 3 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/cpp/cmake/modules/ConfigureCUDA.cmake b/cpp/cmake/modules/ConfigureCUDA.cmake
index 0550aa0b11..d8576cd650 100644
--- a/cpp/cmake/modules/ConfigureCUDA.cmake
+++ b/cpp/cmake/modules/ConfigureCUDA.cmake
@@ -19,6 +19,11 @@ if(DISABLE_DEPRECATION_WARNINGS)
   )
 endif()
 
+if(DISABLE_OPENMP)
+  list(APPEND CUVS_CXX_FLAGS -Wno-unknown-pragmas)
+  list(APPEND CUVS_CUDA_FLAGS -Xcompiler=-Wno-unknown-pragmas)
+endif()
+
 # Be very strict when compiling with GCC as host compiler (and thus more lenient when compiling with
 # clang)
 if(CMAKE_COMPILER_IS_GNUCXX)
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index f5a1486f58..c505146884 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -193,7 +193,8 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
     cagra_index.graph().extent(1) / 2,
     params.ef_construction);
   appr_algo->base_layer_init = false;  // tell hnswlib to build upper layers only
-  auto num_threads           = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+  [[maybe_unused]] auto num_threads =
+    params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
 #pragma omp parallel for num_threads(num_threads)
   for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
     appr_algo->addPoint((void*)(host_dataset_view.data_handle() + i * host_dataset_view.extent(1)),
@@ -544,7 +545,8 @@ void extend(raft::resources const& res,
     const_cast<void*>(idx.get_index()));
   auto current_element_count = hnswlib_index->getCurrentElementCount();
   auto new_element_count     = additional_dataset.extent(0);
-  auto num_threads           = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+  [[maybe_unused]] auto num_threads =
+    params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
 
   hnswlib_index->resizeIndex(current_element_count + new_element_count);
 #pragma omp parallel for num_threads(num_threads)
diff --git a/cpp/src/neighbors/refine/refine_host.hpp b/cpp/src/neighbors/refine/refine_host.hpp
index ca95b89d9e..b813c6002d 100644
--- a/cpp/src/neighbors/refine/refine_host.hpp
+++ b/cpp/src/neighbors/refine/refine_host.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -370,10 +370,12 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
     // taking this into account.
     auto n_elements    = std::max(size_t(512), dim);
     auto max_n_threads = raft::div_rounding_up_safe<size_t>(n_queries * orig_k * dim, n_elements);
-    auto suggested_n_threads_for_distance = std::min(size_t(suggested_n_threads), max_n_threads);
+    [[maybe_unused]] auto suggested_n_threads_for_distance =
+      std::min(size_t(suggested_n_threads), max_n_threads);
 
     // The max number of threads for topk computation is the number of queries.
-    auto suggested_n_threads_for_topk = std::min(size_t(suggested_n_threads), n_queries);
+    [[maybe_unused]] auto suggested_n_threads_for_topk =
+      std::min(size_t(suggested_n_threads), n_queries);
 
     // Compute the refined distance using original dataset vectors
 #pragma omp parallel for collapse(2) num_threads(suggested_n_threads_for_distance)

From 5496b906b1ad88ab584302444eae1e4ce804faea Mon Sep 17 00:00:00 2001
From: Anupam <54245698+aamijar@users.noreply.github.com>
Date: Thu, 2 Oct 2025 09:47:53 -0700
Subject: [PATCH 609/623] Move eigen_solvers from raft (#1402)

Resolves #1344

Authors:
  - Anupam (https://github.com/aamijar)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/1402
---
 .../cluster/detail/spectral/partition.hpp     | 14 ++-
 cpp/src/sparse/cluster/eigen_solvers.cuh      | 62 ++++++++++----
 cpp/tests/sparse/cluster/eigen_solvers.cu     | 85 ++++++++++++++++++-
 3 files changed, 137 insertions(+), 24 deletions(-)

diff --git a/cpp/src/sparse/cluster/detail/spectral/partition.hpp b/cpp/src/sparse/cluster/detail/spectral/partition.hpp
index d0ea793f53..c2849a0d2c 100644
--- a/cpp/src/sparse/cluster/detail/spectral/partition.hpp
+++ b/cpp/src/sparse/cluster/detail/spectral/partition.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2019-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,6 +24,7 @@
 #include "../../cluster_solvers.cuh"
 #include "../../eigen_solvers.cuh"
 #include "spectral_util.cuh"
+#include <raft/sparse/linalg/laplacian.cuh>
 #include <raft/spectral/matrix_wrappers.hpp>
 
 #include <cuda.h>
@@ -40,10 +41,6 @@ namespace cuvs {
 namespace spectral {
 namespace detail {
 
-// =========================================================
-// Spectral partitioner
-// =========================================================
-
 /// Compute spectral graph partition
 /** Compute partition for a weighted undirected graph. This
  *  partition attempts to minimize the cost function:
@@ -100,14 +97,15 @@ std::tuple<vertex_t, weight_t, vertex_t> partition(
   // Compute eigenvectors of Laplacian
 
   // Initialize Laplacian
-  /// sparse_matrix_t<vertex_t, weight_t> A{handle, graph};
-  raft::spectral::matrix::laplacian_matrix_t<vertex_t, weight_t, nnz_t> L{handle, csr_m};
+  auto laplacian =
+    raft::sparse::linalg::compute_graph_laplacian(handle, csr_m.to_csr_matrix_view());
 
   auto eigen_config = eigen_solver.get_config();
   auto nEigVecs     = eigen_config.n_eigVecs;
 
   // Compute smallest eigenvalues and eigenvectors
-  std::get<0>(stats) = eigen_solver.solve_smallest_eigenvectors(handle, L, eigVals, eigVecs);
+  std::get<0>(stats) =
+    eigen_solver.solve_smallest_eigenvectors(handle, laplacian.view(), eigVals, eigVecs);
 
   // Whiten eigenvector matrix
   transform_eigen_matrix(handle, n, nEigVecs, eigVecs);
diff --git a/cpp/src/sparse/cluster/eigen_solvers.cuh b/cpp/src/sparse/cluster/eigen_solvers.cuh
index 6df1e86a8d..a35a9854fd 100644
--- a/cpp/src/sparse/cluster/eigen_solvers.cuh
+++ b/cpp/src/sparse/cluster/eigen_solvers.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2019-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -39,6 +39,8 @@ struct eigen_solver_config_t {
     1234567};  // CAVEAT: this default value is now common to all instances of using seed in
                // Lanczos; was not the case before: there were places where a default seed = 123456
                // was used; this may trigger slightly different # solver iterations
+
+  raft::sparse::solver::LANCZOS_WHICH which{raft::sparse::solver::LANCZOS_WHICH::SA};
 };
 
 template <typename index_type_t, typename value_type_t, typename size_type_t = index_type_t>
@@ -49,27 +51,57 @@ struct lanczos_solver_t {
   {
   }
 
-  index_type_t solve_smallest_eigenvectors(
+  auto solve_smallest_eigenvectors(
     raft::resources const& handle,
     raft::spectral::matrix::sparse_matrix_t<index_type_t, value_type_t, size_type_t> const& A,
     value_type_t* __restrict__ eigVals,
     value_type_t* __restrict__ eigVecs) const
+  {
+    auto csr_structure =
+      raft::make_device_compressed_structure_view<index_type_t, index_type_t, index_type_t>(
+        const_cast<index_type_t*>(A.row_offsets_),
+        const_cast<index_type_t*>(A.col_indices_),
+        A.nrows_,
+        A.ncols_,
+        A.nnz_);
+
+    auto csr_matrix =
+      raft::make_device_csr_matrix_view<value_type_t, index_type_t, index_type_t, index_type_t>(
+        const_cast<value_type_t*>(A.values_), csr_structure);
+    return solve_smallest_eigenvectors(handle, csr_matrix, eigVals, eigVecs);
+  }
+
+  auto solve_smallest_eigenvectors(
+    raft::resources const& handle,
+    raft::device_csr_matrix_view<value_type_t, index_type_t, index_type_t, index_type_t> input,
+    value_type_t* __restrict__ eigVals,
+    value_type_t* __restrict__ eigVecs) const
   {
     RAFT_EXPECTS(eigVals != nullptr, "Null eigVals buffer.");
     RAFT_EXPECTS(eigVecs != nullptr, "Null eigVecs buffer.");
-    index_type_t iters{};
-    raft::sparse::solver::computeSmallestEigenvectors(handle,
-                                                      A,
-                                                      config_.n_eigVecs,
-                                                      config_.maxIter,
-                                                      config_.restartIter,
-                                                      config_.tol,
-                                                      config_.reorthogonalize,
-                                                      iters,
-                                                      eigVals,
-                                                      eigVecs,
-                                                      config_.seed);
-    return iters;
+
+    auto lanczos_config =
+      raft::sparse::solver::lanczos_solver_config<value_type_t>{config_.n_eigVecs,
+                                                                config_.maxIter,
+                                                                config_.restartIter,
+                                                                config_.tol,
+                                                                config_.which,
+                                                                config_.seed};
+    auto v0_opt = std::optional<raft::device_vector_view<value_type_t, uint32_t, raft::row_major>>{
+      std::nullopt};
+    auto input_structure = input.structure_view();
+
+    auto solver_iterations = raft::sparse::solver::lanczos_compute_smallest_eigenvectors(
+      handle,
+      lanczos_config,
+      input,
+      v0_opt,
+      raft::make_device_vector_view<value_type_t, uint32_t, raft::col_major>(eigVals,
+                                                                             config_.n_eigVecs),
+      raft::make_device_matrix_view<value_type_t, uint32_t, raft::col_major>(
+        eigVecs, input_structure.get_n_rows(), config_.n_eigVecs));
+
+    return index_type_t{solver_iterations};
   }
 
   index_type_t solve_largest_eigenvectors(
diff --git a/cpp/tests/sparse/cluster/eigen_solvers.cu b/cpp/tests/sparse/cluster/eigen_solvers.cu
index 9e6626c3a7..93daf50bae 100644
--- a/cpp/tests/sparse/cluster/eigen_solvers.cu
+++ b/cpp/tests/sparse/cluster/eigen_solvers.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,10 +16,13 @@
 
 #include "../../../src/sparse/cluster/eigen_solvers.cuh"
 #include "../../../src/sparse/cluster/partition.cuh"
+#include <raft/core/handle.hpp>
 #include <raft/core/nvtx.hpp>
 #include <raft/core/resource/device_id.hpp>
 #include <raft/core/resources.hpp>
 
+#include <thrust/device_vector.h>
+
 #include <gtest/gtest.h>
 
 #include <cstddef>
@@ -117,5 +120,85 @@ TEST(Raft, SpectralSolvers)
   EXPECT_ANY_THROW(spectral::analyzePartition(h, sm, k, clusters, edgeCut, cost));
 }
 
+TEST(Raft, SpectralPartition)
+{
+  auto offsets = thrust::device_vector<int>(std::vector<int>{
+    0,  16, 25, 35, 41, 44, 48, 52,  56,  61,  63,  66,  67,  69,  74,  76,  78, 80,
+    82, 84, 87, 89, 91, 93, 98, 101, 104, 106, 110, 113, 117, 121, 127, 139, 156});
+  auto indices = thrust::device_vector<int>(std::vector<int>{
+    1,  2,  3,  4,  5,  6,  7,  8,  10, 11, 12, 13, 17, 19, 21, 31, 0,  2,  3,  7,  13, 17, 19,
+    21, 30, 0,  1,  3,  7,  8,  9,  13, 27, 28, 32, 0,  1,  2,  7,  12, 13, 0,  6,  10, 0,  6,
+    10, 16, 0,  4,  5,  16, 0,  1,  2,  3,  0,  2,  30, 32, 33, 2,  33, 0,  4,  5,  0,  0,  3,
+    0,  1,  2,  3,  33, 32, 33, 32, 33, 5,  6,  0,  1,  32, 33, 0,  1,  33, 32, 33, 0,  1,  32,
+    33, 25, 27, 29, 32, 33, 25, 27, 31, 23, 24, 31, 29, 33, 2,  23, 24, 33, 2,  31, 33, 23, 26,
+    32, 33, 1,  8,  32, 33, 0,  24, 25, 28, 32, 33, 2,  8,  14, 15, 18, 20, 22, 23, 29, 30, 31,
+    33, 8,  9,  13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30, 31, 32});
+  auto values  = thrust::device_vector<float>(std::vector<float>{
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0});
+
+  auto num_verts = int(offsets.size() - 1);
+  auto num_edges = indices.size();
+
+  auto result_v = thrust::device_vector<int>(std::vector<int>(num_verts, -1));
+
+  auto constexpr const n_clusters  = 8;
+  auto constexpr const n_eig_vects = std::uint64_t{8};
+
+  auto constexpr const evs_tolerance   = .00001f;
+  auto constexpr const kmean_tolerance = .00001f;
+  auto constexpr const evs_max_iter    = 100;
+  auto constexpr const kmean_max_iter  = 100;
+
+  auto eig_vals  = thrust::device_vector<float>(n_eig_vects);
+  auto eig_vects = thrust::device_vector<float>(n_eig_vects * num_verts);
+
+  auto handle = raft::handle_t{};
+
+  auto restartIter_lanczos = int{15 + n_eig_vects};
+
+  auto seed_eig_solver     = std::uint64_t{1234567};
+  auto seed_cluster_solver = std::uint64_t{12345678};
+
+  auto const adj_matrix = raft::spectral::matrix::sparse_matrix_t<int, float>{handle,
+                                                                              offsets.data().get(),
+                                                                              indices.data().get(),
+                                                                              values.data().get(),
+                                                                              num_verts,
+                                                                              num_verts,
+                                                                              num_edges};
+
+  auto eig_cfg = cuvs::spectral::eigen_solver_config_t<int, float>{
+    n_eig_vects, evs_max_iter, restartIter_lanczos, evs_tolerance, false, seed_eig_solver};
+  auto eigen_solver = cuvs::spectral::lanczos_solver_t<int, float>{eig_cfg};
+
+  auto clust_cfg = cuvs::spectral::cluster_solver_config_t<int, float>{
+    n_clusters, kmean_max_iter, kmean_tolerance, seed_cluster_solver};
+  auto cluster_solver = cuvs::spectral::kmeans_solver_t<int, float>{clust_cfg};
+
+  partition(handle,
+            adj_matrix,
+            eigen_solver,
+            cluster_solver,
+            result_v.data().get(),
+            eig_vals.data().get(),
+            eig_vects.data().get());
+
+  auto edge_cut = float{};
+  auto cost     = float{};
+
+  cuvs::spectral::analyzePartition(
+    handle, adj_matrix, n_clusters, result_v.data().get(), edge_cut, cost);
+
+  ASSERT_LT(edge_cut, 55.0);
+}
+
 }  // namespace spectral
 }  // namespace cuvs

From 88f7f235e039e8a167a4a9e2168aabd2d2e9c05e Mon Sep 17 00:00:00 2001
From: Tarang Jain <40517122+tarang-jain@users.noreply.github.com>
Date: Fri, 3 Oct 2025 11:10:45 -0700
Subject: [PATCH 610/623] CosineExpanded Distance Metric for CAGRA (#197)

Currently only IVF-PQ can be used as the graph building algorithm (NN Descent does not support Cosine). As a result, we are limited by IVF-PQ's restriction of data to be of float / half type for the Cosine metric. This PR also fixes an in-place data modification that was being done by IVF-PQ.
Opportunities for optimization:
NN Descent to support Cosine and compute dataset norms only once -- during NN Descent. Re-use those for CAGRA.

[UPDATE 08/21/2025]: NN Descent now support Cosine. This PR allows the initial CAGRA graph to be built by both methods -- IVF_PQ, NN_DESCENT. The IVF_PQ restriction on data types holds, but uint8 and int8 can be supported with NN Descent as the graph building algorithm. ITERATIVE CAGRA SEARCH is currently disabled for Cosine.

[UPDATE 09/23/2025]: This PR also adds Cosine support for IVF_PQ with uint8 / int8 inputs. The above mentioned restriction with IVF_PQ has been removed. So with this PR CAGRA supports Cosine wholly, for float, uint8 and int8 inputs. ITERATIVE_SEARCH however still has some issues as the graph building method with the Cosine metric and has been disabled.

[UPDATE 09/25/2025]:
Binary size comparison for libcuvs.so (CUDA 12.9, x86):
branch-25.10: 1154.42 MB
This PR: 1160.73 MB

Total CAGRA testing time:
branch-25.10:
```
Start 10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST
19/37 Test #10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST ...   Passed  825.43 sec
      Start 11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST
20/37 Test #11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST ........   Passed    0.58 sec
      Start 12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
21/37 Test #12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST ....   Passed  663.97 sec
      Start 13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST
22/37 Test #13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST ....   Passed  397.57 sec
      Start 14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST
23/37 Test #14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST ...   Passed  408.16 sec
```
This PR:
```
Start 10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST
19/37 Test #10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST ...   Passed  1830.34 sec
      Start 11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST
20/37 Test #11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST ........   Passed    0.45 sec
      Start 12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
21/37 Test #12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST ....   Passed  1444.14 sec
      Start 13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST
22/37 Test #13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST ....   Passed  973.64 sec
      Start 14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST
23/37 Test #14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST ...   Passed  1010.46 sec
```

[UPDATE 09/30/2025]:
Updates to CAGRA C++ tests according to the latest PR reviews.
New total CAGRA testing time:
branch-25.10:
```
      Start  9: NEIGHBORS_ANN_CAGRA_TEST_BUGS
18/37 Test  #9: NEIGHBORS_ANN_CAGRA_TEST_BUGS ...........   Passed   16.99 sec
      Start 10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST
19/37 Test #10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST ...   Passed  803.64 sec
      Start 11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST
20/37 Test #11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST ........   Passed    0.49 sec
      Start 12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
21/37 Test #12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST ....   Passed  667.89 sec
      Start 13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST
22/37 Test #13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST ....   Passed  420.49 sec
      Start 14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST
23/37 Test #14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST ...   Passed  429.57 sec
```
This PR:
```
      Start  9: NEIGHBORS_ANN_CAGRA_TEST_BUGS
18/37 Test  #9: NEIGHBORS_ANN_CAGRA_TEST_BUGS ...........   Passed   26.62 sec
      Start 10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST
19/37 Test #10: NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST ...   Passed  973.23 sec
      Start 11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST
20/37 Test #11: NEIGHBORS_ANN_CAGRA_HELPERS_TEST ........   Passed    0.43 sec
      Start 12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
21/37 Test #12: NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST ....   Passed  702.02 sec
      Start 13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST
22/37 Test #13: NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST ....   Passed  491.65 sec
      Start 14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST
23/37 Test #14: NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST ...   Passed  541.43 sec
```
Fixes #1288
Fixes #389

Authors:
  - Tarang Jain (https://github.com/tarang-jain)
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Tamas Bela Feher (https://github.com/tfeher)
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/cuvs/pull/197
---
 cpp/CMakeLists.txt                            |  12 ++
 cpp/include/cuvs/neighbors/cagra.hpp          |  67 ++++++-
 cpp/src/neighbors/cagra.cuh                   |  35 +++-
 .../neighbors/detail/cagra/cagra_build.cuh    |  10 +-
 .../neighbors/detail/cagra/cagra_search.cuh   |  66 +++++--
 .../detail/cagra/compute_distance-ext.cuh     | 105 ++++++++++-
 .../detail/cagra/compute_distance.cu          |  14 +-
 .../detail/cagra/compute_distance.hpp         |  19 +-
 .../cagra/compute_distance_00_generate.py     |  14 +-
 .../cagra/compute_distance_standard-impl.cuh  |  56 ++++--
 .../cagra/compute_distance_standard.hpp       |  15 +-
 ...d_CosineExpanded_float_uint32_dim128_t8.cu |  38 ++++
 ..._CosineExpanded_float_uint32_dim256_t16.cu |  38 ++++
 ..._CosineExpanded_float_uint32_dim512_t32.cu |  38 ++++
 ...rd_CosineExpanded_half_uint32_dim128_t8.cu |  38 ++++
 ...d_CosineExpanded_half_uint32_dim256_t16.cu |  38 ++++
 ...d_CosineExpanded_half_uint32_dim512_t32.cu |  38 ++++
 ...rd_CosineExpanded_int8_uint32_dim128_t8.cu |  38 ++++
 ...d_CosineExpanded_int8_uint32_dim256_t16.cu |  38 ++++
 ...d_CosineExpanded_int8_uint32_dim512_t32.cu |  38 ++++
 ...d_CosineExpanded_uint8_uint32_dim128_t8.cu |  38 ++++
 ..._CosineExpanded_uint8_uint32_dim256_t16.cu |  38 ++++
 ..._CosineExpanded_uint8_uint32_dim512_t32.cu |  38 ++++
 .../detail/cagra/compute_distance_vpq.hpp     |   5 +-
 cpp/src/neighbors/detail/cagra/factory.cuh    |   8 +-
 cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh     |  64 +++----
 cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh    |   6 -
 cpp/src/neighbors/refine/refine_host.hpp      |  46 ++++-
 cpp/tests/neighbors/ann_cagra.cuh             | 166 +++++++++++-------
 .../ann_ivf_pq/test_int8_t_int64_t.cu         |   9 +-
 .../ann_ivf_pq/test_uint8_t_int64_t.cu        |   6 +-
 python/cuvs/cuvs/neighbors/cagra/cagra.pyx    |   5 +-
 python/cuvs/cuvs/tests/test_cagra.py          |  18 +-
 33 files changed, 1033 insertions(+), 169 deletions(-)
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim512_t32.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim128_t8.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim256_t16.cu
 create mode 100644 cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim512_t32.cu

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index d68c8df37a..2844139da7 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -231,6 +231,9 @@ if(NOT BUILD_CPU_ONLY)
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim128_t8.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim256_t16.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_float_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim512_t32.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim128_t8.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim256_t16.cu
     src/neighbors/detail/cagra/compute_distance_standard_InnerProduct_half_uint32_dim512_t32.cu
@@ -246,6 +249,15 @@ if(NOT BUILD_CPU_ONLY)
     src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim128_t8.cu
     src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim256_t16.cu
     src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_half_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim512_t32.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim128_t8.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim256_t16.cu
+    src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim512_t32.cu
     src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim128_t8.cu
     src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim256_t16.cu
     src/neighbors/detail/cagra/compute_distance_standard_L2Expanded_int8_uint32_dim512_t32.cu
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 1c0890b393..61639d383d 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -339,6 +339,14 @@ struct index : cuvs::neighbors::index {
     return graph_view_;
   }
 
+  /** Dataset norms for cosine distance [size] */
+  [[nodiscard]] inline auto dataset_norms() const noexcept
+    -> std::optional<raft::device_vector_view<const float, int64_t>>
+  {
+    if (dataset_norms_.has_value()) { return raft::make_const_mdspan(dataset_norms_->view()); }
+    return std::nullopt;
+  }
+
   // Don't allow copying the index for performance reasons (try avoiding copying data)
   /** \cond */
   index(const index&)                    = delete;
@@ -354,7 +362,8 @@ struct index : cuvs::neighbors::index {
     : cuvs::neighbors::index(),
       metric_(metric),
       graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
-      dataset_(new cuvs::neighbors::empty_dataset<int64_t>(0))
+      dataset_(new cuvs::neighbors::empty_dataset<int64_t>(0)),
+      dataset_norms_(std::nullopt)
   {
   }
 
@@ -420,12 +429,21 @@ struct index : cuvs::neighbors::index {
     : cuvs::neighbors::index(),
       metric_(metric),
       graph_(raft::make_device_matrix<IdxT, int64_t>(res, 0, 0)),
-      dataset_(make_aligned_dataset(res, dataset, 16))
+      dataset_(make_aligned_dataset(res, dataset, 16)),
+      dataset_norms_(std::nullopt)
   {
     RAFT_EXPECTS(dataset.extent(0) == knn_graph.extent(0),
                  "Dataset and knn_graph must have equal number of rows");
     update_graph(res, knn_graph);
 
+    if (metric_ == cuvs::distance::DistanceType::CosineExpanded) {
+      auto p = dynamic_cast<strided_dataset<T, int64_t>*>(dataset_.get());
+      if (p) {
+        auto dataset_view = p->view();
+        if (dataset_view.extent(0) > 0) { compute_dataset_norms_(res); }
+      }
+    }
+
     raft::resource::sync_stream(res);
   }
 
@@ -435,11 +453,18 @@ struct index : cuvs::neighbors::index {
    * If the new dataset rows are aligned on 16 bytes, then only a reference is stored to the
    * dataset. It is the caller's responsibility to ensure that dataset stays alive as long as the
    * index. It is expected that the same set of vectors are used for update_dataset and index build.
+   *
+   * Note: This will clear any precomputed dataset norms.
    */
   void update_dataset(raft::resources const& res,
                       raft::device_matrix_view<const T, int64_t, raft::row_major> dataset)
   {
     dataset_ = make_aligned_dataset(res, dataset, 16);
+    dataset_norms_.reset();
+
+    if (metric() == cuvs::distance::DistanceType::CosineExpanded) {
+      if (dataset.extent(0) > 0) { compute_dataset_norms_(res); }
+    }
   }
 
   /** Set the dataset reference explicitly to a device matrix view with padding. */
@@ -447,6 +472,11 @@ struct index : cuvs::neighbors::index {
                       raft::device_matrix_view<const T, int64_t, raft::layout_stride> dataset)
   {
     dataset_ = make_aligned_dataset(res, dataset, 16);
+    dataset_norms_.reset();
+
+    if (metric() == cuvs::distance::DistanceType::CosineExpanded) {
+      if (dataset.extent(0) > 0) { compute_dataset_norms_(res); }
+    }
   }
 
   /**
@@ -454,22 +484,38 @@ struct index : cuvs::neighbors::index {
    *
    * We create a copy of the dataset on the device. The index manages the lifetime of this copy. It
    * is expected that the same set of vectors are used for update_dataset and index build.
+   *
+   * Note: This will clear any precomputed dataset norms.
    */
   void update_dataset(raft::resources const& res,
                       raft::host_matrix_view<const T, int64_t, raft::row_major> dataset)
   {
     dataset_ = make_aligned_dataset(res, dataset, 16);
+    dataset_norms_.reset();
+    if (metric() == cuvs::distance::DistanceType::CosineExpanded) {
+      if (dataset.extent(0) > 0) { compute_dataset_norms_(res); }
+    }
   }
 
   /**
    * Replace the dataset with a new dataset. It is expected that the same set of vectors are used
    * for update_dataset and index build.
+   *
+   * Note: This will clear any precomputed dataset norms.
    */
   template <typename DatasetT>
   auto update_dataset(raft::resources const& res, DatasetT&& dataset)
     -> std::enable_if_t<std::is_base_of_v<cuvs::neighbors::dataset<dataset_index_type>, DatasetT>>
   {
     dataset_ = std::make_unique<DatasetT>(std::move(dataset));
+    dataset_norms_.reset();
+    if (metric() == cuvs::distance::DistanceType::CosineExpanded) {
+      auto p = dynamic_cast<strided_dataset<T, int64_t>*>(dataset_.get());
+      if (p) {
+        auto dataset_view = p->view();
+        if (dataset_view.extent(0) > 0) { compute_dataset_norms_(res); }
+      }
+    }
   }
 
   template <typename DatasetT>
@@ -477,6 +523,11 @@ struct index : cuvs::neighbors::index {
     -> std::enable_if_t<std::is_base_of_v<neighbors::dataset<dataset_index_type>, DatasetT>>
   {
     dataset_ = std::move(dataset);
+    dataset_norms_.reset();
+    if (metric() == cuvs::distance::DistanceType::CosineExpanded) {
+      auto dataset_view = this->dataset();
+      if (dataset_view.extent(0) > 0) { compute_dataset_norms_(res); }
+    }
   }
 
   /**
@@ -519,6 +570,10 @@ struct index : cuvs::neighbors::index {
   raft::device_matrix<IdxT, int64_t, raft::row_major> graph_;
   raft::device_matrix_view<const IdxT, int64_t, raft::row_major> graph_view_;
   std::unique_ptr<neighbors::dataset<dataset_index_type>> dataset_;
+  // only float distances supported at the moment
+  std::optional<raft::device_vector<float, int64_t>> dataset_norms_;
+
+  void compute_dataset_norms_(raft::resources const& res);
 };
 /**
  * @}
@@ -539,6 +594,7 @@ struct index : cuvs::neighbors::index {
  * The following distance metrics are supported:
  * - L2
  * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
+ * - CosineExpanded
  *
  * Usage example:
  * @code{.cpp}
@@ -576,6 +632,7 @@ auto build(raft::resources const& res,
  * The following distance metrics are supported:
  * - L2
  * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
+ * - CosineExpanded
  *
  * Usage example:
  * @code{.cpp}
@@ -613,6 +670,7 @@ auto build(raft::resources const& res,
  * The following distance metrics are supported:
  * - L2
  * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
+ * - CosineExpanded (dataset norms are computed as float regardless of input data type)
  *
  * Usage example:
  * @code{.cpp}
@@ -649,6 +707,7 @@ auto build(raft::resources const& res,
  *
  * The following distance metrics are supported:
  * - L2
+ * - CosineExpanded (dataset norms are computed as float regardless of input data type)
  *
  * Usage example:
  * @code{.cpp}
@@ -685,6 +744,7 @@ auto build(raft::resources const& res,
  *
  * The following distance metrics are supported:
  * - L2
+ * - CosineExpanded (dataset norms are computed as float regardless of input data type)
  *
  * Usage example:
  * @code{.cpp}
@@ -722,6 +782,7 @@ auto build(raft::resources const& res,
  * The following distance metrics are supported:
  * - L2
  * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
+ * - CosineExpanded (dataset norms are computed as float regardless of input data type)
  *
  * Usage example:
  * @code{.cpp}
@@ -759,6 +820,7 @@ auto build(raft::resources const& res,
  * The following distance metrics are supported:
  * - L2
  * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
+ * - CosineExpanded (dataset norms are computed as float regardless of input data type)
  *
  * Usage example:
  * @code{.cpp}
@@ -796,6 +858,7 @@ auto build(raft::resources const& res,
  * The following distance metrics are supported:
  * - L2
  * - InnerProduct (currently only supported with IVF-PQ as the build algorithm)
+ * - CosineExpanded (dataset norms are computed as float regardless of input data type)
  *
  * Usage example:
  * @code{.cpp}
diff --git a/cpp/src/neighbors/cagra.cuh b/cpp/src/neighbors/cagra.cuh
index 2e528cec7c..aaf1edb4e3 100644
--- a/cpp/src/neighbors/cagra.cuh
+++ b/cpp/src/neighbors/cagra.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,10 +22,12 @@
 #include "detail/cagra/cagra_search.cuh"
 #include "detail/cagra/graph_core.cuh"
 
+#include "detail/ann_utils.cuh"
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/host_device_accessor.hpp>
 #include <raft/core/mdspan.hpp>
 #include <raft/core/resources.hpp>
+#include <raft/linalg/norm.cuh>
 
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/cagra.hpp>
@@ -36,6 +38,37 @@
 
 namespace cuvs::neighbors::cagra {
 
+// Member function implementations for cagra::index
+template <typename T, typename IdxT>
+void index<T, IdxT>::compute_dataset_norms_(raft::resources const& res)
+{
+  // Get the dataset view
+  auto dataset_view = this->dataset();
+
+  // Allocate norms vector if not already allocated
+  if (!dataset_norms_.has_value() || dataset_norms_->extent(0) != dataset_view.extent(0)) {
+    dataset_norms_.reset();
+    dataset_norms_ = raft::make_device_vector<float, int64_t>(res, dataset_view.extent(0));
+  }
+
+  constexpr float kScale = cuvs::spatial::knn::detail::utils::config<T>::kDivisor /
+                           cuvs::spatial::knn::detail::utils::config<float>::kDivisor;
+
+  // first scale the dataset and then compute norms
+  auto scaled_sq_op = raft::compose_op(
+    raft::sq_op{}, raft::div_const_op<float>{float(kScale)}, raft::cast_op<float>());
+  raft::linalg::reduce<true, true, T, float, int64_t>(dataset_norms_->data_handle(),
+                                                      dataset_view.data_handle(),
+                                                      dataset_view.stride(0),
+                                                      dataset_view.extent(0),
+                                                      (float)0,
+                                                      raft::resource::get_cuda_stream(res),
+                                                      false,
+                                                      scaled_sq_op,
+                                                      raft::add_op(),
+                                                      raft::sqrt_op{});
+}
+
 /**
  * @defgroup cagra CUDA ANN Graph-based nearest neighbor search
  * @{
diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 07c9157b72..e2a43b8a07 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -127,8 +127,9 @@ void build_knn_graph(
   cuvs::neighbors::cagra::graph_build_params::ivf_pq_params pq)
 {
   RAFT_EXPECTS(pq.build_params.metric == cuvs::distance::DistanceType::L2Expanded ||
-                 pq.build_params.metric == cuvs::distance::DistanceType::InnerProduct,
-               "Currently only L2Expanded or InnerProduct metric are supported");
+                 pq.build_params.metric == cuvs::distance::DistanceType::InnerProduct ||
+                 pq.build_params.metric == cuvs::distance::DistanceType::CosineExpanded,
+               "Currently only L2Expanded, InnerProduct and CosineExpanded metrics are supported");
 
   uint32_t node_degree = knn_graph.extent(1);
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
@@ -710,6 +711,11 @@ index<T, IdxT> build(
       std::holds_alternative<cagra::graph_build_params::nn_descent_params>(knn_build_params),
     "IVF_PQ for CAGRA graph build does not support BitwiseHamming as a metric. Please "
     "use nn-descent or the iterative CAGRA search build.");
+  RAFT_EXPECTS(
+    params.metric != cuvs::distance::DistanceType::CosineExpanded ||
+      std::holds_alternative<cagra::graph_build_params::ivf_pq_params>(knn_build_params) ||
+      std::holds_alternative<cagra::graph_build_params::nn_descent_params>(knn_build_params),
+    "CosineExpanded distance is not supported for iterative CAGRA graph build.");
 
   // Validate data type for BitwiseHamming metric
   RAFT_EXPECTS(params.metric != cuvs::distance::DistanceType::BitwiseHamming ||
diff --git a/cpp/src/neighbors/detail/cagra/cagra_search.cuh b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
index 79daf2ad6d..51387c7507 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_search.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_search.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -40,7 +40,9 @@
 // TODO: This shouldn't be calling spatial/knn apis
 #include "../ann_utils.cuh"
 
-#include <rmm/cuda_stream_view.hpp>
+#include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/norm.cuh>
+#include <raft/linalg/unary_op.cuh>
 
 namespace cuvs::neighbors::cagra::detail {
 
@@ -149,9 +151,16 @@ void search_main(raft::resources const& res,
   // Dispatch search parameters based on the dataset kind.
   if (auto* strided_dset = dynamic_cast<const strided_dataset<T, ds_idx_type>*>(&index.data());
       strided_dset != nullptr) {
-    // Search using a plain (strided) row-major dataset
+    RAFT_EXPECTS(index.metric() != cuvs::distance::DistanceType::CosineExpanded ||
+                   index.dataset_norms().has_value(),
+                 "Dataset norms must be provided for CosineExpanded metric");
+
+    const float* dataset_norms_ptr = nullptr;
+    if (index.metric() == cuvs::distance::DistanceType::CosineExpanded) {
+      dataset_norms_ptr = index.dataset_norms().value().data_handle();
+    }
     auto desc = dataset_descriptor_init_with_cache<T, IdxT, DistanceT>(
-      res, params, *strided_dset, index.metric());
+      res, params, *strided_dset, index.metric(), dataset_norms_ptr);
     search_main_core<T, IdxT, DistanceT, CagraSampleFilterT, OutputIdxT>(
       res, params, desc, index.graph(), queries, neighbors, distances, sample_filter);
   } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<float, ds_idx_type>*>(&index.data());
@@ -161,7 +170,7 @@ void search_main(raft::resources const& res,
   } else if (auto* vpq_dset = dynamic_cast<const vpq_dataset<half, ds_idx_type>*>(&index.data());
              vpq_dset != nullptr) {
     auto desc = dataset_descriptor_init_with_cache<T, IdxT, DistanceT>(
-      res, params, *vpq_dset, index.metric());
+      res, params, *vpq_dset, index.metric(), nullptr);
     search_main_core<T, IdxT, DistanceT, CagraSampleFilterT, OutputIdxT>(
       res, params, desc, index.graph(), queries, neighbors, distances, sample_filter);
   } else if (auto* empty_dset = dynamic_cast<const empty_dataset<ds_idx_type>*>(&index.data());
@@ -182,14 +191,45 @@ void search_main(raft::resources const& res,
   // and divide the values by kDivisor. Here we restore the original scale.
   constexpr float kScale = cuvs::spatial::knn::detail::utils::config<T>::kDivisor /
                            cuvs::spatial::knn::detail::utils::config<DistanceT>::kDivisor;
-  cuvs::neighbors::ivf::detail::postprocess_distances(dist_out,
-                                                      dist_in,
-                                                      index.metric(),
-                                                      distances.extent(0),
-                                                      distances.extent(1),
-                                                      kScale,
-                                                      true,
-                                                      raft::resource::get_cuda_stream(res));
+
+  if (index.metric() == cuvs::distance::DistanceType::CosineExpanded) {
+    auto stream      = raft::resource::get_cuda_stream(res);
+    auto query_norms = raft::make_device_vector<DistanceT, int64_t>(res, queries.extent(0));
+
+    // first scale the queries and then compute norms
+    auto scaled_sq_op = raft::compose_op(
+      raft::sq_op{}, raft::div_const_op<DistanceT>{DistanceT(kScale)}, raft::cast_op<DistanceT>());
+    raft::linalg::reduce<true, true, T, DistanceT, int64_t>(query_norms.data_handle(),
+                                                            queries.data_handle(),
+                                                            queries.extent(1),
+                                                            queries.extent(0),
+                                                            (DistanceT)0,
+                                                            stream,
+                                                            false,
+                                                            scaled_sq_op,
+                                                            raft::add_op(),
+                                                            raft::sqrt_op{});
+
+    const auto n_queries = distances.extent(0);
+    const auto k         = distances.extent(1);
+    auto query_norms_ptr = query_norms.data_handle();
+
+    raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
+      res,
+      raft::make_const_mdspan(distances),
+      raft::make_const_mdspan(query_norms.view()),
+      distances,
+      raft::compose_op(raft::add_const_op<DistanceT>{DistanceT(1)}, raft::div_checkzero_op{}));
+  } else {
+    cuvs::neighbors::ivf::detail::postprocess_distances(dist_out,
+                                                        dist_in,
+                                                        index.metric(),
+                                                        distances.extent(0),
+                                                        distances.extent(1),
+                                                        kScale,
+                                                        true,
+                                                        raft::resource::get_cuda_stream(res));
+  }
 }
 /** @} */  // end group cagra
 
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
index faca960808..53d9997243 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance-ext.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -44,6 +44,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 float,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                8,
+                                                128,
+                                                float,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            8,
                                            128,
@@ -74,6 +80,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 float,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                16,
+                                                256,
+                                                float,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            16,
                                            256,
@@ -104,6 +116,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 float,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                32,
+                                                512,
+                                                float,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            32,
                                            512,
@@ -134,6 +152,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 half,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                8,
+                                                128,
+                                                half,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            8,
                                            128,
@@ -164,6 +188,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 half,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                16,
+                                                256,
+                                                half,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            16,
                                            256,
@@ -194,6 +224,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 half,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                32,
+                                                512,
+                                                half,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            32,
                                            512,
@@ -224,6 +260,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 int8_t,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                8,
+                                                128,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            8,
                                            128,
@@ -254,6 +296,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 int8_t,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                16,
+                                                256,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            16,
                                            256,
@@ -284,6 +332,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 int8_t,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                32,
+                                                512,
+                                                int8_t,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            32,
                                            512,
@@ -314,6 +368,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 uint8_t,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                8,
+                                                128,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            8,
                                            128,
@@ -344,6 +404,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 uint8_t,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                16,
+                                                256,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            16,
                                            256,
@@ -374,6 +440,12 @@ extern template struct standard_descriptor_spec<DistanceType::InnerProduct,
                                                 uint8_t,
                                                 uint32_t,
                                                 float>;
+extern template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                                32,
+                                                512,
+                                                uint8_t,
+                                                uint32_t,
+                                                float>;
 extern template struct vpq_descriptor_spec<DistanceType::L2Expanded,
                                            32,
                                            512,
@@ -414,50 +486,62 @@ extern template struct standard_descriptor_spec<DistanceType::BitwiseHamming,
 extern template struct instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::BitwiseHamming, 8, 128, uint8_t, uint32_t, float>,
@@ -467,50 +551,62 @@ extern template struct instance_selector<
 using descriptor_instances = instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::BitwiseHamming, 8, 128, uint8_t, uint32_t, float>,
@@ -520,15 +616,16 @@ using descriptor_instances = instance_selector<
 template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
 auto dataset_descriptor_init(const cagra::search_params& params,
                              const DatasetT& dataset,
-                             cuvs::distance::DistanceType metric)
+                             cuvs::distance::DistanceType metric,
+                             const DistanceT* dataset_norms = nullptr)
   -> dataset_descriptor_host<DataT, IndexT, DistanceT>
 {
   auto [init, priority] =
-    descriptor_instances::select<DataT, IndexT, DistanceT>(params, dataset, metric);
+    descriptor_instances::select<DataT, IndexT, DistanceT>(params, dataset, metric, dataset_norms);
   if (init == nullptr || priority < 0) {
     RAFT_FAIL("No dataset descriptor instance compiled for this parameter combination.");
   }
-  return init(params, dataset, metric);
+  return init(params, dataset, metric, dataset_norms);
 }
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.cu b/cpp/src/neighbors/detail/cagra/compute_distance.cu
index 4158863465..27fb6d3bf6 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.cu
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -32,50 +32,62 @@ using namespace cuvs::distance;
 template struct instance_selector<
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, float, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, float, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, float, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, half, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, half, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, half, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, int8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, int8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, int8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 8, 128, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 8, 128, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 8, 128, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 8, 128, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 16, 256, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 16, 256, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 16, 256, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 16, 256, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::L2Expanded, 32, 512, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::InnerProduct, 32, 512, uint8_t, uint32_t, float>,
+  standard_descriptor_spec<DistanceType::CosineExpanded, 32, 512, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 2, half, uint8_t, uint32_t, float>,
   vpq_descriptor_spec<DistanceType::L2Expanded, 32, 512, 8, 4, half, uint8_t, uint32_t, float>,
   standard_descriptor_spec<DistanceType::BitwiseHamming, 8, 128, uint8_t, uint32_t, float>,
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance.hpp b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
index 2227e4f9ea..2bc8b5674f 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -316,7 +316,7 @@ struct dataset_descriptor_host {
  */
 template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
 using init_desc_type = dataset_descriptor_host<DataT, IndexT, DistanceT> (*)(
-  const cagra::search_params&, const DatasetT&, cuvs::distance::DistanceType);
+  const cagra::search_params&, const DatasetT&, cuvs::distance::DistanceType, const float*);
 
 /**
  * @brief Descriptor instance specification.
@@ -401,7 +401,10 @@ constexpr auto spec_match(const cagra::search_params& params,
 template <typename... Specs>
 struct instance_selector {
   template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
-  static auto select(const cagra::search_params&, const DatasetT&, cuvs::distance::DistanceType)
+  static auto select(const cagra::search_params&,
+                     const DatasetT&,
+                     cuvs::distance::DistanceType,
+                     const float*)
     -> std::tuple<init_desc_type<DataT, IndexT, DistanceT, DatasetT>, double>
   {
     return std::make_tuple(nullptr, -1.0);
@@ -413,25 +416,27 @@ struct instance_selector<Spec, Specs...> {
   template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
   static auto select(const cagra::search_params& params,
                      const DatasetT& dataset,
-                     cuvs::distance::DistanceType metric)
+                     cuvs::distance::DistanceType metric,
+                     const DistanceT* dataset_norms)
     -> std::enable_if_t<spec_sound<Spec, DataT, IndexT, DistanceT, DatasetT>,
                         std::tuple<init_desc_type<DataT, IndexT, DistanceT, DatasetT>, double>>
   {
     auto s0 = spec_match<Spec, DataT, IndexT, DistanceT, DatasetT>(params, dataset, metric);
     auto ss = instance_selector<Specs...>::template select<DataT, IndexT, DistanceT, DatasetT>(
-      params, dataset, metric);
+      params, dataset, metric, dataset_norms);
     return std::get<1>(s0) >= std::get<1>(ss) ? s0 : ss;
   }
 
   template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
   static auto select(const cagra::search_params& params,
                      const DatasetT& dataset,
-                     cuvs::distance::DistanceType metric)
+                     cuvs::distance::DistanceType metric,
+                     const DistanceT* dataset_norms)
     -> std::enable_if_t<!spec_sound<Spec, DataT, IndexT, DistanceT, DatasetT>,
                         std::tuple<init_desc_type<DataT, IndexT, DistanceT, DatasetT>, double>>
   {
     return instance_selector<Specs...>::template select<DataT, IndexT, DistanceT, DatasetT>(
-      params, dataset, metric);
+      params, dataset, metric, dataset_norms);
   }
 };
 
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
index 1c813f1a50..26d2a9ddd7 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_00_generate.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
 import glob
 
 template = """/*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -87,7 +87,8 @@
 for type_path, (data_t, idx_t, distance_t) in search_types.items():
     for (mxdim, team) in mxdim_team:
         # CAGRA
-        for metric in ['L2Expanded', 'InnerProduct']:
+        for metric in ['L2Expanded', 'InnerProduct', 'CosineExpanded']:
+
             path = f"compute_distance_standard_{metric}_{type_path}_dim{mxdim}_t{team}.cu"
             includes = '#include "compute_distance_standard-impl.cuh"'
             params = f"{metric_prefix}{metric}, {team}, {mxdim}, {data_t}, {idx_t}, {distance_t}"
@@ -151,14 +152,15 @@
 template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT>
 auto dataset_descriptor_init(const cagra::search_params& params,
                              const DatasetT& dataset,
-                             cuvs::distance::DistanceType metric)
+                             cuvs::distance::DistanceType metric,
+                             const DistanceT* dataset_norms = nullptr)
   -> dataset_descriptor_host<DataT, IndexT, DistanceT>
 {{
-  auto [init, priority] = descriptor_instances::select<DataT, IndexT, DistanceT>(params, dataset, metric);
+  auto [init, priority] = descriptor_instances::select<DataT, IndexT, DistanceT>(params, dataset, metric, dataset_norms);
   if (init == nullptr || priority < 0) {{
     RAFT_FAIL("No dataset descriptor instance compiled for this parameter combination.");
   }}
-  return init(params, dataset, metric);
+  return init(params, dataset, metric, dataset_norms);
 }}
 '''
     f.write(template.format(includes=includes, content=contents))
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
index fdc8731000..22755a8302 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard-impl.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -35,7 +35,9 @@ RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(DATA_T a, DATA_T b)
 
 template <typename DATA_T, typename DISTANCE_T, cuvs::distance::DistanceType Metric>
 RAFT_DEVICE_INLINE_FUNCTION constexpr auto dist_op(DATA_T a, DATA_T b)
-  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::InnerProduct, DISTANCE_T>
+  -> std::enable_if_t<Metric == cuvs::distance::DistanceType::InnerProduct ||
+                        Metric == cuvs::distance::DistanceType::CosineExpanded,
+                      DISTANCE_T>
 {
   return -static_cast<DISTANCE_T>(a) * static_cast<DISTANCE_T>(b);
 }
@@ -85,6 +87,17 @@ struct standard_dataset_descriptor_t : public dataset_descriptor_base_t<DataT, I
     return (const DATA_T*&)(args.extra_ptr1);
   }
 
+  static constexpr RAFT_INLINE_FUNCTION auto dataset_norms_ptr(const args_t& args) noexcept
+    -> const DISTANCE_T* const&
+  {
+    return (const DISTANCE_T* const&)(args.extra_ptr2);
+  }
+  static constexpr RAFT_INLINE_FUNCTION auto dataset_norms_ptr(args_t& args) noexcept
+    -> const DISTANCE_T*&
+  {
+    return (const DISTANCE_T*&)(args.extra_ptr2);
+  }
+
   static constexpr RAFT_INLINE_FUNCTION auto ld(const args_t& args) noexcept -> const uint32_t&
   {
     return args.extra_word1;
@@ -99,7 +112,8 @@ struct standard_dataset_descriptor_t : public dataset_descriptor_base_t<DataT, I
                                                   const DATA_T* ptr,
                                                   INDEX_T size,
                                                   uint32_t dim,
-                                                  uint32_t ld)
+                                                  uint32_t ld,
+                                                  const DISTANCE_T* dataset_norms = nullptr)
     : base_type(setup_workspace_impl,
                 compute_distance_impl,
                 size,
@@ -107,8 +121,9 @@ struct standard_dataset_descriptor_t : public dataset_descriptor_base_t<DataT, I
                 raft::Pow2<TeamSize>::Log2,
                 get_smem_ws_size_in_bytes(dim))
   {
-    standard_dataset_descriptor_t::ptr(args) = ptr;
-    standard_dataset_descriptor_t::ld(args)  = ld;
+    standard_dataset_descriptor_t::ptr(args)               = ptr;
+    standard_dataset_descriptor_t::ld(args)                = ld;
+    standard_dataset_descriptor_t::dataset_norms_ptr(args) = dataset_norms;
     static_assert(sizeof(*this) == sizeof(base_type));
     static_assert(alignof(standard_dataset_descriptor_t) == alignof(base_type));
   }
@@ -224,10 +239,18 @@ _RAFT_DEVICE __noinline__ auto compute_distance_standard(
   const typename DescriptorT::args_t args, const typename DescriptorT::INDEX_T dataset_index) ->
   typename DescriptorT::DISTANCE_T
 {
-  return compute_distance_standard_worker<DescriptorT>(
+  auto distance = compute_distance_standard_worker<DescriptorT>(
     DescriptorT::ptr(args) + (static_cast<std::uint64_t>(DescriptorT::ld(args)) * dataset_index),
     args.dim,
     args.smem_ws_ptr);
+
+  if constexpr (DescriptorT::kMetric == cuvs::distance::DistanceType::CosineExpanded) {
+    const auto* dataset_norms = DescriptorT::dataset_norms_ptr(args);
+    auto norm                 = dataset_norms[dataset_index];
+    if (norm > 0) { distance = distance / norm; }
+  }
+
+  return distance;
 }
 
 template <cuvs::distance::DistanceType Metric,
@@ -241,7 +264,8 @@ RAFT_KERNEL __launch_bounds__(1, 1)
                                           const DataT* ptr,
                                           IndexT size,
                                           uint32_t dim,
-                                          uint32_t ld)
+                                          uint32_t ld,
+                                          const DistanceT* dataset_norms = nullptr)
 {
   using desc_type =
     standard_dataset_descriptor_t<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>;
@@ -253,7 +277,8 @@ RAFT_KERNEL __launch_bounds__(1, 1)
                       ptr,
                       size,
                       dim,
-                      ld);
+                      ld,
+                      dataset_norms);
 }
 
 template <cuvs::distance::DistanceType Metric,
@@ -264,12 +289,21 @@ template <cuvs::distance::DistanceType Metric,
           typename DistanceT>
 dataset_descriptor_host<DataT, IndexT, DistanceT>
 standard_descriptor_spec<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>::init_(
-  const cagra::search_params& params, const DataT* ptr, IndexT size, uint32_t dim, uint32_t ld)
+  const cagra::search_params& params,
+  const DataT* ptr,
+  IndexT size,
+  uint32_t dim,
+  uint32_t ld,
+  const DistanceT* dataset_norms)
 {
   using desc_type =
     standard_dataset_descriptor_t<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, DistanceT>;
   using base_type = typename desc_type::base_type;
-  desc_type dd_host{nullptr, nullptr, ptr, size, dim, ld};
+
+  RAFT_EXPECTS(Metric != cuvs::distance::DistanceType::CosineExpanded || dataset_norms != nullptr,
+               "Dataset norms must be provided for CosineExpanded metric");
+
+  desc_type dd_host{nullptr, nullptr, ptr, size, dim, ld, dataset_norms};
   return host_type{dd_host,
                    [=](dataset_descriptor_base_t<DataT, IndexT, DistanceT>* dev_ptr,
                        rmm::cuda_stream_view stream) {
@@ -279,7 +313,7 @@ standard_descriptor_spec<Metric, TeamSize, DatasetBlockDim, DataT, IndexT, Dista
                                                              DataT,
                                                              IndexT,
                                                              DistanceT>
-                       <<<1, 1, 0, stream>>>(dev_ptr, ptr, size, dim, ld);
+                       <<<1, 1, 0, stream>>>(dev_ptr, ptr, size, dim, ld, dataset_norms);
                      RAFT_CUDA_TRY(cudaPeekAtLastError());
                    }};
 }
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp b/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
index fec14d7136..9c236cafb5 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -45,13 +45,15 @@ struct standard_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT>
   template <typename DatasetT>
   static auto init(const cagra::search_params& params,
                    const DatasetT& dataset,
-                   cuvs::distance::DistanceType metric) -> host_type
+                   cuvs::distance::DistanceType metric,
+                   const DistanceT* dataset_norms = nullptr) -> host_type
   {
     return init_(params,
                  dataset.view().data_handle(),
                  IndexT(dataset.n_rows()),
                  dataset.dim(),
-                 dataset.stride());
+                 dataset.stride(),
+                 dataset_norms);
   }
 
   template <typename DatasetT>
@@ -68,7 +70,12 @@ struct standard_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT>
 
  private:
   static dataset_descriptor_host<DataT, IndexT, DistanceT> init_(
-    const cagra::search_params& params, const DataT* ptr, IndexT size, uint32_t dim, uint32_t ld);
+    const cagra::search_params& params,
+    const DataT* ptr,
+    IndexT size,
+    uint32_t dim,
+    uint32_t ld,
+    const DistanceT* dataset_norms = nullptr);
 };
 
 }  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..c5d3579d25
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         8,
+                                         128,
+                                         float,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..c1f000700d
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         16,
+                                         256,
+                                         float,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..ea51d0af5c
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_float_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         32,
+                                         512,
+                                         float,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..c8bc8d46e3
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         8,
+                                         128,
+                                         half,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..faa3e5d765
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         16,
+                                         256,
+                                         half,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..1a1eb89630
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_half_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         32,
+                                         512,
+                                         half,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..104059b9ed
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         8,
+                                         128,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..4e057ed800
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         16,
+                                         256,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..5b76a3c17b
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_int8_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         32,
+                                         512,
+                                         int8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim128_t8.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim128_t8.cu
new file mode 100644
index 0000000000..82d7c39886
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim128_t8.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         8,
+                                         128,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim256_t16.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim256_t16.cu
new file mode 100644
index 0000000000..2a5349bcf5
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim256_t16.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         16,
+                                         256,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim512_t32.cu b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim512_t32.cu
new file mode 100644
index 0000000000..7951da6233
--- /dev/null
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_standard_CosineExpanded_uint8_uint32_dim512_t32.cu
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * NOTE: this file is generated by compute_distance_00_generate.py
+ *
+ * Make changes there and run in this directory:
+ *
+ * > python compute_distance_00_generate.py
+ *
+ */
+
+#include "compute_distance_standard-impl.cuh"
+
+namespace cuvs::neighbors::cagra::detail {
+
+using namespace cuvs::distance;
+template struct standard_descriptor_spec<DistanceType::CosineExpanded,
+                                         32,
+                                         512,
+                                         uint8_t,
+                                         uint32_t,
+                                         float>;
+
+}  // namespace cuvs::neighbors::cagra::detail
diff --git a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
index 2985a4d38c..c2223c0d57 100644
--- a/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
+++ b/cpp/src/neighbors/detail/cagra/compute_distance_vpq.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -57,7 +57,8 @@ struct vpq_descriptor_spec : public instance_spec<DataT, IndexT, DistanceT> {
   template <typename DatasetT>
   static auto init(const cagra::search_params& params,
                    const DatasetT& dataset,
-                   cuvs::distance::DistanceType metric) -> host_type
+                   cuvs::distance::DistanceType metric,
+                   const DistanceT* dataset_norms = nullptr) -> host_type
   {
     return init_(params,
                  dataset.data.data_handle(),
diff --git a/cpp/src/neighbors/detail/cagra/factory.cuh b/cpp/src/neighbors/detail/cagra/factory.cuh
index 3d1ea1efc9..cc53bd2690 100644
--- a/cpp/src/neighbors/detail/cagra/factory.cuh
+++ b/cpp/src/neighbors/detail/cagra/factory.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -160,7 +160,8 @@ template <typename DataT, typename IndexT, typename DistanceT, typename DatasetT
 auto dataset_descriptor_init_with_cache(const raft::resources& res,
                                         const cagra::search_params& params,
                                         const DatasetT& dataset,
-                                        cuvs::distance::DistanceType metric)
+                                        cuvs::distance::DistanceType metric,
+                                        const DistanceT* dataset_norms = nullptr)
   -> dataset_descriptor_host<DataT, IndexT, DistanceT>
 {
   auto key = descriptor_cache::make_key(params, dataset, metric);
@@ -169,7 +170,8 @@ auto dataset_descriptor_init_with_cache(const raft::resources& res,
       ->value;
   dataset_descriptor_host<DataT, IndexT, DistanceT> desc;
   if (!cache.get(key, &desc)) {
-    desc = dataset_descriptor_init<DataT, IndexT, DistanceT>(params, dataset, metric);
+    desc =
+      dataset_descriptor_init<DataT, IndexT, DistanceT>(params, dataset, metric, dataset_norms);
     cache.set(key, desc);
   }
   return desc;
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
index a2556dc651..d890945085 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_build.cuh
@@ -135,6 +135,7 @@ void select_residuals(raft::resources const& handle,
  * The residual has the form
  *  `rotation_matrix %* (dataset[:, :] - centers[labels[:], 0:dim])`
  *
+ * For cosine metric, normalizes the data after type conversion before computing residuals.
  */
 template <typename T, typename IdxT>
 void flat_compute_residuals(
@@ -146,21 +147,38 @@ void flat_compute_residuals(
   raft::device_matrix_view<const float, uint32_t, raft::row_major> centers,  // [n_lists, dim_ext]
   const T* dataset,                                                          // [n_rows, dim]
   std::variant<uint32_t, const uint32_t*> labels,                            // [n_rows]
-  rmm::device_async_resource_ref device_memory)
+  rmm::device_async_resource_ref device_memory,
+  cuvs::distance::DistanceType metric = cuvs::distance::DistanceType::L2Expanded)
 {
   auto stream  = raft::resource::get_cuda_stream(handle);
   auto dim     = rotation_matrix.extent(1);
   auto rot_dim = rotation_matrix.extent(0);
   rmm::device_uvector<float> tmp(n_rows * dim, stream, device_memory);
-  auto tmp_view = raft::make_device_vector_view<float, IdxT>(tmp.data(), tmp.size());
-  raft::linalg::map_offset(handle, tmp_view, [centers, dataset, labels, dim] __device__(size_t i) {
-    auto row_ix = i / dim;
-    auto el_ix  = i % dim;
-    auto label  = std::holds_alternative<uint32_t>(labels)
-                    ? std::get<uint32_t>(labels)
-                    : std::get<const uint32_t*>(labels)[row_ix];
-    return utils::mapping<float>{}(dataset[i]) - centers(label, el_ix);
-  });
+  auto tmp_view = raft::make_device_vector_view<float, size_t>(tmp.data(), tmp.size());
+
+  if (metric == cuvs::distance::DistanceType::CosineExpanded) {
+    raft::linalg::map(handle,
+                      tmp_view,
+                      raft::cast_op<float>{},
+                      raft::make_device_vector_view<const T, IdxT>(dataset, n_rows * dim));
+    auto tmp_matrix_view = raft::make_device_matrix_view<float, size_t>(tmp.data(), n_rows, dim);
+    raft::linalg::row_normalize<raft::linalg::L2Norm>(
+      handle, raft::make_const_mdspan(tmp_matrix_view), tmp_matrix_view);
+  } else {
+    raft::linalg::map_offset(handle, tmp_view, [dataset, dim] __device__(size_t i) {
+      return utils::mapping<float>{}(dataset[i]);
+    });
+  }
+
+  raft::linalg::map_offset(
+    handle, tmp_view, [centers, tmp = tmp.data(), labels, dim] __device__(size_t i) {
+      auto row_ix = i / dim;
+      auto el_ix  = i % dim;
+      auto label  = std::holds_alternative<uint32_t>(labels)
+                      ? std::get<uint32_t>(labels)
+                      : std::get<const uint32_t*>(labels)[row_ix];
+      return tmp[i] - centers(label, el_ix);
+    });
 
   float alpha = 1.0f;
   float beta  = 0.0f;
@@ -859,7 +877,8 @@ void encode_list_data(raft::resources const& res,
                                       index->centers(),
                                       new_vectors.data_handle(),
                                       label,
-                                      mr);
+                                      mr,
+                                      index->metric());
 
   constexpr uint32_t kBlockSize  = 256;
   const uint32_t threads_per_vec = std::min<uint32_t>(raft::WarpSize, index->pq_book_size());
@@ -931,7 +950,8 @@ void process_and_fill_codes(raft::resources const& handle,
                                   index.centers(),
                                   new_vectors,
                                   new_labels,
-                                  mr);
+                                  mr,
+                                  index.metric());
 
   launch_process_and_fill_codes_kernel(
     handle, index, new_vectors_residual.view(), src_offset_or_indices, new_labels, n_rows);
@@ -1094,13 +1114,6 @@ void extend(raft::resources const& handle,
                   std::is_same_v<T, int8_t>,
                 "Unsupported data type");
 
-  if (index->metric() == distance::DistanceType::CosineExpanded) {
-    if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>)
-      RAFT_FAIL(
-        "CosineExpanded distance metric is currently not supported for uint8_t and int8_t data "
-        "type");
-  }
-
   rmm::device_async_resource_ref device_memory = raft::resource::get_workspace_resource(handle);
   rmm::device_async_resource_ref large_memory =
     raft::resource::get_large_workspace_resource(handle);
@@ -1267,12 +1280,6 @@ void extend(raft::resources const& handle,
   vec_batches.prefetch_next_batch();
   for (const auto& vec_batch : vec_batches) {
     const auto& idx_batch = *idx_batches++;
-    if (index->metric() == CosineExpanded) {
-      auto vec_batch_view = raft::make_device_matrix_view<T, internal_extents_t>(
-        const_cast<T*>(vec_batch.data()), vec_batch.size(), index->dim());
-      raft::linalg::row_normalize<raft::linalg::L2Norm>(
-        handle, raft::make_const_mdspan(vec_batch_view), vec_batch_view);
-    }
     process_and_fill_codes(handle,
                            *index,
                            vec_batch.data(),
@@ -1321,13 +1328,6 @@ auto build(raft::resources const& handle,
 
   RAFT_EXPECTS(n_rows > 0 && dim > 0, "empty dataset");
   RAFT_EXPECTS(n_rows >= params.n_lists, "number of rows can't be less than n_lists");
-  if (params.metric == distance::DistanceType::CosineExpanded) {
-    // TODO: support int8_t and uint8_t types (https://github.com/rapidsai/cuvs/issues/389)
-    if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>)
-      RAFT_FAIL(
-        "CosineExpanded distance metric is currently not supported for uint8_t and int8_t data "
-        "type");
-  }
 
   auto stream = raft::resource::get_cuda_stream(handle);
 
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
index 15ab026cee..0b059420a2 100644
--- a/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_search.cuh
@@ -849,12 +849,6 @@ inline void search(raft::resources const& handle,
   static_assert(std::is_same_v<T, float> || std::is_same_v<T, half> || std::is_same_v<T, uint8_t> ||
                   std::is_same_v<T, int8_t>,
                 "Unsupported element type.");
-  if (index.metric() == distance::DistanceType::CosineExpanded) {
-    if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>)
-      RAFT_FAIL(
-        "CosineExpanded distance metric is currently not supported for uint8_t and int8_t data "
-        "type");
-  }
   raft::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "ivf_pq::search(n_queries = %u, n_probes = %u, k = %u, dim = %zu)",
     n_queries,
diff --git a/cpp/src/neighbors/refine/refine_host.hpp b/cpp/src/neighbors/refine/refine_host.hpp
index b813c6002d..6bc8271cab 100644
--- a/cpp/src/neighbors/refine/refine_host.hpp
+++ b/cpp/src/neighbors/refine/refine_host.hpp
@@ -18,11 +18,10 @@
 
 #include "../../core/nvtx.hpp"
 #include "refine_common.hpp"
+#include <omp.h>
 #include <raft/core/host_mdspan.hpp>
 #include <raft/util/integer_utils.hpp>
 
-#include <omp.h>
-
 #include <algorithm>
 
 #if defined(__arm__) || defined(__aarch64__)
@@ -69,6 +68,7 @@ DistanceT euclidean_distance_squared_generic(DataT const* a, DataT const* b, siz
 
 struct distance_comp_l2;
 struct distance_comp_inner;
+struct distance_comp_cosine;
 
 // fallback
 template <typename DC, typename DistanceT, typename DataT>
@@ -339,6 +339,26 @@ inline float euclidean_distance_squared<distance_comp_inner, float, ::std::uint8
 //  Refine kernel
 // -----------------------------------------------------------------------------
 
+// Cosine distance: 1 - (a·b)/(||a||·||b||)
+template <typename DistanceT, typename DataT>
+inline DistanceT cosine_distance(DataT const* a, DataT const* b, size_t n)
+{
+  using AccT = double;
+  AccT dot   = AccT{0};
+  AccT na2   = AccT{0};
+  AccT nb2   = AccT{0};
+  for (size_t i = 0; i < n; ++i) {
+    AccT va = static_cast<AccT>(a[i]);
+    AccT vb = static_cast<AccT>(b[i]);
+    dot += va * vb;
+    na2 += va * va;
+    nb2 += vb * vb;
+  }
+  AccT denom = std::sqrt(na2) * std::sqrt(nb2);
+  AccT dist  = denom > AccT{0} ? AccT{1} - (dot / denom) : AccT{1};
+  return static_cast<DistanceT>(dist);
+}
+
 template <typename DC, typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
 [[gnu::optimize(3), gnu::optimize("tree-vectorize")]] void refine_host_impl(
   raft::host_matrix_view<const DataT, ExtentsT, raft::row_major> dataset,
@@ -429,7 +449,11 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
             distance = std::numeric_limits<DistanceT>::max();
           } else {
             const DataT* row = dataset.data_handle() + dim * id;
-            distance         = euclidean_distance_squared<DC, DistanceT, DataT>(query, row, dim);
+            if constexpr (std::is_same_v<DC, distance_comp_cosine>) {
+              distance = cosine_distance<DistanceT, DataT>(query, row, dim);
+            } else {
+              distance = euclidean_distance_squared<DC, DistanceT, DataT>(query, row, dim);
+            }
           }
           refined_pairs[tid][j] = std::make_tuple(distance, id);
         }
@@ -474,6 +498,19 @@ struct distance_comp_inner {
   }
 };
 
+struct distance_comp_cosine {
+  template <typename DistanceT>
+  static inline auto eval(const DistanceT&, const DistanceT&) -> DistanceT
+  {
+    return DistanceT{0};
+  }
+  template <typename DistanceT>
+  static inline auto postprocess(const DistanceT& a) -> DistanceT
+  {
+    return a;
+  }
+};
+
 /**
  * Naive CPU implementation of refine operation
  *
@@ -502,6 +539,9 @@ template <typename IdxT, typename DataT, typename DistanceT, typename ExtentsT>
     case cuvs::distance::DistanceType::InnerProduct:
       return refine_host_impl<distance_comp_inner>(
         dataset, queries, neighbor_candidates, indices, distances);
+    case cuvs::distance::DistanceType::CosineExpanded:
+      return refine_host_impl<distance_comp_cosine>(
+        dataset, queries, neighbor_candidates, indices, distances);
     default: throw raft::logic_error("Unsupported metric");
   }
 }
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index e10e1300ca..027155a69e 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -32,6 +32,7 @@
 #include <raft/core/logger.hpp>
 #include <raft/linalg/add.cuh>
 #include <raft/linalg/matrix_vector_op.cuh>
+#include <raft/linalg/norm.cuh>
 #include <raft/linalg/normalize.cuh>
 #include <raft/linalg/reduce.cuh>
 #include <raft/random/rng.cuh>
@@ -292,6 +293,7 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
       case InnerProduct: return "InnerProduct";
       case L2Expanded: return "L2";
       case BitwiseHamming: return "BitwiseHamming";
+      case CosineExpanded: return "Cosine";
       default: break;
     }
     return "Unknown";
@@ -343,6 +345,12 @@ class AnnCagraTest : public ::testing::TestWithParam<AnnCagraInputs> {
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
         (ps.k * ps.dim * 8 / 5 /*(=magic number)*/ < ps.n_rows))
       GTEST_SKIP();
+    if (ps.metric == cuvs::distance::DistanceType::CosineExpanded) {
+      if (ps.compression.has_value()) { GTEST_SKIP(); }
+      if (ps.build_algo == graph_build_algo::ITERATIVE_CAGRA_SEARCH || ps.dim == 1) {
+        GTEST_SKIP();
+      }
+    }
 
     size_t queries_size = ps.n_queries * ps.k;
     std::vector<SearchIdxT> indices_Cagra(queries_size);
@@ -520,9 +528,12 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
  protected:
   void testCagra()
   {
-    // TODO (tarang-jain): remove when NN Descent index building support InnerProduct. Reference
-    // issue: https://github.com/rapidsai/raft/issues/2276
-    if (ps.metric == InnerProduct && ps.build_algo == graph_build_algo::NN_DESCENT) GTEST_SKIP();
+    if (ps.metric == cuvs::distance::DistanceType::CosineExpanded) {
+      if (ps.compression.has_value()) { GTEST_SKIP(); }
+      if (ps.build_algo == graph_build_algo::ITERATIVE_CAGRA_SEARCH || ps.dim == 1) {
+        GTEST_SKIP();
+      }
+    }
     if (ps.compression != std::nullopt) GTEST_SKIP();
     // IVF_PQ graph build does not support BitwiseHamming
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
@@ -580,8 +591,8 @@ class AnnCagraAddNodesTest : public ::testing::TestWithParam<AnnCagraInputs> {
             }
             break;
           case graph_build_algo::NN_DESCENT: {
-            index_params.graph_build_params =
-              graph_build_params::nn_descent_params(index_params.intermediate_graph_degree);
+            index_params.graph_build_params = graph_build_params::nn_descent_params(
+              index_params.intermediate_graph_degree, index_params.metric);
             break;
           }
           case graph_build_algo::ITERATIVE_CAGRA_SEARCH: {
@@ -730,13 +741,15 @@ class AnnCagraFilterTest : public ::testing::TestWithParam<AnnCagraInputs> {
  protected:
   void testCagra()
   {
-    if (ps.metric == cuvs::distance::DistanceType::InnerProduct &&
-        ps.build_algo == graph_build_algo::NN_DESCENT)
-      GTEST_SKIP();
+    if (ps.metric == cuvs::distance::DistanceType::CosineExpanded) {
+      if (ps.compression.has_value()) { GTEST_SKIP(); }
+      if (ps.build_algo == graph_build_algo::ITERATIVE_CAGRA_SEARCH || ps.dim == 1) {
+        GTEST_SKIP();
+      }
+    }
     // IVF_PQ graph build does not support BitwiseHamming
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
-        ((!std::is_same_v<DataT, uint8_t>) ||
-         (ps.build_algo != graph_build_algo::ITERATIVE_CAGRA_SEARCH)))
+        ((!std::is_same_v<DataT, uint8_t>) || (ps.build_algo == graph_build_algo::IVF_PQ)))
       GTEST_SKIP();
     // If the dataset dimension is small and the dataset size is large, there can be a lot of
     // dataset vectors that have the same distance to the query, especially in the binary Hamming
@@ -940,9 +953,11 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
   template <typename SearchIdxT = IdxT>
   void testCagra()
   {
-    // TODO (tarang-jain): remove when NN Descent index building support InnerProduct. Reference
-    // issue: https://github.com/rapidsai/raft/issues/2276
-    if (ps.metric == InnerProduct && ps.build_algo == graph_build_algo::NN_DESCENT) GTEST_SKIP();
+    if (ps.metric == cuvs::distance::DistanceType::CosineExpanded) {
+      if (ps.build_algo == graph_build_algo::ITERATIVE_CAGRA_SEARCH || ps.dim == 1) {
+        GTEST_SKIP();
+      }
+    }
     if (ps.compression != std::nullopt) GTEST_SKIP();
     // IVF_PQ graph build does not support BitwiseHamming
     if (ps.metric == cuvs::distance::DistanceType::BitwiseHamming &&
@@ -1137,46 +1152,72 @@ class AnnCagraIndexMergeTest : public ::testing::TestWithParam<AnnCagraInputs> {
 inline std::vector<AnnCagraInputs> generate_inputs()
 {
   // TODO(tfeher): test MULTI_CTA kernel with search_width > 1 to allow multiple CTA per queries
-  // Charge graph dim, search algo and max_query parameter
+  // Change graph dim, search algo and max_query parameter
   std::vector<AnnCagraInputs> inputs = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
-    {1, 8, 16},
-    {16},  // k
-    {graph_build_algo::NN_DESCENT,
-     graph_build_algo::ITERATIVE_CAGRA_SEARCH},  // build algo. ITERATIVE_CAGRA_SEARCH is needed to
-                                                 // test BitwiseHamming
+    {1, 16},
+    {16},                                                      // k
+    {graph_build_algo::IVF_PQ, graph_build_algo::NN_DESCENT},  // build algo.
     {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
     {0, 10},  // query size
     {0},
     {256},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {cuvs::distance::DistanceType::L2Expanded,
+     cuvs::distance::DistanceType::InnerProduct,
+     cuvs::distance::DistanceType::BitwiseHamming,
+     cuvs::distance::DistanceType::CosineExpanded},
     {false},
     {true},
     {0.995},
     {std::optional<float>{std::nullopt}},
     {std::optional<vpq_params>{std::nullopt}},
     {std::optional<bool>{std::nullopt}},
-    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
-     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL});
 
-  // Fixed dim, and changing neighbors and query size (output matrix size)
   auto inputs2 = raft::util::itertools::product<AnnCagraInputs>(
-    {1, 100},
+    {100},
     {1000},
-    {8},
-    {1, 16},  // k
-    {graph_build_algo::NN_DESCENT},
-    {search_algo::SINGLE_CTA, search_algo::MULTI_CTA, search_algo::MULTI_KERNEL},
-    {0},  // query size
+    {1, 16},
+    {16},                            // k
+    {graph_build_algo::NN_DESCENT},  // build algo.
+    {search_algo::MULTI_CTA},
+    {10},  // query size
     {0},
     {256},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {cuvs::distance::DistanceType::L2Expanded,
+     cuvs::distance::DistanceType::InnerProduct,
+     cuvs::distance::DistanceType::BitwiseHamming,
+     cuvs::distance::DistanceType::CosineExpanded},
     {false},
     {true},
-    {0.995});
+    {0.995},
+    {std::optional<float>{std::nullopt}},
+    {std::optional<vpq_params>{std::nullopt}},
+    {std::optional<bool>{std::nullopt}},
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+
+  // Additional distances tested with a single search algo.
+  inputs2 =
+    raft::util::itertools::product<AnnCagraInputs>({1, 100},
+                                                   {1000},
+                                                   {8},
+                                                   {1, 16},  // k
+                                                   {graph_build_algo::NN_DESCENT},
+                                                   {search_algo::SINGLE_CTA},
+                                                   {0},  // query size
+                                                   {0},
+                                                   {256},
+                                                   {1},
+                                                   {cuvs::distance::DistanceType::InnerProduct,
+                                                    cuvs::distance::DistanceType::BitwiseHamming,
+                                                    cuvs::distance::DistanceType::CosineExpanded},
+                                                   {false},
+                                                   {true},
+                                                   {0.995});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Corner cases for small datasets
@@ -1240,10 +1281,13 @@ inline std::vector<AnnCagraInputs> generate_inputs()
      graph_build_algo::ITERATIVE_CAGRA_SEARCH},
     {search_algo::AUTO},
     {10},
-    {0, 8, 16, 32},  // team_size
+    {0},  // team_size
     {64},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {cuvs::distance::DistanceType::L2Expanded,
+     cuvs::distance::DistanceType::InnerProduct,
+     cuvs::distance::DistanceType::BitwiseHamming,
+     cuvs::distance::DistanceType::CosineExpanded},
     {false},
     {false},
     {0.995},
@@ -1254,29 +1298,29 @@ inline std::vector<AnnCagraInputs> generate_inputs()
      cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
-  // Varying graph_build_algo, itopk_size
+  // Vary team size only.
   inputs2 = raft::util::itertools::product<AnnCagraInputs>(
     {100},
     {1000},
     {64},
     {16},
-    {graph_build_algo::IVF_PQ,
-     graph_build_algo::NN_DESCENT,
-     graph_build_algo::ITERATIVE_CAGRA_SEARCH},
+    {graph_build_algo::NN_DESCENT},
     {search_algo::AUTO},
     {10},
-    {0},  // team_size
-    {32, 64, 128, 256, 512, 768},
+    {8, 16, 32},  // team_size
+    {64},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
+    {cuvs::distance::DistanceType::L2Expanded,
+     cuvs::distance::DistanceType::InnerProduct,
+     cuvs::distance::DistanceType::BitwiseHamming,
+     cuvs::distance::DistanceType::CosineExpanded},
+    {false},
     {false},
-    {true},
     {0.995},
     {std::optional<float>{std::nullopt}},
     {std::optional<vpq_params>{std::nullopt}},
     {std::optional<bool>{std::nullopt}},
-    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL,
-     cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_LOGICAL});
+    {cuvs::neighbors::MergeStrategy::MERGE_STRATEGY_PHYSICAL});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // Varying n_rows, host_dataset
@@ -1412,21 +1456,23 @@ inline std::vector<AnnCagraInputs> generate_addnode_inputs()
                                                    {0.995});
 
   // testing host and device datasets
-  auto inputs2 = raft::util::itertools::product<AnnCagraInputs>(
-    {100},
-    {10000},
-    {32},
-    {10},
-    {graph_build_algo::AUTO},
-    {search_algo::AUTO},
-    {10},
-    {0},  // team_size
-    {64},
-    {1},
-    {cuvs::distance::DistanceType::L2Expanded, cuvs::distance::DistanceType::InnerProduct},
-    {false, true},
-    {false},
-    {0.985});
+  auto inputs2 =
+    raft::util::itertools::product<AnnCagraInputs>({100},
+                                                   {10000},
+                                                   {32},
+                                                   {10},
+                                                   {graph_build_algo::AUTO},
+                                                   {search_algo::AUTO},
+                                                   {10},
+                                                   {0},  // team_size
+                                                   {64},
+                                                   {1},
+                                                   {cuvs::distance::DistanceType::L2Expanded,
+                                                    cuvs::distance::DistanceType::InnerProduct,
+                                                    cuvs::distance::DistanceType::CosineExpanded},
+                                                   {false, true},
+                                                   {false},
+                                                   {0.985});
   inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
 
   // a few PQ configurations
@@ -1474,9 +1520,7 @@ inline std::vector<AnnCagraInputs> generate_filtering_inputs()
     {0},
     {256},
     {1},
-    {cuvs::distance::DistanceType::L2Expanded,
-     cuvs::distance::DistanceType::InnerProduct,
-     cuvs::distance::DistanceType::BitwiseHamming},
+    {cuvs::distance::DistanceType::L2Expanded},
     {false},
     {true},
     {0.995});
diff --git a/cpp/tests/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu b/cpp/tests/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
index c9e5d4f010..1bbf7c5953 100644
--- a/cpp/tests/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+++ b/cpp/tests/neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,9 +25,12 @@ TEST_BUILD_SEARCH(f32_i08_i64)
 TEST_BUILD_HOST_INPUT_SEARCH(f32_i08_i64)
 TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_i08_i64)
 TEST_BUILD_SERIALIZE_SEARCH(f32_i08_i64)
-INSTANTIATE(f32_i08_i64, defaults() + big_dims() + var_k() + enum_variety_l2() + enum_variety_ip());
+INSTANTIATE(f32_i08_i64,
+            defaults() + big_dims() + var_k() + enum_variety_l2() + enum_variety_ip() +
+              enum_variety_cosine());
 
 TEST_BUILD_SEARCH(f32_i08_i64_filter)
 INSTANTIATE(f32_i08_i64_filter,
-            defaults() + big_dims() + var_k() + enum_variety_l2() + enum_variety_ip());
+            defaults() + big_dims() + var_k() + enum_variety_l2() + enum_variety_ip() +
+              enum_variety_cosine());
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/cpp/tests/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu b/cpp/tests/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
index 6e07322273..4f88586965 100644
--- a/cpp/tests/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+++ b/cpp/tests/neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -27,10 +27,10 @@ TEST_BUILD_HOST_INPUT_OVERLAP_SEARCH(f32_u08_i64)
 TEST_BUILD_EXTEND_SEARCH(f32_u08_i64)
 INSTANTIATE(f32_u08_i64,
             small_dims_per_cluster() + enum_variety() + enum_variety_l2() + enum_variety_l2sqrt() +
-              enum_variety_ip());
+              enum_variety_ip() + enum_variety_cosine());
 
 TEST_BUILD_SEARCH(f32_u08_i64_filter)
 INSTANTIATE(f32_u08_i64_filter,
             small_dims_per_cluster() + enum_variety() + enum_variety_l2() + enum_variety_l2sqrt() +
-              enum_variety_ip());
+              enum_variety_ip() + enum_variety_cosine());
 }  // namespace cuvs::neighbors::ivf_pq
diff --git a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
index 8738e035c3..c46822c522 100644
--- a/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
+++ b/python/cuvs/cuvs/neighbors/cagra/cagra.pyx
@@ -139,12 +139,14 @@ cdef class IndexParams:
 
     metric : str, default = "sqeuclidean"
         String denoting the metric type, valid values for metric are
-        ["sqeuclidean", "inner_product"], where:
+        ["sqeuclidean", "inner_product", "cosine"], where:
 
             - sqeuclidean is the euclidean distance without the square root
               operation, i.e.: distance(a,b) = \\sum_i (a_i - b_i)^2
             - inner_product distance is defined as
               distance(a, b) = \\sum_i a_i * b_i.
+            - cosine distance is defined as
+              distance(a, b) = 1 - \\sum_i a_i * b_i / ( ||a||_2 * ||b||_2).
 
     intermediate_graph_degree : int, default = 128
     graph_degree : int, default = 64
@@ -339,6 +341,7 @@ def build(IndexParams index_params, dataset, resources=None):
     The following distance metrics are supported:
         - L2
         - InnerProduct
+        - Cosine
 
     Parameters
     ----------
diff --git a/python/cuvs/cuvs/tests/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
index 359cec02f5..4ec3c2a8fb 100644
--- a/python/cuvs/cuvs/tests/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -46,7 +46,7 @@ def run_cagra_build_search_test(
     compression=None,
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
-    if metric == "inner_product":
+    if metric == "inner_product" or metric == "cosine":
         if dtype in [np.int8, np.uint8]:
             pytest.skip("skip normalization for int8/uint8 data")
         dataset = normalize(dataset, norm="l2", axis=1)
@@ -112,6 +112,7 @@ def run_cagra_build_search_test(
         "sqeuclidean": "sqeuclidean",
         "inner_product": "cosine",
         "euclidean": "euclidean",
+        "cosine": "cosine",
     }[metric]
     nn_skl = NearestNeighbors(
         n_neighbors=k, algorithm="brute", metric=skl_metric
@@ -150,17 +151,18 @@ def run_cagra_build_search_test(
             search_params, reloaded_index, queries_device, k
         )
         recall = calc_recall(idx_device.copy_to_host(), skl_idx)
-        assert recall > 0.7
+        assert recall > 0.9
 
 
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
-@pytest.mark.parametrize("array_type", ["device", "host"])
+@pytest.mark.parametrize("array_type", ["device"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
-@pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
+@pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product", "cosine"])
 def test_cagra_dataset_dtype_host_device(
     dtype, array_type, inplace, build_algo, metric
 ):
+
     # Note that inner_product tests use normalized input which we cannot
     # represent in int8, therefore we test only sqeuclidean metric here.
     run_cagra_build_search_test(
@@ -204,6 +206,14 @@ def test_filtered_cagra(sparsity):
             "metric": "inner_product",
             "build_algo": "nn_descent",
         },
+        {
+            "intermediate_graph_degree": 64,
+            "graph_degree": 32,
+            "test_extend": True,
+            "k": 10,
+            "metric": "cosine",
+            "build_algo": "ivf_pq",
+        },
     ],
 )
 def test_cagra_index_params(params):

From b9a8f8062d50d9c0ec5ade8752d3027be7ebd1cc Mon Sep 17 00:00:00 2001
From: Jake Awe <jawe@nvidia.com>
Date: Wed, 8 Oct 2025 15:23:48 +0000
Subject: [PATCH 611/623] Update Changelog [skip ci]

---
 CHANGELOG.md | 112 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 112 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2cf7bc0f62..a047a3f68a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,115 @@
+# cuvs 25.10.00 (8 Oct 2025)
+
+## 🚨 Breaking Changes
+
+- [Java] Uniform toHost/toDevice to work across all CuVSMatrix classes ([#1328](https://github.com/rapidsai/cuvs/pull/1328)) [@ldematte](https://github.com/ldematte)
+- Use int64_t for getters in the cagra/ivf_flat c-api ([#1272](https://github.com/rapidsai/cuvs/pull/1272)) [@benfred](https://github.com/benfred)
+- [REVIEW][Java] Rename destroyIndex() to close(), extend AutoCloseable ([#1252](https://github.com/rapidsai/cuvs/pull/1252)) [@ldematte](https://github.com/ldematte)
+- Removing deprecated batching code specific to NN Descent ([#1249](https://github.com/rapidsai/cuvs/pull/1249)) [@jinsolp](https://github.com/jinsolp)
+- MG C API ([#1160](https://github.com/rapidsai/cuvs/pull/1160)) [@viclafargue](https://github.com/viclafargue)
+
+## 🐛 Bug Fixes
+
+- Update `cpp/cmake/config.json` for `ConfigureTest()` ([#1385](https://github.com/rapidsai/cuvs/pull/1385)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Fix k-means++ root rank race condition ([#1359](https://github.com/rapidsai/cuvs/pull/1359)) [@csadorf](https://github.com/csadorf)
+- Fix inadvertent uses of copy constructor in mdarrays across cuVS ([#1330](https://github.com/rapidsai/cuvs/pull/1330)) [@achirkin](https://github.com/achirkin)
+- Refactor CMakeLists to support static only builds of libcuvs ([#1317](https://github.com/rapidsai/cuvs/pull/1317)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix pq_compressed file created by SSD Vamana build ([#1313](https://github.com/rapidsai/cuvs/pull/1313)) [@bkarsin](https://github.com/bkarsin)
+- Revert &quot;[Java]Binary and scalar quantization ([#1104)&quot; (#1274](https://github.com/rapidsai/cuvs/pull/1104)&quot; (#1274)) [@mythrocks](https://github.com/mythrocks)
+- Fix balanced kmeans API ([#1268](https://github.com/rapidsai/cuvs/pull/1268)) [@enp1s0](https://github.com/enp1s0)
+- [BUG] NEIGHBORS_ALL_NEIGHBORS_TEST build ignores --no-mg ([#1230](https://github.com/rapidsai/cuvs/pull/1230)) [@enp1s0](https://github.com/enp1s0)
+- Add error handling for CPU-only bench ([#1203](https://github.com/rapidsai/cuvs/pull/1203)) [@lowener](https://github.com/lowener)
+- Make duplicate removal in all neighbors robust to distance drift across batches ([#1185](https://github.com/rapidsai/cuvs/pull/1185)) [@jinsolp](https://github.com/jinsolp)
+- Fix single GPU sharded search merge ([#1094](https://github.com/rapidsai/cuvs/pull/1094)) [@viclafargue](https://github.com/viclafargue)
+- Processing mutual reachability dist in connect_knn_graph for host data ([#1093](https://github.com/rapidsai/cuvs/pull/1093)) [@jinsolp](https://github.com/jinsolp)
+- [Java][Fix] Multithreaded querying fails without synchronization ([#1082](https://github.com/rapidsai/cuvs/pull/1082)) [@chatman](https://github.com/chatman)
+
+## 📖 Documentation
+
+- Add Vamana C rst Docs to toctree ([#1399](https://github.com/rapidsai/cuvs/pull/1399)) [@tarang-jain](https://github.com/tarang-jain)
+- Regroup overloads in docs ([#1377](https://github.com/rapidsai/cuvs/pull/1377)) [@lowener](https://github.com/lowener)
+- [DOCS] Update IVF Docs with Supported Distance Metrics ([#1365](https://github.com/rapidsai/cuvs/pull/1365)) [@tarang-jain](https://github.com/tarang-jain)
+- Add note about Linux-only support for cuVS pre-compiled packages ([#1306](https://github.com/rapidsai/cuvs/pull/1306)) [@stic](https://github.com/stic)
+- Documentation Fixes ([#1301](https://github.com/rapidsai/cuvs/pull/1301)) [@benfred](https://github.com/benfred)
+- Docs for spectral embedding ([#1299](https://github.com/rapidsai/cuvs/pull/1299)) [@aamijar](https://github.com/aamijar)
+- Improve NN Descent documentation ([#1246](https://github.com/rapidsai/cuvs/pull/1246)) [@jinsolp](https://github.com/jinsolp)
+- Add NN.extend and kmeans to Python doc ([#1217](https://github.com/rapidsai/cuvs/pull/1217)) [@lowener](https://github.com/lowener)
+
+## 🚀 New Features
+
+- [REVIEW] [Java] Option to build fat-jars with native dependencies included ([#1296](https://github.com/rapidsai/cuvs/pull/1296)) [@mythrocks](https://github.com/mythrocks)
+- Updates needed when building with CUDA 13 ([#1219](https://github.com/rapidsai/cuvs/pull/1219)) [@robertmaynard](https://github.com/robertmaynard)
+- [Java] Add CAGRA index graph accessor/build from graph (host memory) ([#1216](https://github.com/rapidsai/cuvs/pull/1216)) [@ldematte](https://github.com/ldematte)
+- [Feat] Add Dockerfile for reproducible installation ([#1195](https://github.com/rapidsai/cuvs/pull/1195)) [@rhdong](https://github.com/rhdong)
+- Vamana C / Python API ([#1112](https://github.com/rapidsai/cuvs/pull/1112)) [@tarang-jain](https://github.com/tarang-jain)
+- Mutual Reachability in all-neighbors API ([#1016](https://github.com/rapidsai/cuvs/pull/1016)) [@jinsolp](https://github.com/jinsolp)
+- CAGRA Build + DiskANN Search cuvs-bench Wrapper ([#899](https://github.com/rapidsai/cuvs/pull/899)) [@tarang-jain](https://github.com/tarang-jain)
+
+## 🛠️ Improvements
+
+- Patch FAISS for missing Thrust includes ([#1398](https://github.com/rapidsai/cuvs/pull/1398)) [@bdice](https://github.com/bdice)
+- Empty commit to trigger a build ([#1363](https://github.com/rapidsai/cuvs/pull/1363)) [@msarahan](https://github.com/msarahan)
+- [Review][Java] Refactor: extract interface from CuVSMatrixBaseImpl ([#1361](https://github.com/rapidsai/cuvs/pull/1361)) [@ldematte](https://github.com/ldematte)
+- use CUDA 13.0.1 CI images ([#1353](https://github.com/rapidsai/cuvs/pull/1353)) [@jameslamb](https://github.com/jameslamb)
+- Treat warnings as errors in doc builds + MG ANNs Python API doc update ([#1350](https://github.com/rapidsai/cuvs/pull/1350)) [@viclafargue](https://github.com/viclafargue)
+- [Java] Support row strides in CuVSMatrix ([#1345](https://github.com/rapidsai/cuvs/pull/1345)) [@ldematte](https://github.com/ldematte)
+- [Java] Adding tests to use CuVSDeviceMatrix (device memory) directly as a CagraIndex input dataset ([#1340](https://github.com/rapidsai/cuvs/pull/1340)) [@ldematte](https://github.com/ldematte)
+- Improve performance of assigning clusters in batched all_neighbors ([#1336](https://github.com/rapidsai/cuvs/pull/1336)) [@jinsolp](https://github.com/jinsolp)
+- Configure repo for automatic release notes generation ([#1334](https://github.com/rapidsai/cuvs/pull/1334)) [@AyodeAwe](https://github.com/AyodeAwe)
+- [Java] Buffered device matrix builder ([#1332](https://github.com/rapidsai/cuvs/pull/1332)) [@ldematte](https://github.com/ldematte)
+- Remove UCX-Py/UCXX from release script ([#1331](https://github.com/rapidsai/cuvs/pull/1331)) [@pentschev](https://github.com/pentschev)
+- [Java] Uniform toHost/toDevice to work across all CuVSMatrix classes ([#1328](https://github.com/rapidsai/cuvs/pull/1328)) [@ldematte](https://github.com/ldematte)
+- [Java] Add `libcuvs` &lt;-&gt; cuvs-java version check ([#1327](https://github.com/rapidsai/cuvs/pull/1327)) [@ldematte](https://github.com/ldematte)
+- update dependencies: use cuda-toolkit wheels ([#1326](https://github.com/rapidsai/cuvs/pull/1326)) [@jameslamb](https://github.com/jameslamb)
+- Add cagra.extend bindings for python ([#1324](https://github.com/rapidsai/cuvs/pull/1324)) [@benfred](https://github.com/benfred)
+- [Java] Make `cudaGetDeviceProperties` compatible with CUDA 12 and 13 based on symbol presence ([#1323](https://github.com/rapidsai/cuvs/pull/1323)) [@ldematte](https://github.com/ldematte)
+- Use branch-25.10 again ([#1319](https://github.com/rapidsai/cuvs/pull/1319)) [@jameslamb](https://github.com/jameslamb)
+- [Review][Java] Add detailed error message for `libcuvs` load failure to UnsupportedProvider/UnsupportedOperationExceptions ([#1316](https://github.com/rapidsai/cuvs/pull/1316)) [@ldematte](https://github.com/ldematte)
+- [Java] Add reason(s) to UnsupportedProvider/UnsupportedOperationExceptions ([#1314](https://github.com/rapidsai/cuvs/pull/1314)) [@ldematte](https://github.com/ldematte)
+- MG Python API ([#1307](https://github.com/rapidsai/cuvs/pull/1307)) [@viclafargue](https://github.com/viclafargue)
+- Fix redundant memset ([#1305](https://github.com/rapidsai/cuvs/pull/1305)) [@vinaydes](https://github.com/vinaydes)
+- Expose pq-centers to C and Python ([#1303](https://github.com/rapidsai/cuvs/pull/1303)) [@benfred](https://github.com/benfred)
+- Port `raft::neighbors::epsilon_neighborhood` to cuvs ([#1294](https://github.com/rapidsai/cuvs/pull/1294)) [@aamijar](https://github.com/aamijar)
+- ScaNN: Overlapped gather for AVQ ([#1286](https://github.com/rapidsai/cuvs/pull/1286)) [@rmaschal](https://github.com/rmaschal)
+- Update rapids-dependency-file-generator ([#1285](https://github.com/rapidsai/cuvs/pull/1285)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- [Review][Java] Expand `CagraIndex#search` to accept more types (int8 and int32) ([#1283](https://github.com/rapidsai/cuvs/pull/1283)) [@ldematte](https://github.com/ldematte)
+- All neighbors C and Python bindings ([#1282](https://github.com/rapidsai/cuvs/pull/1282)) [@viclafargue](https://github.com/viclafargue)
+- Build and test with CUDA 13.0.0 ([#1273](https://github.com/rapidsai/cuvs/pull/1273)) [@jameslamb](https://github.com/jameslamb)
+- Use int64_t for getters in the cagra/ivf_flat c-api ([#1272](https://github.com/rapidsai/cuvs/pull/1272)) [@benfred](https://github.com/benfred)
+- [Java][C] Expose GPUInfo ([#1267](https://github.com/rapidsai/cuvs/pull/1267)) [@ldematte](https://github.com/ldematte)
+- Add rust bindings for kmeans ([#1266](https://github.com/rapidsai/cuvs/pull/1266)) [@benfred](https://github.com/benfred)
+- [REVIEW] Add a public API for CAGRA graph optimize ([#1260](https://github.com/rapidsai/cuvs/pull/1260)) [@abc99lr](https://github.com/abc99lr)
+- Fix debug build ([#1258](https://github.com/rapidsai/cuvs/pull/1258)) [@lowener](https://github.com/lowener)
+- Run `cargo fmt` in the pre-commit hooks ([#1255](https://github.com/rapidsai/cuvs/pull/1255)) [@benfred](https://github.com/benfred)
+- [REVIEW][Java] Rename destroyIndex() to close(), extend AutoCloseable ([#1252](https://github.com/rapidsai/cuvs/pull/1252)) [@ldematte](https://github.com/ldematte)
+- Removing deprecated batching code specific to NN Descent ([#1249](https://github.com/rapidsai/cuvs/pull/1249)) [@jinsolp](https://github.com/jinsolp)
+- Pin Latest Faiss Version ([#1247](https://github.com/rapidsai/cuvs/pull/1247)) [@tarang-jain](https://github.com/tarang-jain)
+- Use build cluster in devcontainers ([#1240](https://github.com/rapidsai/cuvs/pull/1240)) [@trxcllnt](https://github.com/trxcllnt)
+- Use rapids_cuda_enable_fatbin_compression ([#1239](https://github.com/rapidsai/cuvs/pull/1239)) [@robertmaynard](https://github.com/robertmaynard)
+- [Improvement] Replace Calls to thrust::transform with raft::linalg ([#1238](https://github.com/rapidsai/cuvs/pull/1238)) [@tarang-jain](https://github.com/tarang-jain)
+- [Java] CuVSMatrix for device memory ([#1232](https://github.com/rapidsai/cuvs/pull/1232)) [@ldematte](https://github.com/ldematte)
+- Drop log level for CAGRA trace messages ([#1229](https://github.com/rapidsai/cuvs/pull/1229)) [@mythrocks](https://github.com/mythrocks)
+- Update rapids_config to handle user defined branch name ([#1227](https://github.com/rapidsai/cuvs/pull/1227)) [@robertmaynard](https://github.com/robertmaynard)
+- Reduce noise in Java tests ([#1226](https://github.com/rapidsai/cuvs/pull/1226)) [@mythrocks](https://github.com/mythrocks)
+- Update another instance of rapids-build-backend ([#1225](https://github.com/rapidsai/cuvs/pull/1225)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Fix missing sync_stream in ScaNN build ([#1224](https://github.com/rapidsai/cuvs/pull/1224)) [@rmaschal](https://github.com/rmaschal)
+- [Java] Exception-safe RMM Allocations ([#1215](https://github.com/rapidsai/cuvs/pull/1215)) [@mythrocks](https://github.com/mythrocks)
+- Update rapids-build-backend to 0.4.1 ([#1214](https://github.com/rapidsai/cuvs/pull/1214)) [@KyleFromNVIDIA](https://github.com/KyleFromNVIDIA)
+- Removing filter templates in ivfpq for binary size reduction ([#1211](https://github.com/rapidsai/cuvs/pull/1211)) [@jinsolp](https://github.com/jinsolp)
+- ci(labeler): update labeler action to [@v5 ([#1210](https://github.com/rapidsai/cuvs/pull/1210)) @gforsyth](https://github.com/v5 ([#1210](https://github.com/rapidsai/cuvs/pull/1210)) @gforsyth)
+- [Java] Add decorator for synchronized CuVSResource access ([#1209](https://github.com/rapidsai/cuvs/pull/1209)) [@ldematte](https://github.com/ldematte)
+- Remove unused templated instances ([#1204](https://github.com/rapidsai/cuvs/pull/1204)) [@vinaydes](https://github.com/vinaydes)
+- Spectral Embedding precomputed graph api ([#1197](https://github.com/rapidsai/cuvs/pull/1197)) [@aamijar](https://github.com/aamijar)
+- cuvs RAPIDS dependency now use the new rapids-cmake branch info ([#1189](https://github.com/rapidsai/cuvs/pull/1189)) [@robertmaynard](https://github.com/robertmaynard)
+- Allow latest OS in devcontainers ([#1169](https://github.com/rapidsai/cuvs/pull/1169)) [@bdice](https://github.com/bdice)
+- MG C API ([#1160](https://github.com/rapidsai/cuvs/pull/1160)) [@viclafargue](https://github.com/viclafargue)
+- Merge support for tiered index ([#1155](https://github.com/rapidsai/cuvs/pull/1155)) [@benfred](https://github.com/benfred)
+- Update build infra to support new branching strategy ([#1143](https://github.com/rapidsai/cuvs/pull/1143)) [@robertmaynard](https://github.com/robertmaynard)
+- [Java]Binary and scalar quantization ([#1104](https://github.com/rapidsai/cuvs/pull/1104)) [@punAhuja](https://github.com/punAhuja)
+- Improve Vamana index build performance and recall ([#1032](https://github.com/rapidsai/cuvs/pull/1032)) [@bkarsin](https://github.com/bkarsin)
+- Use GCC 14 in conda builds. ([#1030](https://github.com/rapidsai/cuvs/pull/1030)) [@vyasr](https://github.com/vyasr)
+- Update SNMG ANN API testing ([#1023](https://github.com/rapidsai/cuvs/pull/1023)) [@viclafargue](https://github.com/viclafargue)
+
 # cuvs 25.08.00 (6 Aug 2025)
 
 ## 🚨 Breaking Changes

From bc58741d822400bcac47b4d8c06b3d12a97c5fab Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Thu, 9 Oct 2025 01:23:58 -0500
Subject: [PATCH 612/623] Deallocation should be noexcept (#1416)

Update for compatibility with https://github.com/rapidsai/rmm/pull/2077.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1416
---
 cpp/bench/ann/src/common/cuda_huge_page_resource.hpp | 4 ++--
 cpp/bench/ann/src/common/cuda_pinned_resource.hpp    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp b/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
index a4d967dd17..5a8a0be20b 100644
--- a/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
+++ b/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -79,7 +79,7 @@ class cuda_huge_page_resource final : public rmm::mr::device_memory_resource {
    *
    * @param p Pointer to be deallocated
    */
-  void do_deallocate(void* ptr, std::size_t size, rmm::cuda_stream_view) override
+  void do_deallocate(void* ptr, std::size_t size, rmm::cuda_stream_view) noexcept override
   {
     if (munmap(ptr, size) == -1) { RAFT_FAIL("huge_page_resource::munmap"); }
   }
diff --git a/cpp/bench/ann/src/common/cuda_pinned_resource.hpp b/cpp/bench/ann/src/common/cuda_pinned_resource.hpp
index fb977cd34b..5d03253429 100644
--- a/cpp/bench/ann/src/common/cuda_pinned_resource.hpp
+++ b/cpp/bench/ann/src/common/cuda_pinned_resource.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -73,7 +73,7 @@ class cuda_pinned_resource final : public rmm::mr::device_memory_resource {
    *
    * @param p Pointer to be deallocated
    */
-  void do_deallocate(void* ptr, std::size_t, rmm::cuda_stream_view) override
+  void do_deallocate(void* ptr, std::size_t, rmm::cuda_stream_view) noexcept override
   {
     RMM_ASSERT_CUDA_SUCCESS(cudaFreeHost(ptr));
   }

From 471045070dbc25767fc34916bb0635ac7183a44f Mon Sep 17 00:00:00 2001
From: "Artem M. Chirkin" <9253178+achirkin@users.noreply.github.com>
Date: Thu, 9 Oct 2025 17:22:51 +0200
Subject: [PATCH 613/623] ANN_BENCH: Don't throw in noexcept do_deallocate
 (#1417)

A follow-up fix for https://github.com/rapidsai/cuvs/pull/1416

Authors:
  - Artem M. Chirkin (https://github.com/achirkin)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1417
---
 cpp/bench/ann/src/common/cuda_huge_page_resource.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp b/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
index 5a8a0be20b..dd0e015af7 100644
--- a/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
+++ b/cpp/bench/ann/src/common/cuda_huge_page_resource.hpp
@@ -16,6 +16,7 @@
 #pragma once
 
 #include <raft/core/error.hpp>
+#include <raft/core/logger_macros.hpp>
 
 #include <rmm/cuda_stream_view.hpp>
 #include <rmm/mr/device/device_memory_resource.hpp>
@@ -81,7 +82,7 @@ class cuda_huge_page_resource final : public rmm::mr::device_memory_resource {
    */
   void do_deallocate(void* ptr, std::size_t size, rmm::cuda_stream_view) noexcept override
   {
-    if (munmap(ptr, size) == -1) { RAFT_FAIL("huge_page_resource::munmap"); }
+    if (munmap(ptr, size) == -1) { RAFT_LOG_ERROR("huge_page_resource::munmap failed"); }
   }
 
   /**

From 9a2b7280dda8e81af867bc212c51331bbc5f7f28 Mon Sep 17 00:00:00 2001
From: Jinsol Park <jinsolp@nvidia.com>
Date: Fri, 10 Oct 2025 12:00:05 -0700
Subject: [PATCH 614/623] Using `all_neighbors` for mutual reachability (#1234)

Using `all_neighbors` mutual reachability feature to work towards deprecating `cuvs::neighbors::detail::reachability::mutual_reachability_graph` (related issue: https://github.com/rapidsai/cuvs/issues/982).

Also, changes `build_linkage` to use `int64_t`, because `all_neighbors::build` only supports `int64_t` types, and we will change HDBSCAN to use `int64_t` types in cuML as well.

**This PR will break cuML** (which will be solved by https://github.com/rapidsai/cuml/pull/7104)

Waiting for https://github.com/rapidsai/cuvs/pull/1016 to be merged.

Authors:
  - Jinsol Park (https://github.com/jinsolp)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - Tarang Jain (https://github.com/tarang-jain)

URL: https://github.com/rapidsai/cuvs/pull/1234
---
 cpp/include/cuvs/cluster/agglomerative.hpp |  84 +++++++++++++++--
 cpp/src/cluster/detail/single_linkage.cuh  | 105 ++++++++++++++-------
 cpp/src/cluster/single_linkage_float.cu    |  86 +++++++++++++----
 3 files changed, 212 insertions(+), 63 deletions(-)

diff --git a/cpp/include/cuvs/cluster/agglomerative.hpp b/cpp/include/cuvs/cluster/agglomerative.hpp
index eb3099e0d2..a4b333a9af 100644
--- a/cpp/include/cuvs/cluster/agglomerative.hpp
+++ b/cpp/include/cuvs/cluster/agglomerative.hpp
@@ -17,6 +17,7 @@
 #pragma once
 
 #include <cuvs/distance/distance.hpp>
+#include <cuvs/neighbors/all_neighbors.hpp>
 #include <optional>
 #include <variant>
 
@@ -144,15 +145,49 @@ struct mutual_reachability_params {
   /** weight applied when internal distance is chosen for mutual reachability (value of 1.0 disables
    * the weighting) */
   float alpha = 1.0;
+
+  /**
+   * Parameters for building the mutual reachability graph using an underlying KNN algorithm.
+   *
+   * The all-neighbors graph construction algorithm enables building the mutual reachability
+   * graph on datasets larger than device memory by:
+   *   1. Partitioning the dataset into overlapping clusters,
+   *   2. Computing local KNN graphs within each cluster, and
+   *   3. Merging the local graphs into a single global graph.
+   *
+   * Key fields:
+   * - graph_build_params: Selects the KNN construction method (Brute Force or NN Descent)
+   *   and controls algorithm-specific parameters.
+   *
+   * - n_clusters: Number of partitions (batches) to split the data into. Larger `n_clusters`
+   *   reduces memory usage but may reduce accuracy if `overlap_factor` is too low.
+   *   Recommended starting value: `n_clusters = 4`. Increase progressively (4 → 8 → 16 ...)
+   *   to reduce memory usage at the cost of some accuracy. This is independent of `overlap_factor`
+   *   as long as `overlap_factor < n_clusters`.
+   *
+   * - overlap_factor: Number of nearest clusters each data point is assigned to. Higher
+   *   `overlap_factor` improves accuracy at the cost of memory and performance.
+   *   Recommended starting value: `overlap_factor = 2`. Increase gradually (2 → 3 → 4 ...)
+   *   for better accuracy with higher device memory usage.
+   *
+   * - metric: Distance metric to use when computing nearest neighbors.
+   */
+  cuvs::neighbors::all_neighbors::all_neighbors_params all_neighbors_params{
+    cuvs::neighbors::graph_build_params::brute_force_params{}};
 };
 }  // namespace linkage_graph_params
+
 /**
  * Given a dataset, builds the KNN graph, connects graph components and builds a linkage
  * (dendrogram). Returns the Minimum Spanning Tree edges sorted by weight and the dendrogram.
  * @param[in] handle raft handle for resource reuse
- * @param[in] X data points (size n_rows * d)
- * @param[in] linkage_graph_params linkage params or mutual reachability params for building the KNN
- * graph
+ * @param[in] X data points on device memory (size n_rows * d)
+ * @param[in] linkage_graph_params
+ *   Parameters controlling how the KNN graph is built. This can be either:
+ *   - distance_params: standard distance-based KNN graph construction for traditional
+ *     agglomerative clustering.
+ *   - mutual_reachability_params: parameters to compute a mutual reachability graph for
+ *     density-aware hierarchical clustering (e.g. HDBSCAN).
  * @param[in] metric distance metric to use
  * @param[out] out_mst output MST sorted by edge weights (size n_rows - 1)
  * @param[out] dendrogram output dendrogram (size [n_rows - 1] * 2)
@@ -163,15 +198,46 @@ struct mutual_reachability_params {
  */
 void build_linkage(
   raft::resources const& handle,
-  raft::device_matrix_view<const float, int, raft::row_major> X,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X,
+  std::variant<linkage_graph_params::distance_params,
+               linkage_graph_params::mutual_reachability_params> linkage_graph_params,
+  cuvs::distance::DistanceType metric,
+  raft::device_coo_matrix_view<float, int64_t, int64_t, size_t> out_mst,
+  raft::device_matrix_view<int64_t, int64_t> dendrogram,
+  raft::device_vector_view<float, int64_t> out_distances,
+  raft::device_vector_view<int64_t, int64_t> out_sizes,
+  std::optional<raft::device_vector_view<float, int64_t>> core_dists);
+
+/**
+ * Given a dataset, builds the KNN graph, connects graph components and builds a linkage
+ * (dendrogram). Returns the Minimum Spanning Tree edges sorted by weight and the dendrogram.
+ * @param[in] handle raft handle for resource reuse
+ * @param[in] X data points on host memory (size n_rows * d)
+ * @param[in] linkage_graph_params
+ *   Parameters controlling how the KNN graph is built. This can be either:
+ *   - distance_params: standard distance-based KNN graph construction for traditional
+ *     agglomerative clustering.
+ *   - mutual_reachability_params: parameters to compute a mutual reachability graph for
+ *     density-aware hierarchical clustering (e.g. HDBSCAN).
+ * @param[in] metric distance metric to use
+ * @param[out] out_mst output MST sorted by edge weights (size n_rows - 1)
+ * @param[out] dendrogram output dendrogram (size [n_rows - 1] * 2)
+ * @param[out] out_distances distances for output
+ * @param[out] out_sizes cluster sizes of output
+ * @param[out] core_dists (optional) core distances (size m). Must be supplied in the Mutual
+ * Reachability space
+ */
+void build_linkage(
+  raft::resources const& handle,
+  raft::host_matrix_view<const float, int64_t, raft::row_major> X,
   std::variant<linkage_graph_params::distance_params,
                linkage_graph_params::mutual_reachability_params> linkage_graph_params,
   cuvs::distance::DistanceType metric,
-  raft::device_coo_matrix_view<float, int, int, size_t> out_mst,
-  raft::device_matrix_view<int, int> dendrogram,
-  raft::device_vector_view<float, int> out_distances,
-  raft::device_vector_view<int, int> out_sizes,
-  std::optional<raft::device_vector_view<float, int>> core_dists);
+  raft::device_coo_matrix_view<float, int64_t, int64_t, size_t> out_mst,
+  raft::device_matrix_view<int64_t, int64_t> dendrogram,
+  raft::device_vector_view<float, int64_t> out_distances,
+  raft::device_vector_view<int64_t, int64_t> out_sizes,
+  std::optional<raft::device_vector_view<float, int64_t>> core_dists);
 }  // namespace helpers
 /**
  * @}
diff --git a/cpp/src/cluster/detail/single_linkage.cuh b/cpp/src/cluster/detail/single_linkage.cuh
index d64a69f0cc..e6d00b5b0f 100644
--- a/cpp/src/cluster/detail/single_linkage.cuh
+++ b/cpp/src/cluster/detail/single_linkage.cuh
@@ -20,7 +20,11 @@
 #include "agglomerative.cuh"
 #include "connectivities.cuh"
 #include "mst.cuh"
+#include "raft/core/device_mdspan.hpp"
 #include <cuvs/cluster/agglomerative.hpp>
+#include <cuvs/neighbors/all_neighbors.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/mdspan_types.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/sparse/coo.hpp>
 #include <raft/util/cudart_utils.hpp>
@@ -47,39 +51,72 @@ namespace cuvs::cluster::agglomerative::detail {
  * @param[out] out_distances distances of output
  * @param[out] out_sizes cluster sizes of output
  */
-template <typename value_t = float, typename value_idx = int, typename nnz_t = size_t>
-void build_mr_linkage(raft::resources const& handle,
-                      raft::device_matrix_view<const value_t, value_idx, raft::row_major> X,
-                      value_idx min_samples,
-                      float alpha,
-                      cuvs::distance::DistanceType metric,
-                      raft::device_vector_view<value_t, value_idx> core_dists,
-                      raft::device_coo_matrix_view<value_t, value_idx, value_idx, nnz_t> out_mst,
-                      raft::device_matrix_view<value_idx, value_idx> out_dendrogram,
-                      raft::device_vector_view<value_t, value_idx> out_distances,
-                      raft::device_vector_view<value_idx, value_idx> out_sizes)
+template <typename value_t   = float,
+          typename value_idx = int,
+          typename nnz_t     = size_t,
+          typename Accessor  = raft::device_accessor<std::experimental::default_accessor<value_t>>>
+void build_mr_linkage(
+  raft::resources const& handle,
+  raft::mdspan<const value_t, raft::matrix_extent<value_idx>, raft::row_major, Accessor> X,
+  value_idx min_samples,
+  float alpha,
+  cuvs::distance::DistanceType metric,
+  raft::device_vector_view<value_t, value_idx> core_dists,
+  raft::device_coo_matrix_view<value_t, value_idx, value_idx, nnz_t> out_mst,
+  raft::device_matrix_view<value_idx, value_idx> out_dendrogram,
+  raft::device_vector_view<value_t, value_idx> out_distances,
+  raft::device_vector_view<value_idx, value_idx> out_sizes,
+  cuvs::neighbors::all_neighbors::all_neighbors_params all_neighbors_p)
 {
-  size_t m                        = X.extent(0);
-  size_t n                        = X.extent(1);
-  auto mutual_reachability_indptr = raft::make_device_vector<value_idx, value_idx>(handle, m + 1);
-  raft::sparse::COO<value_t, value_idx, nnz_t> mutual_reachability_coo(
-    raft::resource::get_cuda_stream(handle), min_samples * m * 2);
-
-  // Replace this with mutual reachability graph cronstruction from within all_neighbors wrapper.
-  // Reference: https://github.com/rapidsai/cuvs/issues/982
-  cuvs::neighbors::detail::reachability::mutual_reachability_graph<value_idx, value_t, nnz_t>(
+  size_t m    = X.extent(0);
+  size_t n    = X.extent(1);
+  auto stream = raft::resource::get_cuda_stream(handle);
+
+  auto mr_indptr = raft::make_device_vector<value_idx, value_idx>(handle, m + 1);
+  raft::sparse::COO<value_t, value_idx, nnz_t> mr_coo(stream, min_samples * m * 2);
+
+  auto inds  = raft::make_device_matrix<value_idx, value_idx>(handle, m, min_samples);
+  auto dists = raft::make_device_matrix<value_t, value_idx>(handle, m, min_samples);
+
+  if (all_neighbors_p.metric != metric) {
+    RAFT_LOG_WARN("Setting all neighbors metric to given metrix for build_mr_linkage");
+    all_neighbors_p.metric = metric;
+  }
+  cuvs::neighbors::all_neighbors::build(
+    handle, all_neighbors_p, X, inds.view(), dists.view(), core_dists, alpha);
+
+  // self-loops get max distance
+  auto coo_rows = raft::make_device_vector<value_idx, value_idx>(handle, min_samples * m);
+  raft::linalg::map_offset(handle, coo_rows.view(), raft::div_const_op<value_idx>(min_samples));
+
+  raft::sparse::linalg::symmetrize(handle,
+                                   coo_rows.data_handle(),
+                                   inds.data_handle(),
+                                   dists.data_handle(),
+                                   static_cast<value_idx>(m),
+                                   static_cast<value_idx>(m),
+                                   static_cast<nnz_t>(min_samples * m),
+                                   mr_coo);
+
+  raft::sparse::convert::sorted_coo_to_csr(
+    mr_coo.rows(), mr_coo.nnz, mr_indptr.data_handle(), m + 1, stream);
+
+  auto rows_view = raft::make_device_vector_view<const value_idx, nnz_t>(mr_coo.rows(), mr_coo.nnz);
+  auto cols_view = raft::make_device_vector_view<const value_idx, nnz_t>(mr_coo.cols(), mr_coo.nnz);
+  auto vals_in_view =
+    raft::make_device_vector_view<const value_t, nnz_t>(mr_coo.vals(), mr_coo.nnz);
+  auto vals_out_view = raft::make_device_vector_view<value_t, nnz_t>(mr_coo.vals(), mr_coo.nnz);
+
+  raft::linalg::map(
     handle,
-    X.data_handle(),
-    m,
-    n,
-    metric,
-    min_samples,
-    alpha,
-    mutual_reachability_indptr.data_handle(),
-    core_dists.data_handle(),
-    mutual_reachability_coo);
+    vals_out_view,
+    [=] __device__(const value_idx row, const value_idx col, const value_t val) {
+      return row == col ? std::numeric_limits<value_t>::max() : val;
+    },
+    rows_view,
+    cols_view,
+    vals_in_view);
 
-  // auto color = raft::make_device_vector<value_idx, value_idx>(handle, static_cast<value_idx>(m));
   rmm::device_uvector<value_idx> color(m, raft::resource::get_cuda_stream(handle));
   cuvs::sparse::neighbors::MutualReachabilityFixConnectivitiesRedOp<value_idx, value_t>
     reduction_op(core_dists.data_handle(), m);
@@ -88,16 +125,16 @@ void build_mr_linkage(raft::resources const& handle,
 
   detail::build_sorted_mst<value_idx, value_t>(handle,
                                                X.data_handle(),
-                                               mutual_reachability_indptr.data_handle(),
-                                               mutual_reachability_coo.cols(),
-                                               mutual_reachability_coo.vals(),
+                                               mr_indptr.data_handle(),
+                                               mr_coo.cols(),
+                                               mr_coo.vals(),
                                                m,
                                                n,
                                                out_mst.structure_view().get_rows().data(),
                                                out_mst.structure_view().get_cols().data(),
                                                out_mst.get_elements().data(),
                                                color.data(),
-                                               mutual_reachability_coo.nnz,
+                                               mr_coo.nnz,
                                                reduction_op,
                                                metric,
                                                10);
diff --git a/cpp/src/cluster/single_linkage_float.cu b/cpp/src/cluster/single_linkage_float.cu
index 4a33b969e3..b2bf5b8bb3 100644
--- a/cpp/src/cluster/single_linkage_float.cu
+++ b/cpp/src/cluster/single_linkage_float.cu
@@ -43,15 +43,15 @@ void single_linkage(raft::resources const& handle,
 namespace helpers {
 void build_linkage(
   raft::resources const& handle,
-  raft::device_matrix_view<const float, int, raft::row_major> X,
+  raft::device_matrix_view<const float, int64_t, raft::row_major> X,
   std::variant<linkage_graph_params::distance_params,
                linkage_graph_params::mutual_reachability_params> linkage_graph_params,
   cuvs::distance::DistanceType metric,
-  raft::device_coo_matrix_view<float, int, int, size_t> out_mst,
-  raft::device_matrix_view<int, int> out_dendrogram,
-  raft::device_vector_view<float, int> out_distances,
-  raft::device_vector_view<int, int> out_sizes,
-  std::optional<raft::device_vector_view<float, int>> core_dists)
+  raft::device_coo_matrix_view<float, int64_t, int64_t, size_t> out_mst,
+  raft::device_matrix_view<int64_t, int64_t> out_dendrogram,
+  raft::device_vector_view<float, int64_t> out_distances,
+  raft::device_vector_view<int64_t, int64_t> out_sizes,
+  std::optional<raft::device_vector_view<float, int64_t>> core_dists)
 {
   /**
    * Construct MST sorted by weights
@@ -68,30 +68,76 @@ void build_linkage(
     auto mr_params = std::get<
       cuvs::cluster::agglomerative::helpers::linkage_graph_params::mutual_reachability_params>(
       linkage_graph_params);
-    detail::build_mr_linkage<float, int>(handle,
-                                         X,
-                                         mr_params.min_samples,
-                                         mr_params.alpha,
-                                         metric,
-                                         core_dists_mdspan,
-                                         out_mst,
-                                         out_dendrogram,
-                                         out_distances,
-                                         out_sizes);
+    detail::build_mr_linkage<float, int64_t>(handle,
+                                             X,
+                                             mr_params.min_samples,
+                                             mr_params.alpha,
+                                             metric,
+                                             core_dists_mdspan,
+                                             out_mst,
+                                             out_dendrogram,
+                                             out_distances,
+                                             out_sizes,
+                                             mr_params.all_neighbors_params);
   } else {
     auto dist_params =
       std::get<cuvs::cluster::agglomerative::helpers::linkage_graph_params::distance_params>(
         linkage_graph_params);
     if (dist_params.dist_type == cuvs::cluster::agglomerative::Linkage::KNN_GRAPH) {
-      detail::
-        build_dist_linkage<float, int, size_t, cuvs::cluster::agglomerative::Linkage::KNN_GRAPH>(
-          handle, X, dist_params.c, metric, out_mst, out_dendrogram, out_distances, out_sizes);
+      detail::build_dist_linkage<float,
+                                 int64_t,
+                                 size_t,
+                                 cuvs::cluster::agglomerative::Linkage::KNN_GRAPH>(
+        handle, X, dist_params.c, metric, out_mst, out_dendrogram, out_distances, out_sizes);
     } else {
       detail::
-        build_dist_linkage<float, int, size_t, cuvs::cluster::agglomerative::Linkage::PAIRWISE>(
+        build_dist_linkage<float, int64_t, size_t, cuvs::cluster::agglomerative::Linkage::PAIRWISE>(
           handle, X, dist_params.c, metric, out_mst, out_dendrogram, out_distances, out_sizes);
     }
   }
 }
+
+void build_linkage(
+  raft::resources const& handle,
+  raft::host_matrix_view<const float, int64_t, raft::row_major> X,
+  std::variant<linkage_graph_params::distance_params,
+               linkage_graph_params::mutual_reachability_params> linkage_graph_params,
+  cuvs::distance::DistanceType metric,
+  raft::device_coo_matrix_view<float, int64_t, int64_t, size_t> out_mst,
+  raft::device_matrix_view<int64_t, int64_t> out_dendrogram,
+  raft::device_vector_view<float, int64_t> out_distances,
+  raft::device_vector_view<int64_t, int64_t> out_sizes,
+  std::optional<raft::device_vector_view<float, int64_t>> core_dists)
+{
+  /**
+   * Construct MST sorted by weights
+   */
+  if (std::holds_alternative<
+        cuvs::cluster::agglomerative::helpers::linkage_graph_params::mutual_reachability_params>(
+        linkage_graph_params)) {
+    RAFT_EXPECTS(core_dists.has_value(),
+                 "core distances must be pre-allocated to build the linkage with mutual "
+                 "reachability distances");
+    auto core_dists_mdspan = core_dists.value();
+    RAFT_EXPECTS(core_dists_mdspan.extent(0) == X.extent(0),
+                 "core_dists doesn't have expected size");
+    auto mr_params = std::get<
+      cuvs::cluster::agglomerative::helpers::linkage_graph_params::mutual_reachability_params>(
+      linkage_graph_params);
+    detail::build_mr_linkage<float, int64_t>(handle,
+                                             X,
+                                             mr_params.min_samples,
+                                             mr_params.alpha,
+                                             metric,
+                                             core_dists_mdspan,
+                                             out_mst,
+                                             out_dendrogram,
+                                             out_distances,
+                                             out_sizes,
+                                             mr_params.all_neighbors_params);
+  } else {
+    RAFT_FAIL("data must be on device memory to build linkage with distance params");
+  }
+}
 }  // namespace helpers
 }  // namespace cuvs::cluster::agglomerative

From 432cd8037d957a3a1ce71cd63ee62577735ae143 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 13 Oct 2025 13:34:35 -0400
Subject: [PATCH 615/623] Remove unneeded cutlass public build/install
 dependency (#1411)

CMake users of an installed form of CUVS don't need cutlass, so remove it from our build and install export sets.

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)

URL: https://github.com/rapidsai/cuvs/pull/1411
---
 cpp/cmake/thirdparty/get_cutlass.cmake | 30 +-------------------------
 1 file changed, 1 insertion(+), 29 deletions(-)

diff --git a/cpp/cmake/thirdparty/get_cutlass.cmake b/cpp/cmake/thirdparty/get_cutlass.cmake
index 69fec3a1e0..04eaca8940 100644
--- a/cpp/cmake/thirdparty/get_cutlass.cmake
+++ b/cpp/cmake/thirdparty/get_cutlass.cmake
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+# Copyright (c) 2021-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -58,34 +58,6 @@ function(find_and_configure_cutlass)
     add_library(nvidia::cutlass::cutlass ALIAS CUTLASS)
   endif()
 
-  if(NvidiaCutlass_ADDED)
-    rapids_export(
-      BUILD NvidiaCutlass
-      EXPORT_SET NvidiaCutlass
-      GLOBAL_TARGETS nvidia::cutlass::cutlass
-      NAMESPACE nvidia::cutlass::
-    )
-  endif()
-
-  # We generate the cutlass-config files when we built cutlass locally, so always do
-  # `find_dependency`
-  rapids_export_package(
-          BUILD NvidiaCutlass cuvs-exports GLOBAL_TARGETS nvidia::cutlass::cutlass
-  )
-  rapids_export_package(
-          INSTALL NvidiaCutlass cuvs-exports GLOBAL_TARGETS nvidia::cutlass::cutlass
-  )
-
-  # Tell cmake where it can find the generated NvidiaCutlass-config.cmake we wrote.
-  include("${rapids-cmake-dir}/export/find_package_root.cmake")
-  rapids_export_find_package_root(
-          INSTALL NvidiaCutlass [=[${CMAKE_CURRENT_LIST_DIR}/../]=]
-          EXPORT_SET cuvs-exports
-  )
-  rapids_export_find_package_root(
-          BUILD NvidiaCutlass [=[${CMAKE_CURRENT_LIST_DIR}]=]
-          EXPORT_SET cuvs-exports
-  )
 endfunction()
 
 find_and_configure_cutlass()

From 895515c43df0df097e6fce17406b107eba71d774 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 13 Oct 2025 15:23:38 -0400
Subject: [PATCH 616/623] Refactor libcuvs_c header and source locations
 (#1357)

In support of building a stable C interface for libcuvs I have moved all `C` code into a separate directory for both the headers and sources. This will make it easier to install just 'C' language headers, make it easier to apply code rules for C headers, and in the future add more CI checks for our C API.

Authors:
  - Robert Maynard (https://github.com/robertmaynard)
  - MithunR (https://github.com/mythrocks)
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1357
---
 c/CMakeLists.txt                              | 173 +++++++
 {cpp => c}/include/cuvs/cluster/kmeans.h      |   6 +-
 {cpp => c}/include/cuvs/core/c_api.h          |   2 +-
 {cpp => c}/include/cuvs/distance/distance.h   |   0
 .../include/cuvs/distance/pairwise_distance.h |   0
 .../include/cuvs/neighbors/all_neighbors.h    |   0
 .../include/cuvs/neighbors/brute_force.h      |   0
 {cpp => c}/include/cuvs/neighbors/cagra.h     |  18 +-
 {cpp => c}/include/cuvs/neighbors/common.h    |   8 +-
 {cpp => c}/include/cuvs/neighbors/hnsw.h      |   6 +-
 {cpp => c}/include/cuvs/neighbors/ivf_flat.h  |   0
 {cpp => c}/include/cuvs/neighbors/ivf_pq.h    |   0
 {cpp => c}/include/cuvs/neighbors/mg_cagra.h  |   0
 {cpp => c}/include/cuvs/neighbors/mg_common.h |  12 +-
 .../include/cuvs/neighbors/mg_ivf_flat.h      |   0
 {cpp => c}/include/cuvs/neighbors/mg_ivf_pq.h |   0
 .../include/cuvs/neighbors/nn_descent.h       |   0
 {cpp => c}/include/cuvs/neighbors/refine.h    |   0
 .../include/cuvs/neighbors/tiered_index.h     |   6 +-
 {cpp => c}/include/cuvs/neighbors/vamana.h    |   0
 .../cuvs/preprocessing/quantize/binary.h      |   0
 .../cuvs/preprocessing/quantize/scalar.h      |   0
 .../kmeans_c.cpp => c/src/cluster/kmeans.cpp  |   5 +-
 {cpp => c}/src/core/c_api.cpp                 |   3 +-
 .../cuvs => c/src}/core/detail/interop.hpp    |   0
 .../cuvs => c/src}/core/exceptions.hpp        |   4 +-
 {cpp/include/cuvs => c/src}/core/interop.hpp  |   0
 .../src/distance/pairwise_distance.cpp        |   7 +-
 .../src/neighbors/all_neighbors.cpp           |  11 +-
 .../src/neighbors/brute_force.cpp             |   9 +-
 .../cagra_c.cpp => c/src/neighbors/cagra.cpp  |   9 +-
 .../cagra_c.hpp => c/src/neighbors/cagra.hpp  |   0
 .../hnsw_c.cpp => c/src/neighbors/hnsw.cpp    |   9 +-
 .../src/neighbors/ivf_flat.cpp                |   5 +-
 .../src/neighbors/ivf_flat.hpp                |   0
 .../src/neighbors/ivf_pq.cpp                  |   5 +-
 .../src/neighbors/ivf_pq.hpp                  |   2 +
 .../src/neighbors/mg_cagra.cpp                |   9 +-
 .../src/neighbors/mg_ivf_flat.cpp             |  11 +-
 .../src/neighbors/mg_ivf_pq.cpp               |  11 +-
 .../src/neighbors/nn_descent.cpp              |   5 +-
 .../src/neighbors/refine.cpp                  |   7 +-
 .../src/neighbors/tiered_index.cpp            |  11 +-
 .../src/neighbors/vamana.cpp                  |   5 +-
 .../src/preprocessing/quantize/binary.cpp     |   5 +-
 .../src/preprocessing/quantize/scalar.cpp     |   5 +-
 c/tests/CMakeLists.txt                        | 124 +++++
 {cpp => c}/tests/core/c_api.c                 |   0
 {cpp => c}/tests/core/interop.cu              |   7 +-
 .../tests/distance/pairwise_distance_c.cu     |   0
 .../tests/distance/run_pairwise_distance_c.c  |   0
 {cpp => c}/tests/neighbors/all_neighbors_c.cu |   4 +-
 {cpp => c}/tests/neighbors/ann_cagra_c.cu     |   2 +-
 {cpp => c}/tests/neighbors/ann_hnsw_c.cu      |   8 +-
 {cpp => c}/tests/neighbors/ann_ivf_flat_c.cu  |   4 +-
 {cpp => c}/tests/neighbors/ann_ivf_pq_c.cu    |   4 +-
 {cpp => c}/tests/neighbors/ann_mg_c.cu        |   4 +-
 {cpp => c}/tests/neighbors/brute_force_c.cu   |   4 +-
 {cpp => c}/tests/neighbors/c_api.c            |   0
 .../tests/neighbors/run_all_neighbors_c.c     |   0
 .../tests/neighbors/run_brute_force_c.c       |   0
 {cpp => c}/tests/neighbors/run_ivf_flat_c.c   |   0
 {cpp => c}/tests/neighbors/run_ivf_pq_c.c     |   0
 {cpp => c}/tests/neighbors/run_mg_c.c         |   0
 cpp/CMakeLists.txt                            |  96 +---
 cpp/bench/ann/CMakeLists.txt                  |   1 +
 cpp/cmake/config.json                         |   2 +-
 cpp/doxygen/Doxyfile                          |   3 +-
 cpp/include/cuvs/distance/distance.hpp        |   5 +-
 cpp/include/cuvs/neighbors/cagra.hpp          |  10 +-
 cpp/src/neighbors/detail/nn_descent.cuh       |   1 -
 cpp/tests/CMakeLists.txt                      | 490 +++++++-----------
 cpp/tests/neighbors/ann_cagra.cuh             |   9 +-
 docs/source/cpp_api.rst                       |   1 -
 docs/source/cpp_api/core_interop.rst          |  15 -
 java/panama-bindings/generate-bindings.sh     |   2 +-
 rust/cuvs-sys/build.rs                        |   2 +-
 77 files changed, 663 insertions(+), 504 deletions(-)
 create mode 100644 c/CMakeLists.txt
 rename {cpp => c}/include/cuvs/cluster/kmeans.h (99%)
 rename {cpp => c}/include/cuvs/core/c_api.h (99%)
 rename {cpp => c}/include/cuvs/distance/distance.h (100%)
 rename {cpp => c}/include/cuvs/distance/pairwise_distance.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/all_neighbors.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/brute_force.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/cagra.h (99%)
 rename {cpp => c}/include/cuvs/neighbors/common.h (94%)
 rename {cpp => c}/include/cuvs/neighbors/hnsw.h (99%)
 rename {cpp => c}/include/cuvs/neighbors/ivf_flat.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/ivf_pq.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/mg_cagra.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/mg_common.h (87%)
 rename {cpp => c}/include/cuvs/neighbors/mg_ivf_flat.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/mg_ivf_pq.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/nn_descent.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/refine.h (100%)
 rename {cpp => c}/include/cuvs/neighbors/tiered_index.h (98%)
 rename {cpp => c}/include/cuvs/neighbors/vamana.h (100%)
 rename {cpp => c}/include/cuvs/preprocessing/quantize/binary.h (100%)
 rename {cpp => c}/include/cuvs/preprocessing/quantize/scalar.h (100%)
 rename cpp/src/cluster/kmeans_c.cpp => c/src/cluster/kmeans.cpp (99%)
 rename {cpp => c}/src/core/c_api.cpp (99%)
 rename {cpp/include/cuvs => c/src}/core/detail/interop.hpp (100%)
 rename {cpp/include/cuvs => c/src}/core/exceptions.hpp (93%)
 rename {cpp/include/cuvs => c/src}/core/interop.hpp (100%)
 rename cpp/src/distance/pairwise_distance_c.cpp => c/src/distance/pairwise_distance.cpp (97%)
 rename cpp/src/neighbors/all_neighbors_c.cpp => c/src/neighbors/all_neighbors.cpp (99%)
 rename cpp/src/neighbors/brute_force_c.cpp => c/src/neighbors/brute_force.cpp (99%)
 rename cpp/src/neighbors/cagra_c.cpp => c/src/neighbors/cagra.cpp (99%)
 rename cpp/src/neighbors/cagra_c.hpp => c/src/neighbors/cagra.hpp (100%)
 rename cpp/src/neighbors/hnsw_c.cpp => c/src/neighbors/hnsw.cpp (98%)
 rename cpp/src/neighbors/ivf_flat_c.cpp => c/src/neighbors/ivf_flat.cpp (99%)
 rename cpp/src/neighbors/ivf_flat_c.hpp => c/src/neighbors/ivf_flat.hpp (100%)
 rename cpp/src/neighbors/ivf_pq_c.cpp => c/src/neighbors/ivf_pq.cpp (99%)
 rename cpp/src/neighbors/ivf_pq_c.hpp => c/src/neighbors/ivf_pq.hpp (97%)
 rename cpp/src/neighbors/mg_cagra_c.cpp => c/src/neighbors/mg_cagra.cpp (99%)
 rename cpp/src/neighbors/mg_ivf_flat_c.cpp => c/src/neighbors/mg_ivf_flat.cpp (99%)
 rename cpp/src/neighbors/mg_ivf_pq_c.cpp => c/src/neighbors/mg_ivf_pq.cpp (99%)
 rename cpp/src/neighbors/nn_descent_c.cpp => c/src/neighbors/nn_descent.cpp (99%)
 rename cpp/src/neighbors/refine/refine_c.cpp => c/src/neighbors/refine.cpp (98%)
 rename cpp/src/neighbors/tiered_index_c.cpp => c/src/neighbors/tiered_index.cpp (99%)
 rename cpp/src/neighbors/vamana_c.cpp => c/src/neighbors/vamana.cpp (99%)
 rename cpp/src/preprocessing/quantize/binary_c.cpp => c/src/preprocessing/quantize/binary.cpp (99%)
 rename cpp/src/preprocessing/quantize/scalar_c.cpp => c/src/preprocessing/quantize/scalar.cpp (99%)
 create mode 100644 c/tests/CMakeLists.txt
 rename {cpp => c}/tests/core/c_api.c (100%)
 rename {cpp => c}/tests/core/interop.cu (93%)
 rename {cpp => c}/tests/distance/pairwise_distance_c.cu (100%)
 rename {cpp => c}/tests/distance/run_pairwise_distance_c.c (100%)
 rename {cpp => c}/tests/neighbors/all_neighbors_c.cu (99%)
 rename {cpp => c}/tests/neighbors/ann_cagra_c.cu (99%)
 rename {cpp => c}/tests/neighbors/ann_hnsw_c.cu (98%)
 rename {cpp => c}/tests/neighbors/ann_ivf_flat_c.cu (98%)
 rename {cpp => c}/tests/neighbors/ann_ivf_pq_c.cu (98%)
 rename {cpp => c}/tests/neighbors/ann_mg_c.cu (99%)
 rename {cpp => c}/tests/neighbors/brute_force_c.cu (99%)
 rename {cpp => c}/tests/neighbors/c_api.c (100%)
 rename {cpp => c}/tests/neighbors/run_all_neighbors_c.c (100%)
 rename {cpp => c}/tests/neighbors/run_brute_force_c.c (100%)
 rename {cpp => c}/tests/neighbors/run_ivf_flat_c.c (100%)
 rename {cpp => c}/tests/neighbors/run_ivf_pq_c.c (100%)
 rename {cpp => c}/tests/neighbors/run_mg_c.c (100%)
 delete mode 100644 docs/source/cpp_api/core_interop.rst

diff --git a/c/CMakeLists.txt b/c/CMakeLists.txt
new file mode 100644
index 0000000000..acaf3a746c
--- /dev/null
+++ b/c/CMakeLists.txt
@@ -0,0 +1,173 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+cmake_minimum_required(VERSION 3.30.4 FATAL_ERROR)
+include(../cmake/rapids_config.cmake)
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-export)
+include(rapids-find)
+
+project(
+  CUVS_C
+  VERSION "${RAPIDS_VERSION}"
+  LANGUAGES CXX C
+)
+set(CMAKE_INSTALL_MESSAGE LAZY)
+
+# ##################################################################################################
+# * User Options  ------------------------------------------------------------
+
+option(BUILD_SHARED_LIBS "Build cuvs_c shared libraries" ON)
+option(BUILD_TESTS "Build cuvs unit-tests" ON)
+
+option(BUILD_CAGRA_HNSWLIB "Build CAGRA+hnswlib interface" ON)
+option(BUILD_MG_ALGOS "Build with multi-GPU support" ON)
+
+option(CUVSC_STATIC_CUVS_LIBRARY "Link against statical version of libcuvs" OFF)
+
+# Check if cuVS is already available. If so, it is the user's responsibility to ensure that the
+# CMake package is also available at build time of the Python cuvs package.
+if(NOT TARGET cuvs::cuvs)
+  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED)
+else()
+  set(BUILDING_FROM_CUVS ON)
+endif()
+
+# ##################################################################################################
+# * build type ---------------------------------------------------------------
+
+# Set a default build type if none was specified
+rapids_cmake_build_type(Release)
+
+# this is needed for clang-tidy runs
+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
+
+if(NOT DEFINED CUVS_CXX_FLAGS)
+  include(../cpp/cmake/modules/ConfigureCUDA.cmake)
+endif()
+
+# ##################################################################################################
+# * Requirements -------------------------------------------------------------
+
+# add third party dependencies using CPM
+rapids_cpm_init()
+
+# add third party dependencies using CPM
+include(../cpp/cmake/thirdparty/get_dlpack.cmake)
+
+if(BUILD_CAGRA_HNSWLIB)
+  include(../cpp/cmake/thirdparty/get_hnswlib.cmake)
+endif()
+
+# ##################################################################################################
+# * cuvs_c -------------------------------------------------------------------------------
+add_library(
+  cuvs_c SHARED
+  src/core/c_api.cpp
+  src/cluster/kmeans.cpp
+  src/neighbors/brute_force.cpp
+  src/neighbors/ivf_flat.cpp
+  src/neighbors/ivf_pq.cpp
+  src/neighbors/cagra.cpp
+  $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw.cpp>
+  $<$<BOOL:${BUILD_MG_ALGOS}>:src/neighbors/mg_ivf_pq.cpp>
+  $<$<BOOL:${BUILD_MG_ALGOS}>:src/neighbors/mg_ivf_flat.cpp>
+  $<$<BOOL:${BUILD_MG_ALGOS}>:src/neighbors/mg_cagra.cpp>
+  src/neighbors/nn_descent.cpp
+  src/neighbors/vamana.cpp
+  src/neighbors/refine.cpp
+  src/neighbors/tiered_index.cpp
+  src/neighbors/all_neighbors.cpp
+  src/preprocessing/quantize/binary.cpp
+  src/preprocessing/quantize/scalar.cpp
+  src/distance/pairwise_distance.cpp
+)
+add_library(cuvs::c_api ALIAS cuvs_c)
+set_target_properties(
+  cuvs_c
+  PROPERTIES BUILD_RPATH "\$ORIGIN"
+             INSTALL_RPATH "\$ORIGIN"
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             POSITION_INDEPENDENT_CODE ON
+             INTERFACE_POSITION_INDEPENDENT_CODE ON
+             EXPORT_NAME c_api
+)
+
+target_compile_definitions(cuvs_c PUBLIC $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>)
+
+target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
+
+target_include_directories(
+  cuvs_c
+  PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+         "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+  INTERFACE "$<INSTALL_INTERFACE:include>"
+)
+
+target_link_libraries(
+  cuvs_c
+  PUBLIC $<IF:$<BOOL:${CUVSC_STATIC_CUVS_LIBRARY}>,cuvs::cuvs_static,cuvs::cuvs>
+  PRIVATE raft::raft $<TARGET_NAME_IF_EXISTS:hnswlib::hnswlib>
+)
+
+# ##################################################################################################
+# * install targets-----------------------------------------------------------
+if(PROJECT_IS_TOP_LEVEL)
+  rapids_cmake_install_lib_dir(lib_dir)
+  include(GNUInstallDirs)
+  include(CPack)
+
+  # Add CUDAToolkit as an export dependency
+  rapids_export_package(INSTALL CUDAToolkit cuvs-c-exports)
+  rapids_export_package(BUILD CUDAToolkit cuvs-c-exports)
+
+  install(
+    TARGETS cuvs_c
+    DESTINATION ${lib_dir}
+    COMPONENT cuvs_c
+    EXPORT cuvs-c-exports
+  )
+
+  rapids_export(
+    INSTALL cuvs_c
+    VERSION "${RAPIDS_VERSION}"
+    EXPORT_SET cuvs-c-exports
+    GLOBAL_TARGETS cuvs_c
+    NAMESPACE cuvs::
+  )
+  rapids_export(
+    BUILD cuvs_c
+    VERSION "${RAPIDS_VERSION}"
+    EXPORT_SET cuvs-c-exports
+    GLOBAL_TARGETS cuvs_c
+    NAMESPACE cuvs::
+  )
+  install(
+    DIRECTORY include/cuvs
+    COMPONENT cuvs_c
+    DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+  )
+endif()
+
+# ##################################################################################################
+# * build tests ----------------------------------------------------
+if(BUILD_TESTS)
+  enable_language(CUDA)
+  find_package(CUDAToolkit REQUIRED)
+
+  enable_testing()
+  add_subdirectory(tests)
+endif()
diff --git a/cpp/include/cuvs/cluster/kmeans.h b/c/include/cuvs/cluster/kmeans.h
similarity index 99%
rename from cpp/include/cuvs/cluster/kmeans.h
rename to c/include/cuvs/cluster/kmeans.h
index 203ef6b76e..2e748abfb7 100644
--- a/cpp/include/cuvs/cluster/kmeans.h
+++ b/c/include/cuvs/cluster/kmeans.h
@@ -34,17 +34,17 @@ typedef enum {
   /**
    * Sample the centroids using the kmeans++ strategy
    */
-  KMeansPlusPlus,
+  KMeansPlusPlus = 0,
 
   /**
    * Sample the centroids uniformly at random
    */
-  Random,
+  Random = 1,
 
   /**
    * User provides the array of initial centroids
    */
-  Array
+  Array = 2
 } cuvsKMeansInitMethod;
 
 /**
diff --git a/cpp/include/cuvs/core/c_api.h b/c/include/cuvs/core/c_api.h
similarity index 99%
rename from cpp/include/cuvs/core/c_api.h
rename to c/include/cuvs/core/c_api.h
index b47af2c773..6de3f687b7 100644
--- a/cpp/include/cuvs/core/c_api.h
+++ b/c/include/cuvs/core/c_api.h
@@ -33,7 +33,7 @@ extern "C" {
  * @brief An enum denoting error statuses for function calls
  *
  */
-typedef enum { CUVS_ERROR, CUVS_SUCCESS } cuvsError_t;
+typedef enum { CUVS_ERROR = 0, CUVS_SUCCESS = 1 } cuvsError_t;
 
 /** @brief Returns a string describing the last seen error on this thread, or
  *         NULL if the last function succeeded.
diff --git a/cpp/include/cuvs/distance/distance.h b/c/include/cuvs/distance/distance.h
similarity index 100%
rename from cpp/include/cuvs/distance/distance.h
rename to c/include/cuvs/distance/distance.h
diff --git a/cpp/include/cuvs/distance/pairwise_distance.h b/c/include/cuvs/distance/pairwise_distance.h
similarity index 100%
rename from cpp/include/cuvs/distance/pairwise_distance.h
rename to c/include/cuvs/distance/pairwise_distance.h
diff --git a/cpp/include/cuvs/neighbors/all_neighbors.h b/c/include/cuvs/neighbors/all_neighbors.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/all_neighbors.h
rename to c/include/cuvs/neighbors/all_neighbors.h
diff --git a/cpp/include/cuvs/neighbors/brute_force.h b/c/include/cuvs/neighbors/brute_force.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/brute_force.h
rename to c/include/cuvs/neighbors/brute_force.h
diff --git a/cpp/include/cuvs/neighbors/cagra.h b/c/include/cuvs/neighbors/cagra.h
similarity index 99%
rename from cpp/include/cuvs/neighbors/cagra.h
rename to c/include/cuvs/neighbors/cagra.h
index ddf3207aa0..624aa76cb9 100644
--- a/cpp/include/cuvs/neighbors/cagra.h
+++ b/c/include/cuvs/neighbors/cagra.h
@@ -39,13 +39,13 @@ extern "C" {
  */
 enum cuvsCagraGraphBuildAlgo {
   /* Select build algorithm automatically */
-  AUTO_SELECT,
+  AUTO_SELECT = 0,
   /* Use IVF-PQ to build all-neighbors knn graph */
-  IVF_PQ,
+  IVF_PQ = 1,
   /* Experimental, use NN-Descent to build all-neighbors knn graph */
-  NN_DESCENT,
+  NN_DESCENT = 2,
   /* Experimental, use iterative cagra search and optimize to build the knn graph */
-  ITERATIVE_CAGRA_SEARCH
+  ITERATIVE_CAGRA_SEARCH = 3
 };
 
 /** Parameters for VPQ compression. */
@@ -210,18 +210,18 @@ cuvsError_t cuvsCagraExtendParamsDestroy(cuvsCagraExtendParams_t params);
  */
 enum cuvsCagraSearchAlgo {
   /** For large batch sizes. */
-  SINGLE_CTA,
+  SINGLE_CTA = 0,
   /** For small batch sizes. */
-  MULTI_CTA,
-  MULTI_KERNEL,
-  AUTO
+  MULTI_CTA    = 1,
+  MULTI_KERNEL = 2,
+  AUTO         = 3
 };
 
 /**
  * @brief Enum to denote Hash Mode used while searching CAGRA index
  *
  */
-enum cuvsCagraHashMode { HASH, SMALL, AUTO_HASH };
+enum cuvsCagraHashMode { HASH = 0, SMALL = 1, AUTO_HASH = 2 };
 
 /**
  * @brief Supplemental parameters to search CAGRA index
diff --git a/cpp/include/cuvs/neighbors/common.h b/c/include/cuvs/neighbors/common.h
similarity index 94%
rename from cpp/include/cuvs/neighbors/common.h
rename to c/include/cuvs/neighbors/common.h
index 6bcc4db6b0..24152d1aad 100644
--- a/cpp/include/cuvs/neighbors/common.h
+++ b/c/include/cuvs/neighbors/common.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -34,11 +34,11 @@ extern "C" {
  */
 enum cuvsFilterType {
   /* No filter */
-  NO_FILTER,
+  NO_FILTER = 0,
   /* Filter an index with a bitset */
-  BITSET,
+  BITSET = 1,
   /* Filter an index with a bitmap */
-  BITMAP
+  BITMAP = 2
 };
 
 /**
diff --git a/cpp/include/cuvs/neighbors/hnsw.h b/c/include/cuvs/neighbors/hnsw.h
similarity index 99%
rename from cpp/include/cuvs/neighbors/hnsw.h
rename to c/include/cuvs/neighbors/hnsw.h
index c584f41529..edcce942d2 100644
--- a/cpp/include/cuvs/neighbors/hnsw.h
+++ b/c/include/cuvs/neighbors/hnsw.h
@@ -40,11 +40,11 @@ extern "C" {
  */
 enum cuvsHnswHierarchy {
   /* Flat hierarchy, search is base-layer only */
-  NONE,
+  NONE = 0,
   /* Full hierarchy is built using the CPU */
-  CPU,
+  CPU = 1,
   /* Full hierarchy is built using the GPU */
-  GPU
+  GPU = 2
 };
 
 struct cuvsHnswIndexParams {
diff --git a/cpp/include/cuvs/neighbors/ivf_flat.h b/c/include/cuvs/neighbors/ivf_flat.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/ivf_flat.h
rename to c/include/cuvs/neighbors/ivf_flat.h
diff --git a/cpp/include/cuvs/neighbors/ivf_pq.h b/c/include/cuvs/neighbors/ivf_pq.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/ivf_pq.h
rename to c/include/cuvs/neighbors/ivf_pq.h
diff --git a/cpp/include/cuvs/neighbors/mg_cagra.h b/c/include/cuvs/neighbors/mg_cagra.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/mg_cagra.h
rename to c/include/cuvs/neighbors/mg_cagra.h
diff --git a/cpp/include/cuvs/neighbors/mg_common.h b/c/include/cuvs/neighbors/mg_common.h
similarity index 87%
rename from cpp/include/cuvs/neighbors/mg_common.h
rename to c/include/cuvs/neighbors/mg_common.h
index 06b6771573..99bfd21c4a 100644
--- a/cpp/include/cuvs/neighbors/mg_common.h
+++ b/c/include/cuvs/neighbors/mg_common.h
@@ -33,9 +33,9 @@ extern "C" {
  */
 typedef enum {
   /** Index is replicated on each device, favors throughput */
-  CUVS_NEIGHBORS_MG_REPLICATED,
+  CUVS_NEIGHBORS_MG_REPLICATED = 0,
   /** Index is split on several devices, favors scaling */
-  CUVS_NEIGHBORS_MG_SHARDED
+  CUVS_NEIGHBORS_MG_SHARDED = 1
 } cuvsMultiGpuDistributionMode;
 
 /**
@@ -43,9 +43,9 @@ typedef enum {
  */
 typedef enum {
   /** Search queries are split to maintain equal load on GPUs */
-  CUVS_NEIGHBORS_MG_LOAD_BALANCER,
+  CUVS_NEIGHBORS_MG_LOAD_BALANCER = 0,
   /** Each search query is processed by a single GPU in a round-robin fashion */
-  CUVS_NEIGHBORS_MG_ROUND_ROBIN
+  CUVS_NEIGHBORS_MG_ROUND_ROBIN = 1
 } cuvsMultiGpuReplicatedSearchMode;
 
 /**
@@ -53,9 +53,9 @@ typedef enum {
  */
 typedef enum {
   /** Search batches are merged on the root rank */
-  CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK,
+  CUVS_NEIGHBORS_MG_MERGE_ON_ROOT_RANK = 0,
   /** Search batches are merged in a tree reduction fashion */
-  CUVS_NEIGHBORS_MG_TREE_MERGE
+  CUVS_NEIGHBORS_MG_TREE_MERGE = 1
 } cuvsMultiGpuShardedMergeMode;
 
 /**
diff --git a/cpp/include/cuvs/neighbors/mg_ivf_flat.h b/c/include/cuvs/neighbors/mg_ivf_flat.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/mg_ivf_flat.h
rename to c/include/cuvs/neighbors/mg_ivf_flat.h
diff --git a/cpp/include/cuvs/neighbors/mg_ivf_pq.h b/c/include/cuvs/neighbors/mg_ivf_pq.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/mg_ivf_pq.h
rename to c/include/cuvs/neighbors/mg_ivf_pq.h
diff --git a/cpp/include/cuvs/neighbors/nn_descent.h b/c/include/cuvs/neighbors/nn_descent.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/nn_descent.h
rename to c/include/cuvs/neighbors/nn_descent.h
diff --git a/cpp/include/cuvs/neighbors/refine.h b/c/include/cuvs/neighbors/refine.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/refine.h
rename to c/include/cuvs/neighbors/refine.h
diff --git a/cpp/include/cuvs/neighbors/tiered_index.h b/c/include/cuvs/neighbors/tiered_index.h
similarity index 98%
rename from cpp/include/cuvs/neighbors/tiered_index.h
rename to c/include/cuvs/neighbors/tiered_index.h
index 6c54d474b6..be362f8349 100644
--- a/cpp/include/cuvs/neighbors/tiered_index.h
+++ b/c/include/cuvs/neighbors/tiered_index.h
@@ -33,9 +33,9 @@ extern "C" {
  * @brief Enum to hold which ANN algorithm is being used in the tiered index
  */
 typedef enum {
-  CUVS_TIERED_INDEX_ALGO_CAGRA,
-  CUVS_TIERED_INDEX_ALGO_IVF_FLAT,
-  CUVS_TIERED_INDEX_ALGO_IVF_PQ
+  CUVS_TIERED_INDEX_ALGO_CAGRA    = 0,
+  CUVS_TIERED_INDEX_ALGO_IVF_FLAT = 1,
+  CUVS_TIERED_INDEX_ALGO_IVF_PQ   = 2
 } cuvsTieredIndexANNAlgo;
 
 /**
diff --git a/cpp/include/cuvs/neighbors/vamana.h b/c/include/cuvs/neighbors/vamana.h
similarity index 100%
rename from cpp/include/cuvs/neighbors/vamana.h
rename to c/include/cuvs/neighbors/vamana.h
diff --git a/cpp/include/cuvs/preprocessing/quantize/binary.h b/c/include/cuvs/preprocessing/quantize/binary.h
similarity index 100%
rename from cpp/include/cuvs/preprocessing/quantize/binary.h
rename to c/include/cuvs/preprocessing/quantize/binary.h
diff --git a/cpp/include/cuvs/preprocessing/quantize/scalar.h b/c/include/cuvs/preprocessing/quantize/scalar.h
similarity index 100%
rename from cpp/include/cuvs/preprocessing/quantize/scalar.h
rename to c/include/cuvs/preprocessing/quantize/scalar.h
diff --git a/cpp/src/cluster/kmeans_c.cpp b/c/src/cluster/kmeans.cpp
similarity index 99%
rename from cpp/src/cluster/kmeans_c.cpp
rename to c/src/cluster/kmeans.cpp
index 135fcf3245..11f319a256 100644
--- a/cpp/src/cluster/kmeans_c.cpp
+++ b/c/src/cluster/kmeans.cpp
@@ -20,8 +20,9 @@
 #include <cuvs/cluster/kmeans.h>
 #include <cuvs/cluster/kmeans.hpp>
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
+
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
 
 namespace {
 
diff --git a/cpp/src/core/c_api.cpp b/c/src/core/c_api.cpp
similarity index 99%
rename from cpp/src/core/c_api.cpp
rename to c/src/core/c_api.cpp
index 3cf6db457b..74cc371775 100644
--- a/cpp/src/core/c_api.cpp
+++ b/c/src/core/c_api.cpp
@@ -15,7 +15,6 @@
  */
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
 #include <cuvs/version_config.h>
 
 #include <raft/core/device_resources_snmg.hpp>
@@ -32,6 +31,8 @@
 #include <rmm/mr/device/pool_memory_resource.hpp>
 #include <rmm/mr/host/pinned_memory_resource.hpp>
 
+#include "../core/exceptions.hpp"
+
 #include <cstdint>
 #include <memory>
 #include <thread>
diff --git a/cpp/include/cuvs/core/detail/interop.hpp b/c/src/core/detail/interop.hpp
similarity index 100%
rename from cpp/include/cuvs/core/detail/interop.hpp
rename to c/src/core/detail/interop.hpp
diff --git a/cpp/include/cuvs/core/exceptions.hpp b/c/src/core/exceptions.hpp
similarity index 93%
rename from cpp/include/cuvs/core/exceptions.hpp
rename to c/src/core/exceptions.hpp
index 01ee42151d..d7c173be21 100644
--- a/cpp/include/cuvs/core/exceptions.hpp
+++ b/c/src/core/exceptions.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include "c_api.h"
+#include <cuvs/core/c_api.h>
 
 #include <exception>
 
diff --git a/cpp/include/cuvs/core/interop.hpp b/c/src/core/interop.hpp
similarity index 100%
rename from cpp/include/cuvs/core/interop.hpp
rename to c/src/core/interop.hpp
diff --git a/cpp/src/distance/pairwise_distance_c.cpp b/c/src/distance/pairwise_distance.cpp
similarity index 97%
rename from cpp/src/distance/pairwise_distance_c.cpp
rename to c/src/distance/pairwise_distance.cpp
index 121574880b..07996915a0 100644
--- a/cpp/src/distance/pairwise_distance_c.cpp
+++ b/c/src/distance/pairwise_distance.cpp
@@ -1,6 +1,6 @@
 
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -23,10 +23,11 @@
 #include <raft/core/resources.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/distance/distance.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 namespace {
 
 template <typename T, typename DistT, typename LayoutT = raft::row_major>
diff --git a/cpp/src/neighbors/all_neighbors_c.cpp b/c/src/neighbors/all_neighbors.cpp
similarity index 99%
rename from cpp/src/neighbors/all_neighbors_c.cpp
rename to c/src/neighbors/all_neighbors.cpp
index 38f6b5c2a2..1000cb5c38 100644
--- a/cpp/src/neighbors/all_neighbors_c.cpp
+++ b/c/src/neighbors/all_neighbors.cpp
@@ -22,15 +22,16 @@
 #include <raft/util/cudart_utils.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/all_neighbors.h>
-#include <cuvs/neighbors/all_neighbors.hpp>
-#include <cuvs/neighbors/graph_build_types.hpp>
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/nn_descent.h>
+#include <cuvs/neighbors/all_neighbors.hpp>
+#include <cuvs/neighbors/graph_build_types.hpp>
+
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
 
-#include "ivf_pq_c.hpp"
+#include "ivf_pq.hpp"
 
 namespace {
 
diff --git a/cpp/src/neighbors/brute_force_c.cpp b/c/src/neighbors/brute_force.cpp
similarity index 99%
rename from cpp/src/neighbors/brute_force_c.cpp
rename to c/src/neighbors/brute_force.cpp
index 85a46c5b86..9330539487 100644
--- a/cpp/src/neighbors/brute_force_c.cpp
+++ b/c/src/neighbors/brute_force.cpp
@@ -1,6 +1,6 @@
 
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,11 +25,12 @@
 #include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/brute_force.h>
-#include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/brute_force.hpp>
+
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
 
 namespace {
 
diff --git a/cpp/src/neighbors/cagra_c.cpp b/c/src/neighbors/cagra.cpp
similarity index 99%
rename from cpp/src/neighbors/cagra_c.cpp
rename to c/src/neighbors/cagra.cpp
index b3c8a93a91..6c3cbafdad 100644
--- a/cpp/src/neighbors/cagra_c.cpp
+++ b/c/src/neighbors/cagra.cpp
@@ -23,13 +23,14 @@
 #include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/cagra.h>
-#include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/cagra.hpp>
+
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
 
-#include "cagra_c.hpp"
+#include "cagra.hpp"
 #include <fstream>
 
 namespace {
diff --git a/cpp/src/neighbors/cagra_c.hpp b/c/src/neighbors/cagra.hpp
similarity index 100%
rename from cpp/src/neighbors/cagra_c.hpp
rename to c/src/neighbors/cagra.hpp
diff --git a/cpp/src/neighbors/hnsw_c.cpp b/c/src/neighbors/hnsw.cpp
similarity index 98%
rename from cpp/src/neighbors/hnsw_c.cpp
rename to c/src/neighbors/hnsw.cpp
index 3241676770..9fce05eaa3 100644
--- a/cpp/src/neighbors/hnsw_c.cpp
+++ b/c/src/neighbors/hnsw.cpp
@@ -1,6 +1,6 @@
 
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,7 +15,6 @@
  * limitations under the License.
  */
 
-#include "cuvs/distance/distance.h"
 #include <cstdint>
 #include <dlpack/dlpack.h>
 
@@ -25,11 +24,13 @@
 #include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
+#include <cuvs/distance/distance.h>
 #include <cuvs/neighbors/hnsw.h>
 #include <cuvs/neighbors/hnsw.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 namespace {
 
 template <typename T>
diff --git a/cpp/src/neighbors/ivf_flat_c.cpp b/c/src/neighbors/ivf_flat.cpp
similarity index 99%
rename from cpp/src/neighbors/ivf_flat_c.cpp
rename to c/src/neighbors/ivf_flat.cpp
index 5d8f7462fa..c5a249f85a 100644
--- a/cpp/src/neighbors/ivf_flat_c.cpp
+++ b/c/src/neighbors/ivf_flat.cpp
@@ -25,11 +25,12 @@
 #include <raft/util/cudart_utils.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/ivf_flat.h>
 #include <cuvs/neighbors/ivf_flat.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 #include <fstream>
 
 namespace cuvs::neighbors::ivf_flat {
diff --git a/cpp/src/neighbors/ivf_flat_c.hpp b/c/src/neighbors/ivf_flat.hpp
similarity index 100%
rename from cpp/src/neighbors/ivf_flat_c.hpp
rename to c/src/neighbors/ivf_flat.hpp
diff --git a/cpp/src/neighbors/ivf_pq_c.cpp b/c/src/neighbors/ivf_pq.cpp
similarity index 99%
rename from cpp/src/neighbors/ivf_pq_c.cpp
rename to c/src/neighbors/ivf_pq.cpp
index 5c10d4111b..2596666834 100644
--- a/cpp/src/neighbors/ivf_pq_c.cpp
+++ b/c/src/neighbors/ivf_pq.cpp
@@ -23,11 +23,12 @@
 #include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 namespace cuvs::neighbors::ivf_pq {
 void convert_c_index_params(cuvsIvfPqIndexParams params, cuvs::neighbors::ivf_pq::index_params* out)
 {
diff --git a/cpp/src/neighbors/ivf_pq_c.hpp b/c/src/neighbors/ivf_pq.hpp
similarity index 97%
rename from cpp/src/neighbors/ivf_pq_c.hpp
rename to c/src/neighbors/ivf_pq.hpp
index e3591d65be..57ad647173 100644
--- a/cpp/src/neighbors/ivf_pq_c.hpp
+++ b/c/src/neighbors/ivf_pq.hpp
@@ -16,6 +16,8 @@
 #include <cuvs/neighbors/ivf_pq.h>
 #include <cuvs/neighbors/ivf_pq.hpp>
 
+#include "../core/exceptions.hpp"
+
 namespace cuvs::neighbors::ivf_pq {
 /// Converts a cuvsIvfPqIndexParams struct (c) to a ivf_pq::index_params (C++) struct
 void convert_c_index_params(cuvsIvfPqIndexParams params,
diff --git a/cpp/src/neighbors/mg_cagra_c.cpp b/c/src/neighbors/mg_cagra.cpp
similarity index 99%
rename from cpp/src/neighbors/mg_cagra_c.cpp
rename to c/src/neighbors/mg_cagra.cpp
index e661297b97..2695c69f8a 100644
--- a/cpp/src/neighbors/mg_cagra_c.cpp
+++ b/c/src/neighbors/mg_cagra.cpp
@@ -14,17 +14,18 @@
  * limitations under the License.
  */
 
-#include "cagra_c.hpp"
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
+#include "cagra.hpp"
 #include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/mg_cagra.h>
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.hpp>
-#include <cuvs/neighbors/mg_cagra.h>
 #include <dlpack/dlpack.h>
 #include <raft/core/error.hpp>
 #include <raft/core/serialize.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 #include <fstream>
 
 extern "C" cuvsError_t cuvsMultiGpuCagraIndexParamsCreate(
diff --git a/cpp/src/neighbors/mg_ivf_flat_c.cpp b/c/src/neighbors/mg_ivf_flat.cpp
similarity index 99%
rename from cpp/src/neighbors/mg_ivf_flat_c.cpp
rename to c/src/neighbors/mg_ivf_flat.cpp
index bec2fe8149..641d1395f2 100644
--- a/cpp/src/neighbors/mg_ivf_flat_c.cpp
+++ b/c/src/neighbors/mg_ivf_flat.cpp
@@ -14,17 +14,18 @@
  * limitations under the License.
  */
 
-#include "ivf_flat_c.hpp"
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
-#include <cuvs/neighbors/common.hpp>
+#include "ivf_flat.hpp"
 #include <cuvs/neighbors/ivf_flat.h>
-#include <cuvs/neighbors/ivf_flat.hpp>
 #include <cuvs/neighbors/mg_ivf_flat.h>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_flat.hpp>
 #include <dlpack/dlpack.h>
 #include <raft/core/error.hpp>
 #include <raft/core/serialize.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 #include <fstream>
 
 extern "C" cuvsError_t cuvsMultiGpuIvfFlatIndexParamsCreate(
diff --git a/cpp/src/neighbors/mg_ivf_pq_c.cpp b/c/src/neighbors/mg_ivf_pq.cpp
similarity index 99%
rename from cpp/src/neighbors/mg_ivf_pq_c.cpp
rename to c/src/neighbors/mg_ivf_pq.cpp
index 0307a659d1..a16878299a 100644
--- a/cpp/src/neighbors/mg_ivf_pq_c.cpp
+++ b/c/src/neighbors/mg_ivf_pq.cpp
@@ -14,17 +14,18 @@
  * limitations under the License.
  */
 
-#include "ivf_pq_c.hpp"
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
-#include <cuvs/neighbors/common.hpp>
+#include "ivf_pq.hpp"
 #include <cuvs/neighbors/ivf_pq.h>
-#include <cuvs/neighbors/ivf_pq.hpp>
 #include <cuvs/neighbors/mg_ivf_pq.h>
+#include <cuvs/neighbors/common.hpp>
+#include <cuvs/neighbors/ivf_pq.hpp>
 #include <dlpack/dlpack.h>
 #include <raft/core/error.hpp>
 #include <raft/core/serialize.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 #include <fstream>
 
 extern "C" cuvsError_t cuvsMultiGpuIvfPqIndexParamsCreate(
diff --git a/cpp/src/neighbors/nn_descent_c.cpp b/c/src/neighbors/nn_descent.cpp
similarity index 99%
rename from cpp/src/neighbors/nn_descent_c.cpp
rename to c/src/neighbors/nn_descent.cpp
index b0a19879c4..7cad9cb827 100644
--- a/cpp/src/neighbors/nn_descent_c.cpp
+++ b/c/src/neighbors/nn_descent.cpp
@@ -26,11 +26,12 @@
 #include <raft/util/cudart_utils.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/nn_descent.h>
 #include <cuvs/neighbors/nn_descent.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 #include <fstream>
 
 namespace {
diff --git a/cpp/src/neighbors/refine/refine_c.cpp b/c/src/neighbors/refine.cpp
similarity index 98%
rename from cpp/src/neighbors/refine/refine_c.cpp
rename to c/src/neighbors/refine.cpp
index 955eaded41..beb98f9c46 100644
--- a/cpp/src/neighbors/refine/refine_c.cpp
+++ b/c/src/neighbors/refine.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,11 +22,12 @@
 #include <raft/core/resources.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/refine.h>
 #include <cuvs/neighbors/refine.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 namespace {
 
 template <typename T>
diff --git a/cpp/src/neighbors/tiered_index_c.cpp b/c/src/neighbors/tiered_index.cpp
similarity index 99%
rename from cpp/src/neighbors/tiered_index_c.cpp
rename to c/src/neighbors/tiered_index.cpp
index 267c7ba5f0..4aaee52265 100644
--- a/cpp/src/neighbors/tiered_index_c.cpp
+++ b/c/src/neighbors/tiered_index.cpp
@@ -23,16 +23,17 @@
 #include <raft/core/serialize.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/tiered_index.h>
 #include <cuvs/neighbors/tiered_index.hpp>
 
+
 #include <fstream>
 
-#include "cagra_c.hpp"
-#include "ivf_flat_c.hpp"
-#include "ivf_pq_c.hpp"
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+#include "cagra.hpp"
+#include "ivf_flat.hpp"
+#include "ivf_pq.hpp"
 
 namespace {
 using namespace cuvs::neighbors;
diff --git a/cpp/src/neighbors/vamana_c.cpp b/c/src/neighbors/vamana.cpp
similarity index 99%
rename from cpp/src/neighbors/vamana_c.cpp
rename to c/src/neighbors/vamana.cpp
index 4ad497d17e..5f5decb2f1 100644
--- a/cpp/src/neighbors/vamana_c.cpp
+++ b/c/src/neighbors/vamana.cpp
@@ -22,12 +22,13 @@
 #include <raft/core/resources.hpp>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/neighbors/common.h>
 #include <cuvs/neighbors/vamana.h>
 #include <cuvs/neighbors/vamana.hpp>
 
+#include "../core/exceptions.hpp"
+#include "../core/interop.hpp"
+
 namespace {
 
 template <typename T>
diff --git a/cpp/src/preprocessing/quantize/binary_c.cpp b/c/src/preprocessing/quantize/binary.cpp
similarity index 99%
rename from cpp/src/preprocessing/quantize/binary_c.cpp
rename to c/src/preprocessing/quantize/binary.cpp
index 2f795cca4d..4afbc3a758 100644
--- a/cpp/src/preprocessing/quantize/binary_c.cpp
+++ b/c/src/preprocessing/quantize/binary.cpp
@@ -18,11 +18,12 @@
 #include <dlpack/dlpack.h>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/preprocessing/quantize/binary.h>
 #include <cuvs/preprocessing/quantize/binary.hpp>
 
+#include "../../core/exceptions.hpp"
+#include "../../core/interop.hpp"
+
 namespace {
 
 template <typename T, typename OutputT = uint8_t>
diff --git a/cpp/src/preprocessing/quantize/scalar_c.cpp b/c/src/preprocessing/quantize/scalar.cpp
similarity index 99%
rename from cpp/src/preprocessing/quantize/scalar_c.cpp
rename to c/src/preprocessing/quantize/scalar.cpp
index 21a80c3c85..3cd558c4d5 100644
--- a/cpp/src/preprocessing/quantize/scalar_c.cpp
+++ b/c/src/preprocessing/quantize/scalar.cpp
@@ -18,11 +18,12 @@
 #include <dlpack/dlpack.h>
 
 #include <cuvs/core/c_api.h>
-#include <cuvs/core/exceptions.hpp>
-#include <cuvs/core/interop.hpp>
 #include <cuvs/preprocessing/quantize/scalar.h>
 #include <cuvs/preprocessing/quantize/scalar.hpp>
 
+#include "../../core/exceptions.hpp"
+#include "../../core/interop.hpp"
+
 namespace {
 
 template <typename T>
diff --git a/c/tests/CMakeLists.txt b/c/tests/CMakeLists.txt
new file mode 100644
index 0000000000..36e0f15191
--- /dev/null
+++ b/c/tests/CMakeLists.txt
@@ -0,0 +1,124 @@
+# =============================================================================
+# Copyright (c) 2021-2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# ##################################################################################################
+# enable testing ################################################################################
+# ##################################################################################################
+
+include(rapids-test)
+
+if(PROJECT_IS_TOP_LEVEL)
+  rapids_test_init()
+endif()
+
+include(${rapids-cmake-dir}/cpm/gtest.cmake)
+rapids_cpm_gtest(BUILD_STATIC)
+
+function(ConfigureTest)
+
+  set(options)
+  set(oneValueArgs NAME GPUS PERCENT)
+  set(multiValueArgs PATH)
+
+  cmake_parse_arguments(_CUVS_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+  if(NOT DEFINED _CUVS_TEST_GPUS AND NOT DEFINED _CUVS_TEST_PERCENT)
+    set(_CUVS_TEST_GPUS 1)
+    set(_CUVS_TEST_PERCENT 30)
+  endif()
+  if(NOT DEFINED _CUVS_TEST_GPUS)
+    set(_CUVS_TEST_GPUS 1)
+  endif()
+  if(NOT DEFINED _CUVS_TEST_PERCENT)
+    set(_CUVS_TEST_PERCENT 100)
+  endif()
+
+  set(TEST_NAME ${_CUVS_TEST_NAME})
+
+  add_executable(${TEST_NAME} ${_CUVS_TEST_PATH})
+  target_link_libraries(
+    ${TEST_NAME} PRIVATE cuvs::c_api GTest::gtest GTest::gtest_main
+                         $<TARGET_NAME_IF_EXISTS:conda_env>
+  )
+  set_target_properties(
+    ${TEST_NAME}
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUVS_C_BINARY_DIR}/gtests>"
+               INSTALL_RPATH "\$ORIGIN/../../../lib"
+  )
+
+  target_include_directories(
+    ${TEST_NAME} PRIVATE "$<BUILD_INTERFACE:${CUVS_C_SOURCE_DIR}/tests>"
+                         "$<BUILD_INTERFACE:${CUVS_C_SOURCE_DIR}/../cpp/tests>"
+  )
+
+  rapids_test_add(
+    NAME ${TEST_NAME}
+    COMMAND ${TEST_NAME}
+    GPUS ${_CUVS_TEST_GPUS}
+    PERCENT ${_CUVS_TEST_PERCENT}
+    INSTALL_COMPONENT_SET testing
+  )
+endfunction()
+
+ConfigureTest(NAME INTEROP_TEST PATH core/interop.cu)
+ConfigureTest(
+  NAME DISTANCE_C_TEST PATH distance/run_pairwise_distance_c.c distance/pairwise_distance_c.cu
+)
+ConfigureTest(NAME BRUTEFORCE_C_TEST PATH neighbors/run_brute_force_c.c neighbors/brute_force_c.cu)
+ConfigureTest(NAME IVF_FLAT_C_TEST PATH neighbors/run_ivf_flat_c.c neighbors/ann_ivf_flat_c.cu)
+ConfigureTest(NAME IVF_PQ_C_TEST PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu)
+ConfigureTest(NAME CAGRA_C_TEST PATH neighbors/ann_cagra_c.cu)
+ConfigureTest(NAME MG_C_TEST PATH neighbors/run_mg_c.c neighbors/ann_mg_c.cu)
+ConfigureTest(
+  NAME ALL_NEIGHBORS_C_TEST PATH neighbors/run_all_neighbors_c.c neighbors/all_neighbors_c.cu
+)
+
+if(BUILD_CAGRA_HNSWLIB)
+  ConfigureTest(NAME HNSW_C_TEST PATH neighbors/ann_hnsw_c.cu)
+  target_link_libraries(HNSW_C_TEST PRIVATE hnswlib::hnswlib)
+  target_compile_definitions(HNSW_C_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
+endif()
+
+add_executable(cuvs_c_test core/c_api.c)
+target_link_libraries(cuvs_c_test PUBLIC cuvs::c_api)
+
+add_executable(cuvs_c_neighbors_test neighbors/c_api.c)
+target_link_libraries(cuvs_c_neighbors_test PUBLIC cuvs::c_api)
+
+set_target_properties(
+  cuvs_c_test cuvs_c_neighbors_test
+  PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUVS_C_BINARY_DIR}/gtests>"
+             INSTALL_RPATH "\$ORIGIN/../../../lib"
+)
+
+rapids_test_add(
+  NAME cuvs_c_test
+  COMMAND cuvs_c_test
+  GPUS 1
+  PERCENT 100
+  INSTALL_COMPONENT_SET testing
+)
+rapids_test_add(
+  NAME cuvs_c_neighbors_test
+  COMMAND cuvs_c_neighbors_test
+  GPUS 1
+  PERCENT 100
+  INSTALL_COMPONENT_SET testing
+)
+
+# ##################################################################################################
+# Install tests ####################################################################################
+# ##################################################################################################
+if(PROJECT_IS_TOP_LEVEL)
+  rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcuvs)
+endif()
diff --git a/cpp/tests/core/c_api.c b/c/tests/core/c_api.c
similarity index 100%
rename from cpp/tests/core/c_api.c
rename to c/tests/core/c_api.c
diff --git a/cpp/tests/core/interop.cu b/c/tests/core/interop.cu
similarity index 93%
rename from cpp/tests/core/interop.cu
rename to c/tests/core/interop.cu
index 67b853c3c8..8cf49eba73 100644
--- a/cpp/tests/core/interop.cu
+++ b/c/tests/core/interop.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,14 +14,15 @@
  * limitations under the License.
  */
 
-#include "../test_utils.cuh"
+#include "test_utils.cuh"
 #include <raft/core/host_mdspan.hpp>
 #include <raft/core/mdspan_types.hpp>
 #include <raft/core/resources.hpp>
 
-#include <cuvs/core/interop.hpp>
 #include <raft/core/host_mdarray.hpp>
 
+#include "../../src/core/interop.hpp"
+
 #include <gtest/gtest.h>
 #include <sys/types.h>
 
diff --git a/cpp/tests/distance/pairwise_distance_c.cu b/c/tests/distance/pairwise_distance_c.cu
similarity index 100%
rename from cpp/tests/distance/pairwise_distance_c.cu
rename to c/tests/distance/pairwise_distance_c.cu
diff --git a/cpp/tests/distance/run_pairwise_distance_c.c b/c/tests/distance/run_pairwise_distance_c.c
similarity index 100%
rename from cpp/tests/distance/run_pairwise_distance_c.c
rename to c/tests/distance/run_pairwise_distance_c.c
diff --git a/cpp/tests/neighbors/all_neighbors_c.cu b/c/tests/neighbors/all_neighbors_c.cu
similarity index 99%
rename from cpp/tests/neighbors/all_neighbors_c.cu
rename to c/tests/neighbors/all_neighbors_c.cu
index a4c79cc6f2..a13fc5f09d 100644
--- a/cpp/tests/neighbors/all_neighbors_c.cu
+++ b/c/tests/neighbors/all_neighbors_c.cu
@@ -20,8 +20,8 @@
 #include <raft/random/rng.cuh>
 #include <rmm/device_uvector.hpp>
 
-#include "ann_utils.cuh"
-#include "naive_knn.cuh"
+#include "neighbors/ann_utils.cuh"
+#include "neighbors/naive_knn.cuh"
 #include <cuvs/neighbors/all_neighbors.h>
 
 #include <algorithm>
diff --git a/cpp/tests/neighbors/ann_cagra_c.cu b/c/tests/neighbors/ann_cagra_c.cu
similarity index 99%
rename from cpp/tests/neighbors/ann_cagra_c.cu
rename to c/tests/neighbors/ann_cagra_c.cu
index 3123e2b3b3..a39e0553ae 100644
--- a/cpp/tests/neighbors/ann_cagra_c.cu
+++ b/c/tests/neighbors/ann_cagra_c.cu
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-#include "../test_utils.cuh"
+#include "test_utils.cuh"
 #include <cstddef>
 #include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.hpp>
diff --git a/cpp/tests/neighbors/ann_hnsw_c.cu b/c/tests/neighbors/ann_hnsw_c.cu
similarity index 98%
rename from cpp/tests/neighbors/ann_hnsw_c.cu
rename to c/tests/neighbors/ann_hnsw_c.cu
index 2a6401b1dc..83b1523a5c 100644
--- a/cpp/tests/neighbors/ann_hnsw_c.cu
+++ b/c/tests/neighbors/ann_hnsw_c.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,14 +14,14 @@
  * limitations under the License.
  */
 
-#include "../test_utils.cuh"
+#include "test_utils.cuh"
 #include <cuvs/core/c_api.h>
 #include <cuvs/distance/distance.h>
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/hnsw.h>
 #include <dlpack/dlpack.h>
 
 #include <cstdint>
-#include <cuvs/neighbors/cagra.h>
-#include <cuvs/neighbors/hnsw.h>
 
 #include <cuda_runtime.h>
 #include <gtest/gtest.h>
diff --git a/cpp/tests/neighbors/ann_ivf_flat_c.cu b/c/tests/neighbors/ann_ivf_flat_c.cu
similarity index 98%
rename from cpp/tests/neighbors/ann_ivf_flat_c.cu
rename to c/tests/neighbors/ann_ivf_flat_c.cu
index 8c23e6cff8..e1cf0f0402 100644
--- a/cpp/tests/neighbors/ann_ivf_flat_c.cu
+++ b/c/tests/neighbors/ann_ivf_flat_c.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@
 #include <raft/core/handle.hpp>
 #include <raft/random/rng.cuh>
 
-#include "ann_utils.cuh"
+#include "neighbors/ann_utils.cuh"
 #include <cuvs/neighbors/ivf_flat.h>
 
 extern "C" void run_ivf_flat(int64_t n_rows,
diff --git a/cpp/tests/neighbors/ann_ivf_pq_c.cu b/c/tests/neighbors/ann_ivf_pq_c.cu
similarity index 98%
rename from cpp/tests/neighbors/ann_ivf_pq_c.cu
rename to c/tests/neighbors/ann_ivf_pq_c.cu
index 21e4805b90..5c17b2f0c7 100644
--- a/cpp/tests/neighbors/ann_ivf_pq_c.cu
+++ b/c/tests/neighbors/ann_ivf_pq_c.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@
 #include <raft/core/handle.hpp>
 #include <raft/random/rng.cuh>
 
-#include "ann_utils.cuh"
+#include "neighbors/ann_utils.cuh"
 #include <cuvs/neighbors/ivf_pq.h>
 
 extern "C" void run_ivf_pq(int64_t n_rows,
diff --git a/cpp/tests/neighbors/ann_mg_c.cu b/c/tests/neighbors/ann_mg_c.cu
similarity index 99%
rename from cpp/tests/neighbors/ann_mg_c.cu
rename to c/tests/neighbors/ann_mg_c.cu
index 8a82982951..7c85da45a0 100644
--- a/cpp/tests/neighbors/ann_mg_c.cu
+++ b/c/tests/neighbors/ann_mg_c.cu
@@ -20,8 +20,8 @@
 #include <raft/core/handle.hpp>
 #include <raft/random/rng.cuh>
 
-#include "ann_utils.cuh"
-#include "naive_knn.cuh"
+#include "neighbors/ann_utils.cuh"
+#include "neighbors/naive_knn.cuh"
 #include <cuvs/neighbors/brute_force.h>
 #include <cuvs/neighbors/mg_cagra.h>
 #include <cuvs/neighbors/mg_ivf_flat.h>
diff --git a/cpp/tests/neighbors/brute_force_c.cu b/c/tests/neighbors/brute_force_c.cu
similarity index 99%
rename from cpp/tests/neighbors/brute_force_c.cu
rename to c/tests/neighbors/brute_force_c.cu
index d41efb24a2..92b65f7799 100644
--- a/cpp/tests/neighbors/brute_force_c.cu
+++ b/c/tests/neighbors/brute_force_c.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -20,7 +20,7 @@
 #include <raft/random/rng.cuh>
 #include <rmm/device_uvector.hpp>
 
-#include "ann_utils.cuh"
+#include "neighbors/ann_utils.cuh"
 #include <cuvs/neighbors/brute_force.h>
 
 #include <algorithm>
diff --git a/cpp/tests/neighbors/c_api.c b/c/tests/neighbors/c_api.c
similarity index 100%
rename from cpp/tests/neighbors/c_api.c
rename to c/tests/neighbors/c_api.c
diff --git a/cpp/tests/neighbors/run_all_neighbors_c.c b/c/tests/neighbors/run_all_neighbors_c.c
similarity index 100%
rename from cpp/tests/neighbors/run_all_neighbors_c.c
rename to c/tests/neighbors/run_all_neighbors_c.c
diff --git a/cpp/tests/neighbors/run_brute_force_c.c b/c/tests/neighbors/run_brute_force_c.c
similarity index 100%
rename from cpp/tests/neighbors/run_brute_force_c.c
rename to c/tests/neighbors/run_brute_force_c.c
diff --git a/cpp/tests/neighbors/run_ivf_flat_c.c b/c/tests/neighbors/run_ivf_flat_c.c
similarity index 100%
rename from cpp/tests/neighbors/run_ivf_flat_c.c
rename to c/tests/neighbors/run_ivf_flat_c.c
diff --git a/cpp/tests/neighbors/run_ivf_pq_c.c b/c/tests/neighbors/run_ivf_pq_c.c
similarity index 100%
rename from cpp/tests/neighbors/run_ivf_pq_c.c
rename to c/tests/neighbors/run_ivf_pq_c.c
diff --git a/cpp/tests/neighbors/run_mg_c.c b/c/tests/neighbors/run_mg_c.c
similarity index 100%
rename from cpp/tests/neighbors/run_mg_c.c
rename to c/tests/neighbors/run_mg_c.c
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 2844139da7..75e2dc86eb 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -21,7 +21,7 @@ option(BUILD_CPU_ONLY "Build CPU only components. Applies to CUVS benchmarks cur
 
 # workaround for rapids_cuda_init_architectures not working for arch detection with
 # enable_language(CUDA)
-set(lang_list "CXX")
+set(lang_list "CXX" "C")
 
 if(NOT BUILD_CPU_ONLY)
   include(rapids-cuda)
@@ -197,10 +197,6 @@ if(NOT BUILD_CPU_ONLY)
   include(cmake/thirdparty/get_cutlass.cmake)
 endif()
 
-if(BUILD_C_LIBRARY)
-  include(cmake/thirdparty/get_dlpack.cmake)
-endif()
-
 if(BUILD_TESTS OR BUILD_C_TESTS)
   include(${rapids-cmake-dir}/cpm/gtest.cmake)
   rapids_cpm_gtest(BUILD_STATIC)
@@ -315,6 +311,7 @@ if(NOT BUILD_CPU_ONLY)
   target_link_libraries(cuvs-cagra-search PRIVATE raft::raft)
   target_include_directories(
     cuvs-cagra-search PRIVATE "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+                              "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../c/include>"
   )
   target_compile_options(
     cuvs-cagra-search PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>"
@@ -562,8 +559,8 @@ if(NOT BUILD_CPU_ONLY)
 
   target_include_directories(
     cuvs_objs
-    PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-           "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+    PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+           "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../c/include>"
     INTERFACE "$<INSTALL_INTERFACE:include>"
   )
 
@@ -625,8 +622,11 @@ if(NOT BUILD_CPU_ONLY)
     )
 
     target_include_directories(
-      cuvs PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-                  "$<INSTALL_INTERFACE:include>"
+      cuvs
+      PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+             "$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>"
+             "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../c/include>"
+             "$<INSTALL_INTERFACE:include>"
     )
 
     target_link_libraries(
@@ -677,18 +677,16 @@ SECTIONS
                          $<$<BOOL:${CUVS_NVTX}>:NVTX_ENABLED>
     )
 
-    target_include_directories(
-      cuvs_static
-      PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-      INTERFACE "$<INSTALL_INTERFACE:include>"
-    )
+    target_include_directories(cuvs_static INTERFACE "$<INSTALL_INTERFACE:include>")
 
     # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
     target_link_options(cuvs_static PRIVATE $<HOST_LINK:${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld>)
 
     target_include_directories(
-      cuvs_static PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
-                         "$<INSTALL_INTERFACE:include>"
+      cuvs_static
+      PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+             "$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>"
+             "$<INSTALL_INTERFACE:include>"
     )
     target_link_libraries(
       cuvs_static
@@ -729,66 +727,8 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
 
   # ################################################################################################
   # * cuvs_c -------------------------------------------------------------------------------
-
-  if(BUILD_MG_ALGOS)
-    set(CUVS_C_MG_ALGOS src/neighbors/mg_ivf_pq_c.cpp src/neighbors/mg_ivf_flat_c.cpp
-                        src/neighbors/mg_cagra_c.cpp
-    )
-  endif()
-
   if(BUILD_C_LIBRARY)
-    add_library(
-      cuvs_c SHARED
-      src/core/c_api.cpp
-      src/cluster/kmeans_c.cpp
-      src/neighbors/brute_force_c.cpp
-      src/neighbors/ivf_flat_c.cpp
-      src/neighbors/ivf_pq_c.cpp
-      src/neighbors/cagra_c.cpp
-      $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:src/neighbors/hnsw_c.cpp>
-      src/neighbors/nn_descent_c.cpp
-      src/neighbors/vamana_c.cpp
-      src/neighbors/refine/refine_c.cpp
-      src/neighbors/tiered_index_c.cpp
-      src/neighbors/all_neighbors_c.cpp
-      src/preprocessing/quantize/binary_c.cpp
-      src/preprocessing/quantize/scalar_c.cpp
-      src/distance/pairwise_distance_c.cpp
-      ${CUVS_C_MG_ALGOS}
-    )
-    add_library(cuvs::c_api ALIAS cuvs_c)
-    set_target_properties(
-      cuvs_c
-      PROPERTIES BUILD_RPATH "\$ORIGIN"
-                 INSTALL_RPATH "\$ORIGIN"
-                 CXX_STANDARD 17
-                 CXX_STANDARD_REQUIRED ON
-                 POSITION_INDEPENDENT_CODE ON
-                 INTERFACE_POSITION_INDEPENDENT_CODE ON
-                 EXPORT_NAME c_api
-    )
-
-    target_compile_definitions(
-      cuvs_c PUBLIC $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>
-    )
-
-    target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
-
-    target_include_directories(
-      cuvs_c
-      PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
-             "$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>"
-      INTERFACE "$<INSTALL_INTERFACE:include>"
-    )
-
-    target_link_libraries(
-      cuvs_c
-      PUBLIC cuvs::cuvs ${CUVS_CTK_MATH_DEPENDENCIES}
-      PRIVATE raft::raft $<TARGET_NAME_IF_EXISTS:hnswlib::hnswlib>
-    )
-
-    # ensure CUDA symbols aren't relocated to the middle of the debug build binaries
-    target_link_options(cuvs_c PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/fatbin.ld")
+    add_subdirectory(../c c)
   endif()
 
   # ################################################################################################
@@ -811,6 +751,12 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
   )
 
   if(BUILD_C_LIBRARY)
+    install(
+      DIRECTORY ../c/include/cuvs
+      COMPONENT cuvs
+      DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+    )
+
     install(
       TARGETS cuvs_c
       DESTINATION ${lib_dir}
diff --git a/cpp/bench/ann/CMakeLists.txt b/cpp/bench/ann/CMakeLists.txt
index 9f366e7f83..79d72ea71b 100644
--- a/cpp/bench/ann/CMakeLists.txt
+++ b/cpp/bench/ann/CMakeLists.txt
@@ -188,6 +188,7 @@ function(ConfigureAnnBench)
   target_include_directories(
     ${BENCH_NAME}
     PUBLIC "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/include>"
+           "$<BUILD_INTERFACE:${CUVS_SOURCE_DIR}/../c/include>"
     PRIVATE ${ConfigureAnnBench_INCLUDES}
   )
 
diff --git a/cpp/cmake/config.json b/cpp/cmake/config.json
index a10c274cb1..a9f1b53007 100644
--- a/cpp/cmake/config.json
+++ b/cpp/cmake/config.json
@@ -2,7 +2,7 @@
   "parse": {
     "additional_commands": {
       "ConfigureTest": {
-        "flags": ["NOCUDA", "C_LIB", "FETCH_CODEBOOKS"],
+        "flags": ["NOCUDA", "FETCH_CODEBOOKS"],
         "kwargs": {
           "NAME": 1,
           "GPUS": "?",
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
index f9ffb39a20..a1c20422e7 100644
--- a/cpp/doxygen/Doxyfile
+++ b/cpp/doxygen/Doxyfile
@@ -983,7 +983,8 @@ WARN_LOGFILE           =
 # Note: If this tag is empty the current directory is searched.
 
 INPUT                  = main_page.md \
-                         ../include
+                         ../include \
+                         ../../c/include
 
 # This tag can be used to specify the character encoding of the source files
 # that Doxygen parses. Internally Doxygen uses the UTF-8 encoding. Doxygen uses
diff --git a/cpp/include/cuvs/distance/distance.hpp b/cpp/include/cuvs/distance/distance.hpp
index 42c574e581..d1bdd839e5 100644
--- a/cpp/include/cuvs/distance/distance.hpp
+++ b/cpp/include/cuvs/distance/distance.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2021-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,10 +16,9 @@
 
 #pragma once
 
-#include "distance.h"
-
 #include <cstdint>
 #include <cuda_fp16.h>
+#include <cuvs/distance/distance.h>
 #include <raft/core/device_csr_matrix.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/core/resources.hpp>
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 61639d383d..4bf7e26e20 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -129,14 +129,14 @@ struct index_params : cuvs::neighbors::index_params {
 
 enum class search_algo {
   /** For large batch sizes. */
-  SINGLE_CTA,
+  SINGLE_CTA = 0,
   /** For small batch sizes. */
-  MULTI_CTA,
-  MULTI_KERNEL,
-  AUTO
+  MULTI_CTA    = 1,
+  MULTI_KERNEL = 2,
+  AUTO         = 3
 };
 
-enum class hash_mode { HASH, SMALL, AUTO };
+enum class hash_mode { HASH = 0, SMALL = 1, AUTO = 2 };
 
 struct search_params : cuvs::neighbors::search_params {
   /** Maximum number of queries to search at the same time (batch size). Auto select when 0.*/
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 096f071aa9..776f5c417d 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -18,7 +18,6 @@
 
 #include "ann_utils.cuh"
 #include "cagra/device_common.hpp"
-#include "cuvs/distance/distance.h"
 #include "nn_descent_gnnd.hpp"
 
 #include <cuvs/distance/distance.hpp>
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
index 3921bae864..5bcc175f2f 100644
--- a/cpp/tests/CMakeLists.txt
+++ b/cpp/tests/CMakeLists.txt
@@ -21,7 +21,7 @@ rapids_test_init()
 
 function(ConfigureTest)
 
-  set(options NOCUDA C_LIB FETCH_CODEBOOKS)
+  set(options NOCUDA FETCH_CODEBOOKS)
   set(oneValueArgs NAME GPUS PERCENT ADDITIONAL_DEP)
   set(multiValueArgs PATH)
 
@@ -55,7 +55,6 @@ function(ConfigureTest)
             ${CUVS_CTK_MATH_DEPENDENCIES}
             $<TARGET_NAME_IF_EXISTS:OpenMP::OpenMP_CXX>
             $<TARGET_NAME_IF_EXISTS:conda_env>
-            $<$<BOOL:${_CUVS_TEST_C_LIB}>:cuvs::c_api>
             ${_CUVS_TEST_ADDITIONAL_DEP}
   )
   set_target_properties(
@@ -107,308 +106,215 @@ endfunction()
 # test sources ##################################################################################
 # ##################################################################################################
 
-if(BUILD_TESTS)
+ConfigureTest(
+  NAME NEIGHBORS_TEST
+  PATH neighbors/brute_force.cu neighbors/brute_force_prefiltered.cu neighbors/sparse_brute_force.cu
+       neighbors/refine.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_TIERED_INDEX_TEST
+  PATH neighbors/tiered_index.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME CLUSTER_TEST
+  PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu cluster/linkage.cu
+       cluster/connect_knn.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_IVF_FLAT_TEST
+  PATH neighbors/ann_ivf_flat/test_float_int64_t.cu neighbors/ann_ivf_flat/test_half_int64_t.cu
+       neighbors/ann_ivf_flat/test_int8_t_int64_t.cu neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_IVF_PQ_TEST
+  PATH neighbors/ann_ivf_pq/test_float_int64_t.cu neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
+       neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_SCANN_TEST
+  PATH neighbors/ann_scann/test_float_int64_t.cu
+  GPUS 1
+  PERCENT 1
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_BRUTE_FORCE_TEST
+  PATH neighbors/ann_brute_force/test_float.cu neighbors/ann_brute_force/test_half.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_CAGRA_TEST_BUGS
+  PATH neighbors/ann_cagra/bug_extreme_inputs_oob.cu neighbors/ann_cagra/bug_multi_cta_crash.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST
+  PATH neighbors/ann_cagra/test_float_uint32_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_CAGRA_HELPERS_TEST
+  PATH neighbors/ann_cagra/test_optimize_uint32_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
+  PATH neighbors/ann_cagra/test_half_uint32_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST
+  PATH neighbors/ann_cagra/test_int8_t_uint32_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST
+  PATH neighbors/ann_cagra/test_uint8_t_uint32_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_NN_DESCENT_TEST
+  PATH neighbors/ann_nn_descent/test_float_uint32_t.cu
+       neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
+       neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ANN_VAMANA_TEST
+  PATH neighbors/ann_vamana/test_float_uint32_t.cu neighbors/ann_vamana/test_int8_t_uint32_t.cu
+       neighbors/ann_vamana/test_uint8_t_uint32_t.cu
+  GPUS 1
+  PERCENT 100
+  FETCH_CODEBOOKS
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_BALL_COVER_TEST
+  PATH neighbors/ball_cover.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_EPSILON_NEIGHBORHOOD_TEST
+  PATH neighbors/epsilon_neighborhood.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_ALL_NEIGHBORS_TEST
+  PATH neighbors/all_neighbors/test_float.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME NEIGHBORS_DYNAMIC_BATCHING_TEST
+  PATH neighbors/dynamic_batching/test_brute_force.cu neighbors/dynamic_batching/test_cagra.cu
+       neighbors/dynamic_batching/test_ivf_flat.cu neighbors/dynamic_batching/test_ivf_pq.cu
+  GPUS 1
+  PERCENT 100
+)
+
+if(BUILD_CAGRA_HNSWLIB)
   ConfigureTest(
-    NAME NEIGHBORS_TEST
-    PATH neighbors/brute_force.cu neighbors/brute_force_prefiltered.cu
-         neighbors/sparse_brute_force.cu neighbors/refine.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_TIERED_INDEX_TEST
-    PATH neighbors/tiered_index.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME CLUSTER_TEST
-    PATH cluster/kmeans.cu cluster/kmeans_balanced.cu cluster/kmeans_find_k.cu cluster/linkage.cu
-         cluster/connect_knn.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_IVF_FLAT_TEST
-    PATH neighbors/ann_ivf_flat/test_float_int64_t.cu
-         neighbors/ann_ivf_flat/test_half_int64_t.cu
-         neighbors/ann_ivf_flat/test_int8_t_int64_t.cu
-         neighbors/ann_ivf_flat/test_uint8_t_int64_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_IVF_PQ_TEST
-    PATH neighbors/ann_ivf_pq/test_float_int64_t.cu neighbors/ann_ivf_pq/test_int8_t_int64_t.cu
-         neighbors/ann_ivf_pq/test_uint8_t_int64_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_SCANN_TEST
-    PATH neighbors/ann_scann/test_float_int64_t.cu
-    GPUS 1
-    PERCENT 1
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_BRUTE_FORCE_TEST
-    PATH neighbors/ann_brute_force/test_float.cu neighbors/ann_brute_force/test_half.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_TEST_BUGS
-    PATH neighbors/ann_cagra/bug_extreme_inputs_oob.cu neighbors/ann_cagra/bug_multi_cta_crash.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_FLOAT_UINT32_TEST
-    PATH neighbors/ann_cagra/test_float_uint32_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_HELPERS_TEST
-    PATH neighbors/ann_cagra/test_optimize_uint32_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_HALF_UINT32_TEST
-    PATH neighbors/ann_cagra/test_half_uint32_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_INT8_UINT32_TEST
-    PATH neighbors/ann_cagra/test_int8_t_uint32_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_CAGRA_UINT8_UINT32_TEST
-    PATH neighbors/ann_cagra/test_uint8_t_uint32_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_NN_DESCENT_TEST
-    PATH neighbors/ann_nn_descent/test_float_uint32_t.cu
-         neighbors/ann_nn_descent/test_int8_t_uint32_t.cu
-         neighbors/ann_nn_descent/test_uint8_t_uint32_t.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ANN_VAMANA_TEST
-    PATH neighbors/ann_vamana/test_float_uint32_t.cu neighbors/ann_vamana/test_int8_t_uint32_t.cu
-         neighbors/ann_vamana/test_uint8_t_uint32_t.cu
-    GPUS 1
-    PERCENT 100
-    FETCH_CODEBOOKS
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_BALL_COVER_TEST
-    PATH neighbors/ball_cover.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_EPSILON_NEIGHBORHOOD_TEST
-    PATH neighbors/epsilon_neighborhood.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_ALL_NEIGHBORS_TEST
-    PATH neighbors/all_neighbors/test_float.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME NEIGHBORS_DYNAMIC_BATCHING_TEST
-    PATH neighbors/dynamic_batching/test_brute_force.cu neighbors/dynamic_batching/test_cagra.cu
-         neighbors/dynamic_batching/test_ivf_flat.cu neighbors/dynamic_batching/test_ivf_pq.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  if(BUILD_CAGRA_HNSWLIB)
-    ConfigureTest(
-      NAME NEIGHBORS_HNSW_TEST
-      PATH neighbors/hnsw.cu
-      GPUS 1
-      PERCENT 100
-    )
-    target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
-    target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
-  endif()
-
-  if(BUILD_MG_ALGOS)
-    ConfigureTest(
-      NAME NEIGHBORS_MG_TEST
-      PATH neighbors/mg/test_float.cu
-      GPUS 1
-      PERCENT 100
-      ADDITIONAL_DEP NCCL::NCCL
-    )
-  endif()
-
-  ConfigureTest(
-    NAME DISTANCE_TEST
-    PATH distance/dist_canberra.cu
-         distance/dist_correlation.cu
-         distance/dist_cos.cu
-         distance/dist_hamming.cu
-         distance/dist_hellinger.cu
-         distance/dist_inner_product.cu
-         distance/dist_jensen_shannon.cu
-         distance/dist_kl_divergence.cu
-         distance/dist_l1.cu
-         distance/dist_l2_exp.cu
-         distance/dist_l2_sqrt_exp.cu
-         distance/dist_l_inf.cu
-         distance/dist_lp_unexp.cu
-         distance/dist_russell_rao.cu
-         distance/gram.cu
-         distance/masked_nn.cu
-         distance/sparse_distance.cu
-         sparse/neighbors/cross_component_nn.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME SPARSE_TEST
-    PATH sparse/cluster/cluster_solvers.cu sparse/cluster/eigen_solvers.cu
-         sparse/cluster/spectral.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME PREPROCESSING_TEST
-    PATH preprocessing/scalar_quantization.cu preprocessing/binary_quantization.cu
-         preprocessing/spectral_embedding.cu
-    GPUS 1
-    PERCENT 100
-  )
-
-  ConfigureTest(
-    NAME STATS_TEST
-    PATH stats/trustworthiness.cu stats/silhouette_score.cu
+    NAME NEIGHBORS_HNSW_TEST
+    PATH neighbors/hnsw.cu
     GPUS 1
     PERCENT 100
   )
+  target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
+  target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
 endif()
 
-if(TARGET cuvs::c_api)
-  enable_language(C)
-
-  ConfigureTest(
-    NAME INTEROP_TEST
-    PATH core/interop.cu
-    C_LIB
-  )
-  ConfigureTest(
-    NAME DISTANCE_C_TEST
-    PATH distance/run_pairwise_distance_c.c distance/pairwise_distance_c.cu
-    C_LIB
-  )
-
-  ConfigureTest(
-    NAME BRUTEFORCE_C_TEST
-    PATH neighbors/run_brute_force_c.c neighbors/brute_force_c.cu
-    C_LIB
-  )
-
-  ConfigureTest(
-    NAME IVF_FLAT_C_TEST
-    PATH neighbors/run_ivf_flat_c.c neighbors/ann_ivf_flat_c.cu
-    C_LIB
-  )
-
+if(BUILD_MG_ALGOS)
   ConfigureTest(
-    NAME IVF_PQ_C_TEST
-    PATH neighbors/run_ivf_pq_c.c neighbors/ann_ivf_pq_c.cu
-    C_LIB
-  )
-
-  ConfigureTest(
-    NAME CAGRA_C_TEST
-    PATH neighbors/ann_cagra_c.cu
-    C_LIB
-  )
-
-  ConfigureTest(
-    NAME MG_C_TEST
-    PATH neighbors/run_mg_c.c neighbors/ann_mg_c.cu
-    C_LIB
-  )
-
-  ConfigureTest(
-    NAME ALL_NEIGHBORS_C_TEST
-    PATH neighbors/run_all_neighbors_c.c neighbors/all_neighbors_c.cu
-    C_LIB
-  )
-
-  if(BUILD_CAGRA_HNSWLIB)
-    ConfigureTest(
-      NAME HNSW_C_TEST
-      PATH neighbors/ann_hnsw_c.cu
-      C_LIB
-    )
-    target_link_libraries(NEIGHBORS_HNSW_TEST PRIVATE hnswlib::hnswlib)
-    target_compile_definitions(NEIGHBORS_HNSW_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
-  endif()
-
-  add_executable(cuvs_c_test core/c_api.c)
-  target_link_libraries(cuvs_c_test PUBLIC cuvs::c_api)
-
-  add_executable(cuvs_c_neighbors_test neighbors/c_api.c)
-  target_link_libraries(cuvs_c_neighbors_test PUBLIC cuvs::c_api)
-
-  set_target_properties(
-    cuvs_c_test cuvs_c_neighbors_test
-    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUVS_BINARY_DIR}/gtests>"
-               INSTALL_RPATH "\$ORIGIN/../../../lib"
-  )
-
-  rapids_test_add(
-    NAME cuvs_c_test
-    COMMAND cuvs_c_test
+    NAME NEIGHBORS_MG_TEST
+    PATH neighbors/mg/test_float.cu
     GPUS 1
     PERCENT 100
-    INSTALL_COMPONENT_SET testing
-  )
-  rapids_test_add(
-    NAME cuvs_c_neighbors_test
-    COMMAND cuvs_c_neighbors_test
-    GPUS 1
-    PERCENT 100
-    INSTALL_COMPONENT_SET testing
+    ADDITIONAL_DEP NCCL::NCCL
   )
 endif()
 
+ConfigureTest(
+  NAME DISTANCE_TEST
+  PATH distance/dist_canberra.cu
+       distance/dist_correlation.cu
+       distance/dist_cos.cu
+       distance/dist_hamming.cu
+       distance/dist_hellinger.cu
+       distance/dist_inner_product.cu
+       distance/dist_jensen_shannon.cu
+       distance/dist_kl_divergence.cu
+       distance/dist_l1.cu
+       distance/dist_l2_exp.cu
+       distance/dist_l2_sqrt_exp.cu
+       distance/dist_l_inf.cu
+       distance/dist_lp_unexp.cu
+       distance/dist_russell_rao.cu
+       distance/gram.cu
+       distance/masked_nn.cu
+       distance/sparse_distance.cu
+       sparse/neighbors/cross_component_nn.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME SPARSE_TEST
+  PATH sparse/cluster/cluster_solvers.cu sparse/cluster/eigen_solvers.cu sparse/cluster/spectral.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME PREPROCESSING_TEST
+  PATH preprocessing/scalar_quantization.cu preprocessing/binary_quantization.cu
+       preprocessing/spectral_embedding.cu
+  GPUS 1
+  PERCENT 100
+)
+
+ConfigureTest(
+  NAME STATS_TEST
+  PATH stats/trustworthiness.cu stats/silhouette_score.cu
+  GPUS 1
+  PERCENT 100
+)
+
 # ##################################################################################################
 # Install tests ####################################################################################
 # ##################################################################################################
diff --git a/cpp/tests/neighbors/ann_cagra.cuh b/cpp/tests/neighbors/ann_cagra.cuh
index 027155a69e..02a090ea8e 100644
--- a/cpp/tests/neighbors/ann_cagra.cuh
+++ b/cpp/tests/neighbors/ann_cagra.cuh
@@ -299,11 +299,16 @@ inline ::std::ostream& operator<<(::std::ostream& os, const AnnCagraInputs& p)
     return "Unknown";
   };
 
-  std::vector<std::string> algo       = {"single-cta", "multi_cta", "multi_kernel", "auto"};
+  std::map<search_algo, std::string> algo_name = {
+    {search_algo::SINGLE_CTA, "single-cta"},      //
+    {search_algo::MULTI_CTA, "multi_cta"},        //
+    {search_algo::MULTI_KERNEL, "multi_kernel"},  //
+    {search_algo::AUTO, "auto"}                   //
+  };
   std::vector<std::string> build_algo = {"IVF_PQ", "NN_DESCENT", "ITERATIVE_CAGRA_SEARCH", "AUTO"};
   std::vector<std::string> merge_strategy = {"PHYSICAL", "LOGICAL"};
   os << "{n_queries=" << p.n_queries << ", dataset shape=" << p.n_rows << "x" << p.dim
-     << ", k=" << p.k << ", " << algo.at((int)p.algo) << ", max_queries=" << p.max_queries
+     << ", k=" << p.k << ", " << algo_name[p.algo] << ", max_queries=" << p.max_queries
      << ", itopk_size=" << p.itopk_size << ", search_width=" << p.search_width
      << ", metric=" << metric_str(p.metric) << ", " << (p.host_dataset ? "host" : "device")
      << ", build_algo=" << build_algo.at((int)p.build_algo)
diff --git a/docs/source/cpp_api.rst b/docs/source/cpp_api.rst
index 131858ff34..34f48a88f6 100644
--- a/docs/source/cpp_api.rst
+++ b/docs/source/cpp_api.rst
@@ -13,4 +13,3 @@ C++ API Documentation
    cpp_api/preprocessing.rst
    cpp_api/selection.rst
    cpp_api/stats.rst
-   cpp_api/core_interop
diff --git a/docs/source/cpp_api/core_interop.rst b/docs/source/cpp_api/core_interop.rst
deleted file mode 100644
index a8fcb2890b..0000000000
--- a/docs/source/cpp_api/core_interop.rst
+++ /dev/null
@@ -1,15 +0,0 @@
-Interop
-=======
-
-.. role:: py(code)
-   :language: c++
-   :class: highlight
-
-``#include <cuvs/core/interop.hpp>``
-
-namespace *cuvs::core*
-
- .. doxygengroup:: interop
-     :project: cuvs
-     :members:
-     :content-only:
diff --git a/java/panama-bindings/generate-bindings.sh b/java/panama-bindings/generate-bindings.sh
index b7e8ccb067..7e302ce874 100755
--- a/java/panama-bindings/generate-bindings.sh
+++ b/java/panama-bindings/generate-bindings.sh
@@ -35,7 +35,7 @@ fi
 jextract \
  --include-dir "${REPODIR}"/java/internal/build/_deps/dlpack-src/include/ \
  --include-dir "${CUDA_INCLUDE_DIR}" \
- --include-dir "${REPODIR}"/cpp/include \
+ --include-dir "${REPODIR}"/c/include \
  --output "${REPODIR}/java/cuvs-java/src/main/java22/" \
  --target-package ${TARGET_PACKAGE} \
  --library cuvs_c \
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
index 2b9a98cee4..0c79bcd973 100644
--- a/rust/cuvs-sys/build.rs
+++ b/rust/cuvs-sys/build.rs
@@ -73,7 +73,7 @@ fn main() {
     // run bindgen to automatically create rust bindings for the cuvs c-api
     bindgen::Builder::default()
         .header("cuvs_c_wrapper.h")
-        .clang_arg("-I../../cpp/include")
+        .clang_arg("-I../../c/include")
         // needed to find cudaruntime.h
         .clang_args(cmake_cxx_flags.split(' '))
         // include dlpack from the cmake build dependencies

From 6ea5b8fbd074c67a211bdc05ffcf6ea83f2b6bbe Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Tue, 14 Oct 2025 14:34:36 -0400
Subject: [PATCH 617/623] cuvs_static properly adds C include dir to
 target_include_directories (#1426)

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Divye Gala (https://github.com/divyegala)
  - Kyle Edwards (https://github.com/KyleFromNVIDIA)
  - Bradley Dice (https://github.com/bdice)

URL: https://github.com/rapidsai/cuvs/pull/1426
---
 cpp/CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 75e2dc86eb..f7fdf11519 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -686,6 +686,7 @@ SECTIONS
       cuvs_static
       PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
              "$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>"
+             "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../c/include>"
              "$<INSTALL_INTERFACE:include>"
     )
     target_link_libraries(

From 40c53406ba59abe4ba5d45f6eb5e0ba24c4ae99d Mon Sep 17 00:00:00 2001
From: Micka <mide@nvidia.com>
Date: Wed, 15 Oct 2025 04:42:09 +0200
Subject: [PATCH 618/623] Fix C/Python serialization for FP16. Add python tests
 (#1429)

Fix #1419  by aligning with the format present in raft: https://github.com/rapidsai/raft/blob/branch-25.12/cpp/include/raft/core/detail/mdspan_numpy_serializer.hpp#L129-134

Authors:
  - Micka (https://github.com/lowener)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1429
---
 c/src/neighbors/brute_force.cpp            |  2 +-
 c/src/neighbors/cagra.cpp                  |  2 +-
 c/src/neighbors/ivf_flat.cpp               |  2 +-
 c/src/neighbors/mg_cagra.cpp               |  4 ++--
 c/src/neighbors/mg_ivf_flat.cpp            |  4 ++--
 c/src/neighbors/mg_ivf_pq.cpp              |  2 +-
 python/cuvs/cuvs/tests/test_brute_force.py | 21 +++++++++++++++++++--
 python/cuvs/cuvs/tests/test_cagra.py       | 13 ++++++++++++-
 python/cuvs/cuvs/tests/test_ivf_flat.py    | 15 +++++++++++++--
 python/cuvs/cuvs/tests/test_ivf_pq.py      | 13 ++++++++++++-
 python/cuvs/cuvs/tests/test_mg_cagra.py    |  2 +-
 11 files changed, 65 insertions(+), 15 deletions(-)

diff --git a/c/src/neighbors/brute_force.cpp b/c/src/neighbors/brute_force.cpp
index 9330539487..72bb9d3f03 100644
--- a/c/src/neighbors/brute_force.cpp
+++ b/c/src/neighbors/brute_force.cpp
@@ -255,7 +255,7 @@ extern "C" cuvsError_t cuvsBruteForceDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename));
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<half>(res, filename));
     } else {
diff --git a/c/src/neighbors/cagra.cpp b/c/src/neighbors/cagra.cpp
index 6c3cbafdad..8df6e387b3 100644
--- a/c/src/neighbors/cagra.cpp
+++ b/c/src/neighbors/cagra.cpp
@@ -742,7 +742,7 @@ extern "C" cuvsError_t cuvsCagraDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float>(res, filename));
       index->dtype.code = kDLFloat;
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<half>(res, filename));
       index->dtype.code = kDLFloat;
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
diff --git a/c/src/neighbors/ivf_flat.cpp b/c/src/neighbors/ivf_flat.cpp
index c5a249f85a..02cae8f2ae 100644
--- a/c/src/neighbors/ivf_flat.cpp
+++ b/c/src/neighbors/ivf_flat.cpp
@@ -318,7 +318,7 @@ extern "C" cuvsError_t cuvsIvfFlatDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<float, int64_t>(res, filename));
       index->dtype.code = kDLFloat;
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->addr       = reinterpret_cast<uintptr_t>(_deserialize<half, int64_t>(res, filename));
       index->dtype.code = kDLFloat;
       index->dtype.bits = 16;
diff --git a/c/src/neighbors/mg_cagra.cpp b/c/src/neighbors/mg_cagra.cpp
index 2695c69f8a..297fe3359f 100644
--- a/c/src/neighbors/mg_cagra.cpp
+++ b/c/src/neighbors/mg_cagra.cpp
@@ -419,7 +419,7 @@ extern "C" cuvsError_t cuvsMultiGpuCagraDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<float>(res, filename));
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<half>(res, filename));
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
@@ -450,7 +450,7 @@ extern "C" cuvsError_t cuvsMultiGpuCagraDistribute(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<float>(res, filename));
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<half>(res, filename));
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
diff --git a/c/src/neighbors/mg_ivf_flat.cpp b/c/src/neighbors/mg_ivf_flat.cpp
index 641d1395f2..87de0f6148 100644
--- a/c/src/neighbors/mg_ivf_flat.cpp
+++ b/c/src/neighbors/mg_ivf_flat.cpp
@@ -416,7 +416,7 @@ extern "C" cuvsError_t cuvsMultiGpuIvfFlatDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<float>(res, filename));
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<half>(res, filename));
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
@@ -447,7 +447,7 @@ extern "C" cuvsError_t cuvsMultiGpuIvfFlatDistribute(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<float>(res, filename));
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_distribute<half>(res, filename));
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
diff --git a/c/src/neighbors/mg_ivf_pq.cpp b/c/src/neighbors/mg_ivf_pq.cpp
index a16878299a..0015cb7685 100644
--- a/c/src/neighbors/mg_ivf_pq.cpp
+++ b/c/src/neighbors/mg_ivf_pq.cpp
@@ -408,7 +408,7 @@ extern "C" cuvsError_t cuvsMultiGpuIvfPqDeserialize(cuvsResources_t res,
     if (dtype.kind == 'f' && dtype.itemsize == 4) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<float>(res, filename));
-    } else if (dtype.kind == 'f' && dtype.itemsize == 2) {
+    } else if (dtype.kind == 'e' && dtype.itemsize == 2) {
       index->dtype.code = kDLFloat;
       index->addr       = reinterpret_cast<uintptr_t>(_mg_deserialize<half>(res, filename));
     } else if (dtype.kind == 'i' && dtype.itemsize == 1) {
diff --git a/python/cuvs/cuvs/tests/test_brute_force.py b/python/cuvs/cuvs/tests/test_brute_force.py
index 3e0cad3e94..336b337c68 100644
--- a/python/cuvs/cuvs/tests/test_brute_force.py
+++ b/python/cuvs/cuvs/tests/test_brute_force.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,8 @@
 # limitations under the License.
 #
 
+import tempfile
+
 import numpy as np
 import pytest
 from pylibraft.common import device_ndarray
@@ -42,8 +44,17 @@
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("order", ["F", "C"])
 @pytest.mark.parametrize("dtype", [np.float32, np.float16])
+@pytest.mark.parametrize("serialize", [True, False])
 def test_brute_force_knn(
-    n_index_rows, n_query_rows, n_cols, k, inplace, order, metric, dtype
+    n_index_rows,
+    n_query_rows,
+    n_cols,
+    k,
+    inplace,
+    order,
+    metric,
+    dtype,
+    serialize,
 ):
     index = np.random.random_sample((n_index_rows, n_cols))
     index = np.asarray(index, order=order).astype(dtype)
@@ -68,6 +79,12 @@ def test_brute_force_knn(
     prefilter = filters.no_filter()
 
     brute_force_index = brute_force.build(index_device, metric)
+    if serialize:
+        with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+            temp_filename = f.name
+        brute_force.save(temp_filename, brute_force_index)
+        brute_force_index = brute_force.load(temp_filename)
+
     ret_distances, ret_indices = brute_force.search(
         brute_force_index,
         queries_device,
diff --git a/python/cuvs/cuvs/tests/test_cagra.py b/python/cuvs/cuvs/tests/test_cagra.py
index 4ec3c2a8fb..62cc65c333 100644
--- a/python/cuvs/cuvs/tests/test_cagra.py
+++ b/python/cuvs/cuvs/tests/test_cagra.py
@@ -13,6 +13,8 @@
 # limitations under the License.
 #
 
+import tempfile
+
 import cupy as cp
 import numpy as np
 import pytest
@@ -44,6 +46,7 @@ def run_cagra_build_search_test(
     test_extend=False,
     search_params={},
     compression=None,
+    serialize=False,
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     if metric == "inner_product" or metric == "cosine":
@@ -79,6 +82,12 @@ def run_cagra_build_search_test(
         else:
             index = cagra.build(build_params, dataset)
 
+    if serialize:
+        with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+            temp_filename = f.name
+        cagra.save(temp_filename, index)
+        index = cagra.load(temp_filename)
+
     queries = generate_data((n_queries, n_cols), dtype)
     out_idx = np.zeros((n_queries, k), dtype=np.uint32)
     out_dist = np.zeros((n_queries, k), dtype=np.float32)
@@ -159,8 +168,9 @@ def run_cagra_build_search_test(
 @pytest.mark.parametrize("array_type", ["device"])
 @pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
 @pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product", "cosine"])
+@pytest.mark.parametrize("serialize", [True, False])
 def test_cagra_dataset_dtype_host_device(
-    dtype, array_type, inplace, build_algo, metric
+    dtype, array_type, inplace, build_algo, metric, serialize
 ):
 
     # Note that inner_product tests use normalized input which we cannot
@@ -171,6 +181,7 @@ def test_cagra_dataset_dtype_host_device(
         array_type=array_type,
         build_algo=build_algo,
         metric=metric,
+        serialize=serialize,
     )
 
 
diff --git a/python/cuvs/cuvs/tests/test_ivf_flat.py b/python/cuvs/cuvs/tests/test_ivf_flat.py
index ea51f3a02c..09e0efcee4 100644
--- a/python/cuvs/cuvs/tests/test_ivf_flat.py
+++ b/python/cuvs/cuvs/tests/test_ivf_flat.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,8 @@
 # limitations under the License.
 #
 
+import tempfile
+
 import numpy as np
 import pytest
 from pylibraft.common import device_ndarray
@@ -38,6 +40,7 @@ def run_ivf_flat_build_search_test(
     compare=True,
     inplace=True,
     search_params={},
+    serialize=False,
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     if metric == "inner_product":
@@ -51,6 +54,12 @@ def run_ivf_flat_build_search_test(
 
     index = ivf_flat.build(build_params, dataset_device)
 
+    if serialize:
+        with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+            temp_filename = f.name
+        ivf_flat.save(temp_filename, index)
+        index = ivf_flat.load(temp_filename)
+
     if not add_data_on_build:
         dataset_1 = dataset[: n_rows // 2, :]
         dataset_2 = dataset[n_rows // 2 :, :]
@@ -127,7 +136,8 @@ def test_ivf_flat(inplace, dtype, metric):
 
 
 @pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
-def test_extend(dtype):
+@pytest.mark.parametrize("serialize", [True, False])
+def test_extend(dtype, serialize):
     run_ivf_flat_build_search_test(
         n_rows=10000,
         n_cols=10,
@@ -136,6 +146,7 @@ def test_extend(dtype):
         metric="sqeuclidean",
         dtype=dtype,
         add_data_on_build=False,
+        serialize=serialize,
     )
 
 
diff --git a/python/cuvs/cuvs/tests/test_ivf_pq.py b/python/cuvs/cuvs/tests/test_ivf_pq.py
index a7367b9736..a3eb8a9df4 100644
--- a/python/cuvs/cuvs/tests/test_ivf_pq.py
+++ b/python/cuvs/cuvs/tests/test_ivf_pq.py
@@ -13,6 +13,8 @@
 # limitations under the License.
 #
 
+import tempfile
+
 import numpy as np
 import pytest
 from pylibraft.common import device_ndarray
@@ -44,6 +46,7 @@ def run_ivf_pq_build_search_test(
     compare=True,
     inplace=True,
     array_type="device",
+    serialize=False,
 ):
     dataset = generate_data((n_rows, n_cols), dtype)
     if metric == "inner_product":
@@ -67,6 +70,12 @@ def run_ivf_pq_build_search_test(
     else:
         index = ivf_pq.build(build_params, dataset)
 
+    if serialize:
+        with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as f:
+            temp_filename = f.name
+        ivf_pq.save(temp_filename, index)
+        index = ivf_pq.load(temp_filename)
+
     if not add_data_on_build:
         dataset_1 = dataset[: n_rows // 2, :]
         dataset_2 = dataset[n_rows // 2 :, :]
@@ -216,9 +225,11 @@ def test_extend(dtype, array_type):
 @pytest.mark.parametrize("inplace", [True, False])
 @pytest.mark.parametrize("dtype", [np.float32, np.float16, np.int8, np.uint8])
 @pytest.mark.parametrize("array_type", ["host", "device"])
-def test_ivf_pq_dtype(inplace, dtype, array_type):
+@pytest.mark.parametrize("serialize", [True, False])
+def test_ivf_pq_dtype(inplace, dtype, array_type, serialize):
     run_ivf_pq_build_search_test(
         dtype=dtype,
         inplace=inplace,
         array_type=array_type,
+        serialize=serialize,
     )
diff --git a/python/cuvs/cuvs/tests/test_mg_cagra.py b/python/cuvs/cuvs/tests/test_mg_cagra.py
index 16d40f9c17..e03b49605f 100644
--- a/python/cuvs/cuvs/tests/test_mg_cagra.py
+++ b/python/cuvs/cuvs/tests/test_mg_cagra.py
@@ -139,7 +139,7 @@ def run_mg_cagra_build_search_test(
 
 
 @requires_multiple_gpus
-@pytest.mark.parametrize("dtype", [np.float32])
+@pytest.mark.parametrize("dtype", [np.float32, np.float16])
 @pytest.mark.parametrize(
     "metric", ["sqeuclidean"]
 )  # Start with just sqeuclidean

From d91e2252f314a925998485848c14e6dcf7ef308c Mon Sep 17 00:00:00 2001
From: Paul Taylor <178183+trxcllnt@users.noreply.github.com>
Date: Wed, 15 Oct 2025 09:15:25 -0700
Subject: [PATCH 619/623] Enable `sccache-dist` connection pool (#1431)

Authors:
  - Paul Taylor (https://github.com/trxcllnt)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: https://github.com/rapidsai/cuvs/pull/1431
---
 .devcontainer/Dockerfile | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 31e3837840..f5c82fe002 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -42,8 +42,7 @@ ENV HISTFILE="/home/coder/.cache/._bash_history"
 ENV AWS_ROLE_ARN="arn:aws:iam::279114543810:role/nv-gha-token-sccache-devs"
 ENV SCCACHE_REGION="us-east-2"
 ENV SCCACHE_BUCKET="rapids-sccache-devs"
-# 2hr (1 minute longer than sccache-dist request timeout)
-ENV SCCACHE_IDLE_TIMEOUT=7200
+ENV SCCACHE_IDLE_TIMEOUT=0
 
 ###
 # sccache-dist configuration
@@ -54,13 +53,8 @@ ENV DEVCONTAINER_UTILS_ENABLE_SCCACHE_DIST=1
 ENV SCCACHE_DIST_FALLBACK_TO_LOCAL_COMPILE=true
 # Retry transient errors 4 times (for a total of 5 attempts)
 ENV SCCACHE_DIST_MAX_RETRIES=4
-ENV SCCACHE_DIST_CONNECT_TIMEOUT=30
-ENV SCCACHE_DIST_CONNECTION_POOL=false
 # 1hr 59min (to accommodate debug builds)
 ENV SCCACHE_DIST_REQUEST_TIMEOUT=7140
-ENV SCCACHE_DIST_KEEPALIVE_ENABLED=true
-ENV SCCACHE_DIST_KEEPALIVE_INTERVAL=20
-ENV SCCACHE_DIST_KEEPALIVE_TIMEOUT=600
 ENV SCCACHE_DIST_URL="https://${TARGETARCH}.linux.sccache.rapids.nvidia.com"
 
 # Build as much in parallel as possible

From 833299f66228ff52134756f8d77c690e6fd7fa64 Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Fri, 17 Oct 2025 09:56:30 -0400
Subject: [PATCH 620/623] Properly guard usage of openmp function calls (#1435)

Proper support to disable OpenMP also requires that any calls to functions like `omp_get_max_threads` need to be guarded by a `_OPENMP` check.

This is done by adding openmp wrapper functions into `cuvs/core` that properly handle the `_OPENMP` guards and behave as required when OpenMP is disabled.

Required to fix https://github.com/rapidsai/cuvs/issues/1322

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1435
---
 cpp/CMakeLists.txt                            |  2 +-
 .../omp_checks.cpp => core/omp_wrapper.cpp}   | 36 +++++++++++++++----
 cpp/src/core/omp_wrapper.hpp                  | 32 +++++++++++++++++
 .../all_neighbors/all_neighbors_batched.cuh   |  3 +-
 cpp/src/neighbors/detail/cagra/add_nodes.cuh  |  9 +++--
 cpp/src/neighbors/detail/cagra/graph_core.cuh |  2 +-
 cpp/src/neighbors/detail/hnsw.hpp             | 11 +++---
 cpp/src/neighbors/detail/nn_descent.cuh       |  2 +-
 cpp/src/neighbors/mg/snmg.cuh                 | 11 +++---
 cpp/src/neighbors/refine/refine_host.hpp      | 10 +++---
 cpp/src/neighbors/scann/detail/scann_avq.cuh  |  2 +-
 .../neighbors/scann/detail/scann_common.cuh   |  4 ++-
 12 files changed, 92 insertions(+), 32 deletions(-)
 rename cpp/src/{neighbors/mg/omp_checks.cpp => core/omp_wrapper.cpp} (53%)
 create mode 100644 cpp/src/core/omp_wrapper.hpp

diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index f7fdf11519..233e98fb0a 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -332,7 +332,6 @@ if(NOT BUILD_CPU_ONLY)
         src/neighbors/mg/mg_cagra_half_uint32_t.cu
         src/neighbors/mg/mg_cagra_int8_t_uint32_t.cu
         src/neighbors/mg/mg_cagra_uint8_t_uint32_t.cu
-        src/neighbors/mg/omp_checks.cpp
     )
   endif()
 
@@ -359,6 +358,7 @@ if(NOT BUILD_CPU_ONLY)
     src/cluster/kmeans_transform_float.cu
     src/cluster/single_linkage_float.cu
     src/core/bitset.cu
+    src/core/omp_wrapper.cpp
     src/distance/detail/kernels/gram_matrix.cu
     src/distance/detail/kernels/kernel_factory.cu
     src/distance/detail/kernels/kernel_matrices.cu
diff --git a/cpp/src/neighbors/mg/omp_checks.cpp b/cpp/src/core/omp_wrapper.cpp
similarity index 53%
rename from cpp/src/neighbors/mg/omp_checks.cpp
rename to cpp/src/core/omp_wrapper.cpp
index 91a23c8f32..fce85093d3 100644
--- a/cpp/src/neighbors/mg/omp_checks.cpp
+++ b/cpp/src/core/omp_wrapper.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,19 +15,43 @@
  */
 
 #include <omp.h>
+
 #include <raft/core/logger.hpp>
 
-namespace cuvs::neighbors::snmg {
+#include "omp_wrapper.hpp"
+
+namespace cuvs::core::omp {
+
+constexpr bool is_omp_enabled()
+{
+#if defined(_OPENMP)
+  return true;
+#else
+  return false;
+#endif
+}
+
+int get_max_threads() { return is_omp_enabled() ? omp_get_max_threads() : 1; }
+int get_num_procs() { return is_omp_enabled() ? omp_get_num_procs() : 1; }
+int get_num_threads() { return is_omp_enabled() ? omp_get_num_threads() : 1; }
+int get_thread_num() { return is_omp_enabled() ? omp_get_thread_num() : 0; }
+
+void set_nested(int v)
+{
+  (void)v;
+  if constexpr (is_omp_enabled()) { omp_set_nested(v); }
+}
 
-void check_omp_threads(const int requirements)
+void check_threads(const int requirements)
 {
-  const int max_threads = omp_get_max_threads();
-  if (max_threads < requirements)
+  const int max_threads = get_max_threads();
+  if (max_threads < requirements) {
     RAFT_LOG_WARN(
       "OpenMP is only allowed %d threads to run %d GPUs. Please increase the number of OpenMP "
       "threads to avoid NCCL hangs by modifying the environment variable OMP_NUM_THREADS.",
       max_threads,
       requirements);
+  }
 }
 
-}  // namespace cuvs::neighbors::snmg
+}  // namespace cuvs::core::omp
diff --git a/cpp/src/core/omp_wrapper.hpp b/cpp/src/core/omp_wrapper.hpp
new file mode 100644
index 0000000000..869101deec
--- /dev/null
+++ b/cpp/src/core/omp_wrapper.hpp
@@ -0,0 +1,32 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <omp.h>
+
+namespace cuvs::core::omp {
+
+constexpr bool is_omp_enabled();
+
+int get_max_threads();
+int get_num_procs();
+int get_num_threads();
+int get_thread_num();
+
+void set_nested(int v);
+
+void check_threads(const int requirements);
+
+}  // namespace cuvs::core::omp
diff --git a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
index 2649babdc2..27c0568274 100644
--- a/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
+++ b/cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh
@@ -15,6 +15,7 @@
  */
 
 #pragma once
+#include "../../core/omp_wrapper.hpp"
 #include "../detail/reachability.cuh"
 #include "all_neighbors_builder.cuh"
 #include "raft/core/logger_macros.hpp"
@@ -483,7 +484,7 @@ void batch_build(
 {
   if (raft::resource::is_multi_gpu(handle)) {
     // For efficient CPU-computation of omp parallel for regions per GPU
-    omp_set_nested(1);
+    cuvs::core::omp::set_nested(1);
   }
 
   size_t num_rows = static_cast<size_t>(dataset.extent(0));
diff --git a/cpp/src/neighbors/detail/cagra/add_nodes.cuh b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
index 8023d8f9ef..fbfbdac763 100644
--- a/cpp/src/neighbors/detail/cagra/add_nodes.cuh
+++ b/cpp/src/neighbors/detail/cagra/add_nodes.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+#include "../../../core/omp_wrapper.hpp"
 #include "../ann_utils.cuh"
 #include <cuvs/neighbors/cagra.hpp>
 #include <raft/core/device_resources.hpp>
@@ -24,8 +25,6 @@
 
 #include <rmm/device_buffer.hpp>
 
-#include <omp.h>
-
 #include <cstdint>
 
 namespace cuvs::neighbors::cagra {
@@ -165,8 +164,8 @@ void add_node_core(
 #pragma omp parallel
     {
       std::vector<std::pair<IdxT, std::size_t>> detourable_node_count_list(base_degree);
-      for (std::size_t vec_i = omp_get_thread_num(); vec_i < batch.size();
-           vec_i += omp_get_num_threads()) {
+      for (std::size_t vec_i = cuvs::core::omp::get_thread_num(); vec_i < batch.size();
+           vec_i += cuvs::core::omp::get_num_threads()) {
         // Count detourable edges
         for (std::uint32_t i = 0; i < base_degree; i++) {
           std::uint32_t detourable_node_count = 0;
diff --git a/cpp/src/neighbors/detail/cagra/graph_core.cuh b/cpp/src/neighbors/detail/cagra/graph_core.cuh
index 809e3b6dd4..ececc5f5b1 100644
--- a/cpp/src/neighbors/detail/cagra/graph_core.cuh
+++ b/cpp/src/neighbors/detail/cagra/graph_core.cuh
@@ -25,6 +25,7 @@
 
 // TODO: This shouldn't be invoking anything from spatial/knn
 #include "../../../core/nvtx.hpp"
+#include "../../../core/omp_wrapper.hpp"
 #include "../ann_utils.cuh"
 
 #include <raft/util/bitonic_sort.cuh>
@@ -33,7 +34,6 @@
 #include <cuda_fp16.h>
 
 #include <float.h>
-#include <omp.h>
 #include <sys/time.h>
 
 #include <climits>
diff --git a/cpp/src/neighbors/detail/hnsw.hpp b/cpp/src/neighbors/detail/hnsw.hpp
index c505146884..438e3fead1 100644
--- a/cpp/src/neighbors/detail/hnsw.hpp
+++ b/cpp/src/neighbors/detail/hnsw.hpp
@@ -17,6 +17,7 @@
 #pragma once
 
 #include "../../core/nvtx.hpp"
+#include "../../core/omp_wrapper.hpp"
 
 #include <cuvs/neighbors/brute_force.hpp>
 #include <cuvs/neighbors/hnsw.hpp>
@@ -29,7 +30,6 @@
 
 #include <filesystem>
 #include <memory>
-#include <omp.h>
 #include <random>
 #include <thread>
 
@@ -194,7 +194,7 @@ std::enable_if_t<hierarchy == HnswHierarchy::CPU, std::unique_ptr<index<T>>> fro
     params.ef_construction);
   appr_algo->base_layer_init = false;  // tell hnswlib to build upper layers only
   [[maybe_unused]] auto num_threads =
-    params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+    params.num_threads == 0 ? cuvs::core::omp::get_max_threads() : params.num_threads;
 #pragma omp parallel for num_threads(num_threads)
   for (int64_t i = 0; i < host_dataset_view.extent(0); i++) {
     appr_algo->addPoint((void*)(host_dataset_view.data_handle() + i * host_dataset_view.extent(1)),
@@ -284,8 +284,9 @@ std::enable_if_t<hierarchy == HnswHierarchy::GPU, std::unique_ptr<index<T>>> fro
   std::optional<raft::host_matrix_view<const T, int64_t, raft::row_major>> dataset)
 {
   common::nvtx::range<common::nvtx::domain::cuvs> fun_scope("hnsw::from_cagra<GPU>");
-  auto stream      = raft::resource::get_cuda_stream(res);
-  auto num_threads = params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+  auto stream = raft::resource::get_cuda_stream(res);
+  auto num_threads =
+    params.num_threads == 0 ? cuvs::core::omp::get_max_threads() : params.num_threads;
 
   /* Note: NNSW data layout
 
@@ -546,7 +547,7 @@ void extend(raft::resources const& res,
   auto current_element_count = hnswlib_index->getCurrentElementCount();
   auto new_element_count     = additional_dataset.extent(0);
   [[maybe_unused]] auto num_threads =
-    params.num_threads == 0 ? omp_get_max_threads() : params.num_threads;
+    params.num_threads == 0 ? cuvs::core::omp::get_max_threads() : params.num_threads;
 
   hnswlib_index->resizeIndex(current_element_count + new_element_count);
 #pragma omp parallel for num_threads(num_threads)
diff --git a/cpp/src/neighbors/detail/nn_descent.cuh b/cpp/src/neighbors/detail/nn_descent.cuh
index 776f5c417d..7c1eb46dbb 100644
--- a/cpp/src/neighbors/detail/nn_descent.cuh
+++ b/cpp/src/neighbors/detail/nn_descent.cuh
@@ -20,6 +20,7 @@
 #include "cagra/device_common.hpp"
 #include "nn_descent_gnnd.hpp"
 
+#include "../../core/omp_wrapper.hpp"
 #include <cuvs/distance/distance.hpp>
 #include <cuvs/neighbors/nn_descent.hpp>
 
@@ -46,7 +47,6 @@
 #include <cuda_runtime.h>
 
 #include <mma.h>
-#include <omp.h>
 
 #include <limits>
 #include <optional>
diff --git a/cpp/src/neighbors/mg/snmg.cuh b/cpp/src/neighbors/mg/snmg.cuh
index b5658825f0..28c3a4b96c 100644
--- a/cpp/src/neighbors/mg/snmg.cuh
+++ b/cpp/src/neighbors/mg/snmg.cuh
@@ -22,6 +22,7 @@
 #include <raft/linalg/add.cuh>
 #include <raft/util/cuda_dev_essentials.cuh>
 
+#include "../../core/omp_wrapper.hpp"
 #include <cuvs/neighbors/cagra.hpp>
 #include <cuvs/neighbors/common.hpp>
 #include <cuvs/neighbors/ivf_flat.hpp>
@@ -42,10 +43,6 @@ void search(const raft::resources& handle,
             raft::device_matrix_view<float, int64_t, row_major> d_distances);
 }  // namespace cuvs::neighbors
 
-namespace cuvs::neighbors::snmg {
-void check_omp_threads(const int requirements);
-}  // namespace cuvs::neighbors::snmg
-
 namespace cuvs::neighbors::snmg::detail {
 using namespace cuvs::neighbors;
 using namespace raft;
@@ -215,7 +212,8 @@ void sharded_search_with_direct_merge(
       queries.data_handle() + query_offset, n_rows_of_current_batch, n_cols);
 
     const int& requirements = index.num_ranks_;
-    check_omp_threads(requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
+    cuvs::core::omp::check_threads(
+      requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
 #pragma omp parallel for num_threads(index.num_ranks_)
     for (int rank = 0; rank < index.num_ranks_; rank++) {
       const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
@@ -335,7 +333,8 @@ void sharded_search_with_tree_merge(
       queries.data_handle() + query_offset, n_rows_of_current_batch, n_cols);
 
     const int& requirements = index.num_ranks_;
-    check_omp_threads(requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
+    cuvs::core::omp::check_threads(
+      requirements);  // should use at least num_ranks_ threads to avoid NCCL hang
 #pragma omp parallel for num_threads(index.num_ranks_)
     for (int rank = 0; rank < index.num_ranks_; rank++) {
       const raft::resources& dev_res = raft::resource::set_current_device_to_rank(clique, rank);
diff --git a/cpp/src/neighbors/refine/refine_host.hpp b/cpp/src/neighbors/refine/refine_host.hpp
index 6bc8271cab..b74977110c 100644
--- a/cpp/src/neighbors/refine/refine_host.hpp
+++ b/cpp/src/neighbors/refine/refine_host.hpp
@@ -17,8 +17,9 @@
 #pragma once
 
 #include "../../core/nvtx.hpp"
+#include "../../core/omp_wrapper.hpp"
 #include "refine_common.hpp"
-#include <omp.h>
+
 #include <raft/core/host_mdspan.hpp>
 #include <raft/util/integer_utils.hpp>
 
@@ -376,7 +377,8 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
   cuvs::common::nvtx::range<cuvs::common::nvtx::domain::cuvs> fun_scope(
     "neighbors::refine_host(%zu, %zu -> %zu)", n_queries, orig_k, refined_k);
 
-  auto suggested_n_threads = std::max(1, std::min(omp_get_num_procs(), omp_get_max_threads()));
+  auto suggested_n_threads =
+    std::max(1, std::min(cuvs::core::omp::get_num_procs(), cuvs::core::omp::get_max_threads()));
 
   // If the number of queries is small, separate the distance calculation and
   // the top-k calculation into separate loops, and apply finer-grained thread
@@ -438,8 +440,8 @@ template <typename DC, typename IdxT, typename DataT, typename DistanceT, typena
       suggested_n_threads, std::vector<std::tuple<DistanceT, IdxT>>(orig_k));
 #pragma omp parallel num_threads(suggested_n_threads)
     {
-      auto tid = omp_get_thread_num();
-      for (size_t i = tid; i < n_queries; i += omp_get_num_threads()) {
+      auto tid = cuvs::core::omp::get_thread_num();
+      for (size_t i = tid; i < n_queries; i += cuvs::core::omp::get_num_threads()) {
         // Compute the refined distance using original dataset vectors
         const DataT* query = queries.data_handle() + dim * i;
         for (size_t j = 0; j < orig_k; j++) {
diff --git a/cpp/src/neighbors/scann/detail/scann_avq.cuh b/cpp/src/neighbors/scann/detail/scann_avq.cuh
index 1c42f6d2b9..6793acf82f 100644
--- a/cpp/src/neighbors/scann/detail/scann_avq.cuh
+++ b/cpp/src/neighbors/scann/detail/scann_avq.cuh
@@ -547,7 +547,7 @@ class cluster_loader {
       auto pinned_cluster = raft::make_pinned_matrix_view<T, int64_t>(
         cluster_buf_.data_handle(), cluster_vectors.extent(0), cluster_vectors.extent(1));
 
-      int n_threads = std::min<int>(omp_get_max_threads(), 32);
+      int n_threads = std::min<int>(cuvs::core::omp::get_max_threads(), 32);
 #pragma omp parallel for num_threads(n_threads)
       for (int i = 0; i < h_cluster_ids.extent(0); i++) {
         memcpy(pinned_cluster.data_handle() + i * pinned_cluster.extent(1),
diff --git a/cpp/src/neighbors/scann/detail/scann_common.cuh b/cpp/src/neighbors/scann/detail/scann_common.cuh
index 87621af6e4..6a1b2b3fa9 100644
--- a/cpp/src/neighbors/scann/detail/scann_common.cuh
+++ b/cpp/src/neighbors/scann/detail/scann_common.cuh
@@ -16,6 +16,8 @@
 
 #pragma once
 
+#include "../../../core/omp_wrapper.hpp"
+
 #include <raft/core/device_mdarray.hpp>
 #include <raft/core/device_mdspan.hpp>
 #include <raft/matrix/gather.cuh>
@@ -49,7 +51,7 @@ struct gather_functor {
 
     raft::resource::sync_stream(res, stream);
 
-    int n_threads = std::min<int>(omp_get_max_threads(), 32);
+    int n_threads = std::min<int>(cuvs::core::omp::get_max_threads(), 32);
 
 #pragma omp parallel for num_threads(n_threads)
     for (int i = 0; i < h_cluster_ids.extent(0); i++) {

From 399e52ff0368ede96e653db0007e36b0411fd744 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Fri, 17 Oct 2025 13:43:30 -0500
Subject: [PATCH 621/623] Use pinned_host_memory_resource instead of
 pinned_memory_resource. (#1434)

We want to remove RMM's `pinned_memory_resource` in favor of `pinned_host_memory_resource`, because the latter aligns with CCCL's MR design that we will be adopting throughout RAPIDS/RMM.

This PR transitions cuVS to use `pinned_host_memory_resource`.

See https://github.com/rapidsai/rmm/issues/2090 for more information.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - Divye Gala (https://github.com/divyegala)

URL: https://github.com/rapidsai/cuvs/pull/1434
---
 c/src/core/c_api.cpp                          | 6 +++---
 cpp/src/neighbors/detail/ann_utils.cuh        | 3 +--
 cpp/src/neighbors/detail/dynamic_batching.cuh | 4 +---
 3 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/c/src/core/c_api.cpp b/c/src/core/c_api.cpp
index 74cc371775..90be1cee30 100644
--- a/c/src/core/c_api.cpp
+++ b/c/src/core/c_api.cpp
@@ -29,7 +29,7 @@
 #include <rmm/mr/device/owning_wrapper.hpp>
 #include <rmm/mr/device/per_device_resource.hpp>
 #include <rmm/mr/device/pool_memory_resource.hpp>
-#include <rmm/mr/host/pinned_memory_resource.hpp>
+#include <rmm/mr/pinned_host_memory_resource.hpp>
 
 #include "../core/exceptions.hpp"
 
@@ -191,12 +191,12 @@ extern "C" cuvsError_t cuvsRMMMemoryResourceReset()
   });
 }
 
-thread_local std::unique_ptr<rmm::mr::pinned_memory_resource> pinned_mr;
+thread_local std::unique_ptr<rmm::mr::pinned_host_memory_resource> pinned_mr;
 
 extern "C" cuvsError_t cuvsRMMHostAlloc(void** ptr, size_t bytes)
 {
   return cuvs::core::translate_exceptions([=] {
-    if (pinned_mr == nullptr) { pinned_mr = std::make_unique<rmm::mr::pinned_memory_resource>(); }
+    if (pinned_mr == nullptr) { pinned_mr = std::make_unique<rmm::mr::pinned_host_memory_resource>(); }
     *ptr = pinned_mr->allocate(bytes);
   });
 }
diff --git a/cpp/src/neighbors/detail/ann_utils.cuh b/cpp/src/neighbors/detail/ann_utils.cuh
index fa0e2a9217..e4c2468901 100644
--- a/cpp/src/neighbors/detail/ann_utils.cuh
+++ b/cpp/src/neighbors/detail/ann_utils.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -26,7 +26,6 @@
 #include <rmm/cuda_stream_view.hpp>
 #include <rmm/device_scalar.hpp>
 #include <rmm/device_uvector.hpp>
-#include <rmm/mr/host/pinned_memory_resource.hpp>
 #include <rmm/resource_ref.hpp>
 
 #include <cuda_fp16.hpp>
diff --git a/cpp/src/neighbors/detail/dynamic_batching.cuh b/cpp/src/neighbors/detail/dynamic_batching.cuh
index 23c5c07f6a..608917efcc 100644
--- a/cpp/src/neighbors/detail/dynamic_batching.cuh
+++ b/cpp/src/neighbors/detail/dynamic_batching.cuh
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2024, NVIDIA CORPORATION.
+ * Copyright (c) 2024-2025, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -31,8 +31,6 @@
 #include <cooperative_groups.h>
 #include <cuda/atomic>
 #include <cuda/std/atomic>
-#include <rmm/mr/pinned_host_memory_resource.hpp>
-#include <rmm/resource_ref.hpp>
 
 #include <chrono>
 #include <limits>

From 94646de607876d195668dedc618875435b81710a Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 20 Oct 2025 12:08:24 -0400
Subject: [PATCH 622/623] Unify binding headers (#1413)

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1413
---
 c/CMakeLists.txt                              | 30 +++++-
 c/include/cuvs/core/all.h                     | 53 ++++++++++
 c/include/cuvs/neighbors/common.h             |  1 -
 c/include/cuvs/neighbors/mg_common.h          |  1 -
 .../cuvs/preprocessing/quantize/binary.h      | 10 +-
 c/src/preprocessing/quantize/binary.cpp       |  5 +-
 c/tests/CMakeLists.txt                        | 36 +++----
 c/tests/cmake/header_check.cmake              | 99 +++++++++++++++++++
 c/tests/core/headers.c                        | 25 +++++
 cpp/CMakeLists.txt                            |  8 +-
 java/internal/CMakeLists.txt                  | 33 +++++--
 java/panama-bindings/generate-bindings.sh     |  3 +-
 java/panama-bindings/headers.h                | 10 +-
 rust/cuvs-sys/CMakeLists.txt                  | 38 ++++---
 rust/cuvs-sys/build.rs                        |  4 +-
 rust/cuvs-sys/cuvs_c_wrapper.h                | 12 +--
 16 files changed, 296 insertions(+), 72 deletions(-)
 create mode 100644 c/include/cuvs/core/all.h
 create mode 100644 c/tests/cmake/header_check.cmake
 create mode 100644 c/tests/core/headers.c

diff --git a/c/CMakeLists.txt b/c/CMakeLists.txt
index acaf3a746c..5dbef2d89b 100644
--- a/c/CMakeLists.txt
+++ b/c/CMakeLists.txt
@@ -106,7 +106,29 @@ set_target_properties(
              EXPORT_NAME c_api
 )
 
-target_compile_definitions(cuvs_c PUBLIC $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>)
+# Encode what optional components we are building
+set(CUVS_BUILD_CAGRA_HNSWLIB ${BUILD_CAGRA_HNSWLIB})
+set(CUVS_BUILD_MG_ALGOS ${BUILD_MG_ALGOS})
+# cmake-lint: disable=E1126
+file(
+  CONFIGURE
+  OUTPUT
+  "${CMAKE_CURRENT_BINARY_DIR}/include/cuvs/core/c_config.h"
+  CONTENT
+  [=[
+  #pragma once
+  #ifndef CUVS_BUILD_CAGRA_HNSWLIB
+    #cmakedefine CUVS_BUILD_CAGRA_HNSWLIB
+  #endif
+  #ifndef CUVS_BUILD_MG_ALGOS
+    #cmakedefine CUVS_BUILD_MG_ALGOS
+  #endif
+]=]
+)
+target_compile_definitions(
+  cuvs_c PUBLIC $<$<BOOL:${BUILD_CAGRA_HNSWLIB}>:CUVS_BUILD_CAGRA_HNSWLIB>
+                $<$<BOOL:${BUILD_MG_ALGOS}>:CUVS_BUILD_MG_ALGOS>
+)
 
 target_compile_options(cuvs_c PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUVS_CXX_FLAGS}>")
 
@@ -114,6 +136,7 @@ target_include_directories(
   cuvs_c
   PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
          "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+         "$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>"
   INTERFACE "$<INSTALL_INTERFACE:include>"
 )
 
@@ -160,6 +183,11 @@ if(PROJECT_IS_TOP_LEVEL)
     COMPONENT cuvs_c
     DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
   )
+  install(
+    FILES ${CMAKE_CURRENT_BINARY_DIR}/include/cuvs/core/c_config.h
+    COMPONENT cuvs_c
+    DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cuvs/core/
+  )
 endif()
 
 # ##################################################################################################
diff --git a/c/include/cuvs/core/all.h b/c/include/cuvs/core/all.h
new file mode 100644
index 0000000000..254d46197a
--- /dev/null
+++ b/c/include/cuvs/core/all.h
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This is a list of required header files that cuvs_c rust/go/java
+// will bind to.
+
+#pragma once
+
+#include <cuvs/core/c_config.h>
+#include <cuvs/core/c_api.h>
+
+#include <cuvs/cluster/kmeans.h>
+
+#include <cuvs/distance/distance.h>
+#include <cuvs/distance/pairwise_distance.h>
+
+#include <cuvs/neighbors/all_neighbors.h>
+#include <cuvs/neighbors/brute_force.h>
+#include <cuvs/neighbors/cagra.h>
+#include <cuvs/neighbors/common.h>
+#include <cuvs/neighbors/ivf_flat.h>
+#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/neighbors/nn_descent.h>
+#include <cuvs/neighbors/refine.h>
+#include <cuvs/neighbors/tiered_index.h>
+#include <cuvs/neighbors/vamana.h>
+
+#ifdef CUVS_BUILD_CAGRA_HNSWLIB
+  #include <cuvs/neighbors/hnsw.h>
+#endif
+
+#ifdef CUVS_BUILD_MG_ALGOS
+  #include <cuvs/neighbors/mg_cagra.h>
+  #include <cuvs/neighbors/mg_common.h>
+  #include <cuvs/neighbors/mg_ivf_flat.h>
+  #include <cuvs/neighbors/mg_ivf_pq.h>
+#endif
+
+#include <cuvs/preprocessing/quantize/binary.h>
+#include <cuvs/preprocessing/quantize/scalar.h>
diff --git a/c/include/cuvs/neighbors/common.h b/c/include/cuvs/neighbors/common.h
index 24152d1aad..21c701a4fc 100644
--- a/c/include/cuvs/neighbors/common.h
+++ b/c/include/cuvs/neighbors/common.h
@@ -16,7 +16,6 @@
 
 #pragma once
 
-#include <cuvs/distance/distance.h>
 #include <stdint.h>
 
 #ifdef __cplusplus
diff --git a/c/include/cuvs/neighbors/mg_common.h b/c/include/cuvs/neighbors/mg_common.h
index 99bfd21c4a..a9c33e08f0 100644
--- a/c/include/cuvs/neighbors/mg_common.h
+++ b/c/include/cuvs/neighbors/mg_common.h
@@ -16,7 +16,6 @@
 
 #pragma once
 
-#include <cuvs/core/c_api.h>
 #include <stdint.h>
 
 #ifdef __cplusplus
diff --git a/c/include/cuvs/preprocessing/quantize/binary.h b/c/include/cuvs/preprocessing/quantize/binary.h
index 7c6e522c85..f4169085ad 100644
--- a/c/include/cuvs/preprocessing/quantize/binary.h
+++ b/c/include/cuvs/preprocessing/quantize/binary.h
@@ -30,7 +30,11 @@ extern "C" {
  * and sampling_median thresholds are calculated separately for each dimension.
  *
  */
-enum cuvsBinaryQuantizerThreshold { ZERO, MEAN, SAMPLING_MEDIAN };
+enum cuvsBinaryQuantizerThreshold {
+  ZERO=0,
+  MEAN=1,
+  SAMPLING_MEDIAN=2
+};
 
 /**
  * @brief Binary quantizer parameters.
@@ -39,12 +43,12 @@ struct cuvsBinaryQuantizerParams {
   /*
    * specifies the threshold to set a bit in cuvsBinaryQuantizerTransform
    */
-  cuvsBinaryQuantizerThreshold threshold = MEAN;
+  enum cuvsBinaryQuantizerThreshold threshold;
 
   /*
    * specifies the sampling ratio
    */
-  float sampling_ratio = 0.1;
+  float sampling_ratio;
 };
 
 typedef struct cuvsBinaryQuantizerParams* cuvsBinaryQuantizerParams_t;
diff --git a/c/src/preprocessing/quantize/binary.cpp b/c/src/preprocessing/quantize/binary.cpp
index 4afbc3a758..982ab906a8 100644
--- a/c/src/preprocessing/quantize/binary.cpp
+++ b/c/src/preprocessing/quantize/binary.cpp
@@ -105,7 +105,10 @@ void* _train(cuvsResources_t res,
 
 extern "C" cuvsError_t cuvsBinaryQuantizerParamsCreate(cuvsBinaryQuantizerParams_t* params)
 {
-  return cuvs::core::translate_exceptions([=] { *params = new cuvsBinaryQuantizerParams; });
+  return cuvs::core::translate_exceptions([=] {
+    *params =
+        new cuvsBinaryQuantizerParams{.threshold = MEAN, .sampling_ratio = 0.1};
+  });
 }
 
 extern "C" cuvsError_t cuvsBinaryQuantizerParamsDestroy(cuvsBinaryQuantizerParams_t params)
diff --git a/c/tests/CMakeLists.txt b/c/tests/CMakeLists.txt
index 36e0f15191..f0f9780172 100644
--- a/c/tests/CMakeLists.txt
+++ b/c/tests/CMakeLists.txt
@@ -89,36 +89,22 @@ if(BUILD_CAGRA_HNSWLIB)
   target_compile_definitions(HNSW_C_TEST PUBLIC CUVS_BUILD_CAGRA_HNSWLIB)
 endif()
 
-add_executable(cuvs_c_test core/c_api.c)
-target_link_libraries(cuvs_c_test PUBLIC cuvs::c_api)
+# ##################################################################################################
+# * Validate C API ---------------------------------------------------------------
 
-add_executable(cuvs_c_neighbors_test neighbors/c_api.c)
-target_link_libraries(cuvs_c_neighbors_test PUBLIC cuvs::c_api)
+ConfigureTest(NAME cuvs_c_headers PATH core/headers.c)
+ConfigureTest(NAME cuvs_c_test PATH core/c_api.c)
+ConfigureTest(NAME cuvs_c_neighbors_test PATH neighbors/c_api.c)
 
-set_target_properties(
-  cuvs_c_test cuvs_c_neighbors_test
-  PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUVS_C_BINARY_DIR}/gtests>"
-             INSTALL_RPATH "\$ORIGIN/../../../lib"
-)
+# ##################################################################################################
+# * Validate installed headers are listed in `all.h`
 
-rapids_test_add(
-  NAME cuvs_c_test
-  COMMAND cuvs_c_test
-  GPUS 1
-  PERCENT 100
-  INSTALL_COMPONENT_SET testing
-)
-rapids_test_add(
-  NAME cuvs_c_neighbors_test
-  COMMAND cuvs_c_neighbors_test
-  GPUS 1
-  PERCENT 100
-  INSTALL_COMPONENT_SET testing
-)
+include(cmake/header_check.cmake)
+cuvs_c_add_header_check("${CUVS_C_SOURCE_DIR}" "cuvs/core/all.h" INSTALL_COMPONENT_SET testing)
 
 # ##################################################################################################
-# Install tests ####################################################################################
-# ##################################################################################################
+# * Install Tests ---------------------------------------------------------------
+
 if(PROJECT_IS_TOP_LEVEL)
   rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcuvs)
 endif()
diff --git a/c/tests/cmake/header_check.cmake b/c/tests/cmake/header_check.cmake
new file mode 100644
index 0000000000..cf2a868f6d
--- /dev/null
+++ b/c/tests/cmake/header_check.cmake
@@ -0,0 +1,99 @@
+# =============================================================================
+# Copyright (c) 2025, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Build a list of all headers in `c/include/cuvs`
+#
+# cmake-lint: disable=E1126
+function(cuvs_c_add_header_check project_root binding_header COMPONENT_PLACEHOLDER install_set)
+  file(
+    GLOB_RECURSE all_headers_to_match
+    RELATIVE "${project_root}/include/"
+    "${project_root}/include/*.h"
+  )
+
+  set(template_contents
+      [=[
+  set(all_headers_to_match @all_headers_to_match@)
+  set(binding_header_name @binding_header@)
+  set(binary_dir @CMAKE_CURRENT_BINARY_DIR@)
+  set(src_dir @CMAKE_SOURCE_DIR@)
+
+  function(check_binding_header mode header_list_var)
+
+    if(mode STREQUAL BUILD)
+      set(path "${src_dir}/include/${binding_header_name}")
+    else()
+      # Walk up the binary dir till we
+      set(path "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/include/${binding_header_name}")
+    endif()
+
+    if(EXISTS "${path}")
+      file(READ "${path}" binding_header_contents)
+      string(REPLACE "\n" "" binding_header_contents "${binding_header_contents}")
+      foreach(entry ${${header_list_var}})
+        if(NOT entry STREQUAL binding_header_name)
+          string(FIND "${binding_header_contents}" "<${entry}>" contains)
+          if(contains STREQUAL "-1")
+            message(FATAL_ERROR "include \"${entry}\" not found in contents of ${binding_header_name}.")
+          endif()
+        endif()
+      endforeach()
+    else()
+      message(FATAL_ERROR "check_binding_header failed to find ${binding_header_name} on disk.")
+    endif()
+  endfunction()
+
+  function(check_installed_headers )
+    set(path "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/include/")
+    file(GLOB_RECURSE installed_headers RELATIVE "${path}/" "${path}/cuvs/*.h")
+    check_binding_header(INSTALL installed_headers)
+  endfunction()
+
+  if(CMAKE_CURRENT_LIST_DIR STREQUAL binary_dir)
+    # Build directory checks
+    #
+    # 1. Check that binding header content includes all headers
+    #    that we cached
+    #
+    check_binding_header(BUILD all_headers_to_match)
+  else()
+    # Install directory checks
+    #
+    # 1. Check that all headers we have in our cached list are installed
+    # 2. Check that binding header content includes all headers
+    #    that have have installed
+    check_binding_header(INSTALL all_headers_to_match)
+    check_installed_headers()
+  endif()
+
+]=]
+  )
+
+  set(output_path "${CMAKE_CURRENT_BINARY_DIR}/cuvs_header_check.cmake")
+  file(CONFIGURE OUTPUT "${output_path}" CONTENT "${template_contents}" @ONLY)
+
+  # Most likely need to roll all this into a CMake
+  add_test(NAME cuvs_c_verify_install_headers COMMAND ${CMAKE_COMMAND} "-P=${output_path}")
+  install(
+    FILES "${output_path}"
+    COMPONENT testing
+    DESTINATION "."
+    EXCLUDE_FROM_ALL
+  )
+  set_property(
+    TARGET rapids_test_install_${install_set}
+    APPEND
+    PROPERTY "TESTS_TO_RUN" "cuvs_c_verify_install_headers"
+  )
+endfunction()
diff --git a/c/tests/core/headers.c b/c/tests/core/headers.c
new file mode 100644
index 0000000000..f62cb41dda
--- /dev/null
+++ b/c/tests/core/headers.c
@@ -0,0 +1,25 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Basic smoke test to verify non of the C API headers
+// ever use a C++ only construct
+#include <cuvs/core/all.h>
+#include <cuvs/core/all.h> //smoke out missing include guards
+
+int main()
+{
+  return 0;
+}
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
index 233e98fb0a..bd9e29fc17 100644
--- a/cpp/CMakeLists.txt
+++ b/cpp/CMakeLists.txt
@@ -764,12 +764,18 @@ target_compile_definitions(cuvs::cuvs INTERFACE $<$<BOOL:${CUVS_NVTX}>:NVTX_ENAB
       COMPONENT c_api
       EXPORT cuvs-c-exports
     )
+
+    install(
+      FILES ${CUVS_C_BINARY_DIR}/include/cuvs/core/c_config.h
+      COMPONENT cuvs
+      DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cuvs/core/
+    )
   endif()
 
   install(
     FILES ${CMAKE_CURRENT_BINARY_DIR}/include/cuvs/version_config.h
     COMPONENT cuvs
-    DESTINATION include/cuvs
+    DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cuvs
   )
 
   if(TARGET cuvs_c)
diff --git a/java/internal/CMakeLists.txt b/java/internal/CMakeLists.txt
index 20ad54ab89..cc39261fde 100644
--- a/java/internal/CMakeLists.txt
+++ b/java/internal/CMakeLists.txt
@@ -36,14 +36,6 @@ option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulti
 
 if(FIND_CUVS_CPP)
   find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS c_api)
-  if(NOT TARGET cuvs::c_api)
-    message(
-      FATAL_ERROR
-        "Building against a preexisting libcuvs library requires the compiled libcuvs to have been built!"
-    )
-  endif()
-else()
-  set(cuvs_FOUND OFF)
 endif()
 
 if(NOT cuvs_FOUND)
@@ -53,3 +45,28 @@ if(NOT cuvs_FOUND)
 endif()
 
 include(get_dlpack.cmake)
+
+# We are going to copy all the cuvs_c and dlpack headers into a staging location
+if(TARGET cuvs_c)
+  get_target_property(cuvs_c_headers cuvs_c INCLUDE_DIRECTORIES)
+else()
+  get_target_property(cuvs_c_headers cuvs::c_api INTERFACE_INCLUDE_DIRECTORIES)
+endif()
+
+file(
+  INSTALL "${DLPACK_INCLUDE_DIR}"
+  DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/bindings/"
+  FILES_MATCHING
+  PATTERN "*.h"
+)
+
+foreach(cuvs_c_dir IN LISTS cuvs_c_headers)
+  if(EXISTS "${cuvs_c_dir}/cuvs/")
+    file(
+      INSTALL "${cuvs_c_dir}/cuvs"
+      DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/bindings/include/"
+      FILES_MATCHING
+      PATTERN "*.h"
+    )
+  endif()
+endforeach()
diff --git a/java/panama-bindings/generate-bindings.sh b/java/panama-bindings/generate-bindings.sh
index 7e302ce874..8db1a93caf 100755
--- a/java/panama-bindings/generate-bindings.sh
+++ b/java/panama-bindings/generate-bindings.sh
@@ -33,9 +33,8 @@ fi
 
 # Use Jextract utility to generate panama bindings
 jextract \
- --include-dir "${REPODIR}"/java/internal/build/_deps/dlpack-src/include/ \
+ --include-dir "${REPODIR}"/java/internal/build/bindings/include/ \
  --include-dir "${CUDA_INCLUDE_DIR}" \
- --include-dir "${REPODIR}"/c/include \
  --output "${REPODIR}/java/cuvs-java/src/main/java22/" \
  --target-package ${TARGET_PACKAGE} \
  --library cuvs_c \
diff --git a/java/panama-bindings/headers.h b/java/panama-bindings/headers.h
index 57a13cf1b1..6a5bd4c3bd 100644
--- a/java/panama-bindings/headers.h
+++ b/java/panama-bindings/headers.h
@@ -14,15 +14,9 @@
  * limitations under the License.
  */
 
-// Please add the required header files below for which panama FFM API bindings are needed
+#include <cuvs/core/all.h>
 
-#include <cuvs/core/c_api.h>
-#include <cuvs/neighbors/cagra.h>
-#include <cuvs/neighbors/brute_force.h>
-#include <cuvs/neighbors/hnsw.h>
-#include <cuvs/neighbors/ivf_pq.h>
-#include <cuvs/neighbors/common.h>
-#include <cuvs/neighbors/tiered_index.h>
+// Please add any extra required header files below for which panama FFM API bindings are needed
 #include <cuda_runtime.h>
 
 /**
diff --git a/rust/cuvs-sys/CMakeLists.txt b/rust/cuvs-sys/CMakeLists.txt
index db3c0f8d67..3df61a4e20 100644
--- a/rust/cuvs-sys/CMakeLists.txt
+++ b/rust/cuvs-sys/CMakeLists.txt
@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -39,16 +39,7 @@ option(FIND_CUVS_CPP "Search for existing CUVS C++ installations before defaulti
 
 # If the user requested it we attempt to find CUVS.
 if(FIND_CUVS_CPP)
-  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS compiled)
-  if(NOT TARGET cuvs::cuvs)
-    message(
-      FATAL_ERROR
-        "Building against a preexisting libcuvs library requires the compiled libcuvs to have been built!"
-    )
-
-  endif()
-else()
-  set(cuvs_FOUND OFF)
+  find_package(cuvs "${RAPIDS_VERSION}" REQUIRED COMPONENTS c_api)
 endif()
 
 if(NOT cuvs_FOUND)
@@ -59,6 +50,31 @@ endif()
 
 include(get_dlpack.cmake)
 
+# We are going to copy all the cuvs_c and dlpack headers into a staging location
+if(TARGET cuvs_c)
+  get_target_property(cuvs_c_headers cuvs_c INCLUDE_DIRECTORIES)
+else()
+  get_target_property(cuvs_c_headers cuvs::c_api INTERFACE_INCLUDE_DIRECTORIES)
+endif()
+
+file(
+  INSTALL "${DLPACK_INCLUDE_DIR}"
+  DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/bindings/"
+  FILES_MATCHING
+  PATTERN "*.h"
+)
+
+foreach(cuvs_c_dir IN LISTS cuvs_c_headers)
+  if(EXISTS "${cuvs_c_dir}/cuvs/")
+    file(
+      INSTALL "${cuvs_c_dir}/cuvs"
+      DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/bindings/include/"
+      FILES_MATCHING
+      PATTERN "*.h"
+    )
+  endif()
+endforeach()
+
 # add a dummy target here,
 add_library(cuvs-rust INTERFACE)
 target_link_libraries(cuvs-rust INTERFACE cuvs::cuvs)
diff --git a/rust/cuvs-sys/build.rs b/rust/cuvs-sys/build.rs
index 0c79bcd973..0818d83a13 100644
--- a/rust/cuvs-sys/build.rs
+++ b/rust/cuvs-sys/build.rs
@@ -73,9 +73,11 @@ fn main() {
     // run bindgen to automatically create rust bindings for the cuvs c-api
     bindgen::Builder::default()
         .header("cuvs_c_wrapper.h")
-        .clang_arg("-I../../c/include")
         // needed to find cudaruntime.h
         .clang_args(cmake_cxx_flags.split(' '))
+        // include cuvs c headers and dlpack headers we copied
+        // into our staging location
+        .clang_arg(format!("-I{}/build/bindings/include/", out_path.display()))
         // include dlpack from the cmake build dependencies
         .clang_arg(format!(
             "-I{}/build/_deps/dlpack-src/include/",
diff --git a/rust/cuvs-sys/cuvs_c_wrapper.h b/rust/cuvs-sys/cuvs_c_wrapper.h
index aeac04f54e..6c0b0fce30 100644
--- a/rust/cuvs-sys/cuvs_c_wrapper.h
+++ b/rust/cuvs-sys/cuvs_c_wrapper.h
@@ -14,12 +14,6 @@
  * limitations under the License.
  */
 
-// wrapper file containing all the C-API's we should automatically be creating rust
-// bindings for
-#include <cuvs/core/c_api.h>
-#include <cuvs/cluster/kmeans.h>
-#include <cuvs/distance/pairwise_distance.h>
-#include <cuvs/neighbors/brute_force.h>
-#include <cuvs/neighbors/ivf_flat.h>
-#include <cuvs/neighbors/cagra.h>
-#include <cuvs/neighbors/ivf_pq.h>
+#include <cuvs/core/all.h>
+
+// Please add any extra required header files below for which rust bindings are needed

From a05de3ea5e72a966fd26623fa4c56d88929b178e Mon Sep 17 00:00:00 2001
From: Robert Maynard <rmaynard@nvidia.com>
Date: Mon, 20 Oct 2025 12:37:45 -0400
Subject: [PATCH 623/623] Update cagra C API enums to have more long term
 stable values (#1436)

Authors:
  - Robert Maynard (https://github.com/robertmaynard)

Approvers:
  - Ben Frederickson (https://github.com/benfred)

URL: https://github.com/rapidsai/cuvs/pull/1436
---
 c/include/cuvs/neighbors/cagra.h                              | 4 ++--
 cpp/include/cuvs/neighbors/cagra.hpp                          | 4 ++--
 .../src/main/java/com/nvidia/cuvs/CagraSearchParams.java      | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/c/include/cuvs/neighbors/cagra.h b/c/include/cuvs/neighbors/cagra.h
index 624aa76cb9..ed8c69c830 100644
--- a/c/include/cuvs/neighbors/cagra.h
+++ b/c/include/cuvs/neighbors/cagra.h
@@ -214,14 +214,14 @@ enum cuvsCagraSearchAlgo {
   /** For small batch sizes. */
   MULTI_CTA    = 1,
   MULTI_KERNEL = 2,
-  AUTO         = 3
+  AUTO         = 100
 };
 
 /**
  * @brief Enum to denote Hash Mode used while searching CAGRA index
  *
  */
-enum cuvsCagraHashMode { HASH = 0, SMALL = 1, AUTO_HASH = 2 };
+enum cuvsCagraHashMode { HASH = 0, SMALL = 1, AUTO_HASH = 100 };
 
 /**
  * @brief Supplemental parameters to search CAGRA index
diff --git a/cpp/include/cuvs/neighbors/cagra.hpp b/cpp/include/cuvs/neighbors/cagra.hpp
index 4bf7e26e20..c8d1393046 100644
--- a/cpp/include/cuvs/neighbors/cagra.hpp
+++ b/cpp/include/cuvs/neighbors/cagra.hpp
@@ -133,10 +133,10 @@ enum class search_algo {
   /** For small batch sizes. */
   MULTI_CTA    = 1,
   MULTI_KERNEL = 2,
-  AUTO         = 3
+  AUTO         = 100
 };
 
-enum class hash_mode { HASH = 0, SMALL = 1, AUTO = 2 };
+enum class hash_mode { HASH = 0, SMALL = 1, AUTO = 100 };
 
 struct search_params : cuvs::neighbors::search_params {
   /** Maximum number of queries to search at the same time (batch size). Auto select when 0.*/
diff --git a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
index 15073db468..3f23b88d38 100644
--- a/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
+++ b/java/cuvs-java/src/main/java/com/nvidia/cuvs/CagraSearchParams.java
@@ -56,7 +56,7 @@ public enum SearchAlgo {
     /**
      * AUTO
      */
-    AUTO(3);
+    AUTO(100);
 
     /**
      * The value for the enum choice.
@@ -83,7 +83,7 @@ public enum HashMapMode {
     /**
      * AUTO_HASH
      */
-    AUTO_HASH(2);
+    AUTO_HASH(100);
 
     /**
      * The value for the enum choice.